# DataRobot docs > Single Markdown file aggregating public English docs for RAG pipelines and deep LLM context. > Generated: 2026-04-24T16:03:55.851516+00:00 | Locale: en | Pages: 1449 > Overview: [https://docs.datarobot.com/llms.txt](https://docs.datarobot.com/llms.txt) Page boundaries below use `---` separators; each page begins with `#` (title) and a `URL:` line. --- # Environment and commands reference URL: https://docs.datarobot.com/en/docs/agentic-ai/agent-assist/env-and-commands-reference.html > Environment variables, files and directories, and slash commands for DataRobot Agent Assist. # Environment and commands reference This page provides a quick reference for environment variables, important files and directories, and slash commands. ## Environment variables The variables that can be set in the environment or in `.env`. | Variable | Required | Description | Default / notes | | --- | --- | --- | --- | | DATAROBOT_API_TOKEN | Yes (unless provided by DR CLI configuration) | DataRobot API key for LLM gateway (default provider). | — | | DATAROBOT_ENDPOINT | No | DataRobot API endpoint. | https://app.datarobot.com/api/v2 | | AGENT_ASSIST_LLM_BASE_URL | No | Base URL for OpenAI-compatible LLM. | Derived from DATAROBOT_ENDPOINT (for example, …/genai/llmgw) | | AGENT_ASSIST_LLM_MODEL_NAME | No | Model name for the LLM. | anthropic/claude-sonnet-4-5-20250929 | | AGENT_ASSIST_LLM_API_KEY | No | API key for the LLM provider. | Falls back to DATAROBOT_API_TOKEN | | LOGFIRE_TOKEN | No | Logfire token for tracing. | — | | DATAROBOT_CLI_CONFIG | No | Override path to DR CLI configuration file. | Default: ~/.config/datarobot/drconfig.yaml | | AGENT_ASSIST_CONFIG | No | Override path to Agent Assist configuration file. | Default: ~/.config/datarobot/agent_assist_config.yaml | ## Files and directories The paths and files used or created by DataRobot Agent Assist. All paths are relative to the current working directory when you start `dr assist`. > [!WARNING] Run dr assist in an empty directory > Only run `dr assist` from a dedicated and empty directory. Running this command in a directory containing code or other files is unsafe. When you use the agent assist coding workflow, the assistant clones the DataRobot Agent Application Template repository into the current directory. This action can overwrite or conflict with existing files, damaging the existing project and degrading the accuracy of the assistant's output. Before running `dr assist`, if you're not in a dedicated directory, create one and open the terminal there (for example, `mkdir my-agent && cd my-agent`, then run `dr assist`). | Path | Description | | --- | --- | | agent_spec.md | Agent specification file (YAML content) in the current working directory; written by the assistant during design. | | .env | Optional environment file in the current directory; same variable names as above. | | .datarobot/cli/versions.yaml | Used by dependency check; defines minimum tool versions. | | ~/.config/datarobot/drconfig.yaml | DR CLI configuration (token, endpoint). This path can be overridden with DATAROBOT_CLI_CONFIG. | | ~/.config/datarobot/agent_assist_config.yaml | Agent Assist configuration (optional LLM base URL, model name, API key). This path can be overridden with AGENT_ASSIST_CONFIG. | | config.yaml | Optional repository-level configuration; can set repository (url, branch, target_dir, tag) for the template clone. | ## Slash commands The session commands typed after / at the `$` prompt (built-in commands only). | Command | Alias | Description | | --- | --- | --- | | /reset | /new | Clear session state and start fresh (removes agent_spec.md and template directory, clears conversation). | | /help | /? | List commands or show help for a command: /help [command]. | | /quit | /exit | Exit the session (with confirmation). | --- # Agent Assist URL: https://docs.datarobot.com/en/docs/agentic-ai/agent-assist/index.html > Agent Assist (dr-assist) is an interactive AI assistant that helps users design, code, and deploy AI agents through natural conversation. # Agent Assist > [!NOTE] Premium > DataRobot's Agentic AI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. DataRobot Agent Assist ( `dr-assist`) is an interactive AI assistant optimized for the development of AI agents. It helps users design, code, and deploy agents through natural conversation—users describe the agent they want, and the assistant helps build it on the foundation provided by the [Agentic Starter application template](https://github.com/datarobot-community/datarobot-agent-application). DataRobot Agent Assist integrates with the [DataRobot CLI](https://docs.datarobot.com/en/docs/agentic-ai/cli/index.html) as a plugin and uses the [DataRobot LLM gateway](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/dr-llm-gateway.html) for model access. During the design and code cycle, Agent Assist can outline which tools an agent should call based on the proposed functionality—for straightforward tools, it can implement the tool code; for more complex tools (such as those that consume API tokens or write to a database), it can scaffold the initial file structure for the human-in-the-loop to complete in the editor or development environment of their choice. > [!WARNING] Run dr assist in an empty directory > Only run `dr assist` from a dedicated and empty directory. Running this command in a directory containing code or other files is unsafe. When you use the agent assist coding workflow, the assistant clones the DataRobot Agent Application Template repository into the current directory. This action can overwrite or conflict with existing files, damaging the existing project and degrading the accuracy of the assistant's output. Before running `dr assist`, if you're not in a dedicated directory, create one and open the terminal there (for example, `mkdir my-agent && cd my-agent`, then run `dr assist`). DataRobot Agent Assist can: - Design AI agents by helping users think through specifications, ask clarifying questions, and produce an agent specification file ( agent_spec.md ). - Research solutions using file search and analysis (an internal agent can read files, list directories, grep, and glob). - Code AI agents by loading an existing agent_spec.md , cloning the DataRobot agent template repository, and implementing the agent with file edits and shell commands. - Simulate an agent from a specification before coding. In this simulation , the model chooses tools and arguments, but tool calls are not executed. Returns are generated by the LLM so you can validate design (which tools, I/O shapes, model behavior) without calling real deployments or datasets. - Deploy agents to DataRobot following the template’s deployment instructions. | Page | Description | | --- | --- | | Prerequisites and installation | System requirements, required tools and versions, installing the plugin or running standalone, verifying installation. | | Workflows and prompting | Welcome screen, slash commands, Design / Code / Deploy workflows, prompting tips. | | Environment and commands reference | Environment variables table, files and directories, slash commands. | | Troubleshooting | Plugin not discovered, dependency check failed, authentication errors, template bootstrap, LLM API errors, session interruption, and related fixes. | --- # Prerequisites and installation URL: https://docs.datarobot.com/en/docs/agentic-ai/agent-assist/installation.html > System requirements, required tools and versions, and how to install and run DataRobot Agent Assist (plugin or standalone). # Prerequisites and installation This page covers system requirements, prerequisite tools, installation, and configuration for DataRobot Agent Assist. ## System requirements Ensure your system meets the minimum requirements for running DataRobot Agent Assist. - Operating system: macOS or Linux (Windows requires WSL or another supported environment) - Python: 3.10 or higher > [!WARNING] Operating system compatibility > This repository is only compatible with macOS and Linux. On Windows, use a [DataRobot codespace](https://docs.datarobot.com/en/docs/workbench/wb-notebook/codespaces/index.html), [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install), or a virtual machine running a supported OS. ## Prerequisite tools At startup, DataRobot Agent Assist runs a dependency check via the DataRobot CLI and a `.datarobot/cli/versions.yaml` file. The following tools must be installed at the versions indicated. > [!TIP] Install tools system-wide > Install tools system-wide so they are available in all terminal sessions and when the DR CLI runs `dr dependencies check`. | Tool | Version | Description | Installation | | --- | --- | --- | --- | | dr-cli | >= 0.2.50 | The DataRobot CLI. | dr-cli installation | | git | >= 2.30.0 | Version control. | git installation | | uv | >= 0.9.0 | Python package manager. | uv installation | | Pulumi | >= 3.163.0 | Infrastructure as Code. | Pulumi installation | | Taskfile | >= 3.43.3 | Task runner. | Taskfile installation | | Node.js | >= 24 | JavaScript runtime (for example, for template frontend). | Node.js installation | > [!TIP] macOS installation > On macOS, several tools can be installed at once: > > ``` > brew install datarobot-oss/taps/dr-cli uv pulumi/tap/pulumi go-task node git python > ``` ## Install and run DataRobot Agent Assist DataRobot Agent Assist can be run as a DataRobot CLI plugin. Install the plugin so that `dr assist` is available wherever the DataRobot CLI is installed. The plugin is discovered when the `dr-assist` executable is on the `PATH` and responds to `--dr-plugin-manifest`. For the plugin published to the CLI plugin index, run: ``` dr plugin install assist ``` To verify the installation, run the following commands: ``` dr plugin list # Should show "assist" when installed as plugin dr assist --help # Show commands ``` > [!WARNING] Run dr assist in an empty directory > Only run `dr assist` from a dedicated and empty directory. Running this command in a directory containing code or other files is unsafe. When you use the agent assist coding workflow, the assistant clones the DataRobot Agent Application Template repository into the current directory. This action can overwrite or conflict with existing files, damaging the existing project and degrading the accuracy of the assistant's output. Before running `dr assist`, if you're not in a dedicated directory, create one and open the terminal there (for example, `mkdir my-agent && cd my-agent`, then run `dr assist`). ## Set configuration variables If necessary, set the following variables in the environment, in `.env`, or in the configuration files. In many cases, this configuration is handled by [the DataRobot CLI](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/auth.html) through `dr auth login`. | Variable | Required | Description | Default / notes | | --- | --- | --- | --- | | DATAROBOT_API_TOKEN | Yes (unless provided by DR CLI configuration) | DataRobot API key for LLM gateway (default provider). | — | | DATAROBOT_ENDPOINT | No | DataRobot API endpoint. | https://app.datarobot.com/api/v2 | For the full list of variables (including LLM overrides, logging, and config path overrides), see the [Environment and commands reference](https://docs.datarobot.com/en/docs/agentic-ai/agent-assist/env-and-commands-reference.html#environment-variables). ## Startup behavior Before the chat session starts, DataRobot Agent Assist: 1. Verifies the DataRobot CLI is available and runs dr dependencies check (using .datarobot/cli/versions.yaml if present). 2. Checks authentication and can run dr auth login if needed. 3. Ensures DATAROBOT_API_TOKEN (or equivalent from configuration) is set; if not, it displays an error and exits. --- # Troubleshooting URL: https://docs.datarobot.com/en/docs/agentic-ai/agent-assist/troubleshooting.html > Plugin not discovered, dependency check failed, authentication errors, and related fixes. # Troubleshooting This page describes how to resolve common issues with DataRobot Agent Assist, including plugin discovery, dependency checks, authentication and API keys, template bootstrap failures, LLM API errors, and session interruption. Follow the sections below for step-by-step fixes. ## Plugin not discovered If `dr assist` does not run or the plugin does not appear in `dr plugin list`: Check that `dr-assist` is on the PATH ``` which dr-assist ``` If it is not found, reinstall the plugin and ensure the directory containing `dr-assist` is on the `PATH`. Check manifest output The CLI discovers plugins by running the executable with `--dr-plugin-manifest` and reading JSON from stdout. ``` dr-assist --dr-plugin-manifest ``` This should print valid JSON. If it hangs or errors, the plugin may not start correctly. Check manifest speed The manifest must respond within 100 ms for reliable discovery; the CLI uses a 500 ms per-manifest timeout. ``` time dr-assist --dr-plugin-manifest ``` If it is slow, look for heavy imports or startup work before the manifest is printed. Debug with the CLI ``` dr --debug plugin list ``` Use this to see why a plugin might not be listed. Common causes: The manifest responds in over 500 ms, the executable lacks execute permission ( `chmod +x`), or the name conflicts with a built-in command. ## Dependency check failed At startup, DataRobot Agent Assist runs `dr dependencies check`, which uses `.datarobot/cli/versions.yaml`. If the file does not exist, the application creates it with default minimum versions (Node 24, git 2.30, task 3.43.3, pulumi 3.163.0). - Error panel:If the check fails, the application shows a "Dependency Check Failed" panel and prints the dependency error output. - What to do:Install or upgrade the missing tools to at least the versions in the table inPrerequisites and installation. Ensure each tool is on thePATHand reports at least the minimum version (for example,git --version,task --version). Fix any issues reported in the panel (for example, wrong executable name or path). ## Authentication / API key errors DataRobot Agent Assist requires a valid DataRobot API token. Use the following for missing or invalid credentials. Missing API key If `DATAROBOT_API_TOKEN` (or equivalent from configuration) is not set, the application shows a "Configuration Error" panel before starting the chat. Do one of the following: - Set the environment variable: export DATAROBOT_API_TOKEN='your-api-key-here' - Create or update a .env file in the current directory with DATAROBOT_API_TOKEN=... - Get the API key from the URL returned by get_api_key_url() (for example, DataRobot account profile). Invalid or expired key On authentication failure (for example, OpenAI `AuthenticationError`), the application shows an "Authentication Error" panel. Verify the key, run `echo $DATAROBOT_API_TOKEN`, update `.env` if needed, and fetch a new key from the provider URL. Using the DataRobot CLI for authentication Log in with the DataRobot CLI; the token is stored in the DR CLI configuration and reloaded by DataRobot Agent Assist: ``` dr auth login ``` If login fails, the application prints "DataRobot CLI authentication failed" and suggests running `dr auth login` manually. ## Template bootstrap failures > [!WARNING] Run dr assist in an empty directory > Only run `dr assist` from a dedicated and empty directory. Running this command in a directory containing code or other files is unsafe. When you use the agent assist coding workflow, the assistant clones the DataRobot Agent Application Template repository into the current directory. This action can overwrite or conflict with existing files, damaging the existing project and degrading the accuracy of the assistant's output. Before running `dr assist`, if you're not in a dedicated directory, create one and open the terminal there (for example, `mkdir my-agent && cd my-agent`, then run `dr assist`). If you see unexpected files, overwrites, or odd behavior after a clone, you may have started in a non-empty directory. If a git clone fails during the coding workflow (for example, when cloning the DataRobot agent template): Verify git is installed and on the PATH: ``` git --version ``` Check network access to the repository: ``` git ls-remote https://github.com/datarobot-community/datarobot-agent-application.git ``` If using SSH, verify SSH keys are configured: ``` ssh -T git@github.com ``` ## LLM API errors For timeouts, rate limiting, or model-not-available errors: - Check DataRobot service status. - Verify your account has LLM gateway access. - Try a different model if the selected one is unavailable. - For timeouts, the assistant prompts you to retry. ## Session interruption If the session ends unexpectedly: - Session state is not persisted between runs. - The agent_spec.md file is saved to disk and preserved. - Template directory contents are preserved. - Restart the session to continue. ## Getting help For more assistance see the following related documentation: - DataRobot CLI: For CLI installation, configuration file format, and authentication flow: DataRobot CLI documentation . - Plugin build and CI: See the plugin README in the repository for distribution, build, and troubleshooting. --- # Workflows and prompting URL: https://docs.datarobot.com/en/docs/agentic-ai/agent-assist/workflows-and-prompting.html > Welcome screen, slash commands, Design / Code / Deploy workflows, and prompting tips. # Workflows and prompting This page describes the interactive session: the welcome screen, slash commands, and the three main workflows (Design, Code, Deploy), plus prompting tips. ## Start Agent Assist > [!WARNING] Run dr assist in an empty directory > Only run `dr assist` from a dedicated and empty directory. Running this command in a directory containing code or other files is unsafe. When you use the agent assist coding workflow, the assistant clones the DataRobot Agent Application Template repository into the current directory. This action can overwrite or conflict with existing files, damaging the existing project and degrading the accuracy of the assistant's output. Before running `dr assist`, if you're not in a dedicated directory, create one and open the terminal there (for example, `mkdir my-agent && cd my-agent`, then run `dr assist`). To begin, run the `dr assist` command: ``` dr assist ``` When DataRobot Agent Assist is started, the welcome screen shows the welcome message, three options, and the help footer: ``` # Output: Welcome screen $ dr assist 🔌 Running plugin: assist █████████ █████ ███████ ████████ ██ █████████ ██ ███ ██ ██ ██ ██ ██ █████ ██ ██ █████ █████ █████ ██ ██ █████ ███████ █████ █████ ██████████████ ██ ███ ██ ██ ██ █████████ ██ ██ ██ ██ ██ ██ ██ █████ ██ ██ ███████ ██ ███████ ██ ██ ██ ██ ██ ██ ██ ██ ██ █████████ ██ ████ ██ ██ ██ ██ ██ ██ ███ ██ ██ ██ ██ ██ ██ ██ █████ ████████ ████████ ████ ████████ ██ ███ █████ ████████ █████ ████ █████████ Welcome! I help you design, code, and deploy AI agents. What would you like to do? 1. Design an AI agent → Describe your idea 2. Code an AI agent → Load and implement an existing agent_spec.md 3. Deploy an AI agent → Deploy an implemented agent to DataRobot Type /help for commands | /quit to exit ``` Input is collected at the `$` prompt. Type a number (1, 2, or 3) to choose a category, or, describe the goal in natural language. If the first instruction is one of those numbers, the assistant treats it as referring to the predefined categories. > [!TIP] Slash commands > Slash commands use the `/` prefix and are built in. For the full list and descriptions, see the [Environment and commands reference](https://docs.datarobot.com/en/docs/agentic-ai/agent-assist/env-and-commands-reference.html#slash-commands). Typing / shows the list of available commands and suggests typing `/help` for details. The `/quit` command exits the session. ### Design an AI agent Use this workflow when starting a new agent idea. The assistant asks clarifying questions about the agent’s purpose, capabilities, and requirements; suggests tools and models; and produces an agent specification. The specification is displayed and written to `agent_spec.md` in the current directory (YAML format). The specification can be refined over several iterations. The assistant is instructed to limit clarifying questions to about two rounds before proposing an initial draft, then iterate on that draft. Once you have a spec, you can [simulate the agent](https://docs.datarobot.com/en/docs/agentic-ai/agent-assist/workflows-and-prompting.html#simulate-an-agent) to validate the design before coding. ``` # Example conversation: Design a customer support agent $ I want to create a customer support agent for an e-commerce site Agent Assist: I'll help you design that! I can ask some questions to understand your requirements better. What specific customer issues should this agent handle? User: $ It should handle order status, returns, and product questions DataRobot Agent Assist: Great! What data sources will it need access to? User: $ Our order database and product catalog DataRobot Agent Assist: Perfect. Should it escalate complex issues to humans? User: $ Yes, escalate billing and refund requests DataRobot Agent Assist: I'll create a specification for your customer support agent... [Creates and displays agent_spec.md, saves to file] ``` ### Simulate an agent After you have an `agent_spec.md`, you can simulate the agent before implementing it. Simulation is for design validation only: no real tool code runs, and no external services are called. - What happens: The model (LLM) uses your spec to decide which tools to call and with what arguments. When the model requests a tool call, Agent Assist does not run your tool implementation or call DataRobot deployments, datasets, or other APIs. Instead, it uses the LLM to generate simulated return values that match the tool’s described inputs and outputs. You see realistic-looking tool inputs and outputs so you can judge whether the design makes sense. - What it’s for: Validate that the right tools exist, that input/output structures are reasonable, and that the selected model tends to call the right tools for the task—before you spend time implementing real tool code. - What it’s not: Simulation does not call real DataRobot deployments, read real datasets, or hit real APIs. To use live data and deployments, implement the agent and run it in the Code or Deploy workflow. Ask the assistant to simulate your agent (for example, “Simulate my agent” or “Let me try the design”) to begin. Type `/quit` to exit the simulation. ### Code an AI agent Use this workflow when the `agent_spec.md` file already exists and is ready to implement. The assistant loads the specification and—if the template repository is not yet present—uses the `prepare_to_code` tool to clone the [DataRobot Agentic Starter repository](https://github.com/datarobot-community/datarobot-agent-application). It then follows the template’s `AGENTS.md` for setup and implementation. The assistant can run shell commands (with approval), edit files, and manage multi-step work with tools. It suggests testing locally and deploying when the implementation is ready. ``` # Example conversation: Add a tool to an existing specification User: $ Load my existing agent specification and add a tool for inventory checking Agent Assist: I found your agent_spec.md. I can see it's a customer support agent. Let me add an inventory checking tool... [Reads current spec and adds the requested tool] ``` ### Deploy an AI agent Use this workflow when an implemented agent is ready to deploy to DataRobot. The assistant reads the deployment instructions from the template’s `AGENTS.md` and guides users through the steps. It does not run deployment commands; it indicates what to run (in a separate terminal). For interactive or long-running steps (such as `dr start`), the assistant uses the appropriate tools as defined in the codebase. ``` # Example conversation: Deploy the agent User: $ Help me deploy the agent I implemented Agent Assist: I'll read the deployment section of AGENTS.md and walk you through the steps... [Displays deployment commands and instructions] ``` ## Interact with the DataRobot CLI DataRobot Agent Assist runs certain DataRobot CLI ( `dr`) commands on your behalf and can run other shell commands (including additional `dr` commands) with your approval. - At startup: The assistant runs dr dependencies check to verify that required tools (Python, Node, Git, Task, Pulumi, etc.) are installed and meet minimum versions. See Prerequisites and installation . - Code workflow: When you use the assistant to clone and prepare the template (for example, "Code an AI agent" or "prepare to code"), it runs dr start in the template directory to initialize the DataRobot project. You are prompted to approve before the command runs. - Any workflow: The assistant can run shell commands—including dr subcommands—after you approve. It shows the command and description, then prompts for approval before executing. For full DataRobot CLI usage (authorization, deployment, and other commands), see the [DataRobot CLI documentation](https://docs.datarobot.com/en/docs/agentic-ai/cli/index.html). ## Prompting considerations Consider the following guidelines to get better results from the assistant: - Be specific : "Create a customer support agent that handles order inquiries and escalates billing issues" rather than "Create an agent". - Provide context : "I'm building a travel booking agent and need help with the flight search API integration" rather than "Help me with my code". - Ask for explanations : "Explain why this code isn't working and show me how to fix it" rather than "Fix this". - Iterate : "This is good, but can you make it handle edge cases like cancelled orders?" rather than accepting the first solution. --- # Configure evaluation and moderation URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-deploy/agentic-configure-evaluation-moderation.html > How to configure evaluation and moderation guardrails for a custom text generation model and agentic workflows in the workshop. # Configure evaluation and moderation > [!NOTE] Premium > Evaluation and moderation guardrails are a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable Moderation Guardrails ( Premium), Enable Global Models in the Model Registry ( Premium), Enable Additional Custom Model Output in Prediction Responses Evaluation and moderation guardrails help your organization block prompt injection and hateful, toxic, or inappropriate prompts and responses. It can also prevent hallucinations or low-confidence responses and, more generally, keep the model on topic. In addition, these guardrails can safeguard against the sharing of personally identifiable information (PII). Many evaluation and moderation guardrails connect a deployed text generation model (LLM) or agentic workflow to a deployed guard model. These guard models make predictions on LLM prompts and responses and then report these predictions and statistics to the central LLM or agentic workflow deployment. To use evaluation and moderation guardrails, first create and deploy guard models to make predictions on an LLM's prompts or responses; for example, a guard model could identify prompt injection or toxic responses. Then, when you create a custom model with the Text Generation or Agentic Workflow target type, define one or more evaluation and moderation guardrails. ## Select evaluation and moderation guardrails When you create a custom model with the Text Generation or Agentic Workflow target type, define one or more evaluation and moderation guardrails. To select and configure evaluation and moderation guardrails: 1. In theWorkshop, open theAssembletab of a custom model with theText GenerationorAgentic Workflowtarget type andassemble a model, eithermanually from a custom model you created outside of DataRobotorautomatically from a model built in a Use Case's LLM playground: When you assemble a text generation model with moderations, ensure you configure any requiredruntime parameters(for example, credentials) orresource settings(for example, public network access). Finally, set theBase environmentto a moderation-compatible environment; for example,[GenAI] Python 3.12 with Moderations: Resource settingsDataRobot recommends creating the LLM custom model using larger resource bundles with more memory and CPU resources. 2. After you've configured the custom model's required settings, navigate to theEvaluation and moderationsection and clickConfigure: 3. On theConfigure evaluation and moderationpanel, in theConfiguration summary, access the following settings: SettingDescriptionShow workflowReview how evaluations are executed in DataRobot. All evaluations and their respective moderations run in parallel.Moderation settingsSet the following:Set moderation timeout: Configure the maximum wait time (in seconds) for moderations before the system automatically times out.Timeout action: Define what happens if the moderation system times out:Score prompt / responseorBlock prompt / response.NeMo evaluator settingsSet theNeMo evaluator deploymentused by the NeMo Evaluator metrics. The dropdown shows "No options available" until you have created a NeMo evaluator workload and workload deployment via the Workload API. You must complete that step before you can configure the NeMo Evaluator metrics. 4. In theConfigure evaluation and moderationpanel, click one of the following metric cards to configure the required properties. The panel has two sections:All MetricsandNeMo metrics. From theConfiguration summarysidebar you can openShow workflow,Moderation settings, orNeMo evaluator settingsto configure the evaluator deployment used by all NeMo evaluator metrics. All MetricsNeMo metricsEvaluation metricRequiresDescriptionContent SafetyA deployed NIM modelllama-3.1-nemoguard-8b-content-safetyimported fromNVIDIA GPU Cloud (NGC) Catalog.Classify prompts and responses as safe or unsafe; return a list of any unsafe categories detected.CostLLM cost settingsCalculate the cost of generating the LLM response using the provided input cost-per-token, and output cost-per-token values. The cost calculation also includes the cost of citations. For more information, seeCost metric settings.Custom DeploymentCustom deploymentUse any deployment to evaluate and moderate your LLM (supported target types: regression, binary classification,multiclass, text generation).Emotions ClassifierEmotions Classifier deploymentClassify prompt or response text by emotion.FaithfulnessLLM, vector databaseMeasure if the LLM response matches the source to identify possible hallucinations.JailbreakA deployed NIM modelnemoguard-jailbreak-detectimported fromNVIDIA GPU Cloud (NGC) Catalog.Classify jailbreak attempts using NemoGuard JailbreakDetect.PII DetectionPresidio PII DetectionDetect Personally Identifiable Information (PII) in text using the Microsoft Presidio library.Prompt InjectionPrompt Injection ClassifierDetect input manipulations, such as overwriting or altering system prompts, intended to modify the model's output.Prompt tokensN/ATrack the number of tokens associated with the input to the LLM and/or retrieved text from the vector database.Response tokensN/ATrack the number of tokens associated with the output from the LLM and/or retrieved text from the vector database.ROUGE-1Vector databaseCalculate the similarity between the response generated from an LLM blueprint and the documents retrieved from the vector database.ToxicityToxicity ClassifierClassify content toxicity to apply moderation techniques, safeguarding against dissemination of harmful content.Agentic workflow metricsAgent Goal AccuracyLLMEvaluate agentic workflow performance in achieving specified objectives in scenarios without a known benchmark. (This agentic workflow metric is distinct from the NeMo Evaluator metric of the same name underNeMo metrics.)Task AdherenceLLMMeasure whether the agentic workflow response is relevant, complete, and aligned with user expectations.Guideline AdherenceLLM, guideline settingEvaluate how well the response follows the defined guideline using a judge LLM. Returnstruewhen the guideline is followed,falseotherwise. You must supply the guideline and select an LLM (from the gateway or a deployment) when configuring.Global models for evaluation metric deploymentsThe deployments required for PII detection, prompt injection detection, emotion classification, and toxicity classification are available asglobal models in RegistryMulticlass custom deployment metric limitsMulticlasscustom deployment metrics can have:Up to10classes defined in theMatcheslist for moderation criteria.Up to100class names in the guard model.TheNeMo Evaluator metrics(Agent Goal Accuracy, Context Relevance, Faithfulness, LLM Judge, Response Groundedness, Response Relevancy, Topic Adherence) require aNeMo evaluator workload deployment, set inNeMo evaluator settingsin the Configuration summary sidebar. Create the workload and workload deployment via the Workload API before you can select it; theSelect a workload deploymentdropdown shows "No options available" until a deployment exists. Each of these metrics also uses an LLM judge (DataRobot deployment or LLM gateway). Response Relevancy additionally requires an embedding deployment. Topic Adherence and LLM Judge have additional configuration.Stay on topic for inputsandStay on topic for outputdo notuse the NeMo evaluator deployment. They use aNIM deploymentof thellama-3.1-nemoguard-8b-topic-controlmodel (like Content safety and Jailbreak use NIM models). Configure them with LLM typeNIM, select the topic-control NIM deployment, and optionally edit the NeMo guardrails configuration files.Evaluator metricRequiresDescriptionAgent Goal AccuracyEvaluator deployment, LLMEvaluate how well the agent fulfills the user's query. This is distinct from the Agent Goal Accuracy metric underAll metrics(agentic workflow).Context RelevanceEvaluator deployment, LLMMeasure how relevant the provided context is to the response.FaithfulnessEvaluator deployment, LLMEvaluate whether the response stays faithful to the provided context using the NeMo Evaluator. This is distinct from the non-NeMo Faithfulness metric listed underAll metrics.LLM JudgeEvaluator deployment, LLMUse a judge LLM to evaluate a user defined metric.Response GroundednessEvaluator deployment, LLMEvaluate whether the response is grounded in the provided context.Response RelevancyEvaluator deployment, LLM, Embedding deploymentMeasure how relevant the response is to the user's query.Topic AdherenceEvaluator deployment, LLM, Metric mode, Reference topicsAssess whether the response adheres to the expected topics.Topic control metricsStay on topic for inputsNIM deployment ofllama-3.1-nemoguard-8b-topic-control, NVIDIA NeMo guardrails configurationUse NVIDIA NeMo Guardrails to provide topic boundaries, ensuring prompts are topic-relevant and do not use blocked terms.Stay on topic for outputNIM deployment ofllama-3.1-nemoguard-8b-topic-control, NVIDIA NeMo guardrails configurationUse NVIDIA NeMo Guardrails to provide topic boundaries, ensuring responses are topic-relevant and do not use blocked terms.To set theNeMo evaluator deploymentused by the NeMo Evaluator metrics, openNeMo evaluator settingsfrom the Configuration summary sidebar. The evaluator deployment will be applied to all NeMo Evaluator metrics. From theSelect a workload deploymentdropdown list, choose the workload deployment for the NeMo evaluator.NeMo evaluator settings panelThe dropdown shows "No options available" until you have created a NeMo evaluator workload and workload deployment via the Workload API. You must complete that step before you can configure the NeMo Evaluator metrics. 5. Depending on the metric selected above, configure the following fields: FieldDescriptionGeneral settingsNameEnter a unique name if adding multiple instances of the evaluation metric.Apply toSelect one or both ofPromptandResponse, depending on the evaluation metric. Note that when you selectPrompt, it's the user prompt, not the final LLM prompt, that is used for metric calculation. This field is only configurable for metrics that apply to both the prompt and the response.Custom Deployment, PII Detection, Prompt Injection, Emotions Classifier, and Toxicity settingsDeployment nameFor evaluation metrics calculated by a guard model, select the custom model deployment.Custom Deployment settingsInput column nameThis name is defined by the custom model creator. Forglobal models created by DataRobot, the default input column name istext. If the guard model for the custom deployment has themoderations.input_column_namekey valuedefined, this field is populated automatically.Output column nameThis name is defined by the custom model creator, and needs to refer to the target column for the model. The target name is listed on the deployment'sOverviewtab (and often has_PREDICTIONappended to it). You can confirm the column names byexporting and viewing the CSV data from the custom deployment. If the guard model for the custom deployment has themoderations.output_column_namekey valuedefined, this field is populated automatically.Guideline Adherence settingGuidelineThe rule or criteria the agent's response should follow. The selected LLM acts as a judge to evaluate whether the response adheres to this guideline and returnstrue(guideline followed) orfalse(guideline not followed). You must supply the guideline and select an LLM (from the gateway or a deployment) when configuring this metric.Faithfulness, Task Adherence, and Guideline Adherence settingsLLMSelect an LLM to evaluate the selected metric. For Faithfulness, once you select an LLM, you have the option of using your ownuser-providedcredentials instead of DataRobot-provided.NeMo Evaluator metric settingsSelect LLM as a judgeSelect an LLM to evaluate the selected metric.Evaluator deploymentFor the NeMo Evaluator metrics only: set in theNeMo evaluator settingssidebar panel (Select a workload deployment). The NeMo evaluator workload deployment is shared by those metrics. Create the workload and workload deployment via the Workload API before configuring; see the prerequisites above.Topic control settingsLLM TypeSelectAzure OpenAI,OpenAI, orNIM. For theAzure OpenAILLM type, additionally enter anOpenAI API deployment; forNIMenter aNIM deployment. If you use the LLM gateway, the default experience, DataRobot-supplied credentials are provided. When LLM type isAzure OpenAIorOpenAI, clickChange credentialsto provide your own authentication.FilesFor theStay on topicevaluations, next to a file, clickto modify the NeMo guardrails configuration files. In particular, updateprompts.ymlwith allowed and blocked topics andblocked_terms.txtwith the blocked terms, providing rules for NeMo guardrails to enforce. Theblocked_terms.txtfile is shared between the input and output topic control metrics; therefore, modifyingblocked_terms.txtin the input metric modifies it for the output metric and vice versa. Only two topic control metrics can exist in a custom model, one for input and one for output.Moderation settingsConfigure and apply moderationEnable this setting to expand theModerationsection and define the criteria that determine when moderation logic is applied. Cost metric settingsFor theCostmetric, define theInputandOutputcost incurrency amount / tokens amountformat, then clickAdd:TheCostmetric doesn't include theModerationsection toConfigure and apply moderation. 6. In theModerationsection, withConfigure and apply moderationenabled, for each evaluation metric, set the following: SettingDescriptionModeration criteriaIf applicable, set the threshold settings evaluated to trigger moderation logic. For numeric metrics (int or float), you can useless than,greater than, orequals towith a value of your choice. For binary metrics (for example, Agent Goal Accuracy), useequals to0 or 1. For the Emotions Classifier, selectMatchesorDoes not matchand define a list of classes (emotions) to trigger moderation logic.Moderation methodSelectReport,Report and block, orReplace(if applicable).Moderation messageIf you selectReport and block, you can optionally modify the default message. 7. After configuring the required fields, clickAddto save the evaluation and return to the evaluation selection page. Then, select and configure another metric, or clickSave configuration. The guardrails you selected appear in theEvaluation and moderationsection of theAssembletab. After you add guardrails to a text generation custom model, you can [test](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-test-custom-model.html), [register](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-register-cus-models.html), and [deploy](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-deploy-models.html) the model to make predictions in production. After making [predictions](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/index.html), you can view the evaluation metrics on the [Custom metrics](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-custom-metrics.html) tab and prompts, responses, and feedback (if configured) on the [Data exploration](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-data-exploration.html) tab. > [!NOTE] Tracing tab > When you add moderations to an LLM deployment, you can't view custom metric data by row on the [Data exploration > Tracing](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-data-exploration.html) tab. ### Change credentials DataRobot provides credentials for [available LLMs](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html) using the LLM gateway. With certain metrics and LLMs or LLM types, you can instead use your own credentials for authentication. Before proceeding, define user-specified credentials on the [credentials management](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management) page. #### Topic control metrics To change credentials for either Stay on topic for inputs or Stay on topic for output, choose the LLM type and click Change credentials. **LLM type: Azure OpenAI:** Provide the Azure OpenAI API deployment and the OpenAI API base URL. Then, from the dropdown, select the set of credentials to apply. [https://docs.datarobot.com/en/docs/images/change-metric-creds-azure.png](https://docs.datarobot.com/en/docs/images/change-metric-creds-azure.png) **LLM type: OpenAI:** From the dropdown, select the set of credentials to apply. [https://docs.datarobot.com/en/docs/images/change-metric-creds-openai.png](https://docs.datarobot.com/en/docs/images/change-metric-creds-openai.png) **LLM type: NIM:** Select the NIM deployment (for example, the topic-control model). Credentials are typically provided via the deployment configuration. To revert to DataRobot-provided credentials, click Revert credentials. #### Faithfulness metric To change credentials for Faithfulness, select the LLM and click Change credentials. The following table lists the required fields: | Provider | Fields | | --- | --- | | Amazon | AWS account (credentials)AWS region | | Azure OpenAI | OpenAI API deploymentOpenAI API base URLCredentials | | Google | Service account (credentials)Google region | | OpenAI | Credentials | To revert to DataRobot-provided credentials, click Revert credentials. ### Considerations for NeMo Evaluator metrics When using NeMo Evaluator metrics, consider the following: - LLM judge output: The NeMo evaluator expects the LLM judge to return data in the correct JSON schema. Some models (for example, certain Llama versions) may return Python code or other formats instead, which can cause the evaluator to fail. Choose an LLM judge that reliably returns the expected format; newer models are often better at following JSON output instructions. - Rate and token limits: Be aware of rate limits and token limits when using NeMo Evaluator guards; hitting these limits can cause evaluation failures. You can use the [Activity log > Moderation](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-moderation.html) tab and evaluator logs to debug why a request was blocked or why a guard failed. ### Global models for evaluation metric deployments The deployments required for PII detection, prompt injection detection, emotion classification, and toxicity classification are available as [global models in Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-global-models.html). The following global models are available: | Model | Type | Target | Description | | --- | --- | --- | --- | | Prompt Injection Classifier | Binary | injection | Classifies text as prompt injection or legitimate. This guard model requires one column named text, containing the text to classify. For more information, see the deberta-v3-base-injection model details. | | Toxicity Classifier | Binary | toxicity | Classifies text as toxic or non-toxic. This guard model requires one column named text, containing the text to classify. For more information, see the toxic-comment-model details. | | Sentiment Classifier | Binary | sentiment | Classifies text sentiment as positive or negative. This model requires one column named text, containing the text to classify. For more information, see the distilbert-base-uncased-finetuned-sst-2-english model details. | | Emotions Classifier | Multiclass | target | Classifies text by emotion. This is a multilabel model, meaning that multiple emotions can be applied to the text. This model requires one column named text, containing the text to classify. For more information, see the roberta-base-go_emotions-onnx model details. | | Refusal Score | Regression | target | Outputs a maximum similarity score, comparing the input to a list of cases where an LLM has refused to answer a query because the prompt is outside the limits of what the model is configured to answer. | | Presidio PII Detection | Binary | contains_pii | Detects and replaces Personally Identifiable Information (PII) in text. This guard model requires one column named text, containing the text to be classified. The types of PII to detect can optionally be specified in a column, 'entities', as a comma-separated string. If this column is not specified, all supported entities will be detected. Entity types can be found in the PII entities supported by Presidio documentation. In addition to the detection result, the model returns an anonymized_text column, containing an updated version of the input with detected PII replaced with placeholders. For more information, see the Presidio: Data Protection and De-identification SDK documentation. | | Zero-shot Classifier | Binary | target | Performs zero-shot classification on text with user-specified labels. This model requires classified text in a column named text and class labels as a comma-separated string in a column named labels. It expects the same set of labels for all rows; therefore, the labels provided in the first row are used. For more information, see the deberta-v3-large-zeroshot-v1 model details. | | Python Dummy Binary Classification | Binary | target | Always yields 0.75 for the positive class. For more information, see the python3_dummy_binary model template. | ## View evaluation and moderation guardrails When a text generation model with guardrails is registered and deployed, you can view the configured guardrails on the [registered model'sOverview](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-view-manage-reg-models.html#view-version-details) tab and the [deployment'sOverview](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-overview/nxt-overview.html) tab: **Registry:** [https://docs.datarobot.com/en/docs/images/nxt-evaluation-moderation-reg-overview.png](https://docs.datarobot.com/en/docs/images/nxt-evaluation-moderation-reg-overview.png) **Console:** [https://docs.datarobot.com/en/docs/images/nxt-evaluation-moderation-deploy-overview.png](https://docs.datarobot.com/en/docs/images/nxt-evaluation-moderation-deploy-overview.png) > [!NOTE] Evaluation and moderation logs > On the [Activity log > Moderation](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-moderation.html) tab of a deployed LLM with evaluation and moderation configured, you can view a history of evaluation and moderation-related events for the deployment to diagnose issues with a deployment's configured evaluations and moderations. --- # Deploy workflows URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-deploy/agentic-workflow-deploy.html > After an agentic workflow is registered and ready for deployment, you can deploy it to Console and configure deployment settings for monitoring and tracing. # Deploy workflows Once the agentic workflow is registered (listed in the Models tile in Registry), you can deploy it to Console as you would any other model type, providing access to DataRobot monitoring capabilities. To deploy an agentic workflow from Registry: 1. On theRegistry > Modelspage, if the registered agentic workflow isn't already open, click theAgentic workflowtab and locate the workflow to deploy. 2. Click the agentic workflow to open it. If it has theReady for deploymentstatus badge, clickDeploy. 3. Configure the deployment settingsfor the agentic workflow. In particular, review the following sections: Setting configuration post-deploymentYou can configure these settings after the workflow is deployed; however, some settings, like runtime parameters, require temporary deactivation of the deployment. 4. After configuring the deployment settings, clickDeploy model. TheCreating deploymentdialog box appears. Wait for deployment creation, or clickReturn to deploymentsto open Console. --- # Register workflows URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-deploy/agentic-workflow-register.html > After you've connected an agentic workflow custom model to an agentic playground, compared and tested the workflow, and made any required modifications to the workflow, you can register the production-ready agentic workflow for deployment to Console. # Register workflows After experimenting in the agentic playground to build a production-ready agentic workflow, register the custom agentic workflow in the Registry workshop, in preparation for deployment to Console. ## Register from an agentic playground To begin registering an agentic workflow from an agentic playground, find the register option in the comparison chat, or a single-agent chat: **Comparison chat:** Click the actions menu for the agentic workflow you want to register, then click Register agentic workflow. [https://docs.datarobot.com/en/docs/images/agentic-workflow-register-1.png](https://docs.datarobot.com/en/docs/images/agentic-workflow-register-1.png) > [!TIP] Actions menu locations > The actions menu is always available in the Workflows panel for connected agentic workflows. In addition, when a workflow is selected for comparison, the actions menu is available next to the agentic workflow name in the comparison chat area. **Single-agent chat:** Click Register agentic workflow in the upper-right corner of the single-agent chat window. [https://docs.datarobot.com/en/docs/images/agentic-workflow-register-2.png](https://docs.datarobot.com/en/docs/images/agentic-workflow-register-2.png) The agentic workflow opens in the Registry Workshop page. Proceed to [Register from Workshop](https://docs.datarobot.com/en/docs/agentic-ai/agentic-deploy/agentic-workflow-register.html#register-from-workshop). ## Register from Workshop On the Registry > Workshop page, in an open agentic workflow: 1. On theAssembletab, ensure the agentic workflow is fully assembled by reviewing the following sections: LLM gateway access runtime parameter requirementTo use theLLM gatewayfor an agentic workflow, theENABLE_LLM_GATEWAY_INFERENCEruntime parameter must be provided in themodel-metadata.yamlfile and set totrue. 2. (Optional) ClickTest workflowto provide a test dataset andtest the agentic workflow response through the chat and/or score hooks. 3. After the workflow is fully assembled, clickRegister a workflowto open theRegister a workflowpage. 4. UnderConfigure the workflow, theTargetis set based on the workflow you're registering and theTarget typeis set toAgentic Workflow. Select one of the following registration options: 5. ClickRegister a workflow. The agentic workflow version opens on theRegistry > Modelspage with aBuildingstatus. ## Register from the Models page On the Registry > Models page, to register a fully configured agentic workflow: 1. Click theAgentic workflowtab, to filter theModelspage on agentic workflows. 2. Click+ Register a workflow(or thebutton when the registered model or version info panel is open): TheRegister a modelpanel opens to theExternal modeltab. 3. Click theCustom modeltab and then, underConfigure the model, select one of the following options: Then, configure the following fields: FieldDescriptionCustom modelSelect the custom workflow you want to register from theworkshop.Custom model versionSelect the version of the custom workflow to register.Registered model name / Registered modelDo one of the following:Registered model name:When registering a new model, enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, a warning appears.Registered model:When saving as a version of an existing model, select the existing registered model you want to add a new version to.Registered version nameAutomatically populated with the model name, date, and time. Change or modify the name as necessary.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister as a new model.Optional settingsRegistered version descriptionEnter a description of the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add tagand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags added when registering a new model are applied toV1. NoteIf you clickCancelon this page to return to theRegistry, you lose the configuration progress on this page. 4. ClickRegister model. The agentic workflow version opens on theRegistry > Modelspage with aBuildingstatus. ## Custom agentic workflow build troubleshooting If the custom workflow build completes with a Build failed status, you can troubleshoot the failure using the model logs. To access the model logs, in the Insight computation failed warning, click Open the workshop: The Workshop opens to the Versions tab for the custom workflow you registered, with the version panel open to the Insights section. Next to Status, find Logs, then click Model logs to open the model logs console: In the Console Log: Model logs modal, review the timestamped log entries: | | Information | Description | | --- | --- | --- | | (1) | Date / time | The date and time the model log event was recorded. | | (2) | Status | The status the log entry reports: INFO: Reports a successful operation.ERROR: Reports an unsuccessful operation. | | (3) | Message | The description of the successful operation (INFO), or the reason for the failed operation (ERROR). This information can help you troubleshoot the root cause of the error. | > [!NOTE] Model logs consideration > In the Registry, a model package's Model logs only report the operations of the underlying model, not the model package operations (e.g., model package deployment time). If you can't locate the log entry for the error you need to fix, it may be an older log entry not shown in the current view. Click Load older logs to expand the Model logs view. > [!TIP] View older logs > Look for the older log entries at the top of the Model logs; they are added to the top of the existing log history. --- # Deploy URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-deploy/index.html > Register and deploy agentic workflows, and configure evaluation and moderation guardrails. # Deploy > [!NOTE] Premium > DataRobot's Agentic AI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. Register and deploy agentic workflows, and configure evaluation and moderation guardrails. | Topic | Description | | --- | --- | | Register workflows | Register production-ready agentic workflows for deployment to Console. | | Configure evaluation and moderation | Configure evaluation and moderation guardrails for agentic and RAG workflows. | | Deploy workflows | Deploy agentic workflows to Console. | --- # Agent authentication URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-authentication.html > Learn how to implement authentication in DataRobot Agent Templates, covering API tokens, authorization context, OAuth 2.0, and security best practices. # Agent authentication AI agents often need to authenticate to external resources to complete tasks. For example, a deployed agent might need to access external APIs, databases, or cloud services to retrieve data or perform operations. This documentation provides comprehensive guidance for implementing authentication in DataRobot Agent Templates, covering API tokens, MCP server authentication, authorization context, OAuth 2.0, and security best practices. ## Authentication methods This section provides an overview of the different authentication methods available in the framework: | Method | Description | Use Case | | --- | --- | --- | | API token authentication | Simple token-based authentication | External APIs, DataRobot services. | | MCP server authentication | Automatic token-based authentication for MCP servers | MCP tool integration. | | OAuth 2.0 | Standard OAuth for external services | Third-party integrations. | ## API token authentication API token authentication is the most common method for authenticating with DataRobot services and external APIs. It uses bearer tokens passed in headers or environment variables. ### DataRobot API authentication Configure API tokens using environment variables or programmatically. The `DATAROBOT_API_TOKEN` is available in the [API keys and tools section of your account settings](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html). **Environment variables (recommended):** The agent templates automatically use `DATAROBOT_API_TOKEN` and `DATAROBOT_ENDPOINT` environment variables when they are set. This is the recommended approach as it keeps credentials out of your code and works seamlessly with `MyAgent` initialization. The `MyAgent` class automatically falls back to these environment variables if credentials aren't provided explicitly. When using the optional `ToolClient` class for external tool integration, it also uses these environment variables by default. MCP servers also use `DATAROBOT_API_TOKEN` automatically for authentication. ``` DATAROBOT_API_TOKEN= DATAROBOT_ENDPOINT=https://app.datarobot.com ``` **Programmatic:** You can pass API credentials directly when initializing the `MyAgent` class. This is useful when you need to override environment variables or use different credentials for specific instances. The `MyAgent` class will still fall back to environment variables if parameters are not provided. ``` from agent import MyAgent agent = MyAgent( api_key="your_api_key", api_base="https://app.datarobot.com" ) ``` > [!NOTE] ToolClient and MCP tools for external tools > If you're integrating external tools using the `ToolClient` class from the `datarobot-genai` package, you can also pass credentials programmatically. For MCP server-based tool integration, authentication is handled automatically via `DATAROBOT_API_TOKEN`. See the [tool integration documentation](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tools-integrate.html) and [MCP tools documentation](https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-tools-mcp.html) for details. ### External API authentication When creating custom tools that need to authenticate with external APIs, retrieve API keys from environment variables or runtime parameters within your tool's `run()` method. This keeps credentials out of your source code and follows the same security pattern used throughout the agent templates. For local development, use environment variables. For deployed agents, define runtime parameters in `model-metadata.yaml` and retrieve them using `RuntimeParameters.get()` from `datarobot_drum`. Try `os.environ.get()` first (for local development), then fall back to `RuntimeParameters.get()` for deployed agents., as shown in the example below: ``` # custom-tool-example.py import os import requests from crewai.tools import BaseTool from datarobot_drum import RuntimeParameters class ExternalAPITool(BaseTool): def run(self, query: str) -> str: api_key = os.environ.get("EXTERNAL_API_KEY") if not api_key: api_key = RuntimeParameters.get("EXTERNAL_API_KEY")["apiToken"] headers = {"Authorization": f"Bearer {api_key}"} response = requests.get( "https://api.external-service.com/data", headers=headers, params={"query": query} ) return response.json() ``` > [!NOTE] Define runtime parameters for deployment > To use runtime parameters in deployed agents, define them in your `model-metadata.yaml` file. For credential-type parameters (like API keys), use `type: credential`: > > ``` > runtimeParameterDefinitions: > - fieldName: EXTERNAL_API_KEY > type: credential > credentialType: api_token > description: API key for external service authentication > ``` > > See the [runtime parameters documentation](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html) for more details on configuration options. ### MCP server authentication When using MCP (Model Context Protocol) servers to provide tools to your agents, authentication is handled automatically using the `DATAROBOT_API_TOKEN` environment variable. The MCP client in the agent templates automatically uses this token for authenticated requests to the MCP server. Configuration: ``` # .env DATAROBOT_API_TOKEN= DATAROBOT_ENDPOINT=https://app.datarobot.com ``` The MCP client will automatically use `DATAROBOT_API_TOKEN` when making requests to the MCP server. No additional configuration is required beyond setting these environment variables. > [!NOTE] MCP vs. ToolClient authentication > MCP tools use `DATAROBOT_API_TOKEN` directly for server authentication, while `ToolClient` (used for direct tool deployments) can use both API tokens and authorization context. For more information about MCP tool integration, see [Integrate tools using an MCP server](https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-tools-mcp.html). ## Authorization context The framework provides an authorization context system for propagating authentication information to downstream tools and services. This allows tokens and credentials to be automatically passed between agent tools without manual configuration. Initialize authorization context in your agent's `chat()` function using `resolve_authorization_context()` from the `datarobot-genai` package. The function returns the authorization context dictionary, which should be assigned to `completion_create_params["authorization_context"]`. Tools can then retrieve the context using `get_authorization_context()` from the `datarobot` SDK: ``` # custom.py from datarobot_genai.core.chat import resolve_authorization_context from datarobot.models.genai.agent.auth import get_authorization_context def chat(completion_create_params, load_model_result, **kwargs): # Initialize the authorization context for downstream agents and tools completion_create_params["authorization_context"] = resolve_authorization_context( completion_create_params, **kwargs ) # ... rest of chat function # In your tools auth_context = get_authorization_context() access_token = auth_context.get("access_token") ``` > [!NOTE] ToolClient and MCP tools with authorization context > When using the optional `ToolClient` class from the `datarobot-genai` package for external tool integration, it automatically propagates authorization context when calling agent tools. MCP tools use `DATAROBOT_API_TOKEN` directly for authentication and don't require authorization context propagation. See the [tool integration documentation](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tools-integrate.html) and [MCP tools documentation](https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-tools-mcp.html) for details. ## OAuth 2.0 authentication For external services that support OAuth 2.0, implement OAuth flows in your tools. Create an OAuth application in the service's developer console and set environment variables: ``` # .env OAUTH_CLIENT_ID= OAUTH_CLIENT_SECRET= OAUTH_REDIRECT_URI= OAUTH_SCOPE= ``` Then, use those environment variables in the OAuth implementation. This example demonstrates a complete OAuth 2.0 authorization code flow for a CrewAI tool. The tool inherits from `crewai.tools.BaseTool` (the standard base class for tools in CrewAI agent templates), manages its own token lifecycle with in-memory caching, and follows the repository's pattern of retrieving credentials from environment variables. > [!NOTE] Example implementation > Note that this is a CrewAI-specific framework example illustrating a basic OAuth authentication pattern. The `run()` method makes a placeholder API call to demonstrate token usage, but you'll need to implement actual tool logic based on your specific use case. ``` # custom-tool-example.py import os import time import requests from urllib.parse import urlencode from crewai.tools import BaseTool class ExampleToolWithOAuth(BaseTool): def __init__(self): super().__init__() self.client_id = os.getenv("OAUTH_CLIENT_ID") self.client_secret = os.getenv("OAUTH_CLIENT_SECRET") self.redirect_uri = os.getenv("OAUTH_REDIRECT_URI") self._access_token = None self._token_expires_at = None def get_authorization_url(self) -> str: params = { "client_id": self.client_id, "redirect_uri": self.redirect_uri, "response_type": "code", "scope": "read:data" } return f"https://oauth.provider.com/authorize?{urlencode(params)}" def exchange_code_for_token(self, code: str) -> dict: response = requests.post( "https://oauth.provider.com/token", data={ "grant_type": "authorization_code", "client_id": self.client_id, "client_secret": self.client_secret, "code": code, "redirect_uri": self.redirect_uri } ) token_data = response.json() self._access_token = token_data.get("access_token") expires_in = token_data.get("expires_in", 3600) self._token_expires_at = time.time() + expires_in return token_data def get_cached_access_token(self) -> str: if self._access_token and self._token_expires_at and time.time() < self._token_expires_at: return self._access_token # Token expired or not set - refresh or re-authenticate # In production, implement token refresh logic here raise ValueError("Access token expired. Re-authenticate to get a new token.") def run(self, query: str) -> str: access_token = self.get_cached_access_token() response = requests.get( "https://api.provider.com/data", headers={"Authorization": f"Bearer {access_token}"}, params={"query": query} ) return response.json() ``` ## Security best practices Following security best practices is essential when handling authentication in production environments. Adhere to the following guidelines: - API token authentication - Store tokens in environment variables; never hard code secrets in source code. - Use secure token storage solutions in production environments. - Implement token rotation and enforce token expiration policies. - Validate authorization context before using it in tools. - Follow least-privilege access principles. - Log authentication events for audit purposes. - OAuth 2.0 - Use HTTPS for all OAuth communications. - Validate the state parameter to prevent Cross-Site Request Forgery (CSRF) attacks. - Store refresh tokens securely. - Handle token expiration and refresh logic reliably. - Validate authorization context before use. - General Security - Use separate environments for development and production. - Implement robust secret management practices. - Follow container security best practices. - Conduct regular security audits and apply updates. ## Troubleshooting authentication issues This section helps you diagnose and resolve common authentication problems when developing or deploying agents. ### Common issues The following sections describe common authentication errors and how to resolve them: #### Missing API token This error occurs when the DataRobot API token is not configured. Issue: `Error: Missing DataRobot API token. Set the DATAROBOT_API_TOKEN environment variable` Solution: Set the `DATAROBOT_API_TOKEN` environment variable to use your [DataRobot API key](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html). #### Invalid endpoint This error occurs when the DataRobot endpoint is missing or incorrectly configured. Issue: `Error: Missing DataRobot endpoint. Set the DATAROBOT_ENDPOINT environment variable` Solution: Set the correct `DATAROBOT_ENDPOINT` environment variable. #### Authorization context not set This error occurs when tools try to access the authorization context before it has been initialized. Issue: `Error: Authorization context not available for tool` Solution: Ensure `resolve_authorization_context()` is called in your agent's `chat()` function and the result is assigned to `completion_create_params["authorization_context"]`. #### OAuth token expired This error occurs when an OAuth access token has expired and needs to be refreshed. Issue: `Error: 401 Unauthorized` Solution: Implement token refresh logic or re-authenticate. #### MCP server authentication failure This error occurs when the MCP server cannot authenticate the agent's requests. Issue: `Error: 401 Unauthorized` or `MCP connection failures` Solutions: 1. Verify API token : Ensure DATAROBOT_API_TOKEN is set correctly in your environment 2. Check token permissions : Verify the token has necessary permissions for MCP server access 3. Verify MCP server endpoint : Check that the MCP server endpoint is correct 4. For local development : Ensure the MCP server is running and accessible 5. Review MCP configuration : See MCP tools troubleshooting for more details ### Debugging tips Useful techniques for debugging authentication issues: #### Enable verbose logging Enable verbose logging to get detailed information about authentication operations. To do so, search for where `MyAgent` is instantiated in `custom.py` and set `verbose=True`. ``` # custom.py from agent import MyAgent # ... agent = MyAgent(verbose=True, **completion_create_params) ``` #### Check environment variables Verify that required environment variables are set correctly. ``` import os print(f"API Token: {os.getenv('DATAROBOT_API_TOKEN')}") print(f"Endpoint: {os.getenv('DATAROBOT_ENDPOINT')}") ``` #### Test authentication Test authentication using the `AgentEnvironment` class from `datarobot_genai.core.cli`: ``` from datarobot_genai.core.cli import AgentEnvironment try: env = AgentEnvironment() print("Authentication successful") except ValueError as e: print(f"Authentication failed: {e}") ``` #### Validate authorization context Check authorization context using `get_authorization_context()` from `datarobot.models.genai.agent.auth`: ``` # custom-tool-example.py from datarobot.models.genai.agent.auth import get_authorization_context auth_context = get_authorization_context() print(f"Authorization context: {auth_context}") ``` --- # Debugging agents (PyCharm) URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-debugging-pycharm.html > Use PyCharm's built-in Run Agent configuration to debug agent code locally during development. # Debugging agents in PyCharm Debugging agent code is essential for understanding execution flow, inspecting variables, and troubleshooting issues during development. After you clone the repository, PyCharm will automatically have a Run Agent run/debug configuration that points to the `dev.py` script in your agent directory. This script starts the agent development server and gives you an immediate way to run or debug your agent without wiring up a remote debug server. This guide walks you through configuring PyCharm to use your agent's virtual environment, running the autogenerated `Run Agent` configuration, and attaching the debugger while you execute prompts with the CLI. ## Prerequisites Before setting up PyCharm debugging, ensure you have: - PyCharm Professional : Required for full debugging support - Agent application setup : Completed the installation and run dr start to prepare your local environment - Environment configured : Your .env file is properly configured with DATAROBOT_API_TOKEN and DATAROBOT_ENDPOINT ## Configure Python interpreter Before you can debug your agent, you need to configure PyCharm to use the correct Python interpreter for your agent environment. > [!NOTE] Run dr start first > You must run `dr start` before configuring the Python interpreter. This command initializes your workspace and sets up your local virtual environment. ### Set up the Python interpreter 1. Open your agent templates repository in PyCharm. 2. Navigate to PyCharm > Settings . 3. Go to Python > Interpreter . 4. Click the Python Interpreter dropdown (it may showNo interpreter) and selectAdd Interpreter > Add Local Interpreter. 5. In theAdd Python Interpreterdialog, selectSelect existingand choosePythonas the type. Select your agent's virtual environment Python executable in the.venvpath of your agent framework. (The virtual environment is created byuvwhen you rundr startortask install). The example workflow in theDataRobot Agentic Starter repositorycreates two agents:Planner Agent(content planning) andWriter Agent(content writing). For example, the interpreter path for your agent directory should look like: $workspace/agent/.venv/bin/python PyCharm will use this interpreter for running and debugging your agent code, ensuring all dependencies are correctly resolved. ## Use the Run Agent configuration The Run Agent configuration launches the agent development server with your environment variables and interpreter already wired up. 1. Open Run > Edit Configurations to review the configurations for Run Agent . 2. Ensure the configuration points to your agent's dev.py script, sets the working directory to the agent folder, and references your .env file. No additional parameters are required. 3. SelectRun Agentfrom the configuration dropdown and clickRun. PyCharm starts the development server and shows its console output (for example,Running development server on http://127.0.0.1:8842). > [!TIP] Need to recreate the configuration? > If the configuration is missing or out-of-date, create a new Python run configuration with the settings seen in the screenshot. ## Trigger agent execution from the CLI With the development server running in PyCharm, you can execute agent prompts from a terminal window. 1. Open a terminal in your agent application repository root. 2. Run the CLI command for your agent: taskagent:cli--execute--user_prompt"Artificial Intelligence" 3. Watch the PyCharm console for log output as the agent processes the request. Adjust the prompt text or CLI arguments to match your scenario. ## Debug with breakpoints The same Run Agent configuration supports PyCharm's debugger. 1. Set breakpoints in the files you want to inspect (for example, agent/agent/myagent.py ). 2. In PyCharm, choose theRun Agentconfiguration and click theDebugicon (or pressShift+F9). PyCharm launchesdev.pyin debug mode and waits for incoming work. 3. Re-run your CLI command. When execution reaches your breakpoint, PyCharm pauses and opens theDebugtool window so you can inspect state, step through code, and evaluate expressions. ## Use debug features Once execution stops on a breakpoint, you can use PyCharm's debugging capabilities: ### Inspect variables - The Threads & Variables pane shows all variables in the current scope. - Expand objects to see their properties and values. - Right-click variables to add them to Watches for persistent monitoring. ### Evaluate expressions - Click the Evaluate Expression button in the debug toolbar (calculator icon) or right-click in the current context and select Evaluate Expression . - Enter any Python expression to evaluate it in the current context (for example, os.environ ). - Useful for testing conditions, examining complex objects, or calling methods. ### View call stack - The Frames pane shows the call stack, displaying how execution reached the current point. - Click different frames to see variables and code at each level. ## Common issues ### Run Agent configuration is missing Issue: PyCharm does not show the Run Agent configuration. Solution: - Check that your project files (such as .idea/runConfigurations/Run Agent.run.xml ) are not excluded from your VCS or workspace. - Restart PyCharm to force it to reload project metadata. - Re-clone the repository. ### CLI command finishes without hitting breakpoints Issue: The agent finishes processing and never pauses where you expect. Solution: - Make sure you launched the Run Agent configuration in Debug mode, not regular Run. - Verify your breakpoint is active (solid red) and located in code that executes for the selected prompt. - Re-run the CLI command after PyCharm shows that the debugger is listening. ### Environment variables missing Issue: The agent fails due to missing credentials or configuration. Solution: - Confirm your .env file exists at the repository root and includes DATAROBOT_API_TOKEN and DATAROBOT_ENDPOINT . - In the Run Agent configuration, ensure the Paths to ".env" files field points to the correct file. ### Wrong Python version Issue: PyCharm shows "Python version mismatch" errors or modules are missing. Solution: - Confirm you've configured the correct Python interpreter (the one in your agent's virtual environment). - If you recently recreated the environment, click the interpreter dropdown and re-select the .venv Python executable. ### Breakpoints not working Issue: Execution doesn't stop at breakpoints even in Debug mode. Solution: - Confirm the debugger console shows the development server restarted after you clicked Debug. If not, stop and start the configuration again. - Check that you reran the CLI command after entering debug mode—the development server handles one request at a time. - Ensure PyCharm mapped your project correctly. If you're using multiple agent templates, verify you're editing the same copy of the file that the development server runs. --- # Debugging agents (VS Code) URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-debugging-vscode.html > Use VS Code's Run and Debug experience to execute dev.py and inspect agent code locally. # Debugging agents in VS Code VS Code can run the same `dev.py` script that powers the agent development server, so you can step through requests, inspect variables, and replay prompts without wiring up a remote debugger. After cloning the repository, you only need to point VS Code at your agent's virtual environment, create a launch configuration that targets `dev.py`, and execute prompts from the CLI while the debugger listens. This guide mirrors the PyCharm workflow and walks you through preparing VS Code, launching the debugger, and troubleshooting common issues. ## Prerequisites Before configuring VS Code debugging, ensure you have: - VS Code + Python extension : Install the official Microsoft Python extension for debugging support, and see the documentation if you need setup guidance. - Agent application setup : Complete the installation and run dr start to create the agent virtual environment. - Environment variables : Configure .env with DATAROBOT_API_TOKEN , DATAROBOT_ENDPOINT , and any tools-specific variables. - CLI access : You can run task :cli commands from a terminal to trigger executions while the debugger runs. ## Configure Python interpreter > [!NOTE] Run dr start first > `dr start` provisions dependencies and the `.venv` interpreter. Run it before selecting the interpreter in VS Code. 1. Open your agent application folder (for example, datarobot-agent-application ) in VS Code. 2. Press Command+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) to open the Command Palette. 3. Run Python: Select Interpreter . 4. Choose the interpreter that lives inside your agent's.venvpath. For theagentdirectory, the entry typically looks like: $workspace/agent/.venv/bin/python VS Code uses this interpreter for linting, the integrated terminal, and the debugger so that the environment matches what `task` created. ## Configure the Run Agent launch configuration The VS Code debugger uses `.vscode/launch.json`. You can reuse the same layout as the PyCharm guide by creating a Run Agent configuration that points to `dev.py`. 1. Open the Run and Debug view (sidebar play icon) and click create a launch.json file . 2. Select Python Debugger as the debugger. 3. Select Python File as the debug configuration. 4. Replace the autogenerated .vscode/launch.json configuration with something similar to the snippet below, adjusting the paths to your agent: ``` { "version": "0.2.0", "configurations": [ { "name": "Run Agent", "type": "python", "request": "launch", "program": "${workspaceFolder}/agent/dev.py", "cwd": "${workspaceFolder}/agent", "envFile": "${workspaceFolder}/.env", "console": "integratedTerminal", "python": "${workspaceFolder}/agent/.venv/bin/python", "justMyCode": true } ] } ``` - program : Points to your agent's dev.py . - cwd : Ensures relative imports and file paths resolve from the agent directory. - envFile : Loads the same .env settings the CLI uses. - python : Forces the debugger to use the .venv interpreter you selected earlier. If your workspace contains multiple agents, duplicate the configuration—with different names and paths—for each agent you debug regularly. ## Run the development server To start up your development server: 1. Open the Run Agent configuration in the Run and Debug view. 2. Click Run (or press F5) to start the development server under the debugger. 3. Watch the Debug Console or Terminal for the startup message (for example, `Running development server on http://localhost:8842`). VS Code now listens for requests and will pause if you set breakpoints. ## Trigger agent execution from the CLI With the development server listening, you can send prompts from any terminal session. For an example CLI call: ``` task agent:cli -- execute --user_prompt "Artificial Intelligence" ``` Every CLI request flows through the VS Code debugger session, so you can repeat the same prompt or adjust arguments until you isolate the issue. ## Debug with breakpoints To set up debugging with breakpoints: 1. Set breakpoints in files such as `agent/agent/myagent.py`. 2. Click Run > Start Debugging (or press F5) to make sure the debugger is active. 3. Re-run your CLI command. When execution hits a breakpoint, VS Code pauses and highlights the line. 4. Use the debug toolbar to step over, step into, or continue execution. ## Use VS Code debug tools The following sections describe the various tools supported for VS Code debugging. ### Inspect variables - The Variables pane shows locals, globals, and environment data for the paused frame. - Right-click variables to Add to Watch for persistent tracking across frames. ### Evaluate expressions - Use the Debug Console to run Python expressions in the paused context (for example, import os then os.environ["DATAROBOT_ENDPOINT"] ). - Add expressions to Watch when you need to monitor them while stepping through code. ### Review the call stack - The Call Stack view shows every frame leading to the breakpoint. - Selecting a frame updates the editor, Variables pane, and Debug Console context so you can inspect earlier calls. ## Common issues ### Launch configuration missing Issue: The Run Agent option does not appear. Solution: - Confirm .vscode/launch.json exists inside your workspace and contains the configuration. - Use the Command Palette option Python Debugger: Debug using launch.json to regenerate the file if it was deleted. ### Wrong interpreter Issue: VS Code cannot import packages or shows Python version errors. Solution: - Run Python: Select Interpreter and choose the .venv interpreter created by dr start . - If you rebuilt the environment, reload the window ( Developer: Reload Window ) and reselect the interpreter. - Confirm the python entry in .vscode/launch.json points to the same .venv/bin/python path you selected so the debugger starts with the correct runtime. ### Environment variables not loading Issue: The agent fails because credentials are missing during debugging. Solution: - Confirm the .env file exists at the repository root and contains DATAROBOT_API_TOKEN and DATAROBOT_ENDPOINT . - Verify the envFile entry in launch.json matches the .env path. ### CLI finishes without hitting breakpoints Issue: The CLI command completes but the debugger never pauses where expected. Solution: - Make sure the debugger is running (green bar in VS Code) before firing the CLI command. - Verify the breakpoint is solid red (not hollow) and located in code that executes for the chosen prompt. - Rerun the CLI command after restarting the debugger; the dev server handles one request at a time. ### Debugger not attaching Issue: VS Code launches but never shows the server banner or stops at breakpoints. Solution: - Stop the session and start it again to ensure dev.py restarted cleanly. - Check that program and cwd reference the same agent directory; mismatched paths cause VS Code to debug the wrong copy of the code. - Inspect the Debug Console for stack traces indicating missing dependencies—rerun dr start if needed. --- # Customize agents URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-development.html > Learn how to modify agent code, test locally, and deploy agentic workflows for production use. # Customize agents Developing an agent involves editing code in the `agent/agent/` directory (primarily `myagent.py`). A variety of tools and commands are provided to help you test and deploy your agent during the development process. > [!NOTE] Generic base template > You can use the `generic_base` template to build an agent using any framework of your choice; however, you need to implement the agent logic and structure yourself, as this template does not include any pre-defined agent code. ## Modify the agent code The first step in developing your agent is to modify the agent code to implement your desired functionality. The main agent code is located in the `agent/agent` directory in your application project. ``` # agent/agent/ directory agent/agent/ ├── __init__.py # Package initialization ├── myagent.py # Main agent implementation, including prompts ├── custom.py # DataRobot integration hooks ├── config.py # Configuration management ├── mcp_client.py # MCP server integration (optional, for tool use) └── model-metadata.yaml # Agent metadata configuration ``` | File | Description | | --- | --- | | __init__.py | Identifies the directory as a Python package and enables imports. | | model-metadata.yaml | Defines the agent's configuration, runtime parameters, and deployment settings. | | custom.py | Implements DataRobot integration hooks (load_model, chat) for agent execution. | | myagent.py | Contains the main MyAgent class with core workflow logic and framework-specific implementation. | | config.py | Manages configuration loading from environment variables, runtime parameters, and DataRobot credentials. | | mcp_client.py | Provides MCP server connection management for tool integration (optional, only needed when using MCP tools). | The main class you need to modify is in `myagent.py`. See this class for details about the implementation based on the framework for which you are developing. The agent template provides you with a simple example that contains two agents and two tasks. You can modify this code to add more agents, tasks, and tools as needed. Each agent is connected to an LLM provider, which is specified by the `llm` method in the `MyAgent` class in `myagent.py`. You can modify this method (and related configuration) to change the LLM provider or its configuration. See the [Configuring LLM providers](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-llm-providers.html) documentation for more details. For more information on the overall structure of agentic workflow templates, see the [Agent components](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-overview.html) documentation. > [!NOTE] datarobot_genai package > Agent templates use the `datarobot_genai` package to streamline development. This package provides helper functions and base classes that simplify agent implementation, including LLM configuration, response formatting, and integration with DataRobot services. The templates automatically include this package, so you don't need to install it separately. ## Modify agent prompts Each agent template uses different approaches for defining and customizing prompts. Understanding how to modify prompts in your chosen framework is crucial for tailoring agent behavior to your specific use case. **CrewAI:** In CrewAI templates, prompts are defined through several properties in the `MyAgent` class within the `myagent.py` file: Agent prompts : Defined using role , goal , and backstory properties. Task prompts : Defined using description and expected_output properties. ``` @property def agent_planner(self) -> Agent: return Agent( role="Content Planner", goal="Plan engaging and factually accurate content on {topic}", backstory="You're working on planning a blog article about the topic: {topic}. You collect " "information that helps the audience learn something and make informed decisions. Your work is " "the basis for the Content Writer to write an article on this topic.", allow_delegation=False, verbose=self.verbose, llm=self.llm, ) ``` To modify CrewAI agent prompts: Update agent behavior : Modify the role , goal , and backstory properties in agent definitions. Use variables : Leverage {topic} and other variables for dynamic prompt content. ``` @property def task_plan(self) -> Task: return Task( description=( "1. Prioritize the latest trends, key players, and noteworthy news on {topic}.\n" "2. Identify the target audience, considering their interests and pain points.\n" "3. Develop a detailed content outline including an introduction, key points, and a call to action.\n" "4. Include SEO keywords and relevant data or sources." ), expected_output="A comprehensive content plan document with an outline, audience analysis, SEO keywords, " "and resources.", agent=self.agent_planner, ) ``` To modify CrewAI task prompts: Customize task instructions : Update the description property in task definitions. Change expected outputs : Modify the expected_output property to match your requirements. Use variables : Leverage {topic} and other variables for dynamic prompt content. For more advanced CrewAI prompt engineering techniques, see the [CrewAI Agents documentation](https://docs.crewai.com/en/concepts/agents) and [CrewAI Tasks documentation](https://docs.crewai.com/en/concepts/tasks). **LangGraph:** In LangGraph templates, prompts are defined using the `make_system_prompt` helper function from the `datarobot_genai` package and LangChain's `create_agent` (the [Agentic Starter template](https://github.com/datarobot-community/datarobot-agent-application) uses `LangGraphAgent` with `create_agent` nodes in `myagent.py`): > [!NOTE] LangChaincreate_agentvs other LangGraph examples > DataRobot templates use `langchain.agents.create_agent` with `system_prompt=` (and optional `make_system_prompt`). Some community examples use `langgraph.prebuilt.create_react_agent`, which takes a `prompt=` argument instead. Those are different APIs; follow `create_agent` / `system_prompt` when editing DataRobot agent code. ``` from datarobot_genai.core.agents import make_system_prompt from langchain.agents import create_agent @property def agent_planner(self) -> Any: return create_agent( self.llm(), tools=self.mcp_tools, system_prompt=make_system_prompt( "You are a content planner. You are working with a content writer colleague.\n" "You're working on planning a blog article about the topic. You collect information that helps the " "audience learn something and make informed decisions. Your work is the basis for the Content Writer " "to write an article on this topic.\n" "1. Prioritize the latest trends, key players, and noteworthy news on the topic.\n" "2. Identify the target audience, considering their interests and pain points.\n" "3. Develop a detailed content outline including an introduction, key points, and a call to action.\n" "4. Include SEO keywords and relevant data or sources." ), name="planner_agent", ) ``` The [Agentic Starter](https://github.com/datarobot-community/datarobot-agent-application) `myagent.py` passes `tools=self.mcp_tools + self._workflow_tools` so MCP tools and optional workflow tools (for example, A2A) are both included. The example above uses `self.mcp_tools` only for clarity; follow the template file when you need the full list. To modify LangGraph prompts: Update system prompts : Modify the string passed to make_system_prompt() in agent definitions. Add task-specific instructions : Include detailed instructions within the system prompt string. Modify agent behavior : Update the prompt content to change how agents interpret and respond to tasks. For more advanced LangGraph prompt engineering techniques, see the [LangGraph documentation](https://langchain-ai.github.io/langgraph/). **LlamaIndex:** In LlamaIndex templates, prompts are defined using the `system_prompt` parameter in `FunctionAgent` definitions within the `MyAgent` class: ``` @property def research_agent(self) -> FunctionAgent: return FunctionAgent( name="ResearchAgent", description="Useful for finding information on a given topic and recording notes on the topic.", system_prompt=( "You are the ResearchAgent that can find information on a given topic and record notes on the topic. " "Once notes are recorded and you are satisfied, you should hand off control to the " "WriteAgent to write a report on the topic. You should have at least some notes on a topic " "before handing off control to the WriteAgent." ), llm=self.llm, tools=[self.record_notes], can_handoff_to=["WriteAgent"], ) ``` To modify LlamaIndex prompts: Update system prompts : Modify the system_prompt string in FunctionAgent definitions. Customize agent descriptions : Update the description parameter to change how agents are identified. Modify handoff behavior : Update the can_handoff_to list and system prompt to control agent workflow. Add tool-specific instructions : Include instructions about when and how to use specific tools. For more advanced LlamaIndex prompt engineering techniques, see the [LlamaIndex prompt engineering documentation](https://docs.llamaindex.ai/en/stable/module_guides/models/prompts/). **NAT:** In NAT (NVIDIA NeMo Agent Toolkit) templates, prompts are defined in the `workflow.yaml` file using the `system_prompt` field within function definitions: ``` functions: planner: _type: chat_completion llm_name: datarobot_llm system_prompt: | You are a content planner. You are working with a content writer colleague. You're working on planning a blog article about the topic. You collect information that helps the audience learn something and make informed decisions. Your work is the basis for the Content Writer to write an article on this topic. 1. Prioritize the latest trends, key players, and noteworthy news on the topic. 2. Identify the target audience, considering their interests and pain points. 3. Develop a detailed content outline including an introduction, key points, and a call to action. 4. Include SEO keywords and relevant data or sources. ``` To modify NAT prompts: Update system prompts : Modify the system_prompt field in function definitions within workflow.yaml . Configure LLM per function : Set the llm_name field to reference an LLM defined in the llms section of workflow.yaml . Modify workflow structure : Update the workflow section to change the execution order and tool list. Add new functions : Define additional functions in the functions section to extend agent capabilities. For more advanced NAT usage instructions, see the [NVIDIA NeMo Agent Toolkit documentation](https://docs.nvidia.com/nemo/agent-toolkit/latest/index.html). > [!TIP] Best practices for prompt modification > When modifying prompts across any framework: > > Be specific > : Provide clear, detailed instructions for what you want the agent to accomplish. > Use consistent formatting > : Maintain consistent prompt structure across all agents in your workflow. > Test incrementally > : Make small changes and test them before implementing larger modifications. > Consider context > : Ensure prompts work well together in multi-agent workflows. > Document changes > : Keep track of prompt modifications for future reference and team collaboration. ## Enable streaming responses Streaming allows agents to send responses incrementally as they are generated, rather than waiting for the complete response. This provides a better user experience by showing progress in real-time, reducing perceived latency, and enabling users to see agent actions as they happen. Streaming support varies by agent framework. There are three levels of streaming implementation: - Chunk streaming : Each chunk from the LLM is streamed as it's generated (such as tokens/partial text). - Step streaming : Response from each sub-agent is streamed when ready. - Event streaming : Each individual event (starting new step, calling a tool, reasoning) is streamed. | Framework | Streaming | Notes | | --- | --- | --- | | LangGraph | Enabled | Chunk-level streaming is automatically enabled when stream=True is passed. The base LangGraphAgent class handles streaming responses. | | Generic Base | Supported | All streaming levels (chunk, step, event) require custom implementation. Example code is provided in myagent.py for chunk streaming. | | CrewAI | Supported | All streaming levels (chunk, step, event) require custom implementation. Event listeners capture agent execution and tool usage events incrementally, which facilitates step and event streaming with custom code. Chunk streaming requires custom implementation to stream from the LLM directly. | | LlamaIndex | Supported | All streaming levels (chunk, step, event) require custom implementation. The framework executes agents incrementally, which facilitates step streaming with custom code. Chunk and event streaming require custom implementation. | | NAT | Supported | All streaming levels (chunk, step, event) require custom implementation. | > [!NOTE] Infrastructure support > All agent templates include infrastructure in `custom.py` that can handle streaming responses. For frameworks that require custom implementation (Generic Base, CrewAI, LlamaIndex, NAT), you need to modify your agent's `invoke()` method to return an `AsyncGenerator` when streaming is requested. If your agent's `invoke()` method returns an `AsyncGenerator`, the infrastructure automatically converts it to the appropriate streaming response format. The `is_streaming` helper function is available to all framework templates via the `datarobot_genai` package by importing `from datarobot_genai.core.agents import is_streaming`. It checks if `stream=True` is present in the chat completion request body parameters. If streaming is implemented for an agent, enable streaming when testing locally (via CLI) or when making predictions with a deployed agent (via API). **CLI (local testing):** Use the `--stream` flag when running the agent CLI: ``` task agent:cli -- execute --user_prompt 'Write a document about the history of AI.' --stream ``` You can also use streaming with structured queries: ``` task agent:cli -- execute --user_prompt '{"topic":"Generative AI"}' --stream ``` **API (deployed agent):** Set `stream=True` in the completion parameters when making API calls: ``` from openai import OpenAI client = OpenAI( base_url=CHAT_API_URL, api_key=API_KEY, ) completion = client.chat.completions.create( model="datarobot-deployed-llm", messages=[ {"role": "user", "content": "What would it take to colonize Mars?"}, ], stream=True, # Enable streaming ) # Process streaming response for chunk in completion: if chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) ``` ## Testing the agent during local development You can test your agent locally using the development server provided in the template. This allows you to run and debug your agent code without deploying it to DataRobot. To submit a test query to your agent, the development server must be running. Start it manually or use the auto-start option: **Manual start:** Start the development server manually when running multiple tests. The development server runs continuously and blocks the terminal, so start it in one terminal: ``` task agent:dev ``` Keep this terminal running. Then, in a different terminal, run your test commands: ``` task agent:cli -- execute --user_prompt 'Write a document about the history of AI.' ``` You can also send a structured query as a prompt if your agentic workflow requires it: ``` task agent:cli -- execute --user_prompt '{"topic":"Generative AI"}' ``` **Automatic start:** Auto-start the development server for single tests. Use `START_DEV=1` to automatically start and stop the development server: ``` task agent:cli START_DEV=1 -- execute --user_prompt 'Write a document about the history of AI.' ``` You can also send a structured query as a prompt if your agentic workflow requires it: ``` task agent:cli START_DEV=1 -- execute --user_prompt '{"topic":"Generative AI"}' ``` This command will run the agent locally and print the output to the console. You can modify the query to test different inputs and scenarios. > [!TIP] Fast iteration with runtime dependencies > For rapid development, you can add Python dependencies without rebuilding the Docker image using runtime dependencies. This process improves iteration speed. See the [Add Python packages](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-python-packages.html) documentation for details on adding runtime dependencies. ## Build an agent for testing in the DataRobot LLM Playground To create a custom model that can be refined using the DataRobot LLM Playground, deploy development infrastructure (including playground-related resources) from your template project root. This is different from [running the stack locally](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-development.html#testing-the-agent-during-local-development) with `task dev` or `dr run dev`. ``` dr run deploy-dev ``` You can also run `dr task run deploy-dev` (equivalent to `dr run deploy-dev`). This command runs Pulumi with development targets (for example, LLM Playground and related custom model resources) and does not perform a full production deployment. This is significantly faster for iterative cloud development and testing. For command details, see [dr task](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/task.html) and [dr run](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/run.html) in the CLI documentation. For more examples on working with agents in the DataRobot LLM Playground, see the [Agentic playground documentation](https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-playground.html). > [!NOTE] Build command considerations > The `deploy-dev` task can replace or update existing cloud resources for your stack. Full production resources are created with `dr run deploy` (or `dr task run deploy`). If resources are removed or recreated, new deployment IDs may apply. ## Deploy an agent for production use To create a full production-grade deployment: ``` dr run deploy ``` You can also run `dr task run deploy` (equivalent to `dr run deploy`). This matches the [Agentic Starter template README](https://github.com/datarobot-community/datarobot-agent-application#deploy-your-agent) ( `dr run deploy`). The command builds the custom model and creates a production deployment with the necessary infrastructure, which takes longer but provides a complete production environment. See the [CLI task and run commands](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/task.html) for more options. The deployment is a standard DataRobot deployment that includes full monitoring, logging, and scaling capabilities. For more information about DataRobot deployments, see the [Deployment documentation](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-overview/index.html). ### View logs and traces for a deployed agent Once your agent is deployed, you can view OpenTelemetry (OTel) logs and traces in the DataRobot UI. To view logs, on the Deployments tab, locate and click your deployment, click the Activity log tab, and then click Logs. The logs display in OpenTelemetry format and include log levels ( `INFO`, `DEBUG`, `WARN`, and `ERROR`), time-period filtering (Last 15 min, Last hour, Last day, or Custom range), and export capabilities via the OTel logs API for integration with third-party observability tools like Datadog. > [!NOTE] Access and retention > OTel logs are available for all deployment and target types. Only users with Owner and User roles on a deployment can view these logs. Logs data is stored for a retention period of 30 days, after which it is automatically deleted. To view traces, which follow the end-to-end path of requests to your agent, on the Service health tab of your deployment, click Show tracing in the upper-right corner of the Total predictions chart. The tracing table displays trace information including timestamp, status, trace ID, duration, spans count, cost, prompt, and completion. Click a trace row to view detailed spans in Chart or List format, showing individual steps in the agent's execution, including LLM API calls, tool invocations, and agent actions. For more information, see the [logs documentation](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-otel-logs.html) and [tracing documentation](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-data-exploration.html#explore-deployment-data-tracing). ### Manually deploy an agent using Pulumi If needed, you can manually run Pulumi commands to debug or refine Pulumi code. ``` # Load environment variables set -o allexport && source .env # For build mode only (custom model without deployment) export AGENT_DEPLOY=0 # Or for full deployment mode (default) # export AGENT_DEPLOY=1 # Navigate to the infrastructure directory cd ./infra # Run Pulumi deployment pulumi up ``` The `AGENT_DEPLOY` environment variable controls whether Pulumi creates only the custom model ( `AGENT_DEPLOY=0`) or both the custom model and a production deployment ( `AGENT_DEPLOY=1`). If not set, Pulumi defaults to full deployment mode. Pulumi will prompt you to confirm the resources to be created or updated. ## Make predictions with a deployed agentic workflow After the agentic workflow is deployed, access real-time prediction snippets from the deployment's Predictions > Prediction API tab. For more information on deployment predictions, see the [Prediction API snippets documentation](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/nxt-pred-api-snippets.html). Alternatively, you can modify the script below to make predictions with the deployed agentic workflow, replacing the placeholders for the `API_KEY`, `DEPLOYMENT_ID`, and `CHAT_API_URL` variables. ``` # datarobot-llm-chat.py import sys import logging import time import os from openai import OpenAI API_KEY = '' # Your API Key DEPLOYMENT_ID = '' # The agentic workflow deployment ID CHAT_API_URL = '' # The chat API URL for the agentic workflow deployment # For example, 'https://app.datarobot.com/api/v2/deployments/68824e9aa1946013exfc3415/' logging.basicConfig( level=logging.INFO, stream=sys.stdout, format='%(asctime)s %(filename)s:%(lineno)d %(levelname)s %(message)s', ) logger = logging.getLogger(__name__) def main(): openai_client = OpenAI( base_url=CHAT_API_URL, api_key=API_KEY, _strict_response_validation=False ) prompt = "What would it take to colonize Mars?" logging.info(f"Trying Simple prompt first: \"{prompt}\"") completion = openai_client.chat.completions.create( model="datarobot-deployed-llm", messages=[ {"role": "system", "content": "Explain your thoughts using at least 100 words."}, {"role": "user", "content": prompt}, ], max_tokens=512, # omit if you want to use the model's default max ) print(completion.choices[0].message.content) return 0 if __name__ == '__main__': sys.exit(main()) ``` ## Next steps After deployment, your agent will be available in your DataRobot environment. You can: 1. Test your deployed agent using task agent:cli -- execute-deployment . 2. Integrate your agent with other DataRobot services. 3. Monitor usage and performance in the DataRobot dashboard. For agentic platform-specific assistance beyond the scope of the examples provided in this repository, see the official documentation for each framework: - CrewAI - LangGraph - LlamaIndex - NVIDIA NeMo Agent Toolkit You can also find more examples and documentation in the public repositories for specific frameworks to help you build more complex agents, add tools, and define workflows and tasks. - CrewAI GitHub repository - LangGraph GitHub repository - LlamaIndex GitHub repository --- # Installation URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-install.html > Install required components for agentic workflow development. # Installation The [Agentic Starter template repository](https://github.com/datarobot-community/datarobot-agent-application) provides a ready-to-use application template for building and deploying agentic workflows with multi-agent frameworks, a FastAPI backend server, a React frontend, and an MCP server. The template streamlines the process of setting up new agentic applications with minimal configuration requirements and supports local development and testing, as well as one-command deployments to production environments within DataRobot. Review the following sections to install the prerequisite tools and configure your environment. ## System requirements Ensure your system meets the minimum requirements for running the Agentic Starter template: - Operating System: macOS or Linux (Windows is not supported) - Python: Version 3.10 or higher > [!WARNING] Operating system compatibility > This repository is only compatible with macOS and Linux operating systems. If you are using Windows, consider using a [DataRobot codespace](https://docs.datarobot.com/en/docs/workbench/wb-notebook/codespaces/index.html), [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/install), or a virtual machine running a supported OS. ## Install prerequisite tools Before you begin, you'll need the following tools installed. If you already have these tools installed, ensure that they are at the required version (or newer) indicated in the table below. For example commands to install the tools, see the [Detailed installation commands](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-install.html#detailed-installation-commands) section. > [!TIP] Install tools system-wide > Make sure to install the tools system-wide, rather than in a virtual environment, so they are available in your terminal sessions. | Tool | Version | Description | Installation guide | | --- | --- | --- | --- | | dr-cli | >= 0.2.28 | The DataRobot CLI. | DataRobot CLI getting started (install and configure); GitHub dr-cli (alternative) | | git | >= 2.30.0 | A version control system. | git installation guide | | uv | >= 0.9.0 | A Python package manager. | uv installation guide | | Pulumi | >= 3.163.0 | An Infrastructure as Code tool. | Pulumi installation guide | | Taskfile | >= 3.43.3 | A task runner. | Taskfile installation guide | | NodeJS | >= 24 | JavaScript runtime for frontend development. | NodeJS installation guide | ### Detailed installation commands The following sections provide example installation commands for macOS and Linux (Debian/Ubuntu/DataRobot codespace). Click the tab below that corresponds to your operating system: **macOS:** macOS users can install the prerequisite tools using Homebrew. First, install Homebrew if you don't already have it. ``` /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" # If homebrew is not already installed ``` Then, install the prerequisite tools with it: ``` brew install datarobot-oss/taps/dr-cli uv pulumi/tap/pulumi go-task node git ``` **Linux:** Linux users can install the prerequisite tools using the package manager for their distribution. ``` curl https://cli.datarobot.com/install | sh sudo apt-get update sudo apt-get install -y python3 python3-pip python3-venv sudo apt-get install -y git curl -LsSf https://astral.sh/uv/install.sh | sh curl -fsSL https://get.pulumi.com | sh sh -c "$(curl --location https://taskfile.dev/install.sh)" -- -d sudo apt-get install -y nodejs npm ``` > [!NOTE] Local Pulumi login > If you do not have a Pulumi account, use `pulumi login --local` for local login or create a free account at [the Pulumi website](https://app.pulumi.com/signup). ### Restricted network setup In environments without direct internet access (i.e., air-gapped environments), configure Pulumi to install the DataRobot plugin from an internal proxy instead of GitHub. Setting these environment variables redirects all Pulumi plugin downloads to your internal proxy and disables external update checks. Add the following variables to your `.env` file: ``` # .env PULUMI_SKIP_UPDATE_CHECK=1 PULUMI_DATAROBOT_DEFAULT_URL=http://internal-proxy-for-pulumi # OPTIONAL PULUMI_DATAROBOT_PLUGIN_VERSION=v0.10.27 ``` | Environment variable | Required | Description | | --- | --- | --- | | PULUMI_SKIP_UPDATE_CHECK | Yes | Set to 1 to enable air-gapped mode. This disables external update checks and allows the use of a custom plugin server. | | PULUMI_DATAROBOT_DEFAULT_URL | Yes | The base URL of your internal proxy server hosting the DataRobot Pulumi plugin. This replaces the default GitHub releases source. | | PULUMI_DATAROBOT_PLUGIN_VERSION | No | The specific version of the DataRobot Pulumi plugin to install. If not specified, it defaults to the version bundled with the templates. | > [!NOTE] How it works > When `PULUMI_SKIP_UPDATE_CHECK=1` is set, deployment tasks execute `pulumi plugin install resource datarobot --server `. This ensures that plugin downloads are routed through your internal proxy instead of external sources. > [!NOTE] Internal proxy requirements > The internal proxy must host the DataRobot Pulumi plugin files in a structure compatible with Pulumi's plugin installation. It should mirror the directory and file structure of the [official GitHub releases](https://github.com/datarobot-community/pulumi-datarobot/releases). #### Python packages In restricted network environments, `uv sync` operations will fail when attempting to reach the public PyPI. To resolve this, configure `uv` to use your internal PyPI proxy. To configure the proxy, edit the `agent/pyproject.toml` file in your agent project and uncomment the `[tool.uv.pip]` section, replacing the URL with your internal PyPI proxy. For example: ``` # agent/pyproject.toml [tool.uv.pip] extra-index-url = ["https://your-internal-pypi-proxy.example.com/simple/"] ``` > [!NOTE] Configuration impact > Once configured, this setting ensures that all Python package installations are routed through your proxy. This applies to: > > Local development ( > uv sync > ) > Docker image builds > Custom model deployments > Playground operations > Infrastructure deployments > [!TIP] Finding the configuration > The `[tool.uv.pip]` section is located at the end of the `pyproject.toml` file. If it is missing, add it manually. --- # Configure LLM providers with metadata URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-llm-providers-metadata.html > Learn how to configure different LLM providers for your agentic workflows including DataRobot LLM gateway, external APIs, and custom deployments. # Configure LLM providers with metadata One of the key components of an LLM agent is the underlying LLM provider. DataRobot allows users to connect to virtually any LLM backend for their agentic workflows. LLM connections can be simplified by using the DataRobot LLM gateway or a DataRobot deployment (including NIM deployments). Alternatively, you can connect to any external LLM provider that supports the OpenAI API standard. The DataRobot Agentic Starter template provides multiple methods for defining an agent LLM: - Use the DataRobot LLM gateway as the agent LLM, allowing you to use any model available in the gateway. - Connect to use a previously-deployed custom model or NIM using the DataRobot API by providing the deployment ID. - Connect directly to an LLM provider API (such as OpenAI, Anthropic, or Gemini) by providing the necessary API credentials, enabling access to providers supporting a compatible API. This document focuses on configuring LLM providers using environment variables and Pulumi (infrastructure-level configuration). This approach allows you to switch between different LLM provider configurations without modifying your agent code. The infrastructure configuration files use the `build_llm()` helper function from `datarobot_genai` package, which automatically handles deployment detection, gateway configuration, and credential management. This simplifies deployment and credential management by leveraging DataRobot's secure credential system. > [!NOTE] Alternative configuration method > If you prefer to manually create LLM instances directly in your `myagent.py` file for fine-grained control, see [Configure LLM providers with code](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-llm-providers.html) for details on that approach. ## Infrastructure-level LLM configuration You can configure LLM providers at the infrastructure level using environment variables and symlinks. This allows you to switch between different LLM provider configurations without modifying your agent code. Both methods create a symbolic link from `infra/infra/llm.py` to your chosen configuration file in `infra/configurations/llm/`. The available configuration options include: | Configuration File | Description | | --- | --- | | gateway_direct.py | A direct LLM gateway integration. | | deployed_llm.py | An LLM deployed in DataRobot. | | blueprint_with_external_llm.py | An LLM blueprint with external LLMs (Azure OpenAI, Amazon Bedrock, Google Vertex AI, Anthropic). | ### Manual symlink Create the symlink manually for explicit control and immediate visibility of your active configuration. This method is recommended for development. To use this method, navigate to the `infra/infra` folder and create a symbolic link to your chosen configuration: ``` cd infra/infra ln -sf ../configurations/llm/.py ``` Replace `` with one of the available configuration files (e.g., `gateway_direct.py`, `blueprint_with_llm_gateway.py`, etc.). After creating the symlink, you can optionally edit `infra/infra/llm.py` to adjust model parameters ( `temperature`, `top_p`, etc.) or select a specific model. ### Environment variable Set the configuration dynamically using an environment variable. The symlink is automatically created when you run Pulumi commands. This method is recommended for deployment and different environments. To use this method, uncomment the relevant section in your `.env` file. The following examples from `.env.template` show how to configure each LLM provider type. Each configuration may require additional environment variables: > [!TIP] DataRobot credentials in codespaces > If you are using a DataRobot codespace, remove the `DATAROBOT_API_TOKEN` and `DATAROBOT_ENDPOINT` environment variables from the file, as they already exist in the codespace environment. ``` # Your DataRobot API token. # Refer to https://docs.datarobot.com/en/docs/api/api-quickstart/index.html#configure-your-environment for help. DATAROBOT_API_TOKEN= # The URL of your DataRobot instance API. DATAROBOT_ENDPOINT=https://app.datarobot.com/api/v2 # The Pulumi stack name to use for this project. PULUMI_STACK=dev # If empty, a blank passphrase will be used PULUMI_CONFIG_PASSPHRASE=123 # Set to 1 to skip Pulumi update check and prevent issues with rate limiting PULUMI_SKIP_UPDATE_CHECK=0 # If empty, a new use case will be created DATAROBOT_DEFAULT_USE_CASE= # If empty, a new execution environment will be created for each agent using the docker_context folder DATAROBOT_DEFAULT_EXECUTION_ENVIRONMENT="[DataRobot] Python 3.11 GenAI Agents" # This is set to a specific version of `[DataRobot] Python 3.11 GenAI Agents` to preserve compatibility of the templates DATAROBOT_DEFAULT_EXECUTION_ENVIRONMENT_VERSION_ID="6936d3af440bcb12397f4203" # LLM Configuration: # Agent templates support multiple flexible LLM options including: # - LLM Gateway Direct (default) # - LLM Blueprint with an External LLM # - Already deployed LLM in DataRobot # # You can edit the LLM configuration by manually changing which configuration is # active (recommended option). # Simply run `ln -sf ../configurations/llm/.py` # from the `infra/infra` folder # # If you want to do it dynamically however, you can also set it as a configuration value with: # INFRA_ENABLE_LLM= # from the list of options in the infra/configurations/llm folder # Here are some examples of each of those configuration using the dynamic option described above: # If you want to use the LLM gateway direct (default) # INFRA_ENABLE_LLM=gateway_direct.py # If you want to choose an existing LLM Deployment in DataRobot # uncomment and configure these: # LLM_DEPLOYMENT_ID= # INFRA_ENABLE_LLM=deployed_llm.py # If you want to configure an LLM with an external LLM provider # like Azure, Bedrock, Anthropic, or VertexAI (or all 4). Here we provide # an Azure AI example, see: # https://docs.datarobot.com/en/docs/gen-ai/playground-tools/deploy-llm.html # for details on other providers and details: # INFRA_ENABLE_LLM=blueprint_with_external_llm.py # LLM_DEFAULT_MODEL="azure/gpt-4o" # OPENAI_API_VERSION='2024-08-01-preview' # OPENAI_API_BASE='https://.openai.azure.com' # OPENAI_API_DEPLOYMENT_ID='' # OPENAI_API_KEY='' ``` When you run `task infra:build` or `task infra:deploy`, the system reads `INFRA_ENABLE_LLM`, automatically creates or updates the symlink to the specified configuration file, and manages credentials through DataRobot's secure credential system. ## LLM configuration options | Configuration File | Description | | --- | --- | | gateway_direct.py | The default option if not specified in your .env file. Direct LLM gateway integration providing streamlined access to LLMs proxied via DataRobot. Available for both cloud and on-premise users. Requires no additional configuration beyond selecting this file. When using LLM gateway options, your agents can dynamically use any and all models available in the gateway catalog. Each agent or task can specify its own preferred model, allowing you to optimize for different capabilities (e.g., faster models for planning, more capable models for content generation). | | deployed_llm.py | Use a previously deployed DataRobot LLM. Requires LLM_DEPLOYMENT_ID (your deployment ID). | | blueprint_with_external_llm.py | Configure an LLM with an external provider like Azure, Bedrock, Anthropic, or VertexAI. Requires LLM_DEFAULT_MODEL (e.g., "azure/gpt-4o"). Unlike LLM gateway options, all agents use the same model specified in your configuration. The LLM_DEFAULT_MODEL becomes the primary model since you're connecting to a single external deployment. See the DataRobot documentation for details on other providers. | > [!NOTE] When should I edit the default model? > The default model behavior varies by configuration type: > > LLM gateway configurations > ( > gateway_direct.py > ): The default model is automatically configured and rarely needs changing. Agents can dynamically use any model from the catalog. > Deployed models > ( > deployed_llm.py > ): The default model is automatically set to match your deployment. No manual changes needed. > External LLM configurations > ( > blueprint_with_external_llm.py > ): Edit the > default_model > to match your external LLM deployment and add credentials to connect. This is the model all agents will use. ### Configure the DataRobot LLM gateway The LLM gateway provides a streamlined way to access LLMs proxied via DataRobot. The gateway is available for both cloud and on-premise users. You can retrieve a list of available models for your account using the following methods: **cURL:** ``` curl -X GET -H "Authorization: Bearer $DATAROBOT_API_TOKEN" "$DATAROBOT_ENDPOINT/genai/llmgw/catalog/" | jq '[.data[] | select(.isActive == true) | .model]' ``` **Python SDK:** ``` from datarobot.models.genai import LLMGatewayCatalog print("\n".join(LLMGatewayCatalog.get_available_models())) ``` #### LLM gateway: multiple graph nodes and model selection In your `myagent.py` file, the `MyAgent` class provides an `llm()` method that returns a chat model for your graph nodes. With the LLM gateway, the default model comes from your configuration and from helpers such as `infra/configurations/llm/gateway_direct.py`. You can also adjust selection using catalog settings, separate deployments, or custom `llm()` logic (see [Configure LLM providers with code](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-llm-providers.html)). The examples below use the LangGraph style from the [Agentic Starter template](https://github.com/datarobot-community/datarobot-agent-application) ( `create_agent` and `make_system_prompt`). Same model for every node (default pattern).Both `create_agent` calls use `self.llm()`, so they share the same resolved model (typically your configured default). This matches the starter template when you want one gateway model for the whole workflow: ``` from datarobot_genai.core.agents import make_system_prompt from langchain.agents import create_agent @property def agent_planner(self) -> Any: """Content Planner agent.""" return create_agent( self.llm(), tools=self.mcp_tools, system_prompt=make_system_prompt( "You are a content planner. Plan engaging and factually accurate content on {topic}." ), name="planner_agent", ) @property def agent_writer(self) -> Any: """Content Writer agent.""" return create_agent( self.llm(), tools=self.mcp_tools, system_prompt=make_system_prompt( "You are a content writer. Write insightful and factually accurate opinion piece about the topic: {topic}." ), name="writer_agent", ) ``` Different gateway models per node (optional).When you use the LLM gateway, you can point each node at a different catalog model by building a `ChatLiteLLM` with an explicit `model=` string (same API base and credentials as `llm()`, different model id). Use catalog IDs from the gateway (for example, values returned by `LLMGatewayCatalog.get_available_models()` in the [examples above](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-llm-providers-metadata.html#configure-the-datarobot-llm-gateway)): ``` from datarobot_genai.core.agents import make_system_prompt from langchain.agents import create_agent from langchain_litellm.chat_models import ChatLiteLLM def _llm_for_gateway_model(self, model: str) -> ChatLiteLLM: """Chat model for a specific LLM gateway catalog model.""" api_base = self.litellm_api_base(self.config.llm_deployment_id) return ChatLiteLLM( model=model, api_base=api_base, api_key=self.api_key, timeout=self.timeout, streaming=True, max_retries=3, ) @property def agent_planner(self) -> Any: return create_agent( self._llm_for_gateway_model("datarobot/azure/gpt-5-mini-2025-08-07"), tools=self.mcp_tools, system_prompt=make_system_prompt( "You are a content planner. Plan engaging and factually accurate content on {topic}." ), name="planner_agent", ) @property def agent_writer(self) -> Any: return create_agent( self._llm_for_gateway_model("datarobot/azure/gpt-4o-2024-11-20"), tools=self.mcp_tools, system_prompt=make_system_prompt( "You are a content writer. Write insightful and factually accurate opinion piece about the topic: {topic}." ), name="writer_agent", ) ``` Replace the model strings with IDs that are valid for your account and gateway catalog. If you use deployed LLM or external LLM infrastructure configuration instead of the gateway, all nodes typically share one deployment or one external model—see the sections below. The default model in gateway configuration (for example in `gateway_direct.py`) applies when you use `self.llm()` without overriding the model string: ``` default_model: str = "datarobot/azure/gpt-5-mini-2025-08-07" # Used when nodes call self.llm() and for fallbacks ``` ### DataRobot hosted LLM deployments You can easily connect to DataRobot-hosted LLM deployments as an LLM provider for your agents. DataRobot hosted LLMs provide access to moderations, guardrails, and advanced monitoring to help you manage and govern your models. When using a deployed LLM, all agents use the same deployment. The configuration automatically sets the correct model identifier—no manual model configuration needed. You can create LLM deployments in several ways: - From the DataRobot playground : Deploy an LLM from the DataRobot Playground - Hugging Face models : Host Hugging Face models as LLM deployments After deployment, copy the Deployment ID and add it to the `.env` file: ``` LLM_DEPLOYMENT_ID= INFRA_ENABLE_LLM=deployed_llm.py ``` The default model is automatically configured to match the deployment. All agents will use this deployment. > [!NOTE] LLM gateway flag for deployed LLMs > When you configure the agent with the DataRobot Deployed LLM option, `USE_DATAROBOT_LLM_GATEWAY` is automatically set to `0` so inference uses your deployment rather than the LLM gateway. You do not need to set this value manually for that option. ### Configure external LLMs When configured with an external LLM like Azure OpenAI, Amazon Bedrock, etc. all agents in the workflow use the model specified in your configuration. Unlike the LLM gateway, this connection is to a single specific model deployment. **Azure OpenAI:** Azure OpenAI allows you to deploy OpenAI models in your Azure environment. DataRobot can connect to these deployments using the `blueprint_with_external_llm.py` configuration. In the `.env` file, uncomment and provide the following environment variables: ``` # If you want to configure an LLM with an external LLM provider # like Azure, Bedrock, Anthropic, or VertexAI (or all 4). Here we provide # an Azure AI example, see: # https://docs.datarobot.com/en/docs/gen-ai/playground-tools/deploy-llm.html # for details on other providers and details: INFRA_ENABLE_LLM=blueprint_with_external_llm.py LLM_DEFAULT_MODEL="azure/gpt-4o" OPENAI_API_VERSION='2024-08-01-preview' OPENAI_API_BASE='https://.openai.azure.com' OPENAI_API_DEPLOYMENT_ID='' OPENAI_API_KEY='' ``` The `LLM_DEFAULT_MODEL` should match your Azure deployment. For example, if you deployed `gpt-4o` in Azure, use `azure/gpt-4o`. > [!NOTE] Credential management > The API key is securely managed through the DataRobot [Credentials Management](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management) system as an API Token credential type. **Amazon Bedrock:** Amazon Bedrock provides access to foundation models from various providers through AWS. Configuration uses AWS credentials managed securely through DataRobot. In the `.env` file, insert and provide the following environment variables: ``` INFRA_ENABLE_LLM=blueprint_with_external_llm.py AWS_ACCESS_KEY_ID='' AWS_SECRET_ACCESS_KEY='' AWS_REGION_NAME='us-east-1' ``` Then, edit `infra/configurations/llm/blueprint_with_external_llm.py` to specify your Bedrock model: ``` external_model_id: str = "bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0" ``` > [!NOTE] Credential management > The AWS credentials are managed through the DataRobot [Credentials Management](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management) system. This system securely stores your access key, secret key, and optional session token. **Google Vertex AI:** Google Vertex AI provides access to Google's foundation models including Gemini. Configuration uses Google Cloud service account credentials. In the `.env` file, insert and provide the following environment variables: ``` INFRA_ENABLE_LLM=blueprint_with_external_llm.py GOOGLE_SERVICE_ACCOUNT='' # or GOOGLE_APPLICATION_CREDENTIALS='/path/to/service-account.json' GOOGLE_REGION='us-west1' ``` Then, edit `infra/configurations/llm/blueprint_with_external_llm.py` to specify your Vertex AI model: ``` external_model_id: str = "vertex_ai/gemini-2.5-pro" ``` > [!NOTE] Credential management > The Google Cloud Platform credentials are managed through the DataRobot [Credentials Management](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management) system. Provide either the service account JSON content directly ( `GOOGLE_SERVICE_ACCOUNT`) or a path to the JSON file ( `GOOGLE_APPLICATION_CREDENTIALS`). **Anthropic:** Anthropic's Claude models can be accessed directly through their API. In the `.env` file, insert and provide the following environment variables: ``` INFRA_ENABLE_LLM=blueprint_with_external_llm.py ANTHROPIC_API_KEY='' ``` Then, edit `infra/configurations/llm/blueprint_with_external_llm.py`: ``` external_model_id: str = "anthropic/claude-3-5-sonnet-20241022" ``` > [!NOTE] Credential management > The API key is securely managed through the DataRobot [Credentials Management](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management) system as an API Token credential type. ### Other LLM providers The template supports 100+ LLM providers through [LiteLLM](https://docs.litellm.ai/), including: - Cohere : Configure with COHERE_API_KEY - Together AI : Configure with TOGETHERAI_API_KEY - Hugging Face : Configure with appropriate provider credentials - Ollama : For locally hosted models - OpenAI Direct : For direct OpenAI API access For providers not natively supported by the template's configuration files, you can implement direct LiteLLM integration by modifying the agent code and using LLM gateway Direct configuration with custom `def llm()` implementation. #### Direct LiteLLM integration with DataRobot credential management This approach gives you full control over LLM initialization while securely storing credentials in DataRobot's credential management system. The example below illustrates using LangGraph with direct Cohere integration. Select the LLM gateway direct configuration in the `.env` file: ``` INFRA_ENABLE_LLM=gateway_direct.py ``` Add a credential to `model-metadata.yaml` (e.g., `agent/agent/model-metadata.yaml`): ``` runtimeParameterDefinitions: - fieldName: COHERE_API_KEY type: credential - fieldName: COHERE_MODEL type: string defaultValue: "command-r-plus" ``` Update `config.py` to load the credential (e.g., `agent/agent/config.py`): ``` from pydantic import Field class Config(BaseConfig): # ... existing fields ... cohere_api_key: str cohere_model: str = "command-r-plus" ``` Replace the `llm()` method in `agent/agent/myagent.py`: ``` from langchain_openai import ChatOpenAI def llm( self, preferred_model: str | None = None, auto_model_override: bool = True, ) -> ChatOpenAI: """Returns a ChatOpenAI instance configured to use Cohere via LiteLLM. Args: preferred_model: The model to use. If None, uses COHERE_MODEL from config. auto_model_override: Ignored for direct LiteLLM integration. """ return ChatOpenAI( model=self.config.cohere_model, # Override the model with the configured one api_key=self.config.cohere_api_key, base_url="https://api.cohere.ai/v1", # Cohere's OpenAI-compatible endpoint timeout=self.timeout, ) ``` Next, add Pulumi credential management in `infra/configurations/llm/gateway_direct.py`: ``` ...existing code # Create the Cohere credential cohere_credential = datarobot.ApiTokenCredential( resource_name=f"{pulumi.get_project()} Cohere API Key Credential", api_token=os.environ.get("COHERE_API_KEY"), ) # Update the runtime parameters arrays app_runtime_parameters = [ datarobot.ApplicationSourceRuntimeParameterValueArgs( key="COHERE_API_KEY", type="credential", value=cohere_credential.id, ), datarobot.ApplicationSourceRuntimeParameterValueArgs( key="COHERE_MODEL", type="string", value=os.environ.get("COHERE_MODEL", "command-r-plus"), ), ] custom_model_runtime_parameters = [ datarobot.CustomModelRuntimeParameterValueArgs( key="COHERE_API_KEY", type="credential", value=cohere_credential.id, ), datarobot.CustomModelRuntimeParameterValueArgs( key="COHERE_MODEL", type="string", value=os.environ.get("COHERE_MODEL", "command-r-plus"), ), ] ``` Finally, set environment variables in the `.env` file: ``` INFRA_ENABLE_LLM=gateway_direct.py COHERE_API_KEY='' COHERE_MODEL='command-r-plus' ``` This approach gives you full control over LLM initialization and credential management while still using DataRobot's secure credential storage. The credentials are managed as runtime parameters and never hard coded in your application code. ## Deploy configuration changes After changing any LLM configuration (updating `.env` variables, switching configuration files, or modifying configuration parameters), you must deploy the updated configuration before running your agent: ``` task infra:deploy ``` This command updates the deployment with the latest configuration and ensures your agent connects to the proper LLM with the correct credentials. Run this command: - After switching between LLM configurations - After updating credentials or API keys - After modifying model parameters ( temperature , top_p , etc.) - Before running your agent locally with the new configuration ## Advanced LLM configuration In addition to the `.env` file settings, you can directly edit the respective `llm.py` configuration file to make additional changes such as: - Temperature settings (applied to the deployed blueprint) - top_p values (applied to the deployed blueprint) - Timeout configurations (useful for GPU-based models) - Other model-specific parameters --- # Configure LLM providers in code URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-llm-providers.html > Learn how to configure different LLM providers for your agentic workflows including DataRobot Gateway, external APIs, and custom deployments. # Configure LLM providers in code One of the key components of an LLM agent is the underlying LLM provider. DataRobot allows users to connect to virtually any LLM backend for their agentic workflows. LLM connections can be simplified by using the DataRobot LLM gateway or a DataRobot deployment (including NIM deployments). Alternatively, you can connect to any external LLM provider that supports the OpenAI API standard. DataRobot agent templates provide multiple methods for defining an agent LLM: - Use the DataRobot LLM gateway as the agent LLM, allowing you to use any model available in the gateway. - Connect to use a previously deployed custom model or NIM using the DataRobot API by providing the deployment ID. - Connect directly to an LLM provider API (such as OpenAI, Anthropic, or Gemini) by providing the necessary API credentials, enabling access to providers supporting a compatible API. This document focuses on configuring LLM providers by manually creating LLM instances directly in your `myagent.py` file. This approach gives you fine-grained control over LLM initialization and is shown in the framework-specific examples below. > [!NOTE] Alternative configuration method > If you prefer to configure LLM providers using environment variables and Pulumi (infrastructure-level configuration), see [Configure LLM providers with metadata](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-llm-providers-metadata.html). The following sections provide example code snippets for connecting to various LLM providers using the CrewAI, LangGraph, LlamaIndex, and NAT (NVIDIA NeMo Agent Toolkit) frameworks. You can use these snippets as a starting point and modify them as needed to fit your specific use case. ## DataRobot LLM gateway The LLM gateway provides a streamlined way to access LLMs proxied via DataRobot. The gateway is available for both cloud and on-premise users. You can retrieve a list of available models for your account using the following methods: **cURL:** ``` curl -X GET -H "Authorization: Bearer $DATAROBOT_API_TOKEN" "$DATAROBOT_ENDPOINT/genai/llmgw/catalog/" | jq '[.data[] | select(.isActive == true) | .model]' ``` **Python SDK:** ``` from datarobot.models.genai import LLMGatewayCatalog print("\n".join(LLMGatewayCatalog.get_available_models())) ``` The following code examples demonstrate how to programmatically connect to the DataRobot LLM gateway in the CrewAI, LangGraph, and LlamaIndex frameworks. These samples show how to configure the model, API endpoint, and authentication. > [!NOTE] Model format for LLM gateway > When using the DataRobot LLM gateway, the model name format is `datarobot//` (e.g., `datarobot/azure/gpt-5-mini-2025-08-07`). **CrewAI:** ``` from crewai import LLM def llm(self) -> LLM: """Returns a CrewAI LLM instance configured to use DataRobot's LLM gateway.""" return LLM( model="datarobot/azure/gpt-5-mini-2025-08-07", # Define the model name you want to use (format: datarobot//) # Note: The `/chat/completions` endpoint will be automatically appended by LiteLLM api_base="https://app.datarobot.com", # DataRobot endpoint api_key=self.api_key, # Your DataRobot API key timeout=self.timeout, # Optional timeout for requests ) ``` **LangGraph:** ``` from langchain_community.chat_models import ChatLiteLLM def llm(self) -> ChatLiteLLM: """Returns a ChatLiteLLM instance configured to use DataRobot's LLM gateway.""" return ChatLiteLLM( model="datarobot/azure/gpt-5-mini-2025-08-07", # Define the model name you want to use (format: datarobot//) # Note: The `/chat/completions` endpoint will be automatically appended by LiteLLM api_base="https://app.datarobot.com", # DataRobot endpoint api_key=self.api_key, # Your DataRobot API key timeout=self.timeout, # Optional timeout for requests ) ``` **LlamaIndex:** ``` # DataRobotLiteLLM class is included in the `myagent.py` file def llm(self) -> DataRobotLiteLLM: """Returns a DataRobotLiteLLM instance configured to use DataRobot's LLM gateway.""" return DataRobotLiteLLM( model="datarobot/azure/gpt-5-mini-2025-08-07", # Define the model name you want to use (format: datarobot//) # Note: The `/chat/completions` endpoint will be automatically appended by LiteLLM api_base="https://app.datarobot.com", # DataRobot endpoint api_key=self.api_key, # Your DataRobot API key timeout=self.timeout, # Optional timeout for requests ) ``` **NAT:** In NAT templates, LLMs are configured in the `workflow.yaml` file. To use the DataRobot LLM gateway, define an LLM in the `llms` section: ``` llms: datarobot_llm: _type: datarobot-llm-gateway model_name: azure/gpt-4o-mini # Define the model name you want to use temperature: 0.0 ``` Then, define the LLM a specific agent should use through the `llm_name` in the definition of that agent in the `functions` section: ``` functions: planner: _type: chat_completion llm_name: datarobot_llm # Reference the LLM defined below system_prompt: | You are a content planner... ``` If more than one LLM is defined in the `llms` section, the various `functions` can use different LLMs to suit the task. > [!TIP] NAT-provided LLM interfaces > Alternatively, you can use any of the [NAT-provided LLM interfaces](https://docs.nvidia.com/nemo/agent-toolkit/latest/workflows/llms/index.html) instead of the LLM gateway. To use a NAT LLM interface, add the required configuration parameters such as `api_key`, `url`, and other provider-specific settings directly into the `workflow.yaml` file. ## DataRobot hosted LLM deployments You can easily connect to DataRobot-hosted LLM deployments as an LLM provider for your agents. To do this, [Deploy an LLM from the DataRobot Playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/deploy-llm.html) or host [Hugging Face models as LLM deployments on DataRobot](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-open-source-textgen-template.html). DataRobot-hosted LLMs can also provide access to moderations and guardrails for managing and governing models. To use a deployed custom model, manually configure the deployment URL directly in your agent code for `api_base=` param, following the examples below. > [!NOTE] Deployment ID > In the examples below, `DEPLOYMENT_ID` should be replaced with your actual DataRobot deployment ID, which you can obtain from the DataRobot platform. > [!TIP] Model name string construction > DataRobot deployments use an [OpenAI-compatible chat completion endpoint](https://docs.litellm.ai/docs/providers/openai_compatible). Therefore, the `model` name string should start with `openai/` to indicate the use of the OpenAI client. After `openai/`, the model name string should be the name of the model in the deployment. > > For LLMs deployed from the playground, the > model > string should include the provider name and the model name. In the example below, the full model name is > azure/gpt-4o-mini > , provider included, not just > gpt-4o-mini > . This results in a final value of > model="openai/azure/gpt-4o-mini" > . > For NIM models, the > model > string can be found on the NIM deployment's > Predictions > tab or in the NIM documentation. While NIM deployments may work with either > openai > or > meta_llama > interfaces, it's recommended to use > openai > for consistency. **CrewAI:** ``` from crewai import LLM def llm(self) -> LLM: """Returns a CrewAI LLM instance configured to use a DataRobot Deployment.""" return LLM( # Note: For DataRobot deployments, use the openai provider format model="openai/azure/gpt-4o-mini", # Format: openai/ # Note: The `/chat/completions` endpoint will be automatically appended by LiteLLM api_base=f"https://app.datarobot.com/api/v2/deployments/{DEPLOYMENT_ID}/", # Deployment URL api_key=self.api_key, # Your DataRobot API key timeout=self.timeout, # Optional timeout for requests ) ``` **LangGraph:** ``` from langchain_community.chat_models import ChatLiteLLM def llm(self) -> ChatLiteLLM: """Returns a ChatLiteLLM instance configured to use a DataRobot Deployment.""" return ChatLiteLLM( # Note: LangGraph uses datarobot provider format for deployments model="openai/azure/gpt-4o-mini", # Format: openai/ # Note: The `/chat/completions` endpoint will be automatically appended by LiteLLM api_base=f"https://app.datarobot.com/api/v2/deployments/{DEPLOYMENT_ID}/", # Deployment URL api_key=self.api_key, # Your DataRobot API key timeout=self.timeout, # Optional timeout for requests ) ``` **LlamaIndex:** ``` # DataRobotLiteLLM class is included in the `myagent.py` file def llm(self) -> DataRobotLiteLLM: """Returns a DataRobotLiteLLM instance configured to use a DataRobot Deployment.""" return DataRobotLiteLLM( # Note: For DataRobot deployments, use the openai provider format model="openai/azure/gpt-4o-mini", # Format: openai/ # Note: The `/chat/completions` endpoint will be automatically appended by LiteLLM api_base=f"https://app.datarobot.com/api/v2/deployments/{DEPLOYMENT_ID}/", # Deployment URL api_key=self.api_key, # Your DataRobot API key timeout=self.timeout, # Optional timeout for requests ) ``` **NAT:** In NAT templates, LLMs are configured in the `workflow.yaml` file. To use a DataRobot-hosted LLM deployment, define an LLM in the `llms` section: ``` llms: datarobot_deployment: _type: datarobot-llm-deployment model_name: datarobot-deployed-llm # Optional: Define the model name to pass through to the deployment temperature: 0.0 ``` The deployment ID is automatically retrieved from the `LLM_DEPLOYMENT_ID` environment variable or runtime parameter. When you use the DataRobot Deployed LLM option, `USE_DATAROBOT_LLM_GATEWAY` is automatically set to `0` so inference uses your deployment rather than the LLM gateway. To use this deployment, define the LLM a specific agent should use through the `llm_name` in the definition of that agent in the `functions` section: ``` functions: planner: _type: chat_completion llm_name: datarobot_deployment # Reference the LLM defined above system_prompt: | You are a content planner... ``` If more than one LLM is defined in the `llms` section, the various `functions` can use different LLMs to suit the task. > [!TIP] NAT-provided LLM interfaces > Alternatively, you can use any of the [NAT-provided LLM interfaces](https://docs.nvidia.com/nemo/agent-toolkit/latest/workflows/llms/index.html) instead of the LLM gateway. To use a NAT LLM interface, add the required configuration parameters such as `api_key`, `url`, and other provider-specific settings directly in the `workflow.yaml` file. ## DataRobot NIM deployments The template supports using NIM deployments as an LLM provider, which allows you to use any NIM deployment hosted on DataRobot as an LLM provider for your agent. When using LiteLLM with NIM deployments, use the `openai` provider interface. The model name depends on your specific deployment and can be found in the Predictions tab of your deployment in DataRobot. For example, if the deployment uses a model named `meta/llama-3.2-1b-instruct`, use `openai/meta/llama-3.2-1b-instruct` for the model string. This tells LiteLLM to use the `openai` API adapter and the model name `meta/llama-3.2-1b-instruct`. To create a new NIM deployment, you can follow the instructions in the [DataRobot NIM documentation](https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/genai-nvidia-integration.html). > [!NOTE] Deployment ID > In the examples below, `DEPLOYMENT_ID` should be replaced with your actual DataRobot deployment ID, which you can obtain from the DataRobot platform. > [!TIP] Model name string construction > DataRobot deployments use an [OpenAI-compatible chat completion endpoint](https://docs.litellm.ai/docs/providers/openai_compatible). Therefore, the `model` name string should start with `openai/` to indicate the use of the OpenAI client. After `openai/`, the model name string should be the name of the model in the deployment. > > For LLMs deployed from the playground, the > model > string should include the provider name and the model name. In the example below, the full model name is > azure/gpt-4o-mini > , provider included, not just > gpt-4o-mini > . This results in a final value of > model="openai/azure/gpt-4o-mini" > . > For NIM models, the > model > string can be found on the NIM deployment's > Predictions > tab or in the NIM documentation. While NIM deployments may work with either > openai > or > meta_llama > interfaces, it's recommended to use > openai > for consistency. **CrewAI:** ``` from crewai import LLM def llm(self) -> LLM: """Returns a CrewAI LLM instance configured to use a NIM deployed on DataRobot.""" return LLM( # Use the openai provider with the model name from your deployment's Predictions tab model="openai/meta/llama-3.2-1b-instruct", # Format: openai/ api_base=f"https://app.datarobot.com/api/v2/deployments/{DEPLOYMENT_ID}", # NIM Deployment URL api_key=self.api_key, # Your DataRobot API key timeout=self.timeout, # Optional timeout for requests ) ``` **LangGraph:** ``` from langchain_openai import ChatOpenAI def llm(self) -> ChatOpenAI: """Returns a ChatOpenAI instance configured to use a NIM deployed on DataRobot.""" return ChatOpenAI( # Use the model name from your deployment's Predictions tab model="meta/llama-3.2-1b-instruct", # Model name from deployment's Predictions tab api_base=f"https://app.datarobot.com/api/v2/deployments/{DEPLOYMENT_ID}", # NIM deployment URL api_key=self.api_key, # Your DataRobot API key timeout=self.timeout, # Optional timeout for requests ) ``` **LlamaIndex:** ``` from llama_index.llms.openai_like import OpenAILike def llm(self) -> OpenAILike: """Returns an OpenAILike instance configured to use a NIM deployed on DataRobot.""" return OpenAILike( # Use the model name from your deployment's Predictions tab model="meta/llama-3.2-1b-instruct", # Model name from the deployment's Predictions tab api_base=f"https://app.datarobot.com/api/v2/deployments/{DEPLOYMENT_ID}/v1", # NIM deployment URL with /v1 endpoint api_key=self.api_key, # Your DataRobot API key timeout=self.timeout, # Optional timeout for requests is_chat_model=True, # Enable chat model mode for NIM endpoints ) ``` **NAT:** In NAT templates, LLMs are configured in the `workflow.yaml` file. To use a DataRobot NIM deployment, define an LLM in the `llms` section: ``` llms: datarobot_nim: _type: datarobot-nim model_name: meta/llama-3.2-1b-instruct # Optional: Define the model name to pass through to the deployment temperature: 0.0 ``` The deployment ID is automatically retrieved from the `NIM_DEPLOYMENT_ID` environment variable or runtime parameter. To use this deployment, define the LLM a specific agent should use through the `llm_name` in the definition of that agent in the `functions` section: ``` functions: planner: _type: chat_completion llm_name: datarobot_nim # Reference the LLM defined above system_prompt: | You are a content planner... ``` If more than one LLM is defined in the `llms` section, the various `functions` can use different LLMs to suit the task. > [!TIP] NAT-provided LLM interfaces > Alternatively, you can use any of the [NAT-provided LLM interfaces](https://docs.nvidia.com/nemo/agent-toolkit/latest/workflows/llms/index.html) instead of the LLM gateway. To use a NAT LLM interface, add the required configuration parameters such as `api_key`, `url`, and other provider-specific settings directly in the `workflow.yaml` file. ## OpenAI API configuration There are cases where you may want to use an external LLM provider that supports the OpenAI API standard, such as OpenAI itself. The template supports connecting to any OpenAI-compatible LLM provider. Here are examples for directly connecting to OpenAI using the CrewAI and LangGraph frameworks. **CrewAI:** ``` from crewai import LLM def llm(self) -> LLM: """Returns a CrewAI LLM instance configured to use OpenAI.""" return LLM( model="gpt-4o-mini", # Define the OpenAI model name api_key="YOUR_OPENAI_API_KEY", # Your OpenAI API key timeout=self.timeout, # Optional timeout for requests ) ``` **LangGraph:** ``` from langchain_openai import ChatOpenAI def llm(self) -> ChatOpenAI: """Returns a ChatOpenAI instance configured to use OpenAI.""" return ChatOpenAI( model="gpt-4o-mini", # Define the OpenAI model name api_key="YOUR_OPENAI_API_KEY", # Your OpenAI API key timeout=self.timeout, # Optional timeout for requests ) ``` **LlamaIndex:** ``` from llama_index.llms.openai import OpenAI def llm(self) -> OpenAI: """Returns an OpenAI instance configured to use OpenAI.""" return OpenAI( model="gpt-4o-mini", # Define the OpenAI model name api_key="YOUR_OPENAI_API_KEY", # Your OpenAI API key timeout=self.timeout, # Optional timeout for requests ) ``` ## Anthropic API configuration You can connect to Anthropic's Claude models using the Anthropic API. The template supports connecting to Anthropic models through both CrewAI and LangGraph frameworks. You'll need an Anthropic API key to use these models. **CrewAI:** ``` from crewai import LLM def llm(self) -> LLM: """Returns a CrewAI LLM instance configured to use Anthropic.""" return LLM( model="claude-3-5-sonnet-20241022", # Define the Anthropic model name api_key="YOUR_ANTHROPIC_API_KEY", # Your Anthropic API key timeout=self.timeout, # Optional timeout for requests ) ``` **LangGraph:** ``` from langchain_anthropic import ChatAnthropic def llm(self) -> ChatAnthropic: """Returns a ChatAnthropic instance configured to use Anthropic.""" return ChatAnthropic( model="claude-3-5-sonnet-20241022", # Define the Anthropic model name anthropic_api_key="YOUR_ANTHROPIC_API_KEY", # Your Anthropic API key timeout=self.timeout, # Optional timeout for requests ) ``` **LlamaIndex:** ``` from llama_index.llms.anthropic import Anthropic def llm(self) -> Anthropic: """Returns an Anthropic instance configured to use Anthropic.""" return Anthropic( model="claude-3-5-sonnet-20241022", # Define the Anthropic model name api_key="YOUR_ANTHROPIC_API_KEY", # Your Anthropic API key timeout=self.timeout, # Optional timeout for requests ) ``` ## Gemini API configuration You can also connect to Google's Gemini models using the Gemini API. The template supports connecting to Gemini models through both CrewAI and LangGraph frameworks. You'll need a Google AI API key to use these models. **CrewAI:** ``` from crewai import LLM def llm(self) -> LLM: """Returns a CrewAI LLM instance configured to use Gemini.""" return LLM( model="gemini/gemini-1.5-flash", # Define the Gemini model name api_key="YOUR_GEMINI_API_KEY", # Your Google AI API key timeout=self.timeout, # Optional timeout for requests ) ``` **LangGraph:** ``` from langchain_google_genai import ChatGoogleGenerativeAI def llm(self) -> ChatGoogleGenerativeAI: """Returns a ChatGoogleGenerativeAI instance configured to use Gemini.""" return ChatGoogleGenerativeAI( model="gemini-1.5-flash", # Define the Gemini model name google_api_key="YOUR_GEMINI_API_KEY", # Your Google AI API key timeout=self.timeout, # Optional timeout for requests ) ``` **LlamaIndex:** ``` from llama_index.llms.gemini import Gemini def llm(self) -> Gemini: """Returns a Gemini instance configured to use Google's Gemini.""" return Gemini( model="gemini-1.5-flash", # Define the Gemini model name api_key="YOUR_GEMINI_API_KEY", # Your Google AI api key timeout=self.timeout, # Optional timeout for requests ) ``` ## Connect to other providers You can connect to any other LLM provider that supports the OpenAI API standard by following the patterns shown in the examples above. For providers that don't natively support the OpenAI API format, you have several options to help bridge the connection: ### Review framework documentation Each framework provides comprehensive documentation for connecting to various LLM providers: - CrewAI : Visit the CrewAI LLM documentation for detailed examples of connecting to different providers - LangGraph : Check the LangChain LLM integrations for extensive provider support - LlamaIndex : Refer to the LlamaIndex LLM modules for various LLM integrations - NAT : Refer to the NVIDIA NeMo Agent Toolkit documentation for LLM configuration in workflow.yaml ### Use LiteLLM for universal connectivity [LiteLLM](https://docs.litellm.ai/) is a library that provides a unified interface for connecting to 100+ LLM providers. It translates requests to match each provider's specific API format, making it easier to connect to providers like: - Azure OpenAI - AWS Bedrock - Google Vertex AI - Cohere - Hugging Face - Ollama - And more When using LiteLLM, the model string uses a compound format: `provider/model-name` - Provider : The API adapter/provider to use (e.g., openai , azure , etc.). - Model name : The model name to pass to that provider. For example, if the deployment uses a model named `meta/llama-3.2-1b-instruct`, use `openai/meta/llama-3.2-1b-instruct` for the model string. This tells LiteLLM to use the [openaiAPI adapter](https://docs.litellm.ai/docs/providers/openai_compatible) and the model name `meta/llama-3.2-1b-instruct`. This format allows LiteLLM to route requests to the appropriate provider API while using the correct model identifier for that provider. For the most up-to-date list of supported providers and configuration examples, visit the [LiteLLM documentation](https://docs.litellm.ai/docs/providers). --- # Agent components URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-overview.html > This overview details the components required to create an agent using DataRobot's agent framework. # Agent components This overview details the components required to create an agent using DataRobot's agent framework. An agent artifact includes several standard files that contain metadata, hooks/functions, classes, and properties. | Section | Description | | --- | --- | | Agent file structure | Describes important files and their organization for a DataRobot agent. | | Functions and hooks | Details the mandatory functions and integration hooks needed for agent operation. | | Agent class implementation | Details the general structure of the main agent class and its methods and properties. | | Tool integration | Explains how agents use tools via the ToolClient class and framework-specific tool APIs. | ## Agent file structure Every DataRobot agent requires a specific set of files in the `agent/agent/` directory (for example, in the [DataRobot Agentic Starter template](https://github.com/datarobot-community/datarobot-agent-application)). These files work together to create a complete agent that can be deployed and executed by DataRobot. ``` # agent/agent/ directory agent/agent/ ├── __init__.py # Package initialization ├── myagent.py # Main agent implementation, including prompts ├── custom.py # DataRobot integration hooks ├── config.py # Configuration management ├── mcp_client.py # MCP server integration (optional, for tool use) └── model-metadata.yaml # Agent metadata configuration ``` | File | Description | | --- | --- | | __init__.py | Identifies the directory as a Python package and enables imports. | | model-metadata.yaml | Defines the agent's configuration, runtime parameters, and deployment settings. | | custom.py | Implements DataRobot integration hooks (load_model, chat) for agent execution. | | myagent.py | Contains the main MyAgent class with core workflow logic and framework-specific implementation. | | config.py | Manages configuration loading from environment variables, runtime parameters, and DataRobot credentials. | | mcp_client.py | Provides MCP server connection management for tool integration (optional, only needed when using MCP tools). | ### Agent metadata (model-metadata.yaml) The `model-metadata.yaml` file tells DataRobot how to configure and deploy the agent. It defines the agent's type, name, and any required runtime parameters. ``` # model-metadata.yaml --- name: agent_name type: inference targetType: agenticworkflow runtimeParameterDefinitions: - fieldName: LLM_DEPLOYMENT_ID defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string - fieldName: LLM_DEFAULT_MODEL defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string - fieldName: LLM_DEFAULT_MODEL_FRIENDLY_NAME defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string - fieldName: USE_DATAROBOT_LLM_GATEWAY defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string - fieldName: MCP_DEPLOYMENT_ID defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string - fieldName: EXTERNAL_MCP_URL defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string - fieldName: SESSION_SECRET_KEY defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string ``` | Field | Description | | --- | --- | | name | The agent's display name in DataRobot (used for identification and deployment). | | type | The agent model type. Must be inference for all DataRobot agents. | | targetType | The agent target type. Must be agenticworkflow for agentic workflow deployments. | | runtimeParameterDefinitions | Defines optional runtime parameters for LLM configuration, MCP server connections, and other agent settings. | > [!TIP] LLM Provider Configuration > Agents support multiple LLM provider configurations including: > > LLM gateway direct > : Use DataRobot's LLM gateway directly > LLM blueprint with external LLMs > : Connect to external providers (Azure OpenAI, Amazon Bedrock, Google Vertex AI, Anthropic, Cohere, TogetherAI) > Deployed models > : Use a DataRobot-deployed LLM via > LLM_DEPLOYMENT_ID > > When you use the DataRobot Deployed LLM option, `USE_DATAROBOT_LLM_GATEWAY` is automatically set to `0` so the workflow targets your deployment instead of the gateway. ## Functions and hooks (custom.py) Agents use specific function signatures called "hooks" to integrate with DataRobot. The `custom.py` file contains the required functions that DataRobot calls to execute the agent. These functions connect DataRobot and the agent's logic. For more information, see the [structured model hooks documentation](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html). The following DataRobot custom model hooks are implemented in `custom.py`: | Component | Description | | --- | --- | | load_model() | One-time initialization function called when DataRobot starts the agent. | | chat() | Main execution function called for each user interaction/chat message. | > [!TIP] Other DataRobot hooks > The `score()` and `score_unstructured()` functions can be implemented if required for specific use cases. ### load_model() hook The `load_model()` hook is called once to initialize the agent. This is where any one-time configuration can be defined. ``` # custom.py def load_model(code_dir: str) -> tuple[ThreadPoolExecutor, asyncio.AbstractEventLoop]: """The agent is instantiated in this function and returned. Args: code_dir: Path to the agentic workflow directory Returns: tuple[ThreadPoolExecutor, asyncio.AbstractEventLoop]: Thread pool executor and event loop for async operations """ thread_pool_executor = ThreadPoolExecutor(1) event_loop = asyncio.new_event_loop() thread_pool_executor.submit(asyncio.set_event_loop, event_loop).result() return (thread_pool_executor, event_loop) ``` ### chat() hook The main entry point for the agent. DataRobot calls this function every time a user sends a message to the agent. ``` # custom.py def chat( completion_create_params: CompletionCreateParams | CompletionCreateParamsNonStreaming | CompletionCreateParamsStreaming, load_model_result: tuple[ThreadPoolExecutor, asyncio.AbstractEventLoop], **kwargs: Any, ) -> Union[CustomModelChatResponse, Iterator[CustomModelStreamingResponse]]: """Main entry point for agent execution via chat endpoint. Args: completion_create_params: OpenAI-compatible completion parameters load_model_result: Result from load_model() function **kwargs: Additional keyword arguments (e.g., headers) Returns: Union[CustomModelChatResponse, Iterator[CustomModelStreamingResponse]]: Formatted response with agent output """ ``` ## Agent class implementation (myagent.py) The `myagent.py` file contains the `MyAgent` class to implement the workflow logic. This is where you define how the agent behaves, the tools it uses, and how it processes inputs. | Component | Description | | --- | --- | | init() | Method to initialize the agent with credentials, configuration, and framework-specific setup. | | invoke() | Main execution method that processes inputs and returns framework-specific results. | | llm or llm() | Property or method (depending on framework template) that returns the configured LLM instance for agent operations. | Every agent follows this basic pattern, though the specific implementation varies by framework. > [!NOTE] Framework-specific implementations > CrewAI, LangGraph, LlamaIndex, and NAT (NVIDIA NeMo Agent Toolkit) templates include framework-specific `llm` or `llm()` implementations and return types. The Generic Base template provides a minimal implementation that can be customized for any framework. NAT templates configure LLMs in `workflow.yaml` rather than through Python `llm` configuration. ``` # myagent.py class MyAgent: """Agent implementation following DataRobot patterns.""" def __init__( self, api_key: Optional[str] = None, api_base: Optional[str] = None, model: Optional[str] = None, verbose: Optional[Union[bool, str]] = True, timeout: Optional[int] = 90, **kwargs: Any, ): """Initialize agent with credentials and configuration.""" self.api_key = api_key or os.environ.get("DATAROBOT_API_TOKEN") self.api_base = api_base or os.environ.get("DATAROBOT_ENDPOINT") self.model = model self.timeout = timeout # ... other initialization @property def llm(self) -> LLM: # Framework-specific type """Primary LLM configuration.""" if os.environ.get("LLM_DEPLOYMENT_ID"): return self.llm_with_datarobot_deployment else: return self.llm_with_datarobot_llm_gateway def invoke(self, completion_create_params: CompletionCreateParams) -> Union[ Generator[tuple[str, Any | None, dict[str, int]], None, None], tuple[str, Any | None, dict[str, int]], ]: """Main execution method - REQUIRED.""" # Extract inputs inputs = create_inputs_from_completion_params(completion_create_params) # Execute agent workflow # Return results return response_text, pipeline_interactions, usage_metrics ``` ### __init__() method The method that initializes the agent with configuration and credentials from DataRobot and framework-specific setup. ``` # myagent.py class MyAgent: def __init__(self, api_key: Optional[str] = None, api_base: Optional[str] = None, model: Optional[str] = None, verbose: Optional[Union[bool, str]] = True, timeout: Optional[int] = 90, **kwargs: Any): """Initialize agent with DataRobot credentials and configuration.""" ``` ### invoke() method The core execution method that DataRobot calls to run the agent. This method must be implemented and should contain the agent's main workflow logic. All frameworks use the same return type pattern. ``` # myagent.py def invoke(self, completion_create_params: CompletionCreateParams) -> Union[ Generator[tuple[str, Any | None, dict[str, int]], None, None], tuple[str, Any | None, dict[str, int]], ]: """Main execution method - REQUIRED for DataRobot integration. Args: completion_create_params: Input parameters from DataRobot Returns: Union of generator (for streaming) or tuple (for non-streaming): - response_text: str - The agent's response - pipeline_interactions: Any | None - Event tracking data - usage_metrics: dict[str, int] - Token usage statistics """ ``` ### llm property Defines which language model the agent uses for generating responses. The return type and implementation varies by framework. The CrewAI, LangGraph, and LlamaIndex templates implement API base URL logic directly within their `llm` properties: ``` # myagent.py (CrewAI/LangGraph/LlamaIndex) @property def llm(self) -> LLM: # Framework-specific type """Primary LLM instance for agent operations. Returns: Framework-specific LLM type: - CrewAI: LLM - LangGraph: ChatLiteLLM - LlamaIndex: DataRobotLiteLLM """ api_base = urlparse(self.api_base) if os.environ.get("LLM_DEPLOYMENT_ID"): # Handle deployment-specific URL construction # ... implementation details ... return LLM(model="openai/gpt-4o-mini", api_base=deployment_url, ...) else: # Handle LLM gateway URL construction # ... implementation details ... return LLM(model="datarobot/azure/gpt-5-mini-2025-08-07", api_base=api_base.geturl(), ...) ``` The Generic Base template implements the `llm` property as follows: ``` # myagent.py (Generic Base) @property def llm(self) -> Any: """Primary LLM instance for agent operations. Returns: Any: Minimal implementation for custom frameworks """ if os.environ.get("LLM_DEPLOYMENT_ID"): return self.llm_with_datarobot_deployment else: return self.llm_with_datarobot_llm_gateway ``` The NAT template configures LLMs in `workflow.yaml` rather than through a Python property. You can use the DataRobot LLM gateway ( `_type: datarobot-llm-gateway`), DataRobot deployments ( `_type: datarobot-llm-deployment`), or DataRobot NIM deployments ( `_type: datarobot-nim`). The example below uses the DataRobot LLM gateway: ``` # workflow.yaml (NAT) llms: datarobot_llm: _type: datarobot-llm-gateway model_name: azure/gpt-4o-mini # Define the model name you want to use temperature: 0.0 ``` The LLM a specific agent uses is defined through the `llm_name` in the definition of that agent in the `functions` section: ``` # workflow.yaml (NAT) functions: planner: _type: chat_completion llm_name: datarobot_llm # Reference the LLM defined above system_prompt: | You are a content planner... ``` If more than one LLM is defined in the `llms` section, the various `functions` can use different LLMs to suit the task. > [!TIP] NAT-provided LLM interfaces > Alternatively, you can use any of the [NAT-provided LLM interfaces](https://docs.nvidia.com/nemo/agent-toolkit/latest/workflows/llms/index.html) instead of the LLM gateway. To use a NAT LLM interface, add the required configuration parameters such as `api_key`, `url`, and other provider-specific settings directly in the `workflow.yaml` file. For information about using DataRobot deployments with NAT templates, see [Configure LLM providers in code](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-llm-providers.html#datarobot-hosted-llm-deployments). ## Tool integration Agents can use tools to extend their capabilities with the `ToolClient` class, which enables agents to call DataRobot tool deployments. The functionality of `helpers.py` present in previous versions was moved to the [datarobot-genaipackage](https://github.com/datarobot-oss/datarobot-genai) along with other adapter code that connects agents to DataRobot's DRUM server in version 11.3.1. > [!NOTE] ToolClient usage consideration > The `ToolClient` is specifically designed for calling user-deployed global tools within DataRobot. As this client serves that specialized use case, it is not required for the majority of agent tool implementations. ### Framework-specific tools Each framework provides its own native tool APIs for defining custom tools: - CrewAI : Tools are passed to Agent instances via the tools parameter. - LangGraph : Tools are defined as part of graph nodes and edges. - LlamaIndex : Tools are defined as functions and passed to agent constructors. - NAT : Tools are defined in workflow.yaml as functions and referenced in the workflow's tool_list (the available tool types are defined by thenat_toolsubmodules ). > [!TIP] NAT agent configuration > Use [react_agent](https://docs.nvidia.com/nemo/agent-toolkit/latest/workflows/about/react-agent.html) instead of [sequential_executor](https://docs.nvidia.com/nemo/agent-toolkit/latest/workflows/about/sequential-executor.html) for flexible agents that decide which tools to run in which order, depending on the query. ### Authorization context The `resolve_authorization_context()` function from the `datarobot-genai` package is called in `custom.py` to automatically handle authentication for tools that require access tokens. The function returns an authorization context dictionary that is assigned to `completion_create_params["authorization_context"]`. This ensures tools can securely access external services using DataRobot's credential management system. --- # Add Python packages URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-python-packages.html > Add required Python packages to agentic workflows using pyproject.toml dependency management. # Add Python packages To add additional Python packages to your agent environment, add them to your `pyproject.toml` file using uv, a modern Python package and environment manager. > [!WARNING] Keep existing packages > We recommend keeping existing packages in `pyproject.toml` to ensure consistent behavior across playground and deployment environments. > [!TIP] Fast iteration with runtime dependencies > For rapid development, you can add dependencies using [runtime dependencies](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-python-packages.html#add-runtime-dependencies-fast-iteration) without rebuilding the Docker image. This is ideal for quick testing during development. The typical workflow for adding packages is: 1. Add the package: uv add 2. Test locally: dr run dev or task dev (runs the development server) 3. Build Docker image: dr task agent:build-docker-context (if using custom Docker image) 4. Deploy: Use your deployment pipeline > [!NOTE] Automatic synchronization > During `dr run deploy`, `dr run deploy-dev`, or `dr run dev`, the `pyproject.toml` file is automatically synchronized with the agentic workflow code. Package installation is auto-synchronized with `uv` by default. You don't need to manually update the `pyproject.toml` file in these directories. ## Add packages to your agent 1. Navigate to your agent directory and use uv to add the new package: ``` cd agent uv add ``` For example, to add the `requests` package: ``` uv add requests ``` You can also specify version constraints: ``` uv add "requests>=2.32.0" ``` 1. Create a custom execution environment to test your agent in the playground: 2. Open the.envfile. 3. SetDATAROBOT_DEFAULT_EXECUTION_ENVIRONMENT=to empty or delete the line completely. 4. Optional: If you need to test with a custom Docker image, first create the docker context: ``` # Create docker_context directory if needed dr task agent:create-docker-context # Then build and test the Docker image cd agent/docker_context docker build -f Dockerfile . -t docker_context_test ``` After completing these steps, when you run `dr run dev`, `dr run deploy-dev`, or `dr run deploy`, the new environment will be automatically built the first time. Subsequent builds will use the cached environment if the requirements have not changed. The new environment will be automatically linked and used for all your agent components, models, and deployments. You can manually test building your agent image by running the following command, ensuring the new dependency is defined successfully: ``` dr task agent:build-docker-context ``` > [!NOTE] docker_context is optional > The `docker_context` directory is no longer included by default. If you need to build a custom Docker image, first create the docker context using `dr task agent:create-docker-context`. This command downloads the necessary Docker files and creates the `docker_context` directory in your agent folder. The `dr task agent:build-docker-context` command will then copy the updated `pyproject.toml` to the `docker_context/` directory, build the Docker image with the new dependencies, and save the image. > [!WARNING] Don't remove requirements > Don't remove any packages from the `pyproject.toml` file unless you are certain they aren't needed. Removing packages may lead to unexpected behavior with playground or deployment interactions, even if the local execution environment works correctly. ## Agent-specific considerations Different agent types may have different dependency management approaches: | Agent Type | Dependency Management Approach | | --- | --- | | agent_generic_base | Uses pyproject.toml. | | agent_llamaindex | Uses pyproject.toml with auto-generated requirements.in and requirements.txt files. | | agent_crewai | Uses pyproject.toml. | | agent_langgraph | Uses pyproject.toml. | | agent_nat | Uses pyproject.toml. | For all agent types, the recommended approach is to use `uv add ` to modify the `pyproject.toml` file, which will automatically handle dependency resolution and version constraints. The `pyproject.toml` file serves as the single source of truth for all dependencies, and the build process automatically handles the Docker environment setup. ## Add runtime dependencies (Fast iteration) For rapid development and testing, add dependencies at runtime without rebuilding the Docker image. Dependencies added to the `extras` group in your `pyproject.toml` file will be installed when the prompt is first executed in the Playground or when the deployment starts. Runtime dependencies are ideal for: - Quick iteration during development - Testing new packages without rebuilding images - Adding lightweight dependencies that don't require compilation Add runtime dependencies directly using `uv`: ``` cd agent uv add --active --no-upgrade --group extras "chromadb>=1.1.1" ``` > [!TIP] Use --no-upgrade flag > The `--no-upgrade` flag is crucial to minimize the time required to install extra dependencies. It ensures that only new dependencies are added without upgrading existing ones, which helps to keep the runtime installation fast and minimize differences from the execution environment. ### Feature considerations Runtime dependencies have several important limitations to consider: - Internet access required: Since extra dependencies are installed at runtime,internet access is required. In restricted network environments, you mustconfigure an internal PyPI proxyor build custom execution environments to include custom dependencies. - Installation complexity: If a dependency has a sophisticated installation process (for example, compilation of C bindings, setting up cache, or custom build steps), the runtime installation may fail due to restrictions in the execution environment. In such cases, building a custom execution environment is the only option. - Security considerations: Using runtime dependencies makes it possible to quickly fix CVE issues in runtime by upgrading library versions directly in the agent. However, it's also possible to introduce vulnerabilities by downgrading libraries to vulnerable versions, even if the execution environment doesn't have those vulnerabilities. You must update both execution environments andpyproject.toml/uv.lockfiles in your agents to ensure vulnerabilities are properly fixed. Consider the following best practices for using runtime dependencies: - Use runtime dependencies for development and testing. - For production deployments, consider building custom execution environments with all required dependencies installed. - Keep the pyproject.toml file synchronized with all dependencies to ensure consistency across environments. - Minimize upgrades by using the --no-upgrade flag to keep installation times fast. --- # Quickstart URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-quickstart.html > Learn how to build, deploy, and test agentic workflows using DataRobot's pre-built templates for popular AI agent frameworks. # Quickstart This guide covers creating, deploying, and testing an agentic application using DataRobot's pre-built templates, including setting up a development environment, configuring an agent framework, and deploying your workflow to interact with DataRobot's chat interface. ## Prerequisites Before proceeding, [install the required components](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-install.html). > [!WARNING] Installation process > Before starting, complete all installation and setup steps in [agentic-install](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-install.html). Skipping this process can cause errors and prevent your agentic application from running correctly. Do not proceed until you have finished the installation guide. ## Get started Run the following command to start the local development environment: ``` dr start ``` This command starts the DataRobot CLI's interactive wizard ( [dr start](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/start.html) in the [CLI command reference](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/index.html)). It will automatically clone the application repository and create a `.env` file in the root directory populated with environment variables you specify. The wizard provides guidance and context for each step, but for more details, click the dropdown below. > [!NOTE] First-time initialization > When run for the first time, the `dr start` command prepares your development environment to develop and deploy your application. > This includes both environment and agent component configuration. > After this first initialization, future `dr start` operations will only set up your local environment. > For subsequent updates to the configuration of your agent component, please run the `dr component update` command. After `dr start` completes successfully, you should see: - A .env file in your project root - Your application directory created (typically named datarobot-agent-application or based on your application name) Now that your application is configured, proceed to the next section. ## Run your agent > [!WARNING] Running your agent > Do not proceed to this section until you have run `dr start`, detailed in the previous section. Navigate to the application directory created during `dr start`: ``` cd datarobot-agent-application # or the custom directory name you specified during the wizard, if different ``` Then, run the following command to start all components of the application: ``` task dev ``` This starts four processes, running in parallel: - Application frontend - Application backend - Agent - MCP server Once all services are running: 1. Open your web browser and navigate to http://localhost:5173 2. You should see the agent application interface 3. Try sending a test message to verify everything is working From here, you can start customizing your agent by adding your own logic and functionality. See the [Develop your agent](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-quickstart.html#develop-your-agent) section for more details. > [!NOTE] Starting individual services > You can also start individual services in separate terminal windows; for example, `task agent:dev` will start just the agent. ## Develop your agent Now that your agent has been built and tested, you are ready to customize it by adding your own logic and functionality. See the following documentation for more details: - Customize your agent - Add tools to your agent - Configure LLM providers - Add Python requirements - Manage prompts ## Deploy your agent > [!WARNING] Testing your agent > Ensure that you have tested your agent locally before deploying. Next, deploy your agent to DataRobot, which requires a Pulumi login. Run the following command to deploy your agent: ``` dr task run deploy ``` For more on the `task` and `run` commands, see the [CLI task command](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/task.html) and [CLI run command](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/run.html). > [!NOTE] Deployment process > The deployment process will take several minutes to complete. Once deployment is complete, the script displays the deployment details, as shown in the example below. Note that the deployment details will vary based on your configuration. ``` Outputs: AGENT_DEPLOYMENT_ID : "69331fad5e07469e7c4f5c6f" Agent Custom Model Chat Endpoint [apptest] [agent]: "https://datarobot.com/api/v2/genai/agents/fromCustomModel/69331f816e1bf9f1890d5d1d/chat/" Agent Deployment Chat Endpoint [apptest] [agent] : "https://datarobot.com/api/v2/deployments/69331fad5e07469e7c4f5c6f/chat/completions" Agent Execution Environment ID [apptest] [agent] : "680fe4949604e9eba46b1775" Agent Playground URL [apptest] [agent] : "https://datarobot.com/usecases/69331e4c3be0efe3b95a7be0/agentic-playgrounds/69331e4d1c036307186c9b16/comparison/chats" Agentic Starter [apptest] : "https://datarobot.com/custom_applications/6933204a9e21e9b59b5a7bee/" DATABASE_URI : "sqlite+aiosqlite:////tmp/agent_app/.data/agent_app.db" DATAROBOT_APPLICATION_ID : "6933204a9e21e9b59b5a7bee" DATAROBOT_OAUTH_PROVIDERS : (json) [] LLM_DEFAULT_MODEL : "azure/gpt-4o-2024-11-20" SESSION_SECRET_KEY : "secretkey123" USE_DATAROBOT_LLM_GATEWAY : "1" [apptest] [mcp_server] Custom Model Id : "69331eebb49131d3d5430ac7" [apptest] [mcp_server] Deployment Id : "69331f1f30548f83b668d9dc" [apptest] [mcp_server] MCP Server Base Endpoint : "https://datarobot.com/api/v2/deployments/69331f1f30548f83b668d9dc/directAccess/" [apptest] [mcp_server] MCP Server MCP Endpoint : "https://datarobot.com/api/v2/deployments/69331f1f30548f83b668d9dc/directAccess/mcp" ``` > [!NOTE] Note > The sample output above reflects an agent using the LLM gateway ( `USE_DATAROBOT_LLM_GATEWAY` is `"1"`). If you use the DataRobot Deployed LLM option instead, `USE_DATAROBOT_LLM_GATEWAY` is automatically set to `0`. --- # Access request headers URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-request-headers.html > Learn how to access HTTP request headers in your deployed agents for authentication, tracking, and custom metadata. # Access request headers When your agent is deployed, you may need to access HTTP request headers for authentication, tracking, or custom metadata. DataRobot makes headers available to your agent code through the `chat()` function's `**kwargs` parameter. ## Extracting X-Untrusted-* headers Headers with the `X-Untrusted-*` prefix are passed through from the original request. They are available in the `kwargs` dictionary, located in `agent/agent/custom.py`: ``` # agent/agent/custom.py import asyncio from concurrent.futures import ThreadPoolExecutor from typing import Any, Iterator, Union from openai.types.chat import CompletionCreateParams from openai.types.chat.completion_create_params import ( CompletionCreateParamsNonStreaming, CompletionCreateParamsStreaming, ) def chat( completion_create_params: CompletionCreateParams | CompletionCreateParamsNonStreaming | CompletionCreateParamsStreaming, load_model_result: tuple[ThreadPoolExecutor, asyncio.AbstractEventLoop], **kwargs: Any, ) -> Union[CustomModelChatResponse, Iterator[CustomModelStreamingResponse]]: # Extract all headers from kwargs headers = kwargs.get("headers", {}) # Access specific X-Untrusted-* headers authorization_header = headers.get("X-Untrusted-Authorization") custom_header = headers.get("X-Untrusted-Custom-Metadata") # Use headers in your agent logic if authorization_header: # Pass to downstream services, tools, etc. # Do not log raw authorization tokens; log only non-sensitive metadata. request_id = headers.get("X-Untrusted-Request-ID") if request_id: print(f"Processing request {request_id} with authorization header present") else: print("Processing request with authorization header present") # Continue with agent logic... ``` ## Common use cases Request headers can be used for various purposes in your agent workflows. The following examples demonstrate common patterns for extracting and using header information: ### Passing authentication to external services Extract authentication tokens from request headers and pass them to external services. This allows you to forward authentication credentials from incoming requests to downstream tools and services: ``` # agent/agent/custom.py headers = kwargs.get("headers", {}) auth_token = headers.get("X-Untrusted-Authorization") # Use the token to authenticate with external APIs tool_client = MyTool(auth_token=auth_token) ``` ### Tracking request metadata Use headers to track request IDs, user IDs, or other metadata for debugging and analytics. This helps you trace requests through your agent workflow: ``` # agent/agent/custom.py headers = kwargs.get("headers", {}) request_id = headers.get("X-Untrusted-Request-ID") user_id = headers.get("X-Untrusted-User-ID") # Log metadata for debugging or analytics logging.info(f"Processing request {request_id} for user {user_id}") ``` ### Conditional agent behavior Adjust agent behavior based on request context from headers. This enables you to customize agent behavior for different regions, users, or deployment contexts: ``` # agent/agent/custom.py headers = kwargs.get("headers", {}) region = headers.get("X-Untrusted-Region") # Adjust agent behavior based on request context if region == "EU": agent = MyAgent(enable_gdpr_mode=True) ``` ## Important considerations When working with request headers in your agent code, keep the following in mind: - Only headers with the X-Untrusted-* prefix are passed through to your agent code. - Headers are case-sensitive when accessing them from the dictionary. - Always provide default values or check for None when accessing headers that may not be present. - Headers are available in both streaming and non-streaming chat responses. ## Log request headers for debugging Log all available headers to inspect what's being passed to your agent: ``` # agent/agent/custom.py import asyncio import logging from concurrent.futures import ThreadPoolExecutor from typing import Any def chat(completion_create_params, load_model_result: tuple[ThreadPoolExecutor, asyncio.AbstractEventLoop], **kwargs: Any): headers = kwargs.get("headers", {}) # Log all headers to inspect what's available if headers: logging.warning(f"All headers: {dict(headers)}") # Continue with agent logic... ``` --- # DataRobot agentic skills URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-skills.html > Learn how to use DataRobot agentic skills to enhance your AI agent's capabilities. # DataRobot agentic skills This overview details the skills available in the [DataRobot Agentic Skills repository](https://github.com/datarobot-oss/datarobot-agent-skills). Agentic skills are modular, task-specific capability packages that help an AI agent move from general reasoning to reliable execution. Each skill bundles instructions, examples, and supporting resources so that the agent can load only what it needs for the current task, reducing context overload and improving tool use within a given workflow. DataRobot skills are Agent Context Protocol (ACP) definitions for enterprise AI and agent workflows, including building, deploying, and governing agents, as well as AI/ML tasks such as model training, deployment, predictions, feature engineering, and monitoring. They work with major coding agents, including OpenAI Codex, Anthropic Claude Code, Google Gemini CLI, Cursor, and VS Code Copilot. > [!NOTE] Skills nomenclature > "Skills" is an Anthropic term used in Claude AI and Claude Code, but the concept applies more broadly. OpenAI Codex uses `AGENTS.md` to define agent instructions, and Gemini uses `gemini-extension.json` for extensions. This repository is compatible with all of them, and more. ## Quick start Install all DataRobot skills, or only the ones you need, for all your AI agents with one command by using the [universal skills installer](https://github.com/skillcreatorai/Ai-Agent-Skills). For all skills: ``` npx ai-agent-skills install datarobot-oss/datarobot-agent-skills ``` For a specific skill: ``` npx ai-agent-skills install datarobot-oss/datarobot-agent-skills/datarobot-predictions ``` For a specific agent: ``` npx ai-agent-skills install datarobot-oss/datarobot-agent-skills --agent cursor npx ai-agent-skills install datarobot-oss/datarobot-agent-skills --agent claude ``` > [!NOTE] Default behavior > By default, the installer copies skills to all supported agents at the same time. No configuration is required. > For agent-specific installation methods, see the [Installation to your coding agent](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-skills.html#installation-to-your-coding-agent) section below. ### How do skills work? Skills are self-contained folders that package instructions, scripts, and resources for a specific use case. Each folder includes a `SKILL.md` file with YAML frontmatter ( `name` and `description`), followed by the guidance your coding agent uses while the skill is active. > [!NOTE] Skill naming convention > All DataRobot skills follow the naming convention `datarobot-`, where `` describes the skill's focus area. This provides clear identification of DataRobot-specific skills, consistent naming across the skill library, and easy discovery and organization. ### Installation to your coding agent DataRobot skills are compatible with Claude Code, Codex, Gemini CLI, Cursor, and VS Code Copilot. Refer to the section below that corresponds to your coding agent to see the installation instructions. #### Claude Code Register the repository as a plugin marketplace: ``` /plugin marketplace add datarobot-oss/datarobot-agent-skills ``` To install a skill, run: ``` /plugin install @datarobot-skills ``` For example: ``` /plugin install datarobot-model-training@datarobot-skills ``` #### Codex Codex identifies the skills through the `AGENTS.md` file. You can verify that the instructions are loaded by running: ``` codex --ask-for-approval never "Summarize the current instructions." ``` For more details, see the [CodexAGENTS.md](https://developers.openai.com/codex/guides/agents-md) documentation. #### Gemini CLI This repository includes `gemini-extension.json` for Gemini CLI integration. Install locally: ``` gemini extensions install . --consent ``` Or install from the GitHub URL: ``` gemini extensions install https://github.com/datarobot-oss/datarobot-agent-skills.git --consent ``` See the [Gemini CLI extensions](https://geminicli.com/docs/extensions/) documentation for more information. #### Cursor Cursor can automatically detect and use skills from this repository in two main ways: Option 1: Use `AGENTS.md` > NOTE: This option is the recommended approach. When you open this repository as your workspace, Cursor automatically reads the `AGENTS.md` file. The skills are available immediately without additional configuration. To verify that the skills are loaded: 1. Open Cursor in this repository. 2. Open the AI chat panel ( Cmd/Ctrl + L ). 3. Ask: "What DataRobot skills are available?" Option 2: Use `.cursorrules` You can also reference specific skills in your `.cursorrules` file to make sure they are always loaded: ``` # .cursorrules You have access to DataRobot skills in this repository. Available skills (in datarobot-* folders): - datarobot-model-training: Model training and project creation - datarobot-predictions: Making predictions and generating templates - datarobot-model-deployment: Deploying and managing models - datarobot-feature-engineering: Feature analysis and engineering - datarobot-model-monitoring: Model performance monitoring - datarobot-model-explainability: Model explainability and diagnostics - datarobot-data-preparation: Data upload and validation When asked to use a DataRobot skill, read the corresponding SKILL.md file for detailed guidance. ``` Using skills in Cursor: - "Use the datarobot-predictions skill to generate a template for deployment abc123" - "Follow the datarobot-model-training skill to create a new project" - "Check the datarobot-model-monitoring skill to analyze data drift" #### VS Code Copilot (GitHub Copilot) VS Code with GitHub Copilot can automatically detect and use skills from this repository through the `AGENTS.md` file. Setup: 1. Open this repository in VS Code. 2. Ensure that the GitHub Copilot extension is installed and activated. 3. Skills are automatically available through the AGENTS.md file. Verify that the skills are loaded: Open Copilot Chat ( `Cmd/Ctrl + I`) and ask: - "What DataRobot skills are available?" - "List the available skills in this repository" Using skills in VS Code Copilot: In Copilot Chat, reference skills naturally: - "Use the datarobot-predictions skill to generate a template for deployment abc123" - "Following the datarobot-model-training skill, create a new project for customer churn prediction" - "Check the datarobot-model-monitoring skill and help me analyze data drift" > [!TIP] Tip > You can also use the `@workspace` agent in Copilot Chat to give it full context about the repository and available skills. ## Skills This repository contains skills for common DataRobot workflows. You can also contribute your own skills. ### Available skills | Skill Folder | Description | Documentation | | --- | --- | --- | | skills/datarobot-model-training/ | Instructions and utilities for training models, managing projects, and running AutoML experiments. | SKILL.md | | skills/datarobot-model-deployment/ | Tools for deploying models, managing deployments, and configuring prediction environments. | SKILL.md | | skills/datarobot-predictions/ | Guidance for making predictions, batch scoring, real-time predictions, and generating prediction datasets. | SKILL.md | | skills/datarobot-feature-engineering/ | Instructions for feature engineering, feature discovery, and feature importance analysis. | SKILL.md | | skills/datarobot-model-monitoring/ | Tools for monitoring model performance, tracking data drift, and managing model health. | SKILL.md | | skills/datarobot-model-explainability/ | Tools for model explainability, prediction explanations, SHAP values, and model diagnostics. | SKILL.md | | skills/datarobot-data-preparation/ | Utilities for data upload, dataset management, and data validation. | SKILL.md | | skills/datarobot-app-framework-cicd/ | Set up CI/CD pipelines for DataRobot application templates with GitLab and GitHub Actions. | SKILL.md | ## Using skills in your coding agent Once a skill is installed, mention it directly in your instructions to the coding agent: - "Use the DataRobot model training skill to create a new project and start AutoML training." - "Use the DataRobot predictions skill to generate a prediction dataset template for deployment abc123." - "Use the DataRobot feature engineering skill to analyze feature importance for my model." - "Use the DataRobot model monitoring skill to check data drift for deployment xyz789." Your coding agent automatically loads the corresponding `SKILL.md` instructions and any helper scripts it needs while completing the task. ### Helper scripts Some skills include helper scripts that an agent can run directly: - datarobot-predictions : get_deployment_features.py , generate_prediction_data_template.py , validate_prediction_data.py , make_prediction.py - datarobot-model-training : create_project.py , start_training.py , list_models.py - datarobot-data-preparation : upload_dataset.py These scripts are located in each skill's `scripts/` directory and can be executed directly or used as references when writing code. ## Additional references - Browse the latest instructions, scripts, and templates at datarobot-oss/datarobot-agent-skills . - Review the DataRobot documentation for the libraries and workflows referenced in each skill. - See the DataRobot Python SDK documentation for API reference. --- # Add tools to agents URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tools-integrate.html > Learn how to add local tools, predefined tools, and DataRobot global tools to your agentic workflows. # Add tools to agents To add local tools to your agents, you can start by modifying the `myagent.py` file or adding new files to the `agent` directory; the following examples will help you get started. Note that the structure and implementation of a tool is framework-specific. The examples provided here are for CrewAI, LangGraph, LlamaIndex, and NAT (NVIDIA NeMo Agent Toolkit). If you are using another framework, you will need to refer to the framework's documentation for details on how to implement tools. ## Call tools from an agent Once you have defined your tool, you can call it from your agent by adding it to the list of tools available to the agent. This is typically done in the `myagent.py` file where the agent is defined. You will need to import the tool class and add it to the agent's tool list. The following simple examples illustrate one way of doing this for each framework. **CrewAI:** To add a tool to a CrewAI agent, you can modify an agent in the `myagent.py` file in the `agent` directory. An example of modifying the `agent_planner` agent to use the sample tool from the [Local Datetime Tool Example](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tools-integrate.html#local-datetime-tool-example) is shown below: ``` @property def agent_planner(self) -> Agent: datetime_tool = DateTimeTool() # Import and instantiate your tool here return Agent( role="Content Planner", goal="Plan engaging and factually accurate content on {topic}", backstory="...", # truncated for brevity in this example allow_delegation=False, verbose=self.verbose, llm=self.llm(), tools=[datetime_tool] # Add your tool to the tools list here ) ``` **LangGraph:** To add a tool to a LangGraph agent, you can modify an agent in the `myagent.py` file in the `agent` directory. An example of modifying the `agent_planner` agent to use the sample tool from the [Local Datetime Tool Example](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tools-integrate.html#local-datetime-tool-example) is shown below: ``` from datarobot_genai.core.agents import make_system_prompt from langchain.agents import create_agent @property def agent_planner(self) -> Any: datetime_tool = DateTimeTool() # Import and instantiate your tool here return create_agent( self.llm(), tools=[datetime_tool] + self.mcp_tools, # Add your tool to the tools list here system_prompt=make_system_prompt( "...", # truncated for brevity in this example ), name="planner_agent", ) ``` **LlamaIndex:** To add a tool to a LlamaIndex agent, you can modify an agent in the `myagent.py` file in the `agent` directory. An example of modifying the `agent_planner` agent to use the sample tool from the [Local Datetime Tool Example](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tools-integrate.html#local-datetime-tool-example) is shown below: ``` @property def agent_planner(self) -> FunctionAgent: datetime_tool = DateTimeTool() return FunctionAgent( name="PlannerAgent", description="...", # truncated for brevity in this example system_prompt=( "..." # truncated for brevity in this example ), llm=self.llm(), tools=[self.planner_notes_tool, *self.mcp_tools, datetime_tool], can_handoff_to=["WriterAgent"], ) ``` **NAT:** In NAT templates, tools are defined as functions in the `workflow.yaml` file. Functions are defined in the `functions` section with `_type: chat_completion` and referenced in the workflow's `tool_list`. Refer to the [NVIDIA NeMo Agent Toolkit documentation](https://docs.nvidia.com/nemo/agent-toolkit/latest/index.html) for details on implementing tools in NAT. ## Use predefined tools Some frameworks provide predefined tools that you can use directly in your agents. For example, CrewAI provides a `SearchTool` that can be used to perform web searches. You can refer to the framework documentation for a list of predefined tools and how to use them. These tools can be added to your agent by simply importing them from the framework and adding them to the agent's tool list [as shown in the examples above](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tools-integrate.html#call-tools-from-an-agent). > [!NOTE] LangChain compatibility > Most agentic workflow frameworks (including CrewAI, LangGraph, and many others) are natively compatible with LangChain tools. This means you can import and use them without any modifications. You can refer to the [LangChain Tools Documentation](https://python.langchain.com/docs/concepts/tools) for more information on using LangChain tools in your agents. ### Local datetime tool The following examples show how to create a custom local datetime tool for your agents. This tool returns the current date and time, allowing the agent to be aware of and use the current date and time in its responses or actions. This tool does not require any network or file access and can be implemented and run without any additional permissions or credentials. > [!NOTE] Starting point for local tools > This example can be used as a starting point for creating other local tools that do not require external access. **CrewAI:** To add a local datetime tool to a CrewAI agent, you can modify the `myagent.py` file in the `agent` directory. You can add the following code to define the datetime tool: ``` from datetime import datetime from typing import Optional, Type from zoneinfo import ZoneInfo from pydantic import BaseModel, Field from crewai.tools import BaseTool class DateTimeToolInput(BaseModel): """Input schema for DateTimeTool.""" timezone: Optional[str] = Field( None, description="IANA timezone string (e.g., 'America/New_York', 'Europe/London', 'Asia/Tokyo'). " "If not provided, returns local time.", ) class DateTimeTool(BaseTool): name: str = "datetime_tool" description: str = ( "Returns the current date and time. Optionally accepts a timezone parameter as an IANA timezone " "string (e.g., 'America/New_York', 'Europe/London', 'Asia/Tokyo'). If no timezone is provided, " "returns local time.") args_schema: Type[BaseModel] = DateTimeToolInput def _run(self, timezone: Optional[str] = None) -> str: try: # If the agent provides the timezone parameter, use it to get the current time in that timezone if timezone: # Use the specified timezone tz = ZoneInfo(timezone) current_time = datetime.now(tz) return f"{current_time.strftime('%Y-%m-%d %H:%M:%S')} ({timezone})" # Return the current local time if the agent does not have or provide the timezone parameter else: # Use local time return datetime.now().strftime("%Y-%m-%d %H:%M:%S") # Gracefully handle errors and exceptions so the agent can understand the error # and attempt to correct in a followup call of the tool if needed. except Exception: return ( f"Error: Invalid timezone '{timezone}'. " f"Please use a valid IANA timezone string (e.g., 'America/New_York', 'Europe/London')." ) ``` **LangGraph:** To add a local datetime tool to a LangGraph agent, you can modify the `myagent.py` file in the `agent` directory. You can add the following code to define the datetime tool: ``` from datetime import datetime from typing import Optional from zoneinfo import ZoneInfo from langchain.tools import BaseTool class DateTimeTool(BaseTool): name: str = "datetime_tool" description: str = ( "Returns the current date and time. Optionally accepts a timezone parameter as an IANA timezone " "string (e.g., 'America/New_York', 'Europe/London', 'Asia/Tokyo'). If no timezone is provided, " "returns local time.") def _run(self, timezone: Optional[str] = None) -> str: try: # If the agent provides the timezone parameter, use it to get the current time in that timezone if timezone: # Use the specified timezone tz = ZoneInfo(timezone) current_time = datetime.now(tz) return f"{current_time.strftime('%Y-%m-%d %H:%M:%S')} ({timezone})" # Return the current local time if the agent does not have or provide the timezone parameter else: # Use local time return datetime.now().strftime("%Y-%m-%d %H:%M:%S") # Gracefully handle errors and exceptions so the agent can understand the error # and attempt to correct in a followup call of the tool if needed. except Exception: return ( f"Error: Invalid timezone '{timezone}'. " f"Please use a valid IANA timezone string (e.g., 'America/New_York', 'Europe/London')." ) ``` **LlamaIndex:** To add a local datetime tool to a LlamaIndex agent, you can modify the `myagent.py` file in the `agent` directory. You can add the following code to define the datetime tool: ``` from datetime import datetime from typing import Optional from zoneinfo import ZoneInfo from llama_index.core.tools import FunctionTool def _datetime_run(timezone: Optional[str] = None) -> str: """Returns the current date and time. Optionally accepts a timezone parameter as an IANA timezone string (e.g., 'America/New_York', 'Europe/London', 'Asia/Tokyo'). If no timezone is provided, returns local time.""" try: # If the agent provides the timezone parameter, use it to get the current time in that timezone if timezone: # Use the specified timezone tz = ZoneInfo(timezone) current_time = datetime.now(tz) return f"{current_time.strftime('%Y-%m-%d %H:%M:%S')} ({timezone})" # Return the current local time if the agent does not have or provide the timezone parameter else: # Use local time return datetime.now().strftime("%Y-%m-%d %H:%M:%S") # Gracefully handle errors and exceptions so the agent can understand the error # and attempt to correct in a followup call of the tool if needed. except Exception: return ( f"Error: Invalid timezone '{timezone}'. " f"Please use a valid IANA timezone string (e.g., 'America/New_York', 'Europe/London')." ) def DateTimeTool() -> FunctionTool: return FunctionTool.from_defaults( fn=_datetime_run, name="datetime_tool", description=( "Returns the current date and time. Optionally accepts a timezone parameter as an IANA timezone " "string (e.g., 'America/New_York', 'Europe/London', 'Asia/Tokyo'). If no timezone is provided, " "returns local time." ), ) ``` **NAT:** Refer to the [NVIDIA NeMo Agent Toolkit documentation](https://docs.nvidia.com/nemo/agent-toolkit/latest/index.html) for details on implementing local tools in NAT. ### Weather API tool The following examples show how to create a custom tool that fetches weather information from a public API. This tool will require network access to fetch the weather data. You will need to ensure that your agent has the necessary permissions to make network requests and the appropriate API credentials required by the weather service. > [!NOTE] Starting point for external API tools > This example can be used as a starting point for tools that need to communicate with external APIs. **CrewAI:** To add a weather API tool to a CrewAI agent, you can modify the `myagent.py` file in the `agent` directory. You can add the following code to define the weather tool: ``` import requests from typing import Type from pydantic import BaseModel, Field from crewai.tools import BaseTool class WeatherToolInput(BaseModel): """Input schema for WeatherTool.""" city: str = Field( ..., description="The name of the city to fetch weather for (e.g., 'London', 'New York')." ) class WeatherTool(BaseTool): name: str = "weather_tool" description: str = ( "Fetches the current weather for a specified city. Usage: weather_tool(city='City Name'). " "Requires an API key from OpenWeatherMap. Sign up at https://openweathermap.org/api.") args_schema: Type[BaseModel] = WeatherToolInput def _run(self, city: str) -> str: api_key = "YOUR_API_KEY" # Replace with your OpenWeatherMap API key base_url = "http://api.openweathermap.org/data/2.5/weather" params = {"q": city, "appid": api_key, "units": "metric"} try: # Submit a query to an API using requests response = requests.get(base_url, params=params, timeout=10) response.raise_for_status() # Collect and format the response data = response.json() weather = data['weather'][0] main = data['main'] # Format and return the response to the agent return ( f"Current weather in {data['name']}, {data['sys']['country']}:\n" f"Temperature: {main['temp']}°C (feels like {main['feels_like']}°C)\n" f"Condition: {weather['main']} - {weather['description']}\n" f"Humidity: {main['humidity']}%\n" f"Pressure: {main['pressure']} hPa" ) # Gracefully handle errors and exceptions so the agent can understand the error # and attempt to correct in a followup call of the tool if needed. except requests.exceptions.RequestException as e: return f"Error fetching weather data: {str(e)}" except KeyError as e: return f"Error parsing weather data: Missing key {str(e)}" except Exception as e: return f"Unexpected error: {str(e)}" ``` **LangGraph:** To add a weather API tool to a LangGraph agent, you can modify the `myagent.py` file in the `agent` directory. You can add the following code to define the weather tool: ``` import requests from langchain.tools import BaseTool class WeatherTool(BaseTool): name: str = "weather_tool" description: str = ( "Fetches the current weather for a specified city. Usage: weather_tool(city='City Name'). " "Requires an API key from OpenWeatherMap. Sign up at https://openweathermap.org/api.") def _run(self, city: str) -> str: api_key = "YOUR_API_KEY" # Replace with your OpenWeatherMap API key base_url = "http://api.openweathermap.org/data/2.5/weather" params = {"q": city, "appid": api_key, "units": "metric"} try: # Submit a query to an API using requests response = requests.get(base_url, params=params, timeout=10) response.raise_for_status() # Collect and format the response data = response.json() weather = data['weather'][0] main = data['main'] # Format and return the response to the agent return ( f"Current weather in {data['name']}, {data['sys']['country']}:\n" f"Temperature: {main['temp']}°C (feels like {main['feels_like']}°C)\n" f"Condition: {weather['main']} - {weather['description']}\n" f"Humidity: {main['humidity']}%\n" f"Pressure: {main['pressure']} hPa" ) # Gracefully handle errors and exceptions so the agent can understand the error # and attempt to correct in a followup call of the tool if needed. except requests.exceptions.RequestException as e: return f"Error fetching weather data: {str(e)}" except KeyError as e: return f"Error parsing weather data: Missing key {str(e)}" except Exception as e: return f"Unexpected error: {str(e)}" ``` **LlamaIndex:** To add a weather API tool to a LlamaIndex agent, you can modify the `myagent.py` file in the `agent` directory. You can add the following code to define the weather tool: ``` import requests from llama_index.core.tools import FunctionTool def _weather_run(city: str) -> str: """Fetches the current weather for a specified city. Requires a city name as input.""" api_key = "YOUR_API_KEY" # Replace with your OpenWeatherMap API key base_url = "http://api.openweathermap.org/data/2.5/weather" params = {"q": city, "appid": api_key, "units": "metric"} try: # Submit a query to an API using requests response = requests.get(base_url, params=params, timeout=10) response.raise_for_status() # Collect and format the response data = response.json() weather = data['weather'][0] main = data['main'] # Format and return the response to the agent return ( f"Current weather in {data['name']}, {data['sys']['country']}:\n" f"Temperature: {main['temp']}°C (feels like {main['feels_like']}°C)\n" f"Condition: {weather['main']} - {weather['description']}\n" f"Humidity: {main['humidity']}%\n" f"Pressure: {main['pressure']} hPa" ) # Gracefully handle errors and exceptions so the agent can understand the error # and attempt to correct in a followup call of the tool if needed. except requests.exceptions.RequestException as e: return f"Error fetching weather data: {str(e)}" except KeyError as e: return f"Error parsing weather data: Missing key {str(e)}" except Exception as e: return f"Unexpected error: {str(e)}" def WeatherTool() -> FunctionTool: return FunctionTool.from_defaults( fn=_weather_run, name="weather_tool", description=( "Fetches the current weather for a specified city. Usage: weather_tool(city='City Name'). " "Requires an API key from OpenWeatherMap. Sign up at https://openweathermap.org/api." ), ) ``` **NAT:** Refer to the [NVIDIA NeMo Agent Toolkit documentation](https://docs.nvidia.com/nemo/agent-toolkit/latest/index.html) for details on implementing external API tools in NAT. ## Integrate DataRobot deployed tools DataRobot provides a set of global tools that can be used across different agent frameworks. These tools are designed to interact with DataRobot's platform and services. The following examples demonstrate a global tool that searches the DataRobot Data Registry for datasets. This tool will require network access to fetch the data from DataRobot. Before integrating these tools into an agent, [deploy them in DataRobot](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tools.html). > [!NOTE] DataRobot service integration > This example can be modified to create any tool that interacts with DataRobot's services. Source code for many global tools can be found in the [agent-tool-templatesrepository](https://github.com/datarobot-oss/agent-tool-templates). When building agents with external tools—either global tools or custom tools created as custom models in Workshop—the following components are required to call deployed tools from an agent: - The ToolClient class instance from the datarobot-genai package. - The resolve_authorization_context function from the datarobot-genai package (called in custom.py ). - A deployed tool's deployment_id , defined in model-metadata.yaml . - The tool's metadata, defined in a tool module; for example, tool_ai_catalog_search.py . The examples in this documentation show how to add tool integration capabilities to agent templates. The base agent templates provided in this repository do not include tool implementations by default—these examples demonstrate the patterns you can follow to integrate deployed tools into your own agent implementations. To assemble an agentic workflow using agentic tools deployed from Registry, an example workflow could include the following files: | File | Contents | | --- | --- | | __init__.py | Python package initialization file, making the directory a Python package. | | custom.py | The custom model code implementing the Bolt-on Governance API (the chat hook) to call the LLM and also passing those parameters to the agent (defined in myagent.py). | | myagent.py | The agent code, implementing the agentic workflow in the MyAgent class with the required invoke method. Tool integration properties can be added to this class to interface with deployed tools. | | config.py | The code for loading the configuration from environment variables, runtime parameters, and DataRobot credentials. | | mcp_client.py | The code providing MCP server connection management for tool integration (optional, only needed when using MCP tools). | | tool_deployment.py | The BaseTool class code, containing all necessary metadata for implementing tools. | | tool.py | The code for interfacing with the deployed tool, defining the input arguments and schema. Often, this file won't be named tool.py, as you may implement more than one tool. In this example, this functionality is defined in tool_ai_catalog_search.py. | | model-metadata.yaml | The custom model metadata and runtime parameters required by the agentic workflow. | | pyproject.toml | The libraries (and versions) required by the agentic workflow, using modern Python packaging standards. | ### Implement the ToolClient class instance Every agent template and framework requires the `ToolClient` class from the `datarobot-genai` package to offload tool call processing to deployed global tools. The tool client calls the deployed tool and returns the results to the agent. To import the `ToolClient` module into a `myagent.py` file, use the following import statement: ``` # agent/agent/myagent.py from datarobot_genai.core.chat.client import ToolClient ``` The `ToolClient` is available in the [datarobot-genaipackage](https://github.com/datarobot-oss/datarobot-genai). It defines the API endpoint and deployment ID for the deployed tool, gets the authorization context (if required), and provides interfaces for the `score`, `score_unstructured`, and `chat` hooks. After you import the `ToolClient` into `myagent.py`, you can add a `tool_client` property to your `MyAgent` class. The `ToolClient` automatically uses environment variables `DATAROBOT_API_TOKEN` and `DATAROBOT_ENDPOINT` for authentication, but you can also pass these explicitly if needed. ``` # agent/agent/myagent.py from datarobot_genai.core.chat.client import ToolClient class MyAgent: # More agentic workflow code. @property def tool_client(self): """ToolClient instance for calling deployed tools.""" return ToolClient( api_key=self.api_key, base_url=self.api_base, ) # More agentic workflow code. ``` ### (Optional) Initialize authorization context for external tools Authorization context is required to allow downstream agents and tools to retrieve access tokens when connecting to external services. The authorization context functionality is available in the `datarobot-genai` package alongside the `ToolClient` class. The `resolve_authorization_context` function is available in the `datarobot-genai` package and handles resolving the authorization context from the completion parameters and request headers. This function returns an authorization context dictionary that should be assigned to `completion_create_params["authorization_context"]`. The authorization context uses utility methods from the `datarobot` SDK: * `set_authorization_context`: A method to set the authorization context for the current process. * `get_authorization_context`: A method to retrieve the authorization context for the current process. > [!NOTE] OAuth utility method availability > These utility methods are available in the DataRobot Python API client starting with version 3.8.0. You can review the `resolve_authorization_context` function in the [datarobot-genaipackage](https://github.com/datarobot-oss/datarobot-genai). The `resolve_authorization_context` function is available in the `datarobot-genai` package. It resolves the authorization context for the agent, which is required for propagating information needed by downstream agents and tools to retrieve access tokens to connect to external services. When set, authorization context will be automatically propagated when using the `ToolClient` class. In the `custom.py` example below, the `chat()` hook calls `resolve_authorization_context` (imported from `datarobot-genai`) each time a chat request is made to the agentic workflow, and assigns the result to `completion_create_params["authorization_context"]`, providing any credentials required for external tools. ``` # agent/custom.py import asyncio from concurrent.futures import ThreadPoolExecutor from typing import Any, Iterator, Union from agent import MyAgent from config import Config from datarobot_genai.core.chat import ( CustomModelChatResponse, CustomModelStreamingResponse, resolve_authorization_context, to_custom_model_chat_response, to_custom_model_streaming_response, ) from openai.types.chat import CompletionCreateParams from openai.types.chat.completion_create_params import ( CompletionCreateParamsNonStreaming, CompletionCreateParamsStreaming, ) def load_model(code_dir: str) -> tuple[ThreadPoolExecutor, asyncio.AbstractEventLoop]: """The agent is instantiated in this function and returned.""" thread_pool_executor = ThreadPoolExecutor(1) event_loop = asyncio.new_event_loop() thread_pool_executor.submit(asyncio.set_event_loop, event_loop).result() return (thread_pool_executor, event_loop) def chat( completion_create_params: CompletionCreateParams | CompletionCreateParamsNonStreaming | CompletionCreateParamsStreaming, load_model_result: tuple[ThreadPoolExecutor, asyncio.AbstractEventLoop], **kwargs: Any, ) -> Union[CustomModelChatResponse, Iterator[CustomModelStreamingResponse]]: """When using the chat endpoint, this function is called. Agent inputs are in OpenAI message format and defined as the 'user' portion of the input prompt. """ # Load configuration and runtime parameters _ = Config() # Initialize the authorization context for downstream agents and tools to retrieve # access tokens for external services. completion_create_params["authorization_context"] = resolve_authorization_context( completion_create_params, **kwargs ) # Instantiate the agent, all fields from the completion_create_params are passed to the agent # allowing environment variables to be passed during execution agent = MyAgent(**completion_create_params) if completion_create_params.get("stream"): streaming_response_generator = agent.invoke( completion_create_params=completion_create_params ) return to_custom_model_streaming_response( streaming_response_generator, model=completion_create_params.get("model") ) else: # Synchronous non-streaming response response_text, pipeline_interactions, usage_metrics = agent.invoke( completion_create_params=completion_create_params ) return to_custom_model_chat_response( response_text, pipeline_interactions, usage_metrics, model=completion_create_params.get("model"), ) ``` When authorization context is set, it is automatically propagated by the `ToolClient` class from the `datarobot-genai` package. The `ToolClient.call()` method automatically includes the authorization context when calling deployed tools. The `ToolClient` class provides methods to call the custom model tool using various hooks: `score`, `score_unstructured`, and `chat`. When the `authorization_context` is set, the client automatically propagates it to the agent tool. The `authorization_context` is required for retrieving access tokens to connect to external services. If you're implementing a custom tool reliant on an external service, you can use the `@datarobot_tool_auth` decorator to streamline the process of retrieving the authorization context, extracting the relevant data, and connecting to the DataRobot API to obtain the OAuth access token from [an OAuth provider configured in DataRobot](https://docs.datarobot.com/en/docs/platform/acct-settings/manage-oauth.html). When only one OAuth provider is configured the decorator doesn't require the `provider` parameter, as it will use the only available provider; however, if multiple providers are (or will be) available, you should define this parameter. ``` # tool.py from datarobot.models.genai.agent.auth import datarobot_tool_auth, AuthType # More tool code. @datarobot_tool_auth( type=AuthType.OBO, # on-behalf-of provider="google", # required with multiple OAuth providers ) def list_files_in_google_drive(folder_name: str, token: str = "") -> list[dict]: """The value for token parameter will be injected by the decorator.""" # More tool code. ``` ### Interface with tool deployments Global tools and tools custom-built in the Registry workshop must be deployed for the agent to call them. When these tools are deployed, communicating with them requires a deployment ID, used to interface with the tool through the DataRobot API. The primary method for providing a deployed tool's deployment ID to the agent is through environment variables, defined as runtime parameters in the agent's metadata. To provide this metadata, create or modify a `model-metadata.yaml` file to add the runtime parameter for each deployed tool the agent needs to communicate with. Define runtime parameters in `runtimeParameterDefinitions`. ``` # agent/model-metadata.yaml runtimeParameterDefinitions: - fieldName: AI_CATALOG_SEARCH_TOOL_DEPLOYMENT_ID defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string ``` The example below illustrates a `model-metadata.yaml` file configured for an `agenticworkflow` implementing two global tools, Search Data Registry and Get Data Registry Dataset. The field names in the example below are used by DataRobot agent templates implementing these tools; however, the `fieldName` is configurable, and must match the implementation in the agent's code, located in the `myagent.py` file. ``` # agent/model-metadata.yaml --- name: agent_with_tools type: inference targetType: agenticworkflow runtimeParameterDefinitions: - fieldName: OTEL_SDK_ENABLED defaultValue: true type: boolean - fieldName: LLM_DEPLOYMENT_ID defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string - fieldName: DATA_REGISTRY_SEARCH_TOOL_DEPLOYMENT_ID defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string - fieldName: DATA_REGISTRY_READ_TOOL_DEPLOYMENT_ID defaultValue: SET_VIA_PULUMI_OR_MANUALLY type: string ``` Unless you provided the required values as the `defaultValue`, you must set the runtime parameter to provide the deployment IDs to the agent's code. You can do this in two ways: - Manually: Configuring the values in the UI, in the Registry workshop, before deploying the agent. - Automatically: Configuring the values in a Pulumi script and passing them to the custom model. When the parameters are set, they're accessible in the agent code, as long as the `RuntimeParameters` class is imported from `datarobot_drum`. The following example shows how you can add tool properties to your `MyAgent` class to interface with deployed tools: ``` # agent/agent/myagent.py import os from datarobot_drum import RuntimeParameters from datarobot_genai.core.chat.client import ToolClient class MyAgent: # More agentic workflow code. @property def tool_client(self) -> ToolClient: """ToolClient instance for calling deployed tools.""" return ToolClient( api_key=self.api_key, base_url=self.api_base, ) @property def tool_ai_catalog_search(self) -> BaseTool: """Search AI Catalog tool using deployed tool.""" deployment_id = os.environ.get("AI_CATALOG_SEARCH_TOOL_DEPLOYMENT_ID") if not deployment_id: deployment_id = RuntimeParameters.get("AI_CATALOG_SEARCH_TOOL_DEPLOYMENT_ID") return SearchAICatalogTool( tool_client=self.tool_client, deployment_id=deployment_id ) @property def tool_ai_catalog_read(self) -> BaseTool: """Read AI Catalog tool using deployed tool.""" deployment_id = os.environ.get("AI_CATALOG_READ_TOOL_DEPLOYMENT_ID") if not deployment_id: deployment_id = RuntimeParameters.get("AI_CATALOG_READ_TOOL_DEPLOYMENT_ID") return ReadAICatalogTool( tool_client=self.tool_client, deployment_id=deployment_id, ) # More agentic workflow code. ``` ### Define tool metadata When building tools for an agent, the metadata defines how the agent LLM should call the tool. The more details the metadata provides, the more effectively the LLM uses the tool. The metadata includes the tool description and each arguments' schema and related description. Each framework has a unique way to define this metadata; however, in most cases you can leverage `pydantic` to import `BaseModel` to define the tool's arguments. ``` # tool_ai_catalog_search.py from pydantic import BaseModel as PydanticBaseModel, Field class SearchAICatalogArgs(PydanticBaseModel): search_terms: str = Field( default="", description="Terms for the search. Leave blank to return all datasets." ) limit: int = Field( default=20, description="The maximum number of datasets to return. " "Set to -1 to return all." ) ``` The example below implements a simple `BaseTool` class for CrewAI, implemented in `tool_deployment.py`, containing all necessary metadata and available for reuse across multiple CrewAI tools. ``` # tool_deployment.py from abc import ABC from crewai.tools import BaseTool from datarobot_genai.core.chat.client import ToolClient class BaseToolWithDeployment(BaseTool, ABC): model_config = { "arbitrary_types_allowed": True } """Adds support for arbitrary types in Pydantic models, needed for the ToolClient.""" tool_client: ToolClient """The tool client initialized by the agent with access to the ToolClient authorization context.""" deployment_id: str """The DataRobot deployment ID of the custom model executing tool logic.""" ``` The `SearchAICatalogTool`, defined in `tool_ai_catalog_search.py`, invokes `tool_deployment` to build off the `BaseToolWithDeployment` module. ``` # tool_ai_catalog_search.py import json from typing import Dict, List, Type from pydantic import BaseModel as PydanticBaseModel from tool_deployment import BaseToolWithDeployment class SearchAICatalogTool(BaseToolWithDeployment): name: str = "Search Data Registry" description: str = ( "This tool provides a list of all available dataset names and their associated IDs from the Data Registry. " "You should always check to see if the dataset you are looking for can be found here. " "For future queries, you should use the associated dataset ID instead of the name to avoid ambiguity." ) args_schema: Type[PydanticBaseModel] = SearchAICatalogArgs def _run(self, **kwargs) -> List[Dict[str, str]]: # Validate and parse the input arguments using the defined schema. validated_args = self.args_schema(**kwargs) # Call the tool deployment with the generated payload. result = self.tool_client.call( deployment_id=self.deployment_id, payload=validated_args.model_dump() ) # Format and return the results. return json.loads(result.data).get("datasets", []) ``` The example below uses the CrewAI framework to implement a tool through the `BaseTool`, `Agent`, and `Task` classes. The following methods show how you can add tool properties to your `MyAgent` class to initialize the Data Registry searching tool, define an LLM agent to search the Data Registry, and then define a task for the agent: ``` # agent/agent/myagent.py from crewai.tools import BaseTool from crewai import Agent, Task from tool_ai_catalog_search import SearchAICatalogTool from datarobot_genai.core.chat.client import ToolClient class MyAgent: # More agentic workflow code. @property def tool_client(self) -> ToolClient: """ToolClient instance for calling deployed tools.""" return ToolClient( api_key=self.api_key, base_url=self.api_base, ) @property def search_ai_catalog_tool(self) -> BaseTool: """Search AI Catalog tool using deployed tool.""" deployment_id = self.search_ai_catalog_deployment_id if not deployment_id: raise ValueError("Configure a deployment ID for the Search Data Registry tool.") return SearchAICatalogTool( tool_client=self.tool_client, deployment_id=deployment_id ) @property def agent_ai_catalog_searcher(self) -> Agent: """Agent configured to search the AI Catalog.""" return Agent( role="Expert Data Registry Searcher", goal="Search for and retrieve relevant files from Data Registry.", backstory="You are a meticulous analyst that is skilled at examining lists of files and " "determining the most appropriate file based on the context.", verbose=self.verbose, allow_delegation=False, llm=self.llm_with_datarobot_llm_gateway, ) @property def task_ai_catalog_search(self) -> Task: """Task for searching the AI Catalog.""" return Task( description=( "You should search for a relevant dataset id in the Data Registry " "based on the provided dataset topic: {dataset_topic}." ), expected_output=( "Search for a list of relevant files in the Data Registry and " "determine the most relevant dataset id that matches the given topic. " "You should return the entire dataset id." ), agent=self.agent_ai_catalog_searcher, tools=[self.search_ai_catalog_tool], ) # More agentic workflow code. ``` ## Framework-specific documentation You can also refer to the framework repositories and documentation for more information on constructing more advanced tools and agents: - CrewAI tools documentation - LangChain and LangGraph tools documentation - LlamaIndex tools documentation - NVIDIA NeMo Agent Toolkit documentation ## Agentic tool considerations When deploying the application and agent separately from the agentic tool (for example, deploying the application and agent via Pulumi after local development and the tool manually in DataRobot), all components must be deployed by the same user. Custom models use the creator's API key and require a common identity to store and retrieve authentication data from the OAuth Providers Service. --- # Deploy agentic tools URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tools.html > Deploy tools to handle tasks critical to the agent workflow. # Deploy agentic tools When building agents, you often need to integrate tools to handle tasks critical to the agent workflow—typically for complex use cases involving communication with external services. While some tools are embedded directly in the code of an agentic workflow, other tools are deployed externally and called by the agent process. Because externally deployed tools can scale independently, they are well-suited for resource-intensive operations, I/O-bound tasks, and reusable functionality. Deploying tools externally also enables production-ready monitoring, mitigation, and moderation capabilities in Console. ## Global agentic tools The following global tools are available for deployment to Console: > [!TIP] Identifying tools > All global tools are prefixed with the [Tool]identifier. Use this identifier to filter the global models and tools list to show only tools. | Tool | Description | Notes | | --- | --- | --- | | Get Data Registry Dataset | Retrieves datasets from the DataRobot Data Registry using a dataset_id and returns the dataset in CSV format as raw bytes. | N/A | | Make AutoML Predictions | Accepts a pandas.DataFrame and uses that data to return a prediction from the specified predictive model. | The argument columns_to_return_with_predictions tells the tool to return columns from the input dataset. Use this to make sure you can interpret the predictions. For example, you may want to return an ID or other identifying column so that you can see which prediction is which because you can't rely on the index or order of the predictions. | | Make Text Generation Predictions | Accepts a string and returns a prediction from the specified DataRobot text generation model (LLM). | Suitable for tasks like summarization or text completion. This tool should only be used for TextGeneration deployments and not for regression, classification, or other target types. | | Make Time Series Predictions | Returns forecasts from a time series model. | Before using this tool, verify that you have all the data needed. Time series models require a forecast point. They also have specific requirements for the input data. | | Render Plotly Chart | Returns a JSON object containing a rendered Plotly chart object generated based on the provided specification and dataset ID. | When generating the Plotly chart, placeholders in the specification—indicated by double braces enclosing a column name (for example, {{ column_name }})—are replaced by the corresponding values from the specified column in the Data Registry dataset. The Data Registry dataset is identified by the dataset_id input parameter. | | Render Vega-Lite Chart | Generates a Vega-Lite chart by passing in the Vega-Lite specification in JSON format and returns JSON with a base64-encoded image of the chart. | To provide data for the chart, pass in the Data Registry dataset_id for the dataset you want to chart. | | Search Data Registry | Searches for datasets in the DataRobot Data Registry using search terms. Returns matching datasets as a pandas.DataFrame. | The Data Registry does not support partial matching. If this tool doesn't return the expected results, try again with a more specific search query. | | Summarize DataFrame | Provides a detailed summary of a pandas.DataFrame in Markdown format, including statistics and data insights. | N/A | > [!NOTE] Agentic tool target type > All global tools have an Unstructured target type and a Target of `target`. To learn more about a tool, you can access the source code in the [publicagent-tool-templatesrepository](https://github.com/datarobot-oss/agent-tool-templates). Each tool is tagged with the `global.model.source` tag, linking to the directory containing the source files for that tool. This allows you to explore its contents to learn more about the model, review its input and output schema, or use the code as a template for building a customized tool. To find the repository link: 1. Apply theGlobalfilter and look for a[Tool]in the list. 2. Open a version and in the version, scroll down to theKey valuessection. 3. Open theTagspanel and locate theglobal.model.sourcetag. 4. Hover over the tag value to view the full URL, or, click the link to open the repository to the directory for that tool. --- # Implement tracing URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tracing-code.html > Learn how to add OpenTelemetry tracing and custom span instrumentation to your agent tools for monitoring, debugging, and observability. # Implement tracing OpenTelemetry (OTel) provides comprehensive observability for your agents, allowing you to monitor, trace, and debug agent execution in real-time. This guide explains how to add custom tracing to your agent tools to capture detailed execution information. OpenTelemetry tracing helps: - Monitor agent performance and execution flow. - Debug issues by tracking detailed execution traces. - Understand tool execution patterns and timing. - View custom attributes and metadata from your tools. The agent templates already include OpenTelemetry instrumentation for frameworks like CrewAI, LangGraph, and Llama-Index. This instrumentation automatically captures spans for: - Agent execution - Tool invocations - LLM API calls - HTTP requests You can enhance this default tracing by adding custom spans and attributes in your tools. ## Add custom tracing to tools Add custom OpenTelemetry tracing to your tools to capture additional information about tool execution. This allows you to track custom attributes, intermediate outputs, and execution details that are specific to your use case. The basic pattern for adding custom tracing to a tool is: ``` from opentelemetry import trace tracer = trace.get_tracer(__name__) # Within your tool's execution with tracer.start_as_current_span("my_custom_span_name"): current_span = trace.get_current_span() current_span.set_attribute("tool_name", "my_tool_name") current_span.set_attribute("gen_ai.prompt", "input passed to this step") current_span.set_attribute("datarobot.moderation.cost", 0.0) # Your tool logic here result = perform_tool_action() current_span.set_attribute("gen_ai.completion", str(result)) # Optionally add more attributes about the result current_span.set_attribute("result.status", "success") current_span.set_attribute("result.size", len(result)) return result ``` ### Tool examples See the code examples below to learn how to add custom OpenTelemetry tracing to agentic tools: **CrewAI:** ``` import requests from crewai.tools import BaseTool from opentelemetry import trace tracer = trace.get_tracer(__name__) class WeatherTool(BaseTool): name: str = "weather_tool" description: str = ( "Fetches the current weather for a specified city. " "Requires an API key from OpenWeatherMap." ) def _run(self, city: str) -> str: with tracer.start_as_current_span("weather_tool_fetch"): current_span = trace.get_current_span() current_span.set_attribute("tool_name", "weather_tool") current_span.set_attribute("gen_ai.prompt", f"weather lookup for {city}") current_span.set_attribute("datarobot.moderation.cost", 0.0) # Set custom attributes current_span.set_attribute("weather.city", city) current_span.set_attribute("weather.api", "openweathermap") api_key = "YOUR_API_KEY" # Replace with your API key base_url = "http://api.openweathermap.org/data/2.5/weather" params = {"q": city, "appid": api_key, "units": "metric"} try: response = requests.get(base_url, params=params, timeout=10) response.raise_for_status() data = response.json() weather = data['weather'][0] main = data['main'] # Add result attributes current_span.set_attribute("weather.temperature", main['temp']) current_span.set_attribute("weather.condition", weather['main']) result = ( f"Current weather in {data['name']}, {data['sys']['country']}:\n" f"Temperature: {main['temp']}°C (feels like {main['feels_like']}°C)\n" f"Condition: {weather['main']} - {weather['description']}\n" f"Humidity: {main['humidity']}%\n" f"Pressure: {main['pressure']} hPa" ) current_span.set_attribute("gen_ai.completion", result) return result except requests.exceptions.RequestException as e: current_span.set_attribute("weather.error", str(e)) err = f"Error fetching weather data: {str(e)}" current_span.set_attribute("gen_ai.completion", err) return err ``` **LangGraph:** ``` import requests from langchain.tools import BaseTool from opentelemetry import trace tracer = trace.get_tracer(__name__) class WeatherTool(BaseTool): name: str = "weather_tool" description: str = ( "Fetches the current weather for a specified city. " "Requires an API key from OpenWeatherMap." ) def _run(self, city: str) -> str: with tracer.start_as_current_span("weather_tool_fetch"): current_span = trace.get_current_span() current_span.set_attribute("tool_name", "weather_tool") current_span.set_attribute("gen_ai.prompt", f"weather lookup for {city}") current_span.set_attribute("datarobot.moderation.cost", 0.0) # Set custom attributes current_span.set_attribute("weather.city", city) api_key = "YOUR_API_KEY" # Replace with your API key base_url = "http://api.openweathermap.org/data/2.5/weather" params = {"q": city, "appid": api_key, "units": "metric"} try: response = requests.get(base_url, params=params, timeout=10) response.raise_for_status() data = response.json() # Add result attributes current_span.set_attribute("weather.temperature", data['main']['temp']) result = f"Temperature in {city}: {data['main']['temp']}°C" current_span.set_attribute("gen_ai.completion", result) return result except requests.exceptions.RequestException as e: current_span.set_attribute("weather.error", str(e)) err = f"Error: {str(e)}" current_span.set_attribute("gen_ai.completion", err) return err ``` **Llama-Index:** ``` import requests from llama_index.core.tools import FunctionTool from opentelemetry import trace tracer = trace.get_tracer(__name__) def _weather_run(city: str) -> str: """Fetches the current weather for a specified city. Requires an API key from OpenWeatherMap.""" with tracer.start_as_current_span("weather_tool_fetch"): current_span = trace.get_current_span() current_span.set_attribute("tool_name", "weather_tool") current_span.set_attribute("gen_ai.prompt", f"weather lookup for {city}") current_span.set_attribute("datarobot.moderation.cost", 0.0) # Set custom attributes current_span.set_attribute("weather.city", city) api_key = "YOUR_API_KEY" # Replace with your API key base_url = "http://api.openweathermap.org/data/2.5/weather" params = {"q": city, "appid": api_key, "units": "metric"} try: response = requests.get(base_url, params=params, timeout=10) response.raise_for_status() data = response.json() # Add result attributes current_span.set_attribute("weather.temperature", data['main']['temp']) result = f"Temperature in {city}: {data['main']['temp']}°C" current_span.set_attribute("gen_ai.completion", result) return result except requests.exceptions.RequestException as e: current_span.set_attribute("weather.error", str(e)) err = f"Error: {str(e)}" current_span.set_attribute("gen_ai.completion", err) return err def WeatherTool() -> FunctionTool: return FunctionTool.from_defaults( fn=_weather_run, name="weather_tool", description=( "Fetches the current weather for a specified city. " "Requires an API key from OpenWeatherMap." ), ) ``` ## Create nested spans Create nested spans to represent complex tool execution with multiple steps: ``` from opentelemetry import trace tracer = trace.get_tracer(__name__) def complex_tool_workflow(input_data): with tracer.start_as_current_span("complex_tool_main"): current_span = trace.get_current_span() current_span.set_attribute("input.size", len(input_data)) # First step in the workflow with tracer.start_as_current_span("data_processing"): processed_data = process_data(input_data) trace.get_current_span().set_attribute("processed_items", len(processed_data)) # Second step in the workflow with tracer.start_as_current_span("data_validation"): validated_data = validate_data(processed_data) trace.get_current_span().set_attribute("validated_items", len(validated_data)) # Third step in the workflow with tracer.start_as_current_span("result_generation"): result = generate_result(validated_data) current_span.set_attribute("result.size", len(result)) return result ``` ### Add events to spans Add events to your spans to mark important moments in tool execution: ``` from opentelemetry import trace from datetime import datetime tracer = trace.get_tracer(__name__) def tool_with_events(): with tracer.start_as_current_span("tool_execution"): current_span = trace.get_current_span() # Add an event for when processing starts current_span.add_event( "Processing started", {"timestamp": datetime.utcnow().isoformat()} ) # Your tool logic intermediate_result = perform_action() # Add an event for mid-execution current_span.add_event( "Intermediate result ready", {"result_count": len(intermediate_result)} ) # More processing final_result = complete_processing(intermediate_result) # Add final event current_span.add_event( "Processing completed", {"output_size": len(final_result)} ) return final_result ``` ## Add custom tracing to agent You can set up a custom trace to capture how your agent starts up, including configurations and environment details. Follow the steps below to surface runtime parameters (like environment variables) on a span: 1. Update your.envfile to contain the following environment variable so it's available during local development and when you package the model: EXAMPLE_ENV_VAR=my_example_value 2. Add the parameter toagent/model-metadata.yamlso DataRobot can inject it when the agent runs: runtimeParameterDefinitions:-fieldName:EXAMPLE_ENV_VARtype:stringdefaultValue:SET_VIA_PULUMI_OR_MANUALLY 3. Add the parameter to your Config class inagent/agent/config.py(for example,example_env_var: str = "") so the value is available asconfig.example_env_varin your agent code. 4. Updateinfra/infra/llm.pyto forward the runtime parameter into the custom model environment: custom_model_runtime_parameters=[# ...existing parameters...datarobot.CustomModelRuntimeParameterValueArgs(key="EXAMPLE_ENV_VAR",type="string",value=os.environ.get("EXAMPLE_ENV_VAR"),),] 5. Wrap the configuration loading code in a span and attach the values withset_attributeandadd_event. The property name depends on your template (for example,agent_plannerin CrewAI templates): @propertydefagent_planner(self)->Any:withtracer.start_as_current_span("config_variables"):current_span=trace.get_current_span()current_span.set_attribute("config.example_env_var",config.example_env_var)current_span.add_event("config attribute set on span")# ...agent code continued... When you deploy and run the agent, the trace visualizer shows a `config_variables` span with attributes such as `config.example_env_var=my_example_value`. This makes it easy to confirm that runtime parameters and other environment values were loaded correctly. For information on viewing traces in the DataRobot UI after deployment, see [View logs and traces for a deployed agent](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-development.html#view-logs-and-traces-for-a-deployed-agent). ## Map spans and attributes to the tracing table On a deployment, the tracing table displays several columns derived from OpenTelemetry span attributes. The following naming conventions apply across the trace: | Tracing table column | Attribute | How it is derived | | --- | --- | --- | | Cost | datarobot.moderation.cost | Summed across all spans in the trace. | | Prompt | gen_ai.prompt | If multiple spans set this attribute, the first value in trace order is used. | | Completion | gen_ai.completion | If multiple spans set this attribute, the last value in trace order is used. | | Tools | tool_name | Every distinct tool_name found on any span in the trace is listed. | ### Surface tool names in the tracing table The Tools column is populated from the span attribute `tool_name`. Some frameworks set it on tool spans automatically; others do not. If your traces show tool execution in the span timeline, but Tools is empty, create a span around the tool body (or use the active span) and set `tool_name` explicitly. **Inside awithspan:** ``` from opentelemetry import trace tracer = trace.get_tracer(__name__) def my_tool_impl(query: str) -> str: with tracer.start_as_current_span("my_tool"): span = trace.get_current_span() # Attributes that map to deployment Tracing table columns (see above) span.set_attribute("tool_name", "my_tool_name") span.set_attribute("gen_ai.prompt", query) span.set_attribute("datarobot.moderation.cost", 0.0) # numeric; summed per trace # ... tool logic ... result = "result" span.set_attribute("gen_ai.completion", result) return result ``` **Current span only:** If a span is already active (for example, from upstream instrumentation), you can set the attribute on that span: ``` from opentelemetry import trace span = trace.get_current_span() span.set_attribute("tool_name", "my_tool_name") span.set_attribute("gen_ai.prompt", "user input or request text") span.set_attribute("gen_ai.completion", "model or tool output text") span.set_attribute("datarobot.moderation.cost", 0.0) ``` For LangGraph and similar frameworks, tool calls are sometimes wired through callbacks in a way that does not add `tool_name` to spans; manual instrumentation can allow the name to appear in the Tools column. ## Best practices Use descriptive span names: - Use clear, descriptive names for spans (e.g., "weather_fetch" rather than "span1" ). - Include the tool name in the span name when relevant. Set meaningful attributes: - Add attributes that provide context about the execution. - Use consistent attribute naming conventions (e.g., tool.input , tool.output , tool.error ). - Include relevant metadata like sizes, counts, or statuses. - To populate Cost , Prompt , Completion , and Tools in the deployment Tracing table, set datarobot.moderation.cost , gen_ai.prompt , gen_ai.completion , and tool_name on the relevant spans. Keep spans focused: - Create spans for significant operations, not every line of code. - Each span should represent a meaningful unit of work. - Use nested spans to represent sub-operations. --- # Troubleshooting URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-troubleshooting.html > Troubleshoot common issues when working with DataRobot Agent Templates. # Troubleshooting This guide helps you diagnose and resolve common issues when working with DataRobot Agent Templates. ## Prerequisites and setup issues Common issues related to system requirements and initial setup for DataRobot Agent Templates. ### Windows compatibility Issue: Windows is not supported for DataRobot Agent Templates. Solution: Use macOS or Linux for development. ### Missing prerequisites Issue: Required tools are not installed. Solution: Install missing tools following the [prerequisites guide](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-install.html). ### Build tools missing Issue: Build tools are not available on your system. Solution: Install Xcode Command Line Tools (macOS) or build-essential (Linux). ## Environment configuration issues Problems with environment variables and DataRobot endpoint configuration. ### Missing environment variables Issue: Required environment variables are not set. Solution: Create `.env` file with `DATAROBOT_API_TOKEN` and `DATAROBOT_ENDPOINT`. ### Invalid endpoint configuration Issue: Incorrect DataRobot endpoint configured. Solution: Use correct endpoint for your region (cloud) or contact support (on-premise). ## Authentication issues Issues related to API authentication and authorization context setup. ### API token authentication failed Issue: API token is invalid or lacks proper permissions. Solution: Verify API token is valid and has proper permissions. ### Authorization context not set Issue: Authorization context is not initialized in your agent. Solution: Ensure `initialize_authorization_context()` is called in your agent. ## Deployment issues Problems encountered during the deployment process and infrastructure management. ### Pulumi login required Issue: Pulumi authentication is not configured. Solution: Run `pulumi login --local` or `pulumi login`. ### Deployment ID not found Issue: Cannot locate deployment ID after deployment. Solution: Check terminal output or DataRobot UI → Console → Deployments. ## CLI and testing issues Issues with command-line interface usage and local testing of agents. ### CLI command not found Issue: CLI commands are not available. Solution: Ensure the DataRobot CLI is installed and on your PATH. See [Getting started with the DataRobot CLI](https://docs.datarobot.com/en/docs/agentic-ai/cli/getting-started.html) for installation, then run `dr start` to select a framework. For CLI-specific issues, see [CLI troubleshooting](https://docs.datarobot.com/en/docs/agentic-ai/cli/troubleshooting.html). ### Local testing fails Issue: Local testing encounters errors when trying to run your agent during development. Solution: Use the CLI command to test your agent locally. This allows you to run and debug your agent code without deploying it to DataRobot. The `execute` command requires a development server to be running. You can either start it manually with `task agent:dev` (it runs continuously, so use a separate terminal), or use `START_DEV=1` to automatically start and stop it: - Test with a basic text query: task agent:cli START_DEV=1 -- execute --user_prompt "Hello" (or start task agent:dev first, then run task agent:cli -- execute --user_prompt "Hello" ) - Test with JSON input: task agent:cli START_DEV=1 -- execute --user_prompt '{"topic": "Artificial Intelligence"}' - Test with a completion JSON file: task agent:cli START_DEV=1 -- execute --completion_json example-completion.json - Enable verbose logging: Add "verbose": true to the extra_body field in your completion JSON Common issues include missing environment variables ( `DATAROBOT_API_TOKEN`, `DATAROBOT_ENDPOINT`), import issues in `myagent.py` (see [Import issues](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-troubleshooting.html#import-issues-in-myagent-py)), and missing dependencies in `pyproject.toml`. ## Infrastructure issues Problems with containerization, build processes, and infrastructure state management. ### Docker build fails Issue: Docker container build encounters errors. Solution: Test locally and check `pyproject.toml` dependencies. ### Pulumi state issues Issue: Pulumi state is out of sync. Solution: Run `task infra:refresh` to sync state. ### Import issues in myagent.py Issue: Imports in `myagent.py` to files in the same folder cause silent failures in DRUM. Solution: Use relative imports instead of package imports. ## LLM gateway issues Issues specific to LLM gateway connectivity, model access, and configuration. ### Model access error Issue: LLM gateway cannot connect to the model or fails with "no model access" error. Solution: Ensure you have access to the model. If you specified a model you don't have access to (or a retired model), you can connect to the gateway, but then the action fails with a "no model access" error. ### LLM gateway configuration Issue: LLM gateway is not properly configured. Solution: Ensure that your organization has access to the [LLM gateway](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html) and that the `ENABLE_LLM_GATEWAY_INFERENCE` runtime parameter is provided in the `model-metadata.yaml` file and set to `true`. ### Non-gateway model deployment Issue: Non-gateway model deployment fails. Solution: If you are using a non-gateway model, run `task deploy` once even if it fails to get your LLM deployment to run. ## Accessing request headers When your agent is deployed, you may need to access HTTP request headers for authentication, tracking, or custom metadata. DataRobot makes headers available to your agent code through the `chat()` function's `**kwargs` parameter. ### Extracting X-Untrusted-* headers Headers with the `X-Untrusted-*` prefix are passed through from the original request and are available in the `kwargs` dictionary: ``` import asyncio from concurrent.futures import ThreadPoolExecutor from typing import Any, Iterator, Union from openai.types.chat import CompletionCreateParams from openai.types.chat.completion_create_params import ( CompletionCreateParamsNonStreaming, CompletionCreateParamsStreaming, ) def chat( completion_create_params: CompletionCreateParams | CompletionCreateParamsNonStreaming | CompletionCreateParamsStreaming, load_model_result: tuple[ThreadPoolExecutor, asyncio.AbstractEventLoop], **kwargs: Any, ) -> Union[CustomModelChatResponse, Iterator[CustomModelStreamingResponse]]: # Extract all headers from kwargs headers = kwargs.get("headers", {}) # Access specific X-Untrusted-* headers authorization_header = headers.get("X-Untrusted-Authorization") custom_header = headers.get("X-Untrusted-Custom-Metadata") # Use headers in your agent logic if authorization_header: # Pass to downstream services, tools, etc. print(f"Authorization header: {authorization_header}") ``` ### Common use cases Passing authentication to external services: ``` headers = kwargs.get("headers", {}) auth_token = headers.get("X-Untrusted-Authorization") # Use the token to authenticate with external APIs tool_client = MyTool(auth_token=auth_token) ``` Tracking request metadata: ``` headers = kwargs.get("headers", {}) request_id = headers.get("X-Untrusted-Request-ID") user_id = headers.get("X-Untrusted-User-ID") # Log metadata for debugging or analytics logging.info(f"Processing request {request_id} for user {user_id}") ``` Conditional agent behavior: ``` headers = kwargs.get("headers", {}) region = headers.get("X-Untrusted-Region") # Adjust agent behavior based on request context if region == "EU": agent = MyAgent(enable_gdpr_mode=True) ``` ### Important considerations - Only headers with the X-Untrusted-* prefix are passed through to your agent code. - Headers are case-sensitive when accessing them from the dictionary. - Always provide default values or check for None when accessing headers that may not be present. - Headers are available in both streaming and non-streaming chat responses. ## Debugging tips Useful techniques and commands for troubleshooting and debugging agent issues. ### Enable verbose logging ``` agent = MyAgent(verbose=True) ``` ### Test authentication ``` from datarobot_genai.core.cli import AgentEnvironment env = AgentEnvironment() ``` ### Check environment variables ``` echo $DATAROBOT_API_TOKEN echo $DATAROBOT_ENDPOINT ``` ### Log request headers for debugging ``` import asyncio import logging from concurrent.futures import ThreadPoolExecutor from typing import Any def chat(completion_create_params, load_model_result: tuple[ThreadPoolExecutor, asyncio.AbstractEventLoop], **kwargs: Any): headers = kwargs.get("headers", {}) # Log all headers to inspect what's available if headers: logging.warning(f"All headers: {dict(headers)}") # Continue with agent logic... ``` ## Getting help For additional assistance: - Check the documentation for your chosen agentic framework. - Contact DataRobot for support. - Open an issue on the GitHub repository . - Framework Documentation: --- # Build URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/index.html > Build agentic workflows with DataRobot using existing templates or by modifying those templates to suit your specific use case. # Build > [!NOTE] Premium > DataRobot's Agentic AI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. The [DataRobot Agentic Starter template repository](https://github.com/datarobot-community/datarobot-agent-application) provides ready-to-use templates for building and deploying AI agents with multi-agent frameworks. These templates streamline the process of setting up your own agents with minimal configuration requirements and support both local development and testing, as well as deployment to production environments within DataRobot. To start building and deploying AI agents from DataRobot-provided templates, review the [Quickstart](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-quickstart.html) guide. The quickstart uses the [DataRobot CLI](https://docs.datarobot.com/en/docs/agentic-ai/cli/index.html) ( `dr start`, `dr task run`) for setup and deployment—see [Getting started with the DataRobot CLI](https://docs.datarobot.com/en/docs/agentic-ai/cli/getting-started.html) if you need to install or configure the CLI first. | Topic | Description | | --- | --- | | Installation | Install required components for agentic workflow development. | | Quickstart | Build, deploy, and test agentic workflows using DataRobot's pre-built templates. | | Agent components | Learn about the components required to create an agent using DataRobot's agent framework. | | Agent authentication | Learn how to implement authentication in your DataRobot agentic application, covering API tokens, authorization context, OAuth 2.0, and security best practices. | | Customize agents | Customize agent code, test locally, and deploy agentic application for production use. | | Add Python packages | Add required Python packages to agentic application using execution environment or custom model requirements. | | Configure LLM providers in code | Configure different LLM providers for your agentic application including DataRobot gateway, external APIs, and custom deployments. | | Configure LLM providers with metadata | Configure LLM providers using environment variables and Pulumi for infrastructure-level configuration without modifying agent code. | | Add tools to agents | Add local tools, predefined tools, and DataRobot global tools to your agentic application, including detailed integration patterns. | | Deploy agentic tools | Deploy global agentic tools from the DataRobot Registry to handle tasks critical to the agent application. | | DataRobot agentic skills | Install and use DataRobot agentic skills in your agentic application. | | Implement tracing | Add custom tracing to your agent tools for monitoring, debugging, and observability. | | Access request headers | Learn how to access HTTP request headers in your deployed agents for authentication, tracking, and custom metadata. | | Debug agents in PyCharm | Use PyCharm's Run configuration in debug mode to debug agent code locally. | | Debug agents in VS Code | Use VS Code's Run and Debug configuration to start dev.py and step through agent code locally. | | Troubleshooting | Diagnose and resolve common issues when working with DataRobot agentic application. | --- # Chat with agents URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-chatting.html > Describes working with single-agent chats in the playground. # Chat with agents Chatting with an agent primarily involves testing the agent's output against expectations—it allows human evaluation of responses. In the agentic playground chat you provide prompts and assess if the responses align with desired outcomes. Did the playground metrics and tools produce the expected output? Agentic chatting does not include [context-aware](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#context-aware-chatting) capabilities, and so while a "chat" is a collection of chat prompts, each response is individual and not dependent on previous responses. ## Single- vs multi-agent chats A single agent chat is accessed by clicking the agent tile (not the checkbox) in the Agentic playground > Workflows tab. To return to the multi-agent view, click the Agentic playground tile (or the back arrow above). A multi-agent chat: - Is accessed from the Agentic playground > Workflows tab (1). - Uses check boxes to select agents (2). Selecting only one of the agents does not make it a single-agent chat. - Displays Agentic workflow comparison in the top of the chat window (3). There is no functional difference between single- and multi-agent chat responses; however, [tracing](https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-tracing.html) is available only in single-agent chats. ## Entering and sending prompts Enter a prompt to start a chat for either single or multi-agent chatting from the Agentic playground > Workflows tab. Click Send to request a response. ## Agent actions Most actions, with the exception of [tracing](https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-chatting.html#single-agent-tracing), are shared for both single- and multi-agent chats, as described in the sections and tables below. ### Actions menu Click the actions menu either under the Workflows tab or within the chat window to act on the agent. **Workflows tab:** [https://docs.datarobot.com/en/docs/images/agent-actions.png](https://docs.datarobot.com/en/docs/images/agent-actions.png) **Chat window:** [https://docs.datarobot.com/en/docs/images/agent-chat-window-actions.png](https://docs.datarobot.com/en/docs/images/agent-chat-window-actions.png) | Action | Description | Location | | --- | --- | --- | | Edit agentic workflow name | Edit the display name of the agentic workflow. | Workflows tab action menu. | | Open in codespace | Opens the agent in a codespace, where you can directly edit the existing files, upload new files, or use any of the codespace functionality. | Workflows tab action menu Button in the top right of a single-agent chat windowChat window action menu | | Register agentic workflow | After experimenting in the agentic playground to build a production-ready agentic workflow, register the custom agentic workflow in the Registry workshop, in preparation for deployment to Console. | Workflows tab action menu Button in the top right of a single-agent chat windowChat window action menu | | Remove from playground | Delete the agentic workflow from the playground. Removing an agentic workflow deletes it from the Use Case but does not delete it from Workshop. All playground-related information stored with the workflow, including metrics and chats, is also removed. | Workflows tab action menu Chat window action menu | You can remove both the prompt and all responses from the chat history from within the Agentic workflow comparison window: ### Aggregation tools The following table lists the shared [aggregation tools](https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-evaluation-tools.html#add-aggregated-metrics) and tabs available: | | Element | Description | | --- | --- | --- | | (1) | Agentic aggregated metrics | View aggregated metrics and scores calculated not using an evaluation dataset. These metrics originate in the Registry workshop. | | (2) | Evaluation aggregated metrics | View aggregated metrics and scores calculated using an evaluation dataset to provide a baseline for comparison. These metrics originate in the playground. | | (3) | Configure aggregation | Combine metrics across many prompts and/or responses to get a more comprehensive approach to evaluation. | ## Single-agent tracing There are two types of [agentic tracing](https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-tracing.html) available for single-agent chats. The output of the trace depends on the execution location. **Prompt header:** From the actions menu, in the prompt header, select Open tracing table. [https://docs.datarobot.com/en/docs/images/trace-from-chat-window.png](https://docs.datarobot.com/en/docs/images/trace-from-chat-window.png) View a log that traces all components used in LLM response generation. [https://docs.datarobot.com/en/docs/images/tracing-log.png](https://docs.datarobot.com/en/docs/images/tracing-log.png) **Response header:** Click Review tracing in the response header: [https://docs.datarobot.com/en/docs/images/trace-from-response-window.png](https://docs.datarobot.com/en/docs/images/trace-from-response-window.png) The tracing details panel opens. [https://docs.datarobot.com/en/docs/images/tracing-chart.png](https://docs.datarobot.com/en/docs/images/tracing-chart.png) | Location | Option | Description | | --- | --- | --- | | Prompt header actions menu | Open tracing | Opens the tracing table log, which shows all components and prompting activity used in generating agentic responses. | | Response header | Review tracing | Opens the tracing details panel, illustrating the path a single request takes through the agentic workflow. | ## Response feedback Use the response feedback "thumbs" to rate the prompt answer. Responses are recorded in the User feedback column on the [Tracing](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#tracing) tab. The response, as part of the exported feedback sent to the AI Catalog, can be used, for example, to train a predictive model. --- # Evaluate metrics URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-evaluation-tools.html > Configure evaluation metrics, add evaluation datasets, and review tracing for agentic workflows in a playground. # Evaluate metrics The playground's agentic evaluation tools include evaluation metrics and datasets, aggregated metrics, compliance tests, and tracing. The agentic evaluation metric tools include: | Agentic workflow evaluation tool | Description | | --- | --- | | Evaluation metrics | Report an array of performance, safety, and operational metrics for prompts and responses in the playground and define moderation criteria and actions for any configured metrics. | | Evaluation datasets | Upload or generate the evaluation datasets used to evaluate an agentic workflow through evaluation dataset metrics and aggregated metrics. | | Aggregated metrics | Combine evaluation metrics across many prompts and responses to evaluate an agentic workflow at a high level, as only so much can be learned from evaluating a single prompt or response. | | Tracing table | Trace the execution of agentic workflows through a log of all components and prompting activity used in generating responses in the playground. | ## Configure evaluation metrics With evaluation metrics, you can configure performance and operational metrics for agents. You can view these metrics in comparison chats and in chats with individual agents. Playground metrics require reference information provided through an evaluation dataset and are useful for assessing if an agentic workflow is operating as expected. Because they require an evaluation dataset, they are only available in the playground. Agentic workflow metrics don't require reference data, so they are available in production and configured in the [Workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-configure-evaluation-moderation.html). | Playground metrics | Agentic workflow metrics | | --- | --- | | Are configured in a playground. | Are configured in Workshop. | | Require reference data provided as an evaluation dataset. | Don't require reference data. | | Can't be computed in production. | Can be computed in production. | | Can only be applied to the top level agentic workflow. | Can be applied to the top-level agent and sub-agents and sub-tools of the workflow (if they are separate custom models). | > [!NOTE] Agent moderation > Agentic workflow-specific metrics don't support setting moderation criteria. ### View agentic workflow metrics To enable agentic workflow metrics for a workflow, configure [evaluation and moderation in Workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-configure-evaluation-moderation.html). Click the Evaluation tile to see, on the Agentic workflow metrics tab, the configured metrics that are enabled for the agentic workflow. ### Configure playground metrics To enable playground metrics for your workflows, add one or more evaluation metrics to the agentic playground. In addition, you must provide reference data using [evaluation datasets](https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-evaluation-tools.html#add-evaluation-datasets). 1. To select and configure playground evaluation metrics for an agentic playground, do either of the following: Without connected agentsWith connected agentsIf you haven't connected any agents to the playground, in theEvaluate with metricstile, clickConfigure metricsto configure metrics before adding an agent:If you've added one or more agents to the playground, on the side navigation bar click theEvaluationtile: 2. On theEvaluation and moderationpage, click thePlayground metricstab, then clickConfigure metrics. 3. On theConfigure evaluation and moderationpage, click anOperational metric: Playground metricDescriptionAgent latencyTotal latency of running the agent workflow for a given request. Includes time for completions, tool calls, and metrics calculated by the moderations library. Always available when the agent is configured for OTel.Agent total tokensFor agents using the LLM gateway, total tokens are reported in OTel; current templates already do this. For agents using a deployed LLM, that LLM must have token count metrics enabled in themoderations configuration.Agent costAvailable only for calls to deployed LLMs. The deployed LLM must have acost metric configuredin the moderations configuration. Operational metrics and Agentic workflow comparisonAgent Cost, Agent Latency, and Agent Total Tokens use data from theOTel collector, which is configured by default in theagent templates. These metrics aggregate the reported OTel data and require tracing enabled to associate the correct spans with that data. Tracing is not available on the Agentic workflow comparison screen, so these operational evaluation metrics are only available when assessing per-agent responses, not when comparing agentic workflows. Then, optionally modify the metricNameand clickAdd. TheApply tosetting is preconfigured for these metrics. 4. On theConfigure evaluation and moderationpage, click aQuality metric: Playground metricDescriptionAgent Goal Accuracy with ReferenceUse a known benchmark to evaluate agentic workflow performance in achieving specified objectives. Requires an evaluation dataset containing an agent goal column.Tool Call AccuracyMeasure agentic workflow performance when identifying and calling the required tools for a given task. Requires an evaluation dataset containing an expected tool calls column. Evaluation dataset exampleThe example evaluation dataset below includes an expected tool calls column (toolCalls) required by theTool Call Accuracymetric and an agent goal column (agentGoal) required by theAgent Goal Accuracy with Referencemetric:example_evaluation_dataset.csvid,promptText,expectedResponse,toolCalls,agentGoal 1,What is the weather like in New York today?,It is 24 C and sunny in New York today.,"[{""name"":""weather_check"",""args"":{""location"":""New York""}},{""name"":""temperature_conversion"",""args"":{""temperature_fahrenheit"":75}}]",A concise answer to a question about weather. 2,How many planets are in the solar system?,Our solar system has 8 planets.,[],A concise answer to a question about the solar system.In addition, the DataRobot Python client providesutility classes for constructing the expected tool calls column. Then, configure the following settings, depending on the metric you selected: Playground metricDescriptionAgent Goal Accuracy with Reference(Optional) Enter a metricName.Select a playground or deployed LLM to evaluate goal accuracy.Tool Call Accuracy(Optional) Enter a metricName. After configuring the settings, clickAdd. TheApply tosetting is preconfigured for these metrics. 5. Select and configure another metric, or clickSave configuration. Edit configuration summaryAfter you add one or more metrics to the playground configuration, you can edit or delete those metrics. ### Copy metric configurations To copy an evaluation metrics configuration to or from an agentic playground: 1. In the upper-right corner of theEvaluation and moderationpage, next toConfigure metrics, click, and then clickCopy configuration. 2. In theCopy evaluation and moderation configurationmodal, select one of the following options: From an existing playgroundTo an existing playgroundTo a new playgroundIf you selectFrom an existing playground, choose toAdd to existing configurationorReplace existing configurationand then select a playground toCopy from.If you selectTo an existing playground, choose toAdd to existing configurationorReplace existing configurationand then select a playground toCopy to.If you selectTo a new playground, enter aNew playground name. 3. Select if you want toInclude evaluation datasets, and then clickCopy configuration. > [!NOTE] Duplicate evaluation metrics > Selecting Add to existing configuration can result in duplicate metrics. ### Add evaluation datasets To enable playground evaluation metrics and aggregated metrics, you must add one or more evaluation datasets to the playground to serve as reference data. The dataset must be a CSV file, in the Data Registry, and have at least one text or categorical column. 1. To add evaluation datasets in an agentic playground, do either of the following: 2. On theEvaluation and moderationpage, click theEvaluation datasetstab to view any existing datasets, or, clickAdd evaluation datasetfrom any tab, and select one of the following methods: MethodDescriptionSelect an existing datasetClick a dataset in theData Registrytable.Upload a new datasetClickUploadto register and select a new dataset from your local filesystem.ClickUpload from URL, then, enter theURLfor a hosted dataset and clickAdd. After you select a dataset, in theEvaluation dataset configurationright-hand sidebar, define the following columns: ColumnDescriptionPrompt column nameThe name of the reference dataset column containing the user prompt.Response (target) column nameThe name of the reference dataset column containing an expected agent response.Reference goals column nameThe name of the reference dataset column containing a description of the expected (goal) output of the agent. This data is used for theConfigure Agent Goal Accuracy with Referencemetric.Reference tools column nameThe name of the reference dataset column containing the expected agentic tool calls. This data is used for theConfigure Tool Call Accuracymetric. Then, clickAdd evaluation dataset. Evaluation dataset exampleThe example evaluation dataset below includes an expected tool calls column (toolCalls) required by theTool Call Accuracymetric and an agent goal column (agentGoal) required by theAgent Goal Accuracy with Referencemetric:example_evaluation_dataset.csvid,promptText,expectedResponse,toolCalls,agentGoal 1,What is the weather like in New York today?,It is 24 C and sunny in New York today.,"[{""name"":""weather_check"",""args"":{""location"":""New York""}},{""name"":""temperature_conversion"",""args"":{""temperature_fahrenheit"":75}}]",A concise answer to a question about weather. 2,How many planets are in the solar system?,Our solar system has 8 planets.,[],A concise answer to a question about the solar system.In addition, the DataRobot Python client providesutility classes for constructing the expected tool calls column. 3. After you add an evaluation dataset, it appears on theEvaluation datasetstab of theEvaluation and moderationpage, where you can: ## Add aggregated metrics When a playground includes more than one metric, you can begin creating aggregated metrics. Aggregation is the act of combining metrics across many prompts and/or responses, which helps to evaluate agents at a high level (only so much can be learned from evaluating a single prompt/response). Aggregation provides a more comprehensive approach to evaluation. Aggregation either averages the raw scores, counts the boolean values, or surfaces the number of categories in a multiclass model. DataRobot does this by generating the metrics for each individual prompt/response and then aggregating using one of the methods listed, based on the metric. To configure aggregated metrics for an agentic playground: 1. In the agentic playground, clickConfigure aggregationbelow the prompt input (from theWorkflowstab, or in an individual agentChatstab): Workflows tabAgent Chats tabFrom theWorkflowstab, each agentic workflow selected is included in the aggregation job.From theChatstab for a single agent, only the current agentic workflow is included in the aggregation job. Aggregation job run limitOnly one aggregated metric job can run at a time. If an aggregation job is currently running, theConfigure aggregationbutton is disabled and the "Aggregation job in progress; try again when it completes" tooltip appears. 2. On theGenerate aggregated metricspanel, select metrics to include in aggregation and configure theAggregate bysettings. In the right-hand panel, enter a newChat name, select anEvaluation dataset(to generate prompts in the new chat), and select theWorkflowsfor which the metrics should be generated. These fields are pre-populated based on the current playground: Playground vs agentic workflow metricsIn the example below,Agent Goal Accuracy with ReferenceandTool Call Accuracyare playground metrics, whileROUGE-1andResponse Tokens are agentic workflow metrics (fromWorkshop). After you complete theMetrics selectionandConfigurationsections, clickGenerate metrics. This results in a chat, identified as aMetricchat, containing all associated prompts and responses: Aggregated metrics are run against an evaluation dataset, not individual prompts in a standard chat. Therefore, you can only view aggregated metrics in the generatedaggregated metrics chat, added to the agent'sAll Chatslist (on the agent's individualChatstab). Aggregation metric calculation for multiple agentsIf many agents are included in the metric aggregation request, aggregated metrics are computed sequentially, agent-by-agent. 3. Once an aggregated chat is generated, you can explore the resulting aggregated metrics, scores, and related assets on theAgentic aggregated metricstab andEvaluation aggregated metricstab. These tabs are available when comparing agentic chats, and when viewing a single-agent chat. You can filter byAggregation method,Evaluation dataset, andMetric: Agentic aggregated metricsEvaluation aggregated metrics --- # Connect to a playground URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-playground.html > The agentic playground, an asset of a Use Case, is the space for connecting and interacting with agentic workflows. # Connect to a playground An agentic playground, a type of Use Case asset, is the space for connecting and interacting with agentic workflows. Within the playground you compare agentic workflow responses to determine which agent to use in production for solving a business problem. Multiple playgrounds can exist in one Use Case and multiple agentic workflows can exist within a single playground. The suggested, simplified workflow for working with playgrounds is as follows: 1. Add an agentic playground . 2. Connect an agentic workflow . 3. Chat with a single agent to test, tune, and view tracing or with multiple agents to compare results.. 4. Connect additional agentic workflows. 5. Add datasets and metrics to a playground or to individual agentic workflows to evaluate responses. ## Add an agentic playground To add an agentic playground, [create a Use Case](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/usecase-overview.html#create-a-use-case) (if a new Use Case is needed), then, in the left navigation bar, click the Playgrounds tile. From the Playgrounds tab, the following options are available, depending on how many playgrounds already exist: - If you haven't added a playground yet,click theAdd Playgrounddropdown in the center of the page, then clickAdd RAG playground. This button is only available for the first playground added to a Use Case; use theAdd Playgrounddropdown for subsequent playgrounds. - If you've already added one or more playgrounds (RAG or agentic),click theAdd Playgrounddropdown in the upper-right corner of the page, then clickAdd RAG playground. ## Navigate the playground After you create and open an agentic playground, you can access several areas of the playground from the left navigation bar. Click the icons to navigate the playground: | Icon | Component | Description | | --- | --- | --- | | | Agentic playground | Connect, compare, and chat with agentic workflows. | | | Playground info | Display playground summary information including the name, description, creation time and date, creator, last modification time and date, and number of agentic workflows. | | | Evaluation* | Configure evaluation metrics in a playground. | | | Tracing* | Display an exportable log that traces all components used in agent response generation. | * Available only if LLM/agentic assessment is enabled. > [!NOTE] Return to the Playgrounds tile > Also in the left navigation, you can click back to return to the Playgrounds tile in the current Use Case. ## Connect an agentic workflow Agentic workflows can be created manually in the Registry [workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-create-custom-model.html#assemble-an-agentic-workflow), or programmatically using the [agent templates in the publicdatarobot-agent-applicationrepository](https://github.com/datarobot-community/datarobot-agent-application). This repository provides ready-to-use templates for building and deploying AI agents with multi-agent frameworks, streamlining the process of setting up your own agents—with minimal configuration requirements. To connect an agentic workflow to a playground, select the Agentic playground tile and then the Workflows tab. The following options are available: - If you haven't added a workflow yet,clickConnect workflow from workshopin the center of the page. This button is only available for the first workflow added to a playground. - If you've already added one or more workflows,click theConnect agentic workflowdropdown in the upper-left corner of the page. In the Agentic workflow modal, from the Workshop dropdown, select an agentic workflow that you previously assembled in the workshop, then click Connect. ## Manage connected agentic workflows To access management actions for connected agentic workflows, click the actions menu for an agentic workflow, either in the Workflows panel, or in the header of the Agentic workflow comparison, next to the agent name. | Action | Description | | --- | --- | | Edit agentic workflow name | Change the current name for the agentic workflow. The default name is the name defined in the Registry's Workshop. This option is only available in the actions menu in the Workflows panel. | | Open in codespace | Open a codespace containing the agentic workflow's files to fine-tune the agentic workflow by editing the underlying code. | | Register agentic workflow | Open the agentic workflow in the workshop to make any final changes to the configuration, register the workflow, and deploy to production. | | Remove from playground | Disconnect the selected agentic workflow from the current playground. This does not remove the agentic workflow from the workshop, and you can restore the connection at any time. | --- # Review tracing URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-tracing.html > Review tracing for agentic workflows in a playground. # Review tracing Tracing the execution of agentic workflows is a powerful tool for understanding how most parts of the GenAI stack work. The Tracing tile provides a log of all components and prompting activity used in generating agent responses in the playground. Insights from tracing provide full context of everything the agent evaluated, including prompts, vector database chunks, and past interactions within the context window. For example: - DataRobot metadata: Reports the timestamp, status, Use Case, playground, agentic workflow, and the creator name. These help pinpoint the sources of trace records if you need to surface additional information from DataRobot objects interacting with the agentic workflow. - Prompts and responses: Provides a history of user prompts, agentic workflow responses, and user feedback. - Evaluations (if configured): Illustrates how evaluation metrics are scoring prompts or responses. To locate specific information in the Tracing table, click Filters and filter by any combination of Timestamp, User name, LLM Blueprint name, LLM, Vector database, Chat name, and Evaluation dataset. > [!TIP] Send tracing data to the Data Registry > Click Upload to Data Registry to export data from the tracing table to the Data Registry. A warning appears on the tracing table when it includes results from running the toxicity test and the toxicity test results are excluded from the Data Registry upload. ### Individual chat request tracing In addition to the logged information about all prompts and responses on the Tracing tile, you can also access tracing details on the path a single chat request takes through the agentic workflow. Do this from the Chats tab for a single agentic workflow. In a single-agent chat, click Review tracing next to the agent's name. Traces represent the path taken by a request to a model or agentic workflow. DataRobot uses the [OpenTelemetry framework for tracing](https://opentelemetry.io/docs/concepts/signals/traces/). A trace follows the entire end-to-end path of a request, from origin to resolution. Each trace contains one or more spans, starting with the root span. The root span represents the entire path of the request and contains a child span for each individual step in the process. The root (or parent) span and each child span share the same Trace ID. In the tracing details panel header, review the Trace ID for the request, the execution time (in ms), and the span services involved. On the List and Chart tabs, review the [spans](https://opentelemetry.io/docs/concepts/signals/traces/#spans) contained in the trace, along with trace details. The span colors correspond to a Span service. The first span service is the experiment container, sometimes followed by one or more deployments.Restricted span appears when you don’t have access to the deployment or service associated with the span. You can view spans in Chart format or List format. > [!TIP] Span detail controls > From either view, you can click Hide details panel to return to the single-agent-chat. **Chart view:** [https://docs.datarobot.com/en/docs/images/agentic-tracing-table-spans-chart.png](https://docs.datarobot.com/en/docs/images/agentic-tracing-table-spans-chart.png) **List view:** [https://docs.datarobot.com/en/docs/images/agentic-tracing-table-spans-list.png](https://docs.datarobot.com/en/docs/images/agentic-tracing-table-spans-list.png) > [!NOTE] Trace details > In list view, you can click Trace details to view the Input/Output ( Prompt and Completion) and Evaluation details about the trace associated with the current span. For either view, click the Span service name to access the deployment or resource (if you have access). Additional information, dependent on the configuration of the generative AI model or agentic workflow, is available on the Info, Resources, Events, Input/Output, and Error tabs. The Error tab only appears when an error occurs in a trace. --- # Build workflows URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-workflow-build.html > After you've connected an agentic workflow custom model to an agentic playground, as you compare and test the workflow, you can modify the agentic workflow's code in a codespace or in Workshop. # Build workflows After you've [connected an agentic workflow](https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/agentic-playground.html#connect-an-agentic-workflow) custom model to an agentic playground, as you compare and test the workflow, you can modify the workflow's code in a codespace or in the Registry workshop. Changes to the code are reflected in the agentic playground, allowing agentic workflow builders to experiment until they build a production-ready agentic workflow for deployment to Console. ## Agentic workflow components To assemble an agentic workflow, a standard workflow's custom model code could include the following components: | File | Contents | | --- | --- | | __init__.py | Python package initialization file, making the directory a Python package. | | custom.py | The custom model code implementing the Bolt-on Governance API (the chat hook) to call the LLM and also passing those parameters to the agent (defined in myagent.py). | | myagent.py | The agent code, implementing the agentic workflow in the MyAgent class with the required invoke method. Tool integration properties can be added to this class to interface with deployed tools. | | config.py | The code for loading the configuration from environment variables, runtime parameters, and DataRobot credentials. | | mcp_client.py | The code providing MCP server connection management for tool integration (optional, only needed when using MCP tools). | | tool_deployment.py | The BaseTool class code, containing all necessary metadata for implementing tools. | | tool.py | The code for interfacing with the deployed tool, defining the input arguments and schema. Often, this file won't be named tool.py, as you may implement more than one tool. In this example, this functionality is defined in tool_ai_catalog_search.py. | | model-metadata.yaml | The custom model metadata and runtime parameters required by the agentic workflow. | | pyproject.toml | The libraries (and versions) required by the agentic workflow, using modern Python packaging standards. | For more information on assembling agentic workflows, review the following resources: | Resource | Description | | --- | --- | | datarobot-agent-application repository | Documentation and ready-to-use templates for building and deploying AI agents with multi-agent frameworks. These templates streamline the process of setting up your own agents with minimal configuration requirements. | | agent-tool-templates repository | Documentation and source code for the global agentic tools available in the Registry. The source code for these tools can serve as templates for creating your own custom model tools for agentic workflows. | | datarobot-user-models repository | Tools, templates, and information for assembling, debugging, testing, and running your custom models, custom tasks and custom notebook environments with DataRobot. The custom model infrastructure is the foundation of agentic workflows. | | Custom model assembly documentation | Documentation for assembling, testing, and running custom models. | | Workshop documentation | Documentation for using the DataRobot UI to upload model artifacts to create, test, and deploy custom models to Console, a centralized model management and deployment hub. | ## Modify an agentic workflow Agentic workflows connected to the agentic playground can be continuously modified and developed as you prompt, compare, and evaluate metrics. This works for custom tools built manually, and tools built programmatically using the [agent templates in the publicdatarobot-agent-applicationrepository](https://github.com/datarobot-community/datarobot-agent-application). ### Codespace Codespaces are the primary method for developing your agentic workflow while testing in an agentic playground. In DataRobot, a codespace is a development environment you can use to view, modify, and run your agent's files. Changes made in the codespace are passed on to the agentic workflow in the agentic playground, as well as the custom agent in the Registry workshop. To develop an agentic workflow in a codespace, locate the Open in Codespace option in one of the following locations: **Workflows comparison (header):** [https://docs.datarobot.com/en/docs/images/agentic-playground-open-codespace-1.png](https://docs.datarobot.com/en/docs/images/agentic-playground-open-codespace-1.png) **Workflows comparison (sidebar):** [https://docs.datarobot.com/en/docs/images/agentic-playground-open-codespace-2.png](https://docs.datarobot.com/en/docs/images/agentic-playground-open-codespace-2.png) **Single model chat:** [https://docs.datarobot.com/en/docs/images/agentic-playground-open-codespace-3.png](https://docs.datarobot.com/en/docs/images/agentic-playground-open-codespace-3.png) Any of these options opens a codespace in a panel for the selected agentic workflow, where you can modify your agent's code to address issues identified in the playground. > [!NOTE] Codespace loading > The panel displays a Waiting for the codespace to start...message while the agent's files are loaded. When you finish modifying the agent code, click Save to pass those changes to the agentic workflow connected to the playground and in the Registry workshop. ### Workshop Similar to modifying an agentic workflow in a codespace, you can also modify your agent code directly in the Registry's workshop. For more information, see the [Workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/index.html) documentation. When you save the [changes to your agentic workflow](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-create-custom-model.html#assemble-the-custom-model) in the workshop, a new version is created and passed to the agentic workflow connected to the playground. From the workshop you can also [configure evaluation metrics and moderations](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-configure-evaluation-moderation.html) not available in an agentic playground. --- # Evaluate URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-eval/index.html > Chat with a workflow, compare flows, and implement evaluation metrics. # Evaluate > [!NOTE] Premium > DataRobot's Agentic AI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. Create, connect, and test agentic workflows, integrate tools, and implement evaluation metrics. | Topic | Description | | --- | --- | | Connect to a playground | Connect to and interact with agentic workflows. | | Chat with agents | Chat with a single agentic workflow or with multiple workflows for comparison purposes. | | Evaluate metrics | Use the playground's evaluation tools, including evaluation metrics and datasets, aggregated metrics, and compliance tests. | | Review tracing | Review tracing of agentic workflow execution in a playground. | | Build workflows | Modify the agentic workflow's code in a codespace or in the Workshop. | --- # Connect agentic coding environments to MCP servers URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-mcp-clients.html > Configure Cursor, Claude Desktop, and VS Code to use your DataRobot MCP server for tools and resources in agentic coding workflows. # Connect agentic coding environments to MCP servers You can connect your DataRobot MCP server to standard agentic coding environments—such as Cursor, Claude Desktop, and VS Code—to allow AI assistants in those environments to discover and call your MCP tools, prompts, and resources. This enables you to use DataRobot capabilities (e.g., projects, deployments, predictions, third-party tools) directly from your IDE or chat client. This guide explains how to configure each client to use an MCP server that you run locally or that is deployed to DataRobot. It applies to any of the following MCP connection options: - An MCP server deployed using the DataRobot Agentic Starter template ( datarobot-agent-application ). - A standalone MCP server deployed using the DataRobot MCP template ( af-component-datarobot-mcp ). - An MCP server implemented by the DataRobot Global MCP. > [!NOTE] MCP server vs. agent application > This page focuses on connecting MCP clients (Cursor, Claude Desktop, VS Code) to an MCP server. For integrating an MCP server into a DataRobot agentic workflow (e.g., LangGraph agent in the Agentic Starter template), see [Integrate tools using an MCP server](https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-tools-mcp.html). ## Using the DataRobot Global MCP The DataRobot Global MCP is a service automatically deployed to your DataRobot instance that agentic workflows can use to access tools. > [!NOTE] Available tools > The DataRobot Global MCP currently supports tools for predictive AI. This limitation will be removed in a future release. ### Configuration The DataRobot Global MCP requires an API key to authenticate requests. You can obtain your API key from the DataRobot UI by opening the user menu and selecting API keys and tools. See [API key management](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html) for more information. Once you have your API key, configure the MCP client to use the DataRobot Global MCP endpoint. Refer to the steps that correspond to your MCP client in the tabs below. **Cursor:** ``` { "mcpServers": { "datarobot-mcp": { "url": "https://{DATAROBOT_URL}/api/v2/genai/globalmcp/mcp", "headers": { "Authorization": "Bearer " } } } } ``` To verify the connection, save `.cursor/mcp.json` in the correct location, restart Cursor or reload the window, then in Chat or Composer ask the AI to list or use tools from the DataRobot MCP server. **Claude Desktop:** ``` { "mcpServers": { "datarobot-mcp": { "url": "https://{DATAROBOT_URL}/api/v2/genai/globalmcp/mcp", "headers": { "Authorization": "Bearer " } } } } ``` To verify the connection, save `claude_desktop_config.json` in the correct location, restart Claude Desktop, then ask Claude to use tools from the DataRobot MCP server. **VS Code:** ``` { "servers": { "datarobotMcp": { "type": "http", "url": "https://{DATAROBOT_URL}/api/v2/genai/globalmcp/mcp", "headers": { "Authorization": "Bearer " } } } } ``` To verify the connection, save `.vscode/mcp.json` under `.vscode/` or your user profile ( MCP: Open User Configuration in the Command Palette), reload the window if prompted, then in Copilot Chat ask the AI to list or use tools from the DataRobot MCP server. ## Using a standalone MCP server You can also use a standalone MCP server that you deploy to your own infrastructure. To use a standalone MCP server, you need to configure your MCP client to use the standalone MCP server endpoint. ### Prerequisites Before configuring your coding environment, ensure that you have: - A running MCP server (local or deployed): - Local - Deployed - Endpoint and auth - "Authorization": "Bearer " (required for authentication with the MCP server) - "x-datarobot-api-token": " " (required for tool execution) > [!TIP] Finding the deployed MCP endpoint > To find the endpoint of the deployed MCP server: > > For the > DataRobot MCP template > , after deploying run > task infra:info > or check the Pulumi/output step for > MCP_SERVER_MCP_ENDPOINT > . > For the > Agentic Starter template > , the deployment output includes an MCP server endpoint. Use the URL shown there for your client. ### Endpoint reference | Context | Base URL | Notes | | --- | --- | --- | | Agentic Starter (local) | http://localhost:9000/mcp | Default port is 9000; set MCP_SERVER_PORT to change it. | | MCP template (local) | http://localhost:8080/mcp | Default port is 8080; set MCP_SERVER_PORT to change it. | | Deployed to DataRobot | https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp | Use the exact URL from your deployment output. | ### Configure your environment You can configure your MCP client to use the MCP server endpoint. Refer to the steps that correspond to your MCP client in the tabs below. **Cursor:** > [!NOTE] Cursor MCP docs > For Cursor's current MCP options, see [Cursor's MCP documentation](https://cursor.com/docs/context/mcp). Configuration file location: Project-specific: /.cursor/mcp.json Global: ~/.cursor/mcp.json For a local MCP server using the [DataRobot MCP template](https://github.com/datarobot-community/af-component-datarobot-mcp) on port 8080: ``` { "mcpServers": { "datarobot-mcp": { "url": "http://localhost:8080/mcp", "headers": { "Authorization": "Bearer ", "x-datarobot-api-token": "" } } } } ``` For a local MCP server using the [DataRobot Agentic Starter](https://github.com/datarobot-community/datarobot-agent-application) on port 9000: ``` { "mcpServers": { "datarobot-mcp": { "url": "http://localhost:9000/mcp", "headers": { "Authorization": "Bearer ", "x-datarobot-api-token": "" } } } } ``` For a deployed MCP server: ``` { "mcpServers": { "datarobot-mcp": { "url": "https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp", "headers": { "Authorization": "Bearer ", "x-datarobot-api-token": "" } } } } ``` **Claude Desktop:** Claude Desktop can connect to remote MCP servers over HTTP. Configure the MCP server in `claude_desktop_config.json`. > [!NOTE] Claude Desktop MCP docs > For Claude Desktop's current MCP options, see [Claude Desktop's MCP documentation](https://docs.anthropic.com/en/docs/build-with-claude/mcp). Configuration file location: macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json For a local MCP server using the [DataRobot MCP template](https://github.com/datarobot-community/af-component-datarobot-mcp) on port 8080: ``` { "mcpServers": { "datarobot-mcp": { "url": "http://localhost:8080/mcp", "headers": { "Authorization": "Bearer ", "x-datarobot-api-token": "" } } } } ``` For a local MCP server using the [DataRobot Agentic Starter](https://github.com/datarobot-community/datarobot-agent-application) on port 9000: ``` { "mcpServers": { "datarobot-mcp": { "url": "http://localhost:9000/mcp", "headers": { "Authorization": "Bearer ", "x-datarobot-api-token": "" } } } } ``` For a deployed MCP server: ``` { "mcpServers": { "datarobot-mcp": { "url": "https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp", "headers": { "Authorization": "Bearer ", "x-datarobot-api-token": "" } } } } ``` If your deployment requires authentication, add the appropriate headers or token mechanism as supported by Claude Desktop and your MCP server (e.g., environment variables or config fields; check [Claude Desktop documentation](https://docs.anthropic.com/en/docs/build-with-claude/mcp) and your server docs). > [!TIP] Debugging Claude Desktop MCP > On macOS, MCP-related logs are often under `~/Library/Logs/Claude/` (e.g., `mcp*.log`). Use them to troubleshoot connection or auth issues. **VS Code:** VS Code (with GitHub Copilot) includes built-in MCP client support. Configure remote MCP servers in an `mcp.json` file (HTTP servers use `type`, `url`, and optional `headers`). > [!NOTE] VS Code Copilot MCP docs > For VS Code's current MCP options, the configuration schema, and security notes, see [Use MCP servers in VS Code](https://code.visualstudio.com/docs/copilot/customization/mcp-servers) and the [MCP configuration reference](https://code.visualstudio.com/docs/copilot/reference/mcp-configuration). Configuration file location: Workspace: /.vscode/mcp.json (appropriate for shared, non-secret settings that you may commit to source control). User profile: run MCP: Open User Configuration from your coding environment's Command Palette ( ⇧⌘P / Ctrl+Shift+P ). Prefer this location, or another secret-management mechanism, for API keys and other credentials. Do not commit API keys If your MCP configuration includes headers such as Authorization or x-datarobot-api-token , do not commit those secrets to source control. Keep credentialed configuration in your user-profile mcp.json or another secure secret store. You can also use MCP: Add Server in your coding environment's Command Palette for a guided setup. For more options, see [Add and manage MCP servers in VS Code](https://code.visualstudio.com/docs/copilot/customization/mcp-servers). For a local MCP server using the [DataRobot MCP template](https://github.com/datarobot-community/af-component-datarobot-mcp) on port 8080: ``` { "servers": { "datarobotMcp": { "type": "http", "url": "http://localhost:8080/mcp", "headers": { "Authorization": "Bearer ", "x-datarobot-api-token": "" } } } } ``` For a local MCP server using the [DataRobot Agentic Starter](https://github.com/datarobot-community/datarobot-agent-application) on port 9000: ``` { "servers": { "datarobotMcp": { "type": "http", "url": "http://localhost:9000/mcp", "headers": { "Authorization": "Bearer ", "x-datarobot-api-token": "" } } } } ``` For a deployed MCP server: ``` { "servers": { "datarobotMcp": { "type": "http", "url": "https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp", "headers": { "Authorization": "Bearer ", "x-datarobot-api-token": "" } } } } ``` If your deployed server requires a token, configure authentication using HTTP headers supported by your MCP server. Avoid passing tokens in URLs or query parameters to reduce the risk of credential leakage; see [Use MCP servers in VS Code](https://code.visualstudio.com/docs/copilot/customization/mcp-servers) and your server docs. ## Apply and verify To confirm connectivity: 1. Save your MCP configuration in the correct location. 2. Restart your MCP client (or reload the window). 3. In your MCP client, ask the AI to list or use tools from the DataRobot MCP server. ## Troubleshooting ### Client cannot connect to MCP server Symptoms: The IDE or Claude reports that the MCP server is unavailable, or tools do not appear. Solutions: 1. Confirm the MCP server is running: 2. Local: run curl -i http://localhost:9000/ or curl -i http://localhost:8080/ (adjust the port to match your local MCP server setup). A successful response indicates the server is up. 3. Deployed: run curl -i using the exact URL from your deployment output. If the server requires authentication, you may need to pass a bearer token or other headers; see your deployment documentation. A successful response indicates the server is up. 4. Check URL and path: Use the exact base URL and path (e.g., /mcp ) required by your client and server. 5. Confirm firewall and network access: For deployed servers, ensure your network allows outbound HTTPS to the DataRobot host. ### Tools do not appear in the client Symptoms: Connection seems OK, but the client does not list MCP tools. Solutions: 1. Restart the client (Cursor, Claude Desktop, or VS Code) after changing the MCP config. 2. Check client and MCP server logs for errors (e.g., Cursor: Output → MCP Logs; Claude: ~/Library/Logs/Claude/ ). A successful connection will show MCP server logs with tool registration and availability information. ### Authentication errors Symptoms: Requests to the deployed MCP server return 401 or similar. Solutions: 1. Confirm the deployed server's auth requirements (e.g., bearer token). 2. Ensure the client is configured with the correct token or credentials (environment variables or extension settings). 3. For DataRobot deployments, ensure DATAROBOT_API_TOKEN (or the equivalent used by the server) is valid and has access to the deployment. ## Additional resources - Integrate tools using an MCP server — Use an MCP server from a DataRobot agentic workflow (e.g., LangGraph agent). - DataRobot MCP template — Build and deploy standalone MCP servers with DataRobot integration. - DataRobot MCP template — Configure your MCP client — Client setup details from the template repo. - DataRobot Agentic Starter template — Full agentic application including an MCP server. - Model Context Protocol — Official MCP specification. --- # Integrate tools using an MCP server URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-tools-mcp.html > Learn how to integrate tools into your agentic workflows using the Model Context Protocol (MCP) server instead of direct tool deployments. # Integrate tools using an MCP server The Model Context Protocol (MCP) provides a standardized interface for AI agents to interact with external systems, tools, and data sources. Using an MCP server to provide tools to your agents offers several advantages over direct tool deployments: - Centralized tool management : All tools are managed in one MCP server deployment - Standardized interface : Tools follow the MCP protocol standard, making them compatible across different agent frameworks - Dynamic tool registration : Tools can be added or modified without redeploying the agent - Better separation of concerns : Tools are deployed separately from agents, enabling independent scaling and updates This guide explains how to integrate tools using an MCP server with your agentic workflows, as implemented in the [DataRobot Agentic Starter template](https://github.com/datarobot-community/datarobot-agent-application). MCP endpoints can come from the Agentic Starter app, a standalone server built with the [DataRobot MCP template](https://github.com/datarobot-community/af-component-datarobot-mcp), or the [DataRobot Global MCP](https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-mcp-clients.html#using-the-datarobot-global-mcp); this page focuses on connecting your agent application to the MCP URL your deployment uses. > [!NOTE] MCP vs. local tool integration > This documentation covers MCP server-based tool integration. For information about integrating tools using local tools (direct tool deployments), see [Add tools to agents](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tools-integrate.html). To connect MCP clients in Cursor, Claude Desktop, or VS Code—including the DataRobot Global MCP and deployment URLs—see [Connect agentic coding environments to MCP servers](https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-mcp-clients.html). ## Overview The Model Context Protocol (MCP) fundamentally changes how agents access tools. Instead of defining tool logic directly within the agent's code, you deploy a standalone "Tool Server." Your agent then connects to this server as a client, requesting available tools and executing them via a standardized protocol. The [DataRobot Agentic Starter template](https://github.com/datarobot-community/datarobot-agent-application) includes built-in support for MCP tools through the `LangGraphAgent` base class, which provides the `mcp_tools` property that automatically connects to your configured MCP server. Refer to the sections below for more information on how to use MCP tools in your agentic workflows. ### MCP architecture - The agent (client) : The reasoning engine (e.g., a LangGraph application) that plans tasks. It does not contain tool code; it only knows how to ask the MCP server what tools are available. - The MCP server : A web service hosting the logic for tools, resources, and prompts. It handles the actual execution of tasks (e.g., querying a database, calling an external API). - The protocol : A standard interface that allows the agent and MCP server to communicate securely, regardless of the underlying infrastructure. ### Integration workflow To integrate tools using this architecture, follow this high-level process: - Deploy or select an MCP endpoint : Create and deploy an MCP server containing your tool logic (using the DataRobot MCP template ), use the MCP server bundled with the DataRobot Agentic Starter template , or connect through the DataRobot Global MCP when your environment uses that service. - Connect the agent : Configure your agent application to point to the MCP server's URL (for example, a deployment directAccess URL, a gateway URL, or a local dev URL). - Execute : The agent automatically discovers tools at runtime. You do not need to redeploy the agent to add new tools; you only update the MCP server. ## Prerequisites Before integrating MCP tools, ensure you have: - A reachable MCP endpoint: a server built with the DataRobot MCP template , the MCP server included with the DataRobot Agentic Starter template , or the DataRobot Global MCP (depending on your setup). - An agentic workflow based on the DataRobot Agentic Starter template . - The MCP endpoint URL and authentication credentials (if required). For deploying a standalone MCP server, refer to the [MCP template documentation](https://github.com/datarobot-community/af-component-datarobot-mcp/blob/main/README.md). For URL patterns (local ports, deployment `directAccess`, gateway), see [Connect agentic coding environments to MCP servers](https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-mcp-clients.html). ## MCP server deployment The MCP server can be deployed either locally for development or to DataRobot for production use. ### Local deployment For local development, start the MCP server using the development command: ``` cd mcp_server dr task run mcp_server:dev ``` The MCP server will start on the configured port (default: `9000`) and be accessible at `http://localhost:9000/mcp/`. ### Production deployment To deploy the MCP server to DataRobot: 1. Configure deployment settings: Update your Pulumi configuration or deployment settings. 2. Deploy the server: Use the deployment command: drrundeploy You can also usedr task run deploy, which is equivalent. For more on these commands, see the CLItaskandruncommands. 3. Get the deployment endpoint: After deployment, note the deployment ID and construct the MCP endpoint URL: https://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp Some environments use theDataRobot Global MCPinstead of a per-deploymentdirectAccessURL: https://{DATAROBOT_URL}/api/v2/genai/globalmcp/mcp SeeUsing the DataRobot Global MCPfor how Global MCP URLs fit alongside deployment endpoints. 4. Configure the agent: Update your agent's configuration to use the production MCP endpoint (whichever URL your deployment provides). For detailed deployment instructions, refer to the [MCP template README](https://github.com/datarobot-community/af-component-datarobot-mcp/blob/main/README.md). ## Configure MCP server connection To connect your agent to an MCP server, you need to configure the MCP server endpoint and authentication in your agent's environment variables or configuration. ### Local development For local development, configure the MCP server connection in your `.env` file: ``` # .env # MCP Server Configuration MCP_SERVER_PORT=9000 ``` The MCP server uses one of two ports by default: - Port 8080 when deployed using the DataRobot MCP template - Port 9000 when deployed using the DataRobot Agentic Starter template Make sure to use the correct port when configuring the agent. ### Production configuration For production deployments, configure the MCP server using environment variables. This process is handled automatically by the template. > [!NOTE] MCP server authentication > If your MCP server requires authentication, ensure that `DATAROBOT_API_TOKEN` is set in your environment, as the MCP client will use it automatically for authenticated requests. HTTP MCP endpoints often expect both `Authorization: Bearer` and `x-datarobot-api-token` headers; the client typically supplies these from your DataRobot API credentials. For the same headers in Cursor, Claude Desktop, and VS Code, see [Connect agentic coding environments to MCP servers](https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/agentic-mcp-clients.html). ## Use MCP tools in agents The `LangGraphAgent` base class (used in the [DataRobot Agentic Starter template](https://github.com/datarobot-community/datarobot-agent-application)) provides automatic MCP tool integration through the `mcp_tools` property. ### LangGraph agents For LangGraph agents, MCP tools are automatically available through the `mcp_tools` property: ``` # agent/agent/myagent.py from datarobot_genai.core.agents import make_system_prompt from datarobot_genai.langgraph.agent import LangGraphAgent from langchain.agents import create_agent from typing import Any class MyAgent(LangGraphAgent): """Agent that uses MCP tools for external integrations.""" @property def agent_planner(self) -> Any: return create_agent( self.llm(), tools=self.mcp_tools, # MCP tools are automatically available system_prompt=make_system_prompt( "You are a content planner. Use available tools to gather information and plan content." ), name="planner_agent", ) ``` The `mcp_tools` property automatically: - Connects to the configured MCP server - Discovers available tools - Converts MCP tools to LangChain-compatible tools - Handles authentication if required ### Combine MCP tools with local tools You can combine MCP tools with local tools by merging the tool lists: ``` # agent/agent/myagent.py from datarobot_genai.core.agents import make_system_prompt from datarobot_genai.langgraph.agent import LangGraphAgent from langchain.agents import create_agent from langchain_core.tools import Tool from typing import Any class DateTimeTool(Tool): """Local datetime tool.""" name = "datetime_tool" description = "Returns the current date and time." def run(self, query: str = "") -> str: from datetime import datetime return datetime.now().strftime("%Y-%m-%d %H:%M:%S") class MyAgent(LangGraphAgent): """Agent that uses both MCP tools and local tools.""" @property def agent_planner(self) -> Any: # Combine MCP tools with local tools local_tools = [DateTimeTool()] all_tools = list(self.mcp_tools) + local_tools return create_agent( self.llm(), tools=all_tools, system_prompt=make_system_prompt( "You are a content planner. Use available tools to gather information and plan content." ), name="planner_agent", ) ``` ## Create custom MCP tools To add custom tools to your MCP server, use the [DataRobot MCP template](https://github.com/datarobot-community/af-component-datarobot-mcp). The template provides a structured approach for creating tools, resources, and prompts. ### Tool structure Custom MCP tools are defined using the `@dr_mcp_tool` decorator: ``` # dr_mcp/app/recipe/tools/my_custom_tool.py from app.base.core.mcp_instance import dr_mcp_tool from app.base.core.common import get_sdk_client @dr_mcp_tool(tags={"custom", "recipe", "your-domain"}) async def my_custom_tool(input_param: str, optional_param: int = 10) -> str: """ Brief description of what your tool does. This description helps LLMs understand when and how to use your tool. Be specific about the tool's purpose and behavior. Args: input_param: Description of the required parameter optional_param: Description of the optional parameter Returns: Description of what the tool returns """ # Use the DataRobot SDK client for API operations client = get_sdk_client() # Your custom logic here result = f"Processed {input_param} with {optional_param}" return result ``` > [!NOTE] MCP template repository > The path and imports above refer to the [DataRobot MCP template](https://github.com/datarobot-community/af-component-datarobot-mcp) repository structure. ### Tool best practices When creating MCP tools, follow these best practices: - Clear descriptions : Provide detailed docstrings—LLMs use these to understand tool capabilities - Type hints : Always use type hints for parameters and return values - Error handling : Implement proper error handling and return meaningful error messages - Async functions : Tools should be async functions for better performance - Tags : Use descriptive tags to categorize tools (helps with tool filtering) - SDK client : Use get_sdk_client() for DataRobot API access For more information on creating custom MCP tools, see the [MCP template custom tools documentation](https://github.com/datarobot-community/af-component-datarobot-mcp/blob/main/docs/custom_tools.md). ## Comparison: MCP vs. ToolClient | Feature | MCP Server | ToolClient (Direct Deployment) | | --- | --- | --- | | Tool management | Centralized in one server | Each tool deployed separately | | Protocol | MCP standard protocol | DataRobot custom protocol | | Tool discovery | Automatic via MCP | Manual configuration per tool | | Dynamic updates | Tools can be added/modified without agent redeployment | Requires agent redeployment for new tools | | Framework compatibility | Works with any MCP-compatible framework | DataRobot-specific | | Deployment complexity | Single MCP server deployment | Multiple tool deployments | | Scaling | Scale MCP server independently | Scale each tool independently | Choose MCP server integration when: - You want centralized tool management - You need dynamic tool registration - You're using multiple agents that share tools - You want standard protocol compliance Choose ToolClient integration when: - You need fine-grained control over individual tool deployments - Tools have very different scaling requirements - You prefer DataRobot-specific tool management features ## Troubleshooting ### Agent can't connect to MCP server Symptoms: Agent errors mention MCP connection failures or tools not available. Solutions: 1. Verify MCP server is running: # For local developmentcurl-ihttp://localhost:9000/mcp# For production (deployment MCP endpoint)curl-ihttps://{DATAROBOT_URL}/api/v2/deployments/{DEPLOYMENT_ID}/directAccess/mcp# If your environment uses the DataRobot Global MCPcurl-ihttps://{DATAROBOT_URL}/api/v2/genai/globalmcp/mcp 2. Check environment variables: 3. Verify network connectivity: ### Tools not appearing Symptoms: MCP server is connected, but the agent cannot use tools. Solutions: 1. Check tool registration: Verify that tools are properly registered in the MCP server. 2. Review tool metadata: Ensure tool descriptions and schemas are correctly defined. 3. Check server logs: Review MCP server logs for tool registration errors. 4. Verify agent configuration: Confirm that the mcp_tools property is being used correctly. ### Authentication issues Symptoms: MCP server requests fail with authentication errors. Solutions: 1. Verify API token: Ensure DATAROBOT_API_TOKEN is set and valid. 2. Check token permissions: Verify the token has necessary permissions for MCP server access. 3. Review server configuration: Check that the MCP server is configured to accept the authentication method used. ## Additional resources - Connect agentic coding environments to MCP servers — DataRobot Global MCP, deployment and local URLs, and client configuration (Cursor, Claude Desktop, VS Code) - DataRobot MCP template - template for creating and deploying MCP servers - DataRobot Agentic Starter template - Application template with MCP integration - MCP Protocol Documentation - Official MCP protocol specification - Add tools to agents - Documentation for ToolClient-based tool integration - MCP Client Setup Guide - Guide for configuring MCP clients --- # MCP URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-mcp/index.html > Integrate tools using MCP servers and connect agentic coding environments to MCP. # MCP > [!NOTE] Premium > DataRobot's Agentic AI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. The Model Context Protocol (MCP) provides a standardized interface for AI agents to interact with external systems, tools, and data sources. Use MCP to centralize tool management, integrate tools into agentic workflows, and connect IDEs and chat clients to your DataRobot MCP server. | Topic | Description | | --- | --- | | Integrate tools using an MCP server | Integrate tools into your agentic workflows using an MCP server for centralized tool management and a standardized interface. | | Connect agentic coding environments to MCP servers | Configure Cursor, Claude Desktop, and VS Code to use your DataRobot MCP server for tools and resources. | --- # Custom metrics URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-custom-metrics.html > Create and monitor custom metrics and guard metrics for generative and agentic deployments. # Custom metrics On a deployment's Monitoring > Custom metrics tab, you can use the data you collect from the [Data exploration](https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-data-exploration.html) tab (or data calculated through other custom metrics) to compute and monitor custom business or performance metrics. When you configure [evaluation and moderation guardrails](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-configure-evaluation-moderation.html) (including NeMo Evaluator metrics) for a text generation or agentic workflow deployment, the guards also report metrics to this tab—for example, guard latency, average score for prompt or response, and blocked count per guard—so you can monitor and debug guard behavior over time. These metrics are recorded on the configurable Custom metrics summary dashboard, where you monitor, visualize, and export each metric's change over time. This feature allows you to implement your organization's specialized metrics alongside [service health](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-service-health.html) for the deployment. > [!NOTE] Custom metrics limits > You can have up to 50 custom metrics per deployment, and of those 50, 5 can be hosted custom metrics. To view and add custom metrics, in the Console, open the deployment for which you want to create custom metrics and click the Monitoring > Custom metrics tab: ## Add custom metrics To add a metric, in a text generation, agentic workflow, VDB, or MCP deployment, click the Monitoring > Custom metrics tab. Then, on the Custom metrics tab, click + Add custom metric, select one of the following custom metric types, and proceed to the configuration steps linked in the table: **With existing metrics:** [https://docs.datarobot.com/en/docs/images/nxt-new-cus-metric-with-existing.png](https://docs.datarobot.com/en/docs/images/nxt-new-cus-metric-with-existing.png) **Without existing metrics:** [https://docs.datarobot.com/en/docs/images/nxt-new-cus-metric-without-existing.png](https://docs.datarobot.com/en/docs/images/nxt-new-cus-metric-without-existing.png) | Custom metric type | Description | | --- | --- | | New external metric | Add a custom metric where the calculations of the metric are not directly hosted by DataRobot. An external metric is a simple API used to submit a metric value for DataRobot to save and visualize. The metric calculation is handled externally, by the user. External metrics can be combined with other tools in DataRobot like notebooks, jobs, or custom models, or external tools like Airflow or cloud providers to provide the hosting and calculation needed for a particular metric.External custom metrics provide a simple option to save a value from your AI solution for tracking and visualization in DataRobot. For example, you could track the change in LLM cost, calculated by your LLM provider, over time. | | New hosted metric | Add a custom metric where the metric calculations are hosted in a custom job within DataRobot. For hosted metrics, DataRobot orchestrates pulling the data, computing the metric values, saving the values to storage, and visualizing the data. No outside tools or infrastructure are required.Hosted custom metrics provide a complete end-to-end workflow for building business-specific metrics and dashboards in DataRobot. | | Create new from template | Add a custom metric from a template, or ready-to-use example of a hosted custom metric, where DataRobot provides the code and automates the creation process. With metric templates, the result is a hosted metric, without starting from scratch. Templates are provided by DataRobot and can be used as-is or modified to calculate new metrics.Hosted custom metric templates provide the simplest way to get started with custom metrics, where DataRobot provides an example implementation and a complete end-to-end workflow. They are ready to use in just a few clicks. | ### Add external custom metrics External custom metrics allow you to create metrics with calculations occurring outside of DataRobot. With an external metric, you can submit a metric value for DataRobot to save and visualize. External metrics can be combined with other tools in DataRobot like notebooks, jobs, or custom models, or external tools like Airflow or cloud providers to provide the hosting and calculation needed for a particular metric. To add an external custom metric, in the Add custom metric dialog box, configure the metric settings, and then click + Add custom metric: **Numeric:** [https://docs.datarobot.com/en/docs/images/nxt-custom-metric-fields.png](https://docs.datarobot.com/en/docs/images/nxt-custom-metric-fields.png) **Categorical:** [https://docs.datarobot.com/en/docs/images/nxt-custom-metric-fields-categorical.png](https://docs.datarobot.com/en/docs/images/nxt-custom-metric-fields-categorical.png) | Field | Description | | --- | --- | | Name | A descriptive name for the metric. This name appears on the Custom metrics summary dashboard. | | Description | (Optional) A description of the custom metric; for example, you could describe the purpose, calculation method, and more. | | Name of Y-axis (label) | A descriptive name for the dependent variable. This name appears on the custom metric's chart on the Custom Metric Summary dashboard. | | Default interval | The default interval used by the selected Aggregation type. Only HOUR is supported. | | Metric type | The type of metric to create, Numeric or Categorical. The available metric settings change based on this selection. | | Numeric metric settings | | | Baseline | (Optional) The value used as a basis for comparison when calculating the x% better or x% worse values. | | Aggregation type | The type of metric calculation. Select from Sum, Average, or Gauge—a metric with a distinct value measured at single point in time. | | Metric direction | The directionality of the metric, controlling how changes to the metric are visualized. You can select Higher is better or Lower is better. For example, if you choose Lower is better, a 10% decrease in the calculated value of your custom metric will be considered 10% better, and displayed in green. | | Categorical metric settings | | | Class name | For each class added, a descriptive name (maximum of 200 characters). | | Baseline | (Optional) For each class added, the value used as a basis for comparison when calculating the x% better or x% worse values. | | Class direction | For each class added, the directionality of the metric, controlling how changes to the metric are visualized. You can select Higher is better or Lower is better. For example, if you choose Lower is better, a 10% decrease in the calculated value of your custom metric will be considered 10% better, and displayed in green. | | + Add class | To define each class needed for the categorical metric, click + Add class and configure the required class settings listed above. You can add up to ten classes. To remove a class, click Delete class. | | Model specific aggregation setting | | | Is model-specific | When enabled, links the metric to the model with the Model Package ID (the Registered Model Version ID) provided in the dataset. This setting influences when values are aggregated (or uploaded). For example: Model-specific (enabled): Model accuracy metrics are model-specific, so the values are aggregated separately. When you replace a model, the chart for your custom accuracy metric only shows data for the days after the replacement.Not model-specific (disabled): Revenue metrics aren't model-specific, so the values are aggregated together. When you replace a model, the chart for your custom revenue metric doesn't change. This field can't be edited after you create the metric. | | Column name definitions for standard deployments | | | Timestamp column | The column in the dataset containing a timestamp. | | Value column | The column in the dataset containing the values used for custom metric calculation. | | Date format | (Optional) The date format used by the timestamp column. | > [!NOTE] Note > You can override the Column names definition settings when you upload data to a custom metric, [as described below](https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-custom-metrics.html#upload-data-to-custom-metrics). ### Add hosted custom metrics Hosted custom metrics allow you to implement up to 5 of your organization's specialized metrics in a deployment, uploading the custom metric code using [DataRobot Notebooks](https://docs.datarobot.com/en/docs/workbench/wb-notebook/index.html) and hosting the metric calculation on custom jobs infrastructure. After creation, these custom metrics can be reused for other deployments. > [!NOTE] Custom metrics limits > You can have up to 50 custom metrics per deployment, and of those 50, 5 can be hosted custom metrics. > [!WARNING] Time series support > The [DataRobot Model Metrics (DMM)](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/index.html) library does not support time series models, specifically [data export](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-data-sources.html#export-prediction-data) for time series models. To export and retrieve data, use the [DataRobot API client](https://datarobot-public-api-client.readthedocs-hosted.com/en/latest-release/reference/mlops/data_exports.html). To add a hosted custom metric, in the Add Custom Metric dialog box configure the metric settings, and then click Add custom metric from notebook: | Field | Description | | --- | --- | | Name | (Required) A descriptive name for the metric. This name appears on the Custom Metric Summary dashboard. | | Description | A description of the custom metric; for example, you could describe the purpose, calculation method, and more. | | Name of y-axis (label) | (Required) A descriptive name for the dependent variable. This name appears on the custom metric's chart on the Custom Metric Summary dashboard. | | Default interval | Determines the default interval used by the selected Aggregation type. Only HOUR is supported. | | Baseline | Determines the value used as a basis for comparison when calculating the x% better or x% worse values. | | Aggregation type | Determines if the metric is calculated as a Sum, Average, or Gauge—a metric with a distinct value measured at single point in time. | | Metric direction | Determines the directionality of the metric, which controls how changes to the metric are visualized. You can select Higher is better or Lower is better. For example, if you choose Lower is better a 10% decrease in the calculated value of your custom metric will be considered 10% better, displayed in green. | | Is model-specific | When enabled, this setting links the metric to the model with the Model Package ID (Registered Model Version ID) provided in the dataset. This setting influences when values are aggregated (or uploaded). For example: Model-specific (enabled): Model accuracy metrics are model specific, so the values are aggregated completely separately. When you replace a model, the chart for your custom accuracy metric only shows data for the days after the replacement.Not model-specific (disabled): Revenue metrics aren't model specific, so the values are aggregated together. When you replace a model, the chart for your custom revenue metric doesn't change. This field can't be edited after you create the metric. | | Schedule | Defines when the custom metrics are populated. Select a frequency (hourly, daily, monthly, etc.) and a time. Select Use advanced scheduler for more precise scheduling options. | After configuring a custom metric, DataRobot loads the notebook that contains the metric's code. The notebook contains one custom metric cell. A custom metric cell is a unique notebook cell, containing Python code defining how the metric is exported and calculated, code for scoring, and code to populate the metric. Modify the code in the custom metric cell as needed. Then, test the code by clicking Test custom metric code at the bottom of the cell. The test creates a custom job. If the test runs successfully, click Deploy custom metric code to add the custom metric to your deployment. > [!NOTE] Availability information > Notebooks for hosted custom metrics are off by default. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable Notebooks Custom Environments If the code does not run properly, you will receive the Testing custom metric code failed warning after testing completes. Click Open custom metric job to access the job and check the logs to troubleshoot the issue: To troubleshoot a custom metric's code, navigate to the job's Runs tab, containing a log of the failed test. In the failed run, click View log. ### Add hosted custom metrics from the gallery The custom metrics gallery provides a centralized library containing pre-made, reusable, and shareable code implementing a variety of hosted custom metrics for predictive and generative models. These metrics are recorded on the configurable Custom Metric Summary dashboard, alongside any external custom metrics. From this dashboard, you can monitor, visualize, and export each metric's change over time. This feature allows you to implement your organization's specialized metrics, expanding on the insights provided by DataRobot's built-in service health, data drift, and accuracy metrics. To add a pre-made custom metric to a deployment: 1. In theAdd custom metricpanel, select a custom metric template applicable to your use case. DataRobot provides three different categories ofMetric type: Binary ClassificationRegressionLLM (Generative)Agentic workflow / LLMCustom metric templateDescriptionRecall for top x%Measures model performance limited to a certain top fraction of the sorted predicted probabilities. Recall is a measure of a model's performance that calculates the proportion of actual positives that are correctly identified by the model.Precision for top x%Measures model performance limited to a certain top fraction of the sorted predicted probabilities. Precision is a measure of a model's performance that calculates the proportion of correctly predicted positive observations from the total predicted positive.F1 for top x%Measures model performance limited to a certain top fraction of the sorted predicted probabilities. F1 score is a measure of a model's performance which considers both precision and recall.AUC (Area Under the ROC Curve) for top x%Measures model performance limited to a certain top fraction of the sorted predicted probabilities.Custom metric templateDescriptionMean Squared Logarithmic Error (MSLE)Calculates the mean of the squared differences between logarithms of the predicted and actual values. It is a loss function used in regression problems when the target values are expected to have exponential growth, like population counts, average sales of a commodity over a time period, and so on.Median Absolute Error (MedAE)Calculates the median of the absolute differences between the target and the predicted values. It is a robust metric used in regression problems to measure the accuracy of predictions.Custom metric templateDescriptionCompletion Reading TimeEstimates the average time it takes a person to read text generated by the LLM.Completion Tokens MeanCalculates the mean number of tokens in completions for the time period requested. The cl100k_base encoding used only supports OpenAI models: gpt-4, gpt-3.5-turbo, and text-embedding-ada-002. If you use a different model, change the encoding.Cosine Similarity AverageCalculates the mean cosine similarity between each prompt vector and corresponding context vectors.Cosine Similarity MaximumCalculates the maximum cosine similarity between each prompt vector and corresponding context vectors.Cosine Similarity MinimumCalculates the minimum cosine similarity between each prompt vector and corresponding context vectors.CostEstimates the financial cost of using the LLM by calculating the number of tokens in the input, output, and retrieved text, and then applying token pricing. The cl100k_base encoding used only supports OpenAI models: gpt-4, gpt-3.5-turbo, and text-embedding-ada-002. If you use a different model, change the encoding.Dale Chall ReadabilityMeasures the U.S. grade level required to understand a text based on the percentage of difficult words and average sentence length.Euclidean AverageCalculates the mean Euclidean distance between each prompt vector and corresponding context vectors.Euclidean MaximumCalculates the maximum Euclidean distance between each prompt vector and corresponding context vectors.Euclidean MinimumCalculates the minimum Euclidean distance between each prompt vector and corresponding context vectors.Flesch Reading EaseMeasures the readability of text based on the average sentence length and average number of syllables per word.Prompt Injection [sidecar metric]Detects input manipulations, such as overwriting or altering system prompts, that are intended to modify the model's output. This metric requires an additional deployment of the Prompt Injection Classifierglobal model.Prompt Tokens MeanCalculates the mean number of tokens in prompts for the time period requested. The cl100k_base encoding used only supports OpenAI models: gpt-4, gpt-3.5-turbo, and text-embedding-ada-002. If you use a different model, change the encoding.Sentence CountCalculates the total number of sentences in user prompts and text generated by the LLM.SentimentClassifies text sentiment as positive or negativeSentiment [sidecar metric]Classifies text sentiment as positive or negative using a pre-trained sentiment classification model. This metric requires an additional deployment of the Sentiment Classifierglobal model.Syllable CountCalculates the total number of syllables in the words in user prompts and text generated by the LLM.Tokens MeanCalculates the mean of tokens in prompts and completions. The cl100k_base encoding used only supports OpenAI models: gpt-4, gpt-3.5-turbo, and text-embedding-ada-002. If you use a different model, change the encoding.Toxicity [sidecar metric]Measures the toxicity of text using a pre-trained hate speech classification model to safeguard against harmful content. This metric requires an additional deployment of the Toxicity Classifierglobal model.Word CountCalculates the total number of words in user prompts and text generated by the LLM.Japanese text metrics[JP] Character CountCalculates the total number of characters generated while working with the LLM.[JP] PII occurrence countCalculates the total number of PII occurrences while working with the LLM.Custom metric templateDescriptionAgentic completion tokensCalculates the total completion tokens of agent-based LLM calls.Agentic costCalculates the total cost of agent-based LLM calls. Requires that each LLM span reports token usage so the metric can compute cost from the trace.Agentic prompt tokensCalculates the total prompt tokens of agent-based LLM calls. 2. After you select a metric from the list, in theCustom metric configurationsidebar, configure a metric calculation schedule or run the metric calculation immediately, and, optionally, set a metric baseline value. Sidecar metricsIf you selected a[sidecar metric], when you open theAssembletab, navigate to theRuntime Parameterssection to set theSIDECAR_DEPLOYMENT_ID, associating the sidecar metric with the connected deployment required to calculate that metric. If you haven't deployed a model to calculate the metric, you can find pre-defined models for these metrics asglobal models. 3. ClickCreate metric. The new metric appears on theCustom metricsdashboard. 4. After you create a custom metric, you can view the custom job associated with the metric. This job runs on the metric's defined schedule, in the same way ashosted custom metrics(those not from the gallery). To access and manage the associated custom job, click theActions menuand then clickOpen Custom Job: ## Upload data to custom metrics After you create a custom metric, you can provide data to calculate the metric: 1. On theCustom metricstab, locate the custom metric for which you want to upload data and click theUpload Dataicon. 2. In theUpload datadialog box, select an upload method and clickNext: Upload methodDescriptionUse Data RegistryIn theSelect a datasetpanel, upload a dataset or click a dataset from the list, and then clickConfirm. The Data Registry includes datasets from theData explorationtab.Use APIIn theUse API Clientpanel, clickCopy to clipboard, and then modify and use the API snippet to upload a dataset. You can upload up to 10,000 values in one API call. 3. In theSelect dataset columnsdialog box, configure the following: FieldDescriptionTimestamp column(Required) The column in the dataset containing a timestamp.Value column(Required) The column in the dataset containing the values used for custom metric calculation.Association IDThe row containing the association ID required by the custom metric to link predicted values to actuals.Date formatThe date format used by the timestamp column. 4. ClickUpload data. ### Report custom metrics via chat requests For DataRobot-deployed text generation and agentic workflow custom models that implement the `chat()` hook, custom metric values can be reported directly in chat completion requests using the `extra_body` field. This allows reporting custom metrics at the same time as making chat requests, without needing to upload data separately. > [!TIP] Manual chat request construction > The OpenAI client converts the `extra_body` parameter contents to top-level fields in the JSON payload of the chat `POST` request. When manually constructing a chat payload, without the OpenAI client, include `"datarobot_metrics": {...}` in the top level of the payload. To report custom metrics via chat requests: 1. Ensure the deployment has an association ID column defined and moderation configured. These are required for custom metrics to be processed. 2. Define custom metrics on theCustom Metricstab as described inAdd external custom metrics. 3. When making a chat completion request using the OpenAI client, includedatarobot_metricsin theextra_bodyfield with the metric names and values to report: ``` from openai import OpenAI openai_client = OpenAI( base_url="https:///api/v2/deployments/{deployment_id}/", api_key="", ) extra_body = { # These values pass through to the LLM "llm_id": "azure-gpt-6", # If set here, replaces the auto-generated association ID "datarobot_association_id": "my_association_id_0001", # DataRobot captures these for custom metrics "datarobot_metrics": { "field1": 24, "field2": 25 } } completion = openai_client.chat.completions.create( model="datarobot-deployed-llm", messages=[ {"role": "system", "content": "Explain your thoughts using at least 100 words."}, {"role": "user", "content": "What would it take to colonize Mars?"}, ], max_tokens=512, extra_body=extra_body ) print(completion.choices[0].message.content) ``` > [!NOTE] Custom metric requirements > A matching custom metric for each name in > datarobot_metrics > must already be defined for the deployment. > Custom metric values reported this way must be numeric. > The deployed custom model must have an association ID column defined and moderation configured for the metrics to be processed. For more information about using `extra_body` with chat requests, including how to specify association IDs, see the [chat()hook documentation](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#association-id). ## Manage custom metrics On the Custom metrics dashboard, after you've added your custom metrics, you can edit or delete them: On the Custom metrics tab, locate the custom metric you want to manage, and then click the Actions menu: - To edit a metric, clickEdit, update any configurable settings, and then clickUpdate custom metric. - To delete a metric, clickDelete. ## Configure the custom metric dashboard display settings Configure the following settings to specify the custom metric calculations you want to view on the dashboard: > [!TIP] Custom metrics for evaluation and moderation require an association ID > For the metrics added when you configure evaluations and moderations, to view data on the Custom metrics tab, ensure that you set an association ID and enable prediction storage before you start making predictions through the deployed LLM.If you don't set an association ID and provide association IDs alongside the LLM's predictions, the metrics for the moderations won't be calculated on the [Custom metrics](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-custom-metrics.html) tab.After you define the association ID, you can enable automatic association ID generation to ensure these metrics appear on the Custom metrics tab. You can enable this setting [during](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-deploy-models.html#custom-metrics) or [after](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-settings/nxt-custom-metrics-settings.html) deployment. | | Setting | Description | | --- | --- | --- | | (1) | Model | Select the deployment's model, current or previous, to show custom metrics for. | | (2) | Range (UTC) / Date Slider | Select the start and end dates of the period from which you want to view custom metrics. | | (3) | Resolution | Select the granularity of the date slider. Select from hourly, daily, weekly, and monthly granularity based on the time range selected. If the time range is longer than 7 days, hourly granularity is not available. | | (4) | Segment attribute / Segment value | Sets the individual attribute and value to filter the data drift visualizations for segment analysis. | | (5) | Refresh | Refresh the custom metric dashboard. | | (6) | Reset | Reset the custom metric dashboard's display settings to the default. | ### Arrange or hide metrics on the dashboard To arrange or hide metrics on the Custom metrics summary dashboard, locate the custom metric you want to move or hide: - To move a metric, click the grid iconon the left side of the metric tile and then drag the metric to a new location. - To hide a metric chart, clear the checkbox next to the metric name. ## Explore deployment data tracing > [!NOTE] Premium > Tracing is a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. On the Custom metrics tab of a custom or external model deployment, in a custom metric chart's header, click Show tracing to view tracing data for the deployment. Traces represent the path taken by a request to a model or agentic workflow. DataRobot uses the [OpenTelemetry framework for tracing](https://opentelemetry.io/docs/concepts/signals/traces/). A trace follows the entire end-to-end path of a request, from origin to resolution. Each trace contains one or more spans, starting with the root span. The root span represents the entire path of the request and contains a child span for each individual step in the process. The root (or parent) span and each child span share the same Trace ID. > [!NOTE] Access and retention > The tracing table is available for all custom and external model deployments. Tracing data is stored for a retention period of 30 days, after which it is automatically deleted. In the Tracing table, you can review the following fields related to each trace: | Column | Description | | --- | --- | | Timestamp | The date and time of the trace in YYYY-MM-DD HH:MM format. | | Status | The overall status of the trace, including all spans. The Status will be Error if any dependent task fails. | | Trace ID | A unique identifier for the trace. | | Duration | The amount of time, in milliseconds, it took for the trace to complete. This value is equal to the duration of the root span (rounded) and includes all actions represented by child spans. | | Spans count | The number of completed spans (actions) included in the trace. | | Cost | If cost data is provided, the total cost of the trace. | | Prompt | The user prompt related to the trace. | | Completion | The agent or model response (completion) associated with the prompt for the trace. | | Tools | The tool or tools called during the request represented by the trace. | Click Filter to filter by Min span duration, Max span duration, Min trace cost, and Max trace cost. The unit for span filters is nanoseconds (ns), the chart displays spans in milliseconds (ms). > [!TIP] Filter accessibility > The Filter button is hidden when a span is expanded to detail view. To return to the chart view with the filter, click Hide details panel. To review the [spans](https://opentelemetry.io/docs/concepts/signals/traces/#spans) contained in a trace, along with trace details, click a trace row in the Tracing table. The span colors correspond to a Span service, usually a deployment.Restricted span appears when you don’t have access to the deployment or service associated with the span. You can view spans in Chart format or List format. > [!TIP] Span detail controls > From either view, you can click Hide table to collapse the Timestamps table or Hide details panel to return to the expanded Tracing table view. **Chart view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans.png) **List view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans-list.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans-list.png) > [!NOTE] Trace details > In list view, you can click Trace details to view the Input/Output ( Prompt and Completion) and Evaluation details about the trace associated with the current span. For either view, click the Span service name to access the deployment or resource (if you have access). Additional information, dependent on the configuration of the generative AI model or agentic workflow, is available on the Info, Resources, Events, Input/Output, Error, and Logs tabs. The Error tab only appears when an error occurs in a trace. **Chart view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs.png) **List view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs-list-view.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs-list-view.png) ### Filter tracing logs From the list view, you can display OTel logs for a span. The results shown are a subset of the [full deployment logs](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-otel-logs.html), and are accessed as follows: 1. Open the list view and select a span underTrace details. 2. Click theLogstab. 3. ClickShow logs. ### Tracing table OTel attributes For Cost, Prompt, Completion, and Tools, DataRobot reads specific span attributes across all spans that belong to the trace. Other columns (such as Timestamp and Duration) come from trace and span metadata rather than these attributes. | Column | OpenTelemetry mapping | | --- | --- | | Cost | Sums numeric values from the datarobot.moderation.cost attribute on spans in the trace (when that attribute is present). | | Prompt | Uses the gen_ai.prompt attribute. If more than one span includes gen_ai.prompt, the first value encountered in trace order is shown. | | Completion | Uses the gen_ai.completion attribute. If more than one span includes gen_ai.completion, the last value encountered in trace order is shown. | | Tools | Collects every distinct value of the tool_name attribute found on spans in the trace and lists those tool names in the column. | Attribute keys must match exactly (including the underscore in `gen_ai`). Names such as `genai.prompt` or `GenAI.prompt` are not read for the Prompt and Completion columns. Automatic instrumentation (including DataRobot agent templates) often sets `gen_ai.prompt`, `gen_ai.completion`, and sometimes `tool_name`. For custom or external models, frameworks differ: tool execution may not emit `tool_name` even when tools run (for example, some LangGraph callback flows). In that case Prompt and Completion can populate while Tools remains empty until `tool_name` is configured on a span that runs inside the tool—see [Implement tracing](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tracing-code.html#surface-tool-names-in-the-tracing-table). --- # Data exploration and tracing URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-data-exploration.html > Export deployment data, explore traces, and review data quality for generative and agentic deployments. # Data exploration and tracing On a deployment's Monitoring > Data exploration tab, you can interact with a deployment's stored data to gain insight into model or agent performance. You can also download deployment data to use in custom metric calculations. The Data exploration summary includes the following functionality, depending on the deployment type: | Functionality | Description | | --- | --- | | Tracing | For custom model deployments, explore traces from a model or workflow. Each trace contains a visual timeline representing all actions carried out by the model or agent and revealing the order and duration of these actions. | | Data export | For all deployments, download a deployment's stored data including training data, prediction data, actuals, and custom metric data. | | Data quality | For generative AI and agentic workflow deployments, assess the quality of a generative AI model's responses based on user feedback and custom metrics. | > [!NOTE] Data requirements > To use the Data exploration tab, the deployment must store prediction data. Ensure that you [enable prediction row storage](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-settings/nxt-data-exploration-settings.html) in the data exploration (or challenger) settings. The Data exploration tab doesn't store or export Prediction Explanations, even if they are requested with the predictions. ## Configure data exploration range In the deployment from which you want to export stored training data, prediction data, or actuals, click the Monitoring > Data exploration tab and configure the following settings to specify the stored training data, prediction data, or actuals you want to export: | | Setting | Description | | --- | --- | --- | | (1) | Model | Select the deployment's model, current or previous, to export prediction data for. | | (2) | Range (UTC) | Select the start and end dates of the period you want to export prediction data from. | | (3) | Resolution | Select the granularity of the date slider. Select from hourly, daily, weekly, and monthly granularity based on the time range selected. If the time range is longer than 7 days, hourly granularity is not available. | | (4) | Refresh | Refresh the data exploration tab's data. | | (5) | Reset | Reset the data exploration settings to the default. | ## Export deployment data On the Data exploration summary page (or the Data export tab of the Data exploration summary), you can download a deployment's stored data. This can include training data, prediction data, actuals, and custom metric data. Use the exported data to compute and monitor custom business or performance metrics on the [Custom metrics](https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-custom-metrics.html) tab or outside of DataRobot. To export deployment data for custom metrics, verify that the deployment stores prediction data, generate data for a specified time range, and then view or download that data. ### Export a deployment's production data To access deployment data export for prediction data, actuals, or custom metric data, on the Data exploration summary page, locate the Production data panel. On the Production data panel, in the Generate button, click the down arrow and select one of the data generation options. The availability of the following options depends on the data stored in the deployment for the model and time range selected. | Option | Description | | --- | --- | | All production data | For generative AI deployments, generate all available production data (predictions, actuals, custom metrics) for the specified model and time range. | | Custom metrics | For generative AI deployments, generate available custom metric data for the specified model and time range. | > [!NOTE] Premium > Custom metric data export is off by default. Contact your DataRobot representative or administrator for information on enabling this feature. Production data appears in the table below the panels. You can identify the data type in the Exported data column. ### Export a deployment's training data To access deployment data export for training data, on the Data exploration summary page, locate the Training data panel and click Generate training data to generate data for the specified model and time range: Options for interacting with the training data appear in the Training data panel. Click the down arrow to choose between Open training data and Download training data: ### Review and download data After the production or training data are generated, you can view or download the data. Production data appears in the table below the panels, where you can identify the data type in the Exported data column. Training data appears in the Training data panel. | Option | Description | | --- | --- | | | Open the exported data in the Data Registry. | | | Download the exported data. | > [!NOTE] Export to notebook > You can also click Export to notebook to open a [DataRobot notebook](https://docs.datarobot.com/en/docs/workbench/wb-notebook/index.html) with cells for exporting training data, prediction data, and actuals. ## Explore deployment data tracing > [!NOTE] Premium > Tracing is a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. On the Data exploration tab of a custom model deployment, click Tracing to explore traces from the model or agentic workflow. Each trace—identified by a timestamp and a trace ID—contains a visual timeline that represents actions carried out by the model or agent and reveals the order and duration of these actions. Traces represent the path taken by a request to a model or agentic workflow. DataRobot uses the [OpenTelemetry framework for tracing](https://opentelemetry.io/docs/concepts/signals/traces/). A trace follows the entire end-to-end path of a request, from origin to resolution. Each trace contains one or more spans, starting with the root span. The root span represents the entire path of the request and contains a child span for each individual step in the process. The root (or parent) span and each child span share the same Trace ID. > [!NOTE] Access and retention > The tracing table is available for all custom and external model deployments. Tracing data is stored for a retention period of 30 days, after which it is automatically deleted. In the Tracing table, you can review the following fields related to each trace: | Column | Description | | --- | --- | | Timestamp | The date and time of the trace in YYYY-MM-DD HH:MM format. | | Status | The overall status of the trace, including all spans. The Status will be Error if any dependent task fails. | | Trace ID | A unique identifier for the trace. | | Duration | The amount of time, in milliseconds, it took for the trace to complete. This value is equal to the duration of the root span (rounded) and includes all actions represented by child spans. | | Spans count | The number of completed spans (actions) included in the trace. | | Cost | If cost data is provided, the total cost of the trace. | | Prompt | The user prompt related to the trace. | | Completion | The agent or model response (completion) associated with the prompt for the trace. | | Tools | The tool or tools called during the request represented by the trace. | Click Filter to filter by Min span duration, Max span duration, Min trace cost, and Max trace cost. The unit for span filters is nanoseconds (ns), the chart displays spans in milliseconds (ms). > [!TIP] Filter accessibility > The Filter button is hidden when a span is expanded to detail view. To return to the chart view with the filter, click Hide details panel. To review the [spans](https://opentelemetry.io/docs/concepts/signals/traces/#spans) contained in a trace, along with trace details, click a trace row in the Tracing table. The span colors correspond to a Span service, usually a deployment.Restricted span appears when you don’t have access to the deployment or service associated with the span. You can view spans in Chart format or List format. > [!TIP] Span detail controls > From either view, you can click Hide table to collapse the Timestamps table or Hide details panel to return to the expanded Tracing table view. **Chart view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans.png) **List view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans-list.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans-list.png) > [!NOTE] Trace details > In list view, you can click Trace details to view the Input/Output ( Prompt and Completion) and Evaluation details about the trace associated with the current span. For either view, click the Span service name to access the deployment or resource (if you have access). Additional information, dependent on the configuration of the generative AI model or agentic workflow, is available on the Info, Resources, Events, Input/Output, Error, and Logs tabs. The Error tab only appears when an error occurs in a trace. **Chart view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs.png) **List view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs-list-view.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs-list-view.png) ### Filter tracing logs From the list view, you can display OTel logs for a span. The results shown are a subset of the [full deployment logs](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-otel-logs.html), and are accessed as follows: 1. Open the list view and select a span underTrace details. 2. Click theLogstab. 3. ClickShow logs. ### Tracing table OTel attributes For Cost, Prompt, Completion, and Tools, DataRobot reads specific span attributes across all spans that belong to the trace. Other columns (such as Timestamp and Duration) come from trace and span metadata rather than these attributes. | Column | OpenTelemetry mapping | | --- | --- | | Cost | Sums numeric values from the datarobot.moderation.cost attribute on spans in the trace (when that attribute is present). | | Prompt | Uses the gen_ai.prompt attribute. If more than one span includes gen_ai.prompt, the first value encountered in trace order is shown. | | Completion | Uses the gen_ai.completion attribute. If more than one span includes gen_ai.completion, the last value encountered in trace order is shown. | | Tools | Collects every distinct value of the tool_name attribute found on spans in the trace and lists those tool names in the column. | Attribute keys must match exactly (including the underscore in `gen_ai`). Names such as `genai.prompt` or `GenAI.prompt` are not read for the Prompt and Completion columns. Automatic instrumentation (including DataRobot agent templates) often sets `gen_ai.prompt`, `gen_ai.completion`, and sometimes `tool_name`. For custom or external models, frameworks differ: tool execution may not emit `tool_name` even when tools run (for example, some LangGraph callback flows). In that case Prompt and Completion can populate while Tools remains empty until `tool_name` is configured on a span that runs inside the tool—see [Implement tracing](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tracing-code.html#surface-tool-names-in-the-tracing-table). ## Explore deployment data quality > [!NOTE] Premium > Data quality is a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. On the Data exploration tab of a generative AI deployment, click Data quality to explore prompts and responses alongside user ratings and custom metrics, if implemented, providing insight into the quality of the generative AI model. Prompts, responses, and any available metrics are matched by association ID: To configure the rows displayed in the data quality table, click Settings to open the Column management panel, where columns can be selected, hidden, or rearranged. > [!NOTE] Prompt and response matching > To use the data quality table, [define an association ID](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-settings/nxt-custom-metrics-settings.html) to match prompts with responses in the same row. Tracing analysis is only available for prompts and responses matched in the same row by association ID; aggregate custom metric data is excluded. Locate specific rows in the Data quality table by searching. Click Search by and select Prompt values, Response, or Actual values. Then, click Search: In addition, you can filter the Data quality table on a single custom metric value from one of the [custom metrics created for the current deployment](https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-custom-metrics.html). To filter the table, click Filter, select a Metric, enter a Metric value, and then click Apply filters: > [!TIP] Sorting the data quality table > You can sort the Data quality table by clicking the column for Prompt created at, Association ID, or any [custom metrics created for the current deployment](https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-custom-metrics.html). Click the open icon to expand the details panel. The display shows a row's full Prompt and the Response matched with the prompt by association ID. It also shows custom metric values and citations (if configured): To export columns for external use, click Export all in selected range to export every row in the time range defined at the top of the Data quality view, or click Export selected rows if you've selected one or more rows in the table: --- # Moderation events URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-moderation.html > Review evaluation and moderation events for guarded LLM and agentic deployments. # Moderation events For a deployed text generation or agentic workflow model with evaluation and moderation configured, on the deployment's Activity log > Moderation tab, view a history of evaluation and moderation-related events for the deployment. These events can help diagnose issues with a deployment's configured evaluations and moderations. Use this tab together with the [Custom metrics](https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-custom-metrics.html) tab and evaluator logs to debug why a request was blocked or why a guard (including a NeMo Evaluator guard) failed. To view the moderation events log, navigate to the Activity log > Moderation tab. The most recent events appear at the top of the list. Each event shows the time it occurred, a description, and an icon indicating its status. > [!NOTE] Status for moderation events > All moderation events have a failure event type. Moderation events represent deployment actions and can help you review the status of your deployment and the health of the management agent. Currently, the following events can appear as moderation events: | Event type | Description | | --- | --- | | Moderation metric creation error | Reports errors encountered while creating a custom metric definition. For example: Failed to create custom metric. Maximum number of custom metrics reached.Failed to create custom metric for another reason (with details). | | Moderation metric reporting error | Reports errors encountered while reporting a custom metric value. For example:Failed to upload custom metrics. | | Moderation model scoring error | Reports errors encountered during the scoring phase of the model. For example: Failed to execute user score function.Cannot execute postscore guards. | | Moderation model configuration error | Reports errors encountered while configuring moderation for the model. | | Moderation model runtime error | Reports errors encountered while running moderation for the model. For example:Model Guard timeout.Model Guard predictions failed.Faithfulness calculations failed.ROUGE-1 guard configured without citation columns.NeMo guard calculation failed. | --- # OpenTelemetry logs URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-otel-logs.html > View OpenTelemetry log events for deployments. # OpenTelemetry logs A deployment's Logs tab receives logs from models and agentic workflows in the OpenTelemetry (OTel) standard format, centralizing the relevant logging information for deeper analysis, troubleshooting, and understanding of application performance and errors. Additionally, you can filter and [view span-specific logs](https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-data-exploration.html#explore-deployment-data-tracing) on the Monitoring > Data exploration tab. The collected logs provide time-period filtering capabilities and the OTel logs API is available to programmatically export logs with similar filtering capabilities. Because the logs are OTel-compliant, they're standardized for export to third-party observability tools like Datadog. > [!NOTE] Access and retention > OTel logs are available for all deployment and target types. Only users with Owner and User roles on a deployment can view these logs. Logs data is stored for a retention period of 30 days, after which it is automatically deleted. To access the logs for a deployment, on the Deployments tab, locate and click the deployment, click the Activity log tab, and then click Logs. The logging levels available are `INFO`, `DEBUG`, `WARN`, `CRITICAL` and `ERROR`. | Control | Description | | --- | --- | | Range UTC | Select the logging date range Last 15 min, Last hour, Last day, or Custom range. | | Level | Select the logging level to view: Debug, Info, Warning, Error, or Critical. | | Refresh | Refresh the contents of the Logs tab to load new logs. | | Copy logs | Copy the contents of the current Logs tab view. | | Search | Search the text contents of the logs tab. | ## Export OTel logs The code example below uses the OTel logs API to get the OpenTelemetry-compatible logs for a deployment, print a preview and the number of `ERROR` logs, and then write logs to an output file. Before running the code, configure the `entity_id` variable with your deployment ID, replacing `` with the deployment ID from the deployment Overview tab or URL. In addition, you can modify the `export_logs_to_json` function to match your target observability service's expected format. > [!TIP] DataRobot Python Client version > The following script requires `datarobot` version `3.11.0` or higher installed to support OpenTelemetry logging submodules. | Export OTel logs to JSON | | --- | | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | --- # OpenTelemetry metrics URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-otel-metrics.html > Visualize OpenTelemetry metrics alongside DataRobot native metrics. # OpenTelemetry metrics The OTel metrics tab provides comprehensive OpenTelemetry (OTel) metrics monitoring capabilities for your deployment, enabling centralized observability by visualizing external metrics from your applications and agentic workflows alongside DataRobot's native metrics, all in OTel-compliant format for export to third-party observability tools. > [!NOTE] Access and retention > OTel metrics are available for all deployment and target types. Only users with Owner and User roles on a deployment can view and configure these metrics. Metrics data is stored for a retention period of 30 days, after which it is automatically deleted. To access OTel metrics for a deployment, on the Deployments tab, locate and click the deployment, click the Monitoring tab, and then click OTel metrics. ## Select OTel metrics The OTel metrics tab can display up to 50 metrics. A customization dialog box displays all available metrics and supports searching by metric name. From this list, select the most important metrics for the dashboard. To select an OTel metric: 1. On theMonitoring > OTel metricstab, click+ Select OTel metrics(orCustomize tilesif metrics are already added). No metrics on the dashboardMetrics on the dashboard 2. In theCustomize tilesdialog box, in theMetricslist, do any of the following: 3. To add more metrics, click+ Add another metricand repeat the step above. Select up to 50 OTel metrics to display for the deployment. Remove and reorder metricsClick the up arrowand down arrowicons to reorder the metrics on the dashboard. Click the remove iconto remove a metric from the dashboard. 4. ClickSaveto update theOTel metricsdashboard configuration and review the metric visualizations. Supported time resolution settingsThe supported time resolution settings for OTel metric visualization are minute, hour, or day. ## Edit OTel metrics The OTel metrics tab enables the customization of how individual metrics are displayed and aggregated on your monitoring dashboard. After selecting metrics to monitor, fine-tune their presentation by editing display names, choosing aggregation methods, and toggling between trend charts and summary values. To edit an OTel metric: 1. On theMonitoring > OTel metricstab, with metrics already added, click the edit iconin the upper-right corner of a tile. 2. In theEdit metricdialog box, configure the following settings: Counter/gaugeHistogram SettingDescriptionDisplay nameDefines the name displayed on the dashboard tile and chart. The original name is preserved as theKey name, or the name defined in the monitored system.Aggregation typeSets the mathematical method for summarizing OTel metric data points across a given time period. The default aggregation depends on the metric type:Histogram(default for histograms) orAverage(default for counters/gauges). Available aggregation methods:Histogram: Displays the distribution of values as a histogram.Percentile: Calculates percentile values from the metric data.Average: The average of all reported values for the metric within the time period.Sum: The total of all reported values for the metric within the time period.Minimum: The lowest value reported for the metric within the time period.Maximum: The highest value reported for the metric within the time period.Percentile(Available whenHistogramorPercentileaggregation type is selected) Specifies the percentile value to calculate, ranging from 0 to 1. The default value is 0.5 (50th percentile).Show metric values over timeToggle the display of an OTel metric between an "over time" chart for trend analysis and a single, summarized numeric value for an at-a-glance summary. When this setting is disabled, the chart cannot be displayed. 3. To save the new metric settings, clickEdit. --- # Deployment overview URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-overview.html > Review deployment details, lineage, tags, runtime parameters, and evaluation and moderation when guardrails are configured. # Deployment overview When you select a deployment from the Deployments dashboard, DataRobot opens the Overview page for that deployment. The Overview page provides a model- and environment-specific summary that describes the deployment, including the information you supplied when creating the deployment and any model replacement activity. ## Details The Details section of the Overview tab lists an array of information about the deployment, including the deployment's model and environment-specific information. At the top of the Overview page, you can view the deployment name and description; click the edit icon to update this information. > [!NOTE] Note > The information included in this list depends on the registered custom model, target type (for example, text generation or agentic workflow), and environment. | Field | Description | | --- | --- | | Deployment ID | The ID number of the current deployment. Click the copy icon to save it to your clipboard. | | Predictions | A visual representation of the relative prediction frequency, per day, over the past week. | | Importance | The importance level assigned during deployment creation. Click the edit icon to update the deployment importance. | | Approval status | The deployment's approval policy status for governance purposes. | | Prediction environment | The environment on which the deployed model makes predictions. | | Build environment | The build environment used by the deployment's current model (e.g., DataRobot, Python, R, or Java). | | Flags | Indicators providing a variety of deployment metadata, including deployment status—Active, Inactive, Errored, Warning, Launching—and deployment type (for example, LLM, text generation, or agentic workflow). | | Created by | The name of the user who created the model. | | Last prediction | The number of days since the last prediction. Hover over the field to see the full date and time. | | Custom model information | | | Custom model | The name and version of the custom model registered and deployed from the workshop. | | Custom environment | The name and version of the custom model environment on which the registered custom model runs. | | Resource bundle | Preview feature. The CPU or GPU bundle selected for the custom model in the resource settings. | | Resource replicas | Preview feature. The number of replicas defined for the custom model in the resource settings. | | Generative model information | | | Target | The feature name of the target column used by the deployment's current generative model. This feature is the generative model's answer to a prompt; for example, resultText, answer, completion, etc. | | Prompt column name | The feature name of the prompt column used by the deployment's current generative model. This feature is the prompt the generative model responds to; for example, promptText, question, prompt, etc. | ## Lineage The Lineage section provides visibility into the assets and relationships associated with a deployment. This section helps understand the complete context of a deployment, including the models, datasets, experiments, and other MLOps assets connected to it. The Lineage section contains two tabs: - Graph: An interactive, end-to-end visualization of the relationships and dependencies between MLOps assets. This DAG (Directed Acyclic Graph) view helps audit complex workflows, track asset lifecycles, and manage components of agentic and generative AI systems. The graph displays nodes (assets) and edges (relationships/connections), enabling the exploration of connections and navigation through the asset ecosystem. - List: A list of the assets associated with a deployment, including registered models, model versions, experiments, datasets, and other related items. Each item displays its name, ID, creator, and creation date. ClickViewto open any related item, or use the list to quickly identify and access connected assets. For custom model deployments (including text generation, agentic workflows, vector databases, and MCP integrations), the list emphasizes registered models, custom model versions, and related training data. **Graph:** The Lineage section in the Overview tab includes a Graph view that provides an end-to-end visualization of the relationships and dependencies between your MLOps assets. This feature is essential for auditing complex workflows, tracking asset lifecycles, and managing the various components of agentic and generative AI systems. The Graph view serves as a central hub for reviewing your systems. The lineage is presented as a Directed Acyclic Graph (DAG) consisting of nodes (assets) and edges (relationships). When reviewing nodes, the asset you are currently viewing is distinguished by a purple outline. Nodes display key information such as ID, name (or version number), creator, and the last modification information (user and date). When reviewing edges, solid lines represent concrete, persistent relationships within the platform, such as a registered model used to create a deployment.Dashed lines indicate relationships inferred from runtime parameters. These are considered less reliable as they may change if a user modifies the underlying code or parameters. Arrows generally flow from the "ancestor" or container to the "descendant" or content (e.g., Registered model version to Deployment). > [!NOTE] Inaccessible assets > If an asset exists but you do not have permission to view it, the node only displays the asset ID and is marked with an Asset restricted notice. The view is highly interactive, allowing for deep exploration of your asset ecosystem. To interact with the graph area, use the following controls: [https://docs.datarobot.com/en/docs/images/lineage-graph-view-controls.png](https://docs.datarobot.com/en/docs/images/lineage-graph-view-controls.png) Control Description Legend View the legend defining how lines correspond to edges. and Control the magnification level of the graph view. Reset the magnification level and center the graph view on the focused node. Open a fullscreen view of the related items lineage graph. and In fullscreen view, navigate the history of selected nodes (assets/nodes viewed ). Graph area navigation To navigate the graph, click and drag the graph area. To control the zoom level, scroll up and down. To interact with the related item nodes, use the following controls when they appear: [https://docs.datarobot.com/en/docs/images/lineage-graph-node-controls.png](https://docs.datarobot.com/en/docs/images/lineage-graph-node-controls.png) Control Description Navigate to the asset in a new tab. Open a fullscreen view of the related items lineage graph centered on the selected asset node. Copy the asset's associated ID. > [!NOTE] One-to-many list view > If an asset is used by many other assets (e.g., one dataset version used for many projects), in the fullscreen view, the graph shows a preview of the 5 most recent items. Additional assets are viewable in a paginated and searchable list. If you don't have permission to view the ancestor of a paginated group, you can only view the 5 most recent items, without the option to change pages or search. > > [https://docs.datarobot.com/en/docs/images/one-to-many-asset-list.png](https://docs.datarobot.com/en/docs/images/one-to-many-asset-list.png) **List:** The Lineage section in the Overview tab also includes a List view. On the List tab, click Show more to reveal all related items. Each item in the list displays its name, ID, the user who created it, and the date it was created. Click View to open the related item. For custom model deployments (including text generation, agentic workflows, vector databases, and MCP integrations), the list emphasizes registered models, custom model versions, and related training data. [https://docs.datarobot.com/en/docs/images/nxt-overview-tab-items.png](https://docs.datarobot.com/en/docs/images/nxt-overview-tab-items.png) Field Description Registered model The name and ID of the registered model associated with the deployment. Click to open the registered model in Registry. Registered model version The name and ID of the registered model version associated with the deployment. Click to open the registered model version in Registry. Custom model information Custom model The name, version, and ID of the custom model associated with the deployment. Click to open the workshop to the Assemble tab for the custom model. Custom model version The version and ID of the custom model version associated with the deployment. Click to open the workshop to the Versions tab for the custom model. Training dataset The filename and ID of the training dataset used to create the currently deployed custom model. > [!NOTE] Inaccessible related items > If you don't have access to a related item, a lock icon appears at the end of the item's row. ## Evaluation and moderation > [!NOTE] Availability information > Evaluation and moderation guardrails are a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable Moderation Guardrails ( Premium), Enable Global Models in the Model Registry ( Premium), Enable Additional Custom Model Output in Prediction Responses When a text generation or agentic workflow model with guardrails is registered and deployed, you can view the Evaluation and moderation section on the deployment's Overview tab: ## Tags In the Tags section, click + Add new and enter a Name and a Value for each key-value pair you want to tag the deployment with. Deployment tags can help you categorize and search for deployments in the [dashboard](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-overview/nxt-dashboard.html). ## Runtime parameters > [!NOTE] Preview > The ability to edit custom model runtime parameters on a deployment is on by default. > > Feature flag: Enable Editing Custom Model Runtime-Parameters on Deployments On a custom model deployment's Overview tab, you can access the Runtime parameters section. Runtime parameters are injected into containers as standard environment variables, without requiring prefixes or JSON parsing for simple types, meaning that developers can retrieve parameters using standard Python methods (e.g., `os.getenv`) rather than relying on the `datarobot-drum` library and its associated dependencies. Parameters [created via theWorkshopUI](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-create-custom-model.html#define-runtime-parameters) persist and merge when you upload new code versions, ensuring a seamless development flow. From this section, manage these parameters on an inactive deployment. To do this, first make sure the deployment is inactive, then, click Edit: In the Runtime parameters table, edit the Value. To discard an individual change, click Revert changes. If you edit any of the runtime parameters, to save your changes, click Save. For more information on how to define runtime parameters and use them in custom model code, see the [Define custom model runtime parameters](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html) documentation. ## Setup checklist and approval When governance management functionality is enabled for your organization, the Setup checklist panel appears on the deployment Overview. This checklist includes the settings required by your administrator, any additional guidance they provided when configuring the checklist, and the status of the checklist setting: Not enabled, Partially enabled, or Enabled. Complete this checklist before requesting deployment approval from an administrator. Click a tile in the checklist to open the relevant [deployment setting](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-settings/index.html) page. > [!NOTE] Default setup checklist > By default, the Setup checklist displays all available setting groups for the current deployment; however, the default list is customized by the organization-level administrator. If a deployment is subject to a configured approval policy, the deployment is created in a Draft state, with an Approval status of Needs approval, as shown above. After you complete the approval checklist, you can click Request approval in the Draft deployment notice on the deployment Overview page. When you click Request approval, the Submit request for approval dialog box appears, where you can enter Additional comments for the approver. Then, click Request approval to complete your request. After approval, the deployment is automatically moved out of the draft state and activated. On the Deployments tab, draft deployments awaiting approval are shown with a Draft tag and an Inactive tag: > [!NOTE] Draft deployment limitations > With a draft deployment, you can't make predictions, upload actuals or custom metric data, or create scheduled jobs. --- # Prediction API snippets URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-pred-api-snippets.html > Use Prediction API snippets for real-time scoring and chat completions from generative custom model deployments. # Prediction API snippets DataRobot provides sample Python code containing the commands and identifiers required to submit a CSV or JSON file for scoring. You can use this code with the [DataRobot Prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html). To use the Prediction API Scripting Code, open the deployment you want to make predictions through and click Predictions > Prediction API. On the Prediction API Scripting Code page, configure Real-time scripts for chat completions and predictions from text generation, agentic workflow, VDB, and MCP custom model deployments. Follow the sample provided and make the necessary changes when you want to integrate the model, via API, into your production application. > [!NOTE] Dormant prediction servers > Prediction servers become dormant after a prolonged period of inactivity. If you see the Prediction server is dormant alert, contact support@datarobot.com for reactivation. ### Real-time prediction snippet settings To find and access the real-time prediction script required for your use case, configure the following settings: | | Content | Description | | --- | --- | --- | | (1) | Prediction type | Determines the prediction method used. Select Real time. | | (2) | Language | Determines the language of the real-time prediction script generated. Select a format:Python: An example real-time prediction script using DataRobot's Python package.cURL: A script using cURL, a command-line tool for transferring data using various network protocols, available by default in most Linux distributions and macOS. | | (3) | Show secrets | Displays any secrets hidden by ***** in the code snippet. Revealing the secrets in a code snippet can provide a convenient way to retrieve your API key or datarobot-key; however, these secrets are hidden by default for security reasons, so ensure that you handle them carefully. | | (4) | Copy script to clipboard | Copies the entire code snippet to your clipboard. | | (5) | Open in a codespace | Open the snippet in a codespace to edit it, share with others, and incorporate additional files. | | (6) | Code overview screen | Displays the example code you can download and run on your local machine. Edit this code snippet to fit your needs. | ### Open snippets in a codespace You can open a Prediction API code snippet in a [codespace](https://docs.datarobot.com/en/docs/workbench/wb-notebook/codespaces/index.html) to edit the snippet directly, share it with other users, and incorporate additional files. To open a Prediction API snippet, click Open in a codespace. DataRobot generates a codespace instance and populates the snippet inside as a python file. In the codespace, you can upload files and edit the snippet as needed. For example, you may want to add CLI arguments in order to execute the snippet. The codespace allows for full access to file storage. You can use the Upload button to add additional datasets for scoring, and have the prediction output ( `output.json`, `output.csv`, etc.) return to the codespace file directory after executing the snippet. This example uploads `10k_diabetes_small.csv` to the codespace as an input file. To add CLI arguments to the snippet, click Add CLI arguments. This example references `10k_diabetes_small.csv` as the input file for scoring, and names the output file `output.csv`. The snippet is now configured to run and return predictions. When you have finished working in the codespace, click Exit and save codespace. Codespaces belong to [Use Cases](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/usecase-overview.html), so you must specify an existing Use Case or create a new one to save the codespace to. When a Use Case has been selected, click Exit and save codespace again. Your snippet is now saved in a codespace as part of a Use Case. --- # Deployment reports URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-reports.html > Generate on-demand or scheduled deployment reports in Console. # Deployment reports Ongoing monitoring reports are a critical step in the deployment and governance process. DataRobot allows you to download deployment reports from MLOps, compiling deployment status, charts, and overall quality into a shareable report. Deployment reports are compatible with all deployment types. ## Generate a deployment report To generate a report for a deployment, select it from the Deployments inventory, navigate to the Monitoring > Reports tab and click Generate report now: In the Settings for report generation panel, select the Model, Date range, and Date resolution, then click Generate report: When the report generation is finished, click the view icon to open the report in your browser or the download icon to open it locally: ## Schedule deployment reports In addition to manual creation, DataRobot allows you to manage a schedule to generate deployment reports automatically. To schedule report generation for a deployment, select the deployment from the Deployments inventory and navigate to the Monitoring > Reports tab. On the Deployment reports page, click + Create new report schedule: In the report panel, configure the Report Schedule (UTC) and Report Contents and Recipients: > [!TIP] Advanced scheduling > For more granular scheduling controls, you can click Use advanced schedule. After defining the report schedule and recipients, click Save report schedule. The reports automatically generate at the configured dates and times. The generated report appears on the Monitoring > Reports tab. You can edit or delete the report schedule from the list: --- # Resource monitoring URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-resource-monitoring.html > Monitor CPU, memory, and replica utilization for serverless custom model deployments. # Resource monitoring The Monitoring > Resource monitoring tab provides visibility into resource utilization metrics for deployed custom models and agentic workflows, helping you monitor performance, identify bottlenecks, and understand auto-scaling behavior. Use this tab to evaluate resource usage, navigate tradeoffs between speed and cost, and ensure your deployments efficiently utilize available hardware resources. To access Resource monitoring, select a deployment from the Deployments inventory and then click Monitoring > Resource monitoring. The tab displays summary tiles showing aggregated and current values for key metrics, along with interactive charts that visualize resource utilization over time. ## Resource utilization summary tiles The Resource monitoring tab displays summary tiles at the top of the page, showing both the aggregated value over the selected timespan (the primary value) and the current Live value at the time of the request. The aggregated value represents the average value over the selected timespan. Clicking on a metric tile updates the chart below to display that metric over time. The following metrics are displayed as summary tiles: | Metric | Description | | --- | --- | | Replicas | The number of active compute instances (replicas) out of the maximum available for the deployment. For text generation, agentic workflow, VDB, and MCP custom model deployments, this counts custom model pods. | | CPU utilization | The percentage of CPU cores being used across all compute instances for the deployment. | | Memory usage | The amount of memory (in bytes or appropriate units) being used across all compute instances for the deployment. | ## Resource utilization charts The Resource monitoring tab displays interactive charts that visualize resource utilization metrics over time, helping you identify patterns and understand resource consumption trends. The chart displays the selected metric over time, with the following elements: | | Chart element | Description | | --- | --- | --- | | (1) | Time (X-axis) | Displays the time represented by each data point, based on the selected resolution (1 minute, 5 minutes, hourly, or daily). | | (2) | Metric value (Y-axis) | Displays the value (cardinality for Replicas and average for all other metrics) of the selected metric (Replicas, CPU utilization, or Memory usage) for each time period. | | (3) | Containers | For deployments with multiple compute instances, you can filter resource utilization metrics by specific compute instance. | To view additional information on the chart, hover over a data point to see the time range and metric value: You can configure the Resource monitoring dashboard to focus on specific time frames and metrics. The following controls are available: | Control | Description | | --- | --- | | Range (UTC) | Sets the date range displayed for the deployment date slider. You can also drag the date slider to set the range. The range selector only allows you to select dates and times between the start date of the deployment's current version of a model and the current date. | | Resolution | Sets the time granularity of the deployment date slider. The following resolution settings are available, based on the selected range: Hourly: If the range is less than 7 days.Daily: If the range is between 1-60 days (inclusive).Weekly: If the range is between 1-52 weeks (inclusive).Monthly: If the range is at least 1 month and less than 120 months. | | Refresh | Initiates an on-demand update of the dashboard with new data. Otherwise, DataRobot refreshes the dashboard every 15 minutes. | | Reset | Reverts the dashboard controls to the default settings. | > [!NOTE] Time range limitations > The Resource monitoring tab is limited to displaying data from the last 30 days. This limitation ensures optimal performance when querying and displaying resource utilization metrics. ## Filter by compute instance For deployments with multiple compute instances, you can filter resource utilization metrics by specific compute instances. Filtering by compute instance allows you to: - Identify which instances are experiencing high resource utilization. - Troubleshoot issues affecting specific instances. - Understand resource distribution across instances. To filter by compute instance, use the Containers selector in the dashboard controls. Metrics are grouped by compute instance and are filtered by LRS ID or inference ID. > [!NOTE] Compute instance filtering > Compute instance filtering is available for deployments with multiple instances. For single-instance deployments, the filtering selector is not available. ## Understanding resource utilization metrics The following sections provide detailed explanations of each resource utilization metric displayed on the Resource monitoring tab. Understanding these metrics helps you evaluate resource usage, identify bottlenecks, and make informed decisions about resource bundle sizing. ### Replicas The Replicas metric shows the number of active compute instances (replicas) currently running for your deployment, out of the maximum available. This metric helps you: - Monitor changes in the number of replicas over time to understand scaling behavior. - Correlate the number of replicas with resource utilization metrics. - Identify when additional capacity is needed or when resources are underutilized. For custom model deployments on serverless, this metric counts custom model pods. ### CPU utilization The CPU utilization metric shows the percentage of CPU cores being utilized across all compute instances. This metric helps you: - Identify CPU bottlenecks that may be affecting model performance. - Understand CPU usage patterns over time. - Make informed decisions about CPU resource bundle sizing. High CPU utilization may indicate that your deployment needs more CPU resources or that the workload is CPU-intensive. Low CPU utilization may suggest that you can reduce the CPU resource bundle size to optimize costs. ### Memory usage The Memory usage metric shows the amount of memory being used across all compute instances. This metric helps you: - Monitor memory usage to prevent out-of-memory errors. - Identify memory leaks or excessive memory consumption. - Make informed decisions about memory resource allocation. Memory usage is displayed in bytes or appropriate units (KB, MB, GB) based on the scale of usage. --- # Standard output logs URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-runtime-logs.html > View runtime logs from the custom model container for debugging. # Standard output logs When you deploy a custom model, it generates log reports unique to this type of deployment, allowing you to debug custom code and troubleshoot prediction request failures from within DataRobot. These logs are accessible on the Activity logs > Standard output tab. To view the logs for a deployed custom model: - On theDeploymentstab, locate the deployment, click theActions menu(oron the deploymentOverview), and then clickView logs. - In a deployment, click theActivity logtab, and then clickStandard output. From this tab, you can troubleshoot failed prediction requests. The logs are captured from the Docker container running the deployed custom model and contain up to 1MB of data. > [!NOTE] No logs available > Standard output can only be retrieved when the custom model deployment is active; if the deployment is inactive, the Standard output tab and the action menu button are disabled for inactive deployments. > > In addition, even when the Standard output tab is accessible, DataRobot only provides logs from the Docker container running the custom model; therefore, it's possible for specific event logs to be unavailable when a failure occurs outside the Docker container. You can re-request logs by clicking Refresh. Use the Search bar to find specific references within the logs. Click Download Log to save a local copy of the logs. --- # Deployment service health URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-service-health.html > Track latency, throughput, and error rate for generative and agentic custom model deployments. # Deployment service health The Service health tab tracks metrics about a deployment's ability to respond to prediction requests quickly and reliably. This helps identify bottlenecks and assess capacity, which is critical to proper provisioning. For example, if a model seems to have generally slowed in its response times, the Service health tab for the model's deployment can help. You might notice in the tab that median latency goes up with an increase in prediction requests. If latency increases when a new model is switched in, you can consult with your team to determine whether the new model can instead be replaced with one offering better performance. To access Service health, select an individual deployment from the deployment inventory page and then, from the Overview, click Monitoring > Service health. The tab provides informational [tiles and a chart](https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-service-health.html#understand-metric-tiles-and-chart) to help assess the activity level and health of the deployment. > [!NOTE] Time of Prediction > The Time of Prediction value differs between the [Data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html) and [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) tabs and the [Service health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html) tab: > > On the > Service health > tab, the "time of prediction request" is > always > the time the prediction server > received > the prediction request. This method of prediction request tracking accurately represents the prediction service's health for diagnostic purposes. > On the > Data drift > and > Accuracy > tabs, the "time of prediction request" is, > by default > , the time you > submitted > the prediction request, which you can override with the prediction timestamp in the > Prediction History and Service Health > settings. ## Understand metric tiles and chart DataRobot displays informational statistics based on your current settings for model and time frame. That is, tile values correspond to the same units as those selected on the slider. If the slider interval values are weekly, the displayed tile metrics show values corresponding to weeks. Clicking a metric tile updates the chart below. The Service health tab reports the following metrics on the dashboard: > [!NOTE] Service health information for external models and monitoring jobs > Service health information is unavailable for external [agent-monitored deployments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html) and deployments with predictions uploaded through a [prediction monitoring job](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/pred-monitoring-jobs/index.html). | Statistic | Reports (for selected time period) | | --- | --- | | Total Predictions | The number of predictions the deployment has made (per prediction node). | | Total Requests | The number of prediction requests the deployment has received (a single request can contain multiple prediction requests). | | Requests over x ms | The number of requests where the response time was longer than the specified number of milliseconds. The default is 2000 ms; click in the box to enter a time between 10 and 100,000 ms or adjust with the controls. | | Response Time | The time (in milliseconds) DataRobot spent receiving a prediction request, calculating the request, and returning a response to the user. The report does not include time due to network latency. Select the median prediction request time or 90th, 95th, or 99th percentile. The display reports a dash if you have made no requests against it or if it's an external deployment. | | Execution Time | The time (in milliseconds) DataRobot spent calculating a prediction request. Select the median prediction request time or 90th, 95th, or 99th percentile. | | Median/Peak Load | The median and maximum number of requests per minute. | | Data Error Rate | The percentage of requests that result in a 4xx error (problems with the prediction request submission). This is a component of the value reported as the Service Health Summary on the Deployments dashboard top banner. | | System Error Rate | The percentage of well-formed requests that result in a 5xx error (problem with the DataRobot prediction server). This is a component of the value reported as the Service Health Summary on the Deployments dashboard top banner. | | Consumers | The number of distinct users (identified by API key) who have made prediction requests against this deployment. | You can configure the dashboard to focus the visualized statistics on specific segments and time frames. The following controls are available: | Control | Description | | --- | --- | | Model | Updates the dashboard displays to reflect the model you selected from the dropdown. | | Range (UTC) | Sets the date range displayed for the deployment date slider. You can also drag the date slider to set the range. The range selector only allows you to select dates and times between the start date of the deployment's current version of a model and the current date. | | Resolution | Sets the time granularity of the deployment date slider. The following resolution settings are available, based on the selected range: Hourly: If the range is less than 7 days.Daily: If the range is between 1-60 days (inclusive).Weekly: If the range is between 1-52 weeks (inclusive).Monthly: If the range is at least 1 month and less than 120 months. | | Refresh | Initiates an on-demand update of the dashboard with new data. Otherwise, DataRobot refreshes the dashboard every 15 minutes. | | Reset | Reverts the dashboard controls to the default settings. | The chart below the metric tiles displays individual metrics over time, helping to identify patterns in the quality of service. Clicking on a metric tile updates the chart to represent that information; adjusting the data range slider focuses on a specific period: > [!TIP] Export charts > Click Export to download a `.csv` or `.png` file of the currently selected chart, or a `.zip` archive file of both (and a `.json` file). The Median | Peak Load (calls/minute) chart displays two lines, one for Peak load and one for Median load over time: ## Service health status indicators Service health tracks metrics about a deployment’s ability to respond to prediction requests quickly and reliably. You can view the service health status in the [deployment inventory](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-overview/nxt-dashboard.html#health-indicators) and visualize service health on the [Service health](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-service-health.html) tab. Service health monitoring represents the occurrence of 4XX and 5XX errors in your prediction requests or prediction server: - 4xx errors indicate problems with the prediction request submission. - 5xx errors indicate problems with the DataRobot prediction server. | Color | Service Health | Action | | --- | --- | --- | | Green / Passing | Zero 4xx or 5xx errors. | No action needed. | | Yellow / At risk | At least one 4xx error and zero 5xx errors. | Concerns found, but no immediate action needed; monitor. | | Red / Failing | At least one 5xx error. | Immediate action needed. | | Gray / Disabled | Unmonitored deployment. | Enable monitoring and make predictions. | | Gray / Not started | No service health events recorded. | Make predictions. | | Gray / Unknown | No predictions made. | Make predictions. | ## Explore deployment data tracing > [!NOTE] Premium > Tracing is a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. On the Service health tab of a custom model deployment (including text generation, agentic workflow, VDB, and MCP deployments), you can view the tracing table below the Total predictions chart. To view the tracing table, in the upper-right corner of the Total predictions chart, click Show tracing. Traces represent the path taken by a request to a model or agentic workflow. DataRobot uses the [OpenTelemetry framework for tracing](https://opentelemetry.io/docs/concepts/signals/traces/). A trace follows the entire end-to-end path of a request, from origin to resolution. Each trace contains one or more spans, starting with the root span. The root span represents the entire path of the request and contains a child span for each individual step in the process. The root (or parent) span and each child span share the same Trace ID. > [!NOTE] Access and retention > The tracing table is available for all custom and external model deployments. Tracing data is stored for a retention period of 30 days, after which it is automatically deleted. In the Tracing table, you can review the following fields related to each trace: | Column | Description | | --- | --- | | Timestamp | The date and time of the trace in YYYY-MM-DD HH:MM format. | | Status | The overall status of the trace, including all spans. The Status will be Error if any dependent task fails. | | Trace ID | A unique identifier for the trace. | | Duration | The amount of time, in milliseconds, it took for the trace to complete. This value is equal to the duration of the root span (rounded) and includes all actions represented by child spans. | | Spans count | The number of completed spans (actions) included in the trace. | | Cost | If cost data is provided, the total cost of the trace. | | Prompt | The user prompt related to the trace. | | Completion | The agent or model response (completion) associated with the prompt for the trace. | | Tools | The tool or tools called during the request represented by the trace. | Click Filter to filter by Min span duration, Max span duration, Min trace cost, and Max trace cost. The unit for span filters is nanoseconds (ns), the chart displays spans in milliseconds (ms). > [!TIP] Filter accessibility > The Filter button is hidden when a span is expanded to detail view. To return to the chart view with the filter, click Hide details panel. To review the [spans](https://opentelemetry.io/docs/concepts/signals/traces/#spans) contained in a trace, along with trace details, click a trace row in the Tracing table. The span colors correspond to a Span service, usually a deployment.Restricted span appears when you don’t have access to the deployment or service associated with the span. You can view spans in Chart format or List format. > [!TIP] Span detail controls > From either view, you can click Hide table to collapse the Timestamps table or Hide details panel to return to the expanded Tracing table view. **Chart view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans.png) **List view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans-list.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans-list.png) > [!NOTE] Trace details > In list view, you can click Trace details to view the Input/Output ( Prompt and Completion) and Evaluation details about the trace associated with the current span. For either view, click the Span service name to access the deployment or resource (if you have access). Additional information, dependent on the configuration of the generative AI model or agentic workflow, is available on the Info, Resources, Events, Input/Output, Error, and Logs tabs. The Error tab only appears when an error occurs in a trace. **Chart view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs.png) **List view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs-list-view.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs-list-view.png) ### Filter tracing logs From the list view, you can display OTel logs for a span. The results shown are a subset of the [full deployment logs](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-otel-logs.html), and are accessed as follows: 1. Open the list view and select a span underTrace details. 2. Click theLogstab. 3. ClickShow logs. ### Tracing table OTel attributes For Cost, Prompt, Completion, and Tools, DataRobot reads specific span attributes across all spans that belong to the trace. Other columns (such as Timestamp and Duration) come from trace and span metadata rather than these attributes. | Column | OpenTelemetry mapping | | --- | --- | | Cost | Sums numeric values from the datarobot.moderation.cost attribute on spans in the trace (when that attribute is present). | | Prompt | Uses the gen_ai.prompt attribute. If more than one span includes gen_ai.prompt, the first value encountered in trace order is shown. | | Completion | Uses the gen_ai.completion attribute. If more than one span includes gen_ai.completion, the last value encountered in trace order is shown. | | Tools | Collects every distinct value of the tool_name attribute found on spans in the trace and lists those tool names in the column. | Attribute keys must match exactly (including the underscore in `gen_ai`). Names such as `genai.prompt` or `GenAI.prompt` are not read for the Prompt and Completion columns. Automatic instrumentation (including DataRobot agent templates) often sets `gen_ai.prompt`, `gen_ai.completion`, and sometimes `tool_name`. For custom or external models, frameworks differ: tool execution may not emit `tool_name` even when tools run (for example, some LangGraph callback flows). In that case Prompt and Completion can populate while Tools remains empty until `tool_name` is configured on a span that runs inside the tool—see [Implement tracing](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tracing-code.html#surface-tool-names-in-the-tracing-table). --- # Deployment usage URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-usage.html > Monitor quota usage, tokens, and rate limits for agentic workflow and related serverless deployments. # Deployment usage For text generation, VDB, and MCP custom model deployments, the Usage tab follows the standard prediction-processing views described in [Usage](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-usage.html). For agentic workflow (and NIM) deployments, the Quota monitoring experience below is the primary usage view. ## Quota usage monitoring On the Monitoring > Usage tab for agentic workflow and NIM deployments, the Quota monitoring dashboard visualizes historical usage segmented by user or agent. Other serverless generative deployments use the same Console controls when quota monitoring applies. The Quota monitoring dashboard displays three key metric tiles at the top of the page: | Metric | Description | | --- | --- | | Total requests | The total number of requests made during the selected time range, along with the average requests per minute. | | Total rate limited requests | The total number of requests that were rate limited during the selected time range, along with the average rate limited requests per minute. | | Total token count | The total number of tokens consumed during the selected time range, along with the average tokens per minute. | | Average concurrent requests | The average number of simultaneous API calls processed by the agent service over the defined interval, tracked as a key metric for observability and used to enforce the system's quota limit on simultaneous operations. | Each metric displays the value for the selected time frame and the average per minute in green. Click the metric tile to review the corresponding chart below: - Total requests - Total rate limited requests - Total token count - Average concurrent requests You can configure the Quota monitoring dashboard to focus the visualized statistics on specific entities and time frames. The following controls are available: | Filter | Description | | --- | --- | | Model | Select the model version to monitor. The Current option displays data for the active model version. | | Range (UTC) | Select the date and time range for the data displayed. Use the date pickers to set the start and end times in UTC. | | Resolution | Select the time resolution for aggregating data: Hourly, Daily, or Weekly. | | Entity | Filter by entity type: All, User, or Agent. | | Refresh | Updates the dashboard with the latest data based on the current filter settings. | | Reset | Resets all filters to their default values. | ### Quota monitoring charts The Quota monitoring charts display an area chart showing the distribution of requests over time, rate limited requests over time, or token count over time. This chart is a stacked chart (or stacked graph), a chart stacking multiple data series on top of each other to visualize how each entity contributes to the total over time and across categories. Each chart is segmented by entity (user or agent). Each entity is represented by a different color in the chart legend. | | Chart element | Description | | --- | --- | --- | | (1) | Entity filter | Displays all entities (users or agents) included in the selected time range. Each entity is represented by a dot that matches the area in the chart. | | (2) | Entity legend | Displays all entities (users or agents) included in the selected time range. Each entity is represented by a dot that matches the area in the chart. | | (3) | Time range (X-axis) | Displays the time range selected in the filters, showing the date range from start to end. | | (4) | Metric (Y-axis) | Displays the number of requests, rate limited requests, or tokens on the vertical axis. | | (5) | Request areas | Overlapping areas show the volume of requests per entity over time. The height of each area at any point represents the number of requests for that entity at that time. This chart is a stacked chart (or stacked graph), a chart stacking multiple data series on top of each other to visualize how each entity contributes to the total over time and across categories. | | (6) | Tracing | Click Show tracing to view tracing data for the requests. | | (7) | Export | Click Export to download a .csv file. | Hover over the chart to view detailed information about the number of requests for each entity at specific time points. ### Request tracing table > [!NOTE] Premium > Tracing is a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. On any Quota monitoring chart, click Show tracing to view tracing data for the deployment. This tracing chart functions similarly to the tracing chart on the [Data Explorationtab](https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/agent-data-exploration.html#explore-deployment-data-tracing). Traces represent the path taken by a request to a model or agentic workflow. DataRobot uses the [OpenTelemetry framework for tracing](https://opentelemetry.io/docs/concepts/signals/traces/). A trace follows the entire end-to-end path of a request, from origin to resolution. Each trace contains one or more spans, starting with the root span. The root span represents the entire path of the request and contains a child span for each individual step in the process. The root (or parent) span and each child span share the same Trace ID. > [!NOTE] Access and retention > The tracing table is available for all custom and external model deployments. Tracing data is stored for a retention period of 30 days, after which it is automatically deleted. In the Tracing table, you can review the following fields related to each trace: | Column | Description | | --- | --- | | Timestamp | The date and time of the trace in YYYY-MM-DD HH:MM format. | | Status | The overall status of the trace, including all spans. The Status will be Error if any dependent task fails. | | Trace ID | A unique identifier for the trace. | | Duration | The amount of time, in milliseconds, it took for the trace to complete. This value is equal to the duration of the root span (rounded) and includes all actions represented by child spans. | | Spans count | The number of completed spans (actions) included in the trace. | | Cost | If cost data is provided, the total cost of the trace. | | Prompt | The user prompt related to the trace. | | Completion | The agent or model response (completion) associated with the prompt for the trace. | | Tools | The tool or tools called during the request represented by the trace. | Click Filter to filter by Min span duration, Max span duration, Min trace cost, and Max trace cost. The unit for span filters is nanoseconds (ns), the chart displays spans in milliseconds (ms). > [!TIP] Filter accessibility > The Filter button is hidden when a span is expanded to detail view. To return to the chart view with the filter, click Hide details panel. To review the [spans](https://opentelemetry.io/docs/concepts/signals/traces/#spans) contained in a trace, along with trace details, click a trace row in the Tracing table. The span colors correspond to a Span service, usually a deployment.Restricted span appears when you don’t have access to the deployment or service associated with the span. You can view spans in Chart format or List format. > [!TIP] Span detail controls > From either view, you can click Hide table to collapse the Timestamps table or Hide details panel to return to the expanded Tracing table view. **Chart view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans.png) **List view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans-list.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-spans-list.png) > [!NOTE] Trace details > In list view, you can click Trace details to view the Input/Output ( Prompt and Completion) and Evaluation details about the trace associated with the current span. For either view, click the Span service name to access the deployment or resource (if you have access). Additional information, dependent on the configuration of the generative AI model or agentic workflow, is available on the Info, Resources, Events, Input/Output, Error, and Logs tabs. The Error tab only appears when an error occurs in a trace. **Chart view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs.png) **List view:** [https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs-list-view.png](https://docs.datarobot.com/en/docs/images/nxt-tracing-table-span-tabs-list-view.png) ### Filter tracing logs From the list view, you can display OTel logs for a span. The results shown are a subset of the [full deployment logs](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-activity-log/nxt-otel-logs.html), and are accessed as follows: 1. Open the list view and select a span underTrace details. 2. Click theLogstab. 3. ClickShow logs. ### Tracing table OTel attributes For Cost, Prompt, Completion, and Tools, DataRobot reads specific span attributes across all spans that belong to the trace. Other columns (such as Timestamp and Duration) come from trace and span metadata rather than these attributes. | Column | OpenTelemetry mapping | | --- | --- | | Cost | Sums numeric values from the datarobot.moderation.cost attribute on spans in the trace (when that attribute is present). | | Prompt | Uses the gen_ai.prompt attribute. If more than one span includes gen_ai.prompt, the first value encountered in trace order is shown. | | Completion | Uses the gen_ai.completion attribute. If more than one span includes gen_ai.completion, the last value encountered in trace order is shown. | | Tools | Collects every distinct value of the tool_name attribute found on spans in the trace and lists those tool names in the column. | Attribute keys must match exactly (including the underscore in `gen_ai`). Names such as `genai.prompt` or `GenAI.prompt` are not read for the Prompt and Completion columns. Automatic instrumentation (including DataRobot agent templates) often sets `gen_ai.prompt`, `gen_ai.completion`, and sometimes `tool_name`. For custom or external models, frameworks differ: tool execution may not emit `tool_name` even when tools run (for example, some LangGraph callback flows). In that case Prompt and Completion can populate while Tools remains empty until `tool_name` is configured on a span that runs inside the tool—see [Implement tracing](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-tracing-code.html#surface-tool-names-in-the-tracing-table). ### Rate limited requests table The Rate limited requests table provides a detailed breakdown of rate limiting by entity: | | Table element | Description | | --- | --- | --- | | (1) | Entity type filter | Filter the table by entity type (user or agent). | | (2) | Rate limited percentage filter | Filter entities by their rate limited percentage threshold (zero, low, medium, or high). | | (3) | Search box | Search for specific entities by name or identifier. | | (4) | Entity column | Displays the entity identifier (user email or agent name). | | (5) | Rate limited requests column | Shows the number of rate limited requests and the percentage of total requests that were rate limited. The percentage is highlighted in red when it exceeds a threshold, or displayed in gray when it is 0%. | | (6) | Requests column | Displays the number of requests that were rate limited due to exceeding the request quota. | | (7) | Token count column | Displays the number of requests that were rate limited due to exceeding the token quota. | | (8) | Concurrent requests column | Displays the number of requests that were rate limited due to exceeding the concurrent requests quota. | The table helps identify which entities are experiencing rate limiting and to what extent, allowing you to adjust quotas or usage patterns accordingly. --- # Monitor URL: https://docs.datarobot.com/en/docs/agentic-ai/agentic-monitor/index.html > Monitor an agentic artifact deployment's performance and behavior. # Monitor > [!NOTE] Premium > DataRobot's Agentic AI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. After you've deployed an agentic artifact to Console, monitor the deployment's performance and behavior. In Console, on the Deployments page, click the Agentic Workflow tab to view a list of agentic workflow deployments. A fully configured agentic workflow deployment has access to the following Console features: | Feature | Description | | --- | --- | | Overview | | | Deployment overview | Review deployment details, lineage, tags, runtime parameters, and—when guardrails are configured—evaluation and moderation summary information. | | Monitoring | | | Deployment service health | Track model-specific deployment latency, throughput, and error rate. | | Deployment usage | Track prediction processing progress for use in accuracy, data drift, and predictions over time analysis. For agentic workflow deployments, includes quota usage monitoring segmented by user or agent. | | Custom metrics | Create and monitor custom business or performance metrics or add pre-made metrics. When you configure evaluation and moderation for the workflow, guard metrics (for example, guard latency and blocked counts) are reported here. | | Data exploration and tracing | Explore and export stored prediction data, actuals, and training data; assess response quality; and use Tracing to inspect agent traces and timelines. | | Deployment reports | Generate reports, immediately or on a schedule, to summarize the details of a deployment, such as its owner, how the model was built, the model age, and the humility monitoring status. | | Resource monitoring | Monitor CPU, memory, and replica utilization for the serverless deployment. | | OpenTelemetry metrics | Visualize OpenTelemetry metrics from your application alongside DataRobot native metrics. | | Predictions | | | Prediction API snippets | Use downloadable snippets to call the deployment's prediction and chat completion APIs from your application (including real-time scoring integrations). | | Activity log | | | Standard output logs | View runtime logs from the custom model container to debug scoring and request failures. | | OpenTelemetry logs | View OpenTelemetry log events for troubleshooting and deeper analysis (span-related logs can also be filtered from Data exploration). | | Moderation events | When evaluation and moderation guardrails are enabled, review guard-related events to diagnose blocked requests and guard failures. | --- # Authentication management URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/auth.html > Manage authentication with DataRobot. # Authentication management Manage authentication with DataRobot. ## Synopsis ``` dr auth [flags] ``` ## Description The `auth` command provides authentication management for the DataRobot CLI. It handles login, logout, and URL configuration for connecting to your DataRobot instance. ## Commands ### login Authenticate with DataRobot using OAuth. ``` dr auth login ``` Behavior: 1. Starts a local web server (typically on port 8080) 2. Opens your default browser to DataRobot's OAuth page 3. Prompts you to authorize the CLI 4. Receives and stores the API key 5. Closes the browser and server automatically Example: ``` $ dr auth login Opening browser for authentication... Waiting for authentication... ✓ Successfully authenticated! ``` Stored Credentials: - Location: `~/.config/datarobot/drconfig.yaml` (Linux/macOS) or `%USERPROFILE%\.config\datarobot\drconfig.yaml` (Windows) - Format: Encrypted API key Troubleshooting: ``` # If browser doesn't open automatically # The CLI will display a URL to visit manually: $ dr auth login Failed to open browser automatically. Please visit: https://app.datarobot.com/oauth/authorize?client_id=... # Port already in use # The CLI will try alternative ports automatically ``` ### logout Remove stored authentication credentials. ``` dr auth logout ``` Example: ``` $ dr auth logout ✓ Successfully logged out ``` Effect: - Removes API key from config file - Keeps DataRobot URL configuration - Next API call will require re-authentication ### set-url Configure the DataRobot instance URL. ``` dr auth set-url [url] ``` Arguments: - `url` (optional) - DataRobot instance URL Interactive Mode: If no URL is provided, enters interactive mode: ``` $ dr auth set-url Please specify your DataRobot URL, or enter the numbers 1 - 3 if you are using that multi tenant cloud offering Please enter 1 if you are using https://app.datarobot.com Please enter 2 if you are using https://app.eu.datarobot.com Please enter 3 if you are using https://app.jp.datarobot.com Otherwise, please enter the URL you use > _ ``` Direct Mode: Specify URL directly: ``` # Using cloud shortcuts $ dr auth set-url 1 # Sets to https://app.datarobot.com $ dr auth set-url 2 # Sets to https://app.eu.datarobot.com $ dr auth set-url 3 # Sets to https://app.jp.datarobot.com # Using full URL $ dr auth set-url https://app.datarobot.com $ dr auth set-url https://my-company.datarobot.com ``` Validation: ``` $ dr auth set-url invalid-url Error: Invalid URL format ``` ## Global flags These flags work with all `auth` commands: ``` -v, --verbose Enable verbose output --debug Enable debug output --skip-auth Skip authentication checks (for advanced users) -h, --help Show help for command ``` > ⚠️ Warning:The--skip-authflag bypasses all authentication checks. This is intended for advanced use cases where authentication is handled externally or not required. When this flag is used, commands that require authentication may fail with API errors. ## Examples ### Initial setup ``` # Set URL and login (recommended workflow) $ dr auth set-url https://app.datarobot.com ✓ DataRobot URL set to: https://app.datarobot.com $ dr auth login Opening browser for authentication... ✓ Successfully authenticated! ``` ### Using cloud instance shortcuts ``` # US Cloud $ dr auth set-url 1 $ dr auth login # EU Cloud $ dr auth set-url 2 $ dr auth login # Japan Cloud $ dr auth set-url 3 $ dr auth login ``` ### Self-managed instance ``` $ dr auth set-url https://datarobot.mycompany.com $ dr auth login ``` ### Re-authentication ``` # Logout and login again $ dr auth logout ✓ Successfully logged out $ dr auth login Opening browser for authentication... ✓ Successfully authenticated! ``` ### Switching instances ``` # Switch to different DataRobot instance $ dr auth set-url https://staging.datarobot.com $ dr auth login ``` ### Debug authentication issues ``` # Use verbose flag for details $ dr auth login --verbose [INFO] Starting OAuth server on port 8080 [INFO] Opening browser to: https://app.datarobot.com/oauth/... [INFO] Waiting for callback... [INFO] Received authorization code [INFO] Exchanging code for token... [INFO] Token saved successfully ✓ Successfully authenticated! # Use debug flag for even more details $ dr auth login --debug [DEBUG] Config file: /Users/username/.datarobot/config.yaml [DEBUG] Current URL: https://app.datarobot.com [DEBUG] Starting server on: 127.0.0.1:8080 ... ``` ## Authentication flow ``` ┌──────────┐ │ User │ └────┬─────┘ │ │ dr auth login │ v ┌─────────────────┐ ┌──────────────┐ │ Local Server │◄──────┤ Browser │ │ (Port 8080) │ │ │ └────┬────────────┘ └──────▲───────┘ │ │ │ │ Opens │ │ v │ ┌─────────────────┐ │ │ DataRobot │───────────────┘ │ OAuth Server │ └────┬────────────┘ │ │ Returns API Key │ v ┌─────────────────┐ │ Config File │ │ (~/.config/ │ │ datarobot/ │ │ drconfig.yaml)│ └─────────────────┘ ``` ## Configuration file After authentication, credentials are stored in: Location: - Linux/macOS: `~/.config/datarobot/drconfig.yaml` - Windows: `%USERPROFILE%\.config\datarobot\drconfig.yaml` Format: ``` datarobot: endpoint: https://app.datarobot.com token: # User preferences preferences: default_timeout: 30 verify_ssl: true ``` Permissions: - File is created with restricted permissions (0600) - Only the user who created it can read/write ## Security best practices ### 1. Protect your config file ``` # Verify permissions ls -la ~/.config/datarobot/drconfig.yaml # Should show: -rw------- (600) # Fix if needed chmod 600 ~/.config/datarobot/drconfig.yaml ``` ### 2. Do not share credentials Never commit or share: - `~/.config/datarobot/drconfig.yaml` - API keys - OAuth tokens ### 3. Use per-environment authentication ``` # Development export DATAROBOT_CLI_CONFIG=~/.config/datarobot/dev-config.yaml dr auth set-url https://dev.datarobot.com --config $DATAROBOT_CLI_CONFIG dr auth login # Production export DATAROBOT_CLI_CONFIG=~/.config/datarobot/prod-config.yaml dr auth set-url https://prod.datarobot.com --config $DATAROBOT_CLI_CONFIG dr auth login ``` ### 4. Regular re-authentication ``` # Logout when finished dr auth logout # Login only when needed dr auth login ``` ## Environment variables Override configuration with environment variables: ``` # Override URL export DATAROBOT_ENDPOINT=https://app.datarobot.com # Override API key (not recommended) export DATAROBOT_API_TOKEN=your-api-token # Custom config file location export DATAROBOT_CLI_CONFIG=~/.config/datarobot/custom-config.yaml ``` ## Common issues ### Browser doesn't open Problem: Browser fails to open automatically. Solution: ``` # Copy the URL from the output and open manually $ dr auth login Failed to open browser automatically. Please visit: https://app.datarobot.com/oauth/authorize?... ``` ### Port already in use Problem: Port 8080 is already in use. Solution: The CLI automatically tries alternative ports (8081, 8082, etc.) ### Invalid credentials Problem:"Authentication failed" error. Solution: ``` # Clear credentials and try again dr auth logout dr auth login ``` ### Connection refused Problem: Cannot connect to DataRobot. Solution: ``` # Verify URL is correct cat ~/.config/datarobot/drconfig.yaml # Try setting URL again dr auth set-url https://app.datarobot.com # Check network connectivity ping app.datarobot.com ``` ### SSL certificate issues Problem: SSL verification fails. Solution: ``` # For self-signed certificates (not recommended for production) export DATAROBOT_VERIFY_SSL=false dr auth login ``` ## See also - Getting Started - Initial setup guide - Configuration - Configuration file details - templates - Template management commands --- # Shell completion URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/completion.html > Generate shell completion scripts for command auto-completion. # Shell completion Generate shell completion scripts for command auto-completion. ## Synopsis ``` dr self completion ``` ## Description The `self completion` command generates shell completion scripts that enable auto-completion for the DataRobot CLI. Completions provide command, subcommand, and flag suggestions when you press Tab. ## Supported shells - bash —Bourne Again Shell. - zsh —Z Shell. - fish —Friendly Interactive Shell. - powershell —PowerShell. ## Usage ### Bash Linux: ``` # Install system-wide dr self completion bash | sudo tee /etc/bash_completion.d/dr # Reload shell source ~/.bashrc ``` macOS: ``` # Install via Homebrew's bash-completion brew install bash-completion@2 dr self completion bash > $(brew --prefix)/etc/bash_completion.d/dr # Reload shell source ~/.bash_profile ``` Temporary (current session only): ``` source <(dr self completion bash) ``` ### Zsh Setup: First, ensure completion is enabled: ``` # Add to ~/.zshrc if not present autoload -U compinit compinit ``` Installation: ``` # Option 1: User completions directory mkdir -p ~/.zsh/completions dr self completion zsh > ~/.zsh/completions/_dr echo 'fpath=(~/.zsh/completions $fpath)' >> ~/.zshrc # Option 2: System directory dr self completion zsh > "${fpath[1]}/_dr" # Clear cache and reload rm -f ~/.zcompdump source ~/.zshrc ``` Temporary (current session only): ``` source <(dr self completion zsh) ``` ### Fish ``` # Install completion dr self completion fish > ~/.config/fish/completions/dr.fish # Reload Fish source ~/.config/fish/config.fish ``` Temporary (current session only): ``` dr self completion fish | source ``` ### PowerShell Persistent: ``` # Generate completion script dr self completion powershell > dr.ps1 # Add to PowerShell profile Add-Content $PROFILE ". C:\path\to\dr.ps1" # Reload profile . $PROFILE ``` Temporary (current session only): ``` dr self completion powershell | Out-String | Invoke-Expression ``` ## Examples ### Generate completion script ``` # View the generated script dr self completion bash # Save to a file dr self completion bash > dr-completion.bash # Save for all shells dr self completion bash > dr-completion.bash dr self completion zsh > dr-completion.zsh dr self completion fish > dr-completion.fish dr self completion powershell > dr-completion.ps1 ``` ### Install for multiple shells If you use multiple shells: ``` # Bash dr self completion bash > ~/.bash_completions/dr # Zsh dr self completion zsh > ~/.zsh/completions/_dr # Fish dr self completion fish > ~/.config/fish/completions/dr.fish ``` ### Update completions After updating the CLI: ``` # Bash dr self completion bash | sudo tee /etc/bash_completion.d/dr # Zsh dr self completion zsh > ~/.zsh/completions/_dr rm -f ~/.zcompdump exec zsh # Fish dr self completion fish > ~/.config/fish/completions/dr.fish ``` ## Completion behavior ### Command completion ``` $ dr auth completion dotenv run templates version $ dr auth login logout set-url $ dr templates clone list setup status ``` ### Flag completion ``` $ dr run -- --concurrency --dir --exit-code --help --list --parallel --silent --watch --yes $ dr -- --debug --help --verbose ``` ### Argument completion Some commands support argument completion: ``` # Template names (when connected to DataRobot) $ dr templates clone python-streamlit react-frontend fastapi-backend # Task names (when in a template directory) $ dr run build dev deploy lint test ``` ## Troubleshooting ### Completions not working Bash: 1. Verify bash-completion is installed: # macOSbrewlistbash-completion@2# Linuxdpkg-l|grepbash-completion 2. Check if completion script exists: ls-l/etc/bash_completion.d/dr 3. Ensure .bashrc sources completions: grepbash_completion~/.bashrc 4. Reload shell: source~/.bashrc Zsh: 1. Verify compinit is called: grepcompinit~/.zshrc 2. Check fpath includes completion directory: echo$fpath 3. Clear completion cache: rm-f~/.zcompdump* compinit 4. Reload shell: execzsh Fish: 1. Check completion file: ls-l~/.config/fish/completions/dr.fish 2. Verify Fish recognizes it: complete-Cdr 3. Reload Fish: source~/.config/fish/config.fish PowerShell: 1. Check execution policy: Get-ExecutionPolicy If restricted: ``` Set-ExecutionPolicy RemoteSigned -Scope CurrentUser ``` 1. Verify profile loads completion: cat$PROFILE 2. Reload profile: .$PROFILE ### Permission denied Use user-level installation instead of system-wide: ``` # Bash - user level mkdir -p ~/.bash_completions dr self completion bash > ~/.bash_completions/dr echo 'source ~/.bash_completions/dr' >> ~/.bashrc # Zsh - user level mkdir -p ~/.zsh/completions dr self completion zsh > ~/.zsh/completions/_dr echo 'fpath=(~/.zsh/completions $fpath)' >> ~/.zshrc ``` ### Outdated completions After updating the CLI, regenerate completions: ``` # Bash dr self completion bash | sudo tee /etc/bash_completion.d/dr source ~/.bashrc # Zsh dr self completion zsh > ~/.zsh/completions/_dr rm -f ~/.zcompdump exec zsh # Fish dr self completion fish > ~/.config/fish/completions/dr.fish ``` ## Completion features ### Intelligent suggestions Completions are context-aware: ``` # Only shows valid subcommands dr auth # Shows: login logout set-url (not other commands) # Only shows valid flags dr run --l # Shows: --list (not all flags) ``` ### Description support In Fish and PowerShell, completions include descriptions: ``` $ dr templates clone (Clone a template repository) list (List available templates) setup (Interactive template setup wizard) status (Show current template status) ``` ### Dynamic completion Some completions are generated dynamically: ``` # Template names from DataRobot API dr templates clone # Task names from current Taskfile dr run # Available shells dr self completion ``` ## Advanced configuration ### Custom completion scripts You can extend or modify generated completions: ``` # Generate base completion dr self completion bash > ~/dr-completion-custom.bash # Edit to add custom logic vim ~/dr-completion-custom.bash # Source your custom version source ~/dr-completion-custom.bash ``` ### Completion performance For faster completions, especially with dynamic suggestions: ``` # Cache template list dr templates list > ~/.dr-templates-cache # Use cached list in custom completion script ``` ## See also - Shell completion guide —detailed setup instructions. - Getting started —initial setup. - Command completion is powered by Cobra . --- # dotenv command URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/dotenv.html > Manage environment variables and .env files in DataRobot templates. # dotenv command Manage environment variables and `.env` files in DataRobot templates. ## Overview The `dr dotenv` command provides tools for creating, editing, validating, and updating environment configuration files. It includes an interactive wizard for guided setup and a text editor for direct file manipulation. ## Commands ### dr dotenv setup Launch the interactive wizard to configure environment variables. ``` dr dotenv setup ``` Features: - Interactive prompts for all required variables. - Context-aware questions based on template configuration. - Automatic discovery of configuration from .datarobot/prompts.yaml files. - Smart defaults from .env.template . - Secure handling of secret values. - DataRobot authentication integration. - Automatic state tracking of completion timestamp. Prerequisites: - Must be run inside a git repository. - Requires authentication with DataRobot. State tracking: Upon successful completion, `dr dotenv setup` records the timestamp in the state file. This allows `dr templates setup` to intelligently skip dotenv configuration if it has already been completed. The state is stored in the same location as other CLI state (see [Configuration - State tracking](https://docs.datarobot.com/en/docs/agentic-ai/cli/configuration.html#state-tracking)). Keep in mind that `dr dotenv setup` will always prompt for configuration if run manually, regardless of state. To force the setup wizard to run again (ignoring the state file), use the `--force-interactive` flag: ``` dr templates setup --force-interactive ``` This is useful for testing or when you need to reconfigure your environment from scratch. Example: ``` cd my-template dr dotenv setup ``` The wizard guides you through: 1. DataRobot credentials (auto-populated if authenticated). 2. Application-specific configuration. 3. Optional features and integrations. 4. Validation of all inputs. 5. Generation of `.env` file. ### dr dotenv edit Open the `.env` file in an interactive editor or wizard. ``` dr dotenv edit ``` Behavior: - If `.env` exists, opens it in the editor. - If no extra variables are detected, opens text editor mode. - If template prompts are found, offers wizard mode. - Can switch between editor and wizard modes. Editor mode controls: - `e` —edit in text editor. - `w` —switch to wizard mode. - `Enter` —save and exit. - `Esc` —save and exit. Wizard mode controls: - Navigate prompts with arrow keys. - Enter values or select options. - `Esc` —return to previous screen. Example: ``` cd my-template dr dotenv edit ``` ### dr dotenv update Automatically refresh DataRobot credentials in the `.env` file. ``` dr dotenv update ``` Features: - Updates `DATAROBOT_ENDPOINT` and `DATAROBOT_API_TOKEN`. - Preserves all other environment variables. - Automatically authenticates if needed. - Uses current authentication session. Prerequisites: - Must be run inside a git repository. - Must have a `.env` or `.env.template` file. - Requires authentication with DataRobot. Example: ``` cd my-template dr dotenv update ``` Use cases: - Refresh expired API tokens. - Switch DataRobot environments. - Update credentials after re-authentication. ### dr dotenv validate Validate that all required environment variables are properly configured. ``` dr dotenv validate ``` Features: - Validates against template requirements defined in `.datarobot/prompts.yaml`. - Checks both `.env` file and environment variables. - Verifies core DataRobot variables ( `DATAROBOT_ENDPOINT`, `DATAROBOT_API_TOKEN`). - Reports missing or invalid variables with helpful error messages. - Respects conditional requirements based on selected options. Prerequisites: - Must be run inside a git repository. - Must have a `.env` file. Example: ``` cd my-template dr dotenv validate ``` Output: Successful validation: ``` Validating required variables: APP_NAME: my-app DATAROBOT_ENDPOINT: https://app.datarobot.com DATAROBOT_API_TOKEN: *** DATABASE_URL: postgresql://localhost:5432/db Validation passed: all required variables are set. ``` Validation errors: ``` Validating required variables: APP_NAME: my-app DATAROBOT_ENDPOINT: https://app.datarobot.com Validation errors: Error: required variable DATAROBOT_API_TOKEN is not set Description: DataRobot API token for authentication Set this variable in your .env file or run `dr dotenv setup` to configure it. Error: required variable DATABASE_URL is not set Description: PostgreSQL database connection string Set this variable in your .env file or run `dr dotenv setup` to configure it. ``` Use cases: - Verify configuration before running tasks. - Debug missing environment variables. - CI/CD pipeline checks. - Troubleshoot application startup issues. ## File structure ### .env.template Template file committed to version control: ``` # Required Configuration APP_NAME= DATAROBOT_ENDPOINT= DATAROBOT_API_TOKEN= # Optional Configuration # DEBUG=false # PORT=8080 ``` ### .env Generated configuration file (never committed): ``` # Required Configuration APP_NAME=my-awesome-app DATAROBOT_ENDPOINT=https://app.datarobot.com DATAROBOT_API_TOKEN=*** # Optional Configuration DEBUG=true PORT=8000 ``` ## Interactive configuration ### Prompt types The wizard supports multiple input types defined in `.datarobot/prompts.yaml`: Text input: ``` prompts: - key: "app_name" env: "APP_NAME" help: "Enter your application name" ``` Secret string: ``` prompts: - key: "api_key" env: "API_KEY" type: "secret_string" help: "Enter your API key" generate: true # Auto-generate a random secret ``` Single selection: ``` prompts: - key: "environment" env: "ENVIRONMENT" help: "Select deployment environment" options: - name: "Development" value: "dev" - name: "Production" value: "prod" ``` Multiple selection: ``` prompts: - key: "features" env: "ENABLED_FEATURES" help: "Select features to enable" multiple: true options: - name: "Analytics" - name: "Monitoring" ``` ### Conditional prompts Prompts can be shown based on previous selections: ``` prompts: - key: "enable_database" help: "Enable database?" options: - name: "Yes" requires: "database_config" - name: "No" - key: "database_url" section: "database_config" env: "DATABASE_URL" help: "Database connection string" ``` ## Common workflows ### Initial setup Set up a new template with all configuration: ``` cd my-template dr dotenv setup ``` ### Quick updates Update just the DataRobot credentials: ``` dr dotenv update ``` ### Manual editing Edit variables directly: ``` dr dotenv edit # Press 'e' for editor mode # Make changes # Press Enter to save ``` ### Validation Check configuration before running tasks: ``` dr dotenv validate dr run dev ``` ### Switch wizard to editor Start with wizard, switch to editor: ``` dr dotenv edit # Press 'w' for wizard mode # Complete some prompts # Press 'e' to switch to editor for fine-tuning ``` ## Configuration discovery The CLI automatically discovers configuration from: 1. .env.template —base template with variable names. 2. .datarobot/prompts.yaml —interactive prompts and validation. 3. Existing.env —current values (if present). 4. Environment variables —system environment (override .env ). Priority order (highest to lowest): 1. System environment variables. 2. User input from wizard. 3. Existing `.env` file values. 4. Default values from prompts. 5. Template values from `.env.template`. ## Security ### Secret handling - Secret values are masked in the UI. - Variables containing "PASSWORD", "SECRET", "KEY", or "TOKEN" are automatically treated as secrets. - The secret_string prompt type enables secure input with masking. - .env files should never be committed (add to .gitignore ). ### Auto-generation Secret strings with `generate: true` are automatically generated: ``` prompts: - key: "session_secret" env: "SESSION_SECRET" type: "secret_string" generate: true help: "Session encryption key" ``` This generates a cryptographically secure random string when no value exists. ## Error handling ### Not in repository ``` Error: not inside a git repository Run this command from within an application template git repository. To create a new template, run `dr templates setup`. ``` Solution: Navigate to a git repository or use `dr templates setup`. ### Missing .env file ``` Error: .env file does not exist at /path/to/.env Run `dr dotenv setup` to create one. ``` Solution: Run `dr dotenv setup` to create the file. ### Authentication required ``` Error: not authenticated Run `dr auth login` to authenticate. ``` Solution: Authenticate with `dr auth login`. ### Validation failures ``` Validation errors: Error: required variable DATABASE_URL is not set Description: PostgreSQL database connection string Set this variable in your .env file or run `dr dotenv setup` to configure it. ``` Solution: Set the missing variables or run `dr dotenv setup`. ## Exit codes | Code | Meaning | | --- | --- | | 0 | Success. | | 1 | Error (file not found, validation failed, not in repo). | | 130 | Interrupted (Ctrl+C). | ## Examples ### Create configuration from scratch ``` cd my-template dr dotenv setup ``` ### Update after re-authentication ``` dr auth login dr dotenv update ``` ### Validate before deployment ``` dr dotenv validate && dr run deploy ``` ### Edit specific variables ``` dr dotenv edit # Press 'e' for editor mode # Update DATABASE_URL # Press Enter to save ``` ### Check configuration ``` cat .env dr dotenv validate ``` ## Integration with other commands ### With templates ``` dr templates setup # Automatically runs dotenv setup ``` ### With run ``` dr dotenv validate dr run dev ``` ### With auth ``` dr auth login dr dotenv update ``` ## See also - Environment variables guide —managing .env files. - Interactive configuration —configuration wizard details. - Template structure —template organization. - auth command —authentication management. - run command —executing tasks. --- # Command reference URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/index.html > Complete reference documentation for all DataRobot CLI commands. # Command reference Complete reference documentation for all DataRobot CLI commands. ## Global flags These flags are available for all commands: ``` -v, --verbose Enable verbose output (info level logging) --debug Enable debug output (debug level logging) --skip-auth Skip authentication checks (for advanced users) --force-interactive Force the setup wizard to run even if already completed -h, --help Show help information ``` Warning: The `--skip-auth` flag is intended for advanced use cases only. Using this flag will bypass all authentication checks, which may cause API calls to fail. Use with caution. Note: The `--force-interactive` flag forces commands to behave as if setup has never been completed, while still updating the state file. This is useful for testing or forcing re-execution of setup steps. For more on these flags from a configuration perspective, see [Configuration - Advanced flags](https://docs.datarobot.com/en/docs/agentic-ai/cli/configuration.html#advanced-flags) and [Getting started - Getting help](https://docs.datarobot.com/en/docs/agentic-ai/cli/getting-started.html#getting-help). ## Choosing commands - dr start — Use for first-time setup or quickstart: runs task start if present, then a quickstart script from .datarobot/cli/bin/ , or falls back to the template setup wizard. Best when you want one command to "get running." - dr run — Use to run specific tasks (e.g., dr run dev , dr run test ). Use when you already have a template and want to execute a single task. - dr task list — Lists all available tasks. Use when you want to see what tasks a template provides. - dr task compose — Composes a unified Taskfile from component Taskfiles. Use when developing or customizing templates that use multiple Taskfiles. ## Commands ### Main commands | Command | Description | | --- | --- | | auth | Authenticate with DataRobot. | | templates | Manage application templates. | | start | Run the application quickstart process. | | run | Execute application tasks. | | task | Manage Taskfile composition and task execution. | | dotenv | Manage environment variables. | | self | CLI utility commands (update, version, completion). | ### Command tree ``` dr ├── auth Authentication management │ ├── login Log in to DataRobot │ ├── logout Log out from DataRobot │ └── set-url Set DataRobot URL ├── templates Template management │ ├── list List available templates │ ├── clone Clone a template │ ├── setup Interactive setup wizard │ └── status Show template status ├── start Run quickstart process (alias: quickstart) ├── run Task execution ├── task Taskfile composition and execution │ ├── compose Compose unified Taskfile │ ├── list List available tasks │ └── run Execute tasks ├── dotenv Environment configuration └── self CLI utility commands ├── completion Shell completion │ ├── bash Generate bash completion │ ├── zsh Generate zsh completion │ ├── fish Generate fish completion │ └── powershell Generate PowerShell completion ├── config Display configuration settings ├── update Update CLI to latest version └── version Version information ``` ## Quick examples ### Authentication ``` # Set URL and login dr auth set-url https://app.datarobot.com dr auth login # Logout dr auth logout ``` ### Templates ``` # List templates dr templates list # Clone template dr templates clone python-streamlit # Interactive setup dr templates setup # Check status dr templates status ``` ### Quickstart ``` # Run quickstart process (interactive) dr start # Run with auto-yes dr start --yes # Using the alias dr quickstart ``` ### Environment configuration ``` # Interactive wizard dr dotenv setup # Editor mode dr dotenv edit # Validate configuration dr dotenv validate ``` ### Running tasks ``` # List available tasks dr task list # Run a task dr run dev # Run multiple tasks dr run lint test --parallel ``` ### Shell completions ``` # Bash (Linux) dr self completion bash | sudo tee /etc/bash_completion.d/dr # Zsh dr self completion zsh > "${fpath[1]}/_dr" # Fish dr self completion fish > ~/.config/fish/completions/dr.fish ``` ### CLI management ``` # Update to latest version dr self update # Check version dr self version ``` ## Command details For detailed documentation on each command, see: - auth —authentication management. - login —OAuth authentication. - logout —remove credentials. - set-url—configure DataRobot URL. - templates—template operations. - list —list available templates. - clone —clone a template repository. - setup —interactive wizard for full setup. - status—show current template status. - run—task execution. - Execute template tasks (e.g., dr run dev ). - List available tasks via dr task list . - Parallel execution support. - Watch mode for development. - task—Taskfile composition and management. - compose —generate unified Taskfile from components. - list —list all available tasks. - run—execute tasks. - dotenv—environment management. - Interactive configuration wizard. - Direct file editing. - Variable validation. - completion—shell completions. - Bash, Zsh, Fish, PowerShell support. - Auto-complete commands and flags. - version—version information. - Show CLI version. - Build information. - Runtime details. ## Getting help ``` # General help dr --help dr -h # Command help dr auth --help dr templates --help dr run --help # Subcommand help dr auth login --help dr templates clone --help ``` ## Environment variables Global environment variables that affect all commands: ``` # Configuration DATAROBOT_ENDPOINT # DataRobot URL DATAROBOT_API_TOKEN # API token (not recommended) ``` ## Exit codes | Code | Meaning | | --- | --- | | 0 | Success. | | 1 | General error. | | 2 | Command usage error. | | 130 | Interrupted (Ctrl+C). | --- # dr run URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/run.html > Execute tasks defined in application templates. # dr run Execute tasks defined in application templates. ## Synopsis The `dr run` command executes tasks defined in Taskfiles within DataRobot application templates. It automatically discovers component Taskfiles and aggregates them into a unified task execution environment. ``` dr run [TASK_NAME...] [flags] ``` ## Description The `run` command provides a convenient way to execute common application tasks such as starting development servers, running tests, building containers, and deploying applications. It works by discovering Taskfiles in your template directory and generating a consolidated task runner configuration. Key features: - Automatic discovery —finds and aggregates Taskfiles from template components. - Template validation —verifies you're in a DataRobot template directory. - Conflict detection —prevents dotenv directive conflicts in nested Taskfiles. - Parallel execution —run multiple tasks simultaneously. - Watch mode —automatically re-run tasks when files change. ## Template requirements To use `dr run`, your directory must meet these requirements: 1. Contains a .env file —indicates you're in a DataRobot template directory. 2. Contains Taskfiles —component directories with Taskfile.yaml or Taskfile.yml files. 3. No dotenv conflicts —component Taskfiles cannot have their own dotenv directives. If these requirements aren't met, the command provides clear error messages explaining the issue. ## Options ``` -l, --list List all available tasks -d, --dir string Directory to look for tasks (default ".") -p, --parallel Run tasks in parallel -C, --concurrency int Number of concurrent tasks to run (default 2) -w, --watch Enable watch mode for the given task -y, --yes Assume "yes" as answer to all prompts -x, --exit-code Pass-through the exit code of the task command -s, --silent Disable echoing -h, --help Help for run ``` ## Global options ``` -v, --verbose Enable verbose output --debug Enable debug output ``` ## Examples ### List available tasks ``` dr run --list ``` Output: ``` Available tasks: * dev Start development server * test Run tests * lint Run linters * build Build Docker container * deploy Deploy to DataRobot ``` ### Run a single task ``` dr run dev ``` Starts the development server defined in your template's Taskfile. ### Run multiple tasks sequentially ``` dr run lint test ``` Runs the lint task, then the test task in sequence. ### Run multiple tasks in parallel ``` dr run lint test --parallel ``` Runs lint and test tasks simultaneously. ### Run with watch mode ``` dr run dev --watch ``` Runs the development server and automatically restarts it when source files change. ### Control concurrency ``` dr run task1 task2 task3 --parallel --concurrency 3 ``` Runs up to 3 tasks concurrently. ### Silent execution ``` dr run build --silent ``` Runs the build task without echoing commands. ### Pass-through exit codes ``` dr run test --exit-code ``` Exits with the same code as the task command (useful in CI/CD). ## Task discovery The `dr run` command discovers tasks in this order: 1. Check for .env file —verifies you're in a template directory. 2. Scan for Taskfiles —finds Taskfile.yaml or Taskfile.yml files up to 2 levels deep. 3. Validate dotenv directives —ensures component Taskfiles don't have conflicting dotenv declarations. 4. Generate Taskfile.gen.yaml —creates a unified task configuration. 5. Execute tasks —runs the requested tasks using the task binary. ### Directory structure ``` my-template/ ├── .env # Required: template marker ├── Taskfile.gen.yaml # Generated: consolidated tasks ├── backend/ │ ├── Taskfile.yaml # Component tasks (no dotenv) │ └── src/ └── frontend/ ├── Taskfile.yaml # Component tasks (no dotenv) └── src/ ``` ### Generated Taskfile The CLI generates `Taskfile.gen.yaml` with this structure: ``` version: '3' dotenv: [".env"] includes: backend: taskfile: ./backend/Taskfile.yaml dir: ./backend frontend: taskfile: ./frontend/Taskfile.yaml dir: ./frontend ``` This allows you to run component tasks with prefixes: ``` dr run backend:build dr run frontend:dev ``` ## Error handling ### Not in a template directory If you run `dr run` outside a DataRobot template: ``` You don't seem to be in a DataRobot Template directory. This command requires a .env file to be present. ``` Solution: Navigate to a template directory or run `dr templates setup` to create one. ### Dotenv directive conflict If a component Taskfile has its own `dotenv` directive: ``` Error: Cannot generate Taskfile because an existing Taskfile already has a dotenv directive. existing Taskfile already has dotenv directive: backend/Taskfile.yaml ``` Solution: Remove the `dotenv` directive from component Taskfiles. The generated `Taskfile.gen.yaml` handles environment variables. ### Task binary not found If the `task` binary isn't installed: ``` "task" binary not found in PATH. Please install Task from https://taskfile.dev/installation/ ``` Solution: Install Task following the instructions at [taskfile.dev/installation](https://taskfile.dev/installation/). ### No tasks found If no Taskfiles exist in component directories: ``` file does not exist Error: failed to list tasks: exit status 1 ``` Solution: Add Taskfiles to your template components or use `dr templates clone` to start with a pre-configured template. ## Task definitions Tasks are defined in component `Taskfile.yaml` files using Task's syntax. ### Basic task ``` version: '3' tasks: dev: desc: Start development server cmds: - python -m uvicorn src.app.main:app --reload ``` ### Task with dependencies ``` tasks: build: desc: Build Docker container cmds: - docker build -t {{.APP_NAME}} . deploy: desc: Deploy application deps: [build] cmds: - docker push {{.APP_NAME}} - kubectl apply -f deploy.yaml ``` ### Task with environment variables ``` tasks: test: desc: Run tests with coverage env: PYTEST_ARGS: "--cov=src --cov-report=html" cmds: - pytest {{.PYTEST_ARGS}} ``` ### Task with preconditions ``` tasks: deploy: desc: Deploy to production preconditions: - sh: test -f .env msg: ".env file is required" - sh: test -n "$DATAROBOT_ENDPOINT" msg: "DATAROBOT_ENDPOINT must be set" cmds: - ./deploy.sh ``` ## Best practices ### Descriptive task names Use clear, action-oriented task names: ``` tasks: dev: # ✅ Clear and concise desc: Start development server test:unit: # ✅ Namespaced for organization desc: Run unit tests lint:python: # ✅ Specific and descriptive desc: Run Python linters ``` ### Useful descriptions Provide helpful task descriptions: ``` tasks: deploy: desc: Deploy application to DataRobot (requires authentication) cmds: - ./deploy.sh ``` ### Common task names Use standard names for common operations: - dev —start development server. - build —build application or container. - test —run test suite. - lint —run linters and formatters. - deploy —deploy to target environment. - clean —clean build artifacts. ### Environment variable usage Reference `.env` variables in tasks: ``` tasks: deploy: desc: Deploy {{.APP_NAME}} to {{.DEPLOYMENT_TARGET}} cmds: - echo "Deploying to $DATAROBOT_ENDPOINT" - ./deploy.sh ``` ### Silent tasks Use `silent: true` for tasks that don't need output: ``` tasks: check:version: desc: Check CLI version silent: true cmds: - dr version ``` ## Integration with other commands ### With dr templates ``` # Clone and set up template dr templates clone python-streamlit my-app cd my-app # Configure environment dr dotenv setup # Run tasks dr run dev ``` ### With dr dotenv ``` # Update environment variables dr dotenv setup # Run with updated configuration dr run deploy ``` ### In CI/CD pipelines ``` #!/bin/bash # ci-pipeline.sh set -e # Run tests dr run test --exit-code --silent # Run linters dr run lint --exit-code --silent # Build dr run build --silent ``` ## Troubleshooting ### Tasks not found Problem: `dr run --list` shows no tasks. Causes: - No Taskfiles in component directories. - Taskfiles at wrong depth (deeper than 2 levels). Solution: ``` # Check for Taskfiles find . -name "Taskfile.y*ml" -maxdepth 3 # Ensure Taskfiles are in component directories # Correct: ./backend/Taskfile.yaml # Wrong: ./backend/src/Taskfile.yaml ``` ### Environment variables not loading Problem: Tasks can't access environment variables. Causes: - Missing `.env` file. - Variables not exported. Solution: ``` # Verify .env exists ls -la .env # Check variables are set source .env env | grep DATAROBOT ``` ### Task execution fails Problem: Task runs but fails with errors. Solution: ``` # Enable verbose output dr run task-name --verbose # Enable debug output dr run task-name --debug # Check task definition cat component/Taskfile.yaml ``` ### Permission denied errors Problem: Tasks fail with permission errors. Solution: ``` # Make scripts executable chmod +x scripts/*.sh # Check file permissions ls -l scripts/ ``` ## See also - Template system overview —understanding templates. - Task definitions —creating Taskfiles. - Environment variables —managing configuration. - dr dotenv —environment variable management. - Task documentation —official Task runner docs. --- # CLI utility commands URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/self.html > Commands for managing and configuring the DataRobot CLI itself. # CLI utility commands Commands for managing and configuring the DataRobot CLI itself. ## Synopsis ``` dr self ``` ## Description The `self` command provides utility functions for managing the CLI tool itself, including updating to the latest version, checking version information, and setting up shell completion. ## Subcommands ### completion Generate or manage shell completion scripts for command auto-completion. ``` dr self completion ``` See the [completion documentation](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/completion.html) for detailed usage. Quick examples: ``` # Install completions interactively dr self completion install # Generate completions for bash dr self completion bash > /etc/bash_completion.d/dr # Generate completions for zsh dr self completion zsh > "${fpath[1]}/_dr" ``` ### config Display current configuration settings from config file and environment variables. ``` dr self config ``` This command shows all configuration values currently in use by the CLI, including settings from: - Configuration file ( ~/.config/datarobot/drconfig.yaml or custom path) - Environment variables (prefixed with DATAROBOT_CLI_ or standard DATAROBOT_ variables) - Command-line flags Sensitive values like API tokens are automatically redacted for security. Examples: ``` # Display current configuration dr self config ``` Sample output: ``` Configuration initialized. Using config file: /Users/username/.config/datarobot/drconfig.yaml debug: false endpoint: https://app.datarobot.com/api/v2 external_editor: vim token: **** verbose: false ``` Use cases: - Verify which configuration file is being used - Check that environment variables are being recognized - Debug configuration issues - Confirm API endpoint and settings before deployment ### update Update the DataRobot CLI to the latest version. ``` dr self update ``` This command automatically detects your installation method and uses the appropriate update mechanism: - Homebrew (macOS) —uses brew update && upgrade dr-cli if installed via Homebrew - Windows —runs the PowerShell installation script - macOS/Linux —runs the shell installation script The update process will download and install the latest version while preserving your configuration and credentials. Examples: ``` # Update to latest version dr self update ``` Note: This command requires an active internet connection and appropriate permissions to install software on your system. ### version Display version information about the CLI. ``` dr self version ``` Options: - -f, --format —output format ( text or json ) Examples: ``` # Show version (default text format) dr self version # Show version in JSON format dr self version --format json ``` ## Global flags All `dr` global flags are available: - -v, --verbose —enable verbose output - --debug —enable debug output - -h, --help —show help information ## Examples ### Update CLI to latest version ``` $ dr self update Downloading latest version... Installing DataRobot CLI... ✓ Successfully updated to version 1.1.0 ``` ### Check CLI version ``` $ dr self version DataRobot CLI version: 1.0.0 ``` ### View current configuration ``` $ dr self config Configuration initialized. Using config file: /Users/username/.config/datarobot/drconfig.yaml debug: false endpoint: https://app.datarobot.com/api/v2 external_editor: vim token: **** verbose: false ``` ### Install shell completions ``` # Interactive installation $ dr self completion install ✓ Detected shell: zsh ✓ Installing completions to: ~/.zsh/completions/_dr ✓ Completions installed successfully! # Manual installation $ dr self completion bash | sudo tee /etc/bash_completion.d/dr ``` ### Get version in JSON ``` $ dr self version --format json { "version": "1.0.0", "commit": "abc123", "buildDate": "2025-11-10T12:00:00Z" } ``` ## See also - Shell completions guide —detailed completion setup - Completion command —completion command reference - Getting started —initial CLI setup --- # dr start URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/start.html > Run the application quickstart process for the current template. # dr start Run the application quickstart process for the current template. ## Synopsis The `start` command (also available as `quickstart`) provides an automated way to initialize and launch your DataRobot application. It performs several checks and either executes a template-specific quickstart script or seamlessly launches the interactive template setup wizard. It is the main entry point for setting up [Agentic AI](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-quickstart.html) workflows—run `dr start` from the quickstart to clone and configure your agent template. ``` dr start [flags] ``` ## Aliases - dr start - dr quickstart ## Description The `start` command streamlines the process of getting your DataRobot application up and running. It automates the following workflow: 1. Prerequisite checks —verifies that required tools are installed and validates your environment. 2. CLI version check —verifies your CLI version meets the template's minimum requirements. 3. Repository check —checks if you're in a DataRobot repository (if not, launches template setup). 4. Execution —executes a start command in this order: 5. Taskfile —runs task start from the Taskfile (if available). 6. Quickstart script —runs a script from .datarobot/cli/bin/ (if available). 7. Fallback —launches the interactive dr templates setup wizard if neither exists. This command is designed to work intelligently with your template's structure. If you're not in a DataRobot repository or no Taskfile/quickstart exists, the command gracefully falls back to the standard setup wizard. ## Flags ``` -y, --yes Skip confirmation prompts and execute immediately -h, --help Show help information ``` ### Global flags All [global flags](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/index.html#global-flags) are also available. ## Quickstart scripts ### Location Quickstart scripts must be placed in: ``` .datarobot/cli/bin/ ``` ### Naming convention Scripts must start with `quickstart` (case-sensitive): - ✅ quickstart - ✅ quickstart.sh - ✅ quickstart.py - ✅ quickstart-dev - ❌ Quickstart.sh (wrong case) - ❌ start.sh (wrong name) If there are multiple scripts matching the pattern, the first one found in lexicographical order will be executed. ### Platform-specific requirements Unix/Linux/macOS: - Script must have executable permissions ( chmod +x ) - Can be any executable file (shell script, Python, compiled binary, etc.) Windows: - Must have executable extension: .exe , .bat , .cmd , or .ps1 ## Examples ### Basic usage Run the quickstart process interactively: ``` dr start ``` If a quickstart script is found: ``` DataRobot Quickstart ✓ Starting application quickstart process... ✓ Checking template prerequisites... ✓ Locating quickstart script... → Executing quickstart script... Quickstart found at: .datarobot/cli/bin/quickstart.sh. Will proceed with execution... Press 'y' or ENTER to confirm, 'n' to cancel ``` If no quickstart script is found: ``` DataRobot Quickstart ✓ Starting application quickstart process... ✓ Checking template prerequisites... ✓ Locating quickstart script... → Executing quickstart script... No quickstart script found. Will proceed with template setup... ``` The command will then seamlessly launch the interactive setup wizard. ### Non-interactive mode Skip all prompts and execute immediately: ``` dr start --yes ``` or ``` dr start -y ``` This is useful for: - CI/CD pipelines - Automated deployments - Scripted workflows ### Using the alias ``` dr quickstart ``` ## Behavior ### State tracking The `dr start` command automatically tracks when it runs successfully by updating a state file with: - Timestamp of when the command last started (ISO 8601 format) - CLI version used This state information is stored in `.datarobot/cli/state.yaml` within the template directory. State tracking is automatic and transparent. No manual intervention is required. The state file helps other commands (like `dr templates setup`) know that you've already run `dr start`, allowing them to skip redundant setup steps. ### Execution order The command tries the following in order: 1. task start —if a Taskfile with a start task exists, runs it. 2. Quickstart script —if a script is found in .datarobot/cli/bin/ , runs it (after user confirmation unless --yes is used). 3. Setup wizard —if neither is available (or not in a DataRobot repository), launches dr templates setup . ### When a quickstart script runs 1. No task start in Taskfile (or no Taskfile), but script is detected in .datarobot/cli/bin/ 2. User is prompted for confirmation (unless --yes or -y is used) 3. If user confirms (or --yes is specified), script executes with full terminal control 4. Command completes when script finishes 5. State file is updated with current timestamp and CLI version If the user declines to execute the script, the command exits gracefully and still updates the state file. ### When setup wizard runs 1. No task start and no quickstart script (or not in a DataRobot repository) 2. User is notified 3. User is prompted for confirmation (unless --yes or -y is used) 4. If user confirms (or --yes is specified), interactive dr templates setup wizard launches automatically 5. User completes template configuration through the wizard 6. State file is updated with current timestamp and CLI version If the user declines, the command exits gracefully and still updates the state file. ### Prerequisites checked Before proceeding, the command verifies: - ✅ Required tools are installed (Git, etc.) When searching for a quickstart script, the command checks: - ✅ Current directory is within a DataRobot repository (contains .datarobot/ directory) If the repository check fails, the command automatically launches the template setup wizard instead of exiting with an error. ## Error handling ### Not in a DataRobot repository If you're not in a DataRobot repository, the command automatically launches the template setup wizard: ``` $ dr start # Automatically launches: dr templates setup ``` No manual intervention is needed - the command handles this gracefully. ### Missing prerequisites ``` $ dr start Error: required tool 'git' not found # Solution: Install the missing tool ``` ### Script execution failure If a quickstart script fails, the error is displayed and the command exits. Check the script's output for details. ## When to use dr start ### ✅ Good use cases - First-time setup —initializing a newly cloned template or starting from scratch. - Quick restart —restarting development after a break. - Onboarding —helping new team members get started quickly. - CI/CD —automating application initialization in pipelines. - General entry point —universal command that works whether you have a template or not. ### ❌ When not to use - Making configuration changes —use dr dotenv to modify environment variables. - Running specific tasks —use dr run for targeted task execution. ## See also - dr templates setup —interactive template setup wizard. - dr run —execute specific application tasks. - dr dotenv —manage environment configuration. - Template Structure —understanding template organization. ## Tips ### Creating a custom quickstart script 1. Create the directory structure: ``` mkdir -p .datarobot/cli/bin ``` 1. Create your script: ``` # Create the script cat > .datarobot/cli/bin/quickstart.sh <<'EOF' #!/bin/bash echo "Starting my custom quickstart..." dr run build dr run dev EOF ``` 1. Make it executable: ``` chmod +x .datarobot/cli/bin/quickstart.sh ``` 1. Test it: ``` dr start --yes ``` ### Best practices - Keep scripts simple —focus on essential initialization steps. - Provide clear output —use echo statements to show progress. - Handle errors gracefully —use set -e in bash scripts to exit on errors. - Check prerequisites —verify .env exists and required tools are installed. - Make it idempotent —script should be safe to run multiple times. - Document behavior —add comments explaining what the script does. --- # dr task URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/task.html > Manage Taskfile composition and task execution for DataRobot templates. # dr task Manage Taskfile composition and task execution for DataRobot templates. ## Synopsis ``` dr task [command] [flags] ``` ## Description The `task` command group provides utilities for working with Taskfiles in DataRobot application templates. It includes subcommands for composing unified Taskfiles from multiple component Taskfiles and executing tasks. In [Agentic AI](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-quickstart.html) workflows, you use `dr task run deploy` to deploy agents and `dr task run dev` (or `task dev`) for local development. ## Subcommands - compose —compose a unified Taskfile from component Taskfiles. - list —list available tasks. - run —execute template tasks. ## dr task compose Generate a root `Taskfile.yaml` by discovering and aggregating Taskfiles from subdirectories. ### Synopsis ``` dr task compose [flags] ``` ### Description The `compose` command automatically discovers Taskfiles in component directories and generates a unified root `Taskfile.yaml` that includes all components and aggregates common tasks. This allows you to run tasks across multiple components from a single entry point. Key features: - Automatic discovery —finds Taskfiles up to 2 levels deep in subdirectories. - Task aggregation —discovers common tasks (lint, install, dev, deploy) and creates top-level tasks that delegate to components. - Template support —uses customizable Go templates for flexible Taskfile generation. - Auto-discovery —automatically detects .Taskfile.template in the root directory. - Gitignore integration —adds generated Taskfile to .gitignore automatically. ### Options ``` -t, --template string Path to custom Taskfile template -h, --help Help for compose ``` ### Global options ``` -v, --verbose Enable verbose output --debug Enable debug output ``` ### Template requirements To use `dr task compose`, your directory must meet these requirements: 1. Contains a .env file —indicates you're in a DataRobot template directory. 2. Contains component Taskfiles —subdirectories with Taskfile.yaml or Taskfile.yml files. 3. No dotenv conflicts —component Taskfiles cannot have their own dotenv directives. ### Directory structure Expected directory structure: ``` my-template/ ├── .env # Required: template marker ├── .Taskfile.template # Optional: custom template ├── .taskfile-data.yaml # Optional: template configuration ├── Taskfile.yaml # Generated: unified taskfile ├── backend/ │ ├── Taskfile.yaml # Component tasks │ └── src/ ├── frontend/ │ ├── Taskfile.yaml # Component tasks │ └── src/ └── infra/ ├── Taskfile.yaml # Component tasks └── terraform/ ``` ### Examples #### Basic composition Generate a Taskfile using the default embedded template: ``` dr task compose ``` Output: ``` Generated file saved to: Taskfile.yaml Added /Taskfile.yaml line to .gitignore ``` This creates a `Taskfile.yaml` with: - Environment configuration - Includes for all discovered components - Aggregated tasks (lint, install, dev, deploy) #### With auto-discovered template If `.Taskfile.template` exists in the root directory: ``` dr task compose ``` Output: ``` Using auto-discovered template: .Taskfile.template Generated file saved to: Taskfile.yaml ``` The command automatically uses your custom template. #### With custom template Specify a custom template explicitly: ``` dr task compose --template templates/custom.yaml ``` This uses your custom template for generation instead of the embedded default. #### Template in subdirectory ``` dr task compose --template .datarobot/taskfile.template ``` ### Generated Taskfile structure The default generated `Taskfile.yaml` includes: ``` --- # https://taskfile.dev version: '3' env: ENV: testing dotenv: ['.env', '.env.{{.ENV}}'] includes: backend: taskfile: ./backend/Taskfile.yaml dir: ./backend frontend: taskfile: ./frontend/Taskfile.yaml dir: ./frontend infra: taskfile: ./infra/Taskfile.yaml dir: ./infra tasks: default: desc: "ℹ️ Show all available tasks (run `task --list-all` to see hidden tasks)" cmds: - task --list --sort none silent: true start: desc: "💻 Prepare local development environment" cmds: - dr dotenv setup - task: install lint: desc: "🧹 Run linters" cmds: - task: backend:lint - task: frontend:lint install: desc: "🛠️ Install all dependencies" cmds: - task: backend:install - task: frontend:install - task: infra:install test: desc: "🧪 Run tests across all components" cmds: - task: backend:test - task: frontend:test dev: desc: "🚀 Run all services together" cmds: - | task backend:dev & sleep 3 task frontend:dev & sleep 8 echo "✅ All servers started!" wait deploy: desc: "🚀 Deploy all services" cmds: - task: infra:deploy - task: backend:deploy deploy-dev: desc: "🚀 Deploy all services to development" cmds: - task: infra:deploy-dev - task: backend:deploy-dev ``` ### Task aggregation The compose command discovers these common tasks in component Taskfiles: - lint —code linting and formatting. - install —dependency installation. - test —running test suites. - dev —development server startup. - deploy —production deployment operations. - deploy-dev —development deployment operations. For each discovered task type, it creates a top-level task that delegates to all components that have that task. ### Custom templates Create a custom template to control the generated Taskfile structure. #### Template file example Save as `.Taskfile.template`: ``` --- version: '3' env: ENV: production dotenv: ['.env', '.env.{{.ENV}}'] includes: {{- range .Includes }} {{ .Name }}: taskfile: {{ .Taskfile }} dir: {{ .Dir }} {{- end }} tasks: default: desc: "Show available tasks" cmds: - task --list {{- if .HasLint }} lint: desc: "Run linters" cmds: {{- range .LintComponents }} - task: {{ . }}:lint {{- end }} {{- end }} {{- if .HasInstall }} install: desc: "Install dependencies" cmds: {{- range .InstallComponents }} - task: {{ . }}:install {{- end }} {{- end }} {{- if .HasTest }} test: desc: "Run tests" cmds: {{- range .TestComponents }} - task: {{ . }}:test {{- end }} {{- end }} # Custom task check: desc: "Run all checks" cmds: - task: lint - task: test ``` #### Template variables Templates have access to these variables: Includes (array): - `.Name` —component name (e.g., "backend"). - `.Taskfile` —relative path to Taskfile (e.g., "./backend/Taskfile.yaml"). - `.Dir` —relative directory path (e.g., "./backend"). Task flags (boolean): - `.HasLint` —true if any component has a lint task. - `.HasInstall` —true if any component has an install task. - `.HasTest` —true if any component has a test task. - `.HasDev` —true if any component has a dev task. - `.HasDeploy` —true if any component has a deploy task. - `.HasDeployDev` —true if any component has a deploy-dev task. Task components (arrays): - `.LintComponents` —component names with lint tasks. - `.InstallComponents` —component names with install tasks. - `.TestComponents` —component names with test tasks. - `.DevComponents` —component names with dev tasks. - `.DeployComponents` —component names with deploy tasks. - `.DeployDevComponents` —component names with deploy-dev tasks. Development ports (array): - `.DevPorts[].Name` —service name. - `.DevPorts[].Port` —port number. #### Example: minimal template ``` version: '3' dotenv: ['.env'] includes: {{- range .Includes }} {{ .Name }}: taskfile: {{ .Taskfile }} dir: {{ .Dir }} {{- end }} tasks: default: cmds: - task --list ``` #### Example: extensive aggregation ``` version: '3' dotenv: ['.env'] includes: {{- range .Includes }} {{ .Name }}: taskfile: {{ .Taskfile }} dir: {{ .Dir }} {{- end }} tasks: {{- if .HasLint }} lint: desc: "Run all linters" cmds: {{- range .LintComponents }} - task: {{ . }}:lint {{- end }} {{- end }} {{- if .HasInstall }} install: desc: "Install all dependencies" cmds: {{- range .InstallComponents }} - task: {{ . }}:install {{- end }} {{- end }} {{- if .HasTest }} test: desc: "Run all tests" cmds: {{- range .TestComponents }} - task: {{ . }}:test {{- end }} {{- end }} {{- if .HasDev }} dev: desc: "Start all services" cmds: {{- range .DevComponents }} - task: {{ . }}:dev {{- end }} {{- end }} {{- if .HasDeploy }} deploy: desc: "Deploy to production" cmds: {{- range .DeployComponents }} - task: {{ . }}:deploy {{- end }} {{- end }} {{- if .HasDeployDev }} deploy-dev: desc: "Deploy to development" cmds: {{- range .DeployDevComponents }} - task: {{ . }}:deploy-dev {{- end }} {{- end }} ci: desc: "Run CI pipeline" cmds: - task: lint - task: test - task: build ``` ### Gitignore integration The compose command automatically adds the generated Taskfile to `.gitignore`: ``` /Taskfile.yaml ``` This prevents committing the generated file to version control. Each developer generates their own version based on their local component structure. If you want to commit the generated Taskfile, remove it from `.gitignore`. ### Error handling #### Not in a template directory ``` You don't seem to be in a DataRobot Template directory. This command requires a .env file to be present. ``` Solution: Navigate to a template directory or run `dr templates setup`. #### No Taskfiles found ``` no Taskfiles found in child directories ``` Solution: Add Taskfiles to component directories or adjust your directory structure. #### Dotenv conflict ``` Error: Cannot generate Taskfile because an existing Taskfile already has a dotenv directive. existing Taskfile already has dotenv directive: backend/Taskfile.yaml ``` Solution: Remove `dotenv` directives from component Taskfiles. The root Taskfile handles environment loading. #### Template not found ``` Error: template file not found: /path/to/template.yaml ``` Solution: Check the template path and ensure the file exists. ### Best practices #### Keep components independent Each component Taskfile should be self-contained: ``` # backend/Taskfile.yaml version: '3' tasks: dev: desc: Start backend server cmds: - python -m uvicorn src.app.main:app --reload test: desc: Run tests cmds: - pytest lint: desc: Run linters cmds: - ruff check . - mypy . ``` #### Use consistent task names Use the same task names across components for automatic aggregation: - lint —linting. - install —dependency installation. - test —testing. - dev —development server. - build —building artifacts. - deploy —production deployment. - deploy-dev —development deployment. #### Commit custom templates If using a custom template, commit it to version control: ``` git add .Taskfile.template git commit -m "Add custom Taskfile template" ``` #### Configure development ports Optionally create a `.taskfile-data.yaml` file to display service URLs in the dev task. See [Taskfile data configuration](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/task.html#taskfile-data-configuration) for complete documentation. #### Document custom variables If your template uses custom variables, document them: ``` # .Taskfile.template # # Custom variables: # - PROJECT_NAME: Set in .env # - DEPLOY_TARGET: Set in .env # version: '3' # ... ``` #### Test template changes After modifying a template, regenerate and test: ``` dr task compose --template .Taskfile.template task --list task dev ``` ### Taskfile data configuration Template authors can provide additional configuration for Taskfile generation by creating a `.taskfile-data.yaml` file in the template root directory. #### File location ``` my-template/ ├── .env ├── .taskfile-data.yaml # Configuration file ├── Taskfile.yaml # Generated └── components/ ``` #### Configuration format ``` # .taskfile-data.yaml # Optional configuration for dr task compose # Development server ports # Displayed when running the dev task ports: - name: Backend API port: 8080 - name: Frontend port: 5173 - name: Worker Service port: 8842 - name: MCP Server port: 9000 ``` #### Port configuration Purpose: The `ports` array allows template authors to specify which ports their services use. When developers run `task dev`, they see URLs for each service. Example output: When developers run `task dev` with port configuration: ``` task mcp_server:dev & sleep 3 task web:dev & sleep 3 task writer_agent:dev & sleep 3 task frontend_web:dev & sleep 8 ✅ All servers started! 🔗 Backend API: http://localhost:8080 🔗 Frontend: http://localhost:5173 🔗 Worker Service: http://localhost:8842 🔗 MCP Server: http://localhost:9000 ``` DataRobot Notebook integration: The generated dev task automatically detects DataRobot Notebook environments and adjusts URLs: ``` 🔗 Backend API: https://app.datarobot.com/notebook-sessions/abc123/ports/8080 🔗 Frontend: https://app.datarobot.com/notebook-sessions/abc123/ports/5173 ``` This happens automatically when the `NOTEBOOK_ID` environment variable is present. Benefits: - Improved onboarding —new developers immediately know where services are running. - Self-documenting —ports are visible in generated Taskfile and command output. - Notebook support —URLs work correctly in DataRobot Notebooks. - Reduced confusion —no need to check logs or documentation for port numbers. Best practices: 1. List all services —include every service that starts in dev mode. 2. Use descriptive names —"Backend API" is clearer than "Backend". 3. Match actual ports —ensure ports match what's in component Taskfiles. 4. Update when changing —keep configuration in sync with service changes. #### When to use this file Use `.taskfile-data.yaml` when: - Your template has multiple services with different ports. - Services use non-standard ports that aren't obvious. - You want to improve developer experience. - Your template targets DataRobot Notebooks. You can skip it when: - Your template has a single service. - Ports are obvious or standard (e.g., 3000 for Node.js). - You use custom Taskfile templates with hardcoded values. - Port information is already well-documented elsewhere. #### File is optional The `.taskfile-data.yaml` file is completely optional. If not present: - The dev task still works correctly. - Services start normally. - Port URLs simply aren't displayed. This allows template authors to add port configuration incrementally without breaking existing templates. #### Future extensibility The `.taskfile-data.yaml` file uses an extensible format. Future CLI versions may support additional configuration options such as: - Custom environment variables for templates. - Service metadata (descriptions, dependencies). - Deployment configuration. - Build optimization hints. Template authors can future-proof their templates by using this configuration file even if only specifying ports initially. #### Example templates Minimal example: ``` # .taskfile-data.yaml ports: - name: App port: 8000 ``` Full-stack application: ``` # .taskfile-data.yaml ports: - name: Backend API port: 8080 - name: Frontend port: 5173 - name: Database Admin port: 8081 - name: Redis Commander port: 8082 ``` Microservices architecture: ``` # .taskfile-data.yaml ports: - name: API Gateway port: 8080 - name: Auth Service port: 8081 - name: User Service port: 8082 - name: Order Service port: 8083 - name: Frontend port: 3000 - name: Admin Dashboard port: 3001 ``` ### Workflow integration #### Initial setup ``` # Clone template dr templates clone python-fullstack my-app cd my-app # Set up environment dr dotenv setup # Generate Taskfile dr task compose # View available tasks task --list ``` #### Development workflow ``` # Add new component mkdir new-service cat > new-service/Taskfile.yaml << 'EOF' version: '3' tasks: dev: desc: Start new service cmds: - echo "Starting service..." EOF # Regenerate Taskfile dr task compose # Run all services task dev ``` #### Template updates When components change: ``` # Regenerate Taskfile dr task compose # Verify new structure task --list ``` ## dr task list List all available tasks from composed Taskfile. ### Synopsis ``` dr task list [flags] ``` ### Description Lists all tasks available in the current template, including tasks from all component Taskfiles. ### Examples ``` # List all tasks dr task list # Show with full task tree task --list-all ``` ## dr task run Execute template tasks. This is an alias for `dr run`. ### Synopsis ``` dr task run [TASK_NAME...] [flags] ``` ### Description Execute one or more tasks defined in component Taskfiles. See [dr run](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/run.html) for full documentation. ### Examples ``` # Run single task dr task run dev # Run multiple tasks dr task run lint test # Run in parallel dr task run lint test --parallel ``` ## See also - dr run —task execution documentation. - Template system —template structure overview. - Environment variables —configuration management. - Task documentation —official Task runner documentation. --- # Template management URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/templates.html > List, clone, set up, and check status of DataRobot application templates. # Template management ## Template management Manage DataRobot application templates: list available templates, clone repositories, run the interactive setup wizard, and check template status. ## Synopsis ``` dr templates [arguments] [flags] ``` ## Description The `templates` command provides subcommands for discovering, cloning, and configuring application templates from your DataRobot instance. Templates are pre-configured application scaffolds that you customize into your own application. ## Subcommands ### list List available templates from your DataRobot instance. ``` dr templates list ``` Behavior: - Requires authentication. Run dr auth login if not already authenticated. - Fetches templates from the DataRobot API that are available to your user and organization. - Displays template names and descriptions. Example: ``` $ dr templates list Available templates: * python-streamlit - Streamlit application template * react-frontend - React frontend template * fastapi-backend - FastAPI backend template ``` Flags: All [global flags](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/index.html#global-flags) apply (e.g., `--verbose`, `--debug`, `--skip-auth`). ### setup Run the interactive setup wizard to select, clone, and configure a template. ``` dr templates setup ``` Behavior: 1. Displays a list of templates available to you. 2. You select a template and specify a directory name; the wizard clones the template there. 3. Guides you through environment configuration (or skips it if already completed; see State tracking ). 4. Optionally runs dr dotenv setup if the template has environment prompts. For details on the template system and configuration wizard, see [Template system](https://docs.datarobot.com/en/docs/agentic-ai/cli/template-system/index.html) and [Interactive configuration](https://docs.datarobot.com/en/docs/agentic-ai/cli/template-system/interactive-config.html). ### Template selection screen When the setup wizard runs, you see a template list screen: - Navigate the list with the arrow keys (↑/↓). - Filter templates by pressing / and typing a search term. - Select a template by pressing Enter . - At the next prompt, enter the desired directory name for the cloned template and press Enter to clone. Only templates that are available to your user account are shown. After cloning, the wizard continues with configuration steps as needed. Flags: All [global flags](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/index.html#global-flags) apply. Use `--force-interactive` to run the wizard even if setup was previously completed. ### clone Clone a template repository by name (and optional target directory). ``` dr templates clone [directory] ``` Arguments: - template-name — Name or identifier of the template (as shown by dr templates list ). - directory (optional) — Local directory name or path for the clone. If omitted, the template name is used. Examples: ``` # Clone into a directory named after the template dr templates clone python-streamlit # Clone into a custom directory dr templates clone python-streamlit my-app ``` Behavior: - Requires authentication. - Clones the template's Git repository to the current working directory. - After cloning, run dr dotenv setup to configure environment variables, or use dr templates setup to run the full wizard (which includes clone + configuration). ### status Show the current template's status (version, modifications, updates). ``` dr templates status ``` Behavior: - Must be run from within a cloned template directory. - Shows current version, latest available version (if applicable), modified files, and whether updates are available. Example: ``` $ dr templates status Template: python-streamlit Current version: 1.0.0 Modified files: .env (local only) ``` ## Global flags All `dr` [global flags](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/index.html#global-flags) apply to `dr templates` subcommands: - -v, --verbose — Verbose output - --debug — Debug output (creates dr-tui-debug.log in current directory for TUI debugging) - --skip-auth — Skip authentication checks (advanced; API calls may fail) - --force-interactive — Force setup wizard to run even if already completed - -h, --help — Help ## Choosing templates vs. setup - Use dr templates setup when you want a guided, all-in-one flow: select template, clone, and configure in one session. Recommended for most users. - Use dr templates list and dr templates clone when you prefer to clone first and configure manually (e.g., dr dotenv setup afterward). ## See also - Template system — How templates are structured and configured - Interactive configuration — Wizard behavior and keyboard controls - Getting started — Initial setup and first template - dotenv — Environment variable management after cloning --- # Configuration files URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/configuration.html # Configuration files Understanding DataRobot CLI configuration files and settings. ## Configuration location The CLI stores configuration in a platform-specific location: | Platform | Location | | --- | --- | | Linux | ~/.config/datarobot/drconfig.yaml | | macOS | ~/.config/datarobot/drconfig.yaml | | Windows | %USERPROFILE%\.config\datarobot\drconfig.yaml | ## Configuration structure ### Main configuration file `~/.config/datarobot/drconfig.yaml`: ``` # DataRobot Connection endpoint: https://app.datarobot.com token: api key here ``` ### Environment-specific configs You can maintain multiple configurations: ``` # Development ~/.config/datarobot/dev-config.yaml # Staging ~/.config/datarobot/staging-config.yaml # Production ~/.config/datarobot/prod-config.yaml ``` Switch between them: ``` export DATAROBOT_CLI_CONFIG=~/.config/datarobot/dev-config.yaml dr templates list ``` ## Configuration options ### Connection settings ``` # Required: DataRobot instance URL endpoint: https://app.datarobot.com # Required: API authentication key token: api key here ``` ## Environment variables Override configuration with environment variables: ### Connection ``` # DataRobot endpoint URL export DATAROBOT_ENDPOINT=https://app.datarobot.com # API token (not recommended for security) export DATAROBOT_API_TOKEN=your_api_token ``` ### CLI behavior ``` # Custom config file path export DATAROBOT_CLI_CONFIG=~/.config/datarobot/custom-config.yaml # Editor for text editing export EDITOR=nano # Force setup wizard to run even if already completed export DATAROBOT_CLI_FORCE_INTERACTIVE=true ``` ### Environment variables reference | Variable | Purpose | Scope | | --- | --- | --- | | DATAROBOT_ENDPOINT | DataRobot instance URL (API endpoint) | Connection; overrides config | | DATAROBOT_API_TOKEN | API token (not recommended; prefer dr auth login) | Connection; overrides config | | DATAROBOT_CLI_CONFIG | Path to config file | CLI behavior | | DATAROBOT_CLI_FORCE_INTERACTIVE | Force setup wizard to run (e.g., true) | CLI behavior; setup/dotenv | | DATAROBOT_CLI_SKIP_AUTH | Skip authentication checks (e.g., true); advanced use | CLI behavior | | DATAROBOT_VERIFY_SSL | Disable SSL verification if false; not recommended for production | Auth / connection | | DR_TEMPLATES_DIR | Default directory for cloning templates; see Custom templates directory | Templates | | EDITOR | Editor used for text editing (e.g., vim, nano) | dotenv edit | ### Advanced flags The CLI supports advanced command-line flags for special use cases: ``` # Skip authentication checks (advanced users only) dr templates list --skip-auth # Force setup wizard to run (ignore completion state) dr templates setup --force-interactive # Enable verbose logging dr templates list --verbose # Enable debug logging dr templates list --debug ``` > ⚠️ Warning:The--skip-authflag bypasses all authentication checks and should only be used when you understand the implications. Commands requiring API access will likely fail without valid credentials. For the full list of global flags (including `--verbose`, `--debug`, and `--force-interactive`), see [Command reference - Global flags](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/index.html#global-flags). ## Configuration priority Settings are loaded in order of precedence: 1. flags (command-line arguments, i.e. --config ) 2. environment variables (i.e. DATAROBOT_CLI_CONFIG=... ) 3. config files (i.e. ~/.config/datarobot/drconfig.yaml ) 4. defaults (built-in defaults) ## Security best practices ### 1. Protect configuration files ``` # Verify permissions (should be 600) ls -la ~/.config/datarobot/drconfig.yaml # Fix permissions if needed chmod 600 ~/.config/datarobot/drconfig.yaml chmod 700 ~/.config/datarobot/ ``` ### 2. Don't commit credentials Add to `.gitignore`: ``` # DataRobot credentials .config/datarobot/ drconfig.yaml *.yaml !.env.template ``` ### 3. Use environment-specific configs ``` # Never use production credentials in development # Keep separate config files ~/.config/datarobot/ ├── dev-config.yaml # Development ├── staging-config.yaml # Staging └── prod-config.yaml # Production ``` ### 4. Avoid environment variables for secrets ``` # ❌ Don't do this (visible in process list) export DATAROBOT_API_TOKEN=my_secret_token # Do this instead (use config file) dr auth login ``` ## Advanced configuration ### Custom templates directory ``` templates: default_clone_dir: ~/workspace/datarobot ``` Or via environment: ``` export DR_TEMPLATES_DIR=~/workspace/datarobot ``` ### Debugging configuration Enable debug logging: ``` debug: true ``` Or temporarily: ``` dr --debug templates list ``` ## Configuration examples ### Development environment `~/.config/datarobot/dev-config.yaml`: ``` endpoint: https://dev.datarobot.com token: api token for dev ``` Usage: ``` export DATAROBOT_CLI_CONFIG=~/.config/datarobot/dev-config.yaml dr templates list ``` ### Production environment `~/.config/datarobot/prod-config.yaml`: ``` endpoint: https://app.datarobot.com token: api key for prod ``` Usage: ``` export DATAROBOT_CLI_CONFIG=~/.config/datarobot/prod-config.yaml dr run deploy ``` ### Enterprise with proxy `~/.config/datarobot/enterprise-config.yaml`: ``` datarobot: endpoint: https://datarobot.enterprise.com token: enterprise_key proxy: http://proxy.enterprise.com:3128 verify_ssl: true ca_cert_path: /etc/ssl/certs/enterprise-ca.pem timeout: 120 preferences: log_level: warn ``` ## Troubleshooting ### Configuration not loading ``` # Check if config file exists ls -la ~/.config/datarobot/drconfig.yaml # Verify it's readable cat ~/.config/datarobot/drconfig.yaml # Check environment variables env | grep DATAROBOT ``` ### Invalid configuration ``` # The CLI will report syntax errors $ dr templates list Error: Failed to parse config file: yaml: line 5: could not find expected ':' # Fix syntax and try again vim ~/.config/datarobot/drconfig.yaml ``` ### Permission denied ``` # Fix file permissions chmod 600 ~/.config/datarobot/drconfig.yaml # Fix directory permissions chmod 700 ~/.config/datarobot/ ``` ### Multiple configs ``` # List all config files find ~/.config/datarobot -name "*.yaml" # Switch between them export DATAROBOT_CLI_CONFIG=~/.config/datarobot/dev-config.yaml ``` ## State tracking The CLI maintains state information about your interactions with repositories to provide a better user experience. State is tracked per-repository and stores metadata about command executions. ### What counts as a template directory A directory is treated as a DataRobot template directory when it contains a `.env` file (or, for some commands, a `.datarobot/` directory). This affects: - dr run — Requires a .env file in the current directory to discover and run tasks. - dr start — If not in a template directory, launches the template setup wizard instead of running a quickstart. - dr task compose / dr task list — Expect a template directory (e.g., with .env and component Taskfiles). - State tracking — State is stored per template directory in .datarobot/cli/state.yaml within that directory. Cloned templates created by `dr templates setup` or `dr templates clone` include `.datarobot/` and, after configuration, a `.env` file, so they are recognized automatically. ### State file location The CLI stores state locally within each template directory: - .datarobot/cli/state.yaml in the template directory (current working directory) ### Tracked information The state file tracks: - CLI version : Version of the CLI used for the last successful execution - Last start : Timestamp of the last successful dr start execution - Last dotenv setup : Timestamp of the last successful dr dotenv setup execution ### State file format ``` cli_version: "0.2.38" last_start: 2026-01-15T00:02:07.615186Z last_dotenv_setup: 2026-01-15T00:15:30.123456Z ``` All timestamps are in ISO 8601 format (UTC). ### How state is used - dr start : Updates state after successful execution - dr dotenv setup : Records when environment setup was completed - dr templates setup : Skips dotenv setup if it was already completed (based on state) ### Managing state State files are automatically created and updated. To reset state for a template directory: ``` # Remove template state rm .datarobot/cli/state.yaml ``` You can also force the wizard to run without deleting the state file by using the `--force-interactive` flag: ``` # Force re-execution of setup wizard while preserving state dr templates setup --force-interactive # Or via environment variable export DATAROBOT_CLI_FORCE_INTERACTIVE=true dr templates setup ``` This flag makes commands behave as if setup has never been completed, while still updating the state file. This is useful for: - Testing setup flows - Forcing reconfiguration without losing state history - Development and debugging State files are small and do not require manual management under normal circumstances. Each repository maintains its own state independently. ## See also - Getting started —initial setup. - Authentication —managing credentials. --- # Authentication flow URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/development/authentication.html > Reusable authentication mechanism for DataRobot CLI commands. # Authentication flow ## Overview The CLI provides a reusable authentication mechanism that you can use with any command that requires valid DataRobot credentials. Authentication is handled using Cobra's `PreRunE` hooks, which ensure credentials are valid before a command executes. ## Using authentication in commands ### PreRunE hook (recommended) The recommended approach is to use the `auth.EnsureAuthenticatedE()` function in your command's `PreRunE` hook: ``` import "github.com/datarobot/cli/cmd/auth" var MyCmd = &cobra.Command{ Use: "mycommand", Short: "My command description", PreRunE: func(_ *cobra.Command, _ []string) error { return auth.EnsureAuthenticatedE() }, Run: func(_ *cobra.Command, _ []string) { // Command implementation // Authentication is guaranteed to be valid here }, } ``` ### How it works 1. Checks for valid credentials —first checks if a valid API key already exists. 2. Auto-configures URL if missing —if no DataRobot URL is configured, prompts you to set it up. 3. Retrieves new credentials —if credentials are missing or expired, automatically triggers the browser-based login flow. 4. Fails early —if authentication cannot be established, the command won't run and returns an error. ### Direct call (for non-command code) For code that isn't a Cobra command, you can use `auth.EnsureAuthenticated()` directly: ``` import "github.com/datarobot/cli/cmd/auth" func MyFunction() error { // Ensure valid authentication before proceeding. if !auth.EnsureAuthenticated() { return errors.New("authentication failed") } // Continue with authenticated operations. apiKey := config.GetAPIKey() // ... use apiKey for API calls return nil } ``` ### When to use Add authentication to any command that: - Makes API calls to DataRobot endpoints. - Needs to populate DataRobot credentials in configuration files. - Requires valid authentication to function correctly. ### Commands with authentication The following commands use `PreRunE` to ensure authentication: - dr dotenv update —automatically ensures authentication before updating environment variables. - dr templates list —requires authentication to fetch templates from the API. - dr templates clone —requires authentication to fetch template details. ## Skipping authentication For advanced use cases where authentication is handled externally or not required, you can bypass authentication checks using the `--skip-auth` global flag. ### Using the skip-auth flag ``` # Skip authentication for any command dr templates list --skip-auth dr dotenv update --skip-auth # Skip authentication with environment variable DATAROBOT_CLI_SKIP_AUTH=true dr templates setup ``` ### Behavior When `--skip-auth` is enabled: 1. Bypasses all authentication checks —the EnsureAuthenticated() function returns true immediately without validating credentials. 2. Emits a warning —logs a warning message: "Authentication checks are disabled via --skip-auth flag. This may cause API calls to fail." 3. May cause API failures —commands that make API calls will likely fail if no valid credentials are present. ### When to use skip-auth The `--skip-auth` flag is intended for advanced scenarios such as: - Testing —testing command logic without requiring valid credentials. - CI/CD pipelines —when authentication is managed through environment variables ( DATAROBOT_API_TOKEN ). - Offline development —working in environments without internet access or access to DataRobot. - Debugging —isolating authentication issues from other command behavior. > ⚠️ Warning:This flag should only be used when you understand the implications. Most users should rely on the standard authentication flow viadr auth login. ## Manual login You can still manually run `dr auth login` to refresh credentials or change accounts. The `LoginAction()` function provides the interactive login experience with confirmation prompts for overwriting existing credentials. --- # Development guide URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/development/building.html > Building, testing, and developing the DataRobot CLI. # Development guide This guide covers building, testing, and developing the DataRobot CLI. ## Table of contents - Building from source - Project architecture - Coding standards - Development workflow - Testing - Debugging - Release process ## Building from source ### Prerequisites - Go 1.25.3+ — Download . - Git —version control. - Task —task runner ( install ). ### Quick build ``` # Clone repository git clone https://github.com/datarobot-oss/cli.git cd cli # Install development tools task dev-init # Build binary task build # Binary is at ./dist/dr ./dist/dr version ``` ### Available tasks ``` # Show all tasks task --list # Common tasks task build # Build the CLI binary task test # Run all tests task test-coverage # Run tests with coverage task lint # Run linters (includes formatting) task clean # Clean build artifacts task dev-init # Setup development environment task install-tools # Install development tools task run # Run CLI without building ``` ### Build options Always use `task build` for building the CLI. This ensures proper version information and build flags are applied: ``` # Standard build (recommended) task build # Run without building (for quick testing) task run -- templates list ``` The `task build` command automatically includes: - Version information from git - Git commit hash - Build timestamp - Proper ldflags configuration For cross-platform builds and releases, we use GoReleaser (see [Release Process](https://docs.datarobot.com/en/docs/agentic-ai/cli/development/building.html#release-process)). ## Project architecture ### Directory structure ``` cli/ ├── cmd/ # Command implementations (Cobra) │ ├── root.go # Root command and global flags │ ├── auth/ # Authentication commands │ │ ├── cmd.go # Auth command group │ │ ├── login.go # Login command │ │ ├── logout.go # Logout command │ │ └── setURL.go # Set URL command │ ├── dotenv/ # Environment variable management │ │ ├── cmd.go # Dotenv command │ │ ├── model.go # TUI model (Bubble Tea) │ │ ├── promptModel.go # Prompt handling │ │ ├── template.go # Template parsing │ │ └── variables.go # Variable handling │ ├── run/ # Task execution │ │ └── cmd.go # Run command │ ├── templates/ # Template management │ │ ├── cmd.go # Template command group │ │ ├── clone/ # Clone subcommand │ │ ├── list/ # List subcommand │ │ ├── setup/ # Setup wizard │ │ └── status.go # Status command │ └── self/ # CLI utility commands │ ├── cmd.go # Self command group │ ├── completion.go # Completion generation │ └── version.go # Version command ├── internal/ # Private packages (not importable) │ ├── assets/ # Embedded assets │ │ └── templates/ # HTML templates │ ├── config/ # Configuration management │ │ ├── config.go # Config loading/saving │ │ ├── auth.go # Auth config │ │ └── constants.go # Constants │ ├── drapi/ # DataRobot API client │ │ ├── llmGateway.go # LLM gateway API │ │ └── templates.go # Templates API │ ├── envbuilder/ # Environment configuration │ │ ├── builder.go # Env file building │ │ └── discovery.go # Prompt discovery │ ├── task/ # Task runner integration │ │ ├── discovery.go # Taskfile discovery │ │ └── runner.go # Task execution │ └── version/ # Version information │ └── version.go ├── tui/ # Terminal UI shared components │ ├── banner.go # ASCII banner │ └── theme.go # Color theme ├── docs/ # Documentation ├── main.go # Application entry point ├── go.mod # Go module dependencies ├── go.sum # Dependency checksums ├── Taskfile.yaml # Task definitions └── goreleaser.yaml # Release configuration ``` ### Key components #### Command layer (cmd/) The CLI is built using the [Cobra](https://github.com/spf13/cobra) framework. Commands are organized hierarchically, and there should be a one-to-one mapping between commands and files/directories. For example, the `templates` command group is in `cmd/templates/`, with subcommands in their own directories. Code in the `cmd/` folder should primarily handle command-line parsing, argument validation, and orchestrating calls to internal packages. There should be minimal to no business logic here.Consider this the UI layer of the application. ``` // cmd/root.go - Root command definition var RootCmd = &cobra.Command{ Use: "dr", Short: "DataRobot CLI", Long: "Command-line interface for DataRobot", } // Register subcommands RootCmd.AddCommand( auth.Cmd(), templates.Cmd(), // ... ) ``` #### TUI layer (cmd/dotenv/, cmd/templates/setup/) Uses [Bubble Tea](https://github.com/charmbracelet/bubbletea) for interactive UIs: ``` // Bubble Tea Model type Model struct { // State screen screens // Sub-models textInput textinput.Model list list.Model } // Required methods func (m Model) Init() tea.Cmd func (m Model) Update(tea.Msg) (tea.Model, tea.Cmd) func (m Model) View() string ``` #### Internal packages (internal/) Houses core business logic, API clients, configuration management, etc. #### Configuration (internal/config/) Uses [Viper](https://github.com/spf13/viper) for configuration as well as a state registry: ``` // Load config viper.SetConfigName("config") viper.SetConfigType("yaml") viper.AddConfigPath("~/.datarobot") viper.ReadInConfig() // Access values endpoint := viper.GetString("datarobot.endpoint") ``` #### API client (internal/drapi/) HTTP client for DataRobot APIs: ``` // Make API request func GetTemplates() (*TemplateList, error) { resp, err := http.Get(endpoint + "/api/v2/templates") // ... handle response } ``` ### Design patterns #### Command pattern Each command is self-contained: ``` // cmd/templates/list/cmd.go var Cmd = &cobra.Command{ Use: "list", Short: "List templates", GroupID: "core", RunE: func(cmd *cobra.Command, args []string) error { // Implementation return listTemplates() }, } ``` `RunE` is the main execution function. Cobra also provides `PreRunE`, `PostRunE`, and other hooks. Prefer to use these for setup/teardown, validation, etc.: ``` PersistPreRunE: func(cmd *cobra.Command, args []string) error { // Setup logging return setupLogging() }, PreRunE: func(cmd *cobra.Command, args []string) error { // Validate args return validateArgs(args) }, PostRunE: func(cmd *cobra.Command, args []string) error { // Cleanup return nil }, ``` Each command can be assigned to a group via `GroupID` for better organization in `dr help` views. Commands without a `GroupID` are listed under "Additional Commands". #### Model-View-Update (Bubble Tea) Interactive UIs use MVU pattern: ``` // Update handles events func (m Model) Update(msg tea.Msg) (tea.Model, tea.Cmd) { switch msg := msg.(type) { case tea.KeyMsg: return m.handleKey(msg) case dataLoadedMsg: return m.handleData(msg) } return m, nil } // View renders current state func (m Model) View() string { return lipgloss.JoinVertical( lipgloss.Left, m.header(), m.content(), m.footer(), ) } ``` ## Coding standards ### Go style requirements Critical: All code must pass `golangci-lint` with zero errors. Follow these whitespace rules strictly: 1. Never cuddle declarations : Always add a blank line before var , const , type declarations when they follow other statements 2. Separate statement types : Add blank lines between different statement types (assign, if, for, return, etc.) 3. Blank line after block start : Add blank line after opening braces of functions/blocks when they follow declarations 4. Blank line before multi-line statements : Add blank line before if/for/switch statements Example of correct spacing: ``` func example() { x := 1 if x > 0 { y := 2 fmt.Println(y) } var result string result = "done" return result } ``` Common mistakes to avoid: ``` // ❌ BAD: Cuddled declaration func bad() { x := 1 var y int // Missing blank line before declaration } // ✅ GOOD: Properly spaced func good() { x := 1 var y int } ``` ### TUI development standards When building terminal user interfaces: 1. Always wrap TUI models with InterruptibleModel —ensures global Ctrl-C handling: ``` import "github.com/datarobot/cli/tui" // Wrap your model interruptible := tui.NewInterruptibleModel(yourModel) program := tea.NewProgram(interruptible) ``` 1. Reuse existing TUI components—checktui/package first before creating new components. Also explore theBubbles libraryfor pre-built components. 2. Use common lipgloss styles—defined intui/theme.gofor visual consistency: ``` import "github.com/datarobot/cli/tui" // Use theme styles title := tui.TitleStyle.Render("My Title") error := tui.ErrorStyle.Render("Error message") ``` ### Quality tools All code must pass these tools without errors: - go mod tidy —dependency management - go fmt —basic formatting - go vet —suspicious constructs - golangci-lint —comprehensive linting (includes wsl, revive, staticcheck, etc.) - goreleaser check —release configuration validation Before committing code, verify it follows wsl (whitespace) rules. ### Running quality checks ``` # Run all quality checks at once task lint # Individual checks go mod tidy go fmt ./... go vet ./... task install-tools # Install golangci-lint ./tmp/bin/golangci-lint run ./... ./tmp/bin/goreleaser check ``` ## Development workflow ### Important: Use Taskfile, not direct Go commands Always use Taskfile tasks for development operations rather than direct `go` commands. This ensures consistency, proper build flags, and correct environment setup. ``` # ✅ CORRECT: Use task commands task build task test task lint # ❌ INCORRECT: Don't use direct go commands go build go test ``` ### 1. Setup development environment ``` # Clone and setup git clone https://github.com/datarobot-oss/cli.git cd cli task dev-init ``` ### 2. Create feature branch ``` git checkout -b feature/my-feature ``` ### 3. Make changes ``` # Edit code vim cmd/templates/new-feature.go # Run linters (includes formatting) task lint ``` ### 4. Test changes ``` # Run tests task test # Run specific test (direct go test is acceptable for specific tests) go test -run TestMyFeature ./cmd/templates # Test manually using task run task run -- templates list # Or build and test the binary task build ./dist/dr templates list ``` ### 5. Commit and push ``` git add . git commit -m "feat: add new feature" git push origin feature/my-feature ``` ## Testing ### Unit tests ``` // cmd/auth/login_test.go package auth import ( "testing" "github.com/stretchr/testify/assert" ) func TestLogin(t *testing.T) { // Arrange mockAPI := &MockAPI{} // Act err := performLogin(mockAPI) // Assert assert.NoError(t, err) } ``` ### Integration tests ``` // internal/config/config_test.go func TestConfigReadWrite(t *testing.T) { // Create temp config tmpDir := t.TempDir() configPath := filepath.Join(tmpDir, "config.yaml") // Write config err := SaveConfig(configPath, &Config{ Endpoint: "https://test.datarobot.com", }) assert.NoError(t, err) // Read config config, err := LoadConfig(configPath) assert.NoError(t, err) assert.Equal(t, "https://test.datarobot.com", config.Endpoint) } ``` ### TUI tests Using [teatest](https://github.com/charmbracelet/x/tree/main/exp/teatest): ``` // cmd/dotenv/model_test.go func TestDotenvModel(t *testing.T) { m := Model{ // Setup model } tm := teatest.NewTestModel(t, m) // Send keypress tm.Send(tea.KeyMsg{Type: tea.KeyEnter}) // Wait for update teatest.WaitFor(t, tm.Output(), func(bts []byte) bool { return bytes.Contains(bts, []byte("Expected output")) }) } ``` ### Running tests ``` # All tests (recommended) task test # With coverage (opens HTML report) task test-coverage # Specific package (direct go test is fine for targeted testing) go test ./internal/config # Verbose go test -v ./... # With race detection (task test already includes this) go test -race ./... # Specific test go test -run TestLogin ./cmd/auth ``` Note: `task test` automatically runs tests with race detection and coverage enabled. ### Running smoke tests using GitHub Actions We have smoke tests that are not currently run on Pull Requests however can be using PR comments to trigger them. These are the appropriate comments to trigger respective tests: - /trigger-smoke-test or /trigger-test-smoke - Run smoke tests on this PR - /trigger-install-test or /trigger-test-install - Run installation tests on this PR ## Debugging ### Using Delve ``` # Install delve go install github.com/go-delve/delve/cmd/dlv@latest # Debug with arguments dlv debug main.go -- templates list # In debugger (dlv) break main.main (dlv) continue (dlv) print variableName (dlv) next ``` ### Debug logging ``` # Enable debug mode (use task run) task run -- --debug templates list # Or with built binary task build ./dist/dr --debug templates list ``` ### Add debug statements ``` import "github.com/charmbracelet/log" // Debug logging log.Debug("Variable value", "key", value) log.Info("Processing started") log.Warn("Unexpected condition") log.Error("Operation failed", "error", err) ``` ### Quick release ``` # Tag version git tag v1.0.0 git push --tags # GitHub Actions will: # 1. Build for all platforms # 2. Run tests # 3. Create GitHub release # 4. Upload binaries ``` --- # Release process URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/development/releasing.html > How to create and publish releases of the DataRobot CLI. # Release process This document describes how to create and publish releases of the DataRobot CLI. ## Overview The project uses [GoReleaser](https://goreleaser.com/) for automated releases. Releases are triggered by creating and pushing Git tags, which automatically builds binaries for multiple platforms and publishes them to GitHub. ## Prerequisites - Write access to the repository - All changes merged to the main branch - Familiarity with Semantic Versioning ## Versioning We follow [Semantic Versioning](https://semver.org/) (SemVer): - MAJOR.MINOR.PATCH (e.g., v1.2.3 ) - Pre-releases : v1.2.3-rc.1 , v1.2.3-beta.1 , v1.2.3-alpha.1 ### Version guidelines MAJOR version when making incompatible API changes: - Breaking changes to command-line interface - Removing commands or flags - Changing default behavior that breaks existing workflows MINOR version when adding functionality in a backward-compatible manner: - New commands or subcommands - New flags or options - New features PATCH version when making backward-compatible bug fixes: - Bug fixes - Documentation updates - Performance improvements ## Creating a release ### Step 1: Ensure main branch is ready ``` # Switch to main branch git checkout main # Pull latest changes git pull origin main # Verify all tests pass task test # Verify linting passes task lint ``` ### Step 2: Determine next version Review recent changes and decide on the next version number based on SemVer guidelines above. ### Step 3: Create and push tag ``` # Create a new version tag git tag v0.2.0 # Push the tag to trigger the release git push origin v0.2.0 ``` Note: The tag must start with `v` (e.g., `v1.0.0`, not `1.0.0`). ### Step 4: Monitor release process 1. Go to the Actions tab in GitHub 2. Watch the release workflow run 3. The workflow will: 4. Build binaries for multiple platforms (macOS, Linux, Windows) 5. Run tests 6. Generate release notes from commit messages 7. Create a GitHub release 8. Upload artifacts ### Step 5: Verify release Once the workflow completes: 1. Go to Releases 2. Verify the new release appears with: 3. Correct version number 4. Generated release notes 5. Binary artifacts for all platforms 6. Checksums file ### Step 6: Update release notes (optional) Edit the release notes on GitHub to: - Add highlights of major changes - Include upgrade instructions if needed - Add breaking change warnings - Include acknowledgments ## Pre-release versions For testing releases before making them generally available: ``` # Create a pre-release tag git tag v0.2.0-rc.1 # Push the tag git push origin v0.2.0-rc.1 ``` Pre-release versions are marked as "Pre-release" on GitHub and can be used for testing. ## Testing the release process To test the release process without publishing: ``` # Dry run (builds but doesn't publish) goreleaser release --snapshot --clean # Check output in dist/ directory ls -la dist/ ``` This creates build artifacts locally without creating a GitHub release. ## Rollback If a release has issues: ### Delete the tag locally and remotely ``` # Delete local tag git tag -d v0.2.0 # Delete remote tag git push origin :refs/tags/v0.2.0 ``` ### Delete the GitHub release - Go to Releases page - Click on the problematic release - Click "Delete this release" ### Fix the issues and create a new patch release ## Release configuration The release process is configured in `goreleaser.yaml`. Key configurations: - Builds : Defines target platforms and architectures - Archives : Creates distribution archives - Checksums : Generates checksum files - Release notes : Automatic generation from commits - Artifacts : Files to include in the release To validate the configuration: ``` goreleaser check ``` ## Automated release workflow The GitHub Actions workflow ( `.github/workflows/release.yml`) automatically: 1. Triggers on tag push matching v* 2. Checks out the code 3. Sets up Go environment 4. Runs GoReleaser 5. Creates GitHub release 6. Uploads all artifacts ## Best practices 1. Always test before releasing: 2. Run full test suite: task test 3. Run linters: task lint 4. Build locally:task build 5. Use meaningful commit messages: 6. They're used to generate release notes 7. Follow conventional commit format when possible 8. Update CHANGELOG.md: 9. Document significant changes 10. Include migration notes for breaking changes 11. Communicate breaking changes: 12. Update documentation 13. Add prominent notes in release description 14. Consider a major version bump 15. Test installation: 16. Test the install script after release 17. Verify binaries work on target platforms ## Troubleshooting ### Release workflow fails - Check the Actions tab for error messages - Verify goreleaser.yaml is valid: goreleaser check - Ensure all required secrets are configured ### Tag already exists ``` # Delete and recreate if needed git tag -d v0.2.0 git push origin :refs/tags/v0.2.0 git tag v0.2.0 git push origin v0.2.0 ``` ### Missing artifacts - Verify build configuration in goreleaser.yaml - Check build logs in GitHub Actions - Test locally with goreleaser release --snapshot --clean ## Next steps - Setup guide —development environment setup - Building guide —detailed build information --- # Development setup URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/development/setup.html > Setting up your development environment for building and developing the DataRobot CLI. # Development setup This guide covers setting up your development environment for building and developing the DataRobot CLI. ## Prerequisites - Go 1.25.3+ — Download - Git —version control - Task —task runner ( install ) ## Installation ### Installing Task Task is required for running development tasks. #### macOS ``` brew install go-task/tap/go-task ``` #### Linux ``` sh -c "$(curl --location https://taskfile.dev/install.sh)" -- -d -b /usr/local/bin ``` #### Windows ``` choco install go-task ``` ## Setting up the development environment ### Clone the repository ``` git clone https://github.com/datarobot-oss/cli.git cd cli ``` ### Install development tools ``` task dev-init ``` This will install all necessary development tools including linters and code formatters. ### Build the CLI ``` task build ``` The binary will be available at `./dist/dr`. ### Verify the build ``` ./dist/dr version ``` ## Available development tasks View all available tasks: ``` task --list ``` ### Common tasks | Task | Description | | --- | --- | | task build | Build the CLI binary | | task test | Run all tests | | task test-coverage | Run tests with coverage report | | task lint | Run linters and code formatters | | task fmt | Format code | | task clean | Clean build artifacts | | task dev-init | Setup development environment | | task install-tools | Install development tools | | task run | Run CLI without building (e.g., task run -- templates list) | ## Building Always use `task build` for building the CLI.This ensures: - Version information from git is included - Git commit hash is embedded - Build timestamp is recorded - Proper ldflags configuration is applied ``` # Standard build (recommended) task build # Run without building (for quick testing) task run -- templates list ``` ## Running tests ``` # Run all tests task test # Run tests with coverage task test-coverage # Run specific test go test ./cmd/auth/... ``` ## Linting and formatting ``` # Run all linters (includes formatting) task lint # Format code only task fmt ``` The project uses: - golangci-lint for comprehensive linting - go fmt for basic formatting - go vet for suspicious constructs - goreleaser check for release configuration validation ## Next steps - Project structure —understand the codebase organization - Building guide —detailed build information and architecture - Release process —creating releases and publishing --- # Project structure URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/development/structure.html > Organization of the DataRobot CLI codebase. # Project structure This document describes the organization of the DataRobot CLI codebase. ## Directory overview ``` cli/ ├── cmd/ # Command implementations (Cobra) │ ├── root.go # Root command and global flags │ ├── auth/ # Authentication commands │ ├── component/ # Component management commands │ ├── dotenv/ # Environment variable management │ ├── run/ # Task execution │ ├── self/ # Self-management commands │ ├── start/ # Application startup │ ├── task/ # Task commands │ └── templates/ # Template management ├── internal/ # Private application code │ ├── assets/ # Embedded assets │ ├── config/ # Configuration management │ ├── copier/ # Template copying utilities │ ├── drapi/ # DataRobot API client │ ├── envbuilder/ # Environment builder │ ├── misc/ # Miscellaneous utilities │ ├── repo/ # Repository detection │ ├── shell/ # Shell utilities │ ├── task/ # Task discovery and execution │ ├── tools/ # Tool prerequisites │ └── version/ # Version information ├── tui/ # Terminal UI components │ ├── banner.go # Banner display │ ├── interrupt.go # Interrupt handling │ └── theme.go # Visual theme ├── docs/ # Documentation │ ├── commands/ # Command reference │ ├── development/ # Development guides │ ├── template-system/ # Template system docs │ └── user-guide/ # User documentation ├── smoke_test_scripts/ # Smoke tests ├── main.go # Application entry point ├── Taskfile.yaml # Task definitions ├── go.mod # Go module definition └── goreleaser.yaml # Release configuration ``` ## Key directories ### cmd/ Contains all CLI command implementations using the Cobra framework. Each subdirectory represents a command or command group. Structure: - root.go —root command setup and global flags - Each command has its own subdirectory with cmd.go as the entry point - Commands that have subcommands organize them in the same directory Example: - cmd/auth/cmd.go —auth command group - cmd/auth/login.go —login subcommand - cmd/auth/logout.go —logout subcommand ### internal/ Private application code that cannot be imported by other projects. This follows Go's convention for internal packages. #### config/ Configuration management including: - Reading/writing configuration files - Authentication state - User preferences #### drapi/ DataRobot API client implementation for: - Template listing and retrieval - API authentication - API endpoint communication #### envbuilder/ Environment configuration builder that: - Discovers environment variables from templates - Validates configuration - Generates .env files - Provides interactive prompts #### task/ Task discovery and execution: - Taskfile detection - Task parsing - Task running - Output handling ### tui/ Terminal UI components built with Bubble Tea: - Reusable UI models - Theme definitions - Interrupt handling for graceful exits - Banner displays ### docs/ Documentation organized by audience: - commands/ —detailed command reference - development/ —development guides for contributors - template-system/ —template configuration system - user-guide/ —end-user documentation ## Code organization patterns ### Command structure Each command follows this pattern: ``` // cmd/example/cmd.go package example import "github.com/spf13/cobra" var Cmd = &cobra.Command{ Use: "example", Short: "Example command", Long: `Detailed description`, PreRunE: func(cmd *cobra.Command, args []string) error { // Validation and setup return nil }, RunE: func(cmd *cobra.Command, args []string) error { // Command implementation return nil }, } func init() { // Flag definitions Cmd.Flags().StringP("flag", "f", "", "Flag description") } ``` ### TUI models TUI components use the Bubble Tea framework and are wrapped with `InterruptibleModel` for consistent Ctrl-C handling: ``` // cmd/example/model.go package example import ( tea "github.com/charmbracelet/bubbletea" "github.com/datarobot/cli/tui" ) type model struct { // State fields } func (m model) Init() tea.Cmd { return nil } func (m model) Update(msg tea.Msg) (tea.Model, tea.Cmd) { // Handle messages return m, nil } func (m model) View() string { // Render UI return "" } // Usage in command func runInteractive() error { m := model{} wrapped := tui.NewInterruptibleModel(m) _, err := tea.NewProgram(wrapped).Run() return err } ``` ### Configuration Configuration is managed through Viper and stored in: - ~/.config/datarobot/config.yaml —global configuration - ~/.config/datarobot/credentials.json —authentication tokens Access configuration through the `internal/config` package: ``` import "github.com/datarobot/cli/internal/config" // Get configuration values apiKey := config.GetAPIKey() endpoint := config.GetEndpoint() // Set configuration values config.SetAPIKey("new-key") config.SaveConfig() ``` ## Testing structure Tests are colocated with the code they test: - Unit tests: *_test.go files in the same package - Test helpers in same directory when needed - Smoke tests in smoke_test_scripts/ directory ## Build artifacts Generated files and artifacts: - dist/ —build output (created by Task/GoReleaser) - tmp/ —temporary build files - coverage.txt —test coverage report ## Next steps - Setup guide —setting up your development environment - Building guide —detailed build information and architecture --- # Getting started with DataRobot CLI URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/getting-started.html # Getting started with DataRobot CLI This guide will help you install and start using the DataRobot CLI ( `dr`) for managing custom applications. ## Prerequisites Before you begin, ensure you have: - DataRobot account —access to a DataRobot instance (cloud or self-managed). If you don't have an account, sign up at DataRobot or contact your organization's DataRobot administrator. - Git —for cloning templates (version 2.0+). Install Git from git-scm.com if not already installed. Verify installation: git --version - Task —for running tasks. Install Task from taskfile.dev if not already installed. Verify installation: task --version - Terminal —command-line interface access. - macOS/Linux: Use Terminal, iTerm2, or your preferred terminal emulator. - Windows: Use PowerShell, Command Prompt, or Windows Terminal. ## Installation Install the latest version with a single command: macOS/Linux ``` curl https://cli.datarobot.com/install | sh ``` Windows (PowerShell) ``` irm https://cli.datarobot.com/winstall | iex ``` For alternative installation methods (Homebrew, download binary, specific version, or build from source), see the sections below. ### Install via Homebrew / Linuxbrew ``` brew install datarobot-oss/taps/dr-cli ``` ### Download binary Download the latest release for your operating system: #### macOS ``` # Intel Macs curl -LO https://github.com/datarobot-oss/cli/releases/latest/download/dr-darwin-amd64 chmod +x dr-darwin-amd64 sudo mv dr-darwin-amd64 /usr/local/bin/dr # Apple Silicon (M1/M2) curl -LO https://github.com/datarobot-oss/cli/releases/latest/download/dr-darwin-arm64 chmod +x dr-darwin-arm64 sudo mv dr-darwin-arm64 /usr/local/bin/dr ``` #### Linux ``` # x86_64 curl -LO https://github.com/datarobot-oss/cli/releases/latest/download/dr-linux-amd64 chmod +x dr-linux-amd64 sudo mv dr-linux-amd64 /usr/local/bin/dr # ARM64 curl -LO https://github.com/datarobot-oss/cli/releases/latest/download/dr-linux-arm64 chmod +x dr-linux-arm64 sudo mv dr-linux-arm64 /usr/local/bin/dr ``` #### Windows Download `dr-windows-amd64.exe` from the [releases page](https://github.com/datarobot-oss/cli/releases/latest) and add it to your PATH. ### Install a specific version To install a specific version, pass the version number to the installer: #### macOS/Linux ``` curl https://cli.datarobot.com/install | sh -s -- v0.2.38 ``` #### Windows (PowerShell) ``` $env:VERSION = "v0.2.38"; irm https://cli.datarobot.com/winstall | iex ``` ### Build from source If you have Go 1.25.6 or later installed: ``` # Clone the repository git clone https://github.com/datarobot-oss/cli.git cd cli # Install Task (if not already installed) go install github.com/go-task/task/v3/cmd/task@latest # Build task build # The binary will be at ./dist/dr sudo mv ./dist/dr /usr/local/bin/dr ``` ### Verify installation ``` dr --version ``` You should see output similar to: ``` DataRobot CLI version: v0.2.38 ``` ## Updating the CLI To update to the latest version of the DataRobot CLI, use the built-in update command: ``` dr self update ``` This command will automatically: - Detect your installation method (Homebrew, manual installation, etc.) - Download the latest version - Install it using the appropriate method for your system - Preserve your existing configuration and credentials The update process supports: - Homebrew (macOS) —automatically upgrades via brew upgrade --cask dr-cli - Windows —runs the latest PowerShell installation script - macOS/Linux —runs the latest shell installation script After updating, verify the new version: ``` dr self version ``` You can also check the installed version with `dr --version` at any time. ## Uninstalling the CLI How you uninstall depends on how you installed: Installed via install script (curl/irm): - macOS/Linux: Remove the binary (e.g., sudo rm /usr/local/bin/dr if that is where it was installed). Alternatively, run the uninstall script from the CLI repository if you have it cloned. - Windows: Remove the dr executable from your PATH (the install script typically places it in a user directory). Installed via Homebrew: ``` brew uninstall dr-cli ``` Optional: remove configuration and state - User config: Delete ~/.config/datarobot/ (Linux/macOS) or %USERPROFILE%\.config\datarobot\ (Windows) to remove drconfig.yaml and stored credentials. - Template state: In any cloned template directory, you can remove .datarobot/cli/state.yaml to clear local state for that template. ## Initial setup ### 1. Configure DataRobot URL First, configure your DataRobot credentials by setting your DataRobot URL. For steps to locate your DataRobot URL (API endpoint) and manage API keys, see the [DataRobot documentation](https://docs.datarobot.com/). Set your DataRobot instance URL: ``` dr auth set-url ``` You'll be prompted to enter your DataRobot URL. You can use shortcuts for cloud instances: - Enter 1 for https://app.datarobot.com - Enter 2 for https://app.eu.datarobot.com - Enter 3 for https://app.jp.datarobot.com - Or enter your custom URL (e.g., https://your-instance.datarobot.com ) Alternatively, set the URL directly: ``` dr auth set-url https://app.datarobot.com ``` ### 2. Authenticate Log in to DataRobot using OAuth: ``` dr auth login ``` This will: 1. Open your default web browser. 2. Redirect you to the DataRobot login page. 3. Request authorization. 4. Automatically save your credentials. Your API key will be securely stored in `~/.config/datarobot/drconfig.yaml`. ### 3. Verify authentication Check that you're logged in: ``` dr templates list ``` This should display a list of available templates from your DataRobot instance. ## Your first template Now that you're set up, let's create your first application from a template. ### Using the setup wizard (recommended) The easiest way to get started: ``` dr templates setup ``` This interactive wizard will: 1. Display available templates. 2. Help you select and clone a template. 3. Guide you through environment configuration. 4. Set up all required variables. Follow the on-screen prompts to complete the setup. ### Manual setup If you prefer manual control: ``` # 1. List available templates. dr templates list # 2. Set up a template (this clones and configures it). dr templates setup # 3. Navigate to the template directory. cd TEMPLATE_NAME # 4. Configure environment variables (if not done during setup). dr dotenv setup ``` ## Running your application Once your template is set up, you have several options to run it: ### Quick start (recommended) Use the `start` command for automated initialization: ``` dr start ``` This command will: - Check prerequisites and validate your environment. - Verify your CLI version meets the template's minimum requirements. - Check if you're in a DataRobot repository (if not, launches template setup). - Execute a start command in this order: - task start from the Taskfile (if available) - A quickstart script from .datarobot/cli/bin/ (if available) - Fall back to the setup wizard if neither exists. For non-interactive mode (useful in scripts or CI/CD): ``` dr start --yes ``` ### Running specific tasks For more control, execute individual tasks: ``` # List available tasks dr task list # Run the development server dr run dev # Or execute specific tasks dr run build dr run test ``` ## Next steps - Authentication guide —learn about authentication options. - Working with templates —detailed template management. - Shell completions —set up command auto-completion. - Agentic AI quickstart —build and deploy AI agents from DataRobot templates using dr start and dr task run . ## Common issues ### "dr: command not found" Why it happens: The CLI binary isn't in your system's PATH, so your shell can't find it. How to fix: ``` # Check if dr is in PATH which dr # If not found, verify the binary location ls -l /usr/local/bin/dr # Add it to your PATH (for current session) export PATH="/usr/local/bin:$PATH" # For permanent fix, add to your shell config file: # Bash: echo 'export PATH="/usr/local/bin:$PATH"' >> ~/.bashrc # Zsh: echo 'export PATH="/usr/local/bin:$PATH"' >> ~/.zshrc ``` How to prevent: Re-run the installation script or ensure the binary is installed to a directory in your PATH. ### "Failed to read config file" Why it happens: The configuration file doesn't exist yet or is in an unexpected location. This typically occurs on first use before authentication. How to fix: ``` # Set your DataRobot URL (creates config file if missing) dr auth set-url https://app.datarobot.com # Authenticate (saves credentials to config file) dr auth login ``` How to prevent: Run `dr auth set-url` and `dr auth login` as part of your initial setup. The config file is automatically created at `~/.config/datarobot/drconfig.yaml`. ### "Authentication failed" Why it happens: Your API token may have expired, been revoked, or the DataRobot URL may have changed. This can also occur if the config file is corrupted. How to fix: ``` # Clear existing credentials dr auth logout # Re-authenticate dr auth login # If issues persist, verify your DataRobot URL dr auth set-url https://app.datarobot.com # or your instance URL dr auth login ``` How to prevent: Regularly update the CLI ( `dr self update`) and re-authenticate if you change DataRobot instances or if your organization rotates API keys. ## Getting help For additional help: ``` # General help dr --help # Command-specific help dr auth --help dr templates --help dr run --help # Enable verbose output for debugging dr --verbose templates list # Enable debug output for detailed information dr --debug templates list ``` When you enable debug mode, the CLI creates a `dr-tui-debug.log` file in the current directory for terminal UI debug information. For advanced options such as `--skip-auth` (skip authentication checks) and `--force-interactive` (force the setup wizard to run again), see the [Command reference - Global flags](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/index.html#global-flags). ## Configuration location Configuration files are stored in: - Linux/macOS — ~/.config/datarobot/drconfig.yaml . - Windows — %USERPROFILE%\.config\datarobot\drconfig.yaml . See [Configuration files](https://docs.datarobot.com/en/docs/agentic-ai/cli/configuration.html) for more details. --- # CLI URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/index.html > Guides and references for using and developing the DataRobot CLI tool. # CLI This guide provides a comprehensive overview of all available documentation for the DataRobot CLI. The CLI (Command Line Interface) enables you to interact with DataRobot from your terminal, providing powerful automation capabilities for managing projects, templates, and workflows. ## Quick links ### For setup Essential guides to get you started with the DataRobot CLI: - Get started : Installation and initial setup - Quick reference : One-page command and path reference - Shell completions : Set up command auto-completion for your shell - Configuration : Understand and manage config files - Troubleshooting : Common issues and where to find solutions ### For templates Learn how to work with the template system: - Template structure : Understand how templates work - Interactive configuration : Overview of the configuration wizard - Environment variables : Manage .env files ### For agentic workflows Building or deploying AI agents with DataRobot templates: - Agentic AI : Create and evaluate agentic workflows - Agentic quickstart : Set up and run agents with dr start and dr task run - Develop agentic workflows : Installation, customization, tools, and deployment ### For developers Resources for contributing and building: - Building from source : Compile and build the CLI from source - Development setup : Local development environment - Project structure : Codebase organization - Releasing : Release process ## Key features documented ### 1. Shell completions Comprehensive documentation for setting up [auto-completion in multiple shells](https://docs.datarobot.com/en/docs/agentic-ai/cli/shell-completions.html): - Bash (Linux and macOS) - Zsh - Fish - PowerShell ### 2. Template system A detailed explanation of the template system including: - Template repository structure - .datarobot/prompts.yaml format - Interactive configuration wizard - Conditional prompts and sections - Multi-level configuration ### 3. Interactive configuration In-depth coverage of the [interactive configuration system](https://docs.datarobot.com/en/docs/agentic-ai/cli/template-system/interactive-config.html): - Bubble Tea architecture - Prompt types (text, selection, multi-select) - Conditional logic with sections - State management - Keyboard controls - Advanced features ### 4. Environment management A complete guide to managing [environment variables](https://docs.datarobot.com/en/docs/agentic-ai/cli/template-system/environment-variables.html): - .env vs .env.template - Variable types (required, optional, secret) - Interactive wizard - Security best practices - Common patterns ## Getting started If you're new to the DataRobot CLI, start here: 1. Installation and setup : Get the CLI installed and configured 2. Shell completions : Enable command auto-completion for faster workflows 3. Configuration : Understand how to configure the CLI for your environment ## How to contribute to docs We welcome contributions to improve the documentation! Here's how to get started: 1. Fork the repository to create your own copy 2. Edit or create markdown files in docs/ 3. Follow the style guide: 4. Use clear, concise language 5. Include code examples where helpful 6. Add relevant cross-references 7. Use proper markdown formatting 8. Test links to ensure they work correctly 9. Submit a pull request with your changes ## Documentation principles Our documentation follows these key principles to ensure the best experience for all users: ### User-focused - Written from the user's perspective - Task-oriented (how to accomplish something) - Real-world examples and use cases ### Progressive disclosure - Quick start guides for beginners - Deep-dive content for advanced users - Reference material for specific details ### Maintainable - Kept in sync with code changes - Updated with each release - Clear, consistent structure throughout ### Discoverable - Good navigation and organization - Search-friendly content - Well cross-referenced ## Get help Can't find what you're looking for? Here are several ways to get assistance: 1. Search the docs : Use your browser's search function or GitHub's search feature 2. Check examples : Browse code examples in the docs/ directory 3. Ask questions : Open a Discussion on GitHub 4. Report issues : Missing or unclear documentation? Open an issue 5. Email us : Contact oss-community-management@datarobot.com for direct support ## Documentation tools The DataRobot CLI documentation uses: - Markdown : All documentation is written in GitHub-flavored Markdown - MkDocs (future): May add static site generation for better presentation - GitHub Pages (future): May host documentation online for easier access ## Version information - Documentation version : Synchronized with CLI version - CLI version : 0.2.x (latest: v0.2.38) - Status : Active development - Last updated : January 29, 2026 - Releases : GitHub Releases and CHANGELOG for release notes and version history --- # DataRobot CLI overview URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/overview.html > Learn about what the DataRobot CLI is for, how it's used, and how the CLI documentation is organized. # DataRobot CLI overview The DataRobot CLI ( `dr`) is an open source tool for working with DataRobot from your terminal. It is designed for developers and operators who want repeatable, scriptable workflows instead of doing everything from the DataRobot UI. ## What the CLI is for Use the CLI to: - Authenticate against your DataRobot instance (cloud or self-managed) and locally manage credentials. - Work with application templates. Browse, clone, and configure projects built from templates, including an interactive setup when a template defines prompts and environment variables. - Run local development tasks. Execute the same Task-based workflows your template expects (for example, dr run dev , dr run build , dr run test ) so your machine matches how the app is meant to be built and run. - Support Agentic AI workflows. Bootstrap and run agent-oriented setups with commands such as dr start and dr task run . The CLI does not replace the DataRobot web application for every task; it focuses on local development, template-driven apps, and automation (including CI/CD) where a terminal interface fits best. ## Common workflow Most users follow a path like this: 1. Install dr in your workspace (see Quick install or the full Getting started guide). 2. Point the CLI at your environment and sign in (see Authentication management ){ target=_blank }. 3. Clone or set up a template if you are building from an application template (see Working with templates ). 4. Run tasks defined for that project ( dr task list , dr run ) as you develop. If you are building [Agentic AI](https://docs.datarobot.com/en/docs/agentic-ai/index.html) workflows, the [Agentic AI quickstart](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-quickstart.html) walks through `dr start` and `dr task run` end to end. ## Where to go next | If you want to… | Start here | | --- | --- | | Install and configure the CLI for the first time | Getting started | | Look up commands without narrative | Quick reference | | Understand template layout and .env behavior | Template system | | Read per-command details | Command reference (below) | > [!TIP] Building agentic workflows? > The CLI is used to set up, run, and deploy [Agentic AI](https://docs.datarobot.com/en/docs/agentic-ai/index.html) workflows. See the [Agentic AI quickstart](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-quickstart.html) to get started with `dr start` and `dr task run`. ## Quick install Install the latest version with a single command that auto-detects your operating system: macOS/Linux: ``` curl https://cli.datarobot.com/install | sh ``` Windows (PowerShell): ``` irm https://cli.datarobot.com/winstall | iex ``` For more installation options, see [Getting Started](https://docs.datarobot.com/en/docs/agentic-ai/cli/getting-started.html). ## Documentation structure ### User guide End-user documentation for using the CLI: - Getting started —installation and initial setup. - Quick reference —one-page command and path reference. - Authentication —setting up DataRobot credentials. - Working with templates —clone and manage application templates. - Shell completions —set up command auto-completion. - Configuration files —understanding config file structure. - Troubleshooting —common issues and where to find solutions. ### Template system Understanding the interactive template configuration: - Template structure —how templates are organized. - Interactive configuration —the wizard system explained. - Environment variables —managing .env files. ### Command reference The [command reference](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/index.html) lists every `dr` command, global flags, and guidance on when to use `dr start` versus `dr run`, plus links into each topic. Common entry points: - auth —authentication management. - run —task execution. - dotenv —environment variable management. - self —CLI utility commands (version, completion). ### Development guide For contributors and developers: - Building from source —compile and build the CLI. - Development setup —local development environment. - Project structure —codebase organization. - Releasing —release process. ## Getting help If you can't find what you're looking for: 1. Search existing issues . 2. Open a new issue . 3. Email: oss-community-management@datarobot.com. --- # Quick reference URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/quick-reference.html > One-page reference for common DataRobot CLI commands and paths. # Quick reference ## Quick reference One-page reference for the most common DataRobot CLI ( `dr`) commands and paths. ## Installation ``` # macOS/Linux curl https://cli.datarobot.com/install | sh # Windows (PowerShell) irm https://cli.datarobot.com/winstall | iex ``` ## Authentication ``` dr auth set-url https://app.datarobot.com # or 1=US, 2=EU, 3=JP dr auth login dr auth logout ``` ## Templates ``` dr templates list dr templates setup dr templates clone [directory] dr templates status ``` ## Running applications ``` dr start # Quickstart or setup wizard dr task list # List tasks dr run dev # Run dev task dr run build dr run test ``` ## Environment ``` dr dotenv setup dr dotenv edit dr dotenv validate dr dotenv update ``` ## CLI self-management ``` dr --version dr self version dr self update dr self config dr self completion bash | sudo tee /etc/bash_completion.d/dr ``` ## Global flags | Flag | Description | | --- | --- | | -v, --verbose | Verbose output | | --debug | Debug output (creates dr-tui-debug.log) | | --skip-auth | Skip authentication (advanced) | | --force-interactive | Force setup wizard to run again | | -h, --help | Help | ## Config and state paths | Platform | Config file | | --- | --- | | Linux/macOS | ~/.config/datarobot/drconfig.yaml | | Windows | %USERPROFILE%\.config\datarobot\drconfig.yaml | | Location | Purpose | | --- | --- | | .datarobot/cli/state.yaml | Template state (per template directory) | ## See also - Getting started - Command reference - Configuration - Troubleshooting --- # Shell completions URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/shell-completions.html # Shell completions The DataRobot CLI supports auto-completion for Bash, Zsh, Fish, and PowerShell. Shell completions provide: - Command and subcommand suggestions. - Flag and option completions. - Faster command entry via tab completion. - Discovery of available commands. ## Installation ### Automatic installation You can use three different methods to install shell completions: 1. Installation script: Recommended for first-time installs. The installer automatically detects your shell and configures completions. ``` curl -fsSL https://raw.githubusercontent.com/datarobot-oss/cli/main/install.sh | sh ``` 1. Interactive command: Recommended for managing completions. This command detects your shell and installs completions to the appropriate location. ``` dr self completion install ``` 1. Manual installation : Recommended for advanced users. Follow the shell-specific instructions below. ### Interactive commands The CLI provides commands to easily manage completions. ``` # Install completions for your current shell dr self completion install # Force reinstall (useful after updates) dr self completion install --force # Uninstall completions dr self completion uninstall ``` ### Manual installation If you prefer manual installation or the automatic methods do not work, follow the instructions below. ### Bash #### Linux ``` # Generate and install the completion script dr self completion bash | sudo tee /etc/bash_completion.d/dr # Reload your shell source ~/.bashrc ``` ``` # If shell completion is not already enabled in your environment you will need # to enable it. You can execute the following command once: # echo "autoload -U compinit; compinit" >> ~/.zshrc # To load completions for each session, execute the following command once: $ dr self completion zsh > "${fpath[1]}/_dr" ``` #### macOS The default shell in MacOS is `zsh`. Shell completions for `zsh` are typically stored in one of the following directories: - /usr/local/share/zsh/site-functions/ - /opt/homebrew/share/zsh/site-functions/ - ${ZDOTDIR:-$HOME}/.zsh/completions/ Run `echo $fpath` to see all possibilities. For example, if you wish to put CLI completions into ZDOTDIR, then run: ``` dr self completion zsh > ${ZDOTDIR:-$HOME}/.zsh/completions/_dr ``` #### Temporary session For the current session only: ``` source <(dr self completion bash) ``` ### Zsh #### Setup First, ensure completion is enabled in your `~/.zshrc`: ``` # Add these lines if not already present autoload -U compinit compinit ``` #### Installation ``` # Create completions directory if it doesn't exist mkdir -p ~/.zsh/completions # Generate completion script dr self completion zsh > ~/.zsh/completions/_dr # Add to fpath in ~/.zshrc (if not already there) echo 'fpath=(~/.zsh/completions $fpath)' >> ~/.zshrc # Reload your shell source ~/.zshrc ``` #### Alternative (using system directory) ``` # Generate and install the completion script dr self completion zsh > "${fpath[1]}/_dr" # Clear completion cache rm -f ~/.zcompdump # Reload your shell source ~/.zshrc ``` #### Temporary session For the current session only: ``` source <(dr self completion zsh) ``` ### Fish ``` # Generate and install the completion script dr self completion fish > ~/.config/fish/completions/dr.fish # Reload fish configuration source ~/.config/fish/config.fish ``` #### Temporary session For the current session only: ``` dr self completion fish | source ``` ### PowerShell #### Persistent installation To add the CLI to your PowerShell profile: ``` # Generate the completion script dr self completion powershell > dr.ps1 # Find your profile location echo $PROFILE # Add the following line to your profile . C:\path\to\dr.ps1 ``` Alternatively, you can install it directly: ``` # Add to profile dr self completion powershell >> $PROFILE # Reload profile . $PROFILE ``` #### Temporary session For the current session only: ``` dr self completion powershell | Out-String | Invoke-Expression ``` ## Usage Once installed, completions work automatically when you press `Tab`: ### Command completion ``` # Type 'dr' and press Tab to see all commands dr # Shows: auth, completion, dotenv, run, templates, version # Type 'dr auth' and press Tab to see subcommands dr auth # Shows: login, logout, set-url # Type 'dr templates' and press Tab dr templates # Shows: clone, list, setup, status ``` ### Flag completion ``` # Type a command and -- then Tab to see flags dr run -- # Shows: --concurrency, --dir, --exit-code, --help, --list, --parallel, --silent, --watch, --yes # Partial flag matching works too dr run --par # Completes to: dr run --parallel ``` ### Argument completion For commands that support it: ``` # Template names when using clone dr templates clone # Shows available template names from DataRobot # Task names when using run (if in a template directory) dr run # Shows available tasks from Taskfile ``` ## Verification Test that completions are working: ``` # Try command completion dr te # Should complete to: dr templates # Try flag completion dr run --l # Should complete to: dr run --list ``` ## Troubleshooting ### Completions not working #### Bash Important: Bash completions require the `bash-completion` package to be installed first. 1. Install bash-completion if not already installed: ``` # macOS (Homebrew) brew install bash-completion@2 # Ubuntu/Debian sudo apt-get install bash-completion # RHEL/CentOS sudo yum install bash-completion ``` 1. Check that bash-completion is loaded: ``` # macOS brew list bash-completion@2 # Linux (Ubuntu/Debian) dpkg -l | grep bash-completion ``` 1. Verify completion script location: ``` ls -l /etc/bash_completion.d/dr # or on macOS ls -l $(brew --prefix)/etc/bash_completion.d/dr ``` 1. Check .bashrc sources completion: ``` grep bash_completion ~/.bashrc ``` 1. Reload your shell: source~/.bashrc #### Zsh 1. Verifycompinitis in~/.zshrc: grepcompinit~/.zshrc 2. Check completion file location: ``` ls -l ~/.zsh/completions/_dr # or echo $fpath[1] ls -l $fpath[1]/_dr ``` 1. Clear completion cache: rm-f~/.zcompdump 2. Reload Zsh: execzsh #### Fish 1. Check that the completion file exists: ls-l~/.config/fish/completions/dr.fish 2. Verify that Fish recognizes it: complete-Cdr 3. Reload Fish: source~/.config/fish/config.fish #### PowerShell 1. Check execution policy: Get-ExecutionPolicy If it's `Restricted`, change it: ``` Set-ExecutionPolicy RemoteSigned -Scope CurrentUser ``` 1. Verify profile loads completion: cat$PROFILE 2. Reload profile: .$PROFILE ### Permission denied If you get permission errors when installing: ``` # Use sudo for system-wide installation sudo dr completion bash > /etc/bash_completion.d/dr # Or use user-level installation dr completion bash > ~/.bash_completions/dr source ~/.bash_completions/dr ``` ### Completion cache issues For Zsh, if completions are outdated: ``` # Clear cache rm -f ~/.zcompdump* # Rebuild cache compinit ``` ## Advanced configuration ### Custom completion behavior You can customize how completions work by modifying the generated script. For example, in the Bash completion script, you can add custom completion logic: ``` # Extract the generated script dr completion bash > ~/dr-completion.bash # Edit the script to add custom logic vim ~/dr-completion.bash # Source it in your .bashrc source ~/dr-completion.bash ``` ### Multiple shell support If you use multiple shells, install completions for each: ``` # Install for all shells you use dr completion bash > ~/.bash_completions/dr dr completion zsh > ~/.zsh/completions/_dr dr completion fish > ~/.config/fish/completions/dr.fish ``` ## Updating completions When the CLI is updated, regenerate completions: ``` # Bash dr completion bash | sudo tee /etc/bash_completion.d/dr # Zsh dr completion zsh > ~/.zsh/completions/_dr rm -f ~/.zcompdump # Fish dr completion fish > ~/.config/fish/completions/dr.fish # PowerShell dr completion powershell > $PROFILE ``` ## See also - Getting started setup guide - Command reference - Cobra documentation (underlying completion framework) --- # Environment variables URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/template-system/environment-variables.html > Manage environment variables and .env files in DataRobot templates with interactive configuration tools. # Environment variables This page outlines how to manage environment variables and `.env` files in DataRobot templates. DataRobot templates use `.env` files to store configuration variables needed by your application. The CLI provides tools to: - Create .env files from templates - Interactively edit variables - Validate configuration - Securely manage secrets ## File structure ### .env.template The template provided by the repository (committed to Git): ``` # Required configuration APP_NAME= DATAROBOT_ENDPOINT= DATAROBOT_API_TOKEN= # Optional configuration # DEBUG=false # LOG_LEVEL=info # PORT=8080 # Database configuration # DATABASE_URL= # DATABASE_POOL_SIZE=10 # Cache configuration # CACHE_ENABLED=false # CACHE_URL= ``` #### Characteristics - Committed to version control - Contains empty required variables - Comments indicate optional variables - Includes documentation comments ### .env The actual configuration file (never committed): ``` # Required configuration APP_NAME=my-awesome-app DATAROBOT_ENDPOINT=https://app.datarobot.com DATAROBOT_API_TOKEN=*** # Optional configuration DEBUG=true LOG_LEVEL=debug PORT=8000 # Database configuration DATABASE_URL=postgresql://localhost:5432/mydb DATABASE_POOL_SIZE=5 ``` Characteristics: - Generated from `.env.template`. - Contains actual values. - Never committed (in `.gitignore`). - User-specific configuration. ## Create environment files ### Use the wizard The interactive wizard guides you through configuration. ``` # In a template directory dr dotenv setup ``` or ``` # During template setup dr templates setup ``` #### Wizard workflow 1. Loads .env.template . 2. Discovers configuration prompts. 3. Shows interactive questions. 4. Validates inputs. 5. Generates an .env file. ### Manual creation To copy and edit a template manually: ``` # Copy the template cp .env.template .env # Edit the template with your preferred editor vim .env # Alternatively, use the CLI editor dr dotenv ``` ## Manage variables ### Interactive editor Launch the built-in editor to manage variables: ``` dr dotenv ``` #### Features - List all variables - Mask secrets (passwords, API keys) - Start wizard mode - Directly edit variables #### Commands ``` Variables found in .env: APP_NAME: my-awesome-app DATAROBOT_ENDPOINT: https://app.datarobot.com DATAROBOT_API_TOKEN: *** DEBUG: true Press w to set up variables interactively. Press e to edit the file directly. Press enter to finish and exit. ``` ### Wizard mode You can also interactively configure a template with prompts. ``` dr dotenv setup ``` #### Advantages - Guided setup - Built-in validation - Conditional prompts - Help text for each variable ### Direct editing To edit the file directly: ``` dr dotenv edit # Press 'e' to enter editor mode # Or use external editor vim .env ``` ## Variable types ### Required variables The following variables must be set before running the application: ``` # .env.template shows these without comments APP_NAME= DATAROBOT_ENDPOINT= DATAROBOT_API_TOKEN= ``` The wizard enforces that an application name must be provided. ``` Enter your application name > _ (Cannot proceed without entering a value) ``` ### Optional variables The following variables are optional and can be left empty (shown as comments): ``` # .env.template shows these with # prefix # DEBUG=false # LOG_LEVEL=info ``` The wizard allows you to skip binding these variables: ``` Enable debug mode? (optional) > None (leave blank) Yes No ``` ### Secret variables Sensitive values that should be masked during input and display. To define secret variables: ``` # In .datarobot/prompts.yaml prompts: - key: "api_key" env: "API_KEY" type: "secret_string" help: "Enter your API key" ``` #### Auto-detection Variables with names containing `PASSWORD`, `SECRET`, `KEY`, or `TOKEN` are automatically treated as secrets. #### Display behavior - The wizard input's secrets are masked with bullet characters (••••). - The editor view displays secrets as *** . - The actual file contains secrets as plain text values. #### Security best practices - Always add .env to .gitignore . - Use secret_string type for all sensitive values. - Never commit .env files to version control. ### Auto-generated secrets You can cryptographically secure random values for application secrets: ``` prompts: - key: "session_secret" env: "SESSION_SECRET" type: "secret_string" generate: true help: "Session encryption key (auto-generated)" ``` #### Features - Generates 32-character random string if no value exists. - Uses base64 URL-safe encoding. - Preserves existing values (only generates when empty). - User can override secrets with a custom value. ### Conditional variables These variables are only shown or required based on your other selections: ``` # In .datarobot/prompts.yaml prompts: - key: "enable_database" options: - name: "Yes" requires: "database_config" - name: "No" - key: "database_url" section: "database_config" env: "DATABASE_URL" help: "Database connection string" ``` If `Enable database = No`, then `DATABASE_URL` is not shown. ## Environment variable discovery The CLI discovers variables from multiple sources: ### 1. Template file (.env.template) ``` # Variables defined in template APP_NAME= PORT=8080 ``` ### 2. Prompt definitions (.datarobot/prompts.yaml) ``` prompts: - key: "app_name" env: "APP_NAME" help: "Application name" ``` ### 3. Existing .env file ``` # Previously configured values APP_NAME=my-app ``` ### 4. Current environment ``` # Shell environment variables export PORT=3000 ``` ### Merge priority The CLI merges in the following order of priority (highest priority first): 1. User input from wizard. 2. Current shell environment. 3. Existing .env values. 4. Template defaults. ## Common patterns ### Database configuration ``` # PostgreSQL DATABASE_URL=postgresql://user:password@localhost:5432/dbname DATABASE_POOL_SIZE=10 DATABASE_TIMEOUT=30 # MySQL DATABASE_URL=mysql://user:password@localhost:3306/dbname # MongoDB DATABASE_URL=mongodb://localhost:27017/dbname ``` ### Authentication ``` # API Key DATAROBOT_API_TOKEN=your_api_token_here # OAuth AUTH_PROVIDER=oauth2 AUTH_CLIENT_ID=client_id AUTH_CLIENT_SECRET=*** AUTH_REDIRECT_URL=http://localhost:8080/callback # JWT JWT_SECRET=*** JWT_EXPIRATION=3600 ``` ### Feature flags ``` # Enable/disable features FEATURE_ANALYTICS=true FEATURE_MONITORING=false FEATURE_CACHING=true # Or as comma-separated list ENABLED_FEATURES=analytics,caching ``` ### Logging ``` # Log level LOG_LEVEL=debug # debug, info, warn, error # Log format LOG_FORMAT=json # json, text # Log output LOG_OUTPUT=stdout # stdout, file # Log file path LOG_FILE=/var/log/app.log ``` ## Security best practices ### Never commit .env files Ensure that `.gitignore` includes: ``` # Environment variables .env .env.local .env.*.local # Keep templates !.env.template !.env.example ``` ### Use strong secrets ``` # ✓ Good - strong random secret JWT_SECRET=a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6 # ✗ Bad - weak secret JWT_SECRET=secret123 ``` Generate secure secrets: ``` # Random 32-byte hex string openssl rand -hex 32 ``` ### Restrict file permissions ``` # Only the owner can read/write chmod 600 .env # Verify ls -la .env # Should show: -rw------- (600) ``` ### Use different configs per environment ``` # Development .env.development # Staging .env.staging # Production .env.production ``` Load based on the environment: ``` export ENV=production dr run deploy ``` ### Avoid hardcoding in code ``` # ✗ Bad api_token = "abc123" # ✓ Good import os api_token = os.getenv("DATAROBOT_API_TOKEN") ``` ## Validation ### Validate with dr dotenv To validate your environment configuration against template requirements: ``` dr dotenv validate ``` This command validates the following: - All required variables defined in .datarobot/prompts.yaml . - Core DataRobot variables ( DATAROBOT_ENDPOINT , DATAROBOT_API_TOKEN ). - Conditional requirements based on selected options. - Both .env file and environment variables. #### Example output Successful validation: ``` Validating required variables: APP_NAME: my-app DATAROBOT_ENDPOINT: https://app.datarobot.com DATAROBOT_API_TOKEN: *** DATABASE_URL: postgresql://localhost:5432/db Validation passed: all required variables are set. ``` Validation errors: ``` Validating required variables: APP_NAME: my-app DATAROBOT_ENDPOINT: https://app.datarobot.com Validation errors: Error: required variable DATAROBOT_API_TOKEN is not set Description: DataRobot API token for authentication Set this variable in your .env file or run `dr dotenv setup` to configure it. Error: required variable DATABASE_URL is not set Description: PostgreSQL database connection string Set this variable in your .env file or run `dr dotenv setup` to configure it. ``` #### Use cases - Pre-flight checks before running tasks. - CI/CD pipeline validation. - Debugging missing configuration. - Troubleshooting application startup issues. ### Required variables check Commands like `dr run` automatically validate required variables. ``` $ dr run dev Error: Missing required environment variables: - APP_NAME - DATAROBOT_API_TOKEN Please run: dr dotenv setup ``` ### Format validation For variables with specific formats: ``` # URL validation DATAROBOT_ENDPOINT=https://app.datarobot.com # ✓ Valid DATAROBOT_ENDPOINT=not-a-url # ✗ Invalid # Port validation PORT=8080 # ✓ Valid PORT=99999 # ✗ Invalid (out of range) # Email validation EMAIL=user@example.com # ✓ Valid EMAIL=invalid # ✗ Invalid ``` ## Advanced features ### Variable substitution Reference other variables: ``` # Base URL BASE_URL=https://app.datarobot.com # API endpoint uses base URL API_ENDPOINT=${BASE_URL}/api/v2 # Full URL becomes: https://app.datarobot.com/api/v2 ``` ### Multi-line values For long values: ``` # Single line PRIVATE_KEY="-----BEGIN PRIVATE KEY-----\nMIIE..." # Or use actual newlines PRIVATE_KEY="-----BEGIN PRIVATE KEY----- MIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQC... -----END PRIVATE KEY-----" ``` ### Comments Document your configuration: ``` # Application Configuration APP_NAME=my-app # Application identifier PORT=8080 # HTTP server port # Database Configuration # Format: protocol://user:password@host:port/database DATABASE_URL=postgresql://localhost:5432/mydb ``` ## Troubleshooting ### Variables not loading ``` # Check .env exists ls -la .env # Verify format cat .env # Check for syntax errors # Each line should be: KEY=value ``` ### Secrets exposed ``` # Check .gitignore includes .env cat .gitignore | grep .env # Check Git status git status # Should NOT show .env # If .env is tracked, remove it git rm --cached .env git commit -m "Remove .env from tracking" ``` ### Permission errors ``` # Fix permissions chmod 600 .env # Verify ls -la .env ``` ### Variables not expanding ``` # Ensure proper syntax for variable substitution # Works: API_URL=${BASE_URL}/api # Doesn't work: API_URL=$BASE_URL/api # Missing braces ``` ### Configuration not working Use `dr dotenv validate` to diagnose issues: ``` # Validate configuration dr dotenv validate # If validation passes but issues persist, check: # 1. Environment variables override .env env | grep DATAROBOT # 2. Ensure .env is in correct location (repository root) pwd ls -la .env # 3. Check if application is loading .env file # Some applications need explicit .env loading ``` ## Common workflows ### Initial setup ``` cd my-template dr dotenv setup dr dotenv validate dr run dev ``` ### Update credentials ``` dr auth login dr dotenv update dr dotenv validate ``` ### Validate before deployment ``` dr dotenv validate && dr run deploy ``` ### Edit and validate ``` dr dotenv edit dr dotenv validate ``` ## See also - Interactive configuration : Configuration wizard details. - Template structure : Template organization. - dotenv command : dotenv command reference. --- # Template system URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/template-system/index.html > Pre-configured application scaffolds for building and deploying custom applications to DataRobot. # Template system DataRobot templates are pre-configured application scaffolds that help you quickly build and deploy custom applications to DataRobot. Each template includes: - Application source code - Configuration prompts - Environment setup tools - Task definitions - Documentation ## Documentation ### Core concepts - Template structure: How templates are organized. - Interactive configuration: The configuration wizard. - Environment variables: Managing .env files. ## Quickstart ### Using a template ``` # List available templates dr templates list # Interactive setup (recommended) dr templates setup # Manual setup dr templates clone my-template cd my-template dr dotenv setup dr run dev ``` ### Create a template ``` # 1. Create structure mkdir my-template cd my-template # 2. Add metadata mkdir .datarobot cat > .datarobot/prompts.yaml < .env.template < Taskfile.gen.yaml < Interactive configuration wizard for setting up DataRobot templates with smart prompts, validation, and conditional logic. # Interactive configuration system The DataRobot CLI features a powerful interactive configuration system that guides users through setting up application templates with smart prompts, validation, and conditional logic. ## Overview The interactive configuration system is built using [Bubble Tea](https://github.com/charmbracelet/bubbletea), a Go framework for building terminal user interfaces. It provides: - Guided setup: A step-by-step wizard for configuration - Smart prompts: Context-aware questions with validation - Conditional logic: Show/hide prompts based on previous answers - Multiple input types: Text fields, checkboxes, and selection lists - Visual feedback: Beautiful terminal UI with progress indicators ## Architecture ### Components The configuration system consists of three main layers: ``` ┌─────────────────────────────────────────┐ │ User interface layer │ │ (Bubble Tea models and views) │ ├─────────────────────────────────────────┤ │ Business logic layer │ │ (Prompt processing and validation) │ ├─────────────────────────────────────────┤ │ Data layer │ │ (Environment discovery and storage) │ └─────────────────────────────────────────┘ ``` ### Key files - cmd/dotenv/model.go : The main dotenv editor model - cmd/dotenv/promptModel.go : Individual prompt handling - internal/envbuilder/discovery.go : Prompt discovery from templates - cmd/templates/setup/model.go : Template setup wizard orchestration ## Configuration flow ### 1. Template setup wizard When you run `dr templates setup`, the wizard flow is: Template selection: On the template list screen, use the arrow keys to navigate, press `/` to filter by search term, and press Enter to select a template and then enter the target directory name. Only templates available to your user are shown. For full details, see [Templates command - Template selection screen](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/templates.html#template-selection-screen). ``` Welcome screen ↓ DataRobot URL configuration (if needed) ↓ Authentication (if needed) ↓ Template selection ↓ Template cloning ↓ Environment configuration (skipped if previously completed) ↓ Completion ``` State-aware behavior: If `dr dotenv setup` has been successfully run in the past (tracked via state file), the Environment configuration step is automatically skipped. This allows you to re-run the template setup without re-configuring your environment variables. See [Configuration - State tracking](https://docs.datarobot.com/en/docs/agentic-ai/cli/configuration.html#state-tracking) for details. ### 2. Environment configuration The environment configuration phase (dotenv wizard): ``` Load .env template ↓ Discover user prompts (from .datarobot files) ↓ Initialize response map ↓ For each required prompt: ├── Display prompt with help text ├── Show options (if applicable) ├── Capture user input ├── Validate input ├── Update required sections (conditional) └── Move to next prompt ↓ Generate .env file ↓ Save configuration ``` ## Prompt types ### Text input prompts Simple text entry for values: ``` # Example from .datarobot/prompts.yaml prompts: - key: "database_url" env: "DATABASE_URL" help: "Enter your database connection string" default: "postgresql://localhost:5432/mydb" optional: false ``` User experience: ``` Enter your database connection string > postgresql://localhost:5432/mydb█ Default: postgresql://localhost:5432/mydb ``` ### Secret string prompts Secure text entry with input masking for sensitive values: ``` prompts: - key: "api_key" env: "API_KEY" type: "secret_string" help: "Enter your API key" optional: false ``` User experience: ``` Enter your API key > ••••••••••••••••••█ Input is masked for security ``` Features: - Input is masked with bullets (•). - Prevents shoulder-surfing and accidental exposure. - Stored as plain text in .env file (file should be in .gitignore ). ### Auto-generated secrets Secret strings can be automatically generated: ``` prompts: - key: "session_secret" env: "SESSION_SECRET" type: "secret_string" generate: true help: "Session encryption key (auto-generated)" optional: false ``` Behavior: - If no value exists, a cryptographically secure random string is generated. - Generated secrets are 32 characters long. - Uses base64 URL-safe encoding. - Only generates when value is empty (preserves existing secrets). User experience: ``` Session encryption key (auto-generated) > ••••••••••••••••••••••••••••••••█ A random secret was generated. Press Enter to accept or type a custom value. ``` ### Single selection prompts Choose one option from a list: ``` prompts: - key: "environment" env: "ENVIRONMENT" help: "Select your deployment environment" optional: false multiple: false options: - name: "Development" value: "dev" - name: "Staging" value: "staging" - name: "Production" value: "prod" ``` User experience: ``` Select your deployment environment > Development Staging Production ``` ### Multiple selection prompts Choose multiple options (checkboxes): ``` prompts: - key: "features" env: "ENABLED_FEATURES" help: "Select features to enable (space to toggle, enter to confirm)" optional: false multiple: true options: - name: "Analytics" value: "analytics" - name: "Monitoring" value: "monitoring" - name: "Caching" value: "caching" ``` User experience: ``` Select features to enable (Use Space to toggle and Enter to confirm) > [x] Analytics [ ] Monitoring [x] Caching ``` ### Optional prompts Prompts that can be skipped: ``` prompts: - key: "cache_url" env: "CACHE_URL" help: "Enter cache server URL (optional)" optional: true options: - name: "None (leave blank)" blank: true - name: "Redis" value: "redis://localhost:6379" - name: "Memcached" value: "memcached://localhost:11211" ``` ## Conditional prompts Prompts can be shown or hidden based on previous selections using the `requires` and `section` fields. ### Section-based conditions ``` prompts: - key: "enable_database" help: "Do you want to use a database?" multiple: true options: - name: "Yes" value: "yes" requires: "database_config" # Enables this section - name: "No" value: "no" database_config: # Only shown if enabled - key: "database_type" help: "Select database type" options: - name: "PostgreSQL" value: "postgres" - name: "MySQL" value: "mysql" - env: "DATABASE_URL" help: "Enter database connection string" ``` ### How it works 1. Initial state: All sections start as disabled 2. User selection: When you select an option with requires: "section_name" 3. Section activation: That section becomes enabled 4. Prompt display: Prompts with matching section: "section_name" are shown 5. Cascade: Newly shown prompts can activate additional sections ### Example flow ``` Q: Do you want to use a database? [x] Yes ← User selects this (requires: "database_config") → Section "database_config" is now enabled Q: Select database type (Now shown because section is enabled) > PostgreSQL Q: Enter database connection string (Also shown because section is enabled) > postgresql://localhost:5432/db ``` ## Prompt discovery The CLI automatically discovers prompts from `.datarobot` directories in your template. ### Discovery process ``` // From internal/envbuilder/discovery.go func GatherUserPrompts(rootDir string) ([]UserPrompt, []string, error) { // 1. Recursively find all .datarobot directories // 2. Load prompts.yaml from each directory // 3. Parse and validate prompt definitions // 4. Build dependency graph (sections and requires) // 5. Return ordered prompts with root sections } ``` ### Prompt file structure Create `.datarobot/prompts.yaml` in any directory: ``` my-template/ ├── .datarobot/ │ └── prompts.yaml # Root level prompts ├── backend/ │ └── .datarobot/ │ └── prompts.yaml # Backend-specific prompts ├── frontend/ │ └── .datarobot/ │ └── prompts.yaml # Frontend-specific prompts └── .env.template ``` Each `prompts.yaml`: ``` prompts: - key: "unique_key" env: "ENV_VAR_NAME" # Optional: Environment variable to set type: "secret_string" # Optional: "string" (default) or "secret_string" help: "Help text shown to user" default: "default value" # Optional optional: false # Optional: Can be skipped multiple: false # Optional: Allow multiple selections generate: false # Optional: Auto-generate random value (secret_string only) section: "section_name" # Optional: Only show if section enabled options: # Optional: List of choices - name: "Display Name" value: "actual_value" requires: "other_section" # Optional: Enable section if selected ``` ## UI components ### Prompt model Each prompt is rendered by a `promptModel` that handles: - Input capture (text field or list) - Visual rendering - State management - Validation - Success callback ``` type promptModel struct { prompt envbuilder.UserPrompt input textinput.Model // For text prompts list list.Model // For selection prompts Values []string // Captured values successCmd tea.Cmd // Callback when complete } ``` ### List rendering Custom item delegate for beautiful list rendering: ``` type itemDelegate struct { multiple bool // Show checkboxes } func (d itemDelegate) Render(w io.Writer, m list.Model, index int, listItem list.Item) { // Renders items with: // - Checkboxes for multiple selection // - Highlighting for current selection // - Proper spacing and styling } ``` ### State management The main model manages screen transitions: ``` type Model struct { screen screens // Current screen variables []variable // Loaded variables prompts []envbuilder.UserPrompt requires map[string]bool // Active sections envResponses map[string]string // User responses currentPromptIndex int currentPrompt promptModel } ``` ## Keyboard controls ### List navigation - ↑/↓ or j/k - Navigate list items - Space - Toggle checkbox (multiple selection) - Enter - Confirm selection - Esc - Go back to previous screen ### Text input - Type normally to enter text - Enter - Confirm input - Esc - Go back to previous screen ### Editor mode - w - Start wizard mode - e - Open text editor - Enter - Finish and save - Esc - Save and exit editor ## Advanced features ### Default values Prompts can have default values: ``` prompts: - key: "port" env: "PORT" help: "Application port" default: "8080" ``` Shown as: ``` Application port > 8080█ Default: 8080 ``` ### Secret values The CLI provides secure handling for sensitive values using the `secret_string` type: ``` prompts: - key: "api_key" env: "API_KEY" type: "secret_string" help: "Enter your API key" ``` Features: - Input is masked with bullet characters (••••) during entry. - Prevents accidental exposure of sensitive data. - Cryptographic auto-generation secures random values with generate: true . Auto-detection: Variables with names containing "PASSWORD", "SECRET", "KEY", or "TOKEN" are automatically treated as secrets in the editor view, displaying as `***` instead of the actual value. ### Generated secrets You can automatically generate secrets. ``` prompts: - key: "session_secret" env: "SESSION_SECRET" type: "secret_string" generate: true help: "Session encryption key" ``` When `generate: true` is set: - A 32-character cryptographically secure random string is generated if no value exists. - Uses base64 URL-safe encoding. - Preserves existing values (only generates for empty fields). - User can still override with a custom value. ### Merge environment variables The wizard intelligently merges: 1. Existing values from an .env file 2. Environment variables from the current shell 3. User responses from the wizard 4. Template defaults from .env.template Priority (highest to lowest): 1. User wizard responses 2. Current environment variables 3. Existing .env values 4. Template defaults ## Error handling ### Validation Prompts can validate input: ``` func (pm promptModel) submitInput() (promptModel, tea.Cmd) { pm.Values = pm.GetValues() // Don't submit if required and empty if !pm.prompt.Optional && len(pm.Values[0]) == 0 { return pm, nil // Stay on prompt } return pm, pm.successCmd // Proceed } ``` ### User feedback ``` // Visual feedback for errors if err != nil { sb.WriteString(errorStyle.Render("❌ " + err.Error())) } // Success indicators sb.WriteString(successStyle.Render("✓ Configuration saved")) ``` ## Integration example To add the interactive wizard to your template: ### 1. Create a prompts file `.datarobot/prompts.yaml`: ``` prompts: - key: "app_name" env: "APP_NAME" help: "Enter your application name" optional: false - key: "features" help: "Select features to enable" multiple: true options: - name: "Authentication" value: "auth" requires: "auth_config" - name: "Database" value: "database" requires: "db_config" - key: "auth_provider" section: "auth_config" env: "AUTH_PROVIDER" help: "Select authentication provider" options: - name: "OAuth2" value: "oauth2" - name: "SAML" value: "saml" - key: "database_url" section: "db_config" env: "DATABASE_URL" help: "Enter database connection string" default: "postgresql://localhost:5432/myapp" ``` ### 2. Create an environment template `.env.template`: ``` # Application settings APP_NAME= # Features ENABLED_FEATURES= # Authentication (if enabled) # AUTH_PROVIDER= # Database (if enabled) # DATABASE_URL= ``` ### 3. Run setup ``` dr templates setup ``` The wizard automatically discovers and uses your prompts. ## Best practices ### 1. Clear help text ``` # ✓ Good help: "Enter your PostgreSQL connection string (e.g., postgresql://user:pass@host:5432/db)" # ✗ Bad help: "Database URL" ``` ### 2. Sensible defaults ``` # Provide reasonable defaults default: "postgresql://localhost:5432/myapp" ``` ### 3. Organize with sections ``` # Group related prompts - key: "enable_monitoring" options: - name: "Yes" requires: "monitoring_config" - key: "monitoring_url" section: "monitoring_config" help: "Monitoring service URL" ``` ### 4. Use descriptive keys ``` # ✓ Good key: "database_connection_pool_size" # ✗ Bad key: "pool" ``` ### 5. Validate input Use `optional: false` for required fields: ``` prompts: - key: "api_token" env: "API_TOKEN" type: "secret_string" help: "Enter your DataRobot API key" optional: false # Required! ``` ### 6. Use secret types for sensitive data Always use `secret_string` for passwords, API keys, and tokens. ``` # ✓ Good prompts: - key: "database_password" env: "DATABASE_PASSWORD" type: "secret_string" help: "Database password" # ✗ Bad (exposes password during input) prompts: - key: "database_password" env: "DATABASE_PASSWORD" help: "Database password" ``` ### 7. Auto-generate secrets when possible Use `generate: true` for application secrets that don't need to be memorized. ``` # ✓ Good for session keys, encryption keys prompts: - key: "jwt_secret" env: "JWT_SECRET" type: "secret_string" generate: true help: "JWT signing key" # ✗ Don't auto-generate user credentials prompts: - key: "admin_password" env: "ADMIN_PASSWORD" type: "secret_string" help: "Administrator password" help: "Enter your DataRobot API key" optional: false # Required! ``` ## Testing prompts Test your prompt configuration: ``` # Dry run without saving dr dotenv setup # Check discovered prompts dr templates status # View generated .env cat .env ``` ## See also - Template structure : How templates are organized - Environment variables : Manage .env files - Command reference: dotenv : dotenv command documentation --- # Template system structure URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/template-system/structure.html > Understanding how DataRobot organizes and configures application templates. # Template system structure This page provides an understanding of how DataRobot organizes and configures application templates. ## Overview DataRobot templates are Git repositories that contain application code, configuration, and metadata to deploy custom applications to DataRobot. The CLI provides tools to clone, configure, and manage these templates. ## Template repository structure A typical template repository: ``` my-datarobot-template/ ├── .datarobot/ # Template metadata │ ├── prompts.yaml # Configuration prompts │ ├── config.yaml # Template settings │ └── cli/ # CLI-specific files │ └── bin/ # Quickstart scripts │ └── quickstart.sh ├── .env.template # Environment variable template ├── .taskfile-data.yaml # Taskfile configuration (optional) ├── .gitignore ├── README.md ├── Taskfile.gen.yaml # Generated task definitions ├── src/ # Application source code │ ├── app/ │ │ └── main.py │ └── tests/ ├── requirements.txt # Python dependencies └── package.json # Node dependencies (if applicable) ``` ## Template metadata ### .datarobot directory The `.datarobot` directory contains template-specific configuration: ``` .datarobot/ ├── prompts.yaml # User prompts for setup wizard ├── config.yaml # Template metadata └── README.md # Template-specific docs ``` ### prompts.yaml Defines interactive configuration prompts. See [Interactive configuration](https://docs.datarobot.com/en/docs/agentic-ai/cli/template-system/interactive-config.html) for more details. Review the example prompt yaml configuration below. ``` prompts: - key: "app_name" env: "APP_NAME" help: "Enter your application name" default: "my-app" optional: false - key: "deployment_target" env: "DEPLOYMENT_TARGET" help: "Select deployment target" options: - name: "Development" value: "dev" - name: "Production" value: "prod" ``` ### config.yaml Template metadata and settings: ``` name: "My DataRobot Template" version: "1.0.0" description: "A sample DataRobot application template" author: "DataRobot" repository: "https://github.com/datarobot/template-example" # Minimum CLI version required min_cli_version: "0.1.0" # Tags for discovery tags: - python - streamlit - machine-learning # Required DataRobot features requirements: features: - custom_applications permissions: - CREATE_CUSTOM_APPLICATION ``` ## Environment configuration ### .env.template Review a template for environment variables. Note that the commented lines are optional. ``` # Required configuration APP_NAME= DATAROBOT_ENDPOINT= # Optional configuration (commented out by default) # DEBUG=false # LOG_LEVEL=info # Database configuration (conditional) # DATABASE_URL=postgresql://localhost:5432/mydb # DATABASE_POOL_SIZE=10 # Authentication # AUTH_ENABLED=false # AUTH_PROVIDER=oauth2 ``` ### .env (Generated) Created by the CLI during setup, the `.env` file contains actual values. Note that `.env` should be in `.gitignore` and never committed. ``` # Required configuration APP_NAME=my-awesome-app DATAROBOT_ENDPOINT=https://app.datarobot.com # Optional configuration DEBUG=true LOG_LEVEL=debug # Database configuration DATABASE_URL=postgresql://localhost:5432/mydb DATABASE_POOL_SIZE=5 ``` ## Quickstart scripts Templates can optionally provide quickstart scripts to automate application initialization. These scripts are executed by the `dr start` command. Quickstart scripts must be placed in `.datarobot/cli/bin/`. ### Naming conventions Scripts must start with `quickstart` (case-sensitive): - ✅ quickstart - ✅ quickstart.sh - ✅ quickstart.py - ✅ quickstart-dev - ❌ Quickstart.sh (wrong casing) - ❌ start.sh (wrong name) If there are multiple scripts matching the pattern, the first one found in lexicographical order will be executed. ### Platform requirements Review the requirements for different platforms below. #### Unix/Linux/macOS - Must have executable permissions ( chmod +x ) - Can be any executable file (shell script, Python script, compiled binary, etc.) #### Windows - Must have an executable extension: .exe , .bat , .cmd , or .ps1 ### When to use quickstart scripts Quickstart scripts are useful for: - Multi-step initialization: When your application requires several setup steps - Dependency management: Install packages or tools before starting - Environment validation: Check prerequisites before launch - Custom workflows: Template-specific initialization logic ### Fallback behavior If `dr start` does not find a quickstart, it automatically launches the interactive `dr templates setup` wizard instead to ensure that you can always get started even without a custom script. ## Task definitions ### Taskfile.gen.yaml The CLI automatically generates `Taskfile.gen.yaml` to aggregate component tasks. This file includes a `dotenv` directive to load environment variables from `.env`. Important: Component taskfiles cannot have their own `dotenv` directives. The CLI detects conflicts and prevents generation if a component taskfile already has a `dotenv` declaration. The generated structure is shown below. ``` version: '3' dotenv: [".env"] includes: backend: taskfile: ./backend/Taskfile.yaml dir: ./backend frontend: taskfile: ./frontend/Taskfile.yaml dir: ./frontend ``` ### Component taskfiles Component directories define their own tasks: Review the structure of `backend/Taskfile.yaml` below. ``` version: '3' # Note: No dotenv directive are allowed here tasks: dev: desc: Start development server cmds: - python -m uvicorn src.app.main:app --reload test: desc: Run tests cmds: - pytest src/tests/ build: desc: Build application cmds: - docker build -t {{.APP_NAME}} . ``` ### Running tasks The `dr run` command requires a `.env` file to be present: ``` # List all available tasks dr run --list # Run a specific task dr run dev # Run multiple tasks dr run lint test # Run tasks in parallel dr run lint test --parallel ``` If you're not in a DataRobot template directory (no `.env` file), you'll see the following message: ``` You don't seem to be in a DataRobot Template directory. This command requires a .env file to be present. ``` ### Taskfile configuration data Template authors can optionally provide a `.taskfile-data.yaml` file to configure the generated Taskfile. This file allows specifying port numbers for development servers and other configuration data. See [dr task compose documentation](https://docs.datarobot.com/en/docs/agentic-ai/cli/commands/task.html#taskfile-data-configuration) for complete details on the file format and usage. ## Multi-level configuration Templates can have nested `.datarobot` directories for component-specific configuration: ``` my-template/ ├── .datarobot/ │ └── prompts.yaml # Root level prompts ├── backend/ │ ├── .datarobot/ │ │ └── prompts.yaml # Backend prompts │ └── src/ ├── frontend/ │ ├── .datarobot/ │ │ └── prompts.yaml # Frontend prompts │ └── src/ └── .env.template ``` ### Discovery order The CLI discovers prompts in this order: 1. Root .datarobot/prompts.yaml 2. Subdirectory prompts (depth-first search, up to depth 2) 3. Merged and deduplicated ### Example: backend prompts `backend/.datarobot/prompts.yaml`: ``` prompts: - key: "api_port" env: "API_PORT" help: "Backend API port" default: "8000" section: "backend" - key: "database_url" env: "DATABASE_URL" help: "Database connection string" section: "backend" ``` ### Example: frontend prompts `frontend/.datarobot/prompts.yaml`: ``` prompts: - key: "ui_port" env: "UI_PORT" help: "Frontend UI port" default: "3000" section: "frontend" - key: "api_endpoint" env: "API_ENDPOINT" help: "Backend API endpoint" default: "http://localhost:8000" section: "frontend" ``` ## Template lifecycle ### 1. Discovery Templates are discovered from DataRobot: ``` # List available templates dr templates list ``` Output: ``` Available templates: * python-streamlit - Streamlit application template * react-frontend - React frontend template * fastapi-backend - FastAPI backend template ``` ### 2. Cloning Clone a template to your local machine: ``` # Clone a specific template dr templates clone python-streamlit # Clone to a custom directory dr templates clone python-streamlit my-app ``` This: - Clones the Git repository - Sets up directory structure - Initializes configuration files ### 3. Configuration Configure the template interactively: ``` # Full setup wizard dr templates setup # Or configure existing template cd my-template dr dotenv setup ``` ### 4. Development Work on your application: ``` # Run development server (requires .env file) dr run dev # Run tests dr run test # Build for deployment dr run build ``` Note: All `dr run` commands require a `.env` file in the current directory. If you see an error about not being in a template directory, run `dr dotenv setup` to create your `.env` file. ### 5. Deployment Deploy to DataRobot: ``` dr run deploy ``` ## Template types ### Python templates ``` python-template/ ├── .datarobot/ ├── requirements.txt ├── setup.py ├── src/ │ └── app/ │ └── main.py ├── tests/ └── .env.template ``` #### Key features - Python dependencies in requirements.txt - Source code in src/ - Tests in tests/ ### Node.js templates ``` node-template/ ├── .datarobot/ ├── package.json ├── src/ │ └── index.js ├── tests/ └── .env.template ``` #### Key features - Node dependencies in package.json - Source code in src/ - npm scripts integration ### Multi-language templates ``` full-stack-template/ ├── .datarobot/ ├── backend/ │ ├── .datarobot/ │ ├── requirements.txt │ └── src/ ├── frontend/ │ ├── .datarobot/ │ ├── package.json │ └── src/ ├── docker-compose.yml └── .env.template ``` #### Key features - Separate backend and frontend - Component-specific configuration - Docker composition ## Best practices ### Version control Note: Always exclude `.env` and `Taskfile.gen.yaml` from version control. The CLI generates `Taskfile.gen.yaml` automatically. ``` # .gitignore should include: .env Taskfile.gen.yaml *.log __pycache__/ node_modules/ dist/ ``` ### Documentation Include a clear README. ``` # My template ## Quick start {: #quick-start } 1. Clone: `dr templates clone my-template` 2. Configure: `dr templates setup` 3. Run: `dr run dev` ## Available tasks {: #available-tasks } - `dr run dev`: development server. - `dr run test`: run tests. - `dr run build`: build for production. ``` ### Sensible defaults Provide defaults in `.env.template`. ``` # Good defaults for local development API_PORT=8000 DEBUG=true LOG_LEVEL=info ``` ### Clear prompts Use descriptive help text. ``` prompts: - key: "database_url" help: "PostgreSQL connection string (format: postgresql://user:pass@host:5432/dbname)" ``` ### 5. Organized structure Keep related files together. ``` src/ ├── api/ # API endpoints ├── models/ # Data models ├── services/ # Business logic └── utils/ # Utilities ``` ## Template updates ### Checking for updates ``` # Check current template status dr templates status # Shows: # - Current version # - Latest available version # - Modified files # - Available updates ``` ### Updating templates ``` # Update to latest version git pull origin main # Re-run configuration if needed dr dotenv setup ``` ## Creating your own template ### 1. Start with base structure ``` mkdir my-new-template cd my-new-template git init ``` ### 2. Add template files Create the necessary files: ``` # Configuration mkdir .datarobot touch .datarobot/prompts.yaml touch .env.template # Application structure mkdir -p src/app touch src/app/main.py # Tasks touch Taskfile.gen.yaml ``` ### 3. Define prompts `.datarobot/prompts.yaml`: ``` prompts: - key: "app_name" env: "APP_NAME" help: "Enter your application name" optional: false ``` ### 4. Create an environment template `.env.template`: ``` APP_NAME= DATAROBOT_ENDPOINT= ``` ### 5. Define tasks Create component Taskfiles (e.g., `backend/Taskfile.yaml`): ``` version: '3' tasks: dev: desc: Start development server cmds: - echo "Starting {{.APP_NAME}}" ``` ### 6. Configure Taskfile data Optional. Create `.taskfile-data.yaml` to provide additional configuration for the generated root taskfile: ``` # .taskfile-data.yaml # Optional configuration for dr task compose # Ports to display when running dev task ports: - name: Backend port: 8080 - name: Frontend port: 5173 ``` This allows developers using your template to see which ports services run on when they execute `task dev`. ### 7. Test the template ``` # Test the setup locally dr templates setup # Verify configuration dr run --list ``` ### 8. Publish the template ``` # Push to GitHub git add . git commit -m "Initial template" git push origin main # Register with DataRobot (contact your admin) ``` ## See also - Interactive configuration : Configuration wizard details. - Environment variables : Manage .env files. - dr run : Task execution. - dr task compose : Taskfile composition and configuration. - Command reference: templates : Template commands. --- # Troubleshooting URL: https://docs.datarobot.com/en/docs/agentic-ai/cli/troubleshooting.html > Index of common issues and where to find solutions in the DataRobot CLI documentation. # Troubleshooting ## Troubleshooting This page links to troubleshooting and common-issue sections across the CLI documentation. ## Installation and setup | Issue | Where to look | | --- | --- | | "dr: command not found" | Getting started - Common issues: dr: command not found | | "Failed to read config file" | Getting started - Common issues: Failed to read config file | | Uninstalling the CLI | Getting started - Uninstalling the CLI | ## Authentication | Issue | Where to look | | --- | --- | | "Authentication failed" | Getting started - Common issues: Authentication failed | | Browser doesn't open for login | Auth command - Common issues: Browser doesn't open | | Port already in use (OAuth) | Auth command - Common issues: Port already in use | | Invalid credentials, connection refused, SSL issues | Auth command - Common issues | ## Configuration | Issue | Where to look | | --- | --- | | Config not loading, invalid config, permission denied | Configuration - Troubleshooting | | Multiple configs, state tracking | Configuration - State tracking | | Environment variables | Configuration - Environment variables reference | ## Templates and setup wizard | Issue | Where to look | | --- | --- | | Template selection (navigate, filter, directory prompt) | Templates command - Template selection screen | | Interactive configuration and keyboard controls | Interactive configuration - Keyboard controls | ## Tasks and running | Issue | Where to look | | --- | --- | | "Not in a DataRobot Template directory" / no .env | dr run - Error handling, Configuration - What counts as a template directory | | Dotenv directive conflict (Taskfile) | dr run - Dotenv directive conflict | | Task binary not found | dr run - Task binary not found | | Tasks not found, env vars not loading | dr run - Troubleshooting | | dr task compose errors | dr task - Error handling | ## Environment variables (dotenv) | Issue | Where to look | | --- | --- | | Not in repository, missing .env, auth required | dotenv command - Error handling | | Validation failures | dotenv command - Validation failures | ## Debugging | Tip | Where to look | | --- | --- | | Verbose and debug output | Getting started - Getting help | | Global flags (--verbose, --debug, --skip-auth) | Command reference - Global flags | | View current config | dr self config — self command | ## Get help - In-app help: dr --help , dr --help - Issues and discussions: GitHub Issues and GitHub Discussions - Email: oss-community-management@datarobot.com ## See also - Getting started - Command reference - Configuration - Using Agentic AI templates? If you're building or running agentic workflows, see Agentic AI troubleshooting for agent-specific issues (prerequisites, deployment, local testing). --- # Agentic workflow with code URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-code/agentic-example.html # Agentic workflow with code This notebook demonstrates a simple agentic workflow in DataRobot, showing how MLOps can be used to server, monitor, and govern the workflow. After running the notebook, you will have DataRobot MLOps deployments for a simple agent that can also reliably perform basic arithmetic. This example also produces a separate deployment for the calculator tool used by the agent. ## Code asset overview The following code-based assets are used in this workflow, all of which you can download [here](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/simple_agent.zip). - calculator/custom.py : Custom deployment logic for the "calculator" tool that will allow the LLM to reliably perform simple arithmetic operations. - agent/custom.py : Custom deployment logic for the agent that will either respond directly to user prompts or alternatively first delegate to the calculator tool. - agent/requirements.txt : Python dependencies for the agent custom deployment. - agent/model-metadata.yaml : A configuration file for the agent deployment that specifies Azure OpenAI credentials and the identifier of the calculator deployment. - create_deployments.ipynb : This notebook file; includes code for creating and testing the deployments. ## Workflow outline 1. Create the calculator deployment 2. Update the model-metadata.yaml for the agent deployment 3. Create the agent deployment 4. Test and make predictions with the agent deployment ## 1. Create the calculator deployment The following cell deploys the files in the calculator directory ( `calculator/custom.py`). Create a custom model deployment by importing the DataRobot package and using DataRobot MLOps' deployment creation methods. This model functions as a calculator. You can bring two numbers and a mathematic operation to the deployment, and the model will return the answer. ``` import datarobot as dr default_prediction_server_id='5a61d7a0fbd723001a2f70d9' # Specify your prediction server here cm_calc = dr.CustomInferenceModel.create(name='Calculator', target_name='result', target_type='TextGeneration') cmv_calc = dr.CustomModelVersion.create_clean(cm_calc.id, base_environment_id='5e8c889607389fe0f466c72d', # 3.9 drop-in folder_path='./calculator') rmv_calc = dr.RegisteredModelVersion.create_for_custom_model_version(cmv_calc.id) d_calc = dr.Deployment.create_from_registered_model_version(rmv_calc.id, 'Calculator', default_prediction_server_id=default_prediction_server_id) ``` ## 2. Provide credentials In your text editor of choice, update `agent/model-metadata.yaml` with your Azure Open AI credentials and the deployment ID of the calculator deployment created in step 1. In production you should use the DataRobot credential store to expose secrets in the deployment. DataRobot has omitted this step for brevity of this example. ## 3. Create the agent deployment Use the code below to create a deployment for the agent. ``` cm_agent = dr.CustomInferenceModel.create(name='Agent', target_name='completion', target_type='TextGeneration') cmv_agent = dr.CustomModelVersion.create_clean(cm_agent.id, base_environment_id='5e8c889607389fe0f466c72d', # 3.9 drop-in, folder_path='./agent') dr.CustomModelVersionDependencyBuild.start_build(cm_agent.id, cmv_agent.id) rmv_agent = dr.RegisteredModelVersion.create_for_custom_model_version(cmv_agent.id) d_agent = dr.Deployment.create_from_registered_model_version(rmv_agent.id, 'Agent', default_prediction_server_id=default_prediction_server_id) ``` ## 4. Test the deployment The cells below communicate with the deployments by asking the calculator model a math problem ( `what is 4 x 752`) and receiving the answer retrieved by the agent. ``` from datarobot_predict.deployment import predict import pandas as pd import json messages = [ {'role': 'user', 'content': 'what is 4*752',} ] df, _ = predict(d_agent, pd.DataFrame([{'messages': json.dumps(messages)}])) df ``` ``` messages = [ {'role': 'user', 'content': 'hello',} ] df, _ = predict(d_agent, pd.DataFrame([{'messages': json.dumps(messages)}])) df ``` --- # Build and host a ChromaDB vector database URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-code/chromadb-vdb.html # Build and host a ChromaDB vector database The following notebook provides an example of how you can build, validate, and register a vector database to the DataRobot platform using DataRobot's Python client. It describes how to load in and host a ChromaDB in-memory vector store, with metadata filtering, within a custom model. This notebook is designed for use with DataRobot Notebooks; DataRobot recommends downloading this notebook and uploading it for use in the platform. Note that when using ChromaDB-hosted documents with custom models, maximum file size is 1GB per file. ## Setup The following steps outline the necessary configuration to integrate vector databases with the DataRobot platform. 1. This workflow uses the following feature flags. Contact your DataRobot representative or administrator for information on enabling these features. 2. Use a codespace, not a DataRobot Notebook, to ensure this notebook has access to a filesystem. 3. Set the notebook session timeout to 180 minutes. 4. Restart the notebook container using at least a "Medium" (16GB RAM) instance. 5. Optionally, upload your documents archive to the notebook filesystem. ### Install libraries Install the following libraries: ``` # Upgrade pip to fix langchain installation issues !pip install --upgrade pip setuptools ``` ``` !pip install "langchain" \ "langchain-community" \ "langchain-chroma" \ "sentence-transformers==3.0.0" \ "datarobotx" \ "unstructured" \ "pysqlite3-binary" ``` ``` # replace sqlite3 with pysqlite3 to fix chroma issues __import__('pysqlite3') import sys sys.modules['sqlite3'] = sys.modules.pop('pysqlite3') ``` ``` import datarobot as dr import datarobotx as drx from datarobot.models.genai.vector_database import CustomModelVectorDatabaseValidation from datarobot.models.genai.vector_database import VectorDatabase ``` ### Connect to DataRobot Read more about options for [connecting to DataRobot from the Python client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ### Download sample data This example references a sample dataset made from the DataRobot english documentation. To experiment with your own data, modify this section and/or the "Load and split text" section to reference your local dataset. Note: If you are a self-managed user, you must modify code samples that reference `app.datarobot.com` to the appropriate URL for your instance. ``` import requests, zipfile, io SOURCE_DOCUMENTS_ZIP_URL = "https://s3.amazonaws.com/datarobot_public_datasets/ai_accelerators/datarobot_english_documentation_5th_December.zip" UNZIPPED_DOCS_DIR = "datarobot_english_documentation" STORAGE_DIR = "storage" r = requests.get(SOURCE_DOCUMENTS_ZIP_URL) z = zipfile.ZipFile(io.BytesIO(r.content)) z.extractall(f"{STORAGE_DIR}/") ``` ### Load and split text Next, load the DataRobot documentation dataset and split it into chunks. If you are applying this recipe to a different use case, consider the following: - Use additional or alternative document loaders. - Filter out extraneous and noisy documents. - Choose an appropriate chunk_size and overlap . These are counted by number of characters, not tokens. ``` import re from langchain.document_loaders import DirectoryLoader from langchain.text_splitter import RecursiveCharacterTextSplitter SOURCE_DOCUMENTS_DIR = f"{STORAGE_DIR}/{UNZIPPED_DOCS_DIR}/" SOURCE_DOCUMENTS_FILTER = "**/*.txt" loader = DirectoryLoader(f"{SOURCE_DOCUMENTS_DIR}", glob=SOURCE_DOCUMENTS_FILTER) splitter = RecursiveCharacterTextSplitter( chunk_size=128, chunk_overlap=0, ) print(f"Loading {SOURCE_DOCUMENTS_DIR} directory") data = loader.load() print(f"Splitting {len(data)} documents") docs = splitter.split_documents(data) for doc in docs: doc.metadata['source'] = re.sub( rf'{STORAGE_DIR}/{UNZIPPED_DOCS_DIR}/datarobot_docs/en/(.+)\.md', r'https://docs.datarobot.com/en/docs/\1.html', doc.metadata['source'] ) doc.metadata["category"] = doc.metadata["source"].split("|")[-1].replace(".txt", "") print(f"Created {len(docs)} documents") ``` ## Create a vector database from documents Use the following cell to build a vector database from the DataRobot documentation dataset. Note that this notebook uses ChromaDB, an open source, in-memory vector store with metadata filtering support that is compatible with DataRobot Notebooks. Additionally, this notebook uses the HuggingFace `jina-embedding-t-en-v1` [embeddings model](https://huggingface.co/jinaai/jina-embedding-t-en-v1) (open source). ``` from datetime import datetime from langchain_chroma import Chroma from langchain_community.embeddings.sentence_transformer import (SentenceTransformerEmbeddings) CHROMADB_DATA_PATH = f"{STORAGE_DIR}/chromadb" CHROMADB_EMBEDDING_CACHE_FOLDER = STORAGE_DIR + '/sentencetransformers' CHROMADB_EMBEDDING_FUNCTION = SentenceTransformerEmbeddings(model_name="jinaai/jina-embedding-t-en-v1", cache_folder=CHROMADB_EMBEDDING_CACHE_FOLDER) def create_chromadb_from_documents(docs, embedding_function, persist_directory): start_time = datetime.now() print(f'>>> BEGIN ({start_time.strftime("%H:%M:%S")}): Creating ChromaDB from documents') print(f'Embedding function: {embedding_function}') print(f'ChromaDB data directory: {persist_directory}') print(' ') print(f'Documents for loading: {len(docs)}') db = Chroma.from_documents(docs, embedding_function, persist_directory=persist_directory) end_time = datetime.now() print(' ') print(f'>>> END ({end_time.strftime("%H:%M:%S")}): Creating ChromaDB from documents') print(f'Loaded {len(docs)} documents.') print(f"Chroma VectorDB now has {db._collection.count()} documents") total_elapsed_min = (end_time - start_time).total_seconds() / 60 document_average_sec = (end_time - start_time).total_seconds() / len(docs) print("Total Elapsed", "%.2f" % total_elapsed_min, "minutes") print("Document Average", "%.2f" % document_average_sec, "seconds") return db print(f"Created {len(docs)} documents") db = create_chromadb_from_documents(docs, CHROMADB_EMBEDDING_FUNCTION, CHROMADB_DATA_PATH) print(db._collection.count()) ``` ### Test the vector database Use the following cell to test the vector database by having the model perform a similarity search with metadata filtering; it will return the top five documents matching the query provided. ``` question = "What is MLOps?" top_k = 5 metadata_filter = {"category": {"$eq": "index"}} results_with_scores = db.similarity_search_with_score( question, k=top_k, filter=metadata_filter, ) print(len(results_with_scores)) for doc, score in results_with_scores: print("********************************************************************************") print(" ") print("----------") print(f"METADATA: {doc.metadata}, Score: {score}") print(" ") print("----------") print(f"CONTENT: {doc.page_content}") print(" ") ``` ## Define hooks for deploying an unstructured custom model The following cell defines the methods used to deploy an unstructured custom model. These include loading the custom model and using the model for scoring. ``` import os def load_model(input_dir): """Custom model hook for loading our knowledge base.""" import os print("Loading model") STORAGE_DIR = "storage" CHROMADB_DATA_PATH = f"{STORAGE_DIR}/chromadb" CHROMADB_EMBEDDING_CACHE_FOLDER = STORAGE_DIR + '/sentencetransformers' CHROMADB_EMBEDDING_MODEL_NAME = 'jinaai/jina-embedding-t-en-v1' # https://docs.trychroma.com/troubleshooting#sqlite __import__('pysqlite3') import sys sys.modules['sqlite3'] = sys.modules.pop('pysqlite3') from langchain_chroma import Chroma from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings print(f'CHROMADB_DATA_PATH = {CHROMADB_DATA_PATH}') print(f'CHROMADB_EMBEDDING_CACHE_FOLDER = {CHROMADB_EMBEDDING_CACHE_FOLDER}') print(f'CHROMADB_EMBEDDING_MODEL_NAME = {CHROMADB_EMBEDDING_MODEL_NAME}') CHROMADB_EMBEDDING_FUNCTION = SentenceTransformerEmbeddings(model_name="jinaai/jina-embedding-t-en-v1", cache_folder=CHROMADB_EMBEDDING_CACHE_FOLDER) db = Chroma(persist_directory=CHROMADB_DATA_PATH, embedding_function=CHROMADB_EMBEDDING_FUNCTION) print(f'Loaded ChromaDB with {db._collection.count()} chunks') return db def score_unstructured(model, data, **kwargs) -> str: """Custom model hook for retrieving relevant docs with our knowledge base. When requesting predictions from the deployment, pass a dictionary with the following keys: - 'question' the question to be passed to the vector store retriever - 'metadata' the metadata filter to be passed to the vector store retriever - 'top_k' the number of results to return datarobot-user-models (DRUM) handles loading the model and calling this function with the appropriate parameters. Returns: -------- rv : str Json dictionary with keys: - 'question' user's original question - 'relevant' the generated answer to the question - 'metadata' - metadata for each document - 'error' - error message if exception in handling request """ import json try: data_dict = json.loads(data) question = data_dict['question'] top_k = data_dict.get("k", 10) metadata_filter = data_dict.get("filter", None) results_with_scores = model.similarity_search_with_score( question, k=top_k, filter=metadata_filter, ) print(f'Returned {len(results_with_scores)} results') relevant, metadata = [], [] for doc, score in results_with_scores: relevant.append(doc.page_content) doc.metadata["similarity_score"] = score metadata.append(doc.metadata) rv = { "question": question, "relevant": relevant, "metadata": metadata, } except Exception as e: rv = {'error': f"{e.__class__.__name__}: {str(e)}"} return json.dumps(rv), {"mimetype": "application/json", "charset": "utf8"} ``` ### Test hooks locally Before proceeding with deployment, use the cell below to test that the custom model hooks function correctly. ``` import json # Test the hooks locally score_unstructured( load_model(f"{STORAGE_DIR}"), json.dumps( { "question": "How do I replace a custom model on an existing custom environment?", "filter": {"category": {"$eq": "index"}}, "k": 1, } ) ) ``` ## Deploy the knowledge base The cell below uses a convenience method that does the following: - Builds a new custom model environment containing the contents of storage/deploy/ . - Assembles a new custom model with the provided hooks. - Deploys an unstructured custom model to DataRobot. - Returns an object that can be used to make predictions. This example uses a pre-built environment. You can also provide an `environment_id` and instead use an existing custom model environment for shorter iteration cycles on the custom model hooks. See your account's existing pre-built environments from the [DataRobot Workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-environment-workshop/nxt-drop-in-envs.html). ``` genai_environment = dr.ExecutionEnvironment.list(search_for="[GenAI] Python 3.11 with Moderations")[0] deployment = drx.deploy( model="storage/chromadb/", name="External DR Knowledge Base", hooks={ "score_unstructured": score_unstructured, "load_model": load_model }, extra_requirements=[ "langchain_chroma", "pysqlite3-binary", "cloudpickle==2.2.1" ], # Re-use existing environment if you want to change the hook code, # and not requirements environment_id=genai_environment.id ) ``` ### Test the deployment Test that the deployment can successfully provide responses to questions. ``` #deployment = drx.Deployment("ADD_VALUE_HERE") deployment.predict_unstructured( { "question": "How do I replace a custom model on an existing custom environment?", "filter": {"category": {"$eq": "index"}}, "k": 1, } ) ``` ## Validate and create the vector database These methods execute, validate, and integrate the vector database. This example associates a Use Case with the validation and creates the vector database within that Use Case. Set the `use_case_id` to specify an existing Use Case or create a new one with that name. ``` use_case_id = "ADD_VALUE_HERE" use_case = dr.UseCase.get(use_case_id) # UNCOMMENT if you want to create a new Use Case # use_case = dr.UseCase.create() ``` The `CustomModelVectorDatabaseValidation.create` function executes the validation of the vector database. Be sure to provide the deployment ID. ``` external_vdb_validation = CustomModelVectorDatabaseValidation.create( prompt_column_name="question", target_column_name="relevant", deployment_id=deployment.dr_deployment.id, use_case=use_case, wait_for_completion=True ) ``` ``` assert external_vdb_validation.validation_status == "PASSED" ``` After validation completes, use `VectorDatabase.create_from_custom_model()` to integrate the vector database. You must provide the Use Case name (or Use Case ID), a name for the external vector database, and the validation ID returned from the previous cell. ``` vdb = VectorDatabase.create_from_custom_model( name="DR Vector Database", use_case=use_case, validation_id=external_vdb_validation.id ) ``` ``` assert vdb.execution_status == "COMPLETED" ``` ``` print(f"Vector Database ID: {vdb.id}") ``` This vector database ID can now be used in the [GenAI E2E how-to](https://docs.datarobot.com/en/docs/gen-ai/genai-code/genai-e2e.html) to create the LLM blueprint with a vector database. --- # Create and deploy a DataRobot vector database URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-code/create-deploy-vdb-builtin-embeddings.html # Create and deploy a DataRobot vector database This notebook demonstrates how to use the DataRobot Python SDK to create and deploy a vector database (VDB) using DataRobot's built-in embeddings. This is the simplest approach for creating vector databases and doesn't require creating custom models. If you need to use your own embedding model (BYO embeddings), see the [Create vector databases from BYO embeddings](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/create-vdb-byo-embedding.ipynb) notebook for a complete example. This example workflow does the following: - Creates a Use Case and uploads a dataset. - Configures chunking parameters for your documents. - Creates a vector database using DataRobot's built-in embedding models. - Deploys vector databases for use in production. Note: For self-managed users, code samples that reference `app.datarobot.com` need to be changed to the appropriate URL for your instance. ## Setup ### Prerequisites This workflow requires the following feature flags. Contact your DataRobot representative or administrator for information on enabling these features: - Enable MLOps - Enable Public Network Access for all Custom Models (Premium) - Enable Monitoring Support for Generative Models - Enable Custom Inference Models - Enable GenAI Experimentation ### Import libraries This section imports Python libraries needed to interact with DataRobot, configure document chunking, and manage the creation and deployment of vector databases. These libraries supply the necessary interfaces for connection, configuration, and orchestration of the workflow. ``` import datarobot as dr from datarobot.models.genai.vector_database import VectorDatabase from datarobot.models.genai.vector_database import ChunkingParameters from datarobot.enums import VectorDatabaseEmbeddingModel from datarobot.enums import VectorDatabaseChunkingMethod from datarobot.enums import PredictionEnvironmentPlatform from datarobot.enums import PredictionEnvironmentModelFormats import time import requests ``` ### Connect to DataRobot This section managed the connection to the DataRobot client. Read more about different options for [connecting to DataRobot from the Python client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` # Option 1: Use environment variables (recommended) # The client will automatically use DATAROBOT_ENDPOINT and DATAROBOT_API_TOKEN dr.Client() # Option 2: Explicitly provide endpoint and token # endpoint = "https://app.datarobot.com/api/v2" # token = "" # dr.Client(endpoint=endpoint, token=token) ``` ## Create a vector database with DataRobot built-in embeddings This approach uses DataRobot's built-in embedding models to create and deploy a vector database from the uploaded document. ### Get the Use Case Vector databases must be associated with a Use Case in DataRobot. This step uses the Use Case that the notebook is running in (if available), or creates a new one if the notebook is not in a Use Case. Set `USE_CASE_NAME` to define a more specific Use Case name. ``` # Create the Use Case USE_CASE_NAME = "VDB Example Use Case" use_case = dr.UseCase.create(name=USE_CASE_NAME) print(f"Created Use Case: {use_case.name}") ``` ### Upload a dataset This section uploads the documents dataset. The dataset should be a ZIP file containing text files ( `.txt`, `.md`, etc.). Each file is processed and chunked for the vector database. ``` # Get or create the dataset DATASET_NAME = "pirate_resumes.zip" dataset = None # Search for existing dataset by exact name try: all_datasets = dr.Dataset.list(filter_failed=True) for d in all_datasets: if d.name == DATASET_NAME: dataset = d print(f"Found existing dataset: {dataset.name}") break except Exception: pass # Upload if not found if not dataset: dataset_url = "https://s3.amazonaws.com/datarobot_public_datasets/genai/pirate_resumes.zip" dataset = dr.Dataset.create_from_url(dataset_url) print(f"Uploaded new dataset: {dataset.name}") # Add dataset to Use Case if not already added try: use_case_datasets = use_case.get_datasets() dataset_ids = [d.id for d in use_case_datasets] if dataset.id not in dataset_ids: use_case.add(dataset) print(f"Added dataset to Use Case") except Exception: pass # Already added or error adding ``` ### Configure chunking parameters Configure how your documents will be split into chunks. The chunking parameters determine: - Chunk size : Maximum number of characters per chunk. - Chunk overlap : Percentage of overlap between chunks (helps preserve context). - Chunking method : The algorithm used to split text (recursive, fixed, etc.). - Embedding model : The model used to generate embeddings (optional, defaults to Jina). ``` chunking_parameters = ChunkingParameters( embedding_model=VectorDatabaseEmbeddingModel.JINA_EMBEDDING_T_EN_V1, chunking_method=VectorDatabaseChunkingMethod.RECURSIVE, chunk_size=256, chunk_overlap_percentage=25, separators=["\n\n", "\n", " ", ""], ) ``` ### Create the vector database Create the vector database using the dataset and chunking parameters. This process: 1. Splits the documents into chunks. 2. Generates embeddings for each chunk using the specified embedding model. 3. Stores the chunks and embeddings in the vector database. This process typically takes 30-60 seconds depending on the size of the dataset. ``` vdb = VectorDatabase.create( dataset_id=dataset.id, chunking_parameters=chunking_parameters, use_case=use_case, name="My Vector Database" ) ``` Check the status of the vector database until it completes successfully. ``` max_wait_time = 600 check_interval = 5 start_time = time.time() print("Waiting for vector database creation...") while time.time() - start_time < max_wait_time: vdb = VectorDatabase.get(vdb.id) status = vdb.execution_status if status == "COMPLETED": print(f"Vector database created: {vdb.name}") break elif status == "FAILED": error_msg = getattr(vdb, 'error_message', 'Unknown error') raise Exception(f"Vector database creation failed: {error_msg}") else: # Show progress if available percentage = getattr(vdb, 'percentage', None) if percentage is not None: print(f" Status: {status} ({percentage}%)") else: print(f" Status: {status}...") time.sleep(check_interval) else: raise Exception(f"Vector database creation timed out after {max_wait_time} seconds") assert vdb.execution_status == "COMPLETED", f"Vector database creation failed with status: {vdb.execution_status}" ``` ## Deploy the vector database Once the vector database is created, deploy it for production use. There are two main ways to deploy vector databases: - Direct deployment : Deploy the vector database directly to a prediction environment using the Python SDK - Send to Workshop : Register the vector database as a custom model first, then deploy it ### Create a prediction environment First, you need a prediction environment. DataRobot Serverless is typically used for vector database deployments. This section creates a new prediction environment if one doesn't already exist. In addition, this section selects the resource bundle required to run the vector database. ``` PREDICTION_ENVIRONMENT_NAME = "Vector Database Prediction Environment" # Get or create prediction environment prediction_environment = None for env in dr.PredictionEnvironment.list(): if env.name == PREDICTION_ENVIRONMENT_NAME: prediction_environment = env break if prediction_environment is None: prediction_environment = dr.PredictionEnvironment.create( name=PREDICTION_ENVIRONMENT_NAME, platform=PredictionEnvironmentPlatform.DATAROBOT_SERVERLESS, supported_model_formats=[ PredictionEnvironmentModelFormats.DATAROBOT, PredictionEnvironmentModelFormats.CUSTOM_MODEL ], ) print(f"Created prediction environment: {prediction_environment.name}") else: print(f"Using existing prediction environment: {prediction_environment.name}") # Select 3XL resource bundle resource_bundle_id = None try: dr_client = dr.Client() bundles_url = f"{dr_client.endpoint}/mlops/compute/bundles/" headers = {"Authorization": f"Bearer {dr_client.token}"} bundles_response = requests.get(bundles_url, headers=headers, params={"useCases": "customModel"}) if bundles_response.status_code == 200: bundles_data = bundles_response.json() if bundles_data.get("data"): bundles = bundles_data["data"] # Look for 3XL bundle bundle_3xl = next((b for b in bundles if "3XL" in b.get("name", "").upper()), None) if bundle_3xl: resource_bundle_id = bundle_3xl["id"] print(f"Selected 3XL bundle: {bundle_3xl['name']}") else: # Fallback to largest available sorted_bundles = sorted(bundles, key=lambda b: b.get("memoryBytes", 0), reverse=True) if sorted_bundles: resource_bundle_id = sorted_bundles[0]["id"] print(f"Warning: 3XL bundle not found. Using largest available: {sorted_bundles[0]['name']}") else: print("Using memory settings (no resource bundles available)") else: print("Using memory settings (resource bundles not enabled)") except (ImportError, KeyError) as e: print(f"Using memory settings (error checking bundles): {e}") except requests.RequestException as e: print(f"Using memory settings (network error checking bundles): {e}") ``` ### Send vector database to workshop Before deploying, send the vector database to the custom model workshop. This creates a custom model version that can be registered and deployed. The code uses the resource configuration determined when the prediction environment was created or checked (resource bundles if available, otherwise memory settings). ``` assert vdb.execution_status == "COMPLETED", f"Vector database must be completed. Current status: {vdb.execution_status}" # Send to workshop with 3XL bundle or memory settings if resource_bundle_id: custom_model_version = vdb.send_to_custom_model_workshop( resource_bundle_id=resource_bundle_id, replicas=1, network_egress_policy=dr.NETWORK_EGRESS_POLICY.PUBLIC, ) else: custom_model_version = vdb.send_to_custom_model_workshop( maximum_memory=4096*1024*1024, replicas=1, network_egress_policy=dr.NETWORK_EGRESS_POLICY.PUBLIC, ) print(f"Custom model version created: {custom_model_version}") ``` ### Register the model Next, register the custom model version. If a registered model with the same name already exists, this step adds a new version to the existing model instead of creating a duplicate. ``` REGISTERED_MODEL_NAME = f"Vector Database - {vdb.name}" # Register model (adds new version if model already exists) existing_models = [m for m in dr.RegisteredModel.list() if m.name == REGISTERED_MODEL_NAME] if existing_models: registered_model_version = dr.RegisteredModelVersion.create_for_custom_model_version( custom_model_version_id=custom_model_version.id, registered_model_id=existing_models[0].id, ) print(f"Added new version to existing registered model: {REGISTERED_MODEL_NAME}") else: registered_model_version = dr.RegisteredModelVersion.create_for_custom_model_version( custom_model_version_id=custom_model_version.id, registered_model_name=REGISTERED_MODEL_NAME, ) print(f"Created new registered model: {REGISTERED_MODEL_NAME}") ``` ### Wait for model build to complete Wait for the registered model version to finish building before deploying. ``` registered_model = dr.RegisteredModel.get(registered_model_version.registered_model_id) max_wait_time = 600 check_interval = 10 start_time = time.time() print("Waiting for model build to complete...") while time.time() - start_time < max_wait_time: version = registered_model.get_version(registered_model_version.id) build_status = getattr(version, 'build_status', None) or getattr(version, 'buildStatus', None) if build_status in ('READY', 'complete', 'COMPLETE'): print(f"Model build completed (status: {build_status})") break elif build_status in ('FAILED', 'ERROR', 'error'): raise Exception(f"Model build failed. Status: {build_status}") else: print(f" Build status: {build_status}...") time.sleep(check_interval) else: version = registered_model.get_version(registered_model_version.id) build_status = getattr(version, 'build_status', None) or getattr(version, 'buildStatus', None) raise Exception(f"Model build timed out. Current status: {build_status}") # Verify ready status version = registered_model.get_version(registered_model_version.id) final_status = getattr(version, 'build_status', None) or getattr(version, 'buildStatus', None) if final_status not in ('READY', 'complete', 'COMPLETE'): raise Exception(f"Model not ready for deployment. Status: {final_status}") ``` ### Deploy the registered model Deploy the registered model version to the prediction environment created earlier. The model must be in READY status before deployment process can resolve successfully. ``` deployment = dr.Deployment.create_from_registered_model_version( registered_model_version.id, label=f"Vector Database Deployment - {vdb.name}", description="Vector database deployment for RAG applications", prediction_environment_id=prediction_environment.id, max_wait=600, ) print(f"Deployment created: {deployment.id}") ``` ### Use vector databases in LLM Playgrounds Vector databases created through the Python SDK are automatically available in LLM Playgrounds for use in RAG workflows. See the [genai-e2e.ipynb](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/genai-e2e.ipynb) notebook for examples of using vector databases with LLMs. For detailed deployment instructions and UI options, see the [Register and deploy vector databases](https://docs.datarobot.com/en/docs/gen-ai/vector-database/vector-dbs-register-deploy.html) documentation. ## List and manage vector databases Using the command below list all vector databases associated with a Use Case and manage them programmatically. ``` # List all vector databases in a Use Case vdbs = VectorDatabase.list(use_case=use_case) print(f"Found {len(vdbs)} vector database(s) in Use Case '{use_case.name}'") ``` ### Next steps - Use your vector database in an LLM Playground . - Learn about creating vector databases with custom embedding models (BYO embeddings). - Explore external vector databases like ChromaDB. --- # Create vector databases from BYO embeddings URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-code/create-vdb-byo-embedding.html # Create vector databases from BYO embeddings The following notebook outlines how you can build and validate a vector database from a bring-your-own (BYO) embedding and register the vector database to the DataRobot platform using the Python client. This notebook is designed for use with DataRobot notebooks; DataRobot recommends downloading this notebook and uploading it for use in the platform. ## Setup The following steps outline the necessary configuration for integrating vector databases with the DataRobot platform. 1. This workflow uses the following feature flags. Contact your DataRobot representative or administrator for information on enabling these features. 2. Use a codespace, not a DataRobot Notebook, to ensure this notebook has access to a filesystem. 3. Set the notebook session timeout to 180 minutes. 4. Restart the notebook container using at least a "Medium" (16GB RAM) instance. 5. Optionally, upload your documents archive to the notebook filesystem. ### Install requirements Import the following libraries and modules to interface with the DataRobot platform, prediction environments, and vector database functionality. ``` import datarobot as dr from datarobot.enums import PredictionEnvironmentPlatform from datarobot.enums import PredictionEnvironmentModelFormats from datarobot.models.genai.custom_model_embedding_validation import CustomModelEmbeddingValidation from datarobot.models.genai.vector_database import VectorDatabase from datarobot.models.genai.vector_database import ChunkingParameters ``` ### Connect to DataRobot Provide a DataRobot `endpoint` and `token` to connect to DataRobot through the DataRobot Python client. Read more about options for [connecting to DataRobot from the Python client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` # Option 1: Use environment variables (recommended) # The client will automatically use DATAROBOT_ENDPOINT and DATAROBOT_API_TOKEN dr.Client() # Option 2: Explicitly provide endpoint and token # endpoint = "https://app.datarobot.com/api/v2" # token = "" # dr.Client(endpoint=endpoint, token=token) ``` ## Select an environment Using `dr.ExecutionEnvironment.list()`, iterate through the environments available to your organization, selecting the environment named `[DataRobot] Python 3.11 GenAI` to be the base environment of the vector database and assigning it to `base_environment`. ``` execution_environments = dr.ExecutionEnvironment.list() base_environment = None environment_versions = None for execution_environment in execution_environments: # print(execution_environment) if execution_environment.name == "[DataRobot] Python 3.11 GenAI": base_environment = execution_environment environment_versions = dr.ExecutionEnvironmentVersion.list( execution_environment.id ) break environment_version = environment_versions[0] print(base_environment) print(environment_version) ``` ## Create a custom embedding model Using `dr.CustomInferenceModel.list()` search the available custom models for `all-MiniLM-L6-v2-embedding-model`. If the custom model doesn't exist, create it as `custom_model` using `dr.CustomInferenceModel.create()`. If the custom model does exist, assign it to `custom_model`. ``` CUSTOM_MODEL_NAME = "all-MiniLM-L6-v2-embedding-model_20260218-01" if CUSTOM_MODEL_NAME not in [c.name for c in dr.CustomInferenceModel.list()]: # Create a new custom model print("Creating new custom model") custom_model = dr.CustomInferenceModel.create( name=CUSTOM_MODEL_NAME, target_type=dr.TARGET_TYPE.UNSTRUCTURED, is_training_data_for_versions_permanently_enabled=True ) else: print("Custom Model Exists") custom_model = [c for c in dr.CustomInferenceModel.list() if c.name == CUSTOM_MODEL_NAME].pop() ``` ### Write custom embedding model code Create a directory called `custom_embedding_model` to write custom embedding model code into. Write custom embedding model code into the `custom.py` file, creating an unstructured model from `all-MiniLM-L6-v2`. ``` import os os.mkdir('custom_embedding_model') ``` ``` %%writefile ./custom_embedding_model/custom.py import os os.environ["HF_HOME"] = "/tmp/hf_cache" os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache" os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/hf_cache" from sentence_transformers import SentenceTransformer def load_model(input_dir): return SentenceTransformer("all-MiniLM-L6-v2") def score_unstructured(model, data, query, **kwargs): import json data_dict = json.loads(data) outputs = model.encode(data_dict["input"]) return json.dumps( { "result": outputs.tolist(), "device": str(model._target_device) } ) ``` ### Write a requirements file Write the requirements for the custom embedding model into the `requirements.txt` file, ensuring the custom model environment includes the embedding model's dependencies. ``` %%writefile ./custom_embedding_model/requirements.txt sentence-transformers==3.0.0 ``` ### Create a custom model version Using `dr.CustomModelVersion.create_clean`, create a custom model version with the `custom_model`, `base_environment`, and `files` defined in previous steps. In addition, enable public network access using `dr.NETWORK_EGRESS_POLICY.PUBLIC`. ``` # Create a new custom model version in DataRobot print("Upload new model version to DataRobot") model_version = dr.CustomModelVersion.create_clean( custom_model_id=custom_model.id, base_environment_id=base_environment.id, files=[ ("./custom_embedding_model/custom.py", "custom.py"), ("./custom_embedding_model/requirements.txt", "requirements.txt"), ], network_egress_policy=dr.NETWORK_EGRESS_POLICY.PUBLIC, ) ``` ### Build a custom model environment Using `dr.CustomModelVersionDependencyBuild`, build a custom model environment with the required dependencies installed. ``` # Build the custom model environment to ensure dependencies from `requirements.txt` are installed. build_info = dr.CustomModelVersionDependencyBuild.start_build( custom_model_id=custom_model.id, custom_model_version_id=model_version.id, max_wait=60*10, # Set a long timeout ) ``` ## Register the custom model Using `dr.RegisteredModel.list()`, search the available custom models for `all-MiniLM-L6-v2-embedding-model` (assigned to `CUSTOM_MODEL_NAME`). If the registered model doesn't exist, create a `registered_model_version` using `dr.RegisteredModelVersion.create_for_custom_model_version`. If the registered model does exist, assign it to `registered_model` and create a `registered_model_version` using `dr.RegisteredModelVersion.create_for_custom_model_version`. ``` if CUSTOM_MODEL_NAME not in [m.name for m in dr.RegisteredModel.list()]: print("Creating New Registered Model") registered_model_version = dr.RegisteredModelVersion.create_for_custom_model_version( model_version.id, name=CUSTOM_MODEL_NAME, registered_model_name=CUSTOM_MODEL_NAME ) else: print("Using Existing Model") registered_model = [m for m in dr.RegisteredModel.list() if m.name == CUSTOM_MODEL_NAME].pop() registered_model_version = dr.RegisteredModelVersion.create_for_custom_model_version( model_version.id, name=CUSTOM_MODEL_NAME, registered_model_id=registered_model.id ) ``` ## Create a prediction environment for embedding models Using `dr.PredictionEnvironment.list()`, search the available prediction environments for `Prediction environment for BYO embeddings models`. If the prediction environment doesn't exist, create a DataRobot Serverless `prediction_environment` using `dr.PredictionEnvironment.create`. If the prediction environment does exist, assign it to `prediction_environment`. ``` PREDICTION_ENVIRONMENT_NAME = "Prediction environment for BYO embeddings models" prediction_environment = None for _prediction_environment in dr.PredictionEnvironment.list(): if _prediction_environment.name == PREDICTION_ENVIRONMENT_NAME: prediction_environment = _prediction_environment if prediction_environment is None: prediction_environment = dr.PredictionEnvironment.create( name=PREDICTION_ENVIRONMENT_NAME, platform=PredictionEnvironmentPlatform.DATAROBOT_SERVERLESS, supported_model_formats=[ PredictionEnvironmentModelFormats.DATAROBOT, PredictionEnvironmentModelFormats.CUSTOM_MODEL ], ) ``` ## Deploy the custom embedding model Using `dr.Deployment.list()`, search the available deployments for `Deployment for all-MiniLM-L6-v2`. If the prediction environment doesn't exist, create a `deployment`, deploying the `registered_model_version` created in a previous section with `dr.Deployment.create_from_registered_model_version`. If the deployment does exist, assign it to `deployment`. ``` MODEL_DEPLOYMENT_NAME = "Deployment for all-MiniLM-L6-v2" if MODEL_DEPLOYMENT_NAME not in [d.label for d in dr.Deployment.list()]: deployment = dr.Deployment.create_from_registered_model_version( registered_model_version.id, label=MODEL_DEPLOYMENT_NAME, max_wait=1000, prediction_environment_id=prediction_environment.id ) else: deployment = [d for d in dr.Deployment.list() if d.label == MODEL_DEPLOYMENT_NAME][0] ``` ## Create a Use Case for BYO embeddings Using `dr.UseCase.create`, create the Use Case to use the vector database with and assign it to `use_case`. When working with your own vector database, if [PostgreSQL](https://docs.datarobot.com/en/docs/gen-ai/vector-database/vector-dbs.html#connect-to-postgresql) is the connection method, the output dimension must be less than 2000. ``` use_case = dr.UseCase.create(name="For BYO embeddings") ``` ### Upload a dataset to the Use Case Using `dr.Dataset.create_from_url`, upload the example dataset for the vector database and assign it to `dataset`. ``` # this can be updated with any public URL that is pointing to a .zip file # in the expected format dataset_url = "https://s3.amazonaws.com/datarobot_public_datasets/genai/pirate_resumes.zip" # We will use a vector database with our GenAI models. Let's upload a dataset with our documents. # If you wish to use a local file as dataset, change this to # `dataset = dr.Dataset.create_from_file(local_file_path)` dataset = dr.Dataset.create_from_url(dataset_url) ``` Then, add the dataset to the `use_case` created in the previous section. ``` # Attach dataset to use case. use_case.add(dataset) ``` ## Validate and create the custom embedding model The `CustomModelVectorDatabaseValidation.create` function executes the validation of the vector database, setting the required settings and associating the custom embedding model with the `use_case` and `deployment` created earlier in this notebook. This step stores the validation ID in `custom_model_embedding_validation`. ``` # Create BYO embeddings validation using prepared deployment custom_model_embedding_validation = CustomModelEmbeddingValidation.create( prompt_column_name="input", target_column_name="result", deployment_id=deployment.id, use_case = use_case, name="BYO embeddings", wait_for_completion=True, prediction_timeout=300, ) ``` ### Set chunking parameters and create a vector database After validation completes, set the `ChunkingParameter()` and use `VectorDatabase.create()` to integrate the vector database. This step uses the `custom_model_embedding_validation`, `dataset`, and `use_case` defined in previous sections. ``` # Use created validation to set up chunking parameters chunking_parameters = ChunkingParameters( embedding_validation=custom_model_embedding_validation, chunking_method="recursive", chunk_size=256, chunk_overlap_percentage=50, separators=["\n\n", "\n", " ", ""], embedding_model=None, ) vdb = VectorDatabase.create( dataset_id=dataset.id, chunking_parameters=chunking_parameters, use_case=use_case ) ``` ``` from time import sleep vdb = VectorDatabase.get(vdb.id) sleep(30) assert vdb.execution_status == "COMPLETED" ``` --- # Use the DataRobot LLM gateway URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-code/dr-llm-gateway.html # Use the DataRobot LLM gateway The DataRobot LLM gateway is a service that unifies and simplifies LLM access across DataRobot. It provides a DataRobot API endpoint to interface with LLMs hosted by external LLM providers. To request LLM responses from the DataRobot LLM gateway, you can use any API client that supports OpenAI-compatible chat completion API, for example, the [OpenAI Python API library](https://github.com/openai/openai-python). Note: Provisioning LLMs using the LLM gateway is available for DataRobot-managed (Cloud) instances and single-tenant SaaS (STS) self-managed instances with supported pricing plans; it is not available for on-premise installations. Contact your DataRobot representative for details. The gateway itself, as a service for calling LLMs, is available on all platforms. ## Setup The DataRobot LLM gateway is a premium feature; contact your DataRobot representative or administrator for information on enabling it. To use the DataRobot LLM gateway with the OpenAI Python API library, first, make sure the OpenAI client package is installed and imported. You must also import the DataRobot Python client. ``` %pip install openai ``` ``` import datarobot as dr from openai import OpenAI from datarobot.models.genai.llm_gateway_catalog import LLMGatewayCatalog from pprint import pprint ``` ## Connect to the LLM gateway Next, initialize the OpenAI client. The base URL is the DataRobot LLM gateway endpoint. The example below assembles this URL by combining your DataRobot API endpoint (retrieved from [dr_client](https://docs.datarobot.com/en/docs/api/reference/sdk/client-setup.html)) and `/genai/llmgw`. Usually this results in `https://app.datarobot.com/api/v2/genai/llmgw`, `https://app.eu.datarobot.com/api/v2/genai/llmgw`, `https://app.jp.datarobot.com/api/v2/genai/llmgw`, or your organization's DataRobot API endpoint URL with `/genai/llmgw` appended to it. The API key is your [DataRobot API key](https://docs.datarobot.com/en/docs/get-started/acct-mgmt/acct-settings/api-key-mgmt.html). ``` dr_client = dr.Client() DR_API_TOKEN = dr_client.token LLM_GATEWAY_BASE_URL = f"{dr_client.endpoint}/genai/llmgw" client = OpenAI( base_url=LLM_GATEWAY_BASE_URL, api_key=DR_API_TOKEN, ) print(f"Your LLM gateway URL is {LLM_GATEWAY_BASE_URL}.") ``` ## Select models and make requests In your code, you can specify any supported provider LLM and set up the message to send to the LLM as a prompt. An optional argument is `client_id`, where you can specify the caller service to use for metering: `genai-playground`, `custom-model`, or `moderations`. This example calls the LLM gateway catalog endpoint to get one of the latest supported LLMs from each provider. ``` # Get list of catalog model names using the SDK (active, non-deprecated models by default) catalog_entries = LLMGatewayCatalog.get_available_models() first_model_by_provider = {} # Iterate through the available models to select the first supported LLM from each provider for model_string in catalog_entries: # Extract the provider name (the part before the first '/') provider_name = model_string.split('/')[0] # If the provider isn't already recorded, store the full model string if provider_name not in first_model_by_provider: first_model_by_provider[provider_name] = model_string # Store the list of models models = list(first_model_by_provider.values()) print(f"Selected models from {len(first_model_by_provider)} providers:") pprint(first_model_by_provider, sort_dicts=False) ``` After you define the `client`, `models` and `message`, you can make chat completion requests to the LLM gateway. The authentication uses DataRobot-provided credentials. ``` from IPython.display import display, Markdown # Store a message to send to the LLM message = [{"role": "user", "content": "Hello! What is your name and who made you?"}] for model in models: response = client.chat.completions.create( model=model, messages=message, ) response_text = response.choices[0].message.content output_as_markdown = f""" **{model}:** {response_text} --- """ display(Markdown(output_as_markdown)); ``` To further configure your chat completion request when making direct calls to an LLM gateway, specify LLM parameter settings like `temperature`, `max_completion_tokens`, and more. These parameters are also supported for custom models. For more information on the available parameters, see the [OpenAI chat completion documentation](https://platform.openai.com/docs/api-reference/chat/create). ``` model2 = models[0] if len(models) > 0 else "openai/gpt-4o" # Use first model or fallback message2 = [{"role": "user", "content": "Hello! What is your name and who made you? How do you feel about Agentic AI"}] extra_body2 = { "temperature": 0.8, "max_completion_tokens": 2000, } response2 = client.chat.completions.create( model=model2, messages=message2, extra_body=extra_body2, ) response_text2 = response2.choices[0].message.content output_as_markdown2 = f""" **{model2}:** {response_text2} --- """ display(Markdown(output_as_markdown2)); ``` ## Identify supported LLMs To provide a list of LLMs supported by the LLM gateway, this example uses the LLM gateway catalog SDK to get the available models. ``` # Get all available models using the SDK convenience method supported_llms = LLMGatewayCatalog.get_available_models() print(f"Found {len(supported_llms)} available models:") pprint(supported_llms[:10]) # Show first 10 models if len(supported_llms) > 10: print(f"... and {len(supported_llms) - 10} more models") ``` If you try to use an unsupported LLM, the LLM gateway returns an error message, relaying that the specified LLM is not in the LLM catalog. ``` # Verify model availability unsupported_model = "unsupported-provider/random-llm" try: # Check if the model is available before making the request model_entry = LLMGatewayCatalog.verify_model_availability(unsupported_model) print(f"Model {unsupported_model} is available: {model_entry.name}") except ValueError as e: print(f"Model {unsupported_model} is not available: {e}") # Alternative: still show the original error handling for comparison messages3 = [ {"role": "user", "content": "Hello!"} ] try: response = client.chat.completions.create( model=unsupported_model, messages=messages3, ) response.choices[0].message.content except Exception as e: print(f"Direct API call error: {str(e)}") ``` You can also verify if a specific model is available before attempting to use it. This is useful for error handling and validation: ``` # Test different model IDs test_models = [ "azure/gpt-4o-2024-11-20", # Example Azure model "openai/gpt-4o", # Example OpenAI model "non-existent-model" # This should fail ] for model_id in test_models: try: model_entry = LLMGatewayCatalog.verify_model_availability(model_id) print(f"✓ {model_id} is available:") pprint({ "name": model_entry.name, "provider": model_entry.provider, "context_size": f"{model_entry.context_size:,} tokens", "active": model_entry.is_active, "deprecated": model_entry.is_deprecated }, sort_dicts=False) except ValueError as e: print(f"✗ {model_id} is not available: {e}") print() ``` ## Advanced filtering The LLM gateway Catalog SDK provides advanced filtering capabilities to help you find the right models for your needs: ``` # Get all models including deprecated ones all_entries = LLMGatewayCatalog.list( only_active=False, only_non_deprecated=False, limit=20 ) active_count = sum(1 for entry in all_entries if entry.is_active) deprecated_count = sum(1 for entry in all_entries if entry.is_deprecated) print(f"Found {len(all_entries)} total entries:") print(f" - Active: {active_count}") print(f" - Deprecated: {deprecated_count}") # Show deprecated models with replacement info deprecated_models = [e for e in all_entries if e.is_deprecated] if deprecated_models: print("\nDeprecated models with replacements (first 3 models):") for entry in deprecated_models[:3]: deprecated_info = { "model": entry.model, "name": entry.name, "provider": entry.provider } if entry.retirement_date: deprecated_info["retirement_date"] = entry.retirement_date if entry.suggested_replacement: deprecated_info["suggested_replacement"] = entry.suggested_replacement pprint(deprecated_info, sort_dicts=False) print() ``` ``` # Get detailed catalog entries with rich information catalog_entries = LLMGatewayCatalog.list(limit=5) print("Detailed catalog entries:") for entry in catalog_entries: entry_info = { "name": entry.name, "model": entry.model, "provider": entry.provider, "context_size": f"{entry.context_size:,} tokens", "active": entry.is_active, "deprecated": entry.is_deprecated } pprint(entry_info, sort_dicts=False) print() ``` --- # Create external LLMs with code URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-code/ext-llm.html # Create external LLMs with code The following, designed for use with DataRobot Notebooks, outlines how you can build and validate an external LLM using the DataRobot Python client. DataRobot recommends downloading this notebook and uploading it for use in the platform. Note: For self-managed users, when code samples reference `app.datarobot.com`, change them to the appropriate URL for your instance. ## Setup The following steps outline the configuration necessary for integrating an external LLM with the DataRobot platform. 1. This workflow requires that the following feature flags are enabled. Contact your DataRobot representative or administrator for information on enabling these features. 2. Create a new credential in theDataRobot Credentials Management tool: 3. Use a codespace, not a DataRobot Notebook, to ensure this notebook has access to a filesystem. 4. Set the notebook session timeout to 180 minutes. 5. Restart the notebook container using at least a "Medium" (16GB RAM) instance. ## Install libraries Install the following libraries: ``` !pip install openai datarobot-drum datarobot-predict ``` ``` import datarobot as dr from datarobot.models.genai.custom_model_llm_validation import CustomModelLLMValidation ``` ## Connect to DataRobot Read more about different options for [connecting to DataRobot from the Python client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` dr.Client() token = dr.Client().token endpoint = f"{dr.Client().endpoint}" dr.Client(endpoint=endpoint, token=token) ``` ## Create a directory for custom code Create a directory called `custom_model` that will hold your OpenAI wrapper code. ``` !mkdir custom_model ``` ## Define environment variables In the cell below, define each environment variable required to run this notebook. These environment variables are added as runtime parameters to the custom model in the workshop. ``` %%writefile .env # Define required environment variables for the codespace environment OPENAI_API_KEY= # For codespace testing, the API key for the Azure OpenAI API OPENAI_API_VERSION= OPENAI_API_BASE= OPENAI_DEPLOYMENT_NAME= DATAROBOT_CREDENTIAL_OPENAI= # For the deployed model, the ObjectId of the Azure OpenAI API Token credential ``` The value for `DATAROBOT_CREDENTIAL_OPENAI` environment variable must be the `ObjectId` of the Azure OpenAI API Token credential. You can find the ID of the credential using the DataRobot Python API client's credentials list method. ``` dr.Credential.list() ``` ## Define hooks The following cell defines the methods used to deploy a text generation custom model. These include loading the custom model and using the model for scoring. ``` import os import pandas as pd from typing import Any, Iterator, Union, Dict, List from openai import AzureOpenAI from openai.types.chat import ChatCompletion, ChatCompletionChunk # Try to load dotenv for codespace testing try: from dotenv import load_dotenv load_dotenv() except ImportError: pass # Don't load the .env file CompletionCreateParams = Dict[str, Any] def get_config(): # Get configuration from runtime parameters or environment variables try: # Try DataRobot runtime parameters first from datarobot_drum import RuntimeParameters return { "api_key": RuntimeParameters.get("OPENAI_API_KEY")["apiToken"], "api_base": RuntimeParameters.get("OPENAI_API_BASE"), "api_version": RuntimeParameters.get("OPENAI_API_VERSION"), "deployment_name": RuntimeParameters.get("OPENAI_DEPLOYMENT_NAME") } except Exception: # Fallback to environment variables for codespace testing return { "api_key": os.environ.get("OPENAI_API_KEY", ""), "api_base": os.environ.get("OPENAI_API_BASE", ""), "api_version": os.environ.get("OPENAI_API_VERSION", ""), "deployment_name": os.environ.get("OPENAI_DEPLOYMENT_NAME", "") } # Implement the load_model hook. def load_model(*args, **kwargs): config = get_config() return AzureOpenAI( api_key=config["api_key"], azure_endpoint=config["api_base"], api_version=config["api_version"] ) # Load the Azure OpenAI client def load_client(*args, **kwargs): return load_model(*args, **kwargs) # Get supported LLM models def get_supported_llm_models(client: AzureOpenAI) -> List[Dict[str, Any]]: azure_models = client.models.list() model_ids = [m.id for m in azure_models] return model_ids if model_ids else ["datarobot-deployed-llm"] # On-demand chat requests def chat( completion_create_params: CompletionCreateParams, client: AzureOpenAI ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk], Dict]: try: if completion_create_params.get("model") == "datarobot-deployed-llm": config = get_config() completion_create_params["model"] = config["deployment_name"] return client.chat.completions.create(**completion_create_params) except Exception as e: return { "error": f"{e.__class__.__name__}: {str(e)}" } # Batch chat requests PROMPT_COLUMN_NAME = "promptText" COMPLETION_COLUMN_NAME = "resultText" ERROR_COLUMN_NAME = "error" def score(data, client, **kwargs): prompts = data["promptText"].tolist() responses = [] errors = [] for prompt in prompts: try: # Get model config config = get_config() # Attempt to get a completion from the client response = client.chat.completions.create( model=config["deployment_name"], messages=[{"role": "user", "content": f"{prompt}"},], max_tokens=20, temperature=0 ) # On success, append the content and a null error responses.append(response.choices[0].message.content or "") errors.append("") except Exception as e: # On failure, format the error message error = f"{e.__class__.__name__}: {str(e)}" responses.append("") errors.append(error) return pd.DataFrame({ PROMPT_COLUMN_NAME: prompts, COMPLETION_COLUMN_NAME: responses, ERROR_COLUMN_NAME: errors }) ``` ## Test hooks locally Before proceeding with the deployment, use the cell below to test that the custom model hooks function correctly. ``` # Provide test data test_data = pd.DataFrame({PROMPT_COLUMN_NAME: ["What is a large language model (LLM)?"]}) ``` ``` # Test get_supported_llm_models() models = get_supported_llm_models(load_client()) print(f"Available models: {models}") ``` ``` # Test chat() chat( { "model": "datarobot-deployed-llm", "messages": [{"role": "user", "content": "What is a large language model (LLM)?"}], "max_tokens": 20, "temperature": 0, }, client=load_client(), ) ``` ``` # Test score() score(test_data, client=load_client()) ``` ## Save the custom model code Next, save the hooks above as `custom_model/custom.py`. This python file will be executed by DataRobot using your credentials. The following is a copy of the cell where you previously defined the hooks. ``` %%writefile custom_model/custom.py import os import pandas as pd from typing import Any, Iterator, Union, Dict, List from openai import AzureOpenAI from openai.types.chat import ChatCompletion, ChatCompletionChunk # Try to load dotenv for codespace testing try: from dotenv import load_dotenv load_dotenv() except ImportError: pass # Don't load the .env file CompletionCreateParams = Dict[str, Any] def get_config(): # Get configuration from runtime parameters or environment variables try: # Try DataRobot runtime parameters from datarobot_drum import RuntimeParameters return { "api_key": RuntimeParameters.get("OPENAI_API_KEY")["apiToken"], "api_base": RuntimeParameters.get("OPENAI_API_BASE"), "api_version": RuntimeParameters.get("OPENAI_API_VERSION"), "deployment_name": RuntimeParameters.get("OPENAI_DEPLOYMENT_NAME") } except Exception: # Fallback to environment variables for codespace testing return { "api_key": os.environ.get("OPENAI_API_KEY", ""), "api_base": os.environ.get("OPENAI_API_BASE", ""), "api_version": os.environ.get("OPENAI_API_VERSION", ""), "deployment_name": os.environ.get("OPENAI_DEPLOYMENT_NAME", "") } # Implement the load_model hook. def load_model(*args, **kwargs): config = get_config() return AzureOpenAI( api_key=config["api_key"], azure_endpoint=config["api_base"], api_version=config["api_version"] ) # Load the Azure OpenAI client. def load_client(*args, **kwargs): return load_model(*args, **kwargs) # Get supported LLM models def get_supported_llm_models(client: AzureOpenAI) -> List[Dict[str, Any]]: azure_models = client.models.list() model_ids = [m.id for m in azure_models] return model_ids if model_ids else ["datarobot-deployed-llm"] # On-demand chat requests def chat( completion_create_params: CompletionCreateParams, client: AzureOpenAI ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk], Dict]: try: if completion_create_params.get("model") == "datarobot-deployed-llm": config = get_config() completion_create_params["model"] = config["deployment_name"] return client.chat.completions.create(**completion_create_params) except Exception as e: return { "error": f"{e.__class__.__name__}: {str(e)}" } # Batch chat requests PROMPT_COLUMN_NAME = "promptText" COMPLETION_COLUMN_NAME = "resultText" ERROR_COLUMN_NAME = "error" def score(data, client, **kwargs): prompts = data["promptText"].tolist() responses = [] errors = [] for prompt in prompts: try: # Get model config config = get_config() # Attempt to get a completion from the client response = client.chat.completions.create( model=config["deployment_name"], messages=[{"role": "user", "content": f"{prompt}"},], max_tokens=20, temperature=0 ) # On success, append the content and a null error responses.append(response.choices[0].message.content or "") errors.append("") except Exception as e: # On failure, format the error message error = f"{e.__class__.__name__}: {str(e)}" responses.append("") errors.append(error) return pd.DataFrame({ PROMPT_COLUMN_NAME: prompts, COMPLETION_COLUMN_NAME: responses, ERROR_COLUMN_NAME: errors }) ``` Save requirements and metadata files to help describe the model's environment and usage. ``` %%writefile custom_model/requirements.txt openai datarobot-drum pandas python-dotenv ``` ``` %%writefile custom_model/model-metadata.yaml --- name: OpenAI gpt-4o type: inference targetType: textgeneration runtimeParameterDefinitions: - fieldName: OPENAI_API_KEY type: credential credentialType: api_token description: OpenAI API key stored in DataRobot allowEmpty: false - fieldName: OPENAI_API_VERSION type: string description: OpenAI API version string allowEmpty: false - fieldName: OPENAI_API_BASE type: string description: OpenAI API base URL string allowEmpty: false - fieldName: OPENAI_DEPLOYMENT_NAME type: string description: OpenAI API deployment ID string allowEmpty: false ``` ## Test the code locally The DataRobot `DRUM` library allows you to test the code as if DataRobot were running it via a simple CLI. To do this, supply a test file and then run it. ``` # Create the test file test_data.to_csv("custom_model/test_data.csv", index=False) ``` ``` os.putenv("TARGET_NAME", COMPLETION_COLUMN_NAME) !drum score --code-dir custom_model/ --target-type textgeneration --input custom_model/test_data.csv ``` ## Create a custom model in DataRobot The code below performs a few steps to register your code with DataRobot: - Creates a custom model to contain the versioned code. - Creates a custom model version with the code in the custom_model folder and adds the required Runtime Parameters. - Builds the environment to run the model by installing the requirements.txt file. - Tests the entire setup. ``` # List all existing base environments execution_environments = dr.ExecutionEnvironment.list() execution_environments BASE_ENVIRONMENT = None for execution_environment in execution_environments: if execution_environment.name == "[DataRobot] Python 3.11 GenAI": BASE_ENVIRONMENT = execution_environment environment_versions = dr.ExecutionEnvironmentVersion.list( execution_environment.id ) break if BASE_ENVIRONMENT is None: raise ValueError( "Required execution environment '[DataRobot] Python 3.11 GenAI' not found. Please check your DataRobot instance." ) BASE_ENVIRONMENT_VERSION = environment_versions[0] print(BASE_ENVIRONMENT) print(BASE_ENVIRONMENT_VERSION) print(BASE_ENVIRONMENT.id) ``` ``` CUSTOM_MODEL_NAME = "External LLM OpenAI Wrapper Model" if CUSTOM_MODEL_NAME not in [c.name for c in dr.CustomInferenceModel.list()]: # Create a new custom model print("Creating a new custom model") custom_model = dr.CustomInferenceModel.create( name=CUSTOM_MODEL_NAME, target_type=dr.TARGET_TYPE.TEXT_GENERATION, target_name=COMPLETION_COLUMN_NAME, description="Wrapper for OpenAI completion", language="Python", is_training_data_for_versions_permanently_enabled=True, ) else: print("Custom model exists") custom_model = [ c for c in dr.CustomInferenceModel.list() if c.name == CUSTOM_MODEL_NAME ].pop() ``` ``` # Create a new custom model version in DataRobot with required runtime parameters print("Upload new version of model to DataRobot") model_version = dr.CustomModelVersion.create_clean( custom_model_id=custom_model.id, base_environment_id=BASE_ENVIRONMENT.id, files=[ "./custom_model/custom.py", "./custom_model/requirements.txt", "./custom_model/model-metadata.yaml", ], network_egress_policy=dr.NETWORK_EGRESS_POLICY.PUBLIC, runtime_parameter_values=[ dr.models.custom_model_version.RuntimeParameterValue(field_name="OPENAI_API_KEY", type="credential", value=os.environ.get("DATAROBOT_CREDENTIAL_OPENAI", "")), dr.models.custom_model_version.RuntimeParameterValue(field_name="OPENAI_API_VERSION", type="string", value=os.environ.get("OPENAI_API_VERSION", "")), dr.models.custom_model_version.RuntimeParameterValue(field_name="OPENAI_API_BASE", type="string", value=os.environ.get("OPENAI_API_BASE", "")), dr.models.custom_model_version.RuntimeParameterValue(field_name="OPENAI_DEPLOYMENT_NAME", type="string", value=os.environ.get("OPENAI_DEPLOYMENT_NAME", "")), ] ) ``` ``` try: build_info = dr.CustomModelVersionDependencyBuild.start_build( custom_model_id=custom_model.id, custom_model_version_id=model_version.id, max_wait=3600, ) print("Finished new dependency build") except dr.errors.ClientError as e: if "already has a dependency image" in str(e): print("Dependency build already exists, skipping build step") build_info = None else: raise e ``` ## Test the custom model in DataRobot Next, use the environment to run the model with prediction test data to verify that the custom model is functional before deployment. To do this, upload the inference dataset for testing predictions. ``` pred_test_dataset = dr.Dataset.create_from_in_memory_data(test_data) pred_test_dataset.modify(name="LLM Test Data") pred_test_dataset.update() ``` After uploading the inference dataset, you can test the custom model. ``` # Test a new version in DataRobot print("Run test of new version in DataRobot") custom_model_test = dr.CustomModelTest.create( custom_model_id=custom_model.id, custom_model_version_id=model_version.id, dataset_id=pred_test_dataset.id, max_wait=3600, # 1 hour timeout ) custom_model_test.overall_status HOST = "https://app.datarobot.com" for name, test in custom_model_test.detailed_status.items(): print("Test: {}".format(name)) print("Status: {}".format(test["status"])) print("Message: {}".format(test["message"])) print( "Finished testing: " + HOST + "model-registry/custom-models/" + custom_model.id + "/assemble" ) ``` ## Register and deploy the LLM Next, register the model in the Registry. The model registry contains entries from all models (predictive, generative, built in DataRobot, and externally hosted). ``` if CUSTOM_MODEL_NAME not in [m.name for m in dr.RegisteredModel.list()]: print("Creating New Registered Model") registered_model_version = ( dr.RegisteredModelVersion.create_for_custom_model_version( model_version.id, name=CUSTOM_MODEL_NAME, description="LLM Wrapper Example from DataRobot Docs", registered_model_name=CUSTOM_MODEL_NAME, ) ) else: print("Using Existing Model") registered_model = [ m for m in dr.RegisteredModel.list() if m.name == CUSTOM_MODEL_NAME ].pop() registered_model_version = ( dr.RegisteredModelVersion.create_for_custom_model_version( model_version.id, name=CUSTOM_MODEL_NAME, description="LLM Wrapper Example from DataRobot Docs", registered_model_id=registered_model.id, ) ) ``` Now, deploy the model. If you are a DataRobot multitenant SaaS user, you must select a prediction environment. ``` pred_server = [s for s in dr.PredictionServer.list()][0] print(f"Prediction server ID: {pred_server}") ``` ``` MODEL_DEPLOYMENT_NAME = "LLM Wrapper Deployment" if MODEL_DEPLOYMENT_NAME not in [d.label for d in dr.Deployment.list()]: deployment = dr.Deployment.create_from_registered_model_version( registered_model_version.id, label=MODEL_DEPLOYMENT_NAME, description="Your new deployment", max_wait=1000, # Only needed for DataRobot Managed AI Platform default_prediction_server_id=pred_server.id, ) else: deployment = [d for d in dr.Deployment.list() if d.label == MODEL_DEPLOYMENT_NAME][ 0 ] ``` ## Test the deployment Test that the deployment can successfully provide responses to prompts. ``` from datarobot_predict.deployment import predict input_df = pd.DataFrame( { PROMPT_COLUMN_NAME: [ "Give me some context on large language models and their applications?", "What is AutoML?", "Tell me a joke", ], } ) result_df, response_headers = predict(deployment, input_df) result_df ``` ## Validate the external LLM The following methods execute and validate the external LLM. This example associates a Use Case with the validation and creates the vector database within that Use Case. Set the `use_case_id` to specify an existing Use Case or create a new one with that name. ``` # Option 1: Create a new Use Case (default approach) use_case = dr.UseCase.create() # Option 2: Use an existing Use Case (replace with a Use Case ID) # use_case_id = # use_case = dr.UseCase.get(use_case_id) ``` `CustomModelLLMValidation.create` executes the validation of the external LLM. Be sure to provide the deployment ID. ``` external_llm_validation = CustomModelLLMValidation.create( prompt_column_name=PROMPT_COLUMN_NAME, target_column_name=COMPLETION_COLUMN_NAME, deployment_id=deployment.id, name="My External LLM", use_case=use_case, wait_for_completion=True, ) ``` ``` assert external_llm_validation.validation_status == "PASSED" ``` ``` print(f"External LLM Validation ID: {external_llm_validation.id}") ``` This external LLM can now be used in the [GenAI E2E walkthrough](https://docs.datarobot.com/en/docs/gen-ai/genai-code/genai-e2e.html), for example to create the LLM blueprint. --- # Use the Bolt-on Governance API URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-code/genai-chat-completion-api.html # Use the Bolt-on Governance API This notebook outlines how to use the Bolt-on Governance API with [deployed LLM blueprints](https://docs.datarobot.com/en/docs/gen-ai/deploy-llm.html). LLM blueprints deployed from the playground implement the [chat()hook](https://docs.datarobot.com/en/docs/mlops/deployment/custom-models/drum/structured-custom-models.html#chat) in the custom model's `custom.py` file by default. You can use [the official Python library for the OpenAI API](https://github.com/openai/openai-python) to make chat completion requests to DataRobot LLM blueprint deployments: ``` !pip install openai ``` ``` from openai import OpenAI ``` Specify the ID of the LLM blueprint deployment and your DataRobot API token: ``` DEPLOYMENT_ID = "" DATAROBOT_API_TOKEN = "" DEPLOYMENT_URL = f"https://app.datarobot.com/api/v2/deployments/{DEPLOYMENT_ID}" ``` Use the code below to create an OpenAI client: ``` client = OpenAI(base_url=DEPLOYMENT_URL, api_key=DATAROBOT_API_TOKEN) ``` Use the code below to request a chat completion. See the [considerations](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/genai-chat-completion-api.html#considerations) below for more information on specifying the `model` parameter. Specifying the system message in the request overrides the system prompt configured in the LLM blueprint. Specifying other settings in the request, such as `max_completion_tokens`, overrides the settings of the LLM blueprint. ``` completion = client.chat.completions.create( model="datarobot-deployed-llm", messages=[ {"role": "system", "content": "Answer with just a number."}, {"role": "user", "content": "What is 2+3?"}, {"role": "assistant", "content": "5"}, {"role": "user", "content": "Now multiply the result by 4."}, {"role": "assistant", "content": "20"}, {"role": "user", "content": "Now divide the result by 2."}, ], ) ``` ``` print(completion) ``` This returns a `ChatCompletion` object if streaming is disabled and `Iterator[ChatCompletionChunk]` if streaming is enabled. Use the following cell to request a chat completion with a streaming response. ``` streaming_response = client.chat.completions.create( model="datarobot-deployed-llm", messages=[ {"role": "system", "content": "Explain your thoughts using at least 100 words."}, {"role": "user", "content": "What would it take to colonize Mars?"}, ], stream=True, ) ``` ``` for chunk in streaming_response: content = chunk.choices[0].delta.content if content is not None: print(content, end="") ``` To return `citations`, the deployed LLM must have a vector database associated with it.`completion` returns keys related to citations and accessible to custom models. ## Specify association ID and custom metrics When making a chat request to a DataRobot-deployed text generation or agentic workflow custom model, a custom association ID can be optionally specified for chat requests in place of the auto-generated ID by setting `datarobot_association_id` in the `extra_body` field of the chat request. Values can also be reported for arbitrary custom metrics defined for the deployment by setting `datarobot_metrics` in the `extra_body` field. To do this, define these values in the optional `extra_body` field of the chat request. The `extra_body` field is a standard way to add more parameters to an OpenAI chat request, allowing the chat client to pass model-specific parameters to an LLM. If the `datarobot_association_id` field is found in `extra_body`, DataRobot uses that value instead of the automatically generated one. If the `datarobot_metrics` field is found in `extra_body`, DataRobot reports a custom metric for all the `name:value` pairs found inside. A matching custom metric for each name must already be defined for the deployment. Custom metric values reported this way must be numeric. The deployed custom model must have an association ID column defined for DataRobot to process custom metrics from chat requests, regardless of whether `extra_body` is specified. Moderation must be configured for the custom model for the metrics to be processed. ``` extra_body = { # These values pass through to the LLM "llm_id": "azure-gpt-6", # If set here, replaces the auto-generated association ID "datarobot_association_id": "my_association_id_0001", # DataRobot captures these for custom metrics "datarobot_metrics": { "field1": 24, "field2": 25 } } completion = client.chat.completions.create( model="datarobot-deployed-llm", messages=[ {"role": "system", "content": "Explain your thoughts using at least 100 words."}, {"role": "user", "content": "What would it take to colonize Mars?"}, ], max_tokens=512, extra_body=extra_body ) print(completion.choices[0].message.content) ``` ## Moderation and guardrails Moderation guardrails help your organization block prompt injection and hateful, toxic, or inappropriate prompts and responses. To return `datarobot_moderations`, the deployed LLM must be running in an execution environment that has the moderation library installed, and the custom model code directory must contain `moderation_config.yaml` to configure the moderations. When using the Bolt-on Governance API with moderations configured, consider the following: - If there are no guards for the response stage, the moderation library returns the existing stream obtained from the LLM. - Not all response guards are applied to a chunk. The faithfulness, rouge-1, and nemo guards are not applied to chunk. Instead they are applied to the whole response when available because these guards need the whole response to be present in order to evaluate. - If moderation is enabled and the streaming response is requested, the first chunk will always contain the information about prompt guards (if configured) and response guards (excluding faithfulness, rouge-1, and nemo). Access the chunk via chunk.datarobot_moderations . - For every subsequent chunk that is not the last chunk, response guards (excluding faithfulness, rouge-1, and NeMo) are applied and can be accessed from chunk.datarobot_moderations . - The last chunk has all response guards (excluding faithfulness, rouge-1 and nemo) applied to the chunk. Faithfulness, rouge-1, and nemo are applied to the whole response. - If streaming is the aggregration, the following custom metrics are reported: ## Considerations When using the Bolt-on Governance API, consider the following: - If you implement the chat completion hook without modification, the chat() hook behaves differently than the score() hook. Specifically, the unmodified chat() hook passes in themodelparameter through thecompletion_create_paramsargument while the score() hook specifies the model in the custom model code. - If you add a deployed LLM to the playground , the validation uses the value entered into the "Chat model ID" field as the model parameter value. Ensure the LLM deployment accepts this value as the model parameter. Alternatively, you can modify the implementation of the chat() hook to override the value of the model parameter, defining the intended model (for example, using a runtime parameter ). For more information, see GenAI troubleshooting . - The Bolt-on Governance API is also available in GPU environments for custom models running on datarobot-drum>=1.14.3 . --- # End-to-end code-first generative AI experimentation URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-code/genai-e2e.html # End-to-end code-first generative AI experimentation This notebook outlines a comprehensive overview of the generative AI features DataRobot has to offer. Note: For self-managed users, code samples that reference `app.datarobot.com` need to be changed to the appropriate URL for your instance. ``` import datarobot as dr from datarobot.models.genai.vector_database import VectorDatabase from datarobot.models.genai.vector_database import ChunkingParameters from datarobot.enums import PromptType from datarobot.enums import VectorDatabaseEmbeddingModel from datarobot.enums import VectorDatabaseChunkingMethod from datarobot.models.genai.playground import Playground from datarobot.models.genai.llm import LLMDefinition from datarobot.models.genai.llm_blueprint import LLMBlueprint from datarobot.models.genai.llm_blueprint import VectorDatabaseSettings from datarobot.models.genai.chat import Chat from datarobot.models.genai.chat_prompt import ChatPrompt from datarobot.models.genai.vector_database import CustomModelVectorDatabaseValidation from datarobot.models.genai.comparison_chat import ComparisonChat from datarobot.models.genai.comparison_prompt import ComparisonPrompt from datarobot.models.genai.custom_model_llm_validation import CustomModelLLMValidation ``` ### Connect to DataRobot Read more about different options for [connecting to DataRobot from the Python client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` endpoint="https://app.datarobot.com/api/v2" token="" dr.Client(endpoint=endpoint, token=token) ``` ### Create a Use Case Generative AI works within a DataRobot Use Case, so to begin, create a new use case. ``` use_case = dr.UseCase.create() ``` This workflow uses a vector database with GenAI models. Next, you will upload a dataset with a set of documents. ``` # this can be updated with any public URL that is pointing to a .zip file # in the expected format dataset_url = "https://s3.amazonaws.com/datarobot_public_datasets/genai/pirate_resumes.zip" # We will use a vector database with our GenAI models. Let's upload a dataset with our documents. # If you wish to use a local file as dataset, change this to # `dataset = dr.Dataset.create_from_file(local_file_path)` dataset = dr.Dataset.create_from_url(dataset_url) ``` ### Create a vector database ``` chunking_parameters = ChunkingParameters( embedding_model=VectorDatabaseEmbeddingModel.JINA_EMBEDDING_T_EN_V1, chunking_method=VectorDatabaseChunkingMethod.RECURSIVE, chunk_size=128, chunk_overlap_percentage=25, separators=[], ) vdb = VectorDatabase.create(dataset.id, chunking_parameters, use_case) ``` Use the following cell to retrieve the vector database and make sure it has completed. Approximate completion time is 30-60 seconds. ``` vdb = VectorDatabase.get(vdb.id) assert vdb.execution_status == "COMPLETED" ``` ### Create an LLM playground Create an LLM playground that will host large language models (LLMs). ``` playground = Playground.create(name="New playground for example", use_case=use_case) ``` Use the following cell to see what kind of LLMs are available. By default this method returns as a dict for easy readability. It includes a list of allowed settings for each LLM along with any constraints on the values. ``` llms_dict = LLMDefinition.list(use_case=use_case) print(llms_dict) ``` As an example, use GPT 3.5. ``` llms = LLMDefinition.list(use_case=use_case, as_dict=False) gpt = llms[0] ``` To interact with GPT, you need to create an LLM blueprint with the settings that you want. Since the allowed settings depend on the LLM type, take a generic dict with those values that will be validated during the creation of the blueprint. Review the allowed settings for GPT. ``` print([setting.to_dict() for setting in gpt.settings]) ``` ### Create an LLM blueprint ``` llm_settings = { "system_prompt": ( "You are a pirate who begins each response with 'Arrr' " "and ends each response with 'Matey!'" ), "max_completion_length": 256, # Let's ask for short responses "temperature": 2.0, # With a high degree of variability } # PromptType.ONE_TIME_PROMPT is an alternative if you don't wish # for previous chat prompts (history) to be included in each subsequent prompt prompting_strategy = PromptType.CHAT_HISTORY_AWARE ``` Use some vector database settings that could be different from the defaults when the database was created. ``` vector_database_settings = VectorDatabaseSettings( max_documents_retrieved_per_prompt=2, max_tokens=128, ) llm_blueprint = LLMBlueprint.create( playground=playground, name="GPT", llm=gpt, prompt_type=prompting_strategy, llm_settings=llm_settings, vector_database=vdb, vector_database_settings=vector_database_settings, ) ``` ### Create a chat Now create a chat and associate it with the LLM blueprint you just created. A chat is the entity that groups together a set of prompt messages. It can be thought of as a conversation with a particular LLM blueprint. ``` chat = Chat.create( name="Resume review chat with a pirate", llm_blueprint=llm_blueprint ) ``` ### Chat with the LLM blueprint Use the following cell to chat with the LLM blueprint. ``` prompt1 = ChatPrompt.create( chat=chat, text="How can a pirate resume be evaluated?", wait_for_completion=True, ) print(prompt1.result_text) ``` The example cell above returns a truncated output, confirmed by `result_metadata`. ``` print(prompt1.result_metadata.error_message) ``` Try to chat again with a larger `max_completion_length`. LLM settings can be updated within a chat prompt as long as the LLM blueprint hasn't yet been saved. Doing so updates the underlying settings of the LLM blueprint. ``` llm_settings["max_completion_length"] = 1024 prompt2 = ChatPrompt.create( text="Please answer the previous question again.", chat=chat, llm_settings=llm_settings, wait_for_completion=True, ) print(prompt2.result_text) ``` You can keep the new `max_completion_length` and confirm that the LLM blueprint has been updated. ``` llm_blueprint = LLMBlueprint.get(llm_blueprint.id) print(llm_blueprint.llm_settings) ``` Next, lower the temperature to modify the reply, as the response may be unsatisfactory. ``` llm_settings["temperature"] = 0.8 prompt3 = ChatPrompt.create( text="Please summarize the best pirate resume.", chat=chat, llm_settings=llm_settings, wait_for_completion=True, ) print(prompt3.result_text) ``` Confirm that the LLM is using information from the vector database. ``` print([citation.text for citation in prompt3.citations]) ``` Next, create a new LLM blueprint from the existing one so that you can change some settings and compare the two. ``` new_llm_blueprint = LLMBlueprint.create_from_llm_blueprint(llm_blueprint, name="new blueprint") ``` Remove the vector database from the new blueprint, modify the settings as shown below, and save the blueprint. ``` llm_settings["system_prompt"] = "You are an AI assistant who helps to evaluate resumes." new_llm_blueprint = new_llm_blueprint.update( llm_settings=llm_settings, remove_vector_database=True, ) ``` Once you are satisfied with the blueprint, in the UI you can perform an [LLM blueprint comparison](https://docs.datarobot.com/en/docs/gen-ai/compare-llm.html) or [deploy it from the playground](https://docs.datarobot.com/en/docs/gen-ai/deploy-llm.html). ### Add an external vector database Now add an external vector database from a deployment. Note that creating the deployment is outside the scope of this example. To continue with this portion of the example, first create an external vector database deployment, validate and deploy it, and update the `external_vdb_id` value here. You can create an external vector database and deploy it by first following [this guide.](https://docs.datarobot.com/en/docs/gen-ai/genai-code/ext-llm.html) Once you have done that, simply update your external vector database ID here to use it. ``` external_vdb_id = "" ``` ``` # Now create a vector database external_model_vdb = VectorDatabase.get( vector_database_id=external_vdb_id ) assert external_model_vdb.execution_status == "COMPLETED" ``` ### Add an external model LLM Now add an external LLM blueprint from a deployment. Because creating the deployment is outside the scope of this example, it instead assumes a previously deployed text generation model. To continue with this portion of the example, first create an external LLM deployment, validate it, and update the `external_llm_validation_id` value here. You can create an external LLM and validate it by first following [this guide.](https://docs.datarobot.com/en/docs/gen-ai/genai-code/ext-llm.html) Once you have done that, update your external LLM validation ID here to use it. ``` external_llm_validation_id = "" ``` ``` external_llm_validation = CustomModelLLMValidation.get(validation_id=external_llm_validation_id) assert external_llm_validation.validation_status == "PASSED" ``` Now you can create an LLM blueprint using the custom model LLM and the custom model vector database. ``` custom_model_llm_blueprint = LLMBlueprint.create( playground=playground, name="custom model LLM with custom model vdb", llm="custom-model", llm_settings={ "validation_id": external_llm_validation.id, # NOTE - update this value based on the context size in tokens # of the external LLM that you are deploying "external_llm_context_size": 4096 }, vector_database=external_model_vdb, ) ``` ### Create a comparison chat Finally create a `comparison_chat` and associate it with a playground. A comparison chat, analogous to the chat for a chat prompt, is the entity that groups together a set of comparison prompt messages. It can be thought of as a conversation with one or more LLM blueprints. ``` comparison_chat = ComparisonChat.create( name="Resume review comparison chat with a pirate", playground=playground ) ``` Now compare the LLM blueprints. The response of the `custom_model_llm_blueprint` as it is setup in the documentation will not be related to the pirate resume and that is expected. Feel free to modify the external VDB creation and/or external LLM to suit your use case. ``` comparison_prompt = ComparisonPrompt.create( llm_blueprints=[llm_blueprint, new_llm_blueprint, custom_model_llm_blueprint], text="Summarize the best resume", comparison_chat=comparison_chat, wait_for_completion=True, ) print([result.result_text for result in comparison_prompt.results]) ``` --- # Code walkthroughs URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-code/index.html > Use code to create and validate external vector databases and LLMs; walk through a comprehensive GenAI overview. # Code walkthroughs > [!NOTE] Availability information > DataRobot's GenAI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. For code-first users, the following sections provide code samples that create and validate external vector databases and LLMs. Additionally, use the end-to-end notebook to walk through a comprehensive overview of GenAI features. See the [list of considerations](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-consider.html) to keep in mind when working with DataRobot GenAI. | Topic | Description | | --- | --- | | End-to-end code-first generative AI experimentation | A comprehensive overview of the generative AI features DataRobot has to offer with the Python API client. | | Create and deploy a vector database | How to use the Python SDK to create and deploy DataRobot vector databases using built-in embeddings. For custom embedding models (BYO embeddings), see the separate notebook below. | | Create a ChromaDB vector database | How to load in and host a ChromaDB in-memory vector store, with metadata filtering, within a custom model. | | Build and host a Qdrant vector database | How to build, validate, and register a Qdrant vector database to the DataRobot application using DataRobot's Python API client. | | Create vector databases from BYO embeddings | How to build, validate, and register an external vector database from bring-your-own (BYO) embeddings. | | Create external LLMs with code | How to set up and validate an external LLM using DataRobot's Python API client. | | Use the Bolt-on Governance API | How to use the OpenAI Python library to make chat completion requests to a deployed LLM blueprint. | | Use the DataRobot LLM gateway | How to use the OpenAI Python library to make chat completion requests directly to the DataRobot LLM gateway. | --- # Build and host a Qdrant vector database URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-code/qdrantdb-vdb.html # Build and host a Qdrant vector database This page provides an example of how you can build, validate, and register a vector database to the DataRobot application using DataRobot's Python API client. It describes how to load and host a Qdrant vector store with metadata filtering as part of a custom model. This page is designed for use within a DataRobot [codespace](https://docs.datarobot.com/en/docs/workbench/wb-notebook/codespaces/index.html). DataRobot recommends downloading this notebook and uploading it for use in the application. ## Setup The following steps outline the necessary configuration to integrate vector databases with the DataRobot platform. 1. This workflow uses the following feature flags. Contact your DataRobot representative or administrator for information on enabling these features. 2. Use a codespace, not a DataRobot Notebook, to ensure this notebook has access to a filesystem. 3. Usepip installto install the packages outlined in the following section if they are not already in the codespace's environment image. 4. Set the notebook session timeout to 180 minutes. 5. Restart the notebook container using at least a "Medium" (16GB RAM) instance. ### Install libraries Install the following libraries: ``` !pip install "langchain-community==0.4.1" \ "langchain_text_splitters==1.0.0" \ "qdrant-client==1.16.1" \ "sentence-transformers==5.1.2" \ "datarobotx==0.2.0" \ "cloudpickle==2.2.1" ``` ``` import datarobot as dr import datarobotx as drx from datarobot.models.genai.vector_database import CustomModelVectorDatabaseValidation from datarobot.models.genai.vector_database import VectorDatabase import os from pathlib import Path from qdrant_client import QdrantClient, models from sentence_transformers import SentenceTransformer from langchain_community.document_loaders import TextLoader from langchain_text_splitters import RecursiveCharacterTextSplitter import json import requests import zipfile import io import re ``` ### Connect to DataRobot Read more about options for [connecting to DataRobot from the Python client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ### Download sample data This example references a sample dataset made from the DataRobot english documentation. Feel free to use your own data here. Note: If you are a self-managed user, you must modify code samples that reference `app.datarobot.com` to the appropriate URL for your instance. ``` # Configuration QDRANT_DATA_PATH = "qdrant" EMBEDDING_MODEL_NAME = "all-MiniLM-L6-v2" COLLECTION_NAME = "my_documents" SOURCE_DOCUMENTS_ZIP_URL = "https://s3.amazonaws.com/datarobot_public_datasets/ai_accelerators/datarobot_english_documentation_5th_December.zip" UNZIPPED_DOCS_DIR = "datarobot_english_documentation" ``` ``` # Verify docs exist or download them doc_path = Path(UNZIPPED_DOCS_DIR) if doc_path.exists(): txt_files = list(doc_path.rglob("*.txt")) print(f"✓ Found {len(txt_files)} .txt files in {UNZIPPED_DOCS_DIR}/") if txt_files: print(f" Sample files:") for f in txt_files[:5]: print(f" - {f}") else: # Download some example docs r = requests.get(SOURCE_DOCUMENTS_ZIP_URL) z = zipfile.ZipFile(io.BytesIO(r.content)) z.extractall() ``` ## Create a vector database from documents Use the following cell to build a vector database from the DataRobot documentation dataset. Note that this notebook uses Qdrant, an open source vector database with metadata filtering support. Additionally, this notebook uses the HuggingFace `all-MiniLM-L6-v2` [embeddings model](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) (also open source). ``` # Create vectorDB def create_database(): """Create and populate the Qdrant database""" # Initialize encoder encoder = SentenceTransformer(EMBEDDING_MODEL_NAME) # Initialize Qdrant client client = QdrantClient(path=QDRANT_DATA_PATH) try: # Create collection print("Creating collection...") client.create_collection( collection_name=COLLECTION_NAME, vectors_config=models.VectorParams( size=encoder.get_sentence_embedding_dimension(), distance=models.Distance.COSINE, ), ) # Load text files print(f"Loading documents from {UNZIPPED_DOCS_DIR}/...") docs = [] doc_path = Path(UNZIPPED_DOCS_DIR) for file_path in doc_path.rglob("*.txt"): loader = TextLoader(str(file_path)) loaded_docs = loader.load() docs.extend(loaded_docs) print(f"Loaded {len(docs)} documents") # Split documents into chunks splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50) split_docs = splitter.split_documents(docs) print(f"Split into {len(split_docs)} chunks") # Process metadata for doc in split_docs: # Convert file path to documentation URL doc.metadata['source'] = re.sub( r'datarobot_english_documentation/datarobot_docs/en/(.+)\.(txt|md)', r'https://docs.datarobot.com/en/docs/\1.html', doc.metadata.get('source', '') ) # Extract category from source doc.metadata["category"] = doc.metadata["source"].split("/")[-1].split(".")[0] # Batch encode all documents print("Encoding all documents (this may take a few minutes)...") all_contents = [doc.page_content for doc in split_docs] all_vectors = encoder.encode( all_contents, show_progress_bar=False, batch_size=32, convert_to_numpy=True ) # Create points print("Creating point structures...") points_to_upload = [ models.PointStruct( id=idx, vector=all_vectors[idx].tolist(), payload={ "content": doc.page_content, "source": doc.metadata.get("source", ""), "category": doc.metadata.get("category", ""), **doc.metadata } ) for idx, doc in enumerate(split_docs) ] # Upload to Qdrant print(f"Uploading {len(points_to_upload)} points to Qdrant...") client.upload_points( collection_name=COLLECTION_NAME, points=points_to_upload ) # Verify collection_info = client.get_collection(collection_name=COLLECTION_NAME) print(f"✓ Collection '{COLLECTION_NAME}' created with {collection_info.points_count} points") print(f"✓ Data saved to: {QDRANT_DATA_PATH}/") finally: # ALWAYS close the client client.close() print("✓ Database client closed") # Run the creation (only once) create_database() ``` ### Test the vector database Use the following cell to test the vector database by having the model perform a similarity search. It returns the top five documents matching the query provided. ``` # Test a query to the vectorDB def test_database(): """Test the database with a sample query""" encoder = SentenceTransformer(EMBEDDING_MODEL_NAME) client = QdrantClient(path=QDRANT_DATA_PATH) try: question = "What is MLOps?" query_vector = encoder.encode(question).tolist() results = client.query_points( collection_name=COLLECTION_NAME, query=query_vector, limit=5, ).points print(f"Found {len(results)} results for: '{question}'\n") for hit in results: print(f"Score: {hit.score:.4f}") print(f"Content: {hit.payload.get('content', '')[:200]}...") print(f"Source: {hit.payload.get('source', '')}") print(f"Category: {hit.payload.get('category', '')}\n") finally: # ALWAYS close the client client.close() print("✓ Test client closed") # Run the test test_database() ``` ## Define hooks to deploy an unstructured custom model The following cell defines the methods used to deploy an unstructured custom model. These include loading the custom model and using the model for scoring. In this notebook, the vector database is loaded to DataRobot's infrastructure. Alternatively, you could use a proxy endpoint in `load_model` that points to where the vector database is actually stored. ``` def load_model(input_dir): """Custom model hook for loading our Qdrant knowledge base.""" import os from qdrant_client import QdrantClient from sentence_transformers import SentenceTransformer print("Loading model") EMBEDDING_MODEL_NAME = 'all-MiniLM-L6-v2' COLLECTION_NAME = "my_documents" # When deploying model="qdrant/", the files are at input_dir/qdrant/ # not directly at input_dir if input_dir: QDRANT_DATA_PATH = os.path.join(input_dir, "qdrant") else: QDRANT_DATA_PATH = "qdrant" print(f'QDRANT_DATA_PATH = {QDRANT_DATA_PATH}') print(f'EMBEDDING_MODEL_NAME = {EMBEDDING_MODEL_NAME}') print(f'COLLECTION_NAME = {COLLECTION_NAME}') # Initialize the embedding model (downloads if needed) encoder = SentenceTransformer(EMBEDDING_MODEL_NAME) # Initialize Qdrant client client = QdrantClient(path=QDRANT_DATA_PATH) # Get collection info to verify it loaded collection_info = client.get_collection(collection_name=COLLECTION_NAME) print(f'Loaded Qdrant collection "{COLLECTION_NAME}" with {collection_info.points_count} points') return { "client": client, "encoder": encoder, "collection_name": COLLECTION_NAME } def score_unstructured(model, data, **kwargs) -> str: """Custom model hook for retrieving relevant docs with our Qdrant knowledge base. When requesting predictions from the deployment, pass a dictionary with the following keys: - 'question' the question to be passed to the vector store retriever - 'filter' any metadata filter - 'k' the number of results to return (default: 10) datarobot-user-models (DRUM) handles loading the model and calling this function with the appropriate parameters. Returns: -------- rv : str Json dictionary with keys: - 'question' user's original question - 'relevant' the retrieved document contents - 'metadata' - metadata for each document including similarity scores - 'error' - error message if exception in handling request """ import json from qdrant_client import models try: # Loading data data_dict = json.loads(data) question = data_dict['question'] metadata_filter = data_dict.get("filter", None) top_k = data_dict.get("k", 10) # Defining info client = model["client"] encoder = model["encoder"] collection_name = model["collection_name"] # Encode the question query_vector = encoder.encode(question).tolist() # Build query parameters query_params = { "collection_name": collection_name, "query": query_vector, "limit": top_k, } # Only add filter if it exists if metadata_filter is not None: query_params["query_filter"] = models.Filter(**metadata_filter) # Perform the search results = client.query_points(**query_params).points print(f'Returned {len(results)} results') relevant, metadata = [], [] for hit in results: # Extract the content from payload content = hit.payload.get('content', '') relevant.append(content) # Add similarity score to metadata hit_metadata = dict(hit.payload) hit_metadata["similarity_score"] = hit.score metadata.append(hit_metadata) rv = { "question": question, "relevant": relevant, "metadata": metadata, } except Exception as e: rv = {'error': f"{e.__class__.__name__}: {str(e)}"} return json.dumps(rv), {"mimetype": "application/json", "charset": "utf8"} ``` ### Test hooks locally Before proceeding with deployment, use the cell below to test that the custom model hooks function correctly. ``` # Testing to ensure they work locally before deploying def test_hooks(): """Test the DataRobot hooks locally""" # Load the model - pass None so it uses "qdrant" as the path model = load_model(None) try: # Test scoring print("=" * 80) print("TEST: Basic search") print("=" * 80) result = score_unstructured( model, json.dumps({ "question": "What is MLOps?", "filter": models.Filter( must=[ models.FieldCondition( key="category", match=models.MatchValue(value="datarobot_docs|en|more-info|eli5") ) ] ).model_dump(), # converting to be json serializable (will be convert back before scoring) "k": 3, }) ) response = json.loads(result[0]) print(f"Question: {response['question']}") print(f"Found {len(response['relevant'])} results:\n") for idx, (content, meta) in enumerate(zip(response['relevant'], response['metadata'])): print(f"{idx+1}. Score: {meta['similarity_score']:.4f}") print(f" Source: {meta.get('source', 'N/A')}") print(f" Category: {meta.get('category', 'N/A')}") print(f" Content: {content[:150]}...") print() finally: # ALWAYS close the client after testing model["client"].close() print("✓ Test client closed") # Run the test test_hooks() ``` ## Deploy the knowledge base The cell below uses a convenience method that does the following: - Builds a new custom model environment containing the contents of qdrant/ . - Assembles a new custom model with the provided hooks. - Deploys an unstructured custom model to DataRobot. - Returns an object that can be used to make predictions. This example uses a pre-built environment. You can also provide an `environment_id` and instead use an existing custom model environment for shorter iteration cycles on the custom model hooks. See your account's existing pre-built environments from the [DataRobot Workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-environment-workshop/nxt-drop-in-envs.html). ``` # Get GenAI environment genai_environment = dr.ExecutionEnvironment.list(search_for="[GenAI] Python 3.11")[0] # Deploy ONLY the qdrant/ folder (not the parent directory) deployment = drx.deploy( model="qdrant/", name="Qdrant Vector Database", hooks={ "score_unstructured": score_unstructured, "load_model": load_model }, extra_requirements=[ "qdrant-client", "sentence-transformers", ], environment_id=genai_environment.id ) print(f"✓ Deployment complete!") print(f"Deployment ID: {deployment.dr_deployment.id}") ``` ### Test the deployment Test that the deployment can successfully provide responses to questions using the [datarobot-predict](https://datarobot.github.io/datarobot-predict/1.13/deployment/) library. ``` from datarobot_predict.deployment import predict_unstructured # Now with metadata filtering data = { "question": "How do I replace a custom model on an existing custom environment?", "filter": models.Filter( must=[ models.FieldCondition( key="category", match=models.MatchValue(value="datarobot_docs|en|modeling|special-workflows|cml|cml-custom-env") ) ] ).model_dump(), "k": 5, } # Prediction request content, response_headers = predict_unstructured( deployment=deployment.dr_deployment, data=data, ) # Check output content ``` ## Validate and create the vector database These methods execute, validate, and integrate the vector database. This example associates a Use Case with the validation and creates the vector database within that Use Case. Set the `use_case_id` to specify an existing Use Case or create a new one with that name. ``` # Use current use case (assuming your working in a DataRobot Codespace) use_case_id = os.environ['DATAROBOT_DEFAULT_USE_CASE'] use_case = dr.UseCase.get(use_case_id) # UNCOMMENT if you want to create a new Use Case # use_case = dr.UseCase.create() ``` The `CustomModelVectorDatabaseValidation.create` function executes the validation of the vector database. Be sure to provide the deployment ID. ``` external_vdb_validation = CustomModelVectorDatabaseValidation.create( prompt_column_name="question", target_column_name="relevant", deployment_id=deployment.dr_deployment.id, use_case=use_case, wait_for_completion=True ) external_vdb_validation ``` ``` assert external_vdb_validation.validation_status == "PASSED" ``` After validation completes, use `VectorDatabase.create_from_custom_model()` to integrate the vector database. You must provide the Use Case name (or Use Case ID), a name for the external vector database, and the validation ID returned from the previous cell. ``` vdb = VectorDatabase.create_from_custom_model( name="Qdrant Vector Database", use_case=use_case, validation_id=external_vdb_validation.id ) vdb ``` ``` assert vdb.execution_status == "COMPLETED" ``` ``` print(f"Vector Database ID: {vdb.id}") ``` This vector database ID can now be used to alongside [LLM blueprints](https://docs.datarobot.com/en/docs/gen-ai/playground-tools/build-llm-blueprints.html#add-a-vector-database) to create RAG workflows. --- # NVIDIA NIM gallery information URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/genai-nim-gallery-support.html > Comprehensive table of NIM gallery information including model names, types, chat model IDs, playground support, platform support, and documentation links for unstructured models. # NVIDIA NIM gallery information This is a comprehensive table of NVIDIA NIM gallery information, including model names and types, chat model IDs, playground support information, platform support information, and documentation links for unstructured models. | NIM | Type | Chat model ID | Supported in playground | Platform support | | --- | --- | --- | --- | --- | | codellama-13b-instruct | Text Generation | codellama/codellama-13b-instruct | Yes | Cloud, 11.1 and later | | codellama-34b-instruct | Text Generation | codellama/codellama-34b-instruct | Yes | Cloud, 11.1 and later | | codellama-70b-instruct | Text Generation | codellama/codellama-70b-instruct | Yes | Cloud, 11.1 and later | | deepseek-r1-distill-llama-8b | Text Generation | deepseek-ai/deepseek-r1-distill-llama-8b | Yes | Cloud, 11.1 and later | | deepseek-r1-distill-qwen-7b | Text Generation | deepseek-ai/deepseek-r1-distill-qwen-7b | Yes | Cloud, 11.1 and later | | deepseek-r1-distill-qwen-14b | Text Generation | deepseek-ai/deepseek-r1-distill-qwen-14b | Yes | Cloud, 11.1 and later | | deepseek-r1-distill-qwen-32b | Text Generation | deepseek-ai/deepseek-r1-distill-qwen-32b | Yes | Cloud, 11.1 and later | | gemma-2-2b-instruct | Text Generation | google/gemma-2-2b-instruct | Yes | Cloud, 11.1 and later | | gemma-2-9b-it | Text Generation | google/gemma-2-9b-it | Yes | Cloud, 11.1 and later | | gpt-oss-120b | Text Generation | openai/gpt-oss-120b | Yes | Cloud, 11.2 and later | | gpt-oss-20b | Text Generation | openai/gpt-oss-20b | Yes | Cloud, 11.2 and later | | llama-2-13b-chat | Text Generation | meta/llama-2-13b-chat | Yes | Cloud, 11.1 and later | | llama-2-7b-chat | Text Generation | meta/llama-2-7b-chat | Yes | Cloud, 11.1 and later | | llama-2-70b-chat | Text Generation | meta/llama-2-70b-chat | No | 11.1 and later | | llama-3-sqlcoder-8b | Text Generation | defog/llama-3-sqlcoder-8b | Yes | Cloud, 11.1 and later | | llama-3-swallow-70b-instruct-v0.1 | Text Generation | tokyotech-llm/llama-3-swallow-70b-instruct-v0.1 | No | 11.1 and later | | llama-3-taiwan-70b-instruct | Text Generation | yentinglin/llama-3-taiwan-70b-instruct | No | 11.1 and later | | llama-3.1-70b-instruct | Text Generation | meta/llama-3.1-70b-instruct | Yes | Cloud, 11.1 and later | | llama-3.1-8b-instruct | Text Generation | meta/llama-3.1-8b-instruct | Yes | Cloud, 11.1 and later | | llama-3.1-8b-instruct-pb24h2 | Text Generation | meta/llama-3.1-8b-instruct-pb24h2 | Yes | Cloud, 11.1 and later | | llama-3.1-70b-instruct-pb24h2 | Text Generation | meta/llama-3.1-70b-instruct-pb24h2 | Yes | Cloud, 11.1 and later | | llama-3.1-nemotron-nano-8b-v1 | Text Generation | nvidia/llama-3.1-nemotron-nano-8b-v1 | Yes | Cloud, 11.1 and later | | llama-3.1-nemotron-70b-instruct | Text Generation | nvidia/llama-3.1-nemotron-70b-instruct | Yes | Cloud, 11.1 and later | | llama-3.1-nemotron-ultra-253b-v1 | Text Generation | nvidia/llama-3.1-nemotron-ultra-253b-v1 | No | 11.1 and later | | llama-3.1-swallow-70b-instruct-v0.1 | Text Generation | tokyotech-llm/llama-3.1-swallow-70b-instruct-v0.1 | Yes | Cloud, 11.1 and later | | llama-3.2-1b-instruct | Text Generation | meta/llama-3.2-1b-instruct | Yes | Cloud, 11.1 and later | | llama-3.2-3b-instruct | Text Generation | meta/llama-3.2-3b-instruct | Yes | Cloud, 11.1 and later | | llama-3.2-11b-vision-instruct | Text Generation | meta/llama-3.2-11b-vision-instruct | Yes | Cloud, 11.1 and later | | llama-3.2-90b-vision-instruct | Text Generation | meta/llama-3.2-90b-vision-instruct | No | 11.1 and later | | llama-3.3-70b-instruct | Text Generation | meta/llama-3.3-70b-instruct | Yes | Cloud, 11.1 and later | | llama-3.3-nemotron-super-49b-v1 | Text Generation | nvidia/llama-3.3-nemotron-super-49b-v1 | Yes | Cloud, 11.1 and later | | llama-3.3-nemotron-super-49b-v1.5 | Text Generation | nvidia/llama-3-3-nemotron-super-49b-v1-5 | Yes | Cloud, 11.2 and later | | llama-4-scout-17b-16e-instruct | Text Generation | meta/llama-4-scout-17b-16e-instruct | Yes | 11.2 and later | | llama3-70b-instruct | Text Generation | meta/llama3-70b-instruct | No | 11.1 and later | | llama3-8b-instruct | Text Generation | meta/llama3-8b-instruct | Yes | Cloud, 11.1 and later | | mistral-7b-instruct-v0.3 | Text Generation | mistralai/mistral-7b-instruct-v0.3 | Yes | Cloud, 11.1 and later | | mistral-nemo-12b-instruct | Text Generation | mistral-nemo-12b-instruct | Yes | Cloud, 11.1 and later | | mistral-nemo-minitron-8b-8k-instruct | Text Generation | nv-mistralai/mistral-nemo-minitron-8b-8k-instruct | Yes | Cloud, 11.1 and later | | mixtral-8x7b-instruct-v01 | Text Generation | mistralai/mixtral-8x7b-instruct-v0.1 | Yes | Cloud, 11.1 and later | | mixtral-8x22b-instruct-v01 | Text Generation | mistralai/mixtral-8x22b-instruct-v01 | No | 11.1 and later | | nemotron-3-nano | Text Generation | nvidia/nemotron-3-nano | Yes | Cloud | | nemotron-3-super-120b-a12b | Text Generation | nvidia/nemotron-3-super-120b-a12b | Yes | Cloud, 11.7 and later | | nvidia-nemotron-nano-9b-v2 | Text Generation | nvidia/nvidia-nemotron-nano-9b-v2 | Yes | 11.1 and later | | phi-3-mini-4k-instruct | Text Generation | microsoft/phi-3-mini-4k-instruct | Yes | Cloud, 11.1 and later | | qwen-2.5-7b-instruct | Text Generation | qwen/qwen-2.5-7b-instruct | Yes | Cloud, 11.1 and later | | qwen3-32b | Text Generation | qwen/qwen3-32b | Yes | 11.2 and later | | qwen3-next-80b-a3b-thinking | Text Generation | qwen/qwen3-next-80b-a3b-thinking | Yes | 11.2 and later | | starcoder2-7b | Text Generation | bigcode/starcoder2-7b | Yes | Cloud, 11.1 and later | | cosmos-predict1-7b-text2world | Unstructured | - | - | 11.2 and later | | cosmos-predict1-7b-video2world | Unstructured | - | - | 11.2 and later | | cosmos-reason2-2b | Unstructured | - | - | Cloud, 11.7 and later | | cosmos-reason2-8b | Unstructured | - | - | Cloud, 11.7 and later | | cuopt | Unstructured | - | - | Cloud, 11.1 and later | | diffdock | Unstructured | - | - | Cloud, 11.7 and later | | genmol | Unstructured | - | - | Cloud, 11.1 and later | | arctic-embed-l | Embedding/Unstructured | - | - | Cloud, 11.1 and later | | llama-3.1-nemotron-nano-vl-8b-v1 | Unstructured | - | - | 11.2 and later | | llama-3.2-nv-embedqa-1b-v2 | Embedding/Unstructured | - | - | Cloud, 11.1 and later | | nv-embedqa-e5-v5 | Embedding/Unstructured | - | - | Cloud, 11.1 and later | | nv-embedqa-e5-v5-pb24h2 | Embedding/Unstructured | - | - | Cloud, 11.1 and later | | nv-embedqa-mistral-7b-v2 | Embedding/Unstructured | - | - | Cloud, 11.1 and later | | nvclip | Embedding/Unstructured | - | - | Cloud, 11.1 and later | | llama-3.2-nv-rerankqa-1b-v2 | Unstructured | - | - | Cloud, 11.1 and later | | molmim | Unstructured | - | - | Cloud, 11.1 and later | | nemoretriever-graphic-elements-v1 | Unstructured | - | - | Cloud, 11.1 and later | | nemoretriever-page-elements-v2 | Unstructured | - | - | Cloud, 11.1 and later | | nemoretriever-parse | Unstructured | - | - | Cloud, 11.1 and later | | nemoretriever-table-structure-v1 | Unstructured | - | - | Cloud, 11.1 and later | | nv-rerankqa-mistral-4b-v3 | Unstructured | - | - | Cloud, 11.1 and later | | openfold2 | Unstructured | - | - | 11.1 and later | | openfold3 | Unstructured | - | - | Cloud, 11.7 and later | | boltz2 | Unstructured | - | - | Cloud, 11.7 and later | | paddleocr | Unstructured | - | - | Cloud, 11.1 and later | | proteinmpnn | Unstructured | - | - | Cloud, 11.1 and later | | rfdiffusion | Unstructured | - | - | Cloud, 11.1 and later | | llama-3.1-nemoguard-8b-content-safety | Evaluation | - | - | Cloud, 11.1 and later | | llama-3.1-nemoguard-8b-topic-control | Evaluation | - | - | Cloud, 11.1 and later | | nemoguard-jailbreak-detect | Evaluation | - | - | Cloud, 11.1 and later | ## Feature considerations - Chat model ID : For NIM model deployments, the chat model ID can be set to datarobot-deployed-llm for dynamic population, or hard-coded using the values in the table. - Playground support : Models marked as "No" in the playground support column are not supported in the playground. - Embedding/unstructured models with chat support:The following embedding/unstructured models support both direct access endpoint and chat completions endpoint: - Evaluation metrics: --- # NVIDIA AI Enterprise integration URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/genai-nvidia-integration.html > NVIDIA AI Enterprise and DataRobot provide an integrated, pre-built AI stack solution, designed to fit existing infrastructure. # NVIDIA AI Enterprise integration > [!NOTE] Premium > The use of NVIDIA Inference Microservices (NIM) in DataRobot requires access to premium features for GenAI experimentation and GPU inference. Contact your DataRobot representative or administrator for information on enabling the required features. NVIDIA AI Enterprise and DataRobot provide a pre-built AI stack solution, designed to integrate with your organization's existing DataRobot infrastructure, providing access to robust evaluation, governance, and monitoring features. This integration includes a comprehensive array of tools for end-to-end AI orchestration, accelerating your organization's data science pipelines to rapidly deploy production-grade AI applications on NVIDIA GPUs in DataRobot Serverless Compute. In DataRobot, create custom AI applications tailored to your organization's needs by selecting NVIDIA Inference Microservices (NVIDIA NIM) from a gallery of AI applications and agents. NVIDIA NIM provides pre-built and pre-configured microservices within NVIDIA AI Enterprise, designed to accelerate the deployment of generative AI across enterprises. The DataRobot moderation framework provides out-of-the-box guards, allowing you to customize your applications with simple rules, code, or models to ensure GenAI applications perform to your organization's standards. NVIDIA NeMo Guardrails are tightly integrated into DataRobot, providing an easy way to build state-of-the-art guardrails into your application. For more information on the capabilities provided by NVIDIA AI Enterprise and DataRobot, review the documentation listed below, or read the workflow summary on this page. | Task | Description | | --- | --- | | Create an inference endpoint for NVIDIA NIM | Register and deploy with NVIDIA NIM to create inference endpoints accessible through code or the DataRobot UI. | | Evaluate a text generation NVIDIA NIM in the playground | Add a deployed text generation NVIDIA NIM to a blueprint in the playground to access an array of comparison and evaluation tools. | | Use an embedding NVIDIA NIM to create a vector database | Add a registered or deployed embedding NVIDIA NIM to a Use Case with a vector database to enrich prompts in the playground with relevant context before they are sent to the LLM. | | Use NVIDIA NeMo Guardrails in a moderation framework to secure your application | Connect NVIDIA NeMo Guardrails to deployed text generation models to guard against off-topic discussions, unsafe content, and jailbreaking attempts. | | Use a text generation NVIDIA NIM in an application template | Customize application templates from DataRobot to use a registered or deployed NVIDIA NIM text generation model. | ## Create an inference endpoint for NVIDIA NIM The NVIDIA AI Enterprise integration with DataRobot starts in Registry, where you can import NIM containers from the NVIDIA AI Enterprise catalog. The resulting registered model is optimized for deployment to Console and is compatible with the DataRobot monitoring and governance framework. NVIDIA NIM provides optimized foundational models you can add to a playground in Workbench for evaluation and inclusion in agentic blueprints, embedding models used to create vector databases, and NVIDIA NeMo Guardrails used in the DataRobot moderation framework to secure your agentic application. On the Models tab in Registry, register NVIDIA NIM models from the NVIDIA GPU Cloud (NGC) gallery, selecting the model name and performance profile and reviewing the information provided on the model card. After the model is registered, deploy it to a DataRobot Serverless prediction environment. To deploy a registered model to a DataRobot Serverless environment, on the Models tab, locate and click the registered NIM, and then click the version to deploy. Then in the registered model version, you can [review the version information](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-view-manage-reg-models.html#view-version-details) and click Deploy. After the model is deployed to a DataRobot Serverless prediction environment, you can access real-time prediction snippets from the deployment's Predictions tab. The requirements for running the prediction snippet depends on the model type: text generation or unstructured. When you add a NIM to Registry in DataRobot, LLMs are imported as text generation models, allowing you to use the [Bolt-on Governance API](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/genai-chat-completion-api.html) to communicate with the deployed NIM. Other types of models are imported as unstructured models, where endpoints provided by the NIM containers are exposed to communicate with the deployed NIM. This provides the flexibility required to deploy any NIM on GPU infrastructure using DataRobot Serverless Compute. For more information, see the [documentation](https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/nvidia-ngc-nim-import.html). ## Evaluate a text generation NVIDIA NIM in the playground Optimized foundational models are available in Registry through NVIDIA NIM, imported with a text generation target type. In Workbench, you can add these optimized foundational models to a playground, where you can create, interact with, and compare LLM blueprints. For more information, see the [documentation](https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/nvidia-nim-deploy.html). ## Use an embedding NVIDIA NIM to create a vector database Embedding models are available in Registry through NVIDIA NIM. In Workbench, you can add a deployed embedding model to a Use Case as a vector database. Vector databases can optionally be used to ground the LLM responses to specific information and can be assigned to an LLM blueprint to leverage during a Retrieval-Augmented Generation (RAG) operation. For more information, see the [documentation](https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/nvidia-nim-vdb-embed.html). ## Use NVIDIA NeMo Guardrails in a moderation framework to secure your application Connect NVIDIA NeMo Guardrails to deployed text generation models to guard against off-topic discussions, unsafe content, and jailbreaking attempts. To use a deployed NVIDIA NIM with the moderation framework provided by DataRobot, first, register and deploy a NeMo model, then, when you create a custom model with the text generation target type, configure evaluation and moderation. For more information, see the [documentation](https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/nvidia-nim-evaluation-moderation.html). ## Use a text generation NVIDIA NIM in an application template Applications templates can integrate capabilities provided by NVIDIA AI Enterprise. To use this integration, you can customize a DataRobot application template to programmatically generate a GenAI Use Case built on NVIDIA NIM. With minimal edits, the following application templates can be updated to use NVIDIA NIM, selecting a registered or deployed text generation NIM: | Application template | Description | | --- | --- | | Guarded RAG Assistant | Build a RAG-powered chatbot using any knowledge base as its source. The Guarded RAG Assistant template logic contains prompt injection guardrails, sidecar models to evaluate responses, and a customizable interface that is easy to host and share. Example use cases: product documentation, HR policy documentation. | | Predictive Content Generator | Generates prediction content using prediction explanations from a classification model. The Predictive Content Generator template returns natural language-based personalized outreach. Example use cases: next-best-offer, loan approvals, and fraud detection. | | Talk to My Data Agent | Provides a talk-to-your-data experience. Upload a .csv, ask a question, and the agent recommends business analyses. It then produces charts and tables to answer your question (including the source code). This experience is paired with MLOps to host, monitor, and govern the components. | | Forecast Assistant | Leverage predictive and generative AI to analyze a forecast and summarize important factors in predictions. The Forecast Assistant template provides explorable explanations over time and supports "what-if" scenario analysis. Example use case: store sales forecasting. | To use an existing text generation model or deployment with these application templates, select one of the templates above from the Application Gallery. Then, you can make minimal modifications to the template files, locally or in a DataRobot codespace, to customize the template to use a registered or deployed NVIDIA NIM. With the template customized, you can proceed with the standard workflow outlined in the template's `README.md`. For more information, see the [documentation](https://docs.datarobot.com/en/docs/wb-apps/app-templates/index.html). --- # Import and deploy with NVIDIA NIM URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/nvidia-ngc-nim-import.html > Import, register, and deploy models with NVIDIA NIM to create an inference endpoint. Interact with inference endpoints using code or the DataRobot UI. # Import and deploy with NVIDIA NIM > [!NOTE] Premium > The use of NVIDIA Inference Microservices (NIM) in DataRobot requires access to premium features for GenAI experimentation and GPU inference. Contact your DataRobot representative or administrator for information on enabling the required features. The DataRobot integration with the NVIDIA AI Enterprise Suite enables users to perform one-click deployment of NVIDIA Inference Microservices (NIM) on GPUs in DataRobot Serverless Compute. This process starts in Registry, where you can import NIM containers from the NVIDIA AI Enterprise model catalog. The registered model is optimized for deployment to Console and is compatible with the DataRobot monitoring and governance framework. NVIDIA NIM provides optimized foundational models you can add to a playground in Workbench for evaluation and inclusion in agentic blueprints, embedding models used to create vector databases, and NVIDIA NeMo Guardrails used in the DataRobot moderation framework to secure your agentic application. ## Import from NVIDIA GPU Cloud (NGC) On the Models tab in Registry, create a registered model from the gallery of available NIM models, selecting the model name and performance profile and reviewing the information provided on the model card. To import from NVIDIA NGC: 1. On theRegistry > Modelstab, next to+ Register a model, clickand thenImport from NVIDIA NGC. 2. In theImport from NVIDIA NGCpanel, on theSelect NIMtab, click a NIM in the gallery. Search the galleryTo direct your search, you canSearch, filter byPublisher, or clickSort byto order the gallery by date added or alphabetically (ascending or descending). 3. Review the model information from the NVIDIA NGC source, then clickNext. 4. On theRegister modeltab, configure the following fields and clickRegister: FieldDescriptionRegistered model name / Registered modelConfigure one of the following:Registered model name:When registering a new model, enter auniqueand descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, a warning appears.Registered model:When saving as a version of an existing model, select the existing registered model you want to add a new version to.Registered version nameAutomatically populated with the model name and the wordversion. Change the version name or modify the default version name as necessary.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister as a new model.Resource bundleRecommended automatically. If possible, DataRobot translates the GPU requirements for the selected model into a resource bundle. In some cases, DataRobot can't detect a compatible resource bundle. To identify a resource bundle with sufficient VRAM, review the documentation for that NIM.For Managed AI Platform installations, note that theGPU - 5XLresource bundle can be difficult to procure on-demand. If possible, consider a smaller resource bundle.NVIDIA NGC API keySelect the credential associated with your NVIDIA NGC API key. Ensure that the selected NVIDIA NGC API key exists in your DataRobot organization, as cross-organization sharing of NVIDIA NGC API keys is unsupported. In addition, due to this restriction, cross-organization sharing of global models created with NVIDIA NIM is unsupported.Optional settingsRegistered version descriptionEnter a description of the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add tagand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags added when registering a new model are applied toV1. ## Deploy the registered NVIDIA NIM After the NVIDIA NIM is registered, deploy it to a DataRobot Serverless prediction environment. To deploy a registered model to a DataRobot Serverless environment: 1. On theRegistry > Modelstab, locate and click the registered NIM, and then click the version to deploy. 2. In the registered model version, you canreview the version information, then clickDeploy. 3. In thePrediction history and service healthsection, underChoose prediction environment, verify that the correct prediction environment withPlatform: DataRobot Serverlessis selected. Change DataRobot Serverless environmentsIf the correct DataRobot Serverless environment isn't selected, clickChange. On theSelect prediction environmentpanel'sDataRobot Serverlesstab, select a different serverless prediction environment from the list. 4. Optionally, configure additional deployment settings. Then, when the deployment is configured, clickDeploy model. Enable the tracing tableTo enable thetracing tablefor the NIM deployment, ensure that youenable prediction row storagein the data exploration (or challenger) settings and configure the deployment settings required todefine an association ID. ## Make predictions with the deployed NVIDIA NIM After the model is deployed to a DataRobot Serverless prediction environment, you can access real-time prediction snippets from the deployment's Predictions tab. The requirements for running the prediction snippet depend on the model type: text generation or unstructured. **Text generation:** [https://docs.datarobot.com/en/docs/images/nxt-predict-nvidia-nim.png](https://docs.datarobot.com/en/docs/images/nxt-predict-nvidia-nim.png) **Unstructured:** [https://docs.datarobot.com/en/docs/images/nxt-predict-nvidia-nim-unstructured.png](https://docs.datarobot.com/en/docs/images/nxt-predict-nvidia-nim-unstructured.png) When you add a NIM to Registry in DataRobot, LLMs are imported as text generation models, allowing you to use the [Bolt-on Governance API](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/genai-chat-completion-api.html) to communicate with the deployed NIM. Other types of models are imported as unstructured models and endpoints provided by the NIM containers are exposed to communicate with the deployed NIM. This provides the flexibility required to deploy any NIM on GPU infrastructure using DataRobot Serverless Compute. | Target type | Supported endpoint type | Description | | --- | --- | --- | | Text generation | /chat/completions | Deployed text generation NIM models provide access to the /chat/completions endpoint. Use the code snippet provided on the Predictions tab to make predictions. | | Unstructured | /directAccess/nim/ | Deployed unstructured NIM models provide access to the /directAccess/nim/ endpoint. Modify the code snippet provided on the Predictions tab to provide a NIM URL suffix and a properly formed payload. | | Unstructured (embedding model) | Both | Deployed unstructured NIM embedding models can provide access to both the /directAccess/nim/ and /chat/completions endpoints. Modify the code snippet provided on the Predictions tab to suit your intended usage. | > [!NOTE] CSV predictions endpoint use > With an imported text generation NIM, it is also possible to make requests to the `/predictions` endpoint (accepting CSV input). For CSV input submitted to the `/predictions` endpoint, ensure that you use `promptText` as the column name for user prompts to the text generation model. If the CSV input isn't provided in this format, those predictions do not appear in the deployment's [tracing table](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-data-exploration.html#explore-deployment-data-tracing). ### Text generation model endpoints Access the Prediction API scripting code on the deployment's Predictions > Prediction API tab. For a text generation model, the endpoint link required is the base URL of the DataRobot deployment. For more information, see the [Bolt-on Governance API](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/genai-chat-completion-api.html) documentation. ### Unstructured model endpoints Access the Prediction API scripting code from the deployment's Predictions > Prediction API tab. For unstructured models, endpoints provided by the NIM containers are exposed to enable communication with the deployed NIM. To determine how to construct the correct endpoint URL and send a request to a deployed NVIDIA NIM instance, refer to the documentation for the registered and deployed NIM, [listed below](https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/nvidia-ngc-nim-import.html#nim-documentation-list). > [!NOTE] Observability for direct access endpoints > Most unstructured models from NVIDIA NIM only provide access to the `/directAccess/nim/` endpoint. This endpoint is compatible with a limited set of observability features. For example, accuracy and drift tracking is not supported for the `/directAccess/nim/` endpoint. To use the Prediction API scripting code, perform the following steps and use the `send_request` function to communicate with the model: 1. Review the BASE_API_URL (line 4). This is the prefix of the endpoint. It automatically populates with the deployment's base URL. 2. Retrieve the appropriate NIM_SUFFIX (line 10). This is the suffix of the NIM endpoint. Locate this suffix in the NVIDIA NIM documentation for the deployed model . 3. Construct the request payload ( sample_payload , line 45). This request payload must be structured based on the model’s API specifications from the NVIDIA NIM documentation for the deployed model . | Prediction API scripting code | | --- | | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ### Unstructured models with text generation support Embedding models are imported and deployed as unstructured models while maintaining the ability to request chat completions. The following embedding models support both a direct access endpoint and a chat completions endpoint: - arctic-embed-l - llama-3.2-nv-embedqa-1b-v2 - nv-embedqa-e5-v5 - nv-embedqa-e5-v5-pb24h2 - nv-embedqa-mistral-7b-v2 - nvclip Each embedding NIM is deployed as an unstructured model, providing a REST interface at `/directAccess/nim/`. In addition, these models are capable of returning chat completions, so the code snippet provides a `BASE_API_URL` with the `/chat/completions` endpoint used by (structured) text generation models. To use the Prediction API scripting code, review the table below to determine how to modify the prediction snippet to access each endpoint type: | Endpoint type | Requirements | | --- | --- | | Direct access | Update the BASE_API_URL (on line 4), replacing /chat/completions with /directAccess/nim/. To structure the request payload, review the model’s API specifications from the NVIDIA NIM documentation for the deployed model. | | Chat completion | Update the DEPLOYMENT_URL (on line 13), removing /{NIM_SUFFIX} to create DEPLOYMENT_URL = BASE_API_URL. To structure the request payload, review the model’s API specifications from the NVIDIA NIM documentation for the deployed model. | | Prediction API scripting code | | --- | | 1 2 3 4 5 6 7 8 9 10 11 12 13 | --- # Add a text generation NVIDIA NIM to a Playground URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/nvidia-nim-deploy.html > Add a deployed text generation NVIDIA NIM to a blueprint in the playground to access an array of comparison and evaluation tools. # Add a text generation NVIDIA NIM to an LLM blueprint > [!NOTE] Premium > The use of NVIDIA Inference Microservices (NIM) in DataRobot requires access to premium features for GenAI experimentation and GPU inference. Contact your DataRobot representative or administrator for information on enabling the required features. In a Use Case, you can add NVIDIA Inference Microservices (NIM) to the playground for prompting, comparison, and evaluation. A [playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/index.html) is a Use Case asset for creating and interacting with LLM blueprints. LLM blueprints represent the full context for what is needed to generate a response from an LLM, captured in the LLM blueprint settings. Within the playground you compare LLM blueprint responses to determine which blueprint to use in production for solving a business problem. > [!NOTE] Text generation NVIDIA NIM support in the playground > The following text generation models aren't supported in the playground: > > llama-2-70b-chat > llama-3-swallow-70b-instruct-v0.1 > llama-3-taiwan-70b-instruct > llama3-70b-instruct > llama-3.1-nemotron-ultra-253b-v1 > llama-3.2-90b-vision-instruct > mixtral-8x22b-instruct-v01 > nemotron-3-super-120b-a12b To add a deployed text generation NVIDIA NIM to the playground: 1. InWorkbench, select a Use Case from theUse Case directory, and open or create a playground on thePlaygroundstab. 2. On theLLM blueprintstab within a playground, clickCreate LLM blueprintto add a new blueprint. Then, from the playground's blueprintConfigurationpanel, in theLLMdropdown, clickAdd deployed LLM: 3. In theAdd deployed LLMdialog box, enter a deployed LLMName, then select a DataRobot deployment in theDeployment namedropdown. Enter theChat model IDto set themodelparameter for requests from the playground to the deployed LLM, then clickValidate and add. Chat model ID valueTheChat model IDcan be set todatarobot-deployed-llm, allowing the value to populate dynamically. To hard code the value, review theChat model IDtable below, locate the NVIDIA NIM you're adding to the playground, and copy the value from theChat model IDcolumn. 4. After you add a custom LLM and validation is successful, back in the blueprint'sConfigurationpanel, in theLLMdropdown, clickDeployed LLM, and then select theValidation IDof the custom model you added: 5. Configure theVector databaseandPromptingsettings, and clickSave configurationto add the blueprint to the playground. --- # Use NVIDIA NeMo Guardrails with DataRobot moderation URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/nvidia-nim-evaluation-moderation.html > Connect NVIDIA NeMo Guardrails to deployed text generation models to guard against off-topic discussions, unsafe content, and jailbreaking attempts. # Use NVIDIA NeMo Guardrails with DataRobot moderation > [!NOTE] Premium > The use of NVIDIA Inference Microservices (NIM) in DataRobot requires access to premium features for GenAI experimentation and GPU inference. NVIDIA NeMo Guardrails are a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. > > Additional feature flags: Enable Moderation Guardrails ( Premium), Enable Global Models in the Model Registry ( Premium), Enable Additional Custom Model Output in Prediction Responses DataRobot provides out-of-the-box guardrails and lets you customize your applications with simple rules, code, or models. Use NVIDIA Inference Microservices (NIM) to connect NVIDIA NeMo Guardrails to text generation models in DataRobot, allowing you to guard against off-topic discussions, unsafe content, and jailbreaking attempts. The following NVIDIA NeMo Guardrails are available as a NIM and can be implemented using the associated evaluation metric type: | Model name | Evaluation metric type | | --- | --- | | llama-3.1-nemoguard-8b-topic-control | Stay on topic for input / Stay on topic for output | | llama-3.1-nemoguard-8b-content-safety | Content safety | | nemoguard-jailbreak-detect | Jailbreak | In addition, DataRobot provides access to NeMo Evaluator metrics (LLM Judge, Context Relevance, Response Groundedness, Topic Adherence, Agent Goal Accuracy, Response Relevancy, Faithfulness) in the [Configure evaluation and moderation](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-configure-evaluation-moderation.html) panel of the NeMo metrics section. Those metrics require a NeMo evaluator workload deployment (created via the Workload API) and are listed in the NeMo metrics section of that panel. This page covers NVIDIA NeMo Guardrails (Stay on topic, Content safety, Jailbreak) via NIM deployments. ## Use a deployed NIM with NVIDIA NeMo guardrails To use a deployed `llama-3.1-nemoguard-8b-topic-control` NVIDIA NIM with the topic control evaluation metrics, register and deploy the NVIDIA NeMo Guardrail. Once you have created a custom model with the text generation target type, configure the topic control evaluation metric. To select and configure NVIDIA NeMo Guardrails for topic control: 1. In theWorkshop, open theAssembletab of a custom model with theText Generationtarget type andassemble a model, eithermanually from a custom model you created outside DataRobotorautomatically from a model built in a Use Case's LLM playground. When you assemble a text generation model with moderations, ensure you configure any requiredruntime parameters(for example, credentials) orresource settings(for example, public network access). Finally, set theBase environmentto a moderation-compatible environment, such as[GenAI] Python 3.12 with Moderations: Resource settingsDataRobot recommends creating the LLM custom model using larger resource bundles with more memory and CPU resources. 2. After you've configured the custom model's required settings, navigate to theEvaluation and moderationsection and clickConfigure: 3. In theConfigure evaluation and moderationpanel, locate the metrics tagged withNVIDIA NeMo guardrailorNVIDIAand select the metric you want to use. Evaluation metricRequiresDescription1Content safetyA deployed NIM modelllama-3.1-nemoguard-8b-content-safetyimported fromNVIDIA GPU Cloud (NGC) Catalog.Classify prompts and responses as safe or unsafe; return a list of any unsafe categories detected.2JailbreakA deployed NIM modelnemoguard-jailbreak-detectimported fromNVIDIA GPU Cloud (NGC) Catalog.Classify jailbreak attempts using NemoGuard JailbreakDetect.3Stay on topic for inputsNVIDIA NeMo guardrails configurationUse NVIDIA NeMo Guardrails to provide topic boundaries, ensuring prompts are topic-relevant and do not use blocked terms.4Stay on topic for outputNVIDIA NeMo guardrails configurationUse NVIDIA NeMo Guardrails to provide topic boundaries, ensuring responses are topic-relevant and do not use blocked terms. 4. On theConfigure evaluation and moderationpage, set the following fields based on the selected metric: Topic controlContent safetyJailbreakFieldDescriptionNameEnter a descriptive name for the metric you're configuring.Apply forStay on topic for input is applied to the prompt. Stay on topic for output is applied to the response.LLM typeSet the LLM type to NIM.NIM DeploymentSelect an NVIDIA NIM deployment. For more information, seeImport and deploy with NVIDIA NIM.CredentialsSelect a DataRobot API key from the list. Credentials are defined on theCredentials managementpage.Files(Optional) Configure the NeMo files. Next to a file, clickto modify the NeMo guardrails configuration files. In particular, updateprompts.ymlwith allowed and blocked topics andblocked_terms.txtwith the blocked terms, providing rules for NeMo guardrails to enforce. Theblocked_terms.txtfile is shared between the input and output topic control metrics; therefore, modifyingblocked_terms.txtin the input metric modifies it for the output metric and vice versa. Only two topic control metrics can exist in a custom model, one for input and one for output.FieldDescriptionNameEnter a descriptive name for the metric you're configuring.Apply forApply content safety to both the prompt and the response.Deployment nameIn the list, locate the name of thellama-3.1-nemoguard-8b-content-safetymodelregistered and deployed in DataRobotand click the deployment name.FieldDescriptionNameEnter a descriptive name for the metric you're configuring.Apply toApply jailbreak to the prompt.Deployment nameIn the list, locate the name of thenemoguard-jailbreak-detectmodelregistered and deployed in DataRobotand click the deployment name. 5. In theModerationsection, withConfigure and apply moderationenabled, for each evaluation metric, set the following: FieldDescriptionModeration methodSelectReportorReport and block.Moderation messageIf you selectReport and block, you can optionally modify the default message. 6. After configuring the required fields, clickAddto save the evaluation and return to the evaluation selection page. Then,select and configure another metric, or clickSave configuration. The guardrails you selected appear in theEvaluation and moderationsection of theAssembletab. After you add guardrails to a text generation custom model, you can [test](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-test-custom-model.html), [register](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-register-cus-models.html), and [deploy](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-deploy-models.html) the model to make predictions in production. After making [predictions](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/index.html), you can view the evaluation metrics on the [Custom metrics](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-custom-metrics.html) tab and prompts, responses, and feedback (if configured) on the [Data exploration](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-data-exploration.html) tab. --- # Use an embedding NVIDIA NIM to create a vector database URL: https://docs.datarobot.com/en/docs/agentic-ai/genai-integrations/nvidia-nim-vdb-embed.html > Add a deployed embedding NVIDIA NIM to a Use Case with a vector database to enrich prompts in the playground with relevant context before they are sent to the LLM. # Use an embedding NVIDIA NIM to create a vector database > [!NOTE] Premium > The use of NVIDIA Inference Microservices (NIM) in DataRobot requires access to premium features for GenAI experimentation and GPU inference. Contact your DataRobot representative or administrator for information on enabling the required features. The NVIDIA Inference Microservices (NIM) available through Registry include embedding models. A deployed embedding model can be added to a Use Case, creating a collection of unstructured text that is broken into chunks, with embeddings generated for each chunk. Both the chunks and embeddings are stored in the vector database and are available for retrieval. Vector databases can optionally be used to ground the LLM responses to specific information and can be assigned to an LLM blueprint to leverage during a RAG operation. The role of the vector database is to enrich the prompt with relevant context before it is sent to the LLM. Each embedding NVIDIA NIM available is listed below: - arctic-embed-l - llama-3.2-nv-embedqa-1b-v2 - nv-embedqa-e5-v5 - nv-embedqa-e5-v5-pb24h2 - nv-embedqa-mistral-7b-v2 - nvclip ## Create a vector database with a registered embedding NIM After you register an embedding NIM, you can add it to a vector database. DataRobot handles the deployment process automatically. To create a vector database with a registered embedding NVIDIA NIM: 1. On theRegistry > Modelstab, next to+ Register a model, clickand thenImport from NVIDIA NGC. 2. In theImport from NVIDIA NGCpanel, on theSelect NIMtab, click an embedding NIM in the gallery. Search the galleryTo direct your search for an embedding model, you canSearch, filter byPublisher, or clickSort byto order the gallery by date added or alphabetically (ascending or descending). 3. Review the model information from the NVIDIA NGC source, then clickNext. 4. On theRegister modeltab, configure the following fields and clickRegister: FieldDescriptionRegistered model name / Registered modelConfigure one of the following:Registered model name:When registering a new model, enter auniqueand descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, a warning appears.Registered model:When saving as a version of an existing model, select the existing registered model you want to add a new version to.Registered version nameAutomatically populated with the model name and the wordversion. Change the version name or modify the default version name as necessary.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister as a new model.Resource bundleRecommended automatically. If possible, DataRobot translates the GPU requirements for the selected model into a resource bundle. In some cases, DataRobot can't detect a compatible resource bundle. To identify a resource bundle with sufficient VRAM, review the documentation for that NIM.NVIDIA NGC API keySelect the credential associated with your NVIDIA NGC API key.Optional settingsRegistered version descriptionEnter a description of the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add tagand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags added when registering a new model are applied toV1. 5. After the registered model builds, navigate toWorkbenchand open a Use Case. 6. In a Use Case, on theVector databasestab, either: With an existing vector databasesWithout an existing vector databaseIf you have already added one or more vector databases to the Use Case, Click the+ Add vector databasebutton in the upper right.If you haven't added a vector database to the Use Case before, clickCreate vector databasein the center of the page. 7. On theCreate vector databasepanel, enter a descriptiveName. Then, in theData sourcedropdown, select from the data sources associated with the Use Case or clickAdd datato add new data from the Data Registry. 8. In theEmbedding modeldropdown, click the embedding NIM you registered. Then, configure thevector databaseText chunkingsettingsand clickCreate vector database. The selected embedding model is deployed toConsolewhen you create the vector database. If necessary, this process creates a newprediction environmentfor NIM embeddings. After creating a vector database, you can [manage](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#manage-vector-databases) and [version](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html) it, or [add it to an LLM in the playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#add-a-vector-database) to inform responses. ## Create a vector database with a deployed embedding NIM If you've already registered and deployed an embedding NIM, you can add it to a vector database as a deployed embedding model. To create a vector database with a registered and deployed embedding NVIDIA NIM: 1. In a Use Case, on theVector databasestile, either: With an existing vector databasesWithout an existing vector databaseIf you have already added one or more vector databases to the Use Case, Click the+ Add vector databasebutton in the upper right.If you haven't added a vector database to the Use Case before, clickCreate vector databasein the center of the page. 2. On theCreate vector databasepanel, enter a descriptiveName. Then, in theData sourcedropdown, select from the data sources associated with the Use Case or clickAdd datato add new data from the Data Registry. 3. In theEmbedding modeldropdown, clickAdd deployed embedding model. 4. On the next page, configure the following settings to add the NVIDIA NIM embedding model, then clickValidate and add: FieldDescriptionNameEnter a descriptive name for the embedding model you're creating.Deployment nameIn the list, locate the name of the NVIDIA NIM embedding modelregistered and deployed in DataRobotand click the deployment name.Prompt column nameEnterinputas the prompt column name.Response column nameEnterresultas the response column name. Validation processThe validation process can take a few minutes. A notification appears when the process starts and if it succeeds or fails. 5. After the validation of the deployed embedding model succeeds, open theEmbedding modelmenu, then, underDeployed embedding models, select the NVIDIA NIM embedding model. 6. Configure thevector databaseText chunkingsettings, then clickCreate vector database. After creating a vector database, you can [manage](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#manage-vector-databases) and [version](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html) it, or [add it to an LLM in the playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#add-a-vector-database) to inform responses. --- # Agentic AI URL: https://docs.datarobot.com/en/docs/agentic-ai/index.html > Create, connect, and test agentic workflows, integrate tools, and implement evaluation metrics. # Agentic AI Create, connect, and test agentic workflows, integrate tools, and implement evaluation metrics. > [!TIP] New to the DataRobot CLI? > Agentic workflows use the [DataRobot CLI](https://docs.datarobot.com/en/docs/agentic-ai/cli/index.html) ( `dr`) to set up templates, run local development, and deploy. If you haven't installed or used the CLI yet, see [Getting started with the DataRobot CLI](https://docs.datarobot.com/en/docs/agentic-ai/cli/getting-started.html). - Build¶ Build and deploy agents from templates leveraging multi-agent frameworks. - Evaluate¶ Chat with agents, compare responses, and implement evaluation metrics. - Deploy¶ Register and deploy agentic artifacts and configure evaluation and moderation guardrails in Registry. - Monitor & govern¶ Monitor deployment details, usage stats, metrics, logs, and moderation events for agentic artifact deployments in Console. - DataRobot CLI¶ Use the DataRobot CLI to set up templates, run local development, and deploy. - Agent Assist¶ An interactive AI assistant optimized for the development of AI agents. - MCP¶ Integrate tools using MCP servers and connect IDEs and chat clients to your DataRobot MCP server. - Vector databases¶ Create vector databases, LLM blueprints, and GenAI deployments. - Prompt management¶ Create, manage, and share prompts that can be called into agentic workflows. - RAG workflows¶ Create LLM blueprints, moderations, tests, and deployments. - Code walkthroughs¶ Use code walkthroughs to implement GenAI. - NVIDIA AI Enterprise integration¶ Use NVIDIA NIM and NeMo to accelerate GenAI application development. - Feature considerations¶ Review LLM availability, GenAI capabilities, considerations, and troubleshooting tips. --- # Add a text generation NVIDIA NIM to a Playground URL: https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/add-deployed-nvidia-nim.html > Add a deployed text generation NVIDIA NIM to a blueprint in the playground to access an array of comparison and evaluation tools. # Add a text generation NVIDIA NIM to an LLM blueprint > [!NOTE] Premium > The use of NVIDIA Inference Microservices (NIM) in DataRobot requires access to premium features for GenAI experimentation and GPU inference. Contact your DataRobot representative or administrator for information on enabling the required features. In a Use Case, you can add NVIDIA Inference Microservices (NIM) to the playground for prompting, comparison, and evaluation. A [playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/index.html) is a Use Case asset for creating and interacting with LLM blueprints. LLM blueprints represent the full context for what is needed to generate a response from an LLM, captured in the LLM blueprint settings. Within the playground you compare LLM blueprint responses to determine which blueprint to use in production for solving a business problem. > [!NOTE] Text generation NVIDIA NIM support in the playground > The following text generation models aren't supported in the playground: > > llama-2-70b-chat > llama-3-swallow-70b-instruct-v0.1 > llama-3-taiwan-70b-instruct > llama3-70b-instruct > llama-3.1-nemotron-ultra-253b-v1 > llama-3.2-90b-vision-instruct > mixtral-8x22b-instruct-v01 > nemotron-3-super-120b-a12b To add a deployed text generation NVIDIA NIM to the playground: 1. InWorkbench, select a Use Case from theUse Case directory, and open or create a playground on thePlaygroundstab. 2. On theLLM blueprintstab within a playground, clickCreate LLM blueprintto add a new blueprint. Then, from the playground's blueprintConfigurationpanel, in theLLMdropdown, clickAdd deployed LLM: 3. In theAdd deployed LLMdialog box, enter a deployed LLMName, then select a DataRobot deployment in theDeployment namedropdown. Enter theChat model IDto set themodelparameter for requests from the playground to the deployed LLM, then clickValidate and add. Chat model ID valueTheChat model IDcan be set todatarobot-deployed-llm, allowing the value to populate dynamically. To hard code the value, review theChat model IDtable below, locate the NVIDIA NIM you're adding to the playground, and copy the value from theChat model IDcolumn. 4. After you add a custom LLM and validation is successful, back in the blueprint'sConfigurationpanel, in theLLMdropdown, clickDeployed LLM, and then select theValidation IDof the custom model you added: 5. Configure theVector databaseandPromptingsettings, and clickSave configurationto add the blueprint to the playground. --- # Build LLM blueprints URL: https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html > Configure, copy, and register LLM blueprints. # Build LLM blueprints LLM blueprints represent the full context for what is needed to generate a response from an LLM, the resulting output is what can then be compared within the playground. To create an LLM blueprint, select that action from either the LLM blueprints tab or, if it is your first blueprint, the playground welcome screen. Clicking the create button brings you to the configuration and chatting tools: | | Element | Description | | --- | --- | --- | | (1) | Playground summary | Displays a summary of the playground owner and creation date. | | (2) | Chat history | Displays a record of prompts sent to this LLM blueprint, as well as an option to start a new chat. | | (3) | Configuration panel | Provides access to the configuration selections available for creating an LLM blueprint. | | (4) | Prompt entry | Accepts prompts to begin chatting with the LLM blueprint; the configuration must be saved before the entry is activated. | You can also create an LLM blueprint by [copying an existing blueprint](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#copy-llm-blueprint). ## Select an LLM The configuration panel is where you define the LLM blueprint. From here: - Add an LLM. - Set the configuration . - Select or add a vector database . - Set the prompting strategy . DataRobot offers a variety of [preloaded LLMs](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html), with availability dependent on your cluster and account type. Alternatively, you can [add a deployed LLM](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#add-a-deployed-llm) to the playground, which, when validated, is added to the Use Case and available to all associated playgrounds. To add an LLM and begin configuring the LLM blueprint, click Select LLM. A scrollable list of available LLMs appears in a panel to the left. See the [reference documentation](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-reference.html) for brief descriptions of each LLM's characteristics. Use the filtering tools to limit the list so that it shows only LLMs that meet your criteria: - Use one of the multiselect filter options: ProviderFamilyRegionNote that if you have added a deployed LLM,DataRobotbecomes an entry in theProviderfilter. - Toggle to show or hide deprecated LLMs. LLMs marked with aDeprecatedbadge indicate that the end of support date for the LLM falls within 90 days. Retirement dates are set by the provider and are subject to change. While not listed in the selection panel, any LLM blueprints that were built using an LLM that has subsequently been retired are marked in theblueprints tabwith aRetiredbadge. See a list of upcoming deprecations and retired LLMs on theLLM availabilitypage. - Enter a string in the search bar to match all LLMs, from all providers, for that string. Once you select the LLM from the left panel, click Save: ### Add a deployed LLM To select a [custom deployed LLM in DataRobot](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-genai-monitoring.html#create-and-deploy-a-generative-custom-model), click Create LLM blueprint to add a new blueprint to the playground. Then, from the playground's blueprint Configuration panel, click Select LLM and Add deployed LLM: In the Add deployed LLM dialog box, enter a deployed LLM Name, then select a DataRobot deployment in the Deployment name dropdown. If the selected deployment supports the [Bolt-on Governance API](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html), configure the following: **Bolt-on Governance API supported:** When adding a deployed LLM that implements the `chat` function, the playground uses the [Bolt-on Governance API](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#chat) as the preferred communication method. Enter the Chat model ID to set the `model` parameter for requests from the playground to the deployed LLM, then click Validate and add: > [!NOTE] Chat model ID > When using the Bolt-on Governance API with a deployed LLM blueprint, see [LLM availability](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html) for the recommended values of the `model` parameter. Alternatively, specify a reserved value, `datarobot-deployed-llm`, to let the LLM blueprint select the relevant model ID automatically when calling the LLM provider's services. [https://docs.datarobot.com/en/docs/images/add-deployed-llm-bp-fields-api.png](https://docs.datarobot.com/en/docs/images/add-deployed-llm-bp-fields-api.png) To disable the Bolt-on Governance API and use the Prediction API instead, delete the [chatfunction (or hook)](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#chat) from the custom model and redeploy the model. **Bolt-on Governance API not supported:** When adding a deployed LLM that doesn't support the [Bolt-on Governance API](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#chat), the playground uses the Prediction API as the preferred communication method. Enter the Prompt column name and Response column name defined when you [created the custom LLM in the workshop](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-genai-monitoring.html#create-and-deploy-a-generative-custom-model) (for example, `promptText` and `resultText`), then click Validate and add: [https://docs.datarobot.com/en/docs/images/add-deployed-llm-bp-fields.png](https://docs.datarobot.com/en/docs/images/add-deployed-llm-bp-fields.png) To enable the Bolt-on Governance API, modify the custom model code to use the [chatfunction (or hook)](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#chat) and redeploy the model. After you add a custom LLM and validation is successful, back in the blueprint's Configuration panel, in the LLM dropdown, click Deployed LLM, and then select the Validation ID of the custom model you added: Finally, you can configure the [Vector database](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#add-a-vector-database) and [Prompting](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#prompting) settings, and click Save configuration to add the blueprint to the playground. ## Set the configuration Selecting a base LLM exposes some or all of the following configuration options (listed alphabetically). Available options are dependent on the LLM selection. Note that the default value of the option can be dependent on the provider. | Setting | Description | | --- | --- | | Max completion tokens | The maximum number of tokens allowed in the completion. The combined count of this value and prompt tokens must be below the model’s maximum context size, where prompt token count is comprised of system prompt, user prompt, recent chat history, and vector database citations. | | Number of most likely tokens | An integer ranging from 0 to 20 that specifies the number of most likely tokens to consider when generating the response. A value of 0 means all tokens are considered. Note: The Return log probabilities setting must be enabled to use this parameter. | | Random seed | If specified, the system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend. | | Reasoning effort | For OpenAI o-series only (not GPT-4o models) constrains the reasoning effort for reasoning models—either low, medium, or high. Reducing the reasoning effort can result in faster responses and fewer tokens "spent." | | Return log probabilities | If enabled, the log probabilities of each returned output token are returned in the response. Note that this setting is required in order to use the Number of most likely tokens parameter. | | Stop sequences | Up to four strings that, when generated by the model, will stop the generation process. This is useful for controlling the output format or preventing unwanted text from being included in the response. The triggering sequence will not be shown in the returned text. | | Temperature | The temperature controls the randomness of model output. Enter a value (range is LLM-dependent), where higher values return more diverse output and lower values return more deterministic results. A value of 0 may return repetitive results. Temperature is an alternative to Top P for controlling the token selection in the output (see the example below). | | Token frequency penalty | A penalty ranging from -2.0 to 2.0 that is applied to tokens based on their frequency in the context. Positive values increase the penalty, discouraging frequent tokens, while negative values decrease the penalty, allowing for more frequent use of those tokens. | | Token presence penalty | A penalty ranging from -2.0 to 2.0 that is applied to tokens that are already present in the context. Positive values increase the penalty, therefore discouraging repetition, while negative values decrease the penalty, allowing for more repetition. | | Token selection probability cutoff (Top P) | Top P sets a threshold that controls the selection of words included in the response based on a cumulative probability cutoff for token selection. For example, 0.2 considers only the top 20% probability mass. Higher numbers return more diverse options for outputs. Top P is an alternative to Temperature for controlling the token selection in the output (see "Temperature or Top P?" below). | Each base LLM has default configuration settings. As a result, the only required selection before starting to chat is to choose the LLM. ### Add a vector database From the Vector database tab, you can optionally select a [vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html). The selection identifies a database comprised of a collection of [chunks](https://docs.datarobot.com/en/docs/reference/glossary/index.html#chunking) of unstructured text and corresponding text embeddings for each chunk, indexed for easy retrieval. Vector databases are not required for prompting but are used for providing relevant data to the LLM to generate the response. Add a vector database to a playground to experiment with metrics and test responses. The following table describes the fields of the Vector database tab: The dropdown | Field | Description | | --- | --- | | Vector database | Lists all vector databases available in the Use Case (and therefore accessible for use by all of that Use Case's playgrounds). If you select the Add vector database option, the new vector database you add will become available to other LLM blueprints although you must change the LLM blueprint configuration to apply them. | | Vector database version | Select the version of the vector database that the LLM will use. The field is prepopulated with the version you were viewing when you created the playground. Click Vector database version to leave the playground and open the vector database details page. | | Information | Reports configuration information for the selected version. | | Retriever | Sets the method, neighbor chunk inclusion, and retrieval limits that the LLM uses to return chunks from the vector database. | | Retrieval mode | Sets the approach for chunk retrieval, either similarity (most semantically similar) or Maximum Marginal Relevance (relevant yet diverse). | #### Retriever methods The retriever you select defines how the LLM blueprint searches through, and retrieves, the most relevant chunks from the vector database. They inform which information is provided to the language model. Select one of the following methods: | Method | Description | | --- | --- | | Single-Lookup Retriever | Performs a single vector database lookup for each query and returns the most similar documents. | | Conversational Retriever (default) | Rewrites the query based on chat history, returning context-aware responses. In other words, this retriever functions similarly to the Single-Lookup Retriever with the addition of query rewrite as its first step. | | Multi-Step Retriever | Performs the following steps when returning results:Rewrites the query to be chat history-aware and retrieves documents for that query.Creates five new queries based on the result of step 1.Queries the vector database for each of the five new queries.Merges and deduplicates the results, creating one set of returned documents, which are then used for the query. | Use Add Neighbor Chunks to control whether to add neighboring chunks within the vector database to the chunks that the similarity search retrieves. When enabled, the retriever returns `i`, `i-1`, and `i+1` (for example, if the query retrieves chunk number 42, chunks 41 and 43 are also retrieved). Notice also that only the primary chunk has a similarity score. This is because the neighbor chunks are added, not calculated, as part of the response. Also known as context window expansion or context enrichment, this technique includes surrounding chunks adjacent to the retrieved chunk to provide more complete context. Some reasons to enable this include: - A single chunk may be cut off mid-sentence or may miss important context. - Related information might span multiple chunks. - The response might require context from surrounding or chunks. Enter a value to set the Retrieval limits, which control the number of returned documents. The value you set for Top K (nearest neighbors) instructs the LLM on how many relevant chunks to retrieve from the vector database. Chunk selection is based on [similarity scores](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#rouge-scores). Consider: - Larger values provide more comprehensive coverage but also require more processing overhead and may include less relevant results. - Smaller values provide more focused results and faster processing, but may miss relevant information. Max tokens specifies: - The maximum size (in tokens) of each text chunk extracted from the dataset when building the vector database. - The length of the text that is used to create embeddings. - The size of the citations used in RAG operations. #### Retrieval mode Select a retrieval mode to set how matching chunks are selected from the vector database and returned for a query. Set either Similarity or Maximum Marginal Relevance. - Similarity returns the most semantically similar top N chunks, ranked by how close they are in vector space. These are the most similar chunks, regardless of whether they are repetitive or redundant. The Similarity method is faster but can return very similar or duplicate chunks, which can unnecessarily "spend" the context window. - Maximal Marginal Relevance (MMR) balances relevance and diversity, returning results that are both similar to the query and different from each other. If selected, setMaximal marginal relevance lambdato control the balance between relevance (1.0) and diversity (0.0). ### Set prompting strategy The prompting strategy is where you configure context (chat history) settings and optionally add a system prompt. #### Set context state There are two states of context. They control whether chat history is sent with the prompt to include relevant context for responses. | State | Description | | --- | --- | | Context-aware | When sending input, previous chat history is included with the prompt. This state is the default. | | No context | Sends each prompt as independent input, without history from the chat. | > [!NOTE] Note > Consider the context state and how it functions in conjunction with the selected [retriever method](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#retriever). See [context-aware chatting](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#context-aware-chatting) for more information. #### Set system prompt The system prompt, an optional field, is a "universal" prompt prepended to all individual prompts for this LLM blueprint. It instructs and formats the LLM response. The system prompt can impact the structure, tone, format, and content that is created during the generation of the response. See an example of system prompt application in the [LLM blueprint comparison documentation](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html#example-comparison). ## Working with LLM blueprints The left panel of the playground is like a file cabinet of the playground's assets—a list of configured blueprints and a record of the comparison chat history. You can also create a new LLM blueprint, the process described above, from this area. ### LLM blueprints tab The LLM blueprints tab lists all LLM blueprints configured within the playground. It is from this panel that you select LLM blueprints—up to three if you are doing a [comparison](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html). As you select an LLM blueprint via the checkbox, it becomes available in the middle comparison panel. Click the star next to an LLM blueprint name to "favorite" it, which you can later filter on. #### Display controls Use the controls to modify the LLM blueprint listing: The Filter option controls which blueprints are listed in the panel, either by base LLM or status: The small number to the right of the Filter label indicates how many blueprints are displayed as a result of any (or no) applied filtering. Sort by controls the ordering of the blueprints. It is additive, meaning that it is applied on top of any filtering or grouping in place.Group by, also additive, arranges the display by the selected criteria. Labels indicate the group "name" with numbers to indicate the number of member blueprints. #### Actions for LLM blueprints The actions available for an LLM blueprint can be accessed from the Actions menu next to the name in the left-hand LLM blueprints tab or from LLM blueprint actions in a selected LLM blueprint. | Option | Description | | --- | --- | | Configure LLM blueprint | From the LLM blueprints tab only. Opens the configuration settings for the selected blueprint for further tuning. | | Edit LLM blueprint | Provides a modal for changing the LLM blueprint name. Changing the name saves the new name and all saved settings. If any settings have not been saved, they will revert to the last saved version. | | Copy to new LLM blueprint | Creates a new LLM blueprint from all saved settings of the selected blueprint. | | Send to the workshop | Sends the LLM blueprint to Registry where it is added to the workshop. From there it can be deployed as a custom model. | | Delete LLM blueprint | Deletes the LLM blueprint. | ### Chats tab The Chats tab provides access any previous prompts and subsequent made from the playground to the selected LLM blueprint. It also a place from which you can start a new chat. For full details, see either [single blueprint](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html) or [comparison](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html) chatting. ### Copy LLM blueprint You can make a copy of an existing LLM blueprint to inherit the settings. Using this approach makes sense when you want to compare slightly different blueprints, duplicate a blueprint for which you are not the owner in a shared playground, or replace a soon-to-expire LLM. Make a copy in one of two ways: **From an existing blueprint:** In the left-hand panel, click the Actions menu and select Copy to new LLM blueprint to create a copy that inherits the settings of the parent blueprint. [https://docs.datarobot.com/en/docs/images/create-bp-7.png](https://docs.datarobot.com/en/docs/images/create-bp-7.png) The new LLM blueprint opens for further configuration. **From any open LLM blueprint:** Choose LLM blueprint actions and choose Copy to new LLM blueprint. [https://docs.datarobot.com/en/docs/images/create-bp-8.png](https://docs.datarobot.com/en/docs/images/create-bp-8.png) ### Change LLM blueprint configuration To change the configuration of an LLM blueprint, choose Configure LLM blueprint from the actions menu in the LLM blueprints tab. The LLM blueprint configuration and chat history display. Change any of the [configuration settings](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#set-the-configuration) and Save configuration When you make changes to an LLM blueprint, the chat history associated with it, if the configuration is context-aware, is also saved. All the prompts within a chat persist through LLM blueprint changes: - When you submit a prompt, the history included is everything within the most recent chat context. - If you switch the LLM blueprint to No context , each prompt is its own chat context. - If you switch back to Context-aware , that starts a new chat context within the chat. Note that chats in the configuration view are separate from chats in the Comparison view—the histories don't mingle. --- # Multiple LLM blueprint chat comparison URL: https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html > The place to add LLM blueprints to the playground for comparison, submit prompts to these LLM blueprints, and evaluate the rendered responses. # Multiple LLM blueprint chat comparison [https://docs.datarobot.com/en/docs/images/playground-compare-1.png](https://docs.datarobot.com/en/docs/images/playground-compare-1.png) The playground's LLM blueprint tab allows you to: - View all LLM blueprints in the playground. - Filter, group, and sort the LLM blueprint list. - View the playground's chat history . - Create and compare chats (LLM responses). To use the comparison: 1. Create two or more LLM blueprints in the playground. 2. From the LLM blueprints tab, select up to three LLM blueprints for comparison . 3. Send a prompt from the central prompting window. Each of the blueprints receives the prompt and responds, allowing you to compare responses. > [!NOTE] Note > You can only do comparison prompting with workflows that you created. To see the results of prompting another user’s LLM blueprint or agentic flow in a shared Use Case, copy the LLM blueprint or connect to the registered agentic flow. You can chat with the same settings applied. This is intentional behavior because prompting impacts chat history, which can impact the responses that are generated. However, you can provide response feedback on the creator's asset to assist development. #### Example comparison The following example compares three LLM blueprints, each with the same settings except using a different [system prompt](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#set-system-prompt) to influence the style of response. First test the system prompt, when configuring the LLM blueprint, for example: `Describe the novel Game of Thrones`. 1. Enter the system promptAnswer using emojis. 2. Enter the system promptAnswer in the style of a news headline. 3. Enter the system promptAnswer as a haiku. See also the [note on system prompts](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html#consider-system-prompts). ## Compare LLM blueprints To compare multiple LLM blueprints: 1. From theLLM blueprinttab, check the box next to each blueprint you want to compare. 2. Send a prompt (Describe DataRobot). Each LLM blueprint responds in their configured style: 3. Try different prompts (Describe a fish taco, for example) to identify the LLM that best suits the business use case. ### Interpret results One obvious way to compare LLM blueprints is to read the results and see if the responses of one seem more on point. Another helpful measure is to review the evaluation metrics that are returned. Consider: - Which LLM blueprint has the lowest latency? Is that status consistent across prompt/response sets? - Which metrics are excluded from some LLM blueprints and why? - How do results change when you toggle context awareness ? - Do the LLM blueprints use the citations to inform the response effectively? - Do the they respect the system prompt such that the response has the requested tone, format, succinctness, etc.? ### Change selected LLM blueprints You can add blueprints to the comparison at any time, although the maximum allowed for comparison at one time is three. To add an LLM blueprint, select the checkbox to the left of its name. If three are already selected, remove a current selection first. The comparison panel retrieves the comparison history. Because responses have not been returned for the new LLM blueprint, DataRobot provides a button to initiate that action. Click Generate to include the new results. ### Consider system prompts Note that system prompts are not guaranteed to be followed completely, and that wording is very important. For example, consider the comparison using the prompt `Answer using emojis` (EmojiGPT) and `Answer using only emojis` (OnlyEmojiGPT): ### Chats tab A comparison chat groups together one or more comparison prompts, often across multiple blueprints. Use the Chats tab to access any previous comparison prompts made from the playground or start a new chat. In this way, you can select up to three LLM blueprints, query them, and then swap out for other LLM blueprints to send the same prompts and compare results. > [!NOTE] Note > In some cases, you will see a chat named Default chat. This entry contains any chats made in the playground before the new playground functionality was released in April, 2024. If no chats were initiated, the default chat is empty. If the playground was created after that date, the default chat isn't present but an New chat is available for prompting. Rename or delete chats from the entry name. --- # Deploy an LLM from the playground URL: https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/deploy-llm.html > LLM blueprints and all their associated settings are registered in Registry and can be deployed and monitored with the Console. # Deploy an LLM from the playground Use an LLM [playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html) in a [Use Case](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/index.html) to [create an LLM blueprint](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html). Set the blueprint configuration, specifying the base LLM and, optionally, a system prompt and vector database. After testing and tuning the responses, the blueprint is ready for registration and deployment. You can create a text generation custom model by sending the LLM blueprint to [Registry's workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/index.html). The generated custom model automatically implements the [Bolt-on Governance API](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#chat), which is particularly useful for building conversational applications. Follow the steps below to add the LLM blueprint to the workshop: 1. In a Use Case, from thePlaygroundstab, click the playground containing the LLM you want to register as a blueprint. 2. In the playground,compare LLMsto determine which LLM blueprint to send to the workshop, then do either of the following: 3. In theSend to the workshopmodal, do the following, then clickSend to the workshop: 4. ClickSend to the workshop. In the lower-right corner of the LLM playground, notifications appear as the LLM is queued and registered. When notification of the registration's completion appears, clickGo to the workshop: The LLM blueprint opens in the Registry's workshop as a custom model with theText Generationtarget type: 5. On theAssembletab, in theRuntime Parameterssection, configure the key-value pairs required by the LLM, including the LLM service's credentials and other details. To add these values, click the edit iconnext to the available runtime parameters. Premium: DataRobot LLM gatewayIf your organization has access to the DataRobot LLM gateway, you don't need to configure any credentials. Confirm that theENABLE_LLM_GATEWAY_INFERENCEruntime parameter is present and set toTrue. If necessary, configure thePROMPT_COLUMN_NAME(the default column name ispromptText), and then skip to the next step. You can alsomake requests to the DataRobot LLM gateway. To configureCredentialtypeRuntime Parameters, first, add the credentials required for the LLM you're deploying to theCredentials Managementpage of the DataRobot platform: Microsoft-hosted LLMsAmazon-hosted LLMsGoogle-hosted LLMsForMicrosoft-hosted LLMs, use the following:Credential type: API Token (notAzure)Runtime Parameters:KeyDescriptionOPENAI_API_KEYSelect theAPI Tokencredential, created on theCredentials Managementpage, for the Azure OpenAI LLM API endpoint.OPENAI_API_BASEEnter the URL for the Azure OpenAI LLM API endpoint.OPENAI_API_DEPLOYMENT_IDEnter the name of the Azure OpenAI deployment of the LLM, chosen when deploying the LLM to your Azure environment. For more information, see the Azure OpenAI documentation on how toDeploy a model. The default deployment name suggested by DataRobot matches the ID of the LLM in Azure OpenAI (for example, gpt-35-turbo). Modify this parameter if your Azure OpenAI deployment is named differently.OPENAI_API_VERSIONEnter the Azure OpenAI API version to use for this operation, following the YYYY-MM-DD or YYYY-MM-DD-preview format (for example, 2023-05-15). For more information on the supported versions, see theAzure OpenAI API reference documentation.PROMPT_COLUMN_NAMEEnter the prompt column name from the input .csv file. The default column name is promptText.ForAmazon-hosted LLMs, use the following:Credential type: AWSRuntime Parameters:KeyDescriptionAWS_ACCOUNTSelect anAWScredential, created on theCredentials Managementpage, for the AWS account.AWS_REGIONEnter the AWS region of the AWS account. The default is us-west-1.PROMPT_COLUMN_NAMEEnter the prompt column name from the input .csv file. The default column name is promptText.ForGoogle-hosted LLMs, use the following:Credential type: Google Cloud Service AccountRuntime Parametersare:KeyDescriptionGOOGLE_SERVICE_ACCOUNTSelect aGoogle Cloud Service Accountcredential created on theCredentials Managementpage.GOOGLE_REGIONEnter the GCP region of the Google service account. The default is us-west-1.PROMPT_COLUMN_NAMEEnter the prompt column name from the input .csv file. The default column name is promptText. 6. In theSettingssection, ensureNetwork accessis set toPublic. 7. After you complete the custom model assembly configuration, you cantest the modelorcreate new versions. DataRobot recommends testing custom LLMs before deployment. 8. Next, clickRegister a model,provide the registered model or version details, then clickRegister modelagain to add the custom LLM to Registry. The registered model version opens on theRegistry > Modelstab. 9. From theModelstab, in the upper-right corner of the registered model version panel, clickDeployandconfigure the deployment settings. For more information on the deployment functionality available for generative models, seeMonitoring support for generative models. For more information on this process, see the [playground deployment considerations](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-consider.html#playground-deployment-considerations). --- # RAG workflows URL: https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/index.html > Create and compare LLM blueprints, configure metrics, and compare LLM blueprint responses before deployment. # RAG workflows > [!NOTE] Availability information > DataRobot's GenAI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. Create and compare LLM blueprints, configure metrics, and compare LLM blueprint responses before deployment. See the [list of considerations](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-consider.html) to keep in mind when working with DataRobot GenAI. | Topic | Description | | --- | --- | | Playground overview | Navigate through the GenAI playground. | | Create LLM blueprints | Create LLM blueprints and fine-tune results. | | Add a text generation NVIDIA NIM to an LLM blueprint | Premium feature. Add a deployed text generation NVIDIA NIM to a playground. | | Chatting with LLM blueprints | Use single and comparison chats. | | Compare LLM blueprints | Compare LLM blueprint chat results. | | Add evaluation metrics to an LLM blueprint | Configure evaluation and moderation guardrails for LLM blueprints. | | Deploy LLMs to production | Register LLMs for deployment. | --- # Use LLM evaluation tools URL: https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html > Configure evaluation and moderation guardrails for LLM blueprints in a playground. # Use LLM evaluation tools > [!NOTE] Premium > LLM evaluation tools are a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. The playground's LLM evaluation tools include evaluation metrics and datasets, aggregated metrics, compliance tests, and tracing. The LLM evaluation metric tools include: | LLM evaluation tool | Description | | --- | --- | | Evaluation metrics | Report an array of performance, safety, and operational metrics for prompts and responses in the playground and define moderation criteria and actions for any configured metrics. | | Evaluation datasets | Upload or generate the evaluation datasets used to evaluate an LLM blueprint through evaluation dataset metrics, aggregated metrics, and compliance tests. | | Aggregated metrics | Combine evaluation metrics across many prompts and responses to evaluate an LLM blueprint at a high level, as only so much can be learned from evaluating a single prompt or response. | | Compliance tests | Combine an evaluation metric and dataset to automate the detection of compliance issues with pre-configured or custom compliance testing. | | Tracing table | Trace the execution of LLM blueprints through a log of all components and prompting activity used in generating LLM responses in the playground. | ## Configure evaluation metrics With evaluation metrics, you can configure an array of performance, safety, and operational metrics. Configuring these metrics lets you define moderation methods to intervene when prompts and responses meet the moderation criteria you set. This functionality can help detect and block prompt injection and hateful, toxic, or inappropriate prompts and responses. It can also help identify hallucinations or low-confidence responses and safeguard against the sharing of personally identifiable information (PII). > [!TIP] Evaluation deployment metrics > Many evaluation metrics connect a playground-built LLM to a deployed guard model. These guard models make predictions on LLM prompts and responses and then report the predictions and statistics to the playground. If you intend to use any of the Evaluation Deployment type metrics—Custom Deployment, PII Detection, Prompt Injection, Emotions Classifier, and Toxicity—deploy the [required guard models from the NextGen Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-global-models.html) to make predictions on the LLM's prompts or responses. Selecting and configuring evaluation metrics in an LLM playground depends on whether you have already configured LLM blueprints: **With LLM blueprints:** If you've added one or more LLM blueprints to the playground, with or without blueprints selected click the Evaluation tile on the side navigation bar: [https://docs.datarobot.com/en/docs/images/playground-eval-metrics-blueprint.png](https://docs.datarobot.com/en/docs/images/playground-eval-metrics-blueprint.png) **Without LLM blueprints:** If you haven't added any blueprints to the playground, in the Evaluate with metrics section, click Open configure evaluation to configure metrics before adding an LLM blueprint: [https://docs.datarobot.com/en/docs/images/playground-eval-metrics-no-blueprint.png](https://docs.datarobot.com/en/docs/images/playground-eval-metrics-no-blueprint.png) In both cases, the Evaluation and moderation page opens to the Metrics tab. Certain metrics are enabled by default. Note, however, that to report a metric value for Citations and ROUGE-1, you must first associate a vector database with the LLM blueprint. ### Create a new configuration To create a new evaluation metric configuration for the playground: 1. In the upper-right corner of theEvaluation and moderationpage, clickConfigure metrics: 2. In theConfigure evaluation and moderationpanel, click an evaluation metric and then configure the metric settings. The metrics, requirements, and settings are outlined in the tables below. Evaluation metricRequiresDescriptionCostLLM cost settingsCalculate the cost of generating the LLM response using a default or custom LLM, currency, input cost-per-token, and output cost-per-token values. The cost calculation also includes the cost of citations. For more information, seeCost metric settings.Custom DeploymentCustom deploymentUse an existing deployment to evaluate and moderate your LLM (supported target types: regression, binary classification,multiclass, text generation).Emotions ClassifierEmotions Classifier deploymentClassify prompt or response text by emotion.PII DetectionPresidio PII Detection deploymentDetect Personally Identifiable Information (PII) in text using the Microsoft Presidio library.Prompt InjectionPrompt Injection Classifier deploymentDetects input manipulations, such as overwriting or altering system prompts, intended to modify the model's output.ToxicityToxicity Classifier deploymentClassifies content toxicity to apply moderation techniques, safeguarding against dissemination of harmful content.ROUGE-1Vector databaseRecall-Oriented Understudy for Gisting Evaluationcalculates the similarity between the response generated from an LLM blueprint and the documents retrieved from the vector database.CitationsVector databaseReports the documents retrieved by an LLM when prompting a vector database.All tokensN/ATracks the number of tokens associated with the input to the LLM, output from the LLM, and/or retrieved text from the vector database.Prompt tokensN/ATracks the number of tokens associated with the input to the LLM.Response tokensN/ATracks the number of tokens associated with the output from the LLM.Document tokensN/ATracks the number of tokens associated with the retrieved text from the vector database.LatencyN/AReports the response latency of the LLM blueprint.CorrectnessLLM, evaluation dataset, vector databaseUses either a provided or synthetically generated set of prompts or prompt and response pairs to evaluate aggregated metrics against the provided reference dataset. The Correctness metric uses the LlamaIndexCorrectness Evaluator.FaithfulnessLLM, vector databaseMeasures if the LLM response matches the source to identify possible hallucinations. The Faithfulness metric uses the LlamaIndexFaithfulness Evaluator.Topic control metricsStay on topic for inputsNIM deployment ofllama-3.1-nemoguard-8b-topic-control, NVIDIA NeMo guardrails configurationUses NVIDIA NeMo Guardrails to provide topic boundaries, ensuring prompts are topic-relevant and do not use blocked terms.Stay on topic for outputNIM deployment ofllama-3.1-nemoguard-8b-topic-control, NVIDIA NeMo guardrails configurationUses NVIDIA NeMo Guardrails to provide topic boundaries, ensuring responses are topic-relevant and do not use blocked terms. Global models for evaluation metric deploymentsThe deployments required for PII detection, prompt injection detection, emotion classification, and toxicity classification are available asglobal models in Registry Multiclass custom deployment metric limitsMulticlasscustom deployment metrics can have:Up to10classes defined in theMatcheslist for moderation criteria.Up to100class names in the guard model. Depending on the evaluation metric (or evaluation metric type) selected, as well as whether you are using the LLM gateway, different configuration options are required: SettingDescriptionGeneral settingsNameEnter a unique name if adding multiple instances of the evaluation metric.Apply toSelect one or both ofPromptandResponse, depending on the evaluation metric. Note that when you selectPrompt, it's the user prompt, not the final LLM prompt, that is used for metric calculation. This field is only configurable for metrics that apply to both the prompt and the response.Custom Deployment, PII Detection, Prompt Injection, Emotions Classifier, and Toxicity settingsDeployment nameFor evaluation metrics calculated by a guard model deployment, select the custom model deployment.Custom Deployment settingsInput column nameThis name is defined by the custom model creator. Forglobal models created by DataRobot, the default input column name istext. If the guard model for the custom deployment has themoderations.input_column_namekey valuedefined, this field is populated automatically.Output column nameThis name is defined by the custom model creator, and needs to refer to the target column for the model. The target name is listed on the deployment'sOverviewtab (and often has_PREDICTIONappended to it). You can confirm the column names byexporting and viewing the CSV data from the custom deployment. If the guard model for the custom deployment has themoderations.output_column_namekey valuedefined, this field is populated automatically.Correctness and Faithfulness settingsLLMSelect an LLM for evaluation.Topic control metric settingsLLM TypeSelectAzure OpenAI,OpenAI, orNIM. For theAzure OpenAILLM type, additionally enter anOpenAI API base URLandOpenAI API Deployment; forNIMenter aNIM deployment(thellama-3.1-nemoguard-8b-topic-controltopic-control model). If you use the LLM gateway, the default experience, DataRobot-supplied credentials are provided. You can, however, clickChange credentialsto provide your own authentication.FilesFor theStay on topicevaluations, next to a file, clickto modify the NeMo guardrails configuration files. In particular, updateprompts.ymlwith allowed and blocked topics andblocked_terms.txtwith the blocked terms, providing rules for NeMo guardrails to enforce. Theblocked_terms.txtfile is shared between the input and output topic control metrics; therefore, modifyingblocked_terms.txtin the input metric modifies it for the output metric and vice versa. Only two topic control metrics can exist in a playground, one for input and one for output.Moderation settingsConfigure and apply moderationEnable this setting to expand theModerationsection and define the criteria that determines when moderation logic is applied. Cost metric settingsFor theCostmetric, in the row for eachLLMtype, define aCurrencyand theInputandOutputcost incurrency amount / tokens amountformat, then clickAdd:TheCostmetric doesn't include theModerationsection toConfigure and apply moderation. 3. In theModerationsection, withConfigure and apply moderationenabled, for each evaluation metric, set the following: SettingDescriptionModeration criteriaIf applicable, set the threshold settings evaluated to trigger moderation logic. For numeric metrics (int or float), you can useless than,greater than, orequals towith a value of your choice. For binary metrics (for example, Stay on topic for inputs), useequals to0 or 1. For the Emotions Classifier, selectMatchesorDoes not matchand define a list of classes (emotions) to trigger moderation logic.Moderation methodSelectReportorReport and block.Moderation messageIf you selectReport and block, you can optionally modify the default message. 4. After configuring the required fields, clickAddto save the evaluation and return to the evaluation selection page. The metrics you selected appear on theConfigure evaluation and moderationpanel, in theConfiguration summarysidebar. 5. Select and configure another metric, or clickSave configuration. The metrics appear on theEvaluation and moderationpage. If any issues occur during metric configuration, an error message appears below the metric to provide guidance on how to fix the issue. Metric configuration processingError message example ### Change credentials DataRobot provides credentials for [available LLMs](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html) using the LLM gateway. With Azure OpenAI and OpenAI LLM types, you can, however, use your own credentials for authentication. Before proceeding, define user-specified credentials on the [credentials management](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management) page. To change credentials for either Stay on topic for inputs or Stay on topic for output, choose the LLM type and click Change credentials. **LLM type: Azure OpenAI:** Provide the Azure OpenAI API deployment and the OpenAI API base URL. Then, from the dropdown, select the set of credentials to apply. [https://docs.datarobot.com/en/docs/images/change-metric-creds-azure.png](https://docs.datarobot.com/en/docs/images/change-metric-creds-azure.png) **LLM type: OpenAI:** From the dropdown, select the set of credentials to apply. [https://docs.datarobot.com/en/docs/images/change-metric-creds-openai.png](https://docs.datarobot.com/en/docs/images/change-metric-creds-openai.png) **LLM type: NIM:** Select the NIM deployment (for example, the topic-control model). Credentials are typically provided via the deployment configuration. To revert to DataRobot-provided credentials, click Revert credentials. ### Manage configured metrics To edit or remove a configured evaluation metric from the playground: 1. In the upper-right corner of theEvaluation and moderationpage, clickConfigure metrics: 2. In theConfigure evaluation and moderationpanel, in theConfiguration summarysidebar, click the edit iconor the delete icon: 3. If you click edit, you can re-configure the settings for that metric and clickUpdate: ### Copy a metric configuration To copy an evaluation metrics configuration to or from an LLM playground: 1. In the upper-right corner of theEvaluation and moderationpage, next toConfigure metrics, click, and then clickCopy configuration. 2. In theCopy evaluation and moderation configurationmodal, select one of the following options: From an existing playgroundTo an existing playgroundTo a new playgroundIf you selectFrom an existing playground, choose toAdd to existing configurationorReplace existing configurationand then select a playground toCopy from.If you selectTo an existing playground, choose toAdd to existing configurationorReplace existing configurationand then select a playground toCopy to.If you selectTo a new playground, enter aNew playground name. 3. Select if you want toInclude evaluation datasets, and then clickCopy configuration. > [!NOTE] Duplicate evaluation metrics > Selecting Add to existing configuration can result in duplicate metrics, except in the case of NeMo Stay on topic for inputs and Stay on topic for output. Only two topic control metrics can exist, one for input and one for output. ### View metrics in a chat The metrics you configure and add to the playground appear on the LLM responses in the playground. Click the down arrow to open the metric panel for more details. From this panel, click Citation to view the prompt, response, and a list of citations in the Citation dialog box. You can also provide positive or negative feedback for the response. In addition, if a response from the LLM is blocked by the configured moderation criteria and strategy, you can click Show response to view the blocked response: > [!TIP] Multiple moderation messages > If a response from the LLM is blocked by multiple configured moderations, the message for each triggered moderation appears, replacing the LLM response, in the chat. If you configure descriptive moderation messages, this can provide a complete list of reasons for blocking the LLM response. ## Add evaluation datasets To enable evaluation dataset metrics and aggregated metrics, add one or more evaluation datasets to the playground. The dataset must be a CSV file, in the Data Registry, and have at least one text or categorical column. > [!WARNING] When using evaluation datasets with an LLM that includes a vector database > Ensure that no column name exists in both the evaluation dataset and the vector database. If any column name exists in both, those columns are treated as metadata filters, and vector database results are excluded from prompts when you run evaluation dataset aggregation. This situation is most common when the vector database was built from a CSV source document. 1. To select and configure evaluation metrics in an LLM playground, do either of the following: 2. On theEvaluation and moderationpage, click theEvaluation datasetstab to view any existing datasets, then, clickAdd evaluation dataset, and select one of the following methods: Dataset addition methodDescriptionAdd evaluation datasetIn theAdd evaluation datasetpanel, select an existing dataset from theData Registrytable, or upload a new dataset:ClickUploadto register and select a new dataset from your local filesystem.ClickUpload from URL, then, enter theURLfor a hosted dataset and clickAdd.After you select a dataset, in theEvaluation dataset configurationsidebar, define thePrompt column nameandResponse (target) column name, and clickAdd evaluation dataset.Generate synthetic dataEnter aDataset name, select anLLM, setVector database,Vector databaseversion, and theLanguageto use when creating synthetic data. Then, clickGenerate data. For more information, seeGenerate synthetic datasets. 3. After you add an evaluation dataset, it appears on theEvaluation datasetstab of theEvaluation and moderationpage, where you can clickOpen datasetto view the data. You can also click theActions menutoEdit evaluation datasetorDelete evaluation dataset: ## Add aggregated metrics When a playground includes more than one metric, you can begin creating aggregate metrics. Aggregation is the act of combining metrics across many prompts and/or responses, which helps to evaluate a blueprint at a high level (only so much can be learned from evaluating a single prompt/response). Aggregation provides a more comprehensive approach to evaluation. Aggregation either averages the raw scores, counts the boolean values, or surfaces the number of categories in a multiclass model. DataRobot does this by generating the metrics for each individual prompt/response and then aggregating using one of the methods listed, based on the metric. To configure aggregated metrics: 1. In a playground, clickConfigure aggregationbelow the prompt input: Aggregation job run limitOnly one aggregated metric job can run at a time. If an aggregation job is currently running, theConfigure aggregationbutton is disabled and the "Aggregation job in progress; try again when it completes" tooltip appears. 2. On theGenerate aggregated metricspanel, select metrics to calculate in aggregate and configure theAggregate bysettings. Then, enter a newChat name, select anEvaluation dataset(to generate prompts in the new chat), and select theLLM blueprintsfor which the metrics should be generated. These fields are pre-populated based on the current playground: Evaluation dataset selectionIf you select an evaluation dataset metric, likeCorrectness, you must use the evaluation dataset used to create that evaluation dataset metric. After you complete theMetrics selectionandConfigurationsections, clickGenerate metrics. This results in a new chat containing all associated prompts and responses: Aggregated metrics are run against an evaluation dataset, not individual prompts in a standard chat. Therefore, you can only view aggregated metrics in the generatedaggregated metrics chat, added to the LLM blueprint'sAll Chatslist (on the LLM's configuration page). Aggregation metric calculation for multiple blueprintsIf many LLM blueprints are included in the metric aggregation request, aggregated metrics are computed sequentially, blueprint-by-blueprint. 3. Once an aggregated chat is generated, you can explore the resulting aggregated metrics, scores, and related assets on theAggregated metricstab. You can filter byAggregation method,Evaluation dataset, andMetric: In addition, clickCurrent configurationto compare only those metrics calculated for the blueprint configuration currently defined in theLLMtab of theConfigurationsidebar. View related assetsFor each metric in the table, you can clickEvaluation datasetandAggregated chatto view the corresponding asset contributing to the aggregated metric. 4. Returning to the LLMBlueprints comparisonpage, you can now open theAggregated metricstab to view a leaderboard comparing LLM blueprint performance for the generated aggregated metrics: ## Configure compliance testing Combine an evaluation metric and an evaluation dataset to automate the detection of compliance issues through test prompt scenarios. ### Manage compliance testing from the Evaluation tab When you manage compliance testing on the Evaluation tab, you can view pre-defined compliance tests, create and manage custom tests, or modify pre-defined tests to suit your organization's testing requirements. To view all available compliance tests: 1. On the side navigation bar click theEvaluationtile. 2. Click theCompliance teststab. On theCompliance teststab, you can view all the compliance tests available, both DataRobot and custom (if present). The table contains columns for theTestname,Provider, andConfiguration(number of evaluations and evaluation datasets). #### View and customize DataRobot compliance tests Use the View option to review and, optionally: - Customize DataRobot pre-configured compliance tests, including changing the LLM for certain tests. - Manage custom compliance tests. In the table on the Compliance tests tab, click View to open and review any of the compliance tests in which DataRobot is the Provider: | Compliance test | Description | Assessing LLM | Based on | | --- | --- | --- | --- | | Bias Benchmark | Runs LLM question/answer sets that test for bias along eight social dimensions. | GPT-4o | AI Verify Foundation | | Jailbreak | Applies testing scenarios to evaluate whether built-in safeguards enforce LLM jailbreaking compliance standards. | Customizable | jailbreak_llms | | Completeness | Determines whether the LLM response is supplying enough information to comprehensively answer questions. | GPT-4o | Internal | | Personally Identifiable Information (PII) | Determines whether the LLM response contains PII included in the prompt. | Customizable | Internal | | Toxicity | Applies testing scenarios to evaluate whether built-in safeguards enforce toxicity compliance standards. For more information, see the explicit and offensive content warning. | Customizable | Hugging Face | | Japanese Bias Benchmark | Runs LLM question/answer sets in Japanese that test for bias along five social dimensions. | GPT-4o | AI Verify Foundation | > [!WARNING] Explicit and offensive content warning > The [public evaluation dataset for toxicity testing](https://huggingface.co/datasets/allenai/real-toxicity-prompts) contains explicit and offensive content. It is intended to be used exclusively for the purpose of eliminating such content from external models and applications. Any other use is strictly prohibited. When viewing a compliance test from the list, you can review the individual evaluations run as part of the compliance testing process. For all tests, you can review the Name, Metric, LLM, Evaluation dataset, Pass threshold, and Number of prompts. If the test shows `-` in the LLM field, it uses GPT-4o. The following tests default to GPT-4o as the LLM but can be customized: - Jailbreak - Toxicity - PII Use a selected DataRobot test as the foundation for a custom test as follows: 1. SelectViewfor the test you want to modify. 2. ClickCustomize test. 3. From theCreate custom testmodal, modify any of the individual evaluations for the compliance test settings. NoteIn addition to the default metrics and evaluation datasets, you can select any evaluation metrics implemented by a deployed binary classification sidecar model and any evaluation datasets added to the Use Case. SettingDescriptionNameA descriptive name for the custom compliance test.DescriptionA description of the purpose of the compliance test (this is pre-populated when you modify an existing DataRobot test).Test pass thresholdThe minimum percentage (0-100%) of individual evaluations that must pass for the test as a whole to pass.Evaluations*NameThe name of the individual metric.MetricThe criteria to match against.LLMThe LLM used to assess the response. This field is enabled for Jailbreak, Toxicity, and PII compliance tests. All others use GPT-4o.Evaluation datasetThedatasetused for calculating metrics.Pass thresholdThe minimum percentage of responses that must pass for the evaluation to pass.Number of promptsThe number of rows from the dataset used to perform the evaluation.Add evaluationCreate additional evaluations.Copy from existing testCopy the individual evaluations from an existing compliance test. * Use the API-only process,expected_response_column, to validate a sidecar model with metrics you are introducing. It compares the LLM response with an expected response, similar to the pre-providedexact_matchmetric. 4. After you customize the compliance test settings, clickAdd. The new test appears in the table on theCompliance teststab. #### Create custom compliance tests To create a custom compliance test: 1. At the top or bottom of theCompliance teststab, clickCreate custom compliance test. Create compliance tests from anywhere in the Evaluations tabWhen theEvaluationtab is open, you can clickCreate custom compliance testfrom anywhere, not just theCompliance teststab. 2. In theCreate custom testpanel, configure the following settings: SettingDescriptionNameA descriptive name for the custom compliance test.DescriptionA description of the purpose of the compliance test (this is pre-populated when you modify an existing DataRobot test).Test pass thresholdThe minimum percentage (0-100%) of individual evaluations that must pass for the test as a whole to pass.Evaluations*NameThe name of the individual metric.MetricThe criteria to match against.LLMThe LLM used to assess the response. This field is enabled for Jailbreak, Toxicity, and PII compliance tests. All others use GPT-4o. You must set theMetricbefore setting this field.Evaluation datasetThedatasetused for calculating metrics.Pass thresholdThe minimum percentage of responses that must pass for the evaluation to pass.Number of promptsThe number of rows from the dataset used to perform the evaluation.Add evaluationCreate additional evaluations.Copy from existing testCopy the individual evaluations from an existing compliance test. 3. After you configure the compliance test settings, clickAdd. The new test appears in the table on theCompliance teststab. #### Manage custom compliance tests To manage custom compliance tests, locate tests with Custom as the Provider, and choose a management action: - Click the edit icon, then, in theEdit custom testpanel, update the compliance test configuration and clickSave. - Click the delete icon, then clickYes, delete testto remove the test from all playgrounds in the Use Case. ### Run compliance testing from the playground When you perform compliance testing on the Playground tile, you can run the pre-defined compliance tests without modification, create custom tests, or modify the pre-defined tests to suit your organization's testing requirements. To access compliance from the playground tests to run, modify, or create a test: 1. On thePlaygroundtile, in theLLM blueprintslist, click the LLM blueprint you want to test, or, select up to three blueprints for comparison. Access compliance tests from the blueprints comparison pageIf you have two or more LLM blueprints selected, you can click theCompliance teststab from theBlueprints comparisonpage to run compliance tests for multiple LLM blueprints and compare the results. For more information, seeCompare compliance test results 2. In the LLM blueprint, click theCompliance teststab to create or run tests. If you have not run tests before, you receive a message saying no compliance test results are available. If you have run a test before, test results are listed. In either case, clickRun testto open the test panel. 3. TheRun testpanel opens to a list of pre-configured DataRobot compliance tests and custom tests you've created. 4. When you select a compliance test from theAll testslist, you can view the individual evaluations run as part of the compliance testing process. For each test, you can review theName,Metric,Evaluation dataset,Pass threshold, andNumber of prompts. 5. Next, run an existing test, create and run a custom test, or manage custom tests. #### Run existing compliance tests To run an existing, configured compliance test: 1. On theRun testpanel, from theAll testslist, select an availableDataRobotorCustomtest. 2. After selecting a test, clickRun. 3. The test appears on theCompliance teststab with aRunning...status. Cancel a running testIf you need to cancel a test with theRunning...status, clickDelete test results. #### Create and run custom compliance tests To create and run a custom or modified compliance test: 1. On theRun testpanel, from theAll testslist: 2. On theCustom testpanel, configure the following settings: SettingDescriptionNameA descriptive name for the custom compliance test.DescriptionA description of the purpose of the compliance test (this is pre-populated when you modify an existing DataRobot test).Test pass thresholdThe minimum percentage (0-100%) of individual evaluations that must pass for the test as a whole to pass.EvaluationsThe individual evaluations for the compliance test, each consisting of aName,Metric,Evaluation dataset,Pass threshold, andNumber of prompts. In addition to the default metrics and evaluation datasets, you can select any evaluation metrics implemented by a deployed binary classification sidecar model and any evaluation datasets added to the Use Case.Click+ Add evaluationto create additional evaluations.ClickCopy from existing testto copy the individual evaluations from an existing compliance test.There is an API-only process to validate a sidecar model withexpected_response_columnto introduce metrics comparing the LLM response with and expected response, similar to the pre-providedexact_matchmetric. 3. After configuring a custom test, clickSave and run. 4. The test appears on theCompliance teststab with aRunning...status. Cancel a running testIf you need to cancel a test with theRunning...status, clickDelete test results. #### Manage compliance test runs From a running or completed test on the Compliance tests tab: - To delete a completed test run or cancel and delete a running test, click Delete test results . - To view the chat calculating the metric, click the chat name in the Corresponding chat column. - To view the evaluation dataset used to calculate the metric, click the dataset name in the Evaluation dataset column. #### Manage custom compliance tests To manage custom compliance tests, on the Run test panel, from the All tests list, select a custom test, then click Delete test or Edit test. You can't edit or delete pre-configured DataRobot tests. If you select Edit test, update the settings you [configured during compliance test creation](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#create-and-run-custom-compliance-tests). ### Compare compliance test results To compare compliance test results, you can run compliance tests for up to three LLM blueprints at a time. On the Playground tile, in the LLM blueprints list, select up to three LLM blueprint to test, click the Compliance tests tab, and then click Run test. This opens the Run test panel, where you can select and run a test [as you would for a single blueprint](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#run-existing-compliance-tests); however, you can also define the LLM blueprints to run it for. By default, the blueprints selected on the comparison tab are listed here: After the compliance tests run, you can compare them on the Blueprints comparison page. To delete a completed test run, or cancel an in-progress test run, click Delete test results. ## View the tracing table Tracing the execution of LLM blueprints is a powerful tool for understanding how most parts of the GenAI stack work. The Tracing tab provides a log of all components and prompting activity used in generating LLM responses in the playground. Insights from tracing provide full context of everything the LLM evaluated, including prompts, vector database chunks, and past interactions within the context window. For example: - DataRobot metadata: Reports the timestamp, Use Case, playground, vector database, and blueprint IDs, as well as creator name and base LLM. These help pinpoint the sources of trace records if you need to surface additional information from DataRobot objects interacting with the LLM blueprint. - LLM parameters: Shows the parameters used when calling out to an LLM, which is useful for potentially debugging settings like temperature and the system prompts. - Prompts and responses: Provide a history of chats; token count and user feedback provide additional detail. - Latency: Highlights issues orchestrating the parts of the LLM Blueprint. - Token usage: displays the breakdown of token usage to accurately calculate LLM cost. - Evaluations and moderations (if configured): Illustrates how evaluation and moderation metrics are scoring prompts or responses. To locate specific information in the Tracing table, click Filters and filter by User name, LLM, Vector database, LLM Blueprint name, Chat name, Evaluation dataset, and Evaluation status. > [!TIP] Send tracing data to the Data Registry > Click Upload to Data Registry to export data from the tracing table to the Data Registry. A warning appears on the tracing table when it includes results from running the toxicity test and the toxicity test results are excluded from the Data Registry upload. ## Send a metric and compliance test configuration to the workshop After creating an LLM blueprint, setting the blueprint configuration (including evaluations metrics and moderations), and testing and tuning the responses, send the LLM blueprint to the workshop: 1. In a Use Case, from thePlaygroundtile, click the playground containing the LLM you want to register as a blueprint. 2. In the playground,compare LLMsto determine which LLM blueprint to send to the workshop, then, do either of the following: 3. In theSend to the workshopmodal, select up totwelveevaluation metrics (and any configured moderations). Why can't I send all metrics to the workshop?Several metrics are supported by default after you register and deploy an LLM sent to the workshop from the playground, others are configurable using custom metrics. The following table lists the evaluation metrics you cannot select during this process and provides the alternative metric in Console:MetricConsole equivalentCitationsCitations are provided on theData exploration > Tracingtab. If configured in the playground, citations are included in the transfer by default, without the need to select the option in theSend to the workshopmodal. The resulting custom model has theENABLE_CITATION_COLUMNSruntime parameter configured. After deploying that custom model, if theData explorationtab is enabled andassociation IDs are provided, citations are available for a model sent to the workshop.CostCost can be calculated on theMonitoring > Custom metricstab of a deployment.CorrectnessCorrectness is not available for deployed models.LatencyLatency is calculated on theMonitoring > Service healthtab andMonitoring > Custom metricstab.All TokensAll tokens can be calculated on theCustom metricstab, or you can add the prompt tokens and response tokens metrics separately.Document TokensDocument tokens are not available for deployed models. 4. Next, select anyCompliance teststo send. Then, clickSend to the workshop: Compliance tests sent to the workshop are included when you register the custom model andgenerate compliance documentation. Compliance tests in the workshopThe selected compliance test are linked to the custom model in the workshop by theLLM_TEST_SUITE_IDruntime parameter. If you modify the custom model code significantly in the workshop, set theLLM_TEST_SUITE_IDruntime parameter toNoneto avoid running compliance documentation intended for the original model on the modified model. 5. To complete the transfer of evaluation metrics,configure the custom model in the workshop. --- # Playground overview URL: https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html > The playground, an asset of a Use Case, is the space for creating and interacting with LLM blueprints. # Playground overview A playground, another type of Use Case asset, is the space for creating and interacting with LLM blueprints. LLM blueprints represent the full context for what is needed to generate a response from an LLM, captured in the LLM blueprint settings. Within the playground you compare LLM blueprint responses to determine which blueprint to use in production for solving a business problem. You can use playgrounds with or without a [vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/index.html). Multiple playgrounds can exist in one Use Case and multiple LLM blueprints can live within a single playground. The suggested, simplified workflow for working with playgrounds is as follows: 1. Add a playground . 2. Set the LLM blueprint configuration , including the base LLM, prompting settings, and optionally, an associated vector database . 3. Chat to test and tune the LLM blueprint; view the tracing . 4. Build additional LLM blueprints . 5. Compare LLM blueprints side-by-side. 6. Add datasets and metrics to the LLM blueprint to help evaluate responses. ## Add a playground From the Playgrounds tab, the following options are available, depending on how many playgrounds already exist: - If you haven't added a playground yet,click theAdd Playgrounddropdown in the center of the page, then clickAdd RAG playground. This button is only available for the first playground added to a Use Case; use theAdd Playgrounddropdown for subsequent playgrounds. - If you've already added one or more playgrounds (RAG or agentic),click theAdd Playgrounddropdown in the upper-right corner of the page, then clickAdd RAG playground. When you create a playground, the playground opens with two options for [creating and configuring](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#set-the-configuration) an LLM blueprint. From the playground you have access to all the controls for creating LLM blueprints, interacting with and fine-tuning them, and saving them for comparison and potential future deployment. ## Elements of a playground A playground has basic [navigation controls](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html#navigate-the-playground) to take you to specific components: And three major work areas: **Configuration panel:** Use this for setting up the LLM blueprint, including vector database selection and prompting strategies. [https://docs.datarobot.com/en/docs/images/create-bp-4.png](https://docs.datarobot.com/en/docs/images/create-bp-4.png) **Chat window:** Use this for sending prompts and receiving LLM responses. [https://docs.datarobot.com/en/docs/images/chatting-2.png](https://docs.datarobot.com/en/docs/images/chatting-2.png) **Comparison panel:** Use this for working with LLM blueprints and chats. [https://docs.datarobot.com/en/docs/images/playground-compare-5.png](https://docs.datarobot.com/en/docs/images/playground-compare-5.png) Once playgrounds are created, you can switch between them using the breadcrumbs dropdown: ## Navigate the playground On the far left, click the icons to access navigation components: | | Component | Description | | --- | --- | --- | | | Playground | Configure LLM blueprints, compare LLM blueprints, and chat. | | | Playground information | Display playground summary information. | | | LLM evaluation* | Configure evaluation and moderation guardrails for LLM blueprints in a playground. | | | Tracing* | Display an exportable log that traces all components used in LLM response generation. | * Available only if LLM assessment is enabled. ### Playground information The Playground information area provides access to the assets associated with the playground—vector databases, deployed LLMs, and deployed embedding models. It also provides basic playground metadata. Use the tabs, described in the table below, to view additional information. **Playground information:** [https://docs.datarobot.com/en/docs/images/playground-info-1.png](https://docs.datarobot.com/en/docs/images/playground-info-1.png) **Deployed LLMs:** [https://docs.datarobot.com/en/docs/images/playground-info-1b.png](https://docs.datarobot.com/en/docs/images/playground-info-1b.png) **Deployed embedding models:** [https://docs.datarobot.com/en/docs/images/playground-info-1c.png](https://docs.datarobot.com/en/docs/images/playground-info-1c.png) | Tab | Description | | --- | --- | | Vector databases | Lists each unique vector database used in the playground and a variety of configuration metadata. Entries that are grayed out are custom vector databases—not built in DataRobot—and so DataRobot is unable to report configuration specifics. Click an entry to view expanded metadata and listings of related assets, including associated LLM blueprints, deployments, custom models, and registered models. Versioning information is also displayed. | | Deployed LLMs | Lists each deployed LLM that is part of at least one LLM blueprint configuration in the playground. For each entry, metadata reports creation information, deployment name, and the prompt and response column names that were defined when assigning a deployed LLM to the blueprint. | | Deployed embedding models | Lists each deployed embedding model that is part of at least one LLM blueprint configuration in the playground. For each entry, metadata reports creation information, deployment name, and the prompt and response column names that were defined. | > [!NOTE] Note > Regardless of how many LLM blueprint configurations a vector database, deployed LLM, or deployed embedding model is used in, the listing displays a single entry. #### Playground information page tools Click on a playground name or description to make changes. When you add a description, it displays under the playground name at the top of the Playground information page. Click the Actions menu, located to the right of the playground name, to open a menu and delete the playground. Alternatively, you can delete a playground from the Use Case asset listing. Click Settings to expose options that control the display. - Set the columns you want to view by checking or unchecking boxes. - Reorder columns using the arrows to the right of the column name. - Set columns to appear on the far left by clicking the pin icon. --- # Single LLM blueprint chat URL: https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html > Describes using single and comparison chats in the playground. # Single LLM blueprint chat Chatting is the activity of sending prompts and receiving a response from the LLM. A chat is a collection of chat prompts. Once you have set the configuration for your LLM, send it prompts—and follow-up prompts—from the entry box in the lower part of the panel to determine whether further refinements are needed before considering your LLM blueprint for deployment. Chatting within the playground is a "conversation"—you can ask follow-up questions with subsequent prompts. Following is an example of asking the LLM to provide Python code for running DataRobot Autopilot: The results of the follow-up questions are dependent on whether [context awareness](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#context-aware-chatting) is enabled (see continuation of the example). Use the playground to test and tune prompts until you are satisfied with the system prompt and settings. Then, click Save configuration in the bottom of the right-hand panel. ## Context-aware chatting When configuring an LLM blueprint, you set the history awareness in the [Prompting](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#set-system-prompt) tab. There are two states of context. They control whether chat history is sent with the prompt to include relevant context for responses. | State | Description | | --- | --- | | Context-aware | When sending input, previous chat history is included with the prompt. This state is the default. | | No context | Sends each prompt as independent input, without history from the chat. | > [!NOTE] Note > Consider the context state and how it functions in conjunction with the selected [retriever method](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#retriever). You can switch between one-time (no context) and context-aware within a chat. They each become independent sets of history context—going from context-aware, to no context, and back to aware clears the earlier history from the prompt. (This only happens once a new prompt is submitted.) Context state is reported in two ways: 1. A badge, which displays to the right of the LLM blueprint name in both configuration and comparison views, reports the current context state: 2. In the configuration view, dividers show the state of the context setting: Using the example of writing Python code to run Autopilot (above), you could then prompt to make a change to "that code." With context-aware enabled, the LLM responds knowing the code being referenced because it is "aware" of the previous conversation history: See the [prompting reference](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/prompting-reference.html) for information on crafting optimized prompts (including few-shot prompting). ## Single vs comparison chats Chatting with a single LLM blueprint is a good way to tune before starting prompt comparisons with other LLM blueprints.[Comparison](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#comparison-llm-blueprint-chat) lets you compare responses between LLM blueprints to help decide which to move to production. > [!NOTE] Note > You can only do comparison prompting with workflows that you created. To see the results of prompting another user’s LLM blueprint or agentic flow in a shared Use Case, copy the LLM blueprint or connect to the registered agentic flow. You can chat with the same settings applied. This is intentional behavior because prompting impacts chat history, which can impact the responses that are generated. However, you can provide response feedback on the creator's asset to assist development. ### Single LLM blueprint chat When you first configure an [LLM blueprint](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html), part of the creation process includes chatting. Set the configuration, and save, to activate chatting: After seeing chat results, tune the configuration, if desired, and prompt again. Use the additional actions available within each chat result to retrieve more information and the prompt: | Option | Description | | --- | --- | | View configuration | Shows the configuration used by that prompt in the Configuration panel on the right. If you haven't changed configurations while chatting, no change is apparent. Using this tool allows you to recall previous settings and restore the LLM blueprint to those settings. | | Open tracing | Opens the tracing log, which shows all components and prompting activity used in generating LLM responses. | | Delete prompt and response | Removes both the prompt and response from the chat history. If deleted, they are no longer considered as context for future responses. | As you send prompts to the LLM, DataRobot maintains a record of those chats. You can either add to the context of an existing chat or start a new chat, which does not carry over any of the context from other chats in the history: Starting a new chat allows you to have multiple independent conversation threads with a single blueprint. In this way, you can evaluate the LLM blueprint based on different types of topics, without bringing in the history of the previous prompt response, which could "pollute" the answers. While you could also do this by switching context off, submitting a prompt, and then switching it back on, starting a new chat is a simpler solution. Click Start new chat to begin with a clean history; DataRobot will rename the chat from New chat to the words from your prompt once the prompt is submitted. ### Comparison LLM blueprint chat Once you are satisfied, you can compare responses with other LLM blueprints from the LLM blueprints tab. If you determine that further tuning is needed after having started a comparison, you can still modify the configuration of individual LLM blueprints: To compare LLM blueprint chats side-by-side, see the [LLM blueprint comparison](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html) documentation. ## Response feedback Use the response feedback "thumbs" to rate the prompt answer. Responses are recorded in the [Tracing](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#tracing), tab User feedback column. The response, as part of the exported feedback sent to the AI Catalog, can be used, for example, to train a predictive model. ## Citations A citation is [a metric](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#add-and-configure-metrics) and is on by default (as are Latency, Prompt Tokens, and Response Tokens). Citations provide a list of the top reference document chunks, based on relevance to the prompt, retrieved from the vector database. Be aware that the embedding model used to create the vector database in the first place can affect the quality of the citations retrieved. > [!NOTE] Note > Citations only appear when the LLM blueprint being queried has an associated vector database. While citations are one of the available metrics, you do not need the assessment functionality enabled to have citations returned. Use citations as a safety check to validate LLM responses. While they help to validate LLM responses, citations also allow you to validate proper and appropriate retrieval from the vector database—are you retrieving the chunks from your docs that you want to provide as context to the LLM? Additionally, if you enable the Faithfulness metric, which measures whether the LLM response matches the source, it relies on the citation output for its relevance. ### ROUGE scores ROUGE scores, also known as confidence scores, calculates the distance between the response generated from an LLM blueprint and the documents retrieved from the vector database. They indicate how close the response is to the provided context. ROUGE scores are computed using the factual consistency metric approach, where a score is computed using the facts retrieved from the [vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html) and the generated text from the LLM blueprint. The similarity metric used is the [ROUGE-1](https://en.wikipedia.org/wiki/ROUGE_(metric%29) (Recall-Oriented Understudy for Gisting Evaluation) metric. DataRobot GenAI uses an improved version of ROUGE-1 based on insights from ["The limits of automatic summarization according to ROUGE"](https://aclanthology.org/E17-2007.pdf). The ROUGE scoring algorithm is not scaled; instead, DataRobot uses heuristic coefficients. The ROUGE score is reported in the prompt response: ### Similarity score The similarity score is based on the distance of the query embedding to the chunk embedding in the vector space; the larger the similarity score, the better. Do not use this score to compare results between vector databases, only to compare values for retrievals within a single vector databases. The score informs "this chunk is more similar than that chunk to the given query." The similarity score is reported in the citation: ### Metadata filtering Use metadata filtering to limit the citations returned by the prompt query. When configured, the LLM blueprint only returns chunks that include the specified metadata column-value pair. You can add a filter for each metadata column, as needed. Each metadata column can be paired with a single value. > [!NOTE] Note > Vector databases created before the introduction of metadata filtering do not support this feature. To use filtering with them, create a version from the original and configure the LLM blueprint to use the new vector database instead. To create a metadata filter, click Filter metadata below the prompt entry box. All optional metadata column names appear in the dropdown, as well as the option `source`, which is content from the `document_file_path` column. If the vector database includes no optional metadata, only `source` is available for selection. Select a column name and enter a single value. The value must be an exact string match from the vector database (partial matches are not allowed). Use Add filter to add filters for different metadata columns > [!NOTE] Note > If the value entered for a source is not an exact match, while a response is returned there are no citations available. This is because there was not match on the filter. If the LLM blueprint configuration does not include a vector database, clicking Filter metadata displays the following: To enable filtering, add a vector database that includes a minimum of the required `document` and `document_file_path` (shown as `source` in filtering) columns. #### Metadata filtering example The following example, which uses the [DataRobot documentation](https://docs.datarobot.com/en/docs/get-started/how-to/genai-walk-basic.html#prerequisites) as the vector database, compares results to the same prompt ("how do I deploy a model?") with and without a metadata filter applied. The image on the left has no filtering. The image on the right set the value of `source` to `source: datarobot_english_documentation/datarobot_docs|en|mlops|deployment|deploy-methods|deploy-model.txt`. This is a value in the `document_file_path` column of the data source. Notice the differences, particularly in prompt tokens and ROUGE score. When you open citations for the filtered prompt, you can see the source is only the one path: --- # Create and manage prompts URL: https://docs.datarobot.com/en/docs/agentic-ai/prompt-mgmt/create-prompts.html > Create prompts, add variables, version, and share prompts with users or organizations for use in the agent experience. # Create and manage prompts Prompts are a fundamental part of interacting with, and generating outputs from, LLMs and agents. Access the prompt management system from the Prompts tile within Registry for a centralized, version-controlled, and integrated system for prototyping, experimenting, deploying, and monitoring prompts as agent components. The Prompts tile initially lists all saved and shared prompts. Click an entry for configuration details. **Registry listing:** [https://docs.datarobot.com/en/docs/images/manage-prompts-2.png](https://docs.datarobot.com/en/docs/images/manage-prompts-2.png) **Prompt details:** [https://docs.datarobot.com/en/docs/images/prompt-3.png](https://docs.datarobot.com/en/docs/images/prompt-3.png) The system supports: - Creating prompts . - Versioning capabilities , for iterating on to prompts. - Managing prompts by comparing and sharing prompts with users or organizations. - Calling a prompt in an agent . ## Create prompts To create a prompt, open Registry > Prompt. The prompt library opens with a Create prompt button available. **First prompt:** If you have not created any prompts, or none have been shared with you, the library shows an empty state. [https://docs.datarobot.com/en/docs/images/prompt-2.png](https://docs.datarobot.com/en/docs/images/prompt-2.png) **Populated prompt library:** If you have created prompts, or prompts have been shared with you, they are listed on the Prompt tile landing page. [https://docs.datarobot.com/en/docs/images/prompt-1.png](https://docs.datarobot.com/en/docs/images/prompt-1.png) Click Create prompt and complete the fields. All fields support a maximum of 5000 characters. | Field | Description | | --- | --- | | Name | Enter a name for the prompt. The name must contain only letters, numbers, underscores (_), dashes (-), spaces, and brackets. | | Description | Enter a description for the prompt. The description is for developer information; it displays on the prompt library landing page and the individual prompt details page. | | Prompt text | Enter the text that will be included with each individual user prompt, up to 5 million characters. Prompt text can contain variables, as described below. Note that text entered in this field is counted as part of an LLM's max completion token limit. You can use a maximum of 100 variables in a single prompt. | | Variables | Enter a variable name marked with double braces {{ variable_name }}. The variable definition must be included with the agent files and must contain only letters, numbers, and underscores (_). Variable character limits are: Name maximum = 200 characters.Description maximum = 200 characters. | | Comments (optional) | Enter text to display on individual prompt details page. | If you are not using variables, click Create. Otherwise see the section on [adding variables](https://docs.datarobot.com/en/docs/agentic-ai/prompt-mgmt/create-prompts.html#add-variables). Also, see [prompt versioning](https://docs.datarobot.com/en/docs/agentic-ai/prompt-mgmt/create-prompts.html#create-prompt-versions) for details on modifying existing prompts. ### Add variables If you enter a variable in the Prompt text field, the Variables section expands to include a description field. The description is required; the content of this field displays on the individual prompt details page. It does not define the variable.[Variable must be defined](https://docs.datarobot.com/en/docs/agentic-ai/prompt-mgmt/prompt-in-agent.html) in the `MyAgent` class of the `agents.py` file. To include a variable: 1. Define the variable(s) inagents.py. Note that this step does not have to be completed first but must be completed before testing the prompt. 2. Enter the prompt text, indicating a variable using double brackets ({{ }}) around the variable name. With each variable you reference, an entry for that variable is included in theVariablessection. You can use the same variable multiple times in the prompt text. 3. ClickCreate. ### View defined prompts When you click the Prompts tile in Registry, the prompt library opens listing all prompts created by, or shared with, you. You can search for the prompt name or use the Created by filter to search by creator. Click an entry to see details of the newest version of the prompt. A list of all versions is displayed in the right panel. To view a different version, click a version in the panel or use the dropdown. The prompt displayed is read-only. To modify it, [create a new version](https://docs.datarobot.com/en/docs/agentic-ai/prompt-mgmt/create-prompts.html#create-prompt-versions). ## Create prompt versions Prompt versioning is vital for managing changes to prompts over time, as even small alterations can significantly impact an LLM's output. This practice ensures reproducibility, allowing teams to link specific model outputs to the exact prompt version that generated them, and facilitates quick rollbacks to stable versions if new changes degrade performance. To version prompts: 1. Click on the prompt you want to modify from the table list of prompts on thePromptstile in Registry. The display shows the most recent version of the prompt; it is read-only. 2. ClickCreate new version from currentin the upper right to open the canvas for creating a new version of the prompt. The existing prompt text, variables, and comments area are now editable. The name and description are not. 3. Modify the text and variables, as needed. Be sure that all description fields are complete. 4. ClickCreate. The new prompt is displayed in the versions panel. ## Manage prompts Once you have created prompts, or prompts have been shared with you, you can sort, compare, and share them. Access the prompt management system from the Prompts tile within Registry. ### Filter table listing You can filter the prompt table to list only those prompts created by certain users. To do so, select prompt creators from the Created by dropdown. ### Compare versions Use the Compare versions functionality to review changes made between versions. To compare: 1. Select the prompt from the table listing. The latest version of the prompt opens. 2. ClickCompare versionsto open the comparison modal. 3. Using the dropdowns, select the versions to compare. Color helps to identify changes in the prompt text; use the scrollbars to see complete entries. Changes in variable and comments are also indicated. ### Share prompts You can share prompts with a user, group, or organization. An owner can share, change roles, or create new versions from the existing prompt. To share the prompt, click the Action menu and choose Share prompt. The [standard sharing](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/sharing.html) modal appears. --- # Prompt management URL: https://docs.datarobot.com/en/docs/agentic-ai/prompt-mgmt/index.html > Create and version prompts, share prompts with other users, and employ prompts in an agentic playground. # Prompt management > [!NOTE] Premium > DataRobot's GenAI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. Effective prompt management is critical for developing production-grade AI agents. From within Registry, the prompt management system provides a centralized, version-controlled, and deeply integrated system to prototype, experiment, deploy, and monitor prompts as components of agents. Prompt governance establishes a controlled process for the creation, testing, approval, and deployment of prompts. A centralized prompt registry serves as a single source of truth, enabling quality assurance through integrated approval workflows that vet prompts for quality, bias, and adherence to company guidelines. | Topic | Description | | --- | --- | | Create and manage prompts | Create prompts, add variables, and version for prompt lineage. Also compare and share prompts with users or organizations. | | Use saved prompts in an agent | Configure variables, employ the saved prompt in agent code, and test the results in an agentic playground. | --- # Call prompts in an agent URL: https://docs.datarobot.com/en/docs/agentic-ai/prompt-mgmt/prompt-in-agent.html > Employ the saved prompt in agent code; test the results in an agentic playground. # Call prompts in an agent Prompt templates are reusable prompts created in DataRobot, often containing placeholder variables (like `{{ topic }}`) to be defined in the agent code. The prompt template itself is stored in DataRobot and can have multiple versions, allowing for prompt iteration and fine-tuning without changing the agent code. > [!NOTE] Token limits > Prompt templates count towards the LLM's maximum completion token limit. In the agent code, the framework gets the template through the DataRobot API and provides the variable values. These variables are then substituted into the template text to create the final prompt sent to the LLM. Each framework template handles initial input formatting differently, so the method for using DataRobot prompt templates varies. Before modifying the `myagent.py` file, create a prompt template in DataRobot and note the template ID. If needed, also note the version ID of the specific prompt template version to use. Then, in the `myagent.py` file, modify the appropriate method or property in the `MyAgent` class based on the framework. The examples below show modifications to the existing framework templates in this repository. Each example assumes a prompt template exists in DataRobot containing a `{{ topic }}` variable. For example, the prompt template might be: `Write an article about {{ topic }} in 1997.` The user prompt sent to the agent is combined with this template by substituting the user input into the `{{ topic }}` variable. **LangGraph:** LangGraph uses a `prompt_template` property that returns a `ChatPromptTemplate`. Add `import datarobot as dr` at the top of the file, then modify this property to use DataRobot prompt templates: ``` # Added to imports import datarobot as dr # Modified in MyAgent class @property def prompt_template(self) -> ChatPromptTemplate: prompt_template = dr.genai.PromptTemplate.get("PROMPT_TEMPLATE_ID") prompt_template_version = prompt_template.get_latest_version() # To use a specific version instead: # prompt_template_version = prompt_template.get_version("PROMPT_VERSION_ID") # Convert {{ variable }} format to {variable} format for LangGraph's ChatPromptTemplate # The {topic} variable is filled by the framework at runtime prompt_text = prompt_template_version.to_fstring() return ChatPromptTemplate.from_messages( [ ( "user", prompt_text, ), ] ) ``` Replace the prompt template ID ( `"PROMPT_TEMPLATE_ID"`) with the appropriate template ID from DataRobot. The example uses `get_latest_version()` to automatically use the latest version without redeployment. This example uses `to_fstring()` to convert the template's `{{ topic }}` variable to `{topic}` format, which LangGraph's `ChatPromptTemplate` replaces at runtime. If the variables in the prompt template change across versions (for example, if a new version uses `{{ subject }}` instead of `{{ topic }}`), update this code to handle all variables appropriately, otherwise the code may break when fetching a new version. > [!NOTE] Multi-agent workflows > The `prompt_template` property is used for the initial user input. In the [Agentic Starter](https://github.com/datarobot-community/datarobot-agent-application) and related templates, each graph node is built with LangChain's [create_agent](https://python.langchain.com/docs/modules/agents/) and a `system_prompt` argument (often wrapped with `make_system_prompt` from `datarobot_genai`). To ensure all agents follow the prompt template instructions, incorporate the formatted template text into each node's `system_prompt` (not the `prompt=` parameter used by some other LangGraph examples such as `langgraph.prebuilt.create_react_agent`). See [Customize agents](https://docs.datarobot.com/en/docs/agentic-ai/agentic-develop/agentic-development.html#modify-agent-prompts) ( Modify agent prompts, LangGraph tab) for the API used in DataRobot templates. **LlamaIndex:** LlamaIndex uses a `make_input_message` method that returns a string. Add `import datarobot as dr` at the top of the file, then modify this method to use DataRobot prompt templates: ``` # Added to imports import datarobot as dr # Modified in MyAgent class def make_input_message(self, completion_create_params: Any) -> str: user_prompt_content = extract_user_prompt_content(completion_create_params) prompt_template = dr.genai.PromptTemplate.get("PROMPT_TEMPLATE_ID") prompt_template_version = prompt_template.get_latest_version() # To use a specific version instead: # prompt_template_version = prompt_template.get_version("PROMPT_VERSION_ID") # Render the prompt template with variables (assumes {{ topic }} in the template) prompt_text = prompt_template_version.render(topic=user_prompt_content) return prompt_text ``` Replace the prompt template ID ( `"PROMPT_TEMPLATE_ID"`) with the appropriate template ID from DataRobot. The example uses `get_latest_version()` to automatically use the latest version without redeployment. This example assumes the prompt template contains a `{{ topic }}` variable. If the variables in the prompt template change across versions (for example, if a new version uses `{{ subject }}` instead of `{{ topic }}`), update this code to handle all variables appropriately, otherwise the code may break when fetching a new version. > [!NOTE] Multi-agent workflows > The `make_input_message` method affects only the initial input message. Each agent has its own `system_prompt` property. To ensure all agents follow the prompt template instructions, incorporate the formatted prompt template into each agent's `system_prompt` property. **CrewAI:** CrewAI uses agent properties ( `goal`, `backstory`) that can contain prompt templates. Add `import datarobot as dr` at the top of the file, then modify agent properties to use DataRobot prompt templates: ``` # Added to imports import datarobot as dr # Modified in MyAgent class @property def agent_planner(self) -> Agent: prompt_template = dr.genai.PromptTemplate.get("PROMPT_TEMPLATE_ID") prompt_template_version = prompt_template.get_latest_version() # To use a specific version instead: # prompt_template_version = prompt_template.get_version("PROMPT_VERSION_ID") # For properties that use {topic} (f-string format), use to_fstring() prompt_text = prompt_template_version.to_fstring() return Agent( role="Planner", goal=f"Plan engaging and factually accurate content on {{ topic }}. {prompt_text}", backstory=f"You're working on planning a blog article about the topic: {{ topic }}. {prompt_text} " "You collect information that helps the audience learn something and make informed decisions. " "Your work is the basis for the Content Writer to write an article on this topic.", # ... other properties ) ``` Replace the prompt template ID ( `"PROMPT_TEMPLATE_ID"`) with the appropriate template ID from DataRobot. The example uses `get_latest_version()` to automatically use the latest version without redeployment. This example modifies `agent_planner` to use the prompt template in its `goal` and `backstory` properties. Since these properties use `{topic}` (f-string format that CrewAI will fill at runtime), the example uses `to_fstring()` to convert `{{ topic }}` to `{topic}` format so CrewAI can replace it with the user's input. This example assumes the prompt template contains a `{{ topic }}` variable. If the variables in the prompt template change across versions (for example, if a new version uses `{{ subject }}` instead of `{{ topic }}`), update this code to handle all variables appropriately, otherwise the code may break when fetching a new version. > [!NOTE] Multi-agent workflows > Apply prompt templates to each agent's `goal` or `backstory` properties where you want the instructions to be followed. For properties that use `{topic}`, use `to_fstring()`. For plain text properties, use `render()`. --- # ACL hydration URL: https://docs.datarobot.com/en/docs/agentic-ai/vector-database/acl-hydration.html > Learn about fine-grained access control for vector database results based on original document permissions from external sources. # ACL hydration > [!NOTE] Premium > DataRobot's RAG ACL management capabilities are a premium feature; contact your DataRobot representative for enablement information. This functionality is not available in the DataRobot trial experience. Access Control List (ACL) hydration enforces fine-grained authorization for vector database results based on original document permissions from external sources, such as Google Drive and SharePoint. When files are ingested from these sources, DataRobot captures and maintains their access control information, ensuring that users can only access vector database chunks for documents they have permission to view in the original source system. ## Overview ACL hydration enables organizations to maintain the same access control policies in DataRobot that exist in their source systems. This is particularly important for administrators who need to ensure that sensitive documents remain protected when used in vector databases for generative AI applications. For datasets, ACL hydration works as follows: 1. Ingest files from a supported external source. DataRobot registers them in the File Registry . 2. DataRobot captures and caches initial ACLs during ingestion. 3. DataRobot continuously monitors ACL changes in the source system using polling mechanisms: 4. When a user queries a vector database, DataRobot filters results based on the user's permissions in the original source system, ensuring only accessible chunks are returned. See the [feature considerations](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/acl-hydration.html#feature-considerations) for more information. ### Key capabilities ACL hydration provides the following capabilities: | Capability | Description | | --- | --- | | Initial ACL capture | Automatically captures and stores access control information when files are first ingested from supported data sources. | | ACL change monitoring | Continuously polls source systems to detect and apply permission changes as they occur. | | Vector database result filtering | Filters vector database query results to return only chunks from documents the user has permission to access. | | Multi-user support | Supports multiple users with different permission levels, ensuring each user sees only the data they're authorized to access. | ## Supported data sources ACL hydration is currently supported for the following data sources: | Data source | ACL tracking method | | --- | --- | | Google Drive | Drive Activity API | | SharePoint | Delta Query | ## Setting up ACL hydration To enable ACL hydration for your organization, configure admin connections for the supported data sources. ### Prerequisites Before setting up ACL hydration, ensure you have: - An organization administrator account with appropriate permissions. - A connection to a supported data source . - Files ingested from the supported data sources (after the connection is configured). ### Configuration steps 1. As a DataRobot organization admin, go to theData connectionspage. 2. Select an existing connection of the supported data source type or create a new connection (using service accounts for admin connections is highly recommended). 3. Toggle onEnable access control list synchronization. DataRobot will use this connection to poll for ACL changes across all files in your organization. 4. Configure the ACL management details as needed: 5. Admin impersonation email : Set the administrator account to impersonate in the background. This field is mandatory for Google Drive. 6. Domain(_optional): Overwrites the user's email domain with the specified string. For example, ifDomainis set tosharepoint.com, and the user email ismy-account@company.com, DataRobot will trackmy-account@sharepoint.comACLs. 7. ClickSave. There can be only one admin connection per data source type. Any admin user in the DataRobot organization can enable ACL synchronization for a connection they have permission to. Doing so overwrites the existing admin connection for that data source for the entire organization. Organization admin users do not see all admin connections, only those that have been shared with them explicitly or that they own. 8. Retrieve the connection ID to create the data source and begin ingesting files. Initial ACLs are automatically captured during ingestion. NoteFor Google Drive, extract both the Drive ID and Folder ID from the Google Drive URL. For new DataRobot users, an async job automatically refreshes external principals (user IDs) to minimize the no-ACL gap. ## How ACL hydration works ACL hydration captures the access control list, monitors the source system settings, and returns vector database results to each user based on those settings. When files are first ingested, DataRobot captures the current access control list for each file. ACL information is stored in the File Registry, including: * The origin of each file (connector type and external file ID). * Original catalog ID and catalog version ID. * Initial ACLs with user and group permissions. Permission information is preserved even if the catalog item is updated or files are modified. DataRobot continuously monitors for ACL changes in the source system using polling mechanism queries the source APIs at regular intervals, targeting low latency. For example: * For Google Drive, DataRobot uses Drive Activity API to query for permission change events. * For SharePoint, DataRobot uses Delta Query to retrieve incremental changes. When ACL changes are detected, DataRobot updates the stored ACL information in the File Registry. Changes are applied immediately and impact to future vector database queries. When a user queries a vector database: DataRobot checks the user's permissions for each file referenced in the vector database chunks. Only chunks from files the user has permission to access are included in the results. The system uses the stored ACL information ,combined with the user's external principal mapping, to determine access. ## User access levels ACL hydration respects the original source system permissions: | Access level | Behavior | | --- | --- | | Full access | Users with full access to a file in the source system can see all vector database chunks from that file. | | Restricted access | Users with restricted access see only the chunks they're authorized for, based on source system permissions. | | No access | Users without access to a file in the source system cannot see any vector database chunks from that file, even if they have access to the catalog item in DataRobot. | ### File access scenarios The following sample scenarios illustrate how ACL hydration works: Scenario 1: User who ingested files - A user who ingests files from Google Drive has access to all files they ingested. - This user can see all vector database chunks from those files in query results. Scenario 2: User with restricted access - A user who is a member of a specific group has access only to files shared with that group. - This user can see vector database chunks only from files they have permission to access in the source system. - Results from files in folders they don't have access to are filtered out. Scenario 3: Public files - Files marked as public or with "connectivity access" are accessible to all users. - All users can see vector database chunks from these public files. ## Integrate user identities with applications and agents DataRobot supports integration between applications, agents, vector databases, and ACL enforcement. This integration is built so that applications and agents automatically enforce ACL-filtered vector database results for the requesting user. For applications that call DataRobot agents and use ACL-enabled vector databases, you must propagate the requesting user's identity so that the agent can apply the correct ACL filtering. To do so: 1. Read the identity token from the incoming request. Your app receives requests (e.g., from a front end or API client). Read theX-DataRobot-Identity-Tokenheader from each request. 2. Pass theX-DataRobot-Identity-Tokenheader when calling the agent. When your app invokes a DataRobot agent (e.g., for chat or RAG), include the same header in the outbound call so that the agent can identify the user and filter vector database results by that user's permissions. The way you pass the header depends on how your app calls the agent (e.g., as `extra_headers` to an LLM client or as a headers object to a stream manager). Reading the header is the same in all cases: ``` # Example: read the identity token from the request (e.g., FastAPI/Starlette) identity_token = request.headers.get("X-DataRobot-Identity-Token") ``` Then, forward this value in the headers you send when calling the agent. If the identity token is not propagated, the agent cannot apply per-user ACL filtering and behavior may not match the intended access policy. ## Feature considerations - DataRobot supports ACL hydration with Sharepoint and Google Drive. - Locally uploaded files and database connections do not support ACL hydration, as they do not have external access control systems to reference. - ACL hydration enforcement is not automatically applied to existing vector databases and ingested files. It is only applied to newly created vector databases with newly ingested files from supported sources. - DataRobot applies the latest retrieved ACLs regardless of the background synchronization health. If you need to confirm that identity and ACL sync are healthy, use the admin API monitoring described inMonitoring background synchronization. - DataRobot does not support cross-drive links for ACL hydration. For example, if you ingest files using a link pointing to another drive, the ingestion completes successfully but the files are considered inaccessible via ACL. ## Monitoring background synchronization Administrators can monitor the health of background synchronization using the Event Logs API. Two event types are relevant: ### User identity and membership synchronization Use the following to return a report on user identity and membership synchronization with the external source: `GET https://{DATAROBOT_URL}/api/v2/eventLogs/?event=External+principals+synchronized+for+a+connector` The request returns the following response fields: | Field | Description | | --- | --- | | timestamp | The time of the report, represented in UTC. | | context.connectorType | The data source (e.g., gdrive). | | context.mostOutdatedMinutes | The user identity synchronization latency, in minutes. | | context.mode | The job status, either: Create a vector databases, work with versions and related assets, and interact in the playground. # Vector databases > [!NOTE] Premium > DataRobot's GenAI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. A vector database is a collection of unstructured text that is broken into chunks, with embeddings generated for each chunk. Both the chunks and embeddings are stored in a database and are available for retrieval. Vector databases can optionally be used to ground the LLM responses to specific information and can be assigned to an LLM blueprint to leverage during a [RAG](https://docs.datarobot.com/en/docs/reference/glossary/index.html#retrieval-augmented-generation-rag) operation. The role of the vector database is to enrich the prompt with relevant context before it is sent to the LLM. The simplified workflow for working with vector databases is as follows: 1. Create a vector databaseobject. 2. Add anappropriatedata source from theData Registry. 3. Set the configuration,embeddings, andchunking. 4. Create the vector database and add it to an LLM blueprintin the playground. See the [considerations related to vector databases](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-consider.html#vector-database-considerations) for guidance when working with DataRobot GenAI capabilities. Working with vector databases includes the following: | Topic | Description | | --- | --- | | Add data sources | Add internal and external data sources; actions from the Vector databases tab in the Use Case directory. | | Create a vector database | Create and configure a vector database. | | Versioning internal vector databases | Use versioning to modify DataRobot-hosted internal (FAISS-based) vector databases for tracking and fine-tuning. | | Versioning connected vector databases | Add data to an existing Pinecone-, Elasticsearch-, Milvus-, or PostgreSQL-based vector database connection. | | Register and deploy vector databases | Send vector databases from the playground to the workshop for modification and deployment, or deploy the current vector database version directly to Console. | | Use an embedding NVIDIA NIM to create a vector database | Premium feature. Add a registered or deployed embedding NVIDIA NIM to a Use Case with a vector database to enrich prompts in the playground with relevant context before they are sent to the LLM. | | ACL hydration | Premium feature. ACL (Access Control List) hydration enforces fine-grained authorization for vector database results based on original document permissions from external sources such as Google Drive and SharePoint. | --- # Update connected vector databases URL: https://docs.datarobot.com/en/docs/agentic-ai/vector-database/update-connected-vdbs.html > Add data to an existing Pinecone-, Elasticsearch-, Milvus-, or PostgreSQL-based vector database connection. # Update connected vector databases > [!NOTE] Note > Versioning—creating a complete, new vector database—is not available for external, connected vector databases. Instead, you can add data to ("hydrate") a connected vector database without creating a new version. This page describes the ability to add data to a Pinecone, Elasticsearch, Milvus, or PostgreSQL connected vector database. See the section on [versioning a resident vector databases](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html) for details on creating child, related entities based on a single, DataRobot-hosted parent. ## Add data To add data, go to the Vector databases tile in the left panel and select the connected vector database you want to update. From the Actions menu, choose Add data. > [!NOTE] Note > Before adding data, take note of the number of chunks shown in the Number of chunks column. After adding data, you can compare the chunk count of the newly hydrated vector database with this value. > > [https://docs.datarobot.com/en/docs/images/connected-add-4.png](https://docs.datarobot.com/en/docs/images/connected-add-4.png) The Update vector database page opens, providing: - The Data source dropdown, which provides access to all Use Case vector databases and provides an option to Add data . - An optional field for attaching metadata . - A summary of the current vector database configuration. This is the same information provided in the Details section on the vector database listing. From the Data source dropdown, select Add data. The [Data Registry](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html) opens where you can select a dataset to append to the current vector database. Click to select the new dataset and then click Add dataset. You are returned to the Update vector database page; click Update in the upper right to save changes. When you receive a popup notification that data was successfully added, you can confirm the addition by: - Checking your collection at the provider site. - Comparing the number of chunks shown in the Number of chunks column to the number observed prior to adding data. ## Deploy the updated vector database After updating the vector database with new data, you can do the following. Note that you do not need to redeploy to pick up the changes. Because the deployment is simply a "pass-through" to the connected vector database index, existing deployments automatically have access to the added data. - Send it to the model workshop, where it is maintained as a standalone vector database custom model. - Deploy it, which sends it to the model workshop, registers it, and then deploys it. See the section on [registering and deploying](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs-register-deploy.html) vector databases for more information. --- # Vector database data sources URL: https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs-data.html > Learn to create and apply your own text data for use by an LLM in GenAI. # Vector database data sources Generative modeling in DataRobot supports three types of vector databases: - Resident , "in-house" built vector databases, with the Source listed in the application showing DataRobot. Supporting up to 10GB, they are stored in DataRobot and can be found in Vector databases tile for a Use Case. - Connected vector databases up to 100GB, which link out to an external provider. The Source listed in the application is the provider name and they are stored in the provider instance. - External , hosted in Registry workshop for validation and registration, and identified as Read-only connected in the Use Case directory listing. They have no size constraints, since they are hosted outside DataRobot, but must fit within the resource bundle memory of the vector database deployment. ## Dataset requirements When uploading datasets for use in creating a vector database, the supported formats are either `.zip` or `.csv`. Two columns are mandatory for the files— `document` and `document_file_path`. Additional metadata columns, up to 50, can be added for use in filtering during prompt queries. Note that for purposes of [metadata filtering](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#metadata-filtering), `document_file_path` is displayed as `source`. For `.zip` files, DataRobot processes the file to create a `.csv` version that contains text columns ( `document`) with an associated reference ID ( `document_file_path`) column. All content in the text column is treated as strings. The reference ID column is created automatically when the `.zip` is uploaded. All files should be either in the root of the archive or in a single folder inside an archive. Using a folder tree hierarchy is not supported. See the [considerations](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html#supported-dataset-types) for more information on supported file content. ## Internal vector databases Internal vector databases in DataRobot are optimized to maintain retrieval speed while ensuring an acceptable retrieval accuracy. Follow the steps below to prepare and upload the data. 1. Prepare the data as follows. Be sure to see theCSV-specific requirement detailsto ensure the data is in the correct format. 2. Upload the file. You can do this either: Once the data is available on DataRobot, you can [add it as a vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html) for use in the playground. #### CSV-specific requirement details The mandatory columns for CSV are defined as follows: - document can contain any amount (up to file size limitations) of free-text content. - document_file_path is also required and requires a file format suffix (for example, file.txt ). Using a `.csv` file allows you to make use of the [no chunking](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#chunking-settings) option during vector database creation. DataRobot will then treat each row as a chunk and directly generate an embedding on each row. DataRobot vector databases only support using one text column from a CSV for the primary text content. If a CSV has multiple text columns, they must be concatenated into a single `document` column. You can add up to 50 other columns in the CSV as metadata columns. These columns can be used for [metadata filtering](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#metadata-filtering), which limits the citations returned by the prompt query. If the CSV has multiple text columns, you can: - Combine (concatenate) the text columns into one document column. - Convert the CSV rows into individual PDF files (one PDF per row) and then upload the PDFs. For example: Consider a CSV file containing one column with large amounts of free text, `swag`, and a second column with an ID but no text, `InventoryID`. To create a vector database from the data: 1. Rename swag to document . 2. Rename InventoryID to document_file_path . 3. Add "fake" paths to the document_file_path column. For example, change 11223344 to /inventory/11223344.txt . In this way, the column is recognized as containing file paths. ### Export a vector database You can export a vector database, or a specific version of a database, to the Data Registry for re-use in a different Use Case. To export, open the Vector database tile of your Use Case. Click the Actions menu menu and select Export latest vector database version to Data Registry. When you export, you are notified that the job is submitted. Open the Data assets tile to see the dataset registering for use via the Data Registry. It is also saved to the AI Catalog. Once registered, you can preview the dataset or create a new vector database from this dataset. **Preview the dataset:** To preview before creating a new vector database from the export, from the Data assets tile choose Create vector database from the newly added vector database's Actions menu.[Select a provider](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#select-a-provider) and from the Data source dropdown choose Add data. [https://docs.datarobot.com/en/docs/images/vdb-export-3.png](https://docs.datarobot.com/en/docs/images/vdb-export-3.png) The Data Registry opens. Under DataRobot assets choose Datasets and click on the newly exported vector database. The Data preview shows that each [chunk](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#chunking-settings) from the vector database is now a dataset row. [https://docs.datarobot.com/en/docs/images/vdb-export-4.png](https://docs.datarobot.com/en/docs/images/vdb-export-4.png) **Create a new vector database:** From the Actions menu menu select Create vector database. A modal opens to [configure the database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html). [https://docs.datarobot.com/en/docs/images/vdb-3a.png](https://docs.datarobot.com/en/docs/images/vdb-3a.png) You can download the dataset from the [Data Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-data-registry/nxt-explore-data.html), modify it on a chunk level, and then re-upload it, creating a new version or a new vector database. ## External (BYO) vector databases The external "bring-your-own" (BYO) vector database provides the ability to leverage your custom model deployments as vector databases for LLM blueprints, using your own models and data sources. This vector database type is identified as `Read-only connected` in the Use Case directory listing. Using an external vector database cannot be done via the UI; review the [notebook](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/chromadb-vdb.html) that walks through creating a ChromaDB external vector database using DataRobot’s Python client. Key features of external vector databases: - Custom model integration:Incorporate your own custom models as vector databases, enabling greater flexibility and customization. - Input and output format compatibility:External BYO vector databases must adhere to specified input and output formats to ensure seamless integration with LLM blueprints. - Validation and registration:Custom model deployments must be validated to ensure they meet the necessary requirements before being registered as an external vector database. - Seamless integration with LLM blueprints:Once registered, external vector databases can be used with LLM blueprints in the same way as local vector databases. - Error handling and updates:The feature provides error handling and update capabilities, allowing you to revalidate orcreate duplicatesof LLM blueprints to address any issues or changes in custom model deployments. ### Basic external workflow The basic workflow, which is covered in depth in [this notebook](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/chromadb-vdb.html), is as follows: 1. Create the vector database via the API. 2. Create a custom model deployment to bring the vector database into DataRobot. 3. Once the deployment is registered, link to it as part of vector database creation in your notebook. You can view all vector databases (and associated versions) for a Use Case from the Vector database tab within the Use Case. For external vector databases, you can see only the source type. Because these vector databases aren't managed by DataRobot, other data is not available for reporting.. --- # Use an embedding NVIDIA NIM to create a vector database URL: https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs-nvidia-nim-embed.html > Add a deployed embedding NVIDIA NIM to a Use Case with a vector database to enrich prompts in the playground with relevant context before they are sent to the LLM. # Use an embedding NVIDIA NIM to create a vector database > [!NOTE] Premium > The use of NVIDIA Inference Microservices (NIM) in DataRobot requires access to premium features for GenAI experimentation and GPU inference. Contact your DataRobot representative or administrator for information on enabling the required features. The NVIDIA Inference Microservices (NIM) available through Registry include embedding models. A deployed embedding model can be added to a Use Case, creating a collection of unstructured text that is broken into chunks, with embeddings generated for each chunk. Both the chunks and embeddings are stored in the vector database and are available for retrieval. Vector databases can optionally be used to ground the LLM responses to specific information and can be assigned to an LLM blueprint to leverage during a RAG operation. The role of the vector database is to enrich the prompt with relevant context before it is sent to the LLM. Each embedding NVIDIA NIM available is listed below: - arctic-embed-l - llama-3.2-nv-embedqa-1b-v2 - nv-embedqa-e5-v5 - nv-embedqa-e5-v5-pb24h2 - nv-embedqa-mistral-7b-v2 - nvclip ## Create a vector database with a registered embedding NIM After you register an embedding NIM, you can add it to a vector database. DataRobot handles the deployment process automatically. To create a vector database with a registered embedding NVIDIA NIM: 1. On theRegistry > Modelstab, next to+ Register a model, clickand thenImport from NVIDIA NGC. 2. In theImport from NVIDIA NGCpanel, on theSelect NIMtab, click an embedding NIM in the gallery. Search the galleryTo direct your search for an embedding model, you canSearch, filter byPublisher, or clickSort byto order the gallery by date added or alphabetically (ascending or descending). 3. Review the model information from the NVIDIA NGC source, then clickNext. 4. On theRegister modeltab, configure the following fields and clickRegister: FieldDescriptionRegistered model name / Registered modelConfigure one of the following:Registered model name:When registering a new model, enter auniqueand descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, a warning appears.Registered model:When saving as a version of an existing model, select the existing registered model you want to add a new version to.Registered version nameAutomatically populated with the model name and the wordversion. Change the version name or modify the default version name as necessary.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister as a new model.Resource bundleRecommended automatically. If possible, DataRobot translates the GPU requirements for the selected model into a resource bundle. In some cases, DataRobot can't detect a compatible resource bundle. To identify a resource bundle with sufficient VRAM, review the documentation for that NIM.NVIDIA NGC API keySelect the credential associated with your NVIDIA NGC API key.Optional settingsRegistered version descriptionEnter a description of the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add tagand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags added when registering a new model are applied toV1. 5. After the registered model builds, navigate toWorkbenchand open a Use Case. 6. In a Use Case, on theVector databasestab, either: With an existing vector databasesWithout an existing vector databaseIf you have already added one or more vector databases to the Use Case, Click the+ Add vector databasebutton in the upper right.If you haven't added a vector database to the Use Case before, clickCreate vector databasein the center of the page. 7. On theCreate vector databasepanel, enter a descriptiveName. Then, in theData sourcedropdown, select from the data sources associated with the Use Case or clickAdd datato add new data from the Data Registry. 8. In theEmbedding modeldropdown, click the embedding NIM you registered. Then, configure thevector databaseText chunkingsettingsand clickCreate vector database. The selected embedding model is deployed toConsolewhen you create the vector database. If necessary, this process creates a newprediction environmentfor NIM embeddings. After creating a vector database, you can [manage](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#manage-vector-databases) and [version](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html) it, or [add it to an LLM in the playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#add-a-vector-database) to inform responses. ## Create a vector database with a deployed embedding NIM If you've already registered and deployed an embedding NIM, you can add it to a vector database as a deployed embedding model. To create a vector database with a registered and deployed embedding NVIDIA NIM: 1. In a Use Case, on theVector databasestile, either: With an existing vector databasesWithout an existing vector databaseIf you have already added one or more vector databases to the Use Case, Click the+ Add vector databasebutton in the upper right.If you haven't added a vector database to the Use Case before, clickCreate vector databasein the center of the page. 2. On theCreate vector databasepanel, enter a descriptiveName. Then, in theData sourcedropdown, select from the data sources associated with the Use Case or clickAdd datato add new data from the Data Registry. 3. In theEmbedding modeldropdown, clickAdd deployed embedding model. 4. On the next page, configure the following settings to add the NVIDIA NIM embedding model, then clickValidate and add: FieldDescriptionNameEnter a descriptive name for the embedding model you're creating.Deployment nameIn the list, locate the name of the NVIDIA NIM embedding modelregistered and deployed in DataRobotand click the deployment name.Prompt column nameEnterinputas the prompt column name.Response column nameEnterresultas the response column name. Validation processThe validation process can take a few minutes. A notification appears when the process starts and if it succeeds or fails. 5. After the validation of the deployed embedding model succeeds, open theEmbedding modelmenu, then, underDeployed embedding models, select the NVIDIA NIM embedding model. 6. Configure thevector databaseText chunkingsettings, then clickCreate vector database. After creating a vector database, you can [manage](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#manage-vector-databases) and [version](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html) it, or [add it to an LLM in the playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#add-a-vector-database) to inform responses. --- # Register and deploy vector databases URL: https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs-register-deploy.html > Send vector databases from the playground to the workshop for modification and deployment, or deploy the current vector database version directly to the Console. # Register and deploy vector databases The Vector databases tab lists all vector databases associated with a Use Case, both [DataRobot-hosted and connected](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#select-a-provider). For DataRobot-hosted vector databases, entries include information on the versions derived from the parent; see the section on [versioning](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html) for detailed information on vector database versioning. Connected vector databases have only a single version, although you can [add data](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/update-connected-vdbs.html) to that version. **Vector databases tab:** For each vector database on the Vector databases tab, the Actions menu allows you to send the vector database to production in two ways, depending on the provider type. For example: [https://docs.datarobot.com/en/docs/images/vector-db-register-deploy.png](https://docs.datarobot.com/en/docs/images/vector-db-register-deploy.png) **Vector database details:** [https://docs.datarobot.com/en/docs/images/vector-db-register-deploy-2.png](https://docs.datarobot.com/en/docs/images/vector-db-register-deploy-2.png) | Method | Description | | --- | --- | | Send to the workshop | Send the vector database to the workshop for modification and deployment. | | Deploy this version (DataRobot-hosted) | Deploy the latest version of the vector database to the selected prediction environment. | | Deploy vector database (connected) | Deploy the vector database to the selected prediction environment. | ## Send to the workshop To send a vector database from the playground to the workshop, click Send to the workshop and provide the following information: **Resource bundle:** [https://docs.datarobot.com/en/docs/images/vector-db-send-to-workshop.png](https://docs.datarobot.com/en/docs/images/vector-db-send-to-workshop.png) **Memory:** [https://docs.datarobot.com/en/docs/images/vector-db-send-to-workshop-memory.png](https://docs.datarobot.com/en/docs/images/vector-db-send-to-workshop-memory.png) | Field | Description | | --- | --- | | Memory | Determines the maximum amount of memory that can be allocated for a custom inference model. If a model is allocated more than the configured maximum memory value, it is evicted by the system. If this occurs during testing, the test is marked as a failure. If this occurs when the model is deployed, the model is automatically launched again by Kubernetes. | | Bundle | Preview feature If enabled for your organization, selects a Resource bundle—instead of Memory—. Resource bundles allow you to choose from various CPU and GPU hardware platforms for building and testing custom models in the workshop. | | Replicas | Sets the number of replicas executed in parallel to balance workloads when a custom model is running. Increasing the number of replicas may not result in better performance, depending on the custom model's speed. | | Network access | Premium feature. Configures the egress traffic of the custom model: Public: The default setting. The custom model can access any fully qualified domain name (FQDN) in a public network to leverage third-party services.None: The custom model is isolated from the public network and cannot access third-party services. When public network access is enabled, your custom model can use the DATAROBOT_ENDPOINT and DATAROBOT_API_TOKEN environment variables. These environment variables are available for any custom model using a drop-in environment or a custom environment built on DRUM. | > [!NOTE] Premium feature: Network access > Every new custom model you create has public network access by default; however, when you create new versions of any custom model created before October 2023, those new versions remain isolated from public networks (access set to None) until you enable public access for a new version (access set to Public). From this point on, each subsequent version inherits the public access definition from the previous version. > [!NOTE] Preview feature: Resource bundles > Custom model resource bundles and GPU resource bundles are off by default. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable Resource Bundles, Enable Custom Model GPU Inference ( Premium feature) Next, click Send to the workshop. You are redirected to the workshop after the vector database is successfully created. ## Deploy the vector database version To deploy a vector database from the playground to Console, click Deploy this version (DataRobot-hosted) or Deploy vector database (connected) and provide the following information: **Resource bundle:** [https://docs.datarobot.com/en/docs/images/vector-db-deploy.png](https://docs.datarobot.com/en/docs/images/vector-db-deploy.png) **Memory:** [https://docs.datarobot.com/en/docs/images/vector-db-deploy-memory.png](https://docs.datarobot.com/en/docs/images/vector-db-deploy-memory.png) | Field | Description | | --- | --- | | Choose prediction environment | Determines the prediction environment for the deployed vector database. Verify that the correct prediction environment with Platform: DataRobot Serverless is selected. | | Memory | Determines the maximum amount of memory that can be allocated for a custom inference model. If a model is allocated more than the configured maximum memory value, it is evicted by the system. If this occurs during testing, the test is marked as a failure. If this occurs when the model is deployed, the model is automatically launched again by Kubernetes. | | Bundle | Preview feature If enabled for your organization, selects a Resource bundle—instead of Memory—. Resource bundles allow you to choose from various CPU and GPU hardware platforms for building and testing custom models in the workshop. | | Replicas | Sets the number of replicas executed in parallel to balance workloads when a custom model is running. Increasing the number of replicas may not result in better performance, depending on the custom model's speed. | | Network access | Premium feature. Configures the egress traffic of the custom model: Public: The default setting. The custom model can access any fully qualified domain name (FQDN) in a public network to leverage third-party services.None: The custom model is isolated from the public network and cannot access third-party services. When public network access is enabled, your custom model can use the DATAROBOT_ENDPOINT and DATAROBOT_API_TOKEN environment variables. These environment variables are available for any custom model using a drop-in environment or a custom environment built on DRUM. | Next, click Deploy. You are redirected to the Console after the vector database is successfully deployed. > [!NOTE] What monitoring is available for vector database deployments? > DataRobot automatically generates custom metrics relevant to vector databases for deployments with the Vector Database deployment type; for example, Total Documents, Average Documents, Total Citation Tokens, Average Citation Tokens, and VDB Score Latency. Vector database deployments also support service health monitoring. Vector database deployments don't store prediction row-level data for data exploration. --- # Create a vector database URL: https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html > Configure the data source, embeddings, and chunking strategy for a vector database. # Create a vector database > [!NOTE] GPU usage for Self-Managed users > When working with datasets over 1GB, Self-Managed users who do not have GPU usage configured on their cluster may experience serious delays. Email [DataRobot Support](mailto:support@datarobot.com), or visit the [Support site](https://support.datarobot.com/), for installation guidance. The basic steps for creating a vector database for use in a playground are to choose a provider, set the basic configuration, and set text chunking. **Provider:** Choose a [provider](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#select-a-provider) for the output of the vector database creation process, either DataRobot-resident or a connected source. [https://docs.datarobot.com/en/docs/images/vdb-3a1.png](https://docs.datarobot.com/en/docs/images/vdb-3a1.png) **Basic configuration:** Set the [basic configuration](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#set-basic-configuration), including data source from the [Data Registry](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs-data.html) or File Registry and [embedding model](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#set-the-embedding-model). [https://docs.datarobot.com/en/docs/images/vdb-3e.png](https://docs.datarobot.com/en/docs/images/vdb-3e.png) **Text chunking:** Set [text chunking](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#chunking-settings). [https://docs.datarobot.com/en/docs/images/vdb-3f.png](https://docs.datarobot.com/en/docs/images/vdb-3f.png) Use the [Vector databasestile](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#manage-vector-databases) in the Use Case directory to manage built vector databases and deployed embedding models. You will ultimately store the newly created vector database (the output of the vector database creation process) within the Use Case. They are stored either as internal vector databases (FAISS) or on [connected provider instances](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#use-a-connected-vector-database). ## Add a vector database First, add a vector database from one of multiple points within the application. Each method opens the Create vector database modal and uses the same workflow from that point. **TheUse Case assetstile:** From within a Use Case, click the Add dropdown and expand Vector database: [https://docs.datarobot.com/en/docs/images/add-vdb-1.png](https://docs.datarobot.com/en/docs/images/add-vdb-1.png) Choose Create vector database to open the vector database creation modal. Choose Add deployed vector database to add a deployment containing a vector database that you previously registered and deployed . If there are not yet any assets associated with the Use Case, you can add a vector database from the tile landing page. [https://docs.datarobot.com/en/docs/images/add-vdb-2.png](https://docs.datarobot.com/en/docs/images/add-vdb-2.png) **TheData assetstile:** From the Data assets tile, open the Actions menu associated with a Use Case and select Create vector database. [https://docs.datarobot.com/en/docs/images/add-vdb-data-1.png](https://docs.datarobot.com/en/docs/images/add-vdb-data-1.png) The Actions menu is only available if the data is detected as eligible, which means: Processing of the dataset has finished. The data source has the mandatory document and document_file_path columns. There are no more than 50 metadata columns. **TheVector databasestile:** From the Vector databases tile, click the Add dropdown: [https://docs.datarobot.com/en/docs/images/add-vdb-3.png](https://docs.datarobot.com/en/docs/images/add-vdb-3.png) Choose Create vector database to open the vector database creation modal. Choose Add deployed vector database to add a deployment that contains a vector database that you previously registered and deployed . Choose Add external vector database to begin assembling a custom vector database in the Registry workshop , which can then be linked to the Use Case. If there are not yet any vector databases associated with the Use Case, the tile landing page will lead you to create one. [https://docs.datarobot.com/en/docs/images/vdb-3.png](https://docs.datarobot.com/en/docs/images/vdb-3.png) **The playground:** When in a playground, use the [Vector database](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#add-a-vector-database) tab in the configuration section of the LLM blueprint: [https://docs.datarobot.com/en/docs/images/vdb-3b.png](https://docs.datarobot.com/en/docs/images/vdb-3b.png) Once you've selected to add a vector database, start the creation process. ## Select a provider Select a vector database provider, either internal or connected (external) with credentials. This setting determines where the output of the vector database creation process lands. The input to the creation process is always either the [Data or File Registry](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#add-a-data-source). | Provider/type | Description | Max size | | --- | --- | --- | | DataRobot/resident | An internal FAISS-based vector database, hosted locally in the Data Registry. These vector databases can be versioned and do not require credentials. | 10GB | | BYO | A deployment containing a vector database that you previously registered and deployed. Use a notebook to bring-your-own vector database via a custom model deployment. | No constraints, but must fit within the resource bundle memory of the vector database deployment. | | Connected | An external vector database that allows you to use your own Pinecone, Elasticsearch, Milvus, or PostgreSQL instance, with credentials. This option allows you to choose where content is stored but still experiment with RAG pipelines built in DataRobot and leverage DataRobot's out-of-the-box embedding and chunking functionality. | 100GB | ### Use a resident vector database Using a resident vector database means using data that is accessible within the application, either via the Data Registry or a custom model. Internal vector databases in DataRobot are optimized to maintain retrieval speed while ensuring an acceptable retrieval accuracy. See the following for dataset requirements and specific retrieval methods: - SelectDataRobotforan internal vector databasestored in the Data Registry. ### Use an external vector database To use an [external](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs-data.html#external-byo-vector-databases) (BYO) vector database, use the Add > Add deployed vector database option that is available from the Vector database tile. Before adding a vector database this way, develop the vector database externally with the DataRobot Python client, assemble a custom model for it, and then deploy that custom model. See an [example using ChromaDB](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/chromadb-vdb.html). Note that this vector database type is identified as `Read-only connected` in the Use Case directory listing. ### Use a connected vector database DataRobot allows direct connection to external data sources for vector database creation. In this case, the data source is stored locally in the Data Registry, configuration settings are applied, and the created vector database is written back to the provider. The following connections are supported: - Pinecone - Elasticsearch - Milvus - PostgreSQL (pgvector) Select your provider in the Create vector database modal. To use a provider connection, select the provider and enter authentication information. If you choose to use saved credentials, for any of the connected providers, simply select the appropriately named credentials from the dropdown. Available credentials are those that are created and stored in the [credential management system](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management): If you choose to use new credentials, they must first be created in the provider instance. Once entered, they are stored in DataRobot's credential management system for reuse. #### Connect to Pinecone All connection requests to [Pinecone](https://docs.pinecone.io/guides/get-started/overview) must include an API key for connection authentication. If you do not have a Pinecone API key saved to the credential management system, click New credentials. Complete the field for the API key and, optionally, change the display name. In the API token (API key) field, paste the key you created in the [Pinecone console](https://docs.pinecone.io/reference/api/authentication). Once added, DataRobot saves the Pinecone API key in the [credential management system](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management) for reuse when working with Pinecone vector databases. Once the token is input, select a [cloud provider](https://docs.pinecone.io/guides/index-data/create-an-index#cloud-regions) for your Pinecone instance—AWS, Azure, or GCP— and assigned cloud region. After selection, the vector database [configuration options](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#set-basic-configuration) become available. #### Connect to Elasticsearch All connection requests to the [Elasticsearch](https://www.elastic.co/docs/get-started) cluster must include a username and password (basic credentials) or an API key for connection authentication. If you do not have, or wish to add, an Elasticsearch API key saved to the credential management system, click New credentials. There are two types of credentials available for selection in the modal that appears. - Basic: Basic credentials consist of the username and password you use to access the Elasticsearch instance. Enter them here and they will be saved to DataRobot. - API key: In theAPI token (API key)field, paste the key you created, as described in theElasticsearch documentation. Once the credential type is selected, optionally change the display name and select a connection method, either Cloud ID (recommended by Elastic) or URL. See the Elasticsearch documentation for information on [finding your cloud ID](https://www.elastic.co/docs/deploy-manage/deploy/elastic-cloud/find-cloud-id). After selection, the vector database [configuration options](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#set-basic-configuration) become available. #### Connect to Milvus Milvus, a leading open-source vector database project, is distributed under the Apache 2.0 license. All connection requests to [Milvus](https://milvus.io/docs/connect-to-milvus-server.md) must include a username and password (basic credentials) or an API token for connection authentication. If you do not have, or wish to add, a Milvus API key saved to the credential management system, click New credentials. There are two types of credentials available for selection in the modal that appears. - Basic: Basic credentials consist of the username and password you use to access the Milvus instance. Enter them here and they will be saved to DataRobot. - API token: In theAPI token (API key)field, paste the key you created, as described in theMilvus documentation. Once you've selected the credential type, optionally change the display name and [enter a URI](https://milvus.io/docs/connect-to-milvus-server.md) for the Milvus server address. > [!NOTE] Note > When you complete the configuration and create the vector database, it will be available on the Milvus site for your cluster, under the Collections tab. Open the collection and then the Data tab to see the vectors, text, and various metadata. > > [https://docs.datarobot.com/en/docs/images/milvus-collection.png](https://docs.datarobot.com/en/docs/images/milvus-collection.png) After selection, the vector database [configuration options](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#set-basic-configuration) become available. #### Connect to PostgreSQL DataRobot can create vector databases by connecting to a PostgreSQL instance with the [pgvector](https://github.com/pgvector/pgvector) extension enabled, providing vector similarity search, ACID compliance, replication, point-in-time recovery, JOINs, and other PostgreSQL features. > [!NOTE] Note > If using PostgreSQL as the connection method when using a [BYO embedding model](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/create-vdb-byo-embedding.html), the output dimension must be less than 2000. Making connection requests to the PostgreSQL cluster requires a username and password (basic credentials), plus some additional fields. | Field | Description | | --- | --- | | Host | The host name or IP address of the server. | | Database | The database name. Ask your PostgreSQL admin if unknown; often database name is the database with the same name as the user name used to connect to the server. | | Port | The port number the server is listening on. | | Schema | The schema, public by default, used to resolve unqualified object names over this connection. | | SSL mode | The level of protection to provide, either prefer, require (the default), or verify-full. Note that verify-full requires the Postgres server to be configured with a certificate signed by a public certificate authority or a DataRobot installation configured with a private certificate authority. | See the [PostgreSQL documentation](https://jdbc.postgresql.org/documentation/use/#connecting-to-the-database) for more information. > [!NOTE] Note > This PostgreSQL connection is separate from the [data connection added via the Data Registry](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/add-data/connect.html#connect-to-a-data-source), where you can also connect to PostgreSQL. While the fields are similar, a Data Registry connection is not used for vector database creation. Instead, you must configure it as part of the vector database creation flow. After selection, the vector database [configuration options](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#set-basic-configuration) become available. ## Set basic configuration The following table describes the configuration settings used in vector database creation: | Field | Description | | --- | --- | | Name | The name the vector database is saved with. This name displays in the Use Case Vector databases tile and is selectable when configuring playgrounds. | | Data source | The dataset used as the knowledge source for the vector database. The list populates based on the entries in the Use Case's Vector databases tile, if any. If you started the vector database creation from the action menu on the Data assets tile, the field is prepopulated with that dataset. If there are no associated vector databases or none present are applicable, use the Add data option. | | Attach metadata | The name of the file, in the file registry, that is used for appending columns to the vector database to support filtering the citations returned by the prompt query. | | Distance metric (connected providers only) | The vector similarity metric to use in nearest neighbor search, ranking a vector's similarity against the query. | | Embedding model | The model that defines the type of embedding used for encoding data. | ### Add a data source If no data sources are available, or if you want to add new sources, choose Add data in the Data source dropdown. The Add data modal opens. Vector database creation supports ZIP and CSV dataset formats and specific [supported file types](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-consider.html#supported-dataset-types) within the datasets. You can access a supported dataset from either the File Registry or the Data Registry. Vector databases allow ingest from remote sources that support unstructured data, which are outlined on the [Data Sources page](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/index.html). When you use supported remote sources (such as Google Drive or SharePoint), access control information can be propagated and enforced so that VDB results respect the original document permissions—see [ACL hydration](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/acl-hydration.html) for details. | Registry type | Description | | --- | --- | | File | A "general purpose" storage system that can store any type of data. In contrast to the Data Registry, the File Registry does not do CSV conversion on files uploaded to it. In the UI, vector database creation is the only place where the File Registry is applicable, and it is only accessible via the Add data modal. While any file type can be stored there, regardless of registry type the same file types are supported for vector database creation. | | Data | In the Data Registry, a ZIP file is converted into a CSV with the content of each member file stored as row of the CSV. The file path for each file becomes the document_file_path column and the file content (text or base64-encoding of the file) becomes the document column. | > [!NOTE] Supported datastores > For a full list of supported connectors and drivers, see [Supported data stores](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/index.html). Note that some connectors only support unstructured data, which when added, appear in the Files section under Data Registry. Choose a dataset. Datasets from the Data Registry show a preview of the chunk ( `document`) and the file it was sourced from ( `document_file_path`). ### Attach metadata Optionally, you can select an additional file to define the metadata to attach to the chunks in the vector database. That file must reside in Datasets storage in the Data Registry. (It cannot reside in Files storage because the EDA process, which validates that the required columns are present, only runs on Datasets assets.) The file must contain, at minimum, the `document_file_path` column. A `document` column is optional. You can append up to 50 additional columns, which can be used for [filtering during prompt queries](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#metadata-filtering). You can upload a single CSV with document and metadata information in a single file. Alternatively, you can upload data and metadata as separate files and achieve the same final result. > [!NOTE] Note > Vector databases created before the introduction of metadata filtering do not support this feature. To use filtering with them, create a version from the original and configure the LLM blueprint to use the new vector database instead. Either select an available file, or use the Add data modal to add a metadata file to the Data Registry. In this example, the file has one column, `document_file_path`, which defines the file or chunk, as well as a variety of other columns that define the metadata. Once you select a metadata file, you are prompted to choose whether, if you also have metadata in the dataset, DataRobot should keep both sets of metadata or overwrite with the new file. Whether to replace or merge the metadata only applies to if there are duplicate columns between the dataset and metadata dataset. Non-duplicate metadata columns from both are always maintained. ### Set the distance metric When using a connected provider, you can also set a distance vector, also known as a similarity metric (this setting is not available when DataRobot is selected as provider). These metrics measure how similar vectors are; selecting the appropriate metric can substantially boost the effectiveness of classification and clustering tasks. Consider choosing the same similarity metric that was used to train the embedding model. | Provider | Provider documentation | | --- | --- | | Pinecone | Similarity metrics | | Elasticsearch | Similarity parameter | | Milvus | Metric types: float | | PostgreSQL (pgvector) | Similarity metrics | ### Set the embedding model To encode your data, select the embedding model that best suits your Use Case. Use one of the DataRobot-provided embeddings or a [BYO embedding](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#add-a-deployed-embedding-model). DataRobot supports the following types of embeddings; see the full embedding descriptions [here](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html#embeddings-availability). | Embedding type | Description | | --- | --- | | cl-nagoya/sup-simcse-ja-base | A medium-sized language model for Japanese RAG. | | huggingface.co/intfloat/multilingual-e5-base | A medium-sized language model used for multilingual RAG performance across multiple languages. | | huggingface.co/intfloat/multilingual-e5-small | A smaller-sized language model used for multilingual RAG performance with faster performance than the multilingual-e5-base. | | intfloat/e5-base-v2 | A medium-sized language model used for medium-to-high RAG performance. With fewer parameters and a smaller architecture, it is faster than e5_large_v2. | | intfloat/e5-large-v2 | A large language model designed for optimal RAG performance. It is classified as slow due to its architecture and size. | | jinaai/jina-embedding-t-en-v1 | A tiny language model pre-trained on the English corpus and is the fastest, and default, embedding model offered by DataRobot. | | jinaai/jina-embedding-s-en-v2 | Part of the Jina Embeddings v2 family, this embedding model is the optimal choice for long-document embeddings (large chunk sizes, up to 8192). | | sentence-transformers/all-MiniLM-L6-v2 | A small language model fine-tuned on a 1B sentence-pairs dataset that is relatively fast and pre-trained on the English corpus. It is not recommended for RAG, however, as it was trained on old data. | | Add deployed embedding model | Select a deployed embedding model to use during vector database creation. Column names can be found on the deployment's overview page in the Console. | The embedding models that DataRobot provides are based on the [SentenceBERT framework](https://github.com/UKPLab/sentence-transformers), providing an easy way to compute dense vector representations for sentences and paragraphs. The models are based on transformer networks (BERT, RoBERTA, T5) trained on a mixture of supervised and unsupervised data, and achieve state-of-the-art performance in various tasks. Text is embedded in a vector space such that similar text is grouped more closely and can efficiently be found using cosine similarity. #### Add a deployed embedding model While adding a vector database, you can select an embedding model deployed as an unstructured custom model. On the Create vector database panel, click the Embedding model dropdown, then click Add deployed embedding model. On the Add deployed embedding model panel, configure the following settings: | Setting | Description | | --- | --- | | Name | Enter a descriptive name for the embedding model. | | Deployment name | Select the unstructured custom model deployment. | | Prompt column name | Enter the name of the column containing the user prompt, defined when you created the custom embedding model in the workshop (for example, promptText). | | Response (target) column name | Enter the name of the column containing the LLM response, defined when you created the custom embedding model in the workshop (for example, responseText or resultText). | After you configure the deployed embedding model settings, click Validate and add. The deployed embedding model is added to the Embedding model dropdown list: ### Chunking settings Text chunking is the process of splitting a text document into smaller text chunks that are then used to generate [embeddings](https://docs.datarobot.com/en/docs/reference/glossary/index.html#embedding). You can either: - Choose Text chunking and further configure how chunks are derived— method , separators , and other parameters . - Select No chunking . DataRobot will then treat each row as a chunk and directly generate an embedding on each row. **Chunking:** [https://docs.datarobot.com/en/docs/images/vdb-3d.png](https://docs.datarobot.com/en/docs/images/vdb-3d.png) **No chunking:** [https://docs.datarobot.com/en/docs/images/vdb-3c.png](https://docs.datarobot.com/en/docs/images/vdb-3c.png) #### Chunking method The chunking method sets how text from the data source is divided into smaller, more manageable pieces. It is used to improve the efficiency of nearest-neighbor searches so that when queried, the database first identifies the relevant chunks that are likely to contain the nearest neighbors, and then searches within those chunks rather than searching the entire dataset. | Method | Description | | --- | --- | | Recursive | Splits text until chunks are smaller than a specified max size, discards oversized chunks, and if necessary, splits text by individual characters to maintain the chunk size limit. | | Semantic | Splits larger text into smaller, meaningful units based on the semantic content instead of length (chunk size). It is a fully automatic method, meaning that when it is selected, no further chunking configuration is available—it creates chunks where sentences are semantically "closed.". See the deep dive below for more information. | #### Work with separators Separators are "rules" or search patterns ( [not regular expressions](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#use-regular-expressions) although they can be supported) for breaking up text by applying each separator, in order, to divide text into smaller components—they define the tokens by which the documents are split into chunks. Chunks will be large enough to group by topic, with size constraints determined by the model’s configuration. Recursive text chunking is the method applied to the chunking rules. Each vector database starts with four default rules, which define what to split text on: - Double new lines - New lines - Spaces While these rules use a word to identify them for easy understanding, on the backend they are interpreted as individual strings (i.e., `\n\n`, `\n`, `" "`, `""`). There may be cases where none of the separators are present in the document, or there is not enough content to split into the desired chunk size. If this happens, DataRobot applies a "next-best character" fallback rule, moving characters into the next chunk until the chunk fits the defined chunk size. Otherwise, the embedding model would just truncate the chunk if it exceeds the inherent context size. #### Add custom rule You can add up to five custom separators to apply as part of your chunking strategy. This provides a total of nine separators (when considered together with the four defaults). The following applies to custom separators: - Each separator can have a maximum of 20 characters. - There is no "translation logic" that allows use of words as a separator. For example, if you want to chunk on punctuation, you would need to add a separator for each type. - The order of separators matters. To reorder separators, simply click the cell and drag it to the desired location. - To delete separators, whether in fine-tuning your chunking strategy or to free space for additional separators, click the trashcan icon. You cannot delete the default separators. #### Use regular expressions Select Interpret separators as regular expressions to allow regular expressions in separators. It is important to understand that with this feature activated, all separators are treated as regex. This means, for example, that adding "." matches and splits on every character. If you instead want to split on "dots," you must escape the expression (i.e., "`\.`"). This rule applies to all separators, both custom and predefined (which are configured to act this way). ### Chunking parameters Chunking parameters further define the output of the vector database. The default values for chunking parameters are dependent on the embedding model. #### Chunk overlap Overlapping refers to the practice of allowing adjacent chunks to share some amount of data. The Chunk overlap parameter specifies the percentage of overlapping tokens between consecutive chunks. Overlap is useful for maintaining context continuity between chunks when processing the text with language models, at the cost of producing more chunks and increasing the size of the vector database. #### Retrieval limits The value you set for Top K (nearest neighbors) instructs the LLM on how many relevant chunks to retrieve from the vector database. Chunk selection is based on [similarity scores](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#rouge-scores). Consider: - Larger values provide more comprehensive coverage but also require more processing overhead and may include less relevant results. - Smaller values provide more focused results and faster processing, but may miss relevant information. Max tokens specifies: - The maximum size (in tokens) of each text chunk extracted from the dataset when building the vector database. - The length of the text that is used to create embeddings. - The size of the citations used in RAG operations. ## Save the vector database Once the configuration is complete, click Create vector database to make the database [available in the playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#set-the-configuration). ## Manage vector databases The Vector databases tile lists all the vector databases and [deployed embedding models](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#add-a-deployed-embedding-model) associated with a Use Case. Vector database entries include information on the versions derived from the parent; see the section on [versioning](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html) for detailed information on vector database versioning. You can view all vector databases (and associated versions) for a Use Case from the Vector database tab within the Use Case. For external vector databases, you can see only the source type. Because these vector databases aren't managed by DataRobot, other data is not available for reporting. Click on any entry in the Vector databases tile listing to open a new modal where you can view an expanded view of that database's configuration and related items. You can: | | Description | | --- | --- | | (1) | Select a different vector database to explore from the dropdown in the breadcrumbs. | | (2) | Select a different version of the vector database to explore from the dropdown. When you click a version, the details and reported assets (Related items) update to those associated with the specific version. Learn more about versioning. | | (3) | Execute a variety of vector database actions. | | (4) | View the versioning history. When you click a version, the details and reported assets (Related items) update to those associated with the specific version. Learn more about versioning. | | (5) | View items associated with the vector database, such as the related Use Case and LLM blueprints and deployed and registered models that use the vector database. Click on an entity to open it in the corresponding Console tab. | | (6) | Create a playground that uses the selected database. | ### Vector database actions The actions dropdown allows you to apply an action to the vector database you are viewing. The actions available are slightly different depending on the vector database type and where you are accessing the menu from. - Use theActions menufrom the vector database listing accessed fromVector databasestile. - Open a vector database from theVector databasestile—a modal displays an expanded view of that database's configuration. A menu button,Vector database actions, provides access to a set of actions. From the Actions menu, you can: **DataRobot-hosted:** [https://docs.datarobot.com/en/docs/images/vdb-hosted-actions.png](https://docs.datarobot.com/en/docs/images/vdb-hosted-actions.png) Action Description Export latest vector database version to Data Registry Exports the most recent versions of the selected vector database to the Data Registry for re-use in a different Use Case. Create playground from latest version Opens a new playground with the vector database loaded into the LLM configuration. Create new vector database version Creates a new version of the vector database that is based on the version that is currently selected. Edit vector database info Provides an input box for changing the vector database name. Send to the workshop Sends the vector database to the workshop for modification and deployment. For more information, see register and deploy vector databases . Deploy this version Deploys the latest version of the vector database to the selected prediction environment. For more information, see register and deploy vector databases . Delete vector database and all versions Deletes the parent vector database and all versions. Because the vector databases used by deployments are snapshots, deleting a vector database in a Use Case does not affect the deployments using that vector database. The deployment uses an independent snapshot of the vector database. **Connected:** [https://docs.datarobot.com/en/docs/images/vdb-connected-actions.png](https://docs.datarobot.com/en/docs/images/vdb-connected-actions.png) Action Description Add data Appends a selected data source to the current vector database source. Data is added from within DataRobot and then written back to the provider. Create playground Opens a new playground with the vector database loaded into the LLM configuration. Edit vector database info Provides an input box for changing the vector database name. Send to the workshop Sends the vector database to the workshop for modification and deployment. For more information, see register and deploy vector databases . Deploy vector database Deploys the updated vector database to the selected prediction environment. For more information, see register and deploy vector databases . Delete vector database Deletes the vector database instance. Because the vector databases used by deployments are snapshots, deleting a vector database in a Use Case does not affect the deployments using that vector database. The deployment uses an independent snapshot of the vector database. From the Vector database actions dropdown menu, you can: **DataRobot-hosted actions dropdown:** [https://docs.datarobot.com/en/docs/images/vdb-hosted-menu.png](https://docs.datarobot.com/en/docs/images/vdb-hosted-menu.png) Action Description Create playground using this version Opens a new playground with the vector database loaded into the LLM configuration. Create new version from this version Creates a new version of the vector database that is based on the version that is currently selected. Export this version to Data Registry Exports the current vector database version to Data Registry for re-use in a different Use Case. Send to the workshop Sends the vector database to the workshop for modification and deployment. For more information, see register and deploy vector databases . Deploy this version Deploys the latest version of the vector database to the selected prediction environment. For more information, see register and deploy vector databases . Delete vector database Deletes the parent vector database and all versions. Because the vector databases used by deployments are snapshots, deleting a vector database in a Use Case does not affect the deployments using that vector database. The deployment uses an independent snapshot of the vector database. **Connected actions dropdown:** [https://docs.datarobot.com/en/docs/images/vdb-connected-menu.png](https://docs.datarobot.com/en/docs/images/vdb-connected-menu.png) Action Description Create playground using this version Opens a new playground with the vector database loaded into the LLM configuration. Add data Appends a selected data source to the current vector database source. Data is added from within DataRobot and then written back to the provider. Export this version to Data Registry Exports the latest vector database version to Data Registry. It can then be used in different Use Case playgrounds. Send to the workshop Sends the vector database to the workshop for modification and deployment. For more information, see register and deploy vector databases . Deploy vector database Deploys the vector database to the selected prediction environment. For more information, see register and deploy vector databases . Edit authentication Provides a modal where you can change the display name for saved credentials or add new authentication credentials, which are then stored in the credential management system . See the provider-specific credential information above . Delete vector database Deletes the vector database. Deleting a vector database in a Use Case does not affect the deployments using that vector database because the deployment uses an independent snapshot of the vector database. For additional information, see also: - Versioning DataRobot-hosted vector databases . - Updating connected vector databases . ### Vector database details The details section of the vector database expanded view reports information for the selected version, whether you selected the version from the dropdown or the right-hand panel. - Basic vector database metadata: ID, creator and creation date, data source name and size. - Chunking configuration settings: Embedding column and chunking method and settings. - Metadata columns: Names of columns from the data source, which can later be used for metadata filtering . Use this area to quickly compare versions to see how configuration changes impact chunking results. For example, notice how the size and number of chunks changes between the parent version that uses the DataRobot English language documentation: And with the addition of the Japanese language documentation: --- # Update resident vector databases URL: https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html > Use versioning to modify DataRobot-hosted resident vector databases for tracking and fine-tuning. # Update resident vector databases > [!NOTE] Note > Versioning—creating a complete, new vector database—is available for resident DataRobot-hosted (FAISS-based) vector databases. Although versioning is not available for external, connected vector databases, you can add data to ("hydrate") a connected vector database in place without creating a new version. This page describes the ability to version resident (FAISS-based) vector databases—creating child, related entities based on a single parent—brings a host of benefits to agentic solution building. For Pinecone, Elasticsearch, Milvus, or PostgreSQL (pgvector), see the section on [updating connected vector databases](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/update-connected-vdbs.html). Versioning uses metadata in the lineage process to help assess and compare results with previous versions. With versioning you can: - Update the data in your vector database, ensuring the most up-to-date data is available to ground LLM responses. - Create new versions, creating a full vector database lineage, but also select previous versions. This allows you to "update" older versions that are used by downstream assets and to roll back to previous versions, if needed. - Apply "tried and true" chunking and/or embedding parameters from existing vector databases to new data. - Use the dataset's metadata during retrieval, allowing you to more effectively search for chunks in dataset. Versions related to a single parent vector database are displayed in a collapsible right panel. Click any version to update the details and related items to reflect information for the selected version. ## Create a version You can create a vector database version from any parent or child version on which you are an owner—you do not need to have been the vector database creator. (You do have to be the creator to delete a vector database or version, as described in the [considerations](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html#sharing-and-permissions).) On the vector database details screen, use the selector to create a new version from the parent or the Vector database actions dropdown to create from a selected version. **From the parent (1):** From the dropdown that shows the displayed version, click Create new version to create a version based on the parent version: [https://docs.datarobot.com/en/docs/images/vdb-v3.png](https://docs.datarobot.com/en/docs/images/vdb-v3.png) **From selected (2):** From the Vector database actions dropdown, click Create new version from this version to modify the selected version to create a new version. [https://docs.datarobot.com/en/docs/images/vdb-v4.png](https://docs.datarobot.com/en/docs/images/vdb-v4.png) In either case, a version creation window opens with fields dependent on your update selection method—adding or replacing data. | Fields | Description | | --- | --- | | Update vector database version | Select the data and chunking settings for the new child version. | | Current vector database configuration | When adding data. Review the configuration of the vector database version from which the new version is created. | | Test chunking | When replacing data. Set whether to use chunking, and if so, the chunking configuration. | | Related items | When related items are connected to the source of the new version, manage which assets are updated to use the newly created version. | Choose how to update the vector database. You can either [add data](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html#update-vector-database-version-add) to the existing source data or [replace the data source](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html#update-vector-database-version-replace) with entirely new data. ### Select a data source Whether adding or appending data, you select the data source using the Select data dropdown. You can select any data that is associated with the Use Case. If the data you need is not already associated with the Use Case, use the Add data option to open the Data Registry and add a new registered dataset. Any data you add from the Data Registry is handled according to your selection and added to the Use Case, where it can be used in other vector databases. ### Update vector database version: Add Use the fields in this section to select the changes you want made for the new version. The new version is named, by default, `VX`. This name increments by one from the last version created in this vector database lineage. That does not mean that versions can only be built from the immediately previous version. For example, if you have `Parent-vdb`, `V1`, `V2`, and `V3`, and you create a new version from `V2`, that version will be named `V4`, regardless of its basis. If you click the Add data radio button, whichever data source you select is appended to the existing data in the vector database. #### Current vector database configuration When adding new data, the middle section of the window reports the configuration of the vector database this version is built from. This is the same information provided in the Details section on the vector database listing. Note that when selecting the Add data method, you cannot change the chunking configuration. Chunking of the new data uses the same chunking rules as those applied to the data you are appending to. The output reports: - Basic vector database metadata: ID, creator and creation date, data source name and size. - Chunking configuration settings: Embedding column and chunking method and settings. - Metadata columns: Names of columns from the data source, which can later be used for metadata filtering . ### Update vector database version: Replace Choose Replace data and change chunking to replace the data source completely and, optionally, modify the vector database configuration. You are prompted to select the replacement data. When replacing the data source entirely, both the embedding model and the chunking configuration can be changed. Fundamentally, this method rebuilds the vector database but provides you a starting point from an existing version. You may want to do this, for example, to test prompting strategies or to maintain deployed assets. After selecting the data source, configure the [chunking strategy](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#chunking-settings). ### Related items > [!NOTE] Note > The Related items information is only visible if the configuration has associated deployments, custom models, or registered models. The help text under Related items indicates the number of assets related to source vector database that you are creating a new version from ("There are 3 assets connected to this vector database.") Use the radio buttons to set the update method for all assets connected to the source of the new version, either manually or automatically. **Manual update:** When you select to update manually, after saving the new version you are taken to the [vector database details page](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#manage-vector-databases). From there, navigate to the LLM blueprint in the playground and manually [export](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/deploy-llm.html) it to the workshop. Then, register the custom model that uses the new vector database and do a [model replacement](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-overview/nxt-deployment-actions.html#replace-deployed-models) for the deployments that should use the new newly created model package. [https://docs.datarobot.com/en/docs/images/vdb-v10.png](https://docs.datarobot.com/en/docs/images/vdb-v10.png) **Automatic update:** When using the automatic update option, you are prompted to choose exactly which assets you would like DataRobot to update with the new vector database version. [https://docs.datarobot.com/en/docs/images/vdb-v11.png](https://docs.datarobot.com/en/docs/images/vdb-v11.png) Select either: Update all related LLM blueprints to swap the new version into each related LLM blueprint configuration. Update all related deployment assets to swap the new version into all related LLM blueprints, deployments, and custom model and registered model versions that are used by the deployment. ## Comparing versions Use the [Details](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#vector-database-details) section to compare vector database versions and see the results of changes implemented. > [!NOTE] Check back soon > The documentation, like the application, is "continuous deployment." This section will soon be expanded to contain more descriptions, examples, and images. ## Create a playground Use the Vector database actions dropdown to create a playground from the selected version of a vector database. A new playground opens, ready for [configuration](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#set-the-configuration). The playground opens to the Vector database tab, with the version and chunking information preloaded. > [!NOTE] Note > Although this page was reached from within the vector database details page, you are creating a brand new playground. There is no LLM selected, so be sure to set the LLM blueprint in the LLM tab and also consider your prompting strategy. From the Vector database tab you can modify settings and even create a new vector database. See details on [creating vector databases in a playground](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html). Once the vector database is configured and saved you can send text queries. --- # Apache Airflow URL: https://docs.datarobot.com/en/docs/api/code-first-tools/apache-airflow.html > How to use the DataRobot Provider for Apache Airflow to implement a basic DAG orchestrating an end-to-end DataRobot AI pipeline. # DataRobot provider for Apache Airflow The combined capabilities of [DataRobot MLOps](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html) and [Apache Airflow](https://airflow.apache.org/docs/) provide a reliable solution for retraining and redeploying your models. For example, you can retrain and redeploy your models on a schedule, on model performance degradation, or using a sensor that triggers the pipeline in the presence of new data. This quickstart guide on the DataRobot provider for Apache Airflow illustrates the setup and configuration process by implementing a basic [Apache Airflow DAG (Directed Acyclic Graph)](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html) to orchestrate an end-to-end DataRobot AI pipeline. This pipeline includes creating a project, training models, deploying a model, scoring predictions, and returning target and feature drift data. In addition, this guide shows you how to import [example DAG files](https://github.com/datarobot/airflow-provider-datarobot/tree/main/datarobot_provider/example_dags) from the `airflow-provider-datarobot` repository so that you can quickly implement a variety of DataRobot pipelines. The DataRobot provider for Apache Airflow is a Python package built from [source code available in a public GitHub repository](https://github.com/datarobot/airflow-provider-datarobot) and [published in PyPI (The Python Package Index)](https://pypi.org/project/airflow-provider-datarobot/). It is also [listed in the Astronomer Registry](https://registry.astronomer.io/providers/datarobot/versions/latest). For more information on using and developing provider packages, see the [Apache Airflow documentation](https://airflow.apache.org/docs/apache-airflow-providers/index.html). The integration uses [the DataRobot Python API Client](https://pypi.org/project/datarobot/), which communicates with DataRobot instances via REST API. For more information, see [the DataRobot Python package documentation](https://datarobot-public-api-client.readthedocs-hosted.com/en/latest-release/). ## Install the prerequisites The DataRobot provider for Apache Airflow requires an environment with the following dependencies installed: - Apache Airflow>=2.6.0, <3.0 - DataRobot Python API Client>=3.8.0rc1 To install the DataRobot provider, you can run the following command: ``` pip install airflow-provider-datarobot ``` Before you start the tutorial, install the [Astronomer command line interface (CLI) tool](https://github.com/astronomer/astro-cli#readme) to manage your local Airflow instance: **MacOS:** First, install Docker Desktop for [MacOS](https://docs.docker.com/desktop/install/mac-install/). Then, run the following command: ``` brew install astro ``` **Linux:** First, install Docker Desktop for [Linux](https://docs.docker.com/desktop/install/linux-install/). Then, run the following command: ``` curl -sSL https://install.astronomer.io | sudo bash ``` **Windows:** First, install Docker Desktop for [Windows](https://docs.docker.com/desktop/install/windows-install/). Then, see the [Astro CLI README](https://github.com/astronomer/astro-cli#windows). Next, install [pyenv](https://github.com/pyenv/pyenv#simple-python-version-management-pyenv) or another Python version manager. ## Initialize a local Airflow project After you complete the installation prerequisites, you can create a new directory and initialize a local Airflow project there with [AstroCLI](https://github.com/astronomer/astro-cli#get-started): 1. Create a new directory and navigate to it: mkdirairflow-provider-datarobot&&cdairflow-provider-datarobot 2. Run the following command within the new directory, initializing a new project with the required files: astrodevinit 3. Navigate to therequirements.txtfile and add the following content: airflow-provider-datarobot 4. Run the following command to start a local Airflow instance in a Docker container: astrodevstart 5. Once the installation is complete and the web server starts (after approximately one minute), you should be able to access Airflow athttp://localhost:8080/. 6. Sign in to Airflow. The AirflowDAGspage appears. ## Load example DAGs into Airflow The example DAGs don't appear on the DAGs page by default. To make the DataRobot provider for Apache Airflow's example DAGs available: 1. Download the DAG files from theairflow-provider-datarobotrepository. 2. Copy thedatarobot_pipeline_dag.pyAirflow DAG(or the entiredatarobot_provider/example_dagsdirectory) to your project. 3. Wait a minute or two and refresh the page. The example DAGs appear on theDAGspage, including thedatarobot_pipelineDAG: ## Create a connection from Airflow to DataRobot The next step is to create a connection from Airflow to DataRobot: 1. ClickAdmin > Connectionstoadd an Airflow connection. 2. On theList Connectionpage, click+ Add a new record. 3. In theAdd Connectiondialog box, configure the following fields: FieldDescriptionConnection Iddatarobot_default(this name is used by default in all operators)Connection TypeDataRobotAPI KeyA DataRobot API token (locate or create an API key inAPI keys and tools)DataRobot endpoint URLhttps://app.datarobot.com/api/v2by default 4. ClickTestto establish a test connection between Airflow and DataRobot. 5. When the connection test is successful, clickSave. ## Configure the DataRobot pipeline DAG The [datarobot_pipeline Airflow DAG](https://github.com/datarobot/airflow-provider-datarobot/blob/main/datarobot_provider/example_dags/datarobot_pipeline_dag.py) contains operators and sensors that automate the DataRobot pipeline steps. Each operator initiates a specific job, and each sensor waits for a predetermined action to complete: | Operator | Job | | --- | --- | | CreateProjectOperator | Creates a DataRobot project and returns its ID | | TrainModelsOperator | Triggers DataRobot Autopilot to train models | | DeployModelOperator | Deploys a specified model and returns the deployment ID | | DeployRecommendedModelOperator | Deploys a recommended model and returns the deployment ID | | ScorePredictionsOperator | Scores predictions against the deployment and returns a batch prediction job ID | | AutopilotCompleteSensor | Senses if Autopilot completed | | ScoringCompleteSensor | Senses if batch scoring completed | | GetTargetDriftOperator | Returns the target drift from a deployment | | GetFeatureDriftOperator | Returns the feature drift from a deployment | > [!NOTE] Note > This example pipeline doesn't use every available operator or sensor; for more information, see the [Operators](https://github.com/datarobot/airflow-provider-datarobot/tree/main#operators) and [Sensors](https://github.com/datarobot/airflow-provider-datarobot/tree/main#sensors) documentation in the project `README`. Each operator in the DataRobot pipeline requires specific parameters. You define these parameters in a configuration JSON file and provide the JSON when running the DAG. ``` { "training_data": "local-path-to-training-data-or-s3-presigned-url-", "project_name": "Project created from Airflow", "autopilot_settings": { "target": "readmitted", "mode": "quick", "max_wait": 3600 }, "deployment_label": "Deployment created from Airflow", "score_settings": {} } ``` The parameters from `autopilot_settings` are passed directly into the [Project.set_target()](https://datarobot-public-api-client.readthedocs-hosted.com/en/v2.28.0/autodoc/api_reference.html#datarobot.models.Project.set_target) method; you can set any parameter available in this method through the configuration JSON file. Values in the `training_data` and `score_settings` depend on the intake/output type. The parameters from `score_settings` are passed directly into the [BatchPredictionJob.score()](https://datarobot-public-api-client.readthedocs-hosted.com/en/v2.28.0/autodoc/api_reference.html#datarobot.models.BatchPredictionJob.score) method; you can set any parameter available in this method through the configuration JSON file. For example, see the local file intake/output and Amazon AWS S3 intake/output JSON configuration samples below: **Local file example:** Define `training_data` For local file intake, you should provide the local path to the `training_data`: 1 2 3 4 5 6 7 8 9 10 11 Define `score_settings` For the scoring `intake_settings` and `output_settings`, define the `type` and provide the local `path` to the intake and output data locations: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 > [!NOTE] Note > When using the Astro CLI tool to run Airflow, you can place local input files in the `include/` directory. This location is accessible to the Airflow application inside the Docker container. **Amazon AWS S3 example:** Define `training_data` For Amazon AWS S3 intake, you can generate a pre-signed URL for the training data file on S3: In the S3 bucket, click the CSV file. Click Object Actions at the top-right corner of the screen and click Share with a pre-signed URL . Set the expiration time interval and click Create presigned URL . The URL is saved to your clipboard. Paste the URL in the JSON configuration file as the training_data value: 1 2 3 4 5 6 7 8 9 10 11 12 Define `datarobot_aws_credentials` and `score_settings` For scoring data on Amazon AWS S3, you can add your DataRobot AWS credentials to Airflow: Click Admin > Connections to add an Airflow connection . On the List Connection page, click + Add a new record . In the Connection Type list, click DataRobot AWS Credentials . Define a Connection Id and enter your Amazon AWS S3 credentials. Click Test to establish a test connection between Airflow and Amazon AWS S3. When the connection test is successful, click Save . You return to the List Connections page, where you should copy the Conn Id . You can now add the Connection Id / Conn Id value (represented by `connection-id` in this example) to the `datarobot_aws_credentials` field when you [run the DAG](https://docs.datarobot.com/en/docs/api/code-first-tools/apache-airflow.html#run-the-datarobot-pipeline-dag). For the scoring `intake_settings` and `output_settings`, define the `type` and provide the `url` for the AWS S3 intake and output data locations: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 > [!NOTE] Note > Because this pipeline creates a deployment, the output of the deployment creation step provides the `deployment_id` required for scoring. ## Run the DataRobot pipeline DAG After completing the setup steps above, you can run a DataRobot provider DAG in Airflow using the configuration JSON you assembled: 1. On the AirflowDAGspage, locate the DAG pipeline you want to run. 2. Click the run icon for that DAG and clickTrigger DAG w/ config. 3. On theDAG conf parameterspage, enter the JSON configuration data required by the DAG. In this example, the JSON you assembled in the previous step. 4. SelectUnpause DAG when triggered, and then clickTrigger. The DAG starts running: > [!NOTE] Note > While running Airflow in a Docker container (e.g., using the Astro CLI tool), expect the predictions file created inside the container. To make the predictions available in the host machine, specify the output location in the `include/` directory. --- # Feature selection notebook URL: https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-adv-feat.html # Feature selection notebook ## Setup ### Import libraries ``` import datarobot as dr ``` ``` from datarobot_bp_workshop import Workshop, Visualize ``` ``` with open('../api.token', 'r') as f: token = f.read() dr.Client(token=token, endpoint='https://app.datarobot.com/api/v2') ``` ## Initialize ``` w = Workshop() ``` ## Feature selection workflow Note that this section is project-specific. ``` w = Workshop(project_id='5eb9656901f6bb026828f14e') ``` ``` w.Features.Accident_Last2 ``` ``` Single Column Converter: 'Accident_Last2' (SCPICK) Input Summary: Categorical Data Output Method: TaskOutputMethod.TRANSFORM Task Parameters: column_name (cn) = '4163636964656e745f4c61737432' ``` ``` w.Feature('Insurance_Duration') ``` ``` Single Column Converter: 'Insurance_Duration' (SCPICK) Input Summary: Categorical Data Output Method: TaskOutputMethod.TRANSFORM Task Parameters: column_name (cn) = '496e737572616e63655f4475726174696f6e' ``` ``` pni = w.Tasks.PNI2(w.Features.Age) rdt = w.Tasks.RDT5(pni) binning = w.Tasks.BINNING(pni) keras = w.Tasks.KERASC(rdt, binning) keras.set_task_parameters_by_name(learning_rate=0.123) keras_blueprint = w.BlueprintGraph(keras, name='A blueprint I made with the Python API') ``` ``` source_code = keras_blueprint.to_source_code(to_stdout=True) ``` ``` w = Workshop(project_id='5eb9656901f6bb026828f14e') age = w.Features.Age pni2 = w.Tasks.PNI2(age) binning = w.Tasks.BINNING(pni2) rdt5 = w.Tasks.RDT5(pni2) kerasc = w.Tasks.KERASC(binning, rdt5) kerasc.set_task_parameters(learning_rate=0.123) kerasc_blueprint = w.BlueprintGraph(kerasc, name='A blueprint I made with the Python API') ``` ``` exec(compile(source_code, 'blueprint', 'exec'), locals()) ``` ``` kerasc_blueprint.show() ``` ![No description has been 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SKJoihAiFwjlz5kyZPPn0af8mDR4AoHlCngsAAADg3cDlcvX09IRCoXkLc3Mz8xYtzCtegzIzMxMIBIQQqVRaUFBQ8bpT6J07aWnp6enpObk5GekZLSxa/LFtW7UCZTIZh8NJfPRon69vxIMIQoiXt5cKdgwA3iAWi9PT09PT09/8U7t27bZs+b22FSmKogjR1dX18ZlS8QmHi4c+AHiP4JIHAAAA0LzwuDwjY6NX4zgq8lnm5uYmJiYcDocQIpFKsrNeTuXz6NGjinxWenq6SCSqbRZqUYao6j+lUhmXy3n06NG+fb4RERFvY68AQEl0dXXl/JVhGMKyNIcjFosrRnRqaAjeVmgAAKqHPBcAAACA6vXt09e7h5eJmYmpqZmhgUHFh8XFxZmiTFFmxvMXL+7dC8/MzBCJMkUZGXn5+fUtv6SkpKy0VENTs2IMV1RU5IEDB+Li4pW9HwDQ5ExMjBmGoWn61SdSqZTDoSmKLiwoiI9PiHr4MCY2JiE+4dSpk4SQ4qJi1QULAPC2Ic8FAAAAoHrW1tZPnjyJiY4JuB6QnpEhEolEmZnFRcqcDD4zK8vS0vL+/fADBw4lJibKX3jIoIEe7m5K3DoAKIuRkTFFUzKZlMPhsiybnJzy4MGD2NjY6JiYrMxMVUcHAKBiyHMBAAAAqN7efXuDg4KbdBPnz51/+DD68ZPHiixsa2vTpMEAQINpa2tHRUZFRT2MjY2Ji4svLS1VdUQAAM0I8lwAAAAA74VTp0+rOgQAUILdu3erOgQAgOYLeS4AAAAAeCk4KHhY0HBVRwEAAADQQHTdiwBA88JpNXbd6Usnlru//Ty1CjcNdeL1XXfrScyF5c5voXXe5rZAtXDWNxBl2HvV4TNB6/urOhAAAFA6Nb450hYj1p685L/Gi6eM0t52RXHMus/ftP/67bDEmPtRN7ZNsUK6Q2UogeDTQUZHuwv4qtg6Gh6UQuDy2b/h985tGGhEqTqU5kf5laNr1bGjlYEGpYLKVuGmoU5SmYyt+D/12laz18hzvLlfP3HWNwylYdHRsY2Jlvo9AgEAgBrfHCltyw4OVoYaStqzpqyoN3pQfIfPd2z7aqRLa6EGl8PXMaLFBSzHdvrBm6E3t461Rubj7aI4HFsjrpBb0fZUpy7CM5OMl1rTb+ecUUJra3h8fvTs5Xt3w+Jjoh5F3b0fdPbUrg0rZva31+coXAbH4cPtF676LrBTfBX1pz1ia1zcvTNfuxtUPxZ4PX8IehxzdmnnZlRdFEVRFP2WDtvmSKNVn9kb//a7fTcsMTr84a2L/ns2fDOhi2HFad3cKofWsx88b/3OI4G3Q+NjHkTfvnhm78blPp6WAlUH1jDvxu68hascW1RYxLLFBYVVN9tq9uGwJ3Ehu8aaKPUArGlb6oRjs/BExJPIIwvtavwyU9tz5flHceG7R+tW/LuR53iTXSK0B2wMehx3z3eSWQ03e57T8kuRT6J8P7Rsdv0+7RFb4+Ij/D9pp6KbHPokAPDeqenCy201clPgw6ioo4s93ngaeXtRvTtPQ68oKex352akuq54tR6UoNvEyXZ8ceKRT4d72Xfs4thv1YUCllAUTVE0p/k8iamewFznjxHGpyeaXvMxC5hiemG88d8D9D+1F1g28XdzlGLpJ9sOBvtGG04zbNS2lLArHBMbRxuLl4cxR8vAtLWBaevO3sM+/DjSd+XXP11JkdZdBm3QqqNti1w+Dr9qKE2HWZu3pM2YfeCxWNWxyFUe9vtE599VHYXKcFpN2HxsTU/jygso18iyU4+WttwHvscjCNu8KofS6zz751+X9DTnVp5ufKGlg6dlRyft+HMhyeXv2PCcd2d33sJVjklPEzGS/NRM2auPNN2mTe+iQVGCXh9+0PH01mgFLscN3pZaoY3NTGhK0GH24uHH5vulM6/9kWM7eckEKw4lMzQWckihrLEXwKa7RBTfPBeQM2K0+/ABFkcPJL++FwKXYUMt6fK75y+kMrWs/t5CnwQAgNNi0Jq9Pw42Stw/d+5vIXmq61C9M09Dr1NC2O/GzaiurniTbrxaD4o2s2mnT5UH7fn9bGIeS0i5KJsQQhL2Te6+r0njeOdwNLn2+pyXrxNSlLYGx0aDY2OmMbJ96fdXCgJLmmKb7MOInGERiixJ6evyWmszjXzbUVnf4kpj/pjQsWOnth1dHHsMHfPJD7uCU6QGXWb+umOFp27zPjeVxtjYeO3atf3699PS0lJeqayM1fP65rdl3fXfk2p8O4xNTNauXdO3X1/lNBbXacZ8LyMiuvbLvIE93O0cnB29R37wxS+/7L2R0dyeH+kWY9f/sbSXGZV9f//3nwzr42HfyaWz98hJizbt3ed/M7/5ZIUUo2a7owhKt8eSo+EhR5d313vjmsBkJSUXiF68KKv8gDYbOWtEy5KbB08+p2wmzO795ioN9sa2VG3hwgVTpkyxtLRUTnECYzN9SpxXwPGeO8dF47U/UQYDP57hWJ6Xx1BCoaGcKqUFuiZmJoZvvLZW2+dNoeTOuUsihu88bJh1tW+DNboN79+CLrtz5kq6Ki5Tb7MSAACgnmiTXsv2bhxp/vzop3N/vpnbhB0qBW4H7+jT0Dsadj01TVe8oT0oSqDBp9jSrKxi9XsGWLVy5dixY4xNTJRYZmJkdv+DGT0PZgw4mjXreuGZHJavp7moM1+j7lXfAUp7W4GRlItlLCsrL8pKenDt8I+zJ87cE1fGaz116TR7DiGEUEa9l+/zCwq5mxATGR927dyOb8baab922nPaf3Yq8ml89NP46MdBq7rzFFur2aBo2tXV5YvFi//55/CKlSu6d+/O5zV+9j5pxP6t57NbTdu4ZrRFbWNWeT1W33gc47fY/tUClP6Y7fHx9/eOF1a8NmfUY+7mnQcvXA2MjHjwKCYs/MqBzbM87FzHfbPZ9+rN0PjosPBL+9b7OFYdW0tp2wxfvNnv6q3YqLDwK4e2LOhlyass3GnSt7/u9r8UEBUZ8SgqJOTMmiFCju0nRxNjgzd4V9llTev+n/z0z/mAh1H3o0OvXdq/enSr5jLslqaIq6vrl198cfjwoRUrV3h292xUY2lZtjLiMM/PbNkdnJhVLJaKi0SP75z9+3/X0hhCCKlWOdWa48HDm6cPrJnsXO15WcOyz5zvD5y5HhkZmRgZeu+q35E/v5/z5vDnSrW31+uRdv/4q95CknV9+eSZ3x4IjEktLJeUF4oeh57fu/bXCy8fdzVbD1yw4ejFoOio8KhAv72rfdxN3tqxp1jlKG135GyO23XZxUdxIX8M0a1Sy0aTdoc9ifp7egtabp3XeI5QhNRylatXOSYdBg6zMzS0HzHA/s2bvDhgRdeBm8IrB21x2o2e2kNTdMF3w59HI6TCgT5DW1S75Nd1mMk5rqptS+UsLa18fKbs2PHX9u3bx44dY2xs3JjSaKGxEc2Izv25/1GLiZ+MsKhSb1zbD+YPFNzeviuknDY0NqAJeeMcJ7Sw67zfTtwLux0aeCMs/F7EpU3jLOjaP2/KS0TpvVOX0hhex1HDXn8HUNtzdD9jqujWyStZbP1utXWe9YTIPXJqq5za1fsyIvT8aOOfvueuBEVFRjyKCr178eC2L0c5GrzaikK7UM+ztWLXuvis/PNcQEjcw7Dwywe3zveq6X1RAIBmjhL2+Prv3ya1zjj9xewfr2X+92VIfXs+8m8uCt8OFHkakh9e3f3VBsRfl8aHTQh582bEcVzs/yju7p/DX3VQafMpe+Jjgzb0fPWWIMdx8elHsUEbe2kQUkdPuKYdrxYgbdxz5cWIqPv7P3J8Y3iAQl3xavvboKNCsR4UIYQitOHE/z2oqLGnD/dOb0FTRuP3RUXH/TlKr+46r70Pr2q2du0/+uijvX/v+eXnn4cOHaKnp1v3OnVhGSJhCcuSsnJZYkrJppvFiQwRmvCtKaJrrPmZt+HuUSYXJpvdmGzqN1yv4miieNx+Tvo7RptcmWx6ZrRwtaPAvMpZS2vwRrsZ/D3W9OoU0zOjhKs7842rVF7rTsLrPiZLLap8xOV4ddLfOtLk4mTTyxNN9g/QG/hqtyjuzGFmQVPNgqaaBYzTc6l/h6rJvkpl80O2bjg6aNd02yHD7HfERsuI1KCdS3vLivFnOmYdek/f1MlUMuor/yy5+daGraVSHA6nm5u7p4dHeXl5yO2QgMCg8PAwqbSBj4OylPPLvtRts+fDtb/Miv/wfzENGT1BCzsPGNGrY2Vj8wytnMd8s3tMlSX4rbpOWvmnsHjcxyczGEKIluPC3f9b5KxbcURpWHUZ8ekWJ4vPRq0MyGVpU8/x04a+Kk3XxJRXXvTGNgX2c3bsXtqt8sGCb2bjZKVT2txGNxEul6uExipNS86V0Vb9pg05nnAmqbSOpas1B9E2btfjgxVO7TXHTtuTULFlDfs5O3ctdTesfNtc28iyvZFlW374nj2heTW8Jyavvaoup+E5or8pLb6/e9Px57Xso0bHeTt3LXHXf9lwZu17TV7WvWeXL6cu809twBtq9T32FKgcZe6OvM1FBd7Omj7Orbuj4Pytl2OutV29OgtkscFBIqb+50gtl6z6lpMfd/l84oix5OyVuLoOU4HLxLEdqKd/HQ4pTIo9GDzv557jx9kc3ZpQ2Y51HmaKHlfNi7W11cyZM2bNmpWYmHD9RkDgjYC8/Pz6FkIZCA1pNk90x3dP4JSfPpzlfPqHsHJCCKH0+s2dYp/lP9MvfshsVkNopE0RcbXaoM0nrN+6pJceJS3OziiidIwMTHnl+Uytn5Nq3V/lXiLEYSfPPPb5uP2woQ47EiJfHjSUfs/hfQxJ7plTVyvaUrm3WjlHDlVbJchR78uIkdPgMX1fLc81bu00bG6X/oPcFk379kJjBtnKPSMove4rfbfOtH05b6/A2mmoNSGENO0bGwAASkbru322Z8tUu9yLX8/+7nxalVtKA3o+mrXfXGq9J9ZAoaehRnVa6h+/ApombFl8SGjm3AldnO15Z+5KCCFE07lrRx6t5eTclhMYKyOE0MZOTlZ0aeDN++V19oTreqyj9N0+2/XrJIv4/320YE9U9dfZFOiKv0lOl6OxPSjFKKsP/3ZV9C4oirK3t29v137+/PlRUVGXr1y5fet2aWldT58Kb+NVCsrIXHNMK15lPVBCLSKWEMLlzehr+KEJVVF1Ah1evy4GHbXz5oSU5xNC8fkL+xuMN3j5iwN8XV4fXUIIqfW9XQ73gz6Gn5jRLw9ODtXKmKOlvG/Qm/KrxtKIG3fyGbplB1ttQghbeHPT9NGebq42HTp38Bg+a1dkmVGfcb2rfEMtS9gyqnMbO4c2dg7tvL+/JSEKrdUscbgciqI0NDS8e3p9++2qQ4cPffrppx0dOlIN+aUJtihs26Jfwxin+b8udmv4W6BswfllA7t07mzj6DVsxblkGcvk3d22cLynq1N7lwFTt4cVEINeY/ua0oQQTvvpqxY6aYkCtswa2sPewbXb+JXHnsgsRy/0sam8mrCFl78b3tXZyaazp/eE3+9IqldAG59VX7jrlyWcXDltiEtnpw5ufQdP33CxWWYnldBYkrA924KzqFbjfva7dnDtx4PshXUmkCubo51DNy+f9ZfTGe0uPj4uFV8lcNpN/fZLdwPxkzPfTRvs5NjZxrGb17cBJbVWngLt9XJBq47tdWjZ08DglFpSVhybaasWu+mVxR5bMrGvQydn54Fz1l1LoyyGrF4yoOFnXT2OvTorpwl2p5bNld8PvJVPhJ7ejpWtqeni7aHDPA4Ofi5r6DlS/SpX/3LEBcHrx7m4j/vhZoH804nS854y3EIWceJorJSw2RePXM2i20+Y+OodvDoPM4WPq2aGoigOh0tRVHtbuzmzZx84eGDdup/69uurqampeCG0gdCAYgvzC0QX9h5LaTn+w4EV30dxrMfMHqATdfBASFFBfiFLGwqN3riLUrrdBnbTZR7uGOPp2bVnX1dXd48xPweX1Pp5zZR3iZDF+R+PEtOth4xyqpzogDLsN9JLn804d+JmxQ8JKPVWK+/IqV8l1FQhil9Gzi3p6+Dg2K6Th9ekb/53L5fXatQPS/rVY5fqd7ZyO320dLoNv+DB/kXj+zp0cu7S12fRrrtZze7LHQAAOSiBrc+2bbMdxSE/zFtx8rXMRUN6PnJuLvW8HdT5NKSMTkt94lewRCWE/cYDsjjydmgRbeLatW3FIjwHDxctitBturq8HESs7ezhwJM8DLlTRCvWE67tsY7Wd12we/ssmyTfj+dtDX2z41l3V7ymSqn/UVG/o4XJPTLbqaLG2nSa6ZtW7U7c+OdcVaMITdMURXXq1GnxokX//PvPmtWrvby9eA19OYmiKG0NToeWWl9317ahSV6W5PnLpmaD72SP/EfU+3DmxPPFETLS1l53ugmVnVK0xD+z3yHR6PMF5/NZ87baowwIIcSuo+5YA6ooq2Tt+cyBh0RDTuasjRbn1P64YtVeb7YZXZ5X+svlrOGHRf2PZM64Uhj4Kh3MSveezfA+kOF9IKPX8YLw+neomnRIvTQnJ5+laC0dLZoQQlEGjh/8tOf4zTt3I6/7rh5kwSFcsxbGdUTQsLWaDQ6HS1FEW0urf/++mzZuPLDfd+68ufUvRpywf/naa4U2035Y0eAcHysrzBQVlMtk4twYv+0Ho2UUNyvmZmx6kURSnHpz5+7L+SzHqrU1TQjHbsRwe27BlR+/3nn9cV65tEwU5bdmy/Uijm1398qaZ6W5KcnZJRJZeUFqUkb1d6A5bYeP6CSQRG757NuDoc9zyyVlBRkJ9xMym3ePv1pj7ff17T+gv2KrypKOLhr78ZbTMcVGruO+2XIs+PLeH6e5mcmdaqCiORhpUcq9Qz/tfyjlGNnbm9CEEE7bocMc+LK4vxav9A19kS+WycRFmdm1f5WgSHtVoLR1tSnC5Obk1dISHNuRoxz4kqitX649GpFRIhHnzhIwqgAAIABJREFUJd3a+dV3R9JYw94j6/OAWPPO1n3s1Vk5TbE7tW2u5M75wDzKwrtfx4rbnsClbw9D9vGFS49kjT9HXkaopHJqQBkPGDvAoOzW8XPPGUIIKb55/HQyaTl0rLdOxabrOswUP66arZr6AcYmCr3PKNDX16KZosJipvyB74H7/N7TJ9tyCNFwnz7FqeTqrmPPZKSksIilDIQ1vEvMsiwhlIm9u72xBkUIW575NDmPrf3zGinxEiF7fvrEvVLaYvhYj4oXDugWg8Z112aSzh27W9mbUOKtVv6RU69KqKlCFL+MFOXklEgZRlKY8uDMuvmrT2USYb9RvRs8TYr8/eLYDerfmi4P++3LjaeiMkok4oKUB/4HLj1S099pAAA1xbEdNsHTgOREXL1Vbf7whvVY5Nxc6n07kPs0pJROS73iV1QThF1y7/rdEk67bt3MaEIIx7abh3FBdHQK3ambuy5FCBF08XTTlkUH3sqkFOwJ19jVpIQen+/dMbdD8sFP5vxS8xxtdXbFa9SAo6LBnYc3NWHf+22jaZqmaS6H4+zstGzp0n/+Ofz1V1/Vq4T2TkYBU80CfUwvjDfe2Ud3uJCSFZZtjSx/2Ttk2fxiWa6UlcmYjEJZCcXr15rHEZf9cbP4dj4jZtjs7NLfI8tLaK6rGYemeD2tuLRMvCe48HI2U8qwRUWSq/HlSbXVHsXt14bHl0n2BhaczJDly9hyMfM0UyonL1ZfTToFLFco1KdYpqS4hKX0eq7cu2tyK97LE0pgbUUIkdG03AAatlYtvLy9znqfacCKSsHl8gghBoaGo0aOrPhEX1+/HuvLUk98t6Z7x1/Hr1kWELWquJHRyNKSUqWkg4mZAU1KGEIIEacni1jKVEuTIoRn1daSpjUHbQ0dtPX11SwsW9Akq+7yua1sW3OYF6G3njewj79s6VKytGGrKkFFYxkKDd2EXSs+aWVtHRp6V+5K4uTAnZ8H7lvnOnT6hzOm9O06ZcXu/l4/TFlw5HHdwy9lqY+fFbMOOjraFHlVe7duPFLs6wO+/Paq8pYOW1JcSgitb6BPE1FNTcNvZWNJMy/u3HxW5a/F4UH3yyYPbmVjRZMchSKSR/6xV9MKr1VOVU2yO1U3l3rzzPXsEaP797f/OTJaxu88oJcxG/fv2URZXXWuwDnyMkIllfMmuuWIsR6aBVePXKocRSmOOHoyccanfSb0E145lcPWeZgpflwp5uzZpr38FhYW1vYnmqYJITQhXd26vvqQy+XKeUNZV1+XZsXFxWJCmBcnD1yct3nKNM+/txjNGmn+4ug3V/JYQhUXFTN0K7035/ZnC2/5Xc3qM6zXct8rX+YmPXwQFnD6wJ4LicW1fV73Hb1xlwjCZFw4dnWxx7ABY/ptDPLPo9uOGOMmkEb7+T2sqACl3mrlHzlUwyuhakn1vYwQNv/W1XDx6P7W7VrSJK8hu1XHGcEzad2SZpLD71X/3hgA4B0ijTu08ULLmfN7rzjqa7144S/XMyp7UA3osci9udR6r5RzO5DzNFRHeA3qvyrr5qj0sNn8m9ful/V17eVhsP9EvlX37m1K7yzZnrtky+Cerponr4kdvT2ETILvjWQZv3/DO/a0Qf/ZMwiTd+3wwdvZtdza6uyKv6lhR4VSOg8Vmqbv/RbyDJLae60cLpcQoqGh0btP74pPjPkSmiKMYvXDMGypmEnLl0SklJ1MLH9WW++Sw7HSITRXY/VEjdWv/8VUh6ZpuqU2YYokkQrmKWhOaz3CFInDa+2/N1ZT5rk0u/Tupk8zSXGJxUQ4auYYa07unW2rNh4MeZxZyjXut+LkbyPlF0AJ+zdgrdrExcX5nTzZsHUVoaevt+CT+XIWkMlkHA5HJBKZmpoSQvLrOWUMm3Xt+2+Pu+4Yt3pF8KbXX8JlCUOIQEND8a+pGYlYSigej/dqFYlEyhKKepVKrwkl0BQotA2KpilCaitGAX4nT8bFxTV49Trp6+nNn193YxXkF+jp6xFCkp4/V6zg8vQwv41hp3Z0HLv2t5Ujen7+ad9ziy7V/co0KxaLWYqqmGuH5nFpQqRSRXOEireXLCXxaSlr18bDzWR7Ys1TQyq4zf82rtxjr8ZNVK2cqppgd6ptriT03IX00VMGDe68JTrGdfBAM1nE/vNPZMo4R15uTEnlvIFjM2Kck4DmDt1+d2i1TXqPHdTC/3BqnYeZsmNbt359/Veqh0kTJ+rq1jolJ8MwFEVJJJK8/DxTE1NCiPxp+HT1dCm2rKSMJYSwBQF/n0ga7jPzK1bYi/9g3eFIMSGELS0pYykNXV0eIdU6BGzW2eXTiu5PGOLRxcXZ0aVPG9fefezpsQvP1vZ5bp1716hLBCFsfsChs2lDfbw/GNri7DHTiWPtuSW3Dp182e2t7622jrNe/pFTe+XU54ZR78sIISzLsK9uS/W/cNW1XxSHJoTUcJ0CAHiXSEUh274/e/PjzX8tnP6nr8HXH33rnywlpCG9gjpuLg26HdT6NFRXeA247CvxObTBYddWXnbw9fDyHu59PPRPhXn3tJPc+yfwVq5n7sSePbsIAgr6eLcgj09ffSoj/EbclNii8Mthxr179vl2988ls748U9ObiXV3xd/YsYYeFQ3uQb2xX03S927qPAMhZMGCBTw5Hd3/t3ffcU3c/x/AP3eXsIckECIOtODEgQtHXcWqX2fde9S2dlirft21atW2Sqv1Z9Xq9+tqHVW/zrrqwlXQCm7RIEMUZISEhE2A5O5+fwQRIZCEFcTX8+EfPM4b77vP5+4+987nPseyNMPkZGfb2dsTQlRaoSlJrsj7qumPdKb+QMeT0lZpzVDUyzaYyb0dC0YIqrreclWW56Kcu8xcOLoerYs8fyacpb2lUiuSc3HvpsAn+YQQolUpM4p0h+V1Oh1P7OzsXqtdtGvZS5knRZkSHBRc3qWNc5NIyBcGput0WoFAmJGRcfXa1aCg4HBZ+OnTp8q1BT4teP03B/x+H79gTrIdRTJeTucy07N4ul4zb2fqvqoS6oo24XkCxzmf+KTfsisG3nw24S13bcLzBI5u6Ne1ARP2vDxdup48eVKlhSWRGP4ma7HCEovFixctMn/1XLrs+IZDIwcuaOntXZe5EGPe0lp5YgpHN+zo50E/elHGlYdh9KevkfIqKuefSzcz+vfpMn1Wv4tLz5V8jzQ/9mk8R3t2fteTCYt5WXD27Xu0syH5cTHx3Mtr18tNV3rdM09l7U7pcm8fPvl8wvT+H7TfVWdwH0nuzQ2nXrCkfOeIoatcxc81w4Rthw1pZvjqTll3GPZBo0Nb44xVMzPqlUmq9IwmhAweNLjkRJ7jeJ7nCX//3oOrf1+7cf3Gv+fM0ee5yubk5EDxeTkafaXWPjp44NbkJVPH8qlnF/wZrz9c+ZpcnqfsHR0oUvL45L64tnf9tb2EMI7NR67ataJv7/5+dmf+yjY8/bx5u2rqJeK1gG797/iTcV/6jR/VJbXeiIaU6uShvxQFp6xpt1qTz3qjNae0g2PanpSTbWu/VlYkP+F5AkcIMXbhMv9sZVo+jefoRt16e/0aFlnTRvIAADAHl3Z7yxdj5d/vXDV0/R4rwYeLj8fpytFiMX5zKc/toJSnIWPhlaO9aubNsWzlC1tg8AGZEMIlXz59e37XLn17eTn2bcPf+uF6ak5O4PWMkb38O/2Z8X4jPmLzhUi2Yi1hXht9eO5nh+bs2TRp6PcbkpI/+ulWZolDZ6wp/krBgSp/raiUFhSpqrZ3VecZCCGffWpg7COe4whF6Vg2NCQkMPDyvXt3//zzOKlQb5PScWxCNuGsNItPZPxT8sdiShiXRWgnqy7O1BNT3inl2IRsQjtYtXckERnF/o/X8TwhlG3FMlWVNsoKLRAyFCGMlb2rZ9v3xn2z49BvH7ew0T7fH7AnnCWcSqHQEtvOIyZ28HAQUIQWOjjYFImcV8iVPFO37+i+jR0EjI3Iq6OPB2N0qRqN1ekIIbm5ucFB11eu/G7SpMn//c822WNZqUlkU/CZNzas2v/C2cOj6O8RbExYeAZv3f3zrye0c7djaMbG0c2ltLc3TAk94vzFGM51yIqfPu7TUupkxdCMjUt9n96dPE09+GzEuQtPWWHb2ZtWTercyMWGYYQO0ma+zUqO2FwzVLSwrNp9unr+FP9WDV1sGIpibEWNOw2fMawpw+uUCrXZL7Howi9eTuKs283+ecEQH3cHK2sXz04j+rawKjJLvlbHU3Xa9+5S344xp7z41HNbdzzOoz2Gbji4ZcHwTl6udgKasXKUNO08+PN/D29BIk+elOULW3+1ftmoNu52Aitnz26frl05pi6Vdu1UoJovvulKr3vmqYTdMUYnO3bsPlt38LTFU/uJ0i8fPaf/lbE854ihqxwxfz2U47sLD9+9eXhJt5IvzL1k02H4wPq05saSnq0LhuEs+Oc79D/RnLDFsCFNGaPVrOLXAcvheZ7V6Xiej4qO2r5z5+RJU5d/++3lS5dzc039YK2dgx1FNLl5BZWESzyz93Iaxyae2H/l5QgVXG5OLk87OjmUuKwxjf1H+rep52RFU4xQoMvMzCOEoghV2nRzd8+ES0RJbPSxP25qGO9xG5b2E/Fxxw8GF/YTN3qrNe+sL7vmVNZBMIqy9xs14b0mYluB0KlBp6mrV46vT2eHXAxK5024cJl/trIRp06HawUtv9z84/QeXiIbhmZsnF1dSjybAAC8EfKeHvt6ytdnk6X/Ctj2jb+YKkerwMjNpdy3A4NPQ0bCK0971cybY1WEbfgBmRBCeGXgmVCNQ/ePVozuRO6cu6riSc6Nc0Hp7u//e/EgL/7J6XMxLCGErVhLmGdTgn6aOvdYrLDF9LXL/lVipFzjTXGm+IEqZ62oxMbDm9y+LcRzHMeyLKu7e/fe+v/7v3Fjx61evSY0NESrrcpf2njttRc6ztZmzrv274oYB4bQFOXsIOwiYQSEEF4b+Fyro4WTezmN8xDUYQhNUY62tE3pa7sap2MZ4bSeTsPcGWeG0DTl5iJ8x4YQQlQ5HEcx3b1tGggJw9CeEqG7+YVdWQUqaDnzaMTM12Jn08J2L533w40MnhCiunzo0sweg/yX7/df/moeNvLlH3HXLstmtW4zYt3lEYQQQrT3Vw+cvP1F2UvVRPrMiE6nu3kz5MqVK3fv3q3cCsdnhq7/4bj/f0bVLzIxO2jv3vD3v/IZ8P3BAd+/mlzqRzyN0T3a+cOu97ZO7zt3R9+5hVO1937qO2F3rElpG93jnd//t+eWGa2Gfbdn2HcFoWefmtVz1gVTnzOrwavC+ufm5StX790rZ2EJWvaZMGya58hpxVavidqz/byaNzubnHtr27qTfdYNaztl47EpRaYXZs7Z+PAnaXzz5lO2npfM7zT7nBnlpX2yddYiyZYfJrboMSOgx2uvbuoe0ydObtm7akPPHQs6jV57ePTalzuiTTi74sfzat7Apiu77pmportjHBt3ct+1z37uO7gXG7tjf9DLT82U4xwxfJXbYe56mOb9BjVzcaGG9G2+9kaowfpq13XIv9yp9PNHzyqKF7/s2PH7H833HTTYd8taY9Ws4tcBC9C/cRwTExN46VJwULBaXc4h5ezsbSk+L6fw5QI+/ezc7l5zi87Ca3LzeMreyaH4spS488crl3Ur+tEbTn32wq0ccR+D083vx2T0EmEIl3xq7/nZ3Ya7u/K5tw/ue/DqJOWN3aDNPOvLqjlxpRycyu/MRVk1+tfCXf9a+Go7aTcD1p3Wd2IztgvlOFvZyN0r1nXbvtiv/5Id/ZcUCaTcndABACxKF3dq2afukgPzRq1fHzP6k73mtgrKvrmUdq805XZg6GnISKOlHO1Vc2+ORvu+mx92KQ/IcRwhvCrw+KWFPYZ2aJ5zbfmlFJ4Qkn3z3KXUwaPbUZqbu08WvE/DRlW0JcwpL6+esaHRoXkDvv/uZtiMY/FmPlmEx7x+oOaUp1ZUXguKvKHtWz1e/34iTcvCwwMvXbpx/UZWVlZ1BhD5OPNwvTrjGjgENHjV/NUqMydfyEngybMnGdvrunzubvOlv82XRZYq7TSLkmUe8KgzSWw7r6/tvIJp/KW/lSvi+ISEvKg2whZezvu9nAkhhNP+ekp90MyRvCqhfw2rjA57mqTKzNWyPKfVZKS8eHTj7O9r5w7tP3HlxYSCdjevPrt0+rydVx4lZuSxrC4vO1URH/kg5GZ0uv4UY6N2z1rw25WolByW1eWoYu5FKynK6FI1Dcuyd+/cXbfu53HjxgcEBISEVEVWlU8P+mX1X68/v+Y92vjpZz8cvfVMnctyrC43MyUh6u7fZ69Fa8p3oPjMWwGTJszZcvpmlCIjl2W12SmxD6/dfmF69oLPuvPz1Emztvx1+7kqO5/V5qhfyO48zRDWnN+2Cwtr7LjxAT/+WJEUOBdz8sf1f5wNjUxMz2U5TqdJS4i4+eeWxaMmrLtRsoevKStUXlw48Yufjt2KUeXqdLmqmNATgbIcnnB8QbHnXNsw99fAR/LMhPikfDPLi00MXD5uxNTv9567+0yRkcuyrCYj+emDoCPbD1xP5YhG9p/pE2ZsOnM7Vq3R5mcrIv8+sGbS2MUnE1mDm670umeuCu6Ocbz6/L4ziSyf9/DQHw9ePbGW4xwxeJUzez1sxMWzUanpkWcCnxjOalCOvYb0FhHluaPXSvYaZl+cO3Enj67f/4MONkarWcWvA9WJ47jExMT9+w988vH0WbNmnzxxstxJLkKIvR1D8ZqcvDJqMa/RaAjl4ORY4kOgVNLdqw9j1Rodx7Ga1LiHgdsWfbzgtJKUMr0cZ4rRsjMoK3j/4ac6nksL3Hvytfcdjd1qzT3ry6g5pR2cyr9c8NkP/joWFKXM0ely0+MfnN/21fiZv0W9vMgb24XynK2a8O3Tx05bdyQ4Ijkjj2V1uZkpL2ShgUevmfnqOgBATZEr27Xw24sqx85z1n/hY21uq6DMm0vFbgcGnoaMXKLL0V419+ZYBWEbfkDWL5gRdPBMEstnBZ26UjCkWU7IiYsKlsu8euhsYuE2Kt4SJrnhu5b+dCO7Tq+5y4a4l8wdGGmKFztQ5aoVldiCMnrMaywdy8bEPN25a9eUKVMXLlx04fyFak5yEUJ4bf7WC+pVYbn30rgslrAcr87UhijYgtaVTnfgsnrBXc2tVDaLJRzHZ2vYqOS8swmGx8TltfnbA9Urw3IfZnA5LNHquCR1fmw+oQjh0nJWXc/+J43L5YlOx8UpdeVo01PO4lcDFem/hxUaemvj5i3l2fWaat+e3wghwUHBVToQspVQaGNrk5FhPNNYW49zxfn5dZo1cwYhZE1AQJW+5GxiYXXv0f3rxYsJIRs3bzH2vcUqRbmN2Ra0qmPIsj4fHq7Ez60CFFUl1WzWzBl+fp0IIYMMjZ9ViUQikSmJra8XL+7eozshZNKUaUZnfnPgElEU0+SLg3/Nqnvs0/cWBb0ZQ2VV270PAN5CeO4AqDrVk2cgJjd09ed7WKbdH3LDo1HXSq0dcyZKleT1dtQb9CJqTZev1eZX6TuxUHlqeGHRkg4D2pKox88SU9JzBS7vdBg6/4vOVtzTe2E1tCcjvIlqWTWrSO+tN04tKzsAAAAAKMNb1dCtFMhzAdQ41r7jAjYOdCj6kifPJp7euj+yPB+vBDAI1ezNhbIDAAAAACgN8lwANQ1llRZxJfQd3yYNpc7WJC89KSYs6OTuzX+EFB9VHKD8UM3eXCg7AAAAAIBSIc8FUNPw6aE7Zk3ZYekwoHZDNXtzoezKxkZtHd1kq6WjAAAAAAALQZ4LAAAAAKpE8+bNhg8bbukoAIyTPQk/8ecJS0cBAACVAHkuAAAAAKgSrm5u+o+cAtR8J8gbk+fy7+NvJbSShctexL3geXyDBADgNchzAQAAAFhely5daIpWKhXJyQp8WQkAyuDZ0HPUqJGEkByNRiaTPX706PFjWVRUVH5+vqVDAwCwPOS5AAAAACyvffv2PXv0YBiGEKLVaRXJCoVCqVQoFEplcrI8OVmhUCrVKhXLvpEf1ty4eUto6C1LRwFgwL49v1k6BLOpUlJ0LCtgGDtb2w4d2rfz9WUYhmXZZ8+ehYWFPXr8+IksPC093dJhAgBYBvJcAAAAAJa3ZcuW4KBgBwcHqVQqrSuVukvr1pWKRKJ3vN6pX7++jY2NfrasrCy5XC5PksuT5UlJcrU6Va1WvYiPz8vNreaAHRwcsrKyqnmjAEAIUapSBAyj/5silD4/zjCMt7d348aNhw8fTghRKhQPwsIsGSUAgIUgzwUAAABQU2RlZUVHR0dHRxebXpj/EolEYpFI6i719fUdMKCuvb194YL6/JcqVa1WqeXJcnmSPDExMScnp4pCXbZsWVZWxr59+589e1ZFmwAAg1QpqtL+i3mZ/3KVSN7v00f/t5OTc3WEBQBQMyDPBQAAAFDTlZH/EolFIheRVCqtW1cqdZf6tGwpEolcXFwoitIvqFar1Wq1XC5PSpLr81/6KRUMyaOeh0udlp07d7l16/a+ffuePn1awRUCgEH29vZiV7Gbq5vYVezm6uoqdnWXupe9CMuyDMNERkY2bdqUEJKRgXcYAeAtgjwXAAAAwJsqKysrKysrLjau2HQroVAkFkulUn3+SyQSS6VSX19fiURC0zQhJF+rVatUcrlcn/9S63uByeUKhYLjOKPbZRimjrOzPpXWvkM7P79OYWEPf/vt94iIyKrYTYBaz8nJUSQSSdwkIrFYLBZLJG5isavYVSxxcyt8bTkvLy9ZoVCr1KoUFcdx+nO5GH2GKzo6+uDBQ6GhIWfOnK7e/QAAsDzkuQAAAABqm3ytVp/DKjZdKBSKxWKpVKrvBVa3rlSf/3JzcyscAl+VopLLC0b+SkrSr6Z4/kssFhU+YwsYASGkZUuf9evXR0Q8+f33PQ8fPqyuHQV4kxTtgCkSicRikZ6Hh4ednZ1+Hn0OWq1Wq1Xqp0+jVfr+mElytVqdmprK87x+Nt/27UQuLkVXzupYmqHv3b23/8B+ZJwB4G2GPBcAAADA20JbSv5LIBCIXcUSN4nEXSJ1d3dzk0gkbi1aNncVuwqFQv2CKpVK8ZJAKCy2Bn2azMuryZo1qyMiInbv3vPgwYPq2SmAGqVkMkv/h0QiKeyZVdihUq1OjYuLO3v2nD6zLJfLTfy8Q4pSWZjnYlmWpumQkJC9+/bFxRXv3QkA8LZBngsAAADgbafT6ZLlycnyZPL699lomnZxcXF3d5dIJBKJm0QikUgkTZs1c3Gpw/E8TVHF1iMQMIQQb2/v1at/iIiIeBj2qNp2AaA66T8NIRKJRSIX/avBIpGLSCRyl0isi3wdVV0gNTo6uvADqUlJSdnZ2RUMIDk5uUmTJhzLEYq/cOHi4SNHkuXJFd4tAIDaAHkuAAAAADCM4ziVSqVSqWQyWdHp48ePHzt2DF2iV5eevm9X06ZNmzVrRgjhqyFQgCpgMJkllUoL3/Mlr3/q4fFjmf4PdapaqVBqNJqqiy0lRanVas+cOXPs2PGKf1YCAKA2MZDn8vb2njVzRvWH8rbBcS7J5fVRBmqOAf37dfHrZOkoAN483t7elg7BMFx+oeaosfe+sknc3AyOga2n0+lomqZpOi8vz9ramiLE0cGhOsMDMJebm2TkyBFiV1d3N4mL2MXN1a1OnTr6Ss5xXFpamkKhVKtVcbFxd+7cUanUyhSlKiVFrU7VarUWCTgw8NKhQ4czMjJNmRnPHQBvj4a2eROlSktHUX2cBGzJiQbyXCKRix8e6asejvMbpEmTGvqsDgDlg8svQAXV9fAo7M9CCOE5nuU5AcNwHJecnBwe/uTx48eycJmnp+fiRYsIIZmmDTkEYCnNmjetV99DLper1epnMc9uXP+n8COkKSkpOp3O0gEW9/x5rOkz47kD4O3hLGBbO+ZYOgoLw3uLAAAAAGAeqdS9cHyutLS0xzJZuOxJROST6Kjo/Pz8wtkaNmxouRgBzHA9+PrqNWssHQUAAFSC1/JcgwYNtlQcbxUc5zdFcFDwoCAUFkDtsSYggARYOgiANx9FUQkJCUFBweHh4RERESqVytIRAVQUz9fOceTw3AHw9sD5XqjUgRUAAAAAAErief6bb5bu3Lnzxo0bb1mSi/YYsurPC6dWdjc8AH9VoFx6LztwOijgfetq22RVYty7zVi798o/d6Jk98KCjgcMcC3+zU4AAICKQZ4LAAAAAMAUlH39Fj4NXGwqLTdj3X7W/+7e/uvHfuLSVknZeLRs3djNTkCZNn+NZuUz+7+b5w9t30hkI2CsHNyk1nlZtbMbFQAAWA7G5wIAAAAAi7PxfG/Sl1MG9WjV0NWOyktXPntyP/js/m1HHqRaIBHC+Ezb9PMkh1Mzpv0aYeBDTiay6TJ777LBjSUiR3srhs/PSVPGRT0MPn9k99HQpJeDmFEURVE0bXLW6vX5jcbpOGLrtZ/9S+sKpr31w6BxexI4c3aqIqw7jxnfzCo/6tD8f2+8GJNl5ebhkJVXXRsHAIC3BfJcAAAAAGBZjOfo9UdW9nRlCvI3AnH9Vu/WayK4v+foA2KBPBddx7Nlk7qpVhXrNMW4ebf29niZZLJxdG3g49rAp+uACSN2TP94Y0gGT0jenV/GtPvF9FUWm79y4qwutLu3lzOVF7TrlzNRaTwhefLYTEvHBAAAtQ/yXAAAAABgUQLfqTO6i4ni8s/LA47di03TWoka+HTq2UZzNbna+hpVFZ1s87iRW57k8Yyts9S7fb/p878c1PqjVdMuDvxFVv6uYqbKPPZF+2MFfwvaLzx1aJrj0U/fWxSkrfItE0JbO4rr2OgyU1NzdIQQQihrGyuK16SkZONdRQAAqDrIcwEAAACARdnV9xQzXNzpjTuDo1hCCMlXPA058zSk4L8p8bvTv5naq6VXAw9XJzshm5EYfnVEocZJAAAKOklEQVT/5v8+qDds4gf9OjevX4fJTnh0Yfe6gP1haYUZFNtG/T76YvrQbi097LnU2DuXj2z59WCoskhmyegMTNNZJx7OIoQQwikOTvb/7oY+O0TZdflqx/kfmjUU27BpcfcDD/y84eC90t+u5HT5WpbniS4nNf7hpV3z0t3a7JnSuFN7d1qWyDFNvjj416y6x4rknmhR2/EzPp/Yt907YmFO0pN/bqa7vxpQ18D8pcZpEsrZd8zsqf06+Xg3ktaxpTQpsedXTllxjur19frZA5rVd3ey5jUpT2+f37F+8/EIfX6qWHGQ3NTiB4EWdZy+fMln7zd1EVI8r82Mu7jqw0VHEwkhFKFdxmy/P0Y/n/bWt30/2pPElVkWBiNcGdriE7OrBAAAvB2Q5wIAAAAAi9IkxaeydIM+kwccjTwdqyn+37SoTd8hvVq+bLYKXRq0G75o5/Aic1h5dhy7dKsoe+TnfyZzhBCblp9t27HQz7kgQeTetNf4r7v1bDtv0tenElliygxloKwbtu1Y8Ler17vjvvFtajti8q5InUn7yulYjhBC0wa/BkU5dVu6Z9OHTQpGurdu6DuwISGEVNkoVrSk66jJAwuPraObRJiXxRPbOl7tm9a3IoQQ4uDeoveUta0k2g/mn0rhSxQHsS92EGjp6IBNC3s5UbpsVXIW5SCuIxHmpXOEMIZDMFIWBiOkTK8Sn/6ZXGlHCwAA3gT43iIAAAAAWJT2zq7NwSmU58h1xy//serz/s1FJX+K5TPOft2vbZs23q27D/rmr3iW59JubZ45qmsH36bt+07acieD1Ok1wl9CE0IY78nL/t3JKTf8yMIx/j6t2rXrN33N5STKY8CKhX1dKFNmIIQQwkZu/KBN42Y+jZv5ePUo0kmKz7q6Zkx3vw5NWnXuPjHgopyzbztxYnth2btIMVb2ovo+PcZ/v3x0Q4aNu3NXbuCVTEGrjxdP8bbKuL93zih/n1bt2vpPnLPjVkrZL2+WFqfp+MyL3w7u2M7Xu03XHqN/CdESPvP62inDunbq4N2iTYsugz/a8TBX/N7I3q+OTWFxePkUPwiUY+d+nR25R/8d3rVrx57+HTr4dRm+Ljjn5YJc6qFPfPXRNm714Z4kyqSyKBFh0RjKrhLmHw4AAHizIc8FAAAAAJbFxh6eM+LzjSdl2eIOIxdtPBJ88fcfJndyL5rt4tlMpSIjj2XzU2XHt/zxmKUEKbLr4fIsrTY78fq2nRfTeaZBo4Y0IUyToR/4WGnDNs1bdfhBco42Py32xrb53x5K4l16D+3jQhmfoWy8VvE0MiE9V6fNSri9f/XeRzpG3Ly5WymtakGrOSejIx7HyO49+uf86R1Lx/rYa8L3fbvzsYHuX0yz/u83ovPubJj304mw5BxtfkbC/VP7LkRX9ThevC41IV6Vo2XzMhJjk7N5QiiqTutxq3cdvR5y6+GVPSv6ezBE4F7X9dU+viwOTlfiIPA8Twjl1tyvuasNRQifp3wWX+rLgyaWRckIialVoioPHAAA1ETIcwEAAACAxeXH/71t9oj3e05cuvVidL57xwnf7Dz56xgvg2NssEmxiTpi7eZe52VTNl8er+ApWztbihArT+/6NPci5PrzIvmh7LtB93KJlad3A9r4DGZgE58+z+YpBwd7I+kxnud5nnAZt3fMGjJh7XWDiR+hR6N6NBd/93aSRYffp5x6Lv19z5Jx77Vu5O5kLbQVNWzgak0Rmi5twJPXDgKfeeP4pRTKvdeSPYH3b5w+snXlVwOalHp0KqssSq8Spq4BAABqC+S5AAAAAKCGyJPfOf7TzBG9Rq04Gce59Zz9lb+Dodk4bb6OUEKhsDB9otXqeEJRNCGEGOuQZXwGM/D5+fk8RdGlrVP3aMNQ72Y+jZu377/6Zhpx8GouIXml9G6iGJoQUvq6qgclev/D4Q2Z1JDNX47s2sHXu2XHLl8dl5fZp+y1g8CnnFky+aPvd/7v0t043qP9e6Pmrt/500DXUnaqsva11CpRSesHAIA3BvJcAAAAAFCjcOmy4xsOhbO0g7d33VJGLy9dfuzTeI5u0PldzyKL2rfv0c6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) ``` w.set_project(project_id='605ab63ecd8a6669dfd64901') ``` ``` ``` ``` kerasc_blueprint.train(w.project.id) ``` ``` Name: 'A blueprint I made with the Python API' Input Data: Numeric Tasks: Single Column Converter: 'Age' | Missing Values Imputed | Binning of numerical variables | Smooth Ridit Transform | Keras Neural Network Classifier ``` ``` starred_models = w.project.get_models(search_params=dict(is_starred=True)) ``` ``` model_to_clone = starred_models[0].blueprint_id ``` ``` bp = w.clone(blueprint_id=model_to_clone, name='Now featuring selected columns!') ``` ``` bp.show() ``` ![No description has been provided for this image](data:image/png;base64,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) ``` bp.delete() ``` ``` Blueprint deleted. ``` --- # Pass features into a task URL: https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-adv.html > Learn about how features of the same data type may need to be processed differently than others in the Blueprint Workshop. # Pass features into a task Certain features of the same data type may need to be processed differently than others. For example, suppose you are working on solving a problem with a dataset containing text features. One of which lends itself well to using word-grams for preprocessing, while the other uses char-grams. When using Composable ML in DataRobot, you can pass one or more specific features to another task. **Blueprint Workshop:** When using project-specific functionality, DataRobot recommends running the following code. ``` w.set_project(project_id="") # or # w = Workshop(project_id="") ``` In this example, select the Age feature, perform missing value imputation, and pass it to the Keras neural network classifier. Note that similar to other pieces of functionality, you may auto-complete feature names with `w.Features.` to complete available features. ``` features = w.FeatureSelection(w.Features.Age) pni = w.Tasks.PNI2(features) keras = w.Tasks.KERASC(pni) keras_blueprint = w.BlueprintGraph(keras) ``` You may link a blueprint to a specific project if desired, ensuring the blueprint is validated based on the linked project, for example, to confirm that the selected features exist in the dataset associated with the project. ``` # Make sure it is saved at least once, or pass `user_blueprint_id` to `link_to_project` keras_blueprint.save() keras_blueprint.link_to_project(project_id="") ``` **DataRobot UI:** To only pass a desired column into a task, add the Task Single Column Converter or Multiple Column Converter. Then, pick the column name from the original dataset as the parameter column_name or column_names. The following task(s) will only receive the selected column(s). [https://docs.datarobot.com/en/docs/images/bpw-20.png](https://docs.datarobot.com/en/docs/images/bpw-20.png) Click Update and then Save Blueprint to see the new task referencing the chosen column. Here's an example of a blueprint performing specific preprocessing on certain columns. Notice how each column name is observable. [https://docs.datarobot.com/en/docs/images/bpw-21.png](https://docs.datarobot.com/en/docs/images/bpw-21.png) Continuing with this example, you can also pass all columns to another task. To do so, add a new connection from Numeric Variables to the desired task. You may link a blueprint to a specific project if desired, ensuring the blueprint is validated based on the linked project; for example, to confirm that the selected features exist in the dataset associated with the project [https://docs.datarobot.com/en/docs/images/bpw-22.png](https://docs.datarobot.com/en/docs/images/bpw-22.png) Features may also be excluded instead, which is particularly useful when a particular feature should be processed one way, and everything else, processed another way. **Blueprint Workshop:** ``` without_insurance_type = w.FeatureSelection(w.Features.Insurance_Type, exclude=True) only_insurance_type = w.FeatureSelection(w.Features.Insurance_Type) one_hot = w.Tasks.PDM3(without_insurance_type) ordinal = w.Tasks.ORDCAT2(only_insurance_type) keras = w.Tasks.KERASC(one_hot, ordinal) keras_blueprint = w.BlueprintGraph(keras) ``` **DataRobot UI:** To process certain features in different ways, add the Task Multiple Column Converter. This task lets you select columns. You can give it a list with several columns that you want to include and the rest will be dropped (using the parameter column_names). Alternatively, you can instead provide a list of several columns that you would like to use. Next, create an edge from the categorical data to the modeler, insert the alternative processing task, then add a second Multiple Column Converter and pick the same column name and change method to be exclude. Now, one column is processed using one task, and all others are processed with a different task. [https://docs.datarobot.com/en/docs/images/bpw-23.png](https://docs.datarobot.com/en/docs/images/bpw-23.png) [https://docs.datarobot.com/en/docs/images/bpw-24.png](https://docs.datarobot.com/en/docs/images/bpw-24.png) --- # Custom task creation notebook URL: https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-custom-task.html # Custom task creation notebook ## Setup ### Import libraries ``` import datarobot as dr from datarobot.models.execution_environment import ExecutionEnvironment from datarobot_bp_workshop import Workshop, Visualize from datarobot_bp_workshop.magic import * ``` ### Connect to DataRobot Read more about different options for [connecting to DataRobot from the Python client](https://docs.datarobot.com/en/docs/api/dev-learning/api-quickstart.html). ``` with open('../api.token', 'r') as f: token = f.read() dr.Client(token=token, endpoint='https://app.datarobot.com/api/v2') ``` ## Initialize the Blueprint Workshop ``` w = Workshop() ``` Get the Sklearn Drop-In Environment ``` environments = ExecutionEnvironment.list() ``` ``` scikit_env = environments[7] ``` ``` customr = w.create_custom_task( environment_id=scikit_env.id, name="My Custom Ridge Regressor w/ Imputation", target_type="Regression", description="Impute values and perform ridge regression." ) # customr = w.get_custom_task('608e5bc8b66a4934d58d0d4e') ``` ## Iterate on a custom model ``` %%update_custom {customr.id} import pickle import numpy as np import pandas as pd from typing import List, Optional from sklearn.compose import make_column_selector, ColumnTransformer from sklearn.impute import SimpleImputer from sklearn.linear_model import Ridge from sklearn.pipeline import Pipeline def fit( X: pd.DataFrame, y: pd.Series, output_dir: str, class_order: Optional[List[str]] = None, row_weights: Optional[np.ndarray] = None, **kwargs, ): numeric_transformer = ColumnTransformer( transformers=[ ( "imputer", SimpleImputer(strategy="median", add_indicator=True), make_column_selector(dtype_include=np.number), ) ] ) pipeline = Pipeline(steps=[("numeric", numeric_transformer), ("model", Ridge())]) pipeline.fit(X, y) with open("{}/artifact.pkl".format(output_dir), "wb") as fp: pickle.dump(pipeline, fp) ``` ``` '608ef74c5dda651931052422' ``` ``` custom_ridge = w.CustomTask(customr.id)(w.TaskInputs.NUM) custom_ridge_bp = w.BlueprintGraph(custom_ridge, name="My Inline Custom Blueprint") ``` ``` custom_ridge_bp.show() ``` ![No description has been provided for this image](data:image/png;base64,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 ) ``` custom_ridge_bp.save() ``` ``` Name: 'My Inline Custom Blueprint' Input Data: Numeric Tasks: My Custom Ridge Regressor w/ Imputation ``` ``` custom_ridge_bp.train('5eb9656901f6bb026828f14e') ``` ``` Name: 'My Inline Custom Blueprint' Input Data: Numeric Tasks: My Custom Ridge Regressor w/ Imputation ``` --- # Blueprint Workshop overview URL: https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-overview.html > An overview of common usage with the Blueprint Workshop. # Blueprint Workshop overview > [!NOTE] Note > If you are using JupyterLab, "Contextual Help" is supported and assists you with general use of the Blueprint Workshop. You can drag the tab to be side-by-side with your notebook to provide instant documentation on the focus of your text cursor. ## Initialization Before proceeding, ensure you have initialized the workshop and [completed the required setup](https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-setup.html). > [!NOTE] Note > The command will fail unless you have correctly followed the instructions in `configuration`. Use the following code to initialize the workshop. This is necessary for any Python code examples to work correctly. ``` from datarobot_bp_workshop import Workshop w = Workshop() ``` All of the following examples assume you have initialized the workshop as above. ## Understanding blueprints It's important to understand what a "blueprint" is within DataRobot. A blueprint represents the high-level end-to-end procedure for fitting the model, including any preprocessing steps, algorithms, and post-processing. **Blueprint Workshop:** In the Blueprint Workshop, blueprints are represented with a `BlueprintGraph`, which can be created by constructing a DAG via Tasks. ``` pni = w.Tasks.PNI2(w.TaskInputs.NUM) rdt = w.Tasks.RDT5(pni) binning = w.Tasks.BINNING(pni) keras = w.Tasks.KERASC(rdt, binning) keras_blueprint = w.BlueprintGraph(keras, name='A blueprint I made with the Python API') ``` You can save a created blueprint for later use in either the Blueprint Workshop, or the DataRobot UI. ``` keras_blueprint.save() ``` You can also visualize the blueprint ``` keras_blueprint.show() ``` [https://docs.datarobot.com/en/docs/images/bpw-1.png](https://docs.datarobot.com/en/docs/images/bpw-1.png) **DataRobot UI:** In the UI, blueprints are represented graphically with nodes and edges. Both may be selected and provide contextual buttons for performing actions on nodes and edges such as removing, modifying, or adding them. [https://docs.datarobot.com/en/docs/images/bpw-2.png](https://docs.datarobot.com/en/docs/images/bpw-2.png) Each blueprint has a few key components: - The incoming data ("Data"), separated into type (categorical, numeric, text, image, geospatial, etc.). - The tasks performing transformations to the data, for example, "Missing Values Imputed." - The model(s) making predictions or possibly supplying stacked predictions to a subsequent model. - Post-processing steps, such as "Calibration." - The data sent as the final predictions, ("Prediction"). Each blueprint also has nodes and edges (i.e., connections). A node will take in data, perform an operation, and output the data in its new form. An edge is a representation of the flow of data. The image below is a representation of two edges that are received by a single node; the two sets will be stacked horizontally. The column count of the incoming data will be the sum of the two sets of columns, and the row count will remain the same. If two edges are output by a single node, it means that the two copies of the output data are being sent to other nodes. ## Understanding tasks The following sections outline what tasks are and how they are used in DataRobot. ### Types of tasks There are two types of tasks available in DataRobot: - Theestimatorclass predicts a new value(s) (y) by using the input data (x). The final task in any blueprint must be an estimator. Examples of estimator tasks are LogisticRegression, LightGBM regressor, and Calibrate. - Thetransformclass transforms the input data (x) in some way. Examples of transforms are One-hot encoding and Matrix n-gram. These class types share some similarities: - Both class types have afit()method which is used to train them; they learn some characteristics of the data. For example, a binning task requiresfit()to define the bins based on training data, and then applies those bins to all incoming data in the future. - Both transform and estimator can be used for data preprocessing inside a blueprint. For example, Auto-Tuned N-Gram is an estimator and the next task gets its predictions as an input. ### How tasks work together in a blueprint Data is passed through a blueprint sequentially, task by task, left to right. During training: - Once data is passed to an estimator, DataRobot first fits it on the received data, then uses the trained estimator to predict on the same data, then passes the predictions further. To reduce overfit, DataRobot passes stacked predictions when the estimator is not the final step in a blueprint. - Once data is passed to a transform, DataRobot first fits it on the received data, then uses it to transform the training data, and passes the result to the next task. When the trained blueprint is used to make predictions, data is passed through the same steps. However, `fit()` is skipped for data. ## Constructing blueprints Tasks are at the core of the blueprint construction process. Understanding how to add, remove, and modify them in a blueprint is vital to successfully constructing a blueprint. **Blueprint Workshop:** Defining tasks to be used in a blueprint in Python requires knowing the task code to construct it. Fortunately, you can [search tasks](https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-walkthru.html) by name, description, or category, and leverage autocomplete (type `w.Tasks.`, where you press the Tab key at ``) to get started with construction. Once you know the task code, you can instantiate it. ``` binning = w.Tasks.BINNING() ``` **DataRobot UI:** If you will be working with the UI, you will need to start with a blueprint from the Leaderboard, so it is recommended you first read [how to modify an existing blueprint](https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-overview.html#modify-an-existing-blueprint), and then return here. The blueprint editor allows you to add, remove, and modify tasks, their hyperparameters, and their connections. To modify a task, click a node, then on the associated pencil icon, and edit the task or parameters as desired. Click "Update" when satisfied. ( `More details `) To add a task, select the node to be its input or output, and click the associated plus sign button. Once the empty node and task dialog appear, choose the task and configure as desired. ( `More details `) To remove a node, select it and click the trash can icon. [https://docs.datarobot.com/en/docs/images/bpw-5.png](https://docs.datarobot.com/en/docs/images/bpw-5.png) ## Pass data between tasks As mentioned in [Understanding Blueprints](https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-overview.html#understanding-blueprints), data is passed from task to task, determined by the structure of the blueprint. **Blueprint Workshop:** In the following code, you enter a numeric input into a task to perform binning on the numeric input for the blueprint (determined by project and feature list). ``` binning = w.Tasks.BINNING(w.TaskInputs.NUM) ``` Now that you have the `binning` task defined, pass its output to an. ``` kerasc = w.Tasks.KERASC(binning) ``` And now we can save, visualize, or train what you've just created by turning it into a `BlueprintGraph`, as shown in the [example notebook](https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-walkthru.html). ``` keras_bp = w.BlueprintGraph(kerasc) ``` You may pass multiple inputs to a task at construction time, or add more later. The following code will append to the existing input of `kerasc`. ``` impute_missing = w.Tasks.NDC(w.TaskInputs.NUM) kerasc(impute_missing) ``` You may replace the input instead by passing `replace_inputs=True`. ``` kerasc(impute_missing, replace_inputs=True) ``` The `BlueprintGraph` will reflect these changes, as shown by calling `.show()`, but to save the changes, you will need to call `.save()`. ``` keras_bp.save().show() ``` **DataRobot UI:** To add a connection, select the starting node and drag the blue knob to the output point. [https://docs.datarobot.com/en/docs/images/bpw-6.png](https://docs.datarobot.com/en/docs/images/bpw-6.png) To remove a connection, select the edge which you'd like to remove and click the trash can icon. If the trash can icon does not appear, deleting the connection is not permitted. You must ensure the blueprint is still valid even when you remove the connection. [https://docs.datarobot.com/en/docs/images/bpw-7.png](https://docs.datarobot.com/en/docs/images/bpw-7.png) ## Modify a task Modifying a task in the Blueprint Workshop means modifying only the task's parameters; however, in the UI, it can mean modifying the parameters or which task to use in the focused node. This is because you need to edit a task in order to substitute it for another. **Blueprint Workshop:** Use a different task where the substitution is required and save the blueprint. **DataRobot UI:** To modify an existing task, click on the node and then the pencil icon to open the task dialog. [https://docs.datarobot.com/en/docs/images/bpw-8.png](https://docs.datarobot.com/en/docs/images/bpw-8.png) Click on the name of the task for a prompt to choose a new task. [https://docs.datarobot.com/en/docs/images/bpw-9.png](https://docs.datarobot.com/en/docs/images/bpw-9.png) Now you may search and find the specific task you'd like to use. Click Open documentation after selecting a task for details on how the task works. [https://docs.datarobot.com/en/docs/images/bpw-10.png](https://docs.datarobot.com/en/docs/images/bpw-10.png) ## Configure task parameters Tasks have parameters you can configure to modify their behavior. These include, for example, the learning rate in a stochastic gradient descent algorithm, the loss function in a linear regressor, the number of trees in XGBoost, and the max cardinality of a one-hot encoding. **Blueprint Workshop:** The following method is the best way to modify the parameters of a task. DataRobot also recommends [viewing the documentation](https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-walkthru.html) for a task when working with one. If you're using JupyterLab, "Contextual Help" is supported. You can call it using `help(w.Tasks.BINNING)`. You can continue working with the blueprint from the previous step by modifying the `binning` task. Specifically, you can raise the maximum number of bins and lower the number of samples needed to define a bin. ``` binning.set_task_parameters_by_name(max_bins=100, minimum_support=10) ``` There are a number of other ways to work with task parameters, both in terms of retrieving the current values, and modifying them. It's worth understanding that each parameter has both a "name" and "key". The "key" is the source of truth, but it is a highly condensed representation, often one or two characters. So it's often much easier to work with them by name when possible. **DataRobot UI:** You can modify task parameters as well. Parameters display under the header and are dependent on the task type. [https://docs.datarobot.com/en/docs/images/bpw-11.png](https://docs.datarobot.com/en/docs/images/bpw-11.png) Acceptable values are displayed for each parameter as a single value or multiple values in a comma-delimited list. [https://docs.datarobot.com/en/docs/images/bpw-12.png](https://docs.datarobot.com/en/docs/images/bpw-12.png) If the selected value is not valid, DataRobot returns an error: [https://docs.datarobot.com/en/docs/images/bpw-13.png](https://docs.datarobot.com/en/docs/images/bpw-13.png) ## Add or remove data types A project's data is organized into a number of different input data types. When constructing a blueprint, these types may be specifically referenced. When input data of a particular type is passed to a blueprint, only the input types referenced in the blueprint will be used. Similarly, any input types referenced in the blueprint which do not exist in a project will simply not be executed. **Blueprint Workshop:** You can add input data types to tasks in the Blueprint Workshop just like you would [add any other input(s) to a task](https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-overview.html#pass-data-between-tasks). For the sake of demonstration, this code example adds a numeric input and then subsequently adds a date input. However, you can also add them both at once. ``` ndc = w.Tasks.NDC(w.TaskInputs.NUM) ndc(w.TaskInputs.DATE) ``` **DataRobot UI:** If the current blueprint does not have all of the input variable types that you'd like to use, you can add more. Select the Data node, then click the pencil icon to modify the input data types available for the current blueprint. [https://docs.datarobot.com/en/docs/images/bpw-14.png](https://docs.datarobot.com/en/docs/images/bpw-14.png) In this modal, you can select any valid input data, even data not currently visible in the blueprint. You can then drag the connection handle in order to denote data passing to the target task. [https://docs.datarobot.com/en/docs/images/bpw-15.png](https://docs.datarobot.com/en/docs/images/bpw-15.png) ## Modify an existing blueprint Consider that you have run Autopilot and have a Leaderboard of models, each with their performance measured. If you identify a model that you would like to use as the basis for further exploration in the blueprint workshop, you can use the instruction below. **Blueprint Workshop:** First, set the `project_id` of the `Workshop` for the project that contains the model you want to use. ``` w.set_project(project_id=project_id) ``` Retrieve the `blueprint_id` associated with the model you want to use. You can do so in multiple ways: If you are working with DataRobot's Python client, you can retrieve the desired blueprint by searching a [project's menu](https://datarobot-public-api-client.readthedocs-hosted.com/en/latest/entities/blueprint.html#list-blueprints): ``` menu = w.project.get_blueprints() blueprint_id = menu[0].id ``` To visualize the blueprints and find the one you would like to clone, [DataRobot provides an example workflow](https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-walkthru.html). You can also search a project's [Leaderboard](https://datarobot-public-api-client.readthedocs-hosted.com/en/latest/entities/blueprint.html#get-a-blueprint). ``` models = w.project.get_models() blueprint_id = models[0].blueprint_id ``` By navigating to the Leaderboard in the UI, you can obtain a specific model ID via the URL bar, which can be used to [directly retrieve a model](https://datarobot-public-api-client.readthedocs-hosted.com/en/latest/entities/model.html#retrieve-a-known-model), which has a `blueprint_id` field. Once the `blueprint_id` is obtained, it may be used to clone a blueprint. ``` bp = w.clone(blueprint_id=blueprint_id) ``` The source code to create the blueprint from scratch can be retrieved with a simple command: ``` bp.to_source_code() ``` The command may output: ``` keras = w.Tasks.KERASC(...) keras_blueprint = w.BlueprintGraph(keras) ``` This is the code necessary to execute to create the exact same blueprint from scratch. This is useful as it can be modified to create a desired blueprint similar to the cloned blueprint. To make modifications to a blueprint and save in place (as opposed to making another copy), omit the final line which creates a new `BlueprintGraph` (the call to ( `w.BlueprintGraph`)) and instead call the blueprint you'd like to overwrite on the new final task: ``` keras = w.Tasks.KERASC(...) # keras_blueprint = w.BlueprintGraph(...) bp(keras).save() ``` **DataRobot UI:** Navigate to"Describe > Blueprint"and choose Copy and Edit. [https://docs.datarobot.com/en/docs/images/bpw-16.png](https://docs.datarobot.com/en/docs/images/bpw-16.png) DataRobot opens the blueprint editor, where you can directly modify the blueprint to, for example, incorporate new preprocessing or to stack with other models. Alternatively, you can save it to be used with other projects. ## Validation Blueprints have built-in validation and guardrails in both the DataRobot UI and Blueprint Workshop, allowing you to focus on your goals without worrying about remembering the requirements and the ways each task impacts the data from a specification standpoint. This means that the properties of each task are considered for you and you don't need to remember when a task only allows certain data types, requires a certain type of sparsity, handles missing values through imputation, imposes requirements on column count, or anything else. Blueprints automatically validate these properties and requirements, presenting you with warnings or errors if your edits introduce any issues. Furthermore, other structural checks will be performed to ensure that the blueprint is properly connected, contains no cycles, and can be executed. **Blueprint Workshop:** In the Blueprint Workshop, call `.save()` and the blueprint is automatically validated. ``` pni = w.Tasks.PNI2(w.TaskInputs.CAT) binning = w.Tasks.BINNING(pni) keras = w.Tasks.KERASC(binning) invalid_keras_blueprint = w.BlueprintGraph(keras).save() invalid_keras_blueprint.show(vertical=True) ``` [https://docs.datarobot.com/en/docs/images/bpw-17.png](https://docs.datarobot.com/en/docs/images/bpw-17.png) **DataRobot UI:** [https://docs.datarobot.com/en/docs/images/bpw-18.png](https://docs.datarobot.com/en/docs/images/bpw-18.png) ## Constraints Every blueprint is required to have no cycles; the flow of data must be in one direction and never pass through the same node more than once. If a cycle is introduced, DataRobot throws an error in the same fashion as validation, indicating which nodes caused the issue. ## Train a blueprint **Blueprint Workshop:** Use the `keras_bp` from previous examples. We can retrieve our `project_id` by navigating to a project in the UI and copying it from the URL bar `.../project//...`, or by calling `.id` on a DataRobot `Project`, if using the DR Python client. ``` keras_bp.train(project_id=project_id) ``` If the `project_id` is set on the `Workshop`, you may omit the argument to the `train` method. ``` w.set_project(project_id=project_id) keras_bp.train() ``` **DataRobot UI:** Ensure your model is up-to-date, check for any warnings or errors, and click Train. Select any necessary settings. ## Search for a blueprint **Blueprint Workshop:** You may search blueprints by optionally specifying a portion of a title or description of a blueprint, and may specify one or more tags which you have created and tagged blueprints with. By default, the search results will be a python generator, and the actual blueprint data will not be requested until yielded in the generator. You may provide the flag `as_list=True` in order to retrieve all of the blueprints as a list immediately (note this will be slower, but all data will be delivered at once). You may provide the flag `show=True` in order to visualize each blueprint returned which will automatically retrieve all data ( `as_list=True`). ``` shown_bps = w.search_blueprints("Linear Regression", show=True) # bp_generator = w.search_blueprints("Linear Regression") # bps = w.search_blueprints(tag=["deployed"], as_list=True) ``` **DataRobot UI:** Searching for blueprints is done through the AI Catalog, and works just like searching for a dataset. You may filter by a specific tag, or search based on the title or description. ## Share blueprints Building a collection of blueprints for use by many individuals or an entire organization is a fantastic way to ensure maximum benefit and impact for your organization. **Blueprint Workshop:** Sharing a blueprint with other individuals requires calling `share` on the blueprint, and specifying the role to assign (Consumer by default, if omitted). The assigned role can be: Consumer: The user can view and train the blueprint. Editor: The user can view, train, and edit the blueprint. Owner: The user can view, train, edit, delete, and manage permissions, which includes revoking access from any other owners (including you). ``` from datarobot_bp_workshop.utils import Roles keras_bp.share(["", ""],role=Roles.CONSUMER) # keras_bp.share(\[\"\","\"\], role=Roles.EDITOR) # keras_bp.share(\[\"\", \"\"\], role=Roles.OWNER) ``` There are also similar methods to allow for sharing with a group or organization, which will require, respectively, a `group_id` or `organization_id`. ``` from datarobot_bp_workshop.utils import Roles keras_bp.share_with_group([""], role=Roles.CONSUMER) keras_bp.share_with_org([""], role=Roles.CONSUMER) ``` **DataRobot UI:** In the UI, sharing a blueprint is just like sharing a dataset. Navigate to the AI Catalog, search for the blueprint to be shared, and select it. Next, click "Share" and specify an individual(s), group(s), or organization(s), and choose the role you would like to assign. --- # Blueprint Workshop setup URL: https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-setup.html > Learn about the first-time setup necessary for using the Blueprint Workshop. # Blueprint Workshop setup This page explains how to configure the Blueprint Workshop, including installing the required libraries, enabling your DataRobot account, and establishing a connection to DataRobot by providing credentials. ## Installation Run the following steps to install the Blueprint Workshop. 1. mkvirtualenv -p python3.7 blueprint-workshop 2. sudo apt-get install graphviz or brew install graphviz 3. pip install datarobot-bp-workshop ## Connect to DataRobot Use the following sections to connect to DataRobot in order to use the Blueprint Workshop. Each authentication method below specifies credentials for DataRobot, as well as the location of the DataRobot deployment. DataRobot currently supports configuration using a configuration file, by setting environment variables, or within the code itself. ### Credentials Specify an API token and an endpoint in order to use the client. You can manage your API tokens in the DataRobot application by selecting your profile and navigating to API keys and tools. The order of precedence is as follows. Note that the first available option will be used. 1. Set an endpoint and API key in code using datarobot.Client . 2. Set up a config file as specified directly using datarobot.Client . 3. Set up a config file as specified by the environment variable DATAROBOT_CONFIG_FILE . 4. Configure the environment variables DATAROBOT_ENDPOINT and DATAROBOT_API_TOKEN . 5. Search for a config file in the home directory of the current user, at ~/.config/datarobot/drconfig.yaml . For more information, read about the different options for [connecting to DataRobot from the Python client]api-quickstart. > [!NOTE] Note > If you access DataRobot at `https://app.datarobot.com`, the correct endpoint to specify would be `https://app.datarobot.com/api/v2`. If you have a local installation, update the endpoint accordingly to point at the installation of DataRobot available on your local network. ### Set credentials in code To set credentials explicitly in code: ``` import datarobot as dr dr.Client(token='your_token', endpoint='https://app.datarobot.com/api/v2') ``` You can also point to a YAML config file to use: ``` import datarobot as dr dr.Client(config_path='/home/user/my_datarobot_config.yaml') ``` ### Use a configuration file You can use a configuration file to specify the client setup. The following is an example configuration file that should be saved as `~/.config/datarobot/drconfig.yaml`: ``` token: yourtoken endpoint: https://app.datarobot.com/api/v2 ``` You can specify a different location for the DataRobot configuration file by setting the `DATAROBOT_CONFIG_FILE` environment variable. Note that if you specify a file path, you should use an absolute path so that the API client will work when run from any location. ### Set credentials using environment variables Set up an endpoint by setting environment variables in the UNIX shell: ``` export DATAROBOT_ENDPOINT='https://app.datarobot.com/api/v2' export DATAROBOT_API_TOKEN=your_token ``` --- # Blueprint Workshop walkthrough notebook URL: https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/bp-walkthru.html # Blueprint Workshop walkthrough notebook ``` import datarobot as dr ``` ``` from datarobot_bp_workshop import Workshop, Visualize ``` ``` with open('../api.token', 'r') as f: token = f.read() dr.Client(token=token, endpoint='https://app.datarobot.com/api/v2') ``` ## Initialize the workshop ``` w = Workshop() ``` ## Construct a blueprint ``` w.Task('PNI2') ``` ``` Missing Values Imputed (quick median) (PNI2) Input Summary: (None) Output Method: TaskOutputMethod.TRANSFORM ``` ``` w.Tasks.PNI2() ``` ``` Missing Values Imputed (quick median) (PNI2) Input Summary: (None) Output Method: TaskOutputMethod.TRANSFORM ``` ``` pni = w.Tasks.PNI2(w.TaskInputs.NUM) rdt = w.Tasks.RDT5(pni) binning = w.Tasks.BINNING(pni) keras = w.Tasks.KERASC(rdt, binning) keras.set_task_parameters_by_name(learning_rate=0.123) keras_blueprint = w.BlueprintGraph(keras, name='A blueprint I made with the Python API').save() ``` ``` user_blueprint_id = keras_blueprint.user_blueprint_id ``` ## Visualize a blueprint ``` keras_blueprint.show() ``` ![No description has been provided for this image](data:image/png;base64,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 ) ## Inspect tasks ``` pni ``` ``` Missing Values Imputed (quick median) (PNI2) Input Summary: Numeric Data Output Method: TaskOutputMethod.TRANSFORM ``` ``` rdt ``` ``` Smooth Ridit Transform (RDT5) Input Summary: Missing Values Imputed (quick median) (PNI2) Output Method: TaskOutputMethod.TRANSFORM ``` ``` binning ``` ``` Binning of numerical variables (BINNING) Input Summary: Missing Values Imputed (quick median) (PNI2) Output Method: TaskOutputMethod.TRANSFORM ``` ``` keras ``` ``` Keras Neural Network Classifier (KERASC) Input Summary: Smooth Ridit Transform (RDT5) | Binning of numerical variables (BINNING) Output Method: TaskOutputMethod.PREDICT Task Parameters: learning_rate (learning_rate) = 0.123 ``` ``` keras.task_parameters.learning_rate ``` ``` 0.123 ``` ``` keras.task_parameters.batch_size = 32 ``` ``` keras ``` ``` Keras Neural Network Classifier (KERASC) Input Summary: Smooth Ridit Transform (RDT5) | Binning of numerical variables (BINNING) Output Method: TaskOutputMethod.PREDICT Task Parameters: batch_size (batch_size) = 32 learning_rate (learning_rate) = 0.123 ``` ``` keras_blueprint ``` ``` Name: 'A blueprint I made with the Python API' Input Data: Numeric Tasks: Missing Values Imputed (quick median) | Smooth Ridit Transform | Binning of numerical variables | Keras Neural Network Classifier ``` ## Validation Intentionally provide the wrong input data type to test validation. ``` pni = w.Tasks.PNI2(w.TaskInputs.CAT) rdt = w.Tasks.RDT5(pni) binning = w.Tasks.BINNING(pni) keras = w.Tasks.KERASC(rdt, binning) keras.set_task_parameters_by_name(learning_rate=0.123) invalid_keras_blueprint = w.BlueprintGraph(keras) ``` ``` invalid_keras_blueprint.save('A blueprint with warnings (PythonAPI)', user_blueprint_id=user_blueprint_id).show() ``` ![No description has been provided for this 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 ) ``` binning.set_task_parameters_by_name(max_bins=-22) ``` ``` Binning of numerical variables (BINNING) Input Summary: Missing Values Imputed (quick median) (PNI2) Output Method: TaskOutputMethod.TRANSFORM Task Parameters: max_bins (b) = -22 ``` ``` invalid_keras_blueprint.save('A blueprint with warnings (PythonAPI)', user_blueprint_id=user_blueprint_id).show() ``` ``` Binning of numerical variables (BINNING) Invalid value(s) supplied max_bins (b) = -22 - Must be a 'intgrid' parameter defined by: [2, 500] Failed to save: parameter validation failed. ``` ![No description has been provided for this 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 ) ``` keras.validate_task_parameters() ``` ``` Keras Neural Network Classifier (KERASC) All parameters valid! ``` ``` ``` ## Update to the original valid blueprint ``` pni = w.Tasks.PNI2(w.TaskInputs.NUM) rdt = w.Tasks.RDT5(pni) binning = w.Tasks.BINNING(pni) keras = w.Tasks.KERASC(rdt, binning) keras.set_task_parameters_by_name(learning_rate=0.123) keras_blueprint = w.BlueprintGraph(keras) blueprint_graph = keras_blueprint.save('A blueprint I made with the Python API', user_blueprint_id=user_blueprint_id) ``` ## Get help with tasks ``` help(w.Tasks.PNI2) ``` ``` Help on PNI2 in module datarobot_bp_workshop.factories object: class PNI2(datarobot_bp_workshop.friendly_repr.FriendlyRepr) | Missing Values Imputed (quick median) | | Impute missing values on numeric variables with their median and create indicator variables to mark imputed records | | Parameters | ---------- | output_method: string, one of (TaskOutputMethod.TRANSFORM). | task_parameters: dict, which may contain: | | scale_small (s): select, (Default=0) | Possible Values: [False, True] | | threshold (t): int, (Default=10) | Possible Values: [1, 99999] | | Method resolution order: | PNI2 | datarobot_bp_workshop.friendly_repr.FriendlyRepr | builtins.object | | Methods defined here: | | __call__(zelf, *inputs, output_method=None, task_parameters=None, output_method_parameters=None, x_transformations=None, y_transformations=None, freeze=False, version=None) | | __friendly_repr__(zelf) | | documentation(zelf, auto_open=False) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | description = 'Impute missing values on numeric variables with ...eate... | | label = 'Missing Values Imputed (quick median)' | | task_code = 'PNI2' | | task_parameters = scale_small (s): select, (Default=0) | | threshold (t):... | | ---------------------------------------------------------------------- | Methods inherited from datarobot_bp_workshop.friendly_repr.FriendlyRepr: | | __repr__(self) | Return repr(self). | | ---------------------------------------------------------------------- | Data descriptors inherited from datarobot_bp_workshop.friendly_repr.FriendlyRepr: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) ``` ## List task categories ``` w.list_categories(show_tasks=True) ``` ``` Custom - Awesome Model (CUSTOMR_6019ae978cc598a46199cee1) - "My Custom Task" (CUSTOMR_608e42ac186a7242380a6a98) - "My Custom Task" (CUSTOMR_608e42ecd5eb0dc5f28d0dda) - "My Custom Task" (CUSTOMR_608e43fc01f9f466aa8d0d81) - My Custom Ridge Regressor w/ Imputation (CUSTOMR_608e5a4ed5eb0dc5f28d0ea0) - My Custom Ridge Regressor w/ Imputation (CUSTOMR_608e5bc8b66a4934d58d0d4e) - My Custom Ridge Regressor w/ Imputation (CUSTOMR_608ef72b6f13f54305667783) - My Custom Ridge Regressor w/ Imputation (CUSTOMR_608ef74c5dda651931052422) - Second model (CUSTOMC_6019d18adfa83afbad99cdb8) - My Imputation Task (CUSTOMT_6188b0e6fb465717f029fd05) - Image Featurizer (CUSTOMT_61b452e57fd5b0629a2f4fd3) - Maybe Broken? (CUSTOMT_61b7d3f26f8e01a1a8f7bc0c) Preprocessing Numeric Preprocessing Data Quality - Numeric Data Cleansing (NDC) Dimensionality Reducer - Truncated Singular Value Decomposition (SVD2) - Partial Principal Components Analysis (PPCA) - Truncated Singular Value Decomposition (SVD) Scaling - Impose Uniform Transform (UNIF3) - Log Transformer (LOGT) - Smooth Ridit Transform (RDT5) - Standardize (RST) - Search for best transformation including Smooth Ridit (BTRANSF6) - Transparent Search for best transformation (BTRANSF6T) - Transform on the link function scale (LINK) - Ridit Transform (SRDT3) - Standardize (ST) - Sparse Interaction Machine (SPOLY) - Constant Splines (GS) - One-Hot Encoding (PDM3) - Numeric Data Cleansing (NDC) - Missing Values Imputed (quick median) (PNI2) - Missing Values Imputed (arbitrary or quick median) (PNIA4) - Normalizer (NORM) - Search for ratios (RATIO3) - Binning of numerical variables (BINNING) - Search for differences (DIFF3) Categorical Preprocessing - Categorical Embedding (CATEMB) - Category Count (PCCAT) - One-Hot Encoding (PDM3) - Ordinal encoding of categorical variables (ORDCAT2) - Univariate credibility estimates with L2 (CRED1b1) - Buhlmann credibility estimates for high cardinality features (CRED1) Text Preprocessing - TextBlob Sentiment Featurizer (TEXTBLOB_SENTIMENT) - NLTK Sentiment Featurizer (NLTK_SENTIMENT) - One-Hot Encoding (PDM3) - Pretrained TinyBERT Featurizer (TINYBERTFEA) - SpaCy Named Entity Recognition Detector (SPACY_NAMED_ENTITY_RECOGNITION) - Fasttext Word Vectorization and Mean text embedding (TXTEM1) - Keras encoding of text variables (KERAS_TOKENIZER) - Matrix of word-grams occurrences (PTM3) Image Preprocessing - OpenCV Detect Largest Rectangle (OPENCV_DETECT_LARGEST_RECTANGLE) - OpenCV Image Featurizer (OPENCV_FEATURIZER) - Grayscale Downscaled Image Featurizer (IMG_GRAYSCALE_DOWNSCALED_IMAGE_FEATURIZER) - No Post Processing (IMAGE_POST_PROCESSOR) - Pretrained Multi-Level Global Average Pooling Image Featurizer (IMGFEA) Summarized Categorical Preprocessing - Summarized Categorical to Sparse Matrix (CDICT2SP) - Single Column Converter for Summarized Categorical (SCBAGOFCAT2) Geospatial Preprocessing - Spatial Neighborhood Featurizer (GEO_NEIGHBOR_V1) - Geospatial Location Converter (GEO_IN) Models Regression - eXtreme Gradient Boosted Trees Quantile Regressor with Early Stopping (ESQUANTXGBR) - ExtraTrees Regressor (RFR) - Elastic-Net Regressor (L1 / Least-Squares Loss) (ENETCDWC) - Light Gradient Boosted Trees Regressor with Early Stopping (ESLGBMTR) - eXtreme Gradient Boosted Trees Regressor (PXGBR2) - Ridge Regression (RIDGE) - Nystroem Kernel SVM Regressor (ASVMER) - eXtreme Gradient Boosted Trees Regressor with Early Stopping and Unsupervised Learning Features (UESXGBR2) - eXtreme Gradient Boosted Trees Regressor (XGBR2) - eXtreme Gradient Boosted Trees Regressor (XL_PXGBR2) - Nystroem Kernel SVM Regressor (ASVMSKR) - Partial Least-Squares Regression (PLS) - Gaussian Process Regressor with Rational Quadratic Kernel (GPRRQ) - Eureqa Regressor (EQR) - Auto-Tuned Char N-Gram Text Modeler using token counts (CNGER2) - Frequency-Severity Generalized Additive Model (FSGG2) - Hot Spots (XPRIMR) - Linear Regression (GLMCD) - Frequency-Severity ElasticNet (FSEE) - Gradient Boosted Trees Regressor with Early Stopping (Least-Squares Loss) (ESGBR2) - Light Gradient Boosting on ElasticNet Predictions (RES_ESLGBMTR) - Support Vector Regressor (Radial Kernel) (SVMR2) - Regularized Quantile Regressor with Keras (KERAS_REGULARIZED_QUANTILE_REG) - Auto-tuned K-Nearest Neighbors Regressor (Euclidean Distance) (KNNR) - Lasso Regression (LASSO2) - Gaussian Process Regressor with Radial Basis Function Kernel (GPRRBF) - XRuleFit Regressor (XRULEFITR) - Frequency-Severity Light Gradient Boosted Trees (FSLL) - Ridge Regression (RIDGEWC) - Stochastic Gradient Descent Regression (SGDR) - Eureqa Generalized Additive Model (EQ_ESXGBR) - Elastic-Net Regressor (L1 / Least-Squares Loss) with K-Means Distance Features (KMDENETCD) - eXtreme Gradient Boosting on ElasticNet Predictions (RES_XGBR2) - Auto-Tuned Word N-Gram Text Modeler using token counts (WNGER2) - Auto-Tuned Summarized Categorical Modeler (SCENETR) - Keras Neural Network Regressor (KERASR) - Elastic-Net Regressor (L1 / Least-Squares Loss) (ENETCD) - eXtreme Gradient Boosted Trees Regressor (XL_XGBR2) - Gaussian Process Regressor with Dot Product Kernel (GPRDP) - Dropout Additive Regression Trees Regressor (PLGBMDR) - Elastic-Net Regressor (L1 / Least-Squares Loss) with Binned numeric features (BENETCD2) - eXtreme Gradient Boosted Trees Regressor with Early Stopping (XL_ESXGBR2) - Auto-tuned Stochastic Gradient Descent Regression (SGDRA) - RuleFit Regressor (RULEFITR) - Gaussian Process Regressor with Exponential Sine Squared Kernel (GPRESS) - Adaboost Regressor (ABR) - Elastic-Net Regressor (L1 / Least-Squares Loss) with Unsupervised Learning Features (UENETCD) - Gaussian Process Regressor with Matern Kernel (GPRM) - Light Gradient Boosting on ElasticNet Predictions (RES_PLGBMTR) - Gradient Boosted Trees Quantile Regressor with Early Stopping (QESGBR2) - ExtraTrees Regressor (Shallow) (SHAPRFR) - Statsmodels Quantile Regressor (QUANTILER) - eXtreme Gradient Boosted Trees Regressor with Early Stopping (ESXGBR2) - LightGBM Random Forest Regressor (PLGBMRFR) - Frequency-Cost ElasticNet (FCEE) - Frequency-Severity eXtreme Gradient Boosted Trees (FSXX2) - Gradient Boosted Trees Quantile Regressor (QGBR2) - eXtreme Gradient Boosting on ElasticNet Predictions (RES_ESXGBR2) Binary Classification - Stochastic Gradient Descent Classifier (SGDC) - LightGBM Random Forest Classifier (PLGBMRFC) - Bernoulli Naive Bayes classifier (scikit-learn) (BNBC) - Dropout Additive Regression Trees Classifier (PLGBMDC) - Auto-Tuned Char N-Gram Text Modeler using token counts (CNGEC2) - Gaussian Process Classifier with Matern Kernel (GPCM) - Gradient Boosted Trees Classifier with Early Stopping (ESGBC) - XRuleFit Classifier (XRULEFITC) - Support Vector Classifier (Radial Kernel) (SVMC2) - Multinomial Naive Bayes classifier (scikit-learn) (MNBC) - Adaboost Classifier (ABC) - eXtreme Gradient Boosting on ElasticNet Predictions (RES_XGBC2) - Elastic-Net Classifier (L1 / Binomial Deviance) (LENETCDWC) - Gaussian Process Classifier with Radial Basis Function Kernel (GPCRBF) - Light Gradient Boosted Trees Classifier with Early Stopping (ESLGBMTC) - Eureqa Classifier (EQC) - ExtraTrees Classifier (Gini) (SHAPRFC) - Logistic Regression (LR) - Keras Neural Network Classifier (KERASC) - Nystroem Kernel SVM Classifier (ASVMEC) - eXtreme Gradient Boosted Trees Classifier with Early Stopping and Unsupervised Learning Features (UESXGBC2) - RuleFit Classifier (RULEFITC) - Regularized Logistic Regression (L2) (LR1) - Nystroem Kernel SVM Classifier (ASVMSKC) - Naive Bayes combiner classifier (CNBC) - Light Gradient Boosted Trees Classifier with Early Stopping and Unsupervised Learning Features (UESLGBMTC) - Light Gradient Boosting on ElasticNet Predictions (RES_PLGBMTC) - Elastic-Net Classifier (L1 / Binomial Deviance) with K-Means Distance Features (KMDLENETCD) - ExtraTrees Classifier (Gini) (RFC) - Hot Spots (XPRIMC) - Partial Least-Squares Classification (PLSC) - Auto-tuned K-Nearest Neighbors Classifier (Euclidean Distance) (KNNC) - Eureqa Generalized Additive Model Classifier (EQ_ESXGBC) - eXtreme Gradient Boosted Trees Classifier (XL_XGBC2) - Auto-Tuned Summarized Categorical Modeler (SCLENETC) - Light Gradient Boosting on ElasticNet Predictions (RES_ESLGBMTC) - Elastic-Net Classifier with Naive Bayes Feature Weighting (NB_LENETCD) - eXtreme Gradient Boosted Trees Classifier (XGBC2) - Elastic-Net Classifier (L1 / Binomial Deviance) (LENETCD) - Gaussian Naive Bayes classifier (scikit-learn) (GNBC) - Logistic Regression (LRCD) - eXtreme Gradient Boosted Trees Classifier with Early Stopping (ESXGBC2) - eXtreme Gradient Boosted Trees Classifier (PXGBC2) - Auto-Tuned Word N-Gram Text Modeler using token counts (WNGEC2) Multi-class Classification - Stochastic Gradient Descent Classifier (SGDC) - LightGBM Random Forest Classifier (PLGBMRFC) - Dropout Additive Regression Trees Classifier (PLGBMDC) - Gradient Boosted Trees Classifier with Early Stopping (ESGBC) - Light Gradient Boosted Trees Classifier with Early Stopping (ESLGBMTC) - ExtraTrees Classifier (Gini) (SHAPRFC) - Logistic Regression (LR) - Regularized Logistic Regression (L2) (LR1) - Light Gradient Boosted Trees Classifier with Early Stopping and Unsupervised Learning Features (UESLGBMTC) - Light Gradient Boosting on ElasticNet Predictions (RES_PLGBMTC) - Keras Neural Network Classifier (KERASMULTIC) - ExtraTrees Classifier (Gini) (RFC) - Light Gradient Boosting on ElasticNet Predictions (RES_ESLGBMTC) - eXtreme Gradient Boosted Trees Classifier (XGBC2) - Elastic-Net Classifier (L1 / Binomial Deviance) (LENETCD) - Logistic Regression (LRCD) - eXtreme Gradient Boosted Trees Classifier with Early Stopping (ESXGBC2) - eXtreme Gradient Boosted Trees Classifier (PXGBC2) Boosting - eXtreme Gradient Boosted Trees Regressor (XL_PXGBR2) - eXtreme Gradient Boosting on ElasticNet Predictions (RES_XGBC2) - Light Gradient Boosting on ElasticNet Predictions (RES_ESLGBMTR) - eXtreme Gradient Boosting on ElasticNet Predictions (RES_XGBR2) - eXtreme Gradient Boosted Trees Regressor (XL_XGBR2) - Light Gradient Boosting on ElasticNet Predictions (RES_PLGBMTC) - eXtreme Gradient Boosted Trees Regressor with Early Stopping (XL_ESXGBR2) - eXtreme Gradient Boosted Trees Classifier (XL_XGBC2) - Light Gradient Boosting on ElasticNet Predictions (RES_ESLGBMTC) - Light Gradient Boosting on ElasticNet Predictions (RES_PLGBMTR) - eXtreme Gradient Boosting on ElasticNet Predictions (RES_ESXGBR2) Unsupervised Anomaly Detection - Local Outlier Factor Anomaly Detection (ADLOF) - Mahalanobis Distance Ranked Anomaly Detection with PCA and Calibration (ADMAHAL_PCA_CAL) - Keras Autoencoder (KERAS_AUTOENCODER) - Isolation Forest Anomaly Detection (ADISOFOR) - Mahalanobis Distance Ranked Anomaly Detection with PCA (ADMahalPCA) - Keras Autoencoder with Calibration (KERAS_AUTOENCODER_CAL) - Isolation Forest Anomaly Detection with Calibration (ADISOFOR_CAL) - Double Median Absolute Deviation Anomaly Detection (ADDMAD) - Keras Variational Autoencoder (KERAS_VARIATIONAL_AUTOENCODER) - Keras Variational Autoencoder with Calibration (KERAS_VARIATIONAL_AUTOENCODER_CAL) - One-Class SVM Anomaly Detection with Calibration (ADOSVM_CAL) - Local Outlier Factor Anomaly Detection with Calibration (ADLOF_CAL) - Anomaly Detection with Supervised Learning (XGB) (ADXGB) - One-Class SVM Anomaly Detection (ADOSVM) - Anomaly Detection with Supervised Learning (XGB) and Calibration (ADXGB2_CAL) - Double Median Absolute Deviation Anomaly Detection with Calibration (ADDMAD_CAL) Clustering - K-Means Clustering (KMEANS) Calibration - Calibrate predictions with RF (CALIB_V2_RFC) - Text fit on Residuals (L1 / Least-Squares Loss) (XL_ENETCD) - Calibrate predictions: Weighted Calibration (SWCAL) - Calibrate predictions (CALIB) - Text fit on Residuals (L1 / Binomial Deviance) (XL_LENETCD) - Fit High Cardinality and Text (XLF_LENETCD) - Text fit on Residuals (L1 / Least-Squares Loss) (RES_FDENETCD) - Calibrate predictions (CALIB2) - Calibrate predictions: Platt (PLACAL2) - Fit High Cardinality and Text (XLF_ENETCD) Other Column Selection - Converter for Text Mining (SCTXT2) - Single Column Converter for Summarized Categorical (SCBAGOFCAT) - Single Column Converter (SCPICK2) - Single Column Converter (SCPICK) - Converter for Text Mining (SCTXT4) - Multiple Column Selector (MCPICK) Automatic Feature Selection - Feature Selection for Ratios/Differences (FS_RFR2) - Feature Selection for dimensionality reduction (FS_RFCDR2) - Feature Selection for dimensionality reduction (FS_RFCDR_LASSO) - Feature Selection for dimensionality reduction (FS_RFRDR_LASSO) - Feature Selection using L1 Regularization (FS_XL_LASSO2) - Rare Feature Masking (RFMASK) - Feature Selection for Ratios/Differences (FS_RFC2) - Feature Selection for dimensionality reduction (FS_RFRDR2) - Bind branches (BIND) ``` ``` ``` ## Search for tasks by name ``` w.search_tasks('keras') ``` ``` Keras Autoencoder with Calibration: [KERAS_AUTOENCODER_CAL] - Keras Autoencoder for Anomaly Detection with Calibration Keras Autoencoder: [KERAS_AUTOENCODER] - Keras Autoencoder for Anomaly Detection Keras Neural Network Classifier: [KERASC] - Keras Neural Network Classifier Keras Neural Network Classifier: [KERASMULTIC] - Keras Neural Network Multi-Class Classifier Keras Neural Network Regressor: [KERASR] - Keras Neural Network Regressor Keras Variational Autoencoder with Calibration: [KERAS_VARIATIONAL_AUTOENCODER_CAL] - Keras Variational Autoencoder for Anomaly Detection with Calibration Keras Variational Autoencoder: [KERAS_VARIATIONAL_AUTOENCODER] - Keras Variational Autoencoder for Anomaly Detection Keras encoding of text variables: [KERAS_TOKENIZER] - Text encoding based on Keras Tokenizer class Regularized Quantile Regressor with Keras: [KERAS_REGULARIZED_QUANTILE_REG] - Regularized Quantile Regression implemented in Keras ``` ## Search custom tasks ``` w.search_tasks('Awesome') ``` ``` Awesome Model: [CUSTOMR_6019ae978cc598a46199cee1] - This is the best model ever. ``` ## Flexible search ``` w.search_tasks('bins') ``` ``` Binning of numerical variables: [BINNING] - Bin numerical values into non-uniform bins using decision trees Elastic-Net Regressor (L1 / Least-Squares Loss) with Binned numeric features: [BENETCD2] - Bin numerical values into non-uniform bins using decision trees, followed by Elasticnet model using block coordinate descent-- a common form of derivated-free optimization. Based on lightning CDRegressor. ``` ``` w.search_tasks('Pre-proc') ``` ``` ``` ``` [a.task_code for a in w.search_tasks('decision')] ``` ``` ['BINNING', 'BENETCD2', 'RFC', 'RFR'] ``` ``` w.Tasks.RFC ``` ``` ExtraTrees Classifier (Gini): [RFC] - Random Forests based on scikit-learn. Random forests are an ensemble method where hundreds (or thousands) of individual decision trees are fit to bootstrap re-samples of the original dataset. ExtraTrees are a variant of RandomForests with even more randomness. ``` ## Quick description ``` w.Tasks.PDM3.description ``` ``` 'One-Hot (or dummy-variable) transformation of categorical features' ``` ## View documentation for a task ``` binning.documentation() ``` ``` 'https://app.datarobot.com/model-docs/tasks/BINNING-Binning-of-numerical-variables.html' ``` ## View task parameter values As an example, let's look at the Binning Task. ``` binning.get_task_parameter_by_name('max_bins') ``` ``` 20 ``` ## Modify a task parameter ``` binning.set_task_parameters_by_name(max_bins=22) ``` ``` Binning of numerical variables (BINNING) Input Summary: Missing Values Imputed (quick median) (PNI2) Output Method: TaskOutputMethod.TRANSFORM Task Parameters: max_bins (b) = 22 ``` ### Set task parameters with a key Alternatively, use the short name directly. ``` binning.task_parameters.b = 22 ``` ## Validate parameters ``` binning.task_parameters.b = -22 ``` ``` binning.validate_task_parameters() ``` ``` Binning of numerical variables (BINNING) Invalid value(s) supplied max_bins (b) = -22 - Must be a 'intgrid' parameter defined by: [2, 500] ``` ``` ``` ``` binning.set_task_parameters(b=22) ``` ``` Binning of numerical variables (BINNING) Input Summary: Missing Values Imputed (quick median) (PNI2) Output Method: TaskOutputMethod.TRANSFORM Task Parameters: max_bins (b) = 22 ``` ## Validate task parameters ``` binning.validate_task_parameters() ``` ``` Binning of numerical variables (BINNING) All parameters valid! ``` ``` ``` Update an existing blueprint in a personal repository by passing the `user_blueprint_id`. ``` blueprint_graph = keras_blueprint.save('A blueprint I made with the Python API (updated)', user_blueprint_id=user_blueprint_id) ``` ``` assert user_blueprint_id == blueprint_graph.user_blueprint_id ``` ## Retrieve a blueprint You can retrieve a blueprint from your saved blueprints. ``` w.get(user_blueprint_id).show() ``` ![No description has been provided for this 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 ) ## Retrieve blueprints from a personal blueprint repository ``` for bp in w.list(limit=3): bp.show() ``` ![No description has been provided for this image](data:image/png;base64,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) ## Delete a blueprint from your personal repository ``` w.delete(user_blueprint_id) ``` ``` Blueprints deleted. ``` ## Retrieve Leaderboard blueprints via the Workshop ``` project_id = '5eb9656901f6bb026828f14e' project = dr.Project.get(project_id) menu = project.get_blueprints() ``` ``` for bp in menu[6:9]: Visualize.show_dr_blueprint(bp) ``` ![No description has been provided for this 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HxeduPLq5a4pZXVp7I0cO1TC6tv6frN52MOJydOLNqwkXT/zv5/nje5qqNvJuNQJ+Tbx3PejTjm+dJ5Txh3tu3r6/Z7ypXA1HrtNNI+DXxHu3H7/zX+KmQY19mDKhqQOAIuFWYJNGadt9vP7nBb1b8V5eFQT6ZrZuZjYOGvdOXUmt4KRuTeta2HRqnSdQ/hWl4uovoxx/abzySy6disgNGOo62Mf00J5U9o3PVJz8/czoitjTZ9JZCZtL0diRwwuUbvdZG9fP6WnEvGyuKiYdXf06ug4aMfbQik++PZMiVGp8sjWd061u0NQBQKGQgDZhdOvhq7cs7KPHZcfv/m3bgdD4pKxKgZ6ZtbPnIKvnlwqkZ58tS2n0qXOZQ8Y4+vub7//jibjaJ6o9BvdvTZdHBl94Xof8swmgVbQMdFVFRXl5pSJlx/LahAnjBQKV8PDwpKSkBiiOaTt2w6a5btriZ1d2btl+KOx6cgGrYWrtGTB57sfe1qN+2Fb4fPj6hNIG2FNTILq1cfjQ35LEstcEAHhvIQFtutTdP/mqrz7JDl085ouDKS+Sj4rMpJjTSTGnX6xDGfRd9NMc3y5mJtoqXFl2UtzZ7T9tPnqv5HVyynSeffzGbEIIIWzmgQle314WEkrD0n/6zKmDe1oZq5Rl3Is6+sfabRGpr0aYVM36TZgxNdDDztxAjZQXZKU9un/r/F8btsfkvyhWrd2Ajz6dNsTdxlSDzUu+Gnp465YDMVlV11NKx2HUnEkDXGwt27XSVaPKspPPfjNx1cMPD5ya3frI9H5fR77cjZp5/8mffDykV1czbaosL+3exa1Lvz2WLJZ9RDUqizt+7tmHE20C/Ttu33L/9aVdw22otyFVHHbsQjYn4+uSK3LZ4VGCzkOX/vWVZ/cO+kzJ8zuXg7Zv2nnmSZmkwKXUBa3ffdryxTP6d9bjUxwnLEo5v2ry1//WZRy34RkYGPr49B8+fNjz589DQkLCwyPS09PrXJpmn0/nuOtwGWfmj11wPP1F7VUmx5/YnBAZv/jgH2M6j5878sBH/6SyhFAGvaYtmdTHpmNbU0NtdT4pz0tJuLB/w8YD8XnVKkF6C68zaS2fEFLz6VYbch2dpHNHZni0vv2YmZ+M83HsYMAvfZb435WCanNWmE6fVm/q8kUioa/4Mya/dl8sALQ8SECbLreA/sZ0ZfyOdf+mSB76Eul2dOpsJiCEEKJpYt134rquxsLAr4KyJSds6t1m7fhzrqNW1aVHta19wOebHExnBy6NyOMIUbWatm37Qle9l1PBNAzMOhuYdRBc27kzJl9MCFG1mbFt+wJXnRdXLpPOfcYscu9tP2/8oqB0MSG0sduICX42LxuWlpExv6L4nRhUrKb9sWNhD90XhQhMLB3aapaxdTwiQgipvHosOGncJ539/Wz/uH/jxRdG6fQe3E+P5AUfD6m6akorXL7IZYZHaTgMHvHi34K2zv4zHd0dvhk/c9fDmjIRKXVBtRq5+tcFfbQpUUlORjGlaaBrzK8oaBLZZxWWFdM006pVq9GjR48bNy4tLS08PCIkNCTjeUZti3L362tAVcRu/yko/a1hQS7v8paNF/w2DXIY7G26+59UltD6dj4BfV5VE9Ew7Nhr9BKHzmrDJ+y8X1Xv0lt4nclo+Q1CjqOTdO7ICo/Sdl+669fJnV48I6di7uBnTgghFXWORHJfgQQUAGTCQ0hNl01nTVr8+GJUmpSLG1d0ad3EoW4uzpbWdtY9B3+0/Ua5Qb8P+uq9nocmvr8p0K59F9v2XWw7en57Wch0nrhsloN6ZsSmj/x6Wdk69xix9PAjsdnQWeMsGUKYjuOXz3PVrXwUvGLCIIdudpbdengsjyitNqBqOWHZFy7a5XcPLxjlZdvV0XHAtB9Dn1GmvisX+LzeK1d0fsXg7o4OlnZuniN/iX479WLaj1v2patO+f1jSyf4Otk5WLt4DZq45mw2J9cRSSBODPr3ZiXdzjfQQfBiEaXnPcRDh8s4deRSkZxfl4zI5Smh8vGpNR8F9LHp6uQ4YOqq08kiXbf5Xw02ruEApNUFpdVjQA8t9tYfw9zcuvf2cnZ27TlsfVSTvAnNMAwhxNTUdPSHo3Zs3/7zzxuGBA7R0daRv4Qulhq0+GHkpZpmSXAFlyNviijG0qrD66d7uMLTiwbY29l1tO3hMW71+eeshv24cU5Vb7+S3sIl4XWde+Lhmw8GPYpc3kvwagX5Wv7bp5tc+3oUv26goNrnUo9OwrlDywqP13XqwomWgsKE3XNHeNl2dbT3Gjd3e2y29L9opEYiq68AAJAGCWjTpaVBETYvN1/qJYKidLuN/mHnv5eiY2+E7Vo50JQhPJPWhhLrlekSMNiKV3jh+/nbwpLyK0TlmTePfrMprJjp5O5qSDMd/PxtBeLE379YuivmaUGlWFxZnJVTXC3/7DQk0FYgvPnrvFWHrmeUCivzky9v+2rFwWecXt8h3q+uw5woLy01p1QorihMT854+/Y502FwQFcV4Y1Ns5fvjUnJqxCWF2bcj7+fxdbpiF4Rp5w4EldGmw4e3lOdEEII3XrgB+4abPKpw7Hl8n5d0iOXq4SS2CP7w+5nlwkr8pOv/LVo5YE0VqOnj6fuOxmo9LrgOI4QysjK1cpQlSKEq8h6nJrfhK/uFEUxPB5FUZ0sO037+OO9+/b8+OMPxibG8myrpU4RtiCn5vFdriQvr5yj1TQ0Xt+v4cRFWZmFFWJWVJwWt++H3bdEjIGVlRFNpH+rrWxmHXmd+d058ZWt3C+IkLPlNwhpRyfh3KFkhcd0Gdi/HV1xdeO8tcdvZpQKKwvTEoL2nHsofehWaiQy+goAAKlwC77pKikjhNbR1aFJpoSrBKXde+nf28dY8F9c/lTM2xJCxDQtuVoFbTuY0bTawF9jBv76xgdiU7PWNM+wUzuGfXo5vMb7xYQQgYWlGc0+jb5U/UGfkmuR8eVjBllYtqVJrhwHxrPo1I5hn8ZcTnnnuOpwRK+xGWcOh3zR099nmPfayKB8ukPAMBcV0e2jR2+J6l94XUsouxF9s3LCgDbtTGmS9+ZHUuuCKrp8NCS7n3+fxbsuzMtLvpVwNeLEnp1nHsiaDPvCyZPB8h5UneTnS7zH+upNPnZ2dq8WqqmplZVJnAhbVMoRWsdAhybZ7zZ1SkNPT5XiSkskPYQlTk96UsLZampqUET6t9qKKZEUQg0PBlHGH+4OWe5a9T8N0vIl70uyN49O0rkjM7xS03ZtaDb1WtyzOs/iqDESyX0FAIBUSECbrgePy7gu7Xu6GG19UPMD3JR+/8nDzJm86M3L1u69kpRVxjP0XnJs4xBphXKchASGUlFToWg+jyZEJJJ8aWyIkZ6qDKWmQOpyRNVwBRH7Tj7zG+c52q/1ycPGo4Zb8Uov7zv24ppcz8LrWgJF0RQhNb1dUXpdcNknF08ojh/p29PeybGbU7/2zn37WdHDPzuZLU+oP65eLc9qdebj4+Pk6CDpU45lOYriWLYgv0DfQJ8QIiX7JITce1jKWXX0cDP5LemdZ6wobTePrjxO9PCexIyNq6ys5Kq+Z+nfKi9xzXDLzVICkUJpb1d64+gknjuywqMYmhBC1estn29EIruvAACQBglo0/VfyJXCgd49p80ecH7pmawaUlDasFUrASk9v/vXC4mVhBAizMkqrPZIAScSiTiirq5e7aIjTHuSxrI6xz8esCzs3QmFPMf0bJY27+5qSt96WlPSW5mclMrSFj16WTA3H7289Gg4eTqqksqUR6msXJM6hGlP0lja3NWtLXPzjVcmyTyiKgwjsdmWx/7vaOLoz1zHjOiZ12a4OZVz4uCpTK42hUtThxIoHXdvJwGpTH6U9urLeRm/9LoghJQ/jdj9U8RuQhgtqw9W7Vzp03egKzl5Sp5QoyKjanFgtdfduXsNSzki5liaoh48fBAWHhEeHv7ZpzM9PD1klvbf6bAc/0CXaV8GhH59/I0Heig9t8/m+uhS5VdPhsj3/L/Mb7VuZLd8qobTrcFJOndkhleZkpTK0u3c+3bccvN+Q4xZCp/L6CsAAKTCHNCmK+/Mb9tvV9CmQzYe2Dp/mEtHQ3UezQi0jDv3GPzJF8OsGcLmZGYKiVqP4eOcTTV5FKH5mpqq1XIzLvN5Fse09hnp016Tx6jqd+xua0runT3/iDUMWLl2qrdNK20BQzOqema2fV0seIQQ0d3zoc9YFcc5G+YH2JpoClT0LFyG+1i/fkBCfP/EiTuV/G6f/7RshJ2JOk+gY+E+fd03o1pT+RFBF3Lluz0svnfmXJKYbz/n11Xje7TTU2UYvmarLg5dDGhZR0QqhSKO0nXq29NMvebZe+KHR/ZeKWMsR29cOkCfSzl6IKro5UcyC5dJrhIoRsvQQINP0zzN1nb+i7asDDQiuaFBYQXc2/GLpdYF097rAy+7NtoCmmL4PFFRUQUhTfYnPMUiMSEkLT1t1z+7Jk6c9MUX804cP1FYUCjn5kXhv/9yuYBqNWjdvt8XDnfpaKjG56nomtn5ffrzoa1jO/GED/ZuPChnliP9W60z2S2/ptOtwX+DTNK5o/tARnjie0HBd4U8m882r5nm2VFflaEZVR1DvbpnyzL7CgAAqTAC2oQJE3+b/bXx1u/HWXvOXO05s/pHotv08RN3H4ceDJnl6e+1fJ/X8tefie+//EdKROid2d3shq8PHV5VYMIPfhO27/h+Z7/fpvl8ud3ny9e7il/rM/afZLY8dtv6E97rh9pP3HRkYvX9vSr8we5VG3tvn+8yct2hketeLOSEaadXrjkrZ/5JiOj2ju/+6L11Zteh3+4a+u2LMkqCZveefV7GEaXeTcznrKwm/nbW+CuXOWdqGOFiM4J2n53jPszEkCuPO7DneuWrT7gc6YXLJlcJlLbv6hDf1zfAuYrk48vWnM/jaoj/luS6SDHoMfWbZe786oeWe/pcrNzBNjqKokViMY9h0tLSQkJCwiMi6vACphfEKXu/mqO/cf3sHu4zfnSfUf0jriTx0PIZG+PlHs0USflWk+s+VCez5dd8uv2Z8u4ueV3nnng49+2o1waM/e2RzDAknzuywrv/z8r17n8udB24ePvAxdVKrNVNgGpk9hUAANJgBLRJE6dfWD56+KTvdp+59jizsFwsFpcVZiRdjzz85/5LeSzhck8vnTZvR9it9MIKsVhUUZKXmXr/evSVhy9+JUn84J/Z8/8Ke5BdKhaLSnMexT/MoiiuKHb1+LFztwZfeZBZWC4WC0uyk29ExD2tytTYrPMLxn269kjso5xykag851HM8Qt3SjnCci+vo2V3fp82duavJ+OSc8uElSWZ9y/u/3H8hwtP1OZViFzx1Q2Txs/eeiruSU5JpVhYmvv0ztWkQj4l64hKIzZ+ueXCredFaanPKiUUXhy171CSiGPzL+w+8caomazC5YhbeglszvVzQRevP0jPK60Ui0VluSk3zuxYPurDZaczXsTxVvxS6oKinl0Lv5GcWyZiWXFZXsqNC9u+njo/OEv+L7mx5ebmHvn3yGeffTZ9+oz//e9g3bNPQgghXF7spo+GDpv/279Rd1PzSitFFUVZj+PO7V45ddiwZbX7HU7pLbzuZLX8Gk+3eu7zXRLPHZknZtndP6d9OGX94ah7GYUVYrGovCj76Z2YC/9GPKrbLXnZfQUAgGSUjoGRsmNocaqeUI6Jid20eauyY5GJMhq1LXJV9+hl3pMPyT3ECUoye9ZMV1cXQoi//+BG3ZG+vn5eXp7EB35eWrRwYdUc0PETpzRqPNAEvO4rJh2S8VKAPbv+IoRERUY19tNyANBk4RY8vIE2dva1Jw9uP07PLijn6XVwHvLVpz0EbFL8Tfz2PLyWm1uL1w7Be0lKX6Hs0ACgGUACCm9QcRi9epOfZvU7h5w4Pfi3fffxuhUAeA19BQDUBxJQqI4S5N8Li+ng0Mm8lY4KqSh49uhm5Il/Nu+NzsS0LgB4DX0FANQLElCojiuI2T574nZlhwEATRz6CgCoFzwFDwAAAKMneUIAACAASURBVAAKhQQUAAAAABQKCSgAAAAAKBQSUAAAAABQKCSgAAAAAKBQSEABAAAAQKGQgAIAAACAQiEBBQAAAACFQgIKAAAAAAqFBBQAAAAAFAoJKAAAAAAoFBJQAAAAAFAonrIDaLksLS1nz5qp7CjgvWJpaansEGqGpg4AANUhAVUafX09V1cXZUcBoAho6gAAUB1uwQMAAACAQlE6BkbKjgHksmL5Mmfn7gyPkbQCRzjCkX379u/fv5/jOEXGBgAg4PMdHJ08PT3c3HqqqKgkJiZGRkVFRlzMy89XdmgA0OQgAW02err1XLpkCUVRNX4qFotZsXjNunX/Xf5PwYEBAFT3KhN1d3cTCARVmejF8Ij8ggJlhwYATQUS0GaDYZi9+/ZqaWq++5FILCouKl6xYuXDhw8VHxgAQI0EAoGDg6Onp0cvdzc+MlEAqAYJaDPA5/MdHZ28vfp17tLFQF+P4b3x6JiYZR8nPVr5zTd5eXnKihAAQIoaM9Hw8PDCgkJlhwYAyoEEtEkztzAf6DPAy8tLU0vz2rVr165dmz59evUVWI67fOnShg0/VVZWKitIAAA5qaio2Ns7eHv16+HWg6GZGzduhISGRl+JLikpUXZoAKBQSECbIoFA4NrD1XfQIHt7+5ycnPDw8FOnTmVkZBJCtm7dam7elqKoqseM8MgRADRHGpqaPXq4enp4ODo6EorEX4uPjIq68t+V0tJSZYcGAIqABLRpsbS09PUd1KdPHx6fF/1fdEhoWFxcLMuyr1YYEjjk448/JhxhxaK169ZfvnxZidECANRT9UyUIyQhPj4yKuq/y/+VlZUpOzQAaERIQJsETU1NDw+Pwf7+7Tu0T3n6NORCyPlz5wsKa5inr6Ots2vPrqLCwhXLVyY9SlJ8qAAAjUFTU9O1h6unh4eTkxPLcchEAd5vSECViaZpOzs7b29vD49eIpHoypXokJCQhIQE6VuNHj367NmzeOQIAN5LWlpaLq4uLzJRlk1ISIiMirp86XJ5ebmyQwOABoMEVDkMDAz69evn5+tr0srk4cOHp0+fCQ8PR/cKAPDKq0zU2dlZLBZXZaKXLl2uQFcJ0PwhAVUomqZdXV38/PwcHR0LCgtDQ0LPnzv/NPWpsuMCAGi6tLW1uru8yERFItH169cjo6IuRV2qqKhQdmgAUEdIQBXEwMBg0KCBAwYM0NfXT0i4fvr06ZiYGJFIpOy4AACaDW0d7e7du3t6eHTv3l0oFMbGxIaEhsVfuyYUCZUdGgDUDhLQxlU1y9N30KCebj0ryssjo6KOHz+RkpKi7LgAAJoxAwODXh69PD08rK2tS0tKomNiIyOj4uOvCYXyZqIqqqq4lQ+gREhAG4umpqaXt1fgkCGtWrWqmuUZGhqK18UDADQgQ0ND917utc1EtbW1Nv7yy/q16+7cvauwUAGgOiSgDYyiqK5du/r6+bq7uVVWVFwIDT1z+gyGPAEAGpWhkZG7u1tVJlpSUhITExsZGXXt2tUaZzoNHDhw9uzPxWLx9j+3nwgKUny0AIAEtMGoqan16dMnICCgXTuLqiHPsPBw3OIBAFAkI2NjN7eeVZlocXFxbGzcu5no6tU/2HbtSlM0R0jUxciff/kFfTWAgiEBbQDmbdv6+ft7e3sxDBMRHhEcfBKviAcAUC5jY6Oebm6eHh42NjaFhUVxcXGRkVFXr8ZpaGrs3bOHpumq1URicVZW5qpvvsWtKgBFQgJad1UPGAUGDnFxccnIyDh9+sy5c2cLC4uUHRcAALzWpk0bTw8PT0/Pdu3b5eXlPXj4sLuz86sElBAiFotFIvFPP22IirqkxDgBWhQkoHWhq6PT38fHz8/PyMjwxo0bp8+cuXzpcvVfbAcAgKamrVlbz94e3t7exibGNEVX/4jjOEJIUHDwju078II8AAVAAlo7lpaWvr6DvLy8hEJhZGTk8WPHU57iNfIAAM2DjrbOnr27qw9/VseybOK9ez98/wN+6xigsSEBlQufz/f08AgYEtC5c+ekR0nBwScjwiPwIxwAAM3L4MH+M2bMkJSAEkJEYlFxccn3336HNzQBNKo3EtBFCxcqMZTGdifx7vFjx2u7la6Ojq+fn5+fr46OzqVLl4KCgu/cudMY4TU4K6suw4YOU3YU8Iajx44mJt5TdhTNW+DQQBsra2VH0eLUrf9sgtatW2tlbfXW/fe3cBwhhHv06NGz9GeKigsaVxPpe3Fd/nH16lf/5lX/wMPTQ+HBKNRxUosOtEOHDv7+flV32y+EhBw9eiwrM7PxYmtwhkZG732FNjuRl6JIE+gEmzUbK2s0bKWoVf/ZNOnp6VlbW3Mcx3KslEFQiiKEUB07duzYsaMCo4NG1ET6XlyXyev8880EFAghNE137+4SGBhgb2+fnp7+199/nz1zFnfbAQCaO4GKYPPmLYQQjuNKSkteLX+/7/4BNE01JKAxMbGbNm9VfCiNZ8+uv+RZTV1dvb9P/6GBQ6uebV+16rvY2JiqRyObtU2bt8bExCo7ihbN1dVl9qyZyo7ifTN+4hRlh9AiyNl/NgsZzzPOnDlTwwcLCXkfr33QZPvevc+NbhapKzsKxRnXKqubVulbCzECSgghpq1bDx4SMHDAAI7jIiIi8Gw7AAAAQONp6Qmoja1NYMAQ917umZmZ+/cfOHPmTHFxsbKDAgAAAHiftdAEVMDne/T2HPnBCHML8zt37qxZuxZvkgcAAABQjBaXgOrr6w8aNCggIEBNXTXyYtSatWuePElWdlAAAAAALUgLSkA1NTXnzZvXp0/vwsLCoKCg4JPBhQWFyg4KAAAAoMVpQQmog6PD3buJ69dvuHz5Mn7qFwAAAEBZWlACej3h+uIlS5QdBQAAAEBLJ+3nyN4zRUVFyg4BAAAAAFpSAgoAAAAATQESUAAAAABQKCSgAAAAAKBQSEABAAAAQKGQgAIANBZKr++y/cGRq/urNO0yQWFQfdB0UCoqnw80OOSuIlDG3pGAQt2oOM3+37W4U2sGGFDKDgWgyaJUTW26tTdS5zXgafJmmTgTmxTZ1dEYTQKgbiiG6WTA0+dRFCGEUF3t9YM/NFxoTiumcTbAe0BVe87ZvWxwe2N9LQ0BIy4vys9MuXcrJur8v0fDEgvE8pXB2E75dcN4zaCZU7bck3MTaDBv1CBXWVaQ/fThrf8uHNt1KDK5TOJWFEVRFK2gdlpLaJNAiKpFv/GfTfT37GpuqE5VFGQ9TkyIOr1v2+Hredx7VblN+Ux8z7zVVZbmZ6U8uBF19vA//8Y8q3yxjhKrQ57woLlQaaX5k4tqWzVak08xHFdWyT4rEF57Wn70YUVqY/6QDiXfwGQna92lXagLEXm78+q+rwZIQBkjy26Wpi/uJjDqusbtdI3b2Xn6T/nkxq6l83+4kCbHd0XrWth0ap0nQB+qDG/UIFHVNDCzNjCz7jFwzJh/v5j8zdkMtqaNKq7+MsrxFwVGWRtoky0eYzHyp8Pf9DZkXtQfz8Csa682nXgJu/69Trj3qXKb9Jn4nnmrq9QybGtr2NbWzXfs8O3Tpm6KLuSUWx1yhAfNBqPGs9JhXtwZpygNVcZSlbE0UR3SuezbC4UXSxtjn9yt67n+1+VZk9LR4rfTYOt5476hbsGL7mwZaWPTtYONU7defsM+/W57VJpI137yz38scdN6Hzr5psS0desVK1f069tPVVW14UoV3dk8wtqmawdre9ueAwZP//5/t4tVOgxfOctNveH2oVhok83MuHHjPv74I0tLywYoi+cwaaaHAckM3TBjQC/XLraO3TyHjP5yw4a/w2v+ewreU/O+/HL06NGtW7VuuCKrd5UDA2duCH5Uqdnto1VTrJmG20c9NNHwaBUtIxMjPfW3x7wkLW92bGxsFi9e7O7uLuDzG7DYBzdy+u/N6L03w+dQ9kdhRcG5nEBbba6doAGv/UrUYLXOCisqxRxHKoqzkxNCkxPCToXM37nzoy7jF044OHzrXTGhDPou+mmObxczE20Vriw7Ke7s9p82H71X8vqPMqbz7OM3ZleVlnlggte3l4VybNXyMAzP1cXF1cVFKBReuRIdFhZ27do1oVBYz2JZUaVQzHFEVJqXdjti35JUTZsTc2ycnDswl592GzVn0gAXW8t2rXTVqLLs5LPfTFz18MMDp2a3PjK939eRQkIog17TlkzqY9OxramhtjpfXJh+N3zf5j+utxk6LnBADyszXaYk7da5f9av3ncznyOESK9ZSsfh3T1+l/XJyQMTtU/N8Z59tuTld2H95fH/fWYYPL3f4pB3Zgs0VpvUsPSfPnPq4J5WxiplGfeijv6xdltEan2/fiAGBvoDBw4cNmx4xvPnIaGh4eERaWlpdSxL3czCgGFTgjftiHogJoSQysyk6JNJ0dXXqXWH81YjJ+V5KQkX9m/YeCA+73WTofXtx8z8ZJyPYwcDfumzxP+uFJhU+zO/9s1+5elcTmqZTKdPX5+JlM7QHVHf93trXKLyylLvj/dmci2w6bbr0M7L22vChPFJSUkXQkKiIqNyc3PrWWa1rjL1RsjOeQVGdrsmtndxMqHvpLNvVEfV+tKbBCGEqJr1mzBjaqCHnbmBGikvyEp7dP/W+b82bI+p6ixr1+dIDU+O0qQFI7GJSimT1u8+bfniGf076/EpjhMWpZxfNfnrf9NZScsJIUSt3YCPPp02xN3GVIPNS74aenjrlgMxWVWzZSTGUM9qrSc+n9+rl3uvXu7l5eWXLl8KDwtPSLjOsvX9e5djiZAjHCHlFeIHaaXriqlO/pqWRgJzqvKZgdoUa1V7fV4bdVqVcHlF5RsvFEaUE4rP87LVGNVO0FGNKi8TxSWV/H674vnLQGhV/pBuGoFtBeaqpKxEdC2DNaw2HtOuq/5f9szZsOzV6S+/Tx7jYaX5YQdBZw2KFnPP8yp2Xyk8V/WzkhRvsr/JZEIIIWxZ2RdHC6/V8nAb7c8OruDKr2sODdw+sZOvv9Ufd2+LiUi3o1Nns6qeUdPEuu/EdV2NhYFfBWVLbTd126pl4PP57m49PTx6VVRUXPnvSsTFyKtX48TiBprT9noWE23sNmKCn83LtqJlZMyvKH57bX07n4A+r9bh67V1HPb1jmHV1hBYdP9w6W/6JR98ciyDJdJrtsY9Cm9F/pc7YbiLm53g7H9VM5poEyfXdnRFVPS1cjmOqEHapHq3WTv+nOuoVXUFUW1rH/D5JgfT2YFLI/LQJOtNLBYzDGPSqtWHH344duzY9PT0sLDwkNCQjOcZtSuo7Flqnphu6z3B99/7wVJmMr9DarN8s5ETDcOOvUYvceisNnzCzvsiQgihtN2X7vp1cifVqnNHxdzBz5wQQirkK7+mZs/JLlNOLbvpdujQ4WMLi+nTpj14cD8sPCI8PLywoLBBSmZFYpYQQtM13k+UXX2qVtO2bV/oqveyw9UwMOtsYNZBcG3nzph8Malvxb0dnvTSZARTcxOVVibVauTqXxf00aZEJTkZxZSmga4xv6KAJbSE5YQQVZsZ27YvcNV5EbBJ5z5jFrn3tp83flFQuljSaSL7i1AUVVXVvn36ePXzLisrvXgxMiQ05O6duxzXQBFS5FW6aNBKbZgF/+X3QOmrk0ohITz+JC+9KUZU1benosn3tte10cifdqWigBBKIJjVX3eEbtUjR0Sgxe+nRQghEmcIM7zR/fQ+NXnZeBjKwpBRb7gZqI35FHzZ9fDoApZuY91JgxDCFV1aN3Gom4uzpbWddc/BH22/UW7Q74O+eq+Tb/H9TYF27bvYtu9i29Hz28tCItdWLRvD41EUpaqq6uHZa/nypXv27J7xyXQbWxuKquM3RDEqmobmdv3Gfb9msg2Pzbme8KgqoeWKzq8Y3N3RwdLOzXPkL9E1/vHNFZ5eNMDezs6ym4f/klOpYo7Nj908a4Sbs0NnJ5/xW68WEt0+w72M6aryZNXsO3ssjwuNyCNGnr3tX97i0HR06coT3Y6+WiDn2V3fNsl0nrhsloN6ZsSmj/x6Wdk69xix9PAjsdnQWeMsm8b9t/cFwzCEkNatW48ePWrH9u0//7xhSOAQXR0debcXXt25OSqbsvhg/dHQvas+GWil/+7f2nXrcF428o62PTzGrT7/nNWwHzfOqapF8rpOXTjRUlCYsHvuCC/bro72XuPmbo/NrjYqUPtmL7vM6riCYx91s606qPa2A+cGpQrZ0tv7dp7Nplt406Uoqqq37GTZadrUqXt271r1zTde3l51nshEMQINfTNbzzHfLR9pzohTrl57XkOlyKw+puP45fNcdSsfBa+YMMihm51ltx4eyyNKq9+FqVPFSQhPemkygyGEvNtEpZVJafUY0EOLvfXHMDe37r29nJ1dew5bH1VKJC0nhLGcsOwLF+3yu4cXjPKy7eroOGDaj6HPKFPflQt8Xp8n8lyPlIdheBRF1NXV+/f3Wrd27Z7du6bPmF6fyUUURWmoMtZt1Oe7a1jSJD9bmPKiUrio6JwhBzL77s8adbrkuph0sNKaaETlpBUvCMry3pc59HTh6QKuVQeNQF1CCOliozVclyrOLl11OmvAvkzfY7mrbldKGTtu21n7YxO6Ir9sw/nswfsz+x/MmnSh6OKr4R5O9PfJDM89GZ57Mvr8W+vhT9KII6CEECLKzS3gKC11TXWaFLIUpdtt9IIlPW0sWuvzS55lswzhmbQ2pEmutCG7um3V8vB4fEKItra2v6//kIAhObm5D+7fr1UBXeeeeDi3+hKuMiX4281RpVV/cXGivLTUnFIhIcL05EJCaur7OHFRVmZhhZiQvDtHt+79cMCCDtl3Lt19XkoISb+0bcf5MY5D27Yzp8lzVo6afXuPhJTFnArPHT60n0/XDbHxIkIE3Vwd1MQPL0Y9k7vl169NMl0CBlvxCi98P39bWAFHCMm8efSbTR4DN3q7uxpuflDLUTqQhaIohuERQjp36mJp2Wn6tGk3b96UL2MQJx+aOzxj8vy5E3ydP/i6+/DZ6XFHd27ZtD82Q/qf77Kb5atGXpwWt++H3b795ttYWRnRMeks02Vg/3Z0xdWN89YeT2UJISQtIWjPudGTXBxrUf6bzZ6xlV1mjWij/it+XzvY8PH+L6asuZRN2UxC0yWEEPLqCXUnJ0cnZ6fZs2fHREfL2OYNNXSVJXd3rdhxu4aWJbNJMB38/G0F4sRfvli6615VGlWclVNtQE9qn7P1wbtTmqWGJ720R9oygnlR3ltN1EZKmb8FcRwhlJGVq5XhvdiMcq4i63EqIYTial5OmE5DAm0Fwptr5606lCQmhJQmX9721QqL4N/H9B3irXf2cG5NMTRVVddlXT09fz/fwCFDnqU/e/goqVYldHYwiHB4Y4mwqPzXGxUvkkCOKygR54k4QriMIkIovnc7PlNZvuVSSdVdwpycsl9uCHp7qjibMHsK6N5tebS4cmdU0fmqe5jFwpB7FQHWAtsa903xvNvzBWLh7xcLj1V9x2LucVZDTqJv1ASUp6+vQ3FsaUkpR2n3Xvr39jEW/Bd/waiYtyWEiGlaagB120oCD0+Pk57BddiweWF4DCHEQF/foGfPqiVmZm1iYmLl2pjjxOKK0vzs1Ee3Y8JP7j0Y+qCIqznXlEn8LDldRKyNTHRpUsoSQkjl89RMjjJWV6PqXLNl/504/2zoqIGD7NbFXxMyndxd9bnko+GP5P9jpH5tUtC2gxlNqw38NWbgr28eralZa0JkXMU5QhYtXEgWyh1sC8NyLMdK+GOcIjRFE0Ls7OxeLVNXVy8tlfIsaGXqxW1zLv7zo7PfxCmTxnp1H7tkR3+P78Z+djBJUg5a6yYhTk96UsLZampqUIQQvmm7NjSbei1O0h9EdWhyMsuseUdaLnM2bxxpnnVq0dTvLmaxhKjVq+mSZtt/FhQWSPqIomlCCJ/H69WrV9WSNm1MGYaRdxZTVQrFFcXtXLJgS9jjt8cJCSFyVB/PolM7hn16OfyhhEE8qX0OTSQ/U1djeNJL4xnKCKb2EVJFl4+GZPfz77N414V5ecm3Eq5GnNiz88yDEknLBRaWZjT7NPrSk2q1UHItMr58zCALy7Y0qesMXuX2vVWZaGvT1q1NXzwPp8+vxZ1sln3xGqbraeXHHlQ8kVQ/DNNWk9A81ZWjVFe++YmxJk3TdBsNwhYLb5TUuPE7aKadNmGLK68VyR9p7TRmAqpm37eHDs0mJz4oIfqBk4eZM3nRm5et3XslKauMZ+i95NjGIdILoPT712ErSRITE48eO1a3bZsUA32D6dOnSVlBJBLxeLz8/HxdXV1CSGqqPE9yiG5tHD70t6SGG1dmhZUiQvH5/Fd3TYRCEUcoiq5HzZbHHT32eNQnA/y6/3Qtxszd05yk/hNxV/6g69kmJU7koVTUZP+sCUXI0WPHEhMT5Q63ZfH1HdStazdJn7IcSwjFseKCgkJ9fX1CiNTs85WK51ePrr16/A+b4as2Lg3oPedzr1Nzz9U8f7IOzZKrrKzkKKpqTI1iaEIIJfEVkHVp9rLKrAnP4oPVW6bbiGI3zlhyMvXFFJp6NV3SbPvPCRPG62hLnLlRNee4vLy8alg9LS1djuzzVVcp6DRx68FFPTpaGZMKSd+urOqj+TyaEJFI4l6lVlxNxUoNT3ppMoOpQ4Rc9snFE4rjR/r2tHdy7ObUr71z335W9PBZJyUsD22s6XWN3fe2s7AYM2aMlBWqGltWdraRoSEhJFcoV/Z1PyFn2i2RvH99ckTS7XQVhqKoFxND5Z55+WImX+NNsG20BJTS6Tlrwcg2tOj+2ZN3xbRlq1YCUnp+968XEisJIUSYk1VY7SLAiUQijqirq7/R+GhD6VvVTnZWdlRkVF23bkLamrWtcblIJOTx+AWFheHh4VFRUfr6+osWNtHRtrrXrOjuoYMJH3/df5jbL0/NPbpQT/86e0vePyTr3yaFaU/SWFbn+McDloXV6TVsiYmJ70cjbAxOjo41JKAcEXMsTVEPHzyoenbks09nenh61LJstuDO0Y0HP/Cbb2Np2Zo597hROpzKlKRUlm7n3rfjlpv3axijqEv5ssp8B6XlMuf3ZX10Uw5/Mnfn7VePX9W76TbT/vPDD0e9u7Dq2WSO4xLi48MvXrx06fKRfw/XvuzKB3sWL7E/sMl/3vqPE8b+kVhDVcqsPuHz9GyWNu/uakrfelpTmlH3iqspPOml8RxlBFMjmRGWP43Y/VPEbkIYLasPVu1c6dN3oKv6yVMlNS4/8zgplaUtevSyYG6+urWl4eTpqEoqUx6lsnV+cKWx+96iwqIa80+xSMzwmJzc3LDQ0AvnQyzaWzTidZkVp5UQVlC28Hjhf+9eFyl+SjGhtQU9dajEfHmeXxOnlRBaU+CkRe69Pc2BE3EcIZRa/VLIBnsIiebxGYoQRqBhaGHfb/SS7Qf/mmqtKnyyb/Wuu2LC5mRmColaj+HjnE01eRSh+ZqaqtUi5zKfZ3FMa5+RPu01eYyqfsfutqaMzK2AEELEYhEhpKysPPJi1DfffDt+3Phtf2y7c/uOsuOSph41y6YEHwovMRg4atRQr67Mk/MnJeefDd8myb2z5x+xhgEr1071tmmlLWBoRlXPzLaviwWaZYNjxWJCSFp62q5/dk2cOOmLL+adOH5C3ieXBY7Tf/hqoldXcz1VhqIYNf32LsNmDu3McKKszFy2kToc8b2g4LtCns1nm9dM8+yor8rQjKqOod6rLLcu5csq8y2Uft9layZ14W5t/nJ1aA5XvRw0XcIRlmU5jnvw4P6WLVtHjx6zfMXK0JDQinJ5XqJREzbz9KoVh9JUHGd+M826pndyy6w+0d3zoc9YFcc5G+YH2JpoClT0LFyG+1Qrqz4V92540kuTGUyNpJfJtPf6wMuujbaAphg+T1RUVEEIRRFK0nLx/RMn7lTyu33+07IRdibqPIGOhfv0dd+Mak3lRwRdUPa7luQnEgkJIYWFhSdPn5y/YMGkiZP++uvvp6lPG3evnDDiqYhVU53bS6OXPqPJEJqidDT5PY0ZHiGEE154IhTR/Al9tEeb8nQZQlOUlhotcU49JwxPEYkZ/pTe2kNNGB2G0DRlpMfvoEoIITmlLEsxHpaqbfmEYWgLY75J7QevG6rz4dnM+vferDdiF+ff/GfpvO8vF3KEkJzQgyGzPP29lu/zWv56HfHLx2TEKRGhd2Z3sxu+PnQ4IYQQYcIPfhP+fCp9qxat6r6HUCi6fPlSeFh4fEKCSNSYv8/VoDgZ7UH6thd2n5zTf9Rnn3FM4paTdyTeLmqUNrl9x/c7+/02zefL7T5fvtpGGL/WZ+w/yXjDeb3RNF01gSQ9PT3kQkh4RMTz58/rUA7Pxnvs0CkWH0x5czFX9mDXn2dzOcI1Tocjvv/PyvXufy50Hbh4+8DF1T6oGhurU7OXUeZb+E6+fqYMRXX74sjVL16Xkf73JN9VLbjpsmIxRdMPHjwICQmJiozKL5A4MbS2uIKoNd8e99w67NPl486O/+vB292RzOorj922/oT3+qH2EzcdmVjt81eduehWPSrunfCklyYzmBpJKzPFoMfUb5a5V381O5t7+lxsqYF3jctLiPjB7lUbe2+f7zJy3aGR614ehzDt9Mo1Z5t+/ilmxTRFV1RUXIy8GBYafuvWrfq/CrRW7t8uOtRGd3RbzdVtNV8tFGYVTThXmsaRx4mFf7bW+8RE9TMv1c+qbSXpNUwP7hTtN9Udb6A2z0dt3otlXMjFrJUpXFpaxQM7vnVHnX0ddQghhBVuCco9UMvZog2QgIqzHt5Msm5vrKetrsKw5cUF2Sn3b8VdOnfocMid/JenI5d7eum0ec/nTPV17mSiwYjKiwrysp6lXHn44v054gf/zJ6vveLzIT066Akq8lNuPcyiKJlbtVgikfDatfiwsPDo6OiKijrPSlCeetVs2ZW9vqPFygAAHvhJREFUh++OmGUrjjt0vOZJq43XJrmi2NXjx97+aOoYH1ebtgYaTHleelJC3FP80nKDyM3LCwsNDQsPf/L4SX3KYR+dWPOTIKCPi30XcxMtAVdRmJGSGBty9M+/Tt0p4kjjdThld/+c9uG9CdOnDfG0a2eowQhL87OePrqXEPFISOra7KWXKbeW2XTFYvHTp6khISEREeGZmVmNsAcu/+KvG8L6rff6+Eu/EzOD8t/+XFb1sVnnF4z79P7s6SP7dDPXIQUpN6Ieafp4d2a5F4lL/SrurfBypJcmM5ia9yG5TIp6di38RhvnTm10VaiKgrQHV8/s3rIpOIsY17ycI4SU3fl92tjHU2d+PMTN1lSTzXtyNeTwltcvom+6Kisro6NjGuqnYeqGE1b+di73vo3GkLaCTlq0GsUVlIjuZIpfRCMS7Q/NTeqiMbq9wFqbUae4sgo2vVB0J63m4StOWPnnhdwkG43hFgJLDZrPsdmFwuRKQhHC5peuukTNtlNz1KF5YjY9R1SHx8MoHQOjV/9z8mQwISQmJnbT5q11OfSmas+uvwghUZFRP65erexYGoCKigqfzy8ufvtd8G/x8PSommuyafNWeZ+Cby54DotP7RqbtKzPzOM5zeFvEVdXl9mzZhJCfly9ujlOpFMMfX39vLw8mW9sXrRwYdUc0PETp0hfExpEs+4/9fX15fnpo6Z07aOMRm2LXNU9epn35ENKH/JrUsHUhcL6Xg0NDbFYXC5rOser6/Le50Y3i5rtD13X3rhWWd20Sgkh/v6DXy1sQfN/3hsVFRXNctSzvihtI2NxXrZQ3azX1HmjzPLOrQltlj0iSFD/30gEeEvTb1S0sbOvPXlw+3F6dkE5T6+D85CvPu0hYJPibyrhRl+TCqZ5KSmR8+VG8BoSUGgm6NYfbDy1vDufEEI4cXbYip/Di9AnAkCzpuIwevUmP83qD3Bw4vTg3/bdV8Id5yYVDLz3kIBCM0HpqHD5pSI9kvcoOmj7j5tOPkWXCADNGyXIvxcW08Ghk3krHRVSUfDs0c3IE/9s3hudqYQnw5pUMPD+QwIKzYT47u/j+/6u7CgAABoOVxCzffbE7coOo0qTCgbefw32HlAAAAAAAHkgAQUAAAAAhUICCgAAAAAKhQQUAAAAABSqBSWgFFX7XyoFAAAAgIbWghJQ5+7OI0aM0NLSUnYgAAAAAC1aC0pAs7OzR40c8ffff33++Sxzc3NlhwMAAADQQrWg94A+efxk7i+/9OnTZ+iwwIEDB16/fv348aDY2BiZvz0NAAAAAA2oBSWghJCysrIzZ86cO3fOzs4uMHDI8uVLnz17FhQcfPbsuYrycmVHBwAAANAitKwEtArLsgkJCQkJCWZmZn7+fpMnTZowfvz5CxeOHT2amZml7OgAAAAA3nMtMQF9JTU1ddsf2/bu3eft7TV86NCAwYPj4uKOHz+RkJCg7NAAAAAA3lstOgGtUlJcfOL4ieCg4O7dXQIDA77//rukR0mnTp4ODQ2trKxUdnQAAAAA75saElBLS8vZs2YqPhTlYlk2JiY6Jiba0tIyMDDw008/mTBxwoXz54OCgrOzs5UdXb34DhzQ09VF2VG0aHp6esoO4T3UArspqCc+n6+iokII4QgpKS5+69OWee17vzXZvtdDp9BOo0TZUSiOuVrFuwtrSED19fVcW3C+8vDhww0bNuzcudPX13dwgH9gYGD0lehjx4/dvZuo7NDqqFMnS2WHANDwWnI3BXVjbm6+adMvkj5t4dc+UCSLmhKylqYFvQe0VvLy8vbt2zdpwuRNv/7apk2b9evX//LLRi9vLx4PkxYAAJqlpKSkzCw8aQrQJFA6BkbKjqEZsLG1CQwY4ubuVlBQEBoa+h7clwcAaIEmTpw44oPhjOShhKo3QwcFB+/YvkMkEikwNICWhVFV11B2DM1AVlZWVFRUWFi4QCAYMGDA8A+Gm7U1y8vLQxoKANCMFBUV+Q/2l/SpWCwWCkXr1q07euQoy7KKDAygpUECWgvFxcXx8fHHj51IeZrS3dll3LixPd16cBx5mpoqxh/KAABNnqaGplsvN3U19Xc/EovFmZmZixYtvnnzluIDA2hpcAu+7iwtLX19B3l5eQmFwsjIyOPHjqc8farsoAAA4G1t2rTx9PT09PBo177d82fPDI2M3prQzxESdTHy519+wa/iASgGEtD60tXR6e/j4+/vb2hocOPGjdNnzly+dBn3bgAAlM7Y2Kinm5unh4eNjU1hYVFcXFxkZFROTnb1Z+Gruut//tl1+PBh5UUK0OIgAW0YDMP06NFj8GB/Ozu75xnPT586feF8SEFhgbLjAgBocYyMjd3cenp6eFhbWxcXF8fGxkVGRl27dvXVQ0V//rnN1NSUECISi4qLSr7/7rs7d+8qNWSAFgcJaAMzNzf38/Pz8uonUBFcirp06tTp27dvKzsoAID3n6GRkbu7W1XeWVJSEhMT+1be+cq4ceNGfziKUNTdu3d/+P6H/AIMFgAoGhLQRiHg81179vAdNMjBwSE1NfX8+Qvnzp0tLCxSdlwAAO8bQ0ND917ub+Wd8fHXhEKhpE3MLcy3btny779Hdu3aJRaLFRktAFRBAtq4qh5U6tu3L80wMVeiT585k5CQoOygAACaPQMDg14evaryztKSkmg58s7qunXrdvPmzcYOEgAkQQKqCOrq6r179x7s79++Q/uHDx+ePn0mPDy8HM9aAgDUkraOdvfu3T09PLp37y4UCmNjYkNCw+TPOwGgiUACqlA2tjZ+fr4eHh6VFZUXL148d+78/fv3lR0UAEBTp62t1d3FxdPDw9nZWSQSXb9+PTIq6lLUpYoK/KY2QLOEBFQJtHW0vb36DxjQ39zc/MnjJ+fOnQsNCysqwgxRAIA3aGlpubi+yDvFYnFCQkJkVNSlS5fxtk6A5g4JqDJVzRDt06cPj8+L/i86JDQsLi5W5jtEu3TpfP/+g6ofLAYAeP+8yjudnJxYlq3KOy9fuoyZSwDvDSSgyicQCFx7uPoOGmRvb5+TkxMeHn7q1KmMjMwaV+bxeLt3706Ij//5558rMecJAN4jmpqarj1cX+SdHJcQHx8ZFfXf5f/KysqUHRoANDAkoE1IW7O2/X28+/v4aGtpVf2oUvSV6Ldm1vfs2WPp0qUcyz5+/GTFypV5eXnKihYAoEH8v707j6sp//8A/jnn3NuqvSwpMULKUtKqUClLWcq+f5lh/AwxY99p7AzGki8jM6MZfA2yJHukRClLlkkJpUKlvW7de889vz8qwq1uyb3J6/lf95zz+bzP+bzv47z7nOWqN2liZ2fr7ORkZWXFEYK6E+BrgAK0weHz+VZW3dxcXRwcHQQCQURExOng4OfPnpctXbF8mbV1d4bHiMVsYWHBihUrnzx5otB4AQDqonLdSShy5/ad8IiImzduFhcXKzo0APjsUIA2XHp6ei4uLv3792vevHnZy5tiY2MDAvYxDFO2AstKJKx4w6ZNNyJvKDZUAAAZKSsrd+1q6ebqYudgx9BMXFzc5dDQqJtRRUVFig4NAOQHBWhDR9O0lZWlh7uHnYNdfl6+jo4OTdNvl5Y9inTw4KFDhw7hsSQAaLCUlJQsLa2cnZ16ODrwlZTi4+PDIyKuXr2an5ev6NAAQAFQgH4xNLU0t27Z0qxZM4qiPlgk4bjI69d/+WWLUChUSGwAAFJJrTvDroTl5ePn1wG+aihAvxitW5vs2rWrqqUSln369NnKVavwWBIAKNzbutPR0UGpou68djUsNw91JwAQggL0CzJ1yhQvL0+Gx6tqBTErLiwoxGNJAKAoSny+pVU3Z2cnBwd7ZWXlsrozPOxaTm6uokMDgIYFBWgDZWbWwXuI99s/KYqys7fjVV19luE4juO4x/Hxb95kf+YAQTGCTgTFxz9WdBSf5IPchsZBV0/PwMBAV0+Xoem83LysrMysrDdv3yLXCPIWAOpXDQUNKIq+gYGTs1Ntt6IoiqKojubmnyMkaAjCr0eQL/xEXrfchi+IlraWlrZWW1PTt580grwFgPpF17wKAAAAAED9wQxoQ7d9p3909C1FRwEKZmtr4ztjuqKjqGfI7UavUeYtANQLzIACAAAAgFyhAAUAAAAAuUIBCgAAAAByhQIUAAAAAOQKBSgAAAAAyBUKUAAAAACQKxSgAAAAACBXKEABAAAAQK5QgAIAAACAXKEABQAAAAC5QgEKAAAAAHKFAhQAAAAA5AoFKIAsaMNBq09dDF7lxK9iBcbEZ92pC8cX2/LkGhfAe5CHAPBlQAEK8qTczfd/t2NCNnjoUYoOpZbBUOotO3Q00lapelUNY3NzY20VqgHsGXzF3s/DBvWNAwB4BwXoF0994I74+JjgebbaH55h+D1Xhyc9OrOwC6OQwKSiKIqiaLoWJ0N1943hSfExB0Y2k5KsfMvFF+Ke3j8wyagumVz7YEARaE2zft+v33vk2o3ox4/uPrxxPviPjYvHOhgpf74uGYtJ/ucuH/ihQ/1+d2RsVsXE5buNvwfduBWb+PD2g8jzp/dvWDC8q06dEhVJDgANEy7TNAqUqsXkLdtfTvzuryShomOpVmnsryOsfq3VJkXXQ8KyBw6x9XI3/OevVMl7y5S7eQ4woktvnT2XLqli8/oNBuSN0uzy3eat83s251WUUEq6RhYORuaW6o9DbqaWcp+nW1rbxLxdixyleq7bZGmWMRm+5eiqnvpM+Uo8PaNOPVq24909cOweqfXuIskBoIFCAdo4cCyn6bRg26Kn4/wi8z7TOVlRiqNCLmQMGm3l6dnq0J7nbKUlKnZefVrQJeHBl17Vof5sAGhlDT1tFXFBTk6xWNGx1AMtTa158+eFhV2NjLxRVFRUDy3SLXzW71rYS4fLuhO4e+/h0DtJmUIlHaOO1s79zF5dr79Mb0ADwbOcON1Jj2SE/rJ8/fE7ybkiJV1jC5ueXQRXXzeYJG9AhwsAvlgoQBsH8b3AXa/6zhy/cVXciDlB6ay0dfg9Vl48MCJnp8+wrfFlK1Ba3rui1zvcWOI26Wg2Ryi9HlOWTOxl3tbYUF9Tjc/mp/979eDOPfdaDhk72MPOzEibKUp7cOHPzesP3s+tOPVT6qaeU6d/62Vv1lRZ8PpxRNCejXvDUkWEEErLcsSsiR42Fqatm2urUoKs5POrJvg9GXk4xLfF8akuC8JF5U2oturzn2nfDerRyUiTEuSkPb7mv/TnE8mVdkEQc/LCy5ETzAd7tt23K+HdAnWHIW76VOGVE5eyOEIovd6Ltszq38GomaYyJ8hKijm/b8vOoMdFHJE1mGpbKNtbpfZDlv4+17n7N7pM0atHkaf3bd9/7rmgqlGp+uAQWrf7lOWLv+/TXodPcZyoIOWi338WHKvLPG4DQtOUlZWllZXlzJkzY2JiQkNDo6NvCYV1n5RXc5w2t7cuyQpdPPrHIynltU5pRlL02aTosxUrqbb2mPx/UwY5mhuqS3KSY0OP+u86HJ1ZnuHvpzQpyUm5e+nQL9sO38kpH9UqBoIQQgjT3vdknC8hhBBJxuHxrj9HimpIsxq7q6rZSvtsZKLHSFKCtwdEJLKEECLMSIo6kxRVvpjSdZi8cEKvTu3aGDfVVKVKc9IToy4c2RNw+n6u1ORh2v1f5SSXKcKvLW8BQCFQgDYSbNrZRXM02uyf5PfL5MeTfntUUoc2aN0u7gN7mVfkBF/H2Mp7QYB3pTWUTLqPXLpbt2jotBOvJYQQtc4zAn6bbaVRdgOminHXgTO3Wxr6Dl4alsPRTR2GjR/wtjUNg6b80sKP+lQ2m7InYKGddvktnErNTC2Nmwg+OJ8JY08EJ42d1t5zgMWehLjyOoTS6unlokNygk9eLjt3irXbdmtvpEQIIaRJs469J2zq1FQ0eO7pLI4Q2YKproWyPtUtvYZVHAtja8/pVo6Wq8ZNP/BE9FFb1R4cqvnw9Tvm99KkxEVvXhdSTfS0m/JL8xrPWZxhGBsbG1tbWzHLRkdFXboUevt2rFhc69kyh4F9mtLCOwGbjqVUsa2K+fd798231SrPn2bte41e5Niz65xxi06nsx+lNFHXb9tj1BLL9qo+4/cniAmhqxqIqu/RrDbNauhOFoKXqTksbew2vv+xhODkj/65ofUs+3m7vu2Cp9/a0nNq1z59bWaPX36u5jlSGSL8ivMWAOQJDyE1Glxh7M7ZW2MlltO3/mijUed717j8s4s8unbpYtrZyXNJSCrLSXJv7ZwxzMHasn0393H+sflEu5ePa1OaEMK0n7BshqVaRtj2yQN6mFlY2w1bevQpazRkxljTivM3V3BxhVd3K0vTLg7Ow3+N+rBOY9qMXfaTrVZJwoml4/t362LZ0ca134QN57M+vLTKxp8+dl9It+4/2FKp/CNKx22Qkxb3OuT49YLyrq5vmjDEwcbatGOXjvZek/fFlei5DO1d6cmNGoKRpQXhs5ANkwf2Mu/UzcrjW7+zyWJth3lzvZpKOdrVHRxKw87DTkPyYI+3g0P3nq7W1rb23psjims3UA0cTdMURfF5PHt7uxUrlh06fGjunDmWlpZUbd4SYN6+Cc0+uxaRJnVKnxDGdPyyH200S/49On+Eq0UnKyuPKetCX1KG/VfOd383bBUp3dbCzmns+ouvJOpdx47txieE1DAQbML2wV3adLBo08GirXP5PKUMSVJld9U0+44odv/OiCzKZOjmoNC//ab1NdP9eJaAyw+Z72ph0bltJ3unkQt+i8nhmwxePd9N1qeUqovwa89bAJAbFKCNiTAhcLFfaIHp+NVLetftkVlCOLYgMyO/lGWFOY+C/P9+yFK8rEfX/31VKBIVpV/fG3Axj2OMW7eiCWE6DPQy4+VfWjNv75Wk3FJxScb9oFXbrxQy7Rxt9csTixPnpKW+KRaxpfnpya+LPigsmW+8BnZSFsVt913+d3RKTqmoJP91wp2EzI+nVNiUU8djBLShl4+9GiGEELpF36GO6pLkkKO3KiZ7KUq786i1+49dj7oVd+XAyr6GDOE1a6H/LsWrD0amFopuHT90JSFLICrNTb75+6KVh9Mk6vbuzh+9gKCGg8NxHCGUgZmtmb4KRQhXmvksNbeR3bpbgWF4hBA1VdWePZ3XrFn911+Bfdz7yLithjpFJDnZ0i8uE8K0GzTYQkl0f8ccv3/uvS4WCXOTI/fOXXHkJafTe9C7cqwipSXiwrSYg2sDH4gZPTMzA5oQUoeBqDlJqu5OJmzyP7N9pm0/9ahIz3rogu1HIy7+sWa8TbPKZSjHFmZnF4slElFB2t3gddNXnswkum6De2vJ9qWvJkLkLQDICy7BNy5s+vEVqxzNtw5btSjs/rJPfQyEfZmcLiYdDZpp06RYQgghwlepGRzVVE2VIoRv/I0RTav23RHdd8f7mxkataBJVs3t80zatWYkL6IjU6qY4XpH8vrc0cs/2nu6e7ttDD+dS38z0NtGWfwwKOhB2XVDSrPn0j/2jTbhl5+ClVsZE0JYmpY5w+vQgiAu6r5wvEfL1oY0yXl/kVJ1B4cqiAy6nOXi2WvxgUtzcpIf3I0NO/XX/nOJUmrijyxauJAslHWfGhSGxyOEaGtr21h3L/ukdZvW0dG3qtmkSEAIraWtRZMMaRmiZGJqREteRF2v/Gha0e3wOyWj+5mYGtMk++Nt2PSk50WcRZMm6hQhktoORK2T5L3uZCZMvbZ31rU/11kPmDBp4hjX7mOWBPRxWj3mhyNJ0q7jc3mRl28Lh/Rp1bYlTXJr0Y2UCD9b3gIAfAAFaGPDZYX+vPyY9Z6hK5dEbHr/DjKOSAhRVqnmXeofkoiEYkLx+fy3m4hEYo5Q1NvZI2koZVVlmfooez1hVc28j8sLO3jm5YCxzqMGtDhztOkIHzNeceTBE+WlB6Xb5z/erZicqJ3LNv59MylTwNN3W3Ji2yBZWiZ1b4GiaIoQae9YrP7gcFlnFo8vvDO8v33Xbladu7m0se7tYkb7/HCm5qo96MSJ+Ph4WfZI/tTV1X1nzqxmBTEr5jG8goJ8DQ1NQsjzZ8+rbzDxmYDr0MbexsA/Uep7Duoyy88JhUKubOAIqWIgZpzJk7ptHZLkve5qp/RVbNDG2JN7zH38ti0d2HPWTNeQ2RekPvHGcRJO1i9S9RF+trwFAPgACtDGh8uN2LLkkO0fo+fNfq1GkfyKzyUFeYUc3bKDqRZ19009zFmI0p6nSSRaJ7/zWHZFyn1gMrzBW5T2PE1Ct7J1MGbuP69xErTk1v+C4kf9YDt6mH1OS59W1JtTR0IyKp5l1m/eXIkUXwzccSleSAghojeZ+aW12Zs6tEBpObp1UyLC5Kdpkoq7WcouONd4cAgpeREWuCUskBBGw2yo3/6V7r372pIzITXGGR8fHxEeUZs9kx8dbW0irQBlWTFN84oFxeHXwi+HXtbT01u4YIEsDd64fDO/r5v9FF+Pi0vPfXxnhjA5KVVCm9j1MGHuP63IH/VuzlYqRJjyNFUi0y1G0gZC7cwFsVjMETU1tfcKx09OM05qs9WS5D0K2nZk6IB55qamLZgLT6WsotrZtpMSEaY9/zgPa+uz5S0AwAdwD2hjxBVEbvM7+ELL0LDybCf79P6/+Zyy07RFY6yaqTE0o6JhoKNa9zdts4/PX3wq0R+4cuO3bubNNZUYmlHRMbLobWMi67mPfXzuQhLL7zprh984u9Y6KgzDb9K8g2UHPelpyT45/vdNAWM6attSD10uJehwREHFIsmbjAwRUbXzGWtt2IRHEZrfpIlKrU7BMrVAMRr6eup8muY1adHFc9GulYMNSHbo6St5HCFEKBJzlHa33vZGakwNB4dp4zrUtUtLTSWaYvg8cUFBKSGN7yc8WbGY47iSkpLwaxF+fj+PHjV6x44djx4+qnKW7SM553bve1hKGw7adth/nrdNW301Hs0oaTRtb+c17UfvjiTh1KlHQn7nmVuWDevSTI2npGXiOHXTqhEtqNyw05eyZeilioGgCJfxKpNjWrgPd2/ThMeo6LbtbmHIfHqaSW/2PUpWU9fOneDaqZWOCkNRjKpuGxvv6UPaM5w4MyO7vAan1G2HjXFpp6fK42sa20xcu2q0EV0UdTH84zysLeQtAMgLZkAbJ64gesuaINf/DjOq9GFReGDgv31mWvRffbj/6ncf1/k1jeIHAWv2u+ye4v7TPvef3n4qurPRfcyfyTK9m0X8MGD1np7+0zsN+fnAkJ/LQy867dvT94K090hJXp8OPD/L0buZPlcSc/ive+8i596EHrk8w9nTdflB1+XvNmATZN4ZmVqgNPuvv9x//buNSpNPLttwMYcjhLCp/8bncmZmE3afbzrXZta5ag5Oip7dt6uWOVZ6MJpIss9eqO5uyC8Ix3Ecx0kkkqjoqNDQK7G3YkViaa+pkoUofrfvgqb+a8Z2dJ6+3nl65UXih/TJU/6Bftt67ptnM3zTP8M3VfQvSju7csN5WepPqoqBKCKsICz0kW/nLj6bQ33KIrm7dsD43158YpqxKVKbTXn3beGZu40ZMslk6KT3N+QEiQd+O5/NlU0ZUEqt+83f32/+u6hzb67fHJwhJQ8vyhpaueq+1I07bwFAzjAD2lhxeeG/rg3JeK8OLH2wfer3a47depZdwkpYcUlBVlri7Wtnw54I6nj3WMGt9ePGzPYPvpmYkV/CsqKirOS4sJgXspe0XGHsLxPH+fqHxDx/UyRkRcXZLx7FJuXzq5pVKYw4+E+SmJPkXgo89aLyvnHZZ5dOmRNw5UF6finLikuLcjJSE+5F3Xwi86/l1NCC5M29C6ev3UtMzykWsqxYkJ0Sdy5g+YiRy85WvHyxOGzbT7suPXhVkJb6UljtwaGol7evxiVnC8QSCSvISYm7tHfBt/OCM2U+bA2XRCK5e/fO1q1bR40avXbNups3bta9+iSEEMKmX1o+ymfi6sBzt59l5JewLCvIf510L/zob4eu50iI4NF/p4yZvuNMTHK2QCQsyki4dmjduJELT0n/LYYPVTUQHCFs4p++836/kphVzLLi4jdP7zzJpKhPTzPpzVYieXpqw5a/z0YnpOeVsBKJWJCb9vjmCf+Fw8Zsjiyo6IQruhdyPDwxs1gsLslLvXd+78zRM35PLD/OH+RhbX2deQsA8kdp6RkoOgaQwsnZadHChYSQ7Tv9q39SGL4GtrY2vjOmE0LWrV/fYO8B5fP5qmqq+Xn51a+G3P4EH/yyUUP3ReQtACgELsEDQP0QiUSivC+gKgIAAIXDJXgAAAAAkCsUoAAAAAAgV7gEDwDwpWATdw9vt1vRUQAAfDLMgAIAAACAXKEABQAAAAC5QgEKAAAAAHKFAhQAAAAA5AoFKAAAAADIFQpQAAAAAJArFKAAAAAAIFcoQAEAAABArlCAAgAAAIBcoQAFAAAAALlCAQoAAAAAcoUCFAAAAADkiqfoAKAG/ft62NvaKDoKUDAdHR1Fh1D/kNuNXqPMWwCoFyhAG7p27UwVHQLAZ4HcBgD4auESPAAAAADIFaWlZ6DoGAAAAADgK4IZUAAAAACQKxSgAAAAACBXKEABAAAAQK7+H66O6T5fWhm1AAAAAElFTkSuQmCC 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) ## Clone a blueprint from the Leaderboard ``` ridge = menu[7] blueprint_graph = w.clone(blueprint_id=ridge.id, project_id=project_id) blueprint_graph.show() ``` ![No description has been provided for this 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) ``` ridge.id, project_id ``` ``` ('1774086bd8bfd4e1f45c5ff503a99ee2', '5eb9656901f6bb026828f14e') ``` ## Any blueprint can be used as a tutorial ``` source_code = blueprint_graph.to_source_code(to_stdout=True) ``` ``` w = Workshop(user_blueprint_id='61d4dda0addc0e8a29404b9b') rst = w.Tasks.RST(w.TaskInputs.DATE) pdm3 = w.Tasks.PDM3(w.TaskInputs.CAT) pdm3.set_task_parameters(cm=500, sc=25) gs = w.Tasks.GS(w.TaskInputs.NUM) enetcd = w.Tasks.ENETCD(rst, pdm3, gs) enetcd.set_task_parameters(a=0) enetcd_blueprint = w.BlueprintGraph(enetcd, name='Ridge Regressor') ``` ## Execute a blueprint ``` eval(compile(source_code, 'blueprint', 'exec')) ``` ``` enetcd_blueprint.show() ``` ![No description has been provided for this image](data:image/png;base64,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 ) ## Delete the original blueprint directly ``` blueprint_graph.delete() ``` ``` Blueprint deleted. ``` ## Modify the source code ``` #w = Workshop() rst = w.Tasks.RST(w.TaskInputs.DATE) # Use numeric data cleansing instead ndc = w.Tasks.NDC(w.TaskInputs.NUM) pdm3 = w.Tasks.PDM3(w.TaskInputs.CAT) pdm3.set_task_parameters(cm=500, sc=25) enetcd = w.Tasks.ENETCD(rst, ndc, pdm3) enetcd.set_task_parameters(a=0.0) enetcd_blueprint = w.BlueprintGraph(enetcd, name='Ridge Regressor') ``` ``` enetcd_blueprint.show() ``` ![No description has been provided for this image](data:image/png;base64,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 ) ## Add a blueprint to a project and train It ``` project_id = '5eb9656901f6bb026828f14e' ``` ``` enetcd_blueprint.save() ``` ``` Name: 'Ridge Regressor' Input Data: Date | Categorical | Numeric Tasks: Standardize | One-Hot Encoding | Numeric Data Cleansing | Elastic-Net Regressor (L1 / Least-Squares Loss) ``` ``` enetcd_blueprint.train(project_id=project_id) ``` ``` Training requested! Blueprint Id: fa329535f1e5f5465e2c55024aacb910 ``` ``` Name: 'Ridge Regressor' Input Data: Date | Categorical | Numeric Tasks: Standardize | One-Hot Encoding | Numeric Data Cleansing | Elastic-Net Regressor (L1 / Least-Squares Loss) ``` ## Custom Models ### Find tasks ``` w.search_tasks('awesome model') ``` ``` Awesome Model: [CUSTOMR_6019ae978cc598a46199cee1] - This is the best model ever. ``` ``` w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1 ``` ``` Awesome Model: [CUSTOMR_6019ae978cc598a46199cee1] - This is the best model ever. ``` ``` w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1(w.TaskInputs.NUM) ``` ``` Awesome Model (CUSTOMR_6019ae978cc598a46199cee1) Input Summary: Numeric Data Output Method: TaskOutputMethod.PREDICT Task Parameters: version_id (version_id) = latest_6019ae978cc598a46199cee1 ``` ``` w.CustomTask('CUSTOMR_6019ae978cc598a46199cee1') ``` ``` Awesome Model (CUSTOMR_6019ae978cc598a46199cee1) Input Summary: (None) Output Method: TaskOutputMethod.PREDICT Task Parameters: version_id (version_id) = latest_6019ae978cc598a46199cee1 ``` ``` w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1.versions ``` ``` Latest (latest_6019ae978cc598a46199cee1): str v3.0 (6019e2418311cc8207a5f8e1): str v2.10 (6019dff0509159ede309f9c9): str v2.9 (6019dc3b8311cc8207a5f7d9): str v2.8 (6019dbcb4f6322a6283883d9): str v2.7 (6019db4d041c71bd7ea1c670): str v2.6 (6019da5d4f6322a628388364): str v2.5 (6019d924be257008648e3c62): str v2.4 (6019d7db3d7d080b078e3c39): str v2.3 (6019d744356f3c430b38828d): str v2.2 (6019d305be257008648e3c0c): str v2.1 (6019d2e045e619fc03a2eead): str v2.0 (6019d2bd3d7d080b078e3b66): str v1.3 (6019cf0735270cbe238e3c76): str v1.2 (6019b9fdbf5b0a42aba1c6e9): str v1.1 (6019b81729ae9ab5ad8e3c26): str v1.0 (6019afe4dcd97e1e5ebfee13): str ``` ### Build a blueprint ``` pni = w.Tasks.PNI2(w.TaskInputs.NUM) rdt = w.Tasks.RDT5(pni) binning = w.Tasks.BINNING(pni) customr = w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1(rdt, binning) custom_bp = w.BlueprintGraph(customr, name='My Fun Custom Blueprint').save() ``` ### Update task versions ``` customr.version = w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1.versions.v2_7 ``` ``` customr ``` ``` Awesome Model (CUSTOMR_6019ae978cc598a46199cee1) Input Summary: Smooth Ridit Transform (RDT5) | Binning of numerical variables (BINNING) Output Method: TaskOutputMethod.PREDICT Task Parameters: version_id (version_id) = 6019db4d041c71bd7ea1c670 ``` ``` customr.version = w.CustomTasks.CUSTOMR_6019ae978cc598a46199cee1.versions.Latest ``` ``` custom_bp.save() ``` ``` Name: 'My Fun Custom Blueprint' Input Data: Numeric Tasks: Missing Values Imputed (quick median) | Smooth Ridit Transform | Binning of numerical variables | Awesome Model ``` ### Find, View, and Train ``` bps = w.list(limit=3) ``` ``` list(bps)[0].show() ``` ![No description has been provided for this image](data:image/png;base64,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 ) ``` custom_bp.train(project_id=project_id) ``` ``` Training requested! Blueprint Id: 3d753707758ad45b97684811a8756c20 ``` ``` Name: 'My Fun Custom Blueprint' Input Data: Numeric Tasks: Missing Values Imputed (quick median) | Smooth Ridit Transform | Binning of numerical variables | Awesome Model ``` ``` custom_bp.delete() ``` ``` Blueprint deleted. ``` --- # Blueprint Workshop URL: https://docs.datarobot.com/en/docs/api/code-first-tools/bp-workshop/index.html > Learn how to construct and modify DataRobot blueprints through a programmatic interface. # Blueprint Workshop The Blueprint Workshop is built to ensure that constructing and modifying DataRobot blueprints through a programmatic interface is idiomatic, convenient, and powerful. Browse the topics detailed in the table below to get started with the Blueprint Workshop. Some examples in this documentation reference methods from [DataRobot's Python API client](https://docs.datarobot.com/en/docs/api/dev-learning/python/index.html). | Topic | Description | | --- | --- | | Blueprint Workshop setup | The first-time setup necessary for using the Blueprint Workshop. | | Blueprint Workshop overview | An overview of the workshop's functionality. | | Pass features into a task | How to pass one or more specific features to another task. | | Blueprint Workshop API reference | A complete overview of the Blueprint Workshop API. | In addition to the documentation that instructs you on how to use the Blueprint Workshop, DataRobot provides code examples to demonstrate common usage. | Topic | Description | | --- | --- | | Workshop walkthrough notebook | An example workflow of basic Blueprint Workshop functionality. | | Advanced feature selection notebook | How to reference specific columns in a project’s dataset as an input to a task. | | Custom task notebook | How to create a blueprint with a pre-existing custom task. | --- # Covalent URL: https://docs.datarobot.com/en/docs/api/code-first-tools/covalent.html > Learn more about Covalent, a code-first solution that simplifies building and scaling complex AI and high-performance computing applications. # Covalent > [!NOTE] Premium > Support for Covalent is off by default. Contact your DataRobot representative or administrator for information on enabling the feature. DataRobot offers an open-source distributed computing platform, [Covalent](https://www.covalent.xyz/), a code-first solution that simplifies building and scaling complex AI and high-performance computing applications. You can define your compute needs (CPUs, GPUs, storage, deployment, etc.) directly within Python code and Covalent handles the rest, without dealing with the complexities of server management and cloud configurations. Covalent accelerates agentic AI application development with advanced compute orchestration and optimization. As a DataRobot user, you can access the [Covalent SDK](https://docs.covalent.xyz/docs/user-documentation/concepts/covalent-arch/covalent-sdk/) in a Python environment (whether that be in a DataRobot notebook or your own development environment) and use your [DataRobot API key](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html) to leverage all Covalent features, including fine-tuning and model serving. The Covalent SDK enables compute-intensive workloads, such as model training and testing, to run as server-managed workflows. The workload is broken down into tasks that are arranged in a workflow. The tasks and the workflow are Python functions decorated with Covalent’s electron and lattice interfaces, respectively. All documentation for Covalent can be accessed through the [Covalent documentation site](https://docs.covalent.xyz/docs/cloud/covalent_cloud_main). ## Covalent interfaces The tasks and the workflow that Covalent builds are Python functions decorated with Covalent’s [electron](https://docs.datarobot.com/en/docs/api/code-first-tools/covalent.html#electron) and [lattice](https://docs.datarobot.com/en/docs/api/code-first-tools/covalent.html#lattice) interfaces, respectively. ### Electron The simplest unit of computational work in Covalent is a task, called an electron, created in the Covalent API by using the `@covalent.electron` decorator on a function. In discussing object-oriented code, it’s important to distinguish between classes and objects. Here are notational conventions used in this documentation: `Electron` (capital “E”) The [Covalent API class](https://docs.covalent.xyz/docs/user-documentation/api-reference/workflow-components#lattice) representing a computational task that can be run by a Covalent executor. electron (lower-case “e”) An object that is an instantiation of the `Electron` class. `@covalent.electron` The decorator used to: (1) Turn a function into an electron. (2) Wrap a function in an electron. (3) Instantiate an instance of `Electron` containing the decorated function. All three descriptions are equivalent. The `@covalent.electron` decorator makes the function runnable in a Covalent executor. It does not change the function in any other way. The function decorated with `@covalent.electron` can be any Python function; however, it should be thought of, and operate as, a single task. Best practice is to write an electron with a single, well-defined purpose; for example, performing a single transformation of some input or writing or reading a record to a file or database. Here is a simple electron that adds two numbers: ``` import covalent as ct @ct.electron def add(x, y): return x + y ``` An electron is a building block, from which you compose a [lattice](https://docs.datarobot.com/en/docs/api/code-first-tools/covalent.html#lattice). ### Lattice A runnable workflow in Covalent is called a lattice, created with the `@covalent.lattice` decorator. Similar to electrons, here are the notational conventions: `Lattice` (capital “L”) The [Covalent API class](https://docs.covalent.xyz/docs/user-documentation/api-reference/workflow-components#lattice) representing a workflow that can be run by a Covalent dispatcher. `lattice` (lower-case “l”) An object that is an instantiation of the `Lattice` class. `@covalent.lattice` The decorator used to create a lattice by wrapping a function in the `Lattice` class. (The three synonymous descriptions given for electron hold here as well.) The function decorated with `@covalent.lattice` must contain one or more electrons. The lattice is a workflow, a sequence of operations on one or more datasets instantiated in Python code. For Covalent to work properly, the lattice must operate on data only by calling electrons. By “work properly,” we mean “dispatch all tasks to executors.” The flexibility and power of Covalent comes from the ability to assign and reassign tasks (electrons) to executors, which has two main advantages, hardware independence and parallelization. #### Hardware independence The task’s code is decoupled from the details of the hardware it is run on. #### Parallelization Independent tasks can be run in parallel on the same or different backends. Here, independent means that for any two tasks, their inputs are unaffected by each others’ execution outcomes (that is, their outputs or side effects). The Covalent dispatcher can run independent electrons in parallel. For example, in the workflow structure shown below, electron 2 and electron 3 are executed in parallel. ## Example usage Review the code snippet below that creates tasks out of functions and dispatches the resulting workflow. ``` import covalent as ct import covalent_cloud as cc import sklearn import sklearn.svm import yfinance as yf from sklearn.model_selection import train_test_split cc.create_env( name="sklearn", pip=["numpy==2.2.4", "pytz==2025.2", "scikit-learn==1.6.1", "yfinance==0.2.55"], wait=True, ) cpu_ex = cc.CloudExecutor( env="sklearn", num_cpus=2, memory="8GB", time_limit="2 hours" ) # One-task workflow when stacking decorators: @ct.lattice(executor=cpu_ex) @ct.electron def fit_svr_model_and_evaluate(ticker, n_chunks, C=1): ticker_data = yf.download(ticker, start='2022-01-01', end='2023-01-01') data = ticker_data.Close.to_numpy() X = [data[i:i+n_chunks].squeeze() for i in range(len(data) - n_chunks)] y = data[n_chunks:].squeeze() # Split data into train and test sets X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, shuffle=False) # Fit SVR model model = sklearn.svm.SVR(C=C).fit(X_train, y_train) # Predict and calculate MSE predictions = model.predict(X_test) mse = sklearn.metrics.root_mean_squared_error(y_test, predictions) return model, mse # Run the one-task workflow runid = cc.dispatch(fit_svr_model_and_evaluate)('AAPL', n_chunks=6, C=10) model, mse = cc.get_result(runid, wait=True).result.load() print(model, mse) ``` --- # Declarative API URL: https://docs.datarobot.com/en/docs/api/code-first-tools/declarative-api.html > Use the declarative API as a way to programatically provision DataRobot entities. # Declarative API DataRobot offers a Terraform-native declarative API used to programmatically provision DataRobot entities such as models, deployments, applications, and more. The declarative API allows you to: 1. Specify the desired end state of infrastructure, simplifying management and enhancing adaptability across cloud providers. 2. Automate provisioning to ensure consistency across environments and remove concerns about execution order. 3. Simplify version control. 4. Use application templates to reduce workflow duplication and ensure consistency. 5. Integrate with DevOps and CI/CD to ensure predictable, consistent infrastructure and reduce deployment risks. DataRobot recommends using the declarative API as a code-first method to provision DataRobot resources end-to-end in a way that is both repeatable and scalable. ## Declarative API services DataRobot has two services for using the declarative API: Pulumi and Terraform. DataRobot recommends using the service that supports your engineering needs. Pulumi is based on Python, while Terraform is based on yaml. Note that application templates are configured for Pulumi by default. For information on using Pulumi for your declarative API needs, access the [Pulumi registry](https://www.pulumi.com/registry/packages/datarobot/) and review the [installation guide](https://github.com/datarobot-community/pulumi-datarobot?tab=readme-ov-file#datarobot-resource-provider). For information on using Terraform, access the [Terraform registry](https://registry.terraform.io/providers/datarobot-community/datarobot/latest) and review the [installation guide](https://github.com/datarobot-community/terraform-provider-datarobot). Review an example below of how you can use the declarative API to provision DataRobot resources using the Pulumi CLI: ``` import pulumi_datarobot as datarobot import pulumi import os for var in [ "OPENAI_API_KEY", "OPENAI_API_BASE", "OPENAI_API_DEPLOYMENT_ID", "OPENAI_API_VERSION", ]: assert var in os.environ pe = datarobot.PredictionEnvironment( "pulumi_serverless_env", platform="datarobotServerless" ) credential = datarobot.ApiTokenCredential( "pulumi_credential", api_token=os.environ["OPENAI_API_KEY"] ) cm = datarobot.CustomModel( "pulumi_custom_model", base_environment_id="65f9b27eab986d30d4c64268", # GenAI 3.11 w/ moderations folder_path="model/", runtime_parameter_values=[ {"key": "OPENAI_API_KEY", "type": "credential", "value": credential.id}, { "key": "OPENAI_API_BASE", "type": "string", "value": os.environ["OPENAI_API_BASE"], }, { "key": "OPENAI_API_DEPLOYMENT_ID", "type": "string", "value": os.environ["OPENAI_API_DEPLOYMENT_ID"], }, { "key": "OPENAI_API_VERSION", "type": "string", "value": os.environ["OPENAI_API_VERSION"], }, ], target_name="resultText", target_type="TextGeneration", ) rm = datarobot.RegisteredModel( resource_name="pulumi_registered_model", name=None, custom_model_version_id=cm.version_id, ) d = datarobot.Deployment( "pulumi_deployment", label="pulumi_deployment", prediction_environment_id=pe.id, registered_model_version_id=rm.version_id, ) pulumi.export("deployment_id", d.id) ``` --- # Define custom metrics URL: https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-custom-metrics.html > The MetricBase class, along with four additional default classes, provides an interface to define custom metrics. # Define custom metrics The `MetricBase` class provides an interface to define custom metrics. Four additional default classes can help you create custom metrics: `ModelMetricBase`, `DataMetricBase`, `LLMMetricBase`, and `SklearnMetric`. ## Create a metric base In `MetricBase`, define the type of data a metric requires; the custom metric inherits that definition: ``` class MetricBase(object): def __init__( self, name: str, description: str = None, need_predictions: bool = False, need_actuals: bool = False, need_scoring_data: bool = False, need_training_data: bool = False, ): self.name = name self.description = description self._need_predictions = need_predictions self._need_actuals = need_actuals self._need_scoring_data = need_scoring_data self._need_training_data = need_training_data ``` In addition, you must implement the scoring and reduction methods in `MetricBase`: - Scoring (score): Uses initialized data types to calculate a metric. - Reduction (reduce_func): Reduces multiple values in the sameTimeBucketto one value. ``` def score( self, scoring_data: pd.DataFrame, predictions: np.ndarray, actuals: np.ndarray, fit_ctx=None, metadata=None, ) -> float: raise NotImplemented def reduce_func(self) -> callable: return np.mean ``` ## Create metrics calculated with predictions and actuals `ModelMetricBase` is the base class for metrics that require actuals and predictions for metric calculation. ``` class ModelMetricBase(MetricBase): def __init__( self, name: str, description: str = None, need_training_data: bool = False ): super().__init__( name=name, description=description, need_scoring_data=False, need_predictions=True, need_actuals=True, need_training_data=need_training_data, ) def score( self, prediction: np.ndarray, actuals: np.ndarray, fit_context=None, metadata=None, scoring_data=None, ) -> float: raise NotImplemented ``` ## Create metrics calculated with scoring data `DataMetricBase` is the base class for metrics that require scoring data for metric calculation. ``` class DataMetricBase(MetricBase): def __init__( self, name: str, description: str = None, need_training_data: bool = False ): super().__init__( name=name, description=description, need_scoring_data=True, need_predictions=False, need_actuals=False, need_training_data=need_training_data, ) def score( self, scoring_data: pd.DataFrame, fit_ctx=None, metadata=None, predictions=None, actuals=None, ) -> float: raise NotImplemented ``` ## Create LLM metrics `LLMMetricBase` is the base class for LLM metrics that require scoring data and predictions for metric calculation, otherwise known as prompts (the user input) and completions (the LLM response). ``` class LLMMetricBase(MetricBase): def __init__( self, name: str, description: str = None, need_training_data: bool = False ): super().__init__( name=name, description=description, need_scoring_data=True, need_predictions=True, need_actuals=False, need_training_data=need_training_data, ) def score( self, scoring_data: pd.DataFrame, predictions: np.ndarray, fit_ctx=None, metadata=None, actuals=None, ) -> float: raise NotImplemented ``` ## Create Sklearn metrics To accelerate the implementation of custom metrics, you can use ready-made, proven metrics from [Sklearn](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics). Provide the name of a metric, using the `SklearnMetric` class as the base class, to create a custom metric. For example: ``` from dmm.metric.sklearn_metric import SklearnMetric class MedianAbsoluteError(SklearnMetric): """ Metric that calculates the median absolute error of the difference between predictions and actuals """ def __init__(self): super().__init__( metric="median_absolute_error", ) ``` ## PromptSimilarityMetricBase The `PromptSimilarityMetricBase` class compares the LLM prompt and context vectors. This class is generally used with Text Generation models where the prompt and context vectors are populated as described below: The base class pulls the vectors from the `scoring_data`, and iterates over each entry: - The prompt vector is pulled from theprompt_column(which defaults to_LLM_PROMPT_VECTOR) of thescoring_data. - The context vectors are pulled from thecontext_column(which defaults to_LLM_CONTEXT) of thescoring_data. The context column contains a list of context dictionaries, and each context needs to have avectorelement. > [!NOTE] Note > Both the `prompt_column` and `context_column` are expected to be JSON-encoded data. A derived class must implement `calculate_distance()`. For this class, `score()` is already implemented. The `calculate_distance` function returns a single floating point value based on a single `prompt_vector` and a list of `context_vectors`. For an example using the `PromptSimilarityMetricBase`, review the code below calculating the minimum Euclidean distance: ``` from dmm.metric import PromptSimilarityMetricBase class EuclideanMinMetric(PromptSimilarityMetricBase): """Calculate the minimum Euclidean distance between a prompt vector and a list of context vectors""" def calculate_distance(self, prompt_vector: np.ndarray, context_vectors: List[np.ndarray]) -> float: distances = [ np.linalg.norm(prompt_vector - context_vector) for context_vector in context_vectors ] return min(distances) # Instantiation could look like this scorer = EuclideanMinMetric(name=custom_metric.name, description="Euclidean minimum distance between prompt and context vectors") ``` ## Report custom metric values The metrics described above provide the source of the custom metric definitions. Use the `CustomMetric` interface to retrieve the metadata of an existing custom metric in DataRobot and to report data to that custom metric. Initialize the metric by providing the parameters explicitly ( `metric_id`, `deployment_id`, `model_id`, `DataRobotClient()`): ``` from dmm import CustomMetric cm = CustomMetric.from_id(metric_id=METRIC_ID, deployment_id=DEPLOYMENT_ID, model_id=MODEL_ID, client=CLIENT) ``` You can also define these parameters as environment variables: | Parameter | Environment variable | | --- | --- | | metric_id | os.environ["CUSTOM_METRIC_ID"] | | deployment_id | os.environ["DEPLOYMENT_ID"] | | model_id | os.environ["MODEL_ID"] | | DataRobotClient() | os.environ["BASE_URL"] and os.environ["DATAROBOT_ENDPOINT"] | ``` from dmm import CustomMetric cm = CustomMetric.from_id() ``` Optionally, specify batch mode ( `is_batch=True`). ``` from dmm import CustomMetric cm = CustomMetric.from_id(is_batch=True) ``` The `report` method submits custom metric values to a custom metric defined in DataRobot. To use this method, report a DataFrame in the shape of the output from the metric evaluator. ``` print(aggregated_metric_per_time_bucket.to_string()) timestamp samples median_absolute_error 1 01/06/2005 14:00:00.000000 2 0.001 response = cm.report(df=aggregated_metric_per_time_bucket) print(response.status_code) 202 ``` The `dry_run` parameter determines if the custom metric values transfer is a dry run (the values aren't saved in the database) or if it is a production data transfer. This parameter is `False` by default (the values are saved). ``` response = cm.report(df=aggregated_metric_per_time_bucket, dry_run=True) print(response.status_code) 202 ``` --- # Configure data sources URL: https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-data-sources.html > The DataRobotSource and BatchDataRobotSource methods connect to DataRobot to fetch selected data from the DataRobot platform. The DataFrameSource method wraps any pd.DataFrame to create a library-compatible source. # Configure data sources The most commonly used data source is `DataRobotSource`. This data source connects to DataRobot to fetch selected prediction data from the DataRobot platform. Three additional default data sources are available: `DataRobotSource`, `BatchDataRobotSource`, and `DataFrameSource`. > [!WARNING] Time series support > The [DataRobot Model Metrics (DMM)](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/index.html) library does not support time series models, specifically [data export](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-data-sources.html#export-prediction-data) for time series models. To export and retrieve data, use the [DataRobot API client](https://datarobot-public-api-client.readthedocs-hosted.com/en/latest-release/reference/mlops/data_exports.html). ## Configure a DataRobot source `DataRobotSource` connects to DataRobot to fetch selected prediction data from the DataRobot platform. Initialize `DataRobotSource` with the following mandatory parameters: ``` from dmm.data_source import DataRobotSource source = DataRobotSource( base_url=DATAROBOT_ENDPOINT, token=DATAROBOT_API_TOKEN, deployment_id=deployment_id, start=start_of_export_window, end=end_of_export_window, ) ``` You can also provide the `base_url` and `token` parameters as environment variables: `os.environ['DATAROBOT_ENDPOINT']` and `os.environ['DATAROBOT_API_TOKEN']` ``` from dmm.data_source import DataRobotSource source = DataRobotSource( deployment_id=deployment_id, start=start_of_export_window, end=end_of_export_window, ) ``` The following example initializes `DataRobotSource` with all parameters: ``` from dmm.data_source import DataRobotSource source = DataRobotSource( base_url=DATAROBOT_ENDPOINT, token=DATAROBOT_API_TOKEN, client=None, deployment_id=deployment_id, model_id=model_id, start=start_of_export_window, end=end_of_export_window, max_rows=10000, delete_exports=False, use_cache=False, actuals_with_matched_predictions=True, ) ``` | Parameter | Description | | --- | --- | | base_url: str | The DataRobot API URL; for example, https://app.datarobot.com/api/v2. | | token: str | A DataRobot API token from API keys and tools. | | client: Optional[DataRobotClient] | Use the DataRobotClient object instead of base_url and token. | | deployment_id: str | The ID of the deployment evaluated by the custom metric. | | model_id: Optional[str] | The ID of the model evaluated by the custom metric. If you don't specify a model ID, the champion model ID is used. | | start: datetime | The start of the export window. Define the date you want to start retrieving data from. | | end: datetime | The end of the export window. Define the date you want to retrieve data until. | | max_rows: Optional[int] | The maximum number of rows to fetch at once when the requested data doesn't fit into memory. | | delete_exports: Optional[bool] | Whether to automatically delete datasets with exported data created in the AI Catalog. True configures for deletion; the default value is False. | | use_cache: Optional[bool] | Whether to use existing datasets stored in the AI Catalog for time ranges included in previous exports. True uses datasets used in previous exports; the default value is False. | | actuals_with_matched_predictions: Optional[bool] | Whether to allow actuals export without matched predictions. False does not allow unmatched export; the default value is True. | #### Export prediction data The `get_prediction_data` method returns a chunk of prediction data with the appropriate chunk ID; the returned data chunk is a pandas DataFrame with the number of rows respecting the `max_rows` parameter. This method returns data until the data source is exhausted. ``` prediction_df_1, prediction_chunk_id_1 = source.get_prediction_data() print(prediction_df_1.head(5).to_string()) print(f"chunk id: {prediction_chunk_id_1}") DR_RESERVED_PREDICTION_TIMESTAMP DR_RESERVED_PREDICTION_VALUE_high DR_RESERVED_PREDICTION_VALUE_low date_non_unique date_random id 年月日 0 2023-09-13 11:02:51.248000+00:00 0.697782 0.302218 1950-10-01 1949-01-27 1 1949-01-01 1 2023-09-13 11:02:51.252000+00:00 0.581351 0.418649 1959-04-01 1949-02-03 2 1949-02-01 2 2023-09-13 11:02:51.459000+00:00 0.639347 0.360653 1954-05-01 1949-03-28 3 1949-03-01 3 2023-09-13 11:02:51.459000+00:00 0.627727 0.372273 1951-09-01 1949-04-07 4 1949-04-01 4 2023-09-13 11:02:51.664000+00:00 0.591612 0.408388 1951-03-01 1949-05-16 5 1949-05-01 chunk id: 0 ``` When the data source is exhausted, `None` and `-1` are returned: ``` prediction_df_2, prediction_chunk_id_2 = source.get_prediction_data() print(prediction_df_2) print(prediction_chunk_id_2) None chunk id: -1 ``` The `reset` method resets the exhausted data source, allowing it to iterate from the beginning: ``` source.reset() ``` The `get_all_prediction_data` method returns all prediction data available for a data source object in a single DataFrame: ``` prediction_df = source.get_all_prediction_data() ``` ### Export actuals data The `get_actuals_data` method returns a chunk of actuals data with the appropriate chunk ID the returned data chunk is a pandas DataFrame with the number of rows respecting the `max_rows` parameter. This method returns data until the data source is exhausted. ``` actuals_df_1, actuals_chunk_id_1 = source.get_actuals_data() print(actuals_df_1.head(5).to_string()) print(f"chunk id: {actuals_chunk_id_1}") association_id timestamp label actuals predictions predicted_class 0 1 2023-09-13 11:00:00+00:00 low 0 0.302218 high 194 57 2023-09-13 11:00:00+00:00 low 1 0.568564 low 192 56 2023-09-13 11:00:00+00:00 low 1 0.569865 low 190 55 2023-09-13 11:00:00+00:00 low 0 0.473282 high 196 58 2023-09-13 11:00:00+00:00 low 1 0.573861 low chunk id: 0 ``` To return raw data in the format of data from postgresql, set the `return_original_column_names` parameter to `True`: ``` actuals_df_1, actuals_chunk_id_1 = source.get_actuals_data() print(actuals_df_1.head(5).to_string()) print(f"chunk id: {actuals_chunk_id_1}") id timestamp label actuals y predicted_class 0 1 2023-09-13 11:00:00+00:00 low 0 0.302218 high 194 57 2023-09-13 11:00:00+00:00 low 1 0.568564 low 192 56 2023-09-13 11:00:00+00:00 low 1 0.569865 low 190 55 2023-09-13 11:00:00+00:00 low 0 0.473282 high 196 58 2023-09-13 11:00:00+00:00 low 1 0.573861 low chunk id: 0 ``` To return all actuals data available for a source object in a single DataFrame, use the `get_all_actuals_data` method: ``` actuals_df = source.get_all_actuals_data() ``` When the data source is exhausted, `None` and `-1` are returned: ``` actuals_df_2, actuals_chunk_id_2 = source.get_actuals_data() print(actuals_df_2) print(actuals_chunk_id_2) None chunk id: -1 ``` The `reset` method resets the exhausted data source, allowing it to iterate from the beginning: ``` source.reset() ``` ### Export training data The `get_training_data` method returns all data used for training in one call. The returned data is a pandas DataFrame: ``` train_df = source.get_training_data() print(train_df.head(5).to_string()) y date_random date_non_unique 年月日 0 high 1949-01-27 1950-10-01 1949-01-01 1 high 1949-02-03 1959-04-01 1949-02-01 2 low 1949-03-28 1954-05-01 1949-03-01 3 high 1949-04-07 1951-09-01 1949-04-01 4 high 1949-05-16 1951-03-01 1949-05-01 ``` ### Export combined data The `get_data` method returns `combined_data`, which includes merged scoring data, predictions, and matched actuals. This [Metric Evaluator](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-metric-evaluator.html) uses this method as the main data export method. ``` df, chunk_id_1 = source.get_data() print(df.head(5).to_string()) print(f"chunk id: {chunk_id_1}") timestamp predictions date_non_unique date_random association_id 年月日 predicted_class label actuals 0 2023-09-13 11:02:51.248000+00:00 0.302218 1950-10-01 1949-01-27 1 1949-01-01 high low 0 1 2023-09-13 11:02:51.252000+00:00 0.418649 1959-04-01 1949-02-03 2 1949-02-01 high low 0 2 2023-09-13 11:02:51.459000+00:00 0.360653 1954-05-01 1949-03-28 3 1949-03-01 high low 1 3 2023-09-13 11:02:51.459000+00:00 0.372273 1951-09-01 1949-04-07 4 1949-04-01 high low 0 4 2023-09-13 11:02:51.664000+00:00 0.408388 1951-03-01 1949-05-16 5 1949-05-01 high low 0 chunk id: 0 ``` The `get_all_data` returns all combined data available for that source object in a single DataFrame: ``` df = source.get_all_data() ``` ## Configure a DataRobot batch deployment source The `BatchDataRobotSource` interface is for batch deployments. The following example initializes `BatchDataRobotSource` with all parameters: ``` from dmm.data_source import BatchDataRobotSource source = BatchDataRobotSource( base_url=DATAROBOT_ENDPOINT, token=DATAROBOT_API_TOKEN, client=None, deployment_id=deployment_id, model_id=model_id, batch_ids=batch_ids, max_rows=10000, delete_exports=False, use_cache=False, ) ``` The parameters for this method are analogous to those for `DataRobotSource`. The most important difference is that instead of the time range (start and end), you must provide batch IDs. In addition, a batch source doesn't support actuals export. The `get_prediction_data` method returns a chunk of prediction data with the appropriate chunk ID; the returned data chunk is a pandas DataFrame with the number of rows respecting the `max_rows` parameter. This method returns data until the data source is exhausted. ``` prediction_df_1, prediction_chunk_id_1 = source.get_prediction_data() print(prediction_df_1.head(5).to_string()) print(f"chunk id: {prediction_chunk_id_1}") AGE B CHAS CRIM DIS batch_id DR_RESERVED_BATCH_NAME timestamp INDUS LSTAT MEDV NOX PTRATIO RAD RM TAX ZN id 0 65.2 396.90 0 0.00632 4.0900 batch1 2023-06-23 09:47:47.060000+00:00 2.31 4.98 24.0 0.538 15.3 1 6.575 296 18.0 1 1 78.9 396.90 0 0.02731 4.9671 batch1 2023-06-23 09:47:47.060000+00:00 7.07 9.14 21.6 0.469 17.8 2 6.421 242 0.0 2 2 61.1 392.83 0 0.02729 4.9671 batch1 2023-06-23 09:47:47.060000+00:00 7.07 4.03 34.7 0.469 17.8 2 7.185 242 0.0 3 3 45.8 394.63 0 0.03237 6.0622 batch1 2023-06-23 09:47:47.060000+00:00 2.18 2.94 33.4 0.458 18.7 3 6.998 222 0.0 4 4 54.2 396.90 0 0.06905 6.0622 batch1 2023-06-23 09:47:47.060000+00:00 2.18 5.33 36.2 0.458 18.7 3 7.147 222 0.0 5 chunk id: 0 prediction_df = source.get_all_prediction_data() source.reset() df, chunk_id_1 = source.get_data() ``` The `get_training_data` method returns all data used for training in one call. The returned data is a pandas DataFrame: ``` train_df = source.get_training_data() ``` ## Configure a DataFrame source If you aren't exporting data directly from DataRobot, and instead have it downloaded locally (for example), you can load the dataset into `DataFrameSource`. The `DataFrameSource` method wraps any `pd.DataFrame` to create a library-compatible source. This is the easiest way to interact with the library when bringing your own data: ``` source = DataFrameSource( df=pd.read_csv("./data_hour_of_week.csv"), max_rows=10000, timestamp_col="date" ) df, chunk_id_1 = source.get_data() print(df.head(5).to_string()) print(f"chunk id: {chunk_id_1}") date y 0 1959-12-31 23:59:57 -0.183669 1 1960-01-01 01:00:02 0.283993 2 1960-01-01 01:59:52 0.020663 3 1960-01-01 03:00:14 0.404304 4 1960-01-01 03:59:58 1.005252 chunk id: 0 ``` In addition, it is possible to create new data source definitions. To define a new data source, you can customize and implement the `DataSourceBase` interface. ## Set the TimeBucket The `TimeBucket` class enumeration (enum) defines the required data aggregation granularity over time. By default, `TimeBucket` is set to `TimeBucket.ALL`. You can specify any of the following values: `SECOND`, `MINUTE`, `HOUR`, `DAY`, `WEEK`, `MONTH`, `QUARTER`, or `ALL`. To change the `TimeBucket` value, use the `init` method: `source.init(time_bucket)`: ``` # Generate a dummy DataFrame with 2 rows per time bucket (Hour in this scenario) test_df = gen_dataframe_for_accuracy_metric( nr_rows=10, rows_per_time_bucket=2, prediction_value=1, with_actuals=True, with_predictions=True, time_bucket=TimeBucket.HOUR, ) print(test_df) timestamp predictions actuals 0 01/06/2005 13:00:00.000000 1 0.999 1 01/06/2005 13:00:00.000000 1 0.999 2 01/06/2005 14:00:00.000000 1 0.999 3 01/06/2005 14:00:00.000000 1 0.999 4 01/06/2005 15:00:00.000000 1 0.999 5 01/06/2005 15:00:00.000000 1 0.999 6 01/06/2005 16:00:00.000000 1 0.999 7 01/06/2005 16:00:00.000000 1 0.999 8 01/06/2005 17:00:00.000000 1 0.999 9 01/06/2005 17:00:00.000000 1 0.999 # Use DataFrameSource and load created DataFrame source = DataFrameSource( df=test_df, max_rows=10000, timestamp_col="timestamp", ) # Init source with the selected TimeBucket source.init(TimeBucket.HOUR) df, _ = source.get_data() print(df) timestamp predictions actuals 0 01/06/2005 13:00:00.000000 1 0.999 1 01/06/2005 13:00:00.000000 1 0.999 df, _ = source.get_data() print(df) timestamp predictions actuals 2 01/06/2005 14:00:00.000000 1 0.999 3 01/06/2005 14:00:00.000000 1 0.999 source.init(TimeBucket.DAY) df, _ = source.get_data() print(df) timestamp predictions actuals 0 01/06/2005 13:00:00.000000 1 0.999 1 01/06/2005 13:00:00.000000 1 0.999 2 01/06/2005 14:00:00.000000 1 0.999 3 01/06/2005 14:00:00.000000 1 0.999 4 01/06/2005 15:00:00.000000 1 0.999 5 01/06/2005 15:00:00.000000 1 0.999 6 01/06/2005 16:00:00.000000 1 0.999 7 01/06/2005 16:00:00.000000 1 0.999 8 01/06/2005 17:00:00.000000 1 0.999 9 01/06/2005 17:00:00.000000 1 0.999 ``` The returned data chunks follow the selected `TimeBucket`. This is helpful in the `MetricEvaluator`. In addition to `TimeBucket`, the source respects the `max_rows` parameter when generating data chunks; for example, using the same dataset as in the example above (but with `max_rows` set to `3`): ``` source = DataFrameSource( df=test_df, max_rows=3, timestamp_col="timestamp", ) source.init(TimeBucket.DAY) df, chunk_id = source.get_data() print(df) timestamp predictions actuals 0 01/06/2005 13:00:00.000000 1 0.999 1 01/06/2005 13:00:00.000000 1 0.999 2 01/06/2005 14:00:00.000000 1 0.999 ``` In `DataRobotSource`, you can specify the `TimeBucket` and `max_rows` parameters for all export types except training data export, which is returned in one chunk. ## Provide additional DataRobot deployment properties The `Deployment` class is a helper class, providing access to relevant deployment properties. This class is used inside `DataRobotSource` to select the appropriate workflow to work with data. ``` import datarobot as dr from dmm.data_source.datarobot.deployment import Deployment DataRobotClient() deployment = Deployment(deployment_id=deployment_id) deployment_type = deployment.type() target_column = deployment.target_column() positive_class_label = deployment.positive_class_label() negative_class_label = deployment.negative_class_label() prediction_threshold = deployment.prediction_threshold() . . . ``` --- # Use the DR Custom Metrics module URL: https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-dr-custom-metric.html > The DR Custom Metrics module facilitates better synchronization with existing metrics in DataRobot. # Use the DR Custom Metrics module The DR Custom Metrics module facilitates better synchronization with existing metrics in DataRobot. The logic of this module is based on unique names for custom metrics, allowing you to operate on metrics without knowing their IDs. Using this solution, you can define the metric earlier (e.g., before creating the deployment) and synchronize it with DataRobot at the appropriate time. The `DRCustomMetric` class allows you to create new or fetch existing metrics from DataRobot. You can provide custom metrics configuration through YAML, JSON, or a dict. The configuration contains metadata describing the custom metric. The `DRCustomMetric` class contains two methods: - DRCustomMetric.sync(): Retrieves information about existing custom metrics in DataRobot. If a metric is defined locally but not in DataRobot, it is created in DataRobot. - DRCustomMetric.report(): Reports a single value based on a unique name. ``` dr_cm = DRCustomMetric( dr_client=client, deployment_id=deployment_id, model_package_id=model_package_id ) metric_config_yaml = f""" customMetrics: - name: new metric description: metric description type: average timeStep: hour units: count directionality: lowerIsBetter isModelSpecific: yes baselineValue: 0 """ dr_cm.set_config(config_yaml=metric_config_yaml) dr_cm.sync() dr_cm.get_dr_custom_metrics() > [{"name": "existing metric", "id": "65ef19410239ff8015f05a94", ...}, > {"name": "new metric", "id": "65ef197ce5d7b2176ceecf3a", ...}] dr_cm.report_value("existing metric", 1) dr_cm.report_value("new metric", 9) ``` --- # Configure an environment for DMM URL: https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-environment-setup.html > Configure an environment to develop custom metrics with the DataRobot Model Metrics library. # Configure an environment for DMM There are two primary ecosystems where you can develop a custom metric with the DataRobot Model Metrics library (DMM): 1. Within the DataRobot application (via a notebook or a scheduled job). 2. In a local development environment. > [!NOTE] Environment considerations > Installing Python modules: > > When running DMM from within the DataRobot application, the ecosystem is pre-configured with all required Python modules. > When running DMM locally, you need to install the > dmm > module. This automatically updates the Python environment with all required modules. > > Setting DMM parameters: > > When running DMM from within the DataRobot application, the parameters are set through environment variables. > When running DMM locally, it is recommended that you pass values using arguments to set these parameters, rather than setting environment variables. ## Initialize the environment The `CustomMetricArgumentParser` class wraps the standard `argparser.ArgumentParser`. This class provides convenience functions to allow reading values from the environment or normal argument parsing. When `CustomMetricArgumentParser.parse_args()` is called, it checks for missing values. The `log_manager` provides a set of functions to help with logging. The DMM library and the DataRobot public API client use standard Python `logging` primitives. A complete list of log classes with their current levels is available using `get_log_levels()`. The `initialize_loggers()` function initializes all loggers: ``` 2024-08-09 02:19:50 PM - dmm.data_source.datarobot_source - INFO - fetching the next predictions dataframe... 2024-07-15 00:00:00 - 2024-08-09 14:19:46.643722 2024-08-09 02:19:56 PM - urllib3.connectionpool - DEBUG - https://app.datarobot.com:443 "POST /api/v2/deployments/66a90a711zd81645df8c469c/predictionDataExports/ HTTP/1.1" 202 368 ``` The following snippet shows how to set up your runtime environment using the previously mentioned classes: ``` import sys from dmm import CustomMetricArgumentParser from dmm.log_manager import initialize_loggers parser = CustomMetricArgumentParser(description="My new custom metric") parser.add_base_args() # adds standard arguments # Add more with standard ArgumentParser primitives, or some convenience functions such as add_environment_arg() # Parse the program arguments (if any) to an argparse.Namespace. args = parser.parse_args(sys.argv[1:]) # Initialize the logging based on the 'LOG' environment variable, or the --log option initialize_loggers(args.log) ``` The standard/base arguments include the following: | Argument | Description | | --- | --- | | BASE_URL | The URL of the public API. | | API_KEY | The API token used for authentication to the server located at BASE_URL. | | DEPLOYMENT_ID | The deployment ID from the application. | | CUSTOM_METRIC_ID | The custom metric ID from the application. | | DRY_RUN | The flag to indicate whether to report the custom metric result to the deployment. With the DRY_RUN runtime parameter set to 1, the run is a test run and does not report metric data. | | START_TS | The start of the time range for metric calculation. | | END_TS | The end of the time for metric calculation. | | MAX_ROWS | The maximum number of prediction rows to process. | | LOG | The initialization of logging—defaults to setting all dmm and datarobot modules to WARNING. | The following is an example of the help provided using `CustomMetricArgumentParser`: ``` (model-runner) $ python3 custom.py --help usage: custom.py [-h] [--api-key KEY] [--base-url URL] [--deployment-id ID] [--custom-metric-id ID] [--dry-run] [--start-ts TIMESTAMP] [--end-ts TIMESTAMP] [--max-rows ROWS] [--required] [--log [[NAME:]LEVEL ...]] My new custom metric optional arguments: -h, --help show this help message and exit --api-key KEY API key used to authenticate to server. Settable via 'API_KEY', required. --base-url URL URL for server. Settable via 'BASE_URL' (default: https://staging.datarobot.com/api/v2), required. --deployment-id ID Deployment ID. Settable via 'DEPLOYMENT_ID' (default: None), required. --custom-metric-id ID Custom metric ID. Settable via 'CUSTOM_METRIC_ID' (default: None), required. --dry-run Dry run. Settable via 'DRY_RUN' (default: False). --start-ts TIMESTAMP Start timestamp. Settable with 'START_TS', or 'LAST_SUCCESSFUL_RUN_TS' (when not dry run). Default is 2024-08-08 14:27:55.493027 --end-ts TIMESTAMP End timestamp. Settable with 'END_TS' or 'CURRENT_RUN_TS'. Default is 2024-08-09 14:27:55.493044. --max-rows ROWS Maximum number of rows. Settable via 'MAX_ROWS' (default: 100000). --required List the required properties and exit. --log [[NAME:]LEVEL ...] Logging level list. Settable via 'LOG' (default: WARNING). (model-runner) $ ``` ## Using the save_to_csv() utility During development, it is common to run your code over the same data multiple times to see how changes impact the results. The `save_to_csv()` utility allows you to save your results to a CSV file, so you can compare the results between successive runs on the same data. --- # Create a hosted custom metric from a template with code URL: https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-hosted-custom-metrics-quickstart.html # Create a hosted custom metric from a template with code Hosted custom metrics run user-provided code on DataRobot infrastructure to calculate your organization's customized metrics. DataRobot provides a variety of templates for common metrics. These metrics can be used as is, or as a starting point for user-provided metrics. In this tutorial, you create a hosted custom metric using the Python SDK. ## Prerequisites Before you start, import all objects used in this tutorial and initialize the DataRobot client: ``` import datarobot as dr from datarobot.enums import HostedCustomMetricsTemplateMetricTypeQueryParams from datarobot.models.deployment.custom_metrics import HostedCustomMetricTemplate, HostedCustomMetric, \ HostedCustomMetricBlueprint, CustomMetric, MetricTimestampSpoofing, ValueField, SampleCountField, BatchField from datarobot.models.registry import JobRun from datarobot.models.registry.job import Job from datarobot import Deployment from datarobot.models.runtime_parameters import RuntimeParameterValue from datarobot.models.types import Schedule DataRobotClient(token="", endpoint="") gen_ai_deployment_1 = Deployment.get('') ``` ## List hosted custom metrics templates Before creating a hosted custom metric from a template, retrieve the LLM metric template to use as the basis of the new metric. To do this, specify the `metric_type` and, because the deployments are LLM models handling Japanese text, search for the specific metric by name, limiting the search to `1` result. Store the result in templates for the next step. ``` templates = HostedCustomMetricTemplate.list( search="[JP] Character Count", metric_type=HostedCustomMetricsTemplateMetricTypeQueryParams.LLM, limit=1, offset=0, ) ``` ## Create a hosted custom metric in one step Locate the custom metric template to create a hosted custom metric. This method is a shortcut, combining two steps to create a new custom metric from the retrieved template: 1. Create a custom job for a hosted custom metric from the template retrieved in the previous step (stored in templates ). 2. Connect the hosted custom metric job to the deployment defined during the prerequisites step (stored in gen_ai_deployment_1 ). Because we are creating two objects, specify both the job name and custom metric name in addition to the template and deployment IDs. Additionally, define the job schedule and the runtime parameter overrides for the deployment. ``` hosted_custom_metric = HostedCustomMetric.create_from_template( template_id=templates[0].id, deployment_id=gen_ai_deployment_1.id, job_name="Hosted Custom Metric Character Count", custom_metric_name="Character Count", job_description="Hosted Custom Metric", custom_metric_description="LLM Character Count", baseline_value=10, timestamp=MetricTimestampSpoofing( column_name="timestamp", time_format="%Y-%m-%d %H:%M:%S", ), value = ValueField(column_name="value"), sample_count=SampleCountField(column_name='Sample Count'), batch=BatchField(column_name='Batch'), schedule=Schedule( day_of_week=[0], hour=['*'], minute=['*'], day_of_month=[12], month=[1], ), parameter_overrides=[RuntimeParameterValue(field_name='DRY_RUN', value="0", type="string")] ) ``` Once you create the hosted custom metric, initiate the manual run. ``` job_run = JobRun.create( job_id=hosted_custom_metric.custom_job_id runtime_parameter_values=[ RuntimeParameterValue(field_name='DRY_RUN', value="1", type="string"), RuntimeParameterValue(field_name='DEPLOYMENT_ID', value=gen_ai_deployment_1.id, type="deployment"), RuntimeParameterValue(field_name='CUSTOM_METRIC_ID', value=hosted_custom_metric.id, type="customMetric"), ] ) print(job_run.status) ``` ## Create a hosted custom metric in two steps You can also perform these steps manually, in sequence. This is useful if you want to edit the custom metric blueprint before attaching the custom job to the deployment. When you attach the job to the deployment, most settings are copied from the blueprint (unless you provide an override). To create the hosted custom metric manually, first, create a custom job from the template retrieved earlier (stored in `templates`). ``` job = Job.create_from_custom_metric_gallery_template( template_id=templates[0].id, name="Job created from template", description="Job created from template" ) ``` Next, retrieve the default blueprint, provided by the template, and edit it. ``` blueprint = HostedCustomMetricBlueprint.get(job.id) print(f"Original directionality: {blueprint.directionality}") ``` Then, update the parameters of the custom metric in the blueprint. ``` updated_blueprint = blueprint.update( directionality='lowerIsBetter', units='characters', type='gauge', time_step='hour', is_model_specific=False ) print(f"Updated directionality: {updated_blueprint.directionality}") ``` Now create the hosted custom metric. As in the shortcut method, you can provide the job schedule, runtime parameter overrides, and custom metric parameters specific to this deployment. ``` another_hosted_custom_metric = HostedCustomMetric.create_from_custom_job( custom_job_id=job.id, deployment_id=gen_ai_deployment_1.id, name="Custom metric created in 2 steps", ) ``` After creating and configuring the metric, verify that the changes to the blueprint are reflected in the custom metric. ``` another_custom_metric = CustomMetric.get(custom_metric_id=another_hosted_custom_metric.id, deployment_id=gen_ai_deployment_1.id) print(f"Directionality of another custom metric: {another_custom_metric.directionality}") ``` Finally, create a manual job run for the custom metric job. ``` job_run = JobRun.create( job_id=job.id, runtime_parameter_values=[ RuntimeParameterValue(field_name='DRY_RUN', value="1", type="string"), RuntimeParameterValue(field_name='DEPLOYMENT_ID', value=gen_ai_deployment_1.id, type="deployment"), RuntimeParameterValue(field_name='CUSTOM_METRIC_ID', value=another_hosted_custom_metric.id, type="customMetric"), ] ) print(job_run.status) ``` ## List hosted custom metrics associated with a job To list all hosted custom metrics associated with a custom job, use the following code: ``` hosted_custom_metrics = HostedCustomMetric.list(deployment_id=hosted_custom_metric.custom_job_id) for metric in hosted_custom_metrics: print(metric.name) ``` ## Delete hosted custom metrics You can delete the hosted custom metric, removing the custom metric from the deployment but keeping the job, allowing you to create the metric for another deployment. ``` hosted_custom_metric.delete() another_hosted_custom_metric.delete() ``` If necessary, you can delete the entire custom job. If there are any custom metrics associated with that job, they are also deleted. ``` job.delete() ``` --- # Calculate metric values URL: https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-metric-evaluator.html > To calculate custom metric values, you can use MetricEvaluator to calculate metric values over time, BatchMetricEvaluator to calculate metric values per batch, or IndividualMetricEvaluator to evaluate metrics without data aggregation. # Calculate metric values To calculate custom metric values, you can use `MetricEvaluator` to calculate metric values over time, `BatchMetricEvaluator` to calculate metric values per batch, or `IndividualMetricEvaluator` to evaluate metrics without data aggregation. ## Evaluate metrics The `MetricEvaluator` class calculates metric values over time using the selected source. This class is used to "stream" data through the metric object, generating metric values. Initialize the `MetricEvaluator` with the following mandatory parameters: ``` from dmm import MetricEvaluator, TimeBucket from dmm import DataRobotSource from dmm.metric import MedianAbsoluteError source = DataRobotSource( deployment_id=DEPLOYMENT_ID, start=datetime.utcnow() - timedelta(weeks=1), end=datetime.utcnow(), ) metric = MedianAbsoluteError() metric_evaluator = MetricEvaluator(metric=metric, source=source, time_bucket=TimeBucket.MINUTE) ``` To use `MetricEvaluator`, create a metric class implementing the `MetricBase` interface and a source implementing `DataSourceBase`. Then, specify the level of aggregation granularity. Initialize `MetricEvaluator` with all parameters: ``` from dmm import ColumnName, MetricEvaluator, TimeBucket metric_evaluator = MetricEvaluator( metric=metric, source=source, time_bucket=TimeBucket.HOUR, prediction_col=ColumnName.PREDICTIONS, actuals_col=ColumnName.ACTUALS, timestamp_col=ColumnName.TIMESTAMP, filter_actuals=False, filter_predictions=False, filter_scoring_data=False, segment_attribute=None, segment_value=None, ) ``` | Parameter | Description | | --- | --- | | metric: Union[str, MetricBase, List[str], List[MetricBase]] | If a string or list of strings is passed, MetricEvaluator will look for matched Sklearn metrics. If a metric or a list of objects is passed, they must implement the MetricBase interface. | | source: DataSourceBase | The source to pull the data from, DataRobotSource or DataFrameSource or other sources that implement the DataSourceBase interface. | | time_bucket: TimeBucket | The time-bucket size to use for evaluating metrics, determining the granularity of aggregation. | | prediction_col: Optional[str] | The name of the column that contains predictions. | | actuals_col: Optional[str] | The name of the column that contains actuals. | | timestamp_col: Optional[str] | The name of the column that contains timestamps. | | filter_actuals: Optional[bool] | Whether the metric evaluator removes missing actuals values before scoring. True removes missing actuals; the default value is False. | | filter_predictions: Optional[bool] | Whether the metric evaluator removes missing predictions values before scoring. True removes missing predictions; the default value is False. | | filter_scoring_data: Optional[bool] | Whether the metric evaluator removes missing scoring values before scoring. True removes missing scoring values; the default value is False. | | segment_attribute: Optional[str] | The name of the column with segment values. | | segment_value: Optional[Union[str or List[str]]] | A single value or a list of values of the segment attribute to segment on. | The `score` method returns a metric aggregated as defined by `TimeBucket`. The output returned as a pandas DataFrame contains the results per time bucket for all data from the source. ``` source = DataRobotSource( deployment_id=DEPLOYMENT_ID, start=datetime.utcnow() - timedelta(hours=3), end=datetime.utcnow(), ) metric = LogLossFromSklearn() me = MetricEvaluator(metric=metric, source=source, time_bucket=TimeBucket.HOUR) aggregated_metric_per_time_bucket = me.score() print(aggregated_metric_per_time_bucket.to_string()) timestamp samples log_loss 0 2023-09-14 13:29:48.065000+00:00 499 0.539315 1 2023-09-14 14:01:51.484000+00:00 499 0.539397 # we can see the evaluator's statistics stats = me.stats() print(stats) total rows: 998, score calls: 2, reduce calls: 2 ``` To pass more than one metric at a time, do the following: ``` metrics = [LogLossFromSklearn(), AsymmetricError(), RocAuc()] me = MetricEvaluator(metric=metric, source=source, time_bucket=TimeBucket.HOUR) aggregated_metric_per_time_bucket = me.score() stats = me.stats() print(aggregated_metric_per_time_bucket.to_string()) print(stats) timestamp samples log_loss Asymmetric Error roc_auc_score 0 2023-09-14 13:29:48.065000+00:00 499 0.539315 0.365571 0.787030 1 2023-09-14 14:01:51.484000+00:00 499 0.539397 0.365636 0.786837 total rows: 998, score calls: 6, reduce calls: 6 ``` For your data, provide the names of the columns to evaluate: ``` test_df = gen_dataframe_for_accuracy_metric( nr_rows=5, rows_per_time_bucket=1, prediction_value=1, time_bucket=TimeBucket.DAY, prediction_col="my_pred_col", actuals_col="my_actuals_col", timestamp_col="my_timestamp_col" ) print(test_df) my_timestamp_col my_pred_col my_actuals_col 0 01/06/2005 13:00:00.000000 1 0.999 1 02/06/2005 13:00:00.000000 1 0.999 2 03/06/2005 13:00:00.000000 1 0.999 3 04/06/2005 13:00:00.000000 1 0.999 4 05/06/2005 13:00:00.000000 1 0.999 source = DataFrameSource( df=test_df, max_rows=10000, timestamp_col="timestamp", ) metric = LogLossFromSklearn() me = MetricEvaluator(metric=metric, source=source, time_bucket=TimeBucket.DAY, prediction_col="my_pred_col", actuals_col="my_actuals_col", timestamp_col="my_timestamp_col" ) aggregated_metric_per_time_bucket = me.score() ``` ### Configure data filtering If data is missing, use filtering flags. In the following example, the data is missing actuals. In this scenario without a flag, an exception is raised: ``` test_df = gen_dataframe_for_accuracy_metric( nr_rows=10, rows_per_time_bucket=5, prediction_value=1, time_bucket=TimeBucket.HOUR, ) test_df["actuals"].loc[2] = None test_df["actuals"].loc[5] = None print(test_df) timestamp predictions actuals 0 01/06/2005 13:00:00.000000 1 0.999 1 01/06/2005 13:00:00.000000 1 0.999 2 01/06/2005 13:00:00.000000 1 NaN 3 01/06/2005 13:00:00.000000 1 0.999 4 01/06/2005 13:00:00.000000 1 0.999 5 01/06/2005 14:00:00.000000 1 NaN 6 01/06/2005 14:00:00.000000 1 0.999 7 01/06/2005 14:00:00.000000 1 0.999 8 01/06/2005 14:00:00.000000 1 0.999 9 01/06/2005 14:00:00.000000 1 0.999 source = DataFrameSource(df=test_df) metric = MedianAbsoluteError() me = MetricEvaluator(metric=metric, source=source, time_bucket=TimeBucket.HOUR) aggregated_metric_per_time_bucket = me.score() "ValueError: Could not apply metric median_absolute_error, make sure you are passing the right data (see the sklearn docs). The error message was: Input contains NaN." ``` Compare the previous result with the result when you enable the `filter_actuals` flag: ``` me = MetricEvaluator(metric=metric, source=source, time_bucket=TimeBucket.HOUR, filter_actuals=True) aggregated_metric_per_time_bucket = me.score() "removed 1 rows out of 5 in the data chunk before scoring, due to missing values in ['actuals'] data" "removed 1 rows out of 5 in the data chunk before scoring, due to missing values in ['actuals'] data" print(aggregated_metric_per_time_bucket.to_string()) timestamp samples median_absolute_error 0 01/06/2005 13:00:00.000000 4 0.001 1 01/06/2005 14:00:00.000000 4 0.001 ``` Using the `filter_actuals`, `filter_predictions`, and `filter_scoring_data` flags, you can filter out missing values from the data before calculating the metric. By default, these flags are set to `False`. If all data needed to calculate the metric is missing from the data chunk, the data chunk is skipped with the appropriate log: ``` test_df = gen_dataframe_for_accuracy_metric( nr_rows=4, rows_per_time_bucket=2, prediction_value=1, time_bucket=TimeBucket.HOUR, ) test_df["actuals"].loc[0] = None test_df["actuals"].loc[1] = None print(test_df) timestamp predictions actuals 0 01/06/2005 13:00:00.000000 1 NaN 1 01/06/2005 13:00:00.000000 1 NaN 2 01/06/2005 14:00:00.000000 1 0.999 3 01/06/2005 14:00:00.000000 1 0.999 source = DataFrameSource(df=test_df) metric = MedianAbsoluteError() me = MetricEvaluator(metric=metric, source=source, time_bucket=TimeBucket.HOUR, filter_actuals=True) aggregated_metric_per_time_bucket = me.score() "removed 2 rows out of 2 in the data chunk before scoring, due to missing values in ['actuals'] data" "data chunk is empty, skipping scoring..." print(aggregated_metric_per_time_bucket.to_string()) timestamp samples median_absolute_error 1 01/06/2005 14:00:00.000000 2 0.001 ``` ### Perform segmented analysis Perform segmented analysis by defining the `segment_attribute` and each `segment_value`: ``` metrics = LogLossFromSklearn() me = MetricEvaluator(metric=metric, source=source, time_bucket=TimeBucket.HOUR, segment_attribute="insulin", segment_value="Down", ) aggregated_metric_per_time_bucket = me.score() print(aggregated_metric_per_time_bucket.to_string()) timestamp samples log_loss [Down] 0 2023-09-14 13:29:49.737000+00:00 49 0.594483 1 2023-09-14 14:01:52.437000+00:00 49 0.594483 # passing more than one segment value me = MetricEvaluator(metric=metric, source=source, time_bucket=TimeBucket.HOUR, segment_attribute="insulin", segment_value=["Down", "Steady"], ) aggregated_metric_per_time_bucket = me.score() print(aggregated_metric_per_time_bucket.to_string()) timestamp samples log_loss [Down] log_loss [Steady] 0 2023-09-14 13:29:48.502000+00:00 199 0.594483 0.515811 1 2023-09-14 14:01:51.758000+00:00 199 0.594483 0.515811 # passing more than one segment value and more than one metric me = MetricEvaluator(metric=[LogLossFromSklearn(), RocAuc()], source=source, time_bucket=TimeBucket.HOUR, segment_attribute="insulin", segment_value=["Down", "Steady"], ) aggregated_metric_per_time_bucket = me.score() print(aggregated_metric_per_time_bucket.to_string()) timestamp samples log_loss [Down] log_loss [Steady] roc_auc_score [Down] roc_auc_score [Steady] 0 2023-09-14 13:29:48.502000+00:00 199 0.594483 0.515811 0.783333 0.826632 1 2023-09-14 14:01:51.758000+00:00 199 0.594483 0.515811 0.783333 0.826632 ``` ## Evaluate metrics with aggregation-per-batch The `BatchMetricEvaluator` class uses aggregation-per-batch instead of aggregation-over-time. For batches, don't define `TimeBucket`: ``` from dmm.batch_metric_evaluator import BatchMetricEvaluator from dmm.data_source.datarobot_source import BatchDataRobotSource from dmm.metric import MissingValuesFraction source = BatchDataRobotSource( deployment_id=DEPLOYMENT_ID, batch_ids=BATCH_IDS, model_id=MODEL_ID, ) feature_name = 'RAD' metric = MissingValuesFraction(feature_name=feature_name) missing_values_fraction_evaluator = BatchMetricEvaluator(metric=metric, source=source) aggregated_metric_per_batch = missing_values_fraction_evaluator.score() print(aggregated_metric_per_batch.to_string()) batch_id samples Missing Values Fraction 0 506 0.0 1 506 0.0 2 506 0.0 ``` ## Evaluate metrics without data aggregation The `IndividualMetricEvaluator` class is used to evaluate metrics without data aggregation. It performs metric calculations on all exported data and returns a list of individual results. This evaluator allows submitting individual data points with a corresponding association ID, which is useful for cases when you want to visualize your metric results alongside predictions and actuals. To use this evaluator with custom metrics, provide a `score()` method that contains, among others, the following parameters: `timestamps` and `association_ids`. ``` from itertools import zip_longest from typing import List from datetime import datetime from datetime import timedelta from dmm import CustomMetric from dmm import DataRobotSource from dmm import SingleMetricResult from dmm.individual_metric_evaluator import IndividualMetricEvaluator from dmm.metric import LLMMetricBase from nltk import sent_tokenize import numpy as np import pandas as pd source = DataRobotSource( deployment_id=DEPLOYMENT_ID, start=datetime.utcnow() - timedelta(weeks=1), end=datetime.utcnow(), ) custom_metric = CustomMetric.from_id() class SentenceCount(LLMMetricBase): """ Calculates the total number of sentences created while working with the LLM model. Returns the sum of the number of sentences from prompts and completions. """ def __init__(self): super().__init__( name=custom_metric.name, description="Calculates the total number of sentences created while working with the LLM model.", need_training_data=False, ) self.prompt_column = "promptColumn" def score( self, scoring_data: pd.DataFrame, predictions: np.ndarray, timestamps: np.ndarray, association_ids: np.ndarray, **kwargs, ) -> List[SingleMetricResult]: if self.prompt_column not in scoring_data.columns: raise ValueError( f"Prompt column {self.prompt_column} not found in the exported data, " f"modify 'PROMPT_COLUMN' runtime parameter" ) prompts = scoring_data[self.prompt_column].to_numpy() sentence_count = [] for prompt, completion, ts, a_id in zip_longest( prompts, predictions, timestamps, association_ids ): if not isinstance(prompt, str) or not isinstance(completion, str): continue value = len(sent_tokenize(prompt)) + len(sent_tokenize(completion)) sentence_count.append( SingleMetricResult(value=value, timestamp=ts, association_id=a_id) ) return sentence_count sentence_count_evaluator = IndividualMetricEvaluator( metric=SentenceCount(), source=source, ) metric_results = sentence_count_evaluator.score() ``` --- # Model Metrics URL: https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/index.html > Learn how to construct and modify DataRobot blueprints through a programmatic interface. # Model Metrics The `datarobot-model-metrics` (DMM) library provides the tools necessary to compute model metrics over time and produce aggregated metrics. It provides a framework to perform the following operations: | Topic | Description | | --- | --- | | Configure an environment for DMM | Configure an environment to develop custom metrics with the DataRobot Model Metrics library. | | Configure data sources | Connect to DataRobot to fetch selected data from the DataRobot platform. | | Define custom metrics | Define custom metrics using the provided default classes. | | Calculate metric values | Calculate custom metric values over time, by batch, or without data aggregation. | | Use the DR Custom Metrics module | Facilitate synchronization with existing metrics in DataRobot. | | Notebook: Create a hosted custom metric from a template with code | Follow a tutorial to create a hosted custom metric using the Python SDK. | ## Installation The DMM library is published to [PyPI](https://pypi.org/project/datarobot-model-metrics/). To install it, run the following command: ``` pip install datarobot-model-metrics ``` --- # Test custom models locally URL: https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-local-test.html > Use the DataRobot Model Runner tool (DRUM) to test and verify a Python, R, or Java custom model locally, before you upload it to DataRobot. # Test custom models locally > [!NOTE] Availability information > To access the DataRobot Model Runner tool, contact your DataRobot representative. The DataRobot Model Runner is a tool that allows you to test Python, R, and Java custom models locally. The test verifies that a custom model can successfully run and make predictions before you [upload it to DataRobot](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-create-custom-model.html). However, this testing is only for development purposes. DataRobot recommends that you also test custom model you wish to deploy in the [Workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-test-custom-model.html) after uploading it. Before proceeding, reference the guidelines for [setting up a custom model or environment folder](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/index.html). > [!NOTE] Note > The DataRobot Model Runner tool supports Python, R, and Java custom models. Reference the [DRUM readme](https://github.com/datarobot/datarobot-user-models?tab=readme-ov-file#datarobot-user-models) for details about additional functionality, including: - Autocompletion - Custom hooks - Performance tests - Running models with a prediction server - Running models inside a Docker container ### Model requirements In addition to the required folder contents, DRUM requires the following for your serialized model: - Regression models must return a single floating point per row of prediction data. - Binary classification models must return two floating point values that sum to 1.0 per row of prediction data. - The first value must be the positive class probability, and the second the negative class probability. - There is a single pkl/pth/h5 file present. ## Run tests with the DataRobot CM Runner Use the following commands to execute local tests for your custom model: ``` # List all possible arguments drum --help ``` ``` # Test a custom binary classification model drum score -m ~/custom_model/ --input [--positive-class-label ] [--negative-class-label ] [--output ] [--verbose] # Use --verbose for a more detailed output. Make batch predictions with a custom binary classification model. Optionally, specify an output file. Otherwise, predictions are returned to the command line. ``` ``` # Example: Test a custom binary classification model drum score -m ~/custom_model/ --input 10k.csv --positive-class-label yes --negative-class-label no --output 10k-results.csv --verbose ``` ``` # Test a custom regression model drum score -m ~/custom_model/ --input [--output ] [--verbose] # Use --verbose for a more detailed output. Make batch predictions with a custom regression model. Optionally, specify an output file. Otherwise, predictions are returned to the command line. ``` ``` # Example: Test a custom regression model drum score -m ~/custom_model/ --input fast-iron.csv --verbose # This is an example that does not include an output command, so the prediction results return in the command line. ``` --- # Custom model components URL: https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-components.html > Describes custom model support and how to structure a custom model's files. # Custom model components To create and upload a custom model, you need to define two components—the model’s content and an environment where the model’s content will run: - Themodel contentis code written in Python or R. To be correctly parsed by DataRobot, the code must follow certain criteria. The model artifact's structure should match the library used by the model. In addition, it should use the appropriatecustom hooksfor Python, R, and Java models. (Optional) You can add files that will be uploaded and used together with the model’s code (for example, you might want to add a separate file with a dictionary if your custom model contains text preprocessing). - Themodel environmentis defined using a Docker file and additional files that will allow DataRobot to build an image where the model will run. There are a variety of built-in environments; you only need to build your own environment when you need to install Linux packages. For more detailed information, see the section oncustom model environments. At a high level, the steps to define a custom model with these components include: 1. Define and test model content locally (i.e., on your computer). 2. (Optional) Create a container environment where the model will run. 3. Upload the model content and environment (if applicable) into DataRobot. ## Model content To define a custom model, create a local folder containing the files listed in the table below (detailed descriptions follow the table). > [!TIP] Tip > To ensure your assembled custom model folder has the correct contents, you can find examples of these files in the [DataRobot model template repository](https://github.com/datarobot/datarobot-user-models/tree/master/model_templates) on GitHub. | File | Description | Required | | --- | --- | --- | | Model artifact fileorcustom.py/custom.R file | Provide a model artifact and/or a custom code file. Model artifact: a serialized model artifact with a file extension corresponding to the chosen environment language.Custom code: custom capabilities implemented with hooks (or functions) that enable DataRobot to run the code and integrate it with other capabilities. | Yes | | model-metadata.yaml | A file describing a model's metadata, including input/output data requirements and runtime parameters. You can supply a schema that can then be used to validate the model when building and training a blueprint. A schema lets you specify whether a custom model supports or outputs: Certain data typesMissing valuesSparse dataA certain number of columns | Required when a custom model outputs non-numeric data. If not provided, a default schema is used. | | requirements.txt | A list of Python or R packages to add to the base environment. This list pre-installs Python or R packages that the custom model is using but are not a part of the base environment | No | | Additional files | Other files used by the model (for example, a file that defines helper functions used inside custom.py). | No | **requirements.txt Python example:** For Python, provide a list of packages with their versions (1 package per row). For example: ``` numpy>=1.16.0, <1.19.0 pandas==1.1.0 scikit-learn==0.23.1 lightgbm==3.0.0 gensim==3.8.3 sagemaker-scikit-learn-extension==1.1.0 ``` **requirements.txt R example:** For R, provide a list of packages without versions (1 package per row). For example: ``` dplyr stats ``` ### Model code To define a custom model using DataRobot’s framework, your custom model should include a model artifact corresponding to the chosen environment language, custom code in a `custom.py` (for Python models) or `custom.R` (for R models) file, or both. If you provide only the custom code (without a model artifact), you must use the `load_model` hook. A hook is a function called by the custom model framework during a specific time in the custom model lifecycle. The following hooks can be used in your custom code: > [!WARNING] Include all required custom model code in hooks > Custom model hooks are callbacks passed to the custom model. All code required by the custom model must be in a custom model hook—the custom model can't access any code provided outside a defined custom model hook. In addition, you can't modify the input arguments of these hooks as they are predefined. | Hook (Function) | Unstructured/Structured | Purpose | | --- | --- | --- | | init() | Both | Initialize the model run by loading model libraries and reading model files. This hook is executed only once at the beginning of a run. | | load_model() | Both | Load all supported and trained objects from multiple artifacts, or load a trained object stored in an artifact with a format not natively supported by DataRobot. This hook is executed only once at the beginning of a run. | | read_input_data() | Structured | Customize how the model reads data; for example, with encoding and missing value handling. | | transform() | Structured | Define the logic used by custom transformers and estimators to generate transformed data. | | score() | Structured | Define the logic used by custom estimators to generate predictions. | | score_unstructured() | Unstructured | Define the output of a custom estimator and returns predictions on input data. Do not use this hook for transform models. | | chat() | Structured | Define a text generation custom model's handing of real-time chat completion requests. | | get_supported_llm_models() | Structured | Define a text generation custom model's response to the OpenAI "List Models" API. | | post_process() | Structured | Define the post-processing steps applied to the model's predictions. | > [!NOTE] Custom model hook execution order > These hooks are executed in the order listed, as each hook represents a step in the custom model lifecycle. For more information on defining a custom model's code, see the hooks for [structured custom models](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html) or [unstructured custom models](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html). ### Model metadata To define a custom model's [metadata and input validation schema](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-metadata.html), create a `model-metadata.yaml` file and add it to the top level of the model/model directory. The file specifies additional information about a custom model, including [runtime parameters](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html) through `runtimeParameterDefinitions`. ## Model environment There are multiple options for defining the environment where a custom model runs. You can: - Choose from a variety ofdrop-in environments. - Modify a drop-in environment to include missing Python or R packages by specifying the packages in the model'srequirements.txtfile. If provided, therequirements.txtfile must be uploaded together with thecustom.pyorcustom.Rfile in the model content. If model content contains subfolders, it must be placed in the top folder. - Build acustom environmentif you need to install Linux packages. When creating a custom model with a custom environment, the environment used must be compatible with the model contents, as it defines the model's runtime environment. To ensure you follow the compatibility guidelines: - By default, when creating a model version, if the selected execution environment does not change, the version of that execution environment persists from the previous custom model version, even if a newer environment version is available. For more information on how to ensure the custom model version uses the latest version of the execution environment, seeTrigger base execution environment update. --- # DRUM CLI tool URL: https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html > DataRobot Model Runner is a tool that allows you to work with and test Python, R, and Java custom models and custom tasks. # DRUM CLI tool The DataRobot User Models (DRUM) CLI is a tool that allows you to work with Python, R, and Java custom models and to quickly test [custom tasks](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/cml-custom-tasks.html), [custom models](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/index.html), and [custom environments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html) locally before uploading into DataRobot. Because it is also used to run custom tasks and models inside of DataRobot, if they pass local tests with DRUM, they are compatible with DataRobot. You can download DRUM from [PyPI](https://pypi.org/project/datarobot-drum/) and [access DRUM's GitHub repo](https://github.com/datarobot/datarobot-user-models/). DRUM can also: - Run performance and memory usage testing for models. - Perform model validation tests (for example, checking model functionality on corner cases, like null values imputation). - Run models in a Docker container. You can install DRUM for [Ubuntu](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html#drum-on-ubuntu), [Windows](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html#drum-on-windows-with-wsl2), or [MacOS](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html#drum-on-mac). > [!NOTE] Note > DRUM is not regularly tested on Windows or Mac. These steps may differ depending on the configuration of your machine. ## DRUM on Ubuntu The following describes the DRUM installation workflow. Consider the language prerequisites before proceeding. | Language | Prerequisites | Installation command | | --- | --- | --- | | Python | Python 3 required | pip install datarobot-drum | | Java | JRE ≥ 11 | pip install datarobot-drum | | R | Python ≥ 3.6R framework installedDRUM uses the rpy2 package to run R (the latest version is installed by default). You may need to adjust the rpy2 and pandas versions for compatibility. | pip install datarobot-drum[R] | To install the DRUM with support for Python and Java models, use the following command: ``` pip install datarobot-drum ``` To install DRUM with support for R models: ``` pip install datarobot-drum[R] ``` > [!NOTE] Note > If you are using a Conda environment, install the wheels with a `--no-deps` flag. If any dependencies are required for a Conda environment, install them with Conda tools. ## DRUM on Mac The following instructions describe installing DRUM with `conda` (although you can use other tools if you prefer) and then using DRUM to test a task locally. Before you begin, DRUM requires: - An installation ofconda. - A Python environment (also required for R) of 3.7+. ### Install DRUM on Mac 1. Create and activate a virtual environment with Python 3.7+. In the terminal for 3.8, run: condacreate-nDR-custom-taskspython=3.8-y condaactivateDR-custom-tasks 2. Install DRUM: condainstall-cconda-forgeuwsgi-y pipinstalldatarobot-drum 3. To set up the environment, installDocker Desktopand download from GitHub the DataRobotdrop-in environmentswhere your tasks will run. This recommended procedure ensures that your tasks run in the same environment both locally and inside DataRobot. Alternatively, if you plan to run your tasks in a localpythonenvironment, install packages used by your custom task into the same environment as DRUM. ### Use DRUM on Mac To test a task locally, run the `drum fit` command. For example, in a binary classification project: 1. Ensure that thecondaenvironmentDR-custom-tasksis activated. 2. Run thedrum fitcommand (replacing placeholder folder names in< >brackets with actual folder names): drum fit --code-dir --input --target-type binary --target --docker --verbose For example: drum fit --code-dir datarobot-user-models/custom_tasks/examples/python3_sklearn_binary --input datarobot-user-models/tests/testdata/iris_binary_training.csv --target-type binary --target Species --docker datarobot-user-models/public_dropin_environments/python3_sklearn/ --verbose > [!TIP] Tip > To learn more, you can view available parameters by typing `drum fit --help` on the command line. ## DRUM on Windows with WSL2 DRUM can be run on Windows 10 or 11 with WSL2 (Windows Subsystem for Linux), a native extension that is supported by the latest versions of Windows and allows you to easily install and run Linux OS on a Windows machine. With WSL, you can develop custom tasks and custom models locally in an IDE on Windows, and then immediately test and run them on the same machine using DRUM via the Linux command line. > [!TIP] Tip > You can use this [YouTube video](https://www.youtube.com/watch?v=wWFI2Gxtq-8) for instructions on installing WSL into Windows 11 and updating Ubuntu. The following phases are required to complete the Windows DRUM installation: 1. Enable WSL 2. Installpyenv 3. Install DRUM 4. Install Docker Desktop ### Enable Linux (WSL) 1. FromControl Panel > Turn Windows features on or off, check the optionWindows Subsystem for Linux. After making changes, you will be prompted to restart. 2. OpenMicrosoft storeand click to get Ubuntu. 3. Install Ubuntu and launch it from the start prompt. Provide a Unix username and password to complete installation. You can use any credentials but be sure to record them as they will be required in the future. You can access Ubuntu at any time from the Windows start menu. Access files on the C drive under /mnt/c/. ### Install pyenv Because Ubuntu in WSL comes without Python or virtual environments installed, you must install `pyenv`, a Python version management program used on macOS and Linux. (Learn about managing multiple Python environments [here](https://codeburst.io/how-to-install-and-manage-multiple-python-versions-in-wsl2-1131c4e50a58).) In the Ubuntu terminal, run the following commands (you can ignore comments) row by row: ``` cd $HOME sudo apt update --yes sudo apt upgrade --yes sudo apt-get install --yes git git clone https://github.com/pyenv/pyenv.git ~/.pyenv #add pyenv to bashrc echo '# Pyenv environment variables' >> ~/.bashrc echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bashrc echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bashrc echo '# Pyenv initialization' >> ~/.bashrc echo 'if command -v pyenv 1>/dev/null 2>&1; then' >> ~/.bashrc echo ' eval "$(pyenv init -)"' >> ~/.bashrc echo 'fi' >> ~/.bashrc #restart shell exec $SHELL #install pyenv dependencies (copy as a single line) sudo apt-get install --yes libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libgdbm-dev lzma lzma-dev tcl-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev wget curl make build-essential python-openssl #install python 3.7 (it can take awhile) pyenv install 3.7.10 ``` ### Install DRUM on Windows To install DRUM, first you setup a Python environment where DRUM will run, and then install DRUM in that environment. 1. Create and activate apyenvenvironment: cd$HOMEpyenvlocal3.7.10 .pyenv/shims/python3.7-mvenvDR-custom-tasks-pyenvsourceDR-custom-tasks-pyenv/bin/activate 2. Install DRUM and its dependencies into that environment: pipinstalldatarobot-drumexec$SHELL 3. Download container environments, where DRUM will run, from Github. git clone https://github.com/datarobot/datarobot-user-models ### Install Docker Desktop While you can run DRUM directly in the `pyenv` environment, it is preferable to run it in a Docker container. This recommended procedure ensures that your tasks run in the same environment both locally and inside DataRobot, as well as simplifies installation. 1. Download and installDocker Desktop, following the default installation steps. 2. Enable Ubuntu version WSL2 by opening Windows PowerShell and running: wsl.exe--set-versionUbuntu2wsl--set-default-version2 NoteYou may need to download and install anupdate. Follow the instructions in the PowerShell until you see theConversion completemessage. 3. Enable access to Docker Desktop from Ubuntu: ### Use DRUM on Windows 1. From the command line, open an Ubuntu terminal. 2. Use the following commands to activate the environment: cd $HOME source DR-custom-tasks-pyenv/bin/activate 3. Run thedrum fitcommand in an Ubuntu terminal window (replacing placeholder folder names in< >brackets with actual folder names): drum fit --code-dir --input --target-type binary --target --docker --verbose For example: drum fit --code-dir datarobot-user-models/custom_tasks/examples/python3_sklearn_binary --input datarobot-user-models/tests/testdata/iris_binary_training.csv --target-type binary --target Species --docker datarobot-user-models/public_dropin_environments/python3_sklearn/ --verbose --- # Define custom model metadata URL: https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-metadata.html > How to use the model-metadata.yaml file to specify additional information about a custom task or a custom inference model. # Define custom model metadata For structured inference models, `model-metadata.yaml` needs to declare an `inferenceModel` section: binary models need positive/negative class labels; multiclass models need `targetName` and `classLabels` in the same order as the model’s probability outputs. See [Inference model metadata](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-metadata.html#inference-model-metadata) for more information. To define metadata, create a `model-metadata.yaml` file and put it in the top level of the task/model directory. In most cases, it can be skipped, but it is required for custom transform tasks when a custom task outputs non-numeric data. The `model-metadata.yaml` is located in the same folder as `custom.py`. The sections below show how to define metadata for custom models and tasks. For more information, you can review complete examples in the DRUM repository for [custom models](https://github.com/datarobot/datarobot-user-models/blob/master/model_templates/python3_sklearn/model-metadata.yaml) and [tasks](https://github.com/datarobot/datarobot-user-models/blob/master/task_templates/1_transforms/1_python_missing_values/model-metadata.yaml). ## General metadata parameters The following table describes options that are available to tasks and/or inference models. The parameters are required when using `drum push` to supply information about the model/task/version to create. Some of the parameters are also required outside of `drum push` for compatibility reasons. > [!NOTE] Note > The `modelID` parameter adds a new version to a pre-existing custom model or task with the specified ID. Because of this, all options that configure a new base-level custom model or task are ignored when passed alongside this parameter. However, at this time, these parameters still must be included. | Option | When required | Task or inference model | Description | | --- | --- | --- | --- | | name | Always | Both | A string, preferably unique for easy searching, that drum push uses as the custom model title. | | type | Always | Both | A string, either training (for custom tasks) or inference (for custom inference models). | | environmentID | Always | Both | A hash of the execution environment to use while running your custom model or task. You can find a list of available execution environments in Model Registry > Custom Model Workshop > Environments. Expand the environment and click on the Environment Info tab to view and copy the file ID. Required for drum push only. | | targetType | Always | Both | A string indicating the type of target. Must be one of: binaryregressionanomaly unstructured (inference models only)multiclasstextgeneration (inference models only)agenticworkflow (inference models only) transform (transform tasks only) | | modelID | Optional | Both | After creating a model or task, it is best practice to use versioning to add code while iterating. To create a new version instead of a new model or task, use this field to link the custom model/task you created. The ID (hash) is available from the UI, via the URL of the custom model or task. Used with drum push only. | | description | Optional | Both | A searchable field. If modelID is set, use the UI to change a model/task description. Used with drum push only. | | majorVersion | Optional | Both | Specifies whether the model version you are creating should be a major (True, the default) or minor (False) version update. For example, if the previous model version is 2.3, a major version update would create version 3.0; a minor version update would create version 2.4. Used for drum push only. | | targetName | For binary and multiclass (in inferenceModel) | Model | In inferenceModel, the name of the column the model predicts. For multiclass, use the same name as Target name in the Workshop and the same order of classes as Target classes for classLabels. | | positiveClassLabel / negativeClassLabel | For binary classification models | Model | In inferenceModel, when your model predicts probability, the positiveClassLabel dictates what class the prediction corresponds to. | | classLabels | For multiclass classification models | Model | In inferenceModel, a list of class names (strings). The list order must match the order of predicted class probabilities your model returns (for example, the column order of probability outputs). Use the same labels as the Target classes you configure for the custom model in the Workshop. | | predictionThreshold | Optional (binary classification models only). | Model | In inferenceModel, the cutoff point between 0 and 1 that dictates which label will be chosen as the predicted label. | | trainOnProject | Optional | Task | A hash with the ID of the project (PID) to train the model or version on. When using drum push to test and upload a custom estimator task, you have an option to train a single-task blueprint immediately after the estimator is successfully uploaded into DataRobot. The trainOnProject option specifies the project on which to train that blueprint. | ## Inference model metadata (inferenceModel) For structured inference models, target and class-label settings belong under the top-level key `inferenceModel` in `model-metadata.yaml`. If you omit fields that DataRobot or DRUM require for your `targetType`, builds, tests, or deployments can fail. | targetType | Required under inferenceModel | Notes | | --- | --- | --- | | binary | targetName, positiveClassLabel, negativeClassLabel | Optional: predictionThreshold. | | multiclass | targetName, classLabels | classLabels is a YAML list of class names in the same order as your model’s probability outputs. | | regression | (often none) | Many regression templates work without an inferenceModel block; follow your environment and DRUM requirements. | | anomaly, unstructured, textgeneration, … | Follow template / DRUM | See examples for your target type. | Workshop-generated file: On the Registry Workshop Assemble tab, Create model-metadata.yaml produces a starter file for your model’s target type. For multiclass, that file includes `inferenceModel` with `targetName` and `classLabels` (aligned with your Target classes), matching what you need for a successful deployment. In the `model-metadata.yaml` file, you can also [define runtime parameters](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html) to make your custom model code easier to reuse. --- # Define custom model runtime parameters URL: https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html > Add runtime parameters to a custom model through the model metadata, making your custom model code easier to reuse. # Define custom model runtime parameters Add runtime parameters to a custom model through the model metadata, making your custom model code easier to reuse. To define runtime parameters, you can add the following `runtimeParameterDefinitions` in `model-metadata.yaml`: | Key | Description | | --- | --- | | fieldName | Define the name of the runtime parameter. | | type | Define the data type the runtime parameter contains: string, boolean, numeric credential, deployment. | | defaultValue | (Optional) Set the default value for the runtime parameter. For credential type parameters, use defaultValue to reference an existing credential by its credential ID. For other types, set the default string, boolean, or numeric value. If you define a runtime parameter without specifying a defaultValue, the default value is None. | | minValue | (Optional) For numeric runtime parameters, set the minimum numeric value allowed in the runtime parameter. | | maxValue | (Optional) For numeric runtime parameters, set the maximum numeric value allowed in the runtime parameter. | | credentialType | (Optional) For credential runtime parameters, set the type of credentials the parameter must contain. | | allowEmpty | (Optional) Set the empty field policy for the runtime parameter.True: (Default) Allows an empty runtime parameter.False: Enforces providing a value for the runtime parameter before deployment. | | description | (Optional) Provide a description of the purpose or contents of the runtime parameter. | ## DataRobot reserved runtime parameters The following runtime parameter is reserved by DataRobot for custom model configuration: | Runtime parameter | Type | Description | | --- | --- | --- | | CUSTOM_MODEL_WORKERS | numeric | Allows each replica to handle a set number of concurrent processes. This option is intended for process-safe custom models, primarily in generative AI use cases (for more information on process-safe models, see the note below). To determine the appropriate number of concurrent processes to allow per replica, monitor the number of requests and the median response time for the custom model. The median response time for the custom model should be close to the median response time from the LLM. If the response time of the custom model exceeds the LLM's response time, stop increasing the number of concurrent processes and instead increase the number of replicas.Default value: 1 Max value: 40 | > [!WARNING] Custom model process safety > When enabling and configuring `CUSTOM_MODEL_WORKERS`, ensure that your model is process-safe, allowing multiple independent processes to safely interact with shared resources without causing conflicts. This configuration is not intended for general use with custom models to make them more resource efficient. Only process-safe custom models with I/O-bound tasks (like proxy models) benefit from utilizing CPU resources this way. ## Define custom model metadata Before you define `runtimeParameterDefinitions` in `model-metadata.yaml`, [define the custom model metadata](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-metadata.html) required for the target type. For binary and multiclass models, that includes an [inferenceModel](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-metadata.html#inference-model-metadata) block ( `targetName` and class labels; for multiclass, `classLabels`). **Binary classification:** ``` name: binary-example targetType: binary type: inference inferenceModel: targetName: target positiveClassLabel: "1" negativeClassLabel: "0" ``` **Regression:** ``` name: regression-example targetType: regression type: inference ``` **Text generation:** ``` name: textgeneration-example targetType: textgeneration type: inference ``` **Anomaly detection:** ``` name: anomaly-example targetType: anomaly type: inference ``` **Unstructured:** ``` name: unstructured-example targetType: unstructured type: inference ``` **Multiclass:** ``` name: multiclass-example targetType: multiclass type: inference inferenceModel: targetName: class classLabels: - class_a - class_b - class_c ``` Then, below the model information, you can provide the `runtimeParameterDefinitions`: ``` # Example: runtimeParameterDefinitions in model-metadata.yaml name: runtime-parameter-example targetType: regression type: inference runtimeParameterDefinitions: - fieldName: my_first_runtime_parameter type: string description: My first runtime parameter. - fieldName: runtime_parameter_with_default_value type: string defaultValue: Default description: A string-type runtime parameter with a default value. - fieldName: runtime_parameter_boolean type: boolean defaultValue: true description: A boolean-type runtime parameter with a default value of true. - fieldName: runtime_parameter_numeric type: numeric defaultValue: 0 minValue: -100 maxValue: 100 description: A boolean-type runtime parameter with a default value of 0, a minimum value of -100, and a maximum value of 100. - fieldName: runtime_parameter_for_credentials type: credential credentialType: basic allowEmpty: false description: A runtime parameter containing a dictionary of credentials; credentials must be provided before registering the custom model. - fieldName: runtime_parameter_for_connected_deployment type: deployment description: A runtime parameter defined to accept the deployment ID of another deployment to connect to the deployed custom model. ``` ## Provide credentials through runtime parameters The `credential` runtime parameter type supports any `credentialType` value available in the DataRobot REST API. You can provide credentials in two ways: - Reference existing credentials: Use the credential ID as thedefaultValueto reference credentials defined in the DataRobotCredentials managementsection. - Provide credential values directly: Include the full credential structure when defining the runtime parameter (typically used during local development with DRUM). > [!NOTE] Credential types > For more information on the supported credential types, see the [API reference documentation for credentials](https://docs.datarobot.com/en/docs/api/reference/public-api/credentials.html#schemacredentialsbody). ### Reference existing credentials To reference an existing credential, set the `defaultValue` to the credential ID: ``` # Example: Reference an existing credential - fieldName: my_api_token type: credential credentialType: api_token allowEmpty: false defaultValue: description: A runtime parameter referencing an existing API token credential. ``` > [!WARNING] Credential requirements > When you reference an existing credential, the credential must exist in the credential management section before registering the custom model, must match the `credentialType` specified in the runtime parameter definition, and must match the credential ID used as the `defaultValue`. ### Provide credential values directly The credential information required depends on the `credentialType`, as shown in the examples below: | Credential Type | Example | | --- | --- | | basic | basic: credentialType: basic description: string name: string password: string user: string | | azure | azure: credentialType: azure description: string name: string azureConnectionString: string | | gcp | gcp: credentialType: gcp description: string name: string gcpKey: string | | s3 | s3: credentialType: s3 description: string name: string awsAccessKeyId: string awsSecretAccessKey: string awsSessionToken: string | | api_token | api_token: credentialType: api_token apiToken: string name: string | ## Provide override values during local development For local development with DRUM, you can specify a `.yaml` file containing the values of the runtime parameters. The values defined here override the `defaultValue` set in `model-metadata.yaml`: ``` # Example: .runtime-parameters.yaml my_first_runtime_parameter: Hello, world. runtime_parameter_with_default_value: Override the default value. runtime_parameter_for_credentials: credentialType: basic name: credentials password: password1 user: user1 ``` When using DRUM, the `--runtime-params-file` option specifies the file containing the runtime parameter values: ``` # Example: --runtime-params-file drum score --runtime-params-file .runtime-parameters.yaml --code-dir model_templates/python3_sklearn --target-type regression --input tests/testdata/juniors_3_year_stats_regression.csv ``` ## Import and use runtime parameters in custom code To import and access runtime parameters, you can import the `RuntimeParameters` module in your code in `custom.py`: ``` # Example: custom.py from datarobot_drum import RuntimeParameters def mask(value, visible=3): return value[:visible] + ("*" * len(value[visible:])) def transform(data, model): print("Loading the following Runtime Parameters:") parameter1 = RuntimeParameters.get("my_first_runtime_parameter") parameter2 = RuntimeParameters.get("runtime_parameter_with_default_value") print(f"\tParameter 1: {parameter1}") print(f"\tParameter 2: {parameter2}") credentials = RuntimeParameters.get("runtime_parameter_for_credentials") if credentials is not None: credential_type = credentials.pop("credentialType") print( f"\tCredentials (type={credential_type}): " + str({k: mask(v) for k, v in credentials.items()}) ) else: print("No credential data set") return data ``` --- # DataRobot User Models URL: https://docs.datarobot.com/en/docs/api/code-first-tools/drum/index.html > Describes how to assemble custom models and environments. # DataRobot User Models While DataRobot provides hundreds of built-in models, there are situations where you need preprocessing or modeling methods that are not currently supported out of the box. To create a custom inference model, you must provide a model artifact—either defined in a `custom.py` file or a serialized artifact with a file extension corresponding to the chosen environment language and any additional custom code required to use the model. Before adding custom models and environments to DataRobot, you must prepare and structure the files required to run them successfully. The tools and templates necessary to prepare custom models are hosted in the [DataRobot User Models GitHub Repository](https://github.com/datarobot/datarobot-user-models). (Log in to GitHub before clicking this link.) DataRobot recommends understanding the following requirements to prepare your custom model for upload to the Workshop. | Topic | Describes | | --- | --- | | Custom model components | How to identify the components required to run custom inference models. | | Assemble structured custom models | How to assemble and validate structured custom models compatible with DataRobot. | | Assemble unstructured custom models | How to assemble and validate unstructured custom models compatible with DataRobot. | | Define custom model metadata | How to use the model-metadata.yaml file to specify additional information about a custom inference model. | | Define custom model runtime parameters | How to add runtime parameters to a custom model through the model metadata, making your custom model code easier to reuse. | | DRUM CLI tool | How to download and install the DataRobot User Models (DRUM) CLI to work with and test custom models and custom environments locally before uploading to DataRobot. | | Test a custom model locally | How to test custom inference models in your local environment using the DataRobot Model Runner tool. | --- # Assemble structured custom models URL: https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html > DataRobot provides built-in support for a variety of libraries to create models that use conventional target types. # Assemble structured custom models DataRobot provides built-in support for a variety of libraries to create models that use conventional target types. If your model is based on one of these libraries, DataRobot expects your model artifact to have a matching file extension: **Python libraries:** Library File Extension Example Scikit-learn *.pkl sklearn-regressor.pkl Xgboost *.pkl xgboost-regressor.pkl PyTorch *.pth torch-regressor.pth tf.keras (tensorflow>=2.2.1) *.h5 keras-regressor.h5 ONNX *.onnx onnx-regressor.onnx pmml *.pmml pmml-regressor.pmml **R libraries:** Library File Extension Example Caret *.rds brnn-regressor.rds **Java libraries:** Library File Extension Example datarobot-prediction *.jar dr-regressor.jar h2o-genmodel *.java GBM_model_python_1589382591366_1.java (pojo) h2o-genmodel *.zip GBM_model_python_1589382591366_1.zip (mojo) h2o-genmodel-ext-xgboost *.java XGBoost_2_AutoML_20201015_144158.java h2o-genmodel-ext-xgboost *.zip XGBoost_2_AutoML_20201015_144158.zip h2o-ext-mojo-pipeline *.mojo ... > [!NOTE] Note > DRUM supports models with DataRobot-generated Scoring Code and models that implement either the > IClassificationPredictor > or > IRegressionPredictor > interface from the > DataRobot-prediction library > . The model artifact must have a > .jar > extension. > You can define the > DRUM_JAVA_XMX > environment variable to set JVM maximum heap memory size ( > -Xmx > java parameter): > DRUM_JAVA_XMX=512m > . > If you export an H2O model as > POJO > , you cannot rename the file; however, this limitation doesn't apply to models exported as > MOJO > —they may be named in any fashion. > The > h2o-ext-mojo-pipeline > requires an h2o driverless AI license. > Support for DAI Mojo Pipeline has not been incorporated into tests for the build of > datarobot-drum > . If your model doesn't use one of the following libraries, you must create an [unstructured custom model](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html). Compare the characteristics and capabilities of the two types of custom models below: | Model type | Characteristics | Capabilities | | --- | --- | --- | | Structured | Uses a target type known to DataRobot (e.g., regression, binary classification, multiclass, and anomaly detection).Required to conform to a request/response schema.Accepts structured input and output data. | Full deployment capabilities.Accepts training data after deployment. | | Unstructured | Uses a custom target type, unknown to DataRobot.Not required to conform to a request/response schema.Accepts unstructured input and output data. | Limited deployment capabilities. Doesn't support data drift and accuracy statistics, challenger models, or humility rules.Doesn't accept training data after deployment. | ## Structured custom model requirements If your custom model uses one of the supported libraries, make sure it meets the following requirements: - Data sent to a model must be usable for predictions without additional pre-processing. - Regression models must return a single floating point per row of prediction data. - Binary classification models must return one floating point value <= 1.0 or two floating point values that sum to 1.0 per row of prediction data. - There must be a single pkl / pth / h5 file present. > [!NOTE] Data format > When working with structured models DataRobot supports data as files of `csv`, `sparse`, or `arrow` format. DataRobot doesn't sanitize missing or abnormal (containing parentheses, slashes, symbols, etc. ) column names. ## Structured custom model hooks To define a custom model using DataRobot’s framework, your artifact file should contain hooks (or functions) to define how a model is trained and how it scores new data. DataRobot automatically calls each hook and passes the parameters based on the project and blueprint configuration. However, you have full flexibility to define the logic that runs inside each hook. If necessary, you can include these hooks alongside your model artifacts in your model folder in a file called `custom.py` for Python models or `custom.R` for R models. > [!WARNING] Include all required custom model code in hooks > Custom model hooks are callbacks passed to the custom model. All code required by the custom model must be in a custom model hook—the custom model can't access any code provided outside a defined custom model hook. In addition, you can't modify the input arguments of these hooks as they are predefined. > [!NOTE] Note > Training and inference hooks can be defined in the same file. The following sections describe each hook, with examples. ### init() The `init` hook is executed only once at the beginning of the run to allow the model to load libraries and additional files for use in other hooks. ``` init(**kwargs) -> None ``` #### init() input | Input parameter | Description | | --- | --- | | **kwargs | Additional keyword arguments. code_dir is the path where the model code is stored. | #### init() example **Python:** ``` def init(code_dir): global g_code_dir g_code_dir = code_dir ``` **R:** ``` init <- function(...) { library(brnn) library(glmnet) } ``` #### init() output The `init()` hook does not return anything. ### load_model() The `load_model()` hook is executed only once at the beginning of the run to load one or more trained objects from multiple artifacts. It is only required when a trained object is stored in an artifact that uses an unsupported format or when multiple artifacts are used. The `load_model()` hook is not required when there is a single artifact in one of the supported formats: - Python: .pkl , .pth , .h5 , .joblib - Java: .mojo - R: .rds ``` load_model(code_dir: str) -> Any ``` #### load_model() input | Input parameter | Description | | --- | --- | | code_dir | Additional keyword arguments. code_dir is the path where the model code is stored. | #### load_model() example **Python:** ``` def load_model(code_dir): model_path = "model.pkl" model = joblib.load(os.path.join(code_dir, model_path)) return model ``` **R:** ``` load_model <- function(input_dir) { readRDS(file.path(input_dir, "model_name.rds")) } ``` #### load_model() output The `load_model()` hook returns a trained object (of any type). ### read_input_data() The `read_input_data` hook customizes how the model reads data; for example, with encoding and missing value handling. ``` read_input_data(input_binary_data: bytes) -> Any ``` #### read_input_data() input | Input parameter | Description | | --- | --- | | input_binary_data | Data passed through the --input parameter in drum score mode, or a payload submitted to the drum server /predict endpoint. | #### read_input_data() example **Python:** ``` def read_input_data(input_binary_data): global prediction_value prediction_value += 1 return pd.read_csv(io.BytesIO(input_binary_data)) ``` **R:** ``` read_input_data <- function(input_binary_data) { input_text_data <- stri_conv(input_binary_data, "utf8") read.csv(text=gsub("\r","", input_text_data, fixed=TRUE)) } ``` #### read_input_data() output The `read_input_data()` hook must return a pandas `DataFrame` or R `data.frame`; otherwise, you must write your own score method. ### transform() The `transform()` hook defines the output of a custom transform and returns transformed data. Do not use this hook for estimator models. This hook can be used in both transformer and estimator tasks: - For transformers, this hook applies transformations to the data provided and passes it to downstream tasks. - For estimators, this hook applies transformations to the prediction data before making predictions. ``` transform(data: DataFrame, model: Any) -> DataFrame ``` #### transform() input | Input parameter | Description | | --- | --- | | data | A pandas DataFrame (Python) or R data.frame containing the data that the custom model should transform. Missing values are indicated with NaN in Python and NA in R, unless otherwise overridden by the read_input_data hook. | | model | A trained object DataRobot loads from the artifact (typically, a trained transformer) or loaded through the load_model hook. | #### transform() example **Python:** ``` def transform(data, model): data = data.fillna(0) return data ``` **R:** ``` transform <- function(data, model) { data[is.na(data)] <- 0 data } ``` #### transform() output The `transform()` hook returns a pandas `DataFrame` or R `data.frame` with transformed data. ### score() The `score()` hook defines the output of a custom estimator and returns predictions on input data. Do not use this hook for transform models. ``` score(data: DataFrame, model: Any, **kwargs: Dict[str, Any]) -> DataFrame ``` #### score() input | Input parameter | Description | | --- | --- | | data | A pandas DataFrame (Python) or R data.frame containing the data the custom model will score. If the transform hook is used, data will be the transformed data. | | model | A trained object loaded from the artifact by DataRobot or loaded through the load_model hook. | | **kwargs | Additional keyword arguments. For a binary classification model, it contains the positive and negative class labels as the following keys:positive_class_labelnegative_class_label | #### score() examples **Python:** ``` def score(data: pd.DataFrame, model: Any, **kwargs: Dict[str, Any]) -> pd.DataFrame: predictions = model.predict(data) predictions_df = pd.DataFrame(predictions, columns=[kwargs["positive_class_label"]]) predictions_df[kwargs["negative_class_label"]] = ( 1 - predictions_df[kwargs["positive_class_label"]] ) return predictions_df ``` **R:** ``` score <- function(data, model, ...){ scores <- predict(model, newdata = data, type = "prob") names(scores) <- c('0', '1') return(scores) } ``` #### score() output The `score()` hook should return a pandas `DataFrame` (or R `data.frame` or `tibble`) of the following format: - For regression or anomaly detection projects, the output must have a numeric column namedPredictions. - For binary or multiclass projects, the output must have one column per class label, with class names used as column names. Each cell must contain the floating-point class probability of the respective class and these rows must sum up to 1.0. ##### Additional output columns > [!NOTE] Availability information > Additional output in prediction responses for custom models is off by default. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable Additional Custom Model Output in Prediction Responses The `score()` hook can return any number of extra columns, containing data of types `string`, `int`, `float`, `bool`, or `datetime`. When additional columns are returned through the `score()` method, the prediction response is as follows: - For a tabular response (CSV) , the additional columns are returned as part of the response table or dataframe. - For a JSON response , the extraModelOutput key is returned alongside each row. This key is a dictionary containing the values of each additional column in the row. ### chat() The `chat()` hook allows custom models to implement the Bolt-on Governance API to provide access to chat history and streaming response. When using the Bolt-on Governance API with a deployed LLM blueprint, see [LLM availability](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html) for the recommended values of the `model` parameter. Alternatively, specify a reserved value, `model="datarobot-deployed-llm"`, to let the LLM blueprint select the relevant model ID automatically when calling the LLM provider's services. ``` chat(completion_create_params: CompletionCreateParams, model: Any) -> ChatCompletion | Iterator[ChatCompletionChunk] ``` In Workbench, when [adding a deployed LLM](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#add-a-deployed-llm) that implements the `chat` function, the playground uses the Bolt-on Governance API as the preferred communication method. Enter the Chat model ID associated with the LLM blueprint to set the `model` parameter for requests from the playground to the deployed LLM. Alternatively, enter `datarobot-deployed-llm` to let the LLM blueprint select the relevant model ID automatically when calling the LLM provider's services. #### chat() input | Input parameter | Description | | --- | --- | | completion_create_params | An object containing all the parameters required to create the chat completion. For more information, review the following types from the OpenAI Python API library: CompletionCreateParams, ChatCompletion, and ChatCompletionChunk. | | model | The deserialized model loaded by DRUM or by load_model, if supplied. | #### chat() example ``` def chat(completion_create_params, model): openai_client = model return openai_client.chat.completions.create(**completion_create_params) ``` #### chat() output The `chat()` hook returns a `ChatCompletion` object if streaming is disabled and `Iterator[ChatCompletionChunk]` if streaming is enabled. If there are prompt guards configured, the first chunk of the stream contains the prompt guard moderations information (accessible via `datarobot_moderations` on the chunk). For every response guard configured that can be applied to a chunk (all guards except faithfulness, NeMo, Rouge-1, Agent goal accuracy, and task adherence), each intermediate chunk (except the last chunk) has moderations information for those guards accessible via `datarobot_moderations`. For the last chunk, all response guards information is accessible via `datarobot_moderations`. ##### Association ID As of DRUM v1.16.16, every chat completion response automatically generates and returns an association ID (as `datarobot_association_id`). The same association ID gets passed to any other configured custom metrics for the deployed LLM. A custom association ID can be optionally specified for chat requests in place of the auto-generated ID by setting `datarobot_association_id` in the `extra_body` field of the chat request. The `extra_body` field is a standard way to add more parameters to an OpenAI chat request, allowing the chat client to pass model-specific parameters to an LLM. When making a chat request to a DataRobot-deployed text generation or agentic workflow custom model, values can also be reported for arbitrary custom metrics defined for the deployment by setting `datarobot_metrics` in the `extra_body` field. If the field `datarobot_association_id` is found in `extra_body`, DataRobot uses that value instead of the automatically generated one. If the `datarobot_metrics` field is found in `extra_body`, DataRobot reports a custom metric for all the `name:value` pairs found inside. A matching custom metric for each name must already be defined for the deployment. Custom metric values reported this way must be numeric. > [!NOTE] Association ID requirement > The deployed custom model must have an association ID column defined for DataRobot to process custom metrics from chat requests, regardless of whether `extra_body` is specified. Moderation must be configured for the custom model for the metrics to be processed. > [!TIP] Manual chat request construction > The OpenAI client converts the `extra_body` parameter contents to top-level fields in the JSON payload of the chat `POST` request. When manually constructing a chat payload, without the OpenAI client, include `“datarobot_association_id": "my_association_id_0001"` in the top level of the payload. The following example shows how to set the association ID and custom metric values using `extra_body`: ``` from openai import OpenAI openai_client = OpenAI( base_url="https:///api/v2/deployments/{deployment_id}/", api_key="", ) extra_body = { # These values pass through to the LLM "llm_id": "azure-gpt-6", # If set here, replaces the auto-generated association ID "datarobot_association_id": "my_association_id_0001", # DataRobot captures these for custom metrics "datarobot_metrics": { "field1": 24, "field2": 25 } } completion = openai_client.chat.completions.create( model="datarobot-deployed-llm", messages=[ {"role": "system", "content": "Explain your thoughts using at least 100 words."}, {"role": "user", "content": "What would it take to colonize Mars?"}, ], max_tokens=512, extra_body=extra_body ) print(completion.choices[0].message.content) ``` ##### Moderations Moderation guardrails help your organization block prompt injection and hateful, toxic, or inappropriate prompts and responses. Moderation library now supports streaming response. In order for the `chat()` hook to return `datarobot_moderations`, the deployed LLM must be running in an execution environment that has the moderation library installed, and the custom model code directory must contain `moderation_config.yaml` to configure the moderations. The example below shows what is present in `ChatCompletion` if `streaming = False` and in `ChatCompletionChunk` if `streaming = True` and moderation is enabled. ``` datarobot_moderations={ 'Prompt tokens_latency': 0.20357584953308105, 'Prompts_token_count': 8, 'ROUGE-1_latency': 0.028343677520751953, 'Response tokens_latency': 0.0007507801055908203, 'Responses_rouge_1': 1.0, 'Responses_token_count': 1, 'action_promptText': '', 'action_resultText': '', 'association_id': '3d7d525b-9e99-42a4-a641-70254e924a76', 'blocked_promptText': False, 'blocked_resultText': False, 'datarobot_confidence_score': 1.0, 'datarobot_latency': 4.249604940414429, 'datarobot_token_count': 1, 'moderated_promptText': 'Now divide the result by 2.', 'replaced_promptText': False, 'replaced_resultText': False, 'reported_promptText': False, 'reported_resultText': False, 'unmoderated_resultText': '10' } ``` ##### Citations In order for the `chat()` hook to return `citations`, the deployed LLM must have a vector database associated with it. The `chat()` hook returns keys related to citations and accessible to custom models. For example: ``` citations=[ { 'content': 'ISS science results have Earth-based \napplications, including understanding our \nclimate, contributing to the treatment of \ndisease, improving existing materials, and \ninspiring the future generation of scientists, \nclinicians, technologists, engineers, \nmathematicians, artists, and explorers.\nBENEFITS\nFOR HUMANITY\nDISCOVERY\nEXPLORATION', 'link': 'Space_Station_Annual_Highlights/iss_2020_highlights.pdf:10', 'metadata': { 'chunk_id': '953', 'content': 'ISS science results have Earth-based \napplications, including understanding our \nclimate, contributing to the treatment of \ndisease, improving existing materials, and \ninspiring the future generation of scientists, \nclinicians, technologists, engineers, \nmathematicians, artists, and explorers.\nBENEFITS\nFOR HUMANITY\nDISCOVERY\nEXPLORATION', 'page': 10, 'similarity_score': 0.46, 'source': 'Space_Station_Annual_Highlights/iss_2020_highlights.pdf' }, 'vector': None }, ] ``` ### get_supported_llm_models() DataRobot custom models support the [OpenAI "List Models" API](https://platform.openai.com/docs/api-reference/models/list). To customize your model's response to this API, implement the `get_supported_llm_models()` hook in `custom.py`. ``` def get_supported_llm_models(model: Any): ``` #### get_supported_llm_models() input | Input parameter | Description | | --- | --- | | model | Optional. A model ID to compare against. | #### get_supported_llm_models() example ``` def get_supported_llm_models(model: Any): _ = model return [ Model( id="datarobot_llm_id", created=1744854432, object="model", owned_by="tester@datarobot.com", ) ] ``` You can retrieve the supported models for a custom model using the OpenAI client or the DataRobot REST API: **OpenAI client:** ``` from openai import OpenAI API_KEY = '' CHAT_API_URL = 'https://app.datarobot.com/api/v2/deployments//' def list_models(): openai_client = OpenAI( base_url=CHAT_API_URL, api_key=API_KEY, _strict_response_validation=False ) response = openai_client.models.list() print("listing models...") print(response.to_dict()) ``` **DataRobot REST API:** ``` $ curl "https://app.datarobot.com/api/v2/deployments//models" \ -H "Authorization: Bearer " {"data":[{"created":1744854432,"id":"datarobot_llm_id","object":"model","owned_by":"tester@datarobot.com"}],"object":"list"} ``` In the [DRUM repository](https://github.com/datarobot/datarobot-user-models/tree/master/model_templates/python3_dummy_chat), you can view a simple text generation model that supports the OpenAI API [/chat](https://platform.openai.com/docs/api-reference/chat/create) and [/models](https://platform.openai.com/docs/api-reference/models/list) endpoints through the `chat()` and `get_supported_llm_models()` hooks. #### get_supported_llm_models () output If your `custom.py` does not implement `get_supported_llm_models()`, the custom model returns a one item list based on the `LLM_ID` runtime parameter, if it exists. Custom models [exported from Playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/deploy-llm.html) blueprints have this parameter already set to the LLM you selected for the blueprint. If `get_supported_llm_models()` is not defined, and the `LLM_ID` runtime parameter is not defined, then the `/models` API returns an empty list. Support for `/models` is in DRUM 1.16.12 or later. ### post_process() The `post_process` hook formats the prediction data returned by DataRobot or the `score` hook when it doesn't match the output format expectations. ``` post_process(predictions: DataFrame, model: Any) -> DataFrame ``` #### post_process() input | Input parameter | Description | | --- | --- | | predictions | A pandas DataFrame (Python) or R data.frame containing the scored data produced by DataRobot or the score hook. | | model | A trained object loaded from the artifact by DataRobot or loaded through the load_model hook. | #### post_process() example **Python:** ``` def post_process(predictions, model): return predictions + 1 ``` **R:** ``` post_process <- function(predictions, model) { names(predictions) <- c('0', '1') } ``` #### post_process() output The `post_process` hook returns a pandas `DataFrame` (or R `data.frame` or `tibble`) of the following format: - For regression or anomaly detection projects, the output must have a single numeric column namedPredictions. - For binary or multiclass projects, the output must have one column per class, with class names used as column names. Each cell must contain the probability of the respective class, and each row must sum up to 1.0. --- # Assemble unstructured custom models URL: https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html > Unstructured models can use arbitrary data for input and output, allowing you to deploy and monitor models regardless of the target type. # Assemble unstructured custom models If your custom model doesn't use a target type supported by DataRobot, you can create an unstructured model. Unstructured models can use arbitrary ( i.e., unstructured) data for input and output, allowing you to deploy and monitor models regardless of the target type. This characteristic of unstructured models gives you more control over how you read the data from a prediction request and response; however, it requires precise coding to assemble correctly. You must implement [custom hooks to process the unstructured input data](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html#unstructured-custom-model-hooks) and generate a valid response. Compare the characteristics and capabilities of the two types of custom models below: | Model type | Characteristics | Capabilities | | --- | --- | --- | | Structured | Uses a target type known to DataRobot (e.g., regression, binary classification, multiclass, and anomaly detection).Required to conform to a request/response schema.Accepts structured input and output data. | Full deployment capabilities.Accepts training data after deployment. | | Unstructured | Uses a custom target type, unknown to DataRobot.Not required to conform to a request/response schema.Accepts unstructured input and output data. | Limited deployment capabilities. Doesn't support data drift and accuracy statistics, challenger models, or humility rules.Doesn't accept training data after deployment. | Inference models support unstructured mode, where input and output are not verified and can be almost anything. This is your responsibility to verify correctness. For assembly instructions specific to unstructured custom inference models, reference the model templates for [Python](https://github.com/datarobot/datarobot-user-models/tree/master/model_templates/python3_unstructured) and [R](https://github.com/datarobot/datarobot-user-models/tree/master/model_templates/r_unstructured) provided in the DRUM documentation. > [!NOTE] Data format > When working with unstructured models DataRobot supports data as a text or binary file. ## Unstructured custom model hooks Include any necessary hooks in a file called `custom.py` for Python models or `custom.R` for R models alongside your model artifacts in your model folder. > [!WARNING] Include all required custom model code in hooks > Custom model hooks are callbacks passed to the custom model. All code required by the custom model must be in a custom model hook—the custom model can't access any code provided outside a defined custom model hook. In addition, you can't modify the input arguments of these hooks as they are predefined. ### init() The `init` hook is executed only once at the beginning of the run to allow the model to load libraries and additional files for use in other hooks. ``` init(**kwargs) -> None ``` #### init() input | Input parameter | Description | | --- | --- | | **kwargs | An additional keyword argument. code_dir provides a link, passed through the --code_dir parameter, to the folder where the model code is stored. | #### init() example **Python:** ``` def init(code_dir): global g_code_dir g_code_dir = code_dir ``` **R:** ``` init <- function(...) { library(brnn) library(glmnet) } ``` #### init() output The `init()` hook does not return anything. ### load_model() The `load_model()` hook is executed only once at the beginning of the run to load one or more trained objects from multiple artifacts. It is only required when a trained object is stored in an artifact that uses an unsupported format or when multiple artifacts are used. The `load_model()` hook is not required when there is a single artifact in one of the supported formats: - Python: .pkl , .pth , .h5 , .joblib - Java: .mojo - R: .rds ``` load_model(code_dir: str) -> Any ``` #### load_model() input | Input parameter | Description | | --- | --- | | code_dir | A link, passed through the --code_dir parameter, to the directory where the model artifact and additional code are provided. | #### load_model() example **Python:** ``` def load_model(code_dir): model_path = "model.pkl" model = joblib.load(os.path.join(code_dir, model_path)) return model ``` **R:** ``` load_model <- function(input_dir) { readRDS(file.path(input_dir, "model_name.rds")) } ``` #### load_model() output The `load_model()` hook returns a trained object (of any type). ### score_unstructured() The `score_unstructured()` hook defines the output of a custom estimator and returns predictions on input data. Do not use this hook for transform models. ``` score_unstructured(model: Any, data: str/bytes, **kwargs: Dict[str, Any]) -> str/bytes [, Dict[str, str]] ``` #### score_unstructured() input | Input parameter | Description | | --- | --- | | data | Data represented as str or bytes, depending on the provided mimetype. | | model | A trained object loaded from the artifact by DataRobot or loaded through the load_model hook. | | **kwargs | Additional keyword arguments. For a binary classification model, it contains the positive and negative class labels as the following keys:mimetype: str: Indicates the nature and format of the data, taken from request Content-Type header or --content-type CLI argument in batch mode.charset: str: Indicates the encoding for text data, taken from request Content-Type header or --content-type CLI argument in batch mode.query: dict: Parameters passed as query parameters in a HTTP request or the --query CLI argument in batch mode.headers: dict: Request headers passed in the HTTP request. | #### score_unstructured() examples **Python:** The following example processes text input, decodes bytes if necessary, and returns a prediction as a string: ``` def score_unstructured(model, data, query, **kwargs): text_data = data.decode("utf8") if isinstance(data, bytes) else data text_data = text_data.strip() words_count = model.predict(text_data) return str(words_count) ``` ``` curl -X POST "$DATAROBOT_ENDPOINT/api/v2/deployments//predictionsUnstructured/" \ -H "Authorization: Bearer $DATAROBOT_API_TOKEN" \ -H "Content-Type: text/plain" \ -d "This is sample text input" # Expected response: 5 ``` The following example demonstrates parsing JSON input and returning JSON output with the appropriate `Content-Type` header: ``` import json def load_model(code_dir): """Required when no model artifact (.pkl, .h5, etc.) is present.""" return True def score_unstructured(model, data, query, **kwargs): """Parse JSON input and return JSON output with Content-Type header.""" # Parse JSON input input_data = json.loads(data) if data else {} # Your inference logic here result = { "input": input_data, "prediction": "your_prediction_here" } # Return JSON response with Content-Type header return json.dumps(result), {"mimetype": "application/json"} ``` ``` curl -X POST "$DATAROBOT_ENDPOINT/api/v2/deployments//predictionsUnstructured/" \ -H "Authorization: Bearer $DATAROBOT_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"key": "value", "test": 123}' # Expected response: {"input": {"key": "value", "test": 123}, "prediction": "your_prediction_here"} ``` **R:** ``` score_unstructured <- function(model, data, query, ...) { kwargs <- list(...) if (is.raw(data)) { data_text <- stri_conv(data, "utf8") } else { data_text <- data } count <- str_count(data_text, " ") + 1 ret = toString(count) ret } ``` #### score_unstructured() output The `score_unstructured()` hook should return: - A single value return data: str/bytes . - A tuple return data: str/bytes, kwargs: dict[str, str] where kwargs can include {"mimetype": "users/mimetype", "charset": "users/charset"} to build the Content-Type response header from individual components. ## Unstructured model considerations ### Incoming data type resolution The `score_unstructured` hook receives a `data` parameter, which can be of either `str` or `bytes` type. You can use type-checking methods to verify types: - Python:isinstance(data, str)orisinstance(data, bytes) - R:is.character(data)oris.raw(data) DataRobot uses the `Content-Type` header to determine a type to cast `data` to. The `Content-Type` header can be provided in a request or in `--content-type` CLI argument.The `Content-Type` header format is `type/subtype;parameter` (e.g., `text/plain;charset=utf8`). The following rules apply: - Ifcharsetis not defined, defaultutf8charset is used, otherwise provided charset is used to decode data. - IfContent-Typeis not defined, then incomingkwargs={"mimetype": "text/plain", "charset":"utf8"}, so data is treated as text, decoded usingutf8charset and passed asstr. - Ifmimetypestarts withtext/orapplication/json, data is treated as text, decoded using provided charset and passed asstr. - For all othermimetypevalues, data is treated as binary and passed asbytes. ### Outgoing data and kwargs parameters As mentioned above, `score_unstructured` can return: - A single data value:return data. - A tuple (data and additional parameters:return data, {"mimetype": "some/type", "charset": "some_charset"}). #### Server mode In server mode, the following rules apply: - return data: str: The data is treated as text, the defaultContent-Type="text/plain;charset=utf8"header is set in response, and data is encoded and sent using theutf8charset. - return data: bytes: The data is treated as binary, the defaultContent-Type="application/octet-stream;charset=utf8"header is set in response, and data is sent as-is. - return data, kwargs: Ifmimetypevalue is missing inkwargs, the defaultmimetypeis set according to the data typestr/bytes->text/plain/application/octet-stream. Ifcharsetvalue is missing, the defaultutf8charset is set; then, if the data is of typestr, it will be encoded using resolvedcharsetand sent. #### Batch mode The best way to debug in batch mode is to provide `--output` file. The returned data is written to a file according to the type of data returned: - strdata is written to a text file using defaultutf8or returned inkwargscharset. - bytesdata is written to a binary file. The returnedkwargsare not shown in batch mode, but you can still print them during debugging. ### Auxiliaries You may use the `datarobot_drum.RuntimeParameters` in your code (e.g.`custom.py`) to read runtime parameters delivered to the executed custom model. The runtime parameters should be defined in the DataRobot UI. Below is a simple example of how to read a string of credential runtime parameters: ``` from datarobot_drum import RuntimeParameters def load_model(code_dir): target_url = RuntimeParameters.get("TARGET_URL") s3_creds = RuntimeParameters.get("AWS_CREDENTIAL") ... ``` --- # Data science agent URL: https://docs.datarobot.com/en/docs/api/code-first-tools/ds-agent.html > The data science agent executes and iterates on the data preparation process to provide an audit trail for data governance. # Data science agent The data science agent provides an audit trail of data preparation actions that the agent took on your behalf. As it iterates, it creates an audit trail that not only allows you to find and modify based on output, but enhances agent governance by creating snapshots of the data as each tool in the agent makes changes. The result is a data panel artifact —the output created by the agent. The panels create a metadata record that represent each point in the agent's data transformation lifecycle. An artifact can move between steps of a machine learning pipeline, without having to hard-code file paths. In this way, for reproducibility purposes, you don't have to find the original data but can instead use the agent to recreate it based on snapshot IDs. ## Agent overview You can build the Data Science Agent application using a low-code/no-code approach that then allows you to create and deploy autonomous AI agents that can: - Execute multistep tasks. The agent creates workspaces, navigates dashboards, adjusts settings, and saves configurations without step-by-step prompting. - Persist states. Workspaces and agent configurations survive application restarts, maintaining context across sessions. - Interact with data. Agents can connect to datasets and data stores, and run machine learning models to make predictions. - Operate through natural language. Simply describe what you want and the agent calculates the implementation steps. Consider this high-level overview workflow: 1. Create acodespace. 2. Tell the agent what you want to do (creation). 3. Point to thetoolson the MCP server that will implement data prep steps. 4. Review the agent outputand adjust as necessary. The sections below broadly outline the process in DataRobot, using an MCP server for tool calls and human-in-the-loop review for authentication and governance. ### Technical implementation The agent system is powered by: - An underlying LLM. - MCP servers for extensibility. - A backend system with configurable system prompts that define agent behavior. - A workspace-based architecture where agents can be created, customized, and managed through a UI. ### Creation and persistence The Data Science Agent process allows you to spin up an environment where you can instantiate new agents with a single click. - Memory: Unlike a basic LLM, these agents are persistent. That is, if you shut down the session and restart it, the agent and its associated "workspace" remain intact. - Customization: You can modify the system prompt in the backend code to change how the agent behaves, what its goals are, and how it interacts with the user. ### Use tools Agents do more than just respond to prompts. They integrate into an ecosystem, which allows them to: - Leverage MCP servers: With code that includes an MCP (Model Context Protocol) server call, the agent has a standardized way to connect to external tools and data sources securely. - Interact with data: Using the MCP tools, the agent "talks" to data stores, data sets, and even DataRobot. - Execute tasks: The agent can then run machine learning models, make predictions, and manage a "workspace" or dashboard. ### Human/agent collaboration The agent acts as a bridge between the user and complex technical tasks: - UI management: The agent can help build or modify the layout of the results dashboard. - Code-level access: Because the entire agent stack is running in a codespace, the user can go "under the hood" to edit the agent's logic in or change its instructions, while the agent handles the high-level execution. - Panel review: With each snapshot having a unique ID, a review of the output against the snapshots helps identify areas for improvement. ## End-to-end example Consider the following simple agentic workflow: | User input | Agent response | Panel artifact | | --- | --- | --- | | What are the most recently received products? | Based on our product database in 2025, we released 4 color variations of our crew neck t-shirt: Lavender, Jade Green, Mauve, and Burnt Sienna | None | | How are sales? | Sales are good for Lavender but lower for the other new colors | Data table: Results from querying the sales of the four products. Chart: Bar chart of total sales by each new color released. | This two-question inquiry yields two panels. First a data table that shows the sales of each new variation, and second, a chart that shows the results. Fundamentally, a panel is simply YAML output in a specific format that, when recognized by a client, renders the output in particular display format. Here is sample code of the dataset panel referenced above: ``` # panel.yaml id: 6531c type: dataset payload_path: resources/6531c.parquet parents: [] title: | Mexican perishable goods import data with tariff impact calculations - updated query description: null src: |- SELECT "p"."id" AS "product_id", "p"."name" AS "product_name", "p"."category", "s"."id" AS "supplier_id", "s"."name" AS "supplier_name", "s"."country" AS "supplier_country", sum("isi"."quantity") AS "total_import_volume", sum("isi"."total_cost") AS "total_import_cost", avg("isi"."unit_cost") AS "avg_unit_cost", avg("isi"."unit_cost") * 1.25 AS "unit_cost_with_tariff", sum("isi"."total_cost") * 1.25 AS "total_cost_with_tariff", sum("isi"."total_cost") * 0.25 AS "tariff_cost_increase", "s"."lead_time_days" AS "supplier_lead_time", "s"."reliability_score" AS "supplier_reliability", substring("isi"."order_date", 1, 7) AS "order_month" FROM "products" "p" JOIN "inbound_shipment_items" "isi" ON "p"."id" = "isi"."product_id" JOIN "suppliers" "s" ON "isi"."supplier_id" = "s"."id" JOIN "inbound_shipments" "ins" ON "isi"."shipment_id" = "ins"."id" WHERE "s"."country" = 'Mexico' AND "p"."is_perishable" = 1 GROUP BY "p"."id", "p"."name", "p"."category", "s"."id", "s"."name", "s"."country", "s"."lead_time_days", "s"."reliability_score", substring("isi"."order_date", 1, 7) ORDER BY "total_import_cost" DESC src_type: sql vspan: 1 hspan: 2 ``` An agent can invoke the panel display by including conformant YAML in its response. In the reference implementation, YAML in responses is identified in three ways: - Identify xml tags in the response. - Identify three backticks denoting a code block where the content validates as YAML. - Identify a block denoted by two consecutive new lines where the block content validates as YAML. ## Panel class hierarchy All panels derive from a `BasePanel` base class and must contain the following base attributes: ``` # panel.yaml interface BasePanel { id: string; type: "chart" | "dataset" | "text" | "video"; src: string; author: string; created_at: string; // ISO-formatted creation datetime. parents: string[]; // References the parent or linked panel. } ``` Each panel type extends this base interface with additional properties specific to that panel type. ## Installation Add panels with `npm` or `yarn`. Import only the components you need for a minimal bundle size. Then, create your first panel with just a few lines of code. ``` npm install agent-panels # or yarn add agent-panels ``` Once installed, you can import the components you need: ``` import { DatasetPanel, TextPanel, ChartPanel, VideoPanel, usePanel, } from "agent-panels"; ``` Chart and dataset panels can reference payloads that are required to render the panel correctly. Payloads should refer to a file location accessible to the client (like an S3-backed URL) or should reference an MCP resource URI. ## Panel types ### Text panel The text panel component is used for displaying formatted text content with markdown support. It is a good choice for summarizing research or providing a record of agent activity: ``` ``` For example: ``` ``` ### Dataset panel The dataset panel is for displaying and interacting with tabular data with filtering, sorting, and pagination capabilities: ``` ``` ### Chart panel The chart panel represents a graphic that will be presented on the client. Specifically, a chart should reference both its graphical representation (by linking to a payload) and a dataset panel (through a parent panel reference). The chart panel creates visualizations of your data using libraries like Plotly: ``` ``` ### Video panel The video panel is used to display video content with playback controls and annotation markers: ``` ``` ## The usePanel hook The `usePanel` hook simplifies data fetching and state management for all panel types: ``` import { usePanel } from "agent-panels"; function MyComponent() { const panelProps = usePanel({ specUrl: "path/to/panel-spec.yaml", dataUrl: "path/to/data.parquet", }); return ; } ``` --- # Code-first tools URL: https://docs.datarobot.com/en/docs/api/code-first-tools/index.html > Programmatic tools DataRobot has to offer in addition to the APIs. # Code-first tools The table below lists the various programmatic tools DataRobot has to offer in addition to the APIs: | Resource | Description | | --- | --- | | DataRobot User Models (DRUM) | A repository that contains tools, templates, and information for assembling, debugging, testing, and running your custom inference models, custom tasks, and custom notebook environments with DataRobot. | | Blueprint Workshop | Construct and modify DataRobot blueprints and their tasks using a programmatic interface. | | DataRobot Prediction Library | The DataRobot Prediction Library is a Python library for making predictions using various prediction methods supported by DataRobot. It provides a common interface for making predictions, making it easy to swap out the underlying implementation. | | DataRobotX (DRX) | DataRobotX, or DRX, is a collection of DataRobot extensions designed to enhance your data science experience. DRX provides a streamlined experience for common workflows but also offers new, experimental high-level abstractions. | | MLOps agents | The MLOps agents allow you to monitor and manage external models—those running outside of DataRobot MLOps. With this functionality, predictions and information from these models can be reported as part of MLOps deployments. | | Management agent | The MLOps management agent provides a standard mechanism to automate model deployment to any type of infrastructure. It pairs automated deployment with automated monitoring to ease the burden on remote models in production, especially with critical MLOps features such as challenger models and retraining. | | DRApps | DRApps is a simple command line interface (CLI) providing the tools required to host a custom application, such as a Streamlit app, in DataRobot using a DataRobot execution environment. This allows you to run apps without building your own Docker image. Custom applications don't provide any storage; however, you can access the full DataRobot API and other services. | | DataRobot model metrics library (DMM) | A repository that contains a framework to compute model machine learning metrics over time and produce aggregated metrics. In addition, it provides examples of how to run and integrate this library with your custom metrics in DataRobot. You can also review supporting DataRobot documentation. | | MLOps Utilities For Spark | A utilities library to integrate MLOps tasks with Spark. | | Apache Spark API for Scoring Code | Use the Spark API to integrate DataRobot Scoring Code JARs into Spark clusters. | | DataRobot provider for Apache Airflow | This quickstart guide on the DataRobot provider for Apache Airflow illustrates the setup and configuration process by implementing a basic Apache Airflow DAG (Directed Acyclic Graph) to orchestrate an end-to-end DataRobot AI pipeline. | | MLflow integration for DataRobot | How to export a model from MLflow and import it into the DataRobot Model Registry, creating key values from the training parameters, metrics, tags, and artifacts in the MLflow model. | --- # MLflow integration for DataRobot URL: https://docs.datarobot.com/en/docs/api/code-first-tools/mlflow-integration.html > Export a model from MLflow and import it into the DataRobot Model Registry], creating key values from the training parameters, metrics, tags, and artifacts in the MLflow model. # MLflow integration for DataRobot > [!NOTE] Availability information > The MLflow integration for DataRobot is a preview feature. Contact your DataRobot representative or administrator for information on using this feature. The MLflow integration for DataRobot allows you to export a model from MLflow and import it into the DataRobot [Model Registry](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/index.html), creating [key values](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-key-values.html) from the training parameters, metrics, tags, and artifacts in the MLflow model. ## Prerequisites for the MLflow integration The MLflow integration for DataRobot requires the following: - Python >= 3.9 - DataRobot >= 9.0 This integration library uses a preview API endpoint; the DataRobot user associated with your API token must have Owner or User permissions for the DataRobot model package. ## Install the MLflow integration for DataRobot You can install the `datarobot-mlfow` integration with `pip`: ``` # pip installation pip install datarobot-mlflow ``` If you are running the integration on Azure, use the following command: ``` # Azure pip installation pip install "datarobot-mlflow[azure]" ``` ## Configure command line options The following command line options are available for the `drflow_cli`: | Option | Description | | --- | --- | | --mlflow-url | Defines the MLflow tracking URL; for example:Local MLflow: "file:///Users/me/mlflow/examples/mlruns"Azure Databricks MLflow: "azureml://region.api.azureml.ms/mlflow/v1.0/subscriptions/subscription-id/resourceGroups/resource-group-name/providers/Microsoft.MachineLearningServices/workspaces/azure-ml-workspace-name" | | --mlflow-model | Defines the MLflow model name; for example, "cost-model". | | --mlflow-model-version | Defines the MLflow model version; for example, "2". | | --dr-url | Provides the main URL of DataRobot instance; for example, https://app.datarobot.com. | | --dr-model | Defines the ID of the registered model for key value upload; for example, 64227b4bf82db411c90c3209. | | --prefix | Provides a string to prepend to the names of all key values imported to DataRobot. The default value is empty. | | --debug | Sets the Python logging level to logging.DEBUG. The default level is logging.WARNING. | | --verbose | Prints information to stdout during the following processes:Retrieving model from MLflow: prints model information.Setting model data in DataRobot: prints each key value added to DataRobot. | | --with-artifacts | Downloads MLflow model artifacts to /tmp/model. | | --service-provider-type | Defines the service provider for validate-auth. The supported value is azure-databricks for Databricks MLflow in Azure. | | --auth-type | Defines the authentication type for validate-auth. The supported value is azure-service-principal for Azure Service Principal. | | --action | Defines the operation you want the MLflow integration for DataRobot to perform. | The following command line operations are available for the `--action` option: | Action | Description | | --- | --- | | sync | Imports parameters, tags, metrics, and artifacts from an MLflow model into a DataRobot model package as key values. This action requires --mlflow-url, --mlflow-model, --mlflow-model-version, --dr-url, and --dr-model. | | list-mlflow-keys | Lists parameters, tags, metrics, and artifacts in an MLflow model. This action requires --mlflow-url, --mlflow-model, and --mlflow-model-version. | | validate-auth | Validates the Azure AD Service Principal credentials for troubleshooting purposes. This action requires --auth-type and --service-provider-type. | ## Set environment variables In addition to the command line options above, you should also provide any environment variables required for your use case: | Environment variable | Description | | --- | --- | | MLOPS_API_TOKEN | A DataRobot API key, found in the DataRobot API keys and tools. | | AZURE_TENANT_ID | The Azure Tenant ID for your Azure Databricks MLflow instance, found in the Azure portal. | | AZURE_CLIENT_ID | The Azure Client ID for your Azure Databricks MLflow instance, found in the Azure portal. | | AZURE_CLIENT_SECRET | The Azure Client Secret for your Azure Databricks MLflow instance, found in the Azure portal. | You can use `export` to define these environment variables with the information required for your use case: ``` export MLOPS_API_TOKEN="" export AZURE_TENANT_ID="" export AZURE_CLIENT_ID="" export AZURE_CLIENT_SECRET="" ``` ## Run the sync action to import a model from MLflow into DataRobot You can use the command line options and actions defined above to export MLflow model information from MLflow and import it into the DataRobot Model Registry: ``` # Import from MLflow DR_MODEL_ID="" env PYTHONPATH=./ \ python datarobot_mlflow/drflow_cli.py \ --mlflow-url http://localhost:8080 \ --mlflow-model cost-model \ --mlflow-model-version 2 \ --dr-model $DR_MODEL_ID \ --dr-url https://app.datarobot.com \ --with-artifacts \ --verbose \ --action sync ``` After you run this command successfully, you can see MLflow information on the Key Values tab of a Registered Model version: In addition, in the Activity log of the Key Values tab, you can view a record of the key value creation events: ## Troubleshoot Azure AD Service Principal credentials To validate Azure AD Service Principal credentials for troubleshooting purposes, you can use the following command line example: ``` # Validate Azure AD Service Principal credentials export MLOPS_API_TOKEN="n/a" # not used for Azure auth check, but the environment variable must be present env PYTHONPATH=./ \ python datarobot_mlflow/drflow_cli.py \ --verbose \ --auth-type azure-service-principal \ --service-provider-type azure-databricks \ --action validate-auth ``` This command should produce the following output if you haven't [configured the required environment variables](https://docs.datarobot.com/en/docs/api/code-first-tools/mlflow-integration.html#environment-variables): ``` # Example output: missing environment variables Required environment variable is not defined: AZURE_TENANT_ID Required environment variable is not defined: AZURE_CLIENT_ID Required environment variable is not defined: AZURE_CLIENT_SECRET Azure AD Service Principal credentials are not valid; check environment variables ``` If you see this error, provide the required Azure AD Service Principal credentials as environment variables: ``` # Provide Azure AD Service Principal credentials export AZURE_TENANT_ID="" export AZURE_CLIENT_ID="" export AZURE_CLIENT_SECRET="" ``` When the environment variables for the Azure AD Service Principal credentials are defined, you should see the following output: ``` # Example output: successful authentication Azure AD Service Principal credentials are valid for obtaining access token ``` --- # Apache Spark API for Scoring Code URL: https://docs.datarobot.com/en/docs/api/code-first-tools/sc-apache-spark.html > Learn how to use the Spark API for Scoring Code, a library that integrates Scoring Code JARs into Spark clusters. # Apache Spark API for Scoring Code The Spark API for Scoring Code library integrates DataRobot Scoring Code JARs into Spark clusters. It is available as a [PySpark API](https://docs.datarobot.com/en/docs/api/code-first-tools/sc-apache-spark.html#pyspark-api) and a [Spark Scala API](https://docs.datarobot.com/en/docs/api/code-first-tools/sc-apache-spark.html#spark-scala-api). In previous versions, the Spark API for Scoring Code consisted of multiple libraries, each supporting a specific Spark version. Now, one library supports all supported Spark versions. The following Spark versions support this feature: - Spark 2.4.1 or greater - Spark 3.x > [!NOTE] Important > Spark must be compiled for Scala 2.12. For a list of the deprecated, Spark version-specific libraries, see the [Deprecated Spark libraries](https://docs.datarobot.com/en/docs/api/code-first-tools/sc-apache-spark.html#deprecated-spark-libraries) section. ## PySpark API The PySpark API for Scoring Code is included in the [datarobot-predict](https://pypi.org/project/datarobot-predict/) Python package, released on PyPI. The PyPI project description contains documentation and usage examples. ## Spark Scala API The Spark Scala API for Scoring Code is published on Maven as [scoring-code-spark-api](https://central.sonatype.com/artifact/com.datarobot/scoring-code-spark-api). For more information, see the [API reference documentation](https://javadoc.io/doc/com.datarobot/scoring-code-spark-api_3.0.0/latest/com/datarobot/prediction/spark30/Predictors$.html). Before using the Spark API, you must add it to the Spark classpath. For `spark-shell`, use the `--packages` parameter to load the dependencies directly from Maven: ``` spark-shell --conf "spark.driver.memory=2g" \ --packages com.datarobot:scoring-code-spark-api:VERSION \ --jars model.jar ``` ### Score a CSV file The following example illustrates how you can load a CSV file into a Spark DataFrame and score it: ``` import com.datarobot.prediction.sparkapi.Predictors val inputDf = spark.read.option("header", true).csv("input_data.csv") val model = Predictors.getPredictor() val output = model.transform(inputDf) output.show() ``` ### Load models at runtime The following examples illustrate how you can load a model's JAR file at runtime instead of using the spark-shell `--jars` parameter: **From DataRobot:** Define the `PROJECT_ID`, the `MODEL_ID`, and your `API_TOKEN`. ``` val model = Predictors.getPredictorFromServer( "https://app.datarobot.com/projects/PROJECT_ID/models/MODEL_ID/blueprint","API_TOKEN") ``` **From HDFS filesystem:** Define the path to the model JAR file and the `MODEL_ID`. ``` val model = Predictors.getPredictorFromHdfs("path/to/model.jar", spark, "MODEL_ID") ``` ### Time series scoring The following examples illustrate how you can perform time series scoring with the `transform` method, just as you would with non-time series scoring. In addition, you can customize the time series parameters with the `TimeSeriesOptions` builder. If you don't provide additional arguments for a time series model through the `TimeSeriesOptions` builder, the `transform` method returns forecast point predictions for an auto-detected forecast point: ``` val model = Predictors.getPredictor() val forecastPointPredictions = model.transform(timeSeriesDf) ``` To define a forecast point, you can use the `buildSingleForecastPointRequest()` builder method: ``` import com.datarobot.prediction.TimeSeriesOptions val tsOptions = new TimeSeriesOptions.Builder().buildSingleForecastPointRequest("2010-12-05") val model = Predictors.getPredictor(modelId, tsOptions) val output = model.transform(inputDf) ``` To return historical predictions, you can define a start date and end date through the `buildForecastPointRequest()` builder method: ``` val tsOptions = new TimeSeriesOptions.Builder().buildForecastDateRangeRequest("2010-12-05", "2011-01-02") ``` For a complete reference, see [TimeSeriesOptions javadoc](https://javadoc.io/doc/com.datarobot/datarobot-prediction/latest/com/datarobot/prediction/TimeSeriesOptions.Builder.html). ## Deprecated Spark libraries Support for Spark versions earlier than 2.4.1 or Spark compiled for Scala earlier than 2.12 is deprecated. If necessary, you can access deprecated libraries published on Maven Central; however, they will not receive any further updates. The following libraries are deprecated: | Name | Spark version | Scala version | | --- | --- | --- | | scoring-code-spark-api_1.6.0 | 1.6.0 | 2.10 | | scoring-code-spark-api_2.4.3 | 2.4.3 | 2.11 | | scoring-code-spark-api_3.0.0 | 3.0.0 | 2.12 | --- # FIRE feature selection URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/fire.html > Learn about the benefits of Feature Impact Rank Ensembling (FIRE)—a method of advanced feature selection that uses a median rank aggregation of feature impacts across several models created during a run of Autopilot. # FIRE feature selection [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/feature_reduction_with_fire/feature_reduction_with_fire.ipynb) At the heart of machine learning is the "art" of providing a model with the features or variables that are useful for making good predictions. Including redundant or extraneous features can lead to overly complex models that have less predictive power. Striking the right balance is known as feature selection.This page proposes a new, novel method for feature selection, based on using the [feature impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html) scores (also known as feature importance scores in the wider industry) from several different models, which leads to a more robust and powerful result. This accelerator outlines how feature importance rank ensembling (FIRE) can be used to reduce the number of features, based on the feature impact scores DataRobot returns, while maintaining predictive performance. During feature selection, data scientists try to keep "the three Rs" in mind: - Relevant : To reduce generalization risk, the features should be relevant to the business problem at hand. - Redundant : Avoid the use of redundant features—they weaken the interpretability of the model and its predictions. - Reduction : Fewer features mean less complexity, which translates to less time required for model training or inference. Using fewer features decreases the risk of overfitting and may even boost model performance. The chart below shows an example of how feature selection is used to improve a model’s performance. As the number of features are reduced from 501 to 13, the model’s performance improves, as indicated by a higher area under the curve (AUC). This visualization is known as a feature selection curve. ## Feature selection approaches There are three approaches to feature selection. Filter methods select features on the basis of statistical tests. DataRobot users often do this by filtering a dataset from 10,000 features to 1,000 using the feature impact score. This score is based on the alternating conditional expectations (ACE) algorithm and conceptually shows the correlation between the target and the feature. The features are ranked and the top features are retained. One limitation of the DataRobot feature impact score is that it only accounts for the relationship between that feature in isolation and the target. Embedded methods are algorithms that incorporate their own feature selection process. DataRobot uses embedded methods in approaches that include ElasticNet and a proprietary machine learning algorithm, [Eureqa](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/eureqa-classic.html). Wrapper methods are model-agnostic and typically include modeling on a subset of features to help identify which are most impactful. Wrapper methods are widely used and include forward selection, backward elimination, recursive feature elimination, and more sophisticated stochastic techniques, such as random hill climbing and simulated annealing. While wrapper methods tend to provide a more optimal feature list than filter or embedded methods, they are more time-consuming, especially on datasets with hundreds of features. The recursive feature elimination wrapper method is widely used in machine learning to reduce the feature list. A common criteria for removing features is to use the feature impact score, calculated via permutation impact, to remove features with the worst scores and then build a new model. The recursive feature selection approach was used to build the feature selection curve in the chart above. ## Feature importance rank ensembling Building on the recursive feature elimination approach, DataRobot combines the feature impact of multiple diverse models. This approach, known as model ensembling, is based on aggregating ranks of features using the [Feature Impact](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/feature-impact.html) score from several Leaderboard blueprints, as described below. You can apply model ensembling, which provides improved accuracy and robustness, to feature selection. Selecting lists of important features from multiple models and combining them in a way that produces more a robust feature list is the foundation of feature importance rank ensembling (FIRE). While there are many ways to aggregate the results, the following general steps are recommended. See the accelerator for details on completing each step: 1. Calculate feature impact for the top N Leaderboard models against the selected metric. You can calculate feature impact using permutation or SHAP impact. 2. For each model with computed feature impact, get the feature ranking. 3. Compute the median rank of each feature by aggregating the ranks of the features across all models. 4. Sort the aggregated list by the computed median rank. 5. Define the threshold number of features to select. 6. Create a feature list based on the newly selected features. To understand the effect of aggregating features, the graphic below shows the variation in feature impact across four different models trained on the readmission dataset. The aggregated feature impact is derived from four models: - LightGBM - XGBoost - Elastic net linear model - Keras deep learning model As indicated by their high `Normalized Impact` score, the features at the top have consistently performed well across many models. The features at the bottom consistently have little signal (they perform poorly across many models). Some features with wide ranges, like `num_lab_procedures` and `diag_x_desc`, performed well in some models, but not in others. Due to multicollinearity and the inherent nature of models, you see variation. That is, linear models are good at finding linear relationships while tree-based models are good at finding nonlinear relationships. Ensembling the feature impact scores helps identify which features are most important in the dataset views of each model. By iterating with FIRE, you can continue to reduce the feature list and build a feature selection curve. FIRE works best when you use models that have good performance to ensure that the feature impact is useful. ## Results The example below shows results on some wider datasets, several internal for illustrative purposes but also two publicly available—Madelon and KDD 1998. Use the AI accelerator linked at the top of this page to try this. - AE is an internal dataset with 374 features and 25,000 rows. - AF is an internal dataset with 203 features and 400,000 rows. - G is an internal dataset with 478 features and 2,500 rows. - IH is an internal dataset with 283 features and 200,000 rows. - KDD 1998 is a publicly available dataset with 477 features and 76,000 rows. - Madelon is a publicly available dataset with 501 features and 2,000 rows. The example uses Autopilot to return these results and show the scores of the best-performing model. The metrics were selected based on the type of problem and distribution of the target. The example then uses Autopilot to build competing models. Lastly, it uses FIRE to develop new feature lists. The results show the performance of the feature list on the best-performing model along with the standard deviation using 10-fold cross-validation. For [feature lists](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html#automatically-created-feature-lists), `Informative Features` is the default list that includes all features that pass a "reasonableness" check.`DR Reduced Features` is a one-time reduced feature list using permutation impact. FIRE uses the feature impact with the median rank aggregation approach using an adaptive threshold (the N features that possess 95% of total feature impact). The table below shows feature selection results on six wide datasets. The bold formatting in the result rows indicates the best-performing result in terms of the mean cross-validation score (lower values indicate a better score). The chart below compares the performance of feature selection methods. Across all of these datasets, FIRE consistently had similar or better performance than the use of all features. It even outperformed the `DR Reduced Features` method by reducing the feature set without any loss in accuracy. For the Madelon dataset, by looking at the feature selection curve in the graphic at the top of the page, you can see how reducing the number of features provided better performance. As FIRE parsed down features from 501 to 18, the model’s performance improved. Note that if you use FIRE, you must build more models during model training so that you have feature impact across diverse models for ensembling. The information from these other models is very useful for feature selection. ## Conclusion Improved accuracy and parsimony are possible when you use the FIRE method for feature selection. There are many variations left to validate: feature impact (SHAP, permutation), choice of models, perturbing the data or model, and the method for aggregating (median rank, unnormalized impact). --- # Time series to images URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/gramian.html > Generate advanced features used for high frequency data use cases. # Time series to images [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/audio_and_sensors-gramian_angular_fields_for_high_freq_data_to_images/high_frequency_data_classification_using_gramian_angular_fields.ipynb) Prerequisites: [PYTS library](https://pyts.readthedocs.io/) Traditional feature engineering methods like time aware aggregation and spectrograms can have limitations. Spectrograms cannot capture correlations between each segment of the signal with other segments of the signal. If you try to do this with tabular aggregates it becomes a high dimensionality problem. Gramian Angular Field images of signal data can solve the above problem using a matrix which can be used with computer vision models easily without the limitations of dimensionality. --- # Advanced analytics and tools URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/index.html > Advanced usage of the DataRobot API that you can add to your experiment workflow. # Advanced analytics and tools | Topic | Description | | --- | --- | | FIRE feature selection | Learn about the benefits of Feature Impact Rank Ensembling (FIRE)—a method of advanced feature selection that uses a median rank aggregation of feature impacts across several models created during a run of Autopilot. | | Time series to images | Generate advanced features used for high frequency data use cases. | | Use case dependencies | Use an application that allows you to view dependency graphs for DataRobot Use Cases. | | Acoustic data with Visual AI | Generate image features in addition to aggregate numeric features for high frequency data sources. | | Prediction intervals | Learn about the various methods to generate prediction intervals for any DataRobot model. The methods are rooted in conformal inference (also known as conformal prediction). This accelerator focuses on prediction interval generation for regression targets. | | Robust feature selection | This accelerator introduces an approach to select robust features, use multiple seeds for cross validation, add dummy features to compute the median permutation importance, and then select the most robust dummy features. | | Use case explorer | Provides a template for a project management dashboard that collects all the key artifacts in a Use Case and displays them on a timeline. | | Multi-objective optimization | Build a Streamlit application that uses DataRobot deployments to optimize multiple targets simultaneously. | --- # Use Case dependencies URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/lineage.html > Use an application that allows you to view dependency graphs for DataRobot Use Cases. # Use Case dependencies [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced_ml_and_api_approaches/datarobot-lineage-app) This accelerator uses an application that allows you to view dependency graphs for DataRobot Use Cases. Retrieve a Use Case and see how its assets are related. You can run and use the application with `node.js` and Docker. Navigate to `localhost:8000` or `localhost:8080/apps` to begin using it. Usage requires an API key and endpoint to retrieve and view your Use Cases. --- # Acoustic data with Visual AI URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/ml-viz.html > Generate image features in addition to aggregate numeric features for high frequency data sources. # Acoustic data with Visual AI [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/high_frequency_data_classification_using_spectrograms_n_numerics/high_frequency_classification_spectrograms_n_numerics.ipynb) The density of high frequency data presents a challenge for standard machine learning workflows that lack specialized feature engineering techniques to condense the signal, extracting and highlighting its uniqueness. DataRobot's multimodal input capability supports simultaneously leveraging numerics and images, which for this use-case is particularly beneficial for including descriptive spectrograms that enable you to leverage well-established computer vision techniques for complex data understanding. This example notebook shows how to generate image features and aggregate numeric features for high frequency data sources. This approach converts audio wav files from the time domain into the frequency domain to create several types of spectrograms. Statistical numeric features computed from the converted signal add additional descriptors to aid classification of the audio source. --- # Multi-objective optimization URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/multi-objective-opt.html > Implement a Streamlit application using DataRobot deployments to optimize multiple targets at once and explore Pareto-optimal trade-offs. # Multi-objective optimization [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/multi_objective_optimization/run_multi_objective_optimization.ipynb) This accelerator demonstrates how to implement a Streamlit application for multi-objective optimization using DataRobot deployments, allowing users to optimize multiple targets simultaneously. The application acts as a simulation environment: it runs many trials, uses DataRobot’s prediction API as the core of each trial, and supports Monte Carlo–style sampling (e.g., Random and Quasi-Monte Carlo samplers), illustrating how to build custom simulations that integrate with DataRobot and external optimization tools. The notebook outlines how to: 1. Create multiple DataRobot projects : Upload training data, configure project settings for each target, and run Autopilot with cross-validation. 2. Build deployments : Select the top-performing model for each target, create registered model versions, and deploy to a prediction server. 3. Set up the Streamlit application : Upload the application to DataRobot, configure optimization parameters (e.g., trial count, objective directions and weighting), and run simulations to view optimization results. The application uses Optuna to suggest parameters and request predictions from the DataRobot API on each trial, then computes and displays the Pareto front. Current capabilities and features include six types of optimization algorithms, adjustable trial count, numexpr support for individual optimization targets, objective variable weighting, parameter reliability statistics, display of hypervolume and target-vs-feature plots, 2D/3D Pareto front display, localization support (EN/JP), and support for both dedicated prediction servers and serverless deployment. The optimization application process flow is shown below: ``` sequenceDiagram participant User participant App as Streamlit App participant Op as Optuna participant DR as DataRobot API User->>App: Adjust settings User->>App: Click "Simulation Start!" App->>DR: Load deployment infos DR-->>App: Return deployment infos loop For each trial App->>Op: Create study Op->>Op: Suggest parameters Op->>DR: Request predictions DR-->>Op: Return predictions Op->>Op: Update study end App->>App: Calculate Pareto front App->>User: Display optimization results ``` --- # Prediction intervals URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/pred-intervals-inf.html > Designed for DataRobot trial users, experience an end-to-end DataRobot workflow using a use case that predicts flight delays. # Prediction intervals [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/prediction_intervals_via_conformal_inference/prediction_intervals_via_conformal_inference.ipynb) This AI Accelerator demonstrates various ways for generating prediction intervals for any DataRobot model. The methods presented here are rooted in the area of conformal inference (also known as conformal prediction). These types of approaches have become increasingly popular for uncertainty quantification because they do not require strict distributional assumptions to be met in order to achieve proper coverage (i.e., they only require that the testing data is exchangeable with the training data). While conformal inference can be applied across a wide array of prediction problems, the focus in this notebook will be prediction interval generation on regression targets. --- # Robust feature selection URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/rob-fi.html > This accelerator introduces an approach to select robust features, use multiple seeds for cross validation, add dummy features to compute the median permutation importance, and then select the most robust dummy features. # Robust feature selection [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/Robust_Feature_Impact/Robust_Feature_Impact.ipynb) Machine learning models have biases using small data, and some industries (e.g., healthcare and manufacturing) lack labeled data. In light of this, a good approach is to select robust features to build models. This accelerator introduces an approach to select robust features, use multiple seeds for cross validation, add dummy features to compute the median permutation importance, and select the most robust dummy features. This notebook outlines how to: - Connect to DataRobot. - Create multiple projects by multiple seeds and add dummy features. - Create blender models of top-performing models. - Retrieve modeling permutation importance from the top-performing blender models. - Remove features whose permutation importance is lower than dummy features. --- # Use Case explorer URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/adv-analytics-tools/use-case-explorer.html > Provides a template for a project management dashboard that collects all the key artifacts in a Use Case and displays them on a timeline. # Use Case explorer [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/use_case_explorer) DataRobot provides management capabilities for a variety of personas. Using data across Use Cases, experiments, the model registry, and deployments we are able to report on the full lifecycle of an AI project. This AI Accelerator provides a template for a project management dashboard that collects all the key artifacts in a Use Case and displays them on a timeline. It also allows project managers to define a target date and overall status for each Use Case. The left-hand navigation menu will list all of your current Use Cases along with their defined status. It is likely that the status will be undefined for your Use Cases when using the app for the first time. Clicking on a menu item will take you into a detailed view of the Use Case and the associated assets. It also presents a scrollable timeline that illustrates project progress. From this page you can also define or update the project status and target date. These values are fed into the description field of the Use Case object, so they will also be visible inside the DataRobot platform. --- # AWS SageMaker deployment URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/deploy-sagemaker.html > Learn how to programmatically build a model with DataRobot and export and host the model in AWS SageMaker. # AWS SageMaker deployment [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_sagemaker_deployment/dr_model_sagemaker.ipynb) In this accelerator, you deploy a model that has been built in DataRobot to AWS SageMaker. If you already use SageMaker for hosting models, you can still make use of the powerful features of DataRobot, including AutoML and time series modeling. You can integrate DataRobot into your existing deployment processes. Likewise, you can use this workflow to deploy a DataRobot-built model into another type of environment. In this accelerator you will follow the manual steps that are [outlined in DataRobot's documentation](https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/sagemaker/sc-sagemaker.html#use-scoring-code-with-aws-sagemaker), programmatically build a model with DataRobot, and export and host the model in AWS SageMaker. To assist with the setup of AWS services to run the model, this code provisions any extra items that you may not haven yet set up. Review the lists below of what is created in this AI accelerator. ### AWS - ECR Repository - S3 Bucket - IAM Role for SageMaker - SageMaker inference model - SageMaker endpoint configuration - SageMaker endpoint (for real time predictions) - SageMaker batch transform job (for batch predictions) ### DataRobot - DataRobot AutoML Project - DataRobot AutoML Models - Scoring Code JAR file of AutoML Model Once you have run through the code, you will see how you can leverage the power of DataRobot's automated machine learning capabilities to train a model and then make use of the power of AWS to deploy and host that model in SageMaker. --- # Feature Discovery SQL with Spark URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/fd-sql-spark.html > Run Feature Discovery SQL in a new Spark cluster on Docker by setting up a Spark cluster in Docker, registering custom User Defined Functions (UDFs), and executing complex SQL queries across multiple datasets. # Feature Discovery SQL with Spark [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced_ml_and_api_approaches/Using%20Feature%20Discovery%20SQL%20in%20Spark%20clusters) This accelerator outlines an example that runs Feature Discovery SQL in a Docker-based Spark cluster. It walks you through the process of setting up a Spark cluster in Docker, registering custom user-defined functions (UDFs), and executing complex SQL queries for feature engineering across multiple datasets. The same approach can be applied to other Spark environments, such as GCP Dataproc, Amazon EMR, and Cloudera CDP, providing flexibility for running Feature Discovery on various Spark platforms. ## Problem framing Features are commonly split across multiple data assets. Bringing these data assets together can take a lot of work, as it involves joining them and then running machine learning models. It's even more difficult when the datasets are of different granularities, as it requires you to aggregate to join the data successfully. [Feature Discovery](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/enrich-data-using-feature-discovery.html) solves this problem by automating the procedure of joining and aggregating your datasets. After you define how the datasets need to be joined, DataRobot handles feature generation and modeling. Feature Discovery uses Spark to perform joins and aggregations, generating Spark SQL at the end of the process. In some cases, you may want to run this Spark SQL in other Spark clusters to gain more flexibility and scalability for handling larger datasets, without the need to load data directly into the DataRobot environment. This approach allows you to leverage external Spark clusters for more resource-intensive tasks. This accelerator provides an example of running Feature Discovery SQL in Docker-based Spark cluster. ## Prerequisites - Install Docker - Install Docker compose - Download required the datasets, UDFs .jar, and an environment file (optional) ## Compatibility - Feature Discovery SQL is compatible with Spark 3.2(.2), Spark 3.4(.1), and Scala 2.12(.15). Using different Spark & Scala versions might lead to errors. - The UDFs .jar and environment files can be obtained from the following locations. Note that environment file is only required if working with Japanese text. - Spark 3.2.2 - Spark 3.4.1 - Specific Spark versions can be obtained from here . ## File overview The file structure is outlined below: ``` . ├── Using Feature Discovery SQL in other Spark clusters.ipynb ├── apps │ ├── DataRobotRunSSSQL.py │ ├── LC_FD_SQL.sql │ ├── LC_profile.csv │ ├── LC_train.csv │ └── LC_transactions.csv ├── data ├── libs │ ├── spark-udf-assembly-0.1.0.jar │ └── venv.tar.gz ├── docker-compose.yml ├── Dockerfile ├── start-spark.sh └── utils.py ``` - Using Feature Discovery SQL in other Spark clusters.ipynb is the notebook providing a framework for running Feature Discovery SQL in a new Spark cluster on Docker. - docker-compose.yml , Dockerfile , and start-spark.sh are files used by Docker to build and start the Docker container with Spark. - utils.py includes a helper function to download datasets and the UDFs jar. - The app directory includes: - Spark SQL (a file with a .sql extension) - Datasets (files with a .csv extension) - Helper function (files with a .py extension) to parse and execute the SQL - The libs directory includes: - A user-defined functions (UDFs) JAR file - An environment file (only required if datasets include Japanese text, which requires a Mecab tokenizer to handle) - Thedatadirectory is empty, as it is used to store the output result - Note that the datasets, UDFs jar, and environment files are initially unavailable. They have to be downloaded, as described in the accelerator. --- # GraphQL integration URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/graphql.html > Connect a GraphQL server to the DataRobot OpenAPI specification using GraphQL Mesh. # GraphQL integration [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/ecosystem_integration_templates/DataRobot-GraphQL) This accelerator outlines how to integrate GraphQL with DataRobot. In this example implementation, a GraphQL server is connecting to the DataRobot OpenAPI specification using GraphQL Mesh, the currently maintained option. This process requires the following software, available either via the command line or as a URL in the browser: - A working DataRobot login with a valid API key. - node - yarn --- # AI integrations and platforms URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/index.html > Integrations with AI platforms and services to enhance your DataRobot experience. # AI integrations and platforms | Topic | Description | | --- | --- | | AWS SageMaker deployment | Learn how to programmatically build a model with DataRobot and export and host the model in AWS SageMaker. | | Feature Discovery SQL with Spark | Run Feature Discovery SQL in a new Spark cluster on Docker by setting up a Spark cluster in Docker, registering custom User Defined Functions (UDFs), and executing complex SQL queries across multiple datasets. | | GraphQL integration | Connect a GraphQL server to the DataRobot OpenAPI specification using GraphQL Mesh. | | Amazon Athena workflow | Read in an Amazon Athena table to create a project and deploy a model to make predictions with a test dataset. | | AWS workflow | Work with AWS and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions. | | Azure workflow | Work with Azure and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions. | | Databricks workflow | Build models in DataRobot with data acquired and prepared in a Spark-backed notebook environment provided by Databricks. | | Google Cloud and BigQuery workflow | Use Google Collaboratory to source data from BigQuery, build and evaluate a model using DataRobot, and deploy predictions from that model back into BigQuery and GCP. | | SageMaker workflow | Take an ML model that has been built with DataRobot and deploy it to run within AWS SageMaker. | | Snowflake workflow | Work with Snowflake and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions. | | Performance degradation prediction | Use a predictive framework for managing and maintaining your machine learning models with DataRobot MLOps. | | Snowpark integration | Leverage Snowflake for data storage and Snowpark for deployment, feature engineering, and model scoring with DataRobot. | | SAP Hana workflow | Learn how to programmatically build a model with DataRobot using SAP Hana as the data source. | | Speech recognition integration | Use Whisper to transcribe audio files, process them efficiently, and store the transcriptions in a structured format for further analysis or use. | --- # Amazon Athena workflow URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-athena.html > Read in an Amazon Athena table to create a project and deploy a model to make predictions with a test dataset. # Amazon Athena workflow [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_Athena_template/AWS_Athena_End_to_End.ipynb) Being one of the largest cloud providers in the world, AWS has multiple ways of storing data within its cloud. Read more to find out how to integrate DataRobot with your data. In this accelerator integration with Athena, you will create a JDBC data source within DataRobot to connect to Athena and then pull data in via an SQL query. This accelerator notebook covers the following activities: - Read in an Amazon Athena table and upload it to DataRobot's AI Catalog - Create a project with the dataset - Deploy the top-performing model to a DataRobot prediction server - Make batch predictions with a test dataset --- # AWS workflow URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-aws.html > Work with AWS and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions. # AWS workflow Being one of the largest cloud providers in the world, AWS has multiple ways of storing data within its cloud. You can use either of two AI accelerators that allow you to source data from S3 or Athena, build and evaluate a model using DataRobot, and send predictions from that model back to S3. [Access the AI accelerator for S3 on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_S3_template/Amazon_S3_End_to_End.ipynb) [Access the AI accelerator for AWS Athena on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_Athena_template/AWS_Athena_End_to_End.ipynb) Each AI accelerator will perform the following steps to help you integrate DataRobot with your data in AWS: - Import data for training: - Using the DataRobot Python API, you will have DataRobot build up to 50 different machine learning models while also evaluating how those models perform on this dataset. --- # Azure workflow URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-azure.html > Work with Azure and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions. # Azure workflow [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/Azure_template/Azure_End_to_End.ipynb) DataRobot offers an in-depth API that allows you to produce fully automated workflows in your coding environment of choice. This accelerator shows how to enable end-to-end processing of data stored natively in Azure. In this notebook you'll see how data stored in Azure can be used to train a collection of models on DataRobot. You'll then deploy a recommended model and use DataRobot's batch prediction API to produce predictions and write them back to the source Azure container. This accelerator notebook covers the following activities: - Acquire a training dataset from an Azure storage container - Build a new DataRobot project - Deploy a recommended model - Score via DataRobot's batch prediction API - Write results back to the source Azure container --- # Databricks workflow URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-databricks.html > Build models in DataRobot with data acquired and prepared in a Spark-backed notebook environment provided by Databricks. # Databricks workflow [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/Databricks_template/Databricks_End_To_End.ipynb) DataRobot features an in-depth API that allows data scientists to produce fully automated workflows in their coding environment of choice. This accelerator shows how to pair the power of DataRobot with the Spark-backed notebook environment provided by Databricks. In this notebook you'll see how data acquired and prepared in a Databricks notebook can be used to train a collection of models on DataRobot. You'll then deploy a recommended model and use DataRobot's exportable Scoring Code to generate predictions on the Databricks Spark cluster. This accelerator notebook covers the following activities: - Acquiring a training dataset. - Building a new DataRobot project. - Deploying a recommended model. - Scoring via Spark using DataRobot's exportable Java Scoring Code. - Scoring via DataRobot's Prediction API. - Reporting monitoring data to the MLOps agent framework in DataRobot. - Writing results back to a new table. --- # Google Cloud and BigQuery workflow URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-gcp.html > Use Google Collaboratory to source data from BigQuery, build and evaluate a model using DataRobot, and deploy predictions from that model back into BigQuery and GCP. # Google Cloud and BigQuery workflow [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/GCP_template/GCP%20DataRobot%20End%20To%20End.ipynb) DataRobot can integrate directly into your GCP environment, helping to accelerate your use of machine learning across all of the GCP services. In this notebook accelerator, you can use Google Collaboratory or another notebook environment to source data from BigQuery, build and evaluate an ML model using DataRobot, and deploy predictions from that model back into BigQuery and GCP. This accelerator covers the following: 1. Prepare data and ensure connectivity:In the first section of the notebook, you will load a sample dataset to be used for modeling into BigQuery. Once complete, you will connect your BigQuery data with DataRobot. 2. Build and evaluate a model:Using the DataRobot Python API, you will have DataRobot build close to 50 different machine learning models while also evaluating how those models perform on this dataset. 3. Scoring and hosting:In the final section, the entire dataset will be scored on the new model with prediction data written back to BigQuery for use in your GCP applications. --- # SageMaker workflow URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-sagemaker.html > Take an ML model that has been built with DataRobot and deploy it to run within AWS SageMaker. # SageMaker workflow [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/AWS_sagemaker_deployment/dr_model_sagemaker.ipynb) If you already use SageMaker for hosting models, you can still make use of the powerful features of DataRobot, including AutoML and time series modeling. You can integrate DataRobot into your existing deployment processes. Likewise, you can use this accelerator to deploy a DataRobot-built model in another environment. In this accelerator, you will take an ML model that has been built and refined within DataRobot and deploy it to run within AWS SageMaker. To help with the setup of AWS services to run the model, this code will also help provision any extra items that you may not have set up: ### AWS - ECR Repository - S3 Bucket - IAM Role for SageMaker - SageMaker inference model - SageMaker endpoint configuration - SageMaker endpoint (for real time predictions) - SageMaker batch transform job (for batch predictions) ### DataRobot - DataRobot AutoML Project - DataRobot AutoML Models - Scoring Code JAR file of AutoML Model ## What you will learn - Programmatically go through the end-to-end steps of building a model with DataRobot - Export and host the model in AWS SageMaker --- # End-to-end workflow with SAP Hana URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-sap.html > Learn how to programmatically build a model with DataRobot using SAP Hana as the data source. # End-to-end workflow with SAP Hana [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/SAP_template/SAP_End_to_End.ipynb) The scope of this accelerator provides instructions on how to use DataRobot's Python client to build a workflow that will use an existing SAP Hana JDBC driver and: - Create credentials - Create the training data source - Create the predictions data source - Create a dataset used to train the models - Create a dataset used to make predictions - Create a project - Create a deployment - Make batch and real-time predictions - Show the total predictions made so far There is also a playbook at the end of this notebook that describes how to create the back end SAP Hana database that will provide the data required. --- # Snowflake workflow URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/ml-snowflake.html > Work with Snowflake and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions. # Snowflake workflow [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/ecosystem_integration_templates/Snowflake_template/Snowflake%20-%20End-to-end%20Ecommerce%20Churn.ipynb) This AI accelerator walks through how to work with Snowflake (as a data source) and DataRobot's Python client to import data, build and evaluate models, and deploy a model into production to make new predictions. More broadly, the DataRobot API is a critical tool for data scientists to accelerate their machine learning projects with automation while integrating the platform's capabilities into their code-first workflows and coding environments of choice. By using this accelerator, you will: - Connect to DataRobot. - Import data from Snowflake into DataRobot. - Create a DataRobot project and run Autopilot. - Select and evaluate the top performing model. - Deploy the recommended model with MLOps model monitoring. - Orchestrate scheduled batch predictions that write results back to Snowflake. --- # Performance degradation prediction URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/perform-degrade.html > Use a predictive framework for managing and maintaining your machine learning models with DataRobot MLOps. # Performance degradation prediction [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/Prediction%20of%20Model%20Performance%20Degradation%20and%20Service%20Failure/Prediction%20of%20Model%20Performance%20Degradation%20and%20Service%20Failure.ipynb) This notebook is designed to provide a predictive framework for managing and maintaining your machine learning models using DataRobot MLOPs. It focuses on preemptively identifying potential model performance degradation and service failures, enabling your organization to proactively address these issues before they adversely impact operations. This approach ensures the sustained efficiency and reliability of predictions that are integral to your business operations. Early detection of model performance deterioration allows for timely interventions. These interventions could range from adjusting DataRobot's retraining policies to other corrective actions, ensuring the maintenance of optimal model performance. Similarly, predicting potential service infrastructure issues facilitates preemptive maintenance. This not only reduces downtime but also enhances service reliability and provides insights into the root causes of these issues. The notebook demonstrates how to leverage DataRobot MLOps functionality to predict if a machine learning model is likely to degrade within a specific time period and if infrastructure failures may occur. It utilizes DataRobot's Python AI capabilities to collect various characteristics and metrics that DataRobot MLOPs tracks for your deployed model, thereby enabling the construction of a predictive model." This notebook outlines how to: - Establish a connection with DataRobot and access the relevant deployment details - Create a training dataset - Define a target for predicting model performance degradation - Build a TS Model Degradation project - Define a target for predicting Service Failure - Build a TS Service Failure project - Retrieve modeling results from the DataRobot Projects - Augment the original deployment with custom metrics based on these predictions --- # Snowpark integration URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/snowpark-data.html > Leverage Snowflake for data storage and Snowpark for deployment, feature engineering, and model scoring with DataRobot. # Snowpark integration [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/Snowflake_snowpark_template/Native%20integration%20DataRobot%20and%20Snowflake%20Snowpark-Maximizing%20the%20Data%20Cloud.ipynb) If you or your team have tried to develop and productionalize machine learning models with Snowflake using Python and Snowpark but are looking to level up your end-to-end ML lifecycle on the data cloud, then this AI Accelerator is for you. Depending on your role within the organization, This accelerator can address a number of use cases: - Providing technical personnel with a hosted notebook. - Create an improved developer experience. - Improve monitoring capabilities for models within Snowflake. - Provide guidance and insights for business personnel who want action items: next steps for customers, sales, marketing, and more. DataRobot addresses these exact needs, which can be found in this notebook. In addition, it is compatible with the Snowflake data science stack and DataRobot 9.0 to give you advantages in terms of speed, accuracy, security, and cost-effectiveness. --- # Speech recognition integration URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/ai-integrations-platforms/speech-rec.html > Use Whisper to transcribe audio files, process them efficiently, and store the transcriptions in a structured format for further analysis or use. # Speech recognition integration [Access this AI accelerator on GitHub](https://github.com/datarobot/data-science-scripts/blob/master/accelerators_dev/use_cases_and_horizontal_approaches/Speech%20Recognition.ipynb) This accelerator presents a workflow for transcribing audio files using OpenAI's Whisper model. Whisper is a state-of-the-art speech recognition system designed to handle a wide range of audio types and accents. It is highly effective for converting audio files' spoken language into written text. The workflow includes steps to use Whisper to transcribe audio files, process them efficiently, and store the transcriptions in a structured format for further analysis or use. This can be particularly useful for tasks such as generating subtitles, transcribing meetings, or converting speech from various audio sources into text for machine learning. In this example, you take transcribed data and build a classification model with DataRobot. You use DataRobot for model training, selection, deployment, and to evaluate data for insights. This accelerator demonstrates how to use the Python API client to: - Set up the environment (install and import necessary libraries including Whisper and dependencies). - Securely connect to DataRobot. - Get data (publicly available audio files in this example). - Transcribe audio with Whisper. - Use the transcription to create a classification model in DataRobot. - Retrieve and evaluate model performance and insights. --- # Create and deploy a custom model URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/create-custom-model.html > Create, deploy, and monitor a custom inference model with DataRobot's Python client. # Create and deploy a custom model [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/27a757bcf3b10374e70d4c406058ef9cafd28524/ecosystem_integration_templates/Custom%20Model%20End-to-End%20With%20Compliance%20Docs/Custom%20Model%20End-to-End%20With%20Compliance%20Docs.ipynb) This accelerator outlines how to create, deploy, and monitor a custom inference model with DataRobot's Python client. You can use the Custom Model Workshop to upload a model artifact to create, test, and deploy custom inference models to DataRobot’s centralized deployment hub. --- # Custom blueprints with Composable ML URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/custom-bp-nb.html > Customize models on the Leaderboard using the Blueprint Workshop. # Custom blueprints with Composable ML [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/creating_custom_blueprints/create_custom_blueprint.ipynb) [Composable ML](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/index.html) allows you to add pre-defined tasks to a blueprint or to insert their own custom code. You're free to add your data science and subject matter expertise to the models you build. This accelerator shows how to customize the models on the Leaderboard via Composable ML's API, the Blueprint Workshop. It covers the following activities: - Access the Blueprint Workshop - Define and train a custom blueprint using the tasks provided by DataRobot - Insert custom code in the form of a CatBoost classifier into the blueprint --- # GraphSAGE custom transformer URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/custom-transform.html > Convert a tabular dataset into a graph representation, train a GraphSAGE-based neural network, and package the solution as a DataRobot custom transformer. # GraphSAGE custom transformer [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/GDL%20Featurizer/GDL%20Featurizer.ipynb) Tabular data is one of the most common ways data is represented in machine learning. However, it is not the only structure that can be used. Many real-world problems involve relationships between entities that can be better captured using graph structures. Graph data represents entities as nodes and relationships as edges, making it a powerful tool for capturing relational dependencies. Common use cases for graph-based learning include social networks, recommendation systems, fraud detection, and molecular property prediction. In these applications, using [geometric deep learning](https://graphics.stanford.edu/courses/cs233-18-spring/ReferencedPapers/GCNN_Geometric%20deep%20learning-%20going%20beyond%20Euclidean%20data.pdf) (i.e., the application of deep learning approaches on non-Euclidean data like graphs) techniques have grown in popularity in recent years. Deep learning is particularly well-suited for studying this type of information due to their ability to learn representations automatically, especially when it comes to unstructured data. Despite its advantages, graph-based learning techniques are often overlooked for traditional tabular data. This is potentially due to the underlying question: how do you represent tabular data into a graph? Thankfully, methods like [k-Nearest Neighbors (kNN)](https://en.wikipedia.org/wiki/Nearest_neighbor_graph) graphs exist that can do much of the heavy lifting for you. In this accelerator, explore how geometric deep learning can be leveraged to extract graph-based features to enrich datasets for supervised tasks. You can achieve this by: - Converting a tabular dataset into a graph representation using kNN graphs - Training a GraphSAGE -based neural network to generate unsupervised node embeddings - Packaging the solution as a DataRobot Custom Transformer - Evaluating its impact on downstream machine learning tasks in DataRobot. --- # Google Gemini integration URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/gemini-google.html > Leverage LLMs proposed by hyperscalers via the Custom Model Workshop. # Google Gemini integration [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/generative_ai/gemini-app) DataRobot allows you to leverage LLMs proposed by hyperscalers via the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html#custom-model-workshop). This AI Accelerator demonstrates how to implement a Streamlit application based on the Google Gemini LLM and host it on the DataRobot platform. The user of this AI Accelerator is expected to be familiar with the custom model deployment process and custom metrics creation in DataRobot as well as with Google Vertex AI. This accelerator requires the service account for the Vertex AI project. The following steps outline the accelerator workflow. 1. Createcredentialswith a GCP service account (base64 encoded). 2. Optional. Deploy a guard model from theDataRobot global models. 3. Deploya text model (Gemini Pro). 4. Deploya multimodal model (Gemini Pro Vision). 5. Create custom metricsfor both deployments (text and multimodal). 6. Deploy a Streamlit appto DataRobot. --- # GIN financial fraud detection URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/graph-gin.html > Integrate a Graph Isomorphism Network (GIN) as a custom model task in DataRobot using DRUM. # GIN financial fraud detection [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/graph_financial_fraud_classification/Graph_Financial_Fraud_Classification.ipynb) This accelerator demonstrates the end-to-end implementation of a Graph Isomorphism Network (GIN) as a custom model task in DataRobot. It serves multiple purposes, including model development, local testing, and integration with DataRobot’s custom model framework. The primary objectives of this accelerator are to demonstrate the complete pipeline for adding a custom graph-based task to DataRobot, and to outline DataRobot's custom model hooks implementation. These hooks include: - transform : Preprocess JSON graph data into DGL format. DRUM hooks automatically call transform before executing the fit and score hooks. - fit : Train the GIN model and implicitly save it. - score : Score the data in prediction mode. - load_model : Load pre-trained models. The accelerator's workflow includes three major steps: 1. Manually implement the DRUM process. Follow a step-by-step breakdown of data transformation, model training, and prediction viaDRUM. 2. Test models locally with DRUM to validate hooks before integrating the model with DataRobot. 3. Implement a custom task in DataRobot with automated validation and threshold optimization. --- # Custom model development URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/index.html > Custom model development accelerators that you can add to your experiment workflow. # Custom model development | Topic | Description | | --- | --- | | Create and deploy a custom model | How to create, deploy, and monitor a custom inference model with DataRobot's Python client. You can use the Custom Model Workshop to upload a model artifact to create, test, and deploy custom inference models to DataRobot’s centralized deployment hub. | | Custom blueprints with Composable ML | Customize models on the Leaderboard using the Blueprint Workshop. | | GraphSAGE custom transformer | Convert a tabular dataset into a graph representation, train a GraphSAGE-based neural network, and package the solution as a DataRobot custom transformer. | | Google Gemini integration | Implement a Streamlit application based on Google Gemini LLM and host it on the DataRobot platform with Vertex AI integration. | | GIN financial fraud detection | Integrate a Graph Isomorphism Network (GIN) as a custom model task in DataRobot using DRUM. | | Llama 2 on GCP | Host Llama 2 on Google Cloud Platform with cost comparisons, infrastructure details, and integration with DataRobot's custom model framework. | | LLM custom inference template | The LLM custom inference model template enables you to deploy and accelerate your own LLM, along with "batteries-included" LLMs like Azure OpenAI, Google, and AWS. | | Mistral 7B on GCP | Host Mistral 7B on Google Cloud Platform with infrastructure setup, cost considerations, and DataRobot integration for monitoring and deployment. | | Reinforcement learning | Implement a model based on the Q-learning algorithm. | | Scoring Code microservice | Follow a step-by-step procedure to embed Scoring Code in a microservice and prepare it as the Docker container for a deployment on customer infrastructure (it can be self- or hyperscaler-managed K8s). | | Optimize custom model metrics with hyperparameter tuning | Improve DataRobot models using custom loss functions and advanced hyperparameter tuning. | --- # Llama 2 on GCP URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/llama.html > Learn how to integrate Llama 2 on Google GCP and DataRobot. # Llama 2 on GCP [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/Finetuned_Llama/Finetuned%20Llama%202%20on%20Google%20GCP.ipynb) There are a wide variety of open source large language models (LLMs). For example, there has been a lot of interest in [Llama](https://llama.meta.com/) and variations such as Alpaca, Vicuna, Falcon, and Mistral. Because these LLMs require expensive GPUs, users often want to compare cloud providers to find the best hosting option. In this accelerator you will work with Google Cloud Platform to host Llama 2. You may also want to integrate with the cloud provider that hosts your Virtual Private Cloud (VPC) so that you can ensure proper authentication and access it only from within the VPC. While this accelerator uses authentication over the public internet, it is possible to leverage Google's cloud infrastructure to adjust and suit your cloud architectural needs, including provisioning scaleout policies. Finally, by leveraging Vertex AI in a managed format, you can integrate that infrastructure into your existing stack to meet monitoring needs—things like monitoring service health, CPU usage, and low-level alerting to billing, cost attribution, and account management and, using GCP's tools to route information into BigQuery for ad hoc analytics, log exploration, and more. ### Llama 2 For information about Llama 2 you can read: - The model card on HuggingFace . - The paper released on Arxiv . Llama is available from [Meta](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) for download. ### Lllama 13B-Instruct The [Llama-13b-instruct](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) model has been fine-tuned on datasets available from HuggingFace and is designed specifically for instruction-based use cases. It was trained to use `[INST]` and `[/INST]` control tokens around a user message as well as to begin with system ID ( ``). For example: ` [INST] What is your favorite condiment? [/INST]` ### Overview of GCP The GCP instance types listed below can host Llama-13B with acceleration: - g2-standard-8 with 1 L4 GPU: 8 vCPUs, 32 GB of RAM, $623 per month - n1-standard-16 with 2 V100 GPUs: 16 vCPUs, 60GB of RAM, $388 per month - n1-standard-16 with 2 T4 GPUs: 16 vCPUS, 60GB of RAM + 32 GB + 32 GB, $388 per month - a2-highgpu-1g with 1 A100 GPU: 12 vCPUs, 85GB of RAM, $2,682 per month --- # LLM custom inference template URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/llm-template.html > The LLM custom inference model template enables you to deploy and accelerate your own LLM, along with # LLM custom inference template [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/generative_ai/LLM_custom_inference_model_template) There are a wide variety of LLM model such as OpenAI (not Azure), Gemini Pro, Cohere and Claude. Managing and monitoring these LLM models is crucial to effectively using them. Data drift monitoring by DataRobot MLOps enables you to detect the changes in a user prompt and its responses. Sidecar models can prevent a jailbreak, replace Personally Identifiable Information (PII), and evaluate LLM responses with a global model in Registry. Data export functionality shows you of what a user desired to know at each moment and provides the necessary data you should be included in RAG system. Custom metrics indicate your own KPIs which you inform your decisions (e.g., token costs, toxicity, and hallucination). In addition, DataRobot's RAG playground enables you to compare the RAG system of LLM models that you want to try once you deploy the models in MLOps. You can obtain the best LLM model to accelerate your business. The comparison of variety of LLM models is key element to success the RAG system. The LLM custom inference model template enables you to deploy and accelerate your own LLM, along with "batteries-included" LLMs like Azure OpenAI, Google, and AWS. Currently, DataRobot has a template for OpenAI (not Azure), Gemini Pro, Cohere, and Claude. To use this template follow the instructions outlined on GitHub. --- # Mistral 7B on GCP URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/mistral-7b.html > Learn how to integrate Mistral 7B on Google GCP and DataRobot. # Mistral 7B on GCP [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/Mistral%207B%20on%20Google%20GCP/Mistral%207B%20on%20Google%20GCP.ipynb) There are a wide variety of open source large language models (LLMs). For example, there has been a lot of interest in [Llama](https://llama.meta.com/) and variations such as Alpaca, Vicuna, Falcon, and Mistral. Because these LLMs require expensive GPUs, users often want to compare cloud providers to find the best hosting option. In this accelerator, you will work with Google Cloud Platform to host Llama 2. You may also want to integrate with the cloud provider that hosts your Virtual Private Cloud (VPC) so that you can ensure proper authentication and access it only from within the VPC. While this accelerator uses authentication over the public internet, it is possible to leverage Google's cloud infrastructure to adjust and suit your cloud architectural needs, including provisioning scaleout policies. Finally, by leveraging Vertex AI in a managed format, you can integrate that infrastructure into your existing stack to meet monitoring needs—things like monitoring service health, CPU usage, and low-level alerting to billing, cost attribution, and account management and, using GCP's tools to route information into BigQuery for ad hoc analytics, log exploration, and more. For information about Mistral, you can read the model card on [HuggingFace](https://huggingface.co/mistralai/Mistral-7B-v0.1), the [Arxiv page](https://arxiv.org/abs/2310.06825) and the [release announcement](https://mistral.ai/news/announcing-mistral-7b/). It is available under an [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). --- # Optimize custom model metrics with hyperparameter tuning URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/opt-custom-metric.html > Improve DataRobot models using custom loss functions and advanced hyperparameter tuning. # Optimize custom model metrics with hyperparameter tuning [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/Custom_Metrics_Model_Optimization/Custom_Metrics_Model_Optimization.ipynb) This accelerator demonstrates how to improve DataRobot models using custom loss functions and advanced hyperparameter tuning. In many real-world business problems, standard metrics like RMSE or Accuracy do not fully represent the true business cost. For example, in CLV prediction, overpredicting loss-making customers or underpredicting high-value customers can directly impact revenue and retention strategy. Similarly, classification models often need custom objectives such as maximizing recall at specific thresholds or minimizing false negatives. By creating a custom metric and tuning models using that metric, you ensure the model is optimized for business value, not just statistical performance. This approach is essential when: - Business costs are not symmetrical (overprediction and underprediction have different impacts) - False negatives and false positives carry different risk levels - Revenue-driven metrics matter more than standard ML scores - Domain rules must be incorporated into optimization --- # Reinforcement learning URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/reinforce-learn.html > Implement a model based on the Q-learning algorithm. # Reinforcement learning [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/Reinforcement%20learning/Reinforcement_Learning.ipynb) In this accelerator, you implement a very simple model based on the Q-learning algorithm. This accelerator shows a basic form of reinforcement learning that doesn't require a deep understanding of neural networks or advanced mathematics and how one might deploy such a model in DataRobot. This example shows the Grid World problem, where an agent learns to navigate a grid to reach a goal. The accelerator will go through the following steps: 1. Define state and action space 2. Create a Q-table to store expected rewards for each state/action combination 3. Implement a learning algorithm and train a model 4. Evaluate the model 5. Deploy the model to a DataRobot REST API endpoint --- # Scoring Code microservice URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/sc-micro.html > Follow a step-by-step procedure to embed Scoring Code in a microservice and prepare it as the Docker container for a deployment on customer infrastructure (it can be self- or hyperscaler-managed K8s). # Scoring Code microservice [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/ecosystem_integration_templates/scoring-code-as-microservice-w_docker/README.md) This accelerator guides through the step-by-step procedure that makes it possible to embed scoring code in the microservice and to prepare it as the Docker container for the deployment on the customer infrastructure (it can be self or hyperscaler-managed K8s). The K8s configuration and deployment on K8s are out of scope. The accelerator also includes an example Maven project with the Java code. --- # Enrich with Hyperscaler API URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/enrich-hyper.html > Call the GCP API and enrich a modeling dataset that predicts customer churn. # Enrich with Hyperscaler API [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/data_enrichment_gcp_nlp_api/GCP_enrich_sentiment.ipynb) Many companies are recognizing the value of unstructured data, particularly in the form of text, and are looking for ways to extract insights from it. This data includes emails, social media posts, customer feedback, call transcripts, and more. One of the most powerful tools for analyzing text data is sentiment analysis. Sentiment analysis is the process of identifying the emotional tone of a piece of text, such as positive, negative, or neutral. It is a valuable tool to enrich the dataset for building machine learning models. For example, the sentiment expressed through a customer's recent call transcript with customer service could be predictive of the customer's likelihood to churn. However, building sentiment analysis models is not an easy task. It requires a significant investment of time, resources, and expertise, especially in terms of accurately labeled data with large corpus to train. Most companies do not have the resources or expertise to develop their own sentiment analysis models. Fortunately, there are now APIs available that provide sentiment analysis as a service. By using these APIs, companies can save time and money while still gaining the benefits of sentiment analysis. One of the most significant benefits of using hyperscaler APIs for sentiment analysis is their accuracy. The models behind the APIs are trained on large amounts of data, making them highly accurate at identifying emotions and sentiments in text data. This accelerator demonstrates how easy it is to call the GCP API and enrich a customer churn prediction modeling dataset. An improvement in the model performance is based on retrieved sentiment scores from customer call transcripts. --- # GCP sentiment enrichment URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/gcp-enrich.html > Demo the usage of the Google Cloud Natural Language API for sentiment analysis to enrich a customer churn dataset. # GCP sentiment enrichment [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/data_enrichment_gcp_nlp_api/GCP_enrich_sentiment.ipynb) Text data is a valuable source of information for machine learning models, as it allows algorithms to extract insights from large volumes of unstructured text data. Text data can be obtained from various sources, such as social media, news articles, and customer feedback. The benefits of using text data in ML models include its ability to provide valuable insights, such as sentiment analysis, and topic modeling, which can help organizations make informed decisions. However, using text data in ML models can be challenging due to several factors, such as the complexity of natural language, the presence of bias and noise, and the lack of standardization in text data. Additionally, text data requires significant preprocessing and feature engineering to ensure that it can be effectively used in ML models. One common application of text mining is sentiment analysis, where a numerical value is assigned representing whether the text carries a positive, neutral, or negative sentiment. While DataRobot can help efficiently build such models, the training requires a large, accurately labeled corpora that have been accurately labeled, making it a challenging task for users lacking such training dataset. In this accelerator, demo the usage of the Google Cloud Natural Language API for sentiment analysis to enrich a customer churn dataset. The sentiment scores from the Google API help improve the model performance in predicting the likelihood of churn for each customer, without requiring the user to train their own sentiment models. --- # Data enrichment and preparation URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/index.html > Data enrichment and preparation accelerators that you can add to your experiment workflow. # Data enrichment and preparation | Topic | Description | | --- | --- | | Enrich with Hyperscaler API | Call the GCP API and enrich a modeling dataset that predicts customer churn. | | GCP sentiment enrichment | Demo the usage of the Google Cloud Natural Language API for sentiment analysis to enrich a customer churn dataset. | | Churn problem framing | Discover the problem framing and data management steps required to successfully model for churn, using a B2C retail example and a B2B example based on a DataRobot’s churn model. | | Churn insights with Streamlit | Use the Streamlit churn predictor app to present the drivers and predictions of your DataRobot model. | | Synthetic training data | Learn how to generate synthetic datasets that mimic real-world data for training, validation, and testing—enabling safe data sharing and model development when access to real data is limited due to privacy or regulatory constraints. | | Feature engineering for molecular SMILES data | Execute a feature engineering pipeline tailored for SMILES-formatted molecular data. | --- # Churn problem framing URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/ml-churn.html > Discover the problem framing and data management steps required to successfully model for churn, using a B2C retail example and a B2B example based on a DataRobot’s churn model. # Churn problem framing [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/churn_problem_framing_feature_eng/Churn_Before_Modelling.ipynb) Customer retention is central to any successful business and machine learning is frequently proposed as a way of addressing churn. It is tempting to dive right into a churn dataset, but improving outcomes requires correctly framing the problem. Doing so at the start will determine whether the business can take action based on the trained model and whether your hard work is valuable or not. This accelerator blog will teach the problem framing and data management steps required before modeling begins. It uses two examples to illustrate concepts: a B2C retail example, and a B2B example based on DataRobot’s internal churn model. --- # Feature engineering for molecular SMILES data URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/smiles.html > Execute a feature engineering pipeline tailored for SMILES-formatted molecular data. # Feature engineering for molecular SMILES data [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/Feature%20Engineering%20For%20Molecular%20SMILES) SMILES (simplified molecular input line entry system) is a textual representation of molecular structures. While it's compact and widely used in cheminformatics, SMILES strings must be transformed into numerical representations to be used effectively in machine learning models. This accelerator introduces a feature engineering pipeline tailored for SMILES-formatted molecular data. It demonstrates how to convert raw SMILES strings into machine-learning-ready features using RDKit and other tools. It is recommended to run the accelerator in a DataRobot codespace using a GPU environment. This accelerator's workflow is summarized below: 1. Preprocess and visualize SMILES strings using RDKit and py3Dmol. 2. Extract molecular descriptors statistical features (physicochemical properties). 3. Extract TF-IDF features from SMILES strings, and then apply TruncatedSVD to obtain lower-dimensional embeddings. 4. Extract fingerprints features from SMILES strings, then apply TruncatedSVD to obtain lower-dimensional embeddings. 5. Extract semantic representations from pretrained molecular embeddings of ChemBERTa and SMILESBERT (CPU is slow, so GPU is recommended), and then apply PCA to obtain lower-dimensional embeddings. 6. Run Autopilot with these features to compare model performance and create benchmarks. 7. Extract feature contribution (SHAP values). --- # Churn insights with Streamlit URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/streamlit-app.html > Use the Streamlit churn predictor app to present the drivers and predictions of your DataRobot model. # Churn insights with Streamlit [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced_ml_and_api_approaches/Streamlit_template_datarobot_insights/README.md) This app serves as an example of how to present the drivers and predictions of your DataRobot model using a Churn prediction use case. Building a churn predictor app using Streamlit and DataRobot is a great way to leverage the power of machine learning to improve customer retention. The first step in building a churn prediction model is to collect and prepare your data. This typically involves gathering data on your customers' behavior, demographics, and usage patterns. Once you have your data, you can upload it to DataRobot and let the platform do the rest. After training, DataRobot provides detailed insights into the model's performance, including feature importance, model validation, and accuracy metrics. Once you have a model that you're satisfied with, you can generate predictions on new data using DataRobot's prediction API. This workflow assumes that you have already generated these predictions and saved them as a CSV file. To create a Streamlit app for churn prediction, you will need to import the necessary libraries, including Pandas, NumPy, Streamlit, Plotly, and PIL. You can then read in your prediction data and set up your Streamlit app's page configuration. The app itself should allow users to specify criteria for viewing churn scores and top churn reasons. You can accomplish this using sliders and other interactive elements. A workflow of this process for building a Streamlit app using DataRobot predictions can be found in the churn Streamlit app GitHub repository. This workflow can be adapted to present insights from other classification or regression models built in DataRobot. --- # Synthetic training data URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/synth-data.html > Learn how to generate synthetic datasets that mimic real-world data for training, validation, and testing—enabling safe data sharing and model development when access to real data is limited due to privacy or regulatory constraints. # Synthetic training data [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/dr_synth_data/dr_synth_data.ipynb) This notebook provides a powerful code-first accelerator to help generate synthetic datasets in tabular format. It enables you to create synthetic data that mimics the structure and statistical properties of real-world datasets, offering a safe and efficient way to augment existing data or create entirely new datasets. The generated synthetic datasets can be uploaded directly to AI Catalog, where they can be organized, managed, and reused for various machine learning projects. This approach is particularly useful in scenarios where access to real data is limited due to privacy, security, or regulatory constraints. By generating synthetic datasets, users can expand their training data without compromising sensitive information. These synthetic datasets can be used for model training, validation, and testing, allowing for more robust model development and better generalization on unseen data. The notebook outlines how to create a synthetic training data set in a CSV file, with name, address, phone number, company, account number, and credit score. --- # DataRobotX intro URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/exp-track-and-tune/drx-intro.html > Learn how to use the new, agile, DRX package to streamline project creation and configuration. # DataRobotX intro [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/drx_python_package_overview/drx-overview.ipynb) DataRobotX (DRX) is a collection of DataRobot extensions designed to enhance your data science experience. It has clean, scikit-learn-like syntax that makes training models, deploying models, and getting predictions from models easy. It supports any project type including multiclass, time series, multilabel, clustering, and anomaly detection. ## Project goals DRX intends to explore and prototype a programmatic DataRobot experience that is: - Declarative and simple by default - Streamlines common workflows - Uses broadly familiar syntax and verbiage where possible - Unobtrusively customizable - Allows default behaviors and configuration to be easily overridden but not at the expense of complicating the common experience - Accelerates user experimentation - Offers new abstractions and concepts for interacting with DataRobot --- # Experiment tracking and tuning URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/exp-track-and-tune/index.html > Experiment tracking and tuning accelerators that you can add to your experiment workflow. # Experiment tracking and tuning | Topic | Description | | --- | --- | | DataRobotX intro | Learn how to use the new, agile, DRX package to streamline project creation and configuration. | | MLFlow experiment tracking | Automate machine learning experimentation using DataRobot, MLFlow, and Papermill for tracking experiments and logging results. | | Blueprint hyperparameter tuning | Learn how to access, understand, and tune blueprints for both preprocessing and model hyperparameters. | --- # MLFlow experiment tracking URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/exp-track-and-tune/mlflow.html > Automate machine learning experimentation using DataRobot, MLFlow, and Papermill for tracking experiments and logging results. # MLFlow experiment tracking [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced_ml_and_api_approaches/MLFLOW_w_datarobot_experiments/) Experimentation is a mandatory activity in any machine learning developer’s day-to-day activities. For time series projects, the number of parameters and settings to tune for achieving the best model is in itself a vast search space. Many of the experiments in time series use cases are common and repeatable. Tracking these experiments and logging results is a task that needs streamlining. Manual errors and time limitations may lead to selection of suboptimal models leaving better models lost in global minima. The integration of DataRobot API, Papermill, and MLFlow automates machine learning experimentation so that is becomes easier, robust, and easy to share. As illustrated below, you will use the [orchestration notebook](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/MLFLOW_w_datarobot_experiments/orchestration_notebook.ipynb) to design and run the [experiment notebook](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/MLFLOW_w_datarobot_experiments/experiment_notebook.ipynb), with the permutations of parameters handled automatically by DataRobot. At the end of the experiments, copies of the experiment notebook will be available, with the outputs for each permutation for collaboration and reference. You can review [the dependencies](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/MLFLOW_w_datarobot_experiments/requirements.txt) for the accelerator. This accelerator covers the following activities: - Acquiring a training dataset. - Building a new DataRobot project. - Deploying a recommended model. - Scoring via Spark using DataRobot's exportable Java Scoring Code. - Scoring via DataRobot's Prediction API. - Reporting monitoring data to the MLOps agent framework in DataRobot. - Writing results back to a new table. --- # Blueprint hyperparameter tuning URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/exp-track-and-tune/opt-grid.html > Learn how to access, understand, and tune blueprints for both preprocessing and model hyperparameters. # Blueprint hyperparameter tuning [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced_ml_and_api_approaches/Hyperparameter_Optimization/README.md) In machine learning, hyperparameter tuning is the act of adjusting the "settings" (referred to as hyperparameters) in a machine learning algorithm, whether that's the learning rate for an XGBoost model or the activation function in a neural network. Many methods for doing this exist, with the simplest being a brute-force search over every feasible combination. While this requires little effort, it's extremely time-consuming as each combination requires fitting the machine learning algorithm. To this end, practitioners strive to find more efficient ways to search for the best combination of hyperparameters to use in a given prediction problem. DataRobot employs a proprietary version of pattern search for optimization not only for the machine learning algorithm's specific hyperparameters, but also the respective data preprocessing needed to fit the algorithm, with the goal of quickly producing high-performance models tailored to your dataset. While the approach used at DataRobot is sufficient in most cases, you may want to build upon the Autopilot modeling process by custom tuning methods. In this AI Accelerator, you will familiarize yourself with DataRobot's fine-tuning API calls to control DataRobot's pattern search approach as well as implement a modified brute-force grid-search for the text and categorical data pipeline and hyperparameters of an XGBoost model. This accelerator serves as an introductory learning example that other approaches can be built from. Bayesian Optimization, for example, leverages a probabilistic model to judiciously sift through the hyperparameter space to converge on an optimal solution, and will be presented next in this accelerator bundle. Note that as a best practice, it is generally best to wait until the model is in a near-finished state before searching for the best hyperparameters to use. Specifically, the following have already been finalized: - Training data (e.g., data sources) - Model validation method (e.g., group cross-validation, random cross-validation, or backtesting. How the problem is framed influences all subsequent steps, as it changes error minimization.) - Feature engineering (particularly, calculations driven by subject matter expertise) - Preprocessing and data transformations (e.g., word or character tokenizers, PCA, embeddings, normalization, etc.) - Algorithm type (e.g. GLM, tree-based, neural net) These decisions typically have a larger impact on model performance compared to adjusting a machine learning algorithm's hyperparameters (especially when using DataRobot, as the hyperparameters chosen automatically are pretty competitive). This AI Accelerator teaches you how to access, understand, and tune blueprints for both preprocessing and model hyperparameters. You'll programmatically work with DataRobot advanced tuning which you can then adapt to your other projects. You'll learn how to: - Prepare for tuning a model via the DataRobot API - Load a project and model for tuning - Set the validation type for minimizing errors - Extract model metadata - Get model performance - Review hyperparameters - Run a single advanced tuning session - Implement your own custom gridsearch for single and multiple models to evaluate --- # AI accelerators URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html > Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows. # AI accelerators AI Accelerators are designed to help speed up model experimentation, development, and production using the DataRobot API. They codify and package data science expertise in building and delivering successful machine learning projects into repeatable, code-first workflows and modular building blocks. AI Accelerators are ready right out-of-the-box, work with the notebook of your choice, and can be combined to suit your needs. AI accelerators cover a variety of topics, but primarily aim to assist you by: - Providing curated templates for workflows that use best-in-class data science techniques to help frame your business problem (e.g., customize a data visualization to your liking or rank models by ROI). - Getting you started quickly on a new AI or ML project by providing necessary insights, problem-framing, and use cases in notebooks. - Fine-tuning your projects and getting the most value from your existing data and infrastructure investments, including third-party integrations. | Section | Description | | --- | --- | | AI integrations and platforms | Templates for end-to-end API workflows between DataRobot and its ecosystem partners (Snowflake, GCP, Azure, AWS, etc.). | | LLM and GenAI applications | Applications and workflows that leverage large language models (LLM) and generative AI. | | Model building and fine-tuning | Techniques and workflows for building and tuning machine learning models. | | Data enrichment and preparation | Workflows for enhancing and preparing data for machine learning. | | Time series and specific use cases | Workflows and techniques for time series analysis and specific machine learning use cases. | | Model deployment and MLOps | Workflows for deploying models and managing machine learning operations. | | Experiment tracking and tuning | Tools and techniques for tracking experiments and tuning models. | | Model evaluation and metrics | Techniques for evaluating models and understanding their performance metrics. | | Custom model development | Workflows that integrate custom models with DataRobot. | | Advanced analytics and tools | Advanced usage of the DataRobot API that you can add to your experiment workflow. | --- # Adaptive reasoning agent URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/adaptive-agent.html > Showcases an agent's ability to **adapt its reasoning behavior** based on conversation dynamics. # Adaptive reasoning agent [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/generative_ai/adaptive-agent) This accelerator showcases an agent's ability to adapt its reasoning behavior based on conversation dynamics. The agent acts as a customer support representative for the "DataInsight Pro" analytics platform. In this example, the agent uses: - GPT-4o for complex reasoning when corrections are detected. - GPT-4o-mini for fast responses during smooth conversation flow. - GPT-4o-mini as a reflection model to analyze the last three conversation turns and detect user corrections. Specifically, the agent dynamically switches between models based on conversation analysis: | Scenario | Model used | Behavior | | --- | --- | --- | | Conversation flowing smoothly | GPT-4o-mini | Fast, direct responses | | User corrects the agent | GPT-4o | More thorough reasoning | | User rephrases question | GPT-4o | Agent recognizes confusion | | Positive feedback received | GPT-4o-mini | Returns to efficient mode | The following diagram provides an overview that illustrates the agent architecture. ``` ┌─────────────────────────────────────────────────────────────┐ │ Frontend (React) │ │ ┌─────────────┐ ┌──────────────────────────────────────┐ │ │ │ Model Mode │ │ Reflection Log Panel │ │ │ │ Indicator │ │ (shows gpt-4o-mini reasoning) │ │ │ └─────────────┘ └──────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────┘ │ ┌─────────────────────────────────────────────────────────────┐ │ Adaptive Agent │ │ 1. Store conversation history (last 3 turns) │ │ 2. Call reflection model (gpt-4o-mini) before response │ │ 3. Switch model based on correction detection │ │ - Corrections detected → GPT-4o (thorough) │ │ - Smooth conversation → GPT-4o-mini (fast) │ └─────────────────────────────────────────────────────────────┘ ``` The following script, with model adaptation noted, is applied in the accelerator: | Turn | User prompt | Expected behavior | Model | | --- | --- | --- | --- | | 1 | "What pricing plans do you offer?" | Lists 3 tiers (Starter, Pro, Enterprise) | GPT-4o-mini | | 2 | "How do I export data?" | General export explanation | GPT-4o-mini | | 3 | "No, I meant export to CSV specifically, not PDF" | Correction detected! Detailed CSV instructions | GPT-4o | | 4 | "Can I schedule automated exports?" | Thorough answer with plan requirements | GPT-4o | | 5 | "Thanks, that's helpful!" | Positive acknowledgment | GPT-4o-mini | --- # Product feedback automation URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/auto-feedback.html > Use Predictive AI models in tandem with Generative AI models to overcome the limitation of guardrails around automating the summarization and segmentation of sentiment text. # Product feedback automation [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/Product%20Innovation%20GenAI/Product%20Innovation%20using%20Generative%20AI%20and%20DataRobot%20Auto%20ML.ipynb) This accelerator shows how to use Predictive AI models in tandem with Generative AI models and overcome the limitation of guardrails around automating the summarization and segmentation of sentiment text. In a nutshell, it consumes product reviews and ratings and outputs a Design Improvement Report. The voice of the customer is traditionally captured using feedback from sales channels (which is generally reviews). While consolidating product reviews has been done in a semi-automated fashion with the help of summarization, segmentation, and subject matter expertise, it is always a time-consuming and resource intensive process that spans multiple cycles of rework. With DataRobot, you can use the generative AI solution framework to build an automated system to process review text and use Generative AI to produce targeted reports for product design and manufacturing teams. In this accelerator you will: - Extract high impact review keywords from product reviews using DataRobot. - Implement guardrails for selecting models with higher AUC to make sure keywords are robust and correlated to the review sentiment. - Generate product development recommendations for the final report. --- # Teams/Slack chatbots URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/chatbot-teams-slack.html > An accelerator for collaborative app plug-ins, such as bots for Teams and Slack. # Teams/Slack chatbots Slack and Microsoft teams support the creation of applications or bots that can interact with a DataRobot deployment. Using Slack or Teams as a front-end for a LLM-based chat application enables broad availability in an organization, providing the ability to upload documents easily as well as leverage internal knowledge stored in team channels or conversations. **Slack setup:** Obtain permissions from your IT organization to create a Slack App in your Slack organization. Then, [create the app](https://api.slack.com/apps) with the Slack API. **Teams setup:** Obtain permissions from your IT organization to upload a customized app. Then, register for an application and a ot in the [Microsoft Developer Portal](https://login.microsoftonline.com). Within the message send action in both Slack and Teams, export the user question as a prompt into your DataRobot LLM deployment, and provide the response from the REST API call to the user message in your bot. DataRobot enables you to track token usage, prompts, responses, toxicity, and other [custom metrics](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html) to monitor and govern your application internally. The following code can be adapted to the Slack or Teams messaging structure to pass a message as a query to the DataRobot LLM deployment and get a response. ``` import datarobotx as drx # configure drx LLM Deployment RAG = drx.Deployment.from_url( url=f'https://app.datarobot.com/deployments/{RAG_did}/overview' ) def make_datarobot_deployment_unstructured_predictions( data, **kwargs, ): query = json.loads(data) response = RAG.predict_unstructured(query) return json.dumps(response) def make_prediction(prompt, history=None) -> dict: data = { "prompt": prompt, } if history: data["chat_history"] = [ [entry.get("user", ""), entry.get("chatbot", "")] for entry in history ] response = make_datarobot_deployment_unstructured_predictions( json.dumps(data) ) response = json.loads(response) return response ``` --- # AI cluster labeling URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/cluster-genai.html > Use cluster insights provided by DataRobot with ChatGPT to provide business- or domain-specific labels to the clusters using OpenAI and DataRobot APIs. # AI cluster labeling [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/smart_cluster_naming/smart_cluster_labeling.ipynb) The toughest part of unsupervised learning modeling is explaining clusters to end users. DataRobot not only allows users to build clustering models, but also provides insights per cluster to help users analyze the clusters. In most scenarios, the users building the models might not have the subject matter expertise to tailor the cluster labels towards the users consuming the models. This is where you can use Generative AI models to automatically label the clusters with some prompt engineering. Because the Generative AI models have been trained on vast amounts of domain and business datasets, they can understand and label the clusters tuned for end user expertise. This AI Accelerator shows how to extract cluster insights from DataRobot models, use prompt engineering to label clusters, and then rename the clusters in the DataRobot project. You will explore the following: - Use the API to extract Cluster Insights from DataRobot unsupervised learning projects. - Use Generative AI and Prompt Engineering to consume cluster insights and create cluster labels for DataRobot clusters. - Use the API to rename DataRobot clusters. --- # Customer communication AI URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/comm-genai.html > Learn how generative AI models like GPT-3 can be used to augment predictions and provide customer friendly subject matter expert responses. # Customer communication AI [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/customer_communication_datarobot_gen_ai/effective_customer_communication_datarobot_gen_ai.ipynb) In this accelerator, you will see how you can integrate LLM-based agents like ChatGPT with DataRobot Prediction Explanations to quickly implement effective customer communication in AI-based workflows. In banking and fintech, one of the most critical communication provided to the customer is refusal of products and services, like loan application rejection. When a machine learning model predicts high loan default probability, organizations need to relay the rejection to the applicant in an effective way to sustain customer satisfaction, avoid churn, and not reduce the customer lifetime value. Effective communication also needs subject matter expertise, which becomes costly if implemented at the granularity of every application. DataRobot’s Prediction Explanations provide the context for prediction and the LLM agent provides the subject matter expertise to provide effective yet positive responses to adverse event predictions. This allows organizations to effectively communicate their AI-based decisions with their customers while improving costs and productivity related to their expert personnel. --- # Compliance agent URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/compliance-agent.html > Automatically compare your active governance policies against pre-uploaded industry standards or internal benchmarks to identify inconsistenciess. # Compliance agent [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/generative_ai/compliance-agent-mvp-main) The compliance agent, which can be created as a custom application on DataRobot, automatically compares your active governance policies against pre-uploaded industry standards or internal benchmarks to identify inconsistencies. It detects where your custom rules fall short of regulatory requirements and proposes specific recommendations to reconcile these differences. By mapping your organizational policies directly to established frameworks, it ensures your standards are both rigorous and aligned with the latest legal and industry expectations. --- # Support workflow optimization URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/customer-support.html > Use generative AI models to cater to level-one requests, allowing support teams to focus on more pressing and high-visibility requests. # Support workflow optimization [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/Optimizing%20Customer%20Support%20workflows%20with%20Gen%20AI/Optimizing%20Customer%20Support%20workflows%20with%20Gen%20AI.ipynb) Customer support is a crucial part of every organization. With the proliferation of social media, customer-centric organizations are using social media platforms to provide customer support. Irrespective of the platform, support automation has been actively pursued by organizations to improve the customer experience and loyalty while reducing the workload on support teams. While automated prioritization and routing have been solved using predictive models, automated resolution is still an active area of research. The majority of support requests are primarily requests for information (level one requests). Handling these requests and would benefit from automation. In this accelerator, generative AI models cater to level-one requests, allowing support teams to focus on more pressing and high-visibility requests. Learning from historical communications, generative AI responses can maintain the same standard of support communication that customers are used to. Because use of generative AI comes at a cost—per request, compute time, per token—costs can quickly balloon. To mitigate this problem, you can use predictive ML models for routing standard requests to the generative AI model and high-priority severity requests to human staff. --- # Data annotator app URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/data-annotator.html > Leverage the data annotator app to both label new data and label predicted data within an active learning situation after training a model with DataRobot. # Data annotator app [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/data_annotator_app/data_annotator_app.ipynb) High-quality training data is necessary for a top-notch machine learning model. But how you can quickly and easily collect labels from a team of human reviewers? One way is to stand up a Flask app for quick labeling review. This notebook will show you how to leverage the data-annotator app to both (1) label new data and (2) label predicted data within an active learning situation after training a model with DataRobot. The data-annotator app requires two inputs: - img_path : The app is currently configured for labeling images (jpg and png are both supported formats). You need to place these images within a directory and specify that path to the app. - data_path : You need to tell the app all possible labels for your images. - If you are classifying images that have not yet been labeled, you can provide a csv file with at least one column named label that contains all potential classes. See Scenario 1 below for more details. - If you are classifying images that have already been assigned labels, you can provide a csv file with at least two columns named img_path (filename of the image) and label (assigned class for the image). - If you are classifying images that have already been scored within DataRobot, please refer to Scenario 2 below for more details on how to configure the dataset. --- # AI data prep assistant URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/data-prep-assist.html > The AI data preparation assistant is a powerful tool designed to streamline and automate the data preparation process. It combines automated data quality checks with AI-powered data preparation suggestions to help data scientists and analysts prepare their datasets more efficiently. # AI data prep assistant [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/generative_ai/AI_data_prep_assistant) The AI data preparation assistant is a powerful tool designed to streamline and automate the data preparation process. It combines automated data quality checks with AI-powered data preparation suggestions to help data scientists and analysts prepare datasets more efficiently. Data preparation typically consumes 60-80% of a data scientist's time. This involves repetitive tasks like identifying quality issues, cleaning data, and transforming it into a suitable format for analysis. Manual data preparation is not only time-consuming, but prone to inconsistencies and human error. This accelerator provides the following features to assist with data preparation: - An automated data quality assessment across 12 key dimensions. - AI-powered suggestions for data preparation steps. - Automated code generation and execution for data cleaning. - Interactive visualizations of data quality issues. - A real-time data transformation preview. --- # LLM and GenAI applications URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/index.html > LLM and GenAI applications that you can add to your experiment workflow. # LLM and GenAI applications | Topic | Description | | --- | --- | | Adaptive agent | Showcases an agent's ability to adapt its reasoning behavior based on conversation dynamics. | | Direct Preference Optimization for reinforcement learning | Automates the process of fine-tuning an LLM using Direct Preference Optimization (DPO) and then deploying that model to DataRobot. | | Product feedback automation | Use Predictive AI models in tandem with Generative AI models to overcome the limitation of guardrails around automating the summarization and segmentation of sentiment text. | | Teams/Slack chatbots | Build collaborative app plug-ins, such as bots for Teams and Slack. | | AI cluster labeling | Use cluster insights provided by DataRobot with ChatGPT to provide business- or domain-specific labels to the clusters using OpenAI and DataRobot APIs. | | Customer communication AI | How generative AI models, like GPT-3, can be used to augment predictions and provide customer-friendly subject matter expert responses. | | Support workflow optimization | Use generative AI models to cater to level-one requests, allowing support teams to focus on more pressing and high-visibility requests. | | Data annotator app | Leverage the data annotator app to both label new data and label predicted data within an active learning situation after training a model with DataRobot. | | AI data prep assistant | Use the AI data preparation assistant to streamline and automate the data preparation process. | | JITR bot responses | Create a deployment to provide context-aware answers 'on the fly' using "Just In Time Retrieval" (JITR). | | PDF RAG with LLM | Use an LLM as an OCR tool to extract all the text, table, and graph data from a PDF, then build a RAG and playground chat on DataRobot. | | Healthcare conversation agent | Use Retrieval Augmented Generation to build a conversational agent for Healthcare professionals. | | Teams GenAI integration | With DataRobot's Generative AI offerings, organizations can deploy chatbots without the need for an additional front-end or consumption layers. | | Vector chunk visualization | Implement a Streamlit application to gain insights from a vector database of chunks. | | XoT implementation | Implement and evaluate Everything of Thoughts (XoT) in DataRobot, an approach to make generative AI "think like humans." | | Zero-shot error analysis | Use zero-shot text classification with large language models (LLMs), focusing on its application in error analysis of supervised text classification models. | | Compliance agent | Automatically compare your active governance policies against pre-uploaded industry standards or internal benchmarks to identify inconsistencies. | --- # JITR Bot responses URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/jitr-bot.html > Create a deployment to provide context-aware answers 'on the fly' using # JITR bot responses [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/JITR-bot/JITR-bot.ipynb) Retrieval Augmented Generation (RAG) has become an industry standard method for interfacing with large language models by making them 'context-aware'. However, there are a number of situations where a text generation problem is not solved by interacting with large vector database containing many documents. These problems require context but where the context is not known before query time and is often unrelated to existing vector stores. Usually, they are questions about single documents where desirable behavior is to allow the document to be specified at runtime. One application that does this fairly well is DataChad. DataChad works fine for its purpose as a localized web application, but it doesn't generalize. In other words, there is not a good way to interact with the application without opening a browser, uploading whatever files you want to analyze, and hitting a run button. Rather than follow the standard RAG approach of querying an existing vector store, this accelerator creates a deployment that accepts a file as an argument so that it can provide context-aware answers 'on the fly'. DataRobot calls this approach "Just In Time Retrieval", or JITR for short. DataRobot created a Slackbot that uses this deployment as the backend to answer questions when a user uploads a PDF, called the "JITR Bot". --- # PDF RAG with LLM URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/llm-multimodal-pdf.html > Use an LLM as an OCR tool to extract all the text, table, and graph data from a PDF, then build a RAG and playground chat on DataRobot. # PDF RAG with LLM [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/generative_ai/LLM%20Multimodal%20PDF%20RAG) This accelerator introduces an approach to use an LLM as an OCR tool. Supply a PDF and split it into multiple images, then extract the text, table, and graph data from the PDF with an LLM, saving them as Markdown files. Use those files to build a RAG and then create a chat in the DataRobot playground. --- # Healthcare conversation agent URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/med-research.html > Use Retrieval Augmented Generation to build a conversational agent for Healthcare professionals. # Healthcare conversation agent [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/Medical%20Research%20Agent/Medical%20Research%20Conversational%20Agent.ipynb) This accelerator shows how you can use [Retrieval Augmented Generation](https://arxiv.org/abs/2005.11401) to build a conversational agent for healthcare professionals. Healthcare professionals have to constantly stay informed of the latest research in not only their own specialization but also in complimentary fields. This means they have to constantly consume the latest research from trusted sources. Because new research papers are published at an astonishing rate, it is important to filter out irrelevant and untrusted research and focus on trusted research that is important to healthcare in this agent's knowledge base. As this agent's intended use is in healthcare, it is of paramount importance that the agent operates with in the confines of the knowledge base without hallucinations. With DataRobot, this accelerator shows how to use predictive modeling to identify trusted research and then build a knowledge base for the conversational agent using DataRobot's [generative AI offering](https://www.datarobot.com/platform/generative-ai/). This accelerator illustrates the following; - Use predictive models to classify text files - Create a vector store out of research paper abstracts - Use Retrieval Augmented Generation with a generative AI model - Deploy a Generative AI model to the DataRobot platform --- # Teams GenAI integration URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/ms-teams.html > With DataRobot's Generative AI offerings, organizations can deploy chatbots without the need for an additional front-end or consumption layers. # Teams GenAI integration [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/ecosystem_integration_templates/teams_datarobot) Microsoft Teams offers workspace chat and video conferencing, file storage, and application integration to organizations. Workspace chat feature allows you to interact with other users and bots in their day-to-day activities. This feature is useful for deploying Generative AI agents to improve employee productivity. With DataRobot's Generative AI offerings, organizations can deploy chatbots without the need for an additional front-end or consumption layers. ## How it works Most messenger/communication apps support bots. A bot is a program that interacts with the users of the messenger application by ingesting the user message and providing responses. Bot can be static or dynamic depending on the logic encoded. A bot in most cases is a service exposing Rest Endpoints which receive user text and respond back with text. Instead of developers starting from scratch, Microsoft provides an SDK which can be used as building blocks or boilerplate. This SDK supports different languages including python. The code demonstrated here uses this code as boilerplate. --- # Direct Preference Optimization for reinforcement learning URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/rlhf-agent.html > Showcases an agent's ability to **adapt its reasoning behavior** based on conversation dynamics. # Direct Preference Optimization for reinforcement learning [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/Direct%20Preference%20Optimization/DPO.ipynb) [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) is a machine learning technique used to train AI agents, providing a simpler, more stable alternative to traditional Reinforcement Learning from Human Feedback (RLHF). It helps align AI models with human preferences in ways that are hard to capture through pre-training alone, making models more helpful, harmless, and honest. It can improve things like following instructions accurately, avoiding harmful content, and producing more useful responses. Generally, the technique works as follows: 1. A model is trained on large amounts of text data to predict what comes next in sequences (pre-training). 2. Humans evaluate and rank the model prompt outputs. 3. Based on the feedback, a different model learns to predict the preferred response. 4. The main model is fine-tuned using reinforcement learning algorithms to generate higher-scoring outputs. This accelerator automates the process of fine-tuning an LLM using Direct Preference Optimization (DPO) and then deploying that model to DataRobot. Essentially, it takes a base model, teaches it to prefer specific types of responses based on a provided dataset, and prepares it for production use. Specific accelerator actions: Data preparation actions - Downloads a specific preference dataset from the DataRobot Registry. - Uses the Hugging Face datasets library to load the CSV, which typically contains three columns: a prompt, a chosen (preferred) response, and a rejected response. Model training (DPO) - Initializes theQwen2-0.5B-Instructmodel inbfloat16precision to save memory. - Applies DPOTrainer from the TRL (Transformer Reinforcement Learning) library. This is a modern alternative to RLHF that aligns models to human preferences without needing a separate reward model. - For hardware efficiency, the script is configured for FSDP (Fully Sharded Data Parallel). This allows the model to be trained across multiple GPUs by "sharding" the model weights, and it uses Gradient Checkpointing to further reduce VRAM usage. Model consolidation and saving - Weight gathering, which applies a specialized routine to "gather" the FSDP shards back into a single, cohesive model file. - Ensures that only the "Main Process" (Rank 0) handles the final file writing to avoid data corruption or redundant saves. DataRobot deployment - Once the model is saved locally, the script uses the DataRobot API to create a custom model, making the model ready for deployment and accessible via the REST API. --- # Tensile - Enhanced agent reliability through automated test URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/tensile-agent-reliability.html > Use DataRobot's test-driven development framework to improve the reliability, task performance, and policy adherence of AI agents with trajectory logging, replay, and contextual hints. # Tensile: Enhanced agent reliability through automated test Building and maintaining reliable AI agents is challenging. Agents must stay on-task, follow policies, and recover from failures in a way you can measure and improve. This accelerator introduces Tensile, a test-driven development framework in DataRobot for improving the reliability, task performance, and policy adherence of AI agents through automated test synthesis and trajectory analysis. Tensile helps you instrument agents, capture execution trajectories, and turn successes and failures into repeatable tests. You can then evaluate and replay runs, compare system prompt changes, and use clustering and contextual hint injection to remediate issues iteratively. In this accelerator you will: - Instrument an agent with TrajectoryLogger to record execution trajectories. - Analyze trajectories to identify testable moments (successes and failures). - Evaluate and replay runs to quantify improvements and compare system prompt changes. - Configure Tensile with the DataRobot LLM gateway. - Use clustering (Dash app and ClusteringHintInjector ) to explore issues and inject contextual hints. - Apply the Trajectory Analyzer workflow with ProgrammaticHintInjector for iterative improvement. ## Prerequisites Before running the accelerator, ensure you have: - Tensile installed (see the quickstart below). - A config.yaml with LLM and trajectory settings. - For DataRobot: set DATAROBOT_API_TOKEN in test.env (or in your environment). Optionally set DATAROBOT_LLM_GATEWAY_URL and DATAROBOT_TRACE_CONTEXT for observability. Quickstart from the project root: ``` uv venv --python 3.13 uv sync; pre-commit install uv pip install -e . cp config.yaml.sample config.yaml # And fill in credentials tensile # show help ``` ## Instrument an agent for trajectory logging Use `TrajectoryLogger` as the transport for an `httpx` client, then pass that client into your OpenAI-compatible agent. Trajectories are written to `/` (with `trajectory_dir` in `config.yaml`). ``` from tensile.logging import TrajectoryLogger http_client = httpx.AsyncClient( transport=TrajectoryLogger( httpx.AsyncHTTPTransport(), trajectory_subdir= | None ) ) client = AsyncOpenAI( api_key=api_key, base_url=f"{endpoint_url}/v1", http_client=http_client, ) ``` ## Analyze trajectories and evaluate testable moments Run the analysis pipeline (outputs to `analysis_output/` by default): ``` tensile analyze ``` To run testable moments manually (for example, 10 times): ``` tensile test -n 10 ``` ## Replay trajectories Replay steps in a trajectory to collect new LLM responses, spot flukes, or compare behavior after system prompt changes. Omit `output_path` to write to `.replay.jsonl`. ``` tensile replay [output_path] tensile replay --num-replays 5 tensile replay --num-replays 3 --max-concurrency 10 tensile replay --num-replays 3 --system-prompt-path # Examples tensile replay tensile replay -n 5 tensile replay output/replay.jsonl -n 3 ``` ## Configuration ### DataRobot LLM gateway Add the following to your `config.yaml` to use the DataRobot LLM gateway: ``` # config.yaml llm: name: "" # e.g., vertex_ai/gemini-3-pro-preview api_base: "" api_key: "" ``` ## Clustering ### Clustering app Start the Dash app to explore and cluster analysis outputs in the browser. It requires the `dev` dependency group; with `uv`, run: ``` task dev-env task apps:clustering ``` ### Clustering-based hint injection Use `ClusteringHintInjector` with `analysis_dirs` and `trajectories_dirs` pointing at your Tensile outputs and a report store ( `InMemoryReportStore` or `FileSystemReportStore`). Example: ``` from pathlib import Path import httpx from openai import AsyncOpenAI from tensile.logging.hint_injector import ( ClusteringHintConfig, ClusteringHintInjector, InMemoryReportStore, SentenceTransformersEmbeddingBackend, ) base_transport = httpx.AsyncHTTPTransport() embedding_backend = SentenceTransformersEmbeddingBackend( model_name="", ) report_store = InMemoryReportStore() config = ClusteringHintConfig( analysis_dirs=[Path("analysis_output")], trajectories_dirs=[Path("trajectories")], ) hinting_transport = ClusteringHintInjector( base_transport, embedding_backend=embedding_backend, report_store=report_store, config=config, ) http_client = httpx.AsyncClient(transport=hinting_transport) client = AsyncOpenAI( api_key=api_key, base_url=f"{endpoint_url}/v1", http_client=http_client, ) ``` ## Trajectory Analyzer workflow 1. Instrument the agent with ProgrammaticHintInjector and TrajectoryLogger : ``` from tensile.logging import TrajectoryLogger from tensile.logging.hint_injector.programmatic_hint_injector import ProgrammaticHintInjector http_client = httpx.AsyncClient( transport=ProgrammaticHintInjector( wrapped=TrajectoryLogger( wrapped=httpx.AsyncHTTPTransport(), trajectory_subdir=, ), hint_file_path=None, ) ) # It's recommended to start with hint_file_path=None until a hint file is generated by the analyzer ``` 1. Run the agent to produce a trajectory. 2. Run tensile analyze . When analysis finishes, copy the generated hints.json , updated system prompt, and/or updated tool definitions back into your agent. 3. Set hint_file_path to the path of the hints.json file and run the agent again to produce a new trajectory. 4. Run tensile analyze --hints-file to re-analyze with the new hints. 5. Repeat until behavior converges. --- # Vector chunk visualization URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/vectorstore-chunk.html > Implement a Streamlit application to gain insights from a vector database of chunks. # Vector chunk visualization [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/generative_ai/vectorstore-chunk-visualization) This AI Accelerator demonstrates how to implement a Streamlit application to gain insights from a vector database of chunks. A RAG developer can compare similarity between chunks and remove unnecessary data during RAG development. In this workflow you will build a Streamlit application, build a vectorestore, then build and analyze summaries of chunks and clusters in the data. --- # XoT implementation URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/xot-implementation.html > Implement and evaluate Everything of Thoughts (XoT) in DataRobot, an approach to make generative AI # XoT implementation [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/generative_ai/XoT%20Evaluation) Implement and evaluate Everything of Thoughts (XoT) in DataRobot, an approach to make generative AI "think like humans." In the world of generative AI, various methods (called thought generation) are researched to help AI acquire more human-like "thinking patterns." In particular, XoT aims to produce more accurate answers by teaching generative AI the "thinking process." There are two main methods to achieve XoT: 1. Chain-of-Thought (CoT): A method of thinking by connecting multiple thoughts like a chain and reasoning through them 2. Retrieval Augmented Thought Tree (RATT): A method of thinking by expanding multiple possibilities like tree branches and retrieving relevant information from the external knowledge base. This accelerator explains how to implement these methods. Specifically, it introduces how to set up and compare three types of LLM prompts: direct, Chain-of-Thought, and RATT. "Direct" referring to the well-known "you are a helpful assistant." The accelerator also explains how to conduct performance evaluations using sample datasets, comparing the accuracy and efficiency of each method, and analyze using multiple evaluation metrics. --- # Zero-shot error analysis URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/llm-and-genai-apps/zero-shot.html > Use zero-shot text classification with large language models (LLMs), focusing on its application in error analysis of supervised text classification models. # Zero-shot error analysis [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/zero_shot_LMM_error_analysis_NLP/Zero%20Shot%20Text%20Classification%20for%20Error%20Analysis.ipynb) This AI Accelerator which offers a deep dive into the utilization of zero-shot text classification for error analysis in machine learning models. This educational resource is an invaluable asset for those interested in enhancing their understanding and proficiency in the field of machine learning. Building on your existing knowledge and experience with the DataRobot automated machine learning platform, this notebook demonstrates the development of a text classification model. From there, turn your focus towards a crucial, yet sometimes challenging aspect of machine learning - error analysis. Understanding why a supervised machine learning model incorrectly classifies certain examples can be a challenging task. The newly released notebook introduces a novel methodology for identifying and understanding these errors using zero-shot text classification. In this accelerator, make use of three different zero-shot classification methods: Natural Language Inference (NLI), Embedding, and Conversational AI. The distinct capabilities of each method contribute to a comprehensive and enlightening error analysis process. Detailed within the notebook is a thorough explanation of the error analysis procedure. So regardless of your proficiency level in machine learning, the content is structured to cater to a wide range of readers. The application of zero-shot text classification to error analysis could be a significant enhancement to your machine learning practice, particularly with DataRobot. --- # Feature Discovery workflow URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/afd-e2e.html > Use a repeatable framework for end-to-end production machine learning. It includes time-aware feature engineering across multiple tables, training dataset creation, model development, and production deployment. # Feature Discovery workflow [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/Automated_Feature_Discovery_template_ML_pipeline/End-to-end%20Automated%20Feature%20Discovery%20Production%20Workflow.ipynb) This accelerator outlines a repeatable framework for end-to-end production machine learning. It includes time-aware feature engineering across multiple tables, training dataset creation, model development, and production deployment. It is common to build training data from multiple sources, but this process can be time consuming and error prone, especially when you need to create many time-aware features. - Event based data is present in every vertical. For example, customer transactions in retail or banking, medical visits, or production line data in manufacturing. - Summarizing this information at the parent (Entity) level is necessary for most classification and regression use cases. For example, if you are predicting fraud, churn, or propensity to purchase something, you will likely want summary statistics of a customers transactions over a historical window. This raises many practical considerations as a data scientist: How far back in time is relevant for training? Within that training period, which windows are appropriate for features? 30 days? 15? 7? Further, which datasets and variables should you consider for feature engineering? Answering these conceptual questions requires domain expertise or interaction with business SMEs. In practice, especially at the MVP stage, it is common to limit the feature space you explore to what's been created previously or add a few new ideas from domain expertise. - Feature stores can be helpful to quickly try features which were useful in a previous use case, but it is a strong assumption that previously generated lagged features will adapt well across all future use cases. - There are almost always important interactions you haven't evaluated or thought of. Multiple tactical challenges arise as well. Some of the more common ones are: - Time formats are inconsistent between datasets (e.g., minutes vs. days), and need to be handled correctly to avoid target leakage. - Encoding text and categorical data aggregates over varying time horizons across tables is generally painful and prone to error. - Creating a hardened data pipeline for production can take weeks depending on the complexity. - A subtle wrinkle is that short and long-term effects of data matter, particularly with customers/patients/humans, and those effects change over time. It's hard to know apriori which lagged features to create. When data drifts and behavior changes, you very well may need entirely new features post-deployment, and the process starts all over. All of these challenges inject risk into your MVP process. The best case scenario is historical features capture signal in your new use case, and further exploration to new datasets is limited when the model is "good enough". The worst case scenario is you determine the use case isn't worth pursuing, as your features don't capture the new signal. You often end up in the middle, struggling to know how to improve a model you are sure can be better. What if you could radically collapse the cycle time to explore and discover features across any relevant dataset? This notebook provides a template to: - Load data into Snowflake and register with DataRobot's AI Catalog. - Configure and build time aware features across multiple historical time-windows and datasets using Snowflake (applicable to any database). - Build and evaluate multiple feature engineering approaches and algorithms for all data types. - Extract insights and identify the best feature engineering and modeling pipeline. - Test predictions locally. - Deploy the best performing model and all feature engineering in a Docker container, and expose a REST API. - Score from Snowflake and write predictions back to Snowflake. --- # Causal AI for readmission URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/causal-ai.html > Work with data recording hospital readmission outcomes for diabetes patients to evaluate the causal relationship between the diabetes patients' medication status and their subsequent chance of being readmitted to the hospital. # Causal AI for readmission [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/Causal_AI/Causal_AI.ipynb) Predictive AI models are a powerful tool for uncovering subtle predictive relationships between observed variables. But sometimes, you need to draw conclusions about the causal relationship between two variables, not just the observed correlation. To achieve this "Causal AI", you can use the DataRobot platform and a quasi-experimental technique called "Inverse Propensity of Treatment Weighting". This notebook will apply this technique to data on diabetes hospital patient readmission. This notebook outlines how to: - Prepare data for a Propensity of Treatment model - Fit a Propensity of Treatment model with DataRobot - Calculate Inverse Propensity of Treatment Weights - Evaluate the causal relationship using Inverse Propensity of Treatment Weighting - Understanding Inverse Propensity of Treatment Weighting In this notebook, you will be working with data recording hospital readmission outcomes for diabetes patients. You will evaluate the causal relationship between the diabetes patients' medication status and their subsequent chance of being readmitted to the hospital. To evaluate this causal relationship experimentally, you would have to randomly assign patients to the treatment group (those receiving medication) vs. not, and then follow those patients to see whether they get readmitted to the hospital or not. But in the scenario for this notebook, you don't have experimental data! You only have observational data. In other words, some patients walk in taking medication, others don't. You have not controlled the assignment of the "treatment" condition (medication) to the subjects of the study. So while you could use predictive modeling to understand if the medication status of patients walking in is predictive of later readmission, you can't directly use predictive models to make conclusions about whether the medication has a causal effect on readmission. In this scenario, you can use a "quasi-experimental" technique; this is a set of techniques for approximating experimental setups without actually having a true experiment. Specifically, you can use a technique called "Inverse Propensity of Treatment Weighting". Inverse Propensity of Treatment Weighting consists of the following steps: 1. Fit a predictive model to estimate the probability for each study participant being assigned to the treatment group (their "propensity of treatment"). 2. Calculate a special weight for each participant based on their propensity of treatment (the "inverse propensity of treatment weight"), which will adjust the treatment and control groups to become more similar to each other in terms of the observed confounding variables. 3. Evaluate the causal relationship between the treatment and the outcome using the adjusted/weighted populations (pseudopopulations). While this technique is not as valid as the gold standard of a randomized controlled trial, it can bring you a lot closer to obtaining comparable treatment and control groups from which to judge a causal relationship. --- # Custom lift charts URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/custom-lift-chart.html > Leverage popular Python packages with DataRobot's Python client to recreate and augment DataRobot's lift chart visualization. # Custom lift charts [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/customizing_lift_charts/customizing_lift_charts.ipynb) Ever wanted to plot more than 60 bins in DataRobot's lift chart? Ever needed to present this graphic with a specific color palette? Ever required to display more information in the chart due to regulatory reasons? In this AI Accelerator, leverage popular Python packages with DataRobot's Python client to recreate and augment DataRobot's lift chart visualization. These customizations allow you to: - Plot more than 60 bins in DataRobot's lift chart. - Present this lift chart visualizations with a specific color palette. - Display more information in the chart. The steps demonstrated in the accompanying notebook are: 1. Connect to DataRobot 2. Create a DataRobot project 3. Run a single blueprint from the repository 4. Obtain predictions and actuals 5. Recreate DataRobot’s lift chart 6. Add customization to the lift chart --- # Fantasy baseball predictions URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/fantasy-baseball.html > Leverage the DataRobot API to quickly build multiple models that work together to predict common fantasy baseball metrics for each player in the upcoming season. # Fantasy baseball predictions [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/model_factory_selfjoin_fantasy_baseball/fantasy_baseball_predictions_model_factory.ipynb) In this accelerator, you will leverage the DataRobot API to quickly build multiple models that work together to predict common fantasy baseball metrics for each player in the upcoming season. Millions of people play fantasy baseball every year—more than 15 million in the United States and Canada in 2022, according to the Fantasy Sports and Gaming Association. It is the second-most popular fantasy sport in the US and Canada, behind American football, and like most fantasy sports, fantasy team managers typically select players for their team through classic drafts or auction-style processes. Choosing a team of baseball players based on who is your favorite—or even based on last year's performance without any regard for regression to the mean—is likely to field a relatively weak team year in and year out. Baseball is one of the most well documented of all sports, statistics-wise, and with the wealth of data available you can derive a better estimate of each player's true talent level and their likely performance in the coming year using machine learning. This allows for better drafting, helping to avoid overpaying for players coming off of "career" seasons while identifying undervalued players that can effectively fill out a quality team in later rounds of the draft (or for fewer auction dollars). When drafting players for fantasy baseball, you must make decisions based on the player's performance over their career to date, as well as effects like aging, changing positions, changing teams, etc. You will leverage DataRobot to produce better predictions of the players' performances in the next year based on what they have done in prior years, and from patterns you can learn from similar players in the past. ## Learning objectives - How to query a rich dataset of MLB players' statistics from the Fangraphs' API. - How to set up a project with automated time-aware feature engineering (Automated Feature Discovery). - How to update the player data in a Feature Discovery project (i.e., secondary data) to re-predict without building a new project. - How to loop over a project creation function to build many DataRobot projects automatically--in this case, to build one project/model for each of the five common fantasy baseball stats: batting average (AVG), home runs (HR), runs (R), runs batted in (RBI), and stolen bases (SB), though you could repeat the same process on pitching statistics, as well. ## Retrieve baseball data This notebook uses Python's pybaseball module to get data from player-seasons between 2012 and 2023. In this workflow, the machine learning algorithm learns patterns from pre-COVID era data, as well as data from 2020 and 2021. This data should help show how well the top model is able to learn how to work around the shortened 2020 season. Fangraphs provides more than 300 features about hitters each season, from the most superficial statistics like batting average (AVG) and home run counts (HR), to the most in-depth statistics like expected weighted on-base average (xWOBA) and barrel contact percentage (Barrel%). You will use DataRobot to sift through many of these feature to find the ones that best signal future performance. --- # Fine-tune & deploy LLMs URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/finetune-codespace.html > Review an end-to-end workflow for fine-tuning and deployment an LLM using features of Hugging Face, Weights and Biases (W&B), and DataRobot. # Fine-tune & deploy LLMs [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/fine-tuning-in-codespaces/Fine-tuning%20in%20DataRobot%20Codespaces.ipynb) This accelerator illustrates an end-to-end workflow for fine-tuning and deployment an LLM using features of Huggingface, Weights and Biases (W&B), and DataRobot. Specifically, the accelerator walks you through the following steps: - Downloading an LLM from the Hugging Face Hub. - Acquiring a dataset from Hugging Face. - Leveraging DataRobot codespaces, notebooks, and GPU resources to facilitate fine-tuning via Hugging Face and W&B. - Leveraging DataRobot MLOps to register and deploy a model as an inference endpoint. - Leveraging DataRobot's RAG playground to evaluate and compare your fine-tuned LLM against available LLMs. The accelerator uses Hugging Face as a common example that you can modify based on your needs. It uses Weights and Biases to help keep track of your experiments. It is helpful to visualize training loss in real time as well as log prompt results for review during fine-tuning. Also, if you decide to do some hyperparameter tuning, you can do so with W&B Sweeps. ## Considerations This accelerator has been tested in a DataRobot codespace with a GPU resource bundle. requirement.txt has a pinned version of the required libraries. Notebooks images in DataRobot have limited writable space (about 20GB). Therefore, checkpointing models during finetuning is not encouraged, and if you do checkpoint, limit it. This accelerator opts to fine-tune llama-3.2-1B since it is on the smaller side. Use Weights and Biases to track the experiment. The W&B API Key is available in `.env.` If you don't have a W&B account, get one at the [W&B sign up page](https://www.wandb.ai/). --- # Hyperparameter optimization URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/hyperopt.html > Build on the native DataRobot hyperparameter tuning by integrating the hyperopt module into DataRobot workflows. # Hyperparameter optimization [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced_ml_and_api_approaches/Hyperparameter_Optimization) In machine learning, hyperparameter tuning is the act of adjusting the "settings" (referred to as hyperparameters) in a machine learning blueprint or pipeline. For example, adjustable hyperparameters might be the learning rate for an XGBoost model, the activation function in a neural network, or grouping limits in one-hot encoding for categorical features. Many methods for doing this exist, with the simplest being a brute force search over a wide range of possible parameter value combinations. While this requires little effort for the human, it's extremely time-consuming for the machine, as each distinct combination of hyperparameter values requires fitting the blueprint again. To this end, practitioners strive to find more efficient ways to search for the best combination of hyperparameters. This AI Accelerator shows how to leverage open source optimization modules to further tune parameters in DataRobot blueprints. Build on the [native DataRobot hyperparameter tuning functionality](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/Hyperparameter_Optimization/HyperParam_Opt_Core_Concepts.ipynb) by integrating the hyperopt module into the API workflow. The hyperopt module allows for a particular Bayesian approach to parameter tuning known as the Tree-structured Parzen Estimator (TPE), though more generally this accelerator should be seen as an example of how to leverage DataRobot's API to integrate with any parameter tuning optimization framework. You will explore the following: - Identify specific blueprints from a DataRobot project and review their hyperparameters through the API. - Define a search space and optimization algorithm with hyperopt. - Tune hyperparameters with hyperopt's Tree-structured Parzen Estimator (Bayesian) approach. --- # Image data with Databricks URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/image-databricks.html > Import image files using Spark and prepare them into a data frame suitable for ingest into DataRobot. # Image data with Databricks [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/image_dataprep_classification_databricks/Image%20Data%20Preparation.ipynb) Visual AI allows you to leverage images in your models just like any other type of data. In this accelerator, you will import image files using Spark and prepare them into a data frame suitable for ingest into DataRobot. Then you will leverage DataRobot through code to rapidly train and deploy a powerful multiclass image classifier. While there are other methods of ingesting image data into DataRobot, in this notebook you will encode the image data directly into the data frame using base64 encoding. This methodology allows you to keep all of the relevant data in a single data frame, and works well for a Databricks environment. This technique also extends widely to a wide variety of multimodal datasets. Dive in to go from Databricks image data to a deployed classifier. --- # Model building and fine-tuning URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/index.html > Model building and fine-tuning accelerators that you can add to your experiment workflow. # Model building and fine-tuning | Topic | Description | | --- | --- | | Feature Discovery workflow | Use a repeatable framework for end-to-end production machine learning. It includes time-aware feature engineering across multiple tables, training dataset creation, model development, and production deployment. | | Causal AI for readmission | Work with data recording hospital readmission outcomes for diabetes patients to evaluate the causal relationship between the diabetes patients' medication status and their subsequent chance of being readmitted to the hospital. | | Custom lift charts | Leverage popular Python packages with DataRobot's Python client to recreate and augment the lift chart visualization in DataRobot. | | Fantasy baseball predictions | Leverage the DataRobot API to quickly build multiple models that work together to predict common fantasy baseball metrics for each player in the upcoming season. | | Fine-tune & deploy LLMs | Review an end-to-end workflow for fine-tuning and deploying an LLM using features of Hugging Face, Weights and Biases (W&B), and DataRobot. | | Hyperparameter optimization | Build on the native DataRobot hyperparameter tuning by integrating the hyperopt module into DataRobot workflows. | | Image data with Databricks | Import image files using Spark and prepare them into a data frame suitable for ingest into DataRobot. | | Production ML with tables | Explore a repeatable framework for building production ML pipelines that integrate and engineer features from multiple tables. | | Predictions in mobile apps | Learn how to incorporate DataRobot predictions into a mobile app. | | Order quantity prediction | Build a model to improve decisions about initial order quantities using future product details and product sketches. | | Model factory with Python | Learn how to use the Python threading library to build a model factory. | | Symbolic regression (Eureqa) | Apply symbolic regression to your dataset in the form of the Eureqa algorithm. | | Model marketing attribution | Use DataRobot to streamline marketing attribution use cases. | --- # Production ML with tables URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/ml-tables.html > Review an AI accelerator that uses a repeatable framework for a production pipeline from multiple tables. # Production ML with tables [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/Automated_Feature_Discovery_template_ML_pipeline/End-to-end%20Automated%20Feature%20Discovery%20Production%20Workflow.ipynb) We've all been there: data for customer transactions are in one table, but the customer membership history is in another. Or, you have sensor-level data at the sub-second level in one table, machine errors in another table, and production demand in yet another table, all at different time frequencies. Electronic Medical Records (EMRs) are another common instance of this challenge. You have a use case for your business you want to explore, so you build a v0 dataset and use simple aggregations from before, perhaps in a feature store. But moving past v0 is hard. The reality is, the hypothesis space of relevant features explodes when considering multiple data sources with multiple data types in them. By dynamically exploring the feature space across tables, you minimize the risk of missing signal by feature omission and further reduce the burden of a priori knowledge of all possible relevant features. Event-based data is present in every vertical and is becoming more ubiquitous across industries. Building the right features can drastically improve performance. However, understanding which joins and time horizons are best suited to your data is challenging, and also time-consuming and error-prone to explore. In this accelerator, you'll find a repeatable framework for a production pipeline from multiple tables. This code uses Snowflake as a data source, but it can be extended to any supported database. Specifically, the accelerator provides a template to: - Build time-aware features across multiple historical time-windows and datasets using DataRobot and multiple tables in Snowflake (or any database). - Build and evaluate multiple feature engineering approaches and algorithms for all data types. - Extract insights and identify the best feature engineering and modeling pipeline. - Test predictions locally. - Deploy the best-performing model and all data preprocessing/feature engineering in a Docker container, and expose a REST API. - Score from Snowflake and write predictions back to Snowflake. --- # Model marketing attribution URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/model-market.html > Use DataRobot to streamline marketing attribution use cases. # Model marketing attribution [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/marketing_mix_modeling) Marketing attribution can be defined as the process of assigning credit to certain marketing activities (e.g., spending) as they contribute towards a desired goal (e.g., increases in revenue). Understanding how much these marketing activities contribute enables stakeholders to make better budget allocation decisions. However, in today’s world, marketing teams have many avenues to spend their marketing dollars, and it can be a daunting task to figure out which avenues are playing the largest role towards optimizing the key performance indicator (KPI) of interest. Model-based approaches can help sift through all this information in a rigorous and efficient way, leading to faster decision-making. One of the most common ways to understand the attribution from each marketing channel of interest is to do live A/B testing (e.g., incrementality testing). That is, expose one group to marketing spend and another very similar group in nature to no marketing spend. Assuming all the sources of variability between the groups are reasonably satiated, you can measure a KPI (e.g., revenue) for both groups over a window of time and take the difference between them. This difference gives you information as to the lift that your marketing efforts had during the experimental window. While the benefit of this method is deriving attribution via live-testing, some disadvantages include: - A/B testing can be resource and time-intensive (e.g., you need many weeks to get a good picture of the differences, especially in different times of the year). - Derived insights can only be gleaned from the experimental window (i.e., if you’re testing only for 10 weeks, you only have 10 weeks worth of data from the procedure). - Many factors can influence business results such as competition, economy, local events, etc., so it can be difficult to truly isolate the incremental lift from marketing (i.e., experimental design is hard to do outside of a laboratory). ### Model-based approaches In order to emulate the aforementioned A/B testing procedure (and assuming revenue is your KPI of interest), you need to be able to know the difference in revenue between when you expose your customer base to marketing and when you don't. While it’s possible to have days historically where you didn’t have any marketing spend, it’s more likely that you had some level of marketing spend activity each day. Hence, it’s impossible to go back in time to understand what revenue would have been without any marketing spend on each day in the past. This is where model-based approaches can help. For the purposes of this accelerator, you can think of a “model” as “a set of steps a computer takes to find patterns or make decisions.” Different types of models exist, but the focus here will be on machine learning models, which are primarily used for predicting information you don’t know (such as revenue associated with no marketing spend). Specifically for marketing attribution, the steps may include: 1. Acquire historical revenue and spending data at the desired granularity (weekly, monthly, etc.). 2. Build a machine learning model that tries to learn how to predict historical revenue based on your historical spend (and other factors that can help explain revenue, like holidays, promotions, economic indicators, etc.). 3. Once your model has learned all it can from your historical data, you can begin using it to answer what-if questions like, “what would my revenue be if I increase spending in this marketing channel?” or “what would my revenue have been if I didn’t do any marketing spend whatsoever on this day?” 4. After you estimate what revenue would have been with no marketing spend each day in your historical data using the model, you can compare this value to the actual revenue on the given day to understand the total lift in dollars due to your marketing efforts. 5. Once the total lift is estimated, you can allocate this out to the individual marketing channels with the help of explanatory tools like Shapley values . Having this machine learning model gives you the ability to help answer questions you normally wouldn’t be able to know about our historical data. It can also be used to understand future what-if scenarios too (e.g., "if I applied this allocation of spend across my marketing channels on this day in the future, what would my predicted revenue be?") and budget optimization (e.g., "out of the possible allocation strategies I have, which one will increase my revenue the most?"). ### How DataRobot helps DataRobot can help with marketing attribution use cases by: 1. Making building machine learning models incredibly easy. 2. Providing a Python API client that makes building custom workflows repeatable and scalable. 3. Having a suite of interpretability tools for each machine learning model (including SHAPley values). 4. Having connectors to databases such as Snowflake. 5. Having mechanisms to create straightforward applications to consume results (or that can write results back to the database for consumption of an internally-built dashboard) These are just a few examples how DataRobot can streamline this process. Ultimately, a variety of ways exist for tackling marketing attribution and the process described below is just one way DataRobot has helped customers in the past. It is by no means the only way to do marketing attribution use cases. --- # Predictions in mobile apps URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/pred-mobile.html > Learn how to incorporate DataRobot predictions into a mobile app. # Predictions in mobile apps [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/using-datarobot-in-mobile-apps) An AI model can't just be an experiment. AI Predictions need to be in the hands of real users interactive with customers, products, or users. This accelerator demonstrates how to incorporate DataRobot predictions into a mobile app. Included in this accelerator is a Swift Playground App prototype. The playground integrates an app that uses the Iris dataset and calls a DataRobot model to predict the likely sub-species of Iris plant. --- # Order quantity prediction URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/pred-products.html > Build a model to improve decisions about initial order quantities using future product details and product sketches. # Order quantity prediction [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/Retail_Industry_Predicting_Factory_Orders_New_Products/Retail%20Industry%20-%20Predicting%20Factory%20Order%20Quantities%20for%20New%20Products.ipynb) Retailers face many decisions when launching new products. One key decision is the amount of product to order from the manufacturer. Ordering too much wastes working capital and can lead to products being heavily discounted. Ordering too little squanders an opportunity for revenue and may cause customers to purchase other brands. Getting initial orders quantities right is particularly difficult for luxury products where first year demand for a new purse, a new belt or a new shoe can vary by several orders of magnitude based on factors unrelated to the product specifications. This notebook illustrates how to build a model to improve decisions about initial order quantities using future product details and product sketches. --- # Model factory with Python URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/python-multi.html > Learn how to use the Python threading library to build a model factory. # Model factory with Python [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/model-factory-with-python-native-multithreading/Model%20Factory%20with%20Python%20Multithreading.ipynb) Model training in the DataRobot platform is an I/O-bound task that can be time consuming depending on the project configuration and the type of models to be trained. Working under tough deadlines and needing to train tens or hundreds of projects (for example, at an SKU level) requires building model factories and leads to the mandatory requirement to significantly decrease training time. This can be achieved on the base of a multithreaded approach and is demonstrated on the example of this AI Accelerator that leverages a Python multithreading library. --- # Symbolic regression (Eureqa) URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/tune-eureqa.html > Apply symbolic regression to your dataset in the form of the Eureqa algorithm. # Symbolic regression (Eureqa) [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/fine_tuning_with_eureqa/fine_tuning_with_eureqa.ipynb) DataRobot offers the ability to apply symbolic regression to your dataset in the form of the Eureqa algorithm. Eureqa returns human-readable and interpretable analytic expressions and allows us to incorporate DataRobot's own domain expertise about the problem. This accelerator shows how the Eureqa algorithm can "discover" the gravitational constant by finding the correct relationship between the variables from a double-pendulum experiment. This accelerator covers the following activities: - Apply the Eureqa algorithm to your dataset - Tune the model's mathematical building blocks to incorporate DataRobot's domain expertise about the problem - Access the resulting closed-form expression --- # Monitor AWS Sagemaker models with MLOps URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-deploy-mlops/aws-mlops.html > Train and host a SageMaker model that can be monitored in the DataRobot platform. # Monitor AWS Sagemaker models with MLOps [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/ecosystem_integration_templates/AWS_monitor_sagemaker_model_in_DataRobot/AWS_SageMaker_DataRobot_MLOps.ipynb) DataRobot MLOps provides a central hub to deploy, monitor, manage, and govern all your models in production. You can deploy models to the production environment of your choice and continuously monitor the health and accuracy of your models, among other metrics. AWS Sagemaker is a fully managed service that allows data scientists and developers to build, train, and deploy machine learning models. DataRobot MLOps with its AWS Sagemaker integration provides an end-to-end solution for managing machine learning models at scale, you can easily monitor the performance of your machine learning models in real-time, and quickly identify and resolve any issues that arise. --- # Run choice-based conjoint analysis URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-deploy-mlops/conjoint.html > Use conjoint analysis to identify customer preferences for product features through survey-based choice modeling and interpretability with DataRobot. # Run choice-based conjoint analysis [Access this AI accelerator on GitHub](https://github.com/datarobot/data-science-scripts/blob/master/accelerators_dev/use_cases_and_horizontal_approaches/Choice-based-conjoint.ipynb) Conjoint analysis is a tool widely used in marketing research for new product development testing. It's usually executed as an online survey format where a few survey respondents will make one choice out of a set of different alternatives. The output allows researches to accurately identify which product features and combination works best before developing them. This notebook outlines how to run a choice-based conjoint analysis as part of the broader Conjoint Analysis topic, with the focus being on the modeling aspect to derive preference scores. DataRobot's SHAP values add the value of interpretability over traditional methods using a linear regression coefficient scores where negative coefficients make it hard to interpret. From a technical perspective, conjoint analysis is a method to identify respondent (customer) preferences of a product feature, without explicitly asking them about that product feature in a survey. This is done by asking respondents to choose one item out of a set of alternatives. Each alternative is made up of the different feature combinations and permutations you are seeking to test. As you run the survey across a large number of responses, DataRobot helps identify customer latent/unconscious feature preferences, even those they themselves may not realize. --- # Model deployment and MLOps URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-deploy-mlops/index.html > Model deployment and MLOps accelerators that you can add to your experiment workflow. # Model deployment and MLOps | Topic | Description | | --- | --- | | Monitor AWS Sagemaker models with MLOps | Monitor SageMaker models in DataRobot MLOps. | | Run choice-based conjoint analysis | Identify customer feature preferences using conjoint analysis. | | Migrate a model to a new cluster | Move a deployed model between DataRobot clusters. | | Video object detection using Visual AI | Use Visual AI for object detection in video streams. | | MLOps smart audit | Audit and visualize MLOps deployment configurations. | --- # Migrate a model to a new cluster URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-deploy-mlops/model-migrate.html > Move a deployed model between DataRobot clusters. # Migrate a model to a new cluster [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/model_migration_across_dr_instances/Model_Migration_Example.ipynb) Currently under development, an experimental DataRobot API allows administrators to download a deployed model from DataRobot cluster X, upload it to DataRobot cluster Y, and then deploy and make requests from it. Note that this notebook will not work using https://app.datarobot.com. ### Prerequisites - This notebook must be able to write to the model directory, located in the same directory as this accelerator's notebook. For best results, run this notebook from the local file system - Ensure that the model you choose to migrate must be a deployed model. - Provide API keys for both the source and destination clusters. - The Source and Destination users must have the "Enable Experimental API access" feature flag enabled to follow this workflow. - The notebook must have connectivity to the Source and Destination clusters. - DataRobot versions on the clusters must be consistent with the Supported Paths above. - For models on clusters of DataRobot v7.x, you must have SSH access to the App Node of the cluster. - The Source and Destination DataRobot clusters must have the following in the config.yaml: --- # Video object detection using Visual AI URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-deploy-mlops/obj-detection.html > Use Visual AI for object detection in video streams. # Video object detection using Visual AI [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/object_detection_on_video) Object detection (binary and multiclass classification) applied to image and video processing is one of the tasks that can be easily and efficiently implemented with DataRobot [Visual AI](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/visual-ai/index.html), which allows you to train deep learning models intended for Computer Vision based-projects. You can also bring your own Computer Vision model and deploy it in DataRobot via the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html). This accelerator demonstrates how deep learning models trained and deployed with DataRobot can be used for object detection on a video stream. (Consider the example of detection when the person in front of the camera wears glasses.) The Elastic-Net Classifier (L2 / Binomial Deviance) along with Pretrained MobileNetV3-Small-Pruned Multi-Level Global Average Pooling Image Featurizer with no image augmentation are used in this accelerator. The dataset used contains images for two classes: persons with glasses and persons without glasses and is linked in the accelerator on GitHub. A sample of the dataset (100 images for each class) is used for this accelerator. The video stream is captured with OpenCV Computer Vision library. The frontend is implemented as a Streamlit application. --- # MLOps smart audit URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-deploy-mlops/smart-audit.html > Audit and visualize MLOps deployment configurations. # MLOps smart audit [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced_ml_and_api_approaches/mlops_smart_audit) This accelerator outlines a workflow to create an application that provides an interactive dashboard for analyzing the MLOps configuration across multiple machine learning deployments. The application examines each deployment for enabled capabilities (e.g., data drift detection, accuracy monitoring, notifications, etc.) and produces a summarized, interactive view. This helps MLOps administrators assess deployment quality, identify gaps, prioritize improvements, and check compliance scores. The key feature of the accelerator are outlined below: - Deployment overview:Quickly see which MLOps functions are enabled or disabled across your deployments. - Quality and compliance assessment:Each deployment is assigned a quality score based on the percentage of enabled capabilities and a compliance score based on a set of mandatory functions and model risk levels. - Advanced filtering and search:Use sidebar filters to refine deployments by type, owner, capabilities, or score ranges. - LLM-Based insights (optional):When enabled, Azure OpenAI provides natural language summaries and recommendations. - Capability governance:View governance rules for capabilities categorized by importance (Critical, High, Moderate, Low) for both predictive and generative models. --- # Custom metrics for model selection URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/ai-custom-metrics.html > This AI Accelerator demonstrates how one can leverage DataRobot's Python client to extract predictions, compute custom metrics, and sort their DataRobot models accordingly. # Custom metrics for model selection [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/custom_leaderboard_metrics/custom_metrics.ipynb) When it comes to evaluating model performance, DataRobot provides many of the standard metrics [out-of-the box](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html), either on the [Leaderboard](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/leaderboard-ref.html) or as part of a [model insight](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/index.html). However, depending on the industry, you may need to sort your DataRobot leaderboard by a specific metric not natively supported by DataRobot. This AI Accelerator demonstrates how one can leverage DataRobot's Python client to extract predictions, compute custom metrics, and sort their DataRobot models accordingly. The topics covered are as follows: - Setup: import libraries and connect to DataRobot - Build models with Autopilot - Retrieve predictions and actuals - Sort models by Brier Skill Score (BSS) - Sort models by Rate@Top1% - Sort models by return-on-investment (ROI) In addition, although sometimes difficult, assigning the ROI of utilizing machine learning can be vital for use case adoption and model implementation. Creating a payoff matrix uses a compted dollar figure rather than a machine learning metric. --- # t-SNE dimensionality reduction URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/dim-reduction.html > Review examples for taking a DataRobot project and exporting its model insights as both machine readable files and plots in various file formats. # t-SNE dimensionality reduction [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/Dimensionality%20reduction%20in%20DataRobot%20with%20t-SNE/Dimensionality%20reduction%20in%20DataRobot%20with%20t-SNE.ipynb) This accelerator provides examples for taking a DataRobot project and exporting its model insights as both machine readable files and plots in various file formats using t-Distributed Stochastic Neighbor Embedding (t-SNE). t-SNE is a powerful technique for dimensionality reduction that can effectively visualize high-dimensional data in a lower-dimensional space. Dimensionality reduction can improve machine learning results by reducing computational complexity of the algorithms, preventing overfitting, and focusing on the most relevant features in the dataset. Note that this technique should only be used when the number of features is low. --- # Monitor generative AI metrics URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/genai-metrics.html > Monitor LLMs and generative AI solutions to measure alignment, return on investment, provide guardrails. # Monitor generative AI metrics [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/Using%20Custom%20Metrics%20to%20effectively%20monitor%20Generative%20AI/Using%20Custom%20Metrics%20to%20effectively%20monitor%20Generative%20AI.ipynb) While it gets easier to build generative AI solutions with each passing day, it is becoming evident that it is critical to effectively monitor these solutions to measure alignment and ROI, and to provide guardrails. Monitoring generative AI solutions or [LLMOps](https://www.pluralsight.com/resources/blog/data/what-is-llmops) is a multi-faceted endeavor and requires more than simple out-of-the-box metrics. Each business is unique and the solutions they build require customized monitoring metrics. This accelerator illustrates how businesses can use DataRobot to effectively and holistically monitor generative AI solutions, using metrics that segment into themes for covering the monitoring requirements of a majority of businesses. --- # Model evaluation and metrics URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/index.html > Model evaluation and metrics accelerators that you can add to your experiment workflow. # Model evaluation and metrics | Topic | Description | | --- | --- | | Custom metrics for model selection | Demonstrates how to leverage DataRobot's Python client to extract predictions, compute custom metrics, and sort DataRobot models accordingly. | | t-SNE dimensionality reduction | Learn how to use t-SNE for dimensionality reduction and visualization of high-dimensional data, with examples for exporting these insights as files and plots. | | Monitor generative AI metrics | Monitor LLMs and generative AI solutions to measure alignment, return on investment, and provide guardrails using custom metrics. | | Event log viewer | Change the output of the User Activity Monitor to drop or anonymize columns for privacy while maintaining reporting consistency. | | LLM observability | Enable LLMOps or Observability in your existing Generative AI Solutions without refactoring code, with examples for major LLMs. | | Partial dependence plots (PDP/ICE) | Create one-way and two-way partial dependence plots (PDP), and Individual Conditional Expectations (ICE) insights using DataRobot. | | LIME explanations for models | Apply Local Interpretable Model-agnostic Explanations (LIME) to models built and deployed with DataRobot. | | Steel defect detection | Train a highly accurate and robust machine learning model capable of detecting and classifying any-sized scratch present in steel plates. | | Export model insights | Review examples for exporting a variety of DataRobot model insights and performance metrics as both machine-readable files and plots in multiple formats. | --- # Event log viewer URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/log-viewer.html > Change the output of the User Activity Monitor to allow you to drop an entire column of output or change the contents of that column in a way to preserve the anonymity of the column but maintain consistency for reporting. # Event log viewer [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/Simple_Log_Lister/Simple_Log_Lister.ipynb) This accelerator provides you with a method to change the output of the User Activity Monitor to allow you to drop an entire column of output or change the contents of that column in a way to preserve the anonymity of the column but maintain consistency for reporting. For the full list of columns, please refer to [the DataRobot documentation](https://docs.datarobot.com/en/docs/api/reference/public-api/analytics.html#get-apiv2eventlogs). --- # LLM observability URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/observability.html > Enable LLMOps or Observability in your existing Generative AI Solutions without refactoring code. # LLM observability [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/generative_ai/external_monitoring/README.md) This accelerator shows how you can quickly and seamlessly enable LLMOps or [Observability](https://www.datarobot.com/platform/generative-ai/) in your existing Generative AI Solutions without refactoring code. This accelerator includes examples of the industry leading LLMs to show how easy it is to start monitoring an LLM and the solution built on top of it with all the hallmark features available in DataRobot's MLOps platform. The following LLMs are showcased in the accelerator: - PaLM 2 by Google - GPT4 by OpenAI - Titan by AWS Bedrock - Claude by Anthropic --- # Partial dependence plots (PDP/ICE) URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/pdp-ice.html > Create one-way and two-way partial dependence plots (PDP), and Individual Conditional Expectations (ICE) insights using DataRobot. # Partial dependence plots (PDP/ICE) [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/PDP_ICE/PDP%20and%20ICE%20AV.ipynb) This accelerator presents an example workflow to create one-way and two-way partial dependence plots (PDP), and Individual Conditional Expectations (ICE) insights using DataRobot. This accelerator has two parts: 1. Score data against a deployment and join the predictions back with the full dataset. 2. Use the scored dataset to gain insights by generation PDP and ICE plots. --- # LIME explanations for models URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/run-lime.html > Apply Local Interpretable Model-agnostic Explanations (LIME) to models built and deployed with DataRobot. # LIME explanations for models [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/LIME%20with%20DataRobot%20Models/LIME%20analysis%20with%20DataRobot.ipynb) This accelerator shows how you can apply Local Interpretable Model-agnostic Explanations (LIME) to models built and deployed with DataRobot. LIME serves as another method in your toolbox to explain model predictions, complementing the built-in DataRobot capabilities of XEMP and SHAP prediction explanations. The accelerator demonstrates how to: - Connect to DataRobot using the API. - Build and deploy a model in DataRobot. - Execute LIME analysis with the deployed model. --- # Steel defect detection URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/steel-plate.html > Train a highly accurate and robust machine learning model capable of detecting and classifying any-sized scratch present in steel plates. # Steel defect detection [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/faster-rcnn-custom-model/FasterR-CNN_training.ipynb) Modern machine learning techniques are now capable of assisting manufacturers streamline their product development in numerous ways. In this notebook, you are going to focus on detecting and classifying product defects using state of the art computer vision systems. Utilizing machine learning brings immense value to manufacturers, transforming their production processes and giving them an overall competitive edge. By leveraging these advanced methods, manufacturers can streamline product development, enhance defect detection accuracy, optimize operational efficiency, reduce costs, and ultimately deliver higher-quality products to meet the ever-growing demands of the market. In this accelerator, you will leverage computer vision to tackle the task of identifying product defects in hot-rolled steel plates, which are used extensively in construction and agriculture due to their superior strength and high formability. By leveraging an object detection model powered by machine learning, we can achieve precise and efficient detection and classification of one of the most prevalent product defects that steel manufacturers encounter: scratches. In practical applications, the inspection of steel plates is performed visually by an in-factory human examiner, which is time consuming and potentially unreliable. The approach will stand out from traditional techniques that do not utilize machine learning, as it offers the ability to automate the detection process, enhance accuracy, and reduce human effort and error. - Download the data - Perform the necessary data preprocessing - Split the data into training and validation datasets - Create our model - Write custom training and validation loops - Create a visualizer to evaluate model performance and take a look at the model's predictions At the end, you will have successfully trained a highly accurate and robust machine learning model capable of detecting and classifying any sized scratch present in steel plates. --- # Export model insights URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/viz-output.html > Review examples for exporting a variety of DataRobot model insights and performance metrics as both machine-readable files and plots in multiple formats. # Export model insights [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/Viz%20Output/Viz%20Output.ipynb) This accelerator presents some examples for taking a DataRobot project and exporting its model insights as both machine-readable files and plots in various file formats. It will demonstrate how to use the Python API client to: - Securely connect to DataRobot. - Get data. - Start a DataRobot binary classification project. - Retrieve and evaluate model performance and insights. - Package up insights and output them in various file formats. --- # AML alert scoring URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/alert-scoring.html > Develop a machine learning model that utilizes historical data, including customer and transactional information, to identify alerts that resulted in the generation of a Suspicious Activity Report (SAR). # AML alert scoring [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/anti-money-laundering) In this accelerator, delve into the exciting world of machine learning applied to Anti-Money Laundering (AML) alert scoring. The primary goal is to develop a powerful predictive model that utilizes historical customer and transactional data, enabling you to identify suspicious activities and generate crucial Suspicious Activity Reports (SARs). To ensure a smooth and efficient machine learning process, rely on the DataRobot Workbench. This tool allows you to analyze, clean, and curate the data, ensuring its quality and suitability for modeling. By utilizing the DataRobot API, you can seamlessly create and manage experiments, exploring a wide range of machine learning algorithms tailored for the AML alert scoring task. The flexibility and ease-of-use of the API make it a valuable asset for data scientists throughout the process. With just a few lines of code, you can train multiple machine learning models simultaneously, saving valuable time and computational resources. The model insights offered through the API provide invaluable interpretability. Additionally, the DataRobot API allows us to compute predictions on new data before deploying the model into production. This pre-deployment testing phase enables us to evaluate the model's performance in real-world scenarios and make necessary adjustments to address any potential issues. Uncover the incredible potential of machine learning in AML alert scoring, where data-driven insights make a tangible difference in the fight against money laundering. --- # Cold start demand forecasting URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/cold-start.html > This accelerator provides a framework to compare several approaches for cold start modeling on series with limited or no history. # Cold start demand forecasting [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/Demand_forecasting2_cold_start/End_to_end_demand_forecasting_cold_start.ipynb) The cold start demand forecasting problem refers to the challenge of predicting future demand for a new product or service with little or no historical sales data available. This situation typically arises when a company introduces a new product or service to the market or a new product is launched in a store that is already being sold in other stores, and there is no past data available for training a machine learning model to predict future demand. In traditional demand forecasting, historical sales data is used to train a machine learning model that can predict future demand. However, in the case of a new product, there is no historical data available. This presents a significant challenge because accurate demand forecasting is critical for making informed decisions about inventory, pricing, and marketing strategies. This second accelerator of a three-part series on demand forecasting provides the building blocks for cold start modeling workflow on series with limited or no history. This accelerator provides a framework to compare several approaches for cold start modeling. The previous notebook aims to inspect and handle common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation with the tools mentioned above and more. The dataset consists of 50 series (46 SKUs across 22 stores) over a 2 year period with varying series history, typical of a business releasing and removing products over time. The test dataset contains 20 additional series with little or no history which were not present in the training dataset. --- # Demand forecasting with Databricks URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/dbx-forecast.html > How to use DataRobot with Databricks to develop, evaluate, and deploy a multi-series demand forecasting model. # Demand forecasting with Databricks [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/ecosystem_integration_templates/Databricks%20%26%20Datarobot%20-%20Large%20Scale%20Forecasting/Databricks%20%26%20Datarobot%20-%20Large%20Scale%20Forecasting.ipynb) This accelerator is developed for use with Databricks to help you leverage the power of DataRobot for time-series modeling within a Databricks ecosystem. Demand forecasting models are valuable to many businesses because they apply to high-value use cases such as improving inventory management, supply chain processes, and store staffing. However, building forecasting models can be challenging and time-consuming given the amount of experimentation typically required, from performing time series feature engineering to implementing diverse and complex time-series algorithms and evaluating results. The time series capabilities of DataRobot accelerate this process so you can rapidly build and test many modeling approaches and productionalize your models with model monitoring. This accelerator can be imported into Databricks notebooks to walk you through how to use DataRobot with Databricks to develop, evaluate, and deploy a multi-series demand forecasting model. The notebook utilizes the DataRobot API to access DataRobot capabilities while ingesting data from Databricks for model building and scoring. In this accelerator you will: - Connect to DataRobot in a Databricks Notebook - Import data from Databricks into the AI Catalog - Create a time series forecasting project and run Autopilot - Retrieve and evaluate model performances and insights - Make new predictions with a test dataset - Deploy a model with monitoring in DataRobot MLOps - Forecast predictions via the Prediction API --- # Time series demand forecasting URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/demand-flow.html > Perform large-scale demand forecasting using DataRobot's Python package. # Time series demand forecasting [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/Demand_forecasting1_end_to_end/End_to_end_demand_forecasting.ipynb) Demand forecasting models have many common challenges: large quantities of SKUs or series to predict, partial history or irregular history for many SKUs, multiple locations with different local or regional demand patterns, and cold-start prediction requests from the business for new products. The list goes on. Time series in DataRobot, however, has a diverse range of functionality to help tackle these challenges. For example: - Automatic feature engineering and creation of lagged variables across multiple data types, as well as training dataset creation. - Diverse approaches for time series modeling with text data, learning from cross-series interactions and scaling to hundreds or thousands of series. - Feature generation from an uploaded calendar of events file specific to your business or use case. - Automatic backtesting controls for regular and irregular time-series. - Training dataset creation for irregular series via custom aggregations. - Segmented modeling, hierarchical clustering for multi-series models, multimodal modeling, and ensembling. - Periodicity and stationarity detection, and automatic feature list creation with various differencing strategies. - Cold start modeling on series with limited or no history. - Insights for all of the above. In this first installment of a three-part series on demand forecasting, this accelerator provides the building blocks for a time-series experimentation and production workflow. This notebook provides a framework to inspect and handle common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation with the tools mentioned above and more. The dataset consists of 50 series (46 SKUs across 22 stores) over a two year period with varying series history, typical of a business releasing and removing products over time. --- # Demand forecasting retraining URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/df-retrain.html > Implement retraining policies with DataRobot MLOps demand forecast deployments. # Demand forecasting retraining [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/Demand_forecasting3_retraining/End_to_end_demand_forecasting_retraining.ipynb) This accelerator demonstrates retraining policies with DataRobot MLOps demand forecast deployments. This accelerator is a another installment of a series on demand forecasting. The [first accelerator](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/demand-flow.html) focuses on handling common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation. The [second accelerator](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/cold-start.html) provides the building blocks for cold start modeling workflow on series with limited or no history. They can be used as a starting point to create a model deployment for the app. The [third accelerator](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ml-what-if.html) is a what-if app that allows you to adjust certain known in advance variable values to see how changes in those factors might affect the forecasted demand. --- # Financial planning analysis URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/fin-plan.html > This accelerator illustrates an end-to-end financial planning and analysis workflow in DataRobot. # Financial planning analysis [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/FP%26A/FP%26A.ipynb) This accelerator illustrates an end-to-end financial planning and analysis workflow in DataRobot. Time series forecasting in DataRobot has a huge suite of tools and approaches to handle highly complex multiseries problems. DataRobot is used for the model training, selection, deployment, and creation of forecasts. While this example will leverage a snapshot file as a data source, this workflow applies to any data source, e.g. Redshift, S3, Big Query, Synapse, etc. This notebook will demonstrate how to use the Python API client to: - Connect to DataRobot - Import and preparation of data for time series modeling - Create a time series forecasting project and run Autopilot - Retrieve and evaluate model performance and insights - Making forward looking forecasts - Evaluating forecasts vs. historical trends - Deploy a model --- # Flight delay prediction URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/flight-delays.html > Designed for DataRobot trial users, experience an end-to-end DataRobot workflow using a use case that predicts flight delays. # Flight delay prediction [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/Flight%20Delays%20-%20Starter%20Use%20Case%20for%20New%20DataRobot%20Users/Flight%20Delays%20-%20Starter%20Use%20Case%20for%20New%20DataRobot%20Users.ipynb) This accelerator aims to assist DataRobot trial users by providing a guided walkthough of the trial experience. DataRobot suggests that you complete the Flight Delays sample use case in the graphical user interface first, and then return to this accelerator. In this notebook, you will: - Create a Use Case. - Import data from an S3 bucket (this differs from the UI walkthrough). - Perform a data wrangling operation to create the target feature with code (this also differs from the UI walkthrough). - Register the wrangled data set. - Explore the new data set. - Create an experiment and allow DataRobot automation to populate it with many modeling pipelines. - Explore model insights for the best performing model. - View the modeling pipeline for the best performing model. - Register a model in Registry. - Configure a deployment. - Create a deployment. - Make predictions using the deployment. - Review deployment metrics. --- # Fraud detection with Neo4j URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/fraud-detection.html > Build a fraud detection pipeline using Neo4j for storing and querying a knowledge graph. # Fraud detection with Neo4j [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/datarobot-neo4j-knowledge-graph-for-fraud-detection) This accelerator demonstrates how to build a fraud detection pipeline using Neo4j and DataRobot. Use Neo4j to store and query a knowledge graph of clients, loans, addresses, and more. Then, use DataRobot to build a predictive model with graph-based features. The accelerator contains multiple notebooks. The first notebook walks through installing a Neo4j 4.4.11 instance, loading a Neo4j database, and uploading a dump file with the CLI. The second notebook outlines how to extract graph data into training and holdout CSVs, upload training data to DataRobot, and build a classification model for scoring. --- # Time series and specific use cases URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/index.html > Accelerators for time series and other specific use cases that you can add to your experiment workflow. # Time series and specific use cases | Topic | Description | | --- | --- | | AML alert scoring | Develop a machine learning model that utilizes historical data, including customer and transactional information, to identify alerts that resulted in the generation of a Suspicious Activity Report (SAR). | | Cold start demand forecasting | This accelerator provides a framework to compare several approaches for cold start modeling on series with limited or no history. | | Demand forecasting with Databricks | How to use DataRobot with Databricks to develop, evaluate, and deploy a multi-series demand forecasting model. | | Time series demand forecasting | Perform large-scale demand forecasting using DataRobot's Python package. | | Demand forecasting retraining | Implement retraining policies with DataRobot MLOps demand forecast deployments. | | Financial planning analysis | This accelerator illustrates an end-to-end financial planning and analysis workflow in DataRobot. | | Flight delay prediction | Designed for DataRobot trial users, experience an end-to-end DataRobot workflow using a use case that predicts flight delays. | | Fraud detection with Neo4j | Build a fraud detection pipeline using Neo4j for storing and querying a knowledge graph. | | Multi-model analysis | Use Python functions to aggregate DataRobot model insights into visualizations. | | Netlift modeling | Leverage machine learning to find patterns around the types of people for whom marketing campaigns are most effective. | | What-if demand forecasting | Discover how to use a what-if app to adjust known-in-advance variables and explore how changes in factors like promotions, pricing, or seasonality can impact demand forecasts. | | No-show appointment prediction | Build a model that identifies patients most likely to miss appointments, with correlating reasons. | | Lumber price forecasting with Ready Signal | Use Ready Signal to add external control data, such as census and weather data, to improve time series predictions. | | Recommendation engine | Explore how to use historical user purchase data in order to create a recommendation model, which will attempt to guess which products out of a basket of items the customer will be likely to purchase at a given point in time. | | Panel data self-joins | Explore how to implement self-joins in panel data analysis. | | Technical price prediction | Leverage historical insurance claim data for modeling and analysis. | | Statistical tests with Airflow | Review an example workflow for carrying out statistical tests, notify stakeholders of any issues via Slack, and generate automated compliance documentation with the test results. | | Trading volume profile curve | Use a framework to build models that will allow you to predict how much of the next day trading volume will happen at each time interval. | | Hierarchical reconciliation | Learn how to reconcile independent time series forecasts with a hierarchical structure. | | Visual AI for geospatial data | Learn how to use Visual AI to represent geospatial data for enhanced analysis. | --- # Multi-model analysis URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ml-analysis.html > Use Python functions to aggregate DataRobot model insights into visualizations. # Multi-model analysis [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/multi_model_analysis/Multi-Model%20Analysis.ipynb) DataRobot is designed to help you experiment with different modeling approaches, data preparation techniques, and problem framings. You can iterate fast with a tight feedback loop to quickly arrive at the best approach. Sometimes you may wish to break your use case into multiple models, likely across multiple DataRobot projects. Maybe you want to build a separate model for each country or one for different periods of the year. In this case, it helps to bring all of your model performances and insights into one chart. This accelerator shares several Python functions that can take the DataRobot insights—specifically model error, feature effects (partial dependence), and feature importance (SHAP or permutation-based) and bring them together into one chart, allowing you to understand all of your models in one place and more easily share your findings with stakeholders. --- # Netlift modeling URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ml-uplift.html > Leverage machine learning to find patterns around the types of people for whom marketing campaigns are most effective. # Netlift modeling [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/marketing_uplift_modeling/uplift_modeling.ipynb) Uplift modeling, also referred to as "netlift" modeling, is an approach used often in marketing to isolate the impact of a marketing campaign on specific prospective customers’ propensity to purchase something. The underlying example in this DataRobot AI Accelerator is exactly that, but more generally this approach could be used to isolate the impact of any “intervention” on the propensity of any positive response. The key challenge in uplift modeling is to isolate the effect of the campaign, because no individual person can be observed both receiving the campaign and not receiving the campaign. The accelerator addresses this key challenge, as well as other tips and tricks for uplift modeling. In many cases, the historical strategy for determining who received a campaign targeted those already likely to purchase the product (or generally, produce a favorable response). That approach would suggest a simple trend that receiving the campaign increases the likelihood to purchase, but many other features about the customers may be confounding the isolated impact of the campaign. In fact, it's possible that a campaign that targeted already high-probability buyers actually reduced their probability of purchase. These are the so-called "sleeping dogs'' in marketing lingo. From an ROI standpoint, increasing the probability to purchase on one group of prospects from 25% to 50% is just as valuable as increasing that probability on another group from 50% to 75% (assuming the groups are roughly the same size, with the same expected revenue values). So what you're really trying to ask from machine learning models is this: on which prospective customers will the campaign increase the probability of purchase by the greatest amount? This accelerator uses a generic dataset where the favorable outcome is binary: whether or not a product was purchased. The "treatment", or campaign, is simple: a single campaign type that was sent randomly to some prospective buyers, though it also discusses how these methods can be extrapolated to the common case where there was selection bias in the campaign. Leverage machine learning to find patterns around the types of people for whom the campaign is most effective, controlling for their baseline likelihood to purchase in the case that they don't see a campaign. Uplift use cases require some additional post-processing to extract and evaluate the "uplift score", and thus this use case is an ideal candidate for leveraging the DataRobot programmatic API, to seamlessly integrate powerful machine learning with one's typical coding pipeline. While working through the provided Jupyter Notebook, the following concepts and strategies will be reinforced: 1. Data formatting tricks to extract the most from your uplift models. 2. How to leverage DataRobot's API to integrate powerful machine learning into your code-first pipelines. 3. How to extract uplift scores from a single, binary classification model. 4. How to evaluate and understand those uplift scores, and their implied ROI. 5. Considerations for cases where your historical, training data exhibits selection bias, where the campaign was not randomly sent. --- # What-if demand forecasting URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ml-what-if.html > Discover how to use a what-if app to adjust known-in-advance variables and explore how changes in factors like promotions, pricing, or seasonality can impact demand forecasts. # What-if demand forecasting [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/Demand_forecasting4_what_if_app/README.md) This demand forecasting what-if app allows you to adjust certain known in advance variable values to see how changes in those factors might affect the forecasted demand. Some examples of factors that might be adjusted include marketing promotions, pricing, seasonality, or competitor activity. By using the app to explore different scenarios and adjust key inputs, you can make more accurate predictions about future demand and plan accordingly. This app is a third installment of a three-part series on demand forecasting. The [first accelerator](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/Demand_forecasting1_end_to_end/End_to_end_demand_forecasting.ipynb) focuses on handling common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation. The [second accelerator](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/Demand_forecasting2_cold_start/End_to_end_demand_forecasting_cold_start.ipynb) provides the building blocks for cold start modeling workflow on series with limited or no history. They can be used as a starting point to create a model deployment for the app. --- # No-show appointment prediction URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/no-show.html > Build a model that identifies patients most likely to miss appointments, with correlating reasons. # No-show appointment prediction [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/healthcare_appointment_no_show_prediction/no_show.ipynb) Many people are guilty of having canceled a doctor’s appointment. However, although canceling an appointment does not seem too disastrous from the patient’s point of view, no-shows cost outpatient health centers a staggering 14% of anticipated daily revenue (JAOA). Missed appointments trickle into lower utilization rates for not only doctors and nurses but also the overhead costs required to run outpatient centers. In addition, patients missing their appointments risk facing poorer health outcomes as they are unable to access timely care. While outpatient centers employ solutions such as calling patients ahead of time, these high touch resources investments are often not prioritized for patients with the highest risk of no-shows. Low touch solutions such as automated texts are effective tools for mass reminders but do not offer necessary personalization for patients at the highest risk of no-shows. This accelerator shows how to identify clients who are likely to miss appointments ("no-shows") and take action to prevent that from happening. --- # Lumber price forecasting with Ready Signal URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ready-signal.html > Use Ready Signal to add external control data, such as census and weather data, to improve time series predictions. # Lumber price forecasting with Ready Signal [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/data_enrichment_ready_signal_ts/DataRobot_RXA.ipynb) In this accelerator, you will explore how to bring external data from Ready Signal to help augment your time series forecasting accuracy. Ready Signal is an AI-powered data platform that provides access to over 500 normalized, aggregated, and automatically updated data sources for predictive modeling, experimentation, business intelligence, and other data enrichment needs. The data catalog includes micro/macro-economic indicators, labor statistics, demographics, weather, and more. Its AI recommendation engine and auto feature engineering capabilities make it easy to integrate with existing data pipelines and analytics tooling, accelerating and enhancing how relevant third-party data is leveraged. Here, DataRobot provides an example of predicting lumber price combined with the most relevant external data automatically identified by ReadySignal based on correlation with the target variable. The workflow can be applied to any time series forecasting project. --- # Recommendation engine URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/rec-engine.html > Explore how to use historical user purchase data in order to create a recommendation model, which will attempt to guess which products out of a basket of items the customer will be likely to purchase at a given point in time. # Recommendation engine [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/Ecommerce_recommendation_engine/Recommendation%20Engine.ipynb) The accelerator provided in this notebook trains a model on historical customer purchases in order to make recommendations for future visits. The DataRobot features that will be utilized in this notebook are multi-Label modeling and feature discovery. Together the resulting model can provide rank ordered suggestions of content, product, or services that a specific customer might like. In the notebook, you will: - Analyze the datasets required - Create a multilabel dataset for training - Connect to DataRobot - Configure a feature discovery project - Generate features and models - Generate recommendations for new visits --- # Panel data self-joins URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/self-joins.html > Explore how to implement self-joins in panel data analysis. # Panel data self-joins [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/Self_join_technique_for_panel_data) In this accelerator, explore how to implement self-joins in panel data analysis. Regardless of your industry, if you work with panel data, this guide is tailored to help you accelerate feature engineering and extract valuable insights. Panel data, with multiple observations for consistent subjects over time, is ubiquitous in various domains. While panel data is often spread across multiple tables, it can also exist in a single dataset with multiple features suitable as panel dimensions. The self-join technique enables automated, time-aware feature engineering with just one dataset, generating hundreds of candidate features of lagged aggregations and statistics. Combining these features within panel dimensions can substantially improve predictive model performance. The accelerator focuses on predicting airline take-off delays of 30 minutes or more to illustrate the self-join technique. However, this framework applies broadly across verticals and can easily be adapted to your use case. Using a single dataset, join it four times across different features, engineer time-based features from each join, using the AI Catalog for data management. The accelerator covers data preparation with multiple joins and time horizons, how to mitigate target leakage with multiple feature lists as well as time gaps in time-aware joins. Panel data analysis unlocks valuable insights into subjects evolving over time, and is often overlooked when there is a singular dataset. --- # Technical price prediction URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/tech-prices.html > Leverage historical insurance claim data for modeling and analysis. # Technical price prediction [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/insurance_pricing/Code%20first%20Incurred%20Claims%20-%20Insurance%20pricing.ipynb) This accelerator serves as a comprehensive guide to insurance pricing, leveraging historical claims data for modeling and analysis. The primary objective of this notebook is to enable insurance professionals and data scientists to predict insurance pricing accurately and efficiently with DataRobot platform. This accelerator does the following: - Set up the environment for insurance pricing modeling - Import the necessary libraries and emphasize data preparation for use with DataRobot - Visualize the distribution of claim amounts - Create two options for modeling workflows for the insurance pricing project: Pure Premium vs. Frequency and Severity - Explore different feature list and model customization Following the modeling phases, the accelerator transitions to result analysis and business considerations. It discusses testing the models and computing various business metrics and scenarios. The accelerator also covers how to convert from a technical price to a market premium with the inclusion of fixed expenses and variable costs. This part also includes computing loss ratios by various segments, which is crucial for assessing risk and profitability. Finally, the analysis phase includes a dislocation premium chart to visualize premium impact. After thorough analysis and fine-tuning, the accelerator explains how to deploy developed models into production. This is a critical step for implementing the insurance pricing models in real-world scenarios and utilizing them for decision-making. The final section of the accelerator is dedicated to advanced workflows. It introduces feature discovery using secondary claims databases. This advanced approach can significantly reduce the time investment needed from data scientists and engineers, making it a valuable addition to the insurance pricing modeling process. --- # Statistical tests with Airflow URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/test-airflow.html > Review an example workflow for carrying out statistical tests, notify stakeholders of any issues via Slack, and generate automated compliance documentation with the test results. # Statistical tests with Airflow [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/shu/stat-tests/advanced_ml_and_api_approaches/stat_test_airflow/stat_test_airflow.ipynb) This notebook presents an example workflow for carrying out statistical tests, notifying stakeholders of any issues via Slack, and generating automated compliance documentation with the test results. It will demonstrate how to create a pipeline of statistical tests, at different stages of the model development cycle, and integrate with Apache Airflow, including: - Using exploratory statistical tests as part of model training. - Scoring a DataRobot model. - Running any arbitrary statistical tests. - Registering the test results to the model version. - Generating automated compliance documentation using customized templates. - Creating the pipeline with Apache Airflow. --- # Trading volume profile curve URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ts-factory.html > Use a framework to build models that will allow you to predict how much of the next day trading volume will happen at each time interval. # Trading volume profile curve [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/trading_volume_profile_curve_model_factory) In securities trading, it’s often useful to have an idea of how trading volume for a particular instrument will be distributed over the market session. This is done by building a volume curve — essentially, a prediction of how much of the volume will fall within the different time intervals (“time slices”) in a trading day. Volume curves allow traders to better anticipate how to time and pace their orders and are used as inputs into algorithmic execution strategies such as VWAP (volume weighted average price) and IS (implementation shortfall). Historically, volume curves have been built by taking the average share of volume for a particular time slice over the last N trading days (for instance, the share of the daily volume in AAPL that traded between 10:35 and 10:40am on each of the last 20 trading days, on average), with manual adjustments to take account of scheduled events and anticipated differences. Machine learning allows you to do this in a structured, systematic way. The goal of this AI accelerator is to provide a framework to build models that will allow you to predict how much of the next day trading volume will happen at each time interval. The granularity can vary from minute by minute (or even lower) to hourly or daily. If you are working with high granularity, such as minute by minute intervals, having a single time series model to predict the next 1440 minutes (or 480, based on how long the market is open) becomes problematic. Instead, consider a time series model per interval (minute, half hour, hour, etc.) so that each model is only forecasting one step ahead. You can then bring together the predictions of all the models to create the full curve for the next day. Furthermore, while a model is built to predict each time interval, the model isn't restricted to data for that interval, but can leverage a wider window. While the motivation for this repository is a financial markets use case, it should be useful in other scenarios where predictions are required at a high resolution, such as predictive maintenance. ## Challenges - The number of models or deployments can explode, and you need to keep track of all of them. - Each model needs slightly different data. - Even if you are creating a model per minute, you want to use data from earlier and later on in the day. - You want to see a unified result (a single curve for the whole trading day). ## Approach - Train a model per interval, but leverage data outside of the interval by "widening" the time window on which it is trained. - Use a data frame to track all the projects, models and deployments corresponding to each interval. This will make it easy to stitch all the predictions together to build the next day(s) curve. --- # Hierarchical reconciliation URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ts-recon.html > Learn how to reconcile independent time series forecasts with a hierarchical structure. # Hierarchical reconciliation [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/time_series_hierarchical_reconciliation/time_series_hierarchical_reconciliation.ipynb) This AI Accelerator demonstrates how to reconcile (e.g., post-processing to sum appropriately) independent time series forecasts with a hierarchical structure. Reconciling, also known as making ["coherent"](https://otexts.com/fpp3/hierarchical.html) forecasts, is often a requirement when submitting hierarchical forecasts to stakeholders. This notebook leverages the increasingly popular [HierarchicalForecast](https://nixtlaverse.nixtla.io/hierarchicalforecast/index.html) python library to do the reconciliation on forecasts generated from DataRobot time series deployments. The steps demonstrated are as follows: 1. Installing hierarchicalforecast 2. Importing libraries 3. Loading the example dataset 4. Preparing training data for each hierarchy 5. Building models for each level 6. Deploying models for each level 7. Making forecasts 8. Preparing the forecasts 9. Reconcile forecasts 10. Comparing forecasts 11. Conclusion Note that steps 2-6 steps are purely for providing example time series deployments and forecasts from those deployments (in case you don't have any). If you already have a set of forecasts you want to reconcile, feel free to skip to step 7. --- # Visual AI for geospatial data URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/viz-geo.html > Learn how to use Visual AI to represent geospatial data for enhanced analysis. # Visual AI for geospatial data [Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/VisualAI_for_geospatial/Visual%20AI%20for%20geospatial%20data.ipynb) This accelerator shows how you can use Visual AI on geospatial data. Instead of deriving numeric features from the georeferenced data, you look at the geospatial data as images. For example, if you have a map of population distribution, instead of extracting the population that corresponds to each row of the main table you can pass the region of the map that corresponds to that row. This provides more information than a raw count of the population would, as it also encodes the distribution within the region (is it uniform or does it concentrate in some areas? what is the shape?, etc.) The example used to illustrate the approach comes from work done with the Virtue Foundation. As part of the "Data Mapping Initiative", DataRobot has built models to identify suitable locations for new healthcare facilities. By looking at the location of existing hospitals and clinics as a function of several features (road networks, population, terrain, etc.) you find which other areas are suitable in terms of these features (similar to a propensity model). --- # AI consumable assets URL: https://docs.datarobot.com/en/docs/api/dev-learning/ai-assets.html > Structured exports and indexes so AI assistants can use DataRobot documentation reliably. # AI consumable assets Use the following resources to give AI assistants reliable access to DataRobot documentation. | Resource | Description | | --- | --- | | llms.txt | A markdown file that gives LLMs a structured map to find key information in the DataRobot documentation. Start here for a compact index and pointers into the docs. It complements llms-full.md by providing navigation and context instead of the full corpus alone. | | llms-full.md | A markdown file that contains the entirety of the DataRobot documentation in one place for search, retrieval, or grounding. This is a large file. | | docs-llm.zip | A compressed archive of the same full documentation export as llms-full.md, for easier download and offline use. | | Per-page Markdown (*.html.md) | For any published documentation page, you can download a Markdown version at the same path by appending .md after the .html in the URL. For example, this page: ai-assets.html.md. | --- # API Quickstart URL: https://docs.datarobot.com/en/docs/api/dev-learning/api-quickstart.html > Learn how to begin using the DataRobot API to create projects and generate predictions. # API quickstart The DataRobot API provides a programmatic alternative to the web interface for creating and managing DataRobot projects. The API can be used via [REST](https://docs.datarobot.com/en/docs/api/reference/public-api/index.html), or DataRobot's [Python](https://pypi.org/project/datarobot/) or [R](https://cran.r-project.org/web/packages/datarobot/index.html) clients in Windows, UNIX, and OS X environments. This guide walks you through setting up your environment and then you can follow [a sample problem](https://docs.datarobot.com/en/docs/api/dev-learning/api-quickstart.html#use-the-api-predicting-fuel-economy) that outlines an end-to-end workflow for the API. > [!NOTE] Note > The API quickstart guide uses methods for 3.x versions of DataRobot's Python client. If you are a Self-Managed AI Platform user, consult the [Self-Managed AI Platform API resources page](https://docs.datarobot.com/en/docs/api/reference/self-managed.html) to verify which versions of DataRobot's clients are supported for your version of the DataRobot application. ## Prerequisites The DataRobot API requires the following prerequisites, depending on the coding language you choose to use. **Python:** The following prerequisites are for 3.x versions of DataRobot's Python client: Python >=3.7 A registered DataRobot account pip **cURL:** curl jq (for JSON processing) A registered DataRobot account **R:** R >= 3.2 httr (≥ 1.2.0) jsonlite (≥ 1.0) yaml (≥ 2.1.19) A registered DataRobot account ## Install the client Before proceeding, access and install the DataRobot client package for Python or R (instructions are provided below). Review the [API Reference documentation](https://docs.datarobot.com/en/docs/api/reference/index.html) to familiarize yourself with the code-first resources available to you. > [!NOTE] Note > Self-Managed AI Platform users may want to install a previous version of the client in order to match their installed version of the DataRobot application. Reference the [available versions](https://docs.datarobot.com/en/docs/api/index.html#self-managed-ai-platform-api-resources) to map your installation to the correct version of the API client. **Python:** `pip install datarobot datarobot-predict` (Optional) If you would like to build custom blueprints programmatically, install two additional packages: graphviz and blueprint-workshop. For Windows users: [Download the graphviz installer](https://www.graphviz.org/download/#windows) For Ubuntu users: `sudo apt-get install graphviz` For Mac users: `brew install graphviz` Once graphviz is installed, install the workshop: `pip install datarobot-bp-workshop` **R:** `install.packages(“datarobot”)` ## Configure your environment This section walks through how to execute a complete modeling workflow using the DataRobot API, from uploading a dataset to making predictions on a model deployed in a production environment. ### Create a DataRobot API key 1. From the DataRobot UI, click the user icon in the top right corner and selectAPI keys and tools. 2. ClickCreate new key. 3. Name the new key, and clickCreate. The key is activated and ready for use. Once created, each individual key has three pieces of information: | Label | Element | Description | | --- | --- | --- | | (1) | Name | The name of the key, which you can edit. | | (2) | Key | The key value. | | (3) | Date created | The date the key was created. Newly created and not yet used keys display “—”. | | (4) | Last used | The date the key was last used. | ### Retrieve the API endpoint DataRobot provides several deployment options to meet your business requirements. Each deployment type has its own set of endpoints. Choose from the tabs below: **AI Platform (US):** The AI Platform (US) offering is primarily accessed by US users. It can be accessed at [https://app.datarobot.com](https://app.datarobot.com). API endpoint root: `https://app.datarobot.com/api/v2` **AI Platform (EU):** The AI Platform (EU) offering is primarily accessed by EMEA users. It can be accessed at [https://app.eu.datarobot.com](https://app.eu.datarobot.com). API endpoint root: `https://app.eu.datarobot.com/api/v2` **AI Platform (JP):** The AI Platform (JP) offering is primarily accessed by users in Japan. It can be accessed at [https://app.jp.datarobot.com](https://app.jp.datarobot.com). API endpoint root: `https://app.jp.datarobot.com/api/v2` **Self-Managed AI Platform:** For Self-Managed AI Platform users, the API root will be the same as your DataRobot UI root. In the URL below, replace `{datarobot.example.com}` with your deployment endpoint. API endpoint root: `https://{datarobot.example.com}/api/v2` ### Configure API authentication To authenticate with DataRobot's API, your code needs to have access to an endpoint and token from the previous steps. This can be done in three ways: **drconfig.yaml:** DataRobot's recommended authentication method is to use a `drconfig.yaml` file. This is a file that the DataRobot Python and R clients automatically look for. You can instruct the API clients to look for the file in a specific location, `~/.config/datarobot/drconfig.yaml` by default, or under a unique name. Therefore, you can leverage this to have multiple config files. The example below demonstrates the format of the .yaml: ``` endpoint: 'https://app.datarobot.com/api/v2' token: 'NjE3ZjA3Mzk0MmY0MDFmZGFiYjQ0MztergsgsQwOk9G' ``` Once created, you can test your access to the API. For Python: If the config file is located at `~/.config/datarobot/drconfig.yaml`, then all you need to do is import the library: ``` import datarobot as dr ``` Otherwise, use the following command: ``` import datarobot as dr dr.Client(config_path = "") ``` For R: If the config file is located at `~/.config/datarobot/drconfig.yaml`, then all you need to do is load the library: `library(datarobot)` Otherwise, use the following command: ``` ConnectToDataRobot(configPath = "")) ``` For cURL: cURL doesn't natively support YAML files, but you can extract the values from your `drconfig.yaml` file and use them in your cURL commands. This sequence will read the values from your `drconfig.yaml` file and set them as environment variables: ``` # Extract values from drconfig.yaml and set as environment variables export DATAROBOT_ENDPOINT=$(grep 'endpoint:' ~/.config/datarobot/drconfig.yaml | cut -d "'" -f2) export DATAROBOT_API_TOKEN=$(grep 'token:' ~/.config/datarobot/drconfig.yaml | cut -d "'" -f2) ``` Once the environment variables are set, you can use them in your cURL commands: ``` curl --location -X GET "${DATAROBOT_ENDPOINT}/projects" --header "Authorization: Bearer ${DATAROBOT_API_TOKEN}" ``` **Environment variables:** For Windows: For Windows users, open the Command Prompt or PowerShell as an administrator and set the following environment variables: ``` setx DATAROBOT_ENDPOINT "https://app.datarobot.com/api/v2" setx DATAROBOT_API_TOKEN "your_api_token" ``` Once set, close and reopen the Command Prompt or PowerShell for the changes to take effect. To configure persisting environment variables on Windows, search for "Environment Variables" in the Start menu and select Edit the system environment variables. Then, click Environment Variables and, under System variables, click New to add the variables shown above. For macOS and Linux: For macOS and Linux users, open a terminal window and set the following environment variables: ``` export DATAROBOT_ENDPOINT="https://app.datarobot.com/api/v2" export DATAROBOT_API_TOKEN="your_api_token" ``` To configure persisting environment variables on macOS or Linux, edit the shell configuration file ( `~/.bash_profile`, `~/.bashrc`, or `~/.zshrc`) and add the environment variables shown above. Then, save the file and restart your terminal or run `source ~/.bash_profile` (or use any relevant file). Once the environment variables are set, authenticate to connect to DataRobot. For Python: ``` import datarobot as dr dr.Project.list() ``` For cURL: ``` curl --location -X GET "${DATAROBOT_ENDPOINT}/projects" --header "Authorization: Bearer ${DATAROBOT_API_TOKEN}" ``` For R: `library(datarobot)` **Embed in your code:** (Optional) Be cautious to never commit your credentials to Git. For Python: ``` import datarobot as dr dr.Client(endpoint='https://app.datarobot.com/api/v2', token='NjE3ZjA3Mzk0MmY0MDFmZGFiYjQ0MztergsgsQwOk9G') ``` For cURL ``` curl --location --request GET 'https://app.datarobot.com/api/v2/projects/' \ --header 'Authorization: Bearer ' ``` For R: ``` ConnectToDataRobot(endpoint = "https://app.datarobot.com/api/v2", token = 'NjE3ZjA3Mzk0MmY0MDFmZGFiYjQ0MztergsgsQwOk9G') ``` ## Use the API: Predicting fuel economy Once the API credentials, endpoints, and environment are configured, use the DataRobot API to follow this example. The example uses the Python client and the REST API (using cURL), so a basic understanding of Python3 or cURL is required. It progresses through a simple problem: predicting the miles-per-gallon fuel economy from known automobile data (e.g., vehicle weight, number of cylinders, etc.). For additional code examples, reference DataRobot's [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html). > [!NOTE] Note > The following workflow uses methods introduced in version 3.0 of the Python client. Ensure that the client is up-to-date before executing the code included in this example. The following sections provide sample code for Python and cURL that will: 1. Upload a dataset. 2. Train a model to learn from the dataset. 3. Test prediction outcomes on the model with new data. 4. Deploy the model. 5. Predict outcomes on the deployed model using new data. ### Upload a dataset The first step to create a project is uploading a dataset. This example uses the dataset `auto-mpg.csv` and its supporting test dataset, `auto-mpg-test.csv`, both of which can be found in [this .zip file](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/auto.zip). **Python:** ``` import datarobot as dr dr.Client(config_path = "./drconfig.yaml") # Set to the location of your auto-mpg.csv and auto-mpg-test.csv data files # Example: dataset_file_path = '/Users/myuser/Downloads/auto-mpg.csv' training_dataset_file_path = './auto-mpg.csv' test_dataset_file_path = './auto-mpg-test.csv' print("--- Starting DataRobot Model Training Script ---") # Load dataset training_dataset = dr.Dataset.create_from_file(training_dataset_file_path) # Create a new project based on dataset project = dr.Project.create_from_dataset(training_dataset.id, project_name='Auto MPG DR-Client') ``` **cURL:** ``` DATAROBOT_API_TOKEN=${DATAROBOT_API_TOKEN} DATAROBOT_ENDPOINT=${DATAROBOT_ENDPOINT} DATASET_FILE_PATH="./auto-mpg.csv" location=$(curl -Lsi \ -X POST \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -F 'projectName="Auto MPG"' \ -F "file=@${DATASET_FILE_PATH}" \ "${DATAROBOT_ENDPOINT}"/projects/ | grep -i 'Location: .*$' | \ cut -d " " -f2 | tr -d '\r') echo "Uploaded dataset. Checking status of project at: ${location}" while true; do project_id=$(curl -Ls \ -X GET \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" "${location}" \ | grep -Eo 'id":\s"\w+' | cut -d '"' -f3 | tr -d '\r') if [ "${project_id}" = "" ] then echo "Setting up project..." sleep 10 else echo "Project setup complete." echo "Project ID: ${project_id}" break fi done ``` **R:** ``` # Set to the location of your auto-mpg.csv and auto-mpg-test.csv data files # Example: dataset_file_path = '/Users/myuser/Downloads/auto-mpg.csv' training_dataset_file_path <- "./auto-mpg.csv" test_dataset_file_path <- "./auto-mpg-test.csv" # Load dataset using modern DataRobot R client training_dataset <- UploadDataset(training_dataset_file_path) test_dataset <- utils::read.csv(test_dataset_file_path) # Create a new project based on dataset project <- CreateProject(training_dataset, projectName = "Auto MPG DR-Client") ``` ### Train models Now that DataRobot has data, it can use the data to train and build models with Autopilot. Autopilot is DataRobot's "survival of the fittest" modeling mode that automatically selects the best predictive models for the specified target feature and runs them at increasing sample sizes. The outcome of Autopilot is not only a selection of best-suited models, but also identification of a recommended model—the model that best understands how to predict the target feature "mpg". Choosing the best model is a balance of accuracy, metric performance, and model simplicity. You can read more about the [model recommendation process](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html) in the UI documentation. > [!NOTE] Note > This code opens a browser window to display progress in the DataRobot classic UI. Once the window has opened, click the NextGen UI dropdown and select Console to view the deployment once it is complete. > > [https://docs.datarobot.com/en/docs/images/access-nextgen-console.png](https://docs.datarobot.com/en/docs/images/access-nextgen-console.png) **Python:** ``` # Use training data to build models from datarobot import AUTOPILOT_MODE # Set the project's target and initiate Autopilot (runs in Quick mode unless a different mode is specified) project.analyze_and_model(target='mpg', worker_count=-1, mode=AUTOPILOT_MODE.QUICK) print("\nAutopilot is running. This may take some time...") project.wait_for_autopilot() print("Autopilot has completed!") # Open the project in a web browser to view progress print("Opening the project in your default web browser to view real-time events...") project.open_in_browser() # Get the recommended model (the best model for deployment) print("\nRetrieving the best model from the Leaderboard...") best_model = project.recommended_model() print(f"Best Model Found:") print(f" - Model Type: {best_model.model_type}") print(f" - Blueprint ID: {best_model.blueprint_id}") ``` **cURL:** ``` response=$(curl -Lsi \ -X PATCH \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ --data '{"target": "mpg", "mode": "quick"}' \ "${DATAROBOT_ENDPOINT}/projects/${project_id}/aim" | grep 'location: .*$' \ | cut -d " " | tr -d '\r') echo "AI training initiated. Checking status of training at: ${response}" while true; do initial_project_status=$(curl -Ls \ -X GET \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" "${response}" \ | grep -Eo 'stage":\s"\w+' | cut -d '"' -f3 | tr -d '\r') if [ "${initial_project_status}" = "" ] then echo "Setting up AI training..." sleep 10 else echo "Training AI." echo "Grab a coffee or catch up on email." break fi done echo "Polling for Autopilot completion..." while true; do autopilot_done=$(curl -s \ -X GET \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ "${DATAROBOT_ENDPOINT}/projects/${project_id}/" \ | grep -Eo '"autopilotDone":\s*(true|false)' | cut -d ':' -f2 | tr -d ' ') if [ "${autopilot_done}" = "true" ]; then echo "Autopilot training complete. Model ready to deploy." break else echo "Autopilot training in progress... checking again in 60 seconds." sleep 60 fi done # Get the recommended model ID recommended_model_id=$(curl -s \ -X GET \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ "${DATAROBOT_ENDPOINT}/projects/${project_id}/recommendedModels/recommendedModel/" \ | grep -Eo 'modelId":\s"\w+' | cut -d '"' -f3 | tr -d '\r') echo "Recommended model ID: ${recommended_model_id}" ``` **R:** ``` # Set the project target and initiate Autopilot in Quick mode SetTarget(project, target = "mpg") # Start Autopilot in Quick mode (equivalent to Python's AUTOPILOT_MODE.QUICK) StartAutopilot(project, mode = "quick") # Block execution until Autopilot is complete WaitForAutopilot(project) # Open the project in a web browser to view progress OpenProject(project) # Get the recommended model (the best model for deployment) model <- GetRecommendedModel(project, type = RecommendedModelType$RecommendedForDeployment) ``` ### Deploy the model Deployment is the method by which you integrate a machine learning model into an existing production environment to make predictions with live data and generate insights. See the [deployment overview](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-overview/nxt-overview.html) for more information. **Python:** ``` # Deploy the model to a serverless prediction environment print("\nDeploying the model to a serverless prediction environment...") # Find or create a serverless prediction environment serverless_env = None for env in dr.PredictionEnvironment.list(): if env.platform == 'datarobotServerless': serverless_env = env break if serverless_env is None: print("Creating a new serverless prediction environment...") serverless_env = dr.PredictionEnvironment.create( name="Auto MPG Serverless Environment", platform='datarobotServerless' ) # First, register the model to create a registered model version print("Registering the model...") # Check if the registered model already exists registered_model_name = "Auto MPG Registered Model" existing_models = [m for m in dr.RegisteredModel.list() if m.name == registered_model_name] if existing_models: print(f"Using existing registered model: {registered_model_name}") registered_model = existing_models[0] # Create a new version of the existing model registered_model_version = dr.RegisteredModelVersion.create_for_leaderboard_item( best_model.id, name="Auto MPG Model", registered_model_id=registered_model.id ) else: print(f"Creating new registered model: {registered_model_name}") # Create a new registered model registered_model_version = dr.RegisteredModelVersion.create_for_leaderboard_item( best_model.id, name="Auto MPG Model", registered_model_name=registered_model_name ) # Retrieve the newly created registered model object by ID registered_model = dr.RegisteredModel.get(registered_model_version.registered_model_id) # Wait for the model build to complete print("Waiting for model build to complete...") while True: current_version = registered_model.get_version(registered_model_version.id) if current_version.build_status in ('READY', 'complete'): print("Model build completed successfully!") registered_model_version = current_version # Update our reference break elif current_version.build_status == 'FAILED': raise Exception("Model build failed. Please check the model registration.") else: print(f"Build status: {current_version.build_status}. Waiting...") import time time.sleep(30) # Wait 30 seconds before checking again # Deploy the model to the serverless environment using the registered model version deployment = dr.Deployment.create_from_registered_model_version( registered_model_version.id, label="Auto MPG Predictions", description="Deployed with DataRobot client for Auto MPG predictions", prediction_environment_id=serverless_env.id ) print(f"Model deployed successfully! Deployment ID: {deployment.id}") ``` **cURL:** ``` # Use the recommended model ID from training section echo "Using recommended model ID: ${recommended_model_id}" # Find or create a serverless prediction environment echo "Looking for serverless prediction environment..." serverless_env_id=$(curl -s -X GET \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ "${DATAROBOT_ENDPOINT}/predictionEnvironments/" \ | grep -Eo '"id":"[^"]*".*"platform":"datarobotServerless"' \ | grep -Eo '"id":"[^"]*"' | cut -d '"' -f4 | head -1) if [ -z "${serverless_env_id}" ]; then echo "Creating new serverless prediction environment..." serverless_env_response=$(curl -s -X POST \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ --data '{"name":"Auto MPG Serverless Environment","platform":"datarobotServerless"}' \ "${DATAROBOT_ENDPOINT}/predictionEnvironments/") serverless_env_id=$(echo "$serverless_env_response" | grep -Eo '"id":"[^"]*"' | cut -d '"' -f4) echo "Created serverless environment ID: ${serverless_env_id}" else echo "Using existing serverless environment ID: ${serverless_env_id}" fi # Check if registered model already exists registered_model_name="Auto MPG Registered Model" existing_model_id=$(curl -s -X GET \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ "${DATAROBOT_ENDPOINT}/registeredModels/" \ | grep -Eo '"id":"[^"]*".*"'${registered_model_name}'"' \ | grep -Eo '"id":"[^"]*"' | cut -d '"' -f4 | head -1) if [ -n "${existing_model_id}" ]; then echo "Using existing registered model: ${registered_model_name}" # Create new version of existing model model_version_response=$(curl -s -X POST \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ --data "{\"name\":\"Auto MPG Model\",\"registeredModelId\":\"${existing_model_id}\",\"leaderboardItemId\":\"${recommended_model_id}\"}" \ "${DATAROBOT_ENDPOINT}/registeredModels/${existing_model_id}/versions/") else echo "Creating new registered model: ${registered_model_name}" # Create new registered model model_response=$(curl -s -X POST \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ --data "{\"name\":\"${registered_model_name}\"}" \ "${DATAROBOT_ENDPOINT}/registeredModels/") existing_model_id=$(echo "$model_response" | grep -Eo '"id":"[^"]*"' | cut -d '"' -f4) # Create first version model_version_response=$(curl -s -X POST \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ --data "{\"name\":\"Auto MPG Model\",\"registeredModelId\":\"${existing_model_id}\",\"leaderboardItemId\":\"${recommended_model_id}\"}" \ "${DATAROBOT_ENDPOINT}/registeredModels/${existing_model_id}/versions/") fi model_version_id=$(echo "$model_version_response" | grep -Eo '"id":"[^"]*"' | cut -d '"' -f4) echo "Model version ID: ${model_version_id}" # Wait for model build to complete echo "Waiting for model build to complete..." while true; do build_status=$(curl -s -X GET \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ "${DATAROBOT_ENDPOINT}/registeredModels/${existing_model_id}/versions/${model_version_id}/" \ | grep -Eo '"buildStatus":"[^"]*"' | cut -d '"' -f4) if [ "${build_status}" = "READY" ] || [ "${build_status}" = "complete" ]; then echo "Model build completed successfully!" break elif [ "${build_status}" = "FAILED" ]; then echo "Model build failed. Please check the model registration." exit 1 else echo "Build status: ${build_status}. Waiting..." sleep 30 fi done # Deploy the model using the registered model version echo "Deploying the model to the serverless environment..." deployment_response=$(curl -s -X POST \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ --data "{\"label\":\"Auto MPG Predictions\",\"description\":\"Deployed with cURL for Auto MPG predictions\",\"predictionEnvironmentId\":\"${serverless_env_id}\",\"registeredModelVersionId\":\"${model_version_id}\"}" \ "${DATAROBOT_ENDPOINT}/deployments/fromRegisteredModelVersion/") deployment_id=$(echo "$deployment_response" | grep -Eo '"id":"[^"]*"' | cut -d '"' -f4) echo "Model deployed successfully! Deployment ID: ${deployment_id}" # Get the prediction URL for the deployment echo "Retrieving prediction URL for deployment..." prediction_url=$(curl -s -X GET \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ "${DATAROBOT_ENDPOINT}/deployments/${deployment_id}/" \ | grep -Eo '"predictionUrl":"[^"]*"' | cut -d '"' -f4) echo "Prediction URL: ${prediction_url}" ``` **R:** ``` # Deploy the model to a serverless prediction environment cat("Deploying the model to a serverless prediction environment...\n") # Find or create a serverless prediction environment prediction_environments <- ListPredictionEnvironments() serverless_env <- NULL for (env in prediction_environments) { if (env$platform == "datarobotServerless") { serverless_env <- env break } } if (is.null(serverless_env)) { cat("Creating a new serverless prediction environment...\n") serverless_env <- CreatePredictionEnvironment( name = "Auto MPG Serverless Environment", platform = "datarobotServerless" ) } # Register the model to create a registered model version cat("Registering the model...\n") registered_model_name <- "Auto MPG Registered Model" # Check if registered model already exists existing_models <- ListRegisteredModels() existing_model <- NULL for (m in existing_models) { if (m$name == registered_model_name) { existing_model <- m break } } if (!is.null(existing_model)) { cat("Using existing registered model:", registered_model_name, "\n") # Create a new version of the existing model registered_model_version <- CreateRegisteredModelVersion( model_id = model$modelId, name = "Auto MPG Model", registered_model_id = existing_model$id ) } else { cat("Creating new registered model:", registered_model_name, "\n") # Create a new registered model registered_model <- CreateRegisteredModel(name = registered_model_name) # Create first version registered_model_version <- CreateRegisteredModelVersion( model_id = model$modelId, name = "Auto MPG Model", registered_model_id = registered_model$id ) } # Wait for the model build to complete cat("Waiting for model build to complete...\n") while (TRUE) { current_version <- GetRegisteredModelVersion(registered_model_version$id) if (current_version$buildStatus %in% c("READY", "complete")) { cat("Model build completed successfully!\n") break } else if (current_version$buildStatus == "FAILED") { stop("Model build failed. Please check the model registration.") } else { cat("Build status:", current_version$buildStatus, ". Waiting...\n") Sys.sleep(30) # Wait 30 seconds before checking again } } # Deploy the model to the serverless environment using the registered model version deployment <- CreateDeploymentFromRegisteredModelVersion( registered_model_version_id = registered_model_version$id, label = "Auto MPG Predictions", description = "Deployed with DataRobot R client for Auto MPG predictions", prediction_environment_id = serverless_env$id ) cat("Model deployed successfully! Deployment ID:", deployment$id, "\n") ``` ### Make predictions against the deployed model When you have successfully deployed a model, you can use the DataRobot Prediction API to further test the model by making predictions on new data. This allows you to access advanced [model management](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/index.html) features such as data drift, accuracy, and service health statistics. DataRobot offers several methods for making predictions on new data. You can read more about [prediction methods](https://docs.datarobot.com/en/docs/api/dev-learning/python/predictions/index.html) in the UI documentation. You can also reference a Python prediction snippet from the UI. Navigate to the Deployments page, select your deployment, and go to Predictions > Prediction API to reference the snippet for making predictions. **Python:** This code makes predictions on the model using the test set you identified in the first step ( `test_dataset_file_path`), when you uploaded data. ``` # Make predictions on test data print("\nMaking predictions on test data...") # Read the test data directly import pandas as pd from datarobot_predict.deployment import predict test_data = pd.read_csv(test_dataset_file_path) # Use datarobot-predict for deployment predictions predictions, response_headers = predict(deployment, test_data) # Display the results print("\nPrediction Results:") print(predictions.head()) print(f"\nTotal predictions made: {len(predictions)}") ``` **cURL:** This code makes predictions on the deployed model using the test set identified in the first step ( `test_dataset_file_path`), when you uploaded data. ``` # Use the prediction URL from deployment section TEST_DATASET_FILE_PATH="./auto-mpg-test.csv" # Make predictions by sending the CSV data directly predictions=$(curl -s -X POST \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: text/csv; charset=UTF-8" \ --data-binary "@${TEST_DATASET_FILE_PATH}" \ "${prediction_url}") echo "Prediction Results:" echo "$predictions" | jq '.' prediction_count=$(echo "$predictions" | jq '.data | length') echo "Total predictions made: ${prediction_count}" ``` **R:** This code makes predictions on the deployed model using the test set you identified in the first step ( `test_dataset_file_path`), when you uploaded data. ``` # Make predictions on test data cat("Making predictions on test data...\n") # Read the test data directly test_data <- read.csv(test_dataset_file_path) # Use the deployment for predictions (modern approach) predictions <- PredictDeployment(deployment, test_data) # Display the results cat("Prediction Results:\n") print(head(predictions)) cat("Total predictions made:", nrow(predictions), "\n") ``` ## Learn more After getting started with DataRobot's APIs, browse the [developer learning section](https://docs.datarobot.com/en/docs/index.html) for overviews, Jupyter notebooks, and task-based tutorials that help you find complete examples of common data science and machine learning workflows. Browse [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html) to try out repeatable, code-first workflows and modular building blocks. You can also read the [reference documentation](https://docs.datarobot.com/en/docs/api/reference/index.html) available for the REST API and Python API client. --- # Developer learning URL: https://docs.datarobot.com/en/docs/api/dev-learning/index.html > Review the educational resource available for getting started with DataRobot's API and code-first tools. # Developer learning Review the learning resources outlined in the table below to get started with DataRobot's API and code-first tools. | Resource | Description | | --- | --- | | API quickstart | The DataRobot REST API provides a programmatic alternative to the UI for creating and managing DataRobot assets. It allows you to automate processes and iterate more quickly, and lets you use DataRobot with scripted control. The API provides an intuitive modeling and prediction interface. | | AI consumable assets | Structured exports and indexes so AI assistants can reliably use DataRobot documentation. | | Python API client user guide | Review outlines and explanations of the methods that comprise the API client. To access previous version of the Python API client documentation, access ReadTheDocs. | | REST API code examples | Review code examples that outline usage of the Datarobot REST API. | | AI accelerators | Jump-start modeling with code-first workflows. | --- # Credentials URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html # Credentials You can store credentials for use with databases and data connections. To interact with credentials API, use the [Credential](https://docs.datarobot.com/en/docs/api/reference/sdk/credentials.html#credential-api) class. ## List credentials To retrieve the list of all credentials accessible to you, use [Credential.list](https://docs.datarobot.com/en/docs/api/reference/sdk/credentials.html#datarobot.models.Credential.list). ``` import datarobot as dr credentials = dr.Credential.list() ``` Each credential object contains the `credential_id` string field which can be used in, for example, [Batch predictions](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#batch-predictions-s3-creds-usage). ## Basic credentials Use the code below to store generic username and password credentials: ``` >>> import datarobot as dr >>> cred = dr.Credential.create_basic( ... name='my_db_cred', ... user='', ... password='', ... ) >>> cred Credential('5e429d6ecf8a5f36c5693e0f', 'my_db_cred', 'basic'), # Store cred.credential_id >>> cred = dr.Credential.get(credential_id) >>> cred.credential_id '5e429d6ecf8a5f36c5693e0f' ``` Stored credentials can be used in [Batch predictions for JDBC intake or output](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob). ## S3 credentials You can store AWS credentials either using the following three parameters: - aws_access_key_id - aws_secret_access_key - aws_session_token or by using the ID of the saved shared secure configuration: - config_id ``` >>> import datarobot as dr >>> cred = dr.Credential.create_s3( ... name='my_s3_cred', ... aws_access_key_id='', ... aws_secret_access_key='', ... aws_session_token='', ... ) >>> cred Credential('5e429d6ecf8a5f36c5693e03', 'my_s3_cred', 's3'), # Using config_id >>> cred = dr.Credential.dr.Credential.create_s3( ... name='my_s3_cred_with_config_id', ... config_id='', ... ) >>> cred Credential('65ef55ef4cec97f0f733835c', 'my_s3_cred_with_config_id', 's3') # Store cred.credential_id >>> cred = dr.Credential.get(credential_id) >>> cred.credential_id '5e429d6ecf8a5f36c5693e03' ``` Stored credential can be used, for example, in [Batch predictions for S3 intake or output](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#batch-predictions-s3-creds-usage). ## OAuth credentials You can store OAuth credentials in the data store. ``` >>> import datarobot as dr >>> cred = dr.Credential.create_oauth( ... name='my_oauth_cred', ... token='', ... refresh_token='', ... ) >>> cred Credential('5e429d6ecf8a5f36c5693e0f', 'my_oauth_cred', 'oauth'), # Store cred.credential_id >>> cred = dr.Credential.get(credential_id) >>> cred.credential_id '5e429d6ecf8a5f36c5693e0f' ``` ## Snowflake key pair credentials You can store Snowflake key pair credentials in the store. It accepts either of the following parameters: - private_key - passphrase Or you can use the ID of the saved shared secure configuration. - config_id ``` >>> import datarobot as dr >>> cred = dr.Credential.create_snowflake_key_pair( ... name='my_snowflake_key_pair_cred', ... user='', ... private_key="""""", ... passphrase='', ... ) >>> cred Credential('65e9b55e4b0d925c678bb847', 'my_snowflake_key_pair_cred', 'snowflake_key_pair_user_account') >>> cred = dr.Credential.create_snowflake_key_pair( ... name='my_snowflake_key_pair_cred_with_config_id', ... config_id='', ... ) >>> cred Credential('65e9b9494b0d925c678bb84d', 'my_snowflake_key_pair_cred_with_config_id', 'snowflake_key_pair_user_account') ``` ## Databricks access token credentials You can store Databricks access token credentials in the data store. ``` >>> import datarobot as dr >>> cred = dr.Credential.create_databricks_access_token( ... name='my_databricks_access_token_cred', ... databricks_access_token='', ... ) >>> cred Credential('65e9bace4b0d925c678bb850', 'my_databricks_access_token_cred', 'databricks_access_token_account') ``` ## Databricks service principal credentials You can store Databricks service principal credentials in the store. It accepts either of the following parameters: - client_id - client_secret You can also use the ID of the saved shared secure configuration. - config_id ``` >>> import datarobot as dr >>> cred = dr.Credential.create_databricks_service_principal( ... name='my_databricks_service_principal_cred', ... client_id='', ... client_secret='', ... ) >>> cred Credential('65e9bb864b0d925c678bb853', 'my_databricks_service_principal_cred', 'databricks_service_principal_account') >>> cred = dr.Credential.create_databricks_service_principal( ... name='my_databricks_service_principal_cred_with_config_id', ... config_id='', ... ) >>> cred Credential('65e9bcc14b0d925c678bb85e', 'my_databricks_service_principal_cred_with_config_id', 'databricks_service_principal_account') ``` ## Azure Service Principal credentials You can store Azure Service Principal credentials using any of the three following parameters: - client_id - client_secret - azure_tenant_id You can also use the ID of the saved shared secure configuration. - config_id ``` >>> import datarobot as dr >>> cred = dr.Credential.create_azure_service_principal( ... name='my_azure_service_principal_cred', ... client_id='', ... client_secret='', ... azure_tenant_id='', ... ) >>> cred Credential('66c920fc4ef80072a8225e56', 'my_azure_service_principal_cred2', 'azure_service_principal') # Using config_id >>> cred = dr.Credential.dr.Credential.create_azure_service_principal( ... name='my_azure_service_principal_cred_with_config_id', ... config_id='', ... ) >>> cred Credential('66c921aa0ff7aea1ce225e2d', 'my_azure_service_principal_cred_with_config_id', 'azure_service_principal') # Store cred.credential_id >>> cred = dr.Credential.get(credential_id) >>> cred.credential_id '66c921aa0ff7aea1ce225e2d' ``` ## ADLS OAuth credentials You can store ADLS OAuth credentials using any of the three following parameters: - client_id - client_secret - oauth_scopes You can also use the ID of the saved shared secure configuration. - config_id ``` >>> import datarobot as dr >>> cred = dr.Credential.create_adls_oauth( ... name='my_adls_oauth_cred', ... client_id='', ... client_secret='', ... oauth_scopes=[''], ... ) >>> cred Credential('66c9227e3b268d3278225e41', 'my_adls_oauth_cred', 'adls_gen2_oauth') # Using config_id >>> cred = dr.Credential.dr.Credential.create_adls_oauth( ... name='my_adls_oauth_cred_with_config_id', ... config_id='', ... ) >>> cred Credential('66c922b3ae75806f1d126f06', 'my_adls_oauth_cred_with_config_id', 'adls_gen2_oauth') # Store cred.credential_id >>> cred = dr.Credential.get(credential_id) >>> cred.credential_id '66c922b3ae75806f1d126f06' ``` ## Credential data For methods that accept credential data instead of a username/password or credential ID: ``` { "credentialType": "basic", "user": "user123", "password": "pass123", } ``` ``` { "credentialType": "s3", "awsAccessKeyId": "key123", "awsSecretAccessKey": "secret123", } ``` ``` { "credentialType": "s3", "configId": "id123", } ``` ``` { "credentialType": "oauth", "oauthRefreshToken": "token123", "oauthClientId": "client123", "oauthClientSecret": "secret123", } ``` ``` { "credentialType": "snowflake_key_pair_user_account", "user": "user123", "privateKey": "privatekey123", "passphrase": "passphrase123", } ``` ``` { "credentialType": "snowflake_key_pair_user_account", "configId": "id123", } ``` ``` { "credentialType": "databricks_access_token_account", "databricksAccessToken": "token123", } ``` ``` { "credentialType": "databricks_service_principal_account", "clientId": "client123", "clientSecret": "secret123", } ``` ``` { "credentialType": "databricks_service_principal_account", "configId": "id123", } ``` ``` { "credentialType": "azure_service_principal", "clientId": "client123", "clientSecret": "secret123", "azureTenantId": "tenant123" } ``` ``` { "credentialType": "azure_service_principal", "configId": "id123", } ``` ``` { "credentialType": "adls_gen2_oauth", "clientId": "client123", "clientSecret": "secret123", "oauthScopes": ["scope123"] } ``` ``` { "credentialType": "adls_gen2_oauth", "configId": "id123", } ``` --- # Administration URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/index.html # Administration The administration section provides details for users and administrators about managing credentials and sharing permissions. --- # Sharing URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/sharing.html # Sharing Once you have created entities in DataRobot, you may want to share them with collaborators. DataRobot provides an API for sharing the following entities: > Data sources and data stores ( seeDatabase connectivityfor more info on connecting to JDBC databases)DatasetsProjectsCalendar filesModel deployments (seeDeployment sharingfor more information on sharing deployments)Use Cases (Sharing for Use Cases is slightly different than what’s documented on this page. SeeUse Case sharingfor more information and examples.) ## Access levels Entities can be shared at varying access levels. For example, you can allow someone to create projects from a data source you have built without allowing them to delete it. Each entity type uses slightly different permission names intended to specifically convey what kind of actions are available. These roles fall into three categories. These generic role names can be used in the sharing API for any entity. For the complete set of actions granted by each role on a given entity, see the [UI documentation for roles and permissions](https://docs.datarobot.com/en/docs/get-started/acct-mgmt/data-sharing/roles-permissions.html). > OWNERUsed for all entities.Allows any action including deletion.READ_WRITEKnown asEDITORon data sources and data stores.Allows modifications to the state, such as renaming and creating data sources from a data store, butnotdeleting the entity.READ_ONLYKnown asCONSUMERon data sources and data stores.For data sources, enables creating projects and predictions; for data stores, only allows you to view them. When a user’s new role is specified as `None`, their access will be revoked. In addition to the role, some entities (data sources and data stores) allow separate control over whether a new user should be able to share that entity further. When granting access to a user, the `can_share` parameter determines whether that user can, in turn, share this entity with another user. When this parameter is set as false, the user in question has all the access to the entity granted by their role and can remove themselves if desired, but are unable to change the role of any other user. ## Examples Transfer access to the data source from [mailto:old_user@datarobot.com](mailto:old_user@datarobot.com) to [mailto:new_user@datarobot.com](mailto:new_user@datarobot.com). ``` import datarobot as dr new_access = dr.SharingAccess( "new_user@datarobot.com", dr.enums.SHARING_ROLE.OWNER, can_share=True, ) access_list = [dr.SharingAccess("old_user@datarobot.com", None), new_access] dr.DataSource.get('my-data-source-id').share(access_list) ``` To check access to a project: ``` import datarobot as dr project = dr.Project.create('mydata.csv', project_name='My Data') access_list = project.get_access_list() access_list[0].username ``` To transfer ownership of all projects owned by your account to [mailto:new_user@datarobot.com](mailto:new_user@datarobot.com) without sending notifications: ``` import datarobot as dr # Put path to YAML credentials below dr.Client(config_path= '.yaml') # Get all projects for your account and store the ids in a list projects = dr.Project.list() project_ids = [project.id for project in projects] # List of emails to share with share_targets = ['new_user@datarobot.com'] # Target role target_role = dr.enums.SHARING_ROLE.OWNER for pid in project_ids: project = dr.Project.get(project_id=pid) shares = [] for user in share_targets: shares.append(dr.SharingAccess(username=user, role=target_role)) project.share(shares, send_notification=False) ``` --- # Data wrangling URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/data/data_wrangling.html # Recipes To clean, prepare, and wrangle your data into your desired shape, DataRobot provides reusable recipes for data preparation. Each recipe acts like a blueprint, taking one or more datasets or data sources as input and applying a series of operations to filter, modify, join or transform your data. You can then use the recipe to create a dataset ready for consumption. Recipes allow for quick iteration on data prep workflows, and enable re-use via its simple operations API. ## Recipe terminology Recipes use the following terminology: - Recipe : A reusable blueprint for how to create a new dataset by applying operations to transform one or more data inputs. - Recipe dialect : The dialect data wrangling operations should use when working with recipe inputs. For example, use Snowflake dialect when working with data assets from Snowflake. - Input : A dataset or data source providing data to a recipe. A recipe can have multiple inputs. A recipe's inputs must either be all datasets, or all tables from data sources pointing to the same data store. - Primary input : The input used as the base for the recipe. If no operations are applied by the recipe, a dataset identical to the primary input will be output by the recipe. A recipe will only have a single primary input. - Secondary input : An additional input to a recipe. A recipe can have multiple secondary inputs. Data from secondary inputs must be introduced into a recipe via join or other similar operation. - Recipe preview : A sample view of the recipe's data computed by applying the operations in a recipe on its inputs. The data featured in a recipe's preview is generally a sample of the recipe's fully transformed data. - Sampling : A setting through which the number of rows read from a recipe's primary input is modified when computing the recipe's preview. - Downsampling : A setting through which the number of rows written to the dataset published by a recipe is modified. - Operation : A way to modify how a recipe works with data from its inputs. - Wrangling operation : Transformation to apply to a recipe's data. Recipes can stack multiple wrangling operations on top of each other to transform data from its inputs. - Downsampling operation : Modification to the recipe's number of rows to write to a dataset when publishing. Recipes can optionally use a single downsampling operation. - Sampling operation : Modification to the number of rows to read from a recipe's primary data input. Recipes can optionally set a single sampling operation on their primary input. - Publishing : Action to create a new dataset containing the result of applying the recipe's operations on its inputs. Review the recommended workflow to create, iterate, and publish with recipes below. 1. Create a datarobot.Recipe to work on a datarobot.Dataset or data from a datarobot.DataSource . The recipe will belong to a datarobot.Usecase . 2. Modify the recipe by updating its metadata, settings, inputs, operations, or downsampling. 3. Verify the recipe's data by requesting a recipe preview. If you are unhappy with the result, go back to step 2. 4. Publish the recipe to create a new datarobot.Dataset constructed according to the transformations in the recipe. ## Create a recipe There are two ways to create a recipe. You can use either a dataset or a table from a JDBC data source. They become the primary input for the recipe. You will also need a [datarobot.Usecase](https://docs.datarobot.com/en/docs/api/reference/sdk/use-cases.html#datarobot.UseCase), as each recipe will belong to a use case. Choose the [DataWranglingDialect](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.enums.DataWranglingDialect) that best matches the source of the dataset or data source. ### Create a recipe from a dataset Use the [Recipe.from_dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.from_dataset) method to create a recipe from an existing [datarobot.Dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset): ``` >>> import datarobot as dr >>> from datarobot.enums import DataWranglingDialect, RecipeType >>> from datarobot.models.recipe_operation import RandomSamplingOperation >>> >>> # Get your use case and dataset >>> my_use_case = dr.UseCase.list(search_params={"search": "My Use Case"})[0] >>> dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f123') >>> >>> # Create a recipe from the dataset >>> recipe = dr.Recipe.from_dataset( ... use_case=my_use_case, ... dataset=dataset, ... dialect=DataWranglingDialect.SPARK, ... recipe_type=RecipeType.WRANGLING, ... sampling=RandomSamplingOperation(rows=500) ... ) ``` ### Create a recipe from a JDBC table Use the [Recipe.from_data_store](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.from_data_store) method to create a recipe directly from tables in a connected data source: ``` >>> import datarobot as dr >>> from datarobot.enums import DataWranglingDataSourceTypes, DataWranglingDialect, RecipeType >>> from datarobot.models.recipe import DataSourceInput >>> from datarobot.models.recipe_operation import LimitSamplingOperation >>> >>> # Configure your data source input >>> data_source_input = DataSourceInput( ... canonical_name='Sales_Data_Connection', # data connection name ... table='sales_transactions', ... schema='PUBLIC', ... sampling=LimitSamplingOperation(rows=1000) ... ) >>> >>> # Get your use case and data store >>> my_use_case = dr.UseCase.list(search_params={"search": "Sales Analysis"})[0] >>> data_store = dr.DataStore.get('2g33a1b2c9e88f0001e6f657') >>> >>> # Create recipe from data source >>> recipe = dr.Recipe.from_data_store( ... use_case=my_use_case, ... data_store=data_store, ... data_source_type=DataWranglingDataSourceTypes.JDBC, ... dialect=DataWranglingDialect.POSTGRES, ... data_source_inputs=[data_source_input], ... recipe_type=RecipeType.WRANGLING ... ) ``` ## Retrieve recipes You can retrieve a specific recipe by ID, or a list of all recipes, filtering the list as required. ``` >>> import datarobot as dr >>> >>> # Get a specific recipe by ID >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # List all recipes >>> all_recipes = dr.Recipe.list() >>> >>> # Filter recipes. Use any number of params to filter. >>> filtered_recipes = dr.Recipe.list( ... search="My Recipe Name", ... dialect=dr.enums.DataWranglingDialect.SPARK, ... status="draft", ... recipe_type=dr.enums.RecipeType.WRANGLING, ... order_by="-updatedAt", # Most recently updated first ... created_by_username="data_scientist_user" ... ) ``` ### Retrieve information about a recipe The recipe object contains basic information about the recipe that you can query, as shown below. ``` >>> import datarobot as dr >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> recipe.id u'690bbf77aa31530d8287ae5f' >>> recipe.name u"Customer Segmentation Dataset Recipe" ``` You can also retrieve the list of inputs and operations, as well as the settings for downsampling and general recipe settings. ``` >>> import datarobot as dr >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> # Access inputs, operations, downsampling and settings >>> inputs = recipe.inputs >>> primary_input = inputs[0] # First input is the primary input >>> secondary_inputs = inputs[1:] # All others in the list are secondary inputs >>> >>> operations = recipe.operations >>> downsampling_operation = recipe.downsampling >>> settings = recipe.settings ``` ## Update recipe metadata fields You can update the recipe's metadata fields (name, description, etc.) with [Recipe.update](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) as follows: ``` >>> import datarobot as dr >>> >>> # Retrieve an existing recipe >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Update metadata fields >>> recipe.update( ... name="Customer Segmentation Dataset Recipe", ... description="Recipe to create customer segmentation dataset." ... ) ``` ## Update recipe inputs You can update the list of inputs for a recipe with the [Recipe.update](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) method as shown below. By updating the list of inputs, you change the data fed into the recipe to transform. The first input in the list will be the recipe's primary input, with the rest being secondary inputs. > [!NOTE] Recipe input considerations > The [Recipe.update](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) method will replace all existing inputs. If adding inputs, always include the existing primary input to avoid breaking the recipe. > > Data from secondary inputs will not appear in the recipe preview unless somehow joined or combined with data from the primary input. > > Recipe inputs must either be all datasets, or all tables from data sources pointing to the same data store. ``` >>> import datarobot as dr >>> from datarobot.models.recipe import RecipeDatasetInput, JDBCTableDataSourceInput >>> >>> # Get the recipe and additional datasets >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> secondary_dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f456') >>> >>> # Add a secondary dataset input if the primary input is also a dataset >>> recipe.update( ... inputs=[ ... recipe.inputs[0], # Keep the original primary input ... RecipeDatasetInput.from_dataset( ... dataset=secondary_dataset, ... alias='customers_data' ... ) ... ] ... ) >>> >>> # You can also add data from a table in a data store >>> data_store = dr.DataStore.get('5e1b4f8f2a3c4d5e6f7g8h9i') >>> data_source = DataSource.create( ... data_source_type="jdbc", ... canonical_name="My Snowflake connection", ... params=dr.DataSourceParameters( ... data_store_id=data_store.id, ... schema="PUBLIC", ... table="stock_prices" ... ) ... ) >>> table = data_source.create_dataset() >>> # Add data from a table in a data store if the primary input is also a table from the same data store >>> recipe.update( ... inputs=[ ... recipe.inputs[0], # Primary input ... JDBCTableDataSourceInput( ... input_type=RecipeInputType.DATASOURCE, ... data_source_id=data_source.id, ... data_store_id=data_store.id, ... dataset_id=table.id, ... sampling=LimitSamplingOperation(rows=250), ... alias='my_table_alias' ... ) ... ] ... ) ``` ### Update primary input sampling You can choose to limit the number of rows to work with when iterating on your recipe operations. By specifying a [sampling operation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#sampling-operations) on the primary input of your recipe, you enable faster computation of the recipe preview. Sampling operations will not modify the number of rows when publishing to a dataset. You should only specify a sampling operation on the primary input. Since secondary inputs are always joined or combined with the primary input, the primary input is the only input that determines the number of rows to show in the recipe preview. ``` >>> from datarobot.models.recipe_operation import LimitSamplingOperation >>> >>> # Configure sampling for an input >>> my_dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f456') >>> dataset_input = RecipeDatasetInput.from_dataset( ... dataset=my_dataset, ... alias='sampled_data', ... sampling=LimitSamplingOperation(rows=100) ... ) >>> # Update recipe with sampled input >>> recipe.update(inputs=[dataset_input]) ``` ## Update recipe wrangling operations [Wrangling operations](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#wrangling-operations) are the building blocks of your recipe and define the transformations applied to your data. Operations are processed sequentially; the output of one operation becomes the input for the next operation, creating a transformation pipeline. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import * >>> from datarobot.enums import FilterOperationFunctions, AggregationFunctions >>> >>> # Get your recipe >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Create a series of operations >>> operations = [ ... # Filter rows where age > 18 ... FilterOperation( ... conditions=[ ... FilterCondition( ... column="age", ... function=FilterOperationFunctions.GREATER_THAN, ... function_arguments=[18] ... ) ... ], ... keep_rows=True ... ), ... # Then create new column with full name ... ComputeNewOperation( ... expression="CONCAT(first_name, " ", last_name)", ... new_feature_name="full_name" ... ), ... # Then group by department and calculate average salary ... AggregationOperation( ... aggregations=[ ... AggregateFeature( ... feature="salary", ... functions=[AggregationFunctions.AVERAGE] ... ) ... ], ... group_by_columns=["department"] ... ), ... ] >>> >>> # Update the recipe with new list of wrangling operations >>> recipe.update(operations=operations) ``` Below appears the list of available data wrangling operations with an example of their transformation. ### Lags operation The [LagsOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.LagsOperation) creates lagged versions of a column based on datetime ordering. The operation creates new columns for each specified lag. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import LagsOperation >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Create lags for 1 and 2 days for stock price analysis >>> lags_op = LagsOperation( ... column="stock_price", ... orders=[1, 2], ... datetime_partition_column="trade_date", ... multiseries_id_column="ticker_symbol" # For multiseries data (multiple stocks in this example) ... ) >>> >>> # Apply the operation to the recipe >>> recipe.update(operations=[lags_op]) ``` Primary input dataset: | ticker_symbol | trade_date | stock_price | | --- | --- | --- | | AAPL | 2024-01-01 | 150.00 | | AAPL | 2024-01-02 | 152.50 | | AAPL | 2024-01-03 | 149.75 | | AAPL | 2024-01-04 | 153.20 | | MSFT | 2024-01-01 | 380.00 | | MSFT | 2024-01-02 | 385.75 | | MSFT | 2024-01-03 | 382.30 | | MSFT | 2024-01-04 | 388.90 | Recipe preview: | ticker_symbol | trade_date | stock_price | stock_price (1st lag) | stock_price (2nd lag) | | --- | --- | --- | --- | --- | | AAPL | 2024-01-01 | 150.00 | | | | AAPL | 2024-01-02 | 152.50 | 150.00 | | | AAPL | 2024-01-03 | 149.75 | 152.50 | 150.00 | | AAPL | 2024-01-04 | 153.20 | 149.75 | 152.50 | | MSFT | 2024-01-01 | 380.00 | | | | MSFT | 2024-01-02 | 385.75 | 380.00 | | | MSFT | 2024-01-03 | 382.30 | 385.75 | 380.00 | | MSFT | 2024-01-04 | 388.90 | 382.30 | 385.75 | ### Window categorical statistics operation The [WindowCategoricalStatsOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.WindowCategoricalStatsOperation) calculates categorical statistics for a rolling window, creating new columns for each statistical method. This can be used to track trends in categorical data over time. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import WindowCategoricalStatsOperation >>> from datarobot.enums import CategoricalStatsMethods >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Compute most frequent purchase in last 3 purchases >>> window_cat_op = WindowCategoricalStatsOperation( ... column="product_category", ... window_size=3, # Last 3 purchases ... methods=[CategoricalStatsMethods.MOST_FREQUENT], ... datetime_partition_column="purchase_date", ... multiseries_id_column="customer_id" ... ) >>> >>> # Apply the operation to the recipe >>> recipe.update(operations=[window_cat_op]) ``` Primary input dataset: | customer_id | purchase_date | product_category | | --- | --- | --- | | CUST001 | 2024-01-01 | Electronics | | CUST001 | 2024-01-02 | Clothing | | CUST001 | 2024-01-03 | Electronics | | CUST001 | 2024-01-04 | Electronics | | CUST002 | 2024-01-01 | Books | | CUST002 | 2024-01-02 | Books | | CUST002 | 2024-01-03 | Electronics | | CUST002 | 2024-01-04 | Books | Recipe preview: | customer_id | purchase_date | product_category | product_category (3 rows most frequent) | | --- | --- | --- | --- | | CUST001 | 2024-01-01 | Electronics | Electronics | | CUST001 | 2024-01-02 | Clothing | Electronics | | CUST001 | 2024-01-03 | Electronics | Electronics | | CUST001 | 2024-01-04 | Electronics | Electronics | | CUST002 | 2024-01-01 | Books | Books | | CUST002 | 2024-01-02 | Books | Books | | CUST002 | 2024-01-03 | Electronics | Books | | CUST002 | 2024-01-04 | Books | Books | ### Window numerical statistics operation The [WindowNumericStatsOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.WindowNumericStatsOperation) calculates numeric statistics for a rolling window, creating new columns for each statistical method. This operation is useful for computing moving averages, maximums, minimums, and other statistics over time. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import WindowNumericStatsOperation >>> from datarobot.enums import NumericStatsMethods >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Track max and average of last 3 transactions >>> window_num_op = WindowNumericStatsOperation( ... column="sales_amount", ... window_size=3, # Last 3 transactions ... methods=[NumericStatsMethods.AVG, NumericStatsMethods.MAX], ... datetime_partition_column="transaction_date", ... multiseries_id_column="store_id" ... ) >>> >>> # Apply the operation to the recipe >>> recipe.update(operations=[window_num_op]) ``` Primary input dataset: | store_id | transaction_date | sales_amount | | --- | --- | --- | | STORE01 | 2024-01-01 | 100.00 | | STORE01 | 2024-01-02 | 150.00 | | STORE01 | 2024-01-03 | 120.00 | | STORE01 | 2024-01-04 | 200.00 | | STORE02 | 2024-01-01 | 80.00 | | STORE02 | 2024-01-02 | 90.00 | | STORE02 | 2024-01-03 | 110.00 | | STORE02 | 2024-01-04 | 95.00 | Recipe preview: | store_id | transaction_date | sales_amount | sales_amount (3 rows avg) | sales_amount (3 rows max) | | --- | --- | --- | --- | --- | | STORE01 | 2024-01-01 | 100.00 | 100.00 | 100.00 | | STORE01 | 2024-01-02 | 150.00 | 125.00 | 150.00 | | STORE01 | 2024-01-03 | 120.00 | 123.33 | 150.00 | | STORE01 | 2024-01-04 | 200.00 | 156.67 | 200.00 | | STORE02 | 2024-01-01 | 80.00 | 80.00 | 80.00 | | STORE02 | 2024-01-02 | 90.00 | 85.00 | 90.00 | | STORE02 | 2024-01-03 | 110.00 | 93.33 | 110.00 | | STORE02 | 2024-01-04 | 95.00 | 98.33 | 110.00 | ### Time series operation The [TimeSeriesOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.TimeSeriesOperation) generates a dataset ready for time series modeling by creating forecast points, distances, and various time-aware features. By defining a task plan, multiple time-series transformations like lags and rolling statistics are executed and added as features to the recipe data. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import TimeSeriesOperation, TaskPlanElement, Lags >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Define task plan for feature engineering >>> task_plan = [ ... TaskPlanElement( ... column="sales_amount", ... task_list=[Lags(orders=[1])] ... ) ... ] >>> >>> # Create time series operation >>> time_series_op = TimeSeriesOperation( ... target_column="sales_amount", ... datetime_partition_column="sale_date", ... forecast_distances=[1], # Predict 1 period ahead ... task_plan=task_plan ... ) >>> >>> # Apply the operation to the recipe >>> recipe.update(operations=[time_series_op]) ``` Primary input dataset: | store_id | sale_date | sales_amount | | --- | --- | --- | | STORE01 | 2024-01-01 | 1000 | | STORE01 | 2024-01-02 | 1200 | | STORE01 | 2024-01-03 | 1100 | | STORE01 | 2024-01-04 | 1300 | Recipe preview: | store_id (actual) | sale_date (actual) | sales_amount (actual) | Forecast Point | Forecast Distance | sales_amount (1st lag) | sales_amount (naive 1 row seasonal value) | | --- | --- | --- | --- | --- | --- | --- | | STORE01 | 2024-01-02 | 1200 | 2024-01-01 | 1 | 1000 | 1000 | | STORE01 | 2024-01-03 | 1100 | 2024-01-02 | 1 | 1200 | 1200 | | STORE01 | 2024-01-04 | 1300 | 2024-01-03 | 1 | 1100 | 1100 | ### Compute new operation The [ComputeNewOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.ComputeNewOperation) creates a new feature using a SQL expression, allowing you to derive calculated fields from existing columns. This operation can be useful for creating custom business logic, mathematical transformations, and feature combinations. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import ComputeNewOperation >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Create compute new operation to compute total cost, factoring in a discount % >>> compute_op = ComputeNewOperation( ... expression="ROUND(quantity * unit_price * (1 - discount), 2)", ... new_feature_name="total_cost" ... ) >>> >>> # Apply the operation to the recipe >>> recipe.update(operations=[compute_op]) ``` Primary input dataset: | order_id | quantity | unit_price | discount | | --- | --- | --- | --- | | ORD001 | 3 | 25.50 | 0.10 | | ORD002 | 1 | 15.00 | 0.00 | | ORD003 | 2 | 40.00 | 0.15 | | ORD004 | 5 | 12.25 | 0.05 | Recipe preview: | order_id | quantity | unit_price | discount | total_cost | | --- | --- | --- | --- | --- | | ORD001 | 3 | 25.50 | 0.10 | 68.85 | | ORD002 | 1 | 15.00 | 0.00 | 15.00 | | ORD003 | 2 | 40.00 | 0.15 | 68.00 | | ORD004 | 5 | 12.25 | 0.05 | 58.19 | ### Rename column operation The [RenameColumnsOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.RenameColumnsOperation) renames one or more columns. This operation is often useful for standardizing column names, making them more descriptive, or ensuring consistent column naming for specific downstream processes. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import RenameColumnsOperation >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Rename customer id, product name and quantity columns >>> rename_op = RenameColumnsOperation( ... column_mappings={ ... 'cust_id': 'customer_id', ... 'prod_name': 'product_name', ... 'qty': 'quantity' ... } ... ) >>> >>> # Apply the operation to the recipe >>> recipe.update(operations=[rename_op]) ``` Primary input dataset: | cust_id | prod_name | qty | price | | --- | --- | --- | --- | | C001 | Widget A | 3 | 25.99 | | C002 | Gadget B | 1 | 15.50 | | C001 | Tool C | 2 | 45.00 | | C003 | Widget A | 5 | 25.99 | Recipe preview: | customer_id | product_name | quantity | price | | --- | --- | --- | --- | | C001 | Widget A | 3 | 25.99 | | C002 | Gadget B | 1 | 15.50 | | C001 | Tool C | 2 | 45.00 | | C003 | Widget A | 5 | 25.99 | ### Filter operation The [FilterOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.FilterOperation) removes or keeps rows based on one or more filter conditions. Apply multiple conditions with AND/OR logic to create complex filtering rules. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import FilterOperation, FilterCondition >>> from datarobot.enums import FilterOperationFunctions >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Create filter conditions to keep customers over 18 with active status >>> conditions = [ ... FilterCondition( ... column="age", ... function=FilterOperationFunctions.GREATER_THAN_OR_EQUALS, ... function_arguments=[18] ... ), ... FilterCondition( ... column="status", ... function=FilterOperationFunctions.EQUALS, ... function_arguments=["active"] ... ) ... ] >>> >>> # Create filter operation >>> filter_op = FilterOperation( ... conditions=conditions, ... keep_rows=True, # Keep matching rows ... operator="and" # Both conditions must be true ... ) >>> >>> # Apply the operation to the recipe >>> recipe.update(operations=[filter_op]) ``` Primary input dataset: | customer_id | age | status | purchase_amount | | --- | --- | --- | --- | | C001 | 25 | active | 150.00 | | C002 | 17 | active | 75.00 | | C003 | 30 | inactive | 200.00 | | C004 | 22 | active | 95.00 | Recipe preview: | customer_id | age | status | purchase_amount | | --- | --- | --- | --- | | C001 | 25 | active | 150.00 | | C004 | 22 | active | 95.00 | ### Drop columns operation The [DropColumnsOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.DropColumnsOperation) removes one or more columns. This operation is useful for eliminating unnecessary fields, sensitive information, or columns that won't be used in downstream processes. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import DropColumnsOperation >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Create operation to drop 2 extra columns >>> drop_op = DropColumnsOperation( ... columns=['internal_notes', 'legacy_id'] ... ) >>> # Apply the operation to the recipe >>> recipe.update(operations=[drop_op]) ``` Primary input dataset: | customer_id | name | email | internal_notes | legacy_id | | --- | --- | --- | --- | --- | | C001 | John Doe | john@email.com | VIP customer | L001 | | C002 | Jane Doe | jane@email.com | New customer | L002 | | C003 | Bob Lee | bob@email.com | Frequent buyer | L003 | Recipe preview: | customer_id | name | email | | --- | --- | --- | | C001 | John Doe | john@email.com | | C002 | Jane Doe | jane@email.com | | C003 | Bob Lee | bob@email.com | ### Dedupe rows operation The [DedupeRowsOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.DedupeRowsOperation) removes duplicate rows, keeping only unique combinations of values. The operation references values across all columns. This operation helps clean data by eliminating redundant records. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import DedupeRowsOperation >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Create dedupe rows operation >>> dedupe_op = DedupeRowsOperation() >>> # Apply the operation to the recipe >>> recipe.update(operations=[dedupe_op]) ``` Primary input dataset: | customer_id | product | quantity | price | | --- | --- | --- | --- | | C001 | Widget A | 2 | 25.99 | | C002 | Gadget B | 1 | 15.50 | | C001 | Widget A | 2 | 25.99 | | C003 | Tool C | 3 | 45.00 | | C002 | Gadget B | 1 | 15.50 | Recipe preview: | customer_id | product | quantity | price | | --- | --- | --- | --- | | C001 | Widget A | 2 | 25.99 | | C002 | Gadget B | 1 | 15.50 | | C003 | Tool C | 3 | 45.00 | ### Find-and-replace operation The [FindAndReplaceOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.FindAndReplaceOperation) searches for specific strings or patterns in a column and replaces them with new values. The operation supports exact matches, partial matches, or regular expressions for flexible text manipulation. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import FindAndReplaceOperation >>> from datarobot.enums import FindAndReplaceMatchMode >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Replace instances of 'In Progress' (case insensitive) with 'Active' >>> replace_op = FindAndReplaceOperation( ... column="status", ... find="In Progress", ... replace_with="Active", ... match_mode=FindAndReplaceMatchMode.EXACT, ... is_case_sensitive=False ... ) >>> # Apply the operation to the recipe >>> recipe.update(operations=[replace_op]) ``` Primary input dataset: | order_id | status | customer_name | | --- | --- | --- | | ORD001 | In Progress | John Smith | | ORD002 | Completed | Jane Doe | | ORD003 | in progress | Bob Johnson | | ORD004 | Cancelled | Alice Brown | Recipe preview: | order_id | status | customer_name | | --- | --- | --- | | ORD001 | Active | John Smith | | ORD002 | Completed | Jane Doe | | ORD003 | Active | Bob Johnson | | ORD004 | Cancelled | Alice Brown | ### Aggregation operation The [AggregationOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.AggregationOperation) groups data by the specified columns and calculates summary features like sum, average and count. This operation is useful for creating analytical summaries and computing derived features. A new column will be created for each aggregation function applied for each feature chosen for aggregation. ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import AggregationOperation >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Group by customer id and product category >>> # Compute the sum of orders and customer's average order amount >>> agg_op = AggregationOperation( ... group_by_columns=['customer_id', 'product_category'], ... aggregations=[ ... AggregateFeature( ... feature="order_amount", ... functions=[AggregationFunctions.SUM, AggregationFunctions.AVERAGE] ... ) ... ] ... ) >>> # Apply the operation to the recipe >>> recipe.update(operations=[agg_op]) ``` Primary input dataset: | customer_id | product_category | order_id | order_amount | | --- | --- | --- | --- | | C001 | Electronics | ORD001 | 150.00 | | C001 | Electronics | ORD002 | 200.00 | | C001 | Clothing | ORD003 | 75.00 | | C002 | Electronics | ORD004 | 300.00 | | C002 | Electronics | ORD005 | 125.00 | Recipe preview: | customer_id | product_category | order_amount_sum | order_amount_avg | | --- | --- | --- | --- | | C001 | Electronics | 350.00 | 175.00 | | C001 | Clothing | 75.00 | 75.00 | | C002 | Electronics | 425.00 | 212.50 | ### Join operation The [JoinOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.JoinOperation) allows for joining an additional data input to the recipe's current data. This operation can enable you to enrich your primary dataset with additional information from secondary datasets. The join operation only supports one or more equality predicates as the join condition. > [!NOTE] Note > The additional data input is treated as the right side of the join. ``` >>> import datarobot as dr >>> from datarobot.models.recipe import RecipeDatasetInput >>> from datarobot.models.recipe_operation import JoinOperation >>> from datarobot.enums import JoinType >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Get the secondary dataset and add it as an input >>> dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f123') >>> recipe.update( ... inputs=[ ... recipe.inputs[0], # Keep the original primary input ... RecipeDatasetInput.from_dataset( ... dataset=dataset, ... alias='customers' ... ) ... ] ... ) >>> >>> # Join secondary dataset on customer id >>> # Right dataset in join will always be the new dataset >>> join_op = JoinOperation.join_dataset( ... dataset=dataset, ... join_type=JoinTypes.INNER, ... right_prefix='cust_', ... left_keys=['customer_id'], ... right_keys=['id'] ... ) >>> # Apply the operation to the recipe >>> recipe.update(operations=[join_op]) ``` Primary input dataset (orders) | order_id | customer_id | amount | | --- | --- | --- | | ORD001 | C001 | 150.00 | | ORD002 | C002 | 200.00 | | ORD003 | C001 | 75.00 | Secondary input dataset (customers) | id | name | city | | --- | --- | --- | | C001 | John Smith | New York | | C002 | Jane Doe | Los Angeles | | C003 | Bob Lee | Chicago | Recipe preview: | order_id | customer_id | amount | cust_id | cust_name | cust_city | | --- | --- | --- | --- | --- | --- | | ORD001 | C001 | 150.00 | C001 | John Smith | New York | | ORD002 | C002 | 200.00 | C002 | Jane Doe | Los Angeles | | ORD003 | C001 | 75.00 | C001 | John Smith | New York | ## Set recipe SQL transformation directly For advanced use cases, you can set the recipe's transformation using a SQL expression. This provides maximum flexibility for complex operations that may not be available through standard wrangling operations. Important: Setting SQL directly changes the recipe type to SQL and bypasses any existing wrangling operations. ``` >>> import datarobot as dr >>> >>> # Get your recipe >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Define your SQL transformation >>> sql_query = "MY SQL EXPRESSION HERE" >>> # Update the recipe with SQL >>> recipe.update(sql=sql_query) ``` ## Preview recipe data Before publishing your recipe, you can preview the transformed data to validate your transformations and ensure they produce the expected results with [Recipe.get_preview](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.get_preview). ``` >>> import datarobot as dr >>> >>> # Get your recipe >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Generate a preview of the transformed data >>> preview = recipe.get_preview() >>> # View preview data as a DataFrame >>> preview.df ``` ## Update recipe downsampling Downsampling modifies the size of the dataset published by the recipe, which can improve performance for large datasets and speed up development and testing. This is particularly useful when working with millions of rows where a representative sample is sufficient when publishing to a dataset. Downsampling does not affect the number of rows in the recipe preview. Set a recipe's downsampling with a [downsampling operation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#downsampling-operations). ``` >>> import datarobot as dr >>> from datarobot.models.recipe_operation import RandomDownsamplingOperation >>> >>> # Get your recipe >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Configure random downsampling to 50,000 rows >>> downsampling = RandomDownsamplingOperation(max_rows=50_000) >>> # Apply downsampling to the recipe >>> recipe.update(downsampling=downsampling) >>> # Disable downsampling >>> recipe.update(downsampling=None) ``` ## Publish recipe to dataset Once your recipe is complete, you can publish it with [Recipe.publish_to_dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.publish_to_dataset) to create a dataset with your transformed data. ``` >>> import datarobot as dr >>> >>> # Get your recipe >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') >>> >>> # Publish recipe to create a new dataset >>> dataset = recipe.publish_to_dataset( ... name="Customer Segmentation Data", ... do_snapshot=True ... ) >>> >>> # Publish and attach to an existing use case >>> use_case = dr.UseCase.get('5e1b4f8f2a3c4d5e6f7g8h9i') >>> dataset_with_use_case = recipe.publish_to_dataset( ... name="Advanced Customer Analytics", ... use_cases=use_case ... ) ``` --- # Database connectivity URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/data/database_connectivity.html # Build data connections [Databases](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/add-data/connect.html) are a widely used tool to carry valuable business data. To enable integration with a variety of enterprise databases, DataRobot provides a self-service JDBC product for database connectivity setup. Once configured, you can read data from production databases for model building and predictions. This allows you to quickly train and retrain models on that data, and avoids the unnecessary step of exporting data from your enterprise database to a CSV for ingest to DataRobot. With access to more diverse data, you can build more accurate models. ## Database connection terminology Database connection configuration uses the following terminology: - Data store : A configured connection to a database. It has a name, a specified driver, and a JDBC URL. You can register data stores with DataRobot for ease of re-use. A data store has one connector but can have many data sources. - Data source : A configured connection to the backing data store (the location of data within a given endpoint). A data source specifies, via a SQL query or a selected table and schema data, which data to extract from the data store to use for modeling or predictions. A data source has one data store and one connector but can have many datasets. - Data driver : The software that allows the application to interact with a database; each data store is associated with either a driver or a connector (created by the administrator). The driver configuration saves the storage location in the application of the JAR file and any additional dependency files associated with the driver. - Connector : Similarly to data drivers, a connector allows the application to interact with a database; each data store is associated with either a driver or a connector (created by the administrator). The connector configuration saves the storage location in the application of the JAR file and any additional dependency files associated with the connector. - Dataset : Data, a file or the content of a data source, at a particular point in time. A data source can produce multiple datasets; a dataset has exactly one data source. Review the workflow to set up projects or prediction datasets below. 1. An administrator sets up datarobot.DataDriver to access a particular database. For any particular driver, this setup is performed once for the entire system and the resulting driver is used by all users. 2. Users create a datarobot.DataStore which represents an interface to a particular database using that driver. 3. Users create a datarobot.DataSource representing a particular set of data to be extracted from the data store. 4. Users create projects and prediction datasets from a data source. Users can manage their data stores and data sources, while administrators can manage drivers by listing, retrieving, updating, and deleting existing instances of them. ## Create a driver To create a driver, administrators must specify the following: - class_name : The Java class name for the driver if the type is JDBC; otherwise None. - canonical_name : A user-friendly name or resulting driver to display in the API and the GUI. - files :A list of local files which contain the driver if the type is JDBC; otherwise omitted. - typ : The enum for the type of driver. Defaults to dr.enums.DataDriverTypes.JDBC and can also be dr.enums.DataDriverTypes.DR_DATABASE_V1 . - database_driver : The type of native database to use for non-JDBC. For example, dr.enums.DrDatabaseV1Types.BIGQUERY . ``` >>> import datarobot as dr >>> driver = dr.DataDriver.create( ... class_name='org.postgresql.Driver', ... canonical_name='PostgreSQL', ... files=['/tmp/postgresql-42.2.2.jar'] ... ) >>> driver DataDriver('PostgreSQL') ``` Use the code below to create a non-JDBC driver: ``` driver = dr.DataDriver.create(None, "BigQuery Native", typ=dr.enums.DataDriverTypes.DR_DATABASE_V1, database_driver=dr.enums.DrDatabaseV1Types.BIGQUERY) ``` To retrieve information about existing drivers, such as the driver ID for data store creation, you can use `dr.DataDriver.list()`. ## Create a data store After an administrator has created drivers, any user can use them to create a `DataStore`. A data store represents a JDBC database or a non-JDBC database. When creating them, you should specify the following: - type : The type must be either dr.enums.DataStoreTypes.DR_DATABASE_V1 or dr.enums.DataStoreTypes.JDBC . - canonical_name : A user-friendly name to display in the API and GUI for the data store. - driver_id : The ID of the driver to use to connect to the database. - jdbc_url : The full URL specifying the database connection settings such as the database type, server address, port, and database name if the type is JDBC. - fields : The fields used if the type is dr.enums.DataStoreTypes.DR_DATABASE_V1 . A list of dictionary entries, where each entry has an ID, a name, and a value field. > [!NOTE] Note > You can only create data stores with drivers when using the Python client. Drivers and connectors are not interchangeable for this method. To create a data store with a connector, instead use the [REST API](https://docs.datarobot.com/en/docs/api/reference/public-api/data_connectivity.html#create-a-data-store). ``` >>> import datarobot as dr >>> data_store = dr.DataStore.create( ... data_store_type='jdbc', ... canonical_name='Demo DB', ... driver_id='5a6af02eb15372000117c040', ... jdbc_url='jdbc:postgresql://my.db.address.org:5432/perftest' ... ) >>> data_store DataStore('Demo DB') >>> data_store.test(username='username', password='password') {'message': 'Connection successful'} ``` You can create a non-JDBC Data Store using fields instead: ``` >>> fields = [ ... { ... "id": "bq.project_id", ... "name": "Project Id", ... "value": "mldata-358421", ... } ... ] >>> data_store = dr.DataStore.create( ... data_store_type=dr.enums.DataStoreTypes.DR_DATABASE_V1, ... canonical_name='BigQuery Native Connection', ... driver_id=driver_id, ... fields=fields ... ) ``` ### View data stores To view data stores that already exist, use the code below. However, note that not all data stores show up by default with a call to `dr.DataStore.list()`. You must explicitly pass a `typ=dr.enums.DataStoreListTypes.ALL` argument, since the default is to only show JDBC connections. List all JDBC data stores (default): ``` data_stores = dr.DataStore.list() print(f"Found {len(data_stores)} DataStore(s):") for ds in data_stores: print(f" - {ds.canonical_name} (ID: {ds.id}, Type: {ds.data_store_type})") ``` List all data stores: ``` all_stores = dr.DataStore.list(typ=dr.enums.DataStoreListTypes.ALL) ``` ## Create a data source Once you have a data store, you can query datasets via the data source. When creating a data source, first create a [datarobot.DataSourceParameters](https://docs.datarobot.com/en/docs/api/reference/sdk/data-connectivity.html#datarobot.DataSourceParameters) object from a data store's ID and a query. Then, create the data source with the following: - type : The type must be either dr.enums.DataStoreTypes.DR_DATABASE_V1 or dr.enums.DataStoreTypes.JDBC . - canonical_name : A user-friendly name to display in the API and GUI. - params : The DataSourceParameters object. ``` >>> import datarobot as dr >>> params = dr.DataSourceParameters( ... data_store_id='5a8ac90b07a57a0001be501e', ... query='SELECT * FROM airlines10mb WHERE "Year" >= 1995;' ... ) >>> data_source = dr.DataSource.create( ... data_source_type='jdbc', ... canonical_name='airlines stats after 1995', ... params=params ... ) >>> data_source DataSource('airlines stats after 1995') ``` You can create a non-JDBC Data Store with fields. ``` >>> params = dr.DataSourceParameters( ... data_store_id=data_store_id, ... catalog=catalog, ... schema=schema, ... table=table, ... ) >>> data_source = dr.DataSource.create( ... data_source_type=dr.enums.DataStoreTypes.DR_DATABASE_V1, ... canonical_name='BigQuery Data Source', ... driver_id=driver_id, ... params=params ... ) ``` ## Create projects You can create new projects from a data source, demonstrated below. ``` >>> import datarobot as dr >>> project = dr.Project.create_from_data_source( ... data_source_id='5ae6eee9962d740dd7b86886', ... username='username', ... password='password' ... ) ``` As of v3.0 of the Python API client, you can alternatively pass in the `credential_id` of an existing [Dataset.Credential](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#datarobot.models.Credential) object. ``` >>> import datarobot as dr >>> project = dr.Project.create_from_data_source( ... data_source_id='5ae6eee9962d740dd7b86886', ... credential_id='9963d544d5ce3se783r12190' ... ) ``` Alternatively, pass in `credential_data`, which conforms to `CredentialDataSchema`. ``` >>> import datarobot as dr >>> s3_credential_data = {"credentialType": "s3", "awsAccessKeyId": "key123", "awsSecretAccessKey": "secret123"} >>> project = dr.Project.create_from_data_source( ... data_source_id='5ae6eee9962d740dd7b86886', ... credential_data=s3_credential_data ... ) ``` ## Create prediction datasets Given a data source, new prediction datasets can be created for any project. ``` >>> import datarobot as dr >>> project = dr.Project.get('5ae6f296962d740dd7b86887') >>> prediction_dataset = project.upload_dataset_from_data_source( ... data_source_id='5ae6eee9962d740dd7b86886', ... username='username', ... password='password' ... ) ``` --- # Dataset URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/data/dataset.html # Create and manage datasets To create a project and begin modeling, you first need to [upload your data to DataRobot](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/add-data/index.html) to prepare a dataset. ## Create a dataset There are several ways to create a dataset.[Dataset.upload](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.upload) takes either a path to a local file, a streamable file object via external URL, or a Pandas DataFrame. ``` >>> import datarobot as dr >>> # Upload a local file >>> dataset_one = dr.Dataset.upload("./data/examples.csv") >>> # Create a dataset with a URL >>> dataset_two = dr.Dataset.upload("https://raw.githubusercontent.com/curran/data/gh-pages/dbpedia/cities/data.csv") >>> # Create a dataset using a pandas DataFrame >>> dataset_three = dr.Dataset.upload(my_df) >>> # Create a dataset using a local file >>> with open("./data/examples.csv", "rb") as file_pointer: ... dataset_four = dr.Dataset.create_from_file(filelike=file_pointer) ``` [Dataset.create_from_file](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.create_from_file) can take either a path to a local file or any streamable file object. ``` >>> import datarobot as dr >>> dataset = dr.Dataset.create_from_file(file_path='data_dir/my_data.csv') >>> with open('data_dir/my_data.csv', 'rb') as f: ... other_dataset = dr.Dataset.create_from_file(filelike=f) ``` [Dataset.create_from_in_memory_data](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.create_from_in_memory_data) creates a dataset from either a `pandas.Dataframe` or a list of dictionaries representing rows of data. Dictionaries representing rows of data must contain the same keys. ``` >>> import pandas as pd >>> data_frame = pd.read_csv('data_dir/my_data.csv') >>> pandas_dataset = dr.Dataset.create_from_in_memory_data(data_frame=data_frame) >>> in_memory_data = [{'key1': 'value', 'key2': 'other_value', ...}, ... {'key1': 'new_value', 'key2': 'other_new_value', ...}, ...] >>> in_memory_dataset = dr.Dataset.create_from_in_memory_data(records=other_data) ``` [Dataset.create_from_url](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.create_from_url) takes CSV data from a URL. If you have set `DISABLE_CREATE_SNAPSHOT_DATASOURCE`, you must set `do_snapshot=False`. ``` >>> url_dataset = dr.Dataset.create_from_url('https://s3.amazonaws.com/my_data/my_dataset.csv', ... do_snapshot=False) ``` [Dataset.create_from_data_source](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.create_from_data_source) takes data from a data source. If you have set `DISABLE_CREATE_SNAPSHOT_DATASOURCE`, you must set `do_snapshot=False`. ``` >>> data_source_dataset = dr.Dataset.create_from_data_source(data_source.id, do_snapshot=False) ``` or ``` >>> data_source_dataset = data_source.create_dataset(do_snapshot=False) ``` ### Use datasets After creating a dataset, you can create [Projects](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/project.html#projects) from it and begin training models. You can also combine project creation and a dataset upload in one method using [Project.create](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.Project.create). However, using this method means the data is only accessible to the project which created it. ``` >>> project = dataset.create_project(project_name='New Project') >>> project.analyze_and_model('some target') Project(New Project) ``` ## Get information from a dataset The dataset object contains some basic information that you can query, as shown in the snippet below. ``` >>> dataset.id u'5e31cdac39782d0f65842518' >>> dataset.name u'my_data.csv' >>> dataset.categories ["TRAINING", "PREDICTION"] >>> dataset.created_at datetime.datetime(2020, 2, 7, 16, 51, 10, 311000, tzinfo=tzutc()) ``` The snippet below outlines several methods available to retrieve details from a dataset. ``` # Details >>> details = dataset.get_details() >>> details.last_modification_date datetime.datetime(2020, 2, 7, 16, 51, 10, 311000, tzinfo=tzutc()) >>> details.feature_count_by_type [FeatureTypeCount(count=1, feature_type=u'Text'), FeatureTypeCount(count=1, feature_type=u'Boolean'), FeatureTypeCount(count=16, feature_type=u'Numeric'), FeatureTypeCount(count=3, feature_type=u'Categorical')] >>> details.to_dataset().id == details.dataset_id True # Projects >>> dr.Project.create_from_dataset(dataset.id, project_name='Project One') Project(Project One) >>> dr.Project.create_from_dataset(dataset.id, project_name='Project Two') Project(Project Two) >>> dataset.get_projects() [ProjectLocation(url=u'https://app.datarobot.com/api/v2/projects/5e3c94aff86f2d10692497b5/', id=u'5e3c94aff86f2d10692497b5'), ProjectLocation(url=u'https://app.datarobot.com/api/v2/projects/5e3c94eb9525d010a9918ec1/', id=u'5e3c94eb9525d010a9918ec1')] >>> first_id = dataset.get_projects()[0].id >>> dr.Project.get(first_id).project_name 'Project One' # Features >>> all_features = dataset.get_all_features() >>> feature = next(dataset.iterate_all_features(offset=2, limit=1)) >>> feature.name == all_features[2].name True >>> print(feature.name, feature.feature_type, feature.dataset_id) (u'Partition', u'Numeric', u'5e31cdac39782d0f65842518') >>> feature.get_histogram().plot [{'count': 3522, 'target': None, 'label': u'0.0'}, {'count': 3521, 'target': None, 'label': u'1.0'}, ... ] # The raw data >>> with open('myfile.csv', 'wb') as f: ... dataset.get_file(filelike=f) ``` ## Retrieve datasets You can retrieve specific datasets, a list of all datasets, or an iterator that retrieves all or some datasets. ``` >>> dataset_id = '5e387c501a438646ed7bf0f2' >>> dataset = dr.Dataset.get(dataset_id) >>> dataset.id == dataset_id True # A blocking call that returns all datasets >>> dr.Dataset.list() [Dataset(name=u'Untitled Dataset', id=u'5e3c51e0f86f2d1087249728'), Dataset(name=u'my_data.csv', id=u'5e3c2028162e6a5fe9a0d678'), ...] # Avoid listing datasets that fail to properly upload >>> dr.Dataset.list(filter_failed=True) [Dataset(name=u'my_data.csv', id=u'5e3c2028162e6a5fe9a0d678'), Dataset(name=u'my_other_data.csv', id=u'3efc2428g62eaa5f39a6dg7a'), ...] # An iterator that lazily retrieves from the server page-by-page >>> from itertools import islice >>> iterator = dr.Dataset.iterate(offset=2) >>> for element in islice(iterator, 3): ... print(element) Dataset(name='some_data.csv', id='5e8df2f21a438656e7a23d12') Dataset(name='other_data.csv', id='5e8df2e31a438656e7a23d0b') Dataset(name='Untitled Dataset', id='5e6127681a438666cc73c2b0') ``` ## Manage datasets You can modify, delete, and restore datasets. Note that you need the dataset’s ID in order to restore it from deletion. If you do not keep track of the ID, you will be unable to restore a dataset. If your deleted dataset was used to create a project, that project can still access it, but you will not be able to create new projects using that dataset. ``` >>> dataset.modify(name='A Better Name') >>> dataset.name 'A Better Name' >>> new_project = dr.Project.create_from_dataset(dataset.id) >>> stored_id = dataset.id >>> dr.Dataset.delete(dataset.id) # new_project is still ok >>> dr.Project.create_from_dataset(stored_id) Traceback (most recent call last): ... datarobot.errors.ClientError: 410 client error: {u'message': u'Requested Dataset 5e31cdac39782d0f65842518 was previously deleted.'} >>> dr.Dataset.un_delete(stored_id) >>> dr.Project.create_from_dataset(stored_id, project_name='Successful') Project(Successful) ``` You can share a dataset as demonstrated in the following code snippet. ``` >>> from datarobot.enums import SHARING_ROLE >>> from datarobot.models.dataset import Dataset >>> from datarobot.models.sharing import SharingAccess >>> >>> new_access = SharingAccess( >>> "new_user@datarobot.com", >>> SHARING_ROLE.OWNER, >>> can_share=True, >>> ) >>> access_list = [ >>> SharingAccess("old_user@datarobot.com", SHARING_ROLE.OWNER, can_share=True), >>> new_access, >>> ] >>> >>> Dataset.get('my-dataset-id').share(access_list) ``` ## Manage dataset feature lists You can create, modify, and delete custom feature lists on a given dataset. Some feature lists are automatically created by DataRobot and cannot be modified or deleted. Note that you cannot restore a deleted feature list. ``` >>> dataset.get_featurelists() [DatasetFeaturelist(Raw Features), DatasetFeaturelist(universe), DatasetFeaturelist(Informative Features)] >>> dataset_features = [feature.name for feature in dataset.get_all_features()] >>> custom_featurelist = dataset.create_featurelist('Custom Features', dataset_features[:5]) >>> custom_featurelist DatasetFeaturelist(Custom Features) >>> dataset.get_featurelists() [DatasetFeaturelist(Raw Features), DatasetFeaturelist(universe), DatasetFeaturelist(Informative Features), DatasetFeaturelist(Custom Features)] >>> custom_featurelist.update('New Name') >>> custom_featurelist.name 'New Name' >>> custom_featurelist.delete() >>> dataset.get_featurelists() [DatasetFeaturelist(Raw Features), DatasetFeaturelist(universe), DatasetFeaturelist(Informative Features)] ``` ### Use credential data For methods that accept credential data instead of username and password or a credential ID, see the [Credential data](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#id1) section. --- # Feature discovery URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/data/feature_discovery.html # Feature Discovery [Feature Discovery](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/perform-safer.html) allows you to generate features automatically from secondary datasets connected to a primary dataset (training data). You can create this type of connection using relationship configuration. ## Register a primary dataset to create a project To create a Feature Discovery project, upload the primary (training) dataset from a [project](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/project.html#projects). ``` import datarobot as dr primary_dataset = dr.Dataset.create_from_file(file_path='your-training_file.csv') project = dr.Project.create_from_dataset(primary_dataset.id, project_name='Lending Club') ``` ## Register secondary datasets Next, register all the secondary datasets which you want to connect with the primary dataset. You can register the dataset using [Dataset.create_from_file](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.create_from_file), which can take either a path to a local file or any streamable file object. ``` profile_dataset = dr.Dataset.create_from_file(file_path='your_profile_file.csv') transaction_dataset = dr.Dataset.create_from_file(file_path='your_transaction_file.csv') ``` ## Create dataset definitions and relationships Create the [DatasetDefinition](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#dataset-definition) and [Relationship](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#relationship) for the profile and transaction datasets created above using helper functions. ``` profile_catalog_id = profile_dataset.id profile_catalog_version_id = profile_dataset.version_id transac_catalog_id = transaction_dataset.id transac_catalog_version_id = transaction_dataset.version_id profile_dataset_definition = dr.DatasetDefinition( identifier='profile', catalog_id=profile_catalog_id, catalog_version_id=profile_catalog_version_id ) transaction_dataset_definition = dr.DatasetDefinition( identifier='transaction', catalog_id=transac_catalog_id, catalog_version_id=transac_catalog_version_id, primary_temporal_key='Date' ) profile_transaction_relationship = dr.Relationship( dataset1_identifier='profile', dataset2_identifier='transaction', dataset1_keys=['CustomerID'], dataset2_keys=['CustomerID'] ) primary_profile_relationship = dr.Relationship( dataset2_identifier='profile', dataset1_keys=['CustomerID'], dataset2_keys=['CustomerID'], feature_derivation_window_start=-14, feature_derivation_window_end=-1, feature_derivation_window_time_unit='DAY', prediction_point_rounding=1, prediction_point_rounding_time_unit='DAY' ) dataset_definitions = [profile_dataset_definition, transaction_dataset_definition] relationships = [primary_profile_relationship, profile_transaction_relationship] ``` ## Create a relationship configuration Create a relationship configuration using the dataset definitions and relationships created above. ``` # Create the relationships configuration to define connection between the datasets relationship_config = dr.RelationshipsConfiguration.create(dataset_definitions=dataset_definitions, relationships=relationships) ``` ## Create a Feature Discovery project Once you have configured relationships for your datasets, you can create a Feature Discovery project. ``` # Set the datetime partitionining column (`date` in this example) partitioning_spec = dr.DatetimePartitioningSpecification('date') # As of v3.0, use ``Project.set_datetime_partitioning`` instead of passing the spec to ``Project.analyze_and_model`` via ``partitioning_method``. project.set_datetime_partitioning(datetime_partition_spec=partitioning_spec) # Set the target for the project and start Feature discovery (if ``Project.set_datetime_partitioning`` was used there is no need to pass ``partitioning_method``) project.analyze_and_model(target='BadLoan', relationships_configuration_id=relationship_config.id, mode='manual', partitioning_method=partitioning_spec) Project(train.csv) ``` To start training a model, reference the ref: `modeling ` documentation. ## Create secondary dataset configuration for predictions Create configurations for your secondary datasets with [Secondary Dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#secondary-dataset): ``` new_secondary_dataset_config = dr.SecondaryDatasetConfigurations.create( project_id=project.id, name='My config', secondary_datasets=secondary_datasets ) ``` For more details, reference the [Secondary Dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#secondary-dataset) configuration documentation. ## Make predictions with a trained model To make predictions with a trained model, reference the [Predictions documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/predictions/index.html). ``` dataset_from_path = project.upload_dataset( './data_to_predict.csv', secondary_datasets_config_id=new_secondary_dataset_config.id ) predict_job_1 = model.request_predictions(dataset_from_path.id) ``` ### Common errors #### Dataset registration failed You may have a job not successfully complete when registering a dataset: ``` datasetdr.Dataset.create_from_file(file_path='file.csv') datarobot.errors.AsyncProcessUnsuccessfulError: The job did not complete successfully. ``` There are two possible solutions: - Check the internet connectivity. Sometimes network flakiness can cause upload errors. - Check the dataset file size. If a file is too large, you should consider uploading the dataset via a URL rather than uploading the file directly. #### Relationship configuration errors It's possible to submit invalid field data when configuring data relationships: ``` datarobot.errors.ClientError: 422 client error: {u'message': u'Invalid field data', u'errors': {u'datasetDefinitions': {u'1': {u'identifier': u'value cannot contain characters: $ - " . { } / \\'}, u'0': {u'identifier': u'value cannot contain characters: $ - " . { } / \\'}}}} ``` There are two possible solutions: - Check the identifier name passed in datasets_definitions and relationships. - Do not use the name of the dataset if you did not specify it when registering the dataset to the Data Registry. ``` datarobot.errors.ClientError: 422 client error: {u'message': u'Invalid field data', u'errors': {u'datasetDefinitions': {u'1': {u'primaryTemporalKey': u'date column doesnt exist'}, }}} ``` Solution: - Check if the name of the column passed as primaryTemporalKey is correct, as it is case-sensitive. ## Configure relationships A relationship’s configuration specifies additional datasets to be included to a project, how these datasets are related to each other, and the primary dataset. When a relationships configuration is specified for a project, Feature Discovery will create features automatically from these datasets. You can create a relationship configuration from uploaded AI Catalog items. After uploading all the secondary datasets in the AI Catalog: - Create the dataset’s definition to specify which datasets to be used as secondary datasets along with its details - Configure relationships among the above datasets. ``` relationship_config = dr.RelationshipsConfiguration.create(dataset_definitions=dataset_definitions, relationships=relationships) >>> relationship_config.id u'5506fcd38bd88f5953219da0' ``` ## Dataset definitions and relationships using helper functions Create the [DatasetDefinition](https://docs.datarobot.com/en/docs/api/reference/public-api/features.html#dataset-definition) and [Relationship](https://docs.datarobot.com/en/docs/api/reference/public-api/features.html#relationship) for the profile and transaction dataset using helper functions. ``` profile_catalog_id = '5ec4aec1f072bc028e3471ae' profile_catalog_version_id = '5ec4aec2f072bc028e3471b1' transac_catalog_id = '5ec4aec268f0f30289a03901' transac_catalog_version_id = '5ec4aec268f0f30289a03900' profile_dataset_definition = dr.DatasetDefinition( identifier='profile', catalog_id=profile_catalog_id, catalog_version_id=profile_catalog_version_id ) transaction_dataset_definition = dr.DatasetDefinition( identifier='transaction', catalog_id=transac_catalog_id, catalog_version_id=transac_catalog_version_id, primary_temporal_key='Date' ) profile_transaction_relationship = dr.Relationship( dataset1_identifier='profile', dataset2_identifier='transaction', dataset1_keys=['CustomerID'], dataset2_keys=['CustomerID'] ) primary_profile_relationship = dr.Relationship( dataset2_identifier='profile', dataset1_keys=['CustomerID'], dataset2_keys=['CustomerID'], feature_derivation_window_start=-14, feature_derivation_window_end=-1, feature_derivation_window_time_unit='DAY', prediction_point_rounding=1, prediction_point_rounding_time_unit='DAY' ) dataset_definitions = [profile_dataset_definition, transaction_dataset_definition] relationships = [primary_profile_relationship, profile_transaction_relationship] ``` ## Dataset definition and relationship using a dictionary Create the dataset definitions and relationships for the profile and transaction dataset using dict directly. ``` profile_catalog_id = profile_dataset.id profile_catalog_version_id = profile_dataset.version_id transac_catalog_id = transaction_dataset.id transac_catalog_version_id = transaction_dataset.version_id dataset_definitions = [ { 'identifier': 'transaction', 'catalogVersionId': transac_catalog_version_id, 'catalogId': transac_catalog_id, 'primaryTemporalKey': 'Date', 'snapshotPolicy': 'latest', }, { 'identifier': 'profile', 'catalogId': profile_catalog_id, 'catalogVersionId': profile_catalog_version_id, 'snapshotPolicy': 'latest', }, ] relationships = [ { 'dataset2Identifier': 'profile', 'dataset1Keys': ['CustomerID'], 'dataset2Keys': ['CustomerID'], 'featureDerivationWindowStart': -14, 'featureDerivationWindowEnd': -1, 'featureDerivationWindowTimeUnit': 'DAY', 'predictionPointRounding': 1, 'predictionPointRoundingTimeUnit': 'DAY', }, { 'dataset1Identifier': 'profile', 'dataset2Identifier': 'transaction', 'dataset1Keys': ['CustomerID'], 'dataset2Keys': ['CustomerID'], }, ] ``` ## Retrieving relationship configuration You can retrieve a specific relationship’s configuration using the ID of the relationship configuration. ``` relationship_config_id = '5506fcd38bd88f5953219da0' relationship_config = dr.RelationshipsConfiguration(id=relationship_config_id).get() >>> relationship_config.id == relationship_config_id True # Get all the datasets used in this relationship's configuration >> len(relationship_config.dataset_definitions) == 2 True >> relationship_config.dataset_definitions[0] { 'feature_list_id': '5ec4af93603f596525d382d3', 'snapshot_policy': 'latest', 'catalog_id': '5ec4aec268f0f30289a03900', 'catalog_version_id': '5ec4aec268f0f30289a03901', 'primary_temporal_key': 'Date', 'is_deleted': False, 'identifier': 'transaction', 'feature_lists': [ { 'name': 'Raw Features', 'description': 'System created featurelist', 'created_by': 'User1', 'creation_date': datetime.datetime(2020, 5, 20, 4, 18, 27, 150000, tzinfo=tzutc()), 'user_created': False, 'dataset_id': '5ec4aec268f0f30289a03900', 'id': '5ec4af93603f596525d382d1', 'features': [u'CustomerID', u'AccountID', u'Date', u'Amount', u'Description'] }, { 'name': 'universe', 'description': 'System created featurelist', 'created_by': 'User1', 'creation_date': datetime.datetime(2020, 5, 20, 4, 18, 27, 172000, tzinfo=tzutc()), 'user_created': False, 'dataset_id': '5ec4aec268f0f30289a03900', 'id': '5ec4af93603f596525d382d2', 'features': [u'CustomerID', u'AccountID', u'Date', u'Amount', u'Description'] }, { 'features': [u'CustomerID', u'AccountID', u'Date', u'Amount', u'Description'], 'description': 'System created featurelist', 'created_by': u'Garvit Bansal', 'creation_date': datetime.datetime(2020, 5, 20, 4, 18, 27, 179000, tzinfo=tzutc()), 'dataset_version_id': '5ec4aec268f0f30289a03901', 'user_created': False, 'dataset_id': '5ec4aec268f0f30289a03900', 'id': u'5ec4af93603f596525d382d3', 'name': 'Informative Features' } ] } # Get information regarding how the datasets are connected among themselves as well as theprimary dataset >> relationship_config.relationships [ { 'dataset2Identifier': 'profile', 'dataset1Keys': ['CustomerID'], 'dataset2Keys': ['CustomerID'], 'featureDerivationWindowStart': -14, 'featureDerivationWindowEnd': -1, 'featureDerivationWindowTimeUnit': 'DAY', 'predictionPointRounding': 1, 'predictionPointRoundingTimeUnit': 'DAY', }, { 'dataset1Identifier': 'profile', 'dataset2Identifier': 'transaction', 'dataset1Keys': ['CustomerID'], 'dataset2Keys': ['CustomerID'], }, ] ``` ## Update details of a relationship configuration Use the snippet below as an example of how to update the details of the existing relationship configuration. ``` relationship_config_id = '5506fcd38bd88f5953219da0' relationship_config = dr.RelationshipsConfiguration(id=relationship_config_id) # Remove obsolete dataset definitions and its relationships new_datasets_definiton = [ { 'identifier': 'user', 'catalogVersionId': '5c88a37770fc42a2fcc62759', 'catalogId': '5c88a37770fc42a2fcc62759', 'snapshotPolicy': 'latest', }, ] # Get information regarding how the datasets are connected among themselves as well as the primary dataset new_relationships = [ { 'dataset2Identifier': 'user', 'dataset1Keys': ['user_id', 'dept_id'], 'dataset2Keys': ['user_id', 'dept_id'], }, ] new_config = relationship_config.replace(new_datasets_definiton, new_relationships) >>> new_config.id == relationship_config_id True >>> new_config.datasets_definition [ { 'identifier': 'user', 'catalogVersionId': '5c88a37770fc42a2fcc62759', 'catalogId': '5c88a37770fc42a2fcc62759', 'snapshotPolicy': 'latest', }, ] >>> new_config.relationships [ { 'dataset2Identifier': 'user', 'dataset1Keys': ['user_id', 'dept_id'], 'dataset2Keys': ['user_id', 'dept_id'], }, ] ``` ## Delete relationships configuration You can delete a relationship configuration that is not used by any project. ``` relationship_config_id = '5506fcd38bd88f5953219da0' relationship_config = dr.RelationshipsConfiguration(id=relationship_config_id) result = relationship_config.get() >>> result.id == relationship_config_id True # Delete the relationships configuration >>> relationship_config.delete() >>> relationship_config.get() ClientError: Relationships Configuration 5506fcd38bd88f5953219da0 not found ``` ## Secondary dataset configuration Secondary dataset configuration allows you to use the different secondary datasets for a Feature Discovery project when making predictions. ## Secondary datasets using helper functions Create the [Secondary Dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#secondary-dataset) using helper functions. ``` >>> profile_catalog_id = '5ec4aec1f072bc028e3471ae' >>> profile_catalog_version_id = '5ec4aec2f072bc028e3471b1' >>> transac_catalog_id = '5ec4aec268f0f30289a03901' >>> transac_catalog_version_id = '5ec4aec268f0f30289a03900' profile_secondary_dataset = dr.SecondaryDataset( identifier='profile', catalog_id=profile_catalog_id, catalog_version_id=profile_catalog_version_id, snapshot_policy='latest' ) transaction_secondary_dataset = dr.SecondaryDataset( identifier='transaction', catalog_id=transac_catalog_id, catalog_version_id=transac_catalog_version_id, snapshot_policy='latest' ) secondary_datasets = [profile_secondary_dataset, transaction_secondary_dataset] ``` ## Create secondary datasets with dict You can create secondary datasets using raw dict structure. ``` secondary_datasets = [ { 'snapshot_policy': u'latest', 'identifier': u'profile', 'catalog_version_id': u'5fd06b4af24c641b68e4d88f', 'catalog_id': u'5fd06b4af24c641b68e4d88e' }, { 'snapshot_policy': u'dynamic', 'identifier': u'transaction', 'catalog_version_id': u'5fd1e86c589238a4e635e98e', 'catalog_id': u'5fd1e86c589238a4e635e98d' } ] ``` ## Create a secondary dataset configuration Create a secondary dataset configuration for a Feature Discovery Project which uses two secondary datasets: `profile` and `transaction`. ``` import datarobot as dr project = dr.Project.get(project_id='54e639a18bd88f08078ca831') new_secondary_dataset_config = dr.SecondaryDatasetConfigurations.create( project_id=project.id, name='My config', secondary_datasets=secondary_datasets ) >>> new_secondary_dataset_config.id '5fd1e86c589238a4e635e93d' ``` ## Retrieve a secondary dataset configuration You can retrieve specific secondary dataset configurations using the configuration ID. ``` >>> config_id = '5fd1e86c589238a4e635e93d' secondary_dataset_config = dr.SecondaryDatasetConfigurations(id=config_id).get() >>> secondary_dataset_config.id == config_id True >>> secondary_dataset_config { 'created': datetime.datetime(2020, 12, 9, 6, 16, 22, tzinfo=tzutc()), 'creator_full_name': u'abc@datarobot.com', 'creator_user_id': u'asdf4af1gf4bdsd2fba1de0a', 'credential_ids': None, 'featurelist_id': None, 'id': u'5fd1e86c589238a4e635e93d', 'is_default': True, 'name': u'My config', 'project_id': u'5fd06afce2456ec1e9d20457', 'project_version': None, 'secondary_datasets': [ { 'snapshot_policy': u'latest', 'identifier': u'profile', 'catalog_version_id': u'5fd06b4af24c641b68e4d88f', 'catalog_id': u'5fd06b4af24c641b68e4d88e' }, { 'snapshot_policy': u'dynamic', 'identifier': u'transaction', 'catalog_version_id': u'5fd1e86c589238a4e635e98e', 'catalog_id': u'5fd1e86c589238a4e635e98d' } ] } ``` ## List all secondary dataset configurations You can list all secondary dataset configurations created in the project. ``` >>> secondary_dataset_configs = dr.SecondaryDatasetConfigurations.list(project.id) >>> secondary_dataset_configs[0] { 'created': datetime.datetime(2020, 12, 9, 6, 16, 22, tzinfo=tzutc()), 'creator_full_name': u'abc@datarobot.com', 'creator_user_id': u'asdf4af1gf4bdsd2fba1de0a', 'credential_ids': None, 'featurelist_id': None, 'id': u'5fd1e86c589238a4e635e93d', 'is_default': True, 'name': u'My config', 'project_id': u'5fd06afce2456ec1e9d20457', 'project_version': None, 'secondary_datasets': [ { 'snapshot_policy': u'latest', 'identifier': u'profile', 'catalog_version_id': u'5fd06b4af24c641b68e4d88f', 'catalog_id': u'5fd06b4af24c641b68e4d88e' }, { 'snapshot_policy': u'dynamic', 'identifier': u'transaction', 'catalog_version_id': u'5fd1e86c589238a4e635e98e', 'catalog_id': u'5fd1e86c589238a4e635e98d' } ] } ``` --- # Features URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/data/features-python.html # Features Features represent the columns in your dataset that DataRobot uses for modeling. Each feature has properties such as type, statistics, and importance that help you understand your data and make informed modeling decisions. This page describes how to work with features in your projects. ## Retrieve features You can retrieve all features from a project or get a specific feature by name. ### Get all features To retrieve all features from a project, use `Project.get_features()`: ``` >>> import datarobot as dr >>> project = dr.Project.get('5e3c94aff86f2d10692497b5') >>> features = project.get_features() >>> len(features) 21 >>> features[0].name 'Partition' >>> features[0].feature_type 'Numeric' ``` You can also iterate through features using `Project.iterate_features()`: ``` >>> from itertools import islice >>> feature_iterator = project.iterate_features(offset=0, limit=10) >>> for feature in islice(feature_iterator, 5): ... print(feature.name, feature.feature_type) Partition Numeric CustomerID Categorical Age Numeric Income Numeric Education Categorical ``` ### Get a specific feature To retrieve a single feature by name, use `Feature.get()`: ``` >>> feature = dr.Feature.get(project_id=project.id, feature_name='Age') >>> feature.name 'Age' >>> feature.feature_type 'Numeric' >>> feature.project_id '5e3c94aff86f2d10692497b5' ``` ## Explore feature properties Each feature object contains detailed information about the feature's characteristics and distribution. ### Basic feature information ``` >>> feature = project.get_features()[0] >>> feature.name 'Age' >>> feature.feature_type 'Numeric' >>> feature.id 12345 >>> feature.project_id '5e3c94aff86f2d10692497b5' ``` ### Feature statistics For numeric features, you can access summary statistics from the EDA sample: ``` >>> numeric_feature = dr.Feature.get(project_id=project.id, feature_name='Income') >>> numeric_feature.min 25000.0 >>> numeric_feature.max 150000.0 >>> numeric_feature.mean 67500.0 >>> numeric_feature.median 65000.0 >>> numeric_feature.std_dev 18500.5 ``` For date features, summary statistics are expressed as ISO-8601 formatted date strings: ``` >>> date_feature = dr.Feature.get(project_id=project.id, feature_name='TransactionDate') >>> date_feature.min '2020-01-01T00:00:00Z' >>> date_feature.max '2023-12-31T23:59:59Z' ``` ### Feature data quality To check data quality metrics for features: ``` >>> feature = dr.Feature.get(project_id=project.id, feature_name='Email') >>> feature.unique_count 1250 >>> feature.na_count 5 >>> feature.low_information False >>> feature.importance 0.85 ``` The `importance` attribute provides a numeric measure of the strength of relationship between the feature and target, independent of any model. This value may be `None` for non-modeling features such as the partition columns. ### Target leakage detection To check if a feature has [target leakage](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#target-leakage): ``` >>> feature = dr.Feature.get(project_id=project.id, feature_name='CustomerID') >>> feature.target_leakage 'FALSE' ``` Target leakage can return the following values: - FALSE : No target leakage detected. - MODERATE : Moderate risk of target leakage. - HIGH_RISK : High risk of target leakage. - SKIPPED_DETECTION : Target leakage detection was not run on this feature. ### Time series eligibility For time series projects, check if a feature can be used as the datetime partition column. ``` >>> date_feature = dr.Feature.get(project_id=project.id, feature_name='Date') >>> date_feature.time_series_eligible True >>> date_feature.time_series_eligibility_reason 'Suitable for use as datetime partition column' >>> date_feature.time_step 1 >>> date_feature.time_unit 'DAY' ``` ## Get feature histograms Histograms provide a visual representation of feature distributions. To retrieve histogram data for any feature: ``` >>> feature = dr.Feature.get(project_id=project.id, feature_name='Age') >>> histogram = feature.get_histogram() >>> histogram.plot [{'count': 150, 'target': None, 'label': '18-25'}, {'count': 320, 'target': None, 'label': '26-35'}, {'count': 450, 'target': None, 'label': '36-45'}, {'count': 280, 'target': None, 'label': '46-55'}, {'count': 100, 'target': None, 'label': '56+'}] ``` You can specify the maximum number of bins: ``` >>> histogram = feature.get_histogram(bin_limit=20) >>> len(histogram.plot) 20 ``` ## Work with feature lists Feature lists are collections of features used for modeling. You can retrieve feature lists from a project and examine which features they contain. ### Get project feature lists ``` >>> project = dr.Project.get('5e3c94aff86f2d10692497b5') >>> featurelists = project.get_featurelists() >>> featurelists [Featurelist('Raw Features'), Featurelist('Informative Features'), Featurelist('universe')] ``` ### Examine features in a feature list ``` >>> raw_features = project.get_featurelists()[0] >>> raw_features.features ['Partition', 'CustomerID', 'Age', 'Income', 'Education', 'Email'] >>> len(raw_features.features) 21 ``` ### Create a custom feature list You can create custom feature lists from a subset of available features: ``` >>> all_features = project.get_features() >>> selected_feature_names = [f.name for f in all_features if f.feature_type == 'Numeric'] >>> custom_featurelist = project.create_featurelist( ... name='Numeric Features Only', ... features=selected_feature_names ... ) >>> custom_featurelist Featurelist('Numeric Features Only') ``` ## Analyze categorical features For categorical features, you can access additional insights about the distribution of categories. ### Get key summary for categorical features For summarized categorical features, you can retrieve statistics for the top keys: ``` >>> categorical_feature = dr.Feature.get(project_id=project.id, feature_name='ProductCategory') >>> key_summary = categorical_feature.key_summary >>> key_summary[0] {'key': 'Electronics', 'summary': {'min': 0, 'max': 29815.0, 'stdDev': 6498.029, 'mean': 1490.75, 'median': 0.0, 'pctRows': 5.0}} ``` The key summary provides statistics for the top 50 keys, including: - `min`: Minimum value of the key. - `max`: Maximum value of the key. - `mean`: Mean value of the key. - `median`: Median value of the key. - `stdDev`: Standard deviation of the key. - `pctRows`: Percentage occurrence of key in the EDA sample. ## Analyze multicategorical features For multicategorical features, you can retrieve specialized insights about label relationships. ### Get a multicategorical histogram ``` >>> multicat_feature = dr.Feature.get(project_id=project.id, feature_name='Tags') >>> histogram = multicat_feature.get_multicategorical_histogram() >>> histogram MulticategoricalHistogram(...) ``` ### Get pairwise correlations Analyze correlations between labels in a multicategorical feature: ``` >>> correlations = multicat_feature.get_pairwise_correlations() >>> correlations PairwiseCorrelations(...) ``` ### Get pairwise joint probabilities ``` >>> joint_probs = multicat_feature.get_pairwise_joint_probabilities() >>> joint_probs PairwiseJointProbabilities(...) ``` ### Get pairwise conditional probabilities ``` >>> cond_probs = multicat_feature.get_pairwise_conditional_probabilities() >>> cond_probs PairwiseConditionalProbabilities(...) ``` ## Time series feature properties For time series projects, you can retrieve additional properties for features when used with multiseries or cross-series configurations. ### Get multiseries properties Retrieve time series properties for a potential multiseries datetime partition column: ``` >>> date_feature = dr.Feature.get(project_id=project.id, feature_name='Date') >>> properties = date_feature.get_multiseries_properties( ... multiseries_id_columns=['StoreID'] ... ) >>> properties {'time_series_eligible': True, 'time_unit': 'DAY', 'time_step': 1} ``` ### Get cross-series properties To retrieve cross-series properties for multiseries ID columns: ``` >>> multiseries_feature = dr.Feature.get(project_id=project.id, feature_name='StoreID') >>> properties = multiseries_feature.get_cross_series_properties( ... datetime_partition_column='Date', ... cross_series_group_by_columns=['Region'] ... ) >>> properties {'name': 'StoreID', 'eligibility': 'Eligible as cross-series group-by column', 'isEligible': True} ``` ## Filter and search features You can filter features by various criteria to find specific features of interest. ### Filter by feature type ``` >>> all_features = project.get_features() >>> numeric_features = [f for f in all_features if f.feature_type == 'Numeric'] >>> categorical_features = [f for f in all_features if f.feature_type == 'Categorical'] >>> text_features = [f for f in all_features if f.feature_type == 'Text'] ``` ### Find features with missing data ``` >>> features_with_missing = [f for f in project.get_features() ... if f.na_count is not None and f.na_count > 0] >>> for feature in features_with_missing: ... print(f"{feature.name}: {feature.na_count} missing values") Email: 5 missing values Phone: 12 missing values ``` ### Find low-information features ``` >>> low_info_features = [f for f in project.get_features() if f.low_information] >>> [f.name for f in low_info_features] ['ConstantColumn', 'SingleValueColumn'] ``` ### Find features by importance threshold ``` >>> important_features = [f for f in project.get_features() ... if f.importance is not None and f.importance > 0.5] >>> sorted_features = sorted(important_features, key=lambda x: x.importance, reverse=True) >>> for feature in sorted_features[:5]: ... print(f"{feature.name}: {feature.importance:.3f}") Income: 0.892 Age: 0.756 Education: 0.643 ``` ## Common workflows ### Analyze all features in a project ``` >>> project = dr.Project.get('5e3c94aff86f2d10692497b5') >>> features = project.get_features() >>> >>> print(f"Total features: {len(features)}") >>> print(f"Feature types: {set(f.feature_type for f in features)}") >>> >>> for feature in features: ... print(f"\n{feature.name} ({feature.feature_type}):") ... if feature.na_count is not None: ... print(f" Missing values: {feature.na_count}") ... if feature.importance is not None: ... print(f" Importance: {feature.importance:.3f}") ... if feature.target_leakage != 'SKIPPED_DETECTION': ... print(f" Target leakage: {feature.target_leakage}") ``` ### Export feature information to a DataFrame ``` >>> import pandas as pd >>> >>> features = project.get_features() >>> feature_data = [] >>> for feature in features: ... feature_data.append({ ... 'name': feature.name, ... 'type': feature.feature_type, ... 'importance': feature.importance, ... 'missing_count': feature.na_count, ... 'unique_count': feature.unique_count, ... 'low_information': feature.low_information, ... 'target_leakage': feature.target_leakage ... }) >>> >>> df = pd.DataFrame(feature_data) >>> df.to_csv('feature_summary.csv', index=False) ``` ### Compare features across projects ``` >>> project1 = dr.Project.get('5e3c94aff86f2d10692497b5') >>> project2 = dr.Project.get('5e3c94aff86f2d10692497b6') >>> >>> features1 = {f.name: f for f in project1.get_features()} >>> features2 = {f.name: f for f in project2.get_features()} >>> >>> common_features = set(features1.keys()) & set(features2.keys()) >>> print(f"Common features: {len(common_features)}") >>> >>> for feature_name in common_features: ... f1 = features1[feature_name] ... f2 = features2[feature_name] ... if f1.feature_type != f2.feature_type: ... print(f"{feature_name}: type mismatch ({f1.feature_type} vs {f2.feature_type})") ``` ## Considerations - Feature statistics ( min , max , mean , median , std_dev ) are calculated from the EDA sample data. - For non-numeric features or features created prior to summary statistics becoming available, their values will be None . - The importance attribute is independent of any model and measures the relationship strength between the feature and target. - In time series projects, Feature objects represent input features, while ModelingFeature objects represent features used for modeling after partitioning. - Feature histograms are based on the EDA sample and may not reflect the full dataset distribution. --- # Data URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/data/index.html # Data Data integrity and quality are cornerstones for creating highly accurate predictive models. These sections describe the tools and visualizations provided to ensure that your project doesn’t suffer the “garbage in, garbage out” outcome. | Resource | Description | | --- | --- | | Create and manage datasets | Ingest, transform, and store your data for experimentation. | | Build data connections | Integrate with a variety of enterprise databases. | | Recipes | Clean and wrangle data with reusable recipes for data preparation. | | Features | How to work with features and retrieve their statistics in your projects. | | Feature Discovery | Deploy, monitor, manage, and govern all your models in production, regardless of how they were created or when and where they were deployed. | --- # Chats prompting URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/chats-prompting.html # Chats and prompting [Chats](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-prompting.html#datarobot.models.genai.chat.Chat) provide a way to interact with LLMs through prompts, maintaining conversation history and context. Chats allow you to have multi-turn conversations with your LLM applications, where each prompt can reference previous messages in the conversation. ## Create a chat Create a new chat from an LLM blueprint. When creating a chat, you should specify the following: - name : A user-friendly name for the chat. - llm_blueprint : The LLM blueprint ID or LLMBlueprint object to associate the chat with. ``` import datarobot as dr blueprint = dr.genai.LLMBlueprint.get(blueprint_id) chat = dr.genai.Chat.create( name="Customer Support Chat", llm_blueprint=blueprint_id ) chat ``` ## Submit prompts to a chat Send prompts and receive responses using [datarobot.ChatPrompt.submit()](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-prompting.html#datarobot.models.genai.chat_prompt.ChatPrompt.submit): ``` chat = dr.genai.Chat.get(chat_id) prompt = dr.genai.ChatPrompt.create( chat=chat.id, text="What is your return policy?" ) prompt.result_text ``` Submit a follow-up prompt that maintains the conversation history: ``` followup = dr.genai.ChatPrompt.create( chat=chat.id, text="How long does it take to process a return?" ) print(f"Response: {followup.result_text}") ``` ## Retrieve chat history Get all prompts in a chat using [datarobot.ChatPrompt.list()](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-prompting.html#datarobot.models.genai.chat_prompt.ChatPrompt.list): ``` chat = dr.genai.Chat.get(chat_id) prompts = dr.genai.ChatPrompt.list(chat=chat.id) for prompt in prompts: prompt.text prompt.result_text ``` Filter prompts by LLM blueprint: ``` blueprint = dr.genai.LLMBlueprint.get(blueprint_id) blueprint_prompts = dr.genai.ChatPrompt.list(llm_blueprint=blueprint.id) ``` Filter prompts by playground: ``` playground = dr.genai.Playground.get(playground_id) playground_prompts = dr.genai.ChatPrompt.list(playground=playground.id) ``` ## Manage chats List all chats: ``` all_chats = dr.genai.Chat.list() print(f"Found {len(all_chats)} chat(s):") for chat in all_chats: print(f" - {chat.name} (ID: {chat.id})") ``` Filter by LLM blueprint: ``` blueprint = dr.genai.LLMBlueprint.get(blueprint_id) blueprint_chats = dr.genai.Chat.list(llm_blueprint=blueprint.id) ``` Update the chat name: ``` chat = dr.genai.Chat.get(chat_id) chat.update(name="Updated Chat Name") ``` Delete a chat: ``` chat.delete() ``` ## Get chat information Retrieve chat details: ``` chat = dr.genai.Chat.get(chat_id) print(f"Name: {chat.name}") print(f"LLM Blueprint: {chat.llm_blueprint_id}") print(f"Is frozen: {chat.is_frozen}") print(f"Prompts count: {chat.prompts_count}") print(f"Warning: {chat.warning}") ``` --- # Custom model validation URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/custom-model-validation.html # Validate custom model LLMs If you have a custom model deployment that serves as an LLM, you need to validate it before using it in LLM blueprints. Validation ensures the deployment can properly handle LLM requests. Use [datarobot.CustomModelLLMValidation](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-llm-generation.html#datarobot.models.genai.custom_model_llm_validation.CustomModelLLMValidation) to validate your deployments. ## Start LLM validation Validate a deployment as a custom model LLM. Specify the following: - deployment_id : The ID of the deployment to validate. - name : An optional name for the validation. - prompt_column_name : The name of the column the deployed model uses for prompt text input (required for non-chat API deployments). - target_column_name : The name of the column the deployed model uses for prediction output (required for non-chat API deployments). - chat_model_id : The model ID to specify when calling the chat completion API (for deployments that support the chat completion API). - wait_for_completion : If set to True, the code will wait for the validation job to complete before returning results. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id) use_case = dr.UseCase.get(use_case_id) validation = dr.genai.CustomModelLLMValidation.create( deployment_id=deployment.id, use_case=use_case.id, name="My Custom LLM", prompt_column_name="prompt", target_column_name="response", wait_for_completion=True ) validation ``` For deployments that support the chat completion API, use `chat_model_id`: ``` validation = dr.genai.CustomModelLLMValidation.create( deployment_id=deployment.id, name="Chat API LLM", chat_model_id="model-id-from-deployment", wait_for_completion=True ) ``` ## Check validation status If you don't wait for completion, poll for status using [datarobot.CustomModelLLMValidation.get()](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-llm-generation.html#datarobot.models.genai.custom_model_llm_validation.CustomModelLLMValidation.get): ``` validation = dr.genai.CustomModelLLMValidation.create( deployment_id=deployment.id, name="My Custom LLM", prompt_column_name="prompt", target_column_name="response", wait_for_completion=False ) while validation.validation_status == "TESTING": import time time.sleep(5) validation = dr.genai.CustomModelLLMValidation.get(validation.id) if validation.validation_status == "PASSED": print("Validation passed!") print(f"Access data: {validation.deployment_access_data}") else: print(f"Validation failed: {validation.error_message}") ``` ## Use a validated LLM in a blueprint Once validation passes, use the validation ID in an LLM blueprint: ``` validation = dr.genai.CustomModelLLMValidation.get(validation_id) use_case = dr.UseCase.get(use_case_id) playground = dr.genai.Playground.create( name="Custom LLM Playground", use_case=use_case.id ) blueprint = dr.genai.LLMBlueprint.create( playground=playground.id, name="Custom Model Blueprint", llm="custom-model", llm_settings={ "system_prompt": "You are a helpful assistant.", "validation_id": validation.id } ) blueprint ``` ## Update validation settings Modify validation parameters using [datarobot.CustomModelLLMValidation.update()](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-llm-generation.html#datarobot.models.genai.custom_model_llm_validation.CustomModelLLMValidation.update): ``` validation = dr.genai.CustomModelLLMValidation.get(validation_id) validation.update( name="Updated Custom LLM Name", prediction_timeout=60 ) ``` --- # Generative AI URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/index.html # Generative AI How to build, validate, and deploy generative AI applications using LLMs, vector databases, and moderation tools. This section covers creating LLM blueprints, managing chats and prompts, setting up vector databases for RAG (Retrieval-Augmented Generation), and implementing safety measures through moderation. | Topic | Description | | --- | --- | | LLM blueprints | Create and manage LLM blueprints that define how large language models are configured and used in your applications. | | Validate custom model LLMs | Validate deployments as custom model LLMs before using them in LLM blueprints. | | Vector databases | Set up and manage vector databases for RAG (Retrieval-Augmented Generation) workflows. | | Chats and prompting | Manage chat sessions and interact with LLMs through prompts, maintaining conversation history. | | End-to-end RAG application workflow | A complete example of building a RAG application from scratch. | --- # Llm blueprints URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/llm-blueprints.html # LLM blueprints [LLM blueprints](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#working-with-llm-blueprints) define how large language models are configured and used in your generative AI applications. You can use pre-configured LLMs provided by DataRobot or create custom model LLMs from your own deployments. LLM blueprints allow you to configure system prompts, temperature settings, and other parameters that control how the LLM behaves in your applications. ## List available LLMs To see all available LLMs, use [datarobot.LLMDefinition.list()](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-llm-generation.html#datarobot.models.genai.llm.LLMDefinition.list): ``` import datarobot as dr llms = dr.genai.LLMDefinition.list() for llm in llms: print(f"{llm.name}: {llm.description}") print(f" Vendor: {llm.vendor}") print(f" Context size: {llm.context_size}") print(f" Active: {llm.is_active}") ``` You can also filter LLMs by Use Case: ``` use_case = dr.UseCase.get(use_case_id) llms = dr.genai.LLMDefinition.list(use_case=use_case) ``` ## Create a playground Playgrounds are required to create LLM blueprints. A playground is a workspace where you can experiment with different LLM configurations. When creating a playground, you should specify the following: - name : A user-friendly name for the playground. - description : An optional description of the playground's purpose. - use_case : A Use Case to link the playground to. - playground_type : The type of playground, defaults to dr.enums.PlaygroundType.RAG . ``` import datarobot as dr playground = dr.genai.Playground.create( name="My GenAI Playground", use_case=use_case.id ) playground ``` You can also create a playground in a Use Case: ``` use_case = dr.UseCase.get(use_case_id) playground = dr.genai.Playground.create( name="Use Case Playground", use_case=use_case.id ) ``` ## Create an LLM blueprint Create a new LLM blueprint with custom settings. When creating an LLM blueprint, you should specify the following: - playground : The playground ID or Playground object to associate the blueprint with. - name : A user-friendly name for the LLM blueprint. - llm : The LLM definition ID or LLMDefinition object to use. - llm_settings : A dictionary containing LLM configuration settings such as system prompts, temperature, and max completion length. - description : An optional description of the blueprint. - prompt_type : The prompting strategy, defaults to dr.enums.PromptType.CHAT_HISTORY_AWARE . ``` import datarobot as dr llms = dr.genai.LLMDefinition.list() gpt4 = [llm for llm in llms if 'gpt-4' in llm.name.lower()][0] playground = dr.genai.Playground.create(name="Customer Support Playground") blueprint = dr.genai.LLMBlueprint.create( playground=playground.id, name="Customer Support Assistant", description="LLM for customer support queries", llm=gpt4.id, llm_settings={ "system_prompt": "You are a helpful customer support assistant. Be concise and professional.", "temperature": 0.7, "max_completion_length": 500 } ) blueprint ``` ## Retrieve and list LLM blueprints Get a specific blueprint or list all available blueprints using [datarobot.LLMBlueprint.get()](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-llm-generation.html#datarobot.models.genai.llm_blueprint.LLMBlueprint.get) and [datarobot.LLMBlueprint.list()](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-llm-generation.html#datarobot.models.genai.llm_blueprint.LLMBlueprint.list). To retrieve a specific blueprint: ``` blueprint = dr.genai.LLMBlueprint.get(blueprint_id) blueprint ``` To list all blueprints: ``` all_blueprints = dr.genai.LLMBlueprint.list() print(f"Found {len(all_blueprints)} LLM blueprint(s):") for bp in all_blueprints: print(f" - {bp.name} (ID: {bp.id})") ``` To filter blueprints by playground: ``` playground_blueprints = dr.genai.LLMBlueprint.list(playground=playground.id) ``` To filter by LLM type: ``` gpt_blueprints = dr.genai.LLMBlueprint.list(llms=[gpt4.id]) ``` ## Update an LLM blueprint Modify blueprint settings using [datarobot.LLMBlueprint.update()](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-llm-generation.html#datarobot.models.genai.llm_blueprint.LLMBlueprint.update): ``` blueprint = dr.genai.LLMBlueprint.get(blueprint_id) blueprint.update( name="Updated Customer Support Assistant", llm_settings={ "system_prompt": "You are an expert customer support assistant.", "temperature": 0.5 } ) ``` Save the blueprint to lock settings: ``` blueprint.update(is_saved=True) ``` Star the blueprint for easy access: ``` blueprint.update(is_starred=True) ``` ## Create a blueprint from an existing blueprint Create a copy of an existing blueprint to experiment with variations using [datarobot.LLMBlueprint.create_from_llm_blueprint()](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-llm-generation.html#datarobot.models.genai.llm_blueprint.LLMBlueprint.create_from_llm_blueprint): ``` original_blueprint = dr.genai.LLMBlueprint.get(blueprint_id) new_blueprint = dr.genai.LLMBlueprint.create_from_llm_blueprint( llm_blueprint=original_blueprint, name="Experimental Variant", description="Testing different temperature settings" ) new_blueprint ``` --- # Generative AI moderation URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/moderation.html # Generative AI moderation [Moderation configurations](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-moderation.html#datarobot.models.moderation.configuration.ModerationConfiguration) help ensure your generative AI applications produce safe and appropriate content by filtering prompts and responses. Moderation can block or warn on problematic content at different stages of the generation process. ## List moderation templates To retrieve all available moderation templates: ``` import datarobot as dr templates = dr.ModerationTemplate.list() for template in templates: print(f"Template: {template.name}") print(f" Description: {template.description}") ``` ## Create a moderation configuration Create a moderation configuration from a template. When creating a moderation configuration, you should specify the following: - template_id : The ID of the template to base this configuration on. - name : A user-friendly name for the configuration. - description : A description of the configuration. - stages : The stages of moderation where this guard is active (e.g., PROMPT, RESPONSE). - entity_id : The ID of the custom model version or playground this configuration applies to. - entity_type : The type of the associated entity (CUSTOM_MODEL_VERSION or PLAYGROUND). - intervention : The action to take if moderation fails (BLOCK or WARN). - llm_type : The backing LLM this guard uses. ``` templates = dr.ModerationTemplate.list() template = templates[0] custom_model_version = dr.CustomModelVersion.get(version_id) moderation_config = dr.ModerationConfiguration.create( template_id=template.id, name="Content Safety Guard", description="Filters inappropriate content", stages=[dr.ModerationGuardStage.PROMPT, dr.ModerationGuardStage.RESPONSE], entity_id=custom_model_version.id, entity_type=dr.ModerationGuardEntityType.CUSTOM_MODEL_VERSION, intervention=dr.ModerationIntervention.BLOCK, llm_type=dr.ModerationGuardLlmType.DATAROBOT_LLM ) moderation_config ``` You can also create moderation for playgrounds: ``` playground = dr.genai.Playground.get(playground_id) moderation_config = dr.ModerationConfiguration.create( template_id=template.id, name="Playground Safety Guard", description="Filters content in playground", stages=[dr.ModerationGuardStage.PROMPT, dr.ModerationGuardStage.RESPONSE], entity_id=playground.id, entity_type=dr.ModerationGuardEntityType.PLAYGROUND, intervention=dr.ModerationIntervention.WARN, llm_type=dr.ModerationGuardLlmType.DATAROBOT_LLM ) ``` ## List moderation configurations To retrieve configurations for an entity: ``` custom_model_version = dr.CustomModelVersion.get(version_id) configs = dr.ModerationConfiguration.list( entity_id=custom_model_version.id, entity_type=dr.ModerationGuardEntityType.CUSTOM_MODEL_VERSION ) for config in configs: print(f"Config: {config.name}") print(f" Stages: {config.stages}") print(f" Intervention: {config.intervention}") ``` ## Get a moderation configuration To retrieve a specific configuration: ``` config = dr.ModerationConfiguration.get(config_id) print(f"Name: {config.name}") print(f"Description: {config.description}") print(f"Stages: {config.stages}") ``` ## Update moderation configuration To update moderation settings: ``` config = dr.ModerationConfiguration.get(config_id) config.update( name="Updated Safety Guard", description="Enhanced content filtering", intervention=dr.ModerationIntervention.WARN ) ``` ## Get the overall moderation configuration Retrieve the overall moderation configuration for an entity: ``` custom_model_version = dr.CustomModelVersion.get(version_id) overall_config = dr.OverallModerationConfig.get( entity_id=custom_model_version.id, entity_type=dr.ModerationGuardEntityType.CUSTOM_MODEL_VERSION ) if overall_config: print(f"Moderation enabled: {overall_config.is_enabled}") print(f"Configurations: {len(overall_config.configurations)}") ``` ## List the overall moderation configurations To get all of the overall moderation configurations: ``` overall_configs = dr.OverallModerationConfig.list() for config in overall_configs: print(f"Entity: {config.entity_id}") print(f" Enabled: {config.is_enabled}") ``` ## Update the overall moderation configuration To modify the overall moderation settings: ``` overall_config = dr.OverallModerationConfig.get( entity_id=custom_model_version.id, entity_type=dr.ModerationGuardEntityType.CUSTOM_MODEL_VERSION ) overall_config.update(is_enabled=True) ``` --- # End-to-end RAG application workflow URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/rag-workflow.html # End-to-end RAG application workflow This example demonstrates how to build a complete RAG (Retrieval-Augmented Generation) application, combining LLM blueprints, vector databases, moderation, and testing. This workflow shows how to integrate all the components of a production-ready generative AI application. ### Create a playground A playground is a workspace for experimenting with LLM configurations. All LLM blueprints must be associated with a playground. ``` import datarobot as dr playground = dr.genai.Playground.create( name="RAG Application Playground", use_case=use_case.id ) ``` ### Select an LLM Choose from available LLMs provided by DataRobot. You can filter by use case or select based on model capabilities. ``` llms = dr.genai.LLMDefinition.list() llm = [l for l in llms if 'gpt-4' in l.name.lower()][0] ``` ### Build a vector database Upload your documents as a dataset, then create a vector database with appropriate chunking parameters. The vector database stores document embeddings for RAG. ``` dataset = dr.Dataset.upload("company_docs.csv") supported = dr.genai.VectorDatabase.get_supported_embeddings(dataset_id=dataset.id) from datarobot.models.genai.vector_database import ChunkingParameters chunking_params = ChunkingParameters( embedding_model=supported.default_embedding_model, chunking_method="semantic", chunk_size=500, chunk_overlap_percentage=10 ) use_case = dr.UseCase.get(use_case_id) vector_db = dr.genai.VectorDatabase.create( dataset_id=dataset.id, use_case=use_case.id, name="Company Knowledge Base", chunking_parameters=chunking_params ) ``` ### Create an LLM blueprint Combine the LLM with the vector database to create a RAG-enabled blueprint. Configure system prompts and other LLM settings. ``` blueprint = dr.genai.LLMBlueprint.create( playground=playground.id, name="RAG Customer Support", description="RAG-enabled customer support assistant", llm=llm.id, llm_settings={ "system_prompt": "You are a customer support assistant. Use the provided context to answer questions accurately.", "temperature": 0.7 }, vector_database=vector_db.id, vector_database_settings={ "max_documents_retrieved_per_prompt": 3 } ) ``` ### Register custom model Register the blueprint as a custom model version so it can be deployed and used in production. ``` custom_model_version = blueprint.register_custom_model() ``` ### Add moderation Set up content filtering to ensure safe and appropriate responses. Moderation can block or warn on problematic content. ``` template = dr.ModerationTemplate.list()[0] moderation = dr.ModerationConfiguration.create( template_id=template.id, name="Content Safety", description="Filter inappropriate content", stages=[dr.ModerationGuardStage.PROMPT, dr.ModerationGuardStage.RESPONSE], entity_id=custom_model_version.id, entity_type=dr.ModerationGuardEntityType.CUSTOM_MODEL_VERSION, intervention=dr.ModerationIntervention.BLOCK ) ``` ### Interact via chats Create chat sessions to interact with your RAG application. Chats maintain conversation history for context-aware responses. ``` chat = dr.genai.Chat.create( name="Customer Support Session", llm_blueprint=blueprint.id ) prompt = dr.genai.ChatPrompt.create( chat=chat.id, text="What is your return policy?" ) prompt.text prompt.result_text ``` --- # AI robustness testing URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/robustness-testing.html # AI robustness testing [AI robustness tests](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-testing.html#datarobot.models.genai.insights_configuration.InsightsConfiguration) help validate that your generative AI models perform well under various conditions and meet quality standards. Robustness testing allows you to configure insights that measure different aspects of model performance, such as toxicity, relevance, and coherence. ## Configure insights for robustness testing Set up insights to test AI model robustness. When creating an insight configuration, you should specify the following: - custom_model_version_id : The ID of the custom model version to test. - insight_name : A user-friendly name for the insight. - insight_type : The type of insight (e.g., OOTB_METRIC ). - ootb_metric_name : The name of the DataRobot-provided metric (for the OOTB_METRIC type). - stage : The stage when the metric is calculated ( PROMPT or RESPONSE ). ``` import datarobot as dr custom_model_version = dr.CustomModelVersion.get(version_id) insight_config = dr.InsightsConfiguration.create( custom_model_version_id=custom_model_version.id, insight_name="Toxicity Check", insight_type=dr.InsightTypes.OOTB_METRIC, ootb_metric_name="Toxicity", stage=dr.InsightStage.RESPONSE ) insight_config ``` ## Set up test configurations To configure test parameters: ``` eval_dataset = dr.EvaluationDatasetConfiguration.create( name="Robustness Test Dataset", dataset_id=dataset.id ) cost_config = dr.CostConfiguration.create( name="Test Cost Config", cost_per_token=0.0001 ) insight_config.update( evaluation_dataset_configuration_id=eval_dataset.id, cost_configuration_id=cost_config.id ) ``` ## Run robustness tests To execute tests and review results: ``` insight_config = dr.InsightsConfiguration.get(insight_config_id) if insight_config.execution_status == "COMPLETED": results = insight_config.get_results() print(f"Test results: {results}") elif insight_config.execution_status == "ERROR": print(f"Test failed: {insight_config.error_message}") print(f"Resolution: {insight_config.error_resolution}") ``` ## Configure multiple insights To set up multiple tests for comprehensive validation: ``` insights = [ { "insight_name": "Toxicity Check", "ootb_metric_name": "Toxicity", "stage": dr.InsightStage.RESPONSE }, { "insight_name": "Relevance Check", "ootb_metric_name": "Relevance", "stage": dr.InsightStage.RESPONSE }, { "insight_name": "Coherence Check", "ootb_metric_name": "Coherence", "stage": dr.InsightStage.RESPONSE } ] for insight_config in insights: dr.InsightsConfiguration.create( custom_model_version_id=custom_model_version.id, **insight_config ) ``` ## Get an insight configuration To retrieve a specific insight configuration: ``` insight_config = dr.InsightsConfiguration.get(insight_config_id) print(f"Insight name: {insight_config.insight_name}") print(f"Insight type: {insight_config.insight_type}") print(f"Execution status: {insight_config.execution_status}") ``` ## List insight configurations Get all insights for a custom model version: ``` custom_model_version = dr.CustomModelVersion.get(version_id) insights = dr.InsightsConfiguration.list(custom_model_version_id=custom_model_version.id) for insight in insights: print(f"{insight.insight_name}: {insight.execution_status}") ``` --- # Vector databases URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/genai/vector-databases.html # Vector databases [Vector databases](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-vector-databases.html#datarobot.models.genai.vector_database.VectorDatabase) enable RAG (Retrieval-Augmented Generation) workflows by storing document embeddings and retrieving relevant context for LLM prompts. Vector databases allow you to create knowledge bases from your documents and use them to provide context-aware responses in your generative AI applications. ## Validate a deployment as a vector database Before using a deployment as a vector database, validate it using [datarobot.CustomModelVectorDatabaseValidation](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-vector-databases.html#datarobot.models.genai.vector_database.CustomModelVectorDatabaseValidation): ``` import datarobot as dr validation = dr.CustomModelVectorDatabaseValidation.create( deployment_id=deployment.id, name="My Vector Database", prompt_column_name="query", target_column_name="citations", wait_for_completion=True ) if validation.validation_status == "PASSED": print("Vector database validation passed!") ``` ## Get supported embeddings View available embedding models using [datarobot.VectorDatabase.get_supported_embeddings()](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-vector-databases.html#datarobot.models.genai.vector_database.VectorDatabase.get_supported_embeddings): ``` supported = dr.genai.VectorDatabase.get_supported_embeddings() print(f"Default embedding model: {supported.default_embedding_model}") for model in supported.embedding_models: print(f" {model.name}: {model.description}") ``` You can also get recommended embeddings for a specific dataset: ``` dataset = dr.Dataset.get(dataset_id) supported = dr.genai.VectorDatabase.get_supported_embeddings(dataset_id=dataset.id) print(f"Default embedding: {supported.default_embedding_model}") ``` ## Get supported text chunking configurations To view available text chunking options: ``` chunking_configs = dr.genai.VectorDatabase.get_supported_text_chunkings() for config in chunking_configs.text_chunking_configs: print(f"Chunking config: {config}") ``` ## Create a vector database Create a vector database from a dataset containing your documents. When creating a vector database, you should specify the following: - dataset_id : The ID of the dataset used for creation. - chunking_parameters : Parameters defining how documents are split and embedded, including embedding model, chunking method, chunk size, and overlap percentage. - name : An optional user-friendly name for the vector database. - use_case : An optional Use Case to link the vector database to. ``` dataset = dr.Dataset.upload("documents.csv") supported = dr.genai.VectorDatabase.get_supported_embeddings(dataset_id=dataset.id) from datarobot.models.genai.vector_database import ChunkingParameters chunking_params = ChunkingParameters( embedding_model=supported.default_embedding_model, chunking_method="semantic", chunk_size=500, chunk_overlap_percentage=10 ) vector_db = dr.genai.VectorDatabase.create( dataset_id=dataset.id, name="Document Knowledge Base", chunking_parameters=chunking_params, use_case=use_case_id ) vector_db ``` ## Update a vector database Add more documents or update an existing vector database: ``` new_dataset = dr.Dataset.upload("updated_documents.csv") updated_vector_db = dr.genai.VectorDatabase.create( dataset_id=new_dataset.id, parent_vector_database_id=vector_db.id, update_llm_blueprints=True ) ``` ## Link vector database to LLM blueprint Associate a vector database with an LLM blueprint for RAG: ``` vector_db = dr.genai.VectorDatabase.get(vector_db_id) blueprint = dr.genai.LLMBlueprint.get(blueprint_id) blueprint.update( vector_database=vector_db.id, vector_database_settings={ "max_documents_retrieved_per_prompt": 3 } ) ``` ## List and manage vector databases List all vector databases: ``` all_dbs = dr.genai.VectorDatabase.list() print(f"Found {len(all_dbs)} vector database(s):") for db in all_dbs: print(f" - {db.name} (ID: {db.id}, Status: {db.execution_status})") ``` Filter vector databases by Use Case: ``` use_case = dr.UseCase.get(use_case_id) use_case_dbs = dr.genai.VectorDatabase.list(use_case=use_case) ``` Get vector database details: ``` vector_db = dr.genai.VectorDatabase.get(vector_db_id) print(f"Name: {vector_db.name}") print(f"Size: {vector_db.size} bytes") print(f"Status: {vector_db.execution_status}") print(f"Chunks count: {vector_db.chunks_count}") ``` Delete a vector database: ``` vector_db.delete() ``` ## Export a vector database as a dataset To export a vector database as a dataset: ``` vector_db = dr.genai.VectorDatabase.get(vector_db_id) export_job = vector_db.submit_export_dataset_job() exported_dataset_id = export_job.dataset_id ``` --- # Python API client user guide URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/index.html > Review outlines and explanations of the methods that comprise the API client. # Python API client user guide The Python API client user guide outlines and explanations of the methods that comprise the API client. To access previous version of the Python API client documentation, access [ReadTheDocs](https://datarobot-public-api-client.readthedocs-hosted.com/en/latest-release/). | Resource | Description | | --- | --- | | Administration | Manage DataRobot Self-Managed AI Platform installations. | | Data | Ingest, transform, and store your data for experimentation. | | MLOps | Deploy, monitor, manage, and govern all your models in production, regardless of how they were created or when and where they were deployed. | | Modeling | Understand the elements of the basic modeling workflow as well as methods for building additional models in a project. | | Generative AI | Build, validate, and deploy generative AI applications using LLMs, vector databases, and moderation tools. | | Predictions | How to get predictions with new data from a model. | | Use Cases | Group assets that solve a specific business problem. | --- # Agent2Agent protocol URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/a2a.html > Enable interoperability between AI agents in DataRobot. # Agent2Agent protocol The [Agent2Agent (A2A)](https://google.github.io/A2A/) protocol enables interoperability between AI agents. By registering an external agent in the DataRobot model registry and attaching an agent card, you make the agent available for discovery. This page outlines the end-to-end workflow, summarized below: 1. Register an external model in the Model Registry. 2. Create an external prediction environment. 3. Deploy the registered model version. 4. Upload an agent card to the deployment. After completing these steps, the agent is available for A2A interactions within DataRobot. ## Prerequisites ``` import datarobot as dr # Authenticate with the DataRobot API dr.Client(token="YOUR_API_TOKEN", endpoint="https://app.datarobot.com/api/v2") ``` ## Register an external model Use [RegisteredModelVersion.create_for_external](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.model_registry.RegisteredModelVersion.create_for_external) to create a registered model version for an external agent. The `target` parameter uses the [ExternalTarget](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.model_registry.registered_model_version.ExternalTarget) type dict. DataRobot provides a dedicated target type for agents — set `type` to `"AgenticWorkflow"`. ``` import datarobot as dr registered_model_version = dr.RegisteredModelVersion.create_for_external( name="My A2A Agent v1", target={"name": "target", "type": "AgenticWorkflow"}, registered_model_name="My A2A Agent", ) registered_model_version.id >>> '66a1b2c3d4e5f6a7b8c9d0e1' registered_model_version.registered_model_id >>> '66a1b2c3d4e5f6a7b8c9d0e2' ``` If you already have a registered model and want to add a new version, pass `registered_model_id` instead of `registered_model_name`: ``` new_version = dr.RegisteredModelVersion.create_for_external( name="My A2A Agent v2", target={"name": "target", "type": "AgenticWorkflow"}, registered_model_id=registered_model_version.registered_model_id, ) ``` Refer to the [Model Registry documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/model_registry.html#model-registry) for additional details on managing registered models. ## Create an external prediction environment External agentic workflow models must be deployed to an external prediction environment. Deploying to a regular DataRobot prediction server is not supported and will result in a `422` error. Use [PredictionEnvironment.create](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.PredictionEnvironment.create) to create an external prediction environment. Choose a `platform` value that matches where your agent is hosted (e.g.`"aws"`, `"gcp"`, `"azure"`, or `"other"`). ``` import datarobot as dr from datarobot.enums import PredictionEnvironmentPlatform prediction_environment = dr.PredictionEnvironment.create( name="A2A Agent Environment", platform=PredictionEnvironmentPlatform.OTHER, description="External prediction environment for A2A agents", ) prediction_environment.id >>> '66a0b1c2d3e4f5a6b7c8d9e0' ``` If you already have a suitable external prediction environment, you can reuse it: ``` prediction_environments = dr.PredictionEnvironment.list() prediction_environment = prediction_environments[0] ``` ## Deploy the registered model Use [Deployment.create_from_registered_model_version](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.create_from_registered_model_version) to create a deployment from the registered model version. Pass `prediction_environment_id` (not `default_prediction_server_id`) — this is required for agentic workflow targets. ``` import datarobot as dr deployment = dr.Deployment.create_from_registered_model_version( model_package_id=registered_model_version.id, label="My A2A Agent Deployment", description="External A2A agent deployment", prediction_environment_id=prediction_environment.id, ) deployment.id >>> '66b2c3d4e5f6a7b8c9d0e1f2' ``` Refer to the [Deployments documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/deployment.html#deployments) for more information on deployment management. ## Upload an agent card Use [Deployment.upload_agent_card](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.upload_agent_card) to attach an A2A agent card to the deployment. The agent card is a dictionary describing the agent's capabilities and metadata, following the [A2A Agent Card specification](https://google.github.io/A2A/#/documentation?id=agent-card). ``` agent_card = { "name": "My A2A Agent", "description": "An agent that performs data analysis tasks.", "version": "1.0.0", "url": "https://my-agent.example.com/a2a", "capabilities": { "streaming": True, "pushNotifications": False, }, "skills": [ { "id": "data-analysis", "name": "Data Analysis", "description": "Analyzes datasets and provides statistical summaries.", } ], } uploaded_card = deployment.upload_agent_card(agent_card) ``` After uploading, the agent is available in the DataRobot registry as an `A2A` agent. ### Retrieve an agent card Use [Deployment.get_agent_card](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_agent_card) to fetch the agent card from an existing deployment: ``` agent_card = deployment.get_agent_card() agent_card["name"] >>> 'My A2A Agent' ``` ### Delete an agent card Use [Deployment.delete_agent_card](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.delete_agent_card) to remove the agent card from a deployment. This operation is idempotent — it returns successfully even if no agent card exists. ``` deployment.delete_agent_card() ``` ## Discover A2A agent deployments Use the `is_a2a_agent` filter on [DeploymentListFilters](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.DeploymentListFilters) to list only deployments that have an A2A agent card: ``` import datarobot as dr from datarobot.models.deployment import DeploymentListFilters filters = DeploymentListFilters(is_a2a_agent=True) a2a_deployments = dr.Deployment.list(filters=filters) a2a_deployments >>> [Deployment('My A2A Agent Deployment'), Deployment('Another A2A Agent')] ``` You can then retrieve the agent card from any discovered deployment to learn about its capabilities before initiating an A2A interaction: ``` for dep in a2a_deployments: card = dep.get_agent_card() print(f"{card['name']} (v{card['version']}): {card['description']}") ``` ## Full example The following example demonstrates the complete flow of registering an external A2A agent: ``` import datarobot as dr from datarobot.enums import PredictionEnvironmentPlatform from datarobot.models.deployment import DeploymentListFilters # 1. Connect to DataRobot dr.Client(token="YOUR_API_TOKEN", endpoint="https://app.datarobot.com/api/v2") # 2. Register an external model registered_model_version = dr.RegisteredModelVersion.create_for_external( name="Research Assistant Agent v1", target={"name": "target", "type": "AgenticWorkflow"}, registered_model_name="Research Assistant Agent", ) # 3. Create an external prediction environment prediction_environment = dr.PredictionEnvironment.create( name="A2A Agent Environment", platform=PredictionEnvironmentPlatform.OTHER, description="External prediction environment for A2A agents", ) # 4. Deploy the registered model deployment = dr.Deployment.create_from_registered_model_version( model_package_id=registered_model_version.id, label="Research Assistant Agent", description="An A2A agent that assists with research tasks.", prediction_environment_id=prediction_environment.id, ) # 5. Upload the agent card agent_card = { "name": "Research Assistant", "description": "Assists with literature review, summarization, and citation management.", "version": "1.0.0", "url": "https://research-agent.example.com/a2a", "capabilities": { "streaming": True, "pushNotifications": False, }, "skills": [ { "id": "literature-review", "name": "Literature Review", "description": "Searches and summarizes academic papers on a given topic.", }, { "id": "citation-management", "name": "Citation Management", "description": "Formats and organizes citations in various styles.", }, ], } deployment.upload_agent_card(agent_card) # 6. Verify the agent is available for A2A protocol filters = DeploymentListFilters(is_a2a_agent=True) a2a_deployments = dr.Deployment.list(filters=filters) for dep in a2a_deployments: card = dep.get_agent_card() print(f"{card['name']} (v{card['version']}): {card['description']}") ``` --- # Batch monitoring jobs URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/batch-monitoring.html > Manage batch monitoring job definitions and jobs for batch-enabled deployments with the Python API client. # Batch monitoring jobs [Batch monitoring jobs](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/nxt-prediction-jobs.html) let you monitor predictions data over time. This page describes how to manage batch monitoring jobs and job definitions with the Python API client. ## Prerequisites - Data and output configuration for running a monitoring job (e.g., intake and output settings compatible with batch prediction jobs ). ## Batch monitoring job definitions A batch monitoring job definition is a saved configuration for a batch monitoring job. Definitions can be run once on demand or on a schedule. The Python client exposes these via [BatchMonitoringJobDefinition](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/batch-monitoring.html#datarobot.models.batch_monitoring.BatchMonitoringJobDefinition). ### List job definitions List all batch monitoring job definitions. You can also filter them by deployment or name. ``` import datarobot as dr # List all definitions definitions = dr.BatchMonitoringJobDefinition.list() # Filter by deployment definitions = dr.BatchMonitoringJobDefinition.list(deployment_id='5c939e08962d741e34f609f0') # Search by name definitions = dr.BatchMonitoringJobDefinition.list(search_name='Daily Monitoring') ``` Refer to [BatchMonitoringJobDefinition.list](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/batch-monitoring.html#datarobot.models.batch_monitoring.BatchMonitoringJobDefinition.list) for parameters such as `offset` and `limit`. ### Get a job definition Retrieve a single job definition via its ID: ``` import datarobot as dr definition = dr.BatchMonitoringJobDefinition.get(job_definition_id='550e8400-e29b-41d4-a716-446655440000') print(definition.name, definition.deployment_id) ``` ### Create a job definition Create a batch monitoring job definition with the desired intake, output, and deployment. The payload matches the [batch monitoring job create](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-batch-monitoring.html) API (e.g., `deploymentId`, `intakeSettings`, `outputSettings`). You can set `enabled` and `schedule` to run it on a schedule. ``` import datarobot as dr definition = dr.BatchMonitoringJobDefinition.create( deployment_id='5c939e08962d741e34f609f0', name='Weekly batch monitoring', intake_settings={'type': 'localFile', 'url': 'file:///data/predictions.csv'}, output_settings={'type': 'localFile', 'path': '/out/monitoring'}, enabled=False ) ``` Refer to [BatchMonitoringJobDefinition.create](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/batch-monitoring.html#datarobot.models.batch_monitoring.BatchMonitoringJobDefinition.create) and the [Batch Monitoring API](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-batch-monitoring.html) for all supported options (e.g., `monitoringBatchPrefix`, `monitoringColumns`, `chunkSize`). ### Update a job definition Update an existing definition (e.g., name, schedule, or enabled state): ``` import datarobot as dr definition = dr.BatchMonitoringJobDefinition.get(job_definition_id='550e8400-e29b-41d4-a716-446655440000') definition.update(name='Daily batch monitoring', enabled=True) ``` See [BatchMonitoringJobDefinition.update](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/batch-monitoring.html#datarobot.models.batch_monitoring.BatchMonitoringJobDefinition.update) for updatable fields. ### Run a job definition To execute a definition once without changing its schedule: ``` import datarobot as dr definition = dr.BatchMonitoringJobDefinition.get(job_definition_id='550e8400-e29b-41d4-a716-446655440000') job = definition.run_once() print(job.id, job.status) ``` ### Run a job definition on a schedule Start running the definition on its configured schedule (if any): ``` import datarobot as dr definition = dr.BatchMonitoringJobDefinition.get(job_definition_id='550e8400-e29b-41d4-a716-446655440000') definition.run_on_schedule() ``` ### Delete a job definition Remove a batch monitoring job definition. The definition cannot have jobs currently running. ``` import datarobot as dr definition = dr.BatchMonitoringJobDefinition.get(job_definition_id='550e8400-e29b-41d4-a716-446655440000') definition.delete() ``` ## Batch monitoring jobs A batch monitoring job is a single run (either from a single submission or from a definition). Use [BatchMonitoringJob](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/batch-monitoring.html#datarobot.models.batch_monitoring.BatchMonitoringJob) to retrieve the job status and details. ### Get a batch monitoring job Retrieve a job by ID to check status or results (for example, after running a definition with `run_once()`): ``` import datarobot as dr job = dr.BatchMonitoringJob.get(job_id='660e8400-e29b-41d4-a716-446655440001') print(job.status, job.deployment_id) ``` --- # Challenger models URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html > Create and manage challenger models to compare against the deployed champion with the Python API client. # Challenger models [Challenger models](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-mitigation/nxt-challengers.html) are alternative models that you can compare against your currently deployed model (the champion model). This allows you to test new models in production, compare their performance, and make data-driven decisions about whether to replace the champion with a better-performing challenger. This page describes how to create, manage, and work with challenger models. ## Best practices ### When to use challengers - A/B testing: Test new models against the current champion in production. - Model validation: Validate that a new model performs well before replacing the champion. - Performance comparison: Compare multiple model candidates simultaneously. - Risk mitigation: Test models with different characteristics (e.g., different algorithms, feature sets). ### Challenger management 1. Naming convention: Use descriptive names that indicate the model type or purpose (e.g., "XGBoost_v2_Challenger"). 2. Limit active challenger models: Too many challengers can impact prediction performance; typically 2-3 challengers is sufficient. 3. Monitor performance: Regularly review challenger performance metrics before deciding to promote one to champion. 4. Clean up: Remove challengers that are no longer being evaluated to keep your deployment clean. 5. Document: Keep track of why each challenger was created and what makes it different from the champion. ### Prerequisites Before creating a challenger model, ensure you have: - A registered model version (model package) ready to use. - An appropriate prediction environment configured. - Challenger models enabled for the deployment. - An understanding of what you want to test or compare. ## Create a challenger model To create a challenger model, you need a deployment, a model package (registered model version), and a prediction environment. The challenger will use the specified model package and prediction environment to make predictions alongside the champion model. ### Basic challenger model creation ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') # Get a model package (registered model version) to use as challenger project = dr.Project.get('6527eb38b9e5dead5fc12491') model = project.get_models()[0] registered_model_version = dr.RegisteredModelVersion.create_for_leaderboard_item( model_id=model.id, name="Challenger Model Version", registered_model_name='My Registered Model' ) # Get a prediction environment prediction_environments = dr.PredictionEnvironment.list() prediction_environment = prediction_environments[0] # Create the challenger challenger = dr.Challenger.create( deployment_id=deployment.id, model_package_id=registered_model_version.id, prediction_environment_id=prediction_environment.id, name='Elastic-Net Classifier Challenger' ) ``` ### Create a challenger that waits for completion By default, challenger creation is asynchronous. You can specify a maximum wait time for the creation to complete: ``` # Wait up to 600 seconds for creation to complete challenger = dr.Challenger.create( deployment_id=deployment.id, model_package_id=registered_model_version.id, prediction_environment_id=prediction_environment.id, name='Random Forest Challenger', max_wait=600 ) ``` ## List challengers You can retrieve all challengers associated with a deployment. ### List all challengers for a deployment ``` deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') challengers = dr.Challenger.list(deployment_id=deployment.id) ``` You can also use the `list_challengers()` method: ``` deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') challengers = deployment.list_challengers() for challenger in challengers: print(f"{challenger.name}: {challenger.id}") ``` ## Get a specific challenger To retrieve a single challenger by its ID: ``` challenger = dr.Challenger.get( deployment_id='5c939e08962d741e34f609f0', challenger_id='5c939e08962d741e34f609f1' ) ``` ### Access challenger properties ``` challenger = dr.Challenger.get( deployment_id=deployment.id, challenger_id='5c939e08962d741e34f609f1' ) ``` ## Update a challenger model You can update a challenger model's name and prediction environment. ### Update a challenger model name ``` deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') challenger = deployment.list_challengers()[0] challenger.update(name='Updated Challenger Name') ``` ### Update the prediction environment ``` # Get a different prediction environment prediction_environments = dr.PredictionEnvironment.list() new_environment = prediction_environments[1] challenger.update(prediction_environment_id=new_environment.id) ``` To update both the name and prediction environment: ``` challenger.update( name='Final Challenger Name', prediction_environment_id=new_environment.id ) ``` ## Delete a challenger To remove a challenger from a deployment: ``` challenger = dr.Challenger.get( deployment_id=deployment.id, challenger_id='5c939e08962d741e34f609f1' ) challenger.delete() # Verify deletion challengers = deployment.list_challengers() challenger_ids = [c.id for c in challengers] ``` ## Manage challenger settings You can enable or disable challenger models for a deployment and configure challenger-related settings. ### Get challenger settings ``` deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') settings = deployment.get_challenger_models_settings() ``` ### Update challenger settings To enable or disable challenger models for a deployment: ``` deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.update_challenger_models_settings(challenger_models_enabled=True) ``` To disable challenger models: ``` deployment.update_challenger_models_settings(challenger_models_enabled=False) ``` ## Work with challenger predictions Challengers make predictions alongside the champion model, allowing you to compare their performance. ### Understanding challenger predictions When challengers are enabled, predictions made to the deployment will also be scored by the challenger models. This allows you to: - Compare prediction outputs between champion and challengers. - Monitor challenger performance metrics. - Make informed decisions about model replacement. ### Score challenger models You can trigger challenger scoring for existing prediction requests: ``` deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') # Score challengers for predictions with a specific timestamp deployment.score_challenger_predictions(timestamp='2024-01-15T10:00:00Z') ``` ## Common workflows ### Create multiple challengers from different models ``` deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') project = dr.Deployment.get(deployment_id=deployment.id).model['project_id'] project = dr.Project.get(project) # Get top 3 models from the project models = project.get_models()[:3] prediction_environment = dr.PredictionEnvironment.list()[0] challengers = [] for i, model in enumerate(models): registered_model_version = dr.RegisteredModelVersion.create_for_leaderboard_item( model_id=model.id, name=f"Challenger {i+1}", registered_model_name=f'Challenger Model {i+1}' ) challenger = dr.Challenger.create( deployment_id=deployment.id, model_package_id=registered_model_version.id, prediction_environment_id=prediction_environment.id, name=f'{model.model_type} Challenger' ) challengers.append(challenger) print(f"Created {len(challengers)} challengers") ``` ### Compare challenger information ``` deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') challengers = deployment.list_challengers() print("Challenger Comparison:") print(f"{'Name':<40} {'Model Type':<30} {'Model Package ID':<20}") print("-" * 90) for challenger in challengers: model_type = challenger.model.get('type', 'Unknown') model_package_id = challenger.model_package.get('id', 'Unknown') print(f"{challenger.name:<40} {model_type:<30} {model_package_id:<20}") ``` ### Clean up old challenger models ``` deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') challengers = deployment.list_challengers() # Delete challengers older than a certain date or based on criteria # Example: delete challengers with specific naming pattern for challenger in challengers: if 'Old' in challenger.name: print(f"Deleting challenger: {challenger.name}") challenger.delete() ``` ### Replace a challenger with a new model ``` deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') # Get existing challenger old_challenger = deployment.list_challengers()[0] # Create new model version # Get a different model new_model = project.get_models()[1] new_registered_model_version = dr.RegisteredModelVersion.create_for_leaderboard_item( model_id=new_model.id, name="Updated Challenger Version", registered_model_name='Challenger Model' ) # Delete old challenger old_challenger.delete() # Create new challenger with same name but new model new_challenger = dr.Challenger.create( deployment_id=deployment.id, model_package_id=new_registered_model_version.id, prediction_environment_id=old_challenger.prediction_environment['id'], name=old_challenger.name ) ``` ## Considerations - Challenger creation is an asynchronous process. The max_wait parameter controls how long to wait for creation to complete. - A deployment can have multiple challenger models active simultaneously. - Challengers use the same prediction requests as the champion, allowing for direct comparison. - The champion model (the currently deployed model) cannot be deleted while challengers exist that reference it. - Challenger models must use compatible prediction environments with the deployment. - Model packages used for challengers must have the same target type and compatible settings as the champion model. --- # Custom metrics URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_metrics.html > Define and submit custom metrics for deployments with the Python API client. # Custom metrics Custom metrics are used to compute and monitor business or performance metrics. This feature allows you to implement your organization’s specialized metrics, expanding on the insights provided by DataRobot’s built-in service health, data drift, and accuracy metrics. ## Manage custom metrics The following sections outline how to manage custom metrics for deployments. ### Create custom metric To create a custom metric, use `CustomMetric.create`, as shown in the following example. Provide information for all of the required custom metric fields: ``` from datarobot.models.deployment import CustomMetric from datarobot.enums import CustomMetricAggregationType, CustomMetricDirectionality custom_metric = CustomMetric.create( deployment_id="5c939e08962d741e34f609f0", name="My custom metric", units="x", is_model_specific=True, aggregation_type=CustomMetricAggregationType.AVERAGE, directionality=CustomMetricDirectionality.HIGHER_IS_BETTER, ) ``` To set the baseline value during metric creation, use the following example. ``` from datarobot.models.deployment import CustomMetric from datarobot.enums import ( CustomMetricAggregationType, CustomMetricDirectionality, CustomMetricBucketTimeStep, ) custom_metric = CustomMetric.create( deployment_id="5c939e08962d741e34f609f0", name="My custom metric 2", units="y", baseline_value=12, is_model_specific=True, aggregation_type=CustomMetricAggregationType.AVERAGE, directionality=CustomMetricDirectionality.HIGHER_IS_BETTER, time_step=CustomMetricBucketTimeStep.HOUR, ) ``` Define the names of the columns that are used when submitting values from a dataset. ``` from datarobot.models.deployment import CustomMetric from datarobot.enums import CustomMetricAggregationType, CustomMetricDirectionality custom_metric = CustomMetric.create( deployment_id="5c939e08962d741e34f609f0", name="My custom metric 3", units="z", baseline_value=1000, is_model_specific=False, aggregation_type=CustomMetricAggregationType.SUM, directionality=CustomMetricDirectionality.LOWER_IS_BETTER, timestamp_column_name="My Timestamp column", timestamp_format="%d/%m/%y", value_column_name="My Value column", sample_count_column_name="My Sample Count column", ) ``` For batches, see the column configuration below. ``` from datarobot.models.deployment import CustomMetric from datarobot.enums import CustomMetricAggregationType, CustomMetricDirectionality custom_metric = CustomMetric.create( deployment_id="5c939e08962d741e34f609f0", name="My custom metric 4", units="z", baseline_value=1000, is_model_specific=False, aggregation_type=CustomMetricAggregationType.SUM, directionality=CustomMetricDirectionality.LOWER_IS_BETTER, batch_column_name="My Batch column", ) ``` ### List custom metrics To list all custom metrics available for a given deployment, use `CustomMetric.list`. ``` from datarobot.models.deployment import CustomMetric custom_metrics = CustomMetric.list(deployment_id="5c939e08962d741e34f609f0") custom_metrics >>> [CustomMetric('66015bdda7ba87e66baa09ee' | 'My custom metric 2'), CustomMetric('66015bdc5f850c5df3aa09f0' | 'My custom metric')] ``` ### Retrieve custom metrics To get a custom metric by unique identifier, use `CustomMetric.get`. ``` from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113") custom_metric >>> CustomMetric('66015bdc5f850c5df3aa09f0' | 'My custom metric') ``` ### Update custom metrics To retrieve a custom metric by its unique identifier and update it, use `CustomMetric.get()` and then `update()`. ``` from datarobot.models.deployment import CustomMetric from datarobot.enums import CustomMetricAggregationType, CustomMetricDirectionality custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113") custom_metric.update( name="Updated custom metric", units="foo", baseline_value=-12, aggregation_type=CustomMetricAggregationType.SUM, directionality=CustomMetricDirectionality.LOWER_IS_BETTER, ) ``` ### Reset the custom metric baseline To reset the current metric baseline, use `unset_baseline()`, as shown in the example below. ``` from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113") custom_metric.baseline_values >>> [{'value': -12.0}] custom_metric.unset_baseline() custom_metric.baseline_values >>> [] ``` ### Delete custom metrics To delete a custom metric by unique identifier, use `CustomMetric.delete`, as in the following example: ``` from datarobot.models.deployment import CustomMetric CustomMetric.delete(deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113") ``` ## Submit custom metric values The following sections outline how to submit custom metric values from various sources. ### Submit values from JSON To submit aggregated custom metric values from JSON, use the `submit_values` method. Submit data in the form of a list of dictionaries. ``` from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113") data = [{'value': 12, 'sample_size': 3, 'timestamp': '2024-03-15T18:00:00'}, {'value': 11, 'sample_size': 5, 'timestamp': '2024-03-15T17:00:00'}, {'value': 14, 'sample_size': 3, 'timestamp': '2024-03-15T16:00:00'}] custom_metric.submit_values(data=data) # data witch association IDs data = [{'value': 15, 'sample_size': 2, 'timestamp': '2024-03-15T21:00:00', 'association_id': '65f44d04dbe192b552e752aa'}, {'value': 13, 'sample_size': 6, 'timestamp': '2024-03-15T20:00:00', 'association_id': '65f44d04dbe192b552e753bb'}, {'value': 17, 'sample_size': 2, 'timestamp': '2024-03-15T19:00:00', 'association_id': '65f44d04dbe192b552e754cc'}] custom_metric.submit_values(data=data) ``` To submit data in the form of a pandas DataFrame: ``` from datetime import datetime import pandas as pd from datarobot.models.deployment import CustomMetric df = pd.DataFrame( data={ "timestamp": [ datetime(year=2024, month=3, day=10), datetime(year=2024, month=3, day=11), datetime(year=2024, month=3, day=12), datetime(year=2024, month=3, day=13), datetime(year=2024, month=3, day=14), datetime(year=2024, month=3, day=15), ], "value": [28, 34, 29, 1, 2, 13], "sample_size": [1, 2, 3, 4, 1, 2], } ) custom_metric.submit_values(data=df) ``` For deployment-specific metrics, do not provide model information. For model specific metrics set `model_package_id` or `model_id`. ``` custom_metric.submit_values(data=data, model_package_id="6421df32525c58cc6f991f25") custom_metric.submit_values(data=data, model_id="6444482e5583f6ee2e572265") ``` Use a dry run to test uploads without saving metric data in DataRobot. This option is disabled by default. ``` custom_metric.submit_values(data=data, dry_run=True) ``` To send data for a given segment, it must be specified as follows. Note that more than one segment can be specified. ``` segments = [{"name": "custom_seg", "value": "baz"}] custom_metric.submit_values(data=data, segments=segments) ``` Batch mode requires specifying batch IDs. Batches always specify a model by `model_package_id` or `model_id`. ``` from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f600e1", custom_metric_id="65f17bdcd2d66683cdfc2224") data = [{'value': 12, 'sample_size': 3, 'batch': '65f44c93fedc5de16b673aaa'}, {'value': 11, 'sample_size': 5, 'batch': '65f44c93fedc5de16b673bbb'}, {'value': 14, 'sample_size': 3, 'batch': '65f44c93fedc5de16b673ccc'}] custom_metric.submit_values(data=data, model_package_id="6421df32525c58cc6f991f25") ``` ### Submit a single value To report a single metric value at the current moment, use the `submit_single_value` method. View the example below, which uses deployment-specific metrics. ``` from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113") custom_metric.submit_single_value(value=16) ``` For model-specific metrics, set `model_package_id` or `model_id`. ``` from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113") custom_metric.submit_single_value(value=16, model_package_id="6421df32525c58cc6f991f25") custom_metric.submit_single_value(value=16, model_id="6444482e5583f6ee2e572265") ``` Dry run and segments work analogously to report aggregated metric values. ``` custom_metric.submit_single_value(value=16, dry_run=True) segments = [{"name": "custom_seg", "value": "boo"}] custom_metric.submit_single_value(value=16, segments=segments) ``` The sent value timestamp indicates the time the request was sent; the number of sample values is always `1`. This method does not support batch submissions. ### Submit values from a dataset To report aggregated custom metrics values from a dataset in the Data Registry, use the `submit_values_from_catalog` method. ``` from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113") # for deployment specific metrics custom_metric.submit_values_from_catalog(dataset_id="61093144cabd630828bca321") # for model specific metrics set model_package_id or model_id custom_metric.submit_values_from_catalog( dataset_id="61093144cabd630828bca321", model_package_id="6421df32525c58cc6f991f25" ) ``` For segmented analysis, define the name of the column in the dataset and the segment to which it corresponds. ``` segments = [{"name": "custom_seg", "column": "column_with_segment_values"}] custom_metric.submit_values_from_catalog( dataset_id="61093144cabd630828bca321", model_package_id="6421df32525c58cc6f991f25", segments=segments ) ``` For batches, specify the batch IDs in the dataset, or send the entire dataset for a single batch ID. ``` custom_metric.submit_values_from_catalog( dataset_id="61093144cabd630828bca432", model_package_id="6421df32525c58cc6f991f25", batch_id="65f7f71198c2f234b4cb2f7d" ) ``` The names of the columns in the dataset should correspond to the names of the columns that were defined in the custom metric. In addition, the format of the timestamps should also be the same as defined in the metric. If the sample size is not specified, it is treated as a 1 sample by default. The following example shows the shape of a dataset saved in the AI catalog. | timestamp | sample_size | value | | --- | --- | --- | | 12/12/22 | 1 | 22 | | 13/12/22 | 2 | 23 | | 14/12/22 | 3 | 24 | | 15/12/22 | 4 | 25 | The following table shows a sample dataset for batches. | batch | sample_size | value | | --- | --- | --- | | 6572db2c9f9d4ad3b9de33d0 | 1 | 22 | | 6572db2c9f9d4ad3b9de33d0 | 2 | 23 | | 6572db319f9d4ad3b9de33d9 | 3 | 24 | | 6572db319f9d4ad3b9de33d9 | 4 | 25 | ## Retrieve custom metric values over time The following sections outline how to retrieve custom metric values. ### Retrieve values over a time period To retrieve values of a custom metric over a time period, use `get_values_over_time`. ``` from datetime import datetime, timedelta from datarobot.enums import BUCKET_SIZE from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113") now = datetime.now() # specify the time window and bucket size by which results are grouped, the default bucket is 7 days values_over_time = custom_metric.get_values_over_time( start=now - timedelta(days=2), end=now, bucket_size=BUCKET_SIZE.P1D) values_over_time >>> CustomMetricValuesOverTime('2024-03-21 20:15:00+00:00'- '2024-03-23 20:15:00+00:00')" values_over_time.bucket_values >>>{datetime.datetime(2024, 3, 22, 10, 0, tzinfo=tzutc()): 1.0, >>> datetime.datetime(2024, 3, 22, 11, 0, tzinfo=tzutc()): 123.0}} values_over_time.bucket_sample_sizes >>>{datetime.datetime(2024, 3, 22, 10, 0, tzinfo=tzutc()): 1, >>> datetime.datetime(2024, 3, 22, 11, 0, tzinfo=tzutc()): 1}} values_over_time.get_buckets_as_dataframe() >>> start end value sample_size >>> 0 2024-03-21 00:00:00+00:00 2024-03-22 00:00:00+00:00 1.0 1 >>> 1 2024-03-22 00:00:00+00:00 2024-03-23 00:00:00+00:00 123.0 1 ``` For model-specific metrics, set `model_package_id` or `model_id`. ``` values_over_time = custom_metric.get_values_over_time( start=now - timedelta(days=1), end=now, model_package_id="6421df32525c58cc6f991f25") values_over_time = custom_metric.get_values_over_time( start=now - timedelta(days=1), end=now, model_id="6444482e5583f6ee2e572265") ``` To retrieve values for a specific segment, specify the segment name and its value: ``` values_over_time = custom_metric.get_values_over_time( start=now - timedelta(days=1), end=now, segment_attribute="custom_seg", segment_value="val_1") ``` ### Retrieve a summary over a time period To retrieve a summary of a custom metric over a time period, use the `get_summary` method. ``` from datetime import datetime, timedelta from datarobot.enums import BUCKET_SIZE from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113") now = datetime.now() # specify the time window summary = custom_metric.get_summary(start=now - timedelta(days=7), end=now) print(summary) >> "CustomMetricSummary(2024-03-15 15:52:13.392178+00:00 - 2024-03-22 15:52:13.392168+00:00: {'id': '65fd9b1c0c1a840bc6751ce0', 'name': 'My custom metric', 'value': 215.0, 'sample_count': 13, 'baseline_value': 12.0, 'percent_change': 24.02})" ``` For model-specific metrics, set `model_package_id` or `model_id`. ``` summary = custom_metric.get_summary( start=now - timedelta(days=7), end=now, model_package_id="6421df32525c58cc6f991f25") summary = custom_metric.get_summary( start=now - timedelta(days=7), end=now, model_id="6444482e5583f6ee2e572265") ``` To retrieve a summary for a specific segment, specify the segment name and its value. ``` summary = custom_metric.get_summary( start=now - timedelta(days=7), end=now, segment_attribute="custom_seg", segment_value="val_1") ``` ### Retrieve values over a batch To retrieve values of a custom metric over a batch, use `get_values_over_batch`. ``` from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113" ) # All batch metrics, all model-specific values_over_batch = custom_metric.get_values_over_batch(model_package_id='6421df32525c58cc6f991f25') values_over_batch.bucket_values >>> {'6572db2c9f9d4ad3b9de33d0': 35.0, '6572db2c9f9d4ad3b9de44e1': 105.0} values_over_batch.bucket_sample_sizes >>> {'6572db2c9f9d4ad3b9de33d0': 6, '6572db2c9f9d4ad3b9de44e1': 8} values_over_batch.get_buckets_as_dataframe() >>> batch_id batch_name value sample_size >>> 0 6572db2c9f9d4ad3b9de33d0 Batch 1 - 03/26/2024 13:04:46 35.0 6 >>> 1 6572db2c9f9d4ad3b9de44e1 Batch 2 - 03/26/2024 13:06:04 105.0 8 ``` For specific batches, set `batch_ids`. ``` values_over_batch = custom_metric.get_values_over_batch( model_package_id='6421df32525c58cc6f991f25', batch_ids=["65f44c93fedc5de16b673aaa", "65f44c93fedc5de16b673bbb"]) ``` To retrieve values for a specific segment, specify the segment name and its value. ``` values_over_batch = custom_metric.get_values_over_batch( model_package_id='6421df32525c58cc6f991f25', segment_attribute="custom_seg", segment_value="val_1") ``` ### Retrieve a summary over batch To retrieve a summary of a custom metric over batch, use `get_summary`: ``` from datarobot.models.deployment import CustomMetric custom_metric = CustomMetric.get( deployment_id="5c939e08962d741e34f609f0", custom_metric_id="65f17bdcd2d66683cdfc1113" ) # All batch metrics, all model-specific batch_summary = custom_metric.get_batch_summary(model_package_id='6421df32525c58cc6f991f25') print(batch_summary) >> CustomMetricBatchSummary({'id': '6605396413434b3a7b74342c', 'name': 'batch metric', 'value': 41.25, 'sample_count': 28, 'baseline_value': 123.0, 'percent_change': -66.46}) ``` For specific batches, set `batch_ids`. ``` batch_summary = custom_metric.get_batch_summary( model_package_id='6421df32525c58cc6f991f25', batch_ids=["65f44c93fedc5de16b673aaa", "65f44c93fedc5de16b673bbb"]) ``` To retrieve values for a specific segment, specify the segment name and its value. ``` batch_summary = custom_metric.get_batch_summary( model_package_id='6421df32525c58cc6f991f25', segment_attribute="custom_seg", segment_value="val_1") ``` ## Hosted custom metrics Hosted custom metrics allow you to implement up to 5 of your organization’s specialized metrics in a deployment, uploading the custom metric code to DataRobot and hosting the metric calculation on custom jobs infrastructure. After creation, hosted custom metrics can be reused for other deployments. DataRobot provides a variety of templates for common metrics. These metrics can be used as-is, or as a starting point for user-provided metrics. The following sections outline how to create a hosted custom metric using the Python API client. ### Prerequisites Import the necessary objects to create a custom metric and initialize the DataRobot client. ``` import datarobot as dr from datarobot.enums import HostedCustomMetricsTemplateMetricTypeQueryParams from datarobot.models.deployment.custom_metrics import HostedCustomMetricTemplate, HostedCustomMetric, \ HostedCustomMetricBlueprint, CustomMetric, MetricTimestampSpoofing, ValueField, SampleCountField, BatchField from datarobot.models.registry import JobRun from datarobot.models.registry.job import Job from datarobot import Deployment from datarobot.models.runtime_parameters import RuntimeParameterValue from datarobot.models.types import Schedule dr.Client(token="", endpoint="") gen_ai_deployment_1 = Deployment.get('') ``` ### List hosted custom metric templates Before creating a hosted custom metric from a template, retrieve the LLM metric template to use as the basis of the new metric. To do this, specify the `metric_type` and, because the deployments are LLM models handling Japanese text, search for the specific metric by name, limiting the search to 1 result. Store the result in `templates` for the following steps. ``` templates = HostedCustomMetricTemplate.list( search="[JP] Character Count", metric_type=HostedCustomMetricsTemplateMetricTypeQueryParams.LLM, limit=1, offset=0, ) ``` ### Create a hosted custom metric After locating the custom metric template, create the hosted custom metric from that template. This method is a shortcut, combining two steps to create the new custom metric from the retrieved template: 1. Create a custom job for a hosted custom metric from the template previously retrieved (stored in templates ). 2. Connect the hosted custom metric job to the deployment previously defined (stored in gen_ai_deployment_1 ). Specify both the job name and custom metric name in addition to the template and deployment IDs, as you are creating two objects. Additionally, define the job schedule and the runtime parameter overrides for the deployment. ``` hosted_custom_metric = HostedCustomMetric.create_from_template( template_id=templates[0].id, deployment_id=gen_ai_deployment_1.id, job_name="Hosted Custom Metric Character Count", custom_metric_name="Character Count", job_description="Hosted Custom Metric", custom_metric_description="LLM Character Count", baseline_value=10, timestamp=MetricTimestampSpoofing( column_name="timestamp", time_format="%Y-%m-%d %H:%M:%S", ), value = ValueField(column_name="value"), sample_count=SampleCountField(column_name='Sample Count'), batch=BatchField(column_name='Batch'), schedule=Schedule( day_of_week=[0], hour=['*'], minute=['*'], day_of_month=[12], month=[1], ), parameter_overrides=[RuntimeParameterValue(field_name='DRY_RUN', value="0", type="string")] ) ``` Once we have created the hosted custom metric, initiate the manual run. ``` job_run = JobRun.create( job_id=hosted_custom_metric.custom_job_id runtime_parameter_values=[ RuntimeParameterValue(field_name='DRY_RUN', value="1", type="string"), RuntimeParameterValue(field_name='DEPLOYMENT_ID', value=gen_ai_deployment_1.id, type="deployment"), RuntimeParameterValue(field_name='CUSTOM_METRIC_ID', value=hosted_custom_metric.id, type="customMetric"), ] ) print(job_run.status) ``` ### Manually create hosted custom metrics You can alternatively create hosted custom metrics in a manual sequenced process. This is useful if you want to edit the custom metric blueprint before attaching the custom job to the deployment. When you attach the job to the deployment, most settings are copied from the blueprint (unless you provide an override). To create the hosted custom metric manually, first create a custom job from the template (stored in `templates`). ``` job = Job.create_from_custom_metric_gallery_template( template_id=templates[0].id, name="Job created from template", description="Job created from template" ) ``` Next, retrieve the default blueprint provided by the template, and edit it. ``` blueprint = HostedCustomMetricBlueprint.get(job.id) print(f"Original directionality: {blueprint.directionality}") ``` Then, update the parameters of the custom metric in the blueprint. ``` updated_blueprint = blueprint.update( directionality='lowerIsBetter', units='characters', type='gauge', time_step='hour', is_model_specific=False ) print(f"Updated directionality: {updated_blueprint.directionality}") ``` Now, create the hosted custom metric. As in the shortcut method, you can provide the job schedule, runtime parameter overrides, and custom metric parameters specific to this deployment. ``` another_hosted_custom_metric = HostedCustomMetric.create_from_custom_job( custom_job_id=job.id, deployment_id=gen_ai_deployment_1.id, name="Custom metric created in 2 steps", ) ``` After creating and configuring the metric, verify that the changes to the blueprint are reflected in the custom metric. ``` another_custom_metric = CustomMetric.get(custom_metric_id=another_hosted_custom_metric.id, deployment_id=gen_ai_deployment_1.id) print(f"Directionality of another custom metric: {another_custom_metric.directionality}") ``` Finally, create a manual job run for the custom metric job. ``` job_run = JobRun.create( job_id=job.id, runtime_parameter_values=[ RuntimeParameterValue(field_name='DRY_RUN', value="1", type="string"), RuntimeParameterValue(field_name='DEPLOYMENT_ID', value=gen_ai_deployment_1.id, type="deployment"), RuntimeParameterValue(field_name='CUSTOM_METRIC_ID', value=another_hosted_custom_metric.id, type="customMetric"), ] ) print(job_run.status) ``` ### List hosted custom metrics To list all hosted custom metrics associated with a custom job, use the following code: ``` hosted_custom_metrics = HostedCustomMetric.list(deployment_id=hosted_custom_metric.custom_job_id) for metric in hosted_custom_metrics: print(metric.name) ``` ### Delete hosted custom metrics In addition, you can delete the hosted custom metric, which removes it from deployment while keeping the job, allowing you to create the metric for another deployment. ``` hosted_custom_metric.delete() another_hosted_custom_metric.delete() ``` If necessary, you can delete the entire custom job. If there are any custom metrics associated with that job, they are also deleted. ``` job.delete() ``` --- # Custom models URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_model.html > Manage custom inference models and execution environments with the Python API client. # Custom models Custom models provide the ability to run arbitrary modeling code in a user-defined environment. ## Manage execution environments The execution environment defines the runtime environment for custom models. Execution environment version is a revision of execution environment with an actual runtime definition. Refer to [DataRobot User Models repository](https://github.com/datarobot/datarobot-user-models) for sample environments. ### Create an execution environment To create an execution environment: ``` import datarobot as dr execution_environment = dr.ExecutionEnvironment.create( name="Python3 PyTorch Environment", description="This environment contains Python3 pytorch library.", ) execution_environment.id >>> '5b6b2315ca36c0108fc5d41b' ``` #### Create an execution environment version from Docker Context or Docker URI You can create an Execution Environment Version using either a Docker image URI, a Docker context, or both. If you provide both, the environment version is built from the image URI, while the context is uploaded for informational purposes and can be later downloaded. There are two ways to create an execution environment version: synchronously and asynchronously. The synchronous method blocks program execution until the execution environment version is created or creation fails. ``` import datarobot as dr # Use the execution_environment created previously environment_version = dr.ExecutionEnvironmentVersion.create( execution_environment.id, docker_context_path="datarobot-user-models/public_dropin_environments/python3_pytorch", max_wait=3600, # 1 hour timeout ) environment_version.id >>> '5eb538959bc057003b487b2d' environment_version.build_status >>> 'success' ``` The asynchronous method does not block execution, but the execution environment version will not be ready for use until the creation process is finished. In this case, you must manually call [refresh()](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.ExecutionEnvironmentVersion.refresh) for the execution environment version and check if its `build_status` is “success”. To create an execution environment version without blocking a program, set `max_wait` to `None`: ``` import datarobot as dr # Use the execution environment created earlier # create environment version from docker context environment_version = dr.ExecutionEnvironmentVersion.create( execution_environment.id, docker_context_path="datarobot-user-models/public_dropin_environments/python3_pytorch", max_wait=None, # Set None to not block execution on this method ) environment_version.id >>> '5eb538959bc057003b487b2d' environment_version.build_status >>> 'processing' # After some time environment_version.refresh() environment_version.build_status >>> 'success' # now create anoter environment version from docker image URI environment_version = dr.ExecutionEnvironmentVersion.create( execution_environment.id, docker_image_uri="test_org/test_repo:test_tag", max_wait=None, # set None to not block execution on this method ) environment_version.id >>> '5eb538959bc057003b4943d2' environment_version.build_status >>> 'success' environment_version.docker_image_uri 'test_org/test_repo:test_tag' ``` If your environment requires additional metadata to be supplied for models using it, you can create an environment with additional metadata keys. Custom model versions that use this environment must specify values for these keys before they can be used to run tests or make deployments. The values will be baked in as environment variables with `field_name` as the environment variable name. ``` import datarobot as dr from datarobot.models.execution_environment import RequiredMetadataKey execution_environment = dr.ExecutionEnvironment.create( name="Python3 PyTorch Environment", description="This environment contains Python3 pytorch library.", required_metadata_keys=[ RequiredMetadataKey(field_name="MY_VAR", display_name="A value needed by hte environment") ], ) model_version = dr.CustomModelVersion.create_clean( custom_model_id=custom_model.id, base_environment_id=execution_environment.id, folder_path=custom_model_folder, required_metadata={"MY_VAR": "a value"} ) ``` ### List execution environments To list execution environments available to the user: ``` import datarobot as dr execution_environments = dr.ExecutionEnvironment.list() execution_environments >>> [ExecutionEnvironment('[DataRobot] Python 3 PyTorch Drop-In'), ExecutionEnvironment('[DataRobot] Java Drop-In')] environment_versions = dr.ExecutionEnvironmentVersion.list(execution_environment.id) environment_versions >>> [ExecutionEnvironmentVersion('v1')] ``` Refer to [ExecutionEnvironment](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.ExecutionEnvironment) for properties of the execution environment object and [ExecutionEnvironmentVersion](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.ExecutionEnvironmentVersion) for properties of the execution environment object version. You can also filter the execution environments that are returned by passing a string as a `search_for` parameter. Only the execution environments that contain the passed string in `name` or `description` are returned. ``` import datarobot as dr execution_environments = dr.ExecutionEnvironment.list(search_for='java') execution_environments >>> [ExecutionEnvironment('[DataRobot] Java Drop-In')] ``` Execution environment versions can be filtered by build status. ``` import datarobot as dr environment_versions = dr.ExecutionEnvironmentVersion.list( execution_environment.id, dr.EXECUTION_ENVIRONMENT_VERSION_BUILD_STATUS.PROCESSING ) environment_versions >>> [ExecutionEnvironmentVersion('v1')] ``` ### Retrieve an execution environment To retrieve an execution environment and an execution environment version by identifier (rather than list all available environments): ``` import datarobot as dr execution_environment = dr.ExecutionEnvironment.get(execution_environment_id='5506fcd38bd88f5953219da0') execution_environment >>> ExecutionEnvironment('[DataRobot] Python 3 PyTorch Drop-In') environment_version = dr.ExecutionEnvironmentVersion.get( execution_environment_id=execution_environment.id, version_id='5eb538959bc057003b487b2d') environment_version >>> ExecutionEnvironmentVersion('v1') ``` ### Update an execution environment To update name or description of the execution environment: ``` import datarobot as dr execution_environment = dr.ExecutionEnvironment.get(execution_environment_id='5506fcd38bd88f5953219da0') execution_environment.update(name='new name', description='new description') ``` ### Delete Execution Environment To delete the execution environment and execution environment version: ``` import datarobot as dr execution_environment = dr.ExecutionEnvironment.get(execution_environment_id='5506fcd38bd88f5953219da0') execution_environment.delete() ``` ### Get execution environment build logs To get an execution environment version build log: ``` import datarobot as dr environment_version = dr.ExecutionEnvironmentVersion.get( execution_environment_id='5506fcd38bd88f5953219da0', version_id='5eb538959bc057003b487b2d') log, error = environment_version.get_build_log() ``` ## Manage custom models A custom model is user-defined modeling code that supports making predictions against it. Custom models support regression and binary classification target types. To upload actual modeling code, you must create a custom model version for a custom model. See [Custom Model Version documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_model.html#custom-model-versions) for more information. ### Create a custom model #### Regression model To create a regression custom model: ``` import datarobot as dr custom_model = dr.CustomInferenceModel.create( name='Python 3 PyTorch Custom Model', target_type=dr.TARGET_TYPE.REGRESSION, target_name='MEDV', description='This is a Python3-based custom model. It has a simple PyTorch model built on boston housing', language='python' ) custom_model.id >>> '5b6b2315ca36c0108fc5d41b' ``` #### Binary classification model When creating a binary classification custom model, `positive_class_label` and `negative_class_label` must be set: ``` import datarobot as dr custom_model = dr.CustomInferenceModel.create( name='Python 3 PyTorch Custom Model', target_type=dr.TARGET_TYPE.BINARY, target_name='readmitted', positive_class_label='False', negative_class_label='True', description='This is a Python3-based custom model. It has a simple PyTorch model built on 10k_diabetes dataset', language='Python 3' ) custom_model.id >>> '5b6b2315ca36c0108fc5d41b' ``` #### Multiclass model When creating a multiclass classification custom model, you must provide `class_labels`: ``` import datarobot as dr custom_model = dr.CustomInferenceModel.create( name='Python 3 PyTorch Custom Model', target_type=dr.TARGET_TYPE.MULTICLASS, target_name='readmitted', class_labels=['hot dog', 'burrito', 'hoagie', 'reuben'], description='This is a Python3-based custom model. It has a simple PyTorch model built on sandwich dataset', language='Python 3' ) custom_model.id >>> '5b6b2315ca36c0108fc5d41b' ``` Multiclass labels can also be provided as a file in cases where there are many class labels. The file should have each class label separated by a new line. ``` import datarobot as dr custom_model = dr.CustomInferenceModel.create( name='Python 3 PyTorch Custom Model', target_type=dr.TARGET_TYPE.MULTICLASS, target_name='readmitted', class_labels_file='/path/to/classlabels.txt', description='This is a Python3-based custom model. It has a simple PyTorch model built on sandwich dataset', language='Python 3' ) custom_model.id >>> '5b6b2315ca36c0108fc5d41b' ``` #### Unstructured model For unstructured models, the `target_name` parameter is optional and ignored if provided. To create an unstructured custom model: ``` import datarobot as dr custom_model = dr.CustomInferenceModel.create( name='Python 3 Unstructured Custom Model', target_type=dr.TARGET_TYPE.UNSTRUCTURED, description='This is a Python3-based unstructured model', language='python' ) custom_model.id >>> '5b6b2315ca36c0108fc5d41b' ``` #### Anomaly detection model For anomaly detection models, the `target_name` parameter is also optional and is ignored if provided. To create an anomaly detection custom model: ``` import datarobot as dr custom_model = dr.CustomInferenceModel.create( name='Python 3 Unstructured Custom Model', target_type=dr.TARGET_TYPE.ANOMALY, description='This is a Python3-based anomaly detection model', language='python' ) custom_model.id >>> '5b6b2315ca36c0108fc5d41b' ``` #### k8s resources Custom model k8s resources are optional and unless specifically provided, the configured defaults are used. To create a custom model with specific k8s resources: ``` import datarobot as dr custom_model = dr.CustomInferenceModel.create( name='Python 3 PyTorch Custom Model', target_type=dr.TARGET_TYPE.BINARY, target_name='readmitted', positive_class_label='False', negative_class_label='True', description='This is a Python3-based custom model. It has a simple PyTorch model built on 10k_diabetes dataset', language='Python 3', maximum_memory=512*1024*1024, ) ``` ### Assign training data to custom models To create a custom model that enables training data assignment on the model version level, provide the `is_training_data_for_versions_permanently_enabled=True` parameter. For more information, refer to the [Custom model version creation with training data](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_model.html#custom-inference-model-version-training-data) documentation. ``` import datarobot as dr custom_model = dr.CustomInferenceModel.create( name='Python 3 PyTorch Custom Model', target_type=dr.TARGET_TYPE.REGRESSION, target_name='MEDV', description='This is a Python3-based custom model. It has a simple PyTorch model built on boston housing', language='python', is_training_data_for_versions_permanently_enabled=True ) custom_model.id >>> '5b6b2315ca36c0108fc5d41b' ``` ### List custom models To list the custom models available to you: ``` import datarobot as dr dr.CustomInferenceModel.list() >>> [CustomInferenceModel('my model 2'), CustomInferenceModel('my model 1')] # use these parameters to filter results: dr.CustomInferenceModel.list( is_deployed=True, # set to return only deployed models order_by='-updated', # set to define order of returned results search_for='model 1', # return only models containing 'model 1' in name or description ) >>> CustomInferenceModel('my model 1') ``` Refer to [list()](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.CustomInferenceModel.list) for detailed parameter descriptions. ### Retrieve a custom model To retrieve a specific custom model: ``` import datarobot as dr dr.CustomInferenceModel.get('5ebe95044024035cc6a65602') >>> CustomInferenceModel('my model 1') ``` ### Update custom model To update custom model properties: ``` import datarobot as dr custom_model = dr.CustomInferenceModel.get('5ebe95044024035cc6a65602') custom_model.update( name='new name', description='new description', ) ``` Please, refer to [update()](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.CustomInferenceModel.update) for the full list of properties that can be updated. ### Download latest revision of a custom model To download content of the latest Custom Model Version of `CustomInferenceModel` as a ZIP archive: ``` import datarobot as dr path_to_download = '/home/user/Documents/myModel.zip' custom_model = dr.CustomInferenceModel.get('5ebe96b84024035cc6a6560b') custom_model.download_latest_version(path_to_download) ``` ### Assign training data to a custom model This example assigns training data on the model level. To assign training data on the model version level, see the [Custom model version creation with training data](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_model.html#custom-inference-model-version-training-data) documentation. To assign training data to custom inference model: ``` import datarobot as dr path_to_dataset = '/home/user/Documents/trainingDataset.csv' dataset = dr.Dataset.create_from_file(file_path=path_to_dataset) custom_model = dr.CustomInferenceModel.get('5ebe96b84024035cc6a6560b') custom_model.assign_training_data(dataset.id) ``` To assign training data without blocking a program, set `max_wait` to `None`: ``` import datarobot as dr path_to_dataset = '/home/user/Documents/trainingDataset.csv' dataset = dr.Dataset.create_from_file(file_path=path_to_dataset) custom_model = dr.CustomInferenceModel.get('5ebe96b84024035cc6a6560b') cmv = dr.CustomModelVersion.create_from_previous(custom_model_id = custom_model.id, training_dataset_id = dataset.id) cmv.refresh() while cmv.training_data.assignment_in_progress: time.sleep(60) cmv.refresh() ``` Note: training data must be assigned to retrieve feature impact from a custom model version. See the [Custom Model Version documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_model.html#custom-model-version-feature-impact). ## Manage versions Modeling code for custom models can be uploaded by creating a custom model version. When creating a Custom Model Version, the version must be associated with a base execution environment. If the base environment supports additional model dependencies (R or Python environments) and the custom model version contains a valid `requirements.txt` file, the model version will run in an environment based on the base environment with the additional dependencies installed. ### Create a custom model version You can upload custom model content by creating a clean custom model version: ``` import os import datarobot as dr custom_model_folder = "datarobot-user-models/model_templates/python3_pytorch" # Add files from the folder to the custom model model_version = dr.CustomModelVersion.create_clean( custom_model_id=custom_model.id, base_environment_id=execution_environment.id, folder_path=custom_model_folder, ) custom_model.id >>> '5b6b2315ca36c0108fc5d41b' # Alternatively, add a list of files to the custom model model_version_2 = dr.CustomModelVersion.create_clean( custom_model_id=custom_model.id, base_environment_id=execution_environment.id, files=[(os.path.join(custom_model_folder, 'custom.py'), 'custom.py')], ) # You can also set k8s resources to the custom model model_version_3 = dr.CustomModelVersion.create_clean( custom_model_id=custom_model.id, base_environment_id=execution_environment.id, files=[(os.path.join(custom_model_folder, 'custom.py'), 'custom.py')], network_egress_policy=dr.NETWORK_EGRESS_POLICY.PUBLIC, maximum_memory=512*1024*1024, replicas=1, ) ``` To create a new custom model version from a previous one that modifies some files, use the following code. ``` import os import datarobot as dr custom_model_folder = "datarobot-user-models/model_templates/python3_pytorch" file_to_delete = model_version_2.items[0].id model_version_3 = dr.CustomModelVersion.create_from_previous( custom_model_id=custom_model.id, base_environment_id=execution_environment.id, files=[(os.path.join(custom_model_folder, 'custom.py'), 'custom.py')], files_to_delete=[file_to_delete], ) ``` Reference [CustomModelFileItem](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.models.custom_model_version.CustomModelFileItem) for more information about custom model file properties. You can specify a custom environment version when creating a custom model version. By default a version of the same environment does not change between consecutive model versions. However, this behavior can be overridden: ``` import os import datarobot as dr custom_model_folder = "datarobot-user-models/model_templates/python3_pytorch" # Create a clean version and specify an explicit environment version. model_version = dr.CustomModelVersion.create_clean( custom_model_id=custom_model.id, base_environment_id=execution_environment.id, base_environment_version_id="642209acc5638929a9b8dc3d", folder_path=custom_model_folder, ) # Create a version from a previous one, specifying an explicit environment version. model_version_2 = dr.CustomModelVersion.create_from_previous( custom_model_id=custom_model.id, base_environment_id=execution_environment.id, base_environment_version_id="660186775d016eabb290aee9", ) ``` To create a new custom model version from a previous one, with just new k8s resource values: ``` import os import datarobot as dr custom_model_folder = "datarobot-user-models/model_templates/python3_pytorch" file_to_delete = model_version_2.items[0].id model_version_3 = dr.CustomModelVersion.create_from_previous( custom_model_id=custom_model.id, base_environment_id=execution_environment.id, maximum_memory=1024*1024*1024, ) ``` ### Create a custom model version with training data Model version creation allows you to provide training (and holdout) data information. Every custom model has to be explicitly switched to allow training data assignment for model versions. Note that the training data assignment differs for structured and unstructured models, and should be handled differently. #### Enable training data assignment for custom model versions By default, custom model training data is assigned on the model level; for more information, see the [Custom model training data assignment](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_model.html#custom-inference-model-assign-data) documentation. When training data is assigned to a model, the same training data is used for every model version. This method of training data assignment is deprecated and scheduled for removal; however, to avoid introducing issues for existing models, you must individually convert existing models to perform training data assignment by model version. Note that this change is permanent and cannot be undone. Because the conversion process is irreversible, it is highly recommended that you do not convert critical models to the new training data assignment method. Instead, you should duplicate the existing model and test the new method. Use the code below to permanently enable a training data assignment on the model version level for the specified model. ``` import datarobot as dr dr.Client(token=my_token, endpoint=endpoint) custom_model = dr.CustomInferenceModel.get(custom_model_id) custom_model.update(is_training_data_for_versions_permanently_enabled=True) custom_model.is_training_data_for_versions_permanently_enabled # True ``` #### Assign training data for structured models Training data assignment is performed asynchronously, so you can create a version in a blocking or non-blocking way (shown in the examples below). Create a structured model version with blocking (default `max_wait=600`) and wait for the training data assignment result. If the training data assignment fails: - A datarobot.errors.TrainingDataAssignmentError exception is raised. The exception contains the custom model ID, the custom model version ID, and the failure message. - A new custom model version is still created and can be fetched for further processing, but it’s not possible to create a model package from it or deploy it. ``` import datarobot as dr from datarobot.errors import TrainingDataAssignmentError dr.Client(token=my_token, endpoint=endpoint) try: version = dr.CustomModelVersion.create_from_previous( custom_model_id="6444482e5583f6ee2e572265", base_environment_id="642209acc563893014a41e24", training_dataset_id="6421f2149a4f9b1bec6ad6dd", ) except TrainingDataAssignmentError as e: print(e) ``` To fetch the model version in the case of an assignment error: ``` import datarobot as dr from datarobot.errors import TrainingDataAssignmentError dr.Client(token=my_token, endpoint=endpoint) try: version = dr.CustomModelVersion.create_from_previous( custom_model_id="6444482e5583f6ee2e572265", base_environment_id="642209acc563893014a41e24", training_dataset_id="6421f2149a4f9b1bec6ad6dd", ) except TrainingDataAssignmentError as e: version = CustomModelVersion.get( custom_model_id="6444482e5583f6ee2e572265", custom_model_version_id=e.custom_model_version_id, ) print(version.training_data.dataset_id) print(version.training_data.dataset_version_id) print(version.training_data.dataset_name) print(version.training_data.assignment_error) ``` Below is another example of fetching the model version in the case of an assignment error. ``` import datarobot as dr from datarobot.errors import TrainingDataAssignmentError dr.Client(token=my_token, endpoint=endpoint) custom_model = dr.CustomInferenceModel.get("6444482e5583f6ee2e572265") try: version = dr.CustomModelVersion.create_from_previous( custom_model_id="6444482e5583f6ee2e572265", base_environment_id="642209acc563893014a41e24", training_dataset_id="6421f2149a4f9b1bec6ad6dd", ) except TrainingDataAssignmentError as e: pass custom_model.refresh() version = custom_model.latest_version print(version.training_data.dataset_id) print(version.training_data.dataset_version_id) print(version.training_data.dataset_name) print(version.training_data.assignment_error) ``` Create a structured model version with a non-blocking (set `max_wat=None`) training data assignment. In this case, it is the user’s responsibility to poll for `version.training_data.assignment_in_progress`. Once the assignment is finished, check for errors if `version.training_data.assignment_in_progress==False`. If `version.training_data.assignment_error` is None, then there is no error. ``` import datarobot as dr dr.Client(token=my_token, endpoint=endpoint) version = dr.CustomModelVersion.create_from_previous( custom_model_id="6444482e5583f6ee2e572265", base_environment_id="642209acc563893014a41e24", training_dataset_id="6421f2149a4f9b1bec6ad6dd", max_wait=None, ) while version.training_data.assignment_in_progress: time.sleep(10) version.refresh() if version.training_data.assignment_error: print(version.training_data.assignment_error["message"]) ``` #### Assign training data for unstructured models For unstructured models, you can provide the parameters `training_dataset_id` and `holdout_dataset_id`. The training data assignment is performed synchronously and the `max_wait` parameter is ignored. The example below shows how to create an unstructured model version with training and holdout data. ``` import datarobot as dr dr.Client(token=my_token, endpoint=endpoint) version = dr.CustomModelVersion.create_from_previous( custom_model_id="6444482e5583f6ee2e572265", base_environment_id="642209acc563893014a41e24", training_dataset_id="6421f2149a4f9b1bec6ad6dd", holdout_dataset_id="6421f2149a4f9b1bec6ad6ef", ) if version.training_data.assignment_error: print(version.training_data.assignment_error["message"]) ``` #### Remove training data By default, training and holdout data are copied to a new model version from the previous model version. If you don’t want to keep training and holdout data for the new version, set `keep_training_holdout_data` to False. ``` import datarobot as dr dr.Client(token=my_token, endpoint=endpoint) version = dr.CustomModelVersion.create_from_previous( custom_model_id="6444482e5583f6ee2e572265", base_environment_id="642209acc563893014a41e24", keep_training_holdout_data=False, ) ``` ### List custom model versions To list custom model versions available to you: ``` import datarobot as dr dr.CustomModelVersion.list(custom_model.id) >>> [CustomModelVersion('v2.0'), CustomModelVersion('v1.0')] ``` ### Retrieve a custom model version To retrieve a specific custom model version, run the code below. ``` import datarobot as dr dr.CustomModelVersion.get(custom_model.id, custom_model_version_id='5ebe96b84024035cc6a6560b') >>> CustomModelVersion('v2.0') ``` ### Update a version description To update a custom model version description: ``` import datarobot as dr custom_model_version = dr.CustomModelVersion.get( custom_model.id, custom_model_version_id='5ebe96b84024035cc6a6560b', ) custom_model_version.update(description='new description') custom_model_version.description >>> 'new description' ``` ### Download a version To download the contents of a custom model version as a ZIP archive: ``` import datarobot as dr path_to_download = '/home/user/Documents/myModel.zip' custom_model_version = dr.CustomModelVersion.get( custom_model.id, custom_model_version_id='5ebe96b84024035cc6a6560b', ) custom_model_version.download(path_to_download) ``` ### Start custom model inference legacy conversion Custom model versions may include SAS files, with a main program entry point. In order to use a model, a conversion must be run. The conversion can later be fetched and examined by reading the conversion print-outs. By default, a conversion is initiated in a non-blocking mode. If a `max_wait` parameter is provided, than the call is blocked until the conversion is completed. The results can than be read by fetching the conversion entity. ``` import datarobot as dr # Read a custom model version custom_model_version = dr.CustomModelVersion.get(model_id, model_version_id) # Find the main program item ID main_program_item_id = None for item in cm_ver.items: if item.file_name.lower().endswith('.sas'): main_program_item_id = item.id # Execute the conversion if async: # This is a non-blocking call conversion_id = dr.models.CustomModelVersionConversion.run_conversion( custom_model_version.custom_model_id, custom_model_version.id, main_program_item_id, ) else: # This call is blocked until a completion or a timeout conversion_id = dr.models.CustomModelVersionConversion.run_conversion( custom_model_version.custom_model_id, custom_model_version.id, main_program_item_id, max_wait=60, ) ``` #### Monitor model conversion If a custom model version conversion was initiated in a non-blocking mode, it is possible to monitor the progress as follows: ``` import datarobot as dr while True: conversion = dr.models.CustomModelVersionConversion.get( custom_model_id, custom_model_version_id, conversion_id, ) if conversion.conversion_in_progress: logging.info('Conversion is in progress...') time.sleep(1) else: if conversion.conversion_succeeded: logging.info('Conversion succeeded') else: logging.error(f'Conversion failed!\n{conversion.log_message}') break ``` #### Stop conversion It is possible to stop a custom model version conversion that is in progress. The call is non-blocking and you may keep monitoring the conversion progress (see above) until is it completed. ``` import datarobot as dr dr.models.CustomModelVersionConversion.stop_conversion( custom_model_id, custom_model_version_id, conversion_id, ) ``` ### Calculate Feature Impact To trigger the calculation of a custom model version’s Feature Impact, training data must be assigned to a custom model. (For more information about custom model training data, reference the [custom model documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_model.html#custom-inference-model-assign-data).) If training data is assigned, run the following code to trigger the calculation of its Feature Impact: ``` import datarobot as dr version = dr.CustomModelVersion.get(custom_model.id, custom_model_version_id='5ebe96b84024035cc6a6560b') version.calculate_feature_impact() ``` To trigger Feature Impact calculation without blocking a program, set `max_wait` to `None`: ``` import datarobot as dr version = dr.CustomModelVersion.get(custom_model.id, custom_model_version_id='5ebe96b84024035cc6a6560b') version.calculate_feature_impact(max_wait=None) ``` ### Retrieve custom model image Feature Impact To retrieve a custom model image’s Feature Impact, it must be calculated beforehand. Reference the [Custom model version Feature Impact documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_model.html#custom-model-version-calculate-feature-impact) for more information. To get Feature Impact: ``` import datarobot as dr version = dr.CustomModelVersion.get(custom_model.id, custom_model_version_id='5ebe96b84024035cc6a6560b') version.get_feature_impact() >>> [{'featureName': 'B', 'impactNormalized': 1.0, 'impactUnnormalized': 1.1085356209402688, 'redundantWith': 'B'}...] ``` ### Prepare a custom model version for use If your custom model version has dependencies, a dependency build must be completed before the model can be used. The dependency build installs your model’s dependencies into the base environment associated with the model version. ### Start a dependency build To start a custom model version dependency build: ``` import datarobot as dr build_info = dr.CustomModelVersionDependencyBuild.start_build( custom_model_id=custom_model.id, custom_model_version_id=model_version.id, max_wait=3600, # 1 hour timeout ) build_info.build_status >>> 'success' ``` To start a custom model version dependency build without blocking a program until the test finishes, set `max_wait` to `None`: ``` import datarobot as dr build_info = dr.CustomModelVersionDependencyBuild.start_build( custom_model_id=custom_model.id, custom_model_version_id=model_version.id, max_wait=None, ) build_info.build_status >>> 'submitted' # after some time build_info.refresh() build_info.build_status >>> 'success' ``` In case the build fails, or you are just curious, do the following to retrieve the build log once complete: ``` print(build_info.get_log()) ``` To cancel a custom model version dependency build, simply run: ``` build_info.cancel() ``` ## Manage custom model tests A custom model test represents testing performed on custom models. ### Create a custom model test To create a custom model test: ``` import datarobot as dr path_to_dataset = '/home/user/Documents/testDataset.csv' dataset = dr.Dataset.create_from_file(file_path=path_to_dataset) custom_model_test = dr.CustomModelTest.create( custom_model_id=custom_model.id, custom_model_version_id=model_version.id, dataset_id=dataset.id, max_wait=3600, # 1 hour timeout ) custom_model_test.overall_status >>> 'succeeded' ``` or, with k8s resources: ``` import datarobot as dr path_to_dataset = '/home/user/Documents/testDataset.csv' dataset = dr.Dataset.create_from_file(file_path=path_to_dataset) custom_model_test = dr.CustomModelTest.create( custom_model_id=custom_model.id, custom_model_version_id=model_version.id, dataset_id=dataset.id, max_wait=3600, # 1 hour timeout maximum_memory=1024*1024*1024, ) custom_model_test.overall_status >>> 'succeeded' ``` To start a custom model test without blocking a program until the test finishes, set `max_wait` to `None`: ``` import datarobot as dr path_to_dataset = '/home/user/Documents/testDataset.csv' dataset = dr.Dataset.create_from_file(file_path=path_to_dataset) custom_model_test = dr.CustomModelTest.create( custom_model_id=custom_model.id, custom_model_version_id=model_version.id, dataset_id=dataset.id, max_wait=None, ) custom_model_test.overall_status >>> 'in_progress' # after some time custom_model_test.refresh() custom_model_test.overall_status >>> 'succeeded' ``` Running a custom model test uses the custom model version’s base image with its dependencies installed as an execution environment. To start a custom model test using an execution environment as-is, without the model’s dependencies installed, supply an environment ID and (optionally) an environment version ID: ``` import datarobot as dr path_to_dataset = '/home/user/Documents/testDataset.csv' dataset = dr.Dataset.create_from_file(file_path=path_to_dataset) custom_model_test = dr.CustomModelTest.create( custom_model_id=custom_model.id, custom_model_version_id=model_version.id, dataset_id=dataset.id, max_wait=3600, # 1 hour timeout ) custom_model_test.overall_status >>> 'succeeded' ``` In case a test fails, do the following to examine details of the failure: ``` for name, test in custom_model_test.detailed_status.items(): print('Test: {}'.format(name)) print('Status: {}'.format(test['status'])) print('Message: {}'.format(test['message'])) print(custom_model_test.get_log()) ``` To cancel a custom model test: ``` custom_model_test.cancel() ``` To start a custom model test for an unstructured custom model, dataset details should not be provided: ``` import datarobot as dr custom_model_test = dr.CustomModelTest.create( custom_model_id=custom_model.id, custom_model_version_id=model_version.id, ) ``` ### List custom model tests To list the custom model tests available to the user: ``` import datarobot as dr dr.CustomModelTest.list(custom_model_id=custom_model.id) >>> [CustomModelTest('5ec262604024031bed5aaa16')] ``` ### Retrieve a custom model test To retrieve a specific custom model test: ``` import datarobot as dr dr.CustomModelTest.get(custom_model_test_id='5ec262604024031bed5aaa16') >>> CustomModelTest('5ec262604024031bed5aaa16') ``` --- # Data exports URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html > Export prediction, actuals, training, and data quality data from deployments with the Python API client. # Data exports Use deployment data export to retrieve data sent for predictions along with the associated predictions. ## Prediction data export The following sections outline how to manage prediction data exports. ### Create a prediction data export To create a prediction data export, use `PredictionDataExport.create`, defining the time window to include in the export using the `start` and `end` parameters: ``` from datetime import datetime, timedelta from datarobot.models.deployment import PredictionDataExport now=datetime.now() prediction_data_export = PredictionDataExport.create( deployment_id='5c939e08962d741e34f609f0', start=now - timedelta(days=7), end=now) ``` Specify the model ID for export. Otherwise, the champion model ID is used by default: ``` from datetime import datetime, timedelta from datarobot.models.deployment import PredictionDataExport now=datetime.now() prediction_data_export = PredictionDataExport.create( deployment_id='5c939e08962d741e34f609f0', model_id='6444482e5583f6ee2e572265', start=now - timedelta(days=7), end=now ) ``` For deployments in batch mode, provide batch IDs to export prediction data for those batches: ``` from datetime import datetime, timedelta from datarobot.models.deployment import PredictionDataExport now=datetime.now() prediction_data_export = PredictionDataExport.create( deployment_id='5c939e08962d741e34f609f0', model_id='6444482e5583f6ee2e572265', start=now - timedelta(days=7), end=now, batch_ids=['6572db2c9f9d4ad3b9de33d0', '6572db2c9f9d4ad3b9de33d0'] ) ``` The `start` and `end` of the export can be defined as a datetime or string type. ### List prediction data exports To list prediction data exports, use `PredictionDataExport.list`: ``` from datarobot.models.deployment import PredictionDataExport prediction_data_exports = PredictionDataExport.list(deployment_id='5c939e08962d741e34f609f0', limit=0) prediction_data_exports >>> [PredictionDataExport('65fbe59aaa3f847bd5acc75b'), PredictionDataExport('65fbe59aaa3f847bd5acc75c'), PredictionDataExport('65fbe59aaa3f847bd5acc75a')] ``` To list all prediction data exports, set the limit to `0`. Adjust additional parameters to filter the data as needed: ``` from datarobot.enums import ExportStatus from datarobot.models.deployment import PredictionDataExport prediction_data_exports = PredictionDataExport.list(deployment_id='5c939e08962d741e34f609f0', limit=100, offset=100) # Use additional filters prediction_data_exports = PredictionDataExport.list( deployment_id='5c939e08962d741e34f609f0', model_id="6444482e5583f6ee2e572265", batch=False, status=ExportStatus.FAILED ) ``` ### Retrieve a prediction data export To get a prediction data export by identifier, use `PredictionDataExport.get`: ``` from datarobot.models.deployment import PredictionDataExport prediction_data_export = PredictionDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='65fbe59aaa3f847bd5acc75b' ) prediction_data_exports >>> PredictionDataExport('65fbe59aaa3f847bd5acc75b') ``` ### Fetch prediction export datasets To return data from a prediction export as `dr.Dataset`, use the `fetch_data` method. This method can return a list of datasets; however, usually it returns one dataset. There are cases, like time series, when more than one element is returned. The obtained dataset (or datasets) can be transformed into, for example, a pandas DataFrame. ``` from datarobot.models.deployment import PredictionDataExport prediction_data_export = PredictionDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='65fbe59aaa3f847bd5acc75b' ) prediction_datasets = prediction_data_export.fetch_data() prediction_datasets >>> [Dataset(name='Deployment prediction data', id='65f240b0e37a9f1a104bf450')] prediction_dataset = prediction_datasets[0] df = prediction_dataset.get_as_dataframe() df.head(2) >>> DR_RESERVED_PREDICTION_TIMESTAMP ... upstream_x_datarobot_version 0 2024-03-13 23:00:38.998000+00:00 ... predictionapi/X/X 1 2024-03-13 23:00:38.998000+00:00 ... predictionapi/X/X ``` ## Actuals data export The following examples outline how to manage actuals data exports. ### Create actuals data export To create an actuals data export, use `ActualsDataExport.create`, defining the time window to include in the export using the `start` and `end` parameters: ``` from datetime import datetime, timedelta from datarobot.models.deployment import ActualsDataExport now=datetime.now() actuals_data_export = ActualsDataExport.create( deployment_id='5c939e08962d741e34f609f0', start=now - timedelta(days=7), end=now ) ``` Specify the model ID for export. Otherwise, the champion model ID is used by default: ``` from datetime import datetime, timedelta from datarobot.models.deployment import ActualsDataExport now=datetime.now() actuals_data_export = ActualsDataExport.create( deployment_id='5c939e08962d741e34f609f0', model_id="6444482e5583f6ee2e572265", start=now - timedelta(days=7), end=now, ) ``` To export only actuals that are matched to predictions, set `only_matched_predictions` to `True`; by default all available actuals are exported. The `start` and `end` of the export can be defined as a `datetime` or `string` type. ``` from datetime import datetime, timedelta from datarobot.models.deployment import ActualsDataExport now=datetime.now() actuals_data_export = ActualsDataExport.create( deployment_id='5c939e08962d741e34f609f0', only_matched_predictions=True, start=now - timedelta(days=7), end=now, ) ``` ### List actuals data exports To list actuals data exports, use `ActualsDataExport.list`: ``` from datarobot.models.deployment import ActualsDataExport actuals_data_exports = ActualsDataExport.list(deployment_id='5c939e08962d741e34f609f0', limit=0) actuals_data_exports >>> [ActualsDataExport('660456a332d0081029ee5031'), ActualsDataExport('660456a332d0081029ee5032'), ActualsDataExport('660456a332d0081029ee5033')] ``` To list all actuals data exports, set the limit to `0`. Adjust additional parameters to filter the data as needed: ``` from datarobot.enums import ExportStatus from datarobot.models.deployment import ActualsDataExport # use additional filters actuals_data_exports = ActualsDataExport.list( deployment_id='5c939e08962d741e34f609f0', offset=500, limit=50, status=ExportStatus.SUCCEEDED ) ``` ### Retrieve actuals data export To get actuals data export by identifier, use `ActualsDataExport.get`, as in the following example: ``` from datarobot.models.deployment import ActualsDataExport actuals_data_export = ActualsDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='660456a332d0081029ee4031' ) actuals_data_export >>> ActualsDataExport('660456a332d0081029ee4031') ``` ### Fetch actuals export datasets To return data from actuals export as `dr.Dataset`, use the `fetch_data` method: ``` from datarobot.models.deployment import ActualsDataExport actuals_data_export = ActualsDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='660456a332d0081029ee4031' ) actuals_datasets = actuals_data_export.fetch_data() actuals_datasets >>> [Dataset(name='Deployment prediction data', id='65f240b0e37a9f1a104bf450')] actuals_dataset = actuals_datasets[0] df = actuals_dataset.get_as_dataframe() df.head(2) >>> association_id timestamp actuals predictions 0 1 2024-03-20 15:00:00+00:00 21.0 18.125388 1 10 2024-03-20 15:00:00+00:00 12.0 22.805252 ``` This method may return a list of datasets; however, it usually returns one dataset. The obtained dataset (or datasets) can be transformed into, for example, a pandas DataFrame. ## Training data export The following examples outline how to manage training data exports. ### Create training data export To create a training data export, use `TrainingDataExport.create` and define the deployment ID: ``` from datarobot.models.deployment import TrainingDataExport dataset_id = TrainingDataExport.create(deployment_id='5c939e08962d741e34f609f0') ``` Specify the model ID for export. Otherwise, the champion model ID is used by default: ``` from datarobot.models.deployment import TrainingDataExport dataset_id = TrainingDataExport.create( deployment_id='5c939e08962d741e34f609f0', model_id='6444482e5583f6ee2e572265') dataset_id >>> 65fb0c25019ca3333bbb4c10 ``` This method returns the ID of the dataset that contains the training data. This dataset is saved in the AI Catalog. ### List training data exports To list training data exports, use `TrainingDataExport.list`: ``` from datarobot.models.deployment import TrainingDataExport training_data_exports = TrainingDataExport.list(deployment_id='5c939e08962d741e34f609f0') training_data_exports >>> [TrainingDataExport('6565fbf2356124f1daa3acc522')] ``` ### Retrieve a training data export To get training data export by identifier, use `TrainingDataExport.get`. ``` from datarobot.models.deployment import ActualsDataExport training_data_export = TrainingDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='65fbf2356124f1daa3acc522' ) training_data_export >>> TrainingDataExport('6565fbf2356124f1daa3acc522') ``` ### Fetch a training export dataset To return data from the training export as `dr.Dataset`, use `fetch_data`. This method returns a single training dataset. The obtained dataset can be transformed into, for example, a pandas DataFrame. ``` from datarobot.models.deployment import TrainingDataExport training_data_export = TrainingDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='660456a332d0081029ee4031' ) training_dataset = training_data_export.fetch_data() training_dataset >>> [Dataset(name='training-data-10k_diabetes.csv', id='65fb0c25019ca3333bbb4c10')] df = training_dataset.get_as_dataframe() df.head(2) >>> acetohexamide time_in_hospital ... number_outpatient payer_code 0 No 1 ... 0 YY 1 No 2 ... 0 XX ``` ## Data quality export The data-quality exports provide feedback on LLM deployments. It is intended to be used in conjunction with custom-metrics for prompt monitoring. ### Data quality export list To list data quality exports, use `DataQualityExport.list`: The `start` and `end` of the export can be defined as a `datetime` or `string` type. There are many options for filtering and ordering the data. ``` from datetime import datetime, timedelta from datarobot.models.deployment import DataQualityExport now=datetime.now() data_quality_exports = DataQualityExport.list( deployment_id='66903c40f18e6ec90fd7c8c7', start=now - timedelta(days=1), end=now, ) data_quality_exports >>> [DataQualityExport(6447ca39c6a04df6b5b0ed19c6101e3c), ... DataQualityExport(0ff46fd3636545a9ac3e15ee1dbd8638)] data_quality_deports[0].metrics >>> [{'id': '669688f90a23524131e2d301', 'name': 'metric 3', 'value': None}, {'id': '669688e633ae1ffce40eb2f8', 'name': 'metric 2', 'value': 45.0}, {'id': '669688d282c9384ab8068a6c', 'name': 'metric 1', 'value': 178.0}] ``` --- # Deployments URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/deployment.html > Deploy, manage, and monitor models with the Python API client. # Deployments This page outlines how you can deploy, manage, and monitor models with the Python API client. ## Manage deployments The following commands can be used to manage deployments. ### Create a deployment A new deployment can be created from: - A DataRobot model - use create_from_registered_model_version() . Refer to the Model Registry documentation to reference how to create a registered model version. When creating a new deployment, you must provide a DataRobot `registered_model_version_id` (also known as `model_package_id`) and a `label`. Optionally, provide a `description` to document the purpose of the deployment. The default prediction server is used when making predictions against the deployment and is required for creating a deployment on DataRobot in managed SaaS environments. For Self-Managed users, you cannot provide a default prediction server. Instead use a pre-configured prediction server. Refer to [datarobot.PredictionServer.list](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.PredictionServer.list) for more information on retrieving available prediction servers. ``` import datarobot as dr project = dr.Project.get('6527eb38b9e5dead5fc12491') model = project.get_models()[0] prediction_server = dr.PredictionServer.list()[0] registered_model_version = dr.RegisteredModelVersion.create_for_leaderboard_item( model_id=model.id, name="Name of the version(aka model package)", registered_model_name='Name of the registered model unique across the org ' ) deployment = dr.Deployment.create_from_registered_model_version( registered_model_version.id, label='New Deployment', description='A new deployment', default_prediction_server_id=prediction_server.id) >>> Deployment('New Deployment') ``` ### List deployments To list deployments a user can view: ``` import datarobot as dr deployments = dr.Deployment.list() deployments >>> [Deployment('New Deployment'), Deployment('Previous Deployment')] ``` Refer to [Deployment](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment) to learn about the properties of the deployment object. You can also filter the deployments that are returned by passing an instance of the [DeploymentListFilters](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.DeploymentListFilters) class to the `filters` keyword argument. ``` import datarobot as dr filters = dr.models.deployment.DeploymentListFilters( role='OWNER', accuracy_health=dr.enums.DEPLOYMENT_ACCURACY_HEALTH_STATUS.FAILING ) deployments = dr.Deployment.list(filters=filters) deployments >>> [Deployment('Deployment Owned by Me w/ Failing Accuracy 1'), Deployment('Deployment Owned by Me w/ Failing Accuracy 2')] ``` ### Retrieve a deployment It is possible to retrieve a single deployment with its identifier, rather than list all deployments: ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.id >>> '5c939e08962d741e34f609f0' deployment.label >>> 'New Deployment' ``` Refer to [Deployment](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment) to learn about the properties of the deployment object. ### Update a deployment To update a deployment’s label and description: ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.update(label='new label') ``` ### Delete a deployment To mark a deployment as deleted: ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.delete() ``` ### Activate or deactivate a deployment To activate a deployment: ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.activate() deployment.status >>> 'active' ``` To deactivate a deployment: ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.deactivate() deployment.status >>> 'inactive' ``` ### Make batch predictions with a deployment DataRobot provides a small utility function to make batch predictions using a deployment: [Deployment.predict_batch](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.predict_batch). ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') # To note: `source` can be a file path, a file, or a pandas DataFrame prediction_results_as_dataframe = deployment.predict_batch( source="./my_local_file.csv", ) ``` ## Model replacement A deployment’s model can be replaced effortlessly with zero interruption of predictions. Model replacement is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. Predictions made against this deployment will start using the new model as soon as the request is completed. There will be no interruption for predictions throughout the process. The [replace_model()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.replace_model) function won’t return until the asynchronous process is fully finished. Alongside the identifier of the new model, a `reason` is also required. The reason is stored in model history of the deployment for documentation purposes. An enum, `MODEL_REPLACEMENT_REASON`, is provided for convenience. All possible values are documented below: - MODEL_REPLACEMENT_REASON.ACCURACY - MODEL_REPLACEMENT_REASON.DATA_DRIFT - MODEL_REPLACEMENT_REASON.ERRORS - MODEL_REPLACEMENT_REASON.SCHEDULED_REFRESH - MODEL_REPLACEMENT_REASON.SCORING_SPEED - MODEL_REPLACEMENT_REASON.OTHER Below is an example of model replacement: ``` import datarobot as dr from datarobot.enums import MODEL_REPLACEMENT_REASON project = dr.Project.get('5cc899abc191a20104ff446a') model = project.get_models()[0] deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.model['id'], deployment.model['type'] >>> ('5c0a979859b00004ba52e431', 'Decision Tree Classifier (Gini)') deployment.replace_model('5c0a969859b00004ba52e41b', MODEL_REPLACEMENT_REASON.ACCURACY) deployment.model['id'], deployment.model['type'] >>> ('5c0a969859b00004ba52e41b', 'Support Vector Classifier (Linear Kernel)') ``` ### Validation Before initiating the model replacement request, it is usually a good idea to use the [validate_replacement_model()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.validate_replacement_model) function to validate if the new model can be used as a replacement. The [validate_replacement_model()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.validate_replacement_model) function returns the validation status, a message and a checks dictionary. If the status is ‘passing’ or ‘warning’, use [replace_model()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.replace_model) to perform model the replacement. If status is ‘failing’, refer to the `checks` dict for more details on why the new model cannot be used as a replacement. ``` import datarobot as dr project = dr.Project.get('5cc899abc191a20104ff446a') model = project.get_models()[0] deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') status, message, checks = deployment.validate_replacement_model(new_model_id=model.id) status >>> 'passing' # `checks` can be inspected for detail, showing two examples here: checks['target'] >>> {'status': 'passing', 'message': 'Target is compatible.'} checks['permission'] >>> {'status': 'passing', 'message': 'User has permission to replace model.'} ``` ## Monitoring Deployment monitoring can be categorized into several area of concerns: - Service stats over time - Accuracy over time With a [Deployment](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment) object, `get` functions are provided so that you can query monitoring data. Alternatively, it is also possible to retrieve monitoring data directly using a deployment ID: ``` from datarobot.models import Deployment, ServiceStats deployment_id = '5c939e08962d741e34f609f0' # Call `get` functions on a `Deployment` object deployment = Deployment.get(deployment_id) service_stats = deployment.get_service_stats() # Directly fetch without a `Deployment` object service_stats = ServiceStats.get(deployment_id) ``` When querying monitoring data, you can optionally provide a start and end time (accepted as either a `datetime` object or a `string`). Note that only top of the hour datetimes are accepted. For example: `2019-08-01T00:00:00Z`. By default, the end time of the query will be the next top of the hour, the start time will be 7 days before the end time. In the over time variants, an optional `bucket_size` can be provided to specify the resolution of time buckets. For example, if the start time is `2019-08-01T00:00:00Z`, the end time is `2019-08-02T00:00:00Z`, and the `bucket_size` is `T1H`, then 24 time buckets are generated, each providing data calculated over one hour. Use [construct_duration_string()](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) to help construct a bucket size string. > NOTE¶The minimum bucket size is one hour. ### Service stats Service stats are metrics tracking deployment utilization and how well deployments respond to prediction requests. Use `SERVICE_STAT_METRIC.ALL` to retrieve a list of supported metrics. [ServiceStats](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.ServiceStats) retrieves values for all service stats metrics.[ServiceStatsOverTime](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.ServiceStatsOverTime) can be used to fetch how one single metric changes over time. ``` from datetime import datetime from datarobot.enums import SERVICE_STAT_METRIC from datarobot.helpers.partitioning_methods import construct_duration_string from datarobot.models import Deployment deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') service_stats = deployment.get_service_stats( start_time=datetime(2019, 8, 1, hour=15), end_time=datetime(2019, 8, 8, hour=15) ) service_stats[SERVICE_STAT_METRIC.TOTAL_PREDICTIONS] >>> 12597 total_predictions = deployment.get_service_stats_over_time( start_time=datetime(2019, 8, 1, hour=15), end_time=datetime(2019, 8, 8, hour=15), bucket_size=construct_duration_string(days=1), metric=SERVICE_STAT_METRIC.TOTAL_PREDICTIONS ) total_predictions.bucket_values >>> OrderedDict([(datetime.datetime(2019, 8, 1, 15, 0, tzinfo=tzutc()), 1610), (datetime.datetime(2019, 8, 2, 15, 0, tzinfo=tzutc()), 2249), (datetime.datetime(2019, 8, 3, 15, 0, tzinfo=tzutc()), 254), (datetime.datetime(2019, 8, 4, 15, 0, tzinfo=tzutc()), 943), (datetime.datetime(2019, 8, 5, 15, 0, tzinfo=tzutc()), 1967), (datetime.datetime(2019, 8, 6, 15, 0, tzinfo=tzutc()), 2810), (datetime.datetime(2019, 8, 7, 15, 0, tzinfo=tzutc()), 2775)]) ``` ### Data drift Data drift measures how much the distribution of target or a feature has changed comparing to the training data. Deployment’s target drift and feature drift can be retrieved separately using [datarobot.models.deployment.TargetDrift](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.TargetDrift) and [datarobot.models.deployment.FeatureDrift](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.FeatureDrift). Use `DATA_DRIFT_METRIC.ALL` to retrieve a list of supported metrics. ``` from datetime import datetime from datarobot.enums import DATA_DRIFT_METRIC from datarobot.models import Deployment, FeatureDrift deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') target_drift = deployment.get_target_drift( start_time=datetime(2019, 8, 1, hour=15), end_time=datetime(2019, 8, 8, hour=15) ) target_drift.drift_score >>> 0.00408514 feature_drift_data = FeatureDrift.list( deployment_id='5c939e08962d741e34f609f0', start_time=datetime(2019, 8, 1, hour=15), end_time=datetime(2019, 8, 8, hour=15), metric=DATA_DRIFT_METRIC.HELLINGER ) feature_drift = feature_drift_data[0] feature_drift.name >>> 'age' feature_drift.drift_score >>> 4.16981594 ``` #### Predictions over time Predictions over time gives insight on how deployment’s prediction response has changed over time. Different data can be retrieved in each bucket, depending on deployment’s target type: - row_count : The number of rows in the bucket, available for all target types. - mean_predicted_value : The average of the predicted values for all rows in the bucket. Available for regression target type. - mean_probabilities : The mean of the predicted probability for each class. Available for binary or multiclass classification target types. - class_distribution : The count and percent of the predicted class labels. Available for binary or multiclass classification target types. - percentiles : The 10th and 90th percentile of a predicted value or positive class probability. Available for regression and binary target types. ``` from datetime import datetime from datarobot.enums import BUCKET_SIZE from datarobot.models import Deployment # Deployment with regression target type deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') predictions_over_time = deployment.get_predictions_over_time( start_time=datetime(2023, 4, 1), end_time=datetime(2023, 4, 30), bucket_size=BUCKET_SIZE.P1D, ) predicted = [bucket['mean_predicted_value'] for bucket in predictions_over_time.buckets] predicted >>> [0.3772, 0.6642, ...., 0.7937] # Deployment with binary target type deployment = Deployment.get(deployment_id='62fff28a0f5fee488587ce92') predictions_over_time = deployment.get_predictions_over_time( start_time=datetime(2023, 4, 1), end_time=datetime(2023, 4, 22), bucket_size=BUCKET_SIZE.P7D, ) predicted = [ {item['class_name']: item['value'] for item in bucket['mean_probabilities']}.get('True') for bucket in predictions_over_time.buckets ] predicted >>> [0.3955, 0.4274, None] ``` ### Accuracy A collection of metrics are provided to measure the accuracy of a deployment’s predictions. For deployed classification models, use `ACCURACY_METRIC.ALL_CLASSIFICATION` for all supported metrics; for deployed regression models, use `ACCURACY_METRIC.ALL_REGRESSION` instead. [Accuracy](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.Accuracy) and [AccuracyOverTime](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.AccuracyOverTime) are provided to retrieve all default accuracy metrics and measure how one single metric changes over time. ``` from datetime import datetime from datarobot.enums import ACCURACY_METRIC from datarobot.helpers.partitioning_methods import construct_duration_string from datarobot.models import Deployment deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') accuracy = deployment.get_accuracy( start_time=datetime(2019, 8, 1, hour=15), end_time=datetime(2019, 8, 1, 15, 0) ) accuracy[ACCURACY_METRIC.RMSE] >>> 943.225 rmse = deployment.get_accuracy_over_time( start_time=datetime(2019, 8, 1), end_time=datetime(2019, 8, 3), bucket_size=construct_duration_string(days=1), metric=ACCURACY_METRIC.RMSE ) rmse.bucket_values >>> OrderedDict([(datetime.datetime(2019, 8, 1, 15, 0, tzinfo=tzutc()), 1777.190657), (datetime.datetime(2019, 8, 2, 15, 0, tzinfo=tzutc()), 1613.140772)]) ``` It is also possible to retrieve how multiple metrics changed over the same period of time, enabling easier side-by-side comparison across different metrics. ``` from datarobot.enums import ACCURACY_METRIC from datarobot.models import Deployment accuracy_over_time = AccuracyOverTime.get_as_dataframe( ram_app.id, [ACCURACY_METRIC.RMSE, ACCURACY_METRIC.GAMMA_DEVIANCE, ACCURACY_METRIC.MAD]) ``` #### Predictions vs. actuals over time Predictions vs. actuals over time can be used to analyze how deployment’s predictions compare against actuals. Different data can be retrieved in each bucket, depending on deployment’s target type: - row_count_total : The number of rows with or without actuals in the bucket. Available for all target types. - row_count_with_actual : The number of rows with actuals in the bucket. Available for all target types. - mean_predicted_value : The mean of the predicted value for all rows matched with an actual in the bucket. Available for the regression target type. - mean_actual_value : The mean of the actual value for all rows in the bucket. Available for the regression target type. - predicted_class_distribution : The count and percent of predicted class labels. Available for binary and multiclass classification target types. - actual_class_distribution : The count and percent of actual class labels. Available for binary or multiclass classification target types. ``` from datetime import datetime from datarobot.enums import BUCKET_SIZE from datarobot.models import Deployment # Deployment with the regression target type deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') predictions_over_time = deployment.get_predictions_vs_actuals_over_time( start_time=datetime(2023, 4, 1), end_time=datetime(2023, 4, 30), bucket_size=BUCKET_SIZE.P1D, ) predicted = [bucket['mean_actual_value'] for bucket in predictions_over_time.buckets] predicted >>> [0.2806, 0.9170, ...., 0.0314] # Deployment with the binary target type deployment = Deployment.get(deployment_id='62fff28a0f5fee488587ce92') predictions_over_time = deployment.get_predictions_vs_actuals_over_time( start_time=datetime(2023, 4, 1), end_time=datetime(2023, 4, 22), bucket_size=BUCKET_SIZE.P7D, ) predicted = [ {item['class_name']: item['value'] for item in bucket['mean_predicted_value']}.get('True') for bucket in predictions_over_time.buckets ] predicted >>> [0.5822, 0.6305, None] ``` ### Delete data Monitoring data accumulated on a deployment can be deleted using [delete_monitoring_data()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.delete_monitoring_data). A start and end timestamp could be provided to limit data deletion to certain time period. #### WARNING Monitoring data is not recoverable once deleted. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.delete_monitoring_data(model_id=deployment.model['id']) ``` ### List deployment prediction data exports Prediction data exports for a deployment can be retrieved using [list_prediction_data_exports()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.list_prediction_data_exports). ``` from datarobot.enums import ExportStatus from datarobot.models import Deployment deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') prediction_data_exports = deployment.list_prediction_data_exports(limit=0) prediction_data_exports >>> [PredictionDataExport('65fbe59aaa3f847bd5acc75b'), PredictionDataExport('65fbe59aaa3f847bd5acc75c'), PredictionDataExport('65fbe59aaa3f847bd5acc75a')] ``` To list all prediction data exports, set the limit to 0. Adjust additional parameters to filter the data as needed: ``` from datarobot.enums import ExportStatus from datarobot.models import Deployment deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') prediction_data_exports = deployment.list_prediction_data_exports( model_id="6444482e5583f6ee2e572265", batch=False, status=ExportStatus.SUCCEEDED, limit=100, offset=50, ) ``` ### List deployment actuals data exports Actuals data exports for a deployment can be retrieved using [list_actuals_data_exports()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.list_actuals_data_exports). ``` from datarobot.enums import ExportStatus from datarobot.models import Deployment deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') actuals_data_exports = deployment.list_actuals_data_exports(limit=0) actuals_data_exports >>> [ActualsDataExport('660456a332d0081029ee5031'), ActualsDataExport('660456a332d0081029ee5032'), ActualsDataExport('660456a332d0081029ee5033')] ``` To list all actuals data exports, set the limit to `0`. Adjust additional parameters to filter the data as needed: ``` from datarobot.enums import ExportStatus from datarobot.models import Deployment deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') actuals_data_exports = deployment.list_actuals_data_exports( deployment_id='5c939e08962d741e34f609f0', offset=500, limit=50, status=ExportStatus.SUCCEEDED ) ``` ### List deployment training data exports To retrieve successful training data exports for a deployment, use [list_training_data_exports()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.list_training_data_exports). ``` from datarobot.models.deployment import TrainingDataExport training_data_exports = TrainingDataExport.list(deployment_id='5c939e08962d741e34f609f0') training_data_exports >>> [TrainingDataExport('6565fbf2356124f1daa3acc522')] ``` ### List deployment data quality exports To retrieve successful data quality exports for a deployment, use [list_data_quality_exports()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.list_data_quality_exports). ``` from datarobot.models import Deployment deployment = Deployment.get('66903c40f18e6ec90fd7c8c7') data_quality_exports = deployment.list_data_quality_exports(start='2024-07-01', end='2024-08-01') data_quality_exports >>> [DataQualityExport(6447ca39c6a04df6b5b0ed19c6101e3c), ... DataQualityExport(0ff46fd3636545a9ac3e15ee1dbd8638)] ``` There are many filtering and sorting options available. ### Segment analysis Segment analysis is a deployment utility that filters service stats, data drift, and accuracy statistics into unique segment attributes and values. Use [get_segment_attributes()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_segment_attributes) to retrieve segment analysis data. Use [get_segment_values()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_segment_values) to retrieve segment value data. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') segment_attributes_service_health = deployment.get_segment_attributes(DEPLOYMENT_MONITORING_TYPE.SERVICE_HEALTH) >>>['DataRobot-Consumer', 'DataRobot-Host-IP', 'DataRobot-Remote-IP'] segment_attributes_data_drift = deployment.get_segment_attributes(DEPLOYMENT_MONITORING_TYPE.DATA_DRIFT) >>>['DataRobot-Consumer', 'attribute_1', 'attribute_2'] segment_values = deployment.get_segment_values(segmentAttribute=ReservedSegmentAttributes.CONSUMER) >>>['DataRobot-Consumer', 'datarobotuser@email.com'] ``` ## Challengers Challenger models can be used to compare the currently deployed model (the “champion” model) to another model. The following functions can be used to manage deployment’s challenger models: - List: list_challengers() or list() . - Create: create() . - Get: get() . - Update: update() . - Delete: delete() . ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') challenger = deployment.list_challengers()[-1] challenger.update(name='New Challenger Name') challenger.name >>> 'New Challenger Name' ``` ### Settings Use [get_challenger_models_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_challenger_models_settings) and [update_challenger_models_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_challenger_models_settings) to retrieve and update challenger model settings. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.update_challenger_models_settings(challenger_models_enabled=True) settings = deployment.get_challenger_models_settings() settings >>> {'enabled': True} ``` Use [get_challenger_replay_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_challenger_replay_settings) and [update_challenger_replay_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_challenger_replay_settings) to retrieve and update challenger replay settings. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.update_challenger_replay_settings(enabled=True) settings = deployment.get_challenger_replay_settings() settings['enabled'] >>> True ``` ## Settings Review the sections below to learn how to manage a deployment’s settings. ### Drift tracking settings Drift tracking is used to help analyze and monitor the performance of a model after it is deployed. When the model of a deployment is replaced drift tracking status will not be altered. Use [get_drift_tracking_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_drift_tracking_settings) to retrieve the current tracking status for target drift and feature drift. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') settings = deployment.get_drift_tracking_settings() settings >>> {'target_drift': {'enabled': True}, 'feature_drift': {'enabled': True}} ``` Use [update_drift_tracking_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_drift_tracking_settings) to update target drift and feature drift tracking status. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.update_drift_tracking_settings(target_drift_enabled=True, feature_drift_enabled=True) ``` ### Association ID settings Association ID is used to identify predictions, so that when actuals are acquired, accuracy can be calculated. Use [get_association_id_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_association_id_settings) to retrieve current association ID settings. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') settings = deployment.get_association_id_settings() settings >>> {'column_names': ['application_id'], 'required_in_prediction_requests': True} ``` Use [update_association_id_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_association_id_settings) to update association ID settings. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.update_association_id_settings(column_names=['application_id'], required_in_prediction_requests=True) ``` ### Predictions by forecast date Forecast date setting for the deployment. Use [get_predictions_by_forecast_date_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_predictions_by_forecast_date_settings) to retrieve current predictions by forecast date settings. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') settings = deployment.get_predictions_by_forecast_date_settings() settings >>> {'enabled': False, 'column_name': 'date (actual)', 'datetime_format': '%Y-%m-%d'} ``` Use [update_predictions_by_forecast_date_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_predictions_by_forecast_date_settings) to update predictions by forecast date settings. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.update_predictions_by_forecast_date_settings( enable_predictions_by_forecast_date=True, forecast_date_column_name='date (actual)', forecast_date_format='%Y-%m-%d') ``` ### Health settings Health settings APIs can be used to customize definitions for deployment health status. Use [get_health_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_health_settings) to retrieve current health settings, and [get_default_health_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_default_health_settings) to retrieve default health settings. To perform updates, use [update_health_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_health_settings). ``` import datarobot as dr # Get current data drift threshold deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') settings = deployment.get_health_settings() settings['data_drift']['drift_threshold'] >>> 0.15 # Update accuracy health metric settings['accuracy']['metric'] = 'AUC' settings = deployment.update_health_settings(accuracy=settings['accuracy']) settings['accuracy']['metric'] >>> 'AUC' # Set accuracy health metric to default default_settings = deployment.get_default_health_settings() settings = deployment.update_health_settings(accuracy=default_settings['accuracy']) settings['accuracy']['metric'] >>> 'LogLoss' ``` ### Segment analysis settings Segment analysis is a deployment utility that filters data drift and accuracy statistics into unique segment attributes and values. Use [get_segment_analysis_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_segment_analysis_settings) to retrieve current segment analysis settings. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') settings = deployment.get_segment_analysis_settings() settings >>> {'enabled': False, 'attributes': []} ``` Use [update_segment_analysis_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_segment_analysis_settings) to update segment analysis settings. Any categorical column can be a segment attribute. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.update_segment_analysis_settings( segment_analysis_enabled=True, segment_analysis_attributes=["country_code", "is_customer"]) ``` ### Predictions data collection settings Predictions data collection configures whether prediction requests and results should be saved to predictions data storage. Use [get_predictions_data_collection_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_predictions_data_collection_settings) to retrieve current settings of predictions data collection. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') settings = deployment.get_predictions_data_collection_settings() settings >>> {'enabled': True} ``` Use [update_predictions_data_collection_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_predictions_data_collection_settings) to update predictions data collection settings. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.update_predictions_data_collection_settings(enabled=True) ``` ### Prediction warning settings Prediction warning is used to enable Humble AI for a deployment which determines if a model is misbehaving when a prediction goes outside of the calculated boundaries. Use [get_prediction_warning_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_prediction_warning_settings) to retrieve the current prediction warning settings. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') settings = deployment.get_prediction_warning_settings() settings >>> { {'enabled': True}, 'custom_boundaries': {'upper': 1337, 'lower': 0} } ``` Use [update_prediction_warning_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_prediction_warning_settings) to update current prediction warning settings. ``` import datarobot as dr # Set custom boundaries deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') deployment.update_prediction_warning_settings( prediction_warning_enabled=True, use_default_boundaries=False, lower_boundary=1337, upper_boundary=2000, ) # Reset boundaries deployment.update_prediction_warning_settings( prediction_warning_enabled=True, use_default_boundaries=True, ) ``` ### Secondary dataset configuration settings The secondary dataset configuration for a deployed Feature Discovery model can be replaced and retrieved. Secondary dataset configuration is used to specify which secondary datasets to use during prediction for a given deployment. Use [update_secondary_dataset_config()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_secondary_dataset_config) to update the secondary dataset configuration. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') config = deployment.update_secondary_dataset_config(secondary_dataset_config_id='5f48cb94408673683eca0fab') config >>> '5f48cb94408673683eca0fab' ``` Use [get_secondary_dataset_config()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_secondary_dataset_config) to get the secondary dataset config. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') config = deployment.get_secondary_dataset_config() config >>> '5f48cb94408673683eca0fab' ``` ### Share deployments You can grant or revoke other users’ access to a deployment. #### Access levels For deployments, there are 3 access levels: `OWNER` - Allows all actions on a deployment. `USER` - Can see the deployment in the DataRobot UI and see the prediction statistics of the deployment, but cannot edit or delete the deployment. `CONSUMER` - Can only make predictions on the deployment. Cannot see the deployment in the DataRobot UI or retrieve prediction statistics for the deployment in the API. #### Sharing Use [list_shared_roles()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.list_shared_roles) to get a list of users, groups, and organizations that currently have a role on the project. Each role will be returned as a [datarobot.models.deployment.DeploymentSharedRole](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.DeploymentSharedRole). ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') roles = deployment.list_shared_roles() [role.to_dict() for role in roles] >>> [{'role': 'OWNER', 'id': '5c939e08962d741e34f609f0', 'share_recipient_type': 'user', 'name': 'user@datarobot.com'}, {'role': 'USER', 'id': '5c939e08962d741e34f609f1', 'share_recipient_type': 'group', 'name': 'Example Group'}, {'role': 'CONSUMER', 'id': '5c939e08962d741e34f609f2', 'share_recipient_type': 'organization', 'name': 'Example Org'}] ``` Use [update_shared_roles()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_shared_roles) to grant and revoke roles on the deployment. This function takes a list of [datarobot.models.deployment.DeploymentGrantSharedRoleWithId](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.DeploymentGrantSharedRoleWithId) and [datarobot.models.deployment.DeploymentGrantSharedRoleWithUsername](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.DeploymentGrantSharedRoleWithUsername) objects and updates roles accordingly. ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') roles = deployment.list_shared_roles() [role.to_dict() for role in roles] >>> [{'role': 'OWNER', 'id': '5c939e08962d741e34f609f0', 'share_recipient_type': 'user', 'name': 'user@datarobot.com'}] new_role = DeploymentGrantSharedRoleWithUsername(username='user_2@datarobot.com', role='OWNER') response = deployment.update_shared_roles([new_role]) response.status_code >>> 204 roles = deployment.list_shared_roles() [role.to_dict() for role in roles] >>> [{'role': 'OWNER', 'id': '5c939e08962d741e34f609f0', 'share_recipient_type': 'user', 'name': 'user@datarobot.com'}, {'role': 'OWNER', 'id': '5c939e08962d741e34f609f1', 'share_recipient_type': 'user', 'name': 'user_2@datarobot.com'}] revoke_role = DeploymentGrantSharedRoleWithUsername(username='user_2@datarobot.com', role='NO_ROLE') response = deployment.update_shared_roles([revoke_role]) response.status_code >>> 204 roles = deployment.list_shared_roles() [role.to_dict() for role in roles] >>> [{'role': 'OWNER', 'id': '5c939e08962d741e34f609f0', 'share_recipient_type': 'user', 'name': 'user@datarobot.com'}] ``` --- # MLOps URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html > Deploy, monitor, manage, and govern all your models in production with the Python API client. # MLOps DataRobot MLOps provides a central hub to deploy, monitor, manage, and govern all your models in production. | Topic | Description | | --- | --- | | Deployments | Deploy, manage, and monitor models; batch predictions; model replacement; monitoring data. | | Batch monitoring | Manage batch monitoring job definitions and jobs for batch-enabled deployments. | | Challenger models | Create and manage challenger models to compare against the deployed champion. | | Model Registry | Create and manage registered models and versions. | | Custom models | Manage custom inference models and execution environments. | | Custom metrics | Define and submit custom metrics for deployments. | | Data exports | Export prediction, actuals, training, and data quality data. | | Jobs | Run and manage custom jobs. | | Key values | Manage key-value configuration for the platform. | --- # Jobs URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/jobs.html > Run and manage custom jobs for automation with the Python API client. # Jobs You can create custom jobs to implement automation (for example, custom tests and custom metrics) for models and deployments. Each job serves as an automated workload, and the exit code determines if it passed or failed. You can run the custom jobs you create for one or more models or deployments. The automated workloads defined through custom jobs can make prediction requests, fetch inputs, and store outputs using DataRobot’s Public API. ## Manage jobs The sections below outline how to manage custom jobs. ### Create job To create a job, use `dr.registry.Job.create`: ``` import os import datarobot as dr # Add files content using `file_data` argument job = dr.registry.Job.create( "my-job", environment_id="65c4f3ed001d3e27a382608f", file_data={"run.sh": "echo 'hello world'"}, ) # Add files from the folder job_folder = "my-folder/files" job_2 = dr.registry.Job.create( "my-job", environment_id="65c4f3ed001d3e27a382608f", folder_path=job_folder, ) # Add files as a list of individual file paths job_3 = dr.registry.Job.create( "my-job", environment_id="65c4f3ed001d3e27a382608f", files=[(os.path.join(job_folder, 'run.sh'), 'run.sh')], ) # If the files should be added to the root of the job filesystem with # the same names as on the local file system, the above can be simplified to the following: job_4 = dr.registry.Job.create( "my-job", environment_id="65c4f3ed001d3e27a382608f", files=[os.path.join(job_folder, 'run.sh')], ) # Alternatively, a job can be created without the files, # and the files can be added later using the `update` method job_5 = dr.registry.Job.create("my-job") ``` ### Create hosted custom metric job from a template To create a hosted custom metric job from a gallery template, use `dr.registry.Job.create_from_custom_metric_gallery_template`: ``` import datarobot as dr templates = dr.models.deployment.custom_metrics.HostedCustomMetricTemplate.list() template_id = templates[0].id job = dr.registry.Job.create_from_custom_metric_gallery_template( template_id = template_id, name = "my-job", ) ``` ### List jobs To list all jobs available to the current user, use `dr.registry.Job.list`: ``` import datarobot as dr jobs = dr.registry.Job.list() jobs >>> [Job('my-job')] ``` ### Retrieve jobs To get a job by unique identifier, use `dr.registry.Job.get`: ``` import datarobot as dr job = dr.registry.Job.get("65f4453e6ea907cb0405ff7f") job >>> Job('my-job') ``` ### Update jobs To get a job by unique identifier and update it, use `dr.registry.Job.get()` and then `update()`: ``` import datarobot as dr job = dr.registry.Job.get("65f4453e6ea907cb0405ff7f") job.update( environment_id="65c4f3ed001d3e27a382608f", description="My Job", folder_path=job_folder, file_data={"README.md": "My README file"}, ) ``` ### Delete jobs To get a job by unique identifier and delete it, use `dr.registry.Job.get()` and then `delete()`: ``` import datarobot as dr job = dr.registry.Job.get("65f4453e6ea907cb0405ff7f") job.delete() ``` ## Manage job runs Use the following commands to manage job runs. ### Create job runs To create a job run, use `dr.registry.JobRun.create`: ``` import datarobot as dr import time job_id = "65f4453e6ea907cb0405ff7f" # Block until job run is finished job_run = dr.registry.JobRun.create(job_id) # or run job without blocking the thread, and check the job run status manually job_run = dr.registry.JobRun.create(job_id, max_wait=None) while job_run.status == dr.registry.JobRunStatus.RUNNING: time.sleep(1) job_run.refresh() ``` ### List job runs To list all job runs, use `dr.registry.JobRun.list`: ``` import datarobot as dr job_id = "65f4453e6ea907cb0405ff7f" job_runs = dr.registry.JobRun.list(job_id) job_runs >>> [JobRun('65f856957d897d46b0e54b37'), JobRun('65f8567f7d897d46b0e54b32'), JobRun('65f856617d897d46b0e54b2d')] ``` ### Retrieve job runs To get a job run with an identifier, use `dr.registry.JobRun.get`,: ``` import datarobot as dr job_id = "65f4453e6ea907cb0405ff7f" job_run = dr.registry.JobRun.get(job_id, "65f856957d897d46b0e54b37") job_run >>> JobRun('65f856957d897d46b0e54b37') ``` ### Update job runs To get a job run by unique identifier and update it, use `dr.registry.JobRun.get()` and then `update()`: ``` import datarobot as dr job_id = "65f4453e6ea907cb0405ff7f" job_run = dr.registry.JobRun.get(job_id, "65f856957d897d46b0e54b37") job_run.update(description="The description of this job run") ``` ### Cancel a job run To get a running job run by identifier and cancel it, use `dr.registry.JobRun.get()` and then `cancel()`: ``` import datarobot as dr job_id = "65f4453e6ea907cb0405ff7f" job_run = dr.registry.JobRun.get(job_id, "65f856957d897d46b0e54b37") job_run.cancel() ``` ### Retrieve job run logs To get job run logs, use `dr.registry.JobRun.get_logs`: ``` import datarobot as dr job_id = "65f4453e6ea907cb0405ff7f" job_run = dr.registry.JobRun.get(job_id, "65f856957d897d46b0e54b37") job_run.get_logs() >>> 2024-03-18T16:06:46.044946476Z Some log output ``` ### Delete job run logs To delete job run logs, use `dr.registry.JobRun.delete_logs`: ``` import datarobot as dr job_id = "65f4453e6ea907cb0405ff7f" job_run = dr.registry.JobRun.get(job_id, "65f856957d897d46b0e54b37") job_run.delete_logs() ``` --- # Key values URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/key_values.html > Manage key-value configuration for models, deployments, and other entities with the Python API client. # Key values Key values associated with a DataRobot model, deployment, job, or other DataRobot entities are key-value pairs containing information about the related entity. Each key-value pair has the following: - Name : The unique and descriptive name of the key (for the model package or version). - Value type : The data type of the value associated with the key. The possible types are string, numeric, boolean, URL, image, dataset, pickle, binary, JSON, or YAML. - Category : The type of model information provided by the key value. The possible types are training parameter, metric, tag, artifact, and runtime parameter. - Value : The stored data or file. You can include string, numeric, boolean, image, and dataset key values in custom compliance documentation templates. In addition, with key values for registered models, when you generate compliance documentation for a model package and reference a supported key value in the template, DataRobot inserts the matching values from the associated model package. ## Manage key values Use the following commands to manage key values. ### Create a key value To create a key value, use `dr.KeyValue.create`: ``` import datarobot as dr registered_model_id = "65ccb597732422fa2297199e" key_value = dr.KeyValue.create( registered_model_id, dr.KeyValueEntityType.REGISTERED_MODEL, "my-kv-name", dr.KeyValueCategory.TAG, dr.KeyValueType.STRING, "tag-name", ) key_value.id >>> '65f32822be17d11dec9ebdfb' ``` ### List key values To list all key values available to the current user, use `dr.KeyValue.list`: ``` import datarobot as dr registered_model_id = "65ccb597732422fa2297199e" key_values = dr.KeyValue.list(registered_model_id, dr.KeyValueEntityType.REGISTERED_MODEL) key_values >>> [KeyValue('my-kv-name')] ``` ### Retrieve a key value To get a key value by unique identifier, use `dr.KeyValue.get`: ``` import datarobot as dr key_value = dr.KeyValue.get("65f32822be17d11dec9ebdfb") key_value >>> KeyValue('my-kv-name') ``` ### Find key values by name To find a key value by name, use `dr.KeyValue.find`. Provide the entity ID, entity type, and key value name: ``` import datarobot as dr key_value = dr.KeyValue.find("65f32822be17d11dec9ebdfb", dr.KeyValueEntityType.REGISTERED_MODEL, "my-kv-name") key_value >>> KeyValue('my-kv-name') ``` ### Update key values To get a key value by unique identifier and update it, use `dr.KeyValue.get()` and then `update()`: ``` import datarobot as dr key_value = dr.KeyValue.get("65f32822be17d11dec9ebdfb") key_value.update(value=4.7) key_value.update(value_type=dr.KeyValueType.STRING, value="abc") key_value.update(name="new-kv-name") ``` ### Get key value data To get the value from a key value, use `dr.KeyValue.get_value()`. Provide the key value ID: ``` import datarobot as dr key_value = dr.KeyValue.get("65f32822be17d11dec9ebdfb") key_value.update(value=4.7) key_value.get_value() >>> 4.7 key_value.update(value_type=dr.KeyValueType.STRING, value="abc") key_value.get_value() >>> "abc" key_value.update(value_type=dr.KeyValueType.BOOLEAN, value=True) key_value.get_value() >>> True ``` ### Delete key values To get a key value by unique identifier and delete it, use `dr.KeyValue.get()` and then `delete()`: ``` import datarobot as dr key_value = dr.KeyValue.get("65f32822be17d11dec9ebdfb") key_value.delete() ``` --- # Model Registry URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/model_registry.html > Create and manage registered models and versions with the Python API client. # Model Registry Registered models are generic containers that group multiple versions of models which can be deployed, used as a challenger model, or replace a deployed model. Each registered model can have multiple versions. Each version can be created from a DataRobot model, custom model, or external model. Registered models can have versions of different types (Leaderboard, custom, or external) simultaneously as long as they have same target properties and time series settings, where applicable. ## Create a registered model To create a registered model or add a version to an existing model: ``` LEADERBOARD_MODEL_ID = "650c2372c538ffa4480567d1" # Passing registered_model_name creates new version first_version = dr.RegisteredModelVersion.create_for_leaderboard_item( model_id=LEADERBOARD_MODEL_ID, name="Name of the version(aka model package)", registered_model_name='DEMO 3: Name of the registered model unique across the org ' ) # Add custom model as a version # passing registered_model_id adds version to existing registered model CUSTOM_MODEL_VERSION_ID = "619679c86c1abbc2bd628ed1" second_version_from_custom = dr.RegisteredModelVersion.create_for_custom_model_version( custom_model_version_id=CUSTOM_MODEL_VERSION_ID, registered_model_id=first_version.registered_model_id, name="Another Name of the version(aka model package)", ) # Add external model as a version second_version_from_external = dr.RegisteredModelVersion.create_for_external( name='Another name', target={'name': 'Target', 'type': 'Regression'}, registered_model_id=first_version.registered_model_id, ) ``` ## List and filter registered models Use the following command to list registered models. You can filter the registered models that are returned by passing an instance of the [RegisteredModelListFilters](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.model_registry.RegisteredModelListFilters) class to the `filters` keyword argument. You can also filter the registered model versions that are returned by passing an instance of the [RegisteredModelVersionsListFilters](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.model_registry.RegisteredModelVersionsListFilters) class to the `filters` keyword argument. ``` demo_registered_models = dr.RegisteredModel.list(search="DEMO") registered_model_filters = dr.models.model_registry.RegisteredModelListFilters( created_at_start=datetime.datetime(2020, 1, 1), created_at_end=datetime.datetime(2024, 1, 2), modified_at_start=datetime.datetime(2020, 1, 1), modified_at_end=datetime.datetime(2024, 1, 2), target_name='readmitted', target_type='Binary', created_by='john.doe@example.com', compatible_with_model_package_id='650a9f57d3f427ce1cc64747', prediction_threshold=0.5, imported=False, for_challenger=False, ) registered_models = dr.RegisteredModel.list(filters=registered_model_filters, search="10k") registered_model = registered_models[0] versions = registered_model.list_versions() # Similarly to registered models, versions also support fine-grain filtering and search filters = dr.models.model_registry.RegisteredModelVersionsListFilters( target_name='readmitted', ) versions_with_search = registered_model.list_versions(search="Elastic", filters=filters) ``` ## Manage registered models Use the following command to archive registered models. Archiving registered models archives all the versions of the registered model. ``` REGISTERED_MODEL_ID = "651bd2317aed25ed7d4bca7f" dr.RegisteredModel.archive(REGISTERED_MODEL_ID) ``` Use the following command to update registered models. ``` REGISTERED_MODEL_ID = "651bd2317aed25ed7d4bca7f" dr.RegisteredModel.update(REGISTERED_MODEL_ID, name="New name") ``` To share registered models with other users or groups, or retrieve existing roles on the deployment: ``` registered_model = dr.RegisteredModel.get('645b62d5373ed49b485d73e9') # EXISTING ROLES roles = registered_model.get_shared_roles() role = dr.SharingRole( share_recipient_type="user", id='5ca19879a950d002c61ea3e7', role="USER", ) registered_model.share([role]) ``` ## List deployments associated with a registered model Use the following command to list deployments associated with registered model. The deployment is considered associated if one of the versions of the registered model is either a champion or a challenger model. ``` model_with_deployments = dr.RegisteredModel.get('65035d911e9ff5b07f00f2ea') # we can list deployments associated with this registered model. Method is searchable and paginated. model_associated_deployments = model_with_deployments.list_associated_deployments() # we can also list deployments associated with specific version of the registered model version = model_with_deployments.list_versions()[1] version.list_associated_deployments() ``` --- # Blueprints URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/blueprint.html > Learn how to work with DataRobot blueprints, which define computation paths for datasets before producing predictions. # Blueprints Blueprints are a set of computation paths that a dataset passes through before producing predictions from data. A blueprint can be trained on a dataset to generate a model. To modify blueprints using Python, reference the [Blueprint Workshop documentation](https://docs.datarobot.com/en/docs/api/reference/bp-workshop/index.html). The following code block summarizes the interactions available for blueprints. ``` # Get the set of blueprints recommended by datarobot import datarobot as dr my_projects = dr.Project.list() project = my_projects[0] menu = project.get_blueprints() first_blueprint = menu[0] project.train(first_blueprint) ``` ## List blueprints When you upload a file to a project and set a target, you receive a set of recommended blueprints that are appropriate for the task at hand. Use `get_blueprints` to get the list of blueprints recommended for a project: ``` project = dr.Project.get('5506fcd38bd88f5953219da0') menu = project.get_blueprints() blueprint = menu[0] ``` ## Get a blueprint If you already have a `blueprint_id` from a model you can retrieve the blueprint directly. ``` project_id = '5506fcd38bd88f5953219da0' project = dr.Project.get(project_id) models = project.get_models() model = models[0] blueprint = Blueprint.get(project_id, model.blueprint_id) ``` ## Get a blueprint chart You can retrieve charts for all blueprints that are either from a blueprint menu or are already used in a model. You can also get a blueprint’s representation in Graphviz DOT format to render it into the format you need. ``` project_id = '5506fcd38bd88f5953219da0' blueprint_id = '4321fcd38bd88f595321554223' bp_chart = BlueprintChart.get(project_id, blueprint_id) print(bp_chart.to_graphviz()) ``` ## Get blueprint documentation You can retrieve documentation for tasks used in a blueprint. The documentation contains information about the task, its parameters, and links and references to additional sources. All documents are instances of the [BlueprintTaskDocument](https://docs.datarobot.com/en/docs/api/reference/sdk/blueprints.html#datarobot.models.BlueprintTaskDocument) class. ``` project_id = '5506fcd38bd88f5953219da0' blueprint_id = '4321fcd38bd88f595321554223' bp = Blueprint.get(project_id, blueprint_id) docs = bp.get_documents() print(docs[0].task) >>> Average Blend print(docs[0].links[0]['url']) >>> https://en.wikipedia.org/wiki/Ensemble_learning ``` ## Blueprint attributes The `Blueprint` class holds the data required to use the blueprint for modeling. This includes the `blueprint_id` and `project_id`. There are also two attributes that help distinguish blueprints: `model_type` and `processes`. ``` print(blueprint.id) >>> u'8956e1aeecffa0fa6db2b84640fb3848' print(blueprint.project_id) >>> u5506fcd38bd88f5953219da0' print(blueprint.model_type) >>> Logistic Regression print(blueprint.processes) >>> [u'One-Hot Encoding', u'Missing Values Imputed', u'Standardize', u'Logistic Regression'] ``` ## Build a model from a blueprint You can also use a blueprint to train a model. The model is trained on the associated project’s dataset by default. Note that [Project.train](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.train) is used for non-datetime partitioned projects.[Project.train_datetime](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.train_datetime) should be used for datetime partitioned projects. ``` model_job_id = project.train(blueprint) # For datetime partitioned projects model_job = project.train_datetime(blueprint.id) ``` Both [Project.train](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.train) and [Project.train_datetime](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.train_datetime) will put a new modeling job into the queue. However, note that `Project.train` returns the ID of the created [ModelJob](https://docs.datarobot.com/en/docs/api/reference/sdk/jobs.html), while `Project.train_datetime` returns the `ModelJob` object itself. You can pass a ModelJob ID to [wait_for_async_model_creation](https://docs.datarobot.com/en/docs/api/reference/sdk/jobs.html#wait-for-async-model-creation-label) function, which polls the async model creation status and returns the newly created model when it’s finished. --- # Modeling URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/index.html > Information to help you easily navigate the process of building, understanding, and analyzing models. # Modeling The modeling section provides information to help you easily navigate the process of building, understanding, and analyzing models. | Resource | Description | | --- | --- | | Model insights | Information to help you easily navigate the process of building, understanding, and analyzing models. | | Specialized workflows | Alternative workflows for a variety of specialized data types. | | Projects | Create, configure, and manage DataRobot projects for modeling. | | Models | Train, retrieve, and analyze DataRobot models. | | Blueprints | Work with DataRobot blueprints which define computation paths for datasets before producing predictions. | | Jobs | Manage and monitor jobs in DataRobot projects, including model creation jobs and queue management. | | Model recommendation | Learn how to retrieve and work with DataRobot model recommendations for deployment. | | DataRobot Prime | Learn how to use DataRobot Prime to download executable code that approximates models. | --- # Automated documentation URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/automated_documentation.html > Learn how to generate and manage automated documentation for DataRobot models and projects. # Automated documentation DataRobot can generate automated documentation about various entities within the platform, such as specific models or projects. These reports can be downloaded and shared to help with regulatory compliance as well as to provide a general understanding of the AI lifecycle. ## Check available document types Automated documentation is available behind different feature flags set up according to your POC settings or subscription plan.`MODEL_COMPLIANCE` documentation is a premium add-on DataRobot product, while `AUTOPILOT_SUMMARY` report is available behind an optional feature flag for Self-Service and other platforms. ``` import datarobot as dr # Connect to your DataRobot platform with your token dr.Client(token=my_token, endpoint=endpoint) options = dr.AutomatedDocument.list_available_document_types() ``` In response, you get a `data` dictionary with a list of document types that are available for generation with your account. ## Generate automated documents Now that you know which documents you can generate, create one with `AutomatedDocument .generate` method. Note that for `AUTOPILOT_SUMMARY` report, you need to assign a project ID to the `entity_id` parameter, while `MODEL_COMPLIANCE` expects an ID of a model with the `entity_id` parameter. ``` import datarobot as dr dr.Client(token=my_token, endpoint=endpoint) doc_type = "AUTOPILOT_SUMMARY" entity_id = "5e8b6a34d2426053ab9a39ed" # This is an ID of a project file_format="docx" doc = dr.AutomatedDocument(document_type=doc_type, entity_id=entity_id, output_format=file_format) doc.generate() ``` You can specify other attributes. For example, `filepath` presets the file location and name to use when downloading the document. See the API reference for more details. ## Download automated documents If you followed the steps above to generate an automated document, you can use the `AutomatedDocument.download` method right away to get the document. ``` doc.filepath = "Users/jeremy/DR_project_docs/autopilot_report_staff_2021.docx" doc.download() ``` You can set a desired `filepath` (that includes the future file’s name) before you download a document. Otherwise, it will be automatically downloaded to the directory from which you launched your script. Please note that to download the document, you need its ID. When you generate a document with the Python client, the ID is set automatically without your interference. However, if the document has already been generated from the application interface (or REST API) and you want to download it using the Python client, you need to provide the ID of the document you want to download: ``` import datarobot as dr dr.Client(token=my_token, endpoint=endpoint) doc_id = "604f81f0f3d6397d250c35bc" path = "Users/jeremy/DR_project_docs/xgb_model_doc_staff_project_2021.docx" doc = dr.AutomatedDocument(id=doc_id, filepath=path) doc.download() ``` ## List previously generated automated documents You can retrieve information about previously generated documents available for your account. The information includes document ID and type, ID of the entity it was generated for, time of creation, and other information. Documents are sorted by creation time – `created_at` key – from most recent to oldest. ``` import datarobot as dr dr.Client(token=my_token, endpoint=endpoint) docs = dr.AutomatedDocument.list_generated_documents() ``` This returns list of `AutomatedDocument` objects. You can request a list of specific documents. For example, get a list of all `MODEL_COMPLIANCE` documents: ``` model_docs = dr.AutomatedDocument.list_generated_documents(document_types=["MODEL_COMPLIANCE"]) ``` Or get a list of documents created for specific entities: ``` otv_project_reports = dr.AutomatedDocument.list_generated_documents( entity_ids=["604f81f0f3d6397d250c35bc", "5ed60de32f18d97d250c3db5"] ) ``` For more information about all query options, see `AutomatedDocument.list_generated_documents` in the API reference. ## Delete automated documents To delete a document from the DataRobot application, use the `AutomatedDocument.delete` method. ``` import datarobot as dr dr.Client(token=my_token, endpoint=endpoint) doc = dr.AutomatedDocument(id="604f81f0f3d6397d250c35bc") doc.delete() ``` All locally saved automated documents will remain intact. --- # External testset URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/external_testset.html > Learn how to test models with external datasets to evaluate performance and compute metric scores. # Test with external datasets Compute metric scores and insights on an external test dataset to help validate a model's performance and its generalization capabilities before deployment. Evaluating the model against data it hasn't seen during training ("external") provides insights into how well and consistently the model will perform in real-world scenarios. Note that this testing is not available for time series models. ## Request external scores and insights To compute scores and insights on a dataset: - Upload a prediction dataset that contains the target column ( PredictionDataset.contains_target_values == True ). - The dataset must have the same structure as the original project data. ``` import datarobot as dr # Upload dataset project = dr.Project(project_id) dataset = project.upload_dataset('./test_set.csv') dataset.contains_target_values >>>True # request external test to compute metric scores and insights on dataset # select model using project.get_models() external_test_job = model.request_external_test(dataset.id) # once job is complete, scores and insights are ready for retrieving external_test_job.wait_for_completion() ``` ## Retrieve external metric scores and insights After completion of the external test job, metric scores and insights for external test sets will be ready. > [!NOTE] Note > Some notes: > > Check > PredictionDataset.data_quality_warnings > for dataset warnings. > Insights are not available if the dataset is fewer than 10 rows. > The ROC curve cannot be calculated if the dataset has only one class in the target column. ## Retrieve external metric scores ``` import datarobot as dr # retrieving list of external metric scores on multiple datasets metric_scores_list = dr.ExternalScores.list(project_id, model_id) # retrieving external metric scores on one dataset metric_scores = dr.ExternalScores.get(project_id, model_id, dataset_id) ``` ## Retrieve an external lift chart ``` import datarobot as dr # retrieving list of lift charts on multiple datasets lift_list = dr.ExternalLiftChart.list(project_id, model_id) # retrieving one lift chart for dataset lift = dr.ExternalLiftChart.get(project_id, model_id, dataset_id) ``` ## Retrieve an external multiclass lift chart Available for multiclass classification models only. ``` import datarobot as dr # retrieving list of lift charts on multiple datasets lift_list = ExternalMulticlassLiftChart.list(project_id, model_id) # retrieving one lift chart for dataset and a target class lift = ExternalMulticlassLiftChart.get(project_id, model_id, dataset_id, target_class) ``` ## Retrieve an external ROC curve Available for binary classification models only. ``` import datarobot as dr # retrieving list of roc curves on multiple datasets roc_list = ExternalRocCurve.list(project_id, model_id) # retrieving one ROC curve for dataset roc = ExternalRocCurve.get(project_id, model_id, dataset_id) ``` ## Retrieve a multiclass confusion matrix Available for multiclass classification models only. ``` import datarobot as dr # retrieving list of confusion charts on multiple datasets confusion_list = ExternalConfusionChart.list(project_id, model_id) # retrieving one confusion chart for dataset confusion = ExternalConfusionChart.get(project_id, model_id, dataset_id) ``` ## Retrieve a residuals chart Available for regression models only. ``` import datarobot as dr # retrieving list of residuals charts on multiple datasets residuals_list = ExternalResidualsChart.list(project_id, model_id) # retrieving one residuals chart for dataset residuals = ExternalResidualsChart.get(project_id, model_id, dataset_id) ``` --- # Model insights URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/index.html > Information to help you easily navigate the process of building, understanding, and analyzing models. # Model insights The Modeling section provides information to help you easily navigate the process of building, understanding, and analyzing models. | Resource | Description | | --- | --- | | Prediction explanations | Retrieve prediction explanations for DataRobot models. | | SHAP insights | Use SHAP (SHapley Additive exPlanations) insights for explaining model predictions. | | Model performance insights | Compute and retrieve ROC curves, lift charts, and residuals for DataRobot models. | | Automated documentation | Generate and manage automated documentation for DataRobot models and projects. | | External testset | Test models with external datasets to evaluate performance and compute metric scores. | | Rating table | DWownload and upload rating tables for Generalized Additive Models. | --- # Model performance insights URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/model_performance_insights.html > Learn how to compute and retrieve ROC curves, lift charts, and residuals for DataRobot models. # Model performance insights DataRobot provides several insights to help you understand and evaluate model performance: - ROC Curve : Receiver Operating Characteristic curve showing the trade-off between true positive rate and false positive rate at various classification thresholds. Available for binary classification models. - Lift Chart : Depicts how well a model segments the target population and how well the model performs for different ranges of values of the target variable. Available for classification and regression models. - Residuals : Displays the distribution of prediction errors for regression models. These insights can be computed for different data partitions (validation, cross-validation, holdout) and can be filtered by data slices. The following example code assumes that you have a trained model object called `model`. ## ROC Curve The following example shows how to compute and retrieve ROC curves for a binary classification model. ``` from datarobot.insights.roc_curve import RocCurve model_id = model.id # or model_id = 'YOUR_MODEL_ID' # Compute a new ROC curve and wait for it to complete roc = RocCurve.create(entity_id=model_id) # default source is 'validation' # Access ROC curve properties print(roc.auc) >>> 0.85 print(roc.kolmogorov_smirnov_metric) >>> 0.42 print(roc.roc_points[:2]) >>> [{'accuracy': 0.539375, 'f1_score': 0.0, 'false_negative_score': 737, ...}, ...] # Compute ROC curve on a different partition, and return immediately with job reference job = RocCurve.compute(entity_id=model_id, source='holdout') # Wait for the job to complete roc_holdout = job.get_result_when_complete() print(roc_holdout.auc) >>> 0.83 # Get a pre-existing ROC curve (if already computed) existing_roc = RocCurve.get(entity_id=model_id, source='validation') # List all available ROC curves for a model roc_list = RocCurve.list(entity_id=model_id) print(roc_list) >>> [, ...] print([(r.source, r.auc) for r in roc_list]) >>> [('validation', 0.85), ('holdout', 0.83)] ``` ### ROC curve with data slices You can compute ROC curves for specific data slices: ``` from datarobot.insights.roc_curve import RocCurve # Get a pre-existing ROC curve for a specific data slice roc_sliced = RocCurve.get(entity_id=model_id, source='validation', data_slice_id='slice_id_here') # Or compute a new one job = RocCurve.compute(entity_id=model_id, source='validation', data_slice_id='slice_id_here') roc_sliced = job.get_result_when_complete() ``` ## Lift chart Lift charts depict how well a model segments the target population and how well the model performs for different ranges of values of the target variable. ``` from datarobot.insights.lift_chart import LiftChart model_id = model.id # or model_id = 'YOUR_MODEL_ID' # Compute a new lift chart and wait for it to complete lift = LiftChart.create(entity_id=model_id) # default source is 'validation' # Access lift chart bins print(lift.bins[:3]) >>> [{'actual': 0.4, 'predicted': 0.227, 'bin_weight': 5.0}, ...] # Compute lift chart on a different partition, and return immediately with job reference job = LiftChart.compute(entity_id=model_id, source='holdout') # Wait for the job to complete lift_holdout = job.get_result_when_complete() # Get a pre-existing lift chart (if already computed) existing_lift = LiftChart.get(entity_id=model_id, source='validation') # List all available lift charts for a model lift_list = LiftChart.list(entity_id=model_id) print(lift_list) >>> [, ...] print([l.source for l in lift_list]) >>> ['validation', 'holdout', 'crossValidation'] # Get lift chart for a specific data slice lift_sliced = LiftChart.get(entity_id=model_id, source='validation', data_slice_id='slice_id_here') ``` ## Residuals The residuals chart shows the distribution of prediction errors for regression models. ``` from datarobot.insights.residuals import Residuals model_id = model.id # or model_id = 'YOUR_MODEL_ID' # Compute a new residuals chart and wait for it to complete residuals = Residuals.create(entity_id=model_id) # default source is 'validation' # Access residuals properties print(residuals.coefficient_of_determination) >>> 0.85 print(residuals.residual_mean) >>> 0.023 print(residuals.standard_deviation) >>> 1.42 # Access histogram data print(residuals.histogram[:3]) >>> [{'interval_start': -33.37, 'interval_end': -32.52, 'occurrences': 1}, ...] # Access raw chart data (actual, predicted, residual, row number) print(residuals.chart_data[:3]) >>> [[45.2, 43.8, -1.4, 0], [52.1, 51.9, -0.2, 1], ...] # Compute residuals on a different partition, and return immediately with job reference job = Residuals.compute(entity_id=model_id, source='holdout') # Wait for the job to complete residuals_holdout = job.get_result_when_complete() # Get a pre-existing residuals chart (if already computed) existing_residuals = Residuals.get(entity_id=model_id, source='validation') # List all available residuals charts for a model residuals_list = Residuals.list(entity_id=model_id) print(residuals_list) >>> [, ...] print([r.source for r in residuals_list]) >>> ['validation', 'holdout'] # Get residuals for a specific data slice residuals_sliced = Residuals.get(entity_id=model_id, source='validation', data_slice_id='slice_id_here') ``` --- # Prediction explanations URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/prediction_explanations.html > Learn how to compute and retrieve prediction explanations for DataRobot models. # Prediction explanations To compute prediction explanations you need to have [feature impact](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/model.html#feature-impact-label) computed for a model, and predictions for an uploaded dataset computed with a selected model. Computing prediction explanations is a resource-intensive task, but you can configure it with maximum explanations per row and prediction value thresholds to speed up the process. > [!NOTE] Consideration for reading data > Note that when DataRobot is reading data, the `dr_enums.DEFAULT_TIMEOUT.READ` duration is 60 seconds by default. If the data requires more time to be read, consider adjusting that duration manually by setting it to 600 seconds: > > ``` > import datarobot.enums as dr_enums > dr_enums.DEFAULT_TIMEOUT.READ = 600 > ``` ## Quick reference ``` import datarobot as dr # Get project my_projects = dr.Project.list() project = my_projects[0] # Get model models = project.get_models() model = models[0] # Compute feature impact feature_impacts = model.get_or_request_feature_impact() # Upload dataset dataset = project.upload_dataset('./data_to_predict.csv') # Compute predictions predict_job = model.request_predictions(dataset.id) predict_job.wait_for_completion() # Initialize prediction explanations pei_job = dr.PredictionExplanationsInitialization.create(project.id, model.id) pei_job.wait_for_completion() # Compute prediction explanations with default parameters pe_job = dr.PredictionExplanations.create(project.id, model.id, dataset.id) pe = pe_job.get_result_when_complete() # Iterate through predictions with prediction explanations for row in pe.get_rows(): print(row.prediction) print(row.prediction_explanations) # download to a CSV file pe.download_to_csv('prediction_explanations.csv') ``` ## List prediction explanations You can use the `PredictionExplanations.list()` method to return a list of prediction explanations computed for a project’s models: ``` import datarobot as dr prediction_explanations = dr.PredictionExplanations.list('58591727100d2b57196701b3') print(prediction_explanations) >>> [PredictionExplanations(id=585967e7100d2b6afc93b13b, project_id=58591727100d2b57196701b3, model_id=585932c5100d2b7c298b8acf), PredictionExplanations(id=58596bc2100d2b639329eae4, project_id=58591727100d2b57196701b3, model_id=585932c5100d2b7c298b8ac5), PredictionExplanations(id=58763db4100d2b66759cc187, project_id=58591727100d2b57196701b3, model_id=585932c5100d2b7c298b8ac5), ...] pe = prediction_explanations[0] pe.project_id >>> u'58591727100d2b57196701b3' pe.model_id >>> u'585932c5100d2b7c298b8acf' ``` You can pass following parameters to filter the result: - model_id – str, used to filter returned prediction explanations by model_id. - limit – int, limit for number of items returned, default: no limit. - offset – int, number of items to skip, default: 0. List prediction explanations example: ``` project_id = '58591727100d2b57196701b3' model_id = '585932c5100d2b7c298b8acf' dr.PredictionExplanations.list(project_id, model_id=model_id, limit=20, offset=100) ``` ## Initialize prediction explanations In order to compute prediction explanations you have to initialize it for a particular model. ``` dr.PredictionExplanationsInitialization.create(project_id, model_id) ``` ## Compute prediction explanations on new data If all prerequisites are in place, you can compute prediction explanations in the following way: ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' dataset_id = '5506fcd98bd88a8142b725c8' pe_job = dr.PredictionExplanations.create(project_id, model_id, dataset_id, max_explanations=2, threshold_low=0.2, threshold_high=0.8) pe = pe_job.get_result_when_complete() ``` Where: - max_explanations are the maximum number of prediction explanations to compute for each row. - threshold_low and threshold_high are thresholds for the value of the prediction of the row. Prediction explanations will be computed for a row if the row’s prediction value is higher than threshold_high or lower than threshold_low . If no thresholds are specified, prediction explanations will be computed for all rows. ## Compute prediction explanations on training data To compute prediction explanations on training data, use the code snippet below. The prerequisites are generally the same as computing on newly uploaded data: - Feature Impact calculations must have completed. - Prediction explanations must be initialized. - Predictions on training data must first be computed for the model. The `dataset_id` parameter is the ID of the feature list that was used to train the model. ``` import datarobot as dr project_id = '67771742b4d4cf44277b1ff0' model_id = '677859cfeaea57c1bc9a150a' model = dr.Model.get(project_id, model_id) dataset_id = model.featurelist_id # Request feature impact if not done yet. model.request_feature_impact() # Request training predictions for the model if not done yet. # The subset 'all' includes training, validation, and holdout data. model.request_training_predictions(dr.enums.DATA_SUBSET.ALL) # Initialize explanations. dr.PredictionExplanationsInitialization.create(project_id, model_id) # Request and download explanations for the full training data. pe_job = dr.PredictionExplanations.create_on_training_data(project_id, model_id, dataset_id) result = pe_job.get_result_when_done() df = result.get_all_as_dataframe() ``` ## Get previously generated predictions If you don’t have a `PredictJob`, there are two more ways to retrieve predictions from the `Predictions` interface: 1. Get all prediction rows as a pandas.DataFrame object: ``` import datarobot as dr preds = dr.Predictions.get("5b61bd68ca36c04aed8aab7f", prediction_id="5b6b163eca36c0108fc5d411") df = preds.get_all_as_dataframe() df_with_serializer = preds.get_all_as_dataframe(serializer='csv') ``` 1. Download all prediction rows to a file as a CSV: ``` import datarobot as dr preds = dr.Predictions.get("5b61bd68ca36c04aed8aab7f", prediction_id="5b6b163eca36c0108fc5d411") preds.download_to_csv('predictions.csv') preds.download_to_csv('predictions_with_serializer.csv', serializer='csv') ``` ## Retrieve prediction explanations You have three options for retrieving prediction explanations. > [!NOTE] Note > `PredictionExplanations.get_all_as_dataframe()` and `PredictionExplanations.download_to_csv()` reformat prediction explanations to match the schema of the CSV file downloaded from the UI (RowId, Prediction, Explanation 1- N Strength, Explanation 1- N Feature, Explanation 1- N Value). Get prediction explanations rows one by one as [PredictionExplanationsRow](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/prediction_explanations.html#datarobot.models.prediction_explanations.PredictionExplanationsRow) objects: ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' prediction_explanations_id = '5506fcd98bd88f1641a720a3' pe = dr.PredictionExplanations.get(project_id, prediction_explanations_id) for row in pe.get_rows(): print(row.prediction_explanations) ``` Get all rows as `pandas.DataFrame`: ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' prediction_explanations_id = '5506fcd98bd88f1641a720a3' pe = dr.PredictionExplanations.get(project_id, prediction_explanations_id) prediction_explanations_df = pe.get_all_as_dataframe() ``` Download all rows to a file as CSV document: ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' prediction_explanations_id = '5506fcd98bd88f1641a720a3' pe = dr.PredictionExplanations.get(project_id, prediction_explanations_id) pe.download_to_csv('prediction_explanations.csv') ``` ## Adjusted predictions in prediction explanations In some projects such as insurance projects, the prediction adjusted by exposure is more useful compared with raw prediction. For example, the raw prediction (e.g. claim counts) is divided by exposure (e.g. time) in the project with exposure column. The adjusted prediction provides insights with regard to the predicted claim counts per unit of time. To include that information, set `exclude_adjusted_predictions` to False in correspondent method calls. ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' prediction_explanations_id = '5506fcd98bd88f1641a720a3' pe = dr.PredictionExplanations.get(project_id, prediction_explanations_id) pe.download_to_csv('prediction_explanations.csv', exclude_adjusted_predictions=False) prediction_explanations_df = pe.get_all_as_dataframe(exclude_adjusted_predictions=False) ``` ## Multiclass/clustering prediction explanation modes When calculating prediction explanations for the multiclass or clustering model you need to specify which classes should be explained in each row. By default we only explain the predicted class but it can be set with the mode parameter of [PredictionExplanations.create](https://docs.datarobot.com/en/docs/api/reference/sdk/insights.html#datarobot.PredictionExplanations.create) ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' dataset_id = '5506fcd98bd88a8142b725c8' # Explain predicted and second-best class results in each row pe_job = dr.PredictionExplanations.create(project_id, model_id, dataset_id, mode=dr.models.TopPredictionsMode(2)) pe = pe_job.get_result_when_complete() # Explain results for classes "setosa" and "versicolor" in each row pe_job = dr.PredictionExplanations.create(project_id, model_id, dataset_id, mode=dr.models.ClassListMode(["setosa", "versicolor"])) pe = pe_job.get_result_when_complete() ``` ## SHAP based prediction explanations There are two types of SHAP prediction explanations available, universal SHAP explanations and model-specific SHAP explanations. All models support universal SHAP explanations, which use the permutation based explainer algorithm. Selected models support SHAP explanations such as the tree-based explainer or kernel explainer. Universal SHAP explanations can be computed and retrieved very simply and do not require any pre-requisites. They can be computed for any available partition, and can be restricted to specific data slices. ``` import datarobot as dr from datarobot.insights import ShapMatrix project_id = '5ea6d3354cfad121cf33a5e1' model_id = '5ea6d38b4cfad121cf33a60d' project = dr.Project.get(project_id) model = dr.Model.get(project=project_id, model_id=model_id) # Additional parameters can be passed to specify the partition, # data slice, and other parametrers. shap_insight = ShapMatrix.create(model_id) # Get all computed SHAP matrices all_shap_insights = ShapMatrix.list(model_id) # Retrieve the SHAP matrix as a numpy array matrix = shap_insight.matrix # Retrieve the SHAP matrix columns columns = shap_insight.columns # Retrieve the SHAP base value for additivity checks base_value = shap_insight.base_value ``` You can request model-specific SHAP based prediction explanations using previously uploaded scoring dataset for supported models. Unlike for XEMP prediction explanations you do not need to have [feature impact](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/model.html#feature-impact-label) computed for a model, and predictions for an uploaded dataset. See [datarobot.models.ShapMatrix.create()](https://docs.datarobot.com/en/docs/api/reference/sdk/insights.html#datarobot.models.ShapMatrix.create) reference for a description of the types of parameters that can be passed in. ``` import datarobot as dr project_id = '5ea6d3354cfad121cf33a5e1' model_id = '5ea6d38b4cfad121cf33a60d' project = dr.Project.get(project_id) model = dr.Model.get(project=project_id, model_id=model_id) # check if model supports SHAP model_capabilities = model.get_supported_capabilities() print(model_capabilities.get('supportsShap')) >>> True # upload dataset to generate prediction explanations dataset_from_path = project.upload_dataset('./data_to_predict.csv') shap_matrix_job = ShapMatrix.create(project_id=project_id, model_id=model_id, dataset_id=dataset_from_path.id) shap_matrix_job >>> Job(shapMatrix, status=inprogress) # wait for job to finish shap_matrix = shap_matrix_job.get_result_when_complete() shap_matrix >>> ShapMatrix(id='5ea84b624cfad1361c53f65d', project_id='5ea6d3354cfad121cf33a5e1', model_id='5ea6d38b4cfad121cf33a60d', dataset_id='5ea84b464cfad1361c53f655') # retrieve SHAP matrix as pandas.DataFrame df = shap_matrix.get_as_dataframe() # list as available SHAP matrices for a project shap_matrices = dr.ShapMatrix.list(project_id) shap_matrices >>> [ShapMatrix(id='5ea84b624cfad1361c53f65d', project_id='5ea6d3354cfad121cf33a5e1', model_id='5ea6d38b4cfad121cf33a60d', dataset_id='5ea84b464cfad1361c53f655')] shap_matrix = shap_matrices[0] # retrieve SHAP matrix as pandas.DataFrame df = shap_matrix.get_as_dataframe() ``` --- # Rating table URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/rating_table.html > Learn how to download and upload rating tables for Generalized Additive Models. # Rating table A rating table is an exportable csv representation of a Generalized Additive Model. They contain information about the features and coefficients used to make predictions. Users can influence predictions by downloading and editing values in a rating table, then re-uploading the table and using it to create a new model. See the page about interpreting Generalized Additive Models’ output in the DataRobot user guide for more details on how to interpret and edit rating tables. ## Download a rating table You can retrieve a rating table from the list of rating tables in a project: ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' project = dr.Project.get(project_id) rating_tables = project.get_rating_tables() rating_table = rating_tables[0] ``` Or you can retrieve a rating table from a specific model. The model must already exist: ``` import datarobot as dr from datarobot.models import RatingTableModel, RatingTable project_id = '5506fcd38bd88f5953219da0' project = dr.Project.get(project_id) # Get model from list of models with a rating table rating_table_models = project.get_rating_table_models() rating_table_model = rating_table_models[0] # Or retrieve model by id. The model must have a rating table. model_id = '5506fcd98bd88f1641a720a3' rating_table_model = dr.RatingTableModel.get(project=project_id, model_id=model_id) # Then retrieve the rating table from the model rating_table_id = rating_table_model.rating_table_id rating_table = dr.RatingTable.get(projcet_id, rating_table_id) ``` Then you can download the contents of the rating table: ``` rating_table.download('./my_rating_table.csv') ``` ## Upload a rating table After you’ve retrieved the rating table CSV and made the necessary edits, you can re-upload the CSV so you can create a new model from it: ``` job = dr.RatingTable.create(project_id, model_id, './my_rating_table.csv') new_rating_table = job.get_result_when_complete() job = new_rating_table.create_model() model = job.get_result_when_complete() ``` --- # SHAP insights URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/shap_insights.html > Learn how to compute and use SHAP (SHapley Additive exPlanations) insights for explaining model predictions. # SHAP insights SHAP is an open-source method for explaining the predictions from machine learning models. (You can find more information about SHAP at its repository on GitHub: [https://github.com/slundberg/shap](https://github.com/slundberg/shap)) DataRobot supports SHAP computations for all regression and binary classification blueprints. You can compute three different insights: - "SHAP matrix": Raw SHAP values for each feature column and each row. - "SHAP impact": Overall importance for each feature column across all rows, based on aggregated SHAP matrix values. - "SHAP preview": SHAP values for the most important features in each row, presented with the values of the features in that row. The following example code assumes that you have a trained model object called `model`. ``` import datarobot as dr from datarobot.insights.shap_matrix import ShapMatrix from datarobot.insights.shap_impact import ShapImpact from datarobot.insights.shap_preview import ShapPreview model_id = model.id # or model_id = 'YOUR_MODEL_ID' # request SHAP Matrix, and wait for it to complete result = ShapMatrix.create(entity_id=model_id) # default source is 'validation' # view the properties of the SHAP Matrix print(result.columns) >>> ['AUCGUART', 'Color', 'Make', ... print(result.matrix) >>> [[ 1.22604372e-02 1.98424454e-01 2.23308013e-01 ...] ... ] # request SHAP Matrix on a different partition, and return immediately with job reference job = ShapMatrix.compute(entity_id=model_id, source='holdout') # wait for the job to complete result = job.get_result_when_complete() print(result.columns) >>> ['AUCGUART', 'Color', 'Make', ... print(result.matrix) >>> [[-0.11443075 -0.01130723 0.22330801 ... ] ... ] # request SHAP Impact; only works for training currently job = ShapImpact.compute(entity_id=model_id, source='training') result = job.get_result_when_complete() # Impacts are listed as [feature_name, normalized_impact, unnormalized_impact] print(result.shap_impacts) >>> [['AUCGUART', 0.07989059458051094, 0.022147886593333888], ...] # list all matrices computed for this model, including each partition matrix_list = ShapMatrix.list(entity_id=model_id) print(matrix_list) >>> [, ...] print([(matrix_obj, matrix_obj.source) for matrix_obj in matrix_list]) >>> [(, 'validation'), ... ] # upload a file to the AI Catalog dataset = dr.Dataset.upload("./path/to/dataset.csv") # request explanations for that file in the "preview" format job = ShapPreview.compute(entity_id=model_id, source='externalTestSet', external_dataset_id=dataset.id) result = job.get_result_when_complete() print(result.previews[0]) >>> {'row_index': 0, >>> 'prediction_value': 0.3024851286385187, >>> 'preview_values': [{'feature_rank': 1, >>> 'feature_name': 'BYRNO', >>> 'feature_value': '21973', >>> 'shap_value': 0.22025144078391848, >>> 'has_text_explanations': False, >>> 'text_explanations': []}, >>> ... } ``` # SHAP insights for custom models You can compute SHAP insights for custom models, not just native DataRobot models. To do this, first complete the following setup: 1. Create a custom model version with an execution environment and a training dataset; note the version ID. 2. Register the custom model version as a registered model. 3. Initialize the registered model for insights, using the AutomatedDocument.initialize_model_compliance method. At this point, the model is ready for SHAP insights computation. Once these steps are completed for a given registered model version, they do not have to be repeated. As an example, the code snippet below outlines the preparation steps and then requests a ShapMatrix computation on an external dataset via the AI Catalog. It assumes that you have a Scoring Code file, `model.jar`, for the custom model, which you will run using the Java drop-in execution environment, as well as a training dataset called `training.csv`. ``` import datarobot as dr from datarobot.insights.shap_matrix import ShapMatrix # 1: create a custom model version with an execution environment and a training dataset, and note the version id model_args = { "target_type": dr.TARGET_TYPE.REGRESSION, "target_name": "time_in_hospital", "language": "java", } training_dataset = dr.Dataset.create_from_file(file_path="path/to/training.csv") execution_environment = dr.ExecutionEnvironment.list(search_for="java")[0] custom_model = dr.CustomInferenceModel.create( name="model.jar", **model_args, ) custom_model_version = dr.CustomModelVersion.create_clean( custom_model_id=custom_model.id, base_environment_id=execution_environment.id, training_dataset_id=dataset.id, files=[("path/to/model.jar", "model.jar")], ) custom_model_version_id = custom_model_version.id # 2. register the custom model version as a registered model registered_model = dr.RegisteredModelVersion.create_for_custom_model_version( custom_model_version_id=custom_model_version.id, name=model_name, registered_model_name=model_name ) # 3. initialize the registered model for insights autodocs = dr.AutomatedDocument( entity_id=registered_model.id, document_type="MODEL_COMPLIANCE", ) autodocs.initialize_model_compliance() assert autodocs.is_model_compliance_initialized[0] # Add the scoring dataset to the AI catalog scoring_dataset = dr.Dataset.create_from_file(file_path="path/to/scoring_dataset.csv") # Request the ShapMatrix computation, and retrieve results when it finishes job = ShapMatrix.compute( entity_id=custom_model_version_id, source='externalTestSet', external_dataset_id=scoring_dataset.id, entity_type="customModel", ) result = job.get_result_when_complete() print(result.columns) >>> ['AUCGUART', 'Color', 'Make', ... print(result.matrix) >>> [[ 1.22604372e-02 1.98424454e-01 2.23308013e-01 ...] ... ] ``` --- # Jobs URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/job.html > Learn how to manage and monitor jobs in DataRobot projects, including model creation jobs and queue management. # Jobs The [Job](https://docs.datarobot.com/en/docs/api/reference/sdk/jobs.html#jobs-api) class is a generic representation of jobs running in a project's queue. Many tasks for modeling, such as creating a new model or computing Feature Impact for a model, use a job to track the worker usage and progress of the associated task. ## Check the contents of the queue To see what jobs running or waiting in the queue for a project, use the `Project.get_all_jobs` method. ``` from datarobot.enums import QUEUE_STATUS jobs_list = project.get_all_jobs() # gives all jobs queued or inprogress jobs_by_type = {} for job in jobs_list: if job.job_type not in jobs_by_type: jobs_by_type[job.job_type] = [0, 0] if job.status == QUEUE_STATUS.QUEUE: jobs_by_type[job.job_type][0] += 1 else: jobs_by_type[job.job_type][1] += 1 for type in jobs_by_type: (num_queued, num_inprogress) = jobs_by_type[type] print('{} jobs: {} queued, {} inprogress'.format(type, num_queued, num_inprogress)) ``` ## Cancel a job If a job is taking too long to run or no longer necessary, it can be cancelled from the `Job` object. ``` from datarobot.enums import QUEUE_STATUS project.pause_autopilot() bad_jobs = project.get_all_jobs(status=QUEUE_STATUS.QUEUE) for job in bad_jobs: job.cancel() project.unpause_autopilot() ``` ## Retrieve results from a job You can retrieve the results of a job once it is complete. Note that the type of the returned object varies depending on the `job_type`. All return types are documented in `Job.get_result`. ``` from datarobot.enums import JOB_TYPE time_to_wait = 60 * 60 # how long to wait for the job to finish (in seconds) - i.e. an hour assert my_job.job_type == JOB_TYPE.MODEL my_model = my_job.get_result_when_complete(max_wait=time_to_wait) ``` ### Model jobs Model creation is an asynchronous process. This means that when explicitly invoking new model creation (with `project.train` or `model.train` for example), all you get is the ID of the process responsible for model creation. With this ID, you can get info about the model that is being created—or the model itself, once the creation process is finished—by using the `ModelJob` class. ## Get an existing model job To retrieve existing model jobs, use the `ModelJob.get` method. For this, you need the ID of the project from which the model was built and the ID of the model job. The model job is useful if you want to know the parameters for a model’s creation (automatically chosen by the API backend) before the actual model was created. If the model is already created, `ModelJob.get` will raise the `PendingJobFinished` exception. ``` import time import datarobot as dr blueprint_id = '5506fcd38bd88f5953219da0' model_job_id = project.train(blueprint_id) model_job = dr.ModelJob.get(project_id=project.id, model_job_id=model_job_id) model_job.sample_pct >>> 64.0 # wait for model to be created (in a very inefficient way) time.sleep(10 * 60) model_job = dr.ModelJob.get(project_id=project.id, model_job_id=model_job_id) >>> datarobot.errors.PendingJobFinished # get the job attached to the model model_job.model >>> Model('5d518cd3962d741512605e2b') ``` ## Get a created model After a model is created, you can use `ModelJob.get_model` to get the newly-created model. ``` import datarobot as dr model = dr.ModelJob.get_model(project_id=project.id, model_job_id=model_job_id) ``` ## Async model creation If you want to get the created model after getting the model job ID, you can use the [wait_for_async_model_creation](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#wait-for-async-model-creation-api-label) function. It will poll for the status of the model creation process until it’s finished, and then return the newly-created model. Note the differences below between datetime partitioned projects and non-datetime partitioned projects. ``` from datarobot.models.modeljob import wait_for_async_model_creation # Used during training based on blueprint model_job_id = project.train(blueprint, sample_pct=33) new_model = wait_for_async_model_creation( project_id=project.id, model_job_id=model_job_id, ) # Used during training based on existing model model_job_id = existing_model.train(sample_pct=33) new_model = wait_for_async_model_creation( project_id=existing_model.project_id, model_job_id=model_job_id, ) # For datetime-partitioned projects, use project.train_datetime. Note that train_datetime returns a model job instead # of just an ID. model_job = project.train_datetime(blueprint) new_model = wait_for_async_model_creation( project_id=project.id, model_job_id=model_job.id ) ``` --- # Models URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/model.html > Learn how to train, retrieve, and analyze DataRobot models. # Models When a blueprint has been trained on a specific dataset at a specified sample size, the result is a model. Models can be inspected to analyze their accuracy. ## Start training a model To start training a model, use the [Project.train](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.train) method with a blueprint object: ``` import datarobot as dr project = dr.Project.get('5506fcd38bd88f5953219da0') blueprints = project.get_blueprints() model_job_id = project.train(blueprints[0].id) ``` For a datetime partitioned project (see the specialized workflows section), use [Project.train_datetime](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.train_datetime): ``` import datarobot as dr project = dr.Project.get('5506fcd38bd88f5953219da0') blueprints = project.get_blueprints() model_job_id = project.train_datetime(blueprints[0].id) ``` ## List finished models You can use the [Project.get_models](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.get_models) method to return a list of the project models that have finished training: ``` import datarobot as dr project = dr.Project.get('5506fcd38bd88f5953219da0') models = project.get_models() print(models[:5]) >>> [Model(Decision Tree Classifier (Gini)), Model(Auto-tuned K-Nearest Neighbors Classifier (Minkowski Distance)), Model(Gradient Boosted Trees Classifier (R)), Model(Gradient Boosted Trees Classifier), Model(Logistic Regression)] model = models[0] project.id >>> u'5506fcd38bd88f5953219da0' model.id >>> u'5506fcd98bd88f1641a720a3' ``` You can pass following parameters to change the result: - search_params - A dict. Used to filter returned projects. Currently, you can query models by name , sample_pct , and is_starred . - order_by — A str or list. If passed, returned models are ordered by this attribute(s). You can sort by the metric and sample_pct attributes. If the `sort` attribute is preceded by a hyphen, models will be sorted in descending order, otherwise, in ascending order. Multiple `sort` attributes can be included as a comma-delimited string or in a list, e.g., `order_by='sample_pct,-metric'` or `order_by=['sample_pct', '-metric']`. Using `metric` to sort will result in models being sorted according to their validation score by how well they did according to the project metric. - with_metric – A str. If not set as None , the returned models will only have scores for this metric. Otherwise, all the metrics are returned. Review an example of listing models below. ``` import datarobot as dr dr.Project('5506fcd38bd88f5953219da0').get_models(order_by=['sample_pct', '-metric']) # Getting models that contain "Ridge" in name # and with sample_pct more than 64 dr.Project('5506fcd38bd88f5953219da0').get_models( search_params={ 'sample_pct__gt': 64, 'name': "Ridge" }) # Getting models marked as starred dr.Project('5506fcd38bd88f5953219da0').get_models( search_params={ 'is_starred': True }) ``` ## Retrieve a known model If you know the `model_id` and `project_id` values of a model, you can retrieve it directly: ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' model = dr.Model.get(project=project_id, model_id=model_id) ``` You can also use an instance of `Project` as the parameter for [Model.get](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get). ``` model = dr.Model.get(project=project, model_id=model_id) ``` ## Retrieve the highest scoring model for a given metric You can retrieve the highest scoring model for a project based on a metric of your choice. If you decide not to pass a metric to this method or if you pass the default project metric (the value of the `metric` attribute of your project instance), the result of [Project.recommended_model](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.recommended_model) is returned. ``` import datarobot as dr project = dr.Project.get('5506fcd38bd88f5953219da0') top_model_r_squared = project.get_top_model(metric="R Squared") ``` ## Train a model on a different sample size One of the key insights into a model and the data behind it is how its performance varies with more training data. In Autopilot, DataRobot runs at several sample sizes by default, but you can also create a job that will run at a specific sample size, or specify a feature list that should be used for training the new model. The [Model.train](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.train) method of a `Model` instance will put a new modeling job into the queue and return the ID of the created [ModelJob](https://docs.datarobot.com/en/docs/api/reference/sdk/jobs.html). You can pass the model job ID to the [wait_for_async_model_creation](https://docs.datarobot.com/en/docs/api/reference/sdk/jobs.html#wait-for-async-model-creation-label) function, which polls the async model creation status and returns the newly-created model when it’s finished. ``` import datarobot as dr model_job_id = model.train(sample_pct=33) # Retrain a model on a custom featurelist using cross validation. # Note that you can specify a custom value for `sample_pct`. model_job_id = model.train( sample_pct=55, featurelist_id=custom_featurelist.id, scoring_type=dr.SCORING_TYPE.cross_validation, ) ``` ## Cross-validating a model By default, models are evaluated on the first validation partition. To start cross-validation, use [Model.cross_validate](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.cross_validate): ``` import datarobot as dr model_job_id = model.cross_validate() ``` For a :doc:datetime partitioned project , backtesting is the only cross-validation method supported. To run backtesting for a datetime model, use the [DatetimeModel.score_backtests](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.score_backtests) method: ``` import datarobot as dr # `model` here must be an instance of `dr.DatetimeModel`. model_job_id = model.score_backtests() ``` ## Find the features used Because each project can have many associated feature lists, it is important to know which features a model requires in order to run. This helps ensure that the necessary features are provided when generating predictions. ``` feature_names = model.get_features_used() print(feature_names) >>> ['MonthlyIncome', 'VisitsLast8Weeks', 'Age'] ``` ## Feature Impact Feature Impact measures how much worse a model’s error score would be if DataRobot made predictions after randomly shuffling a particular column (a technique sometimes called `Permutation Importance`). The following example code snippet shows how a feature list with just the features with the highest feature impact could be created. ``` import datarobot as dr max_num_features = 10 time_to_wait_for_impact = 4 * 60 # seconds feature_impacts = model.get_or_request_feature_impact(time_to_wait_for_impact) feature_impacts.sort(key=lambda x: x['impactNormalized'], reverse=True) final_names = [f['featureName'] for f in feature_impacts[:max_num_features]] project.create_featurelist('highest_impact', final_names) ``` For datetime-aware models, Feature Impact can be calculated for any backtest and holdout. ``` import datarobot as dr datetime_model = dr.Model.get(project=project_id, model_id=model_id) feature_impacts = datetime_model.get_or_request_feature_impact(backtest=1, with_metadata=True) ``` ## Feature Effects Feature Effects helps to understand how changing a single feature affects the target while holding all other features constant. Feature Effects provides partial dependence plot and prediction vs accuracy plot data. ``` import datarobot as dr feature_effects = model.get_or_request_feature_effect(source='validation') ``` For multiclass models use `request_feature_effects_multiclass` and `get_feature_effects_multiclass` or `get_or_request_feature_effects_multiclass` methods. ``` import datarobot as dr feature_effects = model.get_feature_effect(source='validation') ``` ## Predict new data After creating models, you can use them to generate predictions on new data. See the [predictions documentation](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/index.html#predictions) for further information on how to request predictions from a model. ## Model IDs vs. blueprint IDs Each model has both a `model_id` and a `blueprint_id`. A model is the result of training a blueprint on a dataset at a specified sample percentage. The `blueprint_id` is used to keep track of which blueprint was used to train the model, while the `model_id` is used to locate the trained model in the system. ## Model parameters Some models can have parameters that provide data needed to reproduce their predictions. For additional usage information see [Coefficients](https://docs.datarobot.com/en/docs/workbench/experiments/experiment-insights/ml-coefficients.html#coefficients). ``` import datarobot as dr model = dr.Model.get(project=project, model_id=model_id) mp = model.get_parameters() print(mp.derived_features) >>> [{ 'coefficient': -0.015, 'originalFeature': u'A1Cresult', 'derivedFeature': u'A1Cresult->7', 'type': u'CAT', 'transformations': [{'name': u'One-hot', 'value': u"'>7'"}] }] ``` ## Create a blender model You can blend multiple models; in many cases, the resulting blender model is more accurate than the parent models. To do so, you need to select parent models and a blender method from `datarobot.enums.BLENDER_METHOD`. If this is a time series project, only methods in `datarobot.enums.TS_BLENDER_METHOD` are allowed. Be aware that the tradeoff for better prediction accuracy is bigger resource consumption and slower predictions. ``` import datarobot as dr pr = dr.Project.get(pid) models = pr.get_models() parent_models = [model.id for model in models[:2]] pr.blend(parent_models, dr.enums.BLENDER_METHOD.AVERAGE) ``` ## Lift chart retrieval You can use the `Model` methods `get_lift_chart` and `get_all_lift_charts` to retrieve lift chart data. The first will get it from specific source (validation data, cross validation, or unlocked Holdout) and the second will list all available data. For multiclass models, you can get a list of per-class lift charts using the `Model` method `get_multiclass_lift_chart`. ## ROC curve retrieval Same as with the lift chart, you can use `Model` methods `get_roc_curve` and `get_all_roc_curves` to retrieve ROC curve data. The first gets the curve from a specific source (validation data, cross validation, or unlocked Holdout); the second lists all available data. More information about working with ROC curves can be found in [ROC curve](https://docs.datarobot.com/en/docs/workbench/experiments/experiment-insights/ml-roc-curve.html). To get the best F1 threshold and all threshold data for validation (or holdout) data: ``` import datarobot as dr import pandas as pd p = dr.Project.get("68dc7024820ddddeaa9b5b72") ms = p.get_models() m = ms[1] roc = m.get_roc_curve("validation") # Best threshold (maximal F1 score; same as preselected in the ROC curve tab): roc.get_best_f1_threshold() # All thresholds and metrics: pd.DataFrame(roc.roc_points) ``` ## Residuals chart retrieval Just as with the lift and ROC charts, you can use `Model` methods `get_residuals_chart` and `get_all_residuals_charts` to retrieve residuals chart data. The first will get it from a specific source (validation data, cross-validation data, or unlocked Holdout). The second retrieves all available data. ## Word cloud If your dataset contains text columns, DataRobot can create text processing models that will contain word cloud insight data. An example of such a model is any “Auto-Tuned Word N-Gram Text Modeler” model. You can use the `Model.get_word_cloud` method to retrieve those insights — it provides up to the 200 most important ngrams in the model and coefficients corresponding to their influence. ## Scoring Code A subset of models support code generation. For each of those models, you can download a JAR file with Scoring Code to make predictions locally using `model.download_scoring_code`. For details on how to do so, see [Scoring Code](https://docs.datarobot.com/en/docs/predictions/port-pred/scoring-code/index.html). Optionally, you can download source code in Java to see what calculations those models do internally. Be aware that the source code JAR isn’t compiled so it cannot be used for making predictions. ## Get a model blueprint chart For any model, you can retrieve its blueprint chart. You can also get its representation in graphviz DOT format to render it into the format you need. ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' model = dr.Model.get(project=project_id, model_id=model_id) bp_chart = model.get_model_blueprint_chart() print(bp_chart.to_graphviz()) ``` ## Get a model missing values report For the majority of models, you can retrieve their missing values reports on training data per each numeric and categorical feature. Model needs to have at least one of the supported tasks in the blueprint in order to have a missing values report (blenders are not supported). Report is gathered for Numerical Imputation tasks and Categorical converters like Ordinal Encoding, One-Hot Encoding, etc. Missing values report is available to users with access to full blueprint docs. A report is collected for those features which are considered eligible by a given blueprint task. For instance, a categorical feature with a lot of unique values may not be considered as eligible in the One-Hot encoding task. Please refer to [Missing report attributes description](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#missing-values-report-api) for report interpretation. ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' model = dr.Model.get(project=project_id, model_id=model_id) missing_reports_per_feature = model.get_missing_report_info() for report_per_feature in missing_reports_per_feature: print(report_per_feature) ``` Consider the following example of a Decision Tree Classifier (Gini) blueprint chart representation. A summary of the results is outlined below. ``` print(blueprint_chart.to_graphviz()) >>> digraph "Blueprint Chart" { graph [rankdir=LR] 0 [label="Data"] -2 [label="Numeric Variables"] 2 [label="Missing Values Imputed"] 3 [label="Decision Tree Classifier (Gini)"] 4 [label="Prediction"] -1 [label="Categorical Variables"] 1 [label="Ordinal encoding of categorical variables"] 0 -> -2 -2 -> 2 2 -> 3 3 -> 4 0 -> -1 -1 -> 1 1 -> 3 } ``` And a missing report: ``` print(report_per_feature1) >>> {'feature': 'Veh Year', 'type': 'Numeric', 'missing_count': 150, 'missing_percentage': 50.00, 'tasks': [ {'id': u'2', 'name': u'Missing Values Imputed', 'descriptions': [u'Imputed value: 2006'] } ] } print(report_per_feature2) >>> {'feature': 'Model', 'type': 'Categorical', 'missing_count': 100, 'missing_percentage': 33.33, 'tasks': [ {'id': u'1', 'name': u'Ordinal encoding of categorical variables', 'descriptions': [u'Imputed value: -2'] } ] } ``` The numeric feature “Veh Year” has 150 missing values and, respectively, 50% in training data. It was transformed by the “Missing Values Imputed” task with imputed value 2006. Task has ID 2, and its output goes into Decision Tree Classifier (Gini), which can be inferred from the chart. The “Model” categorical feature was transformed by “Ordinal encoding of categorical variables” task with imputed value -2. ## Get a blueprint's documentation You can retrieve documentation on tasks used to build a model. It will contain information about the task, its parameters and (when available) links and references to additional sources. All documents are instances of `BlueprintTaskDocument` class. ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' model = dr.Model.get(project=project_id, model_id=model_id) docs = model.get_model_blueprint_documents() print(docs[0].task) >>> Average Blend print(docs[0].links[0]['url']) >>> https://en.wikipedia.org/wiki/Ensemble_learning ``` ## Request training predictions You can request a model’s predictions for a particular subset of its training data. See [datarobot.models.Model.request_training_predictions()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_training_predictions) reference for all the valid subsets. See [training predictions reference](https://docs.datarobot.com/en/docs/api/dev-learning/python/predictions/predict_job.html#training-predictions) for more details. ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' model = dr.Model.get(project=project_id, model_id=model_id) training_predictions_job = model.request_training_predictions(dr.enums.DATA_SUBSET.HOLDOUT) training_predictions = training_predictions_job.get_result_when_complete() for row in training_predictions.iterate_rows(): print(row.row_id, row.prediction) ``` ## Advanced tuning You can perform advanced tuning on a model — generate a new model by taking an existing model and rerunning it with modified tuning parameters. The `AdvancedTuningSession` class exists to track the creation of an advanced tuning model on the client. It enables browsing and setting advanced tuning parameters one at a time, and using human-readable parameter names rather than requiring opaque parameter IDs in all cases. No information is sent to the server until the `run()` method is called on the AdvancedTuningSession. See [datarobot.models.Model.get_advanced_tuning_parameters()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_advanced_tuning_parameters) reference for a description of the types of parameters that can be passed in. As of v2.17 of the Python client, all models other than blenders, open source, and user-created models support Advanced Tuning. The use of Advanced Tuning via the API for non-Eureqa models is in beta, but is enabled by default for all users. ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' model = dr.Model.get(project=project_id, model_id=model_id) tune = model.start_advanced_tuning_session() # Get available task names, # and available parameter names for a task name that exists on this model tune.get_task_names() tune.get_parameter_names('Eureqa Generalized Additive Model Classifier (3000 Generations)') tune.set_parameter( task_name='Eureqa Generalized Additive Model Classifier (3000 Generations)', parameter_name='EUREQA_building_block__sine', value=1) job = tune.run() ``` For specific grid search options, pass the grid search arguments when starting the session. ``` import datarobot as dr from datarobot.models.advanced_tuning import GridSearchArguments from datarobot.enums import GridSearchSearchType, GridSearchAlgorithm project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' model = dr.Model.get(project=project_id, model_id=model_id) # Add grid search arguments grid_args = GridSearchArguments( search_type=GridSearchSearchType.SMART, algorithm=GridSearchAlgorithm.PATTERN_SEARCH, batch_size=7, max_iterations=50, random_state=7, wall_clock_time_limit=1000, ) tune = model.start_advanced_tuning_session(grid_search_arguments=grid_args) # Get available task names, # and available parameter names for a task name that exists on this model tune.get_task_names() tune.get_parameter_names('Generalized Additive Model') params_to_tune = { 'colsample_bytree': 0.5, 'learning_rate': [0.08, 0.07], 'max_depth': 6, 'min_child_weight': 2, 'n_estimators':320, } for param, value in params_to_tune.items(): tune.set_parameter( task_name='Generalized Additive Model', parameter_name=param, value=value) job = tune.run() job.get_result_when_complete() ``` ## SHAP Feature Impact SHAP is an open-source method for explaining the predictions from machine learning models. You can find more information about SHAP at its repository on [GitHub](https://github.com/slundberg/shap). DataRobot supports SHAP computations for all regression and binary classification blueprints. You can compute SHAP feature impact, which reports the overall importance for each feature column across all rows, based on aggregated SHAP matrix values. The following example code assumes that you have a trained model object called `model`. ``` import datarobot as dr from datarobot.insights.shap_impact import ShapImpact project_id = '5ec3d6884cfad17cd8c0ed62' model_id = model.id # or model_id = 'YOUR_MODEL_ID' # request SHAP Impact; only works for training currently job = ShapImpact.compute(entity_id=model_id, source='training') result = job.get_result_when_complete() # Impacts are listed as [feature_name, normalized_impact, unnormalized_impact] print(result.shap_impacts) >>> [['AUCGUART', 0.07989059458051094, 0.022147886593333888], ...] # Retrieve a SHAP Impact record that was previously calculated shap_impact = ShapImpact.get(entity_id=model_id, source='training') ``` ## Number of iterations trained Early-stopping models will train a subset of max estimators/iterations that are defined in advanced tuning. This method allows the user to retrieve the actual number of estimators that were trained by an early-stopping tree-based model (currently the only model type supported). The method returns the projectId, modelId, and a list of dictionaries containing the number of iterations trained for each model stage. In the case of single-stage models, this dictionary will contain only one entry. ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' model = dr.Model.get(project=project_id, model_id=model_id) num_iterations = model.get_num_iterations_trained() print(num_iterations) >>> {"projectId": "5506fcd38bd88f5953219da0", "modelId": "5506fcd98bd88f1641a720a3", "data" [{"stage": "FREQ", "numIterations":250}, {"stage":"SEV", "numIterations":50}]} ``` --- # Model recommendation URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/model_recommendation.html > Learn how to retrieve and work with DataRobot model recommendations for deployment. # Model recommendation During Autopilot, DataRobot recommends a model for deployment based on its accuracy and complexity. When running Autopilot in Full or Comprehensive mode, DataRobot uses the following deployment preparation process: 1. First, DataRobot calculates Feature Impact for the selected model and uses it to generate a reduced feature list. 2. Next, DataRobot retrains the selected model on the reduced feature list. If the new model performs better than the original model, DataRobot uses the new model for the next stage. Otherwise, the original model is used. 3. DataRobot then retrains the selected model at an up-to-holdout sample size (typically 80%). As long as the sample is under the frozen threshold (1.5GB), the stage is not frozen. 4. Finally, DataRobot retrains the selected model as a frozen run (hyperparameters are not changed from the up-to-holdout run) using a 100% sample size and selects it as Recommended for Deployment . > [!NOTE] Note > The higher sample size DataRobot uses in Step 3 is either: > > Up to holdout > if the training sample size > does not > exceed the maximum Autopilot size threshold: sample size is the training set plus the validation set (for TVH) or 5-folds (for CV). In this case, DataRobot compares retrained and original models on the holdout score. > Up to validation > if the training sample size > does > exceed the maximum Autopilot size threshold: sample size is the training set (for TVH) or 4-folds (for CV). In this case, DataRobot compares retrained and original models on the validation score. DataRobot gives one model the Recommended for Deployment* badge. This is the most accurate individual, non-blender model on the Leaderboard. After completing the steps described above, it will receive the Prepared for Deployment badge. ## Retrieve all recommendations The following code will return all models recommended for the project. ``` import datarobot as dr recommendations = dr.ModelRecommendation.get_all(project_id) ``` ## Retrieve a default recommendation If you are unsure about the tradeoffs between the various types of recommendations, DataRobot can make this choice for you. The following route will return the “Recommended for Deployment” model to use for predictions for the project. ``` import datarobot as dr recommendation = dr.ModelRecommendation.get(project_id) ``` ## Retrieve a specific recommendation If you know which recommendation you want to use, you can select a specific recommendation using the following code. ``` import datarobot as dr recommendation_type = dr.enums.RECOMMENDED_MODEL_TYPE.RECOMMENDED_FOR_DEPLOYMENT recommendations = dr.ModelRecommendation.get(project_id, recommendation_type) ``` ## Get recommended model You can use method `get_model()` of a recommendation object to retrieve a recommended model. ``` import datarobot as dr recommendation = dr.ModelRecommendation.get(project_id) recommended_model = recommendation.get_model() ``` --- # DataRobot Prime URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/prime.html > Learn how to use DataRobot Prime to download executable code that approximates models. # DataRobot Prime > [!NOTE] Availability information > The ability to create new DataRobot Prime models has been removed. This does not affect existing Prime models or deployments. To export Python code in the future, use the Python code export function in any RuleFit model. DataRobot Prime allows the download of executable code approximating models. For more information about this feature, see the documentation within the DataRobot webapp. Contact your Account Executive or CFDS for information on enabling DataRobot Prime, if needed. ## Approximate a model Given a Model you wish to approximate, `Model.request_approximation` will start a job creating several `Ruleset` objects approximating the parent model. Each of those rulesets will identify how many rules were used to approximate the model, as well as the validation score the approximation achieved. ``` rulesets_job = model.request_approximation() rulesets = rulesets_job.get_result_when_complete() for ruleset in rulesets: info = (ruleset.id, ruleset.rule_count, ruleset.score) print('id: {}, rule_count: {}, score: {}'.format(*info)) ``` ## Prime models vs. models Given a ruleset, you can create a model based on that ruleset. We consider such models to be Prime models. The `PrimeModel` class inherits from the `Model` class, so anything a Model can do, as PrimeModel can do as well. The `PrimeModel` objects available within a `Project` can be listed by `project.get_prime_models`, or a particular one can be retrieve via `PrimeModel.get`. If a ruleset has not yet had a model built for it, `ruleset.request_model` can be used to start a job to make a PrimeModel using a particular ruleset. ``` rulesets = parent_model.get_rulesets() selected_ruleset = sorted(rulesets, key=lambda x: x.score)[-1] if selected_ruleset.model_id: prime_model = PrimeModel.get(selected_ruleset.project_id, selected_ruleset.model_id) else: prime_job = selected_ruleset.request_model() prime_model = prime_job.get_result_when_complete() ``` The `PrimeModel` class has two additional attributes and one additional method. The attributes are `ruleset`, which is the Ruleset used in the PrimeModel, and `parent_model_id` which is the id of the model it approximates. Finally, the new method defined is `request_download_validation` which is used to prepare code download for the model and is discussed later on in this document. ## Retrieving Code from a PrimeModel Given a PrimeModel, you can download the code used to approximate the parent model, and view and execute it locally. The first step is to validate the PrimeModel, which runs some basic validation of the generated code, as well as preparing it for download. We use the `PrimeFile` object to represent code that is ready to download.`PrimeFiles` can be prepared by the `request_download_validation` method on `PrimeModel` objects, and listed from a project with the `get_prime_files` method. Once you have a `PrimeFile` you can check the `is_valid` attribute to verify the code passed basic validation, and then download it to a local file with `download`. ``` validation_job = prime_model.request_download_validation(enums.PRIME_LANGUAGE.PYTHON) prime_file = validation_job.get_result_when_complete() if not prime_file.is_valid: raise ValueError('File was not valid') prime_file.download('/home/myuser/drCode/primeModelCode.py') ``` --- # Projects URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/project.html > Learn how to create, configure, and manage DataRobot projects for modeling. # Projects All of the modeling within DataRobot happens within a project. Each project has one dataset that is used as the source from which to train models. ## Create a project You can create a project from previously-created [Datasets](https://docs.datarobot.com/en/docs/api/dev-learning/python/data/dataset.html#datasets) or directly from a data source. ``` import datarobot as dr dataset = Dataset.create_from_file(file_path='/home/user/data/last_week_data.csv') project = dr.Project.create_from_dataset(dataset.id, project_name='New Project') ``` The following command creates a new project directly from a data source. You must specify a path to data file, file object URL (starting with `http://`, `https://`, `file://`, or `s3://`), raw file contents, or a `pandas.DataFrame` object when creating a new project. Path to file can be either a path to a local file or a publicly accessible URL. ``` import datarobot as dr project = dr.Project.create('/home/user/data/last_week_data.csv', project_name='New Project') ``` You can use the following commands to view the project ID and name: ``` project.id >>> u'5506fcd38bd88f5953219da0' project.project_name >>> u'New Project' ``` ## Select modeling parameters The final information needed to begin modeling includes the target feature, queue mode, metric for comparing models, and optional parameters such as weights, offset, exposure, and downsampling. ### Target The target must be the name of one of the columns of data uploaded to the project. ### Metric The optimization metric used to compare models is an important factor in building accurate models. If a metric is not specified, the default metric recommended by DataRobot will be used. You can use the following code to view a list of valid metrics for a specified target: ``` target_name = 'ItemsPurchased' project.get_metrics(target_name) >>> {'available_metrics': [ 'Gini Norm', 'Weighted Gini Norm', 'Weighted R Squared', 'Weighted RMSLE', 'Weighted MAPE', 'Weighted Gamma Deviance', 'Gamma Deviance', 'RMSE', 'Weighted MAD', 'Tweedie Deviance', 'MAD', 'RMSLE', 'Weighted Tweedie Deviance', 'Weighted RMSE', 'MAPE', 'Weighted Poisson Deviance', 'R Squared', 'Poisson Deviance'], 'feature_name': 'SalePrice'} ``` ### Partitioning method DataRobot projects always have a `Holdout` set used for final model validation. You can use two different approaches for testing prior to the Holdout set: - Split the remaining data into training and validation sets. - Cross-validation, in which the remaining data is split into a number of folds (partitions); each fold serves as a validation set, with models trained on the other folds and evaluated on that fold. There are several other options you can control. To specify a partition method, create an instance of one of the [Partition Classes](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#partitions-api), and pass it as the `partitioning_method` argument in your call to `project.analyze_and_model` or `project.start`. As of v3.0 of the Python client, you can alternately use `project.set_partitioning_method`. See [here](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#set-up-datetime) for more information on using datetime partitioning. Several partitioning methods include parameters for `validation_pct` and `holdout_pct`, specifying desired percentages for the validation and holdout sets. Note that there may be constraints that prevent the actual percentages used from exactly (or some cases, even closely) matching the requested percentages. ### Queue mode You can use the API to set the DataRobot modeling process to run Autopilot in manual, quick, or comprehensive mode. Autopilot mode means that the modeling process will proceed completely automatically, including running recommended models, running at different sample sizes, and blending. Manual mode means that DataRobot will populate a list of recommended models, but will not insert any of them into the queue. This mode lets you specify which models to execute before starting the modeling process. Quick mode means that a smaller set of blueprints is used, so Autopilot finishes faster. ### Weights DataRobot also supports using a `weight` parameter, which are often used to help compensate for rare events in data. You can specify a column name in the project dataset to be used as a `weight` column. ### Offsets Starting with Python client v2.6, DataRobot also supports using an offset parameter. Offsets are commonly used in insurance modeling to include effects that are outside of the training data due to regulatory compliance or constraints. You can specify the names of several columns in the project dataset to be used as the offset columns. ### Exposure Starting with version v2.6, DataRobot also supports using an exposure parameter. Exposure is often used to model insurance premiums where strict proportionality of premiums to duration is required. You can specify the name of the column in the project dataset to be used as an exposure column. ## Start modeling Once you have selected modeling parameters, you can use the following code structure to specify parameters and start the modeling process. ``` import datarobot as dr project.analyze_and_model(target='ItemsPurchased', metric='Tweedie Deviance', mode=dr.AUTOPILOT_MODE.FULL_AUTO) ``` You can also pass additional parameters to `project.analyze_and_model` to change parts of the modeling process. Some of those parameters include: - worker_count - int, sets number of workers used for modeling. - partitioning_method - PartitioningMethod object. - positive_class - str, float, or int; Specifies a level of the target column that should be treated as the positive class for binary classification. May only be specified for binary classification targets. - advanced_options - AdvancedOptions object; Used to set advanced options of modeling process. Can alternatively call set_options on a project instance which will be used automatically if nothing is passed here. - target_type - str; Overrides the automatically selected target_type . An example usage would be setting the target_type=TARGET_TYPE.MULTICLASS when you want to perform a multiclass classification task on a numeric column that has a low cardinality. You can run different Autopilot modes with the `mode` parameter.`AUTOPILOT_MODE.FULL_AUTO` is the default, which will trigger modeling with no further actions necessary. Other accepted modes include `AUTOPILOT_MODE.MANUAL` for manual mode (choose your own models to run rather than use the DataRobot autopilot), `AUTOPILOT_MODE.QUICK` (run on a more limited set of models to get insights more quickly), and `AUTOPILOT_MODE.COMPREHENSIVE` (used to invest more time to find the most accurate model to serve your use case). For a full reference of available parameters, see [Project.analyze_and_model](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.analyze_and_model). ### Clone a project Once a project has been successfully created, you may clone it using the following code structure: ``` new_project = project.clone_project(new_project_name='This is my new project') new_project.project_name >> 'This is my new project' new_project.id != project.id >> True ``` The `new_project_name` attribute is optional. If it is omitted, the default new project name will be ‘Copy of ’. ## Interact with a project The following commands can be used to manage DataRobot projects. ### List projects Returns a list of projects associated with current API user. ``` import datarobot as dr dr.Project.list() >>> [Project(Project One), Project(Two)] dr.Project.list(search_params={'project_name': 'One'}) >>> [Project(One)] ``` You can pass following parameter to change the result: - search_params – dict; Used to filter returned projects. You can only query projects by project_name . ### Get an existing project Rather than querying the full list of projects every time you need to interact with a project, you can retrieve its `ID` value and use that to reference the project. ``` import datarobot as dr project = dr.Project.get(project_id='5506fcd38bd88f5953219da0') project.id >>> '5506fcd38bd88f5953219da0' project.project_name >>> 'Churn Projection' ``` ### Get feature association statistics for an existing project You can retrieve either feature association or correlation statistics and metadata on informative features for a given project. ``` import datarobot as dr project = dr.Project.get(project_id='5506fcd38bd88f5953219da0') association_data = project.get_associations(assoc_type='association', metric='mutualInfo') association_data.keys() >>> ['strengths', 'features'] ``` ### Get whether your featurelists have association statistics Get whether an association matrix job has been run on each of your feature lists. ``` import datarobot as dr project = dr.Project.get(project_id='5506fcd38bd88f5953219da0') featurelists = project.get_association_featurelists() featurelists['featurelists'][0] >>> {"featurelistId": "54e510ef8bd88f5aeb02a3ed", "hasFam": True, "title": "Informative Features"} ``` ### Create association statistics for a featurelist Generate the feature association statistics for all features in a feature list. ``` import datarobot as dr from datarobot.models.feature_association_matrix import FeatureAssociationMatrix project = dr.Project.get(project_id='5506fcd38bd88f5953219da0') featurelist = project.get_featurelist_by_name("Raw Features") status = FeatureAssociationMatrix.create(project.id, featurelist.id) # two ways to wait for completion # option 1 status.wait_for_completion() fam = FeatureAssociationMatrix.get(project_id=project.id, featurelist_id=featurelist.id) # or option 2 # fam = status.get_result_when_complete() ``` ### Get a project's feature list by name Get a feature list by name. ``` import datarobot as dr project = dr.Project.get(project_id='5506fcd38bd88f5953219da0') featurelist = project.get_featurelist_by_name("Raw Features") featurelist >>> Featurelist(Raw Features) # Trying to get feature list that does not exist featurelist = project.get_featurelist_by_name("Flying Circus") featurelist is None >>> True ``` ### Create project feature lists Using a project’s `create_featurelist()` method, you can create feature lists in multiple ways: ``` import datarobot as dr project = dr.Project.get(project_id='5506fcd38bd88f5953219da0') featurelist_one = project.create_featurelist( name="Testing featurelist creation", features=["age", "weight", "number_diagnoses"], ) featurelist_one >>> Featurelist(Testing featurelist creation) featurelist_one.features >>> ['age', 'weight', 'number_diagnoses'] # Create a feature list using another feature list as a starting point (`starting_featurelist`) # To Note: this example passes the `featurelist` object but you can also pass the # id (`starting_featurelist_id`) or the name (`starting_featurelist_name`) featurelist_two = project.create_featurelist( starting_featurelist=featurelist_one, features_to_exclude=["number_diagnoses"], # Please see docs for use of `features_to_include` ) featurelist_two # Note below we have an auto-generated name because we did not pass `name` >>> Featurelist(Testing featurelist creation - 2022-07-12) >>> # Note below we have a new feature list which has `"number_diagnoses"` excluded featurelist_two.features >>> ['age', 'weight'] ``` ### Get values for a pair of features in an existing project Get a sample of the exact values used in the feature association matrix plotting. ``` import datarobot as dr project = dr.Project.get(project_id='5506fcd38bd88f5953219da0') feature_values = project.get_association_matrix_details(feature1='foo', feature2='bar') feature_values.keys() >>> ['features', 'types', 'values'] ``` ### Update a project You can update various attributes of a project. To update the name of the project: ``` project.rename(new_name) ``` To update the number of workers used by your project (this will fail if you request more workers than you have available; the special value `-1` will request your maximum number): ``` project.set_worker_count(num_workers) ``` To unlock the Holdout set, allowing holdout scores to be shown and models to be trained on more data: ``` project.unlock_holdout() ``` To add or change the project description: ``` project.set_project_description(project_description) ``` To add or change the project’s `advanced_options`: ``` # Using kwargs project.set_options(blend_best_models=False) # Using an ``AdvancedOptions`` instance project.set_options(AdvancedOptions(blend_best_models=False)) ``` ### Delete a project Use the following command to delete a project: ``` project.delete() ``` ### Wait for Autopilot to finish Once the modeling Autopilot is started, in some cases you will want to wait for Autopilot to finish: ``` project.wait_for_autopilot() ``` ### Play/Pause Autopilot If your project is running in Autopilot, it will continually use available workers, subject to the number of workers allocated to the project and the total number of simultaneous workers allowed according to the user permissions. To pause a project running in Autopilot: ``` project.pause_autopilot() ``` To resume running a paused project: ``` project.unpause_autopilot() ``` ### Start Autopilot on another feature list You can start Autopilot on an existing feature list. ``` import datarobot as dr featurelist = project.create_featurelist('test', ['feature 1', 'feature 2']) project.start_autopilot(featurelist.id) >>> True # Starting autopilot that is already running on the provided featurelist project.start_autopilot(featurelist.id) >>> dr.errors.AppPlatformError ``` > [!NOTE] Note > This method should be used on a project where the target has already been set. > An error will be raised if autopilot is currently running on or has already finished running on the provided feature list. ### Start preparing a specific model for deployment You can start preparing a specific model for deployment. The model will then go through the various recommendation stages including retraining on a reduced feature list and retraining the model on a higher sample size (recent data for datetime partitioned). ``` # prepare a specific model for deployment and wait for the process to complete project.start_prepare_model_for_deployment(model_id=model.id) project.wait_for_autopilot(check_interval=5, timeout=600) # get the prepared model prepared_for_deployment_model = dr.models.ModelRecommendation.get( project.id, recommendation_type=RECOMMENDED_MODEL_TYPE.PREPARED_FOR_DEPLOYMENT ) prepared_for_deployment_model_id = prepared_for_deployment_model.model_id ``` > [!NOTE] Note > This method should be used on a project where the target has already been set. > An error will be raised if autopilot is currently running on the project or another model in the project is being prepared for deployment. ### Using credential data For methods that accept credential data instead of user/password or credential ID, please see [Credential Data documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#id1). --- # Work with binary data URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/binary_data.html > Learn how to work with binary data files in DataRobot projects. # Work with binary data ## Prepare data for training Working with binary files using the DataRobot API requires prior dataset preparation in one of the supported formats. See [“Prepare the dataset”](https://docs.datarobot.com/en/docs/modeling/special-workflows/visual-ai/vai-model.html#prepare-the-dataset) for more detail. When the dataset is ready, you can start a project following one of the methods described in working with [Datasets](https://docs.datarobot.com/en/docs/api/dev-learning/python/data/dataset.html#datasets) and [Projects](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/project.html#projects). ## Prepare data for predictions For project creation and a lot of the prediction options, DataRobot allows you to upload archives with binary files (e.g. images files). Whenever possible it is recommended to use this option. However, in a few cases the API routes only allow you to upload your dataset in the JSON or CSV format. In these cases, you can add the binary files as base64 strings to your dataset. ## Process images ### Installation To enable support for processing images, install the datarobot library with the `images` option: ``` pip install datarobot[images] ``` This will install all needed dependencies for image processing. ### Process images When working with image files, helper functions may first transform your images before encoding their binary data as base64 strings. Specifically, helper functions will perform these steps: - Retrieve binary data from the file in the specified location (local path or URL). - Resize images to the image size used by DataRobot and save them in a different format - Convert binary data to base64-encoded strings. Working with images locally and located on external servers differs only in the steps related to binary file retrieval. The following steps for transformation and conversion to base64-encoded strings are the same. This examples uses data stored in a folder structure: ``` /home/user/data/predictions ├── images ├  ├──animal01.jpg ├  ├──animal02.jpg ├  ├──animal03.png ├── data.csv ``` As an input for processing, DataRobot needs a collection of image locations. Helper functions will process the images and return base64-encoded strings in the same order. The first example uses the contents of data.csv as an input. This file holds data needed for model predictions and also the image storage locations (in the “image_path” column). Contents of data.csv: ``` weight_in_grams,age_in_months,image_path 5000,34,/home/user/data/predictions/images/animal01.jpg 4300,56,/home/user/data/predictions/images/animal02.jpg 4200,22,/home/user/data/predictions/images/animal03.png ``` This code snippet will read each image from the “image_path” column and store the base64-string with image data in the “image_base64” column. ``` import os import pandas as pd from datarobot.helpers.binary_data_utils import get_encoded_image_contents_from_paths dataset_dir = '/home/user/data/predictions' file_in = os.path.join(dataset_dir, 'data.csv') file_out = os.path.join(dataset_dir, 'out.csv') df = pd.read_csv(file_in) df['image_base64'] = get_encoded_image_contents_from_paths(df['image_path']) df.to_csv(file_out, index=False) ``` The same helper function will work with other iterables: ``` import os from datarobot.helpers.binary_data_utils import get_encoded_image_contents_from_paths images_dir = '/home/user/data/predictions/images' images_absolute_paths = [ os.path.join(images_dir, file) for file in ['animal01.jpg', 'animal02.jpg', 'animal03.png'] ] images_base64 = get_encoded_image_contents_from_paths(images_absolute_paths) ``` Above examples used absolute paths. When working with relative paths, by default the helper function will resolve them relative to the script location. To override this behavior, use `base_path` parameter to specify the base path for relative paths. ``` from datarobot.helpers.binary_data_utils import get_encoded_image_contents_from_paths images_dir = '/home/user/data/predictions/images' images_relative_paths = ['animal01.jpg', 'animal02.jpg', 'animal03.png'] images_base64 = get_encoded_image_contents_from_paths( images_relative_paths, base_path=images_dir ) ``` There is also one helper function to work with remote data. This function retrieves binary content from specified URLs, transforms the images, and returns base64-encoded strings (in the same way as it does for images loaded from local paths). Example: ``` import os from datarobot.helpers.binary_data_utils import get_encoded_image_contents_from_urls image_urls = [ 'https:///animal01.jpg', 'https:///animal02.jpg', 'https:///animal03.png' ] images_base64 = get_encoded_image_contents_from_urls(image_urls) ``` Examples of helper functions up to this points have used default settings. If needed, the following functions allow for further customization by passing explicit parameters related to error handling, image transformations, and request header customization. ### Custom image transformations By default helper functions will apply transformations, which have proven good results. The default values align with the preprocessing used for images uploaded in archives for training. Therefore, using default values should be the first choice when preparing datasets with images for predictions. However, you can also specify custom image transformation settings to override default transformations before converting data into base64 strings. To override the default behavior, create an instance of the `ImageOptions` class and pass it as an additional parameter to the helper function. Note that there is no guarantee that images converted by DataRobot during archive dataset upload match images converted by you on a pixel level, even if the default `ImageOptions` are used. However, if you use `ImageOptions`, you most likely will not be able to visually identify any differences. Examples: ``` import os from datarobot.helpers.image_utils import ImageOptions from datarobot.helpers.binary_data_utils import get_encoded_image_contents_from_paths images_dir = '/home/user/data/predictions/images' images_absolute_paths = [ os.path.join(images_dir, file) for file in ['animal01.jpg', 'animal02.jpg', 'animal03.png'] ] # Override the default behavior for image quality and subsampling, but the images # will still be resized because that's the default behavior. Note: the `keep_quality` # parameter for JPEG files by default preserves the quality of the original images, # so this behavior must be disabled to manually override the quality setting with an # explicit value. image_options = ImageOptions(keep_quality=False, image_quality=80, image_subsampling=0) images_base64 = get_encoded_image_contents_from_paths( paths=images_absolute_paths, image_options=image_options ) # overwrite default behavior for image resizing, this will keep image aspect # ratio and will resize all images using specified size: width=300 and height=300. # Note: if image had different aspect ratio originally it will generate image # thumbnail, not larger than the original, that will fit in requested image size image_options = ImageOptions(image_size=(300, 300)) images_base64 = get_encoded_image_contents_from_paths( paths=images_absolute_paths, image_options=image_options ) # Override the default behavior for image resizing, This will force the image # to be resized to size: width=300 and height=300. When the image originally # had a different aspect ratio - than resizing it using `force_size` parameter # will alter its aspect ratio modifying the image (e.g. stretching) image_options = ImageOptions(image_size=(300, 300), force_size=True) images_base64 = get_encoded_image_contents_from_paths( paths=images_absolute_paths, image_options=image_options ) # overwrite default behavior and retain original image sizes image_options = ImageOptions(should_resize=False) images_base64 = get_encoded_image_contents_from_paths( paths=images_absolute_paths, image_options=image_options ) ``` ### Custom request headers If needed, you can specify custom request headers for downloading binary data. Example: ``` import os from datarobot.helpers.binary_data_utils import get_encoded_image_contents_from_urls token = 'Nl69vmABaEuchUsj88N0eOoH2kfUbhCCByhoFDf4whJyJINTf7NOhhPrNQKqVVJJ' custom_headers = { 'User-Agent': 'My User Agent', 'Authorization': 'Bearer {}'.format(token) } image_urls = [ 'https:///animal01.jpg', 'https:///animal02.jpg', 'https:///animal03.png', ] images_base64 = get_encoded_image_contents_from_urls(image_urls, custom_headers) ``` ### Handling errors When processing multiple images, any error during processing will, by default, stop operations (i.e., the helper function will raise `datarobot.errors.ContentRetrievalTerminatedError` and terminate further processing). In the case of an error during content retrieval (“connectivity issue”, “file not found” etc), you can override this behavior by passing `continue_on_error=True` to the helper function. When specified, processing will continue. In rows where the error was raised, the value``None`` value will be returned instead of a base64-encoded string. This applies only to errors during content retrieval, other errors will always terminate execution. Example: ``` import os from datarobot.helpers.binary_data_utils import get_encoded_image_contents_from_paths images_dir = '/home/user/data/predictions/images' images_absolute_paths = [ os.path.join(images_dir, file) for file in ['animal01.jpg', 'missing.jpg', 'animal03.png'] ] # This execution will print None for missing files and base64 strings for exising files images_base64 = get_encoded_image_contents_from_paths(images_absolute_paths, continue_on_error=True) for value in images_base64: print(value) # This execution will raise error during processing of missing file terminating operation images_base64 = get_encoded_image_contents_from_paths(images_absolute_paths) ``` ## Process other binary files Other binary files can be processed by dedicated functions. These functions work similarly to the functions used for images, although they do not provide functionality for any transformations. Processing follows two steps instead of three: > Retrieve binary data from the file in the specified location (local path or URL).Convert binary data to base64-encoded strings. To process documents into base64-encoded strings use these functions: > To retrieve files from local paths:get_encoded_file_contents_from_paths- tTo retrieve files from locations specified as URLs:get_encoded_file_contents_from_urls- Examples: ``` import os from datarobot.helpers.binary_data_utils import get_encoded_file_contents_from_urls document_urls = [ 'https:///document01.pdf', 'https:///missing.pdf', 'https:///document03.pdf', ] # this call will return base64 strings for existing documents and None for missing files documents_base64 = get_encoded_file_contents_from_urls(document_urls, continue_on_error=True) for value in documents_base64: print(value) # This execution will raise error during processing of missing file terminating operation documents_base64 = get_encoded_file_contents_from_urls(document_urls) ``` --- # Composable ML URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/custom_task.html > Learn how to use custom tasks and composable ML in DataRobot. # Composable ML Composable ML consists of two major components: [the DataRobot Blueprint Workshop](https://blueprint-workshop.datarobot.com/) and custom tasks, detailed below. Custom tasks provide users the ability to train models with arbitrary code in an environment defined by the user. For details on using environments, see: [Manage execution environments](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_model.html#custom-models-execution-environments). ## Manage custom tasks Before you can upload code for a custom task, you need to create the entity that holds all the metadata. ``` import datarobot as dr from datarobot.enums import CUSTOM_TASK_TARGET_TYPE transform = dr.CustomTask.create( name="a convenient display name", # required target_type=CUSTOM_TASK_TARGET_TYPE.TRANSFORM, # required language="python", description="a longer description of the task" ) binary = dr.CustomTask.create( name="this or that", target_type=CUSTOM_TASK_TARGET_TYPE.BINARY, ) ``` A task, by itself is an empty metadata container. Before using your tasks, you need create a [CustomTaskVersion](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/custom_task.html#custom-task-versions) associated with it. A task that is ready for use will have a `latest_version` field populated with this task. ``` binary.latest_version >>> None execution_environment = dr.ExecutionEnvironment.create( name="Python3 PyTorch Environment", description="This environment contains Python3 pytorch library.", ) custom_task_folder = "datarobot-user-tasks/task_templates/python3_pytorch" task_version = dr.CustomTaskVersion.create_clean( custom_task_id=binary.id, base_environment_id=execution_environment.id, folder_path=custom_task_folder, ) binary.refresh() # In order to see the change, you need to GET it from DataRobot binary.latest_version >>> CustomTaskVersion('v1.0') ``` If you create a new version, that will be returned as the `latest_version`. You can download the latest version as a zip file. ``` binary.latest_version >>> CustomTaskVersion('v1.0') custom_task_folder = "/home/my-user-name/tasks/my-updated-task/" task_version = dr.CustomTaskVersion.create_clean( custom_task_id=binary.id, base_environment_id=execution_environment.id, folder_path=custom_task_folder, ) binary.refresh() binary.latest_version >>> CustomTaskVersion('v2.0') binary.download_latest_version("/home/my-user-name/downloads/my-task-files.zip") ``` You can `get`, `list`, `copy`, exactly as you would expect.`copy` makes a complete copy of the task: new copies of the metadata, new copies of the versions, new copies of uploaded files for the new versions. ``` all_tasks = CustomTask.list() assert {el.id for el in all_tasks} == {binary.id, transform.id} new_binary = CustomTask.copy(binary.id) assert new_binary.latest_version.id != binary.latest_version.id original_binary = CustomTask.get(binary.id) assert len(CustomTask.list()) == 3 ``` You can `update` the metadata of a task. When you do this, the object is also updated to the latest data. ``` assert binary.description == new_binary.description binary.update(description="totally new description") assert binary.description != new_binary.description assert original_binary.description != binary.description # hasn't refreshed from the server yet original_binary.refresh() assert original_binary.description == binary.description ``` And finally, you can `delete` only if the task is not in use by any of the following: - Trained models - Deployments - Blueprints in the AI catalog Once you have deleted the objects that use the task, you will be able to delete the task itself. ## Manage custom task versions Code for Custom Tasks can be uploaded by creating a Custom Task Version. When creating a Custom Task Version, the version must be associated with a base execution environment. If the base environment supports additional task dependencies (R or Python environments) and the Custom Task Version contains a valid requirements.txt file, the task version will run in an environment based on the base environment with the additional dependencies installed. ### Create custom task version Upload actual custom task content by creating a clean Custom Task Version: ``` import os from datarobot.enums import CustomTaskOutboundNetworkPolicy custom_task_id = binary.id custom_task_folder = "datarobot-user-tasks/task_templates/python3_pytorch" # add files from the folder to the custom task task_version = dr.CustomTaskVersion.create_clean( custom_task_id=custom_task_id, base_environment_id=execution_environment.id, folder_path=custom_task_folder, outbound_network_policy=CustomTaskOutboundNetworkPolicy.PUBLIC, ) ``` To create a new Custom Task Version from a previous one, with just some files added or removed, do the following: ``` import os import datarobot as dr new_files_folder = "datarobot-user-tasks/task_templates/my_files_to_add_to_pytorch_task" file_to_delete = task_version.items[0].id task_version_2 = dr.CustomTaskVersion.create_from_previous( custom_task_id=custom_task_id, base_environment_id=execution_environment.id, folder_path=new_files_folder, ) ``` Please refer to [CustomTaskFileItem](https://docs.datarobot.com/en/docs/api/reference/sdk/blueprints.html#datarobot.models.custom_task_version.CustomTaskFileItem) for description of custom task file properties. ### List custom task versions Use the following command to list Custom Task Versions available to the user: ``` import datarobot as dr dr.CustomTaskVersion.list(custom_task_id) >>> [CustomTaskVersion('v2.0'), CustomTaskVersion('v1.0')] ``` ### Retrieve custom task version To retrieve a specific Custom Task Version, run: ``` import datarobot as dr dr.CustomTaskVersion.get(custom_task_id, custom_task_version_id='5ebe96b84024035cc6a6560b') >>> CustomTaskVersion('v2.0') ``` ### Update custom task version To update Custom Task Version description execute the following: ``` import datarobot as dr custom_task_version = dr.CustomTaskVersion.get( custom_task_id, custom_task_version_id='5ebe96b84024035cc6a6560b', ) custom_task_version.update(description='new description') custom_task_version.description >>> 'new description' ``` ### Download custom task version Download content of the Custom Task Version as a ZIP archive: ``` import datarobot as dr path_to_download = '/home/user/Documents/myTask.zip' custom_task_version = dr.CustomTaskVersion.get( custom_task_id, custom_task_version_id='5ebe96b84024035cc6a6560b', ) custom_task_version.download(path_to_download) ``` ## Prepare a custom task version for use If your custom task version has dependencies, a dependency build must be completed before the task can be used. The dependency build installs your task’s dependencies into the base environment associated with the task version. see: [Prepare a custom model version for use](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/custom_model.html#custom-models-dependencies) --- # Datetime partitioned projects URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html > Learn how to set up and work with datetime partitioned projects in DataRobot. # Datetime partitioned projects If your dataset is modeling events taking place over time, datetime partitioning may be appropriate. Datetime partitioning ensures that when partitioning the dataset for training and validation, rows are ordered according to the value of the date partition feature. ## Set up datetime partitioned projects After creating a project and before setting the target, create a [DatetimePartitioningSpecification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datetime-part-spec) to define how the project should be partitioned. By passing the specification into `DatetimePartitioning.generate`, the full partitioning can be previewed before finalizing the partitioning. After verifying that the partitioning is correct for the project dataset, pass the specification into `Project.analyze_and_model` via the `partitioning_method` argument. Alternatively, as of v3.0, by using `Project.set_datetime_partitioning()`, the partitioning (and individual options of the partitioning specification) can be updated (with repeated method calls) up until calling `Project.analyze_and_model`. Once modeling begins, the project can be used as normal. The following code block shows the basic workflow for creating datetime partitioned projects. ``` import datarobot as dr project = dr.Project.create('some_data.csv') spec = dr.DatetimePartitioningSpecification('my_date_column') # can customize the spec as needed partitioning_preview = dr.DatetimePartitioning.generate(project.id, spec) # the preview generated is based on the project's data print(partitioning_preview.to_dataframe()) # hmm ... I want more backtests spec.number_of_backtests = 5 partitioning_preview = dr.DatetimePartitioning.generate(project.id, spec) print(partitioning_preview.to_dataframe()) # looks good project.analyze_and_model('target_column') # As of v3.0, ``Project.set_datetime_partitioning()`` and ``Project.list_datetime_partition_spec()`` # are available as an alternative: # view settings project.list_datetime_partition_spec() # maybe I want to also disable holdout before starting modeling project.set_datetime_partitioning(disable_holdout=True) # view settings project.list_datetime_partition_spec() # all of the settings look good # don't need to pass the spec into ``analyze_and_model`` because it's already been set project.analyze_and_model('target_column') # I can retrieve the partitioning settings after the target has been set too partitioning = dr.DatetimePartitioning.get(project.id) ``` ### Configure backtests Backtests are configurable using one of two methods: Method 1: > index (int): The index from zero of this backtest.gap_duration (str): A duration string such as those returned by thepartitioning_methods.construct_duration_stringhelper method. This represents the gap between training and validation scoring data for this backtest.validation_start_date (datetime.datetime): Represents the start date of the validation scoring data for this backtest.validation_duration (str): A duration string such as those returned by thepartitioning_methods.construct_duration_stringhelper method. This represents the desired duration of the validation scoring data for this backtest.importdatarobotasdrfromdatetimeimportdatetimepartitioning_spec=dr.DatetimePartitioningSpecification(backtests=[# modify the first backtest using option 1dr.BacktestSpecification(index=0,gap_duration=dr.partitioning_methods.construct_duration_string(),validation_start_date=datetime(year=2010,month=1,day=1),validation_duration=dr.partitioning_methods.construct_duration_string(years=1),)],# other partitioning settings...) Method 2 (New in version v2.20): > validation_start_date (datetime.datetime): Represents the start date of the validation scoring data for this backtest.validation_end_date (datetime.datetime): Represents the end date of the validation scoring data for this backtest.primary_training_start_date (datetime.datetime): Represents the desired start date of the training partition for this backtest.primary_training_end_date (datetime.datetime): Represents the desired end date of the training partition for this backtest.importdatarobotasdrfromdatetimeimportdatetimepartitioning_spec=dr.DatetimePartitioningSpecification(backtests=[# modify the first backtest using option 2dr.BacktestSpecification(index=0,primary_training_start_date=datetime(year=2005,month=1,day=1),primary_training_end_date=datetime(year=2010,month=1,day=1),validation_start_date=datetime(year=2010,month=1,day=1),validation_end_date=datetime(year=2011,month=1,day=1),)],# other partitioning settings...) Note that Method 2 allows you to directly configure the start and end dates of each partition, including the training partition. The gap partition is calculated as the time between `primary_training_end_date` and `validation_start_date`. Using the same date for both `primary_training_end_date` and `validation_start_date` will result in no gap being created. After configuring backtests, you can set `use_project_settings` to `True` in calls to [Model.train_datetime](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.train_datetime). This will create models that are trained and validated using your custom backtest training partition start and end dates. ## Model with datetime partitioned projects While `Model` objects can still be used to interact with the project, [DatetimeModel](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datetime-mod) objects, which are only retrievable from datetime partitioned projects, provide more information including which date ranges and how many rows are used in training and scoring the model as well as scores and statuses for individual backtests. The autopilot workflow is the same as for other projects, but to manually train a model, `Project.train_datetime` and `Model.train_datetime` should be used in the place of `Project.train` and `Model.train`. To create frozen models, `Model.request_frozen_datetime_model` should be used in place of `DatetimeModel.request_frozen_datetime_model`. Unlike other projects, to trigger computation of scores for all backtests use `DatetimeModel.score_backtests` instead of using the `scoring_type` argument in the `train` methods. ## Accuracy over time plots For datetime partitioned model you can retrieve the Accuracy over Time plot. To do so use [DatetimeModel.get_accuracy_over_time_plot](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_accuracy_over_time_plot). You can also retrieve the detailed metadata using [DatetimeModel.get_accuracy_over_time_plots_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_accuracy_over_time_plots_metadata), and the preview plot using [DatetimeModel.get_accuracy_over_time_plot_preview](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_accuracy_over_time_plot_preview). ## Dates, datetimes, and durations When specifying a date or datetime for datetime partitioning, the client expects to receive and will return a `datetime`. Timezones may be specified, and will be assumed to be UTC if left unspecified. All dates returned from DataRobot are in UTC with a timezone specified. Datetimes may include a time, or specify only a date; however, they may have a non-zero time component only if the partition column included a time component in its date format. If the partition column included only dates like “24/03/2015”, then the time component of any datetimes, if present, must be zero. When date ranges are specified with a start and an end date, the end date is exclusive, so only dates earlier than the end date are included, but the start date is inclusive, so dates equal to or later than the start date are included. If the start and end date are the same, then no dates are included in the range. Durations are specified using a subset of ISO8601. Durations will be of the form `PnYnMnDTnHnMnS` where each “n” may be replaced with an integer value. Within the duration string, > nY represents the number of yearsthe nM following the “P” represents the number of monthsnD represents the number of daysnH represents the number of hoursthe nM following the “T” represents the number of minutesnS represents the number of seconds and “P” is used to indicate that the string represents a period and “T” indicates the beginning of the time component of the string. Any section with a value of 0 may be excluded. As with datetimes, if the partition column did not include a time component in its date format, the time component of any duration must be either unspecified or consist only of zeros. Example Durations: > “P3Y6M” (three years, six months)“P1Y0M0DT0H0M0S” (one year)“P1Y5DT10H” (one year, 5 days, 10 hours) [datarobot.helpers.partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#dur-string-helper) is a helper method that can be used to construct appropriate duration strings. --- # Specialized workflows URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/index.html > Alternative workflows for a variety of specialized data types. # Specialized workflows The following sections describe alternative workflows for a variety of specialized data types. | Resource | Description | | --- | --- | | Time series projects | Learn how to set up and work with time series projects in DataRobot. | | Work with binary data | Learn how to work with binary data files in DataRobot projects. | | Datetime partitioned projects | Learn how to set up and work with datetime partitioned projects in DataRobot. | | Segmented modeling projects | Learn how to create and work with segmented modeling projects in DataRobot. | | Monotonic Constraints | Learn how to use monotonic constraints in DataRobot models. | | Composable ML | Learn how to use custom tasks and composable ML in DataRobot. | | Unsupervised Projects (Anomaly Detection) | Learn how to create and work with unsupervised anomaly detection projects in DataRobot. | | Unsupervised Projects (Clustering) | Learn how to create and work with unsupervised clustering projects in DataRobot. | | Visual AI projects | Learn how to create and work with Visual AI projects using image data in DataRobot. | --- # Monotonic constraints URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/monotonic_constraints.html > Learn how to use monotonic constraints in DataRobot models. # Monotonic constraints Training with monotonic constraints allows users to force models to learn monotonic relationships with respect to some features and the target. This helps users create accurate models that comply with regulations (e.g. insurance, banking). Currently, only certain blueprints (e.g. xgboost) support this feature, and it is only supported for regression and binary classification projects. Typically working with monotonic constraints follows the following two workflows: Workflow one - Running a project with default monotonic constraints - set the target and specify default constraint lists for the project - when running autopilot or manually training models without overriding constraint settings, all blueprints that support monotonic constraints will use the specified default constraint featurelists Workflow two - Running a model with specific monotonic constraints - create featurelists for monotonic constraints - train a blueprint that supports monotonic constraints while specifying monotonic constraint featurelists - the specified constraints will be used, regardless of the defaults on the blueprint ## Create feature lists When specifying monotonic constraints, users must pass a reference to a featurelist containing only the features to be constrained, one for features that should monotonically increase with the target and another for those that should monotonically decrease with the target. ``` import datarobot as dr project = dr.Project.get(project_id) features_mono_up = ['feature_0', 'feature_1'] # features that have monotonically increasing relationship with target features_mono_down = ['feature_2', 'feature_3'] # features that have monotonically decreasing relationship with target flist_mono_up = project.create_featurelist(name='mono_up', features=features_mono_up) flist_mono_down = project.create_featurelist(name='mono_down', features=features_mono_down) ``` ## Specify default monotonic constraints for a project Users can specify default monotonic constraints for the project, to ensure that autopilot models use the desired settings, and optionally to ensure that only blueprints supporting monotonic constraints appear in the project. Regardless of the defaults specified via advanced options selection, the user can override them when manually training a particular model. ``` import datarobot as dr from datarobot.enums import AUTOPILOT_MODE project = dr.Project.get(project_id) # As of v3.0, ``Project.set_options`` may be used as an alternative to passing `advanced_options`` into ``Project.analyze_and_model``. project.set_options( monotonic_increasing_featurelist_id=flist_mono_up.id, monotonic_decreasing_featurelist_id=flist_mono_down.id, only_include_monotonic_blueprints=True ) project.analyze_and_model(target='target', mode=AUTOPILOT_MODE.FULL_AUTO) ``` If `Project.set_options` is not used, alternatively, an advanced options instance may be passed directly to `project.analyze_and_model`: ``` project.analyze_and_model( target='target', mode=AUTOPILOT_MODE.FULL_AUTO, advanced_options=AdvancedOptions(monotonic_increasing_featurelist_id=flist_mono_up.id, monotonic_decreasing_featurelist_id=flist_mono_down.id, only_include_monotonic_blueprints=True) ) ``` ## Retrieve models and blueprints using monotonic constraints When retrieving models, users can inspect to see which supports monotonic constraints, and which actually enforces them. Some models will not support monotonic constraints at all, and some may support constraints but not have any constrained features specified. ``` import datarobot as dr project = dr.Project.get(project_id) models = project.get_models() # retrieve models that support monotonic constraints models_support_mono = [model for model in models if model.supports_monotonic_constraints] # retrieve models that support and enforce monotonic constraints models_enforce_mono = [model for model in models if (model.monotonic_increasing_featurelist_id or model.monotonic_decreasing_featurelist_id)] ``` When retrieving blueprints, users can check if they support monotonic constraints and see what default constraint lists are associated with them. The monotonic featurelist ids associated with a blueprint will be used every time it is trained, unless the user specifically overrides them at model submission time. ``` import datarobot as dr project = dr.Project.get(project_id) blueprints = project.get_blueprints() # retrieve blueprints that support monotonic constraints blueprints_support_mono = [blueprint for blueprint in blueprints if blueprint.supports_monotonic_constraints] # retrieve blueprints that support and enforce monotonic constraints blueprints_enforce_mono = [blueprint for blueprint in blueprints if (blueprint.monotonic_increasing_featurelist_id or blueprint.monotonic_decreasing_featurelist_id)] ``` ## Train a model with specific monotonic constraints Even after specifying default settings for the project, users can override them to train a new model with different constraints, if desired. ``` import datarobot as dr features_mono_up = ['feature_2', 'feature_3'] # features that have monotonically increasing relationship with target features_mono_down = ['feature_0', 'feature_1'] # features that have monotonically decreasing relationship with target project = dr.Project.get(project_id) flist_mono_up = project.create_featurelist(name='mono_up', features=features_mono_up) flist_mono_down = project.create_featurelist(name='mono_down', features=features_mono_down) model_job_id = project.train( blueprint, sample_pct=55, featurelist_id=featurelist.id, monotonic_increasing_featurelist_id=flist_mono_up.id, monotonic_decreasing_featurelist_id=flist_mono_down.id ) ``` --- # Segmented modeling URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/segmented_modeling.html > Learn how to create and work with segmented modeling projects in DataRobot. # Segmented modeling Many [time series](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#time-series) multiseries projects introduce complex forecasting use cases that require using different models for subsets of series (i.e., sales of groceries and clothing can be very different). Within the segmented modeling framework, DataRobot runs multiple time series projects (one per segment / group of series), selects the best models for each segment, and then combines those models to make predictions. ## Segment A segment is a group of series in a multiseries project. For example, given `store` and `country` columns in dataset, you can use the former as the series identifier and the latter as the segment identifier. For the best results, group series with similar patterns into segments (instead of random selection). ## Segmentation task A segmentation task is an entity that defines how input dataset is partitioned. Currently only user-defined segmentation is supported. That is, the dataset must have a separate column that is used to identify segment (and the user must select it). All records within a series must have the same segment identifier. ## Combined model A combined model in a segmented modeling project can be thought of as a meta-model made of references to the best model within each segment. While being quite different from a standard DataRobot model in its creation, its use is very much the same after the model is complete (for example, deploying or making predictions). The following examples illustrate how to set up, run, and manage a segmented modeling project using the Python public API client. For details please refer to [Segmented Modeling API Reference](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#segmented-modeling-api). ### Start a segmentation project with a user-defined segment ID `Time series` modeling must be enabled for your account to run segmented modeling projects. Use the standard method to create a DataRobot project: ``` from datarobot import DatetimePartitioningSpecification from datarobot import enums from datarobot import Project from datarobot import SegmentationTask project_name = "Segmentation Demo with Segmentation ID" project_dataset = "multiseries_segmentation.csv" project = Project.create(project_dataset, project_name=project_name) datetime_partition_column = "timestamp" multiseries_id_column = "series_id" user_defined_segment_id_column = "物类segment_id" target = "target" ``` Create a simple datetime specification for a time series project: ``` spec = DatetimePartitioningSpecification( use_time_series=True, datetime_partition_column=datetime_partition_column, multiseries_id_columns=[multiseries_id_column], ) ``` Create a segmentation task for the project: ``` segmentation_task_results = SegmentationTask.create( project_id=project.id, target=target, use_time_series=True, datetime_partition_column=datetime_partition_column, multiseries_id_columns=[multiseries_id_column], user_defined_segment_id_columns=[user_defined_segment_id_column], ) segmentation_task = segmentation_task_results["completedJobs"][0] ``` Start a segmented project by passing the `segmentation_task_id` argument: ``` project.analyze_and_model( target=target, partitioning_method=spec, mode=enums.AUTOPILOT_MODE.QUICK, worker_count=-1, segmentation_task_id=segmentation_task.id, ) ``` ### Work with combined models Retrieve Combined Models: ``` from datarobot import Project, CombinedModel project_id = "60ff165dde5f3ceacda0f2d6" # Get an existing segmentation project project = Project.get(segmented_project_id) # Retrieve list of all combined models in the project combined_models = project.get_combined_models() # Or just an active (current) combined model current_combined_model = project.get_active_combined_model() ``` Get information about segments in the Combined Model: ``` segments_info = current_combined_model.get_segments_info() # Alternatively this information can be retrieved as a Pandas DataFrame segments_df = current_combined_model.get_segments_as_dataframe() # Or even in CSV format current_combined_model.get_segments_as_csv("combined_model_segments.csv") ``` Ensure Autopilot has completed for all segments: ``` segments_info = current_combined_model.get_segments_info() assert all(segment.autopilot_done for segment in segments_info) ``` Optionally, view a list of all models associated with individual segments: ``` segments_and_child_models = project.get_segments_models(current_combined_model.id) ``` Set a new champion for a segment in the Combined Model, specifying the `project_id` of the segmented project and the `model_id` from that project: ``` segment_project_id = "60ff165dde5f3ceacdaabcde" new_champion_id = "60ff165dde5f3ceacdaa12f7" CombinedModel.set_segment_champion(project_id=segment_project_id, model_id=new_champion_id) ``` If active Combined Model has already been deployed - changing champions is not allowed. In this case, create a copy of Combined Model, make it active, and set champion for it (deployed model remains unchanged): ``` new_combined_model = CombinedModel.set_segment_champion(project_id=segment_project_id, model_id=new_champion_id, clone=True) ``` Run predictions on the Combined Model: ``` prediction_dataset = "multiseries_predictions.csv" # Upload dataset dataset = project.upload_dataset( sourcedata=prediction_dataset, ) # Request predictions predictions_job = current_combined_model.request_predictions( dataset_id=dataset.id, ) predictions = predictions_job.get_result_when_complete() ``` --- # Time series projects URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html > Learn how to set up and work with time series projects in DataRobot. # Time series projects Time series projects, like OTV projects, use [datetime partitioning](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#set-up-datetime), and all the workflow changes that apply to other datetime partitioned projects also apply to them. Unlike other projects, time series projects produce different types of models which forecast multiple future predictions instead of an individual prediction for each row. DataRobot uses a general time series framework to configure how time series features are created and what future values the models will output. This framework consists of a Forecast Point (defining a time a prediction is being made), a Feature Derivation Window (a rolling window used to create features), and a Forecast Window (a rolling window of future values to predict). These components are described in more detail below. Time series projects will automatically transform the dataset provided in order to apply this framework. During the transformation, DataRobot uses the Feature Derivation Window to derive time series features (such as lags and rolling statistics), and uses the Forecast Window to provide examples of forecasting different distances in the future (such as time shifts). After project creation, a new dataset and a new feature list are generated and used to train the models. This process is reapplied automatically at prediction time as well in order to generate future predictions based on the original data features. The `time_unit` and `time_step` used to define the Feature Derivation and Forecast Windows are taken from the datetime partition column, and can be retrieved for a given column in the input data by looking at the corresponding attributes on the [datarobot.models.Feature](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#datarobot.models.Feature) object. If `windows_basis_unit` is set to `ROW`, then Feature Derivation and Forecast Windows will be defined using number of the rows. ## Set up time series projects To set up a time series project, follow the standard [datetime partitioning](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#set-up-datetime) workflow and use the six new time series specific parameters on the [datarobot.DatetimePartitioningSpecification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioningSpecification) Parameter | Description use_time_series | bool, set this to True to enable time series for the project. default_to_known_in_advance | bool, set this to True to default to treating all features as known in advance, or a priori, features. Otherwise, they will not be handled as known in advance features. Individual features can be set to a value different than the default by using the featureSettings parameter. See [the prediction documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#time-series-predict) for more information. default_to_do_not_derive | bool, set this to True to default to excluding all features from feature derivation. Otherwise, they will not be excluded and will be included in the feature derivation process. Individual features can be set to a value different than the default by using the featureSettings parameter. feature_derivation_window_start | int, specifies how many units of the `windows_basis_unit` from the forecast point into the past is the start of the feature derivation window. feature_derivation_window_end | int, specifies how many units of the `windows_basis_unit` from the forecast point into the past is the end of the feature derivation window. forecast_window_start | int, specifies how many units of the `windows_basis_unit` from the forecast point into the future is the start of the forecast window. forecast_window_end | int, specifies how many units of the `windows_basis_unit` from the forecast point into the future is the end of the forecast window windows_basis_unit | string, set this to `ROW` to define feature derivation and forecast windows in terms of the rows, rather than time units. If omitted, will default to the detected time unit (one of the `datarobot.enums.TIME_UNITS`). feature_settings | list of FeatureSettings specifying per feature settings, can be left unspecified. ### Feature derivation window The Feature Derivation window represents the rolling window that is used to derive time series features and lags, relative to the Forecast Point. It is defined in terms of `feature_derivation_window_start` and `feature_derivation_window_end` which are integer values representing datetime offsets in terms of the `time_unit` (e.g. hours or days). The Feature Derivation Window start and end must be less than or equal to zero, indicating they are positioned before the forecast point. Additionally, the window must be specified as an integer multiple of the `time_step` which defines the expected difference in time units between rows in the data. The window is closed, meaning the edges are considered to be inside the window. ### Forecast window The Forecast Window represents the rolling window of future values to predict, relative to the Forecast Point. It is defined in terms of the `forecast_window_start` and `forecast_window_end`, which are positive integer values indicating datetime offsets in terms of the `time_unit` (e.g. hours or days). The Forecast Window start and end must be positive integers, indicating they are positioned after the forecast point. Additionally, the window must be specified as an integer multiple of the `time_step` which defines the expected difference in time units between rows in the data. The window is closed, meaning the edges are considered to be inside the window. ### Multiseries projects Certain time series problems represent multiple separate series of data, e.g. “I have five different stores that all have different customer bases. I want to predict how many units of a particular item will sell, and account for the different behavior of each store”. When setting up the project, a column specifying series ids must be identified, so that each row from the same series has the same value in the multiseries id column. Using a multiseries id column changes which partition columns are eligible for time series, as each series is required to be unique and regular, instead of the entire partition column being required to have those properties. In order to use a multiseries id column for partitioning, a detection job must first be run to analyze the relationship between the partition and multiseries id columns. If needed, it will be automatically triggered by calling [datarobot.models.Feature.get_multiseries_properties()](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#datarobot.models.Feature.get_multiseries_properties) on the desired partition column. The previously computed multiseries properties for a particular partition column can then be accessed via that method. The computation will also be automatically triggered when calling [datarobot.DatetimePartitioning.generate()](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioning.generate) or [datarobot.models.Project.analyze_and_model()](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.analyze_and_model) with a multiseries id column specified. Note that currently only one multiseries id column is supported, but all interfaces accept lists of id columns to ensure multiple id columns will be able to be supported in the future. In order to create a multiseries project: > Set up a datetime partitioning specification with the desired partition column and multiseries id columns.(Optionally) Usedatarobot.models.Feature.get_multiseries_properties()to confirm the inferred time step and time unit of the partition column when used with the specified multiseries id column.(Optionally) Specify the multiseries id column in order to preview the full datetime partitioning settings usingdatarobot.DatetimePartitioning.generate().Specify the multiseries id column when sending the target and partitioning settings viadatarobot.models.Project.analyze_and_model().project=dr.Project.create('path/to/multiseries.csv',project_name='my multiseries project')partitioning_spec=dr.DatetimePartitioningSpecification('timestamp',use_time_series=True,multiseries_id_columns=['multiseries_id'])# manually confirm time step and time unit are as expecteddatetime_feature=dr.Feature.get(project.id,'timestamp')multiseries_props=datetime_feature.get_multiseries_properties(['multiseries_id'])print(multiseries_props)# manually check out the partitioning settings like feature derivation window and backtests# to make sure they make sense before moving onfull_part=dr.DatetimePartitioning.generate(project.id,partitioning_spec)print(full_part.feature_derivation_window_start,full_part.feature_derivation_window_end)print(full_part.to_dataframe())# As of v3.0, can use ``Project.set_datetime_partitioning`` instead of passing the spec into ``Project.analyze_and_model`` via ``partitioning_method``.# The spec options can be passed individually:project.set_datetime_partitioning(use_time_series=True,datetime_partition_column='date',multiseries_id_columns=['series_id'])# Or the whole spec object can be passed:project.set_datetime_partitioning(datetime_partitioning_spec=datetime_spec)# finalize the project and start the autopilotproject.analyze_and_model('target',partitioning_method=partitioning_spec) You can also access optimized partitioning in the API where the target over time is inspected to ensure that the default backtests cover regions of interest and adjust backtests avoid common problems with missing target values or partitions with single values (e.g. zero-inflated datasets). In this case you need to pass the target column when generating the partitioning specification (either by calling `DatetimePartitioning.generate` or `Project.set_datetime_partitioning`) and then pass the full partitioning specification when starting autopilot (if `Project.set_datetime_partitioning` is not used). ``` project = dr.Project.create('path/to/multiseries.csv', project_name='my multiseries project') partitioning_spec = dr.DatetimePartitioningSpecification( 'timestamp', use_time_series=True, multiseries_id_columns=['multiseries_id'] ) # Pass the target column to generate optimized partitions full_part = dr.DatetimePartitioning.generate(project.id, partitioning_spec, 'target') # Or, as of v3.0, call ``Project.set_datetime_partitioning`` after specifying the project target # to generate optimized partitions. project.target = 'target' project.set_datetime_partitioning(datetime_partition_spec=partitioning_spec) # finalize the project and start the autopilot, passing in the full partitioning spec # (if ``Project.set_datetime_partitioning`` was used there is no need to pass ``partitioning_method``) project.analyze_and_model('target', partitioning_method=full_part.to_specification()) ``` ### Feature settings [datarobot.FeatureSettings](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.FeatureSettings) constructor receives `feature_name` and settings. For now settings `known_in_advance` and `do_not_derive` are supported. ``` # I have 10 features, 8 of them are known in advance and two are not # Also, I do not want to derive new features from previous_day_sales not_known_in_advance_features = ['previous_day_sales', 'amount_in_stock'] do_not_derive_features = ['previous_day_sales'] feature_settings = [dr.FeatureSettings(feat_name, known_in_advance=False) for feat_name in not_known_in_advance_features] feature_settings += [dr.FeatureSettings(feat_name, do_not_derive=True) for feat_name in do_not_derive_features] spec = dr.DatetimePartitioningSpecification( # ... default_to_known_in_advance=True, feature_settings=feature_settings ) ``` ## Model data and time series features In time series projects, a new set of modeling features is created after setting the partitioning options. If a featurelist is specified with the partitioning options, it will be used to select which features should be used to derived modeling features; if a featurelist is not specified, the default featurelist will be used. These features are automatically derived from those in the project’s dataset and are the features used for modeling - note that the Project methods `get_featurelists` and `get_modeling_featurelists` will return different data in time series projects. Modeling featurelists are the ones that can be used for modeling and will be accepted by the backend, while regular featurelists will continue to exist but cannot be used. Modeling features are only accessible once the target and partitioning options have been set. In projects that don’t use time series modeling, once the target has been set, modeling and regular features and featurelists will behave the same. ### Restore discarded features [datarobot.models.restore_discarded_features.DiscardedFeaturesInfo](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#datarobot.models.restore_discarded_features.DiscardedFeaturesInfo) can be used to get and restore features that have been removed by the time series feature generation and reduction functionality. ``` project = Project(project_id) discarded_feature_info = project.get_discarded_features() restored_features_info = project.restore_discarded_features(discarded_features_info.features) ``` ## Make predictions Prediction datasets are uploaded [as normal](https://docs.datarobot.com/en/docs/api/dev-learning/python/predictions/predict_job.html#predictions). However, when uploading a prediction dataset, a new parameter `forecast_point` can be specified. The forecast point of a prediction dataset identifies the point in time relative which predictions should be generated, and if one is not specified when uploading a dataset, the server will choose the most recent possible forecast point. The forecast window specified when setting the partitioning options for the project determines how far into the future from the forecast point predictions should be calculated. To simplify the predictions process, starting in version v2.20 a forecast point or prediction start and end dates can be specified when requesting predictions, instead of being specified at dataset upload. Upon uploading a dataset, DataRobot will calculate the range of dates available for use as a forecast point or for batch predictions. To that end, [Predictions](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Predictions) objects now also contain the following new fields: > forecast_point: The default point relative to which predictions will be generatedpredictions_start_date: The start date for bulk historical predictions.predictions_end_date: The end date for bulk historical predictions. Similar settings are provided as part of the [batch prediction API](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#batch-prediction-api) and the [real-time prediction API](https://docs.datarobot.com/en/docs/predictions/api/dr-predapi.html#making-predictions-with-time-series) to make predictions using deployed time series models. datarobot.models.BatchPredictionJob.score When setting up a time series project, input features could be identified as known-in-advance features. These features are not used to generate lags, and are expected to be known for the rows in the forecast window at predict time (e.g. “how much money will have been spent on marketing”, “is this a holiday”). Enough rows of historical data must be provided to cover the span of the effective Feature Derivation Window (which may be longer than the project’s Feature Derivation Window depending on the differencing settings chosen). The effective Feature Derivation Window of any model can be checked via the `effective_feature_derivation_window_start` and `effective_feature_derivation_window_end` attributes of a [DatetimeModel](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel). When uploading datasets to a time series project, the dataset might look something like the following, where “Time” is the datetime partition column, “Target” is the target column, and “Temp.” is an input feature. If the dataset was uploaded with a forecast point of “2017-01-08” and the effective feature derivation window start and end for the model are -5 and -3 and the forecast window start and end were set to 1 and 3, then rows 1 through 3 are historical data, row 6 is the forecast point, and rows 7 though 9 are forecast rows that will have predictions when predictions are computed. ``` Row, Time, Target, Temp. 1, 2017-01-03, 16443, 72 2, 2017-01-04, 3013, 72 3, 2017-01-05, 1643, 68 4, 2017-01-06, , 5, 2017-01-07, , 6, 2017-01-08, , 7, 2017-01-09, , 8, 2017-01-10, , 9, 2017-01-11, , ``` On the other hand, if the project instead used “Holiday” as an a priori input feature, the uploaded dataset might look like the following: ``` Row, Time, Target, Holiday 1, 2017-01-03, 16443, TRUE 2, 2017-01-04, 3013, FALSE 3, 2017-01-05, 1643, FALSE 4, 2017-01-06, , FALSE 5, 2017-01-07, , FALSE 6, 2017-01-08, , FALSE 7, 2017-01-09, , TRUE 8, 2017-01-10, , FALSE 9, 2017-01-11, , FALSE ``` ## Calendars You can upload a [calendar file](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.CalendarFile) containing a list of events relevant to your dataset. When provided, DataRobot automatically derives and creates time series features based on the calendar events (e.g., time until the next event, labeling the most recent event). The calendar file: - Should span the entire training data date range, as well as all future dates in which model will be forecasting. - Must be in csv or xlsx format with a header row. - Must have one date column which has values in the date-only format YYY-MM-DD (i.e., no hour, month, or second). - Can optionally include a second column that provides the event name or type. - Can optionally include a series ID column which specifies which series an event is applicable to. This column name must match the name of the column set as the series ID. Multiseries ID columns are used to add an ability to specify different sets of events for different series, e.g. holidays for different regions.Values of the series ID may be absent for specific events. This means that the event is valid for all series in project dataset (e.g. New Year’s Day is a holiday in all series in the example below).If a multiseries ID column is not provided, all listed events will be applicable to all series in the project dataset. - Cannot be updated in an active project. You must specify all future calendar events at project start. To update the calendar file, you will have to train a new project. An example of a valid calendar file: ``` Date, Name 2019-01-01, New Year's Day 2019-02-14, Valentine's Day 2019-04-01, April Fools 2019-05-05, Cinco de Mayo 2019-07-04, July 4th ``` An example of a valid multiseries calendar file: ``` Date, Name, Country 2019-01-01, New Year's Day, 2019-05-27, Memorial Day, USA 2019-07-04, July 4th, USA 2019-11-28, Thanksgiving, USA 2019-02-04, Constitution Day, Mexico 2019-03-18, Benito Juárez's birth, Mexico 2019-12-25, Christmas Day, ``` Once created, a calendar can be used with a time series project by specifying the `calendar_id` field in the [datarobot.DatetimePartitioningSpecification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioningSpecification) object for the project: ``` import datarobot as dr # create the project project = dr.Project.create('input_data.csv') # create the calendar calendar = dr.CalendarFile.create('calendar_file.csv') # specify the calendar_id in the partitioning specification datetime_spec = dr.DatetimePartitioningSpecification( use_time_series=True, datetime_partition_column='date' calendar_id=calendar.id ) # As of v3.0, can use ``Project.set_datetime_partitioning`` instead of passing the spec into ``Project.analyze_and_model`` via ``partitioning_method``. # The spec options can be passed individually: project.set_datetime_partitioning(use_time_series=True, datetime_partition_column='date', calendar_id=calendar.id) # Or the whole spec object can be passed: project.set_datetime_partitioning(datetime_partitioning_spec=datetime_spec) # start the project, specifying the partitioning method (if ``Project.set_datetime_partitioning`` was used there is no need to pass ``partitioning_method``) project.analyze_and_model( target='project target', partitioning_method=datetime_spec ) ``` As of version v2.23 it is possible to ask DataRobot to generate a calendar file for you using [CalendarFile.create_calendar_from_country_code](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.CalendarFile.create_calendar_from_country_code). This method allows you to provide a country code specifying which country’s holidays to use in generating the calendar, along with a start and end date indicating the bounds of the calendar. Allowed country codes can be retrieved using [CalendarFile.get_allowed_country_codes](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.CalendarFile.get_allowed_country_codes). See the following code block for example usage: ``` import datarobot as dr from datetime import datetime # create the project project = dr.Project.create('input_data.csv') # retrieve the allowed country codes and use the first one country_code = dr.CalendarFile.get_allowed_country_codes()[0]['code'] calendar = dr.CalendarFile.create_calendar_from_country_code( country_code, datetime(2018, 1, 1), datetime(2018, 7, 4) ) # specify the calendar_id in the partitioning specification datetime_spec = dr.DatetimePartitioningSpecification( use_time_series=True, datetime_partition_column='date' calendar_id=calendar.id ) # As of v3.0, can use ``Project.set_datetime_partitioning`` instead of passing the spec into ``Project.analyze_and_model`` via ``partitioning_method``. # The spec options can be passed individually: project.set_datetime_partitioning(use_time_series=True, datetime_partition_column='date', calendar_id=calendar.id) # Or the whole spec object can be passed: project.set_datetime_partitioning(datetime_partitioning_spec=datetime_spec) # Start the project, specifying the partitioning method (if ``Project.set_datetime_partitioning`` was used there is no need to pass ``partitioning_method``) project.analyze_and_model( target='project target', partitioning_method=datetime_spec ) ``` ## Datetime trend plots As a version v2.25, it is possible to retrieve Datetime Trend Plots for time series models to estimate the accuracy of the model. This includes Accuracy over Time and Forecast vs Actual for supervised projects, and Anomaly over Time for unsupervised projects. You can retrieve respective plots using following methods: - DatetimeModel.get_accuracy_over_time_plot - DatetimeModel.get_forecast_vs_actual_plot - DatetimeModel.get_anomaly_over_time_plot By default, the plots would be automatically computed when accessed via retrieval methods. You can compute Datetime Trend Plots separately using a common method [DatetimeModel.compute_datetime_trend_plots](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.compute_datetime_trend_plots). In addition, you can retrieve the respective detailed metadata for each plot type: - DatetimeModel.get_accuracy_over_time_plots_metadata - DatetimeModel.get_forecast_vs_actual_plots_metadata - DatetimeModel.get_anomaly_over_time_plots_metadata And the preview plots: - DatetimeModel.get_accuracy_over_time_plot_preview - DatetimeModel.get_forecast_vs_actual_plot_preview - DatetimeModel.get_anomaly_over_time_plot_preview ## Prediction intervals For each model, prediction intervals estimate the range of values DataRobot expects actual values of the target to fall within. They are similar to a confidence interval of a prediction, but are based on the residual errors measured during the backtesting for the selected model. Note that because calculation depends on the backtesting values, prediction intervals are not available for predictions on models that have not had all backtests completed. To that end, note that creating a prediction with prediction intervals through the API will automatically complete all backtests if they were not already completed. For start-end retrained models, the parent model will be used for backtesting. Additionally, prediction intervals are not available when the number of points per forecast distance is less than 10, due to insufficient data. In a prediction request, users can specify a prediction interval’s size, which specifies the desired probability of actual values falling within the interval range. Larger values are less precise, but more conservative. For example, specifying a size of 80 will result in a lower bound of 10% and an upper bound of 90%. More generally, for a specific `prediction_intervals_size`, the upper and lower bounds will be calculated as follows: - prediction_interval_upper_bound = 50% + ( prediction_intervals_size / 2) - prediction_interval_lower_bound = 50% - ( prediction_intervals_size / 2) Prediction intervals can be calculated for a [DatetimeModel](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel) using the [DatetimeModel.calculate_prediction_intervals](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.calculate_prediction_intervals) method. Users can also retrieve which intervals have already been calculated for the model using the [DatetimeModel.get_calculated_prediction_intervals](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_calculated_prediction_intervals) method. To view prediction intervals data for a prediction, the prediction needs to have been created using the [DatetimeModel.request_predictions](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.request_predictions) method and specifying `include_prediction_intervals = True`. The size for the prediction interval can be specified with the `prediction_intervals_size` parameter for the same function, and will default to 80 if left unspecified. Specifying either of these fields will result in prediction interval bounds being included in the retrieved prediction data for that request (see the [Predictions](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Predictions) class for retrieval methods). Note that if the specified interval size has not already been calculated, this request will automatically calculate the specified size. Prediction intervals are also supported for time series model deployments, and should be specified in deployment settings if desired. Use [Deployment.get_prediction_intervals_settings](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_prediction_intervals_settings) to retrieve current prediction intervals settings for a deployment, and [Deployment.update_prediction_intervals_settings](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_prediction_intervals_settings) to update prediction intervals settings for a deployment. ## Partial history predictions As of version v2.24 it is possible to ask DataRobot to allow to make predictions with incomplete historical data multiseries regression projects. To make predictions in regular project user has to provide enough data for the feature derivation. By setting the datetime partitioning attribute `allow_partial_history_time_series_predictions` to true ( [datarobot.DatetimePartitioningSpecification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioningSpecification) object), the project would be created that allow to make such predictions. The number of models are significantly smaller compared to regular multiseries model, but they are designed to make predictions on unseen series with reasonable accuracy. ## External baseline predictions As of version v2.26 it is possible to ask DataRobot to scale accuracy metric by external predictions. Users can upload data into a Dataset (see [Dataset documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/data/dataset.html#datasets)) and compare the external time series predictions with DataRobot models’ accuracy performance. To use the external predictions dataset in the autopilot, the dataset must be validated first (see [Project.validate_external_time_series_baseline](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.validate_external_time_series_baseline)). Once the dataset is validated, it can be used with a time series project by specifying `external_time_series_baseline_dataset_id` field in [AdvancedOptions](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.AdvancedOptions) and passes the advanced options to the project. See the following code block for example usage: ``` import datarobot as dr from datarobot.helpers import AdvancedOptions from datarobot.models import Dataset # create the project project = dr.Project.create('input_data.csv') # prepare datetime partitioning for external baseline validation datetime_spec = dr.DatetimePartitioningSpecification( use_time_series=True, datetime_partition_column='date', multiseries_id_columns=['series_id'], ) datetime_partitioning = dr.DatetimePartitioning.generate( project_id=project.id, spec=datetime_spec, target='target', ) # create external baseline prediction dataset from local file external_baseline_dataset = Dataset.create_from_file(file_path='external_predictions.csv') # validate the external baseline prediction dataset validation_info = project.validate_external_time_series_baseline( catalog_version_id=external_baseline_dataset.version_id, target='target', datetime_partitioning=datetime_partitioning, ) print( 'External baseline predictions passes validation check:', validation_info.is_external_baseline_dataset_valid ) # As of v3.0, can use ``Project.set_datetime_partitioning`` instead of passing the spec into ``Project.analyze_and_model`` via ``partitioning_method``. # The spec options can be passed individually: project.set_datetime_partitioning(use_time_series=True, datetime_partition_column='date', multiseries_id_columns=['series_id']) # Or the whole spec object can be passed: project.set_datetime_partitioning(datetime_partitioning_spec=datetime_spec) # As of v3.0, add the validated dataset version id into advanced options project.set_options( external_time_series_baseline_dataset_id=external_baseline_dataset.version_id ) # start the project, specifying the partitioning method (if ``Project.set_datetime_partitioning`` and ``Project.set_options`` were not used) project.analyze_and_model( target='target', partitioning_method=datetime_spec advanced_options=AdvancedOptions(external_time_series_baseline_dataset_id) ) ``` ## Time series data prep As of version v2.27 it is possible to prepare a dataset for time series modeling in the AI catalog using the API client. Users can upload unprepped modeling data into a Dataset (see [Dataset documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/data/dataset.html#datasets)) and the prep the dataset for time series modeling by aggregating data to a regular time step and filling gaps via a generated Spark SQL query in the AI catalog. Once the dataset is uploaded, the time series data prep query generator can be created using [DataEngineQueryGenerator.create](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.DataEngineQueryGenerator.create). As of version v3.1 convenience methods have been added to streamline the process of applying time series data prep for predictions. See the following code block for example usage: ``` import datarobot as dr from datarobot.models.data_engine_query_generator import ( QueryGeneratorDataset, QueryGeneratorSettings, ) from datetime import datetime # upload the dataset to the AI Catalog dataset = dr.Dataset.create_from_file('input_data.csv') # create a time series data prep query generator query_generator_dataset = QueryGeneratorDataset( alias='input_data_csv', dataset_id=dataset.id, dataset_version_id=dataset.version_id, ) query_generator_settings = QueryGeneratorSettings( datetime_partition_column="date", time_unit="DAY", time_step=1, default_numeric_aggregation_method="sum", default_categorical_aggregation_method="mostFrequent", target="y", multiseries_id_columns=["id"], default_text_aggregation_method="concat", start_from_series_min_datetime=True, end_to_series_max_datetime=True, ) query_generator = dr.DataEngineQueryGenerator.create( generator_type='TimeSeries', datasets = [query_generator_dataset], generator_settings=query_generator_settings, ) # prep the training dataset training_dataset = query_generator.create_dataset() # create a project project = dr.Project.create_from_dataset(training_dataset.id, project_name='prepped_dataset') # set up datetime partitioning, target, and train model(s) partitioning_spec = dr.DatetimePartitioningSpecification( datetime_partition_column='date', use_time_series=True ) project.analyze_and_model(target='y', mode='manual', partitioning_method=partitioning_spec) blueprints = project.get_blueprints() model_job = project.train_datetime(blueprints[0].id) model = model_job.get_result_when_complete() # query generator can be retrieved from the project if necessary # query_generator = dr.DataEngineQueryGenerator.get(project.query_generator_id) # prep and upload a prediction dataset to the project prediction_dataset = query_generator.prepare_prediction_dataset( 'prediction_data.csv', project.id ) # make predictions within the project # Either forecast point or predictions start/end dates must be specified model.request_predictions(prediction_dataset.id, forecast_point=datetime(2023, 1, 1)) # query generator can be retrieved from a deployed model via project if necessary # deployment = dr.Deployment.get(deployment_id) # project = dr.Project.get(deployment.model['project_id']) # query_generator = dr.DataEngineQueryGenerator.get(project.query_generator_id) # Deploy the model prediction_servers = dr.PredictionServer.list() deployment = dr.Deployment.create_from_learning_model( model.id, 'prepped_deployment', default_prediction_server_id=prediction_servers[0].id ) # Make batch predictions from batch prediction job, supports localFile or dataset for intake # and all types for output timeseries_settings = {'type': 'forecast', 'forecast_point': datetime(2023, 1, 1)} intake_settings = {'type': 'localFile', 'file': 'prediction_data.csv'} output_settings = {'type': 'localFile', 'path': 'predictions_out.csv'} batch_predictions_job = dr.BatchPredictionJob.apply_time_series_data_prep_and_score( deployment, intake_settings, timeseries_settings, output_settings=output_settings ) ``` --- # Unsupervised projects (anomaly detection) URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/unsupervised_anomaly.html > Learn how to create and work with unsupervised anomaly detection projects in DataRobot. # Unsupervised projects (anomaly detection) When the data is not labelled and the problem can be interpreted either as anomaly detection or time series anomaly detection, projects in unsupervised mode become useful. ## Create unsupervised projects In order to create an unsupervised project set `unsupervised_mode` to `True` when setting the target. ``` import datarobot as dr project = Project.create('dataset.csv', project_name='unsupervised') project.analyze_and_model(unsupervised_mode=True) ``` ## Create time series unsupervised projects To create a time series unsupervised project pass `unsupervised_mode=True` to datetime partitioning creation and to project aim. The forecast window will be automatically set to nowcasting, i.e. forecast distance zero (FW = 0, 0). ``` import datarobot as dr project = Project.create('dataset.csv', project_name='unsupervised') spec = DatetimePartitioningSpecification('date', use_time_series=True, unsupervised_mode=True, feature_derivation_window_start=-4, feature_derivation_window_end=0) # this step is optional - preview the default partitioning which will be applied partitioning_preview = DatetimePartitioning.generate(project.id, spec) full_spec = partitioning_preview.to_specification() # As of v3.0, can use ``Project.set_datetime_partitioning`` and ``Project.list_datetime_partitioning_spec`` instead project.set_datetime_partitioning(datetime_partition_spec=spec) project.list_datetime_partitioning_spec() # If ``Project.set_datetime_partitioning`` was used there is no need to pass ``partitioning_method`` in ``Project.analyze_and_model`` project.analyze_and_model(unsupervised_mode=True, partitioning_method=full_spec) ``` ## Unsupervised project metrics In unsupervised projects, metrics are not used for the model optimization. Instead, they are used for the purpose of model ranking. There are two available unsupervised metrics – Synthetic AUC and synthetic LogLoss – both of which are calculated on artificially-labelled validation samples. ## Estimate accuracy of unsupervised anomaly detection datetime partitioned models For datetime partitioned unsupervised model you can retrieve the Anomaly over Time plot. To do so use [DatetimeModel.get_anomaly_over_time_plot](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_anomaly_over_time_plot). You can also retrieve the detailed metadata using [DatetimeModel.get_anomaly_over_time_plots_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_anomaly_over_time_plots_metadata), and the preview plot using [DatetimeModel.get_anomaly_over_time_plot_preview](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_anomaly_over_time_plot_preview). ## Explain unsupervised time series anomaly detection model predictions Within a timeseries unsupervised project for models supporting calculation of Shapley values, Anomaly Assessment insight can be computed to explain anomalies. Example 1: computation, retrieval and deletion of the anomaly assessment insight. ``` import datarobot as dr # Initialize Anomaly Assessment for the backtest 0, training subset and series "series1" model = dr.DatetimeModel.get(project_id, model_id) anomaly_assessment_record = model.initialize_anomaly_assessment(0, "training", "series1") # Get available Anomaly Assessment for the project and model all_records = model.get_anomaly_assessment_records() # Get most recent anomaly assessment explanations all_records[0].get_latest_explanations() # Get anomaly assessment explanations in the range all_records[0].get_explanations(start_date="2020-01-01", points_count=500) # Get anomaly assessment predictions preview all_records[0].get_predictions_preview() # Delete record all_records[0].delete() ``` Example 2: Find explanations for the anomalous regions (regions with maximum anomaly score >=0.6) for the multiseries project. Leave only explanations for the rows with anomaly score >= 0.5. ``` def collect_explanations(model, backtest, source, series_ids): for series in series_ids: try: model.initialize_anomaly_assessment(backtest, source, series) except ClientError: # when insight was already computed pass records_for_series = model.get_anomaly_assessment_records(source=source, backtest=backtest, with_data_only=True, limit=0) result = {} for record in records_for_series: preview = record.get_predictions_preview() anomalous_regions = preview.find_anomalous_regions(max_prediction_threshold=0.6) if anomalous_regions: result[record.series_id] = record.get_explanations_data_in_regions(anomalous_regions, prediction_threshold=0.5) return result import datarobot as dr model = dr.DatetimeModel.get(project_id, model_id) collect_explanations(model, 0, "validation", series_ids) ``` ## Assess unsupervised anomaly detection models on external test sets In unsupervised projects, if there is some labelled data, it may be used to assess anomaly detection models by checking computed classification metrics such as AUC and LogLoss, etc. and insights such as ROC and Lift. Such data is uploaded as a prediction dataset with a specified actual value column name, and, if it is a time series project, a prediction date range. The actual value column can contain only zeros and ones or True/False, and it should not have been seen during training time. ## Request external scores and insights (time series) There are two ways to specify an actual value column and compute scores and insights: 1. Upload a prediction dataset, specifyingpredictions_start_date,predictions_end_date, andactual_value_column, and request predictions on that dataset using a specific model. importdatarobotasdr# Upload datasetproject=dr.Project(project_id)dataset=project.upload_dataset('./data_to_predict.csv',predictions_start_date=datetime(2000,1,1),predictions_end_date=datetime(2015,1,1),actual_value_column='actuals')# run prediction job which also will calculate requested scores and insights.predict_job=model.request_predictions(dataset.id)# prediction output will have column with actualsresult=pred_job.get_result_when_complete() 2. Upload a prediction dataset without specifying any options, and request predictions for a specific model withpredictions_start_date,predictions_end_date, andactual_value_columnspecified. Note, these settings cannot be changed for the dataset after making predictions. ``` import datarobot as dr # Upload dataset project = dr.Project(project_id) dataset = project.upload_dataset('./data_to_predict.csv') # Check which columns are candidates for actual value columns dataset.detected_actual_value_columns [{'missing_count': 25, 'name': 'label_column'}] # run prediction job which also will calculate requested scores and insights. predict_job = model.request_predictions( dataset.id, predictions_start_date=datetime(2000, 1, 1), predictions_end_date=datetime(2015, 1, 1), actual_value_column='label_column' ) result = pred_job.get_result_when_complete() ``` ## Request external scores and insights for AutoML models To compute scores and insights on an external dataset for unsupervised AutoML models (Non Time series) Upload a prediction dataset that contains label column(s), request compute external test on one of `PredictionDataset.detected_actual_value_columns`. ``` import datarobot as dr # Upload dataset project = dr.Project(project_id) dataset = project.upload_dataset('./test_set.csv') dataset.detected_actual_value_columns >>>['label_column_1', 'label_column_2'] # request external test to compute metric scores and insights on dataset external_test_job = model.request_external_test(dataset.id, actual_value_column='label_column_1') # once job is complete, scores and insights are ready for retrieving external_test_job.wait_for_completion() ``` ## Retrieve external scores and insights Upon completion of prediction, external scores and insights can be retrieved to assess model performance. For unsupervised projects Lift Chart and ROC Curve are computed. If the dataset is too small insights will not be computed. If the actual value column contained only one class, the ROC Curve will not be computed. Information about the dataset can be retrieved using `PredictionDataset.get`. ``` import datarobot as dr # Check which columns are candidates for actual value columns scores_list = ExternalScores.list(project_id) scores = ExternalScores.get(project_id, dataset_id=dataset_id, model_id=model_id) lift_list = ExternalLiftChart.list(project_id, model_id) roc = ExternalRocCurve.get(project_id, model, dataset_id) # check dataset warnings, need to be called after predictions are computed. dataset = PredictionDataset.get(project_id, dataset_id) dataset.data_quality_warnings {'single_class_actual_value_column': True, 'insufficient_rows_for_evaluating_models': False, 'has_kia_missing_values_in_forecast_window': False} ``` --- # Unsupervised Projects (Clustering) URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/unsupervised_clustering.html > Learn how to create and work with unsupervised clustering projects in DataRobot. # Unsupervised Projects (Clustering) Use clustering when data is not labelled and the problem can be interpreted as grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). It is a common task in data exploration when finding groups and similarities is needed. ## Create unsupervised projects To create an unsupervised project, set `unsupervised_mode` to `True` when setting the target. To specify clustering, set `unsupervised_type` to `CLUSTERING`. When setting the modeling mode is required, clustering supports either `AUTOPILOT_MODE.COMPREHENSIVE` for DataRobot-run Autopilot or `AUTOPILOT_MODE.MANUAL` for user control of which models/parameters to use. Example: ``` from datarobot import Project from datarobot.enums import UnsupervisedTypeEnum from datarobot.enums import AUTOPILOT_MODE project = Project.create("dataset.csv", project_name="unsupervised clustering") project.analyze_and_model( unsupervised_mode=True, mode=AUTOPILOT_MODE.COMPREHENSIVE, unsupervised_type=UnsupervisedTypeEnum.CLUSTERING, ) ``` You can optionally specify list of explicit cluster numbers. To do this, pass a list of integer values to optional `autopilot_cluster_list` parameter using the `analyze_and_model()` method. ``` project.analyze_and_model( unsupervised_mode=True, mode=AUTOPILOT_MODE.COMPREHENSIVE, unsupervised_type=UnsupervisedTypeEnum.CLUSTERING, autopilot_cluster_list=[7, 9, 11, 15, 19], ) ``` You can also do both in one step using the `Project.start()` method. This method by default will use `AUTOPILOT_MODE.COMPREHENSIVE` mode. ``` from datarobot import Project from datarobot.enums import UnsupervisedTypeEnum project = Project.start( "dataset.csv", unsupervised_mode=True, project_name="unsupervised clustering project", unsupervised_type=UnsupervisedTypeEnum.CLUSTERING, ) ``` ## Unsupervised clustering project metric Unsupervised clustering projects use the `Silhouette Score` metric for model ranking (instead of using it for model optimization). It measures the average similarity of objects within a cluster and their distance to the other objects in the other clusters. ## Retrieve information about clusters In a trained model, you can retrieve information about clusters in along with standard model information. To do this, when training completes, retrieve a model and view basic clustering information: > n_clusters: number of clusters for modelis_n_clusters_dynamically_determined: how clustering model picks number of clusters Here is a code snippet to retrieve information about the number of clusters for model: ``` from datarobot import ClusteringModel model = ClusteringModel.get(project_id, model_id) print("{} clusters found".format(model.n_clusters)) ``` You can retrieve more details about clusters and their data using cluster insights. ## Work with cluster insights You can compute insights to gain deep insights into clusters and their characteristics. This process will perform calculations and return detailed information about each feature and its importance, as well as a detailed per-cluster breakdown. To compute and retrieve cluster insights, use the `ClusteringModel` and its `compute_insights` method. The method starts the cluster insights compute job, waits for its completion for the number of seconds specified in the optional parameter `max_wait` (default: 600), and returns results when insights are ready. If clusters are already computed, access them using the `insights` property of the `ClusteringModel` method. ``` from datarobot import ClusteringModel model = ClusteringModel.get(project_id, model_id) insights = model.compute_insights() ``` This call, with the specified `wait_time`, will run and wait for specified time: ``` from datarobot import ClusteringModel model = ClusteringModel.get(project_id, model_id) insights = model.compute_insights(max_wait=60) ``` If computation fails to finish before `max_wait` expires, the method will raise an `AsyncTimeoutError`. You can retrieve cluster insights after jobs computation finishes. To retrieve cluster insights already computed: ``` from datarobot import ClusteringModel model = ClusteringModel.get(project_id, model_id) for insight in model.insights: print(insight) ``` ## Work with clusters By default, DataRobot names clusters “Cluster 1”, “Cluster 2”, … , “Cluster N” . You can retrieve these names and alter them according to preference. When retrieving clusters before computing insights, clusters will contain only names. After insight computation completes, each cluster will also hold information about the percentage of data that is represented by the Cluster. For example: ``` from datarobot import ClusteringModel model = ClusteringModel.get(project_id, model_id) # helper function def print_summary(name, percent): if not percent: percent = "?" print("'{}' holds {} % of data".format(name, percent)) for cluster in model.clusters: print_summary(cluster.name, cluster.percent) model.compute_insights() for cluster in model.clusters: print_summary(cluster.name, cluster.percent) ``` For a model with three clusters, the code snippet will output: ``` 'Cluster 1' holds ? % of data 'Cluster 2' holds ? % of data 'Cluster 3' holds ? % of data -- Cluster insights computation finished -- 'Cluster 1' holds 27.1704180064 % of data 'Cluster 2' holds 36.9131832797 % of data 'Cluster 3' holds 35.9163987138 % of data ``` Use the following methods of `ClusteringModel` class to alter cluster names: - `update_cluster_names` - changes multiple cluster names using mapping in dictionary - `update_cluster_name` - changes one cluster name After update, each method will return a list of clusters with changed names. For example: ``` from datarobot import ClusteringModel model = ClusteringModel.get(project_id, model_id) # update multiple cluster_name_mappings = [ ("Cluster 1", "AAA"), ("Cluster 2", "BBB"), ("Cluster 3", "CCC") ] clusters = model.update_cluster_names(cluster_name_mappings) # update single clusters = model.update_cluster_name("CCC", "DDD") ``` ## Clustering classes reference ### ClusteringModel ### class datarobot.models.model.ClusteringModel ClusteringModel extends [Model](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model) class. It provides properties and methods specific to clustering projects. #### compute_insights(max_wait=600) Compute and retrieve cluster insights for model. This method awaits completion of job computing cluster insights and returns results after it is finished. If computation takes longer than specified `max_wait` exception will be raised. - Parameters: - project_id ( str ) – Project to start creation in. - model_id ( str ) – Project’s model to start creation in. - max_wait ( int ) – Maximum number of seconds to wait before giving up - Return type: List of ClusterInsight - Raises: - ClientError – Server rejected creation due to client error. Most likely cause is bad project_id or model_id . - AsyncFailureError – If any of the responses from the server are unexpected - AsyncProcessUnsuccessfulError – If the cluster insights computation has failed or was cancelled. - AsyncTimeoutError – If the cluster insights computation did not resolve in time #### property insights : List[ClusterInsight] Return actual list of cluster insights if already computed. - Return type: List of ClusterInsight #### property clusters : List[Cluster] Return actual list of Clusters. - Return type: List of Cluster #### update_cluster_names(cluster_name_mappings) Change many cluster names at once based on list of name mappings. - Parameters: cluster_name_mappings ( List of tuples ) – Cluster names mapping consisting of current cluster name and old cluster name. Example: ``` cluster_name_mappings = [ ("current cluster name 1", "new cluster name 1"), ("current cluster name 2", "new cluster name 2")] ``` #### update_cluster_name(current_name, new_name) Change cluster name from current_name to new_name. - Parameters: - current_name ( str ) – Current cluster name. - new_name ( str ) – New cluster name. - Return type: List of Cluster - Raises: datarobot.errors.ClientError – Server rejected update of cluster names. ### Cluster ### class datarobot.models.model.Cluster Representation of a single cluster. - Variables: - name ( str ) – Current cluster name - percent ( float ) – Percent of data contained in the cluster. This value is reported after cluster insights are computed for the model. #### classmethod list(project_id, model_id) Retrieve a list of clusters in the model. - Parameters: - project_id ( str ) – ID of the project that the model is part of. - model_id ( str ) – ID of the model. - Return type: List of clusters #### classmethod update_multiple_names(project_id, model_id, cluster_name_mappings) Update many clusters at once based on list of name mappings. - Parameters: - project_id ( str ) – ID of the project that the model is part of. - model_id ( str ) – ID of the model. - cluster_name_mappings(Listoftuples) – Cluster name mappings, consisting of current and previous names for each cluster. Example: cluster_name_mappings=[("current cluster name 1","new cluster name 1"),("current cluster name 2","new cluster name 2")] * Return type: List of clusters * Raises: * datarobot.errors.ClientError – Server rejected update of cluster names. * ValueError – Invalid cluster name mapping provided. #### classmethod update_name(project_id, model_id, current_name, new_name) Change cluster name from current_name to new_name - Parameters: - project_id ( str ) – ID of the project that the model is part of. - model_id ( str ) – ID of the model. - current_name ( str ) – Current cluster name - new_name ( str ) – New cluster name - Return type: List of Cluster ### ClusterInsight ### class datarobot.models.model.ClusterInsight Holds data on all insights related to feature as well as breakdown per cluster. - Parameters: - feature_name ( str ) – Name of a feature from the dataset. - feature_type ( str ) – Type of feature. - insights ( List[ClusterInsight] ) – List provides information regarding the importance of a specific feature in relation to each cluster. Results help understand how the model is grouping data and what each cluster represents. - feature_impact ( float ) – Impact of a feature ranging from 0 to 1. #### classmethod compute(project_id, model_id, max_wait=600) Starts creation of cluster insights for the model and if successful, returns computed ClusterInsights. This method allows calculation to continue for a specified time and if not complete, cancels the request. - Parameters: - project_id ( str ) – ID of the project to begin creation of cluster insights for. - model_id ( str ) – ID of the project model to begin creation of cluster insights for. - max_wait ( int ) – Maximum number of seconds to wait canceling the request. - Return type: List[ClusterInsight] - Raises: - ClientError – Server rejected creation due to client error. Most likely cause is bad project_id or model_id . - AsyncFailureError – Indicates whether any of the responses from the server are unexpected. - AsyncProcessUnsuccessfulError – Indicates whether the cluster insights computation failed or was cancelled. - AsyncTimeoutError – Indicates whether the cluster insights computation did not resolve within the specified time limit (max_wait). --- # Visual AI projects URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/visualai.html > Learn how to create and work with Visual AI projects using image data in DataRobot. # Visual AI projects With Visual AI, DataRobot allows you to use image data for modeling. You can create projects with one or multiple image features and also mix them with other DataRobot-supported feature types. You can find more information about [Visual AI](https://docs.datarobot.com/en/docs/modeling/special-workflows/visual-ai/index.html) in the Platform documentation. ## Create a Visual AI project DataRobot offers you different ways to prepare your dataset and to start a Visual AI project. The various ways to do this are covered in detail in the documentation, [Preparing the dataset](https://docs.datarobot.com/en/docs/modeling/special-workflows/visual-ai/vai-model.html#prepare-the-dataset). For the examples given here the images are partitioned into named directories. In the following, images are partitioned into named directories, which serve as labels for the project. For example, to predict on images of cat and dog breeds, labels could be abyssinian, american_bulldog, etc. ``` /home/user/data/imagedataset ├── abyssinian │   ├── abyssinian01.jpg │   ├── abyssinian02.jpg │   ├── … ├── american_bulldog │ ├── american_bulldog01.jpg │ ├── american_bulldog02.jpg │ ├── … ``` You then compress the directory containing the named directories into a ZIP file, creating the dataset used for the project. ``` from datarobot.models import Project, Dataset dataset = Dataset.create_from_file(file_path='/home/user/data/imagedataset.zip') project = Project.create_from_dataset(dataset.id, project_name='My Image Project') ``` ### Target Since this example uses named directories the target name must be `class`, which will contain the name of each directory in the ZIP file. ### Other parameters Setting modeling parameters, such as partitioning method, queue mode, etc, functions in the same way as starting a non-image project. ## Start modeling Once you have set modeling parameters, use the following code snippet to specify parameters and start the modeling process. ``` from datarobot import AUTOPILOT_MODE project.analyze_and_model(target='class', mode=AUTOPILOT_MODE.QUICK) ``` You can also pass optional parameters to `project.analyze_and_model` to change aspects of the modeling process. Some of those parameters include: - worker_count – int, sets the number of workers used for modeling. - partitioning_method – PartitioningMethod object. For a full reference of available parameters, see [Project.analyze_and_model](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.analyze_and_model). You can use the `mode` parameter to set the Autopilot mode.`AUTOPILOT_MODE.FULL_AUTO`, is the default, triggers modeling with no further actions necessary. Other accepted modes include `AUTOPILOT_MODE.MANUAL` for manual mode (choose your own models to run rather than running the full Autopilot) and `AUTOPILOT_MODE.QUICK` to run on a more limited set of models and get insights more quickly (“quick run”). ## Interact with a Visual AI project The following code snippets may be used to access Visual AI images and insights. ### List sample images Sample images allow you to see a subset of images, chosen by DataRobot, in the dataset. The returned `SampleImage` objects have an associated `target_value` that will allow you to categorize the images (abyssinian, american_bulldog, etc). Until you set the target and EDA2 has finished, the `target_value` will be `None`. ``` import io import PIL.Image from datarobot.models.visualai import SampleImage column_name = "image" number_of_images_to_show = 5 for sample in SampleImage.list(project.id, column_name)[:number_of_images_to_show]: # Display the image in the GUI bio = io.BytesIO(sample.image.image_bytes) img = PIL.Image.open(bio) img.show() ``` The results would be images such as: ### List duplicate images Duplicate images, images with different names but are determined by DataRobot to be the same, may exist in a dataset. If this happens, the code returns one of the images and the number of times it occurs in the dataset. ``` from datarobot.models.visualai import DuplicateImage column_name = "image" for duplicate in DuplicateImage.list(project.id, column_name): # To show an image see the previous sample image example print(f"Image id = {duplicate.image.id} has {duplicate.count} duplicates") ``` ### Activation maps Activation maps are overlaid on the images to show which image areas are driving model prediction decisions. Detailed explanations are available in DataRobot Platform documentation, [Model insights](https://docs.datarobot.com/en/docs/modeling/special-workflows/visual-ai/vai-insights.html). #### Compute activation maps To begin, you must first compute activation maps. The following snippet is an example of starting the computation for a Keras model in a Visual AI project. The `compute` method returns a URL that can be used to determine when the computation completes. ``` from datarobot.models.visualai import ImageActivationMap keras_model = project.get_models(search_params={'name': 'Keras'})[0] status_url = ImageActivationMap.compute(project.id, keras_model.id) print(status_url) ``` #### List activation maps After activation maps are computed, you can download them from the DataRobot server. The following snippet is an example of how to get the activation maps and how to plot them. ``` import PIL.Image from datarobot.models.visualai import ImageActivationMap column_name = "image" max_activation_maps = 5 keras_model = project.get_models(search_params={'name': 'Keras'})[0] for activation_map in ImageActivationMap.list(project.id, keras_model.id, column_name)[:max_activation_maps]: bio = io.BytesIO(activation_map.overlay_image.image_bytes) img = PIL.Image.open(bio) img.show() ``` ### Image embeddings Image embeddings allow you to get an impression on how similar two images look to a featurizer network. The embeddings project images from their high-dimensional feature space onto a 2D plane. The closer the images appear in this plane, the more similar they look to the featurizer. Detailed explanations are available in the DataRobot Platform documentation, [Model insights](https://docs.datarobot.com/en/docs/modeling/special-workflows/visual-ai/vai-insights.html). #### Compute image embeddings You must compute image embeddings before retrieving. The following snippet is an example of starting the computation for a Keras model in our Visual AI project. The `compute` method returns a URL that can be used to determine when the computation is complete. ``` from datarobot.models.visualai import ImageEmbedding keras_model = project.get_models(search_params={'name': 'Keras'})[0] status_url = ImageEmbedding.compute(project.id, keras_model.id) print(status_url) ``` #### List image embeddings After image embeddings are computed, you can download them from the DataRobot server. The following snippet is an example of how to get the embeddings for a model and plot them. ``` from matplotlib.offsetbox import OffsetImage, AnnotationBbox import matplotlib.pyplot as plt import numpy as np import PIL.Image from datarobot.models.visualai import ImageEmbedding column_name = "image" keras_model = project.get_models(search_params={'name': 'Keras'})[0] zoom = 0.15 fig, ax = plt.subplots(figsize=(15,10)) for image_embedding in ImageEmbedding.list(project.id, keras_model.id, column_name): image_bytes = image_embedding.image.image_bytes x_position = image_embedding.position_x y_position = image_embedding.position_y image = PIL.Image.open(io.BytesIO(image_bytes)) offset_image = OffsetImage(np.array(image), zoom=zoom) annotation_box = AnnotationBbox(offset_image, (x_position, y_position), xycoords='data', frameon=False) ax.add_artist(annotation_box) ax.update_datalim([(x_position, y_position)]) ax.autoscale() ax.grid(True) fig.show() ``` ### Image augmentation Image Augmentation is a processing step in the DataRobot blueprint that creates new images for training by randomly transforming existing images, thereby increasing the size of (i.e., “augmenting”) the training data. Detailed explanations are available in the DataRobot Platform documentation, [Creating augmented models](https://docs.datarobot.com/en/docs/modeling/special-workflows/visual-ai/tti-augment/ttia-introduction.html). #### Create image augmentation list To create image augmentation samples, you need to provide an image augmentation list. This list holds all information required to compute image augmentation samples. The following snippet shows how to create an image augmentation list. It is then used to compute image augmentation samples. ``` from datarobot.models.visualai import ImageAugmentationList blur_param = {"name": "maximum_filter_size", "currentValue": 10} blur = {"name": "blur", "params": [blur_param]} flip = {"name": "horizontal_flip", "params": []} image_augmentation_list = ImageAugmentationList.create( name="my blur and flip augmentation list", project_id=project.id, feature_name="image", transformation_probability=0.5, number_of_new_images=5, transformations=[blur, flip], ) print(image_augmentation_list) ``` #### List image augmentation lists You can retrieve all available augmentation lists for a project by project_id. ``` from datarobot.models.visualai import ImageAugmentationList image_augmentation_lists = ImageAugmentationList.list( project_id=project.id ) print(image_augmentation_lists) ``` #### Compute and retrieve image augmentation samples You must compute image augmentation samples before retrieving. To compute image augmentation sample, you will need an image augmentation list. This list holds all parameters and transformation information needed to compute samples. You can either create a new one or retrieve an existing one. The following snippet is an example of computing and retrieving image augmentation samples. It uses the previous snippet that creates an image augmentation list, but instead uses it to compute and retrieve image augmentation samples using the `compute_samples` method. ``` from datarobot.models.visualai import ImageAugmentationList, ImageAugmentationSample image_augmentation_list = ImageAugmentationList.get('') for sample in image_augmentation_list.compute_samples(): # Display the image in popup widows bio = io.BytesIO(sample.image.image_bytes) img = PIL.Image.open(bio) img.show() ``` #### List image augmentation samples If image augmentation samples were already computed instead of recomputing them we can retrieve the last sample that was computed for image augmentation list from DataRobot server. The following snippet is an example of how to get the image augmentation samples. ``` import io import PIL.Image from datarobot.models.visualai import ImageAugmentationList image_augmentation_list = ImageAugmentationList.get('') for sample in image_augmentation_list.retrieve_samples(): # Display the image in popup widows bio = io.BytesIO(sample.image.image_bytes) img = PIL.Image.open(bio) img.show() ``` #### Configure augmentations to use during training In order to automatically augment a dataset during training the DataRobot server will look for an augmentation list associated with the project that has the key `initial_list` set to `True`. An augmentation list like this can be created with the following code snippet. If it is created for the project before autopilot is started. It will be used to automatically augment the images in the training dataset. ``` from datarobot.models.visualai import ImageAugmentationList blur_param = {"name": "maximum_filter_size", "currentValue": 10} blur = {"name": "blur", "params": [blur_param]} flip = {"name": "horizontal_flip", "params": []} transforms_to_apply = ImageAugmentationList.create(name="blur and scale", project_id=project.id, feature_name='image', transformation_probability=0.5, number_of_new_images=5, transformations=[blur, flip], initial_list=True) ``` #### Determine available transformations for augmentations The Augmentation List in the example above supports horizontal flip and blur transformations, but DataRobot supports several other transformations. To retrieve the list of supported transformations use the `ImageAugmentationOptions` object as the example below shows. ``` from datarobot.models.visualai import ImageAugmentationOptions options = ImageAugmentationOptions.get(project.id) ``` #### Convert images to base64-encoded strings for predictions If your training dataset contained images, images in the prediction dataset need to be converted to a base64-encoded strings so it can be fully contained in the prediction request (for example, in a CSV file or JSON). For more detail, see: [Work with binary data](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/binary_data.html#binary-data) ### License For the examples here we used the [The Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) licensed under [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/) --- # Batch predictions URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/predictions/batch_predictions.html # Batch predictions The batch prediction API provides a way to score large datasets using flexible options for intake and output on the Prediction Servers you have already deployed. The main features are: - Flexible options for intake and output. - Stream local files and start scoring while still uploading and simultaneously downloading the results. - Score large datasets from and to S3. - Connect to your database using JDBC with bidirectional streaming of scoring data and results. - Intake and output options can be mixed and do not need to match. So scoring from a JDBC source to an S3 target is also an option. - Protection against overloading your prediction servers with the option to control the concurrency level for scoring. - Prediction explanations can be included (with the option to add thresholds). - Passthrough columns are supported to correlate scored data with source data. - You can include prediction warnings in the output. To interact with batch predictions, see the [BatchPredictionJob](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#batch-prediction-api) class. ## Make batch predictions with a deployment DataRobot provides a utility function to make batch predictions using a deployment: [Deployment.predict_batch](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.predict_batch). ``` import datarobot as dr deployment = dr.Deployment.get(deployment_id='5c939e08962d741e34f609f0') # To note: `source` can be a file path, a file or a pandas DataFrame prediction_results_as_dataframe = deployment.predict_batch( source="./my_local_file.csv", ) ``` ## Scoring local CSV files DataRobot provides a utility function for scoring to and from local CSV files: [BatchPredictionJob.score_to_file](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score_to_file). The first parameter can be either: - A path to a CSV dataset - A file-like object - A Pandas DataFrame For larger datasets, you should avoid using a DataFrame, as it loads the entire dataset into memory. The other options do not. ``` import datarobot as dr deployment_id = '5dc5b1015e6e762a6241f9aa' dr.BatchPredictionJob.score_to_file( deployment_id, './data_to_predict.csv', './predicted.csv', ) ``` The input file is streamed to DataRobot’s API and scoring starts immediately. As soon as results start coming in, they start to be downloaded. The entire call is blocked until the file has been scored. ## Scoring from and to S3 DataRobot provides a small utility function for scoring to and from CSV files hosted on S3: [BatchPredictionJob.score_s3](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score_s3). This requires that the intake and output buckets share the same credentials (see [Credentials](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#credentials-api-doc) and [Credential.create_s3](https://docs.datarobot.com/en/docs/api/reference/sdk/credentials.html#datarobot.models.Credential.create_s3)) or that their access policy is set to public: Note that the S3 output functionality has a limit of 100 GB. ``` import datarobot as dr deployment_id = '5dc5b1015e6e762a6241f9aa' cred = dr.Credential.get('5a8ac9ab07a57a0001be501f') job = dr.BatchPredictionJob.score_s3( deployment=deployment_id, source_url='s3://mybucket/data_to_predict.csv', destination_url='s3://mybucket/predicted.csv', credential=cred, ) ``` ## Scoring from and to Azure Cloud Storage DataRobot provides the same support for Azure through the utility function [BatchPredictionJob.score_azure](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score_azure). This requires that you add an Azure connection string to the DataRobot credentials store. (see [Credentials](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#credentials-api-doc) and [Credential.create_azure](https://docs.datarobot.com/en/docs/api/reference/sdk/credentials.html#datarobot.models.Credential.create_azure)) ``` import datarobot as dr deployment_id = '5dc5b1015e6e762a6241f9aa' cred = dr.Credential.get('5a8ac9ab07a57a0001be501f') job = dr.BatchPredictionJob.score_azure( deployment=deployment_id, source_url='https://mybucket.blob.core.windows.net/bucket/data_to_predict.csv', destination_url='https://mybucket.blob.core.windows.net/results/predicted.csv', credential=cred, ) ``` ## Scoring from and to Google Cloud Platform DataRobot provides the same support for GCP through the utility function [BatchPredictionJob.score_gcp](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score_gcp). It requires you to add a GCP connection string to the DataRobot credentials store. (See [Credentials](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#credentials-api-doc) and [Credential.create_gcp](https://docs.datarobot.com/en/docs/api/reference/sdk/credentials.html#datarobot.models.Credential.create_gcp).) ``` import datarobot as dr deployment_id = '5dc5b1015e6e762a6241f9aa' cred = dr.Credential.get('5a8ac9ab07a57a0001be501f') job = dr.BatchPredictionJob.score_gcp( deployment=deployment_id, source_url='gs:/bucket/data_to_predict.csv', destination_url='gs://results/predicted.csv', credential=cred, ) ``` ## Manually configure a batch prediction job If you can’t use any of the utilities above, you are also free to manually configure your job. This requires configuring an intake and output option. Credentials may be created with [Credentials API](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#credentials-api-doc). ``` import datarobot as dr deployment_id = '5dc5b1015e6e762a6241f9aa' dr.BatchPredictionJob.score( deployment_id, intake_settings={ 'type': 's3', 'url': 's3://public-bucket/data_to_predict.csv', 'credential_id': '5a8ac9ab07a57a0001be501f', }, output_settings={ 'type': 'localFile', 'path': './predicted.csv', }, ) ``` ### Supported intake types The following sections outline the supported intake types and describe their configuration parameters: #### Local file intake Local file intake requires you to pass either a path to a CSV dataset, a file-like object, or a Pandas DataFrame as the `file` parameter: ``` intake_settings={ 'type': 'localFile', 'file': './data_to_predict.csv', } ``` #### S3 CSV intake S3 CSV intake requires you to pass an S3 URL to the CSV file to be scored in the `url` parameter: ``` intake_settings={ 'type': 's3', 'url': 's3://public-bucket/data_to_predict.csv', } ``` If the bucket is not publicly accessible, you can supply AWS credentials using the following parameters: - aws_access_key_id - aws_secret_access_key - aws_session_token Save it to the [Credential API](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#s3-creds-usage): ``` import datarobot as dr # get to make sure it exists credential_id = '5a8ac9ab07a57a0001be501f' cred = dr.Credential.get(credential_id) intake_settings={ 'type': 's3', 'url': 's3://private-bucket/data_to_predict.csv', 'credential_id': cred.credential_id, } ``` #### JDBC intake JDBC intake requires you to create a [DataStore](https://docs.datarobot.com/en/docs/api/dev-learning/python/data/database_connectivity.html#database-connectivity-overview) and [Credential](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#basic-creds-usage) for your database: ``` # get to make sure it exists datastore_id = '5a8ac9ab07a57a0001be5010' data_store = dr.DataStore.get(datastore_id) credential_id = '5a8ac9ab07a57a0001be501f' cred = dr.Credential.get(credential_id) intake_settings = { 'type': 'jdbc', 'table': 'table_name', 'schema': 'public', # optional, if supported by database 'catalog': 'master', # optional, if supported by database 'data_store_id': data_store.id, 'credential_id': cred.credential_id, } ``` #### BigQuery intake BigQuery intake requires you to create a GCS [Credential](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#basic-creds-usage) for your database: ``` # get to make sure it exists credential_id = '5a8ac9ab07a57a0001be501f' cred = dr.Credential.get(credential_id) intake_settings = { 'type': 'bigquery', 'dataset': 'dataset_name', 'table': 'table_or_view_name', 'bucket': 'bucket_in_gcs', 'credential_id': cred.credential_id, } ``` #### AI Catalog intake AI Catalog intake requires you to create a [Dataset](https://docs.datarobot.com/en/docs/api/dev-learning/python/data/dataset.html#datasets) and identify the `dataset_id` to use as an input. ``` # get to make sure it exists dataset_id = '5a8ac9ab07a57a0001be501f' dataset = dr.Dataset.get(dataset_id) intake_settings={ 'type': 'dataset', 'dataset': dataset } ``` Or, if you want a `version_id` other than the latest, supply your own. ``` # get to make sure it exists dataset_id = '5a8ac9ab07a57a0001be501f' dataset = dr.Dataset.get(dataset_id) intake_settings={ 'type': 'dataset', 'dataset': dataset, 'dataset_version_id': 'another_version_id' } ``` #### Datasphere intake Datasphere intake requires you to create a [DataStore](https://docs.datarobot.com/en/docs/api/dev-learning/python/data/database_connectivity.html#database-connectivity-overview) and [Credential](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#basic-creds-usage) for your database: ``` # get to make sure it exists datastore_id = '5a8ac9ab07a57a0001be5011' data_store = dr.DataStore.get(datastore_id) credential_id = '5a8ac9ab07a57a0001be501f' cred = dr.Credential.get(credential_id) intake_settings = { 'type': 'datasphere', 'table': 'table_name', 'schema': 'DATASPHERE_SPACE_NAME', 'data_store_id': data_store.id, 'credential_id': cred.credential_id, } ``` ### Supported output types The sections below outline the supported output types and descriptions of their configuration parameters. #### Local file output For local file output, you have two options. 1. You can either pass a path parameter and have the client block and download the scored data concurrently. This is the fastest way to get predictions as it will upload, score, and download concurrently: ``` output_settings={ 'type': 'localFile', 'path': './predicted.csv', } ``` 1. Alternatively, leave out the parameter and subsequently call BatchPredictionJob.download . The BatchPredictionJob.score call will then return as soon as the upload is complete. If the job is not finished scoring, the call to [BatchPredictionJob.download](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.download) will start streaming the data that has been scored so far and block until more data is available. You can poll for job completion using [BatchPredictionJob.get_status](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.get_status) or use [BatchPredictionJob.wait_for_completion](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.wait_for_completion) to wait. ``` import datarobot as dr deployment_id = '5dc5b1015e6e762a6241f9aa' job = dr.BatchPredictionJob.score( deployment_id, intake_settings={ 'type': 'localFile', 'file': './data_to_predict.csv', }, output_settings={ 'type': 'localFile', }, ) job.wait_for_completion() with open('./predicted.csv', 'wb') as f: job.download(f) ``` #### S3 CSV output S3 CSV output requires you to pass an S3 URL to the CSV file where the scored data should be saved in the `url` parameter: ``` output_settings={ 'type': 's3', 'url': 's3://public-bucket/predicted.csv', } ``` Most likely, the bucket is not publicly accessible for writes, but you can supply AWS credentials using these parameters: - aws_access_key_id - aws_secret_access_key - aws_session_token Save it to the [Credential API](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#s3-creds-usage). Here is an example: ``` # get to make sure it exists credential_id = '5a8ac9ab07a57a0001be501f' cred = dr.Credential.get(credential_id) output_settings={ 'type': 's3', 'url': 's3://private-bucket/predicted.csv', 'credential_id': cred.credential_id, } ``` #### JDBC output Just as for the input, JDBC output requires you to create a [DataStore](https://docs.datarobot.com/en/docs/api/dev-learning/python/data/database_connectivity.html#database-connectivity-overview) and [Credential](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#basic-creds-usage) for your database, but for `output_settings` you also need to specify `statement_type`, which should be one of `datarobot.enums.AVAILABLE_STATEMENT_TYPES`: ``` # get to make sure it exists datastore_id = '5a8ac9ab07a57a0001be5010' data_store = dr.DataStore.get(datastore_id) credential_id = '5a8ac9ab07a57a0001be501f' cred = dr.Credential.get(credential_id) output_settings = { 'type': 'jdbc', 'table': 'table_name', 'schema': 'public', # optional, if supported by database 'catalog': 'master', # optional, if supported by database 'statement_type': 'insert', 'data_store_id': data_store.id, 'credential_id': cred.credential_id, } ``` #### BigQuery output Just as for the input, BigQuery requires you to create a GCS [Credential](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#basic-creds-usage) to access BigQuery: ``` # get to make sure it exists credential_id = '5a8ac9ab07a57a0001be501f' cred = dr.Credential.get(credential_id) output_settings = { 'type': 'bigquery', 'dataset': 'dataset_name', 'table': 'table_name', 'bucket': 'bucket_in_gcs', 'credential_id': cred.credential_id, } ``` #### Datasphere output Same as for the input, this requires you to create a [DataStore](https://docs.datarobot.com/en/docs/api/dev-learning/python/data/database_connectivity.html#database-connectivity-overview) and [Credential](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#basic-creds-usage) for your database: ``` # get to make sure it exists datastore_id = '5a8ac9ab07a57a0001be5010' data_store = dr.DataStore.get(datastore_id) credential_id = '5a8ac9ab07a57a0001be501f' cred = dr.Credential.get(credential_id) output_settings = { 'type': 'datasphere', 'table': 'table_name', 'schema': 'DATASPHERE_SPACE_NAME', 'data_store_id': data_store.id, 'credential_id': cred.credential_id, } ``` ## Copy a previously submitted job To submit a job using parameters from a job that was previously submitted, use [BatchPredictionJob.score_from_existing](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score_from_existing). The first parameter is the job ID of another job. ``` import datarobot as dr previously_submitted_job_id = '5dc5b1015e6e762a6241f9aa' dr.BatchPredictionJob.score_from_existing( previously_submitted_job_id, ) ``` ## Scoring an in-memory Pandas DataFrame When working with DataFrames, DataRobot provides a method for scoring the data without first writing it to a CSV file and subsequently reading the data back from a CSV file: `BatchPredictionJob.score_pandas `. This method also joins the computed predictions into the existing DataFrame. The first parameter is the deployment ID and the second is the DataFrame to score. ``` import datarobot as dr import pandas as pd deployment_id = '5dc5b1015e6e762a6241f9aa' df = pd.read_csv('testdata/titanic_predict.csv') job, df = dr.BatchPredictionJob.score_pandas(deployment_id, df) ``` The method returns a copy of the job status and the updated DataFrame with the predictions added. So your DataFrame will now contain the following extra columns: - Survived_1_PREDICTION - Survived_0_PREDICTION - Survived_PREDICTION - THRESHOLD - POSITIVE_CLASS - prediction_status ``` print(df) PassengerId Pclass Name ... Survived_PREDICTION THRESHOLD POSITIVE_CLASS 0 892 3 Kelly, Mr. James ... 0 0.5 1 1 893 3 Wilkes, Mrs. James (Ellen Needs) ... 1 0.5 1 2 894 2 Myles, Mr. Thomas Francis ... 0 0.5 1 3 895 3 Wirz, Mr. Albert ... 0 0.5 1 4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) ... 1 0.5 1 .. ... ... ... ... ... ... ... 413 1305 3 Spector, Mr. Woolf ... 0 0.5 1 414 1306 1 Oliva y Ocana, Dona. Fermina ... 0 0.5 1 415 1307 3 Saether, Mr. Simon Sivertsen ... 0 0.5 1 416 1308 3 Ware, Mr. Frederick ... 0 0.5 1 417 1309 3 Peter, Master. Michael J ... 1 0.5 1 [418 rows x 16 columns] ``` If you don’t want all of them or if you’re not happy with the names of the added columns, they can be modified using column remapping: ``` import datarobot as dr import pandas as pd deployment_id = '5dc5b1015e6e762a6241f9aa' df = pd.read_csv('testdata/titanic_predict.csv') job, df = dr.BatchPredictionJob.score_pandas( deployment_id, df, column_names_remapping={ 'Survived_1_PREDICTION': None, # discard column 'Survived_0_PREDICTION': None, # discard column 'Survived_PREDICTION': 'predicted', # rename column 'THRESHOLD': None, # discard column 'POSITIVE_CLASS': None, # discard column }, ) ``` Any column mapped to `None` will be discarded. Any column mapped to a string will be renamed. Any column not mentioned will be kept in the output untouched. Your DataFrame now contains the following extra columns: - predicted - prediction_status Refer to the documentation for [BatchPredictionJob.score](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score) to see the full range of available options. ## Batch prediction job definitions To submit a working Batch Prediction job, you must supply a variety of elements to the [datarobot.models.BatchPredictionJob.score()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score) request payload depending on what type of prediction is required. Additionally, you must consider the type of intake and output adapters used for a given job. Every time a new batch prediction is created, the same amount of information must be stored somewhere outside of DataRobot and resubmitted every time. #### NOTE The `name` parameter must be unique across your organization. If you attempt to create multiple definitions with the same name, the request will fail. If you wish to free up a name, you must first [datarobot.models.BatchPredictionJobDefinition.delete()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.delete) the existing definition before creating this one. Alternatively, you can just [datarobot.models.BatchPredictionJobDefinition.update()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.update) the existing definition with a new name. For example, a request could look like: ``` import datarobot as dr deployment_id = "5dc5b1015e6e762a6241f9aa" job = dr.BatchPredictionJob.score( deployment_id, intake_settings={ "type": "s3", "url": "s3://bucket/container/file.csv", "credential_id": "5dc5b1015e6e762a6241f9bb" }, output_settings={ "type": "s3", "url": "s3://bucket/container/output.csv", "credential_id": "5dc5b1015e6e762a6241f9bb" }, ) job.wait_for_completion() with open("./predicted.csv", "wb") as f: job.download(f) ``` ## Job definitions If your use case requires the same (or similar) type(s) of predictions to be made multiple times, you can choose to create a Job Definition of the batch prediction job and store it for future use. The method for creating job definitions is [datarobot.models.BatchPredictionJobDefinition.create()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.create), which includes the `enabled`, `name`, and `schedule` parameters. ``` >>> import datarobot as dr >>> job_spec = { ... "num_concurrent": 4, ... "deployment_id": "5dc5b1015e6e762a6241f9aa", ... "intake_settings": { ... "url": "s3://foobar/123", ... "type": "s3", ... "format": "csv", ... "credential_id": "5dc5b1015e6e762a6241f9bb" ... }, ... "output_settings": { ... "url": "s3://foobar/123", ... "type": "s3", ... "format": "csv", ... "credential_id": "5dc5b1015e6e762a6241f9bb" ... }, ...} >>> definition = BatchPredictionJobDefinition.create( ... enabled=False, ... batch_prediction_job=job_spec, ... name="some_definition_name", ... schedule=None ... ) >>> definition BatchPredictionJobDefinition(foobar) ``` ## Execute a job definition ### Manual job execution To submit a stored job definition for scoring, you can either do so on a scheduled basis, described below, or manually submit the definition ID using [datarobot.models.BatchPredictionJobDefinition.run_once()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.run_once): ``` >>> import datarobot as dr >>> definition = dr.BatchPredictionJobDefinition.get("5dc5b1015e6e762a6241f9aa") >>> job = definition.run_once() >>> job.wait_for_completion() ``` ### Scheduled job execution A scheduled batch prediction job works just like a regular batch prediction job, but instead DataRobot handles the execution of the job. In order to schedule the execution of a batch prediction job, a definition must first be created using [datarobot.models.BatchPredictionJobDefinition.create()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.create), or updated using [datarobot.models.BatchPredictionJobDefinition.update()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.update). In this case, `enabled` is set to `True` and a `schedule` payload is provided. Alternatively, use a shorthand version with [datarobot.models.BatchPredictionJobDefinition.run_on_schedule()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.run_on_schedule): ``` >>> import datarobot as dr >>> schedule = { ... "day_of_week": [ ... 1 ... ], ... "month": [ ... "*" ... ], ... "hour": [ ... 16 ... ], ... "minute": [ ... 0 ... ], ... "day_of_month": [ ... 1 ... ] ...} >>> definition = dr.BatchPredictionJob.get("5dc5b1015e6e762a6241f9aa") >>> job = definition.run_on_schedule(schedule) ``` If the created job was not enabled previously, this method will also enable it. ## The schedule payload The `schedule` payload defines at what intervals the job should run, which can be combined in various ways to construct complex scheduling terms if needed. In all of the elements in the objects, you can supply either an asterisk `["*"]` denoting “every” time denomination or an array of integers (e.g.`[1, 2, 3]`) to define a specific interval. #### The schedule payload elements | Key | Possible values | Example | Description | | --- | --- | --- | --- | | minute | ["*"] or [0 ... 59] | [15, 30, 45] | The job will run at these minute values for every hour of the day. | | hour | ["*"] or [0 ... 23] | [12,23] | The hour(s) of the day that the job will run. | | month | ["*"] or [1 ... 12] | ["jan"] | Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., “jan” or “october”).Months that are not compatible with day_of_month are ignored, for example {"day_of_month": [31], "month":["feb"]}. | | day_of_week | ["*"] or [0 ... 6] where (Sunday=0) | ["sun"] | The day(s) of the week that the job will run. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., “sunday”, “Sunday”, “sun”, or “Sun”, all map to [0]).NOTE: This field is additive with day_of_month, meaning the job will run both on the date specified by day_of_month and the day defined in this field. | | day_of_month | ["*"] or [1 ... 31] | [1, 25] | The date(s) of the month that the job will run. Allowed values are either [1 ... 31] or ["*"] for all days of the month.NOTE: This field is additive with day_of_week, meaning the job will run both on the date(s) defined in this field and the day specifiedby day_of_week (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If day_of_month is set to ["*"] and day_of_week is defined,the scheduler will trigger on every day of the month that matches day_of_week (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th).Invalid dates such as February 31st are ignored. | ### Disable a scheduled job Job definitions are only be executed by the scheduler if `enabled` is set to `True`. If you have a job definition that was previously running as a scheduled job, but should now be stopped, simply [datarobot.models.BatchPredictionJobDefinition.delete()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.delete) to remove it completely, or [datarobot.models.BatchPredictionJobDefinition.update()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.update) it with `enabled=False` if you want to keep the definition, but stop the scheduled job from executing at intervals. If a job is currently running, this will finish execution regardless. ``` >>> import datarobot as dr >>> definition = dr.BatchPredictionJobDefinition.get("5dc5b1015e6e762a6241f9aa") >>> definition.delete() ``` --- # Predictions URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/predictions/index.html # Predictions The following sections describe the components to making predictions in DataRobot: - Generate predictions : Initiate a prediction job with the Model.request_predictions() method. This method can use either a training dataset or predictions dataset for scoring. - Batch predictions : Score large sets of data with batch predictions. You can define jobs and their schedule. - Prediction API : Use DataRobot’s Prediction API . to make predictions on both a dedicated and/or a standalone prediction server. - Scoring Code : Qualifying models allow you to export Scoring Code and use DataRobot-generated models outside of the platform --- # Predict job URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/predictions/predict_job.html # Predictions Making predictions is an asynchronous process. This means that when starting predictions with [Model.request_predictions()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_predictions) you will receive a `PredictJob` object in return for tracking the process responsible for fulfilling your request. You can use this object to get information about the predictions generation process before it has finished and be rerouted to the predictions themselves when the process is finished. To do so, use the [PredictJob](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#predict-job-api) class. ## Start making predictions Before actually requesting predictions, you should upload the dataset you wish to predict via `Project.upload_dataset`. Previously uploaded datasets can be viewed using `Project.get_datasets`. When uploading the dataset you can provide the path to a local file, a file object, raw file content, a `pandas.DataFrame` object, or the URL to a publicly available dataset. To start predicting on new data using a finished model, use [Model.request_predictions()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_predictions). It creates a new prediction generation process and returns a `PredictJob` object tracking this process. With it, you can monitor an existing `PredictJob` and retrieve generated predictions when the corresponding `PredictJob` is finished. ``` import datarobot as dr project_id = '5506fcd38bd88f5953219da0' model_id = '5506fcd98bd88f1641a720a3' project = dr.Project.get(project_id) model = dr.Model.get( project=project_id, model_id=model_id, ) # As of v3.0, in addition to passing a ``dataset_id``, you can pass in a ``dataset``, ``file``, ``file_path`` or # ``dataframe`` to `Model.request_predictions`. predict_job = model.request_predictions(file_path='./data_to_predict.csv') # Alternative version uploading the dataset from a local path and passing it by its id dataset_from_path = project.upload_dataset('./data_to_predict.csv') predict_job = model.request_predictions(dataset_id=dataset_from_path.id) # Alternative version: upload the dataset as a file object and pass it by using its dataset id with open('./data_to_predict.csv') as data_to_predict: dataset_from_file = project.upload_dataset(data_to_predict) predict_job = model.request_predictions(dataset_id=dataset_from_file.id) # OR predict_job = model.request_predictions(dataset_id=dataset_from_file.id) ``` ## Listing Predictions Use `Predictions.list()` to return a list of predictions generated on a project: ``` import datarobot as dr predictions = dr.Predictions.list('58591727100d2b57196701b3') print(predictions) >>>[Predictions(prediction_id='5b6b163eca36c0108fc5d411', project_id='5b61bd68ca36c04aed8aab7f', model_id='5b61bd7aca36c05744846630', dataset_id='5b6b1632ca36c03b5875e6a0'), Predictions(prediction_id='5b6b2315ca36c0108fc5d41b', project_id='5b61bd68ca36c04aed8aab7f', model_id='5b61bd7aca36c0574484662e', dataset_id='5b6b1632ca36c03b5875e6a0'), Predictions(prediction_id='5b6b23b7ca36c0108fc5d422', project_id='5b61bd68ca36c04aed8aab7f', model_id='5b61bd7aca36c0574484662e', dataset_id='55b6b1632ca36c03b5875e6a0') ] ``` You can pass following parameters to filter the result: - model_id : A string used to filter returned predictions by model_id . - dataset_id : A string used to filter returned predictions by dataset_id . ## Get an existing PredictJob Use `PredictJob.get` method to retrieve an existing job. This will give you a `PredictJob` matching the latest status of the job if it has not completed. If predictions have finished building, `PredictJob.get` will raise a `PendingJobFinished` exception. ``` import time import datarobot as dr predict_job = dr.PredictJob.get( project_id=project_id, predict_job_id=predict_job_id, ) predict_job.status >>> 'queue' # wait for generation of predictions (in a very inefficient way) time.sleep(10 * 60) predict_job = dr.PredictJob.get( project_id=project_id, predict_job_id=predict_job_id, ) >>> dr.errors.PendingJobFinished # now the predictions are finished predictions = dr.PredictJob.get_predictions( project_id=project.id, predict_job_id=predict_job_id, ) ``` ## Get generated predictions After predictions are generated, use `PredictJob.get_predictions` to get newly-generated predictions. If predictions have not yet been finished, it will raise a `JobNotFinished` exception. ``` import datarobot as dr predictions = dr.PredictJob.get_predictions( project_id=project.id, predict_job_id=predict_job_id, ) ``` ## Retrieve results If you just want to get generated predictions from a `PredictJob`, use `PredictJob.get_result_when_complete`. This function polls the status of the predictions generation process until it has finished, and then will return predictions. ``` dataset = project.get_datasets()[0] predict_job = model.request_predictions(dataset.id) predictions = predict_job.get_result_when_complete() ``` ## Get previously generated predictions If you don’t have a `PredictJob`, there are two more ways to retrieve predictions from the `Predictions` interface: 1. Get all prediction rows as a pandas.DataFrame object: ``` import datarobot as dr preds = dr.Predictions.get("5b61bd68ca36c04aed8aab7f", prediction_id="5b6b163eca36c0108fc5d411") df = preds.get_all_as_dataframe() df_with_serializer = preds.get_all_as_dataframe(serializer='csv') ``` 1. Download all prediction rows to a file as a CSV: ``` import datarobot as dr preds = dr.Predictions.get("5b61bd68ca36c04aed8aab7f", prediction_id="5b6b163eca36c0108fc5d411") preds.download_to_csv('predictions.csv') preds.download_to_csv('predictions_with_serializer.csv', serializer='csv') ``` ### Training predictions The training predictions interface allows you to compute and retrieve out-of-sample predictions for a model using the original project dataset. The predictions can be computed for all the rows, or restricted to validation or holdout data. As the predictions generated will be out-of-sample, they can be expected to have different results than if the project dataset were re-uploaded as a prediction dataset. ## Quick reference Training predictions generation is an asynchronous process. This means that when starting predictions with [datarobot.models.Model.request_training_predictions()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_training_predictions) you will receive back a [datarobot.models.TrainingPredictionsJob](https://docs.datarobot.com/en/docs/api/reference/sdk/jobs.html#datarobot.models.TrainingPredictionsJob) for tracking the process responsible for fulfilling your request. Actual predictions may be obtained with the help of a [datarobot.models.training_predictions.TrainingPredictions](https://docs.datarobot.com/en/docs/api/reference/sdk/training_predictions.html#datarobot.models.training_predictions.TrainingPredictions) object returned as the result of the training predictions job. There are three ways to retrieve training predictions: 1. Iterate prediction rows one by one as named tuples: ```python import datarobot as dr # Calculate new training predictions on all dataset training_predictions_job = model.request_training_predictions(dr.enums.DATA_SUBSET.ALL) training_predictions = training_predictions_job.get_result_when_complete() # Fetch rows from API and print them for prediction in training_predictions.iterate_rows(batch_size=250): print(prediction.row_id, prediction.prediction) ``` 1. Get all prediction rows as a pandas.DataFrame object: ``` import datarobot as dr # Calculate new training predictions on holdout partition of dataset training_predictions_job = model.request_training_predictions(dr.enums.DATA_SUBSET.HOLDOUT) training_predictions = training_predictions_job.get_result_when_complete() # Fetch training predictions as data frame dataframe = training_predictions.get_all_as_dataframe() ``` 1. Download all prediction rows to a file as a CSV document: ``` import datarobot as dr # Calculate new training predictions on all dataset training_predictions_job = model.request_training_predictions(dr.enums.DATA_SUBSET.ALL) training_predictions = training_predictions_job.get_result_when_complete() # Fetch training predictions and save them to file training_predictions.download_to_csv('my-training-predictions.csv') ``` --- # Python code examples URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/index.html > Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows. # Python code examples The API user guide includes overviews and workflows for DataRobot's Python client that outline complete examples of common data science and machine learning workflows. Be sure to review the [API quickstart guide]api-quickstart before using the notebooks below. | Topic | Describes... | | --- | --- | | Modeling and insights examples | Code examples that focus on the model building process and the insights you can generate for models. | | Prediction code examples | Code examples that outline various prediction methods. | | Feature selection examples | Notebooks that outline Feature Importance Rank Ensembling (FIRE) and advanced feature selection with Python. | | Pulumi code examples | How to perform common DataRobot tasks by using Pulumi. | --- # Generate advanced model insights URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/adv-insights.html # Generate advanced model insights This notebook explores model insights available for DataRobot's Python client. You can download this notebook using the icon in the top right of the page. Download the dataset used in this notebook [here](https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/10k_diabetes.csv). ## Setup ### Import libraries Import the libraries in the following snippet. Some of these will assist with the presentation of model insights. ``` %matplotlib inline import datarobot as dr from datarobot.enums import AUTOPILOT_MODE from datarobot.errors import ClientError import matplotlib.pyplot as plt import matplotlib.ticker as mtick import numpy as np import pandas as pd ``` ### Configure DataRobot API authentication Read more about different options for [connecting to DataRobot API from the client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` # If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call # dr.Client(config_path='path-to-drconfig.yaml') ``` ### Import data ``` data_path = "10k_diabetes.csv" df = pd.read_csv(data_path) df.head() ``` | | race | gender | age | weight | admission_type_id | discharge_disposition_id | admission_source_id | time_in_hospital | payer_code | medical_specialty | ... | glipizide_metformin | glimepiride_pioglitazone | metformin_rosiglitazone | metformin_pioglitazone | change | diabetesMed | readmitted | diag_1_desc | diag_2_desc | diag_3_desc | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0 | Caucasian | Female | [50-60) | ? | Elective | Discharged to home | Physician Referral | 1 | CP | Surgery-Neuro | ... | No | No | No | No | No | No | False | Spinal stenosis in cervical region | Spinal stenosis in cervical region | Effusion of joint, site unspecified | | 1 | Caucasian | Female | [20-30) | [50-75) | Urgent | Discharged to home | Physician Referral | 2 | UN | ? | ... | No | No | No | No | No | No | False | First-degree perineal laceration, unspecified ... | Diabetes mellitus of mother, complicating preg... | Sideroblastic anemia | | 2 | Caucasian | Male | [80-90) | ? | Not Available | Discharged/transferred to home with home healt... | NaN | 7 | MC | Family/GeneralPractice | ... | No | No | No | No | No | Yes | True | Pneumococcal pneumonia [Streptococcus pneumoni... | Congestive heart failure, unspecified | Hyperosmolality and/or hypernatremia | | 3 | AfricanAmerican | Female | [50-60) | ? | Emergency | Discharged to home | Transfer from another health care facility | 4 | UN | ? | ... | No | No | No | No | No | Yes | False | Cellulitis and abscess of face | Streptococcus infection in conditions classifi... | Diabetes mellitus without mention of complicat... | | 4 | AfricanAmerican | Female | [50-60) | ? | Emergency | Discharged to home | Emergency Room | 5 | ? | Psychiatry | ... | No | No | No | No | Ch | Yes | False | Bipolar I disorder, single manic episode, unsp... | Diabetes mellitus without mention of complicat... | Depressive type psychosis | 5 rows × 51 columns ## Modeling ### Create a project Create a new project using the `10K_diabetes.csv` dataset, containing the target `readmitted` (framed as a binary classification problem). ``` project = dr.Project.create(data_path, project_name="10K Diabetes Adv Modeling") print("Project ID: {}".format(project.id)) ``` ``` Project ID: 635c2f3ba5c95929466f3cb7 ``` ### Start Autopilot ``` project.analyze_and_model( target="readmitted", worker_count=-1, ) ``` ``` Project(10K Diabetes Adv Modeling) ``` ``` project.wait_for_autopilot() ``` ``` In progress: 14, queued: 0 (waited: 0s) In progress: 14, queued: 0 (waited: 1s) In progress: 14, queued: 0 (waited: 1s) In progress: 14, queued: 0 (waited: 2s) In progress: 14, queued: 0 (waited: 3s) In progress: 14, queued: 0 (waited: 5s) In progress: 11, queued: 0 (waited: 9s) In progress: 10, queued: 0 (waited: 16s) In progress: 6, queued: 0 (waited: 29s) In progress: 1, queued: 0 (waited: 49s) In progress: 7, queued: 0 (waited: 70s) In progress: 1, queued: 0 (waited: 90s) In progress: 16, queued: 0 (waited: 111s) In progress: 10, queued: 0 (waited: 131s) In progress: 6, queued: 0 (waited: 151s) In progress: 2, queued: 0 (waited: 172s) In progress: 0, queued: 0 (waited: 192s) In progress: 5, queued: 0 (waited: 213s) In progress: 1, queued: 0 (waited: 233s) In progress: 4, queued: 0 (waited: 253s) In progress: 1, queued: 0 (waited: 274s) In progress: 1, queued: 0 (waited: 294s) In progress: 0, queued: 0 (waited: 315s) In progress: 0, queued: 0 (waited: 335s) ``` ### Get the top-performing model ``` model = project.get_top_model() ``` ## Model insights The following sections outline the various model insights DataRobot has to offer. Before proceeding, set color constants to replicate the visual style of DataRobot. ``` dr_dark_blue = "#08233F" dr_blue = "#1F77B4" dr_orange = "#FF7F0E" dr_red = "#BE3C28" ``` ### Feature Impact [Feature Impact](https://docs.datarobot.com/en/docs/modeling/analyze-models/understand/feature-impact.html) measures how important a feature is in the context of a model. It measures how much the accuracy of a model would decrease if that feature was removed. Feature Impact is available for all model types and works by altering input data and observing the effect on a model’s score. It is an on-demand feature, meaning that you must initiate a calculation to see the results. Once DataRobot computes the feature impact for a model, that information is saved with the project. ``` feature_impacts = model.get_or_request_feature_impact() ``` ``` # Formats the ticks from a float into a percent percent_tick_fmt = mtick.PercentFormatter(xmax=1.0) impact_df = pd.DataFrame(feature_impacts) impact_df.sort_values(by="impactNormalized", ascending=True, inplace=True) # Positive values are blue, negative are red bar_colors = impact_df.impactNormalized.apply(lambda x: dr_red if x < 0 else dr_blue) ax = impact_df.plot.barh( x="featureName", y="impactNormalized", legend=False, color=bar_colors, figsize=(10, 14) ) ax.xaxis.set_major_formatter(percent_tick_fmt) ax.xaxis.set_tick_params(labeltop=True) ax.xaxis.grid(True, alpha=0.2) ax.set_facecolor(dr_dark_blue) plt.ylabel("") plt.xlabel("Effect") plt.xlim((None, 1)) # Allow for negative impact plt.title("Feature Impact", y=1.04) ``` ``` Text(0.5, 1.04, 'Feature Impact') ``` ### Histogram The [histogram](https://docs.datarobot.com/en/docs/data/analyze-data/histogram.html#histogram-chart) chart "buckets" numeric feature values into equal-sized ranges to show frequency distribution of the variable—the target observation (Y-axis) plotted against the frequency of the value (X-axis). The height of each bar represents the number of rows with values in that range. The helper function below, `matplotlib_pair_histogram`, is used to draw histograms paired with the project's target feature ( `readamitted` in this case). The function includes an orange line in every histogram bin that indicates the average target feature value for rows in that bin. ``` def matplotlib_pair_histogram(labels, counts, target_avgs, bin_count, ax1, feature): # Rotate categorical labels if feature.feature_type in ["Categorical", "Text"]: ax1.tick_params(axis="x", rotation=45) ax1.set_ylabel(feature.name, color=dr_blue) ax1.bar(labels, counts, color=dr_blue) # Instantiate a second axes that shares the same x-axis ax2 = ax1.twinx() ax2.set_ylabel(target_feature_name, color=dr_orange) ax2.plot(labels, target_avgs, marker="o", lw=1, color=dr_orange) ax1.set_facecolor(dr_dark_blue) title = "Histogram for {} ({} bins)".format(feature.name, bin_count) ax1.set_title(title) ``` The next function, `draw_feature_histogram`, gets the histogram data and draws the histogram using the previous helper function. Before using the function, you can retrieve downsampled histogram data using the snippet below: ``` feature = dr.Feature.get(project.id, "num_lab_procedures") feature.get_histogram(bin_limit=6).plot ``` ``` [{'label': '1.0', 'count': 755, 'target': 0.36026490066225164}, {'label': '14.5', 'count': 895, 'target': 0.3240223463687151}, {'label': '28.0', 'count': 1875, 'target': 0.3744}, {'label': '41.5', 'count': 2159, 'target': 0.38490041685965726}, {'label': '55.0', 'count': 1603, 'target': 0.45414847161572053}, {'label': '68.5', 'count': 557, 'target': 0.5080789946140036}] ``` For best accuracy, DataRobot recommends using divisors of 60 for `bin_limit`. Any value less than or equal to 60 can be used. The `target` values are project target input average values for a given bin. ``` def draw_feature_histogram(feature_name, bin_count): feature = dr.Feature.get(project.id, feature_name) # Retrieve downsampled histogram data from server # based on desired bin count data = feature.get_histogram(bin_count).plot labels = [row["label"] for row in data] counts = [row["count"] for row in data] target_averages = [row["target"] for row in data] f, axarr = plt.subplots() f.set_size_inches((10, 4)) matplotlib_pair_histogram(labels, counts, target_averages, bin_count, axarr, feature) ``` Lastly, specify the feature name, target, and desired bin count to create the feature histograms. You can view an example below: ``` feature_name = "num_lab_procedures" target_feature_name = "readmitted" draw_feature_histogram("num_lab_procedures", 12) ``` Categorical and other feature types are supported as well: ``` feature_name = "medical_specialty" draw_feature_histogram("medical_specialty", 10) ``` ### Lift Chart A [lift chart](https://docs.datarobot.com/en/docs/modeling/analyze-models/evaluate/lift-chart.html#lift-chart) shows you how close model predictions are to the actual values of the target in the training data. The lift chart data includes the average predicted value and the average actual values of the target, sorted by the prediction values in ascending order and split into up to 60 bins. ``` lc = model.get_lift_chart("validation") lc ``` ``` LiftChart(validation) ``` ``` bins_df = pd.DataFrame(lc.bins) bins_df.head() ``` | | actual | predicted | bin_weight | | --- | --- | --- | --- | | 0 | 0.000000 | 0.076155 | 27.0 | | 1 | 0.148148 | 0.117283 | 27.0 | | 2 | 0.076923 | 0.146873 | 26.0 | | 3 | 0.148148 | 0.168664 | 27.0 | | 4 | 0.111111 | 0.182873 | 27.0 | The following snippet defines functions for rebinning and plotting. ``` def rebin_df(raw_df, number_of_bins): cols = ["bin", "actual_mean", "predicted_mean", "bin_weight"] new_df = pd.DataFrame(columns=cols) current_prediction_total = 0 current_actual_total = 0 current_row_total = 0 x_index = 1 bin_size = 60 / number_of_bins for rowId, data in raw_df.iterrows(): current_prediction_total += data["predicted"] * data["bin_weight"] current_actual_total += data["actual"] * data["bin_weight"] current_row_total += data["bin_weight"] if (rowId + 1) % bin_size == 0: x_index += 1 bin_properties = { "bin": ((round(rowId + 1) / 60) * number_of_bins), "actual_mean": current_actual_total / current_row_total, "predicted_mean": current_prediction_total / current_row_total, "bin_weight": current_row_total, } new_df = new_df.append(bin_properties, ignore_index=True) current_prediction_total = 0 current_actual_total = 0 current_row_total = 0 return new_df def matplotlib_lift(bins_df, bin_count, ax): grouped = rebin_df(bins_df, bin_count) ax.plot(range(1, len(grouped) + 1), grouped["predicted_mean"], marker="+", lw=1, color=dr_blue) ax.plot(range(1, len(grouped) + 1), grouped["actual_mean"], marker="*", lw=1, color=dr_orange) ax.set_xlim([0, len(grouped) + 1]) ax.set_facecolor(dr_dark_blue) ax.legend(loc="best") ax.set_title("Lift chart {} bins".format(bin_count)) ax.set_xlabel("Sorted Prediction") ax.set_ylabel("Value") return grouped ``` Note that while this method works for any bin count less then 60, the most reliable result can be achieved when the number of bins is a divisor of 60. Additionally, this visualization method does not work for a bin count greater than 60 because DataRobot does not provide enough information for a larger resolution. ``` bin_counts = [10, 12, 15, 20, 30, 60] f, axarr = plt.subplots(len(bin_counts)) f.set_size_inches((8, 4 * len(bin_counts))) rebinned_dfs = [] for i in range(len(bin_counts)): rebinned_dfs.append(matplotlib_lift(bins_df, bin_counts[i], axarr[i])) plt.tight_layout() ``` ``` No handles with labels found to put in legend. No handles with labels found to put in legend. No handles with labels found to put in legend. No handles with labels found to put in legend. No handles with labels found to put in legend. No handles with labels found to put in legend. ``` ### Rebinned Data You can retrieve raw re-binned data for use in third-party tools or for additional evaluation ``` for rebinned in rebinned_dfs: print("Number of bins: {}".format(len(rebinned.index))) print(rebinned) ``` ``` Number of bins: 10 bin actual_mean predicted_mean bin_weight 0 1.0 0.13750 0.159916 160.0 1 2.0 0.17500 0.233332 160.0 2 3.0 0.27500 0.276564 160.0 3 4.0 0.28750 0.317841 160.0 4 5.0 0.41250 0.355449 160.0 5 6.0 0.33750 0.394435 160.0 6 7.0 0.49375 0.436481 160.0 7 8.0 0.54375 0.490176 160.0 8 9.0 0.62500 0.559797 160.0 9 10.0 0.68125 0.697142 160.0 Number of bins: 12 bin actual_mean predicted_mean bin_weight 0 1.0 0.134328 0.151886 134.0 1 2.0 0.180451 0.220872 133.0 2 3.0 0.210526 0.259316 133.0 3 4.0 0.313433 0.294237 134.0 4 5.0 0.293233 0.327699 133.0 5 6.0 0.413534 0.358398 133.0 6 7.0 0.353383 0.390993 133.0 7 8.0 0.440299 0.425269 134.0 8 9.0 0.556391 0.465567 133.0 9 10.0 0.556391 0.515761 133.0 10 11.0 0.609023 0.583067 133.0 11 12.0 0.701493 0.712181 134.0 Number of bins: 15 bin actual_mean predicted_mean bin_weight 0 1.0 0.084112 0.142650 107.0 1 2.0 0.177570 0.206029 107.0 2 3.0 0.207547 0.241613 106.0 3 4.0 0.271028 0.269917 107.0 4 5.0 0.308411 0.297614 107.0 5 6.0 0.264151 0.324330 106.0 6 7.0 0.420561 0.349149 107.0 7 8.0 0.367925 0.374717 106.0 8 9.0 0.336449 0.400959 107.0 9 10.0 0.485981 0.428771 107.0 10 11.0 0.518868 0.460771 106.0 11 12.0 0.551402 0.500419 107.0 12 13.0 0.603774 0.543591 106.0 13 14.0 0.635514 0.610431 107.0 14 15.0 0.719626 0.730594 107.0 Number of bins: 20 bin actual_mean predicted_mean bin_weight 0 1.0 0.0500 0.132253 80.0 1 2.0 0.2250 0.187579 80.0 2 3.0 0.1750 0.221244 80.0 3 4.0 0.1750 0.245419 80.0 4 5.0 0.2500 0.266226 80.0 5 6.0 0.3000 0.286902 80.0 6 7.0 0.3375 0.308215 80.0 7 8.0 0.2375 0.327466 80.0 8 9.0 0.4250 0.346325 80.0 9 10.0 0.4000 0.364573 80.0 10 11.0 0.3625 0.384512 80.0 11 12.0 0.3125 0.404358 80.0 12 13.0 0.4875 0.425218 80.0 13 14.0 0.5000 0.447743 80.0 14 15.0 0.5875 0.474525 80.0 15 16.0 0.5000 0.505826 80.0 16 17.0 0.6250 0.536862 80.0 17 18.0 0.6250 0.582731 80.0 18 19.0 0.6250 0.640753 80.0 19 20.0 0.7375 0.753532 80.0 Number of bins: 30 bin actual_mean predicted_mean bin_weight 0 1.0 0.037037 0.117812 54.0 1 2.0 0.132075 0.167957 53.0 2 3.0 0.245283 0.194772 53.0 3 4.0 0.111111 0.217077 54.0 4 5.0 0.264151 0.234340 53.0 5 6.0 0.150943 0.248885 53.0 6 7.0 0.259259 0.262677 54.0 7 8.0 0.283019 0.277293 53.0 8 9.0 0.283019 0.289984 53.0 9 10.0 0.333333 0.305103 54.0 10 11.0 0.226415 0.317688 53.0 11 12.0 0.301887 0.330972 53.0 12 13.0 0.415094 0.343545 53.0 13 14.0 0.425926 0.354649 54.0 14 15.0 0.396226 0.368169 53.0 15 16.0 0.339623 0.381265 53.0 16 17.0 0.314815 0.394318 54.0 17 18.0 0.358491 0.407725 53.0 18 19.0 0.452830 0.422268 53.0 19 20.0 0.518519 0.435153 54.0 20 21.0 0.509434 0.452046 53.0 21 22.0 0.528302 0.469495 53.0 22 23.0 0.641509 0.489711 53.0 23 24.0 0.462963 0.510929 54.0 24 25.0 0.641509 0.530756 53.0 25 26.0 0.566038 0.556426 53.0 26 27.0 0.666667 0.591609 54.0 27 28.0 0.603774 0.629608 53.0 28 29.0 0.698113 0.676879 53.0 29 30.0 0.740741 0.783314 54.0 Number of bins: 60 bin actual_mean predicted_mean bin_weight 0 1.0 0.037037 0.097886 27.0 1 2.0 0.037037 0.137739 27.0 2 3.0 0.076923 0.162243 26.0 3 4.0 0.185185 0.173459 27.0 4 5.0 0.333333 0.188488 27.0 5 6.0 0.153846 0.201298 26.0 6 7.0 0.148148 0.213213 27.0 7 8.0 0.074074 0.220940 27.0 8 9.0 0.307692 0.229899 26.0 9 10.0 0.222222 0.238617 27.0 10 11.0 0.111111 0.245402 27.0 11 12.0 0.192308 0.252501 26.0 12 13.0 0.259259 0.258865 27.0 13 14.0 0.259259 0.266489 27.0 14 15.0 0.230769 0.273597 26.0 15 16.0 0.333333 0.280852 27.0 16 17.0 0.333333 0.286678 27.0 17 18.0 0.230769 0.293418 26.0 18 19.0 0.259259 0.301547 27.0 19 20.0 0.407407 0.308660 27.0 20 21.0 0.346154 0.314679 26.0 21 22.0 0.111111 0.320585 27.0 22 23.0 0.307692 0.327277 26.0 23 24.0 0.296296 0.334530 27.0 24 25.0 0.407407 0.340926 27.0 25 26.0 0.423077 0.346264 26.0 26 27.0 0.444444 0.351782 27.0 27 28.0 0.407407 0.357515 27.0 28 29.0 0.461538 0.364479 26.0 29 30.0 0.333333 0.371723 27.0 30 31.0 0.407407 0.378530 27.0 31 32.0 0.269231 0.384105 26.0 32 33.0 0.407407 0.390886 27.0 33 34.0 0.222222 0.397751 27.0 34 35.0 0.461538 0.403918 26.0 35 36.0 0.259259 0.411391 27.0 36 37.0 0.481481 0.419135 27.0 37 38.0 0.423077 0.425521 26.0 38 39.0 0.555556 0.431010 27.0 39 40.0 0.481481 0.439296 27.0 40 41.0 0.538462 0.448068 26.0 41 42.0 0.481481 0.455876 27.0 42 43.0 0.576923 0.464854 26.0 43 44.0 0.481481 0.473965 27.0 44 45.0 0.703704 0.484397 27.0 45 46.0 0.576923 0.495230 26.0 46 47.0 0.444444 0.505163 27.0 47 48.0 0.481481 0.516694 27.0 48 49.0 0.615385 0.526190 26.0 49 50.0 0.666667 0.535152 27.0 50 51.0 0.592593 0.548849 27.0 51 52.0 0.538462 0.564293 26.0 52 53.0 0.555556 0.581138 27.0 53 54.0 0.777778 0.602079 27.0 54 55.0 0.576923 0.619633 26.0 55 56.0 0.629630 0.639213 27.0 56 57.0 0.666667 0.662629 27.0 57 58.0 0.730769 0.691678 26.0 58 59.0 0.666667 0.740971 27.0 59 60.0 0.814815 0.825658 27.0 ``` ### ROC Curve The receiver operating characteristic curve, or [ROC curve](https://docs.datarobot.com/en/docs/modeling/analyze-models/evaluate/roc-curve-tab/roc-curve.html#roc-curve), is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. ``` roc = model.get_roc_curve("validation") roc ``` ``` RocCurve(validation) ``` ``` df = pd.DataFrame(roc.roc_points) df.head() ``` | | accuracy | f1_score | false_negative_score | true_negative_score | true_positive_score | false_positive_score | true_negative_rate | false_positive_rate | true_positive_rate | matthews_correlation_coefficient | positive_predictive_value | negative_predictive_value | threshold | fraction_predicted_as_positive | fraction_predicted_as_negative | lift_positive | lift_negative | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0 | 0.603125 | 0.000000 | 635 | 965 | 0 | 0 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000 | 0.603125 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | | 1 | 0.603750 | 0.003145 | 634 | 965 | 1 | 0 | 1.000000 | 0.000000 | 0.001575 | 0.030829 | 1.000 | 0.603502 | 0.878030 | 0.000625 | 0.999375 | 2.519685 | 1.000625 | | 2 | 0.605625 | 0.012520 | 631 | 965 | 4 | 0 | 1.000000 | 0.000000 | 0.006299 | 0.061715 | 1.000 | 0.604637 | 0.849079 | 0.002500 | 0.997500 | 2.519685 | 1.002506 | | 3 | 0.606875 | 0.021773 | 628 | 964 | 7 | 1 | 0.998964 | 0.001036 | 0.011024 | 0.069276 | 0.875 | 0.605528 | 0.788308 | 0.005000 | 0.995000 | 2.204724 | 1.003984 | | 4 | 0.608125 | 0.036866 | 623 | 961 | 12 | 4 | 0.995855 | 0.004145 | 0.018898 | 0.072540 | 0.750 | 0.606692 | 0.764327 | 0.010000 | 0.990000 | 1.889764 | 1.005914 | #### Threshold operations You can get the recommended threshold value with a maximal F1 score using the `RocCurve.get_best_f1_threshold` method. That is the same threshold that is preselected in the DataRobot application when you open the ROC curve tab. ``` threshold = roc.get_best_f1_threshold() threshold ``` ``` 0.3204921984454553 ``` To estimate metrics for different threshold values, pass it to the `RocCurve.estimate_threshold` method. This produces the same results as updating the threshold in the ROC Curve tab. ``` metrics = roc.estimate_threshold(threshold) metrics ``` ``` {'accuracy': 0.606875, 'f1_score': 0.6231276213301378, 'false_negative_score': 115, 'true_negative_score': 451, 'true_positive_score': 520, 'false_positive_score': 514, 'true_negative_rate': 0.46735751295336786, 'false_positive_rate': 0.5326424870466321, 'true_positive_rate': 0.8188976377952756, 'matthews_correlation_coefficient': 0.2929107725430276, 'positive_predictive_value': 0.5029013539651838, 'negative_predictive_value': 0.7968197879858657, 'threshold': 0.3204921984454553, 'fraction_predicted_as_positive': 0.64625, 'fraction_predicted_as_negative': 0.35375, 'lift_positive': 1.2671530178650299, 'lift_negative': 1.3211519800801919} ``` Use the following snippet to plot the ROC curve. ``` roc_df = df dr_roc_green = "#03c75f" white = "#ffffff" dr_purple = "#65147D" dr_dense_green = "#018f4f" threshold = roc.get_best_f1_threshold() fig = plt.figure(figsize=(8, 8)) axes = fig.add_subplot(1, 1, 1, facecolor=dr_dark_blue) plt.scatter(roc_df.false_positive_rate, roc_df.true_positive_rate, color=dr_roc_green) plt.plot(roc_df.false_positive_rate, roc_df.true_positive_rate, color=dr_roc_green) plt.plot([0, 1], [0, 1], color=white, alpha=0.25) plt.title("ROC curve") plt.xlabel("False Positive Rate") plt.xlim([0, 1]) plt.ylabel("True Positive Rate") plt.ylim([0, 1]) ``` ``` (0.0, 1.0) ``` ### Confusion matrix Using keys from the retrieved metrics, you can build a confusion matrix for the selected threshold. ``` roc_df = pd.DataFrame( { "Predicted Negative": [ metrics["true_negative_score"], metrics["false_negative_score"], metrics["true_negative_score"] + metrics["false_negative_score"], ], "Predicted Positive": [ metrics["false_positive_score"], metrics["true_positive_score"], metrics["true_positive_score"] + metrics["false_positive_score"], ], "Total": [ len(roc.negative_class_predictions), len(roc.positive_class_predictions), len(roc.negative_class_predictions) + len(roc.positive_class_predictions), ], } ) roc_df.index = pd.MultiIndex.from_tuples([("Actual", "-"), ("Actual", "+"), ("Total", "")]) roc_df.columns = pd.MultiIndex.from_tuples([("Predicted", "-"), ("Predicted", "+"), ("Total", "")]) roc_df.style.set_properties(**{"text-align": "right"}) roc_df ``` | | | Predicted | Total | | --- | --- | --- | --- | | Actual | - | 511 | 454 | | + | 144 | 491 | 638 | | Total | | 655 | 945 | ### Prediction distribution plot You can use various methods to plot prediction distribution. The method used depends on what packages you have installed. Three different visualizations are outlined below. #### Seaborn ``` import seaborn as sns sns.set_style("whitegrid", {"axes.grid": False}) fig = plt.figure(figsize=(8, 8)) axes = fig.add_subplot(1, 1, 1, facecolor=dr_dark_blue) shared_params = {"shade": True, "clip": (0, 1), "bw": 0.2} sns.kdeplot(np.array(roc.negative_class_predictions), color=dr_purple, **shared_params) sns.kdeplot(np.array(roc.positive_class_predictions), color=dr_dense_green, **shared_params) plt.title("Prediction Distribution") plt.xlabel("Probability of Event") plt.xlim([0, 1]) plt.ylabel("Probability Density") ``` ``` Text(0,0.5,'Probability Density') ``` #### SciPy ``` from scipy.stats import gaussian_kde fig = plt.figure(figsize=(8, 8)) axes = fig.add_subplot(1, 1, 1, facecolor=dr_dark_blue) xs = np.linspace(0, 1, 100) density_neg = gaussian_kde(roc.negative_class_predictions, bw_method=0.2) plt.plot(xs, density_neg(xs), color=dr_purple) plt.fill_between(xs, 0, density_neg(xs), color=dr_purple, alpha=0.3) density_pos = gaussian_kde(roc.positive_class_predictions, bw_method=0.2) plt.plot(xs, density_pos(xs), color=dr_dense_green) plt.fill_between(xs, 0, density_pos(xs), color=dr_dense_green, alpha=0.3) plt.title("Prediction Distribution") plt.xlabel("Probability of Event") plt.xlim([0, 1]) plt.ylabel("Probability Density") ``` ``` Text(0,0.5,'Probability Density') ``` #### Scikit-learn The scikit-learn method is most consistent with how DataRobot displays this plot in the application. This is because scikit-learn supports additional kernel options and you can configure the same kernel used in the application (an epanichkov kernel with size 0.05). The other examples above use a gaussian kernel, so they may slightly differ from the plot in the DataRobot application. ``` from sklearn.neighbors import KernelDensity fig = plt.figure(figsize=(8, 8)) axes = fig.add_subplot(1, 1, 1, facecolor=dr_dark_blue) xs = np.linspace(0, 1, 100) X_neg = np.asarray(roc.negative_class_predictions)[:, np.newaxis] density_neg = KernelDensity(bandwidth=0.05, kernel="epanechnikov").fit(X_neg) plt.plot(xs, np.exp(density_neg.score_samples(xs[:, np.newaxis])), color=dr_purple) plt.fill_between( xs, 0, np.exp(density_neg.score_samples(xs[:, np.newaxis])), color=dr_purple, alpha=0.3 ) X_pos = np.asarray(roc.positive_class_predictions)[:, np.newaxis] density_pos = KernelDensity(bandwidth=0.05, kernel="epanechnikov").fit(X_pos) plt.plot(xs, np.exp(density_pos.score_samples(xs[:, np.newaxis])), color=dr_dense_green) plt.fill_between( xs, 0, np.exp(density_pos.score_samples(xs[:, np.newaxis])), color=dr_dense_green, alpha=0.3 ) plt.title("Prediction Distribution") plt.xlabel("Probability of Event") plt.xlim([0, 1]) plt.ylabel("Probability Density") ``` ``` Text(0,0.5,'Probability Density') ``` ### Word Cloud Text variables often contain words that are highly indicative of the response. The [Word Cloud](https://docs.datarobot.com/en/docs/modeling/analyze-models/understand/word-cloud.html) insight displays the most relevant words and short phrases in word cloud format. This example shows you how to obtain word cloud data and visualize it in similar to how the insight is displayed in the DataRobot application. The example uses `colour` and `wordcloud` packages. First, create a color palette similar to DataRobot's style. ``` from colour import Color import wordcloud ``` ``` colors = [Color("#2458EB")] colors.extend(list(Color("#2458EB").range_to(Color("#31E7FE"), 81))[1:]) colors.extend(list(Color("#31E7FE").range_to(Color("#8da0a2"), 21))[1:]) colors.extend(list(Color("#a18f8c").range_to(Color("#ffad9e"), 21))[1:]) colors.extend(list(Color("#ffad9e").range_to(Color("#d80909"), 81))[1:]) webcolors = [c.get_web() for c in colors] ``` The variable `webcolors` now contains 201 ([-1, 1] interval with step 0.01) colors that will be used in the word cloud. Next, configure the palette. ``` from matplotlib.colors import LinearSegmentedColormap dr_cmap = LinearSegmentedColormap.from_list("DataRobot", webcolors, N=len(colors)) x = np.arange(-1, 1.01, 0.01) y = np.arange(0, 40, 1) X = np.meshgrid(x, y)[0] plt.xticks( [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200], ["-1", "-0.8", "-0.6", "-0.4", "-0.2", "0", "0.2", "0.4", "0.6", "0.8", "1"], ) plt.yticks([], []) im = plt.imshow(X, interpolation="nearest", origin="lower", cmap=dr_cmap) ``` Now you can pick a model that provides a word cloud in the DataRobot. Any "Auto-Tuned Word N-Gram Text Modeler" model will work. ``` models = project.get_models() ``` ``` model_with_word_cloud = None for model in models: try: model.get_word_cloud() model_with_word_cloud = model break except ClientError as e: if e.json["message"] and "No word cloud data" in e.json["message"]: pass else: raise model_with_word_cloud ``` ``` Model(u'Auto-Tuned Word N-Gram Text Modeler using token occurrences - diag_1_desc') ``` ``` wc = model_with_word_cloud.get_word_cloud(exclude_stop_words=True) ``` ``` def word_cloud_plot(wc, font_path=None): # Stopwords usually dominate any word cloud, so we will filter them out dict_freq = { wc_word["ngram"]: wc_word["frequency"] for wc_word in wc.ngrams if not wc_word["is_stopword"] } dict_coef = {wc_word["ngram"]: wc_word["coefficient"] for wc_word in wc.ngrams} def color_func(*args, **kwargs): word = args[0] palette_index = int(round(dict_coef[word] * 100)) + 100 r, g, b = colors[palette_index].get_rgb() return "rgb({:.0f}, {:.0f}, {:.0f})".format(int(r * 255), int(g * 255), int(b * 255)) wc_image = wordcloud.WordCloud( stopwords=set(), width=1024, height=1024, relative_scaling=0.5, prefer_horizontal=1, color_func=color_func, background_color=(0, 10, 29), font_path=font_path, ).fit_words(dict_freq) plt.imshow(wc_image, interpolation="bilinear") plt.axis("off") ``` ``` word_cloud_plot(wc) ``` You can use the word cloud to get information about the most frequent and most important (highest absolute coefficient value) ngrams in your text. ``` wc.most_frequent(5) ``` ``` [{'coefficient': 0.6229774184805059, 'count': 534, 'frequency': 0.21876280213027446, 'is_stopword': False, 'ngram': u'failure'}, {'coefficient': 0.5680375262833832, 'count': 524, 'frequency': 0.21466612044244163, 'is_stopword': False, 'ngram': u'atherosclerosis'}, {'coefficient': 0.37932405511744804, 'count': 505, 'frequency': 0.2068824252355592, 'is_stopword': False, 'ngram': u'infarction'}, {'coefficient': 0.4689734305695615, 'count': 453, 'frequency': 0.18557968045882836, 'is_stopword': False, 'ngram': u'heart'}, {'coefficient': 0.7444542252245913, 'count': 452, 'frequency': 0.18517001229004507, 'is_stopword': False, 'ngram': u'heart failure'}] ``` ``` wc.most_important(5) ``` ``` [{'coefficient': -0.875917913896919, 'count': 38, 'frequency': 0.015567390413764851, 'is_stopword': False, 'ngram': u'obesity unspecified'}, {'coefficient': -0.8655105382141891, 'count': 38, 'frequency': 0.015567390413764851, 'is_stopword': False, 'ngram': u'obesity'}, {'coefficient': 0.8329465952065771, 'count': 9, 'frequency': 0.0036870135190495697, 'is_stopword': False, 'ngram': u'nephroptosis'}, {'coefficient': 0.7444542252245913, 'count': 452, 'frequency': 0.18517001229004507, 'is_stopword': False, 'ngram': u'heart failure'}, {'coefficient': 0.7029270716899754, 'count': 76, 'frequency': 0.031134780827529702, 'is_stopword': False, 'ngram': u'disorders'}] ``` #### Non-ASCII texts The word cloud has full Unicode support, but if you want to visualize it using the code from this notebook you should use the `font_path` parameter that leads to font supporting symbols used in your text. For example, for Japanese text in the model below you should use one of the [CJK fonts](https://en.wikipedia.org/wiki/List_of_CJK_fonts). If you do not have a compatible font, you can download an open-source font [like this one](https://github.com/googlei18n/noto-cjk/raw/master/NotoSansCJKjp-Regular.otf) from [Google's Noto project](https://www.google.com/get/noto/). For this section, download the Japanese-translation version of the "10k_diabetes.csv" dataset [here](https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/jp_10k.csv). ``` jp_project = dr.Project.create("jp_10k.csv", project_name="Japanese 10K") print("Project ID: {}".format(project.id)) ``` ``` Project ID: 5c0008e06523cd0233c49fe4 ``` ``` jp_project.set_target("readmitted_再入院", mode=AUTOPILOT_MODE.QUICK) jp_project.wait_for_autopilot() ``` ``` In progress: 2, queued: 12 (waited: 0s) In progress: 2, queued: 12 (waited: 1s) In progress: 2, queued: 12 (waited: 1s) In progress: 2, queued: 12 (waited: 2s) In progress: 2, queued: 12 (waited: 4s) In progress: 2, queued: 12 (waited: 6s) In progress: 2, queued: 11 (waited: 9s) In progress: 1, queued: 11 (waited: 16s) In progress: 2, queued: 9 (waited: 30s) In progress: 2, queued: 7 (waited: 50s) In progress: 2, queued: 5 (waited: 70s) In progress: 2, queued: 3 (waited: 91s) In progress: 2, queued: 1 (waited: 111s) In progress: 1, queued: 0 (waited: 132s) In progress: 2, queued: 5 (waited: 152s) In progress: 2, queued: 3 (waited: 172s) In progress: 2, queued: 2 (waited: 193s) In progress: 2, queued: 1 (waited: 213s) In progress: 1, queued: 0 (waited: 234s) In progress: 2, queued: 14 (waited: 254s) In progress: 2, queued: 14 (waited: 274s) In progress: 2, queued: 12 (waited: 295s) In progress: 1, queued: 12 (waited: 316s) In progress: 2, queued: 10 (waited: 336s) In progress: 2, queued: 9 (waited: 356s) In progress: 2, queued: 7 (waited: 377s) In progress: 2, queued: 6 (waited: 397s) In progress: 2, queued: 4 (waited: 418s) In progress: 2, queued: 3 (waited: 438s) In progress: 2, queued: 1 (waited: 459s) In progress: 1, queued: 0 (waited: 479s) In progress: 1, queued: 0 (waited: 499s) In progress: 0, queued: 0 (waited: 520s) In progress: 2, queued: 3 (waited: 540s) In progress: 2, queued: 1 (waited: 560s) In progress: 1, queued: 0 (waited: 581s) In progress: 1, queued: 0 (waited: 601s) In progress: 2, queued: 2 (waited: 621s) In progress: 2, queued: 0 (waited: 642s) In progress: 0, queued: 0 (waited: 662s) In progress: 1, queued: 0 (waited: 682s) In progress: 0, queued: 0 (waited: 703s) In progress: 0, queued: 0 (waited: 723s) ``` ``` jp_models = jp_project.get_models() jp_model_with_word_cloud = None for model in jp_models: try: model.get_word_cloud() jp_model_with_word_cloud = model break except ClientError as e: if e.json["message"] and "No word cloud data" in e.json["message"]: pass else: raise jp_model_with_word_cloud ``` ``` Model(u'Auto-Tuned Word N-Gram Text Modeler using token occurrences and tfidf - diag_1_desc_\u8a3a\u65ad1\u8aac\u660e') ``` ``` jp_wc = jp_model_with_word_cloud.get_word_cloud(exclude_stop_words=True) ``` ``` word_cloud_plot(jp_wc, font_path="NotoSansCJKjp-Regular.otf") ``` ### Cumulative gains and lift ROC curve data also contains information necessary for creating cumulative gains and lift charts. Use the fields `fraction_predicted_as_positive` and `fraction_predicted_as_negative` to get X axis and set: - Use true_positive_rate / true_negative_rate as the Y axis for cumulative gains - Use lift_positive / lift_negative as the Y axis for lift. You can use the code for visualizations below, along with baseline/random model (in gray) and ideal (in orange). ``` fig, ((ax_gains_pos, ax_gains_neg), (ax_lift_pos, ax_lift_neg)) = plt.subplots( nrows=2, ncols=2, figsize=(8, 8) ) total_rows = ( df.true_positive_score[0] + df.false_negative_score[0] + df.true_negative_score[0] + df.false_positive_score[0] ) fraction_of_positives = float(df.true_positive_score[0] + df.false_negative_score[0]) / total_rows fraction_of_negatives = 1 - fraction_of_positives # Cumulative gains (positive class) ax_gains_pos.set_facecolor(dr_dark_blue) ax_gains_pos.scatter(df.fraction_predicted_as_positive, df.true_positive_rate, color=dr_roc_green) ax_gains_pos.plot(df.fraction_predicted_as_positive, df.true_positive_rate, color=dr_roc_green) ax_gains_pos.plot([0, 1], [0, 1], color=white, alpha=0.25) ax_gains_pos.plot([0, fraction_of_positives, 1], [0, 1, 1], color=dr_orange) ax_gains_pos.set_title("Cumulative gains (positive class)") ax_gains_pos.set_xlabel("Fraction predicted as positive") ax_gains_pos.set_xlim([0, 1]) ax_gains_pos.set_ylabel("True Positive Rate (Sensitivity)") # Cumulative gains (negative class) ax_gains_neg.set_facecolor(dr_dark_blue) ax_gains_neg.scatter(df.fraction_predicted_as_negative, df.true_negative_rate, color=dr_roc_green) ax_gains_neg.plot(df.fraction_predicted_as_negative, df.true_negative_rate, color=dr_roc_green) ax_gains_neg.plot([0, 1], [0, 1], color=white, alpha=0.25) ax_gains_neg.plot([0, fraction_of_negatives, 1], [0, 1, 1], color=dr_orange) ax_gains_neg.set_title("Cumulative gains (negative class)") ax_gains_neg.set_xlabel("Fraction predicted as negative") ax_gains_neg.set_xlim([0, 1]) ax_gains_neg.set_ylabel("True Negative Rate (Specificity)") # Lift (positive class) ax_lift_pos.set_facecolor(dr_dark_blue) ax_lift_pos.scatter(df.fraction_predicted_as_positive, df.lift_positive, color=dr_roc_green) ax_lift_pos.plot(df.fraction_predicted_as_positive, df.lift_positive, color=dr_roc_green) ax_lift_pos.plot([0, 1], [1, 1], color=white, alpha=0.25) ax_lift_pos.set_title("Lift (positive class)") ax_lift_pos.set_xlabel("Fraction predicted as positive") ax_lift_pos.set_xlim([0, 1]) ax_lift_pos.set_ylabel("Lift") ideal_lift_pos_x = np.arange(0.01, 1.01, 0.01) ideal_lift_pos_y = np.minimum(1 / fraction_of_positives, 1 / ideal_lift_pos_x) ax_lift_pos.plot(ideal_lift_pos_x, ideal_lift_pos_y, color=dr_orange) # Lift (negative class) ax_lift_neg.set_facecolor(dr_dark_blue) ax_lift_neg.scatter(df.fraction_predicted_as_negative, df.lift_negative, color=dr_roc_green) ax_lift_neg.plot(df.fraction_predicted_as_negative, df.lift_negative, color=dr_roc_green) ax_lift_neg.plot([0, 1], [1, 1], color=white, alpha=0.25) # ax_lift_neg.plot([0, fraction_of_positives, 1], [0, 1, 1], color=dr_orange) ax_lift_neg.set_title("Lift (negative class)") ax_lift_neg.set_xlabel("Fraction predicted as negative") ax_lift_neg.set_xlim([0, 1]) ax_lift_neg.set_ylabel("Lift") ideal_lift_neg_x = np.arange(0.01, 1.01, 0.01) ideal_lift_neg_y = np.minimum(1 / fraction_of_negatives, 1 / ideal_lift_neg_x) ax_lift_neg.plot(ideal_lift_neg_x, ideal_lift_neg_y, color=dr_orange) # Adjust spacing for notebook plt.tight_layout() ``` --- # Configure datetime partitioning URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/datetime-v3.html # Configure datetime partitioning This notebook outlines how to use [datetime partitioning](https://docs.datarobot.com/en/docs/api/reference/public-api/datetime_partitioning.html) with version 3.0 of DataRobot's Python client. When dividing your data for model training and validation, DataRobot randomly chooses a set of rows from the training dataset to assign among different cross-validation folds. This process verifies that you have not overfit your model to the training set and that the model can perform well on new data. However, when your data has an intrinsic time-based component, you must be cautious about [target leakage](https://docs.datarobot.com/en/docs/glossary/index.html#target-leakage). Although DataRobot offers datetime partitioning to guard against target leakage, you should always use your domain expertise to evaluate features prior to modeling. The project in this notebook simulates a project with a time-based component that uses out-of-time validation (OTV) modeling). Note that this is not the same as time series modeling, even though the way DataRobot defines backtests for time series is very similar. ### Requirements - Python version 3.7+. - DataRobot API version 3.0+. - A Pandas dataframe (df) with an indicated target feature. Find reference documentation for DataRobot's Python client [here](https://datarobot-public-api-client.readthedocs-hosted.com). ### Import libraries ``` from datetime import datetime import datarobot as dr ``` ### Connect to DataRobot Read more about different options for [connecting to DataRobot from the client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` # If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call # dr.Client(config_path='path-to-drconfig.yaml') ``` ### Configure a project with a datetime partition When configuring a datetime partition, you must specify the durations in strings using the format for the `dr.helpers.partitioning_methods.construct_duration_string()` [method](https://datarobot-public-api-client.readthedocs-hosted.com/en/v2.27.2/autodoc/api_reference.html#dur-string-helper). ``` spec = dr.DatetimePartitioningSpecification( datetime_partition_column="Date", holdout_start_date=datetime(2017, 1, 2), holdout_duration="P1Y0M0DT0H0M0S", number_of_backtests=2, use_time_series=False, ) # Generate a preview based on your project's data partitioning_preview = dr.DatetimePartitioning.generate(project.id, spec) ``` As of v3.0, `Project.set_datetime_partitioning()` and `Project.list_datetime_partition_spec()` are available as an alternative: ``` # View partitioning settings project.list_datetime_partition_spec() # Uncomment to disable holdout before you begin modeling # project.set_datetime_partitioning(disable_holdout=True) ``` ### Create backtest specifications DataRobot provides further control to specify the validation start date as well as the duration. You can view an example in the following cells. The method below is applicable to both time series and out-of-time validation projects. The snippet provided uses `use_time_series = False` in the `dr.DatetimePartitioningSpecification()` [method](https://datarobot-public-api-client.readthedocs-hosted.com/en/v2.27.2/reference/modeling/spec/datetime_partition.html#setting-up-a-datetime-partitioned-project) to initiate an OTV project. The methods used in the snippet below change the backtest specification for the first and second backtests. DataRobot recommends taking advantage of automated partitioning by setting `use_time_series=True` after you specify the number of backtests. ``` # Set duration of the validation backtests duration_1y = "P1Y0M0DT0H0M0S" duration_0s = "P0Y0M0DT0H0M0S" # Note that the dates are not project-specific; they are example dates spec.backtests = [ dr.BacktestSpecification( 0, gap_duration="P0Y0M0DT0H0M0S", validation_start_date=datetime(2016, 1, 2), validation_duration=duration_1y, ), dr.BacktestSpecification( 1, gap_duration="P0Y0M0DT0H0M0S", validation_start_date=datetime(2015, 1, 2), validation_duration=duration_0s, ), ] # Uncomment if you want more backtests # spec.number_of_backtests = 5 # Use the lines below to initiate the project project = dr.Project.create(sourcedata=df, project_name="Project Name") project.analyze_and_model("target_column", partitioning_method=spec) ``` Once backtests are configured for your project, you can proceed to modeling. See the use case for [predicting CO₂ levels](https://docs.datarobot.com/en/docs/api/guide/common-case/python2/otv-nb.html) as an example. --- # Advanced Feature Selection using Feature Importance Rank Ensembling (FIRE) URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/feat-select/Feature-Importance-Rank-Ensembling.html # Advanced Feature Selection using Feature Importance Rank Ensembling (FIRE) This notebook shows the benefits of [FIRE](https://www.datarobot.com/blog/using-feature-importance-rank-ensembling-fire-for-advanced-feature-selection/), advanced feature selection that uses median rank aggregation of feature impacts across several models created during a run of Autopilot. ### Requirements Python version >= 3.7.3 DataRobot API version >= 2.22.1. For additional information, reference the [Python package documentation](https://datarobot-public-api-client.readthedocs-hosted.com). This code example uses the MADLEON dataset from [this paper](https://archive.ics.uci.edu/ml/datasets/Madelon). It can also be found [here](https://s3.amazonaws.com/datarobot_public_datasets/madelon_combined_80.csv). ### Import libraries and connect to DataRobot Read more about different options for [connecting to DataRobot from the client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` import datarobot as dr import numpy as np import pandas as pd ``` ``` # If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call # dr.Client(config_path='path-to-drconfig.yaml') ``` ### FIRE feature selection function ``` def feature_importance_rank_ensembling( project, n_models=5, metric=None, by_partition="validation", feature_list_name=None, ratio=0.95, model_search_params=None, use_ranks=True, ): """ Function that implements the logic of Feature Selection using Feature Importance Rank Ensembling and restarts DR autopilot Parameters: ----------- project: DR project object, n_models: int, get top N best models on the leaderboard to compute feature impact on. Default 5 metric: str, DR metric to check performance against. Default None. If Default, it will use DR project defined metric by_partition: str, whether to use 'validation' or 'crossValidation' partition to get the best model on. Default 'validation' feature_list_name: str, name of the feature list to start iterating from. Default None ratio: float, ratio of total feature impact that new feature list will contain. Default 0.95 model_search_params: dict, dictonary of parameters to search the best model. See official DR python api docs. Default None use_ranks: Boolean, True to use median rank aggregation or False to use total impact unnormalized. Default True Returns: ----------- dr.Model object """ models = get_best_models( project, metric=metric, by_partition=by_partition, start_featurelist_name=feature_list_name, model_search_params=model_search_params, ) models = models.values[:n_models] all_impact = pd.DataFrame() print("Request Feature Impact calculations") # First, kick off all Feature Impact requests and let DataRobot handle parallelizing for model in models: try: model.request_feature_impact() except: pass for model in models: # Allow time for DataRobot to compute Feature Impact feature_impact = pd.DataFrame( model.get_or_request_feature_impact(max_wait=60 * 15) ) # 15min # Track model name and ID for auditing purposes feature_impact["model_type"] = model.model_type feature_impact["model_id"] = model.id # By sorting and re-indexing, the new index becomes our 'ranking' feature_impact = feature_impact.sort_values( by="impactUnnormalized", ascending=False ).reset_index(drop=True) feature_impact["rank"] = feature_impact.index.values # Add to the master list of all models' feature ranks all_impact = pd.concat([all_impact, feature_impact], ignore_index=True) # You need to get a threshold number of features to select. # The threshold is based on the cumulative sum of impact all_impact_agg = ( all_impact.groupby("featureName")[["impactNormalized", "impactUnnormalized"]] .sum() .sort_values("impactUnnormalized", ascending=False) .reset_index() ) # Calculate cumulative Feature Impact and take the first features that possess of total impact all_impact_agg["impactCumulative"] = all_impact_agg["impactUnnormalized"].cumsum() total_impact = all_impact_agg["impactCumulative"].max() * ratio tmp_fl = list( set( all_impact_agg[all_impact_agg.impactCumulative <= total_impact][ "featureName" ].values.tolist() ) ) # The number of features to use n_feats = len(tmp_fl) if use_ranks: # Get the top features based on median rank top_ranked_feats = list( all_impact.groupby("featureName") .median() .sort_values("rank") .head(n_feats) .index.values ) else: # Otherwise, get features based just on the total unnormalized feature impact top_ranked_feats = list(all_impact_agg.featureName.values[:n_feats]) # Create a new feature list featurelist = project.create_modeling_featurelist( f"Reduced FL by Median Rank, top{n_feats}", top_ranked_feats ) featurelist_id = featurelist.id # Start Autopilot print("Starting AutoPilot on a reduced feature list") project.start_autopilot( featurelist_id=featurelist_id, prepare_model_for_deployment=True, blend_best_models=False, ) project.wait_for_autopilot() print("... AutoPilot is completed.") # Return the previous best model return models[0] ``` ### Get the best-performing models Avoid using models trained on higher than 3rd stage of Autopilot sample size (80%, 100%). Blender and Frozen models are ignored, so DataRobot selects models trained on 64% percent of the data. ``` def get_best_models( project, metric=None, by_partition="validation", start_featurelist_name=None, model_search_params=None, ): """ Gets pd.Series of DR model objects sorted by performance. Excludes blenders, frozend and on DR Reduced FL Parameters: ----------- project: DR project object metric: str, metric to use for sorting models on lb, if None, default project metric will be used. Default None by_partiton: boolean, whether to use 'validation' or 'crossValidation' partitioning. Default 'validation' start_featurelist_name: str, initial featurelist name to get models on. Default None model_search_params: dict to pass model search params. Default None Returns: ----------- pd.Series of dr.Model objects, not blender, not frozen and not on DR Reduced Feature List """ # A list of metrics that get better as their value increases desc_metric_list = [ "AUC", "Area Under PR Curve", "Gini Norm", "Kolmogorov-Smirnov", "Max MCC", "Rate@Top5%", "Rate@Top10%", "Rate@TopTenth%", "R Squared", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Accuracy", "Balanced Accuracy", "FVE Multinomial", "FVE Binomial", ] if not metric: metric = project.metric if "Weighted" in metric: desc_metric_list = ["Weighted " + metric for metric in desc_metric_list] asc_flag = False if metric in desc_metric_list else True if project.is_datetime_partitioned: assert by_partition in [ "validation", "backtesting", "holdout", ], "Please specify correct partitioning, in datetime partitioned projects supported options are: 'validation', 'backtesting', 'holdout' " models_df = pd.DataFrame( [ [ model.metrics[metric]["validation"], model.metrics[metric]["backtesting"], model.model_category, model.is_frozen, model.featurelist_name, model, ] for model in project.get_datetime_models() ], columns=[ "validation", "backtesting", "category", "is_frozen", "featurelist_name", "model", ], ).sort_values([by_partition], ascending=asc_flag, na_position="last") else: assert by_partition in [ "validation", "crossValidation", "holdout", ], "Please specify correct partitioning, supported options are: 'validation', 'crossValidation', 'holdout' " models_df = pd.DataFrame( [ [ model.metrics[metric]["crossValidation"], model.metrics[metric]["validation"], model.model_category, model.is_frozen, model.featurelist_name, model, ] for model in project.get_models( with_metric=metric, search_params=model_search_params ) ], columns=[ "crossValidation", "validation", "category", "is_frozen", "featurelist_name", "model", ], ).sort_values([by_partition], ascending=asc_flag, na_position="last") if start_featurelist_name: return models_df.loc[ ( (models_df.category == "model") & (models_df.is_frozen == False) & (models_df.featurelist_name == start_featurelist_name) ), "model", ] else: return models_df.loc[ ( (models_df.category == "model") & (models_df.is_frozen == False) & (models_df.featurelist_name.str.contains("DR Reduced Features M") == False) ), "model", ] ``` ### Primary FIRE function This function automatically executes the FIRE feature selection algorithm on the top N models. Once the reduced feature list is created, DataRobot re-runs Autopilot and waits until it completes. DataRobot then automatically sorts the models based on the project metric, computes Feature Impact, and iterates over again. If the new feature list produces a model that ranks lower based on a metric, it will expend one "life". The algorithm will stop performing feature selection when no lives are available (you start with 3). ``` def main_feature_selection( project_id, start_featurelist_name=None, lifes=2, top_n_models=5, partition="validation", main_scoring_metric=None, initial_impact_reduction_ratio=0.95, best_model_search_params=None, use_ranks=True, ): """ Main function. Meant to get the optimal shortest feature list by repeating the feature selection process until stop criteria is met. Currently supports Binary, Regression, Multiclass, Datetime partitioned (OTV), and AutoTS DataRobot projects. Example usage: >> import datarobot as dr >> dr.Client(config_path='PATH_TO_DR_CONFIG/drconfig.yaml') TIP: set best_model_search_params = {'sample_pct__lte': 65} to avoid using models trained on a higher sample size than the third stage of Autopilot, which is typically ~64% of the data. >> main_feature_reduction('INSERT_PROJECT_ID', start_featurelist_name=None, lifes=3, top_n_models=5, partition='validation', main_scoring_metric=None, initial_impact_reduction_ratio=0.95, best_model_search_params=None, use_ranks=True) Parameters: ----------- project_id: str, id of DR project, start_featurelist_name: str, name of feature list to start iterating from. Default None lifes: int, stopping criteria, if no best model produced after lifes iterations, stop feature reduction. Default 3 top_n_models: int, only for 'Rank Aggregation method', get top N best models on the leaderboard. Default 5 partition: str, whether to use 'validation','crossValidation' or 'backtesting' partition to get the best model on. Default 'validation' main_scoring_metric: str, DR metric to check performance against, If None DR project metric will be used initial_impact_reduction_ratio: float, ratio of total feature impact that new feature list will contain. Default 0.95 best_model_search_params: dict, dictonary of parameters to search the best model. See official DR python api docs. Default None use_ranks: Boolean, True to use median rank aggregation or False to use total impact unnormalized. Default True Returns: ---------- dr.Model object of the best model on the leaderboard """ project = dr.Project.get(project_id) ratio = initial_impact_reduction_ratio assert ratio < 1, "Please specify initial_impact_reduction_ratio < 1" model_search_params = best_model_search_params runs = 0 # Main function loop while lifes > 0: if runs > 0: start_featurelist_name = None try: best_model = feature_importance_rank_ensembling( project, n_models=top_n_models, metric=main_scoring_metric, by_partition=partition, feature_list_name=start_featurelist_name, ratio=ratio, model_search_params=best_model_search_params, use_ranks=use_ranks, ) except dr.errors.ClientError as e: # decay the ratio ratio *= ratio print(e, f"\nWill try again with a ratio decay ... New ratio={ratio:.3f}") continue ############################## ### GET THE NEW BEST MODEL ### ############################## new_best_model = get_best_models( project, metric=main_scoring_metric, by_partition=partition, model_search_params=model_search_params, ).values[0] ################################# ##### PROCESS STOP CRITERIA ##### ################################# if best_model.id == new_best_model.id: # If no better model is produced with a recent run, expend 1 life lifes -= 1 # If no lives left -> stop if lifes <= 0: print( "New model performs worse. No lives left.\nAUTOMATIC FEATURE SELECTION PROCESS HAS BEEN STOPPED" ) return new_best_model # Decay the ratio ratio *= ratio print( f"New model performs worse. One life is burnt.\nRepeat again with decaying the cumulative impact ratio. New ratio={ratio:.3f}" ) runs += 1 print("Run ", runs, " completed") return new_best_model ``` ### Create a project and initiate Autopilot ``` project = dr.Project.create('https://s3.amazonaws.com/datarobot_public_datasets/madelon_combined_80.csv') project.set_target(target='y', project_name = 'FIRE' mode=dr.AUTOPILOT_MODE.QUICK, worker_count=-1, ) # Wait for Autopilot to finish. You can set verbosity to 0 if you do not wish to see progress updates project.wait_for_autopilot(verbosity=1) print(project.id) ``` ### Feature selection When Autopilot completes, perform feature selection. Then, start Autopilot again using a feature list based on the median rank aggregation of Feature Impact across the top 5 models trained on the "Informative Features" feature list. ``` # Adjust the function's parameters for your purposes best_model = main_feature_selection( project.id, partition="crossValidation", best_model_search_params={"sample_pct__lte": 65} ) ``` ### Report the most accurate model ``` print( f"The best model has {project.metric} score = {best_model.metrics[project.metric]['crossValidation']} on the cross-validation partition \ on the list of {len(best_model.get_features_used())} features" ) ``` ``` The best model has LogLoss score = 0.264978 on the cross-validation partition on the list of 13 features ``` --- # Feature selection notebooks URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/feat-select/index.html > Review notebooks that outline feature selection. # Feature selection notebooks DataRobot offers end-to-end code examples via Jupyter notebooks that help you find complete examples of common data science and machine learning workflows. Review the notebooks that outline feature selection below. | Topic | Describes... | | --- | --- | | Feature Importance Rank Ensembling | Learn about the benefits of Feature Importance Rank Ensembling (FIRE)—a method of advanced feature selection that uses a median rank aggregation of feature impacts across several models created during a run of Autopilot. | | Advanced feature selection with Python | Use Python to select features by creating aggregated Feature Impact. | --- # Advanced feature selection with Python URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/feat-select/python-select.html # Advanced feature selection with Python This notebooks shows how you can use DataRobot's Python client to accomplish feature selection by creating aggregated Feature Impact using models created during Autopilot. For more information about the allowed feature transformations, reference the [Python client documentation](https://datarobot-public-api-client.readthedocs-hosted.com/en/latest/autodoc/api_reference.html#datarobot.models.Project.create_type_transform_feature). ## Requirements - Python version 3.7.3. - DataRobot API version 2.14.0. - A DataRobot Project object. - A DataRobot Model object. Small adjustments may be needed depending on the Python version and DataRobot API version you are using. ## Import libraries ``` import datarobot as dr import matplotlib.pyplot as plt import pandas as pd import seaborn as sns sns.set_style("ticks") sns.set_context("poster") ``` ### Connect to DataRobot Read more about different options for [connecting to DataRobot from the client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` # If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call # dr.Client(config_path='path-to-drconfig.yaml') ``` ## Select models For this workflow, select the top five performing models from the project. ``` project = dr.Project.get(project_id="") models = project.get_models() models = models[:5] print(models) ``` ## Create a dataframe Create a dataframe of features' relative rank for the top five models. ``` all_impact = pd.DataFrame() for model in models[0:5]: # This can take about one minute for each model feature_impact = model.get_or_request_feature_impact(max_wait=600) # Ready to be converted to dataframe df = pd.DataFrame(feature_impact) # Track model names and IDs for auditing purposes df["model_type"] = model.model_type df["model_id"] = model.id # By sorting and re-indexing, the new index becomes the 'ranking' df = df.sort_values(by="impactUnnormalized", ascending=False) df = df.reset_index(drop=True) df["rank"] = df.index.values # Add to the master list of all models' feature ranks all_impact = pd.concat([all_impact, df], ignore_index=True) ``` ``` all_impact.head() ``` | | featureName | impactNormalized | impactUnnormalized | redundantWith | model_type | model_id | rank | | --- | --- | --- | --- | --- | --- | --- | --- | | 0 | number_inpatient | 1.000000 | 0.031445 | None | eXtreme Gradient Boosted Trees Classifier with... | 5e620be2d7c7a80c003d16a2 | 0 | | 1 | discharge_disposition_id | 0.950723 | 0.029896 | None | eXtreme Gradient Boosted Trees Classifier with... | 5e620be2d7c7a80c003d16a2 | 1 | | 2 | medical_specialty | 0.828289 | 0.026046 | None | eXtreme Gradient Boosted Trees Classifier with... | 5e620be2d7c7a80c003d16a2 | 2 | | 3 | number_diagnoses | 0.609419 | 0.019163 | None | eXtreme Gradient Boosted Trees Classifier with... | 5e620be2d7c7a80c003d16a2 | 3 | | 4 | num_lab_procedures | 0.543238 | 0.017082 | None | eXtreme Gradient Boosted Trees Classifier with... | 5e620be2d7c7a80c003d16a2 | 4 | ## View rankings and distribution You can find the N features with the highest median ranking and visualize the distributions: ``` from matplotlib.axes._axes import _log as matplotlib_axes_logger matplotlib_axes_logger.setLevel("ERROR") n_feats = 20 top_feats = list( all_impact.groupby("featureName").median().sort_values("rank").head(n_feats).index.values ) top_feat_impact = all_impact.query("featureName in @top_feats").copy() fig, ax = plt.subplots(figsize=(20, 25)) sns.boxenplot(y="featureName", x="rank", data=top_feat_impact, order=top_feats, ax=ax, orient="h") plt.title("Features with highest Feature Impact rating") _ = ax.set_ylabel("Feature Name") _ = ax.set_xlabel("Rank") ``` ## Create a new feature list After analysis, you can create a new feature list with the top features and rerun Autopilot. Note that a feature list can also be created for a dataset and becomes usable across all projects that use that dataset in the future. ``` # Create new featurelist and run autopilot featurelist = project.create_featurelist("consensus-top-features", list(top_feats)) featurelist_id = featurelist.id project.start_autopilot(featurelist_id=featurelist_id) project.wait_for_autopilot() ``` --- # Modeling code examples URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/index.html > Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows for modeling. # Modeling code examples The API user guide includes overviews and workflows for DataRobot's Python client that outline complete examples of common data science and machine learning workflows. Be sure to review the [API quickstart guide]api-quickstart before using the notebooks below. | Topic | Describes... | | --- | --- | | Modeling workflow overview | How to use DataRobot's Python client to train and experiment with models. | | Generate advanced model insights | Model insights available for DataRobot's Python client. | | Build a model factory | A system or a set of procedures that automatically generate predictive models with little to no human intervention. | | Configure datetime partitioning | How to use datetime partitioning to guard a project against time-based target leakage. | | Migrate models | How to transfer models from one DataRobot cluster to another as an .mlpkg file. | | Feature selection examples | Notebooks that outline Feature Importance Rank Ensembling (FIRE) and advanced feature selection with Python. | --- # Migrate a model to a new cluster URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/migrate-nb.html # Migrate a model to a new cluster This notebook demonstrates how to migrate a model from one DataRobot cluster to another of the same version. ## Setup Note that the model you choose to migrate must by using Python 3 or a newer version. Additionally, `This notebook will not work using https://app.datarobot.com`. Reference documentation for this workflow's topics below: - Download -modelPackageFile-) - Upload ### Supported paths The following migration paths are currently known to work by default: - DataRobot v8.x -> 8.x - v8.x -> v9.alpha Note that there will be an extra required process when migrating from v7.3.2+ to v8.0.10+ (shown later in this notebook). ### Prerequisites - This notebook must be able to write to the model directory, located in the same directory where this notebook is run from. For the best results, run this notebook from the local file system. - Ensure that the model you choose to migrate must be a deployed model. - Provide API keys for both the source and destination clusters. - The Source and Destination users must have the " Enable Experimental API access " feature flag enabled to follow this workflow. - The notebook must have connectivity to the Source and Destination clusters - DataRobot versions on the clusters must be consistent with the Supported Paths above. - For models on clusters of DataRobot v7.x, you must have SSH access to the App Node of the cluster. - The Source and Destination DataRobot clusters must have the following in the config.yaml: ``` app_configuration: drenv_override: WHITELIST_EXPERIMENTAL_API: true EXPERIMENTAL_API_ACCESS: true ``` ### Install libraries If you are using VS Code, you can install the required DataRobot packages with the cell below. Otherwise, use the cell that follows it. ``` !{sys.executable} -m pip install --upgrade pip # !{sys.executable} -m pip install "datarobot>=2.28,<2.29" !{sys.executable} -m pip install datarobot !{sys.executable} -m pip install datetime requests --upgrade ``` ``` !pip3 install --upgrade pip # !pip3 install "datarobot>=2.28,<2.29" !pip3 install datarobot !pip3 install datetime requests --upgrade ``` ### Import libraries ``` import datetime from datetime import date import json import os import sys import time from timeit import default_timer import urllib.parse from IPython.display import display, HTML import datarobot as dr import requests print("Started: %s" % (str(datetime.datetime.now()))) ``` ### Configure source settings ``` # Provide the URL protocol, address (IP or FQDN), and path # Example: source_host = "http://1.2.3.4" source_host = "https://source.datarobot.example.com" # Do not use https://app.datarobot.com, because users do not have access to the "Enable Experimental API access" permission # Provide an API key from a user with permission from this cluster source_apikey = "" # Provide the source project ID deployment_id = "" #### Local save file path #### # Saves to the model directory by default, using the deployment_id as the file name model_path = "model/%s.mlpkg" % deployment_id #### Destination Settings #### # Example: destination_host = "http://4.3.2.1" destination_host = "https://destination.datarobot.example.com" # Provide an API key from the nodes referenced above from a user with the permissions referenced above destination_apikey = "" print("DataRobot client version: %s" % dr.__version__) print("Source url: %s | deployment_id: %s" % (source_host, deployment_id)) print("Output path: %s" % (model_path)) print("Destinastion url: %s" % (destination_host)) ``` ``` # Code block to ensure that the model directory exists os.makedirs(os.path.dirname(model_path), exist_ok=True) ``` ## Download the deployed model package The following cell downloads the generated data that represents the deployed model given by the source URL and the deployment_id. It is then saved to `models/{deployment_id}.mlpkg`. ``` # Build the headers and provide the token headers = {} headers["Authorization"] = "Bearer {}".format(source_apikey) # Optional - helps DataRobot track usage of this sample headers["User-Agent"] = "AIA-E2E-MIGRATION-19" # Create a new session session = requests.Session() session.headers.update(headers) print("Downloading the mlpkg file from: %s" % source_host) # Download Code # Makes request to generate an .mlpkg for download on the target server # Returns a URL in the location attribute in response header or None def _request_model_package_download(session, host, deployment_id): apiEndpoint = urllib.parse.urljoin( host, "/api/v2/deployments/%s/modelPackageFileBuilds/" % deployment_id ) print("using download apiEndpoint: %s" % apiEndpoint) ssl_verify = True if (urllib.parse.urlparse(host)).scheme == "https" else False try: r = session.post(apiEndpoint, verify=ssl_verify) r.raise_for_status() return r.headers.get("Location") except requests.exceptions.HTTPError as err: print("Error: %s" % err) return None # Downloads an .mlpkg file to the local system from the target server # Returns the binary data to be downloaded or None def get_model_package(session, host, deployment_id): location = _request_model_package_download(session, host, deployment_id) print("using location: %s" % location) ssl_verify = True if (urllib.parse.urlparse(host)).scheme == "https" else False attempts = 0 wait_length = 30 r = None while attempts <= 10: try: r = session.get(location, verify=ssl_verify) r.raise_for_status() print(r.json()) print("sleeping %s seconds" % wait_length) time.sleep(wait_length) attempts += 1 except ValueError: print("looks like no json, time to download") return r except: attempts += 1 print("exception, sleeping for 60 seconds") time.sleep(60) print( "Number of check attempts exceeded. please check the target instance to see if the package is still being assembled or not" ) return None start = default_timer() output = get_model_package(session, source_host, deployment_id) # if output is None: # print("download failed") print("Saving data to: %s" % model_path) with open(model_path, "wb") as f: f.write(output.content) print( "%s took %s seconds to download %s megs" % ( model_path, default_timer() - start, str(round(os.path.getsize(model_path) / (1024 * 1024), 2)), ) ) ``` ## Upload the model to the Model Registry The following cell uploads the .mlpkg file produced earlier to the `destination_host` provided above. ``` headers = {} headers["Authorization"] = "Bearer {}".format(destination_apikey) # Optional - helps DataRobot track usage of this sample headers["User-Agent"] = "AIA-E2E-MIGRATION-19" session = requests.Session() session.headers.update(headers) model_name = "" # Upload code # Makes a request to upload the .mlpkg file to the target server # Returns a URL in the location attribute of the response header or None def _request_package_upload(session, host, fileLocation): apiEndpoint = urllib.parse.urljoin(host, "/api/v2/modelPackages/fromFile/") print("using upload apiEndpoint: %s" % apiEndpoint) ssl_verify = True if (urllib.parse.urlparse(host)).scheme == "https" else False f = {"file": open(fileLocation, "rb")} try: r = session.post(apiEndpoint, files=f, verify=ssl_verify) r.raise_for_status() return r.headers.get("Location") except requests.exceptions.HTTPError as err: print("ERROR: %s" % err) return None # Uploads the .mlpkg file to the target server # Returns the ID of the new model package or None def upload_model_package(session, host, fileLocation): location = _request_package_upload(session, host, fileLocation) print("Location: %s" % location) ssl_verify = True if (urllib.parse.urlparse(host)).scheme == "https" else False attempts = 0 wait_length = 25 while attempts < 10: try: r = session.get(location, verify=ssl_verify) r.raise_for_status() data = r.json() # Check if you get a status or if it's redirected to the package object if data.get("status") is not None: print(data) else: print("Model Package Uploaded") return data.get("id"), data.get("importance"), data.get("name") attempts += 1 print("sleeping %s seconds" % wait_length) time.sleep(wait_length) except: attempts += 1 print("exception, sleeping 60") time.sleep(60) print( "ERROR: Number of check attempts exceeded. please check the target instance to see if there are errors" ) return None # Upload the .mlpkg start = default_timer() print("Uploading file: %s to: %s" % (model_path, destination_host)) destination_model_id, destination_model_importance, destination_model_name = upload_model_package( session, destination_host, model_path ) if destination_model_id is None: print("upload failed") else: link = urllib.parse.urljoin( destination_host, "/model-registry/model-packages/%s" % destination_model_id ) print("Upload took %s seconds" % (default_timer() - start)) ``` ## Find the dedicated prediction engine ID The next step is to find the prediction server used in the cluster. ``` dpeEndpoint = "%s/api/v2/predictionServers/" % (destination_host) prediction_environment_id = None prediction_environment_url = None ssl_verify = True if (urllib.parse.urlparse(destination_host)).scheme == "https" else False print("finding dpe with: %s" % dpeEndpoint) try: r = session.get(dpeEndpoint, verify=ssl_verify) r.raise_for_status() data = json.loads(r.text) prediction_environment_id = data["data"][data["count"] - 1]["id"] prediction_environment_url = data["data"][data["count"] - 1]["url"] except requests.exceptions.HTTPError as err: print("Error: %s" % err) raise Exception("Error: %s" % err) ## Debug # print("data: %s" % data ) print("Found DPE id: %s | url: %s" % (prediction_environment_id, prediction_environment_url)) ``` ## Create a new deployment from the target model package ``` # Returns Deployment ID or None def deploy_model(session, pid, mid, imp): apiEndpoint = "%s/api/v2/deployments/fromModelPackage/" % destination_host print("deploy from: %s" % apiEndpoint) ssl_verify = True if (urllib.parse.urlparse(destination_host)).scheme == "https" else False body_payload = { "label": "%s" % (destination_model_name), "description": "Cloned from: %s" % (urllib.parse.urlparse(source_host).netloc), "modelPackageId": mid, "importance": imp, } print("deployment settings: %s" % body_payload) try: r = session.post( apiEndpoint, data=json.dumps(body_payload), headers={"Content-Type": "application/json", "Accept": "application/json"}, verify=ssl_verify, ) r.raise_for_status() return r.text except requests.exceptions.HTTPError as err: print("ERROR: %s" % err) print(r.text) print(r.headers) return None start = default_timer() if destination_model_importance is None: destination_model_importance = "LOW" output = deploy_model( session, prediction_environment_id, destination_model_id, destination_model_importance ) print("Deplyment of: %s took: %s seconds" % (output, default_timer() - start)) ``` ## DataRobot v7.x extra steps As mentioned in the prerequisite section, there is an additional required process to finalize the migration. Upon executing the block below, you will have the commands required after SSHing in to the `destination_host`. ``` # # Debug command # destination_model_id = "foo" app_node = (urllib.parse.urlparse(destination_host)).netloc print("# Copy the commands below and paste them to the") print("# ssh command prompt on: %s" % app_node) print("") print("sudo su - datarobot") print( 'docker exec -it app /entrypoint /bin/bash -c "python3 support/upgrade_model_packages.py --save %s"' % destination_model_id ) ``` Upon successfu completion, you should see output like this: ``` Total seconds: 1.097603 | Avg 1.097603 seconds to process a package Successfully updated 1 packages ``` ## Copyright 2023 DataRobot Inc. All Rights Reserved. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or implied --- # Build a model factory URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/model-factory.html # Build a model factory A model factory is a system or set of procedures that automatically generate predictive models with little to no human intervention. Model factories can have multiple layers of complexity, called modules. One module may train models while others can deploy or retrain models. In this example of a model factory, you set up projects and start them in a parallel loop. This allows you to start all projects simultaneously, without unexpected errors. Consider a scenario where you have 20,000 SKUs and you need to do sales forecasting for each one of them. Or, you may have multiple types of customers and you are trying to predict which types will churn. - Can one model handle the high dimensionality that comes with these problems? - Is a single model family able to address the scope of these problems? - Is one preprocessing method sufficient? In this example, use DataRobot to build a single project with the readmitted dataset to predict the probability that a hospital patient may be readmitted after discharge. Then, you will build multiple projects with the `admission id` feature as the target and find the best model for unique value for `admission id`. Lastly, you will prepare the selected models for deployment. ### Import Libraries ``` from time import sleep from dask import compute, delayed # For parallelization import datarobot as dr # Requires version >2.19 import matplotlib.pyplot as plt import pandas as pd import seaborn as sns sns.set(style="whitegrid") ``` ### Import data Download the sample dataset [here](https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/10k-diabetes.csv). ``` data_path = "https://docs.datarobot.com/en/docs/api/guide/python/10k-diabetes.csv" df = pd.read_csv(data_path) ``` ``` # Display the data df.head() ``` | | race | gender | age | weight | admission_type_id | discharge_disposition_id | admission_source_id | time_in_hospital | payer_code | medical_specialty | ... | glipizide_metformin | glimepiride_pioglitazone | metformin_rosiglitazone | metformin_pioglitazone | change | diabetesMed | readmitted | diag_1_desc | diag_2_desc | diag_3_desc | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0 | Caucasian | Female | [50-60) | ? | Elective | Discharged to home | Physician Referral | 1 | CP | Surgery-Neuro | ... | No | No | No | No | No | No | False | Spinal stenosis in cervical region | Spinal stenosis in cervical region | Effusion of joint, site unspecified | | 1 | Caucasian | Female | [20-30) | [50-75) | Urgent | Discharged to home | Physician Referral | 2 | UN | ? | ... | No | No | No | No | No | No | False | First-degree perineal laceration, unspecified ... | Diabetes mellitus of mother, complicating preg... | Sideroblastic anemia | | 2 | Caucasian | Male | [80-90) | ? | Not Available | Discharged/transferred to home with home healt... | NaN | 7 | MC | Family/GeneralPractice | ... | No | No | No | No | No | Yes | True | Pneumococcal pneumonia [Streptococcus pneumoni... | Congestive heart failure, unspecified | Hyperosmolality and/or hypernatremia | | 3 | AfricanAmerican | Female | [50-60) | ? | Emergency | Discharged to home | Transfer from another health care facility | 4 | UN | ? | ... | No | No | No | No | No | Yes | False | Cellulitis and abscess of face | Streptococcus infection in conditions classifi... | Diabetes mellitus without mention of complicat... | | 4 | AfricanAmerican | Female | [50-60) | ? | Emergency | Discharged to home | Emergency Room | 5 | ? | Psychiatry | ... | No | No | No | No | Ch | Yes | False | Bipolar I disorder, single manic episode, unsp... | Diabetes mellitus without mention of complicat... | Depressive type psychosis | 5 rows × 51 columns ### Connect to DataRobot DataRobot recommends providing a configuration file containing your credentials (endpoint and API Key) to connect to DataRobot. ``` # If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call # dr.Client(config_path='path-to-drconfig.yaml') ``` ### Create a project Create a Datarobot project and initiate Autopilot using data from all patients in the dataset. ``` original_proj = dr.Project.start( df, # Pandas dataframe with data project_name="Readmissions", # Name of the project target="readmitted", # Target of the project metric="LogLoss", # Optimization metric (Default is LogLoss) worker_count=-1, ) # Amount of workers to use (-1 means every worker available) original_proj.wait_for_autopilot( verbosity=1 ) # Wait for Autopilot to finish. You can set verbosity to 0 if you do not wish to see progress updates ``` ### Get the best-performing model from the project ``` # Choose the most accurate model best_model = original_proj.get_models()[0] print(best_model) # Print the most accurate model's name best_model.metrics["LogLoss"]["crossValidation"] # Print the crossValidation score ``` ### Model insight functions Use the functions below to plot the ROC curve and Feature Impact for a model. ``` def plot_roc_curve(datarobot_model): """This function plots a roc curve. Input: datarobot_model: """ roc = datarobot_model.get_roc_curve("crossValidation") roc_df = pd.DataFrame(roc.roc_points) auc_score = datarobot_model.metrics["AUC"]["crossValidation"] plt.plot( roc_df["false_positive_rate"], roc_df["true_positive_rate"], "b", label="AUC = %0.2f" % auc_score, ) plt.legend(loc="lower right") plt.plot([0, 1], [0, 1], "r--") plt.xlim([0, 1]) plt.ylim([0, 1]) plt.ylabel("True Positive Rate") plt.xlabel("False Positive Rate") plt.show() def plot_feature_impact(datarobot_model, title=None): """This function plots feature impact Input: datarobot_model: title : --> title of graph """ # Get feature impact feature_impacts = datarobot_model.get_or_request_feature_impact() # Sort feature impact based on normalised impact feature_impacts.sort(key=lambda x: x["impactNormalized"], reverse=True) fi_df = pd.DataFrame(feature_impacts) # Save feature impact in pandas dataframe fig, ax = plt.subplots(figsize=(14, 5)) b = sns.barplot(x="featureName", y="impactNormalized", data=fi_df[0:5], color="b") b.axes.set_title("Feature Impact" if not title else title, fontsize=20) def wait_for_autopilot(proj, wait=120): total_wait = 0 while proj.get_status()["autopilot_done"] == False: sleep(wait) total_wait += wait total_jobs = len(proj.get_all_jobs()) print( "Autopilot still running! {} jobs running and in queue. Total wait time {}s".format( total_jobs, total_wait ) ) ``` ### Visualize the ROC Curve ``` plot_roc_curve(best_model) ``` ### Plot Feature Impact ``` plot_feature_impact(best_model) ``` ### Build a better model Use the `admission_type` feature as a splitting point to create multiple projects. ``` fig, ax = plt.subplots(figsize=(12, 5)) c = sns.countplot(x="admission_type_id", data=df) ``` ## Create a mini model factory Often when DataRobot needs to set up Automated Feature Discovery (AFD), it may take a while to perform Exploratory Data Analysis (EDA). You can save time when running multiple projects by initiating all of them in parallel. Use Python's dask module to do so. ``` def run_dr_factory(segment_num): try: temp_project = dr.Project.start( df.loc[df["admission_type_id"] == segment_num], project_name="Readmission_%s" % segment_num, target="readmitted", metric="LogLoss", worker_count=10, ) return temp_project except: # Catching the case when dataset has fewer than 20 rows. return f"There was an error in segment {segment_num}." ``` ``` delayed_dr_projects = [] # Create one project for each customer type for value in df["admission_type_id"].unique(): temp = delayed(run_dr_factory)(value) delayed_dr_projects.append(temp) projects = compute(delayed_dr_projects)[0] # Filter to the projects that did not throw errors projects_filtered = [project for project in projects if not isinstance(project, str)] ``` ### Get the best-performing model for each admission type Even though accuracy changes may be insignificant for this dataset, in applicable cases a model factory can produce measurable value. This concept becomes increasingly important with a higher cardinality in your data. For example, consider if your business owns a variety of products, and you build a model factory to produce a model for each product. DataRobot saves you large amounts of time by having handling the evaluation of accuracy for separate set of models built for each product. ``` best_models = {} # To save models for key, project in enumerate(projects_filtered): best_models[key] = projects_filtered[key].get_models()[0] print("--------------------------------") print("Best model for admission type id: %s" % project) print(best_models[key]) print(best_models[key].metrics["LogLoss"]["crossValidation"]) print("--------------------------------") ``` ### Generate Feature Impact Observe the differences in Feature Impact outlined below, which could lead to actionable insights. ``` for key, project in enumerate(projects_filtered): plot_feature_impact( best_models[key], title="Feature Impact for admission type id: %s" % project ) ``` ### Deploy the most accurate models After identifying the best-performing models, you can deploy them and use DataRobot's REST API to make HTTP requests with the deployment ID and return predictions. Once deployed, access monitoring capabilities such as: - Service health - Prediction accuracy - Model retraining ``` prediction_server = dr.PredictionServer.list()[0] for key in best_models: temp_deployment = dr.Deployment.create_from_learning_model( best_models[key].id, label="Readmissions_admission_type: %s" % key, description="Test deployment", default_prediction_server_id=prediction_server.id, ) ``` --- # Python modeling workflow overview URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/modeling-code/python-modeling.html # Python modeling workflow overview This code example outlines how to use [DataRobot's Python API client](https://datarobot-public-api-client.readthedocs-hosted.com/) to train and experiment with models. It also offers ideas for integrating DataRobot with other products via the API. Specifically, you will: - Create a project and run Autopilot. - Experiment with feature lists, modeling algorithms, and hyperparameters. - Choose the best model. - Perform an in-depth evaluation of the selected model. - Deploy a model into production in a few lines of code. ## Data used for this example This walkthrough uses a synthetic dataset that illustrates a credit card company’s anti-money laundering (AML) compliance program, with the intent of detecting the following money-laundering scenarios: - A customer spends on the card, but overpays their credit card bill and seeks a cash refund for the difference. - A customer receives credits from a merchant without offsetting transactions, and either spends the money or requests a cash refund from the bank. A rule-based engine is in place to produce an alert when it detects potentially suspicious activity consistent with the scenarios above. The engine triggers an alert whenever a customer requests a refund of any amount. Small refund requests are included because they could be a money launderer’s way of testing the refund mechanism or trying to establish refund requests as a normal pattern for their account. The target feature is `SAR`, suspicious activity reports. It indicates whether or not the alert resulted in an SAR after manual review by investigators, which means that this project is a binary classification problem. The unit of analysis is an individual alert, so the model will be built on the alert level. Each alert will get a score ranging from 0 to 1, indicating the probability of being an alert leading to an SAR. The data consists of a mixture of numeric, categorical, and text data. ## Setup ### Import Libraries ``` import datarobot as dr from datarobot_bp_workshop import Visualize, Workshop import matplotlib.pyplot as plt import pandas as pd %matplotlib inline import time import warnings import graphviz import plotly.express as px import seaborn as sns warnings.filterwarnings("ignore") w = Workshop() # wider .head()s pd.options.display.width = 0 pd.options.display.max_columns = 200 pd.options.display.max_rows = 2000 sns.set_theme(style="darkgrid") ``` ### Connect to DataRobot Read more about different options for [connecting to DataRobot from the client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` # If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call # dr.Client(config_path='path-to-drconfig.yaml') ``` ### Select a training dataset ``` # To read from a local file, uncomment and use: # df = pd.read_csv('./data/DR_Demo_AML_Alert.csv') # To read from an s3 bucket: df = pd.read_csv("https://s3.amazonaws.com/datarobot_public_datasets/DR_Demo_AML_Alert.csv") df.head() ``` ``` # To view target distribution: df_target_summary = ( pd.DataFrame(df["SAR"].value_counts()) .reset_index() .rename(columns={"index": "SAR", "SAR": "Count"}) ) ax = sns.barplot(x="SAR", y="Count", data=df_target_summary) for index, row in df_target_summary.iterrows(): ax.text(row.SAR, row.Count, round(row.Count, 2), color="black", ha="center") plt.show() ``` ## Create a project and train models with Autopilot ``` # Create a project by uploading data. This will take a few minutes. project = dr.Project.create( sourcedata=df, project_name="DR_Demo_API_alert_AML_{}".format(pd.datetime.now().strftime("%Y-%m-%d %H:%M")), ) # Set the project's target and initiate Autopilot in Quick mode. # Wait for Autopilot to finish. You can set verbosity to 0 if you do not wish to see progress updates. project.analyze_and_model(target="SAR", worker_count=-1) # Open the project's Leaderboard to monitor the progress in UI. project.open_in_browser() ``` ### Retrieve and review results from the Leaderboard ``` def get_top_of_leaderboard(project, verbose=True): # A helper method to assemble a dataframe with Leaderboard results and print a summary: leaderboard = [] for m in project.get_models(): leaderboard.append( [ m.blueprint_id, m.featurelist.id, m.id, m.model_type, m.sample_pct, m.metrics["AUC"]["validation"], m.metrics["AUC"]["crossValidation"], ] ) leaderboard_df = pd.DataFrame( columns=[ "bp_id", "featurelist", "model_id", "model", "pct", "validation", "cross_validation", ], data=leaderboard, ) if verbose == True: # Print a Leaderboard summary: print("Unique blueprints tested: " + str(len(leaderboard_df["bp_id"].unique()))) print("Feature lists tested: " + str(len(leaderboard_df["featurelist"].unique()))) print("Models trained: " + str(len(leaderboard_df))) print("Blueprints in the project repository: " + str(len(project.get_blueprints()))) # Print the essential information for top models, sorted by accuracy from validation data: print("\n\nTop models on the Leaderboard:") leaderboard_top = ( leaderboard_df[leaderboard_df["pct"] == 64] .sort_values(by="cross_validation", ascending=False) .head() .reset_index(drop=True) ) display(leaderboard_top.drop(columns=["bp_id", "featurelist"], inplace=False)) # Show blueprints of top models: for index, m in leaderboard_top.iterrows(): Visualize.show_dr_blueprint(dr.Blueprint.get(project.id, m["bp_id"])) return leaderboard_top leaderboard_top = get_top_of_leaderboard(project) ``` ``` Unique blueprints tested: 15 Feature lists tested: 4 Models trained: 28 Blueprints in the project repository: 81 Top models on the Leaderboard: ``` | | model_id | model | pct | validation | cross_validation | | --- | --- | --- | --- | --- | --- | | 0 | 61ae672ab9e0c15c325b05b2 | RandomForest Classifier (Gini) | 64.0 | 0.94577 | 0.945790 | | 1 | 61ae73aa2a2a4649c04e2c73 | RandomForest Classifier (Gini) | 64.0 | 0.94598 | 0.945712 | | 2 | 61ae69df4e24ec75154c24ee | AVG Blender | 64.0 | 0.94573 | 0.945318 | | 3 | 61ae65a640d62771a45b0594 | eXtreme Gradient Boosted Trees Classifier with... | 64.0 | 0.94675 | 0.945166 | | 4 | 61ae65a540d62771a45b0592 | RandomForest Classifier (Gini) | 64.0 | 0.94542 | 0.944690 | ## Experiment to get better results When you run a project using Autopilot, DataRobot first creates blueprints based on the characteristics of your data and puts them in the Repository. Then, it chooses a subset from these to train; when training completes, these are the blueprints you’ll find on the Leaderboard. After the Leaderboard is populated, it can be useful to train some of those blueprints that DataRobot skipped. For example, you can try a more complex Keras blueprint like Keras Residual AutoInt Classifier using Training Schedule (3 Attention Layers with 2 Heads, 2 Layers: 100, 100 Units). In some cases, you may want to directly access the trained model through R and retrain it with a different feature list or tune its hyperparameters. ### Find blueprints trained for the project from the Repository ``` blueprints = project.get_blueprints() # After retrieving the blueprints, you can search for a specific blueprint # In the example below, search for all models that have "Gradient" in their name models_to_run = [] for blueprint in blueprints: if "Gradient" in blueprint.model_type: models_to_run.append(blueprint) ``` ``` models_to_run ``` ### Define and train a custom blueprint If you wish to instead create a custom blueprint rather than finding one from the Repository, use the following snippet. You can read more about composing custom blueprints via code by visiting the [blueprint workshop](https://blueprint-workshop.datarobot.com/) in DataRobot. ``` pdm3 = w.Tasks.PDM3(w.TaskInputs.CAT) pdm3.set_task_parameters(cm=50000, sc=10) ndc = w.Tasks.NDC(w.TaskInputs.NUM) rdt5 = w.Tasks.RDT5(ndc) ptm3 = w.Tasks.PTM3(w.TaskInputs.TXT) ptm3.set_task_parameters(d2=0.2, mxf=20000, d1=5, n="l2", id=True) kerasc = w.Tasks.KERASC(rdt5, pdm3, ptm3) kerasc.set_task_parameters( always_use_test_set=1, epochs=4, hidden_batch_norm=1, hidden_units="list(64)", hidden_use_bias=0, learning_rate=0.03, use_training_schedule=1, ) # Check task documentation: # kerasc.documentation() kerasc_blueprint = w.BlueprintGraph(kerasc, name="A Custom Keras BP (1 Layer: 64 Units)").save() kerasc_blueprint.show() kerasc_blueprint.train(project_id=project.id, sample_pct=64) ``` ``` Training requested! Blueprint Id: 4f5c40cbacfa89b3e37dc2f6d5c169a2 ``` ``` Name: 'A Custom Keras BP (1 Layer: 64 Units)' Input Data: Categorical | Numeric | Text Tasks: One-Hot Encoding | Numeric Data Cleansing | Smooth Ridit Transform | Matrix of word-grams occurrences | Keras Neural Network Classifier ``` ### Train a model with a different feature list ``` # Select a model from the Leaderboard: model = dr.Model.get(project=project.id, model_id=leaderboard_top.iloc[0]["model_id"]) # Retrieve Feature Impact: feature_impact = model.get_or_request_feature_impact() # Create a feature list using the top 25 features based on feature impact: feature_list = [f["featureName"] for f in feature_impact[:25]] new_list = project.create_featurelist("new_feat_list", feature_list) # Retrain models using the new feature list: model.retrain(featurelist_id=new_list.id) ``` ### Tune hyperparameters for a model ``` tune = model.start_advanced_tuning_session() # Get available task names, # and available parameter names for a task name that exists on this model tasks = tune.get_task_names() tune.get_parameter_names(tasks[2]) # Adjust this section as required as it may differ depending on task/parameter names as well as acceptable values tune.set_parameter(task_name=tasks[1], parameter_name="n_estimators", value=200) job = tune.run() ``` ### Select the best model ``` # View the top models on the Leaderboard leaderboard_top = get_top_of_leaderboard(project) ``` ``` Unique blueprints tested: 15 Feature lists tested: 4 Models trained: 28 Blueprints in the project repository: 81 Top models on the Leaderboard: ``` | | model_id | model | pct | validation | cross_validation | | --- | --- | --- | --- | --- | --- | | 0 | 61ae672ab9e0c15c325b05b2 | RandomForest Classifier (Gini) | 64.0 | 0.94577 | 0.945790 | | 1 | 61ae73aa2a2a4649c04e2c73 | RandomForest Classifier (Gini) | 64.0 | 0.94598 | 0.945712 | | 2 | 61ae69df4e24ec75154c24ee | AVG Blender | 64.0 | 0.94573 | 0.945318 | | 3 | 61ae65a640d62771a45b0594 | eXtreme Gradient Boosted Trees Classifier with... | 64.0 | 0.94675 | 0.945166 | | 4 | 61ae65a540d62771a45b0592 | RandomForest Classifier (Gini) | 64.0 | 0.94542 | 0.944690 | ``` # Select the model based on accuracy (AUC) top_model = dr.Model.get(project=project.id, model_id=leaderboard_top.iloc[0]["model_id"]) ``` ## In-depth model evaluation ### Retrieve and plot the ROC curve ``` roc = top_model.get_roc_curve("validation") df_roc = pd.DataFrame(roc.roc_points) dr_dark_blue = "#08233F" dr_roc_green = "#03c75f" white = "#ffffff" fig = plt.figure(figsize=(8, 8)) axes = fig.add_subplot(1, 1, 1, facecolor=dr_dark_blue) plt.scatter(df_roc.false_positive_rate, df_roc.true_positive_rate, color=dr_roc_green) plt.plot(df_roc.false_positive_rate, df_roc.true_positive_rate, color=dr_roc_green) plt.plot([0, 1], [0, 1], color=white, alpha=0.25) plt.title("ROC curve") plt.xlabel("False Positive Rate (Fallout)") plt.xlim([0, 1]) plt.ylabel("True Positive Rate (Sensitivity)") plt.ylim([0, 1]) plt.show() ``` ### Retrieve and plot Feature Impact ``` max_num_features = 15 # Retrieve Feature Impact feature_impacts = top_model.get_or_request_feature_impact() # Plot permutation-based Feature Impact feature_impacts.sort(key=lambda x: x["impactNormalized"], reverse=True) FeatureImpactDF = pd.DataFrame( [ {"Impact Normalized": f["impactNormalized"], "Feature Name": f["featureName"]} for f in feature_impacts[:max_num_features] ] ) FeatureImpactDF["X axis"] = FeatureImpactDF.index g = sns.lmplot(x="Impact Normalized", y="X axis", data=FeatureImpactDF, fit_reg=False) sns.barplot(y=FeatureImpactDF["Feature Name"], x=FeatureImpactDF["Impact Normalized"]) ``` ``` ``` ### Retrieve and plot Feature Effects ``` feature_effects = top_model.get_or_request_feature_effect(source="validation") max_features = 5 for f in feature_effects.feature_effects[:max_features]: plt.figure(figsize=(9, 6)) d = pd.DataFrame(f["partial_dependence"]["data"]) if f["feature_type"] == "numeric": d = d[d["label"] != "nan"] d["label"] = pd.to_numeric(d["label"]) sns.lineplot(x="label", y="dependence", data=d).set_title( f["feature_name"] + ": importance=" + str(round(f["feature_impact_score"], 2)) ) else: sns.scatterplot(x="label", y="dependence", data=d).set_title( f["feature_name"] + ": importance=" + str(round(f["feature_impact_score"], 2)) ) ``` ### Score data before deployment ``` # Use training data to test how the model makes predictions test_data = df.head(50) dataset_from_file = project.upload_dataset(test_data) predict_job_1 = top_model.request_predictions(dataset_from_file.id) predictions = predict_job_1.get_result_when_complete() display(predictions.head()) ``` | | row_id | prediction | positive_probability | prediction_threshold | class_0.0 | class_1.0 | | --- | --- | --- | --- | --- | --- | --- | | 0 | 0 | 0.0 | 0.000192 | 0.5 | 0.999808 | 0.000192 | | 1 | 1 | 0.0 | 0.214922 | 0.5 | 0.785078 | 0.214922 | | 2 | 2 | 0.0 | 0.256123 | 0.5 | 0.743877 | 0.256123 | | 3 | 3 | 0.0 | 0.000051 | 0.5 | 0.999949 | 0.000051 | | 4 | 4 | 0.0 | 0.215951 | 0.5 | 0.784049 | 0.215951 | ### Compute Prediction Explanations ``` # Prepare prediction explanations pe_job = dr.PredictionExplanationsInitialization.create(project.id, top_model.id) pe_job.wait_for_completion() ``` ``` # Compute prediction explanations with default parameters pe_job2 = dr.PredictionExplanations.create( project.id, top_model.id, dataset_from_file.id, max_explanations=3, threshold_low=0.1, threshold_high=0.5, ) pe = pe_job2.get_result_when_complete() display(pe.get_all_as_dataframe().head()) ``` ## Deploy a model After identifying the best-performing models, you can deploy them and use DataRobot's REST API to make HTTP requests and return predictions. You can also configure batch jobs to write back into your environment of choice. Once deployed, access monitoring capabilities such as: - Service health - Prediction accuracy - Model retraining ``` # Copy and paste the model ID from previous steps or from the UI: model_id = top_model.id prediction_server_id = dr.PredictionServer.list()[0].id deployment = dr.Deployment.create_from_learning_model( model_id, label="New Deployment", description="A new deployment", default_prediction_server_id=prediction_server_id, ) deployment ``` --- # Make batch predictions with Azure Blob storage URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/prediction-examples/azure-pred.html > Use the DataRobot Python Client package to set up a batch prediction job that reads an input file for scoring from Azure Blob storage and then writes the results back to Azure. # Make batch predictions with Azure Blob storage The DataRobot Batch Prediction API allows you to take in large datasets and score them against deployed models running on a prediction server. The API also provides flexible options for the intake and output of these files. In this tutorial, you will learn how to use the DataRobot Python Client package (which calls the Batch Prediction API) to set up a batch prediction job. The job reads an input file for scoring from Azure Blob storage and then writes the results back to Azure. This approach also works for Azure Data Lake Storage Gen2 accounts because the underlying storage is the same. ## Requirements In order to use the code provided in this tutorial, make sure you have the following: - Python 2.7 or 3.4+ - The DataRobot Python package (2.21.0+) (pypi) (conda) - A DataRobot deployment - An Azure storage account - An Azure storage container - A scoring dataset in the storage container to use with your DataRobot deployment ## Create stored credentials Running batch prediction jobs requires the appropriate credentials to read and write to Azure Blob storage. You must provide the name of the Azure storage account and an access key. 1. To retrieve these credentials, select theAccess keysmenu in the Azure portal. 2. ClickShow keysto retrieve an access key. You can use either of the keys shown (key1 or key2). 3. Use the following code to create a new credential object within DataRobot that can be used in the batch prediction job to connect to your Azure storage account. AZURE_STORAGE_ACCOUNT="YOUR AZURE STORAGE ACCOUNT NAME"AZURE_STORAGE_ACCESS_KEY="AZURE STORAGE ACCOUNT ACCESS KEY"DR_CREDENTIAL_NAME="Azure_{}".format(AZURE_STORAGE_ACCOUNT)# Create Azure-specific credentials# You can also copy the connection string, which is found below the access key in Azure.credential=dr.Credential.create_azure(name=DR_CREDENTIAL_NAME,azure_connection_string="DefaultEndpointsProtocol=https;AccountName={};AccountKey={};".format(AZURE_STORAGE_ACCOUNT,AZURE_STORAGE_ACCESS_KEY))# Use this code to look up the ID of the credential object created.credential_id=Noneforcredindr.Credential.list():ifcred.name==DR_CREDENTIAL_NAME:credential_id=cred.credential_idbreakprint(credential_id) ## Run the prediction job With a credential object created, you can now configure the batch prediction job as shown in the code sample below: - Setintake_settingsandoutput_settingsto theazuretype. - Forintake_settingsandoutput_settings, seturlto the files in Blob storage that you want to read and write to (the output file does not need to exist already). - Provide the ID of the credential object that was created above. The code sample creates and runs the batch prediction job. Once finished, it provides the status of the job. This code also demonstrates how to configure the job to return both Prediction Explanations and passthrough columns for the scoring data. > [!NOTE] Note > You can find the deployment ID in the sample code output of the [Deployments > Predictions > Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab (with Interface set to "API Client"). ``` DEPLOYMENT_ID = 'YOUR DEPLOYMENT ID' AZURE_STORAGE_ACCOUNT = "YOUR AZURE STORAGE ACCOUNT NAME" AZURE_STORAGE_CONTAINER = "YOUR AZURE STORAGE ACCOUNT CONTAINER" AZURE_INPUT_SCORING_FILE = "YOUR INPUT SCORING FILE NAME" AZURE_OUTPUT_RESULTS_FILE = "YOUR OUTPUT RESULTS FILE NAME" # Set up our batch prediction job # Input: Azure Blob Storage # Output: Azure Blob Storage job = dr.BatchPredictionJob.score( deployment=DEPLOYMENT_ID, intake_settings={ 'type': 'azure', 'url': "https://{}.blob.core.windows.net/{}/{}".format(AZURE_STORAGE_ACCOUNT, AZURE_STORAGE_CONTAINER,AZURE_INPUT_SCORING_FILE), "credential_id": credential_id }, output_settings={ 'type': 'azure', 'url': "https://{}.blob.core.windows.net/{}/{}".format(AZURE_STORAGE_ACCOUNT, AZURE_STORAGE_CONTAINER,AZURE_OUTPUT_RESULTS_FILE), "credential_id": credential_id }, # If explanations are required, uncomment the line below max_explanations=5, # If passthrough columns are required, use this line passthrough_columns=['column1','column2'] ) job.wait_for_completion() job.get_status() ``` When the job completes successfully, you should see the output file in your Azure Blob storage container. ## Documentation - Prediction API overview - DataRobot Batch Prediction API --- # Using the batch prediction API URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/prediction-examples/batch-pred-api.html # Using the batch prediction API This notebook demonstrates how to use DataRobot's batch prediction API to score large datasets with a deployed DataRobot model. The batch prediction API provides flexible intake and output options when scoring large datasets using prediction servers. The API is exposed through the DataRobot Public API and can be consumed using a REST-enabled client or Public API bindings for DataRobot's Python client. Some features of the batch prediction API include: - Intake and output configuration. - Support for streaming local files. - The ability to initiate scoring while still uploading data and simultaneously downloading the results. - Scoring large datasets from and to Amazon S3, Azure Blob, and Google Cloud Storage. - Connecting to external data sources using JDBC with bidirectional streaming of scoring data and results. - A mix of intake and output options; for example, the ability to score from a local file and return results to an S3 target. - Protection against prediction server overload with concurrency and size control level options. - Prediction Explanations (with an option to add thresholds). - Support for passthrough columns to correlate scored data with source data. - Prediction warnings in the output. ## Requirements - Python version 3.7.3 - DataRobot API version 2.26.0 - A deployed DataRobot model object Small adjustments may be required depending on the versions of Python and the DataRobot API you are using. You can also access [full documentation of the Python package](https://docs.datarobot.com/en/docs/predictions/batch/batch-prediction-api/index.html). ## Connect to DataRobot To inititate scoring jobs through the batch prediction API, you need to connect to DataRobot through the `datarobot.Client` command. DataRobot recommends providing a configuration file containing your credentials (endpoint and API Key) to connect to DataRobot. For more information about authentication, reference the [API Quickstart guide]( [https://docs.datarobot.com/en/docs/api/dev-learning/api-quickstart.html](https://docs.datarobot.com/en/docs/api/dev-learning/api-quickstart.html). ``` import datarobot as dr # If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call # dr.Client(config_path='path-to-drconfig.yaml') ``` ## Set the deployment ID Before proceeding, provide the deployed model's deployment ID (retrieved from the deployment's [Overview tab](https://docs.datarobot.com/en/docs/mlops/monitor/dep-overview.html)). ``` deployment_id = "YOUR_DEPLOYMENT_ID" ``` ## Determine input and output options DataRobot's batch prediction API allows you to score data from and to multiple sources. You can take advantage of the credentials and data sources you have already established previously through the UI for easy scoring. Credentials are usernames and passwords, while data sources are any databases with which you have previously established a connection (e.g., Snowflake). View the example code below outlining how to query credentials and data sources. You can reference the full list of DataRobot's supported [input](https://docs.datarobot.com/en/docs/predictions/batch/batch-prediction-api/intake-options.html) and [output options](https://docs.datarobot.com/en/docs/predictions/batch/batch-prediction-api/output-options.html). The snippet below shows how you can query all credentials tied to a DataRobot account. ``` dr.Credential.list() ``` ``` [Credential('5e6696ff820e737a5bd78430', 'adam', 'basic'), Credential('5ed55704397e667bb0caf1c8', 'DATAROBOT', 'basic'), Credential('5ed557e8ae4c4f7ccd1f0fda', 'ta_admin', 'basic'), Credential('5ed55e08397e667c2bcaf137', 'SourceCredentials_PredicitonJob_5ed55e07397e667c2bcaf134', 'basic'), Credential('5ed55e08397e667c2bcaf139', 'TargetCredentials_PredicitonJob_5ed55e07397e667c2bcaf134', 'basic'), Credential('5ed6ba3c397e6611f9caf27d', 'SourceCredentials_PredicitonJob_5ed6ba3c397e6611f9caf27a', 'basic'), Credential('5ed6ba3d397e6611f9caf27f', 'TargetCredentials_PredicitonJob_5ed6ba3c397e6611f9caf27a', 'basic')] ``` The output above returns multiple sets of credentials. The alphanumeric string included in each item of the list is the credentials ID. You can use that ID to access credentials through the API. The snippet below shows how you can query all data sources tied to a DataRobot account. The second line lists each datastore with an alphanumeric string; that is the datastore ID. ``` dr.DataStore.list() print(dr.DataStore.list()[0].id) ``` ``` 5e6696ff820e737a5bd78430 ``` ## Batch prediction scoring examples The snippets below demonstrate how to score data with the Batch Prediction API. Edit the `intake_settings` and `output_settings` to suit your needs. You can mix and match until you get the outcome you prefer. ### Score from CSV to CSV ``` # Scoring without Prediction Explanations if False: dr.BatchPredictionJob.score( deployment_id, intake_settings={ "type": "localFile", "file": "inputfile.csv", # Provide the filepath, Pandas dataframe, or file-like object here }, output_settings={"type": "localFile", "path": "outputfile.csv"}, ) # Scoring with Prediction Explanations if False: dr.BatchPredictionJob.score( deployment_id, intake_settings={ "type": "localFile", "file": "inputfile.csv", # Provide the filepath, Pandas dataframe, or file-like object here }, output_settings={"type": "localFile", "path": "outputfile.csv"}, max_explanations=3, # Compute Prediction Explanations for the amount of features indicated here ) ``` ### Score from S3 to S3 ``` if False: dr.BatchPredictionJob.score( deployment_id, intake_settings={ "type": "s3", "url": "s3://theos-test-bucket/lending_club_scoring.csv", # Provide the URL of your datastore here "credential_id": "YOUR_CREDENTIAL_ID_FROM_ABOVE", # Provide your credentials here }, output_settings={ "type": "s3", "url": "s3://theos-test-bucket/lending_club_scored2.csv", "credential_id": "YOUR_CREDENTIAL_ID_FROM_ABOVE", }, ) ``` ### Score from JDBC to JDBC ``` if False: dr.BatchPredictionJob.score( deployment_id, intake_settings={ "type": "jdbc", "table": "table_name", "schema": "public", "dataStoreId": data_store.id, # Provide the ID of your datastore here "credentialId": cred.credential_id, # Provide your credentials here }, output_settings={ "type": "jdbc", "table": "table_name", "schema": "public", "statementType": "insert", "dataStoreId": data_store.id, "credentialId": cred.credential_id, }, ) ``` --- # ESG score predictions with Python URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/prediction-examples/esg-score.html # ESG score predictions with Python This notebook walks through Python code from an example application that uses DataRobot to predict the Environmental, Societal, and Corporate (ESG) scores for stocks. After completing this lab, you will be able to: - Use the DataRobot Python client to build a model from a training dataset and deploy the model. - Use the DataRobot predictions REST API to calculate predicted values. ### Goals This example workflow creates a model to predict a company's ESG score. [ESG](https://en.wikipedia.org/wiki/Environmental,_social_and_corporate_governance) is a rating of a corporation's Environmental, Societal, and Governance scores. A lower ESG score means a particular company (and its stock) is less exposed to risk in that area. For example, a company that deals with oil extraction might have a very high environmental impact rating, which will in turn increase their overall ESG score. Calculating ESG scores is an extensive process that involves in-depth analysis of a company's publicly available information, as well as data from news sources. As not every company will have their ESG score calculated, you will use DataRobot's ML technology to score a large number of companies across several different stock exchanges that don't have existing ESG scores. This example is part of a larger project — a full demo application called "Harv the Finance Finder." This lab only covers the portions of the application specific to DataRobot AutoML. For an example of how these predictions could be included in an application, [see the full application source](https://github.com/datarobot-community/harv-the-finance-finder) in GitHub. ## Setup ### Prerequisites In order to complete this workflow, you'll need: - A DataRobot account - Basic knowledge of Python - Familiarity with data science concepts and terminology - Python 3 and the DataRobot Python client installed. ## Explore the training data Start by downloading the sample data file: [stock_quotes_esg_train.csv](https://s3.amazonaws.com/datarobot_public/dru/esg/stock_quotes_esg_train.csv) Review the CSV file and note the features: - symbo is the stock ticker symbol — MSFT for Microsoft, V for Visa, GM for General Motors, and so on, as seen in companyName . - open , close , high , low , week52Low , and week52High indicate how the stock price has moved, either today or in the last year. All numbers are in USD. - marketCap tells us the total valuation of the company. - sector is the primary sector that the company operates in, for example, Electronic Technology, Health Services, Transportation, etc. - esg_category is the target feature you'll be training the model on. The companies are lumped into four ESG categories: 1 being the lowest ESG risk (best) and 4 being the highest (worst). ### Data source The application uses data from the [IEX API](http://iexcloud.io/). The stock dataset was created by merging stock data from various industry sectors into a single dataset. The data was collected on 25 May 2020. ESG scores are provided by various agencies, but not in a publicly accessible API. For this showcase application, DataRobot generated fake data to train the model, based on sustainability ratings available in [Yahoo Finance](https://finance.yahoo.com/quote/LULU/sustainability?p=LULU). The script used in the project is available on [GitHub](https://github.com/datarobot-community/harv-the-finance-finder/blob/master/scripts/generate_esg_data.py). ### Connect to DataRobot To read more about the options for connecting to DataRobot from the Python client, [review the API Quickstart guide](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` # If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call # dr.Client(config_path='path-to-drconfig.yaml') ``` ### Configure the Python client The most important package to import is the DataRobot Python Client(opens in a new tab) package, which provides the API to connect your client application to DataRobot. ``` import datarobot as dr ``` Then instantiate the DataRobot client. ``` dr.Client() ``` ## Upload data Upload the project with `dr.Project.create`, passing in the path to the dataset you downloaded above. Specify a project name of your choice. ``` import os, urllib.request DATASET_PATH = "./stock_quotes_esg_train.csv" DATASET_URL = "https://s3.amazonaws.com/datarobot_public/dru/esg/stock_quotes_esg_train.csv" if not os.path.exists(DATASET_PATH): print(f"Downloading dataset from {DATASET_URL} ...") urllib.request.urlretrieve(DATASET_URL, DATASET_PATH) print("Download complete.") project = dr.Project.create( sourcedata=DATASET_PATH, project_name="your project name" ) ``` ## Modeling ### Define the target As a best practice, this application would typically use `esg_category`, a numerical property, as the target feature. However, for learning purposes only, use [multiclass classification](https://docs.datarobot.com/en/docs/modeling/analyze-models/evaluate/multiclass.html#background) to predict ESG scores to be one of four categories, represented by integer values 1 to 4 rather than a numeric value. Do this by transforming the `esg_category` numeric feature to a categorical feature called `esg_category_categorical` using `project.create_type_transform_feature`. ``` # Transform esg_category into a categorical variable type # Note: not best practice, included for learning purposes only project.create_type_transform_feature( "esg_category_categorical", # new feature name "esg_category", # parent name dr.enums.VARIABLE_TYPE_TRANSFORM.CATEGORICAL_INT, ) ``` ### Start Autopilot To start the process of training models on this data, call `project.set_target()`, passing in the target name ( `esg_category_categorical`), which you created in previous steps. You can also pass the mode option, telling DataRobot to do a quick modeling run, building a limited set of models. This can be a long-running process. Call `project.wait_for_autopilot(`), which will print informative output and block the script until the modeling job is finished. ``` # This kicks off modeling using Quick Autopilot mode project.set_target(target="esg_category_categorical", mode=dr.enums.AUTOPILOT_MODE.QUICK) # Time for a cup of tea or a walk - this might take ~15 minutes project.wait_for_autopilot() ``` ### Get the recommended model After Autopilot has finished, you can get a list of all models it has created in your project, ranked by their accuracy. You can get DataRobot's recommendation by calling `dr.ModelRecommendation.get(project.id)`, and get our model from that using `get_model()`. ``` recommendation = dr.ModelRecommendation.get(project.id) recommended_model = recommendation.get_model() print(f"Recommended model is {recommended_model}") ``` ### Deploy the model Now that you have the recommended model, you can deploy it to a production environment to make predictions with new data. Models are not deployed to the same server used to train models; they are deployed to one or more prediction servers. The code below automatically retrieves the first available prediction server (required for DataRobot Cloud and on-premises accounts) and uses it when creating the deployment. After determining the prediction server, use `dr.Deployment.create_from_learning_model` to deploy the model. ``` # Get the prediction server ID. # Required for DataRobot Cloud and on-premises accounts; optional for trial accounts. prediction_servers = dr.PredictionServer.list() prediction_server_id = prediction_servers[0].id if prediction_servers else None deployment = dr.Deployment.create_from_learning_model( model_id=recommended_model.id, label="Financial ESG model", description="Model for scoring financial quote data", default_prediction_server_id=prediction_server_id, ) print(f"Deployment created: {deployment}, deployment id: {deployment.id}") ``` ## Calculate ESG scores for a dataset ### Download prediction data After your model has been deployed you can start making predictions using [DataRobot'sREST API](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi.html). You can use any language to call the API. This notebook uses Python. Start by downloading the dataset we want to calculate ESG scores for: [stock_quotes_all.csv](https://s3.amazonaws.com/datarobot_public/dru/esg/stock_quotes_all.csv). Be sure to save the file in the same location where you saved the training data previously. ### Configure application code In the real world, predictions are likely to happen in a separate application from creating and deploying a model. In this notebook, DataRobot assumes you are working in an interactive environment such as a Python shell or notebook, but this notebook walks through the process you'd go through for adding the code to an application. Make sure the `DATAROBOT_API_TOKEN` and `DATAROBOT_ENDPOINT` are set in your environment as described in the first step of this lab. Then set up your application in Python, which is similar to what you did when setting up for the model building steps: ``` import csv import json import os import sys import urllib.request import datarobot as dr import requests DR_API_KEY = os.environ["DATAROBOT_API_TOKEN"] dr.Client() ``` You might have taken note of the deployment ID when you deployed your model earlier. If you have that ID available, you can use it to find the prediction server for the deployed model. But that value may not be readily accessible in a real application, so use the following code to find the correct deployment using the label you set when deploying the model. Refer back to the earlier steps where you deployed the model to find the label specified and use it here to find the deployment and its associated prediction server. ``` for d in dr.Deployment.list(): if d.label == "Financial ESG model": deployment = d prediction_server_url = deployment.default_prediction_server["url"] ``` ### Make a prediction request Now you have everything you need to make your request. DataRobot's prediction API doesn't come with an SDK so you need to "handcraft" your API requests, using Python's requests. You are sending the following header values: - Content-Type in this case is text/plain as you're sending a CSV file. Alternatively, the API also accepts application/json for JSON payloads. - Authorization takes the same API key we used with the modeling API in the DataRobot Python SDK. - datarobot-key is the key specifically for the prediction server. Note that this value is not used for trial or pay-as-you-go DataRobot accounts. To predict, send a POST request to the prediction server with the data from the file you downloaded above as the payload. Prediction API responds in JSON format, and your predictions will be in the data field. ``` PRED_DATASET_PATH = "./stock_quotes_all.csv" PRED_DATASET_URL = "https://s3.amazonaws.com/datarobot_public/dru/esg/stock_quotes_all.csv" if not os.path.exists(PRED_DATASET_PATH): print(f"Downloading prediction dataset from {PRED_DATASET_URL} ...") urllib.request.urlretrieve(PRED_DATASET_URL, PRED_DATASET_PATH) print("Download complete.") headers = { "Content-Type": "text/plain; charset=UTF-8", "Authorization": f"Bearer {DR_API_KEY}", # comment out line below if using a trial or pay-as-you-go account. "datarobot-key": deployment.default_prediction_server["datarobot-key"], } url = f"{prediction_server_url}/predApi/v1.0/deployments/{deployment.id}/predictions?passthroughColumns=symbol" data = open(PRED_DATASET_PATH, "rb").read() predictions_response = requests.post(url, data=data, headers=headers) predictions = predictions_response.json()["data"] ``` ### Parse and save the prediction response The prediction data payload is a JSON array of objects for all predicted fields, in the same order that you sent it. The actual predicted value is in the prediction field. In this example, you're creating a new list of symbol/category pairs, and filling them by iterating through the returned predictions. ``` # Transform predictions into a CSV file of the format:# symbol, esg_category # where symbol is a value passed through from the prediction request esg_categories = [["symbol", "esg_category"]] for prediction in predictions: symbol = prediction["passthroughValues"]["symbol"] value = int(prediction["prediction"]) esg_entry = [symbol, value] esg_categories.append(esg_entry) # Write the data as a CSV file with open("stocks_esg_scores.csv", mode="w") as out_csv: csv_writer = csv.writer(out_csv) csv_writer.writerows(esg_categories) ``` Review the contents of the output file — `stocks_esg_scores.csv` — to confirm that the `esg_category` column contains ESG categories (i.e. integers 1 through 4). ## Recap In this lab, you walked through Python application code to: - Connect a client application to DataRobot. - Upload a training dataset to DataRobot AutoML. - Identify a target value. - Run Autopilot to generate a set of models . - Deploy the recommended model to a prediction server. - Request predictions from DataRobot for a dataset. --- # Make batch predictions with Google Cloud storage URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/prediction-examples/gcs-pred.html > Learn how to make and write predictions to Google Cloud Storage. # Make batch predictions with Google Cloud storage The DataRobot Batch Prediction API allows you to take in large datasets and score them against deployed models running on a prediction server. The API also provides flexible options for the intake and output of these files. In this tutorial, you will learn how to use the DataRobot Python Client package (which calls the Batch Prediction API) to set up a batch prediction job. The job reads an input file for scoring from Google Cloud Storage (GCS) and then writes the results back to GCS. ## Requirements In order to use the code provided in this tutorial, make sure you have the following: - Python 2.7 or 3.4+ - The DataRobot Python package (version 2.21.0+) - A DataRobot deployment - A GCS bucket - A service account with access to the GCS bucket (detailed below) - A scoring dataset that lives in the GCS bucket to use with your DataRobot deployment ## Configure a GCP service account Running batch prediction jobs requires the appropriate credentials to read and write to GCS. You must create a service account within the Google Cloud Platform that has access to the GCS bucket, then download a key for the account to use in the batch prediction job. 1. To retrieve these credentials, log into the Google Cloud Platform console and selectIAM & Admin > Service Accountsfrom the sidebar. 2. ClickCreate Service Account. Provide a name and description for the account, then clickCreate > Done. 3. On theService Accountpage, find the account that you just created, navigate to theDetailspage, and clickKeys. 4. Go to theAdd Keymenu and clickCreate new key. Select JSON for the key type and clickCreateto generate a key and download a JSON file with the information required for the batch prediction job. 5. Return to your GCS bucket and navigate to thePermissionstab. ClickAdd, enter the email address for the service account user you created, and give the account the “Storage Admin” role. ClickSaveto confirm the changes. This grants your GCP service account access to the GCS bucket. ## Create stored credentials After downloading the JSON key, use the following code to create a new credential object within DataRobot. The credentials will be used in the batch prediction job to connect to the GCS bucket. Open the JSON key file and copy its contents into the key variable. The DataRobot Python client reads the JSON data as a dictionary and parses it accordingly. ``` # Set name for GCP credential in DataRobot DR_CREDENTIAL_NAME = "YOUR GCP DATAROBOT CREDENTIAL NAME" # Create a GCP-specific Credential # NOTE: This cannot be done from the UI # This can be generated and downloaded ready to drop in from within GCP # 1. Go to IAM & Admin -> Service Accounts # 2. Search for the Service Account you want to use (or create a new one) # 3. Go to Keys # 4. Click Add Key -> Create Key # 5. Selection JSON key type # 6. copy the contents of the json file into the gcp_key section of the credential code below key = { "type": "service_account", "project_id": "**********", "private_key_id": "***************", "private_key": "-----BEGIN PRIVATE KEY-----\n********\n-----END PRIVATE KEY-----\n", "client_email": "********", "client_id": "********", "auth_uri": "https://accounts.google.com/o/oauth2/auth", "token_uri": "https://oauth2.googleapis.com/token", "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/*********" } credential = dr.Credential.create_gcp( name=DR_CREDENTIAL_NAME, gcp_key=key ) # Use this code to look up the ID of the credential object created. credential_id = None for cred in dr.Credential.list(): if cred.name == DR_CREDENTIAL_NAME: credential_id = cred.credential_id break print(credential_id) ``` ## Run the prediction job With a credential object created, you can now configure the batch prediction job. Set the `intake_settings` and `output_settings` to the `gcp` type. Provide both attributes with the URL to the files in GCS that you want to read and write to (the output file does not need to exist already). Additionally, provide the ID of the credential object that was created above. The code below creates and runs the batch prediction job. Once finished, it provides the status of the job. This code also demonstrates how to configure the job to return both Prediction Explanations and passthrough columns for the scoring data. > [!NOTE] Note > You can find the deployment ID in the sample code output of the [Deployments > Predictions > Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab (with Interface set to "API Client"). ``` DEPLOYMENT_ID = 'YOUR DEPLOYMENT ID' # Set GCP Info GCP_BUCKET_NAME = "YOUR GCS BUCKET NAME" GCP_INPUT_SCORING_FILE = "YOUR INPUT SCORING FILE NAME" GCP_OUTPUT_RESULTS_FILE = "YOUR OUTPUT RESULTS FILE NAME" # Set up the batch prediction job # Input: Google Cloud Storage # Output: Google Cloud Storage job = dr.BatchPredictionJob.score( deployment=DEPLOYMENT_ID, intake_settings={ 'type': 'gcp', 'url': "gs://{}/{}".format(GCP_BUCKET_NAME,GCP_INPUT_SCORING_FILE), "credential_id": credential_id }, output_settings={ 'type': 'gcp', 'url': "gs://{}/{}".format(GCP_BUCKET_NAME,GCP_OUTPUT_RESULTS_FILE), "credential_id": credential_id }, # If explanations are required, uncomment the line below max_explanations=5, # If passthrough columns are required, use this line passthrough_columns=['column1','column2'] ) job.wait_for_completion() job.get_status() ``` When the job completes successfully, you will see the output file in the GCS bucket. ## Documentation - Prediction API overview - DataRobot Batch Prediction API --- # Prediction code examples URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/prediction-examples/index.html > Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of prediction workflows. # Prediction code examples The API user guide includes overviews and workflows for DataRobot's Python client that outline complete examples of prediction workflows and tasks. Be sure to review the [API quickstart guide]api-quickstart before using the notebooks below. | Topic | Describes... | | --- | --- | | Make batch predictions with Azure Blob storage | How to generate SHAP-based Prediction Explanations with a use case that determines what drives home value in Iowa. | | Using the Batch Prediction API | DataRobot's batch prediction API to score large datasets with a deployed DataRobot model. | | Make batch predictions with Google Cloud Storage | How to read input data from and write predictions back to Google Cloud Storage. | | Make Visual AI predictions via the API | Scripting code for making batch predictions for a Visual AI model via the API. | | ESG score predictions with Python | How to use Python code from an example application that uses DataRobot to predict the Environmental, Societal, and Corporate (ESG) scores for stocks. | | Create and schedule JDBC prediction jobs | How to use DataRobot's Python client to schedule prediction jobs and write them to a JDBC database. | --- # Schedule predictions with a JDBC database URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/prediction-examples/jdbc-nb.html # Schedule predictions with a JDBC database Making predictions on a daily or monthly basis is a manual, time-consuming, and cumbersome process. Batch predictions are commonly used when you have to score new records over a certain frame of time (weeks, months, etc.). For example, you can use batch predictions to score new leads on a monthly basis to predict who will churn, or to predict on a daily basis which products someone is likely to purchase. This notebook outlines how to use DataRobot's Python client to schedule batch prediction jobs and write them to a JDBC database. Specifically, you will: 1. Retrieve existing data stores and credential information. 2. Configure prediction job specifications. 3. Set up a prediction job schedule. 4. Run a test prediction job and enable an automated schedule for scoring. Before proceeding, note that this workflow requires a [deployed DataRobot model](https://docs.datarobot.com/en/docs/mlops/deployment/deploy-methods/index.html) object to use for scoring and an established [data connection](https://docs.datarobot.com/en/docs/data/connect-data/data-conn.html) to read data and host prediction writeback. For more information about the Python client, reference the [documentation](https://datarobot-public-api-client.readthedocs-hosted.com). ### Import libraries ``` import datarobot as dr import pandas as pd ``` ### Connect to DataRobot Read more about different options for [connecting to DataRobot from the client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` # If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call # dr.Client(config_path='path-to-drconfig.yaml') ``` ### List data stores To enable integration with a variety of enterprise databases, DataRobot provides a “self-service” JDBC product for database connectivity setup. Once configured, you can read data from production databases for model building and predictions. This allows you to quickly train and retrain models on that data while avoiding the unnecessary step of exporting data from your enterprise database to a CSV for ingest to DataRobot. It allows access to more diverse data, which results in more accurate models. Use the cell below to query all data sources tied to a DataRobot account. The second line lists each datastore with an alphanumeric string; that is the datastore ID. ``` for d in dr.DataStore.list(): print(d.id, d.canonical_name, d.params) ``` ### Retrieve credentials list You can reference the [DataRobot documentation](https://docs.datarobot.com/en/docs/data/connect-data/stored-creds.html#credentials-management) for more information about managing credentials. ``` dr.Credential.list() ``` The output above returns multiple sets of credentials. The alphanumeric string included in each item of the list is the credentials ID. You can use that ID to access credentials through the API. ### Specify the deployment and data connection Use the snippet below to indicate the deployment you want to use (by binding the deployment ID, retrieved from the deployment's [Overview tab](https://docs.datarobot.com/en/docs/mlops/monitor/dep-overview.html)) and the data store to which you want to write predictions (by providing the data store ID and the corresponding credentials ID). ``` deployment_id = "620219bb18f7f84dec6cec59" datastore_id = "614ca745c7fab1f23da7a632" data_store = dr.DataStore.get(datastore_id) credential_id = "63865454a351b56ce3cb78b3" cred = dr.Credential.get(credential_id) ``` ### Configure intake settings Use the snippet below to configure the intake settings for JDBC scoring. For more information, reference the [batch predictions documentation in the Python client](https://datarobot-public-api-client.readthedocs-hosted.com/en/v2.25.0/entities/batch_predictions.html) and the [intake options documentation](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html). intake_settings = { 'type': 'jdbc', 'table': 'LENDING_CLUB_10K', 'schema': 'TRAINING', # optional, if supported by database 'catalog': 'DEMO', # optional, if supported by database 'data_store_id': data_store.id, 'credential_id': cred.credential_id, } print(intake_settings) ### Configure output settings Use the snippet below to configure the output settings for JDBC scoring. For more information, reference the [output options documentation](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-options.html#statement-types). ``` output_settings = { "type": "jdbc", "table": "LENDING_CLUB_10K_AA_Temp", "schema": "SCORING", # optional, if supported by database "catalog": "SANDBOX", # optional, if supported by database schema "statement_type": "insert", "create_table_if_not_exists": True, "data_store_id": data_store.id, "credential_id": cred.credential_id, } print(output_settings) # Uncomment and use the following lines for local file export: # output_settings={ # 'type': 'localFile', # 'path': './predicted.csv', # } # print(output_settings) ``` Use the code below to retrieve the name of the deployment. ``` deployment = dr.Deployment.get(deployment_id) deployment.label ``` ### Create a schedule Next, set up a [schedule](https://datarobot-public-api-client.readthedocs-hosted.com/en/v2.25.0/entities/batch_prediction_job_definitions.html?highlight=schedule) for making predictions. The snippet below creates a schedule that makes predictions on the first day of every month at 7:59 AM. ``` schedule = { "minute": [59], "hour": [7], "month": ["*"], "dayOfWeek": ["*"], "dayOfMonth": [1], } schedule ``` ### Configure a prediction job ``` job = { "deployment_id": deployment_id, "num_concurrent": 4, "intake_settings": intake_settings, "output_settings": output_settings, "passthroughColumnsSet": "all", } ``` ### Create a prediction job After configuring a prediction job, use the `BatchPredictionJobDefinition.create` method to create a prediction job definition based on the job and scheduled you configured above. ``` definition = dr.BatchPredictionJobDefinition.create( enabled=True, batch_prediction_job=job, name="Monthly Prediction Job JDBC", schedule=schedule ) definition ``` Lastly, the snippet below initiates the prediction job on the schedule. ``` job_run_automatically = definition.run_on_schedule(schedule) ``` --- # Make Visual AI predictions via the API URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/prediction-examples/vai-pred.html > Learn how to make predictions on Visual AI projects with API calls. # Make Visual AI predictions via the API This tutorial outlines how to make predictions on Visual AI projects with API calls. To complete this tutorial, you must have trained and deployed a visual AI model. ## Takeaways This tutorial shows how to: - Configure scripting code for making batch predictions via the API - Make an API call to get batch predictions for a visual AI model - Format images to a base64 format ## Predictions workflow 1. Prepare your data for Visual AI. Before making predictions, convert the images you want to score tobase64 format(the standard format for handling images in API calls). Note that when the model returns prediction results, images will return in base64 format. To convert data, use DataRobot's Python package, described in the guidePreparing binary data for predictions. 2. After training and deploying a Visual AI model, navigate to the deployment and access thePredictions > Prediction APItab. This tab provides the scripting code used to make predictions via the API. 3. To configure the scripting code, selectBatchas the prediction type andAPI Clientas the interface type. 4. Copy the code and save it as a Python script (e.g.,datarobot-predict.py). You can edit the script to incorporate additional steps. For example, add thepassthrough_columns_setargument toBatchPredictionJobif you would like to include columns from the input file (e.g.,image_id) to the output file. 5. Using the scripting code fromstep twoand a base64-converted image file (InputDataConverted.csv), make an API call to get predictions from the deployed model: python datarobot-predict.py InputDataConverted.csv Predictions.csv 6. Access the output file (Predictions.csv) to view prediction results. ## Learn More ## Documentation - Visual AI overview - Making predictions with Visual AI - Prediction API overview --- # Bolt-on governance with Pulumi URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/pulumi-examples/deploy-gov.html # Bolt-on governance with Pulumi When DataRobot refers to "Bolt on Governance", it means wrapping a large language model endpoint in a DataRobot deployment, allowing you to engage with it through a DataRobot API token. In exchange for creating a deployment around the LLM, you get all of the benefits of DataRobot MLOps such as text drift monitoring, request history, and usage statistics. This notebook outlines how to use pulumi to create a deployment endpoint that interfaces with a large language model. ## Initialize the environment As a preliminary step, initialize our environment and make sure the LLM credentials work. ``` import os import datarobot as dr from openai import AzureOpenAI os.environ["PULUMI_CONFIG_PASSPHRASE"] = "default" assert ( "DATAROBOT_API_TOKEN" in os.environ ), "Please set the DATAROBOT_API_TOKEN environment variable" assert "DATAROBOT_ENDPOINT" in os.environ, "Please set the DATAROBOT_ENDPOINT environment variable" assert "OPENAI_API_BASE" in os.environ, "Please set the OPENAI_API_BASE environment variable" assert "OPENAI_API_KEY" in os.environ, "Please set the OPENAI_API_KEY environment variable" assert "OPENAI_API_VERSION" in os.environ, "Please set the OPENAI_API_VERSION environment variable" dr_client = dr.Client() ``` ``` def test_azure_openai_credentials(): """Test the provided OpenAI credentials.""" model_name = os.getenv("OPENAI_API_DEPLOYMENT_ID") try: client = AzureOpenAI( api_key=os.getenv("OPENAI_API_KEY"), azure_endpoint=os.getenv("OPENAI_API_BASE"), api_version=os.getenv("OPENAI_API_VERSION"), ) client.chat.completions.create( messages=[{"role": "user", "content": "hello"}], model=model_name, # type: ignore[arg-type] ) except Exception as e: raise ValueError( f"Unable to run a successful test completion against model '{model_name}' " "with provided Azure OpenAI credentials. Please validate your credentials." ) from e test_azure_openai_credentials() ``` ## Set up a project Configure the functions below to create and build or destroy the Pulumi stack. ``` from pulumi import automation as auto def stack_up(project_name: str, stack_name: str, program: callable) -> auto.Stack: # create (or select if one already exists) a stack that uses our inline program stack = auto.create_or_select_stack( stack_name=stack_name, project_name=project_name, program=program ) stack.refresh(on_output=print) stack.up(on_output=print) return stack def destroy_project(stack: auto.Stack): """Destroy pulumi project""" stack_name = stack.name stack.destroy(on_output=print) stack.workspace.remove_stack(stack_name) print(f"stack {stack_name} in project removed") ``` ## Declarative LLM deployment Deploying a bolt-on governance model isn't complicated, but it is more involved than creating a custom deployment for a standard classification model for three reasons. First, you want to set runtime parameters around the deployment specifying the LLM endpoint and other metadata. Second, you want to set up and apply a credential for model metadata that should be hidden, such as the API Token. The hidden credential you create will actually end up as one of our runtime parameters. Finally, you want to use a special environment called a Serverless Prediction Environment that works well for sending API calls through a deployment. You need to set up one of these specifically for this model. Once you set up your credentials and runtime parameters, put your source code onto DataRobot, register the model and then initialize the deployment. ``` import pulumi import pulumi_datarobot as datarobot def setup_runtime_parameters( credential: datarobot.ApiTokenCredential, ) -> list[datarobot.CustomModelRuntimeParameterValueArgs]: """Setup runtime parameters for bolt on goverance deployment. Each runtime parameter is a tuple trio with the key, type, and value. Args: credential (datarobot.ApiTokenCredential): The DataRobot credential representing the LLM api token """ return [ datarobot.CustomModelRuntimeParameterValueArgs( key=key, type=type_, value=value, # type: ignore[arg-type] ) for key, type_, value in [ ("OPENAI_API_KEY", "credential", credential.id), ("OPENAI_API_BASE", "string", os.getenv("OPENAI_API_BASE")), ("OPENAI_API_VERSION", "string", os.getenv("OPENAI_API_VERSION")), ( "OPENAI_API_DEPLOYMENT_ID", "string", os.getenv("OPENAI_API_DEPLOYMENT_ID"), ), ] ] def make_bolt_on_governance_deployment(): """ Deploy a trained model onto DataRobot's prediction environment. Upload source code to create a custom model version. Then create a registered model and deploy it to a prediction environment. """ # ID for Python 3.11 Moderations Environment python_environment_id = "65f9b27eab986d30d4c64268" custom_model_name = "App Template Minis - OpenAI LLM" registered_model_name = "App Template Minis - OpenAI Registered Model" deployment_name = "App Template Minis - Bolt on Goverance Deployment" prediction_environment = datarobot.PredictionEnvironment( resource_name="App Template Minis - Serverless Environment", platform=dr.enums.PredictionEnvironmentPlatform.DATAROBOT_SERVERLESS, ) llm_credential = datarobot.ApiTokenCredential( resource_name="App Template Minis - OpenAI LLM Credentials", api_token=os.getenv("OPENAI_API_KEY"), ) runtime_parameters = setup_runtime_parameters(llm_credential) deployment_files = [ ("./model_package/requirements.txt", "requirements.txt"), ("./model_package/custom.py", "custom.py"), ("./model_package/model-metadata.yaml", "model-metadata.yaml"), ] custom_model = datarobot.CustomModel( resource_name=custom_model_name, runtime_parameter_values=runtime_parameters, files=deployment_files, base_environment_id=python_environment_id, target_type=dr.enums.TARGET_TYPE.TEXT_GENERATION, target_name="content", language="python", replicas=2, ) registered_model = datarobot.RegisteredModel( resource_name=registered_model_name, custom_model_version_id=custom_model.version_id, ) deployment = datarobot.Deployment( resource_name=deployment_name, label=deployment_name, registered_model_version_id=registered_model.version_id, prediction_environment_id=prediction_environment.id, ) pulumi.export("serverless_environment_id", prediction_environment.id) pulumi.export("custom_model_id", custom_model.id) pulumi.export("registered_model_id", registered_model.id) pulumi.export("deployment_id", deployment.id) ``` ## Run the stack Running the stack takes the files that are in the `model_package` directory, puts them onto DataRobot as a custom model, registers that model, and deploys the result. ``` project_name = "AppTemplateMinis-BoltOnGovernance" stack_name = "MarshallsExtraSpecialLargeLanguageModel" stack = stack_up(project_name, stack_name, program=make_bolt_on_governance_deployment) ``` ### Interact with outputs Now that you have a bolt-on goverance deployment, you can interact with it directly through the OpenAI SDK. The only difference is that you pass the DataRobot API Token instead of your LLM credentials. ``` from pprint import pprint from openai import OpenAI deployment_id = stack.outputs().get("deployment_id").value deployment_chat_base_url = dr_client.endpoint + f"/deployments/{deployment_id}/" client = OpenAI(api_key=dr_client.token, base_url=deployment_chat_base_url) messages = [ {"role": "user", "content": "Why are ducks called ducks?"}, ] response = client.chat.completions.create(messages=messages, model="gpt-4o") pprint(response.choices[0].message.content) ``` ## Clear your work Use the following cell to shut down the stack, thereby deleting any assets created in DataRobot. ``` destroy_project(stack) ``` ### How does scoring code work? The following cell contains code used to upload so that DataRobot knows how to interact with our model. The bolt-on goverance model only requires you to define hooks for `load_model` and `chat1`, but you can add [others too](https://docs.datarobot.com/en/docs/mlops/deployment/custom-models/custom-model-assembly/custom-model-components.html). ``` from IPython.display import Code Code(filename="./model_package/custom.py", language="python") ``` --- # Deploy a custom model with Pulumi URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/pulumi-examples/deploy_custom_inference_model.html # Deploy a custom model with Pulumi This notebook outlines how to use Pulumi to deploy a scikit learn classifier in two easy stages. ## Initialize the environment ``` import os import datarobot as dr os.environ["PULUMI_CONFIG_PASSPHRASE"] = "default" assert ( "DATAROBOT_API_TOKEN" in os.environ ), "Please set the DATAROBOT_API_TOKEN environment variable" assert "DATAROBOT_ENDPOINT" in os.environ, "Please set the DATAROBOT_ENDPOINT environment variable" dr.Client() ``` ## Set up a project Configure the functions below to create and build or destroy the Pulumi stack. ``` from pulumi import automation as auto def stack_up(project_name: str, stack_name: str, program: callable) -> auto.Stack: # create (or select if one already exists) a stack that uses our inline program stack = auto.create_or_select_stack( stack_name=stack_name, project_name=project_name, program=program ) stack.refresh(on_output=print) stack.up(on_output=print) return stack def destroy_project(stack: auto.Stack): """Destroy pulumi project""" stack_name = stack.name stack.destroy(on_output=print) stack.workspace.remove_stack(stack_name) print(f"stack {stack_name} in project removed") ``` ## Declarative custom model deployment To deploy a custom model, you have to put your source code onto DataRobot, register the model, and then initialize the deployment. The `make_custom_deployment` function below shows the declarative way to do this. ``` import pulumi import pulumi_datarobot as datarobot def make_custom_inference_deployment(): """ Deploy a trained model onto DataRobot's prediction environment. Upload source code to create a custom model version. Then create a registered model and deploy it to a prediction environment. """ # ID for Python 3.9 Scikit learn drop in environment base_environment_id = "5e8c889607389fe0f466c72d" # ID for the default prediction server default_prediction_server_id = "5dd7fa2274a35f003102f60d" custom_model_name = "App Template Minis - Readmitted Custom Model" registered_model_name = "App Template Minis - Readmitted Registered Model" deployment_name = "App Template Minis - Readmitted Deployed Model" deployment_files = [ ("./model_package/requirements.txt", "requirements.txt"), ("./model_package/custom.py", "custom.py"), ("./model_package/model.pkl", "model.pkl"), ] custom_model = datarobot.CustomModel( resource_name=custom_model_name, files=deployment_files, base_environment_id=base_environment_id, language="python", target_type="Binary", target_name="readmitted", ) registered_model = datarobot.RegisteredModel( resource_name=registered_model_name, custom_model_version_id=custom_model.version_id, ) deployment = datarobot.Deployment( resource_name=deployment_name, label=deployment_name, registered_model_version_id=registered_model.version_id, prediction_environment_id=default_prediction_server_id, ) pulumi.export("custom_model_id", custom_model.id) pulumi.export("registered_model_id", registered_model.id) pulumi.export("deployment_id", deployment.id) ``` ## Run the stack Running the stack takes the files that are in the `model_package` directory, puts them onto DataRobot as a custom model, registers that model, and deploys the result. ``` project_name = "AppTemplateMinis-CustomInferenceModels" stack_name = "MarshallsCustomReadmissionsPredictor" stack = stack_up(project_name, stack_name, program=make_custom_inference_deployment) ``` ### Interact with outputs ``` from datarobot_predict.deployment import predict import pandas as pd df = pd.read_csv("https://s3.amazonaws.com/datarobot_public_datasets/10k_diabetes.csv").tail(100) deployment_id = stack.outputs().get("deployment_id").value deployment = dr.Deployment.get(deployment_id) predict(deployment, data_frame=df).dataframe.head(10).iloc[:, :2] ``` ## Clear your work Use the following cell to shut down the stack, thereby deleting any assets created in DataRobot. ``` destroy_project(stack) ``` ### How does scoring code work? The following cell contains code used to upload so that DataRobot knows how to interact with our model. Deploying a custom inference model with minimal transformation only requires two hooks to be defined, but you could add [others too](https://docs.datarobot.com/en/docs/mlops/deployment/custom-models/custom-model-assembly/custom-model-components.html). Since the model is a standard scikit-learn binary classifier, DataRobot is smart enough to figure out how to interact with it without you defining any hooks. Since most model artifacts require some custom scoring logic though, you can make a `custom.py` file anyway. ``` from IPython.display import Code Code(filename="./model_package/custom.py", language="python") ``` ### What did I deploy? If you're curious how you got the fitted model in the first place, `fit_custom_model.py` shows the dataset and model fitting code. The following cell displays the code used to train and pickle the model. It's not important for running the template. ``` from IPython.display import Code Code(filename="./fit_custom_model.py", language="python") ``` --- # Deploy a custom application with Pulumi URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/pulumi-examples/deploy_dr_app.html # Deploy a custom application with Pulumi This notebook outlines how to use Pulumi to deploy a custom application in two easy steps. ## Initialize the environment ``` import os import datarobot as dr os.environ["PULUMI_CONFIG_PASSPHRASE"] = "default" assert ( "DATAROBOT_API_TOKEN" in os.environ ), "Please set the DATAROBOT_API_TOKEN environment variable" assert "DATAROBOT_ENDPOINT" in os.environ, "Please set the DATAROBOT_ENDPOINT environment variable" dr.Client() ``` ## Set up a project Configure the functions below to create and build or destroy the Pulumi stack. ``` from pulumi import automation as auto def stack_up(project_name: str, stack_name: str, program: callable) -> auto.Stack: # create (or select if one already exists) a stack that uses our inline program stack = auto.create_or_select_stack( stack_name=stack_name, project_name=project_name, program=program ) stack.refresh(on_output=print) stack.up(on_output=print) return stack def destroy_project(stack: auto.Stack): """Destroy pulumi project""" stack_name = stack.name stack.destroy(on_output=print) stack.workspace.remove_stack(stack_name) print(f"stack {stack_name} in project removed") ``` ## 2. Declarative App Deployment To deploy a custom application, you have to put your source code onto DataRobot and initialize the deployment. The `make_custom_application` function below shows the declarative way to do this. ``` import pulumi import pulumi_datarobot as datarobot def make_custom_application(): """Make a custom app on DataRobot. Upload source code to create source. Then initialize application. """ file_mapping = [ ("frontend/app.py", "app.py"), ("frontend/requirements.txt", "requirements.txt"), ("frontend/start-app.sh", "start-app.sh"), ] app_source = datarobot.ApplicationSource( resource_name="App Template Minis - Custom App Source", files=file_mapping, base_environment_id="6542cd582a9d3d51bf4ac71e", # Python 3.9 streamlit environment ) app = datarobot.CustomApplication( resource_name="App Template Minis - Custom App", source_version_id=app_source.version_id, ) pulumi.export("Application Source Id", app_source.id) pulumi.export("Application Id", app.id) pulumi.export("Application Url", app.application_url) ``` ## Run the stack Running the stack takes the files that are in the `frontend` directory, puts them onto DataRobot, and initializes the application. ``` project_name = "AppTemplateMinis-CustomApplications" stack_name = "MarshallsCustomApplicationDeployer" stack = stack_up(project_name, stack_name, program=make_custom_application) ``` ### Interact with outputs ``` import webbrowser outputs = stack.outputs() app_url = outputs.get("Application Url").value webbrowser.open(app_url) ``` ## Clear your work Use the following cell to shut down the stack, thereby deleting any assets created in DataRobot. ``` destroy_project(stack) ``` --- # Pulumi code examples URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-code-examples/pulumi-examples/index.html > Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of how to execute Pulumi tasks with DataRobot. # Pulumi code examples The API user guide includes overviews and workflows for DataRobot's Python client that outline complete examples of common data science and machine learning workflows. Be sure to review the [API quickstart guide]api-quickstart before using the notebooks below. | Topic | Describes... | | --- | --- | | Bolt-on governance with Pulumi | How to use Pulumi to create a deployment endpoint that interfaces with a large language model. | | Deploy a custom application with Pulumi | How to use Pulumi to deploy a custom application. | | Deploy a custom model with Pulumi | How to use Pulumi to deploy a scikit learn classifier. | --- # Troubleshooting the Python client URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/py-help.html > Review cases that can cause issues with using the Python client and known fixes. # Troubleshooting the Python client This page outlines cases that can cause issues with using the Python client and provides known fixes. ### InsecurePlatformWarning Python versions earlier than 2.7.9 might report an [InsecurePlatformWarning](https://urllib3.readthedocs.org/en/latest/security.html#insecureplatformwarning) in your output. To prevent this warning without updating your Python version, you should install the [pyOpenSSL](https://urllib3.readthedocs.org/en/latest/security.html#pyopenssl) package: `pip install pyopenssl ndg-httpsclient pyasn1` ### Unable to retrieve multiclass metrics The Python client does not compute the confusion matrix metrics for multiclass projects with more than 100 target classes. That is, the metrics object typically obtained using `get_confusion_chart().class_metrics` (as shown in [the API documentation](https://datarobot-public-api-client.readthedocs-hosted.com/en/latest-release/autodoc/api_reference.html?highlight=get_confusion_chart#datarobot.models.BlenderModel.get_confusion_chart)) is empty in such cases. In order to retrieve these metrics, DataRobot recommends using [this code snippet](https://gist.github.com/Templarrr/e40059c00b7d65f1f2c04f85ebb44c17). ### AttributeError: 'EntryPoint' object has no attribute 'resolve' Some earlier versions of [setuptools](https://setuptools.pypa.io/en/latest/) cause an error when importing DataRobot. ``` >>> import datarobot as dr ... File "/home/clark/.local/lib/python2.7/site-packages/trafaret/__init__.py", line 1550, in load_contrib trafaret_class = entrypoint.resolve() AttributeError: 'EntryPoint' object has no attribute 'resolve' ``` The recommended fix is upgrading setuptools to the latest version. `pip install --upgrade setuptools` If you are unable to upgrade, pin [trafaret](https://pypi.python.org/pypi/trafaret/) to version <=7.4 to correct this issue. ### Connection errors `configuration.rst` describes how to configure the DataRobot client with the `max_retries` parameter to fine tune behaviors like the number of attempts to retry failed connections. ### ConnectTimeout If you have a slow connection to your DataRobot installation, you may see a traceback like: ``` ConnectTimeout: HTTPSConnectionPool(host='my-datarobot.com', port=443): Max retries exceeded with url: /api/v2/projects/ (Caused by ConnectTimeoutError(, 'Connection to my-datarobot.com timed out. (connect timeout=6.05)')) ``` ### project.open_leaderboard_browser Calling `project.open_leaderboard_browser` may be blocked if you run it with a text-mode browser or on a server that doesn't have the ability to open a browser. --- # Use Cases URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/use_cases/index.html # Use Cases The Use Cases section provides details on how to utilize and manage DataRobot Use Cases in your Python code. --- # Use cases URL: https://docs.datarobot.com/en/docs/api/dev-learning/python/use_cases/use_cases.html # Use Cases Use Cases are folder-like containers in DataRobot Workbench that allow you to group all assets related to solving a specific business problem inside of a single, manageable entity. These assets include datasets, models, experiments, No-Code AI Apps, and notebooks. You can share entire Use Cases or the individual assets they contain. The primary benefit of a Use Case is that it enables experiment-based, iterative workflows. By housing all key insights in a single location, data scientists have improved navigation of assets and a cleaner interface for experiment creation and model training, review, and evaluation. Specifically, Use Cases allow you to: - Organize your work — group all related datasets, experiments, notebooks, etc. by the problem they solve. - Find assets easily. Use Cases eliminate the need to search through hundreds of unrelated projects or scrape emails for hyperlinks to specific assets. - Share collections of assets. You can share entire Use Cases, containing all the assets your team needs to participate. - Manage access. Add or remove members to a Use Case to control their access. - Monitor changes. Receive notifications when a team member adds, removes, or modifies any asset in a Use Case. Currently, Use Cases in the Python client support interactions with binary classification and regression projects, applications, and datasets. Development is ongoing, so see the release notes for a full list of supported capabilities. For a more in-depth look at Use Cases and the DataRobot Workbench, [refer to the Workbench documentation.](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/index.html) # Add to a Use Case Currently, only project, dataset, and application instances can be added to a Use Case via the Python client. The process of adding a dataset is shown in the example below: ``` import datarobot as dr dr.Client(token="", endpoint="https://app.datarobot.com/api/v2") risk_use_case = dr.UseCase.create( name="Financial Risk Experimentation Environment", description="For running experiments on modeling financial risks to our business.", ) new_dataset = dr.Dataset.create_from_file( file_path="/foo/bar/risk_data.csv", ) risk_use_case.add(entity=new_dataset) risk_use_case.list_datasets() >>> [Dataset(name='risk_data.csv', id='646e8bb507b108ce7b474b27')] ``` You can add an application to a Use Case in a similar way. The primary difference is that you cannot create applications with the Python client. Instead, retrieve an application using its ID or pull it from a retrieved list of applications and then add it to a Use Case: ``` import datarobot as dr dr.Client(token="", endpoint="https://app.datarobot.com/api/v2") risk_use_case = dr.UseCase.create( name="Financial Risk Experimentation Environment", description="For running experiments on modeling financial risks to our business.", ) existing_application = dr.Application.list()[0] risk_use_case.add(entity=existing_application) risk_use_case.list_applications() >>> [Application(name='Financial Risk Detection')] ``` Alternatively, the [UseCaseReferenceEntity](https://docs.datarobot.com/en/docs/api/reference/sdk/use-cases.html#datarobot.models.use_cases.use_case.UseCaseReferenceEntity) returned from [UseCase.add](https://docs.datarobot.com/en/docs/api/reference/sdk/use-cases.html#datarobot.UseCase.add) can be used to share an entity between Use Cases: ``` import datarobot as dr dr.Client(token="", endpoint="https://app.datarobot.com/api/v2") risk_use_case_1 = dr.UseCase.create( name="Financial Risk Experimentation Environment", description="For running experiments on modeling financial risks to our business.", ) risk_use_case_2 = dr.UseCase.create( name="Financial Risk Experimentation Environment 2", description="For running experiments on modeling financial risks to our business.", ) new_dataset = dr.Dataset.create_from_file( file_path="/foo/bar/risk_data.csv", ) dataset_entity = risk_use_case_1.add(entity=new_dataset) risk_use_case_2.add(entity=dataset_entity) risk_use_case_2.list_datasets() >>> [Dataset(name='risk_data.csv', id='646e8bb507b108ce7b474b27')] ``` To add a project to a Use Case, it must meet the following conditions: - It must be binary classification or regression project - The associated dataset must be linked to the same Use Case - Modeling must be in progress (via UI, the analyze_and_model method, or any other methods that initiate modeling) ``` import datarobot as dr dr.Client(token="", endpoint="https://app.datarobot.com/api/v2") risk_use_case = dr.UseCase.create( name="Financial Risk Experimentation Environment", description="For running experiments on modeling financial risks to our business.", ) new_dataset = dr.Dataset.create_from_file( file_path="/foo/bar/risk_data.csv", use_case=risk_use_case ) risk_use_case.add(entity=new_dataset) new_project = dr.Project.create_from_dataset( dataset_id=new_dataset.dataset_id, project_name="Risk Assessment v1", use_case=risk_use_case ) new_project.analyze_and_model(target="credit_risk") risk_use_case.add(entity=new_project) risk_use_case.list_projects() >>> [Project(Risk Assessment v1)] risk_use_case.list_datasets() >>> [Dataset(name='risk_data.csv', id='646e8bb507b108ce7b474b27')] ``` # Configuration There are three primary ways of adding new projects or datasets to Use Cases once they’ve been generated. 1. The easiest method is to directly pass a Use Case to one of the project or dataset creation methods. Passing the use case directly allows for you to finely control what is added to a Use Case in your code. For example, the following code example creates a new Use Case, then creates a new project that is automatically added to the Use Case. ``` import datarobot as dr dr.Client(token="", endpoint="https://app.datarobot.com/api/v2") risk_use_case = dr.UseCase.create( name="Financial Risk Experimentation Environment", description="For running experiments on modeling financial risks to our business.", ) new_project = dr.Project.create( sourcedata="/foo/bar/risk_data.csv", project_name="Risk Assessment v1", use_case=risk_use_case ) risk_use_case.list_projects() >>> [Project(Risk Assessment v1)] ``` 1. You can also use a context manager to perform a series of actions that automatically result in projects or datasets being added to a Use Case without having to manually pass the Use Case yourself. This can be extremely useful if you have a series of calls you want to make that all should be added to a Use Case. For example: ``` import datarobot as dr dr.Client(token="", endpoint="https://app.datarobot.com/api/v2") risk_use_case = dr.UseCase.create( name="Financial Risk Experimentation Environment", description="For running experiments on modeling financial risks to our business.", ) with risk_use_case: new_dataset = dr.Dataset.create_from_file( file_path="/foo/bar/risk_data.csv", ) risk_use_case.list_datasets() >>> [Dataset(name='risk_data.csv', id='646e8bb507b108ce7b474b27')] ``` 1. You can also set a global Use Case to automatically add all project and dataset instances that are created by your code. This is useful if all of the work you are doing should be contained in a single Use Case, but risks accidentally adding projects and datasets that should not be included in your Use Case. Setting a global default Use Case requires knowing the ID of your Use Case ahead of time. For example: ``` import datarobot as dr dr.Client(token="", endpoint="https://app.datarobot.com/api/v2", default_use_case="639ce542862e9b1b1bfa8f1b") new_dataset = dr.Dataset.create_from_file(file_path="/foo/bar/risk_data.csv") risk_use_case = dr.UseCase.get(id="639ce542862e9b1b1bfa8f1b") risk_use_case.list_datasets() >>> [Dataset(name='risk_data.csv', id='646e8bb507b108ce7b474b27')] ``` # Sharing ## Overview Instances of [datarobot.models.sharing.SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) can be created to define a new role grant (or revocation). The [UseCase.share()](https://docs.datarobot.com/en/docs/api/reference/sdk/use-cases.html#datarobot.UseCase.share) instance method takes a list of [SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) as its only argument. Calling this method will apply the list of [SharingRoles](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) to the given [UseCase](https://docs.datarobot.com/en/docs/api/reference/sdk/use-cases.html#datarobot.UseCase). Use Cases support `SHARING_ROLE.OWNER`, `SHARING_ROLE.EDITOR`, `SHARING_ROLE.CONSUMER` and `SHARING_ROLE.NO_ROLE` as possible `role` designations (see `datarobot.enums.SHARING_ROLE`). Currently, the only supported `SHARING_RECIPIENT_TYPE` is `USER`. ## Examples Suppose you had a list of user IDs you wanted to share this Use Case with. You could use a loop to generate a list of SharingRole objects for them, and bulk share this Use Case. ``` >>> from datarobot.models.use_cases.use_case import UseCase >>> from datarobot.models.sharing import SharingRole >>> from datarobot.enums import SHARING_ROLE, SHARING_RECIPIENT_TYPE >>> >>> user_ids = ["60912e09fd1f04e832a575c1", "639ce542862e9b1b1bfa8f1b", "63e185e7cd3a5f8e190c6393"] >>> sharing_roles = [] >>> for user_id in user_ids: ... new_sharing_role = SharingRole( ... role=SHARING_ROLE.CONSUMER, ... share_recipient_type=SHARING_RECIPIENT_TYPE.USER, ... id=user_id, ... can_share=True, ... ) ... sharing_roles.append(new_sharing_role) >>> use_case = UseCase.get(use_case_id="5f33f1fd9071ae13568237b2") >>> use_case.share(roles=sharing_roles) ``` Similarly, a [SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) instance can be used to remove a user’s access if the `role` is set to `SHARING_ROLE.NO_ROLE`, like in this example: ``` >>> from datarobot.models.use_cases.use_case import UseCase >>> from datarobot.models.sharing import SharingRole >>> from datarobot.enums import SHARING_ROLE, SHARING_RECIPIENT_TYPE >>> >>> user_to_remove = "foo.bar@datarobot.com" ... remove_sharing_role = SharingRole( ... role=SHARING_ROLE.NO_ROLE, ... share_recipient_type=SHARING_RECIPIENT_TYPE.USER, ... username=user_to_remove, ... can_share=False, ... ) >>> use_case = UseCase.get(use_case_id="5f33f1fd9071ae13568237b2") >>> use_case.share(roles=[remove_sharing_role]) ``` # Looking beyond a Use Case Use Cases are a powerful tool for organizing your work, and can help if you need to focus only on those resources relevant to a specific business problem. However, occasionally you may want to look outside of a Use Case at other available DataRobot resources. The following code snippet demonstrates how to retrieve all Projects that your user has access to: ``` import datarobot as dr from datarobot.client import client_configuration with client_configuration(default_use_case=[]): all_projects = dr.Project.list() ``` --- # REST API code examples URL: https://docs.datarobot.com/en/docs/api/dev-learning/restapi/index.html > Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows. # REST API code examples The API user guide includes overviews and workflows for DataRobot's REST API that outline complete examples of common data science and machine learning workflows. Be sure to review the [API quickstart guide]api-quickstart before using the notebooks below. | Topic | Describes... | | --- | --- | | Create a multiseries project | How to initiate a DataRobot project for a multiseries time series problem using the DataRobot REST API. | | Create a clustering project | How to create a clustering project and initiate Autopilot in Manual mode via DataRobot's REST API. | | Fetch metadata from prediction jobs | How to retrieve metadata from prediction jobs with DataRobot's REST API. | --- # Create a multiseries project URL: https://docs.datarobot.com/en/docs/api/dev-learning/restapi/multi-rest.html # Create a multiseries project This notebook outlines how to create a DataRobot project and begin modeling for a multiseries time series project with DataRobot's REST API. ### Requirements - DataRobot recommends Python version 3.7 or later. However, this workflow is compatible with earlier versions. - DataRobot API version 2.28.0 Small adjustments may be required depending on the Python version and DataRobot API version you are using. This notebook does not include a dataset and references several unknown columns; however, you can extrapolate to use your own series identifier. You can also reference [documentation for the DataRobot REST API](https://docs.datarobot.com/en/docs/api/reference/public-api/index.html). ### Import libraries ``` import datetime import json import time from pandas.io.json import json_normalize import requests import yaml ``` ### Set credentials ``` FILE_CREDENTIALS = "path-to-drconfig.yaml" parsed_file = yaml.load(open(FILE_CREDENTIALS), Loader=yaml.FullLoader) DR_ENDPOINT = parsed_file["endpoint"] API_TOKEN = parsed_file["token"] AUTH_HEADERS = {"Authorization": "token %s" % API_TOKEN} ``` ### Define functions The functions below handle responses, including asynchronous calls. ``` def wait_for_async_resolution(status_url): status = False while status == False: resp = requests.get(status_url, headers=AUTH_HEADERS) r = json.loads(resp.content) try: statusjob = r["status"].upper() except: statusjob = "" if resp.status_code == 200 and statusjob != "RUNNING" and statusjob != "INITIALIZED": status = True print("Finished: " + str(datetime.datetime.now())) return resp print("Waiting: " + str(datetime.datetime.now())) time.sleep(10) # Delays for 10 seconds. def wait_for_result(response): assert response.status_code in (200, 201, 202), response.content if response.status_code == 200: data = response.json() elif response.status_code == 201: status_url = response.headers["Location"] resp = requests.get(status_url, headers=AUTH_HEADERS) assert resp.status_code == 200, resp.content data = resp.json() elif response.status_code == 202: status_url = response.headers["Location"] resp = wait_for_async_resolution(status_url) data = resp.json() return data ``` ### Create the project Endpoint: `POST /api/v2/projects/` ``` FILE_DATASET = "/Volumes/GoogleDrive/My Drive/Datasets/Store Sales/STORE_SALES-TRAIN-2022-04-25.csv" ``` ``` payload = { # 'projectName': 'TestRESTTimeSeries_1', "file": ("Test_REST_TimeSeries_12", open(FILE_DATASET, "r")) } response = requests.post( "%s/projects/" % (DR_ENDPOINT), headers=AUTH_HEADERS, files=payload, timeout=180 ) response ``` ``` ``` ``` # Wait for async task to complete print("Uploading dataset and creating Project...") projectCreation_response = wait_for_result(response) project_id = projectCreation_response["id"] print("\nProject ID: " + project_id) ``` ``` Uploading dataset and creating Project... Waiting: 2022-07-29 17:55:32.507696 Waiting: 2022-07-29 17:55:43.092965 Waiting: 2022-07-29 17:55:53.670669 Waiting: 2022-07-29 17:56:04.252294 Waiting: 2022-07-29 17:56:14.841809 Finished: 2022-07-29 17:56:25.650896 Project ID: 62e402f1ce8ba47b224fcea3 ``` ### Update the project Endpoint: `PATCH /api/v2/projects/(projectId)/` ``` payload = {"workerCount": 16} response = requests.patch( "%s/projects/%s/" % (DR_ENDPOINT, project_id), headers=AUTH_HEADERS, json=payload, timeout=180 ) response ``` ``` ``` ### Run a detection job For a multiseries project, you must run a detection job to analyze the relationship between the partition and multiseries ID columns. Endpoint: `POST /api/v2/projects/(projectId)/multiseriesProperties/` ``` payload = {"datetimePartitionColumn": "Date", "multiseriesIdColumns": ["Store"]} response = requests.post( "%s/projects/%s/multiseriesProperties/" % (DR_ENDPOINT, project_id), headers=AUTH_HEADERS, json=payload, timeout=180, ) response ``` ``` ``` ``` print("Analyzing multiseries partitions...") multiseries_response = wait_for_result(response) ``` ``` Analyzing multiseries partitions... Waiting: 2022-07-29 17:56:27.571064 Waiting: 2022-07-29 17:56:38.156104 Finished: 2022-07-29 17:56:48.932686 ``` ### Initiate modeling Endpoint: `PATCH /api/v2/projects/(projectId)/aim/` ``` payload = { "target": "Sales", "mode": "quick", "datetimePartitionColumn": "Date", "featureDerivationWindowStart": -25, "featureDerivationWindowEnd": 0, "forecastWindowStart": 1, "forecastWindowEnd": 12, "numberOfBacktests": 2, "useTimeSeries": True, "cvMethod": "datetime", "multiseriesIdColumns": ["Store"], "blendBestModels": False, } response = requests.patch( "%s/projects/%s/aim/" % (DR_ENDPOINT, project_id), headers=AUTH_HEADERS, json=payload, timeout=180, ) response ``` ``` ``` ``` print("Waiting for tasks previous to training to complete...") autopilot_response = wait_for_result(response) ``` ``` Waiting for tasks previous to training to complete... Waiting: 2022-07-29 17:56:51.024036 Waiting: 2022-07-29 17:57:01.746376 Waiting: 2022-07-29 17:57:12.329879 Waiting: 2022-07-29 17:57:22.904449 Waiting: 2022-07-29 17:57:33.679282 Waiting: 2022-07-29 17:57:44.262096 Waiting: 2022-07-29 17:57:54.845494 Waiting: 2022-07-29 17:58:05.427372 Waiting: 2022-07-29 17:58:15.995107 Waiting: 2022-07-29 17:58:26.605621 Waiting: 2022-07-29 17:58:37.188681 Waiting: 2022-07-29 17:58:47.762809 Waiting: 2022-07-29 17:58:58.348806 Waiting: 2022-07-29 17:59:08.925445 Waiting: 2022-07-29 17:59:19.505174 Waiting: 2022-07-29 17:59:30.093026 Waiting: 2022-07-29 17:59:40.670835 Waiting: 2022-07-29 17:59:51.239278 Waiting: 2022-07-29 18:00:01.818356 Waiting: 2022-07-29 18:00:12.395658 Waiting: 2022-07-29 18:00:22.993393 Waiting: 2022-07-29 18:00:33.576738 Waiting: 2022-07-29 18:00:44.166028 Waiting: 2022-07-29 18:00:54.768693 Waiting: 2022-07-29 18:01:05.372862 Waiting: 2022-07-29 18:01:15.981022 Waiting: 2022-07-29 18:01:26.571205 Waiting: 2022-07-29 18:01:37.160074 Waiting: 2022-07-29 18:01:47.741388 Waiting: 2022-07-29 18:01:58.326862 Waiting: 2022-07-29 18:02:08.912622 Finished: 2022-07-29 18:02:19.739789 ``` --- # Fetch metadata from prediction jobs URL: https://docs.datarobot.com/en/docs/api/dev-learning/restapi/pred-metadata.html # Fetch metadata from prediction jobs This notebook outlines how to retrieve metadata from prediction jobs with DataRobot's REST API. In the DataRobot UI, you can see prediction jobs on the Deployments page; this list includes all batch prediction jobs made from REST API code, through DataRobot's Python API client, or from job definitions. Using DataRobot's REST API, you can get more details on each of those predictions; however, you need to use Python to complete this task. ## Setup ### Import libraries ``` import getpass import os import datarobot as dr import pandas as pd import requests print(os.getcwd()) token = getpass.getpass() # Use your own token dr.Client(token=token, endpoint="https://app.datarobot.com/api/v2") ``` ### Connect to DataRobot Read more about different options for [connecting to DataRobot from the client](https://docs.datarobot.com/en/docs/api/api-quickstart/api-qs.html). ``` API_ENDPOINT = "https://app.datarobot.com/api/v2/batchPredictions" # Enter your API key here API_KEY = token session = requests.Session() session.headers = { "Authorization": "Bearer {}".format(API_KEY), } session.close() ``` ### Fetch metadata Use the snippet below to get metadata from your prediction jobs. The following cell displays an example of what the retrieved data looks like. ``` resp = session.get(API_ENDPOINT) print(resp.status_code) df = pd.json_normalize(resp.json()["data"]) df.head() ``` ### Fetch data points ``` log1 = pd.DataFrame(df.iloc[1,]) with pd.option_context( "display.max_rows", 1000, "display.max_columns", 1000 ): # more options can be specified also display(log1) ``` By analyzing the data points above, you can identify numerous insights: - Status details is missing columns - The Source field is using the UI prediction method (i.e., not using job definitions or the Python API client for batch predictions) - DatasetID and DeploymentID are provided, which you can use for further analysis or configuration ### Use metadata for troubleshooting You can use the metadata to inform other users about any issues or failures with predictions job and provide additional useful information to help resolve the issue. DataRobot recommends providing the following to troubleshoot: - URL to the dataset used for prediction - URL to the deployment - URL to the project - URL to the dataset used for training The prediction dataset can be fetched from the prediction job metadata in the cells above. It also provides the dataset ID. Run the following snippet to retrieve a URL that will direct you to the dataset. ``` datasetid = "5ebc89d21b7b850de6ab9a36" dataset = dr.Dataset.get(datasetid) print(dataset) print("https://app.datarobot.com/ai-catalog/" + datasetid) ``` Provide the deployment ID and then run the following snippet to retrieve the URL for the deployment. ``` deploymentid = "6290a642f2d99680864daad8" deployment = dr.Deployment.get(deploymentid) print(deployment) print("https://app.datarobot.com/deployments/" + deploymentid) ``` Use the following cell to get the project ID using the deployment ID. ``` # The deployment ID deploymentid = "6290a642f2d99680864daad8" # Define the API endpoint API_ENDPOINT = "https://app.datarobot.com/api/v2/deployments/" # Provide your API key here API_KEY = token session = requests.Session() session.headers = { "Authorization": "Bearer {}".format(API_KEY), } session.close() ``` Provide the project ID and then run the following snippet to retrieve the URL for the project. ``` projectid = "62908fa8929e0d7ef66e388e" project = dr.Project.get(projectid) print(project) print("https://app.datarobot.com/projects/" + projectid) ``` Lastly, by pulling the URL for the dataset used for training and using the project ID above, you can get the training dataset ID. ``` # The deployment ID projectid = "62908fa8929e0d7ef66e388e" # Define the API endpoint API_ENDPOINT = "https://app.datarobot.com/api/v2/projects/" # Provide your API key here API_KEY = token session = requests.Session() session.headers = { "Authorization": "Bearer {}".format(API_KEY), } resp = session.get(API_ENDPOINT + "?projectId=" + projectid) df = pd.json_normalize(resp.json()) df.T session.close() ``` ``` datasetid = "629086ace265bd23ab9c1de7" print("https://app.datarobot.com/ai-catalog/" + datasetid) ``` Click the link from the output above to access the dataset. --- # Create a clustering project URL: https://docs.datarobot.com/en/docs/api/dev-learning/restapi/rest-cluster.html # Create a clustering project This notebook outlines how to create a clustering project and initiate Autopilot in Manual mode via DataRobot's REST API. Manual mode allows you to select and train specific blueprints for modeling. If you run a clustering project in comprehensive Autopilot mode, some blueprints may take a long time to complete. For example, HDBSCAN is inherently a slow model to train. Because of these time constraints, this notebook only runs one blueprint (K-Means) and tests several clusters. ### Requirements - DataRobot recommends Python version 3.7 or later. - DataRobot API version 2.28.0 ### Import libraries ``` import datetime import json import time from pandas.io.json import json_normalize import requests import yaml ``` ### Set credentials ``` FILE_CREDENTIALS = ( "/Volumes/GoogleDrive/My Drive/rodrigo.miranda/mlops-admin/rodrigo.miranda_drconfig.yaml" ) parsed_file = yaml.load(open(FILE_CREDENTIALS), Loader=yaml.FullLoader) DR_ENDPOINT = parsed_file["endpoint"] API_TOKEN = parsed_file["token"] AUTH_HEADERS = {"Authorization": "token %s" % API_TOKEN} ``` ### Define functions The functions below handle responses, including asynchronous calls. ``` def wait_for_async_resolution(status_url): status = False while status == False: resp = requests.get(status_url, headers=AUTH_HEADERS) r = json.loads(resp.content) try: statusjob = r["status"].upper() except: statusjob = "" if resp.status_code == 200 and statusjob != "RUNNING" and statusjob != "INITIALIZED": status = True print("Finished: " + str(datetime.datetime.now())) return resp print("Waiting: " + str(datetime.datetime.now())) time.sleep(10) # Delays for 10 seconds. def wait_for_result(response): assert response.status_code in (200, 201, 202), response.content if response.status_code == 200: data = response.json() elif response.status_code == 201: status_url = response.headers["Location"] resp = requests.get(status_url, headers=AUTH_HEADERS) assert resp.status_code == 200, resp.content data = resp.json() elif response.status_code == 202: status_url = response.headers["Location"] resp = wait_for_async_resolution(status_url) data = resp.json() return data ``` ### Create a project Endpoint: `POST /api/v2/projects/` ``` FILE_DATASET = ( "/Volumes/GoogleDrive/My Drive/Datasets/Customer Invoices/clustering_customer_invoices.csv" ) ``` ``` payload = {"file": ("Clustering - Customer Invoices 02", open(FILE_DATASET, "r"))} response = requests.post( "%s/projects/" % (DR_ENDPOINT), headers=AUTH_HEADERS, files=payload, timeout=60 ) response ``` ``` ``` ``` # Wait for async task to complete print("Uploading dataset and creating Project...") projectCreation_response = wait_for_result(response) project_id = projectCreation_response["id"] print("\nProject ID: " + project_id) ``` ``` Uploading dataset and creating Project... Waiting: 2022-08-09 14:54:27.008806 Waiting: 2022-08-09 14:54:37.578847 Waiting: 2022-08-09 14:54:48.139574 Waiting: 2022-08-09 14:54:58.846699 Waiting: 2022-08-09 14:55:09.401604 Waiting: 2022-08-09 14:55:19.981831 Waiting: 2022-08-09 14:55:30.551482 Finished: 2022-08-09 14:55:41.361760 Project ID: 62f25900543b1c01e5bdaf59 ``` ### Initiate Autopilot This snippet begins modeling in Manual mode. Endpoint: `PATCH /api/v2/projects/(projectId)/aim/` ``` payload = {"unsupervisedMode": True, "unsupervisedType": "clustering", "mode": "manual"} response = requests.patch( "%s/projects/%s/aim/" % (DR_ENDPOINT, project_id), headers=AUTH_HEADERS, json=payload, timeout=60, ) response ``` ``` ``` ``` print("Creating project in Manual mode...") project_response = wait_for_result(response) ``` ``` Creating project in Manual mode... Waiting: 2022-08-09 14:55:43.398565 Waiting: 2022-08-09 14:55:53.961746 Waiting: 2022-08-09 14:56:04.548413 Waiting: 2022-08-09 14:56:15.131273 Waiting: 2022-08-09 14:56:25.715646 Waiting: 2022-08-09 14:56:36.270683 Waiting: 2022-08-09 14:56:46.828606 Waiting: 2022-08-09 14:56:57.391746 Waiting: 2022-08-09 14:57:08.098635 Waiting: 2022-08-09 14:57:18.650575 Finished: 2022-08-09 14:57:29.453144 ``` ### Retrieve blueprints Endpoint: `GET /api/v2/projects/(projectId)/blueprints/` ``` response = requests.get( "%s/projects/%s/blueprints/" % (DR_ENDPOINT, project_id), headers=AUTH_HEADERS ) response ``` ``` ``` ``` r = json.loads(response.content) r ``` ``` print("Available blueprints:\n") for bp in r: print(bp["modelType"]) print(bp["id"] + "\n") ``` ``` Available blueprints: Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) 500ce93b06e38c4df2800f62ade6650d Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) DBSCAN Hybrid Model 59478b8603dd3e270bd4277a9c1456d7 Gaussian Mixture Model 68a6aa4312a27d1fc55580f7fb1121bc K-Means Clustering 9c08a327281f53fa366bb52c817499d2 ``` ### Build models Endpoint: `POST /api/v2/projects/(projectId)/models/` Next, train 3 K-Means models simultaneously using a different number of clusters. ``` payload = {"blueprintId": "9c08a327281f53fa366bb52c817499d2", "nClusters": 3} response1 = requests.post( "%s/projects/%s/models/" % (DR_ENDPOINT, project_id), headers=AUTH_HEADERS, json=payload, timeout=60, ) response1 ``` ``` ``` ``` payload = {"blueprintId": "9c08a327281f53fa366bb52c817499d2", "nClusters": 5} response2 = requests.post( "%s/projects/%s/models/" % (DR_ENDPOINT, project_id), headers=AUTH_HEADERS, json=payload, timeout=60, ) response2 ``` ``` ``` ``` payload = {"blueprintId": "9c08a327281f53fa366bb52c817499d2", "nClusters": 10} response3 = requests.post( "%s/projects/%s/models/" % (DR_ENDPOINT, project_id), headers=AUTH_HEADERS, json=payload, timeout=60, ) response3 ``` ``` ``` ``` print("Waiting for models training to finish...") ``` ``` Waiting for models training to finish... Finished: 2022-08-09 15:06:54.415661 Finished: 2022-08-09 15:06:55.300248 Finished: 2022-08-09 15:06:56.155223 ``` --- # API reference URL: https://docs.datarobot.com/en/docs/api/dev-learning/workload-api/api-reference.html > REST API reference including endpoints, schemas, and HTTP status codes. # API reference API version: v2.41 | Last updated: December 2025 This section provides the complete REST API reference for the DataRobot Workload API. ## Authentication All API requests require authentication using a Bearer token in the `Authorization` header: ``` Authorization: Bearer YOUR_API_TOKEN ``` ## Base URLs | Endpoint group | Base path | Description | | --- | --- | --- | | Workloads | /api/v2/console/workloads/ | Workload lifecycle management. | | Artifacts | /api/v2/registry/artifacts/ | Container artifact registry. | | Deployments | /api/v2/console/deployments/ | Production deployment management. | | Workload invoke | /api/v2/endpoints/workloads/{workloadId}/ | Invoke a running workload (base URL; append your app path). | | Deployment invoke | /api/v2/endpoints/deployments/{deploymentId}/ | Invoke a deployment (base URL; append your app path). | All endpoint paths in the sections below are relative to the API version root (e.g.`https://app.datarobot.com/api/v2`). Append the path to that base to form the full URL. ## Workload endpoints Endpoints in this section use the Workloads base path: `/api/v2/console/workloads/`. ### POST /console/workloads/ — Create workload Create a new workload from a user-provided artifact. | Field | Type | Required | Description | | --- | --- | --- | --- | | name | string | No | Workload display name. | | artifact | InputArtifact | Conditional | Inline artifact spec (required if no artifactId). | | artifactId | string | Conditional | Existing artifact ID to deploy. | | runtime | WorkloadRuntime | No | Replica count, autoscaling, resource bundle settings. | Response: `201 Created` → WorkloadFormatted ### GET /console/workloads/ — List workloads Retrieve a paginated list of workloads. | Parameter | Type | Default | Description | | --- | --- | --- | --- | | offset | integer | 0 | Number of records to skip. | | limit | integer | 100 | Records per page (max: 100). | | ids | string[] | — | Filter by specific workload IDs. | | search | string | — | Search by workload name. | | sort | string | — | Sort order. | ### GET /console/workloads/{workloadId}/ — Get workload Retrieve details for a specific workload. ### POST /console/workloads/{workloadId}/start — Start workload Restart a stopped workload by scheduling it again. Response: `202 Accepted` ### POST /console/workloads/{workloadId}/stop — Stop workload Stop a running workload. Status transitions to stopping then stopped. Response: `202 Accepted` ### DELETE /console/workloads/{workloadId}/ — Delete workload Soft-delete a workload. Response: `204 No Content` ## Artifact endpoints Endpoints in this section use the Artifacts base path: `/api/v2/registry/artifacts/`. | Method | Endpoint | Description | Response | | --- | --- | --- | --- | | POST | /registry/artifacts/ | Create a new artifact in draft status. | 201 Created → ArtifactFormatted | | GET | /registry/artifacts/ | Retrieve a paginated list of artifacts. | — | | GET | /registry/artifacts/{artifactId}/ | Retrieve artifact details including full container specification. | — | | PUT | /registry/artifacts/{artifactId}/ | Fully replace an artifact's definition. | — | | PATCH | /registry/artifacts/{artifactId}/ | Partially update artifact metadata. | — | | DELETE | /registry/artifacts/{artifactId}/ | Soft-delete an artifact. | — | ## Deployment endpoints Endpoints in this section use the Deployments base path: `/api/v2/console/deployments/`. ### POST /console/deployments/ — Create deployment Create a new workload deployment. | Field | Type | Required | Description | | --- | --- | --- | --- | | name | string | Yes | Deployment display name. | | workloadId | string | Yes | ID of the workload to deploy. | | description | string | No | Deployment description. | | importance | string | No | Priority: critical, high, moderate, low. | ### Other deployment endpoints | Method | Endpoint | Description | | --- | --- | --- | | GET | /console/deployments/ | Retrieve a paginated list of workload deployments. | | GET | /console/deployments/{deploymentId}/ | Retrieve deployment details. | | PATCH | /console/deployments/{deploymentId}/ | Update deployment metadata. | | DELETE | /console/deployments/{deploymentId}/ | Delete a workload deployment. | | GET | /console/deployments/{deploymentId}/stats/ | Retrieve deployment statistics. | ## Schema reference ### WorkloadStatus Workload lifecycle states: | Status | Description | | --- | --- | | unknown | Status cannot be determined. | | submitted | Workload created, not yet scheduled. | | initializing | Containers being pulled and started. | | running | Workload running and healthy. | | stopping | Stop in progress. | | stopped | Workload stopped. | | errored | Error occurred (check statusDetails). | ### ArtifactStatus | Status | Description | | --- | --- | | draft | Artifact can be modified. | | registered | Artifact locked for production use. | ### ResourceRequest Resource requirements for a container: | Field | Type | Required | Description | | --- | --- | --- | --- | | cpu | number | Yes | CPU cores (can be fractional). | | memory | integer | Yes | Memory in bytes. | | gpu | integer | No | Number of GPUs. | | gpuType | string | No | GPU type (e.g., NVIDIA-A10G). | Memory examples: 1 GB = 1073741824, 4 GB = 4294967296, 16 GB = 17179869184 ### ProbeConfig Health probe configuration for containers: | Field | Type | Default | Description | | --- | --- | --- | --- | | path | string | — | HTTP path for health check (required). | | port | integer | 8080 | Port number. | | scheme | string | HTTP | Protocol: HTTP or HTTPS. | | initialDelaySeconds | integer | 30 | Delay before first probe. | | periodSeconds | integer | 30 | Time between probes. | | timeoutSeconds | integer | 30 | Probe timeout. | | failureThreshold | integer | 3 | Number of failures before marking unhealthy. | ### ScalingMetricType Available autoscaling metrics: | Value | Description | | --- | --- | | cpuAverageUtilization | Scale based on average CPU utilization. | | httpRequestsPerHour | Scale based on rolling hourly HTTP request rate. | | httpRequestsInFlight | Scale based on concurrent in-flight requests. | | httpRequestsConcurrency | Scale based on concurrency with scale-to-zero. | ## HTTP status codes | Code | Meaning | When used | | --- | --- | --- | | 200 | OK | Successful GET, PUT, PATCH. | | 201 | Created | Successful POST creating resource. | | 202 | Accepted | Async operation started (start/stop). | | 204 | No Content | Successful DELETE. | | 400 | Bad Request | Malformed request. | | 401 | Unauthorized | Missing or invalid token. | | 403 | Forbidden | Insufficient permissions. | | 404 | Not Found | Resource doesn't exist. | | 422 | Validation Error | Invalid request body/params. | | 500 | Server Error | Internal error. | --- # Best practices and troubleshooting URL: https://docs.datarobot.com/en/docs/api/dev-learning/workload-api/best-practices.html > Container design, production deployments, security, and common issues. # Best practices and troubleshooting This page covers recommended practices for container design, production deployments, security, and troubleshooting steps for common issues. ## Best practices ### Container design - Implement health checks: Always provide liveness, readiness, and startup probes. - Set appropriate timeouts: GPU-heavy workloads need longer startup times. - Use appropriate resource requests: Right-size CPU and memory to avoid overprovisioning. ### Production deployments - Promote artifacts: Lock artifacts before production deployment for immutability. - Enable autoscaling: Configure scaling policies appropriate for your traffic patterns. - Use resource bundles: Leverage predefined resource bundles for consistent GPU allocation. ### Security - Use private registries: Store container images in secure, private registries. - Avoid hardcoded secrets: Use environment variables or secret management. - Implement HTTPS probes: Use scheme: HTTPS for health checks when appropriate. ## Troubleshooting ### Checking workload status details When a workload enters an unexpected state, the `statusDetails` field provides diagnostic information: ``` curl -s -X GET "${DATAROBOT_ENDPOINT}/console/workloads/{workloadId}" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" | jq '.statusDetails' ``` The `statusDetails` object contains two key fields: | Field | Description | | --- | --- | | conditions | Array of Kubernetes-style conditions indicating component states. | | logTail | Array of recent container log lines captured during startup. | ### Common issues #### Workload stuck in initializing status - Check statusDetails.conditions for scheduling issues. - Verify the container image is accessible from the cluster. - Verify resource requests don't exceed cluster capacity. - Review startup probe configuration. #### Workload enters errored status When a workload fails, start by inspecting `statusDetails`: ``` curl -s -X GET "${DATAROBOT_ENDPOINT}/console/workloads/{workloadId}" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" | jq '{status, statusDetails}' ``` --- # Getting started URL: https://docs.datarobot.com/en/docs/api/dev-learning/workload-api/getting-started.html > Deploy your first container. # Getting started: Run a test container This guide walks you through deploying your first container using the Workload API. ## Prerequisites Before you begin, ensure you have: - DataRobot API endpoint (base URL) —your DataRobot instance URL. - Container images hosted in a registry accessible by the DataRobot cluster. - DataRobot API token for authentication. ## Set environment variables Before running any commands, set your DataRobot API credentials: ``` export DATAROBOT_ENDPOINT=https://app.datarobot.com/api/v2 export DATAROBOT_API_TOKEN= ``` Ensure your container: - Runs in unprivileged mode—containers cannot run as root. - Exposes a port higher than 1024—privileged ports (0–1024) are not available. - Implements health check endpoints for readiness probes. - Exposes an HTTP server. ## Step 1: Deploy the whoami container Before deploying your own container, try this example using the `containous/whoami` image—a simple HTTP server that returns request information. Create the workload: ``` curl -X POST "${DATAROBOT_ENDPOINT}/console/workloads/" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{ "name": "test-whoami", "artifact": { "name": "whoami-artifact", "description": "Simple HTTP server for testing", "spec": { "containerGroups": [{ "containers": [{ "imageUri": "containous/whoami:latest", "port": 8080, "primary": true, "resourceRequest": {"cpu": 1, "memory": 536870912}, "entrypoint": ["/whoami", "--port", "8080"], "readinessProbe": { "path": "/", "port": 8080, "initialDelaySeconds": 5 } }] }] } }, "runtime": {"replicaCount": 1} }' ``` > [!NOTE] Draft artifacts > This creates an artifact in draft status. Draft artifacts can be edited and are useful for testing before finalizing your deployment configuration. ## Step 2: Monitor workload status Poll the workload status to track deployment progress. Status progression: submitted → initializing → running. ``` curl -s -X GET "${DATAROBOT_ENDPOINT}/console/workloads/{workloadId}" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" | jq '.status' ``` ## Step 3: Access the running service Once the workload reaches running status, you can access it: ``` curl -X GET "${DATAROBOT_ENDPOINT}/endpoints/workloads/{workloadId}/" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" ``` The invoke URL is a base prefix. Append your application's path to reach specific endpoints (for example, `/health` or `/api/...`). Requests are forwarded to the container with the same path and method. ## Step 4: Stop the running workload When you're done experimenting, stop the running workload: ``` curl -X POST "${DATAROBOT_ENDPOINT}/console/workloads/{workloadId}/stop" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" ``` --- # Workload API URL: https://docs.datarobot.com/en/docs/api/dev-learning/workload-api/index.html > Deploy AI workloads on DataRobot using a unified compute abstraction for containerized services. # Workload API The Workload API provides a unified compute abstraction for running AI workloads on DataRobot. Use it to deploy containerized applications, agents, and inference services with governance, versioning, and observability built in. > [!NOTE] Private preview > The Workload API is available as a private preview. API version: v2.41. ## Documentation overview | Topic | Description | | --- | --- | | Overview | Core concepts, object model, and how components work together . | | Getting started: Run a test container | Deploy your first container using the Workload API. | | Tutorials | Step-by-step guides: deploy production-ready artifact, iterate then deploy. | | Monitoring and observability | OpenTelemetry integration, metrics, logs, and traces. | | Best practices and troubleshooting | Container design, production deployments, and common issues. | | API reference | REST API endpoints, schemas, and HTTP status codes. | ## Quick links - Workloads — /api/v2/console/workloads/ - Artifacts — /api/v2/registry/artifacts/ - Deployments — /api/v2/console/deployments/ - Workload invoke — /api/v2/endpoints/workloads/{workloadId}/ - Deployment invoke — /api/v2/endpoints/deployments/{deploymentId}/ --- # Monitoring and observability URL: https://docs.datarobot.com/en/docs/api/dev-learning/workload-api/monitoring.html > OpenTelemetry integration, metrics, logs, and traces. # Monitoring and observability The Workload API provides built-in monitoring capabilities through OpenTelemetry (OTel) integration. ## Available metrics | Category | Metrics | | --- | --- | | Service health | Number of requests (succeeded / failed), latency, error rate, requests per minute. | | Resource utilization | Number of replicas; CPU and memory consumption by container. | | OTel metrics | OTel-compliant metrics emitted by your application. | ## Accessing logs and traces You can instrument your applications for OpenTelemetry-compliant tracing and leverage the open-source OpenTelemetry (OTel) libraries for seamless auto-instrumentation. ### Tracer configuration example ``` from opentelemetry import trace from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter resource = Resource.create({"service.namespace": "my-service"}) def configure_tracer() -> TracerProvider: trace_exporter = OTLPSpanExporter() trace_provider = TracerProvider(resource=resource) trace_provider.add_span_processor(BatchSpanProcessor(trace_exporter)) trace.set_tracer_provider(trace_provider) return trace_provider trace_provider = configure_tracer() tracer = trace.get_tracer(__name__) # Usage example with tracer.start_as_current_span("Generate Text") as span: span.set_attribute("foo", "bar") span.add_event(name="ack", attributes={"john": "doe"}) ``` ### Logger configuration example ``` import logging from opentelemetry.sdk.resources import Resource from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler from opentelemetry.sdk._logs.export import BatchLogRecordProcessor from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter from opentelemetry._logs import set_logger_provider resource = Resource.create({"service.namespace": "my-service"}) def configure_logging() -> LoggerProvider: log_exporter = OTLPLogExporter() log_provider = LoggerProvider(resource=resource) log_provider.add_log_record_processor(BatchLogRecordProcessor(log_exporter)) set_logger_provider(log_provider) # Bridge Python logging to OTel so logger.info() / logger.warning() are exported via OTLP handler = LoggingHandler(level=logging.NOTSET, logger_provider=log_provider) logging.getLogger().setLevel(logging.NOTSET) logging.getLogger().addHandler(handler) return log_provider log_provider = configure_logging() logger = logging.getLogger(__name__) # Usage logger.info("Logging info.", extra={"extra": "INFO details"}) logger.warning("Logging warning.", extra={"extra": "WARNING details"}) ``` ## API access to telemetry You can retrieve specific traces, logs, and metrics directly via the API: | Endpoint | Description | | --- | --- | | GET /otel/workload/{workloadId}/traces | Get traces for a workload. | | GET /otel/workload/{workloadId}/traces/{traceId} | Get a specific trace. | | GET /otel/workload/{workloadId}/logs | Get logs. | | GET /otel/workload/{workloadId}/metrics/summary | Get metrics summary. | --- # Overview URL: https://docs.datarobot.com/en/docs/api/dev-learning/workload-api/overview.html > Core concepts, object model, and design principles. # Overview The Workload API provides a unified compute abstraction for running AI workloads on DataRobot. This page explains the core architecture, object model, and how the different components work together. ## Core concepts The Workload API uses a layered abstraction that separates what you want to run from how it runs. This design lets you iterate quickly during development while keeping governance and versioning controls for production. ### Object hierarchy The API is built around four primary objects: | Object | Description | | --- | --- | | Artifacts | Define your business logic—container images, runtime configuration, and metadata that describe your application, model, or agent. Artifacts focus on what you're building, not how it scales or where it runs. | | Workloads | Running instances of artifacts. When you start an artifact, the system creates a workload that manages the underlying compute resources, handles scaling, and tracks lifecycle state. | | Deployments | Stable, governed endpoints for production workloads. A deployment wraps a workload with a consistent URL, health monitoring, access controls, and audit logging. | | Artifact collections | Maintain version history. When you register an artifact for production use, it becomes part of a collection that tracks all versions of that artifact over time. | ## Artifact lifecycle Artifacts exist in two states that reflect different stages of the development workflow. ### Draft artifacts Draft artifacts are designed for experimentation. They are mutable, unversioned, and can be updated freely as you iterate on your implementation. Use draft artifacts when you're actively developing and testing. A draft artifact: - Can be modified in place - Does not belong to a version collection - Cannot be attached to a deployment - Is intended for development and testing workflows ### Registered artifacts Registered artifacts are immutable snapshots intended for production. When you register an artifact, the system assigns it a version number and adds it to an artifact collection. A registered artifact: - Is immutable once created - Belongs to an artifact collection with a version sequence (v1, v2, v3, …) - Can be attached to a deployment - Provides a stable reference for production workloads You can create a registered artifact by promoting a draft, or by registering directly without going through the draft stage. ## Artifact types Artifacts are polymorphic—the `type` field determines the expected behavior and constraints. The API treats all artifact types generically, but validation and backend behavior vary by type. > [!NOTE] DataRobot 11.4 > DataRobot 11.4 supports the container type only. Artifact types include: | Type | Description | | --- | --- | | generic | General-purpose containerized service | | agent | AI agents with specific interface requirements | | model | Machine learning models for inference | | graph | Multi-component graph for an application | | nim | NVIDIA NIM containers for optimized inference | The artifact type affects expected container interfaces, telemetry behavior, and deployment compatibility. ## Workloads A workload represents live compute. When you create a workload from an artifact, the system provisions the necessary resources and manages the runtime lifecycle. ### Workload states Workloads progress through a defined lifecycle: | State | Description | | --- | --- | | Submitted | The workload request has been accepted | | Initializing | Resources are being provisioned and containers are starting | | Running | The workload is active and serving requests | | Stopped | The workload has been intentionally stopped | | Errored | The workload encountered a failure | ### Creating workloads You can create workloads in two ways: - From an existing artifact: Reference a previously created artifact by ID. Use this when you want to reuse the same artifact configuration across multiple workloads. - With an inline artifact definition: Include the artifact specification directly in the workload creation request. Use this for rapid iteration without separate artifact management. ### Draft vs. registered workloads A workload's eligibility for production depends on its underlying artifact: - Draft workloads use draft artifacts. They are suited for development and testing but cannot be attached to a deployment. - Registered workloads use registered artifacts. They are eligible for governance controls and can be attached to a deployment for production use. Only a workload whose artifact is registered can be linked to a deployment. After you promote an artifact to registered, any workload using that artifact is no longer draft-capable; the artifact (and thus the workload's configuration) is immutable. ## Deployments Deployments provide the production-facing layer of the Workload API. A deployment wraps a single workload and provides: - A stable URL that persists across workload updates - Health monitoring and status endpoints - Access control and authentication - Audit logging and governance integration Each deployment can have only one active workload at a time and provides a stable external identity for that workload. > [!NOTE] Distinct from model deployments > Workload API deployments are a new concept, separate from DataRobot model deployments. They do not share the same data model or APIs. ## Resource bundles Resource bundles define the compute resources allocated to a workload. Instead of specifying raw CPU, memory, and GPU requirements directly, you select a bundle that matches your workload's needs. Bundles abstract the underlying infrastructure and may include: - CPU allocation - Memory allocation - GPU type and count - GPU memory This abstraction enables workload portability across different infrastructure configurations and simplifies resource management. ## Current capabilities and limitations ### Supported - Bring your own container (BYOC): Containers must be pre-built and published to a registry accessible by the DataRobot cluster. - Container definitions: Ports, entrypoints/commands, environment variables, and Kubernetes-style probes (liveness, readiness, startup). - Artifact lifecycle: Draft → registered, with versioning via artifact collections. - Workload lifecycle: Create, start, and stop workloads. - Production deployments: Stable invoke URLs and a governance layer (access control, audit logging). ### Current limitations - DataRobot Secrets integration is not yet available; use environment variables or your own secret management. - Replacing the workload underlying a deployment (workload swap) is not yet supported. - You cannot build or customize container images via the API; use an external registry and image build pipeline. - Telemetry (metrics, logs, traces) for draft workloads is limited compared to deployment-backed workloads. ## Design principles The Workload API is built around several key design principles: | Principle | Description | | --- | --- | | Separation of concerns | Artifacts define business logic independently from runtime configuration. The same artifact can be reused across environments and deployment scenarios. | | Immutability for production | Registered artifacts are immutable, providing a reliable foundation for auditing, rollbacks, and reproducibility. | | Progressive governance | Draft resources enable rapid experimentation without governance overhead. Governance controls apply only when resources are registered for production use. | | Infrastructure abstraction | The API intentionally hides Kubernetes primitives and infrastructure details. You work with business-level concepts rather than low-level orchestration. | --- # Tutorials URL: https://docs.datarobot.com/en/docs/api/dev-learning/workload-api/tutorials/index.html > Step-by-step tutorials for the Workload API. # Tutorials Step-by-step guides for deploying and managing workloads with the Workload API. | Tutorial | Description | | --- | --- | | Deploy production-ready artifact | Deploy a containerized AI service and link it to a DataRobot Deployment for governance. | | Iterate then deploy | Development-to-production workflow with draft and registered artifacts. | --- # Iterate then deploy URL: https://docs.datarobot.com/en/docs/api/dev-learning/workload-api/tutorials/tutorial-iterate.html > Development-to-production workflow where you iterate on a workload with a draft artifact, then register it and create a governed deployment. # Iterate then deploy This tutorial demonstrates the development-to-production workflow where you iterate on a workload with a draft artifact, then register it and create a governed deployment. ## Workflow overview | Phase | Action | Result | | --- | --- | --- | | Development | POST /console/workloads/ | Creates workload and draft artifacts. | | Iteration | PATCH /registry/artifacts/{artifactId} | Updates the container spec, restarts, tests. | | Lock | PATCH /registry/artifacts/{artifactId} | Sets status: registered to make immutable. | | Production | POST /console/deployments/ | Creates a governed deployment. | ## Step 1: Create a development workload Create a workload with an inline artifact. The artifact is created with `status=draft` by default, allowing iteration. ## Step 2: Iterate on the artifact During development, update the artifact and restart the workload to test changes. ### 2a. Update artifact (partial update with PATCH) Use `PATCH` to update specific metadata fields (for example, name or description) while keeping others unchanged. ### 2b. Replace entire artifact (full update with PUT) Use `PUT` to replace the full artifact definition, including the container spec. For changes to the container specification (image, entrypoint, probes, and so on), `PUT` is required; `PATCH` does not support full artifact updates. ### 2c. Restart workload to apply changes ``` # Stop the workload curl -X POST "${DATAROBOT_ENDPOINT}/console/workloads/${WORKLOAD_ID}/stop" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" # Wait a few seconds, then start curl -X POST "${DATAROBOT_ENDPOINT}/console/workloads/${WORKLOAD_ID}/start" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" ``` ## Step 3: Lock artifact for production When ready for production, change the artifact status from draft to registered: ``` curl -X PATCH "${DATAROBOT_ENDPOINT}/registry/artifacts/${ARTIFACT_ID}" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{"status": "registered"}' ``` > [!NOTE] Immutability > Once registered, the artifact's container specification is locked and cannot be modified. ## Step 4: Create production deployment Create a governed deployment using the `WORKLOAD_ID` from Step 1. ## Key concepts | Concept | Description | | --- | --- | | Draft artifact | Mutable; can be updated during development. | | Registered artifact | Immutable, locked for production use. | | Workload | Runtime instance of containers for development or testing. | | Deployment | Production-grade workload with governance and audit trails. | --- # Deploy a production-ready artifact URL: https://docs.datarobot.com/en/docs/api/dev-learning/workload-api/tutorials/tutorial-production.html > Deploy a containerized AI service via the Workload API and link it to a DataRobot Deployment for governance. # Deploy production-ready artifact This tutorial walks you through deploying a containerized AI service via the Workload API and linking it to a DataRobot Deployment for governance. ## What you'll deploy This tutorial deploys a FastAPI-based agent service that provides: - OpenAI-compatible/chat/completions —Proxies requests to a DataRobot-deployed LLM. - LangGraph/agentendpoint —A ReAct agent with arXiv search for research queries. - Health endpoints — /healthz (liveness), /readyz (readiness), /health (detailed status). - OpenTelemetry logging —Structured logs exported via OTLP for observability. ## Workflow overview 1. Create a workload → Returns workloadId and artifactId 2. Monitor status → Use workloadId to poll until running 3. Test the workload → Invoke endpoints using workloadId 4. Create a deployment → Pass workloadId , returns deploymentId ## API base URLs | API group | Base path | | --- | --- | | Workloads | /api/v2/console/workloads/ | | Deployments | /api/v2/console/deployments/ | | Workload invoke | /api/v2/endpoints/workloads/{workloadId}/ | | Deployment invoke | /api/v2/endpoints/deployments/{deploymentId}/ | ## Step 1: Create a workload Deploy your container by calling the Workload API. This creates a registered artifact and schedules the container on Kubernetes. ``` curl -X POST "${DATAROBOT_ENDPOINT}/console/workloads/" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{ "name": "agent-service", "artifact": { "name": "agent-service-artifact", "status": "registered", "spec": { "containerGroups": [{ "containers": [{ "imageUri": "/agent-service:1.0.0", "port": 8080, "primary": true, "entrypoint": ["python", "server.py"], "resourceRequest": {"cpu": 1, "memory": 536870912}, "environmentVars": [ {"name": "MODEL", "value": "openai/gpt-oss-20b"}, {"name": "DEPLOYMENT_ID", "value": ""} ], "readinessProbe": {"path": "/readyz", "port": 8080} }] }] } }, "runtime": {"replicaCount": 1} }' ``` ## Step 2: Monitor status Poll until status transitions to running: ``` curl -s "${DATAROBOT_ENDPOINT}/console/workloads/${WORKLOAD_ID}" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" | jq '.status' ``` ## Step 3: Test the workload Once running, test the endpoints: Chat completions: ``` curl -X POST "${DATAROBOT_ENDPOINT}/endpoints/workloads/${WORKLOAD_ID}/chat/completions" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{"model": "openai/gpt-oss-20b", "messages": [{"role": "user", "content": "Hello!"}]}' ``` ## Step 4: Create a deployment Link the workload to a Deployment for monitoring, sharing, and audit trails: ``` curl -X POST "${DATAROBOT_ENDPOINT}/console/deployments/" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{ "workloadId": "'${WORKLOAD_ID}'", "name": "agent-service-deployment", "importance": "moderate" }' ``` ## Step 5: Invoke via deployment endpoint After creating the deployment, invoke your service through the governed deployment endpoint: ``` curl -X POST "${DATAROBOT_ENDPOINT}/endpoints/deployments/${DEPLOYMENT_ID}/chat/completions" \ -H "Authorization: Bearer ${DATAROBOT_API_TOKEN}" \ -H "Content-Type: application/json" \ -d '{"model": "openai/gpt-oss-20b", "messages": [{"role": "user", "content": "Hello!"}]}' ``` --- # Developer documentation URL: https://docs.datarobot.com/en/docs/api/index.html > Use the REST and Python APIs and a variety of code-first tools to develop models. # Developer documentation DataRobot supports REST, Python, and R APIs as a programmatic alternative to the UI for creating and managing DataRobot projects. It allows you to automate processes and iterate more quickly, and lets you use DataRobot with scripted control. The API provides an intuitive modeling and prediction interface. You can use the API with DataRobot—supported clients in either R or Python, or with your own custom code. The clients are supported in Windows, UNIX, and OS X environments. Additionally, you can generate predictions with the prediction and batch prediction APIs, and build DataRobot blueprints in the blueprint workshop. - Developer learning¶ Educational resource for getting started with DataRobot APIs. - API reference¶ Access documentation for DataRobot APIs and packages. - DataRobot CLI¶ Access documentation for using and developing the DataRobot CLI. - Agent Assist¶ Access documentation for using the interactive agent-building assistant. - Code-first tools¶ Access documentation for the code-first tools provided by DataRobot. --- # Time series URL: https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/batch-pred-ts.html > Outlines how to set up batch predictions for time series models. Includes settings details and code examples. # Time series Batch predictions for time series models work without any additional configuration. However, in most cases you need to either modify the default configuration or prepare the prediction dataset. ## Time series batch prediction settings The default configuration can be overridden using the `timeseriesSettings` job configuration property: | Parameter | Example | Description | | --- | --- | --- | | type | forecast | Must be either forecast (default) or historical. | | forecastPoint | 2019-02-04T00:00:00Z | (Optional) By default, DataRobot infers the forecast point from the dataset. To configure, type must be set to forecast. | | predictionsStartDate | 2019-01-04T00:00:00Z | (Optional) By default, DataRobot infers the start date from the dataset. To configure, type must be set to historical. | | predictionsEndDate | 2019-02-04T00:00:00Z | (Optional) By default, DataRobot infers the end date from the dataset. To configure, type must be set to historical. | | relaxKnownInAdvanceFeaturesCheck | false | (Optional) If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. Default: false. | Here is a complete example job: ``` { "deploymentId": "5f22ba7ade0f435ba7217bcf", "intakeSettings": {"type": "localFile"}, "outputSettings": {"type": "localFile"}, "timeseriesSettings": { "type": "historical", "predictionsStartDate": "2020-01-01", "predictionsEndDate": "2020-03-31" } } ``` An example using the Python API client: ``` import datarobot as dr dr.Client( endpoint="https://app.datarobot.com/api/v2", token="...", ) deployment_id = "..." input_file = "to_predict.csv" output_file = "predicted.csv" job = dr.BatchPredictionJob.score_to_file( deployment_id, input_file, output_file, timeseries_settings={ "type": "historical", "predictions_start_date": "2020-01-01", "predictions_end_date": "2020-03-31", }, ) print("started scoring...", job) job.wait_for_completion() ``` ## Prediction type When using `forecast` mode, DataRobot makes predictions using `forecastPoint` or rows in the dataset without a target. In `historical` mode, DataRobot enables bulk predictions, which calculates predictions for all possible forecast points and forecast distances within `predictionsStartDate` and `predictionsEndDate` range. ## Requirements for the scoring dataset To ensure the Batch Prediction API can process your time series dataset, you must configure the following: - Sort prediction rows by their timestamps, with the earliest row first. - There is no limit on the number of series DataRobot supports. The only limit is the job timeout as mentioned in Limits . ### Single series forecast dataset example The following is an example forecast dataset for a single series: | date | y | | --- | --- | | 2020-01-01 | 9342.85 | | 2020-01-02 | 4951.33 | | 24 more historical rows | | | 2020-01-27 | 4180.92 | | 2020-01-28 | 5943.11 | | 2020-01-29 | | | 2020-01-30 | | | 2020-01-31 | | | 2020-02-01 | | | 2020-02-02 | | | 2020-02-03 | | | 2020-02-04 | | ### Multiseries forecast dataset example If scoring multiple series, the data must be ordered by series and timestamp: | date | series | y | | --- | --- | --- | | 2020-01-01 | A | 9342.85 | | 2020-01-02 | A | 4951.33 | | 24 more historical rows | | | | 2020-01-27 | A | 4180.92 | | 2020-01-28 | A | 5943.11 | | 2020-01-29 | A | | | 2020-01-30 | A | | | 2020-01-31 | A | | | 2020-02-01 | A | | | 2020-02-02 | A | | | 2020-02-03 | A | | | 2020-02-04 | A | | | 2020-01-01 | B | 8477.22 | | 2020-01-02 | B | 7210.29 | | 24 more historical rows | | | | 2020-01-27 | B | 7400.21 | | 2020-01-28 | B | 8844.71 | | 2020-01-29 | B | | | 2020-01-30 | B | | | 2020-01-31 | B | | | 2020-02-01 | B | | | 2020-02-02 | B | | | 2020-02-03 | B | | | 2020-02-04 | B | | --- # Troubleshooting Batch Prediction jobs URL: https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/batch-pred-tshoot.html > A list of common issues that occur with Batch Prediction jobs, and how to resolve them. # Troubleshooting The following lists some common issues and how to resolve them. ## A job is stuck in INITIALIZING If using local file intake, make sure you have made a `PUT` request with the scoring data for the job after the initial `POST` request. DataRobot only processes one job at a time per prediction instance, so your job may be queued behind other jobs. Check the job log for details: ``` curl -X GET https://app.datarobot.com/api/v2/batchPredictions/:id/ \ -H 'Authorization: Bearer ' ``` ## A job is stuck in RUNNING The job may be running slowly, either because of a slow model or because the scoring data contains errors that the API is trying to identify. You can follow the progress of a job by requesting the job status: ``` curl -X GET https://app.datarobot.com/api/v2/batchPredictions/:id/ \ -H 'Authorization: Bearer ' ``` ## A job was ABORTED When a job is aborted, DataRobot logs the reason to the job status. You can check job status from an individual job URL: ``` curl -X GET https://app.datarobot.com/api/v2/batchPredictions/:id/ \ -H 'Authorization: Bearer ' ``` Or from the listing view of all jobs: ``` curl -X GET https://app.datarobot.com/api/v2/batchPredictions/ \ -H 'Authorization: Bearer ' ``` ## HTTP 406 was returned when uploading a CSV file for local file intake You are missing the `Content-Type: text/csv` header. ## HTTP 422 was returned when uploading a CSV file for local file intake You either: - Already pushed CSV data for this job. To submit new data, create a new job. - Tried to push CSV data for a job that does not require you to push data (e.g., S3 intake). - Didn't encode your CSV data in the UTF-8 character set and didn't specify a custom encoding in csvSettings . - Didn't encode your CSV data in the proper CSV format and didn't specify a custom format in csvSettings . - Tried to push an empty file. In any of the above cases, the response and the job log will contain an explanation. ## Intake stream error due to date format mismatch in Oracle JDBC scoring data Oracle's DATE type contains a time component, which can cause issues with scoring time series data. A model trained using the date format `yyyy-mm-dd` can result in an error for Oracle JDBC scoring data due to Oracle's DATE format. When DataRobot reads dates from Oracle, the dates are returned in the format `yyyy-mm-dd hh:mm:ss` by default. This can cause an error when passed to a model expecting a different format. Use one of the following workarounds to avoid this issue: - Train the model using Oracle as the data source to ensure that the time format is the same when scored from Oracle. - Use the query option instead of table and schema to allow for the use of SQL functions. Oracle's TO_CHAR function can be used to parse time columns before the data is scored. ## The network connection broke while uploading a dataset for local file intake Create a new job and re-upload the dataset. Failed uploads cannot be resumed and will eventually time out. ## The network connection became unavailable while downloading the scoring data for local file output Re-download the job again. The scored data is available for 48 hours on the managed AI Platform (SaaS) and for 48 hours (but configurable) on the Self-Managed AI Platform (VPC or on-prem). ## HTTP 404 was returned while trying to download scored data You either: - Tried to download the scored data for a job that does not have scored data available for download (e.g., S3 output). - Started the download before the job had started scoring. In that case, wait until the download link becomes available in the job links and try again. ## HTTP 406 was returned when trying to download scored data Your client sent an `Accept` header that did not include `text/csv`. Either do not send the `Accept` header or include `text/csv` in it. ## CREATE_TABLE scoring fails due to unsupported output column name formats You may be using a target database as your output adapter that does not support the way DataRobot generates the [output format](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-format.html) column names. Column names such as `name (actual)_PREDICTION` when scoring Time Series models might not be supported with all databases. To work around this issue, you can utilize the [Column Name Remapping](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-format.html#column-name-remapping) functionality to re-write the output column name to some form your target database supports. For instance, if you want to remove the spaces from a column name, you can make a request adding `columnNamesRemapping` as such: ``` { "deploymentId":"", "passthroughColumnsSet":"all", "includePredictionStatus":true, "intakeSettings":{ "type":"localFile" }, "outputSettings":{ "type":"jdbc", "dataStoreId":"", "credentialId":"", "table":"table_name_of_database", "schema":"dbo", "catalog":"test", "statementType":"create_table" }, "columnNamesRemapping":{ "name (actual)_PREDICTION":"name_actual_PREDICTION" } } ``` ## Possible causes for HTTP 422 on job creation These are the possible causes for an `HTTP 422` reply when creating a new Batch Prediction job: - You sent an unknown job parameter - You specified a job parameter with an unexpected type or value - You specified an unknown credential ID in either your intake or output settings - You are attempting to score from/to the same S3/Azure/GCP URL (not supported) - You are attempting to ingest data from the AI Catalog , but your account does not have access to the AI Catalog - You are attempting to ingest data from the AI Catalog and the AI Catalog dataset is not snapshotted (required for predictions) or has not been successfully ingested - You are attempting to use a time series custom model (not currently supported) - You are attempting to use a traditional time series (ARIMA) model (not currently supported) - You requested Prediction Explanations for a multiclass or time series project (not currently supported) - You requested prediction warnings for a project other than a regression project (not currently supported) - You requested prediction warnings for a project that is not properly configured with prediction boundaries --- # Batch Prediction API URL: https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html > The Batch Prediction API provides flexible options for scoring large datasets using the prediction servers you have already deployed. # Batch Prediction API The Batch Prediction API provides flexible options for intake and output when scoring large datasets using the prediction servers you have already deployed. The API is exposed through the DataRobot Public API. The API can be consumed using either any REST-enabled client or the [DataRobot Python Public API bindings](https://datarobot-public-api-client.readthedocs-hosted.com/page/). For more information about Batch Prediction REST API routes, view the [DataRobot REST API reference documentation](https://docs.datarobot.com/en/docs/api/reference/public-api/index.html). The main features of the API are: - Flexible options for intake and output: - Protection against prediction server overload with a concurrency control level option. - Inclusion of Prediction Explanations (with an option to add thresholds). - Support for passthrough columns to correlate scored data with source data. - Addition of prediction warnings in the output. - The ability to make predictions with files greater than 1GB via the API . For more information about making batch prediction settings for time series, [reference the time series documentation](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/batch-pred-ts.html). ## Limits | Item | AI Platform (SaaS) | Self-managed AI Platform (VPC or on-prem) | | --- | --- | --- | | Job runtime limit | 4 hours* | Unlimited | | Local file intake size | Unlimited | Unlimited | | Local file write size | Unlimited | Unlimited | | S3 intake size | Unlimited | Unlimited | | S3 write size | 100GB | 100GB (configurable) | | Azure intake size | 4.75TB | 4.75TB | | Azure write size | 195GB | 195GB | | GCP intake size | 5TB | 5TB | | GCP write size | 5TB | 5TB | | JDBC intake size | Unlimited | Unlimited | | JDBC output size | Unlimited | Unlimited | | Concurrent jobs | 1 per prediction instance | 1 per installation | | Stored data retention time For local file adapters | 48 hours | 48 hours (configurable) | * Feature Discovery projects have a job runtime limit of 6 hours. ## Concurrent jobs To ensure that the prediction server does not get overloaded, DataRobot will only run one job per prediction instance. Further jobs are queued and started as soon as previous jobs complete. ## Data pipeline A Batch Prediction job is a data pipeline consisting of: > Data Intake > Concurrent Scoring > Data Output On creation, the job's `intakeSettings` and `outputSettings` define the data intake and data output part of the pipeline. You can configure any combination of intake and output options. For both, the defaults are local file intake and output, meaning you will have to issue a separate `PUT` request with the data to score and subsequently download the scored data. ### Data sources supported for batch predictions The following table shows the data source support for batch predictions. | Name | Driver version | Intake support | Output support | DataRobot version validated | | --- | --- | --- | --- | --- | | AWS Athena 2.0 | 2.0.35 | yes | no | 7.3 | | AWS S3 | 2022.1.1670354484 | yes | yes | - | | Alibaba Cloud MaxCompute¹ | 3.6.0 | yes | yes | 11.1 | | Databricks² | 2.6.40 | yes | yes | 9.2 | | Exasol | 7.0.14 | yes | yes | 8.0 | | Google BigQuery | 1.2.4 | yes | yes | 7.3 | | InterSystems | 3.2.0 | yes | no | 7.3 | | kdb+ | - | yes | yes | 7.3 | | Microsoft SQL Server | 12.2.0 | yes | yes | 6.0 | | MySQL | 8.0.32 | yes | yes | 6.0 | | Oracle | 11.2.0 | yes | yes | 7.3 | | PostgreSQL | 42.5.1 | yes | yes | 6.0 | | Presto³ | 0.216 | yes | yes | 8.0 | | Redshift | 2.1.0.14 | yes | yes | 6.0 | | SAP HANA | 2.20.17 | yes | yes | 7.3 (intake support only) 10.1 (intake and output support) | | Snowflake | 3.15.1 | yes | yes | 6.2 | | Synapse | 12.4.1 | yes | yes | 7.3 | | Teradata⁴ | 17.10.00.23 | yes | yes | 7.3 | | TreasureData | 0.5.10 | yes | no | 7.3 | ¹ Only the "insert" write strategy is supported. Data table and column names cannot contain special characters. These names can contain letters, digits, and underscores (_); however, they must start with a letter and cannot exceed 128 bytes in length. For more information, see the [feature considerations](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-maxcompute.html#feature-considerations). ² Only the [Databricks JDBC driver](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-databricks.html) supports batch predictions. ³ Presto requires the use of `auto commit: true` for many of the underlying connectors which can delay writes. ⁴ For output to Teradata, DataRobot only supports ANSI mode. For further information, see: - Supported intake options - Supported output options - Output format schema ## Concurrent scoring When scoring, the data you supply is split into chunks and scored concurrently on the prediction instance specified by the deployment. To control the level of concurrency, modify the `numConcurrent` parameter at job creation. ## Job states When working with batch predictions, each prediction job can be in one of four states: - INITIALIZING : The job has been successfully created and is either: - RUNNING : Scoring the dataset on prediction servers has started. - ABORTED : The job was aborted because either: - COMPLETED : The dataset has been scored and: ## Store credentials securely Some sources or targets for scoring may require DataRobot to authenticate on your behalf (for example, if your database requires that you pass a username and password for login). To ensure proper storage of these credentials, you must have [data credentials](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html) enabled. DataRobot uses the following credential types and properties: | Adapter | Credential Type | Property | | --- | --- | --- | | S3 intake / output | s3 | awsAccessKeyId awsSecretAccessKey awsSessionToken (optional) | | JDBC intake / output | basic | username password | To use a stored credential, you must pass the associated `credentialId` in either `intakeSettings` or `outputSettings` as described below for each of the adapters. ## CSV format For any intake or output options that deal with reading or writing CSV files, you can use a custom format by specifying the following in `csvSettings`: | Parameter | Example | Description | | --- | --- | --- | | delimiter | , | (Optional) The delimiter character to use. Default: , (comma). To specify TAB as a delimiter, use the string tab. | | quotechar | " | (Optional) The character to use for quoting fields containing the delimiter. Default: ". | | encoding | utf-8 | (Optional) Encoding for the CSV file. For example (but not limited to): shift_jis, latin_1 or mskanji. Default: utf-8. Any Python supported encoding can be used. | The same format will be used for both intake and output. See a [complete example](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html#end-to-end-scoring-of-csv-files-from-local-files). ## Model monitoring The Batch Prediction API integrates well with DataRobot's model monitoring capabilities: - If you have enabled data drift tracking for your deployment, any predictions run through the Batch Prediction API will be tracked. - If you have enabled target drift tracking for your deployment, the output will contain the desired association ID to be used for reporting actuals. Should you need to run a non-production dataset against your deployment, you can turn off drift and accuracy tracking for a single job by providing the following parameter: | Parameter | Example | Description | | --- | --- | --- | | skipDriftTracking | true | (Optional) Skip data drift, target drift, and accuracy tracking for this job. Default: false. | ## Override the default prediction instance Under normal circumstances, the prediction server used for scoring will be the default prediction server that your model was deployed to. It is however possible to override it If you have access to multiple prediction servers, you can override the default behavior by using the following properties in the `predictionInstance` option: | Parameter | Example | Description | | --- | --- | --- | | hostName | 192.0.2.4 | Sets the hostname to use instead of the default hostname from the prediction server the model was deployed to. | | sslEnabled | false | (Optional) Use SSL (HTTPS) to access the prediction server. Default: true. | | apiKey | NWU...IBn2w | (Optional) Use an API key different from the job creator's key to authenticate against the new prediction server. | | datarobotKey | 154a8abb-cbde-4e73-ab3b-a46c389c337b | (Optional) If running in a managed AI Platform environment, specify the per-organization DataRobot key for the prediction server. Find the key on the Deployments> Predictions > Prediction API tab or by contacting your DataRobot representative. | Here's a complete example: ``` job_details = { 'deploymentId': deployment_id, 'intakeSettings': {'type': 'localFile'}, 'outputSettings': {'type': 'localFile'}, 'predictionInstance': { 'hostName': '192.0.2.4', 'sslEnabled': False, 'apiKey': 'NWUQ9w21UhGgerBtOC4ahN0aqjbjZ0NMhL1e5cSt4ZHIBn2w', 'datarobotKey': '154a8abb-cbde-4e73-ab3b-a46c389c337b', }, } ``` ## Consistent scoring with updated model If you deploy a new model after a job has been queued, DataRobot will still use the model that was deployed at the time of job creation for the entire job. Every row will be scored with the same model. ## Template variables Sometimes it can be useful to specify dynamic parameters in your batch jobs, such as in [Job Definitions](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/job-definitions.html). You can use [jinja's variable syntax](https://jinja.palletsprojects.com/en/3.0.x/templates/#variables) (double curly braces) to print the value of the following parameters: | Variable | Description | | --- | --- | | current_run_time | datetime object for current UTC time (datetime.utcnow()) | | current_run_timestamp | Milliseconds from Unix epoch (integer) | | last_scheduled_run_time | datetime object for the start of last job instantiated from the same job definition | | next_scheduled_run_time | datetime object for the next scheduled start of job from the same job definition | | last_completed_run_time | datetime object for when the previously scheduled job finished scoring | The above variables can be used in the following fields: | Field | Condition | | --- | --- | | intake_settings.query | For JDBC, Synapse, and Snowflake adapters | | output_settings.table | For JDBC, Synapse, Snowflake, and BigQuery adapters, when statement type is create_table or create_table_if_not_exists is marked true | | output_settings.url | For S3, GCP, and Azure adapters | You should specify the URL as: `gs://bucket/output-.csv`. > [!NOTE] Note > To ensure that most databases understand the replacements mentioned above, DataRobot strips microseconds off the ISO-8601 format timestamps. ## API Reference ### The Public API The Batch Prediction API is part of the [DataRobot REST API](https://docs.datarobot.com/en/docs/api/reference/public-api/batch_predictions.html). Reference this documentation for more information about how to work with batch predictions. ### The Python API Client You can use the [Python Public API Client](https://datarobot-public-api-client.readthedocs-hosted.com/) to interface with the Batch Prediction API. --- # Prediction intake options URL: https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html > Configure batch prediction data sources (intake) with the Job Definitions UI or the Batch Prediction API. # Prediction intake options You can configure a prediction source using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-sources) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html). This topic describes both the UI and API intake options. > [!NOTE] Note > For a complete list of supported intake options, see the [data sources supported for batch predictions](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html#data-sources-supported-for-batch-predictions). | Intake option | Description | | --- | --- | | Local file streaming | Stream input data through a URL endpoint for immediate processing when the job moves to a running state. | | HTTP scoring | Stream input data from an absolute URL for scoring. This option can read data from pre-signed URLs for Amazon S3, Azure, and Google Cloud Platform. | | Database connections | | | JDBC scoring | Read prediction data from a JDBC-compatible database with data source details supplied through a job definition or the Batch Prediction API. | | SAP Datasphere scoring | Read prediction data from a SAP Datasphere database with data source details supplied through a job definition or the Batch Prediction API. | | Trino scoring | Read prediction data from a Trino database with data source details supplied through a job definition or the Batch Prediction API. | | Cloud storage connections | | | Azure Blob Storage scoring | Read input data from Azure Blob Storage with DataRobot credentials consisting of an Azure Connection String. | | Google Cloud Storage scoring (GCP) | Read input data from Google Cloud Storage with DataRobot credentials consisting of a JSON-formatted account key. | | Amazon S3 scoring | Read input data from public or private S3 buckets with DataRobot credentials consisting of an access key (ID and key) and a session token (Optional) This is the preferred intake option for larger files. | | Data warehouse connections | | | BigQuery scoring | Score data using BigQuery with data source details supplied through a job definition or the Batch Prediction API. | | Snowflake scoring | Score data using Snowflake with data source details supplied through a job definition or the Batch Prediction API. | | Azure Synapse scoring | Score data using Synapse with data source details supplied through a job definition or the Batch Prediction API. | | Other connections | | | AI Catalog / Data Registry dataset scoring | Read input data from a dataset snapshot in the DataRobot AI Catalog / Data Registry. | | Wrangler Recipe scoring | Read input data from a wrangler recipe created in the DataRobot NextGen Workbench from a Snowflake data connection. | If you are using a custom [CSV format](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html#csv-format), any intake option dealing with CSV will adhere to that format. ## Local file streaming Local file intake does not have any special options. This intake option requires you to upload the job's scoring data using a `PUT` request to the URL specified in the `csvUpload` link in the job data. This starts the job (or queues it for processing if the prediction instance is already occupied). If there is no other queued job for the selected prediction instance, scoring will start while you are still uploading. Refer to [this sample use case](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html#end-to-end-scoring-of-csv-files-from-local-files). > [!NOTE] Note > If you forget to send scoring data, the job remains in the INITIALIZING state. ### Multipart upload Because the local file intake process requires that you upload scoring data for a job using a `PUT` request to the URL specified in the `csvUpload` parameter, by default, a single `PUT` request starts the job (or queues it for processing if the prediction instance is occupied). Multipart upload for batch predictions allows you to override the default behavior to upload scoring data through multiple files. This upload process requires multiple `PUT` requests followed by a single `POST` request ( `finalizeMultipart`) to finalize the multipart upload manually. This feature can be helpful when you want to upload large datasets over a slow connection or if you experience frequent network instability. > [!NOTE] Note > For more information on the batch prediction API and local file intake, see [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) and [Prediction intake options](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html#local-file-streaming). #### Multipart upload endpoints This feature adds the following multipart upload endpoints to the batch prediction API: | Endpoint | Description | | --- | --- | | PUT /api/v2/batchPredictions/:id/csvUpload/part/0/ | Upload scoring data in multiple parts to the URL specified by csvUpload. Increment 0 by 1 in sequential order for each part of the upload. | | POST /api/v2/batchPredictions/:id/csvUpload/finalizeMultipart/ | Finalize the multipart upload process. Make sure each part of the upload has finished before finalizing. | #### Local file intake settings The intake settings for the local file adapter added two new properties to support multipart upload for the batch prediction API: | Property | Type | Default | Description | | --- | --- | --- | --- | | intakeSettings.multipart | boolean | false | true: Requires you to submit multiple files via a PUT request and finalize the process manually via a POST request (finalizeMultipart).false: Finalizes intake after one file is submitted via a PUT request. | | intakeSettings.async | boolean | true | true: Starts the scoring job when the initial PUT request for file intake is made.false: Postpones the scoring job until the PUT request resolves or the POST request for finalizeMultipart resolves. | ##### Multipart intake setting To enable the new multipart upload workflow, configure the `intakeSettings` for the `localFile` adapter as shown in the following sample request: ``` { "intakeSettings": { "type": "localFile", "multipart": true } } ``` - Upload any number of sequentially numbered files. - Finalize the upload to indicate that all required files uploaded successfully. ##### Async intake setting To enable the new multipart upload workflow with async enabled, configure the `intakeSettings` for the `localFile` adapter as shown in the following sample request: > [!NOTE] Note > You can also use the `async` intake setting independently of the `multipart` setting. ``` { "intakeSettings": { "type": "localFile", "multipart": true, "async": false } } ``` A defining feature of batch predictions is that the scoring job starts on the initial file upload, and only one batch prediction job at a time can run for any given prediction instance. This functionality may cause issues when uploading large datasets over a slow connection. In these cases, the client's upload speed could create a bottleneck and block the processing of other jobs. To avoid this potential bottleneck, you can set `async` to `false`, as shown in the example above. This configuration postpones submitting the batch prediction job to the queue. When `"async": false`, the point at which a job enters the batch prediction queue depends on the `multipart` setting: - If"multipart": true, the job is submitted to the queue after thePOSTrequest forfinalizeMultipartresolves. - If"multipart": false, the job is submitted to the queue after the initial file intakePUTrequest resolves. #### Example multipart upload requests The batch prediction API requests required to upload a 3 part multipart batch prediction job would be: ``` PUT /api/v2/batchPredictions/:id/csvUpload/part/0/ PUT /api/v2/batchPredictions/:id/csvUpload/part/1/ PUT /api/v2/batchPredictions/:id/csvUpload/part/2/ POST /api/v2/batchPredictions/:id/csvUpload/finalizeMultipart/ ``` Each uploaded part is a complete CSV file with a header. #### Abort a multipart upload If you start a multipart upload that you don't want to finalize, you can use a `DELETE` request to the existing `batchPredictions` abort route: ``` DELETE /api/v2/batchPredictions/:id/ ``` ## HTTP scoring In addition to the cloud storage adapters, you can also point batch predictions to a regular URL so DataRobot can stream the data for scoring: | Parameter | Example | Description | | --- | --- | --- | | type | http | Use HTTP for intake. | | url | https://example.com/datasets/scoring.csv | An absolute URL for the file to be scored. | The URL can optionally contain a username and password, such as `https://username:password@example.com/datasets/scoring.csv`. The `http` adapter can be used for ingesting data from pre-signed URLs from either [S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/ShareObjectPreSignedURL.html), [Azure](https://docs.microsoft.com/en-us/azure/storage/common/storage-sas-overview), or [GCP](https://cloud.google.com/storage/docs/access-control/signed-urls). ## JDBC scoring DataRobot supports reading from any JDBC-compatible database for Batch Predictions. To use JDBC with the Batch Prediction API, specify `jdbc` as the intake type. Since no file is needed for a `PUT` request, scoring will start immediately, transitioning the job to RUNNING if preliminary validation succeeds. To support this, the Batch Prediction API integrates with [external data sources](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/data-conn.html#add-data-sources) using credentials securely stored in [data credentials](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html). Supply data source details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-sources) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `intakeSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | jdbc | Use a JDBC data store for intake. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | The ID of an external data source. In the UI, select a data connection or click add a new data connection. Complete account and authorization fields. | | Enter credentials | credentialId | 5e4bc5555e6e763beb9db147 | The ID of a stored credential. Refer to storing credentials securely. | | Schemas | schema | public | (Optional) The name of the schema containing the table to be scored. | | Tables | table | scoring_data | (Optional) The name of the database table containing data to be scored. | | SQL query | query | SELECT feature1, feature2, feature3 AS readmitted FROM diabetes | (Optional) A custom query to run against the database. | | Deprecated option | | | | | Fetch size | fetchSize (deprecated) | 1000 | Deprecated: fetchSize is now inferred dynamically for optimal throughput and is no longer needed. (Optional) To balance throughput and memory usage, sets a custom fetchSize (number of rows read at a time). Must be in range [1, 100000]; default 1000. | > [!NOTE] Note > You must specify either `table` and `schema` or `query`. Refer to the [example section](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html#end-to-end-scoring-from-a-jdbc-postgresql-database) for a complete API example. > [!NOTE] Data warehouse connections > Using JDBC to transfer data can be costly in terms of IOPS (input/output operations per second) and expense for data warehouses. The data warehouse adapters reduce the load on database engines during prediction scoring by using cloud storage and bulk insert to create a hybrid JDBC-cloud storage solution. For more information, see the [BigQuery](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html#bigquery-scoring), [Snowflake](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html#snowflake-scoring), and [Synapse](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html#azure-synapse-scoring) data warehouse adapter sections. ### Allowed source IP addresses Any connection initiated from DataRobot originates from an allowed IP addresses. See a full list at [Allowed source IP addresses](https://docs.datarobot.com/en/docs/reference/data-ref/allowed-ips.html). ## SAP Datasphere scoring > [!NOTE] Premium > Support for SAP Datasphere is off by default. Contact your DataRobot representative or administrator for information on enabling the feature. > > Feature flag(s): Enable SAP Datasphere Connector, Enable SAP Datasphere Batch Predictions Integration To use SAP Datasphere for scoring, supply data source details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-sources) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `intakeSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | datasphere | Use a SAP Datasphere database for intake. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | The ID of an external data source. In the UI, select a data connection or click add a new data connection. Refer to the SAP Datasphere connection documentation. | | Enter credentials | credentialId | 5e4bc5555e6e763beb9db147 | The ID of a stored credential for Datasphere. Refer to storing credentials securely. | | | catalog | / | The name of the database catalog containing the table to be scored. | | Schemas | schema | public | The name of the database schema containing the table to be scored. | | Tables | table | scoring_data | The name of the database table containing data to be scored. | ## Databricks scoring To use the [Databricks connector](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-databricks.html) for scoring, supply data source details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-sources) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `intakeSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | databricks | Use a Databricks database for intake. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | The ID of an external data source. In the UI, select a data connection or click add a new data connection. | | + Add credentials | credentialId | 5e96092ef7e8773ddbdbabed | The ID of stored credentials for the external Databricks database connection. | | Catalog | catalog | default | (Optional) The Databricks database catalog containing the source table. | | Schema | schema | public | The Databricks schema containing the source table. | | Table | table | kickcars | The Databricks table from which to read input data. | > [!WARNING] Databricks column name sanitization > For Databricks batch scoring, any column name that contains a dot ( `.`) is automatically sanitized, replacing each dot with an underscore ( `_`). For example, `column.name` becomes `column_name`. To keep intake column names aligned with the training data and avoid unexpected renames, avoid using dots in column names. ## Trino scoring To use the [Trino connector](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-trino.html) for scoring, supply data source details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-sources) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `intakeSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | trino | Use a Trino database for intake. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | The ID of an external data source. In the UI, select a data connection or click add a new data connection. | | + Add credentials | credentialId | 5e96092ef7e8773ddbdbabed | The ID of stored credentials for the external Trino database connection. | | Catalog | catalog | starburst_catalog | The Trino database catalog containing the source table. | | Schema | schema | analytics | The Trino schema containing the source table. | | Table | table | input_data_table | The Trino table from which to read input data. | > [!WARNING] Trino column name case requirement > Use lowercase only for column names in the dataset used to train a project. Trino sanitizes column names automatically (unquoted identifiers are lowercased), so mixed-case or uppercase column names can cause column inconsistency errors when reading from Trino for batch scoring. This applies even when creating tables with quoted column names—Trino still stores them as lowercase. For more information, see [trinodb/trino#17](https://github.com/trinodb/trino/issues/17). ## Azure Blob Storage scoring A scoring option for large files is Azure. To score from Azure Blob Storage, you must configure credentials with DataRobot using an Azure Connection String. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | azure | Use Azure Blob Storage for intake. | | URL | url | https://myaccount.blob.core.windows.net/datasets/scoring.csv | An absolute URL for the file to be scored. | | Format | format | csv | (Optional) Select CSV (csv) or Parquet (parquet). Default value: CSV | | + Add credentials | credentialId | 5e4bc5555e6e763beb488dba | In the UI, enable the + Add credentials field by selecting This URL requires credentials. Refer to storing credentials securely. | Azure credentials are encrypted and are only decrypted when used to set up the client for communication with Azure during scoring. ## Google Cloud Storage scoring DataRobot supports the Google Cloud Storage adapter. To score from Google Cloud Storage, you must set up a credential with DataRobot consisting of a JSON-formatted account key. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | gcp | Use Google Cloud Storage for intake. | | URL | url | gcs://bucket-name/datasets/scoring.csv | An absolute URL for the file to be scored. | | Format | format | csv | (Optional) Select CSV (csv) or Parquet (parquet). Default value: CSV | | + Add credentials | credentialId | 5e4bc5555e6e763beb488dba | In the UI, enable the + Add credentials field by selecting This URL requires credentials. Required if explicit access credentials for this URL are required, otherwise optional. Refer to storing credentials securely. | GCP credentials are encrypted and are only decrypted when used to set up the client for communication with GCP during scoring. ## Amazon S3 scoring For larger files, S3 is the preferred method for intake. DataRobot can ingest files from both public and private buckets. To score from Amazon S3, you must set up a credential with DataRobot consisting of an access key (ID and key) and, optionally, a session token. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | s3 | DataRobot recommends S3 for intake. | | URL | url | s3://bucket-name/datasets/scoring.csv | An absolute URL for the file to be scored. | | Format | format | csv | (Optional) Select CSV (csv) or Parquet (parquet). Default value: CSV | | + Add credentials | credentialId | 5e4bc5555e6e763beb488dba | In the UI, enable the + Add credentials field by selecting This URL requires credentials. Required if explicit access credentials for this URL are required. Refer to storing credentials securely. | AWS credentials are encrypted and only decrypted when used to set up the client for communication with AWS during scoring. > [!NOTE] Note > If running a Private AI Cloud within AWS, it is possible to provide implicit credentials for your application instances using an IAM Instance Profile to access your S3 buckets without supplying explicit credentials in the job data. For more information, see the [AWS documentation](https://docs.aws.amazon.com/codedeploy/latest/userguide/getting-started-create-iam-instance-profile.html). ## BigQuery scoring To use BigQuery for scoring, supply data source details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-sources) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `intakeSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | bigquery | Use the BigQuery API to unload data to Google Cloud Storage and use it as intake. | | Dataset | dataset | my_dataset | The BigQuery dataset to use. | | Table | table | my_table | The BigQuery table or view from the dataset used as intake. | | Bucket | bucket | my-bucket-in-gcs | Bucket where data should be exported. | | + Add credentials | credentialId | 5e4bc5555e6e763beb488dba | Required if explicit access credentials for this bucket are required (otherwise optional).In the UI, enable the + Add credentials field by selecting This connection requires credentials. Refer to storing credentials securely. | Refer to the [example section](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html#end-to-end-scoring-with-bigquery) for a complete API example. ## Snowflake scoring To use Snowflake for scoring, supply data source details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-sources) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `intakeSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | snowflake | Adapter type. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | ID of Snowflake data source. In the UI, select a Snowflake data connection or click add a new data connection. Complete account and authorization fields. | | Enter credentials | credentialId | 5e4bc5555e6e763beb9db147 | The ID of a stored credential for Snowflake. | | Tables | table | SCORING_DATA | (Optional) Name of the Snowflake table containing data to be scored. | | Schemas | schema | PUBLIC | (Optional) Name of the schema containing the table to be scored. | | SQL query | query | SELECT feature1, feature2, feature3 FROM diabetes | (Optional) Custom query to run against the database. | | Cloud storage type | cloudStorageType | s3 | Type of cloud storage backend used in Snowflake external stage. Can be one of 3 cloud storage providers: s3/azure/gcp. Default is s3 | | External stage | externalStage | my_s3_stage | Snowflake external stage. In the UI, toggle on Use external stage to enable the External stage field. | | + Add credentials | cloudStorageCredentialId | 6e4bc5541e6e763beb9db15c | ID of stored credentials for a storage backend (S3/Azure/GCS) used in Snowflake stage. In the UI, enable the + Add credentials field by selecting This URL requires credentials. | Refer to the [example section](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html#end-to-end-scoring-with-snowflake) for a complete API example. ## Azure Synapse scoring To use Synapse for scoring, supply data source details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-sources) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `intakeSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | synapse | Adapter type. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | ID of Synapse data source. In the UI, select a Synapse data connection or click add a new data connection. Complete account and authorization fields. | | External data source | externalDatasource | my_data_source | Name of the Synapse external data source. | | Tables | table | SCORING_DATA | (Optional) Name of the Synapse table containing data to be scored. | | Schemas | schema | dbo | (Optional) Name of the schema containing the table to be scored. | | SQL query | query | SELECT feature1, feature2, feature3 FROM diabetes | (Optional) Custom query to run against the database. | | Enter credentials | credentialId | 5e4bc5555e6e763beb9db147 | The ID of a stored credential for Synapse. Credentials are required if explicit access credentials for this URL are required, otherwise optional. Refer to storing credentials securely. | | + Add credentials | cloudStorageCredentialId | 6e4bc5541e6e763beb9db15c | ID of a stored credential for Azure Blob storage. In the UI, enable the + Add credentials field by selecting This external data source requires credentials. | Refer to the [example section](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html#end-to-end-scoring-with-synapse) for a complete API example. > [!NOTE] Note > Synapse supports fewer collations than the default Microsoft SQL Server. For more information, reference the [Synapse documentation](https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/reference-collation-types). ## AI Catalog / Data Registry dataset scoring To read input data from an [AI Catalog/Data Registry](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html) dataset, the following options are available: | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | dataset | In the UI, select AI Catalog (or Data Registry in NextGen). | | + Select source from AI Catalog | datasetId | 5e4bc5b35e6e763beb9db14a | The AI Catalog dataset ID.In the UI, search for the dataset, select the dataset, then click Use the dataset (or Confirm in NextGen). | | + Select version | datasetVersionId | 5e4bc5555e6e763beb488dba | The AI Catalog dataset version ID (Optional)In the UI, enable the + Select version field by selecting the Use specific version check box. Search for and select the version. If datasetVersionId is not specified, it defaults to the latest version for the specified dataset. | > [!NOTE] Note > For the specified AI Catalog dataset, the version to be scored must have been successfully ingested, and it must be a snapshot. ## Wrangler recipe dataset scoring The following options are available to read input data from a wrangler recipe created in the DataRobot [NextGen Workbench](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/index.html) from a [Snowflake data connection](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/add-data/connect.html): > [!NOTE] Wrangler data connection > Wrangler recipes for batch prediction jobs support data wrangled from a Snowflake data connection or the AI Catalog/Data Registry. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Source type | type | recipe | In the UI, select Wrangler Recipe. | | + Select wrangler recipe | recipeId | 65fb040a42c170ee46230133 | The Wrangler Recipe dataset ID.In the NextGen prediction jobs UI, search for the wrangled dataset, select the dataset, then click Confirm. | --- # Batch Prediction job definitions URL: https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/job-definitions.html > How to submit a working Batch Prediction job. You must supply a variety of elements to the POST request payload depending on the type of prediction. # Batch Prediction job definitions To submit a working Batch Prediction job, you must supply a variety of elements to the `POST` request payload depending on what type of prediction is required. Additionally, you must consider the type of intake and output adapters used for a given job. For more information about Batch Prediction REST API routes, view the [DataRobot REST API reference documentation](https://docs.datarobot.com/en/docs/api/reference/public-api/batch_predictions.html). Every time you make a Batch Prediction, the prediction information is stored outside DataRobot and re-submitted for each prediction request, as described in detail in the [sample use cases section](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html). One such request could be as follows: `POST https://app.datarobot.com/api/v2/batchPredictions` ``` { "deploymentId": "", "intakeSettings": { "type": "dataset", "datasetId": "" }, "outputSettings": { "type": "jdbc", "statementType": "insert", "credentialId": "", "dataStoreId": "", "schema": "public", "table": "example_table", "createTableIfNotExists": false }, "includeProbabilities": true, "includePredictionStatus": true, "passthroughColumnsSet": "all" } ``` ## Job Definitions API If your use case requires the same, or close to the same, type of prediction to be done multiple times, you can choose to create a Job Definition of the Batch Prediction job and store this inside DataRobot for future use. The API for job definitions is identical to the existing `/batchPredictions/` endpoint, and can be used interchangeably by changing the `POST` endpoint to `/batchPredictionJobDefinitions`: `POST https://app.datarobot.com/api/v2/batchPredictionJobDefinitions` ``` { "deploymentId": "", "intakeSettings": { "type": "dataset", "datasetId": "" }, "outputSettings": { "type": "jdbc", "statementType": "insert", "credentialId": "", "dataStoreId": "", "schema": "public", "table": "example_table", "createTableIfNotExists": false }, "includeProbabilities": true, "includePredictionStatus": true, "passthroughColumnsSet": "all" } ``` This definition endpoint will return an accepted payload that verifies the successful storing of the definition to DataRobot. (Optional) You can supply a `name` parameter for easier identification. If you don't supply one, DataRobot will create one for you. > [!WARNING] Warning > The `name` parameter must be unique across your organization. If you attempt to create multiple definitions with the same name, the request will fail. If you wish to free up a name, you must first send a `DELETE` request with the existing job definition ID you wish to delete. ## Execute a Job Definition If you wish to submit a stored job definition for scoring, you can either choose to do so on a scheduled basis, described [here](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/job-scheduling.html), or by manually submitting the definition ID to the endpoint `/batchPredictions/fromJobDefinition` and with the definition ID as the payload, as such: `POST https://app.datarobot.com/api/v2/batchPredictions/fromJobDefinition` ``` { "jobDefinitionId": "" } ``` The endpoint supports regular the CRUD operations, `GET`, `POST`, `DELETE` and `PATCH`. --- # Schedule Batch Prediction jobs URL: https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/job-scheduling.html > How to create a definition and schedule the execution of a Batch Prediction job. # Schedule Batch Prediction jobs After [creating a job definition](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/job-definitions.html), you can choose to execute job definitions on a scheduled basis instead of manually doing so through the `/batchPredictions/fromJobDefinition` endpoint. A Scheduled Batch Prediction job works just like a regular Batch Prediction job, except DataRobot handles the execution of the job. In order to schedule the execution of a Batch Prediction job, a definition must first be created, as described [here](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/job-definitions.html). For more information about Batch Prediction REST API routes, view the [DataRobot REST API reference documentation](https://docs.datarobot.com/en/docs/api/reference/public-api/batch_predictions.html). ## Schedule a job definition The API accepts the keywords `enabled` as well as a `schedule` object, as such: `POST https://app.datarobot.com/api/v2/batchPredictionJobDefinitions` ``` { "deploymentId": "", "intakeSettings": { "type": "dataset", "datasetId": "" }, "outputSettings": { "type": "jdbc", "statementType": "insert", "credentialId": "", "dataStoreId": "", "schema": "public", "table": "example_table", "createTableIfNotExists": false }, "includeProbabilities": true, "includePredictionStatus": true, "passthroughColumnsSet": "all" "enabled": false, "schedule": { "minute": [0], "hour": [1], "month": ["*"] "dayOfWeek": ["*"], "dayOfMonth": ["*"], } } ``` ### Schedule payload The `schedule` payload defines at what intervals the job should run, which can be combined in various ways to construct complex scheduling terms if needed. In all of the elements in the objects, you can supply either an asterisk `["*"]` denoting "every" time denomination or an array of integers (e.g.`[1, 2, 3]`) to define a specific interval. | Key | Possible values | Example | Description | | --- | --- | --- | --- | | minute | ["*"] or [0 ... 59] | [15, 30, 45] | The job will run at these minute values for every hour of the day. | | hour | ["*"] or [0 ... 23] | [12,23] | The hour(s) of the day that the job will run. | | month | ["*"] or [1 ... 12] | ["jan"] | Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., "jan" or "october"). Months that are not compatible with dayOfMonth are ignored, for example {"dayOfMonth": [31], "month":["feb"]}. | | dayOfWeek | ["*"] or [0 ... 6] where (Sunday=0) | ["sun"] | The day(s) of the week that the job will run. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., "sunday", "Sunday", "sun", or "Sun", all map to [0]). NOTE: This field is additive with dayOfMonth, meaning the job will run both on the date specified by dayOfMonth and the day defined in this field. | | dayOfMonth | ["*"] or [1 ... 31] | [1, 25] | The date(s) of the month that the job will run. Allowed values are either [1 ... 31] or ["*"] for all days of the month. NOTE: This field is additive with dayOfWeek, meaning the job will run both on the date(s) defined in this field and the day specified by dayOfWeek (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If dayOfMonth is set to ["*"] and dayOfWeek is defined, the scheduler will trigger on every day of the month that matches dayOfWeek (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored. | > [!NOTE] Note > When specifying a time of day to run jobs, you must use UTC in the `schedule` payload—local time zones are not supported. > To account for DST (daylight savings time), update the schedule according to your local time. ### Examples | Interval | Example | Description | | --- | --- | --- | | Run every 5 minutes | "schedule": { "minute": [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55], "hour": ["*"], "month": ["*"] "dayOfWeek": ["*"], "dayOfMonth": ["*"], } | Executes every time the minute dial of a clock reaches the number(s) defined in minute, since all other fields are with asterisks. | | Run every full hour | "schedule": { "minute": [0], "hour": ["*"], "month": ["*"] "dayOfWeek": ["*"], "dayOfMonth": ["*"], } | Executes every time the clock reaches the minute(s) defined in minute. This example executes every day at 1:00 AM, 2:00 AM, 3:00 AM, and so forth. | | Run right before noon every day | "schedule": { "minute": [59], "hour": [11], "month": ["*"] "dayOfWeek": ["*"], "dayOfMonth": ["*"], } | Executes every time the minute dial of a clock reaches the minutes(s) defined in minute, and the same when the hour dial reaches the number(s) defined in hour. This example executes every day at 11:59 AM. | | Run every full hour once every half year | "schedule": { "minute": [0], "hour": ["*"], "month": [1, 6] "dayOfWeek": ["*"], "dayOfMonth": ["*"], } | Executes every time the minute dial of a clock reaches the minute(s) defined in minute, and only when the month is January (1) or June (6). | | Run every full hour once every half year and only on Mondays and Saturdays | "schedule": { "minute": [0], "hour": ["*"], "month": [1, 6] "dayOfWeek": ["mon", "sun"], "dayOfMonth": ["*"], } | Same as above, but with dayOfWeek specified, the interval is only executed on the days specified. | | Run every full hour once every half year and only on Mondays and Saturdays, but also on the 1st and 10th of the month | "schedule": { "minute": [0], "hour": ["*"], "month": [1, 6] "dayOfWeek": ["mon", "sun"], "dayOfMonth": [1, 10], } | Same as above, but with both dayOfWeek and dayOfMonth specified, these values add to each other, not excluding. This example executes on both the times defined in dayOfWeek and dayOfMonth, and not, as could be believed, only on those years where the 1st and 10th are Mondays and Sundays. | ## Disable a scheduled job Job definitions are only be executed by the scheduler if `enabled` is set to `True`. If you have a job definition that was previously running as a scheduled job, but should now be stopped, simply `PATCH` the endpoint with `enabled` set to `False`. If a job is currently running, this will finish execution regardless. `PATCH https://app.datarobot.com/api/v2/batchPredictionJobDefinitions/` ``` { "enabled": false } ``` ## Limitations The Scheduler has limitations set to how often a job can run and how many jobs can run at once. ### Total runs per day Each organization is limited to a number of job executions per day. If you are a Self-Managed AI Platform user, you can change this limitation by changing the environment variable `BATCH_PREDICTIONS_JOB_SCHEDULER_MAX_NUMBER_OF_RUNS_PER_DAY_PER_ORGANIZATION`. On cloud, this limit is `1000` by default. Note that the limitation is across all scheduled jobs per an organization, so if one scheduled job has a maximum run time of `1000` per day, no more scheduled jobs can be activated by that organization. ### Schedules are best-effort Depending on the load of different definitions running at the same time across the organization, the scheduler cannot guarantee to execute all jobs at the exact second of the schedule. However, in most cases, the scheduler will have resources to trigger the job within 5 seconds of the schedule. ### Running the same definition simultaneously One job definition cannot run more than once on a scheduled basis. This means that if a schedule job is taking long to execute, causing the next interval to trigger before the first one finished, the job will be rejected and aborted. This will continue to happen until the running job finishes. ### Automatic disablement of failing jobs If a user has created a job definition that cannot execute due to misconfiguration and is aborted, this will cause the `enabled` feature to be auto-disabled after `5` consecutive failures. It is therefore recommended that you use the existing `/batchPredictions` endpoint to test if the solution works, before `POST` ing the identical, confirmed working payload to the `/batchPredictionJobDefinitions`. For Self-Managed AI Platform customers, this cut-off point of consecutive failures can be adjusted by changing the `BATCH_PREDICTIONS_JOB_SCHEDULER_FAILURES_BEFORE_ABORT` environment variable. --- # Predictions on large datasets URL: https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/large-preds-api.html > Walk through an example of making predictions on a large dataset using the Batch Prediction API. # Predictions on large datasets [File size limits](https://docs.datarobot.com/en/docs/classic-ui/predictions/pred-file-limits.html) vary depending on the prediction method—for predictions on large datasets, use the Batch Prediction API or real-time Prediction API. The following example shows how to make predictions on a large dataset using the Batch Prediction API. See the [Prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html) for real-time predictions. In this example, the prediction dataset is stored in the AI Catalog. The Batch Prediction API also supports predicting on data sourced from [other locations](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html). Note that for predicting with a dataset from the AI Catalog, the dataset must be snapshotted. In addition to the API key sent in the header of all API requests, you need the following to use the Batch Prediction API: 1. : The deployment ID for the model being used to make predictions against. 2. : The dataset ID of the snapshotted AI Catalog dataset used by the model . The following steps show how to work with files greater than 100MB using the `batchPredictions` API endpoint. In summary, you will: 1. Create a BatchPrediction job indicating the deployed model and dataset to use. 2. Check the status of that BatchPrediction job until it is complete. 3. Download the results. ### 1. Create a Batch Prediction job `POST https://app.datarobot.com/api/v2/batchPredictions` Sample request: ``` { "deploymentId": "", "intakeSettings": { "type": "dataset", "datasetId": "" } } ``` Sample time series request (requires enabling the time series product and the Batch Predictions for time series preview flag): ``` { "deploymentId": "", "intakeSettings": { "type": "dataset", "datasetId": "" }, "timeseriesSettings": { "type": "forecast" } } ``` Sample response: The `links.self` property of the response contains the URL used for the next two steps. ``` { "status": "INITIALIZING", "skippedRows": 0, "failedRows": 0, "elapsedTimeSec": 0, "logs": [ "Job created by user@example.com from 10.1.2.1 at 2020-02-19 22:41:00.865000" ], "links": { "download": null, "self": "https://app.datarobot.com/api/v2/batchPredictions/a1b2c3d4x5y6z7/" }, "jobIntakeSize": null, "scoredRows": 0, "jobOutputSize": null, "jobSpec": { "includeProbabilitiesClasses": [], "maxExplanations": 0, "predictionWarningEnabled": null, "numConcurrent": 4, "thresholdHigh": null, "passthroughColumnsSet": null, "csvSettings": { "quotechar": "\"", "delimiter": ",", "encoding": "utf-8" }, "thresholdLow": null, "outputSettings": { "type": "localFile" }, "includeProbabilities": true, "columnNamesRemapping": {}, "deploymentId": "", "abortOnError": true, "intakeSettings": { "type": "dataset", "datasetId": "" }, "includePredictionStatus": false, "skipDriftTracking": false, "passthroughColumns": null }, "statusDetails": "Job created by user@example.com from 10.1.2.1 at 2020-02-19 22:41:00.865000", "percentageCompleted": 0.0 } ``` The `links.self` property `https://app.datarobot.com/api/v2/batchPredictions/a1b2c3d4x5y6z7/` is the variable `` in the Step 2 GET call, below. ### 2. Check the status of the batch prediction job `GET ` Sample response: ``` { "status": "INITIALIZING", "skippedRows": 0, "failedRows": 0, "elapsedTimeSec": 352, "logs": [ "Job created by user@example.com from 10.1.2.1 at 2020-02-19 22:41:00.865000", "Job started processing at 2020-02-19 22:41:16.192000" ], "links": { "download": "https://app.datarobot.com/api/v2/batchPredictions/a1b2c3d4x5y6z7/download/", "self": "https://app.datarobot.com/api/v2/batchPredictions/a1b2c3d4x5y6z7/" }, "jobIntakeSize": null, "scoredRows": 1982300, "jobOutputSize": null, "jobSpec": { "includeProbabilitiesClasses": [], "maxExplanations": 0, "predictionWarningEnabled": null, "numConcurrent": 4, "thresholdHigh": null, "passthroughColumnsSet": null, "csvSettings": { "quotechar": "\"", "delimiter": ",", "encoding": "utf-8" }, "thresholdLow": null, "outputSettings": { "type": "localFile" }, "includeProbabilities": true, "columnNamesRemapping": {}, "deploymentId": "", "abortOnError": true, "intakeSettings": { "type": "dataset", "datasetId": "" }, "includePredictionStatus": false, "skipDriftTracking": false, "passthroughColumns": null }, "statusDetails": "Job started processing at 2020-02-19 22:41:16.192000", "percentageCompleted": 0.0 } ``` The `links.download` property `https://app.datarobot.com/api/v2/batchPredictions/a1b2c3d4x5y6z7/download/` is the variable `` in the Step 3 GET call, below. ### 3. Download the results of the batch prediction job Continue polling the status URL above until the job status is COMPLETED and error-free. At that point, predictions can be downloaded. `GET ` --- # Output formats URL: https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-format.html > Review the output formats for the predictions DataRobot returns in a columnar table format. # Output format DataRobot returns predictions in a columnar table format. Each example value is followed by the data type it belongs to. The columns returned are determined by model type, as described below. The output schema shares the same format for real-time and batch predictions. > [!NOTE] Note > DataRobot allows prediction output to many different databases that all have unique versions of a string (e.g., some may call it `TEXT` while others may call it `VARCHAR`). > As a result, DataRobot cannot provide implementation-specific data types. ## Regression models | Column name | _PREDICTION | | Data type | Numeric | | Example name | revenue_PREDICTION | | Example value | 493822.12 | | Description | The predicted value. | ## Binary classification models | Positive label | | --- | | Column name | | Data type | | Example name | | Example value | | Description | | Negative label | | --- | | Column name | | Data type | | Example name | | Example value | | Description | | Column name | _PREDICTION | | Data type | Text | | Example name | isbadbuy_PREDICTION | | Example value | 0 | | Description | The predicted label of the classification. | | Column name | THRESHOLD | | Data type | Numeric | | Example name | THRESHOLD | | Example value | 0.5 | | Description | The float prediction threshold used for determining the label. | | Positive class label | | --- | | Column name | | Data type | | Example name | | Example value | | Description | ## Multiclass classification models | Column name | _PREDICTION | | Data type | Text | | Example name | species_PREDICTION | | Example value | lion | | Description | The predicted label of the classification. | | Column name | __PREDICTION | | Data type | Numeric | | Description | The float probability for each class. | | Example name | Example value | | --- | --- | | species_cat_PREDICTION | 0.28 | | species_lion_PREDICTION | 0.24 | | species_lynx_PREDICTION | 0.48 | ## Time series models > [!NOTE] Note > These output columns are available for time series regression, classification, and anomaly detection models. | Time series model columns | Description | Data type | | --- | --- | --- | | | Contains the series ID the row belongs to. Functions as a passthrough column and returns the unaltered column name and values provided in the scoring data. | Text | | FORECAST_POINT | Contains the forecast point timestamp.Unless you request historical time series predictions, the output value is the same for all rows with the same forecast point (but different for each unique forecast distance). | Date | | | Contains the time series timestamp.Functions as a passthrough column and returns the unaltered column name and values provided in the scoring data. (This returns the same value as the originalFormatTimestamp field returned by time series models.) | Date | | FORECAST_DISTANCE | Contains the numeric forecast distance returned by time series models. | Numeric | ## Prediction status | Column name | prediction_status | | Data type | Text | | Description | A row-by-row status containing either OK or a string error message describing why the prediction did not succeed. | | Example value | Could not convert date field to date format YYYY-MM-DD | | Example value | OK | ## Prediction warnings If prediction warnings are enabled for your job, DataRobot returns an additional column. | Column name | IS_OUTLIER_PREDICTION | | Data type | Text | | Description | Whether the prediction is outside the calculated prediction boundaries. | | Column | Example value | | --- | --- | | Data type | Text | | IS_OUTLIER_PREDICTION | True | | IS_OUTLIER_PREDICTION | False | ## Deployment approval status If the approval workflow is enabled for your deployment, the output schema will contain an extra column showing the deployment approval status. | Column name | DEPLOYMENT_APPROVAL_STATUS | | Data type | Text/td> | | Description | Whether the deployment was approved. | | Example value | PENDING | ## Prediction Explanations You can request Prediction Explanations be returned with your predictions by setting the `maxExplanations` job parameter to a non-zero value. You can also set thresholds for computing explanations. If you do not configure a threshold, DataRobot computes explanations for every row. | Job parameter | Description | Example value | Data type | | --- | --- | --- | --- | | maxExplanations | (Optional) Compute up to this number of explanations. | 10 | Integer | | thresholdHigh | (Optional) Limit explanations to predictions above this threshold. | 0.5 | Float | | thresholdLow | (Optional) Limit explanations to predictions below this threshold. | 0.15 | Float | If Prediction Explanations are requested, DataRobot returns four extra columns for each explanation in the format `EXPLANATION__IDENTIFIER` (where `n` is the feature explanation index, from 1 to the maximum number of explanations requested). The returned columns are: | Column | Description | Data type | | --- | --- | --- | | EXPLANATION__FEATURE_NAME | The feature name this explanation covers. | Text | | EXPLANATION__STRENGTH | The feature strength as a float. | Numeric | | EXPLANATION__QUALITATIVE_STRENGTH | The feature strength as a string, a plus or minus indicator from +++ to ---. | Text | | EXPLANATION__ACTUAL_VALUE | The feature associated with this explanation. | Text | ### Prediction Explanation examples | Name | Value | | --- | --- | | EXPLANATION_1_FEATURE_NAME | loan_status | | EXPLANATION_1_ACTUAL_VALUE | Charged Off | | EXPLANATION_1_STRENGTH | 1.380291221709652 | | EXPLANATION_1_QUALITATIVE_STRENGTH | +++ | | Name | Value | | --- | --- | | EXPLANATION_1_FEATURE_NAME | loan_status | | EXPLANATION_1_ACTUAL_VALUE | Fully Paid | | EXPLANATION_1_STRENGTH | -1.2145340858375335 | | EXPLANATION_1_QUALITATIVE_STRENGTH | --- | ## Passthrough columns Passthrough columns you request are passed verbatim. If they conflict with any of the above names, the job is rejected. ## Association ID If your deployment was configured with an [association ID for accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html), all result sets will have that column passed through from the source data automatically. ## Output filters Use the following job configuration properties to control whether to display only specific class probabilities or none at all. | Job parameter | Description | Example value | Data type | | --- | --- | --- | --- | | includeProbabilities | (Optional) Include probabilities for all classes; defaults to true. | true | Boolean | | includeProbabilitiesClasses | (Optional) Include only probabilities for classes listed in the given array; defaults to an empty array []. | ['setosa', 'versicolor'] | Boolean | | includePredictionStatus | (Optional) Include the prediction_status column in the output; defaults to false. | true | Boolean | > [!NOTE] Note > For binary classification, `includeProbabilities` also controls the `THRESHOLD` and `POSITIVE_CLASS` columns. ## Column name remapping If your use case has a strict output schema that does not match the DataRobot output, you can rename and remove any columns from the output using the `columnNamesRemapping` job configuration property. | Job parameter | Description | Example value | | --- | --- | --- | | columnNamesRemapping | (Optional) Provide a list of items to remap (rename or remove columns from) the output from this job. Set an outputName for the column to null or false to ignore it. | [{'inputName': 'isbadbuy_1_PREDICTION', 'outputName':'prediction'}, {'inputName': 'isbadbuy_0_PREDICTION', 'outputName': null}] | --- # Prediction output options URL: https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-options.html > Configure batch prediction destinations (output) with the Job Definitions UI or the Batch Prediction API. # Prediction output options You can configure a prediction destination using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-destinations) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html). This topic describes both the UI and API output options. > [!NOTE] Note > For a complete list of supported output options, see the [data sources supported for batch predictions](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html#data-sources-supported-for-batch-predictions). | Output option | Description | | --- | --- | | Local file streaming | Stream scored data through a URL endpoint for immediate download when the job moves to a running state. | | HTTP write | Stream scored data to an absolute URL for writing. This option can write data to pre-signed URLs for Amazon S3, Azure, and Google Cloud Platform. | | Database connections | | | JDBC write | Write prediction results back to a JDBC data source with data destination details supplied through a job definition or the Batch Prediction API. | | SAP Datasphere write | Write prediction results back to a SAP Datasphere data source with data destination details supplied through a job definition or the Batch Prediction API. | | Trino write | Write prediction results back to a Trino database with data destination details supplied through a job definition or the Batch Prediction API. | | Cloud storage connections | | | Azure Blob Storage write | Write scored data to Azure Blob Storage with a DataRobot credential consisting of an Azure Connection String. | | Google Cloud Storage write | Write scored data to Google Cloud Storage with a DataRobot credential consisting of a JSON-formatted account key. | | Amazon S3 write | Write scored data to public or private S3 buckets with a DataRobot credential consisting of an access key (ID and key) and a session token (Optional) | | Data warehouse connections | | | BigQuery write | Write prediction results to BigQuery with data destination details supplied through a job definition or the Batch Prediction API. | | Snowflake write | Write prediction results to Snowflake with data destination details supplied through a job definition or the Batch Prediction API. | | Azure Synapse write | Write prediction results to Synapse with data destination details supplied through a job definition or the Batch Prediction API. | If you are using a custom [CSV format](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html#csv-format), any output option dealing with CSV will adhere to that format. The columns that appear in the output are documented in the section on [output format](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-format.html). ## Local file streaming If your job is configured with local file streaming as the output option, you can start downloading the scored data as soon as the job moves to a `RUNNING` state. In the example job data JSON below, the URL needed to make the local file streaming request is available in the `download` key of the `links` object: ``` { "elapsedTimeSec": 97, "failedRows": 0, "jobIntakeSize": 1150602342, "jobOutputSize": 107791140, "jobSpec": { "deploymentId": "5dc1a6a9865d6c004dd881ef", "maxExplanations": 0, "numConcurrent": 4, "passthroughColumns": null, "passthroughColumnsSet": null, "predictionWarningEnabled": null, "thresholdHigh": null, "thresholdLow": null }, "links": { "download": "https://app.datarobot.com/api/v2/batchPredictions/5dc45e583c36a100e45276da/download/", "self": "https://app.datarobot.com/api/v2/batchPredictions/5dc45e583c36a100e45276da/" }, "logs": [ "Job created by user@example.org from 203.0.113.42 at 2019-11-07 18:11:36.870000", "Job started processing at 2019-11-07 18:11:49.781000", "Job done processing at 2019-11-07 18:13:14.533000" ], "percentageCompleted": 0.0, "scoredRows": 3000000, "status": "COMPLETED", "statusDetails": "Job done processing at 2019-11-07 18:13:14.533000" } ``` If you download faster than DataRobot can ingest and score your data, the download may appear sluggish because DataRobot streams the scored data as soon as it arrives (in chunks). Refer to the [this sample use case](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html#end-to-end-scoring-of-csv-files-from-local-files) for a complete example. ## HTTP write You can point Batch Predictions at a regular URL, and DataRobot streams the data: | Parameter | Example | Description | | --- | --- | --- | | type | http | Use HTTP for output. | | url | https://example.com/datasets/scored.csv | An absolute URL that designates where the file is written. | The URL can optionally contain a username and password such as: `https://username:password@example.com/datasets/scored.csv`. The `http` adapter can be used for writing data to pre-signed URLs from either [S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/ShareObjectPreSignedURL.html), [Azure](https://docs.microsoft.com/en-us/azure/storage/common/storage-sas-overview), or [GCP](https://cloud.google.com/storage/docs/access-control/signed-urls). ## JDBC write DataRobot supports writing prediction results back to a JDBC data source. For this, the Batch Prediction API integrates with [external data sources](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/data-conn.html#add-data-sources) using [securely stored credentials](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html). Supply data destination details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-destinations) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `outputSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Destination type | type | jdbc | Use a JDBC data store as output. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | The external data source ID. | | Enter credentials | credentialId | 5e4bc5555e6e763beb9db147 | (Optional) The ID of a stored credential. Refer to storing credentials securely. | | Schemas | schema | public | (Optional) The name of the schema where scored data will be written. | | Tables | table | scoring_data | The name of the database table where scored data will be written. | | Database | catalog | output_data | (Optional) The name of the specified database catalog to write output data to. | | Write strategy options | | | | | Write strategy | statementType | update | The statement type, insert, update, or insertUpdate. | | Create table if it does not exist (for Insert or Insert + Update) | create_table_if_not_exists | true | (Optional) If no existing table is detected, attempt to create it before writing data with the strategy defined in the statementType parameter. | | Row identifier (for Update or Insert + Update) | updateColumns | ['index'] | (Optional) A list of strings containing the column names to be updated when statementType is set to update or insertUpdate. | | Row identifier (for Update or Insert + Update) | where_columns | ['refId'] | (Optional) A list of strings containing the column names to be selected when statementType is set to update or insertUpdate. | | Advanced options | | | | | Commit interval | commitInterval | 600 | (Optional) Defines a time interval, in seconds, between commits to the target database. If set to 0, the batch prediction operation will write the entire job before committing. Default: 600 | > [!NOTE] Note > If your target database doesn't support the column naming conventions of DataRobot's [output format](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-format.html), you can use [Column Name Remapping](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-format.html#column-name-remapping) to re-write the output column names to a format your target database supports (e.g., remove spaces from the name). ### Statement types When dealing with Write strategy options, you can use the following statement types to write data, depending on the situation: | Statement type | Description | | --- | --- | | insert | Scored data rows are inserted in the target database as a new entry. Suitable for writing to an empty table. | | update | Scored data entries in the target database matching the row identifier of a result row are updated with the new result (columns identified in updateColumns). Suitable for writing to an existing table. | | insertUpdate | Entries in the target database matching the row identifier of a result row (where_columns) are updated with the new result (update queries). All other result rows are inserted as new entries (insert queries). | | createTable (deprecated) | DataRobot no longer recommends createTable. Use a different option with create_table_if_not_exists set to True. If used, scored data rows are saved to a new table using INSERT queries. The table must not exist before writing. | ### Allowed source IP addresses Any connection initiated from DataRobot originates from an allowed IP addresses. See a full list at [Allowed source IP addresses](https://docs.datarobot.com/en/docs/reference/data-ref/allowed-ips.html). ## SAP Datasphere write > [!NOTE] Premium > Support for SAP Datasphere is off by default. Contact your DataRobot representative or administrator for information on enabling the feature. > > Feature flag(s): Enable SAP Datasphere Connector, Enable SAP Datasphere Batch Predictions Integration To use SAP Datasphere, supply data destination details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-destinations) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `outputSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Destination type | type | datasphere | Use a SAP Datasphere database for output. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | The ID of an external data source. In the UI, select a data connection or click add a new data connection. Refer to the SAP Datasphere connection documentation. | | Enter credentials | credentialId | 5e4bc5555e6e763beb9db147 | The ID of a stored credential for Datasphere. Refer to storing credentials securely. | | | catalog | / | The name of the database catalog containing the table to write to. | | Schemas | schema | public | The name of the database schema containing the table to write to. | | Tables | table | scoring_data | The name of the database table containing data to write to. In the UI, select a table or click Create a table. | ## Databricks write To use the [Databricks connector](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-databricks.html) for output, supply data destination details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-destinations) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `outputSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Destination type | type | databricks | Use a Databricks database for output. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | The ID of an external data source. In the UI, select a data connection or click add a new data connection. | | + Add credentials | credentialId | 5e96092ef7e8773ddbdbabed | The ID of stored credentials for the external Databricks database connection. | | Catalog | catalog | default | (Optional) The Databricks database catalog containing the destination table. | | Schema | schema | public | The Databricks schema containing the destination table. | | Table | table | kickcars_predictions | The Databricks table in which to write output data. | ## Trino write To use the [Trino connector](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-trino.html) for output, supply data destination details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-destinations) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `outputSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Destination type | type | trino | Use a Trino database for output. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | The ID of an external data source. In the UI, select a data connection or click add a new data connection. | | + Credentials | credentialId | 5e96092ef7e8773ddbdbabed | The credentials to use for the external Trino database connection. | | Catalog | catalog | starburst_catalog | The Trino database catalog to store the output table. | | Schema | schema | analytics | The Trino schema to store the output table. | | Table | table | prediction_results | The Trino table in which to write output data. | > [!WARNING] Trino column name case requirement > Use lowercase only for column names in the dataset used to train a project. Trino sanitizes column names automatically (unquoted identifiers are lowercased), so mixed-case or uppercase column names can cause column inconsistency errors when reading from Trino for batch scoring. This applies even when creating tables with quoted column names—Trino still stores them as lowercase. For more information, see [trinodb/trino#17](https://github.com/trinodb/trino/issues/17). ## Azure Blob Storage write Azure Blob Storage is an option for writing large files. To save a dataset to Azure Blob Storage, you must set up a credential with DataRobot consisting of an Azure Connection String. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Destination type | type | azure | Use Azure Blob Storage for output. | | URL | url | https://myaccount.blob.core.windows.net/datasets/scored.csv | An absolute URL for the file to be written. | | Format | format | csv | (Optional) Select CSV (csv) or Parquet (parquet). Default value: CSV | | + Add credentials | credentialId | 5e4bc5555e6e763beb488dba | In the UI, enable the + Add credentials field by selecting This URL requires credentials. Required if explicit access to credentials for this URL are necessary (optional otherwise). Refer to storing credentials securely. | Azure credentials are encrypted and only decrypted when used to set up the client for communication with Azure when writing. ## Google Cloud Storage write DataRobot supports the Google Cloud Storage adapter. To save a dataset to Google Cloud Storage, you must set up a credential with DataRobot consisting of a JSON-formatted account key. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Destination type | type | gcp | Use Google Cloud Storage for output. | | URL | url | gcs://bucket-name/datasets/scored.csv | An absolute URL designating where the file is written. | | Format | format | csv | (Optional) Select CSV (csv) or Parquet (parquet). Default value: CSV | | + Add credentials | credentialId | 5e4bc5555e6e763beb488dba | Required if explicit access credentials for this URL are required, otherwise (Optional) Refer to storing credentials securely. | GCP credentials are encrypted and are only decrypted when used to set up the client for communication with GCP when writing. ## Amazon S3 write DataRobot can save scored data to both public and private buckets. To write to S3, you must set up a credential with DataRobot consisting of an access key (ID and key) and optionally a session token. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Destination type | type | s3 | Use S3 for output. | | URL | url | s3://bucket-name/results/scored.csv | An absolute URL for the file to be written. DataRobot only supports directory scoring when scoring from cloud to cloud. Provide a directory in S3 (or another cloud provider) for the input and a directory ending with / for the output. Using this configuration, all files in the input directory are scored and the results are written to the output directory with the original filenames. When a single file is specified for both the input and the output, the file is overwritten each time the job runs. If you do not wish to overwrite the file, specify a filename template such as s3://bucket-name/results/scored_{{ current_run_time }}.csv. You can review template variable definitions in the documentation. | | Format | format | csv | (Optional) Select CSV (csv) or Parquet (parquet). Default value: CSV | | + Add credentials | credentialId | 5e4bc5555e6e763beb9db147 | In the UI, enable the + Add credentials field by selecting This URL requires credentials. Required if explicit access credentials for this URL are required. Refer to storing credentials securely. | | Advanced options | | | | | Endpoint URL | endpointUrl | https://s3.us-east-1.amazonaws.com | (Optional) Override the endpoint used to connect to S3, for example, to use an API gateway or another S3-compatible storage service. | AWS credentials are encrypted and only decrypted when used to set up the client for communication with AWS when writing. > [!NOTE] Note > If running a Private AI Cloud within AWS, you can provide implicit credentials for your application instances using an IAM Instance Profile to access your S3 buckets without supplying explicit credentials in the job data. For more information, see the AWS article, [Create an IAM Instance Profile](https://docs.aws.amazon.com/codedeploy/latest/userguide/getting-started-create-iam-instance-profile.html). ## BigQuery write To use BigQuery, supply data destination details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-destinations) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `outputSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Destination type | type | bigquery | Use Google Cloud Storage for output and the batch loading job to ingest data from GCS into a BigQuery table. | | Dataset | dataset | my_dataset | The BigQuery dataset to use. | | Table | table | my_table | The BigQuery table from the dataset to use for output. | | Bucket name | bucket | my-bucket-in-gcs | The GCP bucket where data files are stored to be loaded into or unloaded from a BiqQuery table. | | + Add credentials | credentialId | 5e4bc5555e6e763beb488dba | Required if explicit access credentials for this bucket are necessary (otherwise optional). In the UI, enable the + Add credentials field by selecting This connection requires credentials. Refer to storing credentials securely. | > [!NOTE] BigQuery output write strategy > The write strategy for BigQuery output is `insert`. First, the output adapter checks if a BigQuery table exists. If a table exists, the data is inserted. If a table doesn't exist, a table is created and then the data is inserted. Refer to the [example section](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html#end-to-end-scoring-with-bigquery) for a complete API example. ## Snowflake write To use Snowflake, supply data destination details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-destinations) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `outputSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Destination type | type | snowflake | Adapter type. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | ID of Snowflake data source. | | Enter credentials | credentialId | 5e4bc5555e6e763beb9db147 | (Optional) The ID of a stored credential for Snowflake. | | Tables | table | RESULTS | Name of the Snowflake table to store results. | | Schemas | schema | PUBLIC | (Optional) The name of the schema containing the table where results are written. | | Database | catalog | OUTPUT | (Optional) The name of the specified database catalog to write output data to. | | Use external stage options | | | | | Cloud storage type | cloudStorageType | s3 | (Optional) Type of cloud storage backend used in Snowflake external stage. Can be one of 3 cloud storage providers: s3/azure/gcp. The default is s3. In the UI, select Use external stage to enable the Cloud storage type field. | | External stage | externalStage | my_s3_stage | Snowflake external stage. In the UI, select Use external stage to enable the External stage field. | | Endpoint URL (for S3 only) | endpointUrl | https://www.example.com/datasets/ | (Optional) Override the endpoint used to connect to S3, for example, to use an API gateway or another S3-compatible storage service. In the UI, for the S3 option in Cloud storage type click Show advanced options to reveal the Endpoint URL field. | | + Add credentials | cloudStorageCredentialId | 6e4bc5541e6e763beb9db15c | (Optional) ID of stored credentials for a storage backend (S3/Azure/GCS) used in Snowflake stage. In the UI, enable the + Add credentials field by selecting This URL requires credentials. | | Write strategy options (for fallback JDBC connection) | | | | | Write strategy | statementType | insert | If you're using a Snowflake external stage the statementType is insert. However, in the UI you have two configuration options: If you haven't configured an external stage, the connection defaults to JDBC and you can select Insert or Update. If you select Update, you can provide a Row identifier.If you selected Use external stage, the Insert option is required. | | Create table if it does not exist (for Insert) | create_table_if_not_exists | true | (Optional) If no existing table is detected, attempt to create one. | | Advanced options | | | | | Commit interval | commitInterval | 600 | (Optional) Defines a time interval, in seconds, between commits to the target database. If set to 0, the batch prediction operation will write the entire job before committing. Default: 600 | Refer to the [example section](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html#end-to-end-scoring-with-snowflake) for a complete API example. ## Azure Synapse write To use Azure Synapse, supply data destination details using the [Predictions > Job Definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#set-up-prediction-destinations) tab or the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) ( `outputSettings`) as described in the table below. | UI field | Parameter | Example | Description | | --- | --- | --- | --- | | Destination type | type | synapse | Adapter type. | | Data connection parameters | | | | | + Select connection | dataStoreId | 5e4bc5b35e6e763beb9db14a | ID of Synapse data source. | | Enter credentials | credentialId | 5e4bc5555e6e763beb9db147 | (Optional) The ID of a stored credential for Synapse. | | Tables | table | RESULTS | Name of the Synapse table to keep results in. | | Schemas | schema | dbo | (Optional) Name of the schema containing the table where results are written. | | Use external stage options | | | | | External data source | externalDatasource | my_data_source | Name of the identifier created in Synapse for the external data source. | | + Add credentials | cloudStorageCredentialId | 6e4bc5541e6e763beb9db15c | (Optional) ID of a stored credential for Azure Blob storage. | | Write strategy options (for fallback JDBC connection) | | | | | Write strategy | statementType | insert | If you're using a Synapse external stage the statementType is insert. However, in the UI you have two configuration options: If you haven't configured an external stage, the connection defaults to JDBC and you can select Insert, Update, or Insert + Update. If you select Update or Insert + Update, you can provide a Row identifier.If you selected Use external stage, the Insert option is required. | | Create table if it does not exist (for Insert or Insert + Update) | create_table_if_not_exists | true | (Optional) If no existing table is detected, attempt to create it before writing data with the strategy defined in the statementType parameter. | | Create table if it does not exist | create_table_if_not_exists | true | (Optional) Attempt to create the table first if no existing one is detected. | | Advanced options | | | | | Commit interval | commitInterval | 600 | (Optional) Defines a time interval, in seconds, between commits to the target database. If set to 0, the batch prediction operation will write the entire job before committing. Default: 600 | Refer to the [example section](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html#end-to-end-scoring-with-synapse) for a complete API example. --- # Batch prediction use cases URL: https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/pred-examples.html > Examine several end-to-end examples of scoring with API code for both CSV files and external services. # Batch prediction use cases The following provides several end-to-end examples of scoring with API code for both CSV files and external services. - End-to-end scoring of CSV files from local files - End-to-end scoring of CSV files on S3 - AI Catalog-to-CSV file scoring - End-to-end scoring from a JDBC PostgreSQL database - End-to-end scoring with Snowflake - End-to-end scoring with Synapse - End-to-end scoring with BigQuery > [!NOTE] Note > These use cases require the [DataRobot](https://datarobot-public-api-client.readthedocs-hosted.com/) API client to be installed. ## End-to-end scoring of CSV files from local files The following example scores a local CSV file, waits for processing to start, and then initializes the download. ``` import datarobot as dr dr.Client( endpoint="https://app.datarobot.com/api/v2", token="...", ) deployment_id = "..." input_file = "to_predict.csv" output_file = "predicted.csv" job = dr.BatchPredictionJob.score_to_file( deployment_id, input_file, output_file, passthrough_columns_set="all" ) print("started scoring...", job) job.wait_for_completion() ``` ### Prediction Explanations You can include Prediction Explanations by adding the desired [Prediction Explanation parameters](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-format.html#prediction-explanations) to the job configuration: ``` job = dr.BatchPredictionJob.score_to_file( deployment_id, input_file, output_file, max_explanations=10, threshold_high=0.5, threshold_low=0.15, ) ``` ### Custom CSV format If your CSV files does not match the default CSV format, you can modify the expected CSV format by setting `csvSettings`: ``` job = dr.BatchPredictionJob.score_to_file( deployment_id, input_file, output_file, csv_settings={ 'delimiter': ';', 'quotechar': '\'', 'encoding': 'ms_kanji', }, ) ``` ## End-to-end scoring of CSV files on S3 ``` import datarobot as dr dr.Client( endpoint="https://app.datarobot.com/api/v2", token="...", ) deployment_id = "616d01a8ddbd17fc2c75caf4" credential_id = "..." s3_csv_input_file = 's3://my-bucket/data/to_predict.csv' s3_csv_output_file = 's3://my-bucket/data/predicted.csv' job = dr.BatchPredictionJob.score_s3( deployment_id, source_url=s3_csv_input_file, destination_url=s3_csv_output_file, credential=credential_id ) print("started scoring...", job) job.wait_for_completion() ``` The same functionality is available for `score_azure` and `score_gcp`. You can also specify the `credential` object itself, instead of a credential ID: ``` credentials = dr.Credential.get(credential_id) job = dr.BatchPredictionJob.score_s3( deployment_id, source_url=s3_csv_input_file, destination_url=s3_csv_output_file, credential=credentials, ) ``` ### Prediction Explanations You can include Prediction Explanations by adding the desired [Prediction Explanation parameters](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-format.html#prediction-explanations) to the job configuration: ``` job = dr.BatchPredictionJob.score_s3( deployment_id, source_url=s3_csv_input_file, destination_url=s3_csv_output_file, credential=credential_id, max_explanations=10, threshold_high=0.5, threshold_low=0.15, ) ``` ## AI Catalog-to-CSV file scoring When using the [AI Catalog](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html) for intake, you need the `dataset_id` of an already created dataset. ``` import datarobot as dr dr.Client( endpoint="https://app.datarobot.com/api/v2", token="...", ) deployment_id = "616d01a8ddbd17fc2c75caf4" credential_id = "..." dataset_id = "..." dataset = dr.Dataset.get(dataset_id) job = dr.BatchPredictionJob.score( deployment_id, intake_settings={ 'type': 'dataset', 'dataset_id': dataset, }, output_settings={ 'type': 'localFile', }, ) job.wait_for_completion() ``` ## End-to-end scoring from a JDBC PostgreSQL database The following reads a scoring dataset from the table `public.scoring_data` and saves the scored data back to `public.scored_data` (assuming that table already exists). ``` import datarobot as dr dr.Client( endpoint="https://app.datarobot.com/api/v2", token="...", ) deployment_id = "616d01a8ddbd17fc2c75caf4" credential_id = "..." datastore_id = "..." intake_settings = { 'type': 'jdbc', 'table': 'scoring_data', 'schema': 'public', 'data_store_id': datastore_id, 'credential_id': credential_id, } output_settings = { 'type': 'jdbc', 'table': 'scored_data', 'schema': 'public', 'data_store_id': datastore_id, 'credential_id': credential_id, 'statement_type': 'insert' } job = dr.BatchPredictionJob.score( deployment_id, passthrough_columns_set='all', intake_settings=intake_settings, output_settings=output_settings, ) print("started scoring...", job) job.wait_for_completion() ``` More details about JDBC scoring can be found [here](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html#jdbc-scoring). ## End-to-end scoring with Snowflake The following example reads a scoring dataset from the table `public.SCORING_DATA` and saves the scored data back to `public.SCORED_DATA` (assuming that table already exists). ``` import datarobot as dr dr.Client( endpoint="https://app.datarobot.com/api/v2", token="...", ) deployment_id = "616d01a8ddbd17fc2c75caf4" credential_id = "..." cloud_storage_credential_id = "..." datastore_id = "..." intake_settings = { 'type': 'snowflake', 'table': 'SCORING_DATA', 'schema': 'PUBLIC', 'external_stage': 'my_s3_stage_in_snowflake', 'data_store_id': datastore_id, 'credential_id': credential_id, 'cloud_storage_type': 's3', 'cloud_storage_credential_id': cloud_storage_credential_id } output_settings = { 'type': 'snowflake', 'table': 'SCORED_DATA', 'schema': 'PUBLIC', 'statement_type': 'insert' 'external_stage': 'my_s3_stage_in_snowflake', 'data_store_id': datastore_id, 'credential_id': credential_id, 'cloud_storage_type': 's3', 'cloud_storage_credential_id': cloud_storage_credential_id } job = dr.BatchPredictionJob.score( deployment_id, passthrough_columns_set='all', intake_settings=intake_settings, output_settings=output_settings, ) print("started scoring...", job) job.wait_for_completion() ``` More details about Snowflake scoring can be found in [intake](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html#snowflake-scoring) and [output](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-options.html#snowflake-write) documentation. ## End-to-end scoring with Synapse The following example reads a scoring dataset from the table `public.scoring_data` and saves the scored data back to `public.scored_data` (assuming that table already exists). ``` import datarobot as dr dr.Client( endpoint="https://app.datarobot.com/api/v2", token="...", ) deployment_id = "616d01a8ddbd17fc2c75caf4" credential_id = "..." cloud_storage_credential_id = "..." datastore_id = "..." intake_settings = { 'type': 'synapse', 'table': 'SCORING_DATA', 'schema': 'PUBLIC', 'external_data_source': 'some_datastore', 'data_store_id': datastore_id, 'credential_id': credential_id, 'cloud_storage_credential_id': cloud_storage_credential_id } output_settings = { 'type': 'synapse', 'table': 'SCORED_DATA', 'schema': 'PUBLIC', 'statement_type': 'insert' 'external_data_source': 'some_datastore', 'data_store_id': datastore_id, 'credential_id': credential_id, 'cloud_storage_credential_id': cloud_storage_credential_id } job = dr.BatchPredictionJob.score( deployment_id, passthrough_columns_set='all', intake_settings=intake_settings, output_settings=output_settings, ) print("started scoring...", job) job.wait_for_completion() ``` More details about Synapse scoring can be found in the [intake](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html#synapse-scoring) and [output](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-options.html#synapse-write) documentation. ## End-to-end scoring with BigQuery The following example scores data from a BigQuery table and sends results to a BigQuery table. ``` import datarobot as dr dr.Client( endpoint="https://app.datarobot.com/api/v2", token="...", ) deployment_id = "616d01a8ddbd17fc2c75caf4" gcs_credential_id = "6166c01ee91fb6641ecd28bd" intake_settings = { 'type': 'bigquery', 'dataset': 'my-dataset', 'table': 'intake-table', 'bucket': 'my-bucket', 'credential_id': gcs_credential_id, } output_settings = { 'type': 'bigquery', 'dataset': 'my-dataset', 'table': 'output-table', 'bucket': 'my-bucket', 'credential_id': gcs_credential_id, } job = dr.BatchPredictionJob.score( deployment=deployment_id, intake_settings=intake_settings, output_settings=output_settings, include_prediction_status=True, passthrough_columns=["some_col_name"], ) print("started scoring...", job) job.wait_for_completion() ``` More details about BigQuery scoring can be found in the [intake](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html#bigquery-scoring) and [output](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-options.html#bigquery-write) documentation. --- # API changelogs URL: https://docs.datarobot.com/en/docs/api/reference/changelogs/index.html > Reference the changes introduced to new versions of DataRobot's Python client, R client, and REST API. # API changelogs Changelogs contain curated, ordered lists of notable changes for each versioned release for DataRobot's SDKs and REST API. Reference the table below to view changes for DataRobot's newest versions. | Topic | Description | | --- | --- | | REST API changelog | Changes introduced to new versions of the DataRobot REST API. | | Python client changelog | Changes introduced to new versions of the DataRobot Python client. | | R client changelog | Changes introduced to new versions of the DataRobot R client. | --- # Python client changelog URL: https://docs.datarobot.com/en/docs/api/reference/changelogs/py-changelog/index.html # Python client changelog ## 3.15.0 ### New features - Added MemorySpace to provide a basic CRUD functionality for Memory Spaces. - Added Session and Event to simplify building agentic applications with chat interfaces. ### Enhancements ### Bugfixes ### API changes ### Documentation changes ### Experimental changes ## 3.14.0rc2 ### New features - Extended the project advanced options available when setting a target to include new parameter: ‘custom_metrics_losses_info’ (part of the AdvancedOptions object). This parameter allows you to specify the Mongo ID of the custom metrics and losses metadata. - Added JdbcPreview for previewing data from a JDBC URL by executing SQL without creating a data store; use preview to get a JdbcPreviewData . - Promoted DataRobotFileSystem out of experimental. - Promoted FilesDetails out of experimental. - Promoted experimental methods from Files out of experimental. - Added OtelStats to retrieve OpenTelemetry record counts by service. - Added FEATURE_DISCOVERY_PRIVATE_PREVIEW to RecipeType for backward compatibility with legacy recipes that could not be migrated to the new version. ### Enhancements - Added checksum to File . - Added SAP_AI_CORE to PredictionEnvironmentPlatform . ### Bugfixes - Fixed an issue which caused an error when Feature Lists had an empty description in get_relationships_configuration - Fixed deserialization of ProjectOptions when loading project options from the server: feature_engineering_prediction_point , user_partition_col , and each entry in external_predictions are column names returned as strings by the API (the client previously expected Feature -shaped data, which could raise validation errors). ## 3.13.0 ### New features - Added OtelMetrics to retrieve and delete OpenTelemetry metric data. - Added delete to delete OpenTelemetry log entries. - Added the code_challenge_method property in OAuthProviderConfig to support the PKCE authorization code flow. client_secret was also made optional, as some providers do not require it in the PKCE flow. - Added support for Box JWT credentials via Credential.create_box_jwt and the CredentialTypes.BOX_JWT credential type. - Added get_agent_card , upload_agent_card , and delete_agent_card methods to Deployment for managing A2A agent cards. - Added is_a2a_agent filter parameter to DeploymentListFilters to filter deployments by whether they are A2A agents. ### Enhancements - Added MySQL support to the DataWranglingDialect enum. - Improved typing support for RecipePreview attribute result_schema . - Added the runtime_parameters field to CustomModelVersion.create_from_previous and Job.update methods. This supports a new API attribute that enables the creation of runtime parameters without requiring schema definitions in metadata files. - Added created_at to File . ### Bugfixes - Fixed an issue where VectorDatabase.deploy was not sending its parameters to the DataRobot API. - Fixed an issue where OAuthToken.from_dict was not populating id_token . - Fixed an issue where BatchPredictionJob.score sent improperly serialized columnNamesRemapping to the DataRobot API. ### API changes - Revert handling of deprecated parameter inputs on Recipe.from_dataset when an empty list is passed. Prior to release 3.10.0, an empty list was silently ignored, afterwards an empty list would cause an error. This release reverts the prior behavior. - CustomApplicationSource and CustomApplication no longer require the fields creator_first_name and creator_last_name to be set. ### Documentation changes - Add experimental documentation for DataRobotFileSystem , DataRobotFile and DataRobotFSMap . ### Experimental changes - Implement additional methods for DataRobot file system and Files API with fsspec implementation DataRobotFileSystem : - mv : Move files or directories from one path to another. Supports glob patterns, recursive expansion, and list-of-sources. Same-catalog moves use PATCH when possible; otherwise copy then delete. - mv_file : Move a single file or directory from path1 to path2. - glob : Finds files and directories by glob-matching. - sign : Returns a signed URL for a file path. - open : Open a file in the DataRobot file system for reading or writing. - touch : Create or overwrite an empty file at a given path in the DataRobot file system. - cat : Retrieve contents of one or more files. - put_file : Copy file from local to the DataRobot file system. - put_from_url : Load file(s) from a URL into a directory in the DataRobot file system (supports archives and optional subfolder path). - Added upload_from_url to FilesExperimental to load file(s) from a URL into a catalog item. - clone_catalog_item_dir : Clone a catalog item directory to create a new catalog item directory. - get_mapper : Get a mutable mapping object DataRobotFSMap to interact with the DataRobot file system using a key-value pattern. - copy : Copy one or more paths between locations in the DataRobot file system. - put_from_data_source : Upload file or folder from a DataRobot data source DataSource . - created : Get created timestamp for a file. - Create classes for working with user MCP servers: - Created file-like class DataRobotFile to encapsulate and support reading and writing operations on files in the DataRobot file system. - Create user MCP server class ToolInUserMCPServerDeployment to save/list/delete tool metadata of one user MCP server deployment. - Create user MCP server class PromptInUserMCPServerDeployment to save/list/delete prompt metadata of one user MCP server deployment. - Create user MCP server class ResourceInUserMCPServerDeployment to save/list/delete resource metadata of one user MCP server deployment. - Create user MCP server class ToolInUserMCPServerVersion to list tool metadata of one user MCP server version. - Create user MCP server class PromptInUserMCPServerVersion to list prompt metadata of one user MCP server version. - Create user MCP server class ResourceInUserMCPServerVersion to list resource metadata of one user MCP server version. ## 3.12.0 ### Configuration changes - Added pytz and python-dateutil as dependencies. These were implicitly required by the client for date and time handling. - Updated datarobot[core] to require psutil>=7.2.1 (previously unpinned). This ensures compatibility in environments where an older psutil version may already be installed. ### New features - Added OtelSingleMetricValue to retrieve OpenTelemetry metric data for a single metric without configuration. - Added OtelMetricAggregatedValues to retrieve the latest OpenTelemetry metric data for configured metrics. - Added a new class ConfusionMatrix to interact with confusion matrix insights. ### Experimental changes - Added experimental package datarobot._experimental.fs to store DataRobot file system functionality. - Configured datarobot-early-access[fs] as an optional extra add-on to the datarobot-early-access package. - Added initial support for DataRobot file system and Files API with fsspec implementation DataRobotFileSystem . Added implementations for the following methods: - ls : Lists files and directories under a directory path. - info : Gets information about a file or folder. - cp_file : Copies file or directory to another path. - rm_file : Recursively deletes one or more files or folders. - rm : Deletes one or more file or folder paths. Supports use of glob patterns, recursive, and non-recursive search. ### Documentation changes - Added API reference documentation for ConfusionMatrix . - Added usage examples for model performance insights in a new guide showing how to use ConfusionMatrix.get() and ConfusionMatrix.compute() methods. ## 3.11.0 ### New features - Added Recipe.publish_to_dataset to recipe API for easy publishing to dataset. ### Enhancements - Added tag data and create/update/delete functions to Deployment . - Added PromptTemplateVersion.list_all to list prompt template versions across multiple templates with optional filtering by template IDs. - Extended LLM Settings to support creating LLM Blueprints for Agentic Workflows in LLMBlueprint llm_setting parameter now supports - Added the available_litellm_endpoints field to LLMGatewayCatalogEntry to define supported endpoints for each LLM gateway model (includes supports_chat_completions and supports_responses which correspond to /chat/completions and /responses ). - Edited LLMGatewayCatalog.list to include the optional parameter chat_completions_supported_only which filters to only list models that support the /chat/completions route. - Added support for external OAuth provider credentials, including the new CredentialTypes.EXTERNAL_OAUTH_PROVIDER enum value and the creation helper Credential.create_external_oauth_provider . - custom_model_version_id ### Bugfixes - Made the prediction environment in Challenger optional, since it is not always provided by the server. - Restore multipart file uploads via client.request() broken in v3.10.0. ### Documentation changes - Added API reference documentation for RocCurve , LiftChart , and Residuals classes that were previously added in 3.7.0. They replace the deprecated methods Model.request_lift_chart and similar. - Added comprehensive usage examples for model performance insights in a new guide showing how to use RocCurve.get() , LiftChart.get() , and Residuals.get() methods. ## 3.10.0 ### New features - Added PromptTemplate to manage prompt templates with versioning support in the GenAI namespace. - Added PromptTemplateVersion to manage specific versions of prompt templates, including the ability to render prompts with variable substitution. - Added Dataset.get_raw_sample_data to dataset raw data as Pandas DataFrame. - Added the following data wrangling capabilities: - Method Recipe.update to update a recipe using an instance method. Previously, you had to use a class method and explicitly specify a recipe ID. - Method Recipe.list class method to list recipes. - Method Recipe.generate_sql_for_operations class method for generating SQL from an arbitrary list of operations. - Wrangling Operation DedupeRowsOperation to support removing duplicate rows for recipes. - Wrangling Operation FindAndReplaceOperation to support find and replace operations in recipes. - Sampling Operation LimitSamplingOperation to support sampling the first n rows of data from an input. - Sampling Operation TableSampleSamplingOperation to support randomly sampling x% of data from an input using a table sampling method. - Method RecipeDatasetInput.from_dataset to quickly create recipe dataset input using a dataset. - Wrangling Operation AggregationOperation to support applying aggregate transformations in a recipe. - Wrangling Operation JoinOperation to support joining datasets to the data in a recipe. - Method Recipe.set_settings to update the settings of a recipe. Also added support for instance method Recipe.update to update settings with settings parameter. ### Enhancements - Added PromptTemplateVersion.to_fstring to convert prompt templates from {{ variable }} format to Python f-string {variable} format for use with native Python string formatting. - Updated ExecutionEnvironment.list to accept additional parameter for is_public . - Updated MetricInsights.list to add additional parameters with_aggregation_types_only , production_only , and completed_only . - Exposed Recipe for import directly from the datarobot package. - Added RandomDownsamplingOperation and SmartDownsamplingOperation as wrappers to use when setting downsampling on a recipe. - Updated get_authorization_context to return an empty context if no authorization context is set instead of raising an error. - Made variables parameter optional in PromptTemplateVersion.create and PromptTemplate.create_version . Defaults to None , which sends an empty list to the API. ### Bugfixes - Fixed a bug on updating Recipe recipe_type . - Fixed a bug on updating Recipe downsampling to None . - Always camelize PUT request json body under the hood. ### API changes - Warning: Multipart file uploads via client.request() are no longer possible and will be addressed in a future release. - Removed pagination parameters from OtelMetricSummary.list . - Removed pagination values (e.g., count , next , and previous ) from OtelMetricSummary . - Added the name attribute to the Recipe class. - Deprecated the parameter operations from Recipe.get_sql . This should avoid confusion when converting arbitrary operations. This functionality has been duplicated to Recipe.generate_sql_for_operations . The operations parameter will be removed in 3.12. - Deprecated Recipe.retrieve_preview in favor of Recipe.get_preview . Recipe.get_preview will return the wrapper object RecipePreview . The RecipePreview object has a .df attribute containing the preview data as a Pandas DataFrame. - Added optional trace_id and span_id query parameters to OtelLogEntry.list . - Added optional trace_id and span_id to OtelLogEntry . - Deprecated parameter inputs on Recipe.from_dataset . Added new parameter sampling to use instead. ### Configuration changes - Removed black and pylint as dev dependencies. ruff is now used instead. ### Documentation changes - Updated documentation page for Recipes to include new methods. Created the new sections “Recipe Inputs”, “Recipe Operations”, and “Enums and Helpers” to better organize the Recipe components. - Updated documentation for recipe operations, related enums, and helper classes to document arguments and provide examples. - Improved documentation for recipe sampling operations. - Improved documentation for recipe creation methods Recipe.from_dataset and Recipe.from_data_store . - Improved documentation for the recipe input wrapper classes RecipeDatasetInput and JDBCTableDataSourceInput . ## 3.10.0b0 This was an experimental release and is subsumed by 3.10.0rc0. ## 3.9.1 ### New features - Added CustomApplication to manage custom applications with detailed resource information and operational controls. - Added CustomApplicationSource to manage custom application sources (templates for creating custom applications). - Added optional parameter version_id=None to Files.download and Files.list_contained_files to allow non-blocking upload. - Promoted FilesStage out of experimental. - Added Files.clone , Files.create_stage , Files.apply_stage and Files.copy , which were previously experimental. - Added DataRobotAppFrameworkBaseSettings as a Pydantic Settings class for managing configurations for agentic workflows and applications. ### Enhancements - Improved the string representation of RESTClientObject to include endpoint and client version. ## 3.9.0 ### New features - Added OtelMetricConfig to manage and control the display of OpenTelemetry metric configurations for an entity. - Added OtelLogEntry to list the OpenTelemetry logs associated with an entity. - Added OtelMetricSummary to list the reported OpenTelemetry metrics associated with an entity. - Added OtelMetricValue to list the OpenTelemetry metric values associated with an entity. - Added LLMGatewayCatalog to get available LLMs from the LLM Gateway. - Added ResourceBundle to list defined resource bundles. ### Enhancements - Added CustomTemplate.create to allow users to create a new CustomTemplate . - Updated CustomTemplate.list to accept additional parameters for publisher , category , and show_hidden for improved queries. - Updated CustomTemplate.update to accept additional parameters for file , enabled , and is_hidden to set additional fields. - Added is_hidden member to CustomTemplate to align with server data. - Added custom_metric_metadata member to TemplateMetadata to allow creating custom-metric templates using APIObjects. - Added CustomTemplate.download_content to allow users to download content associated with an items file. - Added new attribute spark_instance_size to RecipeSettings . - Added set_default_credential to DataStore.test to set the provided credential as default when the connection test is successful. - Added optional parameter data_type to Connector.list to list connectors which support specified data type. - Added optional parameter data_type to DataStore.list to list data stores which support specified data type. - Added optional parameters retrieval_mode and maximal_marginal_relevance_lambda to VectorDatabaseSettings to select the retrieval mode. - Added optional parameter wait_for_completion=True to Files.upload and Files.create_from_url to allow non-blocking upload. - Added support for file to UseCase.add and UseCase.remove . - Added GridSearchArguments with GridSearchArguments.to_api_payload for creating grid search arguments for advanced tuning jobs. - Added GridSearchSearchType . An enum to define supported grid search types. - Added GridSearchAlgorithm . An enum to define supported grid search algorithms. - Added optional parameters include_agentic , is_agentic , for_playground , and for_production to ModerationTemplate.list to include/filter for agentic templates and to fetch templates specific to playground or production. - Improved equality comparison for APIObject to only look at API related fields. - Added optional parameters tag_keys and tag_values to Deployment.list to filter search results by tags. ### Bugfixes - Fixed validator errors in EvaluationDatasetMetricAggregation . - Fixed Files.download() to download a correct file from the files container. - Fixed the enum values of ModerationGuardOotbType . - Fixed a bug where ModerationTemplate.find was unable to find a template when given a name. - Fixed a bug where OverallModerationConfig.find was unable to find a given entity’s overall moderation config. ### Documentation changes - Added OCREngineSpecificParameters, DataRobotOCREngineType and DataRobotArynOutputFormat to OCR job resources section ## 3.8.0 This release adds support for Notebooks and Codespaces and unstructured data in the Data Registry, and the Chunking Service v2. There are improvements related to playgrounds, vector databases, agentic workflows, incremental learning, and datasets. This release focuses heavily on file management capabilities. There are two new package extras: `auth` and `auth-authlib`. The `auth` extra provides OAuth2 support, while the `auth-authlib` extra provides OAuth2 support using the Authlib library. ### New features #### GenAI - Added AGENTIC_WORKFLOW target type. - Added VectorDatabase.send_to_custom_model_workshop to create a new custom model from a vector database. - Added VectorDatabase.deploy to create a new deployment from a vector database. - Added optional parameters vector_database_default_prediction_server_id , vector_database_prediction_environment_id , vector_database_maximum_memory , vector_database_resource_bundle_id , vector_database_replicas , and vector_database_network_egress_policy to LLMBlueprint.register_custom_model to allow specifying resources in cases where we automatically deploy a vector database when this function is called. - Added ReferenceToolCall for creating tool calls in the evaluation dataset. - Added ReferenceToolCalls to represent a list of tool calls in the evaluation dataset. - Added VectorDatabase.update_connected to add a dataset and optional additional metadata to a connected vector database. #### Notebooks and Codespaces The Notebook and Codespace APIs are now GA and the related classes have been promoted to the stable client. - Changed name of the Notebook run() method to be Notebook.run_as_job . - Added support for Codespaces to the Notebook.is_finished_executing method. - Added the NotebookKernel.get method. - Added the NotebookScheduledJob.cancel method. - Added the NotebookScheduledJob.list method. - Added the Notebook.list_schedules method. #### Unstructured Data The client now supports unstructured data in the Data Registry. - Added the Files class to manage files on the DataRobot platform. The class supports file metadata including the description, creation date, and creator information. - Use Files.get to retrieve file information. - Use Files.upload as a convenient facade method to upload files from URLs, file paths, or file objects (does not support DataFrames). - Use Files.create_from_url to upload a new file from a URL. - Use Files.create_from_file to upload a new file from a local file or file-like object. - Use Files.create_from_data_source to create a new file from a DataSource. - Use Files.list_files to retrieve all individual files contained within a Files object. This is useful for Files objects ingested from archives that contain multiple files. - Use Files.download to download a file’s contents. - Use Files.modify to update a file’s name, description, and/or tags. - Use Files.update to refresh a file object with the latest information from the server. - Use Files.delete to soft-delete a file. - Use Files.un_delete to restore a previously deleted file. - Use Files.search_catalog to search for files in the catalog based on name, tags, or other criteria. - Added the FilesCatalogSearch class to represent file catalog search results with metadata such as catalog name, creator, and tags. - Added the File class to represent individual files within a Files archive. The class provides information about individual files such as name, size, and path within the archive. #### OAuth The client provides better support for OAuth2 authorization workflows in applications using the DataRobot platform. These features are available in the datarobot.auth module. - Added the methods set_authorization_context and get_authorization_context to handle context needed for OAuth access token management. - Added the decorator datarobot_tool_auth to inject OAuth access tokens into the agent tool functions. #### Other Features - Introduced support for Chunking Service V2. The chunking_service_v2 classes have been moved out of the experimental directory and are now available to all users. - Added Model.continue_incremental_learning_from_incremental_model to continue training of the incremental learning model. - Added optional parameter chunk_definition_id in Model.start_incremental_learning_from_sample to begin training using new chunking service. - Added a new attribute snapshot_policy to datarobot.models.RecipeDatasetInput to specify the snapshot policy to use. - Added a new attribute dataset_id to datarobot.models.JDBCTableDataSourceInput to specify the exact dataset ID to use. - Added Dataset.create_version_from_recipe to create a new dataset version based on the Recipe. ### Enhancements - Added the use_tcp_keepalive parameter to Client to enable TCP keep-alive packets when connections are timing out, enabled by default. - Enabled Playground to create agentic playgrounds via input param playground_type=PlaygroundType.AGENTIC . - Extended PlaygroundOOTBMetricConfiguration.create with additional reference column names for agentic metrics. - Updated CustomTemplate.list to return all custom templates when no offset is specified. - Extended MetricInsights.list with the option to pass llm_blueprint_ids . - Extended OOTBMetricConfigurationRequest and OOTBMetricConfigurationResponse with support for extra_metric_settings , which provides an additional configuration option for the Tool Call Accuracy metric. - Extended VectorDatabase.create to support creation of connected vector databases via input param external_vector_database_connection . - Extended VectorDatabase.create to support an additional metadata dataset via input params metadata_dataset_id and metadata_combination_strategy . - Extended VectorDatabase.update to support updating the credential used to access a connected vector database via input param credential_id . - Extended VectorDatabase.download_text_and_embeddings_asset to support downloading additional files via input param part . - Added a new attribute engine_specific_parameters to datarobot.models.OCRJobResource to specify OCR engine specific parameters. - Added docker_image_uri to datarobot.ExecutionEnvironmentVersion . - Added optional parameter docker_image_uri to ExecutionEnvironmentVersion.create . - Changed parameter docker_context_path in ExecutionEnvironmentVersion.create to be optional. - Added a new attribute image_id to datarobot.ExecutionEnvironmentVersion . ### Bugfixes - Fixed PlaygroundOOTBMetricConfiguration.create by using the right payload for customModelLLMValidationId instead of customModelLlmValidationId . - Fixed datarobot.models.RecipeDatasetInput to use correct fields for to_api . - Fixed EvaluationDatasetConfiguration.create to use the correct payload for is_synthetic_dataset . ### Deprecation summary - Remove unreleased Insight configuration routes. These were replaced with the new MetricInsights class, and insight specific configurations. - Deployment.create_from_learning_model method is deprecated. Please first register the leaderboard model with RegisteredModelVersion.create_for_leaderboard_item , then create a deployment with Deployment.create_from_registered_model_version . ### Documentation changes - Updated the example for GenAI to show creation of a metric aggregation job. ### Experimental changes - Added VectorDatabase with a new attribute external_vector_database_connection added to the VectorDatabase.create() method. - Added attribute version to DatasetInfo to identify the analysis version. - Added attribute dataset_definition_info_version to ChunkDefinition to identify the analysis information version. - Added a version query parameter to the DatasetDefinition class, allowing users to specify the analysis version in the get method. - Added DatasetDefinitionInfoHistory with the DatasetDefinitionInfoHistory.list method to retrieve a list of dataset information history records. - Added the DatasetDefinitionInfoHistory.list_versions method to retrieve a list of dataset information records. ## 3.7.0 ### New features - The DataRobot Python Client now supports Python 3.12 and Python 3.13. - Added Deployment.get_retraining_settings to retrieve retraining settings. - Added Deployment.update_retraining_settings to update retraining settings. - Updated RESTClientObject to retry requests when the server returns a 104 connection reset error. - Added support for datasphere as an intake and output type in batch predictions. - Added Deployment.get_accuracy_metrics_settings to retrieve accuracy metrics settings. - Added Deployment.update_accuracy_metrics_settings to update accuracy metrics settings. - Added CustomMetricValuesOverSpace to retrieve custom metric values over space. - Added CustomMetric.get_values_over_space to retrieve custom metric values over space. - Created ComplianceDocTemplateProjectType , an enum to define project type supported by the compliance documentation custom template. - Added attribute project_type to ComplianceDocTemplate to identify the template supported project type. - Added optional parameter project_type in ComplianceDocTemplate.get_default to retrieve the project type’s default template. - Added optional parameter project_type in ComplianceDocTemplate.create to specify the project type supported by the template to create. - Added optional parameter project_type in ComplianceDocTemplate.create_from_json_file to specify the project type supported by the template to create. - Added optional parameter project_type in ComplianceDocTemplate.update to allow updating an existing template’s project type. - Added optional parameter project_type in ComplianceDocTemplate.list to allow to filtering/searching by template’s project type. - Added ShapMatrix.get_as_csv to retrieve SHAP matrix results as a CSV file. - Added ShapMatrix.get_as_dataframe to retrieve SHAP matrix results as a dataframe. - Added a new class LiftChart to interact with lift chart insights. - Added a new class RocCurve to interact with ROC curve insights. - Added a new class Residuals to interact with residuals insights. - Added Project.create_from_recipe to create Feature Discovery projects using recipes. - Added an optional parameter recipe_type to datarobot.models.Recipe.from_dataset() to create Wrangling recipes. - Added an optional parameter recipe_type to datarobot.models.Recipe.from_data_store() to create Wrangling recipes. - Added Recipe.set_recipe_metadata to update recipe metadata. - Added an optional parameter snapshot_policy to datarobot.models.Recipe.from_dataset() to specify the snapshot policy to use. - Added new attributes prediction_point , relationships_configuration_id and feature_discovery_supervised_feature_reduction to RecipeSettings . - Added several optional parameters to ExecutionEnvironment for list , create and update methods. - Added optional parameter metadata_filter to ComparisonPrompt.create . - Added CustomInferenceModel.share to update access control settings for a custom model. - Added CustomInferenceModel.get_access_list to retrieve access control settings for a custom model. - Added new attribute latest_successful_version to ExecutionEnvironment . - Added Dataset.create_from_project to create datasets from project data. - Added ExecutionEnvironment.share to update access control settings for an execution environment. - Added ExecutionEnvironment.get_access_list to retrieve access control settings an execution environment. - Created ModerationTemplate to interact with LLM moderation templates. - Created ModerationConfiguration to interact with LLM moderation configuration. - Created CustomTemplate to interact with custom-templates elements. - Extended the advanced options available when setting a target to include parameter: ‘feature_engineering_prediction_point’(part of the AdvancedOptions object). - Added optional parameter substitute_url_parameters to DataStore for list and get methods. - Added Model.start_incremental_learning_from_sample to initialize the incremental learning model and begin training using the chunking service. Requires the “Project Creation from a Dataset Sample” feature flag. - Added NonChatAwareCustomModelValidation as the base class for CustomModelVectorDatabaseValidation and CustomModelEmbeddingValidation . In contrast, CustomModelLLMValidation now implements the create and update methods differently to interact with the deployments that support the chat completion API. - Added optional parameter chat_model_id to CustomModelLLMValidation.create and CustomModelLLMValidation.update to allow adding deployed LLMs that support the chat completion API. - Fixed ComparisonPrompt not being able to load errored comparison prompt results. - Added optional parameters retirement_date , is_deprecated , and is_active to LLMDefinition and added an optional parameter llm_is_deprecated to the MetricMetadata to expose LLM deprecation and retirement-related information. ### Enhancements - Added Deployment.share as an alias for Deployment.update_shared_roles . - Internally use the existing input argument max_wait in CustomModelVersion.clean_create , to set the READ request timeout. ### Bugfixes - Made user_id and username fields in management_meta optional for PredictionEnvironment to support API responses without these fields. - Fixed the enum values of ComplianceDocTemplateType . - Fixed the enum values of WranglingOperations . - Fixed the enum values of DataWranglingDialect . - Playground id parameter is no longer optional in EvaluationDatasetConfiguration.list - Copy insights path fixed in MetricInsights.copy_to_playground - Missing fields for prompt_type and warning were added to PromptTrace . - Fixed a query parameter name in SidecarModelMetricValidation.list . - Fix typo in attribute VectorDatabase : metadata_columns which was metada_columns - Do not camelCase metadata_filter dict in ChatPrompt.create - Fixed a Use Case query parameter name in CustomModelLLMValidation.list , CustomModelEmbeddingValidation.list , and CustomModelVectorDatabaseValidation.list . - Fixed featureDiscoverySettings parameter name in RelationshipsConfiguration.create and RelationshipsConfiguration.replace . ### API changes - Method CustomModelLLMValidation.create no longer requires the prompt_column_name and target_column_name parameters, and can accept an optional chat_model_id parameter. The parameter order has changed. If the custom model LLM deployment supports the chat completion API, it is recommended to use chat_model_id now instead of (or in addition to) specifying the column names. ### Deprecation summary - Removed the deprecated capabilities attribute of Deployment . - Method Model.request_lift_chart is deprecated and will be removed in favor of LiftChart.compute . - Method Model.get_lift_chart is deprecated and will be removed in favor of LiftChart.get . - Method Model.get_all_lift_charts is deprecated and will be removed in favor of LiftChart.list . - Method Model.request_roc_curve is deprecated and will be removed in favor of RocCurve.compute . - Method Model.get_roc_curve is deprecated and will be removed in favor of RocCurve.get . - Method Model.get_all_roc_curves is deprecated and will be removed in favor of RocCurve.list . - Method Model.request_residuals_chart is deprecated and will be removed in favor of Residuals.compute . - Method Model.get_residuals_chart is deprecated and will be removed in favor of Residuals.get . - Method Model.get_all_residuals_charts is deprecated and will be removed in favor of Residuals.list . ### Documentation changes - Starting with this release, Python client documentation will be available at https://docs.datarobot.com/ as well as on ReadTheDocs. Content has been reorganized to support this change. - Removed numpydoc as a dependency. Docstring parsing has been handled by sphinx.ext.napoleon since 3.6.0. - Fix issues with how the Table of Contents is rendered on ReadTheDocs. sphinx-external-toc is now a dev dependency. - Fix minor issues with formatting across the ReadTheDocs site. - Updated docs on Anomaly Assessment objects to remove duplicate information. ### Experimental changes - Added use_case and deployment_id properties to RetrainingPolicy class. - Added create and update_use_case methods to RetrainingPolicy class. - Renamed the method ‘train_first_incremental_from_sample’ to ‘start_incremental_learning_from_sample’. Added new parameters : ‘early_stopping_rounds’ and ‘first_iteration_only’. - Added the credentials_id parameter to the create method in ChunkDefinition . - Bugfix the next_run_time property of the NotebookScheduledJob class to be nullable. - Added the highlight_whitespace property to the NotebookSettings . - Create new directory specifically for notebooks in the experimental portion of the client. - Added methods to the Notebook class to work with session: start_session() , stop_session() , get_session_status() , is_running() . - Added methods to the Notebook in order to execute and check related execution status: execute() , get_execution_status() , is_finished_executing() . - Added Notebook.create_revision to the Notebook class in order to create revisions. - Moved ModerationTemplate class to ModerationTemplate . - Moved ModerationConfiguration class to ModerationConfiguration to interact with LLM moderation configuration. - Updates to Notebook.run method in the Notebook class in order to encourage proper usage as well as add more descriptive TypedDict as annotation. - Added NotebookScheduledJob.get_most_recent_run to the NotebookScheduledJob class to aid in more idiomatic code when dealing with manual runs. - Updates to Notebook.run method in the Notebook class in order to support Codespace Notebook execution as well as multiple related new classes and methods to expand API coverage which is needed for the underlying execution. - Added ExecutionEnvironment.assign_environment to the ExecutionEnvironment class, which gives the ability to assign or update a notebook’s environment. - Removed deprecated experimental method Model.get_incremental_learning_metadata . - Removed deprecated experimental method Model.start_incremental_learning . ## 3.6.0 ### New features - Added OCRJobResource for running OCR jobs. - Added new Jina V2 embedding model in VectorDatabaseEmbeddingModel. - Added new Small MultiLingual Embedding Model in VectorDatabaseEmbeddingModel. - Added Deployment.get_segment_attributes to retrieve segment attributes. - Added Deployment.get_segment_values to retrieve segment values. - Added AutomatedDocument.list_all_available_document_types to return a list of document types. - Added Model.request_per_class_fairness_insights to return per-class bias & fairness insights. - Added MLOpsEvent to report MLOps Events. Currently supporting moderation MLOps events only - Added Deployment.get_moderation_events to retrieve moderation events for that deployment. - Extended the advanced options available when setting a target to include new parameter: ‘number_of_incremental_learning_iterations_before_best_model_selection’(part of the AdvancedOptions object). This parameter allows you to specify how long top 5 models will run for prior to best model selection. - Add support for ‘connector_type’ in Connector.create . - Deprecate file_path for Connector.create and Connector.update . - Added DataQualityExport and Deployment.list_data_quality_exports to retrieve a list of data quality records. - Added secure config support for Azure Service Principal credentials. - Added support for categorical custom metrics in CustomMetric . - Added NemoConfiguration to manage Nemo configurations. - Added NemoConfiguration.create to create or update a Nemo configuration. - Added NemoConfiguration.get to retrieve a Nemo configuration. - Added a new class ShapDistributions to interact with SHAP distribution insights. - Added the MODEL_COMPLIANCE_GEN_AI value to the attribute document_type from DocumentOption to generate compliance documentation for LLMs in the Registry. - Added new attribute prompts_count to Chat . - Added Recipe modules for Data Wrangling. - Added RecipeOperation and a set of subclasses to represent a single Recipe.operations operation. - Added new attribute similarity_score to Citation . - Added new attributes retriever and add_neighbor_chunks to VectorDatabaseSettings . - Added new attribute metadata to Citation . - Added new attribute metadata_filter to ChatPrompt . - Added new attribute metadata_filter to ComparisonPrompt . - Added new attribute custom_chunking to ChunkingParameters . - Added new attribute custom_chunking to VectorDatabase . - Added a new class LLMTestConfiguration for LLM test configurations. - LLMTestConfiguration.get to retrieve a hosted LLM test configuration. - LLMTestConfiguration.list to list hosted LLM test configurations. - LLMTestConfiguration.create to create an LLM test configuration. - LLMTestConfiguration.update to update an LLM test configuration. - LLMTestConfiguration.delete to delete an LLM test configuration. - Added a new class LLMTestConfigurationSupportedInsights for LLM test configuration supported insights. - LLMTestConfigurationSupportedInsights.list to list hosted LLM test configuration supported insights. - Added a new class LLMTestResult for LLM test results. - LLMTestResult.get to retrieve a hosted LLM test result. - LLMTestResult.list to list hosted LLM test results. - LLMTestResult.create to create an LLM test result. - LLMTestResult.delete to delete an LLM test result. - Added new attribute dataset_name to OOTBDatasetDict . - Added new attribute rows_count to OOTBDatasetDict . - Added new attribute max_num_prompts to DatasetEvaluationDict . - Added new attribute prompt_sampling_strategy to DatasetEvaluationDict . - Added a new class DatasetEvaluationRequestDict for Dataset Evaluations in create/edit requests. - Added new attribute evaluation_dataset_name to InsightEvaluationResult . - Added new attribute chat_name to InsightEvaluationResult . - Added new attribute llm_test_configuration_name to LLMTestResult . - Added new attribute creation_user_name to LLMTestResult . - Added new attribute pass_percentage to LLMTestResult . - Added new attribute evaluation_dataset_name to DatasetEvaluation . - Added new attribute datasets_compatibility to LLMTestConfigurationSupportedInsights . - Added a new class NonOOTBDataset for non out-of-the-box (OOTB) dataset entities. - NonOOTBDataset.list to retrieve non OOTB datasets for compliance testing. - Added a new class OOTBDataset for OOTB dataset entities. - OOTBDataset.list to retrieve OOTB datasets for compliance testing. - Added a new class TraceMetadata to retrieve trace metadata. - Add new attributes to VectorDatabase : parent_id , family_id , metadata_columns , added_dataset_ids , added_dataset_names , and`version`. - VectorDatabase.get_supported_retrieval_settings to retrieve supported retrieval settings. - VectorDatabase.submit_export_dataset_job to submit the vector database as dataset to the AI catalog. - Updated the method VectorDatabase.create to create a new vector database version. - Added a new class SupportedRetrievalSettings for supported vector database retrieval settings. - Added a new class SupportedRetrievalSetting for supported vector database retrieval setting. - Added a new class VectorDatabaseDatasetExportJob for vector database dataset export jobs. - Added new attribute playground_id to CostMetricConfiguration . - Added new attribute name to CostMetricConfiguration . - Added a new class SupportedInsights to support lists. - SupportedInsights.list to list supported insights. - Added a new class MetricInsights for the new metric insights routes. - MetricInsights.list to list metric insights. - MetricInsights.copy_to_playground to copy metrics to another playground. - Added a new class PlaygroundOOTBMetricConfiguration for OOTB metric configurations. - Updated the schema for EvaluationDatasetMetricAggregation to include the new attributes ootb_dataset_name , dataset_id and dataset_name . - Updated the method EvaluationDatasetMetricAggregation.list with additional optional filter parameters. - Added new attribute warning to OOTBDataset . - Added new attribute warning to OOTBDatasetDict . - Added new attribute warnings to LLMTestConfiguration . - Added a new parameter playground_id to SidecarModelMetricValidation.create to support sidecar model metrics transition to playground. - Updated the schema for NemoConfiguration to include the new attributes prompt_pipeline_template_id and response_pipeline_template_id . - Added new attributes to EvaluationDatasetConfiguration : rows_count , playground_id . - Fix retrieving shap_remaining_total when requesting predictions with SHAP insights. This should return the remaining shap values when present. ### API changes - Updated ServerError ’s exc_message to be constructed with a request ID to help with debugging. - Added method Deployment.get_capabilities to retrieve a list of Capability objects containing capability details. - Advanced options parameters: modelGroupId , modelRegimeId , and modelBaselines were renamed into seriesId , forecastDistance , and forecastOffsets . - Added the parameter use_sample_from_dataset from Project.create_from_dataset . This parameter, when set, uses the EDA sample of the dataset to start the project. - Added the parameter quick_compute to functions in the classes ShapMatrix , ShapImpact , and ShapPreview . - Added the parameter copy_insights to Playground.create to copy the insights from existing Playground to the new one. - Added the parameter llm_test_configuration_ids , LLMBlueprint.register_custom_model , to run LLM compliance tests when a blueprint is sent to the custom model workshop. ### Enhancements - Added standard pagination parameters (e.g. limit , offset ) to Deployment.list , allowing you to get deployment data in smaller chunks. - Added the parameter base_path to get_encoded_file_contents_from_paths and get_encoded_image_contents_from_paths , allowing you to better control script behavior when using relative file paths. ### Bugfixes - Fixed field in CustomTaskVersion for controlling network policies. This is changed from outgoing_network_policy to outbound_network_policy . When performing a GET action, this field was incorrect and always resolved to None . When attempting a POST or PATCH action, the incorrect field would result in a 422. Also changed the name of datarobot.enums.CustomTaskOutgoingNetworkPolicy to datarobot.enums.CustomTaskOutboundNetworkPolicy to reflect the proper field name. - Fixed schema for DataSliceSizeInfo , so it now allows an empty list for the messages field. ### Deprecation summary - Removed the parameter in_use from ImageAugmentationList.create . This parameter was deprecated in v3.1.0. - Deprecated AutomatedDocument.list_available_document_types . Please use AutomatedDocument.list_all_available_document_types instead. - Deprecated Model.request_fairness_insights . Please use Model.request_per_class_fairness_insights instead, to return StatusCheckJob instead of status_id . - Deprecated Model.get_prime_eligibility . Prime models are no longer supported. - eligibleForPrime field will no longer be returned from Model.get_supported_capabilities and will be removed after version 3.8 is released. - Deprecated the property ShapImpact.row_count and it will be removed after version 3.7 is released. - Advanced options parameters: modelGroupId , modelRegimeId , and modelBaselines were renamed into seriesId , forecastDistance , and forecastOffsets and are deprecated and they will be removed after version 3.6 is released. - Renamed datarobot.enums.CustomTaskOutgoingNetworkPolicy to datarobot.enums.CustomTaskOutboundNetworkPolicy to reflect bug fix changes. The original enum was unusable. - Removed parameter user_agent_suffix in datarobot.Client . Please use trace_context instead. - Removed deprecated method DataStore.get_access_list . Please use DataStore.get_shared_roles instead. - Removed support for SharingAccess instances in DataStore.update_access_list . Please use SharingRole instances instead. ### Configuration changes - Removed upper bound pin on urllib3 package to allow versions 2.0.2 and above. - Upgraded the Pillow library to version 10.3.0. Users installing DataRobot with the “images” extra ( pip install datarobot[images] ) should note that this is a required library. ### Documentation changes - The API Reference page has been split into multiple sections for better usability. - Fixed docs for Project.refresh to clarify that it does not return a value. - Fixed code example for ExternalScores . - Added copy button to code examples in ReadTheDocs documentation, for convenience. - Removed the outdated ‘examples’ section from the documentation. Please refer to DataRobot’s API Documentation Home for more examples. - Removed the duplicate ‘getting started’ section from the documentation. - Updated to Sphinx RTD Theme v3. - Updated the description for the parameter: ‘number_of_incremental_learning_iterations_before_best_model_selection’ (part of the AdvancedOptions object). ### Experimental changes - Added the force_update parameter to the update method in ChunkDefinition . - Removed attribute select_columns from ChunkDefinition - Added initial experimental support for Chunking Service V2 - DatasetDefinition - DatasetProps - DatasetInfo - DynamicDatasetProps - RowsChunkDefinition - FeaturesChunkDefinition - ChunkDefinitionStats - ChunkDefinition - Added new method update to ChunkDefinition - Added experimental support for time series wrangling, including usage template: - datarobot._experimental.models.time_series_wrangling_template.user_flow_template Experimental changes offer automated time series feature engineering for the data in Snowflake or Postgres. - Added the ability to use the Spark dialect when creating a recipe, allowing data wrangling support for files. - Added new attribute warning to Chat . - Moved all modules from datarobot._experimental.models.genai to datarobot.models.genai . - Added a new method ‘Model.train_first_incremental_from_sample’ that will train first incremental learning iteration from existing sample model. Requires “Project Creation from a Dataset Sample” feature flag. ## 3.5.0 ### New features - Added support for BYO LLMs using serverless predictions in CustomModelLLMValidation . - Added attribute creation_user_name to LLMBlueprint . - Added a new class HostedCustomMetricTemplate for hosted custom metrics templates. - HostedCustomMetricTemplate.get to retrieve a hosted custom metric template. - HostedCustomMetricTemplate.list to list hosted custom metric templates. - Added Job.create_from_custom_metric_gallery_template to create a job from a custom metric gallery template. - Added a new class HostedCustomMetricTemplate for hosted custom metrics. - HostedCustomMetric.list to list hosted custom metrics. - HostedCustomMetric.update to update a hosted custom metrics. - HostedCustomMetric.delete to delete a hosted custom metric. - HostedCustomMetric.create_from_custom_job to create a hosted custom metric from existing custom job. - HostedCustomMetric.create_from_template to create hosted custom metric from template. - Added a new class datarobot.models.deployment.custom_metrics.HostedCustomMetricBlueprint for hosted custom metric blueprints. - HostedCustomMetricBlueprint.get to get a hosted custom metric blueprint. - HostedCustomMetricBlueprint.create to create a hosted custom metric blueprint. - HostedCustomMetricBlueprint.update to update a hosted custom metric blueprint. - Added Job.list_schedules to list job schedules. - Added a new class JobSchedule for the registry job schedule. - JobSchedule.create to create a job schedule. - JobSchedule.update to update a job schedule. - JobSchedule.delete to delete a job schedule. - Added attribute credential_type to RuntimeParameter . - Added a new class EvaluationDatasetConfiguration for configuration of evaluation datasets. - EvaluationDatasetConfiguration.get to get an evaluation dataset configuration. - EvaluationDatasetConfiguration.list to list the evaluation dataset configurations for a Use Case. - EvaluationDatasetConfiguration.create to create an evaluation dataset configuration. - EvaluationDatasetConfiguration.update to update an evaluation dataset configuration. - EvaluationDatasetConfiguration.delete to delete an evaluation dataset configuration. - Added a new class EvaluationDatasetMetricAggregation for metric aggregation results. - EvaluationDatasetMetricAggregation.list to get the metric aggregation results. - EvaluationDatasetMetricAggregation.create to create the metric aggregation job. - EvaluationDatasetMetricAggregation.delete to delete metric aggregation results. - Added a new class SyntheticEvaluationDataset for synthetic dataset generation. Use SyntheticEvaluationDataset.create to create a synthetic evaluation dataset. - Added a new class SidecarModelMetricValidation for sidecar model metric validations. - SidecarModelMetricValidation.create to create a sidecar model metric validation. - SidecarModelMetricValidation.list to list sidecar model metric validations. - SidecarModelMetricValidation.get to get a sidecar model metric validation. - SidecarModelMetricValidation.revalidate to rerun a sidecar model metric validation. - SidecarModelMetricValidation.update to update a sidecar model metric validation. - SidecarModelMetricValidation.delete to delete a sidecar model metric validation. - Added experimental support for Chunking Service: - Added a new attribute, is_descending_order to: ### Bugfixes - Updated the trafaret column prediction from TrainingPredictionsIterator for supporting extra list of strings. ### Configuration changes - Updated black version to 23.1.0. - Removes dependency on package mock , since it is part of the standard library. ### Documentation changes - Removed incorrect can_share parameters in Use Case sharing example - Added usage of external_llm_context_size in llm_settings in genai_example.rst . - Updated doc string for llm_settings to include attribute external_llm_context_size for external LLMs. - Updated genai_example.rst to link to DataRobot doc pages for external vector database and external LLM deployment creation. ### API changes - Remove ImportedModel object since it was API for SSE (standalone scoring engine) which is not part of DataRobot anymore. - Added number_of_clusters parameter to Project.get_model_records to filter models by number of clusters in unsupervised clustering projects. - Remove an unsupported NETWORK_EGRESS_POLICY.DR_API_ACCESS value for custom models. This value was used by a feature that was never released as a GA and is not supported in the current API. - Implemented support for dr-connector-v1 to DataStore and DataSource . - Added a new parameter name to DataStore.list for searching data stores by name. - Added a new parameter entity_type to the compute and create methods of the classes ShapMatrix , ShapImpact , ShapPreview . Insights can be computed for custom models if the parameter entity_type="customModel" is passed. See also the User Guide: :ref: SHAP insights overview . ### Experimental changes - Added experimental api support for Data Wrangling. See Recipe . - Recipe.from_data_store to create a Recipe from data store. - Recipe.retrieve_preview to get a sample of the data after recipe is applied. - Recipe.set_inputs to set inputs to the recipe. - Recipe.set_operations to set operations to the recipe. - Added new experimental DataStore that adds get_spark_session for Databricks databricks-v1 data stores to get a Spark session. - Added attribute chunking_type to DatasetChunkDefinition . - Added OTV attributes to DatasourceDefinition . - Added DatasetChunkDefinition.patch_validation_dates to patch validation dates of OTV datasource definitions after sampling job. ## 3.4.1 ### New features ### Enhancements ### Bugfixes - Updated the validation logic of RelationshipsConfiguration to work with native database connections ### API changes ### Deprecation summary ### Configuration changes ### Documentation changes ### Experimental changes ## 3.4.0 ### New features - Added the following classes for generative AI. Importing these from datarobot._experimental.models.genai is deprecated and will be removed by the release of DataRobot 10.1 and API Client 3.5. - Playground to manage generative AI playgrounds. - LLMDefinition to get information about supported LLMs. - LLMBlueprint to manage LLM blueprints. - Chat to manage chats for LLM blueprints. - ChatPrompt to submit prompts within a chat. - ComparisonChat to manage comparison chats across multiple LLM blueprints within a playground. - ComparisonPrompt to submit a prompt to multiple LLM blueprints within a comparison chat. - VectorDatabase to create vector databases from datasets in the AI Catalog for retrieval augmented generation with an LLM blueprint. - CustomModelVectorDatabaseValidation to validate a deployment for use as a vector database. - CustomModelLLMValidation to validate a deployment for use as an LLM. - UserLimits to get counts of vector databases and LLM requests for a user. - Extended the advanced options available when setting a target to include new parameter: incrementalLearningEarlyStoppingRounds (part of the AdvancedOptions object). This parameter allows you to specify when to stop for incremental learning automation. - Added experimental support for Chunking Service: - DatasetChunkDefinition for defining how chunks are created from a data source. - DatasetChunkDefinition.create to create a new dataset chunk definition. - DatasetChunkDefinition.get to get a specific dataset chunk definition. - DatasetChunkDefinition.list to list all dataset chunk definitions. - DatasetChunkDefinition.get_datasource_definition to retrieve the data source definition. - DatasetChunkDefinition.get_chunk to get specific chunk metadata belonging to a dataset chunk definition. - DatasetChunkDefinition.list_chunks to list all chunk metadata belonging to a dataset chunk definition. - DatasetChunkDefinition.create_chunk to submit a job to retrieve the data from the origin data source. - DatasetChunkDefinition.create_chunk_by_index to submit a job to retrieve data from the origin data source by index. - OriginStorageType - Chunk - ChunkStorageType - ChunkStorage - DatasourceDefinition - DatasourceAICatalogInfo to define the datasource AI catalog information to create a new dataset chunk definition. - DatasourceDataWarehouseInfo to define the datasource data warehouse (snowflake, big query, etc) information to create a new dataset chunk definition. - RuntimeParameter for retrieving runtime parameters assigned to CustomModelVersion . - RuntimeParameterValue to define runtime parameter override value, to be assigned to CustomModelVersion . - Added Snowflake Key Pair authentication for uploading datasets from Snowflake or creating a project from Snowflake data - Added Project.get_model_records to retrieve models. Method Project.get_models is deprecated and will be removed soon in favor of Project.get_model_records . - Extended the advanced options available when setting a target to include new parameter: chunkDefinitionId (part of the AdvancedOptions object). This parameter allows you to specify the chunking definition needed for incremental learning automation. - Extended the advanced options available when setting a target to include new Autopilot parameters: incrementalLearningOnlyMode and incrementalLearningOnBestModel (part of the AdvancedOptions object). These parameters allow you to specify how Autopilot is performed with the chunking service. - Added a new method DatetimeModel.request_lift_chart to support Lift Chart calculations for datetime partitioned projects with support of Sliced Insights. - Added a new method DatetimeModel.get_lift_chart to support Lift chart retrieval for datetime partitioned projects with support of Sliced Insights. - Added a new method DatetimeModel.request_roc_curve to support ROC curve calculation for datetime partitioned projects with support of Sliced Insights. - Added a new method DatetimeModel.get_roc_curve to support ROC curve retrieval for datetime partitioned projects with support of Sliced Insights. - Update method DatetimeModel.request_feature_impact to support use of Sliced Insights. - Update method DatetimeModel.get_feature_impact to support use of Sliced Insights. - Update method DatetimeModel.get_or_request_feature_impact to support use of Sliced Insights. - Update method DatetimeModel.request_feature_effect to support use of Sliced Insights. - Update method DatetimeModel.get_feature_effect to support use of Sliced Insights. - Update method DatetimeModel.get_or_request_feature_effect to support use of Sliced Insights. - Added a new method FeatureAssociationMatrix.create to support the creation of FeatureAssociationMatricies for Featurelists. - Introduced a new method Deployment.perform_model_replace as a replacement for Deployment.replace_model . - Introduced a new property, model_package , which provides an overview of the currently used model package in datarobot.models.Deployment . - Added new parameter prediction_threshold to BatchPredictionJob.score_with_leaderboard_model and BatchPredictionJob.score that automatically assigns the positive class label to any prediction exceeding the threshold. - Added two new enum values to datarobot.models.data_slice.DataSlicesOperators , “BETWEEN” and “NOT_BETWEEN”, which are used to allow slicing. - Added a new class Challenger for interacting with DataRobot challengers to support the following methods: Challenger.get to retrieve challenger objects by ID. Challenger.list to list all challengers. Challenger.create to create a new challenger. Challenger.update to update a challenger. Challenger.delete to delete a challenger. - Added a new method Deployment.get_challenger_replay_settings to retrieve the challenger replay settings of a deployment. - Added a new method Deployment.list_challengers to retrieve the challengers of a deployment. - Added a new method Deployment.get_champion_model_package to retrieve the champion model package from a deployment. - Added a new method Deployment.list_prediction_data_exports to retrieve deployment prediction data exports. - Added a new method Deployment.list_actuals_data_exports to retrieve deployment actuals data exports. - Added a new method Deployment.list_training_data_exports to retrieve deployment training data exports. - Manage deployment health settings with the following methods: - Get health settings Deployment.get_health_settings - Update health settings Deployment.update_health_settings - Get default health settings Deployment.get_default_health_settings - Added new enum value to datarobot.enums._SHARED_TARGET_TYPE to support Text Generation use case. - Added new enum value datarobotServerless to datarobot.enums.PredictionEnvironmentPlatform to support DataRobot Serverless prediction environments. - Added new enum value notApplicable to datarobot.enums.PredictionEnvironmentHealthType to support new health status from DataRobot API. - Added new enum value to datarobot.enums.TARGET_TYPE and datarobot.enums.CUSTOM_MODEL_TARGET_TYPE to support text generation custom inference models. - Updated datarobot.CustomModel to support the creation of text generation custom models. - Added a new class CustomMetric for interacting with DataRobot custom metrics to support the following methods: - CustomMetric.get to retrieve a custom metric object by ID from a given deployment. - CustomMetric.list to list all custom metrics from a given deployment. - CustomMetric.create to create a new custom metric for a given deployment. - CustomMetric.update to update a custom metric for a given deployment. - CustomMetric.delete to delete a custom metric for a given deployment. - CustomMetric.unset_baseline to remove baseline for a given custom metric. - CustomMetric.submit_values to submit aggregated custom metrics values from code. The provided data should be in the form of a dict or a Pandas DataFrame. - CustomMetric.submit_single_value to submit a single custom metric value. - CustomMetric.submit_values_from_catalog to submit aggregated custom metrics values from a dataset via the AI Catalog. - CustomMetric.get_values_over_time to retrieve values of a custom metric over a time period. - CustomMetric.get_summary to retrieve the summary of a custom metric over a time period. - CustomMetric.get_values_over_batch to retrieve values of a custom metric over batches. - CustomMetric.get_batch_summary to retrieve the summary of a custom metric over batches. - Added CustomMetricValuesOverTime to retrieve custom metric over time information. - Added CustomMetricSummary to retrieve custom metric over time summary. - Added CustomMetricValuesOverBatch to retrieve custom metric over batch information. - Added CustomMetricBatchSummary to retrieve custom metric batch summary. - Added Job and JobRun to create, read, update, run, and delete jobs in the Registry. - Added KeyValue to create, read, update, and delete key values. - Added a new class PredictionDataExport for interacting with DataRobot deployment data export to support the following methods: - PredictionDataExport.get to retrieve a prediction data export object by ID from a given deployment. - PredictionDataExport.list to list all prediction data exports from a given deployment. - PredictionDataExport.create to create a new prediction data export for a given deployment. - PredictionDataExport.fetch_data to retrieve a prediction export data as a DataRobot dataset. - Added a new class ActualsDataExport for interacting with DataRobot deployment data export to support the following methods: - ActualsDataExport.get to retrieve an actuals data export object by ID from a given deployment. - ActualsDataExport.list to list all actuals data exports from a given deployment. - ActualsDataExport.create to create a new actuals data export for a given deployment. - ActualsDataExport.fetch_data to retrieve an actuals export data as a DataRobot dataset. - Added a new class TrainingDataExport for interacting with DataRobot deployment data export to support the following methods: - TrainingDataExport.get to retrieve a training data export object by ID from a given deployment. - TrainingDataExport.list to list all training data exports from a given deployment. - TrainingDataExport.create to create a new training data export for a given deployment. - TrainingDataExport.fetch_data to retrieve a training export data as a DataRobot dataset. - Added a new parameter base_environment_version_id to CustomModelVersion.create_clean for overriding the default environment version selection behavior. - Added a new parameter base_environment_version_id to CustomModelVersion.create_from_previous for overriding the default environment version selection behavior. - Added a new class PromptTrace for interacting with DataRobot prompt trace to support the following methods: - PromptTrace.list to list all prompt traces from a given playground. - PromptTrace.export_to_ai_catalog to export prompt traces for the playground to AI catalog. - Added a new class InsightsConfiguration for describing available insights and configured insights for a playground. InsightsConfiguration.list to list the insights that are available to be configured. - Added a new class Insights for configuring insights for a playground. Insights.get to get the current insights configuration for a playground. Insights.create to create or update the insights configuration for a playground. - Added a new class CostMetricConfiguration for describing available cost metrics and configured cost metrics for a Use Case. CostMetricConfiguration.get to get the cost metric configuration. CostMetricConfiguration.create to create a cost metric configuration. CostMetricConfiguration.update to update the cost metric configuration. CostMetricConfiguration.delete to delete the cost metric configuration.Key - Added a new class LLMCostConfiguration for the cost configuration of a specific llm within a Use Case. - Added new classes ShapMatrix , ShapImpact , ShapPreview to interact with SHAP-based insights. See also the User Guide: :ref: SHAP insights overview ### API changes - Parameter Overrides: Users can now override most of the previously set configuration values directly through parameters when initializing the Client. Exceptions: The endpoint and token values must be initialized from one source (client params, environment, or config file) and cannot be overridden individually, for security and consistency reasons. The new configuration priority is as follows: - Client Params - Client config_path param - Environment Variables - Default to reading YAML config file from ~/.config/datarobot/drconfig.yaml - DATAROBOT_API_CONSUMER_TRACKING_ENABLED now always defaults to True . - Added Databricks personal access token and service principal (also shared credentials via secure config) authentication for uploading datasets from Databricks or creating a project from Databricks data. - Added secure config support for AWS long term credentials. - Implemented support for dr-database-v1 to DataStore , DataSource , and DataDriver . Added enum classes to support the changes. - You can retrieve the canonical URI for a Use Case using UseCase.get_uri . - You can open a Use Case in a browser using UseCase.open_in_browser . ### Enhancements - Added a new parameter to Dataset.create_from_url to support fast dataset registration: - sample_size - Added a new parameter to Dataset.create_from_data_source to support fast dataset registration: - sample_size - Job.get_result_when_complete returns datarobot.models.DatetimeModel instead of the datarobot.models.Model if a datetime model was trained. - Dataset.get_as_dataframe can handle downloading parquet files as well as csv files. - Implement support for dr-database-v1 in DataStore - Added two new parameters to BatchPredictionJobDefinition.list for paginating long job definitions lists: - offset - limit - Added two new parameters to BatchPredictionJobDefinition.list for filtering the job definitions: - deployment_id - search_name - Added new parameter to Deployment.validate_replacement_model to support replacement validation based on model package ID: - new_registered_model_version_id - Added support for Native Connectors to Connector for everything other than Connector.create and Connector.update ### Deprecation summary - Removed Model.get_leaderboard_ui_permalink and Model.open_model_browser - Deprecated Project.get_models in favor of Project.get_model_records . - BatchPredictionJobDefinition.list will no longer return all job definitions after version 3.6 is released. To preserve current behavior please pass limit=0. - new_model_id parameter in Deployment.validate_replacement_model will be removed after version 3.6 is released. - Deployment.replace_model will be removed after version 3.6 is released. Method Deployment.perform_model_replace should be used instead. - CustomInferenceModel.assign_training_data was marked as deprecated in v3.2. The deprecation period has been extended, and the feature will now be removed in v3.5. Use CustomModelVersion.create_clean and CustomModelVersion.create_from_previous instead. ### Documentation changes - Updated genai_example.rst to utilize latest genAI features and methods introduced most recently in the API client. ### Experimental changes - Added new attribute, prediction_timeout to CustomModelValidation . - Added new attributes, feedback_result , metrics , and final_prompt to ResultMetadata . - Added use_case_id to CustomModelValidation . - Added llm_blueprints_count and user_name to Playground . - Added custom_model_embedding_validations to SupportedEmbeddings . - Added embedding_validation_id and is_separator_regex to VectorDatabase . - Added optional parameters, use_case , name , and model to CustomModelValidation.create . - Added a method CustomModelValidation.list , to list custom model validations available to a user with several optional parameters to filter the results. - Added a method CustomModelValidation.update , to update a custom model validation. - Added an optional parameter, use_case , to LLMDefinition.list , to include in the returned LLMs the external LLMs available for the specified use_case as well. - Added optional parameter, playground to VectorDatabase.list to list vector databases by playground. - Added optional parameter, comparison_chat , to ComparisonPrompt.list , to list comparison prompts by comparison chat. - Added optional parameter, comparison_chat , to ComparisonPrompt.create , to specify the comparison chat to create the comparison prompt in. - Added optional parameter, feedback_result , to ComparisonPrompt.update , to update a comparison prompt with feedback. - Added optional parameters, is_starred to LLMBlueprint.update to update the LLM blueprint’s starred status. - Added optional parameters, is_starred to LLMBlueprint.list to filter the returned LLM blueprints to those matching is_starred . - Added a new enum PromptType, PromptType to identify the LLMBlueprint’s prompting type. - Added optional parameters, prompt_type to LLMBlueprint.create , to specify the LLM blueprint’s prompting type. This can be set with PromptType . - Added optional parameters, prompt_type to LLMBlueprint.update , to specify the updated LLM blueprint’s prompting type. This can be set with PromptType . - Added a new class, ComparisonChat , for interacting with DataRobot generative AI comparison chats. ComparisonChat.get retrieves a comparison chat object by ID. ComparisonChat.list lists all comparison chats available to the user. ComparisonChat.create creates a new comparison chat. ComparisonChat.update updates the name of a comparison chat. ComparisonChat.delete deletes a single comparison chat. - Added optional parameters, playground and chat to ChatPrompt.list , to list chat prompts by playground and chat. - Added optional parameter, chat to ChatPrompt.create , to specify the chat to create the chat prompt in. - Added a new method, ChatPrompt.update , to update a chat prompt with custom metrics and feedback. - Added a new class, Chat , for interacting with DataRobot generative AI chats. Chat.get retrieves a chat object by ID. Chat.list lists all chats available to the user. Chat.create creates a new chat. Chat.update updates the name of a chat. Chat.delete deletes a single chat. - Removed the model_package module. Use RegisteredModelVersion instead. - Added new class UserLimits - Added support to get the count of users’ LLM API requests. UserLimits.get_llm_requests_count - Added support to get the count of users’ vector databases. UserLimits.get_vector_database_count - Added new methods to the class Notebook which includes Notebook.run and Notebook.download_revision . See the documentation for example usage. - Added new class NotebookScheduledJob . - Added new class NotebookScheduledRun . - Added a new method Model.get_incremental_learning_metadata that retrieves incremental learning metadata for a model. - Added a new method Model.start_incremental_learning that starts incremental learning for a model. - Updated the API endpoint prefix for all GenerativeAI routes to align with the publicly documented routes. ### Bugfixes - Fixed how async url is build in Model.get_or_request_feature_impact - Fixed setting ssl_verify by env variables. - Resolved a problem related to tilde-based paths in the Client’s ‘config_path’ attribute. - Changed the force_size default of ImageOptions to apply the same transformations by default, which are applied when image archive datasets are uploaded to DataRobot. ## 3.3.0 ### New features - Added support for Python 3.11. - Added new library strenum to add StrEnum support while maintaining backwards compatibility with Python 3.7-3.10. DataRobot does not use the native StrEnum class in Python 3.11. - Added a new class PredictionEnvironment for interacting with DataRobot Prediction environments. - Extended the advanced options available when setting a target to include new parameters: modelGroupId , modelRegimeId , and modelBaselines (part of the AdvancedOptions object). These parameters allow you to specify the user columns required to run time series models without feature derivation in OTV projects. - Added a new method PredictionExplanations.create_on_training_data , for computing prediction explanation on training data. - Added a new class RegisteredModel for interacting with DataRobot registered models to support the following methods: - RegisteredModel.get to retrieve RegisteredModel object by ID. - RegisteredModel.list to list all registered models. - RegisteredModel.archive to permanently archive registered model. - RegisteredModel.update to update registered model. - RegisteredModel.get_shared_roles to retrieve access control information for registered model. - RegisteredModel.share to share a registered model. - RegisteredModel.get_version to retrieve RegisteredModelVersion object by ID. - RegisteredModel.list_versions to list registered model versions. - RegisteredModel.list_associated_deployments to list deployments associated with a registered model. - Added a new class RegisteredModelVersion for interacting with DataRobot registered model versions (also known as model packages) to support the following methods: - RegisteredModelVersion.create_for_external to create a new registered model version from an external model. - RegisteredModelVersion.list_associated_deployments to list deployments associated with a registered model version. - RegisteredModelVersion.create_for_leaderboard_item to create a new registered model version from a Leaderboard model. - RegisteredModelVersion.create_for_custom_model_version to create a new registered model version from a custom model version. - Added a new method Deployment.create_from_registered_model_version to support creating deployments from registered model version. - Added a new method Deployment.download_model_package_file to support downloading model package files (.mlpkg) of the currently deployed model. - Added support for retrieving document thumbnails: - DocumentThumbnail - DocumentPageFile - Added support to retrieve document text extraction samples using: - DocumentTextExtractionSample - DocumentTextExtractionSamplePage - DocumentTextExtractionSampleDocument - Added new fields to CustomTaskVersion for controlling network policies. The new fields were also added to the response. This can be set with datarobot.enums.CustomTaskOutgoingNetworkPolicy . - Added a new method BatchPredictionJob.score_with_leaderboard_model to run batch predictions using a Leaderboard model instead of a deployment. - Set IntakeSettings and OutputSettings to use IntakeAdapters and OutputAdapters enum values respectively for the property type . - Added method Deployment.get_predictions_vs_actuals_over_time to retrieve a deployment’s predictions vs actuals over time data. ### Bugfixes - Payload property subset renamed to source in Model.request_feature_effect - Fixed an issue where Context.trace_context was not being set from environment variables or DR config files. - Project.refresh no longer sets Project.advanced_options to a dictionary. - Fixed Dataset.modify to clarify behavior of when to preserve or clear categories. - Fixed an issue with enums in f-strings resulting in the enum class and property being printed instead of the enum property’s value in Python 3.11 environments. ### Deprecation summary - Project.refresh will no longer set Project.advanced_options to a dictionary after version 3.5 is released. : All interactions with Project.advanced_options should be expected to be through the AdvancedOptions class. ### Experimental changes - Added a new class, VectorDatabase , for interacting with DataRobot vector databases. - VectorDatabase.get retrieves a VectorDatabase object by ID. - VectorDatabase.list lists all VectorDatabases available to the user. - VectorDatabase.create creates a new VectorDatabase. - VectorDatabase.create allows you to use a validated deployment of a custom model as your own Vector Database. - VectorDatabase.update updates the name of a VectorDatabase. - VectorDatabase.delete deletes a single VectorDatabase. - VectorDatabase.get_supported_embeddings retrieves all supported embedding models. - VectorDatabase.get_supported_text_chunkings retrieves all supported text chunking configurations. - VectorDatabase.download_text_and_embeddings_asset download a parquet file with internal vector database data. - Added a new class, CustomModelVectorDatabaseValidation , for validating custom model deployments for use as a vector database. - CustomModelVectorDatabaseValidation.get retrieves a CustomModelVectorDatabaseValidation object by ID. - CustomModelVectorDatabaseValidation.get_by_values retrieves a CustomModelVectorDatabaseValidation object by field values. - CustomModelVectorDatabaseValidation.create starts validation of the deployment. - CustomModelVectorDatabaseValidation.revalidate repairs an unlinked external vector database. - Added a new class, Playground , for interacting with DataRobot generative AI playgrounds. - Playground.get retrieves a playground object by ID. - Playground.list lists all playgrounds available to the user. - Playground.create creates a new playground. - Playground.update updates the name and description of a playground. - Playground.delete deletes a single playground. - Added a new class, LLMDefinition , for interacting with DataRobot generative AI LLMs. - LLMDefinition.list lists all LLMs available to the user. - Added a new class, LLMBlueprint , for interacting with DataRobot generative AI LLM blueprints. - LLMBlueprint.get retrieves an LLM blueprint object by ID. - LLMBlueprint.list lists all LLM blueprints available to the user. - LLMBlueprint.create creates a new LLM blueprint. - LLMBlueprint.create_from_llm_blueprint creates a new LLM blueprint from an existing one. - LLMBlueprint.update updates an LLM blueprint. - LLMBlueprint.delete deletes a single LLM blueprint. - Added a new class, ChatPrompt , for interacting with DataRobot generative AI chat prompts. - ChatPrompt.get retrieves a chat prompt object by ID. - ChatPrompt.list lists all chat prompts available to the user. - ChatPrompt.create creates a new chat prompt. - ChatPrompt.delete deletes a single chat prompt. - Added a new class, CustomModelLLMValidation , for validating custom model deployments for use as a custom model LLM. - CustomModelLLMValidation.get retrieves a CustomModelLLMValidation object by ID. - CustomModelLLMValidation.get_by_values retrieves a CustomModelLLMValidation object by field values. - CustomModelLLMValidation.create starts validation of the deployment. - CustomModelLLMValidation.revalidate repairs an unlinked external custom model LLM. - Added a new class, ComparisonPrompt , for interacting with DataRobot generative AI comparison prompts. - ComparisonPrompt.get retrieves a comparison prompt object by ID. - ComparisonPrompt.list lists all comparison prompts available to the user. - ComparisonPrompt.create creates a new comparison prompt. - ComparisonPrompt.update updates a comparison prompt. - ComparisonPrompt.delete deletes a single comparison prompt. - Extended UseCase , adding two new fields to represent the count of vector databases and playgrounds. - Added a new method, ChatPrompt.create_llm_blueprint , to create an LLM blueprint from a chat prompt. - Added a new method, CustomModelLLMValidation.delete , to delete a custom model LLM validation record. - Added a new method, LLMBlueprint.register_custom_model , for registering a custom model from a generative AI LLM blueprint. ## 3.2.0 ### New features - Added new methods to trigger batch monitoring jobs without providing a job definition. - BatchMonitoringJob.run - BatchMonitoringJob.get_status - BatchMonitoringJob.cancel - BatchMonitoringJob.download - Added Deployment.submit_actuals_from_catalog_async to submit actuals from the AI Catalog. - Added a new class StatusCheckJob which represents a job for a status check of submitted async jobs. - Added a new class JobStatusResult represents the result for a status check job of a submitted async task. - Added DatetimePartitioning.datetime_partitioning_log_retrieve to download the datetime partitioning log. - Added method DatetimePartitioning.datetime_partitioning_log_list to list the datetime partitioning log. - Added DatetimePartitioning.get_input_data to retrieve the input data used to create an optimized datetime partitioning. - Added DatetimePartitioningId , which can be passed as a partitioning_method to Project.analyze_and_model . - Added the ability to share deployments. See :ref: deployment sharing for more information on sharing deployments. - Added new methods get_bias_and_fairness_settings and update_bias_and_fairness_settings to retrieve or update bias and fairness settings. - Deployment.get_bias_and_fairness_settings - Deployment.update_bias_and_fairness_settings - Added a new class UseCase for interacting with the DataRobot Use Cases API. - Added a new class Application for retrieving DataRobot Applications available to the user. - Added a new class SharingRole to hold user or organization access rights. - Added a new class BatchMonitoringJob for interacting with batch monitoring jobs. - Added a new class BatchMonitoringJobDefinition for interacting with batch monitoring jobs definitions. - Added a new methods for handling monitoring job definitions: list, get, create, update, delete, run_on_schedule and run_once - BatchMonitoringJobDefinition.list - BatchMonitoringJobDefinition.get - BatchMonitoringJobDefinition.create - BatchMonitoringJobDefinition.update - BatchMonitoringJobDefinition.delete - BatchMonitoringJobDefinition.run_on_schedule - BatchMonitoringJobDefinition.run_once - Added a new method to retrieve a monitoring job - BatchMonitoringJob.get - Added the ability to filter return objects by a Use Case ID passed to the following methods: - Dataset.list - Project.list - Added the ability to automatically add a newly created dataset or project to a Use Case by passing a UseCase, list of UseCase objects, UseCase ID or list of UseCase IDs using the keyword argument use_cases to the following methods: - Dataset.create_from_file - Dataset.create_from_in_memory_data - Dataset.create_from_url - Dataset.create_from_data_source - Dataset.create_from_query_generator - Dataset.create_project - Project.create - Project.create_from_data_source - Project.create_from_dataset - Project.create_segmented_project_from_clustering_model - Project.start - Added the ability to set a default UseCase for requests. It can be set in several ways. - If the user configures the client via Client(...) , then invoke Client(..., default_use_case = ) . - If the user configures the client via dr.config.yaml, then add the property default_use_case: . - If the user configures the client via env vars, then set the env var DATAROBOT_DEFAULT_USE_CASE . - The default use case can also be set programmatically as a context manager via with UseCase.get(): . - Added the ability to configure the collection of client usage metrics to send to DataRobot. Note that this feature only tracks which DataRobot package methods are called and does not collect any user data. You can configure collection with the following settings: - If the user configures the client via Client(...) , then invoke Client(..., enable_api_consumer_tracking = ) . - If the user configures the client via dr.config.yaml, then add the property enable_api_consumer_tracking: . - If the user configures the client via env vars, then set the env var DATAROBOT_API_CONSUMER_TRACKING_ENABLED . Currently the default value for `enable_api_consumer_tracking` is `True`. - Added method [Deployment.get_predictions_over_time](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_predictions_over_time) to retrieve deployment predictions over time data. - Added a new class [FairnessScoresOverTime](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.bias_and_fairness.FairnessScoresOverTime) to retrieve fairness over time information. - Added a new method [Deployment.get_fairness_scores_over_time](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_fairness_scores_over_time) to retrieve fairness scores over time of a deployment. - Added a new `use_gpu` parameter to the method [Project.analyze_and_model](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.Project.analyze_and_model) to set whether the project should allow usage of GPU - Added a new `use_gpu` parameter to the class [Project](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.Project) with information whether project allows usage of GPU - Added a new class [TrainingData](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.models.custom_model_version.TrainingData) for retrieving TrainingData assigned to [CustomModelVersion](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.CustomModelVersion). - Added a new class [HoldoutData](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.models.custom_model_version.HoldoutData) for retrieving HoldoutData assigned to [CustomModelVersion](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.CustomModelVersion). - Added the ability to retrieve the model and blueprint json using the following methods: - [Model.get_model_blueprint_json](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_model_blueprint_json) - [Blueprint.get_json](https://docs.datarobot.com/en/docs/api/reference/public-api/blueprints.html#datarobot.models.Blueprint.get_json) - Added [Credential.update](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#datarobot.models.Credential.update) which allows you to update existing credential resources. - Added a new optional parameter `trace_context` to `datarobot.Client` to provide additional information on the DataRobot code being run. This parameter defaults to `None`. - Updated methods in [Model](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model) to support use of Sliced Insights: - [Model.get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect) - [Model.request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect) - [Model.get_or_request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_or_request_feature_effect) - [Model.get_lift_chart](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_lift_chart) - [Model.get_all_lift_charts](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_all_lift_charts) - [Model.get_residuals_chart](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_residuals_chart) - [Model.get_all_residuals_charts](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_all_residuals_charts) - [Model.request_lift_chart](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_lift_chart) - [Model.request_residuals_chart](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_residuals_chart) - [Model.get_roc_curve](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_roc_curve) - [Model.get_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_impact) - [Model.request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact) - [Model.get_or_request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_or_request_feature_impact) - Added support for [SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) to the following methods: - [DataStore.share](https://docs.datarobot.com/en/docs/api/reference/sdk/data-connectivity.html#datarobot.DataStore.share) - Added new methods for retrieving [SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) information for the following classes: - [DataStore.get_shared_roles](https://docs.datarobot.com/en/docs/api/reference/sdk/data-connectivity.html#datarobot.DataStore.get_shared_roles) - Added new method for calculating sliced roc curve [Model.request_roc_curve](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_roc_curve) - Added new [DataSlice](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.data_slice.DataSlice) to support the following slices methods: - [DataSlice.list](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.data_slice.DataSlice.list) to retrieve all data slices in a project. - [DataSlice.create](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.data_slice.DataSlice.create) to create a new data slice. - [DataSlice.delete](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.data_slice.DataSlice.delete) to delete the data slice calling this method. - [DataSlice.request_size](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.data_slice.DataSlice.request_size) to submit a request to calculate a data slice size on a source. - [DataSlice.get_size_info](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.data_slice.DataSlice.get_size_info) to get the data slice’s info when applied to a source. - [DataSlice.get](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.data_slice.DataSlice.get) to retrieve a specific data slice. - Added new [DataSliceSizeInfo](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.data_slice.DataSliceSizeInfo) to define the result of a data slice applied to a source. - Added new method for retrieving all available feature impacts for the model [Model.get_all_feature_impacts](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_all_feature_impacts). - Added new method for StatusCheckJob to wait and return the completed object once it is generated [datarobot.models.StatusCheckJob.get_result_when_complete()](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.StatusCheckJob.get_result_when_complete) ### Enhancements - Improve error message of SampleImage.list to clarify that a selected parameter cannot be used when a project has not proceeded to the correct stage prior to calling this method. - Extended SampleImage.list by two parameters to filter for a target value range in regression projects. - Added text explanations data to PredictionExplanations and made sure it is returned in both datarobot.PredictionExplanations.get_all_as_dataframe() and datarobot.PredictionExplanations.get_rows() method. - Added two new parameters to Project.upload_dataset_from_catalog : - credential_id - credential_data - Implemented training and holdout data assignment for Custom Model Version creation APIs: - CustomModelVersion.create_clean - CustomModelVersion.create_from_previous - The parameters added to both APIs are: - Extended CustomInferenceModel.create and CustomInferenceModel.update with the parameter is_training_data_for_versions_permanently_enabled . - Added value DR_API_ACCESS to the NETWORK_EGRESS_POLICY enum. - Added new parameter low_memory to Dataset.get_as_dataframe to allow a low memory mode for larger datasets - Added two new parameters to Project.list for paginating long project lists: - offset - limit ### Bugfixes - Fixed incompatibilities with Pandas 2.0 in DatetimePartitioning.to_dataframe . - Fixed a crash when using non-“latin-1” characters in Panda’s DataFrame used as prediction data in BatchPredictionJob.score . - Fixed an issue where failed authentication when invoking datarobot.client.Client() raises a misleading error about client-server compatibility. - Fixed incompatibilities with Pandas 2.0 in AccuracyOverTime.get_as_dataframe . The method will now throw a ValueError if an empty list is passed to the parameter metrics . ### API changes - Added parameter unsupervised_type to the class DatetimePartitioning . - The sliced insight API endpoint GET: api/v2/insights// returns a paginated response. This means that it returns an empty response if no insights data is found, unlike GET: api/v2/projects//models/// , which returns 404 NOT FOUND in this case. To maintain backwards-compatibility, all methods that retrieve insights data raise 404 NOT FOUND if the insights API returns an empty response. ### Deprecation summary - Model.get_feature_fit_metadata() has been removed. Use Model.get_feature_effect_metadata instead. - DatetimeModel.get_feature_fit_metadata() has been removed. Use DatetimeModel.get_feature_effect_metadata instead. - Model.request_feature_fit has been removed. Use Model.request_feature_effect instead. - DatetimeModel.request_feature_fit has been removed. Use DatetimeModel.request_feature_effect instead. - Model.get_feature_fit has been removed. Use Model.get_feature_effect instead. - DatetimeModel.get_feature_fit has been removed. Use DatetimeModel.get_feature_effect instead. - Model.get_or_request_feature_fit has been removed. Use Model.get_or_request_feature_effect instead. - DatetimeModel.get_or_request_feature_fit has been removed. Use DatetimeModel.get_or_request_feature_effect instead. - Deprecated the use of SharingAccess in favor of SharingRole for sharing in the following classes: - DataStore.share - Deprecated the following methods for retrieving SharingAccess information. - DataStore.get_access_list . Please use DataStore.get_shared_roles instead. - CustomInferenceModel.assign_training_data was marked as deprecated and will be removed in v3.4. Use CustomModelVersion.create_clean and CustomModelVersion.create_from_previous instead. ### Configuration changes - Pins dependency on package urllib3 to be less than version 2.0.0. ### Deprecation summary - Deprecated parameter user_agent_suffix in datarobot.Client . user_agent_suffix will be removed in v3.4. Please use trace_context instead. ### Documentation changes - Fixed in-line documentation of DataRobotClientConfig . - Fixed documentation around client configuration from environment variables or config file. ### Experimental changes - Added experimental support for data matching: - DataMatching - DataMatchingQuery - Added new method DataMatchingQuery.get_result for returning data matching query results as pandas dataframes to DataMatchingQuery . - Changed behavior for returning results in the DataMatching . Instead of saving the results as a file, a pandas dataframe will be returned in the following methods: - DataMatching.get_closest_data - DataMatching.get_closest_data_for_model - DataMatching.get_closest_data_for_featurelist - Added experimental support for model lineage: ModelLineage - Changed behavior for methods that search for the closest data points in DataMatching . If the index is missing, instead of throwing the error, methods try to create the index and then query it. This is enabled by default, but if this is not the intended behavior it can be changed by passing False to the new build_index parameter added to the methods: - DataMatching.get_closest_data - DataMatching.get_closest_data_for_model - DataMatching.get_closest_data_for_featurelist - Added a new class Notebook for retrieving DataRobot Notebooks available to the user. - Added experimental support for data wrangling: - Recipe ## 3.1.1 ### Configuration changes - Removes dependency on package contextlib2 since the package is Python 3.7+. - Update typing-extensions to be inclusive of versions from 4.3.0 to < 5.0.0. ## 3.1.0 ### Enhancements - Added new methods BatchPredictionJob.apply_time_series_data_prep_and_score and BatchPredictionJob.apply_time_series_data_prep_and_score_to_file that apply time series data prep to a file or dataset and make batch predictions with a deployment. - Added new methods DataEngineQueryGenerator.prepare_prediction_dataset and DataEngineQueryGenerator.prepare_prediction_dataset_from_catalog that apply time series data prep to a file or catalog dataset and upload the prediction dataset to a project. - Added new max_wait parameter to method Project.create_from_dataset . Values larger than the default can be specified to avoid timeouts when creating a project from Dataset. - Added new method for creating a segmented modeling project from an existing clustering project and model Project.create_segmented_project_from_clustering_model . Please switch to this function if you are previously using ModelPackage for segmented modeling purposes. - Added new method is_unsupervised_clustering_or_multiclass for checking whether the clustering or multiclass parameters are used, quick and efficient without extra API calls. PredictionExplanations.is_unsupervised_clustering_or_multiclass - Retry idempotent requests which result in HTTP 502 and HTTP 504 (in addition to the previous HTTP 413, HTTP 429 and HTTP 503) - Added value PREPARED_FOR_DEPLOYMENT to the RECOMMENDED_MODEL_TYPE enum - Added two new methods to the ImageAugmentationList class: - ImageAugmentationList.list , - ImageAugmentationList.update ### Bugfixes - Added format key to Batch Prediction intake and output settings for S3, GCP and Azure ### API changes - The method PredictionExplanations.is_multiclass now adds an additional API call to check for multiclass target validity, which adds a small delay. - AdvancedOptions parameter blend_best_models defaults to false - AdvancedOptions parameter consider_blenders_in_recommendation defaults to false - DatetimePartitioning has parameter unsupervised_mode ### Deprecation summary - Deprecated method Project.create_from_hdfs . - Deprecated method DatetimePartitioning.generate . - Deprecated parameter in_use from ImageAugmentationList.create as DataRobot will take care of it automatically. - Deprecated property Deployment.capabilities from Deployment . - ImageAugmentationSample.compute was removed in v3.1. You can get the same information with the method ImageAugmentationList.compute_samples . - sample_id parameter removed from ImageAugmentationSample.list . Please use auglist_id instead. ### Documentation changes - Update the documentation to suggest that setting use_backtest_start_end_format of DatetimePartitioning.to_specification to True will mirror the same behavior as the Web UI. - Update the documentation to suggest setting use_start_end_format of Backtest.to_specification to True will mirror the same behavior as the Web UI. ## 3.0.3 ### Bugfixes - Fixed an issue affecting backwards compatibility in datarobot.models.DatetimeModel , where an unexpected keyword from the DataRobot API would break class deserialization. ## 3.0.2 ### Bugfixes - Restored Model.get_leaderboard_ui_permalink , Model.open_model_browser , These methods were accidentally removed instead of deprecated. - Fix for ipykernel < 6.0.0 which does not persist contextvars across cells ### Deprecation summary - Deprecated method Model.get_leaderboard_ui_permalink . Please use Model.get_uri instead. - Deprecated method Model.open_model_browser . Please use Model.open_in_browser instead. ## 3.0.1 ### Bugfixes - Added typing-extensions as a required dependency for the DataRobot Python API client. ## 3.0.0 ### New features - Version 3.0 of the Python client does not support Python 3.6 and earlier versions. Version 3.0 currently supports Python 3.7+. - The default Autopilot mode for project.start_autopilot has changed to Quick mode. - For datetime-aware models, you can now calculate and retrieve feature impact for backtests other than zero and holdout: - DatetimeModel.get_feature_impact - DatetimeModel.request_feature_impact - DatetimeModel.get_or_request_feature_impact - Added a backtest field to feature impact metadata: Model.get_or_request_feature_impact . This field is null for non-datetime-aware models and greater than or equal to zero for holdout in datetime-aware models. - You can use a new method to retrieve the canonical URI for a project, model, deployment, or dataset: - Project.get_uri - Model.get_uri - Deployment.get_uri - Dataset.get_uri - You can use a new method to open a class in a browser based on their URI (project, model, deployment, or dataset): - Project.open_in_browser - Model.open_in_browser - Deployment.open_in_browser - Dataset.open_in_browser - Added a new method for opening DataRobot in a browser: datarobot.rest.RESTClientObject.open_in_browser() . Invoke the method via dr.Client().open_in_browser() . - Altered method Project.create_featurelist to accept five new parameters (please see documentation for information about usage): - starting_featurelist - starting_featurelist_id - starting_featurelist_name - features_to_include - features_to_exclude - Added a new method to retrieve a feature list by name: Project.get_featurelist_by_name . - Added a new convenience method to create datasets: Dataset.upload . - Altered the method Model.request_predictions to accept four new parameters: - dataset - file - file_path - dataframe - Note that the method already supports the parameter dataset_id and all data source parameters are mutually exclusive. - Added a new method to datarobot.models.Dataset , Dataset.get_as_dataframe , which retrieves all the originally uploaded data in a pandas DataFrame. - Added a new method to datarobot.models.Dataset , Dataset.share , which allows the sharing of a dataset with another user. - Added new convenience methods to datarobot.models.Project for dealing with partition classes. Both methods should be called before Project.analyze_and_model . - Project.set_partitioning_method intelligently creates the correct partition class for a regular project, based on input arguments. - Project.set_datetime_partitioning creates the correct partition class for a time series project. - Added a new method to datarobot.models.Project Project.get_top_model which returns the highest scoring model for a metric of your choice. - Use the new method Deployment.predict_batch to pass a file, file path, or DataFrame to datarobot.models.Deployment to easily make batch predictions and return the results as a DataFrame. - Added support for passing in a credentials ID or credentials data to Project.create_from_data_source as an alternative to providing a username and password. - You can now pass in a max_wait value to AutomatedDocument.generate . - Added a new method to datarobot.models.Project Project.get_dataset which retrieves the dataset used during creation of a project. - Added two new properties to datarobot.models.Project : - catalog_id - catalog_version_id - Added a new Autopilot method to datarobot.models.Project Project.analyze_and_model which allows you to initiate Autopilot or data analysis against data uploaded to DataRobot. - Added a new convenience method to datarobot.models.Project Project.set_options which allows you to save AdvancedOptions values for use in modeling. - Added a new convenience method to datarobot.models.Project Project.get_options which allows you to retrieve saved modeling options. ### Enhancements - Refactored the global singleton client connection ( datarobot.client.Client() ) to use ContextVar instead of a global variable for better concurrency support. - Added support for creating monotonic feature lists for time series projects. Set skip_datetime_partition_column to True to create monotonic feature list. For more information see datarobot.models.Project.create_modeling_featurelist() . - Added information about vertex to advanced tuning parameters datarobot.models.Model.get_advanced_tuning_parameters() . - Added the ability to automatically use saved AdvancedOptions set using Project.set_options in Project.analyze_and_model . ### Bugfixes - Dataset.list no longer throws errors when listing datasets with no owner. - Fixed an issue with the creation of BatchPredictionJobDefinitions containing a schedule. - Fixed error handling in datarobot.helpers.partitioning_methods.get_class . - Fixed issue with portions of the payload not using camelCasing in Project.upload_dataset_from_catalog . ### API changes - The Python client now outputs a DataRobotProjectDeprecationWarning when you attempt to access certain resources (projects, models, deployments, etc.) that are deprecated or disabled as a result of the DataRobot platform’s migration to Python 3. - The Python client now raises a TypeError when you try to retrieve a labelwise ROC on a binary model or a binary ROC on a multilabel model. - The method Dataset.create_from_data_source now raises InvalidUsageError if username and password are not passed as a pair together. ### Deprecation summary - Model.get_leaderboard_ui_permalink has been removed. Use Model.get_uri instead. - Model.open_model_browser has been removed. Use Model.open_in_browser instead. - Project.get_leaderboard_ui_permalink has been removed. Use Project.get_uri instead. - Project.open_leaderboard_browser has been removed. Use Project.open_in_browser instead. - Enum VARIABLE_TYPE_TRANSFORM.CATEGORICAL has been removed - Instantiation of Blueprint using a dict has been removed. Use Blueprint.from_data instead. - Specifying an environment to use for testing with CustomModelTest has been removed. - CustomModelVersion ’s required_metadata parameter has been removed. Use required_metadata_values instead. - CustomTaskVersion ’s required_metadata parameter has been removed. Use required_metadata_values instead. - Instantiation of Feature using a dict has been removed. Use Feature.from_data instead. - Instantiation of Featurelist using a dict has been removed. Use Featurelist.from_data instead. - Instantiation of Model using a dict, tuple, or the data parameter has been removed. Use Model.from_data instead. - Instantiation of Project using a dict has been removed. Use Project.from_data instead. - Project ’s quickrun parameter has been removed. Pass AUTOPILOT_MODE.QUICK as the mode instead. - Project ’s scaleout_max_train_pct and scaleout_max_train_rows parameters have been removed. - ComplianceDocumentation has been removed. Use AutomatedDocument instead. - The Deployment method create_from_custom_model_image was removed. Use Deployment.create_from_custom_model_version instead. - PredictJob.create has been removed. Use Model.request_predictions instead. - Model.fetch_resource_data has been removed. Use Model.get instead. - The class CustomInferenceImage was removed. Use CustomModelVersion with base_environment_id instead. - Project.set_target has been deprecated. Use Project.analyze_and_model instead. ### Configuration changes - Added a context manager client_configuration that can be used to change the connection configuration temporarily, for use in asynchronous or multithreaded code. - Upgraded the Pillow library to version 9.2.0. Users installing DataRobot with the “images” extra ( pip install datarobot[images] ) should note that this is a required library. ### Experimental changes - Added experimental support for retrieving document thumbnails: - DocumentThumbnail - DocumentPageFile - Added experimental support to retrieve document text extraction samples using: - DocumentTextExtractionSample - DocumentTextExtractionSamplePage - DocumentTextExtractionSampleDocument - Added experimental deployment improvements: - RetrainingPolicy can be used to manage retraining policies associated with a deployment. - Added an experimental deployment improvement: - Use RetrainingPolicyRun to manage retraining policies run for a retraining policy associated with a deployment. - Added new methods to RetrainingPolicy : - Use RetrainingPolicy.get to get a retraining policy associated with a deployment. - Use RetrainingPolicy.delete to delete a retraining policy associated with a deployment. ## 2.29.0 ### New features - Added support to pass max_ngram_explanations parameter in batch predictions that will trigger the compute of text prediction explanations. - BatchPredictionJob.score - Added support to pass calculation mode to prediction explanations ( mode parameter in PredictionExplanations.create ) as well as batch scoring ( explanations_mode in BatchPredictionJob.score ) for multiclass models. Supported modes: - TopPredictionsMode - ClassListMode - Added method datarobot.CalendarFile.create_calendar_from_dataset() to the calendar file that allows us to create a calendar from a dataset. - Added experimental support for n_clusters parameter in Model.train_datetime and DatetimeModel.retrain that allows to specify number of clusters when creating models in Time Series Clustering project. - Added new parameter clone to datarobot.CombinedModel.set_segment_champion() that allows to set a new champion model in a cloned model instead of the original one, leaving latter unmodified. - Added new property is_active_combined_model to datarobot.CombinedModel that indicates if the selected combined model is currently the active one in the segmented project. - Added new datarobot.models.Project.get_active_combined_model() that allows users to get the currently active combined model in the segmented project. - Added new parameters read_timeout to method ShapMatrix.get_as_dataframe . Values larger than the default can be specified to avoid timeouts when requesting large files. ShapMatrix.get_as_dataframe - Added support for bias mitigation with the following methods - Project.get_bias_mitigated_models - Project.apply_bias_mitigation - Project.request_bias_mitigation_feature_info - Project.get_bias_mitigation_feature_info and by adding new bias mitigation params - bias_mitigation_feature_name - bias_mitigation_technique - include_bias_mitigation_feature_as_predictor_variable to the existing method - Project.start and by adding this enum to supply params to some of the above functionality datarobot.enums.BiasMitigationTechnique - Added new property status to datarobot.models.Deployment that represents model deployment status. - Added new Deployment.activate and Deployment.deactivate that allows deployment activation and deactivation - Added new Deployment.delete_monitoring_data to delete deployment monitoring data. ### Enhancements - Added support for specifying custom endpoint URLs for S3 access in batch predictions: - BatchPredictionJob.score - BatchPredictionJob.score See: `endpoint_url` parameter. - Added guide on :ref: `working with binary data ` - Added multithreading support to binary data helper functions. - Binary data helpers image defaults aligned with application’s image preprocessing. - Added the following accuracy metrics to be retrieved for a deployment - TPR, PPV, F1 and MCC :ref: `Deployment monitoring ` ### Bugfixes - Don’t include holdout start date, end date, or duration in datetime partitioning payload when holdout is disabled. - Removed ICE Plot capabilities from Feature Fit. - Handle undefined calendar_name in CalendarFile.create_calendar_from_dataset - Raise ValueError for submitted calendar names that are not strings ### API changes - version field is removed from ImportedModel object ### Deprecation summary - Reason Codes objects deprecated in 2.13 version were removed. Please use Prediction Explanations instead. ### Configuration changes - The upper version constraint on pandas has been removed. ### Documentation changes - Fixed a minor typo in the example for Dataset.create_from_data_source. - Update the documentation to suggest that feature_derivation_window_end of datarobot.DatetimePartitioningSpecification class should be a negative or zero. ## 2.28.0 ### New features - Added new parameter upload_read_timeout to BatchPredictionJob.score and BatchPredictionJob.score_to_file to indicate how many seconds to wait until intake dataset uploads to server. Default value 600s. - Added the ability to turn off supervised feature reduction for Time Series projects. Option use_supervised_feature_reduction can be set in AdvancedOptions . - Allow maximum_memory to be input for custom tasks versions. This will be used for setting the limit to which a custom task prediction container memory can grow. - Added method datarobot.models.Project.get_multiseries_names() to the project service which will return all the distinct entries in the multiseries column - Added new segmentation_task_id attribute to datarobot.models.Project.set_target() that allows to start project as Segmented Modeling project. - Added new property is_segmented to datarobot.models.Project that indicates if project is a regular one or Segmented Modeling project. - Added method datarobot.models.Project.restart_segment() to the project service that allows to restart single segment that hasn’t reached modeling phase. - Added the ability to interact with Combined Models in Segmented Modeling projects. Available with new class: datarobot.CombinedModel . Functionality: - [datarobot.CombinedModel.get()](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.CombinedModel.get) - [datarobot.CombinedModel.get_segments_info()](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.CombinedModel.get_segments_info) - [datarobot.CombinedModel.get_segments_as_dataframe()](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.CombinedModel.get_segments_as_dataframe) - [datarobot.CombinedModel.get_segments_as_csv()](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.CombinedModel.get_segments_as_csv) - [datarobot.CombinedModel.set_segment_champion()](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.CombinedModel.set_segment_champion) - Added the ability to create and retrieve segmentation tasks used in Segmented Modeling projects. Available with new class: [datarobot.SegmentationTask](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.SegmentationTask). Functionality: - [datarobot.SegmentationTask.create()](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.SegmentationTask.create) - [datarobot.SegmentationTask.list()](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.SegmentationTask.list) - [datarobot.SegmentationTask.get()](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.SegmentationTask.get) - Added new class: [datarobot.SegmentInfo](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.SegmentInfo) that allows to get information on all segments of Segmented modeling projects, i.e. segment project ID, model counts, autopilot status. Functionality: - [datarobot.SegmentInfo.list()](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.SegmentInfo.list) - Added new methods to base `APIObject` to assist with dictionary and json serialization of child objects. Functionality: - `APIObject.to_dict` - `APIObject.to_json` - Added new methods to `ImageAugmentationList` for interacting with image augmentation samples. Functionality: - `ImageAugmentationList.compute_samples` - `ImageAugmentationList.retrieve_samples` - Added the ability to set a prediction threshold when creating a deployment from a learning model. - Added support for governance, owners, predictionEnvironment, and fairnessHealth fields when querying for a Deployment object. - Added helper methods for working with files, images and documents. Methods support conversion of file contents into base64 string representations. Methods for images provide also image resize and transformation support. Functionality: - [get_encoded_file_contents_from_urls](https://docs.datarobot.com/en/docs/api/reference/sdk/binary_data_helpers.html#datarobot.helpers.binary_data_utils.get_encoded_file_contents_from_urls) - [get_encoded_file_contents_from_paths](https://docs.datarobot.com/en/docs/api/reference/sdk/binary_data_helpers.html#datarobot.helpers.binary_data_utils.get_encoded_file_contents_from_paths) - [get_encoded_image_contents_from_paths](https://docs.datarobot.com/en/docs/api/reference/sdk/binary_data_helpers.html#datarobot.helpers.binary_data_utils.get_encoded_image_contents_from_paths) - [get_encoded_image_contents_from_urls](https://docs.datarobot.com/en/docs/api/reference/sdk/binary_data_helpers.html#datarobot.helpers.binary_data_utils.get_encoded_image_contents_from_urls) ### Enhancements - Requesting metadata instead of actual data of datarobot.PredictionExplanations to reduce the amount of data transfer ### Bugfixes - Fix a bug in Job.get_result_when_complete for Prediction Explanations job type to populate all attribute of of datarobot.PredictionExplanations instead of just one - Fix a bug in datarobot.models.ShapImpact where row_count was not optional - Allow blank value for schema and catalog in RelationshipsConfiguration response data - Fix a bug where credentials were incorrectly formatted in Project.upload_dataset_from_catalog and Project.upload_dataset_from_data_source - Rejecting downloads of Batch Prediction data that was not written to the localfile output adapter - Fix a bug in datarobot.models.BatchPredictionJobDefinition.create() where schedule was not optional for all cases ### API changes - User can include ICE plots data in the response when requesting Feature Effects/Feature Fit. Extended methods are - Model.get_feature_effect , - Model.get_feature_fit , - DatetimeModel.get_feature_effect and - DatetimeModel.get_feature_fit . ### Deprecation summary - attrs library is removed from library dependencies - ImageAugmentationSample.compute was marked as deprecated and will be removed in v2.30. You can get the same information with newly introduced method ImageAugmentationList.compute_samples - ImageAugmentationSample.list using sample_id - Deprecating scaleout parameters for projects / models. Includes scaleout_modeling_mode , scaleout_max_train_pct , and scaleout_max_train_rows ### Configuration changes - pandas upper version constraint is updated to include version 1.3.5. ### Documentation changes - Fixed “from datarobot.enums” import in Unsupervised Clustering example provided in docs. ## 2.27.0 ### New features - datarobot.UserBlueprint is now mature with full support of functionality. Users are encouraged to use the Blueprint Workshop instead of this class directly. - Added the arguments attribute in datarobot.CustomTaskVersion . - Added the ability to retrieve detected errors in the potentially multicategorical feature types that prevented the feature to be identified as multicategorical. Project.download_multicategorical_data_format_errors - Added the support of listing/updating user roles on one custom task. - datarobot.CustomTask.get_access_list() - datarobot.CustomTask.share() - Added a method datarobot.models.Dataset.create_from_query_generator() . This creates a dataset in the AI catalog from a datarobot.DataEngineQueryGenerator . - Added the new functionality of creating a user blueprint with a custom task version id. datarobot.UserBlueprint.create_from_custom_task_version_id() . - The DataRobot Python Client is no longer published under the Apache-2.0 software license, but rather under the terms of the DataRobot Tool and Utility Agreement. - Added a new class: datarobot.DataEngineQueryGenerator . This class generates a Spark SQL query to apply time series data prep to a dataset in the AI catalog. Functionality: - [datarobot.DataEngineQueryGenerator.create()](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.DataEngineQueryGenerator.create) - [datarobot.DataEngineQueryGenerator.get()](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.DataEngineQueryGenerator.get) - [datarobot.DataEngineQueryGenerator.create_dataset()](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.DataEngineQueryGenerator.create_dataset) See the :ref: `time series data prep documentation ` for more information. - Added the ability to upload a prediction dataset into a project from the AI catalog [Project.upload_dataset_from_catalog](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.Project.upload_dataset_from_catalog). - Added the ability to specify the number of training rows to use in SHAP based Feature Impact computation. Extended method: - `ShapImpact.create` - Added the ability to retrieve and restore features that have been reduced using the time series feature generation and reduction functionality. The functionality comes with a new class: [datarobot.models.restore_discarded_features.DiscardedFeaturesInfo](https://docs.datarobot.com/en/docs/api/reference/public-api/features.html#datarobot.models.restore_discarded_features.DiscardedFeaturesInfo). Functionality: - [datarobot.models.restore_discarded_features.DiscardedFeaturesInfo.retrieve()](https://docs.datarobot.com/en/docs/api/reference/public-api/features.html#datarobot.models.restore_discarded_features.DiscardedFeaturesInfo.retrieve) - [datarobot.models.restore_discarded_features.DiscardedFeaturesInfo.restore()](https://docs.datarobot.com/en/docs/api/reference/public-api/features.html#datarobot.models.restore_discarded_features.DiscardedFeaturesInfo.restore) - Added the ability to control class mapping aggregation in multiclass projects via [ClassMappingAggregationSettings](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.helpers.ClassMappingAggregationSettings) passed as a parameter to [Project.set_target](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.Project.set_target) - Added support for :ref: `unsupervised clustering projects` - Added the ability to compute and retrieve Feature Effects for a Multiclass model using [datarobot.models.Model.request_feature_effects_multiclass()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effects_multiclass), [datarobot.models.Model.get_feature_effects_multiclass()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effects_multiclass) or [datarobot.models.Model.get_or_request_feature_effects_multiclass()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_or_request_feature_effects_multiclass) methods. For datetime models use following methods [datarobot.models.DatetimeModel.request_feature_effects_multiclass()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.request_feature_effects_multiclass), [datarobot.models.DatetimeModel.get_feature_effects_multiclass()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_feature_effects_multiclass) or [datarobot.models.DatetimeModel.get_or_request_feature_effects_multiclass()](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_or_request_feature_effects_multiclass) with `backtest_index` specified - Added the ability to get and update challenger model settings for deployment class: [datarobot.models.Deployment](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment) Functionality: - [datarobot.models.Deployment.get_challenger_models_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_challenger_models_settings) - [datarobot.models.Deployment.update_challenger_models_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_challenger_models_settings) - Added the ability to get and update segment analysis settings for deployment class: [datarobot.models.Deployment](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment) Functionality: - [datarobot.models.Deployment.get_segment_analysis_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_segment_analysis_settings) - [datarobot.models.Deployment.update_segment_analysis_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_segment_analysis_settings) - Added the ability to get and update predictions by forecast date settings for deployment class: [datarobot.models.Deployment](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment) Functionality: - [datarobot.models.Deployment.get_predictions_by_forecast_date_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.get_predictions_by_forecast_date_settings) - [datarobot.models.Deployment.update_predictions_by_forecast_date_settings()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_predictions_by_forecast_date_settings) - Added the ability to specify multiple feature derivation windows when creating a Relationships Configuration using [RelationshipsConfiguration.create](https://docs.datarobot.com/en/docs/api/reference/public-api/features.html#datarobot.models.RelationshipsConfiguration.create) - Added the ability to manipulate a legacy conversion for a custom inference model, using the class: [CustomModelVersionConversion](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.models.CustomModelVersionConversion) Functionality: - [CustomModelVersionConversion.run_conversion](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.models.CustomModelVersionConversion.run_conversion) - [CustomModelVersionConversion.stop_conversion](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.models.CustomModelVersionConversion.stop_conversion) - [CustomModelVersionConversion.get](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.models.CustomModelVersionConversion.get) - [CustomModelVersionConversion.get_latest](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.models.CustomModelVersionConversion.get_latest) - [CustomModelVersionConversion.list](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html#datarobot.models.CustomModelVersionConversion.list) ### Enhancements - Project.get returns the query_generator_id used for time series data prep when applicable. - Feature Fit & Feature Effects can return datetime instead of numeric for feature_type field for numeric features that are derived from dates. - These methods now provide additional field rowCount in SHAP based Feature Impact results. - ShapImpact.create - ShapImpact.get - Improved performance when downloading prediction dataframes for Multilabel projects using: - Predictions.get_all_as_dataframe - PredictJob.get_predictions - Job.get_result ### Bugfixes - Fix datarobot.CustomTaskVersion and datarobot.CustomModelVersion to correctly format required_metadata_values before sending them via API - Fixed response validation that could cause DataError when using datarobot.models.Dataset for a dataset with a description that is an empty string. ### API changes - RelationshipsConfiguration.create will include a new key data_source_id in data_source field when applicable ### Deprecation summary - Model.get_all_labelwise_roc_curves has been removed. You can get the same information with multiple calls of Model.get_labelwise_roc_curves , one per data source. - Model.get_all_multilabel_lift_charts has been removed. You can get the same information with multiple calls of Model.get_multilabel_lift_charts , one per data source. ### Documentation changes - This release introduces a new documentation organization. The organization has been modified to better reflect the end-to-end modeling workflow. The new “Tutorials” section has 5 major topics that outline the major components of modeling: Data, Modeling, Predictions, MLOps, and Administration. - The Getting Started workflow is now hosted at DataRobot’s API Documentation Home . - Added an example of how to set up optimized datetime partitioning for time series projects. ## 2.26.0 ### New features - Added the ability to use external baseline predictions for time series project. External dataset can be validated using datarobot.models.Project.validate_external_time_series_baseline() . Option can be set in AdvancedOptions to scale datarobot models’ accuracy performance using external dataset’s accuracy performance. See the :ref: external baseline predictions documentation for more information. - Added the ability to generate exponentially weighted moving average features for time series project. Option can be set in AdvancedOptions and controls the alpha parameter used in exponentially weighted moving average operation. - Added the ability to request a specific model be prepared for deployment using Project.start_prepare_model_for_deployment . - Added a new class: datarobot.CustomTask . This class is a custom task that you can use as part (or all) of your blue print for training models. It needs datarobot.CustomTaskVersion before it can properly be used. - Functionality: - Added a new class: datarobot.CustomTaskVersion . This class is for management of specific versions of a custom task. - Functionality: - Added the ability compute batch predictions for an in-memory DataFrame using BatchPredictionJob.score - Added the ability to specify feature discovery settings when creating a Relationships Configuration using RelationshipsConfiguration.create ### Enhancements - Improved performance when downloading prediction dataframes using: - Predictions.get_all_as_dataframe - PredictJob.get_predictions - Job.get_result - Added new max_wait parameter to methods: - Dataset.create_from_url - Dataset.create_from_in_memory_data - Dataset.create_from_data_source - Dataset.create_version_from_in_memory_data - Dataset.create_version_from_url - Dataset.create_version_from_data_source ### Bugfixes - Model.get will return a DatetimeModel instead of Model whenever the project is datetime partitioned. This enables the ModelRecommendation.get_model to return a DatetimeModel instead of Model whenever the project is datetime partitioned. - Try to read Feature Impact result if existing jobId is None in Model.get_or_request_feature_impact . - Set upper version constraints for pandas. - RelationshipsConfiguration.create will return a catalog in data_source field - Argument required_metadata_keys was not properly being sent in the update and create requests for datarobot.ExecutionEnvironment . - Fix issue with datarobot.ExecutionEnvironment create method failing when used against older versions of the application - datarobot.CustomTaskVersion was not properly handling required_metadata_values from the API response ### API changes - Updated Project.start to use AUTOPILOT_MODE.QUICK when the autopilot_on param is set to True. This brings it in line with Project.set_target . - Updated project.start_autopilot to accept the following new GA parameters that are already in the public API: consider_blenders_in_recommendation , run_leakage_removed_feature_list ### Deprecation summary - The required_metadata property of datarobot.CustomModelVersion has been deprecated. required_metadata_values should be used instead. - The required_metadata property of datarobot.CustomTaskVersion has been deprecated. required_metadata_values should be used instead. ### Configuration changes - Now requires dependency on package scikit-learn rather than sklearn . Note: This dependency is only used in example code. See this scikit-learn issue for more information. - Now permits dependency on package attrs to be less than version 21. This fixes compatibility with apache-airflow. - Allow to setup Authorization: type header for OAuth2 Bearer tokens. ### Documentation changes - Update the documentation with respect to the permission that controls AI Catalog dataset snapshot behavior. ## 2.25.0 ### New features - There is a new AnomalyAssessmentRecord object that implements public API routes to work with anomaly assessment insight. This also adds explanations and predictions preview classes. The insight is available for anomaly detection models in time series unsupervised projects which also support calculation of Shapley values. - AnomalyAssessmentPredictionsPreview - AnomalyAssessmentExplanations Functionality: - Initialize an anomaly assessment insight for the specified subset. - [DatetimeModel.initialize_anomaly_assessment](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.initialize_anomaly_assessment) - Get anomaly assessment records, shap explanations, predictions preview: - [DatetimeModel.get_anomaly_assessment_records](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_anomaly_assessment_records) list available records - [AnomalyAssessmentRecord.get_predictions_preview](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.anomaly_assessment.AnomalyAssessmentRecord.get_predictions_preview) get predictions preview for the record - [AnomalyAssessmentRecord.get_latest_explanations](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.anomaly_assessment.AnomalyAssessmentRecord.get_latest_explanations) get latest predictions along with shap explanations for the most anomalous records. - [AnomalyAssessmentRecord.get_explanations](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.anomaly_assessment.AnomalyAssessmentRecord.get_explanations) get predictions along with shap explanations for the most anomalous records for the specified range. - Delete anomaly assessment record: - [AnomalyAssessmentRecord.delete](https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html#datarobot.models.anomaly_assessment.AnomalyAssessmentRecord.delete) delete record - Added an ability to calculate and retrieve Datetime trend plots for [DatetimeModel](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel). This includes Accuracy over Time, Forecast vs Actual, and Anomaly over Time. Plots can be calculated using a common method: - [DatetimeModel.compute_datetime_trend_plots](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.compute_datetime_trend_plots) Metadata for plots can be retrieved using the following methods: - [DatetimeModel.get_accuracy_over_time_plots_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_accuracy_over_time_plots_metadata) - [DatetimeModel.get_forecast_vs_actual_plots_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_forecast_vs_actual_plots_metadata) - [DatetimeModel.get_anomaly_over_time_plots_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_anomaly_over_time_plots_metadata) Plots can be retrieved using the following methods: - [DatetimeModel.get_accuracy_over_time_plot](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_accuracy_over_time_plot) - [DatetimeModel.get_forecast_vs_actual_plot](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_forecast_vs_actual_plot) - [DatetimeModel.get_anomaly_over_time_plot](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_anomaly_over_time_plot) Preview plots can be retrieved using the following methods: - [DatetimeModel.get_accuracy_over_time_plot_preview](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_accuracy_over_time_plot_preview) - [DatetimeModel.get_forecast_vs_actual_plot_preview](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_forecast_vs_actual_plot_preview) - [DatetimeModel.get_anomaly_over_time_plot_preview](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_anomaly_over_time_plot_preview) - Support for Batch Prediction Job Definitions has now been added through the following class: [BatchPredictionJobDefinition](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition). You can create, update, list and delete definitions using the following methods: - [BatchPredictionJobDefinition.list](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.list) - [BatchPredictionJobDefinition.create](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.create) - [BatchPredictionJobDefinition.update](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.update) - [BatchPredictionJobDefinition.delete](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.delete) ### Enhancements - Added a new helper function to create Dataset Definition, Relationship and Secondary Dataset used by Feature Discovery Project. They are accessible via DatasetDefinition Relationship SecondaryDataset - Added new helper function to projects to retrieve the recommended model. Project.recommended_model - Added method to download feature discovery recipe SQLs (limited beta feature). Project.download_feature_discovery_recipe_sqls . - Added docker_context_size and docker_image_size to datarobot.ExecutionEnvironmentVersion ### Bugfixes - Remove the deprecation warnings when using with latest versions of urllib3. - FeatureAssociationMatrix.get is now using correct query param name when featurelist_id is specified. - Handle scalar values in shapBaseValue while converting a predictions response to a data frame. - Ensure that if a configured endpoint ends in a trailing slash, the resulting full URL does not end up with double slashes in the path. - Model.request_frozen_datetime_model is now implementing correct validation of input parameter training_start_date . ### API changes - Arguments secondary_datasets now accept SecondaryDataset to create secondary dataset configurations - SecondaryDatasetConfigurations.create - Arguments dataset_definitions and relationships now accept DatasetDefinition Relationship to create and replace relationships configuration - RelationshipsConfiguration.create creates a new relationships configuration between datasets - RelationshipsConfiguration.retrieve retrieve the requested relationships configuration - Argument required_metadata_keys has been added to datarobot.ExecutionEnvironment . This should be used to define a list of RequiredMetadataKey . datarobot.CustomModelVersion that use a base environment with required_metadata_keys must define values for these fields in their respective required_metadata - Argument required_metadata has been added to datarobot.CustomModelVersion . This should be set with relevant values defined by the base environment’s required_metadata_keys ## 2.24.0 ### New features - Partial history predictions can be made with time series time series multiseries models using the allow_partial_history_time_series_predictions attribute of the datarobot.DatetimePartitioningSpecification . See the :ref: Time Series documentation for more info. - Multicategorical Histograms are now retrievable. They are accessible via MulticategoricalHistogram or Feature.get_multicategorical_histogram . - Add methods to retrieve per-class lift chart data for multilabel models: Model.get_multilabel_lift_charts and Model.get_all_multilabel_lift_charts . - Add methods to retrieve labelwise ROC curves for multilabel models: Model.get_labelwise_roc_curves and Model.get_all_labelwise_roc_curves . - Multicategorical Pairwise Statistics are now retrievable. They are accessible via PairwiseCorrelations , PairwiseJointProbabilities and PairwiseConditionalProbabilities or Feature.get_pairwise_correlations , Feature.get_pairwise_joint_probabilities and Feature.get_pairwise_conditional_probabilities . - Add methods to retrieve prediction results of a deployment: : - Deployment.get_prediction_results - Add method to download scoring code of a deployment using Deployment.download_scoring_code . - Added Automated Documentation: now you can automatically generate documentation about various entities within the platform, such as specific models or projects. Check out the - ref: Automated Documentation overview and also refer to the :ref: API Reference for more details. - Create a new Dataset version for a given dataset by uploading from a file, URL or in-memory datasource. : - Dataset.create_version_from_file ### Enhancements - Added a new status called FAILED to from BatchPredictionJob as this is a new status coming to Batch Predictions in an upcoming version of DataRobot. - Added base_environment_version_id to datarobot.CustomModelVersion . - Support for downloading feature discovery training or prediction dataset using Project.download_feature_discovery_dataset . - Added datarobot.models.FeatureAssociationMatrix , datarobot.models.FeatureAssociationMatrixDetails and datarobot.models.FeatureAssociationFeaturelists that can be used to retrieve feature associations data as an alternative to Project.get_associations , Project.get_association_matrix_details and Project.get_association_featurelists methods. ### Bugfixes - Fixed response validation that could cause DataError when using TrainingPredictions.list and TrainingPredictions.get_all_as_dataframe methods if there are training predictions computed with explanation_algorithm . ### API changes - Remove desired_memory param from the following classes: datarobot.CustomInferenceModel , datarobot.CustomModelVersion , datarobot.CustomModelTest - Remove desired_memory param from the following methods: CustomInferenceModel.create , CustomModelVersion.create_clean , CustomModelVersion.create_from_previous , CustomModelTest.create and CustomModelTest.create ### Deprecation summary - class ComplianceDocumentation will be deprecated in v2.24 and will be removed entirely in v2.27. Use AutomatedDocument instead. To start off, see the - ref: Automated Documentation overview for details. ### Documentation changes - Remove reference to S3 for Project.upload_dataset since it is not supported by the server ## 2.23.0 ### New features - Calendars for time series projects can now be automatically generated by providing a country code to the method CalendarFile.create_calendar_from_country_code . A list of allowed country codes can be retrieved using CalendarFile.get_allowed_country_codes For more information, see the :ref: calendar documentation . - Added calculate_all_series`` param to [ DatetimeModel.compute_series_accuracy`](../../sdk/datarobot-models.md#datarobot.models.DatetimeModel.compute_series_accuracy). This option allows users to compute series accuracy for all available series at once, while by default it is computed for first 1000 series only. - Added ability to specify sampling method when setting target of OTV project. Option can be set in AdvancedOptions and changes a way training data is defined in autopilot steps. - Add support for custom inference model k8s resources management. This new feature enables users to control k8s resources allocation for their executed model in the k8s cluster. It involves in adding the following new parameters: network_egress_policy , desired_memory , maximum_memory , replicas to the following classes: datarobot.CustomInferenceModel , datarobot.CustomModelVersion , datarobot.CustomModelTest - Add support for multiclass custom inference and training models. This enables users to create classification custom models with more than two class labels. The datarobot.CustomInferenceModel class can now use datarobot.TARGET_TYPE.MULTICLASS for their target_type parameter. Class labels for inference models can be set/updated using either a file or as a list of labels. - Support for Listing all the secondary dataset configuration for a given project: : - SecondaryDatasetConfigurations.list - Add support for unstructured custom inference models. The datarobot.CustomInferenceModel class can now use datarobot.TARGET_TYPE.UNSTRUCTURED for its target_type parameter. target_name parameter is optional for UNSTRUCTURED target type. - All per-class lift chart data is now available for multiclass models using Model.get_multiclass_lift_chart . - AUTOPILOT_MODE.COMPREHENSIVE , a new mode , has been added to Project.set_target . - Add support for anomaly detection custom inference models. The datarobot.CustomInferenceModel class can now use datarobot.TARGET_TYPE.ANOMALY for its target_type parameter. target_name parameter is optional for ANOMALY target type. - Support for Updating and retrieving the secondary dataset configuration for a Feature discovery deployment: : - Deployment.update_secondary_dataset_config - Add support for starting and retrieving Feature Impact information for datarobot.CustomModelVersion - Search for interaction features and Supervised Feature reduction for feature discovery project can now be specified : in AdvancedOptions . - Feature discovery projects can now be created using the Project.start method by providing relationships_configuration_id . - Actions applied to input data during automated feature discovery can now be retrieved using FeatureLineage.get Corresponding feature lineage id is available as a new datarobot.models.Feature field feature_lineage_id . - Lift charts and ROC curves are now calculated for backtests 2+ in time series and OTV models. The data can be retrieved for individual backtests using Model.get_lift_chart and Model.get_roc_curve . - The following methods now accept a new argument called credential_data, the credentials to authenticate with the database, to use instead of user/password or credential ID: : - Dataset.create_from_data_source - Add support for DataRobot Connectors, datarobot.Connector provides a simple implementation to interface with connectors. ### Enhancements - Running Autopilot on Leakage Removed feature list can now be specified in AdvancedOptions . By default, Autopilot will always run on Informative Features - Leakage Removed feature list if it exists. If the parameter run_leakage_removed_feature_list is set to False, then Autopilot will run on Informative Features or available custom feature list. - Method Project.upload_dataset and Project.upload_dataset_from_data_source support new optional parameter secondary_datasets_config_id for Feature discovery project. ### Bugfixes - added disable_holdout param in datarobot.DatetimePartitioning - Using Credential.create_gcp produced an incompatible credential - SampleImage.list now supports Regression & Multilabel projects - Using BatchPredictionJob.score could in some circumstances result in a crash from trying to abort the job if it fails to start - Using BatchPredictionJob.score or BatchPredictionJob.score would produce incomplete results in case a job was aborted while downloading. This will now raise an exception. ### API changes - New sampling_method param in Model.train_datetime , Project.train_datetime , Model.train_datetime and Model.train_datetime . - New target_type param in datarobot.CustomInferenceModel - New arguments secondary_datasets , name , creator_full_name , creator_user_id , created , : featurelist_id , credentials_ids , project_version and is_default in datarobot.models.SecondaryDatasetConfigurations - New arguments secondary_datasets , name , featurelist_id to : SecondaryDatasetConfigurations.create - Class FeatureEngineeringGraph has been removed. Use datarobot.models.RelationshipsConfiguration instead. - Param feature_engineering_graphs removed from Project.set_target . - Param config removed from SecondaryDatasetConfigurations.create . ### Deprecation summary - supports_binary_classification and supports_regression are deprecated : for datarobot.CustomInferenceModel and will be removed in v2.24 - Argument config and supports_regression are deprecated : for datarobot.models.SecondaryDatasetConfigurations and will be removed in v2.24 - CustomInferenceImage has been deprecated and will be removed in v2.24. : datarobot.CustomModelVersion with base_environment_id should be used in their place. - environment_id and environment_version_id are deprecated for CustomModelTest.create ### Documentation changes - feature_lineage_id is added as a new parameter in the response for retrieval of a datarobot.models.Feature created by automated feature discovery or time series feature derivation. This id is required to retrieve a datarobot.models.FeatureLineage instance. ## 2.22.1 ### New features - Batch Prediction jobs now support :ref: dataset as intake settings for BatchPredictionJob.score . - Create a Dataset from DataSource: Dataset.create_from_data_sourceDataSource.create_dataset - Added support for Custom Model Dependency Management. Please see :ref: custom model documentation . New features added: Added new argumentbase_environment_idto methodsCustomModelVersion.create_cleanandCustomModelVersion.create_from_previousNew fieldsbase_environment_idanddependenciesto classdatarobot.CustomModelVersionNew classdatarobot.CustomModelVersionDependencyBuildto prepare custom model versions with dependencies.Made argumentenvironment_idofCustomModelTest.createoptional to enable using custom model versions with dependenciesNew fieldimage_typeadded to classdatarobot.CustomModelTestDeployment.create_from_custom_model_versioncan be used to create a deployment from a custom model version. - Added new parameters for starting and re-running Autopilot with customizable settings within Project.start_autopilot . - Added a new method to trigger Feature Impact calculation for a Custom Inference Image: CustomInferenceImage.calculate_feature_impact - Added new method to retrieve number of iterations trained for early stopping models. Currently supports only tree-based models. Model.get_num_iterations_trained . ### Enhancements - A description can now be added or updated for a project. Project.set_project_description . - Added new parameters read_timeout and max_wait to method Dataset.create_from_file . Values larger than the default can be specified for both to avoid timeouts when uploading large files. - Added new parameter metric to datarobot.models.deployment.TargetDrift , datarobot.models.deployment.FeatureDrift , Deployment.get_target_drift and Deployment.get_feature_drift . - Added new parameter timeout to BatchPredictionJob.download to indicate how many seconds to wait for the download to start (in case the job doesn’t start processing immediately). Set to -1 to disable. This parameter can also be sent as download_timeout to BatchPredictionJob.score and BatchPredictionJob.score . If the timeout occurs, the pending job will be aborted. - Added new parameter read_timeout to BatchPredictionJob.download to indicate how many seconds to wait between each downloaded chunk. This parameter can also be sent as download_read_timeout to BatchPredictionJob.score and BatchPredictionJob.score . - Added parameter catalog to BatchPredictionJob to both intake and output adapters for type jdbc . - Consider blenders in recommendation can now be specified in AdvancedOptions . Blenders will be included when autopilot chooses a model to prepare and recommend for deployment. - Added optional parameter max_wait to Deployment.replace_model to indicate the maximum time to wait for model replacement job to complete before erroring. ### Bugfixes - Handle null values in predictionExplanationMetadata["shapRemainingTotal"] while converting a predictions response to a data frame. - Handle null values in customModel["latestVersion"] - Removed an extra column status from BatchPredictionJob as it caused issues with never version of Trafaret validation. - Make predicted_vs_actual optional in Feature Effects data because a feature may have insufficient qualified samples. - Make jdbc_url optional in Data Store data because some data stores will not have it. - The method Project.get_datetime_models now correctly returns all DatetimeModel objects for the project, instead of just the first 100. - Fixed a documentation error related to snake_case vs camelCase in the JDBC settings payload. - Make trafaret validator for datasets use a syntax that works properly with a wider range of trafaret versions. - Handle extra keys in CustomModelTests and CustomModelVersions - ImageEmbedding and ImageActivationMap now supports regression projects. ### API changes - The default value for the mode param in Project.set_target has been changed from AUTOPILOT_MODE.FULL_AUTO to AUTOPILOT_MODE.QUICK ### Documentation changes - Added links to classes with duration parameters such as validation_duration and holdout_duration to provide duration string examples to users. - The :ref: models documentation has been revised to include section on how to train a new model and how to run cross-validation or backtesting for a model. ## 2.21.0 ### New features - Added new arguments explanation_algorithm and max_explanations to method Model.request_training_predictions . New fields explanation_algorithm , max_explanations and shap_warnings have been added to class TrainingPredictions . New fields prediction_explanations and shap_metadata have been added to class TrainingPredictionsIterator that is returned by method TrainingPredictions.iterate_rows . - Added new arguments explanation_algorithm and max_explanations to method Model.request_predictions . New fields explanation_algorithm , max_explanations and shap_warnings have been added to class Predictions . Method Predictions.get_all_as_dataframe has new argument serializer that specifies the retrieval and results validation method ( json or csv ) for the predictions. - Added possibility to compute ShapImpact.create and request ShapImpact.get SHAP impact scores for features in a model. - Added support for accessing Visual AI images and insights. See the DataRobot Python Package documentation, Visual AI Projects, section for details. - User can specify custom row count when requesting Feature Effects. Extended methods are Model.request_feature_effect and Model.get_or_request_feature_effect . - Users can request SHAP based predictions explanations for a models that support SHAP scores using ShapMatrix.create . - Added two new methods to Dataset to lazily retrieve paginated responses. - Dataset.iterate returns an iterator of the datasets that a user can view. - Dataset.iterate_all_features returns an iterator of the features of a dataset. - It’s possible to create an Interaction feature by combining two categorical features together using Project.create_interaction_feature . Operation result represented by models.InteractionFeature. . Specific information about an interaction feature may be retrieved by its name using models.InteractionFeature.get - Added the DatasetFeaturelist class to support featurelists on datasets in the AI Catalog. DatasetFeaturelists can be updated or deleted. Two new methods were also added to Dataset to interact with DatasetFeaturelists. These are Dataset.get_featurelists and Dataset.create_featurelist which list existing featurelists and create new featurelists on a dataset, respectively. - Added model_splits to DatetimePartitioningSpecification and to DatetimePartitioning . This will allow users to control the jobs per model used when building models. A higher number of model_splits will result in less downsampling, allowing the use of more post-processed data. - Added support for :ref: unsupervised projects . - Added support for external test set. Please see :ref: testset documentation - A new workflow is available for assessing models on external test sets in time series unsupervised projects. More information can be found in the :ref: documentation . - Project.upload_dataset and Model.request_predictions now accept actual_value_column - name of the actual value column, can be passed only with date range. - PredictionDataset objects now contain the following new fields: - New warning is added to data_quality_warnings of datarobot.models.PredictionDataset : single_class_actual_value_column . - Scores and insights on external test sets can be retrieved using ExternalScores , ExternalLiftChart , ExternalRocCurve . - Users can create payoff matrices for generating profit curves for binary classification projects using PayoffMatrix.create . - Deployment Improvements: - datarobot.models.deployment.TargetDrift can be used to retrieve target drift information. - datarobot.models.deployment.FeatureDrift can be used to retrieve feature drift information. - Deployment.submit_actuals will submit actuals in batches if the total number of actuals exceeds the limit of one single request. - Deployment.create_from_custom_model_image can be used to create a deployment from a custom model image. - Deployments now support predictions data collection that enables prediction requests and results to be saved in Predictions Data Storage. See Deployment.get_predictions_data_collection_settings and Deployment.update_predictions_data_collection_settings for usage. - New arguments send_notification and include_feature_discovery_entities are added to Project.share . - Now it is possible to specify the number of training rows to use in feature impact computation on supported project types (that is everything except unsupervised, multi-class, time-series). This does not affect SHAP based feature impact. Extended methods: - Model.request_feature_impact - Model.get_or_request_feature_impact - A new class FeatureImpactJob is added to retrieve Feature Impact records with metadata. The regular Job still works as before. - Added support for custom models. Please see :ref: custom model documentation . Classes added: - datarobot.ExecutionEnvironment and datarobot.ExecutionEnvironmentVersion to create and manage custom model executions environments - datarobot.CustomInferenceModel and datarobot.CustomModelVersion to create and manage custom inference models - datarobot.CustomModelTest to perform testing of custom models - Batch Prediction jobs now support forecast and historical Time Series predictions using the new argument timeseries_settings for BatchPredictionJob.score . - Batch Prediction jobs now support scoring to Azure and Google Cloud Storage with methods BatchPredictionJob.score_azure and BatchPredictionJob.score_gcp . - Now it’s possible to create Relationships Configurations to introduce secondary datasets to projects. A configuration specifies additional datasets to be included to a project and how these datasets are related to each other, and the primary dataset. When a relationships configuration is specified for a project, Feature Discovery will create features automatically from these datasets. - RelationshipsConfiguration.create creates a new relationships configuration between datasets - RelationshipsConfiguration.retrieve retrieve the requested relationships configuration - RelationshipsConfiguration.replace replace the relationships configuration details with new one - RelationshipsConfiguration.delete delete the relationships configuration ### Enhancements - Made creating projects from a dataset easier through the new Dataset.create_project . - These methods now provide additional metadata fields in Feature Impact results if called with with_metadata=True . Fields added: rowCount , shapBased , ranRedundancyDetection , count . - Model.get_feature_impact - Model.request_feature_impact - Model.get_or_request_feature_impact - Secondary dataset configuration retrieve and deletion is easier now though new SecondaryDatasetConfigurations.delete soft deletes a Secondary dataset configuration. SecondaryDatasetConfigurations.get retrieve a Secondary dataset configuration. - Retrieve relationships configuration which is applied on the given feature discovery project using Project.get_relationships_configuration . ### Bugfixes - An issue with input validation of the Batch Prediction module - parent_model_id was not visible for all frozen models - Batch Prediction jobs that used other output types than local_file failed when using .wait_for_completion() - A race condition in the Batch Prediction file scoring logic ### API changes - Three new fields were added to the Dataset object. This reflects the updated fields in the public API routes at api/v2/datasets/ . The added fields are: - processing_state: Current ingestion process state of the dataset - row_count: The number of rows in the dataset. - size: The size of the dataset as a CSV in bytes. ### Deprecation summary - datarobot.enums.VARIABLE_TYPE_TRANSFORM.CATEGORICAL for is deprecated for the following and will be removed in v2.22. - Project.batch_features_type_transform() - Project.create_type_transform_feature() ## 2.20.0 ### New features - There is a new Dataset object that implements some of the public API routes at api/v2/datasets/ . This also adds two new feature classes and a details class. - DatasetFeature - DatasetFeatureHistogram - DatasetDetails Functionality: - Create a Dataset by uploading from a file, URL or in-memory datasource. - [Dataset.create_from_file](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.create_from_file) - [Dataset.create_from_in_memory_data](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.create_from_in_memory_data) - [Dataset.create_from_url](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.create_from_url) - Get Datasets or elements of Dataset with: - [Dataset.list](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.list) lists available Datasets - [Dataset.get](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.get) gets a specified Dataset - [Dataset.update](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.get) updates the Dataset with the latest server information. - [Dataset.get_details](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.get_details) gets the DatasetDetails of the Dataset. - [Dataset.get_all_features](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.get_all_features) gets a list of the Dataset’s Features. - [Dataset.get_file](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.get_file) downloads the Dataset as a csv file. - [Dataset.get_projects](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.get_projects) gets a list of Projects that use the Dataset. - Modify, delete or un-delete a Dataset: - [Dataset.modify](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.modify) Changes the name and categories of the Dataset - [Dataset.delete](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.delete) soft deletes a Dataset. - [Dataset.un_delete](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset.un_delete) un-deletes the Dataset. You cannot retrieve the IDs of deleted Datasets, so if you want to un-delete a Dataset, you need to store its ID before deletion. - You can also create a Project using a `Dataset` with: - [Project.create_from_dataset](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.Project.create_from_dataset) - It is possible to create an alternative configuration for the secondary dataset which can be used during the prediction - [SecondaryDatasetConfigurations.create](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.SecondaryDatasetConfigurations.create) allow to create secondary dataset configuration - You can now filter the deployments returned by the [Deployment.list](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.list) command. You can do this by passing an instance of the [DeploymentListFilters](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.deployment.DeploymentListFilters) class to the `filters` keyword argument. The currently supported filters are: - `role` - `service_health` - `model_health` - `accuracy_health` - `execution_environment_type` - `materiality` - A new workflow is available for making predictions in time series projects. To that end, [PredictionDataset](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.PredictionDataset) objects now contain the following new fields: - `forecast_point_range`: The start and end date of the range of dates available for use as the forecast point, detected based on the uploaded prediction dataset - `data_start_date`: A datestring representing the minimum primary date of the prediction dataset - `data_end_date`: A datestring representing the maximum primary date of the prediction dataset - `max_forecast_date`: A datestring representing the maximum forecast date of this prediction dataset Additionally, users no longer need to specify a `forecast_point` or `predictions_start_date` and `predictions_end_date` when uploading datasets for predictions in time series projects. More information can be found in the :ref: `time series predictions` documentation. - Per-class lift chart data is now available for multiclass models using [Model.get_multiclass_lift_chart](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_multiclass_lift_chart). - Unsupervised projects can now be created using the [Project.start](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.Project.start) and [Project.set_target](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.Project.set_target) methods by providing `unsupervised_mode=True`, provided that the user has access to unsupervised machine learning functionality. Contact support for more information. - A new boolean attribute `unsupervised_mode` was added to [datarobot.DatetimePartitioningSpecification](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.DatetimePartitioningSpecification). When it is set to True, datetime partitioning for unsupervised time series projects will be constructed for nowcasting: `forecast_window_start=forecast_window_end=0`. - Users can now configure the start and end of the training partition as well as the end of the validation partition for backtests in a datetime-partitioned project. More information and example usage can be found in the * ref: `backtesting documentation `. ### Enhancements - Updated the user agent header to show which python version. - Model.get_frozen_child_models can be used to retrieve models that are frozen from a given model - Added datarobot.enums.TS_BLENDER_METHOD to make it clearer which blender methods are allowed for use in time series projects. ### Bugfixes - An issue where uploaded CSV’s would loose quotes during serialization causing issues when columns containing line terminators where loaded in a dataframe, has been fixed - Project.get_association_featurelists is now using the correct endpoint name, but the old one will continue to work - Python API PredictionServer supports now on-premise format of API response. ## 2.19.0 ### New features - Projects can be cloned using Project.clone_project - Calendars used in time series projects now support having series-specific events, for instance if a holiday only affects some stores. This can be controlled by using new argument of the CalendarFile.create method. If multiseries id columns are not provided, calendar is considered to be single series and all events are applied to all series. - We have expanded prediction intervals availability to the following use-cases: - Time series model deployments now support prediction intervals. See Deployment.get_prediction_intervals_settings and Deployment.update_prediction_intervals_settings for usage. - Prediction intervals are now supported for model exports for time series. To that end, a new optional parameter prediction_intervals_size has been added to Model.request_transferable_export . More details on prediction intervals can be found in the :ref: `prediction intervals documentation `. - Allowed pairwise interaction groups can now be specified in [AdvancedOptions](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.helpers.AdvancedOptions). They will be used in GAM models during training. - New deployments features: - Update the label and description of a deployment using [Deployment.update](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update). - * ref: `Association ID setting` can be retrieved and updated. - Regression deployments now support :ref: `prediction warnings`. - For multiclass models now it’s possible to get feature impact for each individual target class using [Model.get_multiclass_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_multiclass_feature_impact) - Added support for new :ref: `Batch Prediction API `. - It is now possible to create and retrieve basic, oauth and s3 credentials with [Credential](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/credentials.html#datarobot.models.Credential). - It’s now possible to get feature association statuses for featurelists using [Project.get_association_featurelists](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.Project.get_association_featurelists) - You can also pass a specific featurelist_id into [Project.get_associations](https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html#datarobot.models.Project.get_associations) ### Enhancements - Added documentation to Project.get_metrics to detail the new ascending field that indicates how a metric should be sorted. - Retraining of a model is processed asynchronously and returns a ModelJob immediately. - Blender models can be retrained on a different set of data or a different feature list. - Word cloud ngrams now has variable field representing the source of the ngram. - Method WordCloud.ngrams_per_class can be used to split ngrams for better usability in multiclass projects. - Method Project.set_target support new optional parameters featureEngineeringGraphs and credentials . - Method Project.upload_dataset and Project.upload_dataset_from_data_source support new optional parameter credentials . - Series accuracy retrieval methods ( DatetimeModel.get_series_accuracy_as_dataframe and DatetimeModel.download_series_accuracy_as_csv ) for multiseries time series projects now support additional parameters for specifying what data to retrieve, including: - metric : Which metric to retrieve scores for - multiseries_value : Only returns series with a matching multiseries ID - order_by : An attribute by which to sort the results ### Bugfixes - An issue when using Feature.get and ModelingFeature.get to retrieve summarized categorical feature has been fixed. ### API changes - The datarobot package is now no longer a namespace package . - datarobot.enums.BLENDER_METHOD.FORECAST_DISTANCE is removed (deprecated in 2.18.0). ### Documentation changes - Updated :ref: Residuals charts documentation to reflect that the data rows include row numbers from the source dataset for projects created in DataRobot 5.3 and newer. ## 2.18.0 ### New features - - - - Deployment.submit_actuals can now be used to submit data about actual results from a deployed model, which can be used to calculate accuracy metrics. ### Enhancements - Monotonic constraints are now supported for OTV projects. To that end, the parameters monotonic_increasing_featurelist_id and monotonic_decreasing_featurelist_id can be specified in calls to Model.train_datetime or Project.train_datetime . - When retrieving information about features , information about summarized categorical variables is now available in a new keySummary . - For Word Clouds in multiclass projects, values of the target class for corresponding word or ngram can now be passed using the new class parameter. - Listing deployments using Deployment.list now support sorting and searching the results using the new order_by and search parameters. - You can now get the model associated with a model job by getting the model variable on the model job object . - The Blueprint class can now retrieve the recommended_featurelist_id , which indicates which feature list is recommended for this blueprint. If the field is not present, then there is no recommended feature list for this blueprint. - The Model class now can be used to retrieve the model_number . - The method Model.get_supported_capabilities now has an extra field supportsCodeGeneration to explain whether the model supports code generation. - Calls to Project.start and Project.upload_dataset now support uploading data via S3 URI and pathlib.Path objects. - Errors upon connecting to DataRobot are now clearer when an incorrect API Token is used. - The datarobot package is now a namespace package . ### Deprecation summary - datarobot.enums.BLENDER_METHOD.FORECAST_DISTANCE is deprecated and will be removed in 2.19. Use FORECAST_DISTANCE_ENET instead. ### Documentation changes - Various typo and wording issues have been addressed. - A new notebook showing regression-specific features is now been added to the examples_index. - Documentation for :ref: Access lists has been added. ## 2.17.0 ### New features - - Users can now list available prediction servers using PredictionServer.list . - When specifying datetime partitioning settings , :ref: time series projects can now mark individual features as excluded from feature derivation using the FeatureSettings.do_not_derive attribute. Any features not specified will be assigned according to the DatetimePartitioningSpecification.default_to_do_not_derive value. - Users can now submit multiple feature type transformations in a single batch request using Project.batch_features_type_transform . - - Information on feature clustering and the association strength between pairs of numeric or categorical features is now available. Project.get_associations can be used to retrieve pairwise feature association statistics and Project.get_association_matrix_details can be used to get a sample of the actual values used to measure association strength. ### Enhancements - number_of_do_not_derive_features has been added to the datarobot.DatetimePartitioning class to specify the number of features that are marked as excluded from derivation. - Users with PyYAML>=5.1 will no longer receive a warning when using the datarobot package - It is now possible to use files with unicode names for creating projects and prediction jobs. - Users can now embed DataRobot-generated content in a ComplianceDocTemplate using keyword tags. :ref: See here for more details. - The field calendar_name has been added to datarobot.DatetimePartitioning to display the name of the calendar used for a project. - - Previously, all backtests had to be run before :ref: prediction intervals for a time series project could be requested with predictions. Now, backtests will be computed automatically if needed when prediction intervals are requested. ### Bugfixes - An issue affecting time series project creation for irregularly spaced dates has been fixed. - ComplianceDocTemplate now supports empty text blocks in user sections. - An issue when using Predictions.get to retrieve predictions metadata has been fixed. ### Documentation changes - An overview on working with class ComplianceDocumentation and ComplianceDocTemplate has been created. :ref: See here for more details. ## 2.16.0 ### New features - Three new methods for Series Accuracy have been added to the DatetimeModel class. - Start a request to calculate Series Accuracy with DatetimeModel.compute_series_accuracy - Once computed, Series Accuracy can be retrieved as a pandas.DataFrame using DatetimeModel.get_series_accuracy_as_dataframe - Or saved as a CSV using DatetimeModel.download_series_accuracy_as_csv - Users can now access :ref: prediction intervals data for each prediction with a DatetimeModel . For each model, prediction intervals estimate the range of values DataRobot expects actual values of the target to fall within. They are similar to a confidence interval of a prediction, but are based on the residual errors measured during the backtesting for the selected model. ### Enhancements - Information on the effective feature derivation window is now available for :ref: time series projects to specify the full span of historical data required at prediction time. It may be longer than the feature derivation window of the project depending on the differencing settings used. Additionally, more of the project partitioning settings are also available on the [DatetimeModel](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel) class. The new attributes are: - `effective_feature_derivation_window_start` - `effective_feature_derivation_window_end` - `forecast_window_start` - `forecast_window_end` - `windows_basis_unit` - Prediction metadata is now included in the return of [Predictions.get](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Predictions.get) ### Documentation changes - Various typo and wording issues have been addressed. - The example data that was meant to accompany the Time Series examples has been added to the zip file of the download in the examples_index. ## 2.15.1 ### Enhancements - CalendarFile.get_access_list has been added to the CalendarFile class to return a list of users with access to a calendar file. - A role attribute has been added to the CalendarFile class to indicate the access level a current user has to a calendar file. For more information on the specific access levels, see the :ref: sharing documentation. ### Bugfixes - Previously, attempting to retrieve the calendar_id of a project without a set target would result in an error. This has been fixed to return None instead. ## 2.15.0 ### New features - Previously available for only Eureqa models, Advanced Tuning methods and objects, including Model.start_advanced_tuning_session , Model.get_advanced_tuning_parameters , Model.advanced_tune , and AdvancedTuningSession , now support all models other than blender, open source, and user-created models. Use of Advanced Tuning via API for non-Eureqa models is in beta and not available by default, but can be enabled. - Calendar Files for time series projects can now be created and managed through the CalendarFile class. ### Enhancements - The dataframe returned from datarobot.PredictionExplanations.get_all_as_dataframe() will now have each class label class_X be the same from row to row. - The client is now more robust to networking issues by default. It will retry on more errors and respects Retry-After headers in HTTP 413, 429, and 503 responses. - Added Forecast Distance blender for Time-Series projects configured with more than one Forecast Distance. It blends the selected models creating separate linear models for each Forecast Distance. - Project can now be :ref: shared with other users. - Project.upload_dataset and Project.upload_dataset_from_data_source will return a PredictionDataset with data_quality_warnings if potential problems exist around the uploaded dataset. - relax_known_in_advance_features_check has been added to Project.upload_dataset and Project.upload_dataset_from_data_source to allow missing values from the known in advance features in the forecast window at prediction time. - cross_series_group_by_columns has been added to datarobot.DatetimePartitioning to allow users the ability to indicate how to further split series into related groups. - Information retrieval for ROC Curve has been extended to include fraction_predicted_as_positive , fraction_predicted_as_negative , lift_positive and lift_negative ### Bugfixes - Fixes an issue where the client would not be usable if it could not be sure it was compatible with the configured server ### API changes - Methods for creating datarobot.models.Project : create_from_mysql , create_from_oracle , and create_from_postgresql , deprecated in 2.11, have now been removed. Use datarobot.models.Project.create_from_data_source() instead. - datarobot.FeatureSettings attribute apriori , deprecated in 2.11, has been removed. Use datarobot.FeatureSettings.known_in_advance instead. - datarobot.DatetimePartitioning attribute default_to_a_priori , deprecated in 2.11, has been removed. Use datarobot.DatetimePartitioning.known_in_advance instead. - datarobot.DatetimePartitioningSpecification attribute default_to_a_priori , deprecated in 2.11, has been removed. Use datarobot.DatetimePartitioningSpecification.known_in_advance instead. ### Configuration changes - Now requires dependency on package requests to be at least version 2.21. - Now requires dependency on package urllib3 to be at least version 1.24. ### Documentation changes - Advanced model insights notebook extended to contain information on visualization of cumulative gains and lift charts. ## 2.14.2 ### Bugfixes - Fixed an issue where searches of the HTML documentation would sometimes hang indefinitely ### Documentation changes - Python3 is now the primary interpreter used to build the docs (this does not affect the ability to use the package with Python2) ## 2.14.1 ### Documentation changes - Documentation for the Model Deployment interface has been removed after the corresponding interface was removed in 2.13.0. ## 2.14.0 ### New features - The new method Model.get_supported_capabilities retrieves a summary of the capabilities supported by a particular model, such as whether it is eligible for Prime and whether it has word cloud data available. - New class for working with model compliance documentation feature of DataRobot: class ComplianceDocumentation - New class for working with compliance documentation templates: ComplianceDocTemplate - New class FeatureHistogram has been added to retrieve feature histograms for a requested maximum bin count - Time series projects now support binary classification targets. - Cross series features can now be created within time series multiseries projects using the use_cross_series_features and aggregation_type attributes of the datarobot.DatetimePartitioningSpecification . See the :ref: Time Series documentation for more info. ### Enhancements - Client instantiation now checks the endpoint configuration and provides more informative error messages. It also automatically corrects HTTP to HTTPS if the server responds with a redirect to HTTPS. - Project.upload_dataset and Project.create now accept an optional parameter of dataset_filename to specify a file name for the dataset. This is ignored for url and file path sources. - New optional parameter fallback_to_parent_insights has been added to Model.get_lift_chart , Model.get_all_lift_charts , Model.get_confusion_chart , Model.get_all_confusion_charts , Model.get_roc_curve , and Model.get_all_roc_curves . When True , a frozen model with missing insights will attempt to retrieve the missing insight data from its parent model. - New number_of_known_in_advance_features attribute has been added to the datarobot.DatetimePartitioning class. The attribute specifies number of features that are marked as known in advance. - Project.set_worker_count can now update the worker count on a project to the maximum number available to the user. - - Timeseries projects can now accept feature derivation and forecast windows intervals in terms of number of the rows rather than a fixed time unit. DatetimePartitioningSpecification and Project.set_target support new optional parameter windowsBasisUnit , either ‘ROW’ or detected time unit. - Timeseries projects can now accept feature derivation intervals, forecast windows, forecast points and prediction start/end dates in milliseconds. - DataSources and DataStores can now be :ref: shared with other users. - Training predictions for datetime partitioned projects now support the new data subset dr.enums.DATA_SUBSET.ALL_BACKTESTS for requesting the predictions for all backtest validation folds. ### API changes - The model recommendation type “Recommended” (deprecated in version 2.13.0) has been removed. ### Documentation changes - Example notebooks have been updated: - Notebooks now work in Python 2 and Python 3 - A notebook illustrating time series capability has been added - The financial data example has been replaced with an updated introductory example. - To supplement the embedded Python notebooks in both the PDF and HTML docs bundles, the notebook files and supporting data can now be downloaded from the HTML docs bundle. - Fixed a minor typo in the code sample for get_or_request_feature_impact ## 2.13.0 ### New features - The new method Model.get_or_request_feature_impact functionality will attempt to request feature impact and return the newly created feature impact object or the existing object so two calls are no longer required. - New methods and objects, including Model.start_advanced_tuning_session , Model.get_advanced_tuning_parameters , Model.advanced_tune , and AdvancedTuningSession , were added to support the setting of Advanced Tuning parameters. This is currently supported for Eureqa models only. - New is_starred attribute has been added to the Model class. The attribute specifies whether a model has been marked as starred by user or not. - Model can be marked as starred or being unstarred with Model.star_model and Model.unstar_model . - When listing models with Project.get_models , the model list can now be filtered by the is_starred value. - A custom prediction threshold may now be configured for each model via Model.set_prediction_threshold . When making predictions in binary classification projects, this value will be used when deciding between the positive and negative classes. - Project.check_blendable can be used to confirm if a particular group of models are eligible for blending as some are not, e.g. scaleout models and datetime models with different training lengths. - Individual cross validation scores can be retrieved for new models using Model.get_cross_validation_scores . ### Enhancements - Python 3.7 is now supported. - Feature impact now returns not only the impact score for the features but also whether they were detected to be redundant with other high-impact features. - A new is_blocked attribute has been added to the Job class, specifying whether a job is blocked from execution because one or more dependencies are not yet met. - The Featurelist object now has new attributes reporting its creation time, whether it was created by a user or by DataRobot, and the number of models using the featurelist, as well as a new description field. - Featurelists can now be renamed and have their descriptions updated with Featurelist.update and ModelingFeaturelist.update . - Featurelists can now be deleted with Featurelist.delete and ModelingFeaturelist.delete . - ModelRecommendation.get now accepts an optional parameter of type datarobot.enums.RECOMMENDED_MODEL_TYPE which can be used to get a specific kind of recommendation. - Previously computed predictions can now be listed and retrieved with the Predictions class, without requiring a reference to the original PredictJob . ### Bugfixes - The Model Deployment interface which was previously visible in the client has been removed to allow the interface to mature, although the raw API is available as a “beta” API without full backwards compatibility support. ### API changes - Added support for retrieving the Pareto Front of a Eureqa model. See ParetoFront . - A new recommendation type “Recommended for Deployment” has been added to ModelRecommendation which is now returns as the default recommended model when available. See :ref: model_recommendation . ### Deprecation summary - The feature previously referred to as “Reason Codes” has been renamed to “Prediction Explanations”, to provide increased clarity and accessibility. The old ReasonCodes interface has been deprecated and replaced with PredictionExplanations . - The recommendation type “Recommended” is deprecated and will no longer be returned in v2.14 of the API. ### Documentation changes - Added a new documentation section :ref: model_recommendation . - Time series projects support multiseries as well as single series data. They are now documented in the :ref: Time Series Projects documentation. ## 2.12.0 ### New features - Some models now have Missing Value reports allowing users with access to uncensored blueprints to retrieve a detailed breakdown of how numeric imputation and categorical converter tasks handled missing values. See the :ref: documentation for more information on the report. ## 2.11.0 ### New features - The new ModelRecommendation class can be used to retrieve the recommended models for a project. - A new helper method cross_validate was added to class Model. This method can be used to request Model’s Cross Validation score. - Training a model with monotonic constraints is now supported. Training with monotonic constraints allows users to force models to learn monotonic relationships with respect to some features and the target. This helps users create accurate models that comply with regulations (e.g. insurance, banking). Currently, only certain blueprints (e.g. xgboost) support this feature, and it is only supported for regression and binary classification projects. - DataRobot now supports “Database Connectivity”, allowing databases to be used as the source of data for projects and prediction datasets. The feature works on top of the JDBC standard, so a variety of databases conforming to that standard are available; a list of databases with tested support for DataRobot is available in the user guide in the web application. See :ref: Database Connectivity for details. - Added a new feature to retrieve feature logs for time series projects. Check datarobot.DatetimePartitioning.feature_log_list() and datarobot.DatetimePartitioning.feature_log_retrieve() for details. ### API changes - New attributes supporting monotonic constraints have been added to the AdvancedOptions , Project , Model , and Blueprint classes. See :ref: monotonic constraints for more information on how to configure monotonic constraints. - New parameters predictions_start_date and predictions_end_date added to Project.upload_dataset to support bulk predictions upload for time series projects. ### Deprecation summary - Methods for creating datarobot.models.Project : create_from_mysql , create_from_oracle , and create_from_postgresql , have been deprecated and will be removed in 2.14. Use datarobot.models.Project.create_from_data_source() instead. - datarobot.FeatureSettings attribute apriori , has been deprecated and will be removed in 2.14. Use datarobot.FeatureSettings.known_in_advance instead. - datarobot.DatetimePartitioning attribute default_to_a_priori , has been deprecated and will be removed in 2.14. datarobot.DatetimePartitioning.known_in_advance instead. - datarobot.DatetimePartitioningSpecification attribute default_to_a_priori , has been deprecated and will be removed in 2.14. Use datarobot.DatetimePartitioningSpecification.known_in_advance instead. ### Configuration changes - Retry settings compatible with those offered by urllib3’s Retry interface can now be configured. By default, we will now retry connection errors that prevented requests from arriving at the server. ### Documentation changes - “Advanced Model Insights” example has been updated to properly handle bin weights when rebinning. ## 2.9.0 ### New features - New ModelDeployment class can be used to track status and health of models deployed for predictions. ### Enhancements - DataRobot API now supports creating 3 new blender types - Random Forest, TensorFlow, LightGBM. - Multiclass projects now support blenders creation for 3 new blender types as well as Average and ENET blenders. - Models can be trained by requesting a particular row count using the new training_row_count argument with Project.train , Model.train and Model.request_frozen_model in non-datetime partitioned projects, as an alternative to the previous option of specifying a desired percentage of the project dataset. Specifying model size by row count is recommended when the float precision of sample_pct could be problematic, e.g. when training on a small percentage of the dataset or when training up to partition boundaries. - New attributes max_train_rows , scaleout_max_train_pct , and scaleout_max_train_rows have been added to Project . max_train_rows specified the equivalent value to the existing max_train_pct as a row count. The scaleout fields can be used to see how far scaleout models can be trained on projects, which for projects taking advantage of scalable ingest may exceed the limits on the data available to non-scaleout blueprints. - Individual features can now be marked as a priori or not a priori using the new feature_settings attribute when setting the target or specifying datetime partitioning settings on time series projects. Any features not specified in the feature_settings parameter will be assigned according to the default_to_a_priori value. - Three new options have been made available in the datarobot.DatetimePartitioningSpecification class to fine-tune how time-series projects derive modeling features. treat_as_exponential can control whether data is analyzed as an exponential trend and transformations like log-transform are applied. differencing_method can control which differencing method to use for stationary data. periodicities can be used to specify periodicities occurring within the data. All are optional and defaults will be chosen automatically if they are unspecified. ### API changes - Now training_row_count is available on non-datetime models as well as rowCount based datetime models. It reports the number of rows used to train the model (equivalent to sample_pct ). - Features retrieved from Feature.get now include target_leakage . ## 2.8.1 ### Bugfixes - The documented default connect_timeout will now be correctly set for all configuration mechanisms, so that requests that fail to reach the DataRobot server in a reasonable amount of time will now error instead of hanging indefinitely. If you observe that you have started seeing ConnectTimeout errors, please configure your connect_timeout to a larger value. - Version of trafaret library this package depends on is now pinned to trafaret>=0.7,<1.1 since versions outside that range are known to be incompatible. ## 2.8.0 ### New features - The DataRobot API supports the creation, training, and predicting of multiclass classification projects. DataRobot, by default, handles a dataset with a numeric target column as regression. If your data has a numeric cardinality of fewer than 11 classes, you can override this behavior to instead create a multiclass classification project from the data. To do so, use the set_target function, setting target_type=‘Multiclass’. If DataRobot recognizes your data as categorical, and it has fewer than 11 classes, using multiclass will create a project that classifies which label the data belongs to. - The DataRobot API now includes Rating Tables. A rating table is an exportable csv representation of a model. Users can influence predictions by modifying them and creating a new model with the modified table. See the :ref: documentation for more information on how to use rating tables. - scaleout_modeling_mode has been added to the AdvancedOptions class used when setting a project target. It can be used to control whether scaleout models appear in the autopilot and/or available blueprints. Scaleout models are only supported in the Hadoop environment with the corresponding user permission set. - A new premium add-on product, Time Series, is now available. New projects can be created as time series projects which automatically derive features from past data and forecast the future. See the - ref: time series documentation for more information. - The Feature object now returns the EDA summary statistics (i.e., mean, median, minimum, maximum, and standard deviation) for features where this is available (e.g., numeric, date, time, currency, and length features). These summary statistics will be formatted in the same format as the data it summarizes. - The DataRobot API now supports Training Predictions workflow. Training predictions are made by a model for a subset of data from original dataset. User can start a job which will make those predictions and retrieve them. See the :ref: documentation for more information on how to use training predictions. - DataRobot now supports retrieving a :ref: model blueprint chart and a - ref: model blueprint docs . - With the introduction of Multiclass Classification projects, DataRobot needed a better way to explain the performance of a multiclass model so we created a new Confusion Chart. The API now supports retrieving and interacting with confusion charts. ### Enhancements - DatetimePartitioningSpecification now includes the optional disable_holdout flag that can be used to disable the holdout fold when creating a project with datetime partitioning. - When retrieving reason codes on a project using an exposure column, predictions that are adjusted for exposure can be retrieved. - File URIs can now be used as source data when creating a project or uploading a prediction dataset. The file URI must refer to an allowed location on the server, which is configured as described in the user guide documentation. - The advanced options available when setting the target have been extended to include the new parameter ‘events_count’ as a part of the AdvancedOptions object to allow specifying the events count column. See the user guide documentation in the web app for more information on events count. - PredictJob.get_predictions now returns predicted probability for each class in the dataframe. - PredictJob.get_predictions now accepts prefix parameter to prefix the classes name returned in the predictions dataframe. ### API changes - Add target_type parameter to set_target() and start(), used to override the project default. ## 2.7.2 ### Documentation changes - Updated link to the publicly hosted documentation. ## 2.7.1 ### Documentation changes - Online documentation hosting has migrated from PythonHosted to Read The Docs. Minor code changes have been made to support this. ## 2.7.0 ### New features - Lift chart data for models can be retrieved using the Model.get_lift_chart and Model.get_all_lift_charts methods. - ROC curve data for models in classification projects can be retrieved using the Model.get_roc_curve and Model.get_all_roc_curves methods. - Semi-automatic autopilot mode is removed. - Word cloud data for text processing models can be retrieved using Model.get_word_cloud method. - Scoring code JAR file can be downloaded for models supporting code generation. ### Enhancements - A __repr__ method has been added to the PredictionDataset class to improve readability when using the client interactively. - Model.get_parameters now includes an additional key in the derived features it includes, showing the coefficients for individual stages of multistage models (e.g. Frequency-Severity models). - When training a DatetimeModel on a window of data, a time_window_sample_pct can be specified to take a uniform random sample of the training data instead of using all data within the window. - Installing of DataRobot package now has an “Extra Requirements” section that will install all of the dependencies needed to run the example notebooks. ### Documentation changes - A new example notebook describing how to visualize some of the newly available model insights including lift charts, ROC curves, and word clouds has been added to the examples section. - A new section for Common Issues has been added to Getting Started to help debug issues related to client installation and usage. ## 2.6.1 ### Bugfixes - Fixed a bug with Model.get_parameters raising an exception on some valid parameter values. ### Documentation changes - Fixed sorting order in Feature Impact example code snippet. ## 2.6.0 ### New features - A new partitioning method (datetime partitioning) has been added. The recommended workflow is to preview the partitioning by creating a DatetimePartitioningSpecification and passing it into DatetimePartitioning.generate , inspect the results and adjust as needed for the specific project dataset by adjusting the DatetimePartitioningSpecification and re-generating, and then set the target by passing the final DatetimePartitioningSpecification object to the partitioning_method parameter of Project.set_target . - When interacting with datetime partitioned projects, DatetimeModel can be used to access more information specific to models in datetime partitioned projects. See - ref: the documentation for more information on differences in the modeling workflow for datetime partitioned projects. - The advanced options available when setting the target have been extended to include the new parameters ‘offset’ and ‘exposure’ (part of the AdvancedOptions object) to allow specifying offset and exposure columns to apply to predictions generated by models within the project. See the user guide documentation in the web app for more information on offset and exposure columns. - Blueprints can now be retrieved directly by project_id and blueprint_id via Blueprint.get . - Blueprint charts can now be retrieved directly by project_id and blueprint_id via BlueprintChart.get . If you already have an instance of Blueprint you can retrieve its chart using Blueprint.get_chart . - Model parameters can now be retrieved using ModelParameters.get . If you already have an instance of Model you can retrieve its parameters using Model.get_parameters . - Blueprint documentation can now be retrieved using Blueprint.get_documents . It will contain information about the task, its parameters and (when available) links and references to additional sources. - The DataRobot API now includes Reason Codes. You can now compute reason codes for prediction datasets. You are able to specify thresholds on which rows to compute reason codes for to speed up computation by skipping rows based on the predictions they generate. See the reason codes - ref: documentation for more information. ### Enhancements - A new parameter has been added to the AdvancedOptions used with Project.set_target . By specifying accuracyOptimizedMb=True when creating AdvancedOptions , longer-running models that may have a high accuracy will be included in the autopilot and made available to run manually. - A new option for Project.create_type_transform_feature has been added which explicitly truncates data when casting numerical data as categorical data. - Added 2 new blenders for projects that use MAD or Weighted MAD as a metric. The MAE blender uses BFGS optimization to find linear weights for the blender that minimize mean absolute error (compared to the GLM blender, which finds linear weights that minimize RMSE), and the MAEL1 blender uses BFGS optimization to find linear weights that minimize MAE + a L1 penalty on the coefficients (compared to the ENET blender, which minimizes RMSE + a combination of the L1 and L2 penalty on the coefficients). ### Bugfixes - Fixed a bug (affecting Python 2 only) with printing any model (including frozen and prime models) whose model_type is not ascii. - FrozenModels were unable to correctly use methods inherited from Model. This has been fixed. - When calling get_result for a Job, ModelJob, or PredictJob that has errored, AsyncProcessUnsuccessfulError will now be raised instead of JobNotFinished , consistently with the behavior of get_result_when_complete . ### Deprecation summary - Support for the experimental Recommender Problems projects has been removed. Any code relying on RecommenderSettings or the recommender_settings argument of Project.set_target and Project.start will error. - Project.update , deprecated in v2.2.32, has been removed in favor of specific updates: rename , unlock_holdout , set_worker_count . ### Documentation changes - The link to Configuration from the Quickstart page has been fixed. ## 2.5.1 ### Bugfixes - Fixed a bug (affecting Python 2 only) with printing blueprints whose names are not ascii. - Fixed an issue where the weights column (for weighted projects) did not appear in the advanced_options of a Project . ## 2.5.0 ### New features - Methods to work with blender models have been added. Use Project.blend method to create new blenders, Project.get_blenders to get the list of existing blenders and BlenderModel.get to retrieve a model with blender-specific information. - Projects created via the API can now use smart downsampling when setting the target by passing smart_downsampled and majority_downsampling_rate into the AdvancedOptions object used with Project.set_target . The smart sampling options used with an existing project will be available as part of Project.advanced_options . - Support for frozen models, which use tuning parameters from a parent model for more efficient training, has been added. Use Model.request_frozen_model to create a new frozen model, Project.get_frozen_models to get the list of existing frozen models and FrozenModel.get to retrieve a particular frozen model. ### Enhancements - The inferred date format (e.g. “%Y-%m-%d %H:%M:%S”) is now included in the Feature object. For non-date features, it will be None. - When specifying the API endpoint in the configuration, the client will now behave correctly for endpoints with and without trailing slashes. ## 2.4.0 ### New features - The premium add-on product DataRobot Prime has been added. You can now approximate a model on the leaderboard and download executable code for it. See documentation for further details, or talk to your account representative if the feature is not available on your account. - (Only relevant for on-premise users with a Standalone Scoring cluster.) Methods ( request_transferable_export and download_export ) have been added to the Model class for exporting models (which will only work if model export is turned on). There is a new class ImportedModel for managing imported models on a Standalone Scoring cluster. - It is now possible to create projects from a WebHDFS, PostgreSQL, Oracle or MySQL data source. For more information see the documentation for the relevant Project classmethods: create_from_hdfs , create_from_postgresql , create_from_oracle and create_from_mysql . - Job.wait_for_completion , which waits for a job to complete without returning anything, has been added. ### Enhancements - The client will now check the API version offered by the server specified in configuration, and give a warning if the client version is newer than the server version. The DataRobot server is always backwards compatible with old clients, but new clients may have functionality that is not implemented on older server versions. This issue mainly affects users with on-premise deployments of DataRobot. ### Bugfixes - Fixed an issue where Model.request_predictions might raise an error when predictions finished very quickly instead of returning the job. ### API changes - To set the target with quickrun autopilot, call Project.set_target with mode=AUTOPILOT_MODE.QUICK instead of specifying quickrun=True . ### Deprecation summary - Semi-automatic mode for autopilot has been deprecated and will be removed in 3.0. Use manual or fully automatic instead. - Use of the quickrun argument in Project.set_target has been deprecated and will be removed in 3.0. Use mode=AUTOPILOT_MODE.QUICK instead. ### Configuration changes - It is now possible to control the SSL certificate verification by setting the parameter ssl_verify in the config file. ### Documentation changes - The “Modeling Airline Delay” example notebook has been updated to work with the new 2.3 enhancements. - Documentation for the generic Job class has been added. - Class attributes are now documented in the API Reference section of the documentation. - The changelog now appears in the documentation. - There is a new section dedicated to configuration, which lists all of the configuration options and their meanings. ## 2.3.0 ### New features - The DataRobot API now includes Feature Impact, an approach to measuring the relevance of each feature that can be applied to any model. The Model class now includes methods request_feature_impact (which creates and returns a feature impact job) and get_feature_impact (which can retrieve completed feature impact results). - A new improved workflow for predictions now supports first uploading a dataset via Project.upload_dataset , then requesting predictions via Model.request_predictions . This allows us to better support predictions on larger datasets and non-ascii files. - Datasets previously uploaded for predictions (represented by the PredictionDataset class) can be listed from Project.get_datasets and retrieve and deleted via PredictionDataset.get and PredictionDataset.delete . - You can now create a new feature by re-interpreting the type of an existing feature in a project by using the Project.create_type_transform_feature method. - The Job class now includes a get method for retrieving a job and a cancel method for canceling a job. - All of the jobs classes ( Job , ModelJob , PredictJob ) now include the following new methods: refresh (for refreshing the data in the job object), get_result (for getting the completed resource resulting from the job), and get_result_when_complete (which waits until the job is complete and returns the results, or times out). - A new method Project.refresh can be used to update Project objects with the latest state from the server. - A new function datarobot.async.wait_for_async_resolution can be used to poll for the resolution of any generic asynchronous operation on the server. ### Enhancements - The JOB_TYPE enum now includes FEATURE_IMPACT . - The QUEUE_STATUS enum now includes ABORTED and COMPLETED . - The Project.create method now has a read_timeout parameter which can be used to keep open the connection to DataRobot while an uploaded file is being processed. For very large files this time can be substantial. Appropriately raising this value can help avoid timeouts when uploading large files. - The method Project.wait_for_autopilot has been enhanced to error if the project enters a state where autopilot may not finish. This avoids a situation that existed previously where users could wait indefinitely on their project that was not going to finish. However, users are still responsible to make sure a project has more than zero workers, and that the queue is not paused. - Feature.get now supports retrieving features by feature name. (For backwards compatibility, feature IDs are still supported until 3.0.) - File paths that have unicode directory names can now be used for creating projects and PredictJobs. The filename itself must still be ascii, but containing directory names can have other encodings. - Now raises more specific JobAlreadyRequested exception when we refuse a model fitting request as a duplicate. Users can explicitly catch this exception if they want it to be ignored. - A file_name attribute has been added to the Project class, identifying the file name associated with the original project dataset. Note that if the project was created from a data frame, the file name may not be helpful. - The connect timeout for establishing a connection to the server can now be set directly. This can be done in the yaml configuration of the client, or directly in the code. The default timeout has been lowered from 60 seconds to 6 seconds, which will make detecting a bad connection happen much quicker. ### Bugfixes - Fixed a bug (affecting Python 2 only) with printing features and featurelists whose names are not ascii. ### API changes - Job class hierarchy is rearranged to better express the relationship between these objects. See documentation for datarobot.models.job for details. - Featurelist objects now have a project_id attribute to indicate which project they belong to. Directly accessing the project attribute of a Featurelist object is now deprecated - Support INI-style configuration, which was deprecated in v2.1, has been removed. yaml is the only supported configuration format. - The method Project.get_jobs method, which was deprecated in v2.1, has been removed. Users should use the Project.get_model_jobs method instead to get the list of model jobs. ### Deprecation summary - PredictJob.create has been deprecated in favor of the alternate workflow using Model.request_predictions . - Feature.converter (used internally for object construction) has been made private. - Model.fetch_resource_data has been deprecated and will be removed in 3.0. To fetch a model from its ID, use Model.get. - The ability to use Feature.get with feature IDs (rather than names) is deprecated and will be removed in 3.0. - Instantiating a Project , Model , Blueprint , Featurelist , or Feature instance from a dict of data is now deprecated. Please use the from_data classmethod of these classes instead. Additionally, instantiating a Model from a tuple or by using the keyword argument data is also deprecated. - Use of the attribute Featurelist.project is now deprecated. You can use the project_id attribute of a Featurelist to instantiate a Project instance using Project.get . - Use of the attributes Model.project , Model.blueprint , and Model.featurelist are all deprecated now to avoid use of partially instantiated objects. Please use the ids of these objects instead. - Using a Project instance as an argument in Featurelist.get is now deprecated. Please use a project_id instead. Similarly, using a Project instance in Model.get is also deprecated, and a project_id should be used in its place. ### Configuration changes - Previously it was possible (though unintended) that the client configuration could be mixed through environment variables, configuration files, and arguments to datarobot.Client . This logic is now simpler - please see the Getting Started section of the documentation for more information. ## 2.2.33 ### Bugfixes - Fixed a bug with non-ascii project names using the package with Python 2. - Fixed an error that occurred when printing projects that had been constructed from an ID only or printing printing models that had been constructed from a tuple (which impacted printing PredictJobs). - Fixed a bug with project creation from non-ascii file names. Project creation from non-ascii file names is not supported, so this now raises a more informative exception. The project name is no longer used as the file name in cases where we do not have a file name, which prevents non-ascii project names from causing problems in those circumstances. - Fixed a bug (affecting Python 2 only) with printing projects, features, and featurelists whose names are not ascii. ## 2.2.32 ### New features - Project.get_features and Feature.get methods have been added for feature retrieval. - A generic Job entity has been added for use in retrieving the entire queue at once. Calling Project.get_all_jobs will retrieve all (appropriately filtered) jobs from the queue. Those can be cancelled directly as generic jobs, or transformed into instances of the specific job class using ModelJob.from_job and PredictJob.from_job , which allow all functionality previously available via the ModelJob and PredictJob interfaces. - Model.train now supports featurelist_id and scoring_type parameters, similar to Project.train . ### Enhancements - Deprecation warning filters have been updated. By default, a filter will be added ensuring that usage of deprecated features will display a warning once per new usage location. In order to hide deprecation warnings, a filter like warnings.filterwarnings('ignore', category=DataRobotDeprecationWarning) can be added to a script so no such warnings are shown. Watching for deprecation warnings to avoid reliance on deprecated features is recommended. - If your client is misconfigured and does not specify an endpoint, the cloud production server is no longer used as the default as in many cases this is not the correct default. - This changelog is now included in the distributable of the client. ### Bugfixes - Fixed an issue where updating the global client would not affect existing objects with cached clients. Now the global client is used for every API call. - An issue where mistyping a filepath for use in a file upload has been resolved. Now an error will be raised if it looks like the raw string content for modeling or predictions is just one single line. ### API changes - Use of username and password to authenticate is no longer supported - use an API token instead. - Usage of start_time and finish_time parameters in Project.get_models is not supported both in filtering and ordering of models - Default value of sample_pct parameter of Model.train method is now None instead of 100 . If the default value is used, models will be trained with all of the available training data based on project configuration, rather than with entire dataset including holdout for the previous default value of 100 . - order_by parameter of Project.list which was deprecated in v2.0 has been removed. - recommendation_settings parameter of Project.start which was deprecated in v0.2 has been removed. - Project.status method which was deprecated in v0.2 has been removed. - Project.wait_for_aim_stage method which was deprecated in v0.2 has been removed. - Delay , ConstantDelay , NoDelay , ExponentialBackoffDelay , RetryManager classes from retry module which were deprecated in v2.1 were removed. - Package renamed to datarobot . ### Deprecation summary - Project.update deprecated in favor of specific updates: rename , unlock_holdout , set_worker_count . ### Documentation changes - A new use case involving financial data has been added to the examples directory. - Added documentation for the partition methods. ## 2.1.31 ### Bugfixes - In Python 2, using a unicode token to instantiate the client will now work correctly. ## 2.1.30 ### Bugfixes - The minimum required version of trafaret has been upgraded to 0.7.1 to get around an incompatibility between it and setuptools . ## 2.1.29 ### Enhancements - Minimal used version of requests_toolbelt package changed from 0.4 to 0.6 ## 2.1.28 ### New features - Default to reading YAML config file from ~/.config/datarobot/drconfig.yaml - Allow config_path argument to client - wait_for_autopilot method added to Project. This method can be used to block execution until autopilot has finished running on the project. - Support for specifying which featurelist to use with initial autopilot in Project.set_target - Project.get_predict_jobs method has been added, which looks up all prediction jobs for a project - Project.start_autopilot method has been added, which starts autopilot on specified featurelist - The schema for PredictJob in DataRobot API v2.1 now includes a message . This attribute has been added to the PredictJob class. - PredictJob.cancel now exists to cancel prediction jobs, mirroring ModelJob.cancel - Project.from_async is a new classmethod that can be used to wait for an async resolution in project creation. Most users will not need to know about it as it is used behind the scenes in Project.create and Project.set_target , but power users who may run into periodic connection errors will be able to catch the new ProjectAsyncFailureError and decide if they would like to resume waiting for async process to resolve ### Enhancements - AUTOPILOT_MODE enum now uses string names for autopilot modes instead of numbers ### Deprecation summary - ConstantDelay , NoDelay , ExponentialBackoffDelay , and RetryManager utils are now deprecated - INI-style config files are now deprecated (in favor of YAML config files) - Several functions in the utils submodule are now deprecated (they are being moved elsewhere and are not considered part of the public interface) - Project.get_jobs has been renamed Project.get_model_jobs for clarity and deprecated - Support for the experimental date partitioning has been removed in DataRobot API, so it is being removed from the client immediately. ### API changes - In several places where AppPlatformError was being raised, now TypeError , ValueError or InputNotUnderstoodError are now used. With this change, one can now safely assume that when catching an AppPlatformError it is because of an unexpected response from the server. - AppPlatformError has gained a two new attributes, status_code which is the HTTP status code of the unexpected response from the server, and error_code which is a DataRobot-defined error code. error_code is not used by any routes in DataRobot API 2.1, but will be in the future. In cases where it is not provided, the instance of AppPlatformError will have the attribute error_code set to None . - Two new subclasses of AppPlatformError have been introduced, ClientError (for 400-level response status codes) and ServerError (for 500-level response status codes). These will make it easier to build automated tooling that can recover from periodic connection issues while polling. - If a ClientError or ServerError occurs during a call to Project.from_async , then a ProjectAsyncFailureError (a subclass of AsyncFailureError) will be raised. That exception will have the status_code of the unexpected response from the server, and the location that was being polled to wait for the asynchronous process to resolve. ## 2.0.27 ### New features - PredictJob class was added to work with prediction jobs - wait_for_async_predictions function added to predict_job module ### Deprecation summary - The order_by parameter of the Project.list is now deprecated. ## 0.2.26 ### Enhancements - Project.set_target will re-fetch the project data after it succeeds, keeping the client side in sync with the state of the project on the server - Project.create_featurelist now throws DuplicateFeaturesError exception if passed list of features contains duplicates - Project.get_models now supports snake_case arguments to its order_by keyword ### Deprecation summary - Project.wait_for_aim_stage is now deprecated, as the REST Async flow is a more reliable method of determining that project creation has completed successfully - Project.status is deprecated in favor of Project.get_status - recommendation_settings parameter of Project.start is deprecated in favor of recommender_settings ### Bugfixes - Project.wait_for_aim_stage changed to support Python 3 - Fixed incorrect value of SCORING_TYPE.cross_validation - Models returned by Project.get_models will now be correctly ordered when the order_by keyword is used ## 0.2.25 - Pinned versions of required libraries ## 0.2.24 Official release of v0.2 ## 0.1.24 - Updated documentation - Renamed parameter name of Project.create and Project.start to project_name - Removed Model.predict method - wait_for_async_model_creation function added to modeljob module - wait_for_async_status_service of Project class renamed to _wait_for_async_status_service - Can now use auth_token in config file to configure API Client ## 0.1.23 - Fixes a method that pointed to a removed route ## 0.1.22 - Added featurelist_id attribute to ModelJob class ## 0.1.21 - Removes model attribute from ModelJob class ## 0.1.20 - Project creation raises AsyncProjectCreationError if it was unsuccessful - Removed Model.list_prime_rulesets and Model.get_prime_ruleset methods - Removed Model.predict_batch method - Removed Project.create_prime_model method - Removed PrimeRuleSet model - Adds backwards compatibility bridge for ModelJob async - Adds ModelJob.get and ModelJob.get_model ## 0.1.19 - Minor bugfixes in wait_for_async_status_service ## 0.1.18 - Removes submit_model from Project until server-side implementation is improved - Switches training URLs for new resource-based route at /projects/ /models/ - Job renamed to ModelJob, and using modelJobs route - Fixes an inconsistency in argument order for train methods ## 0.1.17 - wait_for_async_status_service timeout increased from 60s to 600s ## 0.1.16 - Project.create will now handle both async/sync project creation ## 0.1.15 - All routes pluralized to sync with changes in API - Project.get_jobs will request all jobs when no param specified - dataframes from predict method will have pythonic names - Project.get_status created, Project.status now deprecated - Project.unlock_holdout created. - Added quickrun parameter to Project.set_target - Added modelCategory to Model schema - Add permalinks feature to Project and Model objects. - Project.create_prime_model created ## 0.1.14 - Project.set_worker_count fix for compatibility with API change in project update. ## 0.1.13 - Add positive class to set_target . - Change attributes names of Project , Model , Job and Blueprint - features in Model , Job and Blueprint are now processes - dataset_id and dataset_name migrated to featurelist_id and featurelist_name . - samplepct -> sample_pct - Model has now blueprint , project , and featurelist attributes. - Minor bugfixes. ## 0.1.12 - Minor fixes regarding rename Job attributes. features attributes now named processes , samplepct now is sample_pct . ## 0.1.11 (May 27, 2015) - Minor fixes regarding migrating API from under_score names to camelCase. ## 0.1.10 (May 20, 2015) - Remove Project.upload_file , Project.upload_file_from_url and Project.attach_file methods. Moved all logic that uploading file to Project.create method. ## 0.1.9 (May 15, 2015) - Fix uploading file causing a lot of memory usage. Minor bugfixes. --- # R client changelog URL: https://docs.datarobot.com/en/docs/api/reference/changelogs/r-log.html > Reference the changes introduced to new versions of DataRobot's R client. # R client changelog Reference the changes introduced to new versions of DataRobot's R client. ## R client v2.18.7 Version v2.18.7 of the R client is now generally available. It can be accessed via [CRAN](https://cran.r-project.org/web/packages/datarobot/index.html) or [GitHub](https://github.com/datarobot/rsdk/releases/tag/v2.18.7). This is a maintenance release to ensure package compatibility with future versions of R and testthat. ### Enhancements - Test suite updated to replace the deprecated testthat::with_mock() with testthat::local_mocked_bindings() and testthat::with_mocked_bindings() . ## R client v2.18.6 Version v2.18.6 of the R client is now generally available. It can be accessed via [CRAN](https://cran.r-project.org/web/packages/datarobot/index.html) or [GitHub](https://github.com/datarobot/rsdk/releases/tag/v2.18.6). This is a maintenance release to ensure package compatibility with future versions of R. ### Bugfixes - Fixed a small issue with the metadata for the "Introduction to Multiclass" vignette. - Fixed some outstanding code formatting issues in various roxygen docs. ## R client v2.18.5 Version v2.18.5 of the R client is now generally available. It can be accessed via [CRAN](https://cran.r-project.org/web/packages/datarobot/index.html) or [GitHub](https://github.com/datarobot/rsdk/releases/tag/v2.18.5). This is a maintenance release. ### Bugfixes - The functions ListProjects now has NULL default values for limit and offset arguments to maintain backwards compatibility. This fixes compatibility issues with versions of DataRobot before 9.x. ## R client v2.18.4 Version v2.18.4 of the R client is now generally available. It can be accessed via [CRAN](https://cran.r-project.org/web/packages/datarobot/index.html) or [GitHub](https://github.com/datarobot/rsdk/releases/tag/v2.18.4). The `datarobot` package is now dependent on R >= 3.5. ### New features - The R client will now output a warning when you attempt to access certain resources (projects, models, deployments, etc.) that are deprecated or disabled by the DataRobot platform migration to Python 3. - Added support for comprehensive autopilot: usemode = AutopilotMode.Comprehensive. ### Enhancements - The functionRequestFeatureImpactnow accepts arowCountargument, which will change the sample size used for Feature Impact calculations. - The un-exported functiondatarobot:::UploadDatanow takes an optional argumentfileName. ### Bugfixes - Fixed an issue where an undocumented feature incurl==5.0.1is installed that caused any invocation ofdatarobot:::UploadData(i.e.,SetupProject) to fail with the errorNo method asJSON S3 class: form_file. - Loading thedatarobotpackage withsuppressPackageStartupMessages()will now suppress all messages. ### API changes - The functionsListProjectsandas.data.frame.projectSummaryListno longer return fields related to recommender models, which were removed in v2.5.0. - The functionSetTargetnow sets autopilot mode to Quick by default. Additionally, when Quick is passed, the underlying/aimendpoint will no longer be invoked with Auto. ### Deprecations - Thequickrunargument is removed from the functionSetTarget. Users should setmode = AutopilotMode.Quickinstead. - Compliance Documentation was deprecated in favor of the Automated Documentation API. ### Dependency changes - Thedatarobotpackage is now dependent on R >= 3.5 due to changes in the updated "Introduction to DataRobot" vignette. - Added dependency onAmesHousingpackage for updated "Introduction to DataRobot" vignette. - Removed dependency onMASSpackage. - Client documentation is now explicitly generated with Roxygen2 v7.2.3. ### Documentation changes - Updated the "Introduction to DataRobot" vignette to use Ames, Iowa housing data instead of the Boston housing dataset. ## R client v2.31 Version v2.31 of the R client is available for preview. It can be installed via [GitHub](https://github.com/datarobot/rsdk/releases/tag/v2.31.0.9000). This version of the R client addresses an issue where a new feature in the `curl==5.0.1` package caused any invocation of `datarobot:::UploadData` (i.e., `SetupProject`) to fail with the error `No method asJSON S3 class: form_file`. ### Enhancements The unexported function `datarobot:::UploadData` now takes an optional argument `fileName`. ### Bugfixes Loading the `datarobot` package with `suppressPackageStartupMessages()` will now suppress all messages. ### Deprecations - CreateProjectsDatetimeModelsFeatureFit has been removed. Use CreateProjectsDatetimeModelsFeatureEffects instead. - ListProjectsDatetimeModelsFeatureFit has been removed. Use ListProjectsDatetimeModelsFeatureEffects instead. - ListProjectsDatetimeModelsFeatureFitMetadata has been removed. Use ListProjectsDatetimeModelsFeatureEffectsMetadata instead. - CreateProjectsModelsFeatureFit has been removed. Use CreateProjectsModelsFeatureEffects instead. - ListProjectsModelsFeatureFit has been removed. Use ListProjectsModelsFeatureEffects instead. - ListProjectsModelsFeatureFitMetadata has been removed. Use ListProjectsModelsFeatureEffectsMetadata instead. ### Dependency changes Client documentation is now explicitly generated with Roxygen2 v7.2.3. Added Suggests: mockery to improve unit test development experience. --- # REST API changelogs URL: https://docs.datarobot.com/en/docs/api/reference/changelogs/rest-changelog/index.html > Reference the changes introduced to new versions of DataRobot's REST API. # REST API changelogs Changelogs contain curated, ordered lists of notable changes for each versioned release for [DataRobot's REST API client](https://docs.datarobot.com/en/docs/api/reference/public-api/index.html). Reference the changelog below to view changes for DataRobot's newest version, and view previous versions in the table of contents. ## v2.43 changelog Reference for the changes introduced to version 2.43 of DataRobot's REST API. ### New features - Added new endpoints for the REST API: ## v2.42 changelog Reference for the changes introduced to version 2.42 of DataRobot's REST API. ### New features - Added new endpoints for general API usage: - Added an endpoint for limits on deployments:GET /api/v2/deployments/limits/ - Added new endpoints for File Management. Use the endpoints for file uploads, downloads, and management. The feature is comprised of the following endpoints: - Added an endpoint for insights & analytics:POST /api/v2/insights/confusionMatrix/ - Added an endpoint for confusion matrices:GET /api/v2/insights/confusionMatrix/models/{entityId}/ ### API changes - ParameterintakeTypeschema updated forGET /api/v2/batchJobs/. - ParameterintakeTypeschema updated forGET /api/v2/batchPredictions/. - ParameterhardDeleteschema updated forDELETE /api/v2/customApplicationSources/{appSourceId}/. - ParameterhardDeleteschema updated forDELETE /api/v2/customApplications/{applicationId}/. - Parameterreplicasschema updated forGET /api/v2/customModelTests/. - Description updated forGET /api/v2/customTasks/. - Description updated forPOST /api/v2/customTasks/. - Parametereventschema updated forGET /api/v2/eventLogs/. - Parameteridsschema updated forGET /api/v2/externalOAuth/providers/. - Removed parameterskip_consent(query) forPOST /api/v2/externalOAuth/providers/{providerId}/authorize/. - ParameteruseCasesschema updated forGET /api/v2/mlops/compute/bundles/. - Description updated forPOST /api/v2/deployments/fromLearningModel/. - Removed parameterhardDelete(query) forDELETE /api/v2/executionEnvironments/{environmentId}/. - Description updated forGET /api/v2/tenantUsageResources/. - Description updated forGET /api/v2/tenantUsageResources/activeUsers/. - Description updated forGET /api/v2/tenantUsageResources/categories/. - Description updated forGET /api/v2/tenantUsageResources/export/. - Description updated forGET /api/v2/tenants/{tenantId}/resourceCategories/. - ParameterbinarySortMetricschema updated forGET /api/v2/useCases/{useCaseId}/modelsForComparison/. ### Deprecation summary - The API endpoint POST /api/v2/externalOAuth/authorizedProviders/{authorizedProviderId}/accessToken/ is deprecated. ## v2.41 changelog Reference for the changes introduced to version 2.41 of DataRobot's REST API. ### New features - Added an endpoint for user invitations: POST /api/v2/users/invite/ ### API changes - Parameter 'types' schema updated forGET /api/v2/credentials/. - Parameter 'types' schema updated forGET /api/v2/credentials/{credentialId}/associations/. - Parameter 'relatedEntityType' schema updated forGET /api/v2/entityNotificationChannels/{relatedEntityType}/{relatedEntityId}/. - Parameter 'relatedEntityType' schema updated forGET /api/v2/entityNotificationChannels/{relatedEntityType}/{relatedEntityId}/{channelId}/. - Parameter 'relatedEntityType' schema updated forPUT /api/v2/entityNotificationChannels/{relatedEntityType}/{relatedEntityId}/{channelId}/. - Parameter 'relatedEntityType' schema updated forDELETE /api/v2/entityNotificationChannels/{relatedEntityType}/{relatedEntityId}/{channelId}/. - Parameter 'relatedEntityType' schema updated forGET /api/v2/entityNotificationPolicies/{relatedEntityType}/{relatedEntityId}/. - Parameter 'relatedEntityType' schema updated forGET /api/v2/entityNotificationPolicies/{relatedEntityType}/{relatedEntityId}/{policyId}/. - Parameter 'relatedEntityType' schema updated forPUT /api/v2/entityNotificationPolicies/{relatedEntityType}/{relatedEntityId}/{policyId}/. - Parameter 'relatedEntityType' schema updated forDELETE /api/v2/entityNotificationPolicies/{relatedEntityType}/{relatedEntityId}/{policyId}/. - Parameter 'relatedEntityType' schema updated forGET /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/. - Parameter 'relatedEntityType' schema updated forGET /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/. - Parameter 'relatedEntityType' schema updated forPUT /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/. - Parameter 'relatedEntityType' schema updated forDELETE /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/. - Parameter 'relatedEntityType' schema updated forGET /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/relatedPolicies/. - Parameter 'relatedEntityType' schema updated forGET /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/sharedRoles/. - Parameter 'relatedEntityType' schema updated forPATCH /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/sharedRoles/. - Parameter 'event' schema updated forGET /api/v2/eventLogs/. - Parameter 'types' schema updated forPOST /api/v2/externalDataStores/{dataStoreId}/columnsInfo/. - Parameter 'types' schema updated forGET /api/v2/externalDataStores/{dataStoreId}/credentials/. - The new optional parameterprefixhas been added toGET /api/v2/files/{catalogId}/allFiles/. - The new optional parameterprefixhas been added toGET /api/v2/files/{catalogId}/versions/{catalogVersionId}/allFiles/. - Description updated forPOST /api/v2/notificationChannelTemplates/. - Parameter 'relatedEntityType' schema updated forGET /api/v2/notificationEvents/. - Parameter 'startTime' description updated forGET /api/v2/otel/{entityType}/{entityId}/logs/. - Parameter 'startTime' description updated forGET /api/v2/otel/{entityType}/{entityId}/metrics/autocollectedValues/. - Parameter 'startTime' description updated forGET /api/v2/otel/{entityType}/{entityId}/metrics/podInfo/. - Parameter 'segmentValue' description updated forGET /api/v2/otel/{entityType}/{entityId}/metrics/values/segments/{segmentAttribute}/. - Response 403 description updated forPOST /api/v2/otel/{entityType}/{entityId}/metrics/valuesOverTime/segments/. - Parameter 'segmentValue' description updated forGET /api/v2/otel/{entityType}/{entityId}/metrics/valuesOverTime/segments/{segmentAttribute}/. - Parameter 'startTime' description updated forGET /api/v2/otel/{entityType}/{entityId}/traces/. - Parameter 'traceId' description updated forGET /api/v2/otel/{entityType}/{entityId}/traces/{traceId}/. - The new optional parametertagFiltershas been added toGET /api/v2/registeredModels/. - Parameter 'startTime' description updated forGET /api/v2/tracing/{entityType}/{entityId}/. - Parameter 'traceId' description updated forGET /api/v2/tracing/{entityType}/{entityId}/{traceId}/. ## v2.40 changelog Reference for the changes introduced to version 2.40 of DataRobot's REST API. ### New features - Added an endpoint for deployment quota tracking:GET /api/v2/deployments/{deploymentId}/quotaConsumers/ - Added an endpoint for execution environment build management:PATCH /api/v2/executionEnvironments/{environmentId}/versions/{environmentVersionId}/cancelBuild/ - Added new endpoints for OpenTelemetry Metrics Segmentation. Retrieve OpenTelemetry metrics grouped by specific segment attributes for detailed analysis of metric values across different dimensions. This functionality enables users to analyze metrics by custom attributes such as deployment, model version, or other entity characteristics, both for single time periods and over time. Helps identify patterns, anomalies, and performance differences across different segments of operations, enabling more granular observability and troubleshooting. The feature is comprised of the following endpoints: - GET /api/v2/otel/{entityType}/{entityId}/metrics/values/segments/{segmentAttribute}/ - GET /api/v2/otel/{entityType}/{entityId}/metrics/valuesOverTime/segments/{segmentAttribute}/ - Added new endpoints for Tenant Usage and Resource Utilization. Monitor tenant activity and CPU/GPU resource utilization across the platform for administrative oversight and capacity planning. This functionality enables administrators to track active tenants, view aggregated CPU and GPU resource utilization by resource type, and export utilization data for reporting and analysis. Helps administrators understand platform-wide resource consumption, identify tenants with high utilization, plan capacity upgrades, and optimize resource allocation across the organization.. The feature is comprised of the following endpoints: - GET /api/v2/tenantUsageResources/activeTenants/ - GET /api/v2/tenants/utilizationResources/ - GET /api/v2/tenants/utilizationResources/export/ - GET /api/v2/tenants/utilizationResources/{resourceType}/ ### API changes - Parameter 'templateType' schema was updated forGET /api/v2/codeSnippets/. - Parameter 'eventGroup' schema was updated forGET /api/v2/entityNotificationPolicies/{relatedEntityType}/{relatedEntityId}/. - Parameter 'eventGroup' schema was updated forGET /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/. - Parameter 'eventGroup' schema was updated forGET /api/v2/notificationPolicies/. - The new optional parameterinvitedhas been added toGET /api/v2/users/. ## v2.39 changelog Reference for the changes introduced to version 2.39 of DataRobot's REST API. ### New features - Added an endpoint for custom metrics bulk upload:POST /api/v2/deployments/{deploymentId}/customMetrics/bulkUpload/ - Added new endpoints for prompt template management to manage LLMs and prompt templates for generative AI workflows. This functionality enables users to retrieve LLM information, create and manage prompt templates with versioning support, and organize prompts for consistent use across generative AI applications. Prompt templates can be versioned to track changes over time and maintain reproducibility. The endpoints support the full lifecycle of prompt management from creation to version tracking, enabling you to develop, test, and deploy prompts systematically. - Added new endpoints for OpenTelemetry metrics observability. Retrieve OpenTelemetry metrics data to monitor and observe DataRobot entities. This functionality provides access to automatically collected metrics, pod and container information, and metric values over time grouped by attributes. The endpoints enable users to monitor system performance, resource utilization, and application health through standardized OpenTelemetry metrics. They also help troubleshoot performance issues and understand resource consumption patterns across deployments and other entities. - Added new endpoints for Tenant Usage and Resource Management. Use the endpoints to monitor and export tenant resource usage and active user information for administrative oversight and capacity planning. This functionality enables system and organization administrators to track resource consumption across tenants, identify active users, retrieve available resource categories, and export usage data for reporting and analysis. The endpoints support both tenant-specific and cluster-wide usage reporting, helping administrators understand platform utilization, plan capacity, and ensure efficient resource allocation. ### API changes - ParameterentityTypeschema updated forGET /api/v2/comments/{entityType}/{entityId}/. - ParametereventGroupschema updated forGET /api/v2/entityNotificationPolicies/{relatedEntityType}/{relatedEntityId}/. - ParametereventGroupschema updated forGET /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/. - ParametereventGroupschema updated forGET /api/v2/notificationPolicies/. - The new optional parameterskip_consenthas been added toPOST /api/v2/externalOAuth/providers/{providerId}/authorize/. - Parameterlimitschema updated forGET /api/v2/genai/llms/. - The new optional parameterspanIdhas been added toGET /api/v2/otel/{entityType}/{entityId}/logs/. - Description updated forGET /api/v2/tenants/{tenantId}/activeUsers/. - Removed parametertenantId(path) forGET /api/v2/tenants/{tenantId}/resourceCategories/. - Description updated forGET /api/v2/tenants/{tenantId}/usage/. - Description updated forGET /api/v2/tenants/{tenantId}/usageExport/. ## v2.38 changelog ### Deprecation summary - Deprecated thecustomJobIdfield within the trigger property fromPOST /api/v2/deployments/(deploymentId)/retrainingPolicies/andPATCH /api/v2/deployments/(deploymentId)/retrainingPolicies/(retrainingPolicyId)/. This field will be removed in v2.40. - Thecustom_jobtrigger type is no longer supported forPOST /api/v2/deployments/(deploymentId)/retrainingPolicies/andPATCH /api/v2/deployments/(deploymentId)/retrainingPolicies/(retrainingPolicyId)/. To create a custom job retraining policy, usemodel_selection: custom_jobinstead. The option is no longer documented and will be removed in v2.40. ## v2.37 changelog v2.37 of the DataRobot REST API introduced no reported changes. ## v2.36 changelog ### New features - Added new endpoints for role based access management. Uses the following endpoints: - GET /api/v2/accessRoles/ - GET /api/v2/accessRoles/{role_id}/ - POST /api/v2/accessRoles/ - PUT /api/v2/accessRoles/{role_id}/ - DELETE /api/v2/accessRoles/{role_id}/ - GET /api/v2/accessRoles/users/ - Added an endpoint that initializes the incremental learning model and begins training using the chunking servicePOST /api/v2/projects/(projectId)/incrementalLearningModels/fromSampleModel/. To use it, enable the feature flag "Sample Data to Start Project." - Added an endpoint for retrieving themodel_historyof a deploymentGET /api/v2/deployments/(deploymentId)/modelHistory/. - Added new APIs for secure configuration management. Uses the following endpoints: - POST /api/v2/secureConfigs/ - GET /api/v2/secureConfigSchemas/ - GET /api/v2/secureConfigSchemas/(secureConfigSchemaId)/ - PATCH /api/v2/secureConfigs/(secureConfigId)/ - GET /api/v2/secureConfigs/(secureConfigId)/values/ - GET /api/v2/secureConfigs/(secureConfigId)/sharedRoles/ - PATCH /api/v2/secureConfigs/(secureConfigId)/sharedRoles/ - The start and end query parameters are no longer required for GET /api/v2/deployments/(deploymentId)/dataQualityView/ . Defaults will be provided for a one week span when not specified. - The start and end body parameters are no longer required for POST /api/v2/deployments/{deploymentId}/predictionDataExports/ . Defaults will be provided for a one week span when not specified. ### Deprecation summary - Removed the field capabilities from GET /api/v2/deployments/ and GET /api/v2/deployments/(deploymentId)/ , after being deprecated in 2.29. Instead, use GET /api/v2/deployments/(deploymentId)/capabilities/ . - Removed the query param targetClasses from GET /api/v2/deployments/(deploymentId)/accuracy/ and GET /api/v2/deployments/(deploymentId)/accuracyOverTime/ , after being deprecated in 2.31. Instead, use targetClass query param. - Deprecated metrics and modelId fields from GET /api/v2/deployments/(deploymentId)/accuracy/ , use data field in the same endpoint instead. The deprecated fields will be removed in 2.40. ## v2.35 changelog Reference the changes introduced to version 2.35 of DataRobot's REST API. ### New features - Added new endpoints for OCR Jobs. This is a feature for processing datasets with PDFs. It runs OCR on the PDFs in the dataset and replaces all images in those PDFs with text. The feature is comprised of the following endpoints: ### API changes - The new optional parameternumberOfIncrementalLearningIterationsBeforeBestModelSelectionfor projects with the auto-incremental learning option has been added toPATCH /api/v2/projects/(projectId)/aim/. It is used in automated incremental learning Autopilot mode. - Added endpoints to calculate and retrieve the SHAP distributions data: - POST /api/v2/insights/shapDistributions/ - GET /api/v2/insights/shapDistributions/models/(entityId)/ ### Deprecation summary - The API endpoint GET /api/v2/projects/(projectId)/models/(modelId)/primeInfo/ is deprecated, as creating Prime models is no longer supported. - The query params projectId and applicationId have been deprecated for GET /api/v2/useCases/ . ### Documentation changes - The correct expected HTTP response code has been documented for POST /api/v2/notifications/ . It should return a 201 , but 200 was previously documented. - The correct expected HTTP response code has been documented for PATCH /api/v2/ssoConfigurations/(configurationId)/ . It should return a 204 , but 200 was previously documented. in June 2025. ## v2.34 changelog Reference the changes introduced to version 2.34 of DataRobot's REST API. ### New features - Added an endpoint to create a hosted custom metric from a custom job: POST /api/v2/deployments/(deploymentId)/customMetrics/fromCustomJob/ . - Added an endpoint to list hosted custom metrics: GET /api/v2/customJobs/(customJobId)/customMetrics/ . - Added an endpoint to update the hosted custom metric PATCH /api/v2/deployments/(deploymentId)/customMetrics/(customMetricId)/ . - Added an endpoint to delete the hosted custom metric DELETE /api/v2/deployments/(deploymentId)/customMetrics/(customMetricId)/ . - Added an endpoint to create custom job from hosted custom metrics gallery template POST /api/v2/customJobs/fromHostedCustomMetricGalleryTemplate/ . - Added an endpoint to create a blueprint for the hosted custom metric POST /api/v2/customJobs/(customJobId)/hostedCustomMetricTemplate/ . - Added an endpoint to retrieve a blueprint for the hosted custom metric GET /api/v2/customJobs/(customJobId)/hostedCustomMetricTemplate/ . - Added an endpoint to update a blueprint for the hosted custom metric GET /api/v2/customJobs/(customJobId)/hostedCustomMetricTemplate/ . ### Enhancements - Requests for insights at the/api/v2/insights/*group of endpoints now accept theentityTypeparameter, which enables insight computation forcustomModelentities as well as nativedatarobotModelentities. To compute insights on a custom model, you must first initialize insights with a call toPOST /api/v2/modelComplianceDocsInitializations/(entityId)/. - The response from the Get SHAP impact API endpointGET /api/v2/insights/shapImpact/models/(entityId)/has been changed. Now theshapImpactsparameter is a list of key-pair values. Thecappingparameter has been removed from the response. ### Deprecation summary - The API endpoint GET /api/v2/projects/(projectId)/models/ is deprecated. Instead, use GET /api/v2/projects/(projectId)/modelRecords/ . - The API endpoint /projects/(projectId)/featureAssocationFeaturelists/ is removed, after being deprecated in release 2.19. Users should continue to use GET /api/v2/projects/(projectId)/featureAssociationFeaturelists/ . - The field modelsCount in GET /api/v2/useCases/ and GET /api/v2/useCases/(useCaseId)/ is deprecated and will be removed in June 2025. --- # API reference URL: https://docs.datarobot.com/en/docs/api/reference/index.html > Review the reference documentation available for DataRobot's APIs. # API reference The table below outlines the reference documentation available for DataRobot's API, SDKs, and code-first tools. | Resource | Description | | --- | --- | | REST API | The DataRobot REST API provides a programmatic alternative to the UI for creating and managing DataRobot assets. It allows you to automate processes and iterate more quickly, and lets you use DataRobot with scripted control. The API provides an intuitive modeling and prediction interface. | | Python API client | Installation, configuration, and usage guidelines for working with the Python client library. To access previous version of the Python API client documentation, access ReadTheDocs. | | Prediction API | DataRobot's Prediction API provides a mechanism for using your model for real-time predictions on a prediction server. | | Batch prediction API | The Batch Prediction API provides flexible options for scoring large datasets using the prediction servers you have already deployed. | | API changelogs | Changelogs contain curated, ordered lists of notable changes for each versioned release for DataRobot's SDKs and REST API. | | Self-managed resources | Details the resources available for self-managed DataRobot deployments. | | R client | Installation, configuration, and reference documentation for working with the R client library. | | OpenAPI specification | Reference the OpenAPI specification for the DataRobot REST API, which helps automate the generation of a client for languages that DataRobot doesn't directly support. It also assists with the design, implementation, and testing integration with DataRobot's REST API using a variety of automated OpenAPI-compatible tools. Note that accessing the OpenAPI spec requires you to be logged into the DataRobot application. | --- # Make predictions with the API URL: https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html > This section describes how to use DataRobot's Prediction API to make predictions using serverless prediction environments. # Make predictions with the API This section describes how to use DataRobot's Prediction API to make predictions using serverless prediction environments. If you need Prediction API reference documentation, it is available [here](https://docs.datarobot.com/en/docs/api/reference/predapi/pred-ref-serverless/index.html). You can use DataRobot's Prediction API for making predictions on a model deployment (by specifying the deployment ID). This provides access to advanced [model management](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html) features like target or data drift detection. DataRobot's model management features are safely decoupled from the Prediction API so that you can gain their benefit without sacrificing prediction speed or reliability. See the [deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/index.html) section for details on creating a model deployment. Before generating predictions with the Prediction API, review the recommended [best practices](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html#best-practices-for-the-fastest-predictions) to ensure the fastest predictions. ## Making predictions To generate predictions on new data using the Prediction API, you need: - The model's deployment ID. You can find the ID in the sample code output of the Deployments > Predictions > Prediction API tab (with Interface set to "API Client"). - Your API key . > [!WARNING] Warning > If your model is an open-source R script, it will run considerably slower. Prediction requests are submitted as POST requests to the REST API endpoint, for example: ``` curl -i -X POST "https://example.datarobot.com/api/v2/deployments//predictions" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` The order of the prediction response rows is the same as the order of the sent data. The Response returned is similar to: ``` HTTP/1.1 200 OK Content-Type: application/json X-DataRobot-Execution-Time: 38 X-DataRobot-Model-Cache-Hit: true {"data":[...]} ``` > [!NOTE] Note > The example above shows an arbitrary hostname ( `example.datarobot.com`) as the Prediction API URL; be sure to use the correct hostname of your DataRobot instance. The configured (predictions) URL is displayed in the sample code of the [Deployments > Predictions > Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab. See your system administrator for more assistance if needed. ## Using persistent HTTP connections All prediction requests are served over a secure connection (SSL/TLS), which can result in significant connection setup time. Depending on your network latency to the prediction instance, this can be anywhere from 30ms to upwards of 100-150ms. To address this, the Prediction API supports HTTP Keep-Alive, enabling your systems to keep a connection open for up to a minute after the last prediction request. Using the Python `requests` module, run your prediction requests from `requests.Session`: ``` import json import requests data = [ json.dumps({'Feature1': 42, 'Feature2': 'text value 1'}), json.dumps({'Feature1': 60, 'Feature2': 'text value 2'}), ] api_key = '...' api_endpoint = '...' session = requests.Session() session.headers = { 'Authorization': 'Bearer {}'.format(api_key), 'Content-Type': 'text/json', } for row in data: print(session.post(api_endpoint, data=row).json()) ``` Check the documentation of your favorite HTTP library for how to use persistent connections in your integration. ## Prediction inputs The API supports both JSON- and CSV-formatted input data (although JSON can be a safer choice if it is created with a good quality JSON parser). Data can either be posted in the [request body](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html#request-schema) or via a [file upload](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html#file-input) (multipart form). > [!NOTE] Note > When using the Prediction API, the only supported column separator in CSV files and request bodies is the comma ( `,`). ### JSON input The JSON input is formatted as an array of objects where the key is the feature name and the value is the value in the dataset. For example, a CSV file that looks like: ``` a,b,c 1,2,3 7,8,9 ``` Would be represented in JSON as: ``` [ { "a": 1, "b": 2, "c": 3 }, { "a": 7, "b": 8, "c": 9 } ] ``` Submit a JSON array to the Prediction API by sending the data to the `/api/v2/deployments//predictions` endpoint. For example: ``` curl -H "Content-Type: application/json" -X POST --data '[{"a": 4, "b": 5, "c": 6}\]' \ -H "Authorization: Bearer " \ https://example.datarobot.com/api/v2/deployments//predictions ``` ### File input This example assumes a CSV file, `dataset.csv`, that contains a header and the rows of data to predict on. cURL automatically sets the content type. ``` curl -i -X POST "https://example.datarobot.com/api/v2/deployments//predictions" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv HTTP/1.1 200 OK Date: Fri, 08 Feb 2019 10:00:00 GMT Content-Type: application/json Content-Length: 60624 Connection: keep-alive Server: nginx/1.12.2 X-DataRobot-Execution-Time: 39 X-DataRobot-Model-Cache-Hit: true Access-Control-Allow-Methods: OPTIONS, POST Access-Control-Allow-Credentials: true Access-Control-Expose-Headers: Content-Type,Content-Length,X-DataRobot-Execution-Time,X-DataRobot-Model-Cache-Hit,X-DataRobot-Model-Id,X-DataRobot-Request-Id Access-Control-Allow-Headers: Content-Type,Authorization X-DataRobot-Request-ID: 9e61f97bf07903b8c526f4eb47830a86 { "data": [ { "predictionValues": [ { "value": 0.2570950924, "label": 1 }, { "value": 0.7429049076, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 0, "rowId": 0 }, { "predictionValues": [ { "value": 0.7631880558, "label": 1 }, { "value": 0.2368119442, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 1, "rowId": 1 } ] } ``` ### In-body text input This example includes the CSV file content in the request body. With this format, you must set the Content-Type of the form data to `text/plain`. ``` curl -i -X POST "https://example.datarobot.com/api/v2/deployments//predictions" --data-binary $'a,b,c\n1,2,3\n7,8,9\n' -H "content-type: text/plain" \ -H "Authorization: Bearer " \ ``` ## Prediction outputs The Content-Type header value must be set appropriately for the type of data being sent ( `text/csv` or `application/json`); the raw API request responds with JSON by default. Reference the [output format](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-format.html) for more information about the structure of the output schema. The output schema shares the same format for real-time and batch predictions. ### CSV output To return CSV in addition to JSON from the Prediction API for real-time predictions, use `-H "Accept: text/csv"`. ## Prediction objects The following sections describe the content of the various prediction objects. ### Request schema Note that Request schema are standard for any kind of predictions. The following are the accepted headers: | Name | Value(s) | | --- | --- | | Content-Type | text/csv;charset=utf8 | | | application/json | | | multipart/form-data | | Content-Encoding | gzip | | | bz2 | | Authorization | Bearer | Note the following: - If you are submitting predictions as a raw stream of data, you can specify an encoding by adding;charset=to theContent-Typeheader. See thePython standard encodingsfor a list of valid values. DataRobot usesutf8by default. - If you are sending an encoded stream of data, you should specify theContent-Encodingheader. - TheAuthorizationfield is a Bearer authentication HTTP authentication scheme that involves security tokens called bearer tokens. While it is possible to authenticate via pair username + API token (Basic auth) or just via API token, these authentication methods are deprecated and not recommended. You can parameterize a request using URI query parameters: | Parameter name | Type | Notes | | --- | --- | --- | | passthroughColumns | string | List of columns from a scoring dataset to return in the prediction response. | | passthroughColumnsSet | string | If passthroughColumnsSet=all is passed, all columns from the scoring dataset are returned in the prediction response. | Note the following: - The passthroughColumns and passthroughColumnsSet parameters cannot both be passed in the same request. - While there is no limit on the number of column names you can pass with the passthroughColumns query parameter, there is a limit on the HTTP request line (currently 8192 bytes). The following example illustrates the use of multiple passthrough columns: ``` curl -i -X POST "https://example.datarobot.com/api/v2/deployments//predictions?passthroughColumns=Latitude&passthroughColumns=Longitude" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` ### Response schema The following is a sample prediction response body (also see the additional example of a [time series response body](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html#making-predictions-with-time-series)): ``` { "data": [ { "predictionValues": [ { "value": 0.6856798909, "label": 1 }, { "value": 0.3143201091, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 1, "rowId": 0, "passthroughValues": { "Latitude": -25.433508, "Longitude": 22.759397 } }, { "predictionValues": [ { "value": 0.765656753, "label": 1 }, { "value": 0.234343247, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 1, "rowId": 1, "passthroughValues": { "Latitude": 41.051128, "Longitude": 14.49598 } } ] } ``` The table below lists custom DataRobot headers: | Name | Value | Note | | --- | --- | --- | | X-DataRobot-Execution-Time | numeric | Time for compute predictions (ms). | | X-DataRobot-Model-Cache-Hit | true or false | Indication of in-memory presence of model (bool). | | X-DataRobot-Model-Id | ObjectId | ID of the model used to serve the prediction request (only returned for predictions made on model deployments). | | X-DataRobot-Request-Id | uuid | Unique identifier of a prediction request. | The following table describes the Response Prediction Rows of the JSON array: | Name | Type | Note | | --- | --- | --- | | predictionValues | array | An array of predictionValues (schema described below). | | predictionThreshold | float | The threshold used for predictions (applicable to binary classification projects only). | | prediction | float | The output of the model for this row. | | rowId | int | The row described. | | passthroughValues | object | A JSON object where key is a column name and value is a corresponding value for a predicted row from the scoring dataset. This JSON item is only returned if either passthroughColumns or passthroughColumnsSet is passed. | | adjustedPrediction | float | The exposure-adjusted output of the model for this row if the exposure was used during model building. The adjustedPrediction is included in responses if the request parameter excludeAdjustedPredictions is false. | | adjustedPredictionValues | array | An array of exposure-adjusted PredictionValue (schema described below). The adjustedPredictionValues is included in responses if the request parameter excludeAdjustedPredictions is false. | | predictionExplanations | array | An array of PredictionExplanations (schema described below). This JSON item is only returned with Prediction Explanations. | #### Prediction values schema The following table describes the `predictionValues` schema in the JSON Response array: | Name | Type | Note | | --- | --- | --- | | label | - | Describes what the model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a label from the target feature. | | value | float | The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the probability associated with the label that is predicted to be most likely (implying a threshold of 0.5 for binary classification problems). | #### Extra custom model output schema > [!NOTE] Availability information > Additional output in prediction responses for custom models is off by default. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable Additional Custom Model Output in Prediction Responses In some cases, the prediction response from your model may contain extra model output. This is possible for [custom models with additional output columns](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#additional-output-columns) defined in the `score()` hook and for Generative AI (GenAI) models. The `score()` hook can return any number of extra columns, containing data of types `string`, `int`, `float`, `bool`, or `datetime`. When additional columns are returned through the `score()` method, the prediction response is as follows: - For a tabular response (CSV) , the additional columns are returned as part of the response table or dataframe. - For a JSON response , the extraModelOutput key is returned alongside each row. This key is a dictionary containing the values of each additional column in the row. As custom models, deployed GenAI models can return extra columns through the `extraModelOutput` key to provide information about the text generation model (citations, latency, confidence, LLM blueprint ID, token counts, etc.), as shown in the example below: > [!NOTE] Citations in prediction response > For citations to be included in a Gen AI model's prediction response, the [LLM deployed from the playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/deploy-llm.html) must have a [vector database (VDB)](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html) associated with it. ``` # JSON response for GenAI model predictions { "data": [ { "rowId": 0, "prediction": "In the field of biology, there have been some exciting new discoveries made through research conducted on the International Space Station (ISS). Here are three examples:\n\n1. Understanding Plant Root Orientation: Scientists have been studying the growth and development of plants in microgravity. They found that plants grown in space exhibit different root orientation compared to those grown on Earth. This discovery helps us understand how plants adapt and respond to the absence of gravity. This knowledge can be applied to improve agricultural practices and develop innovative techniques for growing plants in challenging environments on Earth.\n\n2. Tissue Damage and Repair: One fascinating area of research on the ISS involves studying how living organisms respond to injuries in space. Scientists have investigated tissue damage and repair mechanisms in various organisms, including humans. By studying the healing processes in microgravity, researchers gained insights into how wounds heal differently in space compared to on Earth. This knowledge has implications for developing new therapies and treatments for wound healing and tissue regeneration.\n\n3. Bubbles, Lightning, and Fire Dynamics: The ISS provides a unique laboratory environment for studying the behavior of bubbles, lightning, and fire in microgravity. Scientists have conducted experiments to understand how these phenomena behave differently without the influence of gravity. These studies have practical applications, such as improving combustion processes, enhancing fire safety measures, and developing more efficient cooling systems.\n\nThese are just a few examples of the exciting discoveries that have been made in the field of biology through research conducted on the ISS. The microgravity environment of space offers a unique perspective and enables researchers to uncover new insights into the workings of living organisms and their interactions with the environment.", "predictionValues": [ { "label": "resultText", "value": "In the field of biology, there have been some exciting new discoveries made through research conducted on the International Space Station (ISS). Here are three examples:\n\n1. Understanding Plant Root Orientation: Scientists have been studying the growth and development of plants in microgravity. They found that plants grown in space exhibit different root orientation compared to those grown on Earth. This discovery helps us understand how plants adapt and respond to the absence of gravity. This knowledge can be applied to improve agricultural practices and develop innovative techniques for growing plants in challenging environments on Earth.\n\n2. Tissue Damage and Repair: One fascinating area of research on the ISS involves studying how living organisms respond to injuries in space. Scientists have investigated tissue damage and repair mechanisms in various organisms, including humans. By studying the healing processes in microgravity, researchers gained insights into how wounds heal differently in space compared to on Earth. This knowledge has implications for developing new therapies and treatments for wound healing and tissue regeneration.\n\n3. Bubbles, Lightning, and Fire Dynamics: The ISS provides a unique laboratory environment for studying the behavior of bubbles, lightning, and fire in microgravity. Scientists have conducted experiments to understand how these phenomena behave differently without the influence of gravity. These studies have practical applications, such as improving combustion processes, enhancing fire safety measures, and developing more efficient cooling systems.\n\nThese are just a few examples of the exciting discoveries that have been made in the field of biology through research conducted on the ISS. The microgravity environment of space offers a unique perspective and enables researchers to uncover new insights into the workings of living organisms and their interactions with the environment." } ], "deploymentApprovalStatus": "APPROVED", "extraModelOutput": { "CITATION_CONTENT_8": "3\nthe research study is received by others and how the \nknowledge is disseminated through citations in other \njournals. For example, six ISS studies have been \npublished in Nature, represented as a small node in the \ngraph. Network analysis shows that findings published \nin Nature are likely to be cited by other similar leading \njournals such as Science and Astrophysical Journal \nLetters (represented in bright yellow links) as well as \nspecialized journals such as Physical Review D and New \nJournal of Physics (represented in a yellow-green link). \nSix publications in Nature led to 512 citations according \nto VOSviewer\u2019s network map (version 1.6.11), an \nincrease of over 8,000% from publication to citation. \nFor comparison purposes, 6 publications in a small \njournal like American Journal of Botany led to 185 \ncitations and 107 publications in Acta Astronautica, \na popular journal among ISS scientists, led to 1,050 \ncitations (Figure 3, panel B). This count of 1,050", "CITATION_CONTENT_9": "Introduction\n4\nFigure 3. Count of publications reported in journals ranked in the top 100 according to global standards of Clarivate. A total of 567 top-tier publications \nthrough the end of FY-23 are shown by year and research category.\nIn this year\u2019s edition of the Annual Highlights of Results, we report findings from a \nwide range of topics in biology and biotechnology, physics, human research, Earth and \nspace science, and technology development \u2013 including investigations about plant root \norientation, tissue damage and repair, bubbles, lightning, fire dynamics, neutron stars, \ncosmic ray nuclei, imaging technology improvements, brain and vascular health, solar \npanel materials, grain flow, as well as satellite and robot control. \nThe findings highlighted here are only a small sample representative of the research \nconducted by the participating space agencies \u2013 ASI (Agenzia Spaziale Italiana), CSA \n(Canadian Space Agency), ESA (European Space Agency), JAXA (Japanese Aerospace", "CITATION_PAGE_3": 4, "CITATION_PAGE_8": 6, "CITATION_CONTENT_5": "23\nPUBLICATION HIGHLIGHTS: \nEARTH AND SPACE SCIENCE\nThe ISS laboratories enable scientific experiments in the biological sciences \nthat explore the complex responses of living organisms to the microgravity \nenvironment. The lab facilities support the exploration of biological systems \nranging from microorganisms and cellular biology to integrated functions \nof multicellular plants and animals. Several recent biological sciences \nexperiments have facilitated new technology developments that allow \ngrowth and maintenance of living cells, tissues, and organisms.\nThe Alpha Magnetic \nSpectrometer-02 (AMS-02) is \na state-of-the-art particle \nphysics detector constructed, \ntested, and operated by an \ninternational team composed \nof 60 institutes from \n16 countries and organized \nunder the United States \nDepartment of Energy (DOE) sponsorship. \nThe AMS-02 uses the unique environment of \nspace to advance knowledge of the universe \nand lead to the understanding of the universe\u2019s", "CITATION_SOURCE_5": "Space_Station_Annual_Highlights/iss_2017_highlights.pdf", "CITATION_CONTENT_3": "Introduction\n2\nExtensive international collaboration in the \nunique environment of LEO as well as procedural \nimprovements to assist researchers in the collection \nof data from the ISS have produced promising \nresults in the areas of protein crystal growth, tissue \nregeneration, vaccine and drug development, 3D \nprinting, and fiber optics, among many others. In \nthis year\u2019s edition of the Annual Highlights of Results, \nwe report findings from a wide range of topics in \nbiotechnology, physics, human research, Earth and \nspace science, and technology development \n\u2013 including investigations about human retinal cells, \nbacterial resistance, black hole detection, space \nanemia, brain health, Bose-Einstein condensates, \nparticle self-assembly, RNA extraction technology, \nand more. The findings highlighted here represent \nonly a sample of the work ISS has contributed to \nsociety during the past 12 months.\nAs of Oct. 1, 2022, we have identified a total of 3,679", "CITATION_SOURCE_8": "Space_Station_Annual_Highlights/iss_2021_highlights.pdf", "CITATION_PAGE_7": 8, "CITATION_PAGE_6": 8, "CITATION_PAGE_2": 4, "CITATION_CONTENT_7": "Biology and Biotechnology Earth and Space Science Educational and Cultural Activities\nHuman Research Physical Science Technology Development and Demonstration", "CITATION_SOURCE_9": "Space_Station_Annual_Highlights/iss_2023_highlights.pdf", "datarobot_latency": 3.1466632366, "blocked_resultText": false, "CITATION_SOURCE_2": "Space_Station_Annual_Highlights/iss_2023_highlights.pdf", "CITATION_SOURCE_6": "Space_Station_Annual_Highlights/iss_2021_highlights.pdf", "CITATION_SOURCE_7": "Space_Station_Annual_Highlights/iss_2023_highlights.pdf", "datarobot_confidence_score": 0.6524822695, "CITATION_PAGE_9": 7, "CITATION_CONTENT_4": "Molecular Life Sciences. 2021 October 29; DOI: \n10.1007/s00018-021-03989-2.\nFigure 7. Immunoflourescent images of human retinal \ncells in different conditions. Image adopted from \nCialdai, Cellular and Molecular Life Sciences.\nThe ISS laboratory provides a platform for investigations in the biological sciences that \nexplores the complex responses of living organisms to the microgravity environment. Lab \nfacilities support the exploration of biological systems, from microorganisms and cellular \nbiology to the integrated functions of multicellular plants and animals.", "CITATION_SOURCE_1": "Space_Station_Annual_Highlights/iss_2023_highlights.pdf", "CITATION_SOURCE_0": "Space_Station_Annual_Highlights/iss_2018_highlights.pdf", "CITATION_SOURCE_3": "Space_Station_Annual_Highlights/iss_2022_highlights.pdf", "CITATION_PAGE_5": 26, "CITATION_PAGE_0": 7, "CITATION_PAGE_1": 11, "LLM_BLUEPRINT_ID": "662ba0062ade64c4fc4c1a1f", "CITATION_PAGE_4": 9, "datarobot_token_count": 320, "CITATION_CONTENT_0": "more effectively in space by addressing \nsuch topics as understanding radiation effects on \ncrew health, combating bone and muscle loss, \nimproving designs of systems that handle fluids \nin microgravity, and determining how to maintain \nenvironmental control efficiently. \nResults from the ISS provide new \ncontributions to the body of scientific \nknowledge in the physical sciences, life \nsciences, and Earth and space sciences \nto advance scientific discoveries in multi\u0002disciplinary ways. \nISS science results have Earth-based \napplications, including understanding our \nclimate, contributing to the treatment of \ndisease, improving existing materials, and inspiring \nthe future generation of scientists, clinicians, \ntechnologists, engineers, mathematicians, artists, \nand explorers.\nBENEFITS\nFOR HUMANITY\nDISCOVERY\nFigure 4. A heat map of all of the countries whose authors have cited scientific results publications from ISS Research through October 1, 2018.\nEXPLORATION", "CITATION_SOURCE_4": "Space_Station_Annual_Highlights/iss_2022_highlights.pdf", "CITATION_CONTENT_2": "capabilities (i.e., facilities), and data delivery are critical to the effective operation \nof scientific projects for accurate results to be shared with the scientific community, \nsponsors, legislators, and the public. \nOver 3,700 investigations have operated since Expedition 1, with more than 250 active \nresearch facilities, the participation of more than 100 countries, the work of more than \n5,000 researchers, and over 4,000 publications. The growth in research (Figure 1) and \ninternational collaboration (Figure 2) has prompted the publication of over 560 research \narticles in top-tier scientific journals with about 75 percent of those groundbreaking studies \noccurring since 2018 (Figure 3). \nBibliometric analyses conducted through VOSviewer1\n measure the impact of space station \nresearch by quantifying and visualizing networks of journals, citations, subject areas, and \ncollaboration between authors, countries, or organizations. Using bibliometrics, a broad", "CITATION_CONTENT_1": "technologists, engineers, mathematicians, artists, and explorers.\nEXPLORATION\nDISCOVERY\nBENEFITS\nFOR HUMANITY", "CITATION_CONTENT_6": "control efficiently. \nResults from the ISS provide new \ncontributions to the body of scientific \nknowledge in the physical sciences, life \nsciences, and Earth and space sciences \nto advance scientific discoveries in multi\u0002disciplinary ways. \nISS science results have Earth-based \napplications, including understanding our \nclimate, contributing to the treatment of \ndisease, improving existing materials, and \ninspiring the future generation of scientists, \nclinicians, technologists, engineers, \nmathematicians, artists and explorers.\nBENEFITS\nFOR HUMANITY\nDISCOVERY\nEXPLORATION" } }, ] } ``` ## Making predictions with time series > [!TIP] Tip > Time series predictions are specific to time series projects, not all time-aware modeling projects. Specifically, the CSV file must follow a specific format, described in the [predictions section](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-predictions.html#make-predictions-tab) of the time series modeling pages. If you are making predictions with the forecast point, you can skip the forecast window in your prediction data as DataRobot generates a forecast point automatically. This is called autoexpansion. Autoexpansion applies automatically if: - Predictions are made for a specific forecast point and not a forecast range. - The time series project has a regular time step and does not use Nowcasting. When using autoexpansion, note the following: - If you have Known in Advance features that are important for your model, it is recommended that you manually create a forecast window to increase prediction accuracy. - If you plan to use an association ID other than the primary date/time column in your deployment to track accuracy, create a forecast window manually. The URL for making predictions with time series deployments and regular non-time series deployments is the same. The only difference is that you can optionally specify forecast point, prediction start/end date, or some other time series specific URL parameters. Using the deployment ID, the server automatically detects the deployed model as a time series deployment and processes it accordingly: ``` curl -i -X POST "https://example.datarobot.com/api/v2/deployments//predictions" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` The following is a sample Response body for a multiseries project: ``` HTTP/1.1 200 OK Content-Type: application/json X-DataRobot-Execution-Time: 1405 X-DataRobot-Model-Cache-Hit: false { "data": [ { "seriesId": 1, "forecastPoint": "2018-01-09T00:00:00Z", "rowId": 365, "timestamp": "2018-01-10T00:00:00.000000Z", "predictionValues": [ { "value": 45180.4041874386, "label": "target (actual)" } ], "forecastDistance": 1, "prediction": 45180.4041874386 }, { "seriesId": 1, "forecastPoint": "2018-01-09T00:00:00Z", "rowId": 366, "timestamp": "2018-01-11T00:00:00.000000Z", "predictionValues": [ { "value": 47742.9432499386, "label": "target (actual)" } ], "forecastDistance": 2, "prediction": 47742.9432499386 }, { "seriesId": 1, "forecastPoint": "2018-01-09T00:00:00Z", "rowId": 367, "timestamp": "2018-01-12T00:00:00.000000Z", "predictionValues": [ { "value": 46394.5698978878, "label": "target (actual)" } ], "forecastDistance": 3, "prediction": 46394.5698978878 }, { "seriesId": 2, "forecastPoint": "2018-01-09T00:00:00Z", "rowId": 697, "timestamp": "2018-01-10T00:00:00.000000Z", "predictionValues": [ { "value": 39794.833199375, "label": "target (actual)" } ] } ] } ``` ### Request parameters You can parameterize the time series prediction request using URI query parameters. For example, overriding the default inferred forecast point can look like this: ``` curl -i -X POST "https://example.datarobot.com/api/v2/deployments//predictions?forecastPoint=1961-01-01T00:00:00?relaxKnownInAdvanceFeaturesCheck=true" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` For the full list of time series-specific parameters, see [Time series predictions for deployments](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/time-pred.html). ### Response schema The [Response schema](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html#response-schema_1) is consistent with [standard predictions](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html#response-schema_2) but adds a number of columns for each `PredictionRow` object: | Name | Type | Notes | | --- | --- | --- | | seriesId | string, int, or None | A multiseries identifier of a predicted row that identifies the series in a multiseries project. | | forecastPoint | string | An ISO 8601 formatted DateTime string corresponding to the forecast point for the prediction request, either user-configured or selected by DataRobot. | | timestamp | string | An ISO 8601 formatted DateTime string corresponding to the DateTime column of the predicted row. | | forecastDistance | int | A forecast distance identifier of the predicted row, or how far it is from forecastPoint in the scoring dataset. | | originalFormatTimestamp | string | A DateTime string corresponding to the DateTime column of the predicted row. Unlike the timestamp column, this column will keep the same DateTime formatting as the uploaded prediction dataset. (This column is shown if enabled by your administrator.) | ## Making Prediction Explanations The DataRobot [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) feature gives insight into which attributes of a particular input cause it to have exceptionally high or exceptionally low predicted values. > [!TIP] Tip > You must run the following two critical dependencies before running Prediction Explanations: > > You must compute > Feature Impact > for the model. > You must generate predictions on the dataset using the selected model. To initialize Prediction Explanations, use the [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) tab. Making Prediction Explanations is very similar to standard prediction requests. First, Prediction Explanations requests are submitted as POST requests to the resource: ``` curl -i -X POST "https://example.datarobot.com/api/v2/deployments//predictionExplanations" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` The following is a sample Response body: ``` HTTP/1.1 200 OK Content-Type: application/json X-DataRobot-Execution-Time: 841 X-DataRobot-Model-Cache-Hit: true { "data": [ { "predictionValues": [ { "value": 0.6634830442, "label": 1 }, { "value": 0.3365169558, "label": 0 } ], "prediction": 1, "rowId": 0, "predictionExplanations": [ { "featureValue": 49, "strength": 0.6194461777, "feature": "driver_age", "qualitativeStrength": "+++", "label": 1 }, { "featureValue": 1, "strength": 0.3501610895, "feature": "territory", "qualitativeStrength": "++", "label": 1 }, { "featureValue": "M", "strength": -0.171075409, "feature": "gender", "qualitativeStrength": "--", "label": 1 } ] }, { "predictionValues": [ { "value": 0.3565584672, "label": 1 }, { "value": 0.6434415328, "label": 0 } ], "prediction": 0, "rowId": 1, "predictionExplanations": [] } ] } ``` ### Request parameters You can parameterize the Prediction Explanations prediction request using URI query parameters: | Parameter name | Type | Notes | | --- | --- | --- | | maxExplanations | int | Maximum number of codes generated per prediction. Default is 3. Previously called maxCodes. | | thresholdLow | float | Prediction Explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) for Prediction Explanations to compute. This value can be null. | | thresholdHigh | float | Prediction Explanation high threshold. Predictions must be above this value (or below the thresholdLow value) for Prediction Explanations to compute. This value can be null. | | excludeAdjustedPredictions | string | Includes or excludes exposure-adjusted predictions in prediction responses if exposure was used during model building. The default value is 'true' (exclude exposure-adjusted predictions). | The following is an example of a parameterized request: ``` curl -i -X POST "https://example.datarobot.com/api/v2/deployments//predictionExplanations?maxExplanations=2&thresholdLow=0.2&thresholdHigh=0.5" -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` DataRobot's headers schema is the same as that for prediction responses. The [Response schema](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html#response-schema_2) is consistent with standard predictions, but adds "predictionExplanations", an array of `PredictionExplanations` for each `PredictionRow` object. #### PredictionExplanations schema Response JSON Array of Objects: | Name | Type | Notes | | --- | --- | --- | | label | – | Describes which output was driven by this Prediction Explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this Prediction Explanation. | | feature | string | Name of the feature contributing to the prediction. | | featureValue | - | Value the feature took on for this row. | | strength | float | Amount this feature's value affected the prediction. | | qualitativeStrength | string | Human-readable description of how strongly the feature affected the prediction (e.g., +++, –, +). | > [!TIP] Tip > The prediction explanation `strength` value is not bounded to the values `[-1, 1]`; its interpretation may change as the number of features in the model changes. For normalized values, use `qualitativeStrength` instead.`qualitativeStrength` expresses the `[-1, 1]` range with visuals, with `---` representing `-1` and `+++` representing `1`. For explanations with the same `qualitativeStrength`, you can then use the `strength` value for ranking. > > See the section on [interpreting Prediction Explanation output](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/xemp-pe.html#interpret-xemp-prediction-explanations) for more information. ## Making predictions with humility monitoring Predictions with [humility monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html) allow you to monitor predictions using user-defined humility rules. When a prediction falls outside the thresholds provided for the "Uncertain Prediction" Trigger, it will default to the action assigned to the trigger. The humility key is added to the body of the prediction response when the trigger is activated. The following is a sample Response body for a Regression project with an `Uncertain Prediction Trigger` with `Action - No Operation`: ``` { "data": [ { "predictionValues": [ { "value": 122.8034057617, "label": "length" } ], "prediction": 122.8034057617, "rowId": 99, "humility": [ { "ruleId": "5ebad4735f11b33a38ff3e0d", "triggered": true, "ruleName": "Uncertain Prediction Trigger" } ] } ] } ``` The following is an example of a Response body for a regression model deployment. It uses the "Uncertain Prediction" trigger with the "Throw Error" action: ``` 480 Error: {"message":"Humility ReturnError action triggered."} ``` The following is an example of a Response body for a regression model deployment. It uses the "Uncertain Prediction" trigger with the "Override Prediction" action: ``` { "data": [ { "predictionValues": [ { "value": 122.8034057617, "label": "length" } ], "prediction": 5220, "rowId": 99, "humility": [ { "ruleId": "5ebad4735f11b33a38ff3e0d", "triggered": true, "ruleName": "Uncertain Prediction Trigger" } ] } ] } ``` ### Response schema The [response schema](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html#response-schema_2) is consistent with [standard predictions](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html#response-schema) but adds a new humility column with a subset of columns for each `Humility` object: | Name | Type | Notes | | --- | --- | --- | | ruleId | string | The ID of the humility rule assigned to the deployment | | triggered | boolean | Returns "True" or "False" depending on if the rule was triggered or not | | ruleName | string | The name of the rule that is either defined by the user or auto-generated with a timestamp | ## Error responses Any error is indicated by a non-200 code attribute. Codes starting with 4XX indicate request errors (e.g., missing columns, wrong credentials, unknown model ID). The message attribute gives a detailed description of the error in the case of a 4XX code. For example: ``` curl -H "Content-Type: application/json" -X POST --data '' \ -H "Authorization: Bearer " \ https://example.datarobot.com/api/v2/deployments//predictions HTTP/1.1 400 BAD REQUEST Date: Fri, 08 Feb 2019 11:00:00 GMT Content-Type: application/json Content-Length: 53 Connection: keep-alive Server: nginx/1.12.2 X-DataRobot-Execution-Time: 332 X-DataRobot-Request-ID: fad6a0b62c1ff30db74c6359648d12fd { "message": "The requested URL was not found on the server. If you entered the URL manually, please check your spelling and try again." } ``` Codes starting with 5XX indicate server-side errors. Retry the request or contact your DataRobot representative. ## Knowing the limitations The following describes the size and timeout boundaries for real-time deployment predictions: - Maximum data submission size is 50MB. - There is no limit on the number of rows, but timeout limits are as follows: If your request exceeds the timeout, or you are trying to score a large file using serverless predictions, consider using thebatch scoring package. - There is a limit on the size of theHTTP request line(currently 8192 bytes). - For managed AI Platform deployments, serverless prediction environments automatically close persistent HTTP connections if they are idle for more than 600 seconds. To use persistent connections, the client side must be able to handle these disconnects correctly. The following example configures Python HTTP libraryrequeststo automatically retry HTTP requests on transport failure: ``` import requests import urllib3 # create a transport adapter that will automatically retry GET/POST/HEAD requests on failures up to 3 times adapter = requests.adapters.HTTPAdapter( max_retries=urllib3.Retry( total=3, method_whitelist=frozenset(['GET', 'POST', 'HEAD']) ) ) # create a Session (a pool of connections) and make it use the given adapter for HTTP and HTTPS requests session = requests.Session() session.mount('http://', adapter) session.mount('https://', adapter) # execute a prediction request that will be retried on transport failures, if needed api_token = '' response = session.post( 'https://example.datarobot.com/api/v2/deployments//predictions', headers={ 'Authorization': 'Bearer %s' % api_token, 'Content-Type': 'text/csv', }, data='', ) print(response.content) ``` ### Model caching Serverless prediction environments fetch models, as needed, from the DataRobot cluster. To speed up subsequent predictions that use the same model, DataRobot stores a certain number of models in memory (cache). When the cache fills, each new model request will require that one of the existing models in the cache be removed. DataRobot removes the least recently used model (which is not necessarily the model that has been in the cache the longest). For Self-Managed AI Platform installations, the default size for the cache is 16 models, but it can vary from installation to installation. Please contact DataRobot support if you have questions regarding the cache size of your specific installation. A serverless prediction environment runs multiple prediction processes, each of which has its own exclusive model cache. Prediction processes do not share between themselves. Because of this, it is possible that you send two consecutive requests to a serverless prediction environment, and each has to download the model data. Each response from the serverless prediction environment includes a header, `X-DataRobot-Model-Cache-Hit`, indicating whether the model used was in the cache. If the model was in the cache, the value of the header is true; if the value is false, the model was not in the cache. ## Best practices for the fastest predictions The following checklist summarizes the suggestions above to help deliver the fastest predictions possible: - Implementpersistent HTTP connections: This reduces network round-trips, and thus latency, to the Prediction API. - Use CSV data:Because JSON serialization of large amounts of data can take longer than using CSV, consider using CSV for yourprediction inputs. - Keep the number of requested models low:This allows the Prediction API to make use ofmodel caching. - Batch data together in chunks:Batch as many rows together as possible without going over the50MB real-time deployment prediction request limit. If scoring larger files, consider using theBatch Prediction APIwhich, in addition to scoring local files, also supports scoring to and from S3 and databases. --- # Prediction API URL: https://docs.datarobot.com/en/docs/api/reference/predapi/index.html > DataRobot's Prediction API provides a mechanism for using your model for real-time predictions via serverless prediction environments. # Prediction API DataRobot's Prediction API provides a mechanism for using your model for real-time predictions via serverless or dedicated prediction environments. Follow [the guidelines](https://docs.datarobot.com/en/docs/api/reference/predapi/dr-predapi-serverless.html) for making serverless predictions with the Prediction API. Serverless predictions use the REST API endpoint `/api/v2/deployments/:id/predictions` and do not require a `datarobot-key` header. To access documentation for the dedicated server Prediction API, navigate to the [Dedicated Prediction API page](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/index.html). | Topic | Description | | --- | --- | | Make predictions with the API (serverless) | Make predictions using serverless prediction environments. | | Make predictions with the Python API client | Use the DataRobot Prediction Library, a Python library for making predictions with various prediction methods. | | Get a prediction server ID | Retrieve a prediction server ID using cURL commands from the REST API or the DataRobot Python client. | | Serverless Prediction API reference | Review Prediction API methods, input and output parameters, and errors for serverless predictions. | | Make predictions with the API (dedicated server) | Legacy documentation for using the Prediction API with dedicated servers. | | Deprecated API routes | Review deprecated Prediction API routes in a reference document listing the deprecated requests and their replacements. | --- # Deprecated API routes URL: https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/deprecated-prediction-api.html > An overview of DataRobot's deprecated Prediction API routes, with a complete list of the specific deprecated POST and GET requests and their replacements. # Deprecated API routes The Prediction API has changed significantly over time and accumulated a number of old routes. Even though these routes are already deprecated, they are still available in some installations since not all users have migrated to newer versions yet. This page describes: - all such deprecated routes - deadlines for their complete removal - new REST endpoints that should be used instead of old ones - how Prediction Admins can capture usages of deprecated routes within their organization to safely upgrade DataRobot Please refer to Prediction API [reference](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/index.html) documentation for details on each specific route. ## Deprecated Prediction API routes The Prediction API has moved from "project/model" routes to "deployment-aware" routes. To support this transition, the following routes have been deprecated: > [!WARNING] Warning > Availability of deprecated routes (those not using the deployment-aware model) is dependent on the initial DataRobot deployment version. > See the table below for complete details. Contact your DataRobot representative if you need help migrating to the new API routes. | Deployment type | Installation timeline | Status and notes | | --- | --- | --- | | Self-Managed AI Platform | New as of v6.0 or later | Disabled | | Self-Managed AI Platform | Upgraded to v6.0 or v.6.1 | Supported | | Self-Managed AI Platform | v6.2 upgrade (future) | All deprecated routes removed entirely | | AI Platform* | Migrated individually, contact your DataRobot representative | Migration is in progress | * Managed AI Platform accounts newer than May 2020 only have access to the new routes. ### The full list of deprecated routes #### Make AutoML predictions Deprecated route: `POST /predApi/v1.0///predict` New route: `POST /predApi/v1.0/deployments//predictions` #### Make time series predictions Deprecated routes: `POST /predApi/v1.0///timeSeriesPredict` `POST /predApi/v1.0/deployments//timeSeriesPredictions` New route: `POST /predApi/v1.0/deployments//predictions` #### Prediction Explanations Deprecated routes: `POST /predApi/v1.0///reasonCodesPredictions` `POST /predApi/v1.0///predictionExplanations` `POST /predApi/v1.0/deployments//predictionExplanations` New route: `POST /predApi/v1.0/deployments//predictions` #### Ping Deprecated route: `GET /api/v1/ping` New route: `GET /predApi/v1.0/ping` #### List models Deprecated route: `GET /api/v1//models` New route: Use Public V2 API to fetch the list of models in a project #### Using tokens Deprecated routes: `GET /api/v1/api_token` `POST /api/v1/api_token` `GET /predApi/v1.0/api_token` New route: API tokens are superseded by API Keys and are managed by the public V2 API only. See the [UI platform documentation](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html) or the Account > API keys and tools section of the public V2 API documentation. ## Request examples for legacy Prediction API routes This section provides examples showing how to make predictions using legacy and soon to be disabled Prediction API routes directly on a model by specifying the model’s project and model ID. See the table above for a [deprecation timeline](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/deprecated-prediction-api.html#tracking-deprecated-routes-usage) based on release status. Generating predictions for classification and regression projects: ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0///predict" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` Generating predictions for time series projects: ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0///timeSeriesPredict" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` Generating Prediction Explanations: ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0///predictionExplanations" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` If you are using he managed AI Platform (SaaS), include the `datarobot-key` in the cURL header: `-H "datarobot-key: xxxx`. ## Tracking deprecated routes usage > [!NOTE] Availability information > This feature is not available for managed AI Platform (SaaS) users. Contact your DataRobot representative for information on handling migrations. > [!NOTE] Note > This feature is available in v6.1 only. In v6.2 it will be deleted along with all deprecated routes. For prediction admins convenience, it is possible to track all deprecated routes usage from a single page. This feature is only available to users who have "Enable Predictions Admin" permission enabled: Prediction admins can access it via Manage Predictions page: In order to access deprecated routes statistics click on the button in the top right corner: The table with statistics will look like this: The table has the following columns: - Last Used : the last time this request was made (UTC) - Request : HTTP request that was made. Includes query parameters, if any - Username : name of the DR user who made this request - Total Use Count : total number of times request was made since v6.1. Notes: 1. The table is sorted by Last Used descending, so that the most recent requests are shown on top. 2. Two different users making the same request are counted separately. 3. The total number of the most recent requests shown is limited by 100. 4. This table only shows requests to deprecated routes. Requests using new routes are not shown.. --- # Make predictions with the API URL: https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html > This section describes how to use DataRobot's Prediction API to make predictions on a dedicated prediction server. # Make predictions with the API This section describes how to use DataRobot's Prediction API to make predictions on a dedicated prediction server. If you need Prediction API reference documentation, it is available [here](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/index.html). You can use DataRobot's Prediction API for making predictions on a model deployment (by specifying the deployment ID). This provides access to advanced [model management](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html) features like target or data drift detection. DataRobot's model management features are safely decoupled from the Prediction API so that you can gain their benefit without sacrificing prediction speed or reliability. See the [deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/index.html) section for details on creating a model deployment. Before generating predictions with the Prediction API, review the recommended [best practices](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html#best-practices-for-the-fastest-predictions) to ensure the fastest predictions. ## Making predictions To generate predictions on new data using the Prediction API, you need: - The model's deployment ID. You can find the ID in the sample code output of the Deployments > Predictions > Prediction API tab (with Interface set to "API Client"). - Your API key . > [!WARNING] Warning > If your model is an open-source R script, it will run considerably slower. Prediction requests are submitted as POST requests to the resource, for example: ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0/deployments//predictions" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` > [!NOTE] Availability information > Managed AI Platform (SaaS) users must include the `datarobot-key` in the cURL header (for example, `curl -H "Content-Type: application/json", -H "datarobot-key: xxxx"`). Find the key by displaying secrets on the [Predictions > Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab or by contacting your DataRobot representative. The order of the prediction response rows is the same as the order of the sent data. The Response returned is similar to: ``` HTTP/1.1 200 OK Content-Type: application/json X-DataRobot-Execution-Time: 38 X-DataRobot-Model-Cache-Hit: true {"data":[...]} ``` > [!NOTE] Note > The example above shows an arbitrary hostname ( `example.datarobot.com`) as the Prediction API URL; be sure to use the correct hostname of your dedicated prediction server. The configured (predictions) URL is displayed in the sample code of the [Deployments > Predictions > Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab. See your system administrator for more assistance if needed. ## Using persistent HTTP connections All prediction requests are served over a secure connection (SSL/TLS), which can result in significant connection setup time. Depending on your network latency to the prediction instance, this can be anywhere from 30ms to upwards of 100-150ms. To address this, the Prediction API supports HTTP Keep-Alive, enabling your systems to keep a connection open for up to a minute after the last prediction request. Using the Python `requests` module, run your prediction requests from `requests.Session`: ``` import json import requests data = [ json.dumps({'Feature1': 42, 'Feature2': 'text value 1'}), json.dumps({'Feature1': 60, 'Feature2': 'text value 2'}), ] api_key = '...' api_endpoint = '...' session = requests.Session() session.headers = { 'Authorization': 'Bearer {}'.format(api_key), 'Content-Type': 'text/json', } for row in data: print(session.post(api_endpoint, data=row).json()) ``` Check the documentation of your favorite HTTP library for how to use persistent connections in your integration. ## Prediction inputs The API supports both JSON- and CSV-formatted input data (although JSON can be a safer choice if it is created with a good quality JSON parser). Data can either be posted in the [request body](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html#request-schema) or via a [file upload](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html#file-input) (multipart form). > [!NOTE] Note > When using the Prediction API, the only supported column separator in CSV files and request bodies is the comma ( `,`). ### JSON input The JSON input is formatted as an array of objects where the key is the feature name and the value is the value in the dataset. For example, a CSV file that looks like: ``` a,b,c 1,2,3 7,8,9 ``` Would be represented in JSON as: ``` [ { "a": 1, "b": 2, "c": 3 }, { "a": 7, "b": 8, "c": 9 } ] ``` Submit a JSON array to the Prediction API by sending the data to the `/predApi/v1.0/deployments//predictions` endpoint. For example: ``` curl -H "Content-Type: application/json" -X POST --data '[{"a": 4, "b": 5, "c": 6}\]' \ -H "Authorization: Bearer " \ https://example.datarobot.com/predApi/v1.0/deployments//predictions ``` ### File input This example assumes a CSV file, `dataset.csv`, that contains a header and the rows of data to predict on. cURL automatically sets the content type. ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0/deployments//predictions" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv HTTP/1.1 200 OK Date: Fri, 08 Feb 2019 10:00:00 GMT Content-Type: application/json Content-Length: 60624 Connection: keep-alive Server: nginx/1.12.2 X-DataRobot-Execution-Time: 39 X-DataRobot-Model-Cache-Hit: true Access-Control-Allow-Methods: OPTIONS, POST Access-Control-Allow-Credentials: true Access-Control-Expose-Headers: Content-Type,Content-Length,X-DataRobot-Execution-Time,X-DataRobot-Model-Cache-Hit,X-DataRobot-Model-Id,X-DataRobot-Request-Id Access-Control-Allow-Headers: Content-Type,Authorization,datarobot-key X-DataRobot-Request-ID: 9e61f97bf07903b8c526f4eb47830a86 { "data": [ { "predictionValues": [ { "value": 0.2570950924, "label": 1 }, { "value": 0.7429049076, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 0, "rowId": 0 }, { "predictionValues": [ { "value": 0.7631880558, "label": 1 }, { "value": 0.2368119442, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 1, "rowId": 1 } ] } ``` ### In-body text input This example includes the CSV file content in the request body. With this format, you must set the Content-Type of the form data to `text/plain`. ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0/deployments//predictions" --data-binary $'a,b,c\n1,2,3\n7,8,9\n' -H "content-type: text/plain" \ -H "Authorization: Bearer " \ ``` ## Prediction outputs The Content-Type header value must be set appropriately for the type of data being sent ( `text/csv` or `application/json`); the raw API request responds with JSON by default. Reference the [output format](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/output-format.html) for more information about the structure of the output schema. The output schema shares the same format for real-time and batch predictions. ### CSV output To return CSV in addition to JSON from the Prediction API for real-time predictions, use `-H "Accept: text/csv"`. ## Prediction objects The following sections describe the content of the various prediction objects. ### Request schema Note that Request schema are standard for any kind of predictions. The following are the accepted headers: | Name | Value(s) | | --- | --- | | Content-Type | text/csv;charset=utf8 | | | application/json | | | multipart/form-data | | Content-Encoding | gzip | | | bz2 | | Authorization | Bearer | Note the following: - If you are submitting predictions as a raw stream of data, you can specify an encoding by adding;charset=to theContent-Typeheader. See thePython standard encodingsfor a list of valid values. DataRobot usesutf8by default. - If you are sending an encoded stream of data, you should specify theContent-Encodingheader. - TheAuthorizationfield is a Bearer authentication HTTP authentication scheme that involves security tokens called bearer tokens. While it is possible to authenticate via pair username + API token (Basic auth) or just via API token, these authentication methods are deprecated and not recommended. You can parameterize a request using URI query parameters: | Parameter name | Type | Notes | | --- | --- | --- | | passthroughColumns | string | List of columns from a scoring dataset to return in the prediction response. | | passthroughColumnsSet | string | If passthroughColumnsSet=all is passed, all columns from the scoring dataset are returned in the prediction response. | Note the following: - The passthroughColumns and passthroughColumnsSet parameters cannot both be passed in the same request. - While there is no limit on the number of column names you can pass with the passthroughColumns query parameter, there is a limit on the HTTP request line (currently 8192 bytes). The following example illustrates the use of multiple passthrough columns: ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0/deployments//predictions?passthroughColumns=Latitude&passthroughColumns=Longitude" \ -H "Authorization: Bearer " \ -H "datarobot-key: " -F \ file=@~/.home/path/to/dataset.csv ``` ### Response schema The following is a sample prediction response body (also see the additional example of a [time series response body](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html#making-predictions-with-time-series)): ``` { "data": [ { "predictionValues": [ { "value": 0.6856798909, "label": 1 }, { "value": 0.3143201091, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 1, "rowId": 0, "passthroughValues": { "Latitude": -25.433508, "Longitude": 22.759397 } }, { "predictionValues": [ { "value": 0.765656753, "label": 1 }, { "value": 0.234343247, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 1, "rowId": 1, "passthroughValues": { "Latitude": 41.051128, "Longitude": 14.49598 } } ] } ``` The table below lists custom DataRobot headers: | Name | Value | Note | | --- | --- | --- | | X-DataRobot-Execution-Time | numeric | Time for compute predictions (ms). | | X-DataRobot-Model-Cache-Hit | true or false | Indication of in-memory presence of model (bool). | | X-DataRobot-Model-Id | ObjectId | ID of the model used to serve the prediction request (only returned for predictions made on model deployments). | | X-DataRobot-Request-Id | uuid | Unique identifier of a prediction request. | The following table describes the Response Prediction Rows of the JSON array: | Name | Type | Note | | --- | --- | --- | | predictionValues | array | An array of predictionValues (schema described below). | | predictionThreshold | float | The threshold used for predictions (applicable to binary classification projects only). | | prediction | float | The output of the model for this row. | | rowId | int | The row described. | | passthroughValues | object | A JSON object where key is a column name and value is a corresponding value for a predicted row from the scoring dataset. This JSON item is only returned if either passthroughColumns or passthroughColumnsSet is passed. | | adjustedPrediction | float | The exposure-adjusted output of the model for this row if the exposure was used during model building. The adjustedPrediction is included in responses if the request parameter excludeAdjustedPredictions is false. | | adjustedPredictionValues | array | An array of exposure-adjusted PredictionValue (schema described below). The adjustedPredictionValues is included in responses if the request parameter excludeAdjustedPredictions is false. | | predictionExplanations | array | An array of PredictionExplanations (schema described below). This JSON item is only returned with Prediction Explanations. | #### Prediction values schema The following table describes the `predictionValues` schema in the JSON Response array: | Name | Type | Note | | --- | --- | --- | | label | - | Describes what the model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a label from the target feature. | | value | float | The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the probability associated with the label that is predicted to be most likely (implying a threshold of 0.5 for binary classification problems). | #### Extra custom model output schema > [!NOTE] Availability information > Additional output in prediction responses for custom models is off by default. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable Additional Custom Model Output in Prediction Responses In some cases, the prediction response from your model may contain extra model output. This is possible for [custom models with additional output columns](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#additional-output-columns) defined in the `score()` hook and for Generative AI (GenAI) models. The `score()` hook can return any number of extra columns, containing data of types `string`, `int`, `float`, `bool`, or `datetime`. When additional columns are returned through the `score()` method, the prediction response is as follows: - For a tabular response (CSV) , the additional columns are returned as part of the response table or dataframe. - For a JSON response , the extraModelOutput key is returned alongside each row. This key is a dictionary containing the values of each additional column in the row. As custom models, deployed GenAI models can return extra columns through the `extraModelOutput` key to provide information about the text generation model (citations, latency, confidence, LLM blueprint ID, token counts, etc.), as shown in the example below: > [!NOTE] Citations in prediction response > For citations to be included in a Gen AI model's prediction response, the [LLM deployed from the playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/deploy-llm.html) must have a [vector database (VDB)](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html) associated with it. ``` # JSON response for GenAI model predictions { "data": [ { "rowId": 0, "prediction": "In the field of biology, there have been some exciting new discoveries made through research conducted on the International Space Station (ISS). Here are three examples:\n\n1. Understanding Plant Root Orientation: Scientists have been studying the growth and development of plants in microgravity. They found that plants grown in space exhibit different root orientation compared to those grown on Earth. This discovery helps us understand how plants adapt and respond to the absence of gravity. This knowledge can be applied to improve agricultural practices and develop innovative techniques for growing plants in challenging environments on Earth.\n\n2. Tissue Damage and Repair: One fascinating area of research on the ISS involves studying how living organisms respond to injuries in space. Scientists have investigated tissue damage and repair mechanisms in various organisms, including humans. By studying the healing processes in microgravity, researchers gained insights into how wounds heal differently in space compared to on Earth. This knowledge has implications for developing new therapies and treatments for wound healing and tissue regeneration.\n\n3. Bubbles, Lightning, and Fire Dynamics: The ISS provides a unique laboratory environment for studying the behavior of bubbles, lightning, and fire in microgravity. Scientists have conducted experiments to understand how these phenomena behave differently without the influence of gravity. These studies have practical applications, such as improving combustion processes, enhancing fire safety measures, and developing more efficient cooling systems.\n\nThese are just a few examples of the exciting discoveries that have been made in the field of biology through research conducted on the ISS. The microgravity environment of space offers a unique perspective and enables researchers to uncover new insights into the workings of living organisms and their interactions with the environment.", "predictionValues": [ { "label": "resultText", "value": "In the field of biology, there have been some exciting new discoveries made through research conducted on the International Space Station (ISS). Here are three examples:\n\n1. Understanding Plant Root Orientation: Scientists have been studying the growth and development of plants in microgravity. They found that plants grown in space exhibit different root orientation compared to those grown on Earth. This discovery helps us understand how plants adapt and respond to the absence of gravity. This knowledge can be applied to improve agricultural practices and develop innovative techniques for growing plants in challenging environments on Earth.\n\n2. Tissue Damage and Repair: One fascinating area of research on the ISS involves studying how living organisms respond to injuries in space. Scientists have investigated tissue damage and repair mechanisms in various organisms, including humans. By studying the healing processes in microgravity, researchers gained insights into how wounds heal differently in space compared to on Earth. This knowledge has implications for developing new therapies and treatments for wound healing and tissue regeneration.\n\n3. Bubbles, Lightning, and Fire Dynamics: The ISS provides a unique laboratory environment for studying the behavior of bubbles, lightning, and fire in microgravity. Scientists have conducted experiments to understand how these phenomena behave differently without the influence of gravity. These studies have practical applications, such as improving combustion processes, enhancing fire safety measures, and developing more efficient cooling systems.\n\nThese are just a few examples of the exciting discoveries that have been made in the field of biology through research conducted on the ISS. The microgravity environment of space offers a unique perspective and enables researchers to uncover new insights into the workings of living organisms and their interactions with the environment." } ], "deploymentApprovalStatus": "APPROVED", "extraModelOutput": { "CITATION_CONTENT_8": "3\nthe research study is received by others and how the \nknowledge is disseminated through citations in other \njournals. For example, six ISS studies have been \npublished in Nature, represented as a small node in the \ngraph. Network analysis shows that findings published \nin Nature are likely to be cited by other similar leading \njournals such as Science and Astrophysical Journal \nLetters (represented in bright yellow links) as well as \nspecialized journals such as Physical Review D and New \nJournal of Physics (represented in a yellow-green link). \nSix publications in Nature led to 512 citations according \nto VOSviewer\u2019s network map (version 1.6.11), an \nincrease of over 8,000% from publication to citation. \nFor comparison purposes, 6 publications in a small \njournal like American Journal of Botany led to 185 \ncitations and 107 publications in Acta Astronautica, \na popular journal among ISS scientists, led to 1,050 \ncitations (Figure 3, panel B). This count of 1,050", "CITATION_CONTENT_9": "Introduction\n4\nFigure 3. Count of publications reported in journals ranked in the top 100 according to global standards of Clarivate. A total of 567 top-tier publications \nthrough the end of FY-23 are shown by year and research category.\nIn this year\u2019s edition of the Annual Highlights of Results, we report findings from a \nwide range of topics in biology and biotechnology, physics, human research, Earth and \nspace science, and technology development \u2013 including investigations about plant root \norientation, tissue damage and repair, bubbles, lightning, fire dynamics, neutron stars, \ncosmic ray nuclei, imaging technology improvements, brain and vascular health, solar \npanel materials, grain flow, as well as satellite and robot control. \nThe findings highlighted here are only a small sample representative of the research \nconducted by the participating space agencies \u2013 ASI (Agenzia Spaziale Italiana), CSA \n(Canadian Space Agency), ESA (European Space Agency), JAXA (Japanese Aerospace", "CITATION_PAGE_3": 4, "CITATION_PAGE_8": 6, "CITATION_CONTENT_5": "23\nPUBLICATION HIGHLIGHTS: \nEARTH AND SPACE SCIENCE\nThe ISS laboratories enable scientific experiments in the biological sciences \nthat explore the complex responses of living organisms to the microgravity \nenvironment. The lab facilities support the exploration of biological systems \nranging from microorganisms and cellular biology to integrated functions \nof multicellular plants and animals. Several recent biological sciences \nexperiments have facilitated new technology developments that allow \ngrowth and maintenance of living cells, tissues, and organisms.\nThe Alpha Magnetic \nSpectrometer-02 (AMS-02) is \na state-of-the-art particle \nphysics detector constructed, \ntested, and operated by an \ninternational team composed \nof 60 institutes from \n16 countries and organized \nunder the United States \nDepartment of Energy (DOE) sponsorship. \nThe AMS-02 uses the unique environment of \nspace to advance knowledge of the universe \nand lead to the understanding of the universe\u2019s", "CITATION_SOURCE_5": "Space_Station_Annual_Highlights/iss_2017_highlights.pdf", "CITATION_CONTENT_3": "Introduction\n2\nExtensive international collaboration in the \nunique environment of LEO as well as procedural \nimprovements to assist researchers in the collection \nof data from the ISS have produced promising \nresults in the areas of protein crystal growth, tissue \nregeneration, vaccine and drug development, 3D \nprinting, and fiber optics, among many others. In \nthis year\u2019s edition of the Annual Highlights of Results, \nwe report findings from a wide range of topics in \nbiotechnology, physics, human research, Earth \nand space science, and technology development \n\u2013 including investigations about human retinal cells, \nbacterial resistance, black hole detection, space \nanemia, brain health, Bose-Einstein condensates, \nparticle self-assembly, RNA extraction technology, \nand more. The findings highlighted here represent \nonly a sample of the work ISS has contributed to \nsociety during the past 12 months.\nAs of Oct. 1, 2022, we have identified a total of 3,679", "CITATION_SOURCE_8": "Space_Station_Annual_Highlights/iss_2021_highlights.pdf", "CITATION_PAGE_7": 8, "CITATION_PAGE_6": 8, "CITATION_PAGE_2": 4, "CITATION_CONTENT_7": "Biology and Biotechnology Earth and Space Science Educational and Cultural Activities\nHuman Research Physical Science Technology Development and Demonstration", "CITATION_SOURCE_9": "Space_Station_Annual_Highlights/iss_2023_highlights.pdf", "datarobot_latency": 3.1466632366, "blocked_resultText": false, "CITATION_SOURCE_2": "Space_Station_Annual_Highlights/iss_2023_highlights.pdf", "CITATION_SOURCE_6": "Space_Station_Annual_Highlights/iss_2021_highlights.pdf", "CITATION_SOURCE_7": "Space_Station_Annual_Highlights/iss_2023_highlights.pdf", "datarobot_confidence_score": 0.6524822695, "CITATION_PAGE_9": 7, "CITATION_CONTENT_4": "Molecular Life Sciences. 2021 October 29; DOI: \n10.1007/s00018-021-03989-2.\nFigure 7. Immunoflourescent images of human retinal \ncells in different conditions. Image adopted from \nCialdai, Cellular and Molecular Life Sciences.\nThe ISS laboratory provides a platform for investigations in the biological sciences that \nexplores the complex responses of living organisms to the microgravity environment. Lab \nfacilities support the exploration of biological systems, from microorganisms and cellular \nbiology to the integrated functions of multicellular plants and animals.", "CITATION_SOURCE_1": "Space_Station_Annual_Highlights/iss_2023_highlights.pdf", "CITATION_SOURCE_0": "Space_Station_Annual_Highlights/iss_2018_highlights.pdf", "CITATION_SOURCE_3": "Space_Station_Annual_Highlights/iss_2022_highlights.pdf", "CITATION_PAGE_5": 26, "CITATION_PAGE_0": 7, "CITATION_PAGE_1": 11, "LLM_BLUEPRINT_ID": "662ba0062ade64c4fc4c1a1f", "CITATION_PAGE_4": 9, "datarobot_token_count": 320, "CITATION_CONTENT_0": "more effectively in space by addressing \nsuch topics as understanding radiation effects on \ncrew health, combating bone and muscle loss, \nimproving designs of systems that handle fluids \nin microgravity, and determining how to maintain \nenvironmental control efficiently. \nResults from the ISS provide new \ncontributions to the body of scientific \nknowledge in the physical sciences, life \nsciences, and Earth and space sciences \nto advance scientific discoveries in multi\u0002disciplinary ways. \nISS science results have Earth-based \napplications, including understanding our \nclimate, contributing to the treatment of \ndisease, improving existing materials, and inspiring \nthe future generation of scientists, clinicians, \ntechnologists, engineers, mathematicians, artists, \nand explorers.\nBENEFITS\nFOR HUMANITY\nDISCOVERY\nFigure 4. A heat map of all of the countries whose authors have cited scientific results publications from ISS Research through October 1, 2018.\nEXPLORATION", "CITATION_SOURCE_4": "Space_Station_Annual_Highlights/iss_2022_highlights.pdf", "CITATION_CONTENT_2": "capabilities (i.e., facilities), and data delivery are critical to the effective operation \nof scientific projects for accurate results to be shared with the scientific community, \nsponsors, legislators, and the public. \nOver 3,700 investigations have operated since Expedition 1, with more than 250 active \nresearch facilities, the participation of more than 100 countries, the work of more than \n5,000 researchers, and over 4,000 publications. The growth in research (Figure 1) and \ninternational collaboration (Figure 2) has prompted the publication of over 560 research \narticles in top-tier scientific journals with about 75 percent of those groundbreaking studies \noccurring since 2018 (Figure 3). \nBibliometric analyses conducted through VOSviewer1\n measure the impact of space station \nresearch by quantifying and visualizing networks of journals, citations, subject areas, and \ncollaboration between authors, countries, or organizations. Using bibliometrics, a broad", "CITATION_CONTENT_1": "technologists, engineers, mathematicians, artists, and explorers.\nEXPLORATION\nDISCOVERY\nBENEFITS\nFOR HUMANITY", "CITATION_CONTENT_6": "control efficiently. \nResults from the ISS provide new \ncontributions to the body of scientific \nknowledge in the physical sciences, life \nsciences, and Earth and space sciences \nto advance scientific discoveries in multi\u0002disciplinary ways. \nISS science results have Earth-based \napplications, including understanding our \nclimate, contributing to the treatment of \ndisease, improving existing materials, and \ninspiring the future generation of scientists, \nclinicians, technologists, engineers, \nmathematicians, artists and explorers.\nBENEFITS\nFOR HUMANITY\nDISCOVERY\nEXPLORATION" } }, ] } ``` ## Making predictions with time series > [!TIP] Tip > Time series predictions are specific to time series projects, not all time-aware modeling projects. Specifically, the CSV file must follow a specific format, described in the [predictions section](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-predictions.html#make-predictions-tab) of the time series modeling pages. If you are making predictions with the forecast point, you can skip the forecast window in your prediction data as DataRobot generates a forecast point automatically. This is called autoexpansion. Autoexpansion applies automatically if: - Predictions are made for a specific forecast point and not a forecast range. - The time series project has a regular time step and does not use Nowcasting. When using autoexpansion, note the following: - If you have Known in Advance features that are important for your model, it is recommended that you manually create a forecast window to increase prediction accuracy. - If you plan to use an association ID other than the primary date/time column in your deployment to track accuracy, create a forecast window manually. The URL for making predictions with time series deployments and regular non-time series deployments is the same. The only difference is that you can optionally specify forecast point, prediction start/end date, or some other time series specific URL parameters. Using the deployment ID, the server automatically detects the deployed model as a time series deployment and processes it accordingly: ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0/deployments//predictions" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` The following is a sample Response body for a multiseries project: ``` HTTP/1.1 200 OK Content-Type: application/json X-DataRobot-Execution-Time: 1405 X-DataRobot-Model-Cache-Hit: false { "data": [ { "seriesId": 1, "forecastPoint": "2018-01-09T00:00:00Z", "rowId": 365, "timestamp": "2018-01-10T00:00:00.000000Z", "predictionValues": [ { "value": 45180.4041874386, "label": "target (actual)" } ], "forecastDistance": 1, "prediction": 45180.4041874386 }, { "seriesId": 1, "forecastPoint": "2018-01-09T00:00:00Z", "rowId": 366, "timestamp": "2018-01-11T00:00:00.000000Z", "predictionValues": [ { "value": 47742.9432499386, "label": "target (actual)" } ], "forecastDistance": 2, "prediction": 47742.9432499386 }, { "seriesId": 1, "forecastPoint": "2018-01-09T00:00:00Z", "rowId": 367, "timestamp": "2018-01-12T00:00:00.000000Z", "predictionValues": [ { "value": 46394.5698978878, "label": "target (actual)" } ], "forecastDistance": 3, "prediction": 46394.5698978878 }, { "seriesId": 2, "forecastPoint": "2018-01-09T00:00:00Z", "rowId": 697, "timestamp": "2018-01-10T00:00:00.000000Z", "predictionValues": [ { "value": 39794.833199375, "label": "target (actual)" } ] } ] } ``` ### Request parameters You can parameterize the time series prediction request using URI query parameters. For example, overriding the default inferred forecast point can look like this: ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0/deployments//predictions?forecastPoint=1961-01-01T00:00:00?relaxKnownInAdvanceFeaturesCheck=true" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` For the full list of time series-specific parameters, see [Time series predictions for deployments](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/time-pred.html). ### Response schema The [Response schema](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html#response-schema_1) is consistent with [standard predictions](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html#response-schema_2) but adds a number of columns for each `PredictionRow` object: | Name | Type | Notes | | --- | --- | --- | | seriesId | string, int, or None | A multiseries identifier of a predicted row that identifies the series in a multiseries project. | | forecastPoint | string | An ISO 8601 formatted DateTime string corresponding to the forecast point for the prediction request, either user-configured or selected by DataRobot. | | timestamp | string | An ISO 8601 formatted DateTime string corresponding to the DateTime column of the predicted row. | | forecastDistance | int | A forecast distance identifier of the predicted row, or how far it is from forecastPoint in the scoring dataset. | | originalFormatTimestamp | string | A DateTime string corresponding to the DateTime column of the predicted row. Unlike the timestamp column, this column will keep the same DateTime formatting as the uploaded prediction dataset. (This column is shown if enabled by your administrator.) | ## Making Prediction Explanations The DataRobot [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) feature gives insight into which attributes of a particular input cause it to have exceptionally high or exceptionally low predicted values. > [!TIP] Tip > You must run the following two critical dependencies before running Prediction Explanations: > > You must compute > Feature Impact > for the model. > You must generate predictions on the dataset using the selected model. To initialize Prediction Explanations, use the [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) tab. Making Prediction Explanations is very similar to standard prediction requests. First, Prediction Explanations requests are submitted as POST requests to the resource: ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0/deployments//predictionExplanations" \ -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` The following is a sample Response body: ``` HTTP/1.1 200 OK Content-Type: application/json X-DataRobot-Execution-Time: 841 X-DataRobot-Model-Cache-Hit: true { "data": [ { "predictionValues": [ { "value": 0.6634830442, "label": 1 }, { "value": 0.3365169558, "label": 0 } ], "prediction": 1, "rowId": 0, "predictionExplanations": [ { "featureValue": 49, "strength": 0.6194461777, "feature": "driver_age", "qualitativeStrength": "+++", "label": 1 }, { "featureValue": 1, "strength": 0.3501610895, "feature": "territory", "qualitativeStrength": "++", "label": 1 }, { "featureValue": "M", "strength": -0.171075409, "feature": "gender", "qualitativeStrength": "--", "label": 1 } ] }, { "predictionValues": [ { "value": 0.3565584672, "label": 1 }, { "value": 0.6434415328, "label": 0 } ], "prediction": 0, "rowId": 1, "predictionExplanations": [] } ] } ``` ### Request parameters You can parameterize the Prediction Explanations prediction request using URI query parameters: | Parameter name | Type | Notes | | --- | --- | --- | | maxExplanations | int | Maximum number of codes generated per prediction. Default is 3. Previously called maxCodes. | | thresholdLow | float | Prediction Explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) for Prediction Explanations to compute. This value can be null. | | thresholdHigh | float | Prediction Explanation high threshold. Predictions must be above this value (or below the thresholdLow value) for Prediction Explanations to compute. This value can be null. | | excludeAdjustedPredictions | string | Includes or excludes exposure-adjusted predictions in prediction responses if exposure was used during model building. The default value is 'true' (exclude exposure-adjusted predictions). | The following is an example of a parameterized request: ``` curl -i -X POST "https://example.datarobot.com/predApi/v1.0/deployments//predictionExplanations?maxExplanations=2&thresholdLow=0.2&thresholdHigh=0.5" -H "Authorization: Bearer " -F \ file=@~/.home/path/to/dataset.csv ``` DataRobot's headers schema is the same as that for prediction responses. The [Response schema](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html#response-schema_2) is consistent with standard predictions, but adds "predictionExplanations", an array of `PredictionExplanations` for each `PredictionRow` object. #### PredictionExplanations schema Response JSON Array of Objects: | Name | Type | Notes | | --- | --- | --- | | label | – | Describes which output was driven by this Prediction Explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this Prediction Explanation. | | feature | string | Name of the feature contributing to the prediction. | | featureValue | - | Value the feature took on for this row. | | strength | float | Amount this feature’s value affected the prediction. | | qualitativeStrength | string | Human-readable description of how strongly the feature affected the prediction (e.g., +++, –, +). | > [!TIP] Tip > The prediction explanation `strength` value is not bounded to the values `[-1, 1]`; its interpretation may change as the number of features in the model changes. For normalized values, use `qualitativeStrength` instead.`qualitativeStrength` expresses the `[-1, 1]` range with visuals, with `---` representing `-1` and `+++` representing `1`. For explanations with the same `qualitativeStrength`, you can then use the `strength` value for ranking. > > See the section on [interpreting Prediction Explanation output](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/xemp-pe.html#interpret-xemp-prediction-explanations) for more information. ## Making predictions with humility monitoring Predictions with [humility monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html) allow you to monitor predictions using user-defined humility rules. When a prediction falls outside the thresholds provided for the "Uncertain Prediction" Trigger, it will default to the action assigned to the trigger. The humility key is added to the body of the prediction response when the trigger is activated. The following is a sample Response body for a Regression project with an `Uncertain Prediction Trigger` with `Action - No Operation`: ``` { "data": [ { "predictionValues": [ { "value": 122.8034057617, "label": "length" } ], "prediction": 122.8034057617, "rowId": 99, "humility": [ { "ruleId": "5ebad4735f11b33a38ff3e0d", "triggered": true, "ruleName": "Uncertain Prediction Trigger" } ] } ] } ``` The following is an example of a Response body for a regression model deployment. It uses the "Uncertain Prediction" trigger with the "Throw Error" action: ``` 480 Error: {"message":"Humility ReturnError action triggered."} ``` The following is an example of a Response body for a regression model deployment. It uses the "Uncertain Prediction" trigger with the "Override Prediction" action: ``` { "data": [ { "predictionValues": [ { "value": 122.8034057617, "label": "length" } ], "prediction": 5220, "rowId": 99, "humility": [ { "ruleId": "5ebad4735f11b33a38ff3e0d", "triggered": true, "ruleName": "Uncertain Prediction Trigger" } ] } ] } ``` ### Response schema The [response schema](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html#response-schema_2) is consistent with [standard predictions](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html#response-schema) but adds a new humility column with a subset of columns for each `Humility` object: | Name | Type | Notes | | --- | --- | --- | | ruleId | string | The ID of the humility rule assigned to the deployment | | triggered | boolean | Returns "True" or "False" depending on if the rule was triggered or not | | ruleName | string | The name of the rule that is either defined by the user or auto-generated with a timestamp | ## Error responses Any error is indicated by a non-200 code attribute. Codes starting with 4XX indicate request errors (e.g., missing columns, wrong credentials, unknown model ID). The message attribute gives a detailed description of the error in the case of a 4XX code. For example: ``` curl -H "Content-Type: application/json" -X POST --data '' \ -H "Authorization: Bearer " \ https://example.datarobot.com/predApi/v1.0/deployments/ HTTP/1.1 400 BAD REQUEST Date: Fri, 08 Feb 2019 11:00:00 GMT Content-Type: application/json Content-Length: 53 Connection: keep-alive Server: nginx/1.12.2 X-DataRobot-Execution-Time: 332 X-DataRobot-Request-ID: fad6a0b62c1ff30db74c6359648d12fd { "message": "The requested URL was not found on the server. If you entered the URL manually, please check your spelling and try again." } ``` Codes starting with 5XX indicate server-side errors. Retry the request or contact your DataRobot representative. ## Knowing the limitations The following describes the size and timeout boundaries for real-time deployment predictions: - Maximum data submission size is 50MB. - There is no limit on the number of rows, but timeout limits are as follows: If your request exceeds the timeout, or you are trying to score a large file using dedicated predictions, consider using thebatch scoring package. - There is a limit on the size of theHTTP request line(currently 8192 bytes). - For managed AI Platform deployments, dedicated Prediction API servers automatically close persistent HTTP connections if they are idle for more than 600 seconds. To use persistent connections, the client side must be able to handle these disconnects correctly. The following example configures Python HTTP libraryrequeststo automatically retry HTTP requests on transport failure: ``` import requests import urllib3 # create a transport adapter that will automatically retry GET/POST/HEAD requests on failures up to 3 times adapter = requests.adapters.HTTPAdapter( max_retries=urllib3.Retry( total=3, method_whitelist=frozenset(['GET', 'POST', 'HEAD']) ) ) # create a Session (a pool of connections) and make it use the given adapter for HTTP and HTTPS requests session = requests.Session() session.mount('http://', adapter) session.mount('https://', adapter) # execute a prediction request that will be retried on transport failures, if needed api_token = '' dr_key = '' response = session.post( 'https://example.datarobot.com/predApi/v1.0/deployments//predictions', headers={ 'Authorization': 'Bearer %s' % api_token, 'DataRobot-Key': dr_key, 'Content-Type': 'text/csv', }, data='', ) print(response.content) ``` ### Model caching The dedicated prediction server fetches models, as needed, from the DataRobot cluster. To speed up subsequent predictions that use the same model, DataRobot stores a certain number of models in memory (cache). When the cache fills, each new model request will require that one of the existing models in the cache be removed. DataRobot removes the least recently used model (which is not necessarily the model that has been in the cache the longest). For Self-Managed AI Platform installations, the default size for the cache is 16 models, but it can vary from installation to installation. Please contact DataRobot support if you have questions regarding the cache size of your specific installation. A prediction server runs multiple prediction processes, each of which has its own exclusive model cache. Prediction processes do not share between themselves. Because of this, it is possible that you send two consecutive requests to a prediction server, and each has to download the model data. Each response from the prediction server includes a header, `X-DataRobot-Model-Cache-Hit`, indicating whether the model used was in the cache. If the model was in the cache, the value of the header is true; if the value is false, the model was not in the cache. ## Best practices for the fastest predictions The following checklist summarizes the suggestions above to help deliver the fastest predictions possible: - Implementpersistent HTTP connections: This reduces network round-trips, and thus latency, to the Prediction API. - Use CSV data:Because JSON serialization of large amounts of data can take longer than using CSV, consider using CSV for yourprediction inputs. - Keep the number of requested models low:This allows the Prediction API to make use ofmodel caching. - Batch data together in chunks:Batch as many rows together as possible without going over the50MB real-time deployment prediction request limit. If scoring larger files, consider using theBatch Prediction APIwhich, in addition to scoring local files, also supports scoring to and from S3 and databases. --- # Dedicated Prediction API URL: https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/index.html > DataRobot's Prediction API provides a mechanism for using your model for real-time predictions on a prediction server. # Prediction API (Dedicated) DataRobot's Prediction API provides a mechanism for using your model for real-time predictions on a dedicated external application server. Follow [the guidelines](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html) for making predictions with the Prediction API. You can also review how to [retrieve a prediction server ID](https://docs.datarobot.com/en/docs/api/reference/predapi/pred-server-id.html) using cURL commands from the REST API or by using the DataRobot Python client to make predictions with a deployment. | Topic | Description | | --- | --- | | Make predictions with the API | Make predictions on a dedicated prediction server. | | Make predictions with the Python API client | Use the DataRobot Prediction Library, a Python library for making predictions with various prediction methods. | | Dedicated Prediction API reference | Review Prediction API methods, input and output parameters, and errors. | | Get a prediction server ID | Retrieve a prediction server ID using cURL commands from the REST API or the DataRobot Python client. | | Deprecated API routes | Review deprecated Prediction API routes in a reference document listing the deprecated requests and their replacements. | --- # Predictions for unstructured model deployments URL: https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/dep-pred-unstructured.html > Using a specified endpoint, calculates predictions based on user-provided data for a specific unstructured model deployment. # Predictions for unstructured model deployments Using the endpoint below, you can provide the data necessary to calculate predictions for a specific unstructured model deployment. If you need to make predictions for a standard model, see [Predictions for deployments](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/dep-pred.html). Endpoint: `/deployments//predictionsUnstructured` Calculates predictions based on user-provided data for a specific unstructured model deployment. This endpoint works only for deployed custom inference models with an unstructured target type. For more information, see [Assemble unstructured custom models](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html). This endpoint does the following: - Calls the/predictUnstructuredroute on the target custom inference model, allowing you to use the custom request and response schema, which may go beyond the standard DataRobot prediction API interface. - Passes any payload and content type (MIME type and charset, if provided) to the model. - Passes any model-returned payload, along with the content type (MIME type and charset, if provided), back to the caller. In the [DRUM library](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html), this call is handled by the [score_unstructured()hook](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html#score). > [!NOTE] Note > You can find the deployment ID in the sample code output of the [Deployments > Predictions > Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab (with Interface set to API Client). Request Method: `POST` Request URL: deployed URL, for example: `https://your-company.orm.datarobot.com/predApi/v1.0` ## Request parameters ### Headers | Key | Description | Example(s) | | --- | --- | --- | | Datarobot-key | Required for managed AI Platform users; string type Once a model is deployed, see the code snippet in the DataRobot UI, Predictions > Prediction API. | DR-key-12345abcdb-xyz6789 | | Authorization | Required; string Three methods are supported: Bearer authentication (deprecated) Basic authentication: User_email and API token (deprecated) API token | Example for Bearer authentication method: Bearer API_key-12345abcdb-xyz6789(deprecated) Example for User_email and API token method: Basic Auth_basic-12345abcdb-xyz6789(deprecated) Example for API token method: Token API_key-12345abcdb-xyz6789 | | Content-Type | Optional; string type Default: application/octet-stream Any provided content type is passed to the model; however, the DRUM library has a built-in decoding mechanism for text content-types using the specified charset. For more information, see Assemble unstructured custom models. | text/plaintext/csvtext/plain; charset=latin1application/json; charset=UTF-8custom/typeapplication/octet-stream | | Content-Encoding | Optional; string type Currently supports only gzip-encoding with the default data extension. | gzip | | Accept | Optional; string type | */* (default) The response is defined by the model output. | | | | | ### Query arguments Currently not supported for the `predictionsUnstructured` endpoint. ### Body | Data | Type | Example(s) | | --- | --- | --- | | Data to pass to the custom model | Bytes | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q{“data”: [{“some”: “json”}]}Custom payload 123 (for example, image data) | ## Response 200 The HTTP Response contains a payload returned by the custom model’s `/predictUnstructured` route and passed back as-is. The `Content-Type` header is passed to the caller. If the `Content-Type` header isn't provided, the `application/octet-stream` default is applied. In the case of a DataRobot-acknowledged error in a request, an `application/json` error message is returned. In the [DRUM library](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html), the response payload and content type are generated by the [score_unstructured()hook](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html#score). ## Errors list | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 400 BAD REQUEST | {"message":"Query parameters not accepted on this endpoint"} | The request passed query parameters to the endpoint. | | 404 NOT FOUND | {"message": "Deployment :deploymentId cannot be found for user :userId"} | The request provided an invalid :deploymentId (a deleted or non-existent deployment). | | 422 UNPROCESSABLE CONTENT | {"message": "Only unstructured custom models can be used with this endpoint. Use /predictions instead.} | The request provided a :deploymentId for a deployment that isn't an unstructured custom inference model deployment. | --- # Predictions for deployments URL: https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/dep-pred.html > Using a specified endpoint, calculates predictions based on user-provided data for a specific deployment. # Predictions for deployments Using the endpoint below, you can provide the data necessary to calculate predictions for a specific deployment. If you need to make predictions for an unstructured custom inference model, see [Predictions for unstructured model deployments](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/dep-pred-unstructured.html). Endpoint: `/deployments//predictions` Calculates predictions based on user-provided data for a specific deployment. Note that this endpoint works only for deployed models. > [!NOTE] Note > You can find the deployment ID in the sample code output of the [Deployments > Predictions > Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab (with Interface set to API Client). Request Method: `POST` Request URL: deployed URL, for example: `https://your-company.orm.datarobot.com/predApi/v1.0` ## Request parameters ### Headers | Key | Description | Example(s) | | --- | --- | --- | | Datarobot-key | A per-organization secret used as an additional authentication factor for prediction servers. Retrieve a datarobot-key programmatically by accessing the /api/v2/predictionServers/ endpoint. The endpoint returns a URL to a prediction server and a corresponding datarobot-key. Required for Self-Managed AI Platform users; string type Once a model is deployed, see the code snippet in the DataRobot UI, Predictions > Prediction API. | DR-key-12345abcdb-xyz6789 | | Authorization | Required; string Three methods are supported: Bearer authentication (deprecated) Basic authentication: User_email and API token (deprecated) API token | Example for Bearer authentication method: Bearer API_key-12345abcdb-xyz6789 (deprecated) Example for User_email and API token method: Basic Auth_basic-12345abcdb-xyz6789 (deprecated) Example for API token method: Token API_key-12345abcdb-xyz6789 | | Content-Type | Optional; string type | text/plain; charset=UTF-8text/csvapplication/jsonmultipart/form-data (for files with data, i.e., .csv, .txt files) | | Content-Encoding | Optional; string type Currently supports only gzip-encoding with the default data extension. | gzip | | Accept | Optional; string type Controls the shape of the response schema. Currently JSON(default) and CSV are supported. See examples. | application/json (default)text/csv (for CSV output) | Datarobot-key: This header is required only with the managed AI Platform. It is used as a precaution to secure user data from other verified DataRobot users. The key can also be retrieved with the following request to the DataRobot API: `GET /api/v2/modelDeployments/` ### Query arguments | Key | Type | Description | Example(s) | | --- | --- | --- | --- | | passthroughColumns | list of strings | (Optional) Controls which columns from a scoring dataset to expose (or copy over) in a prediction response. The request may contain zero, one, or more columns. (There’s no limit on how many column names you can pass.) Column names must be passed as UTF-8 bytes and be percent-encoded (see the HTTP standard for this requirement). Make sure to use the exact name of a column as a value. | /v1.0/deployments//predictions?passthroughColumns=colA&passthroughColumns=colB | | passthroughColumnsSet | string | (Optional) Controls which columns from a scoring dataset to expose (or to copy over) in a prediction response. The only possible option is all and, if passed, all columns from a scoring dataset are exposed. | /v1.0/deployments/deploymentId/predictions?passthroughColumnsSet=all | | predictionWarningEnabled | bool | (Optional) DataRobot monitors unusual or anomalous predictions in real-time and indicates when they are detected. If this argument is set to true, a new key is added to each prediction to specify the result of the Humility check. Otherwise, there are no changes in the prediction response. | /v1.0/deployments/deploymentId/predictions?predictionWarningEnabled=true Response: { "data": [ { "predictionValues": [ { "value": 18.6948852, "label": "y" } ], "isOutlierPrediction": false, "rowId": 0, "prediction": 18.6948852 } ] } | | decimalsNumber | integer | (Optional) Configures the float precision in prediction results by setting the number of digits after the decimal point. If there aren't any digits after the decimal point, rather than adding zeros, the float precision is less than the value set by decimalsNumber. | ?decimalsNumber=15 | > [!NOTE] Note > The `passthroughColumns` and `passthroughColumnsSet` parameters are mutually exclusive and cannot both be passed in the same request. Also, while there isn't a limit on the number of column names you can pass with the `passthroughColumns` query parameter, there is a limit on the size of the [HTTP request line](https://www.w3.org/Protocols/rfc2616/rfc2616-sec5.html#sec5.1) (currently 8192 bytes). ### Body | Data | Type | Example(s) | | --- | --- | --- | | Data to predict | raw text form-data | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q Key: file, value: file_with_data_to_predict.csv | ## Response 200 ### Binary prediction Label: For regression and binary classification tasks, DataRobot API always returns 1 for the positive class and 0 for the negative class. Although the actual values for the classes may be different depending on the data provided (like "yes"/"no"), the DataRobot API will always return 1/0. For multiclass classification, the DataRobot API returns the value itself. Value: Shows the probability of an event happening (where 0 and 1 are min and max probability, respectively). The user can adjust the threshold that links the value with the prediction label. PredictionThreshold ( Applicable to binary classification projects only): Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This can be configured manually through the UI (the [Deploytab](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html#deploy-from-the-leaderboard)), or through the DataRobot API (i.e., the `PATCH /api/v2/projects/(projectId)/models/(modelId)` route). The actual response is dependent on the classification task: binary classification, regression or multiclass task. ### Binary classification example ``` { "data": [ { "predictionValues": [ { "value": 0.2789450715, "label": 1 }, { "value": 0.7210549285, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 0, "rowId": 0 } ] } ``` ### Regression prediction example ``` { "data": [ { "predictionValues": [ { "value": 6754486.5, "label": "revenue" } ], "prediction": 6754486.5, "rowId": 0 } ] } ``` ### Multiclass classification prediction example ``` { "data": [ { "predictionValues": [ { "value": 0.9999997616, "label": "setosa" }, { "value": 2.433e-7, "label": "versicolor" }, { "value": 1.997631915e-16, "label": "virginica" } ], "prediction": "setosa", "rowId": 0 } ] } ``` ## Errors list | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 400 BAD REQUEST | {"message": "Bad request"} | Added external deployments that are unsupported. | | 404 NOT FOUND | {"message": "Deployment :deploymentId cannot be found for user :userId"} | Provided an invalid :deploymentId (deleted deployment). | --- # Prediction Explanations for deployment URL: https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/dep-predex.html > Using a specified endpoint, makes predictions on a given deployment and provides explanations. # Prediction Explanations for deployments Endpoint: `/deployments//predictions?maxExplanations=` Prediction Explanations identify why a given model makes a certain prediction. To calculate Prediction Explanations, use the same endpoint used for calculating bare predictions with the `maxExplanations` URI parameter set to a positive integer value. For specific calculation information, review the main [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) documentation. Prediction Explanations can be either: - XEMP-based (the default). To use XEMP-based explanations, first calculate feature impact and initialize Prediction Explanations to provide aqualitative indicator (qualitativeStrength)of the effect variables have on the predictions. Explanations are computed for the top 50 features, ranked by feature impact scores (not including features with zero feature impact). - SHAP-based. To use SHAP-based explanations, calculating feature impact is not required. ThequalitativeStrengthindicator is not available for SHAP. > [!NOTE] Prediction Explanation considerations > Neither XEMP or SHAP explanations are available for images (that is, no > Image Explanations > ). > SHAP-based Prediction Explanations cannot be generated for multiclass projects. Only XEMP is supported for multiclass projects. > > More information to consider while working with explanations can be found [here](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html#feature-considerations). > [!WARNING] Performance considerations for XEMP-based explanations > XEMP-based explanations can be 100x slower than regular predictions. Avoid them for low-latency critical use cases. SHAP-based explanations are much faster but can add some latency too. Request Method: `POST` Request URL: deployed URL, for example: `https://your-company.orm.datarobot.com/predApi/v1.0` ## Request parameters ### Headers | Key | Description | Example(s) | | --- | --- | --- | | Datarobot-key | A per-organization secret used as an additional authentication factor for prediction servers. Retrieve a datarobot-key programmatically by accessing the /api/v2/predictionServers endpoint. The endpoint returns a URL to a prediction server and a corresponding datarobot-key. Required for Self-Managed AI Platform users; string type Once a model is deployed, see the code snippet in the DataRobot UI, Predictions > Prediction API. | DR-key-12345abcdb-xyz6789 | | Authorization | Required; string Three methods are supported: Bearer authentication (deprecated) Basic authentication: User_email and API token (deprecated) API token | Example for Bearer authentication method: Bearer API_key-12345abcdb-xyz6789 (deprecated) Example for User_email and API token method: Basic Auth_basic-12345abcdb-xyz6789 (deprecated) Example for API token method: Token API_key-12345abcdb-xyz6789 | | Content-Type | Optional; string type | textplain; charset=UTF-8 text/csv application/JSON multipart/form-data (For files with data, i.e., .csv, .txt files) | | Content-Encoding | Optional; string type Currently supports only gzip-encoding with the default data extension. | gzip | Datarobot-key: This header is required only with the managed AI Platform. It is used as a precaution to secure user data from other verified DataRobot users. The key can also be retrieved with the following request to DataRobot API: `GET /api/v2/modelDeployments/` ### Query arguments (explanations specific) > [!NOTE] Note > To trigger prediction explanations, your request must send `maxExplanations=N` where N is greater than `0`. | Key | Type | Description | Example(s) | | --- | --- | --- | --- | | maxExplanations | int OR string | (Optional) Limits the number of explanations returned by server. Previously called maxCodes (deprecated). For SHAP explanations only a special constant all is also accepted. | ?maxExplanations=5?maxExplanations=all | | thresholdLow | float | (Optional) The lower threshold for requiring a Prediction Explanation. Predictions must be below this value (or above the thresholdHigh value) for Prediction Explanations to compute. | ?thresholdLow=0.678 | | thresholdHigh | float | (Optional) The upper threshold for requiring a Prediction Explanation. Predictions must be above this value (or below the thresholdLow value) for Prediction Explanations to compute. | ?thresholdHigh=0.345 | | excludeAdjustedPredictions | bool | (Optional) Includes or excludes exposure-adjusted predictions in prediction responses if exposure was used during model building. The default value is true (exclude exposure-adjusted predictions). | ?excludeAdjustedPredictions=true | | explanationNumTopClasses | int | (Optional) This argument is only for multiclass model explanations, and it is mutually exclusive with explanationClassNames. The number of top predicted classes to explain for each row. The default value is 1. | ?explanationNumTopClasses=5 | | explanationClassNames | list of string types | (Optional) This argument is only for multiclass model explanations, and it is mutually exclusive with explanationNumTopClasses. A list of class names to explain for each row. Class names must be passed as UTF-8 bytes and must be percent-encoded (see the HTTP standard for this requirement). By default, ?explanationNumTopClasses=1 is assumed. | ?explanationClassNames=classA&explanationClassNames=classB | | explanationAlgorithm | string | Defines the Prediction Explanation algorithm used, SHAP or XEMP. | ?explanationAlgorithm=shapexplanationAlgorithm=xemp | The rest of the parameters like `passthroughColumns`, `passthroughColumnsSet`, and `predictionWarningEnabled` can also be used with Prediction Explanations. ### Body | Data | Type | Example(s) | | --- | --- | --- | | Data to predict | raw text form-data | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q Key: file, value: file_with_data_to_predict.csv | ### Response 200 #### Binary XEMP-based explanation response example ``` { "data": [ { "predictionValues": [ { "value": 0.07836511, "label": 1 }, { "value": 0.92163489, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 0, "rowId": 0, "predictionExplanations": [ { "featureValue": "male", "strength": -0.6706725349, "feature": "Sex", "qualitativeStrength": "---", "label": 1 }, { "featureValue": 62, "strength": -0.6325465255, "feature": "Age", "qualitativeStrength": "---", "label": 1 }, { "featureValue": 9.6875, "strength": -0.353000328, "feature": "Fare", "qualitativeStrength": "--", "label": 1 } ] } ] } ``` #### Binary SHAP-based explanation response example ``` { "data":[ { "deploymentApprovalStatus": "APPROVED", "prediction": 0.0, "predictionExplanations": [ { "featureValue": "9", "strength": 0.0534648234, "qualitativeStrength": null, "feature": "number_diagnoses", "label": 1 }, { "featureValue": "0", "strength": -0.0490243586, "qualitativeStrength": null, "feature": "number_inpatient", "label": 1 } ], "rowId": 0, "predictionValues": [ { "value": 0.3111782477, "label": 1 }, { "value": 0.6888217523, "label": 0.0 } ], "predictionThreshold": 0.5, "shapExplanationsMetadata": { "warnings": null, "remainingTotal": -0.089668474, "baseValue": 0.3964062631 } } ] } ``` ### "qualitativeStrength" indicator The "qualitativeStrength" indicates the effect of the feature's value on predictions, based on XEMP calculations. The following table provides an example for a model with two features. See the [XEMP calculation reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/xemp-calc.html) for full calculation details. > [!NOTE] Note > This response is an XEMP-only feature. | Indicator... | Description | | --- | --- | | +++ | Absolute score is > 0.75 and feature has positive impact. | | --- | Absolute score is > 0.75 and feature has negative impact. | | ++ | Absolute score is between (0.25, 0.75) and feature has positive impact. | | -- | Absolute score is between (0.25, 0.75) and feature has negative impact. | | + | Absolute score is between (0.001, 0.25) and feature has positive impact. | | - | Absolute score is between (0.001, 0.25) and feature has negative impact. | | <+ | Absolute score is between (0, 0.001) and feature has positive impact. | | <- | Absolute score is between (0, 0.001) and feature has negative impact. | ## Errors List | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 404 NOT FOUND | {"message": "Not found"} | Provided an invalid :deploymentId (deleted deployment). | | 404 NOT FOUND | {"message": "Bad request"} | Provided the wrong format for :deploymentId. | | 422 UNPROCESSABLE ENTITY | {"message": "{'max_codes': DataError(value can't be converted to int)}"} | Provided maxCodes parameter in unsupported data type (i.e., non-integer values). | | 422 UNPROCESSABLE ENTITY | {"message": "{'threshold_high': DataError(value can't be converted to float)}"} | Provided threshold_high parameter in unsupported data type (i.e., non-integer values). | | 422 UNPROCESSABLE ENTITY | {"message": "{'threshold_low': DataError(value can't be converted to float)}"} | Provided threshold_low parameter in unsupported data type (i.e., non-integer values). | | 422 UNPROCESSABLE ENTITY | {"message": "Multiclass models cannot be used for Prediction Explanations"} | Provided a multiclass classification problem dataset, which is not supported for this endpoint. | | 422 UNPROCESSABLE ENTITY | {"message": "This endpoint does not support predictions on time series models. Please use the timeSeriesPredictions route instead."} | Provided the deploymentId of a time series project, which is not supported for this endpoint. | | 422 UNPROCESSABLE ENTITY | {"message": "{'exclude\_adjusted\_predictions': DataError(value can't be converted to Bool)}"} | Sent an empty or non-Boolean value with the excludeAdjustedPredictions parameter. | | 422 UNPROCESSABLE ENTITY | {"message": "'predictionWarningEnabled': value can't be converted to Bool"} | Provided an invalid (non-boolean) value for predictionWarningEnabled parameter. | --- # Dedicated Prediction API reference URL: https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/index.html > This reference provides additional documentation for the Prediction API. It lists the methods, input and output parameters, and errors that the API may return. # Dedicated Prediction API reference This reference provides additional documentation for the Prediction API to help you successfully use the API. The following pages list the methods, input and output parameters, and errors that may be returned by the API. This reference supplements the information provided in the user guide's Prediction API pages. There, you can also find information about prerequisites and best practices and instructions for obtaining your configured predictions URL. When using these examples, be sure to replace `https://your-company.orm.datarobot.com` with the name of your dedicated prediction instance. If you do not know whether you have a dedicated prediction instance, or its address, contact your DataRobot representative. ## General errors for Prediction API These errors may be returned from any Prediction API calls, depending on the issue. They are common to all endpoints. ### Authorization | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 401 UNAUTHORIZED | {"message": "Invalid API token"} | Provided an invalid or no API token key (Basic Auth). Provided an invalid or no API token key (Bearer Auth). Provided an invalid username with a valid API token key (Basic Auth). | | 401 UNAUTHORIZED | {"message": "Invalid Authorization header. No credentials provided."} | Did not provide an API token key (Bearer Token Auth). | | 401 UNAUTHORIZED | {"message": "The datarobot-key header is missing"} | Did not provide a DataRobot key parameter for a project that requires one. Provided an empty DataRobot key parameter. Provided an invalid DataRobot key parameter. | ### Parameters | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 400 BAD REQUEST | {"message": "passthroughColumns do not match columns, columns expected but not found: [u'Name']"} | Provided the name of a column that does not exist. | | 422 UNPROCESSABLE ENTITY | {"message": "'wd': wd is not allowed key"} | Provided an unsupported parameter, e.g., wd. | | 422 UNPROCESSABLE ENTITY | {"message": "'passthroughColumns': blank value is not allowed"} | Provided an empty value for the passthroughColumns parameter. | | 422 UNPROCESSABLE ENTITY | {"message": "'passthroughColumnsSet': value is not exactly 'all'"} | Needed to provide the all value for the passthroughColumnsSet parameter, and provided some other value (or empty). | | 422 UNPROCESSABLE ENTITY | {"message": "'passthroughColumns' and 'passthroughColumnsSet' cannot be used together"} | Passed parameters for both passthroughColumns and passthroughColumnsSet in the same request. Need to pass parameters in separate requests. | | 422 UNPROCESSABLE ENTITY | {"message": "'predictionWarningEnabled': value can't be converted to Bool"} | Provided an invalid (non-boolean) value for predictionWarningEnabled parameter. | ### Payload | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 400 BAD REQUEST | {"message": "Submitted file '10k\_diabetes.xlsx' has unsupported extension"} | Provided a file with an unsupported extension, e.g., .xlsx. | | 400 BAD REQUEST | {"message": "Bad JSON format"} | Provided raw text with content type application/JSON. | | 400 BAD REQUEST | {"message": "Mimetype '' not supported"} | Provided an empty body with the Text mimetype. | | 400 BAD REQUEST | {"message": "No data was received"} | Provided an empty body with application/JSON content-type. | | 400 BAD REQUEST | {"message": "Mimetype 'application/xml' not supported"} | Provided a request with unsupported mimetype. | | 400 BAD REQUEST | {"message": "Requires non-empty JSON input"} | Provided empty JSON, {}. | | 400 BAD REQUEST | {"message": "JSON uploads must be formatted as an array of objects"} | Provided JSON was malformatted: {"0": {"PassengerId": 892, "Pclass": 3}} | | 400 BAD REQUEST | {"message": "Malformed CSV, please check schema and encoding.\nError tokenizing data. C error: Expected 11 fields in line 5, saw 12\n"} | Provided CSV has issues: 1 row has more fields than expected (in this instance). | | 413 Entity Too Large | {"message": "Request is too large. The request size is $content\_length bytes and the maximum message size allowed by the server is 50MB"} | Provided file is too large. DataRobot accepts files of up to 50MB in size for real-time deployment predictions; if the file size exceeds the limit, the batch-scoring tool should be used. The same limit is applied for archived datasets. | | 422 UNPROCESSABLE ENTITY | {"message": "No data to predict on"} | Provided an empty request payload. | | 422 UNPROCESSABLE ENTITY | {"message": "Missing column(s): Age, Cabin, Embarked, Fare, Name, Parch, PassengerId, Pclass, Sex & SibSp"} | Dataset is missing all required fields. Use a dataset from the project you try to predict on, with expected fields. | ## Prediction API infinity behavior [IEEE-754](https://en.wikipedia.org/wiki/IEEE_754), the standard for floating-point arithmetic, defines finite numbers, infinities, and a special NaN (not-a-number) value. According to [RFC-8259](https://datatracker.ietf.org/doc/html/rfc8259#section-6), infinities and NaN are not allowed in JSON. DataRobot tries to replace these values before they are returned in APIs using the following rules: - Infis replaced with1.7976931348623157e+308(double precision floating-point max). - -Infis replaced with-1.7976931348623157e+308(double precision floating-point min). - NaNis replaced with0.0. The Predictions API rounds floating-point numbers to 10 decimal places. However, the rounding logic changes when the floating point minimum and maximum are rounded below and above the limits, respectively: - 1.7976931348623157e+308(double precision floating-point max) is returned as1.797693135e+308(greater than the maximum limit). - -1.7976931348623157e+308(double precision floating-point min) is returned as-1.797693135e+308(lower than the minimum limit). - Note that CPython’s built-in JSON parser parses such values asinfand-infrespectively, but some other languages may crash. --- # Ping health check URL: https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/ping.html > Health check to determine if the service is # Ping health check Endpoint: `/ping` Health check to determine if the service is "alive". Request Method: `GET` Request URL: deployed URL, example: `https://your-company.orm.datarobot.com/predApi/v1.0` ### Request parameters None required. ### Response 200 | Data | Type | Example(s) | | --- | --- | --- | | response | string | pong | --- # Time series predictions for deployments URL: https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/time-pred.html > Make time series predictions for a deployed model. # Time series predictions for deployments Endpoint: `/deployments//predictions` Makes time series predictions for a deployed model. Request Method: `POST` Request URL: deployed URL, for example: `https://your-company.orm.datarobot.com/predApi/v1.0` ## Request parameters ### Headers | Key | Type | Description | Example(s) | | --- | --- | --- | --- | | Datarobot-key | string | A per-organization secret used as an additional authentication factor for prediction servers. Retrieve a datarobot-key programmatically by accessing the /api/v2/predictionServers endpoint. The endpoint returns a URL to a prediction server and a corresponding datarobot-key. Required for Self-Managed AI Platform users; string type | 33257d41-fcc9-7c01-161c-3467df169a50 | | Authorization | string | Three methods are supported: Bearer authentication(deprecated) Basic authentication: User_email and API token(deprecated) API token | Example for Bearer authentication method: Bearer API_key-12345abcdb-xyz6789(deprecated) Example for User_email and API token method: Basic Auth_basic-12345abcdb-xyz6789(deprecated) Example for API token method: Token API_key-12345abcdb-xyz6789 | Datarobot-key: This header is required only with the managed AI Platform. It is used as a precaution to secure user data from other verified DataRobot users. The key can also be retrieved with the following request to the DataRobot API: `GET /api/v2/modelDeployments/` ### Query arguments (time series models only) | Key | Type | Description | Example(s) | | --- | --- | --- | --- | | forecastPoint | ISO-8601 string | An ISO 8601 formatted DateTime string, without timezone, representing the forecast point. This parameter cannot be used if predictionsStartDate and predictionsEndDate are passed. | ?forecastPoint=2013-12-20T01:30:00Z | | relaxKnownInAdvanceFeaturesCheck | bool | true or false. When true, missing values for known-in-advance features are allowed in the forecast window at prediction time. The default value is false. Note that the absence of known-in-advance values can negatively impact prediction quality. | ?relaxKnownInAdvanceFeaturesCheck=true | | predictionsStartDate | ISO-8601 string | The time in the dataset when bulk predictions begin generating. This parameter must be defined together with predictionsEndDate. The forecastPoint parameter cannot be used if predictionsStartDate and predictionsEndDate are passed. | ?predictionsStartDate=2013-12-20T01:30:00Z&predictionsEndDate=2013-12-20T01:40:00Z | | predictionsEndDate | ISO-8601 string | The time in the dataset when bulk predictions stop generating. This parameter must be defined together with predictionsStartDate. The forecastPoint parameter cannot be used if predictionsStartDate and predictionsEndDate are passed. | See above. | It is possible to use standard URI parameters, including `passthroughColumns`, `passthroughColumnsSet`, and `maxExplanations`. > [!NOTE] XEMP-based explanations support > Time series supports XEMP explanations. See [Prediction Explanations](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/dep-predex.html) for examples of the `maxExplanations` URI parameter. ### Body | Data | Type | Example(s) | | --- | --- | --- | | Historic and prediction data | JSON | Raw data shown in image below. | ### Response 200 #### Regression prediction example ``` { "data": [ { "seriesId": null, "forecastPoint": "2013-12-20T00:00:00Z", "rowId": 35, "timestamp": "2013-12-21T00:00:00.000000Z", "predictionValues": [ { "value": 2.3353628422, "label": "sales (actual)" } ], "forecastDistance": 1, "prediction": 2.3353628422 } ] } ``` #### Binary classification prediction example ``` { "data": [ { "rowId": 147, "prediction": "low", "predictionThreshold": 0.5, "predictionValues": [ {"label": "low", "value": 0.5158823954}, {"label": "high", "value": 0.4841176046}, ], "timestamp": "1961-04-01T00:00:00.000000Z", "forecastDistance": 2, "forecastPoint": "1961-02-01T00:00:00Z", "seriesId": null, } ] } ``` ## Errors List | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 400 BAD REQUEST | {"message": "Based on the forecast point (10/26/08), there are no rows to predict that fall inside of the forecast window (10/27/08 to 11/02/08). Try adjusting the forecast point to an earlier date or appending new future rows to the data."} | No empty rows were provided to predict on. | | 400 BAD REQUEST | {"message": "No valid output rows"} | No historic information was provided; there's only 1 row to predict on. | | 400 BAD REQUEST | {"message": "The \"Time\" feature contains the value 'OCT-27', which does not match the original format %m/%d/%y (e.g., '06/24/19'). To upload this data, first correct the format in your prediction dataset and then try the import again. Because some software automatically converts the format for display, it is best to check the actual format using a text editor."} | Prediction row has a different format than the rest of the data. | | 400 BAD REQUEST | {"message": "The following errors are found:\n • The prediction data must contain historical values spanning more than 35 day(s) into the past. In addition, the target cannot have missing values or missing rows which are used for differencing"} | Provided dataset has fewer than the required 35 rows of historical data. | | 400 BAD REQUEST | {"message": {"forecastPoint": "Invalid RFC 3339 datetime string: "}} | Provided an empty or non-valid forecastPoint. | | 404 NOT FOUND | {"message": "Deployment :deploymentId cannot be found for user :userId"} | Deployment was removed or doesn’t exist. | | 422 UNPROCESSABLE ENTITY | {"message": "Predictions on models that are not time series models are not supported on this endpoint. Please use the predict endpoint instead."} | Provided deploymentId that is not for a time series project. | | 422 UNPROCESSABLE ENTITY | {"message": {"relaxKnownInAdvanceFeaturesCheck": "value can't be converted to Bool"}} | Provided an empty or non-valid value for relaxKnownInAdvanceFeaturesCheck'. | --- # Predictions for deployments (Serverless) URL: https://docs.datarobot.com/en/docs/api/reference/predapi/pred-ref-serverless/dep-pred.html > Using a specified endpoint, calculates predictions based on user-provided data for a specific deployment. # Predictions for deployments (Serverless) Using the endpoint below, you can provide the data necessary to calculate predictions for a specific deployment. If you need to make predictions for an unstructured custom inference model, see [Predictions for unstructured model deployments](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/dep-pred-unstructured.html). Endpoint: `/api/v2/deployments//predictions` Calculates predictions based on user-provided data for a specific deployment. Note that this endpoint works only for deployed models. > [!NOTE] Note > You can find the deployment ID in the sample code output of the [Deployments > Predictions > Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab (with Interface set to API Client). Request Method: `POST` ## Request parameters ### Headers | Key | Description | Example(s) | | --- | --- | --- | | Authorization | Required; string Three methods are supported: Bearer authentication (deprecated) Basic authentication: User_email and API token (deprecated) API token | Example for Bearer authentication method: Bearer API_key-12345abcdb-xyz6789 (deprecated) Example for User_email and API token method: Basic Auth_basic-12345abcdb-xyz6789 (deprecated) Example for API token method: Token API_key-12345abcdb-xyz6789 | | Content-Type | Optional; string type | text/plain; charset=UTF-8text/csvapplication/jsonmultipart/form-data (for files with data, i.e., .csv, .txt files) | | Content-Encoding | Optional; string type Currently supports only gzip-encoding with the default data extension. | gzip | | Accept | Optional; string type Controls the shape of the response schema. Currently JSON(default) and CSV are supported. See examples. | application/json (default)text/csv (for CSV output) | ### Query arguments | Key | Type | Description | Example(s) | | --- | --- | --- | --- | | passthroughColumns | list of strings | (Optional) Controls which columns from a scoring dataset to expose (or copy over) in a prediction response. The request may contain zero, one, or more columns. (There's no limit on how many column names you can pass.) Column names must be passed as UTF-8 bytes and be percent-encoded (see the HTTP standard for this requirement). Make sure to use the exact name of a column as a value. | /api/v2/deployments//predictions?passthroughColumns=colA&passthroughColumns=colB | | passthroughColumnsSet | string | (Optional) Controls which columns from a scoring dataset to expose (or to copy over) in a prediction response. The only possible option is all and, if passed, all columns from a scoring dataset are exposed. | /api/v2/deployments//predictions?passthroughColumnsSet=all | | predictionWarningEnabled | bool | (Optional) DataRobot monitors unusual or anomalous predictions in real-time and indicates when they are detected. If this argument is set to true, a new key is added to each prediction to specify the result of the Humility check. Otherwise, there are no changes in the prediction response. | /api/v2/deployments//predictions?predictionWarningEnabled=true Response: { "data": [ { "predictionValues": [ { "value": 18.6948852, "label": "y" } ], "isOutlierPrediction": false, "rowId": 0, "prediction": 18.6948852 } ] } | | decimalsNumber | integer | (Optional) Configures the float precision in prediction results by setting the number of digits after the decimal point. If there aren't any digits after the decimal point, rather than adding zeros, the float precision is less than the value set by decimalsNumber. | ?decimalsNumber=15 | > [!NOTE] Note > The `passthroughColumns` and `passthroughColumnsSet` parameters are mutually exclusive and cannot both be passed in the same request. Also, while there isn't a limit on the number of column names you can pass with the `passthroughColumns` query parameter, there is a limit on the size of the [HTTP request line](https://www.w3.org/Protocols/rfc2616/rfc2616-sec5.html#sec5.1) (currently 8192 bytes). ### Body | Data | Type | Example(s) | | --- | --- | --- | | Data to predict | raw text form-data | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q Key: file, value: file_with_data_to_predict.csv | ## Response 200 ### Binary prediction Label: For regression and binary classification tasks, DataRobot API always returns 1 for the positive class and 0 for the negative class. Although the actual values for the classes may be different depending on the data provided (like "yes"/"no"), the DataRobot API will always return 1/0. For multiclass classification, the DataRobot API returns the value itself. Value: Shows the probability of an event happening (where 0 and 1 are min and max probability, respectively). The user can adjust the threshold that links the value with the prediction label. PredictionThreshold ( Applicable to binary classification projects only): Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This can be configured manually through the UI (the [Deploytab](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html#deploy-from-the-leaderboard)), or through the DataRobot API (i.e., the `PATCH /api/v2/projects/(projectId)/models/(modelId)` route). The actual response is dependent on the classification task: binary classification, regression or multiclass task. ### Binary classification example ``` { "data": [ { "predictionValues": [ { "value": 0.2789450715, "label": 1 }, { "value": 0.7210549285, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 0, "rowId": 0 } ] } ``` ### Regression prediction example ``` { "data": [ { "predictionValues": [ { "value": 6754486.5, "label": "revenue" } ], "prediction": 6754486.5, "rowId": 0 } ] } ``` ### Multiclass classification prediction example ``` { "data": [ { "predictionValues": [ { "value": 0.9999997616, "label": "setosa" }, { "value": 2.433e-7, "label": "versicolor" }, { "value": 1.997631915e-16, "label": "virginica" } ], "prediction": "setosa", "rowId": 0 } ] } ``` ## Errors list | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 400 BAD REQUEST | {"message": "Bad request"} | Added external deployments that are unsupported. | | 404 NOT FOUND | {"message": "Deployment :deploymentId cannot be found for user :userId"} | Provided an invalid :deploymentId (deleted deployment). | --- # Prediction Explanations for deployment (Serverless) URL: https://docs.datarobot.com/en/docs/api/reference/predapi/pred-ref-serverless/dep-predex.html > Using a specified endpoint, makes predictions on a given deployment and provides explanations. # Prediction Explanations for deployments (Serverless) Endpoint: `/api/v2/deployments//predictions?maxExplanations=` Prediction Explanations identify why a given model makes a certain prediction. To calculate Prediction Explanations, use the same endpoint used for calculating bare predictions with the `maxExplanations` URI parameter set to a positive integer value. For specific calculation information, review the main [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) documentation. Prediction Explanations can be either: - XEMP-based (the default). To use XEMP-based explanations, first calculate feature impact and initialize Prediction Explanations to provide aqualitative indicator (qualitativeStrength)of the effect variables have on the predictions. Explanations are computed for the top 50 features, ranked by feature impact scores (not including features with zero feature impact). - SHAP-based. To use SHAP-based explanations, calculating feature impact is not required. ThequalitativeStrengthindicator is not available for SHAP. > [!NOTE] Prediction Explanation considerations > Neither XEMP or SHAP explanations are available for images (that is, no > Image Explanations > ). > SHAP-based Prediction Explanations cannot be generated for multiclass projects. Only XEMP is supported for multiclass projects. > > More information to consider while working with explanations can be found [here](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html#feature-considerations). > [!WARNING] Performance considerations for XEMP-based explanations > XEMP-based explanations can be 100x slower than regular predictions. Avoid them for low-latency critical use cases. SHAP-based explanations are much faster but can add some latency too. Request Method: `POST` Request URL: REST API URL, for example: `https://your-company.datarobot.com/api/v2` ## Request parameters ### Headers | Key | Description | Example(s) | | --- | --- | --- | | Authorization | Required; string Three methods are supported: Bearer authentication (deprecated) Basic authentication: User_email and API token (deprecated) API token | Example for Bearer authentication method: Bearer API_key-12345abcdb-xyz6789 (deprecated) Example for User_email and API token method: Basic Auth_basic-12345abcdb-xyz6789 (deprecated) Example for API token method: Token API_key-12345abcdb-xyz6789 | | Content-Type | Optional; string type | textplain; charset=UTF-8 text/csv application/JSON multipart/form-data (For files with data, i.e., .csv, .txt files) | | Content-Encoding | Optional; string type Currently supports only gzip-encoding with the default data extension. | gzip | ### Query arguments (explanations specific) > [!NOTE] Note > To trigger prediction explanations, your request must send `maxExplanations=N` where N is greater than `0`. | Key | Type | Description | Example(s) | | --- | --- | --- | --- | | maxExplanations | int OR string | (Optional) Limits the number of explanations returned by server. Previously called maxCodes (deprecated). For SHAP explanations only a special constant all is also accepted. | ?maxExplanations=5?maxExplanations=all | | thresholdLow | float | (Optional) The lower threshold for requiring a Prediction Explanation. Predictions must be below this value (or above the thresholdHigh value) for Prediction Explanations to compute. | ?thresholdLow=0.678 | | thresholdHigh | float | (Optional) The upper threshold for requiring a Prediction Explanation. Predictions must be above this value (or below the thresholdLow value) for Prediction Explanations to compute. | ?thresholdHigh=0.345 | | excludeAdjustedPredictions | bool | (Optional) Includes or excludes exposure-adjusted predictions in prediction responses if exposure was used during model building. The default value is true (exclude exposure-adjusted predictions). | ?excludeAdjustedPredictions=true | | explanationNumTopClasses | int | (Optional) This argument is only for multiclass model explanations, and it is mutually exclusive with explanationClassNames. The number of top predicted classes to explain for each row. The default value is 1. | ?explanationNumTopClasses=5 | | explanationClassNames | list of string types | (Optional) This argument is only for multiclass model explanations, and it is mutually exclusive with explanationNumTopClasses. A list of class names to explain for each row. Class names must be passed as UTF-8 bytes and must be percent-encoded (see the HTTP standard for this requirement). By default, ?explanationNumTopClasses=1 is assumed. | ?explanationClassNames=classA&explanationClassNames=classB | | explanationAlgorithm | string | Defines the Prediction Explanation algorithm used, SHAP or XEMP. | ?explanationAlgorithm=shapexplanationAlgorithm=xemp | The rest of the parameters like `passthroughColumns`, `passthroughColumnsSet`, and `predictionWarningEnabled` can also be used with Prediction Explanations. ### Body | Data | Type | Example(s) | | --- | --- | --- | | Data to predict | raw text form-data | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q Key: file, value: file_with_data_to_predict.csv | ### Response 200 #### Binary XEMP-based explanation response example ``` { "data": [ { "predictionValues": [ { "value": 0.07836511, "label": 1 }, { "value": 0.92163489, "label": 0 } ], "predictionThreshold": 0.5, "prediction": 0, "rowId": 0, "predictionExplanations": [ { "featureValue": "male", "strength": -0.6706725349, "feature": "Sex", "qualitativeStrength": "---", "label": 1 }, { "featureValue": 62, "strength": -0.6325465255, "feature": "Age", "qualitativeStrength": "---", "label": 1 }, { "featureValue": 9.6875, "strength": -0.353000328, "feature": "Fare", "qualitativeStrength": "--", "label": 1 } ] } ] } ``` #### Binary SHAP-based explanation response example ``` { "data":[ { "deploymentApprovalStatus": "APPROVED", "prediction": 0.0, "predictionExplanations": [ { "featureValue": "9", "strength": 0.0534648234, "qualitativeStrength": null, "feature": "number_diagnoses", "label": 1 }, { "featureValue": "0", "strength": -0.0490243586, "qualitativeStrength": null, "feature": "number_inpatient", "label": 1 } ], "rowId": 0, "predictionValues": [ { "value": 0.3111782477, "label": 1 }, { "value": 0.6888217523, "label": 0.0 } ], "predictionThreshold": 0.5, "shapExplanationsMetadata": { "warnings": null, "remainingTotal": -0.089668474, "baseValue": 0.3964062631 } } ] } ``` ### "qualitativeStrength" indicator The "qualitativeStrength" indicates the effect of the feature's value on predictions, based on XEMP calculations. The following table provides an example for a model with two features. See the [XEMP calculation reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/xemp-calc.html) for full calculation details. > [!NOTE] Note > This response is an XEMP-only feature. | Indicator... | Description | | --- | --- | | +++ | Absolute score is > 0.75 and feature has positive impact. | | --- | Absolute score is > 0.75 and feature has negative impact. | | ++ | Absolute score is between (0.25, 0.75) and feature has positive impact. | | -- | Absolute score is between (0.25, 0.75) and feature has negative impact. | | + | Absolute score is between (0.001, 0.25) and feature has positive impact. | | - | Absolute score is between (0.001, 0.25) and feature has negative impact. | | <+ | Absolute score is between (0, 0.001) and feature has positive impact. | | <- | Absolute score is between (0, 0.001) and feature has negative impact. | ## Errors List | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 404 NOT FOUND | {"message": "Not found"} | Provided an invalid :deploymentId (deleted deployment). | | 404 NOT FOUND | {"message": "Bad request"} | Provided the wrong format for :deploymentId. | | 422 UNPROCESSABLE ENTITY | {"message": "{'max_codes': DataError(value can't be converted to int)}"} | Provided maxCodes parameter in unsupported data type (i.e., non-integer values). | | 422 UNPROCESSABLE ENTITY | {"message": "{'threshold_high': DataError(value can't be converted to float)}"} | Provided threshold_high parameter in unsupported data type (i.e., non-integer values). | | 422 UNPROCESSABLE ENTITY | {"message": "{'threshold_low': DataError(value can't be converted to float)}"} | Provided threshold_low parameter in unsupported data type (i.e., non-integer values). | | 422 UNPROCESSABLE ENTITY | {"message": "Multiclass models cannot be used for Prediction Explanations"} | Provided a multiclass classification problem dataset, which is not supported for this endpoint. | | 422 UNPROCESSABLE ENTITY | {"message": "This endpoint does not support predictions on time series models. Please use the timeSeriesPredictions route instead."} | Provided the deploymentId of a time series project, which is not supported for this endpoint. | | 422 UNPROCESSABLE ENTITY | {"message": "{'exclude\_adjusted\_predictions': DataError(value can't be converted to Bool)}"} | Sent an empty or non-Boolean value with the excludeAdjustedPredictions parameter. | | 422 UNPROCESSABLE ENTITY | {"message": "'predictionWarningEnabled': value can't be converted to Bool"} | Provided an invalid (non-boolean) value for predictionWarningEnabled parameter. | --- # Serverless Prediction API reference URL: https://docs.datarobot.com/en/docs/api/reference/predapi/pred-ref-serverless/index.html > This reference provides additional documentation for the Serverless Prediction API. It lists the methods, input and output parameters, and errors that the API may return. # Serverless Prediction API reference This reference provides additional documentation for the Serverless Prediction API to help you successfully use the API. The following pages list the methods, input and output parameters, and errors that may be returned by the API. This reference supplements the information provided in the user guide's Prediction API pages. There, you can also find information about prerequisites and best practices and instructions for using the REST API endpoint. When using these examples, be sure to replace `https://your-company.datarobot.com` with the name of your DataRobot instance. Serverless predictions use the REST API endpoint `/api/v2/deployments/:id/predictions` and do not require a `datarobot-key` header. ## General errors for Prediction API These errors may be returned from any Prediction API calls, depending on the issue. They are common to all endpoints. ### Authorization | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 401 UNAUTHORIZED | {"message": "Invalid API token"} | Provided an invalid or no API token key (Basic Auth). Provided an invalid or no API token key (Bearer Auth). Provided an invalid username with a valid API token key (Basic Auth). | | 401 UNAUTHORIZED | {"message": "Invalid Authorization header. No credentials provided."} | Did not provide an API token key (Bearer Token Auth). | ### Parameters | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 400 BAD REQUEST | {"message": "passthroughColumns do not match columns, columns expected but not found: [u'Name']"} | Provided the name of a column that does not exist. | | 422 UNPROCESSABLE ENTITY | {"message": "'wd': wd is not allowed key"} | Provided an unsupported parameter, e.g., wd. | | 422 UNPROCESSABLE ENTITY | {"message": "'passthroughColumns': blank value is not allowed"} | Provided an empty value for the passthroughColumns parameter. | | 422 UNPROCESSABLE ENTITY | {"message": "'passthroughColumnsSet': value is not exactly 'all'"} | Needed to provide the all value for the passthroughColumnsSet parameter, and provided some other value (or empty). | | 422 UNPROCESSABLE ENTITY | {"message": "'passthroughColumns' and 'passthroughColumnsSet' cannot be used together"} | Passed parameters for both passthroughColumns and passthroughColumnsSet in the same request. Need to pass parameters in separate requests. | | 422 UNPROCESSABLE ENTITY | {"message": "'predictionWarningEnabled': value can't be converted to Bool"} | Provided an invalid (non-boolean) value for predictionWarningEnabled parameter. | ### Payload | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 400 BAD REQUEST | {"message": "Submitted file '10k\_diabetes.xlsx' has unsupported extension"} | Provided a file with an unsupported extension, e.g., .xlsx. | | 400 BAD REQUEST | {"message": "Bad JSON format"} | Provided raw text with content type application/JSON. | | 400 BAD REQUEST | {"message": "Mimetype '' not supported"} | Provided an empty body with the Text mimetype. | | 400 BAD REQUEST | {"message": "No data was received"} | Provided an empty body with application/JSON content-type. | | 400 BAD REQUEST | {"message": "Mimetype 'application/xml' not supported"} | Provided a request with unsupported mimetype. | | 400 BAD REQUEST | {"message": "Requires non-empty JSON input"} | Provided empty JSON, {}. | | 400 BAD REQUEST | {"message": "JSON uploads must be formatted as an array of objects"} | Provided JSON was malformatted: {"0": {"PassengerId": 892, "Pclass": 3}} | | 400 BAD REQUEST | {"message": "Malformed CSV, please check schema and encoding.\nError tokenizing data. C error: Expected 11 fields in line 5, saw 12\n"} | Provided CSV has issues: 1 row has more fields than expected (in this instance). | | 413 Entity Too Large | {"message": "Request is too large. The request size is $content\_length bytes and the maximum message size allowed by the server is 50MB"} | Provided file is too large. DataRobot accepts files of up to 50MB in size for real-time deployment predictions; if the file size exceeds the limit, the batch-scoring tool should be used. The same limit is applied for archived datasets. | | 422 UNPROCESSABLE ENTITY | {"message": "No data to predict on"} | Provided an empty request payload. | | 422 UNPROCESSABLE ENTITY | {"message": "Missing column(s): Age, Cabin, Embarked, Fare, Name, Parch, PassengerId, Pclass, Sex & SibSp"} | Dataset is missing all required fields. Use a dataset from the project you try to predict on, with expected fields. | ## Prediction API infinity behavior [IEEE-754](https://en.wikipedia.org/wiki/IEEE_754), the standard for floating-point arithmetic, defines finite numbers, infinities, and a special NaN (not-a-number) value. According to [RFC-8259](https://datatracker.ietf.org/doc/html/rfc8259#section-6), infinities and NaN are not allowed in JSON. DataRobot tries to replace these values before they are returned in APIs using the following rules: - Infis replaced with1.7976931348623157e+308(double precision floating-point max). - -Infis replaced with-1.7976931348623157e+308(double precision floating-point min). - NaNis replaced with0.0. The Predictions API rounds floating-point numbers to 10 decimal places. However, the rounding logic changes when the floating point minimum and maximum are rounded below and above the limits, respectively: - 1.7976931348623157e+308(double precision floating-point max) is returned as1.797693135e+308(greater than the maximum limit). - -1.7976931348623157e+308(double precision floating-point min) is returned as-1.797693135e+308(lower than the minimum limit). - Note that CPython's built-in JSON parser parses such values asinfand-infrespectively, but some other languages may crash. --- # Time series predictions for deployments (Serverless) URL: https://docs.datarobot.com/en/docs/api/reference/predapi/pred-ref-serverless/time-pred.html > Make time series predictions for a deployed model using serverless prediction environments. # Time series predictions for deployments (Serverless) Endpoint: `/api/v2/deployments//predictions` Makes time series predictions for a deployed model using serverless prediction environments. Request Method: `POST` Request URL: REST API URL, for example: `https://your-company.datarobot.com/api/v2` ## Request parameters ### Headers | Key | Type | Description | Example(s) | | --- | --- | --- | --- | | Authorization | string | Three methods are supported: Bearer authentication(deprecated) Basic authentication: User_email and API token(deprecated) API token | Example for Bearer authentication method: Bearer API_key-12345abcdb-xyz6789(deprecated) Example for User_email and API token method: Basic Auth_basic-12345abcdb-xyz6789(deprecated) Example for API token method: Token API_key-12345abcdb-xyz6789 | ### Query arguments (time series models only) | Key | Type | Description | Example(s) | | --- | --- | --- | --- | | forecastPoint | ISO-8601 string | An ISO 8601 formatted DateTime string, without timezone, representing the forecast point. This parameter cannot be used if predictionsStartDate and predictionsEndDate are passed. | ?forecastPoint=2013-12-20T01:30:00Z | | relaxKnownInAdvanceFeaturesCheck | bool | true or false. When true, missing values for known-in-advance features are allowed in the forecast window at prediction time. The default value is false. Note that the absence of known-in-advance values can negatively impact prediction quality. | ?relaxKnownInAdvanceFeaturesCheck=true | | predictionsStartDate | ISO-8601 string | The time in the dataset when bulk predictions begin generating. This parameter must be defined together with predictionsEndDate. The forecastPoint parameter cannot be used if predictionsStartDate and predictionsEndDate are passed. | ?predictionsStartDate=2013-12-20T01:30:00Z&predictionsEndDate=2013-12-20T01:40:00Z | | predictionsEndDate | ISO-8601 string | The time in the dataset when bulk predictions stop generating. This parameter must be defined together with predictionsStartDate. The forecastPoint parameter cannot be used if predictionsStartDate and predictionsEndDate are passed. | See above. | It is possible to use standard URI parameters, including `passthroughColumns`, `passthroughColumnsSet`, and `maxExplanations`. > [!NOTE] XEMP-based explanations support > Time series supports XEMP explanations. See [Prediction Explanations](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/pred-ref/dep-predex.html) for examples of the `maxExplanations` URI parameter. ### Body | Data | Type | Example(s) | | --- | --- | --- | | Historic and prediction data | JSON | Raw data shown in image below. | ### Response 200 #### Regression prediction example ``` { "data": [ { "seriesId": null, "forecastPoint": "2013-12-20T00:00:00Z", "rowId": 35, "timestamp": "2013-12-21T00:00:00.000000Z", "predictionValues": [ { "value": 2.3353628422, "label": "sales (actual)" } ], "forecastDistance": 1, "prediction": 2.3353628422 } ] } ``` #### Binary classification prediction example ``` { "data": [ { "rowId": 147, "prediction": "low", "predictionThreshold": 0.5, "predictionValues": [ {"label": "low", "value": 0.5158823954}, {"label": "high", "value": 0.4841176046}, ], "timestamp": "1961-04-01T00:00:00.000000Z", "forecastDistance": 2, "forecastPoint": "1961-02-01T00:00:00Z", "seriesId": null, } ] } ``` ## Errors List | HTTP Code | Sample error message | Reason(s) | | --- | --- | --- | | 400 BAD REQUEST | {"message": "Based on the forecast point (10/26/08), there are no rows to predict that fall inside of the forecast window (10/27/08 to 11/02/08). Try adjusting the forecast point to an earlier date or appending new future rows to the data."} | No empty rows were provided to predict on. | | 400 BAD REQUEST | {"message": "No valid output rows"} | No historic information was provided; there's only 1 row to predict on. | | 400 BAD REQUEST | {"message": "The \"Time\" feature contains the value 'OCT-27', which does not match the original format %m/%d/%y (e.g., '06/24/19'). To upload this data, first correct the format in your prediction dataset and then try the import again. Because some software automatically converts the format for display, it is best to check the actual format using a text editor."} | Prediction row has a different format than the rest of the data. | | 400 BAD REQUEST | {"message": "The following errors are found:\n • The prediction data must contain historical values spanning more than 35 day(s) into the past. In addition, the target cannot have missing values or missing rows which are used for differencing"} | Provided dataset has fewer than the required 35 rows of historical data. | | 400 BAD REQUEST | {"message": {"forecastPoint": "Invalid RFC 3339 datetime string: "}} | Provided an empty or non-valid forecastPoint. | | 404 NOT FOUND | {"message": "Deployment :deploymentId cannot be found for user :userId"} | Deployment was removed or doesn't exist. | | 422 UNPROCESSABLE ENTITY | {"message": "Predictions on models that are not time series models are not supported on this endpoint. Please use the predict endpoint instead."} | Provided deploymentId that is not for a time series project. | | 422 UNPROCESSABLE ENTITY | {"message": {"relaxKnownInAdvanceFeaturesCheck": "value can't be converted to Bool"}} | Provided an empty or non-valid value for relaxKnownInAdvanceFeaturesCheck'. | --- # Get a prediction server ID URL: https://docs.datarobot.com/en/docs/api/reference/predapi/pred-server-id.html > Learn how to retrieve a prediction server ID using cURL commands from the REST API or by using the DataRobot Python client. # Get a prediction server ID In order to make predictions from a deployment via DataRobot's [Prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html), you need a prediction server ID. In this tutorial, you'll learn how to retrieve the ID using cURL commands from the REST API or by using the DataRobot Python client. Once obtained, you can use the prediction server ID to deploy a model and make predictions. > [!NOTE] Note > Before proceeding, note that an API key is required for this tutorial. Reference the [Create API keys](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html) tutorial for more information. **cURL:** ``` curl -v \ -H "Authorization: Bearer API_KEY" \ YOUR_DR_URL/api/v2/predictionServers/ Example API_KEY=YOUR_API_KEY ENDPOINT=YOUR_DR_URL/api/v2/predictionServers/ curl -v \ -H "Authorization: Bearer $API_KEY" \ $ENDPOINT ``` **Python:** Before continuing with Python, be sure you have installed the DataRobot Python client and configured your connection to DataRobot as outlined in the [API quickstart guide]api-quickstart. ``` # Set up your environment import os import datarobot as dr API_KEY = os.environ["API_KEY"] YOUR_DR_URL = os.environ["YOUR_DR_URL"] FILE_PATH = os.environ["FILE_PATH"] ENDPOINT = YOUR_DR_URL+"/api/v2" # Instantiate DataRobot instance dr.Client( token=API_KEY, endpoint=ENDPOINT ) prediction_server_id = dr.PredictionServer.list()[0].id print(prediction_server_id) ``` ## Documentation The following provides additional documentation for features mentioned in this tutorial. - API key management - DataRobot Developers portal - DataRobot Prediction API --- # Agents URL: https://docs.datarobot.com/en/docs/api/reference/public-api/agents.html > The following endpoints outline how to manage agents. # Agents The following endpoints outline how to manage agents. ## Request chat completion by custom model ID Operation path: `POST /api/v2/genai/agents/fromCustomModel/{customModelId}/chat/` Authentication requirements: `BearerAuth` Create a chat completion request for an agent using a custom model. ### Body parameter ``` { "additionalProperties": true, "description": "Represents a chat completion request for an agent.", "properties": { "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The version ID of the custom model to use for the chat completion.", "title": "customModelVersionId" }, "messages": { "description": "A list of messages comprising the conversation so far.", "items": { "additionalProperties": true, "description": "Represents a message in a chat conversation.", "properties": { "content": { "anyOf": [ { "maxLength": 50000, "type": "string" }, { "type": "null" } ], "description": "The contents of the message.", "title": "content" }, "role": { "description": "The role of the author of this message.", "title": "role", "type": "string" } }, "required": [ "role" ], "title": "AgentMessage", "type": "object" }, "title": "messages", "type": "array" }, "model": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model identifier to use for completion following OpenAI API notation.", "title": "model" }, "tracingContext": { "anyOf": [ { "description": "Represents a custom tracing context for a chat completion request.", "properties": { "attributes": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "null" } ], "description": "The attributes of the tracing context.", "title": "attributes" }, "entityId": { "description": "The ID of the entity in context of which the agent request is performed. Should be an entity which user has access to.", "title": "entityId", "type": "string" }, "entityType": { "description": "Type of an entity in context of which the agent request is performed.", "enum": [ "deployment", "use_case" ], "title": "TracingContextEntityType", "type": "string" } }, "required": [ "entityId", "entityType", "attributes" ], "title": "AgentTracingContext", "type": "object" }, { "type": "null" } ], "description": "Optional tracing context for the chat completion request." } }, "required": [ "messages" ], "title": "AgentChatCompletionRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of a custom model to use for the chat completion. | | body | body | AgentChatCompletionRequest | true | none | ### Example responses > 202 Response ``` {} ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successful Response | Inline | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ### Response Schema ## Obtain chat completion response by custom model ID Operation path: `GET /api/v2/genai/agents/fromCustomModel/{customModelId}/chat/{chatCompletionId}/` Authentication requirements: `BearerAuth` Obtain chat completion response for a given chat completion ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of a custom model to use for the chat completion. | | chatCompletionId | path | string | true | The ID of a chat completion object. | ### Example responses > 200 Response ``` { "additionalProperties": true, "description": "Chat completion response from an agent.", "properties": { "choices": { "anyOf": [ { "items": { "additionalProperties": true, "description": "Represents a single choice in the chat completion response.", "properties": { "message": { "additionalProperties": true, "description": "Represents a message in a chat conversation.", "properties": { "content": { "anyOf": [ { "maxLength": 50000, "type": "string" }, { "type": "null" } ], "description": "The contents of the message.", "title": "content" }, "role": { "description": "The role of the author of this message.", "title": "role", "type": "string" } }, "required": [ "role" ], "title": "AgentMessage", "type": "object" } }, "required": [ "message" ], "title": "AgentChoice", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "A list of agent choices. Can be more than one. None when failed.", "title": "choices" }, "errorDetails": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Detailed error information if the chat completion failed.", "title": "errorDetails" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Error message if the chat completion failed.", "title": "errorMessage" } }, "title": "AgentChatCompletionResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | AgentChatCompletionResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List search studies by use case ID Operation path: `GET /api/v2/genai/syftrSearch/` Authentication requirements: `BearerAuth` Return all search studies for the specified use case . ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | string | true | Use case ID to retrieve search studies from. | | offset | query | integer | false | Skip the specified number of search studies. | | limit | query | integer | false | Retrieve only the specified number of search studies. | | playgroundId | query | any | false | Playground ID associated with a search study. | | search | query | any | false | Only retrieve the search studies with names matching the search query. | | sort | query | any | false | Apply this sort order to the results.Valid options are 'name'. | ### Example responses > 200 Response ``` { "description": "Paginated list of search studies.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for search study retrieval.", "properties": { "allTrials": { "anyOf": [ { "items": { "description": "Represents a search trial from history.", "properties": { "llmBlueprintId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The correspondent blueprint ID.", "title": "llmBlueprintId" }, "searchParameters": { "additionalProperties": true, "description": "Search parameters of the point.", "title": "searchParameters", "type": "object" }, "values": { "description": "The resulting values of optimization objectives.", "items": { "type": "number" }, "title": "values", "type": "array" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The correspondent vector database ID.", "title": "vectorDatabaseId" } }, "required": [ "llmBlueprintId", "vectorDatabaseId", "values", "searchParameters" ], "title": "HistoryPoint", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Trials history.", "title": "allTrials" }, "datetimeEnd": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "Study end time.", "title": "datetimeEnd" }, "datetimeStart": { "description": "Study start time.", "format": "date-time", "title": "datetimeStart", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Error message if search study fails.", "title": "errorMessage" }, "evalDatasetId": { "description": "The ID of the evaluation dataset.", "title": "evalDatasetId", "type": "string" }, "evalDatasetName": { "description": "The name of evaluation dataset.", "title": "evalDatasetName", "type": "string" }, "evalResults": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The results of the comparative evaluation of LLM blueprints.", "title": "evalResults" }, "existingBlueprintIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The IDs of existing LLM blueprints for comparative evaluation.", "title": "existingBlueprintIds" }, "groundingDatasetId": { "description": "The ID of the dataset the vector databases will be built from.", "title": "groundingDatasetId", "type": "string" }, "groundingDatasetName": { "description": "The name of the grouding dataset.", "title": "groundingDatasetName", "type": "string" }, "jobId": { "anyOf": [ { "format": "uuid4", "type": "string" }, { "type": "null" } ], "description": "The ID of the worker job.", "title": "jobId" }, "name": { "description": "Name of the search study.", "title": "name", "type": "string" }, "numConcurrentTrials": { "description": "The number of simultaneously running trials.", "title": "numConcurrentTrials", "type": "integer" }, "numTrials": { "description": "The number of search trials to sample.", "title": "numTrials", "type": "integer" }, "optimizationObjectives": { "description": "Optimization objectives of a study.", "items": { "maxItems": 2, "minItems": 2, "prefixItems": [ { "description": "List of supported search objectives.", "enum": [ "correctness", "all_tokens" ], "title": "SearchObjective", "type": "string" }, { "description": "Whether to minimize or maximize search objective.", "enum": [ "maximize", "minimize" ], "title": "SearchDirection", "type": "string" } ], "type": "array" }, "title": "optimizationObjectives", "type": "array" }, "paretoFront": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "Pareto frontier of a study.", "title": "paretoFront" }, "playgroundId": { "description": "The ID of the existing playground that will be associated with the search.", "title": "playgroundId", "type": "string" }, "searchSpace": { "anyOf": [ { "description": "Represents full search space.", "properties": { "chunkingParameters": { "description": "Parameters of the text chunkers.", "properties": { "chunkOverlapPercentageMax": { "default": 50, "description": "Maximum value of chunk overlap.", "title": "chunkOverlapPercentageMax", "type": "number" }, "chunkOverlapPercentageMin": { "default": 0, "description": "Minimum value of chunk overlap.", "title": "chunkOverlapPercentageMin", "type": "number" }, "chunkOverlapPercentageStep": { "default": 10, "description": "Step value of chunk overlap.", "title": "chunkOverlapPercentageStep", "type": "number" }, "chunkSizeMaxExp": { "default": 8, "description": "Maximum exponent for chunk size (2^8 = 256).", "title": "chunkSizeMaxExp", "type": "integer" }, "chunkSizeMinExp": { "default": 7, "description": "Minimum exponent for chunk size (2^7 = 128).", "title": "chunkSizeMinExp", "type": "integer" }, "chunkingMethods": { "description": "List of chunking methods to use.", "items": { "type": "string" }, "title": "chunkingMethods", "type": "array" }, "embeddingModelNames": { "description": "List of embedding models to use.", "items": { "type": "string" }, "title": "embeddingModelNames", "type": "array" } }, "required": [ "embeddingModelNames" ], "title": "ChunkingParametersConfig", "type": "object" }, "llmConfig": { "description": "Configuration of LLMs in the search space.", "properties": { "llmNames": { "description": "List of LLM names to use.", "items": { "type": "string" }, "title": "llmNames", "type": "array" }, "temperatureMax": { "default": 1, "description": "Maximum temperature of an LLM.", "title": "temperatureMax", "type": "number" }, "temperatureMin": { "default": 0, "description": "Minimum temperature of an LLM.", "title": "temperatureMin", "type": "number" }, "temperatureStep": { "default": 0.05, "description": "Step size for LLM temperature.", "title": "temperatureStep", "type": "number" }, "topPMax": { "default": 1, "description": "Maximum top_p of an LLM.", "title": "topPMax", "type": "number" }, "topPMin": { "default": 0, "description": "Minimum top_p of an LLM.", "title": "topPMin", "type": "number" }, "topPStep": { "default": 0.05, "description": "Step size for LLM top_p.", "title": "topPStep", "type": "number" } }, "required": [ "llmNames" ], "title": "LLMConfig", "type": "object" }, "vectorDatabaseSettings": { "description": "Settings of the vector database.", "properties": { "addNeighborChunks": { "description": "Add neighboring chunks to those that the similarity search retrieves.", "items": { "type": "boolean" }, "title": "addNeighborChunks", "type": "array" }, "maxDocumentRetrievedPerPromptMax": { "default": 10, "description": "Max value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMax", "type": "integer" }, "maxDocumentRetrievedPerPromptMin": { "default": 1, "description": "Min value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMin", "type": "integer" }, "maxDocumentRetrievedPerPromptStep": { "default": 1, "description": "Step for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptStep", "type": "integer" }, "maxMmrLambdaMax": { "default": 1, "description": "Maximum value of MMR lambda.", "title": "maxMmrLambdaMax", "type": "number" }, "maxMmrLambdaMin": { "default": 0, "description": "Minimum value of MMR lambda.", "title": "maxMmrLambdaMin", "type": "number" }, "maxMmrLambdaStep": { "default": 0.1, "description": "Step value of MMR lambda.", "title": "maxMmrLambdaStep", "type": "number" }, "retrievalModes": { "description": "List of retriever modes to use.", "items": { "type": "string" }, "title": "retrievalModes", "type": "array" }, "retrievers": { "description": "List of retriever types to use.", "items": { "type": "string" }, "title": "retrievers", "type": "array" } }, "required": [ "retrievers", "retrievalModes" ], "title": "VectorDatabaseConfig", "type": "object" } }, "title": "SearchSpace", "type": "object" }, { "type": "null" } ], "description": "Search space for the search." }, "searchStudyId": { "description": "The ID of the search study.", "title": "searchStudyId", "type": "string" }, "studyStatus": { "description": "Represents a search study execution state.", "enum": [ "RUNNING", "COMPLETED", "STOPPED", "FAILED" ], "title": "JobStatus", "type": "string" }, "tempPlaygroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the temp playground.", "title": "tempPlaygroundId" }, "trialsFailed": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of failed trials.", "title": "trialsFailed" }, "trialsRunning": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of currently running trials.", "title": "trialsRunning" }, "trialsSuccess": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of completed trials.", "title": "trialsSuccess" }, "useCaseId": { "description": "The ID of the use case the search study is linked to.", "title": "useCaseId", "type": "string" }, "userId": { "description": "The ID of the user.", "title": "userId", "type": "string" }, "userName": { "description": "The user name of the user who ran the study.", "title": "userName", "type": "string" } }, "required": [ "searchSpace", "useCaseId", "groundingDatasetId", "evalDatasetId", "groundingDatasetName", "evalDatasetName", "userId", "userName", "numTrials", "numConcurrentTrials", "optimizationObjectives", "playgroundId", "tempPlaygroundId", "paretoFront", "datetimeStart", "datetimeEnd", "studyStatus", "searchStudyId", "name", "jobId", "trialsRunning", "trialsFailed", "trialsSuccess", "allTrials", "existingBlueprintIds", "evalResults", "errorMessage" ], "title": "SearchStudyResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListSearchStudyResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Search studies has been successfully retrieved. | ListSearchStudyResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Run agentic search Operation path: `POST /api/v2/genai/syftrSearch/` Authentication requirements: `BearerAuth` Run agentic search. ### Body parameter ``` { "description": "API request for run agentic search request.", "properties": { "evalDatasetId": { "description": "The ID of the evaluation dataset.", "title": "evalDatasetId", "type": "string" }, "groundingDatasetId": { "description": "The ID of the dataset the vector databases will be built from.", "title": "groundingDatasetId", "type": "string" }, "name": { "description": "Name of the search study.", "title": "name", "type": "string" }, "numConcurrentTrials": { "description": "The number of simultaneously running trials.", "title": "numConcurrentTrials", "type": "integer" }, "numTrials": { "description": "The number of search trials to sample.", "title": "numTrials", "type": "integer" }, "optimizationObjectives": { "description": "Optimization objectives of a study.", "items": { "maxItems": 2, "minItems": 2, "prefixItems": [ { "description": "List of supported search objectives.", "enum": [ "correctness", "all_tokens" ], "title": "SearchObjective", "type": "string" }, { "description": "Whether to minimize or maximize search objective.", "enum": [ "maximize", "minimize" ], "title": "SearchDirection", "type": "string" } ], "type": "array" }, "title": "optimizationObjectives", "type": "array" }, "playgroundId": { "description": "The ID of the existing playground that will be associated with the search.", "title": "playgroundId", "type": "string" }, "searchSpace": { "description": "Represents full search space.", "properties": { "chunkingParameters": { "description": "Parameters of the text chunkers.", "properties": { "chunkOverlapPercentageMax": { "default": 50, "description": "Maximum value of chunk overlap.", "title": "chunkOverlapPercentageMax", "type": "number" }, "chunkOverlapPercentageMin": { "default": 0, "description": "Minimum value of chunk overlap.", "title": "chunkOverlapPercentageMin", "type": "number" }, "chunkOverlapPercentageStep": { "default": 10, "description": "Step value of chunk overlap.", "title": "chunkOverlapPercentageStep", "type": "number" }, "chunkSizeMaxExp": { "default": 8, "description": "Maximum exponent for chunk size (2^8 = 256).", "title": "chunkSizeMaxExp", "type": "integer" }, "chunkSizeMinExp": { "default": 7, "description": "Minimum exponent for chunk size (2^7 = 128).", "title": "chunkSizeMinExp", "type": "integer" }, "chunkingMethods": { "description": "List of chunking methods to use.", "items": { "type": "string" }, "title": "chunkingMethods", "type": "array" }, "embeddingModelNames": { "description": "List of embedding models to use.", "items": { "type": "string" }, "title": "embeddingModelNames", "type": "array" } }, "required": [ "embeddingModelNames" ], "title": "ChunkingParametersConfig", "type": "object" }, "llmConfig": { "description": "Configuration of LLMs in the search space.", "properties": { "llmNames": { "description": "List of LLM names to use.", "items": { "type": "string" }, "title": "llmNames", "type": "array" }, "temperatureMax": { "default": 1, "description": "Maximum temperature of an LLM.", "title": "temperatureMax", "type": "number" }, "temperatureMin": { "default": 0, "description": "Minimum temperature of an LLM.", "title": "temperatureMin", "type": "number" }, "temperatureStep": { "default": 0.05, "description": "Step size for LLM temperature.", "title": "temperatureStep", "type": "number" }, "topPMax": { "default": 1, "description": "Maximum top_p of an LLM.", "title": "topPMax", "type": "number" }, "topPMin": { "default": 0, "description": "Minimum top_p of an LLM.", "title": "topPMin", "type": "number" }, "topPStep": { "default": 0.05, "description": "Step size for LLM top_p.", "title": "topPStep", "type": "number" } }, "required": [ "llmNames" ], "title": "LLMConfig", "type": "object" }, "vectorDatabaseSettings": { "description": "Settings of the vector database.", "properties": { "addNeighborChunks": { "description": "Add neighboring chunks to those that the similarity search retrieves.", "items": { "type": "boolean" }, "title": "addNeighborChunks", "type": "array" }, "maxDocumentRetrievedPerPromptMax": { "default": 10, "description": "Max value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMax", "type": "integer" }, "maxDocumentRetrievedPerPromptMin": { "default": 1, "description": "Min value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMin", "type": "integer" }, "maxDocumentRetrievedPerPromptStep": { "default": 1, "description": "Step for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptStep", "type": "integer" }, "maxMmrLambdaMax": { "default": 1, "description": "Maximum value of MMR lambda.", "title": "maxMmrLambdaMax", "type": "number" }, "maxMmrLambdaMin": { "default": 0, "description": "Minimum value of MMR lambda.", "title": "maxMmrLambdaMin", "type": "number" }, "maxMmrLambdaStep": { "default": 0.1, "description": "Step value of MMR lambda.", "title": "maxMmrLambdaStep", "type": "number" }, "retrievalModes": { "description": "List of retriever modes to use.", "items": { "type": "string" }, "title": "retrievalModes", "type": "array" }, "retrievers": { "description": "List of retriever types to use.", "items": { "type": "string" }, "title": "retrievers", "type": "array" } }, "required": [ "retrievers", "retrievalModes" ], "title": "VectorDatabaseConfig", "type": "object" } }, "title": "SearchSpace", "type": "object" }, "useCaseId": { "description": "The ID of the use case the search study is linked to.", "title": "useCaseId", "type": "string" } }, "required": [ "useCaseId", "playgroundId", "groundingDatasetId", "evalDatasetId", "numTrials", "numConcurrentTrials", "optimizationObjectives", "searchSpace", "name" ], "title": "RunAgenticSearchRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | RunAgenticSearchRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for run agentic search request.", "properties": { "jobId": { "description": "The ID of the worker job.", "format": "uuid4", "title": "jobId", "type": "string" }, "searchStudyId": { "description": "The ID of the search study.", "title": "searchStudyId", "type": "string" } }, "required": [ "searchStudyId", "jobId" ], "title": "RunSearchApiResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Agentic search job successfully accepted. Follow the Location header to poll for job execution status. | RunSearchApiResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete existing search study by ID by search study ID Operation path: `DELETE /api/v2/genai/syftrSearch/{searchStudyId}/` Authentication requirements: `BearerAuth` Delete existing search study object from the database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | searchStudyId | path | string | true | The ID of the search study to be retrieved. | ### Example responses > 202 Response ``` { "description": "API response for the deletion of a search study.", "properties": { "jobId": { "description": "The ID of the worker job.", "format": "uuid4", "title": "jobId", "type": "string" }, "searchStudyId": { "description": "The ID of the search study.", "title": "searchStudyId", "type": "string" } }, "required": [ "searchStudyId", "jobId" ], "title": "DeleteSearchApiResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Search study has been successfully deleted. | DeleteSearchApiResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Get existing search study by ID by search study ID Operation path: `GET /api/v2/genai/syftrSearch/{searchStudyId}/` Authentication requirements: `BearerAuth` Return existing search study object from the database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | searchStudyId | path | string | true | The ID of the search study to be retrieved. | ### Example responses > 200 Response ``` { "description": "API response object for search study retrieval.", "properties": { "allTrials": { "anyOf": [ { "items": { "description": "Represents a search trial from history.", "properties": { "llmBlueprintId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The correspondent blueprint ID.", "title": "llmBlueprintId" }, "searchParameters": { "additionalProperties": true, "description": "Search parameters of the point.", "title": "searchParameters", "type": "object" }, "values": { "description": "The resulting values of optimization objectives.", "items": { "type": "number" }, "title": "values", "type": "array" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The correspondent vector database ID.", "title": "vectorDatabaseId" } }, "required": [ "llmBlueprintId", "vectorDatabaseId", "values", "searchParameters" ], "title": "HistoryPoint", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Trials history.", "title": "allTrials" }, "datetimeEnd": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "Study end time.", "title": "datetimeEnd" }, "datetimeStart": { "description": "Study start time.", "format": "date-time", "title": "datetimeStart", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Error message if search study fails.", "title": "errorMessage" }, "evalDatasetId": { "description": "The ID of the evaluation dataset.", "title": "evalDatasetId", "type": "string" }, "evalDatasetName": { "description": "The name of evaluation dataset.", "title": "evalDatasetName", "type": "string" }, "evalResults": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The results of the comparative evaluation of LLM blueprints.", "title": "evalResults" }, "existingBlueprintIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The IDs of existing LLM blueprints for comparative evaluation.", "title": "existingBlueprintIds" }, "groundingDatasetId": { "description": "The ID of the dataset the vector databases will be built from.", "title": "groundingDatasetId", "type": "string" }, "groundingDatasetName": { "description": "The name of the grouding dataset.", "title": "groundingDatasetName", "type": "string" }, "jobId": { "anyOf": [ { "format": "uuid4", "type": "string" }, { "type": "null" } ], "description": "The ID of the worker job.", "title": "jobId" }, "name": { "description": "Name of the search study.", "title": "name", "type": "string" }, "numConcurrentTrials": { "description": "The number of simultaneously running trials.", "title": "numConcurrentTrials", "type": "integer" }, "numTrials": { "description": "The number of search trials to sample.", "title": "numTrials", "type": "integer" }, "optimizationObjectives": { "description": "Optimization objectives of a study.", "items": { "maxItems": 2, "minItems": 2, "prefixItems": [ { "description": "List of supported search objectives.", "enum": [ "correctness", "all_tokens" ], "title": "SearchObjective", "type": "string" }, { "description": "Whether to minimize or maximize search objective.", "enum": [ "maximize", "minimize" ], "title": "SearchDirection", "type": "string" } ], "type": "array" }, "title": "optimizationObjectives", "type": "array" }, "paretoFront": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "Pareto frontier of a study.", "title": "paretoFront" }, "playgroundId": { "description": "The ID of the existing playground that will be associated with the search.", "title": "playgroundId", "type": "string" }, "searchSpace": { "anyOf": [ { "description": "Represents full search space.", "properties": { "chunkingParameters": { "description": "Parameters of the text chunkers.", "properties": { "chunkOverlapPercentageMax": { "default": 50, "description": "Maximum value of chunk overlap.", "title": "chunkOverlapPercentageMax", "type": "number" }, "chunkOverlapPercentageMin": { "default": 0, "description": "Minimum value of chunk overlap.", "title": "chunkOverlapPercentageMin", "type": "number" }, "chunkOverlapPercentageStep": { "default": 10, "description": "Step value of chunk overlap.", "title": "chunkOverlapPercentageStep", "type": "number" }, "chunkSizeMaxExp": { "default": 8, "description": "Maximum exponent for chunk size (2^8 = 256).", "title": "chunkSizeMaxExp", "type": "integer" }, "chunkSizeMinExp": { "default": 7, "description": "Minimum exponent for chunk size (2^7 = 128).", "title": "chunkSizeMinExp", "type": "integer" }, "chunkingMethods": { "description": "List of chunking methods to use.", "items": { "type": "string" }, "title": "chunkingMethods", "type": "array" }, "embeddingModelNames": { "description": "List of embedding models to use.", "items": { "type": "string" }, "title": "embeddingModelNames", "type": "array" } }, "required": [ "embeddingModelNames" ], "title": "ChunkingParametersConfig", "type": "object" }, "llmConfig": { "description": "Configuration of LLMs in the search space.", "properties": { "llmNames": { "description": "List of LLM names to use.", "items": { "type": "string" }, "title": "llmNames", "type": "array" }, "temperatureMax": { "default": 1, "description": "Maximum temperature of an LLM.", "title": "temperatureMax", "type": "number" }, "temperatureMin": { "default": 0, "description": "Minimum temperature of an LLM.", "title": "temperatureMin", "type": "number" }, "temperatureStep": { "default": 0.05, "description": "Step size for LLM temperature.", "title": "temperatureStep", "type": "number" }, "topPMax": { "default": 1, "description": "Maximum top_p of an LLM.", "title": "topPMax", "type": "number" }, "topPMin": { "default": 0, "description": "Minimum top_p of an LLM.", "title": "topPMin", "type": "number" }, "topPStep": { "default": 0.05, "description": "Step size for LLM top_p.", "title": "topPStep", "type": "number" } }, "required": [ "llmNames" ], "title": "LLMConfig", "type": "object" }, "vectorDatabaseSettings": { "description": "Settings of the vector database.", "properties": { "addNeighborChunks": { "description": "Add neighboring chunks to those that the similarity search retrieves.", "items": { "type": "boolean" }, "title": "addNeighborChunks", "type": "array" }, "maxDocumentRetrievedPerPromptMax": { "default": 10, "description": "Max value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMax", "type": "integer" }, "maxDocumentRetrievedPerPromptMin": { "default": 1, "description": "Min value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMin", "type": "integer" }, "maxDocumentRetrievedPerPromptStep": { "default": 1, "description": "Step for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptStep", "type": "integer" }, "maxMmrLambdaMax": { "default": 1, "description": "Maximum value of MMR lambda.", "title": "maxMmrLambdaMax", "type": "number" }, "maxMmrLambdaMin": { "default": 0, "description": "Minimum value of MMR lambda.", "title": "maxMmrLambdaMin", "type": "number" }, "maxMmrLambdaStep": { "default": 0.1, "description": "Step value of MMR lambda.", "title": "maxMmrLambdaStep", "type": "number" }, "retrievalModes": { "description": "List of retriever modes to use.", "items": { "type": "string" }, "title": "retrievalModes", "type": "array" }, "retrievers": { "description": "List of retriever types to use.", "items": { "type": "string" }, "title": "retrievers", "type": "array" } }, "required": [ "retrievers", "retrievalModes" ], "title": "VectorDatabaseConfig", "type": "object" } }, "title": "SearchSpace", "type": "object" }, { "type": "null" } ], "description": "Search space for the search." }, "searchStudyId": { "description": "The ID of the search study.", "title": "searchStudyId", "type": "string" }, "studyStatus": { "description": "Represents a search study execution state.", "enum": [ "RUNNING", "COMPLETED", "STOPPED", "FAILED" ], "title": "JobStatus", "type": "string" }, "tempPlaygroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the temp playground.", "title": "tempPlaygroundId" }, "trialsFailed": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of failed trials.", "title": "trialsFailed" }, "trialsRunning": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of currently running trials.", "title": "trialsRunning" }, "trialsSuccess": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of completed trials.", "title": "trialsSuccess" }, "useCaseId": { "description": "The ID of the use case the search study is linked to.", "title": "useCaseId", "type": "string" }, "userId": { "description": "The ID of the user.", "title": "userId", "type": "string" }, "userName": { "description": "The user name of the user who ran the study.", "title": "userName", "type": "string" } }, "required": [ "searchSpace", "useCaseId", "groundingDatasetId", "evalDatasetId", "groundingDatasetName", "evalDatasetName", "userId", "userName", "numTrials", "numConcurrentTrials", "optimizationObjectives", "playgroundId", "tempPlaygroundId", "paretoFront", "datetimeStart", "datetimeEnd", "studyStatus", "searchStudyId", "name", "jobId", "trialsRunning", "trialsFailed", "trialsSuccess", "allTrials", "existingBlueprintIds", "evalResults", "errorMessage" ], "title": "SearchStudyResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Search study has been successfully retrieved. | SearchStudyResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit search study by search study ID Operation path: `PATCH /api/v2/genai/syftrSearch/{searchStudyId}/` Authentication requirements: `BearerAuth` Edit an existing search study object. ### Body parameter ``` { "description": "The body of the \"Edit search study\" request.", "properties": { "name": { "description": "The new name of the search study.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" } }, "required": [ "name" ], "title": "EditSearchStudyRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | searchStudyId | path | string | true | The ID of the search study to be edited. | | body | body | EditSearchStudyRequest | true | none | ### Example responses > 200 Response ``` {} ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | Inline | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ### Response Schema # Schemas ## AgentChatCompletionRequest ``` { "additionalProperties": true, "description": "Represents a chat completion request for an agent.", "properties": { "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The version ID of the custom model to use for the chat completion.", "title": "customModelVersionId" }, "messages": { "description": "A list of messages comprising the conversation so far.", "items": { "additionalProperties": true, "description": "Represents a message in a chat conversation.", "properties": { "content": { "anyOf": [ { "maxLength": 50000, "type": "string" }, { "type": "null" } ], "description": "The contents of the message.", "title": "content" }, "role": { "description": "The role of the author of this message.", "title": "role", "type": "string" } }, "required": [ "role" ], "title": "AgentMessage", "type": "object" }, "title": "messages", "type": "array" }, "model": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model identifier to use for completion following OpenAI API notation.", "title": "model" }, "tracingContext": { "anyOf": [ { "description": "Represents a custom tracing context for a chat completion request.", "properties": { "attributes": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "null" } ], "description": "The attributes of the tracing context.", "title": "attributes" }, "entityId": { "description": "The ID of the entity in context of which the agent request is performed. Should be an entity which user has access to.", "title": "entityId", "type": "string" }, "entityType": { "description": "Type of an entity in context of which the agent request is performed.", "enum": [ "deployment", "use_case" ], "title": "TracingContextEntityType", "type": "string" } }, "required": [ "entityId", "entityType", "attributes" ], "title": "AgentTracingContext", "type": "object" }, { "type": "null" } ], "description": "Optional tracing context for the chat completion request." } }, "required": [ "messages" ], "title": "AgentChatCompletionRequest", "type": "object" } ``` AgentChatCompletionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelVersionId | any | false | | The version ID of the custom model to use for the chat completion. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | messages | [AgentMessage] | true | | A list of messages comprising the conversation so far. | | model | any | false | | The model identifier to use for completion following OpenAI API notation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | tracingContext | any | false | | Optional tracing context for the chat completion request. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AgentTracingContext | false | | Represents a custom tracing context for a chat completion request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## AgentChatCompletionResponse ``` { "additionalProperties": true, "description": "Chat completion response from an agent.", "properties": { "choices": { "anyOf": [ { "items": { "additionalProperties": true, "description": "Represents a single choice in the chat completion response.", "properties": { "message": { "additionalProperties": true, "description": "Represents a message in a chat conversation.", "properties": { "content": { "anyOf": [ { "maxLength": 50000, "type": "string" }, { "type": "null" } ], "description": "The contents of the message.", "title": "content" }, "role": { "description": "The role of the author of this message.", "title": "role", "type": "string" } }, "required": [ "role" ], "title": "AgentMessage", "type": "object" } }, "required": [ "message" ], "title": "AgentChoice", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "A list of agent choices. Can be more than one. None when failed.", "title": "choices" }, "errorDetails": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Detailed error information if the chat completion failed.", "title": "errorDetails" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Error message if the chat completion failed.", "title": "errorMessage" } }, "title": "AgentChatCompletionResponse", "type": "object" } ``` AgentChatCompletionResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | choices | any | false | | A list of agent choices. Can be more than one. None when failed. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [AgentChoice] | false | | [Represents a single choice in the chat completion response.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorDetails | any | false | | Detailed error information if the chat completion failed. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | Error message if the chat completion failed. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## AgentChoice ``` { "additionalProperties": true, "description": "Represents a single choice in the chat completion response.", "properties": { "message": { "additionalProperties": true, "description": "Represents a message in a chat conversation.", "properties": { "content": { "anyOf": [ { "maxLength": 50000, "type": "string" }, { "type": "null" } ], "description": "The contents of the message.", "title": "content" }, "role": { "description": "The role of the author of this message.", "title": "role", "type": "string" } }, "required": [ "role" ], "title": "AgentMessage", "type": "object" } }, "required": [ "message" ], "title": "AgentChoice", "type": "object" } ``` AgentChoice ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | AgentMessage | true | | The message content of the choice. | ## AgentMessage ``` { "additionalProperties": true, "description": "Represents a message in a chat conversation.", "properties": { "content": { "anyOf": [ { "maxLength": 50000, "type": "string" }, { "type": "null" } ], "description": "The contents of the message.", "title": "content" }, "role": { "description": "The role of the author of this message.", "title": "role", "type": "string" } }, "required": [ "role" ], "title": "AgentMessage", "type": "object" } ``` AgentMessage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | content | any | false | | The contents of the message. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 50000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the author of this message. | ## AgentTracingContext ``` { "description": "Represents a custom tracing context for a chat completion request.", "properties": { "attributes": { "anyOf": [ { "additionalProperties": { "type": "string" }, "type": "object" }, { "type": "null" } ], "description": "The attributes of the tracing context.", "title": "attributes" }, "entityId": { "description": "The ID of the entity in context of which the agent request is performed. Should be an entity which user has access to.", "title": "entityId", "type": "string" }, "entityType": { "description": "Type of an entity in context of which the agent request is performed.", "enum": [ "deployment", "use_case" ], "title": "TracingContextEntityType", "type": "string" } }, "required": [ "entityId", "entityType", "attributes" ], "title": "AgentTracingContext", "type": "object" } ``` AgentTracingContext ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributes | any | true | | The attributes of the tracing context. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | none | | »» additionalProperties | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityId | string | true | | The ID of the entity in context of which the agent request is performed. Should be an entity which user has access to. | | entityType | TracingContextEntityType | true | | The type of an entity in context of which the agent request is performed. Should be an entity which user has access to. | ## ChunkingParametersConfig ``` { "description": "Parameters of the text chunkers.", "properties": { "chunkOverlapPercentageMax": { "default": 50, "description": "Maximum value of chunk overlap.", "title": "chunkOverlapPercentageMax", "type": "number" }, "chunkOverlapPercentageMin": { "default": 0, "description": "Minimum value of chunk overlap.", "title": "chunkOverlapPercentageMin", "type": "number" }, "chunkOverlapPercentageStep": { "default": 10, "description": "Step value of chunk overlap.", "title": "chunkOverlapPercentageStep", "type": "number" }, "chunkSizeMaxExp": { "default": 8, "description": "Maximum exponent for chunk size (2^8 = 256).", "title": "chunkSizeMaxExp", "type": "integer" }, "chunkSizeMinExp": { "default": 7, "description": "Minimum exponent for chunk size (2^7 = 128).", "title": "chunkSizeMinExp", "type": "integer" }, "chunkingMethods": { "description": "List of chunking methods to use.", "items": { "type": "string" }, "title": "chunkingMethods", "type": "array" }, "embeddingModelNames": { "description": "List of embedding models to use.", "items": { "type": "string" }, "title": "embeddingModelNames", "type": "array" } }, "required": [ "embeddingModelNames" ], "title": "ChunkingParametersConfig", "type": "object" } ``` ChunkingParametersConfig ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkOverlapPercentageMax | number | false | | Maximum value of chunk overlap. | | chunkOverlapPercentageMin | number | false | | Minimum value of chunk overlap. | | chunkOverlapPercentageStep | number | false | | Step value of chunk overlap. | | chunkSizeMaxExp | integer | false | | Maximum exponent for chunk size (2^8 = 256). | | chunkSizeMinExp | integer | false | | Minimum exponent for chunk size (2^7 = 128). | | chunkingMethods | [string] | false | | List of chunking methods to use. | | embeddingModelNames | [string] | true | | List of embedding models to use. | ## DeleteSearchApiResponse ``` { "description": "API response for the deletion of a search study.", "properties": { "jobId": { "description": "The ID of the worker job.", "format": "uuid4", "title": "jobId", "type": "string" }, "searchStudyId": { "description": "The ID of the search study.", "title": "searchStudyId", "type": "string" } }, "required": [ "searchStudyId", "jobId" ], "title": "DeleteSearchApiResponse", "type": "object" } ``` DeleteSearchApiResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jobId | string(uuid4) | true | | The ID of the worker job. | | searchStudyId | string | true | | The ID of the search study. | ## EditSearchStudyRequest ``` { "description": "The body of the \"Edit search study\" request.", "properties": { "name": { "description": "The new name of the search study.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" } }, "required": [ "name" ], "title": "EditSearchStudyRequest", "type": "object" } ``` EditSearchStudyRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | The new name of the search study. | ## HTTPValidationErrorResponse ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` HTTPValidationErrorResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | detail | [ValidationError] | false | | none | ## HistoryPoint ``` { "description": "Represents a search trial from history.", "properties": { "llmBlueprintId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The correspondent blueprint ID.", "title": "llmBlueprintId" }, "searchParameters": { "additionalProperties": true, "description": "Search parameters of the point.", "title": "searchParameters", "type": "object" }, "values": { "description": "The resulting values of optimization objectives.", "items": { "type": "number" }, "title": "values", "type": "array" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The correspondent vector database ID.", "title": "vectorDatabaseId" } }, "required": [ "llmBlueprintId", "vectorDatabaseId", "values", "searchParameters" ], "title": "HistoryPoint", "type": "object" } ``` HistoryPoint ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmBlueprintId | any | true | | The correspondent blueprint ID. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | searchParameters | object | true | | Search parameters of the point. | | values | [number] | true | | The resulting values of optimization objectives. | | vectorDatabaseId | any | true | | The correspondent vector database ID. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## JobStatus ``` { "description": "Represents a search study execution state.", "enum": [ "RUNNING", "COMPLETED", "STOPPED", "FAILED" ], "title": "JobStatus", "type": "string" } ``` JobStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | JobStatus | string | false | | Represents a search study execution state. | ### Enumerated Values | Property | Value | | --- | --- | | JobStatus | [RUNNING, COMPLETED, STOPPED, FAILED] | ## LLMConfig ``` { "description": "Configuration of LLMs in the search space.", "properties": { "llmNames": { "description": "List of LLM names to use.", "items": { "type": "string" }, "title": "llmNames", "type": "array" }, "temperatureMax": { "default": 1, "description": "Maximum temperature of an LLM.", "title": "temperatureMax", "type": "number" }, "temperatureMin": { "default": 0, "description": "Minimum temperature of an LLM.", "title": "temperatureMin", "type": "number" }, "temperatureStep": { "default": 0.05, "description": "Step size for LLM temperature.", "title": "temperatureStep", "type": "number" }, "topPMax": { "default": 1, "description": "Maximum top_p of an LLM.", "title": "topPMax", "type": "number" }, "topPMin": { "default": 0, "description": "Minimum top_p of an LLM.", "title": "topPMin", "type": "number" }, "topPStep": { "default": 0.05, "description": "Step size for LLM top_p.", "title": "topPStep", "type": "number" } }, "required": [ "llmNames" ], "title": "LLMConfig", "type": "object" } ``` LLMConfig ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmNames | [string] | true | | List of LLM names to use. | | temperatureMax | number | false | | Maximum temperature of an LLM. | | temperatureMin | number | false | | Minimum temperature of an LLM. | | temperatureStep | number | false | | Step size for LLM temperature. | | topPMax | number | false | | Maximum top_p of an LLM. | | topPMin | number | false | | Minimum top_p of an LLM. | | topPStep | number | false | | Step size for LLM top_p. | ## ListSearchStudyResponse ``` { "description": "Paginated list of search studies.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for search study retrieval.", "properties": { "allTrials": { "anyOf": [ { "items": { "description": "Represents a search trial from history.", "properties": { "llmBlueprintId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The correspondent blueprint ID.", "title": "llmBlueprintId" }, "searchParameters": { "additionalProperties": true, "description": "Search parameters of the point.", "title": "searchParameters", "type": "object" }, "values": { "description": "The resulting values of optimization objectives.", "items": { "type": "number" }, "title": "values", "type": "array" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The correspondent vector database ID.", "title": "vectorDatabaseId" } }, "required": [ "llmBlueprintId", "vectorDatabaseId", "values", "searchParameters" ], "title": "HistoryPoint", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Trials history.", "title": "allTrials" }, "datetimeEnd": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "Study end time.", "title": "datetimeEnd" }, "datetimeStart": { "description": "Study start time.", "format": "date-time", "title": "datetimeStart", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Error message if search study fails.", "title": "errorMessage" }, "evalDatasetId": { "description": "The ID of the evaluation dataset.", "title": "evalDatasetId", "type": "string" }, "evalDatasetName": { "description": "The name of evaluation dataset.", "title": "evalDatasetName", "type": "string" }, "evalResults": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The results of the comparative evaluation of LLM blueprints.", "title": "evalResults" }, "existingBlueprintIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The IDs of existing LLM blueprints for comparative evaluation.", "title": "existingBlueprintIds" }, "groundingDatasetId": { "description": "The ID of the dataset the vector databases will be built from.", "title": "groundingDatasetId", "type": "string" }, "groundingDatasetName": { "description": "The name of the grouding dataset.", "title": "groundingDatasetName", "type": "string" }, "jobId": { "anyOf": [ { "format": "uuid4", "type": "string" }, { "type": "null" } ], "description": "The ID of the worker job.", "title": "jobId" }, "name": { "description": "Name of the search study.", "title": "name", "type": "string" }, "numConcurrentTrials": { "description": "The number of simultaneously running trials.", "title": "numConcurrentTrials", "type": "integer" }, "numTrials": { "description": "The number of search trials to sample.", "title": "numTrials", "type": "integer" }, "optimizationObjectives": { "description": "Optimization objectives of a study.", "items": { "maxItems": 2, "minItems": 2, "prefixItems": [ { "description": "List of supported search objectives.", "enum": [ "correctness", "all_tokens" ], "title": "SearchObjective", "type": "string" }, { "description": "Whether to minimize or maximize search objective.", "enum": [ "maximize", "minimize" ], "title": "SearchDirection", "type": "string" } ], "type": "array" }, "title": "optimizationObjectives", "type": "array" }, "paretoFront": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "Pareto frontier of a study.", "title": "paretoFront" }, "playgroundId": { "description": "The ID of the existing playground that will be associated with the search.", "title": "playgroundId", "type": "string" }, "searchSpace": { "anyOf": [ { "description": "Represents full search space.", "properties": { "chunkingParameters": { "description": "Parameters of the text chunkers.", "properties": { "chunkOverlapPercentageMax": { "default": 50, "description": "Maximum value of chunk overlap.", "title": "chunkOverlapPercentageMax", "type": "number" }, "chunkOverlapPercentageMin": { "default": 0, "description": "Minimum value of chunk overlap.", "title": "chunkOverlapPercentageMin", "type": "number" }, "chunkOverlapPercentageStep": { "default": 10, "description": "Step value of chunk overlap.", "title": "chunkOverlapPercentageStep", "type": "number" }, "chunkSizeMaxExp": { "default": 8, "description": "Maximum exponent for chunk size (2^8 = 256).", "title": "chunkSizeMaxExp", "type": "integer" }, "chunkSizeMinExp": { "default": 7, "description": "Minimum exponent for chunk size (2^7 = 128).", "title": "chunkSizeMinExp", "type": "integer" }, "chunkingMethods": { "description": "List of chunking methods to use.", "items": { "type": "string" }, "title": "chunkingMethods", "type": "array" }, "embeddingModelNames": { "description": "List of embedding models to use.", "items": { "type": "string" }, "title": "embeddingModelNames", "type": "array" } }, "required": [ "embeddingModelNames" ], "title": "ChunkingParametersConfig", "type": "object" }, "llmConfig": { "description": "Configuration of LLMs in the search space.", "properties": { "llmNames": { "description": "List of LLM names to use.", "items": { "type": "string" }, "title": "llmNames", "type": "array" }, "temperatureMax": { "default": 1, "description": "Maximum temperature of an LLM.", "title": "temperatureMax", "type": "number" }, "temperatureMin": { "default": 0, "description": "Minimum temperature of an LLM.", "title": "temperatureMin", "type": "number" }, "temperatureStep": { "default": 0.05, "description": "Step size for LLM temperature.", "title": "temperatureStep", "type": "number" }, "topPMax": { "default": 1, "description": "Maximum top_p of an LLM.", "title": "topPMax", "type": "number" }, "topPMin": { "default": 0, "description": "Minimum top_p of an LLM.", "title": "topPMin", "type": "number" }, "topPStep": { "default": 0.05, "description": "Step size for LLM top_p.", "title": "topPStep", "type": "number" } }, "required": [ "llmNames" ], "title": "LLMConfig", "type": "object" }, "vectorDatabaseSettings": { "description": "Settings of the vector database.", "properties": { "addNeighborChunks": { "description": "Add neighboring chunks to those that the similarity search retrieves.", "items": { "type": "boolean" }, "title": "addNeighborChunks", "type": "array" }, "maxDocumentRetrievedPerPromptMax": { "default": 10, "description": "Max value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMax", "type": "integer" }, "maxDocumentRetrievedPerPromptMin": { "default": 1, "description": "Min value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMin", "type": "integer" }, "maxDocumentRetrievedPerPromptStep": { "default": 1, "description": "Step for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptStep", "type": "integer" }, "maxMmrLambdaMax": { "default": 1, "description": "Maximum value of MMR lambda.", "title": "maxMmrLambdaMax", "type": "number" }, "maxMmrLambdaMin": { "default": 0, "description": "Minimum value of MMR lambda.", "title": "maxMmrLambdaMin", "type": "number" }, "maxMmrLambdaStep": { "default": 0.1, "description": "Step value of MMR lambda.", "title": "maxMmrLambdaStep", "type": "number" }, "retrievalModes": { "description": "List of retriever modes to use.", "items": { "type": "string" }, "title": "retrievalModes", "type": "array" }, "retrievers": { "description": "List of retriever types to use.", "items": { "type": "string" }, "title": "retrievers", "type": "array" } }, "required": [ "retrievers", "retrievalModes" ], "title": "VectorDatabaseConfig", "type": "object" } }, "title": "SearchSpace", "type": "object" }, { "type": "null" } ], "description": "Search space for the search." }, "searchStudyId": { "description": "The ID of the search study.", "title": "searchStudyId", "type": "string" }, "studyStatus": { "description": "Represents a search study execution state.", "enum": [ "RUNNING", "COMPLETED", "STOPPED", "FAILED" ], "title": "JobStatus", "type": "string" }, "tempPlaygroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the temp playground.", "title": "tempPlaygroundId" }, "trialsFailed": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of failed trials.", "title": "trialsFailed" }, "trialsRunning": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of currently running trials.", "title": "trialsRunning" }, "trialsSuccess": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of completed trials.", "title": "trialsSuccess" }, "useCaseId": { "description": "The ID of the use case the search study is linked to.", "title": "useCaseId", "type": "string" }, "userId": { "description": "The ID of the user.", "title": "userId", "type": "string" }, "userName": { "description": "The user name of the user who ran the study.", "title": "userName", "type": "string" } }, "required": [ "searchSpace", "useCaseId", "groundingDatasetId", "evalDatasetId", "groundingDatasetName", "evalDatasetName", "userId", "userName", "numTrials", "numConcurrentTrials", "optimizationObjectives", "playgroundId", "tempPlaygroundId", "paretoFront", "datetimeStart", "datetimeEnd", "studyStatus", "searchStudyId", "name", "jobId", "trialsRunning", "trialsFailed", "trialsSuccess", "allTrials", "existingBlueprintIds", "evalResults", "errorMessage" ], "title": "SearchStudyResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListSearchStudyResponse", "type": "object" } ``` ListSearchStudyResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [SearchStudyResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListSearchStudySortQueryParam ``` { "description": "API object for Sort order values for listiing search studies.", "enum": [ "name", "-name" ], "title": "ListSearchStudySortQueryParam", "type": "string" } ``` ListSearchStudySortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ListSearchStudySortQueryParam | string | false | | API object for Sort order values for listiing search studies. | ### Enumerated Values | Property | Value | | --- | --- | | ListSearchStudySortQueryParam | [name, -name] | ## RunAgenticSearchRequest ``` { "description": "API request for run agentic search request.", "properties": { "evalDatasetId": { "description": "The ID of the evaluation dataset.", "title": "evalDatasetId", "type": "string" }, "groundingDatasetId": { "description": "The ID of the dataset the vector databases will be built from.", "title": "groundingDatasetId", "type": "string" }, "name": { "description": "Name of the search study.", "title": "name", "type": "string" }, "numConcurrentTrials": { "description": "The number of simultaneously running trials.", "title": "numConcurrentTrials", "type": "integer" }, "numTrials": { "description": "The number of search trials to sample.", "title": "numTrials", "type": "integer" }, "optimizationObjectives": { "description": "Optimization objectives of a study.", "items": { "maxItems": 2, "minItems": 2, "prefixItems": [ { "description": "List of supported search objectives.", "enum": [ "correctness", "all_tokens" ], "title": "SearchObjective", "type": "string" }, { "description": "Whether to minimize or maximize search objective.", "enum": [ "maximize", "minimize" ], "title": "SearchDirection", "type": "string" } ], "type": "array" }, "title": "optimizationObjectives", "type": "array" }, "playgroundId": { "description": "The ID of the existing playground that will be associated with the search.", "title": "playgroundId", "type": "string" }, "searchSpace": { "description": "Represents full search space.", "properties": { "chunkingParameters": { "description": "Parameters of the text chunkers.", "properties": { "chunkOverlapPercentageMax": { "default": 50, "description": "Maximum value of chunk overlap.", "title": "chunkOverlapPercentageMax", "type": "number" }, "chunkOverlapPercentageMin": { "default": 0, "description": "Minimum value of chunk overlap.", "title": "chunkOverlapPercentageMin", "type": "number" }, "chunkOverlapPercentageStep": { "default": 10, "description": "Step value of chunk overlap.", "title": "chunkOverlapPercentageStep", "type": "number" }, "chunkSizeMaxExp": { "default": 8, "description": "Maximum exponent for chunk size (2^8 = 256).", "title": "chunkSizeMaxExp", "type": "integer" }, "chunkSizeMinExp": { "default": 7, "description": "Minimum exponent for chunk size (2^7 = 128).", "title": "chunkSizeMinExp", "type": "integer" }, "chunkingMethods": { "description": "List of chunking methods to use.", "items": { "type": "string" }, "title": "chunkingMethods", "type": "array" }, "embeddingModelNames": { "description": "List of embedding models to use.", "items": { "type": "string" }, "title": "embeddingModelNames", "type": "array" } }, "required": [ "embeddingModelNames" ], "title": "ChunkingParametersConfig", "type": "object" }, "llmConfig": { "description": "Configuration of LLMs in the search space.", "properties": { "llmNames": { "description": "List of LLM names to use.", "items": { "type": "string" }, "title": "llmNames", "type": "array" }, "temperatureMax": { "default": 1, "description": "Maximum temperature of an LLM.", "title": "temperatureMax", "type": "number" }, "temperatureMin": { "default": 0, "description": "Minimum temperature of an LLM.", "title": "temperatureMin", "type": "number" }, "temperatureStep": { "default": 0.05, "description": "Step size for LLM temperature.", "title": "temperatureStep", "type": "number" }, "topPMax": { "default": 1, "description": "Maximum top_p of an LLM.", "title": "topPMax", "type": "number" }, "topPMin": { "default": 0, "description": "Minimum top_p of an LLM.", "title": "topPMin", "type": "number" }, "topPStep": { "default": 0.05, "description": "Step size for LLM top_p.", "title": "topPStep", "type": "number" } }, "required": [ "llmNames" ], "title": "LLMConfig", "type": "object" }, "vectorDatabaseSettings": { "description": "Settings of the vector database.", "properties": { "addNeighborChunks": { "description": "Add neighboring chunks to those that the similarity search retrieves.", "items": { "type": "boolean" }, "title": "addNeighborChunks", "type": "array" }, "maxDocumentRetrievedPerPromptMax": { "default": 10, "description": "Max value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMax", "type": "integer" }, "maxDocumentRetrievedPerPromptMin": { "default": 1, "description": "Min value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMin", "type": "integer" }, "maxDocumentRetrievedPerPromptStep": { "default": 1, "description": "Step for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptStep", "type": "integer" }, "maxMmrLambdaMax": { "default": 1, "description": "Maximum value of MMR lambda.", "title": "maxMmrLambdaMax", "type": "number" }, "maxMmrLambdaMin": { "default": 0, "description": "Minimum value of MMR lambda.", "title": "maxMmrLambdaMin", "type": "number" }, "maxMmrLambdaStep": { "default": 0.1, "description": "Step value of MMR lambda.", "title": "maxMmrLambdaStep", "type": "number" }, "retrievalModes": { "description": "List of retriever modes to use.", "items": { "type": "string" }, "title": "retrievalModes", "type": "array" }, "retrievers": { "description": "List of retriever types to use.", "items": { "type": "string" }, "title": "retrievers", "type": "array" } }, "required": [ "retrievers", "retrievalModes" ], "title": "VectorDatabaseConfig", "type": "object" } }, "title": "SearchSpace", "type": "object" }, "useCaseId": { "description": "The ID of the use case the search study is linked to.", "title": "useCaseId", "type": "string" } }, "required": [ "useCaseId", "playgroundId", "groundingDatasetId", "evalDatasetId", "numTrials", "numConcurrentTrials", "optimizationObjectives", "searchSpace", "name" ], "title": "RunAgenticSearchRequest", "type": "object" } ``` RunAgenticSearchRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evalDatasetId | string | true | | The ID of the evaluation dataset. | | groundingDatasetId | string | true | | The ID of the dataset the vector databases will be built from. | | name | string | true | | Name of the search study. | | numConcurrentTrials | integer | true | | The number of simultaneously running trials. | | numTrials | integer | true | | The number of search trials to sample. | | optimizationObjectives | [array] | true | | Optimization objectives of a study. | | playgroundId | string | true | | The ID of the existing playground that will be associated with the search. | | searchSpace | SearchSpace | true | | Search space for the search. | | useCaseId | string | true | | The ID of the use case the search study is linked to. | ## RunSearchApiResponse ``` { "description": "API response object for run agentic search request.", "properties": { "jobId": { "description": "The ID of the worker job.", "format": "uuid4", "title": "jobId", "type": "string" }, "searchStudyId": { "description": "The ID of the search study.", "title": "searchStudyId", "type": "string" } }, "required": [ "searchStudyId", "jobId" ], "title": "RunSearchApiResponse", "type": "object" } ``` RunSearchApiResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jobId | string(uuid4) | true | | The ID of the worker job. | | searchStudyId | string | true | | The ID of the search study. | ## SearchDirection ``` { "description": "Whether to minimize or maximize search objective.", "enum": [ "maximize", "minimize" ], "title": "SearchDirection", "type": "string" } ``` SearchDirection ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | SearchDirection | string | false | | Whether to minimize or maximize search objective. | ### Enumerated Values | Property | Value | | --- | --- | | SearchDirection | [maximize, minimize] | ## SearchObjective ``` { "description": "List of supported search objectives.", "enum": [ "correctness", "all_tokens" ], "title": "SearchObjective", "type": "string" } ``` SearchObjective ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | SearchObjective | string | false | | List of supported search objectives. | ### Enumerated Values | Property | Value | | --- | --- | | SearchObjective | [correctness, all_tokens] | ## SearchSpace ``` { "description": "Represents full search space.", "properties": { "chunkingParameters": { "description": "Parameters of the text chunkers.", "properties": { "chunkOverlapPercentageMax": { "default": 50, "description": "Maximum value of chunk overlap.", "title": "chunkOverlapPercentageMax", "type": "number" }, "chunkOverlapPercentageMin": { "default": 0, "description": "Minimum value of chunk overlap.", "title": "chunkOverlapPercentageMin", "type": "number" }, "chunkOverlapPercentageStep": { "default": 10, "description": "Step value of chunk overlap.", "title": "chunkOverlapPercentageStep", "type": "number" }, "chunkSizeMaxExp": { "default": 8, "description": "Maximum exponent for chunk size (2^8 = 256).", "title": "chunkSizeMaxExp", "type": "integer" }, "chunkSizeMinExp": { "default": 7, "description": "Minimum exponent for chunk size (2^7 = 128).", "title": "chunkSizeMinExp", "type": "integer" }, "chunkingMethods": { "description": "List of chunking methods to use.", "items": { "type": "string" }, "title": "chunkingMethods", "type": "array" }, "embeddingModelNames": { "description": "List of embedding models to use.", "items": { "type": "string" }, "title": "embeddingModelNames", "type": "array" } }, "required": [ "embeddingModelNames" ], "title": "ChunkingParametersConfig", "type": "object" }, "llmConfig": { "description": "Configuration of LLMs in the search space.", "properties": { "llmNames": { "description": "List of LLM names to use.", "items": { "type": "string" }, "title": "llmNames", "type": "array" }, "temperatureMax": { "default": 1, "description": "Maximum temperature of an LLM.", "title": "temperatureMax", "type": "number" }, "temperatureMin": { "default": 0, "description": "Minimum temperature of an LLM.", "title": "temperatureMin", "type": "number" }, "temperatureStep": { "default": 0.05, "description": "Step size for LLM temperature.", "title": "temperatureStep", "type": "number" }, "topPMax": { "default": 1, "description": "Maximum top_p of an LLM.", "title": "topPMax", "type": "number" }, "topPMin": { "default": 0, "description": "Minimum top_p of an LLM.", "title": "topPMin", "type": "number" }, "topPStep": { "default": 0.05, "description": "Step size for LLM top_p.", "title": "topPStep", "type": "number" } }, "required": [ "llmNames" ], "title": "LLMConfig", "type": "object" }, "vectorDatabaseSettings": { "description": "Settings of the vector database.", "properties": { "addNeighborChunks": { "description": "Add neighboring chunks to those that the similarity search retrieves.", "items": { "type": "boolean" }, "title": "addNeighborChunks", "type": "array" }, "maxDocumentRetrievedPerPromptMax": { "default": 10, "description": "Max value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMax", "type": "integer" }, "maxDocumentRetrievedPerPromptMin": { "default": 1, "description": "Min value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMin", "type": "integer" }, "maxDocumentRetrievedPerPromptStep": { "default": 1, "description": "Step for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptStep", "type": "integer" }, "maxMmrLambdaMax": { "default": 1, "description": "Maximum value of MMR lambda.", "title": "maxMmrLambdaMax", "type": "number" }, "maxMmrLambdaMin": { "default": 0, "description": "Minimum value of MMR lambda.", "title": "maxMmrLambdaMin", "type": "number" }, "maxMmrLambdaStep": { "default": 0.1, "description": "Step value of MMR lambda.", "title": "maxMmrLambdaStep", "type": "number" }, "retrievalModes": { "description": "List of retriever modes to use.", "items": { "type": "string" }, "title": "retrievalModes", "type": "array" }, "retrievers": { "description": "List of retriever types to use.", "items": { "type": "string" }, "title": "retrievers", "type": "array" } }, "required": [ "retrievers", "retrievalModes" ], "title": "VectorDatabaseConfig", "type": "object" } }, "title": "SearchSpace", "type": "object" } ``` SearchSpace ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkingParameters | ChunkingParametersConfig | false | | Chunking parameters for RAG. | | llmConfig | LLMConfig | false | | LLM configuration. | | vectorDatabaseSettings | VectorDatabaseConfig | false | | Vector database settings. | ## SearchStudyResponse ``` { "description": "API response object for search study retrieval.", "properties": { "allTrials": { "anyOf": [ { "items": { "description": "Represents a search trial from history.", "properties": { "llmBlueprintId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The correspondent blueprint ID.", "title": "llmBlueprintId" }, "searchParameters": { "additionalProperties": true, "description": "Search parameters of the point.", "title": "searchParameters", "type": "object" }, "values": { "description": "The resulting values of optimization objectives.", "items": { "type": "number" }, "title": "values", "type": "array" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The correspondent vector database ID.", "title": "vectorDatabaseId" } }, "required": [ "llmBlueprintId", "vectorDatabaseId", "values", "searchParameters" ], "title": "HistoryPoint", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Trials history.", "title": "allTrials" }, "datetimeEnd": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "Study end time.", "title": "datetimeEnd" }, "datetimeStart": { "description": "Study start time.", "format": "date-time", "title": "datetimeStart", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Error message if search study fails.", "title": "errorMessage" }, "evalDatasetId": { "description": "The ID of the evaluation dataset.", "title": "evalDatasetId", "type": "string" }, "evalDatasetName": { "description": "The name of evaluation dataset.", "title": "evalDatasetName", "type": "string" }, "evalResults": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The results of the comparative evaluation of LLM blueprints.", "title": "evalResults" }, "existingBlueprintIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The IDs of existing LLM blueprints for comparative evaluation.", "title": "existingBlueprintIds" }, "groundingDatasetId": { "description": "The ID of the dataset the vector databases will be built from.", "title": "groundingDatasetId", "type": "string" }, "groundingDatasetName": { "description": "The name of the grouding dataset.", "title": "groundingDatasetName", "type": "string" }, "jobId": { "anyOf": [ { "format": "uuid4", "type": "string" }, { "type": "null" } ], "description": "The ID of the worker job.", "title": "jobId" }, "name": { "description": "Name of the search study.", "title": "name", "type": "string" }, "numConcurrentTrials": { "description": "The number of simultaneously running trials.", "title": "numConcurrentTrials", "type": "integer" }, "numTrials": { "description": "The number of search trials to sample.", "title": "numTrials", "type": "integer" }, "optimizationObjectives": { "description": "Optimization objectives of a study.", "items": { "maxItems": 2, "minItems": 2, "prefixItems": [ { "description": "List of supported search objectives.", "enum": [ "correctness", "all_tokens" ], "title": "SearchObjective", "type": "string" }, { "description": "Whether to minimize or maximize search objective.", "enum": [ "maximize", "minimize" ], "title": "SearchDirection", "type": "string" } ], "type": "array" }, "title": "optimizationObjectives", "type": "array" }, "paretoFront": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "Pareto frontier of a study.", "title": "paretoFront" }, "playgroundId": { "description": "The ID of the existing playground that will be associated with the search.", "title": "playgroundId", "type": "string" }, "searchSpace": { "anyOf": [ { "description": "Represents full search space.", "properties": { "chunkingParameters": { "description": "Parameters of the text chunkers.", "properties": { "chunkOverlapPercentageMax": { "default": 50, "description": "Maximum value of chunk overlap.", "title": "chunkOverlapPercentageMax", "type": "number" }, "chunkOverlapPercentageMin": { "default": 0, "description": "Minimum value of chunk overlap.", "title": "chunkOverlapPercentageMin", "type": "number" }, "chunkOverlapPercentageStep": { "default": 10, "description": "Step value of chunk overlap.", "title": "chunkOverlapPercentageStep", "type": "number" }, "chunkSizeMaxExp": { "default": 8, "description": "Maximum exponent for chunk size (2^8 = 256).", "title": "chunkSizeMaxExp", "type": "integer" }, "chunkSizeMinExp": { "default": 7, "description": "Minimum exponent for chunk size (2^7 = 128).", "title": "chunkSizeMinExp", "type": "integer" }, "chunkingMethods": { "description": "List of chunking methods to use.", "items": { "type": "string" }, "title": "chunkingMethods", "type": "array" }, "embeddingModelNames": { "description": "List of embedding models to use.", "items": { "type": "string" }, "title": "embeddingModelNames", "type": "array" } }, "required": [ "embeddingModelNames" ], "title": "ChunkingParametersConfig", "type": "object" }, "llmConfig": { "description": "Configuration of LLMs in the search space.", "properties": { "llmNames": { "description": "List of LLM names to use.", "items": { "type": "string" }, "title": "llmNames", "type": "array" }, "temperatureMax": { "default": 1, "description": "Maximum temperature of an LLM.", "title": "temperatureMax", "type": "number" }, "temperatureMin": { "default": 0, "description": "Minimum temperature of an LLM.", "title": "temperatureMin", "type": "number" }, "temperatureStep": { "default": 0.05, "description": "Step size for LLM temperature.", "title": "temperatureStep", "type": "number" }, "topPMax": { "default": 1, "description": "Maximum top_p of an LLM.", "title": "topPMax", "type": "number" }, "topPMin": { "default": 0, "description": "Minimum top_p of an LLM.", "title": "topPMin", "type": "number" }, "topPStep": { "default": 0.05, "description": "Step size for LLM top_p.", "title": "topPStep", "type": "number" } }, "required": [ "llmNames" ], "title": "LLMConfig", "type": "object" }, "vectorDatabaseSettings": { "description": "Settings of the vector database.", "properties": { "addNeighborChunks": { "description": "Add neighboring chunks to those that the similarity search retrieves.", "items": { "type": "boolean" }, "title": "addNeighborChunks", "type": "array" }, "maxDocumentRetrievedPerPromptMax": { "default": 10, "description": "Max value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMax", "type": "integer" }, "maxDocumentRetrievedPerPromptMin": { "default": 1, "description": "Min value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMin", "type": "integer" }, "maxDocumentRetrievedPerPromptStep": { "default": 1, "description": "Step for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptStep", "type": "integer" }, "maxMmrLambdaMax": { "default": 1, "description": "Maximum value of MMR lambda.", "title": "maxMmrLambdaMax", "type": "number" }, "maxMmrLambdaMin": { "default": 0, "description": "Minimum value of MMR lambda.", "title": "maxMmrLambdaMin", "type": "number" }, "maxMmrLambdaStep": { "default": 0.1, "description": "Step value of MMR lambda.", "title": "maxMmrLambdaStep", "type": "number" }, "retrievalModes": { "description": "List of retriever modes to use.", "items": { "type": "string" }, "title": "retrievalModes", "type": "array" }, "retrievers": { "description": "List of retriever types to use.", "items": { "type": "string" }, "title": "retrievers", "type": "array" } }, "required": [ "retrievers", "retrievalModes" ], "title": "VectorDatabaseConfig", "type": "object" } }, "title": "SearchSpace", "type": "object" }, { "type": "null" } ], "description": "Search space for the search." }, "searchStudyId": { "description": "The ID of the search study.", "title": "searchStudyId", "type": "string" }, "studyStatus": { "description": "Represents a search study execution state.", "enum": [ "RUNNING", "COMPLETED", "STOPPED", "FAILED" ], "title": "JobStatus", "type": "string" }, "tempPlaygroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the temp playground.", "title": "tempPlaygroundId" }, "trialsFailed": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of failed trials.", "title": "trialsFailed" }, "trialsRunning": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of currently running trials.", "title": "trialsRunning" }, "trialsSuccess": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The number of completed trials.", "title": "trialsSuccess" }, "useCaseId": { "description": "The ID of the use case the search study is linked to.", "title": "useCaseId", "type": "string" }, "userId": { "description": "The ID of the user.", "title": "userId", "type": "string" }, "userName": { "description": "The user name of the user who ran the study.", "title": "userName", "type": "string" } }, "required": [ "searchSpace", "useCaseId", "groundingDatasetId", "evalDatasetId", "groundingDatasetName", "evalDatasetName", "userId", "userName", "numTrials", "numConcurrentTrials", "optimizationObjectives", "playgroundId", "tempPlaygroundId", "paretoFront", "datetimeStart", "datetimeEnd", "studyStatus", "searchStudyId", "name", "jobId", "trialsRunning", "trialsFailed", "trialsSuccess", "allTrials", "existingBlueprintIds", "evalResults", "errorMessage" ], "title": "SearchStudyResponse", "type": "object" } ``` SearchStudyResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allTrials | any | true | | Trials history. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [HistoryPoint] | false | | [Represents a search trial from history.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimeEnd | any | true | | Study end time. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string(date-time) | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimeStart | string(date-time) | true | | Study start time. | | errorMessage | any | true | | Error message if search study fails. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evalDatasetId | string | true | | The ID of the evaluation dataset. | | evalDatasetName | string | true | | The name of evaluation dataset. | | evalResults | any | true | | The results of the comparative evaluation of LLM blueprints. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [any] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | existingBlueprintIds | any | true | | The IDs of existing LLM blueprints for comparative evaluation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | groundingDatasetId | string | true | | The ID of the dataset the vector databases will be built from. | | groundingDatasetName | string | true | | The name of the grouding dataset. | | jobId | any | true | | The ID of the worker job. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string(uuid4) | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Name of the search study. | | numConcurrentTrials | integer | true | | The number of simultaneously running trials. | | numTrials | integer | true | | The number of search trials to sample. | | optimizationObjectives | [array] | true | | Optimization objectives of a study. | | paretoFront | any | true | | Pareto frontier of a study. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [any] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | playgroundId | string | true | | The ID of the existing playground that will be associated with the search. | | searchSpace | any | true | | Search space for the search. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SearchSpace | false | | Represents full search space. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | searchStudyId | string | true | | The ID of the search study. | | studyStatus | JobStatus | true | | Status of a study (RUNNING, COMPLETED or FAILED). | | tempPlaygroundId | any | true | | The ID of the temp playground. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | trialsFailed | any | true | | The number of failed trials. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | trialsRunning | any | true | | The number of currently running trials. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | trialsSuccess | any | true | | The number of completed trials. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseId | string | true | | The ID of the use case the search study is linked to. | | userId | string | true | | The ID of the user. | | userName | string | true | | The user name of the user who ran the study. | ## TracingContextEntityType ``` { "description": "Type of an entity in context of which the agent request is performed.", "enum": [ "deployment", "use_case" ], "title": "TracingContextEntityType", "type": "string" } ``` TracingContextEntityType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | TracingContextEntityType | string | false | | Type of an entity in context of which the agent request is performed. | ### Enumerated Values | Property | Value | | --- | --- | | TracingContextEntityType | [deployment, use_case] | ## ValidationError ``` { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" } ``` ValidationError ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | loc | [anyOf] | true | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | msg | string | true | | none | | type | string | true | | none | ## VectorDatabaseConfig ``` { "description": "Settings of the vector database.", "properties": { "addNeighborChunks": { "description": "Add neighboring chunks to those that the similarity search retrieves.", "items": { "type": "boolean" }, "title": "addNeighborChunks", "type": "array" }, "maxDocumentRetrievedPerPromptMax": { "default": 10, "description": "Max value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMax", "type": "integer" }, "maxDocumentRetrievedPerPromptMin": { "default": 1, "description": "Min value for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptMin", "type": "integer" }, "maxDocumentRetrievedPerPromptStep": { "default": 1, "description": "Step for the max number of chunks to retrieve from the vector database.", "title": "maxDocumentRetrievedPerPromptStep", "type": "integer" }, "maxMmrLambdaMax": { "default": 1, "description": "Maximum value of MMR lambda.", "title": "maxMmrLambdaMax", "type": "number" }, "maxMmrLambdaMin": { "default": 0, "description": "Minimum value of MMR lambda.", "title": "maxMmrLambdaMin", "type": "number" }, "maxMmrLambdaStep": { "default": 0.1, "description": "Step value of MMR lambda.", "title": "maxMmrLambdaStep", "type": "number" }, "retrievalModes": { "description": "List of retriever modes to use.", "items": { "type": "string" }, "title": "retrievalModes", "type": "array" }, "retrievers": { "description": "List of retriever types to use.", "items": { "type": "string" }, "title": "retrievers", "type": "array" } }, "required": [ "retrievers", "retrievalModes" ], "title": "VectorDatabaseConfig", "type": "object" } ``` VectorDatabaseConfig ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addNeighborChunks | [boolean] | false | | Add neighboring chunks to those that the similarity search retrieves. | | maxDocumentRetrievedPerPromptMax | integer | false | | Max value for the max number of chunks to retrieve from the vector database. | | maxDocumentRetrievedPerPromptMin | integer | false | | Min value for the max number of chunks to retrieve from the vector database. | | maxDocumentRetrievedPerPromptStep | integer | false | | Step for the max number of chunks to retrieve from the vector database. | | maxMmrLambdaMax | number | false | | Maximum value of MMR lambda. | | maxMmrLambdaMin | number | false | | Minimum value of MMR lambda. | | maxMmrLambdaStep | number | false | | Step value of MMR lambda. | | retrievalModes | [string] | true | | List of retriever modes to use. | | retrievers | [string] | true | | List of retriever types to use. | --- # LLM compliance tests URL: https://docs.datarobot.com/en/docs/api/reference/public-api/ai_robustness_tests.html > The following endpoints outline how to manage AI robustness tests. # LLM compliance tests The following endpoints outline how to manage AI robustness tests. ## Create cost metric configuration Operation path: `POST /api/v2/genai/costMetricConfigurations/` Authentication requirements: `BearerAuth` Create a new cost metric configuration. ### Body parameter ``` { "description": "The body of the \"Create cost metric configuration\" request.", "properties": { "costMetricConfigurations": { "description": "The list of cost metric configurations to use.", "items": { "description": "API request/response object for a cost configuration of a single LLM.", "properties": { "currencyCode": { "default": "USD", "description": "The arbitrary code code of the currency of `inputTokenPrice` and `outputTokenPrice`.", "maxLength": 7, "title": "currencyCode", "type": "string" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "inputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceInputTokenCount` input tokens.", "minimum": 0, "title": "inputTokenPrice", "type": "number" }, "llmId": { "description": "The ID of the LLM associated with this cost configuration.", "title": "llmId", "type": "string" }, "outputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceOutputTokenCount` output tokens.", "minimum": 0, "title": "outputTokenPrice", "type": "number" }, "referenceInputTokenCount": { "default": 1000, "description": "The number of input tokens corresponding to `inputTokenPrice`.", "minimum": 0, "title": "referenceInputTokenCount", "type": "integer" }, "referenceOutputTokenCount": { "default": 1000, "description": "The number of output tokens corresponding to `outputTokenPrice`.", "minimum": 0, "title": "referenceOutputTokenCount", "type": "integer" } }, "required": [ "llmId" ], "title": "LLMCostConfigurationResponse", "type": "object" }, "title": "costMetricConfigurations", "type": "array" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name to use for the cost configuration.", "title": "name" }, "playgroundId": { "description": "The ID of the playground to associate with the cost metric configuration.", "title": "playgroundId", "type": "string" }, "useCaseId": { "description": "The ID of the use case to associate with the cost metric configuration.", "title": "useCaseId", "type": "string" } }, "required": [ "useCaseId", "playgroundId", "costMetricConfigurations" ], "title": "CreateCostMetricConfigurationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateCostMetricConfigurationRequest | true | none | ### Example responses > 201 Response ``` { "description": "API response object for a single cost metric configuration.", "properties": { "costConfigurationId": { "description": "The ID of the cost metric configuration.", "title": "costConfigurationId", "type": "string" }, "costMetricConfigurations": { "description": "The list of individual LLM cost configurations that constitute this cost metric configuration.", "items": { "description": "API request/response object for a cost configuration of a single LLM.", "properties": { "currencyCode": { "default": "USD", "description": "The arbitrary code code of the currency of `inputTokenPrice` and `outputTokenPrice`.", "maxLength": 7, "title": "currencyCode", "type": "string" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "inputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceInputTokenCount` input tokens.", "minimum": 0, "title": "inputTokenPrice", "type": "number" }, "llmId": { "description": "The ID of the LLM associated with this cost configuration.", "title": "llmId", "type": "string" }, "outputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceOutputTokenCount` output tokens.", "minimum": 0, "title": "outputTokenPrice", "type": "number" }, "referenceInputTokenCount": { "default": 1000, "description": "The number of input tokens corresponding to `inputTokenPrice`.", "minimum": 0, "title": "referenceInputTokenCount", "type": "integer" }, "referenceOutputTokenCount": { "default": 1000, "description": "The number of output tokens corresponding to `outputTokenPrice`.", "minimum": 0, "title": "referenceOutputTokenCount", "type": "integer" } }, "required": [ "llmId" ], "title": "LLMCostConfigurationResponse", "type": "object" }, "title": "costMetricConfigurations", "type": "array" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name to use for the cost configuration.", "title": "name" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the cost metric configuration.", "title": "playgroundId" }, "useCaseId": { "description": "The ID of the use case associated with the cost metric configuration.", "title": "useCaseId", "type": "string" } }, "required": [ "costConfigurationId", "useCaseId", "costMetricConfigurations" ], "title": "CostMetricConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Cost configuration created successfully | CostMetricConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete cost metric configuration by cost metric configuration ID Operation path: `DELETE /api/v2/genai/costMetricConfigurations/{costMetricConfigurationId}/` Authentication requirements: `BearerAuth` Delete an existing cost metric configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | costMetricConfigurationId | path | string | true | The ID of the cost metric configuration to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Cost metric configuration successfully deleted. | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve cost metric configuration by cost metric configuration ID Operation path: `GET /api/v2/genai/costMetricConfigurations/{costMetricConfigurationId}/` Authentication requirements: `BearerAuth` Retrieve an existing cost metric configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | costMetricConfigurationId | path | string | true | The ID of the cost metric configuration to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single cost metric configuration.", "properties": { "costConfigurationId": { "description": "The ID of the cost metric configuration.", "title": "costConfigurationId", "type": "string" }, "costMetricConfigurations": { "description": "The list of individual LLM cost configurations that constitute this cost metric configuration.", "items": { "description": "API request/response object for a cost configuration of a single LLM.", "properties": { "currencyCode": { "default": "USD", "description": "The arbitrary code code of the currency of `inputTokenPrice` and `outputTokenPrice`.", "maxLength": 7, "title": "currencyCode", "type": "string" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "inputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceInputTokenCount` input tokens.", "minimum": 0, "title": "inputTokenPrice", "type": "number" }, "llmId": { "description": "The ID of the LLM associated with this cost configuration.", "title": "llmId", "type": "string" }, "outputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceOutputTokenCount` output tokens.", "minimum": 0, "title": "outputTokenPrice", "type": "number" }, "referenceInputTokenCount": { "default": 1000, "description": "The number of input tokens corresponding to `inputTokenPrice`.", "minimum": 0, "title": "referenceInputTokenCount", "type": "integer" }, "referenceOutputTokenCount": { "default": 1000, "description": "The number of output tokens corresponding to `outputTokenPrice`.", "minimum": 0, "title": "referenceOutputTokenCount", "type": "integer" } }, "required": [ "llmId" ], "title": "LLMCostConfigurationResponse", "type": "object" }, "title": "costMetricConfigurations", "type": "array" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name to use for the cost configuration.", "title": "name" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the cost metric configuration.", "title": "playgroundId" }, "useCaseId": { "description": "The ID of the use case associated with the cost metric configuration.", "title": "useCaseId", "type": "string" } }, "required": [ "costConfigurationId", "useCaseId", "costMetricConfigurations" ], "title": "CostMetricConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Cost metric configuration successfully retrieved. | CostMetricConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit cost metric configuration by cost metric configuration ID Operation path: `PATCH /api/v2/genai/costMetricConfigurations/{costMetricConfigurationId}/` Authentication requirements: `BearerAuth` Edit an existing cost metric configuration. ### Body parameter ``` { "description": "The body of the \"Edit cost metric configuration\" request.", "properties": { "costMetricConfigurations": { "description": "The list of LLM cost configurations to apply to this cost metric configuration.", "items": { "description": "API request/response object for a cost configuration of a single LLM.", "properties": { "currencyCode": { "default": "USD", "description": "The arbitrary code code of the currency of `inputTokenPrice` and `outputTokenPrice`.", "maxLength": 7, "title": "currencyCode", "type": "string" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "inputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceInputTokenCount` input tokens.", "minimum": 0, "title": "inputTokenPrice", "type": "number" }, "llmId": { "description": "The ID of the LLM associated with this cost configuration.", "title": "llmId", "type": "string" }, "outputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceOutputTokenCount` output tokens.", "minimum": 0, "title": "outputTokenPrice", "type": "number" }, "referenceInputTokenCount": { "default": 1000, "description": "The number of input tokens corresponding to `inputTokenPrice`.", "minimum": 0, "title": "referenceInputTokenCount", "type": "integer" }, "referenceOutputTokenCount": { "default": 1000, "description": "The number of output tokens corresponding to `outputTokenPrice`.", "minimum": 0, "title": "referenceOutputTokenCount", "type": "integer" } }, "required": [ "llmId" ], "title": "LLMCostConfigurationResponse", "type": "object" }, "minItems": 1, "title": "costMetricConfigurations", "type": "array" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name to use for the cost configuration.", "title": "name" } }, "required": [ "costMetricConfigurations" ], "title": "EditCostMetricConfigurationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | costMetricConfigurationId | path | string | true | The ID of the cost metric configuration to edit. | | body | body | EditCostMetricConfigurationRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single cost metric configuration.", "properties": { "costConfigurationId": { "description": "The ID of the cost metric configuration.", "title": "costConfigurationId", "type": "string" }, "costMetricConfigurations": { "description": "The list of individual LLM cost configurations that constitute this cost metric configuration.", "items": { "description": "API request/response object for a cost configuration of a single LLM.", "properties": { "currencyCode": { "default": "USD", "description": "The arbitrary code code of the currency of `inputTokenPrice` and `outputTokenPrice`.", "maxLength": 7, "title": "currencyCode", "type": "string" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "inputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceInputTokenCount` input tokens.", "minimum": 0, "title": "inputTokenPrice", "type": "number" }, "llmId": { "description": "The ID of the LLM associated with this cost configuration.", "title": "llmId", "type": "string" }, "outputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceOutputTokenCount` output tokens.", "minimum": 0, "title": "outputTokenPrice", "type": "number" }, "referenceInputTokenCount": { "default": 1000, "description": "The number of input tokens corresponding to `inputTokenPrice`.", "minimum": 0, "title": "referenceInputTokenCount", "type": "integer" }, "referenceOutputTokenCount": { "default": 1000, "description": "The number of output tokens corresponding to `outputTokenPrice`.", "minimum": 0, "title": "referenceOutputTokenCount", "type": "integer" } }, "required": [ "llmId" ], "title": "LLMCostConfigurationResponse", "type": "object" }, "title": "costMetricConfigurations", "type": "array" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name to use for the cost configuration.", "title": "name" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the cost metric configuration.", "title": "playgroundId" }, "useCaseId": { "description": "The ID of the use case associated with the cost metric configuration.", "title": "useCaseId", "type": "string" } }, "required": [ "costConfigurationId", "useCaseId", "costMetricConfigurations" ], "title": "CostMetricConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Cost metric configuration successfully updated. | CostMetricConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List evaluation dataset configurations Operation path: `GET /api/v2/genai/evaluationDatasetConfigurations/` Authentication requirements: `BearerAuth` List evaluation dataset configurations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | string | true | Only retrieve the evaluation dataset configurations associated with this use case ID. | | playgroundId | query | string | true | Only retrieve the evaluation dataset configuration associated with this playground ID. | | evaluationDatasetConfigurationId | query | any | false | Only retrieve the evaluation dataset configuration with this ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | search | query | any | false | Only retrieve the evaluation dataset configurations matching the search query. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name", "creationUserId", "creationDate", "datasetId", "userName", "datasetName", "promptColumnName", "responseColumnName". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | | correctnessEnabledOnly | query | boolean | false | If true, only retrieve the evaluation dataset configurations with correctness enabled. The default is false. | | completedOnly | query | boolean | false | If true, only retrieve the evaluation dataset configurations where the evaluation dataset is in the completed status. The default is false. | ### Example responses > 200 Response ``` { "description": "Paginated list of evaludation dataset configurations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single evaluation dataset configuration.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals (For agentic workflows).", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "creationDate": { "description": "The creation date of the evaluation dataset configuration (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the evaluation dataset configuration.", "title": "creationUserId", "type": "string" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the evaluation dataset.", "title": "datasetName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration.", "title": "errorMessage" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the evaluation dataset configuration.", "title": "id", "type": "string" }, "name": { "description": "The name of the evaluation dataset configuration.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the evaluation dataset configuration.", "title": "playgroundId" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "rowsCount": { "description": "The rows count of the evaluation dataset.", "title": "rowsCount", "type": "integer" }, "size": { "description": "The size of the evaluation dataset (in bytes).", "title": "size", "type": "integer" }, "tenantId": { "description": "The ID of the DataRobot tenant this evaluation dataset configuration belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "toolCallsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls (For agentic workflows).", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case associated with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created the evaluation dataset configuration.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "size", "rowsCount", "useCaseId", "playgroundId", "datasetId", "datasetName", "promptColumnName", "responseColumnName", "toolCallsColumnName", "agentGoalsColumnName", "userName", "correctnessEnabled", "creationUserId", "creationDate", "tenantId", "executionStatus" ], "title": "EvaluationDatasetConfigurationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListEvaluationDatasetConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Evaluation dataset configurations successfully retrieved. | ListEvaluationDatasetConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create evaluation dataset configuration Operation path: `POST /api/v2/genai/evaluationDatasetConfigurations/` Authentication requirements: `BearerAuth` Create a new evaluation dataset configuration. ### Body parameter ``` { "description": "The body of the \"Create evaluation dataset configuration\" request.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals. It is required to evaluate the AgentGoalAccuracyWithReference metric for agentic workflows.", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "isSyntheticDataset": { "default": false, "description": "Whether the evaluation dataset is synthetic.", "title": "isSyntheticDataset", "type": "boolean" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the evaluation dataset configuration.", "title": "name" }, "playgroundId": { "description": "The ID of the playground to associate with the evaluation dataset configuration.", "title": "playgroundId", "type": "string" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "toolCallsColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls. It is required to evaluate the ToolCallAccuracy metric for agentic workflows.", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case to associate with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" } }, "required": [ "useCaseId", "playgroundId", "datasetId", "promptColumnName" ], "title": "CreateEvaluationDatasetConfigurationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateEvaluationDatasetConfigurationRequest | true | none | ### Example responses > 201 Response ``` { "description": "API response object for a single evaluation dataset configuration.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals (For agentic workflows).", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "creationDate": { "description": "The creation date of the evaluation dataset configuration (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the evaluation dataset configuration.", "title": "creationUserId", "type": "string" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the evaluation dataset.", "title": "datasetName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration.", "title": "errorMessage" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the evaluation dataset configuration.", "title": "id", "type": "string" }, "name": { "description": "The name of the evaluation dataset configuration.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the evaluation dataset configuration.", "title": "playgroundId" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "rowsCount": { "description": "The rows count of the evaluation dataset.", "title": "rowsCount", "type": "integer" }, "size": { "description": "The size of the evaluation dataset (in bytes).", "title": "size", "type": "integer" }, "tenantId": { "description": "The ID of the DataRobot tenant this evaluation dataset configuration belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "toolCallsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls (For agentic workflows).", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case associated with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created the evaluation dataset configuration.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "size", "rowsCount", "useCaseId", "playgroundId", "datasetId", "datasetName", "promptColumnName", "responseColumnName", "toolCallsColumnName", "agentGoalsColumnName", "userName", "correctnessEnabled", "creationUserId", "creationDate", "tenantId", "executionStatus" ], "title": "EvaluationDatasetConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Evaluation dataset configuration successfully created | EvaluationDatasetConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete evaluation dataset configuration by evaluation dataset configuration ID Operation path: `DELETE /api/v2/genai/evaluationDatasetConfigurations/{evaluationDatasetConfigurationId}/` Authentication requirements: `BearerAuth` Delete an existing evaluation dataset configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | path | string | true | The ID of the evaluation dataset configuration to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Evaluation dataset configuration successfully deleted. | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve evaluation dataset configuration by evaluation dataset configuration ID Operation path: `GET /api/v2/genai/evaluationDatasetConfigurations/{evaluationDatasetConfigurationId}/` Authentication requirements: `BearerAuth` Retrieve an existing evaluation dataset configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | path | string | true | The ID of the evaluation dataset configuration to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single evaluation dataset configuration.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals (For agentic workflows).", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "creationDate": { "description": "The creation date of the evaluation dataset configuration (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the evaluation dataset configuration.", "title": "creationUserId", "type": "string" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the evaluation dataset.", "title": "datasetName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration.", "title": "errorMessage" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the evaluation dataset configuration.", "title": "id", "type": "string" }, "name": { "description": "The name of the evaluation dataset configuration.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the evaluation dataset configuration.", "title": "playgroundId" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "rowsCount": { "description": "The rows count of the evaluation dataset.", "title": "rowsCount", "type": "integer" }, "size": { "description": "The size of the evaluation dataset (in bytes).", "title": "size", "type": "integer" }, "tenantId": { "description": "The ID of the DataRobot tenant this evaluation dataset configuration belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "toolCallsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls (For agentic workflows).", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case associated with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created the evaluation dataset configuration.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "size", "rowsCount", "useCaseId", "playgroundId", "datasetId", "datasetName", "promptColumnName", "responseColumnName", "toolCallsColumnName", "agentGoalsColumnName", "userName", "correctnessEnabled", "creationUserId", "creationDate", "tenantId", "executionStatus" ], "title": "EvaluationDatasetConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Evaluation dataset configuration successfully retrieved. | EvaluationDatasetConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit evaluation dataset configuration by evaluation dataset configuration ID Operation path: `PATCH /api/v2/genai/evaluationDatasetConfigurations/{evaluationDatasetConfigurationId}/` Authentication requirements: `BearerAuth` Edit an existing evaluation dataset configuration. ### Body parameter ``` { "description": "The body of the \"Edit evaluation dataset configuration\" request.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the expected name of the dataset column containing expected agent goals. It is required to evaluate the AgentGoalAccuracyWithReference metric for agentic workflows.", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "If specified, enables or disables correctness for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "datasetId": { "default": "000000000000000000000000", "description": "If specified, updates the ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the evaluation dataset configuration to this value.", "title": "name" }, "promptColumnName": { "default": "None", "description": "If specified, changes the expected name of the dataset column containing the prompt text.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the expected name of the dataset column containing the response text.", "title": "responseColumnName" }, "toolCallsColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the expected name of the dataset column containing expected tool calls. It is required to evaluate the ToolCallAccuracy metric for agentic workflows.", "title": "toolCallsColumnName" } }, "title": "EditEvaluationDatasetConfigurationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | path | string | true | The ID of the evaluation dataset configuration to edit. | | body | body | EditEvaluationDatasetConfigurationRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single evaluation dataset configuration.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals (For agentic workflows).", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "creationDate": { "description": "The creation date of the evaluation dataset configuration (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the evaluation dataset configuration.", "title": "creationUserId", "type": "string" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the evaluation dataset.", "title": "datasetName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration.", "title": "errorMessage" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the evaluation dataset configuration.", "title": "id", "type": "string" }, "name": { "description": "The name of the evaluation dataset configuration.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the evaluation dataset configuration.", "title": "playgroundId" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "rowsCount": { "description": "The rows count of the evaluation dataset.", "title": "rowsCount", "type": "integer" }, "size": { "description": "The size of the evaluation dataset (in bytes).", "title": "size", "type": "integer" }, "tenantId": { "description": "The ID of the DataRobot tenant this evaluation dataset configuration belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "toolCallsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls (For agentic workflows).", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case associated with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created the evaluation dataset configuration.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "size", "rowsCount", "useCaseId", "playgroundId", "datasetId", "datasetName", "promptColumnName", "responseColumnName", "toolCallsColumnName", "agentGoalsColumnName", "userName", "correctnessEnabled", "creationUserId", "creationDate", "tenantId", "executionStatus" ], "title": "EvaluationDatasetConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Evaluation dataset configuration successfully updated. | EvaluationDatasetConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete evaluation dataset metric aggregation Operation path: `DELETE /api/v2/genai/evaluationDatasetMetricAggregations/` Authentication requirements: `BearerAuth` Delete the evaluation dataset metric aggregation associated with the specified LLM blueprint IDs and/or chat IDs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmBlueprintIds | query | any | false | The IDs of the LLM blueprints to delete the associated evaluation dataset metric aggregation for. If both llmBlueprintIds and chatIds are specified, will delete the aggregation record only if it matches both criteria. | | chatIds | query | any | false | The IDs of the chats to delete the associated evaluation dataset metric aggregation for. If both llmBlueprintIds and chatIds are specified, will delete the aggregation record only if it matches both criteria. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Evaluation dataset metric aggregation successfully deleted. | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List evaluation dataset metric aggregations Operation path: `GET /api/v2/genai/evaluationDatasetMetricAggregations/` Authentication requirements: `BearerAuth` List evaluation dataset metric aggregations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmBlueprintIds | query | any | false | Only retrieve the evaluation dataset metric aggregations associated with these LLM blueprint IDs. | | chatIds | query | any | false | Only retrieve the evaluation dataset metric aggregations associated with these chat IDs. | | evaluationDatasetConfigurationIds | query | any | false | Only retrieve the evaluation dataset metric aggregations associated with these evaluation dataset configuration IDs. | | metricNames | query | any | false | Only retrieve the evaluation dataset metric aggregations associated with these metric names. | | aggregationTypes | query | any | false | Only retrieve the evaluation dataset metric aggregations associated with these aggregation types. | | currentConfigurationOnly | query | boolean | false | Only retrieve the evaluation dataset metric aggregations associated with the current configuration of the llmblueprints. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name", "creationUserId", "creationDate", "datasetId", "userName", "datasetName", "promptColumnName", "responseColumnName". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | nonErroredOnly | query | boolean | false | If true, only retrieve the evaluation dataset metric aggregations that are in a non-errored status. The default is false. | ### Example responses > 200 Response ``` { "description": "Paginated list of evaluation dataset metric aggregations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single evaluation dataset metric aggregation.", "properties": { "aggregationType": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "items": { "description": "Aggregated record of multiple of the same item across different metric aggregation runs.", "properties": { "count": { "description": "The number of metric aggregation items aggregated.", "title": "count", "type": "integer" }, "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value", "count" ], "title": "AggregatedAggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The aggregated value of the metric.", "title": "aggregationValue" }, "chatId": { "description": "The ID of the chat associated with the metric aggregation.", "title": "chatId", "type": "string" }, "chatLink": { "description": "The link to the chat associated with the metric aggregation.", "title": "chatLink", "type": "string" }, "chatName": { "description": "The name of the chat associated with the metric aggregation.", "title": "chatName", "type": "string" }, "creationDate": { "description": "The creation date of the metric aggregation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the metric aggregation.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user that created the metric aggregation.", "title": "creationUserName", "type": "string" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model's guard the metric aggregation belongs to.", "title": "customModelGuardId" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The dataset ID of the evaluation dataset configuration.", "title": "datasetId" }, "datasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The Data Registry dataset name of the evaluation dataset configuration.", "title": "datasetName" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration associated with the metric aggregation.", "title": "evaluationDatasetConfigurationId" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the metric aggregation.", "title": "llmBlueprintId", "type": "string" }, "metricName": { "description": "The name of the metric associated with the metric aggregation.", "title": "metricName", "type": "string" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "The name of the out-of-the-box dataset." }, "tenantId": { "description": "The ID of the tenant the metric aggregation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" } }, "required": [ "chatId", "chatName", "chatLink", "creationDate", "creationUserId", "creationUserName", "llmBlueprintId", "evaluationDatasetConfigurationId", "ootbDatasetName", "datasetId", "datasetName", "metricName", "aggregationValue", "aggregationType", "tenantId", "customModelGuardId" ], "title": "EvaluationDatasetMetricAggregationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListEvaluationDatasetMetricAggregationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Evaluation dataset metric aggregations successfully retrieved. | ListEvaluationDatasetMetricAggregationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create evaluation dataset metric aggregation Operation path: `POST /api/v2/genai/evaluationDatasetMetricAggregations/` Authentication requirements: `BearerAuth` Create a new evaluation dataset metric aggregation. ### Body parameter ``` { "description": "The body of the \"Create evaluation dataset metric aggregation\" request.", "properties": { "chatName": { "default": "Aggregated chat", "description": "The name for the new chat that will contain the associated prompts and responses.", "maxLength": 5000, "title": "chatName", "type": "string" }, "evaluationDatasetConfigurationId": { "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId", "type": "string" }, "insightsConfiguration": { "description": "The configuration of insights for the metric aggregation.", "items": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "minItems": 1, "title": "insightsConfiguration", "type": "array" }, "llmBlueprintIds": { "description": "The IDs of the LLM blueprints to use for the metric aggregation.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "title": "llmBlueprintIds", "type": "array" } }, "required": [ "llmBlueprintIds", "evaluationDatasetConfigurationId", "insightsConfiguration" ], "title": "CreateEvaluationDatasetMetricAggregationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateEvaluationDatasetMetricAggregationRequest | true | none | ### Example responses > 202 Response ``` { "description": "The body of the \"Create evaluation dataset metric aggregation\" response.", "properties": { "chatIds": { "description": "The IDs of the chats associated with the metric aggregation.", "items": { "type": "string" }, "title": "chatIds", "type": "array" }, "jobId": { "description": "The ID of the evaluation dataset metric aggregation job.", "format": "uuid4", "title": "jobId", "type": "string" } }, "required": [ "jobId", "chatIds" ], "title": "CreateEvaluationDatasetMetricAggregationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Evaluation dataset metric aggregation job successfully accepted. Follow the Location header to poll for job execution status. | CreateEvaluationDatasetMetricAggregationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List evaluation dataset metric aggregations aggregated by llm blueprint Operation path: `GET /api/v2/genai/evaluationDatasetMetricAggregations/aggregateByLLMBlueprint/` Authentication requirements: `BearerAuth` List evaluation dataset metric aggregations aggregated by llm blueprint. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmBlueprintIds | query | any | false | Only retrieve the evaluation dataset metric aggregations associated with these LLM blueprint IDs. | | chatIds | query | any | false | Only retrieve the evaluation dataset metric aggregations associated with these chat IDs. | | evaluationDatasetConfigurationIds | query | any | false | Only retrieve the evaluation dataset metric aggregations associated with these evaluation dataset configuration IDs. | | metricNames | query | any | false | Only retrieve the evaluation dataset metric aggregations associated with these metric names. | | aggregationTypes | query | any | false | Only retrieve the evaluation dataset metric aggregations associated with these aggregation types. | | currentConfigurationOnly | query | boolean | false | Only retrieve the evaluation dataset metric aggregations associated with the current configuration of the llmblueprints. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | nonErroredOnly | query | boolean | false | If true, only retrieve the evaluation dataset metric aggregations that are in a non-errored status. The default is false. | ### Example responses > 200 Response ``` { "description": "Paginated list of evaluation dataset metric aggregations, aggregated by LLM blueprint.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for multiple evaluation dataset metric aggregation\naggregated by llm blueprint.", "properties": { "aggregatedItemCount": { "description": "Number of items aggregated.", "title": "aggregatedItemCount", "type": "integer" }, "aggregatedItemDetails": { "description": "List of details for aggregated items.", "items": { "description": "Details for aggregated items.", "properties": { "chatId": { "description": "The ID of the chat associated with the metric aggregation.", "title": "chatId", "type": "string" }, "chatLink": { "description": "The link to the chat associated with the metric aggregation.", "title": "chatLink", "type": "string" }, "chatName": { "description": "The name of the chat associated with the metric aggregation.", "title": "chatName", "type": "string" }, "creationDate": { "description": "The creation date of the metric aggregation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the metric aggregation.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user that created the metric aggregation.", "title": "creationUserName", "type": "string" } }, "required": [ "chatId", "chatName", "chatLink", "creationDate", "creationUserId", "creationUserName" ], "title": "EvaluationDatasetMetricAggregationChatDetails", "type": "object" }, "title": "aggregatedItemDetails", "type": "array" }, "aggregationType": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "items": { "description": "Aggregated record of multiple of the same item across different metric aggregation runs.", "properties": { "count": { "description": "The number of metric aggregation items aggregated.", "title": "count", "type": "integer" }, "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value", "count" ], "title": "AggregatedAggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The aggregated value of the metric.", "title": "aggregationValue" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model's guard the metric aggregation belongs to.", "title": "customModelGuardId" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The dataset ID of the evaluation dataset configuration.", "title": "datasetId" }, "datasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The Data Registry dataset name of the evaluation dataset configuration.", "title": "datasetName" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration associated with the metric aggregation.", "title": "evaluationDatasetConfigurationId" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the metric aggregation.", "title": "llmBlueprintId", "type": "string" }, "metricName": { "description": "The name of the metric associated with the metric aggregation.", "title": "metricName", "type": "string" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "The name of the out-of-the-box dataset." }, "tenantId": { "description": "The ID of the tenant the metric aggregation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" } }, "required": [ "llmBlueprintId", "evaluationDatasetConfigurationId", "ootbDatasetName", "datasetId", "datasetName", "metricName", "aggregationValue", "aggregationType", "tenantId", "customModelGuardId", "aggregatedItemDetails", "aggregatedItemCount" ], "title": "EvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListEvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Evaluation dataset metric aggregations aggregated by llm blueprint successfully retrieved. | ListEvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List evaluation dataset metric aggregations unique computed metrics by uniquefield Operation path: `GET /api/v2/genai/evaluationDatasetMetricAggregations/uniqueFieldValues/{uniqueField}/` Authentication requirements: `BearerAuth` List evaluation dataset metric aggregations unique computed metrics. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | uniqueField | path | EvaluationDatasetMetricAggregationFieldQueryParam | true | Retrieve the list of this unique field. | | llmBlueprintIds | query | any | false | Only retrieve the list of the unique field associated with these LLM blueprint IDs. | | metricNames | query | any | false | Only retrieve the list of the unique field associated with these metric names. | | chatIds | query | any | false | Only retrieve the list of the unique field associated with these chat IDs. | | evaluationDatasetConfigurationIds | query | any | false | Only retrieve the list of the unique field associated with these evaluation dataset configuration IDs. | | aggregationTypes | query | any | false | Only retrieve the list of the unique field associated with these aggregation types. | | currentConfigurationOnly | query | boolean | false | Only retrieve the evaluation dataset metric aggregations associated with the current configuration of the llmblueprints. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | nonErroredOnly | query | boolean | false | If true, only retrieve the list of the unique field for aggregation records that are in a non-errored status. The default is false. | ### Enumerated Values | Parameter | Value | | --- | --- | | uniqueField | [metricName, llmBlueprintId, aggregationType, evaluationDatasetConfigurationId] | ### Example responses > 200 Response ``` { "description": "Paginated list of evaluation dataset metric aggregations with unique computed metrics.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single unique computed metric.", "properties": { "uniqueFieldValue": { "description": "The unique value associated with the metric aggregation.", "title": "uniqueFieldValue", "type": "string" } }, "required": [ "uniqueFieldValue" ], "title": "EvaluationDatasetMetricAggregationUniqueFieldValuesResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListEvaluationDatasetMetricAggregationUniqueFieldValuesResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Evaluation dataset metric aggregations unique computed metrics successfully retrieved. | ListEvaluationDatasetMetricAggregationUniqueFieldValuesResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List LLM test configuration Operation path: `GET /api/v2/genai/llmTestConfigurations/` Authentication requirements: `BearerAuth` List LLM test configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | any | false | Use Case ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | testConfigType | query | LLMTestConfigurationType | false | Whether to return out-of-the-box (ootb) or custom LLM test configurations in the response. | ### Enumerated Values | Parameter | Value | | --- | --- | | testConfigType | [ootb, custom] | ### Example responses > 200 Response ``` { "description": "Paginated list of LLM test configurations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single LLMTestConfiguration.", "properties": { "creationDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The creation date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationDate" }, "creationUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who created the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationUserId" }, "datasetEvaluations": { "description": "The LLM test dataset evaluations.", "items": { "description": "Dataset evaluation.", "properties": { "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the dataset evaluation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 100, "description": "The max number of prompts to evaluate.", "exclusiveMinimum": 0, "maximum": 5000, "title": "maxNumPrompts", "type": "integer" }, "ootbDataset": { "anyOf": [ { "description": "Out-of-the-box dataset.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "OOTBDataset", "type": "object" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria", "evaluationDatasetName" ], "title": "DatasetEvaluationResponse", "type": "object" }, "title": "datasetEvaluations", "type": "array" }, "description": { "description": "The description of the LLM Test configuration.", "title": "description", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the LLM test configuration.", "title": "errorMessage" }, "id": { "description": "The ID of the LLM Test configuration.", "title": "id", "type": "string" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "lastUpdateDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The last update date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateDate" }, "lastUpdateUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who last updated the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateUserId" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "name": { "description": "The name of the LLM Test configuration.", "title": "name", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, the use case ID associated with the LLM Test configuration.", "title": "useCaseId" }, "warnings": { "description": "Warnings for this LLM test configuration.", "items": { "additionalProperties": { "type": "string" }, "propertyNames": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "type": "object" }, "title": "warnings", "type": "array" } }, "required": [ "id", "name", "description", "datasetEvaluations", "llmTestGradingCriteria", "isOutOfTheBoxTestConfiguration", "warnings" ], "title": "LLMTestConfigurationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMTestConfigurationsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListLLMTestConfigurationsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create LLM test configuration Operation path: `POST /api/v2/genai/llmTestConfigurations/` Authentication requirements: `BearerAuth` Create a new LLM test configuration. ### Body parameter ``` { "description": "Request object for creating a LLMTestConfiguration.", "properties": { "datasetEvaluations": { "description": "Dataset evaluations.", "items": { "description": "Dataset evaluation.", "properties": { "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 0, "description": "The max number of prompts to evaluate.", "maximum": 5000, "minimum": 0, "title": "maxNumPrompts", "type": "integer" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset name. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria" ], "title": "DatasetEvaluationRequest", "type": "object" }, "maxItems": 10, "minItems": 1, "title": "datasetEvaluations", "type": "array" }, "description": { "default": "", "description": "LLM test configuration description.", "maxLength": 5000, "title": "description", "type": "string" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "name": { "description": "LLM test configuration name.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "useCaseId": { "description": "The use case ID associated with the LLM Test configuration.", "title": "useCaseId", "type": "string" } }, "required": [ "name", "useCaseId", "datasetEvaluations", "llmTestGradingCriteria" ], "title": "CreateLLMTestConfigurationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateLLMTestConfigurationRequest | true | none | ### Example responses > 201 Response ``` { "description": "API response object for a single LLMTestConfiguration.", "properties": { "creationDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The creation date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationDate" }, "creationUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who created the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationUserId" }, "datasetEvaluations": { "description": "The LLM test dataset evaluations.", "items": { "description": "Dataset evaluation.", "properties": { "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the dataset evaluation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 100, "description": "The max number of prompts to evaluate.", "exclusiveMinimum": 0, "maximum": 5000, "title": "maxNumPrompts", "type": "integer" }, "ootbDataset": { "anyOf": [ { "description": "Out-of-the-box dataset.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "OOTBDataset", "type": "object" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria", "evaluationDatasetName" ], "title": "DatasetEvaluationResponse", "type": "object" }, "title": "datasetEvaluations", "type": "array" }, "description": { "description": "The description of the LLM Test configuration.", "title": "description", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the LLM test configuration.", "title": "errorMessage" }, "id": { "description": "The ID of the LLM Test configuration.", "title": "id", "type": "string" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "lastUpdateDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The last update date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateDate" }, "lastUpdateUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who last updated the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateUserId" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "name": { "description": "The name of the LLM Test configuration.", "title": "name", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, the use case ID associated with the LLM Test configuration.", "title": "useCaseId" }, "warnings": { "description": "Warnings for this LLM test configuration.", "items": { "additionalProperties": { "type": "string" }, "propertyNames": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "type": "object" }, "title": "warnings", "type": "array" } }, "required": [ "id", "name", "description", "datasetEvaluations", "llmTestGradingCriteria", "isOutOfTheBoxTestConfiguration", "warnings" ], "title": "LLMTestConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successful Response | LLMTestConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List non out-of-the-box datasets Operation path: `GET /api/v2/genai/llmTestConfigurations/nonOotbDatasets/` Authentication requirements: `BearerAuth` List the supported non out-of-the-box datasets that can be used with an LLM test configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | string | true | Use Case ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | search | query | any | false | Only retrieve the datasets with names matching the search query. | ### Example responses > 200 Response ``` { "description": "Paginated list of non-OOTB datasets for use with LLM test configurations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "Non out-of-the-box dataset used with an LLM test configuration.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals (For agentic workflows).", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "creationDate": { "description": "The creation date of the evaluation dataset configuration (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the evaluation dataset configuration.", "title": "creationUserId", "type": "string" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the evaluation dataset.", "title": "datasetName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration.", "title": "errorMessage" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the evaluation dataset configuration.", "title": "id", "type": "string" }, "name": { "description": "The name of the evaluation dataset configuration.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the evaluation dataset configuration.", "title": "playgroundId" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "rowsCount": { "description": "The rows count of the evaluation dataset.", "title": "rowsCount", "type": "integer" }, "size": { "description": "The size of the evaluation dataset (in bytes).", "title": "size", "type": "integer" }, "tenantId": { "description": "The ID of the DataRobot tenant this evaluation dataset configuration belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "toolCallsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls (For agentic workflows).", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case associated with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created the evaluation dataset configuration.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "size", "rowsCount", "useCaseId", "playgroundId", "datasetId", "datasetName", "promptColumnName", "responseColumnName", "toolCallsColumnName", "agentGoalsColumnName", "userName", "correctnessEnabled", "creationUserId", "creationDate", "tenantId", "executionStatus" ], "title": "LLMTestConfigurationNonOOTBDatasetResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMTestConfigurationNonOOTBDatasetsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Non out-of-the-box datasets successfully retrieved. | ListLLMTestConfigurationNonOOTBDatasetsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List out-of-the-box datasets Operation path: `GET /api/v2/genai/llmTestConfigurations/ootbDatasets/` Authentication requirements: `BearerAuth` List the supported out-of-the-box datasets that can be used with an LLM test configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | search | query | any | false | Only retrieve the datasets with names matching the search query. | ### Example responses > 200 Response ``` { "description": "Paginated list of OOTB datasets for use with LLM test configurations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "Out-of-the-box dataset used with an LLM test configuration.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "LLMTestConfigurationOOTBDatasetResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMTestConfigurationOOTBDatasetsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Out-of-the-box datasets successfully retrieved. | ListLLMTestConfigurationOOTBDatasetsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List supported insights Operation path: `GET /api/v2/genai/llmTestConfigurations/supportedInsights/` Authentication requirements: `BearerAuth` List the supported LLM test insight configurations for the specified use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | any | false | If specified, only retrieve the insights supported by this use case ID. | | playgroundId | query | any | false | If specified, only retrieve the insights supported by the use case for which the playgroundId belongs. | ### Example responses > 200 Response ``` { "description": "Response model for supported insights.", "properties": { "datasetsCompatibility": { "description": "The list of insight to evaluation datasets compatibility.", "items": { "description": "Insight to evaluation datasets compatibility.", "properties": { "incompatibleDatasets": { "description": "The list of incompatible datasets.", "items": { "description": "Dataset identifier.", "properties": { "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset, if any.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset.", "title": "datasetName", "type": "string" } }, "required": [ "datasetName", "datasetId" ], "title": "DatasetIdentifier", "type": "object" }, "title": "incompatibleDatasets", "type": "array" }, "insightName": { "description": "The name of the insight.", "title": "insightName", "type": "string" } }, "required": [ "insightName", "incompatibleDatasets" ], "title": "InsightToEvalDatasetsCompatibility", "type": "object" }, "title": "datasetsCompatibility", "type": "array" }, "supportedInsightConfigurations": { "description": "The list of supported insight configurations for the LLM Tests.", "items": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "title": "supportedInsightConfigurations", "type": "array" } }, "required": [ "supportedInsightConfigurations", "datasetsCompatibility" ], "title": "LLMTestConfigurationSupportedInsightsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | LLM test supported insight configurations successfully retrieved. | LLMTestConfigurationSupportedInsightsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete LLM test configuration by LLM test configuration ID Operation path: `DELETE /api/v2/genai/llmTestConfigurations/{llmTestConfigurationId}/` Authentication requirements: `BearerAuth` Delete an existing LLM test configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmTestConfigurationId | path | string | true | The ID of the LLM Test Configuration to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve LLM test configuration by LLM test configuration ID Operation path: `GET /api/v2/genai/llmTestConfigurations/{llmTestConfigurationId}/` Authentication requirements: `BearerAuth` Retrieve an existing LLM test configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmTestConfigurationId | path | string | true | The ID of the LLM Test Configuration to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single LLMTestConfiguration.", "properties": { "creationDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The creation date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationDate" }, "creationUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who created the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationUserId" }, "datasetEvaluations": { "description": "The LLM test dataset evaluations.", "items": { "description": "Dataset evaluation.", "properties": { "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the dataset evaluation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 100, "description": "The max number of prompts to evaluate.", "exclusiveMinimum": 0, "maximum": 5000, "title": "maxNumPrompts", "type": "integer" }, "ootbDataset": { "anyOf": [ { "description": "Out-of-the-box dataset.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "OOTBDataset", "type": "object" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria", "evaluationDatasetName" ], "title": "DatasetEvaluationResponse", "type": "object" }, "title": "datasetEvaluations", "type": "array" }, "description": { "description": "The description of the LLM Test configuration.", "title": "description", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the LLM test configuration.", "title": "errorMessage" }, "id": { "description": "The ID of the LLM Test configuration.", "title": "id", "type": "string" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "lastUpdateDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The last update date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateDate" }, "lastUpdateUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who last updated the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateUserId" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "name": { "description": "The name of the LLM Test configuration.", "title": "name", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, the use case ID associated with the LLM Test configuration.", "title": "useCaseId" }, "warnings": { "description": "Warnings for this LLM test configuration.", "items": { "additionalProperties": { "type": "string" }, "propertyNames": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "type": "object" }, "title": "warnings", "type": "array" } }, "required": [ "id", "name", "description", "datasetEvaluations", "llmTestGradingCriteria", "isOutOfTheBoxTestConfiguration", "warnings" ], "title": "LLMTestConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | LLMTestConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit LLM test configuration by LLM test configuration ID Operation path: `PATCH /api/v2/genai/llmTestConfigurations/{llmTestConfigurationId}/` Authentication requirements: `BearerAuth` Edit an existing LLM test configuration. ### Body parameter ``` { "description": "Request object for editing a LLMTestConfiguration.", "properties": { "datasetEvaluations": { "anyOf": [ { "items": { "description": "Dataset evaluation.", "properties": { "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 0, "description": "The max number of prompts to evaluate.", "maximum": 5000, "minimum": 0, "title": "maxNumPrompts", "type": "integer" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset name. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria" ], "title": "DatasetEvaluationRequest", "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, { "type": "null" } ], "description": "New Dataset evaluations.", "title": "datasetEvaluations" }, "description": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "New LLM test configuration description.", "title": "description" }, "llmTestGradingCriteria": { "anyOf": [ { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, { "type": "null" } ], "description": "New LLM test grading criteria." }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "New LLM test configuration name.", "title": "name" } }, "title": "EditLLMTestConfigurationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmTestConfigurationId | path | string | true | The ID of the LLM Test Configuration to update. | | body | body | EditLLMTestConfigurationRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single LLMTestConfiguration.", "properties": { "creationDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The creation date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationDate" }, "creationUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who created the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationUserId" }, "datasetEvaluations": { "description": "The LLM test dataset evaluations.", "items": { "description": "Dataset evaluation.", "properties": { "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the dataset evaluation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 100, "description": "The max number of prompts to evaluate.", "exclusiveMinimum": 0, "maximum": 5000, "title": "maxNumPrompts", "type": "integer" }, "ootbDataset": { "anyOf": [ { "description": "Out-of-the-box dataset.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "OOTBDataset", "type": "object" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria", "evaluationDatasetName" ], "title": "DatasetEvaluationResponse", "type": "object" }, "title": "datasetEvaluations", "type": "array" }, "description": { "description": "The description of the LLM Test configuration.", "title": "description", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the LLM test configuration.", "title": "errorMessage" }, "id": { "description": "The ID of the LLM Test configuration.", "title": "id", "type": "string" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "lastUpdateDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The last update date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateDate" }, "lastUpdateUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who last updated the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateUserId" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "name": { "description": "The name of the LLM Test configuration.", "title": "name", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, the use case ID associated with the LLM Test configuration.", "title": "useCaseId" }, "warnings": { "description": "Warnings for this LLM test configuration.", "items": { "additionalProperties": { "type": "string" }, "propertyNames": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "type": "object" }, "title": "warnings", "type": "array" } }, "required": [ "id", "name", "description", "datasetEvaluations", "llmTestGradingCriteria", "isOutOfTheBoxTestConfiguration", "warnings" ], "title": "LLMTestConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | LLMTestConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List LLM test results Operation path: `GET /api/v2/genai/llmTestResults/` Authentication requirements: `BearerAuth` List LLM test results. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmTestConfigurationId | query | any | false | LLM Test Configuration ID. | | llmBlueprintId | query | any | false | LLM Blueprint ID. | | llmTestSuiteId | query | any | false | LLM Test Suite ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | ### Example responses > 200 Response ``` { "description": "Paginated list of LLM test results.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single LLMTestResult.", "properties": { "creationDate": { "description": "LLM test result creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user that created this LLM test result.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM result.", "title": "creationUserName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message if the LLM Test Result failed.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error resolution message if the LLM Test Result failed.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result based on the llm test grading criteria. If not specified, execution status is not COMPLETED." }, "id": { "description": "LLM test result ID.", "title": "id", "type": "string" }, "insightEvaluationResults": { "description": "The Insight evaluation results.", "items": { "description": "API response object for a single InsightEvaluationResult.", "properties": { "aggregationType": { "anyOf": [ { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, { "type": "null" } ], "description": "Aggregation type." }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Aggregation value. None indicates that the aggregation failed.", "title": "aggregationValue" }, "chatId": { "description": "Chat ID.", "title": "chatId", "type": "string" }, "chatName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Chat name.", "title": "chatName" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Custom Model LLM Validation ID if using custom model LLM.", "title": "customModelLLMValidationId" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset configuration ID.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "Evaluation name.", "maxLength": 5000, "title": "evaluationName", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result for this insight evaluation result. If not specified, execution status is not COMPLETED." }, "id": { "description": "Insight evaluation result ID.", "title": "id", "type": "string" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "lastUpdateDate": { "description": "Last update date of the insight evaluation result (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM ID used for this insight evaluation result.", "title": "llmId" }, "llmTestResultId": { "description": "LLM test result ID this insight evaluation result is associated to.", "title": "llmTestResultId", "type": "string" }, "maxNumPrompts": { "description": "Number of prompts used in evaluation.", "title": "maxNumPrompts", "type": "integer" }, "metricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name of the metric.", "title": "metricName" }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "id", "llmTestResultId", "maxNumPrompts", "promptSamplingStrategy", "chatId", "chatName", "evaluationName", "insightGradingCriteria", "lastUpdateDate" ], "title": "InsightEvaluationResultResponse", "type": "object" }, "title": "insightEvaluationResults", "type": "array" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "llmBlueprintId": { "description": "LLM Blueprint ID.", "title": "llmBlueprintId", "type": "string" }, "llmBlueprintSnapshot": { "description": "A snapshot in time of a LLMBlueprint's functional parameters.", "properties": { "description": { "description": "The description of the LLMBlueprint at the time of snapshotting.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLMBlueprint for which the snapshot was produced.", "title": "id", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLMBlueprint at the time of snapshotting.", "title": "name", "type": "string" }, "playgroundId": { "description": "The playground id of the LLMBlueprint.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "snapshotDate": { "description": "The date when the snapshot was produced.", "format": "date-time", "title": "snapshotDate", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "id", "name", "description", "playgroundId", "promptType" ], "title": "LLMBlueprintSnapshot", "type": "object" }, "llmTestConfigurationId": { "description": "LLM test configuration ID this LLM result is associated to.", "title": "llmTestConfigurationId", "type": "string" }, "llmTestConfigurationName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "Name of the LLM test configuration this LLM result is associated to.", "title": "llmTestConfigurationName" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "llmTestSuiteId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM test suite ID to which the LLM test configuration is associated to.", "title": "llmTestSuiteId" }, "passPercentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The percentage of underlying insight evaluation results that have a PASS grading result. If not specified, execution status is not COMPLETED.", "title": "passPercentage" }, "useCaseId": { "description": "Use case ID this LLM test result belongs to.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "llmTestConfigurationId", "llmTestConfigurationName", "isOutOfTheBoxTestConfiguration", "useCaseId", "llmBlueprintId", "llmBlueprintSnapshot", "llmTestGradingCriteria", "executionStatus", "insightEvaluationResults", "creationDate", "creationUserId", "creationUserName" ], "title": "LLMTestResultResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMTestResultResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListLLMTestResultResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create LLM test result Operation path: `POST /api/v2/genai/llmTestResults/` Authentication requirements: `BearerAuth` Create a new LLM test result. ### Body parameter ``` { "description": "Request object for creating a LLMTestResult.", "properties": { "llmBlueprintId": { "description": "The LLM Blueprint ID associated with the LLM Test result.", "title": "llmBlueprintId", "type": "string" }, "llmTestConfigurationId": { "description": "The use case ID associated with the LLM Test result.", "title": "llmTestConfigurationId", "type": "string" } }, "required": [ "llmTestConfigurationId", "llmBlueprintId" ], "title": "CreateLLMTestResultRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateLLMTestResultRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for a single LLMTestResult.", "properties": { "creationDate": { "description": "LLM test result creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user that created this LLM test result.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM result.", "title": "creationUserName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message if the LLM Test Result failed.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error resolution message if the LLM Test Result failed.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result based on the llm test grading criteria. If not specified, execution status is not COMPLETED." }, "id": { "description": "LLM test result ID.", "title": "id", "type": "string" }, "insightEvaluationResults": { "description": "The Insight evaluation results.", "items": { "description": "API response object for a single InsightEvaluationResult.", "properties": { "aggregationType": { "anyOf": [ { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, { "type": "null" } ], "description": "Aggregation type." }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Aggregation value. None indicates that the aggregation failed.", "title": "aggregationValue" }, "chatId": { "description": "Chat ID.", "title": "chatId", "type": "string" }, "chatName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Chat name.", "title": "chatName" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Custom Model LLM Validation ID if using custom model LLM.", "title": "customModelLLMValidationId" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset configuration ID.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "Evaluation name.", "maxLength": 5000, "title": "evaluationName", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result for this insight evaluation result. If not specified, execution status is not COMPLETED." }, "id": { "description": "Insight evaluation result ID.", "title": "id", "type": "string" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "lastUpdateDate": { "description": "Last update date of the insight evaluation result (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM ID used for this insight evaluation result.", "title": "llmId" }, "llmTestResultId": { "description": "LLM test result ID this insight evaluation result is associated to.", "title": "llmTestResultId", "type": "string" }, "maxNumPrompts": { "description": "Number of prompts used in evaluation.", "title": "maxNumPrompts", "type": "integer" }, "metricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name of the metric.", "title": "metricName" }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "id", "llmTestResultId", "maxNumPrompts", "promptSamplingStrategy", "chatId", "chatName", "evaluationName", "insightGradingCriteria", "lastUpdateDate" ], "title": "InsightEvaluationResultResponse", "type": "object" }, "title": "insightEvaluationResults", "type": "array" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "llmBlueprintId": { "description": "LLM Blueprint ID.", "title": "llmBlueprintId", "type": "string" }, "llmBlueprintSnapshot": { "description": "A snapshot in time of a LLMBlueprint's functional parameters.", "properties": { "description": { "description": "The description of the LLMBlueprint at the time of snapshotting.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLMBlueprint for which the snapshot was produced.", "title": "id", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLMBlueprint at the time of snapshotting.", "title": "name", "type": "string" }, "playgroundId": { "description": "The playground id of the LLMBlueprint.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "snapshotDate": { "description": "The date when the snapshot was produced.", "format": "date-time", "title": "snapshotDate", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "id", "name", "description", "playgroundId", "promptType" ], "title": "LLMBlueprintSnapshot", "type": "object" }, "llmTestConfigurationId": { "description": "LLM test configuration ID this LLM result is associated to.", "title": "llmTestConfigurationId", "type": "string" }, "llmTestConfigurationName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "Name of the LLM test configuration this LLM result is associated to.", "title": "llmTestConfigurationName" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "llmTestSuiteId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM test suite ID to which the LLM test configuration is associated to.", "title": "llmTestSuiteId" }, "passPercentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The percentage of underlying insight evaluation results that have a PASS grading result. If not specified, execution status is not COMPLETED.", "title": "passPercentage" }, "useCaseId": { "description": "Use case ID this LLM test result belongs to.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "llmTestConfigurationId", "llmTestConfigurationName", "isOutOfTheBoxTestConfiguration", "useCaseId", "llmBlueprintId", "llmBlueprintSnapshot", "llmTestGradingCriteria", "executionStatus", "insightEvaluationResults", "creationDate", "creationUserId", "creationUserName" ], "title": "LLMTestResultResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successful Response | LLMTestResultResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete LLM test result by LLM test result ID Operation path: `DELETE /api/v2/genai/llmTestResults/{llmTestResultId}/` Authentication requirements: `BearerAuth` Delete an existing LLM test result. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmTestResultId | path | string | true | The ID of the LLM Test Result to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve LLM test result by LLM test result ID Operation path: `GET /api/v2/genai/llmTestResults/{llmTestResultId}/` Authentication requirements: `BearerAuth` Retrieve an existing LLM test result. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmTestResultId | path | string | true | The ID of the LLM Test Result to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single LLMTestResult.", "properties": { "creationDate": { "description": "LLM test result creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user that created this LLM test result.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM result.", "title": "creationUserName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message if the LLM Test Result failed.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error resolution message if the LLM Test Result failed.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result based on the llm test grading criteria. If not specified, execution status is not COMPLETED." }, "id": { "description": "LLM test result ID.", "title": "id", "type": "string" }, "insightEvaluationResults": { "description": "The Insight evaluation results.", "items": { "description": "API response object for a single InsightEvaluationResult.", "properties": { "aggregationType": { "anyOf": [ { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, { "type": "null" } ], "description": "Aggregation type." }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Aggregation value. None indicates that the aggregation failed.", "title": "aggregationValue" }, "chatId": { "description": "Chat ID.", "title": "chatId", "type": "string" }, "chatName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Chat name.", "title": "chatName" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Custom Model LLM Validation ID if using custom model LLM.", "title": "customModelLLMValidationId" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset configuration ID.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "Evaluation name.", "maxLength": 5000, "title": "evaluationName", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result for this insight evaluation result. If not specified, execution status is not COMPLETED." }, "id": { "description": "Insight evaluation result ID.", "title": "id", "type": "string" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "lastUpdateDate": { "description": "Last update date of the insight evaluation result (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM ID used for this insight evaluation result.", "title": "llmId" }, "llmTestResultId": { "description": "LLM test result ID this insight evaluation result is associated to.", "title": "llmTestResultId", "type": "string" }, "maxNumPrompts": { "description": "Number of prompts used in evaluation.", "title": "maxNumPrompts", "type": "integer" }, "metricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name of the metric.", "title": "metricName" }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "id", "llmTestResultId", "maxNumPrompts", "promptSamplingStrategy", "chatId", "chatName", "evaluationName", "insightGradingCriteria", "lastUpdateDate" ], "title": "InsightEvaluationResultResponse", "type": "object" }, "title": "insightEvaluationResults", "type": "array" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "llmBlueprintId": { "description": "LLM Blueprint ID.", "title": "llmBlueprintId", "type": "string" }, "llmBlueprintSnapshot": { "description": "A snapshot in time of a LLMBlueprint's functional parameters.", "properties": { "description": { "description": "The description of the LLMBlueprint at the time of snapshotting.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLMBlueprint for which the snapshot was produced.", "title": "id", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLMBlueprint at the time of snapshotting.", "title": "name", "type": "string" }, "playgroundId": { "description": "The playground id of the LLMBlueprint.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "snapshotDate": { "description": "The date when the snapshot was produced.", "format": "date-time", "title": "snapshotDate", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "id", "name", "description", "playgroundId", "promptType" ], "title": "LLMBlueprintSnapshot", "type": "object" }, "llmTestConfigurationId": { "description": "LLM test configuration ID this LLM result is associated to.", "title": "llmTestConfigurationId", "type": "string" }, "llmTestConfigurationName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "Name of the LLM test configuration this LLM result is associated to.", "title": "llmTestConfigurationName" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "llmTestSuiteId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM test suite ID to which the LLM test configuration is associated to.", "title": "llmTestSuiteId" }, "passPercentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The percentage of underlying insight evaluation results that have a PASS grading result. If not specified, execution status is not COMPLETED.", "title": "passPercentage" }, "useCaseId": { "description": "Use case ID this LLM test result belongs to.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "llmTestConfigurationId", "llmTestConfigurationName", "isOutOfTheBoxTestConfiguration", "useCaseId", "llmBlueprintId", "llmBlueprintSnapshot", "llmTestGradingCriteria", "executionStatus", "insightEvaluationResults", "creationDate", "creationUserId", "creationUserName" ], "title": "LLMTestResultResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | LLMTestResultResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List LLM test suites Operation path: `GET /api/v2/genai/llmTestSuites/` Authentication requirements: `BearerAuth` List LLM test suites. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | any | false | Only retrieve the LLM test suites associated with this use case ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name" and "creationDate". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | ### Example responses > 200 Response ``` { "description": "Paginated list of LLM test suites.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "LLMTestSuite object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the LLM test suite.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM test suite.", "title": "id", "type": "string" }, "llmTestConfigurationIds": { "description": "The IDs of the LLM test configurations in this LLM test suite.", "items": { "type": "string" }, "title": "llmTestConfigurationIds", "type": "array" }, "name": { "description": "The name of the LLM test suite.", "title": "name", "type": "string" }, "useCaseId": { "description": "The ID of the use case associated with the LLM test suite.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "llmTestConfigurationIds", "creationDate", "creationUserId" ], "title": "LLMTestSuiteResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMTestSuitesResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListLLMTestSuitesResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create LLM test suite Operation path: `POST /api/v2/genai/llmTestSuites/` Authentication requirements: `BearerAuth` Create a new LLM test suite. ### Body parameter ``` { "description": "The body of the \"Create LLM test suite\" request.", "properties": { "description": { "default": "", "description": "The description of the LLM test suite.", "maxLength": 5000, "title": "description", "type": "string" }, "llmTestConfigurationIds": { "default": [], "description": "The IDs of the LLM test configurations in the LLM test suite.", "items": { "type": "string" }, "maxItems": 100, "title": "llmTestConfigurationIds", "type": "array" }, "name": { "description": "The name of the LLM test suite.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "useCaseId": { "description": "The ID of the use case to associate with the LLM test suite.", "title": "useCaseId", "type": "string" } }, "required": [ "name", "useCaseId" ], "title": "CreateLLMTestSuiteRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateLLMTestSuiteRequest | true | none | ### Example responses > 201 Response ``` { "description": "LLMTestSuite object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the LLM test suite.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM test suite.", "title": "id", "type": "string" }, "llmTestConfigurationIds": { "description": "The IDs of the LLM test configurations in this LLM test suite.", "items": { "type": "string" }, "title": "llmTestConfigurationIds", "type": "array" }, "name": { "description": "The name of the LLM test suite.", "title": "name", "type": "string" }, "useCaseId": { "description": "The ID of the use case associated with the LLM test suite.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "llmTestConfigurationIds", "creationDate", "creationUserId" ], "title": "LLMTestSuiteResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successful Response | LLMTestSuiteResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete LLM test suite by LLM test suite ID Operation path: `DELETE /api/v2/genai/llmTestSuites/{llmTestSuiteId}/` Authentication requirements: `BearerAuth` Delete an existing LLM test suite. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmTestSuiteId | path | string | true | The ID of the LLM test suite to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve LLM test suite by LLM test suite ID Operation path: `GET /api/v2/genai/llmTestSuites/{llmTestSuiteId}/` Authentication requirements: `BearerAuth` Retrieve an existing LLM test suite. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmTestSuiteId | path | string | true | The ID of the LLM test suite to retrieve. | ### Example responses > 200 Response ``` { "description": "LLMTestSuite object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the LLM test suite.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM test suite.", "title": "id", "type": "string" }, "llmTestConfigurationIds": { "description": "The IDs of the LLM test configurations in this LLM test suite.", "items": { "type": "string" }, "title": "llmTestConfigurationIds", "type": "array" }, "name": { "description": "The name of the LLM test suite.", "title": "name", "type": "string" }, "useCaseId": { "description": "The ID of the use case associated with the LLM test suite.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "llmTestConfigurationIds", "creationDate", "creationUserId" ], "title": "LLMTestSuiteResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | LLMTestSuiteResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit LLM test suite by LLM test suite ID Operation path: `PATCH /api/v2/genai/llmTestSuites/{llmTestSuiteId}/` Authentication requirements: `BearerAuth` Edit an existing LLM test suite. ### Body parameter ``` { "description": "The body of the \"Edit LLM test suite\" request.", "properties": { "description": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The description of the LLM test suite.", "title": "description" }, "llmTestConfigurationIds": { "anyOf": [ { "items": { "type": "string" }, "maxItems": 100, "type": "array" }, { "type": "null" } ], "description": "The IDs of the LLM test configurations in the LLM test suite.", "title": "llmTestConfigurationIds" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the LLM test suite.", "title": "name" } }, "title": "EditLLMTestSuiteRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmTestSuiteId | path | string | true | The ID of the LLM test suite to edit. | | body | body | EditLLMTestSuiteRequest | true | none | ### Example responses > 200 Response ``` { "description": "LLMTestSuite object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the LLM test suite.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM test suite.", "title": "id", "type": "string" }, "llmTestConfigurationIds": { "description": "The IDs of the LLM test configurations in this LLM test suite.", "items": { "type": "string" }, "title": "llmTestConfigurationIds", "type": "array" }, "name": { "description": "The name of the LLM test suite.", "title": "name", "type": "string" }, "useCaseId": { "description": "The ID of the use case associated with the LLM test suite.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "llmTestConfigurationIds", "creationDate", "creationUserId" ], "title": "LLMTestSuiteResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | LLMTestSuiteResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete OOTB metric configuration by ootb metric configuration ID Operation path: `DELETE /api/v2/genai/ootbMetricConfigurations/{ootbMetricConfigurationId}/` Authentication requirements: `BearerAuth` Delete single OOTB metric configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ootbMetricConfigurationId | path | string | true | The ID of the metric configuration. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | OOTB metric configuration successfully deleted. | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Get OOTB metric configuration by ootb metric configuration ID Operation path: `GET /api/v2/genai/ootbMetricConfigurations/{ootbMetricConfigurationId}/` Authentication requirements: `BearerAuth` Get OOTB metric configuration from the configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ootbMetricConfigurationId | path | string | true | The ID of the metric configuration. | ### Example responses > 200 Response ``` { "description": "API response object for a single OOTB metric.", "properties": { "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "customOotbMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The custom OOTB metric name to be associated with the OOTB metric.", "title": "customOotbMetricName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the OOTB metric configuration.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "isAgentic": { "default": false, "description": "Whether the OOTB metric configuration is specific to agentic workflows.", "title": "isAgentic", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM to use for `correctness` and `faithfulness` metrics.", "title": "llmId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the OOTB metric." }, "ootbMetricConfigurationId": { "description": "The ID of OOTB metric.", "title": "ootbMetricConfigurationId", "type": "string" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ], "description": "The name of the OOTB metric.", "title": "ootbMetricName" } }, "required": [ "ootbMetricName", "ootbMetricConfigurationId", "executionStatus" ], "title": "OOTBMetricConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OOTB metric configuration | OOTBMetricConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List sidecar model metric validations Operation path: `GET /api/v2/genai/sidecarModelMetricValidations/` Authentication requirements: `BearerAuth` List sidecar model metric validations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | any | false | Only retrieve the sidecar model metric validations associated with these use case IDs. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | search | query | any | false | Only retrieve the sidecar model metric validations matching the search query. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name", "deploymentName", "userName", "creationDate". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | | completedOnly | query | boolean | false | If true, only retrieve the completed sidecar model metric validations. The default is false. | | deploymentId | query | any | false | Only retrieve the sidecar model metric validations associated with this deployment ID. | | modelId | query | any | false | Only retrieve the sidecar model metric validations associated with this model ID. | | promptColumnName | query | any | false | Only retrieve the sidecar model metric validations where the custom model uses this column name for prompt input. | | targetColumnName | query | any | false | Only retrieve the sidecar model metric validations where the custom model uses this column name for prediction output. | | citationsPrefixColumnName | query | any | false | Only retrieve the sidecar model metric validations where the custom model uses this column name prefix for citation inputs. | ### Example responses > 200 Response ``` { "description": "Paginated list of sidecar model metric validations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single sidecar model metric validation.", "properties": { "citationsPrefixColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The column name prefix the custom model uses for citation inputs.", "title": "citationsPrefixColumnName" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the sidecar model metric." }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the sidecar model metric validation.", "title": "playgroundId" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "playgroundId", "citationsPrefixColumnName", "promptColumnName", "responseColumnName", "expectedResponseColumnName" ], "title": "SidecarModelMetricValidationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListSidecarModelMetricValidationnResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Sidecar model metric validations successfully retrieved. | ListSidecarModelMetricValidationnResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Validate sidecar model metric Operation path: `POST /api/v2/genai/sidecarModelMetricValidations/` Authentication requirements: `BearerAuth` Validate a metric hosted in a custom model deployment (also known as a sidecar model metric) for use in the playground. ### Body parameter ``` { "description": "The body of the \"Validate sidecar model metric\" request.", "properties": { "citationsPrefixColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The column name prefix the custom model uses for citation inputs.", "title": "citationsPrefixColumnName" }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "expectedResponseColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for the expected response text input.", "title": "expectedResponseColumnName" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model used in the deployment.", "title": "modelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the sidecar model metric." }, "name": { "default": "Untitled", "description": "The name to use for the validated custom model.", "maxLength": 5000, "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground to associate with the validated custom model.", "title": "playgroundId", "type": "string" }, "predictionTimeout": { "default": 300, "description": "The timeout in seconds for the prediction when validating a custom model. Defaults to 300.", "maximum": 600, "minimum": 1, "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "useCaseId": { "description": "The ID of the use case to associate with the validated custom model.", "title": "useCaseId", "type": "string" } }, "required": [ "deploymentId", "useCaseId", "playgroundId", "targetColumnName" ], "title": "CreateSidecarModelMetricValidationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateSidecarModelMetricValidationRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for a single sidecar model metric validation.", "properties": { "citationsPrefixColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The column name prefix the custom model uses for citation inputs.", "title": "citationsPrefixColumnName" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the sidecar model metric." }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the sidecar model metric validation.", "title": "playgroundId" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "playgroundId", "citationsPrefixColumnName", "promptColumnName", "responseColumnName", "expectedResponseColumnName" ], "title": "SidecarModelMetricValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Sidecar model metric validation job successfully accepted. Follow the Location header to poll for job execution status. | SidecarModelMetricValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete sidecar model metric validation by validation ID Operation path: `DELETE /api/v2/genai/sidecarModelMetricValidations/{validationId}/` Authentication requirements: `BearerAuth` Delete an existing sidecar model metric validation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the sidecar model metric validation to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Sidecar model metric validation successfully deleted. | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve sidecar model metric validation status by validation ID Operation path: `GET /api/v2/genai/sidecarModelMetricValidations/{validationId}/` Authentication requirements: `BearerAuth` Retrieve the status of validating a sidecar model metric. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the sidecar model metric validation to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single sidecar model metric validation.", "properties": { "citationsPrefixColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The column name prefix the custom model uses for citation inputs.", "title": "citationsPrefixColumnName" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the sidecar model metric." }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the sidecar model metric validation.", "title": "playgroundId" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "playgroundId", "citationsPrefixColumnName", "promptColumnName", "responseColumnName", "expectedResponseColumnName" ], "title": "SidecarModelMetricValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Sidecar model metric validation status successfully retrieved. | SidecarModelMetricValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit sidecar model metric validation by validation ID Operation path: `PATCH /api/v2/genai/sidecarModelMetricValidations/{validationId}/` Authentication requirements: `BearerAuth` Edit an existing sidecar model metric validation. ### Body parameter ``` { "description": "The body of the \"Edit sidecar model metric validation\" request.", "properties": { "chatModelId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API.", "title": "chatModelId" }, "citationsPrefixColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the column name prefix that will be used to submit the citation inputs to the sidecar model.", "title": "citationsPrefixColumnName" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the deployment associated with this custom model validation.", "title": "deploymentId" }, "expectedResponseColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to submit the expected response text input to the sidecar model.", "title": "expectedResponseColumnName" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the model associated with this custom model validation.", "title": "modelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the sidecar model metric." }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the custom model validation to this value.", "title": "name" }, "predictionTimeout": { "anyOf": [ { "maximum": 600, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "If specified, sets the timeout in seconds for the prediction when validating a custom model.", "title": "predictionTimeout" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to submit the response text input to the sidecar model.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment.", "title": "targetColumnName" } }, "title": "EditSidecarModelMetricValidationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the sidecar model metric validation to edit. | | body | body | EditSidecarModelMetricValidationRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single sidecar model metric validation.", "properties": { "citationsPrefixColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The column name prefix the custom model uses for citation inputs.", "title": "citationsPrefixColumnName" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the sidecar model metric." }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the sidecar model metric validation.", "title": "playgroundId" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "playgroundId", "citationsPrefixColumnName", "promptColumnName", "responseColumnName", "expectedResponseColumnName" ], "title": "SidecarModelMetricValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Sidecar model metric validation successfully updated. | SidecarModelMetricValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Revalidate sidecar model metric by validation ID Operation path: `POST /api/v2/genai/sidecarModelMetricValidations/{validationId}/revalidate/` Authentication requirements: `BearerAuth` Revalidate an existing sidecar model metric validation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the sidecar model metric validation to revalidate. | ### Example responses > 200 Response ``` { "description": "API response object for a single sidecar model metric validation.", "properties": { "citationsPrefixColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The column name prefix the custom model uses for citation inputs.", "title": "citationsPrefixColumnName" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the sidecar model metric." }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the sidecar model metric validation.", "title": "playgroundId" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "playgroundId", "citationsPrefixColumnName", "promptColumnName", "responseColumnName", "expectedResponseColumnName" ], "title": "SidecarModelMetricValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Sidecar model metric successfully revalidated. | SidecarModelMetricValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Generate synthetic evaluation dataset Operation path: `POST /api/v2/genai/syntheticEvaluationDatasetGenerations/` Authentication requirements: `BearerAuth` Generate a synthetic evaluation dataset. ### Body parameter ``` { "description": "The body of the \"Generate synthetic evaluation dataset\" request.", "properties": { "datasetName": { "anyOf": [ { "maxLength": 255, "type": "string" }, { "type": "null" } ], "description": "The name to use for the generated dataset.", "title": "datasetName" }, "language": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The language to use for the generated dataset.", "title": "language" }, "llmId": { "description": "The ID of the LLM to use for synthetic dataset generation.", "title": "llmId", "type": "string" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "If specified, uses these LLM settings for the prompt and updates the settings of the corresponding chat or LLM blueprint to use these LLM settings.", "title": "llmSettings" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database to use for synthetic dataset generation.", "title": "vectorDatabaseId" } }, "required": [ "llmId" ], "title": "SyntheticEvaluationDatasetGenerationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | SyntheticEvaluationDatasetGenerationRequest | true | none | ### Example responses > 202 Response ``` { "description": "The body of the \"Create synthetic evaluation dataset\" response.", "properties": { "datasetId": { "description": "The ID of the created dataset.", "title": "datasetId", "type": "string" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "description": "The name of the dataset column containing the response text.", "title": "responseColumnName", "type": "string" } }, "required": [ "datasetId", "promptColumnName", "responseColumnName" ], "title": "SyntheticEvaluationDatasetGenerationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Synthetic evaluation data generation job successfully accepted. Follow the Location header to poll for job execution status. | SyntheticEvaluationDatasetGenerationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | # Schemas ## AggregatedAggregationValue ``` { "description": "Aggregated record of multiple of the same item across different metric aggregation runs.", "properties": { "count": { "description": "The number of metric aggregation items aggregated.", "title": "count", "type": "integer" }, "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value", "count" ], "title": "AggregatedAggregationValue", "type": "object" } ``` AggregatedAggregationValue ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of metric aggregation items aggregated. | | item | string | true | | The name of the item. | | value | number | true | | The value associated with the item. | ## AggregationType ``` { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" } ``` AggregationType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | AggregationType | string | false | | The type of the metric aggregation. | ### Enumerated Values | Property | Value | | --- | --- | | AggregationType | [average, percentYes, classPercentCoverage, ngramImportance, guardConditionPercentYes] | ## AggregationValue ``` { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" } ``` AggregationValue ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | item | string | true | | The name of the item. | | value | number | true | | The value associated with the item. | ## ArgumentMatchMode ``` { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } ``` ArgumentMatchMode ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ArgumentMatchMode | string | false | | The different modes for comparing the arguments of tool calls. | ### Enumerated Values | Property | Value | | --- | --- | | ArgumentMatchMode | [exact_match, ignore_arguments] | ## CommonLLMSettings ``` { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" } ``` CommonLLMSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxCompletionLength | any | false | | Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | systemPrompt | any | false | | System prompt guides the style of the LLM response. It is a "universal" prompt, prepended to all individual prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CostMetricConfigurationResponse ``` { "description": "API response object for a single cost metric configuration.", "properties": { "costConfigurationId": { "description": "The ID of the cost metric configuration.", "title": "costConfigurationId", "type": "string" }, "costMetricConfigurations": { "description": "The list of individual LLM cost configurations that constitute this cost metric configuration.", "items": { "description": "API request/response object for a cost configuration of a single LLM.", "properties": { "currencyCode": { "default": "USD", "description": "The arbitrary code code of the currency of `inputTokenPrice` and `outputTokenPrice`.", "maxLength": 7, "title": "currencyCode", "type": "string" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "inputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceInputTokenCount` input tokens.", "minimum": 0, "title": "inputTokenPrice", "type": "number" }, "llmId": { "description": "The ID of the LLM associated with this cost configuration.", "title": "llmId", "type": "string" }, "outputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceOutputTokenCount` output tokens.", "minimum": 0, "title": "outputTokenPrice", "type": "number" }, "referenceInputTokenCount": { "default": 1000, "description": "The number of input tokens corresponding to `inputTokenPrice`.", "minimum": 0, "title": "referenceInputTokenCount", "type": "integer" }, "referenceOutputTokenCount": { "default": 1000, "description": "The number of output tokens corresponding to `outputTokenPrice`.", "minimum": 0, "title": "referenceOutputTokenCount", "type": "integer" } }, "required": [ "llmId" ], "title": "LLMCostConfigurationResponse", "type": "object" }, "title": "costMetricConfigurations", "type": "array" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name to use for the cost configuration.", "title": "name" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the cost metric configuration.", "title": "playgroundId" }, "useCaseId": { "description": "The ID of the use case associated with the cost metric configuration.", "title": "useCaseId", "type": "string" } }, "required": [ "costConfigurationId", "useCaseId", "costMetricConfigurations" ], "title": "CostMetricConfigurationResponse", "type": "object" } ``` CostMetricConfigurationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | costConfigurationId | string | true | | The ID of the cost metric configuration. | | costMetricConfigurations | [LLMCostConfigurationResponse] | true | | The list of individual LLM cost configurations that constitute this cost metric configuration. | | name | any | false | | The name to use for the cost configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | playgroundId | any | false | | The ID of the playground associated with the cost metric configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseId | string | true | | The ID of the use case associated with the cost metric configuration. | ## CreateCostMetricConfigurationRequest ``` { "description": "The body of the \"Create cost metric configuration\" request.", "properties": { "costMetricConfigurations": { "description": "The list of cost metric configurations to use.", "items": { "description": "API request/response object for a cost configuration of a single LLM.", "properties": { "currencyCode": { "default": "USD", "description": "The arbitrary code code of the currency of `inputTokenPrice` and `outputTokenPrice`.", "maxLength": 7, "title": "currencyCode", "type": "string" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "inputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceInputTokenCount` input tokens.", "minimum": 0, "title": "inputTokenPrice", "type": "number" }, "llmId": { "description": "The ID of the LLM associated with this cost configuration.", "title": "llmId", "type": "string" }, "outputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceOutputTokenCount` output tokens.", "minimum": 0, "title": "outputTokenPrice", "type": "number" }, "referenceInputTokenCount": { "default": 1000, "description": "The number of input tokens corresponding to `inputTokenPrice`.", "minimum": 0, "title": "referenceInputTokenCount", "type": "integer" }, "referenceOutputTokenCount": { "default": 1000, "description": "The number of output tokens corresponding to `outputTokenPrice`.", "minimum": 0, "title": "referenceOutputTokenCount", "type": "integer" } }, "required": [ "llmId" ], "title": "LLMCostConfigurationResponse", "type": "object" }, "title": "costMetricConfigurations", "type": "array" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name to use for the cost configuration.", "title": "name" }, "playgroundId": { "description": "The ID of the playground to associate with the cost metric configuration.", "title": "playgroundId", "type": "string" }, "useCaseId": { "description": "The ID of the use case to associate with the cost metric configuration.", "title": "useCaseId", "type": "string" } }, "required": [ "useCaseId", "playgroundId", "costMetricConfigurations" ], "title": "CreateCostMetricConfigurationRequest", "type": "object" } ``` CreateCostMetricConfigurationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | costMetricConfigurations | [LLMCostConfigurationResponse] | true | | The list of cost metric configurations to use. | | name | any | false | | The name to use for the cost configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | playgroundId | string | true | | The ID of the playground to associate with the cost metric configuration. | | useCaseId | string | true | | The ID of the use case to associate with the cost metric configuration. | ## CreateEvaluationDatasetConfigurationRequest ``` { "description": "The body of the \"Create evaluation dataset configuration\" request.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals. It is required to evaluate the AgentGoalAccuracyWithReference metric for agentic workflows.", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "isSyntheticDataset": { "default": false, "description": "Whether the evaluation dataset is synthetic.", "title": "isSyntheticDataset", "type": "boolean" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the evaluation dataset configuration.", "title": "name" }, "playgroundId": { "description": "The ID of the playground to associate with the evaluation dataset configuration.", "title": "playgroundId", "type": "string" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "toolCallsColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls. It is required to evaluate the ToolCallAccuracy metric for agentic workflows.", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case to associate with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" } }, "required": [ "useCaseId", "playgroundId", "datasetId", "promptColumnName" ], "title": "CreateEvaluationDatasetConfigurationRequest", "type": "object" } ``` CreateEvaluationDatasetConfigurationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | agentGoalsColumnName | any | false | | The name of the dataset column containing expected agent goals. It is required to evaluate the AgentGoalAccuracyWithReference metric for agentic workflows. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | correctnessEnabled | any | false | | Whether correctness is enabled for the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the evaluation dataset. | | isSyntheticDataset | boolean | false | | Whether the evaluation dataset is synthetic. | | name | any | false | | The name of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | playgroundId | string | true | | The ID of the playground to associate with the evaluation dataset configuration. | | promptColumnName | string | true | | The name of the dataset column containing the prompt text. | | responseColumnName | any | false | | The name of the dataset column containing the response text. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | toolCallsColumnName | any | false | | The name of the dataset column containing expected tool calls. It is required to evaluate the ToolCallAccuracy metric for agentic workflows. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseId | string | true | | The ID of the use case to associate with the evaluation dataset configuration. | ## CreateEvaluationDatasetMetricAggregationRequest ``` { "description": "The body of the \"Create evaluation dataset metric aggregation\" request.", "properties": { "chatName": { "default": "Aggregated chat", "description": "The name for the new chat that will contain the associated prompts and responses.", "maxLength": 5000, "title": "chatName", "type": "string" }, "evaluationDatasetConfigurationId": { "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId", "type": "string" }, "insightsConfiguration": { "description": "The configuration of insights for the metric aggregation.", "items": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "minItems": 1, "title": "insightsConfiguration", "type": "array" }, "llmBlueprintIds": { "description": "The IDs of the LLM blueprints to use for the metric aggregation.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "title": "llmBlueprintIds", "type": "array" } }, "required": [ "llmBlueprintIds", "evaluationDatasetConfigurationId", "insightsConfiguration" ], "title": "CreateEvaluationDatasetMetricAggregationRequest", "type": "object" } ``` CreateEvaluationDatasetMetricAggregationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatName | string | false | maxLength: 5000 | The name for the new chat that will contain the associated prompts and responses. | | evaluationDatasetConfigurationId | string | true | | The ID of the evaluation dataset configuration. | | insightsConfiguration | [InsightsConfigurationWithAdditionalData] | true | minItems: 1 | The configuration of insights for the metric aggregation. | | llmBlueprintIds | [string] | true | maxItems: 3minItems: 1 | The IDs of the LLM blueprints to use for the metric aggregation. | ## CreateEvaluationDatasetMetricAggregationResponse ``` { "description": "The body of the \"Create evaluation dataset metric aggregation\" response.", "properties": { "chatIds": { "description": "The IDs of the chats associated with the metric aggregation.", "items": { "type": "string" }, "title": "chatIds", "type": "array" }, "jobId": { "description": "The ID of the evaluation dataset metric aggregation job.", "format": "uuid4", "title": "jobId", "type": "string" } }, "required": [ "jobId", "chatIds" ], "title": "CreateEvaluationDatasetMetricAggregationResponse", "type": "object" } ``` CreateEvaluationDatasetMetricAggregationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatIds | [string] | true | | The IDs of the chats associated with the metric aggregation. | | jobId | string(uuid4) | true | | The ID of the evaluation dataset metric aggregation job. | ## CreateLLMTestConfigurationRequest ``` { "description": "Request object for creating a LLMTestConfiguration.", "properties": { "datasetEvaluations": { "description": "Dataset evaluations.", "items": { "description": "Dataset evaluation.", "properties": { "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 0, "description": "The max number of prompts to evaluate.", "maximum": 5000, "minimum": 0, "title": "maxNumPrompts", "type": "integer" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset name. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria" ], "title": "DatasetEvaluationRequest", "type": "object" }, "maxItems": 10, "minItems": 1, "title": "datasetEvaluations", "type": "array" }, "description": { "default": "", "description": "LLM test configuration description.", "maxLength": 5000, "title": "description", "type": "string" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "name": { "description": "LLM test configuration name.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "useCaseId": { "description": "The use case ID associated with the LLM Test configuration.", "title": "useCaseId", "type": "string" } }, "required": [ "name", "useCaseId", "datasetEvaluations", "llmTestGradingCriteria" ], "title": "CreateLLMTestConfigurationRequest", "type": "object" } ``` CreateLLMTestConfigurationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetEvaluations | [DatasetEvaluationRequest] | true | maxItems: 10minItems: 1 | Dataset evaluations. | | description | string | false | maxLength: 5000 | LLM test configuration description. | | llmTestGradingCriteria | LLMTestGradingCriteria | true | | LLM test grading criteria. | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | LLM test configuration name. | | useCaseId | string | true | | The use case ID associated with the LLM Test configuration. | ## CreateLLMTestResultRequest ``` { "description": "Request object for creating a LLMTestResult.", "properties": { "llmBlueprintId": { "description": "The LLM Blueprint ID associated with the LLM Test result.", "title": "llmBlueprintId", "type": "string" }, "llmTestConfigurationId": { "description": "The use case ID associated with the LLM Test result.", "title": "llmTestConfigurationId", "type": "string" } }, "required": [ "llmTestConfigurationId", "llmBlueprintId" ], "title": "CreateLLMTestResultRequest", "type": "object" } ``` CreateLLMTestResultRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmBlueprintId | string | true | | The LLM Blueprint ID associated with the LLM Test result. | | llmTestConfigurationId | string | true | | The use case ID associated with the LLM Test result. | ## CreateLLMTestSuiteRequest ``` { "description": "The body of the \"Create LLM test suite\" request.", "properties": { "description": { "default": "", "description": "The description of the LLM test suite.", "maxLength": 5000, "title": "description", "type": "string" }, "llmTestConfigurationIds": { "default": [], "description": "The IDs of the LLM test configurations in the LLM test suite.", "items": { "type": "string" }, "maxItems": 100, "title": "llmTestConfigurationIds", "type": "array" }, "name": { "description": "The name of the LLM test suite.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "useCaseId": { "description": "The ID of the use case to associate with the LLM test suite.", "title": "useCaseId", "type": "string" } }, "required": [ "name", "useCaseId" ], "title": "CreateLLMTestSuiteRequest", "type": "object" } ``` CreateLLMTestSuiteRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 5000 | The description of the LLM test suite. | | llmTestConfigurationIds | [string] | false | maxItems: 100 | The IDs of the LLM test configurations in the LLM test suite. | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the LLM test suite. | | useCaseId | string | true | | The ID of the use case to associate with the LLM test suite. | ## CreateSidecarModelMetricValidationRequest ``` { "description": "The body of the \"Validate sidecar model metric\" request.", "properties": { "citationsPrefixColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The column name prefix the custom model uses for citation inputs.", "title": "citationsPrefixColumnName" }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "expectedResponseColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for the expected response text input.", "title": "expectedResponseColumnName" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model used in the deployment.", "title": "modelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the sidecar model metric." }, "name": { "default": "Untitled", "description": "The name to use for the validated custom model.", "maxLength": 5000, "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground to associate with the validated custom model.", "title": "playgroundId", "type": "string" }, "predictionTimeout": { "default": 300, "description": "The timeout in seconds for the prediction when validating a custom model. Defaults to 300.", "maximum": 600, "minimum": 1, "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "useCaseId": { "description": "The ID of the use case to associate with the validated custom model.", "title": "useCaseId", "type": "string" } }, "required": [ "deploymentId", "useCaseId", "playgroundId", "targetColumnName" ], "title": "CreateSidecarModelMetricValidationRequest", "type": "object" } ``` CreateSidecarModelMetricValidationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | citationsPrefixColumnName | any | false | | The column name prefix the custom model uses for citation inputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the custom model deployment. | | expectedResponseColumnName | any | false | | The name of the column the custom model uses for the expected response text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | any | false | | The ID of the model used in the deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | moderationConfiguration | any | false | | The moderation configuration to be associated with the sidecar model metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | maxLength: 5000 | The name to use for the validated custom model. | | playgroundId | string | true | | The ID of the playground to associate with the validated custom model. | | predictionTimeout | integer | false | maximum: 600minimum: 1 | The timeout in seconds for the prediction when validating a custom model. Defaults to 300. | | promptColumnName | any | false | | The name of the column the custom model uses for prompt text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responseColumnName | any | false | | The name of the column the custom model uses for response text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetColumnName | string | true | maxLength: 5000 | The name of the column the custom model uses for prediction output. | | useCaseId | string | true | | The ID of the use case to associate with the validated custom model. | ## CustomModelChatLLMSettings ``` { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" } ``` CustomModelChatLLMSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string | true | | The ID of the custom model used via chat completion interface. | | customModelVersionId | any | false | | The ID of the custom model version used via chat completion interface. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | systemPrompt | any | false | | System prompt guides the style of the LLM response. It is a "universal" prompt, prepended to all individual prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CustomModelGuard ``` { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" } ``` CustomModelGuard ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the guard. | | nemoEvaluatorType | any | false | | NeMo Evaluator type of the guard. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelGuardNemoEvaluatorType | false | | NeMo evaluator type as used in the moderation_config.yaml file of the custom model. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbType | any | false | | Out of the box type of the guard. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelGuardOOTBType | false | | OOTB type as used in the moderation_config.yaml file of the custom model. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | CustomModelGuardStage | true | | Stage on which the guard gets applied. | | type | CustomModelGuardType | true | | Type of the guard. | ## CustomModelGuardNemoEvaluatorType ``` { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" } ``` CustomModelGuardNemoEvaluatorType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelGuardNemoEvaluatorType | string | false | | NeMo evaluator type as used in the moderation_config.yaml file of the custom model. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelGuardNemoEvaluatorType | [llm_judge, context_relevance, response_groundedness, topic_adherence, agent_goal_accuracy, response_relevancy, faithfulness] | ## CustomModelGuardOOTBType ``` { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" } ``` CustomModelGuardOOTBType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelGuardOOTBType | string | false | | OOTB type as used in the moderation_config.yaml file of the custom model. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelGuardOOTBType | [token_count, rouge_1, faithfulness, agent_goal_accuracy, custom_metric, cost, task_adherence] | ## CustomModelGuardStage ``` { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" } ``` CustomModelGuardStage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelGuardStage | string | false | | Guard stage as used in the moderation_config.yaml file of the custom model. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelGuardStage | [prompt, response] | ## CustomModelGuardType ``` { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } ``` CustomModelGuardType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelGuardType | string | false | | Guard type as used in the moderation_config.yaml file of the custom model. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelGuardType | [ootb, model, nemo_guardrails, nemo_evaluator] | ## CustomModelLLMSettings ``` { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" } ``` CustomModelLLMSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalLlmContextSize | any | false | | The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 128000minimum: 128 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | systemPrompt | any | false | | System prompt guides the style of the LLM response. It is a "universal" prompt, prepended to all individual prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationId | any | false | | The validation ID of the custom model LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CustomModelValidationStatus ``` { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } ``` CustomModelValidationStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelValidationStatus | string | false | | Status of custom model validation. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelValidationStatus | [TESTING, PASSED, FAILED] | ## DatasetEvaluationRequest ``` { "description": "Dataset evaluation.", "properties": { "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 0, "description": "The max number of prompts to evaluate.", "maximum": 5000, "minimum": 0, "title": "maxNumPrompts", "type": "integer" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset name. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria" ], "title": "DatasetEvaluationRequest", "type": "object" } ``` DatasetEvaluationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | any | false | | The ID of the evaluation dataset configuration for this dataset evaluation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationName | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the evaluation. This name should provide context regarding what is being evaluated. | | insightConfiguration | InsightsConfigurationWithAdditionalData | true | | The configuration of insights with extra data. | | insightGradingCriteria | InsightGradingCriteria | true | | Grading criteria for an insight. | | maxNumPrompts | integer | false | maximum: 5000minimum: 0 | The max number of prompts to evaluate. | | ootbDatasetName | any | false | | Out-of-the-box evaluation dataset name. This applies only to our predefined public evaluation datasets. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBDatasetName | false | | Out-of-the-box dataset name. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptSamplingStrategy | PromptSamplingStrategy | false | | The prompt sampling strategy. Controls how max_num_prompts are sampled. | ## DatasetEvaluationResponse ``` { "description": "Dataset evaluation.", "properties": { "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the dataset evaluation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 100, "description": "The max number of prompts to evaluate.", "exclusiveMinimum": 0, "maximum": 5000, "title": "maxNumPrompts", "type": "integer" }, "ootbDataset": { "anyOf": [ { "description": "Out-of-the-box dataset.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "OOTBDataset", "type": "object" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria", "evaluationDatasetName" ], "title": "DatasetEvaluationResponse", "type": "object" } ``` DatasetEvaluationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the dataset evaluation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | any | false | | The ID of the evaluation dataset configuration for this dataset evaluation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetName | any | true | | Evaluation dataset name. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationName | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the evaluation. This name should provide context regarding what is being evaluated. | | insightConfiguration | InsightsConfigurationWithAdditionalData | true | | The configuration of insights with extra data. | | insightGradingCriteria | InsightGradingCriteria | true | | Grading criteria for an insight. | | maxNumPrompts | integer | false | maximum: 5000 | The max number of prompts to evaluate. | | ootbDataset | any | false | | Out-of-the-box evaluation dataset. This applies only to our predefined public evaluation datasets. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBDataset | false | | Out-of-the-box dataset. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptSamplingStrategy | PromptSamplingStrategy | false | | The prompt sampling strategy. Controls how max_num_prompts are sampled. | ## DatasetIdentifier ``` { "description": "Dataset identifier.", "properties": { "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset, if any.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset.", "title": "datasetName", "type": "string" } }, "required": [ "datasetName", "datasetId" ], "title": "DatasetIdentifier", "type": "object" } ``` DatasetIdentifier ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | any | true | | The ID of the dataset, if any. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetName | string | true | | The name of the dataset. | ## DeploymentAccessData ``` { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" } ``` DeploymentAccessData ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authorizationHeader | string | false | | The Authorization header to use for the deployment. | | chatApiUrl | any | false | | The URL of the deployment's chat API. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datarobotKey | any | true | | The server key associated with the prediction API. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputType | DeploymentInputType | true | | The format of the input data. | | modelType | SupportedDeploymentType | true | | The type of the target output the deployment produces. | | predictionApiUrl | string | true | | The URL of the deployment's prediction API. | ## DeploymentInputType ``` { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" } ``` DeploymentInputType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | DeploymentInputType | string | false | | The format of the input data submitted to a DataRobot deployment. | ### Enumerated Values | Property | Value | | --- | --- | | DeploymentInputType | [CSV, JSON] | ## EditCostMetricConfigurationRequest ``` { "description": "The body of the \"Edit cost metric configuration\" request.", "properties": { "costMetricConfigurations": { "description": "The list of LLM cost configurations to apply to this cost metric configuration.", "items": { "description": "API request/response object for a cost configuration of a single LLM.", "properties": { "currencyCode": { "default": "USD", "description": "The arbitrary code code of the currency of `inputTokenPrice` and `outputTokenPrice`.", "maxLength": 7, "title": "currencyCode", "type": "string" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "inputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceInputTokenCount` input tokens.", "minimum": 0, "title": "inputTokenPrice", "type": "number" }, "llmId": { "description": "The ID of the LLM associated with this cost configuration.", "title": "llmId", "type": "string" }, "outputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceOutputTokenCount` output tokens.", "minimum": 0, "title": "outputTokenPrice", "type": "number" }, "referenceInputTokenCount": { "default": 1000, "description": "The number of input tokens corresponding to `inputTokenPrice`.", "minimum": 0, "title": "referenceInputTokenCount", "type": "integer" }, "referenceOutputTokenCount": { "default": 1000, "description": "The number of output tokens corresponding to `outputTokenPrice`.", "minimum": 0, "title": "referenceOutputTokenCount", "type": "integer" } }, "required": [ "llmId" ], "title": "LLMCostConfigurationResponse", "type": "object" }, "minItems": 1, "title": "costMetricConfigurations", "type": "array" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name to use for the cost configuration.", "title": "name" } }, "required": [ "costMetricConfigurations" ], "title": "EditCostMetricConfigurationRequest", "type": "object" } ``` EditCostMetricConfigurationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | costMetricConfigurations | [LLMCostConfigurationResponse] | true | minItems: 1 | The list of LLM cost configurations to apply to this cost metric configuration. | | name | any | false | | The name to use for the cost configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## EditEvaluationDatasetConfigurationRequest ``` { "description": "The body of the \"Edit evaluation dataset configuration\" request.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the expected name of the dataset column containing expected agent goals. It is required to evaluate the AgentGoalAccuracyWithReference metric for agentic workflows.", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "If specified, enables or disables correctness for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "datasetId": { "default": "000000000000000000000000", "description": "If specified, updates the ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the evaluation dataset configuration to this value.", "title": "name" }, "promptColumnName": { "default": "None", "description": "If specified, changes the expected name of the dataset column containing the prompt text.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the expected name of the dataset column containing the response text.", "title": "responseColumnName" }, "toolCallsColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the expected name of the dataset column containing expected tool calls. It is required to evaluate the ToolCallAccuracy metric for agentic workflows.", "title": "toolCallsColumnName" } }, "title": "EditEvaluationDatasetConfigurationRequest", "type": "object" } ``` EditEvaluationDatasetConfigurationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | agentGoalsColumnName | any | false | | If specified, changes the expected name of the dataset column containing expected agent goals. It is required to evaluate the AgentGoalAccuracyWithReference metric for agentic workflows. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | correctnessEnabled | any | false | | If specified, enables or disables correctness for the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | false | | If specified, updates the ID of the evaluation dataset. | | name | any | false | | If specified, renames the evaluation dataset configuration to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | string | false | maxLength: 5000minLength: 1minLength: 1 | If specified, changes the expected name of the dataset column containing the prompt text. | | responseColumnName | any | false | | If specified, changes the expected name of the dataset column containing the response text. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | toolCallsColumnName | any | false | | If specified, changes the expected name of the dataset column containing expected tool calls. It is required to evaluate the ToolCallAccuracy metric for agentic workflows. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## EditLLMTestConfigurationRequest ``` { "description": "Request object for editing a LLMTestConfiguration.", "properties": { "datasetEvaluations": { "anyOf": [ { "items": { "description": "Dataset evaluation.", "properties": { "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 0, "description": "The max number of prompts to evaluate.", "maximum": 5000, "minimum": 0, "title": "maxNumPrompts", "type": "integer" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset name. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria" ], "title": "DatasetEvaluationRequest", "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, { "type": "null" } ], "description": "New Dataset evaluations.", "title": "datasetEvaluations" }, "description": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "New LLM test configuration description.", "title": "description" }, "llmTestGradingCriteria": { "anyOf": [ { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, { "type": "null" } ], "description": "New LLM test grading criteria." }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "New LLM test configuration name.", "title": "name" } }, "title": "EditLLMTestConfigurationRequest", "type": "object" } ``` EditLLMTestConfigurationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetEvaluations | any | false | | New Dataset evaluations. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [DatasetEvaluationRequest] | false | maxItems: 10minItems: 1 | [Dataset evaluation.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | any | false | | New LLM test configuration description. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmTestGradingCriteria | any | false | | New LLM test grading criteria. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LLMTestGradingCriteria | false | | Grading criteria for the LLM Test configuration. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | any | false | | New LLM test configuration name. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## EditLLMTestSuiteRequest ``` { "description": "The body of the \"Edit LLM test suite\" request.", "properties": { "description": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The description of the LLM test suite.", "title": "description" }, "llmTestConfigurationIds": { "anyOf": [ { "items": { "type": "string" }, "maxItems": 100, "type": "array" }, { "type": "null" } ], "description": "The IDs of the LLM test configurations in the LLM test suite.", "title": "llmTestConfigurationIds" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the LLM test suite.", "title": "name" } }, "title": "EditLLMTestSuiteRequest", "type": "object" } ``` EditLLMTestSuiteRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | any | false | | The description of the LLM test suite. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmTestConfigurationIds | any | false | | The IDs of the LLM test configurations in the LLM test suite. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 100 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | any | false | | The name of the LLM test suite. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## EditSidecarModelMetricValidationRequest ``` { "description": "The body of the \"Edit sidecar model metric validation\" request.", "properties": { "chatModelId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API.", "title": "chatModelId" }, "citationsPrefixColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the column name prefix that will be used to submit the citation inputs to the sidecar model.", "title": "citationsPrefixColumnName" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the deployment associated with this custom model validation.", "title": "deploymentId" }, "expectedResponseColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to submit the expected response text input to the sidecar model.", "title": "expectedResponseColumnName" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the model associated with this custom model validation.", "title": "modelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the sidecar model metric." }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the custom model validation to this value.", "title": "name" }, "predictionTimeout": { "anyOf": [ { "maximum": 600, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "If specified, sets the timeout in seconds for the prediction when validating a custom model.", "title": "predictionTimeout" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to submit the response text input to the sidecar model.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment.", "title": "targetColumnName" } }, "title": "EditSidecarModelMetricValidationRequest", "type": "object" } ``` EditSidecarModelMetricValidationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatModelId | any | false | | The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | citationsPrefixColumnName | any | false | | If specified, changes the column name prefix that will be used to submit the citation inputs to the sidecar model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | any | false | | If specified, changes the ID of the deployment associated with this custom model validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | expectedResponseColumnName | any | false | | If specified, changes the name of the column that will be used to submit the expected response text input to the sidecar model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | any | false | | If specified, changes the ID of the model associated with this custom model validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | moderationConfiguration | any | false | | The moderation configuration to be associated with the sidecar model metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | any | false | | If specified, renames the custom model validation to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionTimeout | any | false | | If specified, sets the timeout in seconds for the prediction when validating a custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 600minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | any | false | | If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responseColumnName | any | false | | If specified, changes the name of the column that will be used to submit the response text input to the sidecar model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetColumnName | any | false | | If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## EvaluationDatasetConfigurationResponse ``` { "description": "API response object for a single evaluation dataset configuration.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals (For agentic workflows).", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "creationDate": { "description": "The creation date of the evaluation dataset configuration (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the evaluation dataset configuration.", "title": "creationUserId", "type": "string" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the evaluation dataset.", "title": "datasetName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration.", "title": "errorMessage" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the evaluation dataset configuration.", "title": "id", "type": "string" }, "name": { "description": "The name of the evaluation dataset configuration.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the evaluation dataset configuration.", "title": "playgroundId" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "rowsCount": { "description": "The rows count of the evaluation dataset.", "title": "rowsCount", "type": "integer" }, "size": { "description": "The size of the evaluation dataset (in bytes).", "title": "size", "type": "integer" }, "tenantId": { "description": "The ID of the DataRobot tenant this evaluation dataset configuration belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "toolCallsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls (For agentic workflows).", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case associated with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created the evaluation dataset configuration.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "size", "rowsCount", "useCaseId", "playgroundId", "datasetId", "datasetName", "promptColumnName", "responseColumnName", "toolCallsColumnName", "agentGoalsColumnName", "userName", "correctnessEnabled", "creationUserId", "creationDate", "tenantId", "executionStatus" ], "title": "EvaluationDatasetConfigurationResponse", "type": "object" } ``` EvaluationDatasetConfigurationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | agentGoalsColumnName | any | true | | The name of the dataset column containing expected agent goals (For agentic workflows). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | correctnessEnabled | any | true | | Whether correctness is enabled for the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the evaluation dataset configuration (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created the evaluation dataset configuration. | | datasetId | string | true | | The ID of the evaluation dataset. | | datasetName | string | true | | The name of the evaluation dataset. | | errorMessage | any | false | | The error message associated with the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | ExecutionStatus | true | | The execution status of the evaluation dataset. | | id | string | true | | The ID of the evaluation dataset configuration. | | name | string | true | | The name of the evaluation dataset configuration. | | playgroundId | any | true | | The ID of the playground associated with the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | string | true | | The name of the dataset column containing the prompt text. | | responseColumnName | any | true | | The name of the dataset column containing the response text. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rowsCount | integer | true | | The rows count of the evaluation dataset. | | size | integer | true | | The size of the evaluation dataset (in bytes). | | tenantId | string(uuid4) | true | | The ID of the DataRobot tenant this evaluation dataset configuration belongs to. | | toolCallsColumnName | any | true | | The name of the dataset column containing expected tool calls (For agentic workflows). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseId | string | true | | The ID of the use case associated with the evaluation dataset configuration. | | userName | string | true | | The name of the user that created the evaluation dataset configuration. | ## EvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse ``` { "description": "API response object for multiple evaluation dataset metric aggregation\naggregated by llm blueprint.", "properties": { "aggregatedItemCount": { "description": "Number of items aggregated.", "title": "aggregatedItemCount", "type": "integer" }, "aggregatedItemDetails": { "description": "List of details for aggregated items.", "items": { "description": "Details for aggregated items.", "properties": { "chatId": { "description": "The ID of the chat associated with the metric aggregation.", "title": "chatId", "type": "string" }, "chatLink": { "description": "The link to the chat associated with the metric aggregation.", "title": "chatLink", "type": "string" }, "chatName": { "description": "The name of the chat associated with the metric aggregation.", "title": "chatName", "type": "string" }, "creationDate": { "description": "The creation date of the metric aggregation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the metric aggregation.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user that created the metric aggregation.", "title": "creationUserName", "type": "string" } }, "required": [ "chatId", "chatName", "chatLink", "creationDate", "creationUserId", "creationUserName" ], "title": "EvaluationDatasetMetricAggregationChatDetails", "type": "object" }, "title": "aggregatedItemDetails", "type": "array" }, "aggregationType": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "items": { "description": "Aggregated record of multiple of the same item across different metric aggregation runs.", "properties": { "count": { "description": "The number of metric aggregation items aggregated.", "title": "count", "type": "integer" }, "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value", "count" ], "title": "AggregatedAggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The aggregated value of the metric.", "title": "aggregationValue" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model's guard the metric aggregation belongs to.", "title": "customModelGuardId" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The dataset ID of the evaluation dataset configuration.", "title": "datasetId" }, "datasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The Data Registry dataset name of the evaluation dataset configuration.", "title": "datasetName" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration associated with the metric aggregation.", "title": "evaluationDatasetConfigurationId" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the metric aggregation.", "title": "llmBlueprintId", "type": "string" }, "metricName": { "description": "The name of the metric associated with the metric aggregation.", "title": "metricName", "type": "string" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "The name of the out-of-the-box dataset." }, "tenantId": { "description": "The ID of the tenant the metric aggregation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" } }, "required": [ "llmBlueprintId", "evaluationDatasetConfigurationId", "ootbDatasetName", "datasetId", "datasetName", "metricName", "aggregationValue", "aggregationType", "tenantId", "customModelGuardId", "aggregatedItemDetails", "aggregatedItemCount" ], "title": "EvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse", "type": "object" } ``` EvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregatedItemCount | integer | true | | Number of items aggregated. | | aggregatedItemDetails | [EvaluationDatasetMetricAggregationChatDetails] | true | | List of details for aggregated items. | | aggregationType | AggregationType | true | | The type of metric aggregation. | | aggregationValue | any | true | | The aggregated value of the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [AggregationValue] | false | | [An individual record in an itemized metric aggregation.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [AggregatedAggregationValue] | false | | [Aggregated record of multiple of the same item across different metric aggregation runs.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelGuardId | any | true | | The ID of the custom model's guard the metric aggregation belongs to. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | any | true | | The dataset ID of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetName | any | true | | The Data Registry dataset name of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | any | true | | The ID of the evaluation dataset configuration associated with the metric aggregation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmBlueprintId | string | true | | The ID of the LLM blueprint associated with the metric aggregation. | | metricName | string | true | | The name of the metric associated with the metric aggregation. | | ootbDatasetName | any | true | | The name of the out-of-the-box dataset. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBDatasetName | false | | Out-of-the-box dataset name. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | tenantId | string(uuid4) | true | | The ID of the tenant the metric aggregation belongs to. | ## EvaluationDatasetMetricAggregationChatDetails ``` { "description": "Details for aggregated items.", "properties": { "chatId": { "description": "The ID of the chat associated with the metric aggregation.", "title": "chatId", "type": "string" }, "chatLink": { "description": "The link to the chat associated with the metric aggregation.", "title": "chatLink", "type": "string" }, "chatName": { "description": "The name of the chat associated with the metric aggregation.", "title": "chatName", "type": "string" }, "creationDate": { "description": "The creation date of the metric aggregation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the metric aggregation.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user that created the metric aggregation.", "title": "creationUserName", "type": "string" } }, "required": [ "chatId", "chatName", "chatLink", "creationDate", "creationUserId", "creationUserName" ], "title": "EvaluationDatasetMetricAggregationChatDetails", "type": "object" } ``` EvaluationDatasetMetricAggregationChatDetails ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatId | string | true | | The ID of the chat associated with the metric aggregation. | | chatLink | string | true | | The link to the chat associated with the metric aggregation. | | chatName | string | true | | The name of the chat associated with the metric aggregation. | | creationDate | string(date-time) | true | | The creation date of the metric aggregation (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created the metric aggregation. | | creationUserName | string | true | | The name of the user that created the metric aggregation. | ## EvaluationDatasetMetricAggregationFieldQueryParam ``` { "description": "Field used for aggregation when listing evaluation dataset metric aggregations.", "enum": [ "metricName", "llmBlueprintId", "aggregationType", "evaluationDatasetConfigurationId" ], "title": "EvaluationDatasetMetricAggregationFieldQueryParam", "type": "string" } ``` EvaluationDatasetMetricAggregationFieldQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | EvaluationDatasetMetricAggregationFieldQueryParam | string | false | | Field used for aggregation when listing evaluation dataset metric aggregations. | ### Enumerated Values | Property | Value | | --- | --- | | EvaluationDatasetMetricAggregationFieldQueryParam | [metricName, llmBlueprintId, aggregationType, evaluationDatasetConfigurationId] | ## EvaluationDatasetMetricAggregationResponse ``` { "description": "API response object for a single evaluation dataset metric aggregation.", "properties": { "aggregationType": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "items": { "description": "Aggregated record of multiple of the same item across different metric aggregation runs.", "properties": { "count": { "description": "The number of metric aggregation items aggregated.", "title": "count", "type": "integer" }, "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value", "count" ], "title": "AggregatedAggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The aggregated value of the metric.", "title": "aggregationValue" }, "chatId": { "description": "The ID of the chat associated with the metric aggregation.", "title": "chatId", "type": "string" }, "chatLink": { "description": "The link to the chat associated with the metric aggregation.", "title": "chatLink", "type": "string" }, "chatName": { "description": "The name of the chat associated with the metric aggregation.", "title": "chatName", "type": "string" }, "creationDate": { "description": "The creation date of the metric aggregation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the metric aggregation.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user that created the metric aggregation.", "title": "creationUserName", "type": "string" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model's guard the metric aggregation belongs to.", "title": "customModelGuardId" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The dataset ID of the evaluation dataset configuration.", "title": "datasetId" }, "datasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The Data Registry dataset name of the evaluation dataset configuration.", "title": "datasetName" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration associated with the metric aggregation.", "title": "evaluationDatasetConfigurationId" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the metric aggregation.", "title": "llmBlueprintId", "type": "string" }, "metricName": { "description": "The name of the metric associated with the metric aggregation.", "title": "metricName", "type": "string" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "The name of the out-of-the-box dataset." }, "tenantId": { "description": "The ID of the tenant the metric aggregation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" } }, "required": [ "chatId", "chatName", "chatLink", "creationDate", "creationUserId", "creationUserName", "llmBlueprintId", "evaluationDatasetConfigurationId", "ootbDatasetName", "datasetId", "datasetName", "metricName", "aggregationValue", "aggregationType", "tenantId", "customModelGuardId" ], "title": "EvaluationDatasetMetricAggregationResponse", "type": "object" } ``` EvaluationDatasetMetricAggregationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregationType | AggregationType | true | | The type of metric aggregation. | | aggregationValue | any | true | | The aggregated value of the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [AggregationValue] | false | | [An individual record in an itemized metric aggregation.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [AggregatedAggregationValue] | false | | [Aggregated record of multiple of the same item across different metric aggregation runs.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatId | string | true | | The ID of the chat associated with the metric aggregation. | | chatLink | string | true | | The link to the chat associated with the metric aggregation. | | chatName | string | true | | The name of the chat associated with the metric aggregation. | | creationDate | string(date-time) | true | | The creation date of the metric aggregation (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created the metric aggregation. | | creationUserName | string | true | | The name of the user that created the metric aggregation. | | customModelGuardId | any | true | | The ID of the custom model's guard the metric aggregation belongs to. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | any | true | | The dataset ID of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetName | any | true | | The Data Registry dataset name of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | any | true | | The ID of the evaluation dataset configuration associated with the metric aggregation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmBlueprintId | string | true | | The ID of the LLM blueprint associated with the metric aggregation. | | metricName | string | true | | The name of the metric associated with the metric aggregation. | | ootbDatasetName | any | true | | The name of the out-of-the-box dataset. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBDatasetName | false | | Out-of-the-box dataset name. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | tenantId | string(uuid4) | true | | The ID of the tenant the metric aggregation belongs to. | ## EvaluationDatasetMetricAggregationUniqueFieldValuesResponse ``` { "description": "API response object for a single unique computed metric.", "properties": { "uniqueFieldValue": { "description": "The unique value associated with the metric aggregation.", "title": "uniqueFieldValue", "type": "string" } }, "required": [ "uniqueFieldValue" ], "title": "EvaluationDatasetMetricAggregationUniqueFieldValuesResponse", "type": "object" } ``` EvaluationDatasetMetricAggregationUniqueFieldValuesResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | uniqueFieldValue | string | true | | The unique value associated with the metric aggregation. | ## ExecutionStatus ``` { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" } ``` ExecutionStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ExecutionStatus | string | false | | Job and entity execution status. | ### Enumerated Values | Property | Value | | --- | --- | | ExecutionStatus | [NEW, RUNNING, COMPLETED, REQUIRES_USER_INPUT, SKIPPED, ERROR] | ## ExtraMetricSettings ``` { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" } ``` ExtraMetricSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | toolCallAccuracy | any | false | | Extra settings for the tool call accuracy metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ToolCallAccuracySettings | false | | Additional arguments for the tool call accuracy metric. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## GradingResult ``` { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" } ``` GradingResult ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | GradingResult | string | false | | Grading result. | ### Enumerated Values | Property | Value | | --- | --- | | GradingResult | [PASS, FAIL] | ## GuardCondition ``` { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" } ``` GuardCondition ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparand | any | true | | The comparand(s) used in the guard condition. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparator | GuardConditionComparator | true | | The comparator used in the guard condition. | ## GuardConditionComparator ``` { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } ``` GuardConditionComparator ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | GuardConditionComparator | string | false | | The comparator used in a guard condition. | ### Enumerated Values | Property | Value | | --- | --- | | GuardConditionComparator | [greaterThan, lessThan, equals, notEquals, is, isNot, matches, doesNotMatch, contains, doesNotContain] | ## HTTPValidationErrorResponse ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` HTTPValidationErrorResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | detail | [ValidationError] | false | | none | ## InsightErrorResolution ``` { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" } ``` InsightErrorResolution ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | InsightErrorResolution | string | false | | Error type linking directly to the field name that is related to the error. | ### Enumerated Values | Property | Value | | --- | --- | | InsightErrorResolution | [ootbMetricName, intervention, guardCondition, sidecarOverall, sidecarRevalidate, sidecarDeploymentId, sidecarInputColumnName, sidecarOutputColumnName, promptPipelineFiles, promptPipelineTemplateId, responsePipelineFiles, responsePipelineTemplateId] | ## InsightEvaluationResultResponse ``` { "description": "API response object for a single InsightEvaluationResult.", "properties": { "aggregationType": { "anyOf": [ { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, { "type": "null" } ], "description": "Aggregation type." }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Aggregation value. None indicates that the aggregation failed.", "title": "aggregationValue" }, "chatId": { "description": "Chat ID.", "title": "chatId", "type": "string" }, "chatName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Chat name.", "title": "chatName" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Custom Model LLM Validation ID if using custom model LLM.", "title": "customModelLLMValidationId" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset configuration ID.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "Evaluation name.", "maxLength": 5000, "title": "evaluationName", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result for this insight evaluation result. If not specified, execution status is not COMPLETED." }, "id": { "description": "Insight evaluation result ID.", "title": "id", "type": "string" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "lastUpdateDate": { "description": "Last update date of the insight evaluation result (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM ID used for this insight evaluation result.", "title": "llmId" }, "llmTestResultId": { "description": "LLM test result ID this insight evaluation result is associated to.", "title": "llmTestResultId", "type": "string" }, "maxNumPrompts": { "description": "Number of prompts used in evaluation.", "title": "maxNumPrompts", "type": "integer" }, "metricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name of the metric.", "title": "metricName" }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "id", "llmTestResultId", "maxNumPrompts", "promptSamplingStrategy", "chatId", "chatName", "evaluationName", "insightGradingCriteria", "lastUpdateDate" ], "title": "InsightEvaluationResultResponse", "type": "object" } ``` InsightEvaluationResultResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregationType | any | false | | Aggregation type. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AggregationType | false | | The type of the metric aggregation. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregationValue | any | false | | Aggregation value. None indicates that the aggregation failed. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [AggregationValue] | false | | [An individual record in an itemized metric aggregation.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatId | string | true | | Chat ID. | | chatName | any | true | | Chat name. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelLLMValidationId | any | false | | Custom Model LLM Validation ID if using custom model LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | any | false | | Evaluation dataset configuration ID. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetName | any | false | | Evaluation dataset name. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationName | string | true | maxLength: 5000 | Evaluation name. | | executionStatus | ExecutionStatus | false | | The execution status of the insight evaluation result. | | gradingResult | any | false | | The grading result for this insight evaluation result. If not specified, execution status is not COMPLETED. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GradingResult | false | | Grading result. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | Insight evaluation result ID. | | insightGradingCriteria | InsightGradingCriteria | true | | Insight grading criteria. | | lastUpdateDate | string(date-time) | true | | Last update date of the insight evaluation result (ISO 8601 formatted). | | llmId | any | false | | LLM ID used for this insight evaluation result. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmTestResultId | string | true | | LLM test result ID this insight evaluation result is associated to. | | maxNumPrompts | integer | true | | Number of prompts used in evaluation. | | metricName | any | false | | Name of the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptSamplingStrategy | PromptSamplingStrategy | true | | Prompt sampling strategy for maxNumPrompts. | ## InsightGradingCriteria ``` { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" } ``` InsightGradingCriteria ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passThreshold | integer | true | maximum: 100minimum: 0 | The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass. | ## InsightToEvalDatasetsCompatibility ``` { "description": "Insight to evaluation datasets compatibility.", "properties": { "incompatibleDatasets": { "description": "The list of incompatible datasets.", "items": { "description": "Dataset identifier.", "properties": { "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset, if any.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset.", "title": "datasetName", "type": "string" } }, "required": [ "datasetName", "datasetId" ], "title": "DatasetIdentifier", "type": "object" }, "title": "incompatibleDatasets", "type": "array" }, "insightName": { "description": "The name of the insight.", "title": "insightName", "type": "string" } }, "required": [ "insightName", "incompatibleDatasets" ], "title": "InsightToEvalDatasetsCompatibility", "type": "object" } ``` InsightToEvalDatasetsCompatibility ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | incompatibleDatasets | [DatasetIdentifier] | true | | The list of incompatible datasets. | | insightName | string | true | | The name of the insight. | ## InsightTypes ``` { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" } ``` InsightTypes ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | InsightTypes | string | false | | The type of insight. | ### Enumerated Values | Property | Value | | --- | --- | | InsightTypes | [Reference, Quality metric, Operational metric, Evaluation deployment, Custom metric, Nemo] | ## InsightsConfigurationWithAdditionalData ``` { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" } ``` InsightsConfigurationWithAdditionalData ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregationTypes | any | true | | The aggregation types used in the insights configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [AggregationType] | false | | [The type of the metric aggregation.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | costConfigurationId | any | false | | The ID of the cost configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customMetricId | any | false | | The ID of the custom metric (if using a custom metric). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelGuard | any | false | | Guard as configured in the custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelGuard | false | | Details of a guard as defined for the custom model. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelLLMValidationId | any | false | | The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | any | false | | The ID of the custom model deployment associated with the insight. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorResolution | any | false | | The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | any | false | | The ID of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | any | false | | The execution status of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExecutionStatus | false | | Job and entity execution status. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | extraMetricSettings | any | false | | Extra settings for the metric that do not reference other entities. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExtraMetricSettings | false | | Extra settings for the metric that do not reference other entities. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | insightName | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the insight. | | insightType | any | false | | The type of the insight. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | InsightTypes | false | | The type of insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isTransferable | boolean | false | | Indicates if insight can be transferred to production. | | llmId | any | false | | The LLM ID for OOTB metrics that use LLMs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmIsActive | any | false | | Whether the LLM is active. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmIsDeprecated | any | false | | Whether the LLM is deprecated and will be removed in a future release. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | any | false | | The ID of the model associated with deploymentId. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelPackageRegisteredModelId | any | false | | The ID of the registered model package associated with deploymentId. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | moderationConfiguration | any | false | | The moderation configuration associated with the insight configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | nemoMetricId | any | false | | The ID of the Nemo configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricId | any | false | | The ID of the ootb metric (if using an ootb metric). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricName | any | false | | The OOTB metric name. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBMetricInsightNames | false | | The Out-Of-The-Box metric name that can be used in the playground. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBAgenticMetricInsightNames | false | | The Out-Of-The-Box metric name that can be used in an Agentic playground. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OTELMetricInsightNames | false | | Metrics that can only be calculated using OTEL Trace/metric data. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | resultUnit | any | false | | The unit of measurement associated with the insight result. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | MetricUnit | false | | The unit of measurement associated with a metric. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sidecarModelMetricMetadata | any | false | | The metadata of the sidecar model metric (if using a sidecar model metric). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SidecarModelMetricMetadata | false | | The metadata of a sidecar model metric. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sidecarModelMetricValidationId | any | false | | The ID of the sidecar model metric validation (if using a sidecar model metric). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | any | false | | The stage (prompt or response) where insight is calculated at. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PipelineStage | false | | Enum that describes at which stage the metric may be calculated. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## Intervention ``` { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } ``` Intervention ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | ModerationAction | true | | The intervention strategy. | | message | string | true | minLength: 1minLength: 1 | The intervention message to replace the prediction when a guard condition is satisfied. | ## LLMBlueprintSnapshot ``` { "description": "A snapshot in time of a LLMBlueprint's functional parameters.", "properties": { "description": { "description": "The description of the LLMBlueprint at the time of snapshotting.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLMBlueprint for which the snapshot was produced.", "title": "id", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLMBlueprint at the time of snapshotting.", "title": "name", "type": "string" }, "playgroundId": { "description": "The playground id of the LLMBlueprint.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "snapshotDate": { "description": "The date when the snapshot was produced.", "format": "date-time", "title": "snapshotDate", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "id", "name", "description", "playgroundId", "promptType" ], "title": "LLMBlueprintSnapshot", "type": "object" } ``` LLMBlueprintSnapshot ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | The description of the LLMBlueprint at the time of snapshotting. | | id | string | true | | The ID of the LLMBlueprint for which the snapshot was produced. | | llmId | any | false | | The ID of the LLM selected for this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmSettings | any | false | | A key/value dictionary of LLM settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CommonLLMSettings | false | | The settings that are available for all non-custom LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelLLMSettings | false | | The settings that are available for custom model LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelChatLLMSettings | false | | The settings that are available for custom model LLMs used via chat completion interface. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the LLMBlueprint at the time of snapshotting. | | playgroundId | string | true | | The playground id of the LLMBlueprint. | | promptType | PromptType | true | | The prompting type of the LLMBlueprint at the time of snapshotting. | | snapshotDate | string(date-time) | false | | The date when the snapshot was produced. | | vectorDatabaseId | any | false | | The ID of the vector database linked to this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseSettings | any | false | | A key/value dictionary of vector database settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseSettings | false | | Vector database retrieval settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## LLMCostConfigurationResponse ``` { "description": "API request/response object for a cost configuration of a single LLM.", "properties": { "currencyCode": { "default": "USD", "description": "The arbitrary code code of the currency of `inputTokenPrice` and `outputTokenPrice`.", "maxLength": 7, "title": "currencyCode", "type": "string" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "inputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceInputTokenCount` input tokens.", "minimum": 0, "title": "inputTokenPrice", "type": "number" }, "llmId": { "description": "The ID of the LLM associated with this cost configuration.", "title": "llmId", "type": "string" }, "outputTokenPrice": { "default": 0.01, "description": "The price of processing `referenceOutputTokenCount` output tokens.", "minimum": 0, "title": "outputTokenPrice", "type": "number" }, "referenceInputTokenCount": { "default": 1000, "description": "The number of input tokens corresponding to `inputTokenPrice`.", "minimum": 0, "title": "referenceInputTokenCount", "type": "integer" }, "referenceOutputTokenCount": { "default": 1000, "description": "The number of output tokens corresponding to `outputTokenPrice`.", "minimum": 0, "title": "referenceOutputTokenCount", "type": "integer" } }, "required": [ "llmId" ], "title": "LLMCostConfigurationResponse", "type": "object" } ``` LLMCostConfigurationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | currencyCode | string | false | maxLength: 7 | The arbitrary code code of the currency of inputTokenPrice and outputTokenPrice. | | customModelLLMValidationId | any | false | | The ID of the custom model LLM validation (if using a custom model LLM). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputTokenPrice | number | false | minimum: 0 | The price of processing referenceInputTokenCount input tokens. | | llmId | string | true | | The ID of the LLM associated with this cost configuration. | | outputTokenPrice | number | false | minimum: 0 | The price of processing referenceOutputTokenCount output tokens. | | referenceInputTokenCount | integer | false | minimum: 0 | The number of input tokens corresponding to inputTokenPrice. | | referenceOutputTokenCount | integer | false | minimum: 0 | The number of output tokens corresponding to outputTokenPrice. | ## LLMTestConfigurationNonOOTBDatasetResponse ``` { "description": "Non out-of-the-box dataset used with an LLM test configuration.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals (For agentic workflows).", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "creationDate": { "description": "The creation date of the evaluation dataset configuration (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the evaluation dataset configuration.", "title": "creationUserId", "type": "string" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the evaluation dataset.", "title": "datasetName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration.", "title": "errorMessage" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the evaluation dataset configuration.", "title": "id", "type": "string" }, "name": { "description": "The name of the evaluation dataset configuration.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the evaluation dataset configuration.", "title": "playgroundId" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "rowsCount": { "description": "The rows count of the evaluation dataset.", "title": "rowsCount", "type": "integer" }, "size": { "description": "The size of the evaluation dataset (in bytes).", "title": "size", "type": "integer" }, "tenantId": { "description": "The ID of the DataRobot tenant this evaluation dataset configuration belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "toolCallsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls (For agentic workflows).", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case associated with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created the evaluation dataset configuration.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "size", "rowsCount", "useCaseId", "playgroundId", "datasetId", "datasetName", "promptColumnName", "responseColumnName", "toolCallsColumnName", "agentGoalsColumnName", "userName", "correctnessEnabled", "creationUserId", "creationDate", "tenantId", "executionStatus" ], "title": "LLMTestConfigurationNonOOTBDatasetResponse", "type": "object" } ``` LLMTestConfigurationNonOOTBDatasetResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | agentGoalsColumnName | any | true | | The name of the dataset column containing expected agent goals (For agentic workflows). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | correctnessEnabled | any | true | | Whether correctness is enabled for the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the evaluation dataset configuration (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created the evaluation dataset configuration. | | datasetId | string | true | | The ID of the evaluation dataset. | | datasetName | string | true | | The name of the evaluation dataset. | | errorMessage | any | false | | The error message associated with the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | ExecutionStatus | true | | The execution status of the evaluation dataset. | | id | string | true | | The ID of the evaluation dataset configuration. | | name | string | true | | The name of the evaluation dataset configuration. | | playgroundId | any | true | | The ID of the playground associated with the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | string | true | | The name of the dataset column containing the prompt text. | | responseColumnName | any | true | | The name of the dataset column containing the response text. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rowsCount | integer | true | | The rows count of the evaluation dataset. | | size | integer | true | | The size of the evaluation dataset (in bytes). | | tenantId | string(uuid4) | true | | The ID of the DataRobot tenant this evaluation dataset configuration belongs to. | | toolCallsColumnName | any | true | | The name of the dataset column containing expected tool calls (For agentic workflows). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseId | string | true | | The ID of the use case associated with the evaluation dataset configuration. | | userName | string | true | | The name of the user that created the evaluation dataset configuration. | ## LLMTestConfigurationOOTBDatasetResponse ``` { "description": "Out-of-the-box dataset used with an LLM test configuration.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "LLMTestConfigurationOOTBDatasetResponse", "type": "object" } ``` LLMTestConfigurationOOTBDatasetResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetName | OOTBDatasetName | true | | The name of the evaluation dataset. | | datasetUrl | any | true | | The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBDatasetUrl | false | | Out-of-the-box dataset URL. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the prompt column. | | responseColumnName | any | true | | The name of the response column, if present. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rowsCount | integer | true | | The number rows in the dataset. | | warning | any | false | | Warning about the content of the dataset. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## LLMTestConfigurationResponse ``` { "description": "API response object for a single LLMTestConfiguration.", "properties": { "creationDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The creation date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationDate" }, "creationUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who created the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationUserId" }, "datasetEvaluations": { "description": "The LLM test dataset evaluations.", "items": { "description": "Dataset evaluation.", "properties": { "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the dataset evaluation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 100, "description": "The max number of prompts to evaluate.", "exclusiveMinimum": 0, "maximum": 5000, "title": "maxNumPrompts", "type": "integer" }, "ootbDataset": { "anyOf": [ { "description": "Out-of-the-box dataset.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "OOTBDataset", "type": "object" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria", "evaluationDatasetName" ], "title": "DatasetEvaluationResponse", "type": "object" }, "title": "datasetEvaluations", "type": "array" }, "description": { "description": "The description of the LLM Test configuration.", "title": "description", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the LLM test configuration.", "title": "errorMessage" }, "id": { "description": "The ID of the LLM Test configuration.", "title": "id", "type": "string" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "lastUpdateDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The last update date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateDate" }, "lastUpdateUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who last updated the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateUserId" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "name": { "description": "The name of the LLM Test configuration.", "title": "name", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, the use case ID associated with the LLM Test configuration.", "title": "useCaseId" }, "warnings": { "description": "Warnings for this LLM test configuration.", "items": { "additionalProperties": { "type": "string" }, "propertyNames": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "type": "object" }, "title": "warnings", "type": "array" } }, "required": [ "id", "name", "description", "datasetEvaluations", "llmTestGradingCriteria", "isOutOfTheBoxTestConfiguration", "warnings" ], "title": "LLMTestConfigurationResponse", "type": "object" } ``` LLMTestConfigurationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | any | false | | The creation date of the LLM Test configuration. For OOTB LLM Test configurations this is null. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string(date-time) | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationUserId | any | false | | The ID of the user who created the LLM Test configuration. For OOTB LLM Test configurations this is null. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetEvaluations | [DatasetEvaluationResponse] | true | | The LLM test dataset evaluations. | | description | string | true | | The description of the LLM Test configuration. | | errorMessage | any | false | | The error message associated with the LLM test configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the LLM Test configuration. | | isOutOfTheBoxTestConfiguration | boolean | true | | Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration. | | lastUpdateDate | any | false | | The last update date of the LLM Test configuration. For OOTB LLM Test configurations this is null. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string(date-time) | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | lastUpdateUserId | any | false | | The ID of the user who last updated the LLM Test configuration. For OOTB LLM Test configurations this is null. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmTestGradingCriteria | LLMTestGradingCriteria | true | | The LLM test grading criteria. | | name | string | true | | The name of the LLM Test configuration. | | useCaseId | any | false | | If specified, the use case ID associated with the LLM Test configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | warnings | [object] | true | | Warnings for this LLM test configuration. | | » additionalProperties | string | false | | none | ## LLMTestConfigurationSupportedInsightsResponse ``` { "description": "Response model for supported insights.", "properties": { "datasetsCompatibility": { "description": "The list of insight to evaluation datasets compatibility.", "items": { "description": "Insight to evaluation datasets compatibility.", "properties": { "incompatibleDatasets": { "description": "The list of incompatible datasets.", "items": { "description": "Dataset identifier.", "properties": { "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset, if any.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset.", "title": "datasetName", "type": "string" } }, "required": [ "datasetName", "datasetId" ], "title": "DatasetIdentifier", "type": "object" }, "title": "incompatibleDatasets", "type": "array" }, "insightName": { "description": "The name of the insight.", "title": "insightName", "type": "string" } }, "required": [ "insightName", "incompatibleDatasets" ], "title": "InsightToEvalDatasetsCompatibility", "type": "object" }, "title": "datasetsCompatibility", "type": "array" }, "supportedInsightConfigurations": { "description": "The list of supported insight configurations for the LLM Tests.", "items": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "title": "supportedInsightConfigurations", "type": "array" } }, "required": [ "supportedInsightConfigurations", "datasetsCompatibility" ], "title": "LLMTestConfigurationSupportedInsightsResponse", "type": "object" } ``` LLMTestConfigurationSupportedInsightsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetsCompatibility | [InsightToEvalDatasetsCompatibility] | true | | The list of insight to evaluation datasets compatibility. | | supportedInsightConfigurations | [InsightsConfigurationWithAdditionalData] | true | | The list of supported insight configurations for the LLM Tests. | ## LLMTestConfigurationType ``` { "description": "Type of LLMTestConfiguration.", "enum": [ "ootb", "custom" ], "title": "LLMTestConfigurationType", "type": "string" } ``` LLMTestConfigurationType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | LLMTestConfigurationType | string | false | | Type of LLMTestConfiguration. | ### Enumerated Values | Property | Value | | --- | --- | | LLMTestConfigurationType | [ootb, custom] | ## LLMTestGradingCriteria ``` { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" } ``` LLMTestGradingCriteria ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passThreshold | integer | true | maximum: 100minimum: 0 | The percentage threshold for Pass results across dataset-insight pairs. | ## LLMTestResultResponse ``` { "description": "API response object for a single LLMTestResult.", "properties": { "creationDate": { "description": "LLM test result creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user that created this LLM test result.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM result.", "title": "creationUserName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message if the LLM Test Result failed.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error resolution message if the LLM Test Result failed.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result based on the llm test grading criteria. If not specified, execution status is not COMPLETED." }, "id": { "description": "LLM test result ID.", "title": "id", "type": "string" }, "insightEvaluationResults": { "description": "The Insight evaluation results.", "items": { "description": "API response object for a single InsightEvaluationResult.", "properties": { "aggregationType": { "anyOf": [ { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, { "type": "null" } ], "description": "Aggregation type." }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Aggregation value. None indicates that the aggregation failed.", "title": "aggregationValue" }, "chatId": { "description": "Chat ID.", "title": "chatId", "type": "string" }, "chatName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Chat name.", "title": "chatName" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Custom Model LLM Validation ID if using custom model LLM.", "title": "customModelLLMValidationId" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset configuration ID.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "Evaluation name.", "maxLength": 5000, "title": "evaluationName", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result for this insight evaluation result. If not specified, execution status is not COMPLETED." }, "id": { "description": "Insight evaluation result ID.", "title": "id", "type": "string" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "lastUpdateDate": { "description": "Last update date of the insight evaluation result (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM ID used for this insight evaluation result.", "title": "llmId" }, "llmTestResultId": { "description": "LLM test result ID this insight evaluation result is associated to.", "title": "llmTestResultId", "type": "string" }, "maxNumPrompts": { "description": "Number of prompts used in evaluation.", "title": "maxNumPrompts", "type": "integer" }, "metricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name of the metric.", "title": "metricName" }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "id", "llmTestResultId", "maxNumPrompts", "promptSamplingStrategy", "chatId", "chatName", "evaluationName", "insightGradingCriteria", "lastUpdateDate" ], "title": "InsightEvaluationResultResponse", "type": "object" }, "title": "insightEvaluationResults", "type": "array" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "llmBlueprintId": { "description": "LLM Blueprint ID.", "title": "llmBlueprintId", "type": "string" }, "llmBlueprintSnapshot": { "description": "A snapshot in time of a LLMBlueprint's functional parameters.", "properties": { "description": { "description": "The description of the LLMBlueprint at the time of snapshotting.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLMBlueprint for which the snapshot was produced.", "title": "id", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLMBlueprint at the time of snapshotting.", "title": "name", "type": "string" }, "playgroundId": { "description": "The playground id of the LLMBlueprint.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "snapshotDate": { "description": "The date when the snapshot was produced.", "format": "date-time", "title": "snapshotDate", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "id", "name", "description", "playgroundId", "promptType" ], "title": "LLMBlueprintSnapshot", "type": "object" }, "llmTestConfigurationId": { "description": "LLM test configuration ID this LLM result is associated to.", "title": "llmTestConfigurationId", "type": "string" }, "llmTestConfigurationName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "Name of the LLM test configuration this LLM result is associated to.", "title": "llmTestConfigurationName" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "llmTestSuiteId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM test suite ID to which the LLM test configuration is associated to.", "title": "llmTestSuiteId" }, "passPercentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The percentage of underlying insight evaluation results that have a PASS grading result. If not specified, execution status is not COMPLETED.", "title": "passPercentage" }, "useCaseId": { "description": "Use case ID this LLM test result belongs to.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "llmTestConfigurationId", "llmTestConfigurationName", "isOutOfTheBoxTestConfiguration", "useCaseId", "llmBlueprintId", "llmBlueprintSnapshot", "llmTestGradingCriteria", "executionStatus", "insightEvaluationResults", "creationDate", "creationUserId", "creationUserName" ], "title": "LLMTestResultResponse", "type": "object" } ``` LLMTestResultResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | LLM test result creation date (ISO 8601 formatted). | | creationUserId | string | true | | ID of the user that created this LLM test result. | | creationUserName | string | true | | The name of the user who created this LLM result. | | errorMessage | any | false | | The error message if the LLM Test Result failed. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorResolution | any | false | | The error resolution message if the LLM Test Result failed. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | ExecutionStatus | true | | The LLM Test execution status. | | gradingResult | any | false | | The grading result based on the llm test grading criteria. If not specified, execution status is not COMPLETED. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GradingResult | false | | Grading result. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | LLM test result ID. | | insightEvaluationResults | [InsightEvaluationResultResponse] | true | | The Insight evaluation results. | | isOutOfTheBoxTestConfiguration | boolean | true | | Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration. | | llmBlueprintId | string | true | | LLM Blueprint ID. | | llmBlueprintSnapshot | LLMBlueprintSnapshot | true | | A snapshot of the llm blueprint entity at the time of LLM Test execution. | | llmTestConfigurationId | string | true | | LLM test configuration ID this LLM result is associated to. | | llmTestConfigurationName | any | true | | Name of the LLM test configuration this LLM result is associated to. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmTestGradingCriteria | LLMTestGradingCriteria | true | | LLM test grading criteria. | | llmTestSuiteId | any | false | | LLM test suite ID to which the LLM test configuration is associated to. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passPercentage | any | false | | The percentage of underlying insight evaluation results that have a PASS grading result. If not specified, execution status is not COMPLETED. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseId | string | true | | Use case ID this LLM test result belongs to. | ## LLMTestSuiteResponse ``` { "description": "LLMTestSuite object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the LLM test suite.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM test suite.", "title": "id", "type": "string" }, "llmTestConfigurationIds": { "description": "The IDs of the LLM test configurations in this LLM test suite.", "items": { "type": "string" }, "title": "llmTestConfigurationIds", "type": "array" }, "name": { "description": "The name of the LLM test suite.", "title": "name", "type": "string" }, "useCaseId": { "description": "The ID of the use case associated with the LLM test suite.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "llmTestConfigurationIds", "creationDate", "creationUserId" ], "title": "LLMTestSuiteResponse", "type": "object" } ``` LLMTestSuiteResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the chat (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created the chat. | | description | string | true | | The description of the LLM test suite. | | id | string | true | | The ID of the LLM test suite. | | llmTestConfigurationIds | [string] | true | | The IDs of the LLM test configurations in this LLM test suite. | | name | string | true | | The name of the LLM test suite. | | useCaseId | string | true | | The ID of the use case associated with the LLM test suite. | ## ListCustomModelValidationSortQueryParam ``` { "description": "Sort order values for listing custom model validations.", "enum": [ "name", "-name", "deploymentName", "-deploymentName", "userName", "-userName", "creationDate", "-creationDate" ], "title": "ListCustomModelValidationSortQueryParam", "type": "string" } ``` ListCustomModelValidationSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ListCustomModelValidationSortQueryParam | string | false | | Sort order values for listing custom model validations. | ### Enumerated Values | Property | Value | | --- | --- | | ListCustomModelValidationSortQueryParam | [name, -name, deploymentName, -deploymentName, userName, -userName, creationDate, -creationDate] | ## ListEvaluationDatasetConfigurationResponse ``` { "description": "Paginated list of evaludation dataset configurations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single evaluation dataset configuration.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals (For agentic workflows).", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "creationDate": { "description": "The creation date of the evaluation dataset configuration (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the evaluation dataset configuration.", "title": "creationUserId", "type": "string" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the evaluation dataset.", "title": "datasetName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration.", "title": "errorMessage" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the evaluation dataset configuration.", "title": "id", "type": "string" }, "name": { "description": "The name of the evaluation dataset configuration.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the evaluation dataset configuration.", "title": "playgroundId" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "rowsCount": { "description": "The rows count of the evaluation dataset.", "title": "rowsCount", "type": "integer" }, "size": { "description": "The size of the evaluation dataset (in bytes).", "title": "size", "type": "integer" }, "tenantId": { "description": "The ID of the DataRobot tenant this evaluation dataset configuration belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "toolCallsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls (For agentic workflows).", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case associated with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created the evaluation dataset configuration.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "size", "rowsCount", "useCaseId", "playgroundId", "datasetId", "datasetName", "promptColumnName", "responseColumnName", "toolCallsColumnName", "agentGoalsColumnName", "userName", "correctnessEnabled", "creationUserId", "creationDate", "tenantId", "executionStatus" ], "title": "EvaluationDatasetConfigurationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListEvaluationDatasetConfigurationResponse", "type": "object" } ``` ListEvaluationDatasetConfigurationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [EvaluationDatasetConfigurationResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListEvaluationDatasetConfigurationsQueryParam ``` { "description": "Sort order values for listing evaluation dataset configurations.", "enum": [ "name", "-name", "creationUserId", "-creationUserId", "creationDate", "-creationDate", "datasetId", "-datasetId", "userName", "-userName", "datasetName", "-datasetName", "promptColumnName", "-promptColumnName", "responseColumnName", "-responseColumnName" ], "title": "ListEvaluationDatasetConfigurationsQueryParam", "type": "string" } ``` ListEvaluationDatasetConfigurationsQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ListEvaluationDatasetConfigurationsQueryParam | string | false | | Sort order values for listing evaluation dataset configurations. | ### Enumerated Values | Property | Value | | --- | --- | | ListEvaluationDatasetConfigurationsQueryParam | [name, -name, creationUserId, -creationUserId, creationDate, -creationDate, datasetId, -datasetId, userName, -userName, datasetName, -datasetName, promptColumnName, -promptColumnName, responseColumnName, -responseColumnName] | ## ListEvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse ``` { "description": "Paginated list of evaluation dataset metric aggregations, aggregated by LLM blueprint.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for multiple evaluation dataset metric aggregation\naggregated by llm blueprint.", "properties": { "aggregatedItemCount": { "description": "Number of items aggregated.", "title": "aggregatedItemCount", "type": "integer" }, "aggregatedItemDetails": { "description": "List of details for aggregated items.", "items": { "description": "Details for aggregated items.", "properties": { "chatId": { "description": "The ID of the chat associated with the metric aggregation.", "title": "chatId", "type": "string" }, "chatLink": { "description": "The link to the chat associated with the metric aggregation.", "title": "chatLink", "type": "string" }, "chatName": { "description": "The name of the chat associated with the metric aggregation.", "title": "chatName", "type": "string" }, "creationDate": { "description": "The creation date of the metric aggregation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the metric aggregation.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user that created the metric aggregation.", "title": "creationUserName", "type": "string" } }, "required": [ "chatId", "chatName", "chatLink", "creationDate", "creationUserId", "creationUserName" ], "title": "EvaluationDatasetMetricAggregationChatDetails", "type": "object" }, "title": "aggregatedItemDetails", "type": "array" }, "aggregationType": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "items": { "description": "Aggregated record of multiple of the same item across different metric aggregation runs.", "properties": { "count": { "description": "The number of metric aggregation items aggregated.", "title": "count", "type": "integer" }, "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value", "count" ], "title": "AggregatedAggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The aggregated value of the metric.", "title": "aggregationValue" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model's guard the metric aggregation belongs to.", "title": "customModelGuardId" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The dataset ID of the evaluation dataset configuration.", "title": "datasetId" }, "datasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The Data Registry dataset name of the evaluation dataset configuration.", "title": "datasetName" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration associated with the metric aggregation.", "title": "evaluationDatasetConfigurationId" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the metric aggregation.", "title": "llmBlueprintId", "type": "string" }, "metricName": { "description": "The name of the metric associated with the metric aggregation.", "title": "metricName", "type": "string" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "The name of the out-of-the-box dataset." }, "tenantId": { "description": "The ID of the tenant the metric aggregation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" } }, "required": [ "llmBlueprintId", "evaluationDatasetConfigurationId", "ootbDatasetName", "datasetId", "datasetName", "metricName", "aggregationValue", "aggregationType", "tenantId", "customModelGuardId", "aggregatedItemDetails", "aggregatedItemCount" ], "title": "EvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListEvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse", "type": "object" } ``` ListEvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [EvaluationDatasetMetricAggregationAggregatedByLLMBlueprintResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListEvaluationDatasetMetricAggregationResponse ``` { "description": "Paginated list of evaluation dataset metric aggregations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single evaluation dataset metric aggregation.", "properties": { "aggregationType": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "items": { "description": "Aggregated record of multiple of the same item across different metric aggregation runs.", "properties": { "count": { "description": "The number of metric aggregation items aggregated.", "title": "count", "type": "integer" }, "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value", "count" ], "title": "AggregatedAggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The aggregated value of the metric.", "title": "aggregationValue" }, "chatId": { "description": "The ID of the chat associated with the metric aggregation.", "title": "chatId", "type": "string" }, "chatLink": { "description": "The link to the chat associated with the metric aggregation.", "title": "chatLink", "type": "string" }, "chatName": { "description": "The name of the chat associated with the metric aggregation.", "title": "chatName", "type": "string" }, "creationDate": { "description": "The creation date of the metric aggregation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the metric aggregation.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user that created the metric aggregation.", "title": "creationUserName", "type": "string" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model's guard the metric aggregation belongs to.", "title": "customModelGuardId" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The dataset ID of the evaluation dataset configuration.", "title": "datasetId" }, "datasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The Data Registry dataset name of the evaluation dataset configuration.", "title": "datasetName" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration associated with the metric aggregation.", "title": "evaluationDatasetConfigurationId" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the metric aggregation.", "title": "llmBlueprintId", "type": "string" }, "metricName": { "description": "The name of the metric associated with the metric aggregation.", "title": "metricName", "type": "string" }, "ootbDatasetName": { "anyOf": [ { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, { "type": "null" } ], "description": "The name of the out-of-the-box dataset." }, "tenantId": { "description": "The ID of the tenant the metric aggregation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" } }, "required": [ "chatId", "chatName", "chatLink", "creationDate", "creationUserId", "creationUserName", "llmBlueprintId", "evaluationDatasetConfigurationId", "ootbDatasetName", "datasetId", "datasetName", "metricName", "aggregationValue", "aggregationType", "tenantId", "customModelGuardId" ], "title": "EvaluationDatasetMetricAggregationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListEvaluationDatasetMetricAggregationResponse", "type": "object" } ``` ListEvaluationDatasetMetricAggregationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [EvaluationDatasetMetricAggregationResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListEvaluationDatasetMetricAggregationSortQueryParam ``` { "description": "Sort order values for listing evaluation dataset metric aggregations.", "enum": [ "metricName", "-metricName", "aggregationValue", "-aggregationValue", "datasetName", "-datasetName", "datasetId", "-datasetId", "creationUserId", "-creationUserId", "creationUserName", "-creationUserName", "creationDate", "-creationDate", "evaluationDatasetConfigurationId", "-evaluationDatasetConfigurationId" ], "title": "ListEvaluationDatasetMetricAggregationSortQueryParam", "type": "string" } ``` ListEvaluationDatasetMetricAggregationSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ListEvaluationDatasetMetricAggregationSortQueryParam | string | false | | Sort order values for listing evaluation dataset metric aggregations. | ### Enumerated Values | Property | Value | | --- | --- | | ListEvaluationDatasetMetricAggregationSortQueryParam | [metricName, -metricName, aggregationValue, -aggregationValue, datasetName, -datasetName, datasetId, -datasetId, creationUserId, -creationUserId, creationUserName, -creationUserName, creationDate, -creationDate, evaluationDatasetConfigurationId, -evaluationDatasetConfigurationId] | ## ListEvaluationDatasetMetricAggregationUniqueFieldValuesResponse ``` { "description": "Paginated list of evaluation dataset metric aggregations with unique computed metrics.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single unique computed metric.", "properties": { "uniqueFieldValue": { "description": "The unique value associated with the metric aggregation.", "title": "uniqueFieldValue", "type": "string" } }, "required": [ "uniqueFieldValue" ], "title": "EvaluationDatasetMetricAggregationUniqueFieldValuesResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListEvaluationDatasetMetricAggregationUniqueFieldValuesResponse", "type": "object" } ``` ListEvaluationDatasetMetricAggregationUniqueFieldValuesResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [EvaluationDatasetMetricAggregationUniqueFieldValuesResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListLLMTestConfigurationNonOOTBDatasetsResponse ``` { "description": "Paginated list of non-OOTB datasets for use with LLM test configurations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "Non out-of-the-box dataset used with an LLM test configuration.", "properties": { "agentGoalsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected agent goals (For agentic workflows).", "title": "agentGoalsColumnName" }, "correctnessEnabled": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "deprecated": true, "description": "Whether correctness is enabled for the evaluation dataset configuration.", "title": "correctnessEnabled" }, "creationDate": { "description": "The creation date of the evaluation dataset configuration (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the evaluation dataset configuration.", "title": "creationUserId", "type": "string" }, "datasetId": { "description": "The ID of the evaluation dataset.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the evaluation dataset.", "title": "datasetName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration.", "title": "errorMessage" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the evaluation dataset configuration.", "title": "id", "type": "string" }, "name": { "description": "The name of the evaluation dataset configuration.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the evaluation dataset configuration.", "title": "playgroundId" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing the response text.", "title": "responseColumnName" }, "rowsCount": { "description": "The rows count of the evaluation dataset.", "title": "rowsCount", "type": "integer" }, "size": { "description": "The size of the evaluation dataset (in bytes).", "title": "size", "type": "integer" }, "tenantId": { "description": "The ID of the DataRobot tenant this evaluation dataset configuration belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "toolCallsColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset column containing expected tool calls (For agentic workflows).", "title": "toolCallsColumnName" }, "useCaseId": { "description": "The ID of the use case associated with the evaluation dataset configuration.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created the evaluation dataset configuration.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "size", "rowsCount", "useCaseId", "playgroundId", "datasetId", "datasetName", "promptColumnName", "responseColumnName", "toolCallsColumnName", "agentGoalsColumnName", "userName", "correctnessEnabled", "creationUserId", "creationDate", "tenantId", "executionStatus" ], "title": "LLMTestConfigurationNonOOTBDatasetResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMTestConfigurationNonOOTBDatasetsResponse", "type": "object" } ``` ListLLMTestConfigurationNonOOTBDatasetsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [LLMTestConfigurationNonOOTBDatasetResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListLLMTestConfigurationOOTBDatasetsResponse ``` { "description": "Paginated list of OOTB datasets for use with LLM test configurations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "Out-of-the-box dataset used with an LLM test configuration.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "LLMTestConfigurationOOTBDatasetResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMTestConfigurationOOTBDatasetsResponse", "type": "object" } ``` ListLLMTestConfigurationOOTBDatasetsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [LLMTestConfigurationOOTBDatasetResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListLLMTestConfigurationsResponse ``` { "description": "Paginated list of LLM test configurations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single LLMTestConfiguration.", "properties": { "creationDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The creation date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationDate" }, "creationUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who created the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "creationUserId" }, "datasetEvaluations": { "description": "The LLM test dataset evaluations.", "items": { "description": "Dataset evaluation.", "properties": { "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the dataset evaluation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration for this dataset evaluation.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "The name of the evaluation. This name should provide context regarding what is being evaluated.", "maxLength": 5000, "minLength": 1, "title": "evaluationName", "type": "string" }, "insightConfiguration": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "maxNumPrompts": { "default": 100, "description": "The max number of prompts to evaluate.", "exclusiveMinimum": 0, "maximum": 5000, "title": "maxNumPrompts", "type": "integer" }, "ootbDataset": { "anyOf": [ { "description": "Out-of-the-box dataset.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "OOTBDataset", "type": "object" }, { "type": "null" } ], "description": "Out-of-the-box evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "evaluationName", "insightConfiguration", "insightGradingCriteria", "evaluationDatasetName" ], "title": "DatasetEvaluationResponse", "type": "object" }, "title": "datasetEvaluations", "type": "array" }, "description": { "description": "The description of the LLM Test configuration.", "title": "description", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the LLM test configuration.", "title": "errorMessage" }, "id": { "description": "The ID of the LLM Test configuration.", "title": "id", "type": "string" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "lastUpdateDate": { "anyOf": [ { "format": "date-time", "type": "string" }, { "type": "null" } ], "description": "The last update date of the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateDate" }, "lastUpdateUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user who last updated the LLM Test configuration. For OOTB LLM Test configurations this is null.", "title": "lastUpdateUserId" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "name": { "description": "The name of the LLM Test configuration.", "title": "name", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, the use case ID associated with the LLM Test configuration.", "title": "useCaseId" }, "warnings": { "description": "Warnings for this LLM test configuration.", "items": { "additionalProperties": { "type": "string" }, "propertyNames": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "type": "object" }, "title": "warnings", "type": "array" } }, "required": [ "id", "name", "description", "datasetEvaluations", "llmTestGradingCriteria", "isOutOfTheBoxTestConfiguration", "warnings" ], "title": "LLMTestConfigurationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMTestConfigurationsResponse", "type": "object" } ``` ListLLMTestConfigurationsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [LLMTestConfigurationResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListLLMTestResultResponse ``` { "description": "Paginated list of LLM test results.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single LLMTestResult.", "properties": { "creationDate": { "description": "LLM test result creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user that created this LLM test result.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM result.", "title": "creationUserName", "type": "string" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message if the LLM Test Result failed.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error resolution message if the LLM Test Result failed.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result based on the llm test grading criteria. If not specified, execution status is not COMPLETED." }, "id": { "description": "LLM test result ID.", "title": "id", "type": "string" }, "insightEvaluationResults": { "description": "The Insight evaluation results.", "items": { "description": "API response object for a single InsightEvaluationResult.", "properties": { "aggregationType": { "anyOf": [ { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, { "type": "null" } ], "description": "Aggregation type." }, "aggregationValue": { "anyOf": [ { "type": "number" }, { "items": { "description": "An individual record in an itemized metric aggregation.", "properties": { "item": { "description": "The name of the item.", "title": "item", "type": "string" }, "value": { "description": "The value associated with the item.", "title": "value", "type": "number" } }, "required": [ "item", "value" ], "title": "AggregationValue", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Aggregation value. None indicates that the aggregation failed.", "title": "aggregationValue" }, "chatId": { "description": "Chat ID.", "title": "chatId", "type": "string" }, "chatName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Chat name.", "title": "chatName" }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Custom Model LLM Validation ID if using custom model LLM.", "title": "customModelLLMValidationId" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset configuration ID.", "title": "evaluationDatasetConfigurationId" }, "evaluationDatasetName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "Evaluation dataset name.", "title": "evaluationDatasetName" }, "evaluationName": { "description": "Evaluation name.", "maxLength": 5000, "title": "evaluationName", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "gradingResult": { "anyOf": [ { "description": "Grading result.", "enum": [ "PASS", "FAIL" ], "title": "GradingResult", "type": "string" }, { "type": "null" } ], "description": "The grading result for this insight evaluation result. If not specified, execution status is not COMPLETED." }, "id": { "description": "Insight evaluation result ID.", "title": "id", "type": "string" }, "insightGradingCriteria": { "description": "Grading criteria for an insight.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass result. Greater than or equal to this threshold indicates a Pass.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "InsightGradingCriteria", "type": "object" }, "lastUpdateDate": { "description": "Last update date of the insight evaluation result (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM ID used for this insight evaluation result.", "title": "llmId" }, "llmTestResultId": { "description": "LLM test result ID this insight evaluation result is associated to.", "title": "llmTestResultId", "type": "string" }, "maxNumPrompts": { "description": "Number of prompts used in evaluation.", "title": "maxNumPrompts", "type": "integer" }, "metricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name of the metric.", "title": "metricName" }, "promptSamplingStrategy": { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } }, "required": [ "id", "llmTestResultId", "maxNumPrompts", "promptSamplingStrategy", "chatId", "chatName", "evaluationName", "insightGradingCriteria", "lastUpdateDate" ], "title": "InsightEvaluationResultResponse", "type": "object" }, "title": "insightEvaluationResults", "type": "array" }, "isOutOfTheBoxTestConfiguration": { "description": "Identifies the LLM Test configuration as an out-of-the-box (OOTB) test configuration.", "title": "isOutOfTheBoxTestConfiguration", "type": "boolean" }, "llmBlueprintId": { "description": "LLM Blueprint ID.", "title": "llmBlueprintId", "type": "string" }, "llmBlueprintSnapshot": { "description": "A snapshot in time of a LLMBlueprint's functional parameters.", "properties": { "description": { "description": "The description of the LLMBlueprint at the time of snapshotting.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLMBlueprint for which the snapshot was produced.", "title": "id", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLMBlueprint at the time of snapshotting.", "title": "name", "type": "string" }, "playgroundId": { "description": "The playground id of the LLMBlueprint.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "snapshotDate": { "description": "The date when the snapshot was produced.", "format": "date-time", "title": "snapshotDate", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "id", "name", "description", "playgroundId", "promptType" ], "title": "LLMBlueprintSnapshot", "type": "object" }, "llmTestConfigurationId": { "description": "LLM test configuration ID this LLM result is associated to.", "title": "llmTestConfigurationId", "type": "string" }, "llmTestConfigurationName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "Name of the LLM test configuration this LLM result is associated to.", "title": "llmTestConfigurationName" }, "llmTestGradingCriteria": { "description": "Grading criteria for the LLM Test configuration.", "properties": { "passThreshold": { "description": "The percentage threshold for Pass results across dataset-insight pairs.", "maximum": 100, "minimum": 0, "title": "passThreshold", "type": "integer" } }, "required": [ "passThreshold" ], "title": "LLMTestGradingCriteria", "type": "object" }, "llmTestSuiteId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "LLM test suite ID to which the LLM test configuration is associated to.", "title": "llmTestSuiteId" }, "passPercentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The percentage of underlying insight evaluation results that have a PASS grading result. If not specified, execution status is not COMPLETED.", "title": "passPercentage" }, "useCaseId": { "description": "Use case ID this LLM test result belongs to.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "llmTestConfigurationId", "llmTestConfigurationName", "isOutOfTheBoxTestConfiguration", "useCaseId", "llmBlueprintId", "llmBlueprintSnapshot", "llmTestGradingCriteria", "executionStatus", "insightEvaluationResults", "creationDate", "creationUserId", "creationUserName" ], "title": "LLMTestResultResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMTestResultResponse", "type": "object" } ``` ListLLMTestResultResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [LLMTestResultResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListLLMTestSuiteSortQueryParam ``` { "description": "Sort order values for listing chats.", "enum": [ "name", "-name", "creationDate", "-creationDate" ], "title": "ListLLMTestSuiteSortQueryParam", "type": "string" } ``` ListLLMTestSuiteSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ListLLMTestSuiteSortQueryParam | string | false | | Sort order values for listing chats. | ### Enumerated Values | Property | Value | | --- | --- | | ListLLMTestSuiteSortQueryParam | [name, -name, creationDate, -creationDate] | ## ListLLMTestSuitesResponse ``` { "description": "Paginated list of LLM test suites.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "LLMTestSuite object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the LLM test suite.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM test suite.", "title": "id", "type": "string" }, "llmTestConfigurationIds": { "description": "The IDs of the LLM test configurations in this LLM test suite.", "items": { "type": "string" }, "title": "llmTestConfigurationIds", "type": "array" }, "name": { "description": "The name of the LLM test suite.", "title": "name", "type": "string" }, "useCaseId": { "description": "The ID of the use case associated with the LLM test suite.", "title": "useCaseId", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "llmTestConfigurationIds", "creationDate", "creationUserId" ], "title": "LLMTestSuiteResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMTestSuitesResponse", "type": "object" } ``` ListLLMTestSuitesResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [LLMTestSuiteResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListSidecarModelMetricValidationnResponse ``` { "description": "Paginated list of sidecar model metric validations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single sidecar model metric validation.", "properties": { "citationsPrefixColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The column name prefix the custom model uses for citation inputs.", "title": "citationsPrefixColumnName" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the sidecar model metric." }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the sidecar model metric validation.", "title": "playgroundId" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "playgroundId", "citationsPrefixColumnName", "promptColumnName", "responseColumnName", "expectedResponseColumnName" ], "title": "SidecarModelMetricValidationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListSidecarModelMetricValidationnResponse", "type": "object" } ``` ListSidecarModelMetricValidationnResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [SidecarModelMetricValidationResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## MetricUnit ``` { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" } ``` MetricUnit ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | MetricUnit | string | false | | The unit of measurement associated with a metric. | ### Enumerated Values | Property | Value | | --- | --- | | MetricUnit | [s, ms, %] | ## ModerationAction ``` { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" } ``` ModerationAction ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ModerationAction | string | false | | The moderation strategy. | ### Enumerated Values | Property | Value | | --- | --- | | ModerationAction | [block, report, reportAndBlock] | ## ModerationConfigurationWithID ``` { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" } ``` ModerationConfigurationWithID ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | guardConditions | [GuardCondition] | true | maxItems: 1minItems: 1 | The guard conditions associated with a metric. | | intervention | Intervention | true | | The intervention specific moderation configuration. | ## ModerationConfigurationWithoutID ``` { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" } ``` ModerationConfigurationWithoutID ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | guardConditions | [GuardCondition] | true | maxItems: 1minItems: 1 | The guard conditions associated with a metric. | | intervention | Intervention | true | | The intervention specific moderation configuration. | ## OOTBAgenticMetricInsightNames ``` { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" } ``` OOTBAgenticMetricInsightNames ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OOTBAgenticMetricInsightNames | string | false | | The Out-Of-The-Box metric name that can be used in an Agentic playground. | ### Enumerated Values | Property | Value | | --- | --- | | OOTBAgenticMetricInsightNames | [tool_call_accuracy, agent_goal_accuracy_with_reference] | ## OOTBDataset ``` { "description": "Out-of-the-box dataset.", "properties": { "datasetName": { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" }, "datasetUrl": { "anyOf": [ { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" }, { "type": "null" } ], "description": "The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets." }, "promptColumnName": { "description": "The name of the prompt column.", "maxLength": 5000, "minLength": 1, "title": "promptColumnName", "type": "string" }, "responseColumnName": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the response column, if present.", "title": "responseColumnName" }, "rowsCount": { "description": "The number rows in the dataset.", "title": "rowsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the content of the dataset.", "title": "warning" } }, "required": [ "datasetName", "datasetUrl", "promptColumnName", "responseColumnName", "rowsCount" ], "title": "OOTBDataset", "type": "object" } ``` OOTBDataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetName | OOTBDatasetName | true | | The name of the evaluation dataset. | | datasetUrl | any | true | | The public URL of the evaluation dataset. This applies only to our predefined public evaluation datasets. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBDatasetUrl | false | | Out-of-the-box dataset URL. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the prompt column. | | responseColumnName | any | true | | The name of the response column, if present. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rowsCount | integer | true | | The number rows in the dataset. | | warning | any | false | | Warning about the content of the dataset. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## OOTBDatasetName ``` { "description": "Out-of-the-box dataset name.", "enum": [ "jailbreak-v1.csv", "bbq-lite-age-v1.csv", "bbq-lite-gender-v1.csv", "bbq-lite-race-ethnicity-v1.csv", "bbq-lite-religion-v1.csv", "bbq-lite-disability-status-v1.csv", "bbq-lite-sexual-orientation-v1.csv", "bbq-lite-nationality-v1.csv", "bbq-lite-ses-v1.csv", "completeness-parent-v1.csv", "completeness-grandparent-v1.csv", "completeness-great-grandparent-v1.csv", "pii-v1.csv", "toxicity-v2.csv", "jbbq-age-v1.csv", "jbbq-gender-identity-v1.csv", "jbbq-physical-appearance-v1.csv", "jbbq-disability-status-v1.csv", "jbbq-sexual-orientation-v1.csv" ], "title": "OOTBDatasetName", "type": "string" } ``` OOTBDatasetName ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OOTBDatasetName | string | false | | Out-of-the-box dataset name. | ### Enumerated Values | Property | Value | | --- | --- | | OOTBDatasetName | [jailbreak-v1.csv, bbq-lite-age-v1.csv, bbq-lite-gender-v1.csv, bbq-lite-race-ethnicity-v1.csv, bbq-lite-religion-v1.csv, bbq-lite-disability-status-v1.csv, bbq-lite-sexual-orientation-v1.csv, bbq-lite-nationality-v1.csv, bbq-lite-ses-v1.csv, completeness-parent-v1.csv, completeness-grandparent-v1.csv, completeness-great-grandparent-v1.csv, pii-v1.csv, toxicity-v2.csv, jbbq-age-v1.csv, jbbq-gender-identity-v1.csv, jbbq-physical-appearance-v1.csv, jbbq-disability-status-v1.csv, jbbq-sexual-orientation-v1.csv] | ## OOTBDatasetUrl ``` { "description": "Out-of-the-box dataset URL.", "enum": [ "https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv", "https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv" ], "title": "OOTBDatasetUrl", "type": "string" } ``` OOTBDatasetUrl ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OOTBDatasetUrl | string | false | | Out-of-the-box dataset URL. | ### Enumerated Values | Property | Value | | --- | --- | | OOTBDatasetUrl | [https://s3.amazonaws.com/datarobot_public_datasets/genai/jailbreak-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-age-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-gender-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-race-ethnicity-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-religion-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-disability-status-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-sexual-orientation-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-nationality-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/bbq-lite-ses-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-parent-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-grandparent-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/completeness-great-grandparent-v1.csv, https://s3.amazonaws.com/datarobot_public_datasets/genai/pii-v1.csv] | ## OOTBMetricConfigurationResponse ``` { "description": "API response object for a single OOTB metric.", "properties": { "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "customOotbMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The custom OOTB metric name to be associated with the OOTB metric.", "title": "customOotbMetricName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the OOTB metric configuration.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "isAgentic": { "default": false, "description": "Whether the OOTB metric configuration is specific to agentic workflows.", "title": "isAgentic", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM to use for `correctness` and `faithfulness` metrics.", "title": "llmId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the OOTB metric." }, "ootbMetricConfigurationId": { "description": "The ID of OOTB metric.", "title": "ootbMetricConfigurationId", "type": "string" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ], "description": "The name of the OOTB metric.", "title": "ootbMetricName" } }, "required": [ "ootbMetricName", "ootbMetricConfigurationId", "executionStatus" ], "title": "OOTBMetricConfigurationResponse", "type": "object" } ``` OOTBMetricConfigurationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelLLMValidationId | any | false | | The ID of the custom model LLM validation (if using a custom model LLM). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customOotbMetricName | any | false | | The custom OOTB metric name to be associated with the OOTB metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the OOTB metric configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorResolution | any | false | | The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [InsightErrorResolution] | false | | [Error type linking directly to the field name that is related to the error.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | ExecutionStatus | true | | The execution status of the OOTB metric configuration. | | extraMetricSettings | any | false | | Extra settings for the metric that do not reference other entities. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExtraMetricSettings | false | | Extra settings for the metric that do not reference other entities. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isAgentic | boolean | false | | Whether the OOTB metric configuration is specific to agentic workflows. | | llmId | any | false | | The ID of the LLM to use for correctness and faithfulness metrics. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | moderationConfiguration | any | false | | The moderation configuration to be associated with the OOTB metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricConfigurationId | string | true | | The ID of OOTB metric. | | ootbMetricName | any | true | | The name of the OOTB metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBMetricInsightNames | false | | The Out-Of-The-Box metric name that can be used in the playground. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBAgenticMetricInsightNames | false | | The Out-Of-The-Box metric name that can be used in an Agentic playground. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OTELMetricInsightNames | false | | Metrics that can only be calculated using OTEL Trace/metric data. | ## OOTBMetricInsightNames ``` { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" } ``` OOTBMetricInsightNames ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OOTBMetricInsightNames | string | false | | The Out-Of-The-Box metric name that can be used in the playground. | ### Enumerated Values | Property | Value | | --- | --- | | OOTBMetricInsightNames | [latency, citations, rouge_1, faithfulness, correctness, prompt_tokens, response_tokens, document_tokens, all_tokens, jailbreak_violation, toxicity_violation, pii_violation, exact_match, starts_with, contains] | ## OTELMetricInsightNames ``` { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ``` OTELMetricInsightNames ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OTELMetricInsightNames | string | false | | Metrics that can only be calculated using OTEL Trace/metric data. | ### Enumerated Values | Property | Value | | --- | --- | | OTELMetricInsightNames | [agent_latency, agent_tokens, agent_cost] | ## PipelineStage ``` { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" } ``` PipelineStage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | PipelineStage | string | false | | Enum that describes at which stage the metric may be calculated. | ### Enumerated Values | Property | Value | | --- | --- | | PipelineStage | [prompt_pipeline, response_pipeline] | ## PromptSamplingStrategy ``` { "description": "The prompt sampling strategy for the evaluation dataset configuration.", "enum": [ "random_without_replacement", "first_n_rows" ], "title": "PromptSamplingStrategy", "type": "string" } ``` PromptSamplingStrategy ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | PromptSamplingStrategy | string | false | | The prompt sampling strategy for the evaluation dataset configuration. | ### Enumerated Values | Property | Value | | --- | --- | | PromptSamplingStrategy | [random_without_replacement, first_n_rows] | ## PromptType ``` { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" } ``` PromptType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | PromptType | string | false | | Determines whether chat history is submitted as context to the user prompt. | ### Enumerated Values | Property | Value | | --- | --- | | PromptType | [CHAT_HISTORY_AWARE, ONE_TIME_PROMPT] | ## RetrievalMode ``` { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" } ``` RetrievalMode ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | RetrievalMode | string | false | | Retrieval modes for vector databases. | ### Enumerated Values | Property | Value | | --- | --- | | RetrievalMode | [similarity, maximal_marginal_relevance] | ## SidecarModelMetricMetadata ``` { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" } ``` SidecarModelMetricMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | expectedResponseColumnName | any | false | | The name of the column the custom model uses for expected response text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | any | false | | The name of the column the custom model uses for prompt text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responseColumnName | any | false | | The name of the column the custom model uses for response text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetColumnName | any | true | | The name of the column the custom model uses for prediction output. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## SidecarModelMetricValidationResponse ``` { "description": "API response object for a single sidecar model metric validation.", "properties": { "citationsPrefixColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The column name prefix the custom model uses for citation inputs.", "title": "citationsPrefixColumnName" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the sidecar model metric." }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "playgroundId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the playground associated with the sidecar model metric validation.", "title": "playgroundId" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "playgroundId", "citationsPrefixColumnName", "promptColumnName", "responseColumnName", "expectedResponseColumnName" ], "title": "SidecarModelMetricValidationResponse", "type": "object" } ``` SidecarModelMetricValidationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | citationsPrefixColumnName | any | true | | The column name prefix the custom model uses for citation inputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the custom model validation (ISO 8601 formatted). | | deploymentAccessData | any | true | | The parameters used for accessing the deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentAccessData | false | | Add authorization_header to avoid breaking change to API. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the custom model deployment. | | deploymentName | any | false | | The name of the custom model deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the validation error (if the validation failed). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorResolution | any | false | | The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [InsightErrorResolution] | false | | [Error type linking directly to the field name that is related to the error.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | expectedResponseColumnName | any | true | | The name of the column the custom model uses for expected response text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model validation. | | modelId | string | true | | The ID of the model used in the deployment. | | moderationConfiguration | any | false | | The moderation configuration associated with the sidecar model metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the validated custom model. | | playgroundId | any | true | | The ID of the playground associated with the sidecar model metric validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionTimeout | integer | true | | The timeout in seconds for the prediction API used in this custom model validation. | | promptColumnName | any | true | | The name of the column the custom model uses for prompt text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responseColumnName | any | true | | The name of the column the custom model uses for response text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetColumnName | string | true | | The name of the column the custom model uses for prediction output. | | tenantId | string(uuid4) | true | | The ID of the tenant the custom model validation belongs to. | | useCaseId | any | true | | The ID of the use case associated with the validated custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | userId | string | true | | The ID of the user that created this custom model validation. | | userName | any | false | | The name of the user that created this custom model validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationStatus | CustomModelValidationStatus | true | | The status of the custom model validation. | ## SupportedDeploymentType ``` { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" } ``` SupportedDeploymentType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | SupportedDeploymentType | string | false | | The type of the target output a DataRobot deployment produces. | ### Enumerated Values | Property | Value | | --- | --- | | SupportedDeploymentType | [TEXT_GENERATION, VECTOR_DATABASE, UNSTRUCTURED, REGRESSION, MULTICLASS, BINARY, NOT_SUPPORTED] | ## SyntheticEvaluationDatasetGenerationRequest ``` { "description": "The body of the \"Generate synthetic evaluation dataset\" request.", "properties": { "datasetName": { "anyOf": [ { "maxLength": 255, "type": "string" }, { "type": "null" } ], "description": "The name to use for the generated dataset.", "title": "datasetName" }, "language": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The language to use for the generated dataset.", "title": "language" }, "llmId": { "description": "The ID of the LLM to use for synthetic dataset generation.", "title": "llmId", "type": "string" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "If specified, uses these LLM settings for the prompt and updates the settings of the corresponding chat or LLM blueprint to use these LLM settings.", "title": "llmSettings" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database to use for synthetic dataset generation.", "title": "vectorDatabaseId" } }, "required": [ "llmId" ], "title": "SyntheticEvaluationDatasetGenerationRequest", "type": "object" } ``` SyntheticEvaluationDatasetGenerationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetName | any | false | | The name to use for the generated dataset. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 255 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | language | any | false | | The language to use for the generated dataset. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmId | string | true | | The ID of the LLM to use for synthetic dataset generation. | | llmSettings | any | false | | If specified, uses these LLM settings for the prompt and updates the settings of the corresponding chat or LLM blueprint to use these LLM settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CommonLLMSettings | false | | The settings that are available for all non-custom LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelLLMSettings | false | | The settings that are available for custom model LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelChatLLMSettings | false | | The settings that are available for custom model LLMs used via chat completion interface. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | any | false | | The ID of the vector database to use for synthetic dataset generation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## SyntheticEvaluationDatasetGenerationResponse ``` { "description": "The body of the \"Create synthetic evaluation dataset\" response.", "properties": { "datasetId": { "description": "The ID of the created dataset.", "title": "datasetId", "type": "string" }, "promptColumnName": { "description": "The name of the dataset column containing the prompt text.", "title": "promptColumnName", "type": "string" }, "responseColumnName": { "description": "The name of the dataset column containing the response text.", "title": "responseColumnName", "type": "string" } }, "required": [ "datasetId", "promptColumnName", "responseColumnName" ], "title": "SyntheticEvaluationDatasetGenerationResponse", "type": "object" } ``` SyntheticEvaluationDatasetGenerationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the created dataset. | | promptColumnName | string | true | | The name of the dataset column containing the prompt text. | | responseColumnName | string | true | | The name of the dataset column containing the response text. | ## ToolCallAccuracySettings ``` { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" } ``` ToolCallAccuracySettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | argumentComparison | ArgumentMatchMode | true | | Setting defining how arguments of tool calls should be compared during the metric computation. | ## ValidationError ``` { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" } ``` ValidationError ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | loc | [anyOf] | true | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | msg | string | true | | none | | type | string | true | | none | ## VectorDatabaseRetrievers ``` { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } ``` VectorDatabaseRetrievers ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | VectorDatabaseRetrievers | string | false | | The method used to retrieve relevant chunks from the vector database. | ### Enumerated Values | Property | Value | | --- | --- | | VectorDatabaseRetrievers | [SINGLE_LOOKUP_RETRIEVER, CONVERSATIONAL_RETRIEVER, MULTI_STEP_RETRIEVER] | ## VectorDatabaseSettings ``` { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" } ``` VectorDatabaseSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addNeighborChunks | boolean | false | | Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1. | | maxDocumentsRetrievedPerPrompt | any | false | | The maximum number of chunks to retrieve from the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 10minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxTokens | any | false | | The maximum number of tokens to retrieve from the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 51200minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maximalMarginalRelevanceLambda | number | false | maximum: 1minimum: 0 | Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0). | | retrievalMode | RetrievalMode | false | | The retrieval mode to use. Similarity search or maximal marginal relevance. | | retriever | VectorDatabaseRetrievers | false | | The method used to retrieve relevant chunks from the vector database. | --- # Analytics URL: https://docs.datarobot.com/en/docs/api/reference/public-api/analytics.html > Use analytics endpoints to retrieve and audit and event logs. # Analytics Use analytics endpoints to retrieve and audit and event logs. ## Retrieve one page of audit log records Operation path: `GET /api/v2/eventLogs/` Authentication requirements: `BearerAuth` Retrieve one page of audit log records. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | query | string | false | The project to select log records for. | | userId | query | string | false | The user to select log records for. | | orgId | query | string | false | The organization to select log records for. | | event | query | string | false | The event type of records. | | minTimestamp | query | string(date-time) | false | The lower bound for timestamps. e.g., '2016-12-13T11:12:13.141516Z'. | | maxTimestamp | query | string(date-time) | false | The upper bound for timestamps. e.g., '2016-12-13T11:12:13.141516Z'. | | offset | query | integer | false | This many results will be skipped. Defaults to 0. | | order | query | string | false | The order of the results. Defaults to descending. | | includeIdentifyingFields | query | string | false | Indicates if identifying information like user names, project names, etc. should be included or not. Defaults to True. | | auditReportType | query | string | false | Indicates which type of event to return - must be one of APP_USAGE for application related events (i.e., Project Created, Dataset Uploaded, etc.) or ADMIN_USAGE for admin related events (i.e., Reset API Token for User, Organization Created, etc.). If not provided, all events will be returned by default. | ### Enumerated Values | Parameter | Value | | --- | --- | | event | [ACL synchronization cycle completed, ACL synchronized for a connector, ADLS OAuth Failed, ADLS OAuth Token Obtained, ADLS OAuth Token Renewal Succeeded, ADLS OAuth User Login Started, ADLS OAuth User Login Succeeded, API Key Created, API Key Deleted, API Key Updated, AZURE OAuth Failed, AZURE OAuth Token Obtained, AZURE OAuth Token Renewal Succeeded, AZURE OAuth User Login Started, AZURE OAuth User Login Succeeded, Abort Autopilot, Access granted to a resource for a subject entity, Access granted to a resource referenced by an experiment container, Access request created, Access revoked to a resource for a subject entity, Access revoked to a resource referenced by an experiment container, Activate account, Activated On First Login, Actuals Uploaded, Add Model, Add New Dataset For Predictions, Add SAML configuration, Advanced Tuning Requested, App Config Changed, App Template Cloned, App Template Created, App Template Deleted, App Template Media Deleted, App Template Media Uploaded, App Template Updated, Approval Workflow Policy Action, Approval Workflow Policy Created, Approval Workflow Policy Deleted, Approval Workflow Policy Updated, Approve account, Association ID Set, Automated Application Access Revoked from the Group, Automated Application Access Revoked from the Organization, Automated Application Access Revoked from the User, Automated Application Created, Automated Application Deleted, Automated Application Domain Prefix Changed, Automated Application Duplicated, Automated Application Shared, Automated Application Shared with Group, Automated Application Shared with Organization, Automated Application Upgraded, Automated Demo Application Created, Automated Document Created, Automated Document Deleted, Automated Document Downloaded, Automated Document Previewed, Automated Document Requested, Automatic Time Series Task Plan Requested, Available Forecast Points Computation Job Started, Base Image Built, Batch Monitoring Disabled, Batch Monitoring Enabled, Batch Prediction Created from Dataset, Batch prediction job aborted, Batch prediction job completed, Batch prediction job created, Batch prediction job failed, Batch prediction job started, Bias And Fairness Cross Class Calculated, Bias And Fairness Insights Calculated, Bias And Fairness Per Class Calculated, Bias and Fairness monitoring settings updated., Bias and Fairness protected features specified., Blending Models Limit Exceeded, Branded Theme Created, Branded Theme Deleted, Branded Theme Updated, Bulk Datasets Deleted, Bulk Datasets Tags Appended, CCM Balancer Terminated, CCM CLUSTER Reprovisioned, CCM Cluster Created, CCM Cluster Terminated, CCM Resource Group Created, Calculation of prediction intervals is requested, Challenger Insight Generation Started, Challenger Model Created, Challenger Model Deleted, Challenger Model Promoted, Challenger Models Disabled, Challenger Models Enabled, Change Request Cancelled, Change Request Created, Change Request Reopened, Change Request Resolved, Change Request Review Added, Change Request Review Requested, Change Request Updated, Change password, Clustering Cluster Names Updated, Code Snippet Created, Codespace Created, Codespace Deleted, Codespace Metadata Edited, Codespace Session Started, Codespace Session Stopped, Comment Created, Comment Deleted, Comment Updated, Completed Feature Discovery Secondary Datasets, Completed Feature Discovery for Primary Dataset, Completed Relationship Quality Assessment, Compliance Doc Deleted, Compliance Doc Downloaded, Compliance Doc Generated, Compliance Doc Previewed, Compute Cluster Added, Compute Cluster Deleted, Compute Cluster Updated, Compute External Insights, Compute Reason Codes, Create Memory Agent, Create Memory Event, Create Memory Session, Create Memory Space, Create account, Created dataset from Data Engine workspace, Created dataset version from Data Engine workspace, Credential Created, Credential Deleted, Credential Updated, Credential Values Retrieved Based On OAuth Configuration ID, Custom Application Access Revoked from the Group, Custom Application Access Revoked from the Organization, Custom Application Access Revoked from the User, Custom Application Created, Custom Application Deleted, Custom Application Failed to Start, Custom Application Managed Image Created, Custom Application Published, Custom Application Renamed, Custom Application Shared with Group, Custom Application Shared with Organization, Custom Application Shared with User, Custom Application Source Access Revoked from the Group, Custom Application Source Access Revoked from the Organization, Custom Application Source Access Revoked from the User, Custom Application Source Shared with Group, Custom Application Source Shared with Organization, Custom Application Source Shared with User, Custom Application Started, Custom Application Stopped, Custom Application Usage Exported, Custom Application Visited, Custom Application Visited by Guest, Custom Job Run Executed, Custom Metric Bulk Upload Succeeded, Custom Metric Creation Succeeded, Custom Metric Dataset Upload Succeeded, Custom Metric JSON Upload Succeeded, Custom Model Conversion Failed, Custom Model Conversion Files Uploaded, Custom Model Conversion Succeeded, Custom Model Updated from Codespace, Custom Model Version Uploaded to Codespace, Custom RBAC Access Role Created, Custom RBAC Access Role Deleted, Custom RBAC Access Role Updated, Custom Registered Model Created, Custom Registered Model Version Added, Custom Task Deploy, Custom Task Fit, Custom inference model added, Custom inference model assign training data request received, Custom inference model updated, Custom inference model version assign training data request received, Custom inference model version created from remote repository content, Custom model item added, Custom model item created from template, Custom task added, Custom task updated, Custom task version added, Data Connection Created, Data Connection Deleted, Data Connection Tested, Data Connection Updated, Data Matching ANN Index Profile Built, Data Matching Query Requested, Data Sample Queried For Wrangling, Data Sampled for Chunk Definition, Data Source is created, Data Sources Permadelete Executed, Data Sources Permadelete Failed, Data Sources Permadelete Submitted, Data Store Config Request Submitted, Data Stores Permadelete Executed, Data Stores Permadelete Failed, Data Stores Permadelete Submitted, Data engine query generator created, Data engine query generator deleted, Data engine workspace created, Data engine workspace deleted, Data engine workspace state previewed, Data engine workspace updated, Dataset Categories Modified, Dataset Column Aliases Modified, Dataset Created, Dataset Deleted, Dataset Description Modified, Dataset Download, Dataset Materialized, Dataset Name Modified, Dataset Reloaded, Dataset Shared, Dataset Sharing Removed, Dataset Tags Modified, Dataset Undeleted, Dataset Upload, Dataset Upload is Completed, Dataset Version Created from Recipe, Dataset Version Deleted, Dataset Version Undeleted, Dataset featurelist created, Dataset featurelist deleted, Dataset featurelist updated, Dataset for predictions with actual value column processed, Dataset refresh job created, Dataset refresh job deleted, Dataset refresh job updated, Dataset relationship created, Dataset relationship updated, Dataset transform created, Datasets Permadelete Executed, Datasets Permadelete Failed, Datasets Permadelete Submitted, Deactivate Account, Decision Flow Created, Decision Flow Model Package Created, Decision Flow Test Downloaded, Decision Flow Version Created, Decision Flow Version Deleted, Default value for Do-Not-Derive is changed, Delete SAML configuration, Delete existing Memory Space, Deny account, Deploy Model To Hadoop, Deployment Activated, Deployment Actuals Export Created, Deployment Added, Deployment Deactivated, Deployment Deleted, Deployment Humility Rule Added, Deployment Humility Rule Deleted, Deployment Humility Rule Submitted, Deployment Humility Rule Updated, Deployment Humility Setting Updated, Deployment Monitoring Batch Created, Deployment Monitoring Timeliness Setting Changed, Deployment Permanently Erased, Deployment Predictions Data Permanently Erased, Deployment Processing Limit Interval Changed, Deployment Statistics Reset, Deployment prediction export created, Deployment prediction warning setting updated, Deployment training data export created, Detected Data Quality: Disguised Missing Values, Detected Data Quality: Excess Zero, Detected Data Quality: Imputation Leakage, Detected Data Quality: Inconsistent Gaps, Detected Data Quality: Inliers, Detected Data Quality: Lagged Features, Detected Data Quality: Leading or Trailing Series, Detected Data Quality: Missing Documents, Detected Data Quality: Missing Images, Detected Data Quality: Multicategorical Invalid Format, Detected Data Quality: New Series in Recent Data, Detected Data Quality: Outliers, Detected Data Quality: Quantile Target Sparsity, Detected Data Quality: Quantile Target Zero Inflation, Detected Data Quality: Target Leakage, Detected Data Quality: Target had infrequent negative values, Do-Not-Derive is used, Documentation Request, Download All Charts, Download Chart, Download Codegen, Download Codegen From Deployment, Download Deployment Chart, Download Model, Download Model Package, Download Model Package From Deployment, Download Predictions, Empty Catalog Item Created, Empty Cluster Status Created, Entitlement Definition Created, Entitlement Definition Deleted, Entitlement Definition Updated, Entitlement Set Created, Entitlement Set Deleted, Entitlement Set Lease Created, Entitlement Set Lease Deleted, Entitlement Set Lease Updated, Entitlement Set Updated, Entitlement Set Updated Entitlements, Entity Notification Channel Created, Entity Notification Channel Deleted, Entity Notification Channel Updated, Entity Notification Policy Created, Entity Notification Policy Deleted, Entity Notification Policy Updated, Entity Tag Created, Entity Tag Deleted, Entity Tag Updated, Entity notification channel created, Ephemeral Session Started, Ephemeral Session Stopped, Experiment Container Created, Experiment Container Dataset Registered, Experiment Container Dataset Unregistered, Experiment Container Deleted, Experiment Container Entity Linked, Experiment Container Entity Migrated, Experiment Container Entity Moved, Experiment Container Entity Unlinked, Experiment Container Reference To Catalog Dataset Removed, Experiment Container Reference To Catalog Dataset Version Removed, Experiment Container Updated, External Predictions Configured, External Registered Model Created, External Registered Model Version Added, External principals synchronized for a connector, External token exchanged, FEAR Predict Job Started, FaaS Function Created, FaaS Function Deleted, FaaS Function Perma Deleted, FaaS Function Updated, Failed Decision Flow Test, Feature Discovery Relationship Quality Assessment Inputs Metrics, Feature Discovery Relationship Quality Assessment Warnings Metrics, Feature Drift Settings Changed, Feature Over Geo Computed, File Deleted, File Download, File Permadelete Executed, File Permadelete Failed, File Permadelete Submitted, File Shared, File Sharing Removed, File Undeleted, File Upload, File Upload is Completed, Finish Autopilot, First Login After DR Account Migration, GenAI Agent Chat Completion Requested, GenAI Chat Created, GenAI Chat Deleted, GenAI Chat Prompt Created, GenAI Chat Prompt Deleted, GenAI Chat Prompt Updated, GenAI Chat Updated, GenAI Comparison Chat Created, GenAI Comparison Chat Deleted, GenAI Comparison Chat Updated, GenAI Comparison Prompt Created, GenAI Comparison Prompt Deleted, GenAI Comparison Prompt Updated, GenAI Cost Metric Configuration Created, GenAI Cost Metric Configuration Deleted, GenAI Cost Metric Configuration Updated, GenAI Evaluation Dataset Configuration Created, GenAI Evaluation Dataset Configuration Deleted, GenAI Evaluation Dataset Configuration Updated, GenAI External Vector Database Updated, GenAI Insights Upserted, GenAI LLM Blueprint Created, GenAI LLM Blueprint Created from Chat Prompt, GenAI LLM Blueprint Created from LLM Blueprint, GenAI LLM Blueprint Deleted, GenAI LLM Blueprint Sent to Model Workshop, GenAI LLM Blueprint Updated, GenAI LLM Test Configuration Created, GenAI LLM Test Configuration Deleted, GenAI LLM Test Configuration Updated, GenAI LLM Test Result Created, GenAI LLM Test Result Deleted, GenAI LLM Test Result Updated, GenAI LLM Test Suite Created, GenAI LLM Test Suite Deleted, GenAI LLM Test Suite Updated, GenAI Metrics Transferred to Model Workshop, GenAI Moderation Config Saved, GenAI Moderation Model Deployed, GenAI Playground Created, GenAI Playground Deleted, GenAI Playground Trace Exported, GenAI Playground Updated, GenAI Prompt Template Created, GenAI Prompt Template Deleted, GenAI Prompt Template Version Created, GenAI Vector Database Created, GenAI Vector Database Deleted, GenAI Vector Database Downloaded, GenAI Vector Database Exported, GenAI Vector Database Updated, General Feedback Submitted, Generic Custom Job Created, Generic Custom Job Manual Run Created, Generic Custom Job Scheduled Run Created, Geometry Over Geo Computed, Geospatial Feature Transform Created, Geospatial Primary Location Column Selected, Global Inbound OAuth Configuration Created, Global Inbound OAuth Configuration Updated, Global SAML Configuration Added, Global SAML Configuration Deleted, Global SAML Configuration Updated, Group Members Updated, Group created, Group deleted, Group updated, Hosted Custom Metric Custom Job Created, Hosted Custom Metric Deployment Connection Created, Inbound OAuth Configuration Created, Inbound OAuth Configuration Updated, Incremental Learning Model Created, Interaction Feature Created, Interaction Feature Deployment Created, Invitation Accepted, Invitation sent, Job definition created, Job definition updated, Login Fail, Login Succeeded Via Global SAML SSO, Login Success Via SAML SSO, Login Successful, Logout, MCP tool is called, MLOPS Integrations Deployment Launched, MLOPS Integrations Deployment Model Replaced, MLOPS Integrations Deployment Stopped, MLOPS Integrations Prediction Environment Created, MLOPS Integrations Prediction Environment Deleted, MLOps Installer Download Request Received, Managed Image Built, Memory Events List Requested, Memory Executor Run Completed, Memory Session Delete Requested, Memory Sessions List Requested, Memory Vector Store Delete, Memory Vector Store Insert, Memory Vector Store Reset, Memory Vector Store Update, Model Deployment Access Revoked, Model Deployment Shared, Model Insights Deleted, Model Insights Job Submitted, Models Starred, Multi-Factor Auth Disable, Multi-Factor Auth Enable, Multilabel Labelwise ROC With Missing TPR Or FPR Requested, Native Registered Model Created, Native Registered Model Version Added, Network Policy Created, Network Policy Deleted, Network Policy Updated, No predictors are left because of Do-Not-Derive, Non Existent Value Tracker Attachment Removed, Notebook Conversion to Codespace Complete, Notebook Conversion to Codespace Initiated, Notebook Created, Notebook Deleted, Notebook Environment Variable Deleted, Notebook Environment Variable Edited, Notebook Environment Variables Created, Notebook Environment Variables Deleted, Notebook Metadata Edited, Notebook Revision Created, Notebook Revision Deleted, Notebook Revision Restored, Notebook Schedule Created, Notebook Schedule Deleted, Notebook Schedule Disabled, Notebook Schedule Enabled, Notebook Schedule Launched, Notebook Session Ports Created, Notebook Session Ports Deleted, Notebook Session Ports Updated, Notebook Session Started, Notebook Session Stopped, Notification Channel Deleted, Notification Channel Template Created, Notification Channel Template Deleted, Notification Channel Template Updated, Notification Custom Job Created, Notification Policy Created, Notification Policy Created From Template, Notification Policy Deleted, Notification Policy Template Created, Notification Policy Template Deleted, Notification Policy Template Updated, Notification Policy Updated, Notification channel created, Notification channel deleted, Notification channel updated, Notification policy created, Notification policy deleted, Notification policy updated, Number of bias mitigation jobs on Autopilot stage., OAuth Provider Access Token Generated, OAuth Provider Authorization Created, OAuth Provider Authorization Revoked, OCR Job Resource Completed, OCR Job Resource Created, OCR Job Resource Started, OIDC Configuration Created, OIDC Configuration Deleted, OIDC Configuration Updated, OTel Logs reset, OTel Metrics reset, OTel Tracing reset, Online Conformal PI Calculation Requested, Organization Perma-Deletion Completed, Organization Perma-Deletion Failed, Organization Perma-Deletion Marked, Organization Perma-Deletion Requested, Organization Perma-Deletion Started, Organization Perma-Deletion Unmarked, Organization created, Organization deleted, Organization updated, Organizations Perma-Deletion Requested, PPS Docker Image Download Request Received, Period accuracy file validation failed, Period accuracy file validation successful, Period accuracy insight computed, Pipeline downsampling build and run started., Pipeline downsampling run failed to start., Predictions by Forecast Date Settings Updated, Prime Downloaded, Prime Run, Project Access Revoked from the Group, Project Access Revoked from the Organization, Project Access Revoked from the User, Project Autopilot Configured, Project Cloned, Project Created, Project Created from Dataset, Project Created from Project Export File, Project Created from Wrangled Dataset, Project Deleted, Project Description Updated, Project Exported as Project Export File, Project Options Retrieved, Project Options Updated, Project Permadelete Executed, Project Permadelete Failed, Project Permadelete Submitted, Project Renamed, Project Restored, Project Shared, Project Shared with Group, Project Shared with Organization, Project Target Selected, Published Recipe Data Uploaded, Rate limit user group changed, Recipe Access Revoked from Group, Recipe Access Revoked from Organization, Recipe Access Revoked from User, Recipe Created, Recipe Deleted, Recipe Operations Added, Recipe Published, Recipe Shared, Recipe Shared with Group, Recipe Shared with Organization, Recipe metadata updated, Registered Model Shared, Registered Model Updated, Registered Model Version Stage Transitioned, Registered Model Version Updated, Remote Repository Registered, Replaced Model, Request External Insights, Request External Insights - All Datasets, Request Model Insights, Restart Autopilot, Restore Reduced Features, Retraining Custom Job Created, Retraining Policy Cancelled, Retraining Policy Created, Retraining Policy Deleted, Retraining Policy Failed, Retraining Policy Started, Retraining Policy Succeeded, RuleFit Code Downloaded, SHAP Impact Computed, SHAP Matrix Computed, SHAP Predictions Explanations Computed, SHAP Predictions Explanations Preview Computed, SHAP Training Predictions Explanations Computed, Secure Configuration Created, Secure Configuration Deleted, Secure Configuration Shared, Secure Configuration Sharing Removed, Secure Configuration Values Updated, Segment Analysis Enabled, Segment Attributes Specified, Select Model Metric, ServiceUser Created, ServiceUser Deleted, ServiceUser Impersonated Token Requested, ServiceUser Token Requested, ServiceUser Updated, Start Autopilot, Successful Decision Flow Test, Successful Login using OIDC flow, Successful Login using OIDC token exchange, Successful Login via Google Idp, Target is set as Do-Not-Derive, Tenant Created, Tenant Encryption Key generated and managed by DataRobot KMS, Tenant Encryption Key rotated, Tenant Encryption Key scheduled for deletion, Tenant Encryption Key was set for the tenant, Tenant Perma-Deletion Completed, Tenant Perma-Deletion Failed, Tenant Perma-Deletion Requested, Tenant Perma-Deletion Started, Tenant Updated, Text prediction explanations computed, Tracing Dependency Graph Requested, Tracing List Requested, Tracing Span Histogram Requested, Train Model, Trial Account Provisioning Completed, Trial Account Provisioning Failed, Trial Account Provisioning Started, Unsupervised Mode Started, Update SAML configuration, Update account, Update existing Memory Space, Update existing chat history event, User Agreement Accepted, User Agreement Declined, User Append Columns Download With Predictions, User Blueprint Added To Repository, User Blueprint Created, User Blueprint Deleted, User Blueprint Deleted In Bulk, User Blueprint Description Modified, User Blueprint Name Modified, User Blueprint Retrieved, User Blueprint Tags Modified, User Blueprint Tasks Retrieved, User Blueprint Updated, User Blueprint Validated, User Blueprints Listed, User Provisioned From JWT, Users Perma-Deletion Canceled, Users Perma-Deletion Canceling, Users Perma-Deletion Completed, Users Perma-Deletion Failed, Users Perma-Deletion Preview Building Canceled, Users Perma-Deletion Preview Building Canceling, Users Perma-Deletion Preview Building Completed, Users Perma-Deletion Preview Building Failed, Users Perma-Deletion Preview Building Started, Users Perma-Deletion Preview Building Submitted, Users Perma-Deletion Started, Users Perma-Deletion Submitted, Value Tracker Attachment Added, Value Tracker Attachment Removed, Value Tracker Created, Value Tracker Stage Changed, Value Tracker Updated, Workspace scheduled batch processing job created, Workspace scheduled batch processing job deleted, Workspace scheduled batch processing job updated, aiAPI Portal Login] | | order | [asc, desc] | | includeIdentifyingFields | [false, False, true, True] | | auditReportType | [APP_USAGE, ADMIN_USAGE] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of returned items.", "type": "integer" }, "data": { "description": "A json array of audit log records structured in the same form as the response [GET /api/v2/eventLogs/{recordId}/][get-apiv2eventlogsrecordid]", "items": { "properties": { "context": { "description": "An object with additional attributes for the record.", "properties": { "orgId": { "description": "The ID of the organization.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "projectName": { "description": "The name of the project.", "type": [ "string", "null" ] }, "projectType": { "description": "The type of the project.", "type": [ "string", "null" ] }, "userProjectRole": { "description": "The role of the user associated with this project.", "type": [ "string", "null" ] } }, "type": "object" }, "event": { "description": "The record event label (e.g., 'Start Autopilot').", "type": "string" }, "id": { "description": "The record ID.", "type": [ "string", "null" ] }, "ip": { "description": "The IP address of the server where the record event happened. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] }, "projectId": { "description": "The record event project ID.", "type": [ "string", "null" ] }, "timestamp": { "description": "The record timestamp.", "format": "date-time", "type": "string" }, "userId": { "description": "The user ID of the user who triggered the record event.", "type": "string" }, "username": { "description": "The username of the record events user. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] } }, "required": [ "context", "event", "id", "projectId", "timestamp", "userId" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL to the next page (if null, no next page exists).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "A URL to the previous page (if null, no previous page exists).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The number of items matching the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of audit log records. | AuditLogsRetrieveResponse | ## Retrieve all the available events Operation path: `GET /api/v2/eventLogs/events/` Authentication requirements: `BearerAuth` Retrieve all the available events. DEPRECATED API. ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Total count of event labels.", "type": "integer" }, "data": { "description": "A JSON array of event labels.", "items": { "description": "The name of the event.", "type": "string" }, "type": "array" } }, "required": [ "count", "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of events. | AuditLogsEventListResponse | ## Retrieve prediction usage data Operation path: `GET /api/v2/eventLogs/predictionUsage/` Authentication requirements: `BearerAuth` Retrieve prediction usage data. The `CAN_ACCESS_USER_ACTIVITY` permission is required. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | query | string | false | The project to retrieve prediction usage for. | | userId | query | string | false | The user to retrieve prediction usage for. | | minTimestamp | query | string(date-time) | true | The lower bound for timestamps. e.g., '2016-12-13T11:12:13.141516Z'. | | maxTimestamp | query | string(date-time) | true | The upper bound for timestamps. Time range should not exceed 24 hours. e.g., '2016-12-13T11:12:13.141516Z'. | | order | query | string | false | The order of prediction usage rows sorted by timestamp. Defaults to descending. | | offset | query | integer | false | This many results will be skipped. Defaults to 0. | | includeIdentifyingFields | query | string | false | Indicates if identifying information like user names, project names, etc., should be included or not. Defaults to True. | ### Enumerated Values | Parameter | Value | | --- | --- | | order | [asc, desc] | | includeIdentifyingFields | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of returned items.", "type": "integer" }, "data": { "description": "A JSON array of prediction usage rows described below.", "items": { "properties": { "avgExecutionTime": { "description": "The average predictions execution time during `timestamp` hour", "type": "number" }, "blenderModelTypes": { "description": "The type of blender model.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "deploymentId": { "description": "The ID of the deployment.", "type": [ "string", "null" ] }, "deploymentType": { "description": "The type of the deployment.", "type": [ "string", "null" ] }, "groupId": { "description": "The ID of the group.", "type": [ "string", "null" ] }, "groupName": { "description": "The name of the group.", "type": [ "string", "null" ] }, "modelId": { "description": "The ID of the model.", "type": [ "string", "null" ] }, "modelType": { "description": "The type of the model.", "type": [ "string", "null" ] }, "orgId": { "description": "The ID of the organization.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "predictionExplanations": { "description": "Whether it contains prediction explanations.", "type": [ "boolean", "null" ] }, "predictionMethod": { "description": "The prediction method that was used.", "type": "string" }, "predictionRowCountPerMinute": { "description": "The list of row counts per minute.", "items": { "type": "integer" }, "type": "array" }, "predictionRowsCount": { "description": "The count of prediction rows during the `timestamp` hour.", "type": "integer" }, "projectId": { "description": "The ID of the project.", "type": [ "string", "null" ] }, "projectType": { "description": "The type of the project.", "type": [ "string", "null" ] }, "pythonVersion": { "description": "The Python version used to build models in the project.", "type": [ "string", "null" ] }, "recommendedModel": { "description": "Whether it is a recommended model.", "type": [ "boolean", "null" ] }, "requestCountPerMinute": { "description": "The list of requests per minute.", "items": { "type": "integer" }, "type": "array" }, "requestsCount": { "description": "The request count during the `timestamp` hour.", "type": "integer" }, "serverErrorCount": { "description": "The server error count during the `timestamp` hour.", "type": "integer" }, "timestamp": { "description": "The timestamp of the event.", "format": "date-time", "type": "string" }, "userErrorCount": { "description": "The user error count during the `timestamp` hour.", "type": "integer" }, "userId": { "description": "The ID of the user.", "type": "string" }, "userProjectRole": { "description": "The role of the user to the related project.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] } }, "required": [ "avgExecutionTime", "predictionExplanations", "predictionMethod", "predictionRowsCount", "requestsCount", "serverErrorCount", "timestamp", "userErrorCount", "userId" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL to the next page (if null, no next page exists).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "A URL to the previous page (if null, no previous page exists).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The number of items matching the query condition.", "type": "integer", "x-versionadded": "v2.23" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of prediction events. | PredictionUsageRetrieveResponse | ## Get the audit record by ID by record ID Operation path: `GET /api/v2/eventLogs/{recordId}/` Authentication requirements: `BearerAuth` Get the audit record by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | includeIdentifyingFields | query | string | false | Indicates if identifying information like user names, project names, etc., should be included or not. Defaults to True. | | recordId | path | string | true | The ID of the audit log. | ### Enumerated Values | Parameter | Value | | --- | --- | | includeIdentifyingFields | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "context": { "description": "An object with additional attributes for the record.", "properties": { "orgId": { "description": "The ID of the organization.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "projectName": { "description": "The name of the project.", "type": [ "string", "null" ] }, "projectType": { "description": "The type of the project.", "type": [ "string", "null" ] }, "userProjectRole": { "description": "The role of the user associated with this project.", "type": [ "string", "null" ] } }, "type": "object" }, "event": { "description": "The record event label (e.g., 'Start Autopilot').", "type": "string" }, "id": { "description": "The record ID.", "type": [ "string", "null" ] }, "ip": { "description": "The IP address of the server where the record event happened. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] }, "projectId": { "description": "The record event project ID.", "type": [ "string", "null" ] }, "timestamp": { "description": "The record timestamp.", "format": "date-time", "type": "string" }, "userId": { "description": "The user ID of the user who triggered the record event.", "type": "string" }, "username": { "description": "The username of the record events user. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] } }, "required": [ "context", "event", "id", "projectId", "timestamp", "userId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The queried audit record. | AuditLogsRetrieveOneResponse | ## Create a customer usage data artifact request Operation path: `POST /api/v2/usageDataExports/` Authentication requirements: `BearerAuth` Create a customer usage data artifact request. Requires "CAN_ACCESS_USER_ACTIVITY" permission. Returns async task status URL as a "Location" header. The async task status should be polled in order to retrieve the artifact ID. ### Body parameter ``` { "properties": { "end": { "description": "The upper bound of stored events timestamp to include within the artifact.", "format": "date-time", "type": [ "string", "null" ] }, "include": { "description": "Additional fields to be included.", "items": { "type": "string" }, "type": "array" }, "includeIdentifyingFields": { "default": true, "description": "Indicates if identifying information like user names, project names, etc. should be included or not. Defaults to True.", "type": "boolean", "x-versionadded": "v2.17" }, "includeReport": { "description": "The list of reports that should be generated. Will default to `None` if not specified.", "items": { "description": "The available 'AuditReportType' enums include 'APP_USAGE', 'ADMIN_USAGE' and 'PREDICTION_USAGE'.", "enum": [ "ADMIN_USAGE", "APP_USAGE", "PREDICTION_USAGE", "SYSTEM_INFO" ], "type": "string" }, "type": "array", "x-versionadded": "v2.17" }, "noCache": { "description": "Switches off caching.", "type": "boolean", "x-versionadded": "v2.20" }, "projectId": { "description": "Only actions that are connected with the project will be retrieved.", "type": "string", "x-versionadded": "v2.18" }, "start": { "description": "The lower bound of stored events timestamp to include within the artifact.", "format": "date-time", "type": [ "string", "null" ] }, "userId": { "description": "Only actions performed by this user will be retrieved.", "type": "string", "x-versionadded": "v2.18" } }, "required": [ "includeIdentifyingFields" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UsageDataExport | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Customer usage data artifact creation started. The status can be tracked at the Location field in the headers. | None | | 400 | Bad Request | Artifact creation process encountered a problem. | None | ## Describe supported available audit events with Operation path: `GET /api/v2/usageDataExports/supportedEvents/` Authentication requirements: `BearerAuth` Describe supported available audit events with which to filter result data. ### Example responses > 200 Response ``` { "properties": { "events": { "additionalProperties": { "type": "string" }, "description": "The available events to use in filtering.", "type": "object", "x-versionadded": "v2.36" } }, "required": [ "events" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of supported available audit events. | UsageDataEventsListResponse | ## Retrieve a prepared customer usage data artifact by artifact ID Operation path: `GET /api/v2/usageDataExports/{artifactId}/` Authentication requirements: `BearerAuth` Retrieve a prepared customer usage data artifact. Requires "CAN_ACCESS_USER_ACTIVITY" permission. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | artifactId | path | string | true | The ID of the generated artifact to retrieve. | ### Example responses > 202 Response ``` { "properties": { "data": { "description": "The requested usage artifact in .zip format.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | An artifact file in .zip format. | UsageDataRetrieveResponse | | 400 | Bad Request | Usage data artifact retrieval failed. | None | | 404 | Not Found | Requested artifact does not exist. | None | ## Reset resource usage by user ID Operation path: `DELETE /api/v2/users/{userId}/rateLimitUsage/` Authentication requirements: `BearerAuth` Reset specified user's rate limit resource usage to zero for all resources. When windows roll over, all limits are automatically reset. Use this route to reset usage sooner. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userId | path | string | true | The user identifier. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The usage has been successfully reset to zero. | None | ## List resource usage by user ID Operation path: `GET /api/v2/users/{userId}/rateLimitUsage/` Authentication requirements: `BearerAuth` List the rate limit resource usage for a user. The usage array returned will have one object corresponding to each rate limit applied to the user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userId | path | string | true | The user identifier. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of resource usage records.", "type": "integer" }, "next": { "description": "URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page.", "type": [ "string", "null" ] }, "usage": { "description": "The resource usage records.", "items": { "properties": { "currentUsage": { "description": "The usage accumulated within the current window.", "type": "integer" }, "maxUsage": { "description": "The maximum value currentUsage can take before requests using this resource are denied.", "type": "integer" }, "resource": { "description": "The rate limit resource this usage applies to.", "type": "string" }, "timeToExpire": { "description": "The number of seconds until this usage resets to zero.", "type": "integer" } }, "required": [ "currentUsage", "maxUsage", "resource", "timeToExpire" ], "type": "object" }, "type": "array" } }, "required": [ "count", "next", "previous", "usage" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ResourceUsageResponse | ## Delete rate limit usage by id Operation path: `DELETE /api/v2/users/{userId}/rateLimitUsage/{resourceName}/` Authentication requirements: `BearerAuth` Reset rate limit resource usage for a user of a specified resource to zero. This will happen automatically when windows roll over. This route can be used to reset a user's rate limits sooner. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userId | path | string | true | The id of the user to reset usage for. | | resourceName | path | string | true | The resource name to reset usage for. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The usage has been successfully reset to zero. | None | # Schemas ## AuditLogContextObjectResponse ``` { "description": "An object with additional attributes for the record.", "properties": { "orgId": { "description": "The ID of the organization.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "projectName": { "description": "The name of the project.", "type": [ "string", "null" ] }, "projectType": { "description": "The type of the project.", "type": [ "string", "null" ] }, "userProjectRole": { "description": "The role of the user associated with this project.", "type": [ "string", "null" ] } }, "type": "object" } ``` An object with additional attributes for the record. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | orgId | string,null | false | | The ID of the organization. | | orgName | string,null | false | | The name of the organization. | | projectName | string,null | false | | The name of the project. | | projectType | string,null | false | | The type of the project. | | userProjectRole | string,null | false | | The role of the user associated with this project. | ## AuditLogsEventListResponse ``` { "properties": { "count": { "description": "Total count of event labels.", "type": "integer" }, "data": { "description": "A JSON array of event labels.", "items": { "description": "The name of the event.", "type": "string" }, "type": "array" } }, "required": [ "count", "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Total count of event labels. | | data | [string] | true | | A JSON array of event labels. | ## AuditLogsRetrieveOneResponse ``` { "properties": { "context": { "description": "An object with additional attributes for the record.", "properties": { "orgId": { "description": "The ID of the organization.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "projectName": { "description": "The name of the project.", "type": [ "string", "null" ] }, "projectType": { "description": "The type of the project.", "type": [ "string", "null" ] }, "userProjectRole": { "description": "The role of the user associated with this project.", "type": [ "string", "null" ] } }, "type": "object" }, "event": { "description": "The record event label (e.g., 'Start Autopilot').", "type": "string" }, "id": { "description": "The record ID.", "type": [ "string", "null" ] }, "ip": { "description": "The IP address of the server where the record event happened. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] }, "projectId": { "description": "The record event project ID.", "type": [ "string", "null" ] }, "timestamp": { "description": "The record timestamp.", "format": "date-time", "type": "string" }, "userId": { "description": "The user ID of the user who triggered the record event.", "type": "string" }, "username": { "description": "The username of the record events user. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] } }, "required": [ "context", "event", "id", "projectId", "timestamp", "userId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | context | AuditLogContextObjectResponse | true | | An object with additional attributes for the record. | | event | string | true | | The record event label (e.g., 'Start Autopilot'). | | id | string,null | true | | The record ID. | | ip | string,null | false | | The IP address of the server where the record event happened. Will be empty if includeIdentifyingFields is True. | | projectId | string,null | true | | The record event project ID. | | timestamp | string(date-time) | true | | The record timestamp. | | userId | string | true | | The user ID of the user who triggered the record event. | | username | string,null | false | | The username of the record events user. Will be empty if includeIdentifyingFields is True. | ## AuditLogsRetrieveResponse ``` { "properties": { "count": { "description": "The number of returned items.", "type": "integer" }, "data": { "description": "A json array of audit log records structured in the same form as the response [GET /api/v2/eventLogs/{recordId}/][get-apiv2eventlogsrecordid]", "items": { "properties": { "context": { "description": "An object with additional attributes for the record.", "properties": { "orgId": { "description": "The ID of the organization.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "projectName": { "description": "The name of the project.", "type": [ "string", "null" ] }, "projectType": { "description": "The type of the project.", "type": [ "string", "null" ] }, "userProjectRole": { "description": "The role of the user associated with this project.", "type": [ "string", "null" ] } }, "type": "object" }, "event": { "description": "The record event label (e.g., 'Start Autopilot').", "type": "string" }, "id": { "description": "The record ID.", "type": [ "string", "null" ] }, "ip": { "description": "The IP address of the server where the record event happened. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] }, "projectId": { "description": "The record event project ID.", "type": [ "string", "null" ] }, "timestamp": { "description": "The record timestamp.", "format": "date-time", "type": "string" }, "userId": { "description": "The user ID of the user who triggered the record event.", "type": "string" }, "username": { "description": "The username of the record events user. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] } }, "required": [ "context", "event", "id", "projectId", "timestamp", "userId" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL to the next page (if null, no next page exists).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "A URL to the previous page (if null, no previous page exists).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The number of items matching the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of returned items. | | data | [AuditLogsRetrieveOneResponse] | true | | A json array of audit log records structured in the same form as the response [GET /api/v2/eventLogs/{recordId}/][get-apiv2eventlogsrecordid] | | next | string,null(uri) | true | | A URL to the next page (if null, no next page exists). | | previous | string,null(uri) | true | | A URL to the previous page (if null, no previous page exists). | | totalCount | integer | true | | The number of items matching the query condition. | ## PredictionUsageObjectResponse ``` { "properties": { "avgExecutionTime": { "description": "The average predictions execution time during `timestamp` hour", "type": "number" }, "blenderModelTypes": { "description": "The type of blender model.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "deploymentId": { "description": "The ID of the deployment.", "type": [ "string", "null" ] }, "deploymentType": { "description": "The type of the deployment.", "type": [ "string", "null" ] }, "groupId": { "description": "The ID of the group.", "type": [ "string", "null" ] }, "groupName": { "description": "The name of the group.", "type": [ "string", "null" ] }, "modelId": { "description": "The ID of the model.", "type": [ "string", "null" ] }, "modelType": { "description": "The type of the model.", "type": [ "string", "null" ] }, "orgId": { "description": "The ID of the organization.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "predictionExplanations": { "description": "Whether it contains prediction explanations.", "type": [ "boolean", "null" ] }, "predictionMethod": { "description": "The prediction method that was used.", "type": "string" }, "predictionRowCountPerMinute": { "description": "The list of row counts per minute.", "items": { "type": "integer" }, "type": "array" }, "predictionRowsCount": { "description": "The count of prediction rows during the `timestamp` hour.", "type": "integer" }, "projectId": { "description": "The ID of the project.", "type": [ "string", "null" ] }, "projectType": { "description": "The type of the project.", "type": [ "string", "null" ] }, "pythonVersion": { "description": "The Python version used to build models in the project.", "type": [ "string", "null" ] }, "recommendedModel": { "description": "Whether it is a recommended model.", "type": [ "boolean", "null" ] }, "requestCountPerMinute": { "description": "The list of requests per minute.", "items": { "type": "integer" }, "type": "array" }, "requestsCount": { "description": "The request count during the `timestamp` hour.", "type": "integer" }, "serverErrorCount": { "description": "The server error count during the `timestamp` hour.", "type": "integer" }, "timestamp": { "description": "The timestamp of the event.", "format": "date-time", "type": "string" }, "userErrorCount": { "description": "The user error count during the `timestamp` hour.", "type": "integer" }, "userId": { "description": "The ID of the user.", "type": "string" }, "userProjectRole": { "description": "The role of the user to the related project.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] } }, "required": [ "avgExecutionTime", "predictionExplanations", "predictionMethod", "predictionRowsCount", "requestsCount", "serverErrorCount", "timestamp", "userErrorCount", "userId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | avgExecutionTime | number | true | | The average predictions execution time during timestamp hour | | blenderModelTypes | string,null | false | | The type of blender model. | | datasetId | string,null | false | | The ID of the dataset. | | deploymentId | string,null | false | | The ID of the deployment. | | deploymentType | string,null | false | | The type of the deployment. | | groupId | string,null | false | | The ID of the group. | | groupName | string,null | false | | The name of the group. | | modelId | string,null | false | | The ID of the model. | | modelType | string,null | false | | The type of the model. | | orgId | string,null | false | | The ID of the organization. | | orgName | string,null | false | | The name of the organization. | | predictionExplanations | boolean,null | true | | Whether it contains prediction explanations. | | predictionMethod | string | true | | The prediction method that was used. | | predictionRowCountPerMinute | [integer] | false | | The list of row counts per minute. | | predictionRowsCount | integer | true | | The count of prediction rows during the timestamp hour. | | projectId | string,null | false | | The ID of the project. | | projectType | string,null | false | | The type of the project. | | pythonVersion | string,null | false | | The Python version used to build models in the project. | | recommendedModel | boolean,null | false | | Whether it is a recommended model. | | requestCountPerMinute | [integer] | false | | The list of requests per minute. | | requestsCount | integer | true | | The request count during the timestamp hour. | | serverErrorCount | integer | true | | The server error count during the timestamp hour. | | timestamp | string(date-time) | true | | The timestamp of the event. | | userErrorCount | integer | true | | The user error count during the timestamp hour. | | userId | string | true | | The ID of the user. | | userProjectRole | string,null | false | | The role of the user to the related project. | | username | string,null | false | | The username of the user. Will be empty if includeIdentifyingFields is True. | ## PredictionUsageRetrieveResponse ``` { "properties": { "count": { "description": "The number of returned items.", "type": "integer" }, "data": { "description": "A JSON array of prediction usage rows described below.", "items": { "properties": { "avgExecutionTime": { "description": "The average predictions execution time during `timestamp` hour", "type": "number" }, "blenderModelTypes": { "description": "The type of blender model.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "deploymentId": { "description": "The ID of the deployment.", "type": [ "string", "null" ] }, "deploymentType": { "description": "The type of the deployment.", "type": [ "string", "null" ] }, "groupId": { "description": "The ID of the group.", "type": [ "string", "null" ] }, "groupName": { "description": "The name of the group.", "type": [ "string", "null" ] }, "modelId": { "description": "The ID of the model.", "type": [ "string", "null" ] }, "modelType": { "description": "The type of the model.", "type": [ "string", "null" ] }, "orgId": { "description": "The ID of the organization.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "predictionExplanations": { "description": "Whether it contains prediction explanations.", "type": [ "boolean", "null" ] }, "predictionMethod": { "description": "The prediction method that was used.", "type": "string" }, "predictionRowCountPerMinute": { "description": "The list of row counts per minute.", "items": { "type": "integer" }, "type": "array" }, "predictionRowsCount": { "description": "The count of prediction rows during the `timestamp` hour.", "type": "integer" }, "projectId": { "description": "The ID of the project.", "type": [ "string", "null" ] }, "projectType": { "description": "The type of the project.", "type": [ "string", "null" ] }, "pythonVersion": { "description": "The Python version used to build models in the project.", "type": [ "string", "null" ] }, "recommendedModel": { "description": "Whether it is a recommended model.", "type": [ "boolean", "null" ] }, "requestCountPerMinute": { "description": "The list of requests per minute.", "items": { "type": "integer" }, "type": "array" }, "requestsCount": { "description": "The request count during the `timestamp` hour.", "type": "integer" }, "serverErrorCount": { "description": "The server error count during the `timestamp` hour.", "type": "integer" }, "timestamp": { "description": "The timestamp of the event.", "format": "date-time", "type": "string" }, "userErrorCount": { "description": "The user error count during the `timestamp` hour.", "type": "integer" }, "userId": { "description": "The ID of the user.", "type": "string" }, "userProjectRole": { "description": "The role of the user to the related project.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user. Will be empty if ``includeIdentifyingFields`` is True.", "type": [ "string", "null" ] } }, "required": [ "avgExecutionTime", "predictionExplanations", "predictionMethod", "predictionRowsCount", "requestsCount", "serverErrorCount", "timestamp", "userErrorCount", "userId" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL to the next page (if null, no next page exists).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "A URL to the previous page (if null, no previous page exists).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The number of items matching the query condition.", "type": "integer", "x-versionadded": "v2.23" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of returned items. | | data | [PredictionUsageObjectResponse] | true | | A JSON array of prediction usage rows described below. | | next | string,null(uri) | true | | A URL to the next page (if null, no next page exists). | | previous | string,null(uri) | true | | A URL to the previous page (if null, no previous page exists). | | totalCount | integer | true | | The number of items matching the query condition. | ## ResourceUsage ``` { "properties": { "currentUsage": { "description": "The usage accumulated within the current window.", "type": "integer" }, "maxUsage": { "description": "The maximum value currentUsage can take before requests using this resource are denied.", "type": "integer" }, "resource": { "description": "The rate limit resource this usage applies to.", "type": "string" }, "timeToExpire": { "description": "The number of seconds until this usage resets to zero.", "type": "integer" } }, "required": [ "currentUsage", "maxUsage", "resource", "timeToExpire" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | currentUsage | integer | true | | The usage accumulated within the current window. | | maxUsage | integer | true | | The maximum value currentUsage can take before requests using this resource are denied. | | resource | string | true | | The rate limit resource this usage applies to. | | timeToExpire | integer | true | | The number of seconds until this usage resets to zero. | ## ResourceUsageResponse ``` { "properties": { "count": { "description": "The number of resource usage records.", "type": "integer" }, "next": { "description": "URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page.", "type": [ "string", "null" ] }, "usage": { "description": "The resource usage records.", "items": { "properties": { "currentUsage": { "description": "The usage accumulated within the current window.", "type": "integer" }, "maxUsage": { "description": "The maximum value currentUsage can take before requests using this resource are denied.", "type": "integer" }, "resource": { "description": "The rate limit resource this usage applies to.", "type": "string" }, "timeToExpire": { "description": "The number of seconds until this usage resets to zero.", "type": "integer" } }, "required": [ "currentUsage", "maxUsage", "resource", "timeToExpire" ], "type": "object" }, "type": "array" } }, "required": [ "count", "next", "previous", "usage" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of resource usage records. | | next | string,null | true | | URL pointing to the next page. | | previous | string,null | true | | URL pointing to the previous page. | | usage | [ResourceUsage] | true | | The resource usage records. | ## UsageDataEventsListResponse ``` { "properties": { "events": { "additionalProperties": { "type": "string" }, "description": "The available events to use in filtering.", "type": "object", "x-versionadded": "v2.36" } }, "required": [ "events" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | events | object | true | | The available events to use in filtering. | | » additionalProperties | string | false | | none | ## UsageDataExport ``` { "properties": { "end": { "description": "The upper bound of stored events timestamp to include within the artifact.", "format": "date-time", "type": [ "string", "null" ] }, "include": { "description": "Additional fields to be included.", "items": { "type": "string" }, "type": "array" }, "includeIdentifyingFields": { "default": true, "description": "Indicates if identifying information like user names, project names, etc. should be included or not. Defaults to True.", "type": "boolean", "x-versionadded": "v2.17" }, "includeReport": { "description": "The list of reports that should be generated. Will default to `None` if not specified.", "items": { "description": "The available 'AuditReportType' enums include 'APP_USAGE', 'ADMIN_USAGE' and 'PREDICTION_USAGE'.", "enum": [ "ADMIN_USAGE", "APP_USAGE", "PREDICTION_USAGE", "SYSTEM_INFO" ], "type": "string" }, "type": "array", "x-versionadded": "v2.17" }, "noCache": { "description": "Switches off caching.", "type": "boolean", "x-versionadded": "v2.20" }, "projectId": { "description": "Only actions that are connected with the project will be retrieved.", "type": "string", "x-versionadded": "v2.18" }, "start": { "description": "The lower bound of stored events timestamp to include within the artifact.", "format": "date-time", "type": [ "string", "null" ] }, "userId": { "description": "Only actions performed by this user will be retrieved.", "type": "string", "x-versionadded": "v2.18" } }, "required": [ "includeIdentifyingFields" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string,null(date-time) | false | | The upper bound of stored events timestamp to include within the artifact. | | include | [string] | false | | Additional fields to be included. | | includeIdentifyingFields | boolean | true | | Indicates if identifying information like user names, project names, etc. should be included or not. Defaults to True. | | includeReport | [string] | false | | The list of reports that should be generated. Will default to None if not specified. | | noCache | boolean | false | | Switches off caching. | | projectId | string | false | | Only actions that are connected with the project will be retrieved. | | start | string,null(date-time) | false | | The lower bound of stored events timestamp to include within the artifact. | | userId | string | false | | Only actions performed by this user will be retrieved. | ## UsageDataRetrieveResponse ``` { "properties": { "data": { "description": "The requested usage artifact in .zip format.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | string(binary) | true | | The requested usage artifact in .zip format. | --- # Application templates URL: https://docs.datarobot.com/en/docs/api/reference/public-api/application_templates.html > Use the endpoints described below to manage application templates. # Application templates Use the endpoints described below to manage application templates. ## List the application templates the user has access to Operation path: `GET /api/v2/applicationTemplates/` Authentication requirements: `BearerAuth` List the application templates the user has access to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of templates.", "items": { "properties": { "createdAt": { "description": "The ISO timestamp indicating when the template was created.", "type": [ "string", "null" ] }, "createdBy": { "description": "The user who created the template.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of the user who created the template.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of the user who created the template.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of the user who created the template.", "type": [ "string", "null" ] }, "description": { "description": "The short description of the template.", "type": "string" }, "editedAt": { "description": "The ISO timestamp indicating when the template was last edited.", "type": [ "string", "null" ] }, "editedBy": { "description": "The user who last edited the template.", "type": [ "string", "null" ] }, "editorFirstName": { "description": "The first name of the user who last edited the template.", "type": [ "string", "null" ] }, "editorLastName": { "description": "The last name of the user who last edited the template.", "type": [ "string", "null" ] }, "editorUserhash": { "description": "The Gravatar hash of the user who last edited the template.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the template.", "type": "string" }, "isGlobal": { "description": "Whether the template is a global template created by DataRobot.", "type": "boolean" }, "isPremium": { "description": "Whether the template is a premium template.", "type": [ "boolean", "null" ], "x-versionadded": "v2.36" }, "mediaUrl": { "description": "The link to the media URL if there is media associated with the application.", "format": "uri", "type": [ "string", "null" ] }, "name": { "description": "The name of the template.", "maxLength": 256, "type": "string" }, "orderIndex": { "description": "The order index of the template.", "type": [ "integer", "null" ], "x-versionadded": "v2.39" }, "ports": { "description": "A list of ports that the application template exposes. Each port has a port number and an optional description.", "items": { "properties": { "description": { "description": "The optional description of the port.", "maxLength": 500, "type": [ "string", "null" ] }, "port": { "description": "The port number to expose.", "maximum": 65535, "minimum": 1024, "type": "integer" } }, "required": [ "port" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 5, "type": "array", "x-versionadded": "v2.38" }, "readme": { "description": "A long-form Markdown readme to be included with the template.", "maxLength": 256000, "type": "string" }, "repository": { "description": "The repository the template is stored in.", "properties": { "branch": { "description": "Optional branch name used when a tag is not provided.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "isPublic": { "description": "Sets whether the repository is public or requires authentication.", "type": "boolean" }, "softPin": { "description": "Optional semantic version constraint (for example, '~=1.1.0') used to resolve tags dynamically.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "tag": { "description": "A reference pointing to where to check out the repository, from either a branch or a commit SHA.", "maxLength": 256, "type": [ "string", "null" ] }, "url": { "description": "The URL to the GitHub repository (e.g., https://github.com/my-org/my-project/).", "format": "uri", "type": "string" } }, "required": [ "isPublic", "url" ], "type": "object", "x-versionadded": "v2.35" }, "tags": { "description": "All tags of the repository.", "items": { "description": "A single tag on the repository such as GenAI or Time Series.", "maxLength": 256, "type": "string" }, "maxItems": 256, "type": "array" } }, "required": [ "createdAt", "createdBy", "description", "editedAt", "editedBy", "editorFirstName", "editorLastName", "editorUserhash", "id", "isGlobal", "isPremium", "mediaUrl", "name", "orderIndex", "readme", "repository", "tags" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ApplicationTemplateListResponse | ## Create an application template Operation path: `POST /api/v2/applicationTemplates/` Authentication requirements: `BearerAuth` Create an application template. ### Body parameter ``` { "properties": { "description": { "description": "The short description of the template.", "type": "string" }, "media": { "description": "The image (.png, .jpg, .svg, .gif) that is displayed alongside the template.", "format": "binary", "type": "string" }, "name": { "description": "The name of the template.", "maxLength": 256, "type": "string" }, "ports": { "description": "A list of ports that the application template exposes. Each port has a port number and an optional description.", "type": "string", "x-versionadded": "v2.38" }, "readme": { "description": "A long-form Markdown readme to be included with the template.", "format": "binary", "type": "string" }, "repository": { "description": "The repository the template is stored in.", "type": "string" }, "tags": { "description": "All tags of the repository.", "maxLength": 256, "type": "string" } }, "required": [ "description", "name", "readme", "repository", "tags" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ApplicationTemplateCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | None | | 403 | Forbidden | Permission settings do not allow creating templates. | None | ## Delete an application template by application template ID Operation path: `DELETE /api/v2/applicationTemplates/{applicationTemplateId}/` Authentication requirements: `BearerAuth` Delete an application template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationTemplateId | path | string | true | The ID of the template. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | Permission settings do not allow deleting templates. | None | | 404 | Not Found | The template is either global, preventing deletion, or does not exist. | None | ## Update an application template by application template ID Operation path: `PATCH /api/v2/applicationTemplates/{applicationTemplateId}/` Authentication requirements: `BearerAuth` Update an application template. ### Body parameter ``` { "properties": { "description": { "description": "The short description of the template.", "type": "string" }, "name": { "description": "The name of the template.", "maxLength": 256, "type": "string" }, "ports": { "description": "A list of ports that the application template exposes. Each port has a port number and an optional description.", "type": "string", "x-versionadded": "v2.38" }, "readme": { "description": "A long-form Markdown readme to be included with the template.", "format": "binary", "type": "string" }, "repository": { "description": "The repository the template is stored in.", "type": "string" }, "tags": { "description": "All tags of the repository.", "maxLength": 256, "type": "string" } }, "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationTemplateId | path | string | true | The ID of the template. | | body | body | ApplicationTemplateUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | Permission settings do not allow updating templates. | None | | 404 | Not Found | The template is either global, preventing updates, or does not exist. | None | ## Clone an application template into a codespace by application template ID Operation path: `POST /api/v2/applicationTemplates/{applicationTemplateId}/clone/` Authentication requirements: `BearerAuth` Clone an application template into a codespace. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationTemplateId | path | string | true | The ID of the template. | ### Example responses > 200 Response ``` { "properties": { "notebookId": { "description": "The ID of the newly created codespace.", "type": "string" }, "useCaseId": { "description": "The ID of the newly created Use Case.", "type": "string" } }, "required": [ "notebookId", "useCaseId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ApplicationTemplateCloneResponse | | 403 | Forbidden | Permission settings do not allow cloning templates. | None | | 404 | Not Found | The template does not exist. | None | | 422 | Unprocessable Entity | An error ocurred when creating the codespace. | None | ## Delete an application template image/gif. by application template ID Operation path: `DELETE /api/v2/applicationTemplates/{applicationTemplateId}/media/` Authentication requirements: `BearerAuth` Delete an application template image/gif. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationTemplateId | path | string | true | The ID of the template. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | Permission settings do not allow allow deleting media from templates. | None | | 404 | Not Found | The template is either global, preventing media deletion, or does not exist. | None | ## Retrieve an application template image by application template ID Operation path: `GET /api/v2/applicationTemplates/{applicationTemplateId}/media/` Authentication requirements: `BearerAuth` Retrieve an application template image. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationTemplateId | path | string | true | The ID of the template. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The media file returned as a FileObject.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ApplicationTemplateMediaResponse | | 404 | Not Found | The template media does not exist or the template does not exist. | None | ## Upload an application template image/gif. by application template ID Operation path: `POST /api/v2/applicationTemplates/{applicationTemplateId}/media/` Authentication requirements: `BearerAuth` Upload an application template image/gif. ### Body parameter ``` { "properties": { "media": { "description": "The image (.png, .jpg, .svg, .gif) that is displayed alongside the template.", "format": "binary", "type": "string" } }, "required": [ "media" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationTemplateId | path | string | true | The ID of the template. | | body | body | ApplicationTemplateMediaUpload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | None | | 403 | Forbidden | Permission settings do not allow allow uploading media to templates. | None | | 404 | Not Found | The template is either global, preventing media upload, or does not exist. | None | ## Get the resolved clone URL by application template ID Operation path: `GET /api/v2/applicationTemplates/{applicationTemplateId}/repositoryUrls/` Authentication requirements: `BearerAuth` Get the resolved clone URL for an application template repository. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationTemplateId | path | string | true | The ID of the template. | ### Example responses > 200 Response ``` { "properties": { "repositoryUrl": { "description": "The resolved clone URL for the template repository with any configured git base URL override applied.", "format": "uri", "type": "string" } }, "required": [ "repositoryUrl" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ApplicationTemplateRepositoryUrlResponse | | 404 | Not Found | The template does not exist. | None | # Schemas ## ApplicationTemplateCloneResponse ``` { "properties": { "notebookId": { "description": "The ID of the newly created codespace.", "type": "string" }, "useCaseId": { "description": "The ID of the newly created Use Case.", "type": "string" } }, "required": [ "notebookId", "useCaseId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notebookId | string | true | | The ID of the newly created codespace. | | useCaseId | string | true | | The ID of the newly created Use Case. | ## ApplicationTemplateCreate ``` { "properties": { "description": { "description": "The short description of the template.", "type": "string" }, "media": { "description": "The image (.png, .jpg, .svg, .gif) that is displayed alongside the template.", "format": "binary", "type": "string" }, "name": { "description": "The name of the template.", "maxLength": 256, "type": "string" }, "ports": { "description": "A list of ports that the application template exposes. Each port has a port number and an optional description.", "type": "string", "x-versionadded": "v2.38" }, "readme": { "description": "A long-form Markdown readme to be included with the template.", "format": "binary", "type": "string" }, "repository": { "description": "The repository the template is stored in.", "type": "string" }, "tags": { "description": "All tags of the repository.", "maxLength": 256, "type": "string" } }, "required": [ "description", "name", "readme", "repository", "tags" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | The short description of the template. | | media | string(binary) | false | | The image (.png, .jpg, .svg, .gif) that is displayed alongside the template. | | name | string | true | maxLength: 256 | The name of the template. | | ports | string | false | | A list of ports that the application template exposes. Each port has a port number and an optional description. | | readme | string(binary) | true | | A long-form Markdown readme to be included with the template. | | repository | string | true | | The repository the template is stored in. | | tags | string | true | maxLength: 256 | All tags of the repository. | ## ApplicationTemplateListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of templates.", "items": { "properties": { "createdAt": { "description": "The ISO timestamp indicating when the template was created.", "type": [ "string", "null" ] }, "createdBy": { "description": "The user who created the template.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of the user who created the template.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of the user who created the template.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of the user who created the template.", "type": [ "string", "null" ] }, "description": { "description": "The short description of the template.", "type": "string" }, "editedAt": { "description": "The ISO timestamp indicating when the template was last edited.", "type": [ "string", "null" ] }, "editedBy": { "description": "The user who last edited the template.", "type": [ "string", "null" ] }, "editorFirstName": { "description": "The first name of the user who last edited the template.", "type": [ "string", "null" ] }, "editorLastName": { "description": "The last name of the user who last edited the template.", "type": [ "string", "null" ] }, "editorUserhash": { "description": "The Gravatar hash of the user who last edited the template.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the template.", "type": "string" }, "isGlobal": { "description": "Whether the template is a global template created by DataRobot.", "type": "boolean" }, "isPremium": { "description": "Whether the template is a premium template.", "type": [ "boolean", "null" ], "x-versionadded": "v2.36" }, "mediaUrl": { "description": "The link to the media URL if there is media associated with the application.", "format": "uri", "type": [ "string", "null" ] }, "name": { "description": "The name of the template.", "maxLength": 256, "type": "string" }, "orderIndex": { "description": "The order index of the template.", "type": [ "integer", "null" ], "x-versionadded": "v2.39" }, "ports": { "description": "A list of ports that the application template exposes. Each port has a port number and an optional description.", "items": { "properties": { "description": { "description": "The optional description of the port.", "maxLength": 500, "type": [ "string", "null" ] }, "port": { "description": "The port number to expose.", "maximum": 65535, "minimum": 1024, "type": "integer" } }, "required": [ "port" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 5, "type": "array", "x-versionadded": "v2.38" }, "readme": { "description": "A long-form Markdown readme to be included with the template.", "maxLength": 256000, "type": "string" }, "repository": { "description": "The repository the template is stored in.", "properties": { "branch": { "description": "Optional branch name used when a tag is not provided.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "isPublic": { "description": "Sets whether the repository is public or requires authentication.", "type": "boolean" }, "softPin": { "description": "Optional semantic version constraint (for example, '~=1.1.0') used to resolve tags dynamically.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "tag": { "description": "A reference pointing to where to check out the repository, from either a branch or a commit SHA.", "maxLength": 256, "type": [ "string", "null" ] }, "url": { "description": "The URL to the GitHub repository (e.g., https://github.com/my-org/my-project/).", "format": "uri", "type": "string" } }, "required": [ "isPublic", "url" ], "type": "object", "x-versionadded": "v2.35" }, "tags": { "description": "All tags of the repository.", "items": { "description": "A single tag on the repository such as GenAI or Time Series.", "maxLength": 256, "type": "string" }, "maxItems": 256, "type": "array" } }, "required": [ "createdAt", "createdBy", "description", "editedAt", "editedBy", "editorFirstName", "editorLastName", "editorUserhash", "id", "isGlobal", "isPremium", "mediaUrl", "name", "orderIndex", "readme", "repository", "tags" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ApplicationTemplateResponse] | true | maxItems: 100 | The list of templates. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ApplicationTemplateMediaResponse ``` { "properties": { "data": { "description": "The media file returned as a FileObject.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | string(binary) | true | | The media file returned as a FileObject. | ## ApplicationTemplateMediaUpload ``` { "properties": { "media": { "description": "The image (.png, .jpg, .svg, .gif) that is displayed alongside the template.", "format": "binary", "type": "string" } }, "required": [ "media" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | media | string(binary) | true | | The image (.png, .jpg, .svg, .gif) that is displayed alongside the template. | ## ApplicationTemplateRepositoryUrlResponse ``` { "properties": { "repositoryUrl": { "description": "The resolved clone URL for the template repository with any configured git base URL override applied.", "format": "uri", "type": "string" } }, "required": [ "repositoryUrl" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | repositoryUrl | string(uri) | true | | The resolved clone URL for the template repository with any configured git base URL override applied. | ## ApplicationTemplateResponse ``` { "properties": { "createdAt": { "description": "The ISO timestamp indicating when the template was created.", "type": [ "string", "null" ] }, "createdBy": { "description": "The user who created the template.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of the user who created the template.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of the user who created the template.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of the user who created the template.", "type": [ "string", "null" ] }, "description": { "description": "The short description of the template.", "type": "string" }, "editedAt": { "description": "The ISO timestamp indicating when the template was last edited.", "type": [ "string", "null" ] }, "editedBy": { "description": "The user who last edited the template.", "type": [ "string", "null" ] }, "editorFirstName": { "description": "The first name of the user who last edited the template.", "type": [ "string", "null" ] }, "editorLastName": { "description": "The last name of the user who last edited the template.", "type": [ "string", "null" ] }, "editorUserhash": { "description": "The Gravatar hash of the user who last edited the template.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the template.", "type": "string" }, "isGlobal": { "description": "Whether the template is a global template created by DataRobot.", "type": "boolean" }, "isPremium": { "description": "Whether the template is a premium template.", "type": [ "boolean", "null" ], "x-versionadded": "v2.36" }, "mediaUrl": { "description": "The link to the media URL if there is media associated with the application.", "format": "uri", "type": [ "string", "null" ] }, "name": { "description": "The name of the template.", "maxLength": 256, "type": "string" }, "orderIndex": { "description": "The order index of the template.", "type": [ "integer", "null" ], "x-versionadded": "v2.39" }, "ports": { "description": "A list of ports that the application template exposes. Each port has a port number and an optional description.", "items": { "properties": { "description": { "description": "The optional description of the port.", "maxLength": 500, "type": [ "string", "null" ] }, "port": { "description": "The port number to expose.", "maximum": 65535, "minimum": 1024, "type": "integer" } }, "required": [ "port" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 5, "type": "array", "x-versionadded": "v2.38" }, "readme": { "description": "A long-form Markdown readme to be included with the template.", "maxLength": 256000, "type": "string" }, "repository": { "description": "The repository the template is stored in.", "properties": { "branch": { "description": "Optional branch name used when a tag is not provided.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "isPublic": { "description": "Sets whether the repository is public or requires authentication.", "type": "boolean" }, "softPin": { "description": "Optional semantic version constraint (for example, '~=1.1.0') used to resolve tags dynamically.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "tag": { "description": "A reference pointing to where to check out the repository, from either a branch or a commit SHA.", "maxLength": 256, "type": [ "string", "null" ] }, "url": { "description": "The URL to the GitHub repository (e.g., https://github.com/my-org/my-project/).", "format": "uri", "type": "string" } }, "required": [ "isPublic", "url" ], "type": "object", "x-versionadded": "v2.35" }, "tags": { "description": "All tags of the repository.", "items": { "description": "A single tag on the repository such as GenAI or Time Series.", "maxLength": 256, "type": "string" }, "maxItems": 256, "type": "array" } }, "required": [ "createdAt", "createdBy", "description", "editedAt", "editedBy", "editorFirstName", "editorLastName", "editorUserhash", "id", "isGlobal", "isPremium", "mediaUrl", "name", "orderIndex", "readme", "repository", "tags" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string,null | true | | The ISO timestamp indicating when the template was created. | | createdBy | string,null | true | | The user who created the template. | | creatorFirstName | string,null | false | | The first name of the user who created the template. | | creatorLastName | string,null | false | | The last name of the user who created the template. | | creatorUserhash | string,null | false | | The Gravatar hash of the user who created the template. | | description | string | true | | The short description of the template. | | editedAt | string,null | true | | The ISO timestamp indicating when the template was last edited. | | editedBy | string,null | true | | The user who last edited the template. | | editorFirstName | string,null | true | | The first name of the user who last edited the template. | | editorLastName | string,null | true | | The last name of the user who last edited the template. | | editorUserhash | string,null | true | | The Gravatar hash of the user who last edited the template. | | id | string | true | | The ID of the template. | | isGlobal | boolean | true | | Whether the template is a global template created by DataRobot. | | isPremium | boolean,null | true | | Whether the template is a premium template. | | mediaUrl | string,null(uri) | true | | The link to the media URL if there is media associated with the application. | | name | string | true | maxLength: 256 | The name of the template. | | orderIndex | integer,null | true | | The order index of the template. | | ports | [Ports] | false | maxItems: 5 | A list of ports that the application template exposes. Each port has a port number and an optional description. | | readme | string | true | maxLength: 256000 | A long-form Markdown readme to be included with the template. | | repository | Repository | true | | The repository the template is stored in. | | tags | [string] | true | maxItems: 256 | All tags of the repository. | ## ApplicationTemplateUpdate ``` { "properties": { "description": { "description": "The short description of the template.", "type": "string" }, "name": { "description": "The name of the template.", "maxLength": 256, "type": "string" }, "ports": { "description": "A list of ports that the application template exposes. Each port has a port number and an optional description.", "type": "string", "x-versionadded": "v2.38" }, "readme": { "description": "A long-form Markdown readme to be included with the template.", "format": "binary", "type": "string" }, "repository": { "description": "The repository the template is stored in.", "type": "string" }, "tags": { "description": "All tags of the repository.", "maxLength": 256, "type": "string" } }, "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | | The short description of the template. | | name | string | false | maxLength: 256 | The name of the template. | | ports | string | false | | A list of ports that the application template exposes. Each port has a port number and an optional description. | | readme | string(binary) | false | | A long-form Markdown readme to be included with the template. | | repository | string | false | | The repository the template is stored in. | | tags | string | false | maxLength: 256 | All tags of the repository. | ## Ports ``` { "properties": { "description": { "description": "The optional description of the port.", "maxLength": 500, "type": [ "string", "null" ] }, "port": { "description": "The port number to expose.", "maximum": 65535, "minimum": 1024, "type": "integer" } }, "required": [ "port" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | maxLength: 500 | The optional description of the port. | | port | integer | true | maximum: 65535minimum: 1024 | The port number to expose. | ## Repository ``` { "description": "The repository the template is stored in.", "properties": { "branch": { "description": "Optional branch name used when a tag is not provided.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "isPublic": { "description": "Sets whether the repository is public or requires authentication.", "type": "boolean" }, "softPin": { "description": "Optional semantic version constraint (for example, '~=1.1.0') used to resolve tags dynamically.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "tag": { "description": "A reference pointing to where to check out the repository, from either a branch or a commit SHA.", "maxLength": 256, "type": [ "string", "null" ] }, "url": { "description": "The URL to the GitHub repository (e.g., https://github.com/my-org/my-project/).", "format": "uri", "type": "string" } }, "required": [ "isPublic", "url" ], "type": "object", "x-versionadded": "v2.35" } ``` The repository the template is stored in. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | branch | string,null | false | maxLength: 256 | Optional branch name used when a tag is not provided. | | isPublic | boolean | true | | Sets whether the repository is public or requires authentication. | | softPin | string,null | false | maxLength: 256 | Optional semantic version constraint (for example, '~=1.1.0') used to resolve tags dynamically. | | tag | string,null | false | maxLength: 256 | A reference pointing to where to check out the repository, from either a branch or a commit SHA. | | url | string(uri) | true | | The URL to the GitHub repository (e.g., https://github.com/my-org/my-project/). | --- # Approval workflows URL: https://docs.datarobot.com/en/docs/api/reference/public-api/approval_workflows.html > This page describes the endpoints for enacting workflow requirements to ensure quality and comply with regulatory obligations. When you create a new or change an existing deployment, if the approval workflow is enabled, an MLOps administrator within your organization must approve your changes. [Approval policies](deploy-approval) affect the users who have permissions to review deployments and provide automated actions when reviews time out. Approval policies also affect users whose deployment events are governed by a configured policy (e.g., new deployment creation, model replacement). # Approval workflows This page describes the endpoints for enacting workflow requirements to ensure quality and comply with regulatory obligations. When you create a new or change an existing deployment, if the approval workflow is enabled, an MLOps administrator within your organization must approve your changes.[Approval policies](https://docs.datarobot.com/en/docs/platform/admin/deploy-approval.html) affect the users who have permissions to review deployments and provide automated actions when reviews time out. Approval policies also affect users whose deployment events are governed by a configured policy (e.g., new deployment creation, model replacement). ## List Approval Policies Operation path: `GET /api/v2/approvalPolicies/` Authentication requirements: `BearerAuth` List Approval Policies. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | entityType | query | string | false | Type of entity to filter policies by. | | namePart | query | string | false | Part of the policy name to search by. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, Deployment, DEPLOYMENT, deploymentModel, DeploymentModel, DEPLOYMENT_MODEL, deploymentConfig, DeploymentConfig, DEPLOYMENT_CONFIG, deploymentStatus, DeploymentStatus, DEPLOYMENT_STATUS, deploymentMonitoringData, DeploymentMonitoringData, DEPLOYMENT_MONITORING_DATA] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of Approval Policies.", "items": { "properties": { "active": { "default": true, "description": "Whether this policy is active.", "type": "boolean" }, "automaticAction": { "description": "An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If ``null``, no automated actions will be taken on the related Change Requests.", "properties": { "action": { "description": "Action of the workflow automation.", "enum": [ "cancel", "Cancel", "CANCEL", "approve", "Approve", "APPROVE" ], "type": "string" }, "period": { "description": "Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled.", "format": "duration", "type": "string" } }, "required": [ "action", "period" ], "type": "object" }, "id": { "description": "ID of the Approval Policy.", "type": "string" }, "name": { "description": "Name of the Approval Policy.", "maxLength": 50, "type": "string" }, "openRequests": { "description": "Number of open Change Requests associated with the policy.", "minimum": 0, "type": "integer" }, "review": { "description": "An object describing review requirements for Change Requests, related to a specific policy. If ``null``, no additional review requirements are added to the related Change Requests.", "properties": { "groups": { "description": "A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "reminderPeriod": { "description": "Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If ``null``, no review reminders are sent to the reviewers.", "format": "duration", "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" } }, "required": [ "active", "id", "name", "openRequests", "trigger" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Approval Policy has been successfully updated. | ApprovalPolicyListResponse | | 403 | Forbidden | Approval Policy management feature is disabled for the user. | None | ## Create a new Approval Policy Operation path: `POST /api/v2/approvalPolicies/` Authentication requirements: `BearerAuth` Create a new Approval Policy. ### Body parameter ``` { "properties": { "active": { "default": true, "description": "Whether this policy is active.", "type": "boolean" }, "automaticAction": { "description": "An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If ``null``, no automated actions will be taken on the related Change Requests.", "properties": { "action": { "description": "Action of the workflow automation.", "enum": [ "cancel", "Cancel", "CANCEL", "approve", "Approve", "APPROVE" ], "type": "string" }, "period": { "description": "Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled.", "format": "duration", "type": "string" } }, "required": [ "action", "period" ], "type": "object" }, "name": { "description": "Name of the Approval Policy.", "maxLength": 50, "type": "string" }, "review": { "description": "An object describing review requirements for Change Requests, related to a specific policy. If ``null``, no additional review requirements are added to the related Change Requests.", "properties": { "groups": { "description": "A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "reminderPeriod": { "description": "Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If ``null``, no review reminders are sent to the reviewers.", "format": "duration", "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" } }, "required": [ "active", "name", "trigger" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ApprovalPolicy | false | none | ### Example responses > 201 Response ``` { "properties": { "active": { "default": true, "description": "Whether this policy is active.", "type": "boolean" }, "automaticAction": { "description": "An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If ``null``, no automated actions will be taken on the related Change Requests.", "properties": { "action": { "description": "Action of the workflow automation.", "enum": [ "cancel", "Cancel", "CANCEL", "approve", "Approve", "APPROVE" ], "type": "string" }, "period": { "description": "Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled.", "format": "duration", "type": "string" } }, "required": [ "action", "period" ], "type": "object" }, "id": { "description": "ID of the Approval Policy.", "type": "string" }, "name": { "description": "Name of the Approval Policy.", "maxLength": 50, "type": "string" }, "openRequests": { "description": "Number of open Change Requests associated with the policy.", "minimum": 0, "type": "integer" }, "review": { "description": "An object describing review requirements for Change Requests, related to a specific policy. If ``null``, no additional review requirements are added to the related Change Requests.", "properties": { "groups": { "description": "A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "reminderPeriod": { "description": "Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If ``null``, no review reminders are sent to the reviewers.", "format": "duration", "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" } }, "required": [ "active", "id", "name", "openRequests", "trigger" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Approval Policy has been successfully created. | ApprovalPolicyResponse | | 403 | Forbidden | Approval Policy management feature is disabled for the user. | None | | 422 | Unprocessable Entity | Approval Policy could not be created with the given input. | None | ## Delete an Approval Policy by approval policy ID Operation path: `DELETE /api/v2/approvalPolicies/{approvalPolicyId}/` Authentication requirements: `BearerAuth` Delete the policy with the given ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | approvalPolicyId | path | string | true | ID of the Approval Policy. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Approval Policy has been successfully deleted. | None | | 403 | Forbidden | Approval Policy management feature is disabled for the user. | None | | 404 | Not Found | Approval Policy does not exist or the user doesn't have access to it. | None | ## Retrieve an Approval Policy by approval policy ID Operation path: `GET /api/v2/approvalPolicies/{approvalPolicyId}/` Authentication requirements: `BearerAuth` Retrieve the policy with the given ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | approvalPolicyId | path | string | true | ID of the Approval Policy. | ### Example responses > 200 Response ``` { "properties": { "active": { "default": true, "description": "Whether this policy is active.", "type": "boolean" }, "automaticAction": { "description": "An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If ``null``, no automated actions will be taken on the related Change Requests.", "properties": { "action": { "description": "Action of the workflow automation.", "enum": [ "cancel", "Cancel", "CANCEL", "approve", "Approve", "APPROVE" ], "type": "string" }, "period": { "description": "Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled.", "format": "duration", "type": "string" } }, "required": [ "action", "period" ], "type": "object" }, "id": { "description": "ID of the Approval Policy.", "type": "string" }, "name": { "description": "Name of the Approval Policy.", "maxLength": 50, "type": "string" }, "openRequests": { "description": "Number of open Change Requests associated with the policy.", "minimum": 0, "type": "integer" }, "review": { "description": "An object describing review requirements for Change Requests, related to a specific policy. If ``null``, no additional review requirements are added to the related Change Requests.", "properties": { "groups": { "description": "A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "reminderPeriod": { "description": "Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If ``null``, no review reminders are sent to the reviewers.", "format": "duration", "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" } }, "required": [ "active", "id", "name", "openRequests", "trigger" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Approval Policy has been successfully retrieved. | ApprovalPolicyResponse | | 403 | Forbidden | Approval Policy management feature is disabled for the user. | None | | 404 | Not Found | Approval Policy does not exist or the user doesn't have access to it. | None | ## Update an Approval Policy by approval policy ID Operation path: `PUT /api/v2/approvalPolicies/{approvalPolicyId}/` Authentication requirements: `BearerAuth` Update the policy with the given ID. ### Body parameter ``` { "properties": { "active": { "default": true, "description": "Whether this policy is active.", "type": "boolean" }, "automaticAction": { "description": "An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If ``null``, no automated actions will be taken on the related Change Requests.", "properties": { "action": { "description": "Action of the workflow automation.", "enum": [ "cancel", "Cancel", "CANCEL", "approve", "Approve", "APPROVE" ], "type": "string" }, "period": { "description": "Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled.", "format": "duration", "type": "string" } }, "required": [ "action", "period" ], "type": "object" }, "name": { "description": "Name of the Approval Policy.", "maxLength": 50, "type": "string" }, "review": { "description": "An object describing review requirements for Change Requests, related to a specific policy. If ``null``, no additional review requirements are added to the related Change Requests.", "properties": { "groups": { "description": "A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "reminderPeriod": { "description": "Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If ``null``, no review reminders are sent to the reviewers.", "format": "duration", "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" } }, "required": [ "active", "name", "trigger" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | approvalPolicyId | path | string | true | ID of the Approval Policy. | | body | body | ApprovalPolicy | false | none | ### Example responses > 200 Response ``` { "properties": { "active": { "default": true, "description": "Whether this policy is active.", "type": "boolean" }, "automaticAction": { "description": "An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If ``null``, no automated actions will be taken on the related Change Requests.", "properties": { "action": { "description": "Action of the workflow automation.", "enum": [ "cancel", "Cancel", "CANCEL", "approve", "Approve", "APPROVE" ], "type": "string" }, "period": { "description": "Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled.", "format": "duration", "type": "string" } }, "required": [ "action", "period" ], "type": "object" }, "id": { "description": "ID of the Approval Policy.", "type": "string" }, "name": { "description": "Name of the Approval Policy.", "maxLength": 50, "type": "string" }, "openRequests": { "description": "Number of open Change Requests associated with the policy.", "minimum": 0, "type": "integer" }, "review": { "description": "An object describing review requirements for Change Requests, related to a specific policy. If ``null``, no additional review requirements are added to the related Change Requests.", "properties": { "groups": { "description": "A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "reminderPeriod": { "description": "Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If ``null``, no review reminders are sent to the reviewers.", "format": "duration", "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" } }, "required": [ "active", "id", "name", "openRequests", "trigger" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Approval Policy has been successfully updated. | ApprovalPolicyResponse | | 403 | Forbidden | Approval Policy management feature is disabled for the user. | None | | 404 | Not Found | Approval Policy does not exist or the user doesn't have access to it. | None | | 422 | Unprocessable Entity | Approval Policy could not be created with the given input. | None | ## Retrieve associated Change Requests Info by approval policy ID Operation path: `GET /api/v2/approvalPolicies/{approvalPolicyId}/shareableChangeRequests/` Authentication requirements: `BearerAuth` Get information about Change Requests submitted for a certain Approval Policy. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | false | Attribute to order Change Requests by. | | approvalPolicyId | path | string | true | ID of the Approval Policy. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of Approval Policies.", "items": { "properties": { "changeRequestId": { "description": "ID of the Change Request.", "type": "string" }, "createDate": { "description": "Change Request creation date.", "format": "date-time", "type": "string" }, "entityId": { "description": "ID of the modified entity.", "type": "string" }, "entityName": { "description": "Name of the modified entity.", "type": [ "string", "null" ] }, "requester": { "description": "Username of the account that initiated a Change Request.", "type": "string" }, "state": { "description": "Status of the Change Request.", "enum": [ "OPENED", "RESOLVED", "CANCELLED" ], "type": "string" }, "updateDate": { "description": "Last date when Change Request was modified.", "format": "date-time", "type": [ "string", "null" ] }, "updatedBy": { "description": "Username of the account that last updated the Change Request.", "type": [ "string", "null" ] } }, "required": [ "changeRequestId", "createDate", "entityId", "entityName", "requester", "state", "updateDate", "updatedBy" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Change Requests information has been successfully retrieved. | ChangeRequestInfoListResponse | | 403 | Forbidden | Approval Policy management feature is disabled for the user. | None | | 404 | Not Found | Approval Policy does not exist or the user doesn't have access to it. | None | ## Get policy ID matching the query Operation path: `GET /api/v2/approvalPolicyMatch/` Authentication requirements: `BearerAuth` Get policy ID matching the query. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | query | string | true | Searched typed of the entity. | | action | query | string | true | Searched policy action. | | fieldName | query | string,null | false | Name of the entity field to filter policies by. | | fieldValue | query | string,null | false | Value of the entity field to filter policies by. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, Deployment, DEPLOYMENT, deploymentModel, DeploymentModel, DEPLOYMENT_MODEL, deploymentConfig, DeploymentConfig, DEPLOYMENT_CONFIG, deploymentStatus, DeploymentStatus, DEPLOYMENT_STATUS, deploymentMonitoringData, DeploymentMonitoringData, DEPLOYMENT_MONITORING_DATA] | | action | [create, Create, CREATE, update, Update, UPDATE, delete, Delete, DELETE] | ### Example responses > 200 Response ``` { "properties": { "action": { "description": "Searched policy action.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "entityType": { "description": "Searched typed of the entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "fieldName": { "description": "Name of the entity field to filter policies by.", "maxLength": 50, "type": [ "string", "null" ] }, "fieldValue": { "description": "Value of the entity field to filter policies by.", "maxLength": 50, "type": [ "string", "null" ] }, "policyId": { "description": "ID of the matching approval policy. ``null`` if no matching policies found.", "type": [ "string", "null" ] } }, "required": [ "action", "entityType", "policyId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Search executed successfully. | ApprovalPolicyMatchResponse | | 403 | Forbidden | Approval Policy management feature is disabled for the user. | None | ## Get a list of available policy triggers Operation path: `GET /api/v2/approvalPolicyTriggers/` Authentication requirements: `BearerAuth` Get a list of available policy triggers. ### Example responses > 200 Response ``` { "properties": { "data": { "description": "List of available Approval Policy Triggers.", "items": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of triggers is successfully generated. | ApprovalWorkflowListTriggerResponse | | 403 | Forbidden | Approval Policy management feature is disabled for the user. | None | ## List Change Requests Operation path: `GET /api/v2/changeRequests/` Authentication requirements: `BearerAuth` List all Change Requests accessible by the user for the given product entity type. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | entityType | query | string | true | Type of the entity to filter requests by. | | entityId | query | any | false | ID of the entity to filter change requests by. | | myRequests | query | string | false | Filter change requests by the owner. If true, only returns change requests owned by the user. If false, only returns change requests owned by other users but accessible to the requester. | | showApproved | query | string | false | Filter change requests by status. If true, only returns approved change requests. If false, only returns not approved change requests. | | showCancelled | query | string | false | Filter change requests by status. If true, only returns cancelled change requests. If false, only returns not cancelled change requests. | | status | query | any | false | Filter change requests by status. | | orderBy | query | string | false | The order that the results should be retrieved in. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, Deployment, DEPLOYMENT] | | myRequests | [false, False, true, True] | | showApproved | [false, False, true, True] | | showCancelled | [false, False, true, True] | | orderBy | [createdAt, -createdAt, processedAt, -processedAt, updatedAt, -updatedAt] | ### Example responses > 201 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of Change Requests", "items": { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "approve", "Approve", "APPROVE", "changeStatus", "ChangeStatus", "CHANGE_STATUS", "changeImportance", "ChangeImportance", "CHANGE_IMPORTANCE", "cleanupStats", "CleanupStats", "CLEANUP_STATS", "delete", "Delete", "DELETE", "replaceModel", "ReplaceModel", "REPLACE_MODEL", "replaceModelPackage", "ReplaceModelPackage", "REPLACE_MODEL_PACKAGE", "updateSecondaryDatasetConfigs", "UpdateSecondaryDatasetConfigs", "UPDATE_SECONDARY_DATASET_CONFIGS" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change that the user wants to apply to the entity. Needs to be provided if the action, like `approve` action for a deployment, requires additional parameters . `null` if the action does not require any additional parameters to be applied. ", "oneOf": [ { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" }, { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" }, { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" }, { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" }, { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" }, { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" }, { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" }, { "type": "null" } ] }, "changeVersionId": { "description": "ID of the current version of change within this Change Request. It's possible to modify the changes that have been requested. At the same time, we need to make sure that review is associated with the correct changes, that's why we implement versioning on the changes and associate user reviews with the specific Change Request versions.", "type": "string" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the request was created.", "format": "date-time", "type": "string" }, "diff": { "description": "The difference between the current entity state and the state of the entity if the Change Request gets applied.", "properties": { "changesFrom": { "description": "List of human readable messages describing the state of the entity before changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" }, "changesTo": { "description": "List of human readable messages describing the state of the entity after changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "changesFrom", "changesTo" ], "type": "object" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" }, "id": { "description": "ID of the Change Request.", "type": "string" }, "numApprovalsRequired": { "description": "Number of approving reviews required for the Change Request to be considered approved.", "minimum": 0, "type": "integer" }, "processedAt": { "description": "Timestamp when the request was processed.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Change Request Status.", "enum": [ "pending", "Pending", "PENDING", "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "resolved", "Resolved", "RESOLVED", "cancelled", "Cancelled", "CANCELLED" ], "type": "string" }, "statusChangedAt": { "description": "Timestamp when the current request status was set. `null` if status is set by the system.", "format": "date-time", "type": [ "string", "null" ] }, "statusChangedBy": { "description": "ID of the user who set the current request status. `null` if the status was set by the system.", "type": [ "string", "null" ] }, "updatedAt": { "description": "Timestamp when the request was last modified.", "format": "date-time", "type": "string" }, "userId": { "description": "ID of the user, who created the Change Request.", "type": "string" }, "userName": { "description": "Email of the user, who created the Change Request", "type": [ "string", "null" ] }, "userOperations": { "description": "A set operations the user can or can not make with the Change Request.", "properties": { "canCancel": { "description": "Whether the user can cancel the Change Request.", "type": "boolean" }, "canComment": { "description": "Whether the user can create commenting review on the Change Request.", "type": "boolean" }, "canResolve": { "description": "Whether the user can resolve the Change Request.", "type": "boolean" }, "canReview": { "description": "Whether the user can review (approve or request updates) the Change Request.", "type": "boolean" }, "canUpdate": { "description": "Whether the user can update the Change Request.", "type": "boolean" } }, "required": [ "canCancel", "canComment", "canResolve", "canReview", "canUpdate" ], "type": "object" } }, "required": [ "action", "autoApply", "change", "changeVersionId", "createdAt", "diff", "entityId", "entityType", "id", "numApprovalsRequired", "processedAt", "status", "statusChangedAt", "statusChangedBy", "updatedAt", "userId", "userName", "userOperations" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | ChangeRequestsListResponse | ## Create Change Request Operation path: `POST /api/v2/changeRequests/` Authentication requirements: `BearerAuth` Request changes for a supported product entity. For now, you can request changes for deployments only. ### Body parameter ``` { "discriminator": { "propertyName": "action" }, "oneOf": [ { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "approve" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "changeStatus" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "changeImportance" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "cleanupStats" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "delete" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Delete a deployment.", "properties": { "defaultDeploymentId": { "description": "Used by management agent to recalculate endpoint traffic. Traffic from the deploymentbeing deleted flipped to a default deployment.", "type": "string" }, "ignoreManagementAgent": { "default": "false", "description": "Do not wait for management agent to delete the deployment first.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "replaceModel" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "replaceModelPackage" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "updateSecondaryDatasetConfigs" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" } ] } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ChangeRequestCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "approve", "Approve", "APPROVE", "changeStatus", "ChangeStatus", "CHANGE_STATUS", "changeImportance", "ChangeImportance", "CHANGE_IMPORTANCE", "cleanupStats", "CleanupStats", "CLEANUP_STATS", "delete", "Delete", "DELETE", "replaceModel", "ReplaceModel", "REPLACE_MODEL", "replaceModelPackage", "ReplaceModelPackage", "REPLACE_MODEL_PACKAGE", "updateSecondaryDatasetConfigs", "UpdateSecondaryDatasetConfigs", "UPDATE_SECONDARY_DATASET_CONFIGS" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change that the user wants to apply to the entity. Needs to be provided if the action, like `approve` action for a deployment, requires additional parameters . `null` if the action does not require any additional parameters to be applied. ", "oneOf": [ { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" }, { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" }, { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" }, { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" }, { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" }, { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" }, { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" }, { "type": "null" } ] }, "changeVersionId": { "description": "ID of the current version of change within this Change Request. It's possible to modify the changes that have been requested. At the same time, we need to make sure that review is associated with the correct changes, that's why we implement versioning on the changes and associate user reviews with the specific Change Request versions.", "type": "string" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the request was created.", "format": "date-time", "type": "string" }, "diff": { "description": "The difference between the current entity state and the state of the entity if the Change Request gets applied.", "properties": { "changesFrom": { "description": "List of human readable messages describing the state of the entity before changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" }, "changesTo": { "description": "List of human readable messages describing the state of the entity after changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "changesFrom", "changesTo" ], "type": "object" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" }, "id": { "description": "ID of the Change Request.", "type": "string" }, "numApprovalsRequired": { "description": "Number of approving reviews required for the Change Request to be considered approved.", "minimum": 0, "type": "integer" }, "processedAt": { "description": "Timestamp when the request was processed.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Change Request Status.", "enum": [ "pending", "Pending", "PENDING", "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "resolved", "Resolved", "RESOLVED", "cancelled", "Cancelled", "CANCELLED" ], "type": "string" }, "statusChangedAt": { "description": "Timestamp when the current request status was set. `null` if status is set by the system.", "format": "date-time", "type": [ "string", "null" ] }, "statusChangedBy": { "description": "ID of the user who set the current request status. `null` if the status was set by the system.", "type": [ "string", "null" ] }, "updatedAt": { "description": "Timestamp when the request was last modified.", "format": "date-time", "type": "string" }, "userId": { "description": "ID of the user, who created the Change Request.", "type": "string" }, "userName": { "description": "Email of the user, who created the Change Request", "type": [ "string", "null" ] }, "userOperations": { "description": "A set operations the user can or can not make with the Change Request.", "properties": { "canCancel": { "description": "Whether the user can cancel the Change Request.", "type": "boolean" }, "canComment": { "description": "Whether the user can create commenting review on the Change Request.", "type": "boolean" }, "canResolve": { "description": "Whether the user can resolve the Change Request.", "type": "boolean" }, "canReview": { "description": "Whether the user can review (approve or request updates) the Change Request.", "type": "boolean" }, "canUpdate": { "description": "Whether the user can update the Change Request.", "type": "boolean" } }, "required": [ "canCancel", "canComment", "canResolve", "canReview", "canUpdate" ], "type": "object" } }, "required": [ "action", "autoApply", "change", "changeVersionId", "createdAt", "diff", "entityId", "entityType", "id", "numApprovalsRequired", "processedAt", "status", "statusChangedAt", "statusChangedBy", "updatedAt", "userId", "userName", "userOperations" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Change Request Created. | ChangeRequestResponse | ## Retrieve Change Request by change request ID Operation path: `GET /api/v2/changeRequests/{changeRequestId}/` Authentication requirements: `BearerAuth` Retrieve Change Request by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | changeRequestId | path | string | true | ID of the Change Request. | ### Example responses > 200 Response ``` { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "approve", "Approve", "APPROVE", "changeStatus", "ChangeStatus", "CHANGE_STATUS", "changeImportance", "ChangeImportance", "CHANGE_IMPORTANCE", "cleanupStats", "CleanupStats", "CLEANUP_STATS", "delete", "Delete", "DELETE", "replaceModel", "ReplaceModel", "REPLACE_MODEL", "replaceModelPackage", "ReplaceModelPackage", "REPLACE_MODEL_PACKAGE", "updateSecondaryDatasetConfigs", "UpdateSecondaryDatasetConfigs", "UPDATE_SECONDARY_DATASET_CONFIGS" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change that the user wants to apply to the entity. Needs to be provided if the action, like `approve` action for a deployment, requires additional parameters . `null` if the action does not require any additional parameters to be applied. ", "oneOf": [ { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" }, { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" }, { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" }, { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" }, { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" }, { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" }, { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" }, { "type": "null" } ] }, "changeVersionId": { "description": "ID of the current version of change within this Change Request. It's possible to modify the changes that have been requested. At the same time, we need to make sure that review is associated with the correct changes, that's why we implement versioning on the changes and associate user reviews with the specific Change Request versions.", "type": "string" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the request was created.", "format": "date-time", "type": "string" }, "diff": { "description": "The difference between the current entity state and the state of the entity if the Change Request gets applied.", "properties": { "changesFrom": { "description": "List of human readable messages describing the state of the entity before changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" }, "changesTo": { "description": "List of human readable messages describing the state of the entity after changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "changesFrom", "changesTo" ], "type": "object" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" }, "id": { "description": "ID of the Change Request.", "type": "string" }, "numApprovalsRequired": { "description": "Number of approving reviews required for the Change Request to be considered approved.", "minimum": 0, "type": "integer" }, "processedAt": { "description": "Timestamp when the request was processed.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Change Request Status.", "enum": [ "pending", "Pending", "PENDING", "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "resolved", "Resolved", "RESOLVED", "cancelled", "Cancelled", "CANCELLED" ], "type": "string" }, "statusChangedAt": { "description": "Timestamp when the current request status was set. `null` if status is set by the system.", "format": "date-time", "type": [ "string", "null" ] }, "statusChangedBy": { "description": "ID of the user who set the current request status. `null` if the status was set by the system.", "type": [ "string", "null" ] }, "updatedAt": { "description": "Timestamp when the request was last modified.", "format": "date-time", "type": "string" }, "userId": { "description": "ID of the user, who created the Change Request.", "type": "string" }, "userName": { "description": "Email of the user, who created the Change Request", "type": [ "string", "null" ] }, "userOperations": { "description": "A set operations the user can or can not make with the Change Request.", "properties": { "canCancel": { "description": "Whether the user can cancel the Change Request.", "type": "boolean" }, "canComment": { "description": "Whether the user can create commenting review on the Change Request.", "type": "boolean" }, "canResolve": { "description": "Whether the user can resolve the Change Request.", "type": "boolean" }, "canReview": { "description": "Whether the user can review (approve or request updates) the Change Request.", "type": "boolean" }, "canUpdate": { "description": "Whether the user can update the Change Request.", "type": "boolean" } }, "required": [ "canCancel", "canComment", "canResolve", "canReview", "canUpdate" ], "type": "object" } }, "required": [ "action", "autoApply", "change", "changeVersionId", "createdAt", "diff", "entityId", "entityType", "id", "numApprovalsRequired", "processedAt", "status", "statusChangedAt", "statusChangedBy", "updatedAt", "userId", "userName", "userOperations" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ChangeRequestResponse | ## Update Change Request by change request ID Operation path: `PATCH /api/v2/changeRequests/{changeRequestId}/` Authentication requirements: `BearerAuth` Update Change Request with the given ID. ### Body parameter ``` { "properties": { "autoApply": { "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change that the user wants to apply to the entity. Needs to be provided if the action, like `approve` action for a deployment, requires additional parameters . `null` if the action does not require any additional parameters to be applied. ", "oneOf": [ { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" }, { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" }, { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" }, { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" }, { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" }, { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" }, { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" } ] }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | changeRequestId | path | string | true | ID of the Change Request. | | body | body | ChangeRequestUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "approve", "Approve", "APPROVE", "changeStatus", "ChangeStatus", "CHANGE_STATUS", "changeImportance", "ChangeImportance", "CHANGE_IMPORTANCE", "cleanupStats", "CleanupStats", "CLEANUP_STATS", "delete", "Delete", "DELETE", "replaceModel", "ReplaceModel", "REPLACE_MODEL", "replaceModelPackage", "ReplaceModelPackage", "REPLACE_MODEL_PACKAGE", "updateSecondaryDatasetConfigs", "UpdateSecondaryDatasetConfigs", "UPDATE_SECONDARY_DATASET_CONFIGS" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change that the user wants to apply to the entity. Needs to be provided if the action, like `approve` action for a deployment, requires additional parameters . `null` if the action does not require any additional parameters to be applied. ", "oneOf": [ { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" }, { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" }, { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" }, { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" }, { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" }, { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" }, { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" }, { "type": "null" } ] }, "changeVersionId": { "description": "ID of the current version of change within this Change Request. It's possible to modify the changes that have been requested. At the same time, we need to make sure that review is associated with the correct changes, that's why we implement versioning on the changes and associate user reviews with the specific Change Request versions.", "type": "string" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the request was created.", "format": "date-time", "type": "string" }, "diff": { "description": "The difference between the current entity state and the state of the entity if the Change Request gets applied.", "properties": { "changesFrom": { "description": "List of human readable messages describing the state of the entity before changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" }, "changesTo": { "description": "List of human readable messages describing the state of the entity after changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "changesFrom", "changesTo" ], "type": "object" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" }, "id": { "description": "ID of the Change Request.", "type": "string" }, "numApprovalsRequired": { "description": "Number of approving reviews required for the Change Request to be considered approved.", "minimum": 0, "type": "integer" }, "processedAt": { "description": "Timestamp when the request was processed.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Change Request Status.", "enum": [ "pending", "Pending", "PENDING", "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "resolved", "Resolved", "RESOLVED", "cancelled", "Cancelled", "CANCELLED" ], "type": "string" }, "statusChangedAt": { "description": "Timestamp when the current request status was set. `null` if status is set by the system.", "format": "date-time", "type": [ "string", "null" ] }, "statusChangedBy": { "description": "ID of the user who set the current request status. `null` if the status was set by the system.", "type": [ "string", "null" ] }, "updatedAt": { "description": "Timestamp when the request was last modified.", "format": "date-time", "type": "string" }, "userId": { "description": "ID of the user, who created the Change Request.", "type": "string" }, "userName": { "description": "Email of the user, who created the Change Request", "type": [ "string", "null" ] }, "userOperations": { "description": "A set operations the user can or can not make with the Change Request.", "properties": { "canCancel": { "description": "Whether the user can cancel the Change Request.", "type": "boolean" }, "canComment": { "description": "Whether the user can create commenting review on the Change Request.", "type": "boolean" }, "canResolve": { "description": "Whether the user can resolve the Change Request.", "type": "boolean" }, "canReview": { "description": "Whether the user can review (approve or request updates) the Change Request.", "type": "boolean" }, "canUpdate": { "description": "Whether the user can update the Change Request.", "type": "boolean" } }, "required": [ "canCancel", "canComment", "canResolve", "canReview", "canUpdate" ], "type": "object" } }, "required": [ "action", "autoApply", "change", "changeVersionId", "createdAt", "diff", "entityId", "entityType", "id", "numApprovalsRequired", "processedAt", "status", "statusChangedAt", "statusChangedBy", "updatedAt", "userId", "userName", "userOperations" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ChangeRequestResponse | ## Request Change Request Review by change request ID Operation path: `POST /api/v2/changeRequests/{changeRequestId}/requestReview/` Authentication requirements: `BearerAuth` Request review for Change Request with the given ID. ### Body parameter ``` { "properties": { "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" } }, "type": "object", "x-versionadded": "v2.38" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | changeRequestId | path | string | true | ID of the Change Request. | | body | body | ChangeRequestRequestReview | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | None | ## List Change Request reviews by change request ID Operation path: `GET /api/v2/changeRequests/{changeRequestId}/reviews/` Authentication requirements: `BearerAuth` List Change Request reviews. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | status | query | string | false | Review status to filter by. | | changeVersionId | query | string | false | ID of the change version to filter by | | changeRequestId | path | string | true | ID of the Change Request. | ### Enumerated Values | Parameter | Value | | --- | --- | | status | [approved, Approved, APPROVED, changesRequested, ChangesRequested, CHANGES_REQUESTED, commented, Commented, COMMENTED] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of change request reviews.", "items": { "properties": { "changeRequestId": { "description": "ID of the Change Request.", "type": "string" }, "changeVersionId": { "description": "ID of the change version.", "type": "string" }, "comment": { "description": "Free form text to comment on the review.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the review was created.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the review.", "type": "string" }, "status": { "description": "Status of the review.", "enum": [ "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "commented", "Commented", "COMMENTED" ], "type": "string" }, "userId": { "description": "ID of the user, who created the review.", "type": "string" }, "userName": { "description": "Email of the user, who created the review", "type": [ "string", "null" ] } }, "required": [ "changeRequestId", "changeVersionId", "createdAt", "id", "status", "userId", "userName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ReviewsListResponse | ## Create review by change request ID Operation path: `POST /api/v2/changeRequests/{changeRequestId}/reviews/` Authentication requirements: `BearerAuth` Review the Change Request. ### Body parameter ``` { "properties": { "changeVersionId": { "description": "ID of the change version.", "type": "string" }, "comment": { "description": "Free form text to comment on the review.", "maxLength": 10000, "type": "string" }, "status": { "description": "Status of the review.", "enum": [ "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "commented", "Commented", "COMMENTED" ], "type": "string" } }, "required": [ "changeVersionId", "status" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | changeRequestId | path | string | true | ID of the Change Request. | | body | body | ReviewCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "changeRequestId": { "description": "ID of the Change Request.", "type": "string" }, "changeVersionId": { "description": "ID of the change version.", "type": "string" }, "comment": { "description": "Free form text to comment on the review.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the review was created.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the review.", "type": "string" }, "status": { "description": "Status of the review.", "enum": [ "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "commented", "Commented", "COMMENTED" ], "type": "string" }, "userId": { "description": "ID of the user, who created the review.", "type": "string" }, "userName": { "description": "Email of the user, who created the review", "type": [ "string", "null" ] } }, "required": [ "changeRequestId", "changeVersionId", "createdAt", "id", "status", "userId", "userName" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | ReviewResponse | ## Retrieve review by change request ID Operation path: `GET /api/v2/changeRequests/{changeRequestId}/reviews/{reviewId}/` Authentication requirements: `BearerAuth` Retrieve a review by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | changeRequestId | path | string | true | ID of the Change Request. | | reviewId | path | string | true | ID of the review. | ### Example responses > 200 Response ``` { "properties": { "changeRequestId": { "description": "ID of the Change Request.", "type": "string" }, "changeVersionId": { "description": "ID of the change version.", "type": "string" }, "comment": { "description": "Free form text to comment on the review.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the review was created.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the review.", "type": "string" }, "status": { "description": "Status of the review.", "enum": [ "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "commented", "Commented", "COMMENTED" ], "type": "string" }, "userId": { "description": "ID of the user, who created the review.", "type": "string" }, "userName": { "description": "Email of the user, who created the review", "type": [ "string", "null" ] } }, "required": [ "changeRequestId", "changeVersionId", "createdAt", "id", "status", "userId", "userName" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ReviewResponse | ## Resolve by change request ID Operation path: `PATCH /api/v2/changeRequests/{changeRequestId}/status/` Authentication requirements: `BearerAuth` Resolve or Cancel the Change Request. ### Body parameter ``` { "properties": { "status": { "description": "Change Request status to set.", "enum": [ "cancelled", "resolving" ], "type": "string" } }, "required": [ "status" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | changeRequestId | path | string | true | ID of the Change Request. | | body | body | ChangeRequestUpdateStatus | false | none | ### Example responses > 200 Response ``` { "properties": { "status": { "description": "Change Request status to set.", "enum": [ "cancelled", "resolving" ], "type": "string" } }, "required": [ "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ChangeRequestUpdateStatus | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Location | string | | A url that can be polled to check the status. | ## List suggested reviewers by change request ID Operation path: `GET /api/v2/changeRequests/{changeRequestId}/suggestedReviewers/` Authentication requirements: `BearerAuth` List users, suggested to review the Change Request. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | changeRequestId | path | string | true | ID of the Change Request. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of suggested change request reviewers.", "items": { "properties": { "firstName": { "description": "First Name.", "type": "string" }, "lastName": { "description": "Last Name.", "type": "string" }, "userId": { "description": "ID of the User.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "required": [ "firstName", "lastName", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SuggestedReviewersResponse | ## Retrieve configured recommended settings by entitytype Operation path: `GET /api/v2/recommendedSettings/{entityType}/` Authentication requirements: `BearerAuth` Retrieve configured recommended settings for an entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to create or get the recommended settings for. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, Deployment, DEPLOYMENT] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of recommended settings.", "items": { "properties": { "hint": { "description": "An optional hint for the setting.", "maxLength": 255, "type": [ "string", "null" ] }, "label": { "description": "The label of the setting.", "maxLength": 80, "type": "string" }, "setting": { "description": "The internal name of the setting.", "maxLength": 30, "type": "string" } }, "required": [ "label", "setting" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 20, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecommendedSettingsResponse | ## Update recommended settings by entitytype Operation path: `PUT /api/v2/recommendedSettings/{entityType}/` Authentication requirements: `BearerAuth` Update recommended settings for an entity. ### Body parameter ``` { "properties": { "data": { "description": "The list of recommended settings to update.", "items": { "properties": { "hint": { "description": "An optional hint for the setting.", "maxLength": 255, "type": [ "string", "null" ] }, "setting": { "description": "The internal name of the setting.", "maxLength": 30, "type": "string" } }, "required": [ "setting" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 20, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to create or get the recommended settings for. | | body | body | RecommendedSettingsUpdate | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, Deployment, DEPLOYMENT] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Retrieve the list of available setting choices by entitytype Operation path: `GET /api/v2/recommendedSettings/{entityType}/choices/` Authentication requirements: `BearerAuth` Retrieve the list of available setting choices for an entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to create or get the recommended settings for. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, Deployment, DEPLOYMENT] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of recommended settings.", "items": { "properties": { "label": { "description": "The label of the setting.", "maxLength": 80, "type": "string" }, "setting": { "description": "The internal name of the setting.", "maxLength": 30, "type": "string" } }, "required": [ "label", "setting" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 20, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecommendedSettingsChoices | # Schemas ## ApprovalPolicy ``` { "properties": { "active": { "default": true, "description": "Whether this policy is active.", "type": "boolean" }, "automaticAction": { "description": "An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If ``null``, no automated actions will be taken on the related Change Requests.", "properties": { "action": { "description": "Action of the workflow automation.", "enum": [ "cancel", "Cancel", "CANCEL", "approve", "Approve", "APPROVE" ], "type": "string" }, "period": { "description": "Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled.", "format": "duration", "type": "string" } }, "required": [ "action", "period" ], "type": "object" }, "name": { "description": "Name of the Approval Policy.", "maxLength": 50, "type": "string" }, "review": { "description": "An object describing review requirements for Change Requests, related to a specific policy. If ``null``, no additional review requirements are added to the related Change Requests.", "properties": { "groups": { "description": "A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "reminderPeriod": { "description": "Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If ``null``, no review reminders are sent to the reviewers.", "format": "duration", "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" } }, "required": [ "active", "name", "trigger" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | true | | Whether this policy is active. | | automaticAction | ApprovalPolicyAutomaticAction | false | | An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If null, no automated actions will be taken on the related Change Requests. | | name | string | true | maxLength: 50 | Name of the Approval Policy. | | review | ApprovalPolicyReview | false | | An object describing review requirements for Change Requests, related to a specific policy. If null, no additional review requirements are added to the related Change Requests. | | trigger | ApprovalPolicyTrigger | true | | An object describing the trigger for the Approval Policy. | ## ApprovalPolicyAutomaticAction ``` { "description": "An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If ``null``, no automated actions will be taken on the related Change Requests.", "properties": { "action": { "description": "Action of the workflow automation.", "enum": [ "cancel", "Cancel", "CANCEL", "approve", "Approve", "APPROVE" ], "type": "string" }, "period": { "description": "Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled.", "format": "duration", "type": "string" } }, "required": [ "action", "period" ], "type": "object" } ``` An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If `null`, no automated actions will be taken on the related Change Requests. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | Action of the workflow automation. | | period | string(duration) | true | | Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled. | ### Enumerated Values | Property | Value | | --- | --- | | action | [cancel, Cancel, CANCEL, approve, Approve, APPROVE] | ## ApprovalPolicyIntendedAction ``` { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" } ``` An object, describing the approvals workflow intended action. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | Type of action to trigger on. | | condition | ApprovalPolicyIntendedActionCondition | false | | An object, describing the condition to trigger on. | ### Enumerated Values | Property | Value | | --- | --- | | action | [create, Create, CREATE, update, Update, UPDATE, delete, Delete, DELETE] | ## ApprovalPolicyIntendedActionCondition ``` { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } ``` An object, describing the condition to trigger on. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | condition | string | true | | Condition for the field content to trigger on. | | fieldName | string | true | maxLength: 50 | Name of the attribute of the entity to filter entities by. An example value is importance attribute for the deployment entity. | | values | [string] | true | maxItems: 100 | Array of field values to apply condition to trigger on. If null for equals condition, then any value for the field is accepted. | ### Enumerated Values | Property | Value | | --- | --- | | condition | [equals, Equals, EQUALS] | ## ApprovalPolicyListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of Approval Policies.", "items": { "properties": { "active": { "default": true, "description": "Whether this policy is active.", "type": "boolean" }, "automaticAction": { "description": "An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If ``null``, no automated actions will be taken on the related Change Requests.", "properties": { "action": { "description": "Action of the workflow automation.", "enum": [ "cancel", "Cancel", "CANCEL", "approve", "Approve", "APPROVE" ], "type": "string" }, "period": { "description": "Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled.", "format": "duration", "type": "string" } }, "required": [ "action", "period" ], "type": "object" }, "id": { "description": "ID of the Approval Policy.", "type": "string" }, "name": { "description": "Name of the Approval Policy.", "maxLength": 50, "type": "string" }, "openRequests": { "description": "Number of open Change Requests associated with the policy.", "minimum": 0, "type": "integer" }, "review": { "description": "An object describing review requirements for Change Requests, related to a specific policy. If ``null``, no additional review requirements are added to the related Change Requests.", "properties": { "groups": { "description": "A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "reminderPeriod": { "description": "Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If ``null``, no review reminders are sent to the reviewers.", "format": "duration", "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" } }, "required": [ "active", "id", "name", "openRequests", "trigger" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ApprovalPolicyResponse] | true | | List of Approval Policies. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ApprovalPolicyMatchResponse ``` { "properties": { "action": { "description": "Searched policy action.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "entityType": { "description": "Searched typed of the entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "fieldName": { "description": "Name of the entity field to filter policies by.", "maxLength": 50, "type": [ "string", "null" ] }, "fieldValue": { "description": "Value of the entity field to filter policies by.", "maxLength": 50, "type": [ "string", "null" ] }, "policyId": { "description": "ID of the matching approval policy. ``null`` if no matching policies found.", "type": [ "string", "null" ] } }, "required": [ "action", "entityType", "policyId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | Searched policy action. | | entityType | string | true | | Searched typed of the entity. | | fieldName | string,null | false | maxLength: 50 | Name of the entity field to filter policies by. | | fieldValue | string,null | false | maxLength: 50 | Value of the entity field to filter policies by. | | policyId | string,null | true | | ID of the matching approval policy. null if no matching policies found. | ### Enumerated Values | Property | Value | | --- | --- | | action | [create, Create, CREATE, update, Update, UPDATE, delete, Delete, DELETE] | | entityType | [deployment, Deployment, DEPLOYMENT, deploymentModel, DeploymentModel, DEPLOYMENT_MODEL, deploymentConfig, DeploymentConfig, DEPLOYMENT_CONFIG, deploymentStatus, DeploymentStatus, DEPLOYMENT_STATUS, deploymentMonitoringData, DeploymentMonitoringData, DEPLOYMENT_MONITORING_DATA] | ## ApprovalPolicyResponse ``` { "properties": { "active": { "default": true, "description": "Whether this policy is active.", "type": "boolean" }, "automaticAction": { "description": "An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If ``null``, no automated actions will be taken on the related Change Requests.", "properties": { "action": { "description": "Action of the workflow automation.", "enum": [ "cancel", "Cancel", "CANCEL", "approve", "Approve", "APPROVE" ], "type": "string" }, "period": { "description": "Period (ISO 8601) after which an action is executed on a Change Request if it is not resolved or cancelled.", "format": "duration", "type": "string" } }, "required": [ "action", "period" ], "type": "object" }, "id": { "description": "ID of the Approval Policy.", "type": "string" }, "name": { "description": "Name of the Approval Policy.", "maxLength": 50, "type": "string" }, "openRequests": { "description": "Number of open Change Requests associated with the policy.", "minimum": 0, "type": "integer" }, "review": { "description": "An object describing review requirements for Change Requests, related to a specific policy. If ``null``, no additional review requirements are added to the related Change Requests.", "properties": { "groups": { "description": "A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "reminderPeriod": { "description": "Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If ``null``, no review reminders are sent to the reviewers.", "format": "duration", "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" } }, "required": [ "active", "id", "name", "openRequests", "trigger" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | true | | Whether this policy is active. | | automaticAction | ApprovalPolicyAutomaticAction | false | | An object describing the automated action on the Change Request that will be performed if the request is not resolved within a given time period after its creation. If null, no automated actions will be taken on the related Change Requests. | | id | string | true | | ID of the Approval Policy. | | name | string | true | maxLength: 50 | Name of the Approval Policy. | | openRequests | integer | true | minimum: 0 | Number of open Change Requests associated with the policy. | | review | ApprovalPolicyReview | false | | An object describing review requirements for Change Requests, related to a specific policy. If null, no additional review requirements are added to the related Change Requests. | | trigger | ApprovalPolicyTrigger | true | | An object describing the trigger for the Approval Policy. | ## ApprovalPolicyReview ``` { "description": "An object describing review requirements for Change Requests, related to a specific policy. If ``null``, no additional review requirements are added to the related Change Requests.", "properties": { "groups": { "description": "A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "reminderPeriod": { "description": "Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If ``null``, no review reminders are sent to the reviewers.", "format": "duration", "type": [ "string", "null" ] } }, "type": "object" } ``` An object describing review requirements for Change Requests, related to a specific policy. If `null`, no additional review requirements are added to the related Change Requests. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | groups | [PolicyUserGroup] | false | maxItems: 100minItems: 1 | A list of user groups that will be added as required reviewers on Change Requests for the entities that match the policy. | | reminderPeriod | string,null(duration) | false | | Duration period in ISO 8601 format that indicates when to send a reminder for reviewing a Change Request after its creation or last reminder if it hasn't been approved yet. If null, no review reminders are sent to the reviewers. | ## ApprovalPolicyTrigger ``` { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" } ``` An object describing the trigger for the Approval Policy. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityType | string | true | | Type of entity to trigger on. | | filterGroups | [PolicyUserGroup] | false | maxItems: 100 | A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If null, approvals workflow will be triggered for all users. | | intendedAction | ApprovalPolicyIntendedAction | true | | An object, describing the approvals workflow intended action. | | labels | ApprovalPolicyTriggerLabel | false | | Trigger Labels. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [deployment, Deployment, DEPLOYMENT, deploymentModel, DeploymentModel, DEPLOYMENT_MODEL, deploymentConfig, DeploymentConfig, DEPLOYMENT_CONFIG, deploymentStatus, DeploymentStatus, DEPLOYMENT_STATUS, deploymentMonitoringData, DeploymentMonitoringData, DEPLOYMENT_MONITORING_DATA] | ## ApprovalPolicyTriggerLabel ``` { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } ``` Trigger Labels. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | groupLabel | string | true | | Group Label. | | label | string | true | | Label. | ## ApprovalWorkflowListTriggerResponse ``` { "properties": { "data": { "description": "List of available Approval Policy Triggers.", "items": { "description": "An object describing the trigger for the Approval Policy.", "properties": { "entityType": { "description": "Type of entity to trigger on.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "deploymentModel", "DeploymentModel", "DEPLOYMENT_MODEL", "deploymentConfig", "DeploymentConfig", "DEPLOYMENT_CONFIG", "deploymentStatus", "DeploymentStatus", "DEPLOYMENT_STATUS", "deploymentMonitoringData", "DeploymentMonitoringData", "DEPLOYMENT_MONITORING_DATA" ], "type": "string" }, "filterGroups": { "description": "A list of user groups to apply Approval Policy for. If User 'A' and User 'B' are both members of the same organisation, and User 'A' is a member of one of the groups listed in this field,but User 'B' is not, then an approval workflow will be triggerred for User 'A' on an action to the entity that matches policy condition, but not for User 'B'. If ``null``, approvals workflow will be triggered for all users.", "items": { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "intendedAction": { "description": "An object, describing the approvals workflow intended action.", "properties": { "action": { "description": "Type of action to trigger on.", "enum": [ "create", "Create", "CREATE", "update", "Update", "UPDATE", "delete", "Delete", "DELETE" ], "type": "string" }, "condition": { "description": "An object, describing the condition to trigger on.", "properties": { "condition": { "description": "Condition for the field content to trigger on.", "enum": [ "equals", "Equals", "EQUALS" ], "type": "string" }, "fieldName": { "description": "Name of the attribute of the entity to filter entities by. An example value is ``importance`` attribute for the deployment entity.", "maxLength": 50, "type": "string" }, "values": { "description": "Array of field values to apply condition to trigger on. If ``null`` for ``equals`` condition, then any value for the field is accepted.", "items": { "maxLength": 50, "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "condition", "fieldName", "values" ], "type": "object" } }, "required": [ "action" ], "type": "object" }, "labels": { "description": "Trigger Labels.", "properties": { "groupLabel": { "description": "Group Label.", "type": "string" }, "label": { "description": "Label.", "type": "string" } }, "required": [ "groupLabel", "label" ], "type": "object" } }, "required": [ "entityType", "intendedAction" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [ApprovalPolicyTrigger] | true | | List of available Approval Policy Triggers. | ## ChangeRequestCreate ``` { "discriminator": { "propertyName": "action" }, "oneOf": [ { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "approve" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "changeStatus" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "changeImportance" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "cleanupStats" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "delete" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Delete a deployment.", "properties": { "defaultDeploymentId": { "description": "Used by management agent to recalculate endpoint traffic. Traffic from the deploymentbeing deleted flipped to a default deployment.", "type": "string" }, "ignoreManagementAgent": { "default": "false", "description": "Do not wait for management agent to delete the deployment first.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "replaceModel" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "replaceModelPackage" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" }, { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "updateSecondaryDatasetConfigs" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" } }, "required": [ "action", "autoApply", "change", "entityId", "entityType" ], "type": "object" } ] } ``` ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » action | string | true | | Actions the user can take on the entity. Each entity type has a specific set of actions. | | » autoApply | boolean | true | | Whether to automatically apply the change when the request is approved. If true, the requested changes will be applied on approval. | | » change | DeploymentApproveChange | true | | Approve a deployment. | | » comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | | » entityId | string | true | | ID of the Product Entity the request is intended to change. | | » entityType | string | true | | Type of the Product Entity that is requested to be changed. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » action | string | true | | Actions the user can take on the entity. Each entity type has a specific set of actions. | | » autoApply | boolean | true | | Whether to automatically apply the change when the request is approved. If true, the requested changes will be applied on approval. | | » change | DeploymentChangeStatusChange | true | | Change deployment status. | | » comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | | » entityId | string | true | | ID of the Product Entity the request is intended to change. | | » entityType | string | true | | Type of the Product Entity that is requested to be changed. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » action | string | true | | Actions the user can take on the entity. Each entity type has a specific set of actions. | | » autoApply | boolean | true | | Whether to automatically apply the change when the request is approved. If true, the requested changes will be applied on approval. | | » change | DeploymentChangeImportanceChange | true | | Change deployment importance. | | » comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | | » entityId | string | true | | ID of the Product Entity the request is intended to change. | | » entityType | string | true | | Type of the Product Entity that is requested to be changed. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » action | string | true | | Actions the user can take on the entity. Each entity type has a specific set of actions. | | » autoApply | boolean | true | | Whether to automatically apply the change when the request is approved. If true, the requested changes will be applied on approval. | | » change | DeploymentCleanupStatsChange | true | | Cleanup deployment stats. | | » comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | | » entityId | string | true | | ID of the Product Entity the request is intended to change. | | » entityType | string | true | | Type of the Product Entity that is requested to be changed. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » action | string | true | | Actions the user can take on the entity. Each entity type has a specific set of actions. | | » autoApply | boolean | true | | Whether to automatically apply the change when the request is approved. If true, the requested changes will be applied on approval. | | » change | DeploymentDeleteChange | false | | Delete a deployment. | | » comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | | » entityId | string | true | | ID of the Product Entity the request is intended to change. | | » entityType | string | true | | Type of the Product Entity that is requested to be changed. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » action | string | true | | Actions the user can take on the entity. Each entity type has a specific set of actions. | | » autoApply | boolean | true | | Whether to automatically apply the change when the request is approved. If true, the requested changes will be applied on approval. | | » change | DeploymentReplaceModelChange | true | | Replace model in deployment. | | » comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | | » entityId | string | true | | ID of the Product Entity the request is intended to change. | | » entityType | string | true | | Type of the Product Entity that is requested to be changed. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » action | string | true | | Actions the user can take on the entity. Each entity type has a specific set of actions. | | » autoApply | boolean | true | | Whether to automatically apply the change when the request is approved. If true, the requested changes will be applied on approval. | | » change | DeploymentReplaceModelPackageChange | true | | Replace model package in deployment. | | » comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | | » entityId | string | true | | ID of the Product Entity the request is intended to change. | | » entityType | string | true | | Type of the Product Entity that is requested to be changed. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » action | string | true | | Actions the user can take on the entity. Each entity type has a specific set of actions. | | » autoApply | boolean | true | | Whether to automatically apply the change when the request is approved. If true, the requested changes will be applied on approval. | | » change | DeploymentUpdateSecondaryDatasetConfigChange | true | | Update secondary dataset config for deployment. | | » comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | | » entityId | string | true | | ID of the Product Entity the request is intended to change. | | » entityType | string | true | | Type of the Product Entity that is requested to be changed. | ### Enumerated Values | Property | Value | | --- | --- | | action | approve | | entityType | [deployment, Deployment, DEPLOYMENT] | | action | changeStatus | | entityType | [deployment, Deployment, DEPLOYMENT] | | action | changeImportance | | entityType | [deployment, Deployment, DEPLOYMENT] | | action | cleanupStats | | entityType | [deployment, Deployment, DEPLOYMENT] | | action | delete | | entityType | [deployment, Deployment, DEPLOYMENT] | | action | replaceModel | | entityType | [deployment, Deployment, DEPLOYMENT] | | action | replaceModelPackage | | entityType | [deployment, Deployment, DEPLOYMENT] | | action | updateSecondaryDatasetConfigs | | entityType | [deployment, Deployment, DEPLOYMENT] | ## ChangeRequestDiff ``` { "description": "The difference between the current entity state and the state of the entity if the Change Request gets applied.", "properties": { "changesFrom": { "description": "List of human readable messages describing the state of the entity before changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" }, "changesTo": { "description": "List of human readable messages describing the state of the entity after changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "changesFrom", "changesTo" ], "type": "object" } ``` The difference between the current entity state and the state of the entity if the Change Request gets applied. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | changesFrom | [string] | true | maxItems: 1000 | List of human readable messages describing the state of the entity before changes are applied. | | changesTo | [string] | true | maxItems: 1000 | List of human readable messages describing the state of the entity after changes are applied. | ## ChangeRequestInfoListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of Approval Policies.", "items": { "properties": { "changeRequestId": { "description": "ID of the Change Request.", "type": "string" }, "createDate": { "description": "Change Request creation date.", "format": "date-time", "type": "string" }, "entityId": { "description": "ID of the modified entity.", "type": "string" }, "entityName": { "description": "Name of the modified entity.", "type": [ "string", "null" ] }, "requester": { "description": "Username of the account that initiated a Change Request.", "type": "string" }, "state": { "description": "Status of the Change Request.", "enum": [ "OPENED", "RESOLVED", "CANCELLED" ], "type": "string" }, "updateDate": { "description": "Last date when Change Request was modified.", "format": "date-time", "type": [ "string", "null" ] }, "updatedBy": { "description": "Username of the account that last updated the Change Request.", "type": [ "string", "null" ] } }, "required": [ "changeRequestId", "createDate", "entityId", "entityName", "requester", "state", "updateDate", "updatedBy" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ChangeRequestInfoResponse] | true | | List of Approval Policies. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ChangeRequestInfoResponse ``` { "properties": { "changeRequestId": { "description": "ID of the Change Request.", "type": "string" }, "createDate": { "description": "Change Request creation date.", "format": "date-time", "type": "string" }, "entityId": { "description": "ID of the modified entity.", "type": "string" }, "entityName": { "description": "Name of the modified entity.", "type": [ "string", "null" ] }, "requester": { "description": "Username of the account that initiated a Change Request.", "type": "string" }, "state": { "description": "Status of the Change Request.", "enum": [ "OPENED", "RESOLVED", "CANCELLED" ], "type": "string" }, "updateDate": { "description": "Last date when Change Request was modified.", "format": "date-time", "type": [ "string", "null" ] }, "updatedBy": { "description": "Username of the account that last updated the Change Request.", "type": [ "string", "null" ] } }, "required": [ "changeRequestId", "createDate", "entityId", "entityName", "requester", "state", "updateDate", "updatedBy" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | changeRequestId | string | true | | ID of the Change Request. | | createDate | string(date-time) | true | | Change Request creation date. | | entityId | string | true | | ID of the modified entity. | | entityName | string,null | true | | Name of the modified entity. | | requester | string | true | | Username of the account that initiated a Change Request. | | state | string | true | | Status of the Change Request. | | updateDate | string,null(date-time) | true | | Last date when Change Request was modified. | | updatedBy | string,null | true | | Username of the account that last updated the Change Request. | ### Enumerated Values | Property | Value | | --- | --- | | state | [OPENED, RESOLVED, CANCELLED] | ## ChangeRequestRequestReview ``` { "properties": { "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" } }, "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | ## ChangeRequestResponse ``` { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "approve", "Approve", "APPROVE", "changeStatus", "ChangeStatus", "CHANGE_STATUS", "changeImportance", "ChangeImportance", "CHANGE_IMPORTANCE", "cleanupStats", "CleanupStats", "CLEANUP_STATS", "delete", "Delete", "DELETE", "replaceModel", "ReplaceModel", "REPLACE_MODEL", "replaceModelPackage", "ReplaceModelPackage", "REPLACE_MODEL_PACKAGE", "updateSecondaryDatasetConfigs", "UpdateSecondaryDatasetConfigs", "UPDATE_SECONDARY_DATASET_CONFIGS" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change that the user wants to apply to the entity. Needs to be provided if the action, like `approve` action for a deployment, requires additional parameters . `null` if the action does not require any additional parameters to be applied. ", "oneOf": [ { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" }, { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" }, { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" }, { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" }, { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" }, { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" }, { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" }, { "type": "null" } ] }, "changeVersionId": { "description": "ID of the current version of change within this Change Request. It's possible to modify the changes that have been requested. At the same time, we need to make sure that review is associated with the correct changes, that's why we implement versioning on the changes and associate user reviews with the specific Change Request versions.", "type": "string" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the request was created.", "format": "date-time", "type": "string" }, "diff": { "description": "The difference between the current entity state and the state of the entity if the Change Request gets applied.", "properties": { "changesFrom": { "description": "List of human readable messages describing the state of the entity before changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" }, "changesTo": { "description": "List of human readable messages describing the state of the entity after changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "changesFrom", "changesTo" ], "type": "object" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" }, "id": { "description": "ID of the Change Request.", "type": "string" }, "numApprovalsRequired": { "description": "Number of approving reviews required for the Change Request to be considered approved.", "minimum": 0, "type": "integer" }, "processedAt": { "description": "Timestamp when the request was processed.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Change Request Status.", "enum": [ "pending", "Pending", "PENDING", "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "resolved", "Resolved", "RESOLVED", "cancelled", "Cancelled", "CANCELLED" ], "type": "string" }, "statusChangedAt": { "description": "Timestamp when the current request status was set. `null` if status is set by the system.", "format": "date-time", "type": [ "string", "null" ] }, "statusChangedBy": { "description": "ID of the user who set the current request status. `null` if the status was set by the system.", "type": [ "string", "null" ] }, "updatedAt": { "description": "Timestamp when the request was last modified.", "format": "date-time", "type": "string" }, "userId": { "description": "ID of the user, who created the Change Request.", "type": "string" }, "userName": { "description": "Email of the user, who created the Change Request", "type": [ "string", "null" ] }, "userOperations": { "description": "A set operations the user can or can not make with the Change Request.", "properties": { "canCancel": { "description": "Whether the user can cancel the Change Request.", "type": "boolean" }, "canComment": { "description": "Whether the user can create commenting review on the Change Request.", "type": "boolean" }, "canResolve": { "description": "Whether the user can resolve the Change Request.", "type": "boolean" }, "canReview": { "description": "Whether the user can review (approve or request updates) the Change Request.", "type": "boolean" }, "canUpdate": { "description": "Whether the user can update the Change Request.", "type": "boolean" } }, "required": [ "canCancel", "canComment", "canResolve", "canReview", "canUpdate" ], "type": "object" } }, "required": [ "action", "autoApply", "change", "changeVersionId", "createdAt", "diff", "entityId", "entityType", "id", "numApprovalsRequired", "processedAt", "status", "statusChangedAt", "statusChangedBy", "updatedAt", "userId", "userName", "userOperations" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | Actions the user can take on the entity. Each entity type has a specific set of actions. | | autoApply | boolean | true | | Whether to automatically apply the change when the request is approved. If true, the requested changes will be applied on approval. | | change | any | true | | Change that the user wants to apply to the entity. Needs to be provided if the action, like approve action for a deployment, requires additional parameters . null if the action does not require any additional parameters to be applied. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentApproveChange | false | | Approve a deployment. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentChangeStatusChange | false | | Change deployment status. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentChangeImportanceChange | false | | Change deployment importance. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentCleanupStatsChange | false | | Cleanup deployment stats. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentReplaceModelChange | false | | Replace model in deployment. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentReplaceModelPackageChange | false | | Replace model package in deployment. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentUpdateSecondaryDatasetConfigChange | false | | Update secondary dataset config for deployment. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | changeVersionId | string | true | | ID of the current version of change within this Change Request. It's possible to modify the changes that have been requested. At the same time, we need to make sure that review is associated with the correct changes, that's why we implement versioning on the changes and associate user reviews with the specific Change Request versions. | | comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | | createdAt | string(date-time) | true | | Timestamp when the request was created. | | diff | ChangeRequestDiff | true | | The difference between the current entity state and the state of the entity if the Change Request gets applied. | | entityId | string | true | | ID of the Product Entity the request is intended to change. | | entityType | string | true | | Type of the Product Entity that is requested to be changed. | | id | string | true | | ID of the Change Request. | | numApprovalsRequired | integer | true | minimum: 0 | Number of approving reviews required for the Change Request to be considered approved. | | processedAt | string,null(date-time) | true | | Timestamp when the request was processed. | | status | string | true | | Change Request Status. | | statusChangedAt | string,null(date-time) | true | | Timestamp when the current request status was set. null if status is set by the system. | | statusChangedBy | string,null | true | | ID of the user who set the current request status. null if the status was set by the system. | | updatedAt | string(date-time) | true | | Timestamp when the request was last modified. | | userId | string | true | | ID of the user, who created the Change Request. | | userName | string,null | true | | Email of the user, who created the Change Request | | userOperations | UserOperations | true | | A set operations the user can or can not make with the Change Request. | ### Enumerated Values | Property | Value | | --- | --- | | action | [approve, Approve, APPROVE, changeStatus, ChangeStatus, CHANGE_STATUS, changeImportance, ChangeImportance, CHANGE_IMPORTANCE, cleanupStats, CleanupStats, CLEANUP_STATS, delete, Delete, DELETE, replaceModel, ReplaceModel, REPLACE_MODEL, replaceModelPackage, ReplaceModelPackage, REPLACE_MODEL_PACKAGE, updateSecondaryDatasetConfigs, UpdateSecondaryDatasetConfigs, UPDATE_SECONDARY_DATASET_CONFIGS] | | entityType | [deployment, Deployment, DEPLOYMENT] | | status | [pending, Pending, PENDING, approved, Approved, APPROVED, changesRequested, ChangesRequested, CHANGES_REQUESTED, resolved, Resolved, RESOLVED, cancelled, Cancelled, CANCELLED] | ## ChangeRequestUpdate ``` { "properties": { "autoApply": { "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change that the user wants to apply to the entity. Needs to be provided if the action, like `approve` action for a deployment, requires additional parameters . `null` if the action does not require any additional parameters to be applied. ", "oneOf": [ { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" }, { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" }, { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" }, { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" }, { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" }, { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" }, { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" } ] }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | autoApply | boolean | false | | Whether to automatically apply the change when the request is approved. If true, the requested changes will be applied on approval. | | change | any | false | | Change that the user wants to apply to the entity. Needs to be provided if the action, like approve action for a deployment, requires additional parameters . null if the action does not require any additional parameters to be applied. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentApproveChange | false | | Approve a deployment. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentChangeStatusChange | false | | Change deployment status. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentChangeImportanceChange | false | | Change deployment importance. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentCleanupStatsChange | false | | Cleanup deployment stats. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentReplaceModelChange | false | | Replace model in deployment. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentReplaceModelPackageChange | false | | Replace model package in deployment. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentUpdateSecondaryDatasetConfigChange | false | | Update secondary dataset config for deployment. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comment | string | false | maxLength: 10000 | Free form text to comment on the requested changes. | ## ChangeRequestUpdateStatus ``` { "properties": { "status": { "description": "Change Request status to set.", "enum": [ "cancelled", "resolving" ], "type": "string" } }, "required": [ "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | status | string | true | | Change Request status to set. | ### Enumerated Values | Property | Value | | --- | --- | | status | [cancelled, resolving] | ## ChangeRequestsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of Change Requests", "items": { "properties": { "action": { "description": "Actions the user can take on the entity. Each entity type has a specific set of actions.", "enum": [ "approve", "Approve", "APPROVE", "changeStatus", "ChangeStatus", "CHANGE_STATUS", "changeImportance", "ChangeImportance", "CHANGE_IMPORTANCE", "cleanupStats", "CleanupStats", "CLEANUP_STATS", "delete", "Delete", "DELETE", "replaceModel", "ReplaceModel", "REPLACE_MODEL", "replaceModelPackage", "ReplaceModelPackage", "REPLACE_MODEL_PACKAGE", "updateSecondaryDatasetConfigs", "UpdateSecondaryDatasetConfigs", "UPDATE_SECONDARY_DATASET_CONFIGS" ], "type": "string" }, "autoApply": { "default": false, "description": "Whether to automatically apply the change when the request is approved. If `true`, the requested changes will be applied on approval.", "type": "boolean" }, "change": { "description": "Change that the user wants to apply to the entity. Needs to be provided if the action, like `approve` action for a deployment, requires additional parameters . `null` if the action does not require any additional parameters to be applied. ", "oneOf": [ { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" }, { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" }, { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" }, { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" }, { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" }, { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" }, { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" }, { "type": "null" } ] }, "changeVersionId": { "description": "ID of the current version of change within this Change Request. It's possible to modify the changes that have been requested. At the same time, we need to make sure that review is associated with the correct changes, that's why we implement versioning on the changes and associate user reviews with the specific Change Request versions.", "type": "string" }, "comment": { "description": "Free form text to comment on the requested changes.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the request was created.", "format": "date-time", "type": "string" }, "diff": { "description": "The difference between the current entity state and the state of the entity if the Change Request gets applied.", "properties": { "changesFrom": { "description": "List of human readable messages describing the state of the entity before changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" }, "changesTo": { "description": "List of human readable messages describing the state of the entity after changes are applied.", "items": { "description": "Single message line.", "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "changesFrom", "changesTo" ], "type": "object" }, "entityId": { "description": "ID of the Product Entity the request is intended to change.", "type": "string" }, "entityType": { "description": "Type of the Product Entity that is requested to be changed.", "enum": [ "deployment", "Deployment", "DEPLOYMENT" ], "type": "string" }, "id": { "description": "ID of the Change Request.", "type": "string" }, "numApprovalsRequired": { "description": "Number of approving reviews required for the Change Request to be considered approved.", "minimum": 0, "type": "integer" }, "processedAt": { "description": "Timestamp when the request was processed.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Change Request Status.", "enum": [ "pending", "Pending", "PENDING", "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "resolved", "Resolved", "RESOLVED", "cancelled", "Cancelled", "CANCELLED" ], "type": "string" }, "statusChangedAt": { "description": "Timestamp when the current request status was set. `null` if status is set by the system.", "format": "date-time", "type": [ "string", "null" ] }, "statusChangedBy": { "description": "ID of the user who set the current request status. `null` if the status was set by the system.", "type": [ "string", "null" ] }, "updatedAt": { "description": "Timestamp when the request was last modified.", "format": "date-time", "type": "string" }, "userId": { "description": "ID of the user, who created the Change Request.", "type": "string" }, "userName": { "description": "Email of the user, who created the Change Request", "type": [ "string", "null" ] }, "userOperations": { "description": "A set operations the user can or can not make with the Change Request.", "properties": { "canCancel": { "description": "Whether the user can cancel the Change Request.", "type": "boolean" }, "canComment": { "description": "Whether the user can create commenting review on the Change Request.", "type": "boolean" }, "canResolve": { "description": "Whether the user can resolve the Change Request.", "type": "boolean" }, "canReview": { "description": "Whether the user can review (approve or request updates) the Change Request.", "type": "boolean" }, "canUpdate": { "description": "Whether the user can update the Change Request.", "type": "boolean" } }, "required": [ "canCancel", "canComment", "canResolve", "canReview", "canUpdate" ], "type": "object" } }, "required": [ "action", "autoApply", "change", "changeVersionId", "createdAt", "diff", "entityId", "entityType", "id", "numApprovalsRequired", "processedAt", "status", "statusChangedAt", "statusChangedBy", "updatedAt", "userId", "userName", "userOperations" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ChangeRequestResponse] | true | maxItems: 1000 | List of Change Requests | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DeploymentApproveChange ``` { "description": "Approve a deployment.", "properties": { "approvalStatus": { "description": "Deployment approval status to set.", "enum": [ "APPROVED" ], "type": "string" } }, "required": [ "approvalStatus" ], "type": "object" } ``` Approve a deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | approvalStatus | string | true | | Deployment approval status to set. | ### Enumerated Values | Property | Value | | --- | --- | | approvalStatus | APPROVED | ## DeploymentChangeImportanceChange ``` { "description": "Change deployment importance.", "properties": { "importance": { "description": "Deployment Importance to set.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string" } }, "required": [ "importance" ], "type": "object" } ``` Change deployment importance. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | importance | string | true | | Deployment Importance to set. | ### Enumerated Values | Property | Value | | --- | --- | | importance | [CRITICAL, HIGH, MODERATE, LOW] | ## DeploymentChangeStatusChange ``` { "description": "Change deployment status.", "properties": { "status": { "description": "Deployment status to set.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" } ``` Change deployment status. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | status | string | true | | Deployment status to set. | ### Enumerated Values | Property | Value | | --- | --- | | status | [active, inactive] | ## DeploymentCleanupStatsChange ``` { "description": "Cleanup deployment stats.", "properties": { "dataType": { "description": "Type of stats to cleanup.", "enum": [ "monitoring" ], "type": "string" }, "end": { "description": "If specified, the stats will be cleaned up to this timestamp. If ``null`` all stats till the deployment end forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "default": null, "description": "ID of the model to remove deployment stats for. If ``null``, the stats will be cleaned up for all models in the mathcing period. Defaults to ``null``.", "type": [ "string", "null" ] }, "start": { "description": "If specified, the stats will be cleaned up from this timestamp. If ``null`` all stats from the deployment start forecast date will be cleaned up. Defaults to ``null``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "dataType", "end", "modelId", "start" ], "type": "object" } ``` Cleanup deployment stats. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataType | string | true | | Type of stats to cleanup. | | end | string,null(date-time) | true | | If specified, the stats will be cleaned up to this timestamp. If null all stats till the deployment end forecast date will be cleaned up. Defaults to null. | | modelId | string,null | true | | ID of the model to remove deployment stats for. If null, the stats will be cleaned up for all models in the mathcing period. Defaults to null. | | start | string,null(date-time) | true | | If specified, the stats will be cleaned up from this timestamp. If null all stats from the deployment start forecast date will be cleaned up. Defaults to null. | ### Enumerated Values | Property | Value | | --- | --- | | dataType | monitoring | ## DeploymentDeleteChange ``` { "description": "Delete a deployment.", "properties": { "defaultDeploymentId": { "description": "Used by management agent to recalculate endpoint traffic. Traffic from the deploymentbeing deleted flipped to a default deployment.", "type": "string" }, "ignoreManagementAgent": { "default": "false", "description": "Do not wait for management agent to delete the deployment first.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "type": "object" } ``` Delete a deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultDeploymentId | string | false | | Used by management agent to recalculate endpoint traffic. Traffic from the deploymentbeing deleted flipped to a default deployment. | | ignoreManagementAgent | string | false | | Do not wait for management agent to delete the deployment first. | ### Enumerated Values | Property | Value | | --- | --- | | ignoreManagementAgent | [false, False, true, True] | ## DeploymentReplaceModelChange ``` { "description": "Replace model in deployment.", "properties": { "modelId": { "description": "ID of the Model to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelId", "replacementReason" ], "type": "object" } ``` Replace model in deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | ID of the Model to deploy. | | replacementReason | string | true | | Reason for replacement. | ### Enumerated Values | Property | Value | | --- | --- | | replacementReason | [accuracy, data_drift, errors, scheduled_refresh, scoring_speed, deprecation, other] | ## DeploymentReplaceModelPackageChange ``` { "description": "Replace model package in deployment.", "properties": { "modelPackageId": { "description": "ID of the Model Package to deploy.", "type": "string" }, "replacementReason": { "default": "other", "description": "Reason for replacement.", "enum": [ "accuracy", "data_drift", "errors", "scheduled_refresh", "scoring_speed", "deprecation", "other" ], "type": "string" } }, "required": [ "modelPackageId", "replacementReason" ], "type": "object" } ``` Replace model package in deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelPackageId | string | true | | ID of the Model Package to deploy. | | replacementReason | string | true | | Reason for replacement. | ### Enumerated Values | Property | Value | | --- | --- | | replacementReason | [accuracy, data_drift, errors, scheduled_refresh, scoring_speed, deprecation, other] | ## DeploymentUpdateSecondaryDatasetConfigChange ``` { "description": "Update secondary dataset config for deployment.", "properties": { "secondaryDatasetsConfigId": { "description": "ID of the secondary dataset configs.", "type": "string" } }, "required": [ "secondaryDatasetsConfigId" ], "type": "object" } ``` Update secondary dataset config for deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | secondaryDatasetsConfigId | string | true | | ID of the secondary dataset configs. | ## PolicyUserGroup ``` { "properties": { "id": { "description": "ID of the user group.", "type": "string" }, "name": { "description": "Name of the user group.", "maxLength": 50, "type": [ "string", "null" ] } }, "required": [ "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the user group. | | name | string,null | false | maxLength: 50 | Name of the user group. | ## RecommendedSetting ``` { "properties": { "hint": { "description": "An optional hint for the setting.", "maxLength": 255, "type": [ "string", "null" ] }, "label": { "description": "The label of the setting.", "maxLength": 80, "type": "string" }, "setting": { "description": "The internal name of the setting.", "maxLength": 30, "type": "string" } }, "required": [ "label", "setting" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | hint | string,null | false | maxLength: 255 | An optional hint for the setting. | | label | string | true | maxLength: 80 | The label of the setting. | | setting | string | true | maxLength: 30 | The internal name of the setting. | ## RecommendedSettingChoice ``` { "properties": { "label": { "description": "The label of the setting.", "maxLength": 80, "type": "string" }, "setting": { "description": "The internal name of the setting.", "maxLength": 30, "type": "string" } }, "required": [ "label", "setting" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | string | true | maxLength: 80 | The label of the setting. | | setting | string | true | maxLength: 30 | The internal name of the setting. | ## RecommendedSettingItem ``` { "properties": { "hint": { "description": "An optional hint for the setting.", "maxLength": 255, "type": [ "string", "null" ] }, "setting": { "description": "The internal name of the setting.", "maxLength": 30, "type": "string" } }, "required": [ "setting" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | hint | string,null | false | maxLength: 255 | An optional hint for the setting. | | setting | string | true | maxLength: 30 | The internal name of the setting. | ## RecommendedSettingsChoices ``` { "properties": { "data": { "description": "The list of recommended settings.", "items": { "properties": { "label": { "description": "The label of the setting.", "maxLength": 80, "type": "string" }, "setting": { "description": "The internal name of the setting.", "maxLength": 30, "type": "string" } }, "required": [ "label", "setting" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 20, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [RecommendedSettingChoice] | true | maxItems: 20 | The list of recommended settings. | ## RecommendedSettingsResponse ``` { "properties": { "data": { "description": "The list of recommended settings.", "items": { "properties": { "hint": { "description": "An optional hint for the setting.", "maxLength": 255, "type": [ "string", "null" ] }, "label": { "description": "The label of the setting.", "maxLength": 80, "type": "string" }, "setting": { "description": "The internal name of the setting.", "maxLength": 30, "type": "string" } }, "required": [ "label", "setting" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 20, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [RecommendedSetting] | true | maxItems: 20 | The list of recommended settings. | ## RecommendedSettingsUpdate ``` { "properties": { "data": { "description": "The list of recommended settings to update.", "items": { "properties": { "hint": { "description": "An optional hint for the setting.", "maxLength": 255, "type": [ "string", "null" ] }, "setting": { "description": "The internal name of the setting.", "maxLength": 30, "type": "string" } }, "required": [ "setting" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 20, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [RecommendedSettingItem] | true | maxItems: 20 | The list of recommended settings to update. | ## ReviewCreate ``` { "properties": { "changeVersionId": { "description": "ID of the change version.", "type": "string" }, "comment": { "description": "Free form text to comment on the review.", "maxLength": 10000, "type": "string" }, "status": { "description": "Status of the review.", "enum": [ "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "commented", "Commented", "COMMENTED" ], "type": "string" } }, "required": [ "changeVersionId", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | changeVersionId | string | true | | ID of the change version. | | comment | string | false | maxLength: 10000 | Free form text to comment on the review. | | status | string | true | | Status of the review. | ### Enumerated Values | Property | Value | | --- | --- | | status | [approved, Approved, APPROVED, changesRequested, ChangesRequested, CHANGES_REQUESTED, commented, Commented, COMMENTED] | ## ReviewResponse ``` { "properties": { "changeRequestId": { "description": "ID of the Change Request.", "type": "string" }, "changeVersionId": { "description": "ID of the change version.", "type": "string" }, "comment": { "description": "Free form text to comment on the review.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the review was created.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the review.", "type": "string" }, "status": { "description": "Status of the review.", "enum": [ "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "commented", "Commented", "COMMENTED" ], "type": "string" }, "userId": { "description": "ID of the user, who created the review.", "type": "string" }, "userName": { "description": "Email of the user, who created the review", "type": [ "string", "null" ] } }, "required": [ "changeRequestId", "changeVersionId", "createdAt", "id", "status", "userId", "userName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | changeRequestId | string | true | | ID of the Change Request. | | changeVersionId | string | true | | ID of the change version. | | comment | string | false | maxLength: 10000 | Free form text to comment on the review. | | createdAt | string(date-time) | true | | Timestamp when the review was created. | | id | string | true | | ID of the review. | | status | string | true | | Status of the review. | | userId | string | true | | ID of the user, who created the review. | | userName | string,null | true | | Email of the user, who created the review | ### Enumerated Values | Property | Value | | --- | --- | | status | [approved, Approved, APPROVED, changesRequested, ChangesRequested, CHANGES_REQUESTED, commented, Commented, COMMENTED] | ## ReviewsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of change request reviews.", "items": { "properties": { "changeRequestId": { "description": "ID of the Change Request.", "type": "string" }, "changeVersionId": { "description": "ID of the change version.", "type": "string" }, "comment": { "description": "Free form text to comment on the review.", "maxLength": 10000, "type": "string" }, "createdAt": { "description": "Timestamp when the review was created.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the review.", "type": "string" }, "status": { "description": "Status of the review.", "enum": [ "approved", "Approved", "APPROVED", "changesRequested", "ChangesRequested", "CHANGES_REQUESTED", "commented", "Commented", "COMMENTED" ], "type": "string" }, "userId": { "description": "ID of the user, who created the review.", "type": "string" }, "userName": { "description": "Email of the user, who created the review", "type": [ "string", "null" ] } }, "required": [ "changeRequestId", "changeVersionId", "createdAt", "id", "status", "userId", "userName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ReviewResponse] | true | maxItems: 1000 | List of change request reviews. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## SuggestedReviewersResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of suggested change request reviewers.", "items": { "properties": { "firstName": { "description": "First Name.", "type": "string" }, "lastName": { "description": "Last Name.", "type": "string" }, "userId": { "description": "ID of the User.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "required": [ "firstName", "lastName", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UserInfo] | true | maxItems: 1000 | List of suggested change request reviewers. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UserInfo ``` { "properties": { "firstName": { "description": "First Name.", "type": "string" }, "lastName": { "description": "Last Name.", "type": "string" }, "userId": { "description": "ID of the User.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "required": [ "firstName", "lastName", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | firstName | string | true | | First Name. | | lastName | string | true | | Last Name. | | userId | string | true | | ID of the User. | | username | string | true | | Username. | ## UserOperations ``` { "description": "A set operations the user can or can not make with the Change Request.", "properties": { "canCancel": { "description": "Whether the user can cancel the Change Request.", "type": "boolean" }, "canComment": { "description": "Whether the user can create commenting review on the Change Request.", "type": "boolean" }, "canResolve": { "description": "Whether the user can resolve the Change Request.", "type": "boolean" }, "canReview": { "description": "Whether the user can review (approve or request updates) the Change Request.", "type": "boolean" }, "canUpdate": { "description": "Whether the user can update the Change Request.", "type": "boolean" } }, "required": [ "canCancel", "canComment", "canResolve", "canReview", "canUpdate" ], "type": "object" } ``` A set operations the user can or can not make with the Change Request. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canCancel | boolean | true | | Whether the user can cancel the Change Request. | | canComment | boolean | true | | Whether the user can create commenting review on the Change Request. | | canResolve | boolean | true | | Whether the user can resolve the Change Request. | | canReview | boolean | true | | Whether the user can review (approve or request updates) the Change Request. | | canUpdate | boolean | true | | Whether the user can update the Change Request. | --- # Batch predictions URL: https://docs.datarobot.com/en/docs/api/reference/public-api/batch_predictions.html > Use the endpoints described below to generate predictions for a project. # Batch predictions Use the endpoints described below to generate predictions for a project. ## List batch jobs Operation path: `GET /api/v2/batchJobs/` Authentication requirements: `BearerAuth` Get a collection of batch jobs by statuses. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | status | query | any | false | Includes only jobs that have the status value that matches this flag. Repeat the parameter for filtering on multiple statuses. | | source | query | any | false | Includes only jobs that have the source value that matches this flag. Repeat the parameter for filtering on multiple statuses.Prefix values with a dash (-) to exclude those sources. | | deploymentId | query | string | false | Includes only jobs for this particular deployment | | modelId | query | string | false | ID of leaderboard model which is used in job for processing predictions dataset | | jobId | query | string | false | Includes only job by specific id | | orderBy | query | string | false | Sort order which will be applied to batch prediction list. Prefix the attribute name with a dash to sort in descending order, e.g. "-created". | | allJobs | query | boolean | false | [DEPRECATED - replaced with RBAC permission model] - No effect | | cutoffHours | query | integer | false | Only list jobs created at most this amount of hours ago. | | startDateTime | query | string(date-time) | false | ISO-formatted datetime of the earliest time the job was added (inclusive). For example "2008-08-24T12:00:00Z". Will ignore cutoffHours if set. | | endDateTime | query | string(date-time) | false | ISO-formatted datetime of the latest time the job was added (inclusive). For example "2008-08-24T12:00:00Z". | | batchPredictionJobDefinitionId | query | string | false | Includes only jobs for this particular definition | | hostname | query | any | false | Includes only jobs for this particular prediction instance hostname | | batchJobType | query | any | false | Includes only jobs that have the batch job type that matches this flag. Repeat the parameter for filtering on multiple types. | | intakeType | query | any | false | Includes only jobs for these particular intakes type | | outputType | query | any | false | Includes only jobs for these particular outputs type | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [created, -created, status, -status] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of jobs", "items": { "properties": { "batchMonitoringJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "id": { "description": "The ID of the Batch job", "type": "string", "x-versionadded": "v2.30" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object", "x-versionadded": "v2.35" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.30" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.30" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.30" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.30" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.30" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of Batch job objects | BatchJobListResponse | ## Launch a Batch job Operation path: `POST /api/v2/batchJobs/fromJobDefinition/` Authentication requirements: `BearerAuth` Launches a one-time batch job based off of the previously supplied definition referring to the job definition ID and puts it on the queue. ### Body parameter ``` { "properties": { "jobDefinitionId": { "description": "ID of the Batch Prediction job definition", "type": "string" } }, "required": [ "jobDefinitionId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | BatchPredictionJobDefinitionId | false | none | ### Example responses > 202 Response ``` { "properties": { "batchMonitoringJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "id": { "description": "The ID of the Batch job", "type": "string", "x-versionadded": "v2.30" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object", "x-versionadded": "v2.35" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.30" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.30" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.30" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.30" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.30" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job details for the created Batch Prediction job | BatchJobResponse | | 404 | Not Found | Job was deleted, never existed or you do not have access to it | None | | 422 | Unprocessable Entity | Could not create a Batch job. Possible reasons: {} | None | ## Cancel a Batch job by batch job ID Operation path: `DELETE /api/v2/batchJobs/{batchJobId}/` Authentication requirements: `BearerAuth` If the job is running, it will be aborted. Then it will be removed, meaning all underlying data will be deleted and the job is removed from the list of jobs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | batchJobId | path | string | true | ID of the Batch job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job cancelled | None | | 404 | Not Found | Job does not exist or was not submitted to the queue. | None | | 409 | Conflict | Job cannot be aborted for some reason. Possible reasons: job is already aborted or completed. | None | ## Retrieve Batch job by batch job ID Operation path: `GET /api/v2/batchJobs/{batchJobId}/` Authentication requirements: `BearerAuth` Retrieve a Batch job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | batchJobId | path | string | true | ID of the Batch job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | ### Example responses > 200 Response ``` { "properties": { "batchMonitoringJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "id": { "description": "The ID of the Batch job", "type": "string", "x-versionadded": "v2.30" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object", "x-versionadded": "v2.35" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.30" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.30" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.30" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.30" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.30" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Job details for the requested Batch job | BatchJobResponse | ## Stream CSV data by batch job ID Operation path: `PUT /api/v2/batchJobs/{batchJobId}/csvUpload/` Authentication requirements: `BearerAuth` Stream CSV data to the job. Only available for jobs thatuses the localFile intake option. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | batchJobId | path | string | true | ID of the Batch job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job data was successfully submitted | None | | 404 | Not Found | Job does not exist or does not require data | None | | 409 | Conflict | Dataset upload has already begun | None | | 415 | Unsupported Media Type | Not acceptable MIME type | None | | 422 | Unprocessable Entity | Job was "ABORTED" due to too many errors in the data | None | ## Download the scored data set of a batch job by batch job ID Operation path: `GET /api/v2/batchJobs/{batchJobId}/download/` Authentication requirements: `BearerAuth` This is only valid for jobs scored using the "localFile" output option. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | batchJobId | path | string | true | ID of the Batch job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Job was downloaded correctly | None | | 404 | Not Found | Job does not exist or is not completed | None | | 406 | Not Acceptable | Not acceptable MIME type | None | | 422 | Unprocessable Entity | Job was "ABORTED" due to too many errors in the data | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ("attachment;filename=result-.csv"). | | 200 | Content-Type | string | | MIME type of the returned data | ## List Batch Prediction job definitions Operation path: `GET /api/v2/batchPredictionJobDefinitions/` Authentication requirements: `BearerAuth` List all Batch Prediction jobs definitions available. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | searchName | query | string | false | A human-readable name for the definition, must be unique across organisations. | | deploymentId | query | string | false | Includes only definitions for this particular deployment | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of scheduled jobs", "items": { "properties": { "batchPredictionJob": { "description": "The Batch Prediction Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "numConcurrent", "redactedFields", "skipDriftTracking" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchPredictionJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of all available jobs | BatchPredictionJobDefinitionsListResponse | | 422 | Unprocessable Entity | Your input data or query arguments did not work together | None | ## Creates a new Batch Prediction job definition Operation path: `POST /api/v2/batchPredictionJobDefinitions/` Authentication requirements: `BearerAuth` Create a Batch Prediction Job definition. A configuration for a Batch Prediction job which can either be executed manually upon request or on scheduled intervals, if enabled. The API payload is the same as for `/batchPredictions` along with optional `enabled` and `schedule` items. ### Body parameter ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "enabled": { "description": "If this job definition is enabled as a scheduled job. Optional if no schedule is supplied.", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you.", "maxLength": 100, "minLength": 1, "type": "string" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "deploymentId", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "skipDriftTracking" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | BatchPredictionJobDefinitionsCreate | false | none | ### Example responses > 202 Response ``` { "properties": { "batchPredictionJob": { "description": "The Batch Prediction Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "numConcurrent", "redactedFields", "skipDriftTracking" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchPredictionJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job details for the created Batch Prediction job definition | BatchPredictionJobDefinitionsResponse | | 403 | Forbidden | You are not authorized to create a job definition on this deployment due to your permissions role | None | | 422 | Unprocessable Entity | You tried to create a job definition with uncompatible or missing parameters to create a fully functioning job definition | None | ## Delete Batch Prediction job definition by job definition ID Operation path: `DELETE /api/v2/batchPredictionJobDefinitions/{jobDefinitionId}/` Authentication requirements: `BearerAuth` Delete a Batch Prediction job definition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobDefinitionId | path | string | true | ID of the Batch Prediction job definition | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | You are not authorized to delete this job definition due to your permissions role | None | | 404 | Not Found | Job was deleted, never existed or you do not have access to it | None | | 409 | Conflict | Job could not be deleted, as there are currently running jobs in the queue. | None | ## Retrieve Batch Prediction job definition by job definition ID Operation path: `GET /api/v2/batchPredictionJobDefinitions/{jobDefinitionId}/` Authentication requirements: `BearerAuth` Retrieve a Batch Prediction job definition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobDefinitionId | path | string | true | ID of the Batch Prediction job definition | ### Example responses > 200 Response ``` { "properties": { "batchPredictionJob": { "description": "The Batch Prediction Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "numConcurrent", "redactedFields", "skipDriftTracking" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchPredictionJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Job details for the requested Batch Prediction job definition | BatchPredictionJobDefinitionsResponse | | 404 | Not Found | Job was deleted, never existed or you do not have access to it | None | ## Update Batch Prediction job definition by job definition ID Operation path: `PATCH /api/v2/batchPredictionJobDefinitions/{jobDefinitionId}/` Authentication requirements: `BearerAuth` Update a Batch Prediction job definition. ### Body parameter ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "enabled": { "description": "If this job definition is enabled as a scheduled job. Optional if no schedule is supplied.", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you.", "maxLength": 100, "minLength": 1, "type": "string" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobDefinitionId | path | string | true | ID of the Batch Prediction job definition | | body | body | BatchPredictionJobDefinitionsUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "batchPredictionJob": { "description": "The Batch Prediction Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "numConcurrent", "redactedFields", "skipDriftTracking" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchPredictionJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Job details for the updated Batch Prediction job definition | BatchPredictionJobDefinitionsResponse | | 403 | Forbidden | You are not authorized to alter the contents of this job definition due to your permissions role | None | | 404 | Not Found | Job was deleted, never existed or you do not have access to it | None | | 409 | Conflict | You chose a name of your job definition that was already existing within your organization | None | | 422 | Unprocessable Entity | Could not update the job definition. Possible reasons: {} | None | ## Retrieve job definition snippet by job definition ID Operation path: `GET /api/v2/batchPredictionJobDefinitions/{jobDefinitionId}/portable/` Authentication requirements: `BearerAuth` Retrieve a Batch Prediction job definition for Portable Batch Predictions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobDefinitionId | path | string | true | ID of the Batch Prediction job definition | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Snippet for Portable Batch Predictions | None | | 404 | Not Found | Job was deleted, never existed or you do not have access to it | None | ## List batch prediction jobs Operation path: `GET /api/v2/batchPredictions/` Authentication requirements: `BearerAuth` Get a collection of batch prediction jobs by statuses. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | status | query | any | false | Includes only jobs that have the status value that matches this flag. Repeat the parameter for filtering on multiple statuses. | | source | query | any | false | Includes only jobs that have the source value that matches this flag. Repeat the parameter for filtering on multiple statuses.Prefix values with a dash (-) to exclude those sources. | | deploymentId | query | string | false | Includes only jobs for this particular deployment | | modelId | query | string | false | ID of leaderboard model which is used in job for processing predictions dataset | | jobId | query | string | false | Includes only job by specific id | | orderBy | query | string | false | Sort order which will be applied to batch prediction list. Prefix the attribute name with a dash to sort in descending order, e.g. "-created". | | allJobs | query | boolean | false | [DEPRECATED - replaced with RBAC permission model] - No effect | | cutoffHours | query | integer | false | Only list jobs created at most this amount of hours ago. | | startDateTime | query | string(date-time) | false | ISO-formatted datetime of the earliest time the job was added (inclusive). For example "2008-08-24T12:00:00Z". Will ignore cutoffHours if set. | | endDateTime | query | string(date-time) | false | ISO-formatted datetime of the latest time the job was added (inclusive). For example "2008-08-24T12:00:00Z". | | batchPredictionJobDefinitionId | query | string | false | Includes only jobs for this particular definition | | hostname | query | any | false | Includes only jobs for this particular prediction instance hostname | | intakeType | query | any | false | Includes only jobs for these particular intakes type | | outputType | query | any | false | Includes only jobs for these particular outputs type | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [created, -created, status, -status] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of jobs", "items": { "properties": { "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.21" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.24" }, "id": { "description": "The ID of the Batch Prediction job", "type": "string", "x-versionadded": "v2.21" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.26" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.24" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.20" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.24" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of Batch Prediction job objects | BatchPredictionJobListResponse | ## Creates a new Batch Prediction job Operation path: `POST /api/v2/batchPredictions/` Authentication requirements: `BearerAuth` Submit the configuration for the job and it will be submitted to the queue. ### Body parameter ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "skipDriftTracking" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | BatchPredictionJobCreate | false | none | ### Example responses > 202 Response ``` { "properties": { "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.21" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.24" }, "id": { "description": "The ID of the Batch Prediction job", "type": "string", "x-versionadded": "v2.21" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.26" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.24" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.20" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.24" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job details for the created Batch Prediction job | BatchPredictionJobResponse | ## Create a new a Batch Prediction job based Operation path: `POST /api/v2/batchPredictions/fromExisting/` Authentication requirements: `BearerAuth` Copies an existing job and submits it to the queue. ### Body parameter ``` { "properties": { "partNumber": { "default": 0, "description": "The number of which csv part is being uploaded when using multipart upload ", "minimum": 0, "type": "integer", "x-versionadded": "v2.27" }, "predictionJobId": { "description": "ID of the Batch Prediction job", "type": "string" } }, "required": [ "partNumber", "predictionJobId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | BatchPredictionJobId | false | none | ### Example responses > 202 Response ``` { "properties": { "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.21" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.24" }, "id": { "description": "The ID of the Batch Prediction job", "type": "string", "x-versionadded": "v2.21" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.26" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.24" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.20" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.24" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job details for the created Batch Prediction job | BatchPredictionJobResponse | ## Launch a Batch Prediction job Operation path: `POST /api/v2/batchPredictions/fromJobDefinition/` Authentication requirements: `BearerAuth` Launches a one-time batch prediction job based off of the previously supplied definition referring to the job definition ID and puts it on the queue. ### Body parameter ``` { "properties": { "jobDefinitionId": { "description": "ID of the Batch Prediction job definition", "type": "string" } }, "required": [ "jobDefinitionId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | BatchPredictionJobDefinitionId | false | none | ### Example responses > 202 Response ``` { "properties": { "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.21" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.24" }, "id": { "description": "The ID of the Batch Prediction job", "type": "string", "x-versionadded": "v2.21" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.26" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.24" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.20" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.24" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job details for the created Batch Prediction job | BatchPredictionJobResponse | | 404 | Not Found | Job was deleted, never existed or you do not have access to it | None | | 422 | Unprocessable Entity | Could not create a Batch job. Possible reasons: {} | None | ## Cancel a Batch Prediction job by prediction job ID Operation path: `DELETE /api/v2/batchPredictions/{predictionJobId}/` Authentication requirements: `BearerAuth` If the job is running, it will be aborted. Then it will be removed, meaning all underlying data will be deleted and the job is removed from the list of jobs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | predictionJobId | path | string | true | ID of the Batch Prediction job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job cancelled | None | | 404 | Not Found | Job does not exist or was not submitted to the queue. | None | | 409 | Conflict | Job cannot be aborted for some reason. Possible reasons: job is already aborted or completed. | None | ## Retrieve Batch Prediction job by prediction job ID Operation path: `GET /api/v2/batchPredictions/{predictionJobId}/` Authentication requirements: `BearerAuth` Retrieve a Batch Prediction job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | predictionJobId | path | string | true | ID of the Batch Prediction job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | ### Example responses > 200 Response ``` { "properties": { "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.21" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.24" }, "id": { "description": "The ID of the Batch Prediction job", "type": "string", "x-versionadded": "v2.21" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.26" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.24" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.20" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.24" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Job details for the requested Batch Prediction job | BatchPredictionJobResponse | ## Update a Batch Prediction job by prediction job ID Operation path: `PATCH /api/v2/batchPredictions/{predictionJobId}/` Authentication requirements: `BearerAuth` If a job has finished execution regardless of the result, it can have parameters changed to ensure better filtering in the job list upon retrieval. Another case: updating job scoring status externally. ### Body parameter ``` { "properties": { "aborted": { "description": "Time when job abortion happened", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "completed": { "description": "Time when job completed scoring", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "failedRows": { "description": "Number of rows that have failed scoring", "type": "integer", "x-versionadded": "v2.26" }, "hidden": { "description": "Hides or unhides the job from the job list", "type": "boolean", "x-versionadded": "v2.24" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "type": [ "integer", "null" ], "x-versionadded": "v2.26" }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "type": [ "integer", "null" ], "x-versionadded": "v2.26" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array", "x-versionadded": "v2.26" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "type": "integer", "x-versionadded": "v2.26" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "type": "integer", "x-versionadded": "v2.26" }, "started": { "description": "Time when job scoring begin", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string", "x-versionadded": "v2.26" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | predictionJobId | path | string | true | ID of the Batch Prediction job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | | body | body | BatchPredictionJobUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.21" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.24" }, "id": { "description": "The ID of the Batch Prediction job", "type": "string", "x-versionadded": "v2.21" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.26" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.24" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.20" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.24" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Job updated | BatchPredictionJobResponse | | 404 | Not Found | Job does not exist or was not submitted to the queue. | None | | 409 | Conflict | Job cannot be hidden for some reason. Possible reasons: job is not in a deletable state. | None | ## Creates a new_model_id Batch Prediction job by prediction job ID Operation path: `PUT /api/v2/batchPredictions/{predictionJobId}/csvUpload/` Authentication requirements: `BearerAuth` Stream CSV data to the prediction job. Only available for jobs thatuses the localFile intake option. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | predictionJobId | path | string | true | ID of the Batch Prediction job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job data was successfully submitted | None | | 404 | Not Found | Job does not exist or does not require data | None | | 406 | Not Acceptable | Not acceptable MIME type | None | | 409 | Conflict | Dataset upload has already begun | None | | 422 | Unprocessable Entity | Job was "ABORTED" due to too many errors in the data | None | ## Finalize a multipart upload by prediction job ID Operation path: `POST /api/v2/batchPredictions/{predictionJobId}/csvUpload/finalizeMultipart/` Authentication requirements: `BearerAuth` Finalize a multipart upload, indicating that no further chunks will be sent. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | predictionJobId | path | string | true | ID of the Batch Prediction job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Acknowledgement that the request was accepted or an error message | None | | 404 | Not Found | Job was deleted, never existed or you do not have access to it | None | | 409 | Conflict | Only multipart jobs can be finalized. | None | | 422 | Unprocessable Entity | No data was uploaded | None | ## Upload CSV data by prediction job ID Operation path: `PUT /api/v2/batchPredictions/{predictionJobId}/csvUpload/part/{partNumber}/` Authentication requirements: `BearerAuth` Stream CSV data to the prediction job in many parts.Only available for jobs that uses the localFile intake option. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | predictionJobId | path | string | true | ID of the Batch Prediction job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job data was successfully submitted | None | | 404 | Not Found | Job does not exist or does not require data | None | | 406 | Not Acceptable | Not acceptable MIME type | None | | 409 | Conflict | Dataset upload has already begun | None | | 422 | Unprocessable Entity | Job was "ABORTED" due to too many errors in the data | None | ## Download the scored data set of a batch prediction job by prediction job ID Operation path: `GET /api/v2/batchPredictions/{predictionJobId}/download/` Authentication requirements: `BearerAuth` This is only valid for jobs scored using the "localFile" output option. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | predictionJobId | path | string | true | ID of the Batch Prediction job | | partNumber | path | integer | true | The number of which csv part is being uploaded when using multipart upload | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Job was downloaded correctly | None | | 404 | Not Found | Job does not exist or is not completed | None | | 406 | Not Acceptable | Not acceptable MIME type | None | | 422 | Unprocessable Entity | Job was "ABORTED" due to too many errors in the data | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ("attachment;filename=result-.csv"). | | 200 | Content-Type | string | | MIME type of the returned data | ## Delete an existing PredictionExplanationsInitialization by project ID Operation path: `DELETE /api/v2/projects/{projectId}/models/{modelId}/predictionExplanationsInitialization/` Authentication requirements: `BearerAuth` Delete an existing PredictionExplanationsInitialization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The deletion was successful. | None | ## Retrieve the current PredictionExplanationsInitialization by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/predictionExplanationsInitialization/` Authentication requirements: `BearerAuth` Retrieve the current PredictionExplanationsInitialization. A PredictionExplanationsInitialization is a pre-requisite for successfully computing prediction explanations using a particular model, and can be used to preview the prediction explanations that would be generated for a complete dataset. ### Body parameter ``` { "properties": { "modelId": { "description": "The model ID.", "type": "string" }, "predictionExplanationsSample": { "description": "Each is a PredictionExplanationsRow. They represent a small sample of prediction explanations that could be generated for a particular dataset. They will have the same schema as the `data` array in the response from [GET /api/v2/projects/{projectId}/predictionExplanations/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationspredictionexplanationsid]. As of v2.21 only difference is that there is no forecastPoint in response for time series projects.", "items": { "properties": { "adjustedPrediction": { "description": "The exposure-adjusted output of the model for this row.", "type": "number", "x-versionadded": "v2.8" }, "adjustedPredictionValues": { "description": "The exposure-adjusted output of the model for this row.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.8" }, "forecastDistance": { "description": "Forecast distance for the row. For time series projects only.", "type": "integer", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "Forecast point for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" }, "prediction": { "description": "The output of the model for this row.", "type": "number" }, "predictionExplanations": { "description": "The list of prediction explanations.", "items": { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "The threshold value used for classification prediction.", "type": [ "number", "null" ] }, "predictionValues": { "description": "The list of prediction values.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row this PredictionExplanationsRow describes.", "type": "integer" }, "seriesId": { "description": "The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "timestamp": { "description": "The timestamp for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "adjustedPrediction", "adjustedPredictionValues", "forecastDistance", "forecastPoint", "prediction", "predictionExplanations", "predictionThreshold", "predictionValues", "rowId", "seriesId", "timestamp" ], "type": "object" }, "type": "array" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "modelId", "predictionExplanationsSample", "projectId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | excludeAdjustedPredictions | query | string | false | Whether to include adjusted predictions in the PredictionExplanationsSample response. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | PredictionExplanationsInitializationRetrieve | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | excludeAdjustedPredictions | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Create a new prediction explanations initialization by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/predictionExplanationsInitialization/` Authentication requirements: `BearerAuth` Create a new prediction explanations initialization. This is a necessary prerequisite for generating prediction explanations. ### Body parameter ``` { "properties": { "maxExplanations": { "default": 3, "description": "The maximum number of prediction explanations to supply per row of the dataset.", "maximum": 10, "minimum": 1, "type": "integer" }, "thresholdHigh": { "default": null, "description": "The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] }, "thresholdLow": { "default": null, "description": "The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | PredictionExplanationsInitializationCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was accepted and will be worked on. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## List all prediction jobs by project ID Operation path: `GET /api/v2/projects/{projectId}/predictJobs/` Authentication requirements: `BearerAuth` List all prediction jobs for a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | status | query | string | false | If provided, only jobs with the same status will be included in the results; otherwise, queued and inprogress jobs (but not errored jobs) will be returned. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | status | [queue, inprogress, error] | ### Example responses > 200 Response ``` { "items": { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if a job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "message": { "description": "An optional message about the job.", "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The status of the job.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" } }, "required": [ "id", "isBlocked", "message", "modelId", "projectId", "status" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of prediction jobs for the project. | Inline | | 404 | Not Found | Job was not found | None | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [PredictJobDetailsResponse] | false | | none | | » id | string | true | | The job ID. | | » isBlocked | boolean | true | | True if a job is waiting for its dependencies to be resolved first. | | » message | string | true | | An optional message about the job. | | » modelId | string | true | | The ID of the model. | | » projectId | string | true | | The project the job belongs to. | | » status | string | true | | The status of the job. | ### Enumerated Values | Property | Value | | --- | --- | | status | [queue, inprogress, error, ABORTED, COMPLETED] | ## Cancel a queued prediction job by project ID Operation path: `DELETE /api/v2/projects/{projectId}/predictJobs/{jobId}/` Authentication requirements: `BearerAuth` Cancel a queued prediction job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | jobId | path | string | true | The job ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The job was successfully cancelled. | None | | 404 | Not Found | Job was not found or the job has already completed | None | ## Look up a particular prediction job by project ID Operation path: `GET /api/v2/projects/{projectId}/predictJobs/{jobId}/` Authentication requirements: `BearerAuth` Look up a particular prediction job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | jobId | path | string | true | The job ID. | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if a job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "message": { "description": "An optional message about the job.", "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The status of the job.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" } }, "required": [ "id", "isBlocked", "message", "modelId", "projectId", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The job has been successfully retrieved and has not yet finished. | PredictJobDetailsResponse | | 303 | See Other | The job has been successfully retrieved and has been completed. See Location header. The response json is also included. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Location | string | url | present only when the requested job has finished - contains a url from which the completed predictions may be retrieved as with [GET /api/v2/projects/{projectId}/predictions/{predictionId}/][get-apiv2projectsprojectidpredictionspredictionid] | ## List prediction datasets uploaded by project ID Operation path: `GET /api/v2/projects/{projectId}/predictionDatasets/` Authentication requirements: `BearerAuth` List prediction datasets uploaded to a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. If 0, all results. | | projectId | path | string | true | The project ID to query. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the dataset individually from [GET /api/v2/projects/{projectId}/predictionDatasets/{datasetId}/][get-apiv2projectsprojectidpredictiondatasetsdatasetid].", "items": { "properties": { "actualValueColumn": { "description": "Optional, only available for unsupervised projects, in case dataset was uploaded with actual value column specified. Name of the column which will be used to calculate the classification metrics and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "catalogId": { "description": "The ID of the AI catalog entry used to create the prediction, dataset or None if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "catalogVersionId": { "description": "The ID of the AI catalog version used to create the prediction dataset, or None if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "containsTargetValues": { "description": "If true, dataset contains target values and can be used to calculate the classification metrics and insights. Only applies for supervised projects.", "type": [ "boolean", "null" ], "x-versionadded": "v2.21" }, "created": { "description": "The date string of when the dataset was created, of the format `YYYY-mm-ddTHH:MM:SS.ssssssZ`, like ``2016-06-09T11:32:34.170338Z``.", "format": "date-time", "type": "string" }, "dataEndDate": { "description": "Only available for time series projects, a date string representing the maximum primary date of the prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "dataQualityWarnings": { "description": "A Json object of available warnings about potential problems in this prediction dataset. Empty if no warnings.", "properties": { "hasKiaMissingValuesInForecastWindow": { "description": "If true, known-in-advance features in this dataset have missing values in the forecast window. Absence of the known-in-advance values can negatively impact prediction quality. Only applies for time series projects.", "type": "boolean", "x-versionadded": "v2.15" }, "insufficientRowsForEvaluatingModels": { "description": "If true, the dataset has a target column present indicating it can be used to evaluate model performance but too few rows to be trustworthy in so doing. If false, either it has no target column at all or it has sufficient rows for model evaluation. Only applies for regression, binary classification, multiclass classification projects and time series unsupervised projects.", "type": "boolean", "x-versionadded": "v2.19" }, "singleClassActualValueColumn": { "description": "If true, actual value column has only one class and such insights as ROC curve can not be calculated. Only applies for binary classification projects or unsupervised projects.", "type": "boolean", "x-versionadded": "v2.21" } }, "type": "object" }, "dataStartDate": { "description": "Only available for time series projects, a date string representing the minimum primary date of the prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "detectedActualValueColumns": { "description": "Only available for unsupervised projects, the list of detected `actualValueColumnInfo` objects which can be used to calculate the classification metrics and insights.", "items": { "properties": { "missingCount": { "description": "Count of the missing values in the column.", "type": "integer", "x-versionadded": "v2.21" }, "name": { "description": "Name of the column.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "missingCount", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "The date string of the forecastPoint of this prediction dataset. Only non-null for time series projects.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "forecastPointRange": { "description": "Only available for time series projects, the start and end of the range of dates available for use as the forecast point, detected based on the uploaded prediction dataset.", "items": { "description": "The date string of a forecast point.", "format": "date-time", "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "id": { "description": "The ID of this dataset.", "type": "string" }, "maxForecastDate": { "description": "Only available for time series projects, a date string representing the maximum forecast date of this prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "name": { "description": "The name of the dataset when it was uploaded.", "type": "string" }, "numColumns": { "description": "The number of columns in this dataset.", "type": "integer" }, "numRows": { "description": "The number of rows in this dataset.", "type": "integer" }, "predictionsEndDate": { "description": "The date string of the prediction end date of this prediction dataset. Used for bulk predictions. Note that this parameter is for generating historical predictions using the training data. Only non-null for time series projects.", "format": "date-time", "type": [ "string", "null" ] }, "predictionsStartDate": { "description": "The date string of the prediction start date of this prediction dataset. Used for bulk predictions. Note that this parameter is for generating historical predictions using the training data. Only non-null for time series projects.", "format": "date-time", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID that owns this dataset.", "type": "string" }, "secondaryDatasetsConfigId": { "description": "Only available for feature discovery projects. The ID of the secondary dataset config used by the dataset for the prediction.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "catalogId", "catalogVersionId", "created", "dataQualityWarnings", "forecastPoint", "id", "name", "numColumns", "numRows", "predictionsEndDate", "predictionsStartDate", "projectId" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Request to list the uploaded predictions datasets was successful. | PredictionDatasetListControllerResponse | ## Upload a dataset by project ID Operation path: `POST /api/v2/projects/{projectId}/predictionDatasets/dataSourceUploads/` Authentication requirements: `BearerAuth` Upload a dataset for predictions from a `DataSource`. ### Body parameter ``` { "properties": { "actualValueColumn": { "description": "The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset.", "type": "string", "x-versionadded": "v2.21" }, "credentialData": { "description": "The credentials to authenticate with the database, to use instead of user/password or credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The credential ID to use for database authentication.", "type": "string", "x-versionadded": "v2.19" }, "credentials": { "description": "A list of credentials for the secondary datasets used in feature discovery project.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" } }, "required": [ "credentialId" ], "type": "object" } ] }, "maxItems": 30, "type": "array", "x-versionadded": "v2.19" }, "dataSourceId": { "description": "The ID of ``DataSource``.", "type": "string" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error.", "format": "date-time", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. DEPRECATED: please use ``credentialId`` or ``credentialData`` instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "predictionsEndDate": { "description": "The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "description": "For time series projects only. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. This value is optional. If omitted or false, missing values are not allowed.", "type": "boolean", "x-versionadded": "v2.15" }, "secondaryDatasetsConfigId": { "description": "For feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction.", "type": "string", "x-versionadded": "v2.19" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication. Default is false.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for database authentication. DEPRECATED: please use ``credentialId`` or ``credentialData`` instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "dataSourceId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID to which the data source will be uploaded to. | | body | body | PredictionDataSource | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Upload successfully started. See the Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create prediction dataset by project ID Operation path: `POST /api/v2/projects/{projectId}/predictionDatasets/datasetUploads/` Authentication requirements: `BearerAuth` Create a prediction dataset from a Dataset Asset referenced by AI Catalog item/version ID. ### Body parameter ``` { "properties": { "actualValueColumn": { "description": "Actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset.", "type": "string", "x-versionadded": "v2.21" }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.19" }, "credentials": { "description": "List of credentials for the secondary datasets used in feature discovery project.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" } }, "required": [ "credentialId" ], "type": "object" } ] }, "maxItems": 30, "type": "array", "x-versionadded": "v2.19" }, "datasetId": { "description": "The ID of the dataset entry to use for prediction dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version to use for the prediction dataset. If not specified - uses latest version associated with datasetId.", "type": "string" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error.", "format": "date-time", "type": "string", "x-versionadded": "v2.8" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "predictionsEndDate": { "description": "The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "description": "For time series projects only. If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or False, missing values are not allowed.", "type": "boolean" }, "secondaryDatasetsConfigId": { "description": "For feature discovery projects only. The Id of the alternative secondary dataset config to use during prediction.", "type": "string", "x-versionadded": "v2.19" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication. Default is false.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for database authentication. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "datasetId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | PredictionFromCatalogDataset | false | none | ### Example responses > 202 Response ``` { "properties": { "datasetId": { "description": "The ID of the newly created prediction dataset.", "type": "string" } }, "required": [ "datasetId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatePredictionDatasetResponse | | 422 | Unprocessable Entity | Target not set yet or cannot specify time series options with a non time series project. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Upload a file by project ID Operation path: `POST /api/v2/projects/{projectId}/predictionDatasets/fileUploads/` Authentication requirements: `BearerAuth` Upload a file for predictions from an attached file. ### Body parameter ``` { "properties": { "actualValueColumn": { "description": "The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset.", "type": "string", "x-versionadded": "v2.21" }, "credentials": { "description": "The list of credentials for the secondary datasets used in feature discovery project.", "type": "string", "x-versionadded": "v2.19" }, "file": { "description": "The dataset file to upload for prediction.", "format": "binary", "type": "string" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions are generated. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error.", "format": "date-time", "type": "string", "x-versionadded": "v2.8" }, "predictionsEndDate": { "description": "Used for time series projects only. The end date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for time series projects only. The start date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "description": "A boolean flag. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or false, missing values are not allowed. For time series projects only.", "enum": [ "false", "False", "true", "True" ], "type": "string", "x-versionadded": "v2.15" }, "secondaryDatasetsConfigId": { "description": "Optional, for feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction.", "type": "string", "x-versionadded": "v2.19" } }, "required": [ "file" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID to which the data will be uploaded for prediction. | | body | body | PredictionFileUpload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Upload successfully started. See the Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create URL uploads by id Operation path: `POST /api/v2/projects/{projectId}/predictionDatasets/urlUploads/` Authentication requirements: `BearerAuth` Upload a file for predictions from a URL. ### Body parameter ``` { "properties": { "actualValueColumn": { "description": "The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. This value is optional.", "type": "string", "x-versionadded": "v2.21" }, "credentials": { "description": "The list of credentials for the secondary datasets used in feature discovery project.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" } }, "required": [ "credentialId" ], "type": "object" } ] }, "maxItems": 30, "type": "array", "x-versionadded": "v2.19" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions are generated. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error.", "format": "date-time", "type": "string", "x-versionadded": "v2.8" }, "predictionsEndDate": { "description": "Used for time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for time series projects only. The start date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "description": "For time series projects only. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. This value is optional. If omitted or false, missing values are not allowed.", "type": "boolean", "x-versionadded": "v2.15" }, "secondaryDatasetsConfigId": { "description": "For feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction.", "type": "string", "x-versionadded": "v2.19" }, "url": { "description": "The URL to download the dataset from.", "format": "url", "type": "string" } }, "required": [ "url" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID to which the data will be uploaded for prediction. | | body | body | PredictionURLUpload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Upload successfully started. See the Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Delete a dataset that was uploaded by project ID Operation path: `DELETE /api/v2/projects/{projectId}/predictionDatasets/{datasetId}/` Authentication requirements: `BearerAuth` Delete a dataset that was uploaded for prediction. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID that owns the data. | | datasetId | path | string | true | The dataset ID to delete. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The dataset has been successfully deleted. | None | | 404 | Not Found | No dataset with the specified datasetId found. | None | ## Get the metadata of a specific dataset by project ID Operation path: `GET /api/v2/projects/{projectId}/predictionDatasets/{datasetId}/` Authentication requirements: `BearerAuth` Get the metadata of a specific dataset. This only works for datasets uploaded to an existing project for prediction. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID that owns the data. | | datasetId | path | string | true | The dataset ID to query for. | ### Example responses > 200 Response ``` { "properties": { "actualValueColumn": { "description": "Optional, only available for unsupervised projects, in case dataset was uploaded with actual value column specified. Name of the column which will be used to calculate the classification metrics and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "catalogId": { "description": "The ID of the AI catalog entry used to create the prediction, dataset or None if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "catalogVersionId": { "description": "The ID of the AI catalog version used to create the prediction dataset, or None if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "containsTargetValues": { "description": "If true, dataset contains target values and can be used to calculate the classification metrics and insights. Only applies for supervised projects.", "type": [ "boolean", "null" ], "x-versionadded": "v2.21" }, "created": { "description": "The date string of when the dataset was created, of the format `YYYY-mm-ddTHH:MM:SS.ssssssZ`, like ``2016-06-09T11:32:34.170338Z``.", "format": "date-time", "type": "string" }, "dataEndDate": { "description": "Only available for time series projects, a date string representing the maximum primary date of the prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "dataQualityWarnings": { "description": "A Json object of available warnings about potential problems in this prediction dataset. Empty if no warnings.", "properties": { "hasKiaMissingValuesInForecastWindow": { "description": "If true, known-in-advance features in this dataset have missing values in the forecast window. Absence of the known-in-advance values can negatively impact prediction quality. Only applies for time series projects.", "type": "boolean", "x-versionadded": "v2.15" }, "insufficientRowsForEvaluatingModels": { "description": "If true, the dataset has a target column present indicating it can be used to evaluate model performance but too few rows to be trustworthy in so doing. If false, either it has no target column at all or it has sufficient rows for model evaluation. Only applies for regression, binary classification, multiclass classification projects and time series unsupervised projects.", "type": "boolean", "x-versionadded": "v2.19" }, "singleClassActualValueColumn": { "description": "If true, actual value column has only one class and such insights as ROC curve can not be calculated. Only applies for binary classification projects or unsupervised projects.", "type": "boolean", "x-versionadded": "v2.21" } }, "type": "object" }, "dataStartDate": { "description": "Only available for time series projects, a date string representing the minimum primary date of the prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "detectedActualValueColumns": { "description": "Only available for unsupervised projects, the list of detected `actualValueColumnInfo` objects which can be used to calculate the classification metrics and insights.", "items": { "properties": { "missingCount": { "description": "Count of the missing values in the column.", "type": "integer", "x-versionadded": "v2.21" }, "name": { "description": "Name of the column.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "missingCount", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "The date string of the forecastPoint of this prediction dataset. Only non-null for time series projects.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "forecastPointRange": { "description": "Only available for time series projects, the start and end of the range of dates available for use as the forecast point, detected based on the uploaded prediction dataset.", "items": { "description": "The date string of a forecast point.", "format": "date-time", "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "id": { "description": "The ID of this dataset.", "type": "string" }, "maxForecastDate": { "description": "Only available for time series projects, a date string representing the maximum forecast date of this prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "name": { "description": "The name of the dataset when it was uploaded.", "type": "string" }, "numColumns": { "description": "The number of columns in this dataset.", "type": "integer" }, "numRows": { "description": "The number of rows in this dataset.", "type": "integer" }, "predictionsEndDate": { "description": "The date string of the prediction end date of this prediction dataset. Used for bulk predictions. Note that this parameter is for generating historical predictions using the training data. Only non-null for time series projects.", "format": "date-time", "type": [ "string", "null" ] }, "predictionsStartDate": { "description": "The date string of the prediction start date of this prediction dataset. Used for bulk predictions. Note that this parameter is for generating historical predictions using the training data. Only non-null for time series projects.", "format": "date-time", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID that owns this dataset.", "type": "string" }, "secondaryDatasetsConfigId": { "description": "Only available for feature discovery projects. The ID of the secondary dataset config used by the dataset for the prediction.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "catalogId", "catalogVersionId", "created", "dataQualityWarnings", "forecastPoint", "id", "name", "numColumns", "numRows", "predictionsEndDate", "predictionsStartDate", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Request to retrieve the metadata of a specified dataset was successful. | PredictionDatasetRetrieveResponse | ## Create a new PredictionExplanations object ( by project ID Operation path: `POST /api/v2/projects/{projectId}/predictionExplanations/` Authentication requirements: `BearerAuth` Create a new PredictionExplanations object (and its accompanying PredictionExplanationsRecord). In order to successfully create PredictionExplanations for a particular model and dataset, you must first - Compute feature impact for the model via [POST /api/v2/projects/{projectId}/models/{modelId}/featureImpact/][post-apiv2projectsprojectidmodelsmodelidfeatureimpact] - Compute a PredictionExplanationsInitialization for the model via [POST /api/v2/projects/{projectId}/models/{modelId}/predictionExplanationsInitialization/][post-apiv2projectsprojectidmodelsmodelidpredictionexplanationsinitialization] - Compute predictions for the model and dataset via [POST /api/v2/projects/{projectId}/predictions/][post-apiv2projectsprojectidpredictions]`thresholdHigh` and `thresholdLow` are optional filters applied to speed up computation. When at least one is specified, only the selected outlier rows will have prediction explanations computed. Rows are considered to be outliers if their predicted value (in case of regression projects) or probability of being the positive class (in case of classification projects) isless than `thresholdLow` or greater than `thresholdHigh`. If neither is specified, prediction explanations will be computed for all rows. ### Body parameter ``` { "properties": { "classNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.29" }, "datasetId": { "description": "The dataset ID.", "type": "string" }, "maxExplanations": { "default": 3, "description": "The maximum number of prediction explanations to supply per row of the dataset.", "maximum": 10, "minimum": 0, "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "thresholdHigh": { "default": null, "description": "The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] }, "thresholdLow": { "default": null, "description": "The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | PredictionExplanationsCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was accepted and will be worked on. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve stored Prediction Explanations by project ID Operation path: `GET /api/v2/projects/{projectId}/predictionExplanations/{predictionExplanationsId}/` Authentication requirements: `BearerAuth` Retrieve stored Prediction Explanations. Each PredictionExplanationsRow retrieved corresponds to a row of the prediction dataset, although some rows may not have had prediction explanations computed depending on the thresholds selected. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. The default may change and a new maximum limit may be imposed without notice. | | excludeAdjustedPredictions | query | string | false | Whether to include adjusted predictions in the PredictionExplanationsRow response. | | projectId | path | string | true | The project ID. | | predictionExplanationsId | path | string | true | The ID of the PredictionExplanationsRecord to retrieve. | ### Enumerated Values | Parameter | Value | | --- | --- | | excludeAdjustedPredictions | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "adjustmentMethod": { "description": "'exposureNormalized' (for regression projects with exposure) or 'N/A' (for classification projects) The value of 'exposureNormalized' indicates that prediction outputs are adjusted (or divided) by exposure. The value of 'N/A' indicates that no adjustments are applied to the adjusted predictions and they are identical to the unadjusted predictions.", "type": "string", "x-versionadded": "v2.8" }, "count": { "description": "The number of rows of prediction explanations returned.", "type": "integer" }, "data": { "description": "Each is a PredictionExplanationsRow corresponding to one row of the prediction dataset.", "items": { "properties": { "adjustedPrediction": { "description": "The exposure-adjusted output of the model for this row.", "type": "number", "x-versionadded": "v2.8" }, "adjustedPredictionValues": { "description": "The exposure-adjusted output of the model for this row.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.8" }, "forecastDistance": { "description": "Forecast distance for the row. For time series projects only.", "type": "integer", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "Forecast point for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" }, "prediction": { "description": "The output of the model for this row.", "type": "number" }, "predictionExplanations": { "description": "The list of prediction explanations.", "items": { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "The threshold value used for classification prediction.", "type": [ "number", "null" ] }, "predictionValues": { "description": "The list of prediction values.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row this PredictionExplanationsRow describes.", "type": "integer" }, "seriesId": { "description": "The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "timestamp": { "description": "The timestamp for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "adjustedPrediction", "adjustedPredictionValues", "forecastDistance", "forecastPoint", "prediction", "predictionExplanations", "predictionThreshold", "predictionValues", "rowId", "seriesId", "timestamp" ], "type": "object" }, "type": "array" }, "id": { "description": "The ID of this group of prediction explanations.", "type": "string" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "predictionExplanationsRecordLocation": { "description": "The URL of the PredictionExplanationsRecord associated with these prediction explanations.", "type": "string" }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "adjustmentMethod", "count", "data", "id", "next", "predictionExplanationsRecordLocation", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | PredictionExplanationsRetrieve | ## List PredictionExplanationsRecord objects by project ID Operation path: `GET /api/v2/projects/{projectId}/predictionExplanationsRecords/` Authentication requirements: `BearerAuth` List PredictionExplanationsRecord objects for a project. These contain metadata about the computed prediction explanations and the location at which the PredictionExplanations can be retrieved. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | modelId | query | string | false | If specified, only prediction explanations records computed for this model will be returned. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the prediction explanations individually from [GET /api/v2/projects/{projectId}/predictionExplanationsRecords/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationsrecordspredictionexplanationsid].", "items": { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "finishTime": { "description": "The timestamp referencing when computation for these prediction explanations finished.", "type": "number" }, "id": { "description": "The PredictionExplanationsRecord ID.", "type": "string" }, "maxExplanations": { "description": "The maximum number of codes generated per prediction.", "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numColumns": { "description": "The number of columns prediction explanations were computed for.", "type": "integer" }, "predictionExplanationsLocation": { "description": "Where to retrieve the prediction explanations.", "type": "string" }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "thresholdHigh": { "description": "The prediction explanation high threshold. Predictions must be above this value (or below the thresholdLow value) to have PredictionExplanations computed.", "type": [ "number", "null" ] }, "thresholdLow": { "description": "The prediction explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) to have PredictionExplanations computed.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "finishTime", "id", "maxExplanations", "modelId", "numColumns", "predictionExplanationsLocation", "predictionThreshold", "projectId", "thresholdHigh", "thresholdLow" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The object was found and returned successfully. | PredictionExplanationsRecordList | ## Delete saved Prediction Explanations by project ID Operation path: `DELETE /api/v2/projects/{projectId}/predictionExplanationsRecords/{predictionExplanationsId}/` Authentication requirements: `BearerAuth` Delete saved Prediction Explanations. Deletes both the actual prediction explanations and the corresponding PredictionExplanationsRecord. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | predictionExplanationsId | path | string | true | The ID of the PredictionExplanationsRecord to retrieve. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The object was deleted successfully. | None | ## Retrieve a PredictionExplanationsRecord object by project ID Operation path: `GET /api/v2/projects/{projectId}/predictionExplanationsRecords/{predictionExplanationsId}/` Authentication requirements: `BearerAuth` Retrieve a PredictionExplanationsRecord object. A PredictionExplanationsRecord contains metadata about the computed prediction explanations and the location at which the PredictionExplanations can be retrieved. ### Body parameter ``` { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "finishTime": { "description": "The timestamp referencing when computation for these prediction explanations finished.", "type": "number" }, "id": { "description": "The PredictionExplanationsRecord ID.", "type": "string" }, "maxExplanations": { "description": "The maximum number of codes generated per prediction.", "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numColumns": { "description": "The number of columns prediction explanations were computed for.", "type": "integer" }, "predictionExplanationsLocation": { "description": "Where to retrieve the prediction explanations.", "type": "string" }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "thresholdHigh": { "description": "The prediction explanation high threshold. Predictions must be above this value (or below the thresholdLow value) to have PredictionExplanations computed.", "type": [ "number", "null" ] }, "thresholdLow": { "description": "The prediction explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) to have PredictionExplanations computed.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "finishTime", "id", "maxExplanations", "modelId", "numColumns", "predictionExplanationsLocation", "predictionThreshold", "projectId", "thresholdHigh", "thresholdLow" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | predictionExplanationsId | path | string | true | The ID of the PredictionExplanationsRecord to retrieve. | | body | body | PredictionExplanationsRecord | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The object was found and returned successfully. | None | ## Get the list of prediction records by project ID Operation path: `GET /api/v2/projects/{projectId}/predictions/` Authentication requirements: `BearerAuth` Get a list of prediction records. .. deprecated:: v2.21 Use [GET /api/v2/projects/{projectId}/predictionsMetadata/][get-apiv2projectsprojectidpredictionsmetadata] instead. The only difference is that parameter `datasetId` is renamed to `predictionDatasetId` both in request and response. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of results to skip. | | limit | query | integer | true | At most this many results are returned. To specify no limit, use 0. The default may change and a maximum limit may be imposed without notice. | | datasetId | query | string | false | The dataset ID used to create the predictions. | | modelId | query | string | false | The model ID. | | projectId | path | string | true | The project of the predictions. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of the metadata records.", "items": { "properties": { "actualValueColumn": { "description": "For time series unsupervised projects only. The actual value column can be used to calculate the classification metrics and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "datasetId": { "description": "Deprecated alias for `predictionDatasetId`.", "type": [ "string", "null" ] }, "explanationAlgorithm": { "description": "The selected algorithm to use for prediction explanations. At present, the only acceptable value is `shap`, which selects the SHapley Additive exPlanations (SHAP) explainer. Defaults to null (no prediction explanations).", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureDerivationWindowCounts": { "description": "For time series projects with partial history only. Indicates how many points were used during feature derivation.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions were generated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "id": { "description": "The ID of the prediction record.", "type": "string" }, "includesPredictionIntervals": { "description": "Whether the predictions include prediction intervals.", "type": "boolean" }, "maxExplanations": { "description": "The maximum number of prediction explanations values to be returned with each row in the `predictions` json array. Null indicates `no limit`. Will be present only if `explanationAlgorithm` was set.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "modelId": { "description": "The model ID used for predictions.", "type": "string" }, "predictionDatasetId": { "description": "The dataset ID where the prediction data comes from. The field is available via `/api/v2/projects//predictionsMetadata/` route and replaced on `datasetId`in deprecated `/api/v2/projects//predictions/` endpoint.", "type": [ "string", "null" ] }, "predictionIntervalsSize": { "description": "For time series projects only. If prediction intervals were computed, what percentile they represent. Will be ``None`` if ``includePredictionIntervals`` is ``False``.", "type": [ "integer", "null" ] }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "type": [ "number", "null" ], "x-versionadded": "v2.22" }, "predictionsEndDate": { "description": "For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter was used for generating historical predictions using the training data, not for future predictions.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "predictionsStartDate": { "description": "For time series projects only. The start date for bulk predictions. Note that this parameter was used for generating historical predictions using the training data, not for future predictions.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "projectId": { "description": "The project ID of the predictions.", "type": "string" }, "shapWarnings": { "description": "Will be present if `explanationAlgorithm` was set to `shap` and there were additivity failures during SHAP values calculation.", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value.", "type": "number", "x-versionadded": "v2.21" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed.", "type": "integer", "x-versionadded": "v2.21" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "url": { "description": "The URL at which you can download the predictions.", "type": "string" } }, "required": [ "id", "includesPredictionIntervals", "modelId", "predictionIntervalsSize", "projectId", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The JSON array of prediction metadata objects. | RetrieveListPredictionMetadataObjectsResponse | ## Make new predictions by project ID Operation path: `POST /api/v2/projects/{projectId}/predictions/` Authentication requirements: `BearerAuth` There are two ways of making predictions. The recommended way is to first upload your dataset to the project, and then using the corresponding datasetId, predict against that dataset. To follow that pattern, send the json request body. Note that requesting prediction intervals will automatically trigger backtesting if backtests were not already completed for this model. The legacy method which is deprecated is to send the file directly with the predictions request. If you need to predict against a file 10MB in size or larger, you will be required to use the above workflow for uploaded datasets. However, the following multipart/form-data can be used with small files: :form file: a dataset to make predictions on :form modelId: the model to use to make predictions .. note:: If using the legacy method of uploading data to this endpoint, a new dataset will be created behind the scenes. For performance reasons, it would be much better to utilize the workflow of creating the dataset first and using the supported method of making predictions of this endpoint. However, to preserve the functionality of existing workflows, the legacy method still exists. ### Body parameter ``` { "properties": { "actualValueColumn": { "description": "For time series projects only. The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. This value is optional.", "type": "string", "x-versionadded": "v2.21" }, "datasetId": { "description": "The dataset to compute predictions for - must have previously been uploaded.", "type": "string" }, "explanationAlgorithm": { "description": "If set to `shap`, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).", "enum": [ "shap" ], "type": "string" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "includeFdwCounts": { "default": false, "description": "For time series projects with partial history only. Indicates if feature derivation window counts `featureDerivationWindowCounts` will be part of the response.", "type": "boolean", "x-versionadded": "v2.24" }, "includePredictionIntervals": { "description": "Specifies whether prediction intervals should be calculated for this request. Defaults to True if `predictionIntervalsSize` is specified, otherwise defaults to False.", "type": "boolean", "x-versionadded": "v2.16" }, "maxExplanations": { "description": "Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of 'shap': If not set, explanations are returned for all features. If the number of features is greater than the 'maxExplanations', the sum of remaining values will also be returned as 'shapRemainingTotal'. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Cannot be set if 'explanationAlgorithm' is omitted.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "modelId": { "description": "The model to make predictions on.", "type": "string" }, "predictionIntervalsSize": { "description": "Represents the percentile to use for the size of the prediction intervals. Defaults to 80 if `includePredictionIntervals` is True.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.16" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions. Accepts values from 0.0 to 1.0. If not specified, model default prediction threshold will be used.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.22" }, "predictionsEndDate": { "description": "The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "predictionsStartDate": { "description": "The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" } }, "required": [ "datasetId", "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project to make predictions within. | | Content-Type | header | string | true | Content types available for making request. multipart/form-data is the legacy deprecated method to send the small file with the prediction request. | | body | body | CreatePredictionFromDataset | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | Content-Type | [application/json, multipart/form-data] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The prediction has successfully been requested. See the Location header. | None | | 422 | Unprocessable Entity | The request cannot be processed. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status of the predictions as with [GET /api/v2/projects/{projectId}/predictJobs/{jobId}/][get-apiv2projectsprojectidpredictjobsjobid] | ## Get a completed set of predictions by project ID Operation path: `GET /api/v2/projects/{projectId}/predictions/{predictionId}/` Authentication requirements: `BearerAuth` Retrieve predictions that have previously been computed. Training predictions encoded either as JSON or CSV. If CSV output was requested, the returned CSV data will contain the following columns: - For regression projects: row_id and prediction . - For binary classification projects: row_id , prediction , class_ and class_ . - For multiclass projects: row_id , prediction and a class_ for each class. - For multilabel projects: row_id and for each class prediction_ and class_ . - For time-series, these additional columns will be added: forecast_point , forecast_distance , timestamp , and series_id . .. minversion:: v2.21 ``` * If `explanationAlgorithm` = 'shap', these additional columns will be added: triplets of (`Explanation__feature_name`, `Explanation__feature_value`, and `Explanation__strength`) for `i` ranging from 1 to `maxExplanations`, `shap_remaining_total` and `shap_base_value`. Binary classification projects will also have `explained_class`, the class for which positive SHAP values imply an increased probability. ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | predictionId | path | string | true | The ID of the prediction record to retrieve. If you have the jobId, you can retrieve the predictionId using [GET /api/v2/projects/{projectId}/predictJobs/{jobId}/][get-apiv2projectsprojectidpredictjobsjobid]. | | projectId | path | string | true | The ID of the project the prediction belongs to. | | Accept | header | string | false | Requested MIME type for the returned data | ### Enumerated Values | Parameter | Value | | --- | --- | | Accept | [application/json, text/csv] | ### Example responses > 200 Response ``` { "properties": { "actualValueColumn": { "description": "For time series unsupervised projects only. Will be present only if the prediction dataset has an actual value column. The name of the column with actuals that was used to calculate the scores and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "explanationAlgorithm": { "description": "The selected algorithm to use for prediction explanations. At present, the only acceptable value is 'shap', which selects the SHapley Additive exPlanations (SHAP) explainer. Defaults to null (no prediction explanations).", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureDerivationWindowCounts": { "description": "For time series projects with partial history only. Indicates how many points were used during feature derivation in the feature derivation window.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "includesPredictionIntervals": { "description": "For time series projects only. Indicates if prediction intervals will be part of the response. Defaults to False.", "type": "boolean", "x-versionadded": "v2.16" }, "maxExplanations": { "description": "The maximum number of prediction explanations values to be returned with each row in the `predictions` json array. Null indicates 'no limit'. Will be present only if `explanationAlgorithm` was set.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "positiveClass": { "description": "For binary classification, the class of the target deemed the positive class. For all other project types this field will be null.", "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "null" } ] }, "predictionIntervalsSize": { "description": "For time series projects only. Will be present only if `includePredictionIntervals` is True. Indicates the percentile used for prediction intervals calculation. Defaults to 80.", "type": [ "integer", "null" ], "x-versionadded": "v2.16" }, "predictions": { "description": "The json array of predictions. The predictions in the response will have slightly different formats, depending on the project type.", "items": { "properties": { "actualValue": { "description": "In the case of an unsupervised time series project with a dataset using ``predictionsStartDate`` and ``predictionsEndDate`` for bulk predictions and a specified actual value column, the predictions will be a json array in the same format as with a forecast point with one additional element - `actualValues`. It is the actual value in the row.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "forecastDistance": { "description": "(if time series project) The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column.", "type": [ "integer", "null" ] }, "forecastPoint": { "description": "(if time series project) The forecastPoint of the predictions. Either provided or inferred.", "format": "date-time", "type": [ "string", "null" ] }, "originalFormatTimestamp": { "description": "The timestamp of this row in the prediction dataset. Unlike the ``timestamp`` field, this field will keep the same DateTime formatting as the uploaded prediction dataset. (This column is shown if enabled by your administrator.)", "type": "string", "x-versionadded": "v2.17" }, "positiveProbability": { "description": "For binary classification, the probability the row belongs to the positive class.", "minimum": 0, "type": [ "number", "null" ] }, "prediction": { "description": "The prediction of the model.", "oneOf": [ { "description": "If using a regressor model, will be the numeric value of the target.", "type": "number" }, { "description": "If using a binary or muliclass classifier model, will be the predicted class.", "type": "string" }, { "description": "If using a multilabel classifier model, will be a list of predicted classes.", "items": { "type": "string" }, "type": "array" } ] }, "predictionExplanationMetadata": { "description": "Array containing algorithm-specific values. Varies depending on the value of `explanationAlgorithm`.", "items": { "description": "Prediction explanation metadata.", "properties": { "shapRemainingTotal": { "description": "Will be present only if `explanationAlgorithm` = 'shap' and `maxExplanations` is nonzero. The total of SHAP values for features beyond the `maxExplanations`. This can be identically 0 in all rows, if `maxExplanations` is greater than the number of features and thus all features are returned.", "type": "integer" } }, "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "predictionExplanations": { "description": "Array contains `predictionExplanation` objects. The total elements in the array are bounded by maxExplanations and feature count. It will be present only if `explanationAlgorithm` is not null (prediction explanations were requested).", "items": { "description": "Prediction explanation result.", "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. The type corresponds to the feature (bool, int, float, str, etc.).", "oneOf": [ { "type": "integer" }, { "type": "boolean" }, { "type": "string" }, { "type": "number" } ] }, "label": { "description": "Describes what output was driven by this prediction explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation. For predictions made using anomaly detection models, it is the `Anomaly Score`.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "strength": { "description": "Algorithm-specific explanation value attributed to `feature` in this row. If `explanationAlgorithm` = `shap`, this is the SHAP value.", "type": [ "number", "null" ] } }, "required": [ "feature", "featureValue", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "predictionIntervalLowerBound": { "description": "Present if ``includePredictionIntervals`` is True. Indicates a lower bound of the estimate of error based on test data.", "type": "number", "x-versionadded": "v2.16" }, "predictionIntervalUpperBound": { "description": "Present if ``includePredictionIntervals`` is True. Indicates an upper bound of the estimate of error based on test data.", "type": "number", "x-versionadded": "v2.16" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionValues": { "description": "The list of predicted values for this row.", "items": { "description": "Predicted values.", "properties": { "label": { "description": "For regression problems this will be the name of the target column, 'Anomaly score' or ignored field. For classification projects this will be the name of the class.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "threshold": { "description": "Threshold used in multilabel classification for this class.", "maximum": 1, "minimum": 0, "type": "number" }, "value": { "description": "The predicted probability of the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row in the prediction dataset this prediction corresponds to.", "minimum": 0, "type": "integer" }, "segmentId": { "description": "The ID of the segment value for a segmented project.", "type": "string", "x-versionadded": "v2.27" }, "seriesId": { "description": "The ID of the series value for a multiseries project. For time series projects that are not a multiseries this will be a NaN.", "type": [ "string", "null" ] }, "target": { "description": "In the case of a time series project with a dataset using predictionsStartDate and predictionsEndDate for bulk predictions, the predictions will be a json array in the same format as with a forecast point with one additional element - `target`. It is the target value in the row.", "type": [ "string", "null" ] }, "timestamp": { "description": "(if time series project) The timestamp of this row in the prediction dataset.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "prediction", "rowId" ], "type": "object" }, "type": "array" }, "shapBaseValue": { "description": "Will be present only if `explanationAlgorithm` = 'shap'. The model's average prediction over the training data. SHAP values are deviations from the base value.", "type": [ "number", "null" ], "x-versionadded": "v2.21" }, "shapWarnings": { "description": "Will be present if `explanationAlgorithm` was set to `shap` and there were additivity failures during SHAP values calculation.", "items": { "description": "Mismatch information.", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value.", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed.", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "task": { "description": "The prediction task.", "enum": [ "Regression", "Binary", "Multiclass", "Multilabel" ], "type": "string" } }, "required": [ "positiveClass", "predictions", "task" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Predictions that have previously been computed. | PredictionRetrieveResponse | | 404 | Not Found | No prediction data found. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Type | string | | MIME type of the returned data | ## Get the list of prediction metadata records by project ID Operation path: `GET /api/v2/projects/{projectId}/predictionsMetadata/` Authentication requirements: `BearerAuth` Use the ID of a metadata object to get the complete set of predictions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of results to skip. | | limit | query | integer | true | At most this many results are returned. To specify no limit, use 0. The default may change and a maximum limit may be imposed without notice. | | predictionDatasetId | query | string | false | The dataset ID used to create the predictions. | | modelId | query | string | false | The model ID. | | projectId | path | string | true | The project of the predictions. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of the metadata records.", "items": { "properties": { "actualValueColumn": { "description": "For time series unsupervised projects only. The actual value column can be used to calculate the classification metrics and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "datasetId": { "description": "Deprecated alias for `predictionDatasetId`.", "type": [ "string", "null" ] }, "explanationAlgorithm": { "description": "The selected algorithm to use for prediction explanations. At present, the only acceptable value is `shap`, which selects the SHapley Additive exPlanations (SHAP) explainer. Defaults to null (no prediction explanations).", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureDerivationWindowCounts": { "description": "For time series projects with partial history only. Indicates how many points were used during feature derivation.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions were generated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "id": { "description": "The ID of the prediction record.", "type": "string" }, "includesPredictionIntervals": { "description": "Whether the predictions include prediction intervals.", "type": "boolean" }, "maxExplanations": { "description": "The maximum number of prediction explanations values to be returned with each row in the `predictions` json array. Null indicates `no limit`. Will be present only if `explanationAlgorithm` was set.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "modelId": { "description": "The model ID used for predictions.", "type": "string" }, "predictionDatasetId": { "description": "The dataset ID where the prediction data comes from. The field is available via `/api/v2/projects//predictionsMetadata/` route and replaced on `datasetId`in deprecated `/api/v2/projects//predictions/` endpoint.", "type": [ "string", "null" ] }, "predictionIntervalsSize": { "description": "For time series projects only. If prediction intervals were computed, what percentile they represent. Will be ``None`` if ``includePredictionIntervals`` is ``False``.", "type": [ "integer", "null" ] }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "type": [ "number", "null" ], "x-versionadded": "v2.22" }, "predictionsEndDate": { "description": "For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter was used for generating historical predictions using the training data, not for future predictions.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "predictionsStartDate": { "description": "For time series projects only. The start date for bulk predictions. Note that this parameter was used for generating historical predictions using the training data, not for future predictions.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "projectId": { "description": "The project ID of the predictions.", "type": "string" }, "shapWarnings": { "description": "Will be present if `explanationAlgorithm` was set to `shap` and there were additivity failures during SHAP values calculation.", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value.", "type": "number", "x-versionadded": "v2.21" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed.", "type": "integer", "x-versionadded": "v2.21" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "url": { "description": "The URL at which you can download the predictions.", "type": "string" } }, "required": [ "id", "includesPredictionIntervals", "modelId", "predictionIntervalsSize", "projectId", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The JSON array of prediction metadata objects. | RetrieveListPredictionMetadataObjectsResponse | ## Retrieve metadata by project ID Operation path: `GET /api/v2/projects/{projectId}/predictionsMetadata/{predictionId}/` Authentication requirements: `BearerAuth` Use the ID of a metadata object to get the complete set of predictions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | predictionId | path | string | true | The ID of the prediction record to retrieve. If you have the jobId, you can retrieve the predictionId using [GET /api/v2/projects/{projectId}/predictJobs/{jobId}/][get-apiv2projectsprojectidpredictjobsjobid]. | | projectId | path | string | true | The ID of the project the prediction belongs to. | ### Example responses > 200 Response ``` { "properties": { "actualValueColumn": { "description": "For time series unsupervised projects only. The actual value column can be used to calculate the classification metrics and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "datasetId": { "description": "Deprecated alias for `predictionDatasetId`.", "type": [ "string", "null" ] }, "explanationAlgorithm": { "description": "The selected algorithm to use for prediction explanations. At present, the only acceptable value is `shap`, which selects the SHapley Additive exPlanations (SHAP) explainer. Defaults to null (no prediction explanations).", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureDerivationWindowCounts": { "description": "For time series projects with partial history only. Indicates how many points were used during feature derivation.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions were generated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "id": { "description": "The ID of the prediction record.", "type": "string" }, "includesPredictionIntervals": { "description": "Whether the predictions include prediction intervals.", "type": "boolean" }, "maxExplanations": { "description": "The maximum number of prediction explanations values to be returned with each row in the `predictions` json array. Null indicates `no limit`. Will be present only if `explanationAlgorithm` was set.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "modelId": { "description": "The model ID used for predictions.", "type": "string" }, "predictionDatasetId": { "description": "The dataset ID where the prediction data comes from. The field is available via `/api/v2/projects//predictionsMetadata/` route and replaced on `datasetId`in deprecated `/api/v2/projects//predictions/` endpoint.", "type": [ "string", "null" ] }, "predictionIntervalsSize": { "description": "For time series projects only. If prediction intervals were computed, what percentile they represent. Will be ``None`` if ``includePredictionIntervals`` is ``False``.", "type": [ "integer", "null" ] }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "type": [ "number", "null" ], "x-versionadded": "v2.22" }, "predictionsEndDate": { "description": "For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter was used for generating historical predictions using the training data, not for future predictions.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "predictionsStartDate": { "description": "For time series projects only. The start date for bulk predictions. Note that this parameter was used for generating historical predictions using the training data, not for future predictions.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "projectId": { "description": "The project ID of the predictions.", "type": "string" }, "shapWarnings": { "description": "Will be present if `explanationAlgorithm` was set to `shap` and there were additivity failures during SHAP values calculation.", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value.", "type": "number", "x-versionadded": "v2.21" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed.", "type": "integer", "x-versionadded": "v2.21" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "url": { "description": "The URL at which you can download the predictions.", "type": "string" } }, "required": [ "id", "includesPredictionIntervals", "modelId", "predictionIntervalsSize", "projectId", "url" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The prediction metadata object. | RetrievePredictionMetadataObject | | 404 | Not Found | Training predictions not found. | None | ## List training prediction jobs by project ID Operation path: `GET /api/v2/projects/{projectId}/trainingPredictions/` Authentication requirements: `BearerAuth` Get a list of training prediction records. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | projectId | path | string | true | Project ID to retrieve training predictions for | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of training prediction jobs", "items": { "description": "A training prediction job", "properties": { "dataSubset": { "description": "Subset of data predicted on", "enum": [ "all", "validationAndHoldout", "holdout", "allBacktests", "validation", "crossValidation" ], "type": "string", "x-enum-versionadded": [ { "value": "validation", "x-versionadded": "v2.21" } ] }, "explanationAlgorithm": { "description": "The method used for calculating prediction explanations", "enum": [ "shap" ], "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "id": { "description": "ID of the training prediction job", "type": "string" }, "maxExplanations": { "description": "the number of top contributors that are included in prediction explanations. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns", "maximum": 100, "minimum": 0, "type": [ "integer", "null" ] }, "modelId": { "description": "ID of the model", "type": "string" }, "shapWarnings": { "description": "Will be present if \"explanationAlgorithm\" was set to \"shap\" and there were additivity failures during SHAP values calculation", "items": { "description": "A training prediction job", "properties": { "partitionName": { "description": "The partition used for the prediction record.", "type": "string" }, "value": { "description": "The warnings related to this partition", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" } }, "required": [ "partitionName", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "url": { "description": "The location of these predictions", "format": "uri", "type": "string" } }, "required": [ "dataSubset", "id", "modelId", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of training prediction jobs | TrainingPredictionsListResponse | ## Submits a job by project ID Operation path: `POST /api/v2/projects/{projectId}/trainingPredictions/` Authentication requirements: `BearerAuth` Create training data predictions. ### Body parameter ``` { "properties": { "dataSubset": { "default": "all", "description": "Subset of data predicted on: The value \"all\" returns predictions for all rows in the dataset including data used for training, validation, holdout and any rows discarded. This is not available for large datasets or projects created with Date/Time partitioning. The value \"validationAndHoldout\" returns predictions for the rows used to calculate the validation score and the holdout score. Not available for large projects or Date/Time projects for models trained into the validation set. The value \"holdout\" returns predictions for the rows used to calculate the holdout score. Not available for projects created without a holdout or for models trained into holdout for large datasets or created with Date/Time partitioning. The value \"allBacktests\" returns predictions for the rows used to calculate the backtesting scores for Date/Time projects. The value \"validation\" returns predictions for the rows used to calculate the validation score.", "enum": [ "all", "validationAndHoldout", "holdout", "allBacktests", "validation", "crossValidation" ], "type": "string", "x-enum-versionadded": [ { "value": "validation", "x-versionadded": "v2.21" } ] }, "explanationAlgorithm": { "description": "If set to \"shap\", the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations)", "type": "string", "x-versionadded": "v2.21" }, "maxExplanations": { "description": "Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of \"shap\": If not set, explanations are returned for all features. If the number of features is greater than the \"maxExplanations\", the sum of remaining values will also be returned as \"shapRemainingTotal\". Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Cannot be set if \"explanationAlgorithm\" is omitted.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "modelId": { "description": "The model to make predictions on", "type": "string" } }, "required": [ "dataSubset", "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | Project ID to compute training predictions for | | body | body | CreateTrainingPrediction | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Submitted successfully. See Location header. | None | | 422 | Unprocessable Entity | - Model/Timeseries/Blender does not support shap based prediction explanations | | | - Error message from StackedPredictionRequestValidationError | | | | | - Could not create training predictions job. Request with same parameters already submitted. | None | | | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking async job status. | ## Retrieve training predictions by project ID Operation path: `GET /api/v2/projects/{projectId}/trainingPredictions/{predictionId}/` Authentication requirements: `BearerAuth` Retrieve training predictions that have previously been computed. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | projectId | path | string | true | Project ID to retrieve training predictions for | | predictionId | path | string | true | Prediction ID to retrieve training predictions for | | Accept | header | string | false | Requested MIME type for the returned data | ### Enumerated Values | Parameter | Value | | --- | --- | | Accept | [application/json, text/csv] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of training prediction rows", "items": { "description": "A training prediction row", "properties": { "forecastDistance": { "description": "(if time series project) The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column.", "type": [ "integer", "null" ] }, "forecastPoint": { "description": "(if time series project) The forecastPoint of the predictions. Either provided or inferred.", "format": "date-time", "type": [ "string", "null" ] }, "partitionId": { "description": "The partition used for the prediction record", "type": "string" }, "prediction": { "description": "The prediction of the model.", "oneOf": [ { "description": "If using a regressor model, will be the numeric value of the target.", "type": "number" }, { "description": "If using a binary or muliclass classifier model, will be the predicted class.", "type": "string" }, { "description": "If using a multilabel classifier model, will be a list of predicted classes.", "items": { "type": "string" }, "type": "array" } ] }, "predictionExplanations": { "description": "Array contains `predictionExplanation` objects. The total elements in the array are bounded by maxExplanations and feature count. It will be present only if `explanationAlgorithm` is not null (prediction explanations were requested).", "items": { "description": "Prediction explanation result.", "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. The type corresponds to the feature (bool, int, float, str, etc.).", "oneOf": [ { "type": "integer" }, { "type": "boolean" }, { "type": "string" }, { "type": "number" } ] }, "label": { "description": "Describes what output was driven by this prediction explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation. For predictions made using anomaly detection models, it is the `Anomaly Score`.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "strength": { "description": "Algorithm-specific explanation value attributed to `feature` in this row. If `explanationAlgorithm` = `shap`, this is the SHAP value.", "type": [ "number", "null" ] } }, "required": [ "feature", "featureValue", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionValues": { "description": "The list of predicted values for this row.", "items": { "description": "Predicted values.", "properties": { "label": { "description": "For regression problems this will be the name of the target column, 'Anomaly score' or ignored field. For classification projects this will be the name of the class.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "threshold": { "description": "Threshold used in multilabel classification for this class.", "maximum": 1, "minimum": 0, "type": "number" }, "value": { "description": "The predicted probability of the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row in the prediction dataset this prediction corresponds to.", "minimum": 0, "type": "integer" }, "seriesId": { "description": "The ID of the series value for a multiseries project. For time series projects that are not a multiseries this will be a NaN.", "type": [ "string", "null" ] }, "shapMetadata": { "description": "The additional information necessary to understand shap based prediction explanations. Only present if explanationAlgorithm=\"shap\" was added in compute request.", "properties": { "shapBaseValue": { "description": "The model's average prediction over the training data. SHAP values are deviations from the base value.", "type": "number" }, "shapRemainingTotal": { "description": "The total of SHAP values for features beyond the maxExplanations. This can be identically 0 in all rows, if maxExplanations is greater than the number of features and thus all features are returned.", "type": "integer" }, "warnings": { "description": "SHAP values calculation warnings", "items": { "description": "The warnings related to this partition", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "type": "array" } }, "required": [ "shapBaseValue", "shapRemainingTotal", "warnings" ], "type": "object" }, "timestamp": { "description": "(if time series project) The timestamp of this row in the prediction dataset.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "partitionId", "prediction", "rowId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Training predictions encoded either as JSON or CSV | string | | 404 | Not Found | Job does not exist or is not completed | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Type | string | | MIME type of the returned data | ## List scheduled deployment batch prediction jobs a user can view Operation path: `GET /api/v2/scheduledJobs/` Authentication requirements: `BearerAuth` Get a list of scheduled batch prediction jobs a user can view. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of scheduled jobs to skip. Defaults to 0. | | limit | query | integer | true | The number of scheduled jobs (max 100) to return. Defaults to 20 | | orderBy | query | string | false | The order to sort the scheduled jobs. Defaults to order by last successful run timestamp in descending order. | | search | query | string | false | Case insensitive search against scheduled jobs name or type name. | | deploymentId | query | string | false | Filter by the prediction integration deployment ID. Ignored for non prediction integration type ID. | | typeId | query | string | false | filter by scheduled job type ID. | | queryByUser | query | string | false | Which user field to filter with. | | filterEnabled | query | string | false | Filter jobs using the enabled field. If true, only enabled jobs are returned, otherwise if false, only disabled jobs are returned. The default returns both enabled and disabled jobs. | ### Enumerated Values | Parameter | Value | | --- | --- | | typeId | datasetRefresh | | queryByUser | [createdBy, updatedBy] | | filterEnabled | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of scheduled jobs", "items": { "properties": { "createdBy": { "description": "User name of the creator", "type": [ "string", "null" ] }, "deploymentId": { "description": "ID of the deployment this scheduled job is created from.", "type": [ "string", "null" ] }, "enabled": { "description": "True if the job is enabled and false if the job is disabled.", "type": "boolean" }, "id": { "description": "ID of scheduled prediction job", "type": "string" }, "name": { "description": "Name of the scheduled job.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "scheduledJobId": { "description": "ID of this scheduled job.", "type": "string" }, "status": { "description": "Object containing status information about the scheduled job.", "properties": { "lastFailedRun": { "description": "Date and time of the last failed run.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRun": { "description": "Date and time of the last successful run.", "format": "date-time", "type": [ "string", "null" ] }, "nextRunTime": { "description": "Date and time of the next run.", "format": "date-time", "type": [ "string", "null" ] }, "queuePosition": { "description": "Position of the job in the queue Job. The value will show 0 if the job is about to run, otherwise, the number will be greater than 0 if currently queued, or None if the job is not currently running.", "minimum": 0, "type": [ "integer", "null" ] }, "running": { "description": "`true` or `false` depending on whether the job is currently running.", "type": "boolean" } }, "required": [ "running" ], "type": "object" }, "typeId": { "description": "Job type of the scheduled job", "type": "string" }, "updatedAt": { "description": "Time of last modification", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "enabled", "id", "schedule", "scheduledJobId", "status", "typeId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" }, "updatedAt": { "description": "Time of last modification", "format": "date-time", "type": "string" }, "updatedBy": { "description": "User ID of last modifier", "type": "string" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of scheduled batch prediction jobs | ScheduledJobsListResponse | # Schemas ## ActualValueColumnInfo ``` { "properties": { "missingCount": { "description": "Count of the missing values in the column.", "type": "integer", "x-versionadded": "v2.21" }, "name": { "description": "Name of the column.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "missingCount", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | missingCount | integer | true | | Count of the missing values in the column. | | name | string | true | | Name of the column. | ## AzureDataStreamer ``` { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Azure ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | azure | ## AzureIntake ``` { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Azure ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | azure | ## AzureOutput ``` { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Save CSV data chunks to Azure Blob Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | format | string | false | | Type of output file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the file or directory | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | azure | ## AzureOutputAdaptor ``` { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Save CSV data chunks to Azure Blob Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | format | string | false | | Type of output file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the file or directory | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | azure | ## AzureServicePrincipalCredentials ``` { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | azureTenantId | string | false | | Tenant ID of the Azure AD service principal. | | clientId | string | false | | Client ID of the Azure AD service principal. | | clientSecret | string | false | | Client Secret of the Azure AD service principal. | | configId | string | false | | The ID of secure configurations of credentials shared by admin. | | credentialType | string | true | | The type of these credentials, 'azure_service_principal' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | azure_service_principal | ## BasicCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'basic' here. | | password | string | true | | The password for database authentication. The password is encrypted at rest and never saved or stored. | | user | string | true | | The username for database authentication. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | basic | ## BatchJobCSVSettings ``` { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" } ``` The CSV settings used for this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | delimiter | any | true | | CSV fields are delimited by this character. Use the string "tab" to denote TSV (TAB separated values). | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 1minLength: 1minLength: 1 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | encoding | string | true | | The encoding to be used for intake and output. For example (but not limited to): "shift_jis", "latin_1" or "mskanji". | | quotechar | string | true | maxLength: 1minLength: 1minLength: 1 | Fields containing the delimiter or newlines must be quoted using this character. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | tab | ## BatchJobCreatedBy ``` { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } ``` Who created this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fullName | string,null | true | | The full name of the user who created this job (if defined by the user) | | userId | string | true | | The User ID of the user who created this job | | username | string | true | | The username (e-mail address) of the user who created this job | ## BatchJobDefinitionResponse ``` { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" } ``` The Batch Prediction Job Definition linking to this job, if any. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | string | true | | The ID of creator of this job definition | | id | string | true | | The ID of the Batch Prediction job definition | | name | string | true | | A human-readable name for the definition, must be unique across organisations | ## BatchJobLinks ``` { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object", "x-versionadded": "v2.35" } ``` Links useful for this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvUpload | string(url) | false | | The URL used to upload the dataset for this job. Only available for localFile intake. | | download | string,null | false | | The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available. | | self | string(url) | true | | The URL used access this job. | ## BatchJobListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of jobs", "items": { "properties": { "batchMonitoringJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "id": { "description": "The ID of the Batch job", "type": "string", "x-versionadded": "v2.30" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object", "x-versionadded": "v2.35" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.30" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.30" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.30" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.30" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.30" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [BatchJobResponse] | true | maxItems: 10000 | An array of jobs | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## BatchJobPredictionInstance ``` { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" } ``` Override the default prediction instance from the deployment when scoring this job. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | apiKey | string | false | | By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users. | | datarobotKey | string | false | | If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key. | | hostName | string | true | | Override the default host name of the deployment with this. | | sslEnabled | boolean | true | | Use SSL (HTTPS) when communicating with the overriden prediction server. | ## BatchJobRemapping ``` { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputName | string | true | | Rename column with this name | | outputName | string,null | true | | Rename column to this name (leave as null to remove from the output) | ## BatchJobResponse ``` { "properties": { "batchMonitoringJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "id": { "description": "The ID of the Batch job", "type": "string", "x-versionadded": "v2.30" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object", "x-versionadded": "v2.35" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.30" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.30" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.30" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.30" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.30" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchMonitoringJobDefinition | BatchJobDefinitionResponse | false | | The Batch Prediction Job Definition linking to this job, if any. | | batchPredictionJobDefinition | BatchJobDefinitionResponse | false | | The Batch Prediction Job Definition linking to this job, if any. | | created | string(date-time) | true | | When was this job created | | createdBy | BatchJobCreatedBy | true | | Who created this job | | elapsedTimeSec | integer | true | minimum: 0 | Number of seconds the job has been processing for | | failedRows | integer | true | minimum: 0 | Number of rows that have failed scoring | | hidden | string(date-time) | false | | When was this job was hidden last, blank if visible | | id | string | true | | The ID of the Batch job | | intakeDatasetDisplayName | string,null | false | | If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset. | | jobIntakeSize | integer,null | true | minimum: 0 | Number of bytes in the intake dataset for this job | | jobOutputSize | integer,null | true | minimum: 0 | Number of bytes in the output dataset for this job | | jobSpec | BatchJobSpecResponse | true | | The job configuration used to create this job | | links | BatchJobLinks | true | | Links useful for this job | | logs | [string] | true | | The job log. | | monitoringBatchId | string,null | true | | Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled. | | percentageCompleted | number | true | maximum: 100minimum: 0 | Indicates job progress which is based on number of already processed rows in dataset | | queuePosition | integer,null | false | minimum: 0 | To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments. | | queued | boolean | true | | The job has been put on the queue for execution. | | resultsDeleted | boolean | false | | Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage) | | scoredRows | integer | true | minimum: 0 | Number of rows that have been used in prediction computation | | skippedRows | integer | true | minimum: 0 | Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows. | | source | string | false | | Source from which batch job was started | | status | string | true | | The current job status | | statusDetails | string | true | | Explanation for current status | ### Enumerated Values | Property | Value | | --- | --- | | status | [INITIALIZING, RUNNING, COMPLETED, ABORTED, FAILED] | ## BatchJobSpecResponse ``` { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" } ``` The job configuration used to create this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | true | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchJobCSVSettings | true | | The CSV settings used for this job | | deploymentId | string | false | | ID of deployment which is used in job for processing predictions dataset | | disableRowLevelErrorHandling | boolean | true | | Skip row by row error handling | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 10minItems: 1 | List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1 | | explanationNumTopClasses | integer | false | maximum: 10minimum: 1 | Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1 | | includePredictionStatus | boolean | true | | Include prediction status column in the output | | includeProbabilities | boolean | true | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | true | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | true | | The response option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureDataStreamer | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageDataStreamer | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogDataStreamer | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPDataStreamer | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryDataStreamer | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3DataStreamer | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeDataStreamer | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseDataStreamer | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSSDataStreamer | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemDataStreamer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPDataStreamer | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCDataStreamer | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileDataStreamer | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksDataStreamer | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereDataStreamer | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoDataStreamer | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | true | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | maxNgramExplanations | any | false | | The maximum number of text ngram explanations to supply per row of the dataset. The default recommended maxNgramExplanations is all (no limit) | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringAggregation | MonitoringAggregation | false | | Defines the aggregation policy for monitoring jobs. | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | monitoringColumns | MonitoringColumnsMapping | false | | Column names mapping for monitoring | | monitoringOutputSettings | MonitoringOutputSettings | false | | Output settings for monitoring jobs | | numConcurrent | integer | false | minimum: 1 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The response option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutputAdaptor | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutputAdaptor | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutputAdaptor | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3OutputAdaptor | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutputAdaptor | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutputAdaptor | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutputAdaptor | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HttpOutputAdaptor | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JdbcOutputAdaptor | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutputAdaptor | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutputAdaptor | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutputAdaptor | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutputAdaptor | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | redactedFields | [string] | true | | A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId | | skipDriftTracking | boolean | true | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsHistorical | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | anonymous | all | | passthroughColumnsSet | all | ## BatchJobTimeSeriesSettingsForecast ``` { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forecastPoint | string(date-time) | false | | Used for forecast predictions in order to override the inferred forecast point from the dataset. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Forecast mode makes predictions using forecastPoint or rows in the dataset without target. | ### Enumerated Values | Property | Value | | --- | --- | | type | forecast | ## BatchJobTimeSeriesSettingsHistorical ``` { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionsEndDate | string(date-time) | false | | Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset. | | predictionsStartDate | string(date-time) | false | | Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range. | ### Enumerated Values | Property | Value | | --- | --- | | type | historical | ## BatchPredictionCreatedBy ``` { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } ``` Who created this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fullName | string,null | true | | The full name of the user who created this job (if defined by the user) | | userId | string | true | | The User ID of the user who created this job | | username | string | true | | The username (e-mail address) of the user who created this job | ## BatchPredictionJobCSVSettings ``` { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" } ``` The CSV settings used for this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | delimiter | any | true | | CSV fields are delimited by this character. Use the string "tab" to denote TSV (TAB separated values). | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 1minLength: 1minLength: 1 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | encoding | string | true | | The encoding to be used for intake and output. For example (but not limited to): "shift_jis", "latin_1" or "mskanji". | | quotechar | string | true | maxLength: 1minLength: 1minLength: 1 | Fields containing the delimiter or newlines must be quoted using this character. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | tab | ## BatchPredictionJobCreate ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "skipDriftTracking" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | true | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchPredictionJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchPredictionJobCSVSettings | true | | The CSV settings used for this job | | deploymentId | string | false | | ID of deployment which is used in job for processing predictions dataset | | disableRowLevelErrorHandling | boolean | true | | Skip row by row error handling | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 100minItems: 1 | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | | explanationNumTopClasses | integer | false | maximum: 100minimum: 1 | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | | includePredictionStatus | boolean | true | | Include prediction status column in the output | | includeProbabilities | boolean | true | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | true | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | true | | The intake option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureIntake | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryIntake | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageIntake | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksIntake | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | Catalog | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereIntake | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSS | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemIntake | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPIntake | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPIntake | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCIntake | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileIntake | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Intake | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeIntake | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseIntake | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoIntake | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | true | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | numConcurrent | integer | false | minimum: 1 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The output option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutput | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutput | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutput | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutput | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutput | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutput | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPOutput | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCOutput | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutput | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Output | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutput | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutput | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutput | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchPredictionJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | secondaryDatasetsConfigId | string | false | | Configuration id for secondary datasets to use when making a prediction. | | skipDriftTracking | boolean | true | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsHistorical | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsTraining | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | passthroughColumnsSet | all | ## BatchPredictionJobDefinitionId ``` { "properties": { "jobDefinitionId": { "description": "ID of the Batch Prediction job definition", "type": "string" } }, "required": [ "jobDefinitionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jobDefinitionId | string | true | | ID of the Batch Prediction job definition | ## BatchPredictionJobDefinitionJobSpecResponse ``` { "description": "The Batch Prediction Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "numConcurrent", "redactedFields", "skipDriftTracking" ], "type": "object" } ``` The Batch Prediction Job specification to be put on the queue in intervals ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | true | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchJobCSVSettings | true | | The CSV settings used for this job | | deploymentId | string | false | | ID of deployment which is used in job for processing predictions dataset | | disableRowLevelErrorHandling | boolean | true | | Skip row by row error handling | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 10minItems: 1 | List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1 | | explanationNumTopClasses | integer | false | maximum: 10minimum: 1 | Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1 | | includePredictionStatus | boolean | true | | Include prediction status column in the output | | includeProbabilities | boolean | true | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | true | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | true | | The response option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureDataStreamer | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageDataStreamer | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogDataStreamer | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPDataStreamer | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryDataStreamer | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3DataStreamer | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeDataStreamer | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseDataStreamer | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSSDataStreamer | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemDataStreamer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPDataStreamer | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCDataStreamer | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileDataStreamer | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksDataStreamer | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereDataStreamer | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoDataStreamer | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | true | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | maxNgramExplanations | any | false | | The maximum number of text ngram explanations to supply per row of the dataset. The default recommended maxNgramExplanations is all (no limit) | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringAggregation | MonitoringAggregation | false | | Defines the aggregation policy for monitoring jobs. | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | monitoringColumns | MonitoringColumnsMapping | false | | Column names mapping for monitoring | | monitoringOutputSettings | MonitoringOutputSettings | false | | Output settings for monitoring jobs | | numConcurrent | integer | true | minimum: 0 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The response option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutputAdaptor | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutputAdaptor | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutputAdaptor | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3OutputAdaptor | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutputAdaptor | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutputAdaptor | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutputAdaptor | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HttpOutputAdaptor | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JdbcOutputAdaptor | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutputAdaptor | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutputAdaptor | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutputAdaptor | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutputAdaptor | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | redactedFields | [string] | true | | A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId | | skipDriftTracking | boolean | true | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsForecastWithPolicy | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsHistorical | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | anonymous | all | | passthroughColumnsSet | all | ## BatchPredictionJobDefinitionResponse ``` { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object" } ``` The Batch Prediction Job Definition linking to this job, if any. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | string | true | | The ID of creator of this job definition | | id | string | true | | The ID of the Batch Prediction job definition | | name | string | true | | A human-readable name for the definition, must be unique across organisations | ## BatchPredictionJobDefinitionsCreate ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "enabled": { "description": "If this job definition is enabled as a scheduled job. Optional if no schedule is supplied.", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you.", "maxLength": 100, "minLength": 1, "type": "string" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "deploymentId", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "skipDriftTracking" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | true | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchPredictionJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchPredictionJobCSVSettings | true | | The CSV settings used for this job | | deploymentId | string | true | | ID of deployment which is used in job for processing predictions dataset | | disableRowLevelErrorHandling | boolean | true | | Skip row by row error handling | | enabled | boolean | false | | If this job definition is enabled as a scheduled job. Optional if no schedule is supplied. | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 100minItems: 1 | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | | explanationNumTopClasses | integer | false | maximum: 100minimum: 1 | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | | includePredictionStatus | boolean | true | | Include prediction status column in the output | | includeProbabilities | boolean | true | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | true | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | true | | The intake option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureIntake | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryIntake | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageIntake | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksIntake | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | Catalog | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereIntake | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSS | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemIntake | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPIntake | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPIntake | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCIntake | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileIntake | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Intake | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeIntake | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseIntake | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoIntake | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | true | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | name | string | false | maxLength: 100minLength: 1minLength: 1 | A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you. | | numConcurrent | integer | false | minimum: 1 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The output option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutput | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutput | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutput | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutput | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutput | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutput | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPOutput | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCOutput | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutput | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Output | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutput | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutput | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutput | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchPredictionJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | secondaryDatasetsConfigId | string | false | | Configuration id for secondary datasets to use when making a prediction. | | skipDriftTracking | boolean | true | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsForecastWithPolicy | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsHistorical | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | passthroughColumnsSet | all | ## BatchPredictionJobDefinitionsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of scheduled jobs", "items": { "properties": { "batchPredictionJob": { "description": "The Batch Prediction Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "numConcurrent", "redactedFields", "skipDriftTracking" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchPredictionJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [BatchPredictionJobDefinitionsResponse] | true | | An array of scheduled jobs | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## BatchPredictionJobDefinitionsResponse ``` { "properties": { "batchPredictionJob": { "description": "The Batch Prediction Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "numConcurrent", "redactedFields", "skipDriftTracking" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchPredictionJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchPredictionJob | BatchPredictionJobDefinitionJobSpecResponse | true | | The Batch Prediction Job specification to be put on the queue in intervals | | created | string(date-time) | true | | When was this job created | | createdBy | BatchJobCreatedBy | true | | Who created this job | | enabled | boolean | true | | If this job definition is enabled as a scheduled job. | | id | string | true | | The ID of the Batch job definition | | lastFailedRunTime | string,null(date-time) | false | | Last time this job had a failed run | | lastScheduledRunTime | string,null(date-time) | false | | Last time this job was scheduled to run (though not guaranteed it actually ran at that time) | | lastStartedJobStatus | string,null | true | | The status of the latest job launched to the queue (if any). | | lastStartedJobTime | string,null(date-time) | true | | The last time (if any) a job was launched. | | lastSuccessfulRunTime | string,null(date-time) | false | | Last time this job had a successful run | | name | string | true | | A human-readable name for the definition, must be unique across organisations | | nextScheduledRunTime | string,null(date-time) | false | | Next time this job is scheduled to run | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | updated | string(date-time) | true | | When was this job last updated | | updatedBy | BatchJobCreatedBy | true | | Who created this job | ### Enumerated Values | Property | Value | | --- | --- | | lastStartedJobStatus | [INITIALIZING, RUNNING, COMPLETED, ABORTED, FAILED] | ## BatchPredictionJobDefinitionsUpdate ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "enabled": { "description": "If this job definition is enabled as a scheduled job. Optional if no schedule is supplied.", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you.", "maxLength": 100, "minLength": 1, "type": "string" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | false | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchPredictionJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchPredictionJobCSVSettings | false | | The CSV settings used for this job | | deploymentId | string | false | | ID of deployment which is used in job for processing predictions dataset | | disableRowLevelErrorHandling | boolean | false | | Skip row by row error handling | | enabled | boolean | false | | If this job definition is enabled as a scheduled job. Optional if no schedule is supplied. | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 100minItems: 1 | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | | explanationNumTopClasses | integer | false | maximum: 100minimum: 1 | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | | includePredictionStatus | boolean | false | | Include prediction status column in the output | | includeProbabilities | boolean | false | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | false | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | false | | The intake option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureIntake | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryIntake | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageIntake | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksIntake | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | Catalog | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereIntake | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSS | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemIntake | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPIntake | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPIntake | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCIntake | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileIntake | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Intake | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeIntake | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseIntake | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoIntake | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | false | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | name | string | false | maxLength: 100minLength: 1minLength: 1 | A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you. | | numConcurrent | integer | false | minimum: 1 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The output option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutput | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutput | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutput | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutput | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutput | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutput | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPOutput | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCOutput | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutput | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Output | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutput | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutput | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutput | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchPredictionJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | secondaryDatasetsConfigId | string | false | | Configuration id for secondary datasets to use when making a prediction. | | skipDriftTracking | boolean | false | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsForecastWithPolicy | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsHistorical | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | passthroughColumnsSet | all | ## BatchPredictionJobId ``` { "properties": { "partNumber": { "default": 0, "description": "The number of which csv part is being uploaded when using multipart upload ", "minimum": 0, "type": "integer", "x-versionadded": "v2.27" }, "predictionJobId": { "description": "ID of the Batch Prediction job", "type": "string" } }, "required": [ "partNumber", "predictionJobId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | partNumber | integer | true | minimum: 0 | The number of which csv part is being uploaded when using multipart upload | | predictionJobId | string | true | | ID of the Batch Prediction job | ## BatchPredictionJobLinks ``` { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object" } ``` Links useful for this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvUpload | string(url) | false | | The URL used to upload the dataset for this job. Only available for localFile intake. | | download | string,null | false | | The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available. | | self | string(url) | true | | The URL used access this job. | ## BatchPredictionJobListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of jobs", "items": { "properties": { "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.21" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.24" }, "id": { "description": "The ID of the Batch Prediction job", "type": "string", "x-versionadded": "v2.21" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.26" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.24" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.20" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.24" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [BatchPredictionJobResponse] | true | | An array of jobs | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## BatchPredictionJobPredictionInstance ``` { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" } ``` Override the default prediction instance from the deployment when scoring this job. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | apiKey | string | false | | By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users. | | datarobotKey | string | false | | If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key. | | hostName | string | true | | Override the default host name of the deployment with this. | | sslEnabled | boolean | true | | Use SSL (HTTPS) when communicating with the overriden prediction server. | ## BatchPredictionJobRemapping ``` { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputName | string | true | | Rename column with this name | | outputName | string,null | true | | Rename column to this name (leave as null to remove from the output) | ## BatchPredictionJobResponse ``` { "properties": { "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.21" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.24" }, "id": { "description": "The ID of the Batch Prediction job", "type": "string", "x-versionadded": "v2.21" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.26" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.24" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.20" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.24" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchPredictionJobDefinition | BatchPredictionJobDefinitionResponse | false | | The Batch Prediction Job Definition linking to this job, if any. | | created | string(date-time) | true | | When was this job created | | createdBy | BatchPredictionCreatedBy | true | | Who created this job | | elapsedTimeSec | integer | true | minimum: 0 | Number of seconds the job has been processing for | | failedRows | integer | true | minimum: 0 | Number of rows that have failed scoring | | hidden | string(date-time) | false | | When was this job was hidden last, blank if visible | | id | string | true | | The ID of the Batch Prediction job | | intakeDatasetDisplayName | string,null | false | | If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset. | | jobIntakeSize | integer,null | true | minimum: 0 | Number of bytes in the intake dataset for this job | | jobOutputSize | integer,null | true | minimum: 0 | Number of bytes in the output dataset for this job | | jobSpec | BatchPredictionJobSpecResponse | true | | The job configuration used to create this job | | links | BatchPredictionJobLinks | true | | Links useful for this job | | logs | [string] | true | | The job log. | | monitoringBatchId | string,null | true | | Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled. | | percentageCompleted | number | true | maximum: 100minimum: 0 | Indicates job progress which is based on number of already processed rows in dataset | | queuePosition | integer,null | false | minimum: 0 | To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments. | | queued | boolean | true | | The job has been put on the queue for execution. | | resultsDeleted | boolean | false | | Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage) | | scoredRows | integer | true | minimum: 0 | Number of rows that have been used in prediction computation | | skippedRows | integer | true | minimum: 0 | Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows. | | source | string | false | | Source from which batch job was started | | status | string | true | | The current job status | | statusDetails | string | true | | Explanation for current status | ### Enumerated Values | Property | Value | | --- | --- | | status | [INITIALIZING, RUNNING, COMPLETED, ABORTED, FAILED] | ## BatchPredictionJobSpecResponse ``` { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.35" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object" } ``` The job configuration used to create this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | true | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchPredictionJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchPredictionJobCSVSettings | true | | The CSV settings used for this job | | deploymentId | string | false | | ID of deployment which is used in job for processing predictions dataset | | disableRowLevelErrorHandling | boolean | true | | Skip row by row error handling | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 100minItems: 1 | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | | explanationNumTopClasses | integer | false | maximum: 100minimum: 1 | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | | includePredictionStatus | boolean | true | | Include prediction status column in the output | | includeProbabilities | boolean | true | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | true | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | true | | The response option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureDataStreamer | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageDataStreamer | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogDataStreamer | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPDataStreamer | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryDataStreamer | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3DataStreamer | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeDataStreamer | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseDataStreamer | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSSDataStreamer | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemDataStreamer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPDataStreamer | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCDataStreamer | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileDataStreamer | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksDataStreamer | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereDataStreamer | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoDataStreamer | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | true | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | numConcurrent | integer | false | minimum: 1 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The response option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutputAdaptor | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutputAdaptor | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutputAdaptor | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3OutputAdaptor | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutputAdaptor | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutputAdaptor | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutputAdaptor | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HttpOutputAdaptor | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JdbcOutputAdaptor | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutputAdaptor | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutputAdaptor | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutputAdaptor | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutputAdaptor | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchPredictionJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | redactedFields | [string] | true | | A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId | | secondaryDatasetsConfigId | string | false | | Configuration id for secondary datasets to use when making a prediction. | | skipDriftTracking | boolean | true | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsHistorical | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsTraining | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | passthroughColumnsSet | all | ## BatchPredictionJobTimeSeriesSettingsForecast ``` { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forecastPoint | string(date-time) | false | | Used for forecast predictions in order to override the inferred forecast point from the dataset. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Forecast mode makes predictions using forecastPoint or rows in the dataset without target. | ### Enumerated Values | Property | Value | | --- | --- | | type | forecast | ## BatchPredictionJobTimeSeriesSettingsForecastWithPolicy ``` { "properties": { "forecastPointPolicy": { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "forecastPointPolicy", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forecastPointPolicy | JobRunTimeBasedForecastPointPolicy | true | | Forecast point policy | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Forecast mode makes predictions using forecastPoint or rows in the dataset without target. | ### Enumerated Values | Property | Value | | --- | --- | | type | forecast | ## BatchPredictionJobTimeSeriesSettingsHistorical ``` { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionsEndDate | string(date-time) | false | | Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset. | | predictionsStartDate | string(date-time) | false | | Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range. | ### Enumerated Values | Property | Value | | --- | --- | | type | historical | ## BatchPredictionJobTimeSeriesSettingsTraining ``` { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Forecast mode used for making predictions on subsets of training data. | ### Enumerated Values | Property | Value | | --- | --- | | type | training | ## BatchPredictionJobUpdate ``` { "properties": { "aborted": { "description": "Time when job abortion happened", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "completed": { "description": "Time when job completed scoring", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "failedRows": { "description": "Number of rows that have failed scoring", "type": "integer", "x-versionadded": "v2.26" }, "hidden": { "description": "Hides or unhides the job from the job list", "type": "boolean", "x-versionadded": "v2.24" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "type": [ "integer", "null" ], "x-versionadded": "v2.26" }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "type": [ "integer", "null" ], "x-versionadded": "v2.26" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array", "x-versionadded": "v2.26" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "type": "integer", "x-versionadded": "v2.26" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "type": "integer", "x-versionadded": "v2.26" }, "started": { "description": "Time when job scoring begin", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string", "x-versionadded": "v2.26" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aborted | string,null(date-time) | false | | Time when job abortion happened | | completed | string,null(date-time) | false | | Time when job completed scoring | | failedRows | integer | false | | Number of rows that have failed scoring | | hidden | boolean | false | | Hides or unhides the job from the job list | | jobIntakeSize | integer,null | false | | Number of bytes in the intake dataset for this job | | jobOutputSize | integer,null | false | | Number of bytes in the output dataset for this job | | logs | [string] | false | | The job log. | | scoredRows | integer | false | | Number of rows that have been used in prediction computation | | skippedRows | integer | false | | Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows. | | started | string,null(date-time) | false | | Time when job scoring begin | | status | string | false | | The current job status | ### Enumerated Values | Property | Value | | --- | --- | | status | [INITIALIZING, RUNNING, COMPLETED, ABORTED, FAILED] | ## BigQueryDataStreamer ``` { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" } ``` Stream CSV data chunks from Big Query using GCS ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bucket | string | true | | The name of gcs bucket for data export | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the GCP credentials | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataset | string | true | | The name of the specified big query dataset to read input data from | | table | string | true | | The name of the specified big query table to read input data from | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | type | bigquery | ## BigQueryIntake ``` { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" } ``` Stream CSV data chunks from Big Query using GCS ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bucket | string | true | | The name of gcs bucket for data export | | credentialId | string | true | | The ID of the GCP credentials | | dataset | string | true | | The name of the specified big query dataset to read input data from | | table | string | true | | The name of the specified big query table to read input data from | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | bigquery | ## BigQueryOutput ``` { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Google BigQuery in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bucket | string | true | | The name of gcs bucket for data loading | | credentialId | string | true | | The ID of the GCP credentials | | dataset | string | true | | The name of the specified big query dataset to write data back | | table | string | true | | The name of the specified big query table to write data back | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | bigquery | ## BigQueryOutputAdaptor ``` { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Google BigQuery in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bucket | string | true | | The name of gcs bucket for data loading | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the GCP credentials | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataset | string | true | | The name of the specified big query dataset to write data back | | table | string | true | | The name of the specified big query table to write data back | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | type | bigquery | ## Catalog ``` { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" } ``` Stream CSV data chunks from AI catalog dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the AI catalog dataset | | datasetVersionId | string | false | | The ID of the AI catalog dataset version | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | dataset | ## CatalogDataStreamer ``` { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" } ``` Stream CSV data chunks from AI catalog dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetVersionId | string | false | | The ID of the AI catalog dataset version | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | type | dataset | ## CreatePredictionDatasetResponse ``` { "properties": { "datasetId": { "description": "The ID of the newly created prediction dataset.", "type": "string" } }, "required": [ "datasetId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the newly created prediction dataset. | ## CreatePredictionFromDataset ``` { "properties": { "actualValueColumn": { "description": "For time series projects only. The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. This value is optional.", "type": "string", "x-versionadded": "v2.21" }, "datasetId": { "description": "The dataset to compute predictions for - must have previously been uploaded.", "type": "string" }, "explanationAlgorithm": { "description": "If set to `shap`, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).", "enum": [ "shap" ], "type": "string" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "includeFdwCounts": { "default": false, "description": "For time series projects with partial history only. Indicates if feature derivation window counts `featureDerivationWindowCounts` will be part of the response.", "type": "boolean", "x-versionadded": "v2.24" }, "includePredictionIntervals": { "description": "Specifies whether prediction intervals should be calculated for this request. Defaults to True if `predictionIntervalsSize` is specified, otherwise defaults to False.", "type": "boolean", "x-versionadded": "v2.16" }, "maxExplanations": { "description": "Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of 'shap': If not set, explanations are returned for all features. If the number of features is greater than the 'maxExplanations', the sum of remaining values will also be returned as 'shapRemainingTotal'. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Cannot be set if 'explanationAlgorithm' is omitted.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "modelId": { "description": "The model to make predictions on.", "type": "string" }, "predictionIntervalsSize": { "description": "Represents the percentile to use for the size of the prediction intervals. Defaults to 80 if `includePredictionIntervals` is True.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.16" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions. Accepts values from 0.0 to 1.0. If not specified, model default prediction threshold will be used.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.22" }, "predictionsEndDate": { "description": "The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "predictionsStartDate": { "description": "The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" } }, "required": [ "datasetId", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string | false | | For time series projects only. The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. This value is optional. | | datasetId | string | true | | The dataset to compute predictions for - must have previously been uploaded. | | explanationAlgorithm | string | false | | If set to shap, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations). | | forecastPoint | string(date-time) | false | | For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error. | | includeFdwCounts | boolean | false | | For time series projects with partial history only. Indicates if feature derivation window counts featureDerivationWindowCounts will be part of the response. | | includePredictionIntervals | boolean | false | | Specifies whether prediction intervals should be calculated for this request. Defaults to True if predictionIntervalsSize is specified, otherwise defaults to False. | | maxExplanations | integer | false | maximum: 100minimum: 1 | Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of 'shap': If not set, explanations are returned for all features. If the number of features is greater than the 'maxExplanations', the sum of remaining values will also be returned as 'shapRemainingTotal'. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Cannot be set if 'explanationAlgorithm' is omitted. | | modelId | string | true | | The model to make predictions on. | | predictionIntervalsSize | integer | false | maximum: 100minimum: 1 | Represents the percentile to use for the size of the prediction intervals. Defaults to 80 if includePredictionIntervals is True. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold used for binary classification in predictions. Accepts values from 0.0 to 1.0. If not specified, model default prediction threshold will be used. | | predictionsEndDate | string(date-time) | false | | The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsStartDate, and cannot be provided with the forecastPoint parameter. | | predictionsStartDate | string(date-time) | false | | The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsEndDate, and cannot be provided with the forecastPoint parameter. | ### Enumerated Values | Property | Value | | --- | --- | | explanationAlgorithm | shap | ## CreateTrainingPrediction ``` { "properties": { "dataSubset": { "default": "all", "description": "Subset of data predicted on: The value \"all\" returns predictions for all rows in the dataset including data used for training, validation, holdout and any rows discarded. This is not available for large datasets or projects created with Date/Time partitioning. The value \"validationAndHoldout\" returns predictions for the rows used to calculate the validation score and the holdout score. Not available for large projects or Date/Time projects for models trained into the validation set. The value \"holdout\" returns predictions for the rows used to calculate the holdout score. Not available for projects created without a holdout or for models trained into holdout for large datasets or created with Date/Time partitioning. The value \"allBacktests\" returns predictions for the rows used to calculate the backtesting scores for Date/Time projects. The value \"validation\" returns predictions for the rows used to calculate the validation score.", "enum": [ "all", "validationAndHoldout", "holdout", "allBacktests", "validation", "crossValidation" ], "type": "string", "x-enum-versionadded": [ { "value": "validation", "x-versionadded": "v2.21" } ] }, "explanationAlgorithm": { "description": "If set to \"shap\", the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations)", "type": "string", "x-versionadded": "v2.21" }, "maxExplanations": { "description": "Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of \"shap\": If not set, explanations are returned for all features. If the number of features is greater than the \"maxExplanations\", the sum of remaining values will also be returned as \"shapRemainingTotal\". Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Cannot be set if \"explanationAlgorithm\" is omitted.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "modelId": { "description": "The model to make predictions on", "type": "string" } }, "required": [ "dataSubset", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSubset | string | true | | Subset of data predicted on: The value "all" returns predictions for all rows in the dataset including data used for training, validation, holdout and any rows discarded. This is not available for large datasets or projects created with Date/Time partitioning. The value "validationAndHoldout" returns predictions for the rows used to calculate the validation score and the holdout score. Not available for large projects or Date/Time projects for models trained into the validation set. The value "holdout" returns predictions for the rows used to calculate the holdout score. Not available for projects created without a holdout or for models trained into holdout for large datasets or created with Date/Time partitioning. The value "allBacktests" returns predictions for the rows used to calculate the backtesting scores for Date/Time projects. The value "validation" returns predictions for the rows used to calculate the validation score. | | explanationAlgorithm | string | false | | If set to "shap", the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations) | | maxExplanations | integer | false | maximum: 100minimum: 1 | Specifies the maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. In the case of "shap": If not set, explanations are returned for all features. If the number of features is greater than the "maxExplanations", the sum of remaining values will also be returned as "shapRemainingTotal". Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns. Cannot be set if "explanationAlgorithm" is omitted. | | modelId | string | true | | The model to make predictions on | ### Enumerated Values | Property | Value | | --- | --- | | dataSubset | [all, validationAndHoldout, holdout, allBacktests, validation, crossValidation] | ## CredentialId ``` { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" } }, "required": [ "credentialId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogVersionId | string | false | | The ID of the latest version of the catalog entry. | | credentialId | string | true | | The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional. | | url | string | false | | The link to retrieve more detailed information about the entity that uses this catalog dataset. | ## DSS ``` { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" } ``` Stream CSV data chunks from DSS dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | false | | The ID of the dataset | | partition | string,null | false | | Partition used to predict | | projectId | string | true | | The ID of the project | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | partition | [holdout, validation, allBacktests, null] | | type | dss | ## DSSDataStreamer ``` { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" } ``` Stream CSV data chunks from DSS dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | partition | string,null | false | | Partition used to predict | | projectId | string | true | | The ID of the project | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | partition | [holdout, validation, allBacktests, null] | | type | dss | ## DataQualityWarningsRecord ``` { "description": "A Json object of available warnings about potential problems in this prediction dataset. Empty if no warnings.", "properties": { "hasKiaMissingValuesInForecastWindow": { "description": "If true, known-in-advance features in this dataset have missing values in the forecast window. Absence of the known-in-advance values can negatively impact prediction quality. Only applies for time series projects.", "type": "boolean", "x-versionadded": "v2.15" }, "insufficientRowsForEvaluatingModels": { "description": "If true, the dataset has a target column present indicating it can be used to evaluate model performance but too few rows to be trustworthy in so doing. If false, either it has no target column at all or it has sufficient rows for model evaluation. Only applies for regression, binary classification, multiclass classification projects and time series unsupervised projects.", "type": "boolean", "x-versionadded": "v2.19" }, "singleClassActualValueColumn": { "description": "If true, actual value column has only one class and such insights as ROC curve can not be calculated. Only applies for binary classification projects or unsupervised projects.", "type": "boolean", "x-versionadded": "v2.21" } }, "type": "object" } ``` A Json object of available warnings about potential problems in this prediction dataset. Empty if no warnings. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | hasKiaMissingValuesInForecastWindow | boolean | false | | If true, known-in-advance features in this dataset have missing values in the forecast window. Absence of the known-in-advance values can negatively impact prediction quality. Only applies for time series projects. | | insufficientRowsForEvaluatingModels | boolean | false | | If true, the dataset has a target column present indicating it can be used to evaluate model performance but too few rows to be trustworthy in so doing. If false, either it has no target column at all or it has sufficient rows for model evaluation. Only applies for regression, binary classification, multiclass classification projects and time series unsupervised projects. | | singleClassActualValueColumn | boolean | false | | If true, actual value column has only one class and such insights as ROC curve can not be calculated. Only applies for binary classification projects or unsupervised projects. | ## DataStageDataStreamer ``` { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" } ``` Stream CSV data chunks from data stage storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStageId | string | true | | The ID of the data stage | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | dataStage | ## DataStageIntake ``` { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" } ``` Stream CSV data chunks from data stage storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStageId | string | true | | The ID of the data stage | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | dataStage | ## DatabricksAccessTokenCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'databricks_access_token_account' here. | | databricksAccessToken | string | true | minLength: 1minLength: 1 | Databricks personal access token. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | databricks_access_token_account | ## DatabricksDataStreamer ``` { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" } ``` Stream CSV data chunks from Databricks using browser-databricks ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with read access to the data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | databricks | ## DatabricksIntake ``` { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" } ``` Stream CSV data chunks from Databricks using browser-databricks ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | string | true | | The ID of the credential holding information about a user with read access to the data source. | | dataStoreId | string | true | | ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | databricks | ## DatabricksOutput ``` { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" } ``` Saves CSV data chunks to Databricks using browser-databricks ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | credentialId | string | true | | The ID of the credential holding information about a user with write access to the data destination. | | dataStoreId | string | true | | The ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | databricks | ## DatabricksOutputAdaptor ``` { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" } ``` Saves CSV data chunks to Databricks using browser-databricks ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with write access to the data destination. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | databricks | ## DatasphereDataStreamer ``` { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` Stream CSV data chunks from Datasphere using browser-datasphere ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with read access to the data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | datasphere | ## DatasphereIntake ``` { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` Stream CSV data chunks from Datasphere using browser-datasphere ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | string | true | | The ID of the credential holding information about a user with read access to the data source. | | dataStoreId | string | true | | ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | datasphere | ## DatasphereOutput ``` { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` Saves CSV data chunks to Datasphere using browser-datasphere ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | credentialId | string | true | | The ID of the credential holding information about a user with write access to the data destination. | | dataStoreId | string | true | | The ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | datasphere | ## DatasphereOutputAdaptor ``` { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" } ``` Saves CSV data chunks to Datasphere using browser-datasphere ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with write access to the data destination. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | datasphere | ## FileSystemDataStreamer ``` { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to data on host filesystem | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | filesystem | ## FileSystemIntake ``` { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to data on host filesystem | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | filesystem | ## FileSystemOutput ``` { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to results on host filesystem | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | filesystem | ## FileSystemOutputAdaptor ``` { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to results on host filesystem | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | filesystem | ## GCPDataStreamer ``` { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Google Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | gcp | ## GCPIntake ``` { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Google Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | gcp | ## GCPKey ``` { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authProviderX509CertUrl | string(uri) | false | | Auth provider X509 certificate URL. | | authUri | string(uri) | false | | Auth URI. | | clientEmail | string | false | | Client email address. | | clientId | string | false | | Client ID. | | clientX509CertUrl | string(uri) | false | | Client X509 certificate URL. | | privateKey | string | false | | Private key. | | privateKeyId | string | false | | Private key ID | | projectId | string | false | | Project ID. | | tokenUri | string(uri) | false | | Token URI. | | type | string | true | | GCP account type. | ### Enumerated Values | Property | Value | | --- | --- | | type | service_account | ## GCPOutput ``` { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Save CSV data chunks to Google Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | format | string | false | | Type of input file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | gcp | ## GCPOutputAdaptor ``` { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Save CSV data chunks to Google Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | format | string | false | | Type of input file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | gcp | ## GoogleServiceAccountCredentials ``` { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configId | string | false | | The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey. | | credentialType | string | true | | The type of these credentials, 'gcp' here. | | gcpKey | GCPKey | false | | The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. | | googleConfigId | string | false | | The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | gcp | ## HTTPDataStreamer ``` { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from HTTP ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | type | http | ## HTTPIntake ``` { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from HTTP ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | type | http | ## HTTPOutput ``` { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" } ``` Save CSV data chunks to HTTP data endpoint ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | headers | object | false | | Extra headers to send with the request | | method | string | true | | Method to use when saving the CSV file | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | method | [POST, PUT] | | type | http | ## HttpOutputAdaptor ``` { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" } ``` Save CSV data chunks to HTTP data endpoint ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | headers | object | false | | Extra headers to send with the request | | method | string | true | | Method to use when saving the CSV file | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | method | [POST, PUT] | | type | http | ## JDBCDataStreamer ``` { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" } ``` Stream CSV data chunks from JDBC ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with read access to the JDBC data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fetchSize | integer | false | maximum: 1000000minimum: 1 | A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21. | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | jdbc | ## JDBCIntake ``` { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" } ``` Stream CSV data chunks from JDBC ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | string,null | false | | The ID of the credential holding information about a user with read access to the JDBC data source. | | dataStoreId | string | true | | ID of the data store to connect to | | fetchSize | integer | false | maximum: 1000000minimum: 1 | A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21. | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | jdbc | ## JDBCOutput ``` { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks via JDBC ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | commitInterval | integer | false | maximum: 86400minimum: 0 | Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing. | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | string,null | false | | The ID of the credential holding information about a user with write access to the JDBC data source. | | dataStoreId | string | true | | ID of the data store to connect to | | schema | string | false | | The name of the specified database schema to write the results to. | | statementType | string | true | | The statement type to use when writing the results. Deprecation Warning: Use of create_table is now discouraged. Use one of the other possibilities along with the parameter createTableIfNotExists set to true. | | table | string | true | | The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | type | string | true | | Type name for this intake type | | updateColumns | [string] | false | maxItems: 100 | The column names to be updated if statementType is set to either update or upsert. | | whereColumns | [string] | false | maxItems: 100 | The column names to be used in the where clause if statementType is set to update or upsert. | ### Enumerated Values | Property | Value | | --- | --- | | statementType | [createTable, create_table, insert, insertUpdate, insert_update, update] | | type | jdbc | ## JdbcOutputAdaptor ``` { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks via JDBC ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | commitInterval | integer | false | maximum: 86400minimum: 0 | Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing. | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with write access to the JDBC data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | false | | The name of the specified database schema to write the results to. | | statementType | string | true | | The statement type to use when writing the results. Deprecation Warning: Use of create_table is now discouraged. Use one of the other possibilities along with the parameter createTableIfNotExists set to true. | | table | string | true | | The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | type | string | true | | Type name for this intake type | | updateColumns | [string] | false | maxItems: 100 | The column names to be updated if statementType is set to either update or upsert. | | whereColumns | [string] | false | maxItems: 100 | The column names to be used in the where clause if statementType is set to update or upsert. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | statementType | [createTable, create_table, insert, insertUpdate, insert_update, update] | | type | jdbc | ## JobRunTimeBasedForecastPointPolicy ``` { "description": "Forecast point policy", "properties": { "configuration": { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" }, "type": { "description": "Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards.", "enum": [ "jobRunTimeBased" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` Forecast point policy ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configuration | JobRunTimeBasedForecastPointPolicySettings | false | | Customize if forecast point based on job run time needs to be shifted. | | type | string | true | | Type of the forecast point policy. Forecast point will be based on the scheduled run time of the job or the current moment in UTC if job was launched manually. Run time can be adjusted backwards or forwards. | ### Enumerated Values | Property | Value | | --- | --- | | type | jobRunTimeBased | ## JobRunTimeBasedForecastPointPolicySettings ``` { "description": "Customize if forecast point based on job run time needs to be shifted.", "properties": { "offset": { "description": "Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M'", "format": "offset", "type": "string" } }, "required": [ "offset" ], "type": "object" } ``` Customize if forecast point based on job run time needs to be shifted. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | offset | string(offset) | true | | Offset to apply to scheduled run time of the job in a ISO-8601 format toobtain a relative forecast point. Example of the positive offset 'P2DT5H3M', example of the negative offset '-P2DT5H4M' | ## LocalFileDataStreamer ``` { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` Stream CSV data chunks from local file storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | async | boolean,null | false | | The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished. | | multipart | boolean | false | | specify if the data will be uploaded in multiple parts instead of a single file | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | [local_file, localFile] | ## LocalFileIntake ``` { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` Stream CSV data chunks from local file storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | async | boolean,null | false | | The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished. | | multipart | boolean | false | | specify if the data will be uploaded in multiple parts instead of a single file | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | [local_file, localFile] | ## LocalFileOutput ``` { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` Save CSV data chunks to local file storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | [local_file, localFile] | ## LocalFileOutputAdaptor ``` { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` Save CSV data chunks to local file storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | [local_file, localFile] | ## MonitoringAggregation ``` { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" } ``` Defines the aggregation policy for monitoring jobs. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | retentionPolicy | string | false | | Monitoring jobs retention policy for aggregation. | | retentionValue | integer | false | | Amount/percentage of samples to retain. | ### Enumerated Values | Property | Value | | --- | --- | | retentionPolicy | [samples, percentage] | ## MonitoringColumnsMapping ``` { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" } ``` Column names mapping for monitoring ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actedUponColumn | string | false | | Name of column that contains value for acted_on. | | actualsTimestampColumn | string | false | | Name of column that contains actual timestamps. | | actualsValueColumn | string | false | | Name of column that contains actuals value. | | associationIdColumn | string | false | | Name of column that contains association Id. | | customMetricId | string | false | | Id of custom metric to process values for. | | customMetricTimestampColumn | string | false | | Name of column that contains custom metric values timestamps. | | customMetricTimestampFormat | string | false | | Format of timestamps from customMetricTimestampColumn. | | customMetricValueColumn | string | false | | Name of column that contains values for custom metric. | | monitoredStatusColumn | string | false | | Column name used to mark monitored rows. | | predictionsColumns | any | false | | Name of the column(s) which contain prediction values. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [PredictionColumMap] | false | maxItems: 100 | Map containing column name(s) and class name(s) for multiclass problem. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Column name that contains the prediction for regressions problem. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | reportDrift | boolean | false | | True to report drift, False otherwise. | | reportPredictions | boolean | false | | True to report prediction, False otherwise. | | uniqueRowIdentifierColumns | [string] | false | maxItems: 100 | Column(s) name of unique row identifiers. | ## MonitoringOutputSettings ``` { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" } ``` Output settings for monitoring jobs ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | monitoredStatusColumn | string | true | | Column name used to mark monitored rows. | | uniqueRowIdentifierColumns | [string] | true | maxItems: 100 | Column(s) name of unique row identifiers. | ## OAuthCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'oauth' here. | | oauthAccessToken | string,null | false | | The OAuth access token. | | oauthClientId | string,null | false | | The OAuth client ID. | | oauthClientSecret | string,null | false | | The OAuth client secret. | | oauthRefreshToken | string | true | | The OAuth refresh token. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | oauth | ## PasswordCredentials ``` { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogVersionId | string | false | | The ID of the latest version of the catalog entry. | | password | string | true | | The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. | | url | string | false | | The link to retrieve more detailed information about the entity that uses this catalog dataset. | | user | string | true | | The username for database authentication. | ## PerNgramTextExplanations ``` { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isUnknown | boolean | true | | Whether the ngram is identifiable by the blueprint or not. | | ngrams | [PredictionExplanationsPredictionValues] | true | maxItems: 1000 | The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information. | | qualitativateStrength | string | true | | A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-'). | | strength | number | true | | The amount these ngrams affected the prediction. | ## PredictJobDetailsResponse ``` { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if a job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "message": { "description": "An optional message about the job.", "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The status of the job.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" } }, "required": [ "id", "isBlocked", "message", "modelId", "projectId", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The job ID. | | isBlocked | boolean | true | | True if a job is waiting for its dependencies to be resolved first. | | message | string | true | | An optional message about the job. | | modelId | string | true | | The ID of the model. | | projectId | string | true | | The project the job belongs to. | | status | string | true | | The status of the job. | ### Enumerated Values | Property | Value | | --- | --- | | status | [queue, inprogress, error, ABORTED, COMPLETED] | ## PredictionArrayObjectValues ``` { "description": "Predicted values.", "properties": { "label": { "description": "For regression problems this will be the name of the target column, 'Anomaly score' or ignored field. For classification projects this will be the name of the class.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "threshold": { "description": "Threshold used in multilabel classification for this class.", "maximum": 1, "minimum": 0, "type": "number" }, "value": { "description": "The predicted probability of the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" } ``` Predicted values. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | any | true | | For regression problems this will be the name of the target column, 'Anomaly score' or ignored field. For classification projects this will be the name of the class. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | threshold | number | false | maximum: 1minimum: 0 | Threshold used in multilabel classification for this class. | | value | number | true | | The predicted probability of the class identified by the label. | ## PredictionColumMap ``` { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | className | string | true | | Class name. | | columnName | string | true | | Column name that contains the prediction for a specific class. | ## PredictionDataSource ``` { "properties": { "actualValueColumn": { "description": "The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset.", "type": "string", "x-versionadded": "v2.21" }, "credentialData": { "description": "The credentials to authenticate with the database, to use instead of user/password or credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The credential ID to use for database authentication.", "type": "string", "x-versionadded": "v2.19" }, "credentials": { "description": "A list of credentials for the secondary datasets used in feature discovery project.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" } }, "required": [ "credentialId" ], "type": "object" } ] }, "maxItems": 30, "type": "array", "x-versionadded": "v2.19" }, "dataSourceId": { "description": "The ID of ``DataSource``.", "type": "string" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error.", "format": "date-time", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. DEPRECATED: please use ``credentialId`` or ``credentialData`` instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "predictionsEndDate": { "description": "The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "description": "For time series projects only. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. This value is optional. If omitted or false, missing values are not allowed.", "type": "boolean", "x-versionadded": "v2.15" }, "secondaryDatasetsConfigId": { "description": "For feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction.", "type": "string", "x-versionadded": "v2.19" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication. Default is false.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for database authentication. DEPRECATED: please use ``credentialId`` or ``credentialData`` instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "dataSourceId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string | false | | The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. | | credentialData | any | false | | The credentials to authenticate with the database, to use instead of user/password or credential ID. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BasicCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Credentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OAuthCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The credential ID to use for database authentication. | | credentials | [oneOf] | false | maxItems: 30 | A list of credentials for the secondary datasets used in feature discovery project. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PasswordCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CredentialId | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSourceId | string | true | | The ID of DataSource. | | forecastPoint | string(date-time) | false | | For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error. | | password | string | false | | The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. DEPRECATED: please use credentialId or credentialData instead. | | predictionsEndDate | string(date-time) | false | | The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsStartDate, and cannot be provided with the forecastPoint parameter. | | predictionsStartDate | string(date-time) | false | | The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsEndDate, and cannot be provided with the forecastPoint parameter. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | For time series projects only. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. This value is optional. If omitted or false, missing values are not allowed. | | secondaryDatasetsConfigId | string | false | | For feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction. | | useKerberos | boolean | false | | If true, use kerberos authentication for database authentication. Default is false. | | user | string | false | | The username for database authentication. DEPRECATED: please use credentialId or credentialData instead. | ## PredictionDatasetListControllerResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the dataset individually from [GET /api/v2/projects/{projectId}/predictionDatasets/{datasetId}/][get-apiv2projectsprojectidpredictiondatasetsdatasetid].", "items": { "properties": { "actualValueColumn": { "description": "Optional, only available for unsupervised projects, in case dataset was uploaded with actual value column specified. Name of the column which will be used to calculate the classification metrics and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "catalogId": { "description": "The ID of the AI catalog entry used to create the prediction, dataset or None if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "catalogVersionId": { "description": "The ID of the AI catalog version used to create the prediction dataset, or None if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "containsTargetValues": { "description": "If true, dataset contains target values and can be used to calculate the classification metrics and insights. Only applies for supervised projects.", "type": [ "boolean", "null" ], "x-versionadded": "v2.21" }, "created": { "description": "The date string of when the dataset was created, of the format `YYYY-mm-ddTHH:MM:SS.ssssssZ`, like ``2016-06-09T11:32:34.170338Z``.", "format": "date-time", "type": "string" }, "dataEndDate": { "description": "Only available for time series projects, a date string representing the maximum primary date of the prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "dataQualityWarnings": { "description": "A Json object of available warnings about potential problems in this prediction dataset. Empty if no warnings.", "properties": { "hasKiaMissingValuesInForecastWindow": { "description": "If true, known-in-advance features in this dataset have missing values in the forecast window. Absence of the known-in-advance values can negatively impact prediction quality. Only applies for time series projects.", "type": "boolean", "x-versionadded": "v2.15" }, "insufficientRowsForEvaluatingModels": { "description": "If true, the dataset has a target column present indicating it can be used to evaluate model performance but too few rows to be trustworthy in so doing. If false, either it has no target column at all or it has sufficient rows for model evaluation. Only applies for regression, binary classification, multiclass classification projects and time series unsupervised projects.", "type": "boolean", "x-versionadded": "v2.19" }, "singleClassActualValueColumn": { "description": "If true, actual value column has only one class and such insights as ROC curve can not be calculated. Only applies for binary classification projects or unsupervised projects.", "type": "boolean", "x-versionadded": "v2.21" } }, "type": "object" }, "dataStartDate": { "description": "Only available for time series projects, a date string representing the minimum primary date of the prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "detectedActualValueColumns": { "description": "Only available for unsupervised projects, the list of detected `actualValueColumnInfo` objects which can be used to calculate the classification metrics and insights.", "items": { "properties": { "missingCount": { "description": "Count of the missing values in the column.", "type": "integer", "x-versionadded": "v2.21" }, "name": { "description": "Name of the column.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "missingCount", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "The date string of the forecastPoint of this prediction dataset. Only non-null for time series projects.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "forecastPointRange": { "description": "Only available for time series projects, the start and end of the range of dates available for use as the forecast point, detected based on the uploaded prediction dataset.", "items": { "description": "The date string of a forecast point.", "format": "date-time", "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "id": { "description": "The ID of this dataset.", "type": "string" }, "maxForecastDate": { "description": "Only available for time series projects, a date string representing the maximum forecast date of this prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "name": { "description": "The name of the dataset when it was uploaded.", "type": "string" }, "numColumns": { "description": "The number of columns in this dataset.", "type": "integer" }, "numRows": { "description": "The number of rows in this dataset.", "type": "integer" }, "predictionsEndDate": { "description": "The date string of the prediction end date of this prediction dataset. Used for bulk predictions. Note that this parameter is for generating historical predictions using the training data. Only non-null for time series projects.", "format": "date-time", "type": [ "string", "null" ] }, "predictionsStartDate": { "description": "The date string of the prediction start date of this prediction dataset. Used for bulk predictions. Note that this parameter is for generating historical predictions using the training data. Only non-null for time series projects.", "format": "date-time", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID that owns this dataset.", "type": "string" }, "secondaryDatasetsConfigId": { "description": "Only available for feature discovery projects. The ID of the secondary dataset config used by the dataset for the prediction.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "catalogId", "catalogVersionId", "created", "dataQualityWarnings", "forecastPoint", "id", "name", "numColumns", "numRows", "predictionsEndDate", "predictionsStartDate", "projectId" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | The number of items returned on this page. | | data | [PredictionDatasetRetrieveResponse] | true | | Each has the same schema as if retrieving the dataset individually from [GET /api/v2/projects/{projectId}/predictionDatasets/{datasetId}/][get-apiv2projectsprojectidpredictiondatasetsdatasetid]. | | next | string,null | true | | A URL pointing to the next page (if null, there is no next page). | | previous | string,null | true | | A URL pointing to the previous page (if null, there is no previous page). | ## PredictionDatasetRetrieveResponse ``` { "properties": { "actualValueColumn": { "description": "Optional, only available for unsupervised projects, in case dataset was uploaded with actual value column specified. Name of the column which will be used to calculate the classification metrics and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "catalogId": { "description": "The ID of the AI catalog entry used to create the prediction, dataset or None if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "catalogVersionId": { "description": "The ID of the AI catalog version used to create the prediction dataset, or None if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "containsTargetValues": { "description": "If true, dataset contains target values and can be used to calculate the classification metrics and insights. Only applies for supervised projects.", "type": [ "boolean", "null" ], "x-versionadded": "v2.21" }, "created": { "description": "The date string of when the dataset was created, of the format `YYYY-mm-ddTHH:MM:SS.ssssssZ`, like ``2016-06-09T11:32:34.170338Z``.", "format": "date-time", "type": "string" }, "dataEndDate": { "description": "Only available for time series projects, a date string representing the maximum primary date of the prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "dataQualityWarnings": { "description": "A Json object of available warnings about potential problems in this prediction dataset. Empty if no warnings.", "properties": { "hasKiaMissingValuesInForecastWindow": { "description": "If true, known-in-advance features in this dataset have missing values in the forecast window. Absence of the known-in-advance values can negatively impact prediction quality. Only applies for time series projects.", "type": "boolean", "x-versionadded": "v2.15" }, "insufficientRowsForEvaluatingModels": { "description": "If true, the dataset has a target column present indicating it can be used to evaluate model performance but too few rows to be trustworthy in so doing. If false, either it has no target column at all or it has sufficient rows for model evaluation. Only applies for regression, binary classification, multiclass classification projects and time series unsupervised projects.", "type": "boolean", "x-versionadded": "v2.19" }, "singleClassActualValueColumn": { "description": "If true, actual value column has only one class and such insights as ROC curve can not be calculated. Only applies for binary classification projects or unsupervised projects.", "type": "boolean", "x-versionadded": "v2.21" } }, "type": "object" }, "dataStartDate": { "description": "Only available for time series projects, a date string representing the minimum primary date of the prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "detectedActualValueColumns": { "description": "Only available for unsupervised projects, the list of detected `actualValueColumnInfo` objects which can be used to calculate the classification metrics and insights.", "items": { "properties": { "missingCount": { "description": "Count of the missing values in the column.", "type": "integer", "x-versionadded": "v2.21" }, "name": { "description": "Name of the column.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "missingCount", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "The date string of the forecastPoint of this prediction dataset. Only non-null for time series projects.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "forecastPointRange": { "description": "Only available for time series projects, the start and end of the range of dates available for use as the forecast point, detected based on the uploaded prediction dataset.", "items": { "description": "The date string of a forecast point.", "format": "date-time", "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "id": { "description": "The ID of this dataset.", "type": "string" }, "maxForecastDate": { "description": "Only available for time series projects, a date string representing the maximum forecast date of this prediction dataset.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "name": { "description": "The name of the dataset when it was uploaded.", "type": "string" }, "numColumns": { "description": "The number of columns in this dataset.", "type": "integer" }, "numRows": { "description": "The number of rows in this dataset.", "type": "integer" }, "predictionsEndDate": { "description": "The date string of the prediction end date of this prediction dataset. Used for bulk predictions. Note that this parameter is for generating historical predictions using the training data. Only non-null for time series projects.", "format": "date-time", "type": [ "string", "null" ] }, "predictionsStartDate": { "description": "The date string of the prediction start date of this prediction dataset. Used for bulk predictions. Note that this parameter is for generating historical predictions using the training data. Only non-null for time series projects.", "format": "date-time", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID that owns this dataset.", "type": "string" }, "secondaryDatasetsConfigId": { "description": "Only available for feature discovery projects. The ID of the secondary dataset config used by the dataset for the prediction.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "catalogId", "catalogVersionId", "created", "dataQualityWarnings", "forecastPoint", "id", "name", "numColumns", "numRows", "predictionsEndDate", "predictionsStartDate", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string,null | false | | Optional, only available for unsupervised projects, in case dataset was uploaded with actual value column specified. Name of the column which will be used to calculate the classification metrics and insights. | | catalogId | string,null | true | | The ID of the AI catalog entry used to create the prediction, dataset or None if not created from the AI catalog. | | catalogVersionId | string,null | true | | The ID of the AI catalog version used to create the prediction dataset, or None if not created from the AI catalog. | | containsTargetValues | boolean,null | false | | If true, dataset contains target values and can be used to calculate the classification metrics and insights. Only applies for supervised projects. | | created | string(date-time) | true | | The date string of when the dataset was created, of the format YYYY-mm-ddTHH:MM:SS.ssssssZ, like 2016-06-09T11:32:34.170338Z. | | dataEndDate | string(date-time) | false | | Only available for time series projects, a date string representing the maximum primary date of the prediction dataset. | | dataQualityWarnings | DataQualityWarningsRecord | true | | A Json object of available warnings about potential problems in this prediction dataset. Empty if no warnings. | | dataStartDate | string(date-time) | false | | Only available for time series projects, a date string representing the minimum primary date of the prediction dataset. | | detectedActualValueColumns | [ActualValueColumnInfo] | false | | Only available for unsupervised projects, the list of detected actualValueColumnInfo objects which can be used to calculate the classification metrics and insights. | | forecastPoint | string,null | true | | The date string of the forecastPoint of this prediction dataset. Only non-null for time series projects. | | forecastPointRange | [string] | false | | Only available for time series projects, the start and end of the range of dates available for use as the forecast point, detected based on the uploaded prediction dataset. | | id | string | true | | The ID of this dataset. | | maxForecastDate | string(date-time) | false | | Only available for time series projects, a date string representing the maximum forecast date of this prediction dataset. | | name | string | true | | The name of the dataset when it was uploaded. | | numColumns | integer | true | | The number of columns in this dataset. | | numRows | integer | true | | The number of rows in this dataset. | | predictionsEndDate | string,null(date-time) | true | | The date string of the prediction end date of this prediction dataset. Used for bulk predictions. Note that this parameter is for generating historical predictions using the training data. Only non-null for time series projects. | | predictionsStartDate | string,null(date-time) | true | | The date string of the prediction start date of this prediction dataset. Used for bulk predictions. Note that this parameter is for generating historical predictions using the training data. Only non-null for time series projects. | | projectId | string | true | | The project ID that owns this dataset. | | secondaryDatasetsConfigId | string | false | | Only available for feature discovery projects. The ID of the secondary dataset config used by the dataset for the prediction. | ## PredictionExplanation ``` { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature | string | true | | The name of the feature contributing to the prediction. | | featureValue | string | true | | The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21). | | imageExplanationUrl | string,null | true | | For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null. | | label | string | true | | Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score. | | perNgramTextExplanations | [PerNgramTextExplanations] | false | maxItems: 10000 | For text features, an array of JSON object containing the per ngram based text prediction explanations. | | qualitativateStrength | string | true | | A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+, medium', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'. | | strength | number | true | | The amount this feature's value affected the prediction. | ## PredictionExplanationsCreate ``` { "properties": { "classNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.29" }, "datasetId": { "description": "The dataset ID.", "type": "string" }, "maxExplanations": { "default": 3, "description": "The maximum number of prediction explanations to supply per row of the dataset.", "maximum": 10, "minimum": 0, "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "thresholdHigh": { "default": null, "description": "The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] }, "thresholdLow": { "default": null, "description": "The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classNames | [string] | false | maxItems: 100 | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | | datasetId | string | true | | The dataset ID. | | maxExplanations | integer | false | maximum: 10minimum: 0 | The maximum number of prediction explanations to supply per row of the dataset. | | modelId | string | true | | The model ID. | | numTopClasses | integer | false | maximum: 100minimum: 1 | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | | thresholdHigh | number,null | false | | The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. | | thresholdLow | number,null | false | | The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. | ## PredictionExplanationsInitializationCreate ``` { "properties": { "maxExplanations": { "default": 3, "description": "The maximum number of prediction explanations to supply per row of the dataset.", "maximum": 10, "minimum": 1, "type": "integer" }, "thresholdHigh": { "default": null, "description": "The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] }, "thresholdLow": { "default": null, "description": "The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | false | maximum: 10minimum: 1 | The maximum number of prediction explanations to supply per row of the dataset. | | thresholdHigh | number,null | false | | The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. | | thresholdLow | number,null | false | | The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. | ## PredictionExplanationsInitializationRetrieve ``` { "properties": { "modelId": { "description": "The model ID.", "type": "string" }, "predictionExplanationsSample": { "description": "Each is a PredictionExplanationsRow. They represent a small sample of prediction explanations that could be generated for a particular dataset. They will have the same schema as the `data` array in the response from [GET /api/v2/projects/{projectId}/predictionExplanations/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationspredictionexplanationsid]. As of v2.21 only difference is that there is no forecastPoint in response for time series projects.", "items": { "properties": { "adjustedPrediction": { "description": "The exposure-adjusted output of the model for this row.", "type": "number", "x-versionadded": "v2.8" }, "adjustedPredictionValues": { "description": "The exposure-adjusted output of the model for this row.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.8" }, "forecastDistance": { "description": "Forecast distance for the row. For time series projects only.", "type": "integer", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "Forecast point for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" }, "prediction": { "description": "The output of the model for this row.", "type": "number" }, "predictionExplanations": { "description": "The list of prediction explanations.", "items": { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "The threshold value used for classification prediction.", "type": [ "number", "null" ] }, "predictionValues": { "description": "The list of prediction values.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row this PredictionExplanationsRow describes.", "type": "integer" }, "seriesId": { "description": "The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "timestamp": { "description": "The timestamp for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "adjustedPrediction", "adjustedPredictionValues", "forecastDistance", "forecastPoint", "prediction", "predictionExplanations", "predictionThreshold", "predictionValues", "rowId", "seriesId", "timestamp" ], "type": "object" }, "type": "array" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "modelId", "predictionExplanationsSample", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | The model ID. | | predictionExplanationsSample | [PredictionExplanationsRow] | true | | Each is a PredictionExplanationsRow. They represent a small sample of prediction explanations that could be generated for a particular dataset. They will have the same schema as the data array in the response from [GET /api/v2/projects/{projectId}/predictionExplanations/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationspredictionexplanationsid]. As of v2.21 only difference is that there is no forecastPoint in response for time series projects. | | projectId | string | true | | The project ID. | ## PredictionExplanationsMetadataValues ``` { "description": "Prediction explanation metadata.", "properties": { "shapRemainingTotal": { "description": "Will be present only if `explanationAlgorithm` = 'shap' and `maxExplanations` is nonzero. The total of SHAP values for features beyond the `maxExplanations`. This can be identically 0 in all rows, if `maxExplanations` is greater than the number of features and thus all features are returned.", "type": "integer" } }, "type": "object" } ``` Prediction explanation metadata. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | shapRemainingTotal | integer | false | | Will be present only if explanationAlgorithm = 'shap' and maxExplanations is nonzero. The total of SHAP values for features beyond the maxExplanations. This can be identically 0 in all rows, if maxExplanations is greater than the number of features and thus all features are returned. | ## PredictionExplanationsObject ``` { "description": "Prediction explanation result.", "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. The type corresponds to the feature (bool, int, float, str, etc.).", "oneOf": [ { "type": "integer" }, { "type": "boolean" }, { "type": "string" }, { "type": "number" } ] }, "label": { "description": "Describes what output was driven by this prediction explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation. For predictions made using anomaly detection models, it is the `Anomaly Score`.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "strength": { "description": "Algorithm-specific explanation value attributed to `feature` in this row. If `explanationAlgorithm` = `shap`, this is the SHAP value.", "type": [ "number", "null" ] } }, "required": [ "feature", "featureValue", "label" ], "type": "object" } ``` Prediction explanation result. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature | string | true | | The name of the feature contributing to the prediction. | | featureValue | any | true | | The value the feature took on for this row. The type corresponds to the feature (bool, int, float, str, etc.). | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | any | true | | Describes what output was driven by this prediction explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation. For predictions made using anomaly detection models, it is the Anomaly Score. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | strength | number,null | false | | Algorithm-specific explanation value attributed to feature in this row. If explanationAlgorithm = shap, this is the SHAP value. | ## PredictionExplanationsPredictionValues ``` { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | string | true | | Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score. | | value | number | true | | The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label. | ## PredictionExplanationsRecord ``` { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "finishTime": { "description": "The timestamp referencing when computation for these prediction explanations finished.", "type": "number" }, "id": { "description": "The PredictionExplanationsRecord ID.", "type": "string" }, "maxExplanations": { "description": "The maximum number of codes generated per prediction.", "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numColumns": { "description": "The number of columns prediction explanations were computed for.", "type": "integer" }, "predictionExplanationsLocation": { "description": "Where to retrieve the prediction explanations.", "type": "string" }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "thresholdHigh": { "description": "The prediction explanation high threshold. Predictions must be above this value (or below the thresholdLow value) to have PredictionExplanations computed.", "type": [ "number", "null" ] }, "thresholdLow": { "description": "The prediction explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) to have PredictionExplanations computed.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "finishTime", "id", "maxExplanations", "modelId", "numColumns", "predictionExplanationsLocation", "predictionThreshold", "projectId", "thresholdHigh", "thresholdLow" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The dataset ID. | | finishTime | number | true | | The timestamp referencing when computation for these prediction explanations finished. | | id | string | true | | The PredictionExplanationsRecord ID. | | maxExplanations | integer | true | | The maximum number of codes generated per prediction. | | modelId | string | true | | The model ID. | | numColumns | integer | true | | The number of columns prediction explanations were computed for. | | predictionExplanationsLocation | string | true | | Where to retrieve the prediction explanations. | | predictionThreshold | number,null | true | | The threshold value used for binary classification prediction. | | projectId | string | true | | The project ID. | | thresholdHigh | number,null | true | | The prediction explanation high threshold. Predictions must be above this value (or below the thresholdLow value) to have PredictionExplanations computed. | | thresholdLow | number,null | true | | The prediction explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) to have PredictionExplanations computed. | ## PredictionExplanationsRecordList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the prediction explanations individually from [GET /api/v2/projects/{projectId}/predictionExplanationsRecords/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationsrecordspredictionexplanationsid].", "items": { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "finishTime": { "description": "The timestamp referencing when computation for these prediction explanations finished.", "type": "number" }, "id": { "description": "The PredictionExplanationsRecord ID.", "type": "string" }, "maxExplanations": { "description": "The maximum number of codes generated per prediction.", "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numColumns": { "description": "The number of columns prediction explanations were computed for.", "type": "integer" }, "predictionExplanationsLocation": { "description": "Where to retrieve the prediction explanations.", "type": "string" }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "thresholdHigh": { "description": "The prediction explanation high threshold. Predictions must be above this value (or below the thresholdLow value) to have PredictionExplanations computed.", "type": [ "number", "null" ] }, "thresholdLow": { "description": "The prediction explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) to have PredictionExplanations computed.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "finishTime", "id", "maxExplanations", "modelId", "numColumns", "predictionExplanationsLocation", "predictionThreshold", "projectId", "thresholdHigh", "thresholdLow" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | The number of items returned on this page. | | data | [PredictionExplanationsRecord] | true | | Each has the same schema as if retrieving the prediction explanations individually from [GET /api/v2/projects/{projectId}/predictionExplanationsRecords/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationsrecordspredictionexplanationsid]. | | next | string,null | true | | A URL pointing to the next page (if null, there is no next page). | | previous | string,null | true | | A URL pointing to the previous page (if null, there is no previous page). | ## PredictionExplanationsRetrieve ``` { "properties": { "adjustmentMethod": { "description": "'exposureNormalized' (for regression projects with exposure) or 'N/A' (for classification projects) The value of 'exposureNormalized' indicates that prediction outputs are adjusted (or divided) by exposure. The value of 'N/A' indicates that no adjustments are applied to the adjusted predictions and they are identical to the unadjusted predictions.", "type": "string", "x-versionadded": "v2.8" }, "count": { "description": "The number of rows of prediction explanations returned.", "type": "integer" }, "data": { "description": "Each is a PredictionExplanationsRow corresponding to one row of the prediction dataset.", "items": { "properties": { "adjustedPrediction": { "description": "The exposure-adjusted output of the model for this row.", "type": "number", "x-versionadded": "v2.8" }, "adjustedPredictionValues": { "description": "The exposure-adjusted output of the model for this row.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.8" }, "forecastDistance": { "description": "Forecast distance for the row. For time series projects only.", "type": "integer", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "Forecast point for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" }, "prediction": { "description": "The output of the model for this row.", "type": "number" }, "predictionExplanations": { "description": "The list of prediction explanations.", "items": { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "The threshold value used for classification prediction.", "type": [ "number", "null" ] }, "predictionValues": { "description": "The list of prediction values.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row this PredictionExplanationsRow describes.", "type": "integer" }, "seriesId": { "description": "The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "timestamp": { "description": "The timestamp for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "adjustedPrediction", "adjustedPredictionValues", "forecastDistance", "forecastPoint", "prediction", "predictionExplanations", "predictionThreshold", "predictionValues", "rowId", "seriesId", "timestamp" ], "type": "object" }, "type": "array" }, "id": { "description": "The ID of this group of prediction explanations.", "type": "string" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "predictionExplanationsRecordLocation": { "description": "The URL of the PredictionExplanationsRecord associated with these prediction explanations.", "type": "string" }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "adjustmentMethod", "count", "data", "id", "next", "predictionExplanationsRecordLocation", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | adjustmentMethod | string | true | | 'exposureNormalized' (for regression projects with exposure) or 'N/A' (for classification projects) The value of 'exposureNormalized' indicates that prediction outputs are adjusted (or divided) by exposure. The value of 'N/A' indicates that no adjustments are applied to the adjusted predictions and they are identical to the unadjusted predictions. | | count | integer | true | | The number of rows of prediction explanations returned. | | data | [PredictionExplanationsRow] | true | | Each is a PredictionExplanationsRow corresponding to one row of the prediction dataset. | | id | string | true | | The ID of this group of prediction explanations. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | predictionExplanationsRecordLocation | string | true | | The URL of the PredictionExplanationsRecord associated with these prediction explanations. | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | ## PredictionExplanationsRow ``` { "properties": { "adjustedPrediction": { "description": "The exposure-adjusted output of the model for this row.", "type": "number", "x-versionadded": "v2.8" }, "adjustedPredictionValues": { "description": "The exposure-adjusted output of the model for this row.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.8" }, "forecastDistance": { "description": "Forecast distance for the row. For time series projects only.", "type": "integer", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "Forecast point for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" }, "prediction": { "description": "The output of the model for this row.", "type": "number" }, "predictionExplanations": { "description": "The list of prediction explanations.", "items": { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "The threshold value used for classification prediction.", "type": [ "number", "null" ] }, "predictionValues": { "description": "The list of prediction values.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row this PredictionExplanationsRow describes.", "type": "integer" }, "seriesId": { "description": "The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "timestamp": { "description": "The timestamp for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "adjustedPrediction", "adjustedPredictionValues", "forecastDistance", "forecastPoint", "prediction", "predictionExplanations", "predictionThreshold", "predictionValues", "rowId", "seriesId", "timestamp" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | adjustedPrediction | number | true | | The exposure-adjusted output of the model for this row. | | adjustedPredictionValues | [PredictionExplanationsPredictionValues] | true | | The exposure-adjusted output of the model for this row. | | forecastDistance | integer | true | | Forecast distance for the row. For time series projects only. | | forecastPoint | string | true | | Forecast point for the row. For time series projects only. | | prediction | number | true | | The output of the model for this row. | | predictionExplanations | [PredictionExplanation] | true | | The list of prediction explanations. | | predictionThreshold | number,null | true | | The threshold value used for classification prediction. | | predictionValues | [PredictionExplanationsPredictionValues] | true | | The list of prediction values. | | rowId | integer | true | | The row this PredictionExplanationsRow describes. | | seriesId | string,null | true | | The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only. | | timestamp | string | true | | The timestamp for the row. For time series projects only. | ## PredictionFileUpload ``` { "properties": { "actualValueColumn": { "description": "The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset.", "type": "string", "x-versionadded": "v2.21" }, "credentials": { "description": "The list of credentials for the secondary datasets used in feature discovery project.", "type": "string", "x-versionadded": "v2.19" }, "file": { "description": "The dataset file to upload for prediction.", "format": "binary", "type": "string" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions are generated. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error.", "format": "date-time", "type": "string", "x-versionadded": "v2.8" }, "predictionsEndDate": { "description": "Used for time series projects only. The end date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for time series projects only. The start date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "description": "A boolean flag. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or false, missing values are not allowed. For time series projects only.", "enum": [ "false", "False", "true", "True" ], "type": "string", "x-versionadded": "v2.15" }, "secondaryDatasetsConfigId": { "description": "Optional, for feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction.", "type": "string", "x-versionadded": "v2.19" } }, "required": [ "file" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string | false | | The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. | | credentials | string | false | | The list of credentials for the secondary datasets used in feature discovery project. | | file | string(binary) | true | | The dataset file to upload for prediction. | | forecastPoint | string(date-time) | false | | For time series projects only. The time in the dataset relative to which predictions are generated. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error. | | predictionsEndDate | string(date-time) | false | | Used for time series projects only. The end date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsStartDate, and cannot be provided with the forecastPoint parameter. | | predictionsStartDate | string(date-time) | false | | Used for time series projects only. The start date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsEndDate, and cannot be provided with the forecastPoint parameter. | | relaxKnownInAdvanceFeaturesCheck | string | false | | A boolean flag. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or false, missing values are not allowed. For time series projects only. | | secondaryDatasetsConfigId | string | false | | Optional, for feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction. | ### Enumerated Values | Property | Value | | --- | --- | | relaxKnownInAdvanceFeaturesCheck | [false, False, true, True] | ## PredictionFromCatalogDataset ``` { "properties": { "actualValueColumn": { "description": "Actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset.", "type": "string", "x-versionadded": "v2.21" }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.19" }, "credentials": { "description": "List of credentials for the secondary datasets used in feature discovery project.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" } }, "required": [ "credentialId" ], "type": "object" } ] }, "maxItems": 30, "type": "array", "x-versionadded": "v2.19" }, "datasetId": { "description": "The ID of the dataset entry to use for prediction dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version to use for the prediction dataset. If not specified - uses latest version associated with datasetId.", "type": "string" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error.", "format": "date-time", "type": "string", "x-versionadded": "v2.8" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "predictionsEndDate": { "description": "The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "description": "For time series projects only. If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or False, missing values are not allowed.", "type": "boolean" }, "secondaryDatasetsConfigId": { "description": "For feature discovery projects only. The Id of the alternative secondary dataset config to use during prediction.", "type": "string", "x-versionadded": "v2.19" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication. Default is false.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for database authentication. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "datasetId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string | false | | Actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. | | credentialData | any | false | | The credentials to authenticate with the database, to be used instead of credential ID. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BasicCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Credentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OAuthCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeKeyPairCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GoogleServiceAccountCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksAccessTokenCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureServicePrincipalCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to authenticate with the database. | | credentials | [oneOf] | false | maxItems: 30 | List of credentials for the secondary datasets used in feature discovery project. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PasswordCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CredentialId | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the dataset entry to use for prediction dataset. | | datasetVersionId | string | false | | The ID of the dataset version to use for the prediction dataset. If not specified - uses latest version associated with datasetId. | | forecastPoint | string(date-time) | false | | For time series projects only. The time in the dataset relative to which predictions are generated. This value is optional. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error. | | password | string | false | | The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.DEPRECATED: please use credentialId or credentialData instead. | | predictionsEndDate | string(date-time) | false | | The end date for bulk predictions, exclusive. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsStartDate, and cannot be provided with the forecastPoint parameter. | | predictionsStartDate | string(date-time) | false | | The start date for bulk predictions. Used for time series projects only. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsEndDate, and cannot be provided with the forecastPoint parameter. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | For time series projects only. If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. If omitted or False, missing values are not allowed. | | secondaryDatasetsConfigId | string | false | | For feature discovery projects only. The Id of the alternative secondary dataset config to use during prediction. | | useKerberos | boolean | false | | If true, use kerberos authentication for database authentication. Default is false. | | user | string | false | | The username for database authentication. DEPRECATED: please use credentialId or credentialData instead. | ## PredictionObject ``` { "properties": { "actualValue": { "description": "In the case of an unsupervised time series project with a dataset using ``predictionsStartDate`` and ``predictionsEndDate`` for bulk predictions and a specified actual value column, the predictions will be a json array in the same format as with a forecast point with one additional element - `actualValues`. It is the actual value in the row.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "forecastDistance": { "description": "(if time series project) The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column.", "type": [ "integer", "null" ] }, "forecastPoint": { "description": "(if time series project) The forecastPoint of the predictions. Either provided or inferred.", "format": "date-time", "type": [ "string", "null" ] }, "originalFormatTimestamp": { "description": "The timestamp of this row in the prediction dataset. Unlike the ``timestamp`` field, this field will keep the same DateTime formatting as the uploaded prediction dataset. (This column is shown if enabled by your administrator.)", "type": "string", "x-versionadded": "v2.17" }, "positiveProbability": { "description": "For binary classification, the probability the row belongs to the positive class.", "minimum": 0, "type": [ "number", "null" ] }, "prediction": { "description": "The prediction of the model.", "oneOf": [ { "description": "If using a regressor model, will be the numeric value of the target.", "type": "number" }, { "description": "If using a binary or muliclass classifier model, will be the predicted class.", "type": "string" }, { "description": "If using a multilabel classifier model, will be a list of predicted classes.", "items": { "type": "string" }, "type": "array" } ] }, "predictionExplanationMetadata": { "description": "Array containing algorithm-specific values. Varies depending on the value of `explanationAlgorithm`.", "items": { "description": "Prediction explanation metadata.", "properties": { "shapRemainingTotal": { "description": "Will be present only if `explanationAlgorithm` = 'shap' and `maxExplanations` is nonzero. The total of SHAP values for features beyond the `maxExplanations`. This can be identically 0 in all rows, if `maxExplanations` is greater than the number of features and thus all features are returned.", "type": "integer" } }, "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "predictionExplanations": { "description": "Array contains `predictionExplanation` objects. The total elements in the array are bounded by maxExplanations and feature count. It will be present only if `explanationAlgorithm` is not null (prediction explanations were requested).", "items": { "description": "Prediction explanation result.", "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. The type corresponds to the feature (bool, int, float, str, etc.).", "oneOf": [ { "type": "integer" }, { "type": "boolean" }, { "type": "string" }, { "type": "number" } ] }, "label": { "description": "Describes what output was driven by this prediction explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation. For predictions made using anomaly detection models, it is the `Anomaly Score`.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "strength": { "description": "Algorithm-specific explanation value attributed to `feature` in this row. If `explanationAlgorithm` = `shap`, this is the SHAP value.", "type": [ "number", "null" ] } }, "required": [ "feature", "featureValue", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "predictionIntervalLowerBound": { "description": "Present if ``includePredictionIntervals`` is True. Indicates a lower bound of the estimate of error based on test data.", "type": "number", "x-versionadded": "v2.16" }, "predictionIntervalUpperBound": { "description": "Present if ``includePredictionIntervals`` is True. Indicates an upper bound of the estimate of error based on test data.", "type": "number", "x-versionadded": "v2.16" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionValues": { "description": "The list of predicted values for this row.", "items": { "description": "Predicted values.", "properties": { "label": { "description": "For regression problems this will be the name of the target column, 'Anomaly score' or ignored field. For classification projects this will be the name of the class.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "threshold": { "description": "Threshold used in multilabel classification for this class.", "maximum": 1, "minimum": 0, "type": "number" }, "value": { "description": "The predicted probability of the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row in the prediction dataset this prediction corresponds to.", "minimum": 0, "type": "integer" }, "segmentId": { "description": "The ID of the segment value for a segmented project.", "type": "string", "x-versionadded": "v2.27" }, "seriesId": { "description": "The ID of the series value for a multiseries project. For time series projects that are not a multiseries this will be a NaN.", "type": [ "string", "null" ] }, "target": { "description": "In the case of a time series project with a dataset using predictionsStartDate and predictionsEndDate for bulk predictions, the predictions will be a json array in the same format as with a forecast point with one additional element - `target`. It is the target value in the row.", "type": [ "string", "null" ] }, "timestamp": { "description": "(if time series project) The timestamp of this row in the prediction dataset.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "prediction", "rowId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValue | string,null | false | | In the case of an unsupervised time series project with a dataset using predictionsStartDate and predictionsEndDate for bulk predictions and a specified actual value column, the predictions will be a json array in the same format as with a forecast point with one additional element - actualValues. It is the actual value in the row. | | forecastDistance | integer,null | false | | (if time series project) The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column. | | forecastPoint | string,null(date-time) | false | | (if time series project) The forecastPoint of the predictions. Either provided or inferred. | | originalFormatTimestamp | string | false | | The timestamp of this row in the prediction dataset. Unlike the timestamp field, this field will keep the same DateTime formatting as the uploaded prediction dataset. (This column is shown if enabled by your administrator.) | | positiveProbability | number,null | false | minimum: 0 | For binary classification, the probability the row belongs to the positive class. | | prediction | any | true | | The prediction of the model. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | If using a regressor model, will be the numeric value of the target. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | If using a binary or muliclass classifier model, will be the predicted class. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | If using a multilabel classifier model, will be a list of predicted classes. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionExplanationMetadata | [PredictionExplanationsMetadataValues] | false | | Array containing algorithm-specific values. Varies depending on the value of explanationAlgorithm. | | predictionExplanations | [PredictionExplanationsObject] | false | | Array contains predictionExplanation objects. The total elements in the array are bounded by maxExplanations and feature count. It will be present only if explanationAlgorithm is not null (prediction explanations were requested). | | predictionIntervalLowerBound | number | false | | Present if includePredictionIntervals is True. Indicates a lower bound of the estimate of error based on test data. | | predictionIntervalUpperBound | number | false | | Present if includePredictionIntervals is True. Indicates an upper bound of the estimate of error based on test data. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold used for binary classification in predictions. | | predictionValues | [PredictionArrayObjectValues] | false | | The list of predicted values for this row. | | rowId | integer | true | minimum: 0 | The row in the prediction dataset this prediction corresponds to. | | segmentId | string | false | | The ID of the segment value for a segmented project. | | seriesId | string,null | false | | The ID of the series value for a multiseries project. For time series projects that are not a multiseries this will be a NaN. | | target | string,null | false | | In the case of a time series project with a dataset using predictionsStartDate and predictionsEndDate for bulk predictions, the predictions will be a json array in the same format as with a forecast point with one additional element - target. It is the target value in the row. | | timestamp | string,null(date-time) | false | | (if time series project) The timestamp of this row in the prediction dataset. | ## PredictionRetrieveResponse ``` { "properties": { "actualValueColumn": { "description": "For time series unsupervised projects only. Will be present only if the prediction dataset has an actual value column. The name of the column with actuals that was used to calculate the scores and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "explanationAlgorithm": { "description": "The selected algorithm to use for prediction explanations. At present, the only acceptable value is 'shap', which selects the SHapley Additive exPlanations (SHAP) explainer. Defaults to null (no prediction explanations).", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureDerivationWindowCounts": { "description": "For time series projects with partial history only. Indicates how many points were used during feature derivation in the feature derivation window.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "includesPredictionIntervals": { "description": "For time series projects only. Indicates if prediction intervals will be part of the response. Defaults to False.", "type": "boolean", "x-versionadded": "v2.16" }, "maxExplanations": { "description": "The maximum number of prediction explanations values to be returned with each row in the `predictions` json array. Null indicates 'no limit'. Will be present only if `explanationAlgorithm` was set.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "positiveClass": { "description": "For binary classification, the class of the target deemed the positive class. For all other project types this field will be null.", "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "null" } ] }, "predictionIntervalsSize": { "description": "For time series projects only. Will be present only if `includePredictionIntervals` is True. Indicates the percentile used for prediction intervals calculation. Defaults to 80.", "type": [ "integer", "null" ], "x-versionadded": "v2.16" }, "predictions": { "description": "The json array of predictions. The predictions in the response will have slightly different formats, depending on the project type.", "items": { "properties": { "actualValue": { "description": "In the case of an unsupervised time series project with a dataset using ``predictionsStartDate`` and ``predictionsEndDate`` for bulk predictions and a specified actual value column, the predictions will be a json array in the same format as with a forecast point with one additional element - `actualValues`. It is the actual value in the row.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "forecastDistance": { "description": "(if time series project) The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column.", "type": [ "integer", "null" ] }, "forecastPoint": { "description": "(if time series project) The forecastPoint of the predictions. Either provided or inferred.", "format": "date-time", "type": [ "string", "null" ] }, "originalFormatTimestamp": { "description": "The timestamp of this row in the prediction dataset. Unlike the ``timestamp`` field, this field will keep the same DateTime formatting as the uploaded prediction dataset. (This column is shown if enabled by your administrator.)", "type": "string", "x-versionadded": "v2.17" }, "positiveProbability": { "description": "For binary classification, the probability the row belongs to the positive class.", "minimum": 0, "type": [ "number", "null" ] }, "prediction": { "description": "The prediction of the model.", "oneOf": [ { "description": "If using a regressor model, will be the numeric value of the target.", "type": "number" }, { "description": "If using a binary or muliclass classifier model, will be the predicted class.", "type": "string" }, { "description": "If using a multilabel classifier model, will be a list of predicted classes.", "items": { "type": "string" }, "type": "array" } ] }, "predictionExplanationMetadata": { "description": "Array containing algorithm-specific values. Varies depending on the value of `explanationAlgorithm`.", "items": { "description": "Prediction explanation metadata.", "properties": { "shapRemainingTotal": { "description": "Will be present only if `explanationAlgorithm` = 'shap' and `maxExplanations` is nonzero. The total of SHAP values for features beyond the `maxExplanations`. This can be identically 0 in all rows, if `maxExplanations` is greater than the number of features and thus all features are returned.", "type": "integer" } }, "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "predictionExplanations": { "description": "Array contains `predictionExplanation` objects. The total elements in the array are bounded by maxExplanations and feature count. It will be present only if `explanationAlgorithm` is not null (prediction explanations were requested).", "items": { "description": "Prediction explanation result.", "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. The type corresponds to the feature (bool, int, float, str, etc.).", "oneOf": [ { "type": "integer" }, { "type": "boolean" }, { "type": "string" }, { "type": "number" } ] }, "label": { "description": "Describes what output was driven by this prediction explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation. For predictions made using anomaly detection models, it is the `Anomaly Score`.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "strength": { "description": "Algorithm-specific explanation value attributed to `feature` in this row. If `explanationAlgorithm` = `shap`, this is the SHAP value.", "type": [ "number", "null" ] } }, "required": [ "feature", "featureValue", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "predictionIntervalLowerBound": { "description": "Present if ``includePredictionIntervals`` is True. Indicates a lower bound of the estimate of error based on test data.", "type": "number", "x-versionadded": "v2.16" }, "predictionIntervalUpperBound": { "description": "Present if ``includePredictionIntervals`` is True. Indicates an upper bound of the estimate of error based on test data.", "type": "number", "x-versionadded": "v2.16" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionValues": { "description": "The list of predicted values for this row.", "items": { "description": "Predicted values.", "properties": { "label": { "description": "For regression problems this will be the name of the target column, 'Anomaly score' or ignored field. For classification projects this will be the name of the class.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "threshold": { "description": "Threshold used in multilabel classification for this class.", "maximum": 1, "minimum": 0, "type": "number" }, "value": { "description": "The predicted probability of the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row in the prediction dataset this prediction corresponds to.", "minimum": 0, "type": "integer" }, "segmentId": { "description": "The ID of the segment value for a segmented project.", "type": "string", "x-versionadded": "v2.27" }, "seriesId": { "description": "The ID of the series value for a multiseries project. For time series projects that are not a multiseries this will be a NaN.", "type": [ "string", "null" ] }, "target": { "description": "In the case of a time series project with a dataset using predictionsStartDate and predictionsEndDate for bulk predictions, the predictions will be a json array in the same format as with a forecast point with one additional element - `target`. It is the target value in the row.", "type": [ "string", "null" ] }, "timestamp": { "description": "(if time series project) The timestamp of this row in the prediction dataset.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "prediction", "rowId" ], "type": "object" }, "type": "array" }, "shapBaseValue": { "description": "Will be present only if `explanationAlgorithm` = 'shap'. The model's average prediction over the training data. SHAP values are deviations from the base value.", "type": [ "number", "null" ], "x-versionadded": "v2.21" }, "shapWarnings": { "description": "Will be present if `explanationAlgorithm` was set to `shap` and there were additivity failures during SHAP values calculation.", "items": { "description": "Mismatch information.", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value.", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed.", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "task": { "description": "The prediction task.", "enum": [ "Regression", "Binary", "Multiclass", "Multilabel" ], "type": "string" } }, "required": [ "positiveClass", "predictions", "task" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string,null | false | | For time series unsupervised projects only. Will be present only if the prediction dataset has an actual value column. The name of the column with actuals that was used to calculate the scores and insights. | | explanationAlgorithm | string,null | false | | The selected algorithm to use for prediction explanations. At present, the only acceptable value is 'shap', which selects the SHapley Additive exPlanations (SHAP) explainer. Defaults to null (no prediction explanations). | | featureDerivationWindowCounts | integer,null | false | | For time series projects with partial history only. Indicates how many points were used during feature derivation in the feature derivation window. | | includesPredictionIntervals | boolean | false | | For time series projects only. Indicates if prediction intervals will be part of the response. Defaults to False. | | maxExplanations | integer,null | false | | The maximum number of prediction explanations values to be returned with each row in the predictions json array. Null indicates 'no limit'. Will be present only if explanationAlgorithm was set. | | positiveClass | any | true | | For binary classification, the class of the target deemed the positive class. For all other project types this field will be null. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionIntervalsSize | integer,null | false | | For time series projects only. Will be present only if includePredictionIntervals is True. Indicates the percentile used for prediction intervals calculation. Defaults to 80. | | predictions | [PredictionObject] | true | | The json array of predictions. The predictions in the response will have slightly different formats, depending on the project type. | | shapBaseValue | number,null | false | | Will be present only if explanationAlgorithm = 'shap'. The model's average prediction over the training data. SHAP values are deviations from the base value. | | shapWarnings | [ShapWarningValues] | false | | Will be present if explanationAlgorithm was set to shap and there were additivity failures during SHAP values calculation. | | task | string | true | | The prediction task. | ### Enumerated Values | Property | Value | | --- | --- | | task | [Regression, Binary, Multiclass, Multilabel] | ## PredictionURLUpload ``` { "properties": { "actualValueColumn": { "description": "The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. This value is optional.", "type": "string", "x-versionadded": "v2.21" }, "credentials": { "description": "The list of credentials for the secondary datasets used in feature discovery project.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" } }, "required": [ "credentialId" ], "type": "object" } ] }, "maxItems": 30, "type": "array", "x-versionadded": "v2.19" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions are generated. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error.", "format": "date-time", "type": "string", "x-versionadded": "v2.8" }, "predictionsEndDate": { "description": "Used for time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsStartDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for time series projects only. The start date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a ``predictionsEndDate``, and cannot be provided with the ``forecastPoint`` parameter.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "description": "For time series projects only. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. This value is optional. If omitted or false, missing values are not allowed.", "type": "boolean", "x-versionadded": "v2.15" }, "secondaryDatasetsConfigId": { "description": "For feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction.", "type": "string", "x-versionadded": "v2.19" }, "url": { "description": "The URL to download the dataset from.", "format": "url", "type": "string" } }, "required": [ "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string | false | | The actual value column name, valid for the prediction files if the project is unsupervised and the dataset is considered as bulk predictions dataset. This value is optional. | | credentials | [oneOf] | false | maxItems: 30 | The list of credentials for the secondary datasets used in feature discovery project. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PasswordCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CredentialId | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forecastPoint | string(date-time) | false | | For time series projects only. The time in the dataset relative to which predictions are generated. If not specified the default value is the value in the row with the latest specified timestamp. Specifying this value for a project that is not a time series project will result in an error. | | predictionsEndDate | string(date-time) | false | | Used for time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsStartDate, and cannot be provided with the forecastPoint parameter. | | predictionsStartDate | string(date-time) | false | | Used for time series projects only. The start date for bulk predictions. Note that this parameter is used for generating historical predictions using the training data, not for future predictions. If not specified, the dataset is not considered as a bulk predictions dataset. This parameter should be provided in conjunction with a predictionsEndDate, and cannot be provided with the forecastPoint parameter. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | For time series projects only. If true, missing values in the known in advance features are allowed in the forecast window at the prediction time. This value is optional. If omitted or false, missing values are not allowed. | | secondaryDatasetsConfigId | string | false | | For feature discovery projects only. The ID of the alternative secondary dataset config to use during prediction. | | url | string(url) | true | | The URL to download the dataset from. | ## RetrieveListPredictionMetadataObjectsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of the metadata records.", "items": { "properties": { "actualValueColumn": { "description": "For time series unsupervised projects only. The actual value column can be used to calculate the classification metrics and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "datasetId": { "description": "Deprecated alias for `predictionDatasetId`.", "type": [ "string", "null" ] }, "explanationAlgorithm": { "description": "The selected algorithm to use for prediction explanations. At present, the only acceptable value is `shap`, which selects the SHapley Additive exPlanations (SHAP) explainer. Defaults to null (no prediction explanations).", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureDerivationWindowCounts": { "description": "For time series projects with partial history only. Indicates how many points were used during feature derivation.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions were generated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "id": { "description": "The ID of the prediction record.", "type": "string" }, "includesPredictionIntervals": { "description": "Whether the predictions include prediction intervals.", "type": "boolean" }, "maxExplanations": { "description": "The maximum number of prediction explanations values to be returned with each row in the `predictions` json array. Null indicates `no limit`. Will be present only if `explanationAlgorithm` was set.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "modelId": { "description": "The model ID used for predictions.", "type": "string" }, "predictionDatasetId": { "description": "The dataset ID where the prediction data comes from. The field is available via `/api/v2/projects//predictionsMetadata/` route and replaced on `datasetId`in deprecated `/api/v2/projects//predictions/` endpoint.", "type": [ "string", "null" ] }, "predictionIntervalsSize": { "description": "For time series projects only. If prediction intervals were computed, what percentile they represent. Will be ``None`` if ``includePredictionIntervals`` is ``False``.", "type": [ "integer", "null" ] }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "type": [ "number", "null" ], "x-versionadded": "v2.22" }, "predictionsEndDate": { "description": "For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter was used for generating historical predictions using the training data, not for future predictions.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "predictionsStartDate": { "description": "For time series projects only. The start date for bulk predictions. Note that this parameter was used for generating historical predictions using the training data, not for future predictions.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "projectId": { "description": "The project ID of the predictions.", "type": "string" }, "shapWarnings": { "description": "Will be present if `explanationAlgorithm` was set to `shap` and there were additivity failures during SHAP values calculation.", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value.", "type": "number", "x-versionadded": "v2.21" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed.", "type": "integer", "x-versionadded": "v2.21" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "url": { "description": "The URL at which you can download the predictions.", "type": "string" } }, "required": [ "id", "includesPredictionIntervals", "modelId", "predictionIntervalsSize", "projectId", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [RetrievePredictionMetadataObject] | true | | An array of the metadata records. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | ## RetrievePredictionMetadataObject ``` { "properties": { "actualValueColumn": { "description": "For time series unsupervised projects only. The actual value column can be used to calculate the classification metrics and insights.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "datasetId": { "description": "Deprecated alias for `predictionDatasetId`.", "type": [ "string", "null" ] }, "explanationAlgorithm": { "description": "The selected algorithm to use for prediction explanations. At present, the only acceptable value is `shap`, which selects the SHapley Additive exPlanations (SHAP) explainer. Defaults to null (no prediction explanations).", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureDerivationWindowCounts": { "description": "For time series projects with partial history only. Indicates how many points were used during feature derivation.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "forecastPoint": { "description": "For time series projects only. The time in the dataset relative to which predictions were generated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "id": { "description": "The ID of the prediction record.", "type": "string" }, "includesPredictionIntervals": { "description": "Whether the predictions include prediction intervals.", "type": "boolean" }, "maxExplanations": { "description": "The maximum number of prediction explanations values to be returned with each row in the `predictions` json array. Null indicates `no limit`. Will be present only if `explanationAlgorithm` was set.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "modelId": { "description": "The model ID used for predictions.", "type": "string" }, "predictionDatasetId": { "description": "The dataset ID where the prediction data comes from. The field is available via `/api/v2/projects//predictionsMetadata/` route and replaced on `datasetId`in deprecated `/api/v2/projects//predictions/` endpoint.", "type": [ "string", "null" ] }, "predictionIntervalsSize": { "description": "For time series projects only. If prediction intervals were computed, what percentile they represent. Will be ``None`` if ``includePredictionIntervals`` is ``False``.", "type": [ "integer", "null" ] }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "type": [ "number", "null" ], "x-versionadded": "v2.22" }, "predictionsEndDate": { "description": "For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter was used for generating historical predictions using the training data, not for future predictions.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "predictionsStartDate": { "description": "For time series projects only. The start date for bulk predictions. Note that this parameter was used for generating historical predictions using the training data, not for future predictions.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "projectId": { "description": "The project ID of the predictions.", "type": "string" }, "shapWarnings": { "description": "Will be present if `explanationAlgorithm` was set to `shap` and there were additivity failures during SHAP values calculation.", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value.", "type": "number", "x-versionadded": "v2.21" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed.", "type": "integer", "x-versionadded": "v2.21" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "url": { "description": "The URL at which you can download the predictions.", "type": "string" } }, "required": [ "id", "includesPredictionIntervals", "modelId", "predictionIntervalsSize", "projectId", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string,null | false | | For time series unsupervised projects only. The actual value column can be used to calculate the classification metrics and insights. | | datasetId | string,null | false | | Deprecated alias for predictionDatasetId. | | explanationAlgorithm | string,null | false | | The selected algorithm to use for prediction explanations. At present, the only acceptable value is shap, which selects the SHapley Additive exPlanations (SHAP) explainer. Defaults to null (no prediction explanations). | | featureDerivationWindowCounts | integer,null | false | | For time series projects with partial history only. Indicates how many points were used during feature derivation. | | forecastPoint | string,null(date-time) | false | | For time series projects only. The time in the dataset relative to which predictions were generated. | | id | string | true | | The ID of the prediction record. | | includesPredictionIntervals | boolean | true | | Whether the predictions include prediction intervals. | | maxExplanations | integer,null | false | | The maximum number of prediction explanations values to be returned with each row in the predictions json array. Null indicates no limit. Will be present only if explanationAlgorithm was set. | | modelId | string | true | | The model ID used for predictions. | | predictionDatasetId | string,null | false | | The dataset ID where the prediction data comes from. The field is available via /api/v2/projects//predictionsMetadata/ route and replaced on datasetIdin deprecated /api/v2/projects//predictions/ endpoint. | | predictionIntervalsSize | integer,null | true | | For time series projects only. If prediction intervals were computed, what percentile they represent. Will be None if includePredictionIntervals is False. | | predictionThreshold | number,null | false | | Threshold used for binary classification in predictions. | | predictionsEndDate | string,null(date-time) | false | | For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter was used for generating historical predictions using the training data, not for future predictions. | | predictionsStartDate | string,null(date-time) | false | | For time series projects only. The start date for bulk predictions. Note that this parameter was used for generating historical predictions using the training data, not for future predictions. | | projectId | string | true | | The project ID of the predictions. | | shapWarnings | ShapWarnings | false | | Will be present if explanationAlgorithm was set to shap and there were additivity failures during SHAP values calculation. | | url | string | true | | The URL at which you can download the predictions. | ## S3Credentials ``` { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | awsAccessKeyId | string | false | | The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified. | | awsSecretAccessKey | string | false | | The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified. | | awsSessionToken | string,null | false | | The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified. | | configId | string | false | | The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken. | | credentialType | string | true | | The type of these credentials, 's3' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | s3 | ## S3DataStreamer ``` { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Amazon Cloud Storage S3 ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endpointUrl | string(url) | false | | Endpoint URL for the S3 connection (omit to use the default) | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | s3 | ## S3Intake ``` { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Amazon Cloud Storage S3 ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | endpointUrl | string(url) | false | | Endpoint URL for the S3 connection (omit to use the default) | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | s3 | ## S3Output ``` { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Saves CSV data chunks to Amazon Cloud Storage S3 ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | endpointUrl | string(url) | false | | Endpoint URL for the S3 connection (omit to use the default) | | format | string | false | | Type of output file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | serverSideEncryption | ServerSideEncryption | false | | Configure Server-Side Encryption for S3 output | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | s3 | ## S3OutputAdaptor ``` { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Saves CSV data chunks to Amazon Cloud Storage S3 ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endpointUrl | string(url) | false | | Endpoint URL for the S3 connection (omit to use the default) | | format | string | false | | Type of output file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | serverSideEncryption | ServerSideEncryption | false | | Configure Server-Side Encryption for S3 output | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | s3 | ## Schedule ``` { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" } ``` The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dayOfMonth | [number,string] | true | maxItems: 31 | The date(s) of the month that the job will run. Allowed values are either [1 ... 31] or ["*"] for all days of the month. This field is additive with dayOfWeek, meaning the job will run both on the date(s) defined in this field and the day specified by dayOfWeek (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If dayOfMonth is set to ["*"] and dayOfWeek is defined, the scheduler will trigger on every day of the month that matches dayOfWeek (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored. | | dayOfWeek | [number,string] | true | maxItems: 7 | The day(s) of the week that the job will run. Allowed values are [0 .. 6], where (Sunday=0), or ["*"], for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., "sunday", "Sunday", "sun", or "Sun", all map to [0]. This field is additive with dayOfMonth, meaning the job will run both on the date specified by dayOfMonth and the day defined in this field. | | hour | [number,string] | true | maxItems: 24 | The hour(s) of the day that the job will run. Allowed values are either ["*"] meaning every hour of the day or [0 ... 23]. | | minute | [number,string] | true | maxItems: 60 | The minute(s) of the day that the job will run. Allowed values are either ["*"] meaning every minute of the day or[0 ... 59]. | | month | [number,string] | true | maxItems: 12 | The month(s) of the year that the job will run. Allowed values are either [1 ... 12] or ["*"] for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., "jan" or "october"). Months that are not compatible with dayOfMonth are ignored, for example {"dayOfMonth": [31], "month":["feb"]}. | ## ScheduledJobResponse ``` { "properties": { "createdBy": { "description": "User name of the creator", "type": [ "string", "null" ] }, "deploymentId": { "description": "ID of the deployment this scheduled job is created from.", "type": [ "string", "null" ] }, "enabled": { "description": "True if the job is enabled and false if the job is disabled.", "type": "boolean" }, "id": { "description": "ID of scheduled prediction job", "type": "string" }, "name": { "description": "Name of the scheduled job.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "scheduledJobId": { "description": "ID of this scheduled job.", "type": "string" }, "status": { "description": "Object containing status information about the scheduled job.", "properties": { "lastFailedRun": { "description": "Date and time of the last failed run.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRun": { "description": "Date and time of the last successful run.", "format": "date-time", "type": [ "string", "null" ] }, "nextRunTime": { "description": "Date and time of the next run.", "format": "date-time", "type": [ "string", "null" ] }, "queuePosition": { "description": "Position of the job in the queue Job. The value will show 0 if the job is about to run, otherwise, the number will be greater than 0 if currently queued, or None if the job is not currently running.", "minimum": 0, "type": [ "integer", "null" ] }, "running": { "description": "`true` or `false` depending on whether the job is currently running.", "type": "boolean" } }, "required": [ "running" ], "type": "object" }, "typeId": { "description": "Job type of the scheduled job", "type": "string" }, "updatedAt": { "description": "Time of last modification", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "enabled", "id", "schedule", "scheduledJobId", "status", "typeId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | string,null | false | | User name of the creator | | deploymentId | string,null | false | | ID of the deployment this scheduled job is created from. | | enabled | boolean | true | | True if the job is enabled and false if the job is disabled. | | id | string | true | | ID of scheduled prediction job | | name | string,null | false | | Name of the scheduled job. | | schedule | Schedule | true | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | scheduledJobId | string | true | | ID of this scheduled job. | | status | ScheduledJobStatus | true | | Object containing status information about the scheduled job. | | typeId | string | true | | Job type of the scheduled job | | updatedAt | string,null(date-time) | false | | Time of last modification | ## ScheduledJobStatus ``` { "description": "Object containing status information about the scheduled job.", "properties": { "lastFailedRun": { "description": "Date and time of the last failed run.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRun": { "description": "Date and time of the last successful run.", "format": "date-time", "type": [ "string", "null" ] }, "nextRunTime": { "description": "Date and time of the next run.", "format": "date-time", "type": [ "string", "null" ] }, "queuePosition": { "description": "Position of the job in the queue Job. The value will show 0 if the job is about to run, otherwise, the number will be greater than 0 if currently queued, or None if the job is not currently running.", "minimum": 0, "type": [ "integer", "null" ] }, "running": { "description": "`true` or `false` depending on whether the job is currently running.", "type": "boolean" } }, "required": [ "running" ], "type": "object" } ``` Object containing status information about the scheduled job. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | lastFailedRun | string,null(date-time) | false | | Date and time of the last failed run. | | lastSuccessfulRun | string,null(date-time) | false | | Date and time of the last successful run. | | nextRunTime | string,null(date-time) | false | | Date and time of the next run. | | queuePosition | integer,null | false | minimum: 0 | Position of the job in the queue Job. The value will show 0 if the job is about to run, otherwise, the number will be greater than 0 if currently queued, or None if the job is not currently running. | | running | boolean | true | | true or false depending on whether the job is currently running. | ## ScheduledJobsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of scheduled jobs", "items": { "properties": { "createdBy": { "description": "User name of the creator", "type": [ "string", "null" ] }, "deploymentId": { "description": "ID of the deployment this scheduled job is created from.", "type": [ "string", "null" ] }, "enabled": { "description": "True if the job is enabled and false if the job is disabled.", "type": "boolean" }, "id": { "description": "ID of scheduled prediction job", "type": "string" }, "name": { "description": "Name of the scheduled job.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "scheduledJobId": { "description": "ID of this scheduled job.", "type": "string" }, "status": { "description": "Object containing status information about the scheduled job.", "properties": { "lastFailedRun": { "description": "Date and time of the last failed run.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRun": { "description": "Date and time of the last successful run.", "format": "date-time", "type": [ "string", "null" ] }, "nextRunTime": { "description": "Date and time of the next run.", "format": "date-time", "type": [ "string", "null" ] }, "queuePosition": { "description": "Position of the job in the queue Job. The value will show 0 if the job is about to run, otherwise, the number will be greater than 0 if currently queued, or None if the job is not currently running.", "minimum": 0, "type": [ "integer", "null" ] }, "running": { "description": "`true` or `false` depending on whether the job is currently running.", "type": "boolean" } }, "required": [ "running" ], "type": "object" }, "typeId": { "description": "Job type of the scheduled job", "type": "string" }, "updatedAt": { "description": "Time of last modification", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "enabled", "id", "schedule", "scheduledJobId", "status", "typeId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" }, "updatedAt": { "description": "Time of last modification", "format": "date-time", "type": "string" }, "updatedBy": { "description": "User ID of last modifier", "type": "string" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ScheduledJobResponse] | true | maxItems: 100 | List of scheduled jobs | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | | updatedAt | string(date-time) | false | | Time of last modification | | updatedBy | string | false | | User ID of last modifier | ## ServerSideEncryption ``` { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" } ``` Configure Server-Side Encryption for S3 output ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | algorithm | string | false | | The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms). | | customerAlgorithm | string | false | | Specifies the algorithm to use to when encrypting the object (for example, AES256). | | customerKey | string | false | | Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string. | | kmsEncryptionContext | string | false | | Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs. | | kmsKeyId | string | false | | Specifies the ID of the symmetric customer managed key to use for object encryption. | ## ShapWarning ``` { "description": "A training prediction job", "properties": { "partitionName": { "description": "The partition used for the prediction record.", "type": "string" }, "value": { "description": "The warnings related to this partition", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" } }, "required": [ "partitionName", "value" ], "type": "object" } ``` A training prediction job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | partitionName | string | true | | The partition used for the prediction record. | | value | ShapWarningItems | true | | The warnings related to this partition | ## ShapWarningItems ``` { "description": "The warnings related to this partition", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" } ``` The warnings related to this partition ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxNormalizedMismatch | number | true | | The maximal relative normalized mismatch value | | mismatchRowCount | integer | true | | The count of rows for which additivity check failed | ## ShapWarningValues ``` { "description": "Mismatch information.", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value.", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed.", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" } ``` Mismatch information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxNormalizedMismatch | number | true | | The maximal relative normalized mismatch value. | | mismatchRowCount | integer | true | | The count of rows for which additivity check failed. | ## ShapWarnings ``` { "description": "Will be present if `explanationAlgorithm` was set to `shap` and there were additivity failures during SHAP values calculation.", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value.", "type": "number", "x-versionadded": "v2.21" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed.", "type": "integer", "x-versionadded": "v2.21" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" } ``` Will be present if `explanationAlgorithm` was set to `shap` and there were additivity failures during SHAP values calculation. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxNormalizedMismatch | number | true | | The maximal relative normalized mismatch value. | | mismatchRowCount | integer | true | | The count of rows for which additivity check failed. | ## SnowflakeDataStreamer ``` { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" } ``` Stream CSV data chunks from Snowflake ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | cloudStorageCredentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with read access to the cloud storage. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageType | string | false | | Type name for cloud storage | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with read access to the Snowflake data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalStage | string | true | | External storage | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | cloudStorageType | [azure, gcp, s3] | | anonymous | [redacted] | | anonymous | [redacted] | | type | snowflake | ## SnowflakeIntake ``` { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" } ``` Stream CSV data chunks from Snowflake ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | cloudStorageCredentialId | string,null | false | | The ID of the credential holding information about a user with read access to the cloud storage. | | cloudStorageType | string | false | | Type name for cloud storage | | credentialId | string,null | false | | The ID of the credential holding information about a user with read access to the Snowflake data source. | | dataStoreId | string | true | | ID of the data store to connect to | | externalStage | string | true | | External storage | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | cloudStorageType | [azure, gcp, s3] | | type | snowflake | ## SnowflakeKeyPairCredentials ``` { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configId | string | false | | The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase. | | credentialType | string | true | | The type of these credentials, 'snowflake_key_pair_user_account' here. | | passphrase | string | false | | Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified. | | privateKeyStr | string | false | | Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified. | | user | string | false | | Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | snowflake_key_pair_user_account | ## SnowflakeOutput ``` { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Snowflake in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | cloudStorageCredentialId | string,null | false | | The ID of the credential holding information about a user with write access to the cloud storage. | | cloudStorageType | string | false | | Type name for cloud storage | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | string,null | false | | The ID of the credential holding information about a user with write access to the Snowflake data source. | | dataStoreId | string | true | | ID of the data store to connect to | | externalStage | string | true | | External storage | | schema | string | false | | The name of the specified database schema to write results to. | | statementType | string | true | | The statement type to use when writing the results. | | table | string | true | | The name of the specified database table to write results to. | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | cloudStorageType | [azure, gcp, s3] | | statementType | [insert, create_table, createTable] | | type | snowflake | ## SnowflakeOutputAdaptor ``` { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Snowflake in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | cloudStorageCredentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with write access to the cloud storage. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageType | string | false | | Type name for cloud storage | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with write access to the Snowflake data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalStage | string | true | | External storage | | schema | string | false | | The name of the specified database schema to write results to. | | statementType | string | true | | The statement type to use when writing the results. | | table | string | true | | The name of the specified database table to write results to. | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | cloudStorageType | [azure, gcp, s3] | | anonymous | [redacted] | | anonymous | [redacted] | | statementType | [insert, create_table, createTable] | | type | snowflake | ## SynapseDataStreamer ``` { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" } ``` Stream CSV data chunks from Azure Synapse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageCredentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the Azure credential holding information about a user with read access to the cloud storage. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with read access to the JDBC data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalDataSource | string | true | | External datasource name | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | anonymous | [redacted] | | type | synapse | ## SynapseIntake ``` { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" } ``` Stream CSV data chunks from Azure Synapse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageCredentialId | string,null | false | | The ID of the Azure credential holding information about a user with read access to the cloud storage. | | credentialId | string,null | false | | The ID of the credential holding information about a user with read access to the JDBC data source. | | dataStoreId | string | true | | ID of the data store to connect to | | externalDataSource | string | true | | External datasource name | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | synapse | ## SynapseOutput ``` { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Azure Synapse in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageCredentialId | string,null | false | | The ID of the credential holding information about a user with write access to the cloud storage. | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | string,null | false | | The ID of the credential holding information about a user with write access to the JDBC data source. | | dataStoreId | string | true | | ID of the data store to connect to | | externalDataSource | string | true | | External data source name | | schema | string | false | | The name of the specified database schema to write results to. | | statementType | string | true | | The statement type to use when writing the results. | | table | string | true | | The name of the specified database table to write results to. | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | statementType | [insert, create_table, createTable] | | type | synapse | ## SynapseOutputAdaptor ``` { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Azure Synapse in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageCredentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with write access to the cloud storage. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with write access to the JDBC data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalDataSource | string | true | | External data source name | | schema | string | false | | The name of the specified database schema to write results to. | | statementType | string | true | | The statement type to use when writing the results. | | table | string | true | | The name of the specified database table to write results to. | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | anonymous | [redacted] | | statementType | [insert, create_table, createTable] | | type | synapse | ## TrainingPredictionsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of training prediction jobs", "items": { "description": "A training prediction job", "properties": { "dataSubset": { "description": "Subset of data predicted on", "enum": [ "all", "validationAndHoldout", "holdout", "allBacktests", "validation", "crossValidation" ], "type": "string", "x-enum-versionadded": [ { "value": "validation", "x-versionadded": "v2.21" } ] }, "explanationAlgorithm": { "description": "The method used for calculating prediction explanations", "enum": [ "shap" ], "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "id": { "description": "ID of the training prediction job", "type": "string" }, "maxExplanations": { "description": "the number of top contributors that are included in prediction explanations. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns", "maximum": 100, "minimum": 0, "type": [ "integer", "null" ] }, "modelId": { "description": "ID of the model", "type": "string" }, "shapWarnings": { "description": "Will be present if \"explanationAlgorithm\" was set to \"shap\" and there were additivity failures during SHAP values calculation", "items": { "description": "A training prediction job", "properties": { "partitionName": { "description": "The partition used for the prediction record.", "type": "string" }, "value": { "description": "The warnings related to this partition", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" } }, "required": [ "partitionName", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "url": { "description": "The location of these predictions", "format": "uri", "type": "string" } }, "required": [ "dataSubset", "id", "modelId", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [TraningPredictions] | true | | A list of training prediction jobs | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## TrainingPredictionsRetrieveResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of training prediction rows", "items": { "description": "A training prediction row", "properties": { "forecastDistance": { "description": "(if time series project) The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column.", "type": [ "integer", "null" ] }, "forecastPoint": { "description": "(if time series project) The forecastPoint of the predictions. Either provided or inferred.", "format": "date-time", "type": [ "string", "null" ] }, "partitionId": { "description": "The partition used for the prediction record", "type": "string" }, "prediction": { "description": "The prediction of the model.", "oneOf": [ { "description": "If using a regressor model, will be the numeric value of the target.", "type": "number" }, { "description": "If using a binary or muliclass classifier model, will be the predicted class.", "type": "string" }, { "description": "If using a multilabel classifier model, will be a list of predicted classes.", "items": { "type": "string" }, "type": "array" } ] }, "predictionExplanations": { "description": "Array contains `predictionExplanation` objects. The total elements in the array are bounded by maxExplanations and feature count. It will be present only if `explanationAlgorithm` is not null (prediction explanations were requested).", "items": { "description": "Prediction explanation result.", "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. The type corresponds to the feature (bool, int, float, str, etc.).", "oneOf": [ { "type": "integer" }, { "type": "boolean" }, { "type": "string" }, { "type": "number" } ] }, "label": { "description": "Describes what output was driven by this prediction explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation. For predictions made using anomaly detection models, it is the `Anomaly Score`.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "strength": { "description": "Algorithm-specific explanation value attributed to `feature` in this row. If `explanationAlgorithm` = `shap`, this is the SHAP value.", "type": [ "number", "null" ] } }, "required": [ "feature", "featureValue", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionValues": { "description": "The list of predicted values for this row.", "items": { "description": "Predicted values.", "properties": { "label": { "description": "For regression problems this will be the name of the target column, 'Anomaly score' or ignored field. For classification projects this will be the name of the class.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "threshold": { "description": "Threshold used in multilabel classification for this class.", "maximum": 1, "minimum": 0, "type": "number" }, "value": { "description": "The predicted probability of the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row in the prediction dataset this prediction corresponds to.", "minimum": 0, "type": "integer" }, "seriesId": { "description": "The ID of the series value for a multiseries project. For time series projects that are not a multiseries this will be a NaN.", "type": [ "string", "null" ] }, "shapMetadata": { "description": "The additional information necessary to understand shap based prediction explanations. Only present if explanationAlgorithm=\"shap\" was added in compute request.", "properties": { "shapBaseValue": { "description": "The model's average prediction over the training data. SHAP values are deviations from the base value.", "type": "number" }, "shapRemainingTotal": { "description": "The total of SHAP values for features beyond the maxExplanations. This can be identically 0 in all rows, if maxExplanations is greater than the number of features and thus all features are returned.", "type": "integer" }, "warnings": { "description": "SHAP values calculation warnings", "items": { "description": "The warnings related to this partition", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "type": "array" } }, "required": [ "shapBaseValue", "shapRemainingTotal", "warnings" ], "type": "object" }, "timestamp": { "description": "(if time series project) The timestamp of this row in the prediction dataset.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "partitionId", "prediction", "rowId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [TraningPredictionRow] | true | | A list of training prediction rows | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## TraningPredictionRow ``` { "description": "A training prediction row", "properties": { "forecastDistance": { "description": "(if time series project) The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column.", "type": [ "integer", "null" ] }, "forecastPoint": { "description": "(if time series project) The forecastPoint of the predictions. Either provided or inferred.", "format": "date-time", "type": [ "string", "null" ] }, "partitionId": { "description": "The partition used for the prediction record", "type": "string" }, "prediction": { "description": "The prediction of the model.", "oneOf": [ { "description": "If using a regressor model, will be the numeric value of the target.", "type": "number" }, { "description": "If using a binary or muliclass classifier model, will be the predicted class.", "type": "string" }, { "description": "If using a multilabel classifier model, will be a list of predicted classes.", "items": { "type": "string" }, "type": "array" } ] }, "predictionExplanations": { "description": "Array contains `predictionExplanation` objects. The total elements in the array are bounded by maxExplanations and feature count. It will be present only if `explanationAlgorithm` is not null (prediction explanations were requested).", "items": { "description": "Prediction explanation result.", "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. The type corresponds to the feature (bool, int, float, str, etc.).", "oneOf": [ { "type": "integer" }, { "type": "boolean" }, { "type": "string" }, { "type": "number" } ] }, "label": { "description": "Describes what output was driven by this prediction explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation. For predictions made using anomaly detection models, it is the `Anomaly Score`.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "strength": { "description": "Algorithm-specific explanation value attributed to `feature` in this row. If `explanationAlgorithm` = `shap`, this is the SHAP value.", "type": [ "number", "null" ] } }, "required": [ "feature", "featureValue", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionValues": { "description": "The list of predicted values for this row.", "items": { "description": "Predicted values.", "properties": { "label": { "description": "For regression problems this will be the name of the target column, 'Anomaly score' or ignored field. For classification projects this will be the name of the class.", "oneOf": [ { "type": "string" }, { "type": "number" } ] }, "threshold": { "description": "Threshold used in multilabel classification for this class.", "maximum": 1, "minimum": 0, "type": "number" }, "value": { "description": "The predicted probability of the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row in the prediction dataset this prediction corresponds to.", "minimum": 0, "type": "integer" }, "seriesId": { "description": "The ID of the series value for a multiseries project. For time series projects that are not a multiseries this will be a NaN.", "type": [ "string", "null" ] }, "shapMetadata": { "description": "The additional information necessary to understand shap based prediction explanations. Only present if explanationAlgorithm=\"shap\" was added in compute request.", "properties": { "shapBaseValue": { "description": "The model's average prediction over the training data. SHAP values are deviations from the base value.", "type": "number" }, "shapRemainingTotal": { "description": "The total of SHAP values for features beyond the maxExplanations. This can be identically 0 in all rows, if maxExplanations is greater than the number of features and thus all features are returned.", "type": "integer" }, "warnings": { "description": "SHAP values calculation warnings", "items": { "description": "The warnings related to this partition", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "type": "array" } }, "required": [ "shapBaseValue", "shapRemainingTotal", "warnings" ], "type": "object" }, "timestamp": { "description": "(if time series project) The timestamp of this row in the prediction dataset.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "partitionId", "prediction", "rowId" ], "type": "object" } ``` A training prediction row ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forecastDistance | integer,null | false | | (if time series project) The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column. | | forecastPoint | string,null(date-time) | false | | (if time series project) The forecastPoint of the predictions. Either provided or inferred. | | partitionId | string | true | | The partition used for the prediction record | | prediction | any | true | | The prediction of the model. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | If using a regressor model, will be the numeric value of the target. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | If using a binary or muliclass classifier model, will be the predicted class. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | If using a multilabel classifier model, will be a list of predicted classes. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionExplanations | [PredictionExplanationsObject] | false | | Array contains predictionExplanation objects. The total elements in the array are bounded by maxExplanations and feature count. It will be present only if explanationAlgorithm is not null (prediction explanations were requested). | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold used for binary classification in predictions. | | predictionValues | [PredictionArrayObjectValues] | false | | The list of predicted values for this row. | | rowId | integer | true | minimum: 0 | The row in the prediction dataset this prediction corresponds to. | | seriesId | string,null | false | | The ID of the series value for a multiseries project. For time series projects that are not a multiseries this will be a NaN. | | shapMetadata | TraningPredictionShapMetadata | false | | The additional information necessary to understand shap based prediction explanations. Only present if explanationAlgorithm="shap" was added in compute request. | | timestamp | string,null(date-time) | false | | (if time series project) The timestamp of this row in the prediction dataset. | ## TraningPredictionShapMetadata ``` { "description": "The additional information necessary to understand shap based prediction explanations. Only present if explanationAlgorithm=\"shap\" was added in compute request.", "properties": { "shapBaseValue": { "description": "The model's average prediction over the training data. SHAP values are deviations from the base value.", "type": "number" }, "shapRemainingTotal": { "description": "The total of SHAP values for features beyond the maxExplanations. This can be identically 0 in all rows, if maxExplanations is greater than the number of features and thus all features are returned.", "type": "integer" }, "warnings": { "description": "SHAP values calculation warnings", "items": { "description": "The warnings related to this partition", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" }, "type": "array" } }, "required": [ "shapBaseValue", "shapRemainingTotal", "warnings" ], "type": "object" } ``` The additional information necessary to understand shap based prediction explanations. Only present if explanationAlgorithm="shap" was added in compute request. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | shapBaseValue | number | true | | The model's average prediction over the training data. SHAP values are deviations from the base value. | | shapRemainingTotal | integer | true | | The total of SHAP values for features beyond the maxExplanations. This can be identically 0 in all rows, if maxExplanations is greater than the number of features and thus all features are returned. | | warnings | [ShapWarningItems] | true | | SHAP values calculation warnings | ## TraningPredictions ``` { "description": "A training prediction job", "properties": { "dataSubset": { "description": "Subset of data predicted on", "enum": [ "all", "validationAndHoldout", "holdout", "allBacktests", "validation", "crossValidation" ], "type": "string", "x-enum-versionadded": [ { "value": "validation", "x-versionadded": "v2.21" } ] }, "explanationAlgorithm": { "description": "The method used for calculating prediction explanations", "enum": [ "shap" ], "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "id": { "description": "ID of the training prediction job", "type": "string" }, "maxExplanations": { "description": "the number of top contributors that are included in prediction explanations. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns", "maximum": 100, "minimum": 0, "type": [ "integer", "null" ] }, "modelId": { "description": "ID of the model", "type": "string" }, "shapWarnings": { "description": "Will be present if \"explanationAlgorithm\" was set to \"shap\" and there were additivity failures during SHAP values calculation", "items": { "description": "A training prediction job", "properties": { "partitionName": { "description": "The partition used for the prediction record.", "type": "string" }, "value": { "description": "The warnings related to this partition", "properties": { "maxNormalizedMismatch": { "description": "The maximal relative normalized mismatch value", "type": "number" }, "mismatchRowCount": { "description": "The count of rows for which additivity check failed", "type": "integer" } }, "required": [ "maxNormalizedMismatch", "mismatchRowCount" ], "type": "object" } }, "required": [ "partitionName", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.21" }, "url": { "description": "The location of these predictions", "format": "uri", "type": "string" } }, "required": [ "dataSubset", "id", "modelId", "url" ], "type": "object" } ``` A training prediction job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSubset | string | true | | Subset of data predicted on | | explanationAlgorithm | string,null | false | | The method used for calculating prediction explanations | | id | string | true | | ID of the training prediction job | | maxExplanations | integer,null | false | maximum: 100minimum: 0 | the number of top contributors that are included in prediction explanations. Defaults to null for datasets narrower than 100 columns, defaults to 100 for datasets wider than 100 columns | | modelId | string | true | | ID of the model | | shapWarnings | [ShapWarning] | false | | Will be present if "explanationAlgorithm" was set to "shap" and there were additivity failures during SHAP values calculation | | url | string(uri) | true | | The location of these predictions | ### Enumerated Values | Property | Value | | --- | --- | | dataSubset | [all, validationAndHoldout, holdout, allBacktests, validation, crossValidation] | | explanationAlgorithm | shap | ## TrinoDataStreamer ``` { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ``` Stream CSV data chunks from Trino using browser-trino ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | The name of the specified database catalog to read input data from. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with read access to the data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | trino | ## TrinoIntake ``` { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ``` Stream CSV data chunks from Trino using browser-trino ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | The name of the specified database catalog to read input data from. | | credentialId | string | true | | The ID of the credential holding information about a user with read access to the data source. | | dataStoreId | string | true | | ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | trino | ## TrinoOutput ``` { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ``` Saves CSV data chunks to Trino using browser-trino ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | The name of the specified database catalog to write output data to. | | credentialId | string | true | | The ID of the credential holding information about a user with write access to the data destination. | | dataStoreId | string | true | | The ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | trino | ## TrinoOutputAdaptor ``` { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ``` Saves CSV data chunks to Trino using browser-trino ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | The name of the specified database catalog to write output data to. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with write access to the data destination. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | trino | --- # Blueprints URL: https://docs.datarobot.com/en/docs/api/reference/public-api/blueprints.html > This page outlines DataRobot's endpoints for blueprints, a graphical representation of the many steps involved in transforming input predictors and targets into a model. # Blueprints This page outlines DataRobot's endpoints for blueprints, a graphical representation of the many steps involved in transforming input predictors and targets into a model. ## List custom tasks Operation path: `GET /api/v2/customTasks/` Authentication requirements: `BearerAuth` Retrieve metadata for all accessible custom tasks. For more information on custom tasks, access the [UI documentation](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/cml-custom-tasks.html). ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | orderBy | query | string | false | Sort order which will be applied to custom task list, valid options are "created", "updated". Prefix the attribute name with a dash to sort in descending order, e.g. orderBy="-created". By default, the orderBy parameter is None which will result in custom tasks being returned in order of creation time descending. | | searchFor | query | string | false | String to search for occurrence in custom task's description, language and name. Search is case insensitive. If not specified, all custom tasks will be returned. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [created, -created, updated, -updated] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom tasks.", "items": { "properties": { "calibratePredictions": { "description": "Determines whether or not predictions should be calibrated by DataRobot. Only applies to anomaly detection.", "type": "boolean" }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "createdBy": { "description": "The username of the custom task creator.", "type": "string" }, "customModelType": { "description": "The type of custom task.", "enum": [ "training", "inference" ], "type": "string", "x-versiondeprecated": "v2.25" }, "description": { "description": "The description of the task.", "type": "string" }, "id": { "description": "The ID of the custom task.", "type": "string" }, "language": { "description": "The programming language used by the task.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The name of the task.", "type": "string" }, "targetType": { "description": "The target type of the custom task.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint" ], "type": "string" }, "updated": { "description": "ISO-8601 timestamp of when task was last updated.", "type": "string" } }, "required": [ "created", "createdBy", "customModelType", "description", "id", "language", "latestVersion", "name", "targetType", "updated" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomTaskListResponse | ## Create a custom task Operation path: `POST /api/v2/customTasks/` Authentication requirements: `BearerAuth` Creates a new custom task and returns the newly created metadata record for it. A custom task may either be an estimator or a transform. Estimators must support a single target type (e.g., binary classification, regression). Regression and anomaly detection models are expected to produce predictions that are arbitrary floating-point or integer numbers. A classification model is expected to return predictions with probability scores for each class. Transforms are expected to return a dataframe or sparse matrix with the same number of rows as the input feature matrix. Only numeric outputs are supported for custom transforms. ### Body parameter ``` { "properties": { "calibratePredictions": { "default": true, "description": "Whether model predictions should be calibrated by DataRobot.Only applies to anomaly detection; we recommend this if you have not already included calibration in your model code.Calibration improves the probability estimates of a model, and modifies the predictions of non-probabilistic models to be interpretable as probabilities. This will facilitate comparison to DataRobot models, and give access to ROC curve insights on external data.", "type": "boolean" }, "description": { "description": "The user-friendly description of the task.", "maxLength": 10000, "type": "string" }, "language": { "description": "Programming language name in which task is written.", "maxLength": 500, "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The user-friendly name for the task.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type of the custom task", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint" ], "type": "string" } }, "required": [ "name", "targetType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomTaskCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "calibratePredictions": { "description": "Determines whether or not predictions should be calibrated by DataRobot. Only applies to anomaly detection.", "type": "boolean" }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "createdBy": { "description": "The username of the custom task creator.", "type": "string" }, "customModelType": { "description": "The type of custom task.", "enum": [ "training", "inference" ], "type": "string", "x-versiondeprecated": "v2.25" }, "description": { "description": "The description of the task.", "type": "string" }, "id": { "description": "The ID of the custom task.", "type": "string" }, "language": { "description": "The programming language used by the task.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The name of the task.", "type": "string" }, "targetType": { "description": "The target type of the custom task.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint" ], "type": "string" }, "updated": { "description": "ISO-8601 timestamp of when task was last updated.", "type": "string" } }, "required": [ "created", "createdBy", "customModelType", "description", "id", "language", "latestVersion", "name", "targetType", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Custom task successfully created. | CustomTaskResponse | | 403 | Forbidden | Custom task creation is not enabled. | None | | 422 | Unprocessable Entity | Input parameters invalid | None | ## Clone custom task Operation path: `POST /api/v2/customTasks/fromCustomTask/` Authentication requirements: `BearerAuth` Creates a copy of the provided custom task, including metadata, versions of that task, and uploaded files. Associates the new versions with files owned by the custom task. ### Body parameter ``` { "properties": { "customTaskId": { "description": "The ID of the custom task to copy.", "type": "string" } }, "required": [ "customTaskId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomTaskCopy | false | none | ### Example responses > 201 Response ``` { "properties": { "calibratePredictions": { "description": "Determines whether or not predictions should be calibrated by DataRobot. Only applies to anomaly detection.", "type": "boolean" }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "createdBy": { "description": "The username of the custom task creator.", "type": "string" }, "customModelType": { "description": "The type of custom task.", "enum": [ "training", "inference" ], "type": "string", "x-versiondeprecated": "v2.25" }, "description": { "description": "The description of the task.", "type": "string" }, "id": { "description": "The ID of the custom task.", "type": "string" }, "language": { "description": "The programming language used by the task.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The name of the task.", "type": "string" }, "targetType": { "description": "The target type of the custom task.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint" ], "type": "string" }, "updated": { "description": "ISO-8601 timestamp of when task was last updated.", "type": "string" } }, "required": [ "created", "createdBy", "customModelType", "description", "id", "language", "latestVersion", "name", "targetType", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successfully created copy. | CustomTaskResponse | ## Delete custom task by custom task ID Operation path: `DELETE /api/v2/customTasks/{customTaskId}/` Authentication requirements: `BearerAuth` Delete a custom task. Only users who have permission to edit custom task can delete it. Only custom tasks which are not currently deployed, used in a blueprint, or in the AIcatalog can be deleted. Relevant CustomTaskImage will be deleted also. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Record deleted. | None | | 409 | Conflict | This custom task is currently deployed, trained, or part of a user blueprint, and cannot be deleted. The response body will contain link where these conflicts can be retrieved. | None | ## Get custom task by custom task ID Operation path: `GET /api/v2/customTasks/{customTaskId}/` Authentication requirements: `BearerAuth` Retrieve metadata for a custom task. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | ### Example responses > 200 Response ``` { "properties": { "calibratePredictions": { "description": "Determines whether or not predictions should be calibrated by DataRobot. Only applies to anomaly detection.", "type": "boolean" }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "createdBy": { "description": "The username of the custom task creator.", "type": "string" }, "customModelType": { "description": "The type of custom task.", "enum": [ "training", "inference" ], "type": "string", "x-versiondeprecated": "v2.25" }, "description": { "description": "The description of the task.", "type": "string" }, "id": { "description": "The ID of the custom task.", "type": "string" }, "language": { "description": "The programming language used by the task.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The name of the task.", "type": "string" }, "targetType": { "description": "The target type of the custom task.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint" ], "type": "string" }, "updated": { "description": "ISO-8601 timestamp of when task was last updated.", "type": "string" } }, "required": [ "created", "createdBy", "customModelType", "description", "id", "language", "latestVersion", "name", "targetType", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomTaskResponse | ## Update custom task by custom task ID Operation path: `PATCH /api/v2/customTasks/{customTaskId}/` Authentication requirements: `BearerAuth` Updates metadata for an existing custom task. All custom tasks must support one target type (e.g. binaryClassification, regression, transform). Setting `positiveClassLabel` and `negativeClassLabel` to null will set the labels to their default values (1 and 0 for positiveClassLabel and negativeClassLabel, respectively). Setting `positiveClassLabel`, `negativeClassLabel`, `targetName` is disabled if task has active deployments. ### Body parameter ``` { "properties": { "description": { "description": "The user-friendly description of the task.", "maxLength": 10000, "type": "string" }, "language": { "description": "Programming language name in which task is written.", "maxLength": 500, "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The user-friendly name for the task.", "maxLength": 255, "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | body | body | CustomTaskUpdate | false | none | ### Example responses > 201 Response ``` { "properties": { "calibratePredictions": { "description": "Determines whether or not predictions should be calibrated by DataRobot. Only applies to anomaly detection.", "type": "boolean" }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "createdBy": { "description": "The username of the custom task creator.", "type": "string" }, "customModelType": { "description": "The type of custom task.", "enum": [ "training", "inference" ], "type": "string", "x-versiondeprecated": "v2.25" }, "description": { "description": "The description of the task.", "type": "string" }, "id": { "description": "The ID of the custom task.", "type": "string" }, "language": { "description": "The programming language used by the task.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The name of the task.", "type": "string" }, "targetType": { "description": "The target type of the custom task.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint" ], "type": "string" }, "updated": { "description": "ISO-8601 timestamp of when task was last updated.", "type": "string" } }, "required": [ "created", "createdBy", "customModelType", "description", "id", "language", "latestVersion", "name", "targetType", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Created. | CustomTaskResponse | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Get a list of users who have access by custom task ID Operation path: `GET /api/v2/customTasks/{customTaskId}/accessControl/` Authentication requirements: `BearerAuth` Get a list of users who have access to this custom task and their roles on it. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | customTaskId | path | string | true | The ID of the custom task. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of items in current page.", "type": "integer" }, "data": { "description": "List of the requested custom task access control entries.", "items": { "properties": { "canShare": { "description": "Whether this user can share this custom task", "type": "boolean" }, "role": { "description": "This users role.", "type": "string" }, "userId": { "description": "This user's userId.", "type": "string" }, "username": { "description": "The username for this user's entry.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of users who have access to this custom task and their roles on it. | CustomTaskAccessControlListResponse | | 400 | Bad Request | Both username and userId were specified. | None | ## Grant access or update roles by custom task ID Operation path: `PATCH /api/v2/customTasks/{customTaskId}/accessControl/` Authentication requirements: `BearerAuth` Grant access or update roles for users on this custom task and appropriate learning data. Up to 100 user roles may be set in a single request. ### Body parameter ``` { "properties": { "data": { "description": "List of sharing roles to update.", "items": { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | body | body | SharingUpdateOrRemoveWithGrant | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | None | | 409 | Conflict | The request would leave the custom task without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid. | None | ## Download the latest custom task version content by custom task ID Operation path: `GET /api/v2/customTasks/{customTaskId}/download/` Authentication requirements: `BearerAuth` Download the latest item bundle from a custom task as a zip compressed archive. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The download succeeded. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ("attachment;filename=model--version-.zip"). | ## List custom task versions by custom task ID Operation path: `GET /api/v2/customTasks/{customTaskId}/versions/` Authentication requirements: `BearerAuth` List custom task versions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | customTaskId | path | string | true | The ID of the custom task. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom task versions.", "items": { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomTaskVersionListResponse | | 400 | Bad Request | Query parameters are invalid. | None | ## Update custom task version files by custom task ID Operation path: `PATCH /api/v2/customTasks/{customTaskId}/versions/` Authentication requirements: `BearerAuth` Create a new custom task version with files added, replaced or deleted. Files from the previous version of a custom task will be used as a basis. ### Body parameter ``` properties: baseEnvironmentId: description: The base environment to use with this custom task version. type: string file: description: 'A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `"main.py"` and `"helpers/helper.py"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.' format: binary type: string filePath: description: The local path of the file being uploaded. See the `file` field explanation for more details. oneOf: - type: string - items: type: string maxItems: 1000 type: array filesToDelete: description: The IDs of the files to be deleted. oneOf: - type: string - items: type: string maxItems: 100 type: array isMajorUpdate: default: "true" description: If set to true, new major version will created, otherwise minor version will be created. enum: - "false" - "False" - "true" - "True" type: string maximumMemory: description: DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. maximum: 15032385536 minimum: 134217728 type: - integer - "null" x-versiondeprecated: 2.32.0 outboundNetworkPolicy: description: What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address. enum: - ISOLATED - PUBLIC type: string x-versionadded: 2.32.0 requiredMetadata: description: Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. type: string x-versionadded: v2.25 x-versiondeprecated: v2.26 requiredMetadataValues: description: Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. type: string x-versionadded: v2.26 required: - baseEnvironmentId - isMajorUpdate type: object ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | body | body | CustomTaskVersionCreateFromLatest | false | none | ### Example responses > 201 Response ``` { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Item successfully created. | CustomTaskVersionResponse | | 413 | Payload Too Large | Item or collection of items was too large in size (bytes). | None | | 422 | Unprocessable Entity | Cannot create the custom task version due to one or more errors. All error responses will have a "message" field and some may have optional fields. The optional fields include: ["errors", "dependencies", "invalidDependencies"] | None | ## Create custom task version by custom task ID Operation path: `POST /api/v2/customTasks/{customTaskId}/versions/` Authentication requirements: `BearerAuth` Create a new custom task version with attached files if supplied. ### Body parameter ``` properties: baseEnvironmentId: description: The base environment to use with this custom task version. type: string file: description: 'A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `"main.py"` and `"helpers/helper.py"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.' format: binary type: string filePath: description: The local path of the file being uploaded. See the `file` field explanation for more details. oneOf: - type: string - items: type: string maxItems: 1000 type: array isMajorUpdate: default: "true" description: If set to true, new major version will created, otherwise minor version will be created. enum: - "false" - "False" - "true" - "True" type: string maximumMemory: description: DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. maximum: 15032385536 minimum: 134217728 type: - integer - "null" x-versiondeprecated: 2.32.0 outboundNetworkPolicy: description: What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address. enum: - ISOLATED - PUBLIC type: string x-versionadded: 2.32.0 requiredMetadata: description: Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. type: string x-versionadded: v2.25 x-versiondeprecated: v2.26 requiredMetadataValues: description: Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. type: string x-versionadded: v2.26 required: - baseEnvironmentId - isMajorUpdate type: object ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | body | body | CustomTaskVersionCreate | false | none | ### Example responses > 201 Response ``` { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Item successfully created. | CustomTaskVersionResponse | | 413 | Payload Too Large | Item or collection of items was too large in size (bytes). | None | | 422 | Unprocessable Entity | Cannot create the custom task version due to one or more errors. All error responses will have a "message" field and some may have optional fields. The optional fields include: ["errors", "dependencies", "invalidDependencies"] | None | ## Create custom task version from remote repository by custom task ID Operation path: `PATCH /api/v2/customTasks/{customTaskId}/versions/fromRepository/` Authentication requirements: `BearerAuth` Create a new custom task version with files added from a remote repository. Files from the previous version of a custom task will be used as a basis. ### Body parameter ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this version.", "type": "string", "x-versionadded": "v2.22" }, "isMajorUpdate": { "default": true, "description": "If set to true, new major version will created, otherwise minor version will be created.", "type": "boolean" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "ref": { "description": "Remote reference (branch, commit, etc). Latest, if not specified.", "type": "string" }, "repositoryId": { "description": "The ID of remote repository used to pull sources. This ID can be found using the /api/v2/remoteRepositories/ endpoint.", "type": "string" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "sourcePath": { "description": "A remote repository file path to be pulled into a custom model or custom task.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" } ] } }, "required": [ "baseEnvironmentId", "repositoryId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | body | body | CustomTaskVersionCreateFromRepository | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Accepted: request placed to a queue for processing. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking async job status. | ## Create a version from a repository Operation path: `POST /api/v2/customTasks/{customTaskId}/versions/fromRepository/` Authentication requirements: `BearerAuth` Create a new custom task version with only files added from the specified remote repository. ### Body parameter ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this version.", "type": "string", "x-versionadded": "v2.22" }, "isMajorUpdate": { "default": true, "description": "If set to true, new major version will created, otherwise minor version will be created.", "type": "boolean" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "ref": { "description": "Remote reference (branch, commit, etc). Latest, if not specified.", "type": "string" }, "repositoryId": { "description": "The ID of remote repository used to pull sources. This ID can be found using the /api/v2/remoteRepositories/ endpoint.", "type": "string" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "sourcePath": { "description": "A remote repository file path to be pulled into a custom model or custom task.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" } ] } }, "required": [ "baseEnvironmentId", "repositoryId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | body | body | CustomTaskVersionCreateFromRepository | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Accepted: request placed to a queue for processing. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking async job status. | ## Get custom task version by custom task ID Operation path: `GET /api/v2/customTasks/{customTaskId}/versions/{customTaskVersionId}/` Authentication requirements: `BearerAuth` Display a requested version of a custom task along with the files attached to it. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | customTaskVersionId | path | string | true | The ID of the custom task version. | ### Example responses > 200 Response ``` { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomTaskVersionResponse | ## Update custom task version by custom task ID Operation path: `PATCH /api/v2/customTasks/{customTaskId}/versions/{customTaskVersionId}/` Authentication requirements: `BearerAuth` Edit metadata of a specific task version. ### Body parameter ``` { "properties": { "description": { "description": "New description for the custom task or model.", "maxLength": 10000, "type": "string" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | customTaskVersionId | path | string | true | The ID of the custom task version. | | body | body | CustomTaskVersionMetadataUpdate | false | none | ### Example responses > 200 Response ``` { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The edit was successful. | CustomTaskVersionResponse | | 404 | Not Found | Custom task not found or user does not have edit permissions. | None | ## Cancel dependency build by custom task ID Operation path: `DELETE /api/v2/customTasks/{customTaskId}/versions/{customTaskVersionId}/dependencyBuild/` Authentication requirements: `BearerAuth` Cancel the custom task version's dependency build. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | customTaskVersionId | path | string | true | The ID of the custom task version. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Custom task version's dependency build was cancelled. | None | | 409 | Conflict | Custom task dependency build has reached a terminal state and cannot be cancelled. | None | | 422 | Unprocessable Entity | No custom task dependency build started for specified version or dependency image is in use and cannot be deleted | None | ## Retrieve the custom task version's dependency build status by custom task ID Operation path: `GET /api/v2/customTasks/{customTaskId}/versions/{customTaskVersionId}/dependencyBuild/` Authentication requirements: `BearerAuth` Retrieve the custom task version's dependency build status. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | customTaskVersionId | path | string | true | The ID of the custom task version. | ### Example responses > 200 Response ``` { "properties": { "buildEnd": { "description": "The ISO-8601 encoded time when this build completed.", "type": [ "string", "null" ] }, "buildLogLocation": { "description": "The URL to download the build logs from this build.", "format": "uri", "type": [ "string", "null" ] }, "buildStart": { "description": "The ISO-8601 encoded time when this build started.", "type": "string" }, "buildStatus": { "description": "The current status of the dependency build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "customTaskId": { "description": "The ID of custom task.", "type": "string" }, "customTaskVersionId": { "description": "The ID of custom task version.", "type": "string" } }, "required": [ "buildEnd", "buildLogLocation", "buildStart", "buildStatus", "customTaskId", "customTaskVersionId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The metadata from the custom task version's dependency build. | CustomTaskVersionDependencyBuildMetadataResponse | | 422 | Unprocessable Entity | Custom task dependency build has not started. | None | ## Start a custom task version's dependency build by custom task ID Operation path: `POST /api/v2/customTasks/{customTaskId}/versions/{customTaskVersionId}/dependencyBuild/` Authentication requirements: `BearerAuth` Start a custom task version's dependency build. This is required to test, deploy, or train custom tasks. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | customTaskVersionId | path | string | true | The ID of the custom task version. | ### Example responses > 202 Response ``` { "properties": { "buildEnd": { "description": "The ISO-8601 encoded time when this build completed.", "type": [ "string", "null" ] }, "buildLogLocation": { "description": "The URL to download the build logs from this build.", "format": "uri", "type": [ "string", "null" ] }, "buildStart": { "description": "The ISO-8601 encoded time when this build started.", "type": "string" }, "buildStatus": { "description": "The current status of the dependency build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "customTaskId": { "description": "The ID of custom task.", "type": "string" }, "customTaskVersionId": { "description": "The ID of custom task version.", "type": "string" } }, "required": [ "buildEnd", "buildLogLocation", "buildStart", "buildStatus", "customTaskId", "customTaskVersionId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Custom task version's dependency build has started. | CustomTaskVersionDependencyBuildMetadataResponse | | 422 | Unprocessable Entity | Custom task dependency build has failed. | None | ## Retrieve the custom task version's dependency build log by custom task ID Operation path: `GET /api/v2/customTasks/{customTaskId}/versions/{customTaskVersionId}/dependencyBuildLog/` Authentication requirements: `BearerAuth` Retrieve the custom task version's dependency build log. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | customTaskVersionId | path | string | true | The ID of the custom task version. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The log file generated during the custom task version's dependency build. | None | | 404 | Not Found | Dependency build is in progress or could not be found. | None | | 422 | Unprocessable Entity | Custom task dependency build has not started. | None | ## Download custom task version content by custom task ID Operation path: `GET /api/v2/customTasks/{customTaskId}/versions/{customTaskVersionId}/download/` Authentication requirements: `BearerAuth` Download a specific item bundle from a custom task as a zip compressed archive. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTaskId | path | string | true | The ID of the custom task. | | customTaskVersionId | path | string | true | The ID of the custom task version. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The download succeeded. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ("attachment;filename=model--version-.zip"). | ## List training blueprints Operation path: `GET /api/v2/customTrainingBlueprints/` Authentication requirements: `BearerAuth` List custom training blueprints. This route retrieves the metadata for all custom training blueprints a user has access to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | customModelId | query | string | false | The list of blueprints for a specific model. Default: all. | | reverse | query | string | false | List blueprints in reverse order. | | targetTypes | query | array[string] | false | Custom model target types to return. | ### Enumerated Values | Parameter | Value | | --- | --- | | reverse | [false, False, true, True] | | targetTypes | [Binary, Regression, Multiclass, Anomaly, Transform, TextGeneration, GeoPoint, Unstructured, VectorDatabase, AgenticWorkflow, MCP] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of training model blueprints.", "items": { "properties": { "createdAt": { "description": "ISO-8601 timestamp of when the blueprint was created.", "type": "string" }, "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "targetType": { "description": "The target type of the training model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "trainingHistory": { "description": "List of instances of this blueprint having been trained.", "items": { "properties": { "creationDate": { "description": "ISO-8601 timestamp of when the project the blueprint was trained on was created.", "type": "string" }, "lid": { "description": "The leaderboard ID the blueprint was trained on.", "type": "string" }, "pid": { "description": "The project ID the blueprint was trained on.", "type": "string" }, "projectModelsCount": { "description": "Number of models in the project the blueprint was trained on.", "type": "integer" }, "projectName": { "description": "The project name the blueprint was trained on.", "type": "string" }, "targetName": { "description": "The target name of the project the blueprint was trained on.", "type": "string" } }, "required": [ "creationDate", "lid", "pid", "projectModelsCount", "projectName", "targetName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "userBlueprintId": { "description": "User Blueprint ID that can be used to train the model.", "type": "string" } }, "required": [ "createdAt", "customModel", "customModelVersion", "executionEnvironment", "executionEnvironmentVersion", "targetType", "trainingHistory", "userBlueprintId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom training blueprint list returned. | CustomTrainingBlueprintListResponse | ## Create a blueprint Operation path: `POST /api/v2/customTrainingBlueprints/` Authentication requirements: `BearerAuth` This route creates a blueprint from a custom training estimator with an environment so that it can be trained via blueprint ID. ### Body parameter ``` { "properties": { "customModelVersionId": { "description": "The ID of the specific model version from which to create a custom training blueprint.", "type": "string" } }, "required": [ "customModelVersionId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomTrainingBlueprintCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "createdAt": { "description": "ISO-8601 timestamp of when the blueprint was created.", "type": "string" }, "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "targetType": { "description": "The target type of the training model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "trainingHistory": { "description": "List of instances of this blueprint having been trained.", "items": { "properties": { "creationDate": { "description": "ISO-8601 timestamp of when the project the blueprint was trained on was created.", "type": "string" }, "lid": { "description": "The leaderboard ID the blueprint was trained on.", "type": "string" }, "pid": { "description": "The project ID the blueprint was trained on.", "type": "string" }, "projectModelsCount": { "description": "Number of models in the project the blueprint was trained on.", "type": "integer" }, "projectName": { "description": "The project name the blueprint was trained on.", "type": "string" }, "targetName": { "description": "The target name of the project the blueprint was trained on.", "type": "string" } }, "required": [ "creationDate", "lid", "pid", "projectModelsCount", "projectName", "targetName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "userBlueprintId": { "description": "User Blueprint ID that can be used to train the model.", "type": "string" } }, "required": [ "createdAt", "customModel", "customModelVersion", "executionEnvironment", "executionEnvironmentVersion", "targetType", "trainingHistory", "userBlueprintId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Blueprint successfully created. | CustomTrainingBlueprintResponse | | 404 | Not Found | Any of the entities in the request cannot be retrieved. | None | | 422 | Unprocessable Entity | Input parameters are invalid: either the custom model is for inference or no environment version ID was specified and the given environment has no versions. | None | ## List of augmentation lists Operation path: `GET /api/v2/imageAugmentationLists/` Authentication requirements: `BearerAuth` List of augmentation lists that match the specified query. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | query | string | true | Project ID to retrieve augmentation lists from | | featureName | query | string | false | Name of the image feature that the augmentation list is associated with | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned. To specify no limit, use 0. The default may change without notice. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The content in the form of an augmentation lists", "items": { "properties": { "featureName": { "description": "The name of the image feature containing the data to be augmented", "type": "string" }, "id": { "description": "Image Augmentation list ID", "type": "string" }, "inUse": { "default": false, "description": "This is set to true when the Augmentation List has been used to train a model", "type": "boolean" }, "initialList": { "default": false, "description": "Whether this list will be used during autopilot to perform image augmentation", "type": "boolean" }, "name": { "description": "The name of the image augmentation list", "type": "string" }, "numberOfNewImages": { "description": "Number of new rows to add for each existing row", "minimum": 1, "type": "integer" }, "projectId": { "description": "The project containing the image data to be augmented", "type": "string" }, "samplesId": { "default": null, "description": "Image Augmentation list samples ID", "type": [ "string", "null" ] }, "transformationProbability": { "description": "Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always.", "maximum": 1, "minimum": 0, "type": "number" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "id", "name", "numberOfNewImages", "projectId", "transformationProbability", "transformations" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Image Augmentation Lists | ImageAugmentationListsResponse | ## Creates a new augmentation list based Operation path: `POST /api/v2/imageAugmentationLists/` Authentication requirements: `BearerAuth` Creates a new augmentation list based on the posted payload data. ### Body parameter ``` { "properties": { "featureName": { "description": "The name of the image feature containing the data to be augmented", "type": "string" }, "inUse": { "default": false, "description": "This parameter was deprecated. You can still pass a value, but it will be ignored. DataRobot will takes care of the value itself.", "type": "boolean", "x-versiondeprecated": "2.30.0" }, "initialList": { "default": false, "description": "Whether this list will be used during autopilot to perform image augmentation", "type": "boolean" }, "name": { "description": "The name of the image augmentation list", "type": "string" }, "numberOfNewImages": { "description": "Number of new rows to add for each existing row", "minimum": 1, "type": "integer" }, "projectId": { "description": "The project containing the image data to be augmented", "type": "string" }, "samplesId": { "default": null, "description": "Image Augmentation list samples ID", "type": [ "string", "null" ] }, "transformationProbability": { "description": "Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always.", "maximum": 1, "minimum": 0, "type": "number" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name", "numberOfNewImages", "projectId", "transformationProbability", "transformations" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ImageAugmentationListsCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "augmentationListId": { "description": "\n Id of the newly created augmentation list which can be used to:\n - retrieve the full augmentation list details;\n - retrieve augmentation previews;\n - to set the list id of a model in advanced tuning;\n - to delete or rename the list.\n ", "type": "string" } }, "required": [ "augmentationListId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Augmentation list id | ImageAugmentationListCreateResponse | | 422 | Unprocessable Entity | Unable to create image augmentation list with the given input | None | ## Delete an existing augmentation lists by id by augmentation ID Operation path: `DELETE /api/v2/imageAugmentationLists/{augmentationId}/` Authentication requirements: `BearerAuth` Delete an existing augmentation lists by id. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | augmentationId | path | string | true | The id of the augmentation list to fetch | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The successful response is empty | None | | 400 | Bad Request | Cannot delete an augmentation list that is marked as in_use. | None | ## Returns a single augmentation list by augmentation ID Operation path: `GET /api/v2/imageAugmentationLists/{augmentationId}/` Authentication requirements: `BearerAuth` Returns a single augmentation list with the specified id. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | augmentationId | path | string | true | The id of the augmentation list to fetch | ### Example responses > 200 Response ``` { "properties": { "featureName": { "description": "The name of the image feature containing the data to be augmented", "type": "string" }, "id": { "description": "Image Augmentation list ID", "type": "string" }, "inUse": { "default": false, "description": "This is set to true when the Augmentation List has been used to train a model", "type": "boolean" }, "initialList": { "default": false, "description": "Whether this list will be used during autopilot to perform image augmentation", "type": "boolean" }, "name": { "description": "The name of the image augmentation list", "type": "string" }, "numberOfNewImages": { "description": "Number of new rows to add for each existing row", "minimum": 1, "type": "integer" }, "projectId": { "description": "The project containing the image data to be augmented", "type": "string" }, "samplesId": { "default": null, "description": "Image Augmentation list samples ID", "type": [ "string", "null" ] }, "transformationProbability": { "description": "Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always.", "maximum": 1, "minimum": 0, "type": "number" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "id", "name", "numberOfNewImages", "projectId", "transformationProbability", "transformations" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns an augmentation list object | ImageAugmentationListsRetrieve | ## Update an existing augmentation list by augmentation ID Operation path: `PATCH /api/v2/imageAugmentationLists/{augmentationId}/` Authentication requirements: `BearerAuth` Update an existing augmentation list, with passed in values. ### Body parameter ``` { "properties": { "featureName": { "description": "The name of the image feature containing the data to be augmented", "type": "string" }, "inUse": { "description": "This parameter was deprecated. You can still pass a value, but it will be ignored. DataRobot will takes care of the value itself.", "type": "boolean", "x-versiondeprecated": "2.30.0" }, "initialList": { "description": "Whether this list will be used during autopilot to perform image augmentation", "type": "boolean" }, "name": { "description": "The name of the image augmentation list", "type": "string" }, "numberOfNewImages": { "description": "Number of new rows to add for each existing row", "minimum": 0, "type": "integer" }, "projectId": { "description": "This parameter was deprecated. You can still pass a value, but it will be ignored. If you want to move an augmentation list to another project you can create a new list in the other project and delete the list in this project.", "type": "string", "x-versiondeprecated": "2.30.0" }, "transformationProbability": { "description": "Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always.", "maximum": 1, "minimum": 0, "type": "number" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | augmentationId | path | string | true | The id of the augmentation list to fetch | | body | body | ImageAugmentationListPatchParam | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The successful response is empty | None | ## Retrieve latest Augmentation Samples generated by augmentation ID Operation path: `GET /api/v2/imageAugmentationLists/{augmentationId}/samples/` Authentication requirements: `BearerAuth` Retrieve latest Augmentation Samples generated for list. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | augmentationId | path | string | true | The id of the augmentation list | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Augmentation samples list", "items": { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The augmented image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "originalImageId": { "description": "Id of the original image that was transformed to produce the augmented image. If this is an original image (from the original training dataset) this value will be null. The id can be used to retrieve the original image file with: [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "originalImageId", "projectId", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ImageAugmentationListsRetrieveSamplesResponse | | 404 | Not Found | Augmentation Samples not found | None | ## Requests the creation of sample augmentations based by augmentation ID Operation path: `POST /api/v2/imageAugmentationLists/{augmentationId}/samples/` Authentication requirements: `BearerAuth` This endpoint will schedule a job to augment the specified images from a project's dataset and return a link to monitor the status of the job, as well as a link to retrieve the resulting augmented images. ### Body parameter ``` { "properties": { "numberOfRows": { "description": "Number of images from the original dataset to be augmented", "minimum": 1, "type": "integer" } }, "required": [ "numberOfRows" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | augmentationId | path | string | true | The id of the augmentation list | | body | body | ImageAugmentationListsCreateSamples | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "Id of the newly created augmentation samples which can be used to retrieve the full set of sample images.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Image Augmentation Samples generation has been successfully requested | ImageAugmentationSamplesResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL that can be polled to check the status of the operation. | ## Augmentation list of all available transformations that are supported by project ID Operation path: `GET /api/v2/imageAugmentationOptions/{projectId}/` Authentication requirements: `BearerAuth` Augmentation list of all available transformations that are supported in the system. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "currentNumberOfNewImages": { "description": "Number of new images to be created for each original image during training", "type": "integer" }, "currentTransformationProbability": { "description": "Probability that each transformation included in an augmentation list will be applied to an image, if `affectedByTransformationProbability` for that transformation is True", "type": "number" }, "featureName": { "description": "Name of the image feature that the augmentation list is associated with", "type": "string" }, "id": { "description": "Augmentation list id", "type": "string" }, "maxNumberOfNewImages": { "description": "Maximum number of new images per original image to be generated during training", "type": "integer" }, "maxTransformationProbability": { "description": "Maximum probability that each enabled augmentation will be applied to an image", "maximum": 1, "type": "number" }, "minNumberOfNewImages": { "description": "Minimum number of new images per original image to be generated during training", "type": "integer" }, "minTransformationProbability": { "description": "Minimum probability that each enabled augmentation will be applied to an image", "minimum": 0, "type": "number" }, "name": { "description": "The name of the augmentation list", "type": "string" }, "projectId": { "description": "The id of the project containing the image data to be augmented", "type": "string" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "affectedByTransformationProbability": { "description": "If true, whenever this transformation is included in an augmentation list, this transformation will be applied to each image with probability set by the Transformation Probability. If false, whenever this transformation is included in an augmentation list, this transformation will be applied to each image with probability described in the Platform Documentation here: https://docs.datarobot.com/en/docs/modeling/special-workflows/visual-ai/tti-augment/ttia-lists.html#transformations", "type": "boolean" }, "enabledByDefault": { "description": "Determines if the parameter should be default selected in the UI", "type": "boolean" }, "name": { "description": "The name of the transformation", "type": "string" }, "params": { "description": "The list of parameters that control the transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" }, "translatedName": { "description": "Translated name of the parameter", "type": "string" }, "type": { "description": "The type of the parameter (int, float, etc...)", "type": "string" } }, "required": [ "name", "translatedName", "type" ], "type": "object" }, "type": "array" }, "translatedName": { "description": "Translated name of the transformation", "type": "string" } }, "required": [ "affectedByTransformationProbability", "enabledByDefault", "name", "params", "translatedName" ], "type": "object" }, "type": "array" } }, "required": [ "currentNumberOfNewImages", "currentTransformationProbability", "id", "maxNumberOfNewImages", "maxTransformationProbability", "minNumberOfNewImages", "minTransformationProbability", "name", "projectId", "transformations" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Augmentation list object | ImageAugmentationOptionsResponse | ## (Deprecated in v2 Operation path: `POST /api/v2/imageAugmentationSamples/` Authentication requirements: `BearerAuth` (Deprecated in v2.28) This endpoint will schedule a job to augment the specified images from a project's dataset and return a link to monitor the status of the job, as well as a link to retrieve the resulting augmented images. ### Body parameter ``` { "properties": { "featureName": { "description": "The name of the image feature containing the data to be augmented", "type": "string" }, "id": { "description": "Augmentation list id", "type": "string" }, "inUse": { "default": false, "description": "This is set to true when the Augmentation List has been used to train a model", "type": "boolean" }, "initialList": { "default": false, "description": "Whether this list will be used during autopilot to perform image augmentation", "type": "boolean" }, "name": { "description": "The name of the image augmentation list", "type": "string" }, "numberOfNewImages": { "description": "Number of new rows to add for each existing row", "minimum": 1, "type": "integer" }, "numberOfRows": { "description": "Number of images from the original dataset to be augmented", "minimum": 1, "type": "integer" }, "projectId": { "description": "The project containing the image data to be augmented", "type": "string" }, "samplesId": { "default": null, "description": "Image Augmentation list samples ID", "type": [ "string", "null" ] }, "transformationProbability": { "description": "Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always.", "maximum": 1, "minimum": 0, "type": "number" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name", "numberOfNewImages", "numberOfRows", "projectId", "transformationProbability", "transformations" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ImageAugmentationSamplesRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "Id of the newly created augmentation samples which can be used to retrieve the full set of sample images.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Image Augmentation Samples generation has been successfully requested | ImageAugmentationSamplesResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL that can be polled to check the status of the operation. | ## (Deprecated in v2 by samples ID Operation path: `GET /api/v2/imageAugmentationSamples/{samplesId}/` Authentication requirements: `BearerAuth` (Deprecated in v2.28) Retrieve previously generated Augmentation Samples. This route is being deprecated please use `GET /imageAugmentationLists//samples` route instead. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | samplesId | path | string | true | Id of the augmentation sample | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Augmentation samples list", "items": { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The augmented image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "originalImageId": { "description": "Id of the original image that was transformed to produce the augmented image. If this is an original image (from the original training dataset) this value will be null. The id can be used to retrieve the original image file with: [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "originalImageId", "projectId", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ImageAugmentationRetrieveSamplesResponse | | 404 | Not Found | Augmentation Samples not found | None | ## List blueprints by project ID Operation path: `GET /api/v2/projects/{projectId}/blueprints/` Authentication requirements: `BearerAuth` List appropriate blueprints for the project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "items": { "properties": { "blueprintCategory": { "description": "describes the category of the blueprint and indicates the kind of model this blueprint produces. Will be either \"DataRobot\" or \"Scaleout DataRobot\".", "type": "string", "x-versionadded": "v2.6" }, "id": { "description": "the blueprint ID of this blueprint - note that this is not an ObjectId.", "type": "string" }, "isCustomModelBlueprint": { "description": "Whether blueprint contains custom task.", "type": "boolean" }, "modelType": { "description": "the model this blueprint will produce.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "monotonicIncreasingFeaturelistId": { "description": "null or str, the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "processes": { "description": "a list of strings representing processes the blueprint uses.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "the project the blueprint belongs to.", "type": "string" }, "recommendedFeaturelistId": { "description": "The ID of the feature list recommended for this blueprint. If this field is not present, then there is no recommended feature list.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "supportsComposableMl": { "description": "indicates whether this blueprint is supported in Composable ML.", "type": "boolean", "x-versionadded": "v2.26" }, "supportsIncrementalLearning": { "description": "Whether blueprint supports incremental learning.", "type": "boolean", "x-versionadded": "v2.32" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints.", "type": "boolean", "x-versionadded": "v2.11" } }, "required": [ "blueprintCategory", "id", "isCustomModelBlueprint", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "processes", "projectId", "supportsComposableMl", "supportsIncrementalLearning", "supportsMonotonicConstraints" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of blueprints | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [BlueprintResponse] | false | | none | | » blueprintCategory | string | true | | describes the category of the blueprint and indicates the kind of model this blueprint produces. Will be either "DataRobot" or "Scaleout DataRobot". | | » id | string | true | | the blueprint ID of this blueprint - note that this is not an ObjectId. | | » isCustomModelBlueprint | boolean | true | | Whether blueprint contains custom task. | | » modelType | string | true | | the model this blueprint will produce. | | » monotonicDecreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | » monotonicIncreasingFeaturelistId | string,null | true | | null or str, the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | » processes | [string] | true | | a list of strings representing processes the blueprint uses. | | » projectId | string | true | | the project the blueprint belongs to. | | » recommendedFeaturelistId | string,null | false | | The ID of the feature list recommended for this blueprint. If this field is not present, then there is no recommended feature list. | | » supportsComposableMl | boolean | true | | indicates whether this blueprint is supported in Composable ML. | | » supportsIncrementalLearning | boolean | true | | Whether blueprint supports incremental learning. | | » supportsMonotonicConstraints | boolean | true | | whether this model supports enforcing monotonic constraints. | ## Retrieve a blueprint by its ID Operation path: `GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/` Authentication requirements: `BearerAuth` Retrieve a blueprint by its ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | blueprintId | path | string | true | The blueprint ID. | ### Example responses > 200 Response ``` { "properties": { "blueprintCategory": { "description": "describes the category of the blueprint and indicates the kind of model this blueprint produces. Will be either \"DataRobot\" or \"Scaleout DataRobot\".", "type": "string", "x-versionadded": "v2.6" }, "id": { "description": "the blueprint ID of this blueprint - note that this is not an ObjectId.", "type": "string" }, "isCustomModelBlueprint": { "description": "Whether blueprint contains custom task.", "type": "boolean" }, "modelType": { "description": "the model this blueprint will produce.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "monotonicIncreasingFeaturelistId": { "description": "null or str, the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "processes": { "description": "a list of strings representing processes the blueprint uses.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "the project the blueprint belongs to.", "type": "string" }, "recommendedFeaturelistId": { "description": "The ID of the feature list recommended for this blueprint. If this field is not present, then there is no recommended feature list.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "supportsComposableMl": { "description": "indicates whether this blueprint is supported in Composable ML.", "type": "boolean", "x-versionadded": "v2.26" }, "supportsIncrementalLearning": { "description": "Whether blueprint supports incremental learning.", "type": "boolean", "x-versionadded": "v2.32" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints.", "type": "boolean", "x-versionadded": "v2.11" } }, "required": [ "blueprintCategory", "id", "isCustomModelBlueprint", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "processes", "projectId", "supportsComposableMl", "supportsIncrementalLearning", "supportsMonotonicConstraints" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A blueprint based on its blueprint ID. | BlueprintResponse | | 404 | Not Found | This resource does not exist. | None | ## Retrieve a blueprint chart by blueprint id Operation path: `GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/blueprintChart/` Authentication requirements: `BearerAuth` Retrieve a blueprint chart by blueprint id. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | blueprintId | path | string | true | The blueprint ID. | ### Example responses > 200 Response ``` { "properties": { "edges": { "description": "An array of chart edges - tuples of (start_id, end_id).", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "nodes": { "description": "An array of node descriptions.", "items": { "properties": { "id": { "description": "The ID of the node.", "type": "string" }, "label": { "description": "The label of the node.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "type": "array" } }, "required": [ "edges", "nodes" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A blueprint chart based on the blueprint id. | BlueprintChartRetrieveResponse | | 404 | Not Found | No blueprint data found. | None | ## Retrieve blueprint tasks documentation by project ID Operation path: `GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/blueprintDocs/` Authentication requirements: `BearerAuth` Retrieve blueprint tasks documentation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | blueprintId | path | string | true | The blueprint ID. | ### Example responses > 200 Response ``` { "items": { "properties": { "description": { "description": "The task description.", "type": "string" }, "links": { "description": "A list of external documentation links.", "items": { "properties": { "name": { "description": "The name of the documentation at the link.", "type": "string" }, "url": { "description": "The URL at which external documentation can be found.", "type": [ "string", "null" ] } }, "required": [ "name", "url" ], "type": "object" }, "type": "array" }, "parameters": { "description": "An array of task parameters.", "items": { "properties": { "description": { "description": "A description of what the parameter does.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "type": { "description": "The type (and default value) of the parameter.", "type": "string" } }, "required": [ "description", "name", "type" ], "type": "object" }, "type": "array" }, "references": { "description": "A list of reference links.", "items": { "properties": { "name": { "description": "The name of the reference.", "type": "string" }, "url": { "description": "The URL at which the reference can be found.", "type": [ "string", "null" ] } }, "required": [ "name", "url" ], "type": "object" }, "type": "array" }, "task": { "description": "The task described in document.", "type": "string" }, "title": { "description": "The document title.", "type": "string" } }, "required": [ "description", "links", "parameters", "references", "task", "title" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The task documentation for a blueprint. | Inline | | 404 | Not Found | Model document missing. | None | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [BlueprintListDocumentsResponse] | false | | none | | » description | string | true | | The task description. | | » links | [BlueprintDocLinks] | true | | A list of external documentation links. | | »» name | string | true | | The name of the documentation at the link. | | »» url | string,null | true | | The URL at which external documentation can be found. | | » parameters | [BlueprintDocParameters] | true | | An array of task parameters. | | »» description | string | true | | A description of what the parameter does. | | »» name | string | true | | The name of the parameter. | | »» type | string | true | | The type (and default value) of the parameter. | | » references | [BlueprintDocReferences] | true | | A list of reference links. | | »» name | string | true | | The name of the reference. | | »» url | string,null | true | | The URL at which the reference can be found. | | » task | string | true | | The task described in document. | | » title | string | true | | The document title. | ## Retrieve the JSON representation of a datarobot blueprint by project ID Operation path: `GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/json/` Authentication requirements: `BearerAuth` Retrieve a blueprint json by its ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | blueprintId | path | string | true | The blueprint ID. | ### Example responses > 200 Response ``` { "properties": { "blueprint": { "description": "JSON blueprint representation of the model.", "example": "\n {\n \"1\": [[\"NUM\"], [\"PNI2\"], \"T\"],\n \"2\": [[\"1\"], [\"LASSO2\"], \"P\"],\n }\n ", "type": "object", "x-versionadded": "v2.31" } }, "required": [ "blueprint" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Blueprint json based on its blueprint ID. | BlueprintJsonResponse | | 404 | Not Found | This resource does not exist. | None | ## Retrieve a reduced model blueprint chart by model id Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/blueprintChart/` Authentication requirements: `BearerAuth` Retrieve a reduced model blueprint chart by model id. The model blueprint charts are reduced from the full blueprint charts to show only those sections of the blueprint that were actually used in the model, given the selected featurelist. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "edges": { "description": "An array of chart edges - tuples of (start_id, end_id).", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "nodes": { "description": "An array of node descriptions.", "items": { "properties": { "id": { "description": "The ID of the node.", "type": "string" }, "label": { "description": "The label of the node.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "type": "array" } }, "required": [ "edges", "nodes" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A reduced model blueprint chart based on the model id. | BlueprintChartRetrieveResponse | | 404 | Not Found | No model found for given projectId and modelId. | None | ## Retrieve task documentation by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/blueprintDocs/` Authentication requirements: `BearerAuth` Retrieve task documentation for a reduced model blueprint. The model blueprint is reduced from the full blueprint to show only those sections of the blueprint that were actually used in the model, given the selected featurelist. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "items": { "properties": { "description": { "description": "The task description.", "type": "string" }, "links": { "description": "A list of external documentation links.", "items": { "properties": { "name": { "description": "The name of the documentation at the link.", "type": "string" }, "url": { "description": "The URL at which external documentation can be found.", "type": [ "string", "null" ] } }, "required": [ "name", "url" ], "type": "object" }, "type": "array" }, "parameters": { "description": "An array of task parameters.", "items": { "properties": { "description": { "description": "A description of what the parameter does.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "type": { "description": "The type (and default value) of the parameter.", "type": "string" } }, "required": [ "description", "name", "type" ], "type": "object" }, "type": "array" }, "references": { "description": "A list of reference links.", "items": { "properties": { "name": { "description": "The name of the reference.", "type": "string" }, "url": { "description": "The URL at which the reference can be found.", "type": [ "string", "null" ] } }, "required": [ "name", "url" ], "type": "object" }, "type": "array" }, "task": { "description": "The task described in document.", "type": "string" }, "title": { "description": "The document title.", "type": "string" } }, "required": [ "description", "links", "parameters", "references", "task", "title" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The task documentation for a reduced model blueprint. | Inline | | 404 | Not Found | No model found for given projectId and modelId. | None | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [BlueprintListDocumentsResponse] | false | | none | | » description | string | true | | The task description. | | » links | [BlueprintDocLinks] | true | | A list of external documentation links. | | »» name | string | true | | The name of the documentation at the link. | | »» url | string,null | true | | The URL at which external documentation can be found. | | » parameters | [BlueprintDocParameters] | true | | An array of task parameters. | | »» description | string | true | | A description of what the parameter does. | | »» name | string | true | | The name of the parameter. | | »» type | string | true | | The type (and default value) of the parameter. | | » references | [BlueprintDocReferences] | true | | A list of reference links. | | »» name | string | true | | The name of the reference. | | »» url | string,null | true | | The URL at which the reference can be found. | | » task | string | true | | The task described in document. | | » title | string | true | | The document title. | ## Delete user blueprints Operation path: `DELETE /api/v2/userBlueprints/` Authentication requirements: `BearerAuth` Delete user blueprints, specified by `userBlueprintIds`. ### Body parameter ``` { "properties": { "userBlueprintIds": { "description": "The list of IDs of user blueprints to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ] } }, "required": [ "userBlueprintIds" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintsBulkDelete | false | none | ### Example responses > 200 Response ``` { "properties": { "failedToDelete": { "description": "The list of IDs of User Blueprints which failed to be deleted.", "items": { "description": "An ID of a User Blueprint which failed to be deleted.", "type": "string" }, "type": "array" }, "successfullyDeleted": { "description": "The list of IDs of User Blueprints successfully deleted.", "items": { "description": "An ID of a User Blueprint successfully deleted.", "type": "string" }, "type": "array" } }, "required": [ "failedToDelete", "successfullyDeleted" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of user blueprints successfully and unsuccessfully deleted. | UserBlueprintsBulkDeleteResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## List user blueprints Operation path: `GET /api/v2/userBlueprints/` Authentication requirements: `BearerAuth` Fetch the list of the user blueprints the current user has access to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of results to skip (for pagination). | | limit | query | integer | true | The max number of results to return. | | projectId | query | string | false | The ID of the project, used to filter for original project_id. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of records on this page.", "type": "integer" }, "data": { "description": "The list of user blueprints.", "items": { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" } }, "required": [ "blender", "blueprintId", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL to the next page, or `null` if there is no such page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL to the previous page, or `null` if there is no such page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of records.", "type": "integer" } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Fetched the list of the accessible user blueprints successfully. | UserBlueprintsListResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | ## Create a user blueprint Operation path: `POST /api/v2/userBlueprints/` Authentication requirements: `BearerAuth` Create a user blueprint. ### Body parameter ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "blueprint", "decompressedBlueprint", "isInplaceEditor", "saveToCatalog" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Created the user blueprint successfully. | UserBlueprintsDetailedItem | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Project not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Clone a blueprint Operation path: `POST /api/v2/userBlueprints/fromBlueprintId/` Authentication requirements: `BearerAuth` Clone a blueprint from a project. ### Body parameter ``` { "properties": { "blueprintId": { "description": "The ID associated with the blueprint to create the user blueprint from.", "type": "string" }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active.", "type": "string" }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "blueprintId", "decompressedBlueprint", "isInplaceEditor", "projectId", "saveToCatalog" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintCreateFromBlueprintId | false | none | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Created the user blueprint successfully. | UserBlueprintsDetailedItem | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Project or blueprint not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Create a user blueprint Operation path: `POST /api/v2/userBlueprints/fromCustomTaskVersionId/` Authentication requirements: `BearerAuth` Create a user blueprint from a single custom task. ### Body parameter ``` { "properties": { "customTaskVersionId": { "description": "The ID of a custom task version.", "type": "string" }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description for the user blueprint that will be created from this CustomTaskVersion.", "type": "string" }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "customTaskVersionId", "decompressedBlueprint", "saveToCatalog" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintCreateFromCustomTaskVersionIdPayload | false | none | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Created the user blueprint successfully | UserBlueprintsDetailedItem | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Custom task version or custom task not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Clone a user blueprint Operation path: `POST /api/v2/userBlueprints/fromUserBlueprintId/` Authentication requirements: `BearerAuth` Clone a user blueprint. ### Body parameter ``` { "properties": { "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" }, "userBlueprintId": { "description": "The ID of the existing user blueprint to copy.", "type": "string" } }, "required": [ "decompressedBlueprint", "isInplaceEditor", "saveToCatalog", "userBlueprintId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintCreateFromUserBlueprintId | false | none | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Created the user blueprint successfully. | UserBlueprintsDetailedItem | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | User blueprint or project not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Delete a user blueprint by user blueprint ID Operation path: `DELETE /api/v2/userBlueprints/{userBlueprintId}/` Authentication requirements: `BearerAuth` Delete a user blueprint, specified by the `userBlueprintId`. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userBlueprintId | path | string | true | Used to identify a specific user-owned blueprint. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully deleted the specified blueprint, if it existed. | None | ## Retrieve a user blueprint by user blueprint ID Operation path: `GET /api/v2/userBlueprints/{userBlueprintId}/` Authentication requirements: `BearerAuth` Retrieve a user blueprint. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | editMode | query | boolean | true | Whether to retrieve the extra blueprint metadata for editing. | | decompressedBlueprint | query | boolean | true | Whether to retrieve the blueprint in the decompressed format. | | projectId | query | string | false | String representation of ObjectId for the currently active project. The user blueprint is retrieved when this project is active. | | isInplaceEditor | query | boolean | true | Whether the request is sent from the in place user BP editor. | | getDynamicLabels | query | boolean | false | Whether to add dynamic labels to a decompressed blueprint. | | userBlueprintId | path | string | true | Used to identify a specific user-owned blueprint. | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprintId", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieved the user blueprint successfully. | UserBlueprintsRetrieveResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Referenced project or user blueprint not found. | None | ## Update a user blueprint by user blueprint ID Operation path: `PATCH /api/v2/userBlueprints/{userBlueprintId}/` Authentication requirements: `BearerAuth` Update a user blueprint. ### Body parameter ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "decompressedBlueprint", "isInplaceEditor", "saveToCatalog" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userBlueprintId | path | string | true | Used to identify a specific user-owned blueprint. | | body | body | UserBlueprintUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Updated the user blueprint successfully. | UserBlueprintsDetailedItem | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | User blueprint not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Get the list of users, groups and organizations that have access by user blueprint ID Operation path: `GET /api/v2/userBlueprints/{userBlueprintId}/sharedRoles/` Authentication requirements: `BearerAuth` Get the list of users, groups and organizations that have access to this user blueprint. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | userBlueprintId | path | string | true | Used to identify a specific user-owned blueprint. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of SharedRoles objects.", "items": { "properties": { "id": { "description": "The ID of the recipient organization, group or user.", "type": "string" }, "name": { "description": "The name of the recipient organization, group or user.", "type": "string" }, "role": { "description": "The role of the org/group/user on this dataset or \"NO_ROLE\" for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully retrieved roles. | UserBlueprintSharedRolesListResponse | ## Share a user blueprint by user blueprint ID Operation path: `PATCH /api/v2/userBlueprints/{userBlueprintId}/sharedRoles/` Authentication requirements: `BearerAuth` Share a user blueprint with a user, group, or organization. ### Body parameter ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userBlueprintId | path | string | true | Used to identify a specific user-owned blueprint. | | body | body | SharedRolesUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | None | | 400 | Bad Request | Bad Request | None | | 403 | Forbidden | User can view entity but does not have permission to grant these roles on the entity. | None | | 404 | Not Found | Either the entity does not exist or the user does not have permissions to view the entity. | None | | 409 | Conflict | The request would leave the entity without an owner. | None | | 422 | Unprocessable Entity | The request was formatted improperly. | None | ## Validate many user blueprints Operation path: `POST /api/v2/userBlueprintsBulkValidations/` Authentication requirements: `BearerAuth` Validate many user blueprints, optionally using a specific project. Any non-existent or inaccessible user blueprints will be ignored. ### Body parameter ``` { "properties": { "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": "string" }, "userBlueprintIds": { "description": "The IDs of the user blueprints to validate in bulk.", "items": { "description": "The ID of one user blueprint to validate.", "type": "string" }, "type": "array" } }, "required": [ "userBlueprintIds" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintBulkValidationRequest | false | none | ### Example responses > 201 Response ``` { "properties": { "data": { "description": "A list of validation responses with their associated User Blueprint ID.", "items": { "properties": { "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "userBlueprintId", "vertexContext" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Validated many user blueprints successfully. | UserBlueprintsBulkValidationResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Referenced project was not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Retrieve input types Operation path: `GET /api/v2/userBlueprintsInputTypes/` Authentication requirements: `BearerAuth` Retrieve the input types which can be used with User Blueprints. ### Example responses > 200 Response ``` { "properties": { "inputTypes": { "description": "The list of associated pairs of an input type and their human-readable names.", "items": { "properties": { "name": { "description": "The human-readable name of an input type.", "type": "string" }, "type": { "description": "The unique identifier of an input type.", "type": "string" } }, "required": [ "name", "type" ], "type": "object" }, "type": "array" } }, "required": [ "inputTypes" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully retrieved the input types. | UserBlueprintsInputTypesResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | ## Add user blueprints Operation path: `POST /api/v2/userBlueprintsProjectBlueprints/` Authentication requirements: `BearerAuth` Add the list of user blueprints, by ID, to a specified (by ID) project's repository. ### Body parameter ``` { "properties": { "deleteAfter": { "default": false, "description": "Whether to delete the user blueprint(s) after adding it (them) to the project menu.", "type": "boolean" }, "describeFailures": { "default": false, "description": "Whether to include extra fields to describe why any blueprints were not added to the chosen project.", "type": "boolean", "x-versionadded": "v2.27" }, "projectId": { "description": "The projectId of the project for the repository to add the specified user blueprints to.", "type": "string" }, "userBlueprintIds": { "description": "The IDs of the user blueprints to add to the specified project's repository.", "items": { "description": "An ID of one user blueprint to add to the specified project's repository.", "type": "string" }, "type": "array" } }, "required": [ "deleteAfter", "describeFailures", "projectId", "userBlueprintIds" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintAddToMenu | false | none | ### Example responses > 200 Response ``` { "properties": { "addedToMenu": { "description": "The list of userBlueprintId and blueprintId pairs representing blueprints successfully added to the project repository.", "items": { "properties": { "blueprintId": { "description": "The blueprintId representing the blueprint which was added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprintId added to the project repository.", "type": "string" } }, "required": [ "blueprintId", "userBlueprintId" ], "type": "object" }, "type": "array" }, "message": { "description": "A success message or a list of reasons why the list of blueprints could not be added to the project repository.", "type": "string", "x-versionadded": "2.27" }, "notAddedToMenu": { "description": "The list of userBlueprintId and error message representing blueprints which failed to be added to the project repository.", "items": { "properties": { "error": { "description": "The error message representing why the blueprint was not added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprint which was not added to the project repository.", "type": "string" } }, "required": [ "error", "userBlueprintId" ], "type": "object" }, "type": "array", "x-versionadded": "2.27" } }, "required": [ "addedToMenu" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully added the user blueprints to the project's repository. | UserBlueprintAddToMenuResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Referenced project not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Validate task parameters Operation path: `POST /api/v2/userBlueprintsTaskParameters/` Authentication requirements: `BearerAuth` Validate that each value assigned to specified task parameters are valid. ### Body parameter ``` { "properties": { "outputMethod": { "description": "The method representing how the task will output data.", "enum": [ "P", "Pm", "S", "Sm", "T", "TS" ], "type": "string" }, "projectId": { "description": "The projectId representing the project where this user blueprint is edited.", "type": [ "string", "null" ] }, "taskCode": { "description": "The task code representing the task to validate parameter values.", "type": "string" }, "taskParameters": { "description": "The list of task parameters and proposed values to be validated.", "items": { "properties": { "newValue": { "description": "The proposed value for the task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "paramName": { "description": "The name of the task parameter to be validated.", "type": "string" } }, "required": [ "newValue", "paramName" ], "type": "object" }, "type": "array" } }, "required": [ "outputMethod", "taskCode", "taskParameters" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintTaskParameterValidation | false | none | ### Example responses > 200 Response ``` { "properties": { "errors": { "description": "A list of the task parameters, their proposed values, and messages describing why each is not valid.", "items": { "properties": { "message": { "description": "The description of the issue with the proposed task parameter value.", "type": "string" }, "paramName": { "description": "The name of the validated task parameter.", "type": "string" }, "value": { "description": "The invalid value proposed for the validated task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "message", "paramName", "value" ], "type": "object" }, "type": "array" } }, "required": [ "errors" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Accepted validation parameters for a task in the context of User Blueprints. | UserBlueprintsValidateTaskParametersResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Custom task version not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Retrieve tasks Operation path: `GET /api/v2/userBlueprintsTasks/` Authentication requirements: `BearerAuth` Retrieve the available tasks, organized into categories, which can be used to create or modify User Blueprints. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | query | string | false | The project ID to use for task retrieval. | | blueprintId | query | string | false | The blueprint ID to use for task retrieval. | | userBlueprintId | query | string | false | The user blueprint ID to use for task retrieval. | ### Example responses > 200 Response ``` { "properties": { "categories": { "description": "The list of the available task categories, sub-categories, and tasks.", "items": { "properties": { "name": { "description": "The name of the category.", "type": "string" }, "subcategories": { "description": "The list of the available task category items.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "taskCodes": { "description": "A list of task codes representing the tasks in this category.", "items": { "type": "string" }, "type": "array" } }, "required": [ "name", "taskCodes" ], "type": "object" }, "type": "array" }, "tasks": { "description": "The list of task codes and their task definitions.", "items": { "properties": { "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "taskDefinition": { "description": "A definition of a task in terms of label, arguments, description, and other metadata.", "oneOf": [ { "properties": { "arguments": { "description": "The list of definitions of each argument which can be set for the task.", "items": { "properties": { "argument": { "description": "The definition of a task argument, used to specify a certain aspect of the task.", "oneOf": [ { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ] }, "key": { "description": "The unique key of the argument.", "type": "string" } }, "required": [ "argument", "key" ], "type": "object" }, "type": "array" }, "categories": { "description": "The categories which the task is in.", "items": { "type": "string" }, "type": "array" }, "colnamesAndTypes": { "description": "The column names, their types, and their hex representation, available in the specified project for the task.", "items": { "properties": { "colname": { "description": "The column name.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "type": { "description": "The data type of the column.", "type": "string" } }, "required": [ "colname", "hex", "type" ], "type": "object" }, "type": "array" }, "customTaskId": { "description": "The ID of the custom task, if it is a custom task.", "type": [ "string", "null" ] }, "customTaskVersions": { "description": "Metadata for all of the custom task's versions.", "items": { "properties": { "id": { "description": "Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task.", "type": "string" }, "label": { "description": "The name of the custom task version.", "type": "string" }, "versionMajor": { "description": "Major version of the custom task.", "type": "integer" }, "versionMinor": { "description": "Minor version of the custom task.", "type": "integer" } }, "required": [ "id", "label", "versionMajor", "versionMinor" ], "type": "object" }, "type": "array" }, "description": { "description": "The description of the task.", "type": "string" }, "icon": { "description": "The integer representing the ID to be displayed when the blueprint is trained.", "type": "integer" }, "isCommonTask": { "default": false, "description": "Whether the task is a common task.", "type": "boolean" }, "isCustomTask": { "description": "Whether the task is custom code written by the user.", "type": "boolean" }, "isVisibleInComposableMl": { "default": true, "description": "Whether the task is visible in the ComposableML menu.", "type": "boolean" }, "label": { "description": "The generic / default title or label for the task.", "type": "string" }, "outputMethods": { "description": "The methods which the task can use to produce output.", "items": { "type": "string" }, "type": "array" }, "supportsScoringCode": { "description": "Whether the task supports Scoring Code.", "type": "boolean" }, "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "timeSeriesOnly": { "description": "Whether the task can only be used with time series projects.", "type": "boolean" }, "url": { "description": "The URL of the documentation of the task.", "oneOf": [ { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, { "type": "string" } ] }, "validInputs": { "description": "The supported input types of the task.", "items": { "description": "A specific supported input type.", "type": "string" }, "type": "array" } }, "required": [ "arguments", "categories", "description", "icon", "label", "outputMethods", "supportsScoringCode", "taskCode", "timeSeriesOnly", "url", "validInputs" ], "type": "object" } ] } }, "required": [ "taskCode", "taskDefinition" ], "type": "object" }, "type": "array" } }, "required": [ "categories", "tasks" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully retrieved the tasks. | UserBlueprintTasksResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Referenced project or user blueprint not found. | None | ## Validate a user blueprint Operation path: `POST /api/v2/userBlueprintsValidations/` Authentication requirements: `BearerAuth` Validate a user blueprint. ### Body parameter ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] } }, "required": [ "blueprint", "isInplaceEditor" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintValidation | false | none | ### Example responses > 201 Response ``` { "properties": { "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Validated the user blueprint successfully. | UserBlueprintsValidationResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Referenced project not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Retrieve an archive (tar by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/logs/` Authentication requirements: `BearerAuth` Retrieve an archive (tar.gz) of the logs produced and persisted by a model. Note that only blueprints with custom tasks create persistent logs - this will not work with any other type of model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "An archive (tar.gz) of the logs produced and persisted by a model.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | An archive (tar.gz) of the logs produced and persisted by a model. | PersistentLogsForModelWithCustomTasksRetrieveResponse | | 403 | Forbidden | User does not have permissions to fetch model logs. | None | | 404 | Not Found | Logs for this model could not be found. | None | ## Retrieve training artifact by id Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/trainingArtifact/` Authentication requirements: `BearerAuth` Retrieve an archive (tar.gz) of the artifacts produced and persisted by a model. Note that only blueprints with custom tasks create these artifacts - this will not work with any other type of model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "An archive (tar.gz) of the artifacts produced and persisted by a model.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | An archive (tar.gz) of the artifacts produced by this model. | ArtifactsForModelWithCustomTasksRetrieveResponse | | 403 | Forbidden | User does not have permissions to fetch this artifact. | None | | 404 | Not Found | The model with this modelID does not have any artifacts. | None | # Schemas ## AllowExtra ``` { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ``` The parameters submitted by the user to the failed job. ### Properties None ## ArtifactsForModelWithCustomTasksRetrieveResponse ``` { "properties": { "data": { "description": "An archive (tar.gz) of the artifacts produced and persisted by a model.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | string(binary) | true | | An archive (tar.gz) of the artifacts produced and persisted by a model. | ## BlueprintChartRetrieveResponse ``` { "properties": { "edges": { "description": "An array of chart edges - tuples of (start_id, end_id).", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "nodes": { "description": "An array of node descriptions.", "items": { "properties": { "id": { "description": "The ID of the node.", "type": "string" }, "label": { "description": "The label of the node.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "type": "array" } }, "required": [ "edges", "nodes" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | edges | [array] | true | | An array of chart edges - tuples of (start_id, end_id). | | nodes | [NodeDescription] | true | | An array of node descriptions. | ## BlueprintDocLinks ``` { "properties": { "name": { "description": "The name of the documentation at the link.", "type": "string" }, "url": { "description": "The URL at which external documentation can be found.", "type": [ "string", "null" ] } }, "required": [ "name", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the documentation at the link. | | url | string,null | true | | The URL at which external documentation can be found. | ## BlueprintDocParameters ``` { "properties": { "description": { "description": "A description of what the parameter does.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "type": { "description": "The type (and default value) of the parameter.", "type": "string" } }, "required": [ "description", "name", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | A description of what the parameter does. | | name | string | true | | The name of the parameter. | | type | string | true | | The type (and default value) of the parameter. | ## BlueprintDocReferences ``` { "properties": { "name": { "description": "The name of the reference.", "type": "string" }, "url": { "description": "The URL at which the reference can be found.", "type": [ "string", "null" ] } }, "required": [ "name", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the reference. | | url | string,null | true | | The URL at which the reference can be found. | ## BlueprintJsonResponse ``` { "properties": { "blueprint": { "description": "JSON blueprint representation of the model.", "example": "\n {\n \"1\": [[\"NUM\"], [\"PNI2\"], \"T\"],\n \"2\": [[\"1\"], [\"LASSO2\"], \"P\"],\n }\n ", "type": "object", "x-versionadded": "v2.31" } }, "required": [ "blueprint" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprint | object | true | | JSON blueprint representation of the model. | ## BlueprintListDocumentsResponse ``` { "properties": { "description": { "description": "The task description.", "type": "string" }, "links": { "description": "A list of external documentation links.", "items": { "properties": { "name": { "description": "The name of the documentation at the link.", "type": "string" }, "url": { "description": "The URL at which external documentation can be found.", "type": [ "string", "null" ] } }, "required": [ "name", "url" ], "type": "object" }, "type": "array" }, "parameters": { "description": "An array of task parameters.", "items": { "properties": { "description": { "description": "A description of what the parameter does.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "type": { "description": "The type (and default value) of the parameter.", "type": "string" } }, "required": [ "description", "name", "type" ], "type": "object" }, "type": "array" }, "references": { "description": "A list of reference links.", "items": { "properties": { "name": { "description": "The name of the reference.", "type": "string" }, "url": { "description": "The URL at which the reference can be found.", "type": [ "string", "null" ] } }, "required": [ "name", "url" ], "type": "object" }, "type": "array" }, "task": { "description": "The task described in document.", "type": "string" }, "title": { "description": "The document title.", "type": "string" } }, "required": [ "description", "links", "parameters", "references", "task", "title" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | The task description. | | links | [BlueprintDocLinks] | true | | A list of external documentation links. | | parameters | [BlueprintDocParameters] | true | | An array of task parameters. | | references | [BlueprintDocReferences] | true | | A list of reference links. | | task | string | true | | The task described in document. | | title | string | true | | The document title. | ## BlueprintResponse ``` { "properties": { "blueprintCategory": { "description": "describes the category of the blueprint and indicates the kind of model this blueprint produces. Will be either \"DataRobot\" or \"Scaleout DataRobot\".", "type": "string", "x-versionadded": "v2.6" }, "id": { "description": "the blueprint ID of this blueprint - note that this is not an ObjectId.", "type": "string" }, "isCustomModelBlueprint": { "description": "Whether blueprint contains custom task.", "type": "boolean" }, "modelType": { "description": "the model this blueprint will produce.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "monotonicIncreasingFeaturelistId": { "description": "null or str, the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "processes": { "description": "a list of strings representing processes the blueprint uses.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "the project the blueprint belongs to.", "type": "string" }, "recommendedFeaturelistId": { "description": "The ID of the feature list recommended for this blueprint. If this field is not present, then there is no recommended feature list.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "supportsComposableMl": { "description": "indicates whether this blueprint is supported in Composable ML.", "type": "boolean", "x-versionadded": "v2.26" }, "supportsIncrementalLearning": { "description": "Whether blueprint supports incremental learning.", "type": "boolean", "x-versionadded": "v2.32" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints.", "type": "boolean", "x-versionadded": "v2.11" } }, "required": [ "blueprintCategory", "id", "isCustomModelBlueprint", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "processes", "projectId", "supportsComposableMl", "supportsIncrementalLearning", "supportsMonotonicConstraints" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintCategory | string | true | | describes the category of the blueprint and indicates the kind of model this blueprint produces. Will be either "DataRobot" or "Scaleout DataRobot". | | id | string | true | | the blueprint ID of this blueprint - note that this is not an ObjectId. | | isCustomModelBlueprint | boolean | true | | Whether blueprint contains custom task. | | modelType | string | true | | the model this blueprint will produce. | | monotonicDecreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | monotonicIncreasingFeaturelistId | string,null | true | | null or str, the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | processes | [string] | true | | a list of strings representing processes the blueprint uses. | | projectId | string | true | | the project the blueprint belongs to. | | recommendedFeaturelistId | string,null | false | | The ID of the feature list recommended for this blueprint. If this field is not present, then there is no recommended feature list. | | supportsComposableMl | boolean | true | | indicates whether this blueprint is supported in Composable ML. | | supportsIncrementalLearning | boolean | true | | Whether blueprint supports incremental learning. | | supportsMonotonicConstraints | boolean | true | | whether this model supports enforcing monotonic constraints. | ## BpData ``` { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | children | [AllowExtra] | false | | A nested dictionary representation of the blueprint DAG. | | id | string | true | | The type of the node (e.g., "start", "input", "task"). | | inputs | [string] | false | | The inputs to the current node. | | output | any | false | | IDs describing the destination of any outgoing edges. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | IDs describing the destination of any outgoing edges. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | taskMap | AllowExtra | true | | The parameters submitted by the user to the failed job. | | taskParameters | string | false | | A stringified JSON object describing the parameters and their values for a task. | | tasks | [string] | true | | The task defining the current node. | | type | string | true | | A unique ID to represent the current node. | ## ColnameAndType ``` { "properties": { "colname": { "description": "The column name.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "type": { "description": "The data type of the column.", "type": "string" } }, "required": [ "colname", "hex", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | colname | string | true | | The column name. | | hex | string | true | | A safe hex representation of the column name. | | type | string | true | | The data type of the column. | ## CustomModelShortResponse ``` { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` Custom model associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model. | | name | string | true | | User-friendly name of the model. | ## CustomModelVersionShortResponse ``` { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" } ``` Custom model version associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model version. | | label | string | true | | User-friendly name of the model version. | ## CustomTaskAccessControlListResponse ``` { "properties": { "count": { "description": "Number of items in current page.", "type": "integer" }, "data": { "description": "List of the requested custom task access control entries.", "items": { "properties": { "canShare": { "description": "Whether this user can share this custom task", "type": "boolean" }, "role": { "description": "This users role.", "type": "string" }, "userId": { "description": "This user's userId.", "type": "string" }, "username": { "description": "The username for this user's entry.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Number of items in current page. | | data | [CustomTaskAccessControlResponse] | true | maxItems: 1000 | List of the requested custom task access control entries. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page) | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page | ## CustomTaskAccessControlResponse ``` { "properties": { "canShare": { "description": "Whether this user can share this custom task", "type": "boolean" }, "role": { "description": "This users role.", "type": "string" }, "userId": { "description": "This user's userId.", "type": "string" }, "username": { "description": "The username for this user's entry.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | Whether this user can share this custom task | | role | string | true | | This users role. | | userId | string | true | | This user's userId. | | username | string | true | | The username for this user's entry. | ## CustomTaskCopy ``` { "properties": { "customTaskId": { "description": "The ID of the custom task to copy.", "type": "string" } }, "required": [ "customTaskId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customTaskId | string | true | | The ID of the custom task to copy. | ## CustomTaskCreate ``` { "properties": { "calibratePredictions": { "default": true, "description": "Whether model predictions should be calibrated by DataRobot.Only applies to anomaly detection; we recommend this if you have not already included calibration in your model code.Calibration improves the probability estimates of a model, and modifies the predictions of non-probabilistic models to be interpretable as probabilities. This will facilitate comparison to DataRobot models, and give access to ROC curve insights on external data.", "type": "boolean" }, "description": { "description": "The user-friendly description of the task.", "maxLength": 10000, "type": "string" }, "language": { "description": "Programming language name in which task is written.", "maxLength": 500, "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The user-friendly name for the task.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type of the custom task", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint" ], "type": "string" } }, "required": [ "name", "targetType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | calibratePredictions | boolean | false | | Whether model predictions should be calibrated by DataRobot.Only applies to anomaly detection; we recommend this if you have not already included calibration in your model code.Calibration improves the probability estimates of a model, and modifies the predictions of non-probabilistic models to be interpretable as probabilities. This will facilitate comparison to DataRobot models, and give access to ROC curve insights on external data. | | description | string | false | maxLength: 10000 | The user-friendly description of the task. | | language | string | false | maxLength: 500 | Programming language name in which task is written. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. | | name | string | true | maxLength: 255 | The user-friendly name for the task. | | targetType | string | true | | The target type of the custom task | ### Enumerated Values | Property | Value | | --- | --- | | targetType | [Binary, Regression, Multiclass, Anomaly, Transform, TextGeneration, GeoPoint] | ## CustomTaskListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom tasks.", "items": { "properties": { "calibratePredictions": { "description": "Determines whether or not predictions should be calibrated by DataRobot. Only applies to anomaly detection.", "type": "boolean" }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "createdBy": { "description": "The username of the custom task creator.", "type": "string" }, "customModelType": { "description": "The type of custom task.", "enum": [ "training", "inference" ], "type": "string", "x-versiondeprecated": "v2.25" }, "description": { "description": "The description of the task.", "type": "string" }, "id": { "description": "The ID of the custom task.", "type": "string" }, "language": { "description": "The programming language used by the task.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The name of the task.", "type": "string" }, "targetType": { "description": "The target type of the custom task.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint" ], "type": "string" }, "updated": { "description": "ISO-8601 timestamp of when task was last updated.", "type": "string" } }, "required": [ "created", "createdBy", "customModelType", "description", "id", "language", "latestVersion", "name", "targetType", "updated" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomTaskResponse] | true | maxItems: 1000 | List of custom tasks. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomTaskResponse ``` { "properties": { "calibratePredictions": { "description": "Determines whether or not predictions should be calibrated by DataRobot. Only applies to anomaly detection.", "type": "boolean" }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "createdBy": { "description": "The username of the custom task creator.", "type": "string" }, "customModelType": { "description": "The type of custom task.", "enum": [ "training", "inference" ], "type": "string", "x-versiondeprecated": "v2.25" }, "description": { "description": "The description of the task.", "type": "string" }, "id": { "description": "The ID of the custom task.", "type": "string" }, "language": { "description": "The programming language used by the task.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The name of the task.", "type": "string" }, "targetType": { "description": "The target type of the custom task.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint" ], "type": "string" }, "updated": { "description": "ISO-8601 timestamp of when task was last updated.", "type": "string" } }, "required": [ "created", "createdBy", "customModelType", "description", "id", "language", "latestVersion", "name", "targetType", "updated" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | calibratePredictions | boolean | false | | Determines whether or not predictions should be calibrated by DataRobot. Only applies to anomaly detection. | | created | string | true | | ISO-8601 timestamp of when the task was created. | | createdBy | string | true | | The username of the custom task creator. | | customModelType | string | true | | The type of custom task. | | description | string | true | | The description of the task. | | id | string | true | | The ID of the custom task. | | language | string | true | | The programming language used by the task. | | latestVersion | CustomTaskVersionResponse | true | | The latest version for the custom task (if this field is empty the task is not ready for use). | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. | | name | string | true | | The name of the task. | | targetType | string | true | | The target type of the custom task. | | updated | string | true | | ISO-8601 timestamp of when task was last updated. | ### Enumerated Values | Property | Value | | --- | --- | | customModelType | [training, inference] | | targetType | [Binary, Regression, Multiclass, Anomaly, Transform, TextGeneration, GeoPoint] | ## CustomTaskUpdate ``` { "properties": { "description": { "description": "The user-friendly description of the task.", "maxLength": 10000, "type": "string" }, "language": { "description": "Programming language name in which task is written.", "maxLength": 500, "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "name": { "description": "The user-friendly name for the task.", "maxLength": 255, "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 10000 | The user-friendly description of the task. | | language | string | false | maxLength: 500 | Programming language name in which task is written. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. | | name | string | false | maxLength: 255 | The user-friendly name for the task. | ## CustomTaskVersionCreate ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this custom task version.", "type": "string" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "isMajorUpdate": { "default": "true", "description": "If set to true, new major version will created, otherwise minor version will be created.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "string", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "type": "string", "x-versionadded": "v2.26" } }, "required": [ "baseEnvironmentId", "isMajorUpdate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string | true | | The base environment to use with this custom task version. | | file | string(binary) | false | | A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding filePath supplied that shows the relative location of the file. For example, you have two files: /home/username/custom-task/main.py and /home/username/custom-task/helpers/helper.py. When uploading these files, you would also need to include two filePath fields of, "main.py" and "helpers/helper.py". If the supplied file already exists at the supplied filePath, the old file is replaced by the new file. | | filePath | any | false | | The local path of the file being uploaded. See the file field explanation for more details. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isMajorUpdate | string | true | | If set to true, new major version will created, otherwise minor version will be created. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. | | outboundNetworkPolicy | string | false | | What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address. | | requiredMetadata | string | false | | Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. | | requiredMetadataValues | string | false | | Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. | ### Enumerated Values | Property | Value | | --- | --- | | isMajorUpdate | [false, False, true, True] | | outboundNetworkPolicy | [ISOLATED, PUBLIC] | ## CustomTaskVersionCreateFromLatest ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this custom task version.", "type": "string" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "filesToDelete": { "description": "The IDs of the files to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" } ] }, "isMajorUpdate": { "default": "true", "description": "If set to true, new major version will created, otherwise minor version will be created.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "string", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "type": "string", "x-versionadded": "v2.26" } }, "required": [ "baseEnvironmentId", "isMajorUpdate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string | true | | The base environment to use with this custom task version. | | file | string(binary) | false | | A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding filePath supplied that shows the relative location of the file. For example, you have two files: /home/username/custom-task/main.py and /home/username/custom-task/helpers/helper.py. When uploading these files, you would also need to include two filePath fields of, "main.py" and "helpers/helper.py". If the supplied file already exists at the supplied filePath, the old file is replaced by the new file. | | filePath | any | false | | The local path of the file being uploaded. See the file field explanation for more details. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | filesToDelete | any | false | | The IDs of the files to be deleted. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 100 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isMajorUpdate | string | true | | If set to true, new major version will created, otherwise minor version will be created. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. | | outboundNetworkPolicy | string | false | | What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address. | | requiredMetadata | string | false | | Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. | | requiredMetadataValues | string | false | | Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. | ### Enumerated Values | Property | Value | | --- | --- | | isMajorUpdate | [false, False, true, True] | | outboundNetworkPolicy | [ISOLATED, PUBLIC] | ## CustomTaskVersionCreateFromRepository ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this version.", "type": "string", "x-versionadded": "v2.22" }, "isMajorUpdate": { "default": true, "description": "If set to true, new major version will created, otherwise minor version will be created.", "type": "boolean" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "ref": { "description": "Remote reference (branch, commit, etc). Latest, if not specified.", "type": "string" }, "repositoryId": { "description": "The ID of remote repository used to pull sources. This ID can be found using the /api/v2/remoteRepositories/ endpoint.", "type": "string" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "sourcePath": { "description": "A remote repository file path to be pulled into a custom model or custom task.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" } ] } }, "required": [ "baseEnvironmentId", "repositoryId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string | true | | The base environment to use with this version. | | isMajorUpdate | boolean | false | | If set to true, new major version will created, otherwise minor version will be created. | | outboundNetworkPolicy | string | false | | What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address. | | ref | string | false | | Remote reference (branch, commit, etc). Latest, if not specified. | | repositoryId | string | true | | The ID of remote repository used to pull sources. This ID can be found using the /api/v2/remoteRepositories/ endpoint. | | requiredMetadata | object | false | | Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. | | requiredMetadataValues | [RequiredMetadataValue] | false | maxItems: 100 | Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. | | sourcePath | any | false | | A remote repository file path to be pulled into a custom model or custom task. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 100 | none | ### Enumerated Values | Property | Value | | --- | --- | | outboundNetworkPolicy | [ISOLATED, PUBLIC] | ## CustomTaskVersionDependencyBuildMetadataResponse ``` { "properties": { "buildEnd": { "description": "The ISO-8601 encoded time when this build completed.", "type": [ "string", "null" ] }, "buildLogLocation": { "description": "The URL to download the build logs from this build.", "format": "uri", "type": [ "string", "null" ] }, "buildStart": { "description": "The ISO-8601 encoded time when this build started.", "type": "string" }, "buildStatus": { "description": "The current status of the dependency build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "customTaskId": { "description": "The ID of custom task.", "type": "string" }, "customTaskVersionId": { "description": "The ID of custom task version.", "type": "string" } }, "required": [ "buildEnd", "buildLogLocation", "buildStart", "buildStatus", "customTaskId", "customTaskVersionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildEnd | string,null | true | | The ISO-8601 encoded time when this build completed. | | buildLogLocation | string,null(uri) | true | | The URL to download the build logs from this build. | | buildStart | string | true | | The ISO-8601 encoded time when this build started. | | buildStatus | string | true | | The current status of the dependency build. | | customTaskId | string | true | | The ID of custom task. | | customTaskVersionId | string | true | | The ID of custom task version. | ### Enumerated Values | Property | Value | | --- | --- | | buildStatus | [submitted, processing, failed, success, aborted] | ## CustomTaskVersionListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom task versions.", "items": { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomTaskVersionResponse] | true | maxItems: 1000 | List of custom task versions. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomTaskVersionMetadataUpdate ``` { "properties": { "description": { "description": "New description for the custom task or model.", "maxLength": 10000, "type": "string" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 10000 | New description for the custom task or model. | | requiredMetadata | object | false | | Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. | | requiredMetadataValues | [RequiredMetadataValue] | false | maxItems: 100 | Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. | ## CustomTaskVersionResponse ``` { "description": "The latest version for the custom task (if this field is empty the task is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this task version.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this task version.", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the task was created.", "type": "string" }, "customModelId": { "description": "an alias for customTaskId", "type": "string", "x-versiondeprecated": "v2.25" }, "customTaskId": { "description": "the ID of the custom task.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom task version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "description": { "description": "Description of a custom task version.", "type": [ "string", "null" ] }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "2.32.0" }, "outboundNetworkPolicy": { "description": "What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address.", "enum": [ "ISOLATED", "PUBLIC" ], "type": "string", "x-versionadded": "2.32.0" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" }, "warning": { "description": "Warnings about the custom task version", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "created", "customModelId", "customTaskId", "description", "id", "isFrozen", "items", "label", "outboundNetworkPolicy", "versionMajor", "versionMinor" ], "type": "object" } ``` The latest version for the custom task (if this field is empty the task is not ready for use). ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string,null | false | | The base environment to use with this task version. | | baseEnvironmentVersionId | string,null | false | | The base environment version to use with this task version. | | created | string | true | | ISO-8601 timestamp of when the task was created. | | customModelId | string | true | | an alias for customTaskId | | customTaskId | string | true | | the ID of the custom task. | | dependencies | [DependencyResponse] | false | maxItems: 1000 | The parsed dependencies of the custom task version if the version has a valid requirements.txt file. | | description | string,null | true | | Description of a custom task version. | | id | string | true | | the ID of the custom model version created. | | isFrozen | boolean | true | | If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded). | | items | [WorkspaceItemResponse] | true | maxItems: 100 | List of file items. | | label | string | true | | A semantic version number of the major and minor version. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | DEPRECATED! The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. | | outboundNetworkPolicy | string | true | | What kind of outbound network calls are allowed. If ISOLATED, then no outbound network calls are allowed. If PUBLIC, then network calls are allowed to any public ip address. | | requiredMetadata | object | false | | Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you want to change them, make a new version. | | requiredMetadataValues | [RequiredMetadataValue] | false | maxItems: 100 | Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. | | versionMajor | integer | true | | The major version number, incremented on deployments or larger file changes. | | versionMinor | integer | true | | The minor version number, incremented on general file changes. | | warning | [string] | false | maxItems: 100 | Warnings about the custom task version | ### Enumerated Values | Property | Value | | --- | --- | | outboundNetworkPolicy | [ISOLATED, PUBLIC] | ## CustomTrainingBlueprintCreate ``` { "properties": { "customModelVersionId": { "description": "The ID of the specific model version from which to create a custom training blueprint.", "type": "string" } }, "required": [ "customModelVersionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelVersionId | string | true | | The ID of the specific model version from which to create a custom training blueprint. | ## CustomTrainingBlueprintListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of training model blueprints.", "items": { "properties": { "createdAt": { "description": "ISO-8601 timestamp of when the blueprint was created.", "type": "string" }, "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "targetType": { "description": "The target type of the training model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "trainingHistory": { "description": "List of instances of this blueprint having been trained.", "items": { "properties": { "creationDate": { "description": "ISO-8601 timestamp of when the project the blueprint was trained on was created.", "type": "string" }, "lid": { "description": "The leaderboard ID the blueprint was trained on.", "type": "string" }, "pid": { "description": "The project ID the blueprint was trained on.", "type": "string" }, "projectModelsCount": { "description": "Number of models in the project the blueprint was trained on.", "type": "integer" }, "projectName": { "description": "The project name the blueprint was trained on.", "type": "string" }, "targetName": { "description": "The target name of the project the blueprint was trained on.", "type": "string" } }, "required": [ "creationDate", "lid", "pid", "projectModelsCount", "projectName", "targetName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "userBlueprintId": { "description": "User Blueprint ID that can be used to train the model.", "type": "string" } }, "required": [ "createdAt", "customModel", "customModelVersion", "executionEnvironment", "executionEnvironmentVersion", "targetType", "trainingHistory", "userBlueprintId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomTrainingBlueprintResponse] | true | maxItems: 1000 | List of training model blueprints. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomTrainingBlueprintResponse ``` { "properties": { "createdAt": { "description": "ISO-8601 timestamp of when the blueprint was created.", "type": "string" }, "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "targetType": { "description": "The target type of the training model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "trainingHistory": { "description": "List of instances of this blueprint having been trained.", "items": { "properties": { "creationDate": { "description": "ISO-8601 timestamp of when the project the blueprint was trained on was created.", "type": "string" }, "lid": { "description": "The leaderboard ID the blueprint was trained on.", "type": "string" }, "pid": { "description": "The project ID the blueprint was trained on.", "type": "string" }, "projectModelsCount": { "description": "Number of models in the project the blueprint was trained on.", "type": "integer" }, "projectName": { "description": "The project name the blueprint was trained on.", "type": "string" }, "targetName": { "description": "The target name of the project the blueprint was trained on.", "type": "string" } }, "required": [ "creationDate", "lid", "pid", "projectModelsCount", "projectName", "targetName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "userBlueprintId": { "description": "User Blueprint ID that can be used to train the model.", "type": "string" } }, "required": [ "createdAt", "customModel", "customModelVersion", "executionEnvironment", "executionEnvironmentVersion", "targetType", "trainingHistory", "userBlueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string | true | | ISO-8601 timestamp of when the blueprint was created. | | customModel | CustomModelShortResponse | true | | Custom model associated with this deployment. | | customModelVersion | CustomModelVersionShortResponse | true | | Custom model version associated with this deployment. | | executionEnvironment | ExecutionEnvironmentShortResponse | true | | Execution environment associated with this deployment. | | executionEnvironmentVersion | ExecutionEnvironmentVersionShortResponse | true | | Execution environment version associated with this deployment. | | targetType | string | true | | The target type of the training model. | | trainingHistory | [TrainingHistoryEntry] | true | maxItems: 1000 | List of instances of this blueprint having been trained. | | userBlueprintId | string | true | | User Blueprint ID that can be used to train the model. | ### Enumerated Values | Property | Value | | --- | --- | | targetType | [Binary, Regression, Multiclass, Anomaly, Transform, TextGeneration, GeoPoint, Unstructured, VectorDatabase, AgenticWorkflow, MCP] | ## DependencyConstraint ``` { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | constraintType | string | true | | The constraint type to apply to the version. | | version | string | true | | The version label to use in the constraint. | ### Enumerated Values | Property | Value | | --- | --- | | constraintType | [<, <=, ==, >=, >] | ## DependencyResponse ``` { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | constraints | [DependencyConstraint] | true | maxItems: 100 | Constraints that should be applied to the dependency when installed. | | extras | string | false | | The dependency's package extras. | | line | string | true | | The original line from the requirements.txt file. | | lineNumber | integer | true | | The line number the requirement was on in requirements.txt. | | packageName | string | true | | The dependency's package name. | ## ExecutionEnvironmentShortResponse ``` { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` Execution environment associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the execution environment. | | name | string | true | | User-friendly name of the execution environment. | ## ExecutionEnvironmentVersionShortResponse ``` { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" } ``` Execution environment version associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the execution environment version. | | label | string | true | | User-friendly name of the execution environment version. | ## GrantAccessControlWithId ``` { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## GrantAccessControlWithUsername ``` { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | Username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## ImageAugmentationListCreateResponse ``` { "properties": { "augmentationListId": { "description": "\n Id of the newly created augmentation list which can be used to:\n - retrieve the full augmentation list details;\n - retrieve augmentation previews;\n - to set the list id of a model in advanced tuning;\n - to delete or rename the list.\n ", "type": "string" } }, "required": [ "augmentationListId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | augmentationListId | string | true | | Id of the newly created augmentation list which can be used to: - retrieve the full augmentation list details; - retrieve augmentation previews; - to set the list id of a model in advanced tuning; - to delete or rename the list. | ## ImageAugmentationListPatchParam ``` { "properties": { "featureName": { "description": "The name of the image feature containing the data to be augmented", "type": "string" }, "inUse": { "description": "This parameter was deprecated. You can still pass a value, but it will be ignored. DataRobot will takes care of the value itself.", "type": "boolean", "x-versiondeprecated": "2.30.0" }, "initialList": { "description": "Whether this list will be used during autopilot to perform image augmentation", "type": "boolean" }, "name": { "description": "The name of the image augmentation list", "type": "string" }, "numberOfNewImages": { "description": "Number of new rows to add for each existing row", "minimum": 0, "type": "integer" }, "projectId": { "description": "This parameter was deprecated. You can still pass a value, but it will be ignored. If you want to move an augmentation list to another project you can create a new list in the other project and delete the list in this project.", "type": "string", "x-versiondeprecated": "2.30.0" }, "transformationProbability": { "description": "Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always.", "maximum": 1, "minimum": 0, "type": "number" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | false | | The name of the image feature containing the data to be augmented | | inUse | boolean | false | | This parameter was deprecated. You can still pass a value, but it will be ignored. DataRobot will takes care of the value itself. | | initialList | boolean | false | | Whether this list will be used during autopilot to perform image augmentation | | name | string | false | | The name of the image augmentation list | | numberOfNewImages | integer | false | minimum: 0 | Number of new rows to add for each existing row | | projectId | string | false | | This parameter was deprecated. You can still pass a value, but it will be ignored. If you want to move an augmentation list to another project you can create a new list in the other project and delete the list in this project. | | transformationProbability | number | false | maximum: 1minimum: 0 | Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always. | | transformations | [Transformation] | false | | List of Transformations to possibly apply to each image | ## ImageAugmentationListsCreate ``` { "properties": { "featureName": { "description": "The name of the image feature containing the data to be augmented", "type": "string" }, "inUse": { "default": false, "description": "This parameter was deprecated. You can still pass a value, but it will be ignored. DataRobot will takes care of the value itself.", "type": "boolean", "x-versiondeprecated": "2.30.0" }, "initialList": { "default": false, "description": "Whether this list will be used during autopilot to perform image augmentation", "type": "boolean" }, "name": { "description": "The name of the image augmentation list", "type": "string" }, "numberOfNewImages": { "description": "Number of new rows to add for each existing row", "minimum": 1, "type": "integer" }, "projectId": { "description": "The project containing the image data to be augmented", "type": "string" }, "samplesId": { "default": null, "description": "Image Augmentation list samples ID", "type": [ "string", "null" ] }, "transformationProbability": { "description": "Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always.", "maximum": 1, "minimum": 0, "type": "number" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name", "numberOfNewImages", "projectId", "transformationProbability", "transformations" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | false | | The name of the image feature containing the data to be augmented | | inUse | boolean | false | | This parameter was deprecated. You can still pass a value, but it will be ignored. DataRobot will takes care of the value itself. | | initialList | boolean | false | | Whether this list will be used during autopilot to perform image augmentation | | name | string | true | | The name of the image augmentation list | | numberOfNewImages | integer | true | minimum: 1 | Number of new rows to add for each existing row | | projectId | string | true | | The project containing the image data to be augmented | | samplesId | string,null | false | | Image Augmentation list samples ID | | transformationProbability | number | true | maximum: 1minimum: 0 | Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always. | | transformations | [Transformation] | true | | List of Transformations to possibly apply to each image | ## ImageAugmentationListsCreateSamples ``` { "properties": { "numberOfRows": { "description": "Number of images from the original dataset to be augmented", "minimum": 1, "type": "integer" } }, "required": [ "numberOfRows" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | numberOfRows | integer | true | minimum: 1 | Number of images from the original dataset to be augmented | ## ImageAugmentationListsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The content in the form of an augmentation lists", "items": { "properties": { "featureName": { "description": "The name of the image feature containing the data to be augmented", "type": "string" }, "id": { "description": "Image Augmentation list ID", "type": "string" }, "inUse": { "default": false, "description": "This is set to true when the Augmentation List has been used to train a model", "type": "boolean" }, "initialList": { "default": false, "description": "Whether this list will be used during autopilot to perform image augmentation", "type": "boolean" }, "name": { "description": "The name of the image augmentation list", "type": "string" }, "numberOfNewImages": { "description": "Number of new rows to add for each existing row", "minimum": 1, "type": "integer" }, "projectId": { "description": "The project containing the image data to be augmented", "type": "string" }, "samplesId": { "default": null, "description": "Image Augmentation list samples ID", "type": [ "string", "null" ] }, "transformationProbability": { "description": "Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always.", "maximum": 1, "minimum": 0, "type": "number" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "id", "name", "numberOfNewImages", "projectId", "transformationProbability", "transformations" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ImageAugmentationListsRetrieve] | true | | The content in the form of an augmentation lists | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ImageAugmentationListsRetrieve ``` { "properties": { "featureName": { "description": "The name of the image feature containing the data to be augmented", "type": "string" }, "id": { "description": "Image Augmentation list ID", "type": "string" }, "inUse": { "default": false, "description": "This is set to true when the Augmentation List has been used to train a model", "type": "boolean" }, "initialList": { "default": false, "description": "Whether this list will be used during autopilot to perform image augmentation", "type": "boolean" }, "name": { "description": "The name of the image augmentation list", "type": "string" }, "numberOfNewImages": { "description": "Number of new rows to add for each existing row", "minimum": 1, "type": "integer" }, "projectId": { "description": "The project containing the image data to be augmented", "type": "string" }, "samplesId": { "default": null, "description": "Image Augmentation list samples ID", "type": [ "string", "null" ] }, "transformationProbability": { "description": "Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always.", "maximum": 1, "minimum": 0, "type": "number" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "id", "name", "numberOfNewImages", "projectId", "transformationProbability", "transformations" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | false | | The name of the image feature containing the data to be augmented | | id | string | true | | Image Augmentation list ID | | inUse | boolean | false | | This is set to true when the Augmentation List has been used to train a model | | initialList | boolean | false | | Whether this list will be used during autopilot to perform image augmentation | | name | string | true | | The name of the image augmentation list | | numberOfNewImages | integer | true | minimum: 1 | Number of new rows to add for each existing row | | projectId | string | true | | The project containing the image data to be augmented | | samplesId | string,null | false | | Image Augmentation list samples ID | | transformationProbability | number | true | maximum: 1minimum: 0 | Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always. | | transformations | [Transformation] | true | | List of Transformations to possibly apply to each image | ## ImageAugmentationListsRetrieveSamplesResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Augmentation samples list", "items": { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The augmented image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "originalImageId": { "description": "Id of the original image that was transformed to produce the augmented image. If this is an original image (from the original training dataset) this value will be null. The id can be used to retrieve the original image file with: [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "originalImageId", "projectId", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ImageAugmentationRetrieveSamplesItem] | true | | Augmentation samples list | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ImageAugmentationOptionsResponse ``` { "properties": { "currentNumberOfNewImages": { "description": "Number of new images to be created for each original image during training", "type": "integer" }, "currentTransformationProbability": { "description": "Probability that each transformation included in an augmentation list will be applied to an image, if `affectedByTransformationProbability` for that transformation is True", "type": "number" }, "featureName": { "description": "Name of the image feature that the augmentation list is associated with", "type": "string" }, "id": { "description": "Augmentation list id", "type": "string" }, "maxNumberOfNewImages": { "description": "Maximum number of new images per original image to be generated during training", "type": "integer" }, "maxTransformationProbability": { "description": "Maximum probability that each enabled augmentation will be applied to an image", "maximum": 1, "type": "number" }, "minNumberOfNewImages": { "description": "Minimum number of new images per original image to be generated during training", "type": "integer" }, "minTransformationProbability": { "description": "Minimum probability that each enabled augmentation will be applied to an image", "minimum": 0, "type": "number" }, "name": { "description": "The name of the augmentation list", "type": "string" }, "projectId": { "description": "The id of the project containing the image data to be augmented", "type": "string" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "affectedByTransformationProbability": { "description": "If true, whenever this transformation is included in an augmentation list, this transformation will be applied to each image with probability set by the Transformation Probability. If false, whenever this transformation is included in an augmentation list, this transformation will be applied to each image with probability described in the Platform Documentation here: https://docs.datarobot.com/en/docs/modeling/special-workflows/visual-ai/tti-augment/ttia-lists.html#transformations", "type": "boolean" }, "enabledByDefault": { "description": "Determines if the parameter should be default selected in the UI", "type": "boolean" }, "name": { "description": "The name of the transformation", "type": "string" }, "params": { "description": "The list of parameters that control the transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" }, "translatedName": { "description": "Translated name of the parameter", "type": "string" }, "type": { "description": "The type of the parameter (int, float, etc...)", "type": "string" } }, "required": [ "name", "translatedName", "type" ], "type": "object" }, "type": "array" }, "translatedName": { "description": "Translated name of the transformation", "type": "string" } }, "required": [ "affectedByTransformationProbability", "enabledByDefault", "name", "params", "translatedName" ], "type": "object" }, "type": "array" } }, "required": [ "currentNumberOfNewImages", "currentTransformationProbability", "id", "maxNumberOfNewImages", "maxTransformationProbability", "minNumberOfNewImages", "minTransformationProbability", "name", "projectId", "transformations" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | currentNumberOfNewImages | integer | true | | Number of new images to be created for each original image during training | | currentTransformationProbability | number | true | | Probability that each transformation included in an augmentation list will be applied to an image, if affectedByTransformationProbability for that transformation is True | | featureName | string | false | | Name of the image feature that the augmentation list is associated with | | id | string | true | | Augmentation list id | | maxNumberOfNewImages | integer | true | | Maximum number of new images per original image to be generated during training | | maxTransformationProbability | number | true | maximum: 1 | Maximum probability that each enabled augmentation will be applied to an image | | minNumberOfNewImages | integer | true | | Minimum number of new images per original image to be generated during training | | minTransformationProbability | number | true | minimum: 0 | Minimum probability that each enabled augmentation will be applied to an image | | name | string | true | | The name of the augmentation list | | projectId | string | true | | The id of the project containing the image data to be augmented | | transformations | [ImageAugmentationOptionsTransformation] | true | | List of Transformations to possibly apply to each image | ## ImageAugmentationOptionsTransformation ``` { "properties": { "affectedByTransformationProbability": { "description": "If true, whenever this transformation is included in an augmentation list, this transformation will be applied to each image with probability set by the Transformation Probability. If false, whenever this transformation is included in an augmentation list, this transformation will be applied to each image with probability described in the Platform Documentation here: https://docs.datarobot.com/en/docs/modeling/special-workflows/visual-ai/tti-augment/ttia-lists.html#transformations", "type": "boolean" }, "enabledByDefault": { "description": "Determines if the parameter should be default selected in the UI", "type": "boolean" }, "name": { "description": "The name of the transformation", "type": "string" }, "params": { "description": "The list of parameters that control the transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" }, "translatedName": { "description": "Translated name of the parameter", "type": "string" }, "type": { "description": "The type of the parameter (int, float, etc...)", "type": "string" } }, "required": [ "name", "translatedName", "type" ], "type": "object" }, "type": "array" }, "translatedName": { "description": "Translated name of the transformation", "type": "string" } }, "required": [ "affectedByTransformationProbability", "enabledByDefault", "name", "params", "translatedName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | affectedByTransformationProbability | boolean | true | | If true, whenever this transformation is included in an augmentation list, this transformation will be applied to each image with probability set by the Transformation Probability. If false, whenever this transformation is included in an augmentation list, this transformation will be applied to each image with probability described in the Platform Documentation here: https://docs.datarobot.com/en/docs/modeling/special-workflows/visual-ai/tti-augment/ttia-lists.html#transformations | | enabledByDefault | boolean | true | | Determines if the parameter should be default selected in the UI | | name | string | true | | The name of the transformation | | params | [ImageAugmentationOptionsTransformationParam] | true | | The list of parameters that control the transformation | | translatedName | string | true | | Translated name of the transformation | ## ImageAugmentationOptionsTransformationParam ``` { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" }, "translatedName": { "description": "Translated name of the parameter", "type": "string" }, "type": { "description": "The type of the parameter (int, float, etc...)", "type": "string" } }, "required": [ "name", "translatedName", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | currentValue | any | false | | Current transformation value | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxValue | any | false | | Max transformation value | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | minValue | any | false | | Min transformation value | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Transformation param name | | translatedName | string | true | | Translated name of the parameter | | type | string | true | | The type of the parameter (int, float, etc...) | ## ImageAugmentationRetrieveSamplesItem ``` { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The augmented image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "originalImageId": { "description": "Id of the original image that was transformed to produce the augmented image. If this is an original image (from the original training dataset) this value will be null. The id can be used to retrieve the original image file with: [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "originalImageId", "projectId", "width" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | height | integer | true | | Height of the image in pixels | | imageId | string | true | | Id of the image. The augmented image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile] | | originalImageId | string,null | true | | Id of the original image that was transformed to produce the augmented image. If this is an original image (from the original training dataset) this value will be null. The id can be used to retrieve the original image file with: [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile] | | projectId | string | true | | The project ID. | | width | integer | true | | Width of the image in pixels | ## ImageAugmentationRetrieveSamplesResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Augmentation samples list", "items": { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The augmented image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "originalImageId": { "description": "Id of the original image that was transformed to produce the augmented image. If this is an original image (from the original training dataset) this value will be null. The id can be used to retrieve the original image file with: [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "originalImageId", "projectId", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ImageAugmentationRetrieveSamplesItem] | true | | Augmentation samples list | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ImageAugmentationSamplesRequest ``` { "properties": { "featureName": { "description": "The name of the image feature containing the data to be augmented", "type": "string" }, "id": { "description": "Augmentation list id", "type": "string" }, "inUse": { "default": false, "description": "This is set to true when the Augmentation List has been used to train a model", "type": "boolean" }, "initialList": { "default": false, "description": "Whether this list will be used during autopilot to perform image augmentation", "type": "boolean" }, "name": { "description": "The name of the image augmentation list", "type": "string" }, "numberOfNewImages": { "description": "Number of new rows to add for each existing row", "minimum": 1, "type": "integer" }, "numberOfRows": { "description": "Number of images from the original dataset to be augmented", "minimum": 1, "type": "integer" }, "projectId": { "description": "The project containing the image data to be augmented", "type": "string" }, "samplesId": { "default": null, "description": "Image Augmentation list samples ID", "type": [ "string", "null" ] }, "transformationProbability": { "description": "Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always.", "maximum": 1, "minimum": 0, "type": "number" }, "transformations": { "description": "List of Transformations to possibly apply to each image", "items": { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name", "numberOfNewImages", "numberOfRows", "projectId", "transformationProbability", "transformations" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | false | | The name of the image feature containing the data to be augmented | | id | string | false | | Augmentation list id | | inUse | boolean | false | | This is set to true when the Augmentation List has been used to train a model | | initialList | boolean | false | | Whether this list will be used during autopilot to perform image augmentation | | name | string | true | | The name of the image augmentation list | | numberOfNewImages | integer | true | minimum: 1 | Number of new rows to add for each existing row | | numberOfRows | integer | true | minimum: 1 | Number of images from the original dataset to be augmented | | projectId | string | true | | The project containing the image data to be augmented | | samplesId | string,null | false | | Image Augmentation list samples ID | | transformationProbability | number | true | maximum: 1minimum: 0 | Probability that each enabled transformation will be applied to an image. This does not apply to Horizontal or Vertical Flip, which are set to 50% always. | | transformations | [Transformation] | true | | List of Transformations to possibly apply to each image | ## ImageAugmentationSamplesResponse ``` { "properties": { "statusId": { "description": "Id of the newly created augmentation samples which can be used to retrieve the full set of sample images.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | Id of the newly created augmentation samples which can be used to retrieve the full set of sample images. | ## NodeDescription ``` { "properties": { "id": { "description": "The ID of the node.", "type": "string" }, "label": { "description": "The label of the node.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the node. | | label | string | true | | The label of the node. | ## ParamValuePair ``` { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | param | string | true | | The name of a field associated with the value. | | value | any | true | | Any value. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | ## PersistentLogsForModelWithCustomTasksRetrieveResponse ``` { "properties": { "data": { "description": "An archive (tar.gz) of the logs produced and persisted by a model.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | string(binary) | true | | An archive (tar.gz) of the logs produced and persisted by a model. | ## RequiredMetadataValue ``` { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fieldName | string | true | | The required field name. This value will be added as an environment variable when running custom models. | | value | string | true | maxLength: 100 | The value for the given field. | ## SharedRolesUpdate ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | Name of the action being taken. The only operation is 'updateRoles'. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | Array of GrantAccessControl objects., up to maximum 100 objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithUsername | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithId | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## SharingUpdateOrRemoveWithGrant ``` { "properties": { "data": { "description": "List of sharing roles to update.", "items": { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [UserRoleWithGrant] | true | maxItems: 100 | List of sharing roles to update. | ## TrainingHistoryEntry ``` { "properties": { "creationDate": { "description": "ISO-8601 timestamp of when the project the blueprint was trained on was created.", "type": "string" }, "lid": { "description": "The leaderboard ID the blueprint was trained on.", "type": "string" }, "pid": { "description": "The project ID the blueprint was trained on.", "type": "string" }, "projectModelsCount": { "description": "Number of models in the project the blueprint was trained on.", "type": "integer" }, "projectName": { "description": "The project name the blueprint was trained on.", "type": "string" }, "targetName": { "description": "The target name of the project the blueprint was trained on.", "type": "string" } }, "required": [ "creationDate", "lid", "pid", "projectModelsCount", "projectName", "targetName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string | true | | ISO-8601 timestamp of when the project the blueprint was trained on was created. | | lid | string | true | | The leaderboard ID the blueprint was trained on. | | pid | string | true | | The project ID the blueprint was trained on. | | projectModelsCount | integer | true | | Number of models in the project the blueprint was trained on. | | projectName | string | true | | The project name the blueprint was trained on. | | targetName | string | true | | The target name of the project the blueprint was trained on. | ## Transformation ``` { "properties": { "enabled": { "default": false, "description": "Whether this transformation is enabled by default", "type": "boolean" }, "name": { "description": "Transformation name", "type": "string" }, "params": { "description": "Config values for transformation", "items": { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | false | | Whether this transformation is enabled by default | | name | string | true | | Transformation name | | params | [TransformationParam] | false | | Config values for transformation | ## TransformationParam ``` { "properties": { "currentValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Current transformation value" }, "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Max transformation value" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "number" } ], "description": "Min transformation value" }, "name": { "description": "Transformation param name", "type": "string" } }, "required": [ "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | currentValue | any | false | | Current transformation value | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxValue | any | false | | Max transformation value | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | minValue | any | false | | Min transformation value | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Transformation param name | ## UserBlueprintAddToMenu ``` { "properties": { "deleteAfter": { "default": false, "description": "Whether to delete the user blueprint(s) after adding it (them) to the project menu.", "type": "boolean" }, "describeFailures": { "default": false, "description": "Whether to include extra fields to describe why any blueprints were not added to the chosen project.", "type": "boolean", "x-versionadded": "v2.27" }, "projectId": { "description": "The projectId of the project for the repository to add the specified user blueprints to.", "type": "string" }, "userBlueprintIds": { "description": "The IDs of the user blueprints to add to the specified project's repository.", "items": { "description": "An ID of one user blueprint to add to the specified project's repository.", "type": "string" }, "type": "array" } }, "required": [ "deleteAfter", "describeFailures", "projectId", "userBlueprintIds" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deleteAfter | boolean | true | | Whether to delete the user blueprint(s) after adding it (them) to the project menu. | | describeFailures | boolean | true | | Whether to include extra fields to describe why any blueprints were not added to the chosen project. | | projectId | string | true | | The projectId of the project for the repository to add the specified user blueprints to. | | userBlueprintIds | [string] | true | | The IDs of the user blueprints to add to the specified project's repository. | ## UserBlueprintAddToMenuResponse ``` { "properties": { "addedToMenu": { "description": "The list of userBlueprintId and blueprintId pairs representing blueprints successfully added to the project repository.", "items": { "properties": { "blueprintId": { "description": "The blueprintId representing the blueprint which was added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprintId added to the project repository.", "type": "string" } }, "required": [ "blueprintId", "userBlueprintId" ], "type": "object" }, "type": "array" }, "message": { "description": "A success message or a list of reasons why the list of blueprints could not be added to the project repository.", "type": "string", "x-versionadded": "2.27" }, "notAddedToMenu": { "description": "The list of userBlueprintId and error message representing blueprints which failed to be added to the project repository.", "items": { "properties": { "error": { "description": "The error message representing why the blueprint was not added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprint which was not added to the project repository.", "type": "string" } }, "required": [ "error", "userBlueprintId" ], "type": "object" }, "type": "array", "x-versionadded": "2.27" } }, "required": [ "addedToMenu" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addedToMenu | [UserBlueprintAddedToMenuItem] | true | | The list of userBlueprintId and blueprintId pairs representing blueprints successfully added to the project repository. | | message | string | false | | A success message or a list of reasons why the list of blueprints could not be added to the project repository. | | notAddedToMenu | [UserBlueprintFailedToAddToMenuItem] | false | | The list of userBlueprintId and error message representing blueprints which failed to be added to the project repository. | ## UserBlueprintAddedToMenuItem ``` { "properties": { "blueprintId": { "description": "The blueprintId representing the blueprint which was added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprintId added to the project repository.", "type": "string" } }, "required": [ "blueprintId", "userBlueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The blueprintId representing the blueprint which was added to the project repository. | | userBlueprintId | string | true | | The userBlueprintId associated with the blueprintId added to the project repository. | ## UserBlueprintBulkValidationRequest ``` { "properties": { "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": "string" }, "userBlueprintIds": { "description": "The IDs of the user blueprints to validate in bulk.", "items": { "description": "The ID of one user blueprint to validate.", "type": "string" }, "type": "array" } }, "required": [ "userBlueprintIds" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | projectId | string | false | | String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks. | | userBlueprintIds | [string] | true | | The IDs of the user blueprints to validate in bulk. | ## UserBlueprintCreate ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "blueprint", "decompressedBlueprint", "isInplaceEditor", "saveToCatalog" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprint | any | true | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [UserBlueprintsBlueprintTask] | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | decompressedBlueprint | boolean | true | | Whether to retrieve the blueprint in the decompressed format. | | description | string | false | | The description to give to the blueprint. | | getDynamicLabels | boolean | false | | Whether to add dynamic labels to a decompressed blueprint. | | isInplaceEditor | boolean | true | | Whether the request is sent from the in place user BP editor. | | modelType | string | false | maxLength: 1000 | The title to give to the blueprint. | | projectId | string,null | false | | String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks. | | saveToCatalog | boolean | true | | Whether to save the blueprint to the catalog. | ## UserBlueprintCreateFromBlueprintId ``` { "properties": { "blueprintId": { "description": "The ID associated with the blueprint to create the user blueprint from.", "type": "string" }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active.", "type": "string" }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "blueprintId", "decompressedBlueprint", "isInplaceEditor", "projectId", "saveToCatalog" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The ID associated with the blueprint to create the user blueprint from. | | decompressedBlueprint | boolean | true | | Whether to retrieve the blueprint in the decompressed format. | | description | string | false | | The description to give to the blueprint. | | getDynamicLabels | boolean | false | | Whether to add dynamic labels to a decompressed blueprint. | | isInplaceEditor | boolean | true | | Whether the request is sent from the in place user BP editor. | | modelType | string | false | maxLength: 1000 | The title to give to the blueprint. | | projectId | string | true | | String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. | | saveToCatalog | boolean | true | | Whether to save the blueprint to the catalog. | ## UserBlueprintCreateFromCustomTaskVersionIdPayload ``` { "properties": { "customTaskVersionId": { "description": "The ID of a custom task version.", "type": "string" }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description for the user blueprint that will be created from this CustomTaskVersion.", "type": "string" }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "customTaskVersionId", "decompressedBlueprint", "saveToCatalog" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customTaskVersionId | string | true | | The ID of a custom task version. | | decompressedBlueprint | boolean | true | | Whether to retrieve the blueprint in the decompressed format. | | description | string | false | | The description for the user blueprint that will be created from this CustomTaskVersion. | | saveToCatalog | boolean | true | | Whether to save the blueprint to the catalog. | ## UserBlueprintCreateFromUserBlueprintId ``` { "properties": { "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" }, "userBlueprintId": { "description": "The ID of the existing user blueprint to copy.", "type": "string" } }, "required": [ "decompressedBlueprint", "isInplaceEditor", "saveToCatalog", "userBlueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | decompressedBlueprint | boolean | true | | Whether to retrieve the blueprint in the decompressed format. | | description | string | false | | The description to give to the blueprint. | | getDynamicLabels | boolean | false | | Whether to add dynamic labels to a decompressed blueprint. | | isInplaceEditor | boolean | true | | Whether the request is sent from the in place user BP editor. | | modelType | string | false | maxLength: 1000 | The title to give to the blueprint. | | projectId | string,null | false | | String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks. | | saveToCatalog | boolean | true | | Whether to save the blueprint to the catalog. | | userBlueprintId | string | true | | The ID of the existing user blueprint to copy. | ## UserBlueprintFailedToAddToMenuItem ``` { "properties": { "error": { "description": "The error message representing why the blueprint was not added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprint which was not added to the project repository.", "type": "string" } }, "required": [ "error", "userBlueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | error | string | true | | The error message representing why the blueprint was not added to the project repository. | | userBlueprintId | string | true | | The userBlueprintId associated with the blueprint which was not added to the project repository. | ## UserBlueprintSharedRolesListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of SharedRoles objects.", "items": { "properties": { "id": { "description": "The ID of the recipient organization, group or user.", "type": "string" }, "name": { "description": "The name of the recipient organization, group or user.", "type": "string" }, "role": { "description": "The role of the org/group/user on this dataset or \"NO_ROLE\" for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UserBlueprintSharedRolesResponse] | true | | The list of SharedRoles objects. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UserBlueprintSharedRolesResponse ``` { "properties": { "id": { "description": "The ID of the recipient organization, group or user.", "type": "string" }, "name": { "description": "The name of the recipient organization, group or user.", "type": "string" }, "role": { "description": "The role of the org/group/user on this dataset or \"NO_ROLE\" for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient organization, group or user. | | name | string | true | | The name of the recipient organization, group or user. | | role | string | true | | The role of the org/group/user on this dataset or "NO_ROLE" for removing access when used with route to modify access. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [CONSUMER, EDITOR, OWNER] | | shareRecipientType | [user, group, organization] | ## UserBlueprintTask ``` { "properties": { "arguments": { "description": "The list of definitions of each argument which can be set for the task.", "items": { "properties": { "argument": { "description": "The definition of a task argument, used to specify a certain aspect of the task.", "oneOf": [ { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ] }, "key": { "description": "The unique key of the argument.", "type": "string" } }, "required": [ "argument", "key" ], "type": "object" }, "type": "array" }, "categories": { "description": "The categories which the task is in.", "items": { "type": "string" }, "type": "array" }, "colnamesAndTypes": { "description": "The column names, their types, and their hex representation, available in the specified project for the task.", "items": { "properties": { "colname": { "description": "The column name.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "type": { "description": "The data type of the column.", "type": "string" } }, "required": [ "colname", "hex", "type" ], "type": "object" }, "type": "array" }, "customTaskId": { "description": "The ID of the custom task, if it is a custom task.", "type": [ "string", "null" ] }, "customTaskVersions": { "description": "Metadata for all of the custom task's versions.", "items": { "properties": { "id": { "description": "Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task.", "type": "string" }, "label": { "description": "The name of the custom task version.", "type": "string" }, "versionMajor": { "description": "Major version of the custom task.", "type": "integer" }, "versionMinor": { "description": "Minor version of the custom task.", "type": "integer" } }, "required": [ "id", "label", "versionMajor", "versionMinor" ], "type": "object" }, "type": "array" }, "description": { "description": "The description of the task.", "type": "string" }, "icon": { "description": "The integer representing the ID to be displayed when the blueprint is trained.", "type": "integer" }, "isCommonTask": { "default": false, "description": "Whether the task is a common task.", "type": "boolean" }, "isCustomTask": { "description": "Whether the task is custom code written by the user.", "type": "boolean" }, "isVisibleInComposableMl": { "default": true, "description": "Whether the task is visible in the ComposableML menu.", "type": "boolean" }, "label": { "description": "The generic / default title or label for the task.", "type": "string" }, "outputMethods": { "description": "The methods which the task can use to produce output.", "items": { "type": "string" }, "type": "array" }, "supportsScoringCode": { "description": "Whether the task supports Scoring Code.", "type": "boolean" }, "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "timeSeriesOnly": { "description": "Whether the task can only be used with time series projects.", "type": "boolean" }, "url": { "description": "The URL of the documentation of the task.", "oneOf": [ { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, { "type": "string" } ] }, "validInputs": { "description": "The supported input types of the task.", "items": { "description": "A specific supported input type.", "type": "string" }, "type": "array" } }, "required": [ "arguments", "categories", "description", "icon", "label", "outputMethods", "supportsScoringCode", "taskCode", "timeSeriesOnly", "url", "validInputs" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | arguments | [UserBlueprintTaskArgument] | true | | The list of definitions of each argument which can be set for the task. | | categories | [string] | true | | The categories which the task is in. | | colnamesAndTypes | [ColnameAndType] | false | | The column names, their types, and their hex representation, available in the specified project for the task. | | customTaskId | string,null | false | | The ID of the custom task, if it is a custom task. | | customTaskVersions | [UserBlueprintTaskCustomTaskMetadataWithArguments] | false | | Metadata for all of the custom task's versions. | | description | string | true | | The description of the task. | | icon | integer | true | | The integer representing the ID to be displayed when the blueprint is trained. | | isCommonTask | boolean | false | | Whether the task is a common task. | | isCustomTask | boolean | false | | Whether the task is custom code written by the user. | | isVisibleInComposableMl | boolean | false | | Whether the task is visible in the ComposableML menu. | | label | string | true | | The generic / default title or label for the task. | | outputMethods | [string] | true | | The methods which the task can use to produce output. | | supportsScoringCode | boolean | true | | Whether the task supports Scoring Code. | | taskCode | string | true | | The unique code which represents the task to be constructed and executed | | timeSeriesOnly | boolean | true | | Whether the task can only be used with time series projects. | | url | any | true | | The URL of the documentation of the task. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validInputs | [string] | true | | The supported input types of the task. | ## UserBlueprintTaskArgument ``` { "properties": { "argument": { "description": "The definition of a task argument, used to specify a certain aspect of the task.", "oneOf": [ { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ] }, "key": { "description": "The unique key of the argument.", "type": "string" } }, "required": [ "argument", "key" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | argument | UserBlueprintTaskArgumentDefinition | true | | The definition of a task argument, used to specify a certain aspect of the task. | | key | string | true | | The unique key of the argument. | ## UserBlueprintTaskArgumentDefinition ``` { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | default | any | false | | The default value of the argument. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the argument. | | recommended | any | false | | The recommended value, based on frequently used values. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | tunable | boolean | false | | Whether the argument is tunable by the end-user. | | type | string | true | | The type of the argument (e.g., "int", "float", "select", "intgrid", "multi", etc.). | | values | any | true | | The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | ## UserBlueprintTaskCategoryItem ``` { "properties": { "name": { "description": "The name of the category.", "type": "string" }, "subcategories": { "description": "The list of the available task category items.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "taskCodes": { "description": "A list of task codes representing the tasks in this category.", "items": { "type": "string" }, "type": "array" } }, "required": [ "name", "taskCodes" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the category. | | subcategories | [AllowExtra] | false | | The list of the available task category items. | | taskCodes | [string] | true | | A list of task codes representing the tasks in this category. | ## UserBlueprintTaskCustomTaskMetadataWithArguments ``` { "properties": { "id": { "description": "Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task.", "type": "string" }, "label": { "description": "The name of the custom task version.", "type": "string" }, "versionMajor": { "description": "Major version of the custom task.", "type": "integer" }, "versionMinor": { "description": "Minor version of the custom task.", "type": "integer" } }, "required": [ "id", "label", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task. | | label | string | true | | The name of the custom task version. | | versionMajor | integer | true | | Major version of the custom task. | | versionMinor | integer | true | | Minor version of the custom task. | ## UserBlueprintTaskLookupEntry ``` { "properties": { "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "taskDefinition": { "description": "A definition of a task in terms of label, arguments, description, and other metadata.", "oneOf": [ { "properties": { "arguments": { "description": "The list of definitions of each argument which can be set for the task.", "items": { "properties": { "argument": { "description": "The definition of a task argument, used to specify a certain aspect of the task.", "oneOf": [ { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ] }, "key": { "description": "The unique key of the argument.", "type": "string" } }, "required": [ "argument", "key" ], "type": "object" }, "type": "array" }, "categories": { "description": "The categories which the task is in.", "items": { "type": "string" }, "type": "array" }, "colnamesAndTypes": { "description": "The column names, their types, and their hex representation, available in the specified project for the task.", "items": { "properties": { "colname": { "description": "The column name.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "type": { "description": "The data type of the column.", "type": "string" } }, "required": [ "colname", "hex", "type" ], "type": "object" }, "type": "array" }, "customTaskId": { "description": "The ID of the custom task, if it is a custom task.", "type": [ "string", "null" ] }, "customTaskVersions": { "description": "Metadata for all of the custom task's versions.", "items": { "properties": { "id": { "description": "Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task.", "type": "string" }, "label": { "description": "The name of the custom task version.", "type": "string" }, "versionMajor": { "description": "Major version of the custom task.", "type": "integer" }, "versionMinor": { "description": "Minor version of the custom task.", "type": "integer" } }, "required": [ "id", "label", "versionMajor", "versionMinor" ], "type": "object" }, "type": "array" }, "description": { "description": "The description of the task.", "type": "string" }, "icon": { "description": "The integer representing the ID to be displayed when the blueprint is trained.", "type": "integer" }, "isCommonTask": { "default": false, "description": "Whether the task is a common task.", "type": "boolean" }, "isCustomTask": { "description": "Whether the task is custom code written by the user.", "type": "boolean" }, "isVisibleInComposableMl": { "default": true, "description": "Whether the task is visible in the ComposableML menu.", "type": "boolean" }, "label": { "description": "The generic / default title or label for the task.", "type": "string" }, "outputMethods": { "description": "The methods which the task can use to produce output.", "items": { "type": "string" }, "type": "array" }, "supportsScoringCode": { "description": "Whether the task supports Scoring Code.", "type": "boolean" }, "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "timeSeriesOnly": { "description": "Whether the task can only be used with time series projects.", "type": "boolean" }, "url": { "description": "The URL of the documentation of the task.", "oneOf": [ { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, { "type": "string" } ] }, "validInputs": { "description": "The supported input types of the task.", "items": { "description": "A specific supported input type.", "type": "string" }, "type": "array" } }, "required": [ "arguments", "categories", "description", "icon", "label", "outputMethods", "supportsScoringCode", "taskCode", "timeSeriesOnly", "url", "validInputs" ], "type": "object" } ] } }, "required": [ "taskCode", "taskDefinition" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | taskCode | string | true | | The unique code which represents the task to be constructed and executed | | taskDefinition | UserBlueprintTask | true | | A definition of a task in terms of label, arguments, description, and other metadata. | ## UserBlueprintTaskParameterValidation ``` { "properties": { "outputMethod": { "description": "The method representing how the task will output data.", "enum": [ "P", "Pm", "S", "Sm", "T", "TS" ], "type": "string" }, "projectId": { "description": "The projectId representing the project where this user blueprint is edited.", "type": [ "string", "null" ] }, "taskCode": { "description": "The task code representing the task to validate parameter values.", "type": "string" }, "taskParameters": { "description": "The list of task parameters and proposed values to be validated.", "items": { "properties": { "newValue": { "description": "The proposed value for the task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "paramName": { "description": "The name of the task parameter to be validated.", "type": "string" } }, "required": [ "newValue", "paramName" ], "type": "object" }, "type": "array" } }, "required": [ "outputMethod", "taskCode", "taskParameters" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | outputMethod | string | true | | The method representing how the task will output data. | | projectId | string,null | false | | The projectId representing the project where this user blueprint is edited. | | taskCode | string | true | | The task code representing the task to validate parameter values. | | taskParameters | [UserBlueprintTaskParameterValidationRequestParamItem] | true | | The list of task parameters and proposed values to be validated. | ### Enumerated Values | Property | Value | | --- | --- | | outputMethod | [P, Pm, S, Sm, T, TS] | ## UserBlueprintTaskParameterValidationRequestParamItem ``` { "properties": { "newValue": { "description": "The proposed value for the task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "paramName": { "description": "The name of the task parameter to be validated.", "type": "string" } }, "required": [ "newValue", "paramName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | newValue | any | true | | The proposed value for the task parameter. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | paramName | string | true | | The name of the task parameter to be validated. | ## UserBlueprintTasksResponse ``` { "properties": { "categories": { "description": "The list of the available task categories, sub-categories, and tasks.", "items": { "properties": { "name": { "description": "The name of the category.", "type": "string" }, "subcategories": { "description": "The list of the available task category items.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "taskCodes": { "description": "A list of task codes representing the tasks in this category.", "items": { "type": "string" }, "type": "array" } }, "required": [ "name", "taskCodes" ], "type": "object" }, "type": "array" }, "tasks": { "description": "The list of task codes and their task definitions.", "items": { "properties": { "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "taskDefinition": { "description": "A definition of a task in terms of label, arguments, description, and other metadata.", "oneOf": [ { "properties": { "arguments": { "description": "The list of definitions of each argument which can be set for the task.", "items": { "properties": { "argument": { "description": "The definition of a task argument, used to specify a certain aspect of the task.", "oneOf": [ { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ] }, "key": { "description": "The unique key of the argument.", "type": "string" } }, "required": [ "argument", "key" ], "type": "object" }, "type": "array" }, "categories": { "description": "The categories which the task is in.", "items": { "type": "string" }, "type": "array" }, "colnamesAndTypes": { "description": "The column names, their types, and their hex representation, available in the specified project for the task.", "items": { "properties": { "colname": { "description": "The column name.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "type": { "description": "The data type of the column.", "type": "string" } }, "required": [ "colname", "hex", "type" ], "type": "object" }, "type": "array" }, "customTaskId": { "description": "The ID of the custom task, if it is a custom task.", "type": [ "string", "null" ] }, "customTaskVersions": { "description": "Metadata for all of the custom task's versions.", "items": { "properties": { "id": { "description": "Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task.", "type": "string" }, "label": { "description": "The name of the custom task version.", "type": "string" }, "versionMajor": { "description": "Major version of the custom task.", "type": "integer" }, "versionMinor": { "description": "Minor version of the custom task.", "type": "integer" } }, "required": [ "id", "label", "versionMajor", "versionMinor" ], "type": "object" }, "type": "array" }, "description": { "description": "The description of the task.", "type": "string" }, "icon": { "description": "The integer representing the ID to be displayed when the blueprint is trained.", "type": "integer" }, "isCommonTask": { "default": false, "description": "Whether the task is a common task.", "type": "boolean" }, "isCustomTask": { "description": "Whether the task is custom code written by the user.", "type": "boolean" }, "isVisibleInComposableMl": { "default": true, "description": "Whether the task is visible in the ComposableML menu.", "type": "boolean" }, "label": { "description": "The generic / default title or label for the task.", "type": "string" }, "outputMethods": { "description": "The methods which the task can use to produce output.", "items": { "type": "string" }, "type": "array" }, "supportsScoringCode": { "description": "Whether the task supports Scoring Code.", "type": "boolean" }, "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "timeSeriesOnly": { "description": "Whether the task can only be used with time series projects.", "type": "boolean" }, "url": { "description": "The URL of the documentation of the task.", "oneOf": [ { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, { "type": "string" } ] }, "validInputs": { "description": "The supported input types of the task.", "items": { "description": "A specific supported input type.", "type": "string" }, "type": "array" } }, "required": [ "arguments", "categories", "description", "icon", "label", "outputMethods", "supportsScoringCode", "taskCode", "timeSeriesOnly", "url", "validInputs" ], "type": "object" } ] } }, "required": [ "taskCode", "taskDefinition" ], "type": "object" }, "type": "array" } }, "required": [ "categories", "tasks" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [UserBlueprintTaskCategoryItem] | true | | The list of the available task categories, sub-categories, and tasks. | | tasks | [UserBlueprintTaskLookupEntry] | true | | The list of task codes and their task definitions. | ## UserBlueprintUpdate ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "decompressedBlueprint", "isInplaceEditor", "saveToCatalog" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprint | any | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [UserBlueprintsBlueprintTask] | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | decompressedBlueprint | boolean | true | | Whether to retrieve the blueprint in the decompressed format. | | description | string | false | | The description to give to the blueprint. | | getDynamicLabels | boolean | false | | Whether to add dynamic labels to a decompressed blueprint. | | isInplaceEditor | boolean | true | | Whether the request is sent from the in place user BP editor. | | modelType | string | false | maxLength: 1000 | The title to give to the blueprint. | | projectId | string,null | false | | String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks. | | saveToCatalog | boolean | true | | Whether to save the blueprint to the catalog. | ## UserBlueprintValidation ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] } }, "required": [ "blueprint", "isInplaceEditor" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprint | any | true | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [UserBlueprintsBlueprintTask] | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isInplaceEditor | boolean | true | | Whether the request is sent from the in place user BP editor. | | projectId | string,null | false | | String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks. | ## UserBlueprintsBlueprintTask ``` { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | taskData | UserBlueprintsBlueprintTaskData | true | | The data defining the task / vertex in the blueprint. | | taskId | string | true | | The identifier of a task / vertex in the blueprint. | ## UserBlueprintsBlueprintTaskData ``` { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputs | [string] | true | | The IDs or input data types which will be inputs to the task. | | outputMethod | string | true | | The method which the task will use to produce output. | | outputMethodParameters | [ParamValuePair] | true | | The parameters which further define how output will be produced. | | taskCode | string,null | true | | The unique code representing the python class which will be instantiated and executed. | | taskParameters | [ParamValuePair] | true | | The parameters which further define the behavior of the task. | | xTransformations | [ParamValuePair] | true | | The transformations to apply to the input data before fitting or predicting. | | yTransformations | [ParamValuePair] | true | | The transformations to apply to the input target before fitting or predicting. | ## UserBlueprintsBulkDelete ``` { "properties": { "userBlueprintIds": { "description": "The list of IDs of user blueprints to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ] } }, "required": [ "userBlueprintIds" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | userBlueprintIds | any | true | | The list of IDs of user blueprints to be deleted. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## UserBlueprintsBulkDeleteResponse ``` { "properties": { "failedToDelete": { "description": "The list of IDs of User Blueprints which failed to be deleted.", "items": { "description": "An ID of a User Blueprint which failed to be deleted.", "type": "string" }, "type": "array" }, "successfullyDeleted": { "description": "The list of IDs of User Blueprints successfully deleted.", "items": { "description": "An ID of a User Blueprint successfully deleted.", "type": "string" }, "type": "array" } }, "required": [ "failedToDelete", "successfullyDeleted" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | failedToDelete | [string] | true | | The list of IDs of User Blueprints which failed to be deleted. | | successfullyDeleted | [string] | true | | The list of IDs of User Blueprints successfully deleted. | ## UserBlueprintsBulkValidationResponse ``` { "properties": { "data": { "description": "A list of validation responses with their associated User Blueprint ID.", "items": { "properties": { "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "userBlueprintId", "vertexContext" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [UserBlueprintsBulkValidationResponseItem] | true | | A list of validation responses with their associated User Blueprint ID. | ## UserBlueprintsBulkValidationResponseItem ``` { "properties": { "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "userBlueprintId", "vertexContext" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | userBlueprintId | string | true | | The unique ID associated with the user blueprint. | | vertexContext | [VertexContextItem] | true | | Info about, warnings about, and errors with a specific vertex in the blueprint. | ## UserBlueprintsDetailedItem ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blender | boolean | true | | Whether the blueprint is a blender. | | blueprint | any | true | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [UserBlueprintsBlueprintTask] | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The deterministic ID of the blueprint, based on its content. | | bpData | BpData | true | | Additional blueprint metadata used to render the blueprint in the UI | | customTaskVersionMetadata | [array] | false | | An association of custom entity ids and task ids. | | decompressedFormat | boolean | true | | Whether the blueprint is in the decompressed format. | | diagram | string | true | | The diagram used by the UI to display the blueprint. | | features | [string] | true | | The list of the names of tasks used in the blueprint. | | featuresText | string | true | | The description of the blueprint via the names of tasks used. | | hexColumnNameLookup | [UserBlueprintsHexColumnNameLookupEntry] | false | | The lookup between hex values and data column names used in the blueprint. | | hiddenFromCatalog | boolean | false | | If true, the blueprint will not show up in the catalog. | | icons | [integer] | true | | The icon(s) associated with the blueprint. | | insights | string | true | | An indication of the insights generated by the blueprint. | | isTimeSeries | boolean | true | | Whether the blueprint contains time-series tasks. | | linkedToProjectId | boolean | false | | Whether the user blueprint is linked to a project. | | modelType | string | true | | The generated or provided title of the blueprint. | | projectId | string,null | false | | The ID of the project the blueprint was originally created with, if applicable. | | referenceModel | boolean | true | | Whether the blueprint is a reference model. | | shapSupport | boolean | true | | Whether the blueprint supports shapley additive explanations. | | supportedTargetTypes | [string] | true | | The list of supported targets of the current blueprint. | | supportsGpu | boolean | true | | Whether the blueprint supports execution on the GPU. | | supportsNewSeries | boolean | false | | Whether the blueprint supports new series. | | userBlueprintId | string | true | | The unique ID associated with the user blueprint. | | userId | string | true | | The ID of the user who owns the blueprint. | | vertexContext | [VertexContextItem] | true | | Info about, warnings about, and errors with a specific vertex in the blueprint. | ## UserBlueprintsHexColumnNameLookupEntry ``` { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | colname | string | true | | The name of the column. | | hex | string | true | | A safe hex representation of the column name. | | projectId | string | false | | The ID of the project from which the column name originates. | ## UserBlueprintsInputType ``` { "properties": { "name": { "description": "The human-readable name of an input type.", "type": "string" }, "type": { "description": "The unique identifier of an input type.", "type": "string" } }, "required": [ "name", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The human-readable name of an input type. | | type | string | true | | The unique identifier of an input type. | ## UserBlueprintsInputTypesResponse ``` { "properties": { "inputTypes": { "description": "The list of associated pairs of an input type and their human-readable names.", "items": { "properties": { "name": { "description": "The human-readable name of an input type.", "type": "string" }, "type": { "description": "The unique identifier of an input type.", "type": "string" } }, "required": [ "name", "type" ], "type": "object" }, "type": "array" } }, "required": [ "inputTypes" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputTypes | [UserBlueprintsInputType] | true | | The list of associated pairs of an input type and their human-readable names. | ## UserBlueprintsListItem ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" } }, "required": [ "blender", "blueprintId", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blender | boolean | true | | Whether the blueprint is a blender. | | blueprintId | string | true | | The deterministic ID of the blueprint, based on its content. | | customTaskVersionMetadata | [array] | false | | An association of custom entity ids and task ids. | | decompressedFormat | boolean | true | | Whether the blueprint is in the decompressed format. | | diagram | string | true | | The diagram used by the UI to display the blueprint. | | features | [string] | true | | The list of the names of tasks used in the blueprint. | | featuresText | string | true | | The description of the blueprint via the names of tasks used. | | hexColumnNameLookup | [UserBlueprintsHexColumnNameLookupEntry] | false | | The lookup between hex values and data column names used in the blueprint. | | hiddenFromCatalog | boolean | false | | If true, the blueprint will not show up in the catalog. | | icons | [integer] | true | | The icon(s) associated with the blueprint. | | insights | string | true | | An indication of the insights generated by the blueprint. | | isTimeSeries | boolean | true | | Whether the blueprint contains time-series tasks. | | linkedToProjectId | boolean | false | | Whether the user blueprint is linked to a project. | | modelType | string | true | | The generated or provided title of the blueprint. | | projectId | string,null | false | | The ID of the project the blueprint was originally created with, if applicable. | | referenceModel | boolean | true | | Whether the blueprint is a reference model. | | shapSupport | boolean | true | | Whether the blueprint supports shapley additive explanations. | | supportedTargetTypes | [string] | true | | The list of supported targets of the current blueprint. | | supportsGpu | boolean | true | | Whether the blueprint supports execution on the GPU. | | supportsNewSeries | boolean | false | | Whether the blueprint supports new series. | | userBlueprintId | string | true | | The unique ID associated with the user blueprint. | | userId | string | true | | The ID of the user who owns the blueprint. | ## UserBlueprintsListResponse ``` { "properties": { "count": { "description": "The number of records on this page.", "type": "integer" }, "data": { "description": "The list of user blueprints.", "items": { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" } }, "required": [ "blender", "blueprintId", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL to the next page, or `null` if there is no such page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL to the previous page, or `null` if there is no such page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of records.", "type": "integer" } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [UserBlueprintsListItem] | true | maxItems: 100 | The list of user blueprints. | | next | string,null | true | | The URL to the next page, or null if there is no such page. | | previous | string,null | true | | The URL to the previous page, or null if there is no such page. | | totalCount | integer | false | | The total number of records. | ## UserBlueprintsRetrieveResponse ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprintId", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blender | boolean | true | | Whether the blueprint is a blender. | | blueprint | any | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [UserBlueprintsBlueprintTask] | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The deterministic ID of the blueprint, based on its content. | | bpData | BpData | false | | Additional blueprint metadata used to render the blueprint in the UI | | customTaskVersionMetadata | [array] | false | | An association of custom entity ids and task ids. | | decompressedFormat | boolean | true | | Whether the blueprint is in the decompressed format. | | diagram | string | true | | The diagram used by the UI to display the blueprint. | | features | [string] | true | | The list of the names of tasks used in the blueprint. | | featuresText | string | true | | The description of the blueprint via the names of tasks used. | | hexColumnNameLookup | [UserBlueprintsHexColumnNameLookupEntry] | false | | The lookup between hex values and data column names used in the blueprint. | | hiddenFromCatalog | boolean | false | | If true, the blueprint will not show up in the catalog. | | icons | [integer] | true | | The icon(s) associated with the blueprint. | | insights | string | true | | An indication of the insights generated by the blueprint. | | isTimeSeries | boolean | true | | Whether the blueprint contains time-series tasks. | | linkedToProjectId | boolean | false | | Whether the user blueprint is linked to a project. | | modelType | string | true | | The generated or provided title of the blueprint. | | projectId | string,null | false | | The ID of the project the blueprint was originally created with, if applicable. | | referenceModel | boolean | true | | Whether the blueprint is a reference model. | | shapSupport | boolean | true | | Whether the blueprint supports shapley additive explanations. | | supportedTargetTypes | [string] | true | | The list of supported targets of the current blueprint. | | supportsGpu | boolean | true | | Whether the blueprint supports execution on the GPU. | | supportsNewSeries | boolean | false | | Whether the blueprint supports new series. | | userBlueprintId | string | true | | The unique ID associated with the user blueprint. | | userId | string | true | | The ID of the user who owns the blueprint. | | vertexContext | [VertexContextItem] | false | | Info about, warnings about, and errors with a specific vertex in the blueprint. | ## UserBlueprintsValidateTaskParameter ``` { "properties": { "message": { "description": "The description of the issue with the proposed task parameter value.", "type": "string" }, "paramName": { "description": "The name of the validated task parameter.", "type": "string" }, "value": { "description": "The invalid value proposed for the validated task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "message", "paramName", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | The description of the issue with the proposed task parameter value. | | paramName | string | true | | The name of the validated task parameter. | | value | any | true | | The invalid value proposed for the validated task parameter. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | ## UserBlueprintsValidateTaskParametersResponse ``` { "properties": { "errors": { "description": "A list of the task parameters, their proposed values, and messages describing why each is not valid.", "items": { "properties": { "message": { "description": "The description of the issue with the proposed task parameter value.", "type": "string" }, "paramName": { "description": "The name of the validated task parameter.", "type": "string" }, "value": { "description": "The invalid value proposed for the validated task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "message", "paramName", "value" ], "type": "object" }, "type": "array" } }, "required": [ "errors" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errors | [UserBlueprintsValidateTaskParameter] | true | | A list of the task parameters, their proposed values, and messages describing why each is not valid. | ## UserBlueprintsValidationResponse ``` { "properties": { "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "vertexContext" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vertexContext | [VertexContextItem] | true | | Info about, warnings about, and errors with a specific vertex in the blueprint. | ## UserRoleWithGrant ``` { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If role is NO_ROLE canShare is ignored. | | role | string,null | true | | The role to set on the entity. When it is None, the role of this user will be removedfrom this entity. | | username | string | true | | The username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | ## VertexContextItem ``` { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | information | VertexContextItemInfo | false | | A specification of requirements of the inputs and expectations of the output of the vertex. | | messages | VertexContextItemMessages | false | | Warnings about and errors with a specific vertex in the blueprint. | | taskId | string | true | | The ID associated with a specific vertex in the blueprint. | ## VertexContextItemInfo ``` { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputs | [string] | true | | A specification of requirements of the inputs of the vertex. | | outputs | [string] | true | | A specification of expectations of the output of the vertex. | ## VertexContextItemMessages ``` { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errors | [string] | false | | Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail. | | warnings | [string] | false | | Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly. | ## WorkspaceItemResponse ``` { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commitSha | string,null | false | | SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories). | | created | string | true | | ISO-8601 timestamp of when the file item was created. | | fileName | string | true | | The name of the file item. | | filePath | string | true | | The path of the file item. | | fileSource | string | true | | The source of the file item. | | id | string | true | | ID of the file item. | | ref | string,null | false | | Remote reference (branch, commit, tag). Branch "master", if not specified. | | repositoryFilePath | string,null | false | | Full path to the file in the remote repository. | | repositoryLocation | string,null | false | | URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name). | | repositoryName | string,null | false | | Name of the repository from which the file was pulled. | --- # Compliance documentation URL: https://docs.datarobot.com/en/docs/api/reference/public-api/compliance_documentation.html > DataRobot automates many critical compliance tasks associated with developing a model and, by doing so, decreases the time-to-deployment in highly regulated industries. You can generate, for each model, individualized documentation to provide comprehensive guidance on what constitutes effective model risk management. Then, you can download the report as an editable Microsoft Word document (.docx). The generated report includes the appropriate level of information and transparency necessitated by regulatory compliance demands. # Compliance documentation DataRobot automates many critical compliance tasks associated with developing a model and, by doing so, decreases the time-to-deployment in highly regulated industries. You can generate, for each model, individualized documentation to provide comprehensive guidance on what constitutes effective model risk management. Then, you can download the report as an editable Microsoft Word document (.docx). The generated report includes the appropriate level of information and transparency necessitated by regulatory compliance demands. ## List all available document types Operation path: `GET /api/v2/automatedDocumentOptions/` Authentication requirements: `BearerAuth` Check which document types and locales are available for generation with your account. ### Example responses > 200 Response ``` { "properties": { "data": { "description": "List of document types available for generation.", "items": { "properties": { "documentType": { "description": "Type of document available for generation.", "enum": [ "AUTOPILOT_SUMMARY", "MODEL_COMPLIANCE", "DEPLOYMENT_REPORT", "MODEL_COMPLIANCE_GEN_AI" ], "type": "string", "x-enum-versionadded": [ { "value": "DEPLOYMENT_REPORT", "x-versionadded": "v2.24" }, { "value": "MODEL_COMPLIANCE_GEN_AI", "x-versionadded": "v2.35" } ] }, "locale": { "description": "Locale available for the document generation.", "enum": [ "EN_US", "JA_JP" ], "type": "string" } }, "required": [ "documentType", "locale" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Available document types and locales retrieved. | AutomatedDocOptionsResponse | ## List all generated documents Operation path: `GET /api/v2/automatedDocuments/` Authentication requirements: `BearerAuth` Get information about all previously generated documents available for your account. The information includes document ID and type, ID of the entity it was generated for, time of creation, and other information. Example request to get a list of all documents: .. code-block:: text ``` GET https://app.datarobot.com/api/v2/automatedDocuments/ HTTP/1.1 Authorization: Bearer DnwzBUSTOtKBO5Sp1hoUByG4YgZwCCw4 ``` Query for specific documents. For example, request a list of documents generated for a specific model in `docx` and `html` formats: .. code-block:: text ``` GET https://app.datarobot.com/api/v2/automatedDocuments?entityId= 5ec4ea7e41054c158c5b002f&outputFormat=docx&outputFormat=html HTTP/1.1 Authorization: Bearer DnwzBUSTOtKBO5Sp1hoUByG4YgZwCCw4 ``` In response body, you will get a page with queried documents. This is an example response with one document returned: .. code-block:: json ``` { "totalCount": 1, "count": 1, "previous": null, "next": null, "data": [ { "id": "5ebdb5e911a5fb85edff2b3c", "documentType": "MODEL_COMPLIANCE", "entityId": "5ebbb5e7d9d7b96e3d48e3b5", "templateId": "5bd812e5f750edd392fa880f", "locale": "EN_US", "outputFormat": "DOCX", "createdAt": "2019-11-07T11:12:13.141516Z" } ] } ``` If there are no matching documents, you will get a page with an empty data array. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | Number of items to skip. Defaults to 0 if not provided. | | limit | query | integer | true | Number of items to return, defaults to 100 if not provided. | | documentType | query | any | false | Query for one or more document types. | | outputFormat | query | any | false | Query for one or more output formats. | | locale | query | any | false | Query generated documents by one or more locales. | | entityId | query | any | false | Query generated documents by one or more entity IDs. For Model Compliance docs, the entity ID is a model ID. For Autopilot Summary reports, query by project IDs. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of generated documents.", "items": { "properties": { "createdAt": { "description": "Timestamp for the document creation time.", "format": "date-time", "type": "string" }, "docTypeSpecificInfo": { "description": "Information that is specific for a certain document type.", "oneOf": [ { "properties": { "endDate": { "description": "End date of selected data in the document.", "format": "date-time", "type": "string" }, "modelName": { "description": "Name of the deployed model.", "type": "string" }, "startDate": { "description": "Start date of selected data in the document.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "startDate" ], "type": "object" }, { "type": "object" } ] }, "documentType": { "description": "Type of the generated document.", "enum": [ "AUTOPILOT_SUMMARY", "MODEL_COMPLIANCE", "DEPLOYMENT_REPORT", "MODEL_COMPLIANCE_GEN_AI" ], "type": "string", "x-enum-versionadded": [ { "value": "DEPLOYMENT_REPORT", "x-versionadded": "v2.24" }, { "value": "MODEL_COMPLIANCE_GEN_AI", "x-versionadded": "v2.35" } ] }, "entityId": { "description": "Unique identifier of the entity the document was generated for.", "type": "string" }, "id": { "description": "Unique identifier of the generated document.", "type": "string" }, "locale": { "description": "Locale of the generated document.", "enum": [ "EN_US", "JA_JP" ], "type": "string" }, "outputFormat": { "description": "File format of the generated document.", "enum": [ "docx", "html" ], "type": "string" }, "templateId": { "description": "Unique identifier of the template used for the document outline.", "type": "string" } }, "required": [ "createdAt", "documentType", "entityId", "id", "outputFormat", "templateId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of found documents retrieved. | AutomatedDocListResponse | ## Request generation of automated document Operation path: `POST /api/v2/automatedDocuments/` Authentication requirements: `BearerAuth` Request generation of an automated document that's available for your account. Below is an example request body to generate Model Compliance documentation: .. sourcecode:: json ``` { "documentType": "MODEL_COMPLIANCE", "entityId": "507f191e810c19729de860ea", "outputFormat": "docx" } ``` For Autopilot Summary, set a corresponding document type, `AUTOPILOT_SUMMARY`, and assign a needed project ID to the `entityId` value. After the request is sent, the jobs needed for document generation are queued. You can see the status of the generation by polling the URL in the `Location` headers. After the generation is complete, the status URL will automatically redirect you to the resource location to download the document. ### Body parameter ``` { "properties": { "documentType": { "description": "Type of the automated document you want to generate.", "enum": [ "AUTOPILOT_SUMMARY", "MODEL_COMPLIANCE", "DEPLOYMENT_REPORT", "MODEL_COMPLIANCE_GEN_AI" ], "type": "string", "x-enum-versionadded": [ { "value": "DEPLOYMENT_REPORT", "x-versionadded": "v2.24" }, { "value": "MODEL_COMPLIANCE_GEN_AI", "x-versionadded": "v2.35" } ] }, "documentTypeSpecificParameters": { "description": "Set parameters unique for a specific document type. Currently, only these document types can have document-specific parameters: ['DEPLOYMENT_REPORT']", "oneOf": [ { "properties": { "bucketSize": { "description": "You can regulate the size of the buckets in charts. One bucket reflects some duration period and you can set the exact duration with this parameter. Bucket size gets defaulted to either a month, a week, or a day, based on the time range of the report. We use `ISO 8601 `_ duration format to specify values.", "format": "duration", "type": "string" }, "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "description": "Provide a model ID to generate a report for a previously deployed model.", "type": "string" }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, { "type": "object" }, { "type": "null" } ], "x-versionadded": "v.2.24" }, "entityId": { "description": "ID of the entity to generate the document for. It can be a model ID, a project ID.", "type": "string" }, "locale": { "description": "Localization of the document. Defaults to EN_US.", "enum": [ "EN_US", "JA_JP" ], "type": "string" }, "outputFormat": { "description": "Format to generate the document in.", "enum": [ "docx", "html" ], "type": "string" }, "templateId": { "description": "Template ID to use for the document outline. Defaults to standard Datarobot template.", "type": "string" } }, "required": [ "documentType", "entityId", "outputFormat" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | AutomatedDocCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Document generation request accepted. | None | | 422 | Unprocessable Entity | Unable to process document generation request. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL to poll document generation status: [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] | ## Delete automated document by document ID Operation path: `DELETE /api/v2/automatedDocuments/{documentId}/` Authentication requirements: `BearerAuth` Delete a document using its ID. Example request: ``` .. code-block:: text DELETE https://app.datarobot.com/api/v2/automatedDocuments/5ec4ea7e41054c158c5b002f/ HTTP/1.1 Authorization: Bearer DnwzBUSTOtKBO5Sp1hoUByG4YgZwCCw4. ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | documentId | path | string | true | Unique identifier of the generated document. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Document successfully deleted. | None | | 404 | Not Found | Provided document ID not found. | None | ## Download generated document by document ID Operation path: `GET /api/v2/automatedDocuments/{documentId}/` Authentication requirements: `BearerAuth` Download a generated Automated Documentation file. .. code-block:: text ``` GET https://app.datarobot.com/api/v2/automatedDocuments/5ec4ea7e41054c158c5b002f/ HTTP/1.1 Authorization: Bearer DnwzBUSTOtKBO5Sp1hoUByG4YgZwCCw4 ``` In response, you will get a file containing the generated documentation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | documentId | path | string | true | Unique identifier of the generated document. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Download request succeeded. | None | | 404 | Not Found | Documentation record not found. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Autogenerated filename ("attachment;filename=report_name.outputFormat"). | | 200 | Content-Type | string | | MIME type corresponding to document file format | ## List compliance documentation templates Operation path: `GET /api/v2/complianceDocTemplates/` Authentication requirements: `BearerAuth` List user's custom-built compliance documentation templates. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | namePart | query | string | false | When present, only return templates with names that contain the given substring. | | orderBy | query | string | false | Sort order to apply to the dataset list. Prefix the attribute name with a dash to sort in descending order (e.g., orderBy='-id'). | | labels | query | string,null | false | Name of labels to filter by. | | projectType | query | string,null | false | Type of project templates to search for. If not specified, returns all project templates types. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [id, -id] | | projectType | [autoMl, textGeneration, timeSeries] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of templates.", "items": { "properties": { "creatorId": { "description": "The ID of the user who created the template", "type": "string" }, "creatorUsername": { "description": "The username of the user who created the template", "type": "string" }, "dateModified": { "description": "Last date/time of template modification.", "format": "date-time", "type": [ "string", "null" ] }, "description": { "description": "An overview of the template", "type": [ "string", "null" ] }, "id": { "description": "The ID of the template accessible by the user", "type": "string" }, "instructions": { "description": "Currently always returns ``null``. Maintained as a placeholder for future functionality.", "type": [ "string", "null" ] }, "labels": { "description": "User-added filtering labels for the template", "items": { "type": "string" }, "type": "array" }, "name": { "description": "The name of the template", "type": "string" }, "orgId": { "description": "The ID of the organization the user who created the template belongs to", "type": "string" }, "permissions": { "description": "The level of permissions for the user viewing this template.", "properties": { "CAN_DELETE_TEMPLATE": { "description": "Whether the current user can delete this template", "type": "boolean" }, "CAN_EDIT_TEMPLATE": { "description": "Whether the current user can edit this template", "type": "boolean" }, "CAN_SHARE": { "description": "Whether the current user can share this template", "type": "boolean" }, "CAN_USE_TEMPLATE": { "description": "Whether the current user can generate documents with this template", "type": "boolean" }, "CAN_VIEW": { "description": "Whether the current user can view this template", "type": "boolean" } }, "required": [ "CAN_DELETE_TEMPLATE", "CAN_EDIT_TEMPLATE", "CAN_SHARE", "CAN_USE_TEMPLATE", "CAN_VIEW" ], "type": "object" }, "projectType": { "description": "Type of project template.", "enum": [ "autoMl", "textGeneration", "timeSeries" ], "type": [ "string", "null" ] }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. ", "items": { "oneOf": [ { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ] }, "type": "array" } }, "required": [ "creatorId", "creatorUsername", "dateModified", "description", "id", "instructions", "labels", "name", "orgId", "permissions", "projectType", "sections" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully returned list of templates | ComplianceDocTemplateListResponse | ## Create a new compliance documentation template Operation path: `POST /api/v2/complianceDocTemplates/` Authentication requirements: `BearerAuth` Create a new compliance documentation template. One can retrieve the default DataRobot template via `GET /api/v2/complianceDocTemplates/default/` endpoint. ### Body parameter ``` { "properties": { "labels": { "description": "Names of the labels to assign to the template.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.22" }, "name": { "description": "Name of the new template. Must be unique among templates created by the user.", "type": "string" }, "projectType": { "description": "Type of project template.", "enum": [ "autoMl", "textGeneration", "timeSeries" ], "type": "string", "x-versionadded": "v2.22" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. ", "items": { "oneOf": [ { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ] }, "type": "array" } }, "required": [ "name", "sections" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComplianceDocTemplateCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "dateModified": { "description": "Timestamp of the created template", "format": "date-time", "type": "string" }, "id": { "description": "Created template Id", "type": "string" } }, "required": [ "dateModified", "id" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Model compliance documentation template created successfully | ComplianceDocTemplateCreateResponse | | 422 | Unprocessable Entity | Template cannot be created, e.g., invalid sections or name already exists | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 201 | Location | string | url | URL location of the newly created template. | ## Retrieve the default documentation template Operation path: `GET /api/v2/complianceDocTemplates/default/` Authentication requirements: `BearerAuth` Retrieve the default documentation template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | type | query | string | true | Specifies the type of the default template to retrieve. The normal template is applicable for all AutoML projects that are not time series. The timeSeries template is only applicable to time series projects.The textGeneration template is only applicable to text generation registry models. | ### Enumerated Values | Parameter | Value | | --- | --- | | type | [normal, textGeneration, timeSeries] | ### Example responses > 200 Response ``` { "properties": { "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. ", "items": { "oneOf": [ { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ] }, "type": "array" } }, "required": [ "sections" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully retrieved default template | ComplianceDocTemplateDefaultRetrieveResponse | ## Delete a compliance documentation template by template ID Operation path: `DELETE /api/v2/complianceDocTemplates/{templateId}/` Authentication requirements: `BearerAuth` Delete a compliance documentation template. Documentation previously generated using this template will remain unchanged. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | templateId | path | string | true | The Id of a model compliance document template accessible by the user | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Template deleted successfully | None | | 403 | Forbidden | Insufficient permissions to delete template | None | | 404 | Not Found | Template not found | None | ## Retrieve a documentation template by template ID Operation path: `GET /api/v2/complianceDocTemplates/{templateId}/` Authentication requirements: `BearerAuth` Retrieve a JSON representation of a custom Compliance Documentation template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | templateId | path | string | true | The Id of a model compliance document template accessible by the user | ### Example responses > 200 Response ``` { "properties": { "creatorId": { "description": "The ID of the user who created the template", "type": "string" }, "creatorUsername": { "description": "The username of the user who created the template", "type": "string" }, "dateModified": { "description": "Last date/time of template modification.", "format": "date-time", "type": [ "string", "null" ] }, "description": { "description": "An overview of the template", "type": [ "string", "null" ] }, "id": { "description": "The ID of the template accessible by the user", "type": "string" }, "instructions": { "description": "Currently always returns ``null``. Maintained as a placeholder for future functionality.", "type": [ "string", "null" ] }, "labels": { "description": "User-added filtering labels for the template", "items": { "type": "string" }, "type": "array" }, "name": { "description": "The name of the template", "type": "string" }, "orgId": { "description": "The ID of the organization the user who created the template belongs to", "type": "string" }, "permissions": { "description": "The level of permissions for the user viewing this template.", "properties": { "CAN_DELETE_TEMPLATE": { "description": "Whether the current user can delete this template", "type": "boolean" }, "CAN_EDIT_TEMPLATE": { "description": "Whether the current user can edit this template", "type": "boolean" }, "CAN_SHARE": { "description": "Whether the current user can share this template", "type": "boolean" }, "CAN_USE_TEMPLATE": { "description": "Whether the current user can generate documents with this template", "type": "boolean" }, "CAN_VIEW": { "description": "Whether the current user can view this template", "type": "boolean" } }, "required": [ "CAN_DELETE_TEMPLATE", "CAN_EDIT_TEMPLATE", "CAN_SHARE", "CAN_USE_TEMPLATE", "CAN_VIEW" ], "type": "object" }, "projectType": { "description": "Type of project template.", "enum": [ "autoMl", "textGeneration", "timeSeries" ], "type": [ "string", "null" ] }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. ", "items": { "oneOf": [ { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ] }, "type": "array" } }, "required": [ "creatorId", "creatorUsername", "dateModified", "description", "id", "instructions", "labels", "name", "orgId", "permissions", "projectType", "sections" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully retrieved template | TemplateResponse | | 404 | Not Found | No matching template owned by this user was found | None | ## Update an existing model compliance documentation template by template ID Operation path: `PATCH /api/v2/complianceDocTemplates/{templateId}/` Authentication requirements: `BearerAuth` Update an existing model compliance documentation template with the given `templateId`. The template must be accessible by the user. If the `templateId` is not found for the user, the update cannot be performed. For a description of the template `sections` object options, see the sample `sections` on the schema below. ### Body parameter ``` { "properties": { "description": { "description": "New description for the template.", "type": "string" }, "labels": { "description": "Names of the labels to assign to the template.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.22" }, "name": { "description": "New name for the template. Must be unique among templates created by the user.", "type": "string" }, "projectType": { "description": "Type of project template.", "enum": [ "autoMl", "textGeneration", "timeSeries" ], "type": "string", "x-versionadded": "v2.22" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. ", "items": { "oneOf": [ { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ] }, "type": "array" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | templateId | path | string | true | The Id of a model compliance document template accessible by the user | | body | body | ComplianceDocTemplateUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Model compliance documentation template updated successfully | None | | 403 | Forbidden | Not enough permissions to edit template | None | | 404 | Not Found | Template not found | None | ## Get the template's access control list by template ID Operation path: `GET /api/v2/complianceDocTemplates/{templateId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations who have access to this template and their roles on the template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | templateId | path | string | true | The template identifier | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The template's access control list. | SharingListV2Response | | 404 | Not Found | Either the template does not exist or the user does not have permissions to view the template. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## Update the template's access controls by template ID Operation path: `PATCH /api/v2/complianceDocTemplates/{templateId}/sharedRoles/` Authentication requirements: `BearerAuth` Set roles for users on this template. ### Body parameter ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | templateId | path | string | true | The template identifier | | body | body | SharedRolesUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | None | | 409 | Conflict | The request would leave the template without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid | None | ## Check if compliance documentation pre-processing is initialized by entity ID Operation path: `GET /api/v2/modelComplianceDocsInitializations/{entityId}/` Authentication requirements: `BearerAuth` Check if compliance documentation pre-processing is initialized for the current model. This is only required for custom models. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityId | path | string | true | The ID of the model or model package the document corresponds to. | ### Example responses > 200 Response ``` { "properties": { "initialized": { "description": "Whether compliance documentation pre-preprocessing is initialized for the model", "type": "boolean" }, "status": { "description": "Compliance documentation pre-processing initialization status", "enum": [ "initialized", "initializationInProgress", "notRequested", "noTrainingData", "trainingDataAssignmentInProgress", "initializationError" ], "type": "string" } }, "required": [ "initialized", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve information about the status of the compliance documentation pre-preprocessing initialization. | ModelComplianceDocsInitializationsResponse | | 404 | Not Found | Model not found. | None | ## Initialize compliance documentation pre-processing by entity ID Operation path: `POST /api/v2/modelComplianceDocsInitializations/{entityId}/` Authentication requirements: `BearerAuth` Initialize compliance documentation pre-processing for the current model. This route must be called before generating documentation for a custom model. ### Body parameter ``` { "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityId | path | string | true | The ID of the model or model package the document corresponds to. | | body | body | Empty | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job to initialize compliance documentation pre-processing successfully started. | None | | 404 | Not Found | Model not found. | None | | 422 | Unprocessable Entity | Cannot prepare model for compliance document generation. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL to poll for getting a status of the job: [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] | # Schemas ## AccessControlV2 ``` { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the recipient. | | name | string | true | | The name of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | The type of the recipient. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## AutomatedDocCreate ``` { "properties": { "documentType": { "description": "Type of the automated document you want to generate.", "enum": [ "AUTOPILOT_SUMMARY", "MODEL_COMPLIANCE", "DEPLOYMENT_REPORT", "MODEL_COMPLIANCE_GEN_AI" ], "type": "string", "x-enum-versionadded": [ { "value": "DEPLOYMENT_REPORT", "x-versionadded": "v2.24" }, { "value": "MODEL_COMPLIANCE_GEN_AI", "x-versionadded": "v2.35" } ] }, "documentTypeSpecificParameters": { "description": "Set parameters unique for a specific document type. Currently, only these document types can have document-specific parameters: ['DEPLOYMENT_REPORT']", "oneOf": [ { "properties": { "bucketSize": { "description": "You can regulate the size of the buckets in charts. One bucket reflects some duration period and you can set the exact duration with this parameter. Bucket size gets defaulted to either a month, a week, or a day, based on the time range of the report. We use `ISO 8601 `_ duration format to specify values.", "format": "duration", "type": "string" }, "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "description": "Provide a model ID to generate a report for a previously deployed model.", "type": "string" }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, { "type": "object" }, { "type": "null" } ], "x-versionadded": "v.2.24" }, "entityId": { "description": "ID of the entity to generate the document for. It can be a model ID, a project ID.", "type": "string" }, "locale": { "description": "Localization of the document. Defaults to EN_US.", "enum": [ "EN_US", "JA_JP" ], "type": "string" }, "outputFormat": { "description": "Format to generate the document in.", "enum": [ "docx", "html" ], "type": "string" }, "templateId": { "description": "Template ID to use for the document outline. Defaults to standard Datarobot template.", "type": "string" } }, "required": [ "documentType", "entityId", "outputFormat" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | documentType | string | true | | Type of the automated document you want to generate. | | documentTypeSpecificParameters | any | false | | Set parameters unique for a specific document type. Currently, only these document types can have document-specific parameters: ['DEPLOYMENT_REPORT'] | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AutomatedDocCreateDeploymentReport | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AutomatedDocCreateModelComplianceDocs | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityId | string | true | | ID of the entity to generate the document for. It can be a model ID, a project ID. | | locale | string | false | | Localization of the document. Defaults to EN_US. | | outputFormat | string | true | | Format to generate the document in. | | templateId | string | false | | Template ID to use for the document outline. Defaults to standard Datarobot template. | ### Enumerated Values | Property | Value | | --- | --- | | documentType | [AUTOPILOT_SUMMARY, MODEL_COMPLIANCE, DEPLOYMENT_REPORT, MODEL_COMPLIANCE_GEN_AI] | | locale | [EN_US, JA_JP] | | outputFormat | [docx, html] | ## AutomatedDocCreateDeploymentReport ``` { "properties": { "bucketSize": { "description": "You can regulate the size of the buckets in charts. One bucket reflects some duration period and you can set the exact duration with this parameter. Bucket size gets defaulted to either a month, a week, or a day, based on the time range of the report. We use `ISO 8601 `_ duration format to specify values.", "format": "duration", "type": "string" }, "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "description": "Provide a model ID to generate a report for a previously deployed model.", "type": "string" }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bucketSize | string(duration) | false | | You can regulate the size of the buckets in charts. One bucket reflects some duration period and you can set the exact duration with this parameter. Bucket size gets defaulted to either a month, a week, or a day, based on the time range of the report. We use ISO 8601 _ duration format to specify values. | | end | string,null(date-time) | false | | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | modelId | string | false | | Provide a model ID to generate a report for a previously deployed model. | | start | string,null(date-time) | false | | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | ## AutomatedDocCreateModelComplianceDocs ``` { "type": "object" } ``` ### Properties None ## AutomatedDocItem ``` { "properties": { "createdAt": { "description": "Timestamp for the document creation time.", "format": "date-time", "type": "string" }, "docTypeSpecificInfo": { "description": "Information that is specific for a certain document type.", "oneOf": [ { "properties": { "endDate": { "description": "End date of selected data in the document.", "format": "date-time", "type": "string" }, "modelName": { "description": "Name of the deployed model.", "type": "string" }, "startDate": { "description": "Start date of selected data in the document.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "startDate" ], "type": "object" }, { "type": "object" } ] }, "documentType": { "description": "Type of the generated document.", "enum": [ "AUTOPILOT_SUMMARY", "MODEL_COMPLIANCE", "DEPLOYMENT_REPORT", "MODEL_COMPLIANCE_GEN_AI" ], "type": "string", "x-enum-versionadded": [ { "value": "DEPLOYMENT_REPORT", "x-versionadded": "v2.24" }, { "value": "MODEL_COMPLIANCE_GEN_AI", "x-versionadded": "v2.35" } ] }, "entityId": { "description": "Unique identifier of the entity the document was generated for.", "type": "string" }, "id": { "description": "Unique identifier of the generated document.", "type": "string" }, "locale": { "description": "Locale of the generated document.", "enum": [ "EN_US", "JA_JP" ], "type": "string" }, "outputFormat": { "description": "File format of the generated document.", "enum": [ "docx", "html" ], "type": "string" }, "templateId": { "description": "Unique identifier of the template used for the document outline.", "type": "string" } }, "required": [ "createdAt", "documentType", "entityId", "id", "outputFormat", "templateId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | Timestamp for the document creation time. | | docTypeSpecificInfo | any | false | | Information that is specific for a certain document type. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DocTypeSpecificInfoDeploymentReport | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DocTypeSpecificInfoModelCompliance | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | documentType | string | true | | Type of the generated document. | | entityId | string | true | | Unique identifier of the entity the document was generated for. | | id | string | true | | Unique identifier of the generated document. | | locale | string | false | | Locale of the generated document. | | outputFormat | string | true | | File format of the generated document. | | templateId | string | true | | Unique identifier of the template used for the document outline. | ### Enumerated Values | Property | Value | | --- | --- | | documentType | [AUTOPILOT_SUMMARY, MODEL_COMPLIANCE, DEPLOYMENT_REPORT, MODEL_COMPLIANCE_GEN_AI] | | locale | [EN_US, JA_JP] | | outputFormat | [docx, html] | ## AutomatedDocListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of generated documents.", "items": { "properties": { "createdAt": { "description": "Timestamp for the document creation time.", "format": "date-time", "type": "string" }, "docTypeSpecificInfo": { "description": "Information that is specific for a certain document type.", "oneOf": [ { "properties": { "endDate": { "description": "End date of selected data in the document.", "format": "date-time", "type": "string" }, "modelName": { "description": "Name of the deployed model.", "type": "string" }, "startDate": { "description": "Start date of selected data in the document.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "startDate" ], "type": "object" }, { "type": "object" } ] }, "documentType": { "description": "Type of the generated document.", "enum": [ "AUTOPILOT_SUMMARY", "MODEL_COMPLIANCE", "DEPLOYMENT_REPORT", "MODEL_COMPLIANCE_GEN_AI" ], "type": "string", "x-enum-versionadded": [ { "value": "DEPLOYMENT_REPORT", "x-versionadded": "v2.24" }, { "value": "MODEL_COMPLIANCE_GEN_AI", "x-versionadded": "v2.35" } ] }, "entityId": { "description": "Unique identifier of the entity the document was generated for.", "type": "string" }, "id": { "description": "Unique identifier of the generated document.", "type": "string" }, "locale": { "description": "Locale of the generated document.", "enum": [ "EN_US", "JA_JP" ], "type": "string" }, "outputFormat": { "description": "File format of the generated document.", "enum": [ "docx", "html" ], "type": "string" }, "templateId": { "description": "Unique identifier of the template used for the document outline.", "type": "string" } }, "required": [ "createdAt", "documentType", "entityId", "id", "outputFormat", "templateId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [AutomatedDocItem] | true | | List of generated documents. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## AutomatedDocOptions ``` { "properties": { "documentType": { "description": "Type of document available for generation.", "enum": [ "AUTOPILOT_SUMMARY", "MODEL_COMPLIANCE", "DEPLOYMENT_REPORT", "MODEL_COMPLIANCE_GEN_AI" ], "type": "string", "x-enum-versionadded": [ { "value": "DEPLOYMENT_REPORT", "x-versionadded": "v2.24" }, { "value": "MODEL_COMPLIANCE_GEN_AI", "x-versionadded": "v2.35" } ] }, "locale": { "description": "Locale available for the document generation.", "enum": [ "EN_US", "JA_JP" ], "type": "string" } }, "required": [ "documentType", "locale" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | documentType | string | true | | Type of document available for generation. | | locale | string | true | | Locale available for the document generation. | ### Enumerated Values | Property | Value | | --- | --- | | documentType | [AUTOPILOT_SUMMARY, MODEL_COMPLIANCE, DEPLOYMENT_REPORT, MODEL_COMPLIANCE_GEN_AI] | | locale | [EN_US, JA_JP] | ## AutomatedDocOptionsResponse ``` { "properties": { "data": { "description": "List of document types available for generation.", "items": { "properties": { "documentType": { "description": "Type of document available for generation.", "enum": [ "AUTOPILOT_SUMMARY", "MODEL_COMPLIANCE", "DEPLOYMENT_REPORT", "MODEL_COMPLIANCE_GEN_AI" ], "type": "string", "x-enum-versionadded": [ { "value": "DEPLOYMENT_REPORT", "x-versionadded": "v2.24" }, { "value": "MODEL_COMPLIANCE_GEN_AI", "x-versionadded": "v2.35" } ] }, "locale": { "description": "Locale available for the document generation.", "enum": [ "EN_US", "JA_JP" ], "type": "string" } }, "required": [ "documentType", "locale" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [AutomatedDocOptions] | true | | List of document types available for generation. | ## ComplianceDocTemplateCreate ``` { "properties": { "labels": { "description": "Names of the labels to assign to the template.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.22" }, "name": { "description": "Name of the new template. Must be unique among templates created by the user.", "type": "string" }, "projectType": { "description": "Type of project template.", "enum": [ "autoMl", "textGeneration", "timeSeries" ], "type": "string", "x-versionadded": "v2.22" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. ", "items": { "oneOf": [ { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ] }, "type": "array" } }, "required": [ "name", "sections" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | labels | [string] | false | | Names of the labels to assign to the template. | | name | string | true | | Name of the new template. Must be unique among templates created by the user. | | projectType | string | false | | Type of project template. | | sections | [oneOf] | true | | List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionDataRobot | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionUser | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionCustom | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionTableOfContents | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | projectType | [autoMl, textGeneration, timeSeries] | ## ComplianceDocTemplateCreateResponse ``` { "properties": { "dateModified": { "description": "Timestamp of the created template", "format": "date-time", "type": "string" }, "id": { "description": "Created template Id", "type": "string" } }, "required": [ "dateModified", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dateModified | string(date-time) | true | | Timestamp of the created template | | id | string | true | | Created template Id | ## ComplianceDocTemplateDefaultRetrieveResponse ``` { "properties": { "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. ", "items": { "oneOf": [ { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ] }, "type": "array" } }, "required": [ "sections" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sections | [oneOf] | true | | List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionDataRobot | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionUser | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionCustom | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionTableOfContents | false | | none | ## ComplianceDocTemplateListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of templates.", "items": { "properties": { "creatorId": { "description": "The ID of the user who created the template", "type": "string" }, "creatorUsername": { "description": "The username of the user who created the template", "type": "string" }, "dateModified": { "description": "Last date/time of template modification.", "format": "date-time", "type": [ "string", "null" ] }, "description": { "description": "An overview of the template", "type": [ "string", "null" ] }, "id": { "description": "The ID of the template accessible by the user", "type": "string" }, "instructions": { "description": "Currently always returns ``null``. Maintained as a placeholder for future functionality.", "type": [ "string", "null" ] }, "labels": { "description": "User-added filtering labels for the template", "items": { "type": "string" }, "type": "array" }, "name": { "description": "The name of the template", "type": "string" }, "orgId": { "description": "The ID of the organization the user who created the template belongs to", "type": "string" }, "permissions": { "description": "The level of permissions for the user viewing this template.", "properties": { "CAN_DELETE_TEMPLATE": { "description": "Whether the current user can delete this template", "type": "boolean" }, "CAN_EDIT_TEMPLATE": { "description": "Whether the current user can edit this template", "type": "boolean" }, "CAN_SHARE": { "description": "Whether the current user can share this template", "type": "boolean" }, "CAN_USE_TEMPLATE": { "description": "Whether the current user can generate documents with this template", "type": "boolean" }, "CAN_VIEW": { "description": "Whether the current user can view this template", "type": "boolean" } }, "required": [ "CAN_DELETE_TEMPLATE", "CAN_EDIT_TEMPLATE", "CAN_SHARE", "CAN_USE_TEMPLATE", "CAN_VIEW" ], "type": "object" }, "projectType": { "description": "Type of project template.", "enum": [ "autoMl", "textGeneration", "timeSeries" ], "type": [ "string", "null" ] }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. ", "items": { "oneOf": [ { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ] }, "type": "array" } }, "required": [ "creatorId", "creatorUsername", "dateModified", "description", "id", "instructions", "labels", "name", "orgId", "permissions", "projectType", "sections" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [TemplateResponse] | true | | List of templates. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ComplianceDocTemplateUpdate ``` { "properties": { "description": { "description": "New description for the template.", "type": "string" }, "labels": { "description": "Names of the labels to assign to the template.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.22" }, "name": { "description": "New name for the template. Must be unique among templates created by the user.", "type": "string" }, "projectType": { "description": "Type of project template.", "enum": [ "autoMl", "textGeneration", "timeSeries" ], "type": "string", "x-versionadded": "v2.22" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. ", "items": { "oneOf": [ { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ] }, "type": "array" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | | New description for the template. | | labels | [string] | false | | Names of the labels to assign to the template. | | name | string | false | | New name for the template. Must be unique among templates created by the user. | | projectType | string | false | | Type of project template. | | sections | [oneOf] | false | | List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionDataRobot | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionUser | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionCustom | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionTableOfContents | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | projectType | [autoMl, textGeneration, timeSeries] | ## DocTypeSpecificInfoDeploymentReport ``` { "properties": { "endDate": { "description": "End date of selected data in the document.", "format": "date-time", "type": "string" }, "modelName": { "description": "Name of the deployed model.", "type": "string" }, "startDate": { "description": "Start date of selected data in the document.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string(date-time) | true | | End date of selected data in the document. | | modelName | string | false | | Name of the deployed model. | | startDate | string(date-time) | true | | Start date of selected data in the document. | ## DocTypeSpecificInfoModelCompliance ``` { "type": "object" } ``` ### Properties None ## Empty ``` { "type": "object" } ``` ### Properties None ## GrantAccessControlWithId ``` { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## GrantAccessControlWithUsername ``` { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | Username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## InstructionsField ``` { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" } ``` Section instructions ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | owner | string | true | | Instructions owner | | user | string | true | | Instructions user | ## ModelComplianceDocsInitializationsResponse ``` { "properties": { "initialized": { "description": "Whether compliance documentation pre-preprocessing is initialized for the model", "type": "boolean" }, "status": { "description": "Compliance documentation pre-processing initialization status", "enum": [ "initialized", "initializationInProgress", "notRequested", "noTrainingData", "trainingDataAssignmentInProgress", "initializationError" ], "type": "string" } }, "required": [ "initialized", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | initialized | boolean | true | | Whether compliance documentation pre-preprocessing is initialized for the model | | status | string | true | | Compliance documentation pre-processing initialization status | ### Enumerated Values | Property | Value | | --- | --- | | status | [initialized, initializationInProgress, notRequested, noTrainingData, trainingDataAssignmentInProgress, initializationError] | ## SampleSection ``` { "type": "object" } ``` ### Properties None ## SectionCustom ``` { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | | Section description | | highlightedText | string | true | maxLength: 5000 | Highlighted text of the section, optionally separated by \n to split paragraphs. | | instructions | InstructionsField | false | | Section instructions | | locked | boolean | false | | Locked section flag | | regularText | string | true | maxLength: 5000 | Regular text of the section, optionally separated by \n to split paragraphs. | | sections | [SampleSection] | false | | List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. | | title | string | true | maxLength: 500 | Section Title | | type | string | true | | Section with user-defined content. It can be a section title or summary. | ### Enumerated Values | Property | Value | | --- | --- | | type | custom | ## SectionDataRobot ``` { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | contentId | string | true | | The identifier of the content in the section. This attribute identifies what is going to be rendered in the section. | | description | string,null | false | | Section description | | instructions | InstructionsField | false | | Section instructions | | locked | boolean | false | | Locked section flag | | sections | [SampleSection] | false | | List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. | | title | string | true | maxLength: 500 | Section Title | | type | string | true | | Section owned by DataRobot. The content of this section type is controlled by DataRobot (see contentId attribute). Users can add sub-sections to it. | ### Enumerated Values | Property | Value | | --- | --- | | contentId | [MODEL_DESCRIPTION_AND_OVERVIEW, MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION, OVERVIEW_OF_MODEL_RESULTS, MODEL_DEVELOPMENT_OVERVIEW_SUB, TS_MODEL_DEVELOPMENT_OVERVIEW_SUB, AD_MODEL_DEVELOPMENT_OVERVIEW_SUB, MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION, MODEL_METHODOLOGY, MODEL_METHODOLOGY_EXTERNAL_PREDICTION, LITERATURE_REVIEW_AND_REFERENCES, ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED, PERSONALLY_IDENTIFIABLE_INFORMATION, DATA_PARTITIONING_METHODOLOGY, AD_DATA_PARTITIONING_METHODOLOGY, TS_DATA_PARTITIONING_METHODOLOGY, QUANTITATIVE_ANALYSIS, FINAL_MODEL_VARIABLES, VALIDATION_STABILITY, MODEL_PERFORMANCE, ACCURACY_LIFT_CHART, MULTICLASS_ACCURACY_LIFT_CHART, SENSITIVITY_ANALYSIS, SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION, AD_SENSITIVITY_ANALYSIS, ACCURACY_OVER_TIME, ANOMALY_OVER_TIME, ACCURACY_ROC, MULTICLASS_CONFUSION, MODEL_VERSION_CONTROL, CUSTOM_INFERENCE_VERSION_CONTROL, FEATURE_ASSOCIATION_MATRIX, BIAS_AND_FAIRNESS, BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION, HOW_TO_USE, PREFACE, TS_MODEL_PERFORMANCE, EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW, MODEL_DATA_OVERVIEW, MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY, MODEL_PERFORMANCE_AND_STABILITY, CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY, SENSITIVITY_TESTING_AND_ANALYSIS, MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING, MODEL_STAKEHOLDERS, MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE, MODEL_INTERDEPENDENCIES, DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS, INPUT_DATA_EXTRACTION_PREPARATION, DATA_ASSUMPTIONS, CUSTOM_INFERENCE_MODELING_FEATURES, MODEL_ASSUMPTIONS, VARIABLE_SELECTION, VARIABLE_SELECTION_EXTERNAL_PREDICTION, TS_VARIABLE_SELECTION, EXPERT_JUDGEMENT_AND_VAR_SELECTION, KEY_RELATIONSHIPS, AD_KEY_RELATIONSHIPS, LLM_SYSTEM_STAKEHOLDERS, LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE, GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW, LLM_DATA_OVERVIEW, LLM_RAG_DATA, LLM_FINE_TUNING_DATA, LLM_OVERVIEW, LLM_MODEL_CARD, LLM_RISKS_AND_LIMITATIONS, LLM_INTERDEPENDENCIES, LLM_LITERATURE_REFERENCES, LLM_EVALUATION, LLM_COPYRIGHT_CONCERNS, LLM_REGISTRY_GOVERNANCE, LLM_REGISTRY_VERSION_CONTROL] | | type | datarobot | ## SectionTableOfContents ``` { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | locked | boolean | false | | Locked section flag | | title | string | true | maxLength: 500 | Section Title | | type | string | true | | Table of contents. This section has no additional attributes. | ### Enumerated Values | Property | Value | | --- | --- | | type | table_of_contents | ## SectionUser ``` { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | | Section description | | highlightedText | string | true | maxLength: 5000 | Highlighted text of the section, optionally separated by \n to split paragraphs. | | instructions | InstructionsField | false | | Section instructions | | locked | boolean | false | | Locked section flag | | regularText | string | true | maxLength: 5000 | Regular text of the section, optionally separated by \n to split paragraphs. | | sections | [SampleSection] | false | | List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. | | title | string | true | maxLength: 500 | Section Title | | type | string | true | | Section with user-defined content. Those sections may contain text generated by the user. | ### Enumerated Values | Property | Value | | --- | --- | | type | user | ## SharedRolesUpdate ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | Name of the action being taken. The only operation is 'updateRoles'. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | Array of GrantAccessControl objects., up to maximum 100 objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithUsername | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithId | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## SharingListV2Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControlV2] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of items matching the condition. | ## TemplatePermissionsResponse ``` { "description": "The level of permissions for the user viewing this template.", "properties": { "CAN_DELETE_TEMPLATE": { "description": "Whether the current user can delete this template", "type": "boolean" }, "CAN_EDIT_TEMPLATE": { "description": "Whether the current user can edit this template", "type": "boolean" }, "CAN_SHARE": { "description": "Whether the current user can share this template", "type": "boolean" }, "CAN_USE_TEMPLATE": { "description": "Whether the current user can generate documents with this template", "type": "boolean" }, "CAN_VIEW": { "description": "Whether the current user can view this template", "type": "boolean" } }, "required": [ "CAN_DELETE_TEMPLATE", "CAN_EDIT_TEMPLATE", "CAN_SHARE", "CAN_USE_TEMPLATE", "CAN_VIEW" ], "type": "object" } ``` The level of permissions for the user viewing this template. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CAN_DELETE_TEMPLATE | boolean | true | | Whether the current user can delete this template | | CAN_EDIT_TEMPLATE | boolean | true | | Whether the current user can edit this template | | CAN_SHARE | boolean | true | | Whether the current user can share this template | | CAN_USE_TEMPLATE | boolean | true | | Whether the current user can generate documents with this template | | CAN_VIEW | boolean | true | | Whether the current user can view this template | ## TemplateResponse ``` { "properties": { "creatorId": { "description": "The ID of the user who created the template", "type": "string" }, "creatorUsername": { "description": "The username of the user who created the template", "type": "string" }, "dateModified": { "description": "Last date/time of template modification.", "format": "date-time", "type": [ "string", "null" ] }, "description": { "description": "An overview of the template", "type": [ "string", "null" ] }, "id": { "description": "The ID of the template accessible by the user", "type": "string" }, "instructions": { "description": "Currently always returns ``null``. Maintained as a placeholder for future functionality.", "type": [ "string", "null" ] }, "labels": { "description": "User-added filtering labels for the template", "items": { "type": "string" }, "type": "array" }, "name": { "description": "The name of the template", "type": "string" }, "orgId": { "description": "The ID of the organization the user who created the template belongs to", "type": "string" }, "permissions": { "description": "The level of permissions for the user viewing this template.", "properties": { "CAN_DELETE_TEMPLATE": { "description": "Whether the current user can delete this template", "type": "boolean" }, "CAN_EDIT_TEMPLATE": { "description": "Whether the current user can edit this template", "type": "boolean" }, "CAN_SHARE": { "description": "Whether the current user can share this template", "type": "boolean" }, "CAN_USE_TEMPLATE": { "description": "Whether the current user can generate documents with this template", "type": "boolean" }, "CAN_VIEW": { "description": "Whether the current user can view this template", "type": "boolean" } }, "required": [ "CAN_DELETE_TEMPLATE", "CAN_EDIT_TEMPLATE", "CAN_SHARE", "CAN_USE_TEMPLATE", "CAN_VIEW" ], "type": "object" }, "projectType": { "description": "Type of project template.", "enum": [ "autoMl", "textGeneration", "timeSeries" ], "type": [ "string", "null" ] }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. ", "items": { "oneOf": [ { "properties": { "contentId": { "description": "The identifier of the content in the section. This attribute identifies what is going to be rendered in the section.", "enum": [ "MODEL_DESCRIPTION_AND_OVERVIEW", "MODEL_DESCRIPTION_AND_OVERVIEW_EXTERNAL_PREDICTION", "OVERVIEW_OF_MODEL_RESULTS", "MODEL_DEVELOPMENT_OVERVIEW_SUB", "TS_MODEL_DEVELOPMENT_OVERVIEW_SUB", "AD_MODEL_DEVELOPMENT_OVERVIEW_SUB", "MODEL_DEVELOPMENT_OVERVIEW_SUB_EXTERNAL_PREDICTION", "MODEL_METHODOLOGY", "MODEL_METHODOLOGY_EXTERNAL_PREDICTION", "LITERATURE_REVIEW_AND_REFERENCES", "ALTERNATIVE_MODEL_FRAMEWORKS_AND_THEORIES_CONSIDERED", "PERSONALLY_IDENTIFIABLE_INFORMATION", "DATA_PARTITIONING_METHODOLOGY", "AD_DATA_PARTITIONING_METHODOLOGY", "TS_DATA_PARTITIONING_METHODOLOGY", "QUANTITATIVE_ANALYSIS", "FINAL_MODEL_VARIABLES", "VALIDATION_STABILITY", "MODEL_PERFORMANCE", "ACCURACY_LIFT_CHART", "MULTICLASS_ACCURACY_LIFT_CHART", "SENSITIVITY_ANALYSIS", "SENSITIVITY_ANALYSIS_NO_NULL_IMPUTATION", "AD_SENSITIVITY_ANALYSIS", "ACCURACY_OVER_TIME", "ANOMALY_OVER_TIME", "ACCURACY_ROC", "MULTICLASS_CONFUSION", "MODEL_VERSION_CONTROL", "CUSTOM_INFERENCE_VERSION_CONTROL", "FEATURE_ASSOCIATION_MATRIX", "BIAS_AND_FAIRNESS", "BIAS_AND_FAIRNESS_EXTERNAL_PREDICTION", "HOW_TO_USE", "PREFACE", "TS_MODEL_PERFORMANCE", "EXECUTIVE_SUMMARY_AND_MODEL_OVERVIEW", "MODEL_DATA_OVERVIEW", "MODEL_THEORETICAL_FRAMEWORK_AND_METHODOLOGY", "MODEL_PERFORMANCE_AND_STABILITY", "CUSTOM_INFERENCE_PERFORMANCE_AND_STABILITY", "SENSITIVITY_TESTING_AND_ANALYSIS", "MODEL_IMPLEMENTATION_AND_OUTPUT_REPORTING", "MODEL_STAKEHOLDERS", "MODEL_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "MODEL_INTERDEPENDENCIES", "DATA_SOURCE_OVERVIEW_AND_APPROPRIATENESS", "INPUT_DATA_EXTRACTION_PREPARATION", "DATA_ASSUMPTIONS", "CUSTOM_INFERENCE_MODELING_FEATURES", "MODEL_ASSUMPTIONS", "VARIABLE_SELECTION", "VARIABLE_SELECTION_EXTERNAL_PREDICTION", "TS_VARIABLE_SELECTION", "EXPERT_JUDGEMENT_AND_VAR_SELECTION", "KEY_RELATIONSHIPS", "AD_KEY_RELATIONSHIPS", "LLM_SYSTEM_STAKEHOLDERS", "LLM_DEVELOPMENT_PURPOSE_AND_INTENDED_USE", "GEN_AI_LLM_SYSTEM_DESCRIPTION_AND_OVERVIEW", "LLM_DATA_OVERVIEW", "LLM_RAG_DATA", "LLM_FINE_TUNING_DATA", "LLM_OVERVIEW", "LLM_MODEL_CARD", "LLM_RISKS_AND_LIMITATIONS", "LLM_INTERDEPENDENCIES", "LLM_LITERATURE_REFERENCES", "LLM_EVALUATION", "LLM_COPYRIGHT_CONCERNS", "LLM_REGISTRY_GOVERNANCE", "LLM_REGISTRY_VERSION_CONTROL" ], "type": "string" }, "description": { "description": "Section description", "type": [ "string", "null" ] }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section owned by DataRobot. The content of this section type is controlled by DataRobot (see ``contentId`` attribute). Users can add sub-sections to it.", "enum": [ "datarobot" ], "type": "string" } }, "required": [ "contentId", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. Those sections may contain text generated by the user.", "enum": [ "user" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "description": { "description": "Section description", "type": [ "string", "null" ] }, "highlightedText": { "description": "Highlighted text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "instructions": { "description": "Section instructions", "properties": { "owner": { "description": "Instructions owner", "type": "string" }, "user": { "description": "Instructions user", "type": "string" } }, "required": [ "owner", "user" ], "type": "object" }, "locked": { "description": "Locked section flag", "type": "boolean" }, "regularText": { "description": "Regular text of the section, optionally separated by ``\\n`` to split paragraphs.", "maxLength": 5000, "type": "string" }, "sections": { "description": "List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, e.g. the structure is recursive. The limit of nesting sections is 5. Total number of sections is limited to 500. ", "items": { "type": "object" }, "type": "array" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Section with user-defined content. It can be a section title or summary. ", "enum": [ "custom" ], "type": "string" } }, "required": [ "highlightedText", "regularText", "title", "type" ], "type": "object" }, { "properties": { "locked": { "description": "Locked section flag", "type": "boolean" }, "title": { "description": "Section Title", "maxLength": 500, "type": "string" }, "type": { "description": "Table of contents. This section has no additional attributes.", "enum": [ "table_of_contents" ], "type": "string" } }, "required": [ "title", "type" ], "type": "object" } ] }, "type": "array" } }, "required": [ "creatorId", "creatorUsername", "dateModified", "description", "id", "instructions", "labels", "name", "orgId", "permissions", "projectType", "sections" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creatorId | string | true | | The ID of the user who created the template | | creatorUsername | string | true | | The username of the user who created the template | | dateModified | string,null(date-time) | true | | Last date/time of template modification. | | description | string,null | true | | An overview of the template | | id | string | true | | The ID of the template accessible by the user | | instructions | string,null | true | | Currently always returns null. Maintained as a placeholder for future functionality. | | labels | [string] | true | | User-added filtering labels for the template | | name | string | true | | The name of the template | | orgId | string | true | | The ID of the organization the user who created the template belongs to | | permissions | TemplatePermissionsResponse | true | | The level of permissions for the user viewing this template. | | projectType | string,null | true | | Type of project template. | | sections | [oneOf] | true | | List of section objects representing the structure of the document. Each section can have sub-sections that have the same schema as the parent section, i.e., the structure is recursive. The number of nested sections allowed is 5. The total number of sections allowed is 500. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionDataRobot | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionUser | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionCustom | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SectionTableOfContents | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | projectType | [autoMl, textGeneration, timeSeries] | --- # Credentials URL: https://docs.datarobot.com/en/docs/api/reference/public-api/credentials.html > The following endpoints outline how to manage user credentials. # Credentials The following endpoints outline how to manage user credentials. ## List credentials Operation path: `GET /api/v2/credentials/` Authentication requirements: `BearerAuth` List all sets of credentials available for given user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | types | query | any | false | Includes only credentials of the specified type. Repeat the parameter for filtering on multiple statuses. | | orderBy | query | string | false | The order to sort the credentials. Defaults to the order by the creation_date in descending order. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [creationDate, -creationDate] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of credentials.", "items": { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "name": { "description": "Name of these credentials.", "type": "string" } }, "required": [ "creationDate", "credentialId", "name" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of credentials. | CredentialsListResponse | ## Store a new set of credentials which can be used Operation path: `POST /api/v2/credentials/` Authentication requirements: `BearerAuth` Store a new set of credentials. ### Body parameter ``` { "discriminator": { "propertyName": "credentialType" }, "oneOf": [ { "properties": { "credentialType": { "description": "Credentials type.", "enum": [ "basic" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "password": { "description": "Password to store for this credentials.", "type": "string" }, "snowflakeAccountName": { "description": "Snowflake account name.", "type": "string", "x-versionadded": "v2.21" }, "user": { "description": "Username to store for this credentials.", "type": "string" } }, "required": [ "name", "password", "user" ], "type": "object" }, { "properties": { "credentialType": { "description": "Credentials type.", "enum": [ "oauth" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "refreshToken": { "description": "OAUTH refresh token.", "type": "string" }, "token": { "description": "OAUTH token.", "type": "string" } }, "required": [ "credentialType", "name", "refreshToken", "token" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "AWS access key ID.", "type": "string", "x-versionadded": "v2.20" }, "awsSecretAccessKey": { "description": "AWS secret access key.", "type": "string", "x-versionadded": "v2.20" }, "awsSessionToken": { "description": "AWS session token.", "type": "string", "x-versionadded": "v2.20" }, "configId": { "description": "ID of Secure configurations to share S3 or GCP credentials by admin. Alternative to googleConfigId (deprecated).", "type": "string", "x-versionadded": "v2.32" }, "credentialType": { "description": "Credentials type.", "enum": [ "s3" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "configId": { "description": "ID of Secure configurations to share S3 or GCP credentials by admin. Alternative to googleConfigId (deprecated).", "type": "string", "x-versionadded": "v2.32" }, "credentialType": { "description": "Credentials type.", "enum": [ "gcp" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "ID of Google configurations shared by admin (deprecated). Please use configId instead.", "type": "string", "x-versionadded": "v2.30" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "azureConnectionString": { "description": "Azure connection string.", "type": "string", "x-versionadded": "v2.21" }, "credentialType": { "description": "Credentials type.", "enum": [ "azure" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "azureConnectionString", "credentialType", "name" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string", "x-versionadded": "v2.30" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string", "x-versionadded": "v2.30" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string", "x-versionadded": "v2.30" }, "configId": { "description": "ID of secure configuration to share Azure service principal credentials by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "Credentials type.", "enum": [ "azure_service_principal" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "clientId": { "description": "Snowflake OAUTH client ID.", "type": "string", "x-versionadded": "v2.23" }, "clientSecret": { "description": "Snowflake OAUTH client secret.", "type": "string", "x-versionadded": "v2.23" }, "credentialType": { "description": "Credentials type.", "enum": [ "snowflake_oauth_user_account" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "oauthConfigId": { "description": "ID of snowflake OAuth configurations shared by admin.", "type": "string", "x-versionadded": "v2.30" }, "oauthIssuerType": { "description": "OAuth issuer type.", "enum": [ "azure", "okta", "snowflake" ], "type": "string", "x-versionadded": "v2.27" }, "oauthIssuerUrl": { "description": "OAuth issuer URL.", "format": "uri", "type": "string", "x-versionadded": "v2.27" }, "oauthScopes": { "description": "OAuth scopes.", "items": { "description": "OAuth scope.", "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.27" }, "snowflakeAccountName": { "description": "Snowflake account name.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "clientId": { "description": "ADLS Gen2 OAuth client ID.", "type": "string", "x-versionadded": "v2.27" }, "clientSecret": { "description": "ADLS Gen2 OAuth client secret.", "type": "string", "x-versionadded": "v2.27" }, "configId": { "description": "ID of secure configuration to share ADLS OAuth credentials by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "Credentials type.", "enum": [ "adls_gen2_oauth" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "oauthScopes": { "description": "ADLS Gen2 OAuth scopes.", "items": { "description": "ADLS Gen2 OAuth scope.", "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.27" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "clientId": { "description": "Azure OAuth client ID.", "type": "string", "x-versionadded": "v2.38" }, "clientSecret": { "description": "Azure OAuth client secret.", "type": "string", "x-versionadded": "v2.38" }, "credentialType": { "description": "Credentials type.", "enum": [ "azure_oauth" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "oauthScopes": { "description": "Azure OAuth scopes.", "items": { "description": "Azure OAuth scope.", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.38" } }, "required": [ "clientId", "clientSecret", "credentialType", "name", "oauthScopes" ], "type": "object" }, { "properties": { "authenticationId": { "description": "Authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service.", "type": "string" }, "credentialType": { "description": "Credentials type for DataRobot managed external OAuth provider service integration.", "enum": [ "external_oauth_provider" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "authenticationId", "credentialType", "name" ], "type": "object" }, { "properties": { "configId": { "description": "ID of Snowflake Key Pair Credentials Secure configurations to share by admin", "type": "string", "x-versionadded": "v2.32" }, "credentialType": { "description": "Credentials type.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "passphrase": { "description": "Optional passphrase to encrypt private key.", "minLength": 1, "type": "string", "x-versionadded": "v2.30" }, "privateKeyStr": { "description": "Private key for key pair authentication.", "minLength": 1, "type": "string", "x-versionadded": "v2.30" }, "user": { "description": "Username for this credential.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "credentialType": { "description": "Credentials type.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string", "x-versionadded": "v2.31" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "databricksAccessToken", "name" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks Service Principal.", "type": "string", "x-versionadded": "v2.33" }, "clientSecret": { "description": "Client secret for Databricks Service Principal.", "type": "string", "x-versionadded": "v2.33" }, "configId": { "description": "ID of Databricks Service Principal Credentials Secure configuration to share by admin", "type": "string", "x-versionadded": "v2.33" }, "credentialType": { "description": "Credentials type.", "enum": [ "databricks_service_principal_account" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "apiToken": { "description": "API token.", "minLength": 1, "type": "string", "x-versionadded": "v2.31" }, "credentialType": { "description": "Credentials type.", "enum": [ "api_token" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "apiToken", "credentialType", "name" ], "type": "object" }, { "properties": { "authUrl": { "description": "The URL used for SAP authentication.", "format": "uri", "type": "string", "x-versionadded": "v2.35" }, "clientId": { "description": "SAP OAUTH client ID.", "type": "string", "x-versionadded": "v2.35" }, "clientSecret": { "description": "SAP OAUTH client secret.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "Credentials type.", "enum": [ "sap_oauth" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "sapAiApiUrl": { "description": "The URL used for SAP AI API service.", "format": "uri", "type": "string", "x-versionadded": "v2.35" } }, "required": [ "authUrl", "clientId", "clientSecret", "credentialType", "name", "sapAiApiUrl" ], "type": "object" }, { "properties": { "clientId": { "description": "Box JWT client ID.", "type": "string", "x-versionadded": "v2.41" }, "clientSecret": { "description": "Box JWT client secret.", "type": "string", "x-versionadded": "v2.41" }, "configId": { "description": "ID of secure configuration to share Box JWT credentials by admin.", "type": "string", "x-versionadded": "v2.41" }, "credentialType": { "description": "Credentials type.", "enum": [ "box_jwt" ], "type": "string", "x-versionadded": "v2.41" }, "description": { "description": "Credentials description.", "type": "string" }, "enterpriseId": { "description": "Box enterprise identifier.", "type": "string", "x-versionadded": "v2.41" }, "name": { "description": "Credentials name.", "type": "string" }, "passphrase": { "description": "Passphrase for the Box JWT private key.", "minLength": 1, "type": "string", "x-versionadded": "v2.41" }, "privateKeyStr": { "description": "RSA private key for Box JWT.", "minLength": 1, "type": "string", "x-versionadded": "v2.41" }, "publicKeyId": { "description": "Box public key identifier.", "type": "string", "x-versionadded": "v2.41" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for integrations that use a client ID and client secret (e.g. OAuth client credentials).", "type": "string", "x-versionadded": "v2.42" }, "clientSecret": { "description": "Client secret for integrations that use a client ID and client secret (e.g. OAuth client credentials).", "type": "string", "x-versionadded": "v2.42" }, "configId": { "description": "ID of shared secure configuration for client ID and client secret, managed by admin.", "type": "string", "x-versionadded": "v2.42" }, "credentialType": { "description": "Credentials type.", "enum": [ "client_id_and_secret" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" } ] } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CredentialsBody | false | none | ### Example responses > 201 Response ``` { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "name": { "description": "Name of these credentials.", "type": "string" } }, "required": [ "creationDate", "credentialId", "name" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Credentials stored successfully. | CreateCredentialsResponse | | 409 | Conflict | The specified name is already in use. | None | ## List credentials associated by association ID Operation path: `GET /api/v2/credentials/associations/{associationId}/` Authentication requirements: `BearerAuth` Returns a list of credentials associated with the specified object for the given session user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | false | Sort order which will be applied to credential associations list. Prefix the attribute name with a dash to sort in descending order, e.g. "-is_default". Absent by default. | | associationId | path | string | true | The compound ID of the data connection. == : Where: of ObjectId type - the ID of the data connection; of string type - the type of data connection from CredentialMappingResourceTypes Enum: dataconnection, batch_prediction_job_definition. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [isDefault, -isDefault] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of credentials associations.", "items": { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "isDefault": { "description": "Whether this credential association with the given object is default for given session user.", "type": "boolean" }, "name": { "description": "Name of these credentials.", "type": "string" }, "objectId": { "description": "Associated object ID.", "type": "string" }, "objectType": { "description": "Associated object type.", "enum": [ "batchPredictionJobDefinition", "dataconnection" ], "type": "string" } }, "required": [ "creationDate", "credentialId", "name", "objectId", "objectType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of credentials associated with the object for the session user. | ListCredentialsAssociationsResponse | ## Delete the credentials set by credential ID Operation path: `DELETE /api/v2/credentials/{credentialId}/` Authentication requirements: `BearerAuth` Delete the credential set matching the specified ID if it is not used by any data connection or batch prediction job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | credentialId | path | string | true | Credentials entity ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Credentials deleted successfully. | None | | 409 | Conflict | Credentials are in use by one or more data connections or batch prediction jobs. | None | ## Retrieve the credentials set by credential ID Operation path: `GET /api/v2/credentials/{credentialId}/` Authentication requirements: `BearerAuth` Return the credential set matching the specified ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | credentialId | path | string | true | Credentials entity ID. | ### Example responses > 200 Response ``` { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "name": { "description": "Name of these credentials.", "type": "string" } }, "required": [ "creationDate", "credentialId", "name" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Credentials retrieved successfully. | CreateCredentialsResponse | ## Update specified credentials by credential ID Operation path: `PATCH /api/v2/credentials/{credentialId}/` Authentication requirements: `BearerAuth` Update specified credentials. ### Body parameter ``` { "properties": { "apiToken": { "description": "API token.", "minLength": 1, "type": "string", "x-versionadded": "v2.31" }, "authUrl": { "description": "The URL used for SAP OAuth authentication.", "format": "uri", "type": "string", "x-versionadded": "v2.35" }, "authenticationId": { "description": "Authorized external OAuth provider identifier.", "type": "string", "x-versionadded": "v2.41" }, "awsAccessKeyId": { "description": "AWS access key ID (applicable for credentialType `s3` only).", "type": "string" }, "awsSecretAccessKey": { "description": "The AWS secret access key (applicable for credentialType `s3` only).", "type": "string" }, "awsSessionToken": { "description": "The AWS session token (applicable for credentialType `s3` only).", "type": "string" }, "azureConnectionString": { "description": "Azure connection string (applicable for credentialType `azure` only).", "type": "string", "x-versionadded": "v2.21" }, "azureTenantId": { "description": "Tenant ID of the Azure AD service principal (applicable for credentialType `azure_service_principal` only).", "type": "string", "x-versionadded": "v2.30" }, "clientId": { "description": "OAUTH client ID (applicable for credentialType `snowflake_oauth_user_account`, `adls_gen2_oauth`, `sap_oauth_account`, `azure_service_principal`, `azure_oauth` and `client_id_and_secret`).", "type": "string", "x-versionadded": "v2.23" }, "clientSecret": { "description": "OAUTH client secret (applicable for credentialType `snowflake_oauth_user_account`, `adls_gen2_oauth`, `sap_oauth_account`, `azure_service_principal`, `azure_oauth` and `client_id_and_secret`).", "type": "string", "x-versionadded": "v2.23" }, "configId": { "description": "ID of secure configuration credentials to share by admin. Alternative to googleConfigId (deprecated).", "type": "string", "x-versionadded": "v2.32" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string", "x-versionadded": "v2.31" }, "description": { "description": "Description of credentials. If omitted, and name is not omitted, clears any previous description for that name.", "type": "string" }, "enterpriseId": { "description": "Box enterprise identifier (applicable for credentialType `box_jwt` only).", "type": "string", "x-versionadded": "v2.41" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "ID of Google configurations shared by admin (deprecated). Please use configId instead.", "type": "string", "x-versionadded": "v2.30" }, "name": { "description": "Name of credentials.", "type": "string" }, "oauthConfigId": { "description": "ID of snowflake OAuth configurations shared by admin.", "type": "string", "x-versionadded": "v2.30" }, "oauthIssuerType": { "description": "Snowflake IDP issuer type (applicable for credentialType `snowflake_oauth_user_account` only).", "enum": [ "azure", "okta", "snowflake" ], "type": "string", "x-versionadded": "v2.27" }, "oauthIssuerUrl": { "description": "Snowflake External IDP issuer URL (applicable for Snowflake External OAUTH connections only).", "format": "uri", "type": "string", "x-versionadded": "v2.27" }, "oauthScopes": { "description": "External OAUTH scopes (applicable for Snowflake External OAUTH connections, credentialType `snowflake_oauth_user_account`, `adls_gen2_oauth`, and `azure_oauth`).", "items": { "description": "AUTH scope.", "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.27" }, "passphrase": { "description": "Optional passphrase to encrypt private key.(applicable for credentialType `snowflake_key_pair_user_account` only).", "minLength": 1, "type": "string", "x-versionadded": "v2.30" }, "password": { "description": "Password to update for this set of credentials (applicable for credentialType `basic` only).", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication.(applicable for credentialType `snowflake_key_pair_user_account` only).", "minLength": 1, "type": "string", "x-versionadded": "v2.30" }, "publicKeyId": { "description": "Box public key identifier (applicable for credentialType `box_jwt` only).", "type": "string", "x-versionadded": "v2.41" }, "refreshToken": { "description": "OAUTH refresh token (applicable for credentialType `oauth` only).", "type": "string" }, "sapAiApiUrl": { "description": "The URL used for SAP AI API service.", "format": "uri", "type": "string", "x-versionadded": "v2.35" }, "snowflakeAccountName": { "description": "Snowflake account name (applicable for `snowflake_oauth_user_account` only).", "type": "string", "x-versionadded": "v2.21" }, "token": { "description": "OAUTH token (applicable for credentialType `oauth` only).", "type": "string" }, "user": { "description": "Username to update for this set of credentials (applicable for credentialType `basic` and `snowflake_key_pair_user_account` only).", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | credentialId | path | string | true | Credentials entity ID. | | body | body | CredentialsUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Set of credentials updated | None | | 404 | Not Found | Requested credentials not found | None | | 409 | Conflict | The name specified for credentials is already in use. | None | | 422 | Unprocessable Entity | Must specify at least one field to update | None | ## List all objects associated by credential ID Operation path: `GET /api/v2/credentials/{credentialId}/associations/` Authentication requirements: `BearerAuth` List all objects associated with specified credentials for the current user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | types | query | any | false | Includes only credentials of the specified type. Repeat the parameter for filtering on multiple statuses. | | orderBy | query | string | false | The order to sort the credentials. Defaults to the order by the creation_date in descending order. | | credentialId | path | string | true | Credentials entity ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [creationDate, -creationDate] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Objects associated with the specified credentials.", "items": { "properties": { "isDefault": { "description": "Whether this credential association with the given object is default for given session user.", "type": "boolean" }, "link": { "description": "Link to get more details about associated object.", "format": "uri", "type": [ "string", "null" ] }, "objectId": { "description": "Associated object ID.", "type": "string" }, "objectType": { "description": "Associated object type.", "type": "string" } }, "required": [ "link", "objectId", "objectType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Objects associated with the specified credentials for the current user. | CredentialsListAssociationsResponse | ## Add objects associated by credential ID Operation path: `PATCH /api/v2/credentials/{credentialId}/associations/` Authentication requirements: `BearerAuth` Add objects associated with credentials. ### Body parameter ``` { "properties": { "credentialsToAdd": { "description": "Objects to associate with given credentials.", "items": { "properties": { "objectId": { "description": "Object ID identifying the object to be associated with the credentials.", "type": "string" }, "objectType": { "description": "Type of object associated with the credentials.", "enum": [ "dataconnection" ], "type": "string" } }, "required": [ "objectId", "objectType" ], "type": "object" }, "minItems": 1, "type": "array" }, "credentialsToRemove": { "description": "Object IDs, each of which identifies an object to be disassociated from this credential. To see which objects are currently associated, see the response from [GET /api/v2/credentials/{credentialId}/associations/][get-apiv2credentialscredentialidassociations].", "items": { "description": "Object ID to be disassociated from given credentials.", "type": "string" }, "minItems": 1, "type": "array" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | credentialId | path | string | true | Credentials entity ID. | | body | body | CredentialsAssociationUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | Credentials association updated | None | | 404 | Not Found | Requested credentials not found | None | | 409 | Conflict | One or more associations already exists when adding or one or more associations do not exist | None | | 422 | Unprocessable Entity | Must specify a field to update or must specify only one of credentialsToAdd or credentialsToRemove. | None | ## Set default credentials by credential ID Operation path: `PUT /api/v2/credentials/{credentialId}/associations/{associationId}/` Authentication requirements: `BearerAuth` Set (create or update) the credentials' association for the given Data Connection for the given session Owner for the given credentials. ### Body parameter ``` { "properties": { "isDefault": { "default": false, "description": "Whether this credentials' association with the given object is default for given session user.", "type": "boolean" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | credentialId | path | string | true | Credentials entity ID. | | associationId | path | string | true | The compound ID of the data connection. == : Where: of ObjectId type - the ID of the data connection; of string type - the type of data connection from CredentialMappingResourceTypes Enum: dataconnection, batch_prediction_job_definition. | | body | body | SetCredentialsAssociationRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "isDefault": { "description": "Whether this credential association with the given object is default for given session user.", "type": "boolean" }, "name": { "description": "Name of these credentials.", "type": "string" }, "objectId": { "description": "Associated object ID.", "type": "string" }, "objectType": { "description": "Associated object type.", "enum": [ "batchPredictionJobDefinition", "dataconnection" ], "type": "string" } }, "required": [ "creationDate", "credentialId", "name", "objectId", "objectType" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Credentials association was successfully set (created or updated). | RetrieveCredentialAssociationResponse | | 201 | Created | New credential association was created. | None | # Schemas ## CreateCredentialsResponse ``` { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "name": { "description": "Name of these credentials.", "type": "string" } }, "required": [ "creationDate", "credentialId", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | ISO-8601 formatted date/time when these credentials were created. | | credentialId | string | true | | ID of these credentials. | | credentialType | string | false | | Type of credentials. | | description | string | false | | Description of these credentials. | | name | string | true | | Name of these credentials. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | [adls_gen2_oauth, api_token, azure, azure_oauth, azure_service_principal, basic, bearer, box_jwt, client_id_and_secret, databricks_access_token_account, databricks_service_principal_account, external_oauth_provider, gcp, oauth, rsa, s3, sap_oauth, snowflake_key_pair_user_account, snowflake_oauth_user_account, tableau_access_token] | ## CredentialsAssociationUpdate ``` { "properties": { "credentialsToAdd": { "description": "Objects to associate with given credentials.", "items": { "properties": { "objectId": { "description": "Object ID identifying the object to be associated with the credentials.", "type": "string" }, "objectType": { "description": "Type of object associated with the credentials.", "enum": [ "dataconnection" ], "type": "string" } }, "required": [ "objectId", "objectType" ], "type": "object" }, "minItems": 1, "type": "array" }, "credentialsToRemove": { "description": "Object IDs, each of which identifies an object to be disassociated from this credential. To see which objects are currently associated, see the response from [GET /api/v2/credentials/{credentialId}/associations/][get-apiv2credentialscredentialidassociations].", "items": { "description": "Object ID to be disassociated from given credentials.", "type": "string" }, "minItems": 1, "type": "array" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialsToAdd | [CredentialsToAdd] | false | minItems: 1 | Objects to associate with given credentials. | | credentialsToRemove | [string] | false | minItems: 1 | Object IDs, each of which identifies an object to be disassociated from this credential. To see which objects are currently associated, see the response from [GET /api/v2/credentials/{credentialId}/associations/][get-apiv2credentialscredentialidassociations]. | ## CredentialsAssociationsData ``` { "properties": { "isDefault": { "description": "Whether this credential association with the given object is default for given session user.", "type": "boolean" }, "link": { "description": "Link to get more details about associated object.", "format": "uri", "type": [ "string", "null" ] }, "objectId": { "description": "Associated object ID.", "type": "string" }, "objectType": { "description": "Associated object type.", "type": "string" } }, "required": [ "link", "objectId", "objectType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isDefault | boolean | false | | Whether this credential association with the given object is default for given session user. | | link | string,null(uri) | true | | Link to get more details about associated object. | | objectId | string | true | | Associated object ID. | | objectType | string | true | | Associated object type. | ## CredentialsBody ``` { "discriminator": { "propertyName": "credentialType" }, "oneOf": [ { "properties": { "credentialType": { "description": "Credentials type.", "enum": [ "basic" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "password": { "description": "Password to store for this credentials.", "type": "string" }, "snowflakeAccountName": { "description": "Snowflake account name.", "type": "string", "x-versionadded": "v2.21" }, "user": { "description": "Username to store for this credentials.", "type": "string" } }, "required": [ "name", "password", "user" ], "type": "object" }, { "properties": { "credentialType": { "description": "Credentials type.", "enum": [ "oauth" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "refreshToken": { "description": "OAUTH refresh token.", "type": "string" }, "token": { "description": "OAUTH token.", "type": "string" } }, "required": [ "credentialType", "name", "refreshToken", "token" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "AWS access key ID.", "type": "string", "x-versionadded": "v2.20" }, "awsSecretAccessKey": { "description": "AWS secret access key.", "type": "string", "x-versionadded": "v2.20" }, "awsSessionToken": { "description": "AWS session token.", "type": "string", "x-versionadded": "v2.20" }, "configId": { "description": "ID of Secure configurations to share S3 or GCP credentials by admin. Alternative to googleConfigId (deprecated).", "type": "string", "x-versionadded": "v2.32" }, "credentialType": { "description": "Credentials type.", "enum": [ "s3" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "configId": { "description": "ID of Secure configurations to share S3 or GCP credentials by admin. Alternative to googleConfigId (deprecated).", "type": "string", "x-versionadded": "v2.32" }, "credentialType": { "description": "Credentials type.", "enum": [ "gcp" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "ID of Google configurations shared by admin (deprecated). Please use configId instead.", "type": "string", "x-versionadded": "v2.30" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "azureConnectionString": { "description": "Azure connection string.", "type": "string", "x-versionadded": "v2.21" }, "credentialType": { "description": "Credentials type.", "enum": [ "azure" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "azureConnectionString", "credentialType", "name" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string", "x-versionadded": "v2.30" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string", "x-versionadded": "v2.30" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string", "x-versionadded": "v2.30" }, "configId": { "description": "ID of secure configuration to share Azure service principal credentials by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "Credentials type.", "enum": [ "azure_service_principal" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "clientId": { "description": "Snowflake OAUTH client ID.", "type": "string", "x-versionadded": "v2.23" }, "clientSecret": { "description": "Snowflake OAUTH client secret.", "type": "string", "x-versionadded": "v2.23" }, "credentialType": { "description": "Credentials type.", "enum": [ "snowflake_oauth_user_account" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "oauthConfigId": { "description": "ID of snowflake OAuth configurations shared by admin.", "type": "string", "x-versionadded": "v2.30" }, "oauthIssuerType": { "description": "OAuth issuer type.", "enum": [ "azure", "okta", "snowflake" ], "type": "string", "x-versionadded": "v2.27" }, "oauthIssuerUrl": { "description": "OAuth issuer URL.", "format": "uri", "type": "string", "x-versionadded": "v2.27" }, "oauthScopes": { "description": "OAuth scopes.", "items": { "description": "OAuth scope.", "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.27" }, "snowflakeAccountName": { "description": "Snowflake account name.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "clientId": { "description": "ADLS Gen2 OAuth client ID.", "type": "string", "x-versionadded": "v2.27" }, "clientSecret": { "description": "ADLS Gen2 OAuth client secret.", "type": "string", "x-versionadded": "v2.27" }, "configId": { "description": "ID of secure configuration to share ADLS OAuth credentials by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "Credentials type.", "enum": [ "adls_gen2_oauth" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "oauthScopes": { "description": "ADLS Gen2 OAuth scopes.", "items": { "description": "ADLS Gen2 OAuth scope.", "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.27" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "clientId": { "description": "Azure OAuth client ID.", "type": "string", "x-versionadded": "v2.38" }, "clientSecret": { "description": "Azure OAuth client secret.", "type": "string", "x-versionadded": "v2.38" }, "credentialType": { "description": "Credentials type.", "enum": [ "azure_oauth" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "oauthScopes": { "description": "Azure OAuth scopes.", "items": { "description": "Azure OAuth scope.", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.38" } }, "required": [ "clientId", "clientSecret", "credentialType", "name", "oauthScopes" ], "type": "object" }, { "properties": { "authenticationId": { "description": "Authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service.", "type": "string" }, "credentialType": { "description": "Credentials type for DataRobot managed external OAuth provider service integration.", "enum": [ "external_oauth_provider" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "authenticationId", "credentialType", "name" ], "type": "object" }, { "properties": { "configId": { "description": "ID of Snowflake Key Pair Credentials Secure configurations to share by admin", "type": "string", "x-versionadded": "v2.32" }, "credentialType": { "description": "Credentials type.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "passphrase": { "description": "Optional passphrase to encrypt private key.", "minLength": 1, "type": "string", "x-versionadded": "v2.30" }, "privateKeyStr": { "description": "Private key for key pair authentication.", "minLength": 1, "type": "string", "x-versionadded": "v2.30" }, "user": { "description": "Username for this credential.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "credentialType": { "description": "Credentials type.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string", "x-versionadded": "v2.31" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "databricksAccessToken", "name" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks Service Principal.", "type": "string", "x-versionadded": "v2.33" }, "clientSecret": { "description": "Client secret for Databricks Service Principal.", "type": "string", "x-versionadded": "v2.33" }, "configId": { "description": "ID of Databricks Service Principal Credentials Secure configuration to share by admin", "type": "string", "x-versionadded": "v2.33" }, "credentialType": { "description": "Credentials type.", "enum": [ "databricks_service_principal_account" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "apiToken": { "description": "API token.", "minLength": 1, "type": "string", "x-versionadded": "v2.31" }, "credentialType": { "description": "Credentials type.", "enum": [ "api_token" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "apiToken", "credentialType", "name" ], "type": "object" }, { "properties": { "authUrl": { "description": "The URL used for SAP authentication.", "format": "uri", "type": "string", "x-versionadded": "v2.35" }, "clientId": { "description": "SAP OAUTH client ID.", "type": "string", "x-versionadded": "v2.35" }, "clientSecret": { "description": "SAP OAUTH client secret.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "Credentials type.", "enum": [ "sap_oauth" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" }, "sapAiApiUrl": { "description": "The URL used for SAP AI API service.", "format": "uri", "type": "string", "x-versionadded": "v2.35" } }, "required": [ "authUrl", "clientId", "clientSecret", "credentialType", "name", "sapAiApiUrl" ], "type": "object" }, { "properties": { "clientId": { "description": "Box JWT client ID.", "type": "string", "x-versionadded": "v2.41" }, "clientSecret": { "description": "Box JWT client secret.", "type": "string", "x-versionadded": "v2.41" }, "configId": { "description": "ID of secure configuration to share Box JWT credentials by admin.", "type": "string", "x-versionadded": "v2.41" }, "credentialType": { "description": "Credentials type.", "enum": [ "box_jwt" ], "type": "string", "x-versionadded": "v2.41" }, "description": { "description": "Credentials description.", "type": "string" }, "enterpriseId": { "description": "Box enterprise identifier.", "type": "string", "x-versionadded": "v2.41" }, "name": { "description": "Credentials name.", "type": "string" }, "passphrase": { "description": "Passphrase for the Box JWT private key.", "minLength": 1, "type": "string", "x-versionadded": "v2.41" }, "privateKeyStr": { "description": "RSA private key for Box JWT.", "minLength": 1, "type": "string", "x-versionadded": "v2.41" }, "publicKeyId": { "description": "Box public key identifier.", "type": "string", "x-versionadded": "v2.41" } }, "required": [ "credentialType", "name" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for integrations that use a client ID and client secret (e.g. OAuth client credentials).", "type": "string", "x-versionadded": "v2.42" }, "clientSecret": { "description": "Client secret for integrations that use a client ID and client secret (e.g. OAuth client credentials).", "type": "string", "x-versionadded": "v2.42" }, "configId": { "description": "ID of shared secure configuration for client ID and client secret, managed by admin.", "type": "string", "x-versionadded": "v2.42" }, "credentialType": { "description": "Credentials type.", "enum": [ "client_id_and_secret" ], "type": "string" }, "description": { "description": "Credentials description.", "type": "string" }, "name": { "description": "Credentials name.", "type": "string" } }, "required": [ "credentialType", "name" ], "type": "object" } ] } ``` ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » credentialType | string | false | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | | » password | string | true | | Password to store for this credentials. | | » snowflakeAccountName | string | false | | Snowflake account name. | | » user | string | true | | Username to store for this credentials. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | | » refreshToken | string | true | | OAUTH refresh token. | | » token | string | true | | OAUTH token. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » awsAccessKeyId | string | false | | AWS access key ID. | | » awsSecretAccessKey | string | false | | AWS secret access key. | | » awsSessionToken | string | false | | AWS session token. | | » configId | string | false | | ID of Secure configurations to share S3 or GCP credentials by admin. Alternative to googleConfigId (deprecated). | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » configId | string | false | | ID of Secure configurations to share S3 or GCP credentials by admin. Alternative to googleConfigId (deprecated). | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » gcpKey | GCPKey | false | | The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. | | » googleConfigId | string | false | | ID of Google configurations shared by admin (deprecated). Please use configId instead. | | » name | string | true | | Credentials name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » azureConnectionString | string | true | | Azure connection string. | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » azureTenantId | string | false | | Tenant ID of the Azure AD service principal. | | » clientId | string | false | | Client ID of the Azure AD service principal. | | » clientSecret | string | false | | Client Secret of the Azure AD service principal. | | » configId | string | false | | ID of secure configuration to share Azure service principal credentials by admin. | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » clientId | string | false | | Snowflake OAUTH client ID. | | » clientSecret | string | false | | Snowflake OAUTH client secret. | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | | » oauthConfigId | string | false | | ID of snowflake OAuth configurations shared by admin. | | » oauthIssuerType | string | false | | OAuth issuer type. | | » oauthIssuerUrl | string(uri) | false | | OAuth issuer URL. | | » oauthScopes | [string] | false | minItems: 1 | OAuth scopes. | | » snowflakeAccountName | string | false | | Snowflake account name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » clientId | string | false | | ADLS Gen2 OAuth client ID. | | » clientSecret | string | false | | ADLS Gen2 OAuth client secret. | | » configId | string | false | | ID of secure configuration to share ADLS OAuth credentials by admin. | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | | » oauthScopes | [string] | false | minItems: 1 | ADLS Gen2 OAuth scopes. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » clientId | string | true | | Azure OAuth client ID. | | » clientSecret | string | true | | Azure OAuth client secret. | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | | » oauthScopes | [string] | true | maxItems: 100minItems: 1 | Azure OAuth scopes. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » authenticationId | string | true | | Authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service. | | » credentialType | string | true | | Credentials type for DataRobot managed external OAuth provider service integration. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » configId | string | false | | ID of Snowflake Key Pair Credentials Secure configurations to share by admin | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | | » passphrase | string | false | minLength: 1minLength: 1 | Optional passphrase to encrypt private key. | | » privateKeyStr | string | false | minLength: 1minLength: 1 | Private key for key pair authentication. | | » user | string | false | | Username for this credential. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » credentialType | string | true | | Credentials type. | | » databricksAccessToken | string | true | minLength: 1minLength: 1 | Databricks personal access token. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » clientId | string | false | | Client ID for Databricks Service Principal. | | » clientSecret | string | false | | Client secret for Databricks Service Principal. | | » configId | string | false | | ID of Databricks Service Principal Credentials Secure configuration to share by admin | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » apiToken | string | true | minLength: 1minLength: 1 | API token. | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » authUrl | string(uri) | true | | The URL used for SAP authentication. | | » clientId | string | true | | SAP OAUTH client ID. | | » clientSecret | string | true | | SAP OAUTH client secret. | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | | » sapAiApiUrl | string(uri) | true | | The URL used for SAP AI API service. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » clientId | string | false | | Box JWT client ID. | | » clientSecret | string | false | | Box JWT client secret. | | » configId | string | false | | ID of secure configuration to share Box JWT credentials by admin. | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » enterpriseId | string | false | | Box enterprise identifier. | | » name | string | true | | Credentials name. | | » passphrase | string | false | minLength: 1minLength: 1 | Passphrase for the Box JWT private key. | | » privateKeyStr | string | false | minLength: 1minLength: 1 | RSA private key for Box JWT. | | » publicKeyId | string | false | | Box public key identifier. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » clientId | string | false | | Client ID for integrations that use a client ID and client secret (e.g. OAuth client credentials). | | » clientSecret | string | false | | Client secret for integrations that use a client ID and client secret (e.g. OAuth client credentials). | | » configId | string | false | | ID of shared secure configuration for client ID and client secret, managed by admin. | | » credentialType | string | true | | Credentials type. | | » description | string | false | | Credentials description. | | » name | string | true | | Credentials name. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | basic | | credentialType | oauth | | credentialType | s3 | | credentialType | gcp | | credentialType | azure | | credentialType | azure_service_principal | | credentialType | snowflake_oauth_user_account | | oauthIssuerType | [azure, okta, snowflake] | | credentialType | adls_gen2_oauth | | credentialType | azure_oauth | | credentialType | external_oauth_provider | | credentialType | snowflake_key_pair_user_account | | credentialType | databricks_access_token_account | | credentialType | databricks_service_principal_account | | credentialType | api_token | | credentialType | sap_oauth | | credentialType | box_jwt | | credentialType | client_id_and_secret | ## CredentialsListAssociationsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Objects associated with the specified credentials.", "items": { "properties": { "isDefault": { "description": "Whether this credential association with the given object is default for given session user.", "type": "boolean" }, "link": { "description": "Link to get more details about associated object.", "format": "uri", "type": [ "string", "null" ] }, "objectId": { "description": "Associated object ID.", "type": "string" }, "objectType": { "description": "Associated object type.", "type": "string" } }, "required": [ "link", "objectId", "objectType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CredentialsAssociationsData] | true | | Objects associated with the specified credentials. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CredentialsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of credentials.", "items": { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "name": { "description": "Name of these credentials.", "type": "string" } }, "required": [ "creationDate", "credentialId", "name" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CreateCredentialsResponse] | true | | List of credentials. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CredentialsToAdd ``` { "properties": { "objectId": { "description": "Object ID identifying the object to be associated with the credentials.", "type": "string" }, "objectType": { "description": "Type of object associated with the credentials.", "enum": [ "dataconnection" ], "type": "string" } }, "required": [ "objectId", "objectType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | objectId | string | true | | Object ID identifying the object to be associated with the credentials. | | objectType | string | true | | Type of object associated with the credentials. | ### Enumerated Values | Property | Value | | --- | --- | | objectType | dataconnection | ## CredentialsUpdate ``` { "properties": { "apiToken": { "description": "API token.", "minLength": 1, "type": "string", "x-versionadded": "v2.31" }, "authUrl": { "description": "The URL used for SAP OAuth authentication.", "format": "uri", "type": "string", "x-versionadded": "v2.35" }, "authenticationId": { "description": "Authorized external OAuth provider identifier.", "type": "string", "x-versionadded": "v2.41" }, "awsAccessKeyId": { "description": "AWS access key ID (applicable for credentialType `s3` only).", "type": "string" }, "awsSecretAccessKey": { "description": "The AWS secret access key (applicable for credentialType `s3` only).", "type": "string" }, "awsSessionToken": { "description": "The AWS session token (applicable for credentialType `s3` only).", "type": "string" }, "azureConnectionString": { "description": "Azure connection string (applicable for credentialType `azure` only).", "type": "string", "x-versionadded": "v2.21" }, "azureTenantId": { "description": "Tenant ID of the Azure AD service principal (applicable for credentialType `azure_service_principal` only).", "type": "string", "x-versionadded": "v2.30" }, "clientId": { "description": "OAUTH client ID (applicable for credentialType `snowflake_oauth_user_account`, `adls_gen2_oauth`, `sap_oauth_account`, `azure_service_principal`, `azure_oauth` and `client_id_and_secret`).", "type": "string", "x-versionadded": "v2.23" }, "clientSecret": { "description": "OAUTH client secret (applicable for credentialType `snowflake_oauth_user_account`, `adls_gen2_oauth`, `sap_oauth_account`, `azure_service_principal`, `azure_oauth` and `client_id_and_secret`).", "type": "string", "x-versionadded": "v2.23" }, "configId": { "description": "ID of secure configuration credentials to share by admin. Alternative to googleConfigId (deprecated).", "type": "string", "x-versionadded": "v2.32" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string", "x-versionadded": "v2.31" }, "description": { "description": "Description of credentials. If omitted, and name is not omitted, clears any previous description for that name.", "type": "string" }, "enterpriseId": { "description": "Box enterprise identifier (applicable for credentialType `box_jwt` only).", "type": "string", "x-versionadded": "v2.41" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "ID of Google configurations shared by admin (deprecated). Please use configId instead.", "type": "string", "x-versionadded": "v2.30" }, "name": { "description": "Name of credentials.", "type": "string" }, "oauthConfigId": { "description": "ID of snowflake OAuth configurations shared by admin.", "type": "string", "x-versionadded": "v2.30" }, "oauthIssuerType": { "description": "Snowflake IDP issuer type (applicable for credentialType `snowflake_oauth_user_account` only).", "enum": [ "azure", "okta", "snowflake" ], "type": "string", "x-versionadded": "v2.27" }, "oauthIssuerUrl": { "description": "Snowflake External IDP issuer URL (applicable for Snowflake External OAUTH connections only).", "format": "uri", "type": "string", "x-versionadded": "v2.27" }, "oauthScopes": { "description": "External OAUTH scopes (applicable for Snowflake External OAUTH connections, credentialType `snowflake_oauth_user_account`, `adls_gen2_oauth`, and `azure_oauth`).", "items": { "description": "AUTH scope.", "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.27" }, "passphrase": { "description": "Optional passphrase to encrypt private key.(applicable for credentialType `snowflake_key_pair_user_account` only).", "minLength": 1, "type": "string", "x-versionadded": "v2.30" }, "password": { "description": "Password to update for this set of credentials (applicable for credentialType `basic` only).", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication.(applicable for credentialType `snowflake_key_pair_user_account` only).", "minLength": 1, "type": "string", "x-versionadded": "v2.30" }, "publicKeyId": { "description": "Box public key identifier (applicable for credentialType `box_jwt` only).", "type": "string", "x-versionadded": "v2.41" }, "refreshToken": { "description": "OAUTH refresh token (applicable for credentialType `oauth` only).", "type": "string" }, "sapAiApiUrl": { "description": "The URL used for SAP AI API service.", "format": "uri", "type": "string", "x-versionadded": "v2.35" }, "snowflakeAccountName": { "description": "Snowflake account name (applicable for `snowflake_oauth_user_account` only).", "type": "string", "x-versionadded": "v2.21" }, "token": { "description": "OAUTH token (applicable for credentialType `oauth` only).", "type": "string" }, "user": { "description": "Username to update for this set of credentials (applicable for credentialType `basic` and `snowflake_key_pair_user_account` only).", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | apiToken | string | false | minLength: 1minLength: 1 | API token. | | authUrl | string(uri) | false | | The URL used for SAP OAuth authentication. | | authenticationId | string | false | | Authorized external OAuth provider identifier. | | awsAccessKeyId | string | false | | AWS access key ID (applicable for credentialType s3 only). | | awsSecretAccessKey | string | false | | The AWS secret access key (applicable for credentialType s3 only). | | awsSessionToken | string | false | | The AWS session token (applicable for credentialType s3 only). | | azureConnectionString | string | false | | Azure connection string (applicable for credentialType azure only). | | azureTenantId | string | false | | Tenant ID of the Azure AD service principal (applicable for credentialType azure_service_principal only). | | clientId | string | false | | OAUTH client ID (applicable for credentialType snowflake_oauth_user_account, adls_gen2_oauth, sap_oauth_account, azure_service_principal, azure_oauth and client_id_and_secret). | | clientSecret | string | false | | OAUTH client secret (applicable for credentialType snowflake_oauth_user_account, adls_gen2_oauth, sap_oauth_account, azure_service_principal, azure_oauth and client_id_and_secret). | | configId | string | false | | ID of secure configuration credentials to share by admin. Alternative to googleConfigId (deprecated). | | databricksAccessToken | string | false | minLength: 1minLength: 1 | Databricks personal access token. | | description | string | false | | Description of credentials. If omitted, and name is not omitted, clears any previous description for that name. | | enterpriseId | string | false | | Box enterprise identifier (applicable for credentialType box_jwt only). | | gcpKey | GCPKey | false | | The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. | | googleConfigId | string | false | | ID of Google configurations shared by admin (deprecated). Please use configId instead. | | name | string | false | | Name of credentials. | | oauthConfigId | string | false | | ID of snowflake OAuth configurations shared by admin. | | oauthIssuerType | string | false | | Snowflake IDP issuer type (applicable for credentialType snowflake_oauth_user_account only). | | oauthIssuerUrl | string(uri) | false | | Snowflake External IDP issuer URL (applicable for Snowflake External OAUTH connections only). | | oauthScopes | [string] | false | minItems: 1 | External OAUTH scopes (applicable for Snowflake External OAUTH connections, credentialType snowflake_oauth_user_account, adls_gen2_oauth, and azure_oauth). | | passphrase | string | false | minLength: 1minLength: 1 | Optional passphrase to encrypt private key.(applicable for credentialType snowflake_key_pair_user_account only). | | password | string | false | | Password to update for this set of credentials (applicable for credentialType basic only). | | privateKeyStr | string | false | minLength: 1minLength: 1 | Private key for key pair authentication.(applicable for credentialType snowflake_key_pair_user_account only). | | publicKeyId | string | false | | Box public key identifier (applicable for credentialType box_jwt only). | | refreshToken | string | false | | OAUTH refresh token (applicable for credentialType oauth only). | | sapAiApiUrl | string(uri) | false | | The URL used for SAP AI API service. | | snowflakeAccountName | string | false | | Snowflake account name (applicable for snowflake_oauth_user_account only). | | token | string | false | | OAUTH token (applicable for credentialType oauth only). | | user | string | false | | Username to update for this set of credentials (applicable for credentialType basic and snowflake_key_pair_user_account only). | ### Enumerated Values | Property | Value | | --- | --- | | oauthIssuerType | [azure, okta, snowflake] | ## GCPKey ``` { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authProviderX509CertUrl | string(uri) | false | | Auth provider X509 certificate URL. | | authUri | string(uri) | false | | Auth URI. | | clientEmail | string | false | | Client email address. | | clientId | string | false | | Client ID. | | clientX509CertUrl | string(uri) | false | | Client X509 certificate URL. | | privateKey | string | false | | Private key. | | privateKeyId | string | false | | Private key ID | | projectId | string | false | | Project ID. | | tokenUri | string(uri) | false | | Token URI. | | type | string | true | | GCP account type. | ### Enumerated Values | Property | Value | | --- | --- | | type | service_account | ## ListCredentialsAssociationsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of credentials associations.", "items": { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "isDefault": { "description": "Whether this credential association with the given object is default for given session user.", "type": "boolean" }, "name": { "description": "Name of these credentials.", "type": "string" }, "objectId": { "description": "Associated object ID.", "type": "string" }, "objectType": { "description": "Associated object type.", "enum": [ "batchPredictionJobDefinition", "dataconnection" ], "type": "string" } }, "required": [ "creationDate", "credentialId", "name", "objectId", "objectType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrieveCredentialAssociationResponse] | true | | List of credentials associations. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveCredentialAssociationResponse ``` { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "isDefault": { "description": "Whether this credential association with the given object is default for given session user.", "type": "boolean" }, "name": { "description": "Name of these credentials.", "type": "string" }, "objectId": { "description": "Associated object ID.", "type": "string" }, "objectType": { "description": "Associated object type.", "enum": [ "batchPredictionJobDefinition", "dataconnection" ], "type": "string" } }, "required": [ "creationDate", "credentialId", "name", "objectId", "objectType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | ISO-8601 formatted date/time when these credentials were created. | | credentialId | string | true | | ID of these credentials. | | credentialType | string | false | | Type of credentials. | | description | string | false | | Description of these credentials. | | isDefault | boolean | false | | Whether this credential association with the given object is default for given session user. | | name | string | true | | Name of these credentials. | | objectId | string | true | | Associated object ID. | | objectType | string | true | | Associated object type. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | [adls_gen2_oauth, api_token, azure, azure_oauth, azure_service_principal, basic, bearer, box_jwt, client_id_and_secret, databricks_access_token_account, databricks_service_principal_account, external_oauth_provider, gcp, oauth, rsa, s3, sap_oauth, snowflake_key_pair_user_account, snowflake_oauth_user_account, tableau_access_token] | | objectType | [batchPredictionJobDefinition, dataconnection] | ## SetCredentialsAssociationRequest ``` { "properties": { "isDefault": { "default": false, "description": "Whether this credentials' association with the given object is default for given session user.", "type": "boolean" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isDefault | boolean | false | | Whether this credentials' association with the given object is default for given session user. | --- # Custom applications URL: https://docs.datarobot.com/en/docs/api/reference/public-api/custom_applications.html > Use the endpoints described below to manage custom applications. # Custom applications Use the endpoints described below to manage custom applications. ## The list of custom application sources created Operation path: `GET /api/v2/customApplicationSources/` Authentication requirements: `BearerAuth` The list of custom application sources. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | orderBy | query | string | false | The sort order applied to the list of custom application sources. Prefix the attribute name with a dash to sort in descending order, e.g. "-createdAt". | | name | query | string | false | Allows for searching custom application sources by name. | | createdBy | query | string | false | Filter custom application sources to return only those created by the specified user. | | updatedAtStartTs | query | string(date-time) | false | Filter application sources modified on or after this timestamp. | | updatedAtEndTs | query | string(date-time) | false | Filter application sources modified before this timestamp. | | createdAtStartTs | query | string(date-time) | false | Filter application sources created on or after this timestamp. | | createdAtEndTs | query | string(date-time) | false | Filter application sources created before this timestamp. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [name, -name, createdAt, -createdAt, updatedAt, -updatedAt] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The array of custom application source objects.", "items": { "properties": { "createdAt": { "description": "The timestamp when the application source was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source ID.", "type": "string" }, "latestVersion": { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The name of the custom application source.", "maxLength": 255, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application source.", "items": { "enum": [ "CAN_PUBLISH_NEW_IMAGE", "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_VIEW", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE" ], "type": "string" }, "maxItems": 100, "type": "array" }, "updatedAt": { "description": "The timestamp when the application source was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "createdAt", "id", "latestVersion", "name", "orgId", "permissions", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationSourceListResponse | ## Create a custom application source Operation path: `POST /api/v2/customApplicationSources/` Authentication requirements: `BearerAuth` Create a custom application source. ### Body parameter ``` { "properties": { "name": { "description": "The name of the custom application source.", "maxLength": 255, "minLength": 1, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomApplicationSourceCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "The timestamp when the application source was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source ID.", "type": "string" }, "latestVersion": { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The name of the custom application source.", "maxLength": 255, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application source.", "items": { "enum": [ "CAN_PUBLISH_NEW_IMAGE", "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_VIEW", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE" ], "type": "string" }, "maxItems": 100, "type": "array" }, "updatedAt": { "description": "The timestamp when the application source was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "createdAt", "id", "latestVersion", "name", "orgId", "permissions", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationSource | | 202 | Accepted | Creation has successfully started. | None | | 403 | Forbidden | User does not have permission to create a source. | None | | 422 | Unprocessable Entity | Custom application source could not be created with the given input. | None | ## Create a custom application source Operation path: `POST /api/v2/customApplicationSources/fromCustomTemplate/` Authentication requirements: `BearerAuth` Create a custom application source from a template. ### Body parameter ``` { "properties": { "customTemplateId": { "description": "The custom template ID for the custom application.", "type": "string" } }, "required": [ "customTemplateId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomApplicationSourceFromGalleryTemplateCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "The timestamp when the application source was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source ID.", "type": "string" }, "latestVersion": { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The name of the custom application source.", "maxLength": 255, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application source.", "items": { "enum": [ "CAN_PUBLISH_NEW_IMAGE", "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_VIEW", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE" ], "type": "string" }, "maxItems": 100, "type": "array" }, "updatedAt": { "description": "The timestamp when the application source was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "createdAt", "id", "latestVersion", "name", "orgId", "permissions", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationSource | | 202 | Accepted | The custom application source creation process has successfully started. See the location header. | None | | 403 | Forbidden | The current user does not have permission to create a custom application source. | None | | 422 | Unprocessable Entity | A custom application source could not be created with the selected custom template. | None | ## Delete a custom application source by app source ID Operation path: `DELETE /api/v2/customApplicationSources/{appSourceId}/` Authentication requirements: `BearerAuth` Delete a custom application source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | hardDelete | query | string | false | Marks that this application source should be hard deleted instead of soft deleted. | | appSourceId | path | string | true | The ID of the application source. | ### Enumerated Values | Parameter | Value | | --- | --- | | hardDelete | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The source has been deleted. | None | ## Retrieve a custom application source by app source ID Operation path: `GET /api/v2/customApplicationSources/{appSourceId}/` Authentication requirements: `BearerAuth` Retrieve a source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "The timestamp when the application source was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source ID.", "type": "string" }, "latestVersion": { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The name of the custom application source.", "maxLength": 255, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application source.", "items": { "enum": [ "CAN_PUBLISH_NEW_IMAGE", "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_VIEW", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE" ], "type": "string" }, "maxItems": 100, "type": "array" }, "updatedAt": { "description": "The timestamp when the application source was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "createdAt", "id", "latestVersion", "name", "orgId", "permissions", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationSource | ## Update a custom application source's name by app source ID Operation path: `PATCH /api/v2/customApplicationSources/{appSourceId}/` Authentication requirements: `BearerAuth` Update a source's name. ### Body parameter ``` { "properties": { "name": { "description": "The name of the custom application source.", "maxLength": 255, "minLength": 1, "type": "string" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | | body | body | CustomApplicationSourceUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The source has been updated. | None | ## Get a list of users, groups, and organizations with access by app source ID Operation path: `GET /api/v2/customApplicationSources/{appSourceId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups, and organizations with access to this application source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | appSourceId | path | string | true | The ID of the application source. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SharingListV2Response | ## Share an application source by app source ID Operation path: `PATCH /api/v2/customApplicationSources/{appSourceId}/sharedRoles/` Authentication requirements: `BearerAuth` Share an application source with a user, group, or organization. ### Body parameter ``` { "properties": { "note": { "default": "", "description": "A note to go with the project share", "type": "string" }, "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" }, "sendNotification": { "default": false, "description": "Send a notification?", "type": "boolean" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | | body | body | ApplicationSharingUpdateOrRemove | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The roles were updated successfully. | None | | 422 | Unprocessable Entity | The request was formatted improperly. | None | ## Paginated list of custom application source versions of the specified by app source ID Operation path: `GET /api/v2/customApplicationSources/{appSourceId}/versions/` Authentication requirements: `BearerAuth` List of custom application source versions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | appSourceId | path | string | true | The ID of the application source. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of custom application source version objects.", "items": { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationSourceVersionListResponse | ## Create a custom application source version by app source ID Operation path: `POST /api/v2/customApplicationSources/{appSourceId}/versions/` Authentication requirements: `BearerAuth` Create a custom application source. ### Body parameter ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this source version.", "type": "string" }, "baseEnvironmentVersionId": { "description": "The base environment version ID to use with this source version.", "type": "string" }, "baseVersion": { "description": "The ID of the version used as the source for parameter duplication.", "type": "string" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "filesToDelete": { "description": "The IDs of the files to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "label": { "description": "The label for new Custom App Source Version.", "maxLength": 255, "minLength": 1, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | | body | body | CustomApplicationSourceVersionCreate | false | none | ### Example responses > 200 Response ``` { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationSourceVersion | | 201 | Created | Source version was successfully created. | None | | 403 | Forbidden | User does not have permission to create a new source version. | None | | 422 | Unprocessable Entity | Custom application source version could not be created with the given input. | None | ## Delete a custom application source version if it is still mutable by app source ID Operation path: `DELETE /api/v2/customApplicationSources/{appSourceId}/versions/{appSourceVersionId}/` Authentication requirements: `BearerAuth` Delete a custom application source version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | | appSourceVersionId | path | string | true | The ID of the application source version. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The source version has been deleted. | None | ## Retrieve a custom application source version by app source ID Operation path: `GET /api/v2/customApplicationSources/{appSourceId}/versions/{appSourceVersionId}/` Authentication requirements: `BearerAuth` Retrieve a source version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | | appSourceVersionId | path | string | true | The ID of the application source version. | ### Example responses > 200 Response ``` { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationSourceVersion | ## Update a custom application source version by app source ID Operation path: `PATCH /api/v2/customApplicationSources/{appSourceId}/versions/{appSourceVersionId}/` Authentication requirements: `BearerAuth` Update the source version. ### Body parameter ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this source version.", "type": "string" }, "baseEnvironmentVersionId": { "description": "The base environment version ID to use with this source version.", "type": "string" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "filesToDelete": { "description": "The IDs of the files to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "label": { "description": "The label for new Custom App Source Version.", "maxLength": 255, "minLength": 1, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | | appSourceVersionId | path | string | true | The ID of the application source version. | | body | body | CustomApplicationSourceVersionUpdate | false | none | ### Example responses > 200 Response ``` { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationSourceVersion | ## Download Custom Application Source version files as a zip archive by app source ID Operation path: `GET /api/v2/customApplicationSources/{appSourceId}/versions/{appSourceVersionId}/archive/` Authentication requirements: `BearerAuth` Download all files from a Custom Application Source version as a zip archive. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | | appSourceVersionId | path | string | true | The ID of the application source version. | ### Example responses > 200 Response ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A zip archive containing all files in the source version. | string | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Specifies the zip filename derived from the version label (e.g. "attachment; filename=v1.0.zip"). | ## Update the custom application source version by app source ID Operation path: `POST /api/v2/customApplicationSources/{appSourceId}/versions/{appSourceVersionId}/fromCodespace/` Authentication requirements: `BearerAuth` Update files in the source version from Codespace. ### Body parameter ``` { "properties": { "codespaceId": { "description": "The ID of the Codespace that should be used as source for files.", "type": "string" }, "label": { "description": "The label for new Custom App Source Version in case current version is frozen and new should be created.", "maxLength": 255, "minLength": 1, "type": "string" } }, "required": [ "codespaceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | | appSourceVersionId | path | string | true | The ID of the application source version. | | body | body | CustomApplicationSourceVersionFromCodespace | false | none | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "The custom application source version ID.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationSourceVersionFromCodespaceResponse | | 202 | Accepted | Task for updating source applied. | None | ## Retrieve a file by app source ID Operation path: `GET /api/v2/customApplicationSources/{appSourceId}/versions/{appSourceVersionId}/items/{itemId}/` Authentication requirements: `BearerAuth` Retrieve a file stored inside a custom application source version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | | appSourceVersionId | path | string | true | The ID of the application source version. | | itemId | path | string | true | The ID of file item inside of the application source version. | ### Example responses > 200 Response ``` { "properties": { "content": { "description": "The textual content of the file item.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The full internal path of the file item.", "type": "string" }, "id": { "description": "The ID of the file item.", "type": "string" } }, "required": [ "content", "fileName", "filePath", "id" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationItemRetrieve | ## Update a codespace by app source ID Operation path: `POST /api/v2/customApplicationSources/{appSourceId}/versions/{appSourceVersionId}/toCodespace/` Authentication requirements: `BearerAuth` Update a codespace with files from the source version. ### Body parameter ``` { "properties": { "codespaceId": { "description": "The ID of the Codespace that should be used as source for files.", "type": "string" } }, "required": [ "codespaceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | appSourceId | path | string | true | The ID of the application source. | | appSourceVersionId | path | string | true | The ID of the application source version. | | body | body | CustomApplicationSourceVersionToCodespace | false | none | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "The custom application source version ID.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationSourceVersionFromCodespaceResponse | | 202 | Accepted | Task for uploading files to codespace applied. | None | ## The list of applications created by the currently authenticated user Operation path: `GET /api/v2/customApplications/` Authentication requirements: `BearerAuth` The list of applications created by the currently authenticated user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | orderBy | query | string | false | The sort order applied to the list of custom applications. Prefix the attribute name with a dash to sort in descending order, e.g. "-createdAt". Additional sorting options include "bundleSize" and "replicas". | | name | query | string | false | Allows for searching custom applications by name. | | customApplicationSourceId | query | any | false | Allows you to get custom applications created only from specific sources. To find apps not linked to a custom application source, use the value "null". | | includeSourceLabels | query | boolean | false | Whether or not you want to include the name of the application source andthe label of the source version. | | requireSource | query | boolean | false | Whether we should only fetch apps created from a custom application source. | | createdBy | query | string | false | Filter custom applications to return only those created by the specified user. | | status | query | any | false | Filter applications by status. | | updatedAtStartTs | query | string(date-time) | false | Filter applications modified on or after this timestamp. | | updatedAtEndTs | query | string(date-time) | false | Filter applications modified before this timestamp. | | externalAccessEnabled | query | boolean | false | Filter applications by external access enablement status. | | resourceLabel | query | string | false | Filter applications by resource bundle label. | | replicasMin | query | integer | false | Filter applications with replica count greater than or equal to this value. | | replicasMax | query | integer | false | Filter applications with replica count less than or equal to this value. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [name, -name, createdAt, -createdAt, updatedAt, -updatedAt, bundleSize, -bundleSize, replicas, -replicas] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The array of custom application objects.", "items": { "properties": { "allowAutoStopping": { "description": "Determines if apps are auto-paused to save resources.", "type": [ "boolean", "null" ] }, "applicationUrl": { "description": "The URL for accessing application endpoints", "format": "uri", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp when the application was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application", "type": [ "string", "null" ] }, "customApplicationSourceId": { "description": "The custom application source used in app.", "type": [ "string", "null" ] }, "customApplicationSourceVersionId": { "description": "The custom application source version used in app.", "type": [ "string", "null" ] }, "envVersionId": { "description": "The execution environment version used in app", "type": [ "string", "null" ] }, "expiresAt": { "description": "ISO-8601 formatted date of the custom application removing date", "format": "date-time", "type": [ "string", "null" ] }, "externalAccessEnabled": { "description": "Determines if sharing with guest users is allowed.", "type": [ "boolean", "null" ] }, "externalAccessRecipients": { "description": "The external users and domains allowed to view this app.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "id": { "description": "The custom application ID.", "type": "string" }, "lrsId": { "description": "The Long Running Service ID associated with app.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "name": { "description": "The name of the custom application.", "maxLength": 512, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_DELETE", "CAN_PUBLISH_NEW_IMAGE", "CAN_SEE_SOURCE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "maxItems": 100, "type": "array" }, "resources": { "description": "The resource configuration for the application, including CPU, memory, replicas, etc.", "properties": { "cpuLimit": { "description": "The CPU core limit for a container.", "type": "number" }, "cpuRequest": { "description": "The requested CPU cores for a container.", "type": "number" }, "healthEndpointPath": { "description": "The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from `serviceWebRequestsOnRootPath`. Use this to expose a dedicated health endpoint (e.g., `/healthz`) instead of probing the root path.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "memoryLimit": { "description": "The memory limit for a container in bytes.", "type": "integer" }, "memoryRequest": { "description": "The requested memory for a container in bytes.", "type": "integer" }, "replicas": { "description": "The number of running application replicas.", "type": "integer" }, "resourceLabel": { "description": "The ID of the resource request bundle used for custom application.", "type": "string" }, "serviceWebRequestsOnRootPath": { "description": "Sets whether applications made from this source version expect to receive requests on `/` or on `/apps/{ID}` by default.", "type": "boolean" }, "sessionAffinity": { "description": "The session affinity for an application.", "type": "boolean" }, "storageLimit": { "description": "The ephemeral storage limit for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" }, "storageRequest": { "description": "The requested ephemeral storage for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" } }, "required": [ "cpuLimit", "cpuRequest", "healthEndpointPath", "memoryLimit", "memoryRequest", "replicas", "resourceLabel", "serviceWebRequestsOnRootPath", "sessionAffinity", "storageLimit", "storageRequest" ], "type": "object", "x-versionadded": "v2.37" }, "sourceName": { "description": "The name of the custom app source.", "type": "string" }, "sourceVersionLabel": { "description": "The label of the source version.", "type": "string" }, "status": { "description": "The state of application in LRS", "enum": [ "created", "failed", "initializing", "paused", "publishing", "running" ], "type": "string" }, "updatedAt": { "description": "The timestamp when the application was updated.", "type": "string" }, "userId": { "description": "Creator's ID", "type": "string" } }, "required": [ "allowAutoStopping", "applicationUrl", "createdAt", "customApplicationSourceId", "customApplicationSourceVersionId", "envVersionId", "expiresAt", "externalAccessEnabled", "externalAccessRecipients", "id", "lrsId", "name", "orgId", "permissions", "status", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationListResponse | ## Create a custom application Operation path: `POST /api/v2/customApplications/` Authentication requirements: `BearerAuth` Create a custom application. ### Body parameter ``` { "properties": { "applicationSourceId": { "description": "The ID of the custom application source to be used for the new application. The latest version version will be chosen.", "type": "string" }, "applicationSourceVersionId": { "description": "The ID of the custom application source version to be used for the new application.", "type": "string" }, "environmentId": { "description": "The execution environment ID for the application.", "type": "string" }, "name": { "description": "The name of the custom application.", "maxLength": 512, "type": [ "string", "null" ] }, "resources": { "description": "Resources required for running custom application.", "properties": { "healthEndpointPath": { "description": "The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from `serviceWebRequestsOnRootPath`. Use this to expose a dedicated health endpoint (e.g., `/healthz`) instead of probing the root path.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "replicas": { "description": "The number of running application replicas.", "minimum": 0, "type": "integer" }, "resourceLabel": { "description": "The ID of the resource request bundle used for custom application.", "type": "string" }, "serviceWebRequestsOnRootPath": { "description": "Sets whether applications made from this source version expect to receive requests on `/` or on `/apps/{ID}` by default.", "type": [ "boolean", "null" ] }, "sessionAffinity": { "description": "The Session affinity of an application source version.", "type": [ "boolean", "null" ] } }, "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomApplicationCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "allowAutoStopping": { "description": "Determines if apps are auto-paused to save resources.", "type": [ "boolean", "null" ] }, "applicationUrl": { "description": "The URL for accessing application endpoints", "format": "uri", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp when the application was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application", "type": [ "string", "null" ] }, "customApplicationSourceId": { "description": "The custom application source used in app.", "type": [ "string", "null" ] }, "customApplicationSourceVersionId": { "description": "The custom application source version used in app.", "type": [ "string", "null" ] }, "envVersionId": { "description": "The execution environment version used in app", "type": [ "string", "null" ] }, "expiresAt": { "description": "ISO-8601 formatted date of the custom application removing date", "format": "date-time", "type": [ "string", "null" ] }, "externalAccessEnabled": { "description": "Determines if sharing with guest users is allowed.", "type": [ "boolean", "null" ] }, "externalAccessRecipients": { "description": "The external users and domains allowed to view this app.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "id": { "description": "The custom application ID.", "type": "string" }, "lrsId": { "description": "The Long Running Service ID associated with app.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "name": { "description": "The name of the custom application.", "maxLength": 512, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_DELETE", "CAN_PUBLISH_NEW_IMAGE", "CAN_SEE_SOURCE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "maxItems": 100, "type": "array" }, "resources": { "description": "The resource configuration for the application, including CPU, memory, replicas, etc.", "properties": { "cpuLimit": { "description": "The CPU core limit for a container.", "type": "number" }, "cpuRequest": { "description": "The requested CPU cores for a container.", "type": "number" }, "healthEndpointPath": { "description": "The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from `serviceWebRequestsOnRootPath`. Use this to expose a dedicated health endpoint (e.g., `/healthz`) instead of probing the root path.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "memoryLimit": { "description": "The memory limit for a container in bytes.", "type": "integer" }, "memoryRequest": { "description": "The requested memory for a container in bytes.", "type": "integer" }, "replicas": { "description": "The number of running application replicas.", "type": "integer" }, "resourceLabel": { "description": "The ID of the resource request bundle used for custom application.", "type": "string" }, "serviceWebRequestsOnRootPath": { "description": "Sets whether applications made from this source version expect to receive requests on `/` or on `/apps/{ID}` by default.", "type": "boolean" }, "sessionAffinity": { "description": "The session affinity for an application.", "type": "boolean" }, "storageLimit": { "description": "The ephemeral storage limit for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" }, "storageRequest": { "description": "The requested ephemeral storage for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" } }, "required": [ "cpuLimit", "cpuRequest", "healthEndpointPath", "memoryLimit", "memoryRequest", "replicas", "resourceLabel", "serviceWebRequestsOnRootPath", "sessionAffinity", "storageLimit", "storageRequest" ], "type": "object", "x-versionadded": "v2.37" }, "sourceName": { "description": "The name of the custom app source.", "type": "string" }, "sourceVersionLabel": { "description": "The label of the source version.", "type": "string" }, "status": { "description": "The state of application in LRS", "enum": [ "created", "failed", "initializing", "paused", "publishing", "running" ], "type": "string" }, "updatedAt": { "description": "The timestamp when the application was updated.", "type": "string" }, "userId": { "description": "Creator's ID", "type": "string" } }, "required": [ "allowAutoStopping", "applicationUrl", "createdAt", "customApplicationSourceId", "customApplicationSourceVersionId", "envVersionId", "expiresAt", "externalAccessEnabled", "externalAccessRecipients", "id", "lrsId", "name", "orgId", "permissions", "status", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplication | | 202 | Accepted | Creation has successfully started. See the Location header. | None | | 403 | Forbidden | User does not have permission to launch application of provided type. | None | | 422 | Unprocessable Entity | Application could not be created with the given input. | None | ## Delete an application by application ID Operation path: `DELETE /api/v2/customApplications/{applicationId}/` Authentication requirements: `BearerAuth` Delete an application. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | hardDelete | query | string | false | Marks that this application should be hard deleted instead of soft deleted. | | applicationId | path | string | true | The ID of the application | ### Enumerated Values | Parameter | Value | | --- | --- | | hardDelete | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The application has been deleted. | None | ## Retrieve an application by application ID Operation path: `GET /api/v2/customApplications/{applicationId}/` Authentication requirements: `BearerAuth` Retrieve an application. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | includeSourceLabels | query | boolean | false | Whether or not you want to include the name of the application source andthe label of the source version. | | applicationId | path | string | true | The ID of the application | ### Example responses > 200 Response ``` { "properties": { "allowAutoStopping": { "description": "Determines if apps are auto-paused to save resources.", "type": [ "boolean", "null" ] }, "applicationUrl": { "description": "The URL for accessing application endpoints", "format": "uri", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp when the application was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application", "type": [ "string", "null" ] }, "customApplicationSourceId": { "description": "The custom application source used in app.", "type": [ "string", "null" ] }, "customApplicationSourceVersionId": { "description": "The custom application source version used in app.", "type": [ "string", "null" ] }, "envVersionId": { "description": "The execution environment version used in app", "type": [ "string", "null" ] }, "expiresAt": { "description": "ISO-8601 formatted date of the custom application removing date", "format": "date-time", "type": [ "string", "null" ] }, "externalAccessEnabled": { "description": "Determines if sharing with guest users is allowed.", "type": [ "boolean", "null" ] }, "externalAccessRecipients": { "description": "The external users and domains allowed to view this app.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "id": { "description": "The custom application ID.", "type": "string" }, "lrsId": { "description": "The Long Running Service ID associated with app.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "name": { "description": "The name of the custom application.", "maxLength": 512, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_DELETE", "CAN_PUBLISH_NEW_IMAGE", "CAN_SEE_SOURCE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "maxItems": 100, "type": "array" }, "resources": { "description": "The resource configuration for the application, including CPU, memory, replicas, etc.", "properties": { "cpuLimit": { "description": "The CPU core limit for a container.", "type": "number" }, "cpuRequest": { "description": "The requested CPU cores for a container.", "type": "number" }, "healthEndpointPath": { "description": "The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from `serviceWebRequestsOnRootPath`. Use this to expose a dedicated health endpoint (e.g., `/healthz`) instead of probing the root path.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "memoryLimit": { "description": "The memory limit for a container in bytes.", "type": "integer" }, "memoryRequest": { "description": "The requested memory for a container in bytes.", "type": "integer" }, "replicas": { "description": "The number of running application replicas.", "type": "integer" }, "resourceLabel": { "description": "The ID of the resource request bundle used for custom application.", "type": "string" }, "serviceWebRequestsOnRootPath": { "description": "Sets whether applications made from this source version expect to receive requests on `/` or on `/apps/{ID}` by default.", "type": "boolean" }, "sessionAffinity": { "description": "The session affinity for an application.", "type": "boolean" }, "storageLimit": { "description": "The ephemeral storage limit for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" }, "storageRequest": { "description": "The requested ephemeral storage for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" } }, "required": [ "cpuLimit", "cpuRequest", "healthEndpointPath", "memoryLimit", "memoryRequest", "replicas", "resourceLabel", "serviceWebRequestsOnRootPath", "sessionAffinity", "storageLimit", "storageRequest" ], "type": "object", "x-versionadded": "v2.37" }, "sourceName": { "description": "The name of the custom app source.", "type": "string" }, "sourceVersionLabel": { "description": "The label of the source version.", "type": "string" }, "status": { "description": "The state of application in LRS", "enum": [ "created", "failed", "initializing", "paused", "publishing", "running" ], "type": "string" }, "updatedAt": { "description": "The timestamp when the application was updated.", "type": "string" }, "userId": { "description": "Creator's ID", "type": "string" } }, "required": [ "allowAutoStopping", "applicationUrl", "createdAt", "customApplicationSourceId", "customApplicationSourceVersionId", "envVersionId", "expiresAt", "externalAccessEnabled", "externalAccessRecipients", "id", "lrsId", "name", "orgId", "permissions", "status", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplication | ## Update an application's name by application ID Operation path: `PATCH /api/v2/customApplications/{applicationId}/` Authentication requirements: `BearerAuth` Update an application's name. ### Body parameter ``` { "properties": { "allowAutoStopping": { "description": "Determines if the custom app should be stopped automatically.", "type": "boolean" }, "customApplicationSourceVersionId": { "description": "The ID of the custom application source version to set this app to.", "type": "string" }, "externalAccessEnabled": { "description": "Determines if the custom app can be shared with guest users.", "type": "boolean" }, "externalAccessRecipients": { "description": "Who should be able to access the custom app", "items": { "description": "The email address, or email domain of who can use an app", "maxLength": 512, "minLength": 0, "type": "string" }, "maxItems": 2048, "type": "array" }, "name": { "description": "The name of the custom application.", "maxLength": 512, "minLength": 1, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationId | path | string | true | The ID of the application | | body | body | CustomApplicationUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The application has been updated. | None | ## Retrieve an application's publication history by application ID Operation path: `GET /api/v2/customApplications/{applicationId}/history/` Authentication requirements: `BearerAuth` Retrieve an application's publication history. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | applicationId | path | string | true | The ID of the application | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of custom application soure versions published to this custom application.", "items": { "properties": { "createdAt": { "description": "The date and time that the user published a new version of the app.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who published the application.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of the user who published the application.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of the user who published the application.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of the user who published the application.", "type": [ "string", "null" ] }, "sourceId": { "description": "The custom application source ID of the record.", "type": "string" }, "sourceName": { "description": "The name of the custom app source.", "type": "string" }, "sourceVersionId": { "description": "The custom application source version ID of the record.", "type": "string" }, "sourceVersionLabel": { "description": "The label of the source version.", "type": "string" } }, "required": [ "createdAt", "sourceId", "sourceVersionId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationsHistoryListResponse | ## Retrieve an application's logs by application ID Operation path: `GET /api/v2/customApplications/{applicationId}/logs/` Authentication requirements: `BearerAuth` Retrieve an application's logs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationId | path | string | true | The ID of the application | ### Example responses > 200 Response ``` { "properties": { "buildError": { "description": "The build error of the custom application.", "type": "string" }, "buildLog": { "description": "The build log of the custom application.", "type": "string" }, "buildStatus": { "description": "The build status of the custom application.", "type": "string" }, "logs": { "description": "The logs of the custom application.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "logs" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationLogs | ## Get a list of users, groups and organizations that have an access by application ID Operation path: `GET /api/v2/customApplications/{applicationId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations that have an access to this application. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | applicationId | path | string | true | The ID of the application | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SharingListV2Response | ## Share an application by application ID Operation path: `PATCH /api/v2/customApplications/{applicationId}/sharedRoles/` Authentication requirements: `BearerAuth` Share an application with a user, group, or organization. ### Body parameter ``` { "properties": { "note": { "default": "", "description": "A note to go with the project share", "type": "string" }, "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" }, "sendNotification": { "default": false, "description": "Send a notification?", "type": "boolean" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationId | path | string | true | The ID of the application | | body | body | ApplicationSharingUpdateOrRemove | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The roles were updated successfully. | None | | 422 | Unprocessable Entity | The request was formatted improperly. | None | ## Retrieve an application's usages by application ID Operation path: `GET /api/v2/customApplications/{applicationId}/usages/` Authentication requirements: `BearerAuth` Retrieve an application's usages. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | start | query | string(date-time) | false | Filter visits on or after this UTC timestamp (inclusive). ISO 8601 format. | | end | query | string(date-time) | false | Filter visits on or before this UTC timestamp (inclusive). ISO 8601 format. | | applicationId | path | string | true | The ID of the application | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of visits to the custom application.", "items": { "properties": { "userId": { "description": "The ID of the user (or null for a guest).", "type": [ "string", "null" ] }, "userType": { "description": "Determines whether the user was a creator, viewer, or guest at the time of visit.", "enum": [ "guest", "viewer", "creator" ], "type": [ "string", "null" ] }, "username": { "description": "The name of the user.", "maxLength": 512, "minLength": 1, "type": "string" }, "visitTimestamp": { "description": "The date and time that user last visited the app.", "format": "date-time", "type": "string" } }, "required": [ "userId", "userType", "username", "visitTimestamp" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CustomApplicationsUsagesListResponse | ## Download an application's access logs by application ID Operation path: `GET /api/v2/customApplications/{applicationId}/usages/download/` Authentication requirements: `BearerAuth` Download an application's access logs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date-time) | true | Filter visits on or after this UTC timestamp (inclusive). ISO 8601 format. Required. | | end | query | string(date-time) | true | Filter visits on or before this UTC timestamp (inclusive). ISO 8601 format. Required. | | applicationId | path | string | true | The ID of the application | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Retrieve a list of custom templates Operation path: `GET /api/v2/customTemplates/` Authentication requirements: `BearerAuth` Retrieve a list of custom templates. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | false | The order to sort the custom templates. | | search | query | string | false | Only return custom templates whose name or description contain this text. | | tag | query | string | false | Only return custom templates with a matching tag. | | templateSubType | query | string | false | Only return custom templates of this sub-type. | | templateType | query | string | false | Only return custom templates of this type. | | publisher | query | string | false | Only return custom templates with this publisher. | | category | query | string | false | Only return custom templates with this category (use case). | | showHidden | query | boolean | false | Hidden templates are not visible in the UI. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [name, -name, createdAt, -createdAt, templateType, -templateType, templateSubType, -templateSubType] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of custom templates.", "items": { "properties": { "defaultEnvironment": { "description": "Specifies the default environment for the custom template.", "properties": { "environmentId": { "description": "The ID the environment to use for the public custom metric image.", "type": "string" }, "environmentVersionId": { "description": "The ID of the specific environment version to use with the public custom metric image.", "type": "string" } }, "required": [ "environmentId", "environmentVersionId" ], "type": "object", "x-versionadded": "v2.36" }, "defaultResourceBundleId": { "description": "Specifies the default resource bundle for the custom metric template.", "enum": [ "cpu.nano", "cpu.micro", "cpu.small", "cpu.medium", "cpu.large", "cpu.xlarge", "cpu.2xlarge", "cpu.3xlarge", "cpu.4xlarge", "cpu.5xlarge", "cpu.6xlarge", "cpu.7xlarge", "cpu.8xlarge", "cpu.16xlarge", "DRAWSR6i.4xlargeFrac8Regular", "DRAWSR6i.4xlargeFrac4Regular", "DRAWSG4dn.xlargeFrac1Regular", "DRAWSG4dn.2xlargeFrac1Regular", "DRAWSG5.2xlargeFrac1Regular", "DRAWSG5.12xlargeFrac1Regular", "DRAWSG5.48xlargeFrac1Regular", "DRAWSG6e.xlargeFrac1Regular", "DRAWSG6e.12xlargeFrac1Regular", "DRAWSG6e.48xlargeFrac1Regular", "gpu.small", "gpu.medium", "gpu.large", "gpu.xlarge", "gpu.2xlarge", "gpu.3xlarge", "gpu.5xlarge", "gpu.7xlarge", "starter", "basic", "basic.8x", "train.l", "infer.s", "infer.m", "infer.l" ], "type": [ "string", "null" ] }, "description": { "description": "A description of the custom template.", "maxLength": 10000, "type": "string" }, "enabled": { "description": "Determines whether the template is enabled.", "type": "boolean" }, "id": { "description": "The ID of the custom template.", "type": "string" }, "items": { "description": "A list of custom files.", "items": { "properties": { "id": { "description": "The ID of the custom template file.", "type": "string" }, "name": { "description": "Name of the custom template file.", "maxLength": 255, "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The name of the custom template.", "maxLength": 255, "type": "string" }, "templateMetadata": { "description": "Specifies permanent metadata for the custom template.", "properties": { "classLabels": { "description": "List of class names in case of creating a Binary or a multiclass custom model.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "readme": { "description": "Content of README.md file of the template.", "maxLength": 1048576, "type": [ "string", "null" ] }, "resourceBundleIds": { "description": "Custom template resource bundle IDs list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "source": { "description": "Custom template source repo.", "type": "object", "x-versionadded": "v2.36" }, "tags": { "description": "Custom template tags list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "templateTypeSpecificResources": { "description": "Specifies resources for the custom template.", "properties": { "serviceWebRequestsOnRootPath": { "default": false, "description": "Whether the 'service_web_requests_on_root_path' resource should be enabled on the custom app.", "type": [ "boolean", "null" ] } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" }, "templateSubType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": [ "string", "null" ] }, "templateType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": "string" } }, "required": [ "defaultEnvironment", "defaultResourceBundleId", "description", "enabled", "id", "items", "name", "templateMetadata", "templateSubType", "templateType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The paginated list of custom templates. | CustomTemplateListResponse | | 403 | Forbidden | User does not have permission to access custom templates. | None | ## Create a custom template Operation path: `POST /api/v2/customTemplates/` Authentication requirements: `BearerAuth` Create a custom template. ### Body parameter ``` { "properties": { "defaultEnvironment": { "description": "Specifies the default environment for the custom metric template.", "type": "string" }, "defaultResourceBundleId": { "description": "Specifies the default resource bundle for the custom metric template.", "enum": [ "cpu.nano", "cpu.micro", "cpu.small", "cpu.medium", "cpu.large", "cpu.xlarge", "cpu.2xlarge", "cpu.3xlarge", "cpu.4xlarge", "cpu.5xlarge", "cpu.6xlarge", "cpu.7xlarge", "cpu.8xlarge", "cpu.16xlarge", "DRAWSR6i.4xlargeFrac8Regular", "DRAWSR6i.4xlargeFrac4Regular", "DRAWSG4dn.xlargeFrac1Regular", "DRAWSG4dn.2xlargeFrac1Regular", "DRAWSG5.2xlargeFrac1Regular", "DRAWSG5.12xlargeFrac1Regular", "DRAWSG5.48xlargeFrac1Regular", "DRAWSG6e.xlargeFrac1Regular", "DRAWSG6e.12xlargeFrac1Regular", "DRAWSG6e.48xlargeFrac1Regular", "gpu.small", "gpu.medium", "gpu.large", "gpu.xlarge", "gpu.2xlarge", "gpu.3xlarge", "gpu.5xlarge", "gpu.7xlarge", "starter", "basic", "basic.8x", "train.l", "infer.s", "infer.m", "infer.l" ], "type": "string" }, "description": { "description": "A description of the custom template.", "maxLength": 10000, "type": "string" }, "enabled": { "default": true, "description": "Disabled templates remain visible in the UI but cannot be used.", "type": "boolean", "x-versionadded": "v2.36" }, "file": { "description": "The file to be used to create the custom metric template.", "format": "binary", "type": "string" }, "isHidden": { "default": false, "description": "Hidden templates are not visible in the UI.", "type": "boolean", "x-versionadded": "v2.37" }, "name": { "description": "The name of the custom template.", "maxLength": 255, "type": "string" }, "templateMetadata": { "description": "Specifies permanent metadata for the custom template.", "type": [ "string", "null" ] }, "templateSubType": { "description": "Defines sub-type of the custom template.", "maxLength": 255, "type": "string" }, "templateType": { "description": "Defines type of the custom template.", "maxLength": 255, "type": "string" } }, "required": [ "defaultEnvironment", "description", "file", "name", "templateSubType", "templateType" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomTemplateCreatePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The custom template was successfully created. | None | | 403 | Forbidden | User does not have permission to create a custom template. | None | ## Delete a custom template by custom template ID Operation path: `DELETE /api/v2/customTemplates/{customTemplateId}/` Authentication requirements: `BearerAuth` Delete a custom template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTemplateId | path | string | true | The ID of the custom template. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The requested custom template was successfully deleted. | None | | 403 | Forbidden | User does not have permission to delete a custom template. | None | | 404 | Not Found | Custom template was not found. | None | ## Retrieve a single custom template by custom template ID Operation path: `GET /api/v2/customTemplates/{customTemplateId}/` Authentication requirements: `BearerAuth` Retrieve a single custom template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTemplateId | path | string | true | The ID of the custom template. | ### Example responses > 200 Response ``` { "properties": { "defaultEnvironment": { "description": "Specifies the default environment for the custom template.", "properties": { "environmentId": { "description": "The ID the environment to use for the public custom metric image.", "type": "string" }, "environmentVersionId": { "description": "The ID of the specific environment version to use with the public custom metric image.", "type": "string" } }, "required": [ "environmentId", "environmentVersionId" ], "type": "object", "x-versionadded": "v2.36" }, "defaultResourceBundleId": { "description": "Specifies the default resource bundle for the custom metric template.", "enum": [ "cpu.nano", "cpu.micro", "cpu.small", "cpu.medium", "cpu.large", "cpu.xlarge", "cpu.2xlarge", "cpu.3xlarge", "cpu.4xlarge", "cpu.5xlarge", "cpu.6xlarge", "cpu.7xlarge", "cpu.8xlarge", "cpu.16xlarge", "DRAWSR6i.4xlargeFrac8Regular", "DRAWSR6i.4xlargeFrac4Regular", "DRAWSG4dn.xlargeFrac1Regular", "DRAWSG4dn.2xlargeFrac1Regular", "DRAWSG5.2xlargeFrac1Regular", "DRAWSG5.12xlargeFrac1Regular", "DRAWSG5.48xlargeFrac1Regular", "DRAWSG6e.xlargeFrac1Regular", "DRAWSG6e.12xlargeFrac1Regular", "DRAWSG6e.48xlargeFrac1Regular", "gpu.small", "gpu.medium", "gpu.large", "gpu.xlarge", "gpu.2xlarge", "gpu.3xlarge", "gpu.5xlarge", "gpu.7xlarge", "starter", "basic", "basic.8x", "train.l", "infer.s", "infer.m", "infer.l" ], "type": [ "string", "null" ] }, "description": { "description": "A description of the custom template.", "maxLength": 10000, "type": "string" }, "enabled": { "description": "Determines whether the template is enabled.", "type": "boolean" }, "id": { "description": "The ID of the custom template.", "type": "string" }, "items": { "description": "A list of custom files.", "items": { "properties": { "id": { "description": "The ID of the custom template file.", "type": "string" }, "name": { "description": "Name of the custom template file.", "maxLength": 255, "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The name of the custom template.", "maxLength": 255, "type": "string" }, "templateMetadata": { "description": "Specifies permanent metadata for the custom template.", "properties": { "classLabels": { "description": "List of class names in case of creating a Binary or a multiclass custom model.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "readme": { "description": "Content of README.md file of the template.", "maxLength": 1048576, "type": [ "string", "null" ] }, "resourceBundleIds": { "description": "Custom template resource bundle IDs list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "source": { "description": "Custom template source repo.", "type": "object", "x-versionadded": "v2.36" }, "tags": { "description": "Custom template tags list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "templateTypeSpecificResources": { "description": "Specifies resources for the custom template.", "properties": { "serviceWebRequestsOnRootPath": { "default": false, "description": "Whether the 'service_web_requests_on_root_path' resource should be enabled on the custom app.", "type": [ "boolean", "null" ] } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" }, "templateSubType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": [ "string", "null" ] }, "templateType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": "string" } }, "required": [ "defaultEnvironment", "defaultResourceBundleId", "description", "enabled", "id", "items", "name", "templateMetadata", "templateSubType", "templateType" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A given custom template. | CustomTemplateEntity | | 403 | Forbidden | User does not have permission to access a particular custom template. | None | ## Update the given custom template by custom template ID Operation path: `PATCH /api/v2/customTemplates/{customTemplateId}/` Authentication requirements: `BearerAuth` Update the given custom template. ### Body parameter ``` { "properties": { "defaultEnvironment": { "description": "Specifies the default environment for the custom template.", "type": "string" }, "defaultResourceBundleId": { "description": "Specifies the default resource bundle for the custom metric template.", "enum": [ "cpu.nano", "cpu.micro", "cpu.small", "cpu.medium", "cpu.large", "cpu.xlarge", "cpu.2xlarge", "cpu.3xlarge", "cpu.4xlarge", "cpu.5xlarge", "cpu.6xlarge", "cpu.7xlarge", "cpu.8xlarge", "cpu.16xlarge", "DRAWSR6i.4xlargeFrac8Regular", "DRAWSR6i.4xlargeFrac4Regular", "DRAWSG4dn.xlargeFrac1Regular", "DRAWSG4dn.2xlargeFrac1Regular", "DRAWSG5.2xlargeFrac1Regular", "DRAWSG5.12xlargeFrac1Regular", "DRAWSG5.48xlargeFrac1Regular", "DRAWSG6e.xlargeFrac1Regular", "DRAWSG6e.12xlargeFrac1Regular", "DRAWSG6e.48xlargeFrac1Regular", "gpu.small", "gpu.medium", "gpu.large", "gpu.xlarge", "gpu.2xlarge", "gpu.3xlarge", "gpu.5xlarge", "gpu.7xlarge", "starter", "basic", "basic.8x", "train.l", "infer.s", "infer.m", "infer.l" ], "type": "string" }, "description": { "description": "A description of the custom template.", "maxLength": 10000, "type": "string" }, "enabled": { "default": true, "description": "Disabled templates remain visible in the UI but cannot be used.", "type": "boolean", "x-versionadded": "v2.36" }, "file": { "description": "The file to be used to create the custom template.", "format": "binary", "type": "string" }, "isHidden": { "default": false, "description": "Hidden templates are not visible in the UI.", "type": "boolean", "x-versionadded": "v2.37" }, "name": { "description": "The name of the custom template.", "maxLength": 255, "type": "string" }, "templateMetadata": { "description": "Specifies permanent metadata for the custom template.", "type": [ "string", "null" ] }, "templateSubType": { "description": "Defines the sub-type of the custom template.", "maxLength": 255, "type": "string" }, "templateType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": "string" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTemplateId | path | string | true | The ID of the custom template. | | body | body | CustomTemplateUpdatePayload | false | none | ### Example responses > 200 Response ``` { "properties": { "defaultEnvironment": { "description": "Specifies the default environment for the custom template.", "properties": { "environmentId": { "description": "The ID the environment to use for the public custom metric image.", "type": "string" }, "environmentVersionId": { "description": "The ID of the specific environment version to use with the public custom metric image.", "type": "string" } }, "required": [ "environmentId", "environmentVersionId" ], "type": "object", "x-versionadded": "v2.36" }, "defaultResourceBundleId": { "description": "Specifies the default resource bundle for the custom metric template.", "enum": [ "cpu.nano", "cpu.micro", "cpu.small", "cpu.medium", "cpu.large", "cpu.xlarge", "cpu.2xlarge", "cpu.3xlarge", "cpu.4xlarge", "cpu.5xlarge", "cpu.6xlarge", "cpu.7xlarge", "cpu.8xlarge", "cpu.16xlarge", "DRAWSR6i.4xlargeFrac8Regular", "DRAWSR6i.4xlargeFrac4Regular", "DRAWSG4dn.xlargeFrac1Regular", "DRAWSG4dn.2xlargeFrac1Regular", "DRAWSG5.2xlargeFrac1Regular", "DRAWSG5.12xlargeFrac1Regular", "DRAWSG5.48xlargeFrac1Regular", "DRAWSG6e.xlargeFrac1Regular", "DRAWSG6e.12xlargeFrac1Regular", "DRAWSG6e.48xlargeFrac1Regular", "gpu.small", "gpu.medium", "gpu.large", "gpu.xlarge", "gpu.2xlarge", "gpu.3xlarge", "gpu.5xlarge", "gpu.7xlarge", "starter", "basic", "basic.8x", "train.l", "infer.s", "infer.m", "infer.l" ], "type": [ "string", "null" ] }, "description": { "description": "A description of the custom template.", "maxLength": 10000, "type": "string" }, "enabled": { "description": "Determines whether the template is enabled.", "type": "boolean" }, "id": { "description": "The ID of the custom template.", "type": "string" }, "items": { "description": "A list of custom files.", "items": { "properties": { "id": { "description": "The ID of the custom template file.", "type": "string" }, "name": { "description": "Name of the custom template file.", "maxLength": 255, "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The name of the custom template.", "maxLength": 255, "type": "string" }, "templateMetadata": { "description": "Specifies permanent metadata for the custom template.", "properties": { "classLabels": { "description": "List of class names in case of creating a Binary or a multiclass custom model.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "readme": { "description": "Content of README.md file of the template.", "maxLength": 1048576, "type": [ "string", "null" ] }, "resourceBundleIds": { "description": "Custom template resource bundle IDs list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "source": { "description": "Custom template source repo.", "type": "object", "x-versionadded": "v2.36" }, "tags": { "description": "Custom template tags list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "templateTypeSpecificResources": { "description": "Specifies resources for the custom template.", "properties": { "serviceWebRequestsOnRootPath": { "default": false, "description": "Whether the 'service_web_requests_on_root_path' resource should be enabled on the custom app.", "type": [ "boolean", "null" ] } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" }, "templateSubType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": [ "string", "null" ] }, "templateType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": "string" } }, "required": [ "defaultEnvironment", "defaultResourceBundleId", "description", "enabled", "id", "items", "name", "templateMetadata", "templateSubType", "templateType" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The custom template was successfully updated. | CustomTemplateEntity | | 403 | Forbidden | User does not have permission to update a custom template. | None | ## Retrieve a single custom template file by custom template ID Operation path: `GET /api/v2/customTemplates/{customTemplateId}/files/{fileId}/` Authentication requirements: `BearerAuth` Retrieve a single custom template file. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customTemplateId | path | string | true | The ID of the custom template. | | fileId | path | string | true | The ID of the file. | ### Example responses > 200 Response ``` { "properties": { "content": { "description": "The content of the chosen file.", "type": "string" }, "contentEncoding": { "description": "The encoding of the content field. Either \"utf-8\" for text files or \"base64\" for binary files such as images.", "type": "string", "x-versionadded": "v2.43" }, "fileName": { "description": "The name of the chosen file.", "type": "string" }, "id": { "description": "The ID of the file.", "type": "string" } }, "required": [ "content", "contentEncoding", "fileName", "id" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A given custom template file. | CustomTemplateFileResponse | | 403 | Forbidden | User does not have permission to access a particular custom template file. | None | | 404 | Not Found | File not found. | None | # Schemas ## AccessControlV2 ``` { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the recipient. | | name | string | true | | The name of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | The type of the recipient. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## ApplicationSharingUpdateOrRemove ``` { "properties": { "note": { "default": "", "description": "A note to go with the project share", "type": "string" }, "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" }, "sendNotification": { "default": false, "description": "Send a notification?", "type": "boolean" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | note | string | false | | A note to go with the project share | | operation | string | true | | Name of the action being taken. The only operation is 'updateRoles'. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | Array of GrantAccessControl objects., up to maximum 100 objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithUsername | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithId | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sendNotification | boolean | false | | Send a notification? | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## CustomAppHistory ``` { "properties": { "createdAt": { "description": "The date and time that the user published a new version of the app.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who published the application.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of the user who published the application.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of the user who published the application.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of the user who published the application.", "type": [ "string", "null" ] }, "sourceId": { "description": "The custom application source ID of the record.", "type": "string" }, "sourceName": { "description": "The name of the custom app source.", "type": "string" }, "sourceVersionId": { "description": "The custom application source version ID of the record.", "type": "string" }, "sourceVersionLabel": { "description": "The label of the source version.", "type": "string" } }, "required": [ "createdAt", "sourceId", "sourceVersionId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The date and time that the user published a new version of the app. | | createdBy | string,null | false | | The username of the user who published the application. | | creatorFirstName | string,null | false | | The first name of the user who published the application. | | creatorLastName | string,null | false | | The last name of the user who published the application. | | creatorUserhash | string,null | false | | The Gravatar hash of the user who published the application. | | sourceId | string | true | | The custom application source ID of the record. | | sourceName | string | false | | The name of the custom app source. | | sourceVersionId | string | true | | The custom application source version ID of the record. | | sourceVersionLabel | string | false | | The label of the source version. | ## CustomAppUsage ``` { "properties": { "userId": { "description": "The ID of the user (or null for a guest).", "type": [ "string", "null" ] }, "userType": { "description": "Determines whether the user was a creator, viewer, or guest at the time of visit.", "enum": [ "guest", "viewer", "creator" ], "type": [ "string", "null" ] }, "username": { "description": "The name of the user.", "maxLength": 512, "minLength": 1, "type": "string" }, "visitTimestamp": { "description": "The date and time that user last visited the app.", "format": "date-time", "type": "string" } }, "required": [ "userId", "userType", "username", "visitTimestamp" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | userId | string,null | true | | The ID of the user (or null for a guest). | | userType | string,null | true | | Determines whether the user was a creator, viewer, or guest at the time of visit. | | username | string | true | maxLength: 512minLength: 1minLength: 1 | The name of the user. | | visitTimestamp | string(date-time) | true | | The date and time that user last visited the app. | ### Enumerated Values | Property | Value | | --- | --- | | userType | [guest, viewer, creator] | ## CustomApplication ``` { "properties": { "allowAutoStopping": { "description": "Determines if apps are auto-paused to save resources.", "type": [ "boolean", "null" ] }, "applicationUrl": { "description": "The URL for accessing application endpoints", "format": "uri", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp when the application was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application", "type": [ "string", "null" ] }, "customApplicationSourceId": { "description": "The custom application source used in app.", "type": [ "string", "null" ] }, "customApplicationSourceVersionId": { "description": "The custom application source version used in app.", "type": [ "string", "null" ] }, "envVersionId": { "description": "The execution environment version used in app", "type": [ "string", "null" ] }, "expiresAt": { "description": "ISO-8601 formatted date of the custom application removing date", "format": "date-time", "type": [ "string", "null" ] }, "externalAccessEnabled": { "description": "Determines if sharing with guest users is allowed.", "type": [ "boolean", "null" ] }, "externalAccessRecipients": { "description": "The external users and domains allowed to view this app.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "id": { "description": "The custom application ID.", "type": "string" }, "lrsId": { "description": "The Long Running Service ID associated with app.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "name": { "description": "The name of the custom application.", "maxLength": 512, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_DELETE", "CAN_PUBLISH_NEW_IMAGE", "CAN_SEE_SOURCE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "maxItems": 100, "type": "array" }, "resources": { "description": "The resource configuration for the application, including CPU, memory, replicas, etc.", "properties": { "cpuLimit": { "description": "The CPU core limit for a container.", "type": "number" }, "cpuRequest": { "description": "The requested CPU cores for a container.", "type": "number" }, "healthEndpointPath": { "description": "The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from `serviceWebRequestsOnRootPath`. Use this to expose a dedicated health endpoint (e.g., `/healthz`) instead of probing the root path.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "memoryLimit": { "description": "The memory limit for a container in bytes.", "type": "integer" }, "memoryRequest": { "description": "The requested memory for a container in bytes.", "type": "integer" }, "replicas": { "description": "The number of running application replicas.", "type": "integer" }, "resourceLabel": { "description": "The ID of the resource request bundle used for custom application.", "type": "string" }, "serviceWebRequestsOnRootPath": { "description": "Sets whether applications made from this source version expect to receive requests on `/` or on `/apps/{ID}` by default.", "type": "boolean" }, "sessionAffinity": { "description": "The session affinity for an application.", "type": "boolean" }, "storageLimit": { "description": "The ephemeral storage limit for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" }, "storageRequest": { "description": "The requested ephemeral storage for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" } }, "required": [ "cpuLimit", "cpuRequest", "healthEndpointPath", "memoryLimit", "memoryRequest", "replicas", "resourceLabel", "serviceWebRequestsOnRootPath", "sessionAffinity", "storageLimit", "storageRequest" ], "type": "object", "x-versionadded": "v2.37" }, "sourceName": { "description": "The name of the custom app source.", "type": "string" }, "sourceVersionLabel": { "description": "The label of the source version.", "type": "string" }, "status": { "description": "The state of application in LRS", "enum": [ "created", "failed", "initializing", "paused", "publishing", "running" ], "type": "string" }, "updatedAt": { "description": "The timestamp when the application was updated.", "type": "string" }, "userId": { "description": "Creator's ID", "type": "string" } }, "required": [ "allowAutoStopping", "applicationUrl", "createdAt", "customApplicationSourceId", "customApplicationSourceVersionId", "envVersionId", "expiresAt", "externalAccessEnabled", "externalAccessRecipients", "id", "lrsId", "name", "orgId", "permissions", "status", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowAutoStopping | boolean,null | true | | Determines if apps are auto-paused to save resources. | | applicationUrl | string,null(uri) | true | | The URL for accessing application endpoints | | createdAt | string | true | | The timestamp when the application was created. | | createdBy | string,null | false | | The username of who created the application | | creatorFirstName | string,null | false | | The first name of who created the application | | creatorLastName | string,null | false | | The last name of who created the application | | creatorUserhash | string,null | false | | The Gravatar hash of user who created the application | | customApplicationSourceId | string,null | true | | The custom application source used in app. | | customApplicationSourceVersionId | string,null | true | | The custom application source version used in app. | | envVersionId | string,null | true | | The execution environment version used in app | | expiresAt | string,null(date-time) | true | | ISO-8601 formatted date of the custom application removing date | | externalAccessEnabled | boolean,null | true | | Determines if sharing with guest users is allowed. | | externalAccessRecipients | [string] | true | maxItems: 100 | The external users and domains allowed to view this app. | | id | string | true | | The custom application ID. | | lrsId | string,null | true | | The Long Running Service ID associated with app. | | name | string | true | maxLength: 512minLength: 1minLength: 1 | The name of the custom application. | | orgId | string,null | true | | The ID of the creator's organization. | | permissions | [string] | true | maxItems: 100 | The list of permitted actions, which the authenticated user can perform on this application. | | resources | CustomApplicationResourcesResponse | false | | The resource configuration for the application, including CPU, memory, replicas, etc. | | sourceName | string | false | | The name of the custom app source. | | sourceVersionLabel | string | false | | The label of the source version. | | status | string | true | | The state of application in LRS | | updatedAt | string | true | | The timestamp when the application was updated. | | userId | string | true | | Creator's ID | ### Enumerated Values | Property | Value | | --- | --- | | status | [created, failed, initializing, paused, publishing, running] | ## CustomApplicationCreate ``` { "properties": { "applicationSourceId": { "description": "The ID of the custom application source to be used for the new application. The latest version version will be chosen.", "type": "string" }, "applicationSourceVersionId": { "description": "The ID of the custom application source version to be used for the new application.", "type": "string" }, "environmentId": { "description": "The execution environment ID for the application.", "type": "string" }, "name": { "description": "The name of the custom application.", "maxLength": 512, "type": [ "string", "null" ] }, "resources": { "description": "Resources required for running custom application.", "properties": { "healthEndpointPath": { "description": "The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from `serviceWebRequestsOnRootPath`. Use this to expose a dedicated health endpoint (e.g., `/healthz`) instead of probing the root path.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "replicas": { "description": "The number of running application replicas.", "minimum": 0, "type": "integer" }, "resourceLabel": { "description": "The ID of the resource request bundle used for custom application.", "type": "string" }, "serviceWebRequestsOnRootPath": { "description": "Sets whether applications made from this source version expect to receive requests on `/` or on `/apps/{ID}` by default.", "type": [ "boolean", "null" ] }, "sessionAffinity": { "description": "The Session affinity of an application source version.", "type": [ "boolean", "null" ] } }, "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | applicationSourceId | string | false | | The ID of the custom application source to be used for the new application. The latest version version will be chosen. | | applicationSourceVersionId | string | false | | The ID of the custom application source version to be used for the new application. | | environmentId | string | false | | The execution environment ID for the application. | | name | string,null | false | maxLength: 512 | The name of the custom application. | | resources | CustomApplicationResources | false | | Resources required for running custom application. | ## CustomApplicationItemRetrieve ``` { "properties": { "content": { "description": "The textual content of the file item.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The full internal path of the file item.", "type": "string" }, "id": { "description": "The ID of the file item.", "type": "string" } }, "required": [ "content", "fileName", "filePath", "id" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | content | string | true | | The textual content of the file item. | | fileName | string | true | | The name of the file item. | | filePath | string | true | | The full internal path of the file item. | | id | string | true | | The ID of the file item. | ## CustomApplicationListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The array of custom application objects.", "items": { "properties": { "allowAutoStopping": { "description": "Determines if apps are auto-paused to save resources.", "type": [ "boolean", "null" ] }, "applicationUrl": { "description": "The URL for accessing application endpoints", "format": "uri", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp when the application was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application", "type": [ "string", "null" ] }, "customApplicationSourceId": { "description": "The custom application source used in app.", "type": [ "string", "null" ] }, "customApplicationSourceVersionId": { "description": "The custom application source version used in app.", "type": [ "string", "null" ] }, "envVersionId": { "description": "The execution environment version used in app", "type": [ "string", "null" ] }, "expiresAt": { "description": "ISO-8601 formatted date of the custom application removing date", "format": "date-time", "type": [ "string", "null" ] }, "externalAccessEnabled": { "description": "Determines if sharing with guest users is allowed.", "type": [ "boolean", "null" ] }, "externalAccessRecipients": { "description": "The external users and domains allowed to view this app.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "id": { "description": "The custom application ID.", "type": "string" }, "lrsId": { "description": "The Long Running Service ID associated with app.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "name": { "description": "The name of the custom application.", "maxLength": 512, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_DELETE", "CAN_PUBLISH_NEW_IMAGE", "CAN_SEE_SOURCE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "maxItems": 100, "type": "array" }, "resources": { "description": "The resource configuration for the application, including CPU, memory, replicas, etc.", "properties": { "cpuLimit": { "description": "The CPU core limit for a container.", "type": "number" }, "cpuRequest": { "description": "The requested CPU cores for a container.", "type": "number" }, "healthEndpointPath": { "description": "The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from `serviceWebRequestsOnRootPath`. Use this to expose a dedicated health endpoint (e.g., `/healthz`) instead of probing the root path.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "memoryLimit": { "description": "The memory limit for a container in bytes.", "type": "integer" }, "memoryRequest": { "description": "The requested memory for a container in bytes.", "type": "integer" }, "replicas": { "description": "The number of running application replicas.", "type": "integer" }, "resourceLabel": { "description": "The ID of the resource request bundle used for custom application.", "type": "string" }, "serviceWebRequestsOnRootPath": { "description": "Sets whether applications made from this source version expect to receive requests on `/` or on `/apps/{ID}` by default.", "type": "boolean" }, "sessionAffinity": { "description": "The session affinity for an application.", "type": "boolean" }, "storageLimit": { "description": "The ephemeral storage limit for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" }, "storageRequest": { "description": "The requested ephemeral storage for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" } }, "required": [ "cpuLimit", "cpuRequest", "healthEndpointPath", "memoryLimit", "memoryRequest", "replicas", "resourceLabel", "serviceWebRequestsOnRootPath", "sessionAffinity", "storageLimit", "storageRequest" ], "type": "object", "x-versionadded": "v2.37" }, "sourceName": { "description": "The name of the custom app source.", "type": "string" }, "sourceVersionLabel": { "description": "The label of the source version.", "type": "string" }, "status": { "description": "The state of application in LRS", "enum": [ "created", "failed", "initializing", "paused", "publishing", "running" ], "type": "string" }, "updatedAt": { "description": "The timestamp when the application was updated.", "type": "string" }, "userId": { "description": "Creator's ID", "type": "string" } }, "required": [ "allowAutoStopping", "applicationUrl", "createdAt", "customApplicationSourceId", "customApplicationSourceVersionId", "envVersionId", "expiresAt", "externalAccessEnabled", "externalAccessRecipients", "id", "lrsId", "name", "orgId", "permissions", "status", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomApplication] | true | maxItems: 100 | The array of custom application objects. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomApplicationLogs ``` { "properties": { "buildError": { "description": "The build error of the custom application.", "type": "string" }, "buildLog": { "description": "The build log of the custom application.", "type": "string" }, "buildStatus": { "description": "The build status of the custom application.", "type": "string" }, "logs": { "description": "The logs of the custom application.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "logs" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildError | string | false | | The build error of the custom application. | | buildLog | string | false | | The build log of the custom application. | | buildStatus | string | false | | The build status of the custom application. | | logs | [string] | true | maxItems: 1000 | The logs of the custom application. | ## CustomApplicationResources ``` { "description": "Resources required for running custom application.", "properties": { "healthEndpointPath": { "description": "The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from `serviceWebRequestsOnRootPath`. Use this to expose a dedicated health endpoint (e.g., `/healthz`) instead of probing the root path.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "replicas": { "description": "The number of running application replicas.", "minimum": 0, "type": "integer" }, "resourceLabel": { "description": "The ID of the resource request bundle used for custom application.", "type": "string" }, "serviceWebRequestsOnRootPath": { "description": "Sets whether applications made from this source version expect to receive requests on `/` or on `/apps/{ID}` by default.", "type": [ "boolean", "null" ] }, "sessionAffinity": { "description": "The Session affinity of an application source version.", "type": [ "boolean", "null" ] } }, "type": "object", "x-versionadded": "v2.37" } ``` Resources required for running custom application. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | healthEndpointPath | string,null | false | maxLength: 255 | The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from serviceWebRequestsOnRootPath. Use this to expose a dedicated health endpoint (e.g., /healthz) instead of probing the root path. | | replicas | integer | false | minimum: 0 | The number of running application replicas. | | resourceLabel | string | false | | The ID of the resource request bundle used for custom application. | | serviceWebRequestsOnRootPath | boolean,null | false | | Sets whether applications made from this source version expect to receive requests on / or on /apps/{ID} by default. | | sessionAffinity | boolean,null | false | | The Session affinity of an application source version. | ## CustomApplicationResourcesResponse ``` { "description": "The resource configuration for the application, including CPU, memory, replicas, etc.", "properties": { "cpuLimit": { "description": "The CPU core limit for a container.", "type": "number" }, "cpuRequest": { "description": "The requested CPU cores for a container.", "type": "number" }, "healthEndpointPath": { "description": "The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from `serviceWebRequestsOnRootPath`. Use this to expose a dedicated health endpoint (e.g., `/healthz`) instead of probing the root path.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "memoryLimit": { "description": "The memory limit for a container in bytes.", "type": "integer" }, "memoryRequest": { "description": "The requested memory for a container in bytes.", "type": "integer" }, "replicas": { "description": "The number of running application replicas.", "type": "integer" }, "resourceLabel": { "description": "The ID of the resource request bundle used for custom application.", "type": "string" }, "serviceWebRequestsOnRootPath": { "description": "Sets whether applications made from this source version expect to receive requests on `/` or on `/apps/{ID}` by default.", "type": "boolean" }, "sessionAffinity": { "description": "The session affinity for an application.", "type": "boolean" }, "storageLimit": { "description": "The ephemeral storage limit for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" }, "storageRequest": { "description": "The requested ephemeral storage for a container in bytes.", "type": "integer", "x-versionadded": "v2.42" } }, "required": [ "cpuLimit", "cpuRequest", "healthEndpointPath", "memoryLimit", "memoryRequest", "replicas", "resourceLabel", "serviceWebRequestsOnRootPath", "sessionAffinity", "storageLimit", "storageRequest" ], "type": "object", "x-versionadded": "v2.37" } ``` The resource configuration for the application, including CPU, memory, replicas, etc. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cpuLimit | number | true | | The CPU core limit for a container. | | cpuRequest | number | true | | The requested CPU cores for a container. | | healthEndpointPath | string,null | true | maxLength: 255 | The path used by the Kubernetes health probe for liveness and readiness checks. When set, this takes precedence over the path derived from serviceWebRequestsOnRootPath. Use this to expose a dedicated health endpoint (e.g., /healthz) instead of probing the root path. | | memoryLimit | integer | true | | The memory limit for a container in bytes. | | memoryRequest | integer | true | | The requested memory for a container in bytes. | | replicas | integer | true | | The number of running application replicas. | | resourceLabel | string | true | | The ID of the resource request bundle used for custom application. | | serviceWebRequestsOnRootPath | boolean | true | | Sets whether applications made from this source version expect to receive requests on / or on /apps/{ID} by default. | | sessionAffinity | boolean | true | | The session affinity for an application. | | storageLimit | integer | true | | The ephemeral storage limit for a container in bytes. | | storageRequest | integer | true | | The requested ephemeral storage for a container in bytes. | ## CustomApplicationSource ``` { "properties": { "createdAt": { "description": "The timestamp when the application source was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source ID.", "type": "string" }, "latestVersion": { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The name of the custom application source.", "maxLength": 255, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application source.", "items": { "enum": [ "CAN_PUBLISH_NEW_IMAGE", "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_VIEW", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE" ], "type": "string" }, "maxItems": 100, "type": "array" }, "updatedAt": { "description": "The timestamp when the application source was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "createdAt", "id", "latestVersion", "name", "orgId", "permissions", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string | true | | The timestamp when the application source was created. | | createdBy | string,null | false | | The username of who created the application source. | | creatorFirstName | string,null | false | | The first name of who created the application source. | | creatorLastName | string,null | false | | The last name of who created the application source. | | creatorUserhash | string,null | false | | The Gravatar hash of user who created the application source. | | id | string | true | | The custom application source ID. | | latestVersion | CustomApplicationSourceVersion | true | | The latest version of the source. | | name | string | true | maxLength: 255minLength: 1minLength: 1 | The name of the custom application source. | | orgId | string,null | true | | The ID of the creator's organization. | | permissions | [string] | true | maxItems: 100 | The list of permitted actions, which the authenticated user can perform on this application source. | | updatedAt | string | true | | The timestamp when the application source was modified. | | userId | string | true | | Creator's ID. | ## CustomApplicationSourceCreate ``` { "properties": { "name": { "description": "The name of the custom application source.", "maxLength": 255, "minLength": 1, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | maxLength: 255minLength: 1minLength: 1 | The name of the custom application source. | ## CustomApplicationSourceFromGalleryTemplateCreate ``` { "properties": { "customTemplateId": { "description": "The custom template ID for the custom application.", "type": "string" } }, "required": [ "customTemplateId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customTemplateId | string | true | | The custom template ID for the custom application. | ## CustomApplicationSourceListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The array of custom application source objects.", "items": { "properties": { "createdAt": { "description": "The timestamp when the application source was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source ID.", "type": "string" }, "latestVersion": { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The name of the custom application source.", "maxLength": 255, "minLength": 1, "type": "string" }, "orgId": { "description": "The ID of the creator's organization.", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application source.", "items": { "enum": [ "CAN_PUBLISH_NEW_IMAGE", "CAN_CHANGE_EXTERNAL_ACCESS", "CAN_VIEW", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE" ], "type": "string" }, "maxItems": 100, "type": "array" }, "updatedAt": { "description": "The timestamp when the application source was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "createdAt", "id", "latestVersion", "name", "orgId", "permissions", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomApplicationSource] | true | maxItems: 100 | The array of custom application source objects. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomApplicationSourceUpdate ``` { "properties": { "name": { "description": "The name of the custom application source.", "maxLength": 255, "minLength": 1, "type": "string" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 255minLength: 1minLength: 1 | The name of the custom application source. | ## CustomApplicationSourceVersion ``` { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` The latest version of the source. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string,null | true | | The ID of the environment used for this source. | | baseEnvironmentVersionId | string,null | true | | The ID of the environment version used for this source. | | createdAt | string | true | | The timestamp of when the application source version was created. | | createdBy | string,null | false | | The username of who created the application source version. | | creatorFirstName | string,null | false | | The first name of who created the application source version. | | creatorLastName | string,null | false | | The last name of who created the application source version. | | creatorUserhash | string,null | false | | The Gravatar hash of user who created the application source version. | | id | string | true | | The custom application source version ID. | | isFrozen | boolean | true | | Marks that this version has become immutable. | | items | [WorkspaceItemResponse] | true | maxItems: 1000 | The list of file items. | | label | string,null | true | maxLength: 255minLength: 1minLength: 1 | The label of the custom application source version. | | updatedAt | string | true | | The timestamp when the application source version was modified. | | userId | string | true | | Creator's ID. | ## CustomApplicationSourceVersionCreate ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this source version.", "type": "string" }, "baseEnvironmentVersionId": { "description": "The base environment version ID to use with this source version.", "type": "string" }, "baseVersion": { "description": "The ID of the version used as the source for parameter duplication.", "type": "string" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "filesToDelete": { "description": "The IDs of the files to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "label": { "description": "The label for new Custom App Source Version.", "maxLength": 255, "minLength": 1, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string | false | | The base environment to use with this source version. | | baseEnvironmentVersionId | string | false | | The base environment version ID to use with this source version. | | baseVersion | string | false | | The ID of the version used as the source for parameter duplication. | | file | string(binary) | false | | A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding filePath supplied that shows the relative location of the file. For example, you have two files: /home/username/custom-task/main.py and /home/username/custom-task/helpers/helper.py. When uploading these files, you would also need to include two filePath fields of, "main.py" and "helpers/helper.py". If the supplied file already exists at the supplied filePath, the old file is replaced by the new file. | | filePath | any | false | | The local path of the file being uploaded. See the file field explanation for more details. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | filesToDelete | any | false | | The IDs of the files to be deleted. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | string | false | maxLength: 255minLength: 1minLength: 1 | The label for new Custom App Source Version. | ## CustomApplicationSourceVersionFromCodespace ``` { "properties": { "codespaceId": { "description": "The ID of the Codespace that should be used as source for files.", "type": "string" }, "label": { "description": "The label for new Custom App Source Version in case current version is frozen and new should be created.", "maxLength": 255, "minLength": 1, "type": "string" } }, "required": [ "codespaceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | codespaceId | string | true | | The ID of the Codespace that should be used as source for files. | | label | string | false | maxLength: 255minLength: 1minLength: 1 | The label for new Custom App Source Version in case current version is frozen and new should be created. | ## CustomApplicationSourceVersionFromCodespaceResponse ``` { "properties": { "id": { "description": "The custom application source version ID.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The custom application source version ID. | ## CustomApplicationSourceVersionListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of custom application source version objects.", "items": { "description": "The latest version of the source.", "properties": { "baseEnvironmentId": { "description": "The ID of the environment used for this source.", "type": [ "string", "null" ] }, "baseEnvironmentVersionId": { "description": "The ID of the environment version used for this source.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp of when the application source version was created.", "type": "string" }, "createdBy": { "description": "The username of who created the application source version.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of who created the application source version.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of who created the application source version.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of user who created the application source version.", "type": [ "string", "null" ] }, "id": { "description": "The custom application source version ID.", "type": "string" }, "isFrozen": { "description": "Marks that this version has become immutable.", "type": "boolean" }, "items": { "description": "The list of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "label": { "description": "The label of the custom application source version.", "maxLength": 255, "minLength": 1, "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp when the application source version was modified.", "type": "string" }, "userId": { "description": "Creator's ID.", "type": "string" } }, "required": [ "baseEnvironmentId", "baseEnvironmentVersionId", "createdAt", "id", "isFrozen", "items", "label", "updatedAt", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomApplicationSourceVersion] | true | maxItems: 100 | An array of custom application source version objects. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomApplicationSourceVersionToCodespace ``` { "properties": { "codespaceId": { "description": "The ID of the Codespace that should be used as source for files.", "type": "string" } }, "required": [ "codespaceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | codespaceId | string | true | | The ID of the Codespace that should be used as source for files. | ## CustomApplicationSourceVersionUpdate ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this source version.", "type": "string" }, "baseEnvironmentVersionId": { "description": "The base environment version ID to use with this source version.", "type": "string" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "filesToDelete": { "description": "The IDs of the files to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "label": { "description": "The label for new Custom App Source Version.", "maxLength": 255, "minLength": 1, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string | false | | The base environment to use with this source version. | | baseEnvironmentVersionId | string | false | | The base environment version ID to use with this source version. | | file | string(binary) | false | | A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding filePath supplied that shows the relative location of the file. For example, you have two files: /home/username/custom-task/main.py and /home/username/custom-task/helpers/helper.py. When uploading these files, you would also need to include two filePath fields of, "main.py" and "helpers/helper.py". If the supplied file already exists at the supplied filePath, the old file is replaced by the new file. | | filePath | any | false | | The local path of the file being uploaded. See the file field explanation for more details. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | filesToDelete | any | false | | The IDs of the files to be deleted. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | string | false | maxLength: 255minLength: 1minLength: 1 | The label for new Custom App Source Version. | ## CustomApplicationUpdate ``` { "properties": { "allowAutoStopping": { "description": "Determines if the custom app should be stopped automatically.", "type": "boolean" }, "customApplicationSourceVersionId": { "description": "The ID of the custom application source version to set this app to.", "type": "string" }, "externalAccessEnabled": { "description": "Determines if the custom app can be shared with guest users.", "type": "boolean" }, "externalAccessRecipients": { "description": "Who should be able to access the custom app", "items": { "description": "The email address, or email domain of who can use an app", "maxLength": 512, "minLength": 0, "type": "string" }, "maxItems": 2048, "type": "array" }, "name": { "description": "The name of the custom application.", "maxLength": 512, "minLength": 1, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowAutoStopping | boolean | false | | Determines if the custom app should be stopped automatically. | | customApplicationSourceVersionId | string | false | | The ID of the custom application source version to set this app to. | | externalAccessEnabled | boolean | false | | Determines if the custom app can be shared with guest users. | | externalAccessRecipients | [string] | false | maxItems: 2048 | Who should be able to access the custom app | | name | string | false | maxLength: 512minLength: 1minLength: 1 | The name of the custom application. | ## CustomApplicationsHistoryListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of custom application soure versions published to this custom application.", "items": { "properties": { "createdAt": { "description": "The date and time that the user published a new version of the app.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who published the application.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "The first name of the user who published the application.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "The last name of the user who published the application.", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "The Gravatar hash of the user who published the application.", "type": [ "string", "null" ] }, "sourceId": { "description": "The custom application source ID of the record.", "type": "string" }, "sourceName": { "description": "The name of the custom app source.", "type": "string" }, "sourceVersionId": { "description": "The custom application source version ID of the record.", "type": "string" }, "sourceVersionLabel": { "description": "The label of the source version.", "type": "string" } }, "required": [ "createdAt", "sourceId", "sourceVersionId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomAppHistory] | true | maxItems: 100 | The list of custom application soure versions published to this custom application. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomApplicationsUsagesListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of visits to the custom application.", "items": { "properties": { "userId": { "description": "The ID of the user (or null for a guest).", "type": [ "string", "null" ] }, "userType": { "description": "Determines whether the user was a creator, viewer, or guest at the time of visit.", "enum": [ "guest", "viewer", "creator" ], "type": [ "string", "null" ] }, "username": { "description": "The name of the user.", "maxLength": 512, "minLength": 1, "type": "string" }, "visitTimestamp": { "description": "The date and time that user last visited the app.", "format": "date-time", "type": "string" } }, "required": [ "userId", "userType", "username", "visitTimestamp" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomAppUsage] | true | maxItems: 100 | The list of visits to the custom application. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomTemplateCreatePayload ``` { "properties": { "defaultEnvironment": { "description": "Specifies the default environment for the custom metric template.", "type": "string" }, "defaultResourceBundleId": { "description": "Specifies the default resource bundle for the custom metric template.", "enum": [ "cpu.nano", "cpu.micro", "cpu.small", "cpu.medium", "cpu.large", "cpu.xlarge", "cpu.2xlarge", "cpu.3xlarge", "cpu.4xlarge", "cpu.5xlarge", "cpu.6xlarge", "cpu.7xlarge", "cpu.8xlarge", "cpu.16xlarge", "DRAWSR6i.4xlargeFrac8Regular", "DRAWSR6i.4xlargeFrac4Regular", "DRAWSG4dn.xlargeFrac1Regular", "DRAWSG4dn.2xlargeFrac1Regular", "DRAWSG5.2xlargeFrac1Regular", "DRAWSG5.12xlargeFrac1Regular", "DRAWSG5.48xlargeFrac1Regular", "DRAWSG6e.xlargeFrac1Regular", "DRAWSG6e.12xlargeFrac1Regular", "DRAWSG6e.48xlargeFrac1Regular", "gpu.small", "gpu.medium", "gpu.large", "gpu.xlarge", "gpu.2xlarge", "gpu.3xlarge", "gpu.5xlarge", "gpu.7xlarge", "starter", "basic", "basic.8x", "train.l", "infer.s", "infer.m", "infer.l" ], "type": "string" }, "description": { "description": "A description of the custom template.", "maxLength": 10000, "type": "string" }, "enabled": { "default": true, "description": "Disabled templates remain visible in the UI but cannot be used.", "type": "boolean", "x-versionadded": "v2.36" }, "file": { "description": "The file to be used to create the custom metric template.", "format": "binary", "type": "string" }, "isHidden": { "default": false, "description": "Hidden templates are not visible in the UI.", "type": "boolean", "x-versionadded": "v2.37" }, "name": { "description": "The name of the custom template.", "maxLength": 255, "type": "string" }, "templateMetadata": { "description": "Specifies permanent metadata for the custom template.", "type": [ "string", "null" ] }, "templateSubType": { "description": "Defines sub-type of the custom template.", "maxLength": 255, "type": "string" }, "templateType": { "description": "Defines type of the custom template.", "maxLength": 255, "type": "string" } }, "required": [ "defaultEnvironment", "description", "file", "name", "templateSubType", "templateType" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultEnvironment | string | true | | Specifies the default environment for the custom metric template. | | defaultResourceBundleId | string | false | | Specifies the default resource bundle for the custom metric template. | | description | string | true | maxLength: 10000 | A description of the custom template. | | enabled | boolean | false | | Disabled templates remain visible in the UI but cannot be used. | | file | string(binary) | true | | The file to be used to create the custom metric template. | | isHidden | boolean | false | | Hidden templates are not visible in the UI. | | name | string | true | maxLength: 255 | The name of the custom template. | | templateMetadata | string,null | false | | Specifies permanent metadata for the custom template. | | templateSubType | string | true | maxLength: 255 | Defines sub-type of the custom template. | | templateType | string | true | maxLength: 255 | Defines type of the custom template. | ### Enumerated Values | Property | Value | | --- | --- | | defaultResourceBundleId | [cpu.nano, cpu.micro, cpu.small, cpu.medium, cpu.large, cpu.xlarge, cpu.2xlarge, cpu.3xlarge, cpu.4xlarge, cpu.5xlarge, cpu.6xlarge, cpu.7xlarge, cpu.8xlarge, cpu.16xlarge, DRAWSR6i.4xlargeFrac8Regular, DRAWSR6i.4xlargeFrac4Regular, DRAWSG4dn.xlargeFrac1Regular, DRAWSG4dn.2xlargeFrac1Regular, DRAWSG5.2xlargeFrac1Regular, DRAWSG5.12xlargeFrac1Regular, DRAWSG5.48xlargeFrac1Regular, DRAWSG6e.xlargeFrac1Regular, DRAWSG6e.12xlargeFrac1Regular, DRAWSG6e.48xlargeFrac1Regular, gpu.small, gpu.medium, gpu.large, gpu.xlarge, gpu.2xlarge, gpu.3xlarge, gpu.5xlarge, gpu.7xlarge, starter, basic, basic.8x, train.l, infer.s, infer.m, infer.l] | ## CustomTemplateEntity ``` { "properties": { "defaultEnvironment": { "description": "Specifies the default environment for the custom template.", "properties": { "environmentId": { "description": "The ID the environment to use for the public custom metric image.", "type": "string" }, "environmentVersionId": { "description": "The ID of the specific environment version to use with the public custom metric image.", "type": "string" } }, "required": [ "environmentId", "environmentVersionId" ], "type": "object", "x-versionadded": "v2.36" }, "defaultResourceBundleId": { "description": "Specifies the default resource bundle for the custom metric template.", "enum": [ "cpu.nano", "cpu.micro", "cpu.small", "cpu.medium", "cpu.large", "cpu.xlarge", "cpu.2xlarge", "cpu.3xlarge", "cpu.4xlarge", "cpu.5xlarge", "cpu.6xlarge", "cpu.7xlarge", "cpu.8xlarge", "cpu.16xlarge", "DRAWSR6i.4xlargeFrac8Regular", "DRAWSR6i.4xlargeFrac4Regular", "DRAWSG4dn.xlargeFrac1Regular", "DRAWSG4dn.2xlargeFrac1Regular", "DRAWSG5.2xlargeFrac1Regular", "DRAWSG5.12xlargeFrac1Regular", "DRAWSG5.48xlargeFrac1Regular", "DRAWSG6e.xlargeFrac1Regular", "DRAWSG6e.12xlargeFrac1Regular", "DRAWSG6e.48xlargeFrac1Regular", "gpu.small", "gpu.medium", "gpu.large", "gpu.xlarge", "gpu.2xlarge", "gpu.3xlarge", "gpu.5xlarge", "gpu.7xlarge", "starter", "basic", "basic.8x", "train.l", "infer.s", "infer.m", "infer.l" ], "type": [ "string", "null" ] }, "description": { "description": "A description of the custom template.", "maxLength": 10000, "type": "string" }, "enabled": { "description": "Determines whether the template is enabled.", "type": "boolean" }, "id": { "description": "The ID of the custom template.", "type": "string" }, "items": { "description": "A list of custom files.", "items": { "properties": { "id": { "description": "The ID of the custom template file.", "type": "string" }, "name": { "description": "Name of the custom template file.", "maxLength": 255, "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The name of the custom template.", "maxLength": 255, "type": "string" }, "templateMetadata": { "description": "Specifies permanent metadata for the custom template.", "properties": { "classLabels": { "description": "List of class names in case of creating a Binary or a multiclass custom model.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "readme": { "description": "Content of README.md file of the template.", "maxLength": 1048576, "type": [ "string", "null" ] }, "resourceBundleIds": { "description": "Custom template resource bundle IDs list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "source": { "description": "Custom template source repo.", "type": "object", "x-versionadded": "v2.36" }, "tags": { "description": "Custom template tags list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "templateTypeSpecificResources": { "description": "Specifies resources for the custom template.", "properties": { "serviceWebRequestsOnRootPath": { "default": false, "description": "Whether the 'service_web_requests_on_root_path' resource should be enabled on the custom app.", "type": [ "boolean", "null" ] } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" }, "templateSubType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": [ "string", "null" ] }, "templateType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": "string" } }, "required": [ "defaultEnvironment", "defaultResourceBundleId", "description", "enabled", "id", "items", "name", "templateMetadata", "templateSubType", "templateType" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultEnvironment | DefaultEnvironment | true | | Specifies the default environment for the custom template. | | defaultResourceBundleId | string,null | true | | Specifies the default resource bundle for the custom metric template. | | description | string | true | maxLength: 10000 | A description of the custom template. | | enabled | boolean | true | | Determines whether the template is enabled. | | id | string | true | | The ID of the custom template. | | items | [CustomTemplateFile] | true | maxItems: 1000 | A list of custom files. | | name | string | true | maxLength: 255 | The name of the custom template. | | templateMetadata | TemplateMetadata | true | | Specifies permanent metadata for the custom template. | | templateSubType | string,null | true | maxLength: 255 | Defines the type of the custom template. | | templateType | string | true | maxLength: 255 | Defines the type of the custom template. | ### Enumerated Values | Property | Value | | --- | --- | | defaultResourceBundleId | [cpu.nano, cpu.micro, cpu.small, cpu.medium, cpu.large, cpu.xlarge, cpu.2xlarge, cpu.3xlarge, cpu.4xlarge, cpu.5xlarge, cpu.6xlarge, cpu.7xlarge, cpu.8xlarge, cpu.16xlarge, DRAWSR6i.4xlargeFrac8Regular, DRAWSR6i.4xlargeFrac4Regular, DRAWSG4dn.xlargeFrac1Regular, DRAWSG4dn.2xlargeFrac1Regular, DRAWSG5.2xlargeFrac1Regular, DRAWSG5.12xlargeFrac1Regular, DRAWSG5.48xlargeFrac1Regular, DRAWSG6e.xlargeFrac1Regular, DRAWSG6e.12xlargeFrac1Regular, DRAWSG6e.48xlargeFrac1Regular, gpu.small, gpu.medium, gpu.large, gpu.xlarge, gpu.2xlarge, gpu.3xlarge, gpu.5xlarge, gpu.7xlarge, starter, basic, basic.8x, train.l, infer.s, infer.m, infer.l] | ## CustomTemplateFile ``` { "properties": { "id": { "description": "The ID of the custom template file.", "type": "string" }, "name": { "description": "Name of the custom template file.", "maxLength": 255, "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom template file. | | name | string | true | maxLength: 255 | Name of the custom template file. | ## CustomTemplateFileResponse ``` { "properties": { "content": { "description": "The content of the chosen file.", "type": "string" }, "contentEncoding": { "description": "The encoding of the content field. Either \"utf-8\" for text files or \"base64\" for binary files such as images.", "type": "string", "x-versionadded": "v2.43" }, "fileName": { "description": "The name of the chosen file.", "type": "string" }, "id": { "description": "The ID of the file.", "type": "string" } }, "required": [ "content", "contentEncoding", "fileName", "id" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | content | string | true | | The content of the chosen file. | | contentEncoding | string | true | | The encoding of the content field. Either "utf-8" for text files or "base64" for binary files such as images. | | fileName | string | true | | The name of the chosen file. | | id | string | true | | The ID of the file. | ## CustomTemplateListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of custom templates.", "items": { "properties": { "defaultEnvironment": { "description": "Specifies the default environment for the custom template.", "properties": { "environmentId": { "description": "The ID the environment to use for the public custom metric image.", "type": "string" }, "environmentVersionId": { "description": "The ID of the specific environment version to use with the public custom metric image.", "type": "string" } }, "required": [ "environmentId", "environmentVersionId" ], "type": "object", "x-versionadded": "v2.36" }, "defaultResourceBundleId": { "description": "Specifies the default resource bundle for the custom metric template.", "enum": [ "cpu.nano", "cpu.micro", "cpu.small", "cpu.medium", "cpu.large", "cpu.xlarge", "cpu.2xlarge", "cpu.3xlarge", "cpu.4xlarge", "cpu.5xlarge", "cpu.6xlarge", "cpu.7xlarge", "cpu.8xlarge", "cpu.16xlarge", "DRAWSR6i.4xlargeFrac8Regular", "DRAWSR6i.4xlargeFrac4Regular", "DRAWSG4dn.xlargeFrac1Regular", "DRAWSG4dn.2xlargeFrac1Regular", "DRAWSG5.2xlargeFrac1Regular", "DRAWSG5.12xlargeFrac1Regular", "DRAWSG5.48xlargeFrac1Regular", "DRAWSG6e.xlargeFrac1Regular", "DRAWSG6e.12xlargeFrac1Regular", "DRAWSG6e.48xlargeFrac1Regular", "gpu.small", "gpu.medium", "gpu.large", "gpu.xlarge", "gpu.2xlarge", "gpu.3xlarge", "gpu.5xlarge", "gpu.7xlarge", "starter", "basic", "basic.8x", "train.l", "infer.s", "infer.m", "infer.l" ], "type": [ "string", "null" ] }, "description": { "description": "A description of the custom template.", "maxLength": 10000, "type": "string" }, "enabled": { "description": "Determines whether the template is enabled.", "type": "boolean" }, "id": { "description": "The ID of the custom template.", "type": "string" }, "items": { "description": "A list of custom files.", "items": { "properties": { "id": { "description": "The ID of the custom template file.", "type": "string" }, "name": { "description": "Name of the custom template file.", "maxLength": 255, "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The name of the custom template.", "maxLength": 255, "type": "string" }, "templateMetadata": { "description": "Specifies permanent metadata for the custom template.", "properties": { "classLabels": { "description": "List of class names in case of creating a Binary or a multiclass custom model.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "readme": { "description": "Content of README.md file of the template.", "maxLength": 1048576, "type": [ "string", "null" ] }, "resourceBundleIds": { "description": "Custom template resource bundle IDs list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "source": { "description": "Custom template source repo.", "type": "object", "x-versionadded": "v2.36" }, "tags": { "description": "Custom template tags list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "templateTypeSpecificResources": { "description": "Specifies resources for the custom template.", "properties": { "serviceWebRequestsOnRootPath": { "default": false, "description": "Whether the 'service_web_requests_on_root_path' resource should be enabled on the custom app.", "type": [ "boolean", "null" ] } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" }, "templateSubType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": [ "string", "null" ] }, "templateType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": "string" } }, "required": [ "defaultEnvironment", "defaultResourceBundleId", "description", "enabled", "id", "items", "name", "templateMetadata", "templateSubType", "templateType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomTemplateEntity] | true | maxItems: 100 | A list of custom templates. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomTemplateUpdatePayload ``` { "properties": { "defaultEnvironment": { "description": "Specifies the default environment for the custom template.", "type": "string" }, "defaultResourceBundleId": { "description": "Specifies the default resource bundle for the custom metric template.", "enum": [ "cpu.nano", "cpu.micro", "cpu.small", "cpu.medium", "cpu.large", "cpu.xlarge", "cpu.2xlarge", "cpu.3xlarge", "cpu.4xlarge", "cpu.5xlarge", "cpu.6xlarge", "cpu.7xlarge", "cpu.8xlarge", "cpu.16xlarge", "DRAWSR6i.4xlargeFrac8Regular", "DRAWSR6i.4xlargeFrac4Regular", "DRAWSG4dn.xlargeFrac1Regular", "DRAWSG4dn.2xlargeFrac1Regular", "DRAWSG5.2xlargeFrac1Regular", "DRAWSG5.12xlargeFrac1Regular", "DRAWSG5.48xlargeFrac1Regular", "DRAWSG6e.xlargeFrac1Regular", "DRAWSG6e.12xlargeFrac1Regular", "DRAWSG6e.48xlargeFrac1Regular", "gpu.small", "gpu.medium", "gpu.large", "gpu.xlarge", "gpu.2xlarge", "gpu.3xlarge", "gpu.5xlarge", "gpu.7xlarge", "starter", "basic", "basic.8x", "train.l", "infer.s", "infer.m", "infer.l" ], "type": "string" }, "description": { "description": "A description of the custom template.", "maxLength": 10000, "type": "string" }, "enabled": { "default": true, "description": "Disabled templates remain visible in the UI but cannot be used.", "type": "boolean", "x-versionadded": "v2.36" }, "file": { "description": "The file to be used to create the custom template.", "format": "binary", "type": "string" }, "isHidden": { "default": false, "description": "Hidden templates are not visible in the UI.", "type": "boolean", "x-versionadded": "v2.37" }, "name": { "description": "The name of the custom template.", "maxLength": 255, "type": "string" }, "templateMetadata": { "description": "Specifies permanent metadata for the custom template.", "type": [ "string", "null" ] }, "templateSubType": { "description": "Defines the sub-type of the custom template.", "maxLength": 255, "type": "string" }, "templateType": { "description": "Defines the type of the custom template.", "maxLength": 255, "type": "string" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultEnvironment | string | false | | Specifies the default environment for the custom template. | | defaultResourceBundleId | string | false | | Specifies the default resource bundle for the custom metric template. | | description | string | false | maxLength: 10000 | A description of the custom template. | | enabled | boolean | false | | Disabled templates remain visible in the UI but cannot be used. | | file | string(binary) | false | | The file to be used to create the custom template. | | isHidden | boolean | false | | Hidden templates are not visible in the UI. | | name | string | false | maxLength: 255 | The name of the custom template. | | templateMetadata | string,null | false | | Specifies permanent metadata for the custom template. | | templateSubType | string | false | maxLength: 255 | Defines the sub-type of the custom template. | | templateType | string | false | maxLength: 255 | Defines the type of the custom template. | ### Enumerated Values | Property | Value | | --- | --- | | defaultResourceBundleId | [cpu.nano, cpu.micro, cpu.small, cpu.medium, cpu.large, cpu.xlarge, cpu.2xlarge, cpu.3xlarge, cpu.4xlarge, cpu.5xlarge, cpu.6xlarge, cpu.7xlarge, cpu.8xlarge, cpu.16xlarge, DRAWSR6i.4xlargeFrac8Regular, DRAWSR6i.4xlargeFrac4Regular, DRAWSG4dn.xlargeFrac1Regular, DRAWSG4dn.2xlargeFrac1Regular, DRAWSG5.2xlargeFrac1Regular, DRAWSG5.12xlargeFrac1Regular, DRAWSG5.48xlargeFrac1Regular, DRAWSG6e.xlargeFrac1Regular, DRAWSG6e.12xlargeFrac1Regular, DRAWSG6e.48xlargeFrac1Regular, gpu.small, gpu.medium, gpu.large, gpu.xlarge, gpu.2xlarge, gpu.3xlarge, gpu.5xlarge, gpu.7xlarge, starter, basic, basic.8x, train.l, infer.s, infer.m, infer.l] | ## DefaultEnvironment ``` { "description": "Specifies the default environment for the custom template.", "properties": { "environmentId": { "description": "The ID the environment to use for the public custom metric image.", "type": "string" }, "environmentVersionId": { "description": "The ID of the specific environment version to use with the public custom metric image.", "type": "string" } }, "required": [ "environmentId", "environmentVersionId" ], "type": "object", "x-versionadded": "v2.36" } ``` Specifies the default environment for the custom template. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | environmentId | string | true | | The ID the environment to use for the public custom metric image. | | environmentVersionId | string | true | | The ID of the specific environment version to use with the public custom metric image. | ## GrantAccessControlWithId ``` { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## GrantAccessControlWithUsername ``` { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | Username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## SharingListV2Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControlV2] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of items matching the condition. | ## TemplateMetadata ``` { "description": "Specifies permanent metadata for the custom template.", "properties": { "classLabels": { "description": "List of class names in case of creating a Binary or a multiclass custom model.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "readme": { "description": "Content of README.md file of the template.", "maxLength": 1048576, "type": [ "string", "null" ] }, "resourceBundleIds": { "description": "Custom template resource bundle IDs list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.36" }, "source": { "description": "Custom template source repo.", "type": "object", "x-versionadded": "v2.36" }, "tags": { "description": "Custom template tags list.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "templateTypeSpecificResources": { "description": "Specifies resources for the custom template.", "properties": { "serviceWebRequestsOnRootPath": { "default": false, "description": "Whether the 'service_web_requests_on_root_path' resource should be enabled on the custom app.", "type": [ "boolean", "null" ] } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" } ``` Specifies permanent metadata for the custom template. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classLabels | [string] | false | maxItems: 1000 | List of class names in case of creating a Binary or a multiclass custom model. | | readme | string,null | false | maxLength: 1048576 | Content of README.md file of the template. | | resourceBundleIds | [string] | false | maxItems: 1000 | Custom template resource bundle IDs list. | | source | TemplateSource | false | | Custom template source repo. | | tags | [string] | false | maxItems: 1000 | Custom template tags list. | | templateTypeSpecificResources | TemplateTypeSpecificResources | false | | Specifies resources for the custom template. | ## TemplateSource ``` { "description": "Custom template source repo.", "type": "object", "x-versionadded": "v2.36" } ``` Custom template source repo. ### Properties None ## TemplateTypeSpecificResources ``` { "description": "Specifies resources for the custom template.", "properties": { "serviceWebRequestsOnRootPath": { "default": false, "description": "Whether the 'service_web_requests_on_root_path' resource should be enabled on the custom app.", "type": [ "boolean", "null" ] } }, "type": "object", "x-versionadded": "v2.36" } ``` Specifies resources for the custom template. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | serviceWebRequestsOnRootPath | boolean,null | false | | Whether the 'service_web_requests_on_root_path' resource should be enabled on the custom app. | ## WorkspaceItemResponse ``` { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commitSha | string,null | false | | SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories). | | created | string | true | | ISO-8601 timestamp of when the file item was created. | | fileName | string | true | | The name of the file item. | | filePath | string | true | | The path of the file item. | | fileSource | string | true | | The source of the file item. | | id | string | true | | ID of the file item. | | ref | string,null | false | | Remote reference (branch, commit, tag). Branch "master", if not specified. | | repositoryFilePath | string,null | false | | Full path to the file in the remote repository. | | repositoryLocation | string,null | false | | URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name). | | repositoryName | string,null | false | | Name of the repository from which the file was pulled. | --- # Custom jobs URL: https://docs.datarobot.com/en/docs/api/reference/public-api/custom_jobs.html > Use the endpoints described below to manage custom jobs. Use custom jobs to implement automation (for example, custom tests) for your models and deployments. Each job serves as an automated workload, and the exit code determines if it passed or failed. You can run the custom jobs you create for one or more models or deployments. The automated workloads you define through custom jobs can make prediction requests, fetch inputs, and store outputs using DataRobot's Public API. # Custom jobs Use the endpoints described below to manage custom jobs. Use custom jobs to implement automation (for example, custom tests) for your models and deployments. Each job serves as an automated workload, and the exit code determines if it passed or failed. You can run the custom jobs you create for one or more models or deployments. The automated workloads you define through custom jobs can make prediction requests, fetch inputs, and store outputs using DataRobot's Public API. ## Retrieve custom job limits Operation path: `GET /api/v2/customJobLimits/` Authentication requirements: `BearerAuth` Retrieve custom job limits. ### Example responses > 200 Response ``` { "properties": { "maxCustomJobRuns": { "description": "Number of custom jobs allowed to run in parallel.", "type": "integer", "x-versionadded": "v2.33" }, "maxCustomJobTimeout": { "description": "Execution time limit for the custom job in seconds.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "maxCustomJobRuns", "maxCustomJobTimeout" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom job limits | CustomJobLimitsResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | ## List custom jobs Operation path: `GET /api/v2/customJobs/` Authentication requirements: `BearerAuth` List custom jobs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | onlyRunning | query | string | false | Whether only custom jobs that are currently being run should be returned. | | search | query | string | false | If supplied, only include custom jobs whose name or description contain this string. | | jobType | query | array[string] | false | The type of the custom job to filter by. | ### Enumerated Values | Parameter | Value | | --- | --- | | onlyRunning | [false, False, true, True] | | jobType | [default, hostedCustomMetric, notification, retraining] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of custom jobs.", "items": { "properties": { "created": { "description": "ISO-8601 timestamp of when the custom job was created.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobType": { "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "lastRun": { "description": "The last custom job run.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "updated": { "description": "ISO-8601 timestamp of when custom job was last updated.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "environmentId", "environmentVersionId", "id", "items", "jobType", "lastRun", "name", "resources", "updated" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of custom jobs. | CustomJobListResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | ## Create a custom job Operation path: `POST /api/v2/customJobs/` Authentication requirements: `BearerAuth` Create a custom job. ### Body parameter ``` { "properties": { "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment to use for this custom job.", "type": "string", "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version to use for this custom job. If not provided, the latest execution environment version will be used.", "type": "string", "x-versionadded": "v2.33" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string", "x-versionadded": "v2.33" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ], "x-versionadded": "v2.33" }, "jobType": { "default": "default", "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the custom job.", "maxLength": 255, "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameterValues": { "description": "Ability to inject values into a custom job at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the metadata.yaml file. This list will be merged with any existing runtime values set from the prior version when issuing a PATCH request so it is possible to specify a `null` value to unset specific parameters and fall back to the defaultValue from the definition.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateCustomJob | false | none | ### Example responses > 201 Response ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the custom job was created.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobType": { "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "lastRun": { "description": "The last custom job run.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "updated": { "description": "ISO-8601 timestamp of when custom job was last updated.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "environmentId", "environmentVersionId", "id", "items", "jobType", "lastRun", "name", "resources", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Created. | CustomJobResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Create a custom jobs from a gallery template Operation path: `POST /api/v2/customJobs/fromGalleryTemplate/` Authentication requirements: `BearerAuth` Create a custom job. ### Body parameter ``` { "properties": { "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string" }, "environmentId": { "description": "The ID of the execution environment to use for this custom job.", "type": "string" }, "environmentVersionId": { "description": "The ID of the execution environment version to use for this custom job. If not provided, the latest execution environment version will be used.", "type": "string" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "jobType": { "default": "default", "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameterValues": { "description": "Ability to inject values into a custom job at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the metadata.yaml file. This list will be merged with any existing runtime values set from the prior version when issuing a PATCH request so it is possible to specify a `null` value to unset specific parameters and fall back to the defaultValue from the definition.", "type": "string" }, "templateId": { "description": "Custom Job Template ID.", "type": "string" } }, "required": [ "templateId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateCustomJobFromTemplateGallery | false | none | ### Example responses > 201 Response ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the custom job was created.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobType": { "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "lastRun": { "description": "The last custom job run.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "updated": { "description": "ISO-8601 timestamp of when custom job was last updated.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "environmentId", "environmentVersionId", "id", "items", "jobType", "lastRun", "name", "resources", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Created. | CustomJobResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Delete custom job by custom job ID Operation path: `DELETE /api/v2/customJobs/{customJobId}/` Authentication requirements: `BearerAuth` Delete custom job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Custom job deleted. | None | | 403 | Forbidden | Custom jobs are not enabled. | None | ## Retrieve custom job by custom job ID Operation path: `GET /api/v2/customJobs/{customJobId}/` Authentication requirements: `BearerAuth` Retrieve custom job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the custom job was created.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobType": { "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "lastRun": { "description": "The last custom job run.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "updated": { "description": "ISO-8601 timestamp of when custom job was last updated.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "environmentId", "environmentVersionId", "id", "items", "jobType", "lastRun", "name", "resources", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom job | CustomJobResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | ## Update custom job by custom job ID Operation path: `PATCH /api/v2/customJobs/{customJobId}/` Authentication requirements: `BearerAuth` Update custom job. ### Body parameter ``` { "properties": { "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": "string", "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment to use for this custom job.", "type": "string", "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version to use for this custom job. If not provided, the latest execution environment version will be used.", "type": "string", "x-versionadded": "v2.33" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string", "x-versionadded": "v2.33" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ], "x-versionadded": "v2.33" }, "filesToDelete": { "description": "The IDs of the files to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" } ], "x-versionadded": "v2.33" }, "name": { "description": "Name of the custom job.", "maxLength": 255, "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameterValues": { "description": "Ability to inject values into a custom job at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the metadata.yaml file. This list will be merged with any existing runtime values set from the prior version when issuing a PATCH request so it is possible to specify a `null` value to unset specific parameters and fall back to the defaultValue from the definition.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | body | body | UpdateCustomJob | false | none | ### Example responses > 201 Response ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the custom job was created.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobType": { "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "lastRun": { "description": "The last custom job run.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "updated": { "description": "ISO-8601 timestamp of when custom job was last updated.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "environmentId", "environmentVersionId", "id", "items", "jobType", "lastRun", "name", "resources", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Updated custom job | CustomJobResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Retrieve custom job file content by custom job ID Operation path: `GET /api/v2/customJobs/{customJobId}/items/{itemId}/` Authentication requirements: `BearerAuth` Retrieve custom job file content. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | itemId | path | string | true | ID of the file item. | ### Example responses > 200 Response ``` { "properties": { "content": { "description": "Content of the chosen file.", "type": "string", "x-versionadded": "v2.33" }, "fileName": { "description": "Name of the chosen file.", "type": "string", "x-versionadded": "v2.33" }, "filePath": { "description": "Path of the chosen file.", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "content", "fileName", "filePath", "id" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom job file content | CustomJobFileResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | | 404 | Not Found | No file found. | None | | 422 | Unprocessable Entity | File is not utf-8 encoded. | None | ## List custom job runs by custom job ID Operation path: `GET /api/v2/customJobs/{customJobId}/runs/` Authentication requirements: `BearerAuth` List custom job runs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | scheduledJobId | query | string | false | If supplied, only include custom job runs that are scheduled with this scheduled job id. | | customJobId | path | string | true | ID of the custom job. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of custom job runs.", "items": { "properties": { "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string", "x-versionadded": "v2.33" }, "customJobId": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job run.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "duration": { "description": "Duration of the custom test run is seconds.", "type": "number", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The entry point file item ID in the custom job's workspace.", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobStatusId": { "description": "ID to track the custom job run execution status.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "status": { "description": "The status of the custom job run.", "enum": [ "succeeded", "failed", "running", "interrupted", "canceling", "canceled" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "customJobId", "duration", "id", "items", "jobStatusId", "resources", "status" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of custom job runs. | CustomJobRunListResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | ## Create a custom job run by custom job ID Operation path: `POST /api/v2/customJobs/{customJobId}/runs/` Authentication requirements: `BearerAuth` Create a custom job run. ### Body parameter ``` { "properties": { "description": { "description": "The description of the custom job run.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "runtimeParameterValues": { "description": "Ability to inject values at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the custom job metadata.yaml file. It has a priority over an existing runtime parameter overrides defined at the custom job level.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" }, "value": { "anyOf": [ { "description": "The value for the given field.", "maxLength": 4096, "type": [ "string", "null" ] }, { "default": false, "description": "The boolean value for the field (default False)", "type": "boolean" }, { "default": null, "description": "The numeric value for the field", "type": [ "number", "null" ] } ], "description": "The string, boolean or numeric value for the given field.", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | body | body | CreateCustomJobRun | false | none | ### Example responses > 201 Response ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string", "x-versionadded": "v2.33" }, "customJobId": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job run.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "duration": { "description": "Duration of the custom test run is seconds.", "type": "number", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The entry point file item ID in the custom job's workspace.", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobStatusId": { "description": "ID to track the custom job run execution status.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "status": { "description": "The status of the custom job run.", "enum": [ "succeeded", "failed", "running", "interrupted", "canceling", "canceled" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "customJobId", "duration", "id", "items", "jobStatusId", "resources", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Created. | CustomJobRunResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Cancel custom job run by custom job ID Operation path: `DELETE /api/v2/customJobs/{customJobId}/runs/{customJobRunId}/` Authentication requirements: `BearerAuth` Cancel custom job run. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | customJobRunId | path | string | true | ID of the custom job run. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Custom job run canceled | None | | 403 | Forbidden | Custom jobs are not enabled. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Retrieve custom job run by custom job ID Operation path: `GET /api/v2/customJobs/{customJobId}/runs/{customJobRunId}/` Authentication requirements: `BearerAuth` Retrieve custom job run. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | customJobRunId | path | string | true | ID of the custom job run. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string", "x-versionadded": "v2.33" }, "customJobId": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job run.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "duration": { "description": "Duration of the custom test run is seconds.", "type": "number", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The entry point file item ID in the custom job's workspace.", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobStatusId": { "description": "ID to track the custom job run execution status.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "status": { "description": "The status of the custom job run.", "enum": [ "succeeded", "failed", "running", "interrupted", "canceling", "canceled" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "customJobId", "duration", "id", "items", "jobStatusId", "resources", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom job run | CustomJobRunResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | ## Update custom job run by custom job ID Operation path: `PATCH /api/v2/customJobs/{customJobId}/runs/{customJobRunId}/` Authentication requirements: `BearerAuth` Update custom job run. ### Body parameter ``` { "properties": { "description": { "description": "The description of the custom job run.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "runtimeParameterValues": { "description": "Ability to inject values at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the custom job metadata.yaml file. It has a priority over an existing runtime parameter overrides defined at the custom job level.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" }, "value": { "anyOf": [ { "description": "The value for the given field.", "maxLength": 4096, "type": [ "string", "null" ] }, { "default": false, "description": "The boolean value for the field (default False)", "type": "boolean" }, { "default": null, "description": "The numeric value for the field", "type": [ "number", "null" ] } ], "description": "The string, boolean or numeric value for the given field.", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | customJobRunId | path | string | true | ID of the custom job run. | | body | body | CreateCustomJobRun | false | none | ### Example responses > 201 Response ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string", "x-versionadded": "v2.33" }, "customJobId": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job run.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "duration": { "description": "Duration of the custom test run is seconds.", "type": "number", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The entry point file item ID in the custom job's workspace.", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobStatusId": { "description": "ID to track the custom job run execution status.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "status": { "description": "The status of the custom job run.", "enum": [ "succeeded", "failed", "running", "interrupted", "canceling", "canceled" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "customJobId", "duration", "id", "items", "jobStatusId", "resources", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Updated custom job run | CustomJobRunResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Retrieve custom job run file content by custom job ID Operation path: `GET /api/v2/customJobs/{customJobId}/runs/{customJobRunId}/items/{itemId}/` Authentication requirements: `BearerAuth` Retrieve custom job run file content. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | customJobRunId | path | string | true | ID of the custom job run. | | itemId | path | string | true | ID of the file item. | ### Example responses > 200 Response ``` { "properties": { "content": { "description": "Content of the chosen file.", "type": "string", "x-versionadded": "v2.33" }, "fileName": { "description": "Name of the chosen file.", "type": "string", "x-versionadded": "v2.33" }, "filePath": { "description": "Path of the chosen file.", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "content", "fileName", "filePath", "id" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom job run file content | CustomJobFileResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | | 404 | Not Found | No file found. | None | | 422 | Unprocessable Entity | File is not utf-8 encoded. | None | ## De,ete custom job run logs by custom job ID Operation path: `DELETE /api/v2/customJobs/{customJobId}/runs/{customJobRunId}/logs/` Authentication requirements: `BearerAuth` Delete custom job run logs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | customJobRunId | path | string | true | ID of the custom job run. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Custom job run logs deleted. | None | | 404 | Not Found | No log found. | None | ## Retrieve custom job run logs by custom job ID Operation path: `GET /api/v2/customJobs/{customJobId}/runs/{customJobRunId}/logs/` Authentication requirements: `BearerAuth` Retrieve custom job run logs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | customJobRunId | path | string | true | ID of the custom job run. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The log file download. | None | | 404 | Not Found | No log found. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ("attachment;filename=custom-job-run--.log"). | ## Get Custom Job's access control list by custom job ID Operation path: `GET /api/v2/customJobs/{customJobId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations who have access to this custom job and their roles on the custom job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | customJobId | path | string | true | ID of the custom job. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The Custom Job's access control list. | SharingListV2Response | | 404 | Not Found | Either the Custom Job does not exist or the user does not have permissions to view the Custom Job. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## Update Custom Job's controls by custom job ID Operation path: `PATCH /api/v2/customJobs/{customJobId}/sharedRoles/` Authentication requirements: `BearerAuth` Set roles for users on this custom job. ### Body parameter ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | body | body | SharedRolesUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | None | | 409 | Conflict | The request would leave the custom job without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid | None | ## Permanently delete custom job by custom job ID Operation path: `POST /api/v2/customJobsCleanup/{customJobId}/` Authentication requirements: `BearerAuth` Permanently delete custom job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Custom job permanently deleted. | None | | 401 | Unauthorized | Permadelete not enabled or user not permitted to permadelete. | None | | 403 | Forbidden | Custom jobs are not enabled. | None | | 409 | Conflict | At least one of the custom job componenets are not soft-deleted, therefore can not permanently delete. | None | ## List deleted custom jobs Operation path: `GET /api/v2/deletedCustomJobs/` Authentication requirements: `BearerAuth` List deleted custom jobs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of custom jobs.", "items": { "properties": { "created": { "description": "ISO-8601 timestamp of when the custom job was created.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobType": { "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "lastRun": { "description": "The last custom job run.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "updated": { "description": "ISO-8601 timestamp of when custom job was last updated.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "environmentId", "environmentVersionId", "id", "items", "jobType", "lastRun", "name", "resources", "updated" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of deleted custom jobs. | CustomJobListResponse | | 403 | Forbidden | Custom jobs are not enabled. | None | # Schemas ## AccessControlV2 ``` { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the recipient. | | name | string | true | | The name of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | The type of the recipient. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## CreateCustomJob ``` { "properties": { "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment to use for this custom job.", "type": "string", "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version to use for this custom job. If not provided, the latest execution environment version will be used.", "type": "string", "x-versionadded": "v2.33" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string", "x-versionadded": "v2.33" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ], "x-versionadded": "v2.33" }, "jobType": { "default": "default", "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the custom job.", "maxLength": 255, "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameterValues": { "description": "Ability to inject values into a custom job at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the metadata.yaml file. This list will be merged with any existing runtime values set from the prior version when issuing a PATCH request so it is possible to specify a `null` value to unset specific parameters and fall back to the defaultValue from the definition.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 10000 | The description of the custom job. | | environmentId | string | false | | The ID of the execution environment to use for this custom job. | | environmentVersionId | string | false | | The ID of the execution environment version to use for this custom job. If not provided, the latest execution environment version will be used. | | file | string(binary) | false | | A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding filePath supplied that shows the relative location of the file. For example, you have two files: /home/username/custom-task/main.py and /home/username/custom-task/helpers/helper.py. When uploading these files, you would also need to include two filePath fields of, "main.py" and "helpers/helper.py". If the supplied file already exists at the supplied filePath, the old file is replaced by the new file. | | filePath | any | false | | The local path of the file being uploaded. See the file field explanation for more details. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jobType | string | false | | Type of the custom job. | | name | string | true | maxLength: 255 | Name of the custom job. | | resources | CustomJobResources | false | | The custom job resources that will be applied in the k8s cluster. | | runtimeParameterValues | string | false | | Ability to inject values into a custom job at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the metadata.yaml file. This list will be merged with any existing runtime values set from the prior version when issuing a PATCH request so it is possible to specify a null value to unset specific parameters and fall back to the defaultValue from the definition. | ### Enumerated Values | Property | Value | | --- | --- | | jobType | [default, hostedCustomMetric, notification, retraining] | ## CreateCustomJobFromTemplateGallery ``` { "properties": { "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string" }, "environmentId": { "description": "The ID of the execution environment to use for this custom job.", "type": "string" }, "environmentVersionId": { "description": "The ID of the execution environment version to use for this custom job. If not provided, the latest execution environment version will be used.", "type": "string" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "jobType": { "default": "default", "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameterValues": { "description": "Ability to inject values into a custom job at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the metadata.yaml file. This list will be merged with any existing runtime values set from the prior version when issuing a PATCH request so it is possible to specify a `null` value to unset specific parameters and fall back to the defaultValue from the definition.", "type": "string" }, "templateId": { "description": "Custom Job Template ID.", "type": "string" } }, "required": [ "templateId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 10000 | The description of the custom job. | | environmentId | string | false | | The ID of the execution environment to use for this custom job. | | environmentVersionId | string | false | | The ID of the execution environment version to use for this custom job. If not provided, the latest execution environment version will be used. | | file | string(binary) | false | | A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding filePath supplied that shows the relative location of the file. For example, you have two files: /home/username/custom-task/main.py and /home/username/custom-task/helpers/helper.py. When uploading these files, you would also need to include two filePath fields of, "main.py" and "helpers/helper.py". If the supplied file already exists at the supplied filePath, the old file is replaced by the new file. | | filePath | any | false | | The local path of the file being uploaded. See the file field explanation for more details. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jobType | string | false | | Type of the custom job. | | resources | CustomJobResources | false | | The custom job resources that will be applied in the k8s cluster. | | runtimeParameterValues | string | false | | Ability to inject values into a custom job at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the metadata.yaml file. This list will be merged with any existing runtime values set from the prior version when issuing a PATCH request so it is possible to specify a null value to unset specific parameters and fall back to the defaultValue from the definition. | | templateId | string | true | | Custom Job Template ID. | ### Enumerated Values | Property | Value | | --- | --- | | jobType | [default, hostedCustomMetric, notification, retraining] | ## CreateCustomJobRun ``` { "properties": { "description": { "description": "The description of the custom job run.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "runtimeParameterValues": { "description": "Ability to inject values at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the custom job metadata.yaml file. It has a priority over an existing runtime parameter overrides defined at the custom job level.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" }, "value": { "anyOf": [ { "description": "The value for the given field.", "maxLength": 4096, "type": [ "string", "null" ] }, { "default": false, "description": "The boolean value for the field (default False)", "type": "boolean" }, { "default": null, "description": "The numeric value for the field", "type": [ "number", "null" ] } ], "description": "The string, boolean or numeric value for the given field.", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 10000 | The description of the custom job run. | | runtimeParameterValues | [RuntimeParameterValue] | false | maxItems: 100 | Ability to inject values at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the custom job metadata.yaml file. It has a priority over an existing runtime parameter overrides defined at the custom job level. | ## CustomJobFileResponse ``` { "properties": { "content": { "description": "Content of the chosen file.", "type": "string", "x-versionadded": "v2.33" }, "fileName": { "description": "Name of the chosen file.", "type": "string", "x-versionadded": "v2.33" }, "filePath": { "description": "Path of the chosen file.", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "content", "fileName", "filePath", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | content | string | true | | Content of the chosen file. | | fileName | string | true | | Name of the chosen file. | | filePath | string | true | | Path of the chosen file. | | id | string | true | | The ID of the custom job. | ## CustomJobLimitsResponse ``` { "properties": { "maxCustomJobRuns": { "description": "Number of custom jobs allowed to run in parallel.", "type": "integer", "x-versionadded": "v2.33" }, "maxCustomJobTimeout": { "description": "Execution time limit for the custom job in seconds.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "maxCustomJobRuns", "maxCustomJobTimeout" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxCustomJobRuns | integer | true | | Number of custom jobs allowed to run in parallel. | | maxCustomJobTimeout | integer | true | | Execution time limit for the custom job in seconds. | ## CustomJobListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of custom jobs.", "items": { "properties": { "created": { "description": "ISO-8601 timestamp of when the custom job was created.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobType": { "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "lastRun": { "description": "The last custom job run.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "updated": { "description": "ISO-8601 timestamp of when custom job was last updated.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "environmentId", "environmentVersionId", "id", "items", "jobType", "lastRun", "name", "resources", "updated" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomJobResponse] | true | maxItems: 1000 | List of custom jobs. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomJobResources ``` { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" } ``` The custom job resources that will be applied in the k8s cluster. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | egressNetworkPolicy | string | true | | Egress network policy. | | resourceBundleId | string,null | false | | A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | ### Enumerated Values | Property | Value | | --- | --- | | egressNetworkPolicy | [none, public] | ## CustomJobResponse ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the custom job was created.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobType": { "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "lastRun": { "description": "The last custom job run.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "updated": { "description": "ISO-8601 timestamp of when custom job was last updated.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "environmentId", "environmentVersionId", "id", "items", "jobType", "lastRun", "name", "resources", "updated" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string | true | | ISO-8601 timestamp of when the custom job was created. | | description | string | false | maxLength: 10000 | The description of the custom job. | | entryPoint | string,null | false | | The ID of the entry point file to use. | | environmentId | string,null | true | | The ID of the execution environment used for this custom job. | | environmentVersionId | string,null | true | | The ID of the execution environment version used for this custom job. | | id | string | true | | The ID of the custom job. | | items | [WorkspaceItemResponse] | true | maxItems: 1000 | List of file items. | | jobType | string | true | | Type of the custom job. | | lastRun | string,null | true | | The last custom job run. | | name | string | true | | The name of the custom job. | | resources | CustomJobResources | true | | The custom job resources that will be applied in the k8s cluster. | | runtimeParameters | [RuntimeParameterUnified] | false | maxItems: 100 | Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition. | | updated | string | true | | ISO-8601 timestamp of when custom job was last updated. | ### Enumerated Values | Property | Value | | --- | --- | | jobType | [default, hostedCustomMetric, notification, retraining] | ## CustomJobRunListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of custom job runs.", "items": { "properties": { "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string", "x-versionadded": "v2.33" }, "customJobId": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job run.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "duration": { "description": "Duration of the custom test run is seconds.", "type": "number", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The entry point file item ID in the custom job's workspace.", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobStatusId": { "description": "ID to track the custom job run execution status.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "status": { "description": "The status of the custom job run.", "enum": [ "succeeded", "failed", "running", "interrupted", "canceling", "canceled" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "customJobId", "duration", "id", "items", "jobStatusId", "resources", "status" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomJobRunResponse] | true | maxItems: 1000 | List of custom job runs. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomJobRunResponse ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string", "x-versionadded": "v2.33" }, "customJobId": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job run.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "duration": { "description": "Duration of the custom test run is seconds.", "type": "number", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The entry point file item ID in the custom job's workspace.", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobStatusId": { "description": "ID to track the custom job run execution status.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "status": { "description": "The status of the custom job run.", "enum": [ "succeeded", "failed", "running", "interrupted", "canceling", "canceled" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "customJobId", "duration", "id", "items", "jobStatusId", "resources", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string | true | | ISO-8601 timestamp of when the model was created. | | customJobId | string | true | | The ID of the custom job. | | description | string | false | maxLength: 10000 | The description of the custom job run. | | duration | number | true | | Duration of the custom test run is seconds. | | entryPoint | string | false | | The entry point file item ID in the custom job's workspace. | | id | string | true | | The ID of the custom job. | | items | [WorkspaceItemResponse] | true | maxItems: 1000 | List of file items. | | jobStatusId | string,null | true | | ID to track the custom job run execution status. | | resources | CustomJobResources | true | | The custom job resources that will be applied in the k8s cluster. | | runtimeParameters | [RuntimeParameterUnified] | false | maxItems: 100 | Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition. | | status | string | true | | The status of the custom job run. | ### Enumerated Values | Property | Value | | --- | --- | | status | [succeeded, failed, running, interrupted, canceling, canceled] | ## GrantAccessControlWithId ``` { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## GrantAccessControlWithUsername ``` { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | Username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## RuntimeParameterUnified ``` { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowEmpty | boolean | false | | Indicates whether the param must be set before registration | | credentialType | string,null | false | | The type of credential, required only for credentials parameters. | | currentValue | any | false | | Given the default and the override, this is the actual current value of the parameter. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultValue | any | false | | The default value for the given field. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | | Description how this parameter impacts the running model. | | fieldName | string | true | | The parameter name. This value will be added as an environment variable when running custom models. | | keyValueId | string,null | false | | The ID of the key-value entry storing this parameter value. | | maxValue | number,null | false | | The maximum value for a numeric field. | | minValue | number,null | false | | The minimum value for a numeric field. | | overrideValue | any | false | | Value set by the user that overrides the default set in the definition. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | The type of this value. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | [adls_gen2_oauth, api_token, azure, azure_oauth, azure_service_principal, basic, bearer, box_jwt, client_id_and_secret, databricks_access_token_account, databricks_service_principal_account, external_oauth_provider, gcp, oauth, rsa, s3, sap_oauth, snowflake_key_pair_user_account, snowflake_oauth_user_account, tableau_access_token] | | type | [boolean, credential, customMetric, deployment, modelPackage, numeric, string] | ## RuntimeParameterValue ``` { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" }, "value": { "anyOf": [ { "description": "The value for the given field.", "maxLength": 4096, "type": [ "string", "null" ] }, { "default": false, "description": "The boolean value for the field (default False)", "type": "boolean" }, { "default": null, "description": "The numeric value for the field", "type": [ "number", "null" ] } ], "description": "The string, boolean or numeric value for the given field.", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fieldName | string | true | | The required field name. This value will be added as an environment variable when running custom models. | | type | string | true | | The type of this value. | | value | any | true | | The string, boolean or numeric value for the given field. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | maxLength: 4096 | The value for the given field. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | The boolean value for the field (default False) | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number,null | false | | The numeric value for the field | ### Enumerated Values | Property | Value | | --- | --- | | type | [boolean, credential, customMetric, deployment, modelPackage, numeric, string] | ## SharedRolesUpdate ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | Name of the action being taken. The only operation is 'updateRoles'. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | Array of GrantAccessControl objects., up to maximum 100 objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithUsername | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithId | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## SharingListV2Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControlV2] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of items matching the condition. | ## UpdateCustomJob ``` { "properties": { "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": "string", "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment to use for this custom job.", "type": "string", "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version to use for this custom job. If not provided, the latest execution environment version will be used.", "type": "string", "x-versionadded": "v2.33" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string", "x-versionadded": "v2.33" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ], "x-versionadded": "v2.33" }, "filesToDelete": { "description": "The IDs of the files to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" } ], "x-versionadded": "v2.33" }, "name": { "description": "Name of the custom job.", "maxLength": 255, "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameterValues": { "description": "Ability to inject values into a custom job at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the metadata.yaml file. This list will be merged with any existing runtime values set from the prior version when issuing a PATCH request so it is possible to specify a `null` value to unset specific parameters and fall back to the defaultValue from the definition.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 10000 | The description of the custom job. | | entryPoint | string | false | | The ID of the entry point file to use. | | environmentId | string | false | | The ID of the execution environment to use for this custom job. | | environmentVersionId | string | false | | The ID of the execution environment version to use for this custom job. If not provided, the latest execution environment version will be used. | | file | string(binary) | false | | A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding filePath supplied that shows the relative location of the file. For example, you have two files: /home/username/custom-task/main.py and /home/username/custom-task/helpers/helper.py. When uploading these files, you would also need to include two filePath fields of, "main.py" and "helpers/helper.py". If the supplied file already exists at the supplied filePath, the old file is replaced by the new file. | | filePath | any | false | | The local path of the file being uploaded. See the file field explanation for more details. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | filesToDelete | any | false | | The IDs of the files to be deleted. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 100 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | maxLength: 255 | Name of the custom job. | | resources | CustomJobResources | false | | The custom job resources that will be applied in the k8s cluster. | | runtimeParameterValues | string | false | | Ability to inject values into a custom job at runtime. The fieldName must match a fieldName that is listed in the runtimeParameterDefinitions section of the metadata.yaml file. This list will be merged with any existing runtime values set from the prior version when issuing a PATCH request so it is possible to specify a null value to unset specific parameters and fall back to the defaultValue from the definition. | ## WorkspaceItemResponse ``` { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commitSha | string,null | false | | SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories). | | created | string | true | | ISO-8601 timestamp of when the file item was created. | | fileName | string | true | | The name of the file item. | | filePath | string | true | | The path of the file item. | | fileSource | string | true | | The source of the file item. | | id | string | true | | ID of the file item. | | ref | string,null | false | | Remote reference (branch, commit, tag). Branch "master", if not specified. | | repositoryFilePath | string,null | false | | Full path to the file in the remote repository. | | repositoryLocation | string,null | false | | URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name). | | repositoryName | string,null | false | | Name of the repository from which the file was pulled. | --- # Custom models URL: https://docs.datarobot.com/en/docs/api/reference/public-api/custom_models.html > Assemble, test and deploy your own custom model with a custom environment. # Custom models Assemble, test and deploy your own custom model with a custom environment. ## Get custom model feature impact by image ID Operation path: `GET /api/v2/customInferenceImages/{imageId}/featureImpact/` Authentication requirements: `BearerAuth` Retrieve feature impact scores for features in a custom inference model image. .. minversion:: v2.23 DEPRECATED: please use version route instead: [GET /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/featureImpact/][get-apiv2custommodelscustommodelidversionscustommodelversionidfeatureimpact] This route is a counterpart of a corresponding endpoint for native models: [GET /api/v2/projects/{projectId}/models/{modelId}/featureImpact/][get-apiv2projectsprojectidmodelsmodelidfeatureimpact]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | imageId | path | string | true | ID of the image of the custom inference model to retrieve feature impact from. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of feature impact records in a given batch.", "type": "integer" }, "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL for the next page of results, or null if there is no next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL for the previous page of results, or null if there is no previous page.", "format": "uri", "type": [ "string", "null" ] }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact.", "type": "boolean" }, "rowCount": { "description": "The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the ``rowCount``, we return ``null`` here.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "shapBased": { "description": "Indicates whether feature impact was calculated using Shapley values. True for anomaly detection models when the project is unsupervised, as permutation approach is not applicable. Note that supervised projects must use an alternative route for SHAP impact: /api/v2/projects/(projectId)/models/(modelId)/shapImpact/", "type": "boolean", "x-versionadded": "v2.18" } }, "required": [ "count", "featureImpacts", "next", "previous", "ranRedundancyDetection", "rowCount", "shapBased" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model feature impact returned. | FeatureImpactResponse | | 404 | Not Found | No feature impact data found for custom model. | None | | 422 | Unprocessable Entity | Cannot retrieve feature impact scores: (1) if custom model is not an inference model, (2) if training data is not assigned, (3) if feature impact job is in progress for custom model. | None | ## Create custom model feature impact by image ID Operation path: `POST /api/v2/customInferenceImages/{imageId}/featureImpact/` Authentication requirements: `BearerAuth` Add a request to calculate feature impact for a custom inference model image to the queue. .. minversion:: v2.23 DEPRECATED: please use version route instead: [POST /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/featureImpact/][post-apiv2custommodelscustommodelidversionscustommodelversionidfeatureimpact] This route is a counterpart of a corresponding endpoint for native models: [POST /api/v2/projects/{projectId}/models/{modelId}/featureImpact/][post-apiv2projectsprojectidmodelsmodelidfeatureimpact]. ### Body parameter ``` { "properties": { "rowCount": { "description": "The sample size to use for Feature Impact computation. It is possible to re-compute Feature Impact with a different row count.", "maximum": 100000, "minimum": 10, "type": "integer", "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | imageId | path | string | true | ID of the image of the custom inference model to submit feature impact job for. | | body | body | FeatureImpactCreatePayload | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] for tracking job status.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Feature impact request has been successfully submitted. | FeatureImpactCreateResponse | | 404 | Not Found | If feature impact has already been submitted. The response will include jobId property which can be used for tracking its progress. | None | | 422 | Unprocessable Entity | If job cannot be submitted because of invalid input or model state: (1) if image id does not correspond to a custom inference model, (2) if training data is not yet assigned or assignment is in progress, (3) if the rowCount exceeds the minimum or maximum value for this model's training data. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | Contains a url for tracking job status: [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid]. | ## Get custom model resource limits Operation path: `GET /api/v2/customModelLimits/` Authentication requirements: `BearerAuth` Retrieve custom model resource limits the user has access to. ### Example responses > 200 Response ``` { "properties": { "desiredCustomModelContainerSize": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "maxCustomModelContainerSize": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "maxCustomModelReplicasPerDeployment": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "maxCustomModelTestingParallelUsers": { "description": "The maximum number of parallel users that can be used for Custom Model Testing checks", "exclusiveMinimum": 0, "maximum": 20, "type": "integer" }, "minCustomModelContainerSize": { "description": "The minimum memory that might be allocated by the custom-model.", "maximum": 134217728, "minimum": 134217728, "type": [ "integer", "null" ], "x-versionadded": "v2.37" } }, "required": [ "maxCustomModelTestingParallelUsers" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomModelResourceLimits | ## List custom model tests Operation path: `GET /api/v2/customModelTests/` Authentication requirements: `BearerAuth` Retrieve the testing history for a model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | resourceBundleId | query | string,null | false | A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | | maximumMemory | query | integer,null | false | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId. | | networkEgressPolicy | query | string,null | false | Network egress policy. | | desiredMemory | query | integer,null | false | The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId. | | replicas | query | integer,null | false | A fixed number of replicas that will be set for the given custom-model. | | requiresHa | query | boolean,null | false | Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. | | customModelId | query | string | true | ID of the Custom Model to retrieve testing history for. | ### Enumerated Values | Parameter | Value | | --- | --- | | networkEgressPolicy | [NONE, PUBLIC] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom model tests.", "items": { "properties": { "completedAt": { "description": "ISO-8601 timestamp of when the testing attempt was completed.", "type": "string" }, "created": { "description": "ISO-8601 timestamp of when the testing entry was created.", "type": "string" }, "createdBy": { "description": "The username of the user that started the custom model test.", "type": "string" }, "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelImageId": { "description": "If testing was successful, ID of the custom inference model image that can be used for a deployment, otherwise null.", "type": [ "string", "null" ] }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "datasetId": { "description": "ID of the dataset used for testing.", "type": "string" }, "datasetVersionId": { "description": "ID of the specific dataset version used for testing.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "ID of the testing history entry.", "type": "string" }, "imageType": { "description": "The type of the image, either customModelImage if the testing attempt is using a customModelImage as its model or customModelVersion if the testing attempt is using a customModelVersion with dependency management.", "enum": [ "customModelImage", "customModelVersion" ], "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "overallStatus": { "description": "The overall status of the testing history entry.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "testingStatus": { "description": "Maps the testing types to their status and message for the testing entry. Testing type represents a single check executed during the test.", "properties": { "errorCheck": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "longRunningService": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "nullValueImputation": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "sideEffects": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } }, "type": "object" } }, "required": [ "completedAt", "created", "createdBy", "customModel", "customModelImageId", "customModelVersion", "datasetId", "datasetVersionId", "executionEnvironment", "executionEnvironmentVersion", "id", "overallStatus", "testingStatus" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomModelTestsListResponse | ## Create custom model test Operation path: `POST /api/v2/customModelTests/` Authentication requirements: `BearerAuth` Test a custom inference model. This will start a job to check that the custom model can make predictions against the supplied dataset without breaking. ### Body parameter ``` { "properties": { "configuration": { "description": "Key value map of Testing type and Testing type config.", "properties": { "errorCheck": { "default": "fail", "description": "Ensures that the model can make predictions on the provided test dataset.", "enum": [ "fail", "skip" ], "type": "string" }, "longRunningService": { "default": "fail", "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "enum": [ "fail" ], "type": "string" }, "nullValueImputation": { "default": "warn", "description": "Verifies that the model can impute null values. Required for Feature Impact.", "enum": [ "skip", "warn", "fail" ], "type": "string" }, "sideEffects": { "default": "warn", "description": "Verifies that predictions made on the dataset match row-wise predictions for the same dataset. Fails if the predictions do not match.", "enum": [ "skip", "warn", "fail" ], "type": "string" } }, "type": "object" }, "customModelId": { "description": "The ID of the custom model to test.", "type": "string" }, "customModelVersionId": { "description": "The ID of custom model version to use.", "type": "string" }, "datasetId": { "description": "The ID of the dataset to use for testing. Dataset ID is required for regular (non-unstructured) custom models.", "type": "string" }, "datasetVersionId": { "description": "The ID of the version of the dataset item to use as the testing dataset. Defaults to the latest version.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "environmentId": { "description": "The ID of environment to use. If not specified, the customModelVersion's dependency environment will be used.", "type": "string", "x-versiondeprecated": "v2.23" }, "environmentVersionId": { "description": "The ID of environment version to use. Defaults to the latest successfully built version.", "type": "string", "x-versiondeprecated": "v2.23" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "customModelId", "customModelVersionId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomModelTests | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Model testing job successfully started. | CustomModelAsyncOperationResponse | | 403 | Forbidden | No access to use data for testing custom model. | None | | 404 | Not Found | Custom model or dataset not found. | None | | 422 | Unprocessable Entity | Custom Model Testing cannot be submitted because of invalid input or model state: (1) if user does not have permission to create legacy conversion environment, (2) testing is already in progress for the custom model, (3) dataset used for testing is not snapshotted,(4) other cases. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL that can be polled to check the status. | ## Cancel custom model test by custom model test ID Operation path: `DELETE /api/v2/customModelTests/{customModelTestId}/` Authentication requirements: `BearerAuth` Cancel custom inference model testing. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelTestId | path | string | true | ID of the testing history attempt. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Testing canceled. | None | | 404 | Not Found | Testing attempt not found. | None | | 409 | Conflict | Testing attempt has already reached a terminal state. | None | ## Get custom model test by custom model test ID Operation path: `GET /api/v2/customModelTests/{customModelTestId}/` Authentication requirements: `BearerAuth` Retrieve a specific testing history entry for a custom model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelTestId | path | string | true | ID of the testing history attempt. | ### Example responses > 200 Response ``` { "properties": { "completedAt": { "description": "ISO-8601 timestamp of when the testing attempt was completed.", "type": "string" }, "created": { "description": "ISO-8601 timestamp of when the testing entry was created.", "type": "string" }, "createdBy": { "description": "The username of the user that started the custom model test.", "type": "string" }, "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelImageId": { "description": "If testing was successful, ID of the custom inference model image that can be used for a deployment, otherwise null.", "type": [ "string", "null" ] }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "datasetId": { "description": "ID of the dataset used for testing.", "type": "string" }, "datasetVersionId": { "description": "ID of the specific dataset version used for testing.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "ID of the testing history entry.", "type": "string" }, "imageType": { "description": "The type of the image, either customModelImage if the testing attempt is using a customModelImage as its model or customModelVersion if the testing attempt is using a customModelVersion with dependency management.", "enum": [ "customModelImage", "customModelVersion" ], "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "overallStatus": { "description": "The overall status of the testing history entry.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "testingStatus": { "description": "Maps the testing types to their status and message for the testing entry. Testing type represents a single check executed during the test.", "properties": { "errorCheck": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "longRunningService": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "nullValueImputation": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "sideEffects": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } }, "type": "object" } }, "required": [ "completedAt", "created", "createdBy", "customModel", "customModelImageId", "customModelVersion", "datasetId", "datasetVersionId", "executionEnvironment", "executionEnvironmentVersion", "id", "overallStatus", "testingStatus" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomModelTestsResponse | ## Get custom model test log by custom model test ID Operation path: `GET /api/v2/customModelTests/{customModelTestId}/log/` Authentication requirements: `BearerAuth` Retrieve the logs from a model testing attempt. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelTestId | path | string | true | ID of the testing history attempt. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The log file will be downloaded. | None | | 404 | Not Found | No testing log found. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ("attachment;filename=testing-.log"). | ## Get custom model test log tail by custom model test ID Operation path: `GET /api/v2/customModelTests/{customModelTestId}/tail/` Authentication requirements: `BearerAuth` Retrieve the last N lines of logs from a model testing attempt. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | lines | query | integer | true | Number of lines from the log to retrieve (1-1000, default 100). | | customModelTestId | path | string | true | ID of the testing history attempt. | ### Example responses > 200 Response ``` { "properties": { "log": { "description": "The N lines of the log.", "type": "string" } }, "required": [ "log" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The log tail was retrieved. | CustomModelTestsLogTailResponse | | 400 | Bad Request | Requested number of lines is invalid. | None | | 404 | Not Found | The testing history entry cannot be found. | None | ## List custom models Operation path: `GET /api/v2/customModels/` Authentication requirements: `BearerAuth` Retrieve metadata for all custom models the user has access to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | customModelType | query | string | false | If "training" specified, only Custom Training Tasks will be returned. If "inference" specified, only Custom Inference Models will be returned. If not specified, all custom models will be returned. After deprecation, only Custom Inference Models will be returned | | targetType | query | string | false | The target type of the custom model. | | isDeployed | query | string | false | If TRUE, only deployed custom models will be returned. If FALSE, only not deployed custom models will be returned. If not specified, all custom models will be returned. | | orderBy | query | string | false | Sort order which will be applied to custom model list, valid options are "created", "updated". Prefix the attribute name with a dash to sort in descending order, e.g. orderBy="-created". By default, the orderBy parameter is None which will result in custom models being returned in order of creation time descending. | | searchFor | query | string | false | String to search for occurrence in custom model's description, language and name. Search is case insensitive. If not specified, all custom models will be returned. | | tagKeys | query | string | false | List of tag keys to filter by. | | tagValues | query | string | false | List of tag values to filter by. | ### Enumerated Values | Parameter | Value | | --- | --- | | customModelType | [training, inference] | | targetType | [Binary, Regression, Multiclass, Anomaly, Transform, TextGeneration, GeoPoint, Unstructured, VectorDatabase, AgenticWorkflow, MCP] | | isDeployed | [false, False, true, True] | | orderBy | [created, -created, updated, -updated] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom models.", "items": { "properties": { "calibratePredictions": { "description": "Determines whether ot not predictions should be calibrated by DataRobot.Only applies to anomaly detection.", "type": "boolean" }, "classLabels": { "description": "If the model is a multiclass classifier, these are the model's class labels", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "createdBy": { "description": "The username of the custom model creator.", "type": "string" }, "customModelType": { "description": "The type of custom model.", "enum": [ "training", "inference" ], "type": "string" }, "deploymentsCount": { "description": "The number of models deployed.", "type": "integer" }, "description": { "description": "The description of the model.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "id": { "description": "The ID of the custom model.", "type": "string" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Assigning training data on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled and return HTTP 422 for this particular model. Use training data assignment at the version level: [POST /api/v2/customModels/{customModelId}/versions/][post-apiv2custommodelscustommodelidversions] [PATCH /api/v2/customModels/{customModelId}/versions/][patch-apiv2custommodelscustommodelidversions] ", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "The programming language used to write the model.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The name of the model.", "type": "string" }, "negativeClassLabel": { "description": "If the model is a binary classifier, this is the negative class label.", "type": "string" }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "playgroundId": { "description": "The ID of the GenAI Playground associated with the given custom inference model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "positiveClassLabel": { "description": "If the model is a binary classifier, this is the positive class label.", "type": "string" }, "predictionThreshold": { "description": "If the model is a binary classifier, this is the prediction threshold.", "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "supportsAnomalyDetection": { "description": "Whether the model supports anomaly detection.", "type": "boolean" }, "supportsBinaryClassification": { "description": "Whether the model supports binary classification.", "type": "boolean" }, "supportsRegression": { "description": "Whether the model supports regression.", "type": "boolean" }, "tags": { "description": "A list of the custom model tag.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "type": "string" }, "value": { "description": "The value of the tag.", "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.39" }, "targetName": { "description": "The name of the target for labeling predictions.", "type": "string" }, "targetType": { "description": "The target type of custom model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "template": { "description": "If not null, the template used to create the custom model.", "properties": { "modelType": { "description": "The type of template the model was created from.", "enum": [ "nimModel", "invalid" ], "type": "string", "x-versionadded": "v2.36" }, "templateId": { "description": "The ID of the template used to create this custom model.", "type": "string", "x-versionadded": "v2.36" } }, "required": [ "modelType", "templateId" ], "type": "object", "x-versionadded": "v2.36" }, "trainingDataAssignmentInProgress": { "description": "Indicates if training data is currently being assigned to the custom model.", "type": "boolean" }, "trainingDataFileName": { "description": "The name of the file that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDataPartitionColumn": { "description": "The name of the column containing the partition assignments in training data if it was assigned previously and partitioning was provided.", "type": [ "string", "null" ] }, "trainingDatasetId": { "description": "The ID of the dataset that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDatasetVersionId": { "description": "The ID of the dataset version that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "updated": { "description": "ISO-8601 timestamp of when model was last updated.", "type": "string" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "maxLength": 100, "type": "string" } }, "required": [ "created", "createdBy", "deploymentsCount", "description", "id", "language", "latestVersion", "name", "supportsBinaryClassification", "supportsRegression", "targetType", "template", "updated" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomModelListResponse | ## Create custom model Operation path: `POST /api/v2/customModels/` Authentication requirements: `BearerAuth` Creates a new custom model and returns the newly created metadata record for it. All custom models must support at least one target type (binaryClassification, regression). Custom inference models can only support a single target type. A regression model is expected to produce predictions that are arbitrary floating-point or integer numbers. A classification model is expected to return predictions with probability scores for each class. For example, a binary classification model might return: .. code:: Python ``` { positiveClassLabel: probability, negativeClassLabel: 1.0 - probability } ``` For Custom Inference Models, the `file` parameter must be either a tarball or zip archive containing, at minimum, a script named `start_server.sh`. It may contain additional files, including scripts and precompiled binaries as well as data files.`start_server.sh` may execute these scripts and/or binaries. When this script is executed, it is run as part of an Environment (specified via subsequent API calls), and all included scripts and binaries can take advantage of any programming language interpreters, compilers, libraries, or other tools included in the Environment.`start_server.sh` must be marked as executable ( `chmod +x`). When `start_server.sh` is launched, it must launch and maintain (in the foreground) a Web server that listens on two URLs: - GET $URL_PREFIX/ This route must return a 200 response code with an empty body immediately if the server is ready to respond to prediction requests. Otherwise it should either not accept the request, not respond to the request, or return a 503 response code. - POST $URL_PREFIX/predict_no_state/This route must accept as input a JSON object of the form: .. code-block:: Python { 'X': { 'col1': [...col1_data...], 'col2': [...col2_data...], 'col3': [...col3_data...], ... } } The data lists will all be the same length. It must return a JSON object of the form: .. code-block:: Python { 'predictions': [...predictions data...] } The predictions data must correspond 1:1 to the rows in the input data lists. $URL_PREFIXis provided as an environment variable. The Web server process must re-read its value every time the process starts, as it may change. It is an opaque string that is guaranteed to be a valid URL component, but may contain path separators (/). ### Body parameter ``` { "properties": { "calibratePredictions": { "default": true, "description": "Whether model predictions should be calibrated by DataRobot.Only applies to anomaly detection training tasks; we recommend this if you have not already included calibration in your model code.Calibration improves the probability estimates of a model, and modifies the predictions of non-probabilistic models to be interpretable as probabilities. This will facilitate comparison to DataRobot models, and give access to ROC curve insights on external data.", "type": "boolean", "x-versionadded": "v2.23" }, "classLabels": { "description": "The class labels for multiclass classification. Required for multiclass inference models. If using one of the [DataRobot] base environments and your model produces an ndarray of unlabeled class probabilities, the order of the labels should match the order of the predicted output", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "customModelType": { "description": "The type of custom model.", "enum": [ "training", "inference" ], "type": "string", "x-versiondeprecated": "v2.28" }, "description": { "description": "The user-friendly description of the model.", "maxLength": 10000, "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Training data assignment on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled for this particular model.", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "Programming language name in which model is written.", "maxLength": 500, "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The user-friendly name for the model.", "maxLength": 255, "type": "string" }, "negativeClassLabel": { "description": "The negative class label for custom models that support binary classification. If specified, `positiveClassLabel` must also be specified. Default value is \"0\".", "maxLength": 500, "type": [ "string", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "playgroundId": { "description": "The ID of the GenAI Playground associated with the given custom inference model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "positiveClassLabel": { "description": "The positive class label for custom models that support binary classification. If specified, `negativeClassLabel` must also be specified. Default value is \"1\".", "maxLength": 500, "type": [ "string", "null" ] }, "predictionThreshold": { "default": 0.5, "description": "The prediction threshold which will be used for binary classification custom model.", "maximum": 1, "minimum": 0, "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "supportsBinaryClassification": { "description": "Whether the model supports binary classification.", "type": "boolean", "x-versiondeprecated": "v2.23" }, "supportsRegression": { "description": "Whether the model supports regression.", "type": "boolean", "x-versiondeprecated": "v2.23" }, "tags": { "description": "The list of a tag's name/value pairs.", "items": { "properties": { "name": { "description": "The name of the tag.", "maxLength": 50, "minLength": 1, "type": "string" }, "value": { "description": "The value of the tag.", "maxLength": 256, "minLength": 1, "type": "string" } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 50, "minItems": 1, "type": "array", "x-versionadded": "v2.39" }, "targetName": { "description": "The name of the target for labeling predictions. Required for model type 'inference'. Specifying this value for a model type 'training' will result in an error.", "maxLength": 500, "type": [ "string", "null" ] }, "targetType": { "description": "The target type of the custom model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string", "x-versionadded": "v2.23" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "maxLength": 100, "type": "string", "x-versionadded": "v2.29" } }, "required": [ "customModelType", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomModelCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "calibratePredictions": { "description": "Determines whether ot not predictions should be calibrated by DataRobot.Only applies to anomaly detection.", "type": "boolean" }, "classLabels": { "description": "If the model is a multiclass classifier, these are the model's class labels", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "createdBy": { "description": "The username of the custom model creator.", "type": "string" }, "customModelType": { "description": "The type of custom model.", "enum": [ "training", "inference" ], "type": "string" }, "deploymentsCount": { "description": "The number of models deployed.", "type": "integer" }, "description": { "description": "The description of the model.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "id": { "description": "The ID of the custom model.", "type": "string" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Assigning training data on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled and return HTTP 422 for this particular model. Use training data assignment at the version level: [POST /api/v2/customModels/{customModelId}/versions/][post-apiv2custommodelscustommodelidversions] [PATCH /api/v2/customModels/{customModelId}/versions/][patch-apiv2custommodelscustommodelidversions] ", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "The programming language used to write the model.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The name of the model.", "type": "string" }, "negativeClassLabel": { "description": "If the model is a binary classifier, this is the negative class label.", "type": "string" }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "playgroundId": { "description": "The ID of the GenAI Playground associated with the given custom inference model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "positiveClassLabel": { "description": "If the model is a binary classifier, this is the positive class label.", "type": "string" }, "predictionThreshold": { "description": "If the model is a binary classifier, this is the prediction threshold.", "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "supportsAnomalyDetection": { "description": "Whether the model supports anomaly detection.", "type": "boolean" }, "supportsBinaryClassification": { "description": "Whether the model supports binary classification.", "type": "boolean" }, "supportsRegression": { "description": "Whether the model supports regression.", "type": "boolean" }, "tags": { "description": "A list of the custom model tag.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "type": "string" }, "value": { "description": "The value of the tag.", "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.39" }, "targetName": { "description": "The name of the target for labeling predictions.", "type": "string" }, "targetType": { "description": "The target type of custom model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "template": { "description": "If not null, the template used to create the custom model.", "properties": { "modelType": { "description": "The type of template the model was created from.", "enum": [ "nimModel", "invalid" ], "type": "string", "x-versionadded": "v2.36" }, "templateId": { "description": "The ID of the template used to create this custom model.", "type": "string", "x-versionadded": "v2.36" } }, "required": [ "modelType", "templateId" ], "type": "object", "x-versionadded": "v2.36" }, "trainingDataAssignmentInProgress": { "description": "Indicates if training data is currently being assigned to the custom model.", "type": "boolean" }, "trainingDataFileName": { "description": "The name of the file that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDataPartitionColumn": { "description": "The name of the column containing the partition assignments in training data if it was assigned previously and partitioning was provided.", "type": [ "string", "null" ] }, "trainingDatasetId": { "description": "The ID of the dataset that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDatasetVersionId": { "description": "The ID of the dataset version that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "updated": { "description": "ISO-8601 timestamp of when model was last updated.", "type": "string" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "maxLength": 100, "type": "string" } }, "required": [ "created", "createdBy", "deploymentsCount", "description", "id", "language", "latestVersion", "name", "supportsBinaryClassification", "supportsRegression", "targetType", "template", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Created. | CustomModelResponse | | 403 | Forbidden | Custom model creation is not enabled. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Clone custom model Operation path: `POST /api/v2/customModels/fromCustomModel/` Authentication requirements: `BearerAuth` Creates a copy of the provided custom model, including metadata, versions of that model, and uploaded files. Associates the new versions with files owned by the custom model. ### Body parameter ``` { "properties": { "customModelId": { "description": "The ID of the custom model to copy.", "type": "string" } }, "required": [ "customModelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomModelCopy | false | none | ### Example responses > 201 Response ``` { "properties": { "calibratePredictions": { "description": "Determines whether ot not predictions should be calibrated by DataRobot.Only applies to anomaly detection.", "type": "boolean" }, "classLabels": { "description": "If the model is a multiclass classifier, these are the model's class labels", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "createdBy": { "description": "The username of the custom model creator.", "type": "string" }, "customModelType": { "description": "The type of custom model.", "enum": [ "training", "inference" ], "type": "string" }, "deploymentsCount": { "description": "The number of models deployed.", "type": "integer" }, "description": { "description": "The description of the model.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "id": { "description": "The ID of the custom model.", "type": "string" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Assigning training data on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled and return HTTP 422 for this particular model. Use training data assignment at the version level: [POST /api/v2/customModels/{customModelId}/versions/][post-apiv2custommodelscustommodelidversions] [PATCH /api/v2/customModels/{customModelId}/versions/][patch-apiv2custommodelscustommodelidversions] ", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "The programming language used to write the model.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The name of the model.", "type": "string" }, "negativeClassLabel": { "description": "If the model is a binary classifier, this is the negative class label.", "type": "string" }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "playgroundId": { "description": "The ID of the GenAI Playground associated with the given custom inference model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "positiveClassLabel": { "description": "If the model is a binary classifier, this is the positive class label.", "type": "string" }, "predictionThreshold": { "description": "If the model is a binary classifier, this is the prediction threshold.", "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "supportsAnomalyDetection": { "description": "Whether the model supports anomaly detection.", "type": "boolean" }, "supportsBinaryClassification": { "description": "Whether the model supports binary classification.", "type": "boolean" }, "supportsRegression": { "description": "Whether the model supports regression.", "type": "boolean" }, "tags": { "description": "A list of the custom model tag.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "type": "string" }, "value": { "description": "The value of the tag.", "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.39" }, "targetName": { "description": "The name of the target for labeling predictions.", "type": "string" }, "targetType": { "description": "The target type of custom model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "template": { "description": "If not null, the template used to create the custom model.", "properties": { "modelType": { "description": "The type of template the model was created from.", "enum": [ "nimModel", "invalid" ], "type": "string", "x-versionadded": "v2.36" }, "templateId": { "description": "The ID of the template used to create this custom model.", "type": "string", "x-versionadded": "v2.36" } }, "required": [ "modelType", "templateId" ], "type": "object", "x-versionadded": "v2.36" }, "trainingDataAssignmentInProgress": { "description": "Indicates if training data is currently being assigned to the custom model.", "type": "boolean" }, "trainingDataFileName": { "description": "The name of the file that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDataPartitionColumn": { "description": "The name of the column containing the partition assignments in training data if it was assigned previously and partitioning was provided.", "type": [ "string", "null" ] }, "trainingDatasetId": { "description": "The ID of the dataset that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDatasetVersionId": { "description": "The ID of the dataset version that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "updated": { "description": "ISO-8601 timestamp of when model was last updated.", "type": "string" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "maxLength": 100, "type": "string" } }, "required": [ "created", "createdBy", "deploymentsCount", "description", "id", "language", "latestVersion", "name", "supportsBinaryClassification", "supportsRegression", "targetType", "template", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successfully created copy. | CustomModelResponse | ## Create a custom model Operation path: `POST /api/v2/customModels/fromModelTemplate/` Authentication requirements: `BearerAuth` Create a custom model from a template. ### Body parameter ``` { "properties": { "nimContainerTagOverride": { "description": "Allows to override the NIM container image tag.", "maxLength": 100, "type": "string", "x-versionadded": "v2.37" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": "string" }, "secretConfigId": { "description": "A secret configuration that is used by a custom model.", "type": [ "string", "null" ] }, "templateId": { "description": "The id of the custom model template.", "type": "string" } }, "required": [ "resourceBundleId", "templateId" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomModelCreateFromTemplatePayload | false | none | ### Example responses > 201 Response ``` { "properties": { "customModelId": { "description": "The id of the created custom model.", "type": "string" }, "customModelVersionId": { "description": "The id of the created custom model version.", "type": "string" } }, "required": [ "customModelId", "customModelVersionId" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Custom model created from template | CustomModelCreateFromTemplateResponse | | 403 | Forbidden | UXR Custom Model Workshop is not enabled. | None | | 422 | Unprocessable Entity | Invalid template data. | None | ## Create a new prediction explanations initialization Operation path: `POST /api/v2/customModels/predictionExplanationsInitialization/` Authentication requirements: `BearerAuth` Create a new prediction explanations initialization for custom model. This is a necessary prerequisite for generating prediction explanations. .. minversion:: v2.23 DEPRECATED please use custom model version route instead: [POST /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/predictionExplanationsInitialization/][post-apiv2custommodelscustommodelidversionscustommodelversionidpredictionexplanationsinitialization]. ### Body parameter ``` { "properties": { "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "customModelVersionId": { "description": "The ID of the custom model version.", "type": "string" }, "environmentId": { "description": "The ID of environment to use. If not specified, the customModelVersion's dependency environment will be used.", "type": "string" }, "environmentVersionId": { "description": "The ID of environment version to use. Defaults to the latest successfully built version.", "type": "string" } }, "required": [ "customModelId", "customModelVersionId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomModelPredictionExplanations | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was accepted and will be worked on. | None | | 422 | Unprocessable Entity | Specified custom model is not valid for prediction explanations. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL to poll to track the prediction explanation initialization has finished. | ## Delete custom model by custom model ID Operation path: `DELETE /api/v2/customModels/{customModelId}/` Authentication requirements: `BearerAuth` Delete a custom model. Only users who have permission to edit custom model can delete it. Only custom models which are not currently deployed or undergoing custom model testing can be deleted. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Record deleted. | None | | 409 | Conflict | This custom model is currently deployed and cannot be deleted. The response body will contain link where those deployments can be retrieved. | None | ## Get custom model by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/` Authentication requirements: `BearerAuth` Retrieve metadata for a custom model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | ### Example responses > 200 Response ``` { "properties": { "calibratePredictions": { "description": "Determines whether ot not predictions should be calibrated by DataRobot.Only applies to anomaly detection.", "type": "boolean" }, "classLabels": { "description": "If the model is a multiclass classifier, these are the model's class labels", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "createdBy": { "description": "The username of the custom model creator.", "type": "string" }, "customModelType": { "description": "The type of custom model.", "enum": [ "training", "inference" ], "type": "string" }, "deploymentsCount": { "description": "The number of models deployed.", "type": "integer" }, "description": { "description": "The description of the model.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "id": { "description": "The ID of the custom model.", "type": "string" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Assigning training data on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled and return HTTP 422 for this particular model. Use training data assignment at the version level: [POST /api/v2/customModels/{customModelId}/versions/][post-apiv2custommodelscustommodelidversions] [PATCH /api/v2/customModels/{customModelId}/versions/][patch-apiv2custommodelscustommodelidversions] ", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "The programming language used to write the model.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The name of the model.", "type": "string" }, "negativeClassLabel": { "description": "If the model is a binary classifier, this is the negative class label.", "type": "string" }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "playgroundId": { "description": "The ID of the GenAI Playground associated with the given custom inference model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "positiveClassLabel": { "description": "If the model is a binary classifier, this is the positive class label.", "type": "string" }, "predictionThreshold": { "description": "If the model is a binary classifier, this is the prediction threshold.", "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "supportsAnomalyDetection": { "description": "Whether the model supports anomaly detection.", "type": "boolean" }, "supportsBinaryClassification": { "description": "Whether the model supports binary classification.", "type": "boolean" }, "supportsRegression": { "description": "Whether the model supports regression.", "type": "boolean" }, "tags": { "description": "A list of the custom model tag.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "type": "string" }, "value": { "description": "The value of the tag.", "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.39" }, "targetName": { "description": "The name of the target for labeling predictions.", "type": "string" }, "targetType": { "description": "The target type of custom model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "template": { "description": "If not null, the template used to create the custom model.", "properties": { "modelType": { "description": "The type of template the model was created from.", "enum": [ "nimModel", "invalid" ], "type": "string", "x-versionadded": "v2.36" }, "templateId": { "description": "The ID of the template used to create this custom model.", "type": "string", "x-versionadded": "v2.36" } }, "required": [ "modelType", "templateId" ], "type": "object", "x-versionadded": "v2.36" }, "trainingDataAssignmentInProgress": { "description": "Indicates if training data is currently being assigned to the custom model.", "type": "boolean" }, "trainingDataFileName": { "description": "The name of the file that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDataPartitionColumn": { "description": "The name of the column containing the partition assignments in training data if it was assigned previously and partitioning was provided.", "type": [ "string", "null" ] }, "trainingDatasetId": { "description": "The ID of the dataset that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDatasetVersionId": { "description": "The ID of the dataset version that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "updated": { "description": "ISO-8601 timestamp of when model was last updated.", "type": "string" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "maxLength": 100, "type": "string" } }, "required": [ "created", "createdBy", "deploymentsCount", "description", "id", "language", "latestVersion", "name", "supportsBinaryClassification", "supportsRegression", "targetType", "template", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomModelResponse | ## Update custom model by custom model ID Operation path: `PATCH /api/v2/customModels/{customModelId}/` Authentication requirements: `BearerAuth` Updates metadata for an existing custom model. All custom models must support at least one target type (binaryClassification, regression). Custom inference models can only support a single target type. Setting `positiveClassLabel` and `negativeClassLabel` to null will set the labels to their default values (1 and 0 for positiveClassLabel and negativeClassLabel, respectively). Setting `positiveClassLabel`, `negativeClassLabel`, 'targetName` is disabled if model has active deployments or assigned training data. ### Body parameter ``` { "properties": { "classLabels": { "description": "The class labels for multiclass classification. Required for multiclass inference models. If using one of the [DataRobot] base environments and your model produces an ndarray of unlabeled class probabilities, the order of the labels should match the order of the predicted output", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "description": { "description": "The user-friendly description of the model.", "maxLength": 10000, "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Training data assignment on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled for this particular model.", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "Programming language name in which model is written.", "maxLength": 500, "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The user-friendly name for the model.", "maxLength": 255, "type": "string" }, "negativeClassLabel": { "description": "The negative class label for custom models that support binary classification. If specified, `positiveClassLabel` must also be specified. Default value is \"0\".", "maxLength": 500, "type": [ "string", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "positiveClassLabel": { "description": "The positive class label for custom models that support binary classification. If specified, `negativeClassLabel` must also be specified. Default value is \"1\".", "maxLength": 500, "type": [ "string", "null" ] }, "predictionThreshold": { "default": 0.5, "description": "The prediction threshold which will be used for binary classification custom model.", "maximum": 1, "minimum": 0, "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "targetName": { "description": "The name of the target for labeling predictions. Required for model type 'inference'. Specifying this value for a model type 'training' will result in an error.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | body | body | CustomModelUpdate | false | none | ### Example responses > 201 Response ``` { "properties": { "calibratePredictions": { "description": "Determines whether ot not predictions should be calibrated by DataRobot.Only applies to anomaly detection.", "type": "boolean" }, "classLabels": { "description": "If the model is a multiclass classifier, these are the model's class labels", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "createdBy": { "description": "The username of the custom model creator.", "type": "string" }, "customModelType": { "description": "The type of custom model.", "enum": [ "training", "inference" ], "type": "string" }, "deploymentsCount": { "description": "The number of models deployed.", "type": "integer" }, "description": { "description": "The description of the model.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "id": { "description": "The ID of the custom model.", "type": "string" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Assigning training data on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled and return HTTP 422 for this particular model. Use training data assignment at the version level: [POST /api/v2/customModels/{customModelId}/versions/][post-apiv2custommodelscustommodelidversions] [PATCH /api/v2/customModels/{customModelId}/versions/][patch-apiv2custommodelscustommodelidversions] ", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "The programming language used to write the model.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The name of the model.", "type": "string" }, "negativeClassLabel": { "description": "If the model is a binary classifier, this is the negative class label.", "type": "string" }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "playgroundId": { "description": "The ID of the GenAI Playground associated with the given custom inference model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "positiveClassLabel": { "description": "If the model is a binary classifier, this is the positive class label.", "type": "string" }, "predictionThreshold": { "description": "If the model is a binary classifier, this is the prediction threshold.", "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "supportsAnomalyDetection": { "description": "Whether the model supports anomaly detection.", "type": "boolean" }, "supportsBinaryClassification": { "description": "Whether the model supports binary classification.", "type": "boolean" }, "supportsRegression": { "description": "Whether the model supports regression.", "type": "boolean" }, "tags": { "description": "A list of the custom model tag.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "type": "string" }, "value": { "description": "The value of the tag.", "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.39" }, "targetName": { "description": "The name of the target for labeling predictions.", "type": "string" }, "targetType": { "description": "The target type of custom model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "template": { "description": "If not null, the template used to create the custom model.", "properties": { "modelType": { "description": "The type of template the model was created from.", "enum": [ "nimModel", "invalid" ], "type": "string", "x-versionadded": "v2.36" }, "templateId": { "description": "The ID of the template used to create this custom model.", "type": "string", "x-versionadded": "v2.36" } }, "required": [ "modelType", "templateId" ], "type": "object", "x-versionadded": "v2.36" }, "trainingDataAssignmentInProgress": { "description": "Indicates if training data is currently being assigned to the custom model.", "type": "boolean" }, "trainingDataFileName": { "description": "The name of the file that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDataPartitionColumn": { "description": "The name of the column containing the partition assignments in training data if it was assigned previously and partitioning was provided.", "type": [ "string", "null" ] }, "trainingDatasetId": { "description": "The ID of the dataset that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDatasetVersionId": { "description": "The ID of the dataset version that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "updated": { "description": "ISO-8601 timestamp of when model was last updated.", "type": "string" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "maxLength": 100, "type": "string" } }, "required": [ "created", "createdBy", "deploymentsCount", "description", "id", "language", "latestVersion", "name", "supportsBinaryClassification", "supportsRegression", "targetType", "template", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Created. | CustomModelResponse | | 403 | Forbidden | Custom inference model modification is not enabled for the user. | None | | 409 | Conflict | Custom model cannot be updated while it is being validated or some fields cannot be updated after deployment or assigning training data. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Get a list of users who have access by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/accessControl/` Authentication requirements: `BearerAuth` Get a list of users who have access to this custom model and their roles on it. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | customModelId | path | string | true | The ID of the custom model. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of items in current page.", "type": "integer" }, "data": { "description": "List of the requested custom model access control entries.", "items": { "properties": { "canShare": { "description": "Whether this user can share this custom model", "type": "boolean" }, "role": { "description": "This users role.", "type": "string" }, "userId": { "description": "This user's userId.", "type": "string" }, "username": { "description": "The username for this user's entry.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of users who have access to this custom model and their roles on it. | CustomModelAccessControlListResponse | | 400 | Bad Request | Both username and userId were specified. | None | ## Grant access or update roles by custom model ID Operation path: `PATCH /api/v2/customModels/{customModelId}/accessControl/` Authentication requirements: `BearerAuth` Grant access or update roles for users on this custom model and appropriate learning data. Up to 100 user roles may be set in a single request. ### Body parameter ``` { "properties": { "data": { "description": "List of sharing roles to update.", "items": { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | body | body | SharingUpdateOrRemoveWithGrant | false | none | ### Example responses > 204 Response ``` { "properties": { "data": { "description": "Roles were successfully updated.", "items": { "properties": { "canShare": { "description": "Whether this user can share this custom model", "type": "boolean" }, "role": { "description": "This users role.", "type": "string" }, "userId": { "description": "This user's userId.", "type": "string" }, "username": { "description": "The username for this user's entry.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | CustomModelAccessControlUpdateResponse | | 409 | Conflict | The request would leave the custom model without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid. | None | ## Download the latest custom model version content by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/download/` Authentication requirements: `BearerAuth` Download the latest item bundle from a custom model as a zip compressed archive. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | pps | query | string | false | Download model version from PPS tab.If "true" specified, model archive includes dependencies install script. If "false" specified, dependencies script is not included. If not specified -> "false" behavior. | | customModelId | path | string | true | The ID of the custom model. | ### Enumerated Values | Parameter | Value | | --- | --- | | pps | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The download succeeded. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ("attachment;filename=model--version-.zip"). | ## Assign training data by custom model ID Operation path: `PATCH /api/v2/customModels/{customModelId}/trainingData/` Authentication requirements: `BearerAuth` The current API is deprecated and scheduled for removal in v2.34. Training data assignment is implemented in CustomModelVersionCreateController and CustomModelVersionCreateFromLatestController. Assigns the specified dataset to the specified custom model as training data. For each of the custom model's deployments, the training data from the specified project provides a baseline to enable drift tracking. The API is disabled and returns HTTP 422 when a custom model is converted to assign training data at the version level. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] and [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] Use training data assignment at the version level: [POST /api/v2/customModels/{customModelId}/versions/][post-apiv2custommodelscustommodelidversions] [PATCH /api/v2/customModels/{customModelId}/versions/][patch-apiv2custommodelscustommodelidversions]. ### Body parameter ``` { "properties": { "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | body | body | DeprecatedTrainingDataForModelsAssignment | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was accepted and will be worked on. | None | | 409 | Conflict | Custom model has assigned training data already and is deployed. | None | | 410 | Gone | The requested Dataset has been deleted. | None | | 422 | Unprocessable Entity | Dataset ingest must finish before assigning training data or provided dataset is incompatible with the custom model. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL to poll to track the training data assignment has finished. | ## List custom model versions by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/versions/` Authentication requirements: `BearerAuth` List custom model versions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | mainBranchCommitSha | query | string | false | Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version. | | customModelId | path | string | true | The ID of the custom model. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom model versions.", "items": { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomModelVersionListResponse | | 400 | Bad Request | Query parameters are invalid. | None | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Update custom model version files by custom model ID Operation path: `PATCH /api/v2/customModels/{customModelId}/versions/` Authentication requirements: `BearerAuth` Create a new custom model version with files added, replaced or deleted. Files from the previous version of a custom models will be used as a basis. ### Body parameter ``` properties: baseEnvironmentId: description: The base environment to use with this model version. At least one of "baseEnvironmentId" and "baseEnvironmentVersionId" must be provided. If both are specified, the version must belong to the environment. type: string x-versionadded: v2.22 baseEnvironmentVersionId: description: 'The base environment version ID to use with this model version. At least one of "baseEnvironmentId" and "baseEnvironmentVersionId" must be provided. If both are specified, the version must belong to the environment. If not specified: in the case where the previous model versions exist, the value from the latest model version is inherited, otherwise, the latest successfully built version of the environment specified in "baseEnvironmentId" is used.' type: string x-versionadded: v2.29 desiredMemory: description: The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId. maximum: 15032385536 minimum: 134217728 type: - integer - "null" x-versiondeprecated: v2.24 file: description: 'A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `"main.py"` and `"helpers/helper.py"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.' format: binary type: string filePath: description: The local path of the file being uploaded. See the `file` field explanation for more details. oneOf: - type: string - items: type: string maxItems: 1000 type: array filesToDelete: description: The IDs of the files to be deleted. oneOf: - type: string - items: type: string maxItems: 100 type: array gitModelVersion: description: Contains git related attributes that are associated with a custom model version. properties: commitUrl: description: A URL to the commit page in GitHub repository. format: uri type: string mainBranchCommitSha: description: Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version. maxLength: 40 minLength: 40 type: string pullRequestCommitSha: default: null description: Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version. maxLength: 40 minLength: 40 type: - string - "null" refName: description: The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch. type: string required: - commitUrl - mainBranchCommitSha - pullRequestCommitSha - refName type: object holdoutData: description: "Holdout data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] an\ d [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] \ " type: - string - "null" x-versionadded: "2.31" isMajorUpdate: default: "true" description: If set to true, new major version will created, otherwise minor version will be created. enum: - "false" - "False" - "true" - "True" type: string keepTrainingHoldoutData: default: true description: If the version should inherit training and holdout data from the previous version. Defaults to true.This field is only applicable if the model has training data for versions enabled. Otherwise the field value will be ignored. type: boolean x-versionadded: v2.30 maximumMemory: description: The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId. maximum: 15032385536 minimum: 134217728 type: - integer - "null" networkEgressPolicy: description: Network egress policy. enum: - NONE - PUBLIC type: - string - "null" replicas: description: A fixed number of replicas that will be set for the given custom-model. exclusiveMinimum: 0 maximum: 25 type: - integer - "null" requiredMetadata: description: Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. type: string x-versionadded: v2.25 x-versiondeprecated: v2.26 requiredMetadataValues: description: "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. Field names and values are exposed as environment variables with values when running the custom model. Example: \"required_metadata_values\": [{\"field_name\": \"hi\", \"value\": \"there\"}]," type: string x-versionadded: v2.26 requiresHa: description: Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. type: - boolean - "null" x-versionadded: v2.26 resourceBundleId: description: "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint." type: - string - "null" x-versionadded: v2.33 trainingData: description: "Training data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] an\ d [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] \ " type: - string - "null" x-versionadded: "2.31" required: - isMajorUpdate type: object ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | body | body | CustomModelVersionCreateFromLatest | false | none | ### Example responses > 201 Response ``` { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Item successfully created. | CustomModelVersionResponse | | 403 | Forbidden | User does not have permissions to use requested Dataset | None | | 404 | Not Found | Either custom model or dataset not found or user does not have edit permissions. | None | | 413 | Payload Too Large | Item or collection of items was too large in size (bytes). | None | | 422 | Unprocessable Entity | Cannot create the custom task version due to one or more errors. All error responses will have a "message" field and some may have optional fields. The optional fields include: ["errors", "dependencies", "invalidDependencies"] | None | ## Create custom model version by custom model ID Operation path: `POST /api/v2/customModels/{customModelId}/versions/` Authentication requirements: `BearerAuth` Create a new custom model version with attached files if supplied. ### Body parameter ``` properties: baseEnvironmentId: description: The base environment to use with this model version. At least one of "baseEnvironmentId" and "baseEnvironmentVersionId" must be provided. If both are specified, the version must belong to the environment. type: string x-versionadded: v2.22 baseEnvironmentVersionId: description: 'The base environment version ID to use with this model version. At least one of "baseEnvironmentId" and "baseEnvironmentVersionId" must be provided. If both are specified, the version must belong to the environment. If not specified: in the case where the previous model versions exist, the value from the latest model version is inherited, otherwise, the latest successfully built version of the environment specified in "baseEnvironmentId" is used.' type: string x-versionadded: v2.29 desiredMemory: description: The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId. maximum: 15032385536 minimum: 134217728 type: - integer - "null" x-versiondeprecated: v2.24 file: description: 'A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `"main.py"` and `"helpers/helper.py"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.' format: binary type: string filePath: description: The local path of the file being uploaded. See the `file` field explanation for more details. oneOf: - type: string - items: type: string maxItems: 1000 type: array gitModelVersion: description: Contains git related attributes that are associated with a custom model version. properties: commitUrl: description: A URL to the commit page in GitHub repository. format: uri type: string mainBranchCommitSha: description: Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version. maxLength: 40 minLength: 40 type: string pullRequestCommitSha: default: null description: Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version. maxLength: 40 minLength: 40 type: - string - "null" refName: description: The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch. type: string required: - commitUrl - mainBranchCommitSha - pullRequestCommitSha - refName type: object holdoutData: description: "Holdout data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] an\ d [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] \ " type: - string - "null" x-versionadded: "2.31" isMajorUpdate: default: "true" description: If set to true, new major version will created, otherwise minor version will be created. enum: - "false" - "False" - "true" - "True" type: string keepTrainingHoldoutData: default: true description: If the version should inherit training and holdout data from the previous version. Defaults to true.This field is only applicable if the model has training data for versions enabled. Otherwise the field value will be ignored. type: boolean x-versionadded: v2.30 maximumMemory: description: The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId. maximum: 15032385536 minimum: 134217728 type: - integer - "null" networkEgressPolicy: description: Network egress policy. enum: - NONE - PUBLIC type: - string - "null" replicas: description: A fixed number of replicas that will be set for the given custom-model. exclusiveMinimum: 0 maximum: 25 type: - integer - "null" requiredMetadata: description: Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. type: string x-versionadded: v2.25 x-versiondeprecated: v2.26 requiredMetadataValues: description: "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. Field names and values are exposed as environment variables with values when running the custom model. Example: \"required_metadata_values\": [{\"field_name\": \"hi\", \"value\": \"there\"}]," type: string x-versionadded: v2.26 requiresHa: description: Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. type: - boolean - "null" x-versionadded: v2.26 resourceBundleId: description: "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint." type: - string - "null" x-versionadded: v2.33 trainingData: description: "Training data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] an\ d [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] \ " type: - string - "null" x-versionadded: "2.31" required: - isMajorUpdate type: object ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | body | body | CustomModelVersionCreate | false | none | ### Example responses > 201 Response ``` { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Item successfully created. | CustomModelVersionResponse | | 403 | Forbidden | User does not have permissions to use requested Dataset | None | | 404 | Not Found | Either custom model or dataset not found or user does not have edit permissions. | None | | 413 | Payload Too Large | Item or collection of items was too large in size (bytes). | None | | 422 | Unprocessable Entity | Cannot create the custom task version due to one or more errors. All error responses will have a "message" field and some may have optional fields. The optional fields include: ["errors", "dependencies", "invalidDependencies"] | None | ## Create a new custom model version by custom model ID Operation path: `POST /api/v2/customModels/{customModelId}/versions/fromCodespace/` Authentication requirements: `BearerAuth` Create a new custom model version with files from the codespace. ### Body parameter ``` { "properties": { "codespaceId": { "description": "The ID of the codespace that is the source for the custom model version files.", "type": "string" } }, "required": [ "codespaceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | body | body | CustomModelVersionFromCodespace | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 202 | Accepted | Task for creating a new custom model version applied. | None | ## Create custom model version from remote repository by custom model ID Operation path: `PATCH /api/v2/customModels/{customModelId}/versions/fromRepository/` Authentication requirements: `BearerAuth` Create a new custom model version with files added from a remote repository. Files from the previous version of a custom models will be used as a basis. ### Body parameter ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this version.", "type": "string", "x-versionadded": "v2.22" }, "isMajorUpdate": { "default": true, "description": "If set to true, new major version will created, otherwise minor version will be created.", "type": "boolean" }, "keepTrainingHoldoutData": { "default": true, "description": "If the version should inherit training and holdout data from the previous version. Defaults to true.This field is only applicable if the model has training data for versions enabled. Otherwise the field value will be ignored.", "type": "boolean", "x-versionadded": "v2.30" }, "ref": { "description": "Remote reference (branch, commit, etc). Latest, if not specified.", "type": "string" }, "repositoryId": { "description": "The ID of remote repository used to pull sources. This ID can be found using the /api/v2/remoteRepositories/ endpoint.", "type": "string" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "sourcePath": { "description": "A remote repository file path to be pulled into a custom model or custom task.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" } ] } }, "required": [ "repositoryId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | body | body | CustomModelVersionCreateFromRepository | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Accepted: request placed to a queue for processing. | None | | 422 | Unprocessable Entity | Custom Model version cannot be created: (1) input parameters are invalid, (2) if user does not have permission to create legacy conversion environment. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking async job status. | ## Create a version from a repository Operation path: `POST /api/v2/customModels/{customModelId}/versions/fromRepository/` Authentication requirements: `BearerAuth` Create a new custom model version with only files added from the specified remote repository. ### Body parameter ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this version.", "type": "string", "x-versionadded": "v2.22" }, "isMajorUpdate": { "default": true, "description": "If set to true, new major version will created, otherwise minor version will be created.", "type": "boolean" }, "keepTrainingHoldoutData": { "default": true, "description": "If the version should inherit training and holdout data from the previous version. Defaults to true.This field is only applicable if the model has training data for versions enabled. Otherwise the field value will be ignored.", "type": "boolean", "x-versionadded": "v2.30" }, "ref": { "description": "Remote reference (branch, commit, etc). Latest, if not specified.", "type": "string" }, "repositoryId": { "description": "The ID of remote repository used to pull sources. This ID can be found using the /api/v2/remoteRepositories/ endpoint.", "type": "string" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "sourcePath": { "description": "A remote repository file path to be pulled into a custom model or custom task.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" } ] } }, "required": [ "repositoryId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | body | body | CustomModelVersionCreateFromRepository | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Accepted: request placed to a queue for processing. | None | | 422 | Unprocessable Entity | Custom Model version cannot be created: (1) input parameters are invalid, (2) if user does not have permission to create legacy conversion environment. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking async job status. | ## Add or replace training and holdout data by custom model ID Operation path: `PATCH /api/v2/customModels/{customModelId}/versions/withTrainingData/` Authentication requirements: `BearerAuth` Current API is deprecated and will be removed in v2.33. Training data assignment is implemented in CustomModelVersionCreateController and CustomModelVersionCreateFromLatestController. Creates a new custom model version (bumping it up a minor version) with the specified training and holdout data. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] and [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] This API is going to be deprecated. Use model version creation APIs to assign training data at the version level: [POST /api/v2/customModels/{customModelId}/versions/][post-apiv2custommodelscustommodelidversions] [PATCH /api/v2/customModels/{customModelId}/versions/][patch-apiv2custommodelscustommodelidversions]. ### Body parameter ``` { "properties": { "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ], "x-versiondeprecated": "v2.33" } }, "type": "object" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean", "x-versiondeprecated": "v2.33" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" } }, "type": "object" } }, "required": [ "trainingData" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | body | body | DeprecatedCustomModelVersionTrainingDataUpdate | false | none | ### Example responses > 202 Response ``` { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was accepted and will be worked on. | CustomModelVersionResponse | | 404 | Not Found | Custom model not found or user does not have edit permissions. | None | | 410 | Gone | The requested Dataset has been deleted. | None | | 422 | Unprocessable Entity | Cannot update custom model training data. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL to poll to track the training data assignment has finished. | ## Get custom model version by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/` Authentication requirements: `BearerAuth` Display a requested version of a custom model along with the files attached to it. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | ### Example responses > 200 Response ``` { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomModelVersionResponse | ## Update custom model version by custom model ID Operation path: `PATCH /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/` Authentication requirements: `BearerAuth` Edit metadata of a specific model version. ### Body parameter ``` { "properties": { "description": { "description": "New description for the custom task or model.", "maxLength": 10000, "type": "string" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | | body | body | CustomModelVersionMetadataUpdate | false | none | ### Example responses > 200 Response ``` { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The edit was successful. | CustomModelVersionResponse | | 404 | Not Found | Custom model not found or user does not have edit permissions. | None | | 422 | Unprocessable Entity | Cannot update custom model metadata. | None | ## Get a list by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/conversions/` Authentication requirements: `BearerAuth` Get the list of custom model conversions that are associated with the given custom model. Alternatively, it can return a single item list of the latest custom model conversion that is associated with the given custom model version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | isLatest | query | string | false | Whether to return only the latest associated custom model conversion or all of the associated ones. | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | ### Enumerated Values | Parameter | Value | | --- | --- | | isLatest | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom model conversions.", "items": { "properties": { "conversionInProgress": { "description": "Whether a custom model conversion is in progress or not.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "conversionSucceeded": { "description": "Indication for a successful custom model conversion.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "created": { "description": "ISO-8601 timestamp of when the custom model conversion created.", "type": "string" }, "customModelVersionId": { "description": "ID of the custom model version.", "type": "string" }, "generatedMetadata": { "description": "Custom model conversion output metadata.", "properties": { "outputColumns": { "description": "A list of column lists that are associated with the output datasets from the custom model conversion process.", "items": { "description": "A columns list belong to a single dataset output from a custom model conversion process", "items": { "description": "A column name belong to a single dataset output from a custom model conversion process", "maxLength": 1024, "minLength": 1, "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" }, "maxItems": 50, "type": "array" }, "outputDatasets": { "description": "A list of output datasets from the custom model conversion process.", "items": { "description": "An output dataset name from the custom model conversion process", "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" } }, "required": [ "outputColumns", "outputDatasets" ], "type": "object" }, "id": { "description": "ID of the custom model version.", "type": "string" }, "logMessage": { "description": "The output log message from the custom model conversion process.", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "mainProgramItemId": { "description": "The main program file item ID.", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "shouldStop": { "default": false, "description": "Whether the user requested to stop the given conversion.", "type": "boolean", "x-versionadded": "v2.26" } }, "required": [ "created", "customModelVersionId", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ConversionListResponse | ## Generates JAR file by custom model ID Operation path: `POST /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/conversions/` Authentication requirements: `BearerAuth` Converts files in the given custom model version to a JAR file. ### Body parameter ``` { "properties": { "mainProgramItemId": { "description": "The main program file item ID.", "type": "string", "x-versionadded": "v2.26" } }, "required": [ "mainProgramItemId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | | body | body | ConversionCreateQuery | false | none | ### Example responses > 202 Response ``` { "properties": { "conversionId": { "description": "ID that can be used to stop a given conversion.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status", "type": "string" } }, "required": [ "conversionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was accepted and will be worked on. | CustomModelConversionAsyncOperationResponse | | 422 | Unprocessable Entity | Input parameters are invalid. | None | ## Stop a given custom model conversion by custom model ID Operation path: `DELETE /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/conversions/{conversionId}/` Authentication requirements: `BearerAuth` Stop a running conversion for given model and model version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | | conversionId | path | string | true | ID of the custom model conversion. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 422 | Unprocessable Entity | The given conversion is not active. | None | ## Get a given custom model conversion by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/conversions/{conversionId}/` Authentication requirements: `BearerAuth` Get a given custom model conversion. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | | conversionId | path | string | true | ID of the custom model conversion. | ### Example responses > 200 Response ``` { "properties": { "conversionInProgress": { "description": "Whether a custom model conversion is in progress or not.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "conversionSucceeded": { "description": "Indication for a successful custom model conversion.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "created": { "description": "ISO-8601 timestamp of when the custom model conversion created.", "type": "string" }, "customModelVersionId": { "description": "ID of the custom model version.", "type": "string" }, "generatedMetadata": { "description": "Custom model conversion output metadata.", "properties": { "outputColumns": { "description": "A list of column lists that are associated with the output datasets from the custom model conversion process.", "items": { "description": "A columns list belong to a single dataset output from a custom model conversion process", "items": { "description": "A column name belong to a single dataset output from a custom model conversion process", "maxLength": 1024, "minLength": 1, "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" }, "maxItems": 50, "type": "array" }, "outputDatasets": { "description": "A list of output datasets from the custom model conversion process.", "items": { "description": "An output dataset name from the custom model conversion process", "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" } }, "required": [ "outputColumns", "outputDatasets" ], "type": "object" }, "id": { "description": "ID of the custom model version.", "type": "string" }, "logMessage": { "description": "The output log message from the custom model conversion process.", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "mainProgramItemId": { "description": "The main program file item ID.", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "shouldStop": { "default": false, "description": "Whether the user requested to stop the given conversion.", "type": "boolean", "x-versionadded": "v2.26" } }, "required": [ "created", "customModelVersionId", "id" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ConversionResponse | ## Cancel dependency build by custom model ID Operation path: `DELETE /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/dependencyBuild/` Authentication requirements: `BearerAuth` Cancel the custom model version's dependency build. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Custom model version's dependency build was cancelled. | None | | 409 | Conflict | Custom model dependency build has reached a terminal state and cannot be cancelled. | None | | 422 | Unprocessable Entity | No custom model dependency build started for specified version or dependency image is in use and cannot be deleted | None | ## Retrieve the custom model version's dependency build status by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/dependencyBuild/` Authentication requirements: `BearerAuth` Retrieve the custom model version's dependency build status. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | ### Example responses > 200 Response ``` { "properties": { "buildEnd": { "description": "The ISO-8601 encoded time when this build completed.", "type": [ "string", "null" ] }, "buildLogLocation": { "description": "The URL to download the build logs from this build.", "format": "uri", "type": [ "string", "null" ] }, "buildStart": { "description": "The ISO-8601 encoded time when this build started.", "type": "string" }, "buildStatus": { "description": "The current status of the dependency build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" } }, "required": [ "buildEnd", "buildLogLocation", "buildStart", "buildStatus" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The metadata from the custom model version's dependency build. | BaseDependencyBuildMetadataResponse | | 422 | Unprocessable Entity | Custom model dependency build has not started. | None | ## Start a custom model version's dependency build by custom model ID Operation path: `POST /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/dependencyBuild/` Authentication requirements: `BearerAuth` Start a custom model version's dependency build. This is required to test, deploy, or train custom models. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | ### Example responses > 202 Response ``` { "properties": { "buildEnd": { "description": "The ISO-8601 encoded time when this build completed.", "type": [ "string", "null" ] }, "buildLogLocation": { "description": "The URL to download the build logs from this build.", "format": "uri", "type": [ "string", "null" ] }, "buildStart": { "description": "The ISO-8601 encoded time when this build started.", "type": "string" }, "buildStatus": { "description": "The current status of the dependency build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" } }, "required": [ "buildEnd", "buildLogLocation", "buildStart", "buildStatus" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Custom model version's dependency build has started. | BaseDependencyBuildMetadataResponse | | 422 | Unprocessable Entity | Custom model dependency build has failed. | None | ## Retrieve the custom model version's dependency build log by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/dependencyBuildLog/` Authentication requirements: `BearerAuth` Retrieve the custom model version's dependency build log. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The custom model version's dependency build log in tar.gz format.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The log file generated during the custom model version's dependency build. | DependencyBuildLogResponse | | 404 | Not Found | Dependency build is in progress or could not be found. | None | | 422 | Unprocessable Entity | Custom model dependency build has not started. | None | ## Download custom model version content by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/download/` Authentication requirements: `BearerAuth` Download a specific item bundle from a custom model as a zip compressed archive. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | pps | query | string | false | Download model version from PPS tab.If "true" specified, model archive includes dependencies install script. If "false" specified, dependencies script is not included. If not specified -> "false" behavior. | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | ### Enumerated Values | Parameter | Value | | --- | --- | | pps | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The download succeeded. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ("attachment;filename=model--version-.zip"). | ## Get custom model feature impact by custom model ID Operation path: `GET /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/featureImpact/` Authentication requirements: `BearerAuth` Retrieve feature impact scores for features in a custom inference model image. This route is a counterpart of a corresponding endpoint for native models: [GET /api/v2/projects/{projectId}/models/{modelId}/featureImpact/][get-apiv2projectsprojectidmodelsmodelidfeatureimpact]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of feature impact records in a given batch.", "type": "integer" }, "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL for the next page of results, or null if there is no next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL for the previous page of results, or null if there is no previous page.", "format": "uri", "type": [ "string", "null" ] }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact.", "type": "boolean" }, "rowCount": { "description": "The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the ``rowCount``, we return ``null`` here.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "shapBased": { "description": "Indicates whether feature impact was calculated using Shapley values. True for anomaly detection models when the project is unsupervised, as permutation approach is not applicable. Note that supervised projects must use an alternative route for SHAP impact: /api/v2/projects/(projectId)/models/(modelId)/shapImpact/", "type": "boolean", "x-versionadded": "v2.18" } }, "required": [ "count", "featureImpacts", "next", "previous", "ranRedundancyDetection", "rowCount", "shapBased" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model feature impact returned. | FeatureImpactResponse | | 404 | Not Found | No feature impact data found for custom model. | None | | 422 | Unprocessable Entity | Cannot retrieve feature impact scores: (1) if custom model is not an inference model, (2) if training data is not assigned, (3) if feature impact job is in progress for custom model. | None | ## Create custom model feature impact by custom model ID Operation path: `POST /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/featureImpact/` Authentication requirements: `BearerAuth` Add a request to calculate feature impact for a custom inference model image to the queue. This route is a counterpart of a corresponding endpoint for native models: [POST /api/v2/projects/{projectId}/models/{modelId}/featureImpact/][post-apiv2projectsprojectidmodelsmodelidfeatureimpact]. ### Body parameter ``` { "properties": { "rowCount": { "description": "The sample size to use for Feature Impact computation. It is possible to re-compute Feature Impact with a different row count.", "maximum": 100000, "minimum": 10, "type": "integer", "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | | body | body | FeatureImpactCreatePayload | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] for tracking job status.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Feature impact request has been successfully submitted. | FeatureImpactCreateResponse | | 404 | Not Found | If feature impact has already been submitted. The response will include jobId property which can be used for tracking its progress. | None | | 422 | Unprocessable Entity | If job cannot be submitted because of invalid input or model state: (1) if image id does not correspond to a custom inference model, (2) if training data is not yet assigned or assignment is in progress, (3) if the rowCount exceeds the minimum or maximum value for this model's training data. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | Contains a url for tracking job status: [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid]. | ## Create a new prediction explanations initialization by custom model ID Operation path: `POST /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/predictionExplanationsInitialization/` Authentication requirements: `BearerAuth` Create a new prediction explanations initialization for custom model version. This is a necessary prerequisite for generating prediction explanations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was accepted and will be worked on. | None | | 422 | Unprocessable Entity | Specified custom model is not valid for prediction explanations. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL to poll to track the prediction explanation initialization has finished. | ## Update a codespace by custom model ID Operation path: `POST /api/v2/customModels/{customModelId}/versions/{customModelVersionId}/toCodespace/` Authentication requirements: `BearerAuth` Update a codespace with files from the custom model version. ### Body parameter ``` { "properties": { "codespaceId": { "description": "The ID of the codespace that should be updated with custom model version files.", "type": "string" } }, "required": [ "codespaceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customModelId | path | string | true | The ID of the custom model. | | customModelVersionId | path | string | true | The ID of the custom model version. | | body | body | CustomModelVersionToCodespace | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 202 | Accepted | Task for uploading applied. | None | ## List execution environments Operation path: `GET /api/v2/executionEnvironments/` Authentication requirements: `BearerAuth` List all execution environments sorted by creation time descending. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | searchFor | query | string | false | String to search for occurence in all execution environment's description and label. Search is case insensitive. If not specified, all execution environments will be returned. | | useCases | query | string | false | If specified, only execution environments with this use case are returned. | | isPublic | query | boolean | false | If set, only return execution environments matching this setting. | ### Enumerated Values | Parameter | Value | | --- | --- | | useCases | [customModel, notebook, gpu, customApplication, sparkApplication, customJob] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of execution environments.", "items": { "properties": { "created": { "description": "ISO-8601 environment creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "deploymentsCount": { "description": "Number of deployments in environment.", "type": "integer" }, "description": { "description": "The description of the environment.", "type": "string" }, "id": { "description": "The ID of the environment.", "type": "string" }, "isPublic": { "description": "If the environment is public.", "type": "boolean", "x-versionadded": "v2.23" }, "latestSuccessfulVersion": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "latestVersion": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the environment.", "type": "string" }, "programmingLanguage": { "description": "The programming language of the environment to be created.", "enum": [ "python", "r", "java", "julia", "legacy", "other" ], "type": "string" }, "useCases": { "description": "The list of use cases supported by the environment", "items": { "enum": [ "customModel", "notebook", "gpu", "customApplication", "sparkApplication", "customJob" ], "type": "string" }, "maxItems": 6, "type": "array", "x-versionadded": "v2.36" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "created", "description", "id", "isPublic", "latestSuccessfulVersion", "latestVersion", "name", "programmingLanguage", "username" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The request was successful. | ExecutionEnvironmentListResponse | ## Create an execution environment Operation path: `POST /api/v2/executionEnvironments/` Authentication requirements: `BearerAuth` Create a new execution environment. ### Body parameter ``` { "properties": { "description": { "default": "", "description": "The description of the environment to be created.", "maxLength": 10000, "type": "string" }, "environmentId": { "description": "The ID the new environment should use. Only admins can create environments with pre-defined IDs", "type": "string" }, "isPublic": { "description": "True/False public access for the environment. Public environments can be used by all users without sharing. Public access to environments can only be added by an admin", "type": "boolean" }, "name": { "description": "The name of the environment to be created.", "maxLength": 255, "type": "string" }, "programmingLanguage": { "default": "other", "description": "The programming language of the environment to be created.", "enum": [ "python", "r", "java", "julia", "legacy", "other" ], "type": "string" }, "useCases": { "description": "The list of use cases supported by the environment", "items": { "enum": [ "customModel", "notebook", "gpu", "customApplication", "sparkApplication", "customJob" ], "type": "string" }, "maxItems": 6, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ExecutionEnvironmentCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "id": { "description": "The ID of the newly created environment.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The new environment has been successfully created. | ExecutionEnvironmentCreateResponse | | 409 | Conflict | An environment with the supplied ID already exists | None | | 422 | Unprocessable Entity | User does not have permission to create legacy conversion environment | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 201 | Location | string | | URL from which the new environment can be retrieved. | ## Destroy an execution environment by environment ID Operation path: `DELETE /api/v2/executionEnvironments/{environmentId}/` Authentication requirements: `BearerAuth` Mark a specified execution environment as deleted. Environments are never deleted from database, however marked environments don't appear in any API. Relevant CustomModelImage will be deleted. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | The ID of the environment. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The execution environment has been successfully deleted. | None | | 409 | Conflict | This environment is currently deployed and cannot be deleted. The response body will contain link where those deployments can be retrieved. | None | ## Get an execution environment by environment ID Operation path: `GET /api/v2/executionEnvironments/{environmentId}/` Authentication requirements: `BearerAuth` Retrieve a single execution environment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | The ID of the environment. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "ISO-8601 environment creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "deploymentsCount": { "description": "Number of deployments in environment.", "type": "integer" }, "description": { "description": "The description of the environment.", "type": "string" }, "id": { "description": "The ID of the environment.", "type": "string" }, "isPublic": { "description": "If the environment is public.", "type": "boolean", "x-versionadded": "v2.23" }, "latestSuccessfulVersion": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "latestVersion": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the environment.", "type": "string" }, "programmingLanguage": { "description": "The programming language of the environment to be created.", "enum": [ "python", "r", "java", "julia", "legacy", "other" ], "type": "string" }, "useCases": { "description": "The list of use cases supported by the environment", "items": { "enum": [ "customModel", "notebook", "gpu", "customApplication", "sparkApplication", "customJob" ], "type": "string" }, "maxItems": 6, "type": "array", "x-versionadded": "v2.36" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "created", "description", "id", "isPublic", "latestSuccessfulVersion", "latestVersion", "name", "programmingLanguage", "username" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The request was successful. | ExecutionEnvironmentResponse | ## Update an execution environment by environment ID Operation path: `PATCH /api/v2/executionEnvironments/{environmentId}/` Authentication requirements: `BearerAuth` Update an execution environment. ### Body parameter ``` { "properties": { "description": { "description": "The new description of the environment.", "maxLength": 10000, "type": "string" }, "isPublic": { "description": "If the environment is public.", "type": "boolean", "x-versionadded": "v2.39" }, "name": { "description": "New execution environment name.", "maxLength": 255, "type": "string" }, "programmingLanguage": { "description": "The new programming language of the environment.", "enum": [ "python", "r", "java", "julia", "legacy", "other" ], "type": "string" }, "useCases": { "description": "The list of use cases supported by the environment", "items": { "enum": [ "customModel", "notebook", "gpu", "customApplication", "sparkApplication", "customJob" ], "type": "string" }, "maxItems": 6, "type": "array", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | The ID of the environment. | | body | body | ExecutionEnvironmentUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "ISO-8601 environment creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "deploymentsCount": { "description": "Number of deployments in environment.", "type": "integer" }, "description": { "description": "The description of the environment.", "type": "string" }, "id": { "description": "The ID of the environment.", "type": "string" }, "isPublic": { "description": "If the environment is public.", "type": "boolean", "x-versionadded": "v2.23" }, "latestSuccessfulVersion": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "latestVersion": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the environment.", "type": "string" }, "programmingLanguage": { "description": "The programming language of the environment to be created.", "enum": [ "python", "r", "java", "julia", "legacy", "other" ], "type": "string" }, "useCases": { "description": "The list of use cases supported by the environment", "items": { "enum": [ "customModel", "notebook", "gpu", "customApplication", "sparkApplication", "customJob" ], "type": "string" }, "maxItems": 6, "type": "array", "x-versionadded": "v2.36" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "created", "description", "id", "isPublic", "latestSuccessfulVersion", "latestVersion", "name", "programmingLanguage", "username" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The execution environment has been successfully updated. | ExecutionEnvironmentResponse | ## Get a list of users who have access by environment ID Operation path: `GET /api/v2/executionEnvironments/{environmentId}/accessControl/` Authentication requirements: `BearerAuth` Get a list of users who have access to this execution environment and their roles. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | username | query | string | false | Optional, only return the access control information for a user with this username. | | userId | query | string | false | Optional, only return the access control information for a user with this user ID. | | environmentId | path | string | true | The ID of the environment. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SharingListResponse | | 400 | Bad Request | Bad Request. Both username and userId were specified | None | | 403 | Forbidden | Forbidden. The user does not have permissions to view the execution environment access list. | None | | 404 | Not Found | Execution environment not found. Either the execution environment does not exist or user does not have permission to view it. | None | ## Grant access or update roles by environment ID Operation path: `PATCH /api/v2/executionEnvironments/{environmentId}/accessControl/` Authentication requirements: `BearerAuth` Grant access or update roles for users on this execution environment. Up to 100 user roles may be set in a single request. ### Body parameter ``` { "properties": { "data": { "description": "List of sharing roles to update.", "items": { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | The ID of the environment. | | body | body | SharingUpdateOrRemoveWithGrant | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | Roles updated successfully. | None | | 403 | Forbidden | User can view execution environment but does not have permission to grant these roles on the execution environment. | None | | 404 | Not Found | Either the execution environment does not exist or the user does not have permissions to view the execution environment. | None | | 409 | Conflict | The request would leave the execution environment without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid. | None | ## List execution environment versions by environment ID Operation path: `GET /api/v2/executionEnvironments/{environmentId}/versions/` Authentication requirements: `BearerAuth` List all execution environment versions sorted by creation time descending. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | buildStatus | query | string | false | Build status of execution environment version to filter. If not specified, all versions will be returned. | | search | query | string | false | String to search for occurrence in execution environment versions by version id, image_id, search in label and description. Search is case insensitive.If not specified, all execution environment versions will be returned. | | environmentId | path | string | true | The ID of the environment. | ### Enumerated Values | Parameter | Value | | --- | --- | | buildStatus | [submitted, processing, failed, success, aborted] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of execution environment versions.", "items": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The request was successful. | ExecutionEnvironmentVersionListResponse | ## Create an execution environment version by environment ID Operation path: `POST /api/v2/executionEnvironments/{environmentId}/versions/` Authentication requirements: `BearerAuth` Create a new execution environment version. ### Body parameter ``` properties: contextUrl: default: "" description: Docker context URL of the environment version. It is not intended to be used for automatic processing. maxLength: 1000 type: string x-versionadded: v2.37 description: default: "" description: The description of the environment version. maxLength: 10000 type: string dockerContext: description: Context tar.gz or zip file. The Docker Context file should be a tar.gz or zip file containing at minimum a single top-level file named "Dockerfile". This file should be a Docker-compatible Dockerfile using syntax as defined in the `official Docker documentation `_. The archive may contain additional files as well. These files may be added into the final container using `ADD `_ and/or `COPY `_ directives in the Dockerfile. format: binary type: string dockerImage: description: The pre-built Docker image saved as a .tar archive. If not supplied, the environment will be built from the supplied dockerContext. format: binary type: string dockerImageUri: description: The URI of the Docker image that is used to build the environment version. Parameter dockerContext may also be provided to upload context, but the image URI is used for the build. type: string x-versionadded: v2.37 environmentVersionId: description: The ID the new environment version should use. Only admins can create environment versions with pre-defined IDs type: string label: default: "" description: Human readable version indicator. maxLength: 50 type: string type: object x-versionadded: v2.36 ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | The ID of the environment. | | body | body | ExecutionEnvironmentVersionCreate | false | none | ### Example responses > 202 Response ``` { "properties": { "id": { "description": "The ID of the newly created environment.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The new environment version has been successfully accepted to be built. | ExecutionEnvironmentCreateResponse | | 409 | Conflict | An environment version with the supplied ID already exists. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL from which the new environment version can be retrieved. | ## Get an execution environment version by environment ID Operation path: `GET /api/v2/executionEnvironments/{environmentId}/versions/{environmentVersionId}/` Authentication requirements: `BearerAuth` Retrieve an execution environment version by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | The execution environment ID. | | environmentVersionId | path | string | true | The execution environment version ID. | ### Example responses > 200 Response ``` { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The request was successful. | ExecutionEnvironmentVersionResponse | ## Download the execution environment build log by environment ID Operation path: `GET /api/v2/executionEnvironments/{environmentId}/versions/{environmentVersionId}/buildLog/` Authentication requirements: `BearerAuth` Retrieve execution environment version build log. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | The execution environment ID. | | environmentVersionId | path | string | true | The execution environment version ID. | ### Example responses > 200 Response ``` { "properties": { "error": { "description": "The message specifying why the build failed. Empty if the build finished successfully.", "type": "string" }, "log": { "description": "The full console output of the execution environment build. Logs are empty unless version build is finished.", "type": "string" } }, "required": [ "error", "log" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The build log content. | ExecutionEnvironmentVersionBuildLogResponse | | 400 | Bad Request | Bad request. | None | | 404 | Not Found | Execution environment or execution environment version not found. Either the execution environment or its version does not exist or the user does not have permission to view the execution environment. | None | ## Stop the execution environment build by environment ID Operation path: `PATCH /api/v2/executionEnvironments/{environmentId}/versions/{environmentVersionId}/cancelBuild/` Authentication requirements: `BearerAuth` Stop the execution environment version build. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | The execution environment ID. | | environmentVersionId | path | string | true | The execution environment version ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Build stopped successfully. | None | | 404 | Not Found | Execution environment or execution environment version not found. Either the execution environment or its version does not exist or the user does not have permission to view the execution environment. | None | | 409 | Conflict | Build has reached a terminal state and cannot be stopped. | None | ## Submit image tarball build by environment ID Operation path: `GET /api/v2/executionEnvironments/{environmentId}/versions/{environmentVersionId}/download/` Authentication requirements: `BearerAuth` Submit image tarball build for URI based environment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | imageFile | query | string | false | If true, the built Docker image will be downloaded as a tar archive, otherwise the Docker context will be returned | | environmentId | path | string | true | The ID of the environment. | | environmentVersionId | path | string | true | The ID of the environment version. | ### Enumerated Values | Parameter | Value | | --- | --- | | imageFile | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The download succeeded. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download. | ## Request on-demand image build by environment ID Operation path: `POST /api/v2/executionEnvironments/{environmentId}/versions/{environmentVersionId}/download/` Authentication requirements: `BearerAuth` Request on-demand image build for an execution environment version based on image URI. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | The ID of the environment. | | environmentVersionId | path | string | true | The ID of the environment version. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Image build submitted | None | ## Permanently delete execution environments Operation path: `POST /api/v2/executionEnvironmentsPermadelete/` Authentication requirements: `BearerAuth` Permanently delete records and files associated with previously soft deleted execution environments. ### Body parameter ``` { "properties": { "executionEnvironmentIds": { "description": "List of custom environments to be permanently deleted.", "items": { "description": "An ID for a custom environment to be permanently deleted.", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "executionEnvironmentIds" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ExecutionEnvironmentPermadelete | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | At least one environment was not found. | None | | 409 | Conflict | At least one environment has not been soft deleted. | None | # Schemas ## AccessControl ``` { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | Whether the recipient can share the role further. | | role | string | true | | The role of the user on this entity. | | userId | string | true | | The identifier of the user that has access to this entity. | | username | string | true | | The username of the user that has access to the entity. | ## BaseDependencyBuildMetadataResponse ``` { "properties": { "buildEnd": { "description": "The ISO-8601 encoded time when this build completed.", "type": [ "string", "null" ] }, "buildLogLocation": { "description": "The URL to download the build logs from this build.", "format": "uri", "type": [ "string", "null" ] }, "buildStart": { "description": "The ISO-8601 encoded time when this build started.", "type": "string" }, "buildStatus": { "description": "The current status of the dependency build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" } }, "required": [ "buildEnd", "buildLogLocation", "buildStart", "buildStatus" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildEnd | string,null | true | | The ISO-8601 encoded time when this build completed. | | buildLogLocation | string,null(uri) | true | | The URL to download the build logs from this build. | | buildStart | string | true | | The ISO-8601 encoded time when this build started. | | buildStatus | string | true | | The current status of the dependency build. | ### Enumerated Values | Property | Value | | --- | --- | | buildStatus | [submitted, processing, failed, success, aborted] | ## ConversionCreateQuery ``` { "properties": { "mainProgramItemId": { "description": "The main program file item ID.", "type": "string", "x-versionadded": "v2.26" } }, "required": [ "mainProgramItemId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | mainProgramItemId | string | true | | The main program file item ID. | ## ConversionListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom model conversions.", "items": { "properties": { "conversionInProgress": { "description": "Whether a custom model conversion is in progress or not.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "conversionSucceeded": { "description": "Indication for a successful custom model conversion.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "created": { "description": "ISO-8601 timestamp of when the custom model conversion created.", "type": "string" }, "customModelVersionId": { "description": "ID of the custom model version.", "type": "string" }, "generatedMetadata": { "description": "Custom model conversion output metadata.", "properties": { "outputColumns": { "description": "A list of column lists that are associated with the output datasets from the custom model conversion process.", "items": { "description": "A columns list belong to a single dataset output from a custom model conversion process", "items": { "description": "A column name belong to a single dataset output from a custom model conversion process", "maxLength": 1024, "minLength": 1, "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" }, "maxItems": 50, "type": "array" }, "outputDatasets": { "description": "A list of output datasets from the custom model conversion process.", "items": { "description": "An output dataset name from the custom model conversion process", "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" } }, "required": [ "outputColumns", "outputDatasets" ], "type": "object" }, "id": { "description": "ID of the custom model version.", "type": "string" }, "logMessage": { "description": "The output log message from the custom model conversion process.", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "mainProgramItemId": { "description": "The main program file item ID.", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "shouldStop": { "default": false, "description": "Whether the user requested to stop the given conversion.", "type": "boolean", "x-versionadded": "v2.26" } }, "required": [ "created", "customModelVersionId", "id" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ConversionResponse] | true | maxItems: 1000 | List of custom model conversions. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ConversionResponse ``` { "properties": { "conversionInProgress": { "description": "Whether a custom model conversion is in progress or not.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "conversionSucceeded": { "description": "Indication for a successful custom model conversion.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "created": { "description": "ISO-8601 timestamp of when the custom model conversion created.", "type": "string" }, "customModelVersionId": { "description": "ID of the custom model version.", "type": "string" }, "generatedMetadata": { "description": "Custom model conversion output metadata.", "properties": { "outputColumns": { "description": "A list of column lists that are associated with the output datasets from the custom model conversion process.", "items": { "description": "A columns list belong to a single dataset output from a custom model conversion process", "items": { "description": "A column name belong to a single dataset output from a custom model conversion process", "maxLength": 1024, "minLength": 1, "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" }, "maxItems": 50, "type": "array" }, "outputDatasets": { "description": "A list of output datasets from the custom model conversion process.", "items": { "description": "An output dataset name from the custom model conversion process", "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" } }, "required": [ "outputColumns", "outputDatasets" ], "type": "object" }, "id": { "description": "ID of the custom model version.", "type": "string" }, "logMessage": { "description": "The output log message from the custom model conversion process.", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "mainProgramItemId": { "description": "The main program file item ID.", "type": [ "string", "null" ], "x-versionadded": "v2.26" }, "shouldStop": { "default": false, "description": "Whether the user requested to stop the given conversion.", "type": "boolean", "x-versionadded": "v2.26" } }, "required": [ "created", "customModelVersionId", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | conversionInProgress | boolean,null | false | | Whether a custom model conversion is in progress or not. | | conversionSucceeded | boolean,null | false | | Indication for a successful custom model conversion. | | created | string | true | | ISO-8601 timestamp of when the custom model conversion created. | | customModelVersionId | string | true | | ID of the custom model version. | | generatedMetadata | GeneratedMetadata | false | | Custom model conversion output metadata. | | id | string | true | | ID of the custom model version. | | logMessage | string,null | false | | The output log message from the custom model conversion process. | | mainProgramItemId | string,null | false | | The main program file item ID. | | shouldStop | boolean | false | | Whether the user requested to stop the given conversion. | ## CustomModelAccessControlListResponse ``` { "properties": { "count": { "description": "Number of items in current page.", "type": "integer" }, "data": { "description": "List of the requested custom model access control entries.", "items": { "properties": { "canShare": { "description": "Whether this user can share this custom model", "type": "boolean" }, "role": { "description": "This users role.", "type": "string" }, "userId": { "description": "This user's userId.", "type": "string" }, "username": { "description": "The username for this user's entry.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Number of items in current page. | | data | [CustomModelAccessControlResponse] | true | maxItems: 1000 | List of the requested custom model access control entries. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page) | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page | ## CustomModelAccessControlResponse ``` { "properties": { "canShare": { "description": "Whether this user can share this custom model", "type": "boolean" }, "role": { "description": "This users role.", "type": "string" }, "userId": { "description": "This user's userId.", "type": "string" }, "username": { "description": "The username for this user's entry.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | Whether this user can share this custom model | | role | string | true | | This users role. | | userId | string | true | | This user's userId. | | username | string | true | | The username for this user's entry. | ## CustomModelAccessControlUpdateResponse ``` { "properties": { "data": { "description": "Roles were successfully updated.", "items": { "properties": { "canShare": { "description": "Whether this user can share this custom model", "type": "boolean" }, "role": { "description": "This users role.", "type": "string" }, "userId": { "description": "This user's userId.", "type": "string" }, "username": { "description": "The username for this user's entry.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [CustomModelAccessControlResponse] | true | maxItems: 1000 | Roles were successfully updated. | ## CustomModelAsyncOperationResponse ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status | ## CustomModelConversionAsyncOperationResponse ``` { "properties": { "conversionId": { "description": "ID that can be used to stop a given conversion.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status", "type": "string" } }, "required": [ "conversionId", "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | conversionId | string | true | | ID that can be used to stop a given conversion. | | statusId | string | true | | ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status | ## CustomModelCopy ``` { "properties": { "customModelId": { "description": "The ID of the custom model to copy.", "type": "string" } }, "required": [ "customModelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string | true | | The ID of the custom model to copy. | ## CustomModelCreate ``` { "properties": { "calibratePredictions": { "default": true, "description": "Whether model predictions should be calibrated by DataRobot.Only applies to anomaly detection training tasks; we recommend this if you have not already included calibration in your model code.Calibration improves the probability estimates of a model, and modifies the predictions of non-probabilistic models to be interpretable as probabilities. This will facilitate comparison to DataRobot models, and give access to ROC curve insights on external data.", "type": "boolean", "x-versionadded": "v2.23" }, "classLabels": { "description": "The class labels for multiclass classification. Required for multiclass inference models. If using one of the [DataRobot] base environments and your model produces an ndarray of unlabeled class probabilities, the order of the labels should match the order of the predicted output", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "customModelType": { "description": "The type of custom model.", "enum": [ "training", "inference" ], "type": "string", "x-versiondeprecated": "v2.28" }, "description": { "description": "The user-friendly description of the model.", "maxLength": 10000, "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Training data assignment on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled for this particular model.", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "Programming language name in which model is written.", "maxLength": 500, "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The user-friendly name for the model.", "maxLength": 255, "type": "string" }, "negativeClassLabel": { "description": "The negative class label for custom models that support binary classification. If specified, `positiveClassLabel` must also be specified. Default value is \"0\".", "maxLength": 500, "type": [ "string", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "playgroundId": { "description": "The ID of the GenAI Playground associated with the given custom inference model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "positiveClassLabel": { "description": "The positive class label for custom models that support binary classification. If specified, `negativeClassLabel` must also be specified. Default value is \"1\".", "maxLength": 500, "type": [ "string", "null" ] }, "predictionThreshold": { "default": 0.5, "description": "The prediction threshold which will be used for binary classification custom model.", "maximum": 1, "minimum": 0, "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "supportsBinaryClassification": { "description": "Whether the model supports binary classification.", "type": "boolean", "x-versiondeprecated": "v2.23" }, "supportsRegression": { "description": "Whether the model supports regression.", "type": "boolean", "x-versiondeprecated": "v2.23" }, "tags": { "description": "The list of a tag's name/value pairs.", "items": { "properties": { "name": { "description": "The name of the tag.", "maxLength": 50, "minLength": 1, "type": "string" }, "value": { "description": "The value of the tag.", "maxLength": 256, "minLength": 1, "type": "string" } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 50, "minItems": 1, "type": "array", "x-versionadded": "v2.39" }, "targetName": { "description": "The name of the target for labeling predictions. Required for model type 'inference'. Specifying this value for a model type 'training' will result in an error.", "maxLength": 500, "type": [ "string", "null" ] }, "targetType": { "description": "The target type of the custom model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string", "x-versionadded": "v2.23" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "maxLength": 100, "type": "string", "x-versionadded": "v2.29" } }, "required": [ "customModelType", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | calibratePredictions | boolean | false | | Whether model predictions should be calibrated by DataRobot.Only applies to anomaly detection training tasks; we recommend this if you have not already included calibration in your model code.Calibration improves the probability estimates of a model, and modifies the predictions of non-probabilistic models to be interpretable as probabilities. This will facilitate comparison to DataRobot models, and give access to ROC curve insights on external data. | | classLabels | [string] | false | maxItems: 100 | The class labels for multiclass classification. Required for multiclass inference models. If using one of the [DataRobot] base environments and your model produces an ndarray of unlabeled class probabilities, the order of the labels should match the order of the predicted output | | customModelType | string | true | | The type of custom model. | | description | string | false | maxLength: 10000 | The user-friendly description of the model. | | desiredMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The amount of memory that is expected to be allocated by the custom model. | | gitModelVersion | GitModelVersion | false | | Contains git related attributes that are associated with a custom model version. | | isTrainingDataForVersionsPermanentlyEnabled | boolean | false | | Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Training data assignment on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled for this particular model. | | language | string | false | maxLength: 500 | Programming language name in which model is written. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed | | name | string | true | maxLength: 255 | The user-friendly name for the model. | | negativeClassLabel | string,null | false | maxLength: 500 | The negative class label for custom models that support binary classification. If specified, positiveClassLabel must also be specified. Default value is "0". | | networkEgressPolicy | string,null | false | | Network egress policy. | | playgroundId | string,null | false | | The ID of the GenAI Playground associated with the given custom inference model. | | positiveClassLabel | string,null | false | maxLength: 500 | The positive class label for custom models that support binary classification. If specified, negativeClassLabel must also be specified. Default value is "1". | | predictionThreshold | number | false | maximum: 1minimum: 0 | The prediction threshold which will be used for binary classification custom model. | | replicas | integer,null | false | maximum: 25 | A fixed number of replicas that will be set for the given custom-model. | | requiresHa | boolean,null | false | | Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. | | supportsBinaryClassification | boolean | false | | Whether the model supports binary classification. | | supportsRegression | boolean | false | | Whether the model supports regression. | | tags | [CustomModelTagCreate] | false | maxItems: 50minItems: 1 | The list of a tag's name/value pairs. | | targetName | string,null | false | maxLength: 500 | The name of the target for labeling predictions. Required for model type 'inference'. Specifying this value for a model type 'training' will result in an error. | | targetType | string | false | | The target type of the custom model. | | userProvidedId | string | false | maxLength: 100 | A user-provided unique ID associated with the given custom inference model. | ### Enumerated Values | Property | Value | | --- | --- | | customModelType | [training, inference] | | networkEgressPolicy | [NONE, DR_API_ACCESS, PUBLIC] | | targetType | [Binary, Regression, Multiclass, Anomaly, Transform, TextGeneration, GeoPoint, Unstructured, VectorDatabase, AgenticWorkflow, MCP] | ## CustomModelCreateFromTemplatePayload ``` { "properties": { "nimContainerTagOverride": { "description": "Allows to override the NIM container image tag.", "maxLength": 100, "type": "string", "x-versionadded": "v2.37" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": "string" }, "secretConfigId": { "description": "A secret configuration that is used by a custom model.", "type": [ "string", "null" ] }, "templateId": { "description": "The id of the custom model template.", "type": "string" } }, "required": [ "resourceBundleId", "templateId" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | nimContainerTagOverride | string | false | maxLength: 100 | Allows to override the NIM container image tag. | | resourceBundleId | string | true | | A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | | secretConfigId | string,null | false | | A secret configuration that is used by a custom model. | | templateId | string | true | | The id of the custom model template. | ## CustomModelCreateFromTemplateResponse ``` { "properties": { "customModelId": { "description": "The id of the created custom model.", "type": "string" }, "customModelVersionId": { "description": "The id of the created custom model version.", "type": "string" } }, "required": [ "customModelId", "customModelVersionId" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string | true | | The id of the created custom model. | | customModelVersionId | string | true | | The id of the created custom model version. | ## CustomModelListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom models.", "items": { "properties": { "calibratePredictions": { "description": "Determines whether ot not predictions should be calibrated by DataRobot.Only applies to anomaly detection.", "type": "boolean" }, "classLabels": { "description": "If the model is a multiclass classifier, these are the model's class labels", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "createdBy": { "description": "The username of the custom model creator.", "type": "string" }, "customModelType": { "description": "The type of custom model.", "enum": [ "training", "inference" ], "type": "string" }, "deploymentsCount": { "description": "The number of models deployed.", "type": "integer" }, "description": { "description": "The description of the model.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "id": { "description": "The ID of the custom model.", "type": "string" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Assigning training data on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled and return HTTP 422 for this particular model. Use training data assignment at the version level: [POST /api/v2/customModels/{customModelId}/versions/][post-apiv2custommodelscustommodelidversions] [PATCH /api/v2/customModels/{customModelId}/versions/][patch-apiv2custommodelscustommodelidversions] ", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "The programming language used to write the model.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The name of the model.", "type": "string" }, "negativeClassLabel": { "description": "If the model is a binary classifier, this is the negative class label.", "type": "string" }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "playgroundId": { "description": "The ID of the GenAI Playground associated with the given custom inference model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "positiveClassLabel": { "description": "If the model is a binary classifier, this is the positive class label.", "type": "string" }, "predictionThreshold": { "description": "If the model is a binary classifier, this is the prediction threshold.", "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "supportsAnomalyDetection": { "description": "Whether the model supports anomaly detection.", "type": "boolean" }, "supportsBinaryClassification": { "description": "Whether the model supports binary classification.", "type": "boolean" }, "supportsRegression": { "description": "Whether the model supports regression.", "type": "boolean" }, "tags": { "description": "A list of the custom model tag.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "type": "string" }, "value": { "description": "The value of the tag.", "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.39" }, "targetName": { "description": "The name of the target for labeling predictions.", "type": "string" }, "targetType": { "description": "The target type of custom model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "template": { "description": "If not null, the template used to create the custom model.", "properties": { "modelType": { "description": "The type of template the model was created from.", "enum": [ "nimModel", "invalid" ], "type": "string", "x-versionadded": "v2.36" }, "templateId": { "description": "The ID of the template used to create this custom model.", "type": "string", "x-versionadded": "v2.36" } }, "required": [ "modelType", "templateId" ], "type": "object", "x-versionadded": "v2.36" }, "trainingDataAssignmentInProgress": { "description": "Indicates if training data is currently being assigned to the custom model.", "type": "boolean" }, "trainingDataFileName": { "description": "The name of the file that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDataPartitionColumn": { "description": "The name of the column containing the partition assignments in training data if it was assigned previously and partitioning was provided.", "type": [ "string", "null" ] }, "trainingDatasetId": { "description": "The ID of the dataset that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDatasetVersionId": { "description": "The ID of the dataset version that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "updated": { "description": "ISO-8601 timestamp of when model was last updated.", "type": "string" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "maxLength": 100, "type": "string" } }, "required": [ "created", "createdBy", "deploymentsCount", "description", "id", "language", "latestVersion", "name", "supportsBinaryClassification", "supportsRegression", "targetType", "template", "updated" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomModelResponse] | true | maxItems: 1000 | List of custom models. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomModelPredictionExplanations ``` { "properties": { "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "customModelVersionId": { "description": "The ID of the custom model version.", "type": "string" }, "environmentId": { "description": "The ID of environment to use. If not specified, the customModelVersion's dependency environment will be used.", "type": "string" }, "environmentVersionId": { "description": "The ID of environment version to use. Defaults to the latest successfully built version.", "type": "string" } }, "required": [ "customModelId", "customModelVersionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string | true | | The ID of the custom model. | | customModelVersionId | string | true | | The ID of the custom model version. | | environmentId | string | false | | The ID of environment to use. If not specified, the customModelVersion's dependency environment will be used. | | environmentVersionId | string | false | | The ID of environment version to use. Defaults to the latest successfully built version. | ## CustomModelResourceLimits ``` { "properties": { "desiredCustomModelContainerSize": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "maxCustomModelContainerSize": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "maxCustomModelReplicasPerDeployment": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "maxCustomModelTestingParallelUsers": { "description": "The maximum number of parallel users that can be used for Custom Model Testing checks", "exclusiveMinimum": 0, "maximum": 20, "type": "integer" }, "minCustomModelContainerSize": { "description": "The minimum memory that might be allocated by the custom-model.", "maximum": 134217728, "minimum": 134217728, "type": [ "integer", "null" ], "x-versionadded": "v2.37" } }, "required": [ "maxCustomModelTestingParallelUsers" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | desiredCustomModelContainerSize | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed | | maxCustomModelContainerSize | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed | | maxCustomModelReplicasPerDeployment | integer,null | false | maximum: 25 | A fixed number of replicas that will be set for the given custom-model. | | maxCustomModelTestingParallelUsers | integer | true | maximum: 20 | The maximum number of parallel users that can be used for Custom Model Testing checks | | minCustomModelContainerSize | integer,null | false | maximum: 134217728minimum: 134217728 | The minimum memory that might be allocated by the custom-model. | ## CustomModelResponse ``` { "properties": { "calibratePredictions": { "description": "Determines whether ot not predictions should be calibrated by DataRobot.Only applies to anomaly detection.", "type": "boolean" }, "classLabels": { "description": "If the model is a multiclass classifier, these are the model's class labels", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "createdBy": { "description": "The username of the custom model creator.", "type": "string" }, "customModelType": { "description": "The type of custom model.", "enum": [ "training", "inference" ], "type": "string" }, "deploymentsCount": { "description": "The number of models deployed.", "type": "integer" }, "description": { "description": "The description of the model.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "id": { "description": "The ID of the custom model.", "type": "string" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Assigning training data on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled and return HTTP 422 for this particular model. Use training data assignment at the version level: [POST /api/v2/customModels/{customModelId}/versions/][post-apiv2custommodelscustommodelidversions] [PATCH /api/v2/customModels/{customModelId}/versions/][patch-apiv2custommodelscustommodelidversions] ", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "The programming language used to write the model.", "type": "string" }, "latestVersion": { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The name of the model.", "type": "string" }, "negativeClassLabel": { "description": "If the model is a binary classifier, this is the negative class label.", "type": "string" }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "playgroundId": { "description": "The ID of the GenAI Playground associated with the given custom inference model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "positiveClassLabel": { "description": "If the model is a binary classifier, this is the positive class label.", "type": "string" }, "predictionThreshold": { "description": "If the model is a binary classifier, this is the prediction threshold.", "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "supportsAnomalyDetection": { "description": "Whether the model supports anomaly detection.", "type": "boolean" }, "supportsBinaryClassification": { "description": "Whether the model supports binary classification.", "type": "boolean" }, "supportsRegression": { "description": "Whether the model supports regression.", "type": "boolean" }, "tags": { "description": "A list of the custom model tag.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "type": "string" }, "value": { "description": "The value of the tag.", "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.39" }, "targetName": { "description": "The name of the target for labeling predictions.", "type": "string" }, "targetType": { "description": "The target type of custom model.", "enum": [ "Binary", "Regression", "Multiclass", "Anomaly", "Transform", "TextGeneration", "GeoPoint", "Unstructured", "VectorDatabase", "AgenticWorkflow", "MCP" ], "type": "string" }, "template": { "description": "If not null, the template used to create the custom model.", "properties": { "modelType": { "description": "The type of template the model was created from.", "enum": [ "nimModel", "invalid" ], "type": "string", "x-versionadded": "v2.36" }, "templateId": { "description": "The ID of the template used to create this custom model.", "type": "string", "x-versionadded": "v2.36" } }, "required": [ "modelType", "templateId" ], "type": "object", "x-versionadded": "v2.36" }, "trainingDataAssignmentInProgress": { "description": "Indicates if training data is currently being assigned to the custom model.", "type": "boolean" }, "trainingDataFileName": { "description": "The name of the file that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDataPartitionColumn": { "description": "The name of the column containing the partition assignments in training data if it was assigned previously and partitioning was provided.", "type": [ "string", "null" ] }, "trainingDatasetId": { "description": "The ID of the dataset that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "trainingDatasetVersionId": { "description": "The ID of the dataset version that was used as training data if it was assigned previously.", "type": [ "string", "null" ] }, "updated": { "description": "ISO-8601 timestamp of when model was last updated.", "type": "string" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "maxLength": 100, "type": "string" } }, "required": [ "created", "createdBy", "deploymentsCount", "description", "id", "language", "latestVersion", "name", "supportsBinaryClassification", "supportsRegression", "targetType", "template", "updated" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | calibratePredictions | boolean | false | | Determines whether ot not predictions should be calibrated by DataRobot.Only applies to anomaly detection. | | classLabels | [string] | false | maxItems: 100 | If the model is a multiclass classifier, these are the model's class labels | | created | string | true | | ISO-8601 timestamp of when the model was created. | | createdBy | string | true | | The username of the custom model creator. | | customModelType | string | false | | The type of custom model. | | deploymentsCount | integer | true | | The number of models deployed. | | description | string | true | | The description of the model. | | desiredMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The amount of memory that is expected to be allocated by the custom model. | | gitModelVersion | GitModelVersion | false | | Contains git related attributes that are associated with a custom model version. | | id | string | true | | The ID of the custom model. | | isTrainingDataForVersionsPermanentlyEnabled | boolean | false | | Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Assigning training data on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled and return HTTP 422 for this particular model. Use training data assignment at the version level: [POST /api/v2/customModels/{customModelId}/versions/][post-apiv2custommodelscustommodelidversions] [PATCH /api/v2/customModels/{customModelId}/versions/][patch-apiv2custommodelscustommodelidversions] | | language | string | true | | The programming language used to write the model. | | latestVersion | CustomModelVersionResponse | true | | The latest version for the custom model (if this field is empty, the model is not ready for use). | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed | | name | string | true | | The name of the model. | | negativeClassLabel | string | false | | If the model is a binary classifier, this is the negative class label. | | networkEgressPolicy | string,null | false | | Network egress policy. | | playgroundId | string,null | false | | The ID of the GenAI Playground associated with the given custom inference model. | | positiveClassLabel | string | false | | If the model is a binary classifier, this is the positive class label. | | predictionThreshold | number | false | | If the model is a binary classifier, this is the prediction threshold. | | replicas | integer,null | false | maximum: 25 | A fixed number of replicas that will be set for the given custom-model. | | requiresHa | boolean,null | false | | Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. | | resourceBundleId | string,null | false | | A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | | supportsAnomalyDetection | boolean | false | | Whether the model supports anomaly detection. | | supportsBinaryClassification | boolean | true | | Whether the model supports binary classification. | | supportsRegression | boolean | true | | Whether the model supports regression. | | tags | [CustomModelTagRetrieveResponse] | false | maxItems: 1000 | A list of the custom model tag. | | targetName | string | false | | The name of the target for labeling predictions. | | targetType | string | true | | The target type of custom model. | | template | Template | true | | If not null, the template used to create the custom model. | | trainingDataAssignmentInProgress | boolean | false | | Indicates if training data is currently being assigned to the custom model. | | trainingDataFileName | string,null | false | | The name of the file that was used as training data if it was assigned previously. | | trainingDataPartitionColumn | string,null | false | | The name of the column containing the partition assignments in training data if it was assigned previously and partitioning was provided. | | trainingDatasetId | string,null | false | | The ID of the dataset that was used as training data if it was assigned previously. | | trainingDatasetVersionId | string,null | false | | The ID of the dataset version that was used as training data if it was assigned previously. | | updated | string | true | | ISO-8601 timestamp of when model was last updated. | | userProvidedId | string | false | maxLength: 100 | A user-provided unique ID associated with the given custom inference model. | ### Enumerated Values | Property | Value | | --- | --- | | customModelType | [training, inference] | | networkEgressPolicy | [NONE, DR_API_ACCESS, PUBLIC] | | targetType | [Binary, Regression, Multiclass, Anomaly, Transform, TextGeneration, GeoPoint, Unstructured, VectorDatabase, AgenticWorkflow, MCP] | ## CustomModelShortResponse ``` { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` Custom model associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model. | | name | string | true | | User-friendly name of the model. | ## CustomModelTagCreate ``` { "properties": { "name": { "description": "The name of the tag.", "maxLength": 50, "minLength": 1, "type": "string" }, "value": { "description": "The value of the tag.", "maxLength": 256, "minLength": 1, "type": "string" } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 50minLength: 1minLength: 1 | The name of the tag. | | value | string | true | maxLength: 256minLength: 1minLength: 1 | The value of the tag. | ## CustomModelTagRetrieveResponse ``` { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "type": "string" }, "value": { "description": "The value of the tag.", "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the tag. | | name | string | true | | The name of the tag. | | value | string | true | | The value of the tag. | ## CustomModelTesting ``` { "description": "Maps the testing types to their status and message for the testing entry. Testing type represents a single check executed during the test.", "properties": { "errorCheck": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "longRunningService": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "nullValueImputation": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "sideEffects": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } }, "type": "object" } ``` Maps the testing types to their status and message for the testing entry. Testing type represents a single check executed during the test. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorCheck | TestingStatus | false | | Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted. | | longRunningService | TestingStatus | false | | Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted. | | nullValueImputation | TestingStatus | false | | Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted. | | sideEffects | TestingStatus | false | | Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted. | ## CustomModelTests ``` { "properties": { "configuration": { "description": "Key value map of Testing type and Testing type config.", "properties": { "errorCheck": { "default": "fail", "description": "Ensures that the model can make predictions on the provided test dataset.", "enum": [ "fail", "skip" ], "type": "string" }, "longRunningService": { "default": "fail", "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "enum": [ "fail" ], "type": "string" }, "nullValueImputation": { "default": "warn", "description": "Verifies that the model can impute null values. Required for Feature Impact.", "enum": [ "skip", "warn", "fail" ], "type": "string" }, "sideEffects": { "default": "warn", "description": "Verifies that predictions made on the dataset match row-wise predictions for the same dataset. Fails if the predictions do not match.", "enum": [ "skip", "warn", "fail" ], "type": "string" } }, "type": "object" }, "customModelId": { "description": "The ID of the custom model to test.", "type": "string" }, "customModelVersionId": { "description": "The ID of custom model version to use.", "type": "string" }, "datasetId": { "description": "The ID of the dataset to use for testing. Dataset ID is required for regular (non-unstructured) custom models.", "type": "string" }, "datasetVersionId": { "description": "The ID of the version of the dataset item to use as the testing dataset. Defaults to the latest version.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "environmentId": { "description": "The ID of environment to use. If not specified, the customModelVersion's dependency environment will be used.", "type": "string", "x-versiondeprecated": "v2.23" }, "environmentVersionId": { "description": "The ID of environment version to use. Defaults to the latest successfully built version.", "type": "string", "x-versiondeprecated": "v2.23" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "customModelId", "customModelVersionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configuration | CustomModelTestsConfig | false | | Key value map of Testing type and Testing type config. | | customModelId | string | true | | The ID of the custom model to test. | | customModelVersionId | string | true | | The ID of custom model version to use. | | datasetId | string | false | | The ID of the dataset to use for testing. Dataset ID is required for regular (non-unstructured) custom models. | | datasetVersionId | string | false | | The ID of the version of the dataset item to use as the testing dataset. Defaults to the latest version. | | desiredMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId. | | environmentId | string | false | | The ID of environment to use. If not specified, the customModelVersion's dependency environment will be used. | | environmentVersionId | string | false | | The ID of environment version to use. Defaults to the latest successfully built version. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId. | | networkEgressPolicy | string,null | false | | Network egress policy. | | replicas | integer,null | false | maximum: 25 | A fixed number of replicas that will be set for the given custom-model. | | requiresHa | boolean,null | false | | Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. | | resourceBundleId | string,null | false | | A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | ### Enumerated Values | Property | Value | | --- | --- | | networkEgressPolicy | [NONE, PUBLIC] | ## CustomModelTestsConfig ``` { "description": "Key value map of Testing type and Testing type config.", "properties": { "errorCheck": { "default": "fail", "description": "Ensures that the model can make predictions on the provided test dataset.", "enum": [ "fail", "skip" ], "type": "string" }, "longRunningService": { "default": "fail", "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "enum": [ "fail" ], "type": "string" }, "nullValueImputation": { "default": "warn", "description": "Verifies that the model can impute null values. Required for Feature Impact.", "enum": [ "skip", "warn", "fail" ], "type": "string" }, "sideEffects": { "default": "warn", "description": "Verifies that predictions made on the dataset match row-wise predictions for the same dataset. Fails if the predictions do not match.", "enum": [ "skip", "warn", "fail" ], "type": "string" } }, "type": "object" } ``` Key value map of Testing type and Testing type config. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorCheck | string | false | | Ensures that the model can make predictions on the provided test dataset. | | longRunningService | string | false | | Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted. | | nullValueImputation | string | false | | Verifies that the model can impute null values. Required for Feature Impact. | | sideEffects | string | false | | Verifies that predictions made on the dataset match row-wise predictions for the same dataset. Fails if the predictions do not match. | ### Enumerated Values | Property | Value | | --- | --- | | errorCheck | [fail, skip] | | longRunningService | fail | | nullValueImputation | [skip, warn, fail] | | sideEffects | [skip, warn, fail] | ## CustomModelTestsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom model tests.", "items": { "properties": { "completedAt": { "description": "ISO-8601 timestamp of when the testing attempt was completed.", "type": "string" }, "created": { "description": "ISO-8601 timestamp of when the testing entry was created.", "type": "string" }, "createdBy": { "description": "The username of the user that started the custom model test.", "type": "string" }, "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelImageId": { "description": "If testing was successful, ID of the custom inference model image that can be used for a deployment, otherwise null.", "type": [ "string", "null" ] }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "datasetId": { "description": "ID of the dataset used for testing.", "type": "string" }, "datasetVersionId": { "description": "ID of the specific dataset version used for testing.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "ID of the testing history entry.", "type": "string" }, "imageType": { "description": "The type of the image, either customModelImage if the testing attempt is using a customModelImage as its model or customModelVersion if the testing attempt is using a customModelVersion with dependency management.", "enum": [ "customModelImage", "customModelVersion" ], "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "overallStatus": { "description": "The overall status of the testing history entry.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "testingStatus": { "description": "Maps the testing types to their status and message for the testing entry. Testing type represents a single check executed during the test.", "properties": { "errorCheck": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "longRunningService": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "nullValueImputation": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "sideEffects": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } }, "type": "object" } }, "required": [ "completedAt", "created", "createdBy", "customModel", "customModelImageId", "customModelVersion", "datasetId", "datasetVersionId", "executionEnvironment", "executionEnvironmentVersion", "id", "overallStatus", "testingStatus" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomModelTestsResponse] | true | maxItems: 1000 | List of custom model tests. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomModelTestsLogTailResponse ``` { "properties": { "log": { "description": "The N lines of the log.", "type": "string" } }, "required": [ "log" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | log | string | true | | The N lines of the log. | ## CustomModelTestsResponse ``` { "properties": { "completedAt": { "description": "ISO-8601 timestamp of when the testing attempt was completed.", "type": "string" }, "created": { "description": "ISO-8601 timestamp of when the testing entry was created.", "type": "string" }, "createdBy": { "description": "The username of the user that started the custom model test.", "type": "string" }, "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelImageId": { "description": "If testing was successful, ID of the custom inference model image that can be used for a deployment, otherwise null.", "type": [ "string", "null" ] }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "datasetId": { "description": "ID of the dataset used for testing.", "type": "string" }, "datasetVersionId": { "description": "ID of the specific dataset version used for testing.", "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "ID of the testing history entry.", "type": "string" }, "imageType": { "description": "The type of the image, either customModelImage if the testing attempt is using a customModelImage as its model or customModelVersion if the testing attempt is using a customModelVersion with dependency management.", "enum": [ "customModelImage", "customModelVersion" ], "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "overallStatus": { "description": "The overall status of the testing history entry.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "testingStatus": { "description": "Maps the testing types to their status and message for the testing entry. Testing type represents a single check executed during the test.", "properties": { "errorCheck": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "longRunningService": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "nullValueImputation": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "sideEffects": { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } }, "type": "object" } }, "required": [ "completedAt", "created", "createdBy", "customModel", "customModelImageId", "customModelVersion", "datasetId", "datasetVersionId", "executionEnvironment", "executionEnvironmentVersion", "id", "overallStatus", "testingStatus" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | completedAt | string | true | | ISO-8601 timestamp of when the testing attempt was completed. | | created | string | true | | ISO-8601 timestamp of when the testing entry was created. | | createdBy | string | true | | The username of the user that started the custom model test. | | customModel | CustomModelShortResponse | true | | Custom model associated with this deployment. | | customModelImageId | string,null | true | | If testing was successful, ID of the custom inference model image that can be used for a deployment, otherwise null. | | customModelVersion | CustomModelVersionShortResponse | true | | Custom model version associated with this deployment. | | datasetId | string | true | | ID of the dataset used for testing. | | datasetVersionId | string | true | | ID of the specific dataset version used for testing. | | desiredMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId. | | executionEnvironment | ExecutionEnvironmentShortResponse | true | | Execution environment associated with this deployment. | | executionEnvironmentVersion | ExecutionEnvironmentVersionShortResponse | true | | Execution environment version associated with this deployment. | | id | string | true | | ID of the testing history entry. | | imageType | string | false | | The type of the image, either customModelImage if the testing attempt is using a customModelImage as its model or customModelVersion if the testing attempt is using a customModelVersion with dependency management. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId. | | networkEgressPolicy | string,null | false | | Network egress policy. | | overallStatus | string | true | | The overall status of the testing history entry. | | replicas | integer,null | false | maximum: 25 | A fixed number of replicas that will be set for the given custom-model. | | requiresHa | boolean,null | false | | Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. | | resourceBundleId | string,null | false | | A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | | testingStatus | CustomModelTesting | true | | Maps the testing types to their status and message for the testing entry. Testing type represents a single check executed during the test. | ### Enumerated Values | Property | Value | | --- | --- | | imageType | [customModelImage, customModelVersion] | | networkEgressPolicy | [NONE, PUBLIC] | | overallStatus | [not_tested, queued, failed, canceled, succeeded, in_progress, aborted, warning, skipped] | ## CustomModelUpdate ``` { "properties": { "classLabels": { "description": "The class labels for multiclass classification. Required for multiclass inference models. If using one of the [DataRobot] base environments and your model produces an ndarray of unlabeled class probabilities, the order of the labels should match the order of the predicted output", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "description": { "description": "The user-friendly description of the model.", "maxLength": 10000, "type": "string" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "isTrainingDataForVersionsPermanentlyEnabled": { "description": "Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Training data assignment on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled for this particular model.", "type": "boolean", "x-versionadded": "v2.30" }, "language": { "description": "Programming language name in which model is written.", "maxLength": 500, "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "name": { "description": "The user-friendly name for the model.", "maxLength": 255, "type": "string" }, "negativeClassLabel": { "description": "The negative class label for custom models that support binary classification. If specified, `positiveClassLabel` must also be specified. Default value is \"0\".", "maxLength": 500, "type": [ "string", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "DR_API_ACCESS", "PUBLIC" ], "type": [ "string", "null" ], "x-versiondeprecated": "v2.30" }, "positiveClassLabel": { "description": "The positive class label for custom models that support binary classification. If specified, `negativeClassLabel` must also be specified. Default value is \"1\".", "maxLength": 500, "type": [ "string", "null" ] }, "predictionThreshold": { "default": 0.5, "description": "The prediction threshold which will be used for binary classification custom model.", "maximum": 1, "minimum": 0, "type": "number" }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.30" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26", "x-versiondeprecated": "v2.30" }, "targetName": { "description": "The name of the target for labeling predictions. Required for model type 'inference'. Specifying this value for a model type 'training' will result in an error.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classLabels | [string] | false | maxItems: 100 | The class labels for multiclass classification. Required for multiclass inference models. If using one of the [DataRobot] base environments and your model produces an ndarray of unlabeled class probabilities, the order of the labels should match the order of the predicted output | | description | string | false | maxLength: 10000 | The user-friendly description of the model. | | desiredMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The amount of memory that is expected to be allocated by the custom model. | | gitModelVersion | GitModelVersion | false | | Contains git related attributes that are associated with a custom model version. | | isTrainingDataForVersionsPermanentlyEnabled | boolean | false | | Indicates that training data assignment is now permanently at the version level only for the custom model. Once enabled, this cannot be disabled. Training data assignment on the model level [PATCH /api/v2/customModels/{customModelId}/trainingData/][patch-apiv2custommodelscustommodelidtrainingdata] will be permanently disabled for this particular model. | | language | string | false | maxLength: 500 | Programming language name in which model is written. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed | | name | string | false | maxLength: 255 | The user-friendly name for the model. | | negativeClassLabel | string,null | false | maxLength: 500 | The negative class label for custom models that support binary classification. If specified, positiveClassLabel must also be specified. Default value is "0". | | networkEgressPolicy | string,null | false | | Network egress policy. | | positiveClassLabel | string,null | false | maxLength: 500 | The positive class label for custom models that support binary classification. If specified, negativeClassLabel must also be specified. Default value is "1". | | predictionThreshold | number | false | maximum: 1minimum: 0 | The prediction threshold which will be used for binary classification custom model. | | replicas | integer,null | false | maximum: 25 | A fixed number of replicas that will be set for the given custom-model. | | requiresHa | boolean,null | false | | Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. | | targetName | string,null | false | maxLength: 500 | The name of the target for labeling predictions. Required for model type 'inference'. Specifying this value for a model type 'training' will result in an error. | ### Enumerated Values | Property | Value | | --- | --- | | networkEgressPolicy | [NONE, DR_API_ACCESS, PUBLIC] | ## CustomModelVersionCreate ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version. At least one of \"baseEnvironmentId\" and \"baseEnvironmentVersionId\" must be provided. If both are specified, the version must belong to the environment.", "type": "string", "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version ID to use with this model version. At least one of \"baseEnvironmentId\" and \"baseEnvironmentVersionId\" must be provided. If both are specified, the version must belong to the environment. If not specified: in the case where the previous model versions exist, the value from the latest model version is inherited, otherwise, the latest successfully built version of the environment specified in \"baseEnvironmentId\" is used.", "type": "string", "x-versionadded": "v2.29" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] and [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] ", "type": [ "string", "null" ], "x-versionadded": "2.31" }, "isMajorUpdate": { "default": "true", "description": "If set to true, new major version will created, otherwise minor version will be created.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "keepTrainingHoldoutData": { "default": true, "description": "If the version should inherit training and holdout data from the previous version. Defaults to true.This field is only applicable if the model has training data for versions enabled. Otherwise the field value will be ignored.", "type": "boolean", "x-versionadded": "v2.30" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "string", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. Field names and values are exposed as environment variables with values when running the custom model. Example: \"required_metadata_values\": [{\"field_name\": \"hi\", \"value\": \"there\"}],", "type": "string", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] and [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] ", "type": [ "string", "null" ], "x-versionadded": "2.31" } }, "required": [ "isMajorUpdate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string | false | | The base environment to use with this model version. At least one of "baseEnvironmentId" and "baseEnvironmentVersionId" must be provided. If both are specified, the version must belong to the environment. | | baseEnvironmentVersionId | string | false | | The base environment version ID to use with this model version. At least one of "baseEnvironmentId" and "baseEnvironmentVersionId" must be provided. If both are specified, the version must belong to the environment. If not specified: in the case where the previous model versions exist, the value from the latest model version is inherited, otherwise, the latest successfully built version of the environment specified in "baseEnvironmentId" is used. | | desiredMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId. | | file | string(binary) | false | | A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding filePath supplied that shows the relative location of the file. For example, you have two files: /home/username/custom-task/main.py and /home/username/custom-task/helpers/helper.py. When uploading these files, you would also need to include two filePath fields of, "main.py" and "helpers/helper.py". If the supplied file already exists at the supplied filePath, the old file is replaced by the new file. | | filePath | any | false | | The local path of the file being uploaded. See the file field explanation for more details. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | gitModelVersion | GitModelVersion | false | | Contains git related attributes that are associated with a custom model version. | | holdoutData | string,null | false | | Holdout data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] and [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] | | isMajorUpdate | string | true | | If set to true, new major version will created, otherwise minor version will be created. | | keepTrainingHoldoutData | boolean | false | | If the version should inherit training and holdout data from the previous version. Defaults to true.This field is only applicable if the model has training data for versions enabled. Otherwise the field value will be ignored. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId. | | networkEgressPolicy | string,null | false | | Network egress policy. | | replicas | integer,null | false | maximum: 25 | A fixed number of replicas that will be set for the given custom-model. | | requiredMetadata | string | false | | Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. | | requiredMetadataValues | string | false | | Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. Field names and values are exposed as environment variables with values when running the custom model. Example: "required_metadata_values": [{"field_name": "hi", "value": "there"}], | | requiresHa | boolean,null | false | | Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. | | resourceBundleId | string,null | false | | A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | | trainingData | string,null | false | | Training data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] and [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] | ### Enumerated Values | Property | Value | | --- | --- | | isMajorUpdate | [false, False, true, True] | | networkEgressPolicy | [NONE, PUBLIC] | ## CustomModelVersionCreateFromLatest ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version. At least one of \"baseEnvironmentId\" and \"baseEnvironmentVersionId\" must be provided. If both are specified, the version must belong to the environment.", "type": "string", "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version ID to use with this model version. At least one of \"baseEnvironmentId\" and \"baseEnvironmentVersionId\" must be provided. If both are specified, the version must belong to the environment. If not specified: in the case where the previous model versions exist, the value from the latest model version is inherited, otherwise, the latest successfully built version of the environment specified in \"baseEnvironmentId\" is used.", "type": "string", "x-versionadded": "v2.29" }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "file": { "description": "A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding `filePath` supplied that shows the relative location of the file. For example, you have two files: `/home/username/custom-task/main.py` and `/home/username/custom-task/helpers/helper.py`. When uploading these files, you would _also_ need to include two `filePath` fields of, `\"main.py\"` and `\"helpers/helper.py\"`. If the supplied `file` already exists at the supplied `filePath`, the old file is replaced by the new file.", "format": "binary", "type": "string" }, "filePath": { "description": "The local path of the file being uploaded. See the `file` field explanation for more details.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 1000, "type": "array" } ] }, "filesToDelete": { "description": "The IDs of the files to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" } ] }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] and [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] ", "type": [ "string", "null" ], "x-versionadded": "2.31" }, "isMajorUpdate": { "default": "true", "description": "If set to true, new major version will created, otherwise minor version will be created.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "keepTrainingHoldoutData": { "default": true, "description": "If the version should inherit training and holdout data from the previous version. Defaults to true.This field is only applicable if the model has training data for versions enabled. Otherwise the field value will be ignored.", "type": "boolean", "x-versionadded": "v2.30" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "string", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. Field names and values are exposed as environment variables with values when running the custom model. Example: \"required_metadata_values\": [{\"field_name\": \"hi\", \"value\": \"there\"}],", "type": "string", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] and [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] ", "type": [ "string", "null" ], "x-versionadded": "2.31" } }, "required": [ "isMajorUpdate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string | false | | The base environment to use with this model version. At least one of "baseEnvironmentId" and "baseEnvironmentVersionId" must be provided. If both are specified, the version must belong to the environment. | | baseEnvironmentVersionId | string | false | | The base environment version ID to use with this model version. At least one of "baseEnvironmentId" and "baseEnvironmentVersionId" must be provided. If both are specified, the version must belong to the environment. If not specified: in the case where the previous model versions exist, the value from the latest model version is inherited, otherwise, the latest successfully built version of the environment specified in "baseEnvironmentId" is used. | | desiredMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId. | | file | string(binary) | false | | A file with code for a custom task or a custom model. For each file supplied as form data, you must have a corresponding filePath supplied that shows the relative location of the file. For example, you have two files: /home/username/custom-task/main.py and /home/username/custom-task/helpers/helper.py. When uploading these files, you would also need to include two filePath fields of, "main.py" and "helpers/helper.py". If the supplied file already exists at the supplied filePath, the old file is replaced by the new file. | | filePath | any | false | | The local path of the file being uploaded. See the file field explanation for more details. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 1000 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | filesToDelete | any | false | | The IDs of the files to be deleted. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 100 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | gitModelVersion | GitModelVersion | false | | Contains git related attributes that are associated with a custom model version. | | holdoutData | string,null | false | | Holdout data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] and [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] | | isMajorUpdate | string | true | | If set to true, new major version will created, otherwise minor version will be created. | | keepTrainingHoldoutData | boolean | false | | If the version should inherit training and holdout data from the previous version. Defaults to true.This field is only applicable if the model has training data for versions enabled. Otherwise the field value will be ignored. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId. | | networkEgressPolicy | string,null | false | | Network egress policy. | | replicas | integer,null | false | maximum: 25 | A fixed number of replicas that will be set for the given custom-model. | | requiredMetadata | string | false | | Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. | | requiredMetadataValues | string | false | | Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. Field names and values are exposed as environment variables with values when running the custom model. Example: "required_metadata_values": [{"field_name": "hi", "value": "there"}], | | requiresHa | boolean,null | false | | Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. | | resourceBundleId | string,null | false | | A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | | trainingData | string,null | false | | Training data configuration may be supplied for version. This functionality has to be explicitly enabled for the current model. See isTrainingDataForVersionsPermanentlyEnabled parameter for [POST /api/v2/customModels/][post-apiv2custommodels] and [PATCH /api/v2/customModels/{customModelId}/][patch-apiv2custommodelscustommodelid] | ### Enumerated Values | Property | Value | | --- | --- | | isMajorUpdate | [false, False, true, True] | | networkEgressPolicy | [NONE, PUBLIC] | ## CustomModelVersionCreateFromRepository ``` { "properties": { "baseEnvironmentId": { "description": "The base environment to use with this version.", "type": "string", "x-versionadded": "v2.22" }, "isMajorUpdate": { "default": true, "description": "If set to true, new major version will created, otherwise minor version will be created.", "type": "boolean" }, "keepTrainingHoldoutData": { "default": true, "description": "If the version should inherit training and holdout data from the previous version. Defaults to true.This field is only applicable if the model has training data for versions enabled. Otherwise the field value will be ignored.", "type": "boolean", "x-versionadded": "v2.30" }, "ref": { "description": "Remote reference (branch, commit, etc). Latest, if not specified.", "type": "string" }, "repositoryId": { "description": "The ID of remote repository used to pull sources. This ID can be found using the /api/v2/remoteRepositories/ endpoint.", "type": "string" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "sourcePath": { "description": "A remote repository file path to be pulled into a custom model or custom task.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" } ] } }, "required": [ "repositoryId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string | false | | The base environment to use with this version. | | isMajorUpdate | boolean | false | | If set to true, new major version will created, otherwise minor version will be created. | | keepTrainingHoldoutData | boolean | false | | If the version should inherit training and holdout data from the previous version. Defaults to true.This field is only applicable if the model has training data for versions enabled. Otherwise the field value will be ignored. | | ref | string | false | | Remote reference (branch, commit, etc). Latest, if not specified. | | repositoryId | string | true | | The ID of remote repository used to pull sources. This ID can be found using the /api/v2/remoteRepositories/ endpoint. | | requiredMetadata | object | false | | Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. | | requiredMetadataValues | [RequiredMetadataValue] | false | maxItems: 100 | Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. | | sourcePath | any | false | | A remote repository file path to be pulled into a custom model or custom task. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 100 | none | ## CustomModelVersionFromCodespace ``` { "properties": { "codespaceId": { "description": "The ID of the codespace that is the source for the custom model version files.", "type": "string" } }, "required": [ "codespaceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | codespaceId | string | true | | The ID of the codespace that is the source for the custom model version files. | ## CustomModelVersionListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom model versions.", "items": { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomModelVersionResponse] | true | maxItems: 1000 | List of custom model versions. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomModelVersionMetadataUpdate ``` { "properties": { "description": { "description": "New description for the custom task or model.", "maxLength": 10000, "type": "string" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 10000 | New description for the custom task or model. | | gitModelVersion | GitModelVersion | false | | Contains git related attributes that are associated with a custom model version. | | requiredMetadata | object | false | | Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. | | requiredMetadataValues | [RequiredMetadataValue] | false | maxItems: 100 | Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. | ## CustomModelVersionResponse ``` { "description": "The latest version for the custom model (if this field is empty, the model is not ready for use).", "properties": { "baseEnvironmentId": { "description": "The base environment to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "baseEnvironmentVersionId": { "description": "The base environment version to use with this model version.", "type": [ "string", "null" ], "x-versionadded": "v2.25" }, "created": { "description": "ISO-8601 timestamp of when the model was created.", "type": "string" }, "customModelId": { "description": "The ID of the custom model.", "type": "string" }, "dependencies": { "description": "The parsed dependencies of the custom model version if the version has a valid requirements.txt file.", "items": { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.22" }, "description": { "description": "Description of a custom model version.", "type": [ "string", "null" ] }, "desiredMemory": { "description": "The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ], "x-versiondeprecated": "v2.24" }, "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" }, "id": { "description": "the ID of the custom model version created.", "type": "string" }, "isFrozen": { "description": "If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded).", "type": "boolean", "x-versiondeprecated": "v2.34" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "label": { "description": "A semantic version number of the major and minor version.", "type": "string" }, "maximumMemory": { "description": "The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "networkEgressPolicy": { "description": "Network egress policy.", "enum": [ "NONE", "PUBLIC" ], "type": [ "string", "null" ] }, "replicas": { "description": "A fixed number of replicas that will be set for the given custom-model.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "requiredMetadata": { "description": "Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version.", "type": "object", "x-versionadded": "v2.25", "x-versiondeprecated": "v2.26" }, "requiredMetadataValues": { "description": "Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys.", "items": { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "requiresHa": { "description": "Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" }, "versionMajor": { "description": "The major version number, incremented on deployments or larger file changes.", "type": "integer" }, "versionMinor": { "description": "The minor version number, incremented on general file changes.", "type": "integer" } }, "required": [ "created", "customModelId", "description", "id", "isFrozen", "items", "label", "versionMajor", "versionMinor" ], "type": "object" } ``` The latest version for the custom model (if this field is empty, the model is not ready for use). ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseEnvironmentId | string,null | false | | The base environment to use with this model version. | | baseEnvironmentVersionId | string,null | false | | The base environment version to use with this model version. | | created | string | true | | ISO-8601 timestamp of when the model was created. | | customModelId | string | true | | The ID of the custom model. | | dependencies | [DependencyResponse] | false | maxItems: 1000 | The parsed dependencies of the custom model version if the version has a valid requirements.txt file. | | description | string,null | true | | Description of a custom model version. | | desiredMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The amount of memory that is expected to be allocated by the custom model. This setting is incompatible with setting the resourceBundleId. | | gitModelVersion | GitModelVersion | false | | Contains git related attributes that are associated with a custom model version. | | holdoutData | HoldoutDataResponse | false | | Holdout data configuration. | | id | string | true | | the ID of the custom model version created. | | isFrozen | boolean | true | | If the version is frozen and immutable (i.e. it is either deployed or has been edited, causing a newer version to be yielded). | | items | [DeprecatedCustomModelWorkspaceItemResponse] | true | maxItems: 100 | List of file items. | | label | string | true | | A semantic version number of the major and minor version. | | maximumMemory | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. If exceeded, the custom-model will be killed. This setting is incompatible with setting the resourceBundleId. | | networkEgressPolicy | string,null | false | | Network egress policy. | | replicas | integer,null | false | maximum: 25 | A fixed number of replicas that will be set for the given custom-model. | | requiredMetadata | object | false | | Additional parameters required by the execution environment. The required keys are defined by the fieldNames in the base environment's requiredMetadataKeys. Once set, they cannot be changed. If you to change them, make a new version. | | requiredMetadataValues | [RequiredMetadataValue] | false | maxItems: 100 | Additional parameters required by the execution environment. The required fieldNames are defined by the fieldNames in the base environment's requiredMetadataKeys. | | requiresHa | boolean,null | false | | Require all custom model replicas to be deployed on different Kubernetes nodes for predictions fault tolerance. | | resourceBundleId | string,null | false | | A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | | trainingData | TrainingDataResponse | false | | Training data configuration. | | versionMajor | integer | true | | The major version number, incremented on deployments or larger file changes. | | versionMinor | integer | true | | The minor version number, incremented on general file changes. | ### Enumerated Values | Property | Value | | --- | --- | | networkEgressPolicy | [NONE, PUBLIC] | ## CustomModelVersionShortResponse ``` { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" } ``` Custom model version associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model version. | | label | string | true | | User-friendly name of the model version. | ## CustomModelVersionToCodespace ``` { "properties": { "codespaceId": { "description": "The ID of the codespace that should be updated with custom model version files.", "type": "string" } }, "required": [ "codespaceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | codespaceId | string | true | | The ID of the codespace that should be updated with custom model version files. | ## DependencyBuildLogResponse ``` { "properties": { "data": { "description": "The custom model version's dependency build log in tar.gz format.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | string(binary) | true | | The custom model version's dependency build log in tar.gz format. | ## DependencyConstraint ``` { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | constraintType | string | true | | The constraint type to apply to the version. | | version | string | true | | The version label to use in the constraint. | ### Enumerated Values | Property | Value | | --- | --- | | constraintType | [<, <=, ==, >=, >] | ## DependencyResponse ``` { "properties": { "constraints": { "description": "Constraints that should be applied to the dependency when installed.", "items": { "properties": { "constraintType": { "description": "The constraint type to apply to the version.", "enum": [ "<", "<=", "==", ">=", ">" ], "type": "string" }, "version": { "description": "The version label to use in the constraint.", "type": "string" } }, "required": [ "constraintType", "version" ], "type": "object" }, "maxItems": 100, "type": "array" }, "extras": { "description": "The dependency's package extras.", "type": "string" }, "line": { "description": "The original line from the requirements.txt file.", "type": "string" }, "lineNumber": { "description": "The line number the requirement was on in requirements.txt.", "type": "integer" }, "packageName": { "description": "The dependency's package name.", "type": "string" } }, "required": [ "constraints", "line", "lineNumber", "packageName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | constraints | [DependencyConstraint] | true | maxItems: 100 | Constraints that should be applied to the dependency when installed. | | extras | string | false | | The dependency's package extras. | | line | string | true | | The original line from the requirements.txt file. | | lineNumber | integer | true | | The line number the requirement was on in requirements.txt. | | packageName | string | true | | The dependency's package name. | ## DeprecatedCustomModelVersionTrainingDataUpdate ``` { "properties": { "gitModelVersion": { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" }, "holdoutData": { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ], "x-versiondeprecated": "v2.33" } }, "type": "object" }, "trainingData": { "description": "Training data configuration.", "properties": { "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean", "x-versiondeprecated": "v2.33" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" } }, "type": "object" } }, "required": [ "trainingData" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | gitModelVersion | GitModelVersion | false | | Contains git related attributes that are associated with a custom model version. | | holdoutData | DeprecatedHoldoutDataAssignment | false | | Holdout data configuration. | | trainingData | DeprecatedTrainingDataForVersionsAssignment | true | | Training data configuration. | ## DeprecatedCustomModelWorkspaceItemResponse ``` { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] }, "storagePath": { "description": "Storage path of the file item.", "type": "string", "x-versiondeprecated": "2.25" }, "workspaceId": { "description": "The workspace ID of the file item.", "type": "string", "x-versiondeprecated": "2.25" } }, "required": [ "created", "fileName", "filePath", "fileSource", "id", "storagePath", "workspaceId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commitSha | string,null | false | | SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories). | | created | string | true | | ISO-8601 timestamp of when the file item was created. | | fileName | string | true | | The name of the file item. | | filePath | string | true | | The path of the file item. | | fileSource | string | true | | The source of the file item. | | id | string | true | | ID of the file item. | | ref | string,null | false | | Remote reference (branch, commit, tag). Branch "master", if not specified. | | repositoryFilePath | string,null | false | | Full path to the file in the remote repository. | | repositoryLocation | string,null | false | | URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name). | | repositoryName | string,null | false | | Name of the repository from which the file was pulled. | | storagePath | string | true | | Storage path of the file item. | | workspaceId | string | true | | The workspace ID of the file item. | ## DeprecatedHoldoutDataAssignment ``` { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ], "x-versiondeprecated": "v2.33" } }, "type": "object" } ``` Holdout data configuration. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string,null | false | | The ID of the dataset. | | datasetName | string,null | false | maxLength: 255 | A user-friendly name for the dataset. | | datasetVersionId | string,null | false | | The ID of the dataset version. | | partitionColumn | string,null | false | maxLength: 500 | The name of the column containing the partition assignments. | ## DeprecatedTrainingDataForModelsAssignment ``` { "properties": { "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string,null | false | | The ID of the dataset. | | datasetName | string,null | false | maxLength: 255 | A user-friendly name for the dataset. | | datasetVersionId | string,null | false | | The ID of the dataset version. | | partitionColumn | string,null | false | maxLength: 500 | The name of the column containing the partition assignments. | ## DeprecatedTrainingDataForVersionsAssignment ``` { "description": "Training data configuration.", "properties": { "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean", "x-versiondeprecated": "v2.33" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ], "x-versiondeprecated": "v2.34" } }, "type": "object" } ``` Training data configuration. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | assignmentInProgress | boolean | false | | Indicates if training data is currently being assigned to the custom model. Only for structured models. | | datasetId | string,null | false | | The ID of the dataset. | | datasetName | string,null | false | maxLength: 255 | A user-friendly name for the dataset. | | datasetVersionId | string,null | false | | The ID of the dataset version. | ## ExecutionEnvironmentCreate ``` { "properties": { "description": { "default": "", "description": "The description of the environment to be created.", "maxLength": 10000, "type": "string" }, "environmentId": { "description": "The ID the new environment should use. Only admins can create environments with pre-defined IDs", "type": "string" }, "isPublic": { "description": "True/False public access for the environment. Public environments can be used by all users without sharing. Public access to environments can only be added by an admin", "type": "boolean" }, "name": { "description": "The name of the environment to be created.", "maxLength": 255, "type": "string" }, "programmingLanguage": { "default": "other", "description": "The programming language of the environment to be created.", "enum": [ "python", "r", "java", "julia", "legacy", "other" ], "type": "string" }, "useCases": { "description": "The list of use cases supported by the environment", "items": { "enum": [ "customModel", "notebook", "gpu", "customApplication", "sparkApplication", "customJob" ], "type": "string" }, "maxItems": 6, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 10000 | The description of the environment to be created. | | environmentId | string | false | | The ID the new environment should use. Only admins can create environments with pre-defined IDs | | isPublic | boolean | false | | True/False public access for the environment. Public environments can be used by all users without sharing. Public access to environments can only be added by an admin | | name | string | true | maxLength: 255 | The name of the environment to be created. | | programmingLanguage | string | false | | The programming language of the environment to be created. | | useCases | [string] | false | maxItems: 6 | The list of use cases supported by the environment | ### Enumerated Values | Property | Value | | --- | --- | | programmingLanguage | [python, r, java, julia, legacy, other] | ## ExecutionEnvironmentCreateResponse ``` { "properties": { "id": { "description": "The ID of the newly created environment.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the newly created environment. | ## ExecutionEnvironmentListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of execution environments.", "items": { "properties": { "created": { "description": "ISO-8601 environment creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "deploymentsCount": { "description": "Number of deployments in environment.", "type": "integer" }, "description": { "description": "The description of the environment.", "type": "string" }, "id": { "description": "The ID of the environment.", "type": "string" }, "isPublic": { "description": "If the environment is public.", "type": "boolean", "x-versionadded": "v2.23" }, "latestSuccessfulVersion": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "latestVersion": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the environment.", "type": "string" }, "programmingLanguage": { "description": "The programming language of the environment to be created.", "enum": [ "python", "r", "java", "julia", "legacy", "other" ], "type": "string" }, "useCases": { "description": "The list of use cases supported by the environment", "items": { "enum": [ "customModel", "notebook", "gpu", "customApplication", "sparkApplication", "customJob" ], "type": "string" }, "maxItems": 6, "type": "array", "x-versionadded": "v2.36" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "created", "description", "id", "isPublic", "latestSuccessfulVersion", "latestVersion", "name", "programmingLanguage", "username" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ExecutionEnvironmentResponse] | true | maxItems: 1000 | The list of execution environments. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ExecutionEnvironmentPermadelete ``` { "properties": { "executionEnvironmentIds": { "description": "List of custom environments to be permanently deleted.", "items": { "description": "An ID for a custom environment to be permanently deleted.", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "executionEnvironmentIds" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionEnvironmentIds | [string] | true | maxItems: 10minItems: 1 | List of custom environments to be permanently deleted. | ## ExecutionEnvironmentResponse ``` { "properties": { "created": { "description": "ISO-8601 environment creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "deploymentsCount": { "description": "Number of deployments in environment.", "type": "integer" }, "description": { "description": "The description of the environment.", "type": "string" }, "id": { "description": "The ID of the environment.", "type": "string" }, "isPublic": { "description": "If the environment is public.", "type": "boolean", "x-versionadded": "v2.23" }, "latestSuccessfulVersion": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "latestVersion": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the environment.", "type": "string" }, "programmingLanguage": { "description": "The programming language of the environment to be created.", "enum": [ "python", "r", "java", "julia", "legacy", "other" ], "type": "string" }, "useCases": { "description": "The list of use cases supported by the environment", "items": { "enum": [ "customModel", "notebook", "gpu", "customApplication", "sparkApplication", "customJob" ], "type": "string" }, "maxItems": 6, "type": "array", "x-versionadded": "v2.36" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "created", "description", "id", "isPublic", "latestSuccessfulVersion", "latestVersion", "name", "programmingLanguage", "username" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string | true | | ISO-8601 environment creation timestamp. Example: 2016-12-13T11:12:13.141516Z. | | deploymentsCount | integer | false | | Number of deployments in environment. | | description | string | true | | The description of the environment. | | id | string | true | | The ID of the environment. | | isPublic | boolean | true | | If the environment is public. | | latestSuccessfulVersion | ExecutionEnvironmentVersionResponse | true | | Latest version build for this environment. | | latestVersion | ExecutionEnvironmentVersionResponse | true | | Latest version build for this environment. | | name | string | true | | The name of the environment. | | programmingLanguage | string | true | | The programming language of the environment to be created. | | useCases | [string] | false | maxItems: 6 | The list of use cases supported by the environment | | username | string | true | | The username of the user. | ### Enumerated Values | Property | Value | | --- | --- | | programmingLanguage | [python, r, java, julia, legacy, other] | ## ExecutionEnvironmentShortResponse ``` { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` Execution environment associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the execution environment. | | name | string | true | | User-friendly name of the execution environment. | ## ExecutionEnvironmentUpdate ``` { "properties": { "description": { "description": "The new description of the environment.", "maxLength": 10000, "type": "string" }, "isPublic": { "description": "If the environment is public.", "type": "boolean", "x-versionadded": "v2.39" }, "name": { "description": "New execution environment name.", "maxLength": 255, "type": "string" }, "programmingLanguage": { "description": "The new programming language of the environment.", "enum": [ "python", "r", "java", "julia", "legacy", "other" ], "type": "string" }, "useCases": { "description": "The list of use cases supported by the environment", "items": { "enum": [ "customModel", "notebook", "gpu", "customApplication", "sparkApplication", "customJob" ], "type": "string" }, "maxItems": 6, "type": "array", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 10000 | The new description of the environment. | | isPublic | boolean | false | | If the environment is public. | | name | string | false | maxLength: 255 | New execution environment name. | | programmingLanguage | string | false | | The new programming language of the environment. | | useCases | [string] | false | maxItems: 6 | The list of use cases supported by the environment | ### Enumerated Values | Property | Value | | --- | --- | | programmingLanguage | [python, r, java, julia, legacy, other] | ## ExecutionEnvironmentVersionBuildLogResponse ``` { "properties": { "error": { "description": "The message specifying why the build failed. Empty if the build finished successfully.", "type": "string" }, "log": { "description": "The full console output of the execution environment build. Logs are empty unless version build is finished.", "type": "string" } }, "required": [ "error", "log" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | error | string | true | | The message specifying why the build failed. Empty if the build finished successfully. | | log | string | true | | The full console output of the execution environment build. Logs are empty unless version build is finished. | ## ExecutionEnvironmentVersionCreate ``` { "properties": { "contextUrl": { "default": "", "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.37" }, "description": { "default": "", "description": "The description of the environment version.", "maxLength": 10000, "type": "string" }, "dockerContext": { "description": "Context tar.gz or zip file. The Docker Context file should be a tar.gz or zip file containing at minimum a single top-level file named \"Dockerfile\". This file should be a Docker-compatible Dockerfile using syntax as defined in the `official Docker documentation `_. The archive may contain additional files as well. These files may be added into the final container using `ADD `_ and/or `COPY `_ directives in the Dockerfile.", "format": "binary", "type": "string" }, "dockerImage": { "description": "The pre-built Docker image saved as a .tar archive. If not supplied, the environment will be built from the supplied dockerContext.", "format": "binary", "type": "string" }, "dockerImageUri": { "description": "The URI of the Docker image that is used to build the environment version. Parameter dockerContext may also be provided to upload context, but the image URI is used for the build.", "type": "string", "x-versionadded": "v2.37" }, "environmentVersionId": { "description": "The ID the new environment version should use. Only admins can create environment versions with pre-defined IDs", "type": "string" }, "label": { "default": "", "description": "Human readable version indicator.", "maxLength": 50, "type": "string" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | contextUrl | string | false | maxLength: 1000 | Docker context URL of the environment version. It is not intended to be used for automatic processing. | | description | string | false | maxLength: 10000 | The description of the environment version. | | dockerContext | string(binary) | false | | Context tar.gz or zip file. The Docker Context file should be a tar.gz or zip file containing at minimum a single top-level file named "Dockerfile". This file should be a Docker-compatible Dockerfile using syntax as defined in the official Docker documentation . The archive may contain additional files as well. These files may be added into the final container using ADD and/or COPY _ directives in the Dockerfile. | | dockerImage | string(binary) | false | | The pre-built Docker image saved as a .tar archive. If not supplied, the environment will be built from the supplied dockerContext. | | dockerImageUri | string | false | | The URI of the Docker image that is used to build the environment version. Parameter dockerContext may also be provided to upload context, but the image URI is used for the build. | | environmentVersionId | string | false | | The ID the new environment version should use. Only admins can create environment versions with pre-defined IDs | | label | string | false | maxLength: 50 | Human readable version indicator. | ## ExecutionEnvironmentVersionListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of execution environment versions.", "items": { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ExecutionEnvironmentVersionResponse] | true | maxItems: 1000 | The list of execution environment versions. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ExecutionEnvironmentVersionResponse ``` { "description": "Latest version build for this environment.", "properties": { "buildId": { "description": "The environment version image build ID.", "type": [ "string", "null" ] }, "buildStatus": { "description": "The status of the build.", "enum": [ "submitted", "processing", "failed", "success", "aborted" ], "type": "string" }, "contextUrl": { "description": "Docker context URL of the environment version. It is not intended to be used for automatic processing.", "type": "string", "x-versionadded": "v2.37" }, "created": { "description": "ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z.", "type": "string" }, "description": { "description": "The description of the environment version.", "type": "string" }, "dockerContext": { "description": "A URL that can be used to download the Docker context of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerContextSize": { "description": "The size of the uploaded Docker context in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "dockerImage": { "description": "A URL that can be used to download the built Docker image as a tarball of the environment version, if available or `null`", "type": [ "string", "null" ] }, "dockerImageSize": { "description": "The size of the built Docker image in bytes if available, or `null` if not.", "type": [ "integer", "null" ] }, "environmentId": { "description": "The ID of the environment.", "type": "string" }, "id": { "description": "The ID of the environment version.", "type": "string" }, "imageId": { "description": "The Docker image ID of the environment version.", "type": "string" }, "label": { "description": "Human readable version indicator.", "type": "string" }, "sourceDockerImageUri": { "description": "The image URI that was used as a base to build the environment.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "buildStatus", "contextUrl", "created", "description", "dockerContext", "dockerContextSize", "dockerImage", "dockerImageSize", "environmentId", "id", "imageId", "label" ], "type": "object", "x-versionadded": "v2.36" } ``` Latest version build for this environment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildId | string,null | false | | The environment version image build ID. | | buildStatus | string | true | | The status of the build. | | contextUrl | string | true | | Docker context URL of the environment version. It is not intended to be used for automatic processing. | | created | string | true | | ISO-8601 environment version creation timestamp. Example: 2016-12-13T11:12:13.141516Z. | | description | string | true | | The description of the environment version. | | dockerContext | string,null | true | | A URL that can be used to download the Docker context of the environment version, if available or null | | dockerContextSize | integer,null | true | | The size of the uploaded Docker context in bytes if available, or null if not. | | dockerImage | string,null | true | | A URL that can be used to download the built Docker image as a tarball of the environment version, if available or null | | dockerImageSize | integer,null | true | | The size of the built Docker image in bytes if available, or null if not. | | environmentId | string | true | | The ID of the environment. | | id | string | true | | The ID of the environment version. | | imageId | string | true | | The Docker image ID of the environment version. | | label | string | true | | Human readable version indicator. | | sourceDockerImageUri | string,null | false | | The image URI that was used as a base to build the environment. | ### Enumerated Values | Property | Value | | --- | --- | | buildStatus | [submitted, processing, failed, success, aborted] | ## ExecutionEnvironmentVersionShortResponse ``` { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" } ``` Execution environment version associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the execution environment version. | | label | string | true | | User-friendly name of the execution environment version. | ## FeatureImpactCreatePayload ``` { "properties": { "rowCount": { "description": "The sample size to use for Feature Impact computation. It is possible to re-compute Feature Impact with a different row count.", "maximum": 100000, "minimum": 10, "type": "integer", "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rowCount | integer | false | maximum: 100000minimum: 10 | The sample size to use for Feature Impact computation. It is possible to re-compute Feature Impact with a different row count. | ## FeatureImpactCreateResponse ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] for tracking job status.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] for tracking job status. | ## FeatureImpactItem ``` { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | true | | The name of the feature. | | impactNormalized | number | true | maximum: 1 | The same as impactUnnormalized, but normalized such that the highest value is 1. | | impactUnnormalized | number | true | | How much worse the error metric score is when making predictions on modified data. | | parentFeatureName | string,null | false | | The name of the parent feature. | | redundantWith | string,null | true | | Name of feature that has the highest correlation with this feature. | ## FeatureImpactResponse ``` { "properties": { "count": { "description": "Number of feature impact records in a given batch.", "type": "integer" }, "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL for the next page of results, or null if there is no next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL for the previous page of results, or null if there is no previous page.", "format": "uri", "type": [ "string", "null" ] }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact.", "type": "boolean" }, "rowCount": { "description": "The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the ``rowCount``, we return ``null`` here.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "shapBased": { "description": "Indicates whether feature impact was calculated using Shapley values. True for anomaly detection models when the project is unsupervised, as permutation approach is not applicable. Note that supervised projects must use an alternative route for SHAP impact: /api/v2/projects/(projectId)/models/(modelId)/shapImpact/", "type": "boolean", "x-versionadded": "v2.18" } }, "required": [ "count", "featureImpacts", "next", "previous", "ranRedundancyDetection", "rowCount", "shapBased" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Number of feature impact records in a given batch. | | featureImpacts | [FeatureImpactItem] | true | maxItems: 1000 | A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned. | | next | string,null(uri) | true | | The URL for the next page of results, or null if there is no next page. | | previous | string,null(uri) | true | | The URL for the previous page of results, or null if there is no previous page. | | ranRedundancyDetection | boolean | true | | Indicates whether redundant feature identification was run while calculating this feature impact. | | rowCount | integer,null | true | | The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the rowCount, we return null here. | | shapBased | boolean | true | | Indicates whether feature impact was calculated using Shapley values. True for anomaly detection models when the project is unsupervised, as permutation approach is not applicable. Note that supervised projects must use an alternative route for SHAP impact: /api/v2/projects/(projectId)/models/(modelId)/shapImpact/ | ## GeneratedMetadata ``` { "description": "Custom model conversion output metadata.", "properties": { "outputColumns": { "description": "A list of column lists that are associated with the output datasets from the custom model conversion process.", "items": { "description": "A columns list belong to a single dataset output from a custom model conversion process", "items": { "description": "A column name belong to a single dataset output from a custom model conversion process", "maxLength": 1024, "minLength": 1, "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" }, "maxItems": 50, "type": "array" }, "outputDatasets": { "description": "A list of output datasets from the custom model conversion process.", "items": { "description": "An output dataset name from the custom model conversion process", "type": "string" }, "maxItems": 50, "minItems": 1, "type": "array" } }, "required": [ "outputColumns", "outputDatasets" ], "type": "object" } ``` Custom model conversion output metadata. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | outputColumns | [array] | true | maxItems: 50 | A list of column lists that are associated with the output datasets from the custom model conversion process. | | outputDatasets | [string] | true | maxItems: 50minItems: 1 | A list of output datasets from the custom model conversion process. | ## GitModelVersion ``` { "description": "Contains git related attributes that are associated with a custom model version.", "properties": { "commitUrl": { "description": "A URL to the commit page in GitHub repository.", "format": "uri", "type": "string" }, "mainBranchCommitSha": { "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": "string" }, "pullRequestCommitSha": { "default": null, "description": "Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version.", "maxLength": 40, "minLength": 40, "type": [ "string", "null" ] }, "refName": { "description": "The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch.", "type": "string" } }, "required": [ "commitUrl", "mainBranchCommitSha", "pullRequestCommitSha", "refName" ], "type": "object" } ``` Contains git related attributes that are associated with a custom model version. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commitUrl | string(uri) | true | | A URL to the commit page in GitHub repository. | | mainBranchCommitSha | string | true | maxLength: 40minLength: 40minLength: 40 | Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version. | | pullRequestCommitSha | string,null | true | maxLength: 40minLength: 40minLength: 40 | Specifies the commit SHA-1 in GitHub repository from the main branch that corresponds to a given custom model version. | | refName | string | true | | The branch or tag name that triggered the workflow run. For workflows triggered by push, this is the branch or tag ref that was pushed. For workflows triggered by pull_request, this is the pull request merge branch. | ## HoldoutDataResponse ``` { "description": "Holdout data configuration.", "properties": { "datasetId": { "description": "The ID of the dataset. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version. Should be provided only for unstructured models. ", "type": [ "string", "null" ] }, "partitionColumn": { "description": "The name of the column containing the partition assignments.", "maxLength": 500, "type": [ "string", "null" ] } }, "type": "object" } ``` Holdout data configuration. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string,null | false | | The ID of the dataset. Should be provided only for unstructured models. | | datasetName | string,null | false | maxLength: 255 | A user-friendly name for the dataset. | | datasetVersionId | string,null | false | | The ID of the dataset version. Should be provided only for unstructured models. | | partitionColumn | string,null | false | maxLength: 500 | The name of the column containing the partition assignments. | ## RequiredMetadataValue ``` { "properties": { "fieldName": { "description": "The required field name. This value will be added as an environment variable when running custom models.", "type": "string" }, "value": { "description": "The value for the given field.", "maxLength": 100, "type": "string" } }, "required": [ "fieldName", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fieldName | string | true | | The required field name. This value will be added as an environment variable when running custom models. | | value | string | true | maxLength: 100 | The value for the given field. | ## SharingListResponse ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControl] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | ## SharingUpdateOrRemoveWithGrant ``` { "properties": { "data": { "description": "List of sharing roles to update.", "items": { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [UserRoleWithGrant] | true | maxItems: 100 | List of sharing roles to update. | ## Template ``` { "description": "If not null, the template used to create the custom model.", "properties": { "modelType": { "description": "The type of template the model was created from.", "enum": [ "nimModel", "invalid" ], "type": "string", "x-versionadded": "v2.36" }, "templateId": { "description": "The ID of the template used to create this custom model.", "type": "string", "x-versionadded": "v2.36" } }, "required": [ "modelType", "templateId" ], "type": "object", "x-versionadded": "v2.36" } ``` If not null, the template used to create the custom model. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelType | string | true | | The type of template the model was created from. | | templateId | string | true | | The ID of the template used to create this custom model. | ### Enumerated Values | Property | Value | | --- | --- | | modelType | [nimModel, invalid] | ## TestingStatus ``` { "description": "Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted.", "properties": { "message": { "description": "Test message.", "type": "string" }, "status": { "description": "Test status.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } ``` Ensures that the custom model image can build and launch. If it cannot, the test is marked as Failed and subsequent test are aborted. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | Test message. | | status | string | true | | Test status. | ### Enumerated Values | Property | Value | | --- | --- | | status | [not_tested, queued, failed, canceled, succeeded, in_progress, aborted, warning, skipped] | ## TrainingDataAssignmentErrorResponse ``` { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" } ``` Training data configuration. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string,null | false | maxLength: 10000 | Training data assignment error message | ## TrainingDataResponse ``` { "description": "Training data configuration.", "properties": { "assignmentError": { "description": "Training data configuration.", "properties": { "message": { "description": "Training data assignment error message", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "assignmentInProgress": { "default": false, "description": "Indicates if training data is currently being assigned to the custom model. Only for structured models.", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": [ "string", "null" ] }, "datasetName": { "description": "A user-friendly name for the dataset.", "maxLength": 255, "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": [ "string", "null" ] } }, "type": "object" } ``` Training data configuration. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | assignmentError | TrainingDataAssignmentErrorResponse | false | | Training data configuration. | | assignmentInProgress | boolean | false | | Indicates if training data is currently being assigned to the custom model. Only for structured models. | | datasetId | string,null | false | | The ID of the dataset. | | datasetName | string,null | false | maxLength: 255 | A user-friendly name for the dataset. | | datasetVersionId | string,null | false | | The ID of the dataset version. | ## UserRoleWithGrant ``` { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If role is NO_ROLE canShare is ignored. | | role | string,null | true | | The role to set on the entity. When it is None, the role of this user will be removedfrom this entity. | | username | string | true | | The username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | --- # Data connectivity URL: https://docs.datarobot.com/en/docs/api/reference/public-api/data_connectivity.html > This page outlines how to integrate with a variety of enterprise databases, using a 'self-service' JDBC platform. # Data connectivity This page outlines how to integrate with a variety of enterprise databases, using a 'self-service' JDBC platform. ## List all ACL management connections Operation path: `GET /api/v2/aclConnections/` Authentication requirements: `BearerAuth` A list of all ACL management connections for the user organization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.43" }, "data": { "description": "The list of ACL management connections for the user organization.", "items": { "properties": { "adminEmail": { "default": null, "description": "The user to impersonate for the admin-level operations. Currently only applicable to Google Drive.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ACL management connection ID.", "type": "string" }, "domain": { "default": null, "description": "The custom domain for the connection. Currently only applicable to SharePoint.", "type": [ "string", "null" ] }, "externalConnectorType": { "description": "The data provider being configured.", "enum": [ "gdrive", "sharepoint" ], "type": "string" } }, "required": [ "dataStoreId", "externalConnectorType" ], "type": "object", "x-versionadded": "v2.43" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.43" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ACLAdminConnectionList | ## Define the connection as ACL management Operation path: `PUT /api/v2/aclConnections/` Authentication requirements: `BearerAuth` Create or update the ACL management connection. Default credentials must be defined for the data store in advance. ### Body parameter ``` { "properties": { "adminEmail": { "default": null, "description": "The user to impersonate for the admin-level operations. Currently only applicable to Google Drive.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ACL management connection ID.", "type": "string" }, "domain": { "default": null, "description": "The custom domain for the connection. Currently only applicable to SharePoint.", "type": [ "string", "null" ] } }, "required": [ "dataStoreId" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ACLAdminConnectionBase | false | none | ### Example responses > 200 Response ``` { "properties": { "adminEmail": { "default": null, "description": "The user to impersonate for the admin-level operations. Currently only applicable to Google Drive.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ACL management connection ID.", "type": "string" }, "domain": { "default": null, "description": "The custom domain for the connection. Currently only applicable to SharePoint.", "type": [ "string", "null" ] }, "externalConnectorType": { "description": "The data provider being configured.", "enum": [ "gdrive", "sharepoint" ], "type": "string" } }, "required": [ "dataStoreId", "externalConnectorType" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ACLAdminConnectionResponse | ## Deactivate ACL management by data store ID Operation path: `DELETE /api/v2/aclConnections/{dataStoreId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ACL management connection ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | ## Create a data stage Operation path: `POST /api/v2/dataStages/` Authentication requirements: `BearerAuth` Create a data stage. ### Body parameter ``` { "properties": { "filename": { "description": "The filename associated with the stage.", "type": "string" } }, "required": [ "filename" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateDataStageRequest | false | none | ### Example responses > 201 Response ``` { "properties": { "id": { "description": "The ID of the data stage object.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Created data stage | CreateDataStageResponse | | 403 | Forbidden | Permission denied. | None | | 422 | Unprocessable Entity | The request cannot be processed. | None | ## Finalize a data stage by data stage ID Operation path: `POST /api/v2/dataStages/{dataStageId}/finalize/` Authentication requirements: `BearerAuth` Finalize a data stage. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStageId | path | string | true | The ID of the data stage. | ### Example responses > 200 Response ``` { "properties": { "parts": { "description": "The size of the part.", "items": { "properties": { "checksum": { "description": "The checksum of the part.", "type": "string" }, "number": { "description": "The number of the part.", "type": "integer" }, "size": { "description": "The size of the part.", "type": "integer" } }, "required": [ "checksum", "number", "size" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "parts" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Finalize data stage | DataStageFinalizeResponse | | 403 | Forbidden | Permission denied. | None | | 404 | Not Found | The entity is not found. | None | | 406 | Not Acceptable | The request is not acceptable. | None | | 409 | Conflict | The request conflicts with the server state. | None | | 410 | Gone | The requested entity has been deleted. | None | | 422 | Unprocessable Entity | The request cannot be processed. | None | ## Upload a part by data stage ID Operation path: `PUT /api/v2/dataStages/{dataStageId}/parts/{partNumber}/` Authentication requirements: `BearerAuth` Upload a part to the data stage. ### Body parameter ``` { "properties": { "file": { "description": "The part file to upload to the data stage.", "format": "binary", "type": "string" } }, "required": [ "file" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStageId | path | string | true | The ID of the data stage. | | partNumber | path | integer | true | The part number associated with the part. | | body | body | PartUploadRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "checksum": { "description": "The checksum of the part.", "type": "string" }, "size": { "description": "The size of the part.", "type": "integer" } }, "required": [ "checksum", "size" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Upload part to a data stage | PartUploadResponse | | 403 | Forbidden | Permission denied. | None | | 404 | Not Found | The entity is not found. | None | | 409 | Conflict | The request conflicts with the server state. | None | | 410 | Gone | The requested entity has been deleted. | None | | 422 | Unprocessable Entity | The request cannot be processed. | None | ## Upload JDBC driver Operation path: `POST /api/v2/externalDataDriverFile/` Authentication requirements: `BearerAuth` Upload JDBC driver from file. Only Java archive (.jar) files are supported. ### Body parameter ``` { "properties": { "file": { "description": "JDBC driver JAR file to upload.", "format": "binary", "type": "string" } }, "required": [ "file" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DriverUploadRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "localUrl": { "description": "URL of uploaded driver file.", "type": "string" } }, "required": [ "localUrl" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | DriverUploadResponse | | 413 | Payload Too Large | JDBC driver JAR file size exceeds the configured limit. | None | ## List drivers Operation path: `GET /api/v2/externalDataDrivers/` Authentication requirements: `BearerAuth` Fetch all drivers a user has access to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | type | query | string | true | Driver type. Either 'jdbc' or 'dr-database-v1'. | ### Enumerated Values | Parameter | Value | | --- | --- | | type | [all, dr-connector-v1, dr-database-v1, jdbc] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of drivers.", "items": { "properties": { "associatedAuthTypes": { "description": "Authentication types associated with this connector configuration.", "items": { "enum": [ "basic", "googleOauthUserAccount", "snowflakeKeyPairUserAccount", "snowflakeOauthUserAccount" ], "type": "string" }, "type": "array" }, "baseNames": { "description": "Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls'", "items": { "description": "Original file name of the uploaded JAR file.", "type": "string" }, "type": "array" }, "canonicalName": { "description": "User-friendly driver name.", "type": "string" }, "className": { "description": "Driver class name. For example 'com.amazon.redshift.jdbc.Driver'", "type": [ "string", "null" ] }, "configurationId": { "description": "Driver configuration ID if it was provided during driver upload.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "creator": { "description": "The ID of the user who uploaded the driver.", "type": "string" }, "databaseDriver": { "description": "The type of database of the driver. Only required of 'dr-database-v1' drivers.", "enum": [ "bigquery-v1", "databricks-v1", "datasphere-v1", "trino-v1" ], "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "id": { "description": "Driver ID.", "type": "string" }, "type": { "default": "jdbc", "description": "Driver type. Either 'jdbc' or 'dr-database-v1'.", "enum": [ "dr-database-v1", "jdbc" ], "type": "string", "x-versionadded": "v2.30" }, "version": { "description": "Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload.", "type": "string", "x-versionadded": "v2.18" } }, "required": [ "creator", "id" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Drivers accessible by the user. | DriverListResponse | ## Create a new JDBC driver Operation path: `POST /api/v2/externalDataDrivers/` Authentication requirements: `BearerAuth` Create a new JDBC driver. To create connections from fields, rather than supplying a JDBC URL, use 'configurationId'. When using 'configurationId', do not include 'canonicalName' or 'className' as they are part of the driver configuration. Specifying a 'version' is required as it is used in the construction of the canonicalName. ### Body parameter ``` { "properties": { "baseNames": { "description": "Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls'", "items": { "description": "Original file name of the uploaded JAR file.", "type": "string" }, "type": "array" }, "canonicalName": { "description": "User-friendly driver name.", "type": "string" }, "className": { "description": "Driver class name. For example 'com.amazon.redshift.jdbc.Driver'", "type": [ "string", "null" ] }, "configurationId": { "description": "Driver configuration ID if it was provided during driver upload.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "databaseDriver": { "description": "The type of database of the driver. Only required of 'dr-database-v1' drivers.", "enum": [ "bigquery-v1", "databricks-v1", "datasphere-v1", "trino-v1" ], "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "localJarUrls": { "description": "File path(s) for the driver files.This path is returned in the response as 'local_url' by the driverUpload route.If there are multiple JAR files required for the driver, each uploaded JAR must be present in this list. If specified, values will replace any previous settings.", "items": { "description": "File path for the driver file. This path is returned by the driverUpload route in the 'local_url' response.", "type": "string" }, "minItems": 1, "type": "array" }, "type": { "default": "jdbc", "description": "Driver type. Either 'jdbc' or 'dr-database-v1'.", "enum": [ "dr-database-v1", "jdbc" ], "type": "string", "x-versionadded": "v2.30" }, "version": { "description": "Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload.", "type": "string", "x-versionadded": "v2.18" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateDriverRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "associatedAuthTypes": { "description": "Authentication types associated with this connector configuration.", "items": { "enum": [ "basic", "googleOauthUserAccount", "snowflakeKeyPairUserAccount", "snowflakeOauthUserAccount" ], "type": "string" }, "type": "array" }, "baseNames": { "description": "Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls'", "items": { "description": "Original file name of the uploaded JAR file.", "type": "string" }, "type": "array" }, "canonicalName": { "description": "User-friendly driver name.", "type": "string" }, "className": { "description": "Driver class name. For example 'com.amazon.redshift.jdbc.Driver'", "type": [ "string", "null" ] }, "configurationId": { "description": "Driver configuration ID if it was provided during driver upload.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "creator": { "description": "The ID of the user who uploaded the driver.", "type": "string" }, "databaseDriver": { "description": "The type of database of the driver. Only required of 'dr-database-v1' drivers.", "enum": [ "bigquery-v1", "databricks-v1", "datasphere-v1", "trino-v1" ], "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "id": { "description": "Driver ID.", "type": "string" }, "type": { "default": "jdbc", "description": "Driver type. Either 'jdbc' or 'dr-database-v1'.", "enum": [ "dr-database-v1", "jdbc" ], "type": "string", "x-versionadded": "v2.30" }, "version": { "description": "Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload.", "type": "string", "x-versionadded": "v2.18" } }, "required": [ "creator", "id" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Details about new driver entity. | DriverResponse | ## Delete the driver by driver ID Operation path: `DELETE /api/v2/externalDataDrivers/{driverId}/` Authentication requirements: `BearerAuth` Delete the driver with given ID if it is not used by any data store or data source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | driverId | path | string | true | Driver ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Driver deleted successfully. | None | | 409 | Conflict | Driver is in use by one or more data store or data source | None | ## Retrieve driver details by driver ID Operation path: `GET /api/v2/externalDataDrivers/{driverId}/` Authentication requirements: `BearerAuth` Retrieve driver details by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | driverId | path | string | true | Driver ID. | ### Example responses > 200 Response ``` { "properties": { "associatedAuthTypes": { "description": "Authentication types associated with this connector configuration.", "items": { "enum": [ "basic", "googleOauthUserAccount", "snowflakeKeyPairUserAccount", "snowflakeOauthUserAccount" ], "type": "string" }, "type": "array" }, "baseNames": { "description": "Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls'", "items": { "description": "Original file name of the uploaded JAR file.", "type": "string" }, "type": "array" }, "canonicalName": { "description": "User-friendly driver name.", "type": "string" }, "className": { "description": "Driver class name. For example 'com.amazon.redshift.jdbc.Driver'", "type": [ "string", "null" ] }, "configurationId": { "description": "Driver configuration ID if it was provided during driver upload.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "creator": { "description": "The ID of the user who uploaded the driver.", "type": "string" }, "databaseDriver": { "description": "The type of database of the driver. Only required of 'dr-database-v1' drivers.", "enum": [ "bigquery-v1", "databricks-v1", "datasphere-v1", "trino-v1" ], "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "id": { "description": "Driver ID.", "type": "string" }, "type": { "default": "jdbc", "description": "Driver type. Either 'jdbc' or 'dr-database-v1'.", "enum": [ "dr-database-v1", "jdbc" ], "type": "string", "x-versionadded": "v2.30" }, "version": { "description": "Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload.", "type": "string", "x-versionadded": "v2.18" } }, "required": [ "creator", "id" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Driver details retrieved successfully. | DriverResponse | ## Update properties of an existing JDBC Driver by driver ID Operation path: `PATCH /api/v2/externalDataDrivers/{driverId}/` Authentication requirements: `BearerAuth` Update properties of an existing JDBC driver. To change the canonicalName and className, you must first remove the driver configuration, if it exists, as its properties will otherwise override name changes. ### Body parameter ``` { "properties": { "baseNames": { "description": "Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls'", "items": { "description": "Original file name of the uploaded JAR file.", "type": "string" }, "type": "array" }, "canonicalName": { "description": "User-friendly driver name.", "type": "string" }, "className": { "description": "Driver class name. For example 'com.amazon.redshift.jdbc.Driver'", "type": [ "string", "null" ] }, "configurationId": { "description": "Driver configuration ID if it was provided during driver upload.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "localJarUrls": { "description": "File path(s) for the driver files.This path is returned in the response as 'local_url' by the driverUpload route.If there are multiple JAR files required for the driver, each uploaded JAR must be present in this list. If specified, values will replace any previous settings.", "items": { "description": "File path for the driver file. This path is returned by the driverUpload route in the 'local_url' response.", "type": "string" }, "minItems": 1, "type": "array" }, "removeConfig": { "description": "Pass `True` to remove configuration currently associated with this driver. Note: must pass `canonicalName` and `className` if removing the configuration.", "type": "boolean", "x-versionadded": "v2.18" }, "skipConfigVerification": { "description": "Pass `True` to skip `jdbc_url` verification.", "type": "boolean", "x-versionadded": "v2.18" }, "version": { "description": "Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload.", "type": "string", "x-versionadded": "v2.18" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | driverId | path | string | true | Driver ID. | | body | body | UpdateDriverRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "associatedAuthTypes": { "description": "Authentication types associated with this connector configuration.", "items": { "enum": [ "basic", "googleOauthUserAccount", "snowflakeKeyPairUserAccount", "snowflakeOauthUserAccount" ], "type": "string" }, "type": "array" }, "baseNames": { "description": "Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls'", "items": { "description": "Original file name of the uploaded JAR file.", "type": "string" }, "type": "array" }, "canonicalName": { "description": "User-friendly driver name.", "type": "string" }, "className": { "description": "Driver class name. For example 'com.amazon.redshift.jdbc.Driver'", "type": [ "string", "null" ] }, "configurationId": { "description": "Driver configuration ID if it was provided during driver upload.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "creator": { "description": "The ID of the user who uploaded the driver.", "type": "string" }, "databaseDriver": { "description": "The type of database of the driver. Only required of 'dr-database-v1' drivers.", "enum": [ "bigquery-v1", "databricks-v1", "datasphere-v1", "trino-v1" ], "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "id": { "description": "Driver ID.", "type": "string" }, "type": { "default": "jdbc", "description": "Driver type. Either 'jdbc' or 'dr-database-v1'.", "enum": [ "dr-database-v1", "jdbc" ], "type": "string", "x-versionadded": "v2.30" }, "version": { "description": "Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload.", "type": "string", "x-versionadded": "v2.18" } }, "required": [ "creator", "id" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Driver properties updated successfully. | DriverResponse | ## Driver configuration details by driver ID Operation path: `GET /api/v2/externalDataDrivers/{driverId}/configuration/` Authentication requirements: `BearerAuth` Retrieve the driver configuration with the specified ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | driverId | path | string | true | Driver ID. | ### Example responses > 200 Response ``` { "properties": { "associatedAuthTypes": { "description": "The list of authentication types supported by this driver configuration.", "items": { "type": "string" }, "type": "array" }, "className": { "description": "Java class name of the driver to be used (e.g., org.postgresql.Driver).", "type": "string" }, "creator": { "description": "The user ID of the creator of this configuration.", "type": "string" }, "id": { "description": "The driver configuration ID.", "type": "string" }, "jdbcFieldSchemas": { "description": "Description of which fields to show when creating a datastore, their defaults, and whether or not they are required.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "jdbcUrlPathDelimiter": { "description": "Separator of address from path in the JDBC URL (e.g., \"/\").", "type": "string" }, "jdbcUrlPrefix": { "description": "Prefix of the JDBC URL (e.g., \"jdbc:mssql://\" or \"jdbc:oracle:thin:@\").", "type": "string" }, "jdbcUrlQueryDelimiter": { "description": "Separator of path from the list of query parameters in the JDBC URL (e.g., \"?\").", "type": "string" }, "jdbcUrlQueryParamDelimiter": { "description": "Separator of each set of query parameter key-value pairs in the JDBC URL (e.g., \"&\").", "type": "string" }, "jdbcUrlQueryParamKeyValueDelimiter": { "description": "Separator of the key and value in a query parameter pair (e.g., \"=\").", "type": "string" }, "standardizedName": { "description": "The plain text name for the driver (e.g., PostgreSQL).", "type": "string" }, "statements": { "description": "List of supported statments for this driver configuration.", "items": { "enum": [ "insert", "update", "upsert" ], "type": "string" }, "type": "array" }, "updated": { "description": "ISO-8601 formatted time/date when this configuration was most recently updated.", "type": "string" } }, "required": [ "associatedAuthTypes", "className", "creator", "id", "jdbcFieldSchemas", "jdbcUrlPathDelimiter", "jdbcUrlPrefix", "jdbcUrlQueryDelimiter", "jdbcUrlQueryParamDelimiter", "jdbcUrlQueryParamKeyValueDelimiter", "standardizedName", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Driver configuration retrieved successfully. | DriverConfigurationRetrieveResponse | ## List data sources Operation path: `GET /api/v2/externalDataSources/` Authentication requirements: `BearerAuth` Return the detailed list of available data sources. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | type | query | string | false | The data source type to filter by. | ### Enumerated Values | Parameter | Value | | --- | --- | | type | [all, databases, dr-connector-v1, dr-database-v1, jdbc] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of data sources.", "items": { "properties": { "canDelete": { "description": "True if the user can delete the data source.", "type": "boolean", "x-versionadded": "v2.23" }, "canShare": { "description": "True if the user can share the data source.", "type": "boolean", "x-versionadded": "v2.22" }, "canonicalName": { "description": "The data source canonical name.", "type": "string" }, "creator": { "description": "The ID of the user who created the data source.", "type": "string" }, "driverClassType": { "description": "The type of driver used to create this data source.", "type": "string" }, "id": { "description": "The data source ID.", "type": "string" }, "params": { "description": "The data source configuration.", "oneOf": [ { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string" } }, "required": [ "dataStoreId", "table" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "required": [ "dataStoreId", "query" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "required": [ "dataStoreId", "path" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data source.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data source type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data source update.", "type": "string" } }, "required": [ "canDelete", "canShare", "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Data sources retrieved successfully. | DataSourceListResponse | ## Create a data source Operation path: `POST /api/v2/externalDataSources/` Authentication requirements: `BearerAuth` Create a fully configured source of data which could be used for datasets and projects creation. A data source specifies, via SQL query or selected table and schema data, which data to extract from the data connection (the location of data within a given endpoint) to use for modeling or predictions. A data source has one data connection and one connector but can have many datasets. To test the SQL query before creating the data source, use [POST /api/v2/externalDataStores/{dataStoreId}/verifySQL/][post-apiv2externaldatastoresdatastoreidverifysql]. ### Body parameter ``` { "properties": { "canonicalName": { "description": "The data source canonical name.", "type": "string" }, "params": { "description": "The data source configuration.", "oneOf": [ { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string" } }, "required": [ "dataStoreId", "table" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "required": [ "dataStoreId", "query" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "required": [ "dataStoreId", "path" ], "type": "object" } ] }, "type": { "description": "The data source type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" } }, "required": [ "canonicalName", "params", "type" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DataSourceCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "canonicalName": { "description": "The data source canonical name.", "type": "string" }, "creator": { "description": "The ID of the user who created the data source.", "type": "string" }, "driverClassType": { "description": "The type of driver used to create this data source.", "type": "string" }, "id": { "description": "The data source ID.", "type": "string" }, "params": { "description": "The data source configuration.", "oneOf": [ { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string" } }, "required": [ "dataStoreId", "table" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "required": [ "dataStoreId", "query" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "required": [ "dataStoreId", "path" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data source.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data source type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data source update.", "type": "string" } }, "required": [ "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Data source created successfully. | DataSourceRetrieveResponse | ## Delete the data source by data source ID Operation path: `DELETE /api/v2/externalDataSources/{dataSourceId}/` Authentication requirements: `BearerAuth` Delete the data source with given ID if it is not used by any dataset. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataSourceId | path | string | true | The ID of the Data Source. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Data source deleted successfully. | None | | 409 | Conflict | Data source is in use by one or more datasets | None | ## Data source details by data source ID Operation path: `GET /api/v2/externalDataSources/{dataSourceId}/` Authentication requirements: `BearerAuth` Return the details of the existing data source with the given ID, including SQL query or selected table and schema data, which fully describe which data to extract and from which location. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataSourceId | path | string | true | The ID of the Data Source. | ### Example responses > 200 Response ``` { "properties": { "canonicalName": { "description": "The data source canonical name.", "type": "string" }, "creator": { "description": "The ID of the user who created the data source.", "type": "string" }, "driverClassType": { "description": "The type of driver used to create this data source.", "type": "string" }, "id": { "description": "The data source ID.", "type": "string" }, "params": { "description": "The data source configuration.", "oneOf": [ { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string" } }, "required": [ "dataStoreId", "table" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "required": [ "dataStoreId", "query" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "required": [ "dataStoreId", "path" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data source.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data source type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data source update.", "type": "string" } }, "required": [ "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Data source details retrieved successfully. | DataSourceRetrieveResponse | ## Update the data source by data source ID Operation path: `PATCH /api/v2/externalDataSources/{dataSourceId}/` Authentication requirements: `BearerAuth` Update the data source with given ID. To test the SQL query before updating the data source,use [POST /api/v2/externalDataStores/{dataStoreId}/verifySQL/][post-apiv2externaldatastoresdatastoreidverifysql]. ### Body parameter ``` { "properties": { "canonicalName": { "description": "The data source's canonical name.", "type": "string" }, "params": { "description": "Data source configuration.", "oneOf": [ { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if supported.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database.", "maxLength": 256, "type": "string" } }, "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "type": "object" } ] } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataSourceId | path | string | true | The ID of the Data Source. | | body | body | DataSourceUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Data source updated successfully. | None | ## Get the data source's access control list by data source ID Operation path: `GET /api/v2/externalDataSources/{dataSourceId}/accessControl/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations who have access to this data source and their roles. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | username | query | string | false | Optional, only return the access control information for a user with this username. | | userId | query | string | false | Optional, only return the access control information for a user with this user ID. | | dataSourceId | path | string | true | The ID of the Data Source. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The data source's access control list. | SharingListResponse | | 404 | Not Found | Either the data source does not exist or the user does not have permissions to view the data source. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## Update the data source's access controls by data source ID Operation path: `PATCH /api/v2/externalDataSources/{dataSourceId}/accessControl/` Authentication requirements: `BearerAuth` Set roles for users on this data source. Note that when granting access to a data source, access to the corresponding data store as a "CONSUMER" will also be granted. ### Body parameter ``` { "properties": { "data": { "description": "List of sharing roles to update.", "items": { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataSourceId | path | string | true | The ID of the Data Source. | | body | body | SharingUpdateOrRemoveWithGrant | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | None | | 409 | Conflict | The request would leave the data source without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid | None | ## Describe data source permissions by data source ID Operation path: `GET /api/v2/externalDataSources/{dataSourceId}/permissions/` Authentication requirements: `BearerAuth` Describe what permissions current user has for given data source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataSourceId | path | string | true | The ID of the Data Source. | ### Example responses > 200 Response ``` { "properties": { "canCreatePredictions": { "description": "True if the user can create predictions from the data source.", "type": "boolean" }, "canCreateProject": { "description": "True if the user can create project from the data source.", "type": "boolean" }, "canDelete": { "description": "True if the user can delete the data source.", "type": "boolean" }, "canEdit": { "description": "True if the user can edit data source info.", "type": "boolean" }, "canSetRoles": { "description": "Roles the user can grant or revoke from other users, groups, or organizations.", "items": { "description": "The role the user can grant or revoke from users, groups or organizations.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "type": "array" }, "canShare": { "description": "True if the user can share the data source.", "type": "boolean" }, "canView": { "description": "True if the user can view data source info.", "type": "boolean" }, "dataSourceId": { "description": "The ID of the data source.", "type": "string" }, "userId": { "description": "The ID of the user identified by username.", "type": "string" }, "username": { "description": "`username` of a user with access to this data source.", "type": "string" } }, "required": [ "canCreatePredictions", "canCreateProject", "canDelete", "canEdit", "canSetRoles", "canShare", "canView", "dataSourceId", "userId", "username" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The data source permissions. | DataSourceDescribePermissionsResponse | ## Retrieve shared roles by id Operation path: `GET /api/v2/externalDataSources/{dataSourceId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations who have access to this data source and their roles. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | dataSourceId | path | string | true | The ID of the Data Source. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" }, "userFullName": { "description": "The full name of the recipient user.", "type": "string" } }, "required": [ "canShare", "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of user/group/organization roles. | SharedRolesWithGrantListResponse | ## Modify data source shared roles by data source ID Operation path: `PATCH /api/v2/externalDataSources/{dataSourceId}/sharedRoles/` Authentication requirements: `BearerAuth` Grant access, remove access or update roles for organizations, groups or users on this data source. Up to 100 roles may be set per array in a single request. ### Body parameter ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "An array of RoleRequest objects. May contain at most 100 such objects.", "items": { "oneOf": [ { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataSourceId | path | string | true | The ID of the Data Source. | | body | body | SharedRolesUpdateWithGrant | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | None | | 409 | Conflict | Duplicate entry for the org/group/user in roles listor the request would leave the data source without an owner. | None | | 422 | Unprocessable Entity | Request is unprocessable. For example, name is stated for not user recipient. | None | ## List data stores Operation path: `GET /api/v2/externalDataStores/` Authentication requirements: `BearerAuth` Return the list with details of the existing data stores available for the user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | type | query | string | false | Includes only data stores of the specified type or any if set to all. | | databaseType | query | any | false | Includes only data stores of the specified database type. For JDBC based data stores, the database_type value is the string between the first and the second colons of a jdbc url. For example, a snowflake jdbc url is jdbc//, the database_type is hence snowflake. If an empty string is used, or if the string contains only whitespace, no filtering occurs. | | connectorType | query | any | false | Includes only data stores of the specified connector type. | | showHidden | query | string | false | Specifies whether non-visible OAuth fields are shown. | | name | query | string | false | Search for data stores whose canonicalName matches or contains the specified name.The search is case insensitive. | | substituteUrlParameters | query | string | false | Specifies whether dynamic parameters in the URL will be substituted. | | dataType | query | string | false | Includes only data stores which support the specified data type or all data stores if set to all. | ### Enumerated Values | Parameter | Value | | --- | --- | | type | [all, databases, dr-connector-v1, dr-database-v1, jdbc] | | showHidden | [false, False, true, True] | | substituteUrlParameters | [false, False, true, True] | | dataType | [all, structured, unstructured] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of data stores.", "items": { "properties": { "associatedAuthTypes": { "description": "The supported authentication types for the JDBC configuration.", "items": { "enum": [ "adls_gen2_oauth", "azure_service_principal", "basic", "box_jwt", "databricks_access_token_account", "databricks_service_principal_account", "gcp", "google_oauth_user_account", "kerberos", "oauth", "s3", "snowflake_key_pair_user_account", "snowflake_oauth_user_account" ], "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "canonicalName": { "description": "The user-friendly name of the data store.", "type": "string" }, "creator": { "description": "The ID of the user who created the data store.", "type": "string" }, "id": { "description": "The data store ID.", "type": "string" }, "params": { "description": "The data store configuration.", "oneOf": [ { "properties": { "driverId": { "description": "The driver ID.", "type": "string" }, "jdbcFieldSchemas": { "default": [], "description": "The fields to show when creating a data store, their defaults, whether or not they are required, and more.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "maxItems": 100, "type": "array" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "required": [ "driverId" ], "type": "object" }, { "properties": { "connectorId": { "description": "The ID of the connector.", "type": "string" }, "fieldSchemas": { "description": "The connector field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "connectorId", "fields" ], "type": "object" }, { "properties": { "driverId": { "description": "The database driver ID.", "type": "string" }, "fieldSchemas": { "description": "The database driver field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The database driver fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "driverId", "fields" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data store.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data store type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data store update.", "type": "string" } }, "required": [ "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Data stores retrieved successfully. | DataStoreListResponse | | 422 | Unprocessable Entity | Filtering by database type is currently not supported for dr-connector-v1 data stores. | None | ## Create a data store Operation path: `POST /api/v2/externalDataStores/` Authentication requirements: `BearerAuth` Create a data store which includes a name and a driver ID or a connector ID. The driver would be configured by a JDBC URL or by jdbc fields; the connector would be configured by connection fields. ### Body parameter ``` { "properties": { "canonicalName": { "description": "The user-friendly name of the data store.", "type": "string" }, "params": { "description": "The data store configuration.", "oneOf": [ { "properties": { "driverId": { "description": "Driver ID.", "type": "string" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "required": [ "driverId" ], "type": "object" }, { "properties": { "connectorId": { "description": "The ID of the connector.", "type": "string" }, "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "connectorId", "fields" ], "type": "object" }, { "properties": { "driverId": { "description": "The database driver ID.", "type": "string" }, "fields": { "description": "The database driver fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "driverId", "fields" ], "type": "object" } ] }, "type": { "description": "The data store type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" } }, "required": [ "canonicalName", "params", "type" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DataStoreCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "associatedAuthTypes": { "description": "The supported authentication types for the JDBC configuration.", "items": { "enum": [ "adls_gen2_oauth", "azure_service_principal", "basic", "box_jwt", "databricks_access_token_account", "databricks_service_principal_account", "gcp", "google_oauth_user_account", "kerberos", "oauth", "s3", "snowflake_key_pair_user_account", "snowflake_oauth_user_account" ], "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "canonicalName": { "description": "The user-friendly name of the data store.", "type": "string" }, "creator": { "description": "The ID of the user who created the data store.", "type": "string" }, "id": { "description": "The data store ID.", "type": "string" }, "params": { "description": "The data store configuration.", "oneOf": [ { "properties": { "driverId": { "description": "The driver ID.", "type": "string" }, "jdbcFieldSchemas": { "default": [], "description": "The fields to show when creating a data store, their defaults, whether or not they are required, and more.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "maxItems": 100, "type": "array" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "required": [ "driverId" ], "type": "object" }, { "properties": { "connectorId": { "description": "The ID of the connector.", "type": "string" }, "fieldSchemas": { "description": "The connector field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "connectorId", "fields" ], "type": "object" }, { "properties": { "driverId": { "description": "The database driver ID.", "type": "string" }, "fieldSchemas": { "description": "The database driver field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The database driver fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "driverId", "fields" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data store.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data store type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data store update.", "type": "string" } }, "required": [ "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Data store created successfully. | DataStoreRetrieveResponse | ## Delete the data store by data store ID Operation path: `DELETE /api/v2/externalDataStores/{dataStoreId}/` Authentication requirements: `BearerAuth` Delete the data store with given ID if it is not used by any data source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Data store deleted successfully. | None | | 409 | Conflict | Data store is in use by one or more data source | None | ## Data store details by data store ID Operation path: `GET /api/v2/externalDataStores/{dataStoreId}/` Authentication requirements: `BearerAuth` A configured connection to a database; it has a name and a specified driver. The driver may be specified by a JDBC URL or connection parameters if the driver was created with the parameter configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | substituteUrlParameters | query | string | false | Specifies whether dynamic parameters in the URL will be substituted. | | dataStoreId | path | string | true | The ID of the data store. | ### Enumerated Values | Parameter | Value | | --- | --- | | substituteUrlParameters | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "associatedAuthTypes": { "description": "The supported authentication types for the JDBC configuration.", "items": { "enum": [ "adls_gen2_oauth", "azure_service_principal", "basic", "box_jwt", "databricks_access_token_account", "databricks_service_principal_account", "gcp", "google_oauth_user_account", "kerberos", "oauth", "s3", "snowflake_key_pair_user_account", "snowflake_oauth_user_account" ], "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "canonicalName": { "description": "The user-friendly name of the data store.", "type": "string" }, "creator": { "description": "The ID of the user who created the data store.", "type": "string" }, "id": { "description": "The data store ID.", "type": "string" }, "params": { "description": "The data store configuration.", "oneOf": [ { "properties": { "driverId": { "description": "The driver ID.", "type": "string" }, "jdbcFieldSchemas": { "default": [], "description": "The fields to show when creating a data store, their defaults, whether or not they are required, and more.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "maxItems": 100, "type": "array" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "required": [ "driverId" ], "type": "object" }, { "properties": { "connectorId": { "description": "The ID of the connector.", "type": "string" }, "fieldSchemas": { "description": "The connector field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "connectorId", "fields" ], "type": "object" }, { "properties": { "driverId": { "description": "The database driver ID.", "type": "string" }, "fieldSchemas": { "description": "The database driver field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The database driver fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "driverId", "fields" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data store.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data store type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data store update.", "type": "string" } }, "required": [ "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Data store details retrieved successfully. | DataStoreRetrieveResponse | ## Update a data store configuration by data store ID Operation path: `PATCH /api/v2/externalDataStores/{dataStoreId}/` Authentication requirements: `BearerAuth` Update a data store configuration. ### Body parameter ``` { "properties": { "canonicalName": { "description": "The user-friendly name of the data store.", "type": "string" }, "params": { "description": "The data store configuration.", "oneOf": [ { "properties": { "driverId": { "description": "The driver ID.", "type": "string" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "type": "object" }, { "properties": { "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "type": "object" } ] } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | | body | body | DataStoreUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "associatedAuthTypes": { "description": "The supported authentication types for the JDBC configuration.", "items": { "enum": [ "adls_gen2_oauth", "azure_service_principal", "basic", "box_jwt", "databricks_access_token_account", "databricks_service_principal_account", "gcp", "google_oauth_user_account", "kerberos", "oauth", "s3", "snowflake_key_pair_user_account", "snowflake_oauth_user_account" ], "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "canonicalName": { "description": "The user-friendly name of the data store.", "type": "string" }, "creator": { "description": "The ID of the user who created the data store.", "type": "string" }, "id": { "description": "The data store ID.", "type": "string" }, "params": { "description": "The data store configuration.", "oneOf": [ { "properties": { "driverId": { "description": "The driver ID.", "type": "string" }, "jdbcFieldSchemas": { "default": [], "description": "The fields to show when creating a data store, their defaults, whether or not they are required, and more.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "maxItems": 100, "type": "array" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "required": [ "driverId" ], "type": "object" }, { "properties": { "connectorId": { "description": "The ID of the connector.", "type": "string" }, "fieldSchemas": { "description": "The connector field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "connectorId", "fields" ], "type": "object" }, { "properties": { "driverId": { "description": "The database driver ID.", "type": "string" }, "fieldSchemas": { "description": "The database driver field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The database driver fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "driverId", "fields" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data store.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data store type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data store update.", "type": "string" } }, "required": [ "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The updated data store entry. | DataStoreRetrieveResponse | ## Update the data store's access controls by data store ID Operation path: `PATCH /api/v2/externalDataStores/{dataStoreId}/accessControl/` Authentication requirements: `BearerAuth` Set roles for users on this data store. ### Body parameter ``` { "properties": { "data": { "description": "List of sharing roles to update.", "items": { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | | body | body | SharingUpdateOrRemoveWithGrant | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | None | | 409 | Conflict | The request would leave the data store without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid | None | ## Retrieves a data store's data columns by data store ID Operation path: `POST /api/v2/externalDataStores/{dataStoreId}/columns/` Authentication requirements: `BearerAuth` Retrieves a data store's data columns. ### Body parameter ``` { "properties": { "catalog": { "description": "The name of the specified database catalog.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential mapping.", "type": "string" }, "password": { "description": "The password for the db connection.", "type": "string" }, "query": { "description": "The schema query.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" }, "user": { "description": "The username for the db connection.", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | | body | body | DataStoreColumnsList | false | none | ### Example responses > 200 Response ``` { "properties": { "columns": { "description": "The list of data store columns.", "items": { "properties": { "dataType": { "description": "The data type of the column.", "type": "string" }, "name": { "description": "The name of the column.", "type": "string" }, "precision": { "description": "The precision of the column.", "type": [ "string", "null" ] }, "scale": { "description": "The scale of the column.", "type": [ "string", "null" ] } }, "required": [ "dataType", "name", "precision", "scale" ], "type": "object" }, "type": "array" } }, "required": [ "columns" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response with array of column objects. | DataStoreColumnsListResponse | | 400 | Bad Request | The error thrown by DSS system when retrieving columns. | None | | 403 | Forbidden | Incorrect permissions to access catalog item through DSS. | None | ## Retrieves a data store's column metadata by data store ID Operation path: `POST /api/v2/externalDataStores/{dataStoreId}/columnsInfo/` Authentication requirements: `BearerAuth` Retrieves a data store's column metadata. ### Body parameter ``` { "properties": { "catalog": { "description": "The name of the specified database catalog.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string", "x-versionadded": "v2.19" }, "password": { "description": "The password for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" }, "useKerberos": { "default": false, "description": "Whether to use Kerberos for data store authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "table" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | types | query | any | false | Includes only credentials of the specified type. Repeat the parameter for filtering on multiple statuses. | | orderBy | query | string | false | The order to sort the credentials. Defaults to the order by the creation_date in descending order. | | dataStoreId | path | string | true | The ID of the data store. | | body | body | DataStoreColumnsInfoList | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [creationDate, -creationDate] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of data store columns metadata.", "items": { "properties": { "columnDefaultValue": { "description": "The default value of the column.", "type": [ "string", "null" ] }, "comment": { "description": "The comment of the column.", "type": [ "string", "null" ] }, "dataType": { "description": "The data type of the column.", "type": "string" }, "dataTypeInt": { "description": "The integer value of the column data type.", "type": "integer" }, "exportedKeys": { "description": "The foreign key columns that reference this table's primary key columns.", "items": { "properties": { "foreignKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" }, "keyType": { "description": "The type of this key.", "enum": [ "1", "2", "3" ], "type": "string" }, "name": { "description": "The name of this key.", "type": "string" }, "primaryKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" } }, "required": [ "keyType", "primaryKey" ], "type": "object" }, "type": "array" }, "importedKeys": { "description": "The primary key columns that are referenced by this table's foreign key columns.", "items": { "properties": { "foreignKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" }, "keyType": { "description": "The type of this key.", "enum": [ "1", "2", "3" ], "type": "string" }, "name": { "description": "The name of this key.", "type": "string" }, "primaryKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" } }, "required": [ "keyType", "primaryKey" ], "type": "object" }, "type": "array" }, "isInPrimaryKey": { "description": "True if the column is in the primary key.", "type": [ "boolean", "null" ] }, "isNullable": { "description": "Whether the column values can be null.", "enum": [ "NO", "UNKNOWN", "YES" ], "type": [ "string", "null" ] }, "name": { "description": "The name of the column.", "type": "string" }, "precision": { "description": "The precision of the column.", "type": [ "integer", "null" ] }, "primaryKeys": { "description": "The primary key columns of the table.", "items": { "type": "string" }, "type": "array" }, "scale": { "description": "The scale of the column.", "type": [ "integer", "null" ] } }, "required": [ "columnDefaultValue", "comment", "dataType", "dataTypeInt", "exportedKeys", "importedKeys", "isInPrimaryKey", "isNullable", "name", "precision", "primaryKeys", "scale" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a data store's column metadata. | DataStoreColumnsInfoListResponse | | 409 | Conflict | This operation is only allowed for JDBC data stores. | None | ## List credentials associated by data store ID Operation path: `GET /api/v2/externalDataStores/{dataStoreId}/credentials/` Authentication requirements: `BearerAuth` Returns a list of credentials associated with the specified data store. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | types | query | any | false | Includes only credentials of the specified type. Repeat the parameter for filtering on multiple statuses. | | orderBy | query | string | false | The order to sort the credentials. Defaults to the order by the creation_date in descending order. | | dataStoreId | path | string | true | The ID of the data store. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [creationDate, -creationDate] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of credentials.", "items": { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "name": { "description": "Name of these credentials.", "type": "string" } }, "required": [ "creationDate", "credentialId", "name" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of credentials associated with the data store. | CredentialsListResponse | ## Describe data store permissions by data store ID Operation path: `GET /api/v2/externalDataStores/{dataStoreId}/permissions/` Authentication requirements: `BearerAuth` Describe what permissions current user has for given data store. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | ### Example responses > 200 Response ``` { "properties": { "canCreateDataSource": { "description": "True if the user can create data source from this data store.", "type": "boolean" }, "canDelete": { "description": "True if the user can delete the data store.", "type": "boolean" }, "canEdit": { "description": "True if the user can edit data store info.", "type": "boolean" }, "canScanDatabase": { "description": "True if the user can scan data store database.", "type": "boolean" }, "canSetRoles": { "description": "Roles the user can grant or revoke from other users, groups, or organizations.", "items": { "description": "The role the user can grant or revoke from users, groups or organizations.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "type": "array" }, "canShare": { "description": "True if the user can share the data store.", "type": "boolean" }, "canTestConnection": { "description": "True if the user can test data store database connection.", "type": "boolean" }, "canView": { "description": "True if the user can view data store info.", "type": "boolean" }, "dataStoreId": { "description": "The ID of the data store.", "type": "string" }, "userId": { "description": "The ID of the user identified by username.", "type": "string" }, "username": { "description": "`username` of a user with access to this data store.", "type": "string" } }, "required": [ "canCreateDataSource", "canDelete", "canEdit", "canScanDatabase", "canSetRoles", "canShare", "canTestConnection", "canView", "dataStoreId", "userId", "username" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The data store permissions. | DataStoreDescribePermissionsResponse | ## Retrieves a data store's data schemas by data store ID Operation path: `POST /api/v2/externalDataStores/{dataStoreId}/schemas/` Authentication requirements: `BearerAuth` Retrieves a data store's data schemas. ### Body parameter ``` { "properties": { "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string", "x-versionadded": "v2.19" }, "password": { "description": "The password for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" }, "useKerberos": { "default": false, "description": "Whether to use Kerberos for data store authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | | body | body | DataStoreCredentials | false | none | ### Example responses > 200 Response ``` { "properties": { "catalog": { "description": "The name of the catalog associated with the schemas. If more than one catalog is queried, the returned value will be the catalog name for the first record returned.", "type": "string" }, "catalogs": { "description": "The list of catalogs associated with each retrieved schema. If no catalog is found, this value is null.", "items": { "description": "The catalog names for the schema associated with the data store.", "type": [ "string", "null" ] }, "type": "array" }, "schemas": { "description": "The list of schemas available in the data store.", "items": { "type": "string" }, "type": "array" } }, "required": [ "catalog", "catalogs", "schemas" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response with schemas and catalog. | DataStoreSchemasList | ## Get the data store's access control list by data store ID Operation path: `GET /api/v2/externalDataStores/{dataStoreId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users who have access to this data store and their roles on the data store. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | dataStoreId | path | string | true | The ID of the data store. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" }, "userFullName": { "description": "The full name of the recipient user.", "type": "string" } }, "required": [ "canShare", "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The data store's access control list. | SharedRolesWithGrantListResponse | | 404 | Not Found | Either the data store does not exist or the user does not have permissions to view the data store. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## Modify data store shared roles by data store ID Operation path: `PATCH /api/v2/externalDataStores/{dataStoreId}/sharedRoles/` Authentication requirements: `BearerAuth` Grant access, remove access or update roles for organizations, groups or users on this data store. Up to 100 roles may be set per array in a single request. ### Body parameter ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "An array of RoleRequest objects. May contain at most 100 such objects.", "items": { "oneOf": [ { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | | body | body | SharedRolesUpdateWithGrant | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The resource was successfully modified. | None | | 409 | Conflict | Duplicate entry for the org/group/user in roles listor the request would leave the data store without an owner. | None | | 422 | Unprocessable Entity | Request is unprocessable. For example, name is stated for not user recipient. | None | ## Retrieve detected standard user-defined functions by data store ID Operation path: `GET /api/v2/externalDataStores/{dataStoreId}/standardUserDefinedFunctions/` Authentication requirements: `BearerAuth` Retrieve detected standard user-defined functions. Raise a 404 error if detection needs to be run first. Results might not be up-to-date; run detection with the `force=True` option to refresh them. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | credentialId | query | string | false | The ID of the set of credentials to use instead of username and password. | | functionType | query | string | true | The standard user-defined function type. | | schema | query | string | true | The schema to create or detect user-defined functions in. | | functionName | query | string | false | The standard user-defined function name to filter results by. | | dataStoreId | path | string | true | The ID of the data store. | ### Enumerated Values | Parameter | Value | | --- | --- | | functionType | [rolling_median, rolling_most_frequent] | ### Example responses > 200 Response ``` { "properties": { "detectedFunctions": { "description": "The detected standard user-defined functions.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "functionType": { "description": "The requested standard user-defined function type.", "type": "string" }, "schema": { "description": "The requested schema.", "type": "string" } }, "required": [ "detectedFunctions", "functionType", "schema" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Return detected standard user-defined functions for the given data store, credentials, function type, and schema. | DetectedStandardUdfsRetrieveResponse | ## Start a job to create a standard user-defined function of the given type by data store ID Operation path: `POST /api/v2/externalDataStores/{dataStoreId}/standardUserDefinedFunctions/` Authentication requirements: `BearerAuth` Start the creation of the standard user-defined function. Since this is an asynchronous process, this endpoint returns a status ID to use with the status endpoint as well as a location header that includes URL that can be polled for status. Once completed, the status URL redirects to the endpoint with the created function name. ### Body parameter ``` { "properties": { "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string" }, "functionType": { "description": "The standard user-defined function type.", "enum": [ "rolling_median", "rolling_most_frequent" ], "type": "string" }, "schema": { "description": "The schema to create or detect user-defined functions in.", "type": "string" } }, "required": [ "functionType", "schema" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | | body | body | StandardUdfCreate | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "The ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the job's status.", "type": "string" } }, "required": [ "statusId" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | StatusIdResponse | ## Start the job that detects standard user-defined functions for the given data store, credentials by data store ID Operation path: `POST /api/v2/externalDataStores/{dataStoreId}/standardUserDefinedFunctions/detect/` Authentication requirements: `BearerAuth` Start the detection process for the standard user-defined functions. Since this is an asynchronous process this endpoint returns a status ID to use with the status endpoint as well as a location header that includes the URL that can be polled for status. Once completed, the status URL redirects to the endpoint with the detected function names. ### Body parameter ``` { "properties": { "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string" }, "force": { "default": false, "description": "Forces detection to be submitted, even if a cache with detected standard user-defined functions for given parameters is already present.", "type": "boolean" }, "functionType": { "description": "The standard user-defined function type.", "enum": [ "rolling_median", "rolling_most_frequent" ], "type": "string" }, "schema": { "description": "The schema to create or detect user-defined functions in.", "type": "string" } }, "required": [ "force", "functionType", "schema" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | | body | body | DetectUdfs | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "The ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the job's status.", "type": "string" } }, "required": [ "statusId" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | StatusIdResponse | ## Retrieves a data store's database tables (including views) by data store ID Operation path: `POST /api/v2/externalDataStores/{dataStoreId}/tables/` Authentication requirements: `BearerAuth` Retrieves a data store's database tables (including views). ### Body parameter ``` { "properties": { "catalog": { "description": "Only show tables in this catalog.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string", "x-versionadded": "v2.19" }, "password": { "description": "The password for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" }, "schema": { "description": "Only show tables in this schema.", "type": "string" }, "useKerberos": { "default": false, "description": "Whether to use Kerberos for data store authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | | body | body | DataStoreTables | false | none | ### Example responses > 200 Response ``` { "properties": { "catalog": { "description": "The catalog associated with schemas.", "type": [ "string", "null" ] }, "tables": { "description": "The list of tables in the data store.", "items": { "properties": { "catalog": { "description": "The name of the catalog.", "type": [ "string", "null" ] }, "name": { "description": "The name of the table.", "type": "string" }, "schema": { "description": "The schema of the table.", "type": "string" }, "type": { "description": "The type of table.", "enum": [ "TABLE", "VIEW" ], "type": "string" } }, "required": [ "name", "type" ], "type": "object" }, "type": "array" } }, "required": [ "catalog", "tables" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of tables available in the retrieved data store. | DataStoreTablesList | ## Test data store connection by data store ID Operation path: `POST /api/v2/externalDataStores/{dataStoreId}/test/` Authentication requirements: `BearerAuth` Test the ability to connect to a data store with specified authentication. ### Body parameter ``` { "properties": { "credentialData": { "description": "The type of credentials to use with the data store.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "authenticationId": { "description": "The authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'external_oauth_provider' here.", "enum": [ "external_oauth_provider" ], "type": "string" } }, "required": [ "authenticationId", "credentialType" ], "type": "object" }, { "properties": { "clientId": { "description": "Box JWT client ID.", "type": "string", "x-versionadded": "v2.41" }, "clientSecret": { "description": "Box JWT client secret.", "type": "string", "x-versionadded": "v2.41" }, "configId": { "description": "ID of secure configuration to share Box JWT credentials by admin.", "type": "string", "x-versionadded": "v2.41" }, "credentialType": { "description": "Credentials type.", "enum": [ "box_jwt" ], "type": "string", "x-versionadded": "v2.41" }, "enterpriseId": { "description": "Box enterprise identifier.", "type": "string", "x-versionadded": "v2.41" }, "passphrase": { "description": "Passphrase for the Box JWT private key.", "minLength": 1, "type": "string", "x-versionadded": "v2.41" }, "privateKeyStr": { "description": "RSA private key for Box JWT.", "minLength": 1, "type": "string", "x-versionadded": "v2.41" }, "publicKeyId": { "description": "Box public key identifier.", "type": "string", "x-versionadded": "v2.41" } }, "required": [ "credentialType" ], "type": "object", "x-versionadded": "v2.42" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string", "x-versionadded": "v2.19" }, "password": { "description": "The password for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" }, "useKerberos": { "default": false, "description": "Whether to use Kerberos for data store authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | | body | body | DataStoreCredentialsWithCredentialsTypeSupport | false | none | ### Example responses > 200 Response ``` { "properties": { "message": { "description": "The connection attempt results.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The outcome of the connection test. | DataStoreTestResponse | | 400 | Bad Request | The details on why the connect call failed. | None | ## Verify a SQL query by data store ID Operation path: `POST /api/v2/externalDataStores/{dataStoreId}/verifySQL/` Authentication requirements: `BearerAuth` Execute the SQL query on the data store, returning a small number of rows (max 999). Use this for quick query execution validation and exploring results, not for capturing an entire result set. ### Body parameter ``` { "properties": { "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string", "x-versionadded": "v2.19" }, "maxRows": { "description": "The maximum number of rows of data to return if successful.", "maximum": 999, "minimum": 0, "type": "integer" }, "password": { "description": "The password for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" }, "query": { "description": "The SQL query to verify.", "type": "string" }, "useKerberos": { "default": false, "description": "Whether to use Kerberos for data store authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "maxRows", "query" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataStoreId | path | string | true | The ID of the data store. | | body | body | DataStoreSQLVerify | false | none | ### Example responses > 200 Response ``` { "properties": { "columns": { "description": "The list of columns for the returned set of records. Column order matches the order of values in a record.", "items": { "description": "The name of the column.", "type": "string" }, "maxItems": 20000, "type": "array" }, "records": { "description": "The list of records output by the query.", "items": { "description": "The list of values for a single database record, ordered as the columns are ordered.", "items": { "description": "The string representation of the column's value.", "type": "string" }, "type": "array" }, "maxItems": 10000, "type": "array" } }, "required": [ "columns", "records" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns the names of columns and a limited number of results from the SQL query. | DataStoreSQLVerifyResponse | | 400 | Bad Request | The details explaining why the SQL query is invalid. | None | ## List available driver configurations Operation path: `GET /api/v2/externalDriverConfigurations/` Authentication requirements: `BearerAuth` Retrieve matching driver configurations based on query. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | type | query | string | false | Type of driver configurations to return. | | showHidden | query | string | false | If True, include hidden configurations in response. | ### Enumerated Values | Parameter | Value | | --- | --- | | type | [all, dr-connector-v1, dr-database-v1, jdbc] | | showHidden | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of driver configurations available to the user.", "items": { "properties": { "associatedAuthTypes": { "description": "The list of authentication types supported by this driver configuration.", "items": { "type": "string" }, "type": "array" }, "className": { "description": "Java class name of the driver to be used (e.g., org.postgresql.Driver).", "type": "string" }, "creator": { "description": "The user ID of the creator of this configuration.", "type": "string" }, "id": { "description": "The driver configuration ID.", "type": "string" }, "jdbcFieldSchemas": { "description": "Description of which fields to show when creating a datastore, their defaults, and whether or not they are required.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "jdbcUrlPathDelimiter": { "description": "Separator of address from path in the JDBC URL (e.g., \"/\").", "type": "string" }, "jdbcUrlPrefix": { "description": "Prefix of the JDBC URL (e.g., \"jdbc:mssql://\" or \"jdbc:oracle:thin:@\").", "type": "string" }, "jdbcUrlQueryDelimiter": { "description": "Separator of path from the list of query parameters in the JDBC URL (e.g., \"?\").", "type": "string" }, "jdbcUrlQueryParamDelimiter": { "description": "Separator of each set of query parameter key-value pairs in the JDBC URL (e.g., \"&\").", "type": "string" }, "jdbcUrlQueryParamKeyValueDelimiter": { "description": "Separator of the key and value in a query parameter pair (e.g., \"=\").", "type": "string" }, "standardizedName": { "description": "The plain text name for the driver (e.g., PostgreSQL).", "type": "string" }, "statements": { "description": "List of supported statments for this driver configuration.", "items": { "enum": [ "insert", "update", "upsert" ], "type": "string" }, "type": "array" }, "updated": { "description": "ISO-8601 formatted time/date when this configuration was most recently updated.", "type": "string" } }, "required": [ "associatedAuthTypes", "className", "creator", "id", "jdbcFieldSchemas", "jdbcUrlPathDelimiter", "jdbcUrlPrefix", "jdbcUrlQueryDelimiter", "jdbcUrlQueryParamDelimiter", "jdbcUrlQueryParamKeyValueDelimiter", "standardizedName", "updated" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Driver configurations retrieved successfully. | DriverConfigurationListResponse | ## Driver configuration details by configuration ID Operation path: `GET /api/v2/externalDriverConfigurations/{configurationId}/` Authentication requirements: `BearerAuth` Retrieve the driver configuration with the specified ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | configurationId | path | string | true | The driver configuration ID. | ### Example responses > 200 Response ``` { "properties": { "associatedAuthTypes": { "description": "The list of authentication types supported by this driver configuration.", "items": { "type": "string" }, "type": "array" }, "className": { "description": "Java class name of the driver to be used (e.g., org.postgresql.Driver).", "type": "string" }, "creator": { "description": "The user ID of the creator of this configuration.", "type": "string" }, "id": { "description": "The driver configuration ID.", "type": "string" }, "jdbcFieldSchemas": { "description": "Description of which fields to show when creating a datastore, their defaults, and whether or not they are required.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "jdbcUrlPathDelimiter": { "description": "Separator of address from path in the JDBC URL (e.g., \"/\").", "type": "string" }, "jdbcUrlPrefix": { "description": "Prefix of the JDBC URL (e.g., \"jdbc:mssql://\" or \"jdbc:oracle:thin:@\").", "type": "string" }, "jdbcUrlQueryDelimiter": { "description": "Separator of path from the list of query parameters in the JDBC URL (e.g., \"?\").", "type": "string" }, "jdbcUrlQueryParamDelimiter": { "description": "Separator of each set of query parameter key-value pairs in the JDBC URL (e.g., \"&\").", "type": "string" }, "jdbcUrlQueryParamKeyValueDelimiter": { "description": "Separator of the key and value in a query parameter pair (e.g., \"=\").", "type": "string" }, "standardizedName": { "description": "The plain text name for the driver (e.g., PostgreSQL).", "type": "string" }, "statements": { "description": "List of supported statments for this driver configuration.", "items": { "enum": [ "insert", "update", "upsert" ], "type": "string" }, "type": "array" }, "updated": { "description": "ISO-8601 formatted time/date when this configuration was most recently updated.", "type": "string" } }, "required": [ "associatedAuthTypes", "className", "creator", "id", "jdbcFieldSchemas", "jdbcUrlPathDelimiter", "jdbcUrlPrefix", "jdbcUrlQueryDelimiter", "jdbcUrlQueryParamDelimiter", "jdbcUrlQueryParamKeyValueDelimiter", "standardizedName", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Driver configuration retrieved successfully. | DriverConfigurationRetrieveResponse | ## Preview data Operation path: `POST /api/v2/jdbcDataPreview/` Authentication requirements: `BearerAuth` Execute SQL against the selected JDBC URL and return a row-limited preview. Specify credentials and connection parameters in the JDBC URL and/or in the `parameters` field. ### Body parameter ``` { "properties": { "jdbcUrl": { "description": "The JDBC URL.", "type": "string" }, "maxRows": { "default": 1000, "description": "The row limit for the preview. The default is 1,000 rows, and a maximum of 10,000 per request.", "maximum": 10000, "minimum": 0, "type": "integer" }, "parameters": { "description": "Optional connection parameters and credential properties as key-value pairs (e.g. user, password, ssl, timeout).", "type": "object", "x-versionadded": "v2.43" }, "sql": { "description": "The SQL to execute (e.g. SELECT ...).", "maxLength": 320000, "type": "string" } }, "required": [ "jdbcUrl", "sql" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | JdbcDataPreview | false | none | ### Example responses > 200 Response ``` { "properties": { "columns": { "description": "The list of columns for the returned set of records. Column order matches the order of values in a record.", "items": { "description": "The name of the column.", "type": "string" }, "maxItems": 20000, "type": "array" }, "records": { "description": "The list of records output by the query.", "items": { "description": "The list of values for a single database record, ordered as the columns are ordered.", "items": { "description": "The string representation of the column's value.", "type": "string" }, "type": "array" }, "maxItems": 10000, "type": "array" }, "resultSchema": { "description": "The JDBC result schema.", "items": { "description": "The JDBC result column description.", "properties": { "dataType": { "description": "The data type of the column.", "type": "string" }, "dataTypeInt": { "description": "The integer value of the column data type.", "type": [ "integer", "null" ] }, "name": { "description": "The name of the column.", "type": "string" }, "precision": { "description": "The precision of the column.", "type": [ "integer", "null" ] }, "scale": { "description": "The scale of the column.", "type": [ "integer", "null" ] } }, "required": [ "dataType", "dataTypeInt", "name", "precision", "scale" ], "type": "object", "x-versionadded": "v2.43" }, "maxItems": 20000, "type": "array" } }, "required": [ "columns", "records", "resultSchema" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns columns, records, and resultSchema matching the data preview shape. | DataStorePreviewResponse | # Schemas ## ACLAdminConnectionBase ``` { "properties": { "adminEmail": { "default": null, "description": "The user to impersonate for the admin-level operations. Currently only applicable to Google Drive.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ACL management connection ID.", "type": "string" }, "domain": { "default": null, "description": "The custom domain for the connection. Currently only applicable to SharePoint.", "type": [ "string", "null" ] } }, "required": [ "dataStoreId" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | adminEmail | string,null | false | | The user to impersonate for the admin-level operations. Currently only applicable to Google Drive. | | dataStoreId | string | true | | The ACL management connection ID. | | domain | string,null | false | | The custom domain for the connection. Currently only applicable to SharePoint. | ## ACLAdminConnectionList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.43" }, "data": { "description": "The list of ACL management connections for the user organization.", "items": { "properties": { "adminEmail": { "default": null, "description": "The user to impersonate for the admin-level operations. Currently only applicable to Google Drive.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ACL management connection ID.", "type": "string" }, "domain": { "default": null, "description": "The custom domain for the connection. Currently only applicable to SharePoint.", "type": [ "string", "null" ] }, "externalConnectorType": { "description": "The data provider being configured.", "enum": [ "gdrive", "sharepoint" ], "type": "string" } }, "required": [ "dataStoreId", "externalConnectorType" ], "type": "object", "x-versionadded": "v2.43" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.43" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ACLAdminConnectionResponse] | true | maxItems: 100 | The list of ACL management connections for the user organization. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ACLAdminConnectionResponse ``` { "properties": { "adminEmail": { "default": null, "description": "The user to impersonate for the admin-level operations. Currently only applicable to Google Drive.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ACL management connection ID.", "type": "string" }, "domain": { "default": null, "description": "The custom domain for the connection. Currently only applicable to SharePoint.", "type": [ "string", "null" ] }, "externalConnectorType": { "description": "The data provider being configured.", "enum": [ "gdrive", "sharepoint" ], "type": "string" } }, "required": [ "dataStoreId", "externalConnectorType" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | adminEmail | string,null | false | | The user to impersonate for the admin-level operations. Currently only applicable to Google Drive. | | dataStoreId | string | true | | The ACL management connection ID. | | domain | string,null | false | | The custom domain for the connection. Currently only applicable to SharePoint. | | externalConnectorType | string | true | | The data provider being configured. | ### Enumerated Values | Property | Value | | --- | --- | | externalConnectorType | [gdrive, sharepoint] | ## AccessControl ``` { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | Whether the recipient can share the role further. | | role | string | true | | The role of the user on this entity. | | userId | string | true | | The identifier of the user that has access to this entity. | | username | string | true | | The username of the user that has access to the entity. | ## AccessControlWithGrant ``` { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" }, "userFullName": { "description": "The full name of the recipient user.", "type": "string" } }, "required": [ "canShare", "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | Whether the recipient can share the role further. | | id | string | true | | The identifier of the recipient. | | name | string | true | | The name of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | The type of the recipient. | | userFullName | string | false | | The full name of the recipient user. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## AzureServicePrincipalCredentials ``` { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | azureTenantId | string | false | | Tenant ID of the Azure AD service principal. | | clientId | string | false | | Client ID of the Azure AD service principal. | | clientSecret | string | false | | Client Secret of the Azure AD service principal. | | configId | string | false | | The ID of secure configurations of credentials shared by admin. | | credentialType | string | true | | The type of these credentials, 'azure_service_principal' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | azure_service_principal | ## BOXJWTCredentialsFields ``` { "properties": { "clientId": { "description": "Box JWT client ID.", "type": "string", "x-versionadded": "v2.41" }, "clientSecret": { "description": "Box JWT client secret.", "type": "string", "x-versionadded": "v2.41" }, "configId": { "description": "ID of secure configuration to share Box JWT credentials by admin.", "type": "string", "x-versionadded": "v2.41" }, "credentialType": { "description": "Credentials type.", "enum": [ "box_jwt" ], "type": "string", "x-versionadded": "v2.41" }, "enterpriseId": { "description": "Box enterprise identifier.", "type": "string", "x-versionadded": "v2.41" }, "passphrase": { "description": "Passphrase for the Box JWT private key.", "minLength": 1, "type": "string", "x-versionadded": "v2.41" }, "privateKeyStr": { "description": "RSA private key for Box JWT.", "minLength": 1, "type": "string", "x-versionadded": "v2.41" }, "publicKeyId": { "description": "Box public key identifier.", "type": "string", "x-versionadded": "v2.41" } }, "required": [ "credentialType" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clientId | string | false | | Box JWT client ID. | | clientSecret | string | false | | Box JWT client secret. | | configId | string | false | | ID of secure configuration to share Box JWT credentials by admin. | | credentialType | string | true | | Credentials type. | | enterpriseId | string | false | | Box enterprise identifier. | | passphrase | string | false | minLength: 1minLength: 1 | Passphrase for the Box JWT private key. | | privateKeyStr | string | false | minLength: 1minLength: 1 | RSA private key for Box JWT. | | publicKeyId | string | false | | Box public key identifier. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | box_jwt | ## BasicCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'basic' here. | | password | string | true | | The password for database authentication. The password is encrypted at rest and never saved or stored. | | user | string | true | | The username for database authentication. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | basic | ## ConnectorFieldSchema ``` { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | choices | [string] | true | | If non-empty, the list of all possible values for a parameter. | | default | string | true | | The default value of the parameter. | | description | string | true | | The description of the parameter. | | name | string | true | | The name of the parameter. | | required | boolean | true | | Whether or not the parameter is required for a connection. | | visibleByDefault | boolean | true | | Whether or not the parameter should be shown in the UI by default. | ## CreateCredentialsResponse ``` { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "name": { "description": "Name of these credentials.", "type": "string" } }, "required": [ "creationDate", "credentialId", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | ISO-8601 formatted date/time when these credentials were created. | | credentialId | string | true | | ID of these credentials. | | credentialType | string | false | | Type of credentials. | | description | string | false | | Description of these credentials. | | name | string | true | | Name of these credentials. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | [adls_gen2_oauth, api_token, azure, azure_oauth, azure_service_principal, basic, bearer, box_jwt, client_id_and_secret, databricks_access_token_account, databricks_service_principal_account, external_oauth_provider, gcp, oauth, rsa, s3, sap_oauth, snowflake_key_pair_user_account, snowflake_oauth_user_account, tableau_access_token] | ## CreateDataStageRequest ``` { "properties": { "filename": { "description": "The filename associated with the stage.", "type": "string" } }, "required": [ "filename" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | filename | string | true | | The filename associated with the stage. | ## CreateDataStageResponse ``` { "properties": { "id": { "description": "The ID of the data stage object.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the data stage object. | ## CreateDriverRequest ``` { "properties": { "baseNames": { "description": "Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls'", "items": { "description": "Original file name of the uploaded JAR file.", "type": "string" }, "type": "array" }, "canonicalName": { "description": "User-friendly driver name.", "type": "string" }, "className": { "description": "Driver class name. For example 'com.amazon.redshift.jdbc.Driver'", "type": [ "string", "null" ] }, "configurationId": { "description": "Driver configuration ID if it was provided during driver upload.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "databaseDriver": { "description": "The type of database of the driver. Only required of 'dr-database-v1' drivers.", "enum": [ "bigquery-v1", "databricks-v1", "datasphere-v1", "trino-v1" ], "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "localJarUrls": { "description": "File path(s) for the driver files.This path is returned in the response as 'local_url' by the driverUpload route.If there are multiple JAR files required for the driver, each uploaded JAR must be present in this list. If specified, values will replace any previous settings.", "items": { "description": "File path for the driver file. This path is returned by the driverUpload route in the 'local_url' response.", "type": "string" }, "minItems": 1, "type": "array" }, "type": { "default": "jdbc", "description": "Driver type. Either 'jdbc' or 'dr-database-v1'.", "enum": [ "dr-database-v1", "jdbc" ], "type": "string", "x-versionadded": "v2.30" }, "version": { "description": "Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload.", "type": "string", "x-versionadded": "v2.18" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseNames | [string] | false | | Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls' | | canonicalName | string | false | | User-friendly driver name. | | className | string,null | false | | Driver class name. For example 'com.amazon.redshift.jdbc.Driver' | | configurationId | string,null | false | | Driver configuration ID if it was provided during driver upload. | | databaseDriver | string,null | false | | The type of database of the driver. Only required of 'dr-database-v1' drivers. | | localJarUrls | [string] | false | minItems: 1 | File path(s) for the driver files.This path is returned in the response as 'local_url' by the driverUpload route.If there are multiple JAR files required for the driver, each uploaded JAR must be present in this list. If specified, values will replace any previous settings. | | type | string | false | | Driver type. Either 'jdbc' or 'dr-database-v1'. | | version | string | false | | Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload. | ### Enumerated Values | Property | Value | | --- | --- | | databaseDriver | [bigquery-v1, databricks-v1, datasphere-v1, trino-v1] | | type | [dr-database-v1, jdbc] | ## CredentialsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of credentials.", "items": { "properties": { "creationDate": { "description": "ISO-8601 formatted date/time when these credentials were created.", "format": "date-time", "type": "string" }, "credentialId": { "description": "ID of these credentials.", "type": "string" }, "credentialType": { "default": "basic", "description": "Type of credentials.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": "string" }, "description": { "description": "Description of these credentials.", "type": "string" }, "name": { "description": "Name of these credentials.", "type": "string" } }, "required": [ "creationDate", "credentialId", "name" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CreateCredentialsResponse] | true | | List of credentials. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DRConnectorV1Create ``` { "properties": { "connectorId": { "description": "The ID of the connector.", "type": "string" }, "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "connectorId", "fields" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | connectorId | string | true | | The ID of the connector. | | fields | [DRConnectorV1Field] | true | | The connector fields. | ## DRConnectorV1DataSource ``` { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "required": [ "dataStoreId", "path" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | string | true | | The data store ID for this data source. | | filter | string | false | maxLength: 320000 | An optional filter string for narrowing results, which overrides path when specified. Only supported by the Jira connector. | | path | string | true | | The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like /foldername/filename.csv. | ## DRConnectorV1Details ``` { "properties": { "connectorId": { "description": "The ID of the connector.", "type": "string" }, "fieldSchemas": { "description": "The connector field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "connectorId", "fields" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | connectorId | string | true | | The ID of the connector. | | fieldSchemas | [ConnectorFieldSchema] | false | | The connector field schemas. | | fields | [DRConnectorV1Field] | true | | The connector fields. | ## DRConnectorV1Field ``` { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | false | | The field name. | | name | string | true | | The user-friendly displayable field name. | | value | string | true | | The field value. | ## DRConnectorV1Update ``` { "properties": { "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fields | [DRConnectorV1Field] | false | | The connector fields. | ## DataSourceCreate ``` { "properties": { "canonicalName": { "description": "The data source canonical name.", "type": "string" }, "params": { "description": "The data source configuration.", "oneOf": [ { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string" } }, "required": [ "dataStoreId", "table" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "required": [ "dataStoreId", "query" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "required": [ "dataStoreId", "path" ], "type": "object" } ] }, "type": { "description": "The data source type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" } }, "required": [ "canonicalName", "params", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canonicalName | string | true | | The data source canonical name. | | params | any | true | | The data source configuration. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabaseTableDataSource | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabaseQueryDataSource | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DRConnectorV1DataSource | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | The data source type. | ### Enumerated Values | Property | Value | | --- | --- | | type | [dr-connector-v1, dr-database-v1, jdbc] | ## DataSourceDescribePermissionsResponse ``` { "properties": { "canCreatePredictions": { "description": "True if the user can create predictions from the data source.", "type": "boolean" }, "canCreateProject": { "description": "True if the user can create project from the data source.", "type": "boolean" }, "canDelete": { "description": "True if the user can delete the data source.", "type": "boolean" }, "canEdit": { "description": "True if the user can edit data source info.", "type": "boolean" }, "canSetRoles": { "description": "Roles the user can grant or revoke from other users, groups, or organizations.", "items": { "description": "The role the user can grant or revoke from users, groups or organizations.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "type": "array" }, "canShare": { "description": "True if the user can share the data source.", "type": "boolean" }, "canView": { "description": "True if the user can view data source info.", "type": "boolean" }, "dataSourceId": { "description": "The ID of the data source.", "type": "string" }, "userId": { "description": "The ID of the user identified by username.", "type": "string" }, "username": { "description": "`username` of a user with access to this data source.", "type": "string" } }, "required": [ "canCreatePredictions", "canCreateProject", "canDelete", "canEdit", "canSetRoles", "canShare", "canView", "dataSourceId", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canCreatePredictions | boolean | true | | True if the user can create predictions from the data source. | | canCreateProject | boolean | true | | True if the user can create project from the data source. | | canDelete | boolean | true | | True if the user can delete the data source. | | canEdit | boolean | true | | True if the user can edit data source info. | | canSetRoles | [string] | true | | Roles the user can grant or revoke from other users, groups, or organizations. | | canShare | boolean | true | | True if the user can share the data source. | | canView | boolean | true | | True if the user can view data source info. | | dataSourceId | string | true | | The ID of the data source. | | userId | string | true | | The ID of the user identified by username. | | username | string | true | | username of a user with access to this data source. | ## DataSourceInList ``` { "properties": { "canDelete": { "description": "True if the user can delete the data source.", "type": "boolean", "x-versionadded": "v2.23" }, "canShare": { "description": "True if the user can share the data source.", "type": "boolean", "x-versionadded": "v2.22" }, "canonicalName": { "description": "The data source canonical name.", "type": "string" }, "creator": { "description": "The ID of the user who created the data source.", "type": "string" }, "driverClassType": { "description": "The type of driver used to create this data source.", "type": "string" }, "id": { "description": "The data source ID.", "type": "string" }, "params": { "description": "The data source configuration.", "oneOf": [ { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string" } }, "required": [ "dataStoreId", "table" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "required": [ "dataStoreId", "query" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "required": [ "dataStoreId", "path" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data source.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data source type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data source update.", "type": "string" } }, "required": [ "canDelete", "canShare", "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canDelete | boolean | true | | True if the user can delete the data source. | | canShare | boolean | true | | True if the user can share the data source. | | canonicalName | string | true | | The data source canonical name. | | creator | string | true | | The ID of the user who created the data source. | | driverClassType | string | false | | The type of driver used to create this data source. | | id | string | true | | The data source ID. | | params | any | true | | The data source configuration. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabaseTableDataSource | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabaseQueryDataSource | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DRConnectorV1DataSource | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the user making the request on this data source. | | type | string | true | | The data source type. | | updated | string | true | | The ISO 8601-formatted date/time of the data source update. | ### Enumerated Values | Property | Value | | --- | --- | | role | [CONSUMER, EDITOR, OWNER] | | type | [dr-connector-v1, dr-database-v1, jdbc] | ## DataSourceListResponse ``` { "properties": { "data": { "description": "The list of data sources.", "items": { "properties": { "canDelete": { "description": "True if the user can delete the data source.", "type": "boolean", "x-versionadded": "v2.23" }, "canShare": { "description": "True if the user can share the data source.", "type": "boolean", "x-versionadded": "v2.22" }, "canonicalName": { "description": "The data source canonical name.", "type": "string" }, "creator": { "description": "The ID of the user who created the data source.", "type": "string" }, "driverClassType": { "description": "The type of driver used to create this data source.", "type": "string" }, "id": { "description": "The data source ID.", "type": "string" }, "params": { "description": "The data source configuration.", "oneOf": [ { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string" } }, "required": [ "dataStoreId", "table" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "required": [ "dataStoreId", "query" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "required": [ "dataStoreId", "path" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data source.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data source type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data source update.", "type": "string" } }, "required": [ "canDelete", "canShare", "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [DataSourceInList] | true | | The list of data sources. | ## DataSourceRetrieveResponse ``` { "properties": { "canonicalName": { "description": "The data source canonical name.", "type": "string" }, "creator": { "description": "The ID of the user who created the data source.", "type": "string" }, "driverClassType": { "description": "The type of driver used to create this data source.", "type": "string" }, "id": { "description": "The data source ID.", "type": "string" }, "params": { "description": "The data source configuration.", "oneOf": [ { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string" } }, "required": [ "dataStoreId", "table" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "required": [ "dataStoreId", "query" ], "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "required": [ "dataStoreId", "path" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data source.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data source type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data source update.", "type": "string" } }, "required": [ "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canonicalName | string | true | | The data source canonical name. | | creator | string | true | | The ID of the user who created the data source. | | driverClassType | string | false | | The type of driver used to create this data source. | | id | string | true | | The data source ID. | | params | any | true | | The data source configuration. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabaseTableDataSource | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabaseQueryDataSource | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DRConnectorV1DataSource | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the user making the request on this data source. | | type | string | true | | The data source type. | | updated | string | true | | The ISO 8601-formatted date/time of the data source update. | ### Enumerated Values | Property | Value | | --- | --- | | role | [CONSUMER, EDITOR, OWNER] | | type | [dr-connector-v1, dr-database-v1, jdbc] | ## DataSourceUpdate ``` { "properties": { "canonicalName": { "description": "The data source's canonical name.", "type": "string" }, "params": { "description": "Data source configuration.", "oneOf": [ { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if supported.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database.", "maxLength": 256, "type": "string" } }, "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "type": "object" }, { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "type": "object" } ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canonicalName | string | false | | The data source's canonical name. | | params | any | false | | Data source configuration. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OptionalDatabaseTableDataSource | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OptionalDatabaseQueryDataSource | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OptionalDRConnectorV1DataSource | false | | none | ## DataStageFinalizeResponse ``` { "properties": { "parts": { "description": "The size of the part.", "items": { "properties": { "checksum": { "description": "The checksum of the part.", "type": "string" }, "number": { "description": "The number of the part.", "type": "integer" }, "size": { "description": "The size of the part.", "type": "integer" } }, "required": [ "checksum", "number", "size" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "parts" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parts | [PartFinalizeResponse] | true | maxItems: 1000minItems: 1 | The size of the part. | ## DataStoreColumnResponse ``` { "properties": { "dataType": { "description": "The data type of the column.", "type": "string" }, "name": { "description": "The name of the column.", "type": "string" }, "precision": { "description": "The precision of the column.", "type": [ "string", "null" ] }, "scale": { "description": "The scale of the column.", "type": [ "string", "null" ] } }, "required": [ "dataType", "name", "precision", "scale" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataType | string | true | | The data type of the column. | | name | string | true | | The name of the column. | | precision | string,null | true | | The precision of the column. | | scale | string,null | true | | The scale of the column. | ## DataStoreColumnsInfoList ``` { "properties": { "catalog": { "description": "The name of the specified database catalog.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string", "x-versionadded": "v2.19" }, "password": { "description": "The password for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" }, "useKerberos": { "default": false, "description": "Whether to use Kerberos for data store authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "table" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string,null | false | | The name of the specified database catalog. | | credentialId | string | false | | The ID of the set of credentials to use instead of username and password. | | password | string | false | | The password for data store authentication. | | schema | string | false | | The schema name. | | table | string | true | | The table name. | | useKerberos | boolean | false | | Whether to use Kerberos for data store authentication. | | user | string | false | | The username for data store authentication. | ## DataStoreColumnsInfoListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of data store columns metadata.", "items": { "properties": { "columnDefaultValue": { "description": "The default value of the column.", "type": [ "string", "null" ] }, "comment": { "description": "The comment of the column.", "type": [ "string", "null" ] }, "dataType": { "description": "The data type of the column.", "type": "string" }, "dataTypeInt": { "description": "The integer value of the column data type.", "type": "integer" }, "exportedKeys": { "description": "The foreign key columns that reference this table's primary key columns.", "items": { "properties": { "foreignKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" }, "keyType": { "description": "The type of this key.", "enum": [ "1", "2", "3" ], "type": "string" }, "name": { "description": "The name of this key.", "type": "string" }, "primaryKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" } }, "required": [ "keyType", "primaryKey" ], "type": "object" }, "type": "array" }, "importedKeys": { "description": "The primary key columns that are referenced by this table's foreign key columns.", "items": { "properties": { "foreignKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" }, "keyType": { "description": "The type of this key.", "enum": [ "1", "2", "3" ], "type": "string" }, "name": { "description": "The name of this key.", "type": "string" }, "primaryKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" } }, "required": [ "keyType", "primaryKey" ], "type": "object" }, "type": "array" }, "isInPrimaryKey": { "description": "True if the column is in the primary key.", "type": [ "boolean", "null" ] }, "isNullable": { "description": "Whether the column values can be null.", "enum": [ "NO", "UNKNOWN", "YES" ], "type": [ "string", "null" ] }, "name": { "description": "The name of the column.", "type": "string" }, "precision": { "description": "The precision of the column.", "type": [ "integer", "null" ] }, "primaryKeys": { "description": "The primary key columns of the table.", "items": { "type": "string" }, "type": "array" }, "scale": { "description": "The scale of the column.", "type": [ "integer", "null" ] } }, "required": [ "columnDefaultValue", "comment", "dataType", "dataTypeInt", "exportedKeys", "importedKeys", "isInPrimaryKey", "isNullable", "name", "precision", "primaryKeys", "scale" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DataStoreExtendedColumnResponse] | true | | The list of data store columns metadata. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DataStoreColumnsList ``` { "properties": { "catalog": { "description": "The name of the specified database catalog.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential mapping.", "type": "string" }, "password": { "description": "The password for the db connection.", "type": "string" }, "query": { "description": "The schema query.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" }, "user": { "description": "The username for the db connection.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string,null | false | | The name of the specified database catalog. | | credentialId | string | false | | The ID of the credential mapping. | | password | string | false | | The password for the db connection. | | query | string | false | | The schema query. | | schema | string | false | | The schema name. | | table | string | false | | The table name. | | user | string | false | | The username for the db connection. | ## DataStoreColumnsListResponse ``` { "properties": { "columns": { "description": "The list of data store columns.", "items": { "properties": { "dataType": { "description": "The data type of the column.", "type": "string" }, "name": { "description": "The name of the column.", "type": "string" }, "precision": { "description": "The precision of the column.", "type": [ "string", "null" ] }, "scale": { "description": "The scale of the column.", "type": [ "string", "null" ] } }, "required": [ "dataType", "name", "precision", "scale" ], "type": "object" }, "type": "array" } }, "required": [ "columns" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columns | [DataStoreColumnResponse] | true | | The list of data store columns. | ## DataStoreCreate ``` { "properties": { "canonicalName": { "description": "The user-friendly name of the data store.", "type": "string" }, "params": { "description": "The data store configuration.", "oneOf": [ { "properties": { "driverId": { "description": "Driver ID.", "type": "string" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "required": [ "driverId" ], "type": "object" }, { "properties": { "connectorId": { "description": "The ID of the connector.", "type": "string" }, "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "connectorId", "fields" ], "type": "object" }, { "properties": { "driverId": { "description": "The database driver ID.", "type": "string" }, "fields": { "description": "The database driver fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "driverId", "fields" ], "type": "object" } ] }, "type": { "description": "The data store type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" } }, "required": [ "canonicalName", "params", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canonicalName | string | true | | The user-friendly name of the data store. | | params | any | true | | The data store configuration. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCDataStoreCreate | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DRConnectorV1Create | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabaseDataStoreCreate | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | The data store type. | ### Enumerated Values | Property | Value | | --- | --- | | type | [dr-connector-v1, dr-database-v1, jdbc] | ## DataStoreCredentials ``` { "properties": { "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string", "x-versionadded": "v2.19" }, "password": { "description": "The password for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" }, "useKerberos": { "default": false, "description": "Whether to use Kerberos for data store authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to use instead of username and password. | | password | string | false | | The password for data store authentication. | | useKerberos | boolean | false | | Whether to use Kerberos for data store authentication. | | user | string | false | | The username for data store authentication. | ## DataStoreCredentialsWithCredentialsTypeSupport ``` { "properties": { "credentialData": { "description": "The type of credentials to use with the data store.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "authenticationId": { "description": "The authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'external_oauth_provider' here.", "enum": [ "external_oauth_provider" ], "type": "string" } }, "required": [ "authenticationId", "credentialType" ], "type": "object" }, { "properties": { "clientId": { "description": "Box JWT client ID.", "type": "string", "x-versionadded": "v2.41" }, "clientSecret": { "description": "Box JWT client secret.", "type": "string", "x-versionadded": "v2.41" }, "configId": { "description": "ID of secure configuration to share Box JWT credentials by admin.", "type": "string", "x-versionadded": "v2.41" }, "credentialType": { "description": "Credentials type.", "enum": [ "box_jwt" ], "type": "string", "x-versionadded": "v2.41" }, "enterpriseId": { "description": "Box enterprise identifier.", "type": "string", "x-versionadded": "v2.41" }, "passphrase": { "description": "Passphrase for the Box JWT private key.", "minLength": 1, "type": "string", "x-versionadded": "v2.41" }, "privateKeyStr": { "description": "RSA private key for Box JWT.", "minLength": 1, "type": "string", "x-versionadded": "v2.41" }, "publicKeyId": { "description": "Box public key identifier.", "type": "string", "x-versionadded": "v2.41" } }, "required": [ "credentialType" ], "type": "object", "x-versionadded": "v2.42" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string", "x-versionadded": "v2.19" }, "password": { "description": "The password for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" }, "useKerberos": { "default": false, "description": "Whether to use Kerberos for data store authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialData | any | false | | The type of credentials to use with the data store. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BasicCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Credentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OAuthCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeKeyPairCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GoogleServiceAccountCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksAccessTokenCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksServicePrincipalCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureServicePrincipalCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExternalOAuthProviderCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BOXJWTCredentialsFields | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to use instead of username and password. | | password | string | false | | The password for data store authentication. | | useKerberos | boolean | false | | Whether to use Kerberos for data store authentication. | | user | string | false | | The username for data store authentication. | ## DataStoreDescribePermissionsResponse ``` { "properties": { "canCreateDataSource": { "description": "True if the user can create data source from this data store.", "type": "boolean" }, "canDelete": { "description": "True if the user can delete the data store.", "type": "boolean" }, "canEdit": { "description": "True if the user can edit data store info.", "type": "boolean" }, "canScanDatabase": { "description": "True if the user can scan data store database.", "type": "boolean" }, "canSetRoles": { "description": "Roles the user can grant or revoke from other users, groups, or organizations.", "items": { "description": "The role the user can grant or revoke from users, groups or organizations.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "type": "array" }, "canShare": { "description": "True if the user can share the data store.", "type": "boolean" }, "canTestConnection": { "description": "True if the user can test data store database connection.", "type": "boolean" }, "canView": { "description": "True if the user can view data store info.", "type": "boolean" }, "dataStoreId": { "description": "The ID of the data store.", "type": "string" }, "userId": { "description": "The ID of the user identified by username.", "type": "string" }, "username": { "description": "`username` of a user with access to this data store.", "type": "string" } }, "required": [ "canCreateDataSource", "canDelete", "canEdit", "canScanDatabase", "canSetRoles", "canShare", "canTestConnection", "canView", "dataStoreId", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canCreateDataSource | boolean | true | | True if the user can create data source from this data store. | | canDelete | boolean | true | | True if the user can delete the data store. | | canEdit | boolean | true | | True if the user can edit data store info. | | canScanDatabase | boolean | true | | True if the user can scan data store database. | | canSetRoles | [string] | true | | Roles the user can grant or revoke from other users, groups, or organizations. | | canShare | boolean | true | | True if the user can share the data store. | | canTestConnection | boolean | true | | True if the user can test data store database connection. | | canView | boolean | true | | True if the user can view data store info. | | dataStoreId | string | true | | The ID of the data store. | | userId | string | true | | The ID of the user identified by username. | | username | string | true | | username of a user with access to this data store. | ## DataStoreExtendedColumnResponse ``` { "properties": { "columnDefaultValue": { "description": "The default value of the column.", "type": [ "string", "null" ] }, "comment": { "description": "The comment of the column.", "type": [ "string", "null" ] }, "dataType": { "description": "The data type of the column.", "type": "string" }, "dataTypeInt": { "description": "The integer value of the column data type.", "type": "integer" }, "exportedKeys": { "description": "The foreign key columns that reference this table's primary key columns.", "items": { "properties": { "foreignKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" }, "keyType": { "description": "The type of this key.", "enum": [ "1", "2", "3" ], "type": "string" }, "name": { "description": "The name of this key.", "type": "string" }, "primaryKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" } }, "required": [ "keyType", "primaryKey" ], "type": "object" }, "type": "array" }, "importedKeys": { "description": "The primary key columns that are referenced by this table's foreign key columns.", "items": { "properties": { "foreignKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" }, "keyType": { "description": "The type of this key.", "enum": [ "1", "2", "3" ], "type": "string" }, "name": { "description": "The name of this key.", "type": "string" }, "primaryKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" } }, "required": [ "keyType", "primaryKey" ], "type": "object" }, "type": "array" }, "isInPrimaryKey": { "description": "True if the column is in the primary key.", "type": [ "boolean", "null" ] }, "isNullable": { "description": "Whether the column values can be null.", "enum": [ "NO", "UNKNOWN", "YES" ], "type": [ "string", "null" ] }, "name": { "description": "The name of the column.", "type": "string" }, "precision": { "description": "The precision of the column.", "type": [ "integer", "null" ] }, "primaryKeys": { "description": "The primary key columns of the table.", "items": { "type": "string" }, "type": "array" }, "scale": { "description": "The scale of the column.", "type": [ "integer", "null" ] } }, "required": [ "columnDefaultValue", "comment", "dataType", "dataTypeInt", "exportedKeys", "importedKeys", "isInPrimaryKey", "isNullable", "name", "precision", "primaryKeys", "scale" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnDefaultValue | string,null | true | | The default value of the column. | | comment | string,null | true | | The comment of the column. | | dataType | string | true | | The data type of the column. | | dataTypeInt | integer | true | | The integer value of the column data type. | | exportedKeys | [JdbcForeignKey] | true | | The foreign key columns that reference this table's primary key columns. | | importedKeys | [JdbcForeignKey] | true | | The primary key columns that are referenced by this table's foreign key columns. | | isInPrimaryKey | boolean,null | true | | True if the column is in the primary key. | | isNullable | string,null | true | | Whether the column values can be null. | | name | string | true | | The name of the column. | | precision | integer,null | true | | The precision of the column. | | primaryKeys | [string] | true | | The primary key columns of the table. | | scale | integer,null | true | | The scale of the column. | ### Enumerated Values | Property | Value | | --- | --- | | isNullable | [NO, UNKNOWN, YES] | ## DataStoreListResponse ``` { "properties": { "data": { "description": "The list of data stores.", "items": { "properties": { "associatedAuthTypes": { "description": "The supported authentication types for the JDBC configuration.", "items": { "enum": [ "adls_gen2_oauth", "azure_service_principal", "basic", "box_jwt", "databricks_access_token_account", "databricks_service_principal_account", "gcp", "google_oauth_user_account", "kerberos", "oauth", "s3", "snowflake_key_pair_user_account", "snowflake_oauth_user_account" ], "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "canonicalName": { "description": "The user-friendly name of the data store.", "type": "string" }, "creator": { "description": "The ID of the user who created the data store.", "type": "string" }, "id": { "description": "The data store ID.", "type": "string" }, "params": { "description": "The data store configuration.", "oneOf": [ { "properties": { "driverId": { "description": "The driver ID.", "type": "string" }, "jdbcFieldSchemas": { "default": [], "description": "The fields to show when creating a data store, their defaults, whether or not they are required, and more.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "maxItems": 100, "type": "array" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "required": [ "driverId" ], "type": "object" }, { "properties": { "connectorId": { "description": "The ID of the connector.", "type": "string" }, "fieldSchemas": { "description": "The connector field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "connectorId", "fields" ], "type": "object" }, { "properties": { "driverId": { "description": "The database driver ID.", "type": "string" }, "fieldSchemas": { "description": "The database driver field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The database driver fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "driverId", "fields" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data store.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data store type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data store update.", "type": "string" } }, "required": [ "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [DataStoreRetrieveResponse] | true | | The list of data stores. | ## DataStorePreviewResponse ``` { "properties": { "columns": { "description": "The list of columns for the returned set of records. Column order matches the order of values in a record.", "items": { "description": "The name of the column.", "type": "string" }, "maxItems": 20000, "type": "array" }, "records": { "description": "The list of records output by the query.", "items": { "description": "The list of values for a single database record, ordered as the columns are ordered.", "items": { "description": "The string representation of the column's value.", "type": "string" }, "type": "array" }, "maxItems": 10000, "type": "array" }, "resultSchema": { "description": "The JDBC result schema.", "items": { "description": "The JDBC result column description.", "properties": { "dataType": { "description": "The data type of the column.", "type": "string" }, "dataTypeInt": { "description": "The integer value of the column data type.", "type": [ "integer", "null" ] }, "name": { "description": "The name of the column.", "type": "string" }, "precision": { "description": "The precision of the column.", "type": [ "integer", "null" ] }, "scale": { "description": "The scale of the column.", "type": [ "integer", "null" ] } }, "required": [ "dataType", "dataTypeInt", "name", "precision", "scale" ], "type": "object", "x-versionadded": "v2.43" }, "maxItems": 20000, "type": "array" } }, "required": [ "columns", "records", "resultSchema" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columns | [string] | true | maxItems: 20000 | The list of columns for the returned set of records. Column order matches the order of values in a record. | | records | [array] | true | maxItems: 10000 | The list of records output by the query. | | resultSchema | [DataStoreResultSchemaResponse] | true | maxItems: 20000 | The JDBC result schema. | ## DataStoreResultSchemaResponse ``` { "description": "The JDBC result column description.", "properties": { "dataType": { "description": "The data type of the column.", "type": "string" }, "dataTypeInt": { "description": "The integer value of the column data type.", "type": [ "integer", "null" ] }, "name": { "description": "The name of the column.", "type": "string" }, "precision": { "description": "The precision of the column.", "type": [ "integer", "null" ] }, "scale": { "description": "The scale of the column.", "type": [ "integer", "null" ] } }, "required": [ "dataType", "dataTypeInt", "name", "precision", "scale" ], "type": "object", "x-versionadded": "v2.43" } ``` The JDBC result column description. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataType | string | true | | The data type of the column. | | dataTypeInt | integer,null | true | | The integer value of the column data type. | | name | string | true | | The name of the column. | | precision | integer,null | true | | The precision of the column. | | scale | integer,null | true | | The scale of the column. | ## DataStoreRetrieveResponse ``` { "properties": { "associatedAuthTypes": { "description": "The supported authentication types for the JDBC configuration.", "items": { "enum": [ "adls_gen2_oauth", "azure_service_principal", "basic", "box_jwt", "databricks_access_token_account", "databricks_service_principal_account", "gcp", "google_oauth_user_account", "kerberos", "oauth", "s3", "snowflake_key_pair_user_account", "snowflake_oauth_user_account" ], "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "canonicalName": { "description": "The user-friendly name of the data store.", "type": "string" }, "creator": { "description": "The ID of the user who created the data store.", "type": "string" }, "id": { "description": "The data store ID.", "type": "string" }, "params": { "description": "The data store configuration.", "oneOf": [ { "properties": { "driverId": { "description": "The driver ID.", "type": "string" }, "jdbcFieldSchemas": { "default": [], "description": "The fields to show when creating a data store, their defaults, whether or not they are required, and more.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "maxItems": 100, "type": "array" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "required": [ "driverId" ], "type": "object" }, { "properties": { "connectorId": { "description": "The ID of the connector.", "type": "string" }, "fieldSchemas": { "description": "The connector field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "connectorId", "fields" ], "type": "object" }, { "properties": { "driverId": { "description": "The database driver ID.", "type": "string" }, "fieldSchemas": { "description": "The database driver field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The database driver fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "driverId", "fields" ], "type": "object" } ] }, "role": { "description": "The role of the user making the request on this data store.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "type": { "description": "The data store type.", "enum": [ "dr-connector-v1", "dr-database-v1", "jdbc" ], "type": "string" }, "updated": { "description": "The ISO 8601-formatted date/time of the data store update.", "type": "string" } }, "required": [ "canonicalName", "creator", "id", "params", "role", "type", "updated" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associatedAuthTypes | [string] | false | | The supported authentication types for the JDBC configuration. | | canonicalName | string | true | | The user-friendly name of the data store. | | creator | string | true | | The ID of the user who created the data store. | | id | string | true | | The data store ID. | | params | any | true | | The data store configuration. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCDataStoreDetails | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DRConnectorV1Details | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabaseDataStoreDetails | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the user making the request on this data store. | | type | string | true | | The data store type. | | updated | string | true | | The ISO 8601-formatted date/time of the data store update. | ### Enumerated Values | Property | Value | | --- | --- | | role | [CONSUMER, EDITOR, OWNER] | | type | [dr-connector-v1, dr-database-v1, jdbc] | ## DataStoreSQLVerify ``` { "properties": { "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string", "x-versionadded": "v2.19" }, "maxRows": { "description": "The maximum number of rows of data to return if successful.", "maximum": 999, "minimum": 0, "type": "integer" }, "password": { "description": "The password for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" }, "query": { "description": "The SQL query to verify.", "type": "string" }, "useKerberos": { "default": false, "description": "Whether to use Kerberos for data store authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "maxRows", "query" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to use instead of username and password. | | maxRows | integer | true | maximum: 999minimum: 0 | The maximum number of rows of data to return if successful. | | password | string | false | | The password for data store authentication. | | query | string | true | | The SQL query to verify. | | useKerberos | boolean | false | | Whether to use Kerberos for data store authentication. | | user | string | false | | The username for data store authentication. | ## DataStoreSQLVerifyResponse ``` { "properties": { "columns": { "description": "The list of columns for the returned set of records. Column order matches the order of values in a record.", "items": { "description": "The name of the column.", "type": "string" }, "maxItems": 20000, "type": "array" }, "records": { "description": "The list of records output by the query.", "items": { "description": "The list of values for a single database record, ordered as the columns are ordered.", "items": { "description": "The string representation of the column's value.", "type": "string" }, "type": "array" }, "maxItems": 10000, "type": "array" } }, "required": [ "columns", "records" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columns | [string] | true | maxItems: 20000 | The list of columns for the returned set of records. Column order matches the order of values in a record. | | records | [array] | true | maxItems: 10000 | The list of records output by the query. | ## DataStoreSchemasList ``` { "properties": { "catalog": { "description": "The name of the catalog associated with the schemas. If more than one catalog is queried, the returned value will be the catalog name for the first record returned.", "type": "string" }, "catalogs": { "description": "The list of catalogs associated with each retrieved schema. If no catalog is found, this value is null.", "items": { "description": "The catalog names for the schema associated with the data store.", "type": [ "string", "null" ] }, "type": "array" }, "schemas": { "description": "The list of schemas available in the data store.", "items": { "type": "string" }, "type": "array" } }, "required": [ "catalog", "catalogs", "schemas" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | The name of the catalog associated with the schemas. If more than one catalog is queried, the returned value will be the catalog name for the first record returned. | | catalogs | [string,null] | true | | The list of catalogs associated with each retrieved schema. If no catalog is found, this value is null. | | schemas | [string] | true | | The list of schemas available in the data store. | ## DataStoreTables ``` { "properties": { "catalog": { "description": "Only show tables in this catalog.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string", "x-versionadded": "v2.19" }, "password": { "description": "The password for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" }, "schema": { "description": "Only show tables in this schema.", "type": "string" }, "useKerberos": { "default": false, "description": "Whether to use Kerberos for data store authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for data store authentication.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | Only show tables in this catalog. | | credentialId | string | false | | The ID of the set of credentials to use instead of username and password. | | password | string | false | | The password for data store authentication. | | schema | string | false | | Only show tables in this schema. | | useKerberos | boolean | false | | Whether to use Kerberos for data store authentication. | | user | string | false | | The username for data store authentication. | ## DataStoreTablesList ``` { "properties": { "catalog": { "description": "The catalog associated with schemas.", "type": [ "string", "null" ] }, "tables": { "description": "The list of tables in the data store.", "items": { "properties": { "catalog": { "description": "The name of the catalog.", "type": [ "string", "null" ] }, "name": { "description": "The name of the table.", "type": "string" }, "schema": { "description": "The schema of the table.", "type": "string" }, "type": { "description": "The type of table.", "enum": [ "TABLE", "VIEW" ], "type": "string" } }, "required": [ "name", "type" ], "type": "object" }, "type": "array" } }, "required": [ "catalog", "tables" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string,null | true | | The catalog associated with schemas. | | tables | [TableDescription] | true | | The list of tables in the data store. | ## DataStoreTestResponse ``` { "properties": { "message": { "description": "The connection attempt results.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | The connection attempt results. | ## DataStoreUpdate ``` { "properties": { "canonicalName": { "description": "The user-friendly name of the data store.", "type": "string" }, "params": { "description": "The data store configuration.", "oneOf": [ { "properties": { "driverId": { "description": "The driver ID.", "type": "string" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "type": "object" }, { "properties": { "fields": { "description": "The connector fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "type": "object" } ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canonicalName | string | false | | The user-friendly name of the data store. | | params | any | false | | The data store configuration. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCDataStoreUpdate | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DRConnectorV1Update | false | | none | ## DatabaseDataStoreCreate ``` { "properties": { "driverId": { "description": "The database driver ID.", "type": "string" }, "fields": { "description": "The database driver fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "driverId", "fields" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | driverId | string | true | | The database driver ID. | | fields | [DRConnectorV1Field] | true | | The database driver fields. | ## DatabaseDataStoreDetails ``` { "properties": { "driverId": { "description": "The database driver ID.", "type": "string" }, "fieldSchemas": { "description": "The database driver field schemas.", "items": { "properties": { "choices": { "description": "If non-empty, the list of all possible values for a parameter.", "items": { "type": "string" }, "type": "array" }, "default": { "description": "The default value of the parameter.", "type": "string" }, "description": { "description": "The description of the parameter.", "type": "string" }, "name": { "description": "The name of the parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "fields": { "description": "The database driver fields.", "items": { "properties": { "id": { "description": "The field name.", "type": "string" }, "name": { "description": "The user-friendly displayable field name.", "type": "string" }, "value": { "description": "The field value.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "driverId", "fields" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | driverId | string | true | | The database driver ID. | | fieldSchemas | [ConnectorFieldSchema] | false | | The database driver field schemas. | | fields | [DRConnectorV1Field] | true | | The database driver fields. | ## DatabaseQueryDataSource ``` { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "required": [ "dataStoreId", "query" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | string | true | | The data store ID for this data source. | | fetchSize | integer | false | maximum: 20000minimum: 1 | The user-specified fetch size. | | query | string | true | maxLength: 320000 | The user-specified SQL query. If this is used, then catalog, schema and table will not be used. | ## DatabaseTableDataSource ``` { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string" } }, "required": [ "dataStoreId", "table" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | maxLength: 256 | The catalog name in the database if supported. | | dataStoreId | string | true | | The data store ID for this data source. | | fetchSize | integer | false | maximum: 20000minimum: 1 | The user-specified fetch size. | | partitionColumn | string | false | | The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects. | | schema | string | false | maxLength: 256 | The schema associated with the table or view in the database if the data source is not query based. | | table | string | true | maxLength: 256 | The table or view name in the database if the data source is not query based. | ## DatabricksAccessTokenCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'databricks_access_token_account' here. | | databricksAccessToken | string | true | minLength: 1minLength: 1 | Databricks personal access token. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | databricks_access_token_account | ## DatabricksServicePrincipalCredentials ``` { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clientId | string | false | minLength: 1minLength: 1 | Client ID for Databricks service principal. | | clientSecret | string | false | minLength: 1minLength: 1 | Client secret for Databricks service principal. | | configId | string | false | | The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret. | | credentialType | string | true | | The type of these credentials, 'databricks_service_principal_account' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | databricks_service_principal_account | ## DetectUdfs ``` { "properties": { "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string" }, "force": { "default": false, "description": "Forces detection to be submitted, even if a cache with detected standard user-defined functions for given parameters is already present.", "type": "boolean" }, "functionType": { "description": "The standard user-defined function type.", "enum": [ "rolling_median", "rolling_most_frequent" ], "type": "string" }, "schema": { "description": "The schema to create or detect user-defined functions in.", "type": "string" } }, "required": [ "force", "functionType", "schema" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to use instead of username and password. | | force | boolean | true | | Forces detection to be submitted, even if a cache with detected standard user-defined functions for given parameters is already present. | | functionType | string | true | | The standard user-defined function type. | | schema | string | true | | The schema to create or detect user-defined functions in. | ### Enumerated Values | Property | Value | | --- | --- | | functionType | [rolling_median, rolling_most_frequent] | ## DetectedStandardUdfsRetrieveResponse ``` { "properties": { "detectedFunctions": { "description": "The detected standard user-defined functions.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "functionType": { "description": "The requested standard user-defined function type.", "type": "string" }, "schema": { "description": "The requested schema.", "type": "string" } }, "required": [ "detectedFunctions", "functionType", "schema" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | detectedFunctions | [string] | true | maxItems: 1000 | The detected standard user-defined functions. | | functionType | string | true | | The requested standard user-defined function type. | | schema | string | true | | The requested schema. | ## DriverConfigurationListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of driver configurations available to the user.", "items": { "properties": { "associatedAuthTypes": { "description": "The list of authentication types supported by this driver configuration.", "items": { "type": "string" }, "type": "array" }, "className": { "description": "Java class name of the driver to be used (e.g., org.postgresql.Driver).", "type": "string" }, "creator": { "description": "The user ID of the creator of this configuration.", "type": "string" }, "id": { "description": "The driver configuration ID.", "type": "string" }, "jdbcFieldSchemas": { "description": "Description of which fields to show when creating a datastore, their defaults, and whether or not they are required.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "jdbcUrlPathDelimiter": { "description": "Separator of address from path in the JDBC URL (e.g., \"/\").", "type": "string" }, "jdbcUrlPrefix": { "description": "Prefix of the JDBC URL (e.g., \"jdbc:mssql://\" or \"jdbc:oracle:thin:@\").", "type": "string" }, "jdbcUrlQueryDelimiter": { "description": "Separator of path from the list of query parameters in the JDBC URL (e.g., \"?\").", "type": "string" }, "jdbcUrlQueryParamDelimiter": { "description": "Separator of each set of query parameter key-value pairs in the JDBC URL (e.g., \"&\").", "type": "string" }, "jdbcUrlQueryParamKeyValueDelimiter": { "description": "Separator of the key and value in a query parameter pair (e.g., \"=\").", "type": "string" }, "standardizedName": { "description": "The plain text name for the driver (e.g., PostgreSQL).", "type": "string" }, "statements": { "description": "List of supported statments for this driver configuration.", "items": { "enum": [ "insert", "update", "upsert" ], "type": "string" }, "type": "array" }, "updated": { "description": "ISO-8601 formatted time/date when this configuration was most recently updated.", "type": "string" } }, "required": [ "associatedAuthTypes", "className", "creator", "id", "jdbcFieldSchemas", "jdbcUrlPathDelimiter", "jdbcUrlPrefix", "jdbcUrlQueryDelimiter", "jdbcUrlQueryParamDelimiter", "jdbcUrlQueryParamKeyValueDelimiter", "standardizedName", "updated" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DriverConfigurationRetrieveResponse] | true | | List of driver configurations available to the user. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DriverConfigurationRetrieveResponse ``` { "properties": { "associatedAuthTypes": { "description": "The list of authentication types supported by this driver configuration.", "items": { "type": "string" }, "type": "array" }, "className": { "description": "Java class name of the driver to be used (e.g., org.postgresql.Driver).", "type": "string" }, "creator": { "description": "The user ID of the creator of this configuration.", "type": "string" }, "id": { "description": "The driver configuration ID.", "type": "string" }, "jdbcFieldSchemas": { "description": "Description of which fields to show when creating a datastore, their defaults, and whether or not they are required.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "type": "array" }, "jdbcUrlPathDelimiter": { "description": "Separator of address from path in the JDBC URL (e.g., \"/\").", "type": "string" }, "jdbcUrlPrefix": { "description": "Prefix of the JDBC URL (e.g., \"jdbc:mssql://\" or \"jdbc:oracle:thin:@\").", "type": "string" }, "jdbcUrlQueryDelimiter": { "description": "Separator of path from the list of query parameters in the JDBC URL (e.g., \"?\").", "type": "string" }, "jdbcUrlQueryParamDelimiter": { "description": "Separator of each set of query parameter key-value pairs in the JDBC URL (e.g., \"&\").", "type": "string" }, "jdbcUrlQueryParamKeyValueDelimiter": { "description": "Separator of the key and value in a query parameter pair (e.g., \"=\").", "type": "string" }, "standardizedName": { "description": "The plain text name for the driver (e.g., PostgreSQL).", "type": "string" }, "statements": { "description": "List of supported statments for this driver configuration.", "items": { "enum": [ "insert", "update", "upsert" ], "type": "string" }, "type": "array" }, "updated": { "description": "ISO-8601 formatted time/date when this configuration was most recently updated.", "type": "string" } }, "required": [ "associatedAuthTypes", "className", "creator", "id", "jdbcFieldSchemas", "jdbcUrlPathDelimiter", "jdbcUrlPrefix", "jdbcUrlQueryDelimiter", "jdbcUrlQueryParamDelimiter", "jdbcUrlQueryParamKeyValueDelimiter", "standardizedName", "updated" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associatedAuthTypes | [string] | true | | The list of authentication types supported by this driver configuration. | | className | string | true | | Java class name of the driver to be used (e.g., org.postgresql.Driver). | | creator | string | true | | The user ID of the creator of this configuration. | | id | string | true | | The driver configuration ID. | | jdbcFieldSchemas | [JDBCFieldSchemas] | true | | Description of which fields to show when creating a datastore, their defaults, and whether or not they are required. | | jdbcUrlPathDelimiter | string | true | | Separator of address from path in the JDBC URL (e.g., "/"). | | jdbcUrlPrefix | string | true | | Prefix of the JDBC URL (e.g., "jdbc:mssql://" or "jdbc:oracle:thin:@"). | | jdbcUrlQueryDelimiter | string | true | | Separator of path from the list of query parameters in the JDBC URL (e.g., "?"). | | jdbcUrlQueryParamDelimiter | string | true | | Separator of each set of query parameter key-value pairs in the JDBC URL (e.g., "&"). | | jdbcUrlQueryParamKeyValueDelimiter | string | true | | Separator of the key and value in a query parameter pair (e.g., "="). | | standardizedName | string | true | | The plain text name for the driver (e.g., PostgreSQL). | | statements | [string] | false | | List of supported statments for this driver configuration. | | updated | string | true | | ISO-8601 formatted time/date when this configuration was most recently updated. | ## DriverListResponse ``` { "properties": { "data": { "description": "The list of drivers.", "items": { "properties": { "associatedAuthTypes": { "description": "Authentication types associated with this connector configuration.", "items": { "enum": [ "basic", "googleOauthUserAccount", "snowflakeKeyPairUserAccount", "snowflakeOauthUserAccount" ], "type": "string" }, "type": "array" }, "baseNames": { "description": "Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls'", "items": { "description": "Original file name of the uploaded JAR file.", "type": "string" }, "type": "array" }, "canonicalName": { "description": "User-friendly driver name.", "type": "string" }, "className": { "description": "Driver class name. For example 'com.amazon.redshift.jdbc.Driver'", "type": [ "string", "null" ] }, "configurationId": { "description": "Driver configuration ID if it was provided during driver upload.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "creator": { "description": "The ID of the user who uploaded the driver.", "type": "string" }, "databaseDriver": { "description": "The type of database of the driver. Only required of 'dr-database-v1' drivers.", "enum": [ "bigquery-v1", "databricks-v1", "datasphere-v1", "trino-v1" ], "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "id": { "description": "Driver ID.", "type": "string" }, "type": { "default": "jdbc", "description": "Driver type. Either 'jdbc' or 'dr-database-v1'.", "enum": [ "dr-database-v1", "jdbc" ], "type": "string", "x-versionadded": "v2.30" }, "version": { "description": "Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload.", "type": "string", "x-versionadded": "v2.18" } }, "required": [ "creator", "id" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [DriverResponse] | true | | The list of drivers. | ## DriverResponse ``` { "properties": { "associatedAuthTypes": { "description": "Authentication types associated with this connector configuration.", "items": { "enum": [ "basic", "googleOauthUserAccount", "snowflakeKeyPairUserAccount", "snowflakeOauthUserAccount" ], "type": "string" }, "type": "array" }, "baseNames": { "description": "Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls'", "items": { "description": "Original file name of the uploaded JAR file.", "type": "string" }, "type": "array" }, "canonicalName": { "description": "User-friendly driver name.", "type": "string" }, "className": { "description": "Driver class name. For example 'com.amazon.redshift.jdbc.Driver'", "type": [ "string", "null" ] }, "configurationId": { "description": "Driver configuration ID if it was provided during driver upload.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "creator": { "description": "The ID of the user who uploaded the driver.", "type": "string" }, "databaseDriver": { "description": "The type of database of the driver. Only required of 'dr-database-v1' drivers.", "enum": [ "bigquery-v1", "databricks-v1", "datasphere-v1", "trino-v1" ], "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "id": { "description": "Driver ID.", "type": "string" }, "type": { "default": "jdbc", "description": "Driver type. Either 'jdbc' or 'dr-database-v1'.", "enum": [ "dr-database-v1", "jdbc" ], "type": "string", "x-versionadded": "v2.30" }, "version": { "description": "Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload.", "type": "string", "x-versionadded": "v2.18" } }, "required": [ "creator", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associatedAuthTypes | [string] | false | | Authentication types associated with this connector configuration. | | baseNames | [string] | false | | Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls' | | canonicalName | string | false | | User-friendly driver name. | | className | string,null | false | | Driver class name. For example 'com.amazon.redshift.jdbc.Driver' | | configurationId | string,null | false | | Driver configuration ID if it was provided during driver upload. | | creator | string | true | | The ID of the user who uploaded the driver. | | databaseDriver | string,null | false | | The type of database of the driver. Only required of 'dr-database-v1' drivers. | | id | string | true | | Driver ID. | | type | string | false | | Driver type. Either 'jdbc' or 'dr-database-v1'. | | version | string | false | | Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload. | ### Enumerated Values | Property | Value | | --- | --- | | databaseDriver | [bigquery-v1, databricks-v1, datasphere-v1, trino-v1] | | type | [dr-database-v1, jdbc] | ## DriverUploadRequest ``` { "properties": { "file": { "description": "JDBC driver JAR file to upload.", "format": "binary", "type": "string" } }, "required": [ "file" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | file | string(binary) | true | | JDBC driver JAR file to upload. | ## DriverUploadResponse ``` { "properties": { "localUrl": { "description": "URL of uploaded driver file.", "type": "string" } }, "required": [ "localUrl" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | localUrl | string | true | | URL of uploaded driver file. | ## ExternalOAuthProviderCredentials ``` { "properties": { "authenticationId": { "description": "The authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'external_oauth_provider' here.", "enum": [ "external_oauth_provider" ], "type": "string" } }, "required": [ "authenticationId", "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authenticationId | string | true | | The authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service. | | credentialType | string | true | | The type of these credentials, 'external_oauth_provider' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | external_oauth_provider | ## GCPKey ``` { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authProviderX509CertUrl | string(uri) | false | | Auth provider X509 certificate URL. | | authUri | string(uri) | false | | Auth URI. | | clientEmail | string | false | | Client email address. | | clientId | string | false | | Client ID. | | clientX509CertUrl | string(uri) | false | | Client X509 certificate URL. | | privateKey | string | false | | Private key. | | privateKeyId | string | false | | Private key ID | | projectId | string | false | | Project ID. | | tokenUri | string(uri) | false | | Token URI. | | type | string | true | | GCP account type. | ### Enumerated Values | Property | Value | | --- | --- | | type | service_account | ## GoogleServiceAccountCredentials ``` { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configId | string | false | | The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey. | | credentialType | string | true | | The type of these credentials, 'gcp' here. | | gcpKey | GCPKey | false | | The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. | | googleConfigId | string | false | | The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | gcp | ## GrantAccessControlWithIdWithGrant ``` { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If role is NO_ROLE canShare is ignored. | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## GrantAccessControlWithUsernameWithGrant ``` { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If role is NO_ROLE canShare is ignored. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | The username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## JDBCDataStoreCreate ``` { "properties": { "driverId": { "description": "Driver ID.", "type": "string" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "required": [ "driverId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | driverId | string | true | | Driver ID. | | jdbcFields | [JDBCFields] | false | maxItems: 100 | The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: {"address": localhost:5432, "database": "fooBar", "connectTimeout": 10}. In most cases, all keys in jdbcFields should be defined by a schema listed in jdbcFieldSchemas from DriverConfiguration. The request will be rejected if there are required parameters (as defined by jdbcFieldSchemas) that are not provided. | | jdbcUrl | string | false | | The JDBC URL. | ## JDBCDataStoreDetails ``` { "properties": { "driverId": { "description": "The driver ID.", "type": "string" }, "jdbcFieldSchemas": { "default": [], "description": "The fields to show when creating a data store, their defaults, whether or not they are required, and more.", "items": { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" }, "maxItems": 100, "type": "array" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "required": [ "driverId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | driverId | string | true | | The driver ID. | | jdbcFieldSchemas | [JDBCFieldSchemas] | false | maxItems: 100 | The fields to show when creating a data store, their defaults, whether or not they are required, and more. | | jdbcFields | [JDBCFields] | false | maxItems: 100 | The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: {"address": localhost:5432, "database": "fooBar", "connectTimeout": 10}. In most cases, all keys in jdbcFields should be defined by a schema listed in jdbcFieldSchemas from DriverConfiguration. The request will be rejected if there are required parameters (as defined by jdbcFieldSchemas) that are not provided. | | jdbcUrl | string | false | | The JDBC URL. | ## JDBCDataStoreUpdate ``` { "properties": { "driverId": { "description": "The driver ID.", "type": "string" }, "jdbcFields": { "description": "The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: `{\"address\": localhost:5432, \"database\": \"fooBar\", \"connectTimeout\": 10}`. In most cases, all keys in `jdbcFields` should be defined by a schema listed in `jdbcFieldSchemas` from `DriverConfiguration`. The request will be rejected if there are required parameters (as defined by `jdbcFieldSchemas`) that are not provided.", "items": { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.18" }, "jdbcUrl": { "description": "The JDBC URL.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | driverId | string | false | | The driver ID. | | jdbcFields | [JDBCFields] | false | maxItems: 100 | The fields used to create the JDBC URL, in the form of a JSON object where parameter name/value pairs are the items in the object. For example: {"address": localhost:5432, "database": "fooBar", "connectTimeout": 10}. In most cases, all keys in jdbcFields should be defined by a schema listed in jdbcFieldSchemas from DriverConfiguration. The request will be rejected if there are required parameters (as defined by jdbcFieldSchemas) that are not provided. | | jdbcUrl | string | false | | The JDBC URL. | ## JDBCFieldSchemas ``` { "properties": { "choices": { "default": [], "description": "If non-empty, a list of all possible values for this parameter.", "items": { "description": "Possible value for this parameter.", "type": "string" }, "type": "array" }, "default": { "description": "Default value of the JDBC parameter.", "type": "string" }, "description": { "default": "", "description": "Description of this parameter.", "type": "string" }, "index": { "description": "Sort order within one `kind`.", "type": "integer" }, "kind": { "description": "Use of this parameter in constructing the JDBC URL.", "enum": [ "ADDRESS", "EXTENDED_PATH_PARAM", "PATH_PARAM", "QUERY_PARAM" ], "type": "string" }, "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "required": { "description": "Whether or not the parameter is required for a connection.", "type": "boolean" }, "visibleByDefault": { "description": "Whether or not the parameter should be shown in the UI by default.", "type": "boolean" } }, "required": [ "choices", "default", "description", "kind", "name", "required", "visibleByDefault" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | choices | [string] | true | | If non-empty, a list of all possible values for this parameter. | | default | string | true | | Default value of the JDBC parameter. | | description | string | true | | Description of this parameter. | | index | integer | false | | Sort order within one kind. | | kind | string | true | | Use of this parameter in constructing the JDBC URL. | | name | string | true | | The name of the JDBC parameter. | | required | boolean | true | | Whether or not the parameter is required for a connection. | | visibleByDefault | boolean | true | | Whether or not the parameter should be shown in the UI by default. | ### Enumerated Values | Property | Value | | --- | --- | | kind | [ADDRESS, EXTENDED_PATH_PARAM, PATH_PARAM, QUERY_PARAM] | ## JDBCFields ``` { "properties": { "name": { "description": "The name of the JDBC parameter.", "type": "string" }, "value": { "description": "The value of the JDBC parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the JDBC parameter. | | value | string | true | | The value of the JDBC parameter. | ## JdbcConnectionParameters ``` { "description": "Optional connection parameters and credential properties as key-value pairs (e.g. user, password, ssl, timeout).", "type": "object", "x-versionadded": "v2.43" } ``` Optional connection parameters and credential properties as key-value pairs (e.g. user, password, ssl, timeout). ### Properties None ## JdbcDataPreview ``` { "properties": { "jdbcUrl": { "description": "The JDBC URL.", "type": "string" }, "maxRows": { "default": 1000, "description": "The row limit for the preview. The default is 1,000 rows, and a maximum of 10,000 per request.", "maximum": 10000, "minimum": 0, "type": "integer" }, "parameters": { "description": "Optional connection parameters and credential properties as key-value pairs (e.g. user, password, ssl, timeout).", "type": "object", "x-versionadded": "v2.43" }, "sql": { "description": "The SQL to execute (e.g. SELECT ...).", "maxLength": 320000, "type": "string" } }, "required": [ "jdbcUrl", "sql" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jdbcUrl | string | true | | The JDBC URL. | | maxRows | integer | false | maximum: 10000minimum: 0 | The row limit for the preview. The default is 1,000 rows, and a maximum of 10,000 per request. | | parameters | JdbcConnectionParameters | false | | Optional connection parameters and credential properties as key-value pairs (e.g. user, password, ssl, timeout). | | sql | string | true | maxLength: 320000 | The SQL to execute (e.g. SELECT ...). | ## JdbcForeignKey ``` { "properties": { "foreignKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" }, "keyType": { "description": "The type of this key.", "enum": [ "1", "2", "3" ], "type": "string" }, "name": { "description": "The name of this key.", "type": "string" }, "primaryKey": { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" } }, "required": [ "keyType", "primaryKey" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | foreignKey | JdbcKeyInfo | false | | The referred primary key. | | keyType | string | true | | The type of this key. | | name | string | false | | The name of this key. | | primaryKey | JdbcKeyInfo | true | | The referred primary key. | ### Enumerated Values | Property | Value | | --- | --- | | keyType | [1, 2, 3] | ## JdbcKeyInfo ``` { "description": "The referred primary key.", "properties": { "catalog": { "description": "The catalog name.", "type": "string" }, "column": { "description": "The column name.", "type": "string" }, "schema": { "description": "The schema name.", "type": "string" }, "table": { "description": "The table name.", "type": "string" } }, "required": [ "catalog", "column", "table" ], "type": "object" } ``` The referred primary key. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | The catalog name. | | column | string | true | | The column name. | | schema | string | false | | The schema name. | | table | string | true | | The table name. | ## OAuthCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'oauth' here. | | oauthAccessToken | string,null | false | | The OAuth access token. | | oauthClientId | string,null | false | | The OAuth client ID. | | oauthClientSecret | string,null | false | | The OAuth client secret. | | oauthRefreshToken | string | true | | The OAuth refresh token. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | oauth | ## OptionalDRConnectorV1DataSource ``` { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string", "x-versionadded": "v2.22" }, "filter": { "description": "An optional filter string for narrowing results, which overrides `path` when specified. Only supported by the Jira connector.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.39" }, "path": { "description": "The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like `/foldername/filename.csv`.", "type": "string", "x-versionadded": "v2.22" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | string | false | | The data store ID for this data source. | | filter | string | false | maxLength: 320000 | An optional filter string for narrowing results, which overrides path when specified. Only supported by the Jira connector. | | path | string | false | | The path to the dataset within whatever filesystem data source is using. For example, for S3 the path will look something like /foldername/filename.csv. | ## OptionalDatabaseQueryDataSource ``` { "properties": { "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "query": { "description": "The user-specified SQL query. If this is used, then catalog, schema and table will not be used.", "maxLength": 320000, "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | string | false | | The data store ID for this data source. | | fetchSize | integer | false | maximum: 20000minimum: 1 | The user-specified fetch size. | | query | string | false | maxLength: 320000 | The user-specified SQL query. If this is used, then catalog, schema and table will not be used. | ## OptionalDatabaseTableDataSource ``` { "properties": { "catalog": { "description": "The catalog name in the database if supported.", "maxLength": 256, "type": "string" }, "dataStoreId": { "description": "The data store ID for this data source.", "type": "string" }, "fetchSize": { "description": "The user-specified fetch size.", "maximum": 20000, "minimum": 1, "type": "integer" }, "partitionColumn": { "description": "The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects.", "type": "string" }, "schema": { "description": "The schema associated with the table or view in the database if supported.", "maxLength": 256, "type": "string" }, "table": { "description": "The table or view name in the database.", "maxLength": 256, "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | maxLength: 256 | The catalog name in the database if supported. | | dataStoreId | string | false | | The data store ID for this data source. | | fetchSize | integer | false | maximum: 20000minimum: 1 | The user-specified fetch size. | | partitionColumn | string | false | | The name of the partition column. It is needed to allow parallel execution for the 10GB+ projects. | | schema | string | false | maxLength: 256 | The schema associated with the table or view in the database if supported. | | table | string | false | maxLength: 256 | The table or view name in the database. | ## PartFinalizeResponse ``` { "properties": { "checksum": { "description": "The checksum of the part.", "type": "string" }, "number": { "description": "The number of the part.", "type": "integer" }, "size": { "description": "The size of the part.", "type": "integer" } }, "required": [ "checksum", "number", "size" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | checksum | string | true | | The checksum of the part. | | number | integer | true | | The number of the part. | | size | integer | true | | The size of the part. | ## PartUploadRequest ``` { "properties": { "file": { "description": "The part file to upload to the data stage.", "format": "binary", "type": "string" } }, "required": [ "file" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | file | string(binary) | true | | The part file to upload to the data stage. | ## PartUploadResponse ``` { "properties": { "checksum": { "description": "The checksum of the part.", "type": "string" }, "size": { "description": "The size of the part.", "type": "integer" } }, "required": [ "checksum", "size" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | checksum | string | true | | The checksum of the part. | | size | integer | true | | The size of the part. | ## S3Credentials ``` { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | awsAccessKeyId | string | false | | The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified. | | awsSecretAccessKey | string | false | | The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified. | | awsSessionToken | string,null | false | | The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified. | | configId | string | false | | The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken. | | credentialType | string | true | | The type of these credentials, 's3' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | s3 | ## SharedRolesUpdateWithGrant ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "An array of RoleRequest objects. May contain at most 100 such objects.", "items": { "oneOf": [ { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | Name of the action being taken. The only operation is 'updateRoles'. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | An array of RoleRequest objects. May contain at most 100 such objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithUsernameWithGrant | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithIdWithGrant | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## SharedRolesWithGrantListResponse ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" }, "userFullName": { "description": "The full name of the recipient user.", "type": "string" } }, "required": [ "canShare", "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControlWithGrant] | true | | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of items matching the condition. | ## SharingListResponse ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControl] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | ## SharingUpdateOrRemoveWithGrant ``` { "properties": { "data": { "description": "List of sharing roles to update.", "items": { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [UserRoleWithGrant] | true | maxItems: 100 | List of sharing roles to update. | ## SnowflakeKeyPairCredentials ``` { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configId | string | false | | The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase. | | credentialType | string | true | | The type of these credentials, 'snowflake_key_pair_user_account' here. | | passphrase | string | false | | Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified. | | privateKeyStr | string | false | | Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified. | | user | string | false | | Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | snowflake_key_pair_user_account | ## StandardUdfCreate ``` { "properties": { "credentialId": { "description": "The ID of the set of credentials to use instead of username and password.", "type": "string" }, "functionType": { "description": "The standard user-defined function type.", "enum": [ "rolling_median", "rolling_most_frequent" ], "type": "string" }, "schema": { "description": "The schema to create or detect user-defined functions in.", "type": "string" } }, "required": [ "functionType", "schema" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to use instead of username and password. | | functionType | string | true | | The standard user-defined function type. | | schema | string | true | | The schema to create or detect user-defined functions in. | ### Enumerated Values | Property | Value | | --- | --- | | functionType | [rolling_median, rolling_most_frequent] | ## StatusIdResponse ``` { "properties": { "statusId": { "description": "The ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the job's status.", "type": "string" } }, "required": [ "statusId" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | The ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the job's status. | ## TableDescription ``` { "properties": { "catalog": { "description": "The name of the catalog.", "type": [ "string", "null" ] }, "name": { "description": "The name of the table.", "type": "string" }, "schema": { "description": "The schema of the table.", "type": "string" }, "type": { "description": "The type of table.", "enum": [ "TABLE", "VIEW" ], "type": "string" } }, "required": [ "name", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string,null | false | | The name of the catalog. | | name | string | true | | The name of the table. | | schema | string | false | | The schema of the table. | | type | string | true | | The type of table. | ### Enumerated Values | Property | Value | | --- | --- | | type | [TABLE, VIEW] | ## UpdateDriverRequest ``` { "properties": { "baseNames": { "description": "Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls'", "items": { "description": "Original file name of the uploaded JAR file.", "type": "string" }, "type": "array" }, "canonicalName": { "description": "User-friendly driver name.", "type": "string" }, "className": { "description": "Driver class name. For example 'com.amazon.redshift.jdbc.Driver'", "type": [ "string", "null" ] }, "configurationId": { "description": "Driver configuration ID if it was provided during driver upload.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "localJarUrls": { "description": "File path(s) for the driver files.This path is returned in the response as 'local_url' by the driverUpload route.If there are multiple JAR files required for the driver, each uploaded JAR must be present in this list. If specified, values will replace any previous settings.", "items": { "description": "File path for the driver file. This path is returned by the driverUpload route in the 'local_url' response.", "type": "string" }, "minItems": 1, "type": "array" }, "removeConfig": { "description": "Pass `True` to remove configuration currently associated with this driver. Note: must pass `canonicalName` and `className` if removing the configuration.", "type": "boolean", "x-versionadded": "v2.18" }, "skipConfigVerification": { "description": "Pass `True` to skip `jdbc_url` verification.", "type": "boolean", "x-versionadded": "v2.18" }, "version": { "description": "Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload.", "type": "string", "x-versionadded": "v2.18" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseNames | [string] | false | | Original file name(s) of the uploaded JAR file(s). If there are multiple JAR files required for the driver, each of the original file names should be present in the list in the same order as found in 'localJarUrls' | | canonicalName | string | false | | User-friendly driver name. | | className | string,null | false | | Driver class name. For example 'com.amazon.redshift.jdbc.Driver' | | configurationId | string,null | false | | Driver configuration ID if it was provided during driver upload. | | localJarUrls | [string] | false | minItems: 1 | File path(s) for the driver files.This path is returned in the response as 'local_url' by the driverUpload route.If there are multiple JAR files required for the driver, each uploaded JAR must be present in this list. If specified, values will replace any previous settings. | | removeConfig | boolean | false | | Pass True to remove configuration currently associated with this driver. Note: must pass canonicalName and className if removing the configuration. | | skipConfigVerification | boolean | false | | Pass True to skip jdbc_url verification. | | version | string | false | | Driver version, which is used to construct the canonical name if driver configuration ID was provided during driver upload. | ## UserRoleWithGrant ``` { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If role is NO_ROLE canShare is ignored. | | role | string,null | true | | The role to set on the entity. When it is None, the role of this user will be removedfrom this entity. | | username | string | true | | The username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | --- # Data Exports And Tracing URL: https://docs.datarobot.com/en/docs/api/reference/public-api/data_exports_and_tracing.html > Use the endpoints described below to manage data exports and tracing. # Data Exports And Tracing Use the endpoints described below to manage data exports and tracing. ## Gets OTel statistics Operation path: `GET /api/v2/otel/stats/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | serviceName | query | any | false | Service name, or list of service names, of the process. | | userId | query | any | false | User IDs to view (must be admin to use this field). | | startTime | query | string(date-time) | false | The start time of the trace | | endTime | query | string(date-time) | false | The end time of the trace | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "OTel entity statistics.", "items": { "properties": { "logCount": { "description": "The number of logs used by this entity.", "type": "integer" }, "metricCount": { "description": "The number of metrics used by this entity.", "type": "integer" }, "serviceName": { "description": "Service name of the process.", "type": "string" }, "spanCount": { "description": "The number of spans used by this entity.", "type": "integer" }, "userId": { "description": "The user ID.", "type": "string" } }, "required": [ "logCount", "metricCount", "serviceName", "spanCount", "userId" ], "type": "object", "x-versionadded": "v2.43" }, "maxItems": 10000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelStatsResponse | ## Delete all OpenTelemetry logs by entitytype Operation path: `DELETE /api/v2/otel/{entityType}/{entityId}/logs/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | startTime | query | string(date-time) | false | The start time of the logs to delete. | | endTime | query | string(date-time) | false | The end time of the logs to delete. | | entityType | path | string | true | The type of the entity to which the logs belong. | | entityId | path | string | true | The ID of the entity to which the logs belong. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | User does not have permission to delete OpenTelemetry logs. | None | ## Retrieve OpenTelemetry logs by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/logs/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | startTime | query | string(date-time) | false | The start time of the log list. | | endTime | query | string(date-time) | false | The end time of the log list. | | level | query | string | false | The minimum log level of logs to include. | | includes | query | any | false | The list of strings that must be included in the log entry. | | excludes | query | any | false | The list of values that must be excluded from the log entry. | | spanId | query | string | false | The OTel span ID the logs must be associated with (if any). | | traceId | query | string | false | The OTel trace ID the logs must be associated with (if any). | | entityType | path | string | true | The type of the entity to which the logs belong. | | entityId | path | string | true | The ID of the entity to which the logs belong. | ### Enumerated Values | Parameter | Value | | --- | --- | | level | [debug, info, warn, warning, error, critical] | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of OpenTelemetry log entries.", "items": { "properties": { "level": { "description": "The log level.", "type": "string" }, "message": { "description": "The log message.", "type": "string" }, "spanId": { "description": "The OTel span ID with which the log is associated.", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "stacktrace": { "description": "The stack trace (if any).", "type": "string" }, "timestamp": { "description": "The log timestamp.", "format": "date-time", "type": "string" }, "traceId": { "description": "The OTel trace ID with which the log is associated.", "type": [ "string", "null" ], "x-versionadded": "v2.39" } }, "required": [ "level", "message", "timestamp" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelLoggingListResponse | ## Delete all OpenTelemetry metrics by entitytype Operation path: `DELETE /api/v2/otel/{entityType}/{entityId}/metrics/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | startTime | query | string(date-time) | false | The start time of the metric values to delete. | | endTime | query | string(date-time) | false | The end time of the metric values to delete. | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Get aggregated values of OpenTelemetry metrics that DataRobot automatically collects by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/metrics/autocollectedValues/` Authentication requirements: `BearerAuth` Get aggregated values of OpenTelemetry metrics that DataRobot automatically collects for the specified entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | startTime | query | string(date-time) | false | The start time of the metric list. | | endTime | query | string(date-time) | false | The end time of the metric list. | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of automatically collected OTel metric values.", "items": { "properties": { "aggregatedValue": { "description": "The aggregated metric value over the period.", "type": [ "number", "null" ] }, "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "currentValue": { "description": "The current metric value at request time.", "type": [ "number", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "level": { "description": "Indicates whether this metric is logged for an entity, a pod, or a container.", "enum": [ "entity", "pod", "container" ], "type": "string" }, "maximumMetricOtelName": { "description": "The name of a metric that indicates the maximum value for this metric.", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] } }, "required": [ "level", "otelName" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricAutocollectedValuesResponse | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## List the OpenTelemetry metric configurations by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/metrics/configs/` Authentication requirements: `BearerAuth` List the OpenTelemetry metric configurations for the specified entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of OpenTelemetry metrics.", "items": { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": [ "boolean", "null" ] }, "id": { "description": "The ID of the metric.", "type": "string" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "displayName", "enabled", "id", "otelName" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricConfigListResponse | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Create an OpenTelemetry metric configuration by entitytype Operation path: `POST /api/v2/otel/{entityType}/{entityId}/metrics/configs/` Authentication requirements: `BearerAuth` Create an OpenTelemetry metric configuration for the specified entity. ### Body parameter ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": "boolean" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | | body | body | OtelMetricConfigCreatePayload | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 201 Response ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": [ "boolean", "null" ] }, "id": { "description": "The ID of the metric.", "type": "string" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "displayName", "enabled", "id", "otelName" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The resource was created. | OtelMetricConfigObject | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Set all the OpenTelemetry metric configurations by entitytype Operation path: `PUT /api/v2/otel/{entityType}/{entityId}/metrics/configs/` Authentication requirements: `BearerAuth` Set all the OpenTelemetry metric configurations for the specified entity. ### Body parameter ``` { "properties": { "values": { "description": "The list of OTel metric configurations.", "items": { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": "boolean" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 50, "type": "array" } }, "required": [ "values" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | | body | body | OtelMetricConfigSetPayload | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The metric configurations were set. | None | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Delete an OpenTelemetry metric configuration by entitytype Operation path: `DELETE /api/v2/otel/{entityType}/{entityId}/metrics/configs/{otelMetricId}/` Authentication requirements: `BearerAuth` Delete an OpenTelemetry metric configuration for the specified entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | | otelMetricId | path | string | true | The ID of the OpenTelemetry metric configuration. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Get the OpenTelemetry metric configuration by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/metrics/configs/{otelMetricId}/` Authentication requirements: `BearerAuth` Get an OpenTelemetry metric configuration for the specified entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | | otelMetricId | path | string | true | The ID of the OpenTelemetry metric configuration. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": [ "boolean", "null" ] }, "id": { "description": "The ID of the metric.", "type": "string" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "displayName", "enabled", "id", "otelName" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricConfigObject | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Update an OpenTelemetry metric configuration by entitytype Operation path: `PATCH /api/v2/otel/{entityType}/{entityId}/metrics/configs/{otelMetricId}/` Authentication requirements: `BearerAuth` Update an OpenTelemetry metric configuration for the specified entity. ### Body parameter ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": "string" }, "enabled": { "description": "Whether the OTel metric is enabled.", "type": "boolean" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.41" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | | otelMetricId | path | string | true | The ID of the OpenTelemetry metric configuration. | | body | body | OtelMetricConfigUpdatePayload | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": [ "boolean", "null" ] }, "id": { "description": "The ID of the metric.", "type": "string" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "displayName", "enabled", "id", "otelName" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricConfigObject | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## List pods and containers found by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/metrics/podInfo/` Authentication requirements: `BearerAuth` List pods and containers found in OpenTelemetry metrics of the specified entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | startTime | query | string(date-time) | false | The start time of the metric list. | | endTime | query | string(date-time) | false | The end time of the metric list. | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of information about pods in OTel metrics.", "items": { "properties": { "containers": { "description": "The list of containers in the pod.", "items": { "properties": { "name": { "description": "The name of the container.", "type": "string" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The name of the pod.", "type": "string" } }, "required": [ "containers", "name" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricPodsInfoResponse | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## List reported OpenTelemetry metrics of the specified entity by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/metrics/summary/` Authentication requirements: `BearerAuth` List reported OpenTelemetry metrics of the specified entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | search | query | string | false | Only show reported metrics whose name contains this string. | | metricType | query | string | false | Only show reported metrics of this type. | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of information about available OTel metrics.", "items": { "properties": { "description": { "description": "The description of the reported metric.", "type": [ "string", "null" ] }, "metricType": { "description": "The reported metric type (e.g., counter, gauge, histogram).", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "type": "string" }, "units": { "description": "The units of the reported metric.", "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricSummaryResponse | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Get a single OpenTelemetry metric value of the specified entity over time by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/metrics/valueOverTime/` Authentication requirements: `BearerAuth` Get a single OpenTelemetry metric value of the specified entity over time. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | startTime | query | string(date-time) | false | The start time of the metric list. | | endTime | query | string(date-time) | false | The end time of the metric list. | | resolution | query | string | false | The period for values of the metric list. | | otelName | query | string | true | The OTel key of the metric. | | aggregation | query | string | true | The aggregation method used for metric display. | | units | query | string,null | false | The unit of measurement for the metric. | | displayName | query | string,null | false | The display name of the metric. | | percentile | query | number,null | false | The metric percentile for the percentile aggregation of histograms. | | bucketInterval | query | number,null | false | The bucket size used for histogram aggregation. | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | resolution | [PT1M, PT5M, PT1H, P1D, P7D] | | aggregation | [sum, average, min, max, cardinality, percentiles, histogram] | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "id": { "description": "The metric configuration ID (if any).", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "timeBuckets": { "description": "The list of OTel metric value periods.", "items": { "properties": { "buckets": { "description": "The histogram bucket values.", "items": { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "delta": { "description": "The difference from the previous period (if any).", "type": [ "number", "null" ] }, "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "samples": { "description": "The number of OTel metric values for the period.", "type": "integer" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" }, "value": { "description": "The metric value for the period.", "type": [ "number", "null" ] } }, "required": [ "endTime", "samples", "startTime" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 1000, "type": "array" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] } }, "required": [ "otelName", "timeBuckets" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelSingleMetricValueOverTimeResponse | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Get OpenTelemetry metrics values of the specified entity over a single time by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/metrics/values/` Authentication requirements: `BearerAuth` Get OpenTelemetry metrics values of the specified entity over a single time period. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | startTime | query | string(date-time) | false | The start time of the metric list. | | endTime | query | string(date-time) | false | The end time of the metric list. | | histogramBuckets | query | string | false | Return histograms as buckets instead of percentile values. | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | histogramBuckets | [false, False, true, True] | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "metricAggregations": { "description": "The list of OTel metric value periods.", "items": { "properties": { "aggregatedValue": { "description": "The aggregated metric value over the period.", "type": [ "number", "null" ] }, "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "buckets": { "description": "The histogram bucket values.", "items": { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "currentValue": { "description": "The current metric value at request time.", "type": [ "number", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" } }, "required": [ "metricAggregations" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricAggregatedValuesResponse | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Get OpenTelemetry metric values, grouped by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/metrics/values/segments/{segmentAttribute}/` Authentication requirements: `BearerAuth` Get OpenTelemetry metric values for the specified entity, grouped by the segment attribute name and values over a single time period. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | segmentValue | query | any | false | The values for grouping metrics by segment. | | segmentLimit | query | integer | false | The maximum number of segment values to return when segmentValues is not provided. | | otelName | query | any | false | The OTel key of the metric. | | aggregation | query | any | false | The aggregation method used for metric display. | | startTime | query | string(date-time) | false | The start time of the metric list. | | endTime | query | string(date-time) | false | The end time of the metric list. | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | | segmentAttribute | path | string | true | The name of the attribute by which to group results. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "metricAggregations": { "description": "The list of OTel metric value periods.", "items": { "properties": { "aggregatedValues": { "description": "The aggregated metric values segmented by attribute.", "items": { "type": "number" }, "maxItems": 50, "type": "array" }, "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 50, "type": "array" }, "segment": { "description": "The segment definition.", "properties": { "attribute": { "description": "The segment attribute name.", "maxLength": 255, "type": "string" }, "values": { "description": "The segment values.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "attribute", "values" ], "type": "object", "x-versionadded": "v2.4" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" } }, "required": [ "endTime", "metricAggregations", "segment", "startTime" ], "type": "object", "x-versionadded": "v2.4" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricAggregatedValuesSegmentedResponse | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Get OpenTelemetry configured metrics values of the specified entity over time by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/metrics/valuesOverTime/` Authentication requirements: `BearerAuth` Get OpenTelemetry configured metrics values of the specified entity over time. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | startTime | query | string(date-time) | false | The start time of the metric list. | | endTime | query | string(date-time) | false | The end time of the metric list. | | resolution | query | string | false | The period for values of the metric list. | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | resolution | [PT1M, PT5M, PT1H, P1D, P7D] | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of OTel metric value periods.", "items": { "properties": { "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" }, "values": { "description": "The list of OTel metric values for the period.", "items": { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "buckets": { "description": "The histogram bucket values.", "items": { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "delta": { "description": "The difference from the previous period (if any).", "type": [ "number", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "id": { "description": "The metric configuration ID (if any).", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "samples": { "description": "The number of OTel metric values for the period.", "type": "integer" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] }, "value": { "description": "The metric value for the period.", "type": [ "number", "null" ] } }, "required": [ "otelName", "samples" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 50, "type": "array" } }, "required": [ "endTime", "startTime", "values" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricValuesOverTimeResponse | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Get OpenTelemetry metric values by entitytype Operation path: `POST /api/v2/otel/{entityType}/{entityId}/metrics/valuesOverTime/segments/` Authentication requirements: `BearerAuth` Get OpenTelemetry metric values for the specified entity, grouped by multiple attributes. ### Body parameter ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "endTime": { "description": "The end time of the metric list.", "format": "date-time", "type": "string" }, "interval": { "default": "PT1H", "description": "The interval for the metric values.", "enum": [ "PT1M", "PT5M", "PT1H", "P1D", "P7D" ], "type": "string" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "segments": { "description": "The list of attributes to segment results by.", "items": { "properties": { "attributes": { "description": "The list of attribute name-value pairs.", "items": { "properties": { "attributeName": { "description": "The name of this attribute.", "maxLength": 255, "type": "string" }, "attributeValue": { "description": "The value of this attribute.", "maxLength": 255, "type": "string" } }, "required": [ "attributeName", "attributeValue" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10, "type": "array" } }, "required": [ "attributes" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 50, "type": "array" }, "startTime": { "description": "The start time of the metric list.", "format": "date-time", "type": "string" } }, "required": [ "aggregation", "otelName", "segments" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | | body | body | OtelMetricValuesOverTimeSegmentByMultipleAttrsRequest | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of metric values over time segmented by the requested attributes.", "items": { "properties": { "attributes": { "description": "The list of attributes for this segment.", "items": { "properties": { "attributeName": { "description": "The name of this attribute.", "maxLength": 255, "type": "string" }, "attributeValue": { "description": "The value of this attribute.", "maxLength": 255, "type": "string" } }, "required": [ "attributeName", "attributeValue" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "values": { "description": "The time buckets with metric values for this segment.", "items": { "properties": { "endTime": { "description": "The end time of the metric list.", "format": "date-time", "type": "string" }, "startTime": { "description": "The start time of the metric list.", "format": "date-time", "type": "string" }, "value": { "description": "The aggregated metric value over the period.", "type": [ "number", "null" ] } }, "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "attributes", "values" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricValuesOverTimeSegmentByMultipleAttrsResponse | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Get OpenTelemetry metric values, grouped by segment attribute by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/metrics/valuesOverTime/segments/{segmentAttribute}/` Authentication requirements: `BearerAuth` Get OpenTelemetry metric values for the specified entity, grouped by the segment attribute name and values. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | segmentValue | query | any | false | The values for grouping metrics by segment. | | segmentLimit | query | integer | false | The maximum number of segment values to return when segmentValues is not provided. | | otelName | query | any | false | The OTel key of the metric. | | aggregation | query | any | false | The aggregation method used for metric display. | | startTime | query | string(date-time) | false | The start time of the metric list. | | endTime | query | string(date-time) | false | The end time of the metric list. | | resolution | query | string | false | The period for values of the metric list. | | entityType | path | string | true | The type of the entity to which the metric belongs. | | entityId | path | string | true | The ID of the entity to which the metric belongs. | | segmentAttribute | path | string | true | The name of the attribute by which to group results. | ### Enumerated Values | Parameter | Value | | --- | --- | | resolution | [PT1M, PT5M, PT1H, P1D, P7D] | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of segmented metric value periods.", "items": { "properties": { "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" }, "values": { "description": "The list of OTel metric segment values for the period.", "items": { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "segmentValues": { "description": "The values for each segment.", "items": { "type": "number" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "otelName", "segmentValues" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 1000, "type": "array" } }, "required": [ "endTime", "startTime", "values" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 200, "type": "array" }, "interval": { "description": "The interval for the metric values.", "enum": [ "PT1M", "PT5M", "PT1H", "P1D", "P7D" ], "type": "string" }, "segment": { "description": "The segment definition.", "properties": { "attribute": { "description": "The segment attribute name.", "maxLength": 255, "type": "string" }, "values": { "description": "The segment values.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "attribute", "values" ], "type": "object", "x-versionadded": "v2.4" } }, "required": [ "data", "interval", "segment" ], "type": "object", "x-versionadded": "v2.4" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OtelMetricSegmentedValueResponse | | 403 | Forbidden | User does not have permission to access OpenTelemetry metrics. | None | ## Delete OpenTelemetry traces by entitytype Operation path: `DELETE /api/v2/otel/{entityType}/{entityId}/traces/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | startTime | query | string(date-time) | false | The start time of the traces to delete. | | endTime | query | string(date-time) | false | The end time of the traces to delete. | | entityType | path | string | true | The type of the entity to which the trace belongs. | | entityId | path | string | true | The ID of the entity to which the trace belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | ## List OpenTelemetry traces by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/traces/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | startTime | query | string(date-time) | false | The start time of the trace. | | endTime | query | string(date-time) | false | The end time of the trace. | | minSpanDuration | query | integer | false | The minimum duration of the span in nanoseconds. | | maxSpanDuration | query | integer | false | The maximum duration of the span in nanoseconds. | | minTraceDuration | query | integer | false | The minimum duration of the trace in nanoseconds. | | searchKeys | query | any | false | The list of search keys. | | searchValues | query | any | false | The list of search values. | | minTraceCost | query | integer | false | The minimum cost of the trace. | | maxTraceCost | query | integer | false | The maximum cost of the trace. | | rootSpanName | query | any | false | Filter by root span name. Accepts a single value or a list of values. | | status | query | string | false | Filter traces by status. Use 'error' to return only traces with at least one error span, or 'ok' to return only traces with no error spans. | | sortBy | query | string | false | Field to sort traces by. One of: 'timestamp', 'duration', 'cost'. Defaults to 'timestamp'. | | sortDirection | query | string | false | Sort direction. One of: 'asc', 'desc'. Defaults to 'desc'. | | entityType | path | string | true | The type of the entity to which the trace belongs. | | entityId | path | string | true | The ID of the entity to which the trace belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | status | [error, ok] | | sortBy | [timestamp, duration, cost] | | sortDirection | [asc, desc] | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.44" }, "data": { "description": "The list of traces.", "items": { "properties": { "completion": { "description": "The completion of the trace.", "type": [ "string", "null" ] }, "cost": { "description": "The cost of the trace.", "type": "number" }, "duration": { "description": "The duration of the trace.", "type": "number" }, "errorSpansCount": { "description": "The number of error spans.", "type": "integer" }, "prompt": { "description": "The prompt of the trace.", "type": [ "string", "null" ] }, "rootServiceName": { "description": "The root service name.", "type": "string" }, "rootSpanName": { "description": "The root span name.", "type": "string" }, "spansCount": { "description": "The number of spans.", "type": "integer" }, "timestamp": { "description": "The timestamp of the trace.", "type": "number" }, "tools": { "default": null, "description": "A list of tool names used in the trace. Extracted from span attributes, includes all tools encountered in the trace.", "items": { "properties": { "callCount": { "description": "The number of times tools were used in a trace.", "type": "integer" }, "name": { "description": "The name of the tool.", "type": "string" } }, "required": [ "callCount", "name" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.39" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 32, "type": "string" } }, "required": [ "completion", "cost", "duration", "errorSpansCount", "prompt", "rootServiceName", "rootSpanName", "spansCount", "timestamp", "tools", "traceId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "total": { "description": "The total number of traces.", "type": "integer", "x-versiondeprecated": "v2.44" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.44" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | TracingListResponse | ## Retrieve the specified OpenTelemetry trace by entitytype Operation path: `GET /api/v2/otel/{entityType}/{entityId}/traces/{traceId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to which the trace belongs. | | entityId | path | string | true | The ID of the entity to which the trace belongs. | | traceId | path | string | true | The OTel trace ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "duration": { "description": "The duration of the trace.", "type": [ "number", "null" ] }, "metrics": { "description": "Metric values produced by DataRobot moderations.", "properties": { "promptGuards": { "additionalProperties": { "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "boolean" } ] }, "description": "Prompt guard values produced by DataRobot moderations.", "type": "object" }, "responseGuards": { "additionalProperties": { "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "boolean" } ] }, "description": "Prompt guard values produced by DataRobot moderations.", "type": "object" } }, "type": "object", "x-versionadded": "v2.37" }, "rootServiceName": { "description": "The root service name.", "type": [ "string", "null" ] }, "rootSpanName": { "description": "The root span name.", "type": [ "string", "null" ] }, "spanCount": { "description": "The number of spans.", "type": "integer" }, "spans": { "description": "The list of spans.", "items": { "description": "The span object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the span.", "type": "object" }, "duration": { "description": "The duration of the span.", "type": "number" }, "events": { "description": "The list of events.", "items": { "description": "The event object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the event.", "type": "object" }, "name": { "description": "The name of the event.", "type": "string" } }, "required": [ "attributes", "name" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "hasPermission": { "description": "Whether the user has permission to view the span.", "type": "boolean" }, "kind": { "description": "The kind of the span.", "type": "string" }, "links": { "description": "The list of links.", "items": { "description": "The link object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the link.", "type": "object" }, "spanId": { "description": "The span ID.", "maxLength": 32, "minLength": 16, "type": "string" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 16, "type": "string" } }, "required": [ "attributes", "spanId", "traceId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The span name.", "type": "string" }, "parentSpanId": { "description": "The parent span ID.", "maxLength": 32, "minLength": 16, "type": [ "string", "null" ] }, "resource": { "description": "The resource of the span.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the resource.", "type": "object" } }, "required": [ "attributes" ], "type": "object", "x-versionadded": "v2.37" }, "scope": { "additionalProperties": { "type": "string" }, "description": "The scope of the span.", "type": "object" }, "serviceName": { "description": "The service name of the span.", "type": "string" }, "spanId": { "description": "The span ID.", "maxLength": 32, "minLength": 16, "type": "string" }, "startTime": { "description": "The start time of the span", "type": "number" }, "statusCode": { "description": "The status code of the span.", "type": [ "string", "null" ] }, "statusMessage": { "description": "The status message of the span.", "type": [ "string", "null" ] }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 16, "type": "string" } }, "required": [ "attributes", "duration", "events", "hasPermission", "kind", "links", "name", "resource", "scope", "serviceName", "spanId", "startTime", "traceId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 32, "type": "string" } }, "required": [ "duration", "rootServiceName", "rootSpanName", "spanCount", "spans", "traceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | TracingRetrieveResponse | ## Deprecated means by entitytype Operation path: `GET /api/v2/tracing/{entityType}/{entityId}/` Authentication requirements: `BearerAuth` Deprecated means to list OpenTelemetry traces. The current means uses /api/v2/otel/ //traces/. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | startTime | query | string(date-time) | false | The start time of the trace. | | endTime | query | string(date-time) | false | The end time of the trace. | | minSpanDuration | query | integer | false | The minimum duration of the span in nanoseconds. | | maxSpanDuration | query | integer | false | The maximum duration of the span in nanoseconds. | | minTraceDuration | query | integer | false | The minimum duration of the trace in nanoseconds. | | searchKeys | query | any | false | The list of search keys. | | searchValues | query | any | false | The list of search values. | | minTraceCost | query | integer | false | The minimum cost of the trace. | | maxTraceCost | query | integer | false | The maximum cost of the trace. | | rootSpanName | query | any | false | Filter by root span name. Accepts a single value or a list of values. | | status | query | string | false | Filter traces by status. Use 'error' to return only traces with at least one error span, or 'ok' to return only traces with no error spans. | | sortBy | query | string | false | Field to sort traces by. One of: 'timestamp', 'duration', 'cost'. Defaults to 'timestamp'. | | sortDirection | query | string | false | Sort direction. One of: 'asc', 'desc'. Defaults to 'desc'. | | entityType | path | string | true | The type of the entity to which the trace belongs. | | entityId | path | string | true | The ID of the entity to which the trace belongs. | ### Enumerated Values | Parameter | Value | | --- | --- | | status | [error, ok] | | sortBy | [timestamp, duration, cost] | | sortDirection | [asc, desc] | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.44" }, "data": { "description": "The list of traces.", "items": { "properties": { "completion": { "description": "The completion of the trace.", "type": [ "string", "null" ] }, "cost": { "description": "The cost of the trace.", "type": "number" }, "duration": { "description": "The duration of the trace.", "type": "number" }, "errorSpansCount": { "description": "The number of error spans.", "type": "integer" }, "prompt": { "description": "The prompt of the trace.", "type": [ "string", "null" ] }, "rootServiceName": { "description": "The root service name.", "type": "string" }, "rootSpanName": { "description": "The root span name.", "type": "string" }, "spansCount": { "description": "The number of spans.", "type": "integer" }, "timestamp": { "description": "The timestamp of the trace.", "type": "number" }, "tools": { "default": null, "description": "A list of tool names used in the trace. Extracted from span attributes, includes all tools encountered in the trace.", "items": { "properties": { "callCount": { "description": "The number of times tools were used in a trace.", "type": "integer" }, "name": { "description": "The name of the tool.", "type": "string" } }, "required": [ "callCount", "name" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.39" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 32, "type": "string" } }, "required": [ "completion", "cost", "duration", "errorSpansCount", "prompt", "rootServiceName", "rootSpanName", "spansCount", "timestamp", "tools", "traceId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "total": { "description": "The total number of traces.", "type": "integer", "x-versiondeprecated": "v2.44" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.44" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | TracingListResponse | ## Retrieve tracing by id Operation path: `GET /api/v2/tracing/{entityType}/{entityId}/{traceId}/` Authentication requirements: `BearerAuth` Deprecated means to get an OpenTelemetry trace. The current means uses /api/v2/otel/ //traces//. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityType | path | string | true | The type of the entity to which the trace belongs. | | entityId | path | string | true | The ID of the entity to which the trace belongs. | | traceId | path | string | true | The OTel trace ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [deployment, use_case, experiment_container, custom_application, workload, workload_deployment] | ### Example responses > 200 Response ``` { "properties": { "duration": { "description": "The duration of the trace.", "type": [ "number", "null" ] }, "metrics": { "description": "Metric values produced by DataRobot moderations.", "properties": { "promptGuards": { "additionalProperties": { "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "boolean" } ] }, "description": "Prompt guard values produced by DataRobot moderations.", "type": "object" }, "responseGuards": { "additionalProperties": { "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "boolean" } ] }, "description": "Prompt guard values produced by DataRobot moderations.", "type": "object" } }, "type": "object", "x-versionadded": "v2.37" }, "rootServiceName": { "description": "The root service name.", "type": [ "string", "null" ] }, "rootSpanName": { "description": "The root span name.", "type": [ "string", "null" ] }, "spanCount": { "description": "The number of spans.", "type": "integer" }, "spans": { "description": "The list of spans.", "items": { "description": "The span object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the span.", "type": "object" }, "duration": { "description": "The duration of the span.", "type": "number" }, "events": { "description": "The list of events.", "items": { "description": "The event object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the event.", "type": "object" }, "name": { "description": "The name of the event.", "type": "string" } }, "required": [ "attributes", "name" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "hasPermission": { "description": "Whether the user has permission to view the span.", "type": "boolean" }, "kind": { "description": "The kind of the span.", "type": "string" }, "links": { "description": "The list of links.", "items": { "description": "The link object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the link.", "type": "object" }, "spanId": { "description": "The span ID.", "maxLength": 32, "minLength": 16, "type": "string" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 16, "type": "string" } }, "required": [ "attributes", "spanId", "traceId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The span name.", "type": "string" }, "parentSpanId": { "description": "The parent span ID.", "maxLength": 32, "minLength": 16, "type": [ "string", "null" ] }, "resource": { "description": "The resource of the span.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the resource.", "type": "object" } }, "required": [ "attributes" ], "type": "object", "x-versionadded": "v2.37" }, "scope": { "additionalProperties": { "type": "string" }, "description": "The scope of the span.", "type": "object" }, "serviceName": { "description": "The service name of the span.", "type": "string" }, "spanId": { "description": "The span ID.", "maxLength": 32, "minLength": 16, "type": "string" }, "startTime": { "description": "The start time of the span", "type": "number" }, "statusCode": { "description": "The status code of the span.", "type": [ "string", "null" ] }, "statusMessage": { "description": "The status message of the span.", "type": [ "string", "null" ] }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 16, "type": "string" } }, "required": [ "attributes", "duration", "events", "hasPermission", "kind", "links", "name", "resource", "scope", "serviceName", "spanId", "startTime", "traceId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 32, "type": "string" } }, "required": [ "duration", "rootServiceName", "rootSpanName", "spanCount", "spans", "traceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | TracingRetrieveResponse | # Schemas ## Event ``` { "description": "The event object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the event.", "type": "object" }, "name": { "description": "The name of the event.", "type": "string" } }, "required": [ "attributes", "name" ], "type": "object", "x-versionadded": "v2.37" } ``` The event object. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributes | object | true | | The attributes of the event. | | » additionalProperties | string | false | | none | | name | string | true | | The name of the event. | ## LinkView ``` { "description": "The link object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the link.", "type": "object" }, "spanId": { "description": "The span ID.", "maxLength": 32, "minLength": 16, "type": "string" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 16, "type": "string" } }, "required": [ "attributes", "spanId", "traceId" ], "type": "object", "x-versionadded": "v2.37" } ``` The link object. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributes | object | true | | The attributes of the link. | | » additionalProperties | string | false | | none | | spanId | string | true | maxLength: 32minLength: 16minLength: 16 | The span ID. | | traceId | string | true | maxLength: 32minLength: 16minLength: 16 | The OTel trace ID. | ## OtelAutocollectedMetricValue ``` { "properties": { "aggregatedValue": { "description": "The aggregated metric value over the period.", "type": [ "number", "null" ] }, "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "currentValue": { "description": "The current metric value at request time.", "type": [ "number", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "level": { "description": "Indicates whether this metric is logged for an entity, a pod, or a container.", "enum": [ "entity", "pod", "container" ], "type": "string" }, "maximumMetricOtelName": { "description": "The name of a metric that indicates the maximum value for this metric.", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] } }, "required": [ "level", "otelName" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregatedValue | number,null | false | | The aggregated metric value over the period. | | aggregation | string,null | false | | The aggregation method used for metric display. | | currentValue | number,null | false | | The current metric value at request time. | | displayName | string,null | false | maxLength: 127 | The display name of the metric. | | level | string | true | | Indicates whether this metric is logged for an entity, a pod, or a container. | | maximumMetricOtelName | string,null | false | | The name of a metric that indicates the maximum value for this metric. | | otelName | string | true | maxLength: 255 | The OTel key of the metric. | | unit | string,null | false | | The unit of measurement for the metric. | ### Enumerated Values | Property | Value | | --- | --- | | level | [entity, pod, container] | | unit | [bytes, nanocores, percentage] | ## OtelLogEntry ``` { "properties": { "level": { "description": "The log level.", "type": "string" }, "message": { "description": "The log message.", "type": "string" }, "spanId": { "description": "The OTel span ID with which the log is associated.", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "stacktrace": { "description": "The stack trace (if any).", "type": "string" }, "timestamp": { "description": "The log timestamp.", "format": "date-time", "type": "string" }, "traceId": { "description": "The OTel trace ID with which the log is associated.", "type": [ "string", "null" ], "x-versionadded": "v2.39" } }, "required": [ "level", "message", "timestamp" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | level | string | true | | The log level. | | message | string | true | | The log message. | | spanId | string,null | false | | The OTel span ID with which the log is associated. | | stacktrace | string | false | | The stack trace (if any). | | timestamp | string(date-time) | true | | The log timestamp. | | traceId | string,null | false | | The OTel trace ID with which the log is associated. | ## OtelLoggingListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of OpenTelemetry log entries.", "items": { "properties": { "level": { "description": "The log level.", "type": "string" }, "message": { "description": "The log message.", "type": "string" }, "spanId": { "description": "The OTel span ID with which the log is associated.", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "stacktrace": { "description": "The stack trace (if any).", "type": "string" }, "timestamp": { "description": "The log timestamp.", "format": "date-time", "type": "string" }, "traceId": { "description": "The OTel trace ID with which the log is associated.", "type": [ "string", "null" ], "x-versionadded": "v2.39" } }, "required": [ "level", "message", "timestamp" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [OtelLogEntry] | true | maxItems: 1000 | The list of OpenTelemetry log entries. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## OtelMetricAggregatedValue ``` { "properties": { "aggregatedValue": { "description": "The aggregated metric value over the period.", "type": [ "number", "null" ] }, "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "buckets": { "description": "The histogram bucket values.", "items": { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "currentValue": { "description": "The current metric value at request time.", "type": [ "number", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregatedValue | number,null | false | | The aggregated metric value over the period. | | aggregation | string,null | false | | The aggregation method used for metric display. | | buckets | [OtelMetricHistogramBucketValue] | false | maxItems: 100 | The histogram bucket values. | | currentValue | number,null | false | | The current metric value at request time. | | displayName | string,null | false | maxLength: 127 | The display name of the metric. | | otelName | string | true | maxLength: 255 | The OTel key of the metric. | | unit | string,null | false | | The unit of measurement for the metric. | ### Enumerated Values | Property | Value | | --- | --- | | unit | [bytes, nanocores, percentage] | ## OtelMetricAggregatedValueSegmented ``` { "properties": { "aggregatedValues": { "description": "The aggregated metric values segmented by attribute.", "items": { "type": "number" }, "maxItems": 50, "type": "array" }, "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.4" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregatedValues | [number] | false | maxItems: 50 | The aggregated metric values segmented by attribute. | | aggregation | string,null | false | | The aggregation method used for metric display. | | displayName | string,null | false | maxLength: 127 | The display name of the metric. | | otelName | string | true | maxLength: 255 | The OTel key of the metric. | ## OtelMetricAggregatedValuesResponse ``` { "properties": { "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "metricAggregations": { "description": "The list of OTel metric value periods.", "items": { "properties": { "aggregatedValue": { "description": "The aggregated metric value over the period.", "type": [ "number", "null" ] }, "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "buckets": { "description": "The histogram bucket values.", "items": { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "currentValue": { "description": "The current metric value at request time.", "type": [ "number", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" } }, "required": [ "metricAggregations" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endTime | string(date-time) | false | | The end time of the metric value period. | | metricAggregations | [OtelMetricAggregatedValue] | true | maxItems: 100 | The list of OTel metric value periods. | | startTime | string(date-time) | false | | The start time of the metric value period. | ## OtelMetricAggregatedValuesSegmentedResponse ``` { "properties": { "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "metricAggregations": { "description": "The list of OTel metric value periods.", "items": { "properties": { "aggregatedValues": { "description": "The aggregated metric values segmented by attribute.", "items": { "type": "number" }, "maxItems": 50, "type": "array" }, "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 50, "type": "array" }, "segment": { "description": "The segment definition.", "properties": { "attribute": { "description": "The segment attribute name.", "maxLength": 255, "type": "string" }, "values": { "description": "The segment values.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "attribute", "values" ], "type": "object", "x-versionadded": "v2.4" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" } }, "required": [ "endTime", "metricAggregations", "segment", "startTime" ], "type": "object", "x-versionadded": "v2.4" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endTime | string(date-time) | true | | The end time of the metric value period. | | metricAggregations | [OtelMetricAggregatedValueSegmented] | true | maxItems: 50 | The list of OTel metric value periods. | | segment | OtelMetricSegment | true | | The segment definition. | | startTime | string(date-time) | true | | The start time of the metric value period. | ## OtelMetricAttribute ``` { "properties": { "attributeName": { "description": "The name of this attribute.", "maxLength": 255, "type": "string" }, "attributeValue": { "description": "The value of this attribute.", "maxLength": 255, "type": "string" } }, "required": [ "attributeName", "attributeValue" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributeName | string | true | maxLength: 255 | The name of this attribute. | | attributeValue | string | true | maxLength: 255 | The value of this attribute. | ## OtelMetricAttributeList ``` { "properties": { "attributes": { "description": "The list of attribute name-value pairs.", "items": { "properties": { "attributeName": { "description": "The name of this attribute.", "maxLength": 255, "type": "string" }, "attributeValue": { "description": "The value of this attribute.", "maxLength": 255, "type": "string" } }, "required": [ "attributeName", "attributeValue" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10, "type": "array" } }, "required": [ "attributes" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributes | [OtelMetricAttribute] | true | maxItems: 10 | The list of attribute name-value pairs. | ## OtelMetricAttributeSegmentedValue ``` { "properties": { "endTime": { "description": "The end time of the metric list.", "format": "date-time", "type": "string" }, "startTime": { "description": "The start time of the metric list.", "format": "date-time", "type": "string" }, "value": { "description": "The aggregated metric value over the period.", "type": [ "number", "null" ] } }, "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endTime | string(date-time) | false | | The end time of the metric list. | | startTime | string(date-time) | false | | The start time of the metric list. | | value | number,null | false | | The aggregated metric value over the period. | ## OtelMetricAttributeWithValuesList ``` { "properties": { "attributes": { "description": "The list of attributes for this segment.", "items": { "properties": { "attributeName": { "description": "The name of this attribute.", "maxLength": 255, "type": "string" }, "attributeValue": { "description": "The value of this attribute.", "maxLength": 255, "type": "string" } }, "required": [ "attributeName", "attributeValue" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "values": { "description": "The time buckets with metric values for this segment.", "items": { "properties": { "endTime": { "description": "The end time of the metric list.", "format": "date-time", "type": "string" }, "startTime": { "description": "The start time of the metric list.", "format": "date-time", "type": "string" }, "value": { "description": "The aggregated metric value over the period.", "type": [ "number", "null" ] } }, "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "attributes", "values" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributes | [OtelMetricAttribute] | true | maxItems: 1000 | The list of attributes for this segment. | | values | [OtelMetricAttributeSegmentedValue] | true | maxItems: 1000 | The time buckets with metric values for this segment. | ## OtelMetricAutocollectedValuesResponse ``` { "properties": { "data": { "description": "The list of automatically collected OTel metric values.", "items": { "properties": { "aggregatedValue": { "description": "The aggregated metric value over the period.", "type": [ "number", "null" ] }, "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "currentValue": { "description": "The current metric value at request time.", "type": [ "number", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "level": { "description": "Indicates whether this metric is logged for an entity, a pod, or a container.", "enum": [ "entity", "pod", "container" ], "type": "string" }, "maximumMetricOtelName": { "description": "The name of a metric that indicates the maximum value for this metric.", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] } }, "required": [ "level", "otelName" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [OtelAutocollectedMetricValue] | true | maxItems: 1000 | The list of automatically collected OTel metric values. | ## OtelMetricConfigCreatePayload ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": "boolean" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregation | string | false | | The aggregation method used for metric display. | | displayName | string,null | false | maxLength: 127 | The display name of the metric. | | enabled | boolean | false | | Whether the OTel metric is enabled. | | otelName | string | true | maxLength: 255 | The OTel key of the metric. | | percentile | number,null | false | maximum: 1 | The metric percentile for the percentile aggregation of histograms. | | unit | string,null | false | | The unit of measurement for the metric. | ### Enumerated Values | Property | Value | | --- | --- | | aggregation | [sum, average, min, max, cardinality, percentiles, histogram] | | unit | [bytes, nanocores, percentage, null] | ## OtelMetricConfigListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of OpenTelemetry metrics.", "items": { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": [ "boolean", "null" ] }, "id": { "description": "The ID of the metric.", "type": "string" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "displayName", "enabled", "id", "otelName" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [OtelMetricConfigObject] | true | maxItems: 1000 | The list of OpenTelemetry metrics. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## OtelMetricConfigObject ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": [ "boolean", "null" ] }, "id": { "description": "The ID of the metric.", "type": "string" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "displayName", "enabled", "id", "otelName" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregation | string | false | | The aggregation method used for metric display. | | displayName | string,null | true | maxLength: 127 | The display name of the metric. | | enabled | boolean,null | true | | Whether the OTel metric is enabled. | | id | string | true | | The ID of the metric. | | otelName | string | true | maxLength: 255 | The OTel key of the metric. | | percentile | number,null | false | maximum: 1 | The metric percentile for the percentile aggregation of histograms. | | unit | string,null | false | | The unit of measurement for the metric. | ### Enumerated Values | Property | Value | | --- | --- | | aggregation | [sum, average, min, max, cardinality, percentiles, histogram] | | unit | [bytes, nanocores, percentage, null] | ## OtelMetricConfigPayload ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": "boolean" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregation | string | false | | The aggregation method used for metric display. | | displayName | string,null | false | maxLength: 127 | The display name of the metric. | | enabled | boolean | false | | Whether the OTel metric is enabled. | | otelName | string | true | maxLength: 255 | The OTel key of the metric. | | percentile | number,null | false | maximum: 1 | The metric percentile for the percentile aggregation of histograms. | | unit | string,null | false | | The unit of measurement for the metric. | ### Enumerated Values | Property | Value | | --- | --- | | aggregation | [sum, average, min, max, cardinality, percentiles, histogram] | | unit | [bytes, nanocores, percentage, null] | ## OtelMetricConfigSetPayload ``` { "properties": { "values": { "description": "The list of OTel metric configurations.", "items": { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "enabled": { "default": true, "description": "Whether the OTel metric is enabled.", "type": "boolean" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 50, "type": "array" } }, "required": [ "values" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | values | [OtelMetricConfigPayload] | true | maxItems: 50 | The list of OTel metric configurations. | ## OtelMetricConfigUpdatePayload ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": "string" }, "enabled": { "description": "Whether the OTel metric is enabled.", "type": "boolean" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "percentile": { "description": "The metric percentile for the percentile aggregation of histograms.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "unit": { "default": null, "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage", null ], "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregation | string | false | | The aggregation method used for metric display. | | displayName | string | false | maxLength: 127 | The display name of the metric. | | enabled | boolean | false | | Whether the OTel metric is enabled. | | otelName | string | false | maxLength: 255 | The OTel key of the metric. | | percentile | number,null | false | maximum: 1 | The metric percentile for the percentile aggregation of histograms. | | unit | string,null | false | | The unit of measurement for the metric. | ### Enumerated Values | Property | Value | | --- | --- | | aggregation | [sum, average, min, max, cardinality, percentiles, histogram] | | unit | [bytes, nanocores, percentage, null] | ## OtelMetricContainerInfo ``` { "properties": { "name": { "description": "The name of the container.", "type": "string" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the container. | ## OtelMetricHistogramBucketValue ``` { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The count of the bucket values. | | value | number | true | | The value of the bucket. | ## OtelMetricPodInfo ``` { "properties": { "containers": { "description": "The list of containers in the pod.", "items": { "properties": { "name": { "description": "The name of the container.", "type": "string" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The name of the pod.", "type": "string" } }, "required": [ "containers", "name" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | containers | [OtelMetricContainerInfo] | true | maxItems: 1000 | The list of containers in the pod. | | name | string | true | | The name of the pod. | ## OtelMetricPodsInfoResponse ``` { "properties": { "data": { "description": "The list of information about pods in OTel metrics.", "items": { "properties": { "containers": { "description": "The list of containers in the pod.", "items": { "properties": { "name": { "description": "The name of the container.", "type": "string" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The name of the pod.", "type": "string" } }, "required": [ "containers", "name" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [OtelMetricPodInfo] | true | maxItems: 1000 | The list of information about pods in OTel metrics. | ## OtelMetricSegment ``` { "description": "The segment definition.", "properties": { "attribute": { "description": "The segment attribute name.", "maxLength": 255, "type": "string" }, "values": { "description": "The segment values.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "attribute", "values" ], "type": "object", "x-versionadded": "v2.4" } ``` The segment definition. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attribute | string | true | maxLength: 255 | The segment attribute name. | | values | [string] | true | maxItems: 10minItems: 1 | The segment values. | ## OtelMetricSegmentPeriod ``` { "properties": { "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" }, "values": { "description": "The list of OTel metric segment values for the period.", "items": { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "segmentValues": { "description": "The values for each segment.", "items": { "type": "number" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "otelName", "segmentValues" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 1000, "type": "array" } }, "required": [ "endTime", "startTime", "values" ], "type": "object", "x-versionadded": "v2.4" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endTime | string(date-time) | true | | The end time of the metric value period. | | startTime | string(date-time) | true | | The start time of the metric value period. | | values | [OtelMetricSegmentValue] | true | maxItems: 1000 | The list of OTel metric segment values for the period. | ## OtelMetricSegmentValue ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "segmentValues": { "description": "The values for each segment.", "items": { "type": "number" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "otelName", "segmentValues" ], "type": "object", "x-versionadded": "v2.4" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregation | string,null | false | | The aggregation method used for metric display. | | displayName | string,null | false | maxLength: 127 | The display name of the metric. | | otelName | string | true | maxLength: 255 | The OTel key of the metric. | | segmentValues | [number] | true | maxItems: 10minItems: 1 | The values for each segment. | ## OtelMetricSegmentedValueResponse ``` { "properties": { "data": { "description": "The list of segmented metric value periods.", "items": { "properties": { "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" }, "values": { "description": "The list of OTel metric segment values for the period.", "items": { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "segmentValues": { "description": "The values for each segment.", "items": { "type": "number" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "otelName", "segmentValues" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 1000, "type": "array" } }, "required": [ "endTime", "startTime", "values" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 200, "type": "array" }, "interval": { "description": "The interval for the metric values.", "enum": [ "PT1M", "PT5M", "PT1H", "P1D", "P7D" ], "type": "string" }, "segment": { "description": "The segment definition.", "properties": { "attribute": { "description": "The segment attribute name.", "maxLength": 255, "type": "string" }, "values": { "description": "The segment values.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "attribute", "values" ], "type": "object", "x-versionadded": "v2.4" } }, "required": [ "data", "interval", "segment" ], "type": "object", "x-versionadded": "v2.4" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [OtelMetricSegmentPeriod] | true | maxItems: 200 | The list of segmented metric value periods. | | interval | string | true | | The interval for the metric values. | | segment | OtelMetricSegment | true | | The segment definition. | ### Enumerated Values | Property | Value | | --- | --- | | interval | [PT1M, PT5M, PT1H, P1D, P7D] | ## OtelMetricSummary ``` { "properties": { "description": { "description": "The description of the reported metric.", "type": [ "string", "null" ] }, "metricType": { "description": "The reported metric type (e.g., counter, gauge, histogram).", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "type": "string" }, "units": { "description": "The units of the reported metric.", "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | | The description of the reported metric. | | metricType | string,null | false | | The reported metric type (e.g., counter, gauge, histogram). | | otelName | string | true | | The OTel key of the metric. | | units | string,null | false | | The units of the reported metric. | ## OtelMetricSummaryResponse ``` { "properties": { "data": { "description": "The list of information about available OTel metrics.", "items": { "properties": { "description": { "description": "The description of the reported metric.", "type": [ "string", "null" ] }, "metricType": { "description": "The reported metric type (e.g., counter, gauge, histogram).", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "type": "string" }, "units": { "description": "The units of the reported metric.", "type": [ "string", "null" ] } }, "required": [ "otelName" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [OtelMetricSummary] | true | maxItems: 1000 | The list of information about available OTel metrics. | ## OtelMetricTimeBucketValue ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "buckets": { "description": "The histogram bucket values.", "items": { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "delta": { "description": "The difference from the previous period (if any).", "type": [ "number", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "id": { "description": "The metric configuration ID (if any).", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "samples": { "description": "The number of OTel metric values for the period.", "type": "integer" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] }, "value": { "description": "The metric value for the period.", "type": [ "number", "null" ] } }, "required": [ "otelName", "samples" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregation | string,null | false | | The aggregation method used for metric display. | | buckets | [OtelMetricHistogramBucketValue] | false | maxItems: 100 | The histogram bucket values. | | delta | number,null | false | | The difference from the previous period (if any). | | displayName | string,null | false | maxLength: 127 | The display name of the metric. | | id | string,null | false | | The metric configuration ID (if any). | | otelName | string | true | maxLength: 255 | The OTel key of the metric. | | samples | integer | true | | The number of OTel metric values for the period. | | unit | string,null | false | | The unit of measurement for the metric. | | value | number,null | false | | The metric value for the period. | ### Enumerated Values | Property | Value | | --- | --- | | unit | [bytes, nanocores, percentage] | ## OtelMetricValuePeriod ``` { "properties": { "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" }, "values": { "description": "The list of OTel metric values for the period.", "items": { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "buckets": { "description": "The histogram bucket values.", "items": { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "delta": { "description": "The difference from the previous period (if any).", "type": [ "number", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "id": { "description": "The metric configuration ID (if any).", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "samples": { "description": "The number of OTel metric values for the period.", "type": "integer" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] }, "value": { "description": "The metric value for the period.", "type": [ "number", "null" ] } }, "required": [ "otelName", "samples" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 50, "type": "array" } }, "required": [ "endTime", "startTime", "values" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endTime | string(date-time) | true | | The end time of the metric value period. | | startTime | string(date-time) | true | | The start time of the metric value period. | | values | [OtelMetricTimeBucketValue] | true | maxItems: 50 | The list of OTel metric values for the period. | ## OtelMetricValuesOverTimeResponse ``` { "properties": { "data": { "description": "The list of OTel metric value periods.", "items": { "properties": { "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" }, "values": { "description": "The list of OTel metric values for the period.", "items": { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "buckets": { "description": "The histogram bucket values.", "items": { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "delta": { "description": "The difference from the previous period (if any).", "type": [ "number", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "id": { "description": "The metric configuration ID (if any).", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "samples": { "description": "The number of OTel metric values for the period.", "type": "integer" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] }, "value": { "description": "The metric value for the period.", "type": [ "number", "null" ] } }, "required": [ "otelName", "samples" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 50, "type": "array" } }, "required": [ "endTime", "startTime", "values" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [OtelMetricValuePeriod] | true | maxItems: 1000 | The list of OTel metric value periods. | ## OtelMetricValuesOverTimeSegmentByMultipleAttrsRequest ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "enum": [ "sum", "average", "min", "max", "cardinality", "percentiles", "histogram" ], "type": "string" }, "endTime": { "description": "The end time of the metric list.", "format": "date-time", "type": "string" }, "interval": { "default": "PT1H", "description": "The interval for the metric values.", "enum": [ "PT1M", "PT5M", "PT1H", "P1D", "P7D" ], "type": "string" }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "segments": { "description": "The list of attributes to segment results by.", "items": { "properties": { "attributes": { "description": "The list of attribute name-value pairs.", "items": { "properties": { "attributeName": { "description": "The name of this attribute.", "maxLength": 255, "type": "string" }, "attributeValue": { "description": "The value of this attribute.", "maxLength": 255, "type": "string" } }, "required": [ "attributeName", "attributeValue" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10, "type": "array" } }, "required": [ "attributes" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 50, "type": "array" }, "startTime": { "description": "The start time of the metric list.", "format": "date-time", "type": "string" } }, "required": [ "aggregation", "otelName", "segments" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregation | string | true | | The aggregation method used for metric display. | | endTime | string(date-time) | false | | The end time of the metric list. | | interval | string | false | | The interval for the metric values. | | otelName | string | true | maxLength: 255 | The OTel key of the metric. | | segments | [OtelMetricAttributeList] | true | maxItems: 50 | The list of attributes to segment results by. | | startTime | string(date-time) | false | | The start time of the metric list. | ### Enumerated Values | Property | Value | | --- | --- | | aggregation | [sum, average, min, max, cardinality, percentiles, histogram] | | interval | [PT1M, PT5M, PT1H, P1D, P7D] | ## OtelMetricValuesOverTimeSegmentByMultipleAttrsResponse ``` { "properties": { "data": { "description": "The list of metric values over time segmented by the requested attributes.", "items": { "properties": { "attributes": { "description": "The list of attributes for this segment.", "items": { "properties": { "attributeName": { "description": "The name of this attribute.", "maxLength": 255, "type": "string" }, "attributeValue": { "description": "The value of this attribute.", "maxLength": 255, "type": "string" } }, "required": [ "attributeName", "attributeValue" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "values": { "description": "The time buckets with metric values for this segment.", "items": { "properties": { "endTime": { "description": "The end time of the metric list.", "format": "date-time", "type": "string" }, "startTime": { "description": "The start time of the metric list.", "format": "date-time", "type": "string" }, "value": { "description": "The aggregated metric value over the period.", "type": [ "number", "null" ] } }, "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "attributes", "values" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [OtelMetricAttributeWithValuesList] | true | maxItems: 1000 | The list of metric values over time segmented by the requested attributes. | ## OtelSingleMetricTimeBucketValue ``` { "properties": { "buckets": { "description": "The histogram bucket values.", "items": { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "delta": { "description": "The difference from the previous period (if any).", "type": [ "number", "null" ] }, "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "samples": { "description": "The number of OTel metric values for the period.", "type": "integer" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" }, "value": { "description": "The metric value for the period.", "type": [ "number", "null" ] } }, "required": [ "endTime", "samples", "startTime" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [OtelMetricHistogramBucketValue] | false | maxItems: 100 | The histogram bucket values. | | delta | number,null | false | | The difference from the previous period (if any). | | endTime | string(date-time) | true | | The end time of the metric value period. | | samples | integer | true | | The number of OTel metric values for the period. | | startTime | string(date-time) | true | | The start time of the metric value period. | | value | number,null | false | | The metric value for the period. | ## OtelSingleMetricValueOverTimeResponse ``` { "properties": { "aggregation": { "description": "The aggregation method used for metric display.", "type": [ "string", "null" ] }, "displayName": { "description": "The display name of the metric.", "maxLength": 127, "type": [ "string", "null" ] }, "id": { "description": "The metric configuration ID (if any).", "type": [ "string", "null" ] }, "otelName": { "description": "The OTel key of the metric.", "maxLength": 255, "type": "string" }, "timeBuckets": { "description": "The list of OTel metric value periods.", "items": { "properties": { "buckets": { "description": "The histogram bucket values.", "items": { "properties": { "count": { "description": "The count of the bucket values.", "type": "integer" }, "value": { "description": "The value of the bucket.", "type": "number" } }, "required": [ "count", "value" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 100, "type": "array" }, "delta": { "description": "The difference from the previous period (if any).", "type": [ "number", "null" ] }, "endTime": { "description": "The end time of the metric value period.", "format": "date-time", "type": "string" }, "samples": { "description": "The number of OTel metric values for the period.", "type": "integer" }, "startTime": { "description": "The start time of the metric value period.", "format": "date-time", "type": "string" }, "value": { "description": "The metric value for the period.", "type": [ "number", "null" ] } }, "required": [ "endTime", "samples", "startTime" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 1000, "type": "array" }, "unit": { "description": "The unit of measurement for the metric.", "enum": [ "bytes", "nanocores", "percentage" ], "type": [ "string", "null" ] } }, "required": [ "otelName", "timeBuckets" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregation | string,null | false | | The aggregation method used for metric display. | | displayName | string,null | false | maxLength: 127 | The display name of the metric. | | id | string,null | false | | The metric configuration ID (if any). | | otelName | string | true | maxLength: 255 | The OTel key of the metric. | | timeBuckets | [OtelSingleMetricTimeBucketValue] | true | maxItems: 1000 | The list of OTel metric value periods. | | unit | string,null | false | | The unit of measurement for the metric. | ### Enumerated Values | Property | Value | | --- | --- | | unit | [bytes, nanocores, percentage] | ## OtelStats ``` { "properties": { "logCount": { "description": "The number of logs used by this entity.", "type": "integer" }, "metricCount": { "description": "The number of metrics used by this entity.", "type": "integer" }, "serviceName": { "description": "Service name of the process.", "type": "string" }, "spanCount": { "description": "The number of spans used by this entity.", "type": "integer" }, "userId": { "description": "The user ID.", "type": "string" } }, "required": [ "logCount", "metricCount", "serviceName", "spanCount", "userId" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | logCount | integer | true | | The number of logs used by this entity. | | metricCount | integer | true | | The number of metrics used by this entity. | | serviceName | string | true | | Service name of the process. | | spanCount | integer | true | | The number of spans used by this entity. | | userId | string | true | | The user ID. | ## OtelStatsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "OTel entity statistics.", "items": { "properties": { "logCount": { "description": "The number of logs used by this entity.", "type": "integer" }, "metricCount": { "description": "The number of metrics used by this entity.", "type": "integer" }, "serviceName": { "description": "Service name of the process.", "type": "string" }, "spanCount": { "description": "The number of spans used by this entity.", "type": "integer" }, "userId": { "description": "The user ID.", "type": "string" } }, "required": [ "logCount", "metricCount", "serviceName", "spanCount", "userId" ], "type": "object", "x-versionadded": "v2.43" }, "maxItems": 10000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [OtelStats] | true | maxItems: 10000 | OTel entity statistics. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## Resource ``` { "description": "The resource of the span.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the resource.", "type": "object" } }, "required": [ "attributes" ], "type": "object", "x-versionadded": "v2.37" } ``` The resource of the span. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributes | object | true | | The attributes of the resource. | | » additionalProperties | string | false | | none | ## SpanView ``` { "description": "The span object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the span.", "type": "object" }, "duration": { "description": "The duration of the span.", "type": "number" }, "events": { "description": "The list of events.", "items": { "description": "The event object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the event.", "type": "object" }, "name": { "description": "The name of the event.", "type": "string" } }, "required": [ "attributes", "name" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "hasPermission": { "description": "Whether the user has permission to view the span.", "type": "boolean" }, "kind": { "description": "The kind of the span.", "type": "string" }, "links": { "description": "The list of links.", "items": { "description": "The link object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the link.", "type": "object" }, "spanId": { "description": "The span ID.", "maxLength": 32, "minLength": 16, "type": "string" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 16, "type": "string" } }, "required": [ "attributes", "spanId", "traceId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The span name.", "type": "string" }, "parentSpanId": { "description": "The parent span ID.", "maxLength": 32, "minLength": 16, "type": [ "string", "null" ] }, "resource": { "description": "The resource of the span.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the resource.", "type": "object" } }, "required": [ "attributes" ], "type": "object", "x-versionadded": "v2.37" }, "scope": { "additionalProperties": { "type": "string" }, "description": "The scope of the span.", "type": "object" }, "serviceName": { "description": "The service name of the span.", "type": "string" }, "spanId": { "description": "The span ID.", "maxLength": 32, "minLength": 16, "type": "string" }, "startTime": { "description": "The start time of the span", "type": "number" }, "statusCode": { "description": "The status code of the span.", "type": [ "string", "null" ] }, "statusMessage": { "description": "The status message of the span.", "type": [ "string", "null" ] }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 16, "type": "string" } }, "required": [ "attributes", "duration", "events", "hasPermission", "kind", "links", "name", "resource", "scope", "serviceName", "spanId", "startTime", "traceId" ], "type": "object", "x-versionadded": "v2.37" } ``` The span object. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributes | object | true | | The attributes of the span. | | » additionalProperties | string | false | | none | | duration | number | true | | The duration of the span. | | events | [Event] | true | maxItems: 1000 | The list of events. | | hasPermission | boolean | true | | Whether the user has permission to view the span. | | kind | string | true | | The kind of the span. | | links | [LinkView] | true | maxItems: 1000 | The list of links. | | name | string | true | | The span name. | | parentSpanId | string,null | false | maxLength: 32minLength: 16minLength: 16 | The parent span ID. | | resource | Resource | true | | The resource of the span. | | scope | object | true | | The scope of the span. | | » additionalProperties | string | false | | none | | serviceName | string | true | | The service name of the span. | | spanId | string | true | maxLength: 32minLength: 16minLength: 16 | The span ID. | | startTime | number | true | | The start time of the span | | statusCode | string,null | false | | The status code of the span. | | statusMessage | string,null | false | | The status message of the span. | | traceId | string | true | maxLength: 32minLength: 16minLength: 16 | The OTel trace ID. | ## ToolField ``` { "properties": { "callCount": { "description": "The number of times tools were used in a trace.", "type": "integer" }, "name": { "description": "The name of the tool.", "type": "string" } }, "required": [ "callCount", "name" ], "type": "object", "x-versionadded": "v2.4" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | callCount | integer | true | | The number of times tools were used in a trace. | | name | string | true | | The name of the tool. | ## TraceView ``` { "properties": { "completion": { "description": "The completion of the trace.", "type": [ "string", "null" ] }, "cost": { "description": "The cost of the trace.", "type": "number" }, "duration": { "description": "The duration of the trace.", "type": "number" }, "errorSpansCount": { "description": "The number of error spans.", "type": "integer" }, "prompt": { "description": "The prompt of the trace.", "type": [ "string", "null" ] }, "rootServiceName": { "description": "The root service name.", "type": "string" }, "rootSpanName": { "description": "The root span name.", "type": "string" }, "spansCount": { "description": "The number of spans.", "type": "integer" }, "timestamp": { "description": "The timestamp of the trace.", "type": "number" }, "tools": { "default": null, "description": "A list of tool names used in the trace. Extracted from span attributes, includes all tools encountered in the trace.", "items": { "properties": { "callCount": { "description": "The number of times tools were used in a trace.", "type": "integer" }, "name": { "description": "The name of the tool.", "type": "string" } }, "required": [ "callCount", "name" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.39" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 32, "type": "string" } }, "required": [ "completion", "cost", "duration", "errorSpansCount", "prompt", "rootServiceName", "rootSpanName", "spansCount", "timestamp", "tools", "traceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | completion | string,null | true | | The completion of the trace. | | cost | number | true | | The cost of the trace. | | duration | number | true | | The duration of the trace. | | errorSpansCount | integer | true | | The number of error spans. | | prompt | string,null | true | | The prompt of the trace. | | rootServiceName | string | true | | The root service name. | | rootSpanName | string | true | | The root span name. | | spansCount | integer | true | | The number of spans. | | timestamp | number | true | | The timestamp of the trace. | | tools | [ToolField] | true | maxItems: 100 | A list of tool names used in the trace. Extracted from span attributes, includes all tools encountered in the trace. | | traceId | string | true | maxLength: 32minLength: 32minLength: 32 | The OTel trace ID. | ## TracingEvaluationMetrics ``` { "description": "Metric values produced by DataRobot moderations.", "properties": { "promptGuards": { "additionalProperties": { "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "boolean" } ] }, "description": "Prompt guard values produced by DataRobot moderations.", "type": "object" }, "responseGuards": { "additionalProperties": { "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "boolean" } ] }, "description": "Prompt guard values produced by DataRobot moderations.", "type": "object" } }, "type": "object", "x-versionadded": "v2.37" } ``` Metric values produced by DataRobot moderations. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptGuards | object | false | | Prompt guard values produced by DataRobot moderations. | | » additionalProperties | any | false | | none | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responseGuards | object | false | | Prompt guard values produced by DataRobot moderations. | | » additionalProperties | any | false | | none | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | ## TracingListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.44" }, "data": { "description": "The list of traces.", "items": { "properties": { "completion": { "description": "The completion of the trace.", "type": [ "string", "null" ] }, "cost": { "description": "The cost of the trace.", "type": "number" }, "duration": { "description": "The duration of the trace.", "type": "number" }, "errorSpansCount": { "description": "The number of error spans.", "type": "integer" }, "prompt": { "description": "The prompt of the trace.", "type": [ "string", "null" ] }, "rootServiceName": { "description": "The root service name.", "type": "string" }, "rootSpanName": { "description": "The root span name.", "type": "string" }, "spansCount": { "description": "The number of spans.", "type": "integer" }, "timestamp": { "description": "The timestamp of the trace.", "type": "number" }, "tools": { "default": null, "description": "A list of tool names used in the trace. Extracted from span attributes, includes all tools encountered in the trace.", "items": { "properties": { "callCount": { "description": "The number of times tools were used in a trace.", "type": "integer" }, "name": { "description": "The name of the tool.", "type": "string" } }, "required": [ "callCount", "name" ], "type": "object", "x-versionadded": "v2.4" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.39" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 32, "type": "string" } }, "required": [ "completion", "cost", "duration", "errorSpansCount", "prompt", "rootServiceName", "rootSpanName", "spansCount", "timestamp", "tools", "traceId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "total": { "description": "The total number of traces.", "type": "integer", "x-versiondeprecated": "v2.44" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.44" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [TraceView] | true | maxItems: 1000 | The list of traces. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | total | integer | false | | The total number of traces. | | totalCount | integer | true | | The total number of items across all pages. | ## TracingRetrieveResponse ``` { "properties": { "duration": { "description": "The duration of the trace.", "type": [ "number", "null" ] }, "metrics": { "description": "Metric values produced by DataRobot moderations.", "properties": { "promptGuards": { "additionalProperties": { "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "boolean" } ] }, "description": "Prompt guard values produced by DataRobot moderations.", "type": "object" }, "responseGuards": { "additionalProperties": { "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "boolean" } ] }, "description": "Prompt guard values produced by DataRobot moderations.", "type": "object" } }, "type": "object", "x-versionadded": "v2.37" }, "rootServiceName": { "description": "The root service name.", "type": [ "string", "null" ] }, "rootSpanName": { "description": "The root span name.", "type": [ "string", "null" ] }, "spanCount": { "description": "The number of spans.", "type": "integer" }, "spans": { "description": "The list of spans.", "items": { "description": "The span object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the span.", "type": "object" }, "duration": { "description": "The duration of the span.", "type": "number" }, "events": { "description": "The list of events.", "items": { "description": "The event object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the event.", "type": "object" }, "name": { "description": "The name of the event.", "type": "string" } }, "required": [ "attributes", "name" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "hasPermission": { "description": "Whether the user has permission to view the span.", "type": "boolean" }, "kind": { "description": "The kind of the span.", "type": "string" }, "links": { "description": "The list of links.", "items": { "description": "The link object.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the link.", "type": "object" }, "spanId": { "description": "The span ID.", "maxLength": 32, "minLength": 16, "type": "string" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 16, "type": "string" } }, "required": [ "attributes", "spanId", "traceId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "name": { "description": "The span name.", "type": "string" }, "parentSpanId": { "description": "The parent span ID.", "maxLength": 32, "minLength": 16, "type": [ "string", "null" ] }, "resource": { "description": "The resource of the span.", "properties": { "attributes": { "additionalProperties": { "type": "string" }, "description": "The attributes of the resource.", "type": "object" } }, "required": [ "attributes" ], "type": "object", "x-versionadded": "v2.37" }, "scope": { "additionalProperties": { "type": "string" }, "description": "The scope of the span.", "type": "object" }, "serviceName": { "description": "The service name of the span.", "type": "string" }, "spanId": { "description": "The span ID.", "maxLength": 32, "minLength": 16, "type": "string" }, "startTime": { "description": "The start time of the span", "type": "number" }, "statusCode": { "description": "The status code of the span.", "type": [ "string", "null" ] }, "statusMessage": { "description": "The status message of the span.", "type": [ "string", "null" ] }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 16, "type": "string" } }, "required": [ "attributes", "duration", "events", "hasPermission", "kind", "links", "name", "resource", "scope", "serviceName", "spanId", "startTime", "traceId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "traceId": { "description": "The OTel trace ID.", "maxLength": 32, "minLength": 32, "type": "string" } }, "required": [ "duration", "rootServiceName", "rootSpanName", "spanCount", "spans", "traceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | duration | number,null | true | | The duration of the trace. | | metrics | TracingEvaluationMetrics | false | | Metric values produced by DataRobot moderations. | | rootServiceName | string,null | true | | The root service name. | | rootSpanName | string,null | true | | The root span name. | | spanCount | integer | true | | The number of spans. | | spans | [SpanView] | true | maxItems: 1000 | The list of spans. | | traceId | string | true | maxLength: 32minLength: 32minLength: 32 | The OTel trace ID. | --- # Data Registry URL: https://docs.datarobot.com/en/docs/api/reference/public-api/data_registry.html > Read below to learn about DataRobot's endpoints for managing data, including topics such as the Data Registry, datasets, feature lists, and blueprints. Reference the [Glossary](glossary/index) and UI documentation for more information about these topics. # Data Registry Read below to learn about DataRobot's endpoints for managing data, including topics such as the Data Registry, datasets, feature lists, and blueprints. Reference the [Glossary](https://docs.datarobot.com/en/docs/reference/glossary/index.html) and UI documentation for more information about these topics. ## List all catalog items accessible by the user Operation path: `GET /api/v2/catalogItems/` Authentication requirements: `BearerAuth` List all catalog items accessible by the user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | Specifies the number of results to skip for pagination. | | limit | query | integer | true | Sets the maximum number of results returned. Enter 0 to specify no limit. | | initialCacheSize | query | integer | true | The initial cache size, for Mongo search only. | | useCache | query | string | false | Sets whether to use the cache, for Mongo search only. | | orderBy | query | string | false | The attribute sort order applied to the returned catalog list: 'catalogName', 'originalName', 'description', 'created', or 'relevance'. For all options other than 'relevance', prefix the attribute name with a dash to sort in descending order. e.g., orderBy='-catalogName'. Defaults to '-created'. | | searchFor | query | string | false | A value to search for in the dataset's name, description, tags, column names, categories, and latest errors. The search is case insensitive. If no value is provided, or if the empty string is used, or if the string contains only whitespace, no filtering occurs. Partial matching is performed on the dataset name and description fields; all other fields require an exact match. | | tag | query | any | false | Filter results to display only items with the specified catalog item tags, in lower case, with no spaces. | | accessType | query | string | false | Access type used to filter returned results. Valid options are 'owner', 'shared', 'created', and 'any' (the default): 'owner' items are owned by the requester, 'shared' items have been shared with the requester, 'created' items have been created by the requester, and 'any' items matches all. | | datasourceType | query | any | false | Data source types used for filtering. | | category | query | any | false | Category type(s) used for filtering. Searches are case sensitive and support '&' and 'OR' operators. | | filterFailed | query | string | false | Sets whether to exclude from the search results all catalog items that failed during import. If True, invalid catalog items will be excluded; default is False. | | ownerUserId | query | any | false | Filter results to display only those owned by user(s) identified by the specified UID. | | ownerUsername | query | any | false | Filter results to display only those owned by user(s) identified by the specified username. | | type | query | string | false | Filter results by catalog type. The 'dataset' option matches both 'snapshot_dataset' and 'remote_dataset'. | | isUxrPreviewable | query | boolean | false | Filter results by catalogType = 'snapshot_dataset' and catalogType = 'remote_dataset' and data_origin in ['snowflake', 'bigquery-v1'] | ### Enumerated Values | Parameter | Value | | --- | --- | | useCache | [false, False, true, True] | | orderBy | [originalName, -originalName, catalogName, -catalogName, description, -description, created, -created, relevance, -relevance] | | accessType | [owner, shared, any, created] | | filterFailed | [false, False, true, True] | | type | [dataset, snapshot_dataset, remote_dataset, user_blueprint, files] | ### Example responses > 200 Response ``` { "properties": { "cacheHit": { "description": "Indicates if the catalog item is returned from the cache.", "type": [ "boolean", "null" ] }, "count": { "description": "Number of catalog items returned on this page.", "type": "integer" }, "data": { "description": "Detailed information for every found catalog item.", "items": { "properties": { "canShareDatasetData": { "description": "Indicates if the dataset data can be shared.", "type": "boolean", "x-versionadded": "v2.30" }, "canUseDatasetData": { "description": "Indicates if the dataset data can be used.", "type": "boolean", "x-versionadded": "v2.23" }, "catalogName": { "description": "Catalog item name.", "type": "string" }, "catalogType": { "description": "Catalog item type.", "enum": [ "unknown_dataset_type", "snapshot_dataset", "remote_dataset", "unknown_catalog_type", "user_blueprint", "files" ], "type": "string" }, "dataEngineQueryId": { "description": "The ID of the catalog item data engine query.", "type": [ "string", "null" ] }, "dataOrigin": { "description": "Data origin of the datasource for this catalog item.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the catalog item data source.", "type": [ "string", "null" ] }, "description": { "description": "Catalog item description.", "type": [ "string", "null" ] }, "error": { "description": "The latest error of the catalog item.", "type": [ "string", "null" ] }, "id": { "description": "Catalog item ID.", "type": "string" }, "infoCreationDate": { "description": "The creation date of the catalog item.", "type": "string" }, "infoCreatorFullName": { "description": "The creator of the catalog item.", "type": "string" }, "infoModificationDate": { "description": "The date when the dataset metadata was last modified. This field is only applicable if the catalog item is a dataset.", "type": "string" }, "infoModifierFullName": { "description": "The user that last modified the dataset metadata. This field is only applicable if the catalog item is a dataset.", "type": [ "string", "null" ] }, "isDataEngineEligible": { "description": "Indicates if the catalog item is eligible for use by the data engine.", "type": "boolean", "x-versionadded": "v2.21" }, "isFirstVersion": { "description": "Indicates if the catalog item is the first version.", "type": "boolean" }, "lastModificationDate": { "description": "The date when the catalog item was last modified.", "type": "string" }, "lastModifierFullName": { "description": "The user that last modified the catalog item.", "type": "string" }, "originalName": { "description": "Catalog item original name.", "type": "string" }, "processingState": { "description": "The latest processing state of the catalog item.", "type": [ "integer", "null" ] }, "projectsUsedInCount": { "description": "The number of projects that use the catalog item.", "type": "integer" }, "recipeId": { "description": "The ID of the catalog item recipe.", "type": [ "string", "null" ] }, "relevance": { "description": "ElasticSearch score value or null if search done in Mongo.", "type": [ "number", "null" ] }, "tags": { "description": "List of catalog item tags in the lower case with no spaces.", "items": { "type": "string" }, "type": "array" }, "uri": { "description": "The URI to the datasource from which the catalog item was created, if it is a dataset.", "type": [ "string", "null" ] }, "userBlueprintId": { "description": "The ID by which a user blueprint is referenced in User Blueprint API.", "type": [ "string", "null" ], "x-versionadded": "v2.25" } }, "required": [ "canShareDatasetData", "canUseDatasetData", "catalogName", "catalogType", "dataEngineQueryId", "dataOrigin", "dataSourceId", "description", "error", "id", "infoCreationDate", "infoCreatorFullName", "infoModificationDate", "infoModifierFullName", "isDataEngineEligible", "lastModificationDate", "lastModifierFullName", "originalName", "processingState", "projectsUsedInCount", "recipeId", "relevance", "tags", "uri", "userBlueprintId" ], "type": "object" }, "type": "array" }, "next": { "description": "Location of the next page.", "type": [ "string", "null" ] }, "previous": { "description": "Location of the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "Total number of catalog items.", "type": "integer" } }, "required": [ "cacheHit", "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Paginated list of catalog items is returned. | CatalogListSearchResponse | ## Retrieves latest version information, by ID by catalog ID Operation path: `GET /api/v2/catalogItems/{catalogId}/` Authentication requirements: `BearerAuth` Retrieves latest version information, by ID, for catalog items. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | Catalog item ID. | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "The ISO 8601-formatted date and time indicating when this item was created in the catalog.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The full name or username of the user who added this item to the catalog.", "type": "string" }, "description": { "description": "Catalog item description.", "type": [ "string", "null" ] }, "id": { "description": "Catalog item ID.", "type": "string" }, "message": { "description": "Details of exception(s) raised during ingestion process, if any.", "type": [ "string", "null" ] }, "modifiedAt": { "description": "The ISO 8601-formatted date and time indicating changes to the Info field(s) of this catalog item.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "The full name or username of the user who last modified the Info field(s) of this catalog item.", "type": "string" }, "name": { "description": "Catalog item name.", "type": "string" }, "status": { "description": "For datasets, the current ingestion process state of this catalog item.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": [ "string", "null" ] }, "tags": { "description": "List of catalog item tags in the lower case with no spaces.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "Catalog item type.", "enum": [ "unknown_dataset_type", "snapshot_dataset", "remote_dataset", "unknown_catalog_type", "user_blueprint", "files" ], "type": "string" } }, "required": [ "createdAt", "createdBy", "description", "id", "message", "modifiedAt", "modifiedBy", "name", "status", "tags", "type" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Catalog item details retrieved successfully. | CatalogDetailsRetrieveResponse | ## Update the name, description, or tags by catalog ID Operation path: `PATCH /api/v2/catalogItems/{catalogId}/` Authentication requirements: `BearerAuth` Update the name, description, or tags for the requested catalog item. ### Body parameter ``` { "properties": { "description": { "description": "New catalog item description", "maxLength": 1000, "type": "string" }, "name": { "description": "New catalog item name", "maxLength": 255, "type": "string" }, "tags": { "description": "New catalog item tags. Tags must be the lower case, without spaces,and cannot include -$.,{}\"#' special characters.", "items": { "maxLength": 255, "minLength": 0, "type": "string" }, "type": "array" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | Catalog item ID. | | body | body | UpdateCatalogMetadata | false | none | ### Example responses > 200 Response ``` { "properties": { "canShareDatasetData": { "description": "Indicates if the dataset data can be shared.", "type": "boolean", "x-versionadded": "v2.30" }, "canUseDatasetData": { "description": "Indicates if the dataset data can be used.", "type": "boolean", "x-versionadded": "v2.23" }, "catalogName": { "description": "Catalog item name.", "type": "string" }, "catalogType": { "description": "Catalog item type.", "enum": [ "unknown_dataset_type", "snapshot_dataset", "remote_dataset", "unknown_catalog_type", "user_blueprint", "files" ], "type": "string" }, "dataEngineQueryId": { "description": "The ID of the catalog item data engine query.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the catalog item data source.", "type": [ "string", "null" ] }, "description": { "description": "Catalog item description.", "type": [ "string", "null" ] }, "error": { "description": "The latest error of the catalog item.", "type": [ "string", "null" ] }, "id": { "description": "Catalog item ID.", "type": "string" }, "infoCreationDate": { "description": "The creation date of the catalog item.", "type": "string" }, "infoCreatorFullName": { "description": "The creator of the catalog item.", "type": "string" }, "infoModificationDate": { "description": "The date when the dataset metadata was last modified. This field is only applicable if the catalog item is a dataset.", "type": "string" }, "infoModifierFullName": { "description": "The user that last modified the dataset metadata. This field is only applicable if the catalog item is a dataset.", "type": [ "string", "null" ] }, "isDataEngineEligible": { "description": "Indicates if the catalog item is eligible for use by the data engine.", "type": "boolean", "x-versionadded": "v2.21" }, "isFirstVersion": { "description": "Indicates if the catalog item is the first version.", "type": "boolean" }, "lastModificationDate": { "description": "The date when the catalog item was last modified.", "type": "string" }, "lastModifierFullName": { "description": "The user that last modified the catalog item.", "type": "string" }, "originalName": { "description": "Catalog item original name.", "type": "string" }, "processingState": { "description": "The latest processing state of the catalog item.", "type": [ "integer", "null" ] }, "projectsUsedInCount": { "description": "The number of projects that use the catalog item.", "type": "integer" }, "recipeId": { "description": "The ID of the catalog item recipe.", "type": [ "string", "null" ] }, "relevance": { "description": "ElasticSearch score value or null if search done in Mongo.", "type": [ "number", "null" ] }, "tags": { "description": "List of catalog item tags in the lower case with no spaces.", "items": { "type": "string" }, "type": "array" }, "uri": { "description": "The URI to the datasource from which the catalog item was created, if it is a dataset.", "type": [ "string", "null" ] }, "userBlueprintId": { "description": "The ID by which a user blueprint is referenced in User Blueprint API.", "type": [ "string", "null" ], "x-versionadded": "v2.25" } }, "required": [ "canShareDatasetData", "canUseDatasetData", "catalogName", "catalogType", "dataEngineQueryId", "dataSourceId", "description", "error", "id", "infoCreationDate", "infoCreatorFullName", "infoModificationDate", "infoModifierFullName", "isDataEngineEligible", "lastModificationDate", "lastModifierFullName", "originalName", "processingState", "projectsUsedInCount", "recipeId", "relevance", "tags", "uri", "userBlueprintId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Extended details of the updated catalog item. | CatalogExtendedDetailsResponse | | 403 | Forbidden | User does not have permission to update this catalog item. | None | | 410 | Gone | Requested catalog item was previously deleted. | None | ## Create a data engine query generator Operation path: `POST /api/v2/dataEngineQueryGenerators/` Authentication requirements: `BearerAuth` Create a data engine query generator. ### Body parameter ``` { "properties": { "datasets": { "description": "Source datasets in the Data Engine workspace.", "items": { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" } }, "required": [ "alias" ], "type": "object" }, "maxItems": 32, "type": "array" }, "generatorSettings": { "description": "Data engine generator settings of the given `generator_type`.", "properties": { "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column in time series project.", "type": "string" }, "defaultCategoricalAggregationMethod": { "description": "Default aggregation method used for categorical feature.", "enum": [ "last", "mostFrequent" ], "type": "string" }, "defaultNumericAggregationMethod": { "description": "Default aggregation method used for numeric feature.", "enum": [ "mean", "sum" ], "type": "string" }, "defaultTextAggregationMethod": { "description": "Default aggregation method used for text feature.", "enum": [ "concat", "last", "meanLength", "mostFrequent", "totalLength" ], "type": "string" }, "endToSeriesMaxDatetime": { "default": true, "description": "A boolean value indicating whether generates post-aggregated series up to series maximum datetime or global maximum datetime.", "type": "boolean" }, "multiseriesIdColumns": { "description": "An array with the names of columns identifying the series to which row of the output dataset belongs. Currently, only one multiseries ID column is supported.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "startFromSeriesMinDatetime": { "default": true, "description": "A boolean value indicating whether post-aggregated series starts from series minimum datetime or global minimum datetime.", "type": "boolean" }, "target": { "description": "The name of target for the output dataset.", "type": "string" }, "timeStep": { "description": "Number of time steps for the output dataset.", "exclusiveMinimum": 0, "type": "integer" }, "timeUnit": { "description": "Indicates which unit is a basis for time steps of the output dataset.", "enum": [ "DAY", "HOUR", "MINUTE", "MONTH", "QUARTER", "WEEK", "YEAR" ], "type": "string" } }, "required": [ "datetimePartitionColumn", "defaultCategoricalAggregationMethod", "defaultNumericAggregationMethod", "timeStep", "timeUnit" ], "type": "object" }, "generatorType": { "description": "Type of data engine query generator", "enum": [ "TimeSeries" ], "type": "string" } }, "required": [ "datasets", "generatorSettings", "generatorType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateDataEngineQueryGenerator | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 422 | Unprocessable Entity | Unable to process data engine query generation. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve a data engine query generator by ID Operation path: `GET /api/v2/dataEngineQueryGenerators/{dataEngineQueryGeneratorId}/` Authentication requirements: `BearerAuth` Retrieve a data engine query generator. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataEngineQueryGeneratorId | path | string | true | The ID of the data engine query generator. | ### Example responses > 200 Response ``` { "properties": { "datasets": { "description": "Source datasets in the Data Engine workspace.", "items": { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" } }, "required": [ "alias" ], "type": "object" }, "maxItems": 32, "type": "array" }, "generatorSettings": { "description": "Data engine generator settings of the given `generator_type`.", "properties": { "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column in time series project.", "type": "string" }, "defaultCategoricalAggregationMethod": { "description": "Default aggregation method used for categorical feature.", "enum": [ "last", "mostFrequent" ], "type": "string" }, "defaultNumericAggregationMethod": { "description": "Default aggregation method used for numeric feature.", "enum": [ "mean", "sum" ], "type": "string" }, "defaultTextAggregationMethod": { "description": "Default aggregation method used for text feature.", "enum": [ "concat", "last", "meanLength", "mostFrequent", "totalLength" ], "type": "string" }, "endToSeriesMaxDatetime": { "default": true, "description": "A boolean value indicating whether generates post-aggregated series up to series maximum datetime or global maximum datetime.", "type": "boolean" }, "multiseriesIdColumns": { "description": "An array with the names of columns identifying the series to which row of the output dataset belongs. Currently, only one multiseries ID column is supported.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "startFromSeriesMinDatetime": { "default": true, "description": "A boolean value indicating whether post-aggregated series starts from series minimum datetime or global minimum datetime.", "type": "boolean" }, "target": { "description": "The name of target for the output dataset.", "type": "string" }, "timeStep": { "description": "Number of time steps for the output dataset.", "exclusiveMinimum": 0, "type": "integer" }, "timeUnit": { "description": "Indicates which unit is a basis for time steps of the output dataset.", "enum": [ "DAY", "HOUR", "MINUTE", "MONTH", "QUARTER", "WEEK", "YEAR" ], "type": "string" } }, "required": [ "datetimePartitionColumn", "defaultCategoricalAggregationMethod", "defaultNumericAggregationMethod", "timeStep", "timeUnit" ], "type": "object" }, "generatorType": { "description": "Type of data engine query generator", "enum": [ "TimeSeries" ], "type": "string" }, "id": { "description": "The ID of the data engine query generator.", "type": "string" }, "query": { "description": "Generated SparkSQL query.", "type": "string" } }, "required": [ "datasets", "generatorSettings", "generatorType", "id", "query" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RetrieveDataEngineQueryResponse | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Specified query generator was not found. | None | ## Create Data Engine workspace state Operation path: `POST /api/v2/dataEngineWorkspaceStates/` Authentication requirements: `BearerAuth` Create Data Engine workspace state in database. ### Body parameter ``` { "properties": { "datasets": { "description": "The source datasets in the data engine workspace.", "items": { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" } }, "required": [ "alias" ], "type": "object" }, "type": "array" }, "language": { "description": "The language of the data engine query.", "enum": [ "SQL" ], "type": "string" }, "query": { "description": "The actual body of the data engine query.", "maxLength": 320000, "type": "string" } }, "required": [ "language", "query" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateWorkspaceState | false | none | ### Example responses > 200 Response ``` { "properties": { "workspaceStateId": { "description": "The ID of the data engine workspace state.", "type": "string" } }, "required": [ "workspaceStateId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The Data Engine workspace state. | WorkspaceSourceCreatedResponse | | 410 | Gone | Specified workspace state was already deleted. | None | ## Create Data Engine workspace state Operation path: `POST /api/v2/dataEngineWorkspaceStates/fromDataEngineQueryGenerator/` Authentication requirements: `BearerAuth` Create Data Engine workspace state in database from a query generator. ### Body parameter ``` { "properties": { "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" }, "queryGeneratorId": { "description": "The ID of the query generator.", "type": "string" } }, "required": [ "queryGeneratorId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateWorkspaceStateFromQueryGenerator | false | none | ### Example responses > 201 Response ``` { "properties": { "datasets": { "description": "Source datasets in the Data Engine workspace.", "items": { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" } }, "required": [ "alias" ], "type": "object" }, "maxItems": 32, "type": "array" }, "language": { "description": "Language of the Data Engine query.", "type": "string" }, "query": { "description": "Actual body of the Data Engine query.", "type": "string" }, "queryGeneratorId": { "description": "Query generator id.", "type": [ "string", "null" ] }, "workspaceStateId": { "description": "Data Engine workspace state ID.", "type": "string" } }, "required": [ "datasets", "language", "query", "queryGeneratorId", "workspaceStateId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The Data Engine Workspace state | WorkspaceStateCreatedFromQueryGeneratorResponse | ## Read Data Engine workspace state by workspace state ID Operation path: `GET /api/v2/dataEngineWorkspaceStates/{workspaceStateId}/` Authentication requirements: `BearerAuth` Read and return previously stored Data Engine workspace state. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | workspaceStateId | path | string | true | The ID of the data engine workspace state. | ### Example responses > 200 Response ``` { "properties": { "datasets": { "description": "The source datasets in the data engine workspace.", "items": { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "ID of a dataset in the catalog.", "type": "string" }, "datasetVersionId": { "description": "ID of a dataset version in the catalog.", "type": "string" }, "needsCredentials": { "description": "Whether a user must provide credentials for source datasets.", "type": "boolean" } }, "required": [ "alias", "datasetId", "datasetVersionId", "needsCredentials" ], "type": "object" }, "type": "array" }, "language": { "description": "The language of the data engine query.", "type": "string" }, "query": { "description": "The actual SQL statement of the data engine query.", "type": "string" }, "queryGeneratorId": { "description": "The query generator ID.", "type": [ "string", "null" ] }, "runTime": { "description": "The execution time of the data engine query.", "type": [ "number", "null" ] } }, "required": [ "datasets", "language", "query", "queryGeneratorId", "runTime" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The Data Engine workspace state. | WorkspaceStateResponse | | 410 | Gone | Specified workspace state was already deleted. | None | ## List datasets Operation path: `GET /api/v2/datasets/` Authentication requirements: `BearerAuth` List all datasets accessible by the user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | category | query | string | false | If specified, only dataset versions that have the specified category will be included in the results. Categories identify the intended use of the dataset. | | orderBy | query | string | false | Sorting order which will be applied to catalog list. | | limit | query | integer | true | At most this many results are returned. | | offset | query | integer | true | This many results will be skipped. | | filterFailed | query | string | false | Whether datasets that failed during import should be excluded from the results. If True invalid datasets will be excluded. | | datasetVersionIds | query | any | false | If specified will only return datasets that are associated with specified dataset versions. Cannot be used as the same time with experiment_container_ids. | ### Enumerated Values | Parameter | Value | | --- | --- | | category | [TRAINING, PREDICTION, SAMPLE] | | orderBy | [created, -created] | | filterFailed | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of dataset details.", "items": { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "columnCount": { "description": "The number of columns in the dataset.", "type": "integer", "x-versionadded": "v2.30" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes.", "type": "integer", "x-versionadded": "v2.21" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "rowCount": { "description": "The number of rows in the dataset.", "type": "integer", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "columnCount", "createdBy", "creationDate", "dataPersisted", "datasetId", "datasetSize", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "name", "processingState", "rowCount", "timeSeriesProperties", "versionId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of datasets | DatasetListResponse | ## Execute bulk dataset action Operation path: `PATCH /api/v2/datasets/` Authentication requirements: `BearerAuth` Execute the specified bulk action on multiple datasets. ### Body parameter ``` { "properties": { "datasetIds": { "description": "The dataset IDs to execute the bulk action on.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "payload": { "description": "indicate which action to run and with what parameters.", "oneOf": [ { "properties": { "action": { "description": "The action to execute on the datasets. Has to be 'delete' for this payload.", "enum": [ "delete" ], "type": "string" } }, "required": [ "action" ], "type": "object" }, { "properties": { "action": { "description": "The action to execute on the datasets. Has to be 'tag' for this payload.", "enum": [ "tag" ], "type": "string" }, "tags": { "description": "The tags to append to the datasets. Tags will not be duplicated.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "action", "tags" ], "type": "object" }, { "properties": { "action": { "description": "The action to execute on the datasets. Has to be 'updateRoles' for this payload.", "enum": [ "updateRoles" ], "type": "string" }, "applyGrantToLinkedObjects": { "default": false, "description": "If `true` for any users being granted access to the entity, grant the user read access to any linked objects such as `DataSources` and `DataStores` that may be used by this entity. Ignored if no such objects are relevant for the entity. This will not result in access being lowered for a user if the user already has higher access to linked objects than read access. However, if the target user does not have sharing permissions to the linked object, they will be given sharing access without lowering existing permissions. May result in an error if the user making the call does not have sufficient permissions to complete the grant. Default value is false.", "type": "boolean" }, "roles": { "description": "An array of `RoleRequest` objects. Maximum number of such objects is 100.", "items": { "oneOf": [ { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "name": { "description": "Name of the user/group/org to update the access role for.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "id": { "description": "The org/group/user ID.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "action", "roles" ], "type": "object" } ] } }, "required": [ "datasetIds", "payload" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | BulkDatasetAction | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully executed | None | | 409 | Conflict | Cannot delete a dataset that has refresh jobs. | None | ## Create dataset Operation path: `POST /api/v2/datasets/fromDataEngineWorkspaceState/` Authentication requirements: `BearerAuth` Create a dataset from a Data Engine workspace state. ### Body parameter ``` { "properties": { "credentials": { "description": "The JDBC credentials.", "type": "string" }, "datasetName": { "description": "The custom name for the created dataset.", "type": "string" }, "doSnapshot": { "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "workspaceStateId": { "description": "The ID of the workspace state to use as the source of data.", "type": "string" } }, "required": [ "workspaceStateId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DatasetCreateFromWorkspaceState | false | none | ### Example responses > 202 Response ``` { "properties": { "datasetId": { "description": "The ID of the output dataset item.", "type": "string" }, "datasetVersionId": { "description": "The ID of the output dataset version item.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "datasetId", "datasetVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetDataEngineResponse | | 410 | Gone | Specified query output was already deleted. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a dataset from a data source Operation path: `POST /api/v2/datasets/fromDataSource/` Authentication requirements: `BearerAuth` Create a Dataset Item from a data source. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.19" }, "dataSourceId": { "description": "The identifier for the DataSource to use as the source of data.", "type": "string" }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "persistDataAfterIngestion": { "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error.", "type": "boolean" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for database authentication. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "dataSourceId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | Datasource | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a dataset from a file Operation path: `POST /api/v2/datasets/fromFile/` Authentication requirements: `BearerAuth` Create a dataset from a file. ### Body parameter ``` properties: categories: description: An array of strings describing the intended use of the dataset. oneOf: - enum: - BATCH_PREDICTIONS - MULTI_SERIES_CALENDAR - PREDICTION - SAMPLE - SINGLE_SERIES_CALENDAR - TRAINING type: string - items: enum: - BATCH_PREDICTIONS - MULTI_SERIES_CALENDAR - PREDICTION - SAMPLE - SINGLE_SERIES_CALENDAR - TRAINING type: string type: array file: description: The data to be used for the creation. format: binary type: string required: - file type: object ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DatasetFromFile | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | | 422 | Unprocessable Entity | The request cannot be processed. The request did not contain file contents. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a dataset from a hdfs Operation path: `POST /api/v2/datasets/fromHDFS/` Authentication requirements: `BearerAuth` Create a Dataset Item from an HDFS URL. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "namenodeWebhdfsPort": { "description": "The port of the HDFS name node.", "type": "integer" }, "password": { "description": "The password (in cleartext) for authenticating to HDFS using Kerberos. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "persistDataAfterIngestion": { "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error", "type": "boolean" }, "url": { "description": "The HDFS url to use as the source of data for the dataset being created.", "format": "uri", "type": "string" }, "user": { "description": "The username for authenticating to HDFS using Kerberos.", "type": "string" } }, "required": [ "url" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | Hdfs | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Create a dataset from a recipe Operation path: `POST /api/v2/datasets/fromRecipe/` Authentication requirements: `BearerAuth` Create a dataset item and version from a recipe.During publishing, an immutable copy of the recipe is created, as well as a copy of the recipe's data source. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ], "x-versionadded": "v2.33" }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" } ], "x-versionadded": "v2.33" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.33" }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean", "x-versionadded": "v2.33" }, "materializationDestination": { "description": "Destination table information to create and materialize the recipe to. If None, the recipe will be materialized in DataRobot.", "properties": { "catalog": { "description": "Database to materialize the recipe to.", "type": "string", "x-versionadded": "v2.33" }, "schema": { "description": "Schema to materialize the recipe to.", "type": "string", "x-versionadded": "v2.33" }, "table": { "description": "Table name to create and materialize the recipe to. This table should not already exist.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "catalog", "schema", "table" ], "type": "object" }, "name": { "description": "Name to be assigned to the new dataset.", "type": "string", "x-versionadded": "v2.33" }, "persistDataAfterIngestion": { "default": true, "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error.", "type": "boolean", "x-versionadded": "v2.33" }, "recipeId": { "description": "The identifier for the Wrangling Recipe to use as the source of data.", "type": "string", "x-versionadded": "v2.33" }, "skipDuplicateDatesValidation": { "default": false, "description": "By default, if a recipe contains time series or a time series resampling operation, publishing fails if there are date duplicates to prevent data quality issues and ambiguous transformations. If set to True, then validation will be skipped.", "type": "boolean", "x-versionadded": "v2.36" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication.", "type": "boolean", "x-versionadded": "v2.33" } }, "required": [ "recipeId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateFromRecipe | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a dataset from a stage Operation path: `POST /api/v2/datasets/fromStage/` Authentication requirements: `BearerAuth` Create a dataset from a data stage. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "maxItems": 100, "type": "array" } ] }, "stageId": { "description": "The ID of the data stage which will be used to create the dataset item & version.", "type": "string" } }, "required": [ "stageId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DatasetFromStage | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | | 403 | Forbidden | You do not have permission to use data stages. | None | | 404 | Not Found | Data Stage not found. | None | | 409 | Conflict | Data Stage not finalized | None | | 410 | Gone | Data Stage failed | None | | 422 | Unprocessable Entity | The request cannot be processed. The request did not contain data stage. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a dataset from an URL Operation path: `POST /api/v2/datasets/fromURL/` Authentication requirements: `BearerAuth` Create a Dataset Item from a URL. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "persistDataAfterIngestion": { "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error.", "type": "boolean" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "url": { "description": "The URL to download the dataset used to create the dataset item and version.", "format": "url", "type": "string" } }, "required": [ "url" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | Url | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Delete dataset by dataset ID Operation path: `DELETE /api/v2/datasets/{datasetId}/` Authentication requirements: `BearerAuth` Marks the dataset with the given ID as deleted. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully deleted | None | | 409 | Conflict | Cannot delete a dataset that has refresh jobs. | None | ## Get dataset details by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/` Authentication requirements: `BearerAuth` Retrieves the details of the dataset with given ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "columnCount": { "description": "The number of columns in the dataset.", "type": "integer", "x-versionadded": "v2.30" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataEngineQueryId": { "description": "The ID of the source data engine query.", "type": [ "string", "null" ] }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "dataSourceId": { "description": "The ID of the datasource used as the source of the dataset.", "type": [ "string", "null" ] }, "dataSourceType": { "description": "The type of the datasource that was used as the source of the dataset.", "type": "string" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes.", "type": "integer", "x-versionadded": "v2.21" }, "description": { "description": "The description of the dataset.", "type": [ "string", "null" ] }, "eda1ModificationDate": { "description": "The ISO 8601 formatted date and time when the EDA1 for the dataset was updated.", "format": "date-time", "type": "string" }, "eda1ModifierFullName": { "description": "The user who was the last to update EDA1 for the dataset.", "type": "string" }, "entityCountByType": { "description": "Number of different type entities that use the dataset.", "properties": { "numCalendars": { "description": "The number of calendars that use the dataset", "type": "integer" }, "numExperimentContainer": { "description": "The number of experiment containers that use the dataset.", "type": "integer", "x-versionadded": "v2.37" }, "numExternalModelPackages": { "description": "The number of external model packages that use the dataset", "type": "integer" }, "numFeatureDiscoveryConfigs": { "description": "The number of feature discovery configs that use the dataset", "type": "integer" }, "numPredictionDatasets": { "description": "The number of prediction datasets that use the dataset", "type": "integer" }, "numProjects": { "description": "The number of projects that use the dataset", "type": "integer" }, "numSparkSqlQueries": { "description": "The number of spark sql queries that use the dataset", "type": "integer" } }, "required": [ "numCalendars", "numExperimentContainer", "numExternalModelPackages", "numFeatureDiscoveryConfigs", "numPredictionDatasets", "numProjects", "numSparkSqlQueries" ], "type": "object" }, "error": { "description": "Details of exception raised during ingestion process, if any.", "type": "string" }, "featureCount": { "description": "Total number of features in the dataset.", "type": "integer" }, "featureCountByType": { "description": "Number of features in the dataset grouped by feature type.", "items": { "properties": { "count": { "description": "The number of features of this type in the dataset", "type": "integer" }, "featureType": { "description": "The data type grouped in this count", "type": "string" } }, "required": [ "count", "featureType" ], "type": "object" }, "type": "array" }, "featureDiscoveryProjectId": { "description": "Feature Discovery project ID used to create the dataset.", "type": "string", "x-versionadded": "v2.35" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean", "x-versionadded": "2.30.0" }, "lastModificationDate": { "description": "The ISO 8601 formatted date and time when the dataset was last modified.", "format": "date-time", "type": "string" }, "lastModifierFullName": { "description": "Full name of user who was the last to modify the dataset.", "type": "string" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "recipeId": { "description": "The ID of the source recipe.", "type": [ "string", "null" ] }, "rowCount": { "description": "The number of rows in the dataset.", "type": "integer", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "tags": { "description": "List of tags attached to the item.", "items": { "type": "string" }, "type": "array" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "uri": { "description": "The URI to datasource. For example, `file_name.csv`, or `jdbc:DATA_SOURCE_GIVEN_NAME/SCHEMA.TABLE_NAME`, or `jdbc:DATA_SOURCE_GIVEN_NAME/` for `query` based datasources, or`https://s3.amazonaws.com/dr-pr-tst-data/kickcars-sample-200.csv`, etc.", "type": "string" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "columnCount", "createdBy", "creationDate", "dataEngineQueryId", "dataPersisted", "dataSourceId", "dataSourceType", "datasetId", "datasetSize", "description", "eda1ModificationDate", "eda1ModifierFullName", "error", "featureCount", "featureCountByType", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "isWranglingEligible", "lastModificationDate", "lastModifierFullName", "name", "processingState", "recipeId", "rowCount", "tags", "timeSeriesProperties", "uri", "versionId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The dataset details | FullDatasetDetailsResponse | ## Modify dataset by dataset ID Operation path: `PATCH /api/v2/datasets/{datasetId}/` Authentication requirements: `BearerAuth` Modifies the specified dataset. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset. If any categories were previously specified for the dataset, they will be overwritten.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "name": { "description": "The new name of the dataset.", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | PatchDataset | false | none | ### Example responses > 200 Response ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "createdBy", "creationDate", "dataPersisted", "datasetId", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "name", "processingState", "timeSeriesProperties", "versionId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Dataset successfully modified | BasicDatasetDetailsResponse | | 422 | Unprocessable Entity | The categories are not applicable to the dataset. | None | ## List dataset access by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/accessControl/` Authentication requirements: `BearerAuth` List the users and their associated roles for the specified dataset. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userId | query | string | false | Only return the access control information for a user with this user ID. | | username | query | string | false | Only return the access control information for a user with this username. | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of DatasetAccessControl objects.", "items": { "properties": { "canShare": { "description": "True if this user can share with other users", "type": "boolean" }, "canUseData": { "description": "True if the user can view, download and process data (use to create projects, predictions, etc)", "type": "boolean" }, "role": { "description": "The role of the user on this data source.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "userFullName": { "description": "The full name of a user with access to this dataset. If the full name is not available, username is returned instead.", "type": "string" }, "username": { "description": "`username` of a user with access to this dataset.", "type": "string" } }, "required": [ "canShare", "canUseData", "role", "userFullName", "username" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The paginated list of user permissions. | DatasetAccessControlListResponse | ## Modify dataset access by dataset ID Operation path: `PATCH /api/v2/datasets/{datasetId}/accessControl/` Authentication requirements: `BearerAuth` Grant access to the dataset at the specified role level, or remove access to the dataset. ### Body parameter ``` { "properties": { "applyGrantToLinkedObjects": { "default": false, "description": "If `true` for any users being granted access to the entity, grant the user read access to any linked objects such as `DataSources` and `DataStores` that may be used by this entity. Ignored if no such objects are relevant for the entity. This will not result in access being lowered for a user if the user already has higher access to linked objects than read access. However, if the target user does not have sharing permissions to the linked object, they will be given sharing access without lowering existing permissions. May result in an error if the user making the call does not have sufficient permissions to complete the grant. Default value is false.", "type": "boolean" }, "data": { "description": "array of DatasetAccessControl objects.", "items": { "properties": { "canShare": { "description": "whether the user should be able to share with other users. If `true`, the user will be able to grant any role (up to and including their own) to other users. If `role` is empty `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "role": { "description": "the role to grant to the user, or \"\" (empty string) to remove the users access", "enum": [ "CONSUMER", "EDITOR", "OWNER", "" ], "type": "string" }, "username": { "description": "username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "minItems": 1, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | DatasetAccessSet | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully modified. | None | | 409 | Conflict | Duplicate entry for a user in permission list or the request would leave the dataset without an owner. | None | ## Get dataset features by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/allFeaturesDetails/` Authentication requirements: `BearerAuth` Return detailed information on all the features and transforms for this dataset.If the Dataset Item has attribute snapshot = True, all optional fields also appear. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | At most this many results are returned. The default may change and a maximum limit may be imposed without notice. | | offset | query | integer | true | This many results will be skipped. | | orderBy | query | string | true | How the features should be ordered. | | includePlot | query | string | false | Include histogram plot data in the response. | | searchFor | query | string | false | A value to search for in the feature name. The search is case insensitive. If no value is provided, an empty string is used, or the string contains only whitespace, no filtering occurs. | | featurelistId | query | string | false | The ID of a featurelist. If specified, only returns features that are present in the specified featurelist. | | includeDataQuality | query | string | false | Include detected data quality issue types in the response. | | datasetId | path | string | true | The ID of the dataset. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [featureType, name, id, unique, missing, stddev, mean, median, min, max, dataQualityIssues, -featureType, -name, -id, -unique, -missing, -stddev, -mean, -median, -min, -max, -dataQualityIssues] | | includePlot | [false, False, true, True] | | includeDataQuality | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of features related to the requested dataset.", "items": { "properties": { "dataQualityIssues": { "description": "The status of data quality issue detection.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": [ "string", "null" ] }, "dataQualityIssuesTypes": { "description": "Data quality issue types.", "items": { "description": "Data quality issue type.", "enum": [ "disguised_missing_values", "excess_zero", "external_feature_derivation", "few_negative_values", "imputation_leakage", "inconsistent_gaps", "inliers", "lagged_features", "leading_trailing_zeros", "missing_documents", "missing_images", "missing_values", "multicategorical_invalid_format", "new_series_recent_data", "outliers", "quantile_target_sparsity", "quantile_target_zero_inflation", "target_leakage", "unusual_repeated_values" ], "type": "string" }, "maxItems": 20, "type": "array" }, "datasetId": { "description": "The ID of the dataset the feature belongs to", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version the feature belongs to.", "type": "string" }, "dateFormat": { "description": "The date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ] }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string" }, "id": { "description": "The number of the column in the dataset.", "type": "integer" }, "isZeroInflated": { "description": "whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ] }, "language": { "description": "Detected language of the feature.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "Whether feature has too few values to be informative.", "type": "boolean" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "max": { "description": "Maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "Maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "Maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "mean": { "description": "Arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "median": { "description": "Median of the EDA sample of the feature.", "oneOf": [ { "description": "Median of the EDA sample of the feature.", "type": "string" }, { "description": "Median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "min": { "description": "Minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "Minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "Minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "naCount": { "description": "Number of missing values.", "type": [ "integer", "null" ] }, "name": { "description": "Feature name", "type": "string" }, "plot": { "description": "Plot data based on feature values.", "items": { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.30" }, "sampleRows": { "description": "The number of rows in the sample used to calculate the statistics.", "type": "integer", "x-versionadded": "v2.35" }, "stdDev": { "description": "Standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "Standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "Standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "timeSeriesEligibilityReason": { "description": "why the feature is ineligible for time series projects, or 'suitable' if it is eligible.", "type": [ "string", "null" ] }, "timeSeriesEligibilityReasonAggregation": { "description": "why the feature is ineligible for aggregation, or 'suitable' if it is eligible.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "timeSeriesEligible": { "description": "whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.", "type": "boolean" }, "timeSeriesEligibleAggregation": { "description": "whether this feature can be used as a datetime feature for aggregationfor time series data prep. Always false for non-date features.", "type": "boolean", "x-versionadded": "v2.24" }, "timeStep": { "description": "The minimum time step that can be used to specify time series windows. The units for this value are the ``timeUnit``. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.", "type": [ "integer", "null" ] }, "timeStepAggregation": { "description": "The minimum time step that can be used to aggregate using this feature for time series data prep. The units for this value are the ``timeUnit``. Only present for date features that are eligible for aggregation in time series data prep and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "timeUnit": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.", "type": [ "string", "null" ] }, "timeUnitAggregation": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. Only present for date features eligible for aggregation, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "uniqueCount": { "description": "Number of unique values.", "type": [ "integer", "null" ] }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "datasetId", "datasetVersionId", "dateFormat", "featureType", "id", "name", "sampleRows" ], "type": "object", "x-versionadded": "v2.35" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of feature info | DatasetFeaturesListResponse | ## Recover deleted dataset by dataset ID Operation path: `PATCH /api/v2/datasets/{datasetId}/deleted/` Authentication requirements: `BearerAuth` Recover the dataset item with given datasetId from `deleted`. ### Body parameter ``` { "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | UpdateDatasetDeleted | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Item was not deleted: nothing to recover. | None | | 204 | No Content | Successfully recovered | None | ## Get dataset feature histogram by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/featureHistograms/{featureName}/` Authentication requirements: `BearerAuth` Get histogram chart data for a specific feature in the specified dataset. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | binLimit | query | integer | true | Maximum number of bins in the returned plot. | | key | query | string | false | Only required for the Summarized categorical feature. The name of the top 50 key for which plot to be retrieved. | | usePlot2 | query | string | false | Use frequent values plot data instead of histogram for supported feature types. | | datasetId | path | string | true | The ID of the dataset entry to retrieve. | | featureName | path | string | true | The name of the feature. | ### Example responses > 200 Response ``` { "properties": { "plot": { "description": "Plot data based on feature values.", "items": { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array" } }, "required": [ "plot" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The feature histogram | DatasetFeatureHistogramResponse | ## List dataset feature transforms by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/featureTransforms/` Authentication requirements: `BearerAuth` Retrieves the transforms of the dataset with given ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | At most this many results are returned. The default may change and a maximum limit may be imposed without notice. | | offset | query | integer | true | This many results will be skipped. | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of transforms' details.", "items": { "properties": { "dateExtraction": { "description": "The value to extract from the date column.", "enum": [ "year", "yearDay", "month", "monthDay", "week", "weekDay" ], "type": [ "string", "null" ] }, "name": { "description": "The feature name.", "type": "string" }, "parentName": { "description": "The name of the parent feature.", "type": "string" }, "replacement": { "anyOf": [ { "type": "string" }, { "type": "boolean" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The replacement in case of a failed transformation." }, "variableType": { "description": "The type of the transform.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "dateExtraction", "name", "parentName", "replacement", "variableType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of feature transforms | DatasetTransformListResponse | | 410 | Gone | Dataset deleted. | None | ## Create dataset feature transform by dataset ID Operation path: `POST /api/v2/datasets/{datasetId}/featureTransforms/` Authentication requirements: `BearerAuth` Create a new feature by changing the type of an existing one. ### Body parameter ``` { "properties": { "dateExtraction": { "description": "The value to extract from the date column, of these options: `[year|yearDay|month|monthDay|week|weekDay]`. Required for transformation of a date column. Otherwise must not be provided.", "enum": [ "year", "yearDay", "month", "monthDay", "week", "weekDay" ], "type": "string" }, "name": { "description": "The name of the new feature. Must not be the same as any existing features for this project. Must not contain '/' character.", "type": "string" }, "parentName": { "description": "The name of the parent feature.", "type": "string" }, "replacement": { "anyOf": [ { "type": [ "string", "null" ] }, { "type": [ "boolean", "null" ] }, { "type": [ "number", "null" ] }, { "type": [ "integer", "null" ] } ], "description": "The replacement in case of a failed transformation." }, "variableType": { "description": "The type of the new feature. Must be one of `text`, `categorical` (Deprecated in version v2.21), `numeric`, or `categoricalInt`. See the description of this method for more information.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "name", "parentName", "variableType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | FeatureTransform | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | None | | 409 | Conflict | Feature name already exists. | None | | 410 | Gone | Dataset deleted. | None | | 422 | Unprocessable Entity | In case of an invalid transformation or when dataset does not have profile data or sample files available. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Get dataset feature transform by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/featureTransforms/{featureName}/` Authentication requirements: `BearerAuth` Retrieve the specified feature with descriptive information. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The dataset to select feature from. | | featureName | path | string | true | The name of the feature. Note that DataRobot renames some features, so the feature name may not be the one from your original data. Non-ascii features names should be utf-8-encoded (before URL-quoting). | ### Example responses > 200 Response ``` { "properties": { "dateExtraction": { "description": "The value to extract from the date column.", "enum": [ "year", "yearDay", "month", "monthDay", "week", "weekDay" ], "type": [ "string", "null" ] }, "name": { "description": "The feature name.", "type": "string" }, "parentName": { "description": "The name of the parent feature.", "type": "string" }, "replacement": { "anyOf": [ { "type": "string" }, { "type": "boolean" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The replacement in case of a failed transformation." }, "variableType": { "description": "The type of the transform.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "dateExtraction", "name", "parentName", "replacement", "variableType" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The feature transform | DatasetTransformResponse | | 410 | Gone | Dataset deleted. | None | ## Retrieve dataset featurelists by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/featurelists/` Authentication requirements: `BearerAuth` Retrieves the featurelists of the dataset with given ID and the latest dataset version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | At most this many results are returned. The default may change and a maximum limit may be imposed without notice. | | offset | query | integer | true | This many results will be skipped. | | orderBy | query | string | true | How the feature lists should be ordered. | | searchFor | query | string | false | A value to search for in the featurelist name. The search is case insensitive. If no value is provided, an empty string is used, or the string contains only whitespace, no filtering occurs. | | datasetId | path | string | true | The ID of the dataset. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [name, description, featuresNumber, creationDate, userCreated, -name, -description, -featuresNumber, -creationDate, -userCreated] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of featurelists' details.", "items": { "properties": { "createdBy": { "description": "`username` of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "the ISO 8601 formatted date and time when the dataset was created.", "format": "date-time", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version if the featurelist is associated with a specific dataset version, for example Informative Features, or null otherwise.", "type": [ "string", "null" ] }, "features": { "description": "Features in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "The ID of the featurelist", "type": "string" }, "name": { "description": "The name of the featurelist", "type": "string" }, "userCreated": { "description": "True if the featurelist was created by a user vs the system.", "type": "boolean" } }, "required": [ "createdBy", "creationDate", "datasetId", "datasetVersionId", "features", "id", "name", "userCreated" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of featurelists | DatasetFeaturelistListResponse | ## Create dataset featurelist by dataset ID Operation path: `POST /api/v2/datasets/{datasetId}/featurelists/` Authentication requirements: `BearerAuth` Create featurelist for specified dataset. ### Body parameter ``` { "properties": { "description": { "description": "The description of the featurelist.", "type": "string" }, "features": { "description": "The list of names of features to be included in the new featurelist. All features listed must be part of the universe. All features for this dataset for the request to succeed.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "name": { "description": "The name of the featurelist to be created.", "type": "string" } }, "required": [ "features", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | FeatureListCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "createdBy": { "description": "`username` of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "the ISO 8601 formatted date and time when the dataset was created.", "format": "date-time", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version if the featurelist is associated with a specific dataset version, for example Informative Features, or null otherwise.", "type": [ "string", "null" ] }, "features": { "description": "Features in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "The ID of the featurelist", "type": "string" }, "name": { "description": "The name of the featurelist", "type": "string" }, "userCreated": { "description": "True if the featurelist was created by a user vs the system.", "type": "boolean" } }, "required": [ "createdBy", "creationDate", "datasetId", "datasetVersionId", "features", "id", "name", "userCreated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successfully created | DatasetFeaturelistResponse | | 409 | Conflict | Feature list with specified name already exists | None | | 422 | Unprocessable Entity | One or more of the specified features does not exist in the dataset | None | ## Delete dataset featurelist by dataset ID Operation path: `DELETE /api/v2/datasets/{datasetId}/featurelists/{featurelistId}/` Authentication requirements: `BearerAuth` Deletes the indicated featurelist of the dataset with given ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | featurelistId | path | string | true | The ID of the featurelist. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully deleted | None | ## Get dataset featurelist by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/featurelists/{featurelistId}/` Authentication requirements: `BearerAuth` Retrieves the specified featurelist of the dataset with given ID and the latest dataset version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | featurelistId | path | string | true | The ID of the featurelist. | ### Example responses > 200 Response ``` { "properties": { "createdBy": { "description": "`username` of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "the ISO 8601 formatted date and time when the dataset was created.", "format": "date-time", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version if the featurelist is associated with a specific dataset version, for example Informative Features, or null otherwise.", "type": [ "string", "null" ] }, "features": { "description": "Features in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "The ID of the featurelist", "type": "string" }, "name": { "description": "The name of the featurelist", "type": "string" }, "userCreated": { "description": "True if the featurelist was created by a user vs the system.", "type": "boolean" } }, "required": [ "createdBy", "creationDate", "datasetId", "datasetVersionId", "features", "id", "name", "userCreated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The featurelist | DatasetFeaturelistResponse | ## Update dataset featurelist by dataset ID Operation path: `PATCH /api/v2/datasets/{datasetId}/featurelists/{featurelistId}/` Authentication requirements: `BearerAuth` Modifies the indicated featurelist of the dataset with given ID. ### Body parameter ``` { "properties": { "description": { "description": "The new description of the featurelist.", "type": [ "string", "null" ] }, "name": { "description": "The new name of the featurelist.", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | featurelistId | path | string | true | The ID of the featurelist. | | body | body | FeatureListModify | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully modified | None | ## Retrieve original dataset data by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/file/` Authentication requirements: `BearerAuth` Retrieve all the originally uploaded data, in CSV form. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The original dataset data | string | | 409 | Conflict | Ingest info is missing for dataset version. | None | | 422 | Unprocessable Entity | Dataset cannot be downloaded. Possible reasons include "dataPersisted" being false for the dataset, the dataset not being a snapshot, and this dataset is too big to be downloaded (maximum download size depends on a config of your installation). | None | ## Describe dataset permissions by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/permissions/` Authentication requirements: `BearerAuth` Describe what permissions current user has for given dataset. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ``` { "properties": { "canCreateFeaturelist": { "description": "True if the user can create a new featurelist for this dataset.", "type": "boolean" }, "canDeleteDataset": { "description": "True if the user can delete dataset.", "type": "boolean" }, "canDownloadDatasetData": { "description": "True if the user can download data.", "type": "boolean" }, "canGetCatalogItemInfo": { "description": "True if the user can view catalog info.", "type": "boolean" }, "canGetDatasetInfo": { "description": "True if the user can view dataset info.", "type": "boolean" }, "canGetDatasetPermissions": { "description": "True if the user can view dataset permissions.", "type": "boolean" }, "canGetFeatureInfo": { "description": "True if the user can retrieve feature info of dataset.", "type": "boolean" }, "canGetFeaturelists": { "description": "True if the user can view featurelist for this dataset.", "type": "boolean" }, "canPatchCatalogInfo": { "description": "True if the user can modify catalog info.", "type": "boolean" }, "canPatchDatasetAliases": { "description": "True if the user can modify dataset feature aliases.", "type": "boolean" }, "canPatchDatasetInfo": { "description": "True if the user can modify dataset info.", "type": "boolean" }, "canPatchDatasetPermissions": { "description": "True if the user can modify dataset permissions.", "type": "boolean" }, "canPatchFeaturelists": { "description": "True if the user can modify featurelists for this dataset.", "type": "boolean" }, "canPostDataset": { "description": "True if the user can create a new dataset.", "type": "boolean" }, "canReloadDataset": { "description": "True if the user can reload dataset.", "type": "boolean" }, "canShareDataset": { "description": "True if the user can share the dataset.", "type": "boolean" }, "canSnapshotDataset": { "description": "True if the user can save snapshot of dataset.", "type": "boolean" }, "canUndeleteDataset": { "description": "True if the user can undelete dataset.", "type": "boolean" }, "canUseDatasetData": { "description": "True if the user can use dataset data to create projects, train custom models or provide predictions.", "type": "boolean" }, "canUseFeaturelists": { "description": "True if the user can use featurelists for this dataset. (for project creation)", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "uid": { "description": "The ID of the user identified by username.", "type": "string" }, "username": { "description": "`username` of a user with access to this dataset.", "type": "string" } }, "required": [ "canCreateFeaturelist", "canDeleteDataset", "canDownloadDatasetData", "canGetCatalogItemInfo", "canGetDatasetInfo", "canGetDatasetPermissions", "canGetFeatureInfo", "canGetFeaturelists", "canPatchCatalogInfo", "canPatchDatasetAliases", "canPatchDatasetInfo", "canPatchDatasetPermissions", "canPatchFeaturelists", "canPostDataset", "canReloadDataset", "canShareDataset", "canSnapshotDataset", "canUndeleteDataset", "canUseDatasetData", "canUseFeaturelists", "datasetId", "uid", "username" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The dataset permissions. | DatasetDescribePermissionsResponse | ## Get dataset projects by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/projects/` Authentication requirements: `BearerAuth` Retrieves a dataset's projects by dataset ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | Only this many items are returned. | | offset | query | integer | true | Skip this many items. | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of information about dataset's projects", "items": { "properties": { "id": { "description": "The dataset's project ID.", "type": "string" }, "url": { "description": "The link to retrieve more information about the dataset's project.", "type": "string" } }, "required": [ "id", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of projects | DatasetProjectListResponse | ## Information about scheduled jobs by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/refreshJobs/` Authentication requirements: `BearerAuth` Paginated list of scheduled jobs descriptions for a specific dataset with given dataset ID, sorted by time of the last update. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | Only this many items are returned. | | offset | query | integer | true | Skip this many items. | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of information about scheduled dataset refresh jobs. Results are based on updatedAt value and returned in descending order (latest returned first).", "items": { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "createdBy": { "description": "The user who created this dataset refresh job.", "type": "string" }, "credentialId": { "description": "ID used to validate with Kerberos authentication service if Kerberos is enabled.", "type": [ "string", "null" ] }, "credentials": { "description": "A JSON string describing the data engine queries credentials to use when refreshing.", "type": "string" }, "datasetId": { "description": "The ID of the dataset the user scheduled job applies to.", "type": "string" }, "enabled": { "description": "Indicates whether the scheduled job is active (true) or inactive(false).", "type": "boolean" }, "jobId": { "description": "The scheduled job ID.", "type": "string" }, "name": { "description": "The scheduled job's name.", "type": "string" }, "schedule": { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" }, "scheduleReferenceDate": { "description": "The UTC reference date in RFC-3339 format of when the schedule starts from. Can be used to help build a more intuitive schedule picker.", "format": "date-time", "type": "string" }, "updatedAt": { "description": "The UTC date in RFC-3339 format of when the job was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The user who last modified this dataset refresh job.", "type": "string" }, "useKerberos": { "description": "Boolean (true) if the Kerberos authentication service is needed when refreshing a job.", "type": "boolean" } }, "required": [ "categories", "createdBy", "credentialId", "credentials", "datasetId", "enabled", "jobId", "name", "schedule", "scheduleReferenceDate", "updatedAt", "updatedBy", "useKerberos" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of a dataset's scheduled job information retrieved successfully. | DatasetRefreshJobsListResponse | ## Schedule dataset refresh by dataset ID Operation path: `POST /api/v2/datasets/{datasetId}/refreshJobs/` Authentication requirements: `BearerAuth` Create a dataset refresh job that will automatically create dataset snapshots on a schedule. Optionally if the limit of enabled jobs per user is reached the following metadata will be added to the default error response payload: - datasetsWithJob ( array ) - The list of datasets IDs that have at least one enabled job. - errorType ( string ) - (New in version v2.21) The type of error that happened, possible values include (but are not limited to): Generic Limit Reached , Max Job Limit Reached for Dataset , and Max Job Limit Reached for User . ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset. The supported options are ``TRAINING``, and ``PREDICTION``.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialId": { "default": null, "description": "The ID of the set of credentials to use to run the scheduled job when the Kerberos authentication service is utilized. Required when useKerberos is true.", "type": [ "string", "null" ] }, "credentials": { "description": "A JSON string describing the data engine queries credentials to use when refreshing.", "type": "string" }, "enabled": { "default": true, "description": "Boolean for whether the scheduled job is active (true) or inactive (false).", "type": "boolean" }, "name": { "description": "The scheduled job name.", "maxLength": 256, "type": "string" }, "schedule": { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" }, "scheduleReferenceDate": { "description": "The UTC reference date in RFC-3339 format of when the schedule starts from. This value is returned in `/api/v2/datasets/(datasetId)/refreshJobs/(jobId)/` to help build a more intuitive schedule picker. The default is the current time.", "format": "date-time", "type": "string" }, "useKerberos": { "default": false, "description": "If true, the Kerberos authentication system is used in conjunction with a credential ID.", "type": "boolean" } }, "required": [ "name", "schedule" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | DatasetRefreshJobCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "createdBy": { "description": "The user who created this dataset refresh job.", "type": "string" }, "credentialId": { "description": "ID used to validate with Kerberos authentication service if Kerberos is enabled.", "type": [ "string", "null" ] }, "credentials": { "description": "A JSON string describing the data engine queries credentials to use when refreshing.", "type": "string" }, "datasetId": { "description": "The ID of the dataset the user scheduled job applies to.", "type": "string" }, "enabled": { "description": "Indicates whether the scheduled job is active (true) or inactive(false).", "type": "boolean" }, "jobId": { "description": "The scheduled job ID.", "type": "string" }, "name": { "description": "The scheduled job's name.", "type": "string" }, "schedule": { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" }, "scheduleReferenceDate": { "description": "The UTC reference date in RFC-3339 format of when the schedule starts from. Can be used to help build a more intuitive schedule picker.", "format": "date-time", "type": "string" }, "updatedAt": { "description": "The UTC date in RFC-3339 format of when the job was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The user who last modified this dataset refresh job.", "type": "string" }, "useKerberos": { "description": "Boolean (true) if the Kerberos authentication service is needed when refreshing a job.", "type": "boolean" } }, "required": [ "categories", "createdBy", "credentialId", "credentials", "datasetId", "enabled", "jobId", "name", "schedule", "scheduleReferenceDate", "updatedAt", "updatedBy", "useKerberos" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Dataset refresh job created. | DatasetRefreshJobResponse | | 409 | Conflict | The maximum number of enabled jobs is reached. | None | | 422 | Unprocessable Entity | Refresh job could not be created. Possible reasons include, the job does not belong to the given dataset, credential ID required when Kerberos authentication enabled, or the schedule is not valid or cannot be understood. | None | ## Deletes an existing dataset refresh job by dataset ID Operation path: `DELETE /api/v2/datasets/{datasetId}/refreshJobs/{jobId}/` Authentication requirements: `BearerAuth` Deletes an existing dataset refresh job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The dataset associated with the scheduled refresh job. | | jobId | path | string | true | The ID of the user scheduled dataset refresh job. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Scheduled Job deleted. | None | | 422 | Unprocessable Entity | Invalid job ID or dataset ID provided. | None | ## Gets configuration of a user scheduled dataset refresh job by job ID Operation path: `GET /api/v2/datasets/{datasetId}/refreshJobs/{jobId}/` Authentication requirements: `BearerAuth` Gets configuration of a user scheduled dataset refresh job by job ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The dataset associated with the scheduled refresh job. | | jobId | path | string | true | The ID of the user scheduled dataset refresh job. | ### Example responses > 200 Response ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "createdBy": { "description": "The user who created this dataset refresh job.", "type": "string" }, "credentialId": { "description": "ID used to validate with Kerberos authentication service if Kerberos is enabled.", "type": [ "string", "null" ] }, "credentials": { "description": "A JSON string describing the data engine queries credentials to use when refreshing.", "type": "string" }, "datasetId": { "description": "The ID of the dataset the user scheduled job applies to.", "type": "string" }, "enabled": { "description": "Indicates whether the scheduled job is active (true) or inactive(false).", "type": "boolean" }, "jobId": { "description": "The scheduled job ID.", "type": "string" }, "name": { "description": "The scheduled job's name.", "type": "string" }, "schedule": { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" }, "scheduleReferenceDate": { "description": "The UTC reference date in RFC-3339 format of when the schedule starts from. Can be used to help build a more intuitive schedule picker.", "format": "date-time", "type": "string" }, "updatedAt": { "description": "The UTC date in RFC-3339 format of when the job was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The user who last modified this dataset refresh job.", "type": "string" }, "useKerberos": { "description": "Boolean (true) if the Kerberos authentication service is needed when refreshing a job.", "type": "boolean" } }, "required": [ "categories", "createdBy", "credentialId", "credentials", "datasetId", "enabled", "jobId", "name", "schedule", "scheduleReferenceDate", "updatedAt", "updatedBy", "useKerberos" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Job information retrieved successfully. | DatasetRefreshJobResponse | ## Update a dataset refresh job by dataset ID Operation path: `PATCH /api/v2/datasets/{datasetId}/refreshJobs/{jobId}/` Authentication requirements: `BearerAuth` Update a dataset refresh job. Optionally if the limit of enabled jobs per user is reached the following metadata will be added to the default error response payload: - datasetsWithJob ( array ) - The list of datasets IDs that have at least one enabled job. - errorType ( string ) - (New in version v2.21) The type of error that happened, possible values include (but are not limited to): Generic Limit Reached , Max Job Limit Reached for Dataset , and Max Job Limit Reached for User . ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset. The supported options are ``TRAINING``, and ``PREDICTION``.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialId": { "description": "The ID of the set of credentials to use to run the scheduled job when the Kerberos authentication service is utilized. Required when useKerberos is true.", "type": [ "string", "null" ] }, "credentials": { "description": "A JSON string describing the data engine queries credentials to use when refreshing.", "type": "string" }, "enabled": { "description": "Boolean for whether the scheduled job is active (true) or inactive (false).", "type": "boolean" }, "name": { "description": "The scheduled job name.", "maxLength": 256, "type": "string" }, "schedule": { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" }, "scheduleReferenceDate": { "description": "The UTC reference date in RFC-3339 format of when the schedule starts from. This value is returned in `/api/v2/datasets/(datasetId)/refreshJobs/(jobId)/` to help build a more intuitive schedule picker. Required when ``schedule`` is being updated. The default is the current time.", "format": "date-time", "type": "string" }, "useKerberos": { "description": "If true, the Kerberos authentication system is used in conjunction with a credential ID.", "type": "boolean" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The dataset associated with the scheduled refresh job. | | jobId | path | string | true | The ID of the user scheduled dataset refresh job. | | body | body | DatasetRefreshJobUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "createdBy": { "description": "The user who created this dataset refresh job.", "type": "string" }, "credentialId": { "description": "ID used to validate with Kerberos authentication service if Kerberos is enabled.", "type": [ "string", "null" ] }, "credentials": { "description": "A JSON string describing the data engine queries credentials to use when refreshing.", "type": "string" }, "datasetId": { "description": "The ID of the dataset the user scheduled job applies to.", "type": "string" }, "enabled": { "description": "Indicates whether the scheduled job is active (true) or inactive(false).", "type": "boolean" }, "jobId": { "description": "The scheduled job ID.", "type": "string" }, "name": { "description": "The scheduled job's name.", "type": "string" }, "schedule": { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" }, "scheduleReferenceDate": { "description": "The UTC reference date in RFC-3339 format of when the schedule starts from. Can be used to help build a more intuitive schedule picker.", "format": "date-time", "type": "string" }, "updatedAt": { "description": "The UTC date in RFC-3339 format of when the job was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The user who last modified this dataset refresh job.", "type": "string" }, "useKerberos": { "description": "Boolean (true) if the Kerberos authentication service is needed when refreshing a job.", "type": "boolean" } }, "required": [ "categories", "createdBy", "credentialId", "credentials", "datasetId", "enabled", "jobId", "name", "schedule", "scheduleReferenceDate", "updatedAt", "updatedBy", "useKerberos" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Scheduled Job configuration updated. | DatasetRefreshJobResponse | | 409 | Conflict | The maximum number of enabled jobs is reached. | None | | 422 | Unprocessable Entity | Refresh job could not be updated. Possible reasons include, the job does not belong to the given dataset, credential ID required when Kerberos authentication enabled, or the schedule is not valid or cannot be understood. | None | ## Results of dataset refresh job by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/refreshJobs/{jobId}/executionResults/` Authentication requirements: `BearerAuth` Paginated list of execution results for refresh job with the given ID and dataset with the given ID, sorted from newest to oldest. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | false | Maximum number of results returned. The default may change and a maximum limit may be imposed without notice. | | offset | query | integer | false | The number of results that will be skipped. | | datasetId | path | string | true | The dataset associated with the scheduled refresh job. | | jobId | path | string | true | The ID of the user scheduled dataset refresh job. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Array of dataset refresh results, returned latest first. ", "items": { "properties": { "completedAt": { "description": "UTC completion date, in RFC-3339 format.", "format": "date-time", "type": "string" }, "datasetId": { "description": "The dataset ID associated with this result.", "type": "string" }, "datasetVersionId": { "description": "The dataset version ID associated with this result.", "type": "string" }, "executionId": { "description": "The result ID.", "type": "string" }, "jobId": { "description": "The job ID associated with this result.", "type": "string" }, "message": { "description": "The current status of execution.", "type": "string" }, "startedAt": { "description": "UTC start date, in RFC-3339 format.", "format": "date-time", "type": "string" }, "status": { "description": "The status of this dataset refresh.", "enum": [ "INITIALIZING", "REFRESHING", "SUCCESS", "ERROR" ], "type": "string" } }, "required": [ "completedAt", "datasetId", "datasetVersionId", "executionId", "jobId", "message", "startedAt", "status" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Paginated list of dataset refresh job results, sorted from latest to oldest. | DatasetRefreshJobRetrieveExecutionResultsResponse | ## List related datasets by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/relationships/` Authentication requirements: `BearerAuth` Retrieve a list of the dataset relationships for a specific dataset. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | At most this many results are returned. | | offset | query | integer | true | This many results will be skipped. | | linkedDatasetId | query | string | false | Providing linkedDatasetId will filter such that only relationships between datasetId (from the path) and linkedDatasetId will be returned. | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of relationships' details.", "items": { "properties": { "createdBy": { "description": "The username of the user that created this relationship.", "type": "string" }, "creationDate": { "description": "ISO-8601 formatted time/date that this record was created.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the dataset relationship", "type": "string" }, "linkedDatasetId": { "description": "ID of the linked dataset.", "type": "string" }, "linkedFeatures": { "description": "List of features belonging to the linked dataset.", "items": { "type": "string" }, "type": "array" }, "modificationDate": { "description": "ISO-8601 formatted time/date that this record was last updated.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "The username of the user that modified this relationship most recently.", "type": "string" }, "sourceDatasetId": { "description": "ID of the source dataset.", "type": "string" }, "sourceFeatures": { "description": "List of features belonging to the source dataset.", "items": { "type": "string" }, "type": "array" } }, "required": [ "createdBy", "creationDate", "id", "linkedDatasetId", "linkedFeatures", "modificationDate", "modifiedBy", "sourceDatasetId", "sourceFeatures" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of dataset relationships | DatasetRelationshipListResponse | | 410 | Gone | Dataset deleted | None | ## Create dataset relationship by dataset ID Operation path: `POST /api/v2/datasets/{datasetId}/relationships/` Authentication requirements: `BearerAuth` Create a dataset relationship. ### Body parameter ``` { "properties": { "linkedDatasetId": { "description": "The ID of another dataset with which to create relationships.", "type": "string" }, "linkedFeatures": { "description": "List of features belonging to the linked dataset.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "sourceFeatures": { "description": "List of features belonging to the source dataset.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "linkedDatasetId", "linkedFeatures", "sourceFeatures" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | DatasetRelationshipCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "createdBy": { "description": "The username of the user that created this relationship.", "type": "string" }, "creationDate": { "description": "ISO-8601 formatted time/date that this record was created.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the dataset relationship", "type": "string" }, "linkedDatasetId": { "description": "ID of the linked dataset.", "type": "string" }, "linkedFeatures": { "description": "List of features belonging to the linked dataset.", "items": { "type": "string" }, "type": "array" }, "modificationDate": { "description": "ISO-8601 formatted time/date that this record was last updated.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "The username of the user that modified this relationship most recently.", "type": "string" }, "sourceDatasetId": { "description": "ID of the source dataset.", "type": "string" }, "sourceFeatures": { "description": "List of features belonging to the source dataset.", "items": { "type": "string" }, "type": "array" } }, "required": [ "createdBy", "creationDate", "id", "linkedDatasetId", "linkedFeatures", "modificationDate", "modifiedBy", "sourceDatasetId", "sourceFeatures" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successfully created | DatasetRelationshipResponse | | 409 | Conflict | Relationship already exists. | None | | 410 | Gone | Dataset deleted. | None | | 422 | Unprocessable Entity | Missing or unrecognized fields. | None | ## Delete dataset relationship by dataset ID Operation path: `DELETE /api/v2/datasets/{datasetId}/relationships/{datasetRelationshipId}/` Authentication requirements: `BearerAuth` Delete a dataset relationship. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | datasetRelationshipId | path | string | true | The ID of the dataset relationship to delete. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully deleted | None | | 410 | Gone | Dataset deleted. | None | ## Update dataset relationship by dataset ID Operation path: `PATCH /api/v2/datasets/{datasetId}/relationships/{datasetRelationshipId}/` Authentication requirements: `BearerAuth` Update a dataset relationship. ### Body parameter ``` { "properties": { "linkedDatasetId": { "description": "id of another dataset with which to create relationships", "type": "string" }, "linkedFeatures": { "description": "list of features belonging to the linked dataset", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "sourceFeatures": { "description": "list of features belonging to the source dataset", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | datasetRelationshipId | path | string | true | The ID of the dataset relationship to delete. | | body | body | DatasetRelationshipUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "createdBy": { "description": "The username of the user that created this relationship.", "type": "string" }, "creationDate": { "description": "ISO-8601 formatted time/date that this record was created.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the dataset relationship", "type": "string" }, "linkedDatasetId": { "description": "ID of the linked dataset.", "type": "string" }, "linkedFeatures": { "description": "List of features belonging to the linked dataset.", "items": { "type": "string" }, "type": "array" }, "modificationDate": { "description": "ISO-8601 formatted time/date that this record was last updated.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "The username of the user that modified this relationship most recently.", "type": "string" }, "sourceDatasetId": { "description": "ID of the source dataset.", "type": "string" }, "sourceFeatures": { "description": "List of features belonging to the source dataset.", "items": { "type": "string" }, "type": "array" } }, "required": [ "createdBy", "creationDate", "id", "linkedDatasetId", "linkedFeatures", "modificationDate", "modifiedBy", "sourceDatasetId", "sourceFeatures" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully updated | DatasetRelationshipResponse | | 409 | Conflict | Relationship already exists | None | | 410 | Gone | Dataset deleted | None | | 422 | Unprocessable Entity | Bad payload: missing or unrecognized fields | None | ## List dataset shared roles by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations who have access to this dataset and their roles. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return the access control information for a organization, group or user with this ID. | | name | query | string | false | Only return the access control information for a organization, group or user with this name. | | shareRecipientType | query | string | false | The recipient type. | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | datasetId | path | string | true | The ID of the dataset. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of SharedRoles objects.", "items": { "properties": { "canShare": { "description": "True if this user can share with other users", "type": "boolean" }, "canUseData": { "description": "True if the user can view, download and process data (use to create projects, predictions, etc)", "type": "boolean" }, "id": { "description": "The ID of the recipient organization, group or user.", "type": "string" }, "name": { "description": "The name of the recipient organization, group or user.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entry or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" }, "userFullName": { "description": "If the recipient type is a user, the full name of the user if available.", "type": "string" } }, "required": [ "canShare", "canUseData", "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The paginated list of user permissions. | SharedRolesListResponse | ## Modify dataset shared roles by dataset ID Operation path: `PATCH /api/v2/datasets/{datasetId}/sharedRoles/` Authentication requirements: `BearerAuth` Grant access, remove access or update roles for organizations, groups or users on this dataset. Up to 100 roles may be set per array in a single request. ### Body parameter ``` { "properties": { "applyGrantToLinkedObjects": { "default": false, "description": "If `true` for any users being granted access to the entity, grant the user read access to any linked objects such as `DataSources` and `DataStores` that may be used by this entity. Ignored if no such objects are relevant for the entity. This will not result in access being lowered for a user if the user already has higher access to linked objects than read access. However, if the target user does not have sharing permissions to the linked object, they will be given sharing access without lowering existing permissions. May result in an error if the user making the call does not have sufficient permissions to complete the grant. Default value is false.", "type": "boolean" }, "operation": { "description": "The name of the action being taken. The only operation is `updateRoles`.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "An array of `RoleRequest` objects. Maximum number of such objects is 100.", "items": { "oneOf": [ { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "name": { "description": "Name of the user/group/org to update the access role for.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "id": { "description": "The org/group/user ID.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | CatalogSharedRoles | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully modified. | None | | 409 | Conflict | Duplicate entry for the org/group/user in permission listor the request would leave the dataset without an owner. | None | | 422 | Unprocessable Entity | Request is unprocessable. For example, name is stated for not user recipient. | None | ## List dataset versions by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/versions/` Authentication requirements: `BearerAuth` List all versions associated with given datasetId and which match the specified query parameters. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | category | query | string | false | If specified, only dataset versions that have the specified category will be included in the results. Categories identify the intended use of the dataset. | | orderBy | query | string | false | Sorting order which will be applied to catalog list. | | limit | query | integer | true | At most this many results are returned. | | offset | query | integer | true | This many results will be skipped. | | filterFailed | query | string | false | Whether datasets that failed during import should be excluded from the results. If True invalid datasets will be excluded. | | datasetId | path | string | true | The ID of the dataset. | ### Enumerated Values | Parameter | Value | | --- | --- | | category | [TRAINING, PREDICTION, SAMPLE] | | orderBy | [created, -created] | | filterFailed | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of dataset details.", "items": { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "columnCount": { "description": "The number of columns in the dataset.", "type": "integer", "x-versionadded": "v2.30" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes.", "type": "integer", "x-versionadded": "v2.21" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "rowCount": { "description": "The number of rows in the dataset.", "type": "integer", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "columnCount", "createdBy", "creationDate", "dataPersisted", "datasetId", "datasetSize", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "name", "processingState", "rowCount", "timeSeriesProperties", "versionId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of dataset versions | DatasetListResponse | ## Create dataset version by dataset ID Operation path: `POST /api/v2/datasets/{datasetId}/versions/fromDataEngineWorkspaceState/` Authentication requirements: `BearerAuth` Create a new dataset version for a specified dataset from a Data Engine workspace state. The new dataset version should have the same schema as the specified dataset. ### Body parameter ``` { "properties": { "credentials": { "description": "JDBC credentials.", "type": "string" }, "doSnapshot": { "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "workspaceStateId": { "description": "The ID of the workspace state to use as the source of data.", "type": "string" } }, "required": [ "workspaceStateId" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | DatasetCreateVersionFromWorkspaceState | false | none | ### Example responses > 202 Response ``` { "properties": { "datasetId": { "description": "The ID of the output dataset item.", "type": "string" }, "datasetVersionId": { "description": "The ID of the output dataset version item.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "datasetId", "datasetVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetDataEngineResponse | | 410 | Gone | Specified workspace was already deleted. | None | | 422 | Unprocessable Entity | Type of new dataset version is incompatible with specified dataset. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a version from a data source Operation path: `POST /api/v2/datasets/{datasetId}/versions/fromDataSource/` Authentication requirements: `BearerAuth` Create a new version for the specified dataset from specified Data Source. The dataset must have been created from a compatible data source originally. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.19" }, "dataSourceId": { "description": "The identifier for the DataSource to use as the source of data.", "type": "string" }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "persistDataAfterIngestion": { "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error.", "type": "boolean" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for database authentication. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "dataSourceId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | Datasource | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a version from a file Operation path: `POST /api/v2/datasets/{datasetId}/versions/fromFile/` Authentication requirements: `BearerAuth` Create a new version for the specified dataset from a file. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "file": { "description": "The data to be used for the creation.", "format": "binary", "type": "string" } }, "required": [ "file" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | DatasetFromFile | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a version from a hdfs Operation path: `POST /api/v2/datasets/{datasetId}/versions/fromHDFS/` Authentication requirements: `BearerAuth` Create a new version for the specified dataset from a HDFS URL. The dataset must have been created from the same HDFS URL originally. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "namenodeWebhdfsPort": { "description": "The port of the HDFS name node.", "type": "integer" }, "password": { "description": "The password (in cleartext) for authenticating to HDFS using Kerberos. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "persistDataAfterIngestion": { "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error", "type": "boolean" }, "url": { "description": "The HDFS url to use as the source of data for the dataset being created.", "format": "uri", "type": "string" }, "user": { "description": "The username for authenticating to HDFS using Kerberos.", "type": "string" } }, "required": [ "url" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | Hdfs | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Create a version from a latest version Operation path: `POST /api/v2/datasets/{datasetId}/versions/fromLatestVersion/` Authentication requirements: `BearerAuth` Create a new version of the specified dataset from the latest dataset version. This will reuse the same source of the data that was previously used. Not supported for datasets that were previously loaded from an uploaded file. If the dataset is currently a remote dataset, it will be converted to a snapshot dataset. NOTE: if the current version uses a Data Source, the `user` and `password` must be specified so the data can be accessed. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.19" }, "credentials": { "description": "A list of credentials to use if this is a Spark dataset that requires credentials.", "type": "string", "x-versionadded": "v2.20" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server-side HTTP request and never saved or stored. Required only if the previous data source was a data source. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "useKerberos": { "default": false, "description": "If true, use Kerberos for database authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "useLatestSuccess": { "default": false, "description": "If true, use the latest version that was successfully ingested instead of the latest version, which might be in an errored state. If no successful version is present, the latest errored version is used and the operation fails.", "type": "boolean", "x-versionadded": "v2.20" }, "user": { "description": "The username for database authentication. Required only if the dataset was initially created from a data source. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | FromLatest | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | | 409 | Conflict | The latest version of the dataset is in an errored state. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a version from a recipe Operation path: `POST /api/v2/datasets/{datasetId}/versions/fromRecipe/` Authentication requirements: `BearerAuth` Create a new dataset version for a specified dataset from a wrangler recipe. ### Body parameter ``` { "properties": { "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" } ], "x-versionadded": "v2.37" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe to use as the source of data.", "type": "string" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "recipeId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | DatasetCreateVersionFromRecipe | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | | 410 | Gone | Specified recipe was already deleted. | None | | 422 | Unprocessable Entity | Type of new dataset version is incompatible with specified dataset. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a version from a stage Operation path: `POST /api/v2/datasets/{datasetId}/versions/fromStage/` Authentication requirements: `BearerAuth` Create a new version for the specified dataset from a data stage. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "maxItems": 100, "type": "array" } ] }, "stageId": { "description": "The ID of the data stage which will be used to create the dataset item & version.", "type": "string" } }, "required": [ "stageId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | DatasetFromStage | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | | 403 | Forbidden | You do not have permission to use data stages. | None | | 404 | Not Found | Data Stage not found or Dataset not found | None | | 409 | Conflict | Data Stage not finalized | None | | 410 | Gone | Data Stage failed | None | | 422 | Unprocessable Entity | The request cannot be processed. Possible reasons include the request did not contain data stage, dataset was previously created from a non data stage source | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a version from an URL Operation path: `POST /api/v2/datasets/{datasetId}/versions/fromURL/` Authentication requirements: `BearerAuth` Create a new version for the specified dataset from specified URL. The dataset must have been created from the same URL originally. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "persistDataAfterIngestion": { "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error.", "type": "boolean" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "url": { "description": "The URL to download the dataset used to create the dataset item and version.", "format": "url", "type": "string" } }, "required": [ "url" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | body | body | Url | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Delete dataset version by dataset ID Operation path: `DELETE /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/` Authentication requirements: `BearerAuth` Marks the dataset version with the given ID as deleted. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset entry. | | datasetVersionId | path | string | true | The ID of the dataset version. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully deleted | None | ## Get dataset details by version by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/` Authentication requirements: `BearerAuth` Retrieves the details of the dataset with given ID and version ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset entry. | | datasetVersionId | path | string | true | The ID of the dataset version. | ### Example responses > 200 Response ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "columnCount": { "description": "The number of columns in the dataset.", "type": "integer", "x-versionadded": "v2.30" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataEngineQueryId": { "description": "The ID of the source data engine query.", "type": [ "string", "null" ] }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "dataSourceId": { "description": "The ID of the datasource used as the source of the dataset.", "type": [ "string", "null" ] }, "dataSourceType": { "description": "The type of the datasource that was used as the source of the dataset.", "type": "string" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes.", "type": "integer", "x-versionadded": "v2.21" }, "description": { "description": "The description of the dataset.", "type": [ "string", "null" ] }, "eda1ModificationDate": { "description": "The ISO 8601 formatted date and time when the EDA1 for the dataset was updated.", "format": "date-time", "type": "string" }, "eda1ModifierFullName": { "description": "The user who was the last to update EDA1 for the dataset.", "type": "string" }, "entityCountByType": { "description": "Number of different type entities that use the dataset.", "properties": { "numCalendars": { "description": "The number of calendars that use the dataset", "type": "integer" }, "numExperimentContainer": { "description": "The number of experiment containers that use the dataset.", "type": "integer", "x-versionadded": "v2.37" }, "numExternalModelPackages": { "description": "The number of external model packages that use the dataset", "type": "integer" }, "numFeatureDiscoveryConfigs": { "description": "The number of feature discovery configs that use the dataset", "type": "integer" }, "numPredictionDatasets": { "description": "The number of prediction datasets that use the dataset", "type": "integer" }, "numProjects": { "description": "The number of projects that use the dataset", "type": "integer" }, "numSparkSqlQueries": { "description": "The number of spark sql queries that use the dataset", "type": "integer" } }, "required": [ "numCalendars", "numExperimentContainer", "numExternalModelPackages", "numFeatureDiscoveryConfigs", "numPredictionDatasets", "numProjects", "numSparkSqlQueries" ], "type": "object" }, "error": { "description": "Details of exception raised during ingestion process, if any.", "type": "string" }, "featureCount": { "description": "Total number of features in the dataset.", "type": "integer" }, "featureCountByType": { "description": "Number of features in the dataset grouped by feature type.", "items": { "properties": { "count": { "description": "The number of features of this type in the dataset", "type": "integer" }, "featureType": { "description": "The data type grouped in this count", "type": "string" } }, "required": [ "count", "featureType" ], "type": "object" }, "type": "array" }, "featureDiscoveryProjectId": { "description": "Feature Discovery project ID used to create the dataset.", "type": "string", "x-versionadded": "v2.35" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean", "x-versionadded": "2.30.0" }, "lastModificationDate": { "description": "The ISO 8601 formatted date and time when the dataset was last modified.", "format": "date-time", "type": "string" }, "lastModifierFullName": { "description": "Full name of user who was the last to modify the dataset.", "type": "string" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "recipeId": { "description": "The ID of the source recipe.", "type": [ "string", "null" ] }, "rowCount": { "description": "The number of rows in the dataset.", "type": "integer", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "tags": { "description": "List of tags attached to the item.", "items": { "type": "string" }, "type": "array" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "uri": { "description": "The URI to datasource. For example, `file_name.csv`, or `jdbc:DATA_SOURCE_GIVEN_NAME/SCHEMA.TABLE_NAME`, or `jdbc:DATA_SOURCE_GIVEN_NAME/` for `query` based datasources, or`https://s3.amazonaws.com/dr-pr-tst-data/kickcars-sample-200.csv`, etc.", "type": "string" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "columnCount", "createdBy", "creationDate", "dataEngineQueryId", "dataPersisted", "dataSourceId", "dataSourceType", "datasetId", "datasetSize", "description", "eda1ModificationDate", "eda1ModifierFullName", "error", "featureCount", "featureCountByType", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "isWranglingEligible", "lastModificationDate", "lastModifierFullName", "name", "processingState", "recipeId", "rowCount", "tags", "timeSeriesProperties", "uri", "versionId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The dataset details | FullDatasetDetailsResponse | ## Retrieve all features details by id Operation path: `GET /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/allFeaturesDetails/` Authentication requirements: `BearerAuth` Return detailed information on all the features and transforms for this dataset.If the Dataset Item has attribute snapshot = True, all optional fields also appear. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | At most this many results are returned. The default may change and a maximum limit may be imposed without notice. | | offset | query | integer | true | This many results will be skipped. | | orderBy | query | string | true | How the features should be ordered. | | includePlot | query | string | false | Include histogram plot data in the response. | | searchFor | query | string | false | A value to search for in the feature name. The search is case insensitive. If no value is provided, an empty string is used, or the string contains only whitespace, no filtering occurs. | | featurelistId | query | string | false | The ID of a featurelist. If specified, only returns features that are present in the specified featurelist. | | includeDataQuality | query | string | false | Include detected data quality issue types in the response. | | datasetId | path | string | true | The ID of the dataset entry. | | datasetVersionId | path | string | true | The ID of the dataset version. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [featureType, name, id, unique, missing, stddev, mean, median, min, max, dataQualityIssues, -featureType, -name, -id, -unique, -missing, -stddev, -mean, -median, -min, -max, -dataQualityIssues] | | includePlot | [false, False, true, True] | | includeDataQuality | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of features related to the requested dataset.", "items": { "properties": { "dataQualityIssues": { "description": "The status of data quality issue detection.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": [ "string", "null" ] }, "dataQualityIssuesTypes": { "description": "Data quality issue types.", "items": { "description": "Data quality issue type.", "enum": [ "disguised_missing_values", "excess_zero", "external_feature_derivation", "few_negative_values", "imputation_leakage", "inconsistent_gaps", "inliers", "lagged_features", "leading_trailing_zeros", "missing_documents", "missing_images", "missing_values", "multicategorical_invalid_format", "new_series_recent_data", "outliers", "quantile_target_sparsity", "quantile_target_zero_inflation", "target_leakage", "unusual_repeated_values" ], "type": "string" }, "maxItems": 20, "type": "array" }, "datasetId": { "description": "The ID of the dataset the feature belongs to", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version the feature belongs to.", "type": "string" }, "dateFormat": { "description": "The date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ] }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string" }, "id": { "description": "The number of the column in the dataset.", "type": "integer" }, "isZeroInflated": { "description": "whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ] }, "language": { "description": "Detected language of the feature.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "Whether feature has too few values to be informative.", "type": "boolean" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "max": { "description": "Maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "Maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "Maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "mean": { "description": "Arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "median": { "description": "Median of the EDA sample of the feature.", "oneOf": [ { "description": "Median of the EDA sample of the feature.", "type": "string" }, { "description": "Median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "min": { "description": "Minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "Minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "Minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "naCount": { "description": "Number of missing values.", "type": [ "integer", "null" ] }, "name": { "description": "Feature name", "type": "string" }, "plot": { "description": "Plot data based on feature values.", "items": { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.30" }, "sampleRows": { "description": "The number of rows in the sample used to calculate the statistics.", "type": "integer", "x-versionadded": "v2.35" }, "stdDev": { "description": "Standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "Standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "Standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "timeSeriesEligibilityReason": { "description": "why the feature is ineligible for time series projects, or 'suitable' if it is eligible.", "type": [ "string", "null" ] }, "timeSeriesEligibilityReasonAggregation": { "description": "why the feature is ineligible for aggregation, or 'suitable' if it is eligible.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "timeSeriesEligible": { "description": "whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.", "type": "boolean" }, "timeSeriesEligibleAggregation": { "description": "whether this feature can be used as a datetime feature for aggregationfor time series data prep. Always false for non-date features.", "type": "boolean", "x-versionadded": "v2.24" }, "timeStep": { "description": "The minimum time step that can be used to specify time series windows. The units for this value are the ``timeUnit``. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.", "type": [ "integer", "null" ] }, "timeStepAggregation": { "description": "The minimum time step that can be used to aggregate using this feature for time series data prep. The units for this value are the ``timeUnit``. Only present for date features that are eligible for aggregation in time series data prep and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "timeUnit": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.", "type": [ "string", "null" ] }, "timeUnitAggregation": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. Only present for date features eligible for aggregation, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "uniqueCount": { "description": "Number of unique values.", "type": [ "integer", "null" ] }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "datasetId", "datasetVersionId", "dateFormat", "featureType", "id", "name", "sampleRows" ], "type": "object", "x-versionadded": "v2.35" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of feature info | DatasetFeaturesListResponse | ## Recover deleted dataset version by dataset ID Operation path: `PATCH /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/deleted/` Authentication requirements: `BearerAuth` Recover the dataset version item with given datasetId and datasetVersionId from `deleted`. ### Body parameter ``` { "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset entry. | | datasetVersionId | path | string | true | The ID of the dataset version. | | body | body | UpdateDatasetDeleted | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The item was not deleted: nothing to recover. | None | | 204 | No Content | Successfully recovered | None | ## Retrieve feature histograms by id Operation path: `GET /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/featureHistograms/{featureName}/` Authentication requirements: `BearerAuth` Get histogram chart data for a specific feature in the specified dataset. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | binLimit | query | integer | true | Maximum number of bins in the returned plot. | | key | query | string | false | Only required for the Summarized categorical feature. The name of the top 50 key for which plot to be retrieved. | | usePlot2 | query | string | false | Use frequent values plot data instead of histogram for supported feature types. | | datasetId | path | string | true | The ID of the dataset entry to retrieve. | | datasetVersionId | path | string | true | The ID of the dataset version to retrieve. | | featureName | path | string | true | The name of the feature. | ### Example responses > 200 Response ``` { "properties": { "plot": { "description": "Plot data based on feature values.", "items": { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array" } }, "required": [ "plot" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The feature histogram | DatasetFeatureHistogramResponse | ## Retrieve featurelists by id Operation path: `GET /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/featurelists/` Authentication requirements: `BearerAuth` Retrieves the featurelists of the dataset with given ID and the latest dataset version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | At most this many results are returned. The default may change and a maximum limit may be imposed without notice. | | offset | query | integer | true | This many results will be skipped. | | orderBy | query | string | true | How the feature lists should be ordered. | | searchFor | query | string | false | A value to search for in the featurelist name. The search is case insensitive. If no value is provided, an empty string is used, or the string contains only whitespace, no filtering occurs. | | datasetId | path | string | true | The ID of the dataset entry. | | datasetVersionId | path | string | true | The ID of the dataset version. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [name, description, featuresNumber, creationDate, userCreated, -name, -description, -featuresNumber, -creationDate, -userCreated] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of featurelists' details.", "items": { "properties": { "createdBy": { "description": "`username` of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "the ISO 8601 formatted date and time when the dataset was created.", "format": "date-time", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version if the featurelist is associated with a specific dataset version, for example Informative Features, or null otherwise.", "type": [ "string", "null" ] }, "features": { "description": "Features in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "The ID of the featurelist", "type": "string" }, "name": { "description": "The name of the featurelist", "type": "string" }, "userCreated": { "description": "True if the featurelist was created by a user vs the system.", "type": "boolean" } }, "required": [ "createdBy", "creationDate", "datasetId", "datasetVersionId", "features", "id", "name", "userCreated" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of featurelists | DatasetFeaturelistListResponse | ## Retrieve featurelists by id Operation path: `GET /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/featurelists/{featurelistId}/` Authentication requirements: `BearerAuth` Retrieves the specified featurelist of the dataset with given ID and the latest dataset version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset to retrieve featurelist for. | | datasetVersionId | path | string | true | The ID of the dataset version to retrieve featurelists for. | | featurelistId | path | string | true | The ID of the featurelist. | ### Example responses > 200 Response ``` { "properties": { "createdBy": { "description": "`username` of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "the ISO 8601 formatted date and time when the dataset was created.", "format": "date-time", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version if the featurelist is associated with a specific dataset version, for example Informative Features, or null otherwise.", "type": [ "string", "null" ] }, "features": { "description": "Features in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "The ID of the featurelist", "type": "string" }, "name": { "description": "The name of the featurelist", "type": "string" }, "userCreated": { "description": "True if the featurelist was created by a user vs the system.", "type": "boolean" } }, "required": [ "createdBy", "creationDate", "datasetId", "datasetVersionId", "features", "id", "name", "userCreated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The featurelist | DatasetFeaturelistResponse | ## Retrieve file by id Operation path: `GET /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/file/` Authentication requirements: `BearerAuth` Retrieve all the originally uploaded data, in CSV form. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset entry. | | datasetVersionId | path | string | true | The ID of the dataset version. | ### Example responses > 200 Response ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The original dataset data | string | | 409 | Conflict | Ingest info is missing for dataset version. | None | | 422 | Unprocessable Entity | Dataset version cannot be downloaded. Possible reasons include dataPersisted being false for the dataset, the dataset not being a snapshot, and this dataset version is too big to be downloaded (maximum download size depends on a config of your installation). | None | ## Create a version from a version Operation path: `POST /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/fromVersion/` Authentication requirements: `BearerAuth` Create a new version of the specified dataset from the specified dataset version. This will reuse the same source of the data that was previously used. Not supported for datasets that were previously loaded from an uploaded file. If the dataset is currently a remote dataset, it will be converted to a snapshot dataset. NOTE: If the specified version uses a Data Source, the `user` and `password` must be specified so the data can be accessed. ### Body parameter ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string" }, "credentials": { "description": "A list of credentials to use if this is a Spark dataset that requires credentials.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server-side HTTP request and never saved or stored. Required only if the previous data source was a data source. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "useKerberos": { "default": false, "description": "If true, use Kerberos for database authentication.", "type": "boolean" }, "user": { "description": "The username for database authentication. Required only if the dataset was initially created from a data source. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset entry. | | datasetVersionId | path | string | true | The ID of the dataset version. | | body | body | FromSpecific | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedDatasetResponse | | 409 | Conflict | The dataset item's specified version is in an errored state. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Get dataset projects by version by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/projects/` Authentication requirements: `BearerAuth` Retrieves a dataset's projects for the specified catalog dataset and dataset version id. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | Only this many items are returned. | | offset | query | integer | true | Skip this many items. | | datasetId | path | string | true | The ID of the dataset entry. | | datasetVersionId | path | string | true | The ID of the dataset version. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Array of project references.", "items": { "properties": { "id": { "description": "The dataset's project ID.", "type": "string" }, "url": { "description": "The link to retrieve more information about the dataset version's project.", "type": "string" } }, "required": [ "id", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of projects | GetDatasetVersionProjectsResponse | ## Create an empty files catalog item Operation path: `POST /api/v2/files/` Authentication requirements: `BearerAuth` Create an empty files catalog item. ### Example responses > 201 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The details of the created catalog item. | BaseFilesVersionResponse | ## Create a files catalog item Operation path: `POST /api/v2/files/fromDataSource/` Authentication requirements: `BearerAuth` Create a files catalog item from a data source. ### Body parameter ``` properties: credentialData: description: The credentials to be used to authenticate with the database in place of the credential id. oneOf: - properties: awsAccessKeyId: description: The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified. type: string awsSecretAccessKey: description: The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified. type: string awsSessionToken: default: null description: The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified. type: - string - "null" configId: description: The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken. type: string credentialType: description: The type of these credentials, 's3' here. enum: - s3 type: string required: - credentialType type: object - properties: authenticationId: description: The authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service. type: string credentialType: description: The type of these credentials, 'external_oauth_provider' here. enum: - external_oauth_provider type: string required: - authenticationId - credentialType type: object credentialId: description: The identifier for the set of credentials to use to authenticate with the database. type: string dataSourceId: description: The identifier of the DataSource to use as the source of data. type: string useArchiveContents: default: "True" description: If true, extract archive contents and associate them with the catalog entity. enum: - "false" - "False" - "true" - "True" type: string required: - dataSourceId type: object x-versionadded: v2.37 ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | FilesFromDataSource | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedFilesResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a file from a file Operation path: `POST /api/v2/files/fromFile/` Authentication requirements: `BearerAuth` Create a files catalog item from a file. ### Body parameter ``` properties: file: description: The file to upload. format: binary type: string originalCatalogId: default: null description: If the contents of the file being uploaded are derived from a file in a catalog entity which was used to clone, the current catalog entity, the original catalog entity ID in which the file exists. type: - string - "null" x-versionadded: v2.38 originalFileName: default: null description: If the contents of the file being uploaded are derived from a file in the catalog entity, the name of the file it is derived from. type: - string - "null" x-versionadded: v2.38 useArchiveContents: default: "True" description: If true, extract archive contents and associate with the catalog entity. enum: - "false" - "False" - "true" - "True" type: string required: - file type: object x-versionadded: v2.37 ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | FilesFromFile | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedFilesResponse | | 422 | Unprocessable Entity | The request cannot be processed. The request did not contain file contents. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a file from an URL Operation path: `POST /api/v2/files/fromURL/` Authentication requirements: `BearerAuth` Create a files catalog item from a URL. ### Body parameter ``` { "properties": { "url": { "description": "The URL to download the file used to create the catalog entity.", "format": "url", "type": "string" }, "useArchiveContents": { "default": "True", "description": "If true, extract archive contents and associate them with the catalog entity.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "url" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | FilesFromUrl | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedFilesResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Delete the file by catalog ID Operation path: `DELETE /api/v2/files/{catalogId}/` Authentication requirements: `BearerAuth` Marks the file with the given ID as deleted. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully deleted. | None | | 409 | Conflict | Cannot delete a dataset that has refresh jobs. | None | ## Delete files or folders by catalog ID Operation path: `DELETE /api/v2/files/{catalogId}/allFiles/` Authentication requirements: `BearerAuth` Recursively delete one or more files or folders inside a catalog item. Folder names should end with "/". ### Body parameter ``` { "properties": { "paths": { "description": "File and folder paths to delete. Folder paths should end with slash '/'.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "paths" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | DeleteFilesOrFolders | false | none | ### Example responses > 200 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog version item.", "type": "string" }, "numFiles": { "description": "The new number of files in the file entity.", "type": "integer" }, "results": { "description": "The number of files deleted for each path provided.", "items": { "properties": { "numFilesDeleted": { "description": "The number of files that were deleted for the path.", "type": "integer" }, "path": { "description": "The file or folder path that was deleted.", "type": "string" } }, "required": [ "numFilesDeleted", "path" ], "type": "object", "x-versionadded": "v2.42" }, "maxItems": 1000, "type": "array" } }, "required": [ "catalogId", "catalogVersionId", "numFiles", "results" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | DeleteFilesOrFoldersResponse | ## List all files associated by catalog ID Operation path: `GET /api/v2/files/{catalogId}/allFiles/` Authentication requirements: `BearerAuth` Lists all files associated with the catalog item. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | fileType | query | any | false | If specified, will only return files that match the specified type(s). | | prefix | query | string | false | If specified, will only return files with paths that start with the given folder prefix. Must end with '/'. | | recursive | query | string | false | Whether to list all files recursively. If a prefix is specific, whether to list all files under that prefix recursively. | | catalogId | path | string | true | The catalog item ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | recursive | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of file metadata.", "items": { "properties": { "fileChecksum": { "description": "The checksum of the file.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "fileCreatedAt": { "description": "The date when the file was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "fileName": { "description": "The name of the file. The actual file can be retrieved with [GET /api/v2/files/{catalogId}/file/][get-apiv2filescatalogidfile].", "type": "string" }, "fileSize": { "description": "The size of the file, in bytes.", "type": "integer" }, "fileType": { "description": "The file type, if known.", "type": "string" }, "ingestErrors": { "description": "Any errors associated with the file.", "type": [ "string", "null" ] } }, "required": [ "fileChecksum", "fileCreatedAt", "fileName", "fileSize", "fileType", "ingestErrors" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The paginated list of files. | FilesListResponse | ## Rename a file by catalog ID Operation path: `PATCH /api/v2/files/{catalogId}/allFiles/` Authentication requirements: `BearerAuth` Rename a file or folder within the same catalog item. Folder names should end with "/". ### Body parameter ``` { "properties": { "fromPath": { "description": "The file or folder path to rename. Folder paths should end with \"/\".", "type": "string" }, "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict with an existing file or folder with the same name.\nRENAME (default): rename the file or folder using “ (n).ext” or \" (n)\" pattern.\nREPLACE: prefer the renamed file or folder.\nSKIP: prefer the existing file or folder.\nERROR: return \"HTTP 409 Conflict\" response in case of a naming conflict. ", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "toPath": { "description": "The new path for the file or folder. Folder paths should end with \"/\".", "type": "string" } }, "required": [ "fromPath", "overwrite", "toPath" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | RenameFileOrFolderBody | false | none | ### Example responses > 201 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "numFiles": { "description": "The number of files in the file entity.", "type": "integer" } }, "required": [ "catalogId", "catalogVersionId", "numFiles" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | FilesVersionResponse | | 404 | Not Found | The specified file or folder to rename was not found in the catalog item. | None | | 422 | Unprocessable Entity | Rename operation cannot be executed. Cannot rename a folder to a file path. | None | ## Create a duplicate files collection by catalog ID Operation path: `POST /api/v2/files/{catalogId}/clone/` Authentication requirements: `BearerAuth` Copy all files from the original catalog item into a newly created one. Optionally, omit some files. ### Body parameter ``` { "properties": { "omit": { "default": null, "description": "File names to skip when cloning the files.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object", "x-versionadded": "v2.38" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | CloneFiles | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The catalog item ID.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "statusId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | FilesAsyncResponse | ## Copy a file by catalog ID Operation path: `POST /api/v2/files/{catalogId}/copy/` Authentication requirements: `BearerAuth` Copy a file or folder within the same catalog item. Folder names should end with "/". ### Body parameter ``` { "properties": { "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict in the target location.\nRENAME (default): rename a duplicate file using “ (n).ext” pattern.\nREPLACE: prefer the file you copy.\nSKIP: prefer the file existing in the *target*.\nERROR: fail with an error in case of a naming conflict. ", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "source": { "description": "The file or folder path to copy. Folder paths should end with '/'.", "type": "string" }, "target": { "description": "The new file or folder path to copy to. Folders paths should end with '/'.", "type": "string" } }, "required": [ "overwrite", "source", "target" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | CopyFileOrFolder | false | none | ### Example responses > 201 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "numFiles": { "description": "The number of files in the file entity.", "type": "integer" } }, "required": [ "catalogId", "catalogVersionId", "numFiles" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | FilesVersionResponse | | 404 | Not Found | The specified source file or folder was not found in the catalog item. | None | | 422 | Unprocessable Entity | Copy operation cannot be executed. Cannot copy a folder to a file path. | None | ## Copy multiple files by catalog ID Operation path: `POST /api/v2/files/{catalogId}/copyBatch/` Authentication requirements: `BearerAuth` Copy one or multiple paths across different catalog items or within the same catalog item. ### Body parameter ``` { "properties": { "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict in the target location.\nRENAME (default): rename a duplicate file using “ (n).ext” pattern.\nREPLACE: prefer files you copy.\nSKIP: prefer files existing in the *target*.\nERROR: fail with an error in case of a naming conflict. ", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "sources": { "description": "File and folder names to copy. Folders are identified by ending with '/'.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" }, "target": { "default": null, "description": "Either a target folder to copy *sources* into or a target file name to copy a single file and rename it on copy.Folders are identified by ending with '/'.", "type": [ "string", "null" ] }, "targetCatalogId": { "default": null, "description": "Target catalog ID to copy files into. Copy files within the same catalog item if no *targetCatalogId* is provided.", "type": [ "string", "null" ] } }, "required": [ "overwrite", "sources" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | CopyBatch | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The catalog item ID.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "statusId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | FilesAsyncResponse | ## Recover a deleted file by catalog ID Operation path: `PATCH /api/v2/files/{catalogId}/deleted/` Authentication requirements: `BearerAuth` Recover the file item with given catalogId from `deleted`. ### Body parameter ``` { "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | UpdateFileDeleted | false | none | ### Example responses > 204 Response ``` { "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The files were successfully recovered. | FilesEmpty | ## Retrieve data by catalog ID Operation path: `POST /api/v2/files/{catalogId}/downloads/` Authentication requirements: `BearerAuth` Creates a URL to download specified data from the AI catalog (using either /files/{catalogId}/file/ or URL to external storage via signed URL) andredirects to the URL via HTTP 303. ### Body parameter ``` { "properties": { "duration": { "default": 60, "description": "Access ttl in seconds (maximum value is 300s).", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "fileName": { "description": "The name of a file to download from an unstructured dataset. If not specified, it will download either the original archive file or, if there is only one file, it will download that.", "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | FilesDuration | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 303 | See Other | URL was successfully provided and 303 sent. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 303 | Location | string | | Download the file by this URL. | ## Retrieve the requested data by streaming it by catalog ID Operation path: `GET /api/v2/files/{catalogId}/file/` Authentication requirements: `BearerAuth` Retrieve the requested data from the catalog entity by streaming it. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | fileName | query | string | false | The name of a file to download from an unstructured dataset. If not specified, it will download either the original archive file or, if there is only one file, it will download that. | | catalogId | path | string | true | The catalog item ID. | ### Example responses > 200 Response ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The file data associated with the catalog entity. | string | | 422 | Unprocessable Entity | Data cannot be downloaded. This may be because the file is too big to be downloaded (maximum download size depends on the configuration of your installation). | None | ## Add file(s) into an existing files catalog item by catalog ID Operation path: `POST /api/v2/files/{catalogId}/fromDataSource/` Authentication requirements: `BearerAuth` Add file(s) into an existing files catalog item from a data source. ### Body parameter ``` { "properties": { "credentialData": { "description": "The credentials to be used to authenticate with the database in place of the credential ID.", "oneOf": [ { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ] }, "credentialId": { "description": "The identifier for the set of credentials to use to authenticate with the database.", "type": "string" }, "dataSourceId": { "description": "The identifier of the data source to use as the source for the data.", "type": "string" }, "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict between an existing file and an uploaded one.\nRENAME (default): rename an uploaded file using “ (n).ext” pattern.\nREPLACE: prefer an uploaded file.\nSKIP: prefer an existing file.\nERROR: return \"HTTP 409 Conflict\" response in case of a naming conflict.", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "prefix": { "description": "Folder path to prepend to uploaded file paths. Must end with \"/\".", "type": "string" }, "useArchiveContents": { "default": "True", "description": "If true, extract archive contents and associate them with the catalog entity.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "dataSourceId", "overwrite" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | AddFilesIntoContainerFromDataSource | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedFilesResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a file from a file Operation path: `POST /api/v2/files/{catalogId}/fromFile/` Authentication requirements: `BearerAuth` Add file(s) to an existing files catalog item. ### Body parameter ``` properties: file: description: The file to upload. format: binary type: string overwrite: default: RENAME description: >- How to deal with a name conflict between an existing file and an uploaded one. RENAME (default): rename an uploaded file using “ (n).ext” pattern. REPLACE: prefer an uploaded file. SKIP: prefer an existing file. ERROR: return "HTTP 409 Conflict" response in case of a naming conflict. enum: - rename - Rename - RENAME - replace - Replace - REPLACE - skip - Skip - SKIP - error - Error - ERROR type: string prefix: description: Folder path to prepend to uploaded file paths. Must end with "/". type: string useArchiveContents: default: "True" description: If true, extract archive contents and associate them with the catalog entity. enum: - "false" - "False" - "true" - "True" type: string required: - file - overwrite type: object x-versionadded: v2.42 ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | AddFilesIntoContainerFromFile | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedFilesResponse | | 422 | Unprocessable Entity | The request cannot be processed. The request did not contain file contents. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Apply staged files by catalog ID Operation path: `POST /api/v2/files/{catalogId}/fromStage/` Authentication requirements: `BearerAuth` Add files from the stage into the catalog item. In the case of naming conflicts, rename files according to overwrite strategy. ### Body parameter ``` { "properties": { "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict between an existing file and an uploaded one.\nRENAME (default): rename an uploaded file using “ (n).ext” pattern.\nREPLACE: prefer an uploaded file.\nSKIP: prefer an existing file.\nERROR: return \"HTTP 409 Conflict\" response in case of a naming conflict. ", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "stageId": { "description": "The ID of the stage.", "type": "string" } }, "required": [ "overwrite", "stageId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | AddFromStage | false | none | ### Example responses > 200 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "numFiles": { "description": "The number of files in the file entity.", "type": "integer" } }, "required": [ "catalogId", "catalogVersionId", "numFiles" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | FilesVersionResponse | ## Create a file from an URL Operation path: `POST /api/v2/files/{catalogId}/fromURL/` Authentication requirements: `BearerAuth` Add file(s) into an existing files catalog item from a URL. ### Body parameter ``` { "properties": { "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict between an existing file and an uploaded one.\nRENAME (default): rename an uploaded file using “ (n).ext” pattern.\nREPLACE: prefer an uploaded file.\nSKIP: prefer an existing file.\nERROR: return \"HTTP 409 Conflict\" response in case of a naming conflict.", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "prefix": { "description": "Folder path to prepend to uploaded file paths. Must end with \"/\".", "type": "string" }, "url": { "description": "The URL to download the file(s) to add to the catalog entity.", "format": "url", "type": "string" }, "useArchiveContents": { "default": "True", "description": "If true, extract archive contents and associate them with the catalog entity.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "overwrite", "url" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | AddFilesIntoContainerFromUrl | false | none | ### Example responses > 202 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | CreatedFilesResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create links by id Operation path: `POST /api/v2/files/{catalogId}/links/` Authentication requirements: `BearerAuth` Creates URLs to download specified data from the AI catalog (using either /files/{catalogId}/file/ or URL to external storage via signed URL). ### Body parameter ``` { "properties": { "duration": { "default": 600, "description": "Access ttl in seconds (maximum value is 3000s).", "exclusiveMinimum": 0, "maximum": 3000, "type": "integer" }, "fileNames": { "description": "The names of files to download from an unstructured dataset. If not specified, it will download either the original archive file or, if there is only one file, it will download that.", "items": { "type": [ "string", "null" ] }, "maxItems": 100, "type": "array" } }, "required": [ "fileNames" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | body | body | FilesDurationAndFiles | false | none | ### Example responses > 200 Response ``` { "properties": { "links": { "description": "The list of generated links", "items": { "properties": { "fileName": { "description": "The name of the file associated with the generated link. ", "type": [ "string", "null" ] }, "url": { "description": "The generated link associated with the requested file. ", "type": "string" } }, "required": [ "fileName", "url" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" } }, "required": [ "links" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | URLs were successfully provided. | CreatedLinksResponse | ## Create an empty stage by catalog ID Operation path: `POST /api/v2/files/{catalogId}/stages/` Authentication requirements: `BearerAuth` Create an empty stage entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | ### Example responses > 201 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "stageId": { "description": "The ID of the stage.", "type": "string" } }, "required": [ "catalogId", "stageId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | FilesStageResponse | ## Stage file by catalog ID Operation path: `POST /api/v2/files/{catalogId}/stages/{stageId}/upload/` Authentication requirements: `BearerAuth` Stage file for a batch upload. This endpoint won't unzip an archive for you. Use `POST /files//fromFile/` (no stage) if you would like to unzip the archive on upload. ### Body parameter ``` properties: file: description: The file to upload. format: binary type: string originalCatalogId: default: null description: If the contents of the file being uploaded are derived from a file in a catalog entity which was used to clone, the current catalog entity, the original catalog entity ID in which the file exists. type: - string - "null" x-versionadded: v2.38 originalFileName: default: null description: If the contents of the file being uploaded are derived from a file in the catalog entity, the name of the file it is derived from. type: - string - "null" required: - file type: object x-versionadded: v2.38 ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | stageId | path | string | true | The stage ID. | | body | body | FileUpload | false | none | ### Example responses > 201 Response ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "stageId": { "description": "The ID of the stage.", "type": "string" } }, "required": [ "catalogId", "stageId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | FilesStageResponse | ## List catalog versions by catalog ID Operation path: `GET /api/v2/files/{catalogId}/versions/` Authentication requirements: `BearerAuth` List all versions associated with given catalogId and which match the specified query parameters. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | false | Sorting order which will be applied to catalog list. | | catalogId | path | string | true | The catalog item ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [created, -created] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of catalog item and version IDs.", "items": { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "createdBy": { "description": "The name or email of the user who created the catalog entity version.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the catalog entity version was created.", "format": "date-time", "type": "string" }, "isLatest": { "description": "Whether this is the latest version of the catalog entity.", "type": "boolean" }, "isStage": { "description": "Whether this is a staged version of the catalog entity.", "type": "boolean" }, "numFiles": { "description": "The number of files in the file entity.", "type": "integer" }, "size": { "description": "The total size of all files associated with the catalog entity version, in bytes.", "type": "integer" } }, "required": [ "catalogId", "catalogVersionId", "createdBy", "creationDate", "isLatest", "isStage", "numFiles", "size" ], "type": "object", "x-versionadded": "v2.44" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.44" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of catalog versions | CatalogListResponse | ## Retrieve all files by id Operation path: `GET /api/v2/files/{catalogId}/versions/{catalogVersionId}/allFiles/` Authentication requirements: `BearerAuth` List all files associated with the catalog item and version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | fileType | query | any | false | If specified, will only return files that match the specified type(s). | | prefix | query | string | false | If specified, will only return files with paths that start with the given folder prefix. Must end with '/'. | | recursive | query | string | false | Whether to list all files recursively. If a prefix is specific, whether to list all files under that prefix recursively. | | catalogId | path | string | true | The catalog item ID. | | catalogVersionId | path | string | true | The catalog version item ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | recursive | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of file metadata.", "items": { "properties": { "fileChecksum": { "description": "The checksum of the file.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "fileCreatedAt": { "description": "The date when the file was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "fileName": { "description": "The name of the file. The actual file can be retrieved with [GET /api/v2/files/{catalogId}/file/][get-apiv2filescatalogidfile].", "type": "string" }, "fileSize": { "description": "The size of the file, in bytes.", "type": "integer" }, "fileType": { "description": "The file type, if known.", "type": "string" }, "ingestErrors": { "description": "Any errors associated with the file.", "type": [ "string", "null" ] } }, "required": [ "fileChecksum", "fileCreatedAt", "fileName", "fileSize", "fileType", "ingestErrors" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The paginated list of files. | FilesListResponse | ## Create downloads by id Operation path: `POST /api/v2/files/{catalogId}/versions/{catalogVersionId}/downloads/` Authentication requirements: `BearerAuth` Creates a URL to download specified data from the AI catalog (using either /files/{catalogId}/versions/{catalogVersionId}/file/ or URL to external storage via signed URL) andredirects to the URL via HTTP 303. ### Body parameter ``` { "properties": { "duration": { "default": 60, "description": "Access ttl in seconds (maximum value is 300s).", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "fileName": { "description": "The name of a file to download from an unstructured dataset. If not specified, it will download either the original archive file or, if there is only one file, it will download that.", "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | catalogVersionId | path | string | true | The catalog version item ID. | | body | body | FilesDuration | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 303 | See Other | URL was successfully provided and 303 sent. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 303 | Location | string | | Download the file by this URL. | ## Retrieve file by id Operation path: `GET /api/v2/files/{catalogId}/versions/{catalogVersionId}/file/` Authentication requirements: `BearerAuth` Retrieve the requested data from the catalog entity and version by streaming it. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | fileName | query | string | false | The name of a file to download from an unstructured dataset. If not specified, it will download either the original archive file or, if there is only one file, it will download that. | | catalogId | path | string | true | The catalog item ID. | | catalogVersionId | path | string | true | The catalog version item ID. | ### Example responses > 200 Response ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The file data associated with the catalog entity and version. | string | | 422 | Unprocessable Entity | Data cannot be downloaded. This may be because the file is too big to be downloaded (maximum download size depends on the configuration of your installation). | None | ## Create links by id Operation path: `POST /api/v2/files/{catalogId}/versions/{catalogVersionId}/links/` Authentication requirements: `BearerAuth` Creates URLs to download specified data from the AI catalog (using either /files/{catalogId}/versions/{catalogVersionId}/file/ or URL to external storage via signed URL). ### Body parameter ``` { "properties": { "duration": { "default": 600, "description": "Access ttl in seconds (maximum value is 3000s).", "exclusiveMinimum": 0, "maximum": 3000, "type": "integer" }, "fileNames": { "description": "The names of files to download from an unstructured dataset. If not specified, it will download either the original archive file or, if there is only one file, it will download that.", "items": { "type": [ "string", "null" ] }, "maxItems": 100, "type": "array" } }, "required": [ "fileNames" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | catalogId | path | string | true | The catalog item ID. | | catalogVersionId | path | string | true | The catalog version item ID. | | body | body | FilesDurationAndFiles | false | none | ### Example responses > 200 Response ``` { "properties": { "links": { "description": "The list of generated links", "items": { "properties": { "fileName": { "description": "The name of the file associated with the generated link. ", "type": [ "string", "null" ] }, "url": { "description": "The generated link associated with the requested file. ", "type": "string" } }, "required": [ "fileName", "url" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" } }, "required": [ "links" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | URLs were successfully provided. | CreatedLinksResponse | ## Perform a permanent deletion dry run of a file Operation path: `POST /api/v2/filesPermadeleteDryRuns/` Authentication requirements: `BearerAuth` Perform a permanent deletion dry run of an AI catalog file. Detect if there will be any errors or warnings. Display file versions and associated files that would be deleted if the deletion were executed. ### Body parameter ``` { "properties": { "catalogIds": { "description": "The catalog IDs to be deleted.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "catalogIds" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | FilePermadelete | false | none | ### Example responses > 200 Response ``` { "properties": { "fileVersionsToDelete": { "description": "The IDs of file versions to be deleted for each corresponding file.", "items": { "properties": { "catalogId": { "description": "The ID of a file to be deleted.", "type": "string" }, "versionIds": { "description": "The IDs of associated file versions to be deleted.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "catalogId", "versionIds" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "filesToDelete": { "description": "The IDs of files to be deleted.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "warnings": { "description": "The warnings on potential issues that may occur with some or all of the file deletions.", "items": { "properties": { "catalogIds": { "description": "The IDs of the files associated with the warning.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "warning": { "description": "The warning on potential issues if the listed files were deleted.", "type": "string" } }, "required": [ "catalogIds", "warning" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" } }, "required": [ "fileVersionsToDelete", "filesToDelete" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The dry run is complete. | FilePermadeleteDryRunResponse | | 422 | Unprocessable Entity | Unable to permanently delete the files. | None | ## Permanently delete the files Operation path: `POST /api/v2/filesPermadeleteJobs/` Authentication requirements: `BearerAuth` Permanently deletes the list of files from the AI catalog. ### Body parameter ``` { "properties": { "catalogIds": { "description": "The catalog IDs to be deleted.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "catalogIds" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | FilePermadelete | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the permadelete job's status.", "type": "string" } }, "required": [ "statusId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | FilePermadeleteResponse | | 422 | Unprocessable Entity | Unable to permanently delete the files. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Delete user blueprints Operation path: `DELETE /api/v2/userBlueprints/` Authentication requirements: `BearerAuth` Delete user blueprints, specified by `userBlueprintIds`. ### Body parameter ``` { "properties": { "userBlueprintIds": { "description": "The list of IDs of user blueprints to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ] } }, "required": [ "userBlueprintIds" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintsBulkDelete | false | none | ### Example responses > 200 Response ``` { "properties": { "failedToDelete": { "description": "The list of IDs of User Blueprints which failed to be deleted.", "items": { "description": "An ID of a User Blueprint which failed to be deleted.", "type": "string" }, "type": "array" }, "successfullyDeleted": { "description": "The list of IDs of User Blueprints successfully deleted.", "items": { "description": "An ID of a User Blueprint successfully deleted.", "type": "string" }, "type": "array" } }, "required": [ "failedToDelete", "successfullyDeleted" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of user blueprints successfully and unsuccessfully deleted. | UserBlueprintsBulkDeleteResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## List user blueprints Operation path: `GET /api/v2/userBlueprints/` Authentication requirements: `BearerAuth` Fetch the list of the user blueprints the current user has access to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of results to skip (for pagination). | | limit | query | integer | true | The max number of results to return. | | projectId | query | string | false | The ID of the project, used to filter for original project_id. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of records on this page.", "type": "integer" }, "data": { "description": "The list of user blueprints.", "items": { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" } }, "required": [ "blender", "blueprintId", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL to the next page, or `null` if there is no such page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL to the previous page, or `null` if there is no such page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of records.", "type": "integer" } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Fetched the list of the accessible user blueprints successfully. | UserBlueprintsListResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | ## Create a user blueprint Operation path: `POST /api/v2/userBlueprints/` Authentication requirements: `BearerAuth` Create a user blueprint. ### Body parameter ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "blueprint", "decompressedBlueprint", "isInplaceEditor", "saveToCatalog" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Created the user blueprint successfully. | UserBlueprintsDetailedItem | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Project not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Clone a blueprint Operation path: `POST /api/v2/userBlueprints/fromBlueprintId/` Authentication requirements: `BearerAuth` Clone a blueprint from a project. ### Body parameter ``` { "properties": { "blueprintId": { "description": "The ID associated with the blueprint to create the user blueprint from.", "type": "string" }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active.", "type": "string" }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "blueprintId", "decompressedBlueprint", "isInplaceEditor", "projectId", "saveToCatalog" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintCreateFromBlueprintId | false | none | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Created the user blueprint successfully. | UserBlueprintsDetailedItem | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Project or blueprint not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Create a user blueprint Operation path: `POST /api/v2/userBlueprints/fromCustomTaskVersionId/` Authentication requirements: `BearerAuth` Create a user blueprint from a single custom task. ### Body parameter ``` { "properties": { "customTaskVersionId": { "description": "The ID of a custom task version.", "type": "string" }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description for the user blueprint that will be created from this CustomTaskVersion.", "type": "string" }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "customTaskVersionId", "decompressedBlueprint", "saveToCatalog" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintCreateFromCustomTaskVersionIdPayload | false | none | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Created the user blueprint successfully | UserBlueprintsDetailedItem | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Custom task version or custom task not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Clone a user blueprint Operation path: `POST /api/v2/userBlueprints/fromUserBlueprintId/` Authentication requirements: `BearerAuth` Clone a user blueprint. ### Body parameter ``` { "properties": { "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" }, "userBlueprintId": { "description": "The ID of the existing user blueprint to copy.", "type": "string" } }, "required": [ "decompressedBlueprint", "isInplaceEditor", "saveToCatalog", "userBlueprintId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintCreateFromUserBlueprintId | false | none | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Created the user blueprint successfully. | UserBlueprintsDetailedItem | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | User blueprint or project not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Delete a user blueprint by user blueprint ID Operation path: `DELETE /api/v2/userBlueprints/{userBlueprintId}/` Authentication requirements: `BearerAuth` Delete a user blueprint, specified by the `userBlueprintId`. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userBlueprintId | path | string | true | Used to identify a specific user-owned blueprint. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully deleted the specified blueprint, if it existed. | None | ## Retrieve a user blueprint by user blueprint ID Operation path: `GET /api/v2/userBlueprints/{userBlueprintId}/` Authentication requirements: `BearerAuth` Retrieve a user blueprint. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | editMode | query | boolean | true | Whether to retrieve the extra blueprint metadata for editing. | | decompressedBlueprint | query | boolean | true | Whether to retrieve the blueprint in the decompressed format. | | projectId | query | string | false | String representation of ObjectId for the currently active project. The user blueprint is retrieved when this project is active. | | isInplaceEditor | query | boolean | true | Whether the request is sent from the in place user BP editor. | | getDynamicLabels | query | boolean | false | Whether to add dynamic labels to a decompressed blueprint. | | userBlueprintId | path | string | true | Used to identify a specific user-owned blueprint. | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprintId", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieved the user blueprint successfully. | UserBlueprintsRetrieveResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Referenced project or user blueprint not found. | None | ## Update a user blueprint by user blueprint ID Operation path: `PATCH /api/v2/userBlueprints/{userBlueprintId}/` Authentication requirements: `BearerAuth` Update a user blueprint. ### Body parameter ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "decompressedBlueprint", "isInplaceEditor", "saveToCatalog" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userBlueprintId | path | string | true | Used to identify a specific user-owned blueprint. | | body | body | UserBlueprintUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Updated the user blueprint successfully. | UserBlueprintsDetailedItem | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | User blueprint not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Get the list of users, groups and organizations by user blueprint ID Operation path: `GET /api/v2/userBlueprints/{userBlueprintId}/sharedRoles/` Authentication requirements: `BearerAuth` Get the list of users, groups and organizations that have access to this user blueprint. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | userBlueprintId | path | string | true | Used to identify a specific user-owned blueprint. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of SharedRoles objects.", "items": { "properties": { "id": { "description": "The ID of the recipient organization, group or user.", "type": "string" }, "name": { "description": "The name of the recipient organization, group or user.", "type": "string" }, "role": { "description": "The role of the org/group/user on this dataset or \"NO_ROLE\" for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully retrieved roles. | UserBlueprintSharedRolesListResponse | ## Share a user blueprint by user blueprint ID Operation path: `PATCH /api/v2/userBlueprints/{userBlueprintId}/sharedRoles/` Authentication requirements: `BearerAuth` Share a user blueprint with a user, group, or organization. ### Body parameter ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userBlueprintId | path | string | true | Used to identify a specific user-owned blueprint. | | body | body | SharedRolesUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | None | | 400 | Bad Request | Bad Request | None | | 403 | Forbidden | User can view entity but does not have permission to grant these roles on the entity. | None | | 404 | Not Found | Either the entity does not exist or the user does not have permissions to view the entity. | None | | 409 | Conflict | The request would leave the entity without an owner. | None | | 422 | Unprocessable Entity | The request was formatted improperly. | None | ## Validate many user blueprints Operation path: `POST /api/v2/userBlueprintsBulkValidations/` Authentication requirements: `BearerAuth` Validate many user blueprints, optionally using a specific project. Any non-existent or inaccessible user blueprints will be ignored. ### Body parameter ``` { "properties": { "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": "string" }, "userBlueprintIds": { "description": "The IDs of the user blueprints to validate in bulk.", "items": { "description": "The ID of one user blueprint to validate.", "type": "string" }, "type": "array" } }, "required": [ "userBlueprintIds" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintBulkValidationRequest | false | none | ### Example responses > 201 Response ``` { "properties": { "data": { "description": "A list of validation responses with their associated User Blueprint ID.", "items": { "properties": { "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "userBlueprintId", "vertexContext" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Validated many user blueprints successfully. | UserBlueprintsBulkValidationResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Referenced project was not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Retrieve input types Operation path: `GET /api/v2/userBlueprintsInputTypes/` Authentication requirements: `BearerAuth` Retrieve the input types which can be used with User Blueprints. ### Example responses > 200 Response ``` { "properties": { "inputTypes": { "description": "The list of associated pairs of an input type and their human-readable names.", "items": { "properties": { "name": { "description": "The human-readable name of an input type.", "type": "string" }, "type": { "description": "The unique identifier of an input type.", "type": "string" } }, "required": [ "name", "type" ], "type": "object" }, "type": "array" } }, "required": [ "inputTypes" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully retrieved the input types. | UserBlueprintsInputTypesResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | ## Add user blueprints Operation path: `POST /api/v2/userBlueprintsProjectBlueprints/` Authentication requirements: `BearerAuth` Add the list of user blueprints, by ID, to a specified (by ID) project's repository. ### Body parameter ``` { "properties": { "deleteAfter": { "default": false, "description": "Whether to delete the user blueprint(s) after adding it (them) to the project menu.", "type": "boolean" }, "describeFailures": { "default": false, "description": "Whether to include extra fields to describe why any blueprints were not added to the chosen project.", "type": "boolean", "x-versionadded": "v2.27" }, "projectId": { "description": "The projectId of the project for the repository to add the specified user blueprints to.", "type": "string" }, "userBlueprintIds": { "description": "The IDs of the user blueprints to add to the specified project's repository.", "items": { "description": "An ID of one user blueprint to add to the specified project's repository.", "type": "string" }, "type": "array" } }, "required": [ "deleteAfter", "describeFailures", "projectId", "userBlueprintIds" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintAddToMenu | false | none | ### Example responses > 200 Response ``` { "properties": { "addedToMenu": { "description": "The list of userBlueprintId and blueprintId pairs representing blueprints successfully added to the project repository.", "items": { "properties": { "blueprintId": { "description": "The blueprintId representing the blueprint which was added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprintId added to the project repository.", "type": "string" } }, "required": [ "blueprintId", "userBlueprintId" ], "type": "object" }, "type": "array" }, "message": { "description": "A success message or a list of reasons why the list of blueprints could not be added to the project repository.", "type": "string", "x-versionadded": "2.27" }, "notAddedToMenu": { "description": "The list of userBlueprintId and error message representing blueprints which failed to be added to the project repository.", "items": { "properties": { "error": { "description": "The error message representing why the blueprint was not added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprint which was not added to the project repository.", "type": "string" } }, "required": [ "error", "userBlueprintId" ], "type": "object" }, "type": "array", "x-versionadded": "2.27" } }, "required": [ "addedToMenu" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully added the user blueprints to the project's repository. | UserBlueprintAddToMenuResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Referenced project not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Validate task parameters Operation path: `POST /api/v2/userBlueprintsTaskParameters/` Authentication requirements: `BearerAuth` Validate that each value assigned to specified task parameters are valid. ### Body parameter ``` { "properties": { "outputMethod": { "description": "The method representing how the task will output data.", "enum": [ "P", "Pm", "S", "Sm", "T", "TS" ], "type": "string" }, "projectId": { "description": "The projectId representing the project where this user blueprint is edited.", "type": [ "string", "null" ] }, "taskCode": { "description": "The task code representing the task to validate parameter values.", "type": "string" }, "taskParameters": { "description": "The list of task parameters and proposed values to be validated.", "items": { "properties": { "newValue": { "description": "The proposed value for the task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "paramName": { "description": "The name of the task parameter to be validated.", "type": "string" } }, "required": [ "newValue", "paramName" ], "type": "object" }, "type": "array" } }, "required": [ "outputMethod", "taskCode", "taskParameters" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintTaskParameterValidation | false | none | ### Example responses > 200 Response ``` { "properties": { "errors": { "description": "A list of the task parameters, their proposed values, and messages describing why each is not valid.", "items": { "properties": { "message": { "description": "The description of the issue with the proposed task parameter value.", "type": "string" }, "paramName": { "description": "The name of the validated task parameter.", "type": "string" }, "value": { "description": "The invalid value proposed for the validated task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "message", "paramName", "value" ], "type": "object" }, "type": "array" } }, "required": [ "errors" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Accepted validation parameters for a task in the context of User Blueprints. | UserBlueprintsValidateTaskParametersResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Custom task version not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | ## Retrieve tasks Operation path: `GET /api/v2/userBlueprintsTasks/` Authentication requirements: `BearerAuth` Retrieve the available tasks, organized into categories, which can be used to create or modify User Blueprints. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | query | string | false | The project ID to use for task retrieval. | | blueprintId | query | string | false | The blueprint ID to use for task retrieval. | | userBlueprintId | query | string | false | The user blueprint ID to use for task retrieval. | ### Example responses > 200 Response ``` { "properties": { "categories": { "description": "The list of the available task categories, sub-categories, and tasks.", "items": { "properties": { "name": { "description": "The name of the category.", "type": "string" }, "subcategories": { "description": "The list of the available task category items.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "taskCodes": { "description": "A list of task codes representing the tasks in this category.", "items": { "type": "string" }, "type": "array" } }, "required": [ "name", "taskCodes" ], "type": "object" }, "type": "array" }, "tasks": { "description": "The list of task codes and their task definitions.", "items": { "properties": { "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "taskDefinition": { "description": "A definition of a task in terms of label, arguments, description, and other metadata.", "oneOf": [ { "properties": { "arguments": { "description": "The list of definitions of each argument which can be set for the task.", "items": { "properties": { "argument": { "description": "The definition of a task argument, used to specify a certain aspect of the task.", "oneOf": [ { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ] }, "key": { "description": "The unique key of the argument.", "type": "string" } }, "required": [ "argument", "key" ], "type": "object" }, "type": "array" }, "categories": { "description": "The categories which the task is in.", "items": { "type": "string" }, "type": "array" }, "colnamesAndTypes": { "description": "The column names, their types, and their hex representation, available in the specified project for the task.", "items": { "properties": { "colname": { "description": "The column name.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "type": { "description": "The data type of the column.", "type": "string" } }, "required": [ "colname", "hex", "type" ], "type": "object" }, "type": "array" }, "customTaskId": { "description": "The ID of the custom task, if it is a custom task.", "type": [ "string", "null" ] }, "customTaskVersions": { "description": "Metadata for all of the custom task's versions.", "items": { "properties": { "id": { "description": "Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task.", "type": "string" }, "label": { "description": "The name of the custom task version.", "type": "string" }, "versionMajor": { "description": "Major version of the custom task.", "type": "integer" }, "versionMinor": { "description": "Minor version of the custom task.", "type": "integer" } }, "required": [ "id", "label", "versionMajor", "versionMinor" ], "type": "object" }, "type": "array" }, "description": { "description": "The description of the task.", "type": "string" }, "icon": { "description": "The integer representing the ID to be displayed when the blueprint is trained.", "type": "integer" }, "isCommonTask": { "default": false, "description": "Whether the task is a common task.", "type": "boolean" }, "isCustomTask": { "description": "Whether the task is custom code written by the user.", "type": "boolean" }, "isVisibleInComposableMl": { "default": true, "description": "Whether the task is visible in the ComposableML menu.", "type": "boolean" }, "label": { "description": "The generic / default title or label for the task.", "type": "string" }, "outputMethods": { "description": "The methods which the task can use to produce output.", "items": { "type": "string" }, "type": "array" }, "supportsScoringCode": { "description": "Whether the task supports Scoring Code.", "type": "boolean" }, "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "timeSeriesOnly": { "description": "Whether the task can only be used with time series projects.", "type": "boolean" }, "url": { "description": "The URL of the documentation of the task.", "oneOf": [ { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, { "type": "string" } ] }, "validInputs": { "description": "The supported input types of the task.", "items": { "description": "A specific supported input type.", "type": "string" }, "type": "array" } }, "required": [ "arguments", "categories", "description", "icon", "label", "outputMethods", "supportsScoringCode", "taskCode", "timeSeriesOnly", "url", "validInputs" ], "type": "object" } ] } }, "required": [ "taskCode", "taskDefinition" ], "type": "object" }, "type": "array" } }, "required": [ "categories", "tasks" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully retrieved the tasks. | UserBlueprintTasksResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Referenced project or user blueprint not found. | None | ## Validate a user blueprint Operation path: `POST /api/v2/userBlueprintsValidations/` Authentication requirements: `BearerAuth` Validate a user blueprint. ### Body parameter ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] } }, "required": [ "blueprint", "isInplaceEditor" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBlueprintValidation | false | none | ### Example responses > 201 Response ``` { "properties": { "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "vertexContext" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Validated the user blueprint successfully. | UserBlueprintsValidationResponse | | 401 | Unauthorized | User is not authorized. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Referenced project not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity. | None | # Schemas ## AddFilesIntoContainerFromDataSource ``` { "properties": { "credentialData": { "description": "The credentials to be used to authenticate with the database in place of the credential ID.", "oneOf": [ { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ] }, "credentialId": { "description": "The identifier for the set of credentials to use to authenticate with the database.", "type": "string" }, "dataSourceId": { "description": "The identifier of the data source to use as the source for the data.", "type": "string" }, "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict between an existing file and an uploaded one.\nRENAME (default): rename an uploaded file using “ (n).ext” pattern.\nREPLACE: prefer an uploaded file.\nSKIP: prefer an existing file.\nERROR: return \"HTTP 409 Conflict\" response in case of a naming conflict.", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "prefix": { "description": "Folder path to prepend to uploaded file paths. Must end with \"/\".", "type": "string" }, "useArchiveContents": { "default": "True", "description": "If true, extract archive contents and associate them with the catalog entity.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "dataSourceId", "overwrite" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialData | S3Credentials | false | | The credentials to be used to authenticate with the database in place of the credential ID. | | credentialId | string | false | | The identifier for the set of credentials to use to authenticate with the database. | | dataSourceId | string | true | | The identifier of the data source to use as the source for the data. | | overwrite | string | true | | How to deal with a name conflict between an existing file and an uploaded one.RENAME (default): rename an uploaded file using “ (n).ext” pattern.REPLACE: prefer an uploaded file.SKIP: prefer an existing file.ERROR: return "HTTP 409 Conflict" response in case of a naming conflict. | | prefix | string | false | | Folder path to prepend to uploaded file paths. Must end with "/". | | useArchiveContents | string | false | | If true, extract archive contents and associate them with the catalog entity. | ### Enumerated Values | Property | Value | | --- | --- | | overwrite | [rename, Rename, RENAME, replace, Replace, REPLACE, skip, Skip, SKIP, error, Error, ERROR] | | useArchiveContents | [false, False, true, True] | ## AddFilesIntoContainerFromFile ``` { "properties": { "file": { "description": "The file to upload.", "format": "binary", "type": "string" }, "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict between an existing file and an uploaded one.\nRENAME (default): rename an uploaded file using “ (n).ext” pattern.\nREPLACE: prefer an uploaded file.\nSKIP: prefer an existing file.\nERROR: return \"HTTP 409 Conflict\" response in case of a naming conflict.", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "prefix": { "description": "Folder path to prepend to uploaded file paths. Must end with \"/\".", "type": "string" }, "useArchiveContents": { "default": "True", "description": "If true, extract archive contents and associate them with the catalog entity.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "file", "overwrite" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | file | string(binary) | true | | The file to upload. | | overwrite | string | true | | How to deal with a name conflict between an existing file and an uploaded one.RENAME (default): rename an uploaded file using “ (n).ext” pattern.REPLACE: prefer an uploaded file.SKIP: prefer an existing file.ERROR: return "HTTP 409 Conflict" response in case of a naming conflict. | | prefix | string | false | | Folder path to prepend to uploaded file paths. Must end with "/". | | useArchiveContents | string | false | | If true, extract archive contents and associate them with the catalog entity. | ### Enumerated Values | Property | Value | | --- | --- | | overwrite | [rename, Rename, RENAME, replace, Replace, REPLACE, skip, Skip, SKIP, error, Error, ERROR] | | useArchiveContents | [false, False, true, True] | ## AddFilesIntoContainerFromUrl ``` { "properties": { "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict between an existing file and an uploaded one.\nRENAME (default): rename an uploaded file using “ (n).ext” pattern.\nREPLACE: prefer an uploaded file.\nSKIP: prefer an existing file.\nERROR: return \"HTTP 409 Conflict\" response in case of a naming conflict.", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "prefix": { "description": "Folder path to prepend to uploaded file paths. Must end with \"/\".", "type": "string" }, "url": { "description": "The URL to download the file(s) to add to the catalog entity.", "format": "url", "type": "string" }, "useArchiveContents": { "default": "True", "description": "If true, extract archive contents and associate them with the catalog entity.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "overwrite", "url" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | overwrite | string | true | | How to deal with a name conflict between an existing file and an uploaded one.RENAME (default): rename an uploaded file using “ (n).ext” pattern.REPLACE: prefer an uploaded file.SKIP: prefer an existing file.ERROR: return "HTTP 409 Conflict" response in case of a naming conflict. | | prefix | string | false | | Folder path to prepend to uploaded file paths. Must end with "/". | | url | string(url) | true | | The URL to download the file(s) to add to the catalog entity. | | useArchiveContents | string | false | | If true, extract archive contents and associate them with the catalog entity. | ### Enumerated Values | Property | Value | | --- | --- | | overwrite | [rename, Rename, RENAME, replace, Replace, REPLACE, skip, Skip, SKIP, error, Error, ERROR] | | useArchiveContents | [false, False, true, True] | ## AddFromStage ``` { "properties": { "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict between an existing file and an uploaded one.\nRENAME (default): rename an uploaded file using “ (n).ext” pattern.\nREPLACE: prefer an uploaded file.\nSKIP: prefer an existing file.\nERROR: return \"HTTP 409 Conflict\" response in case of a naming conflict. ", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "stageId": { "description": "The ID of the stage.", "type": "string" } }, "required": [ "overwrite", "stageId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | overwrite | string | true | | How to deal with a name conflict between an existing file and an uploaded one.RENAME (default): rename an uploaded file using “ (n).ext” pattern.REPLACE: prefer an uploaded file.SKIP: prefer an existing file.ERROR: return "HTTP 409 Conflict" response in case of a naming conflict. | | stageId | string | true | | The ID of the stage. | ### Enumerated Values | Property | Value | | --- | --- | | overwrite | [rename, Rename, RENAME, replace, Replace, REPLACE, skip, Skip, SKIP, error, Error, ERROR] | ## AllowExtra ``` { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ``` The parameters submitted by the user to the failed job. ### Properties None ## AzureServicePrincipalCredentials ``` { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | azureTenantId | string | false | | Tenant ID of the Azure AD service principal. | | clientId | string | false | | Client ID of the Azure AD service principal. | | clientSecret | string | false | | Client Secret of the Azure AD service principal. | | configId | string | false | | The ID of secure configurations of credentials shared by admin. | | credentialType | string | true | | The type of these credentials, 'azure_service_principal' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | azure_service_principal | ## BaseFilesVersionResponse ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | The ID of the catalog entry. | | catalogVersionId | string | true | | The ID of the catalog entry version. | ## BasicCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'basic' here. | | password | string | true | | The password for database authentication. The password is encrypted at rest and never saved or stored. | | user | string | true | | The username for database authentication. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | basic | ## BasicDatasetDetailsResponse ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "createdBy", "creationDate", "dataPersisted", "datasetId", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "name", "processingState", "timeSeriesProperties", "versionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [string] | true | | An array of strings describing the intended use of the dataset. | | createdBy | string,null | true | | Username of the user who created the dataset. | | creationDate | string(date-time) | true | | The date when the dataset was created. | | dataPersisted | boolean | true | | If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available. | | datasetId | string | true | | The ID of this dataset. | | isDataEngineEligible | boolean | true | | Whether this dataset can be a data source of a data engine query. | | isLatestVersion | boolean | true | | Whether this dataset version is the latest version of this dataset. | | isSnapshot | boolean | true | | Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot. | | name | string | true | | The name of this dataset in the catalog. | | processingState | string | true | | Current ingestion process state of dataset. | | sampleSize | SampleSize | false | | Ingest size to use during dataset registration. Default behavior is to ingest full dataset. | | timeSeriesProperties | TimeSeriesProperties | true | | Properties related to time series data prep. | | versionId | string | true | | The object ID of the catalog_version the dataset belongs to. | ### Enumerated Values | Property | Value | | --- | --- | | processingState | [COMPLETED, ERROR, RUNNING] | ## BasicDatasetWithSizeResponse ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "columnCount": { "description": "The number of columns in the dataset.", "type": "integer", "x-versionadded": "v2.30" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes.", "type": "integer", "x-versionadded": "v2.21" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "rowCount": { "description": "The number of rows in the dataset.", "type": "integer", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "columnCount", "createdBy", "creationDate", "dataPersisted", "datasetId", "datasetSize", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "name", "processingState", "rowCount", "timeSeriesProperties", "versionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [string] | true | | An array of strings describing the intended use of the dataset. | | columnCount | integer | true | | The number of columns in the dataset. | | createdBy | string,null | true | | Username of the user who created the dataset. | | creationDate | string(date-time) | true | | The date when the dataset was created. | | dataPersisted | boolean | true | | If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available. | | datasetId | string | true | | The ID of this dataset. | | datasetSize | integer | true | | The size of the dataset as a CSV in bytes. | | isDataEngineEligible | boolean | true | | Whether this dataset can be a data source of a data engine query. | | isLatestVersion | boolean | true | | Whether this dataset version is the latest version of this dataset. | | isSnapshot | boolean | true | | Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot. | | name | string | true | | The name of this dataset in the catalog. | | processingState | string | true | | Current ingestion process state of dataset. | | rowCount | integer | true | | The number of rows in the dataset. | | sampleSize | SampleSize | false | | Ingest size to use during dataset registration. Default behavior is to ingest full dataset. | | timeSeriesProperties | TimeSeriesProperties | true | | Properties related to time series data prep. | | versionId | string | true | | The object ID of the catalog_version the dataset belongs to. | ### Enumerated Values | Property | Value | | --- | --- | | processingState | [COMPLETED, ERROR, RUNNING] | ## BpData ``` { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | children | [AllowExtra] | false | | A nested dictionary representation of the blueprint DAG. | | id | string | true | | The type of the node (e.g., "start", "input", "task"). | | inputs | [string] | false | | The inputs to the current node. | | output | any | false | | IDs describing the destination of any outgoing edges. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | IDs describing the destination of any outgoing edges. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | taskMap | AllowExtra | true | | The parameters submitted by the user to the failed job. | | taskParameters | string | false | | A stringified JSON object describing the parameters and their values for a task. | | tasks | [string] | true | | The task defining the current node. | | type | string | true | | A unique ID to represent the current node. | ## BulkCatalogAppendTagsPayload ``` { "properties": { "action": { "description": "The action to execute on the datasets. Has to be 'tag' for this payload.", "enum": [ "tag" ], "type": "string" }, "tags": { "description": "The tags to append to the datasets. Tags will not be duplicated.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "action", "tags" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | The action to execute on the datasets. Has to be 'tag' for this payload. | | tags | [string] | true | minItems: 1 | The tags to append to the datasets. Tags will not be duplicated. | ### Enumerated Values | Property | Value | | --- | --- | | action | tag | ## BulkCatalogDeletePayload ``` { "properties": { "action": { "description": "The action to execute on the datasets. Has to be 'delete' for this payload.", "enum": [ "delete" ], "type": "string" } }, "required": [ "action" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | The action to execute on the datasets. Has to be 'delete' for this payload. | ### Enumerated Values | Property | Value | | --- | --- | | action | delete | ## BulkCatalogSharePayload ``` { "properties": { "action": { "description": "The action to execute on the datasets. Has to be 'updateRoles' for this payload.", "enum": [ "updateRoles" ], "type": "string" }, "applyGrantToLinkedObjects": { "default": false, "description": "If `true` for any users being granted access to the entity, grant the user read access to any linked objects such as `DataSources` and `DataStores` that may be used by this entity. Ignored if no such objects are relevant for the entity. This will not result in access being lowered for a user if the user already has higher access to linked objects than read access. However, if the target user does not have sharing permissions to the linked object, they will be given sharing access without lowering existing permissions. May result in an error if the user making the call does not have sufficient permissions to complete the grant. Default value is false.", "type": "boolean" }, "roles": { "description": "An array of `RoleRequest` objects. Maximum number of such objects is 100.", "items": { "oneOf": [ { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "name": { "description": "Name of the user/group/org to update the access role for.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "id": { "description": "The org/group/user ID.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "action", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | The action to execute on the datasets. Has to be 'updateRoles' for this payload. | | applyGrantToLinkedObjects | boolean | false | | If true for any users being granted access to the entity, grant the user read access to any linked objects such as DataSources and DataStores that may be used by this entity. Ignored if no such objects are relevant for the entity. This will not result in access being lowered for a user if the user already has higher access to linked objects than read access. However, if the target user does not have sharing permissions to the linked object, they will be given sharing access without lowering existing permissions. May result in an error if the user making the call does not have sufficient permissions to complete the grant. Default value is false. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | An array of RoleRequest objects. Maximum number of such objects is 100. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogRolesWithNames | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogRolesWithId | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | action | updateRoles | ## BulkDatasetAction ``` { "properties": { "datasetIds": { "description": "The dataset IDs to execute the bulk action on.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "payload": { "description": "indicate which action to run and with what parameters.", "oneOf": [ { "properties": { "action": { "description": "The action to execute on the datasets. Has to be 'delete' for this payload.", "enum": [ "delete" ], "type": "string" } }, "required": [ "action" ], "type": "object" }, { "properties": { "action": { "description": "The action to execute on the datasets. Has to be 'tag' for this payload.", "enum": [ "tag" ], "type": "string" }, "tags": { "description": "The tags to append to the datasets. Tags will not be duplicated.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "action", "tags" ], "type": "object" }, { "properties": { "action": { "description": "The action to execute on the datasets. Has to be 'updateRoles' for this payload.", "enum": [ "updateRoles" ], "type": "string" }, "applyGrantToLinkedObjects": { "default": false, "description": "If `true` for any users being granted access to the entity, grant the user read access to any linked objects such as `DataSources` and `DataStores` that may be used by this entity. Ignored if no such objects are relevant for the entity. This will not result in access being lowered for a user if the user already has higher access to linked objects than read access. However, if the target user does not have sharing permissions to the linked object, they will be given sharing access without lowering existing permissions. May result in an error if the user making the call does not have sufficient permissions to complete the grant. Default value is false.", "type": "boolean" }, "roles": { "description": "An array of `RoleRequest` objects. Maximum number of such objects is 100.", "items": { "oneOf": [ { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "name": { "description": "Name of the user/group/org to update the access role for.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "id": { "description": "The org/group/user ID.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "action", "roles" ], "type": "object" } ] } }, "required": [ "datasetIds", "payload" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetIds | [string] | true | minItems: 1 | The dataset IDs to execute the bulk action on. | | payload | any | true | | indicate which action to run and with what parameters. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BulkCatalogDeletePayload | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BulkCatalogAppendTagsPayload | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BulkCatalogSharePayload | false | | none | ## CatalogDetailsRetrieveResponse ``` { "properties": { "createdAt": { "description": "The ISO 8601-formatted date and time indicating when this item was created in the catalog.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The full name or username of the user who added this item to the catalog.", "type": "string" }, "description": { "description": "Catalog item description.", "type": [ "string", "null" ] }, "id": { "description": "Catalog item ID.", "type": "string" }, "message": { "description": "Details of exception(s) raised during ingestion process, if any.", "type": [ "string", "null" ] }, "modifiedAt": { "description": "The ISO 8601-formatted date and time indicating changes to the Info field(s) of this catalog item.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "The full name or username of the user who last modified the Info field(s) of this catalog item.", "type": "string" }, "name": { "description": "Catalog item name.", "type": "string" }, "status": { "description": "For datasets, the current ingestion process state of this catalog item.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": [ "string", "null" ] }, "tags": { "description": "List of catalog item tags in the lower case with no spaces.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "Catalog item type.", "enum": [ "unknown_dataset_type", "snapshot_dataset", "remote_dataset", "unknown_catalog_type", "user_blueprint", "files" ], "type": "string" } }, "required": [ "createdAt", "createdBy", "description", "id", "message", "modifiedAt", "modifiedBy", "name", "status", "tags", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The ISO 8601-formatted date and time indicating when this item was created in the catalog. | | createdBy | string | true | | The full name or username of the user who added this item to the catalog. | | description | string,null | true | | Catalog item description. | | id | string | true | | Catalog item ID. | | message | string,null | true | | Details of exception(s) raised during ingestion process, if any. | | modifiedAt | string(date-time) | true | | The ISO 8601-formatted date and time indicating changes to the Info field(s) of this catalog item. | | modifiedBy | string | true | | The full name or username of the user who last modified the Info field(s) of this catalog item. | | name | string | true | | Catalog item name. | | status | string,null | true | | For datasets, the current ingestion process state of this catalog item. | | tags | [string] | true | | List of catalog item tags in the lower case with no spaces. | | type | string | true | | Catalog item type. | ### Enumerated Values | Property | Value | | --- | --- | | status | [COMPLETED, ERROR, RUNNING] | | type | [unknown_dataset_type, snapshot_dataset, remote_dataset, unknown_catalog_type, user_blueprint, files] | ## CatalogExtendedDetailsDataOriginResponse ``` { "properties": { "canShareDatasetData": { "description": "Indicates if the dataset data can be shared.", "type": "boolean", "x-versionadded": "v2.30" }, "canUseDatasetData": { "description": "Indicates if the dataset data can be used.", "type": "boolean", "x-versionadded": "v2.23" }, "catalogName": { "description": "Catalog item name.", "type": "string" }, "catalogType": { "description": "Catalog item type.", "enum": [ "unknown_dataset_type", "snapshot_dataset", "remote_dataset", "unknown_catalog_type", "user_blueprint", "files" ], "type": "string" }, "dataEngineQueryId": { "description": "The ID of the catalog item data engine query.", "type": [ "string", "null" ] }, "dataOrigin": { "description": "Data origin of the datasource for this catalog item.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the catalog item data source.", "type": [ "string", "null" ] }, "description": { "description": "Catalog item description.", "type": [ "string", "null" ] }, "error": { "description": "The latest error of the catalog item.", "type": [ "string", "null" ] }, "id": { "description": "Catalog item ID.", "type": "string" }, "infoCreationDate": { "description": "The creation date of the catalog item.", "type": "string" }, "infoCreatorFullName": { "description": "The creator of the catalog item.", "type": "string" }, "infoModificationDate": { "description": "The date when the dataset metadata was last modified. This field is only applicable if the catalog item is a dataset.", "type": "string" }, "infoModifierFullName": { "description": "The user that last modified the dataset metadata. This field is only applicable if the catalog item is a dataset.", "type": [ "string", "null" ] }, "isDataEngineEligible": { "description": "Indicates if the catalog item is eligible for use by the data engine.", "type": "boolean", "x-versionadded": "v2.21" }, "isFirstVersion": { "description": "Indicates if the catalog item is the first version.", "type": "boolean" }, "lastModificationDate": { "description": "The date when the catalog item was last modified.", "type": "string" }, "lastModifierFullName": { "description": "The user that last modified the catalog item.", "type": "string" }, "originalName": { "description": "Catalog item original name.", "type": "string" }, "processingState": { "description": "The latest processing state of the catalog item.", "type": [ "integer", "null" ] }, "projectsUsedInCount": { "description": "The number of projects that use the catalog item.", "type": "integer" }, "recipeId": { "description": "The ID of the catalog item recipe.", "type": [ "string", "null" ] }, "relevance": { "description": "ElasticSearch score value or null if search done in Mongo.", "type": [ "number", "null" ] }, "tags": { "description": "List of catalog item tags in the lower case with no spaces.", "items": { "type": "string" }, "type": "array" }, "uri": { "description": "The URI to the datasource from which the catalog item was created, if it is a dataset.", "type": [ "string", "null" ] }, "userBlueprintId": { "description": "The ID by which a user blueprint is referenced in User Blueprint API.", "type": [ "string", "null" ], "x-versionadded": "v2.25" } }, "required": [ "canShareDatasetData", "canUseDatasetData", "catalogName", "catalogType", "dataEngineQueryId", "dataOrigin", "dataSourceId", "description", "error", "id", "infoCreationDate", "infoCreatorFullName", "infoModificationDate", "infoModifierFullName", "isDataEngineEligible", "lastModificationDate", "lastModifierFullName", "originalName", "processingState", "projectsUsedInCount", "recipeId", "relevance", "tags", "uri", "userBlueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShareDatasetData | boolean | true | | Indicates if the dataset data can be shared. | | canUseDatasetData | boolean | true | | Indicates if the dataset data can be used. | | catalogName | string | true | | Catalog item name. | | catalogType | string | true | | Catalog item type. | | dataEngineQueryId | string,null | true | | The ID of the catalog item data engine query. | | dataOrigin | string,null | true | | Data origin of the datasource for this catalog item. | | dataSourceId | string,null | true | | The ID of the catalog item data source. | | description | string,null | true | | Catalog item description. | | error | string,null | true | | The latest error of the catalog item. | | id | string | true | | Catalog item ID. | | infoCreationDate | string | true | | The creation date of the catalog item. | | infoCreatorFullName | string | true | | The creator of the catalog item. | | infoModificationDate | string | true | | The date when the dataset metadata was last modified. This field is only applicable if the catalog item is a dataset. | | infoModifierFullName | string,null | true | | The user that last modified the dataset metadata. This field is only applicable if the catalog item is a dataset. | | isDataEngineEligible | boolean | true | | Indicates if the catalog item is eligible for use by the data engine. | | isFirstVersion | boolean | false | | Indicates if the catalog item is the first version. | | lastModificationDate | string | true | | The date when the catalog item was last modified. | | lastModifierFullName | string | true | | The user that last modified the catalog item. | | originalName | string | true | | Catalog item original name. | | processingState | integer,null | true | | The latest processing state of the catalog item. | | projectsUsedInCount | integer | true | | The number of projects that use the catalog item. | | recipeId | string,null | true | | The ID of the catalog item recipe. | | relevance | number,null | true | | ElasticSearch score value or null if search done in Mongo. | | tags | [string] | true | | List of catalog item tags in the lower case with no spaces. | | uri | string,null | true | | The URI to the datasource from which the catalog item was created, if it is a dataset. | | userBlueprintId | string,null | true | | The ID by which a user blueprint is referenced in User Blueprint API. | ### Enumerated Values | Property | Value | | --- | --- | | catalogType | [unknown_dataset_type, snapshot_dataset, remote_dataset, unknown_catalog_type, user_blueprint, files] | ## CatalogExtendedDetailsResponse ``` { "properties": { "canShareDatasetData": { "description": "Indicates if the dataset data can be shared.", "type": "boolean", "x-versionadded": "v2.30" }, "canUseDatasetData": { "description": "Indicates if the dataset data can be used.", "type": "boolean", "x-versionadded": "v2.23" }, "catalogName": { "description": "Catalog item name.", "type": "string" }, "catalogType": { "description": "Catalog item type.", "enum": [ "unknown_dataset_type", "snapshot_dataset", "remote_dataset", "unknown_catalog_type", "user_blueprint", "files" ], "type": "string" }, "dataEngineQueryId": { "description": "The ID of the catalog item data engine query.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the catalog item data source.", "type": [ "string", "null" ] }, "description": { "description": "Catalog item description.", "type": [ "string", "null" ] }, "error": { "description": "The latest error of the catalog item.", "type": [ "string", "null" ] }, "id": { "description": "Catalog item ID.", "type": "string" }, "infoCreationDate": { "description": "The creation date of the catalog item.", "type": "string" }, "infoCreatorFullName": { "description": "The creator of the catalog item.", "type": "string" }, "infoModificationDate": { "description": "The date when the dataset metadata was last modified. This field is only applicable if the catalog item is a dataset.", "type": "string" }, "infoModifierFullName": { "description": "The user that last modified the dataset metadata. This field is only applicable if the catalog item is a dataset.", "type": [ "string", "null" ] }, "isDataEngineEligible": { "description": "Indicates if the catalog item is eligible for use by the data engine.", "type": "boolean", "x-versionadded": "v2.21" }, "isFirstVersion": { "description": "Indicates if the catalog item is the first version.", "type": "boolean" }, "lastModificationDate": { "description": "The date when the catalog item was last modified.", "type": "string" }, "lastModifierFullName": { "description": "The user that last modified the catalog item.", "type": "string" }, "originalName": { "description": "Catalog item original name.", "type": "string" }, "processingState": { "description": "The latest processing state of the catalog item.", "type": [ "integer", "null" ] }, "projectsUsedInCount": { "description": "The number of projects that use the catalog item.", "type": "integer" }, "recipeId": { "description": "The ID of the catalog item recipe.", "type": [ "string", "null" ] }, "relevance": { "description": "ElasticSearch score value or null if search done in Mongo.", "type": [ "number", "null" ] }, "tags": { "description": "List of catalog item tags in the lower case with no spaces.", "items": { "type": "string" }, "type": "array" }, "uri": { "description": "The URI to the datasource from which the catalog item was created, if it is a dataset.", "type": [ "string", "null" ] }, "userBlueprintId": { "description": "The ID by which a user blueprint is referenced in User Blueprint API.", "type": [ "string", "null" ], "x-versionadded": "v2.25" } }, "required": [ "canShareDatasetData", "canUseDatasetData", "catalogName", "catalogType", "dataEngineQueryId", "dataSourceId", "description", "error", "id", "infoCreationDate", "infoCreatorFullName", "infoModificationDate", "infoModifierFullName", "isDataEngineEligible", "lastModificationDate", "lastModifierFullName", "originalName", "processingState", "projectsUsedInCount", "recipeId", "relevance", "tags", "uri", "userBlueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShareDatasetData | boolean | true | | Indicates if the dataset data can be shared. | | canUseDatasetData | boolean | true | | Indicates if the dataset data can be used. | | catalogName | string | true | | Catalog item name. | | catalogType | string | true | | Catalog item type. | | dataEngineQueryId | string,null | true | | The ID of the catalog item data engine query. | | dataSourceId | string,null | true | | The ID of the catalog item data source. | | description | string,null | true | | Catalog item description. | | error | string,null | true | | The latest error of the catalog item. | | id | string | true | | Catalog item ID. | | infoCreationDate | string | true | | The creation date of the catalog item. | | infoCreatorFullName | string | true | | The creator of the catalog item. | | infoModificationDate | string | true | | The date when the dataset metadata was last modified. This field is only applicable if the catalog item is a dataset. | | infoModifierFullName | string,null | true | | The user that last modified the dataset metadata. This field is only applicable if the catalog item is a dataset. | | isDataEngineEligible | boolean | true | | Indicates if the catalog item is eligible for use by the data engine. | | isFirstVersion | boolean | false | | Indicates if the catalog item is the first version. | | lastModificationDate | string | true | | The date when the catalog item was last modified. | | lastModifierFullName | string | true | | The user that last modified the catalog item. | | originalName | string | true | | Catalog item original name. | | processingState | integer,null | true | | The latest processing state of the catalog item. | | projectsUsedInCount | integer | true | | The number of projects that use the catalog item. | | recipeId | string,null | true | | The ID of the catalog item recipe. | | relevance | number,null | true | | ElasticSearch score value or null if search done in Mongo. | | tags | [string] | true | | List of catalog item tags in the lower case with no spaces. | | uri | string,null | true | | The URI to the datasource from which the catalog item was created, if it is a dataset. | | userBlueprintId | string,null | true | | The ID by which a user blueprint is referenced in User Blueprint API. | ### Enumerated Values | Property | Value | | --- | --- | | catalogType | [unknown_dataset_type, snapshot_dataset, remote_dataset, unknown_catalog_type, user_blueprint, files] | ## CatalogIdAndVersionMetadata ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "createdBy": { "description": "The name or email of the user who created the catalog entity version.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the catalog entity version was created.", "format": "date-time", "type": "string" }, "isLatest": { "description": "Whether this is the latest version of the catalog entity.", "type": "boolean" }, "isStage": { "description": "Whether this is a staged version of the catalog entity.", "type": "boolean" }, "numFiles": { "description": "The number of files in the file entity.", "type": "integer" }, "size": { "description": "The total size of all files associated with the catalog entity version, in bytes.", "type": "integer" } }, "required": [ "catalogId", "catalogVersionId", "createdBy", "creationDate", "isLatest", "isStage", "numFiles", "size" ], "type": "object", "x-versionadded": "v2.44" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | The ID of the catalog entry. | | catalogVersionId | string | true | | The ID of the catalog entry version. | | createdBy | string,null | true | | The name or email of the user who created the catalog entity version. | | creationDate | string(date-time) | true | | The date when the catalog entity version was created. | | isLatest | boolean | true | | Whether this is the latest version of the catalog entity. | | isStage | boolean | true | | Whether this is a staged version of the catalog entity. | | numFiles | integer | true | | The number of files in the file entity. | | size | integer | true | | The total size of all files associated with the catalog entity version, in bytes. | ## CatalogListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of catalog item and version IDs.", "items": { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "createdBy": { "description": "The name or email of the user who created the catalog entity version.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the catalog entity version was created.", "format": "date-time", "type": "string" }, "isLatest": { "description": "Whether this is the latest version of the catalog entity.", "type": "boolean" }, "isStage": { "description": "Whether this is a staged version of the catalog entity.", "type": "boolean" }, "numFiles": { "description": "The number of files in the file entity.", "type": "integer" }, "size": { "description": "The total size of all files associated with the catalog entity version, in bytes.", "type": "integer" } }, "required": [ "catalogId", "catalogVersionId", "createdBy", "creationDate", "isLatest", "isStage", "numFiles", "size" ], "type": "object", "x-versionadded": "v2.44" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.44" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CatalogIdAndVersionMetadata] | true | maxItems: 1000 | List of catalog item and version IDs. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CatalogListSearchResponse ``` { "properties": { "cacheHit": { "description": "Indicates if the catalog item is returned from the cache.", "type": [ "boolean", "null" ] }, "count": { "description": "Number of catalog items returned on this page.", "type": "integer" }, "data": { "description": "Detailed information for every found catalog item.", "items": { "properties": { "canShareDatasetData": { "description": "Indicates if the dataset data can be shared.", "type": "boolean", "x-versionadded": "v2.30" }, "canUseDatasetData": { "description": "Indicates if the dataset data can be used.", "type": "boolean", "x-versionadded": "v2.23" }, "catalogName": { "description": "Catalog item name.", "type": "string" }, "catalogType": { "description": "Catalog item type.", "enum": [ "unknown_dataset_type", "snapshot_dataset", "remote_dataset", "unknown_catalog_type", "user_blueprint", "files" ], "type": "string" }, "dataEngineQueryId": { "description": "The ID of the catalog item data engine query.", "type": [ "string", "null" ] }, "dataOrigin": { "description": "Data origin of the datasource for this catalog item.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the catalog item data source.", "type": [ "string", "null" ] }, "description": { "description": "Catalog item description.", "type": [ "string", "null" ] }, "error": { "description": "The latest error of the catalog item.", "type": [ "string", "null" ] }, "id": { "description": "Catalog item ID.", "type": "string" }, "infoCreationDate": { "description": "The creation date of the catalog item.", "type": "string" }, "infoCreatorFullName": { "description": "The creator of the catalog item.", "type": "string" }, "infoModificationDate": { "description": "The date when the dataset metadata was last modified. This field is only applicable if the catalog item is a dataset.", "type": "string" }, "infoModifierFullName": { "description": "The user that last modified the dataset metadata. This field is only applicable if the catalog item is a dataset.", "type": [ "string", "null" ] }, "isDataEngineEligible": { "description": "Indicates if the catalog item is eligible for use by the data engine.", "type": "boolean", "x-versionadded": "v2.21" }, "isFirstVersion": { "description": "Indicates if the catalog item is the first version.", "type": "boolean" }, "lastModificationDate": { "description": "The date when the catalog item was last modified.", "type": "string" }, "lastModifierFullName": { "description": "The user that last modified the catalog item.", "type": "string" }, "originalName": { "description": "Catalog item original name.", "type": "string" }, "processingState": { "description": "The latest processing state of the catalog item.", "type": [ "integer", "null" ] }, "projectsUsedInCount": { "description": "The number of projects that use the catalog item.", "type": "integer" }, "recipeId": { "description": "The ID of the catalog item recipe.", "type": [ "string", "null" ] }, "relevance": { "description": "ElasticSearch score value or null if search done in Mongo.", "type": [ "number", "null" ] }, "tags": { "description": "List of catalog item tags in the lower case with no spaces.", "items": { "type": "string" }, "type": "array" }, "uri": { "description": "The URI to the datasource from which the catalog item was created, if it is a dataset.", "type": [ "string", "null" ] }, "userBlueprintId": { "description": "The ID by which a user blueprint is referenced in User Blueprint API.", "type": [ "string", "null" ], "x-versionadded": "v2.25" } }, "required": [ "canShareDatasetData", "canUseDatasetData", "catalogName", "catalogType", "dataEngineQueryId", "dataOrigin", "dataSourceId", "description", "error", "id", "infoCreationDate", "infoCreatorFullName", "infoModificationDate", "infoModifierFullName", "isDataEngineEligible", "lastModificationDate", "lastModifierFullName", "originalName", "processingState", "projectsUsedInCount", "recipeId", "relevance", "tags", "uri", "userBlueprintId" ], "type": "object" }, "type": "array" }, "next": { "description": "Location of the next page.", "type": [ "string", "null" ] }, "previous": { "description": "Location of the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "Total number of catalog items.", "type": "integer" } }, "required": [ "cacheHit", "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cacheHit | boolean,null | true | | Indicates if the catalog item is returned from the cache. | | count | integer | true | | Number of catalog items returned on this page. | | data | [CatalogExtendedDetailsDataOriginResponse] | true | | Detailed information for every found catalog item. | | next | string,null | true | | Location of the next page. | | previous | string,null | true | | Location of the previous page. | | totalCount | integer | false | | Total number of catalog items. | ## CatalogRolesWithId ``` { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "id": { "description": "The org/group/user ID.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others. If true, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If role is NO_ROLE, canShare is ignored. | | canUseData | boolean | false | | Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For OWNER, canUseData is always True. If role is empty, canUseData is ignored. | | id | string | true | | The org/group/user ID. | | role | string | true | | The role of the org/group/user on this catalog entity or NO_ROLE for removing access when used with route to modify access. | | shareRecipientType | string | true | | The recipient type. | ### Enumerated Values | Property | Value | | --- | --- | | role | [CONSUMER, EDITOR, OWNER, NO_ROLE] | | shareRecipientType | [user, group, organization] | ## CatalogRolesWithNames ``` { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "name": { "description": "Name of the user/group/org to update the access role for.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others. If true, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If role is NO_ROLE, canShare is ignored. | | canUseData | boolean | false | | Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For OWNER, canUseData is always True. If role is empty, canUseData is ignored. | | name | string | true | | Name of the user/group/org to update the access role for. | | role | string | true | | The role of the org/group/user on this catalog entity or NO_ROLE for removing access when used with route to modify access. | | shareRecipientType | string | true | | The recipient type. | ### Enumerated Values | Property | Value | | --- | --- | | role | [CONSUMER, EDITOR, OWNER, NO_ROLE] | | shareRecipientType | [user, group, organization] | ## CatalogSharedRoles ``` { "properties": { "applyGrantToLinkedObjects": { "default": false, "description": "If `true` for any users being granted access to the entity, grant the user read access to any linked objects such as `DataSources` and `DataStores` that may be used by this entity. Ignored if no such objects are relevant for the entity. This will not result in access being lowered for a user if the user already has higher access to linked objects than read access. However, if the target user does not have sharing permissions to the linked object, they will be given sharing access without lowering existing permissions. May result in an error if the user making the call does not have sufficient permissions to complete the grant. Default value is false.", "type": "boolean" }, "operation": { "description": "The name of the action being taken. The only operation is `updateRoles`.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "An array of `RoleRequest` objects. Maximum number of such objects is 100.", "items": { "oneOf": [ { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "name": { "description": "Name of the user/group/org to update the access role for.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "canShare": { "default": false, "description": "Whether the org/group/user should be able to share with others. If `true`, the org/group/user will be able to grant any role (up to and including their own) to other orgs/groups/user. If `role` is `NO_ROLE`, `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user/group/org should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "id": { "description": "The org/group/user ID.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entity or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.37" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | applyGrantToLinkedObjects | boolean | false | | If true for any users being granted access to the entity, grant the user read access to any linked objects such as DataSources and DataStores that may be used by this entity. Ignored if no such objects are relevant for the entity. This will not result in access being lowered for a user if the user already has higher access to linked objects than read access. However, if the target user does not have sharing permissions to the linked object, they will be given sharing access without lowering existing permissions. May result in an error if the user making the call does not have sufficient permissions to complete the grant. Default value is false. | | operation | string | true | | The name of the action being taken. The only operation is updateRoles. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | An array of RoleRequest objects. Maximum number of such objects is 100. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogRolesWithNames | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogRolesWithId | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## CloneFiles ``` { "properties": { "omit": { "default": null, "description": "File names to skip when cloning the files.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | omit | [string] | false | maxItems: 1000minItems: 1 | File names to skip when cloning the files. | ## ColnameAndType ``` { "properties": { "colname": { "description": "The column name.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "type": { "description": "The data type of the column.", "type": "string" } }, "required": [ "colname", "hex", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | colname | string | true | | The column name. | | hex | string | true | | A safe hex representation of the column name. | | type | string | true | | The data type of the column. | ## CopyBatch ``` { "properties": { "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict in the target location.\nRENAME (default): rename a duplicate file using “ (n).ext” pattern.\nREPLACE: prefer files you copy.\nSKIP: prefer files existing in the *target*.\nERROR: fail with an error in case of a naming conflict. ", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "sources": { "description": "File and folder names to copy. Folders are identified by ending with '/'.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" }, "target": { "default": null, "description": "Either a target folder to copy *sources* into or a target file name to copy a single file and rename it on copy.Folders are identified by ending with '/'.", "type": [ "string", "null" ] }, "targetCatalogId": { "default": null, "description": "Target catalog ID to copy files into. Copy files within the same catalog item if no *targetCatalogId* is provided.", "type": [ "string", "null" ] } }, "required": [ "overwrite", "sources" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | overwrite | string | true | | How to deal with a name conflict in the target location.RENAME (default): rename a duplicate file using “ (n).ext” pattern.REPLACE: prefer files you copy.SKIP: prefer files existing in the target.ERROR: fail with an error in case of a naming conflict. | | sources | [string] | true | maxItems: 100minItems: 1 | File and folder names to copy. Folders are identified by ending with '/'. | | target | string,null | false | | Either a target folder to copy sources into or a target file name to copy a single file and rename it on copy.Folders are identified by ending with '/'. | | targetCatalogId | string,null | false | | Target catalog ID to copy files into. Copy files within the same catalog item if no targetCatalogId is provided. | ### Enumerated Values | Property | Value | | --- | --- | | overwrite | [rename, Rename, RENAME, replace, Replace, REPLACE, skip, Skip, SKIP, error, Error, ERROR] | ## CopyFileOrFolder ``` { "properties": { "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict in the target location.\nRENAME (default): rename a duplicate file using “ (n).ext” pattern.\nREPLACE: prefer the file you copy.\nSKIP: prefer the file existing in the *target*.\nERROR: fail with an error in case of a naming conflict. ", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "source": { "description": "The file or folder path to copy. Folder paths should end with '/'.", "type": "string" }, "target": { "description": "The new file or folder path to copy to. Folders paths should end with '/'.", "type": "string" } }, "required": [ "overwrite", "source", "target" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | overwrite | string | true | | How to deal with a name conflict in the target location.RENAME (default): rename a duplicate file using “ (n).ext” pattern.REPLACE: prefer the file you copy.SKIP: prefer the file existing in the target.ERROR: fail with an error in case of a naming conflict. | | source | string | true | | The file or folder path to copy. Folder paths should end with '/'. | | target | string | true | | The new file or folder path to copy to. Folders paths should end with '/'. | ### Enumerated Values | Property | Value | | --- | --- | | overwrite | [rename, Rename, RENAME, replace, Replace, REPLACE, skip, Skip, SKIP, error, Error, ERROR] | ## CreateDataEngineQueryGenerator ``` { "properties": { "datasets": { "description": "Source datasets in the Data Engine workspace.", "items": { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" } }, "required": [ "alias" ], "type": "object" }, "maxItems": 32, "type": "array" }, "generatorSettings": { "description": "Data engine generator settings of the given `generator_type`.", "properties": { "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column in time series project.", "type": "string" }, "defaultCategoricalAggregationMethod": { "description": "Default aggregation method used for categorical feature.", "enum": [ "last", "mostFrequent" ], "type": "string" }, "defaultNumericAggregationMethod": { "description": "Default aggregation method used for numeric feature.", "enum": [ "mean", "sum" ], "type": "string" }, "defaultTextAggregationMethod": { "description": "Default aggregation method used for text feature.", "enum": [ "concat", "last", "meanLength", "mostFrequent", "totalLength" ], "type": "string" }, "endToSeriesMaxDatetime": { "default": true, "description": "A boolean value indicating whether generates post-aggregated series up to series maximum datetime or global maximum datetime.", "type": "boolean" }, "multiseriesIdColumns": { "description": "An array with the names of columns identifying the series to which row of the output dataset belongs. Currently, only one multiseries ID column is supported.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "startFromSeriesMinDatetime": { "default": true, "description": "A boolean value indicating whether post-aggregated series starts from series minimum datetime or global minimum datetime.", "type": "boolean" }, "target": { "description": "The name of target for the output dataset.", "type": "string" }, "timeStep": { "description": "Number of time steps for the output dataset.", "exclusiveMinimum": 0, "type": "integer" }, "timeUnit": { "description": "Indicates which unit is a basis for time steps of the output dataset.", "enum": [ "DAY", "HOUR", "MINUTE", "MONTH", "QUARTER", "WEEK", "YEAR" ], "type": "string" } }, "required": [ "datetimePartitionColumn", "defaultCategoricalAggregationMethod", "defaultNumericAggregationMethod", "timeStep", "timeUnit" ], "type": "object" }, "generatorType": { "description": "Type of data engine query generator", "enum": [ "TimeSeries" ], "type": "string" } }, "required": [ "datasets", "generatorSettings", "generatorType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasets | [DataEngineDataset] | true | maxItems: 32 | Source datasets in the Data Engine workspace. | | generatorSettings | GeneratorSettings | true | | Data engine generator settings of the given generator_type. | | generatorType | string | true | | Type of data engine query generator | ### Enumerated Values | Property | Value | | --- | --- | | generatorType | TimeSeries | ## CreateFromRecipe ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ], "x-versionadded": "v2.33" }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" } ], "x-versionadded": "v2.33" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.33" }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean", "x-versionadded": "v2.33" }, "materializationDestination": { "description": "Destination table information to create and materialize the recipe to. If None, the recipe will be materialized in DataRobot.", "properties": { "catalog": { "description": "Database to materialize the recipe to.", "type": "string", "x-versionadded": "v2.33" }, "schema": { "description": "Schema to materialize the recipe to.", "type": "string", "x-versionadded": "v2.33" }, "table": { "description": "Table name to create and materialize the recipe to. This table should not already exist.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "catalog", "schema", "table" ], "type": "object" }, "name": { "description": "Name to be assigned to the new dataset.", "type": "string", "x-versionadded": "v2.33" }, "persistDataAfterIngestion": { "default": true, "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error.", "type": "boolean", "x-versionadded": "v2.33" }, "recipeId": { "description": "The identifier for the Wrangling Recipe to use as the source of data.", "type": "string", "x-versionadded": "v2.33" }, "skipDuplicateDatesValidation": { "default": false, "description": "By default, if a recipe contains time series or a time series resampling operation, publishing fails if there are date duplicates to prevent data quality issues and ambiguous transformations. If set to True, then validation will be skipped.", "type": "boolean", "x-versionadded": "v2.36" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication.", "type": "boolean", "x-versionadded": "v2.33" } }, "required": [ "recipeId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialData | any | false | | The credentials to authenticate with the database, to be used instead of credential ID. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BasicCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Credentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OAuthCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to authenticate with the database. | | doSnapshot | boolean | false | | If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, Enable Create Snapshot Data Source. | | materializationDestination | MaterializationDestination | false | | Destination table information to create and materialize the recipe to. If None, the recipe will be materialized in DataRobot. | | name | string | false | | Name to be assigned to the new dataset. | | persistDataAfterIngestion | boolean | false | | If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and doSnapshot to true will result in an error. | | recipeId | string | true | | The identifier for the Wrangling Recipe to use as the source of data. | | skipDuplicateDatesValidation | boolean | false | | By default, if a recipe contains time series or a time series resampling operation, publishing fails if there are date duplicates to prevent data quality issues and ambiguous transformations. If set to True, then validation will be skipped. | | useKerberos | boolean | false | | If true, use kerberos authentication for database authentication. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SAMPLE, SINGLE_SERIES_CALENDAR, TRAINING] | ## CreateWorkspaceState ``` { "properties": { "datasets": { "description": "The source datasets in the data engine workspace.", "items": { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" } }, "required": [ "alias" ], "type": "object" }, "type": "array" }, "language": { "description": "The language of the data engine query.", "enum": [ "SQL" ], "type": "string" }, "query": { "description": "The actual body of the data engine query.", "maxLength": 320000, "type": "string" } }, "required": [ "language", "query" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasets | [DataEngineDataset] | false | | The source datasets in the data engine workspace. | | language | string | true | | The language of the data engine query. | | query | string | true | maxLength: 320000 | The actual body of the data engine query. | ### Enumerated Values | Property | Value | | --- | --- | | language | SQL | ## CreateWorkspaceStateFromQueryGenerator ``` { "properties": { "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" }, "queryGeneratorId": { "description": "The ID of the query generator.", "type": "string" } }, "required": [ "queryGeneratorId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | false | | The ID of the dataset. | | datasetVersionId | string | false | | The ID of the dataset version. | | queryGeneratorId | string | true | | The ID of the query generator. | ## CreatedDatasetDataEngineResponse ``` { "properties": { "datasetId": { "description": "The ID of the output dataset item.", "type": "string" }, "datasetVersionId": { "description": "The ID of the output dataset version item.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "datasetId", "datasetVersionId", "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the output dataset item. | | datasetVersionId | string | true | | The ID of the output dataset version item. | | statusId | string | true | | ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status. | ## CreatedDatasetResponse ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | The ID of the catalog entry. | | catalogVersionId | string | true | | The ID of the latest version of the catalog entry. | | statusId | string | true | | ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status. | ## CreatedFilesResponse ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "statusId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | The ID of the catalog entry. | | catalogVersionId | string | true | | The ID of the catalog entry version. | | statusId | string | true | | ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status. | ## CreatedLinksResponse ``` { "properties": { "links": { "description": "The list of generated links", "items": { "properties": { "fileName": { "description": "The name of the file associated with the generated link. ", "type": [ "string", "null" ] }, "url": { "description": "The generated link associated with the requested file. ", "type": "string" } }, "required": [ "fileName", "url" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" } }, "required": [ "links" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | links | [Link] | true | maxItems: 100 | The list of generated links | ## DataEngineDataset ``` { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" } }, "required": [ "alias" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | alias | string | true | | Alias to be used as the table name. | | datasetId | string | false | | The ID of the dataset. | | datasetVersionId | string | false | | The ID of the dataset version. | ## DatabricksAccessTokenCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'databricks_access_token_account' here. | | databricksAccessToken | string | true | minLength: 1minLength: 1 | Databricks personal access token. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | databricks_access_token_account | ## DatabricksServicePrincipalCredentials ``` { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clientId | string | false | minLength: 1minLength: 1 | Client ID for Databricks service principal. | | clientSecret | string | false | minLength: 1minLength: 1 | Client secret for Databricks service principal. | | configId | string | false | | The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret. | | credentialType | string | true | | The type of these credentials, 'databricks_service_principal_account' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | databricks_service_principal_account | ## DatasetAccessControlListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of DatasetAccessControl objects.", "items": { "properties": { "canShare": { "description": "True if this user can share with other users", "type": "boolean" }, "canUseData": { "description": "True if the user can view, download and process data (use to create projects, predictions, etc)", "type": "boolean" }, "role": { "description": "The role of the user on this data source.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "userFullName": { "description": "The full name of a user with access to this dataset. If the full name is not available, username is returned instead.", "type": "string" }, "username": { "description": "`username` of a user with access to this dataset.", "type": "string" } }, "required": [ "canShare", "canUseData", "role", "userFullName", "username" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetAccessControlResponse] | true | | The list of DatasetAccessControl objects. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetAccessControlResponse ``` { "properties": { "canShare": { "description": "True if this user can share with other users", "type": "boolean" }, "canUseData": { "description": "True if the user can view, download and process data (use to create projects, predictions, etc)", "type": "boolean" }, "role": { "description": "The role of the user on this data source.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "userFullName": { "description": "The full name of a user with access to this dataset. If the full name is not available, username is returned instead.", "type": "string" }, "username": { "description": "`username` of a user with access to this dataset.", "type": "string" } }, "required": [ "canShare", "canUseData", "role", "userFullName", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | True if this user can share with other users | | canUseData | boolean | true | | True if the user can view, download and process data (use to create projects, predictions, etc) | | role | string | true | | The role of the user on this data source. | | userFullName | string | true | | The full name of a user with access to this dataset. If the full name is not available, username is returned instead. | | username | string | true | | username of a user with access to this dataset. | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, EDITOR, CONSUMER] | ## DatasetAccessInner ``` { "properties": { "canShare": { "description": "whether the user should be able to share with other users. If `true`, the user will be able to grant any role (up to and including their own) to other users. If `role` is empty `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "role": { "description": "the role to grant to the user, or \"\" (empty string) to remove the users access", "enum": [ "CONSUMER", "EDITOR", "OWNER", "" ], "type": "string" }, "username": { "description": "username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | whether the user should be able to share with other users. If true, the user will be able to grant any role (up to and including their own) to other users. If role is empty canShare is ignored. | | canUseData | boolean | false | | Whether the user should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For OWNER, canUseData is always True. If role is empty, canUseData is ignored. | | role | string | true | | the role to grant to the user, or "" (empty string) to remove the users access | | username | string | true | | username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [CONSUMER, EDITOR, OWNER, ``] | ## DatasetAccessSet ``` { "properties": { "applyGrantToLinkedObjects": { "default": false, "description": "If `true` for any users being granted access to the entity, grant the user read access to any linked objects such as `DataSources` and `DataStores` that may be used by this entity. Ignored if no such objects are relevant for the entity. This will not result in access being lowered for a user if the user already has higher access to linked objects than read access. However, if the target user does not have sharing permissions to the linked object, they will be given sharing access without lowering existing permissions. May result in an error if the user making the call does not have sufficient permissions to complete the grant. Default value is false.", "type": "boolean" }, "data": { "description": "array of DatasetAccessControl objects.", "items": { "properties": { "canShare": { "description": "whether the user should be able to share with other users. If `true`, the user will be able to grant any role (up to and including their own) to other users. If `role` is empty `canShare` is ignored.", "type": "boolean" }, "canUseData": { "description": "Whether the user should be able to view, download and process (e.g., use it to create projects, predictions, etc) data. For `OWNER`, `canUseData` is always `True`. If `role` is empty, `canUseData` is ignored.", "type": "boolean" }, "role": { "description": "the role to grant to the user, or \"\" (empty string) to remove the users access", "enum": [ "CONSUMER", "EDITOR", "OWNER", "" ], "type": "string" }, "username": { "description": "username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "minItems": 1, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | applyGrantToLinkedObjects | boolean | false | | If true for any users being granted access to the entity, grant the user read access to any linked objects such as DataSources and DataStores that may be used by this entity. Ignored if no such objects are relevant for the entity. This will not result in access being lowered for a user if the user already has higher access to linked objects than read access. However, if the target user does not have sharing permissions to the linked object, they will be given sharing access without lowering existing permissions. May result in an error if the user making the call does not have sufficient permissions to complete the grant. Default value is false. | | data | [DatasetAccessInner] | true | minItems: 1 | array of DatasetAccessControl objects. | ## DatasetCreateFromWorkspaceState ``` { "properties": { "credentials": { "description": "The JDBC credentials.", "type": "string" }, "datasetName": { "description": "The custom name for the created dataset.", "type": "string" }, "doSnapshot": { "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "workspaceStateId": { "description": "The ID of the workspace state to use as the source of data.", "type": "string" } }, "required": [ "workspaceStateId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentials | string | false | | The JDBC credentials. | | datasetName | string | false | | The custom name for the created dataset. | | doSnapshot | boolean | false | | If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, Enable Create Snapshot Data Source. | | workspaceStateId | string | true | | The ID of the workspace state to use as the source of data. | ## DatasetCreateVersionFromRecipe ``` { "properties": { "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" } ], "x-versionadded": "v2.37" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe to use as the source of data.", "type": "string" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "recipeId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialData | any | false | | The credentials to authenticate with the database, to be used instead of credential ID. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BasicCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Credentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OAuthCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to authenticate with the database. | | recipeId | string | true | | The ID of the recipe to use as the source of data. | | useKerberos | boolean | false | | If true, use kerberos authentication for database authentication. | ## DatasetCreateVersionFromWorkspaceState ``` { "properties": { "credentials": { "description": "JDBC credentials.", "type": "string" }, "doSnapshot": { "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "workspaceStateId": { "description": "The ID of the workspace state to use as the source of data.", "type": "string" } }, "required": [ "workspaceStateId" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentials | string | false | | JDBC credentials. | | doSnapshot | boolean | false | | If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, Enable Create Snapshot Data Source. | | workspaceStateId | string | true | | The ID of the workspace state to use as the source of data. | ## DatasetDescribePermissionsResponse ``` { "properties": { "canCreateFeaturelist": { "description": "True if the user can create a new featurelist for this dataset.", "type": "boolean" }, "canDeleteDataset": { "description": "True if the user can delete dataset.", "type": "boolean" }, "canDownloadDatasetData": { "description": "True if the user can download data.", "type": "boolean" }, "canGetCatalogItemInfo": { "description": "True if the user can view catalog info.", "type": "boolean" }, "canGetDatasetInfo": { "description": "True if the user can view dataset info.", "type": "boolean" }, "canGetDatasetPermissions": { "description": "True if the user can view dataset permissions.", "type": "boolean" }, "canGetFeatureInfo": { "description": "True if the user can retrieve feature info of dataset.", "type": "boolean" }, "canGetFeaturelists": { "description": "True if the user can view featurelist for this dataset.", "type": "boolean" }, "canPatchCatalogInfo": { "description": "True if the user can modify catalog info.", "type": "boolean" }, "canPatchDatasetAliases": { "description": "True if the user can modify dataset feature aliases.", "type": "boolean" }, "canPatchDatasetInfo": { "description": "True if the user can modify dataset info.", "type": "boolean" }, "canPatchDatasetPermissions": { "description": "True if the user can modify dataset permissions.", "type": "boolean" }, "canPatchFeaturelists": { "description": "True if the user can modify featurelists for this dataset.", "type": "boolean" }, "canPostDataset": { "description": "True if the user can create a new dataset.", "type": "boolean" }, "canReloadDataset": { "description": "True if the user can reload dataset.", "type": "boolean" }, "canShareDataset": { "description": "True if the user can share the dataset.", "type": "boolean" }, "canSnapshotDataset": { "description": "True if the user can save snapshot of dataset.", "type": "boolean" }, "canUndeleteDataset": { "description": "True if the user can undelete dataset.", "type": "boolean" }, "canUseDatasetData": { "description": "True if the user can use dataset data to create projects, train custom models or provide predictions.", "type": "boolean" }, "canUseFeaturelists": { "description": "True if the user can use featurelists for this dataset. (for project creation)", "type": "boolean" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "uid": { "description": "The ID of the user identified by username.", "type": "string" }, "username": { "description": "`username` of a user with access to this dataset.", "type": "string" } }, "required": [ "canCreateFeaturelist", "canDeleteDataset", "canDownloadDatasetData", "canGetCatalogItemInfo", "canGetDatasetInfo", "canGetDatasetPermissions", "canGetFeatureInfo", "canGetFeaturelists", "canPatchCatalogInfo", "canPatchDatasetAliases", "canPatchDatasetInfo", "canPatchDatasetPermissions", "canPatchFeaturelists", "canPostDataset", "canReloadDataset", "canShareDataset", "canSnapshotDataset", "canUndeleteDataset", "canUseDatasetData", "canUseFeaturelists", "datasetId", "uid", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canCreateFeaturelist | boolean | true | | True if the user can create a new featurelist for this dataset. | | canDeleteDataset | boolean | true | | True if the user can delete dataset. | | canDownloadDatasetData | boolean | true | | True if the user can download data. | | canGetCatalogItemInfo | boolean | true | | True if the user can view catalog info. | | canGetDatasetInfo | boolean | true | | True if the user can view dataset info. | | canGetDatasetPermissions | boolean | true | | True if the user can view dataset permissions. | | canGetFeatureInfo | boolean | true | | True if the user can retrieve feature info of dataset. | | canGetFeaturelists | boolean | true | | True if the user can view featurelist for this dataset. | | canPatchCatalogInfo | boolean | true | | True if the user can modify catalog info. | | canPatchDatasetAliases | boolean | true | | True if the user can modify dataset feature aliases. | | canPatchDatasetInfo | boolean | true | | True if the user can modify dataset info. | | canPatchDatasetPermissions | boolean | true | | True if the user can modify dataset permissions. | | canPatchFeaturelists | boolean | true | | True if the user can modify featurelists for this dataset. | | canPostDataset | boolean | true | | True if the user can create a new dataset. | | canReloadDataset | boolean | true | | True if the user can reload dataset. | | canShareDataset | boolean | true | | True if the user can share the dataset. | | canSnapshotDataset | boolean | true | | True if the user can save snapshot of dataset. | | canUndeleteDataset | boolean | true | | True if the user can undelete dataset. | | canUseDatasetData | boolean | true | | True if the user can use dataset data to create projects, train custom models or provide predictions. | | canUseFeaturelists | boolean | true | | True if the user can use featurelists for this dataset. (for project creation) | | datasetId | string | true | | The ID of the dataset. | | uid | string | true | | The ID of the user identified by username. | | username | string | true | | username of a user with access to this dataset. | ## DatasetFeatureHistogramResponse ``` { "properties": { "plot": { "description": "Plot data based on feature values.", "items": { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array" } }, "required": [ "plot" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | plot | [DatasetFeaturePlotDataResponse] | true | | Plot data based on feature values. | ## DatasetFeaturePlotDataResponse ``` { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | number | true | | Number of values in the bin. | | label | string | true | | Bin start for numerical/uncapped, or string value for categorical. The bin ==Missing== is created for rows that did not have the feature. | ## DatasetFeatureResponseWithDataQuality ``` { "properties": { "dataQualityIssues": { "description": "The status of data quality issue detection.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": [ "string", "null" ] }, "dataQualityIssuesTypes": { "description": "Data quality issue types.", "items": { "description": "Data quality issue type.", "enum": [ "disguised_missing_values", "excess_zero", "external_feature_derivation", "few_negative_values", "imputation_leakage", "inconsistent_gaps", "inliers", "lagged_features", "leading_trailing_zeros", "missing_documents", "missing_images", "missing_values", "multicategorical_invalid_format", "new_series_recent_data", "outliers", "quantile_target_sparsity", "quantile_target_zero_inflation", "target_leakage", "unusual_repeated_values" ], "type": "string" }, "maxItems": 20, "type": "array" }, "datasetId": { "description": "The ID of the dataset the feature belongs to", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version the feature belongs to.", "type": "string" }, "dateFormat": { "description": "The date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ] }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string" }, "id": { "description": "The number of the column in the dataset.", "type": "integer" }, "isZeroInflated": { "description": "whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ] }, "language": { "description": "Detected language of the feature.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "Whether feature has too few values to be informative.", "type": "boolean" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "max": { "description": "Maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "Maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "Maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "mean": { "description": "Arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "median": { "description": "Median of the EDA sample of the feature.", "oneOf": [ { "description": "Median of the EDA sample of the feature.", "type": "string" }, { "description": "Median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "min": { "description": "Minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "Minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "Minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "naCount": { "description": "Number of missing values.", "type": [ "integer", "null" ] }, "name": { "description": "Feature name", "type": "string" }, "plot": { "description": "Plot data based on feature values.", "items": { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.30" }, "sampleRows": { "description": "The number of rows in the sample used to calculate the statistics.", "type": "integer", "x-versionadded": "v2.35" }, "stdDev": { "description": "Standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "Standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "Standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "timeSeriesEligibilityReason": { "description": "why the feature is ineligible for time series projects, or 'suitable' if it is eligible.", "type": [ "string", "null" ] }, "timeSeriesEligibilityReasonAggregation": { "description": "why the feature is ineligible for aggregation, or 'suitable' if it is eligible.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "timeSeriesEligible": { "description": "whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.", "type": "boolean" }, "timeSeriesEligibleAggregation": { "description": "whether this feature can be used as a datetime feature for aggregationfor time series data prep. Always false for non-date features.", "type": "boolean", "x-versionadded": "v2.24" }, "timeStep": { "description": "The minimum time step that can be used to specify time series windows. The units for this value are the ``timeUnit``. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.", "type": [ "integer", "null" ] }, "timeStepAggregation": { "description": "The minimum time step that can be used to aggregate using this feature for time series data prep. The units for this value are the ``timeUnit``. Only present for date features that are eligible for aggregation in time series data prep and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "timeUnit": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.", "type": [ "string", "null" ] }, "timeUnitAggregation": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. Only present for date features eligible for aggregation, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "uniqueCount": { "description": "Number of unique values.", "type": [ "integer", "null" ] }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "datasetId", "datasetVersionId", "dateFormat", "featureType", "id", "name", "sampleRows" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataQualityIssues | string,null | false | | The status of data quality issue detection. | | dataQualityIssuesTypes | [string] | false | maxItems: 20 | Data quality issue types. | | datasetId | string | true | | The ID of the dataset the feature belongs to | | datasetVersionId | string | true | | The ID of the dataset version the feature belongs to. | | dateFormat | string,null | true | | The date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime . | | featureType | string | true | | Feature type. | | id | integer | true | | The number of the column in the dataset. | | isZeroInflated | boolean,null | false | | whether feature has an excessive number of zeros | | keySummary | any | false | | Per key summaries for Summarized Categorical or Multicategorical columns | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FeatureKeySummaryResponseValidatorSummarizedCategorical | false | | For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [FeatureKeySummaryResponseValidatorMultilabel] | false | | For a Multicategorical columns, this will contain statistics for the top classes | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | language | string | false | | Detected language of the feature. | | lowInformation | boolean | false | | Whether feature has too few values to be informative. | | lowerQuartile | any | false | | Lower quartile point of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Lower quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Lower quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | max | any | false | | Maximum value of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Maximum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Maximum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | mean | any | false | | Arithmetic mean of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Arithmetic mean of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Arithmetic mean of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | median | any | false | | Median of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Median of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Median of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | min | any | false | | Minimum value of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Minimum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Minimum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | naCount | integer,null | false | | Number of missing values. | | name | string | true | | Feature name | | plot | [DatasetFeaturePlotDataResponse] | false | | Plot data based on feature values. | | sampleRows | integer | true | | The number of rows in the sample used to calculate the statistics. | | stdDev | any | false | | Standard deviation of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Standard deviation of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Standard deviation of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timeSeriesEligibilityReason | string,null | false | | why the feature is ineligible for time series projects, or 'suitable' if it is eligible. | | timeSeriesEligibilityReasonAggregation | string,null | false | | why the feature is ineligible for aggregation, or 'suitable' if it is eligible. | | timeSeriesEligible | boolean | false | | whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements. | | timeSeriesEligibleAggregation | boolean | false | | whether this feature can be used as a datetime feature for aggregationfor time series data prep. Always false for non-date features. | | timeStep | integer,null | false | | The minimum time step that can be used to specify time series windows. The units for this value are the timeUnit. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise. | | timeStepAggregation | integer,null | false | | The minimum time step that can be used to aggregate using this feature for time series data prep. The units for this value are the timeUnit. Only present for date features that are eligible for aggregation in time series data prep and null otherwise. | | timeUnit | string,null | false | | The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise. | | timeUnitAggregation | string,null | false | | The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. Only present for date features eligible for aggregation, and null otherwise. | | uniqueCount | integer,null | false | | Number of unique values. | | upperQuartile | any | false | | Upper quartile point of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Upper quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Upper quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | dataQualityIssues | [ISSUES_FOUND, NOT_ANALYZED, NO_ISSUES_FOUND] | | featureType | [Boolean, Categorical, Currency, Date, Date Duration, Document, Image, Interaction, Length, Location, Multicategorical, Numeric, Percentage, Summarized Categorical, Text, Time] | ## DatasetFeaturelistListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of featurelists' details.", "items": { "properties": { "createdBy": { "description": "`username` of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "the ISO 8601 formatted date and time when the dataset was created.", "format": "date-time", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version if the featurelist is associated with a specific dataset version, for example Informative Features, or null otherwise.", "type": [ "string", "null" ] }, "features": { "description": "Features in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "The ID of the featurelist", "type": "string" }, "name": { "description": "The name of the featurelist", "type": "string" }, "userCreated": { "description": "True if the featurelist was created by a user vs the system.", "type": "boolean" } }, "required": [ "createdBy", "creationDate", "datasetId", "datasetVersionId", "features", "id", "name", "userCreated" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetFeaturelistResponse] | true | | An array of featurelists' details. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetFeaturelistResponse ``` { "properties": { "createdBy": { "description": "`username` of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "the ISO 8601 formatted date and time when the dataset was created.", "format": "date-time", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version if the featurelist is associated with a specific dataset version, for example Informative Features, or null otherwise.", "type": [ "string", "null" ] }, "features": { "description": "Features in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "The ID of the featurelist", "type": "string" }, "name": { "description": "The name of the featurelist", "type": "string" }, "userCreated": { "description": "True if the featurelist was created by a user vs the system.", "type": "boolean" } }, "required": [ "createdBy", "creationDate", "datasetId", "datasetVersionId", "features", "id", "name", "userCreated" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | string,null | true | | username of the user who created the dataset. | | creationDate | string(date-time) | true | | the ISO 8601 formatted date and time when the dataset was created. | | datasetId | string | true | | The ID of the dataset. | | datasetVersionId | string,null | true | | The ID of the dataset version if the featurelist is associated with a specific dataset version, for example Informative Features, or null otherwise. | | features | [string] | true | | Features in the featurelist. | | id | string | true | | The ID of the featurelist | | name | string | true | | The name of the featurelist | | userCreated | boolean | true | | True if the featurelist was created by a user vs the system. | ## DatasetFeaturesListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of features related to the requested dataset.", "items": { "properties": { "dataQualityIssues": { "description": "The status of data quality issue detection.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": [ "string", "null" ] }, "dataQualityIssuesTypes": { "description": "Data quality issue types.", "items": { "description": "Data quality issue type.", "enum": [ "disguised_missing_values", "excess_zero", "external_feature_derivation", "few_negative_values", "imputation_leakage", "inconsistent_gaps", "inliers", "lagged_features", "leading_trailing_zeros", "missing_documents", "missing_images", "missing_values", "multicategorical_invalid_format", "new_series_recent_data", "outliers", "quantile_target_sparsity", "quantile_target_zero_inflation", "target_leakage", "unusual_repeated_values" ], "type": "string" }, "maxItems": 20, "type": "array" }, "datasetId": { "description": "The ID of the dataset the feature belongs to", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version the feature belongs to.", "type": "string" }, "dateFormat": { "description": "The date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ] }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string" }, "id": { "description": "The number of the column in the dataset.", "type": "integer" }, "isZeroInflated": { "description": "whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ] }, "language": { "description": "Detected language of the feature.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "Whether feature has too few values to be informative.", "type": "boolean" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "max": { "description": "Maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "Maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "Maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "mean": { "description": "Arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "median": { "description": "Median of the EDA sample of the feature.", "oneOf": [ { "description": "Median of the EDA sample of the feature.", "type": "string" }, { "description": "Median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "min": { "description": "Minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "Minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "Minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "naCount": { "description": "Number of missing values.", "type": [ "integer", "null" ] }, "name": { "description": "Feature name", "type": "string" }, "plot": { "description": "Plot data based on feature values.", "items": { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.30" }, "sampleRows": { "description": "The number of rows in the sample used to calculate the statistics.", "type": "integer", "x-versionadded": "v2.35" }, "stdDev": { "description": "Standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "Standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "Standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "timeSeriesEligibilityReason": { "description": "why the feature is ineligible for time series projects, or 'suitable' if it is eligible.", "type": [ "string", "null" ] }, "timeSeriesEligibilityReasonAggregation": { "description": "why the feature is ineligible for aggregation, or 'suitable' if it is eligible.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "timeSeriesEligible": { "description": "whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.", "type": "boolean" }, "timeSeriesEligibleAggregation": { "description": "whether this feature can be used as a datetime feature for aggregationfor time series data prep. Always false for non-date features.", "type": "boolean", "x-versionadded": "v2.24" }, "timeStep": { "description": "The minimum time step that can be used to specify time series windows. The units for this value are the ``timeUnit``. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.", "type": [ "integer", "null" ] }, "timeStepAggregation": { "description": "The minimum time step that can be used to aggregate using this feature for time series data prep. The units for this value are the ``timeUnit``. Only present for date features that are eligible for aggregation in time series data prep and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "timeUnit": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.", "type": [ "string", "null" ] }, "timeUnitAggregation": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. Only present for date features eligible for aggregation, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "uniqueCount": { "description": "Number of unique values.", "type": [ "integer", "null" ] }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "datasetId", "datasetVersionId", "dateFormat", "featureType", "id", "name", "sampleRows" ], "type": "object", "x-versionadded": "v2.35" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetFeatureResponseWithDataQuality] | true | | The list of features related to the requested dataset. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetFromFile ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "file": { "description": "The data to be used for the creation.", "format": "binary", "type": "string" } }, "required": [ "file" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | file | string(binary) | true | | The data to be used for the creation. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SAMPLE, SINGLE_SERIES_CALENDAR, TRAINING] | ## DatasetFromStage ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "maxItems": 100, "type": "array" } ] }, "stageId": { "description": "The ID of the data stage which will be used to create the dataset item & version.", "type": "string" } }, "required": [ "stageId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 100 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stageId | string | true | | The ID of the data stage which will be used to create the dataset item & version. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SAMPLE, SINGLE_SERIES_CALENDAR, TRAINING] | ## DatasetListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of dataset details.", "items": { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "columnCount": { "description": "The number of columns in the dataset.", "type": "integer", "x-versionadded": "v2.30" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes.", "type": "integer", "x-versionadded": "v2.21" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "rowCount": { "description": "The number of rows in the dataset.", "type": "integer", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "columnCount", "createdBy", "creationDate", "dataPersisted", "datasetId", "datasetSize", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "name", "processingState", "rowCount", "timeSeriesProperties", "versionId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [BasicDatasetWithSizeResponse] | true | | An array of dataset details. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetProject ``` { "properties": { "id": { "description": "The dataset's project ID.", "type": "string" }, "url": { "description": "The link to retrieve more information about the dataset version's project.", "type": "string" } }, "required": [ "id", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The dataset's project ID. | | url | string | true | | The link to retrieve more information about the dataset version's project. | ## DatasetProjectListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of information about dataset's projects", "items": { "properties": { "id": { "description": "The dataset's project ID.", "type": "string" }, "url": { "description": "The link to retrieve more information about the dataset's project.", "type": "string" } }, "required": [ "id", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetProjectResponse] | true | | An array of information about dataset's projects | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetProjectResponse ``` { "properties": { "id": { "description": "The dataset's project ID.", "type": "string" }, "url": { "description": "The link to retrieve more information about the dataset's project.", "type": "string" } }, "required": [ "id", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The dataset's project ID. | | url | string | true | | The link to retrieve more information about the dataset's project. | ## DatasetRefresh ``` { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" } ``` Schedule describing when to refresh the dataset. The smallest schedule allowed is daily. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dayOfMonth | [number,string] | false | maxItems: 31 | The date(s) of the month that the job will run. Allowed values are either [1 ... 31] or ["*"] for all days of the month. This field is additive with dayOfWeek, meaning the job will run both on the date(s) defined in this field and the day specified by dayOfWeek (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If dayOfMonth is set to ["*"] and dayOfWeek is defined, the scheduler will trigger on every day of the month that matches dayOfWeek (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored. | | dayOfWeek | [number,string] | false | maxItems: 7 | The day(s) of the week that the job will run. Allowed values are [0 .. 6], where (Sunday=0), or ["*"], for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., "sunday", "Sunday", "sun", or "Sun", all map to [0]. This field is additive with dayOfMonth, meaning the job will run both on the date specified by dayOfMonth and the day defined in this field. | | hour | any | true | | The hour(s) of the day that the job will run. Allowed values are [0 ... 23]. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | minute | any | true | | The minute(s) of the day that the job will run. Allowed values are [0 ... 59]. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | month | [number,string] | false | maxItems: 12 | The month(s) of the year that the job will run. Allowed values are either [1 ... 12] or ["*"] for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., "jan" or "october"). Months that are not compatible with dayOfMonth are ignored, for example {"dayOfMonth": [31], "month":["feb"]}. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23] | | anonymous | [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59] | ## DatasetRefreshExecutionResult ``` { "properties": { "completedAt": { "description": "UTC completion date, in RFC-3339 format.", "format": "date-time", "type": "string" }, "datasetId": { "description": "The dataset ID associated with this result.", "type": "string" }, "datasetVersionId": { "description": "The dataset version ID associated with this result.", "type": "string" }, "executionId": { "description": "The result ID.", "type": "string" }, "jobId": { "description": "The job ID associated with this result.", "type": "string" }, "message": { "description": "The current status of execution.", "type": "string" }, "startedAt": { "description": "UTC start date, in RFC-3339 format.", "format": "date-time", "type": "string" }, "status": { "description": "The status of this dataset refresh.", "enum": [ "INITIALIZING", "REFRESHING", "SUCCESS", "ERROR" ], "type": "string" } }, "required": [ "completedAt", "datasetId", "datasetVersionId", "executionId", "jobId", "message", "startedAt", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | completedAt | string(date-time) | true | | UTC completion date, in RFC-3339 format. | | datasetId | string | true | | The dataset ID associated with this result. | | datasetVersionId | string | true | | The dataset version ID associated with this result. | | executionId | string | true | | The result ID. | | jobId | string | true | | The job ID associated with this result. | | message | string | true | | The current status of execution. | | startedAt | string(date-time) | true | | UTC start date, in RFC-3339 format. | | status | string | true | | The status of this dataset refresh. | ### Enumerated Values | Property | Value | | --- | --- | | status | [INITIALIZING, REFRESHING, SUCCESS, ERROR] | ## DatasetRefreshJobCreate ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset. The supported options are ``TRAINING``, and ``PREDICTION``.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialId": { "default": null, "description": "The ID of the set of credentials to use to run the scheduled job when the Kerberos authentication service is utilized. Required when useKerberos is true.", "type": [ "string", "null" ] }, "credentials": { "description": "A JSON string describing the data engine queries credentials to use when refreshing.", "type": "string" }, "enabled": { "default": true, "description": "Boolean for whether the scheduled job is active (true) or inactive (false).", "type": "boolean" }, "name": { "description": "The scheduled job name.", "maxLength": 256, "type": "string" }, "schedule": { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" }, "scheduleReferenceDate": { "description": "The UTC reference date in RFC-3339 format of when the schedule starts from. This value is returned in `/api/v2/datasets/(datasetId)/refreshJobs/(jobId)/` to help build a more intuitive schedule picker. The default is the current time.", "format": "date-time", "type": "string" }, "useKerberos": { "default": false, "description": "If true, the Kerberos authentication system is used in conjunction with a credential ID.", "type": "boolean" } }, "required": [ "name", "schedule" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. The supported options are TRAINING, and PREDICTION. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | The ID of the set of credentials to use to run the scheduled job when the Kerberos authentication service is utilized. Required when useKerberos is true. | | credentials | string | false | | A JSON string describing the data engine queries credentials to use when refreshing. | | enabled | boolean | false | | Boolean for whether the scheduled job is active (true) or inactive (false). | | name | string | true | maxLength: 256 | The scheduled job name. | | schedule | DatasetRefresh | true | | Schedule describing when to refresh the dataset. The smallest schedule allowed is daily. | | scheduleReferenceDate | string(date-time) | false | | The UTC reference date in RFC-3339 format of when the schedule starts from. This value is returned in /api/v2/datasets/(datasetId)/refreshJobs/(jobId)/ to help build a more intuitive schedule picker. The default is the current time. | | useKerberos | boolean | false | | If true, the Kerberos authentication system is used in conjunction with a credential ID. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SINGLE_SERIES_CALENDAR, TRAINING] | ## DatasetRefreshJobResponse ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "createdBy": { "description": "The user who created this dataset refresh job.", "type": "string" }, "credentialId": { "description": "ID used to validate with Kerberos authentication service if Kerberos is enabled.", "type": [ "string", "null" ] }, "credentials": { "description": "A JSON string describing the data engine queries credentials to use when refreshing.", "type": "string" }, "datasetId": { "description": "The ID of the dataset the user scheduled job applies to.", "type": "string" }, "enabled": { "description": "Indicates whether the scheduled job is active (true) or inactive(false).", "type": "boolean" }, "jobId": { "description": "The scheduled job ID.", "type": "string" }, "name": { "description": "The scheduled job's name.", "type": "string" }, "schedule": { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" }, "scheduleReferenceDate": { "description": "The UTC reference date in RFC-3339 format of when the schedule starts from. Can be used to help build a more intuitive schedule picker.", "format": "date-time", "type": "string" }, "updatedAt": { "description": "The UTC date in RFC-3339 format of when the job was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The user who last modified this dataset refresh job.", "type": "string" }, "useKerberos": { "description": "Boolean (true) if the Kerberos authentication service is needed when refreshing a job.", "type": "boolean" } }, "required": [ "categories", "createdBy", "credentialId", "credentials", "datasetId", "enabled", "jobId", "name", "schedule", "scheduleReferenceDate", "updatedAt", "updatedBy", "useKerberos" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | true | | An array of strings describing the intended use of the dataset. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | string | true | | The user who created this dataset refresh job. | | credentialId | string,null | true | | ID used to validate with Kerberos authentication service if Kerberos is enabled. | | credentials | string | true | | A JSON string describing the data engine queries credentials to use when refreshing. | | datasetId | string | true | | The ID of the dataset the user scheduled job applies to. | | enabled | boolean | true | | Indicates whether the scheduled job is active (true) or inactive(false). | | jobId | string | true | | The scheduled job ID. | | name | string | true | | The scheduled job's name. | | schedule | DatasetRefresh | true | | Schedule describing when to refresh the dataset. The smallest schedule allowed is daily. | | scheduleReferenceDate | string(date-time) | true | | The UTC reference date in RFC-3339 format of when the schedule starts from. Can be used to help build a more intuitive schedule picker. | | updatedAt | string(date-time) | true | | The UTC date in RFC-3339 format of when the job was last updated. | | updatedBy | string | true | | The user who last modified this dataset refresh job. | | useKerberos | boolean | true | | Boolean (true) if the Kerberos authentication service is needed when refreshing a job. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SINGLE_SERIES_CALENDAR, TRAINING] | ## DatasetRefreshJobRetrieveExecutionResultsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Array of dataset refresh results, returned latest first. ", "items": { "properties": { "completedAt": { "description": "UTC completion date, in RFC-3339 format.", "format": "date-time", "type": "string" }, "datasetId": { "description": "The dataset ID associated with this result.", "type": "string" }, "datasetVersionId": { "description": "The dataset version ID associated with this result.", "type": "string" }, "executionId": { "description": "The result ID.", "type": "string" }, "jobId": { "description": "The job ID associated with this result.", "type": "string" }, "message": { "description": "The current status of execution.", "type": "string" }, "startedAt": { "description": "UTC start date, in RFC-3339 format.", "format": "date-time", "type": "string" }, "status": { "description": "The status of this dataset refresh.", "enum": [ "INITIALIZING", "REFRESHING", "SUCCESS", "ERROR" ], "type": "string" } }, "required": [ "completedAt", "datasetId", "datasetVersionId", "executionId", "jobId", "message", "startedAt", "status" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetRefreshExecutionResult] | true | | Array of dataset refresh results, returned latest first. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetRefreshJobUpdate ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset. The supported options are ``TRAINING``, and ``PREDICTION``.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialId": { "description": "The ID of the set of credentials to use to run the scheduled job when the Kerberos authentication service is utilized. Required when useKerberos is true.", "type": [ "string", "null" ] }, "credentials": { "description": "A JSON string describing the data engine queries credentials to use when refreshing.", "type": "string" }, "enabled": { "description": "Boolean for whether the scheduled job is active (true) or inactive (false).", "type": "boolean" }, "name": { "description": "The scheduled job name.", "maxLength": 256, "type": "string" }, "schedule": { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" }, "scheduleReferenceDate": { "description": "The UTC reference date in RFC-3339 format of when the schedule starts from. This value is returned in `/api/v2/datasets/(datasetId)/refreshJobs/(jobId)/` to help build a more intuitive schedule picker. Required when ``schedule`` is being updated. The default is the current time.", "format": "date-time", "type": "string" }, "useKerberos": { "description": "If true, the Kerberos authentication system is used in conjunction with a credential ID.", "type": "boolean" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. The supported options are TRAINING, and PREDICTION. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | The ID of the set of credentials to use to run the scheduled job when the Kerberos authentication service is utilized. Required when useKerberos is true. | | credentials | string | false | | A JSON string describing the data engine queries credentials to use when refreshing. | | enabled | boolean | false | | Boolean for whether the scheduled job is active (true) or inactive (false). | | name | string | false | maxLength: 256 | The scheduled job name. | | schedule | DatasetRefresh | false | | Schedule describing when to refresh the dataset. The smallest schedule allowed is daily. | | scheduleReferenceDate | string(date-time) | false | | The UTC reference date in RFC-3339 format of when the schedule starts from. This value is returned in /api/v2/datasets/(datasetId)/refreshJobs/(jobId)/ to help build a more intuitive schedule picker. Required when schedule is being updated. The default is the current time. | | useKerberos | boolean | false | | If true, the Kerberos authentication system is used in conjunction with a credential ID. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SINGLE_SERIES_CALENDAR, TRAINING] | ## DatasetRefreshJobsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of information about scheduled dataset refresh jobs. Results are based on updatedAt value and returned in descending order (latest returned first).", "items": { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "createdBy": { "description": "The user who created this dataset refresh job.", "type": "string" }, "credentialId": { "description": "ID used to validate with Kerberos authentication service if Kerberos is enabled.", "type": [ "string", "null" ] }, "credentials": { "description": "A JSON string describing the data engine queries credentials to use when refreshing.", "type": "string" }, "datasetId": { "description": "The ID of the dataset the user scheduled job applies to.", "type": "string" }, "enabled": { "description": "Indicates whether the scheduled job is active (true) or inactive(false).", "type": "boolean" }, "jobId": { "description": "The scheduled job ID.", "type": "string" }, "name": { "description": "The scheduled job's name.", "type": "string" }, "schedule": { "description": "Schedule describing when to refresh the dataset. The smallest schedule allowed is daily.", "properties": { "dayOfMonth": { "default": [ "*" ], "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "default": [ "*" ], "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "default": [ 0 ], "description": "The hour(s) of the day that the job will run. Allowed values are ``[0 ... 23]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23" ], "type": "string" }, "type": "array" } ] }, "minute": { "default": [ 0 ], "description": "The minute(s) of the day that the job will run. Allowed values are ``[0 ... 59]``.", "oneOf": [ { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, { "items": { "enum": [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59" ], "type": "string" }, "type": "array" } ] }, "month": { "default": [ "*" ], "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "hour", "minute" ], "type": "object" }, "scheduleReferenceDate": { "description": "The UTC reference date in RFC-3339 format of when the schedule starts from. Can be used to help build a more intuitive schedule picker.", "format": "date-time", "type": "string" }, "updatedAt": { "description": "The UTC date in RFC-3339 format of when the job was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The user who last modified this dataset refresh job.", "type": "string" }, "useKerberos": { "description": "Boolean (true) if the Kerberos authentication service is needed when refreshing a job.", "type": "boolean" } }, "required": [ "categories", "createdBy", "credentialId", "credentials", "datasetId", "enabled", "jobId", "name", "schedule", "scheduleReferenceDate", "updatedAt", "updatedBy", "useKerberos" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetRefreshJobResponse] | true | | An array of information about scheduled dataset refresh jobs. Results are based on updatedAt value and returned in descending order (latest returned first). | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetRelationshipCreate ``` { "properties": { "linkedDatasetId": { "description": "The ID of another dataset with which to create relationships.", "type": "string" }, "linkedFeatures": { "description": "List of features belonging to the linked dataset.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "sourceFeatures": { "description": "List of features belonging to the source dataset.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "linkedDatasetId", "linkedFeatures", "sourceFeatures" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | linkedDatasetId | string | true | | The ID of another dataset with which to create relationships. | | linkedFeatures | [string] | true | minItems: 1 | List of features belonging to the linked dataset. | | sourceFeatures | [string] | true | minItems: 1 | List of features belonging to the source dataset. | ## DatasetRelationshipListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of relationships' details.", "items": { "properties": { "createdBy": { "description": "The username of the user that created this relationship.", "type": "string" }, "creationDate": { "description": "ISO-8601 formatted time/date that this record was created.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the dataset relationship", "type": "string" }, "linkedDatasetId": { "description": "ID of the linked dataset.", "type": "string" }, "linkedFeatures": { "description": "List of features belonging to the linked dataset.", "items": { "type": "string" }, "type": "array" }, "modificationDate": { "description": "ISO-8601 formatted time/date that this record was last updated.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "The username of the user that modified this relationship most recently.", "type": "string" }, "sourceDatasetId": { "description": "ID of the source dataset.", "type": "string" }, "sourceFeatures": { "description": "List of features belonging to the source dataset.", "items": { "type": "string" }, "type": "array" } }, "required": [ "createdBy", "creationDate", "id", "linkedDatasetId", "linkedFeatures", "modificationDate", "modifiedBy", "sourceDatasetId", "sourceFeatures" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetRelationshipResponse] | true | | An array of relationships' details. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetRelationshipResponse ``` { "properties": { "createdBy": { "description": "The username of the user that created this relationship.", "type": "string" }, "creationDate": { "description": "ISO-8601 formatted time/date that this record was created.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the dataset relationship", "type": "string" }, "linkedDatasetId": { "description": "ID of the linked dataset.", "type": "string" }, "linkedFeatures": { "description": "List of features belonging to the linked dataset.", "items": { "type": "string" }, "type": "array" }, "modificationDate": { "description": "ISO-8601 formatted time/date that this record was last updated.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "The username of the user that modified this relationship most recently.", "type": "string" }, "sourceDatasetId": { "description": "ID of the source dataset.", "type": "string" }, "sourceFeatures": { "description": "List of features belonging to the source dataset.", "items": { "type": "string" }, "type": "array" } }, "required": [ "createdBy", "creationDate", "id", "linkedDatasetId", "linkedFeatures", "modificationDate", "modifiedBy", "sourceDatasetId", "sourceFeatures" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | string | true | | The username of the user that created this relationship. | | creationDate | string(date-time) | true | | ISO-8601 formatted time/date that this record was created. | | id | string | true | | ID of the dataset relationship | | linkedDatasetId | string | true | | ID of the linked dataset. | | linkedFeatures | [string] | true | | List of features belonging to the linked dataset. | | modificationDate | string(date-time) | true | | ISO-8601 formatted time/date that this record was last updated. | | modifiedBy | string | true | | The username of the user that modified this relationship most recently. | | sourceDatasetId | string | true | | ID of the source dataset. | | sourceFeatures | [string] | true | | List of features belonging to the source dataset. | ## DatasetRelationshipUpdate ``` { "properties": { "linkedDatasetId": { "description": "id of another dataset with which to create relationships", "type": "string" }, "linkedFeatures": { "description": "list of features belonging to the linked dataset", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "sourceFeatures": { "description": "list of features belonging to the source dataset", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | linkedDatasetId | string | false | | id of another dataset with which to create relationships | | linkedFeatures | [string] | false | minItems: 1 | list of features belonging to the linked dataset | | sourceFeatures | [string] | false | minItems: 1 | list of features belonging to the source dataset | ## DatasetTransformListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of transforms' details.", "items": { "properties": { "dateExtraction": { "description": "The value to extract from the date column.", "enum": [ "year", "yearDay", "month", "monthDay", "week", "weekDay" ], "type": [ "string", "null" ] }, "name": { "description": "The feature name.", "type": "string" }, "parentName": { "description": "The name of the parent feature.", "type": "string" }, "replacement": { "anyOf": [ { "type": "string" }, { "type": "boolean" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The replacement in case of a failed transformation." }, "variableType": { "description": "The type of the transform.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "dateExtraction", "name", "parentName", "replacement", "variableType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetTransformResponse] | true | | An array of transforms' details. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetTransformResponse ``` { "properties": { "dateExtraction": { "description": "The value to extract from the date column.", "enum": [ "year", "yearDay", "month", "monthDay", "week", "weekDay" ], "type": [ "string", "null" ] }, "name": { "description": "The feature name.", "type": "string" }, "parentName": { "description": "The name of the parent feature.", "type": "string" }, "replacement": { "anyOf": [ { "type": "string" }, { "type": "boolean" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The replacement in case of a failed transformation." }, "variableType": { "description": "The type of the transform.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "dateExtraction", "name", "parentName", "replacement", "variableType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dateExtraction | string,null | true | | The value to extract from the date column. | | name | string | true | | The feature name. | | parentName | string | true | | The name of the parent feature. | | replacement | any | true | | The replacement in case of a failed transformation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | variableType | string | true | | The type of the transform. | ### Enumerated Values | Property | Value | | --- | --- | | dateExtraction | [year, yearDay, month, monthDay, week, weekDay] | | variableType | [text, categorical, numeric, categoricalInt] | ## Datasource ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.19" }, "dataSourceId": { "description": "The identifier for the DataSource to use as the source of data.", "type": "string" }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "persistDataAfterIngestion": { "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error.", "type": "boolean" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "useKerberos": { "default": false, "description": "If true, use kerberos authentication for database authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for database authentication. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "required": [ "dataSourceId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialData | any | false | | The credentials to authenticate with the database, to be used instead of credential ID. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BasicCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Credentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OAuthCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeKeyPairCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GoogleServiceAccountCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksAccessTokenCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksServicePrincipalCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureServicePrincipalCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to authenticate with the database. | | dataSourceId | string | true | | The identifier for the DataSource to use as the source of data. | | doSnapshot | boolean | false | | If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, Enable Create Snapshot Data Source. | | password | string | false | | The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. DEPRECATED: please use credentialId or credentialData instead. | | persistDataAfterIngestion | boolean | false | | If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and doSnapshot to true will result in an error. | | sampleSize | SampleSize | false | | Ingest size to use during dataset registration. Default behavior is to ingest full dataset. | | useKerberos | boolean | false | | If true, use kerberos authentication for database authentication. | | user | string | false | | The username for database authentication. DEPRECATED: please use credentialId or credentialData instead. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SAMPLE, SINGLE_SERIES_CALENDAR, TRAINING] | ## DeleteFilesOrFolders ``` { "properties": { "paths": { "description": "File and folder paths to delete. Folder paths should end with slash '/'.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "paths" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | paths | [string] | true | maxItems: 1000minItems: 1 | File and folder paths to delete. Folder paths should end with slash '/'. | ## DeleteFilesOrFoldersResponse ``` { "properties": { "catalogId": { "description": "The ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog version item.", "type": "string" }, "numFiles": { "description": "The new number of files in the file entity.", "type": "integer" }, "results": { "description": "The number of files deleted for each path provided.", "items": { "properties": { "numFilesDeleted": { "description": "The number of files that were deleted for the path.", "type": "integer" }, "path": { "description": "The file or folder path that was deleted.", "type": "string" } }, "required": [ "numFilesDeleted", "path" ], "type": "object", "x-versionadded": "v2.42" }, "maxItems": 1000, "type": "array" } }, "required": [ "catalogId", "catalogVersionId", "numFiles", "results" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | The ID of the catalog item. | | catalogVersionId | string | true | | The ID of the catalog version item. | | numFiles | integer | true | | The new number of files in the file entity. | | results | [DeletedFilesCountPerPathItemResponse] | true | maxItems: 1000 | The number of files deleted for each path provided. | ## DeletedFilesCountPerPathItemResponse ``` { "properties": { "numFilesDeleted": { "description": "The number of files that were deleted for the path.", "type": "integer" }, "path": { "description": "The file or folder path that was deleted.", "type": "string" } }, "required": [ "numFilesDeleted", "path" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | numFilesDeleted | integer | true | | The number of files that were deleted for the path. | | path | string | true | | The file or folder path that was deleted. | ## EntityCountByTypeResponse ``` { "description": "Number of different type entities that use the dataset.", "properties": { "numCalendars": { "description": "The number of calendars that use the dataset", "type": "integer" }, "numExperimentContainer": { "description": "The number of experiment containers that use the dataset.", "type": "integer", "x-versionadded": "v2.37" }, "numExternalModelPackages": { "description": "The number of external model packages that use the dataset", "type": "integer" }, "numFeatureDiscoveryConfigs": { "description": "The number of feature discovery configs that use the dataset", "type": "integer" }, "numPredictionDatasets": { "description": "The number of prediction datasets that use the dataset", "type": "integer" }, "numProjects": { "description": "The number of projects that use the dataset", "type": "integer" }, "numSparkSqlQueries": { "description": "The number of spark sql queries that use the dataset", "type": "integer" } }, "required": [ "numCalendars", "numExperimentContainer", "numExternalModelPackages", "numFeatureDiscoveryConfigs", "numPredictionDatasets", "numProjects", "numSparkSqlQueries" ], "type": "object" } ``` Number of different type entities that use the dataset. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | numCalendars | integer | true | | The number of calendars that use the dataset | | numExperimentContainer | integer | true | | The number of experiment containers that use the dataset. | | numExternalModelPackages | integer | true | | The number of external model packages that use the dataset | | numFeatureDiscoveryConfigs | integer | true | | The number of feature discovery configs that use the dataset | | numPredictionDatasets | integer | true | | The number of prediction datasets that use the dataset | | numProjects | integer | true | | The number of projects that use the dataset | | numSparkSqlQueries | integer | true | | The number of spark sql queries that use the dataset | ## ExternalOAuthProviderCredentials ``` { "properties": { "authenticationId": { "description": "The authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'external_oauth_provider' here.", "enum": [ "external_oauth_provider" ], "type": "string" } }, "required": [ "authenticationId", "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authenticationId | string | true | | The authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service. | | credentialType | string | true | | The type of these credentials, 'external_oauth_provider' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | external_oauth_provider | ## FeatureCountByTypeResponse ``` { "properties": { "count": { "description": "The number of features of this type in the dataset", "type": "integer" }, "featureType": { "description": "The data type grouped in this count", "type": "string" } }, "required": [ "count", "featureType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of features of this type in the dataset | | featureType | string | true | | The data type grouped in this count | ## FeatureKeySummaryDetailsResponseValidatorMultilabel ``` { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } ``` Statistics of the key. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | max | number | true | | Maximum value of the key. | | mean | number | true | | Mean value of the key. | | median | number | true | | Median value of the key. | | min | number | true | | Minimum value of the key. | | pctRows | number | true | | Percentage occurrence of key in the EDA sample of the feature. | | stdDev | number | true | | Standard deviation of the key. | ## FeatureKeySummaryDetailsResponseValidatorSummarizedCategorical ``` { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } ``` Statistics of the key. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataQualities | string | true | | The indicator of data quality assessment of the feature. | | max | number | true | | Maximum value of the key. | | mean | number | true | | Mean value of the key. | | median | number | true | | Median value of the key. | | min | number | true | | Minimum value of the key. | | pctRows | number | true | | Percentage occurrence of key in the EDA sample of the feature. | | stdDev | number | true | | Standard deviation of the key. | ### Enumerated Values | Property | Value | | --- | --- | | dataQualities | [ISSUES_FOUND, NOT_ANALYZED, NO_ISSUES_FOUND] | ## FeatureKeySummaryResponseValidatorMultilabel ``` { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | key | string | true | | Name of the key. | | summary | FeatureKeySummaryDetailsResponseValidatorMultilabel | true | | Statistics of the key. | ## FeatureKeySummaryResponseValidatorSummarizedCategorical ``` { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" } ``` For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters) ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | key | string | true | | Name of the key. | | summary | FeatureKeySummaryDetailsResponseValidatorSummarizedCategorical | true | | Statistics of the key. | ## FeatureListCreate ``` { "properties": { "description": { "description": "The description of the featurelist.", "type": "string" }, "features": { "description": "The list of names of features to be included in the new featurelist. All features listed must be part of the universe. All features for this dataset for the request to succeed.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "name": { "description": "The name of the featurelist to be created.", "type": "string" } }, "required": [ "features", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | | The description of the featurelist. | | features | [string] | true | minItems: 1 | The list of names of features to be included in the new featurelist. All features listed must be part of the universe. All features for this dataset for the request to succeed. | | name | string | true | | The name of the featurelist to be created. | ## FeatureListModify ``` { "properties": { "description": { "description": "The new description of the featurelist.", "type": [ "string", "null" ] }, "name": { "description": "The new name of the featurelist.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | | The new description of the featurelist. | | name | string | false | | The new name of the featurelist. | ## FeatureTransform ``` { "properties": { "dateExtraction": { "description": "The value to extract from the date column, of these options: `[year|yearDay|month|monthDay|week|weekDay]`. Required for transformation of a date column. Otherwise must not be provided.", "enum": [ "year", "yearDay", "month", "monthDay", "week", "weekDay" ], "type": "string" }, "name": { "description": "The name of the new feature. Must not be the same as any existing features for this project. Must not contain '/' character.", "type": "string" }, "parentName": { "description": "The name of the parent feature.", "type": "string" }, "replacement": { "anyOf": [ { "type": [ "string", "null" ] }, { "type": [ "boolean", "null" ] }, { "type": [ "number", "null" ] }, { "type": [ "integer", "null" ] } ], "description": "The replacement in case of a failed transformation." }, "variableType": { "description": "The type of the new feature. Must be one of `text`, `categorical` (Deprecated in version v2.21), `numeric`, or `categoricalInt`. See the description of this method for more information.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "name", "parentName", "variableType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dateExtraction | string | false | | The value to extract from the date column, of these options: [year\|yearDay\|month\|monthDay\|week\|weekDay]. Required for transformation of a date column. Otherwise must not be provided. | | name | string | true | | The name of the new feature. Must not be the same as any existing features for this project. Must not contain '/' character. | | parentName | string | true | | The name of the parent feature. | | replacement | any | false | | The replacement in case of a failed transformation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean,null | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number,null | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer,null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | variableType | string | true | | The type of the new feature. Must be one of text, categorical (Deprecated in version v2.21), numeric, or categoricalInt. See the description of this method for more information. | ### Enumerated Values | Property | Value | | --- | --- | | dateExtraction | [year, yearDay, month, monthDay, week, weekDay] | | variableType | [text, categorical, numeric, categoricalInt] | ## FilePermadelete ``` { "properties": { "catalogIds": { "description": "The catalog IDs to be deleted.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "catalogIds" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogIds | [string] | true | maxItems: 10minItems: 1 | The catalog IDs to be deleted. | ## FilePermadeleteDryRunResponse ``` { "properties": { "fileVersionsToDelete": { "description": "The IDs of file versions to be deleted for each corresponding file.", "items": { "properties": { "catalogId": { "description": "The ID of a file to be deleted.", "type": "string" }, "versionIds": { "description": "The IDs of associated file versions to be deleted.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "catalogId", "versionIds" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "filesToDelete": { "description": "The IDs of files to be deleted.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "warnings": { "description": "The warnings on potential issues that may occur with some or all of the file deletions.", "items": { "properties": { "catalogIds": { "description": "The IDs of the files associated with the warning.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "warning": { "description": "The warning on potential issues if the listed files were deleted.", "type": "string" } }, "required": [ "catalogIds", "warning" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" } }, "required": [ "fileVersionsToDelete", "filesToDelete" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fileVersionsToDelete | [FileToVersionMap] | true | maxItems: 1000 | The IDs of file versions to be deleted for each corresponding file. | | filesToDelete | [string] | true | maxItems: 1000 | The IDs of files to be deleted. | | warnings | [FilePermadeleteWarnings] | false | maxItems: 1000 | The warnings on potential issues that may occur with some or all of the file deletions. | ## FilePermadeleteResponse ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the permadelete job's status.", "type": "string" } }, "required": [ "statusId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the permadelete job's status. | ## FilePermadeleteWarnings ``` { "properties": { "catalogIds": { "description": "The IDs of the files associated with the warning.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" }, "warning": { "description": "The warning on potential issues if the listed files were deleted.", "type": "string" } }, "required": [ "catalogIds", "warning" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogIds | [string] | true | maxItems: 1000 | The IDs of the files associated with the warning. | | warning | string | true | | The warning on potential issues if the listed files were deleted. | ## FileResponse ``` { "properties": { "fileChecksum": { "description": "The checksum of the file.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "fileCreatedAt": { "description": "The date when the file was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "fileName": { "description": "The name of the file. The actual file can be retrieved with [GET /api/v2/files/{catalogId}/file/][get-apiv2filescatalogidfile].", "type": "string" }, "fileSize": { "description": "The size of the file, in bytes.", "type": "integer" }, "fileType": { "description": "The file type, if known.", "type": "string" }, "ingestErrors": { "description": "Any errors associated with the file.", "type": [ "string", "null" ] } }, "required": [ "fileChecksum", "fileCreatedAt", "fileName", "fileSize", "fileType", "ingestErrors" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fileChecksum | string,null | true | | The checksum of the file. | | fileCreatedAt | string,null(date-time) | true | | The date when the file was created. | | fileName | string | true | | The name of the file. The actual file can be retrieved with [GET /api/v2/files/{catalogId}/file/][get-apiv2filescatalogidfile]. | | fileSize | integer | true | | The size of the file, in bytes. | | fileType | string | true | | The file type, if known. | | ingestErrors | string,null | true | | Any errors associated with the file. | ## FileToVersionMap ``` { "properties": { "catalogId": { "description": "The ID of a file to be deleted.", "type": "string" }, "versionIds": { "description": "The IDs of associated file versions to be deleted.", "items": { "type": "string" }, "maxItems": 1000, "type": "array" } }, "required": [ "catalogId", "versionIds" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | The ID of a file to be deleted. | | versionIds | [string] | true | maxItems: 1000 | The IDs of associated file versions to be deleted. | ## FileUpload ``` { "properties": { "file": { "description": "The file to upload.", "format": "binary", "type": "string" }, "originalCatalogId": { "default": null, "description": "If the contents of the file being uploaded are derived from a file in a catalog entity which was used to clone, the current catalog entity, the original catalog entity ID in which the file exists.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "originalFileName": { "default": null, "description": "If the contents of the file being uploaded are derived from a file in the catalog entity, the name of the file it is derived from.", "type": [ "string", "null" ] } }, "required": [ "file" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | file | string(binary) | true | | The file to upload. | | originalCatalogId | string,null | false | | If the contents of the file being uploaded are derived from a file in a catalog entity which was used to clone, the current catalog entity, the original catalog entity ID in which the file exists. | | originalFileName | string,null | false | | If the contents of the file being uploaded are derived from a file in the catalog entity, the name of the file it is derived from. | ## FilesAsyncResponse ``` { "properties": { "catalogId": { "description": "The catalog item ID.", "type": "string" }, "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "catalogId", "statusId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | The catalog item ID. | | statusId | string | true | | ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status. | ## FilesDuration ``` { "properties": { "duration": { "default": 60, "description": "Access ttl in seconds (maximum value is 300s).", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "fileName": { "description": "The name of a file to download from an unstructured dataset. If not specified, it will download either the original archive file or, if there is only one file, it will download that.", "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | duration | integer | false | maximum: 300 | Access ttl in seconds (maximum value is 300s). | | fileName | string | false | | The name of a file to download from an unstructured dataset. If not specified, it will download either the original archive file or, if there is only one file, it will download that. | ## FilesDurationAndFiles ``` { "properties": { "duration": { "default": 600, "description": "Access ttl in seconds (maximum value is 3000s).", "exclusiveMinimum": 0, "maximum": 3000, "type": "integer" }, "fileNames": { "description": "The names of files to download from an unstructured dataset. If not specified, it will download either the original archive file or, if there is only one file, it will download that.", "items": { "type": [ "string", "null" ] }, "maxItems": 100, "type": "array" } }, "required": [ "fileNames" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | duration | integer | false | maximum: 3000 | Access ttl in seconds (maximum value is 3000s). | | fileNames | [string,null] | true | maxItems: 100 | The names of files to download from an unstructured dataset. If not specified, it will download either the original archive file or, if there is only one file, it will download that. | ## FilesEmpty ``` { "type": "object", "x-versionadded": "v2.37" } ``` ### Properties None ## FilesFromDataSource ``` { "properties": { "credentialData": { "description": "The credentials to be used to authenticate with the database in place of the credential id.", "oneOf": [ { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "authenticationId": { "description": "The authentication ID for external OAuth provider. Used to retrieve tokens from DataRobot OAuth service.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'external_oauth_provider' here.", "enum": [ "external_oauth_provider" ], "type": "string" } }, "required": [ "authenticationId", "credentialType" ], "type": "object" } ] }, "credentialId": { "description": "The identifier for the set of credentials to use to authenticate with the database.", "type": "string" }, "dataSourceId": { "description": "The identifier of the DataSource to use as the source of data.", "type": "string" }, "useArchiveContents": { "default": "True", "description": "If true, extract archive contents and associate them with the catalog entity.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "dataSourceId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialData | any | false | | The credentials to be used to authenticate with the database in place of the credential id. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Credentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExternalOAuthProviderCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The identifier for the set of credentials to use to authenticate with the database. | | dataSourceId | string | true | | The identifier of the DataSource to use as the source of data. | | useArchiveContents | string | false | | If true, extract archive contents and associate them with the catalog entity. | ### Enumerated Values | Property | Value | | --- | --- | | useArchiveContents | [false, False, true, True] | ## FilesFromFile ``` { "properties": { "file": { "description": "The file to upload.", "format": "binary", "type": "string" }, "originalCatalogId": { "default": null, "description": "If the contents of the file being uploaded are derived from a file in a catalog entity which was used to clone, the current catalog entity, the original catalog entity ID in which the file exists.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "originalFileName": { "default": null, "description": "If the contents of the file being uploaded are derived from a file in the catalog entity, the name of the file it is derived from.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "useArchiveContents": { "default": "True", "description": "If true, extract archive contents and associate with the catalog entity.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "file" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | file | string(binary) | true | | The file to upload. | | originalCatalogId | string,null | false | | If the contents of the file being uploaded are derived from a file in a catalog entity which was used to clone, the current catalog entity, the original catalog entity ID in which the file exists. | | originalFileName | string,null | false | | If the contents of the file being uploaded are derived from a file in the catalog entity, the name of the file it is derived from. | | useArchiveContents | string | false | | If true, extract archive contents and associate with the catalog entity. | ### Enumerated Values | Property | Value | | --- | --- | | useArchiveContents | [false, False, true, True] | ## FilesFromUrl ``` { "properties": { "url": { "description": "The URL to download the file used to create the catalog entity.", "format": "url", "type": "string" }, "useArchiveContents": { "default": "True", "description": "If true, extract archive contents and associate them with the catalog entity.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "url" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | url | string(url) | true | | The URL to download the file used to create the catalog entity. | | useArchiveContents | string | false | | If true, extract archive contents and associate them with the catalog entity. | ### Enumerated Values | Property | Value | | --- | --- | | useArchiveContents | [false, False, true, True] | ## FilesListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of file metadata.", "items": { "properties": { "fileChecksum": { "description": "The checksum of the file.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "fileCreatedAt": { "description": "The date when the file was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "fileName": { "description": "The name of the file. The actual file can be retrieved with [GET /api/v2/files/{catalogId}/file/][get-apiv2filescatalogidfile].", "type": "string" }, "fileSize": { "description": "The size of the file, in bytes.", "type": "integer" }, "fileType": { "description": "The file type, if known.", "type": "string" }, "ingestErrors": { "description": "Any errors associated with the file.", "type": [ "string", "null" ] } }, "required": [ "fileChecksum", "fileCreatedAt", "fileName", "fileSize", "fileType", "ingestErrors" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [FileResponse] | true | maxItems: 1000 | List of file metadata. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## FilesStageResponse ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "stageId": { "description": "The ID of the stage.", "type": "string" } }, "required": [ "catalogId", "stageId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | The ID of the catalog entry. | | stageId | string | true | | The ID of the stage. | ## FilesVersionResponse ``` { "properties": { "catalogId": { "description": "The ID of the catalog entry.", "type": "string" }, "catalogVersionId": { "description": "The ID of the catalog entry version.", "type": "string" }, "numFiles": { "description": "The number of files in the file entity.", "type": "integer" } }, "required": [ "catalogId", "catalogVersionId", "numFiles" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | The ID of the catalog entry. | | catalogVersionId | string | true | | The ID of the catalog entry version. | | numFiles | integer | true | | The number of files in the file entity. | ## FromLatest ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string", "x-versionadded": "v2.19" }, "credentials": { "description": "A list of credentials to use if this is a Spark dataset that requires credentials.", "type": "string", "x-versionadded": "v2.20" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server-side HTTP request and never saved or stored. Required only if the previous data source was a data source. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "useKerberos": { "default": false, "description": "If true, use Kerberos for database authentication.", "type": "boolean", "x-versionadded": "v2.19" }, "useLatestSuccess": { "default": false, "description": "If true, use the latest version that was successfully ingested instead of the latest version, which might be in an errored state. If no successful version is present, the latest errored version is used and the operation fails.", "type": "boolean", "x-versionadded": "v2.20" }, "user": { "description": "The username for database authentication. Required only if the dataset was initially created from a data source. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialData | any | false | | The credentials to authenticate with the database, to be used instead of credential ID. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BasicCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Credentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OAuthCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeKeyPairCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GoogleServiceAccountCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksAccessTokenCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksServicePrincipalCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureServicePrincipalCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to authenticate with the database. | | credentials | string | false | | A list of credentials to use if this is a Spark dataset that requires credentials. | | password | string | false | | The password (in cleartext) for database authentication. The password will be encrypted on the server-side HTTP request and never saved or stored. Required only if the previous data source was a data source. DEPRECATED: please use credentialId or credentialData instead. | | useKerberos | boolean | false | | If true, use Kerberos for database authentication. | | useLatestSuccess | boolean | false | | If true, use the latest version that was successfully ingested instead of the latest version, which might be in an errored state. If no successful version is present, the latest errored version is used and the operation fails. | | user | string | false | | The username for database authentication. Required only if the dataset was initially created from a data source. DEPRECATED: please use credentialId or credentialData instead. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SAMPLE, SINGLE_SERIES_CALENDAR, TRAINING] | ## FromSpecific ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database.", "type": "string" }, "credentials": { "description": "A list of credentials to use if this is a Spark dataset that requires credentials.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server-side HTTP request and never saved or stored. Required only if the previous data source was a data source. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "useKerberos": { "default": false, "description": "If true, use Kerberos for database authentication.", "type": "boolean" }, "user": { "description": "The username for database authentication. Required only if the dataset was initially created from a data source. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialData | any | false | | The credentials to authenticate with the database, to be used instead of credential ID. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BasicCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Credentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OAuthCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeKeyPairCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GoogleServiceAccountCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksAccessTokenCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksServicePrincipalCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureServicePrincipalCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to authenticate with the database. | | credentials | string | false | | A list of credentials to use if this is a Spark dataset that requires credentials. | | password | string | false | | The password (in cleartext) for database authentication. The password will be encrypted on the server-side HTTP request and never saved or stored. Required only if the previous data source was a data source. DEPRECATED: please use credentialId or credentialData instead. | | useKerberos | boolean | false | | If true, use Kerberos for database authentication. | | user | string | false | | The username for database authentication. Required only if the dataset was initially created from a data source. DEPRECATED: please use credentialId or credentialData instead. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SAMPLE, SINGLE_SERIES_CALENDAR, TRAINING] | ## FullDatasetDetailsResponse ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "columnCount": { "description": "The number of columns in the dataset.", "type": "integer", "x-versionadded": "v2.30" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataEngineQueryId": { "description": "The ID of the source data engine query.", "type": [ "string", "null" ] }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "dataSourceId": { "description": "The ID of the datasource used as the source of the dataset.", "type": [ "string", "null" ] }, "dataSourceType": { "description": "The type of the datasource that was used as the source of the dataset.", "type": "string" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes.", "type": "integer", "x-versionadded": "v2.21" }, "description": { "description": "The description of the dataset.", "type": [ "string", "null" ] }, "eda1ModificationDate": { "description": "The ISO 8601 formatted date and time when the EDA1 for the dataset was updated.", "format": "date-time", "type": "string" }, "eda1ModifierFullName": { "description": "The user who was the last to update EDA1 for the dataset.", "type": "string" }, "entityCountByType": { "description": "Number of different type entities that use the dataset.", "properties": { "numCalendars": { "description": "The number of calendars that use the dataset", "type": "integer" }, "numExperimentContainer": { "description": "The number of experiment containers that use the dataset.", "type": "integer", "x-versionadded": "v2.37" }, "numExternalModelPackages": { "description": "The number of external model packages that use the dataset", "type": "integer" }, "numFeatureDiscoveryConfigs": { "description": "The number of feature discovery configs that use the dataset", "type": "integer" }, "numPredictionDatasets": { "description": "The number of prediction datasets that use the dataset", "type": "integer" }, "numProjects": { "description": "The number of projects that use the dataset", "type": "integer" }, "numSparkSqlQueries": { "description": "The number of spark sql queries that use the dataset", "type": "integer" } }, "required": [ "numCalendars", "numExperimentContainer", "numExternalModelPackages", "numFeatureDiscoveryConfigs", "numPredictionDatasets", "numProjects", "numSparkSqlQueries" ], "type": "object" }, "error": { "description": "Details of exception raised during ingestion process, if any.", "type": "string" }, "featureCount": { "description": "Total number of features in the dataset.", "type": "integer" }, "featureCountByType": { "description": "Number of features in the dataset grouped by feature type.", "items": { "properties": { "count": { "description": "The number of features of this type in the dataset", "type": "integer" }, "featureType": { "description": "The data type grouped in this count", "type": "string" } }, "required": [ "count", "featureType" ], "type": "object" }, "type": "array" }, "featureDiscoveryProjectId": { "description": "Feature Discovery project ID used to create the dataset.", "type": "string", "x-versionadded": "v2.35" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean", "x-versionadded": "2.30.0" }, "lastModificationDate": { "description": "The ISO 8601 formatted date and time when the dataset was last modified.", "format": "date-time", "type": "string" }, "lastModifierFullName": { "description": "Full name of user who was the last to modify the dataset.", "type": "string" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "recipeId": { "description": "The ID of the source recipe.", "type": [ "string", "null" ] }, "rowCount": { "description": "The number of rows in the dataset.", "type": "integer", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "tags": { "description": "List of tags attached to the item.", "items": { "type": "string" }, "type": "array" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "uri": { "description": "The URI to datasource. For example, `file_name.csv`, or `jdbc:DATA_SOURCE_GIVEN_NAME/SCHEMA.TABLE_NAME`, or `jdbc:DATA_SOURCE_GIVEN_NAME/` for `query` based datasources, or`https://s3.amazonaws.com/dr-pr-tst-data/kickcars-sample-200.csv`, etc.", "type": "string" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "columnCount", "createdBy", "creationDate", "dataEngineQueryId", "dataPersisted", "dataSourceId", "dataSourceType", "datasetId", "datasetSize", "description", "eda1ModificationDate", "eda1ModifierFullName", "error", "featureCount", "featureCountByType", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "isWranglingEligible", "lastModificationDate", "lastModifierFullName", "name", "processingState", "recipeId", "rowCount", "tags", "timeSeriesProperties", "uri", "versionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [string] | true | | An array of strings describing the intended use of the dataset. | | columnCount | integer | true | | The number of columns in the dataset. | | createdBy | string,null | true | | Username of the user who created the dataset. | | creationDate | string(date-time) | true | | The date when the dataset was created. | | dataEngineQueryId | string,null | true | | The ID of the source data engine query. | | dataPersisted | boolean | true | | If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available. | | dataSourceId | string,null | true | | The ID of the datasource used as the source of the dataset. | | dataSourceType | string | true | | The type of the datasource that was used as the source of the dataset. | | datasetId | string | true | | The ID of this dataset. | | datasetSize | integer | true | | The size of the dataset as a CSV in bytes. | | description | string,null | true | | The description of the dataset. | | eda1ModificationDate | string(date-time) | true | | The ISO 8601 formatted date and time when the EDA1 for the dataset was updated. | | eda1ModifierFullName | string | true | | The user who was the last to update EDA1 for the dataset. | | entityCountByType | EntityCountByTypeResponse | false | | Number of different type entities that use the dataset. | | error | string | true | | Details of exception raised during ingestion process, if any. | | featureCount | integer | true | | Total number of features in the dataset. | | featureCountByType | [FeatureCountByTypeResponse] | true | | Number of features in the dataset grouped by feature type. | | featureDiscoveryProjectId | string | false | | Feature Discovery project ID used to create the dataset. | | isDataEngineEligible | boolean | true | | Whether this dataset can be a data source of a data engine query. | | isLatestVersion | boolean | true | | Whether this dataset version is the latest version of this dataset. | | isSnapshot | boolean | true | | Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot. | | isWranglingEligible | boolean | true | | Whether the source of the dataset can support wrangling. | | lastModificationDate | string(date-time) | true | | The ISO 8601 formatted date and time when the dataset was last modified. | | lastModifierFullName | string | true | | Full name of user who was the last to modify the dataset. | | name | string | true | | The name of this dataset in the catalog. | | processingState | string | true | | Current ingestion process state of dataset. | | recipeId | string,null | true | | The ID of the source recipe. | | rowCount | integer | true | | The number of rows in the dataset. | | sampleSize | SampleSize | false | | Ingest size to use during dataset registration. Default behavior is to ingest full dataset. | | tags | [string] | true | | List of tags attached to the item. | | timeSeriesProperties | TimeSeriesProperties | true | | Properties related to time series data prep. | | uri | string | true | | The URI to datasource. For example, file_name.csv, or jdbc:DATA_SOURCE_GIVEN_NAME/SCHEMA.TABLE_NAME, or jdbc:DATA_SOURCE_GIVEN_NAME/ for query based datasources, orhttps://s3.amazonaws.com/dr-pr-tst-data/kickcars-sample-200.csv, etc. | | versionId | string | true | | The object ID of the catalog_version the dataset belongs to. | ### Enumerated Values | Property | Value | | --- | --- | | processingState | [COMPLETED, ERROR, RUNNING] | ## GCPKey ``` { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authProviderX509CertUrl | string(uri) | false | | Auth provider X509 certificate URL. | | authUri | string(uri) | false | | Auth URI. | | clientEmail | string | false | | Client email address. | | clientId | string | false | | Client ID. | | clientX509CertUrl | string(uri) | false | | Client X509 certificate URL. | | privateKey | string | false | | Private key. | | privateKeyId | string | false | | Private key ID | | projectId | string | false | | Project ID. | | tokenUri | string(uri) | false | | Token URI. | | type | string | true | | GCP account type. | ### Enumerated Values | Property | Value | | --- | --- | | type | service_account | ## GeneratorSettings ``` { "description": "Data engine generator settings of the given `generator_type`.", "properties": { "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column in time series project.", "type": "string" }, "defaultCategoricalAggregationMethod": { "description": "Default aggregation method used for categorical feature.", "enum": [ "last", "mostFrequent" ], "type": "string" }, "defaultNumericAggregationMethod": { "description": "Default aggregation method used for numeric feature.", "enum": [ "mean", "sum" ], "type": "string" }, "defaultTextAggregationMethod": { "description": "Default aggregation method used for text feature.", "enum": [ "concat", "last", "meanLength", "mostFrequent", "totalLength" ], "type": "string" }, "endToSeriesMaxDatetime": { "default": true, "description": "A boolean value indicating whether generates post-aggregated series up to series maximum datetime or global maximum datetime.", "type": "boolean" }, "multiseriesIdColumns": { "description": "An array with the names of columns identifying the series to which row of the output dataset belongs. Currently, only one multiseries ID column is supported.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "startFromSeriesMinDatetime": { "default": true, "description": "A boolean value indicating whether post-aggregated series starts from series minimum datetime or global minimum datetime.", "type": "boolean" }, "target": { "description": "The name of target for the output dataset.", "type": "string" }, "timeStep": { "description": "Number of time steps for the output dataset.", "exclusiveMinimum": 0, "type": "integer" }, "timeUnit": { "description": "Indicates which unit is a basis for time steps of the output dataset.", "enum": [ "DAY", "HOUR", "MINUTE", "MONTH", "QUARTER", "WEEK", "YEAR" ], "type": "string" } }, "required": [ "datetimePartitionColumn", "defaultCategoricalAggregationMethod", "defaultNumericAggregationMethod", "timeStep", "timeUnit" ], "type": "object" } ``` Data engine generator settings of the given `generator_type`. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimePartitionColumn | string | true | | The date column that will be used as a datetime partition column in time series project. | | defaultCategoricalAggregationMethod | string | true | | Default aggregation method used for categorical feature. | | defaultNumericAggregationMethod | string | true | | Default aggregation method used for numeric feature. | | defaultTextAggregationMethod | string | false | | Default aggregation method used for text feature. | | endToSeriesMaxDatetime | boolean | false | | A boolean value indicating whether generates post-aggregated series up to series maximum datetime or global maximum datetime. | | multiseriesIdColumns | [string] | false | maxItems: 1minItems: 1 | An array with the names of columns identifying the series to which row of the output dataset belongs. Currently, only one multiseries ID column is supported. | | startFromSeriesMinDatetime | boolean | false | | A boolean value indicating whether post-aggregated series starts from series minimum datetime or global minimum datetime. | | target | string | false | | The name of target for the output dataset. | | timeStep | integer | true | | Number of time steps for the output dataset. | | timeUnit | string | true | | Indicates which unit is a basis for time steps of the output dataset. | ### Enumerated Values | Property | Value | | --- | --- | | defaultCategoricalAggregationMethod | [last, mostFrequent] | | defaultNumericAggregationMethod | [mean, sum] | | defaultTextAggregationMethod | [concat, last, meanLength, mostFrequent, totalLength] | | timeUnit | [DAY, HOUR, MINUTE, MONTH, QUARTER, WEEK, YEAR] | ## GetDatasetVersionProjectsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Array of project references.", "items": { "properties": { "id": { "description": "The dataset's project ID.", "type": "string" }, "url": { "description": "The link to retrieve more information about the dataset version's project.", "type": "string" } }, "required": [ "id", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetProject] | true | | Array of project references. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## GoogleServiceAccountCredentials ``` { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configId | string | false | | The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey. | | credentialType | string | true | | The type of these credentials, 'gcp' here. | | gcpKey | GCPKey | false | | The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. | | googleConfigId | string | false | | The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | gcp | ## GrantAccessControlWithId ``` { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## GrantAccessControlWithUsername ``` { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | Username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## Hdfs ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "namenodeWebhdfsPort": { "description": "The port of the HDFS name node.", "type": "integer" }, "password": { "description": "The password (in cleartext) for authenticating to HDFS using Kerberos. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "persistDataAfterIngestion": { "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error", "type": "boolean" }, "url": { "description": "The HDFS url to use as the source of data for the dataset being created.", "format": "uri", "type": "string" }, "user": { "description": "The username for authenticating to HDFS using Kerberos.", "type": "string" } }, "required": [ "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | doSnapshot | boolean | false | | If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, Enable Create Snapshot Data Source. | | namenodeWebhdfsPort | integer | false | | The port of the HDFS name node. | | password | string | false | | The password (in cleartext) for authenticating to HDFS using Kerberos. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. | | persistDataAfterIngestion | boolean | false | | If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and doSnapshot to true will result in an error | | url | string(uri) | true | | The HDFS url to use as the source of data for the dataset being created. | | user | string | false | | The username for authenticating to HDFS using Kerberos. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SAMPLE, SINGLE_SERIES_CALENDAR, TRAINING] | ## Link ``` { "properties": { "fileName": { "description": "The name of the file associated with the generated link. ", "type": [ "string", "null" ] }, "url": { "description": "The generated link associated with the requested file. ", "type": "string" } }, "required": [ "fileName", "url" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fileName | string,null | true | | The name of the file associated with the generated link. | | url | string | true | | The generated link associated with the requested file. | ## MaterializationDestination ``` { "description": "Destination table information to create and materialize the recipe to. If None, the recipe will be materialized in DataRobot.", "properties": { "catalog": { "description": "Database to materialize the recipe to.", "type": "string", "x-versionadded": "v2.33" }, "schema": { "description": "Schema to materialize the recipe to.", "type": "string", "x-versionadded": "v2.33" }, "table": { "description": "Table name to create and materialize the recipe to. This table should not already exist.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "catalog", "schema", "table" ], "type": "object" } ``` Destination table information to create and materialize the recipe to. If None, the recipe will be materialized in DataRobot. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | Database to materialize the recipe to. | | schema | string | true | | Schema to materialize the recipe to. | | table | string | true | | Table name to create and materialize the recipe to. This table should not already exist. | ## OAuthCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'oauth' here. | | oauthAccessToken | string,null | false | | The OAuth access token. | | oauthClientId | string,null | false | | The OAuth client ID. | | oauthClientSecret | string,null | false | | The OAuth client secret. | | oauthRefreshToken | string | true | | The OAuth refresh token. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | oauth | ## ParamValuePair ``` { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | param | string | true | | The name of a field associated with the value. | | value | any | true | | Any value. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | ## PatchDataset ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset. If any categories were previously specified for the dataset, they will be overwritten.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "name": { "description": "The new name of the dataset.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. If any categories were previously specified for the dataset, they will be overwritten. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | | The new name of the dataset. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SAMPLE, SINGLE_SERIES_CALENDAR, TRAINING] | ## RenameFileOrFolderBody ``` { "properties": { "fromPath": { "description": "The file or folder path to rename. Folder paths should end with \"/\".", "type": "string" }, "overwrite": { "default": "RENAME", "description": "How to deal with a name conflict with an existing file or folder with the same name.\nRENAME (default): rename the file or folder using “ (n).ext” or \" (n)\" pattern.\nREPLACE: prefer the renamed file or folder.\nSKIP: prefer the existing file or folder.\nERROR: return \"HTTP 409 Conflict\" response in case of a naming conflict. ", "enum": [ "rename", "Rename", "RENAME", "replace", "Replace", "REPLACE", "skip", "Skip", "SKIP", "error", "Error", "ERROR" ], "type": "string" }, "toPath": { "description": "The new path for the file or folder. Folder paths should end with \"/\".", "type": "string" } }, "required": [ "fromPath", "overwrite", "toPath" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fromPath | string | true | | The file or folder path to rename. Folder paths should end with "/". | | overwrite | string | true | | How to deal with a name conflict with an existing file or folder with the same name.RENAME (default): rename the file or folder using “ (n).ext” or " (n)" pattern.REPLACE: prefer the renamed file or folder.SKIP: prefer the existing file or folder.ERROR: return "HTTP 409 Conflict" response in case of a naming conflict. | | toPath | string | true | | The new path for the file or folder. Folder paths should end with "/". | ### Enumerated Values | Property | Value | | --- | --- | | overwrite | [rename, Rename, RENAME, replace, Replace, REPLACE, skip, Skip, SKIP, error, Error, ERROR] | ## RetrieveDataEngineQueryResponse ``` { "properties": { "datasets": { "description": "Source datasets in the Data Engine workspace.", "items": { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" } }, "required": [ "alias" ], "type": "object" }, "maxItems": 32, "type": "array" }, "generatorSettings": { "description": "Data engine generator settings of the given `generator_type`.", "properties": { "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column in time series project.", "type": "string" }, "defaultCategoricalAggregationMethod": { "description": "Default aggregation method used for categorical feature.", "enum": [ "last", "mostFrequent" ], "type": "string" }, "defaultNumericAggregationMethod": { "description": "Default aggregation method used for numeric feature.", "enum": [ "mean", "sum" ], "type": "string" }, "defaultTextAggregationMethod": { "description": "Default aggregation method used for text feature.", "enum": [ "concat", "last", "meanLength", "mostFrequent", "totalLength" ], "type": "string" }, "endToSeriesMaxDatetime": { "default": true, "description": "A boolean value indicating whether generates post-aggregated series up to series maximum datetime or global maximum datetime.", "type": "boolean" }, "multiseriesIdColumns": { "description": "An array with the names of columns identifying the series to which row of the output dataset belongs. Currently, only one multiseries ID column is supported.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "startFromSeriesMinDatetime": { "default": true, "description": "A boolean value indicating whether post-aggregated series starts from series minimum datetime or global minimum datetime.", "type": "boolean" }, "target": { "description": "The name of target for the output dataset.", "type": "string" }, "timeStep": { "description": "Number of time steps for the output dataset.", "exclusiveMinimum": 0, "type": "integer" }, "timeUnit": { "description": "Indicates which unit is a basis for time steps of the output dataset.", "enum": [ "DAY", "HOUR", "MINUTE", "MONTH", "QUARTER", "WEEK", "YEAR" ], "type": "string" } }, "required": [ "datetimePartitionColumn", "defaultCategoricalAggregationMethod", "defaultNumericAggregationMethod", "timeStep", "timeUnit" ], "type": "object" }, "generatorType": { "description": "Type of data engine query generator", "enum": [ "TimeSeries" ], "type": "string" }, "id": { "description": "The ID of the data engine query generator.", "type": "string" }, "query": { "description": "Generated SparkSQL query.", "type": "string" } }, "required": [ "datasets", "generatorSettings", "generatorType", "id", "query" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasets | [DataEngineDataset] | true | maxItems: 32 | Source datasets in the Data Engine workspace. | | generatorSettings | GeneratorSettings | true | | Data engine generator settings of the given generator_type. | | generatorType | string | true | | Type of data engine query generator | | id | string | true | | The ID of the data engine query generator. | | query | string | true | | Generated SparkSQL query. | ### Enumerated Values | Property | Value | | --- | --- | | generatorType | TimeSeries | ## S3Credentials ``` { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | awsAccessKeyId | string | false | | The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified. | | awsSecretAccessKey | string | false | | The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified. | | awsSessionToken | string,null | false | | The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified. | | configId | string | false | | The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken. | | credentialType | string | true | | The type of these credentials, 's3' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | s3 | ## SampleSize ``` { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" } ``` Ingest size to use during dataset registration. Default behavior is to ingest full dataset. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | The sample size can be specified only as a number of rows for now. | | value | integer | true | maximum: 1000000 | Number of rows to ingest during dataset registration. | ### Enumerated Values | Property | Value | | --- | --- | | type | rows | ## SharedRolesListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of SharedRoles objects.", "items": { "properties": { "canShare": { "description": "True if this user can share with other users", "type": "boolean" }, "canUseData": { "description": "True if the user can view, download and process data (use to create projects, predictions, etc)", "type": "boolean" }, "id": { "description": "The ID of the recipient organization, group or user.", "type": "string" }, "name": { "description": "The name of the recipient organization, group or user.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entry or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" }, "userFullName": { "description": "If the recipient type is a user, the full name of the user if available.", "type": "string" } }, "required": [ "canShare", "canUseData", "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [SharedRolesResponse] | true | | The list of SharedRoles objects. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## SharedRolesResponse ``` { "properties": { "canShare": { "description": "True if this user can share with other users", "type": "boolean" }, "canUseData": { "description": "True if the user can view, download and process data (use to create projects, predictions, etc)", "type": "boolean" }, "id": { "description": "The ID of the recipient organization, group or user.", "type": "string" }, "name": { "description": "The name of the recipient organization, group or user.", "type": "string" }, "role": { "description": "The role of the org/group/user on this catalog entry or `NO_ROLE` for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" }, "userFullName": { "description": "If the recipient type is a user, the full name of the user if available.", "type": "string" } }, "required": [ "canShare", "canUseData", "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | True if this user can share with other users | | canUseData | boolean | true | | True if the user can view, download and process data (use to create projects, predictions, etc) | | id | string | true | | The ID of the recipient organization, group or user. | | name | string | true | | The name of the recipient organization, group or user. | | role | string | true | | The role of the org/group/user on this catalog entry or NO_ROLE for removing access when used with route to modify access. | | shareRecipientType | string | true | | The recipient type. | | userFullName | string | false | | If the recipient type is a user, the full name of the user if available. | ### Enumerated Values | Property | Value | | --- | --- | | role | [CONSUMER, EDITOR, OWNER, NO_ROLE] | | shareRecipientType | [user, group, organization] | ## SharedRolesUpdate ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | Name of the action being taken. The only operation is 'updateRoles'. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | Array of GrantAccessControl objects., up to maximum 100 objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithUsername | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithId | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## SnowflakeKeyPairCredentials ``` { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configId | string | false | | The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase. | | credentialType | string | true | | The type of these credentials, 'snowflake_key_pair_user_account' here. | | passphrase | string | false | | Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified. | | privateKeyStr | string | false | | Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified. | | user | string | false | | Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | snowflake_key_pair_user_account | ## TimeSeriesProperties ``` { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" } ``` Properties related to time series data prep. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isMostlyImputed | boolean,null | true | | Whether more than half of the rows are imputed. | ## UpdateCatalogMetadata ``` { "properties": { "description": { "description": "New catalog item description", "maxLength": 1000, "type": "string" }, "name": { "description": "New catalog item name", "maxLength": 255, "type": "string" }, "tags": { "description": "New catalog item tags. Tags must be the lower case, without spaces,and cannot include -$.,{}\"#' special characters.", "items": { "maxLength": 255, "minLength": 0, "type": "string" }, "type": "array" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 1000 | New catalog item description | | name | string | false | maxLength: 255 | New catalog item name | | tags | [string] | false | | New catalog item tags. Tags must be the lower case, without spaces,and cannot include -$.,{}"#' special characters. | ## UpdateDatasetDeleted ``` { "type": "object" } ``` ### Properties None ## UpdateFileDeleted ``` { "type": "object", "x-versionadded": "v2.37" } ``` ### Properties None ## Url ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "oneOf": [ { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, { "items": { "enum": [ "BATCH_PREDICTIONS", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" } ] }, "doSnapshot": { "default": true, "description": "If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, `Enable Create Snapshot Data Source`.", "type": "boolean" }, "persistDataAfterIngestion": { "description": "If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and `doSnapshot` to true will result in an error.", "type": "boolean" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "url": { "description": "The URL to download the dataset used to create the dataset item and version.", "format": "url", "type": "string" } }, "required": [ "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | any | false | | An array of strings describing the intended use of the dataset. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | doSnapshot | boolean | false | | If true, create a snapshot dataset; if false, create a remote dataset. Creating snapshots from non-file sources requires an additional permission, Enable Create Snapshot Data Source. | | persistDataAfterIngestion | boolean | false | | If true, will enforce saving all data (for download and sampling) and will allow a user to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.). If false, will not enforce saving data. The data schema (feature names and types) still will be available. Specifying this parameter to false and doSnapshot to true will result in an error. | | sampleSize | SampleSize | false | | Ingest size to use during dataset registration. Default behavior is to ingest full dataset. | | url | string(url) | true | | The URL to download the dataset used to create the dataset item and version. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [BATCH_PREDICTIONS, MULTI_SERIES_CALENDAR, PREDICTION, SAMPLE, SINGLE_SERIES_CALENDAR, TRAINING] | ## UserBlueprintAddToMenu ``` { "properties": { "deleteAfter": { "default": false, "description": "Whether to delete the user blueprint(s) after adding it (them) to the project menu.", "type": "boolean" }, "describeFailures": { "default": false, "description": "Whether to include extra fields to describe why any blueprints were not added to the chosen project.", "type": "boolean", "x-versionadded": "v2.27" }, "projectId": { "description": "The projectId of the project for the repository to add the specified user blueprints to.", "type": "string" }, "userBlueprintIds": { "description": "The IDs of the user blueprints to add to the specified project's repository.", "items": { "description": "An ID of one user blueprint to add to the specified project's repository.", "type": "string" }, "type": "array" } }, "required": [ "deleteAfter", "describeFailures", "projectId", "userBlueprintIds" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deleteAfter | boolean | true | | Whether to delete the user blueprint(s) after adding it (them) to the project menu. | | describeFailures | boolean | true | | Whether to include extra fields to describe why any blueprints were not added to the chosen project. | | projectId | string | true | | The projectId of the project for the repository to add the specified user blueprints to. | | userBlueprintIds | [string] | true | | The IDs of the user blueprints to add to the specified project's repository. | ## UserBlueprintAddToMenuResponse ``` { "properties": { "addedToMenu": { "description": "The list of userBlueprintId and blueprintId pairs representing blueprints successfully added to the project repository.", "items": { "properties": { "blueprintId": { "description": "The blueprintId representing the blueprint which was added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprintId added to the project repository.", "type": "string" } }, "required": [ "blueprintId", "userBlueprintId" ], "type": "object" }, "type": "array" }, "message": { "description": "A success message or a list of reasons why the list of blueprints could not be added to the project repository.", "type": "string", "x-versionadded": "2.27" }, "notAddedToMenu": { "description": "The list of userBlueprintId and error message representing blueprints which failed to be added to the project repository.", "items": { "properties": { "error": { "description": "The error message representing why the blueprint was not added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprint which was not added to the project repository.", "type": "string" } }, "required": [ "error", "userBlueprintId" ], "type": "object" }, "type": "array", "x-versionadded": "2.27" } }, "required": [ "addedToMenu" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addedToMenu | [UserBlueprintAddedToMenuItem] | true | | The list of userBlueprintId and blueprintId pairs representing blueprints successfully added to the project repository. | | message | string | false | | A success message or a list of reasons why the list of blueprints could not be added to the project repository. | | notAddedToMenu | [UserBlueprintFailedToAddToMenuItem] | false | | The list of userBlueprintId and error message representing blueprints which failed to be added to the project repository. | ## UserBlueprintAddedToMenuItem ``` { "properties": { "blueprintId": { "description": "The blueprintId representing the blueprint which was added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprintId added to the project repository.", "type": "string" } }, "required": [ "blueprintId", "userBlueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The blueprintId representing the blueprint which was added to the project repository. | | userBlueprintId | string | true | | The userBlueprintId associated with the blueprintId added to the project repository. | ## UserBlueprintBulkValidationRequest ``` { "properties": { "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": "string" }, "userBlueprintIds": { "description": "The IDs of the user blueprints to validate in bulk.", "items": { "description": "The ID of one user blueprint to validate.", "type": "string" }, "type": "array" } }, "required": [ "userBlueprintIds" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | projectId | string | false | | String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks. | | userBlueprintIds | [string] | true | | The IDs of the user blueprints to validate in bulk. | ## UserBlueprintCreate ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "blueprint", "decompressedBlueprint", "isInplaceEditor", "saveToCatalog" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprint | any | true | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [UserBlueprintsBlueprintTask] | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | decompressedBlueprint | boolean | true | | Whether to retrieve the blueprint in the decompressed format. | | description | string | false | | The description to give to the blueprint. | | getDynamicLabels | boolean | false | | Whether to add dynamic labels to a decompressed blueprint. | | isInplaceEditor | boolean | true | | Whether the request is sent from the in place user BP editor. | | modelType | string | false | maxLength: 1000 | The title to give to the blueprint. | | projectId | string,null | false | | String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks. | | saveToCatalog | boolean | true | | Whether to save the blueprint to the catalog. | ## UserBlueprintCreateFromBlueprintId ``` { "properties": { "blueprintId": { "description": "The ID associated with the blueprint to create the user blueprint from.", "type": "string" }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active.", "type": "string" }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "blueprintId", "decompressedBlueprint", "isInplaceEditor", "projectId", "saveToCatalog" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The ID associated with the blueprint to create the user blueprint from. | | decompressedBlueprint | boolean | true | | Whether to retrieve the blueprint in the decompressed format. | | description | string | false | | The description to give to the blueprint. | | getDynamicLabels | boolean | false | | Whether to add dynamic labels to a decompressed blueprint. | | isInplaceEditor | boolean | true | | Whether the request is sent from the in place user BP editor. | | modelType | string | false | maxLength: 1000 | The title to give to the blueprint. | | projectId | string | true | | String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. | | saveToCatalog | boolean | true | | Whether to save the blueprint to the catalog. | ## UserBlueprintCreateFromCustomTaskVersionIdPayload ``` { "properties": { "customTaskVersionId": { "description": "The ID of a custom task version.", "type": "string" }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description for the user blueprint that will be created from this CustomTaskVersion.", "type": "string" }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "customTaskVersionId", "decompressedBlueprint", "saveToCatalog" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customTaskVersionId | string | true | | The ID of a custom task version. | | decompressedBlueprint | boolean | true | | Whether to retrieve the blueprint in the decompressed format. | | description | string | false | | The description for the user blueprint that will be created from this CustomTaskVersion. | | saveToCatalog | boolean | true | | Whether to save the blueprint to the catalog. | ## UserBlueprintCreateFromUserBlueprintId ``` { "properties": { "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" }, "userBlueprintId": { "description": "The ID of the existing user blueprint to copy.", "type": "string" } }, "required": [ "decompressedBlueprint", "isInplaceEditor", "saveToCatalog", "userBlueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | decompressedBlueprint | boolean | true | | Whether to retrieve the blueprint in the decompressed format. | | description | string | false | | The description to give to the blueprint. | | getDynamicLabels | boolean | false | | Whether to add dynamic labels to a decompressed blueprint. | | isInplaceEditor | boolean | true | | Whether the request is sent from the in place user BP editor. | | modelType | string | false | maxLength: 1000 | The title to give to the blueprint. | | projectId | string,null | false | | String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks. | | saveToCatalog | boolean | true | | Whether to save the blueprint to the catalog. | | userBlueprintId | string | true | | The ID of the existing user blueprint to copy. | ## UserBlueprintFailedToAddToMenuItem ``` { "properties": { "error": { "description": "The error message representing why the blueprint was not added to the project repository.", "type": "string" }, "userBlueprintId": { "description": "The userBlueprintId associated with the blueprint which was not added to the project repository.", "type": "string" } }, "required": [ "error", "userBlueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | error | string | true | | The error message representing why the blueprint was not added to the project repository. | | userBlueprintId | string | true | | The userBlueprintId associated with the blueprint which was not added to the project repository. | ## UserBlueprintSharedRolesListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of SharedRoles objects.", "items": { "properties": { "id": { "description": "The ID of the recipient organization, group or user.", "type": "string" }, "name": { "description": "The name of the recipient organization, group or user.", "type": "string" }, "role": { "description": "The role of the org/group/user on this dataset or \"NO_ROLE\" for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UserBlueprintSharedRolesResponse] | true | | The list of SharedRoles objects. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UserBlueprintSharedRolesResponse ``` { "properties": { "id": { "description": "The ID of the recipient organization, group or user.", "type": "string" }, "name": { "description": "The name of the recipient organization, group or user.", "type": "string" }, "role": { "description": "The role of the org/group/user on this dataset or \"NO_ROLE\" for removing access when used with route to modify access.", "enum": [ "CONSUMER", "EDITOR", "OWNER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient organization, group or user. | | name | string | true | | The name of the recipient organization, group or user. | | role | string | true | | The role of the org/group/user on this dataset or "NO_ROLE" for removing access when used with route to modify access. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [CONSUMER, EDITOR, OWNER] | | shareRecipientType | [user, group, organization] | ## UserBlueprintTask ``` { "properties": { "arguments": { "description": "The list of definitions of each argument which can be set for the task.", "items": { "properties": { "argument": { "description": "The definition of a task argument, used to specify a certain aspect of the task.", "oneOf": [ { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ] }, "key": { "description": "The unique key of the argument.", "type": "string" } }, "required": [ "argument", "key" ], "type": "object" }, "type": "array" }, "categories": { "description": "The categories which the task is in.", "items": { "type": "string" }, "type": "array" }, "colnamesAndTypes": { "description": "The column names, their types, and their hex representation, available in the specified project for the task.", "items": { "properties": { "colname": { "description": "The column name.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "type": { "description": "The data type of the column.", "type": "string" } }, "required": [ "colname", "hex", "type" ], "type": "object" }, "type": "array" }, "customTaskId": { "description": "The ID of the custom task, if it is a custom task.", "type": [ "string", "null" ] }, "customTaskVersions": { "description": "Metadata for all of the custom task's versions.", "items": { "properties": { "id": { "description": "Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task.", "type": "string" }, "label": { "description": "The name of the custom task version.", "type": "string" }, "versionMajor": { "description": "Major version of the custom task.", "type": "integer" }, "versionMinor": { "description": "Minor version of the custom task.", "type": "integer" } }, "required": [ "id", "label", "versionMajor", "versionMinor" ], "type": "object" }, "type": "array" }, "description": { "description": "The description of the task.", "type": "string" }, "icon": { "description": "The integer representing the ID to be displayed when the blueprint is trained.", "type": "integer" }, "isCommonTask": { "default": false, "description": "Whether the task is a common task.", "type": "boolean" }, "isCustomTask": { "description": "Whether the task is custom code written by the user.", "type": "boolean" }, "isVisibleInComposableMl": { "default": true, "description": "Whether the task is visible in the ComposableML menu.", "type": "boolean" }, "label": { "description": "The generic / default title or label for the task.", "type": "string" }, "outputMethods": { "description": "The methods which the task can use to produce output.", "items": { "type": "string" }, "type": "array" }, "supportsScoringCode": { "description": "Whether the task supports Scoring Code.", "type": "boolean" }, "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "timeSeriesOnly": { "description": "Whether the task can only be used with time series projects.", "type": "boolean" }, "url": { "description": "The URL of the documentation of the task.", "oneOf": [ { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, { "type": "string" } ] }, "validInputs": { "description": "The supported input types of the task.", "items": { "description": "A specific supported input type.", "type": "string" }, "type": "array" } }, "required": [ "arguments", "categories", "description", "icon", "label", "outputMethods", "supportsScoringCode", "taskCode", "timeSeriesOnly", "url", "validInputs" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | arguments | [UserBlueprintTaskArgument] | true | | The list of definitions of each argument which can be set for the task. | | categories | [string] | true | | The categories which the task is in. | | colnamesAndTypes | [ColnameAndType] | false | | The column names, their types, and their hex representation, available in the specified project for the task. | | customTaskId | string,null | false | | The ID of the custom task, if it is a custom task. | | customTaskVersions | [UserBlueprintTaskCustomTaskMetadataWithArguments] | false | | Metadata for all of the custom task's versions. | | description | string | true | | The description of the task. | | icon | integer | true | | The integer representing the ID to be displayed when the blueprint is trained. | | isCommonTask | boolean | false | | Whether the task is a common task. | | isCustomTask | boolean | false | | Whether the task is custom code written by the user. | | isVisibleInComposableMl | boolean | false | | Whether the task is visible in the ComposableML menu. | | label | string | true | | The generic / default title or label for the task. | | outputMethods | [string] | true | | The methods which the task can use to produce output. | | supportsScoringCode | boolean | true | | Whether the task supports Scoring Code. | | taskCode | string | true | | The unique code which represents the task to be constructed and executed | | timeSeriesOnly | boolean | true | | Whether the task can only be used with time series projects. | | url | any | true | | The URL of the documentation of the task. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validInputs | [string] | true | | The supported input types of the task. | ## UserBlueprintTaskArgument ``` { "properties": { "argument": { "description": "The definition of a task argument, used to specify a certain aspect of the task.", "oneOf": [ { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ] }, "key": { "description": "The unique key of the argument.", "type": "string" } }, "required": [ "argument", "key" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | argument | UserBlueprintTaskArgumentDefinition | true | | The definition of a task argument, used to specify a certain aspect of the task. | | key | string | true | | The unique key of the argument. | ## UserBlueprintTaskArgumentDefinition ``` { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | default | any | false | | The default value of the argument. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the argument. | | recommended | any | false | | The recommended value, based on frequently used values. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | tunable | boolean | false | | Whether the argument is tunable by the end-user. | | type | string | true | | The type of the argument (e.g., "int", "float", "select", "intgrid", "multi", etc.). | | values | any | true | | The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | ## UserBlueprintTaskCategoryItem ``` { "properties": { "name": { "description": "The name of the category.", "type": "string" }, "subcategories": { "description": "The list of the available task category items.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "taskCodes": { "description": "A list of task codes representing the tasks in this category.", "items": { "type": "string" }, "type": "array" } }, "required": [ "name", "taskCodes" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the category. | | subcategories | [AllowExtra] | false | | The list of the available task category items. | | taskCodes | [string] | true | | A list of task codes representing the tasks in this category. | ## UserBlueprintTaskCustomTaskMetadataWithArguments ``` { "properties": { "id": { "description": "Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task.", "type": "string" }, "label": { "description": "The name of the custom task version.", "type": "string" }, "versionMajor": { "description": "Major version of the custom task.", "type": "integer" }, "versionMinor": { "description": "Minor version of the custom task.", "type": "integer" } }, "required": [ "id", "label", "versionMajor", "versionMinor" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task. | | label | string | true | | The name of the custom task version. | | versionMajor | integer | true | | Major version of the custom task. | | versionMinor | integer | true | | Minor version of the custom task. | ## UserBlueprintTaskLookupEntry ``` { "properties": { "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "taskDefinition": { "description": "A definition of a task in terms of label, arguments, description, and other metadata.", "oneOf": [ { "properties": { "arguments": { "description": "The list of definitions of each argument which can be set for the task.", "items": { "properties": { "argument": { "description": "The definition of a task argument, used to specify a certain aspect of the task.", "oneOf": [ { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ] }, "key": { "description": "The unique key of the argument.", "type": "string" } }, "required": [ "argument", "key" ], "type": "object" }, "type": "array" }, "categories": { "description": "The categories which the task is in.", "items": { "type": "string" }, "type": "array" }, "colnamesAndTypes": { "description": "The column names, their types, and their hex representation, available in the specified project for the task.", "items": { "properties": { "colname": { "description": "The column name.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "type": { "description": "The data type of the column.", "type": "string" } }, "required": [ "colname", "hex", "type" ], "type": "object" }, "type": "array" }, "customTaskId": { "description": "The ID of the custom task, if it is a custom task.", "type": [ "string", "null" ] }, "customTaskVersions": { "description": "Metadata for all of the custom task's versions.", "items": { "properties": { "id": { "description": "Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task.", "type": "string" }, "label": { "description": "The name of the custom task version.", "type": "string" }, "versionMajor": { "description": "Major version of the custom task.", "type": "integer" }, "versionMinor": { "description": "Minor version of the custom task.", "type": "integer" } }, "required": [ "id", "label", "versionMajor", "versionMinor" ], "type": "object" }, "type": "array" }, "description": { "description": "The description of the task.", "type": "string" }, "icon": { "description": "The integer representing the ID to be displayed when the blueprint is trained.", "type": "integer" }, "isCommonTask": { "default": false, "description": "Whether the task is a common task.", "type": "boolean" }, "isCustomTask": { "description": "Whether the task is custom code written by the user.", "type": "boolean" }, "isVisibleInComposableMl": { "default": true, "description": "Whether the task is visible in the ComposableML menu.", "type": "boolean" }, "label": { "description": "The generic / default title or label for the task.", "type": "string" }, "outputMethods": { "description": "The methods which the task can use to produce output.", "items": { "type": "string" }, "type": "array" }, "supportsScoringCode": { "description": "Whether the task supports Scoring Code.", "type": "boolean" }, "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "timeSeriesOnly": { "description": "Whether the task can only be used with time series projects.", "type": "boolean" }, "url": { "description": "The URL of the documentation of the task.", "oneOf": [ { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, { "type": "string" } ] }, "validInputs": { "description": "The supported input types of the task.", "items": { "description": "A specific supported input type.", "type": "string" }, "type": "array" } }, "required": [ "arguments", "categories", "description", "icon", "label", "outputMethods", "supportsScoringCode", "taskCode", "timeSeriesOnly", "url", "validInputs" ], "type": "object" } ] } }, "required": [ "taskCode", "taskDefinition" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | taskCode | string | true | | The unique code which represents the task to be constructed and executed | | taskDefinition | UserBlueprintTask | true | | A definition of a task in terms of label, arguments, description, and other metadata. | ## UserBlueprintTaskParameterValidation ``` { "properties": { "outputMethod": { "description": "The method representing how the task will output data.", "enum": [ "P", "Pm", "S", "Sm", "T", "TS" ], "type": "string" }, "projectId": { "description": "The projectId representing the project where this user blueprint is edited.", "type": [ "string", "null" ] }, "taskCode": { "description": "The task code representing the task to validate parameter values.", "type": "string" }, "taskParameters": { "description": "The list of task parameters and proposed values to be validated.", "items": { "properties": { "newValue": { "description": "The proposed value for the task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "paramName": { "description": "The name of the task parameter to be validated.", "type": "string" } }, "required": [ "newValue", "paramName" ], "type": "object" }, "type": "array" } }, "required": [ "outputMethod", "taskCode", "taskParameters" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | outputMethod | string | true | | The method representing how the task will output data. | | projectId | string,null | false | | The projectId representing the project where this user blueprint is edited. | | taskCode | string | true | | The task code representing the task to validate parameter values. | | taskParameters | [UserBlueprintTaskParameterValidationRequestParamItem] | true | | The list of task parameters and proposed values to be validated. | ### Enumerated Values | Property | Value | | --- | --- | | outputMethod | [P, Pm, S, Sm, T, TS] | ## UserBlueprintTaskParameterValidationRequestParamItem ``` { "properties": { "newValue": { "description": "The proposed value for the task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "paramName": { "description": "The name of the task parameter to be validated.", "type": "string" } }, "required": [ "newValue", "paramName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | newValue | any | true | | The proposed value for the task parameter. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | paramName | string | true | | The name of the task parameter to be validated. | ## UserBlueprintTasksResponse ``` { "properties": { "categories": { "description": "The list of the available task categories, sub-categories, and tasks.", "items": { "properties": { "name": { "description": "The name of the category.", "type": "string" }, "subcategories": { "description": "The list of the available task category items.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "taskCodes": { "description": "A list of task codes representing the tasks in this category.", "items": { "type": "string" }, "type": "array" } }, "required": [ "name", "taskCodes" ], "type": "object" }, "type": "array" }, "tasks": { "description": "The list of task codes and their task definitions.", "items": { "properties": { "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "taskDefinition": { "description": "A definition of a task in terms of label, arguments, description, and other metadata.", "oneOf": [ { "properties": { "arguments": { "description": "The list of definitions of each argument which can be set for the task.", "items": { "properties": { "argument": { "description": "The definition of a task argument, used to specify a certain aspect of the task.", "oneOf": [ { "properties": { "default": { "description": "The default value of the argument.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "name": { "description": "The name of the argument.", "type": "string" }, "recommended": { "description": "The recommended value, based on frequently used values.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "type": "null" } ] }, "tunable": { "description": "Whether the argument is tunable by the end-user.", "type": "boolean" }, "type": { "description": "The type of the argument (e.g., \"int\", \"float\", \"select\", \"intgrid\", \"multi\", etc.).", "type": "string" }, "values": { "description": "The possible values of the argument, which may be a range, a list, or a dictionary of ranges or lists keyed by type.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] } }, "required": [ "name", "type", "values" ], "type": "object" } ] }, "key": { "description": "The unique key of the argument.", "type": "string" } }, "required": [ "argument", "key" ], "type": "object" }, "type": "array" }, "categories": { "description": "The categories which the task is in.", "items": { "type": "string" }, "type": "array" }, "colnamesAndTypes": { "description": "The column names, their types, and their hex representation, available in the specified project for the task.", "items": { "properties": { "colname": { "description": "The column name.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "type": { "description": "The data type of the column.", "type": "string" } }, "required": [ "colname", "hex", "type" ], "type": "object" }, "type": "array" }, "customTaskId": { "description": "The ID of the custom task, if it is a custom task.", "type": [ "string", "null" ] }, "customTaskVersions": { "description": "Metadata for all of the custom task's versions.", "items": { "properties": { "id": { "description": "Id of the custom task version. The ID can be latest_ which implies to use the latest version of that custom task.", "type": "string" }, "label": { "description": "The name of the custom task version.", "type": "string" }, "versionMajor": { "description": "Major version of the custom task.", "type": "integer" }, "versionMinor": { "description": "Minor version of the custom task.", "type": "integer" } }, "required": [ "id", "label", "versionMajor", "versionMinor" ], "type": "object" }, "type": "array" }, "description": { "description": "The description of the task.", "type": "string" }, "icon": { "description": "The integer representing the ID to be displayed when the blueprint is trained.", "type": "integer" }, "isCommonTask": { "default": false, "description": "Whether the task is a common task.", "type": "boolean" }, "isCustomTask": { "description": "Whether the task is custom code written by the user.", "type": "boolean" }, "isVisibleInComposableMl": { "default": true, "description": "Whether the task is visible in the ComposableML menu.", "type": "boolean" }, "label": { "description": "The generic / default title or label for the task.", "type": "string" }, "outputMethods": { "description": "The methods which the task can use to produce output.", "items": { "type": "string" }, "type": "array" }, "supportsScoringCode": { "description": "Whether the task supports Scoring Code.", "type": "boolean" }, "taskCode": { "description": "The unique code which represents the task to be constructed and executed", "type": "string" }, "timeSeriesOnly": { "description": "Whether the task can only be used with time series projects.", "type": "boolean" }, "url": { "description": "The URL of the documentation of the task.", "oneOf": [ { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, { "type": "string" } ] }, "validInputs": { "description": "The supported input types of the task.", "items": { "description": "A specific supported input type.", "type": "string" }, "type": "array" } }, "required": [ "arguments", "categories", "description", "icon", "label", "outputMethods", "supportsScoringCode", "taskCode", "timeSeriesOnly", "url", "validInputs" ], "type": "object" } ] } }, "required": [ "taskCode", "taskDefinition" ], "type": "object" }, "type": "array" } }, "required": [ "categories", "tasks" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [UserBlueprintTaskCategoryItem] | true | | The list of the available task categories, sub-categories, and tasks. | | tasks | [UserBlueprintTaskLookupEntry] | true | | The list of task codes and their task definitions. | ## UserBlueprintUpdate ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "decompressedBlueprint": { "default": false, "description": "Whether to retrieve the blueprint in the decompressed format.", "type": "boolean" }, "description": { "description": "The description to give to the blueprint.", "type": "string" }, "getDynamicLabels": { "default": false, "description": "Whether to add dynamic labels to a decompressed blueprint.", "type": "boolean" }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "modelType": { "description": "The title to give to the blueprint.", "maxLength": 1000, "type": "string" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] }, "saveToCatalog": { "default": true, "description": "Whether to save the blueprint to the catalog.", "type": "boolean" } }, "required": [ "decompressedBlueprint", "isInplaceEditor", "saveToCatalog" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprint | any | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [UserBlueprintsBlueprintTask] | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | decompressedBlueprint | boolean | true | | Whether to retrieve the blueprint in the decompressed format. | | description | string | false | | The description to give to the blueprint. | | getDynamicLabels | boolean | false | | Whether to add dynamic labels to a decompressed blueprint. | | isInplaceEditor | boolean | true | | Whether the request is sent from the in place user BP editor. | | modelType | string | false | maxLength: 1000 | The title to give to the blueprint. | | projectId | string,null | false | | String representation of ObjectId for the currently active project. The user blueprint is created when this project is active. Necessary in the event of project specific tasks, such as column selection tasks. | | saveToCatalog | boolean | true | | Whether to save the blueprint to the catalog. | ## UserBlueprintValidation ``` { "properties": { "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "isInplaceEditor": { "default": false, "description": "Whether the request is sent from the in place user BP editor.", "type": "boolean" }, "projectId": { "description": "String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks.", "type": [ "string", "null" ] } }, "required": [ "blueprint", "isInplaceEditor" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprint | any | true | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [UserBlueprintsBlueprintTask] | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isInplaceEditor | boolean | true | | Whether the request is sent from the in place user BP editor. | | projectId | string,null | false | | String representation of ObjectId for the currently active project. The user blueprint is validated when this project is active. Necessary in the event of project specific tasks, such as column selection tasks. | ## UserBlueprintsBlueprintTask ``` { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | taskData | UserBlueprintsBlueprintTaskData | true | | The data defining the task / vertex in the blueprint. | | taskId | string | true | | The identifier of a task / vertex in the blueprint. | ## UserBlueprintsBlueprintTaskData ``` { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputs | [string] | true | | The IDs or input data types which will be inputs to the task. | | outputMethod | string | true | | The method which the task will use to produce output. | | outputMethodParameters | [ParamValuePair] | true | | The parameters which further define how output will be produced. | | taskCode | string,null | true | | The unique code representing the python class which will be instantiated and executed. | | taskParameters | [ParamValuePair] | true | | The parameters which further define the behavior of the task. | | xTransformations | [ParamValuePair] | true | | The transformations to apply to the input data before fitting or predicting. | | yTransformations | [ParamValuePair] | true | | The transformations to apply to the input target before fitting or predicting. | ## UserBlueprintsBulkDelete ``` { "properties": { "userBlueprintIds": { "description": "The list of IDs of user blueprints to be deleted.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ] } }, "required": [ "userBlueprintIds" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | userBlueprintIds | any | true | | The list of IDs of user blueprints to be deleted. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## UserBlueprintsBulkDeleteResponse ``` { "properties": { "failedToDelete": { "description": "The list of IDs of User Blueprints which failed to be deleted.", "items": { "description": "An ID of a User Blueprint which failed to be deleted.", "type": "string" }, "type": "array" }, "successfullyDeleted": { "description": "The list of IDs of User Blueprints successfully deleted.", "items": { "description": "An ID of a User Blueprint successfully deleted.", "type": "string" }, "type": "array" } }, "required": [ "failedToDelete", "successfullyDeleted" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | failedToDelete | [string] | true | | The list of IDs of User Blueprints which failed to be deleted. | | successfullyDeleted | [string] | true | | The list of IDs of User Blueprints successfully deleted. | ## UserBlueprintsBulkValidationResponse ``` { "properties": { "data": { "description": "A list of validation responses with their associated User Blueprint ID.", "items": { "properties": { "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "userBlueprintId", "vertexContext" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [UserBlueprintsBulkValidationResponseItem] | true | | A list of validation responses with their associated User Blueprint ID. | ## UserBlueprintsBulkValidationResponseItem ``` { "properties": { "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "userBlueprintId", "vertexContext" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | userBlueprintId | string | true | | The unique ID associated with the user blueprint. | | vertexContext | [VertexContextItem] | true | | Info about, warnings about, and errors with a specific vertex in the blueprint. | ## UserBlueprintsDetailedItem ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprint", "blueprintId", "bpData", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId", "vertexContext" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blender | boolean | true | | Whether the blueprint is a blender. | | blueprint | any | true | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [UserBlueprintsBlueprintTask] | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The deterministic ID of the blueprint, based on its content. | | bpData | BpData | true | | Additional blueprint metadata used to render the blueprint in the UI | | customTaskVersionMetadata | [array] | false | | An association of custom entity ids and task ids. | | decompressedFormat | boolean | true | | Whether the blueprint is in the decompressed format. | | diagram | string | true | | The diagram used by the UI to display the blueprint. | | features | [string] | true | | The list of the names of tasks used in the blueprint. | | featuresText | string | true | | The description of the blueprint via the names of tasks used. | | hexColumnNameLookup | [UserBlueprintsHexColumnNameLookupEntry] | false | | The lookup between hex values and data column names used in the blueprint. | | hiddenFromCatalog | boolean | false | | If true, the blueprint will not show up in the catalog. | | icons | [integer] | true | | The icon(s) associated with the blueprint. | | insights | string | true | | An indication of the insights generated by the blueprint. | | isTimeSeries | boolean | true | | Whether the blueprint contains time-series tasks. | | linkedToProjectId | boolean | false | | Whether the user blueprint is linked to a project. | | modelType | string | true | | The generated or provided title of the blueprint. | | projectId | string,null | false | | The ID of the project the blueprint was originally created with, if applicable. | | referenceModel | boolean | true | | Whether the blueprint is a reference model. | | shapSupport | boolean | true | | Whether the blueprint supports shapley additive explanations. | | supportedTargetTypes | [string] | true | | The list of supported targets of the current blueprint. | | supportsGpu | boolean | true | | Whether the blueprint supports execution on the GPU. | | supportsNewSeries | boolean | false | | Whether the blueprint supports new series. | | userBlueprintId | string | true | | The unique ID associated with the user blueprint. | | userId | string | true | | The ID of the user who owns the blueprint. | | vertexContext | [VertexContextItem] | true | | Info about, warnings about, and errors with a specific vertex in the blueprint. | ## UserBlueprintsHexColumnNameLookupEntry ``` { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | colname | string | true | | The name of the column. | | hex | string | true | | A safe hex representation of the column name. | | projectId | string | false | | The ID of the project from which the column name originates. | ## UserBlueprintsInputType ``` { "properties": { "name": { "description": "The human-readable name of an input type.", "type": "string" }, "type": { "description": "The unique identifier of an input type.", "type": "string" } }, "required": [ "name", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The human-readable name of an input type. | | type | string | true | | The unique identifier of an input type. | ## UserBlueprintsInputTypesResponse ``` { "properties": { "inputTypes": { "description": "The list of associated pairs of an input type and their human-readable names.", "items": { "properties": { "name": { "description": "The human-readable name of an input type.", "type": "string" }, "type": { "description": "The unique identifier of an input type.", "type": "string" } }, "required": [ "name", "type" ], "type": "object" }, "type": "array" } }, "required": [ "inputTypes" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputTypes | [UserBlueprintsInputType] | true | | The list of associated pairs of an input type and their human-readable names. | ## UserBlueprintsListItem ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" } }, "required": [ "blender", "blueprintId", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blender | boolean | true | | Whether the blueprint is a blender. | | blueprintId | string | true | | The deterministic ID of the blueprint, based on its content. | | customTaskVersionMetadata | [array] | false | | An association of custom entity ids and task ids. | | decompressedFormat | boolean | true | | Whether the blueprint is in the decompressed format. | | diagram | string | true | | The diagram used by the UI to display the blueprint. | | features | [string] | true | | The list of the names of tasks used in the blueprint. | | featuresText | string | true | | The description of the blueprint via the names of tasks used. | | hexColumnNameLookup | [UserBlueprintsHexColumnNameLookupEntry] | false | | The lookup between hex values and data column names used in the blueprint. | | hiddenFromCatalog | boolean | false | | If true, the blueprint will not show up in the catalog. | | icons | [integer] | true | | The icon(s) associated with the blueprint. | | insights | string | true | | An indication of the insights generated by the blueprint. | | isTimeSeries | boolean | true | | Whether the blueprint contains time-series tasks. | | linkedToProjectId | boolean | false | | Whether the user blueprint is linked to a project. | | modelType | string | true | | The generated or provided title of the blueprint. | | projectId | string,null | false | | The ID of the project the blueprint was originally created with, if applicable. | | referenceModel | boolean | true | | Whether the blueprint is a reference model. | | shapSupport | boolean | true | | Whether the blueprint supports shapley additive explanations. | | supportedTargetTypes | [string] | true | | The list of supported targets of the current blueprint. | | supportsGpu | boolean | true | | Whether the blueprint supports execution on the GPU. | | supportsNewSeries | boolean | false | | Whether the blueprint supports new series. | | userBlueprintId | string | true | | The unique ID associated with the user blueprint. | | userId | string | true | | The ID of the user who owns the blueprint. | ## UserBlueprintsListResponse ``` { "properties": { "count": { "description": "The number of records on this page.", "type": "integer" }, "data": { "description": "The list of user blueprints.", "items": { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" } }, "required": [ "blender", "blueprintId", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL to the next page, or `null` if there is no such page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL to the previous page, or `null` if there is no such page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of records.", "type": "integer" } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [UserBlueprintsListItem] | true | maxItems: 100 | The list of user blueprints. | | next | string,null | true | | The URL to the next page, or null if there is no such page. | | previous | string,null | true | | The URL to the previous page, or null if there is no such page. | | totalCount | integer | false | | The total number of records. | ## UserBlueprintsRetrieveResponse ``` { "properties": { "blender": { "default": false, "description": "Whether the blueprint is a blender.", "type": "boolean" }, "blueprint": { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "oneOf": [ { "description": "The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator.", "items": { "properties": { "taskData": { "description": "The data defining the task / vertex in the blueprint.", "oneOf": [ { "properties": { "inputs": { "description": "The IDs or input data types which will be inputs to the task.", "items": { "description": "A specific input data type.", "type": "string" }, "type": "array" }, "outputMethod": { "description": "The method which the task will use to produce output.", "type": "string" }, "outputMethodParameters": { "default": [], "description": "The parameters which further define how output will be produced.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "taskCode": { "description": "The unique code representing the python class which will be instantiated and executed.", "type": [ "string", "null" ] }, "taskParameters": { "default": [], "description": "The parameters which further define the behavior of the task.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "xTransformations": { "default": [], "description": "The transformations to apply to the input data before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" }, "yTransformations": { "default": [], "description": "The transformations to apply to the input target before fitting or predicting.", "items": { "properties": { "param": { "description": "The name of a field associated with the value.", "type": "string" }, "value": { "description": "Any value.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "param", "value" ], "type": "object" }, "type": "array" } }, "required": [ "inputs", "outputMethod", "outputMethodParameters", "taskCode", "taskParameters", "xTransformations", "yTransformations" ], "type": "object" } ] }, "taskId": { "description": "The identifier of a task / vertex in the blueprint.", "type": "string" } }, "required": [ "taskData", "taskId" ], "type": "object" }, "type": "array" }, { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ] }, "blueprintId": { "description": "The deterministic ID of the blueprint, based on its content.", "type": "string" }, "bpData": { "description": "Additional blueprint metadata used to render the blueprint in the UI", "oneOf": [ { "properties": { "children": { "description": "A nested dictionary representation of the blueprint DAG.", "items": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "type": "array" }, "id": { "description": "The type of the node (e.g., \"start\", \"input\", \"task\").", "type": "string" }, "inputs": { "description": "The inputs to the current node.", "items": { "type": "string" }, "type": "array" }, "output": { "description": "IDs describing the destination of any outgoing edges.", "oneOf": [ { "description": "IDs describing the destination of any outgoing edges.", "type": "string" }, { "items": { "type": "string" }, "maxItems": 0, "type": "array" } ] }, "taskMap": { "description": "The parameters submitted by the user to the failed job.", "type": "object" }, "taskParameters": { "description": "A stringified JSON object describing the parameters and their values for a task.", "type": "string" }, "tasks": { "description": "The task defining the current node.", "items": { "type": "string" }, "type": "array" }, "type": { "description": "A unique ID to represent the current node.", "type": "string" } }, "required": [ "id", "taskMap", "tasks", "type" ], "type": "object" } ] }, "customTaskVersionMetadata": { "description": "An association of custom entity ids and task ids.", "items": { "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "type": "array" }, "decompressedFormat": { "default": false, "description": "Whether the blueprint is in the decompressed format.", "type": "boolean" }, "diagram": { "description": "The diagram used by the UI to display the blueprint.", "type": "string" }, "features": { "description": "The list of the names of tasks used in the blueprint.", "items": { "type": "string" }, "type": "array" }, "featuresText": { "description": "The description of the blueprint via the names of tasks used.", "type": "string" }, "hexColumnNameLookup": { "description": "The lookup between hex values and data column names used in the blueprint.", "items": { "properties": { "colname": { "description": "The name of the column.", "type": "string" }, "hex": { "description": "A safe hex representation of the column name.", "type": "string" }, "projectId": { "description": "The ID of the project from which the column name originates.", "type": "string" } }, "required": [ "colname", "hex" ], "type": "object" }, "type": "array" }, "hiddenFromCatalog": { "description": "If true, the blueprint will not show up in the catalog.", "type": "boolean" }, "icons": { "description": "The icon(s) associated with the blueprint.", "items": { "type": "integer" }, "type": "array" }, "insights": { "description": "An indication of the insights generated by the blueprint.", "type": "string" }, "isTimeSeries": { "default": false, "description": "Whether the blueprint contains time-series tasks.", "type": "boolean" }, "linkedToProjectId": { "description": "Whether the user blueprint is linked to a project.", "type": "boolean" }, "modelType": { "description": "The generated or provided title of the blueprint.", "type": "string" }, "projectId": { "description": "The ID of the project the blueprint was originally created with, if applicable.", "type": [ "string", "null" ] }, "referenceModel": { "default": false, "description": "Whether the blueprint is a reference model.", "type": "boolean" }, "shapSupport": { "default": false, "description": "Whether the blueprint supports shapley additive explanations.", "type": "boolean" }, "supportedTargetTypes": { "description": "The list of supported targets of the current blueprint.", "items": { "enum": [ "binary", "multiclass", "multilabel", "nonnegative", "regression", "unsupervised", "unsupervisedClustering" ], "type": "string" }, "type": "array" }, "supportsGpu": { "default": false, "description": "Whether the blueprint supports execution on the GPU.", "type": "boolean" }, "supportsNewSeries": { "description": "Whether the blueprint supports new series.", "type": "boolean" }, "userBlueprintId": { "description": "The unique ID associated with the user blueprint.", "type": "string" }, "userId": { "description": "The ID of the user who owns the blueprint.", "type": "string" }, "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "blender", "blueprintId", "decompressedFormat", "diagram", "features", "featuresText", "icons", "insights", "isTimeSeries", "modelType", "referenceModel", "shapSupport", "supportedTargetTypes", "supportsGpu", "userBlueprintId", "userId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blender | boolean | true | | Whether the blueprint is a blender. | | blueprint | any | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [UserBlueprintsBlueprintTask] | false | | The representation of a directed acyclic graph defining a pipeline of data through tasks and a final estimator. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AllowExtra | false | | The parameters submitted by the user to the failed job. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The deterministic ID of the blueprint, based on its content. | | bpData | BpData | false | | Additional blueprint metadata used to render the blueprint in the UI | | customTaskVersionMetadata | [array] | false | | An association of custom entity ids and task ids. | | decompressedFormat | boolean | true | | Whether the blueprint is in the decompressed format. | | diagram | string | true | | The diagram used by the UI to display the blueprint. | | features | [string] | true | | The list of the names of tasks used in the blueprint. | | featuresText | string | true | | The description of the blueprint via the names of tasks used. | | hexColumnNameLookup | [UserBlueprintsHexColumnNameLookupEntry] | false | | The lookup between hex values and data column names used in the blueprint. | | hiddenFromCatalog | boolean | false | | If true, the blueprint will not show up in the catalog. | | icons | [integer] | true | | The icon(s) associated with the blueprint. | | insights | string | true | | An indication of the insights generated by the blueprint. | | isTimeSeries | boolean | true | | Whether the blueprint contains time-series tasks. | | linkedToProjectId | boolean | false | | Whether the user blueprint is linked to a project. | | modelType | string | true | | The generated or provided title of the blueprint. | | projectId | string,null | false | | The ID of the project the blueprint was originally created with, if applicable. | | referenceModel | boolean | true | | Whether the blueprint is a reference model. | | shapSupport | boolean | true | | Whether the blueprint supports shapley additive explanations. | | supportedTargetTypes | [string] | true | | The list of supported targets of the current blueprint. | | supportsGpu | boolean | true | | Whether the blueprint supports execution on the GPU. | | supportsNewSeries | boolean | false | | Whether the blueprint supports new series. | | userBlueprintId | string | true | | The unique ID associated with the user blueprint. | | userId | string | true | | The ID of the user who owns the blueprint. | | vertexContext | [VertexContextItem] | false | | Info about, warnings about, and errors with a specific vertex in the blueprint. | ## UserBlueprintsValidateTaskParameter ``` { "properties": { "message": { "description": "The description of the issue with the proposed task parameter value.", "type": "string" }, "paramName": { "description": "The name of the validated task parameter.", "type": "string" }, "value": { "description": "The invalid value proposed for the validated task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "message", "paramName", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | The description of the issue with the proposed task parameter value. | | paramName | string | true | | The name of the validated task parameter. | | value | any | true | | The invalid value proposed for the validated task parameter. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | ## UserBlueprintsValidateTaskParametersResponse ``` { "properties": { "errors": { "description": "A list of the task parameters, their proposed values, and messages describing why each is not valid.", "items": { "properties": { "message": { "description": "The description of the issue with the proposed task parameter value.", "type": "string" }, "paramName": { "description": "The name of the validated task parameter.", "type": "string" }, "value": { "description": "The invalid value proposed for the validated task parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "message", "paramName", "value" ], "type": "object" }, "type": "array" } }, "required": [ "errors" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errors | [UserBlueprintsValidateTaskParameter] | true | | A list of the task parameters, their proposed values, and messages describing why each is not valid. | ## UserBlueprintsValidationResponse ``` { "properties": { "vertexContext": { "description": "Info about, warnings about, and errors with a specific vertex in the blueprint.", "items": { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" }, "type": "array" } }, "required": [ "vertexContext" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vertexContext | [VertexContextItem] | true | | Info about, warnings about, and errors with a specific vertex in the blueprint. | ## VertexContextItem ``` { "properties": { "information": { "description": "A specification of requirements of the inputs and expectations of the output of the vertex.", "oneOf": [ { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ] }, "messages": { "description": "Warnings about and errors with a specific vertex in the blueprint.", "oneOf": [ { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ] }, "taskId": { "description": "The ID associated with a specific vertex in the blueprint.", "type": "string" } }, "required": [ "taskId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | information | VertexContextItemInfo | false | | A specification of requirements of the inputs and expectations of the output of the vertex. | | messages | VertexContextItemMessages | false | | Warnings about and errors with a specific vertex in the blueprint. | | taskId | string | true | | The ID associated with a specific vertex in the blueprint. | ## VertexContextItemInfo ``` { "properties": { "inputs": { "description": "A specification of requirements of the inputs of the vertex.", "items": { "type": "string" }, "type": "array" }, "outputs": { "description": "A specification of expectations of the output of the vertex.", "items": { "type": "string" }, "type": "array" } }, "required": [ "inputs", "outputs" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputs | [string] | true | | A specification of requirements of the inputs of the vertex. | | outputs | [string] | true | | A specification of expectations of the output of the vertex. | ## VertexContextItemMessages ``` { "properties": { "errors": { "description": "Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errors | [string] | false | | Errors with a specific vertex in the blueprint. Execution of the vertex is expected to fail. | | warnings | [string] | false | | Warnings about a specific vertex in the blueprint. Execution of the vertex may fail or behave unexpectedly. | ## WorkspaceSourceCreatedResponse ``` { "properties": { "workspaceStateId": { "description": "The ID of the data engine workspace state.", "type": "string" } }, "required": [ "workspaceStateId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | workspaceStateId | string | true | | The ID of the data engine workspace state. | ## WorkspaceSourceDatasetWithCredsResponse ``` { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "ID of a dataset in the catalog.", "type": "string" }, "datasetVersionId": { "description": "ID of a dataset version in the catalog.", "type": "string" }, "needsCredentials": { "description": "Whether a user must provide credentials for source datasets.", "type": "boolean" } }, "required": [ "alias", "datasetId", "datasetVersionId", "needsCredentials" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | alias | string | true | | Alias to be used as the table name. | | datasetId | string | true | | ID of a dataset in the catalog. | | datasetVersionId | string | true | | ID of a dataset version in the catalog. | | needsCredentials | boolean | true | | Whether a user must provide credentials for source datasets. | ## WorkspaceStateCreatedFromQueryGeneratorResponse ``` { "properties": { "datasets": { "description": "Source datasets in the Data Engine workspace.", "items": { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "The ID of the dataset.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version.", "type": "string" } }, "required": [ "alias" ], "type": "object" }, "maxItems": 32, "type": "array" }, "language": { "description": "Language of the Data Engine query.", "type": "string" }, "query": { "description": "Actual body of the Data Engine query.", "type": "string" }, "queryGeneratorId": { "description": "Query generator id.", "type": [ "string", "null" ] }, "workspaceStateId": { "description": "Data Engine workspace state ID.", "type": "string" } }, "required": [ "datasets", "language", "query", "queryGeneratorId", "workspaceStateId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasets | [DataEngineDataset] | true | maxItems: 32 | Source datasets in the Data Engine workspace. | | language | string | true | | Language of the Data Engine query. | | query | string | true | | Actual body of the Data Engine query. | | queryGeneratorId | string,null | true | | Query generator id. | | workspaceStateId | string | true | | Data Engine workspace state ID. | ## WorkspaceStateResponse ``` { "properties": { "datasets": { "description": "The source datasets in the data engine workspace.", "items": { "properties": { "alias": { "description": "Alias to be used as the table name.", "type": "string" }, "datasetId": { "description": "ID of a dataset in the catalog.", "type": "string" }, "datasetVersionId": { "description": "ID of a dataset version in the catalog.", "type": "string" }, "needsCredentials": { "description": "Whether a user must provide credentials for source datasets.", "type": "boolean" } }, "required": [ "alias", "datasetId", "datasetVersionId", "needsCredentials" ], "type": "object" }, "type": "array" }, "language": { "description": "The language of the data engine query.", "type": "string" }, "query": { "description": "The actual SQL statement of the data engine query.", "type": "string" }, "queryGeneratorId": { "description": "The query generator ID.", "type": [ "string", "null" ] }, "runTime": { "description": "The execution time of the data engine query.", "type": [ "number", "null" ] } }, "required": [ "datasets", "language", "query", "queryGeneratorId", "runTime" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasets | [WorkspaceSourceDatasetWithCredsResponse] | true | | The source datasets in the data engine workspace. | | language | string | true | | The language of the data engine query. | | query | string | true | | The actual SQL statement of the data engine query. | | queryGeneratorId | string,null | true | | The query generator ID. | | runTime | number,null | true | | The execution time of the data engine query. | --- # Data wrangling URL: https://docs.datarobot.com/en/docs/api/reference/public-api/data_wrangling.html > This page outlines the operations and endpoints for data wrangling (preparation). # Data wrangling This page outlines the operations and endpoints for data wrangling (preparation). ## List recipes Operation path: `GET /api/v2/recipes/` Authentication requirements: `BearerAuth` Get a list of the recipes available for given user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | false | The attribute sort order applied to the returned recipes list: 'recipe_id', 'name', 'description', 'dialect', 'status', 'recipe_type', 'created_at', 'created_by', 'updated_at', 'updated_by'. Prefix the attribute name with a dash to sort in descending order. e.g., orderBy='-name'. Defaults to '-created'. | | search | query | string | false | Only return recipes with names that contain the specified string. | | dialect | query | any | false | SQL dialect for Query Generator. | | status | query | any | false | Status used for filtering recipes. | | recipeType | query | any | false | Type of the recipe workflow. | | creatorUserId | query | any | false | Filter results to display only those created by user(s) associated with the specified ID. | | creatorUsername | query | any | false | Filter results to display only those created by user(s) associated with the specified username. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [recipeId, -recipeId, name, -name, description, -description, dialect, -dialect, status, -status, recipeType, -recipeType, createdAt, -createdAt, createdBy, -createdBy, updatedAt, -updatedAt, updatedBy, -updatedBy] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "A list of the datasets in this Use Case.", "items": { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecipesListResponse | ## Create a recipe and a data source Operation path: `POST /api/v2/recipes/fromDataStore/` Authentication requirements: `BearerAuth` Create a recipe which could be used for wrangling from a created fully reconfigured source of data. A `data source` specifies, via SQL query or selected table and schema data, which data to extract from the `data connection` (the location of data within a given endpoint) to use for modeling or predictions. A `data source` has one `data connection` and one `connector` but can have many `datasets`. ### Body parameter ``` { "properties": { "dataSourceType": { "description": "Data source type.", "enum": [ "dr-database-v1", "jdbc" ], "type": "string", "x-versionadded": "v2.33" }, "dataStoreId": { "description": "Data store ID for this data source.", "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "experimentContainerId": { "description": "[DEPRECATED - replaced with use_case_id] ID assigned to the Use Case, which is an experimental container for the recipe.", "type": "string", "x-versionadded": "v2.33" }, "inputs": { "description": "List of recipe inputs", "items": { "description": "Data source configuration.", "properties": { "canonicalName": { "description": "Data source canonical name.", "type": "string", "x-versionadded": "v2.33" }, "catalog": { "description": "Catalog name in the database if supported.", "maxLength": 256, "type": "string", "x-versionadded": "v2.33" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "schema": { "description": "Schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string", "x-versionadded": "v2.33" }, "table": { "description": "Table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string", "x-versionadded": "v2.33" } }, "required": [ "canonicalName", "table" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "recipeType": { "default": "wrangling", "description": "Type of the recipe workflow.", "enum": [ "sql", "wrangling" ], "type": "string", "x-versionadded": "v2.36" }, "useCaseId": { "description": "ID of the Use Case associated with the recipe.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "dataSourceType", "dataStoreId", "dialect", "inputs", "recipeType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | RecipeFromDataSourceCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Data source and recipe created successfully. | RecipeResponse | ## Create a recipe given dataset Operation path: `POST /api/v2/recipes/fromDataset/` Authentication requirements: `BearerAuth` Create a recipe which could be used for wrangling from given dataset. Deepcopy the dataset's recipe if available.Otherwise create a new recipe reusing the dataset's data source.A `data source` specifies, via SQL query or selected table and schema data, which data to extract from the `data connection` (the location of data within a given endpoint) to use for modeling or predictions. A `data source` has one `data connection` and one `connector` but can have many `datasets`. ### Body parameter ``` { "discriminator": { "propertyName": "status" }, "oneOf": [ { "properties": { "datasetId": { "description": "Dataset ID to create a Recipe from.", "type": "string" }, "dialect": { "description": "Source type data was retrieved from. Should be omitted for dataset rewrangling.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "status": { "description": "Preview recipe", "enum": [ "preview" ], "type": "string" } }, "required": [ "datasetId", "dialect", "status" ], "type": "object" }, { "properties": { "datasetId": { "description": "Dataset ID to create a Recipe from.", "type": "string" }, "datasetVersionId": { "default": null, "description": "Dataset version ID to create a Recipe from.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from. Should be omitted for dataset rewrangling and feature discovery recipes.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "experimentContainerId": { "description": "[DEPRECATED - replaced with use_case_id] ID assigned to the Use Case, which is an experimental container for the recipe.", "type": "string" }, "inputs": { "description": "List of recipe inputs. Should be omitted on dataset wrangling when dataset is created from recipe.", "items": { "description": "Dataset configuration.", "properties": { "sampling": { "description": "Sampling data transformation.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "type": "object" }, "maxItems": 1, "minItems": 1, "type": "array" }, "recipeType": { "default": "WRANGLING", "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string" }, "snapshotPolicy": { "description": "Snapshot policy to use the created recipe.", "enum": [ "fixed", "latest" ], "type": "string" }, "status": { "default": "draft", "description": "Wrangling recipe", "enum": [ "draft" ], "type": "string" }, "useCaseId": { "description": "ID of the Use Case associated with the recipe.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datasetId", "recipeType" ], "type": "object" } ] } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | GenericRecipeFromDataset | false | none | ### Example responses > 201 Response ``` { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Recipe created successfully. | RecipeResponse | | 422 | Unprocessable Entity | You can't specify dialect or inputs when source Recipe is available. | None | ## Clone given wrangling recipe Operation path: `POST /api/v2/recipes/fromRecipe/` Authentication requirements: `BearerAuth` Shallow copy the given recipe, reusing existing data sources. Implicitly creates duplicate of wrangling session. ### Body parameter ``` { "properties": { "name": { "description": "The recipe name.", "maxLength": 255, "type": "string", "x-versionadded": "v2.33" }, "recipeId": { "description": "Recipe ID to create a Recipe from.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "recipeId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | RecipeFromRecipeCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Recipe created successfully. | RecipeResponse | ## Deletes the wrangling recipe by recipe ID Operation path: `DELETE /api/v2/recipes/{recipeId}/` Authentication requirements: `BearerAuth` Marks the wrangling recipe with a given ID as deleted. ### Body parameter ``` { "properties": { "featureDiscoverySupervisedFeatureReduction": { "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "[Deprecated] No effect. The relationships configuration ID field is immutable.", "type": [ "string", "null" ], "x-versionadded": "v2.33", "x-versiondeprecated": "v2.34" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | body | body | RecipeSettingsUpdate | false | none | ### Example responses > 204 Response ``` { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully deleted. | RecipeSettingsResponse | ## Retrieve a wrangling recipe by recipe ID Operation path: `GET /api/v2/recipes/{recipeId}/` Authentication requirements: `BearerAuth` Retrieve a wrangling recipe given ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecipeResponse | ## Patched wrangling recipe by recipe ID Operation path: `PATCH /api/v2/recipes/{recipeId}/` Authentication requirements: `BearerAuth` Patch a wrangling recipe name and description. ### Body parameter ``` { "properties": { "description": { "description": "New recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "New recipe name.", "maxLength": 255, "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "The recipe workflow type.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.36" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.36" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | body | body | PatchRecipe | false | none | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecipeResponse | ## Updates the downsampling by recipe ID Operation path: `PUT /api/v2/recipes/{recipeId}/downsampling/` Authentication requirements: `BearerAuth` Updates the downsampling directive in the recipe.Downsampling will be applied on top of the recipe during publishing. ### Body parameter ``` { "properties": { "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] } }, "required": [ "downsampling" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | body | body | RecipeDownsamplingUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecipeResponse | | 422 | Unprocessable Entity | Cannot modify published recipe. | None | ## Gets inputs by recipe ID Operation path: `GET /api/v2/recipes/{recipeId}/inputs/` Authentication requirements: `BearerAuth` Gets inputs of the given recipe. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | ### Example responses > 200 Response ``` { "properties": { "inputs": { "description": "List of recipe inputs", "items": { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "columnCount": { "description": "Number of features in original (not sampled) data source", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "connectionName": { "description": "The user-friendly name of the data store.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "datasetId": { "description": "The ID of the input data source.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "datasetVersionId": { "description": "The ID of the input data source,", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "inputType": { "description": "Source type data came from", "enum": [ "datasource", "dataset" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Combination of \"catalog\", \"schema\" and \"table\" from data source", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rowCount": { "description": "Number of rows in original (not sampled) data source", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string", "x-versionadded": "v2.36" }, "status": { "description": "Input preparation status", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "alias", "columnCount", "connectionName", "dataSourceId", "dataStoreId", "inputType", "name", "rowCount", "status" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "inputs" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecipeInputsResponse | ## Sets the input of the given recipe by recipe ID Operation path: `PUT /api/v2/recipes/{recipeId}/inputs/` Authentication requirements: `BearerAuth` Set the inputs on a recipe to change the configuration. Implicitly restart the initial sampling job which calculates:1) Column names;2) resulting size of the sample in bytes;3) resulting size of the sample in rows. ### Body parameter ``` { "properties": { "inputs": { "description": "List of data sources and their sampling configurations.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "datasetId", "inputType" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "inputs" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | body | body | RecipeInputUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecipeResponse | ## Retrieve recipe insights by recipe ID Operation path: `GET /api/v2/recipes/{recipeId}/insights/` Authentication requirements: `BearerAuth` Retrieve recipe insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | At most this many results are returned. The default may change and a maximum limit may be imposed without notice. | | offset | query | integer | true | This many results will be skipped. | | numberOfOperationsToUse | query | integer | false | The number indicating how many operations from the beginning to return insights for. | | recipeId | path | string | true | The ID of the recipe. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The list of features related to the requested dataset.", "items": { "properties": { "datasetId": { "description": "The ID of the dataset the feature belongs to", "type": "string", "x-versionadded": "v2.33" }, "datasetVersionId": { "description": "The ID of the dataset version the feature belongs to.", "type": "string", "x-versionadded": "v2.33" }, "dateFormat": { "description": "The date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The number of the column in the dataset.", "type": "integer", "x-versionadded": "v2.33" }, "isZeroInflated": { "description": "whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ], "x-versionadded": "v2.33" }, "language": { "description": "Detected language of the feature.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "Whether feature has too few values to be informative.", "type": "boolean", "x-versionadded": "v2.33" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "majorityClassCount": { "description": "The number of rows with a majority class value if smart downsampling is applicable to this feature.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "max": { "description": "Maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "Maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "Maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "mean": { "description": "Arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "median": { "description": "Median of the EDA sample of the feature.", "oneOf": [ { "description": "Median of the EDA sample of the feature.", "type": "string" }, { "description": "Median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "min": { "description": "Minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "Minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "Minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "minorityClassCount": { "description": "The number of rows with neither null nor majority class value if smart downsampling is applicable to this feature.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "naCount": { "description": "Number of missing values.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Feature name", "type": "string", "x-versionadded": "v2.33" }, "plot": { "description": "Plot data based on feature values.", "items": { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.30" }, "sampleRows": { "description": "The number of rows in the sample used to calculate the statistics.", "type": "integer", "x-versionadded": "v2.35", "x-versiondeprecated": "v2.36" }, "stdDev": { "description": "Standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "Standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "Standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "timeSeriesEligibilityReason": { "description": "why the feature is ineligible for time series projects, or 'suitable' if it is eligible.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "timeSeriesEligibilityReasonAggregation": { "description": "why the feature is ineligible for aggregation, or 'suitable' if it is eligible.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "timeSeriesEligible": { "description": "whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.", "type": "boolean", "x-versionadded": "v2.33" }, "timeSeriesEligibleAggregation": { "description": "whether this feature can be used as a datetime feature for aggregationfor time series data prep. Always false for non-date features.", "type": "boolean", "x-versionadded": "v2.24" }, "timeStep": { "description": "The minimum time step that can be used to specify time series windows. The units for this value are the ``timeUnit``. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "timeStepAggregation": { "description": "The minimum time step that can be used to aggregate using this feature for time series data prep. The units for this value are the ``timeUnit``. Only present for date features that are eligible for aggregation in time series data prep and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "timeUnit": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "timeUnitAggregation": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. Only present for date features eligible for aggregation, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "uniqueCount": { "description": "Number of unique values.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "datasetId", "datasetVersionId", "dateFormat", "featureType", "id", "majorityClassCount", "minorityClassCount", "name", "sampleRows" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "message": { "description": "The status message.", "type": "string", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "status": { "description": "The job status.", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string", "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RefinexInsightsResponse | ## Update the operations by recipe ID Operation path: `PUT /api/v2/recipes/{recipeId}/operations/` Authentication requirements: `BearerAuth` Updates the operations in a recipe by saving new directives. To validate the new operations, run preview validation and SQL generation. To apply them to the new recipe, you must run a request to preview the results must be run. ### Body parameter ``` { "properties": { "force": { "default": false, "description": "If `true` then operations are stored even if they contain errors", "type": "boolean" }, "operations": { "description": "List of directives to run for the recipe.", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array" } }, "required": [ "operations" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | body | body | RecipeOperationsUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecipeResponse | | 409 | Conflict | Operations can't be applied due to a wrangling session state. | None | | 422 | Unprocessable Entity | Cannot modify published recipe. | None | ## Retrieve a recipe operation details by recipe ID Operation path: `GET /api/v2/recipes/{recipeId}/operations/{operationIndex}/` Authentication requirements: `BearerAuth` Returns an operation configuration with an additional inputColumns field to show the list of columns available at that stage. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | operationIndex | path | integer | true | The zero-based index of the operation. | ### Example responses > 200 Response ``` { "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OperationDetails | ## Retrieve a wrangling preview by recipe ID Operation path: `GET /api/v2/recipes/{recipeId}/preview/` Authentication requirements: `BearerAuth` Retrieve a wrangling preview given ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | numberOfOperationsToUse | query | integer | false | The number indicating how many operations from the beginning to retrieve a preview for. | | recipeId | path | string | true | The ID of the recipe. | ### Example responses > 200 Response ``` { "properties": { "byteSize": { "description": "Data memory usage", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "columns": { "description": "List of columns in data preview", "items": { "description": "Column name", "type": "string" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.33" }, "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of records output by the query.", "items": { "description": "List of values for a single database record, ordered as the columns are ordered.", "items": { "description": "String representation of the column's value.", "type": "string" }, "maxItems": 10000, "type": "array" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "estimatedSizeExceedsLimit": { "description": "Defines if downsampling should be done based on sample size", "type": "boolean", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "resultSchema": { "description": "JDBC result schema", "items": { "description": "JDBC result column description", "properties": { "columnDefaultValue": { "description": "Default value of the column.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataType": { "description": "DataType of the column.", "type": "string", "x-versionadded": "v2.33" }, "dataTypeInt": { "description": "Integer value of the column data type.", "type": "integer", "x-versionadded": "v2.33" }, "isInPrimaryKey": { "description": "True if the column is in the primary key .", "type": "boolean", "x-versionadded": "v2.33" }, "isNullable": { "description": "If the column values can be null.", "enum": [ "NO", "UNKNOWN", "YES" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Name of the column.", "type": "string", "x-versionadded": "v2.33" }, "precision": { "description": "Precision of the column.", "type": "integer", "x-versionadded": "v2.33" }, "scale": { "description": "Scale of the column.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "dataType", "name" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.33" }, "storedCount": { "description": "The number of rows available for preview.", "minimum": 0, "type": "integer", "x-versionadded": "v2.37" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "byteSize", "columns", "data", "estimatedSizeExceedsLimit", "next", "previous", "resultSchema", "storedCount", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecipePreviewResponse | ## Start the job by recipe ID Operation path: `POST /api/v2/recipes/{recipeId}/preview/` Authentication requirements: `BearerAuth` Starts the preview process for the recipe. Since this is an asynchronous process this endpoint returns a status ID to use with the status endpoint and a location header with the URL that can be polled for status.Launch WranglingJob, which includes: 1. InitialSamplingJob if it hasn’t been launched before 2. Preview query itself 3. Launch recipe eda job Insights computation is launched implicitly if there was sampling specified and no operations specified. ### Body parameter ``` { "properties": { "credentialId": { "description": "The ID of the credentials to use for the connection. If not given, the default credentials for the connection will be used.", "type": "string" }, "numberOfOperationsToUse": { "description": "The number indicating how many operations from the beginning to compute a preview for.", "minimum": 0, "type": "integer", "x-versionadded": "v2.35" } }, "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | body | body | RecipeRunPreviewAsync | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "statusId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | StatusResponse | | 422 | Unprocessable Entity | Credentials were not provided and default credentials were not found. | None | ## Submit a relationship quality assessment job by recipe ID Operation path: `POST /api/v2/recipes/{recipeId}/relationshipQualityAssessments/` Authentication requirements: `BearerAuth` Submit a job to assess the quality of the relationship configuration within a Feature Discovery session in Workbench. ### Body parameter ``` { "properties": { "credentials": { "description": "Credentials for dynamic policy secondary datasets.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "credentialId": { "description": "ID of the credentials object in credential store.Can only be used along with catalogVersionId.", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": [ "string", "null" ] } }, "required": [ "credentialId" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. ", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" } ] }, "maxItems": 30, "type": "array" }, "datetimePartitionColumn": { "description": "If a datetime partition column was used, the name of the column.", "type": [ "string", "null" ] }, "featureEngineeringPredictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "type": [ "string", "null" ] }, "relationshipsConfiguration": { "description": "Object describing how secondary datasets are related to the primary dataset", "properties": { "datasetDefinitions": { "description": "The list of datasets.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "maxItems": 30, "minItems": 1, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "Id of the relationship configuration", "type": "string" }, "relationships": { "description": "The list of relationships.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "maxItems": 70, "minItems": 1, "type": "array" }, "snowflakePushDownCompatible": { "description": "Flag indicating if the relationships configuration is compatible with Snowflake push down processing.", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "id", "relationships" ], "type": "object" }, "userId": { "description": "Mongo Id of the User who created the request", "type": "string" } }, "required": [ "relationshipsConfiguration" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | body | body | RelationshipQualityAssessmentsCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Relationship quality assessment has successfully started. See the Location header. | None | | 422 | Unprocessable Entity | Unable to process the request | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Updates recipe settings by recipe ID Operation path: `PATCH /api/v2/recipes/{recipeId}/settings/` Authentication requirements: `BearerAuth` Updates some recipe settings applicable in the modeling stage. ### Body parameter ``` { "properties": { "featureDiscoverySupervisedFeatureReduction": { "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "[Deprecated] No effect. The relationships configuration ID field is immutable.", "type": [ "string", "null" ], "x-versionadded": "v2.33", "x-versiondeprecated": "v2.34" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | body | body | RecipeSettingsUpdate | false | none | ### Example responses > 200 Response ``` { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RecipeSettingsResponse | ## Build SQL query by recipe ID Operation path: `POST /api/v2/recipes/{recipeId}/sql/` Authentication requirements: `BearerAuth` Builds a SQL query for the recipe. Overrides operations to get the adjusted query without changing the recipe. ### Body parameter ``` { "properties": { "inputsAsAliases": { "default": false, "description": "Produce the SQL that uses the input aliases instead of the real table names.", "type": "boolean", "x-versionadded": "v2.36" }, "operations": { "description": "List of operations to override the recipe operations when building SQL with default *null*. It doesn't modify the recipe itself. Missing *operations* field or *null* give original recipe SQL. Empty *operations* list produces basic query of a format: `SELECT FROM `", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | body | body | BuildRecipeSql | false | none | ### Example responses > 201 Response ``` { "properties": { "sql": { "description": "Generated sql.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "sql" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | BuildRecipeSqlResponse | | 409 | Conflict | Input source data is not ready yet. | None | | 422 | Unprocessable Entity | Failed to build SQL. | None | ## Generate a time series transformation plan by recipe ID Operation path: `POST /api/v2/recipes/{recipeId}/timeseriesTransformationPlans/` Authentication requirements: `BearerAuth` Generate a list of recipe operations, which serve as the plan to transform a regular dataset into a time series dataset. ### Body parameter ``` { "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "doNotDeriveColumns": { "default": [], "description": "Columns to exclude from derivation; for them only the first lag is suggested.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "excludeLowInfoColumns": { "default": true, "description": "Whether to ignore columns with low signal (only include features that pass a \"reasonableness\" check that determines whether they contain information useful for building a generalizable model).", "type": "boolean" }, "featureDerivationWindows": { "description": "A list of rolling windows of past values, defined in terms of rows, that are used to derive features for the modeling dataset.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 5, "minItems": 1, "type": "array" }, "featureReductionThreshold": { "default": 0.9, "description": "Threshold for feature reduction. For example, 0.9 means that features which cumulatively reach 90 % of importance are returned. Additionally, no more than 200 features are returned.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "maxLagOrder": { "description": "The maximum lag order. This value cannot be greater than the largest feature derivation window.", "exclusiveMinimum": 0, "maximum": 100, "type": [ "integer", "null" ] }, "multiseriesIdColumn": { "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "numberOfOperationsToUse": { "description": "If set, a transformation plan is suggested after the specified number of operations.", "type": "integer" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" } }, "required": [ "datetimePartitionColumn", "featureDerivationWindows", "forecastDistances", "targetColumn" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | body | body | GenerateTransformationPlan | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "statusId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | StatusResponse | ## Retrieve generated time series transformation plan by recipe ID Operation path: `GET /api/v2/recipes/{recipeId}/timeseriesTransformationPlans/{id}/` Authentication requirements: `BearerAuth` Returns a list of recipe operations, which serve as the plan to transform a regular dataset into a time series dataset. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | recipeId | path | string | true | The ID of the recipe. | | id | path | string | true | The ID of the transformation plan. | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "The identifier of the transformation plan.", "type": "string" }, "inputParameters": { "description": "The input parameters corresponding to the suggested operations.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "doNotDeriveColumns": { "default": [], "description": "Columns to exclude from derivation; for them only the first lag is suggested.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "excludeLowInfoColumns": { "default": true, "description": "Whether to ignore columns with low signal (only include features that pass a \"reasonableness\" check that determines whether they contain information useful for building a generalizable model).", "type": "boolean" }, "featureDerivationWindows": { "description": "A list of rolling windows of past values, defined in terms of rows, that are used to derive features for the modeling dataset.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 5, "minItems": 1, "type": "array" }, "featureReductionThreshold": { "default": 0.9, "description": "Threshold for feature reduction. For example, 0.9 means that features which cumulatively reach 90 % of importance are returned. Additionally, no more than 200 features are returned.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "maxLagOrder": { "description": "The maximum lag order. This value cannot be greater than the largest feature derivation window.", "exclusiveMinimum": 0, "maximum": 100, "type": [ "integer", "null" ] }, "multiseriesIdColumn": { "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "numberOfOperationsToUse": { "description": "If set, a transformation plan is suggested after the specified number of operations.", "type": "integer" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" } }, "required": [ "datetimePartitionColumn", "featureDerivationWindows", "forecastDistances", "targetColumn" ], "type": "object", "x-versionadded": "v2.35" }, "status": { "description": "Transformation preparation status", "enum": [ "INITIALIZED", "COMPLETED", "ERROR" ], "type": "string" }, "suggestedOperations": { "description": "The list of operations to apply to a recipe to get the dataset ready for time series modeling.", "items": { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10, "type": "array" } }, "required": [ "id", "inputParameters", "status", "suggestedOperations" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | TransformationPlanResponse | ## Stop wrangling session Operation path: `DELETE /api/v2/sparkSessions/` Authentication requirements: `BearerAuth` Stop the underlying interactive Spark session, if applicable. ### Body parameter ``` { "properties": { "size": { "default": "small", "description": "The Spark instance size to stop.", "enum": [ "small", "medium", "large" ], "type": "string" } }, "type": "object", "x-versionadded": "v2.38" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | StopSession | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | # Schemas ## AggregateDirectiveArguments ``` { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" } ``` The aggregation description. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregations | [Aggregation] | true | minItems: 1 | The aggregations. | | groupBy | [string] | true | minItems: 1 | The column(s) to group by. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ## Aggregation ``` { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature | string,null | false | | The feature. | | functions | [string] | true | minItems: 1 | The functions. | ## BaseCategoricalStatsArguments ``` { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" } ``` Task arguments. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | methods | [string] | true | maxItems: 10minItems: 1 | Window method: most-frequent | | skip | boolean | true | | If True, this directive will be skipped during processing. | | windowSize | integer | true | maximum: 300 | Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive. | ## BaseDirectiveArguments ``` { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" } ``` The transformation description. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | skip | boolean | true | | If True, this directive will be skipped during processing. | ## BaseLagsArguments ``` { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" } ``` Task arguments. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | orders | [integer] | true | maxItems: 100minItems: 1 | Lag orders. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ## BaseNumericStatsArguments ``` { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" } ``` Task arguments. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | methods | [string] | true | maxItems: 10minItems: 1 | Methods to apply in a rolling window. | | skip | boolean | true | | If True, this directive will be skipped during processing. | | windowSize | integer | true | maximum: 300 | Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive. | ## BuildRecipeSql ``` { "properties": { "inputsAsAliases": { "default": false, "description": "Produce the SQL that uses the input aliases instead of the real table names.", "type": "boolean", "x-versionadded": "v2.36" }, "operations": { "description": "List of operations to override the recipe operations when building SQL with default *null*. It doesn't modify the recipe itself. Missing *operations* field or *null* give original recipe SQL. Empty *operations* list produces basic query of a format: `SELECT FROM
`", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputsAsAliases | boolean | false | | Produce the SQL that uses the input aliases instead of the real table names. | | operations | [OneOfDirective] | false | maxItems: 1000 | List of operations to override the recipe operations when building SQL with default null. It doesn't modify the recipe itself. Missing operations field or null give original recipe SQL. Empty operations list produces basic query of a format: SELECT FROM
| ## BuildRecipeSqlResponse ``` { "properties": { "sql": { "description": "Generated sql.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "sql" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sql | string | true | | Generated sql. | ## CatalogPasswordCredentials ``` { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. ", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogVersionId | string | false | | Identifier of the catalog version | | password | string | true | | The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. | | url | string | false | | URL that is subject to credentials. | | user | string | true | | The username for database authentication. | ## ComputeNewDirectiveArguments ``` { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" } ``` The transformation description. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | expression | string | true | | The expression for new feature computation. | | newFeatureName | string | true | | The new feature name which will hold results of expression evaluation. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ## DataStoreExtendedColumnNoKeysResponse ``` { "description": "JDBC result column description", "properties": { "columnDefaultValue": { "description": "Default value of the column.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataType": { "description": "DataType of the column.", "type": "string", "x-versionadded": "v2.33" }, "dataTypeInt": { "description": "Integer value of the column data type.", "type": "integer", "x-versionadded": "v2.33" }, "isInPrimaryKey": { "description": "True if the column is in the primary key .", "type": "boolean", "x-versionadded": "v2.33" }, "isNullable": { "description": "If the column values can be null.", "enum": [ "NO", "UNKNOWN", "YES" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Name of the column.", "type": "string", "x-versionadded": "v2.33" }, "precision": { "description": "Precision of the column.", "type": "integer", "x-versionadded": "v2.33" }, "scale": { "description": "Scale of the column.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "dataType", "name" ], "type": "object" } ``` JDBC result column description ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnDefaultValue | string,null | false | | Default value of the column. | | dataType | string | true | | DataType of the column. | | dataTypeInt | integer | false | | Integer value of the column data type. | | isInPrimaryKey | boolean | false | | True if the column is in the primary key . | | isNullable | string,null | false | | If the column values can be null. | | name | string | true | | Name of the column. | | precision | integer | false | | Precision of the column. | | scale | integer | false | | Scale of the column. | ### Enumerated Values | Property | Value | | --- | --- | | isNullable | [NO, UNKNOWN, YES] | ## DatasetDefinition ``` { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | ID of the catalog item. | | catalogVersionId | string | true | | ID of the catalog item version. | | featureListId | string,null | false | | ID of the feature list. This decides which columns in the dataset are used for feature generation. | | identifier | string | true | maxLength: 20minLength: 1minLength: 1 | Short name of the dataset (used directly as part of the generated feature names). | | primaryTemporalKey | string,null | false | | Name of the column indicating time of record creation. | | snapshotPolicy | string | false | | Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets). | ### Enumerated Values | Property | Value | | --- | --- | | snapshotPolicy | [specified, latest, dynamic] | ## DatasetFeaturePlotDataResponse ``` { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | number | true | | Number of values in the bin. | | label | string | true | | Bin start for numerical/uncapped, or string value for categorical. The bin ==Missing== is created for rows that did not have the feature. | ## DatasetInputCreate ``` { "description": "Dataset configuration.", "properties": { "sampling": { "description": "Sampling data transformation.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "type": "object" } ``` Dataset configuration. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sampling | DatasetInputSampling | false | | Sampling data transformation. | ## DatasetInputSampling ``` { "description": "Sampling data transformation.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } ``` Sampling data transformation. ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | RandomSampleArgumentsCreate | false | | The interactive sampling config. | | » directive | string | true | | The directive name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | LimitDirectiveArguments | true | | The interactive sampling config. | | » directive | string | true | | The directive name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | DatetimeSampleArgumentsCreate | false | | The interactive sampling config. | | » directive | string | true | | The directive name. | ### Enumerated Values | Property | Value | | --- | --- | | directive | random-sample | | directive | limit | | directive | datetime-sample | ## DatetimeSampleArgumentsCreate ``` { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" } ``` The interactive sampling config. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimePartitionColumn | string | true | | The datetime partition column to order by. | | multiseriesIdColumn | string,null | false | | The series ID column, if present. | | rows | integer | false | maximum: 10000minimum: 1 | The number of rows to be sampled. | | selectedSeries | [string] | false | maxItems: 1000minItems: 1 | The selected series to be sampled. Requires "multiseriesIdColumn". | | skip | boolean | true | | If True, this directive will be skipped during processing. | | strategy | string | true | | Sets whether to take the latest or earliest rows relative to the datetime partition column. | ### Enumerated Values | Property | Value | | --- | --- | | strategy | [earliest, latest] | ## DownsamplingRandomDirectiveArguments ``` { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" } ``` The downsampling configuration. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rows | integer | true | | The number of sampled rows. | | seed | integer,null | false | | The start number of the random number generator | | skip | boolean | true | | If True, this directive will be skipped during processing. | ## DropColumnsArguments ``` { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" } ``` The transformation description. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columns | [string] | true | maxItems: 1000minItems: 1 | The list of columns. | | keepColumns | boolean | false | | If True, keep only the specified columns. If False (default), drop the specified columns. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ## EfficientRowBasedSampleArgumentsCreate ``` { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" } ``` The sampling config. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | samplingMethod | string | true | | The sampling method. If the sampling method is "rows", the size is the number of rows to be sampled. If the sampling method is "percent", the size is the percent of the table to be sampled. | | seed | integer | false | minimum: 0 | The starting value used to initialize the pseudo random number generator. | | size | number | false | minimum: 0.01 | The number of rows or percent of the table to be sampled. The value is dependent on the sampling method. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ### Enumerated Values | Property | Value | | --- | --- | | samplingMethod | [percent, rows] | ## ExperimentContainerUserResponse ``` { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } ``` A user associated with a use case. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string,null | true | | The email address of the user. | | fullName | string,null | false | | The full name of the user. | | id | string | true | | The ID of the user. | | userhash | string,null | false | | The user's gravatar hash. | | username | string,null | false | | The username of the user. | ## FeatureDerivationWindow ``` { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | integer | true | maximum: 0 | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | start | integer | true | | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | unit | string | true | | Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | ### Enumerated Values | Property | Value | | --- | --- | | unit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | ## FeatureDiscoverySettingResponse ``` { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | Description of this feature discovery setting | | family | string | true | | Family of this feature discovery setting | | name | string | true | maxLength: 100 | Name of this feature discovery setting | | settingType | string | true | | Type of this feature discovery setting | | value | boolean | true | | Value of this feature discovery setting | | verboseName | string | true | | Human readable name of this feature discovery setting | ## FeatureKeySummaryDetailsResponseValidatorMultilabel ``` { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } ``` Statistics of the key. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | max | number | true | | Maximum value of the key. | | mean | number | true | | Mean value of the key. | | median | number | true | | Median value of the key. | | min | number | true | | Minimum value of the key. | | pctRows | number | true | | Percentage occurrence of key in the EDA sample of the feature. | | stdDev | number | true | | Standard deviation of the key. | ## FeatureKeySummaryDetailsResponseValidatorSummarizedCategorical ``` { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } ``` Statistics of the key. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataQualities | string | true | | The indicator of data quality assessment of the feature. | | max | number | true | | Maximum value of the key. | | mean | number | true | | Mean value of the key. | | median | number | true | | Median value of the key. | | min | number | true | | Minimum value of the key. | | pctRows | number | true | | Percentage occurrence of key in the EDA sample of the feature. | | stdDev | number | true | | Standard deviation of the key. | ### Enumerated Values | Property | Value | | --- | --- | | dataQualities | [ISSUES_FOUND, NOT_ANALYZED, NO_ISSUES_FOUND] | ## FeatureKeySummaryResponseValidatorMultilabel ``` { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | key | string | true | | Name of the key. | | summary | FeatureKeySummaryDetailsResponseValidatorMultilabel | true | | Statistics of the key. | ## FeatureKeySummaryResponseValidatorSummarizedCategorical ``` { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" } ``` For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters) ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | key | string | true | | Name of the key. | | summary | FeatureKeySummaryDetailsResponseValidatorSummarizedCategorical | true | | Statistics of the key. | ## FilterCondition ``` { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | column | string | true | | The column name. | | function | string | true | | The function used to evaluate each value. | | functionArguments | [anyOf] | false | maxItems: 2 | The arguments to use with the function. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | function | [between, contains, eq, gt, gte, lt, lte, neq, notnull, null] | ## FilterDirectiveArguments ``` { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" } ``` The transformation description. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | conditions | [FilterCondition] | true | maxItems: 1000 | The list of conditions. | | keepRows | boolean | true | | Determines whether matching rows should be kept or dropped. | | operator | string | true | | The operator to apply on multiple conditions. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ### Enumerated Values | Property | Value | | --- | --- | | operator | [and, or] | ## GenerateTransformationPlan ``` { "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "doNotDeriveColumns": { "default": [], "description": "Columns to exclude from derivation; for them only the first lag is suggested.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "excludeLowInfoColumns": { "default": true, "description": "Whether to ignore columns with low signal (only include features that pass a \"reasonableness\" check that determines whether they contain information useful for building a generalizable model).", "type": "boolean" }, "featureDerivationWindows": { "description": "A list of rolling windows of past values, defined in terms of rows, that are used to derive features for the modeling dataset.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 5, "minItems": 1, "type": "array" }, "featureReductionThreshold": { "default": 0.9, "description": "Threshold for feature reduction. For example, 0.9 means that features which cumulatively reach 90 % of importance are returned. Additionally, no more than 200 features are returned.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "maxLagOrder": { "description": "The maximum lag order. This value cannot be greater than the largest feature derivation window.", "exclusiveMinimum": 0, "maximum": 100, "type": [ "integer", "null" ] }, "multiseriesIdColumn": { "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "numberOfOperationsToUse": { "description": "If set, a transformation plan is suggested after the specified number of operations.", "type": "integer" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" } }, "required": [ "datetimePartitionColumn", "featureDerivationWindows", "forecastDistances", "targetColumn" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselinePeriods | [integer] | false | maxItems: 10minItems: 1 | A list of periodicities used to calculate naive target features. | | datetimePartitionColumn | string | true | | The column that is used to order the data. | | doNotDeriveColumns | [string] | false | maxItems: 200 | Columns to exclude from derivation; for them only the first lag is suggested. | | excludeLowInfoColumns | boolean | false | | Whether to ignore columns with low signal (only include features that pass a "reasonableness" check that determines whether they contain information useful for building a generalizable model). | | featureDerivationWindows | [integer] | true | maxItems: 5minItems: 1 | A list of rolling windows of past values, defined in terms of rows, that are used to derive features for the modeling dataset. | | featureReductionThreshold | number,null | false | maximum: 1 | Threshold for feature reduction. For example, 0.9 means that features which cumulatively reach 90 % of importance are returned. Additionally, no more than 200 features are returned. | | forecastDistances | [integer] | true | maxItems: 20minItems: 1 | A list of forecast distances, which defines the number of rows into the future to predict. | | knownInAdvanceColumns | [string] | false | maxItems: 200 | Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time. | | maxLagOrder | integer,null | false | maximum: 100 | The maximum lag order. This value cannot be greater than the largest feature derivation window. | | multiseriesIdColumn | string,null | false | | The series ID column, if present. This column partitions data to create a multiseries modeling project. | | numberOfOperationsToUse | integer | false | | If set, a transformation plan is suggested after the specified number of operations. | | targetColumn | string | true | | The column intended to be used as the target for modeling. This parameter is required for generating naive features. | ## GenericRecipeFromDataset ``` { "discriminator": { "propertyName": "status" }, "oneOf": [ { "properties": { "datasetId": { "description": "Dataset ID to create a Recipe from.", "type": "string" }, "dialect": { "description": "Source type data was retrieved from. Should be omitted for dataset rewrangling.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "status": { "description": "Preview recipe", "enum": [ "preview" ], "type": "string" } }, "required": [ "datasetId", "dialect", "status" ], "type": "object" }, { "properties": { "datasetId": { "description": "Dataset ID to create a Recipe from.", "type": "string" }, "datasetVersionId": { "default": null, "description": "Dataset version ID to create a Recipe from.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from. Should be omitted for dataset rewrangling and feature discovery recipes.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "experimentContainerId": { "description": "[DEPRECATED - replaced with use_case_id] ID assigned to the Use Case, which is an experimental container for the recipe.", "type": "string" }, "inputs": { "description": "List of recipe inputs. Should be omitted on dataset wrangling when dataset is created from recipe.", "items": { "description": "Dataset configuration.", "properties": { "sampling": { "description": "Sampling data transformation.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "type": "object" }, "maxItems": 1, "minItems": 1, "type": "array" }, "recipeType": { "default": "WRANGLING", "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string" }, "snapshotPolicy": { "description": "Snapshot policy to use the created recipe.", "enum": [ "fixed", "latest" ], "type": "string" }, "status": { "default": "draft", "description": "Wrangling recipe", "enum": [ "draft" ], "type": "string" }, "useCaseId": { "description": "ID of the Use Case associated with the recipe.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datasetId", "recipeType" ], "type": "object" } ] } ``` ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » datasetId | string | true | | Dataset ID to create a Recipe from. | | » dialect | string | true | | Source type data was retrieved from. Should be omitted for dataset rewrangling. | | » status | string | true | | Preview recipe | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » datasetId | string | true | | Dataset ID to create a Recipe from. | | » datasetVersionId | string,null | false | | Dataset version ID to create a Recipe from. | | » dialect | string | false | | Source type data was retrieved from. Should be omitted for dataset rewrangling and feature discovery recipes. | | » experimentContainerId | string | false | | [DEPRECATED - replaced with use_case_id] ID assigned to the Use Case, which is an experimental container for the recipe. | | » inputs | [DatasetInputCreate] | false | maxItems: 1minItems: 1 | List of recipe inputs. Should be omitted on dataset wrangling when dataset is created from recipe. | | » recipeType | string | true | | Type of the recipe workflow. | | » snapshotPolicy | string | false | | Snapshot policy to use the created recipe. | | » status | string | false | | Wrangling recipe | | » useCaseId | string | false | | ID of the Use Case associated with the recipe. | ### Enumerated Values | Property | Value | | --- | --- | | dialect | [snowflake, bigquery, databricks, spark, postgres] | | status | preview | | dialect | [snowflake, bigquery, databricks, spark, postgres] | | recipeType | [sql, Sql, SQL, wrangling, Wrangling, WRANGLING, featureDiscovery, FeatureDiscovery, FEATURE_DISCOVERY, featureDiscoveryPrivatePreview, FeatureDiscoveryPrivatePreview, FEATURE_DISCOVERY_PRIVATE_PREVIEW] | | snapshotPolicy | [fixed, latest] | | status | draft | ## InputParametersResponse ``` { "description": "The input parameters corresponding to the suggested operations.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "doNotDeriveColumns": { "default": [], "description": "Columns to exclude from derivation; for them only the first lag is suggested.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "excludeLowInfoColumns": { "default": true, "description": "Whether to ignore columns with low signal (only include features that pass a \"reasonableness\" check that determines whether they contain information useful for building a generalizable model).", "type": "boolean" }, "featureDerivationWindows": { "description": "A list of rolling windows of past values, defined in terms of rows, that are used to derive features for the modeling dataset.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 5, "minItems": 1, "type": "array" }, "featureReductionThreshold": { "default": 0.9, "description": "Threshold for feature reduction. For example, 0.9 means that features which cumulatively reach 90 % of importance are returned. Additionally, no more than 200 features are returned.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "maxLagOrder": { "description": "The maximum lag order. This value cannot be greater than the largest feature derivation window.", "exclusiveMinimum": 0, "maximum": 100, "type": [ "integer", "null" ] }, "multiseriesIdColumn": { "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "numberOfOperationsToUse": { "description": "If set, a transformation plan is suggested after the specified number of operations.", "type": "integer" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" } }, "required": [ "datetimePartitionColumn", "featureDerivationWindows", "forecastDistances", "targetColumn" ], "type": "object", "x-versionadded": "v2.35" } ``` The input parameters corresponding to the suggested operations. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselinePeriods | [integer] | false | maxItems: 10minItems: 1 | A list of periodicities used to calculate naive target features. | | datetimePartitionColumn | string | true | | The column that is used to order the data. | | doNotDeriveColumns | [string] | false | maxItems: 200 | Columns to exclude from derivation; for them only the first lag is suggested. | | excludeLowInfoColumns | boolean | false | | Whether to ignore columns with low signal (only include features that pass a "reasonableness" check that determines whether they contain information useful for building a generalizable model). | | featureDerivationWindows | [integer] | true | maxItems: 5minItems: 1 | A list of rolling windows of past values, defined in terms of rows, that are used to derive features for the modeling dataset. | | featureReductionThreshold | number,null | false | maximum: 1 | Threshold for feature reduction. For example, 0.9 means that features which cumulatively reach 90 % of importance are returned. Additionally, no more than 200 features are returned. | | forecastDistances | [integer] | true | maxItems: 20minItems: 1 | A list of forecast distances, which defines the number of rows into the future to predict. | | knownInAdvanceColumns | [string] | false | maxItems: 200 | Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time. | | maxLagOrder | integer,null | false | maximum: 100 | The maximum lag order. This value cannot be greater than the largest feature derivation window. | | multiseriesIdColumn | string,null | false | | The series ID column, if present. This column partitions data to create a multiseries modeling project. | | numberOfOperationsToUse | integer | false | | If set, a transformation plan is suggested after the specified number of operations. | | targetColumn | string | true | | The column intended to be used as the target for modeling. This parameter is required for generating naive features. | ## JDBCTableDataSourceInputCreate ``` { "description": "Data source configuration.", "properties": { "canonicalName": { "description": "Data source canonical name.", "type": "string", "x-versionadded": "v2.33" }, "catalog": { "description": "Catalog name in the database if supported.", "maxLength": 256, "type": "string", "x-versionadded": "v2.33" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "schema": { "description": "Schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string", "x-versionadded": "v2.33" }, "table": { "description": "Table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string", "x-versionadded": "v2.33" } }, "required": [ "canonicalName", "table" ], "type": "object" } ``` Data source configuration. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canonicalName | string | true | | Data source canonical name. | | catalog | string | false | maxLength: 256 | Catalog name in the database if supported. | | sampling | SampleDirectiveCreate | false | | The input data transformation steps. | | schema | string | false | maxLength: 256 | Schema associated with the table or view in the database if the data source is not query based. | | table | string | true | maxLength: 256 | Table or view name in the database if the data source is not query based. | ## JoinArguments ``` { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" } ``` The transformation description. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | joinType | string | true | | The join type between primary and secondary data sources. | | leftKeys | [string] | false | maxItems: 10000minItems: 1 | The list of columns to be used in the "ON" clause from the primary data source. Required for inner, left joins, not used for cartesian joins. | | rightDataSourceId | string | true | | The ID of the input data source. | | rightKeys | [string] | false | maxItems: 10000minItems: 1 | The list of columns to be used in the "ON" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins. | | rightPrefix | string,null | false | | Optional prefix to be added to all column names from the right table in the join result. | | skip | boolean | true | | If True, this directive will be skipped during processing. | | source | string | true | | The source type. | ### Enumerated Values | Property | Value | | --- | --- | | joinType | [inner, left, cartesian] | | source | table | ## LimitDirectiveArguments ``` { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" } ``` The interactive sampling config. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rows | integer | true | maximum: 10000minimum: 1 | The number of rows to be selected. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ## OneOfDirective ``` { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] } ``` ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | FilterDirectiveArguments | true | | The transformation description. | | » directive | string | true | | The single data transformation step. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | ReplaceDirectiveArguments | true | | The transformation description. | | » directive | string | true | | The single data transformation step. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | ComputeNewDirectiveArguments | true | | The transformation description. | | » directive | string | true | | The single data transformation step. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | BaseDirectiveArguments | false | | The transformation description. | | » directive | string | true | | The single data transformation step. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | DropColumnsArguments | true | | The transformation description. | | » directive | string | true | | The single data transformation step. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | RenameColumnsArguments | true | | The transformation description. | | » directive | string | true | | The single data transformation step. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | JoinArguments | true | | The transformation description. | | » directive | string | true | | The single data transformation step. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | AggregateDirectiveArguments | true | | The aggregation description. | | » directive | string | true | | The single data transformation step. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | TimeSeriesDirectiveArguments | true | | Time series directive arguments. | | » directive | string | true | | Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based. | ### Enumerated Values | Property | Value | | --- | --- | | directive | filter | | directive | replace | | directive | compute-new | | directive | dedupe-rows | | directive | drop-columns | | directive | rename-columns | | directive | join | | directive | aggregate | | directive | time-series | ## OneOfDownsamplingDirective ``` { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] } ``` Data transformation step. ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | DownsamplingRandomDirectiveArguments | true | | The downsampling configuration. | | » directive | string | true | | The downsampling method. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | SmartDownsamplingArguments | true | | The downsampling configuration. | | » directive | string | true | | The downsampling method. | ### Enumerated Values | Property | Value | | --- | --- | | directive | random-sample | | directive | smart-downsampling | ## OneOfTransforms ``` { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" } ``` ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | BaseNumericStatsArguments | true | | Task arguments. | | » name | string | true | | Task name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | BaseCategoricalStatsArguments | true | | Task arguments. | | » name | string | true | | Task name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | BaseLagsArguments | true | | Task arguments. | | » name | string | true | | Task name. | ### Enumerated Values | Property | Value | | --- | --- | | name | numeric-stats | | name | categorical-stats | | name | lags | ## OperationDetails ``` { "type": "object" } ``` ### Properties None ## PatchRecipe ``` { "properties": { "description": { "description": "New recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "New recipe name.", "maxLength": 255, "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "The recipe workflow type.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.36" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": "string", "x-versionadded": "v2.36" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 1000 | New recipe description. | | name | string | false | maxLength: 255 | New recipe name. | | recipeType | string | false | | The recipe workflow type. | | sql | string | false | maxLength: 320000 | Recipe SQL query. | ### Enumerated Values | Property | Value | | --- | --- | | recipeType | [sql, Sql, SQL, wrangling, Wrangling, WRANGLING, featureDiscovery, FeatureDiscovery, FEATURE_DISCOVERY, featureDiscoveryPrivatePreview, FeatureDiscoveryPrivatePreview, FEATURE_DISCOVERY_PRIVATE_PREVIEW] | ## RandomSampleArgumentsCreate ``` { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" } ``` The interactive sampling config. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rows | integer | false | maximum: 10000minimum: 1 | The number of rows to be sampled. | | seed | integer | false | minimum: 0 | The starting number of the random number generator. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ## RecipeDownsamplingUpdate ``` { "properties": { "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] } }, "required": [ "downsampling" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | downsampling | OneOfDownsamplingDirective | true | | Data transformation step. | ## RecipeFromDataSourceCreate ``` { "properties": { "dataSourceType": { "description": "Data source type.", "enum": [ "dr-database-v1", "jdbc" ], "type": "string", "x-versionadded": "v2.33" }, "dataStoreId": { "description": "Data store ID for this data source.", "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "experimentContainerId": { "description": "[DEPRECATED - replaced with use_case_id] ID assigned to the Use Case, which is an experimental container for the recipe.", "type": "string", "x-versionadded": "v2.33" }, "inputs": { "description": "List of recipe inputs", "items": { "description": "Data source configuration.", "properties": { "canonicalName": { "description": "Data source canonical name.", "type": "string", "x-versionadded": "v2.33" }, "catalog": { "description": "Catalog name in the database if supported.", "maxLength": 256, "type": "string", "x-versionadded": "v2.33" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "schema": { "description": "Schema associated with the table or view in the database if the data source is not query based.", "maxLength": 256, "type": "string", "x-versionadded": "v2.33" }, "table": { "description": "Table or view name in the database if the data source is not query based.", "maxLength": 256, "type": "string", "x-versionadded": "v2.33" } }, "required": [ "canonicalName", "table" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "recipeType": { "default": "wrangling", "description": "Type of the recipe workflow.", "enum": [ "sql", "wrangling" ], "type": "string", "x-versionadded": "v2.36" }, "useCaseId": { "description": "ID of the Use Case associated with the recipe.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "dataSourceType", "dataStoreId", "dialect", "inputs", "recipeType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSourceType | string | true | | Data source type. | | dataStoreId | string | true | | Data store ID for this data source. | | dialect | string | true | | Source type data was retrieved from. | | experimentContainerId | string | false | | [DEPRECATED - replaced with use_case_id] ID assigned to the Use Case, which is an experimental container for the recipe. | | inputs | [JDBCTableDataSourceInputCreate] | true | maxItems: 1000minItems: 1 | List of recipe inputs | | recipeType | string | true | | Type of the recipe workflow. | | useCaseId | string | false | | ID of the Use Case associated with the recipe. | ### Enumerated Values | Property | Value | | --- | --- | | dataSourceType | [dr-database-v1, jdbc] | | dialect | [snowflake, bigquery, databricks, spark, postgres] | | recipeType | [sql, wrangling] | ## RecipeFromRecipeCreate ``` { "properties": { "name": { "description": "The recipe name.", "maxLength": 255, "type": "string", "x-versionadded": "v2.33" }, "recipeId": { "description": "Recipe ID to create a Recipe from.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "recipeId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | maxLength: 255 | The recipe name. | | recipeId | string | true | | Recipe ID to create a Recipe from. | ## RecipeInput ``` { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "datasetId", "inputType" ], "type": "object" } ], "x-versionadded": "v2.35" } ``` ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » alias | string,null | false | maxLength: 256 | The alias for the data source table. | | » dataSourceId | string,null | false | | The ID of the input data source. | | » dataStoreId | string,null | false | | The ID of the input data store. | | » datasetId | string,null | false | | The ID of the input dataset. | | » inputType | string | true | | The data that comes from a database connection. | | » sampling | SampleDirectiveCreate | false | | The input data transformation steps. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » alias | string,null | false | maxLength: 256 | The alias for the data source table. | | » datasetId | string | true | | The ID of the input dataset. | | » datasetVersionId | string,null | false | | The version ID of the input dataset. | | » inputType | string | true | | The data that comes from the Data Registry. | | » sampling | SampleDirectiveCreate | false | | The input data transformation steps. | | » snapshotPolicy | string | false | | Snapshot policy to use for this input. | ### Enumerated Values | Property | Value | | --- | --- | | inputType | datasource | | inputType | dataset | | snapshotPolicy | [fixed, latest] | ## RecipeInputResponse ``` { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] } ``` ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » alias | string,null | true | maxLength: 256 | The alias for the data source table. | | » dataSourceId | string,null | true | | The ID of the input data source. | | » dataStoreId | string,null | true | | The ID of the input data store. | | » datasetId | string,null | true | | The ID of the input dataset. | | » inputType | string | true | | The data that comes from a database connection. | | » sampling | SampleDirectiveCreate | false | | The input data transformation steps. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » alias | string,null | true | maxLength: 256 | The alias for the data source table. | | » datasetId | string | true | | The ID of the input dataset. | | » datasetVersionId | string,null | true | | The version ID of the input dataset. | | » inputType | string | true | | The data that comes from the Data Registry. | | » sampling | SampleDirectiveCreate | false | | The input data transformation steps. | | » snapshotPolicy | string | true | | Snapshot policy to use for this input. | ### Enumerated Values | Property | Value | | --- | --- | | inputType | datasource | | inputType | dataset | | snapshotPolicy | [fixed, latest] | ## RecipeInputStatsResponse ``` { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "columnCount": { "description": "Number of features in original (not sampled) data source", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "connectionName": { "description": "The user-friendly name of the data store.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "datasetId": { "description": "The ID of the input data source.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "datasetVersionId": { "description": "The ID of the input data source,", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "inputType": { "description": "Source type data came from", "enum": [ "datasource", "dataset" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Combination of \"catalog\", \"schema\" and \"table\" from data source", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rowCount": { "description": "Number of rows in original (not sampled) data source", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string", "x-versionadded": "v2.36" }, "status": { "description": "Input preparation status", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "alias", "columnCount", "connectionName", "dataSourceId", "dataStoreId", "inputType", "name", "rowCount", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | alias | string,null | true | maxLength: 256 | The alias for the data source table. | | columnCount | integer,null | true | | Number of features in original (not sampled) data source | | connectionName | string,null | true | | The user-friendly name of the data store. | | dataSourceId | string,null | true | | The ID of the input data source. | | dataStoreId | string,null | true | | The ID of the input data store. | | datasetId | string,null | false | | The ID of the input data source. | | datasetVersionId | string,null | false | | The ID of the input data source, | | inputType | string | true | | Source type data came from | | name | string,null | true | | Combination of "catalog", "schema" and "table" from data source | | rowCount | integer,null | true | | Number of rows in original (not sampled) data source | | sampling | SampleDirectiveCreate | false | | The input data transformation steps. | | snapshotPolicy | string | false | | Snapshot policy to use for this input. | | status | string | true | | Input preparation status | ### Enumerated Values | Property | Value | | --- | --- | | inputType | [datasource, dataset] | | snapshotPolicy | [fixed, latest] | | status | [ABORTED, COMPLETED, ERROR, EXPIRED, INITIALIZED, RUNNING] | ## RecipeInputUpdate ``` { "properties": { "inputs": { "description": "List of data sources and their sampling configurations.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "datasetId", "inputType" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "inputs" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputs | [RecipeInput] | true | maxItems: 1000minItems: 1 | List of data sources and their sampling configurations. | ## RecipeInputsResponse ``` { "properties": { "inputs": { "description": "List of recipe inputs", "items": { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "columnCount": { "description": "Number of features in original (not sampled) data source", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "connectionName": { "description": "The user-friendly name of the data store.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "datasetId": { "description": "The ID of the input data source.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "datasetVersionId": { "description": "The ID of the input data source,", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "inputType": { "description": "Source type data came from", "enum": [ "datasource", "dataset" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Combination of \"catalog\", \"schema\" and \"table\" from data source", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rowCount": { "description": "Number of rows in original (not sampled) data source", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string", "x-versionadded": "v2.36" }, "status": { "description": "Input preparation status", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "alias", "columnCount", "connectionName", "dataSourceId", "dataStoreId", "inputType", "name", "rowCount", "status" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "inputs" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputs | [RecipeInputStatsResponse] | true | maxItems: 1000minItems: 1 | List of recipe inputs | ## RecipeOperationsUpdate ``` { "properties": { "force": { "default": false, "description": "If `true` then operations are stored even if they contain errors", "type": "boolean" }, "operations": { "description": "List of directives to run for the recipe.", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array" } }, "required": [ "operations" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | force | boolean | false | | If true then operations are stored even if they contain errors | | operations | [OneOfDirective] | true | maxItems: 1000 | List of directives to run for the recipe. | ## RecipePreviewResponse ``` { "properties": { "byteSize": { "description": "Data memory usage", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "columns": { "description": "List of columns in data preview", "items": { "description": "Column name", "type": "string" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.33" }, "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of records output by the query.", "items": { "description": "List of values for a single database record, ordered as the columns are ordered.", "items": { "description": "String representation of the column's value.", "type": "string" }, "maxItems": 10000, "type": "array" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "estimatedSizeExceedsLimit": { "description": "Defines if downsampling should be done based on sample size", "type": "boolean", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "resultSchema": { "description": "JDBC result schema", "items": { "description": "JDBC result column description", "properties": { "columnDefaultValue": { "description": "Default value of the column.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataType": { "description": "DataType of the column.", "type": "string", "x-versionadded": "v2.33" }, "dataTypeInt": { "description": "Integer value of the column data type.", "type": "integer", "x-versionadded": "v2.33" }, "isInPrimaryKey": { "description": "True if the column is in the primary key .", "type": "boolean", "x-versionadded": "v2.33" }, "isNullable": { "description": "If the column values can be null.", "enum": [ "NO", "UNKNOWN", "YES" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Name of the column.", "type": "string", "x-versionadded": "v2.33" }, "precision": { "description": "Precision of the column.", "type": "integer", "x-versionadded": "v2.33" }, "scale": { "description": "Scale of the column.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "dataType", "name" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.33" }, "storedCount": { "description": "The number of rows available for preview.", "minimum": 0, "type": "integer", "x-versionadded": "v2.37" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "byteSize", "columns", "data", "estimatedSizeExceedsLimit", "next", "previous", "resultSchema", "storedCount", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | byteSize | integer | true | minimum: 0 | Data memory usage | | columns | [string] | true | maxItems: 10000 | List of columns in data preview | | count | integer | false | | The number of items returned on this page. | | data | [array] | true | maxItems: 1000 | List of records output by the query. | | estimatedSizeExceedsLimit | boolean | true | | Defines if downsampling should be done based on sample size | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | resultSchema | [DataStoreExtendedColumnNoKeysResponse] | true | maxItems: 10000 | JDBC result schema | | storedCount | integer | true | minimum: 0 | The number of rows available for preview. | | totalCount | integer | true | | The total number of items across all pages. | ## RecipeResponse ``` { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string,null(date-time) | true | | ISO 8601-formatted date/time when the recipe was created. | | createdBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | | description | string | true | maxLength: 1000 | The recipe description. | | dialect | string | true | | Source type data was retrieved from. | | downsampling | OneOfDownsamplingDirective | true | | Data transformation step. | | errorMessage | string,null | true | | Error message related to the specific operation | | failedOperationsIndex | integer,null | true | | Index of the first operation where error appears. | | inputs | [RecipeInputResponse] | true | maxItems: 1000 | List of data sources. | | name | string,null | true | maxLength: 255 | The recipe name. | | operations | [OneOfDirective] | true | maxItems: 1000 | List of transformations | | originalDatasetId | string,null | true | | The ID of the dataset used to create the recipe. | | recipeId | string | true | | The ID of the recipe. | | recipeType | string | true | | Type of the recipe workflow. | | settings | RecipeSettingsResponse | true | | Recipe settings reusable at a modeling stage. | | sql | string,null | true | maxLength: 320000 | Recipe SQL query. | | status | string | true | | Recipe publication status. | | updatedAt | string,null(date-time) | true | | ISO 8601-formatted date/time when the recipe was last updated. | | updatedBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | ### Enumerated Values | Property | Value | | --- | --- | | dialect | [snowflake, bigquery, spark-feature-discovery, databricks, spark, postgres] | | recipeType | [sql, Sql, SQL, wrangling, Wrangling, WRANGLING, featureDiscovery, FeatureDiscovery, FEATURE_DISCOVERY, featureDiscoveryPrivatePreview, FeatureDiscoveryPrivatePreview, FEATURE_DISCOVERY_PRIVATE_PREVIEW] | | status | [draft, preview, published] | ## RecipeRunPreviewAsync ``` { "properties": { "credentialId": { "description": "The ID of the credentials to use for the connection. If not given, the default credentials for the connection will be used.", "type": "string" }, "numberOfOperationsToUse": { "description": "The number indicating how many operations from the beginning to compute a preview for.", "minimum": 0, "type": "integer", "x-versionadded": "v2.35" } }, "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the credentials to use for the connection. If not given, the default credentials for the connection will be used. | | numberOfOperationsToUse | integer | false | minimum: 0 | The number indicating how many operations from the beginning to compute a preview for. | ## RecipeSettingsResponse ``` { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" } ``` Recipe settings reusable at a modeling stage. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureDiscoveryProjectId | string,null | true | | Associated feature discovery project ID. | | featureDiscoverySupervisedFeatureReduction | boolean,null | true | | Run supervised feature reduction for Feature Discovery. | | predictionPoint | string,null | true | maxLength: 255 | The date column to be used as the prediction point for time-based feature engineering. | | relationshipsConfigurationId | string,null | true | | Associated relationships configuration ID. | | sparkInstanceSize | string | true | | The Spark instance size to use, if applicable. | | target | string,null | false | | The feature to use as the target at the modeling stage. | | weightsFeature | string,null | false | | The weights feature. | ### Enumerated Values | Property | Value | | --- | --- | | sparkInstanceSize | [small, medium, large] | ## RecipeSettingsUpdate ``` { "properties": { "featureDiscoverySupervisedFeatureReduction": { "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "[Deprecated] No effect. The relationships configuration ID field is immutable.", "type": [ "string", "null" ], "x-versionadded": "v2.33", "x-versiondeprecated": "v2.34" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureDiscoverySupervisedFeatureReduction | boolean,null | false | | Run supervised feature reduction for Feature Discovery. | | predictionPoint | string,null | false | maxLength: 255 | The date column to be used as the prediction point for time-based feature engineering. | | relationshipsConfigurationId | string,null | false | | [Deprecated] No effect. The relationships configuration ID field is immutable. | | sparkInstanceSize | string | false | | The Spark instance size to use, if applicable. | | target | string,null | false | | The feature to use as the target at the modeling stage. | | weightsFeature | string,null | false | | The weights feature. | ### Enumerated Values | Property | Value | | --- | --- | | sparkInstanceSize | [small, medium, large] | ## RecipesListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "A list of the datasets in this Use Case.", "items": { "properties": { "createdAt": { "description": "ISO 8601-formatted date/time when the recipe was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The recipe description.", "maxLength": 1000, "type": "string", "x-versionadded": "v2.33" }, "dialect": { "description": "Source type data was retrieved from.", "enum": [ "snowflake", "bigquery", "spark-feature-discovery", "databricks", "spark", "postgres" ], "type": "string", "x-versionadded": "v2.33" }, "downsampling": { "description": "Data transformation step.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "rows": { "description": "The number of sampled rows.", "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The start number of the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" }, "directive": { "description": "The downsampling method.", "enum": [ "smart-downsampling" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "errorMessage": { "default": null, "description": "Error message related to the specific operation", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "failedOperationsIndex": { "default": null, "description": "Index of the first operation where error appears.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "inputs": { "description": "List of data sources.", "items": { "discriminator": { "propertyName": "inputType" }, "oneOf": [ { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "dataSourceId": { "description": "The ID of the input data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "The ID of the input data store.", "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from a database connection.", "enum": [ "datasource" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } }, "required": [ "alias", "dataSourceId", "dataStoreId", "datasetId", "inputType" ], "type": "object" }, { "properties": { "alias": { "description": "The alias for the data source table.", "maxLength": 256, "type": [ "string", "null" ] }, "datasetId": { "description": "The ID of the input dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the input dataset.", "type": [ "string", "null" ] }, "inputType": { "description": "The data that comes from the Data Registry.", "enum": [ "dataset" ], "type": "string" }, "sampling": { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] }, "snapshotPolicy": { "description": "Snapshot policy to use for this input.", "enum": [ "fixed", "latest" ], "type": "string" } }, "required": [ "alias", "datasetId", "datasetVersionId", "inputType", "snapshotPolicy" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "name": { "description": "The recipe name.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "operations": { "description": "List of transformations", "items": { "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The transformation description.", "properties": { "conditions": { "description": "The list of conditions.", "items": { "properties": { "column": { "description": "The column name.", "type": "string", "x-versionadded": "v2.33" }, "function": { "description": "The function used to evaluate each value.", "enum": [ "between", "contains", "eq", "gt", "gte", "lt", "lte", "neq", "notnull", "null" ], "type": "string", "x-versionadded": "v2.33" }, "functionArguments": { "default": [], "description": "The arguments to use with the function.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 2, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "column", "function" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "keepRows": { "default": true, "description": "Determines whether matching rows should be kept or dropped.", "type": "boolean", "x-versionadded": "v2.33" }, "operator": { "default": "and", "description": "The operator to apply on multiple conditions.", "enum": [ "and", "or" ], "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "conditions", "keepRows", "operator", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "filter" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "replace" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "expression": { "description": "The expression for new feature computation.", "type": "string", "x-versionadded": "v2.33" }, "newFeatureName": { "description": "The new feature name which will hold results of expression evaluation.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "expression", "newFeatureName", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "compute-new" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "skip" ], "type": "object", "x-versionadded": "v2.37" }, "directive": { "description": "The single data transformation step.", "enum": [ "dedupe-rows" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columns": { "description": "The list of columns.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "keepColumns": { "default": false, "description": "If True, keep only the specified columns. If False (default), drop the specified columns.", "type": "boolean", "x-versionadded": "v2.37" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columns", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "drop-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "rename-columns" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The transformation description.", "discriminator": { "propertyName": "source" }, "oneOf": [ { "properties": { "joinType": { "description": "The join type between primary and secondary data sources.", "enum": [ "inner", "left", "cartesian" ], "type": "string" }, "leftKeys": { "description": "The list of columns to be used in the \"ON\" clause from the primary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightDataSourceId": { "description": "The ID of the input data source.", "type": "string" }, "rightKeys": { "description": "The list of columns to be used in the \"ON\" clause from a secondary data source. Required for inner, left joins, not used for cartesian joins.", "items": { "type": "string" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "rightPrefix": { "description": "Optional prefix to be added to all column names from the right table in the join result.", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" }, "source": { "description": "The source type.", "enum": [ "table" ], "type": "string" } }, "required": [ "joinType", "rightDataSourceId", "skip", "source" ], "type": "object" } ], "x-versionadded": "v2.34" }, "directive": { "description": "The single data transformation step.", "enum": [ "join" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The aggregation description.", "properties": { "aggregations": { "description": "The aggregations.", "items": { "properties": { "feature": { "default": null, "description": "The feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "functions": { "description": "The functions.", "items": { "enum": [ "sum", "min", "max", "count", "count-distinct", "stddev", "avg", "most-frequent", "median" ], "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "functions" ], "type": "object" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "groupBy": { "description": "The column(s) to group by.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "aggregations", "groupBy", "skip" ], "type": "object" }, "directive": { "description": "The single data transformation step.", "enum": [ "aggregate" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" } ] }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "originalDatasetId": { "description": "The ID of the dataset used to create the recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "recipeId": { "description": "The ID of the recipe.", "type": "string", "x-versionadded": "v2.33" }, "recipeType": { "description": "Type of the recipe workflow.", "enum": [ "sql", "Sql", "SQL", "wrangling", "Wrangling", "WRANGLING", "featureDiscovery", "FeatureDiscovery", "FEATURE_DISCOVERY", "featureDiscoveryPrivatePreview", "FeatureDiscoveryPrivatePreview", "FEATURE_DISCOVERY_PRIVATE_PREVIEW" ], "type": "string", "x-versionadded": "v2.33" }, "settings": { "description": "Recipe settings reusable at a modeling stage.", "properties": { "featureDiscoveryProjectId": { "description": "Associated feature discovery project ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoverySupervisedFeatureReduction": { "default": null, "description": "Run supervised feature reduction for Feature Discovery.", "type": [ "boolean", "null" ], "x-versionadded": "v2.34" }, "predictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relationshipsConfigurationId": { "description": "Associated relationships configuration ID.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "sparkInstanceSize": { "default": "small", "description": "The Spark instance size to use, if applicable.", "enum": [ "small", "medium", "large" ], "type": "string", "x-versionadded": "v2.38" }, "target": { "description": "The feature to use as the target at the modeling stage.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "weightsFeature": { "description": "The weights feature.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "featureDiscoveryProjectId", "featureDiscoverySupervisedFeatureReduction", "predictionPoint", "relationshipsConfigurationId", "sparkInstanceSize" ], "type": "object" }, "sql": { "description": "Recipe SQL query.", "maxLength": 320000, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "status": { "description": "Recipe publication status.", "enum": [ "draft", "preview", "published" ], "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "ISO 8601-formatted date/time when the recipe was last updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "description", "dialect", "downsampling", "errorMessage", "failedOperationsIndex", "inputs", "name", "operations", "originalDatasetId", "recipeId", "recipeType", "settings", "sql", "status", "updatedAt", "updatedBy" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RecipeResponse] | true | | A list of the datasets in this Use Case. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RefinexInsightsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The list of features related to the requested dataset.", "items": { "properties": { "datasetId": { "description": "The ID of the dataset the feature belongs to", "type": "string", "x-versionadded": "v2.33" }, "datasetVersionId": { "description": "The ID of the dataset version the feature belongs to.", "type": "string", "x-versionadded": "v2.33" }, "dateFormat": { "description": "The date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The number of the column in the dataset.", "type": "integer", "x-versionadded": "v2.33" }, "isZeroInflated": { "description": "whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ], "x-versionadded": "v2.33" }, "language": { "description": "Detected language of the feature.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "Whether feature has too few values to be informative.", "type": "boolean", "x-versionadded": "v2.33" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "majorityClassCount": { "description": "The number of rows with a majority class value if smart downsampling is applicable to this feature.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "max": { "description": "Maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "Maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "Maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "mean": { "description": "Arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "median": { "description": "Median of the EDA sample of the feature.", "oneOf": [ { "description": "Median of the EDA sample of the feature.", "type": "string" }, { "description": "Median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "min": { "description": "Minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "Minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "Minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "minorityClassCount": { "description": "The number of rows with neither null nor majority class value if smart downsampling is applicable to this feature.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "naCount": { "description": "Number of missing values.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Feature name", "type": "string", "x-versionadded": "v2.33" }, "plot": { "description": "Plot data based on feature values.", "items": { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.30" }, "sampleRows": { "description": "The number of rows in the sample used to calculate the statistics.", "type": "integer", "x-versionadded": "v2.35", "x-versiondeprecated": "v2.36" }, "stdDev": { "description": "Standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "Standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "Standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "timeSeriesEligibilityReason": { "description": "why the feature is ineligible for time series projects, or 'suitable' if it is eligible.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "timeSeriesEligibilityReasonAggregation": { "description": "why the feature is ineligible for aggregation, or 'suitable' if it is eligible.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "timeSeriesEligible": { "description": "whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.", "type": "boolean", "x-versionadded": "v2.33" }, "timeSeriesEligibleAggregation": { "description": "whether this feature can be used as a datetime feature for aggregationfor time series data prep. Always false for non-date features.", "type": "boolean", "x-versionadded": "v2.24" }, "timeStep": { "description": "The minimum time step that can be used to specify time series windows. The units for this value are the ``timeUnit``. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "timeStepAggregation": { "description": "The minimum time step that can be used to aggregate using this feature for time series data prep. The units for this value are the ``timeUnit``. Only present for date features that are eligible for aggregation in time series data prep and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "timeUnit": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "timeUnitAggregation": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. Only present for date features eligible for aggregation, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "uniqueCount": { "description": "Number of unique values.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "datasetId", "datasetVersionId", "dateFormat", "featureType", "id", "majorityClassCount", "minorityClassCount", "name", "sampleRows" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "message": { "description": "The status message.", "type": "string", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "status": { "description": "The job status.", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string", "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [WranglingFeatureResponse] | true | maxItems: 100 | The list of features related to the requested dataset. | | message | string | false | | The status message. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | status | string | false | | The job status. | | totalCount | integer | true | | The total number of items across all pages. | ### Enumerated Values | Property | Value | | --- | --- | | status | [ABORTED, COMPLETED, ERROR, EXPIRED, INITIALIZED, RUNNING] | ## Relationship ``` { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataset1Identifier | string,null | false | maxLength: 20minLength: 1minLength: 1 | Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset. | | dataset1Keys | [string] | true | maxItems: 10minItems: 1 | column(s) in the first dataset that are used to join to the second dataset. | | dataset2Identifier | string | true | maxLength: 20minLength: 1minLength: 1 | Identifier of the second dataset in the relationship. | | dataset2Keys | [string] | true | maxItems: 10minItems: 1 | column(s) in the second dataset that are used to join to the first dataset. | | featureDerivationWindowEnd | integer | false | maximum: 0 | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | featureDerivationWindowStart | integer | false | | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | featureDerivationWindowTimeUnit | string | false | | Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | featureDerivationWindows | [FeatureDerivationWindow] | false | maxItems: 3 | The list of feature derivation window definitions that will be used. | | predictionPointRounding | integer | false | maximum: 30 | Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided. | | predictionPointRoundingTimeUnit | string | false | | Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided. | ### Enumerated Values | Property | Value | | --- | --- | | featureDerivationWindowTimeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | | predictionPointRoundingTimeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | ## RelationshipQualityAssessmentsCreate ``` { "properties": { "credentials": { "description": "Credentials for dynamic policy secondary datasets.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "credentialId": { "description": "ID of the credentials object in credential store.Can only be used along with catalogVersionId.", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": [ "string", "null" ] } }, "required": [ "credentialId" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. ", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" } ] }, "maxItems": 30, "type": "array" }, "datetimePartitionColumn": { "description": "If a datetime partition column was used, the name of the column.", "type": [ "string", "null" ] }, "featureEngineeringPredictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "type": [ "string", "null" ] }, "relationshipsConfiguration": { "description": "Object describing how secondary datasets are related to the primary dataset", "properties": { "datasetDefinitions": { "description": "The list of datasets.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "maxItems": 30, "minItems": 1, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "Id of the relationship configuration", "type": "string" }, "relationships": { "description": "The list of relationships.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "maxItems": 70, "minItems": 1, "type": "array" }, "snowflakePushDownCompatible": { "description": "Flag indicating if the relationships configuration is compatible with Snowflake push down processing.", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "id", "relationships" ], "type": "object" }, "userId": { "description": "Mongo Id of the User who created the request", "type": "string" } }, "required": [ "relationshipsConfiguration" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentials | [oneOf] | false | maxItems: 30 | Credentials for dynamic policy secondary datasets. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | StoredCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogPasswordCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimePartitionColumn | string,null | false | | If a datetime partition column was used, the name of the column. | | featureEngineeringPredictionPoint | string,null | false | | The date column to be used as the prediction point for time-based feature engineering. | | relationshipsConfiguration | RelationshipsConfigPayload | true | | Object describing how secondary datasets are related to the primary dataset | | userId | string | false | | Mongo Id of the User who created the request | ## RelationshipsConfigPayload ``` { "description": "Object describing how secondary datasets are related to the primary dataset", "properties": { "datasetDefinitions": { "description": "The list of datasets.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "maxItems": 30, "minItems": 1, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "Id of the relationship configuration", "type": "string" }, "relationships": { "description": "The list of relationships.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "maxItems": 70, "minItems": 1, "type": "array" }, "snowflakePushDownCompatible": { "description": "Flag indicating if the relationships configuration is compatible with Snowflake push down processing.", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "id", "relationships" ], "type": "object" } ``` Object describing how secondary datasets are related to the primary dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetDefinitions | [DatasetDefinition] | true | maxItems: 30minItems: 1 | The list of datasets. | | featureDiscoveryMode | string,null | false | | Mode of feature discovery. Supported values are 'default' and 'manual'. | | featureDiscoverySettings | [FeatureDiscoverySettingResponse] | false | maxItems: 100 | The list of feature discovery settings used to customize the feature discovery process. | | id | string | true | | Id of the relationship configuration | | relationships | [Relationship] | true | maxItems: 70minItems: 1 | The list of relationships. | | snowflakePushDownCompatible | boolean,null | false | | Flag indicating if the relationships configuration is compatible with Snowflake push down processing. | ### Enumerated Values | Property | Value | | --- | --- | | featureDiscoveryMode | [default, manual] | ## RenameColumn ``` { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | newName | string | true | | The new column name. | | originalName | string | true | | The original column name. | ## RenameColumnsArguments ``` { "description": "The transformation description.", "properties": { "columnMappings": { "description": "The list of name mappings.", "items": { "properties": { "newName": { "description": "The new column name.", "type": "string", "x-versionadded": "v2.33" }, "originalName": { "description": "The original column name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "newName", "originalName" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "columnMappings", "skip" ], "type": "object" } ``` The transformation description. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnMappings | [RenameColumn] | true | maxItems: 1000minItems: 1 | The list of name mappings. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ## ReplaceDirectiveArguments ``` { "description": "The transformation description.", "properties": { "isCaseSensitive": { "default": true, "description": "The flag indicating if the \"search_for\" value is case-sensitive.", "type": "boolean", "x-versionadded": "v2.33" }, "matchMode": { "description": "The match mode to use when detecting \"search_for\" values.", "enum": [ "partial", "exact", "regex" ], "type": "string", "x-versionadded": "v2.33" }, "origin": { "description": "The place name to look for in values.", "type": "string", "x-versionadded": "v2.33" }, "replacement": { "default": "", "description": "The replacement value.", "type": "string", "x-versionadded": "v2.33" }, "searchFor": { "description": "Indicates what needs to be replaced.", "type": "string", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "matchMode", "origin", "searchFor", "skip" ], "type": "object" } ``` The transformation description. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isCaseSensitive | boolean | false | | The flag indicating if the "search_for" value is case-sensitive. | | matchMode | string | true | | The match mode to use when detecting "search_for" values. | | origin | string | true | | The place name to look for in values. | | replacement | string | false | | The replacement value. | | searchFor | string | true | | Indicates what needs to be replaced. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ### Enumerated Values | Property | Value | | --- | --- | | matchMode | [partial, exact, regex] | ## SampleDirectiveCreate ``` { "description": "The input data transformation steps.", "discriminator": { "propertyName": "directive" }, "oneOf": [ { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "random-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "datetimePartitionColumn": { "description": "The datetime partition column to order by.", "type": "string", "x-versionadded": "v2.33" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rows": { "default": 10000, "description": "The number of rows to be sampled.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "selectedSeries": { "description": "The selected series to be sampled. Requires \"multiseriesIdColumn\".", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "strategy": { "default": "earliest", "description": "Sets whether to take the latest or earliest rows relative to the datetime partition column.", "enum": [ "earliest", "latest" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "datetimePartitionColumn", "skip", "strategy" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "datetime-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The interactive sampling config.", "properties": { "rows": { "default": 1000, "description": "The number of rows to be selected.", "maximum": 10000, "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "rows", "skip" ], "type": "object" }, "directive": { "description": "The directive name.", "enum": [ "limit" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "The directive name.", "enum": [ "tablesample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" }, { "properties": { "arguments": { "description": "The sampling config.", "properties": { "samplingMethod": { "default": "rows", "description": "The sampling method. If the sampling method is \"rows\", the size is the number of rows to be sampled. If the sampling method is \"percent\", the size is the percent of the table to be sampled.", "enum": [ "percent", "rows" ], "type": "string" }, "seed": { "default": 0, "description": "The starting value used to initialize the pseudo random number generator.", "minimum": 0, "type": "integer" }, "size": { "description": "The number of rows or percent of the table to be sampled. The value is dependent on the sampling method.", "minimum": 0.01, "type": "number" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean" } }, "required": [ "samplingMethod", "skip" ], "type": "object", "x-versionadded": "v2.38" }, "directive": { "description": "The directive name.", "enum": [ "efficient-rowbased-sample" ], "type": "string" } }, "required": [ "directive" ], "type": "object" } ] } ``` The input data transformation steps. ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | RandomSampleArgumentsCreate | false | | The interactive sampling config. | | » directive | string | true | | The directive name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | DatetimeSampleArgumentsCreate | false | | The interactive sampling config. | | » directive | string | true | | The directive name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | LimitDirectiveArguments | true | | The interactive sampling config. | | » directive | string | true | | The directive name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | TableSampleArgumentsCreate | false | | The sampling config. | | » directive | string | true | | The directive name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » arguments | EfficientRowBasedSampleArgumentsCreate | false | | The sampling config. | | » directive | string | true | | The directive name. | ### Enumerated Values | Property | Value | | --- | --- | | directive | random-sample | | directive | datetime-sample | | directive | limit | | directive | tablesample | | directive | efficient-rowbased-sample | ## SmartDownsamplingArguments ``` { "description": "The downsampling configuration.", "properties": { "method": { "description": "The smart downsampling method.", "enum": [ "binary", "zero-inflated" ], "type": "string", "x-versionadded": "v2.33" }, "rows": { "description": "The number of sampled rows.", "minimum": 2, "type": "integer", "x-versionadded": "v2.33" }, "seed": { "default": null, "description": "The starting number for the random number generator", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "method", "rows", "skip" ], "type": "object" } ``` The downsampling configuration. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | method | string | true | | The smart downsampling method. | | rows | integer | true | minimum: 2 | The number of sampled rows. | | seed | integer,null | false | | The starting number for the random number generator | | skip | boolean | true | | If True, this directive will be skipped during processing. | ### Enumerated Values | Property | Value | | --- | --- | | method | [binary, zero-inflated] | ## StatusResponse ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status.", "type": "string" } }, "required": [ "statusId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status. | ## StopSession ``` { "properties": { "size": { "default": "small", "description": "The Spark instance size to stop.", "enum": [ "small", "medium", "large" ], "type": "string" } }, "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | size | string | false | | The Spark instance size to stop. | ### Enumerated Values | Property | Value | | --- | --- | | size | [small, medium, large] | ## StoredCredentials ``` { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "credentialId": { "description": "ID of the credentials object in credential store.Can only be used along with catalogVersionId.", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": [ "string", "null" ] } }, "required": [ "credentialId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogVersionId | string | false | | Identifier of the catalog version | | credentialId | string | true | | ID of the credentials object in credential store.Can only be used along with catalogVersionId. | | url | string,null | false | | URL that is subject to credentials. | ## TableSampleArgumentsCreate ``` { "description": "The sampling config.", "properties": { "percent": { "description": "The percent of the table to be sampled.", "maximum": 100, "minimum": 0, "type": "number" }, "seed": { "default": 0, "description": "The starting number of the random number generator.", "minimum": 0, "type": "integer" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "percent", "skip" ], "type": "object", "x-versionadded": "v2.35" } ``` The sampling config. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | percent | number | true | maximum: 100minimum: 0 | The percent of the table to be sampled. | | seed | integer | false | minimum: 0 | The starting number of the random number generator. | | skip | boolean | true | | If True, this directive will be skipped during processing. | ## TaskPlanItem ``` { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | column | string | true | | Column to apply transformations to. | | taskList | [OneOfTransforms] | true | maxItems: 15minItems: 1 | Tasks to apply to the specific column. | ## TimeSeriesDirective ``` { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | arguments | TimeSeriesDirectiveArguments | true | | Time series directive arguments. | | directive | string | true | | Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based. | ### Enumerated Values | Property | Value | | --- | --- | | directive | time-series | ## TimeSeriesDirectiveArguments ``` { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" } ``` Time series directive arguments. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselinePeriods | [integer] | false | maxItems: 10minItems: 1 | A list of periodicities used to calculate naive target features. | | datetimePartitionColumn | string | true | | The column that is used to order the data. | | forecastDistances | [integer] | true | maxItems: 20minItems: 1 | A list of forecast distances, which defines the number of rows into the future to predict. | | forecastPoint | string(date-time) | false | | Filter output by given forecast point. Can be applied only at the prediction time. | | knownInAdvanceColumns | [string] | false | maxItems: 200 | Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time. | | multiseriesIdColumn | string,null | false | | The series ID column, if present. This column partitions data to create a multiseries modeling project. | | rollingMedianUserDefinedFunction | string,null | false | | To optimize rolling median calculation with relational database sources, pass qualified path to the UDF, as follows: "DB_NAME.SCHEMA_NAME.FUNCTION_NAME". Contact DataRobot Support to fetch a suggested SQL function. | | rollingMostFrequentUserDefinedFunction | string,null | false | | To optimize rolling most frequent calculation with relational database sources, pass qualified path to the UDF, as follows: "DB_NAME.SCHEMA_NAME.FUNCTION_NAME". Contact DataRobot Support to fetch a suggested SQL function. | | skip | boolean | true | | If True, this directive will be skipped during processing. | | targetColumn | string | true | | The column intended to be used as the target for modeling. This parameter is required for generating naive features. | | taskPlan | [TaskPlanItem] | true | maxItems: 200minItems: 1 | Task plan to describe time series specific transformations. | ## TransformationPlanResponse ``` { "properties": { "id": { "description": "The identifier of the transformation plan.", "type": "string" }, "inputParameters": { "description": "The input parameters corresponding to the suggested operations.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "doNotDeriveColumns": { "default": [], "description": "Columns to exclude from derivation; for them only the first lag is suggested.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "excludeLowInfoColumns": { "default": true, "description": "Whether to ignore columns with low signal (only include features that pass a \"reasonableness\" check that determines whether they contain information useful for building a generalizable model).", "type": "boolean" }, "featureDerivationWindows": { "description": "A list of rolling windows of past values, defined in terms of rows, that are used to derive features for the modeling dataset.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 5, "minItems": 1, "type": "array" }, "featureReductionThreshold": { "default": 0.9, "description": "Threshold for feature reduction. For example, 0.9 means that features which cumulatively reach 90 % of importance are returned. Additionally, no more than 200 features are returned.", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "maxLagOrder": { "description": "The maximum lag order. This value cannot be greater than the largest feature derivation window.", "exclusiveMinimum": 0, "maximum": 100, "type": [ "integer", "null" ] }, "multiseriesIdColumn": { "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "numberOfOperationsToUse": { "description": "If set, a transformation plan is suggested after the specified number of operations.", "type": "integer" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" } }, "required": [ "datetimePartitionColumn", "featureDerivationWindows", "forecastDistances", "targetColumn" ], "type": "object", "x-versionadded": "v2.35" }, "status": { "description": "Transformation preparation status", "enum": [ "INITIALIZED", "COMPLETED", "ERROR" ], "type": "string" }, "suggestedOperations": { "description": "The list of operations to apply to a recipe to get the dataset ready for time series modeling.", "items": { "properties": { "arguments": { "description": "Time series directive arguments.", "properties": { "baselinePeriods": { "default": [ 1 ], "description": "A list of periodicities used to calculate naive target features.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 10, "minItems": 1, "type": "array" }, "datetimePartitionColumn": { "description": "The column that is used to order the data.", "type": "string" }, "forecastDistances": { "description": "A list of forecast distances, which defines the number of rows into the future to predict.", "items": { "exclusiveMinimum": 0, "type": "integer" }, "maxItems": 20, "minItems": 1, "type": "array" }, "forecastPoint": { "description": "Filter output by given forecast point. Can be applied only at the prediction time.", "format": "date-time", "type": "string" }, "knownInAdvanceColumns": { "default": [], "description": "Columns that are known in advance (future values are known). Values for these known columns must be specified at prediction time.", "items": { "type": "string" }, "maxItems": 200, "type": "array" }, "multiseriesIdColumn": { "default": null, "description": "The series ID column, if present. This column partitions data to create a multiseries modeling project.", "type": [ "string", "null" ] }, "rollingMedianUserDefinedFunction": { "default": null, "description": "To optimize rolling median calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "rollingMostFrequentUserDefinedFunction": { "default": null, "description": "To optimize rolling most frequent calculation with relational database sources,\n pass qualified path to the UDF, as follows: \"DB_NAME.SCHEMA_NAME.FUNCTION_NAME\".\n Contact DataRobot Support to fetch a suggested SQL function.\n ", "type": [ "string", "null" ] }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "targetColumn": { "description": "The column intended to be used as the target for modeling. This parameter is required for generating naive features.", "type": "string" }, "taskPlan": { "description": "Task plan to describe time series specific transformations.", "items": { "properties": { "column": { "description": "Column to apply transformations to.", "type": "string" }, "taskList": { "description": "Tasks to apply to the specific column.", "items": { "discriminator": { "propertyName": "name" }, "oneOf": [ { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Methods to apply in a rolling window.", "items": { "enum": [ "avg", "max", "median", "min", "stddev" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "numeric-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "methods": { "description": "Window method: most-frequent", "items": { "enum": [ "most-frequent" ], "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" }, "windowSize": { "description": "Rolling window size, defined in terms of rows. Left end is exclusive, right end is inclusive.", "exclusiveMinimum": 0, "maximum": 300, "type": "integer" } }, "required": [ "methods", "skip", "windowSize" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "categorical-stats" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" }, { "properties": { "arguments": { "description": "Task arguments.", "properties": { "orders": { "description": "Lag orders.", "items": { "exclusiveMinimum": 0, "maximum": 300, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "skip": { "default": false, "description": "If True, this directive will be skipped during processing.", "type": "boolean", "x-versionadded": "v2.37" } }, "required": [ "orders", "skip" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Task name.", "enum": [ "lags" ], "type": "string" } }, "required": [ "arguments", "name" ], "type": "object" } ], "x-versionadded": "v2.35" }, "maxItems": 15, "minItems": 1, "type": "array" } }, "required": [ "column", "taskList" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn", "forecastDistances", "skip", "targetColumn", "taskPlan" ], "type": "object", "x-versionadded": "v2.35" }, "directive": { "description": "Time series processing directive, which prepares the dataset for time series modeling. All windows are row-based.", "enum": [ "time-series" ], "type": "string" } }, "required": [ "arguments", "directive" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10, "type": "array" } }, "required": [ "id", "inputParameters", "status", "suggestedOperations" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the transformation plan. | | inputParameters | InputParametersResponse | true | | The input parameters corresponding to the suggested operations. | | status | string | true | | Transformation preparation status | | suggestedOperations | [TimeSeriesDirective] | true | maxItems: 10 | The list of operations to apply to a recipe to get the dataset ready for time series modeling. | ### Enumerated Values | Property | Value | | --- | --- | | status | [INITIALIZED, COMPLETED, ERROR] | ## WranglingFeatureResponse ``` { "properties": { "datasetId": { "description": "The ID of the dataset the feature belongs to", "type": "string", "x-versionadded": "v2.33" }, "datasetVersionId": { "description": "The ID of the dataset version the feature belongs to.", "type": "string", "x-versionadded": "v2.33" }, "dateFormat": { "description": "The date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "The number of the column in the dataset.", "type": "integer", "x-versionadded": "v2.33" }, "isZeroInflated": { "description": "whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ], "x-versionadded": "v2.33" }, "language": { "description": "Detected language of the feature.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "Whether feature has too few values to be informative.", "type": "boolean", "x-versionadded": "v2.33" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "majorityClassCount": { "description": "The number of rows with a majority class value if smart downsampling is applicable to this feature.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "max": { "description": "Maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "Maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "Maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "mean": { "description": "Arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "Arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "median": { "description": "Median of the EDA sample of the feature.", "oneOf": [ { "description": "Median of the EDA sample of the feature.", "type": "string" }, { "description": "Median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "min": { "description": "Minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "Minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "Minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "minorityClassCount": { "description": "The number of rows with neither null nor majority class value if smart downsampling is applicable to this feature.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "naCount": { "description": "Number of missing values.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Feature name", "type": "string", "x-versionadded": "v2.33" }, "plot": { "description": "Plot data based on feature values.", "items": { "properties": { "count": { "description": "Number of values in the bin.", "type": "number" }, "label": { "description": "Bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array", "x-versionadded": "v2.30" }, "sampleRows": { "description": "The number of rows in the sample used to calculate the statistics.", "type": "integer", "x-versionadded": "v2.35", "x-versiondeprecated": "v2.36" }, "stdDev": { "description": "Standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "Standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "Standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.33" }, "timeSeriesEligibilityReason": { "description": "why the feature is ineligible for time series projects, or 'suitable' if it is eligible.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "timeSeriesEligibilityReasonAggregation": { "description": "why the feature is ineligible for aggregation, or 'suitable' if it is eligible.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "timeSeriesEligible": { "description": "whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.", "type": "boolean", "x-versionadded": "v2.33" }, "timeSeriesEligibleAggregation": { "description": "whether this feature can be used as a datetime feature for aggregationfor time series data prep. Always false for non-date features.", "type": "boolean", "x-versionadded": "v2.24" }, "timeStep": { "description": "The minimum time step that can be used to specify time series windows. The units for this value are the ``timeUnit``. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "timeStepAggregation": { "description": "The minimum time step that can be used to aggregate using this feature for time series data prep. The units for this value are the ``timeUnit``. Only present for date features that are eligible for aggregation in time series data prep and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.24" }, "timeUnit": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "timeUnitAggregation": { "description": "The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. Only present for date features eligible for aggregation, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "uniqueCount": { "description": "Number of unique values.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "datasetId", "datasetVersionId", "dateFormat", "featureType", "id", "majorityClassCount", "minorityClassCount", "name", "sampleRows" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the dataset the feature belongs to | | datasetVersionId | string | true | | The ID of the dataset version the feature belongs to. | | dateFormat | string,null | true | | The date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime . | | featureType | string | true | | Feature type. | | id | integer | true | | The number of the column in the dataset. | | isZeroInflated | boolean,null | false | | whether feature has an excessive number of zeros | | keySummary | any | false | | Per key summaries for Summarized Categorical or Multicategorical columns | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FeatureKeySummaryResponseValidatorSummarizedCategorical | false | | For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [FeatureKeySummaryResponseValidatorMultilabel] | false | | For a Multicategorical columns, this will contain statistics for the top classes | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | language | string | false | | Detected language of the feature. | | lowInformation | boolean | false | | Whether feature has too few values to be informative. | | lowerQuartile | any | false | | Lower quartile point of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Lower quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Lower quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | majorityClassCount | integer,null | true | | The number of rows with a majority class value if smart downsampling is applicable to this feature. | | max | any | false | | Maximum value of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Maximum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Maximum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | mean | any | false | | Arithmetic mean of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Arithmetic mean of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Arithmetic mean of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | median | any | false | | Median of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Median of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Median of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | min | any | false | | Minimum value of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Minimum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Minimum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | minorityClassCount | integer,null | true | | The number of rows with neither null nor majority class value if smart downsampling is applicable to this feature. | | naCount | integer,null | false | | Number of missing values. | | name | string | true | | Feature name | | plot | [DatasetFeaturePlotDataResponse] | false | | Plot data based on feature values. | | sampleRows | integer | true | | The number of rows in the sample used to calculate the statistics. | | stdDev | any | false | | Standard deviation of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Standard deviation of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Standard deviation of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timeSeriesEligibilityReason | string,null | false | | why the feature is ineligible for time series projects, or 'suitable' if it is eligible. | | timeSeriesEligibilityReasonAggregation | string,null | false | | why the feature is ineligible for aggregation, or 'suitable' if it is eligible. | | timeSeriesEligible | boolean | false | | whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements. | | timeSeriesEligibleAggregation | boolean | false | | whether this feature can be used as a datetime feature for aggregationfor time series data prep. Always false for non-date features. | | timeStep | integer,null | false | | The minimum time step that can be used to specify time series windows. The units for this value are the timeUnit. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise. | | timeStepAggregation | integer,null | false | | The minimum time step that can be used to aggregate using this feature for time series data prep. The units for this value are the timeUnit. Only present for date features that are eligible for aggregation in time series data prep and null otherwise. | | timeUnit | string,null | false | | The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise. | | timeUnitAggregation | string,null | false | | The unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. Only present for date features eligible for aggregation, and null otherwise. | | uniqueCount | integer,null | false | | Number of unique values. | | upperQuartile | any | false | | Upper quartile point of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Upper quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Upper quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | featureType | [Boolean, Categorical, Currency, Date, Date Duration, Document, Image, Interaction, Length, Location, Multicategorical, Numeric, Percentage, Summarized Categorical, Text, Time] | --- # DataRobot models URL: https://docs.datarobot.com/en/docs/api/reference/public-api/datarobot_models.html > Use the endpoints described below to manage DataRobot models. # DataRobot models Use the endpoints described below to manage DataRobot models. ## List of bias mitigated models by project ID Operation path: `GET /api/v2/projects/{projectId}/biasMitigatedModels/` Authentication requirements: `BearerAuth` List of bias mitigated models for the selected project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | parentModelId | query | string | false | Retrieve a list of mitigated models for the parent model if specified. Otherwise retrieve a list of all mitigated models for the project. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Retrieve list of mitigated models for project.", "items": { "properties": { "biasMitigationTechnique": { "description": "Method applied to perform bias mitigation.", "enum": [ "preprocessingReweighing", "postProcessingRejectionOptionBasedClassification" ], "type": "string", "x-versionadded": "v2.27" }, "includeBiasMitigationFeatureAsPredictorVariable": { "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.", "type": "boolean", "x-versionadded": "v2.27" }, "modelId": { "description": "Mitigated model ID", "type": "string" }, "parentModelId": { "description": "Parent model ID", "type": [ "string", "null" ] }, "protectedFeature": { "description": "Protected feature that will be used in a bias mitigation task to mitigate bias", "type": "string" } }, "required": [ "biasMitigationTechnique", "includeBiasMitigationFeatureAsPredictorVariable", "modelId", "parentModelId", "protectedFeature" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns Bias Mitigated models results. | BiasMitigatedModelsListResponse | ## Add a request by project ID Operation path: `POST /api/v2/projects/{projectId}/biasMitigatedModels/` Authentication requirements: `BearerAuth` Add a request to the queue to train a model with bias mitigation applied. If the job has been previously submitted, the request will return the `jobId` of the previously submitted job. Use this `jobId` to check status of the previously submitted job. ### Body parameter ``` { "properties": { "biasMitigationFeature": { "description": "The name of the protected feature used to mitigate bias on models.", "minLength": 1, "type": "string", "x-versionadded": "v2.27" }, "biasMitigationParentLid": { "description": "The ID of the model to modify with a bias-mitigation task.", "type": "string" }, "biasMitigationTechnique": { "description": "Method applied to perform bias mitigation.", "enum": [ "preprocessingReweighing", "postProcessingRejectionOptionBasedClassification" ], "type": "string", "x-versionadded": "v2.27" }, "includeBiasMitigationFeatureAsPredictorVariable": { "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.", "type": "boolean", "x-versionadded": "v2.27" } }, "required": [ "biasMitigationFeature", "biasMitigationParentLid", "biasMitigationTechnique", "includeBiasMitigationFeatureAsPredictorVariable" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | BiasMitigationModelCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The model training request has been successfully submitted. See Location header. | None | ## Get bias mitigation data quality information by project ID Operation path: `GET /api/v2/projects/{projectId}/biasMitigationFeatureInfo/` Authentication requirements: `BearerAuth` Get bias mitigation data quality information for a given projectId and featureName. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | featureName | query | string | true | Name of feature for mitigation info. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "messages": { "description": "List of data quality messages. The list may include reports on more than one data quality issue, if present.", "items": { "properties": { "additionalInfo": { "description": "Zero or more text strings for secondary display after user clicks for more information.", "items": { "type": "string" }, "maxItems": 50, "type": "array" }, "messageLevel": { "description": "Message severity level.", "enum": [ "CRITICAL", "INFORMATIONAL", "NO_ISSUES", "WARNING" ], "type": "string" }, "messageText": { "description": "Text for primary display in UI.", "maxLength": 500, "minLength": 1, "type": "string" } }, "required": [ "messageLevel", "messageText" ], "type": "object" }, "maxItems": 50, "minItems": 1, "type": "array" } }, "required": [ "messages" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Dictionary with one entry ("messages") with list of data quality information about a feature. | MessagesInfo | ## Submit a job by project ID Operation path: `POST /api/v2/projects/{projectId}/biasMitigationFeatureInfo/{featureName}/` Authentication requirements: `BearerAuth` Submit a job to create bias mitigation data quality information for a given projectId and featureName. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID | | featureName | path | string | true | Name of feature for mitigation info. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | A URI of the newly submitted job in the "Location" header. | None | ## List all blenders by project ID Operation path: `GET /api/v2/projects/{projectId}/blenderModels/` Authentication requirements: `BearerAuth` List all blenders in a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. If 0, all results. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/blenderModels/{modelId}/][get-apiv2projectsprojectidblendermodelsmodelid].", "items": { "properties": { "blenderMethod": { "description": "Method used to blend results of underlying models.", "type": "string" }, "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelIds": { "description": "List of models used in blender.", "items": { "type": "string" }, "type": "array" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderMethod", "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelIds", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of all of the blenders in a project. | BlenderListResponse | | 404 | Not Found | This resource does not exist. | None | ## Create a blender by project ID Operation path: `POST /api/v2/projects/{projectId}/blenderModels/` Authentication requirements: `BearerAuth` Create a blender from other models using a specified blender method. Note: Time Series projects only allow the following blender methods: "AVG", "MED", "FORECAST_DISTANCE_ENET", and "FORECAST_DISTANCE_AVG". ### Body parameter ``` { "properties": { "blenderMethod": { "description": "The blender method, one of \"PLS\", \"GLM\", \"AVG\", \"ENET\", \"MED\", \"MAE\", \"MAEL1\", \"TF\", \"RF\", \"LGBM\", \"FORECAST_DISTANCE_ENET\" (new in v2.18), \"FORECAST_DISTANCE_AVG\" (new in v2.18), \"MIN\", \"MAX\".", "enum": [ "PLS", "GLM", "ENET", "AVG", "MED", "MAE", "MAEL1", "FORECAST_DISTANCE_AVG", "FORECAST_DISTANCE_ENET", "MAX", "MIN" ], "type": "string" }, "modelIds": { "description": "The list of models to use in blender.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "blenderMethod", "modelIds" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | BlenderCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Model job successfully added to queue. See the Location header. | None | | 404 | Not Found | This resource does not exist. | None | | 422 | Unprocessable Entity | Unable to create a blender or request is not supported in this context. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | A url that can be polled to check the status of the job. | ## Check if models can be blended by project ID Operation path: `POST /api/v2/projects/{projectId}/blenderModels/blendCheck/` Authentication requirements: `BearerAuth` Check if models can be blended. ### Body parameter ``` { "properties": { "blenderMethod": { "description": "The blender method, one of \"PLS\", \"GLM\", \"AVG\", \"ENET\", \"MED\", \"MAE\", \"MAEL1\", \"TF\", \"RF\", \"LGBM\", \"FORECAST_DISTANCE_ENET\" (new in v2.18), \"FORECAST_DISTANCE_AVG\" (new in v2.18), \"MIN\", \"MAX\".", "enum": [ "PLS", "GLM", "ENET", "AVG", "MED", "MAE", "MAEL1", "FORECAST_DISTANCE_AVG", "FORECAST_DISTANCE_ENET", "MAX", "MIN" ], "type": "string" }, "modelIds": { "description": "The list of models to use in blender.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "blenderMethod", "modelIds" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | BlenderCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "blendable": { "description": "If True, the models can be blended.", "type": "boolean" }, "reason": { "description": "Useful info as to why a model can't be blended.", "type": "string" } }, "required": [ "blendable", "reason" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Information on whether models can be blended and why. | BlenderInfoRetrieveResponse | | 404 | Not Found | This resource does not exist. | None | | 422 | Unprocessable Entity | Unable to process request. | None | ## Retrieve a blender by project ID Operation path: `GET /api/v2/projects/{projectId}/blenderModels/{modelId}/` Authentication requirements: `BearerAuth` Retrieve a blender. Blenders are a special type of models, so the response includes all attributes that would be in a response to [GET /api/v2/projects/{projectId}/models/{modelId}/][get-apiv2projectsprojectidmodelsmodelid] as well as some additional ones. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "blenderMethod": { "description": "Method used to blend results of underlying models.", "type": "string" }, "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelIds": { "description": "List of models used in blender.", "items": { "type": "string" }, "type": "array" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderMethod", "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelIds", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The blender model. | BlenderRetrieveResponse | | 404 | Not Found | Specified blender not found. | None | ## Retrieve all existing combined models by project ID Operation path: `GET /api/v2/projects/{projectId}/combinedModels/` Authentication requirements: `BearerAuth` Retrieve all existing combined models for this project. .. note:: ``` To retrieve information on the segments for a combined model, retrieve the combined model using [GET /api/v2/projects/{projectId}/combinedModels/{combinedModelId}/][get-apiv2projectsprojectidcombinedmodelscombinedmodelid]. ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of combined models.", "items": { "properties": { "combinedModelId": { "description": "The ID of the combined model.", "type": "string" }, "isActiveCombinedModel": { "default": false, "description": "Indicates whether this model is the active one in segmented modeling project.", "type": "boolean", "x-versionadded": "v2.29" }, "modelCategory": { "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.", "enum": [ "combined" ], "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "segmentationTaskId": { "description": "The ID of the segmentation task used to generate this combined model.", "type": "string" } }, "required": [ "combinedModelId", "isActiveCombinedModel", "modelCategory", "projectId", "segmentationTaskId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CombinedModelListResponse | ## Retrieve an existing combined model by project ID Operation path: `GET /api/v2/projects/{projectId}/combinedModels/{combinedModelId}/` Authentication requirements: `BearerAuth` Retrieve an existing combined model. If available, contains information on which champion model is used for each segment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The ID of the project. | | combinedModelId | path | string | true | The ID of the combined model. | ### Example responses > 200 Response ``` { "properties": { "combinedModelId": { "description": "The ID of the combined model.", "type": "string" }, "isActiveCombinedModel": { "default": false, "description": "Indicates whether this model is the active one in segmented modeling project.", "type": "boolean", "x-versionadded": "v2.29" }, "modelCategory": { "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.", "enum": [ "combined" ], "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "segmentationTaskId": { "description": "The ID of the segmentation task used to generate this combined model.", "type": "string" }, "segments": { "description": "Information for each segment. Maps each segment to the project and model used for it.", "items": { "properties": { "modelId": { "description": "The ID of the segment champion model.", "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project used for this segment.", "type": [ "string", "null" ] }, "segment": { "description": "Segment name.", "type": "string" } }, "required": [ "modelId", "projectId", "segment" ], "type": "object" }, "type": "array" } }, "required": [ "combinedModelId", "isActiveCombinedModel", "modelCategory", "projectId", "segmentationTaskId", "segments" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CombinedModelResponse | ## Retrieve Combined Model segments info by project ID Operation path: `GET /api/v2/projects/{projectId}/combinedModels/{combinedModelId}/segments/` Authentication requirements: `BearerAuth` Retrieve Combined Model segments info (name, related project & model details). ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | searchSegmentName | query | string | false | Case-insensitive search against segment name. | | projectId | path | string | true | The ID of the project. | | combinedModelId | path | string | true | The ID of the combined model. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of combined model segment info.", "items": { "properties": { "autopilotDone": { "description": "Whether autopilot is done for the project.", "type": [ "boolean", "null" ] }, "holdoutUnlocked": { "description": "Whether holdout is unlocked for the project.", "type": [ "boolean", "null" ] }, "isFrozen": { "description": "Indicates whether the segment champion model is frozen, i.e., uses tuning parameters from a parent model.", "type": [ "boolean", "null" ] }, "modelAssignedBy": { "description": "Who assigned the model as segment champion. The default is ``DataRobot``.", "type": [ "string", "null" ] }, "modelAwardTime": { "description": "The time when the model was awarded as segment champion.", "format": "date-time", "type": [ "string", "null" ] }, "modelCount": { "description": "The count of trained models in the project.", "type": [ "integer", "null" ] }, "modelIcon": { "description": "The number for the icon representing the given champion model.", "items": { "type": "integer" }, "type": "array" }, "modelId": { "description": "The ID of the segment champion model.", "type": [ "string", "null" ] }, "modelMetrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or ``null`` if they have not been computed.", "type": [ "object", "null" ] }, "modelType": { "description": "The description of the model type of the given champion model.", "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project.", "type": [ "string", "null" ] }, "projectPaused": { "description": "Whether the project is paused right now.", "type": [ "boolean", "null" ] }, "projectStage": { "description": "The current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed.", "enum": [ "modeling", "aim", "fasteda", "eda", "eda2", "empty" ], "type": [ "string", "null" ] }, "projectStageDescription": { "description": "A description of the current stage of the project.", "type": [ "string", "null" ] }, "projectStatusError": { "description": "Project status error message.", "type": [ "string", "null" ] }, "rowCount": { "description": "The count of rows in the project's dataset.", "type": [ "integer", "null" ] }, "rowPercentage": { "description": "The percentage of rows in the segment project's dataset compared to the original dataset.", "type": [ "number", "null" ] }, "segment": { "description": "Segment name.", "type": "string" } }, "required": [ "autopilotDone", "holdoutUnlocked", "isFrozen", "modelAssignedBy", "modelAwardTime", "modelCount", "modelIcon", "modelId", "modelMetrics", "modelType", "projectId", "projectStage", "projectStageDescription", "rowCount", "rowPercentage", "segment" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CombinedModelSegmentsPaginatedResponse | ## Download Combined Model segments info by project ID Operation path: `GET /api/v2/projects/{projectId}/combinedModels/{combinedModelId}/segments/download/` Authentication requirements: `BearerAuth` Download Combined Model segments info (name, related project & model details) as a CSV. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The ID of the project. | | combinedModelId | path | string | true | The ID of the combined model. | ### Example responses > 200 Response ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | string | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto-generated filename for this download. | ## List datetime partitioned project models by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/` Authentication requirements: `BearerAuth` List all the models from a datetime partitioned project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | bulkOperationId | query | string | false | the ID of the bulk model operation. If specified, only models submitted in scope of this operation will be shown. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/][get-apiv2projectsprojectiddatetimemodelsmodelid].", "items": { "properties": { "backtests": { "description": "information on each backtesting fold of the model", "items": { "properties": { "index": { "description": "the index of the fold", "type": "integer" }, "score": { "description": "the score of the model for this backtesting fold, if computed", "type": [ "number", "null" ] }, "status": { "description": "the status of the current backtest model job", "enum": [ "COMPLETED", "NOT_COMPLETED", "INSUFFICIENT_DATA", "ERRORED", "BACKTEST_BOUNDARIES_EXCEEDED" ], "type": "string" }, "trainingDuration": { "description": "the duration of the data used to train the model for this backtesting fold", "format": "duration", "type": "string" }, "trainingEndDate": { "description": "the end date of the training for this backtesting fold", "format": "date-time", "type": "string" }, "trainingRowCount": { "description": "the number of rows used to train the model for this backtesting fold", "type": "integer" }, "trainingStartDate": { "description": "the start date of the training for this backtesting fold", "format": "date-time", "type": "string" } }, "required": [ "index", "score", "status", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" }, "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "effectiveFeatureDerivationWindowEnd": { "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.16" }, "effectiveFeatureDerivationWindowStart": { "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the `featureDerivationWindowStart` set on the project due to the differencing method and period selected.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "v2.16" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "forecastWindowEnd": { "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.16" }, "forecastWindowStart": { "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.16" }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "holdoutScore": { "description": "the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked", "type": [ "number", "null" ] }, "holdoutStatus": { "description": "the status of the holdout fold", "enum": [ "COMPLETED", "INSUFFICIENT_DATA", "HOLDOUT_BOUNDARIES_EXCEEDED" ], "type": "string" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "Object where each metric has validation, backtesting, backtestingScores and holdout scores reported, or null if they have not been computed. The `validation` score will be the score of the first backtest, which will be computed during initial model training. The `backtesting` and `backtestingScores` scores are computed when requested via [POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/][post-apiv2projectsprojectiddatetimemodelsmodelidbacktests]. The `backtesting` score is the average score across all backtests. The `backtestingScores` is an array of scores for each backtest, with the scores reported as null if the backtest score is unavailable. The `holdout` score is the score against the holdout data, using the training data defined in `trainingInfo`.", "example": "\n {\n \"metrics\": {\n \"FVE Poisson\": {\n \"holdout\": null,\n \"validation\": 0.56269,\n \"backtesting\": 0.50166,\n \"backtestingScores\": [0.51206, 0.49436, null, 0.62516],\n \"crossValidation\": null\n },\n \"RMSE\": {\n \"holdout\": null,\n \"validation\": 21.0836,\n \"backtesting\": 23.361932,\n \"backtestingScores\": [0.4403, 0.4213, null, 0.5132],\n \"crossValidation\": null\n }\n }\n }\n", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "always null for datetime models", "enum": [ null ], "type": "string" }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingInfo": { "description": "holdout and prediction training data details", "properties": { "holdoutTrainingDuration": { "description": "the duration of the data used to train a model to score the holdout", "format": "duration", "type": "string" }, "holdoutTrainingEndDate": { "description": "the end date of the data used to train a model to score the holdout", "format": "date-time", "type": "string" }, "holdoutTrainingRowCount": { "description": "the number of rows used to train a model to score the holdout", "type": "integer" }, "holdoutTrainingStartDate": { "description": "the start date of data used to train a model to score the holdout", "format": "date-time", "type": "string" }, "predictionTrainingDuration": { "description": "the duration of the data used to train a model to make predictions", "format": "duration", "type": "string" }, "predictionTrainingEndDate": { "description": "the end date of the data used to train a model to make predictions", "format": "date-time", "type": "string" }, "predictionTrainingRowCount": { "description": "the number of rows used to train a model to make predictions", "type": "integer" }, "predictionTrainingStartDate": { "description": "the start date of data used to train a model to make predictions", "format": "date-time", "type": "string" } }, "required": [ "holdoutTrainingDuration", "holdoutTrainingEndDate", "holdoutTrainingRowCount", "holdoutTrainingStartDate", "predictionTrainingDuration", "predictionTrainingEndDate", "predictionTrainingRowCount", "predictionTrainingStartDate" ], "type": "object" }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.16" } }, "required": [ "backtests", "blenderModels", "blueprintId", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "externalPredictionModel", "featurelistId", "featurelistName", "forecastWindowEnd", "forecastWindowStart", "frozenPct", "hasCodegen", "holdoutScore", "holdoutStatus", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingInfo", "trainingRowCount", "trainingStartDate", "windowsBasisUnit" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The datetime partitioned project's models | DatetimeModelsResponse | ## Train a new datetime model by project ID Operation path: `POST /api/v2/projects/{projectId}/datetimeModels/` Authentication requirements: `BearerAuth` Train a new datetime model. All durations and datetimes should be specified in accordance with the :ref: `timestamp and duration formatting rules`. ### Body parameter ``` { "properties": { "blueprintId": { "description": "The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models.", "type": "string" }, "featurelistId": { "description": "If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "monotonicIncreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "maximum": 100, "minimum": 2, "type": "integer", "x-versionadded": "v2.27" }, "samplingMethod": { "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.", "enum": [ "random", "latest" ], "type": "string", "x-versionadded": "v2.20" }, "sourceProjectId": { "description": "The project the blueprint comes from. Required only if the `blueprintId` comes from a different project.", "type": "string" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.7" }, "trainingDuration": { "description": "A duration string representing the training duration for the submitted model.", "format": "duration", "type": "string" }, "trainingRowCount": { "description": "The number of rows of data that should be used when training this model.", "exclusiveMinimum": 0, "type": "integer" }, "useProjectSettings": { "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings.", "type": "boolean", "x-versionadded": "v2.19" } }, "required": [ "blueprintId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | TrainDatetimeModel | false | none | ### Example responses > 202 Response ``` { "properties": { "message": { "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The model has been successfully submitted. | DatetimeModelSubmissionResponse | | 422 | Unprocessable Entity | There was an error submitting the specified job. See the message field for more details. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrain an existing datetime model by project ID Operation path: `POST /api/v2/projects/{projectId}/datetimeModels/fromModel/` Authentication requirements: `BearerAuth` Retrain an existing datetime model using a new training period for the model training set (with optional time window sampling) or different feature list. All durations and datetimes should be specified in accordance with the :ref: `timestamp and duration formatting rules`. Note that only one of `trainingDuration` or `trainingRowCount` or `trainingStartDate` and `trainingEndDate` should be specified. If `trainingStartDate` and `trainingEndDate` are specified, the source model must be frozen. ### Body parameter ``` { "properties": { "featurelistId": { "description": "If specified, the new model will be trained using this featurelist. Otherwise, the model will be trained on the same feature list as the source model.", "type": "string" }, "modelId": { "description": "The ID of an existing model to use as the source for the training parameters.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "monotonicIncreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "maximum": 100, "minimum": 2, "type": "integer", "x-versionadded": "v2.27" }, "samplingMethod": { "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.", "enum": [ "random", "latest" ], "type": "string", "x-versionadded": "v2.20" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": "integer" }, "trainingDuration": { "description": "A duration string representing the training duration to use for training the new model. If specified, the model will be trained using the specified training duration. Otherwise, the original model's duration will be used. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "format": "duration", "type": "string" }, "trainingEndDate": { "description": "A datetime string representing the end date of the data to use for training this model. Note that only one of `trainingDuration` or `trainingRowCount` or `trainingStartDate` and `trainingEndDate` should be specified. If `trainingStartDate` and `trainingEndDate` are specified, the source model must be frozen.", "format": "date-time", "type": "string" }, "trainingRowCount": { "description": "The number of rows of data that should be used to train the model. If not specified, the original model's row count will be used. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "exclusiveMinimum": 0, "type": "integer" }, "trainingStartDate": { "description": "A datetime string representing the start date of the data to use for training this model. Note that only one of `trainingDuration` or `trainingRowCount` or `trainingStartDate` and `trainingEndDate` should be specified. If `trainingStartDate` and `trainingEndDate` are specified, the source model must be frozen.", "format": "date-time", "type": "string" }, "useProjectSettings": { "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "type": "boolean", "x-versionadded": "v2.24" } }, "required": [ "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | RetrainDatetimeModel | false | none | ### Example responses > 202 Response ``` { "properties": { "message": { "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Retrain an existing datetime model using a new sample size and/or feature list. | DatetimeModelSubmissionResponse | | 403 | Forbidden | User does not have permissions to manage models. | None | | 404 | Not Found | Model with specified modelId doesn't exist, or user does not have access to the project. | None | | 422 | Unprocessable Entity | Model with specified modelId is deprecated, or it doesn't support retraining with specified parameters. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Get datetime model by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/` Authentication requirements: `BearerAuth` Look up a particular datetime model All durations and datetimes are specified in accordance with :ref: `timestamp and duration formatting rules `. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "backtests": { "description": "information on each backtesting fold of the model", "items": { "properties": { "index": { "description": "the index of the fold", "type": "integer" }, "score": { "description": "the score of the model for this backtesting fold, if computed", "type": [ "number", "null" ] }, "status": { "description": "the status of the current backtest model job", "enum": [ "COMPLETED", "NOT_COMPLETED", "INSUFFICIENT_DATA", "ERRORED", "BACKTEST_BOUNDARIES_EXCEEDED" ], "type": "string" }, "trainingDuration": { "description": "the duration of the data used to train the model for this backtesting fold", "format": "duration", "type": "string" }, "trainingEndDate": { "description": "the end date of the training for this backtesting fold", "format": "date-time", "type": "string" }, "trainingRowCount": { "description": "the number of rows used to train the model for this backtesting fold", "type": "integer" }, "trainingStartDate": { "description": "the start date of the training for this backtesting fold", "format": "date-time", "type": "string" } }, "required": [ "index", "score", "status", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" }, "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "effectiveFeatureDerivationWindowEnd": { "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.16" }, "effectiveFeatureDerivationWindowStart": { "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the `featureDerivationWindowStart` set on the project due to the differencing method and period selected.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "v2.16" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "forecastWindowEnd": { "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.16" }, "forecastWindowStart": { "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.16" }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "holdoutScore": { "description": "the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked", "type": [ "number", "null" ] }, "holdoutStatus": { "description": "the status of the holdout fold", "enum": [ "COMPLETED", "INSUFFICIENT_DATA", "HOLDOUT_BOUNDARIES_EXCEEDED" ], "type": "string" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "Object where each metric has validation, backtesting, backtestingScores and holdout scores reported, or null if they have not been computed. The `validation` score will be the score of the first backtest, which will be computed during initial model training. The `backtesting` and `backtestingScores` scores are computed when requested via [POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/][post-apiv2projectsprojectiddatetimemodelsmodelidbacktests]. The `backtesting` score is the average score across all backtests. The `backtestingScores` is an array of scores for each backtest, with the scores reported as null if the backtest score is unavailable. The `holdout` score is the score against the holdout data, using the training data defined in `trainingInfo`.", "example": "\n {\n \"metrics\": {\n \"FVE Poisson\": {\n \"holdout\": null,\n \"validation\": 0.56269,\n \"backtesting\": 0.50166,\n \"backtestingScores\": [0.51206, 0.49436, null, 0.62516],\n \"crossValidation\": null\n },\n \"RMSE\": {\n \"holdout\": null,\n \"validation\": 21.0836,\n \"backtesting\": 23.361932,\n \"backtestingScores\": [0.4403, 0.4213, null, 0.5132],\n \"crossValidation\": null\n }\n }\n }\n", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "always null for datetime models", "enum": [ null ], "type": "string" }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingInfo": { "description": "holdout and prediction training data details", "properties": { "holdoutTrainingDuration": { "description": "the duration of the data used to train a model to score the holdout", "format": "duration", "type": "string" }, "holdoutTrainingEndDate": { "description": "the end date of the data used to train a model to score the holdout", "format": "date-time", "type": "string" }, "holdoutTrainingRowCount": { "description": "the number of rows used to train a model to score the holdout", "type": "integer" }, "holdoutTrainingStartDate": { "description": "the start date of data used to train a model to score the holdout", "format": "date-time", "type": "string" }, "predictionTrainingDuration": { "description": "the duration of the data used to train a model to make predictions", "format": "duration", "type": "string" }, "predictionTrainingEndDate": { "description": "the end date of the data used to train a model to make predictions", "format": "date-time", "type": "string" }, "predictionTrainingRowCount": { "description": "the number of rows used to train a model to make predictions", "type": "integer" }, "predictionTrainingStartDate": { "description": "the start date of data used to train a model to make predictions", "format": "date-time", "type": "string" } }, "required": [ "holdoutTrainingDuration", "holdoutTrainingEndDate", "holdoutTrainingRowCount", "holdoutTrainingStartDate", "predictionTrainingDuration", "predictionTrainingEndDate", "predictionTrainingRowCount", "predictionTrainingStartDate" ], "type": "object" }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.16" } }, "required": [ "backtests", "blenderModels", "blueprintId", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "externalPredictionModel", "featurelistId", "featurelistName", "forecastWindowEnd", "forecastWindowStart", "frozenPct", "hasCodegen", "holdoutScore", "holdoutStatus", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingInfo", "trainingRowCount", "trainingStartDate", "windowsBasisUnit" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Datetime model | DatetimeModelDetailsResponse | ## Score all the available backtests of a datetime model by project ID Operation path: `POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/` Authentication requirements: `BearerAuth` Score all the available backtests of a datetime model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Scoring of all the available backtests of a datetime model has been successfully requested. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Prepare a model by project ID Operation path: `POST /api/v2/projects/{projectId}/deploymentReadyModels/` Authentication requirements: `BearerAuth` Prepare a specific model for deployment. This model will go through the recommendation stages. ### Body parameter ``` { "properties": { "modelId": { "description": "The model to prepare for deployment.", "type": "string" } }, "required": [ "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | PrepareForDeployment | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Start preparing the model for deployment. | None | | 422 | Unprocessable Entity | An error occurred when submitting the model job | None | ## Retrieve Eureqa model details plot by project ID Operation path: `GET /api/v2/projects/{projectId}/eureqaDistributionPlot/{solutionId}/` Authentication requirements: `BearerAuth` Retrieve Eureqa model details plot. Available for classification projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | solutionId | path | string | true | The ID of the solution to return data for. | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "The distribution plot data.", "items": { "properties": { "binEnd": { "description": "The end of the numeric range for the current bin. Note that `binEnd` - `binStart` should be a constant, modulo floating-point rounding error, for all bins in a single plot.", "type": "number" }, "binStart": { "description": "The start of the numeric range for the current bin. Must be equal to the `binEnd` of the previous bin.", "type": "number" }, "negatives": { "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is negative.", "type": "integer" }, "positives": { "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is positive.", "type": "integer" } }, "required": [ "binEnd", "binStart", "negatives", "positives" ], "type": "object" }, "type": "array" }, "complexity": { "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.", "type": "integer" }, "error": { "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.", "type": [ "number", "null" ] }, "errorMetric": { "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.", "type": "string" }, "eureqaSolutionId": { "description": "The ID of the solution.", "type": "string" }, "expression": { "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.", "type": "string" }, "expressionAnnotated": { "description": "The `expression`, rendered with additional tags to assist in automatic parsing.", "type": "string" }, "threshold": { "description": "Classifier threshold selected by the backend, used to determine which model values are binned as positive and which are binned as negative. Must have a value between the `binStart` of the first bin and `binEnd` of the last bin.", "type": "number" } }, "required": [ "bins", "complexity", "error", "errorMetric", "eureqaSolutionId", "expression", "expressionAnnotated", "threshold" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The Eureqa model details plot. | EureqaDistributionDetailResponse | | 404 | Not Found | Data was not found. | None | ## Retrieve eureqa model detail by id Operation path: `GET /api/v2/projects/{projectId}/eureqaModelDetail/{solutionId}/` Authentication requirements: `BearerAuth` Retrieve Eureqa model details plot. Available for regression projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | solutionId | path | string | true | The ID of the solution to return data for. | ### Example responses > 200 Response ``` { "properties": { "complexity": { "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.", "type": "integer" }, "error": { "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.", "type": [ "number", "null" ] }, "errorMetric": { "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.", "type": "string" }, "eureqaSolutionId": { "description": "The ID of the solution.", "type": "string" }, "expression": { "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.", "type": "string" }, "expressionAnnotated": { "description": "The `expression`, rendered with additional tags to assist in automatic parsing.", "type": "string" }, "plotData": { "description": "The plot data.", "items": { "properties": { "actual": { "description": "The actual value of the target variable for the specified row.", "type": "number" }, "predicted": { "description": "The predicted value of the target by the solution for the specified row.", "type": "number" }, "row": { "description": "The row number from the raw source data. Used as the X axis for the plot when rendered in the web application.", "type": "integer" } }, "required": [ "actual", "predicted", "row" ], "type": "object" }, "type": "array" } }, "required": [ "complexity", "error", "errorMetric", "eureqaSolutionId", "expression", "expressionAnnotated", "plotData" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The Eureqa model details plot. | EureqaModelDetailResponse | | 404 | Not Found | Data was not found. | None | ## Create a new model by project ID Operation path: `POST /api/v2/projects/{projectId}/eureqaModels/` Authentication requirements: `BearerAuth` Create a new model from an existing eureqa solution. ### Body parameter ``` { "properties": { "parentModelId": { "description": "The ID of the model to clone from. If omitted, will automatically search for and find the first leaderboard model created by the blueprint run that also created the solution associated with `solutionId`.", "type": "string" }, "solutionId": { "description": "The ID of the solution to be cloned.", "type": "string" } }, "required": [ "solutionId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | EureqaLeaderboardEntryPayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Request accepted, creation is underway. | None | | 404 | Not Found | Data not found. | None | | 422 | Unprocessable Entity | Model for this solution already exists. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Location | string | | The location at which the new model can be retrieved. | ## Retrieve the pareto front by project ID Operation path: `GET /api/v2/projects/{projectId}/eureqaModels/{modelId}/` Authentication requirements: `BearerAuth` Retrieve the pareto front for the specified Eureqa model. Only the best solution in the pareto front will have a corresponding model initially. Models can be created for other solutions using [POST /api/v2/projects/{projectId}/eureqaModels/][post-apiv2projectsprojectideureqamodels]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "errorMetric": { "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.", "type": "string" }, "hyperparameters": { "description": "The hyperparameters used by this run of the Eureqa blueprint.", "oneOf": [ { "properties": { "buildingBlocks": { "description": "Mathematical operators and other components that comprise Eureqa Expressions.", "type": [ "object", "null" ] }, "errorMetric": { "description": "Error Metric Eureqa used internally, to evaluate which models to keep on its internal Pareto Front. ", "type": [ "string", "null" ] }, "maxGenerations": { "description": "The maximum number of evolutionary generations to run.", "minimum": 32, "type": [ "integer", "null" ] }, "numThreads": { "description": "The number of threads Eureqa will run with.", "minimum": 0, "type": [ "integer", "null" ] }, "priorSolutions": { "description": "Prior Eureqa Solutions.", "items": { "description": "Prior solution.", "type": "string" }, "type": "array" }, "randomSeed": { "description": "Constant to seed Eureqa's pseudo-random number generator.", "minimum": 0, "type": [ "integer", "null" ] }, "splitMode": { "description": "Whether to perform in-order (2) or random (1) splitting within the training set, for evolutionary re-training and re-validatoon.", "enum": [ "custom", "1", "2" ], "type": [ "string", "null" ] }, "syncMigrations": { "description": "Whether Eureqa's migrations are synchronized.", "type": [ "boolean", "null" ] }, "targetExpressionFormat": { "description": "Constrain the target expression to the specified format.", "enum": [ "None", "exponential", "featureInteraction" ], "type": [ "string", "null" ] }, "targetExpressionString": { "description": "Eureqa Expression to constrain the form of the models that Eureqa will consider.", "type": [ "string", "null" ] }, "timeoutSec": { "description": "The duration of time to run the Eureqa search algorithm for Eureqa will run until either of max_generations or timeout_sec is reached.", "minimum": 0, "type": [ "number", "null" ] }, "trainingFraction": { "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary training.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "trainingSplitExpr": { "description": "Valid Eureqa Expression to do Eureqa internal training splits.", "type": [ "string", "null" ] }, "validationFraction": { "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary validation.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "validationSplitExpr": { "description": "Valid Eureqa Expression to do Eureqa internal validation splits.", "type": [ "string", "null" ] }, "weightExpr": { "description": "Eureqa Weight Expression.", "type": [ "string", "null" ] } }, "required": [ "buildingBlocks", "maxGenerations", "numThreads", "priorSolutions", "randomSeed", "splitMode", "syncMigrations", "targetExpressionFormat", "targetExpressionString", "timeoutSec", "trainingFraction", "trainingSplitExpr", "validationFraction", "validationSplitExpr", "weightExpr" ], "type": "object" }, { "type": "null" } ] }, "projectId": { "description": "The project ID of the Eureqa model.", "type": "string" }, "solutions": { "description": "The Eureqa model solutions.", "items": { "properties": { "bestModel": { "description": "True if this solution generates the best model.", "type": "boolean" }, "complexity": { "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.", "type": "integer" }, "error": { "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.", "type": [ "number", "null" ] }, "eureqaSolutionId": { "description": "The ID of the solution.", "type": "string" }, "expression": { "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.", "type": "string" }, "expressionAnnotated": { "description": "The `expression`, rendered with additional tags to assist in automatic parsing.", "type": "string" } }, "required": [ "bestModel", "complexity", "error", "eureqaSolutionId", "expression", "expressionAnnotated" ], "type": "object" }, "type": "array" }, "targetType": { "description": "The type of the target variable.", "enum": [ "Regression", "Binary" ], "type": "string" } }, "required": [ "errorMetric", "hyperparameters", "projectId", "solutions", "targetType" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The Pareto front for the Eureqa model. | ParetoFrontResponse | | 404 | Not Found | Data was not found. | None | ## Train a frozen datetime model by project ID Operation path: `POST /api/v2/projects/{projectId}/frozenDatetimeModels/` Authentication requirements: `BearerAuth` Train a frozen datetime model. If no training data is specified, the frozen datetime model will be trained on the most recent data using an amount of data that is equivalent to the original model. However, if the equivalent duration does not provide enough rows for training, then the duration will be extended until the minimum is met. Note that this will require the holdout of the project to be unlocked. All durations and datetimes should be specified in accordance with the :ref: `timestamp and duration formatting rules`. Note that only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified. ### Body parameter ``` { "properties": { "modelId": { "description": "The ID of an existing model to use as the source for the training parameters.", "type": "string" }, "samplingMethod": { "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.", "enum": [ "random", "latest" ], "type": "string" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": "integer" }, "trainingDuration": { "description": "A duration string representing the training duration for the submitted model. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "format": "duration", "type": "string" }, "trainingEndDate": { "description": "A datetime string representing the end date of the data to use for training this model. If specified, `trainingStartDate` must also be specified. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "format": "date-time", "type": "string" }, "trainingRowCount": { "description": "The number of rows of data that should be used when training this model. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "exclusiveMinimum": 0, "type": "integer" }, "trainingStartDate": { "description": "A datetime string representing the start date of the data to use for training this model. If specified, `trainingEndDate` must also be specified. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "format": "date-time", "type": "string" }, "useProjectSettings": { "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "type": "boolean", "x-versionadded": "v2.24" } }, "required": [ "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | TrainDatetimeFrozenModel | false | none | ### Example responses > 202 Response ``` { "properties": { "message": { "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The model has been successfully submitted. | DatetimeModelSubmissionResponse | | 403 | Forbidden | User does not have permissions to manage models. | None | | 404 | Not Found | Model with specified modelId does not exist, or user does not have access to the project. | None | | 422 | Unprocessable Entity | Model with specified modelId is deprecated, or it does not support retraining with specified parameters. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## List all frozen models by project ID Operation path: `GET /api/v2/projects/{projectId}/frozenModels/` Authentication requirements: `BearerAuth` List all frozen models from a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. If 0, all results. | | withMetric | query | string | false | If specified, the returned models will only have scores for this metric. If not, all metrics will be included. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of the frozen models in a project.", "items": { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of frozen models in the project. | FrozenModelListResponse | | 404 | Not Found | This resource does not exist. | None | ## Train a new frozen model by project ID Operation path: `POST /api/v2/projects/{projectId}/frozenModels/` Authentication requirements: `BearerAuth` Train a new frozen model with parameters from an existing model. Frozen models use tuning parameters from another model on the leaderboard, allowing them to be retrained on a larger amount of the training data more efficiently. To specify the amount of data to use to train the model, use either `samplePct` to express a percentage of the rows of the dataset to use or `trainingRowCount` to express the number of rows to use. If neither `samplePct` or `trainingRowCount` is specified, the model will be trained on the maximum available training data that can be used to train an in-memory model. For projects using smart sampling, `samplePct` and `trainingRowCount` will be interpreted as a percent or number of rows of the minority class. When configuring retraining sample sizes for models in projects with large row counts, DataRobot recommends requesting sample sizes using integer row counts instead of percentages. This is because percentages map to many actual possible row counts and only one of which is the actual sample size for up to validation. For example, if a project has 199,408 rows and you request a 64% sample size, any number of rows between 126,625 rows and 128,618 rows maps to 64% of the data. Using actual integer row counts (or `project.max_training_rows`) avoids ambiguity around how many rows of data you want the model to use. ### Body parameter ``` { "properties": { "modelId": { "description": "The ID of an existing model to use as a source of training parameters.", "type": "string" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "maximum": 100, "minimum": 2, "type": "integer", "x-versionadded": "v2.27" }, "samplePct": { "description": "The percentage of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified. The specified percentage should be between 0.0 and 100.0.", "type": "number" }, "trainingRowCount": { "description": "The integer number of rows of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified.", "type": "integer" } }, "required": [ "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | FrozenModelCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The frozen model has been successfully submitted. | None | | 404 | Not Found | This resource does not exist. | None | | 422 | Unprocessable Entity | Unable to process request. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 204 | Location | string | url | Contains a url at which the job processing the model can be retrieved as with [GET /api/v2/projects/{projectId}/modelJobs/{jobId}/][get-apiv2projectsprojectidmodeljobsjobid].. | ## Look up a particular frozen model by project ID Operation path: `GET /api/v2/projects/{projectId}/frozenModels/{modelId}/` Authentication requirements: `BearerAuth` Look up a particular frozen model. If model with given ID exists but it's not frozen, returns 404 Not Found. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The frozen model. | ModelDetailsResponse | | 404 | Not Found | No such frozen model found. | None | ## Train a new incremental learning model based on an existing model and external data, that was not by project ID Operation path: `POST /api/v2/projects/{projectId}/incrementalLearningModels/fromModel/` Authentication requirements: `BearerAuth` Train a new incremental learning model based on an existing model and external data, that was not seen during partitioning. ### Body parameter ``` { "properties": { "dataStageCompression": { "description": "The file compression for the data stage. Only supports CSV storage type.", "enum": [ "zip", "bz2", "gzip" ], "type": "string", "x-versionadded": "v2.33" }, "dataStageDelimiter": { "description": "The file delimiter for the data stage specified as a string.", "type": "string", "x-versionadded": "v2.33" }, "dataStageEncoding": { "description": "The file encoding for the data stage.", "enum": [ "UTF-8", "ASCII", "WINDOWS-1252" ], "type": "string", "x-versionadded": "v2.33" }, "dataStageId": { "description": "The data stage ID. The data stage must be finalized and not expired.", "type": "string", "x-versionadded": "v2.33" }, "dataStageStorageType": { "default": "csv", "description": "The file type of the data stage contents.", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The parent model ID. This model must support incremental learning.", "type": "string", "x-versionadded": "v2.33" }, "trainingDataName": { "description": "String identifier for the current iteration.", "maxLength": 500, "type": "string", "x-versionadded": "v2.33" } }, "required": [ "dataStageId", "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | TrainIncrementalLearningModel | false | none | ### Example responses > 202 Response ``` { "properties": { "message": { "description": "any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The incremental learning model submission response. | ModelRetrainResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## List modeling jobs by project ID Operation path: `GET /api/v2/projects/{projectId}/modelJobs/` Authentication requirements: `BearerAuth` List modeling jobs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | status | query | string | false | If provided, only jobs with the same status will be included in the results; otherwise, queued and inprogress jobs (but not errored jobs) will be returned. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | status | [queue, inprogress, error] | ### Example responses > 200 Response ``` { "items": { "properties": { "data": { "description": "List of modeling jobs.", "items": { "properties": { "blueprintId": { "description": "The blueprint used by the model - note that this is not an ObjectId.", "type": "string" }, "featurelistId": { "description": "The ID of the featurelist the model is using.", "type": "string" }, "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if a job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the job was trained using GPU capabilities.", "type": "boolean" }, "modelCategory": { "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.", "enum": [ "model", "prime", "blend" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelType": { "description": "The type of model used by the job.", "type": "string" }, "processes": { "description": "The list of processes the modeling job includes.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset the job is using.", "type": "number" }, "status": { "description": "The status of the job.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" } }, "required": [ "blueprintId", "featurelistId", "id", "isBlocked", "modelCategory", "modelId", "modelType", "processes", "projectId", "status" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of modeling jobs. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [ModelingJobListResponse] | false | | none | | » data | [ModelJobResponse] | true | | List of modeling jobs. | | »» blueprintId | string | true | | The blueprint used by the model - note that this is not an ObjectId. | | »» featurelistId | string | true | | The ID of the featurelist the model is using. | | »» id | string | true | | The job ID. | | »» isBlocked | boolean | true | | True if a job is waiting for its dependencies to be resolved first. | | »» isTrainedOnGpu | boolean | false | | Whether the job was trained using GPU capabilities. | | »» modelCategory | string | true | | Indicates what kind of model this is. Will be combined for combined models. | | »» modelId | string | true | | The ID of the model. | | »» modelType | string | true | | The type of model used by the job. | | »» processes | [string] | true | | The list of processes the modeling job includes. | | »» projectId | string | true | | The project the job belongs to. | | »» samplePct | number | false | | The percentage of the dataset the job is using. | | »» status | string | true | | The status of the job. | ### Enumerated Values | Property | Value | | --- | --- | | modelCategory | [model, prime, blend] | | status | [queue, inprogress, error, ABORTED, COMPLETED] | ## Cancel a modeling job by project ID Operation path: `DELETE /api/v2/projects/{projectId}/modelJobs/{jobId}/` Authentication requirements: `BearerAuth` Cancel a modeling job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | jobId | path | string | true | The job ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The job has been successfully cancelled. | None | | 404 | Not Found | no job with jobId found, or the job has already completed | None | ## Look up a specific modeling job by project ID Operation path: `GET /api/v2/projects/{projectId}/modelJobs/{jobId}/` Authentication requirements: `BearerAuth` Look up a particular modeling job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | jobId | path | string | true | The job ID. | ### Example responses > 200 Response ``` { "properties": { "blueprintId": { "description": "The blueprint used by the model - note that this is not an ObjectId.", "type": "string" }, "featurelistId": { "description": "The ID of the featurelist the model is using.", "type": "string" }, "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if a job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the job was trained using GPU capabilities.", "type": "boolean" }, "modelCategory": { "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.", "enum": [ "model", "prime", "blend" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelType": { "description": "The type of model used by the job.", "type": "string" }, "processes": { "description": "The list of processes the modeling job includes.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset the job is using.", "type": "number" }, "status": { "description": "The status of the job.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" } }, "required": [ "blueprintId", "featurelistId", "id", "isBlocked", "modelCategory", "modelId", "modelType", "processes", "projectId", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The modeling job. | ModelJobResponse | | 303 | See Other | Task is completed, see Location header for the location of a new resource | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Location | string | url | if a status code 303 was returned, will contain a url at which the completed model can be retrieved` | ## Retrieve model records, supports filtering by project ID Operation path: `GET /api/v2/projects/{projectId}/modelRecords/` Authentication requirements: `BearerAuth` Retrieve model records, supports filtering and search. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | withMetric | query | string | false | If specified, the returned models will only have scores for this metric. If not, all metrics will be included. | | showInSampleScores | query | boolean | false | If specified, will return metric scores for models trained into validation/holdout for projects that do not have stacked predictions. | | characteristics | query | array[string] | false | A characteristics to filter models with. Only those models which have all specified characteristics are returned. | | searchTerm | query | string | false | Filter models by the string expression in the description, case insensitive. | | labels | query | array[string] | false | Filter models by labels. Only those models which have all specified labels are returned. | | blueprints | query | string | false | Filter models by blueprint IDs. | | families | query | array[string] | false | Filter models by families. | | featurelists | query | string | false | Filter models by featurelist names. | | trainingFilters | query | string | false | Filter models by training length or type. Could be training duration in string representation, 'Start/end date', 'Project settings' constants or number of rows. Training duration for datetime partitioned projects may have up to three parts: --.Example of the training duration: P6Y0M0D-78-Random, returns models trained on6 years, with sampling rate 78%, randomly taken from the training window. Example of the row count with sampling method: 150-Random | | numberOfClusters | query | string | false | Filter models by number of clusters. Applicable only in unsupervised clustering projects. | | sortByMetric | query | string | false | Metric to order models by. If omitted, the project metric is used. | | sortByPartition | query | string | false | Partition to use when ordering models by metric. If omitted, the validation partition is used. | | offset | query | integer | true | The number of results that are skipped. | | limit | query | integer | true | The maximum number of models to return. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | characteristics | [frozen, new series optimized, trained on gpu, with exportable coefficients, with mono constraints, with rating table, with scoring code] | | labels | [prepared for deployment, starred] | | families | [Adaptive Boosting, Anomaly Detection, Blender, Calibrator, Clustering, Custom, Decision Tree, Document, Naive, ElasticNet Generalized Linear Model, Eureqa, Eureqa Time-Series, External Prediction Model, Factorization Machine, Generalized Additive Model, Generalized Linear Model, Gradient Boosting Machine, Image, K Nearest Neighbors, Naive Bayes, Neural Network, Other, Random Forest, Rule Induction, Summarized Categorical, Support Vector Machine, Text Mining, Time-Series Neural Network, Traditional Time-Series, Two Stage, Vowpal Wabbit] | | sortByPartition | [backtesting, crossValidation, validation, holdout] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of models returned on this page.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "Model records.", "items": { "oneOf": [ { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string", "x-versionadded": "v2.33" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean", "x-versionadded": "v2.33" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object", "x-versionadded": "v2.33" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string", "x-versionadded": "v2.33" }, "modelFamily": { "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).", "type": "string", "x-versionadded": "v2.33" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string", "x-versionadded": "v2.33" }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string", "x-versionadded": "v2.33" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "metrics", "modelCategory", "modelFamily", "modelNumber", "modelType", "parentModelId", "processes", "projectId", "samplePct", "trainingRowCount" ], "type": "object" }, { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string", "x-versionadded": "v2.33" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string", "x-versionadded": "v2.33" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean", "x-versionadded": "v2.33" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object", "x-versionadded": "v2.33" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string", "x-versionadded": "v2.33" }, "modelFamily": { "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).", "type": "string", "x-versionadded": "v2.33" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string", "x-versionadded": "v2.33" }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string", "x-versionadded": "v2.33" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string", "x-versionadded": "v2.33" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "metrics", "modelCategory", "modelFamily", "modelNumber", "modelType", "parentModelId", "processes", "projectId", "samplePct", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).", "type": "string" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "numberOfClusters": { "description": "The number of clusters in the unsupervised clustering model. Only present in unsupervised clustering projects.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "metrics", "modelCategory", "modelFamily", "modelNumber", "modelType", "numberOfClusters", "parentModelId", "processes", "projectId", "samplePct", "trainingRowCount" ], "type": "object", "x-versionadded": "v2.34" } ] }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "Total number of models after filters applied.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelRecordsResponse | ## List project models by project ID Operation path: `GET /api/v2/projects/{projectId}/models/` Authentication requirements: `BearerAuth` Lists all the models from a project. .. minversion:: v2.34 .. deprecated:: v2.34 ``` Use [GET /api/v2/projects/{projectId}/modelRecords/][get-apiv2projectsprojectidmodelrecords] instead. Fewer attributes are returned in the response of model records route. Removed attributes: monotonic_increasing_featurelist_id -- Retrievable from the blueprint level monotonic_decreasing_featurelist_id -- Retrievable from the blueprint level. supports_composable_ml -- Retrievable from the blueprint level. supports_monotonic_constraints -- Retrievable from the blueprint level. has_empty_clusters -- Retrievable from the individual model level. is_n_clusters_dynamically_determined -- Retrievable from the individual model level. prediction_threshold -- Retrievable from the individual model level. prediction_threshold_read_only - Retrievable from the individual model level. Changed attributes: n_clusters becomes number_of_clusters and is returned for unsupervised clustering models. ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | withMetric | query | string | false | If specified, the returned models will only have scores for this metric. If not, all metrics will be included. | | showInSampleScores | query | boolean | false | If specified, will return metric scores for models trained into validation/holdout for projects that do not have stacked predictions. | | name | query | string | false | If specified, filters for models with a model type matching name. | | samplePct | query | number | false | If specified, filters for models with a matching sample percentage. | | isStarred | query | string | false | If specified, filters for models marked as starred. | | orderBy | query | string | false | A comma-separated list of metrics to sort by. If metric is prefixed with a '-', models are sorted by this metric in descending order, otherwise are sorted in ascending order. Valid sorting metrics are metric and samplePct. Use of metric sorts models by metric value selected for this project using the validation score. Use of the prefix accounts for the direction of the metric, so -metric will sort in order of decreasing 'goodness', which may be opposite to the natural numerical order. If not specified, -metric will be used. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | isStarred | [false, False, true, True] | | orderBy | [metric, -metric, samplePct, -samplePct] | ### Example responses > 200 Response ``` { "items": { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The project's models. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [ModelDetailsResponse] | false | | none | | » blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. | | » blueprintId | string | true | | The blueprint used to construct the model. | | » dataSelectionMethod | string | false | | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. | | » externalPredictionModel | boolean | true | | If the model is an external prediction model. | | » featurelistId | string,null | true | | The ID of the feature list used by the model. | | » featurelistName | string,null | true | | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. | | » frozenPct | number,null | true | | The training percent used to train the frozen model. | | » hasCodegen | boolean | true | | If the model has a codegen JAR file. | | » hasFinetuners | boolean | false | | Whether a model has fine tuners. | | » icons | integer,null | true | | The icons associated with the model. | | » id | string | true | | The ID of the model. | | » isAugmented | boolean | false | | Whether a model was trained using augmentation. | | » isBlender | boolean | true | | If the model is a blender. | | » isCustom | boolean | true | | If the model contains custom tasks. | | » isFrozen | boolean | true | | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. | | » isNClustersDynamicallyDetermined | boolean | false | | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. | | » isStarred | boolean | true | | Indicates whether the model has been starred. | | » isTrainedIntoHoldout | boolean | true | | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. | | » isTrainedIntoValidation | boolean | true | | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. | | » isTrainedOnGpu | boolean | true | | Whether the model was trained using GPU workers. | | » isTransparent | boolean | true | | If the model is a transparent model with exposed coefficients. | | » isUserModel | boolean | true | | If the model was created with Composable ML. | | » lifecycle | ModelLifecycle | true | | Object returning model lifecycle. | | »» reason | string,null | true | | The reason for the lifecycle stage. None if the model is active. | | »» stage | string | true | | The model lifecycle stage. | | » linkFunction | string,null | true | | The link function the final modeler uses in the blueprint. If no link function exists, returns null. | | » metrics | object | true | | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. | | » modelCategory | string | true | | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. | | » modelFamily | string | true | | The family the model belongs to, e.g., SVM, GBM, etc. | | » modelFamilyFullName | string | true | | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. | | » modelNumber | integer,null | true | | The model number from the Leaderboard. | | » modelType | string | true | | Identifies the model (e.g., Nystroem Kernel SVM Regressor). | | » monotonicDecreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | » monotonicIncreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | » nClusters | integer,null | false | | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | » parentModelId | string,null | true | | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. | | » predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. | | » predictionThresholdReadOnly | boolean | true | | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. | | » processes | [string] | true | maxItems: 100 | The list of processes used by the model. | | » projectId | string | true | | The ID of the project to which the model belongs. | | » samplePct | number,null | true | | The percentage of the dataset used in training the model. | | » samplingMethod | string | false | | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. | | » supportsComposableMl | boolean | true | | indicates whether this model is supported in Composable ML. | | » supportsMonotonicConstraints | boolean | true | | whether this model supports enforcing monotonic constraints | | » timeWindowSamplePct | integer,null | false | | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. | | » trainingDuration | string,null | true | | the duration spanned by the dates in the partition column for the data used to train the model | | » trainingEndDate | string,null(date-time) | true | | the end date of the dates in the partition column for the data used to train the model | | » trainingRowCount | integer,null | true | | The number of rows used to train the model. | | » trainingStartDate | string,null(date-time) | true | | the start date of the dates in the partition column for the data used to train the model | ### Enumerated Values | Property | Value | | --- | --- | | dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] | | stage | [active, deprecated, disabled] | | modelCategory | [model, prime, blend, combined, incrementalLearning] | | samplingMethod | [random, latest] | ## Train a new model by project ID Operation path: `POST /api/v2/projects/{projectId}/models/` Authentication requirements: `BearerAuth` Train a new model. To specify the amount of data to use to train the model, use either `samplePct` to express a percentage of the rows of the dataset to use or `trainingRowCount` to express the number of rows to use. If neither `samplePct` or `trainingRowCount` is specified, the model will be trained on the maximum available training data that can be used to train an in-memory model. For projects using smart sampling, samplePct and trainingRowCount will be interpreted as a percent or number of rows of the minority class. When configuring retraining sample sizes for models in projects with large row counts, DataRobot recommends requesting sample sizes using integer row counts instead of percentages. This is because percentages map to many actual possible row counts and only one of which is the actual sample size for up to validation. For example, if a project has 199,408 rows and you request a 64% sample size, any number of rows between 126,625 rows and 128,618 rows maps to 64% of the data. Using actual integer row counts (or `project.max_training_rows`) avoids ambiguity around how many rows of data you want the model to use. ### Body parameter ``` { "properties": { "blueprintId": { "description": "The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models.", "type": "string" }, "featurelistId": { "description": "If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used.", "type": [ "string", "null" ] }, "monotonicIncreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used.", "type": [ "string", "null" ] }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "maximum": 100, "minimum": 2, "type": "integer", "x-versionadded": "v2.27" }, "samplePct": { "description": "The percentage of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified. The specified percentage should be between 0 and 100.", "exclusiveMinimum": 0, "maximum": 100, "type": "number" }, "scoringType": { "description": "Validation is available for any partitioning. If the project uses cross validation, `crossValidation` may be used to indicate that all available training/validation combinations should be used.", "enum": [ "validation", "crossValidation" ], "type": "string" }, "sourceProjectId": { "description": "The project the blueprint comes from. Required only if the `blueprintId` comes from a different project.", "type": "string" }, "trainingRowCount": { "description": "An integer representing the number of rows of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified.", "type": "integer" } }, "required": [ "blueprintId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | TrainModel | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | None | | 422 | Unprocessable Entity | Could not create new job. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrain a model by project ID Operation path: `POST /api/v2/projects/{projectId}/models/fromModel/` Authentication requirements: `BearerAuth` Retrain an existing model using a new sample size and/or feature list.When configuring retraining sample sizes for models in projects with large row counts, DataRobot recommends requesting sample sizes using integer row counts instead of percentages. This is because percentages map to many actual possible row counts and only one of which is the actual sample size for up to validation. For example, if a project has 199,408 rows and you request a 64% sample size, any number of rows between 126,625 rows and 128,618 rows maps to 64% of the data. Using actual integer row counts (or `project.max_training_rows`) avoids ambiguity around how many rows of data you want the model to use. Note that only one of `samplePct` or `trainingRowCount` should be specified. ### Body parameter ``` { "properties": { "featurelistId": { "description": "If specified, the model will be trained using that featurelist, otherwise the model will be trained on the same feature list as before.", "type": "string" }, "modelId": { "description": "The model to be retrained.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "monotonicIncreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "maximum": 100, "minimum": 2, "type": "integer", "x-versionadded": "v2.27" }, "samplePct": { "description": "The percentage of the dataset to use to use to train the model. The specified percentage should be between 0 and 100. If not specified, original model sample percent will be used.", "exclusiveMinimum": 0, "maximum": 100, "type": "number" }, "scoringType": { "description": "Validation is available for any partitioning. If the project uses cross validation, `crossValidation` may be used to indicate that all available training/validation combinations should be used.", "enum": [ "validation", "crossValidation" ], "type": "string", "x-versionadded": "v2.23" }, "trainingRowCount": { "description": "The number of rows to use to train the model. If not specified, original model training row count will be used.", "exclusiveMinimum": 0, "type": "integer" } }, "required": [ "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | RetrainModel | false | none | ### Example responses > 202 Response ``` { "properties": { "message": { "description": "any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Retrain an existing model using a new sample size and/or feature list. | ModelRetrainResponse | | 422 | Unprocessable Entity | model with specified modelId is deprecated, or it doesn't support retraining using a new sample size and/or feature list | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Delete a model by project ID Operation path: `DELETE /api/v2/projects/{projectId}/models/{modelId}/` Authentication requirements: `BearerAuth` Delete a model from the leaderboard. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The model has been successfully deleted. | None | | 404 | Not Found | This resource does not exist. | None | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Get model by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/` Authentication requirements: `BearerAuth` Look up a particular model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The model. | ModelDetailsResponse | ## Update a model's attributes by project ID Operation path: `PATCH /api/v2/projects/{projectId}/models/{modelId}/` Authentication requirements: `BearerAuth` Update a model's attributes. ### Body parameter ``` { "properties": { "isStarred": { "description": "Mark model either as starred or unstarred.", "type": "boolean" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions. Default value is 0.5.", "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | ModelUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The model has been successfully updated with new attributes. | None | | 404 | Not Found | This resource does not exist. | None | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Create advanced tuning by id Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/advancedTuning/` Authentication requirements: `BearerAuth` Submit a job to make a new version of the model with different advanced tuning parameters. Note: This route currently supports all models other than: OSS, blenders, prime, scaleout, baseline and user-created. Currently, only single-stage models (most simple models) are supported. Blueprints that run multiple steps, for example one step to predict zero vs nonzero and a second step to determine the value of nonzero predictions, are not supported. (:ref: `Advanced Tuning documentation `). Parameters may be omitted from this endpoint. If a parameter is omitted, its `currentValue` will be used. To see the possible parameter IDs and constraints on possible values, see [GET /api/v2/projects/{projectId}/models/{modelId}/advancedTuning/parameters/][get-apiv2projectsprojectidmodelsmodelidadvancedtuningparameters]. ### Body parameter ``` { "properties": { "gridSearchArguments": { "description": "The details of the grid search configuration.", "properties": { "algorithm": { "default": "Pattern Search", "description": "The algorithm to apply when running the grid search.", "enum": [ "Pattern Search", "Accelerated Search", "Exhaustive Search", "Greedy Exhaustive Search", "Bayesian TPE Search", "Bayesian GP Search" ], "type": "string" }, "batchSize": { "default": 2, "description": "The number of iterations to perform in each batch.", "maximum": 8, "minimum": 1, "type": "integer" }, "maxIterations": { "default": 100, "description": "Sets the maximum number of iterations to perform.", "maximum": 10000, "minimum": 1, "type": "integer" }, "randomState": { "default": 42, "description": "The random state/seed used for the grid search.", "minimum": 0, "type": "integer" }, "searchType": { "default": "smart", "description": "The type of grid search to be performed. If not specified, DataRobot performs Smart Search.", "enum": [ "full", "smart", "bayesian" ], "type": "string" }, "wallClockTimeLimit": { "default": 300, "description": "The wall clock time limit, in seconds. The model with the best score, at this point, is selected.", "maximum": 604800, "minimum": 1, "type": "integer" } }, "type": "object", "x-versionadded": "v2.38" }, "tuningDescription": { "description": "Human-readable description of this advanced-tuning request.", "type": "string" }, "tuningParameters": { "description": "Parameters to tune.", "items": { "properties": { "parameterId": { "description": "ID of the parameter whose value to set.", "type": "string" }, "value": { "description": "Value for the specified parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "parameterId", "value" ], "type": "object" }, "type": "array" } }, "required": [ "tuningParameters" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | ModelAdvancedTuning | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The job has been successfully submitted. See the Location header. | None | | 403 | Forbidden | Permission denied creating advanced tuned model | None | | 404 | Not Found | This resource does not exist. | None | | 413 | Payload Too Large | Tuning request is too large | None | | 422 | Unprocessable Entity | Could not create new job. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | A url at which the job processing the model can be retrieved. | ## Retrieve information about all advanced tuning parameters available by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/advancedTuning/parameters/` Authentication requirements: `BearerAuth` Retrieve information about all advanced tuning parameters available for the specified model. Note: This route currently supports all models other than: OSS, blenders, prime, scaleout, baseline and user-created. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "tuningDescription": { "description": "Human-readable description of the tuned model, if specified by the user. `null` if unspecified.", "type": [ "string", "null" ] }, "tuningParameters": { "description": "An array of objects containing information about tuning parameters that are supported by the specified model.", "items": { "properties": { "constraints": { "description": "Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format.", "properties": { "ascii": { "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "supportsGridSearch" ], "type": "object" }, "float": { "description": "Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).", "properties": { "max": { "description": "Maximum value for the parameter.", "type": "number" }, "min": { "description": "Minimum value for the parameter.", "type": "number" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "max", "min", "supportsGridSearch" ], "type": "object" }, "floatList": { "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).", "properties": { "maxLength": { "description": "Maximum permitted length of the list.", "minimum": 0, "type": "integer" }, "maxVal": { "description": "Maximum permitted value.", "type": "number" }, "minLength": { "description": "Minimum permitted length of the list.", "minimum": 0, "type": "integer" }, "minVal": { "description": "Minimum permitted value.", "type": "number" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "maxLength", "maxVal", "minLength", "minVal", "supportsGridSearch" ], "type": "object" }, "int": { "description": "Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.", "properties": { "max": { "description": "Maximum value for the parameter.", "type": "integer" }, "min": { "description": "Minimum value for the parameter.", "type": "integer" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "max", "min", "supportsGridSearch" ], "type": "object" }, "intList": { "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.", "properties": { "maxLength": { "description": "Maximum permitted length of the list.", "minimum": 0, "type": "integer" }, "maxVal": { "description": "Maximum permitted value.", "type": "integer" }, "minLength": { "description": "Minimum permitted length of the list.", "minimum": 0, "type": "integer" }, "minVal": { "description": "Minimum permitted value.", "type": "integer" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "maxLength", "maxVal", "minLength", "minVal", "supportsGridSearch" ], "type": "object" }, "select": { "description": "Indicates that the value can be one selected from a list of known values.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" }, "values": { "description": "List of valid values for this field.", "items": { "type": "string" }, "type": "array" } }, "required": [ "supportsGridSearch", "values" ], "type": "object" }, "selectgrid": { "description": "Indicates that the value can be one selected from a list of known values.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" }, "values": { "description": "List of valid values for this field.", "items": { "type": "string" }, "type": "array" } }, "required": [ "supportsGridSearch", "values" ], "type": "object" }, "unicode": { "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "supportsGridSearch" ], "type": "object" } }, "type": "object" }, "currentValue": { "description": "The single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "defaultValue": { "description": "The actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on `current_value`).", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "parameterId": { "description": "Unique (per-blueprint) identifier of this parameter. This is the identifier used to specify which parameter to tune when make a new advanced tuning request.", "type": "string" }, "parameterName": { "description": "Name of the parameter.", "type": "string" }, "taskName": { "description": "Human-readable name of the task that this parameter belongs to.", "type": "string" }, "vertexId": { "description": "Id of the vertex this parameter belongs to.", "type": "string", "x-versionadded": "v2.29" } }, "required": [ "constraints", "currentValue", "defaultValue", "parameterId", "parameterName", "taskName", "vertexId" ], "type": "object" }, "type": "array" } }, "required": [ "tuningDescription", "tuningParameters" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The information about all advanced tuning parameters available for the specified model. | AdvancedTuningArgumentsRetrieveResponse | | 403 | Forbidden | Permission denied creating advanced tuned model. | None | | 404 | Not Found | This resource does not exist. | None | | 422 | Unprocessable Entity | This model does not support advanced tuning. | None | ## Retrieve cluster names assigned by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/clusterNames/` Authentication requirements: `BearerAuth` Retrieve all cluster names assigned to an unsupervised cluster model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "clusters": { "description": "A list of the model's cluster information entries.", "items": { "properties": { "name": { "description": "A cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "percent": { "description": "The percentage of rows in the dataset this cluster contains.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "name" ], "type": "object" }, "maxItems": 100, "minItems": 2, "type": "array" }, "modelId": { "description": "The model ID", "type": "string" }, "projectId": { "description": "The project ID", "type": "string" } }, "required": [ "clusters", "modelId", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve all cluster names for the model | ClusterNamesResponse | | 404 | Not Found | Could not find unsupervised clustering model. Possible reasons include: 1. Provided model id points to a model that does not exist in specified project. 2. Provided model has incompatible type. Method requires model to be unsupervised clustering model. | None | ## Update cluster names assigned by project ID Operation path: `PATCH /api/v2/projects/{projectId}/models/{modelId}/clusterNames/` Authentication requirements: `BearerAuth` Update and then retrieve all cluster names assigned to an unsupervised cluster model. ### Body parameter ``` { "properties": { "clusterNameMappings": { "description": "\n A list of the mappings from a cluster's current name to its new name.\n After update, value passed as a new name will become cluster's current name.\n All cluster names should be unique and should identify one and only one cluster.\n ", "items": { "properties": { "currentName": { "description": "Current cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "newName": { "description": "New cluster name.", "maxLength": 50, "minLength": 1, "type": "string" } }, "required": [ "currentName", "newName" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "clusterNameMappings" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | ClusterNamesUpdateParam | false | none | ### Example responses > 200 Response ``` { "properties": { "clusters": { "description": "A list of the model's cluster information entries.", "items": { "properties": { "name": { "description": "A cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "percent": { "description": "The percentage of rows in the dataset this cluster contains.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "name" ], "type": "object" }, "maxItems": 100, "minItems": 2, "type": "array" }, "modelId": { "description": "The model ID", "type": "string" }, "projectId": { "description": "The project ID", "type": "string" } }, "required": [ "clusters", "modelId", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Update cluster names and then retrieve all cluster names for the model | ClusterNamesResponse | | 404 | Not Found | Could not find unsupervised clustering model. Possible reasons include: 1. Provided model id points to a model which does not exists in specified project. 2. Provided model has incompatible type. Method requires model to be unsupervised clustering model. | None | | 422 | Unprocessable Entity | The request cannot be processed. Possible reasons include: 1. Mapping contains invalid current cluster name and referenced cluster was not found. 2. Mapping is invalid as after update, clusters will not be uniquely identifiable by name. | None | ## Run cross validation by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/crossValidation/` Authentication requirements: `BearerAuth` Run cross validation on a model. ### Body parameter ``` { "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | Empty | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The model has been successfully submitted. | None | | 422 | Unprocessable Entity | Unable to process the request. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | Contains a url at which the job processing the model can be retrieved | ## Get cross validation scores by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/crossValidationScores/` Authentication requirements: `BearerAuth` Get Cross Validation scores for each partition in a model. .. note:: Individual partition scores are only available for newer models; older models that have cross validation score calculated will need to be retrained. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | metric | query | string | false | Set to the name of a metric to only return results for that metric. | | partition | query | number | false | Set to a value such as 1.0, 2.0 to only return results for the specified partition. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "cvScores": { "description": "A dictionary `cvScores` with sub-dictionary keyed by `partition_id`, each `partition_id` is itself a dictionary keyed by `metric_name` where the value is the reading for that particular metric for the partition_id.", "example": "\n {\n \"cvScores\": {\n \"FVE Gamma\": {\n \"0.0\": 0.24334,\n \"1.0\": 0.17757,\n \"2.0\": 0.21803,\n \"3.0\": 0.20185,\n \"4.0\": 0.20576\n },\n \"FVE Poisson\": {\n \"0.0\": 0.24527,\n \"1.0\": 0.22092,\n \"2.0\": 0.22451,\n \"3.0\": 0.24417,\n \"4.0\": 0.21654\n }\n }\n }\n", "type": "object" } }, "required": [ "cvScores" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The Cross Validation scores for each partition in a model. | CrossValidationRetrieveResponse | | 404 | Not Found | Not found. | None | ## List the features used by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/features/` Authentication requirements: `BearerAuth` List the features used in a model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "aPrioriFeatureNames": { "description": "(Deprecated in version v2.11) Renamed to `knownInAdvanceFeatureNames`. This parameter always has the same value as `knownInAdvanceFeatureNames` and will be removed in a future release.", "items": { "type": "string" }, "type": "array" }, "featureNames": { "description": "An array of the names of all features used by the specified model.", "items": { "type": "string" }, "type": "array" }, "knownInAdvanceFeatureNames": { "description": "An array of the names of time series known-in-advance features used by the specified model.", "items": { "type": "string" }, "type": "array" } }, "required": [ "aPrioriFeatureNames", "featureNames", "knownInAdvanceFeatureNames" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The features used in a model. | ModelFeatureListResponse | | 404 | Not Found | This resource does not exist. | None | ## Retrieve grid search scores by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/gridSearchScores/` Authentication requirements: `BearerAuth` Retrieve grid search scores. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | source | query | string | false | The source for the scores. If omitted, the validation partition is used. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | validation | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "The grid search scores metadata ID.", "type": "string" }, "modelId": { "description": "The ID of the model that scores belong to.", "type": "string" }, "parameters": { "description": "The list of grid search parameters.", "items": { "items": { "properties": { "parameterName": { "description": "The name of the parameter.", "type": "string" }, "parameterValue": { "description": "The value of the parameter.", "type": "number" } }, "required": [ "parameterName", "parameterValue" ], "type": "object", "x-versionadded": "v2.39" }, "type": "array" }, "maxItems": 100000, "minItems": 1, "type": "array" }, "projectId": { "description": "The project the model belongs to.", "type": "string" }, "scores": { "description": "The list of grid search scores.", "items": { "type": "number" }, "maxItems": 100000, "minItems": 1, "type": "array" }, "source": { "description": "The source for the scores. If omitted, the validation partition is used.", "enum": [ "validation" ], "type": "string" } }, "required": [ "id", "modelId", "parameters", "projectId", "scores", "source" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of grid search scores. | GridSearchScoresRetrieveResponse | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Retrieve a summary of how the model's subtasks handle missing values by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/missingReport/` Authentication requirements: `BearerAuth` Retrieve a summary of how the model's subtasks handle missing values Only models built after the missing value report feature was added will have reports, and only models with at least one imputation or encoding task, e.g. ordinal encoding, missing value imputation. Blenders and scaleout models do not support Missing Value reports. ``` The report will describe how each feature's missing values were treated, and report how many missing values were present in the training data. Features which were not processed by a given blueprint task will not mention it: for instance, a categorical feature with many unique values may not be considered eligible for processing by a One-Hot Encoding Report is collected for those features which are considered eligible by given blueprint task. For instance, categorical feature with a lot of unique values may not be considered as eligible in One-Hot Encoding Task. ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "missingValuesReport": { "description": "Missing values report, which contains an array of reports for individual features.", "items": { "properties": { "feature": { "description": "The name of the feature.", "type": "string" }, "missingCount": { "description": "The number of missing values in the training data.", "type": "integer" }, "missingPercentage": { "description": "The percentage of missing values in the training data.", "maximum": 1, "minimum": 0, "type": "number" }, "tasks": { "additionalProperties": { "properties": { "descriptions": { "description": "Human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored.", "items": { "type": "string" }, "type": "array" }, "name": { "description": "Task name, e.g., 'Ordinal encoding of categorical variables'.", "type": "string" } }, "required": [ "descriptions", "name" ], "type": "object" }, "description": "Information on individual tasks of the model which were used to process the feature. The names of properties will be task ids (which correspond to the ids used in the blueprint chart endpoints like [GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/blueprintChart/][get-apiv2projectsprojectidblueprintsblueprintidblueprintchart]) The corresponding value for each task will be of the form `task` described.", "type": "object" }, "type": { "description": "The type of the feature, e.g., `Categorical` or `Numeric`.", "type": "string" } }, "required": [ "feature", "missingCount", "missingPercentage", "tasks", "type" ], "type": "object" }, "type": "array" } }, "required": [ "missingValuesReport" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve a summary of how the model's subtasks handle missing values. | MissingReportRetrieve | | 404 | Not Found | Could not find missing value report for provided project and model ID. | None | ## Get number of iterations trained by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/numIterationsTrained/` Authentication requirements: `BearerAuth` Retrieve the actual number of iterations or estimators trained by a tree-based early stopping model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "Number of estimators or iterations for a single model stage", "items": { "properties": { "numIterations": { "description": "The number of iterations run in this stage of modeling.", "minimum": 0, "type": "integer" }, "stage": { "description": "Modeling stage or None if it is a single-stage model", "type": [ "string", "null" ] } }, "required": [ "numIterations", "stage" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "data", "modelId", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The number of estimators/iterations trained | NumIterationsTrainedResponse | | 404 | Not Found | Cannot retrieve early stopping information for this model. | None | ## Retrieve model parameters by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/parameters/` Authentication requirements: `BearerAuth` Retrieve model parameters. These are the parameters that appear in the webapp on the `Coefficients` tab. Note that they are only available for some models. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "derivedFeatures": { "description": "An array of preprocessing information about derived features used in the model.", "items": { "properties": { "coefficient": { "description": "The coefficient for this feature.", "type": "number" }, "derivedFeature": { "description": "The name of the derived feature.", "type": "string" }, "originalFeature": { "description": "The name of the feature used to derive this feature.", "type": "string" }, "stageCoefficients": { "description": "An array of json objects describing separate coefficients for every stage of model (empty for single stage models).", "items": { "properties": { "coefficient": { "description": "The corresponding value of the coefficient for that stage.", "type": "number" }, "stage": { "description": "The name of the stage.", "type": "string" } }, "required": [ "coefficient", "stage" ], "type": "object" }, "type": "array" }, "transformations": { "description": "An array of json objects describing the transformations applied to create this derived feature.", "items": { "properties": { "name": { "description": "The name of the transformation.", "type": "string" }, "value": { "description": "The value used in carrying it out.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" }, "type": { "description": "The type of this feature.", "type": "string" } }, "required": [ "coefficient", "derivedFeature", "originalFeature", "stageCoefficients", "transformations", "type" ], "type": "object" }, "type": "array" }, "parameters": { "description": "An array of parameters that are related to the whole model.", "items": { "properties": { "name": { "description": "The name of the parameter identifying what it means for the model, e.g. \"Intercept\".", "type": "string" }, "value": { "description": "The value of the parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "derivedFeatures", "parameters" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The parameters of the model. | ModelParametersRetrieveResponse | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Retrieve prediction intervals that are already calculated by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/predictionIntervals/` Authentication requirements: `BearerAuth` Retrieve prediction intervals (in descending order) that are already calculated for this model. Note that the project this model belongs to must be a time series project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | projectId | path | string | true | The project to retrieve prediction intervals for. Must be a time series project. | | modelId | path | string | true | The model to retrieve prediction intervals for. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A descending-ordered array of already-calculated prediction intervals percentiles.", "items": { "exclusiveMinimum": 0, "maximum": 100, "type": "integer" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Request was successful. | PredictionIntervalsListResponse | ## Calculate prediction intervals by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/predictionIntervals/` Authentication requirements: `BearerAuth` Submit a job to calculate prediction intervals for the specified percentiles for this model. Note that the project this model belongs to must be a time series project. ### Body parameter ``` { "properties": { "percentiles": { "description": "The list of prediction intervals percentiles to calculate. Currently we only allow requesting one interval at a time.", "items": { "exclusiveMinimum": 0, "maximum": 100, "type": "integer" }, "maxItems": 1, "minItems": 1, "type": "array" } }, "required": [ "percentiles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project to calculate prediction intervals for. Must be a time series project. | | modelId | path | string | true | The model to calculate prediction intervals for. | | body | body | PredictionIntervalsCreate | false | none | ### Example responses > 202 Response ``` { "properties": { "message": { "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job was successfully submitted. See Location header. | PredictionIntervalsCreateResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Check a Model for Prime Eligibility by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/primeInfo/` Authentication requirements: `BearerAuth` Check if a model can be approximated by DataRobot Prime. Deprecated in v2.35. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project the model belongs to. | | modelId | path | string | true | The model to check. | ### Example responses > 200 Response ``` { "properties": { "canMakePrime": { "description": "Indicates whether the requested model is a valid input for creating a Prime model.", "type": "boolean" }, "message": { "description": "May contain details about why a model is not eligible for DataRobot Prime.", "type": "string" }, "messageId": { "description": "An error code representing the reason the model cannot be approximated with DataRobot Prime; 0 for eligible models.", "type": "integer" } }, "required": [ "canMakePrime", "message", "messageId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | PrimeInfoRetrieveResponse | ## List rulesets by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/primeRulesets/` Authentication requirements: `BearerAuth` List all the rulesets approximating a model When rulesets are created for the parent model, all of the rulesets are created at once, but not all rulesets have corresponding Prime models (until they are directly requested). ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project the model belongs to. | | modelId | path | string | true | The model to find approximating rulesets for. | ### Example responses > 200 Response ``` { "items": { "properties": { "modelId": { "description": "The ID of the Prime model using this ruleset (if it exists) or null.", "type": "string" }, "parentModelId": { "description": "The ID of the model this ruleset approximates.", "type": "string" }, "projectId": { "description": "The project this ruleset belongs to.", "type": "string" }, "ruleCount": { "description": "The number of rules used by this ruleset.", "type": "integer" }, "rulesetId": { "description": "The ID of the ruleset.", "type": "integer" }, "score": { "description": "The validation score of the ruleset.", "type": "number" } }, "required": [ "modelId", "parentModelId", "projectId", "ruleCount", "rulesetId", "score" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [PrimeRulesetsListResponse] | false | | none | | » modelId | string | true | | The ID of the Prime model using this ruleset (if it exists) or null. | | » parentModelId | string | true | | The ID of the model this ruleset approximates. | | » projectId | string | true | | The project this ruleset belongs to. | | » ruleCount | integer | true | | The number of rules used by this ruleset. | | » rulesetId | integer | true | | The ID of the ruleset. | | » score | number | true | | The validation score of the ruleset. | ## Create Rulesets by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/primeRulesets/` Authentication requirements: `BearerAuth` Approximate an existing model on the leaderboard with DataRobot Prime. A request body should be an empty JSON {}. Deprecated in v2.35. ### Body parameter ``` { "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project the model to approximate belongs to. | | modelId | path | string | true | The model to approximate. | | body | body | PrimeRulesetsCreatePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was understood and accepted, and is now being worked on. See the Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | A URL that can be polled to check the status of the job. | ## Retrieve scoring code by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/scoringCode/` Authentication requirements: `BearerAuth` Retrieve Scoring Code for making new predictions from an existing model offline. You need the "Scoring Code" feature enabled to use this route. By default, returns a compiled executable JAR that can be executed locally to calculate model predictions, or it can be used as a library for a Java application. Execute it with the '--help` parameters to learn how to use it as a command-line utility. See model API documentation (https://javadoc.io/doc/com.datarobot/datarobot-prediction/latest/index.html) to be able to use it inside an existing Java application. With the sourceCode query parameter set to 'true', returns a source code archive that can be used to review internal calculations of the model. This JAR is NOT executable. See "https://docs.datarobot.com/en/docs/predictions/port-pred/scoring-code/index.html" in DataRobot application for more information. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | sourceCode | query | string | false | If set to "true", the downloaded JAR file will contain only the source code and will not be executable. | | projectId | path | string | true | The project that created the model. | | modelId | path | string | true | The model to use. | ### Enumerated Values | Parameter | Value | | --- | --- | | sourceCode | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The JAR file. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | attachment; filename="<"filename">".jar The suggested filename for the scoring code is dynamically generated | | 200 | Content-Type | string | | application/java-archive | ## Get supported capabilities by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/supportedCapabilities/` Authentication requirements: `BearerAuth` Get supported capabilities for a model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "eligibleForPrime": { "description": "`True` if the model is eligible for prime. Use [GET /api/v2/projects/{projectId}/models/{modelId}/primeInfo/][get-apiv2projectsprojectidmodelsmodelidprimeinfo] to request additional details if the model is not eligible.", "type": "boolean", "x-versiondeprecated": "v2.35" }, "hasParameters": { "description": "`True` if the model has parameters that can be retrieved. Use [GET /api/v2/projects/{projectId}/models/{modelId}/parameters/][get-apiv2projectsprojectidmodelsmodelidparameters] to retrieve the model parameters.", "type": "boolean" }, "hasWordCloud": { "description": "True` if the model has word cloud data available. Use [GET /api/v2/projects/{projectId}/models/{modelId}/wordCloud/][get-apiv2projectsprojectidmodelsmodelidwordcloud] to retrieve a word cloud.", "type": "boolean" }, "reasons": { "description": "Information on why the capability is unsupported for the model.", "properties": { "supportsAccuracyOverTime": { "description": "If present, the reason why Accuracy Over Time plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsAnomalyAssessment": { "description": "If present, the reason why the Anomaly Assessment insight cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsAnomalyOverTime": { "description": "If present, the reason why Anomaly Over Time plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsClusterInsights": { "description": "If present, the reason why Cluster Insights cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsConfusionMatrix": { "description": "If present, the reason why Confusion Matrix cannot be generated for the model. There are some cases where Confusion Matrix is available but it was calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.33" }, "supportsDocumentTextExtractionSampleInsight": { "description": "If present, the reason document text extraction sample insights are not supported for the model.", "type": "string", "x-versionadded": "v2.29" }, "supportsForecastAccuracy": { "description": "If present, the reason why Forecast Accuracy plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsForecastVsActual": { "description": "If present, the reason why Forecast vs Actual plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsImageActivationMaps": { "description": "If present, the reason image activation maps are not supported for the model.", "type": "string" }, "supportsImageEmbedding": { "description": "If present, the reason image embeddings are not supported for the model.", "type": "string" }, "supportsLiftChart": { "description": "If present, the reason why Lift Chart cannot be generated for the model. There are some cases where Lift Chart is available but it was calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.31" }, "supportsPeriodAccuracy": { "description": "If present, the reason why Period Accuracy insights cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsPredictionExplanations": { "description": "If present, the reason why Prediction Explanations cannot be computed for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsPredictionIntervals": { "description": "If present, the reason why Prediction Intervals cannot be computed for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsResiduals": { "description": "If present, the reason why residuals are not available for the model. There are some cases where Residuals are available but they were calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.30" }, "supportsRocCurve": { "description": "If present, the reason why ROC Curve cannot be generated for the model. There are some cases where ROC Curve is available but it was calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.32" }, "supportsSeriesInsights": { "description": "If present, the reason why Series Insights cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsStability": { "description": "If present, the reason why Stability plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" } }, "type": "object" }, "supportsAccuracyOverTime": { "description": "`True` if Accuracy Over Time plots can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsAdvancedTuning": { "description": "`True` if model supports Advanced Tuning.", "type": "boolean", "x-versionadded": "v2.36" }, "supportsAnomalyAssessment": { "description": "`True` if Anomaly Assessment insights can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsAnomalyOverTime": { "description": "`True` if Anomaly Over Time plots can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsBlending": { "description": "`True` if the model supports blending. See [POST /api/v2/projects/{projectId}/blenderModels/blendCheck/][post-apiv2projectsprojectidblendermodelsblendcheck] to check specific blending combinations.", "type": "boolean" }, "supportsClusterInsights": { "description": "`True` if Cluster Insights can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsCodeGeneration": { "description": "`True` if the model supports export of model's source code or compiled Java executable.", "type": "boolean", "x-versionadded": "v2.18" }, "supportsCoefficients": { "description": "`True` if model coefficients are available.", "type": "boolean", "x-versionadded": "v2.32" }, "supportsConfusionMatrix": { "description": "`True` if Confusion Matrix can be generated.", "type": "boolean", "x-versionadded": "v2.33" }, "supportsDocumentTextExtractionSampleInsight": { "description": "`True` if the model has document column(s) and document text extraction samples can be generated.", "type": "boolean", "x-versionadded": "v2.29" }, "supportsEarlyStopping": { "description": "`True` if this is an early stopping tree-based model and number of trained iterations can be retrieved.", "type": "boolean", "x-versionadded": "v2.22" }, "supportsForecastAccuracy": { "description": "`True` if Forecast Accuracy plots can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsForecastVsActual": { "description": "`True` if Forecast vs Actual plots can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsImageActivationMaps": { "description": "`True` if the model has image column(s) and activation maps can be generated.", "type": "boolean", "x-versionadded": "v2.19" }, "supportsImageEmbedding": { "description": "`True` if the model has image column(s) and image embeddings can be generated.", "type": "boolean", "x-versionadded": "v2.19" }, "supportsLiftChart": { "description": "`True` if Lift Chart can be generated.", "type": "boolean", "x-versionadded": "v2.31" }, "supportsModelPackageExport": { "description": "`True` if the model can be exported as a model package.", "type": "boolean", "x-versionadded": "v2.19" }, "supportsModelTrainingMetrics": { "description": "When `True` , the model will track and save key training metrics in an effort to communicate model accuracy throughout training, rather than at training completion.", "type": "boolean", "x-versionadded": "v2.21" }, "supportsMonotonicConstraints": { "description": "`True` if the model supports monotonic constraints.", "type": "boolean" }, "supportsNNVisualizations": { "description": "`True` if the model supports neuralNetworkVisualizations.", "type": "boolean", "x-versionadded": "v2.19" }, "supportsPerLabelMetrics": { "description": "`True` if the experiment qualifies as a multilabel project.", "type": "boolean", "x-versionadded": "v2.36" }, "supportsPeriodAccuracy": { "description": "`True` if Period Accuracy insights can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsPredictionExplanations": { "description": "`True` if the model supports Prediction Explanations.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsPredictionIntervals": { "description": "`True` if Prediction Intervals can be computed for predictions generated by this model.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsResiduals": { "description": "When `True`, the model supports residuals and residuals data can be retrieved.", "type": "boolean", "x-versionadded": "v2.30" }, "supportsRocCurve": { "description": "`True` if ROC Curve can be generated.", "type": "boolean", "x-versionadded": "v2.32" }, "supportsSeriesInsights": { "description": "`True` if Series Insights can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsShap": { "description": "`True` if the model supports Shapley package, i.e., Shapley-based feature importance.", "type": "boolean", "x-versionadded": "v2.18" }, "supportsStability": { "description": "`True` if Stability plots can be generated.", "type": "boolean", "x-versionadded": "v2.34" } }, "required": [ "eligibleForPrime", "hasParameters", "hasWordCloud", "supportsAccuracyOverTime", "supportsAdvancedTuning", "supportsAnomalyAssessment", "supportsAnomalyOverTime", "supportsBlending", "supportsClusterInsights", "supportsCodeGeneration", "supportsCoefficients", "supportsConfusionMatrix", "supportsDocumentTextExtractionSampleInsight", "supportsForecastAccuracy", "supportsForecastVsActual", "supportsImageActivationMaps", "supportsImageEmbedding", "supportsLiftChart", "supportsModelTrainingMetrics", "supportsMonotonicConstraints", "supportsNNVisualizations", "supportsPerLabelMetrics", "supportsPredictionExplanations", "supportsPredictionIntervals", "supportsResiduals", "supportsRocCurve", "supportsSeriesInsights", "supportsShap", "supportsStability" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successfully returned model capability information. | ModelCapabilitiesRetrieveResponse | | 404 | Not Found | Resource not found. | None | ## Get Prime files by project ID Operation path: `GET /api/v2/projects/{projectId}/primeFiles/` Authentication requirements: `BearerAuth` List all DataRobot Prime files available for download. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. To specify no limit, use 0. The default may change and a maximum limit may be imposed without notice. | | parentModelId | query | string | false | If specified, only Prime files approximating the specified parent model will be returned; otherwise all applicable Prime files will be returned. | | modelId | query | string | false | If specified, only Prime files with code used in the specified prime model will be returned; otherwise all applicable Prime files will be returned. | | projectId | path | string | true | The project to list available files for. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/.", "items": { "properties": { "id": { "description": "The ID of the file.", "type": "string" }, "isValid": { "description": "Whether the code passed basic validation checks.", "type": "boolean" }, "language": { "description": "The language the code is written in (e.g., Python).", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The ID of the Prime model.", "type": "string" }, "parentModelId": { "description": "The ID of the model this code approximates.", "type": "string" }, "projectId": { "description": "The ID of the project the file belongs to.", "type": "string" }, "rulesetId": { "description": "The ID of the ruleset this code uses to approximate the parent model.", "type": "integer" } }, "required": [ "id", "isValid", "language", "modelId", "parentModelId", "projectId", "rulesetId" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | PrimeFileListResponse | ## Create a Prime File by project ID Operation path: `POST /api/v2/projects/{projectId}/primeFiles/` Authentication requirements: `BearerAuth` Request creation and validation of source code from a Prime model. Deprecated in v2.35. ### Body parameter ``` { "properties": { "language": { "description": "The desired language of the generated code.", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The Prime model to generate code for.", "type": "string" } }, "required": [ "language", "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project the Prime model belongs to. | | body | body | PrimeFileCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The prime validation job was added to queue. See the Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | A URL that can be polled to check the status of the prime validation job. | ## Retrieve metadata about a DataRobot Prime file by project ID Operation path: `GET /api/v2/projects/{projectId}/primeFiles/{primeFileId}/` Authentication requirements: `BearerAuth` Retrieve metadata about a DataRobot Prime file available for download. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project the file belongs to. | | primeFileId | path | string | true | The file to retrieve. | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "The ID of the file.", "type": "string" }, "isValid": { "description": "Whether the code passed basic validation checks.", "type": "boolean" }, "language": { "description": "The language the code is written in (e.g., Python).", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The ID of the Prime model.", "type": "string" }, "parentModelId": { "description": "The ID of the model this code approximates.", "type": "string" }, "projectId": { "description": "The ID of the project the file belongs to.", "type": "string" }, "rulesetId": { "description": "The ID of the ruleset this code uses to approximate the parent model.", "type": "integer" } }, "required": [ "id", "isValid", "language", "modelId", "parentModelId", "projectId", "rulesetId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | PrimeFileResponse | ## Download code by project ID Operation path: `GET /api/v2/projects/{projectId}/primeFiles/{primeFileId}/download/` Authentication requirements: `BearerAuth` Download code from an existing Prime file. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project the file belongs to. | | primeFileId | path | string | true | The Prime file to download code from. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a file with the executable code from the Prime file. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Will be attachment; filename="". The suggested filename will depend on the language the Prime file was generated for. | ## List all Prime models by project ID Operation path: `GET /api/v2/projects/{projectId}/primeModels/` Authentication requirements: `BearerAuth` List all Prime models in a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | projectId | path | string | true | The project to list models from. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/.", "items": { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "ruleCount": { "description": "The number of rules used to create this model.", "type": "integer" }, "rulesetId": { "description": "The ID of the ruleset this model uses.", "type": "integer" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "score": { "description": "The validation score of the model's ruleset.", "type": "number" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "ruleCount", "rulesetId", "samplePct", "score", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | PrimeModelListResponse | ## Create a Prime Model from a Ruleset by project ID Operation path: `POST /api/v2/projects/{projectId}/primeModels/` Authentication requirements: `BearerAuth` Create a Prime model using a particular ruleset. DataRobot Prime is not available for multiclass projects. Once rulesets approximating a parent model have been created, using POST /api/v2/projects/(projectId)/models/(modelId)/primeRulesets/, this route will allow creation of a Prime model using one of those rulesets. Available rulesets can be retrieved via GET /api/v2/projects/(projectId)/models/(modelId)/primeRulesets/. Deprecated in v2.35. ### Body parameter ``` { "properties": { "parentModelId": { "description": "The model being approximated.", "type": "string" }, "rulesetId": { "description": "The ID of the ruleset to use.", "type": "integer" } }, "required": [ "parentModelId", "rulesetId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project in which to create the model. | | body | body | PrimeModelCreatePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Prime model creation job successfully added to queue. See the Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | a url that can be polled to check the status of the job | ## Retrieve Prime model details by project ID Operation path: `GET /api/v2/projects/{projectId}/primeModels/{modelId}/` Authentication requirements: `BearerAuth` Retrieve Prime model details. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project to retrieve the model from. | | modelId | path | string | true | The model to retrieve. | ### Example responses > 200 Response ``` { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "ruleCount": { "description": "The number of rules used to create this model.", "type": "integer" }, "rulesetId": { "description": "The ID of the ruleset this model uses.", "type": "integer" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "score": { "description": "The validation score of the model's ruleset.", "type": "number" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "ruleCount", "rulesetId", "samplePct", "score", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Prime models are an extension of models, so the response includes all attributes that would be in a response to GET /api/v2/projects/(projectId)/models/(modelId)/ as well as some additional ones. | PrimeModelDetailsRetrieveResponse | ## List recommended models by project ID Operation path: `GET /api/v2/projects/{projectId}/recommendedModels/` Authentication requirements: `BearerAuth` Retrieves all of the current recommended models for the project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "items": { "properties": { "modelId": { "description": "The ID of the recommended model.", "type": "string" }, "recommendationType": { "description": "The type of model recommendation.", "enum": [ "MOSTACCURATE", "LIMITEDACCURATE", "FASTACCURATE", "RECOMMENDEDFORDEPLOYMENT", "PREPAREDFORDEPLOYMENT" ], "type": "string" } }, "required": [ "modelId", "recommendationType" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of recommended models. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [RecommendedModelResponse] | false | | none | | » modelId | string | true | | The ID of the recommended model. | | » recommendationType | string | true | | The type of model recommendation. | ### Enumerated Values | Property | Value | | --- | --- | | recommendationType | [MOSTACCURATE, LIMITEDACCURATE, FASTACCURATE, RECOMMENDEDFORDEPLOYMENT, PREPAREDFORDEPLOYMENT] | ## Get the recommended model by project ID Operation path: `GET /api/v2/projects/{projectId}/recommendedModels/recommendedModel/` Authentication requirements: `BearerAuth` This route returns the simplest recommended model available. To see all the available recommended models, use [GET /api/v2/projects/{projectId}/recommendedModels/][get-apiv2projectsprojectidrecommendedmodels]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "modelId": { "description": "The ID of the recommended model.", "type": "string" }, "recommendationType": { "description": "The type of model recommendation.", "enum": [ "MOSTACCURATE", "LIMITEDACCURATE", "FASTACCURATE", "RECOMMENDEDFORDEPLOYMENT", "PREPAREDFORDEPLOYMENT" ], "type": "string" } }, "required": [ "modelId", "recommendationType" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The recommended model. | RecommendedModelResponse | ## Get RuleFit code files by project ID Operation path: `GET /api/v2/projects/{projectId}/ruleFitFiles/` Authentication requirements: `BearerAuth` List all RuleFit code files available for download. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | modelId | query | string | false | If specified, only RuleFit code files with code used in the specified RuleFit model will be returned; otherwise, all applicable RuleFit files will be returned. | | offset | query | integer | true | This many files will be skipped. | | limit | query | integer | true | At most this many files are returned. | | projectId | path | string | true | The project to list available files for. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Each item has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/ruleFitFiles/(ruleFitFileId)/.", "items": { "properties": { "id": { "description": "The ID of the file.", "type": "string" }, "isValid": { "description": "Whether the code passed basic validation checks.", "type": "boolean" }, "language": { "description": "The language the code is written in (e.g., Python).", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The ID of the RuleFit model.", "type": "string" }, "projectId": { "description": "The ID of the project the file belongs to.", "type": "string" } }, "required": [ "id", "isValid", "language", "modelId", "projectId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RuleFitCodeFileListResponse | ## Create a RuleFit code file by project ID Operation path: `POST /api/v2/projects/{projectId}/ruleFitFiles/` Authentication requirements: `BearerAuth` Request creation and validation of source code from a RuleFit model. ### Body parameter ``` { "properties": { "language": { "description": "The desired language of the generated code.", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The RuleFit model to generate code for.", "type": "string" } }, "required": [ "language", "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project the file belongs to. | | body | body | RuleFitCodeFileCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The RuleFit code validation job was added to the queue. See the Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | A URL that can be polled to check the status of the RuleFit code validation job. | ## Get RuleFit code file information by project ID Operation path: `GET /api/v2/projects/{projectId}/ruleFitFiles/{ruleFitFileId}/` Authentication requirements: `BearerAuth` Get information about a RuleFit code file available for download. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ruleFitFileId | path | string | true | The ID of the file. | | projectId | path | string | true | The project to list available files for. | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "The ID of the file.", "type": "string" }, "isValid": { "description": "Whether the code passed basic validation checks.", "type": "boolean" }, "language": { "description": "The language the code is written in (e.g., Python).", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The ID of the RuleFit model.", "type": "string" }, "projectId": { "description": "The ID of the project the file belongs to.", "type": "string" } }, "required": [ "id", "isValid", "language", "modelId", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RuleFitCodeFileResponse | ## Download RuleFit code by project ID Operation path: `GET /api/v2/projects/{projectId}/ruleFitFiles/{ruleFitFileId}/download/` Authentication requirements: `BearerAuth` Download code from an existing RuleFit file. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ruleFitFileId | path | string | true | The ID of the file. | | projectId | path | string | true | The project to list available files for. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a file with the executable code from the RuleFit model. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Will be attachment;filename="<"filename">". The suggested filename will depend on the language RuleFit file was generated for. | ## Update champion model by project ID Operation path: `PUT /api/v2/projects/{projectId}/segmentChampion/` Authentication requirements: `BearerAuth` Update champion model for a segment project. ### Body parameter ``` { "properties": { "clone": { "default": false, "description": "Clone current combined model and assign champion to the new combined model.", "type": "boolean", "x-versionadded": "v2.29" }, "modelId": { "description": "The ID of segment champion model.", "type": "string" } }, "required": [ "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | SegmentChampionModelUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "combinedModelId": { "description": "The ID of the combined model that has been updated.", "type": "string" } }, "required": [ "combinedModelId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The ID of the combined model that has been updated with the new segment champion model. | SegmentChampionModelUpdateResponse | # Schemas ## AdvancedTuningArgumentsRetrieveResponse ``` { "properties": { "tuningDescription": { "description": "Human-readable description of the tuned model, if specified by the user. `null` if unspecified.", "type": [ "string", "null" ] }, "tuningParameters": { "description": "An array of objects containing information about tuning parameters that are supported by the specified model.", "items": { "properties": { "constraints": { "description": "Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format.", "properties": { "ascii": { "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "supportsGridSearch" ], "type": "object" }, "float": { "description": "Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).", "properties": { "max": { "description": "Maximum value for the parameter.", "type": "number" }, "min": { "description": "Minimum value for the parameter.", "type": "number" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "max", "min", "supportsGridSearch" ], "type": "object" }, "floatList": { "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).", "properties": { "maxLength": { "description": "Maximum permitted length of the list.", "minimum": 0, "type": "integer" }, "maxVal": { "description": "Maximum permitted value.", "type": "number" }, "minLength": { "description": "Minimum permitted length of the list.", "minimum": 0, "type": "integer" }, "minVal": { "description": "Minimum permitted value.", "type": "number" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "maxLength", "maxVal", "minLength", "minVal", "supportsGridSearch" ], "type": "object" }, "int": { "description": "Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.", "properties": { "max": { "description": "Maximum value for the parameter.", "type": "integer" }, "min": { "description": "Minimum value for the parameter.", "type": "integer" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "max", "min", "supportsGridSearch" ], "type": "object" }, "intList": { "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.", "properties": { "maxLength": { "description": "Maximum permitted length of the list.", "minimum": 0, "type": "integer" }, "maxVal": { "description": "Maximum permitted value.", "type": "integer" }, "minLength": { "description": "Minimum permitted length of the list.", "minimum": 0, "type": "integer" }, "minVal": { "description": "Minimum permitted value.", "type": "integer" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "maxLength", "maxVal", "minLength", "minVal", "supportsGridSearch" ], "type": "object" }, "select": { "description": "Indicates that the value can be one selected from a list of known values.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" }, "values": { "description": "List of valid values for this field.", "items": { "type": "string" }, "type": "array" } }, "required": [ "supportsGridSearch", "values" ], "type": "object" }, "selectgrid": { "description": "Indicates that the value can be one selected from a list of known values.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" }, "values": { "description": "List of valid values for this field.", "items": { "type": "string" }, "type": "array" } }, "required": [ "supportsGridSearch", "values" ], "type": "object" }, "unicode": { "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "supportsGridSearch" ], "type": "object" } }, "type": "object" }, "currentValue": { "description": "The single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "defaultValue": { "description": "The actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on `current_value`).", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "parameterId": { "description": "Unique (per-blueprint) identifier of this parameter. This is the identifier used to specify which parameter to tune when make a new advanced tuning request.", "type": "string" }, "parameterName": { "description": "Name of the parameter.", "type": "string" }, "taskName": { "description": "Human-readable name of the task that this parameter belongs to.", "type": "string" }, "vertexId": { "description": "Id of the vertex this parameter belongs to.", "type": "string", "x-versionadded": "v2.29" } }, "required": [ "constraints", "currentValue", "defaultValue", "parameterId", "parameterName", "taskName", "vertexId" ], "type": "object" }, "type": "array" } }, "required": [ "tuningDescription", "tuningParameters" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | tuningDescription | string,null | true | | Human-readable description of the tuned model, if specified by the user. null if unspecified. | | tuningParameters | [TuningParameters] | true | | An array of objects containing information about tuning parameters that are supported by the specified model. | ## BacktestStatusResponse ``` { "properties": { "index": { "description": "the index of the fold", "type": "integer" }, "score": { "description": "the score of the model for this backtesting fold, if computed", "type": [ "number", "null" ] }, "status": { "description": "the status of the current backtest model job", "enum": [ "COMPLETED", "NOT_COMPLETED", "INSUFFICIENT_DATA", "ERRORED", "BACKTEST_BOUNDARIES_EXCEEDED" ], "type": "string" }, "trainingDuration": { "description": "the duration of the data used to train the model for this backtesting fold", "format": "duration", "type": "string" }, "trainingEndDate": { "description": "the end date of the training for this backtesting fold", "format": "date-time", "type": "string" }, "trainingRowCount": { "description": "the number of rows used to train the model for this backtesting fold", "type": "integer" }, "trainingStartDate": { "description": "the start date of the training for this backtesting fold", "format": "date-time", "type": "string" } }, "required": [ "index", "score", "status", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | index | integer | true | | the index of the fold | | score | number,null | true | | the score of the model for this backtesting fold, if computed | | status | string | true | | the status of the current backtest model job | | trainingDuration | string(duration) | true | | the duration of the data used to train the model for this backtesting fold | | trainingEndDate | string(date-time) | true | | the end date of the training for this backtesting fold | | trainingRowCount | integer | true | | the number of rows used to train the model for this backtesting fold | | trainingStartDate | string(date-time) | true | | the start date of the training for this backtesting fold | ### Enumerated Values | Property | Value | | --- | --- | | status | [COMPLETED, NOT_COMPLETED, INSUFFICIENT_DATA, ERRORED, BACKTEST_BOUNDARIES_EXCEEDED] | ## BaseConstraintType ``` { "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "supportsGridSearch" ], "type": "object" } ``` Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | supportsGridSearch | boolean | true | | When True, Grid Search is supported for this parameter. | ## BiasMitigatedModelsDataResponse ``` { "properties": { "biasMitigationTechnique": { "description": "Method applied to perform bias mitigation.", "enum": [ "preprocessingReweighing", "postProcessingRejectionOptionBasedClassification" ], "type": "string", "x-versionadded": "v2.27" }, "includeBiasMitigationFeatureAsPredictorVariable": { "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.", "type": "boolean", "x-versionadded": "v2.27" }, "modelId": { "description": "Mitigated model ID", "type": "string" }, "parentModelId": { "description": "Parent model ID", "type": [ "string", "null" ] }, "protectedFeature": { "description": "Protected feature that will be used in a bias mitigation task to mitigate bias", "type": "string" } }, "required": [ "biasMitigationTechnique", "includeBiasMitigationFeatureAsPredictorVariable", "modelId", "parentModelId", "protectedFeature" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | biasMitigationTechnique | string | true | | Method applied to perform bias mitigation. | | includeBiasMitigationFeatureAsPredictorVariable | boolean | true | | Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task. | | modelId | string | true | | Mitigated model ID | | parentModelId | string,null | true | | Parent model ID | | protectedFeature | string | true | | Protected feature that will be used in a bias mitigation task to mitigate bias | ### Enumerated Values | Property | Value | | --- | --- | | biasMitigationTechnique | [preprocessingReweighing, postProcessingRejectionOptionBasedClassification] | ## BiasMitigatedModelsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Retrieve list of mitigated models for project.", "items": { "properties": { "biasMitigationTechnique": { "description": "Method applied to perform bias mitigation.", "enum": [ "preprocessingReweighing", "postProcessingRejectionOptionBasedClassification" ], "type": "string", "x-versionadded": "v2.27" }, "includeBiasMitigationFeatureAsPredictorVariable": { "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.", "type": "boolean", "x-versionadded": "v2.27" }, "modelId": { "description": "Mitigated model ID", "type": "string" }, "parentModelId": { "description": "Parent model ID", "type": [ "string", "null" ] }, "protectedFeature": { "description": "Protected feature that will be used in a bias mitigation task to mitigate bias", "type": "string" } }, "required": [ "biasMitigationTechnique", "includeBiasMitigationFeatureAsPredictorVariable", "modelId", "parentModelId", "protectedFeature" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [BiasMitigatedModelsDataResponse] | true | | Retrieve list of mitigated models for project. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## BiasMitigationModelCreate ``` { "properties": { "biasMitigationFeature": { "description": "The name of the protected feature used to mitigate bias on models.", "minLength": 1, "type": "string", "x-versionadded": "v2.27" }, "biasMitigationParentLid": { "description": "The ID of the model to modify with a bias-mitigation task.", "type": "string" }, "biasMitigationTechnique": { "description": "Method applied to perform bias mitigation.", "enum": [ "preprocessingReweighing", "postProcessingRejectionOptionBasedClassification" ], "type": "string", "x-versionadded": "v2.27" }, "includeBiasMitigationFeatureAsPredictorVariable": { "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.", "type": "boolean", "x-versionadded": "v2.27" } }, "required": [ "biasMitigationFeature", "biasMitigationParentLid", "biasMitigationTechnique", "includeBiasMitigationFeatureAsPredictorVariable" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | biasMitigationFeature | string | true | minLength: 1minLength: 1 | The name of the protected feature used to mitigate bias on models. | | biasMitigationParentLid | string | true | | The ID of the model to modify with a bias-mitigation task. | | biasMitigationTechnique | string | true | | Method applied to perform bias mitigation. | | includeBiasMitigationFeatureAsPredictorVariable | boolean | true | | Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task. | ### Enumerated Values | Property | Value | | --- | --- | | biasMitigationTechnique | [preprocessingReweighing, postProcessingRejectionOptionBasedClassification] | ## BlenderCreate ``` { "properties": { "blenderMethod": { "description": "The blender method, one of \"PLS\", \"GLM\", \"AVG\", \"ENET\", \"MED\", \"MAE\", \"MAEL1\", \"TF\", \"RF\", \"LGBM\", \"FORECAST_DISTANCE_ENET\" (new in v2.18), \"FORECAST_DISTANCE_AVG\" (new in v2.18), \"MIN\", \"MAX\".", "enum": [ "PLS", "GLM", "ENET", "AVG", "MED", "MAE", "MAEL1", "FORECAST_DISTANCE_AVG", "FORECAST_DISTANCE_ENET", "MAX", "MIN" ], "type": "string" }, "modelIds": { "description": "The list of models to use in blender.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "blenderMethod", "modelIds" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blenderMethod | string | true | | The blender method, one of "PLS", "GLM", "AVG", "ENET", "MED", "MAE", "MAEL1", "TF", "RF", "LGBM", "FORECAST_DISTANCE_ENET" (new in v2.18), "FORECAST_DISTANCE_AVG" (new in v2.18), "MIN", "MAX". | | modelIds | [string] | true | minItems: 1 | The list of models to use in blender. | ### Enumerated Values | Property | Value | | --- | --- | | blenderMethod | [PLS, GLM, ENET, AVG, MED, MAE, MAEL1, FORECAST_DISTANCE_AVG, FORECAST_DISTANCE_ENET, MAX, MIN] | ## BlenderInfoRetrieveResponse ``` { "properties": { "blendable": { "description": "If True, the models can be blended.", "type": "boolean" }, "reason": { "description": "Useful info as to why a model can't be blended.", "type": "string" } }, "required": [ "blendable", "reason" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blendable | boolean | true | | If True, the models can be blended. | | reason | string | true | | Useful info as to why a model can't be blended. | ## BlenderListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/blenderModels/{modelId}/][get-apiv2projectsprojectidblendermodelsmodelid].", "items": { "properties": { "blenderMethod": { "description": "Method used to blend results of underlying models.", "type": "string" }, "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelIds": { "description": "List of models used in blender.", "items": { "type": "string" }, "type": "array" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderMethod", "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelIds", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [BlenderRetrieveResponse] | true | | Each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/blenderModels/{modelId}/][get-apiv2projectsprojectidblendermodelsmodelid]. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## BlenderRetrieveResponse ``` { "properties": { "blenderMethod": { "description": "Method used to blend results of underlying models.", "type": "string" }, "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelIds": { "description": "List of models used in blender.", "items": { "type": "string" }, "type": "array" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderMethod", "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelIds", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blenderMethod | string | true | | Method used to blend results of underlying models. | | blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. | | blueprintId | string | true | | The blueprint used to construct the model. | | dataSelectionMethod | string | false | | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. | | externalPredictionModel | boolean | true | | If the model is an external prediction model. | | featurelistId | string,null | true | | The ID of the feature list used by the model. | | featurelistName | string,null | true | | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. | | frozenPct | number,null | true | | The training percent used to train the frozen model. | | hasCodegen | boolean | true | | If the model has a codegen JAR file. | | hasFinetuners | boolean | false | | Whether a model has fine tuners. | | icons | integer,null | true | | The icons associated with the model. | | id | string | true | | The ID of the model. | | isAugmented | boolean | false | | Whether a model was trained using augmentation. | | isBlender | boolean | true | | If the model is a blender. | | isCustom | boolean | true | | If the model contains custom tasks. | | isFrozen | boolean | true | | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. | | isNClustersDynamicallyDetermined | boolean | false | | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. | | isStarred | boolean | true | | Indicates whether the model has been starred. | | isTrainedIntoHoldout | boolean | true | | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. | | isTrainedIntoValidation | boolean | true | | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. | | isTrainedOnGpu | boolean | true | | Whether the model was trained using GPU workers. | | isTransparent | boolean | true | | If the model is a transparent model with exposed coefficients. | | isUserModel | boolean | true | | If the model was created with Composable ML. | | lifecycle | ModelLifecycle | true | | Object returning model lifecycle. | | linkFunction | string,null | true | | The link function the final modeler uses in the blueprint. If no link function exists, returns null. | | metrics | object | true | | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. | | modelCategory | string | true | | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. | | modelFamily | string | true | | The family the model belongs to, e.g., SVM, GBM, etc. | | modelFamilyFullName | string | true | | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. | | modelIds | [string] | true | | List of models used in blender. | | modelNumber | integer,null | true | | The model number from the Leaderboard. | | modelType | string | true | | Identifies the model (e.g., Nystroem Kernel SVM Regressor). | | monotonicDecreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | monotonicIncreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | nClusters | integer,null | false | | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | parentModelId | string,null | true | | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. | | predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. | | predictionThresholdReadOnly | boolean | true | | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. | | processes | [string] | true | maxItems: 100 | The list of processes used by the model. | | projectId | string | true | | The ID of the project to which the model belongs. | | samplePct | number,null | true | | The percentage of the dataset used in training the model. | | samplingMethod | string | false | | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. | | supportsComposableMl | boolean | true | | indicates whether this model is supported in Composable ML. | | supportsMonotonicConstraints | boolean | true | | whether this model supports enforcing monotonic constraints | | timeWindowSamplePct | integer,null | false | | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. | | trainingDuration | string,null | true | | the duration spanned by the dates in the partition column for the data used to train the model | | trainingEndDate | string,null(date-time) | true | | the end date of the dates in the partition column for the data used to train the model | | trainingRowCount | integer,null | true | | The number of rows used to train the model. | | trainingStartDate | string,null(date-time) | true | | the start date of the dates in the partition column for the data used to train the model | ### Enumerated Values | Property | Value | | --- | --- | | dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] | | modelCategory | [model, prime, blend, combined, incrementalLearning] | | samplingMethod | [random, latest] | ## ClassificationBinDataResponse ``` { "properties": { "binEnd": { "description": "The end of the numeric range for the current bin. Note that `binEnd` - `binStart` should be a constant, modulo floating-point rounding error, for all bins in a single plot.", "type": "number" }, "binStart": { "description": "The start of the numeric range for the current bin. Must be equal to the `binEnd` of the previous bin.", "type": "number" }, "negatives": { "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is negative.", "type": "integer" }, "positives": { "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is positive.", "type": "integer" } }, "required": [ "binEnd", "binStart", "negatives", "positives" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | binEnd | number | true | | The end of the numeric range for the current bin. Note that binEnd - binStart should be a constant, modulo floating-point rounding error, for all bins in a single plot. | | binStart | number | true | | The start of the numeric range for the current bin. Must be equal to the binEnd of the previous bin. | | negatives | integer | true | | The number of records in the dataset where the model's predicted value falls into this bin and the target is negative. | | positives | integer | true | | The number of records in the dataset where the model's predicted value falls into this bin and the target is positive. | ## ClusterInfoList ``` { "properties": { "name": { "description": "A cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "percent": { "description": "The percentage of rows in the dataset this cluster contains.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 50minLength: 1minLength: 1 | A cluster name. | | percent | number | false | maximum: 100minimum: 0 | The percentage of rows in the dataset this cluster contains. | ## ClusterNamesMappingValidation ``` { "properties": { "currentName": { "description": "Current cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "newName": { "description": "New cluster name.", "maxLength": 50, "minLength": 1, "type": "string" } }, "required": [ "currentName", "newName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | currentName | string | true | maxLength: 50minLength: 1minLength: 1 | Current cluster name. | | newName | string | true | maxLength: 50minLength: 1minLength: 1 | New cluster name. | ## ClusterNamesResponse ``` { "properties": { "clusters": { "description": "A list of the model's cluster information entries.", "items": { "properties": { "name": { "description": "A cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "percent": { "description": "The percentage of rows in the dataset this cluster contains.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "name" ], "type": "object" }, "maxItems": 100, "minItems": 2, "type": "array" }, "modelId": { "description": "The model ID", "type": "string" }, "projectId": { "description": "The project ID", "type": "string" } }, "required": [ "clusters", "modelId", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clusters | [ClusterInfoList] | true | maxItems: 100minItems: 2 | A list of the model's cluster information entries. | | modelId | string | true | | The model ID | | projectId | string | true | | The project ID | ## ClusterNamesUpdateParam ``` { "properties": { "clusterNameMappings": { "description": "\n A list of the mappings from a cluster's current name to its new name.\n After update, value passed as a new name will become cluster's current name.\n All cluster names should be unique and should identify one and only one cluster.\n ", "items": { "properties": { "currentName": { "description": "Current cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "newName": { "description": "New cluster name.", "maxLength": 50, "minLength": 1, "type": "string" } }, "required": [ "currentName", "newName" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "clusterNameMappings" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clusterNameMappings | [ClusterNamesMappingValidation] | true | maxItems: 100 | A list of the mappings from a cluster's current name to its new name. After update, value passed as a new name will become cluster's current name. All cluster names should be unique and should identify one and only one cluster. | ## CombinedModelListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of combined models.", "items": { "properties": { "combinedModelId": { "description": "The ID of the combined model.", "type": "string" }, "isActiveCombinedModel": { "default": false, "description": "Indicates whether this model is the active one in segmented modeling project.", "type": "boolean", "x-versionadded": "v2.29" }, "modelCategory": { "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.", "enum": [ "combined" ], "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "segmentationTaskId": { "description": "The ID of the segmentation task used to generate this combined model.", "type": "string" } }, "required": [ "combinedModelId", "isActiveCombinedModel", "modelCategory", "projectId", "segmentationTaskId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CommonGetAndListCombinedModel] | true | | The list of combined models. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CombinedModelResponse ``` { "properties": { "combinedModelId": { "description": "The ID of the combined model.", "type": "string" }, "isActiveCombinedModel": { "default": false, "description": "Indicates whether this model is the active one in segmented modeling project.", "type": "boolean", "x-versionadded": "v2.29" }, "modelCategory": { "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.", "enum": [ "combined" ], "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "segmentationTaskId": { "description": "The ID of the segmentation task used to generate this combined model.", "type": "string" }, "segments": { "description": "Information for each segment. Maps each segment to the project and model used for it.", "items": { "properties": { "modelId": { "description": "The ID of the segment champion model.", "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project used for this segment.", "type": [ "string", "null" ] }, "segment": { "description": "Segment name.", "type": "string" } }, "required": [ "modelId", "projectId", "segment" ], "type": "object" }, "type": "array" } }, "required": [ "combinedModelId", "isActiveCombinedModel", "modelCategory", "projectId", "segmentationTaskId", "segments" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | combinedModelId | string | true | | The ID of the combined model. | | isActiveCombinedModel | boolean | true | | Indicates whether this model is the active one in segmented modeling project. | | modelCategory | string | true | | Indicates what kind of model this is. Will be combined for combined models. | | projectId | string | true | | The ID of the project. | | segmentationTaskId | string | true | | The ID of the segmentation task used to generate this combined model. | | segments | [SegmentProjectModelResponse] | true | | Information for each segment. Maps each segment to the project and model used for it. | ### Enumerated Values | Property | Value | | --- | --- | | modelCategory | combined | ## CombinedModelSegmentsPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of combined model segment info.", "items": { "properties": { "autopilotDone": { "description": "Whether autopilot is done for the project.", "type": [ "boolean", "null" ] }, "holdoutUnlocked": { "description": "Whether holdout is unlocked for the project.", "type": [ "boolean", "null" ] }, "isFrozen": { "description": "Indicates whether the segment champion model is frozen, i.e., uses tuning parameters from a parent model.", "type": [ "boolean", "null" ] }, "modelAssignedBy": { "description": "Who assigned the model as segment champion. The default is ``DataRobot``.", "type": [ "string", "null" ] }, "modelAwardTime": { "description": "The time when the model was awarded as segment champion.", "format": "date-time", "type": [ "string", "null" ] }, "modelCount": { "description": "The count of trained models in the project.", "type": [ "integer", "null" ] }, "modelIcon": { "description": "The number for the icon representing the given champion model.", "items": { "type": "integer" }, "type": "array" }, "modelId": { "description": "The ID of the segment champion model.", "type": [ "string", "null" ] }, "modelMetrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or ``null`` if they have not been computed.", "type": [ "object", "null" ] }, "modelType": { "description": "The description of the model type of the given champion model.", "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project.", "type": [ "string", "null" ] }, "projectPaused": { "description": "Whether the project is paused right now.", "type": [ "boolean", "null" ] }, "projectStage": { "description": "The current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed.", "enum": [ "modeling", "aim", "fasteda", "eda", "eda2", "empty" ], "type": [ "string", "null" ] }, "projectStageDescription": { "description": "A description of the current stage of the project.", "type": [ "string", "null" ] }, "projectStatusError": { "description": "Project status error message.", "type": [ "string", "null" ] }, "rowCount": { "description": "The count of rows in the project's dataset.", "type": [ "integer", "null" ] }, "rowPercentage": { "description": "The percentage of rows in the segment project's dataset compared to the original dataset.", "type": [ "number", "null" ] }, "segment": { "description": "Segment name.", "type": "string" } }, "required": [ "autopilotDone", "holdoutUnlocked", "isFrozen", "modelAssignedBy", "modelAwardTime", "modelCount", "modelIcon", "modelId", "modelMetrics", "modelType", "projectId", "projectStage", "projectStageDescription", "rowCount", "rowPercentage", "segment" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CombinedModelSegmentsResponse] | true | | The list of combined model segment info. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CombinedModelSegmentsResponse ``` { "properties": { "autopilotDone": { "description": "Whether autopilot is done for the project.", "type": [ "boolean", "null" ] }, "holdoutUnlocked": { "description": "Whether holdout is unlocked for the project.", "type": [ "boolean", "null" ] }, "isFrozen": { "description": "Indicates whether the segment champion model is frozen, i.e., uses tuning parameters from a parent model.", "type": [ "boolean", "null" ] }, "modelAssignedBy": { "description": "Who assigned the model as segment champion. The default is ``DataRobot``.", "type": [ "string", "null" ] }, "modelAwardTime": { "description": "The time when the model was awarded as segment champion.", "format": "date-time", "type": [ "string", "null" ] }, "modelCount": { "description": "The count of trained models in the project.", "type": [ "integer", "null" ] }, "modelIcon": { "description": "The number for the icon representing the given champion model.", "items": { "type": "integer" }, "type": "array" }, "modelId": { "description": "The ID of the segment champion model.", "type": [ "string", "null" ] }, "modelMetrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or ``null`` if they have not been computed.", "type": [ "object", "null" ] }, "modelType": { "description": "The description of the model type of the given champion model.", "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project.", "type": [ "string", "null" ] }, "projectPaused": { "description": "Whether the project is paused right now.", "type": [ "boolean", "null" ] }, "projectStage": { "description": "The current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed.", "enum": [ "modeling", "aim", "fasteda", "eda", "eda2", "empty" ], "type": [ "string", "null" ] }, "projectStageDescription": { "description": "A description of the current stage of the project.", "type": [ "string", "null" ] }, "projectStatusError": { "description": "Project status error message.", "type": [ "string", "null" ] }, "rowCount": { "description": "The count of rows in the project's dataset.", "type": [ "integer", "null" ] }, "rowPercentage": { "description": "The percentage of rows in the segment project's dataset compared to the original dataset.", "type": [ "number", "null" ] }, "segment": { "description": "Segment name.", "type": "string" } }, "required": [ "autopilotDone", "holdoutUnlocked", "isFrozen", "modelAssignedBy", "modelAwardTime", "modelCount", "modelIcon", "modelId", "modelMetrics", "modelType", "projectId", "projectStage", "projectStageDescription", "rowCount", "rowPercentage", "segment" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | autopilotDone | boolean,null | true | | Whether autopilot is done for the project. | | holdoutUnlocked | boolean,null | true | | Whether holdout is unlocked for the project. | | isFrozen | boolean,null | true | | Indicates whether the segment champion model is frozen, i.e., uses tuning parameters from a parent model. | | modelAssignedBy | string,null | true | | Who assigned the model as segment champion. The default is DataRobot. | | modelAwardTime | string,null(date-time) | true | | The time when the model was awarded as segment champion. | | modelCount | integer,null | true | | The count of trained models in the project. | | modelIcon | [integer] | true | | The number for the icon representing the given champion model. | | modelId | string,null | true | | The ID of the segment champion model. | | modelMetrics | object,null | true | | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. | | modelType | string,null | true | | The description of the model type of the given champion model. | | projectId | string,null | true | | The ID of the project. | | projectPaused | boolean,null | false | | Whether the project is paused right now. | | projectStage | string,null | true | | The current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed. | | projectStageDescription | string,null | true | | A description of the current stage of the project. | | projectStatusError | string,null | false | | Project status error message. | | rowCount | integer,null | true | | The count of rows in the project's dataset. | | rowPercentage | number,null | true | | The percentage of rows in the segment project's dataset compared to the original dataset. | | segment | string | true | | Segment name. | ### Enumerated Values | Property | Value | | --- | --- | | projectStage | [modeling, aim, fasteda, eda, eda2, empty] | ## CommonGetAndListCombinedModel ``` { "properties": { "combinedModelId": { "description": "The ID of the combined model.", "type": "string" }, "isActiveCombinedModel": { "default": false, "description": "Indicates whether this model is the active one in segmented modeling project.", "type": "boolean", "x-versionadded": "v2.29" }, "modelCategory": { "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.", "enum": [ "combined" ], "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "segmentationTaskId": { "description": "The ID of the segmentation task used to generate this combined model.", "type": "string" } }, "required": [ "combinedModelId", "isActiveCombinedModel", "modelCategory", "projectId", "segmentationTaskId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | combinedModelId | string | true | | The ID of the combined model. | | isActiveCombinedModel | boolean | true | | Indicates whether this model is the active one in segmented modeling project. | | modelCategory | string | true | | Indicates what kind of model this is. Will be combined for combined models. | | projectId | string | true | | The ID of the project. | | segmentationTaskId | string | true | | The ID of the segmentation task used to generate this combined model. | ### Enumerated Values | Property | Value | | --- | --- | | modelCategory | combined | ## Constraints ``` { "description": "Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format.", "properties": { "ascii": { "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "supportsGridSearch" ], "type": "object" }, "float": { "description": "Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).", "properties": { "max": { "description": "Maximum value for the parameter.", "type": "number" }, "min": { "description": "Minimum value for the parameter.", "type": "number" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "max", "min", "supportsGridSearch" ], "type": "object" }, "floatList": { "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).", "properties": { "maxLength": { "description": "Maximum permitted length of the list.", "minimum": 0, "type": "integer" }, "maxVal": { "description": "Maximum permitted value.", "type": "number" }, "minLength": { "description": "Minimum permitted length of the list.", "minimum": 0, "type": "integer" }, "minVal": { "description": "Minimum permitted value.", "type": "number" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "maxLength", "maxVal", "minLength", "minVal", "supportsGridSearch" ], "type": "object" }, "int": { "description": "Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.", "properties": { "max": { "description": "Maximum value for the parameter.", "type": "integer" }, "min": { "description": "Minimum value for the parameter.", "type": "integer" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "max", "min", "supportsGridSearch" ], "type": "object" }, "intList": { "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.", "properties": { "maxLength": { "description": "Maximum permitted length of the list.", "minimum": 0, "type": "integer" }, "maxVal": { "description": "Maximum permitted value.", "type": "integer" }, "minLength": { "description": "Minimum permitted length of the list.", "minimum": 0, "type": "integer" }, "minVal": { "description": "Minimum permitted value.", "type": "integer" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "maxLength", "maxVal", "minLength", "minVal", "supportsGridSearch" ], "type": "object" }, "select": { "description": "Indicates that the value can be one selected from a list of known values.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" }, "values": { "description": "List of valid values for this field.", "items": { "type": "string" }, "type": "array" } }, "required": [ "supportsGridSearch", "values" ], "type": "object" }, "selectgrid": { "description": "Indicates that the value can be one selected from a list of known values.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" }, "values": { "description": "List of valid values for this field.", "items": { "type": "string" }, "type": "array" } }, "required": [ "supportsGridSearch", "values" ], "type": "object" }, "unicode": { "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "supportsGridSearch" ], "type": "object" } }, "type": "object" } ``` Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ascii | BaseConstraintType | false | | Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that ascii fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines. | | float | Float | false | | Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point). | | floatList | FloatList | false | | Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point). | | int | Int | false | | Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer. | | intList | IntList | false | | Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers. | | select | Select | false | | Indicates that the value can be one selected from a list of known values. | | selectgrid | Select | false | | Indicates that the value can be one selected from a list of known values. | | unicode | BaseConstraintType | false | | Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that ascii fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines. | ## CrossValidationRetrieveResponse ``` { "properties": { "cvScores": { "description": "A dictionary `cvScores` with sub-dictionary keyed by `partition_id`, each `partition_id` is itself a dictionary keyed by `metric_name` where the value is the reading for that particular metric for the partition_id.", "example": "\n {\n \"cvScores\": {\n \"FVE Gamma\": {\n \"0.0\": 0.24334,\n \"1.0\": 0.17757,\n \"2.0\": 0.21803,\n \"3.0\": 0.20185,\n \"4.0\": 0.20576\n },\n \"FVE Poisson\": {\n \"0.0\": 0.24527,\n \"1.0\": 0.22092,\n \"2.0\": 0.22451,\n \"3.0\": 0.24417,\n \"4.0\": 0.21654\n }\n }\n }\n", "type": "object" } }, "required": [ "cvScores" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cvScores | object | true | | A dictionary cvScores with sub-dictionary keyed by partition_id, each partition_id is itself a dictionary keyed by metric_name where the value is the reading for that particular metric for the partition_id. | ## DatetimeModelDetailsResponse ``` { "properties": { "backtests": { "description": "information on each backtesting fold of the model", "items": { "properties": { "index": { "description": "the index of the fold", "type": "integer" }, "score": { "description": "the score of the model for this backtesting fold, if computed", "type": [ "number", "null" ] }, "status": { "description": "the status of the current backtest model job", "enum": [ "COMPLETED", "NOT_COMPLETED", "INSUFFICIENT_DATA", "ERRORED", "BACKTEST_BOUNDARIES_EXCEEDED" ], "type": "string" }, "trainingDuration": { "description": "the duration of the data used to train the model for this backtesting fold", "format": "duration", "type": "string" }, "trainingEndDate": { "description": "the end date of the training for this backtesting fold", "format": "date-time", "type": "string" }, "trainingRowCount": { "description": "the number of rows used to train the model for this backtesting fold", "type": "integer" }, "trainingStartDate": { "description": "the start date of the training for this backtesting fold", "format": "date-time", "type": "string" } }, "required": [ "index", "score", "status", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" }, "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "effectiveFeatureDerivationWindowEnd": { "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.16" }, "effectiveFeatureDerivationWindowStart": { "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the `featureDerivationWindowStart` set on the project due to the differencing method and period selected.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "v2.16" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "forecastWindowEnd": { "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.16" }, "forecastWindowStart": { "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.16" }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "holdoutScore": { "description": "the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked", "type": [ "number", "null" ] }, "holdoutStatus": { "description": "the status of the holdout fold", "enum": [ "COMPLETED", "INSUFFICIENT_DATA", "HOLDOUT_BOUNDARIES_EXCEEDED" ], "type": "string" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "Object where each metric has validation, backtesting, backtestingScores and holdout scores reported, or null if they have not been computed. The `validation` score will be the score of the first backtest, which will be computed during initial model training. The `backtesting` and `backtestingScores` scores are computed when requested via [POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/][post-apiv2projectsprojectiddatetimemodelsmodelidbacktests]. The `backtesting` score is the average score across all backtests. The `backtestingScores` is an array of scores for each backtest, with the scores reported as null if the backtest score is unavailable. The `holdout` score is the score against the holdout data, using the training data defined in `trainingInfo`.", "example": "\n {\n \"metrics\": {\n \"FVE Poisson\": {\n \"holdout\": null,\n \"validation\": 0.56269,\n \"backtesting\": 0.50166,\n \"backtestingScores\": [0.51206, 0.49436, null, 0.62516],\n \"crossValidation\": null\n },\n \"RMSE\": {\n \"holdout\": null,\n \"validation\": 21.0836,\n \"backtesting\": 23.361932,\n \"backtestingScores\": [0.4403, 0.4213, null, 0.5132],\n \"crossValidation\": null\n }\n }\n }\n", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "always null for datetime models", "enum": [ null ], "type": "string" }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingInfo": { "description": "holdout and prediction training data details", "properties": { "holdoutTrainingDuration": { "description": "the duration of the data used to train a model to score the holdout", "format": "duration", "type": "string" }, "holdoutTrainingEndDate": { "description": "the end date of the data used to train a model to score the holdout", "format": "date-time", "type": "string" }, "holdoutTrainingRowCount": { "description": "the number of rows used to train a model to score the holdout", "type": "integer" }, "holdoutTrainingStartDate": { "description": "the start date of data used to train a model to score the holdout", "format": "date-time", "type": "string" }, "predictionTrainingDuration": { "description": "the duration of the data used to train a model to make predictions", "format": "duration", "type": "string" }, "predictionTrainingEndDate": { "description": "the end date of the data used to train a model to make predictions", "format": "date-time", "type": "string" }, "predictionTrainingRowCount": { "description": "the number of rows used to train a model to make predictions", "type": "integer" }, "predictionTrainingStartDate": { "description": "the start date of data used to train a model to make predictions", "format": "date-time", "type": "string" } }, "required": [ "holdoutTrainingDuration", "holdoutTrainingEndDate", "holdoutTrainingRowCount", "holdoutTrainingStartDate", "predictionTrainingDuration", "predictionTrainingEndDate", "predictionTrainingRowCount", "predictionTrainingStartDate" ], "type": "object" }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.16" } }, "required": [ "backtests", "blenderModels", "blueprintId", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "externalPredictionModel", "featurelistId", "featurelistName", "forecastWindowEnd", "forecastWindowStart", "frozenPct", "hasCodegen", "holdoutScore", "holdoutStatus", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingInfo", "trainingRowCount", "trainingStartDate", "windowsBasisUnit" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtests | [BacktestStatusResponse] | true | | information on each backtesting fold of the model | | blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. | | blueprintId | string | true | | The blueprint used to construct the model. | | dataSelectionMethod | string | false | | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. | | effectiveFeatureDerivationWindowEnd | integer | true | maximum: 0 | Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end. | | effectiveFeatureDerivationWindowStart | integer | true | | Only available for time series projects. How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the featureDerivationWindowStart set on the project due to the differencing method and period selected. | | externalPredictionModel | boolean | true | | If the model is an external prediction model. | | featurelistId | string,null | true | | The ID of the feature list used by the model. | | featurelistName | string,null | true | | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. | | forecastWindowEnd | integer | true | minimum: 0 | Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end. | | forecastWindowStart | integer | true | minimum: 0 | Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start. | | frozenPct | number,null | true | | The training percent used to train the frozen model. | | hasCodegen | boolean | true | | If the model has a codegen JAR file. | | hasFinetuners | boolean | false | | Whether a model has fine tuners. | | holdoutScore | number,null | true | | the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked | | holdoutStatus | string | true | | the status of the holdout fold | | icons | integer,null | true | | The icons associated with the model. | | id | string | true | | The ID of the model. | | isAugmented | boolean | false | | Whether a model was trained using augmentation. | | isBlender | boolean | true | | If the model is a blender. | | isCustom | boolean | true | | If the model contains custom tasks. | | isFrozen | boolean | true | | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. | | isNClustersDynamicallyDetermined | boolean | false | | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. | | isStarred | boolean | true | | Indicates whether the model has been starred. | | isTrainedIntoHoldout | boolean | true | | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. | | isTrainedIntoValidation | boolean | true | | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. | | isTrainedOnGpu | boolean | true | | Whether the model was trained using GPU workers. | | isTransparent | boolean | true | | If the model is a transparent model with exposed coefficients. | | isUserModel | boolean | true | | If the model was created with Composable ML. | | lifecycle | ModelLifecycle | true | | Object returning model lifecycle. | | linkFunction | string,null | true | | The link function the final modeler uses in the blueprint. If no link function exists, returns null. | | metrics | object | true | | Object where each metric has validation, backtesting, backtestingScores and holdout scores reported, or null if they have not been computed. The validation score will be the score of the first backtest, which will be computed during initial model training. The backtesting and backtestingScores scores are computed when requested via [POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/][post-apiv2projectsprojectiddatetimemodelsmodelidbacktests]. The backtesting score is the average score across all backtests. The backtestingScores is an array of scores for each backtest, with the scores reported as null if the backtest score is unavailable. The holdout score is the score against the holdout data, using the training data defined in trainingInfo. | | modelCategory | string | true | | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. | | modelFamily | string | true | | The family the model belongs to, e.g., SVM, GBM, etc. | | modelFamilyFullName | string | true | | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. | | modelNumber | integer,null | true | | The model number from the Leaderboard. | | modelType | string | true | | Identifies the model (e.g., Nystroem Kernel SVM Regressor). | | monotonicDecreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | monotonicIncreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | nClusters | integer,null | false | | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | parentModelId | string,null | true | | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. | | predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. | | predictionThresholdReadOnly | boolean | true | | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. | | processes | [string] | true | maxItems: 100 | The list of processes used by the model. | | projectId | string | true | | The ID of the project to which the model belongs. | | samplePct | string | true | | always null for datetime models | | samplingMethod | string | false | | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. | | supportsComposableMl | boolean | true | | indicates whether this model is supported in Composable ML. | | supportsMonotonicConstraints | boolean | true | | whether this model supports enforcing monotonic constraints | | timeWindowSamplePct | integer,null | false | | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. | | trainingDuration | string,null | true | | the duration spanned by the dates in the partition column for the data used to train the model | | trainingEndDate | string,null(date-time) | true | | the end date of the dates in the partition column for the data used to train the model | | trainingInfo | TrainingInfoResponse | true | | holdout and prediction training data details | | trainingRowCount | integer,null | true | | The number of rows used to train the model. | | trainingStartDate | string,null(date-time) | true | | the start date of the dates in the partition column for the data used to train the model | | windowsBasisUnit | string | true | | Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window. | ### Enumerated Values | Property | Value | | --- | --- | | dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] | | holdoutStatus | [COMPLETED, INSUFFICIENT_DATA, HOLDOUT_BOUNDARIES_EXCEEDED] | | modelCategory | [model, prime, blend, combined, incrementalLearning] | | samplePct | null | | samplingMethod | [random, latest] | | windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] | ## DatetimeModelRecordResponse ``` { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string", "x-versionadded": "v2.33" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string", "x-versionadded": "v2.33" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean", "x-versionadded": "v2.33" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object", "x-versionadded": "v2.33" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string", "x-versionadded": "v2.33" }, "modelFamily": { "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).", "type": "string", "x-versionadded": "v2.33" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string", "x-versionadded": "v2.33" }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string", "x-versionadded": "v2.33" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string", "x-versionadded": "v2.33" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "metrics", "modelCategory", "modelFamily", "modelNumber", "modelType", "parentModelId", "processes", "projectId", "samplePct", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. | | blueprintId | string | true | | The blueprint used to construct the model. | | dataSelectionMethod | string | false | | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. | | externalPredictionModel | boolean | true | | If the model is an external prediction model. | | featurelistId | string,null | true | | The ID of the feature list used by the model. | | featurelistName | string,null | true | | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. | | frozenPct | number,null | true | | The training percent used to train the frozen model. | | hasCodegen | boolean | true | | If the model has a codegen JAR file. | | icons | integer,null | true | | The icons associated with the model. | | id | string | true | | The ID of the model. | | isBlender | boolean | true | | If the model is a blender. | | isCustom | boolean | true | | If the model contains custom tasks. | | isFrozen | boolean | true | | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. | | isStarred | boolean | true | | Indicates whether the model has been starred. | | isTrainedIntoHoldout | boolean | true | | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. | | isTrainedIntoValidation | boolean | true | | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. | | isTrainedOnGpu | boolean | true | | Whether the model was trained using GPU workers. | | isTransparent | boolean | true | | If the model is a transparent model with exposed coefficients. | | isUserModel | boolean | true | | If the model was created with Composable ML. | | metrics | object | true | | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. | | modelCategory | string | true | | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. | | modelFamily | string | true | | The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.). | | modelNumber | integer,null | true | | The model number from the Leaderboard. | | modelType | string | true | | Identifies the model (e.g., Nystroem Kernel SVM Regressor). | | parentModelId | string,null | true | | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. | | processes | [string] | true | maxItems: 100 | The list of processes used by the model. | | projectId | string | true | | The ID of the project to which the model belongs. | | samplePct | number,null | true | | The percentage of the dataset used in training the model. | | samplingMethod | string | false | | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. | | timeWindowSamplePct | integer,null | false | | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. | | trainingDuration | string,null | true | | the duration spanned by the dates in the partition column for the data used to train the model | | trainingEndDate | string,null(date-time) | true | | the end date of the dates in the partition column for the data used to train the model | | trainingRowCount | integer,null | true | | The number of rows used to train the model. | | trainingStartDate | string,null(date-time) | true | | the start date of the dates in the partition column for the data used to train the model | ### Enumerated Values | Property | Value | | --- | --- | | dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] | | modelCategory | [model, prime, blend, combined, incrementalLearning] | | samplingMethod | [random, latest] | ## DatetimeModelSubmissionResponse ``` { "properties": { "message": { "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created. | ## DatetimeModelsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/][get-apiv2projectsprojectiddatetimemodelsmodelid].", "items": { "properties": { "backtests": { "description": "information on each backtesting fold of the model", "items": { "properties": { "index": { "description": "the index of the fold", "type": "integer" }, "score": { "description": "the score of the model for this backtesting fold, if computed", "type": [ "number", "null" ] }, "status": { "description": "the status of the current backtest model job", "enum": [ "COMPLETED", "NOT_COMPLETED", "INSUFFICIENT_DATA", "ERRORED", "BACKTEST_BOUNDARIES_EXCEEDED" ], "type": "string" }, "trainingDuration": { "description": "the duration of the data used to train the model for this backtesting fold", "format": "duration", "type": "string" }, "trainingEndDate": { "description": "the end date of the training for this backtesting fold", "format": "date-time", "type": "string" }, "trainingRowCount": { "description": "the number of rows used to train the model for this backtesting fold", "type": "integer" }, "trainingStartDate": { "description": "the start date of the training for this backtesting fold", "format": "date-time", "type": "string" } }, "required": [ "index", "score", "status", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" }, "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "effectiveFeatureDerivationWindowEnd": { "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.16" }, "effectiveFeatureDerivationWindowStart": { "description": "Only available for time series projects. How many timeUnits into the past relative to the forecast point the user needs to provide history for at prediction time. This can differ from the `featureDerivationWindowStart` set on the project due to the differencing method and period selected.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "v2.16" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "forecastWindowEnd": { "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.16" }, "forecastWindowStart": { "description": "Only available for time series projects. How many timeUnits into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.16" }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "holdoutScore": { "description": "the holdout score of the model according to the project metric, if the score is available and the holdout is unlocked", "type": [ "number", "null" ] }, "holdoutStatus": { "description": "the status of the holdout fold", "enum": [ "COMPLETED", "INSUFFICIENT_DATA", "HOLDOUT_BOUNDARIES_EXCEEDED" ], "type": "string" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "Object where each metric has validation, backtesting, backtestingScores and holdout scores reported, or null if they have not been computed. The `validation` score will be the score of the first backtest, which will be computed during initial model training. The `backtesting` and `backtestingScores` scores are computed when requested via [POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtests/][post-apiv2projectsprojectiddatetimemodelsmodelidbacktests]. The `backtesting` score is the average score across all backtests. The `backtestingScores` is an array of scores for each backtest, with the scores reported as null if the backtest score is unavailable. The `holdout` score is the score against the holdout data, using the training data defined in `trainingInfo`.", "example": "\n {\n \"metrics\": {\n \"FVE Poisson\": {\n \"holdout\": null,\n \"validation\": 0.56269,\n \"backtesting\": 0.50166,\n \"backtestingScores\": [0.51206, 0.49436, null, 0.62516],\n \"crossValidation\": null\n },\n \"RMSE\": {\n \"holdout\": null,\n \"validation\": 21.0836,\n \"backtesting\": 23.361932,\n \"backtestingScores\": [0.4403, 0.4213, null, 0.5132],\n \"crossValidation\": null\n }\n }\n }\n", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "always null for datetime models", "enum": [ null ], "type": "string" }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingInfo": { "description": "holdout and prediction training data details", "properties": { "holdoutTrainingDuration": { "description": "the duration of the data used to train a model to score the holdout", "format": "duration", "type": "string" }, "holdoutTrainingEndDate": { "description": "the end date of the data used to train a model to score the holdout", "format": "date-time", "type": "string" }, "holdoutTrainingRowCount": { "description": "the number of rows used to train a model to score the holdout", "type": "integer" }, "holdoutTrainingStartDate": { "description": "the start date of data used to train a model to score the holdout", "format": "date-time", "type": "string" }, "predictionTrainingDuration": { "description": "the duration of the data used to train a model to make predictions", "format": "duration", "type": "string" }, "predictionTrainingEndDate": { "description": "the end date of the data used to train a model to make predictions", "format": "date-time", "type": "string" }, "predictionTrainingRowCount": { "description": "the number of rows used to train a model to make predictions", "type": "integer" }, "predictionTrainingStartDate": { "description": "the start date of data used to train a model to make predictions", "format": "date-time", "type": "string" } }, "required": [ "holdoutTrainingDuration", "holdoutTrainingEndDate", "holdoutTrainingRowCount", "holdoutTrainingStartDate", "predictionTrainingDuration", "predictionTrainingEndDate", "predictionTrainingRowCount", "predictionTrainingStartDate" ], "type": "object" }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "Only available for time series projects. Indicates which unit is the basis for the feature derivation window and the forecast window.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.16" } }, "required": [ "backtests", "blenderModels", "blueprintId", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "externalPredictionModel", "featurelistId", "featurelistName", "forecastWindowEnd", "forecastWindowStart", "frozenPct", "hasCodegen", "holdoutScore", "holdoutStatus", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingInfo", "trainingRowCount", "trainingStartDate", "windowsBasisUnit" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatetimeModelDetailsResponse] | true | | each has the same schema as if retrieving the model individually from [GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/][get-apiv2projectsprojectiddatetimemodelsmodelid]. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## DerivedFeatures ``` { "properties": { "coefficient": { "description": "The coefficient for this feature.", "type": "number" }, "derivedFeature": { "description": "The name of the derived feature.", "type": "string" }, "originalFeature": { "description": "The name of the feature used to derive this feature.", "type": "string" }, "stageCoefficients": { "description": "An array of json objects describing separate coefficients for every stage of model (empty for single stage models).", "items": { "properties": { "coefficient": { "description": "The corresponding value of the coefficient for that stage.", "type": "number" }, "stage": { "description": "The name of the stage.", "type": "string" } }, "required": [ "coefficient", "stage" ], "type": "object" }, "type": "array" }, "transformations": { "description": "An array of json objects describing the transformations applied to create this derived feature.", "items": { "properties": { "name": { "description": "The name of the transformation.", "type": "string" }, "value": { "description": "The value used in carrying it out.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" }, "type": { "description": "The type of this feature.", "type": "string" } }, "required": [ "coefficient", "derivedFeature", "originalFeature", "stageCoefficients", "transformations", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | coefficient | number | true | | The coefficient for this feature. | | derivedFeature | string | true | | The name of the derived feature. | | originalFeature | string | true | | The name of the feature used to derive this feature. | | stageCoefficients | [StageCoefficients] | true | | An array of json objects describing separate coefficients for every stage of model (empty for single stage models). | | transformations | [Transformations] | true | | An array of json objects describing the transformations applied to create this derived feature. | | type | string | true | | The type of this feature. | ## Empty ``` { "type": "object" } ``` ### Properties None ## EureqaDistributionDetailResponse ``` { "properties": { "bins": { "description": "The distribution plot data.", "items": { "properties": { "binEnd": { "description": "The end of the numeric range for the current bin. Note that `binEnd` - `binStart` should be a constant, modulo floating-point rounding error, for all bins in a single plot.", "type": "number" }, "binStart": { "description": "The start of the numeric range for the current bin. Must be equal to the `binEnd` of the previous bin.", "type": "number" }, "negatives": { "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is negative.", "type": "integer" }, "positives": { "description": "The number of records in the dataset where the model's predicted value falls into this bin and the target is positive.", "type": "integer" } }, "required": [ "binEnd", "binStart", "negatives", "positives" ], "type": "object" }, "type": "array" }, "complexity": { "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.", "type": "integer" }, "error": { "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.", "type": [ "number", "null" ] }, "errorMetric": { "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.", "type": "string" }, "eureqaSolutionId": { "description": "The ID of the solution.", "type": "string" }, "expression": { "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.", "type": "string" }, "expressionAnnotated": { "description": "The `expression`, rendered with additional tags to assist in automatic parsing.", "type": "string" }, "threshold": { "description": "Classifier threshold selected by the backend, used to determine which model values are binned as positive and which are binned as negative. Must have a value between the `binStart` of the first bin and `binEnd` of the last bin.", "type": "number" } }, "required": [ "bins", "complexity", "error", "errorMetric", "eureqaSolutionId", "expression", "expressionAnnotated", "threshold" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [ClassificationBinDataResponse] | true | | The distribution plot data. | | complexity | integer | true | | The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters. | | error | number,null | true | | The error for the current solution, as computed by eureqa using the errorMetric error metric. None if Eureqa model refitted existing solutions. | | errorMetric | string | true | | The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently. | | eureqaSolutionId | string | true | | The ID of the solution. | | expression | string | true | | The eureqa "solution string". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa. | | expressionAnnotated | string | true | | The expression, rendered with additional tags to assist in automatic parsing. | | threshold | number | true | | Classifier threshold selected by the backend, used to determine which model values are binned as positive and which are binned as negative. Must have a value between the binStart of the first bin and binEnd of the last bin. | ## EureqaLeaderboardEntryPayload ``` { "properties": { "parentModelId": { "description": "The ID of the model to clone from. If omitted, will automatically search for and find the first leaderboard model created by the blueprint run that also created the solution associated with `solutionId`.", "type": "string" }, "solutionId": { "description": "The ID of the solution to be cloned.", "type": "string" } }, "required": [ "solutionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parentModelId | string | false | | The ID of the model to clone from. If omitted, will automatically search for and find the first leaderboard model created by the blueprint run that also created the solution associated with solutionId. | | solutionId | string | true | | The ID of the solution to be cloned. | ## EureqaModelDetailResponse ``` { "properties": { "complexity": { "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.", "type": "integer" }, "error": { "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.", "type": [ "number", "null" ] }, "errorMetric": { "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.", "type": "string" }, "eureqaSolutionId": { "description": "The ID of the solution.", "type": "string" }, "expression": { "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.", "type": "string" }, "expressionAnnotated": { "description": "The `expression`, rendered with additional tags to assist in automatic parsing.", "type": "string" }, "plotData": { "description": "The plot data.", "items": { "properties": { "actual": { "description": "The actual value of the target variable for the specified row.", "type": "number" }, "predicted": { "description": "The predicted value of the target by the solution for the specified row.", "type": "number" }, "row": { "description": "The row number from the raw source data. Used as the X axis for the plot when rendered in the web application.", "type": "integer" } }, "required": [ "actual", "predicted", "row" ], "type": "object" }, "type": "array" } }, "required": [ "complexity", "error", "errorMetric", "eureqaSolutionId", "expression", "expressionAnnotated", "plotData" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | complexity | integer | true | | The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters. | | error | number,null | true | | The error for the current solution, as computed by eureqa using the errorMetric error metric. None if Eureqa model refitted existing solutions. | | errorMetric | string | true | | The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently. | | eureqaSolutionId | string | true | | The ID of the solution. | | expression | string | true | | The eureqa "solution string". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa. | | expressionAnnotated | string | true | | The expression, rendered with additional tags to assist in automatic parsing. | | plotData | [PlotDataResponse] | true | | The plot data. | ## Float ``` { "description": "Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).", "properties": { "max": { "description": "Maximum value for the parameter.", "type": "number" }, "min": { "description": "Minimum value for the parameter.", "type": "number" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "max", "min", "supportsGridSearch" ], "type": "object" } ``` Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point). ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | max | number | true | | Maximum value for the parameter. | | min | number | true | | Minimum value for the parameter. | | supportsGridSearch | boolean | true | | When True, Grid Search is supported for this parameter. | ## FloatList ``` { "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).", "properties": { "maxLength": { "description": "Maximum permitted length of the list.", "minimum": 0, "type": "integer" }, "maxVal": { "description": "Maximum permitted value.", "type": "number" }, "minLength": { "description": "Minimum permitted length of the list.", "minimum": 0, "type": "integer" }, "minVal": { "description": "Minimum permitted value.", "type": "number" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "maxLength", "maxVal", "minLength", "minVal", "supportsGridSearch" ], "type": "object" } ``` Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point). ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxLength | integer | true | minimum: 0 | Maximum permitted length of the list. | | maxVal | number | true | | Maximum permitted value. | | minLength | integer | true | minimum: 0 | Minimum permitted length of the list. | | minVal | number | true | | Minimum permitted value. | | supportsGridSearch | boolean | true | | When True, Grid Search is supported for this parameter. | ## FrozenModelCreate ``` { "properties": { "modelId": { "description": "The ID of an existing model to use as a source of training parameters.", "type": "string" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "maximum": 100, "minimum": 2, "type": "integer", "x-versionadded": "v2.27" }, "samplePct": { "description": "The percentage of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified. The specified percentage should be between 0.0 and 100.0.", "type": "number" }, "trainingRowCount": { "description": "The integer number of rows of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified.", "type": "integer" } }, "required": [ "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | The ID of an existing model to use as a source of training parameters. | | nClusters | integer | false | maximum: 100minimum: 2 | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | samplePct | number | false | | The percentage of the dataset to use with the model. Only one of samplePct and trainingRowCount should be specified. The specified percentage should be between 0.0 and 100.0. | | trainingRowCount | integer | false | | The integer number of rows of the dataset to use with the model. Only one of samplePct and trainingRowCount should be specified. | ## FrozenModelListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of the frozen models in a project.", "items": { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ModelDetailsResponse] | true | | An array of the frozen models in a project. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | false | | The total number of items across all pages. | ## GridSearchArgument ``` { "description": "The details of the grid search configuration.", "properties": { "algorithm": { "default": "Pattern Search", "description": "The algorithm to apply when running the grid search.", "enum": [ "Pattern Search", "Accelerated Search", "Exhaustive Search", "Greedy Exhaustive Search", "Bayesian TPE Search", "Bayesian GP Search" ], "type": "string" }, "batchSize": { "default": 2, "description": "The number of iterations to perform in each batch.", "maximum": 8, "minimum": 1, "type": "integer" }, "maxIterations": { "default": 100, "description": "Sets the maximum number of iterations to perform.", "maximum": 10000, "minimum": 1, "type": "integer" }, "randomState": { "default": 42, "description": "The random state/seed used for the grid search.", "minimum": 0, "type": "integer" }, "searchType": { "default": "smart", "description": "The type of grid search to be performed. If not specified, DataRobot performs Smart Search.", "enum": [ "full", "smart", "bayesian" ], "type": "string" }, "wallClockTimeLimit": { "default": 300, "description": "The wall clock time limit, in seconds. The model with the best score, at this point, is selected.", "maximum": 604800, "minimum": 1, "type": "integer" } }, "type": "object", "x-versionadded": "v2.38" } ``` The details of the grid search configuration. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | algorithm | string | false | | The algorithm to apply when running the grid search. | | batchSize | integer | false | maximum: 8minimum: 1 | The number of iterations to perform in each batch. | | maxIterations | integer | false | maximum: 10000minimum: 1 | Sets the maximum number of iterations to perform. | | randomState | integer | false | minimum: 0 | The random state/seed used for the grid search. | | searchType | string | false | | The type of grid search to be performed. If not specified, DataRobot performs Smart Search. | | wallClockTimeLimit | integer | false | maximum: 604800minimum: 1 | The wall clock time limit, in seconds. The model with the best score, at this point, is selected. | ### Enumerated Values | Property | Value | | --- | --- | | algorithm | [Pattern Search, Accelerated Search, Exhaustive Search, Greedy Exhaustive Search, Bayesian TPE Search, Bayesian GP Search] | | searchType | [full, smart, bayesian] | ## GridSearchScoresRetrieveResponse ``` { "properties": { "id": { "description": "The grid search scores metadata ID.", "type": "string" }, "modelId": { "description": "The ID of the model that scores belong to.", "type": "string" }, "parameters": { "description": "The list of grid search parameters.", "items": { "items": { "properties": { "parameterName": { "description": "The name of the parameter.", "type": "string" }, "parameterValue": { "description": "The value of the parameter.", "type": "number" } }, "required": [ "parameterName", "parameterValue" ], "type": "object", "x-versionadded": "v2.39" }, "type": "array" }, "maxItems": 100000, "minItems": 1, "type": "array" }, "projectId": { "description": "The project the model belongs to.", "type": "string" }, "scores": { "description": "The list of grid search scores.", "items": { "type": "number" }, "maxItems": 100000, "minItems": 1, "type": "array" }, "source": { "description": "The source for the scores. If omitted, the validation partition is used.", "enum": [ "validation" ], "type": "string" } }, "required": [ "id", "modelId", "parameters", "projectId", "scores", "source" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The grid search scores metadata ID. | | modelId | string | true | | The ID of the model that scores belong to. | | parameters | [array] | true | maxItems: 100000minItems: 1 | The list of grid search parameters. | | projectId | string | true | | The project the model belongs to. | | scores | [number] | true | maxItems: 100000minItems: 1 | The list of grid search scores. | | source | string | true | | The source for the scores. If omitted, the validation partition is used. | ### Enumerated Values | Property | Value | | --- | --- | | source | validation | ## HyperparametersResponse ``` { "properties": { "buildingBlocks": { "description": "Mathematical operators and other components that comprise Eureqa Expressions.", "type": [ "object", "null" ] }, "errorMetric": { "description": "Error Metric Eureqa used internally, to evaluate which models to keep on its internal Pareto Front. ", "type": [ "string", "null" ] }, "maxGenerations": { "description": "The maximum number of evolutionary generations to run.", "minimum": 32, "type": [ "integer", "null" ] }, "numThreads": { "description": "The number of threads Eureqa will run with.", "minimum": 0, "type": [ "integer", "null" ] }, "priorSolutions": { "description": "Prior Eureqa Solutions.", "items": { "description": "Prior solution.", "type": "string" }, "type": "array" }, "randomSeed": { "description": "Constant to seed Eureqa's pseudo-random number generator.", "minimum": 0, "type": [ "integer", "null" ] }, "splitMode": { "description": "Whether to perform in-order (2) or random (1) splitting within the training set, for evolutionary re-training and re-validatoon.", "enum": [ "custom", "1", "2" ], "type": [ "string", "null" ] }, "syncMigrations": { "description": "Whether Eureqa's migrations are synchronized.", "type": [ "boolean", "null" ] }, "targetExpressionFormat": { "description": "Constrain the target expression to the specified format.", "enum": [ "None", "exponential", "featureInteraction" ], "type": [ "string", "null" ] }, "targetExpressionString": { "description": "Eureqa Expression to constrain the form of the models that Eureqa will consider.", "type": [ "string", "null" ] }, "timeoutSec": { "description": "The duration of time to run the Eureqa search algorithm for Eureqa will run until either of max_generations or timeout_sec is reached.", "minimum": 0, "type": [ "number", "null" ] }, "trainingFraction": { "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary training.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "trainingSplitExpr": { "description": "Valid Eureqa Expression to do Eureqa internal training splits.", "type": [ "string", "null" ] }, "validationFraction": { "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary validation.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "validationSplitExpr": { "description": "Valid Eureqa Expression to do Eureqa internal validation splits.", "type": [ "string", "null" ] }, "weightExpr": { "description": "Eureqa Weight Expression.", "type": [ "string", "null" ] } }, "required": [ "buildingBlocks", "maxGenerations", "numThreads", "priorSolutions", "randomSeed", "splitMode", "syncMigrations", "targetExpressionFormat", "targetExpressionString", "timeoutSec", "trainingFraction", "trainingSplitExpr", "validationFraction", "validationSplitExpr", "weightExpr" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildingBlocks | object,null | true | | Mathematical operators and other components that comprise Eureqa Expressions. | | errorMetric | string,null | false | | Error Metric Eureqa used internally, to evaluate which models to keep on its internal Pareto Front. | | maxGenerations | integer,null | true | minimum: 32 | The maximum number of evolutionary generations to run. | | numThreads | integer,null | true | minimum: 0 | The number of threads Eureqa will run with. | | priorSolutions | [string] | true | | Prior Eureqa Solutions. | | randomSeed | integer,null | true | minimum: 0 | Constant to seed Eureqa's pseudo-random number generator. | | splitMode | string,null | true | | Whether to perform in-order (2) or random (1) splitting within the training set, for evolutionary re-training and re-validatoon. | | syncMigrations | boolean,null | true | | Whether Eureqa's migrations are synchronized. | | targetExpressionFormat | string,null | true | | Constrain the target expression to the specified format. | | targetExpressionString | string,null | true | | Eureqa Expression to constrain the form of the models that Eureqa will consider. | | timeoutSec | number,null | true | minimum: 0 | The duration of time to run the Eureqa search algorithm for Eureqa will run until either of max_generations or timeout_sec is reached. | | trainingFraction | number,null | true | maximum: 1minimum: 0 | The fraction of the DataRobot training data to use for Eureqa evolutionary training. | | trainingSplitExpr | string,null | true | | Valid Eureqa Expression to do Eureqa internal training splits. | | validationFraction | number,null | true | maximum: 1minimum: 0 | The fraction of the DataRobot training data to use for Eureqa evolutionary validation. | | validationSplitExpr | string,null | true | | Valid Eureqa Expression to do Eureqa internal validation splits. | | weightExpr | string,null | true | | Eureqa Weight Expression. | ### Enumerated Values | Property | Value | | --- | --- | | splitMode | [custom, 1, 2] | | targetExpressionFormat | [None, exponential, featureInteraction] | ## Int ``` { "description": "Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.", "properties": { "max": { "description": "Maximum value for the parameter.", "type": "integer" }, "min": { "description": "Minimum value for the parameter.", "type": "integer" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "max", "min", "supportsGridSearch" ], "type": "object" } ``` Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | max | integer | true | | Maximum value for the parameter. | | min | integer | true | | Minimum value for the parameter. | | supportsGridSearch | boolean | true | | When True, Grid Search is supported for this parameter. | ## IntList ``` { "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.", "properties": { "maxLength": { "description": "Maximum permitted length of the list.", "minimum": 0, "type": "integer" }, "maxVal": { "description": "Maximum permitted value.", "type": "integer" }, "minLength": { "description": "Minimum permitted length of the list.", "minimum": 0, "type": "integer" }, "minVal": { "description": "Minimum permitted value.", "type": "integer" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "maxLength", "maxVal", "minLength", "minVal", "supportsGridSearch" ], "type": "object" } ``` Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxLength | integer | true | minimum: 0 | Maximum permitted length of the list. | | maxVal | integer | true | | Maximum permitted value. | | minLength | integer | true | minimum: 0 | Minimum permitted length of the list. | | minVal | integer | true | | Minimum permitted value. | | supportsGridSearch | boolean | true | | When True, Grid Search is supported for this parameter. | ## MessagesInfo ``` { "properties": { "messages": { "description": "List of data quality messages. The list may include reports on more than one data quality issue, if present.", "items": { "properties": { "additionalInfo": { "description": "Zero or more text strings for secondary display after user clicks for more information.", "items": { "type": "string" }, "maxItems": 50, "type": "array" }, "messageLevel": { "description": "Message severity level.", "enum": [ "CRITICAL", "INFORMATIONAL", "NO_ISSUES", "WARNING" ], "type": "string" }, "messageText": { "description": "Text for primary display in UI.", "maxLength": 500, "minLength": 1, "type": "string" } }, "required": [ "messageLevel", "messageText" ], "type": "object" }, "maxItems": 50, "minItems": 1, "type": "array" } }, "required": [ "messages" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | messages | [OneMessageInfo] | true | maxItems: 50minItems: 1 | List of data quality messages. The list may include reports on more than one data quality issue, if present. | ## MissingReportRetrieve ``` { "properties": { "missingValuesReport": { "description": "Missing values report, which contains an array of reports for individual features.", "items": { "properties": { "feature": { "description": "The name of the feature.", "type": "string" }, "missingCount": { "description": "The number of missing values in the training data.", "type": "integer" }, "missingPercentage": { "description": "The percentage of missing values in the training data.", "maximum": 1, "minimum": 0, "type": "number" }, "tasks": { "additionalProperties": { "properties": { "descriptions": { "description": "Human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored.", "items": { "type": "string" }, "type": "array" }, "name": { "description": "Task name, e.g., 'Ordinal encoding of categorical variables'.", "type": "string" } }, "required": [ "descriptions", "name" ], "type": "object" }, "description": "Information on individual tasks of the model which were used to process the feature. The names of properties will be task ids (which correspond to the ids used in the blueprint chart endpoints like [GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/blueprintChart/][get-apiv2projectsprojectidblueprintsblueprintidblueprintchart]) The corresponding value for each task will be of the form `task` described.", "type": "object" }, "type": { "description": "The type of the feature, e.g., `Categorical` or `Numeric`.", "type": "string" } }, "required": [ "feature", "missingCount", "missingPercentage", "tasks", "type" ], "type": "object" }, "type": "array" } }, "required": [ "missingValuesReport" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | missingValuesReport | [PerFeatureMissingReport] | true | | Missing values report, which contains an array of reports for individual features. | ## ModelAdvancedTuning ``` { "properties": { "gridSearchArguments": { "description": "The details of the grid search configuration.", "properties": { "algorithm": { "default": "Pattern Search", "description": "The algorithm to apply when running the grid search.", "enum": [ "Pattern Search", "Accelerated Search", "Exhaustive Search", "Greedy Exhaustive Search", "Bayesian TPE Search", "Bayesian GP Search" ], "type": "string" }, "batchSize": { "default": 2, "description": "The number of iterations to perform in each batch.", "maximum": 8, "minimum": 1, "type": "integer" }, "maxIterations": { "default": 100, "description": "Sets the maximum number of iterations to perform.", "maximum": 10000, "minimum": 1, "type": "integer" }, "randomState": { "default": 42, "description": "The random state/seed used for the grid search.", "minimum": 0, "type": "integer" }, "searchType": { "default": "smart", "description": "The type of grid search to be performed. If not specified, DataRobot performs Smart Search.", "enum": [ "full", "smart", "bayesian" ], "type": "string" }, "wallClockTimeLimit": { "default": 300, "description": "The wall clock time limit, in seconds. The model with the best score, at this point, is selected.", "maximum": 604800, "minimum": 1, "type": "integer" } }, "type": "object", "x-versionadded": "v2.38" }, "tuningDescription": { "description": "Human-readable description of this advanced-tuning request.", "type": "string" }, "tuningParameters": { "description": "Parameters to tune.", "items": { "properties": { "parameterId": { "description": "ID of the parameter whose value to set.", "type": "string" }, "value": { "description": "Value for the specified parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "parameterId", "value" ], "type": "object" }, "type": "array" } }, "required": [ "tuningParameters" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | gridSearchArguments | GridSearchArgument | false | | The details of the grid search configuration. | | tuningDescription | string | false | | Human-readable description of this advanced-tuning request. | | tuningParameters | [TuningParameter] | true | | Parameters to tune. | ## ModelCapabilitiesRetrieveResponse ``` { "properties": { "eligibleForPrime": { "description": "`True` if the model is eligible for prime. Use [GET /api/v2/projects/{projectId}/models/{modelId}/primeInfo/][get-apiv2projectsprojectidmodelsmodelidprimeinfo] to request additional details if the model is not eligible.", "type": "boolean", "x-versiondeprecated": "v2.35" }, "hasParameters": { "description": "`True` if the model has parameters that can be retrieved. Use [GET /api/v2/projects/{projectId}/models/{modelId}/parameters/][get-apiv2projectsprojectidmodelsmodelidparameters] to retrieve the model parameters.", "type": "boolean" }, "hasWordCloud": { "description": "True` if the model has word cloud data available. Use [GET /api/v2/projects/{projectId}/models/{modelId}/wordCloud/][get-apiv2projectsprojectidmodelsmodelidwordcloud] to retrieve a word cloud.", "type": "boolean" }, "reasons": { "description": "Information on why the capability is unsupported for the model.", "properties": { "supportsAccuracyOverTime": { "description": "If present, the reason why Accuracy Over Time plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsAnomalyAssessment": { "description": "If present, the reason why the Anomaly Assessment insight cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsAnomalyOverTime": { "description": "If present, the reason why Anomaly Over Time plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsClusterInsights": { "description": "If present, the reason why Cluster Insights cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsConfusionMatrix": { "description": "If present, the reason why Confusion Matrix cannot be generated for the model. There are some cases where Confusion Matrix is available but it was calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.33" }, "supportsDocumentTextExtractionSampleInsight": { "description": "If present, the reason document text extraction sample insights are not supported for the model.", "type": "string", "x-versionadded": "v2.29" }, "supportsForecastAccuracy": { "description": "If present, the reason why Forecast Accuracy plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsForecastVsActual": { "description": "If present, the reason why Forecast vs Actual plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsImageActivationMaps": { "description": "If present, the reason image activation maps are not supported for the model.", "type": "string" }, "supportsImageEmbedding": { "description": "If present, the reason image embeddings are not supported for the model.", "type": "string" }, "supportsLiftChart": { "description": "If present, the reason why Lift Chart cannot be generated for the model. There are some cases where Lift Chart is available but it was calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.31" }, "supportsPeriodAccuracy": { "description": "If present, the reason why Period Accuracy insights cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsPredictionExplanations": { "description": "If present, the reason why Prediction Explanations cannot be computed for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsPredictionIntervals": { "description": "If present, the reason why Prediction Intervals cannot be computed for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsResiduals": { "description": "If present, the reason why residuals are not available for the model. There are some cases where Residuals are available but they were calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.30" }, "supportsRocCurve": { "description": "If present, the reason why ROC Curve cannot be generated for the model. There are some cases where ROC Curve is available but it was calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.32" }, "supportsSeriesInsights": { "description": "If present, the reason why Series Insights cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsStability": { "description": "If present, the reason why Stability plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" } }, "type": "object" }, "supportsAccuracyOverTime": { "description": "`True` if Accuracy Over Time plots can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsAdvancedTuning": { "description": "`True` if model supports Advanced Tuning.", "type": "boolean", "x-versionadded": "v2.36" }, "supportsAnomalyAssessment": { "description": "`True` if Anomaly Assessment insights can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsAnomalyOverTime": { "description": "`True` if Anomaly Over Time plots can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsBlending": { "description": "`True` if the model supports blending. See [POST /api/v2/projects/{projectId}/blenderModels/blendCheck/][post-apiv2projectsprojectidblendermodelsblendcheck] to check specific blending combinations.", "type": "boolean" }, "supportsClusterInsights": { "description": "`True` if Cluster Insights can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsCodeGeneration": { "description": "`True` if the model supports export of model's source code or compiled Java executable.", "type": "boolean", "x-versionadded": "v2.18" }, "supportsCoefficients": { "description": "`True` if model coefficients are available.", "type": "boolean", "x-versionadded": "v2.32" }, "supportsConfusionMatrix": { "description": "`True` if Confusion Matrix can be generated.", "type": "boolean", "x-versionadded": "v2.33" }, "supportsDocumentTextExtractionSampleInsight": { "description": "`True` if the model has document column(s) and document text extraction samples can be generated.", "type": "boolean", "x-versionadded": "v2.29" }, "supportsEarlyStopping": { "description": "`True` if this is an early stopping tree-based model and number of trained iterations can be retrieved.", "type": "boolean", "x-versionadded": "v2.22" }, "supportsForecastAccuracy": { "description": "`True` if Forecast Accuracy plots can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsForecastVsActual": { "description": "`True` if Forecast vs Actual plots can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsImageActivationMaps": { "description": "`True` if the model has image column(s) and activation maps can be generated.", "type": "boolean", "x-versionadded": "v2.19" }, "supportsImageEmbedding": { "description": "`True` if the model has image column(s) and image embeddings can be generated.", "type": "boolean", "x-versionadded": "v2.19" }, "supportsLiftChart": { "description": "`True` if Lift Chart can be generated.", "type": "boolean", "x-versionadded": "v2.31" }, "supportsModelPackageExport": { "description": "`True` if the model can be exported as a model package.", "type": "boolean", "x-versionadded": "v2.19" }, "supportsModelTrainingMetrics": { "description": "When `True` , the model will track and save key training metrics in an effort to communicate model accuracy throughout training, rather than at training completion.", "type": "boolean", "x-versionadded": "v2.21" }, "supportsMonotonicConstraints": { "description": "`True` if the model supports monotonic constraints.", "type": "boolean" }, "supportsNNVisualizations": { "description": "`True` if the model supports neuralNetworkVisualizations.", "type": "boolean", "x-versionadded": "v2.19" }, "supportsPerLabelMetrics": { "description": "`True` if the experiment qualifies as a multilabel project.", "type": "boolean", "x-versionadded": "v2.36" }, "supportsPeriodAccuracy": { "description": "`True` if Period Accuracy insights can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsPredictionExplanations": { "description": "`True` if the model supports Prediction Explanations.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsPredictionIntervals": { "description": "`True` if Prediction Intervals can be computed for predictions generated by this model.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsResiduals": { "description": "When `True`, the model supports residuals and residuals data can be retrieved.", "type": "boolean", "x-versionadded": "v2.30" }, "supportsRocCurve": { "description": "`True` if ROC Curve can be generated.", "type": "boolean", "x-versionadded": "v2.32" }, "supportsSeriesInsights": { "description": "`True` if Series Insights can be generated.", "type": "boolean", "x-versionadded": "v2.34" }, "supportsShap": { "description": "`True` if the model supports Shapley package, i.e., Shapley-based feature importance.", "type": "boolean", "x-versionadded": "v2.18" }, "supportsStability": { "description": "`True` if Stability plots can be generated.", "type": "boolean", "x-versionadded": "v2.34" } }, "required": [ "eligibleForPrime", "hasParameters", "hasWordCloud", "supportsAccuracyOverTime", "supportsAdvancedTuning", "supportsAnomalyAssessment", "supportsAnomalyOverTime", "supportsBlending", "supportsClusterInsights", "supportsCodeGeneration", "supportsCoefficients", "supportsConfusionMatrix", "supportsDocumentTextExtractionSampleInsight", "supportsForecastAccuracy", "supportsForecastVsActual", "supportsImageActivationMaps", "supportsImageEmbedding", "supportsLiftChart", "supportsModelTrainingMetrics", "supportsMonotonicConstraints", "supportsNNVisualizations", "supportsPerLabelMetrics", "supportsPredictionExplanations", "supportsPredictionIntervals", "supportsResiduals", "supportsRocCurve", "supportsSeriesInsights", "supportsShap", "supportsStability" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | eligibleForPrime | boolean | true | | True if the model is eligible for prime. Use [GET /api/v2/projects/{projectId}/models/{modelId}/primeInfo/][get-apiv2projectsprojectidmodelsmodelidprimeinfo] to request additional details if the model is not eligible. | | hasParameters | boolean | true | | True if the model has parameters that can be retrieved. Use [GET /api/v2/projects/{projectId}/models/{modelId}/parameters/][get-apiv2projectsprojectidmodelsmodelidparameters] to retrieve the model parameters. | | hasWordCloud | boolean | true | | True` if the model has word cloud data available. Use [GET /api/v2/projects/{projectId}/models/{modelId}/wordCloud/][get-apiv2projectsprojectidmodelsmodelidwordcloud] to retrieve a word cloud. | | reasons | Reasons | false | | Information on why the capability is unsupported for the model. | | supportsAccuracyOverTime | boolean | true | | True if Accuracy Over Time plots can be generated. | | supportsAdvancedTuning | boolean | true | | True if model supports Advanced Tuning. | | supportsAnomalyAssessment | boolean | true | | True if Anomaly Assessment insights can be generated. | | supportsAnomalyOverTime | boolean | true | | True if Anomaly Over Time plots can be generated. | | supportsBlending | boolean | true | | True if the model supports blending. See [POST /api/v2/projects/{projectId}/blenderModels/blendCheck/][post-apiv2projectsprojectidblendermodelsblendcheck] to check specific blending combinations. | | supportsClusterInsights | boolean | true | | True if Cluster Insights can be generated. | | supportsCodeGeneration | boolean | true | | True if the model supports export of model's source code or compiled Java executable. | | supportsCoefficients | boolean | true | | True if model coefficients are available. | | supportsConfusionMatrix | boolean | true | | True if Confusion Matrix can be generated. | | supportsDocumentTextExtractionSampleInsight | boolean | true | | True if the model has document column(s) and document text extraction samples can be generated. | | supportsEarlyStopping | boolean | false | | True if this is an early stopping tree-based model and number of trained iterations can be retrieved. | | supportsForecastAccuracy | boolean | true | | True if Forecast Accuracy plots can be generated. | | supportsForecastVsActual | boolean | true | | True if Forecast vs Actual plots can be generated. | | supportsImageActivationMaps | boolean | true | | True if the model has image column(s) and activation maps can be generated. | | supportsImageEmbedding | boolean | true | | True if the model has image column(s) and image embeddings can be generated. | | supportsLiftChart | boolean | true | | True if Lift Chart can be generated. | | supportsModelPackageExport | boolean | false | | True if the model can be exported as a model package. | | supportsModelTrainingMetrics | boolean | true | | When True , the model will track and save key training metrics in an effort to communicate model accuracy throughout training, rather than at training completion. | | supportsMonotonicConstraints | boolean | true | | True if the model supports monotonic constraints. | | supportsNNVisualizations | boolean | true | | True if the model supports neuralNetworkVisualizations. | | supportsPerLabelMetrics | boolean | true | | True if the experiment qualifies as a multilabel project. | | supportsPeriodAccuracy | boolean | false | | True if Period Accuracy insights can be generated. | | supportsPredictionExplanations | boolean | true | | True if the model supports Prediction Explanations. | | supportsPredictionIntervals | boolean | true | | True if Prediction Intervals can be computed for predictions generated by this model. | | supportsResiduals | boolean | true | | When True, the model supports residuals and residuals data can be retrieved. | | supportsRocCurve | boolean | true | | True if ROC Curve can be generated. | | supportsSeriesInsights | boolean | true | | True if Series Insights can be generated. | | supportsShap | boolean | true | | True if the model supports Shapley package, i.e., Shapley-based feature importance. | | supportsStability | boolean | true | | True if Stability plots can be generated. | ## ModelDetailsResponse ``` { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. | | blueprintId | string | true | | The blueprint used to construct the model. | | dataSelectionMethod | string | false | | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. | | externalPredictionModel | boolean | true | | If the model is an external prediction model. | | featurelistId | string,null | true | | The ID of the feature list used by the model. | | featurelistName | string,null | true | | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. | | frozenPct | number,null | true | | The training percent used to train the frozen model. | | hasCodegen | boolean | true | | If the model has a codegen JAR file. | | hasFinetuners | boolean | false | | Whether a model has fine tuners. | | icons | integer,null | true | | The icons associated with the model. | | id | string | true | | The ID of the model. | | isAugmented | boolean | false | | Whether a model was trained using augmentation. | | isBlender | boolean | true | | If the model is a blender. | | isCustom | boolean | true | | If the model contains custom tasks. | | isFrozen | boolean | true | | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. | | isNClustersDynamicallyDetermined | boolean | false | | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. | | isStarred | boolean | true | | Indicates whether the model has been starred. | | isTrainedIntoHoldout | boolean | true | | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. | | isTrainedIntoValidation | boolean | true | | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. | | isTrainedOnGpu | boolean | true | | Whether the model was trained using GPU workers. | | isTransparent | boolean | true | | If the model is a transparent model with exposed coefficients. | | isUserModel | boolean | true | | If the model was created with Composable ML. | | lifecycle | ModelLifecycle | true | | Object returning model lifecycle. | | linkFunction | string,null | true | | The link function the final modeler uses in the blueprint. If no link function exists, returns null. | | metrics | object | true | | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. | | modelCategory | string | true | | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. | | modelFamily | string | true | | The family the model belongs to, e.g., SVM, GBM, etc. | | modelFamilyFullName | string | true | | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. | | modelNumber | integer,null | true | | The model number from the Leaderboard. | | modelType | string | true | | Identifies the model (e.g., Nystroem Kernel SVM Regressor). | | monotonicDecreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | monotonicIncreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | nClusters | integer,null | false | | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | parentModelId | string,null | true | | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. | | predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. | | predictionThresholdReadOnly | boolean | true | | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. | | processes | [string] | true | maxItems: 100 | The list of processes used by the model. | | projectId | string | true | | The ID of the project to which the model belongs. | | samplePct | number,null | true | | The percentage of the dataset used in training the model. | | samplingMethod | string | false | | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. | | supportsComposableMl | boolean | true | | indicates whether this model is supported in Composable ML. | | supportsMonotonicConstraints | boolean | true | | whether this model supports enforcing monotonic constraints | | timeWindowSamplePct | integer,null | false | | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. | | trainingDuration | string,null | true | | the duration spanned by the dates in the partition column for the data used to train the model | | trainingEndDate | string,null(date-time) | true | | the end date of the dates in the partition column for the data used to train the model | | trainingRowCount | integer,null | true | | The number of rows used to train the model. | | trainingStartDate | string,null(date-time) | true | | the start date of the dates in the partition column for the data used to train the model | ### Enumerated Values | Property | Value | | --- | --- | | dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] | | modelCategory | [model, prime, blend, combined, incrementalLearning] | | samplingMethod | [random, latest] | ## ModelFeatureListResponse ``` { "properties": { "aPrioriFeatureNames": { "description": "(Deprecated in version v2.11) Renamed to `knownInAdvanceFeatureNames`. This parameter always has the same value as `knownInAdvanceFeatureNames` and will be removed in a future release.", "items": { "type": "string" }, "type": "array" }, "featureNames": { "description": "An array of the names of all features used by the specified model.", "items": { "type": "string" }, "type": "array" }, "knownInAdvanceFeatureNames": { "description": "An array of the names of time series known-in-advance features used by the specified model.", "items": { "type": "string" }, "type": "array" } }, "required": [ "aPrioriFeatureNames", "featureNames", "knownInAdvanceFeatureNames" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aPrioriFeatureNames | [string] | true | | (Deprecated in version v2.11) Renamed to knownInAdvanceFeatureNames. This parameter always has the same value as knownInAdvanceFeatureNames and will be removed in a future release. | | featureNames | [string] | true | | An array of the names of all features used by the specified model. | | knownInAdvanceFeatureNames | [string] | true | | An array of the names of time series known-in-advance features used by the specified model. | ## ModelJobResponse ``` { "properties": { "blueprintId": { "description": "The blueprint used by the model - note that this is not an ObjectId.", "type": "string" }, "featurelistId": { "description": "The ID of the featurelist the model is using.", "type": "string" }, "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if a job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the job was trained using GPU capabilities.", "type": "boolean" }, "modelCategory": { "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.", "enum": [ "model", "prime", "blend" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelType": { "description": "The type of model used by the job.", "type": "string" }, "processes": { "description": "The list of processes the modeling job includes.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset the job is using.", "type": "number" }, "status": { "description": "The status of the job.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" } }, "required": [ "blueprintId", "featurelistId", "id", "isBlocked", "modelCategory", "modelId", "modelType", "processes", "projectId", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The blueprint used by the model - note that this is not an ObjectId. | | featurelistId | string | true | | The ID of the featurelist the model is using. | | id | string | true | | The job ID. | | isBlocked | boolean | true | | True if a job is waiting for its dependencies to be resolved first. | | isTrainedOnGpu | boolean | false | | Whether the job was trained using GPU capabilities. | | modelCategory | string | true | | Indicates what kind of model this is. Will be combined for combined models. | | modelId | string | true | | The ID of the model. | | modelType | string | true | | The type of model used by the job. | | processes | [string] | true | | The list of processes the modeling job includes. | | projectId | string | true | | The project the job belongs to. | | samplePct | number | false | | The percentage of the dataset the job is using. | | status | string | true | | The status of the job. | ### Enumerated Values | Property | Value | | --- | --- | | modelCategory | [model, prime, blend] | | status | [queue, inprogress, error, ABORTED, COMPLETED] | ## ModelLifecycle ``` { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" } ``` Object returning model lifecycle. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | reason | string,null | true | | The reason for the lifecycle stage. None if the model is active. | | stage | string | true | | The model lifecycle stage. | ### Enumerated Values | Property | Value | | --- | --- | | stage | [active, deprecated, disabled] | ## ModelParametersRetrieveResponse ``` { "properties": { "derivedFeatures": { "description": "An array of preprocessing information about derived features used in the model.", "items": { "properties": { "coefficient": { "description": "The coefficient for this feature.", "type": "number" }, "derivedFeature": { "description": "The name of the derived feature.", "type": "string" }, "originalFeature": { "description": "The name of the feature used to derive this feature.", "type": "string" }, "stageCoefficients": { "description": "An array of json objects describing separate coefficients for every stage of model (empty for single stage models).", "items": { "properties": { "coefficient": { "description": "The corresponding value of the coefficient for that stage.", "type": "number" }, "stage": { "description": "The name of the stage.", "type": "string" } }, "required": [ "coefficient", "stage" ], "type": "object" }, "type": "array" }, "transformations": { "description": "An array of json objects describing the transformations applied to create this derived feature.", "items": { "properties": { "name": { "description": "The name of the transformation.", "type": "string" }, "value": { "description": "The value used in carrying it out.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" }, "type": { "description": "The type of this feature.", "type": "string" } }, "required": [ "coefficient", "derivedFeature", "originalFeature", "stageCoefficients", "transformations", "type" ], "type": "object" }, "type": "array" }, "parameters": { "description": "An array of parameters that are related to the whole model.", "items": { "properties": { "name": { "description": "The name of the parameter identifying what it means for the model, e.g. \"Intercept\".", "type": "string" }, "value": { "description": "The value of the parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" } }, "required": [ "derivedFeatures", "parameters" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | derivedFeatures | [DerivedFeatures] | true | | An array of preprocessing information about derived features used in the model. | | parameters | [Parameters] | true | | An array of parameters that are related to the whole model. | ## ModelRecordResponse ``` { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string", "x-versionadded": "v2.33" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean", "x-versionadded": "v2.33" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object", "x-versionadded": "v2.33" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string", "x-versionadded": "v2.33" }, "modelFamily": { "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).", "type": "string", "x-versionadded": "v2.33" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string", "x-versionadded": "v2.33" }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string", "x-versionadded": "v2.33" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "metrics", "modelCategory", "modelFamily", "modelNumber", "modelType", "parentModelId", "processes", "projectId", "samplePct", "trainingRowCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. | | blueprintId | string | true | | The blueprint used to construct the model. | | externalPredictionModel | boolean | true | | If the model is an external prediction model. | | featurelistId | string,null | true | | The ID of the feature list used by the model. | | featurelistName | string,null | true | | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. | | frozenPct | number,null | true | | The training percent used to train the frozen model. | | hasCodegen | boolean | true | | If the model has a codegen JAR file. | | icons | integer,null | true | | The icons associated with the model. | | id | string | true | | The ID of the model. | | isBlender | boolean | true | | If the model is a blender. | | isCustom | boolean | true | | If the model contains custom tasks. | | isFrozen | boolean | true | | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. | | isStarred | boolean | true | | Indicates whether the model has been starred. | | isTrainedIntoHoldout | boolean | true | | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. | | isTrainedIntoValidation | boolean | true | | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. | | isTrainedOnGpu | boolean | true | | Whether the model was trained using GPU workers. | | isTransparent | boolean | true | | If the model is a transparent model with exposed coefficients. | | isUserModel | boolean | true | | If the model was created with Composable ML. | | metrics | object | true | | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. | | modelCategory | string | true | | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. | | modelFamily | string | true | | The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.). | | modelNumber | integer,null | true | | The model number from the Leaderboard. | | modelType | string | true | | Identifies the model (e.g., Nystroem Kernel SVM Regressor). | | parentModelId | string,null | true | | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. | | processes | [string] | true | maxItems: 100 | The list of processes used by the model. | | projectId | string | true | | The ID of the project to which the model belongs. | | samplePct | number,null | true | | The percentage of the dataset used in training the model. | | trainingRowCount | integer,null | true | | The number of rows used to train the model. | ### Enumerated Values | Property | Value | | --- | --- | | modelCategory | [model, prime, blend, combined, incrementalLearning] | ## ModelRecordsResponse ``` { "properties": { "count": { "description": "The number of models returned on this page.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "Model records.", "items": { "oneOf": [ { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string", "x-versionadded": "v2.33" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean", "x-versionadded": "v2.33" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object", "x-versionadded": "v2.33" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string", "x-versionadded": "v2.33" }, "modelFamily": { "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).", "type": "string", "x-versionadded": "v2.33" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string", "x-versionadded": "v2.33" }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string", "x-versionadded": "v2.33" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "metrics", "modelCategory", "modelFamily", "modelNumber", "modelType", "parentModelId", "processes", "projectId", "samplePct", "trainingRowCount" ], "type": "object" }, { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string", "x-versionadded": "v2.33" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string", "x-versionadded": "v2.33" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean", "x-versionadded": "v2.33" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean", "x-versionadded": "v2.33" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object", "x-versionadded": "v2.33" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string", "x-versionadded": "v2.33" }, "modelFamily": { "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).", "type": "string", "x-versionadded": "v2.33" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string", "x-versionadded": "v2.33" }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string", "x-versionadded": "v2.33" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string", "x-versionadded": "v2.33" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "metrics", "modelCategory", "modelFamily", "modelNumber", "modelType", "parentModelId", "processes", "projectId", "samplePct", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).", "type": "string" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "numberOfClusters": { "description": "The number of clusters in the unsupervised clustering model. Only present in unsupervised clustering projects.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "metrics", "modelCategory", "modelFamily", "modelNumber", "modelType", "numberOfClusters", "parentModelId", "processes", "projectId", "samplePct", "trainingRowCount" ], "type": "object", "x-versionadded": "v2.34" } ] }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "Total number of models after filters applied.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | The number of models returned on this page. | | data | [oneOf] | true | | Model records. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModelRecordResponse | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatetimeModelRecordResponse | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | UnsupervisedClusteringModelRecordResponse | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | next | string,null | true | | A URL pointing to the next page (if null, there is no next page). | | previous | string,null | true | | A URL pointing to the previous page (if null, there is no previous page). | | totalCount | integer | true | minimum: 0 | Total number of models after filters applied. | ## ModelRetrainResponse ``` { "properties": { "message": { "description": "any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created. | ## ModelUpdate ``` { "properties": { "isStarred": { "description": "Mark model either as starred or unstarred.", "type": "boolean" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions. Default value is 0.5.", "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isStarred | boolean | false | | Mark model either as starred or unstarred. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold used for binary classification in predictions. Default value is 0.5. | ## ModelingJobListResponse ``` { "properties": { "data": { "description": "List of modeling jobs.", "items": { "properties": { "blueprintId": { "description": "The blueprint used by the model - note that this is not an ObjectId.", "type": "string" }, "featurelistId": { "description": "The ID of the featurelist the model is using.", "type": "string" }, "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if a job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the job was trained using GPU capabilities.", "type": "boolean" }, "modelCategory": { "description": "Indicates what kind of model this is. Will be ``combined`` for combined models.", "enum": [ "model", "prime", "blend" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelType": { "description": "The type of model used by the job.", "type": "string" }, "processes": { "description": "The list of processes the modeling job includes.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset the job is using.", "type": "number" }, "status": { "description": "The status of the job.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" } }, "required": [ "blueprintId", "featurelistId", "id", "isBlocked", "modelCategory", "modelId", "modelType", "processes", "projectId", "status" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [ModelJobResponse] | true | | List of modeling jobs. | ## NumIterationsTrainedData ``` { "properties": { "numIterations": { "description": "The number of iterations run in this stage of modeling.", "minimum": 0, "type": "integer" }, "stage": { "description": "Modeling stage or None if it is a single-stage model", "type": [ "string", "null" ] } }, "required": [ "numIterations", "stage" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | numIterations | integer | true | minimum: 0 | The number of iterations run in this stage of modeling. | | stage | string,null | true | | Modeling stage or None if it is a single-stage model | ## NumIterationsTrainedResponse ``` { "properties": { "data": { "description": "Number of estimators or iterations for a single model stage", "items": { "properties": { "numIterations": { "description": "The number of iterations run in this stage of modeling.", "minimum": 0, "type": "integer" }, "stage": { "description": "Modeling stage or None if it is a single-stage model", "type": [ "string", "null" ] } }, "required": [ "numIterations", "stage" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "data", "modelId", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [NumIterationsTrainedData] | true | | Number of estimators or iterations for a single model stage | | modelId | string | true | | The model ID. | | projectId | string | true | | The project ID. | ## OneMessageInfo ``` { "properties": { "additionalInfo": { "description": "Zero or more text strings for secondary display after user clicks for more information.", "items": { "type": "string" }, "maxItems": 50, "type": "array" }, "messageLevel": { "description": "Message severity level.", "enum": [ "CRITICAL", "INFORMATIONAL", "NO_ISSUES", "WARNING" ], "type": "string" }, "messageText": { "description": "Text for primary display in UI.", "maxLength": 500, "minLength": 1, "type": "string" } }, "required": [ "messageLevel", "messageText" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | additionalInfo | [string] | false | maxItems: 50 | Zero or more text strings for secondary display after user clicks for more information. | | messageLevel | string | true | | Message severity level. | | messageText | string | true | maxLength: 500minLength: 1minLength: 1 | Text for primary display in UI. | ### Enumerated Values | Property | Value | | --- | --- | | messageLevel | [CRITICAL, INFORMATIONAL, NO_ISSUES, WARNING] | ## ParameterItem ``` { "properties": { "parameterName": { "description": "The name of the parameter.", "type": "string" }, "parameterValue": { "description": "The value of the parameter.", "type": "number" } }, "required": [ "parameterName", "parameterValue" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parameterName | string | true | | The name of the parameter. | | parameterValue | number | true | | The value of the parameter. | ## Parameters ``` { "properties": { "name": { "description": "The name of the parameter identifying what it means for the model, e.g. \"Intercept\".", "type": "string" }, "value": { "description": "The value of the parameter.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the parameter identifying what it means for the model, e.g. "Intercept". | | value | string | true | | The value of the parameter. | ## ParetoFrontResponse ``` { "properties": { "errorMetric": { "description": "The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently.", "type": "string" }, "hyperparameters": { "description": "The hyperparameters used by this run of the Eureqa blueprint.", "oneOf": [ { "properties": { "buildingBlocks": { "description": "Mathematical operators and other components that comprise Eureqa Expressions.", "type": [ "object", "null" ] }, "errorMetric": { "description": "Error Metric Eureqa used internally, to evaluate which models to keep on its internal Pareto Front. ", "type": [ "string", "null" ] }, "maxGenerations": { "description": "The maximum number of evolutionary generations to run.", "minimum": 32, "type": [ "integer", "null" ] }, "numThreads": { "description": "The number of threads Eureqa will run with.", "minimum": 0, "type": [ "integer", "null" ] }, "priorSolutions": { "description": "Prior Eureqa Solutions.", "items": { "description": "Prior solution.", "type": "string" }, "type": "array" }, "randomSeed": { "description": "Constant to seed Eureqa's pseudo-random number generator.", "minimum": 0, "type": [ "integer", "null" ] }, "splitMode": { "description": "Whether to perform in-order (2) or random (1) splitting within the training set, for evolutionary re-training and re-validatoon.", "enum": [ "custom", "1", "2" ], "type": [ "string", "null" ] }, "syncMigrations": { "description": "Whether Eureqa's migrations are synchronized.", "type": [ "boolean", "null" ] }, "targetExpressionFormat": { "description": "Constrain the target expression to the specified format.", "enum": [ "None", "exponential", "featureInteraction" ], "type": [ "string", "null" ] }, "targetExpressionString": { "description": "Eureqa Expression to constrain the form of the models that Eureqa will consider.", "type": [ "string", "null" ] }, "timeoutSec": { "description": "The duration of time to run the Eureqa search algorithm for Eureqa will run until either of max_generations or timeout_sec is reached.", "minimum": 0, "type": [ "number", "null" ] }, "trainingFraction": { "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary training.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "trainingSplitExpr": { "description": "Valid Eureqa Expression to do Eureqa internal training splits.", "type": [ "string", "null" ] }, "validationFraction": { "description": "The fraction of the DataRobot training data to use for Eureqa evolutionary validation.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "validationSplitExpr": { "description": "Valid Eureqa Expression to do Eureqa internal validation splits.", "type": [ "string", "null" ] }, "weightExpr": { "description": "Eureqa Weight Expression.", "type": [ "string", "null" ] } }, "required": [ "buildingBlocks", "maxGenerations", "numThreads", "priorSolutions", "randomSeed", "splitMode", "syncMigrations", "targetExpressionFormat", "targetExpressionString", "timeoutSec", "trainingFraction", "trainingSplitExpr", "validationFraction", "validationSplitExpr", "weightExpr" ], "type": "object" }, { "type": "null" } ] }, "projectId": { "description": "The project ID of the Eureqa model.", "type": "string" }, "solutions": { "description": "The Eureqa model solutions.", "items": { "properties": { "bestModel": { "description": "True if this solution generates the best model.", "type": "boolean" }, "complexity": { "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.", "type": "integer" }, "error": { "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.", "type": [ "number", "null" ] }, "eureqaSolutionId": { "description": "The ID of the solution.", "type": "string" }, "expression": { "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.", "type": "string" }, "expressionAnnotated": { "description": "The `expression`, rendered with additional tags to assist in automatic parsing.", "type": "string" } }, "required": [ "bestModel", "complexity", "error", "eureqaSolutionId", "expression", "expressionAnnotated" ], "type": "object" }, "type": "array" }, "targetType": { "description": "The type of the target variable.", "enum": [ "Regression", "Binary" ], "type": "string" } }, "required": [ "errorMetric", "hyperparameters", "projectId", "solutions", "targetType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMetric | string | true | | The Eureqa error metric identifier used to compute error metrics for this search. Note that Eureqa error metrics do not correspond 1:1 with DataRobot error metrics - the available metrics are not the same, and even equivalent metrics may be computed slightly differently. | | hyperparameters | any | true | | The hyperparameters used by this run of the Eureqa blueprint. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HyperparametersResponse | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | projectId | string | true | | The project ID of the Eureqa model. | | solutions | [SolutionResponse] | true | | The Eureqa model solutions. | | targetType | string | true | | The type of the target variable. | ### Enumerated Values | Property | Value | | --- | --- | | targetType | [Regression, Binary] | ## PerFeatureMissingReport ``` { "properties": { "feature": { "description": "The name of the feature.", "type": "string" }, "missingCount": { "description": "The number of missing values in the training data.", "type": "integer" }, "missingPercentage": { "description": "The percentage of missing values in the training data.", "maximum": 1, "minimum": 0, "type": "number" }, "tasks": { "additionalProperties": { "properties": { "descriptions": { "description": "Human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored.", "items": { "type": "string" }, "type": "array" }, "name": { "description": "Task name, e.g., 'Ordinal encoding of categorical variables'.", "type": "string" } }, "required": [ "descriptions", "name" ], "type": "object" }, "description": "Information on individual tasks of the model which were used to process the feature. The names of properties will be task ids (which correspond to the ids used in the blueprint chart endpoints like [GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/blueprintChart/][get-apiv2projectsprojectidblueprintsblueprintidblueprintchart]) The corresponding value for each task will be of the form `task` described.", "type": "object" }, "type": { "description": "The type of the feature, e.g., `Categorical` or `Numeric`.", "type": "string" } }, "required": [ "feature", "missingCount", "missingPercentage", "tasks", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature | string | true | | The name of the feature. | | missingCount | integer | true | | The number of missing values in the training data. | | missingPercentage | number | true | maximum: 1minimum: 0 | The percentage of missing values in the training data. | | tasks | object | true | | Information on individual tasks of the model which were used to process the feature. The names of properties will be task ids (which correspond to the ids used in the blueprint chart endpoints like [GET /api/v2/projects/{projectId}/blueprints/{blueprintId}/blueprintChart/][get-apiv2projectsprojectidblueprintsblueprintidblueprintchart]) The corresponding value for each task will be of the form task described. | | » additionalProperties | PerFeatureTaskMissingReport | false | | none | | type | string | true | | The type of the feature, e.g., Categorical or Numeric. | ## PerFeatureTaskMissingReport ``` { "properties": { "descriptions": { "description": "Human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored.", "items": { "type": "string" }, "type": "array" }, "name": { "description": "Task name, e.g., 'Ordinal encoding of categorical variables'.", "type": "string" } }, "required": [ "descriptions", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | descriptions | [string] | true | | Human readable aggregated information about how the task handles missing values. The following descriptions may be present: what value is imputed for missing values, whether the feature being missing is treated as a feature by the task, whether missing values are treated as infrequent values, whether infrequent values are treated as missing values, and whether missing values are ignored. | | name | string | true | | Task name, e.g., 'Ordinal encoding of categorical variables'. | ## PlotDataResponse ``` { "properties": { "actual": { "description": "The actual value of the target variable for the specified row.", "type": "number" }, "predicted": { "description": "The predicted value of the target by the solution for the specified row.", "type": "number" }, "row": { "description": "The row number from the raw source data. Used as the X axis for the plot when rendered in the web application.", "type": "integer" } }, "required": [ "actual", "predicted", "row" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actual | number | true | | The actual value of the target variable for the specified row. | | predicted | number | true | | The predicted value of the target by the solution for the specified row. | | row | integer | true | | The row number from the raw source data. Used as the X axis for the plot when rendered in the web application. | ## PredictionIntervalsCreate ``` { "properties": { "percentiles": { "description": "The list of prediction intervals percentiles to calculate. Currently we only allow requesting one interval at a time.", "items": { "exclusiveMinimum": 0, "maximum": 100, "type": "integer" }, "maxItems": 1, "minItems": 1, "type": "array" } }, "required": [ "percentiles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | percentiles | [integer] | true | maxItems: 1minItems: 1 | The list of prediction intervals percentiles to calculate. Currently we only allow requesting one interval at a time. | ## PredictionIntervalsCreateResponse ``` { "properties": { "message": { "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created. | ## PredictionIntervalsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A descending-ordered array of already-calculated prediction intervals percentiles.", "items": { "exclusiveMinimum": 0, "maximum": 100, "type": "integer" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [integer] | true | | A descending-ordered array of already-calculated prediction intervals percentiles. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## PrepareForDeployment ``` { "properties": { "modelId": { "description": "The model to prepare for deployment.", "type": "string" } }, "required": [ "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | The model to prepare for deployment. | ## PrimeFileCreate ``` { "properties": { "language": { "description": "The desired language of the generated code.", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The Prime model to generate code for.", "type": "string" } }, "required": [ "language", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | language | string | true | | The desired language of the generated code. | | modelId | string | true | | The Prime model to generate code for. | ### Enumerated Values | Property | Value | | --- | --- | | language | [Python, Java] | ## PrimeFileListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/.", "items": { "properties": { "id": { "description": "The ID of the file.", "type": "string" }, "isValid": { "description": "Whether the code passed basic validation checks.", "type": "boolean" }, "language": { "description": "The language the code is written in (e.g., Python).", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The ID of the Prime model.", "type": "string" }, "parentModelId": { "description": "The ID of the model this code approximates.", "type": "string" }, "projectId": { "description": "The ID of the project the file belongs to.", "type": "string" }, "rulesetId": { "description": "The ID of the ruleset this code uses to approximate the parent model.", "type": "integer" } }, "required": [ "id", "isValid", "language", "modelId", "parentModelId", "projectId", "rulesetId" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [PrimeFileResponse] | true | | Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | ## PrimeFileResponse ``` { "properties": { "id": { "description": "The ID of the file.", "type": "string" }, "isValid": { "description": "Whether the code passed basic validation checks.", "type": "boolean" }, "language": { "description": "The language the code is written in (e.g., Python).", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The ID of the Prime model.", "type": "string" }, "parentModelId": { "description": "The ID of the model this code approximates.", "type": "string" }, "projectId": { "description": "The ID of the project the file belongs to.", "type": "string" }, "rulesetId": { "description": "The ID of the ruleset this code uses to approximate the parent model.", "type": "integer" } }, "required": [ "id", "isValid", "language", "modelId", "parentModelId", "projectId", "rulesetId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the file. | | isValid | boolean | true | | Whether the code passed basic validation checks. | | language | string | true | | The language the code is written in (e.g., Python). | | modelId | string | true | | The ID of the Prime model. | | parentModelId | string | true | | The ID of the model this code approximates. | | projectId | string | true | | The ID of the project the file belongs to. | | rulesetId | integer | true | | The ID of the ruleset this code uses to approximate the parent model. | ### Enumerated Values | Property | Value | | --- | --- | | language | [Python, Java] | ## PrimeInfoRetrieveResponse ``` { "properties": { "canMakePrime": { "description": "Indicates whether the requested model is a valid input for creating a Prime model.", "type": "boolean" }, "message": { "description": "May contain details about why a model is not eligible for DataRobot Prime.", "type": "string" }, "messageId": { "description": "An error code representing the reason the model cannot be approximated with DataRobot Prime; 0 for eligible models.", "type": "integer" } }, "required": [ "canMakePrime", "message", "messageId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canMakePrime | boolean | true | | Indicates whether the requested model is a valid input for creating a Prime model. | | message | string | true | | May contain details about why a model is not eligible for DataRobot Prime. | | messageId | integer | true | | An error code representing the reason the model cannot be approximated with DataRobot Prime; 0 for eligible models. | ## PrimeModelCreatePayload ``` { "properties": { "parentModelId": { "description": "The model being approximated.", "type": "string" }, "rulesetId": { "description": "The ID of the ruleset to use.", "type": "integer" } }, "required": [ "parentModelId", "rulesetId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parentModelId | string | true | | The model being approximated. | | rulesetId | integer | true | | The ID of the ruleset to use. | ## PrimeModelDetailsRetrieveResponse ``` { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "ruleCount": { "description": "The number of rules used to create this model.", "type": "integer" }, "rulesetId": { "description": "The ID of the ruleset this model uses.", "type": "integer" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "score": { "description": "The validation score of the model's ruleset.", "type": "number" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "ruleCount", "rulesetId", "samplePct", "score", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. | | blueprintId | string | true | | The blueprint used to construct the model. | | dataSelectionMethod | string | false | | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. | | externalPredictionModel | boolean | true | | If the model is an external prediction model. | | featurelistId | string,null | true | | The ID of the feature list used by the model. | | featurelistName | string,null | true | | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. | | frozenPct | number,null | true | | The training percent used to train the frozen model. | | hasCodegen | boolean | true | | If the model has a codegen JAR file. | | hasFinetuners | boolean | false | | Whether a model has fine tuners. | | icons | integer,null | true | | The icons associated with the model. | | id | string | true | | The ID of the model. | | isAugmented | boolean | false | | Whether a model was trained using augmentation. | | isBlender | boolean | true | | If the model is a blender. | | isCustom | boolean | true | | If the model contains custom tasks. | | isFrozen | boolean | true | | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. | | isNClustersDynamicallyDetermined | boolean | false | | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. | | isStarred | boolean | true | | Indicates whether the model has been starred. | | isTrainedIntoHoldout | boolean | true | | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. | | isTrainedIntoValidation | boolean | true | | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. | | isTrainedOnGpu | boolean | true | | Whether the model was trained using GPU workers. | | isTransparent | boolean | true | | If the model is a transparent model with exposed coefficients. | | isUserModel | boolean | true | | If the model was created with Composable ML. | | lifecycle | ModelLifecycle | true | | Object returning model lifecycle. | | linkFunction | string,null | true | | The link function the final modeler uses in the blueprint. If no link function exists, returns null. | | metrics | object | true | | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. | | modelCategory | string | true | | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. | | modelFamily | string | true | | The family the model belongs to, e.g., SVM, GBM, etc. | | modelFamilyFullName | string | true | | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. | | modelNumber | integer,null | true | | The model number from the Leaderboard. | | modelType | string | true | | Identifies the model (e.g., Nystroem Kernel SVM Regressor). | | monotonicDecreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | monotonicIncreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | nClusters | integer,null | false | | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | parentModelId | string,null | true | | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. | | predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. | | predictionThresholdReadOnly | boolean | true | | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. | | processes | [string] | true | maxItems: 100 | The list of processes used by the model. | | projectId | string | true | | The ID of the project to which the model belongs. | | ruleCount | integer | true | | The number of rules used to create this model. | | rulesetId | integer | true | | The ID of the ruleset this model uses. | | samplePct | number,null | true | | The percentage of the dataset used in training the model. | | samplingMethod | string | false | | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. | | score | number | true | | The validation score of the model's ruleset. | | supportsComposableMl | boolean | true | | indicates whether this model is supported in Composable ML. | | supportsMonotonicConstraints | boolean | true | | whether this model supports enforcing monotonic constraints | | timeWindowSamplePct | integer,null | false | | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. | | trainingDuration | string,null | true | | the duration spanned by the dates in the partition column for the data used to train the model | | trainingEndDate | string,null(date-time) | true | | the end date of the dates in the partition column for the data used to train the model | | trainingRowCount | integer,null | true | | The number of rows used to train the model. | | trainingStartDate | string,null(date-time) | true | | the start date of the dates in the partition column for the data used to train the model | ### Enumerated Values | Property | Value | | --- | --- | | dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] | | modelCategory | [model, prime, blend, combined, incrementalLearning] | | samplingMethod | [random, latest] | ## PrimeModelListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/.", "items": { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "ruleCount": { "description": "The number of rules used to create this model.", "type": "integer" }, "rulesetId": { "description": "The ID of the ruleset this model uses.", "type": "integer" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "score": { "description": "The validation score of the model's ruleset.", "type": "number" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "ruleCount", "rulesetId", "samplePct", "score", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [PrimeModelDetailsRetrieveResponse] | true | | Each has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/primeFiles/(primeFileId)/. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | ## PrimeRulesetsCreatePayload ``` { "type": "object" } ``` ### Properties None ## PrimeRulesetsListResponse ``` { "properties": { "modelId": { "description": "The ID of the Prime model using this ruleset (if it exists) or null.", "type": "string" }, "parentModelId": { "description": "The ID of the model this ruleset approximates.", "type": "string" }, "projectId": { "description": "The project this ruleset belongs to.", "type": "string" }, "ruleCount": { "description": "The number of rules used by this ruleset.", "type": "integer" }, "rulesetId": { "description": "The ID of the ruleset.", "type": "integer" }, "score": { "description": "The validation score of the ruleset.", "type": "number" } }, "required": [ "modelId", "parentModelId", "projectId", "ruleCount", "rulesetId", "score" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | The ID of the Prime model using this ruleset (if it exists) or null. | | parentModelId | string | true | | The ID of the model this ruleset approximates. | | projectId | string | true | | The project this ruleset belongs to. | | ruleCount | integer | true | | The number of rules used by this ruleset. | | rulesetId | integer | true | | The ID of the ruleset. | | score | number | true | | The validation score of the ruleset. | ## Reasons ``` { "description": "Information on why the capability is unsupported for the model.", "properties": { "supportsAccuracyOverTime": { "description": "If present, the reason why Accuracy Over Time plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsAnomalyAssessment": { "description": "If present, the reason why the Anomaly Assessment insight cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsAnomalyOverTime": { "description": "If present, the reason why Anomaly Over Time plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsClusterInsights": { "description": "If present, the reason why Cluster Insights cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsConfusionMatrix": { "description": "If present, the reason why Confusion Matrix cannot be generated for the model. There are some cases where Confusion Matrix is available but it was calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.33" }, "supportsDocumentTextExtractionSampleInsight": { "description": "If present, the reason document text extraction sample insights are not supported for the model.", "type": "string", "x-versionadded": "v2.29" }, "supportsForecastAccuracy": { "description": "If present, the reason why Forecast Accuracy plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsForecastVsActual": { "description": "If present, the reason why Forecast vs Actual plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsImageActivationMaps": { "description": "If present, the reason image activation maps are not supported for the model.", "type": "string" }, "supportsImageEmbedding": { "description": "If present, the reason image embeddings are not supported for the model.", "type": "string" }, "supportsLiftChart": { "description": "If present, the reason why Lift Chart cannot be generated for the model. There are some cases where Lift Chart is available but it was calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.31" }, "supportsPeriodAccuracy": { "description": "If present, the reason why Period Accuracy insights cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsPredictionExplanations": { "description": "If present, the reason why Prediction Explanations cannot be computed for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsPredictionIntervals": { "description": "If present, the reason why Prediction Intervals cannot be computed for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsResiduals": { "description": "If present, the reason why residuals are not available for the model. There are some cases where Residuals are available but they were calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.30" }, "supportsRocCurve": { "description": "If present, the reason why ROC Curve cannot be generated for the model. There are some cases where ROC Curve is available but it was calculated using stacked predictions or in-sample predictions.", "type": "string", "x-versionadded": "v2.32" }, "supportsSeriesInsights": { "description": "If present, the reason why Series Insights cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" }, "supportsStability": { "description": "If present, the reason why Stability plots cannot be generated for the model.", "type": "string", "x-versionadded": "v2.34" } }, "type": "object" } ``` Information on why the capability is unsupported for the model. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | supportsAccuracyOverTime | string | false | | If present, the reason why Accuracy Over Time plots cannot be generated for the model. | | supportsAnomalyAssessment | string | false | | If present, the reason why the Anomaly Assessment insight cannot be generated for the model. | | supportsAnomalyOverTime | string | false | | If present, the reason why Anomaly Over Time plots cannot be generated for the model. | | supportsClusterInsights | string | false | | If present, the reason why Cluster Insights cannot be generated for the model. | | supportsConfusionMatrix | string | false | | If present, the reason why Confusion Matrix cannot be generated for the model. There are some cases where Confusion Matrix is available but it was calculated using stacked predictions or in-sample predictions. | | supportsDocumentTextExtractionSampleInsight | string | false | | If present, the reason document text extraction sample insights are not supported for the model. | | supportsForecastAccuracy | string | false | | If present, the reason why Forecast Accuracy plots cannot be generated for the model. | | supportsForecastVsActual | string | false | | If present, the reason why Forecast vs Actual plots cannot be generated for the model. | | supportsImageActivationMaps | string | false | | If present, the reason image activation maps are not supported for the model. | | supportsImageEmbedding | string | false | | If present, the reason image embeddings are not supported for the model. | | supportsLiftChart | string | false | | If present, the reason why Lift Chart cannot be generated for the model. There are some cases where Lift Chart is available but it was calculated using stacked predictions or in-sample predictions. | | supportsPeriodAccuracy | string | false | | If present, the reason why Period Accuracy insights cannot be generated for the model. | | supportsPredictionExplanations | string | false | | If present, the reason why Prediction Explanations cannot be computed for the model. | | supportsPredictionIntervals | string | false | | If present, the reason why Prediction Intervals cannot be computed for the model. | | supportsResiduals | string | false | | If present, the reason why residuals are not available for the model. There are some cases where Residuals are available but they were calculated using stacked predictions or in-sample predictions. | | supportsRocCurve | string | false | | If present, the reason why ROC Curve cannot be generated for the model. There are some cases where ROC Curve is available but it was calculated using stacked predictions or in-sample predictions. | | supportsSeriesInsights | string | false | | If present, the reason why Series Insights cannot be generated for the model. | | supportsStability | string | false | | If present, the reason why Stability plots cannot be generated for the model. | ## RecommendedModelResponse ``` { "properties": { "modelId": { "description": "The ID of the recommended model.", "type": "string" }, "recommendationType": { "description": "The type of model recommendation.", "enum": [ "MOSTACCURATE", "LIMITEDACCURATE", "FASTACCURATE", "RECOMMENDEDFORDEPLOYMENT", "PREPAREDFORDEPLOYMENT" ], "type": "string" } }, "required": [ "modelId", "recommendationType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | The ID of the recommended model. | | recommendationType | string | true | | The type of model recommendation. | ### Enumerated Values | Property | Value | | --- | --- | | recommendationType | [MOSTACCURATE, LIMITEDACCURATE, FASTACCURATE, RECOMMENDEDFORDEPLOYMENT, PREPAREDFORDEPLOYMENT] | ## RetrainDatetimeModel ``` { "properties": { "featurelistId": { "description": "If specified, the new model will be trained using this featurelist. Otherwise, the model will be trained on the same feature list as the source model.", "type": "string" }, "modelId": { "description": "The ID of an existing model to use as the source for the training parameters.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "monotonicIncreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "maximum": 100, "minimum": 2, "type": "integer", "x-versionadded": "v2.27" }, "samplingMethod": { "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.", "enum": [ "random", "latest" ], "type": "string", "x-versionadded": "v2.20" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": "integer" }, "trainingDuration": { "description": "A duration string representing the training duration to use for training the new model. If specified, the model will be trained using the specified training duration. Otherwise, the original model's duration will be used. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "format": "duration", "type": "string" }, "trainingEndDate": { "description": "A datetime string representing the end date of the data to use for training this model. Note that only one of `trainingDuration` or `trainingRowCount` or `trainingStartDate` and `trainingEndDate` should be specified. If `trainingStartDate` and `trainingEndDate` are specified, the source model must be frozen.", "format": "date-time", "type": "string" }, "trainingRowCount": { "description": "The number of rows of data that should be used to train the model. If not specified, the original model's row count will be used. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "exclusiveMinimum": 0, "type": "integer" }, "trainingStartDate": { "description": "A datetime string representing the start date of the data to use for training this model. Note that only one of `trainingDuration` or `trainingRowCount` or `trainingStartDate` and `trainingEndDate` should be specified. If `trainingStartDate` and `trainingEndDate` are specified, the source model must be frozen.", "format": "date-time", "type": "string" }, "useProjectSettings": { "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings. Only one of `trainingRowCount`, `trainingDuration`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "type": "boolean", "x-versionadded": "v2.24" } }, "required": [ "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featurelistId | string | false | | If specified, the new model will be trained using this featurelist. Otherwise, the model will be trained on the same feature list as the source model. | | modelId | string | true | | The ID of an existing model to use as the source for the training parameters. | | monotonicDecreasingFeaturelistId | string,null | false | | The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | monotonicIncreasingFeaturelistId | string,null | false | | The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | nClusters | integer | false | maximum: 100minimum: 2 | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | samplingMethod | string | false | | Defines how training data is selected if subsampling is used (e.g., if timeWindowSamplePct is specified). Can be either random or latest. If omitted, defaults to latest if trainingRowCount is used and random for other cases (e.g., if trainingDuration or useProjectSettings is specified). May only be specified for OTV projects. | | timeWindowSamplePct | integer | false | | An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the samplingMethod option. If specified, trainingRowCount may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of trainingDuration or trainingStartDate and trainingEndDate must be specified. | | trainingDuration | string(duration) | false | | A duration string representing the training duration to use for training the new model. If specified, the model will be trained using the specified training duration. Otherwise, the original model's duration will be used. Only one of trainingRowCount, trainingDuration, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. | | trainingEndDate | string(date-time) | false | | A datetime string representing the end date of the data to use for training this model. Note that only one of trainingDuration or trainingRowCount or trainingStartDate and trainingEndDate should be specified. If trainingStartDate and trainingEndDate are specified, the source model must be frozen. | | trainingRowCount | integer | false | | The number of rows of data that should be used to train the model. If not specified, the original model's row count will be used. Only one of trainingRowCount, trainingDuration, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. | | trainingStartDate | string(date-time) | false | | A datetime string representing the start date of the data to use for training this model. Note that only one of trainingDuration or trainingRowCount or trainingStartDate and trainingEndDate should be specified. If trainingStartDate and trainingEndDate are specified, the source model must be frozen. | | useProjectSettings | boolean | false | | If True, the model will be trained using the previously-specified custom backtest training settings. Only one of trainingRowCount, trainingDuration, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. | ### Enumerated Values | Property | Value | | --- | --- | | samplingMethod | [random, latest] | ## RetrainModel ``` { "properties": { "featurelistId": { "description": "If specified, the model will be trained using that featurelist, otherwise the model will be trained on the same feature list as before.", "type": "string" }, "modelId": { "description": "The model to be retrained.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "monotonicIncreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "maximum": 100, "minimum": 2, "type": "integer", "x-versionadded": "v2.27" }, "samplePct": { "description": "The percentage of the dataset to use to use to train the model. The specified percentage should be between 0 and 100. If not specified, original model sample percent will be used.", "exclusiveMinimum": 0, "maximum": 100, "type": "number" }, "scoringType": { "description": "Validation is available for any partitioning. If the project uses cross validation, `crossValidation` may be used to indicate that all available training/validation combinations should be used.", "enum": [ "validation", "crossValidation" ], "type": "string", "x-versionadded": "v2.23" }, "trainingRowCount": { "description": "The number of rows to use to train the model. If not specified, original model training row count will be used.", "exclusiveMinimum": 0, "type": "integer" } }, "required": [ "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featurelistId | string | false | | If specified, the model will be trained using that featurelist, otherwise the model will be trained on the same feature list as before. | | modelId | string | true | | The model to be retrained. | | monotonicDecreasingFeaturelistId | string,null | false | | The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | monotonicIncreasingFeaturelistId | string,null | false | | The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | nClusters | integer | false | maximum: 100minimum: 2 | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | samplePct | number | false | maximum: 100 | The percentage of the dataset to use to use to train the model. The specified percentage should be between 0 and 100. If not specified, original model sample percent will be used. | | scoringType | string | false | | Validation is available for any partitioning. If the project uses cross validation, crossValidation may be used to indicate that all available training/validation combinations should be used. | | trainingRowCount | integer | false | | The number of rows to use to train the model. If not specified, original model training row count will be used. | ### Enumerated Values | Property | Value | | --- | --- | | scoringType | [validation, crossValidation] | ## RuleFitCodeFileCreate ``` { "properties": { "language": { "description": "The desired language of the generated code.", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The RuleFit model to generate code for.", "type": "string" } }, "required": [ "language", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | language | string | true | | The desired language of the generated code. | | modelId | string | true | | The RuleFit model to generate code for. | ### Enumerated Values | Property | Value | | --- | --- | | language | [Python, Java] | ## RuleFitCodeFileListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Each item has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/ruleFitFiles/(ruleFitFileId)/.", "items": { "properties": { "id": { "description": "The ID of the file.", "type": "string" }, "isValid": { "description": "Whether the code passed basic validation checks.", "type": "boolean" }, "language": { "description": "The language the code is written in (e.g., Python).", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The ID of the RuleFit model.", "type": "string" }, "projectId": { "description": "The ID of the project the file belongs to.", "type": "string" } }, "required": [ "id", "isValid", "language", "modelId", "projectId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [RuleFitCodeFileResponse] | true | | Each item has the same schema as if retrieving the file individually from GET /api/v2/projects/(projectId)/ruleFitFiles/(ruleFitFileId)/. | | next | string,null(uri) | true | | The URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL pointing to the previous page (if null, there is no previous page). | ## RuleFitCodeFileResponse ``` { "properties": { "id": { "description": "The ID of the file.", "type": "string" }, "isValid": { "description": "Whether the code passed basic validation checks.", "type": "boolean" }, "language": { "description": "The language the code is written in (e.g., Python).", "enum": [ "Python", "Java" ], "type": "string" }, "modelId": { "description": "The ID of the RuleFit model.", "type": "string" }, "projectId": { "description": "The ID of the project the file belongs to.", "type": "string" } }, "required": [ "id", "isValid", "language", "modelId", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the file. | | isValid | boolean | true | | Whether the code passed basic validation checks. | | language | string | true | | The language the code is written in (e.g., Python). | | modelId | string | true | | The ID of the RuleFit model. | | projectId | string | true | | The ID of the project the file belongs to. | ### Enumerated Values | Property | Value | | --- | --- | | language | [Python, Java] | ## SegmentChampionModelUpdate ``` { "properties": { "clone": { "default": false, "description": "Clone current combined model and assign champion to the new combined model.", "type": "boolean", "x-versionadded": "v2.29" }, "modelId": { "description": "The ID of segment champion model.", "type": "string" } }, "required": [ "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clone | boolean | false | | Clone current combined model and assign champion to the new combined model. | | modelId | string | true | | The ID of segment champion model. | ## SegmentChampionModelUpdateResponse ``` { "properties": { "combinedModelId": { "description": "The ID of the combined model that has been updated.", "type": "string" } }, "required": [ "combinedModelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | combinedModelId | string | true | | The ID of the combined model that has been updated. | ## SegmentProjectModelResponse ``` { "properties": { "modelId": { "description": "The ID of the segment champion model.", "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project used for this segment.", "type": [ "string", "null" ] }, "segment": { "description": "Segment name.", "type": "string" } }, "required": [ "modelId", "projectId", "segment" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string,null | true | | The ID of the segment champion model. | | projectId | string,null | true | | The ID of the project used for this segment. | | segment | string | true | | Segment name. | ## Select ``` { "description": "Indicates that the value can be one selected from a list of known values.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" }, "values": { "description": "List of valid values for this field.", "items": { "type": "string" }, "type": "array" } }, "required": [ "supportsGridSearch", "values" ], "type": "object" } ``` Indicates that the value can be one selected from a list of known values. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | supportsGridSearch | boolean | true | | When True, Grid Search is supported for this parameter. | | values | [string] | true | | List of valid values for this field. | ## SolutionResponse ``` { "properties": { "bestModel": { "description": "True if this solution generates the best model.", "type": "boolean" }, "complexity": { "description": "The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters.", "type": "integer" }, "error": { "description": "The error for the current solution, as computed by eureqa using the `errorMetric` error metric. None if Eureqa model refitted existing solutions.", "type": [ "number", "null" ] }, "eureqaSolutionId": { "description": "The ID of the solution.", "type": "string" }, "expression": { "description": "The eureqa \"solution string\". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa.", "type": "string" }, "expressionAnnotated": { "description": "The `expression`, rendered with additional tags to assist in automatic parsing.", "type": "string" } }, "required": [ "bestModel", "complexity", "error", "eureqaSolutionId", "expression", "expressionAnnotated" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bestModel | boolean | true | | True if this solution generates the best model. | | complexity | integer | true | | The complexity score for this solution. Complexity score is a function of the mathematical operators used in the current solution. The complexity calculation can be tuned via model hyperparameters. | | error | number,null | true | | The error for the current solution, as computed by eureqa using the errorMetric error metric. None if Eureqa model refitted existing solutions. | | eureqaSolutionId | string | true | | The ID of the solution. | | expression | string | true | | The eureqa "solution string". This is a mathematical expression; human-readable but with strict syntax specifications defined by Eureqa. | | expressionAnnotated | string | true | | The expression, rendered with additional tags to assist in automatic parsing. | ## StageCoefficients ``` { "properties": { "coefficient": { "description": "The corresponding value of the coefficient for that stage.", "type": "number" }, "stage": { "description": "The name of the stage.", "type": "string" } }, "required": [ "coefficient", "stage" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | coefficient | number | true | | The corresponding value of the coefficient for that stage. | | stage | string | true | | The name of the stage. | ## TrainDatetimeFrozenModel ``` { "properties": { "modelId": { "description": "The ID of an existing model to use as the source for the training parameters.", "type": "string" }, "samplingMethod": { "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.", "enum": [ "random", "latest" ], "type": "string" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": "integer" }, "trainingDuration": { "description": "A duration string representing the training duration for the submitted model. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "format": "duration", "type": "string" }, "trainingEndDate": { "description": "A datetime string representing the end date of the data to use for training this model. If specified, `trainingStartDate` must also be specified. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "format": "date-time", "type": "string" }, "trainingRowCount": { "description": "The number of rows of data that should be used when training this model. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "exclusiveMinimum": 0, "type": "integer" }, "trainingStartDate": { "description": "A datetime string representing the start date of the data to use for training this model. If specified, `trainingEndDate` must also be specified. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "format": "date-time", "type": "string" }, "useProjectSettings": { "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings. Only one of `trainingDuration`, `trainingRowCount`, `trainingStartDate` and `trainingEndDate`, or `useProjectSettings` may be specified.", "type": "boolean", "x-versionadded": "v2.24" } }, "required": [ "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | The ID of an existing model to use as the source for the training parameters. | | samplingMethod | string | false | | Defines how training data is selected if subsampling is used (e.g., if timeWindowSamplePct is specified). Can be either random or latest. If omitted, defaults to latest if trainingRowCount is used and random for other cases (e.g., if trainingDuration or useProjectSettings is specified). May only be specified for OTV projects. | | timeWindowSamplePct | integer | false | | An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the samplingMethod option. If specified, trainingRowCount may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of trainingDuration or trainingStartDate and trainingEndDate must be specified. | | trainingDuration | string(duration) | false | | A duration string representing the training duration for the submitted model. Only one of trainingDuration, trainingRowCount, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. | | trainingEndDate | string(date-time) | false | | A datetime string representing the end date of the data to use for training this model. If specified, trainingStartDate must also be specified. Only one of trainingDuration, trainingRowCount, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. | | trainingRowCount | integer | false | | The number of rows of data that should be used when training this model. Only one of trainingDuration, trainingRowCount, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. | | trainingStartDate | string(date-time) | false | | A datetime string representing the start date of the data to use for training this model. If specified, trainingEndDate must also be specified. Only one of trainingDuration, trainingRowCount, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. | | useProjectSettings | boolean | false | | If True, the model will be trained using the previously-specified custom backtest training settings. Only one of trainingDuration, trainingRowCount, trainingStartDate and trainingEndDate, or useProjectSettings may be specified. | ### Enumerated Values | Property | Value | | --- | --- | | samplingMethod | [random, latest] | ## TrainDatetimeModel ``` { "properties": { "blueprintId": { "description": "The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models.", "type": "string" }, "featurelistId": { "description": "If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "monotonicIncreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects.", "type": [ "string", "null" ], "x-versionadded": "v2.18" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "maximum": 100, "minimum": 2, "type": "integer", "x-versionadded": "v2.27" }, "samplingMethod": { "description": "Defines how training data is selected if subsampling is used (e.g., if `timeWindowSamplePct` is specified). Can be either ``random`` or ``latest``. If omitted, defaults to ``latest`` if `trainingRowCount` is used and ``random`` for other cases (e.g., if `trainingDuration` or `useProjectSettings` is specified). May only be specified for OTV projects.", "enum": [ "random", "latest" ], "type": "string", "x-versionadded": "v2.20" }, "sourceProjectId": { "description": "The project the blueprint comes from. Required only if the `blueprintId` comes from a different project.", "type": "string" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the `samplingMethod` option. If specified, `trainingRowCount` may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of `trainingDuration` or `trainingStartDate` and `trainingEndDate` must be specified.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.7" }, "trainingDuration": { "description": "A duration string representing the training duration for the submitted model.", "format": "duration", "type": "string" }, "trainingRowCount": { "description": "The number of rows of data that should be used when training this model.", "exclusiveMinimum": 0, "type": "integer" }, "useProjectSettings": { "description": "If ``True``, the model will be trained using the previously-specified custom backtest training settings.", "type": "boolean", "x-versionadded": "v2.19" } }, "required": [ "blueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models. | | featurelistId | string | false | | If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used. | | monotonicDecreasingFeaturelistId | string,null | false | | The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects. | | monotonicIncreasingFeaturelistId | string,null | false | | The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no constraints will be enforced. If omitted, the project default is used. May only be specified for OTV projects. | | nClusters | integer | false | maximum: 100minimum: 2 | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | samplingMethod | string | false | | Defines how training data is selected if subsampling is used (e.g., if timeWindowSamplePct is specified). Can be either random or latest. If omitted, defaults to latest if trainingRowCount is used and random for other cases (e.g., if trainingDuration or useProjectSettings is specified). May only be specified for OTV projects. | | sourceProjectId | string | false | | The project the blueprint comes from. Required only if the blueprintId comes from a different project. | | timeWindowSamplePct | integer | false | | An integer between 1 and 99 indicating the percentage of sampling within the time window. The points kept are determined by the value provided for the samplingMethod option. If specified, trainingRowCount may not be specified, and the specified model must either be a duration or selectedDateRange model, or one of trainingDuration or trainingStartDate and trainingEndDate must be specified. | | trainingDuration | string(duration) | false | | A duration string representing the training duration for the submitted model. | | trainingRowCount | integer | false | | The number of rows of data that should be used when training this model. | | useProjectSettings | boolean | false | | If True, the model will be trained using the previously-specified custom backtest training settings. | ### Enumerated Values | Property | Value | | --- | --- | | samplingMethod | [random, latest] | ## TrainIncrementalLearningModel ``` { "properties": { "dataStageCompression": { "description": "The file compression for the data stage. Only supports CSV storage type.", "enum": [ "zip", "bz2", "gzip" ], "type": "string", "x-versionadded": "v2.33" }, "dataStageDelimiter": { "description": "The file delimiter for the data stage specified as a string.", "type": "string", "x-versionadded": "v2.33" }, "dataStageEncoding": { "description": "The file encoding for the data stage.", "enum": [ "UTF-8", "ASCII", "WINDOWS-1252" ], "type": "string", "x-versionadded": "v2.33" }, "dataStageId": { "description": "The data stage ID. The data stage must be finalized and not expired.", "type": "string", "x-versionadded": "v2.33" }, "dataStageStorageType": { "default": "csv", "description": "The file type of the data stage contents.", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The parent model ID. This model must support incremental learning.", "type": "string", "x-versionadded": "v2.33" }, "trainingDataName": { "description": "String identifier for the current iteration.", "maxLength": 500, "type": "string", "x-versionadded": "v2.33" } }, "required": [ "dataStageId", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStageCompression | string | false | | The file compression for the data stage. Only supports CSV storage type. | | dataStageDelimiter | string | false | | The file delimiter for the data stage specified as a string. | | dataStageEncoding | string | false | | The file encoding for the data stage. | | dataStageId | string | true | | The data stage ID. The data stage must be finalized and not expired. | | dataStageStorageType | string | false | | The file type of the data stage contents. | | modelId | string | true | | The parent model ID. This model must support incremental learning. | | trainingDataName | string | false | maxLength: 500 | String identifier for the current iteration. | ### Enumerated Values | Property | Value | | --- | --- | | dataStageCompression | [zip, bz2, gzip] | | dataStageEncoding | [UTF-8, ASCII, WINDOWS-1252] | | dataStageStorageType | [csv, parquet] | ## TrainModel ``` { "properties": { "blueprintId": { "description": "The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models.", "type": "string" }, "featurelistId": { "description": "If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used.", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used.", "type": [ "string", "null" ] }, "monotonicIncreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If ``null``, no constraints will be enforced. If omitted, the project default is used.", "type": [ "string", "null" ] }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "maximum": 100, "minimum": 2, "type": "integer", "x-versionadded": "v2.27" }, "samplePct": { "description": "The percentage of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified. The specified percentage should be between 0 and 100.", "exclusiveMinimum": 0, "maximum": 100, "type": "number" }, "scoringType": { "description": "Validation is available for any partitioning. If the project uses cross validation, `crossValidation` may be used to indicate that all available training/validation combinations should be used.", "enum": [ "validation", "crossValidation" ], "type": "string" }, "sourceProjectId": { "description": "The project the blueprint comes from. Required only if the `blueprintId` comes from a different project.", "type": "string" }, "trainingRowCount": { "description": "An integer representing the number of rows of the dataset to use with the model. Only one of `samplePct` and `trainingRowCount` should be specified.", "type": "integer" } }, "required": [ "blueprintId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blueprintId | string | true | | The ID of a blueprint to use to generate the model. Allowed blueprints can be retrieved using [GET /api/v2/projects/{projectId}/blueprints/][get-apiv2projectsprojectidblueprints] or taken from existing models. | | featurelistId | string | false | | If specified, the model will be trained using this featurelist. If not specified, the recommended featurelist for the specified blueprint will be used. If there is no recommended featurelist, the project's default will be used. | | monotonicDecreasingFeaturelistId | string,null | false | | The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no constraints will be enforced. If omitted, the project default is used. | | monotonicIncreasingFeaturelistId | string,null | false | | The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no constraints will be enforced. If omitted, the project default is used. | | nClusters | integer | false | maximum: 100minimum: 2 | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | samplePct | number | false | maximum: 100 | The percentage of the dataset to use with the model. Only one of samplePct and trainingRowCount should be specified. The specified percentage should be between 0 and 100. | | scoringType | string | false | | Validation is available for any partitioning. If the project uses cross validation, crossValidation may be used to indicate that all available training/validation combinations should be used. | | sourceProjectId | string | false | | The project the blueprint comes from. Required only if the blueprintId comes from a different project. | | trainingRowCount | integer | false | | An integer representing the number of rows of the dataset to use with the model. Only one of samplePct and trainingRowCount should be specified. | ### Enumerated Values | Property | Value | | --- | --- | | scoringType | [validation, crossValidation] | ## TrainingInfoResponse ``` { "description": "holdout and prediction training data details", "properties": { "holdoutTrainingDuration": { "description": "the duration of the data used to train a model to score the holdout", "format": "duration", "type": "string" }, "holdoutTrainingEndDate": { "description": "the end date of the data used to train a model to score the holdout", "format": "date-time", "type": "string" }, "holdoutTrainingRowCount": { "description": "the number of rows used to train a model to score the holdout", "type": "integer" }, "holdoutTrainingStartDate": { "description": "the start date of data used to train a model to score the holdout", "format": "date-time", "type": "string" }, "predictionTrainingDuration": { "description": "the duration of the data used to train a model to make predictions", "format": "duration", "type": "string" }, "predictionTrainingEndDate": { "description": "the end date of the data used to train a model to make predictions", "format": "date-time", "type": "string" }, "predictionTrainingRowCount": { "description": "the number of rows used to train a model to make predictions", "type": "integer" }, "predictionTrainingStartDate": { "description": "the start date of data used to train a model to make predictions", "format": "date-time", "type": "string" } }, "required": [ "holdoutTrainingDuration", "holdoutTrainingEndDate", "holdoutTrainingRowCount", "holdoutTrainingStartDate", "predictionTrainingDuration", "predictionTrainingEndDate", "predictionTrainingRowCount", "predictionTrainingStartDate" ], "type": "object" } ``` holdout and prediction training data details ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | holdoutTrainingDuration | string(duration) | true | | the duration of the data used to train a model to score the holdout | | holdoutTrainingEndDate | string(date-time) | true | | the end date of the data used to train a model to score the holdout | | holdoutTrainingRowCount | integer | true | | the number of rows used to train a model to score the holdout | | holdoutTrainingStartDate | string(date-time) | true | | the start date of data used to train a model to score the holdout | | predictionTrainingDuration | string(duration) | true | | the duration of the data used to train a model to make predictions | | predictionTrainingEndDate | string(date-time) | true | | the end date of the data used to train a model to make predictions | | predictionTrainingRowCount | integer | true | | the number of rows used to train a model to make predictions | | predictionTrainingStartDate | string(date-time) | true | | the start date of data used to train a model to make predictions | ## Transformations ``` { "properties": { "name": { "description": "The name of the transformation.", "type": "string" }, "value": { "description": "The value used in carrying it out.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the transformation. | | value | string | true | | The value used in carrying it out. | ## TuningParameter ``` { "properties": { "parameterId": { "description": "ID of the parameter whose value to set.", "type": "string" }, "value": { "description": "Value for the specified parameter.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] } }, "required": [ "parameterId", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parameterId | string | true | | ID of the parameter whose value to set. | | value | any | true | | Value for the specified parameter. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | ## TuningParameters ``` { "properties": { "constraints": { "description": "Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format.", "properties": { "ascii": { "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "supportsGridSearch" ], "type": "object" }, "float": { "description": "Numeric constraints on a floating-point value. If present, indicates that this parameter's value may be a JSON number (integer or floating point).", "properties": { "max": { "description": "Maximum value for the parameter.", "type": "number" }, "min": { "description": "Minimum value for the parameter.", "type": "number" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "max", "min", "supportsGridSearch" ], "type": "object" }, "floatList": { "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of numbers (integer or floating point).", "properties": { "maxLength": { "description": "Maximum permitted length of the list.", "minimum": 0, "type": "integer" }, "maxVal": { "description": "Maximum permitted value.", "type": "number" }, "minLength": { "description": "Minimum permitted length of the list.", "minimum": 0, "type": "integer" }, "minVal": { "description": "Minimum permitted value.", "type": "number" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "maxLength", "maxVal", "minLength", "minVal", "supportsGridSearch" ], "type": "object" }, "int": { "description": "Numeric constraints on an integer value. If present, indicates that this parameter's value may be a JSON integer.", "properties": { "max": { "description": "Maximum value for the parameter.", "type": "integer" }, "min": { "description": "Minimum value for the parameter.", "type": "integer" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "max", "min", "supportsGridSearch" ], "type": "object" }, "intList": { "description": "Numeric constraints on a value of an array of floating-point numbers. If present, indicates that this parameter's value may be a JSON array of integers.", "properties": { "maxLength": { "description": "Maximum permitted length of the list.", "minimum": 0, "type": "integer" }, "maxVal": { "description": "Maximum permitted value.", "type": "integer" }, "minLength": { "description": "Minimum permitted length of the list.", "minimum": 0, "type": "integer" }, "minVal": { "description": "Minimum permitted value.", "type": "integer" }, "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "maxLength", "maxVal", "minLength", "minVal", "supportsGridSearch" ], "type": "object" }, "select": { "description": "Indicates that the value can be one selected from a list of known values.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" }, "values": { "description": "List of valid values for this field.", "items": { "type": "string" }, "type": "array" } }, "required": [ "supportsGridSearch", "values" ], "type": "object" }, "selectgrid": { "description": "Indicates that the value can be one selected from a list of known values.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" }, "values": { "description": "List of valid values for this field.", "items": { "type": "string" }, "type": "array" } }, "required": [ "supportsGridSearch", "values" ], "type": "object" }, "unicode": { "description": "Indicates that the value can contain free-form ASCII text. If present, is an empty object. Note that `ascii` fields must be valid ASCII-encoded strings. Additionally, they may not contain semicolons or newlines.", "properties": { "supportsGridSearch": { "description": "When True, Grid Search is supported for this parameter.", "type": "boolean" } }, "required": [ "supportsGridSearch" ], "type": "object" } }, "type": "object" }, "currentValue": { "description": "The single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range.", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "defaultValue": { "description": "The actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on `current_value`).", "oneOf": [ { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "boolean" }, { "type": "number" }, { "type": "null" } ] }, "type": "array" } ] }, "parameterId": { "description": "Unique (per-blueprint) identifier of this parameter. This is the identifier used to specify which parameter to tune when make a new advanced tuning request.", "type": "string" }, "parameterName": { "description": "Name of the parameter.", "type": "string" }, "taskName": { "description": "Human-readable name of the task that this parameter belongs to.", "type": "string" }, "vertexId": { "description": "Id of the vertex this parameter belongs to.", "type": "string", "x-versionadded": "v2.29" } }, "required": [ "constraints", "currentValue", "defaultValue", "parameterId", "parameterName", "taskName", "vertexId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | constraints | Constraints | true | | Constraints on valid values for this parameter. Note that any of these fields may be omitted but at least one will always be present. The presence of a field indicates that the parameter in question will accept values in the corresponding format. | | currentValue | any | true | | The single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultValue | any | true | | The actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on current_value). | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [anyOf] | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parameterId | string | true | | Unique (per-blueprint) identifier of this parameter. This is the identifier used to specify which parameter to tune when make a new advanced tuning request. | | parameterName | string | true | | Name of the parameter. | | taskName | string | true | | Human-readable name of the task that this parameter belongs to. | | vertexId | string | true | | Id of the vertex this parameter belongs to. | ## UnsupervisedClusteringModelRecordResponse ``` { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.).", "type": "string" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "numberOfClusters": { "description": "The number of clusters in the unsupervised clustering model. Only present in unsupervised clustering projects.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "metrics", "modelCategory", "modelFamily", "modelNumber", "modelType", "numberOfClusters", "parentModelId", "processes", "projectId", "samplePct", "trainingRowCount" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. | | blueprintId | string | true | | The blueprint used to construct the model. | | externalPredictionModel | boolean | true | | If the model is an external prediction model. | | featurelistId | string,null | true | | The ID of the feature list used by the model. | | featurelistName | string,null | true | | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. | | frozenPct | number,null | true | | The training percent used to train the frozen model. | | hasCodegen | boolean | true | | If the model has a codegen JAR file. | | icons | integer,null | true | | The icons associated with the model. | | id | string | true | | The ID of the model. | | isBlender | boolean | true | | If the model is a blender. | | isCustom | boolean | true | | If the model contains custom tasks. | | isFrozen | boolean | true | | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. | | isStarred | boolean | true | | Indicates whether the model has been starred. | | isTrainedIntoHoldout | boolean | true | | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. | | isTrainedIntoValidation | boolean | true | | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. | | isTrainedOnGpu | boolean | true | | Whether the model was trained using GPU workers. | | isTransparent | boolean | true | | If the model is a transparent model with exposed coefficients. | | isUserModel | boolean | true | | If the model was created with Composable ML. | | metrics | object | true | | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. | | modelCategory | string | true | | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. | | modelFamily | string | true | | The full name of the family that the model belongs to (e.g., Support Vector Machine, Gradient Boosting Machine, etc.). | | modelNumber | integer,null | true | | The model number from the Leaderboard. | | modelType | string | true | | Identifies the model (e.g., Nystroem Kernel SVM Regressor). | | numberOfClusters | integer,null | true | | The number of clusters in the unsupervised clustering model. Only present in unsupervised clustering projects. | | parentModelId | string,null | true | | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. | | processes | [string] | true | maxItems: 100 | The list of processes used by the model. | | projectId | string | true | | The ID of the project to which the model belongs. | | samplePct | number,null | true | | The percentage of the dataset used in training the model. | | trainingRowCount | integer,null | true | | The number of rows used to train the model. | ### Enumerated Values | Property | Value | | --- | --- | | modelCategory | [model, prime, blend, combined, incrementalLearning] | --- # Dataset Definition URL: https://docs.datarobot.com/en/docs/api/reference/public-api/dataset_definition.html > The endpoints below outline how to create and manage data source definitions. # Dataset Definition The endpoints below outline how to create and manage data source definitions. ## List all dataset definitions Operation path: `GET /api/v2/datasetDefinitions/` Authentication requirements: `BearerAuth` List all dataset definitions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of user-defined dataset definitions.", "items": { "properties": { "creatorUserId": { "description": "The ID of the user who created the dataset definition.", "type": "string" }, "datasetInfo": { "description": "Information about the dataset.", "properties": { "columns": { "description": "The list of the dataset column names. This field is auto-generated by the analysis job.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 1000, "minItems": 2, "type": "array" }, "dataSourceId": { "default": null, "description": "The ID of the SQL table query and the database path, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dataStoreId": { "default": null, "description": "The ID of the SQL data store, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from, this field is auto-generated by the analysis job.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "estimatedSizePerRow": { "description": "Estimated byte size per row of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "sourceSize": { "description": "Total dataset byte size, this field is auto-generated by the analysis job.", "type": "integer" }, "totalRows": { "description": "Total rows of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "version": { "description": "The version of the dataset definition information.", "type": "integer" } }, "required": [ "columns", "dataSourceId", "dataStoreId", "dialect", "estimatedSizePerRow", "sourceSize", "totalRows", "version" ], "type": "object", "x-versionadded": "v2.37" }, "datasetProps": { "description": "Dataset properties.", "properties": { "datasetId": { "description": "The ID of the AI Catalog dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the AI Catalog dataset.", "type": "string" } }, "required": [ "datasetId", "datasetVersionId" ], "type": "object", "x-versionadded": "v2.37" }, "dynamicDatasetProps": { "description": "Dynamic dataset additional properties.", "properties": { "credentialsId": { "default": null, "description": "The ID of the credentials to access the data store.", "type": [ "string", "null" ] } }, "required": [ "credentialsId" ], "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the dataset definition.", "type": "string" }, "name": { "description": "The name of the dataset definition.", "type": "string" } }, "required": [ "creatorUserId", "datasetInfo", "datasetProps", "dynamicDatasetProps", "id", "name" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | DatasetDefPaginatedResponse | ## Create a dataset definition Operation path: `POST /api/v2/datasetDefinitions/` Authentication requirements: `BearerAuth` Create a dataset definition. ### Body parameter ``` { "properties": { "credentialsId": { "description": "The ID of the credentials to access the data store.", "type": "string" }, "datasetId": { "description": "The ID of the AI Catalog dataset.", "type": "string" }, "datasetVersionId": { "default": null, "description": "The version ID of the AI Catalog dataset.", "type": [ "string", "null" ] }, "name": { "default": null, "description": "The name of the dataset definition.", "type": [ "string", "null" ] } }, "required": [ "datasetId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DatasetDefCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "creatorUserId": { "description": "The ID of the user who created the dataset definition.", "type": "string" }, "datasetInfo": { "description": "Information about the dataset.", "properties": { "columns": { "description": "The list of the dataset column names. This field is auto-generated by the analysis job.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 1000, "minItems": 2, "type": "array" }, "dataSourceId": { "default": null, "description": "The ID of the SQL table query and the database path, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dataStoreId": { "default": null, "description": "The ID of the SQL data store, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from, this field is auto-generated by the analysis job.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "estimatedSizePerRow": { "description": "Estimated byte size per row of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "sourceSize": { "description": "Total dataset byte size, this field is auto-generated by the analysis job.", "type": "integer" }, "totalRows": { "description": "Total rows of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "version": { "description": "The version of the dataset definition information.", "type": "integer" } }, "required": [ "columns", "dataSourceId", "dataStoreId", "dialect", "estimatedSizePerRow", "sourceSize", "totalRows", "version" ], "type": "object", "x-versionadded": "v2.37" }, "datasetProps": { "description": "Dataset properties.", "properties": { "datasetId": { "description": "The ID of the AI Catalog dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the AI Catalog dataset.", "type": "string" } }, "required": [ "datasetId", "datasetVersionId" ], "type": "object", "x-versionadded": "v2.37" }, "dynamicDatasetProps": { "description": "Dynamic dataset additional properties.", "properties": { "credentialsId": { "default": null, "description": "The ID of the credentials to access the data store.", "type": [ "string", "null" ] } }, "required": [ "credentialsId" ], "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the dataset definition.", "type": "string" }, "name": { "description": "The name of the dataset definition.", "type": "string" } }, "required": [ "creatorUserId", "datasetInfo", "datasetProps", "dynamicDatasetProps", "id", "name" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Dataset definition created successfully. | DatasetDefResponse | ## Soft delete a dataset definition based by dataset definition ID Operation path: `DELETE /api/v2/datasetDefinitions/{datasetDefinitionId}/` Authentication requirements: `BearerAuth` Soft delete a dataset definition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetDefinitionId | path | string | true | The ID of the dataset definition. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | ## Retrieve a dataset definition based by dataset definition ID Operation path: `GET /api/v2/datasetDefinitions/{datasetDefinitionId}/` Authentication requirements: `BearerAuth` Retrieve a dataset definition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | version | query | integer | false | The version of the dataset definition information. | | datasetDefinitionId | path | string | true | The ID of the dataset definition. | ### Example responses > 200 Response ``` { "properties": { "creatorUserId": { "description": "The ID of the user who created the dataset definition.", "type": "string" }, "datasetInfo": { "description": "Information about the dataset.", "properties": { "columns": { "description": "The list of the dataset column names. This field is auto-generated by the analysis job.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 1000, "minItems": 2, "type": "array" }, "dataSourceId": { "default": null, "description": "The ID of the SQL table query and the database path, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dataStoreId": { "default": null, "description": "The ID of the SQL data store, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from, this field is auto-generated by the analysis job.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "estimatedSizePerRow": { "description": "Estimated byte size per row of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "sourceSize": { "description": "Total dataset byte size, this field is auto-generated by the analysis job.", "type": "integer" }, "totalRows": { "description": "Total rows of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "version": { "description": "The version of the dataset definition information.", "type": "integer" } }, "required": [ "columns", "dataSourceId", "dataStoreId", "dialect", "estimatedSizePerRow", "sourceSize", "totalRows", "version" ], "type": "object", "x-versionadded": "v2.37" }, "datasetProps": { "description": "Dataset properties.", "properties": { "datasetId": { "description": "The ID of the AI Catalog dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the AI Catalog dataset.", "type": "string" } }, "required": [ "datasetId", "datasetVersionId" ], "type": "object", "x-versionadded": "v2.37" }, "dynamicDatasetProps": { "description": "Dynamic dataset additional properties.", "properties": { "credentialsId": { "default": null, "description": "The ID of the credentials to access the data store.", "type": [ "string", "null" ] } }, "required": [ "credentialsId" ], "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the dataset definition.", "type": "string" }, "name": { "description": "The name of the dataset definition.", "type": "string" } }, "required": [ "creatorUserId", "datasetInfo", "datasetProps", "dynamicDatasetProps", "id", "name" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | DatasetDefResponse | ## Analyze a dataset definition by dataset definition ID Operation path: `POST /api/v2/datasetDefinitions/{datasetDefinitionId}/analyze/` Authentication requirements: `BearerAuth` Analyze a dataset definition. ### Body parameter ``` { "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetDefinitionId | path | string | true | The ID of the dataset definition. | | body | body | Empty | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | ## Retrieve a list chunk definitions by dataset definition ID Operation path: `GET /api/v2/datasetDefinitions/{datasetDefinitionId}/chunkDefinitions/` Authentication requirements: `BearerAuth` Retrieve a list chunk definitions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | datasetDefinitionId | path | string | true | The ID of the dataset definition. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of chunk definitions.", "items": { "properties": { "chunkDefinitionStats": { "description": "Chunk definition stats. This field is auto-generated by the analysis job.", "properties": { "expectedChunkSize": { "description": "Expected chunk size. this field is auto-generated by the analysis job.", "type": "integer" }, "numberOfRowsPerChunk": { "description": "Number of rows per chunk. This field is auto-generated by the analysis job.", "type": "integer" }, "totalNumberOfChunks": { "description": "Total rows of the chunks. This field is auto-generated by the analysis job.", "type": "integer" } }, "required": [ "expectedChunkSize", "numberOfRowsPerChunk", "totalNumberOfChunks" ], "type": "object", "x-versionadded": "v2.37" }, "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "datasetDefinitionId": { "description": "The dataset definition ID the definition belongs.", "type": "string" }, "datasetDefinitionInfoVersion": { "description": "The version of the dataset definition information.", "type": "integer" }, "featuresChunkDefinition": { "description": "Feature chunk definition properties.", "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the chunk definition.", "type": "string" }, "isReadonly": { "default": false, "description": "Flag the allows or prevents updates.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": "string" }, "partitionMethod": { "default": "random", "description": "The partition method.", "enum": [ "random", "stratified", "date" ], "type": "string" }, "rowsChunkDefinition": { "description": "Row chunk definition properties.", "properties": { "datetimePartitionColumn": { "description": "Date partition column name.", "type": [ "string", "null" ] }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvEarliestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvLatestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationDownsamplingPct": { "description": "Percent by which to downsample the validation data.", "type": [ "number", "null" ] }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "targetClass": { "description": "Target Class.", "type": [ "string", "null" ] }, "targetColumn": { "description": "Target column name.", "type": [ "string", "null" ] }, "userGroupColumn": { "description": "User group column name.", "type": [ "string", "null" ] } }, "required": [ "datetimePartitionColumn", "isDescendingOrder", "orderByColumns", "otvEarliestTimestamp", "otvLatestTimestamp", "otvTrainingEndDate", "otvValidationDownsamplingPct", "otvValidationEndDate", "otvValidationStartDate", "targetClass", "targetColumn", "userGroupColumn" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "chunkDefinitionStats", "chunkingStrategyType", "datasetDefinitionId", "datasetDefinitionInfoVersion", "featuresChunkDefinition", "id", "isReadonly", "name", "partitionMethod", "rowsChunkDefinition" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ChunkDefinitionPaginatedResponse | ## Create a chunk definition based by dataset definition ID Operation path: `POST /api/v2/datasetDefinitions/{datasetDefinitionId}/chunkDefinitions/` Authentication requirements: `BearerAuth` Create a chunk definition. ### Body parameter ``` { "discriminator": { "propertyName": "partitionMethod" }, "oneOf": [ { "properties": { "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": [ "string", "null" ] }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "partitionMethod": { "description": "The partition method.", "enum": [ "random" ], "type": "string" } }, "required": [ "partitionMethod" ], "type": "object" }, { "properties": { "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": [ "string", "null" ] }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "partitionMethod": { "description": "The partition method.", "enum": [ "stratified" ], "type": "string" }, "targetClass": { "description": "Target Class.", "type": "string" }, "targetColumn": { "description": "Target column name.", "type": "string" } }, "required": [ "partitionMethod", "targetClass", "targetColumn" ], "type": "object" }, { "properties": { "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "datetimePartitionColumn": { "description": "Date partition column name.", "type": "string" }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": [ "string", "null" ] }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "partitionMethod": { "description": "The partition method.", "enum": [ "date" ], "type": "string" } }, "required": [ "datetimePartitionColumn", "partitionMethod" ], "type": "object" } ], "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetDefinitionId | path | string | true | The ID of the dataset definition. | | body | body | ChunkDefinitionCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "chunkDefinitionStats": { "description": "Chunk definition stats. This field is auto-generated by the analysis job.", "properties": { "expectedChunkSize": { "description": "Expected chunk size. this field is auto-generated by the analysis job.", "type": "integer" }, "numberOfRowsPerChunk": { "description": "Number of rows per chunk. This field is auto-generated by the analysis job.", "type": "integer" }, "totalNumberOfChunks": { "description": "Total rows of the chunks. This field is auto-generated by the analysis job.", "type": "integer" } }, "required": [ "expectedChunkSize", "numberOfRowsPerChunk", "totalNumberOfChunks" ], "type": "object", "x-versionadded": "v2.37" }, "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "datasetDefinitionId": { "description": "The dataset definition ID the definition belongs.", "type": "string" }, "datasetDefinitionInfoVersion": { "description": "The version of the dataset definition information.", "type": "integer" }, "featuresChunkDefinition": { "description": "Feature chunk definition properties.", "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the chunk definition.", "type": "string" }, "isReadonly": { "default": false, "description": "Flag the allows or prevents updates.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": "string" }, "partitionMethod": { "default": "random", "description": "The partition method.", "enum": [ "random", "stratified", "date" ], "type": "string" }, "rowsChunkDefinition": { "description": "Row chunk definition properties.", "properties": { "datetimePartitionColumn": { "description": "Date partition column name.", "type": [ "string", "null" ] }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvEarliestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvLatestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationDownsamplingPct": { "description": "Percent by which to downsample the validation data.", "type": [ "number", "null" ] }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "targetClass": { "description": "Target Class.", "type": [ "string", "null" ] }, "targetColumn": { "description": "Target column name.", "type": [ "string", "null" ] }, "userGroupColumn": { "description": "User group column name.", "type": [ "string", "null" ] } }, "required": [ "datetimePartitionColumn", "isDescendingOrder", "orderByColumns", "otvEarliestTimestamp", "otvLatestTimestamp", "otvTrainingEndDate", "otvValidationDownsamplingPct", "otvValidationEndDate", "otvValidationStartDate", "targetClass", "targetColumn", "userGroupColumn" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "chunkDefinitionStats", "chunkingStrategyType", "datasetDefinitionId", "datasetDefinitionInfoVersion", "featuresChunkDefinition", "id", "isReadonly", "name", "partitionMethod", "rowsChunkDefinition" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Chunk definition created successfully. | ChunkDefinitionResponse | ## Soft delete a chunk definition based by dataset definition ID Operation path: `DELETE /api/v2/datasetDefinitions/{datasetDefinitionId}/chunkDefinitions/{chunkDefinitionId}/` Authentication requirements: `BearerAuth` Soft delete a chunk definition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetDefinitionId | path | string | true | The ID of the dataset definition. | | chunkDefinitionId | path | string | true | The ID of the chunk definition. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | ## Retrieve a chunk definition based by dataset definition ID Operation path: `GET /api/v2/datasetDefinitions/{datasetDefinitionId}/chunkDefinitions/{chunkDefinitionId}/` Authentication requirements: `BearerAuth` Retrieve a chunk definition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetDefinitionId | path | string | true | The ID of the dataset definition. | | chunkDefinitionId | path | string | true | The ID of the chunk definition. | ### Example responses > 200 Response ``` { "properties": { "chunkDefinitionStats": { "description": "Chunk definition stats. This field is auto-generated by the analysis job.", "properties": { "expectedChunkSize": { "description": "Expected chunk size. this field is auto-generated by the analysis job.", "type": "integer" }, "numberOfRowsPerChunk": { "description": "Number of rows per chunk. This field is auto-generated by the analysis job.", "type": "integer" }, "totalNumberOfChunks": { "description": "Total rows of the chunks. This field is auto-generated by the analysis job.", "type": "integer" } }, "required": [ "expectedChunkSize", "numberOfRowsPerChunk", "totalNumberOfChunks" ], "type": "object", "x-versionadded": "v2.37" }, "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "datasetDefinitionId": { "description": "The dataset definition ID the definition belongs.", "type": "string" }, "datasetDefinitionInfoVersion": { "description": "The version of the dataset definition information.", "type": "integer" }, "featuresChunkDefinition": { "description": "Feature chunk definition properties.", "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the chunk definition.", "type": "string" }, "isReadonly": { "default": false, "description": "Flag the allows or prevents updates.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": "string" }, "partitionMethod": { "default": "random", "description": "The partition method.", "enum": [ "random", "stratified", "date" ], "type": "string" }, "rowsChunkDefinition": { "description": "Row chunk definition properties.", "properties": { "datetimePartitionColumn": { "description": "Date partition column name.", "type": [ "string", "null" ] }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvEarliestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvLatestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationDownsamplingPct": { "description": "Percent by which to downsample the validation data.", "type": [ "number", "null" ] }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "targetClass": { "description": "Target Class.", "type": [ "string", "null" ] }, "targetColumn": { "description": "Target column name.", "type": [ "string", "null" ] }, "userGroupColumn": { "description": "User group column name.", "type": [ "string", "null" ] } }, "required": [ "datetimePartitionColumn", "isDescendingOrder", "orderByColumns", "otvEarliestTimestamp", "otvLatestTimestamp", "otvTrainingEndDate", "otvValidationDownsamplingPct", "otvValidationEndDate", "otvValidationStartDate", "targetClass", "targetColumn", "userGroupColumn" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "chunkDefinitionStats", "chunkingStrategyType", "datasetDefinitionId", "datasetDefinitionInfoVersion", "featuresChunkDefinition", "id", "isReadonly", "name", "partitionMethod", "rowsChunkDefinition" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ChunkDefinitionResponse | ## Update a chunk definition based by dataset definition ID Operation path: `PATCH /api/v2/datasetDefinitions/{datasetDefinitionId}/chunkDefinitions/{chunkDefinitionId}/` Authentication requirements: `BearerAuth` Update a chunk definition. ### Body parameter ``` { "properties": { "operations": { "description": "Operations to perform on the update chunk definition.", "properties": { "forceUpdate": { "default": false, "description": "Force update the chunk definition. If set to true, the analysis will be reset.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.37" }, "updates": { "description": "Fields to be updated in the chunk definition.", "properties": { "datetimePartitionColumn": { "description": "Date partition column name.", "type": "string" }, "isDescendingOrder": { "description": "The sorting order.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": "string" }, "orderByColumns": { "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": "string" }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": "string" }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": "string" }, "targetClass": { "description": "Target Class.", "type": "string" }, "targetColumn": { "description": "Target column name.", "type": "string" }, "userGroupColumn": { "description": "User group column name.", "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } }, "required": [ "updates" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetDefinitionId | path | string | true | The ID of the dataset definition. | | chunkDefinitionId | path | string | true | The ID of the chunk definition. | | body | body | ChunkDefinitionRowsUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "chunkDefinitionStats": { "description": "Chunk definition stats. This field is auto-generated by the analysis job.", "properties": { "expectedChunkSize": { "description": "Expected chunk size. this field is auto-generated by the analysis job.", "type": "integer" }, "numberOfRowsPerChunk": { "description": "Number of rows per chunk. This field is auto-generated by the analysis job.", "type": "integer" }, "totalNumberOfChunks": { "description": "Total rows of the chunks. This field is auto-generated by the analysis job.", "type": "integer" } }, "required": [ "expectedChunkSize", "numberOfRowsPerChunk", "totalNumberOfChunks" ], "type": "object", "x-versionadded": "v2.37" }, "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "datasetDefinitionId": { "description": "The dataset definition ID the definition belongs.", "type": "string" }, "datasetDefinitionInfoVersion": { "description": "The version of the dataset definition information.", "type": "integer" }, "featuresChunkDefinition": { "description": "Feature chunk definition properties.", "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the chunk definition.", "type": "string" }, "isReadonly": { "default": false, "description": "Flag the allows or prevents updates.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": "string" }, "partitionMethod": { "default": "random", "description": "The partition method.", "enum": [ "random", "stratified", "date" ], "type": "string" }, "rowsChunkDefinition": { "description": "Row chunk definition properties.", "properties": { "datetimePartitionColumn": { "description": "Date partition column name.", "type": [ "string", "null" ] }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvEarliestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvLatestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationDownsamplingPct": { "description": "Percent by which to downsample the validation data.", "type": [ "number", "null" ] }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "targetClass": { "description": "Target Class.", "type": [ "string", "null" ] }, "targetColumn": { "description": "Target column name.", "type": [ "string", "null" ] }, "userGroupColumn": { "description": "User group column name.", "type": [ "string", "null" ] } }, "required": [ "datetimePartitionColumn", "isDescendingOrder", "orderByColumns", "otvEarliestTimestamp", "otvLatestTimestamp", "otvTrainingEndDate", "otvValidationDownsamplingPct", "otvValidationEndDate", "otvValidationStartDate", "targetClass", "targetColumn", "userGroupColumn" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "chunkDefinitionStats", "chunkingStrategyType", "datasetDefinitionId", "datasetDefinitionInfoVersion", "featuresChunkDefinition", "id", "isReadonly", "name", "partitionMethod", "rowsChunkDefinition" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Chunk definition updated successfully. | ChunkDefinitionResponse | ## Analyze a chunk definition by dataset definition ID Operation path: `POST /api/v2/datasetDefinitions/{datasetDefinitionId}/chunkDefinitions/{chunkDefinitionId}/analyze/` Authentication requirements: `BearerAuth` Analyze a chunk definition. ### Body parameter ``` { "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetDefinitionId | path | string | true | The ID of the dataset definition. | | chunkDefinitionId | path | string | true | The ID of the chunk definition. | | body | body | Empty | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | ## List all dataset definition versions by dataset definition ID Operation path: `GET /api/v2/datasetDefinitions/{datasetDefinitionId}/versions/` Authentication requirements: `BearerAuth` List all dataset definition versions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | datasetDefinitionId | path | string | true | The ID of the dataset definition. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the dataset definition versions.", "items": { "properties": { "datasetInfo": { "description": "Information about the dataset.", "properties": { "columns": { "description": "The list of the dataset column names. This field is auto-generated by the analysis job.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 1000, "minItems": 2, "type": "array" }, "dataSourceId": { "default": null, "description": "The ID of the SQL table query and the database path, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dataStoreId": { "default": null, "description": "The ID of the SQL data store, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from, this field is auto-generated by the analysis job.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "estimatedSizePerRow": { "description": "Estimated byte size per row of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "sourceSize": { "description": "Total dataset byte size, this field is auto-generated by the analysis job.", "type": "integer" }, "totalRows": { "description": "Total rows of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "version": { "description": "The version of the dataset definition information.", "type": "integer" } }, "required": [ "columns", "dataSourceId", "dataStoreId", "dialect", "estimatedSizePerRow", "sourceSize", "totalRows", "version" ], "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the dataset definition version.", "type": "string" } }, "required": [ "datasetInfo", "id" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | DatasetDefVersionPaginatedResponse | # Schemas ## ChunkDefinitionCreate ``` { "discriminator": { "propertyName": "partitionMethod" }, "oneOf": [ { "properties": { "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": [ "string", "null" ] }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "partitionMethod": { "description": "The partition method.", "enum": [ "random" ], "type": "string" } }, "required": [ "partitionMethod" ], "type": "object" }, { "properties": { "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": [ "string", "null" ] }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "partitionMethod": { "description": "The partition method.", "enum": [ "stratified" ], "type": "string" }, "targetClass": { "description": "Target Class.", "type": "string" }, "targetColumn": { "description": "Target column name.", "type": "string" } }, "required": [ "partitionMethod", "targetClass", "targetColumn" ], "type": "object" }, { "properties": { "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "datetimePartitionColumn": { "description": "Date partition column name.", "type": "string" }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": [ "string", "null" ] }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "partitionMethod": { "description": "The partition method.", "enum": [ "date" ], "type": "string" } }, "required": [ "datetimePartitionColumn", "partitionMethod" ], "type": "object" } ], "x-versionadded": "v2.37" } ``` ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » chunkingStrategyType | string | false | | The partition method. | | » isDescendingOrder | boolean | false | | The sorting order. | | » name | string,null | false | | The name of the chunk definition. | | » orderByColumns | [string] | false | maxItems: 10 | The list of the sorting column names. | | » partitionMethod | string | true | | The partition method. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » chunkingStrategyType | string | false | | The partition method. | | » isDescendingOrder | boolean | false | | The sorting order. | | » name | string,null | false | | The name of the chunk definition. | | » orderByColumns | [string] | false | maxItems: 10 | The list of the sorting column names. | | » partitionMethod | string | true | | The partition method. | | » targetClass | string | true | | Target Class. | | » targetColumn | string | true | | Target column name. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » chunkingStrategyType | string | false | | The partition method. | | » datetimePartitionColumn | string | true | | Date partition column name. | | » isDescendingOrder | boolean | false | | The sorting order. | | » name | string,null | false | | The name of the chunk definition. | | » orderByColumns | [string] | false | maxItems: 10 | The list of the sorting column names. | | » otvTrainingEndDate | string,null(date-time) | false | | The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationEndDate, one must specify ValidationStartDate. This attribute cannot be patched for non-OTV incremental learning projects. | | » otvValidationEndDate | string,null(date-time) | false | | The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationEndDate, one must specify ValidationStartDate. This attribute cannot be patched for non-OTV incremental learning projects. | | » otvValidationStartDate | string,null(date-time) | false | | The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationStartDate, one must specify ValidationEndDate. This attribute cannot be patched for non-OTV incremental learning projects. | | » partitionMethod | string | true | | The partition method. | ### Enumerated Values | Property | Value | | --- | --- | | chunkingStrategyType | [features, rows] | | partitionMethod | random | | chunkingStrategyType | [features, rows] | | partitionMethod | stratified | | chunkingStrategyType | [features, rows] | | partitionMethod | date | ## ChunkDefinitionPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of chunk definitions.", "items": { "properties": { "chunkDefinitionStats": { "description": "Chunk definition stats. This field is auto-generated by the analysis job.", "properties": { "expectedChunkSize": { "description": "Expected chunk size. this field is auto-generated by the analysis job.", "type": "integer" }, "numberOfRowsPerChunk": { "description": "Number of rows per chunk. This field is auto-generated by the analysis job.", "type": "integer" }, "totalNumberOfChunks": { "description": "Total rows of the chunks. This field is auto-generated by the analysis job.", "type": "integer" } }, "required": [ "expectedChunkSize", "numberOfRowsPerChunk", "totalNumberOfChunks" ], "type": "object", "x-versionadded": "v2.37" }, "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "datasetDefinitionId": { "description": "The dataset definition ID the definition belongs.", "type": "string" }, "datasetDefinitionInfoVersion": { "description": "The version of the dataset definition information.", "type": "integer" }, "featuresChunkDefinition": { "description": "Feature chunk definition properties.", "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the chunk definition.", "type": "string" }, "isReadonly": { "default": false, "description": "Flag the allows or prevents updates.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": "string" }, "partitionMethod": { "default": "random", "description": "The partition method.", "enum": [ "random", "stratified", "date" ], "type": "string" }, "rowsChunkDefinition": { "description": "Row chunk definition properties.", "properties": { "datetimePartitionColumn": { "description": "Date partition column name.", "type": [ "string", "null" ] }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvEarliestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvLatestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationDownsamplingPct": { "description": "Percent by which to downsample the validation data.", "type": [ "number", "null" ] }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "targetClass": { "description": "Target Class.", "type": [ "string", "null" ] }, "targetColumn": { "description": "Target column name.", "type": [ "string", "null" ] }, "userGroupColumn": { "description": "User group column name.", "type": [ "string", "null" ] } }, "required": [ "datetimePartitionColumn", "isDescendingOrder", "orderByColumns", "otvEarliestTimestamp", "otvLatestTimestamp", "otvTrainingEndDate", "otvValidationDownsamplingPct", "otvValidationEndDate", "otvValidationStartDate", "targetClass", "targetColumn", "userGroupColumn" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "chunkDefinitionStats", "chunkingStrategyType", "datasetDefinitionId", "datasetDefinitionInfoVersion", "featuresChunkDefinition", "id", "isReadonly", "name", "partitionMethod", "rowsChunkDefinition" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ChunkDefinitionResponse] | true | maxItems: 100 | The list of chunk definitions. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ChunkDefinitionResponse ``` { "properties": { "chunkDefinitionStats": { "description": "Chunk definition stats. This field is auto-generated by the analysis job.", "properties": { "expectedChunkSize": { "description": "Expected chunk size. this field is auto-generated by the analysis job.", "type": "integer" }, "numberOfRowsPerChunk": { "description": "Number of rows per chunk. This field is auto-generated by the analysis job.", "type": "integer" }, "totalNumberOfChunks": { "description": "Total rows of the chunks. This field is auto-generated by the analysis job.", "type": "integer" } }, "required": [ "expectedChunkSize", "numberOfRowsPerChunk", "totalNumberOfChunks" ], "type": "object", "x-versionadded": "v2.37" }, "chunkingStrategyType": { "default": "rows", "description": "The partition method.", "enum": [ "features", "rows" ], "type": "string" }, "datasetDefinitionId": { "description": "The dataset definition ID the definition belongs.", "type": "string" }, "datasetDefinitionInfoVersion": { "description": "The version of the dataset definition information.", "type": "integer" }, "featuresChunkDefinition": { "description": "Feature chunk definition properties.", "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the chunk definition.", "type": "string" }, "isReadonly": { "default": false, "description": "Flag the allows or prevents updates.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": "string" }, "partitionMethod": { "default": "random", "description": "The partition method.", "enum": [ "random", "stratified", "date" ], "type": "string" }, "rowsChunkDefinition": { "description": "Row chunk definition properties.", "properties": { "datetimePartitionColumn": { "description": "Date partition column name.", "type": [ "string", "null" ] }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvEarliestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvLatestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationDownsamplingPct": { "description": "Percent by which to downsample the validation data.", "type": [ "number", "null" ] }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "targetClass": { "description": "Target Class.", "type": [ "string", "null" ] }, "targetColumn": { "description": "Target column name.", "type": [ "string", "null" ] }, "userGroupColumn": { "description": "User group column name.", "type": [ "string", "null" ] } }, "required": [ "datetimePartitionColumn", "isDescendingOrder", "orderByColumns", "otvEarliestTimestamp", "otvLatestTimestamp", "otvTrainingEndDate", "otvValidationDownsamplingPct", "otvValidationEndDate", "otvValidationStartDate", "targetClass", "targetColumn", "userGroupColumn" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "chunkDefinitionStats", "chunkingStrategyType", "datasetDefinitionId", "datasetDefinitionInfoVersion", "featuresChunkDefinition", "id", "isReadonly", "name", "partitionMethod", "rowsChunkDefinition" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkDefinitionStats | ChunkDefinitionStatsResponse | true | | Chunk definition stats. This field is auto-generated by the analysis job. | | chunkingStrategyType | string | true | | The partition method. | | datasetDefinitionId | string | true | | The dataset definition ID the definition belongs. | | datasetDefinitionInfoVersion | integer | true | | The version of the dataset definition information. | | featuresChunkDefinition | FeaturesChunkDefinitionResponse | true | | Feature chunk definition properties. | | id | string | true | | The ID of the chunk definition. | | isReadonly | boolean | true | | Flag the allows or prevents updates. | | name | string | true | | The name of the chunk definition. | | partitionMethod | string | true | | The partition method. | | rowsChunkDefinition | RowsChunkDefinitionResponse | true | | Row chunk definition properties. | ### Enumerated Values | Property | Value | | --- | --- | | chunkingStrategyType | [features, rows] | | partitionMethod | [random, stratified, date] | ## ChunkDefinitionRowsUpdate ``` { "properties": { "operations": { "description": "Operations to perform on the update chunk definition.", "properties": { "forceUpdate": { "default": false, "description": "Force update the chunk definition. If set to true, the analysis will be reset.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.37" }, "updates": { "description": "Fields to be updated in the chunk definition.", "properties": { "datetimePartitionColumn": { "description": "Date partition column name.", "type": "string" }, "isDescendingOrder": { "description": "The sorting order.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": "string" }, "orderByColumns": { "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": "string" }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": "string" }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": "string" }, "targetClass": { "description": "Target Class.", "type": "string" }, "targetColumn": { "description": "Target column name.", "type": "string" }, "userGroupColumn": { "description": "User group column name.", "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } }, "required": [ "updates" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operations | ChunkDefinitionUpdateOperation | false | | Operations to perform on the update chunk definition. | | updates | ChunkDefinitionRowsUpdateFields | true | | Fields to be updated in the chunk definition. | ## ChunkDefinitionRowsUpdateFields ``` { "description": "Fields to be updated in the chunk definition.", "properties": { "datetimePartitionColumn": { "description": "Date partition column name.", "type": "string" }, "isDescendingOrder": { "description": "The sorting order.", "type": "boolean" }, "name": { "description": "The name of the chunk definition.", "type": "string" }, "orderByColumns": { "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": "string" }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": "string" }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": "string" }, "targetClass": { "description": "Target Class.", "type": "string" }, "targetColumn": { "description": "Target column name.", "type": "string" }, "userGroupColumn": { "description": "User group column name.", "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` Fields to be updated in the chunk definition. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimePartitionColumn | string | false | | Date partition column name. | | isDescendingOrder | boolean | false | | The sorting order. | | name | string | false | | The name of the chunk definition. | | orderByColumns | [string] | false | maxItems: 10 | The list of the sorting column names. | | otvTrainingEndDate | string(date-time) | false | | The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationEndDate, one must specify ValidationStartDate. This attribute cannot be patched for non-OTV incremental learning projects. | | otvValidationEndDate | string(date-time) | false | | The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationEndDate, one must specify ValidationStartDate. This attribute cannot be patched for non-OTV incremental learning projects. | | otvValidationStartDate | string(date-time) | false | | The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationStartDate, one must specify ValidationEndDate. This attribute cannot be patched for non-OTV incremental learning projects. | | targetClass | string | false | | Target Class. | | targetColumn | string | false | | Target column name. | | userGroupColumn | string | false | | User group column name. | ## ChunkDefinitionStatsResponse ``` { "description": "Chunk definition stats. This field is auto-generated by the analysis job.", "properties": { "expectedChunkSize": { "description": "Expected chunk size. this field is auto-generated by the analysis job.", "type": "integer" }, "numberOfRowsPerChunk": { "description": "Number of rows per chunk. This field is auto-generated by the analysis job.", "type": "integer" }, "totalNumberOfChunks": { "description": "Total rows of the chunks. This field is auto-generated by the analysis job.", "type": "integer" } }, "required": [ "expectedChunkSize", "numberOfRowsPerChunk", "totalNumberOfChunks" ], "type": "object", "x-versionadded": "v2.37" } ``` Chunk definition stats. This field is auto-generated by the analysis job. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | expectedChunkSize | integer | true | | Expected chunk size. this field is auto-generated by the analysis job. | | numberOfRowsPerChunk | integer | true | | Number of rows per chunk. This field is auto-generated by the analysis job. | | totalNumberOfChunks | integer | true | | Total rows of the chunks. This field is auto-generated by the analysis job. | ## ChunkDefinitionUpdateOperation ``` { "description": "Operations to perform on the update chunk definition.", "properties": { "forceUpdate": { "default": false, "description": "Force update the chunk definition. If set to true, the analysis will be reset.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.37" } ``` Operations to perform on the update chunk definition. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forceUpdate | boolean | false | | Force update the chunk definition. If set to true, the analysis will be reset. | ## DatasetDefCreate ``` { "properties": { "credentialsId": { "description": "The ID of the credentials to access the data store.", "type": "string" }, "datasetId": { "description": "The ID of the AI Catalog dataset.", "type": "string" }, "datasetVersionId": { "default": null, "description": "The version ID of the AI Catalog dataset.", "type": [ "string", "null" ] }, "name": { "default": null, "description": "The name of the dataset definition.", "type": [ "string", "null" ] } }, "required": [ "datasetId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialsId | string | false | | The ID of the credentials to access the data store. | | datasetId | string | true | | The ID of the AI Catalog dataset. | | datasetVersionId | string,null | false | | The version ID of the AI Catalog dataset. | | name | string,null | false | | The name of the dataset definition. | ## DatasetDefPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of user-defined dataset definitions.", "items": { "properties": { "creatorUserId": { "description": "The ID of the user who created the dataset definition.", "type": "string" }, "datasetInfo": { "description": "Information about the dataset.", "properties": { "columns": { "description": "The list of the dataset column names. This field is auto-generated by the analysis job.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 1000, "minItems": 2, "type": "array" }, "dataSourceId": { "default": null, "description": "The ID of the SQL table query and the database path, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dataStoreId": { "default": null, "description": "The ID of the SQL data store, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from, this field is auto-generated by the analysis job.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "estimatedSizePerRow": { "description": "Estimated byte size per row of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "sourceSize": { "description": "Total dataset byte size, this field is auto-generated by the analysis job.", "type": "integer" }, "totalRows": { "description": "Total rows of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "version": { "description": "The version of the dataset definition information.", "type": "integer" } }, "required": [ "columns", "dataSourceId", "dataStoreId", "dialect", "estimatedSizePerRow", "sourceSize", "totalRows", "version" ], "type": "object", "x-versionadded": "v2.37" }, "datasetProps": { "description": "Dataset properties.", "properties": { "datasetId": { "description": "The ID of the AI Catalog dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the AI Catalog dataset.", "type": "string" } }, "required": [ "datasetId", "datasetVersionId" ], "type": "object", "x-versionadded": "v2.37" }, "dynamicDatasetProps": { "description": "Dynamic dataset additional properties.", "properties": { "credentialsId": { "default": null, "description": "The ID of the credentials to access the data store.", "type": [ "string", "null" ] } }, "required": [ "credentialsId" ], "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the dataset definition.", "type": "string" }, "name": { "description": "The name of the dataset definition.", "type": "string" } }, "required": [ "creatorUserId", "datasetInfo", "datasetProps", "dynamicDatasetProps", "id", "name" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetDefResponse] | true | maxItems: 100 | The list of user-defined dataset definitions. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetDefResponse ``` { "properties": { "creatorUserId": { "description": "The ID of the user who created the dataset definition.", "type": "string" }, "datasetInfo": { "description": "Information about the dataset.", "properties": { "columns": { "description": "The list of the dataset column names. This field is auto-generated by the analysis job.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 1000, "minItems": 2, "type": "array" }, "dataSourceId": { "default": null, "description": "The ID of the SQL table query and the database path, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dataStoreId": { "default": null, "description": "The ID of the SQL data store, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from, this field is auto-generated by the analysis job.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "estimatedSizePerRow": { "description": "Estimated byte size per row of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "sourceSize": { "description": "Total dataset byte size, this field is auto-generated by the analysis job.", "type": "integer" }, "totalRows": { "description": "Total rows of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "version": { "description": "The version of the dataset definition information.", "type": "integer" } }, "required": [ "columns", "dataSourceId", "dataStoreId", "dialect", "estimatedSizePerRow", "sourceSize", "totalRows", "version" ], "type": "object", "x-versionadded": "v2.37" }, "datasetProps": { "description": "Dataset properties.", "properties": { "datasetId": { "description": "The ID of the AI Catalog dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the AI Catalog dataset.", "type": "string" } }, "required": [ "datasetId", "datasetVersionId" ], "type": "object", "x-versionadded": "v2.37" }, "dynamicDatasetProps": { "description": "Dynamic dataset additional properties.", "properties": { "credentialsId": { "default": null, "description": "The ID of the credentials to access the data store.", "type": [ "string", "null" ] } }, "required": [ "credentialsId" ], "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the dataset definition.", "type": "string" }, "name": { "description": "The name of the dataset definition.", "type": "string" } }, "required": [ "creatorUserId", "datasetInfo", "datasetProps", "dynamicDatasetProps", "id", "name" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creatorUserId | string | true | | The ID of the user who created the dataset definition. | | datasetInfo | DatasetInformationResponse | true | | Information about the dataset. | | datasetProps | DatasetPropsResponse | true | | Dataset properties. | | dynamicDatasetProps | DynamicDatasetPropsResponse | true | | Dynamic dataset additional properties. | | id | string | true | | The ID of the dataset definition. | | name | string | true | | The name of the dataset definition. | ## DatasetDefVersionPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the dataset definition versions.", "items": { "properties": { "datasetInfo": { "description": "Information about the dataset.", "properties": { "columns": { "description": "The list of the dataset column names. This field is auto-generated by the analysis job.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 1000, "minItems": 2, "type": "array" }, "dataSourceId": { "default": null, "description": "The ID of the SQL table query and the database path, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dataStoreId": { "default": null, "description": "The ID of the SQL data store, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from, this field is auto-generated by the analysis job.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "estimatedSizePerRow": { "description": "Estimated byte size per row of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "sourceSize": { "description": "Total dataset byte size, this field is auto-generated by the analysis job.", "type": "integer" }, "totalRows": { "description": "Total rows of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "version": { "description": "The version of the dataset definition information.", "type": "integer" } }, "required": [ "columns", "dataSourceId", "dataStoreId", "dialect", "estimatedSizePerRow", "sourceSize", "totalRows", "version" ], "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the dataset definition version.", "type": "string" } }, "required": [ "datasetInfo", "id" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DatasetDefVersionResponse] | true | maxItems: 100 | The list of the dataset definition versions. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatasetDefVersionResponse ``` { "properties": { "datasetInfo": { "description": "Information about the dataset.", "properties": { "columns": { "description": "The list of the dataset column names. This field is auto-generated by the analysis job.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 1000, "minItems": 2, "type": "array" }, "dataSourceId": { "default": null, "description": "The ID of the SQL table query and the database path, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dataStoreId": { "default": null, "description": "The ID of the SQL data store, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from, this field is auto-generated by the analysis job.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "estimatedSizePerRow": { "description": "Estimated byte size per row of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "sourceSize": { "description": "Total dataset byte size, this field is auto-generated by the analysis job.", "type": "integer" }, "totalRows": { "description": "Total rows of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "version": { "description": "The version of the dataset definition information.", "type": "integer" } }, "required": [ "columns", "dataSourceId", "dataStoreId", "dialect", "estimatedSizePerRow", "sourceSize", "totalRows", "version" ], "type": "object", "x-versionadded": "v2.37" }, "id": { "description": "The ID of the dataset definition version.", "type": "string" } }, "required": [ "datasetInfo", "id" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetInfo | DatasetInformationResponse | true | | Information about the dataset. | | id | string | true | | The ID of the dataset definition version. | ## DatasetInformationResponse ``` { "description": "Information about the dataset.", "properties": { "columns": { "description": "The list of the dataset column names. This field is auto-generated by the analysis job.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 1000, "minItems": 2, "type": "array" }, "dataSourceId": { "default": null, "description": "The ID of the SQL table query and the database path, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dataStoreId": { "default": null, "description": "The ID of the SQL data store, this field is auto-generated by the analysis job.", "type": [ "string", "null" ] }, "dialect": { "description": "Source type data was retrieved from, this field is auto-generated by the analysis job.", "enum": [ "snowflake", "bigquery", "databricks", "spark", "postgres" ], "type": "string" }, "estimatedSizePerRow": { "description": "Estimated byte size per row of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "sourceSize": { "description": "Total dataset byte size, this field is auto-generated by the analysis job.", "type": "integer" }, "totalRows": { "description": "Total rows of the dataset, this field is auto-generated by the analysis job.", "type": "integer" }, "version": { "description": "The version of the dataset definition information.", "type": "integer" } }, "required": [ "columns", "dataSourceId", "dataStoreId", "dialect", "estimatedSizePerRow", "sourceSize", "totalRows", "version" ], "type": "object", "x-versionadded": "v2.37" } ``` Information about the dataset. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columns | [string] | true | maxItems: 1000minItems: 2 | The list of the dataset column names. This field is auto-generated by the analysis job. | | dataSourceId | string,null | true | | The ID of the SQL table query and the database path, this field is auto-generated by the analysis job. | | dataStoreId | string,null | true | | The ID of the SQL data store, this field is auto-generated by the analysis job. | | dialect | string | true | | Source type data was retrieved from, this field is auto-generated by the analysis job. | | estimatedSizePerRow | integer | true | | Estimated byte size per row of the dataset, this field is auto-generated by the analysis job. | | sourceSize | integer | true | | Total dataset byte size, this field is auto-generated by the analysis job. | | totalRows | integer | true | | Total rows of the dataset, this field is auto-generated by the analysis job. | | version | integer | true | | The version of the dataset definition information. | ### Enumerated Values | Property | Value | | --- | --- | | dialect | [snowflake, bigquery, databricks, spark, postgres] | ## DatasetPropsResponse ``` { "description": "Dataset properties.", "properties": { "datasetId": { "description": "The ID of the AI Catalog dataset.", "type": "string" }, "datasetVersionId": { "description": "The version ID of the AI Catalog dataset.", "type": "string" } }, "required": [ "datasetId", "datasetVersionId" ], "type": "object", "x-versionadded": "v2.37" } ``` Dataset properties. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the AI Catalog dataset. | | datasetVersionId | string | true | | The version ID of the AI Catalog dataset. | ## DynamicDatasetPropsResponse ``` { "description": "Dynamic dataset additional properties.", "properties": { "credentialsId": { "default": null, "description": "The ID of the credentials to access the data store.", "type": [ "string", "null" ] } }, "required": [ "credentialsId" ], "type": "object", "x-versionadded": "v2.37" } ``` Dynamic dataset additional properties. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialsId | string,null | true | | The ID of the credentials to access the data store. | ## Empty ``` { "type": "object" } ``` ### Properties None ## FeaturesChunkDefinitionResponse ``` { "description": "Feature chunk definition properties.", "type": "object", "x-versionadded": "v2.37" } ``` Feature chunk definition properties. ### Properties None ## RowsChunkDefinitionResponse ``` { "description": "Row chunk definition properties.", "properties": { "datetimePartitionColumn": { "description": "Date partition column name.", "type": [ "string", "null" ] }, "isDescendingOrder": { "default": false, "description": "The sorting order.", "type": "boolean" }, "orderByColumns": { "default": [], "description": "The list of the sorting column names.", "items": { "description": "Dataset column name.", "type": "string" }, "maxItems": 10, "type": "array" }, "otvEarliestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvLatestTimestamp": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvTrainingEndDate": { "description": "The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationDownsamplingPct": { "description": "Percent by which to downsample the validation data.", "type": [ "number", "null" ] }, "otvValidationEndDate": { "description": "The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationEndDate`, one must specify `ValidationStartDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "otvValidationStartDate": { "description": "The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying `ValidationStartDate`, one must specify `ValidationEndDate`. This attribute cannot be patched for non-OTV incremental learning projects.", "format": "date-time", "type": [ "string", "null" ] }, "targetClass": { "description": "Target Class.", "type": [ "string", "null" ] }, "targetColumn": { "description": "Target column name.", "type": [ "string", "null" ] }, "userGroupColumn": { "description": "User group column name.", "type": [ "string", "null" ] } }, "required": [ "datetimePartitionColumn", "isDescendingOrder", "orderByColumns", "otvEarliestTimestamp", "otvLatestTimestamp", "otvTrainingEndDate", "otvValidationDownsamplingPct", "otvValidationEndDate", "otvValidationStartDate", "targetClass", "targetColumn", "userGroupColumn" ], "type": "object", "x-versionadded": "v2.37" } ``` Row chunk definition properties. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimePartitionColumn | string,null | true | | Date partition column name. | | isDescendingOrder | boolean | true | | The sorting order. | | orderByColumns | [string] | true | maxItems: 10 | The list of the sorting column names. | | otvEarliestTimestamp | string,null(date-time) | true | | The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationEndDate, one must specify ValidationStartDate. This attribute cannot be patched for non-OTV incremental learning projects. | | otvLatestTimestamp | string,null(date-time) | true | | The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationEndDate, one must specify ValidationStartDate. This attribute cannot be patched for non-OTV incremental learning projects. | | otvTrainingEndDate | string,null(date-time) | true | | The end date of training data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationEndDate, one must specify ValidationStartDate. This attribute cannot be patched for non-OTV incremental learning projects. | | otvValidationDownsamplingPct | number,null | true | | Percent by which to downsample the validation data. | | otvValidationEndDate | string,null(date-time) | true | | The end date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationEndDate, one must specify ValidationStartDate. This attribute cannot be patched for non-OTV incremental learning projects. | | otvValidationStartDate | string,null(date-time) | true | | The start date of validation scoring data in string format. Format can be '%Y-%m-%d %H:%M%S' or '%Y-%m-%d', the timezone defaults to UTC.When specifying ValidationStartDate, one must specify ValidationEndDate. This attribute cannot be patched for non-OTV incremental learning projects. | | targetClass | string,null | true | | Target Class. | | targetColumn | string,null | true | | Target column name. | | userGroupColumn | string,null | true | | User group column name. | --- # Deployment Configuration URL: https://docs.datarobot.com/en/docs/api/reference/public-api/deployment_configuration.html > Use the endpoints described below to manage deployment configuration. Configure capabilities of your current deployment based on the data provided, for example, training data, prediction data, or actuals. After you create and configure a deployment, you can use the deployment settings to add or update deployment functionality that wasn't configured during deployment creation. # Deployment Configuration Use the endpoints described below to manage deployment configuration. Configure capabilities of your current deployment based on the data provided, for example, training data, prediction data, or actuals. After you create and configure a deployment, you can use the deployment settings to add or update deployment functionality that wasn't configured during deployment creation. ## Retrieve deployment health settings by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/healthSettings/` Authentication requirements: `BearerAuth` Retrieve deployment health settings. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "accuracy": { "description": "Accuracy health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute accuracy health status, only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "failingThreshold": { "description": "Threshold for failing status.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "measurement": { "description": "Measurement for calculating accuracy health status.", "enum": [ "percent", "value" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "metric": { "description": "Metric used for calculating accuracy health status.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "warningThreshold": { "description": "Threshold for warning status.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "type": "object" }, "actualsTimeliness": { "description": "Predictions timeliness health setting.", "properties": { "enabled": { "description": "Indicates if timeliness status is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "expectedFrequency": { "description": "Expected frequency for receiving data as an ISO 8601 duration string.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "customMetrics": { "description": "Custom metrics health status setting.", "properties": { "failingConditions": { "description": "Conditions for failing status.", "items": { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.33" }, "warningConditions": { "description": "Conditions for warning status.", "items": { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.33" } }, "type": "object" }, "dataDrift": { "description": "Data drift health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute data drift health status, only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "driftThreshold": { "description": "The drift metric threshold above which the feature is considered as drifted.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.33" }, "excludedFeatures": { "description": "Features that are excluded from data drift status consideration.", "items": { "maxLength": 256, "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "highImportanceFailingCount": { "description": "Threshold of drifted high importance feature count for failing status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "highImportanceWarningCount": { "description": "Threshold of drifted high importance feature count for warning status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "importanceThreshold": { "description": "The feature importance threshold above which the feature is considered as important.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.33" }, "lowImportanceFailingCount": { "description": "Threshold of drifted low importance feature count for failing status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "lowImportanceWarningCount": { "description": "Threshold of drifted low importance feature count for warning status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "starredFeatures": { "description": "Features that are deemed to be of high importance, regardless of how their feature importance values compare against the threshold.", "items": { "maxLength": 256, "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "timeInterval": { "description": "Time duration used to compute data drift health status, only applicable to deployments with batch monitoring disabled.", "enum": [ "T2H", "P1D", "P7D", "P30D", "P90D", "P180D", "P365D" ], "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "fairness": { "description": "Fairness health status setting. Only available if deployment supports fairness.", "properties": { "protectedClassFailingCount": { "description": "Number of protected class below fairness threshold for failing status.", "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "protectedClassWarningCount": { "description": "Number of protected class below fairness threshold for warning status.", "minimum": 1, "type": "integer", "x-versionadded": "v2.33" } }, "type": "object" }, "predictionsTimeliness": { "description": "Predictions timeliness health setting.", "properties": { "enabled": { "description": "Indicates if timeliness status is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "expectedFrequency": { "description": "Expected frequency for receiving data as an ISO 8601 duration string.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "service": { "description": "Service health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute the service health status. Only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "failingConditions": { "description": "Conditions for \"failing\" status.", "items": { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.41" }, "timeInterval": { "description": "Amount of time used to compute service health status. Only applicable to deployments with batch monitoring disabled.", "enum": [ "T1H", "T2H", "P1D", "P7D", "P30D", "P90D", "P180D", "P365D" ], "type": "string", "x-versionadded": "v2.41" }, "warningConditions": { "description": "Conditions for \"warning\" status.", "items": { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.41" } }, "type": "object" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Deployment health settings. | HealthSettings | ## Update deployment health settings by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/healthSettings/` Authentication requirements: `BearerAuth` Update deployment health settings. ### Body parameter ``` { "properties": { "accuracy": { "description": "Accuracy health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute accuracy health status, only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "failingThreshold": { "description": "Threshold for failing status.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "measurement": { "description": "Measurement for calculating accuracy health status.", "enum": [ "percent", "value" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "metric": { "description": "Metric used for calculating accuracy health status.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "warningThreshold": { "description": "Threshold for warning status.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "type": "object" }, "actualsTimeliness": { "description": "Predictions timeliness health setting.", "properties": { "enabled": { "description": "Indicates if timeliness status is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "expectedFrequency": { "description": "Expected frequency for receiving data as an ISO 8601 duration string.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "customMetrics": { "description": "Custom metrics health status setting.", "properties": { "failingConditions": { "description": "Conditions for failing status.", "items": { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.33" }, "warningConditions": { "description": "Conditions for warning status.", "items": { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.33" } }, "type": "object" }, "dataDrift": { "description": "Data drift health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute data drift health status, only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "driftThreshold": { "description": "The drift metric threshold above which the feature is considered as drifted.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.33" }, "excludedFeatures": { "description": "Features that are excluded from data drift status consideration.", "items": { "maxLength": 256, "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "highImportanceFailingCount": { "description": "Threshold of drifted high importance feature count for failing status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "highImportanceWarningCount": { "description": "Threshold of drifted high importance feature count for warning status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "importanceThreshold": { "description": "The feature importance threshold above which the feature is considered as important.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.33" }, "lowImportanceFailingCount": { "description": "Threshold of drifted low importance feature count for failing status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "lowImportanceWarningCount": { "description": "Threshold of drifted low importance feature count for warning status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "starredFeatures": { "description": "Features that are deemed to be of high importance, regardless of how their feature importance values compare against the threshold.", "items": { "maxLength": 256, "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "timeInterval": { "description": "Time duration used to compute data drift health status, only applicable to deployments with batch monitoring disabled.", "enum": [ "T2H", "P1D", "P7D", "P30D", "P90D", "P180D", "P365D" ], "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "fairness": { "description": "Fairness health status setting. Only available if deployment supports fairness.", "properties": { "protectedClassFailingCount": { "description": "Number of protected class below fairness threshold for failing status.", "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "protectedClassWarningCount": { "description": "Number of protected class below fairness threshold for warning status.", "minimum": 1, "type": "integer", "x-versionadded": "v2.33" } }, "type": "object" }, "predictionsTimeliness": { "description": "Predictions timeliness health setting.", "properties": { "enabled": { "description": "Indicates if timeliness status is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "expectedFrequency": { "description": "Expected frequency for receiving data as an ISO 8601 duration string.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "service": { "description": "Service health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute the service health status. Only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "failingConditions": { "description": "Conditions for \"failing\" status.", "items": { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.41" }, "timeInterval": { "description": "Amount of time used to compute service health status. Only applicable to deployments with batch monitoring disabled.", "enum": [ "T1H", "T2H", "P1D", "P7D", "P30D", "P90D", "P180D", "P365D" ], "type": "string", "x-versionadded": "v2.41" }, "warningConditions": { "description": "Conditions for \"warning\" status.", "items": { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.41" } }, "type": "object" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | HealthSettings | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Updated deployment health settings. | None | ## Retrieve default deployment health settings by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/healthSettings/defaults/` Authentication requirements: `BearerAuth` Retrieve default deployment health settings. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "accuracy": { "description": "Accuracy health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute accuracy health status, only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "failingThreshold": { "description": "Threshold for failing status.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "measurement": { "description": "Measurement for calculating accuracy health status.", "enum": [ "percent", "value" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "metric": { "description": "Metric used for calculating accuracy health status.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "warningThreshold": { "description": "Threshold for warning status.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "type": "object" }, "actualsTimeliness": { "description": "Predictions timeliness health setting.", "properties": { "enabled": { "description": "Indicates if timeliness status is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "expectedFrequency": { "description": "Expected frequency for receiving data as an ISO 8601 duration string.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "customMetrics": { "description": "Custom metrics health status setting.", "properties": { "failingConditions": { "description": "Conditions for failing status.", "items": { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.33" }, "warningConditions": { "description": "Conditions for warning status.", "items": { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.33" } }, "type": "object" }, "dataDrift": { "description": "Data drift health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute data drift health status, only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "driftThreshold": { "description": "The drift metric threshold above which the feature is considered as drifted.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.33" }, "excludedFeatures": { "description": "Features that are excluded from data drift status consideration.", "items": { "maxLength": 256, "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "highImportanceFailingCount": { "description": "Threshold of drifted high importance feature count for failing status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "highImportanceWarningCount": { "description": "Threshold of drifted high importance feature count for warning status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "importanceThreshold": { "description": "The feature importance threshold above which the feature is considered as important.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.33" }, "lowImportanceFailingCount": { "description": "Threshold of drifted low importance feature count for failing status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "lowImportanceWarningCount": { "description": "Threshold of drifted low importance feature count for warning status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "starredFeatures": { "description": "Features that are deemed to be of high importance, regardless of how their feature importance values compare against the threshold.", "items": { "maxLength": 256, "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "timeInterval": { "description": "Time duration used to compute data drift health status, only applicable to deployments with batch monitoring disabled.", "enum": [ "T2H", "P1D", "P7D", "P30D", "P90D", "P180D", "P365D" ], "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "fairness": { "description": "Fairness health status setting. Only available if deployment supports fairness.", "properties": { "protectedClassFailingCount": { "description": "Number of protected class below fairness threshold for failing status.", "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "protectedClassWarningCount": { "description": "Number of protected class below fairness threshold for warning status.", "minimum": 1, "type": "integer", "x-versionadded": "v2.33" } }, "type": "object" }, "predictionsTimeliness": { "description": "Predictions timeliness health setting.", "properties": { "enabled": { "description": "Indicates if timeliness status is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "expectedFrequency": { "description": "Expected frequency for receiving data as an ISO 8601 duration string.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "service": { "description": "Service health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute the service health status. Only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "failingConditions": { "description": "Conditions for \"failing\" status.", "items": { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.41" }, "timeInterval": { "description": "Amount of time used to compute service health status. Only applicable to deployments with batch monitoring disabled.", "enum": [ "T1H", "T2H", "P1D", "P7D", "P30D", "P90D", "P180D", "P365D" ], "type": "string", "x-versionadded": "v2.41" }, "warningConditions": { "description": "Conditions for \"warning\" status.", "items": { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.41" } }, "type": "object" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Default deployment health settings. | HealthSettings | ## Retrieve secondary datasets configuration by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/model/secondaryDatasetConfiguration/` Authentication requirements: `BearerAuth` Retrieve the secondary datasets configuration used by a deployed Feature Discovery model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "DR-formatted datetime, null for legacy (before DR 6.0) db records.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorFullName": { "description": "Full name or email of the user who created this config. Null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorUserId": { "description": "ID of the user who created this config, null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "credentialIds": { "description": "The list of credentials used by the secondary datasets if the datasets used in the configuration are from a datasource.", "items": { "properties": { "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "credentialId": { "description": "ID of the credential store to be used for the given catalog version.", "type": "string" }, "url": { "description": "The URL of the datasource.", "type": [ "string", "null" ] } }, "required": [ "catalogVersionId", "credentialId" ], "type": "object" }, "type": "array" }, "featurelistId": { "description": "ID of the feature list.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "isDefault": { "description": "Whether the secondary datasets config is the default config.", "type": "boolean" }, "isDeleted": { "description": "Whether the secondary datasets config is soft-deleted.", "type": "boolean" }, "name": { "description": "Name of the secondary datasets config.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project.", "type": [ "string", "null" ] }, "secondaryDatasetConfigId": { "description": "ID of the secondary datasets configuration.", "type": "string" }, "secondaryDatasets": { "description": "The list of secondary datasets used in the config.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "description": "Policy to use by a dataset while making prediction.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } }, "required": [ "created", "creatorFullName", "creatorUserId", "featurelistId", "isDefault", "isDeleted", "name", "projectId", "secondaryDatasetConfigId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Secondary datasets configuration. | SecondaryDatasetConfigResponse | | 404 | Not Found | Deployment or secondary datasets configuration cannot be found for the deployed model. | None | ## Update the secondary datasets configuration by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/model/secondaryDatasetConfiguration/` Authentication requirements: `BearerAuth` Update the secondary datasets configuration used by the deployed feature discovery model. ### Body parameter ``` { "properties": { "credentialsIds": { "description": "The list of credentials used by the secondary datasets.", "items": { "properties": { "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "credentialId": { "description": "ID of the credential store to be used for the given catalog version.", "type": "string" }, "url": { "description": "The URL of the datasource.", "type": [ "string", "null" ] } }, "required": [ "catalogVersionId", "credentialId" ], "type": "object" }, "type": "array" }, "secondaryDatasetConfigId": { "description": "ID of the secondary datasets configuration to be used at the time of prediction.", "type": "string" } }, "required": [ "secondaryDatasetConfigId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | SecondaryDatasetConfigUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Secondary Datasets Configuration updated successfully. | None | | 403 | Forbidden | Invalid credentials for secondary datasets configuration. | None | ## List the secondary datasets configuration history by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/model/secondaryDatasetConfigurationHistory/` Authentication requirements: `BearerAuth` List all the secondary datasets configuration used by a given Feature Discovery deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | Number of items to skip. Defaults to 0 if not provided. | | limit | query | integer | true | Number of items to return, defaults to 100 if not provided. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of items.", "type": "integer" }, "data": { "description": "Secondary datasets configuration history.", "items": { "properties": { "configId": { "description": "ID of the secondary datasets configuration.", "type": "string" }, "configName": { "description": "The name of the secondary datasets config.", "type": "string" }, "updated": { "description": "Timestamp when configuration was updated on the given deployment.", "type": "string" }, "username": { "description": "The name of the user who made the update.", "type": "string" } }, "required": [ "configId", "configName", "updated", "username" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Secondary Datasets Configuration history. | SecondaryDatasetsConfigListResponse | ## Retrieve deployment settings by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/settings/` Authentication requirements: `BearerAuth` Retrieve deployment settings. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "associationId": { "description": "The association ID setting for the deployment.", "properties": { "columnNames": { "description": "List of column names used to represent an association ID.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "requiredInPredictionRequests": { "description": "Indicates whether the association ID is required in prediction requests.Note that you may not change an association ID's column names after they have been set and predictions including those columns have been made.", "type": "boolean" } }, "type": "object" }, "automaticActuals": { "description": "Automatic actuals setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether automatic actuals is enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object", "x-versionadded": "v2.25" }, "biasAndFairness": { "description": "Bias and fairness setting for the deployment.", "properties": { "fairnessMetricsSet": { "description": "A set of fairness metrics to use for calculating fairness.", "enum": [ "proportionalParity", "equalParity", "predictionBalance", "trueFavorableAndUnfavorableRateParity", "favorableAndUnfavorablePredictiveValueParity" ], "type": "string" }, "fairnessThreshold": { "description": "Threshold value of the fairness metric.", "maximum": 1, "minimum": 0, "type": "number" }, "preferableTargetValue": { "anyOf": [ { "type": "boolean" }, { "type": "integer" }, { "type": "string" } ], "description": "A target value that should be treated as a positive outcome for the prediction." }, "protectedFeatures": { "description": "A list of features to mark as protected for bias and fairness measurement.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "fairnessMetricsSet", "fairnessThreshold", "preferableTargetValue", "protectedFeatures" ], "type": "object" }, "challengerModels": { "description": "Challenger models setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether challenger models are enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "featureDrift": { "description": "The feature drift setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether feature drift tracking is enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "humility": { "description": "Humility setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether humility rules are enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "predictionIntervals": { "description": "The prediction intervals setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether prediction intervals are enabled for this deployment. ", "type": "boolean" }, "percentiles": { "description": "The percentiles used for this deployment. Currently, we support at most one percentile at a time.", "items": { "type": "integer" }, "type": "array" } }, "required": [ "enabled" ], "type": "object" }, "predictionWarning": { "description": "The prediction warning setting for the deployment.", "properties": { "customBoundaries": { "description": "Null if default boundaries for the model are used", "properties": { "lower": { "description": "All predictions less than provided value are considered anomalous.", "type": "number" }, "upper": { "description": "All predictions greater than provided value are considered anomalous.", "type": "number" } }, "required": [ "lower", "upper" ], "type": "object" }, "enabled": { "description": "Indicates whether prediction warnings are enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "predictionsByForecastDate": { "description": "Forecast date setting for the deployment.", "properties": { "columnName": { "description": "The column name in prediction datasets to be used as forecast date", "type": [ "string", "null" ] }, "datetimeFormat": { "description": "The datetime format of the forecast date column in prediction datasets.\nFor time series deployments using the datetime format `%Y-%m-%d %H:%M:%S.%f`,\nDataRobot automatically populates a `v2` in front of the timestamp format.\nDate/time values submitted in prediction data should not include this `v2` prefix.\nOther timestamp formats are not affected.\n", "type": [ "string", "null" ] }, "enabled": { "description": "Indicates whether predictions by forecast dates is enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object", "x-versionadded": "v2.35" }, "predictionsDataCollection": { "description": "The predictions data collection setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether incoming prediction requests and results should be stored in record-level storage.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "processingLimits": { "description": "Processing limits setting for the deployment.", "properties": { "interval": { "description": "A time interval that is applied when calculating processing limits.", "enum": [ "hour", "day", "week" ], "type": "string" } }, "required": [ "interval" ], "type": "object" }, "segmentAnalysis": { "description": "The segment analysis setting for the deployment.", "properties": { "attributes": { "description": "The segment attributes to be tracked. Note that only categorical columns can be specified as tracked segment attributes. The target column may not be specified", "items": { "type": "string" }, "type": "array" }, "enabled": { "description": "Indicates whether service health, drift, and accuracy are tracked for segments of prediction data.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "targetDrift": { "description": "The target drift setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether target drift tracking is enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } }, "required": [ "associationId", "featureDrift", "predictionIntervals", "targetDrift" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The deployment settings | DeploymentSettingsResponse | ## Update deployment settings by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/settings/` Authentication requirements: `BearerAuth` Updates deployment settings. ### Body parameter ``` { "properties": { "associationId": { "description": "The association ID setting for the deployment.", "properties": { "columnNames": { "description": "List of column names used to represent an association ID.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "requiredInPredictionRequests": { "description": "Indicates whether the association ID is required in prediction requests.Note that you may not change an association ID's column names after they have been set and predictions including those columns have been made.", "type": "boolean" } }, "type": "object" }, "automaticActuals": { "description": "Automatic actuals setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether automatic actuals is enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object", "x-versionadded": "v2.25" }, "biasAndFairness": { "description": "Bias and fairness setting for the deployment.", "properties": { "fairnessMetricsSet": { "description": "A set of fairness metrics to use for calculating fairness.", "enum": [ "proportionalParity", "equalParity", "predictionBalance", "trueFavorableAndUnfavorableRateParity", "favorableAndUnfavorablePredictiveValueParity" ], "type": "string" }, "fairnessThreshold": { "description": "Threshold value of the fairness metric.", "maximum": 1, "minimum": 0, "type": "number" }, "preferableTargetValue": { "anyOf": [ { "type": "boolean" }, { "type": "integer" }, { "type": "string" } ], "description": "A target value that should be treated as a positive outcome for the prediction." }, "protectedFeatures": { "description": "A list of features to mark as protected for bias and fairness measurement.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "fairnessMetricsSet", "fairnessThreshold", "preferableTargetValue", "protectedFeatures" ], "type": "object" }, "challengerModels": { "description": "Challenger models setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether challenger models are enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "featureDrift": { "description": "The feature drift setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether feature drift tracking is enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "humility": { "description": "Humility setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether humility rules are enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "predictionIntervals": { "description": "The prediction intervals setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether prediction intervals are enabled for this deployment. ", "type": "boolean" }, "percentiles": { "description": "The percentiles used for this deployment. Currently, we support at most one percentile at a time.", "items": { "type": "integer" }, "type": "array" } }, "required": [ "enabled" ], "type": "object" }, "predictionWarning": { "description": "The prediction warning setting for the deployment.", "properties": { "customBoundaries": { "description": "Null if default boundaries for the model are used", "properties": { "lower": { "description": "All predictions less than provided value are considered anomalous.", "type": "number" }, "upper": { "description": "All predictions greater than provided value are considered anomalous.", "type": "number" } }, "required": [ "lower", "upper" ], "type": "object" }, "enabled": { "description": "Indicates whether prediction warnings are enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "predictionsByForecastDate": { "description": "Forecast date setting for the deployment.", "properties": { "columnName": { "description": "The column name in prediction datasets to be used as forecast date", "type": [ "string", "null" ] }, "datetimeFormat": { "description": "The datetime format of the forecast date column in prediction datasets.\nFor time series deployments using the datetime format `%Y-%m-%d %H:%M:%S.%f`,\nDataRobot automatically populates a `v2` in front of the timestamp format.\nDate/time values submitted in prediction data should not include this `v2` prefix.\nOther timestamp formats are not affected.\n", "type": [ "string", "null" ] }, "enabled": { "description": "Indicates whether predictions by forecast dates is enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object", "x-versionadded": "v2.35" }, "predictionsDataCollection": { "description": "The predictions data collection setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether incoming prediction requests and results should be stored in record-level storage.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "processingLimits": { "description": "Processing limits setting for the deployment.", "properties": { "interval": { "description": "A time interval that is applied when calculating processing limits.", "enum": [ "hour", "day", "week" ], "type": "string" } }, "required": [ "interval" ], "type": "object" }, "segmentAnalysis": { "description": "The segment analysis setting for the deployment.", "properties": { "attributes": { "description": "The segment attributes to be tracked. Note that only categorical columns can be specified as tracked segment attributes. The target column may not be specified", "items": { "type": "string" }, "type": "array" }, "enabled": { "description": "Indicates whether service health, drift, and accuracy are tracked for segments of prediction data.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "targetDrift": { "description": "The target drift setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether target drift tracking is enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | DeploymentSettingsUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | # Schemas ## AccuracyHealth ``` { "description": "Accuracy health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute accuracy health status, only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "failingThreshold": { "description": "Threshold for failing status.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "measurement": { "description": "Measurement for calculating accuracy health status.", "enum": [ "percent", "value" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "metric": { "description": "Metric used for calculating accuracy health status.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "warningThreshold": { "description": "Threshold for warning status.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "type": "object" } ``` Accuracy health status setting. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchCount | integer | false | | Number of recent batches used to compute accuracy health status, only applicable to deployments with batch monitoring enabled. | | failingThreshold | number,null | false | | Threshold for failing status. | | measurement | string,null | false | | Measurement for calculating accuracy health status. | | metric | string,null | false | | Metric used for calculating accuracy health status. | | warningThreshold | number,null | false | | Threshold for warning status. | ### Enumerated Values | Property | Value | | --- | --- | | batchCount | [1, 5, 10, 50, 100, 1000, 10000] | | measurement | [percent, value] | | metric | [AUC, Accuracy, Balanced Accuracy, F1, FPR, FVE Binomial, FVE Gamma, FVE Multinomial, FVE Poisson, FVE Tweedie, Gamma Deviance, Gini Norm, Kolmogorov-Smirnov, LogLoss, MAE, MAPE, MCC, NPV, PPV, Poisson Deviance, R Squared, RMSE, RMSLE, Rate@Top10%, Rate@Top5%, TNR, TPR, Tweedie Deviance, WGS84 MAE, WGS84 RMSE] | ## AssociationID ``` { "description": "The association ID setting for the deployment.", "properties": { "columnNames": { "description": "List of column names used to represent an association ID.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "requiredInPredictionRequests": { "description": "Indicates whether the association ID is required in prediction requests.Note that you may not change an association ID's column names after they have been set and predictions including those columns have been made.", "type": "boolean" } }, "type": "object" } ``` The association ID setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNames | [string] | false | maxItems: 1minItems: 1 | List of column names used to represent an association ID. | | requiredInPredictionRequests | boolean | false | | Indicates whether the association ID is required in prediction requests.Note that you may not change an association ID's column names after they have been set and predictions including those columns have been made. | ## AutomaticActuals ``` { "description": "Automatic actuals setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether automatic actuals is enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object", "x-versionadded": "v2.25" } ``` Automatic actuals setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | true | | Indicates whether automatic actuals is enabled for the deployment. | ## BiasAndFairness ``` { "description": "Bias and fairness setting for the deployment.", "properties": { "fairnessMetricsSet": { "description": "A set of fairness metrics to use for calculating fairness.", "enum": [ "proportionalParity", "equalParity", "predictionBalance", "trueFavorableAndUnfavorableRateParity", "favorableAndUnfavorablePredictiveValueParity" ], "type": "string" }, "fairnessThreshold": { "description": "Threshold value of the fairness metric.", "maximum": 1, "minimum": 0, "type": "number" }, "preferableTargetValue": { "anyOf": [ { "type": "boolean" }, { "type": "integer" }, { "type": "string" } ], "description": "A target value that should be treated as a positive outcome for the prediction." }, "protectedFeatures": { "description": "A list of features to mark as protected for bias and fairness measurement.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "fairnessMetricsSet", "fairnessThreshold", "preferableTargetValue", "protectedFeatures" ], "type": "object" } ``` Bias and fairness setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fairnessMetricsSet | string | true | | A set of fairness metrics to use for calculating fairness. | | fairnessThreshold | number | true | maximum: 1minimum: 0 | Threshold value of the fairness metric. | | preferableTargetValue | any | true | | A target value that should be treated as a positive outcome for the prediction. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | protectedFeatures | [string] | true | maxItems: 10minItems: 1 | A list of features to mark as protected for bias and fairness measurement. | ### Enumerated Values | Property | Value | | --- | --- | | fairnessMetricsSet | [proportionalParity, equalParity, predictionBalance, trueFavorableAndUnfavorableRateParity, favorableAndUnfavorablePredictiveValueParity] | ## Challengers ``` { "description": "Challenger models setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether challenger models are enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } ``` Challenger models setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | true | | Indicates whether challenger models are enabled for the deployment. | ## CustomBoundaries ``` { "description": "Null if default boundaries for the model are used", "properties": { "lower": { "description": "All predictions less than provided value are considered anomalous.", "type": "number" }, "upper": { "description": "All predictions greater than provided value are considered anomalous.", "type": "number" } }, "required": [ "lower", "upper" ], "type": "object" } ``` Null if default boundaries for the model are used ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | lower | number | true | | All predictions less than provided value are considered anomalous. | | upper | number | true | | All predictions greater than provided value are considered anomalous. | ## CustomMetricCondition ``` { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categoryName | string,null | false | maxLength: 256 | Category name for the custom metric. Only relevant for categorical metrics. | | compareOperator | string | true | | Defines how the custom metric value is compared against the threshold. | | metricId | string | true | | Custom metric ID. | | threshold | number | true | | Threshold value used to determine if the condition is met. | ### Enumerated Values | Property | Value | | --- | --- | | compareOperator | [lt, lte, gt, gte] | ## CustomMetricsHealth ``` { "description": "Custom metrics health status setting.", "properties": { "failingConditions": { "description": "Conditions for failing status.", "items": { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.33" }, "warningConditions": { "description": "Conditions for warning status.", "items": { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.33" } }, "type": "object" } ``` Custom metrics health status setting. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | failingConditions | [CustomMetricCondition] | false | maxItems: 25 | Conditions for failing status. | | warningConditions | [CustomMetricCondition] | false | maxItems: 25 | Conditions for warning status. | ## DataDriftHealth ``` { "description": "Data drift health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute data drift health status, only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "driftThreshold": { "description": "The drift metric threshold above which the feature is considered as drifted.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.33" }, "excludedFeatures": { "description": "Features that are excluded from data drift status consideration.", "items": { "maxLength": 256, "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "highImportanceFailingCount": { "description": "Threshold of drifted high importance feature count for failing status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "highImportanceWarningCount": { "description": "Threshold of drifted high importance feature count for warning status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "importanceThreshold": { "description": "The feature importance threshold above which the feature is considered as important.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.33" }, "lowImportanceFailingCount": { "description": "Threshold of drifted low importance feature count for failing status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "lowImportanceWarningCount": { "description": "Threshold of drifted low importance feature count for warning status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "starredFeatures": { "description": "Features that are deemed to be of high importance, regardless of how their feature importance values compare against the threshold.", "items": { "maxLength": 256, "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "timeInterval": { "description": "Time duration used to compute data drift health status, only applicable to deployments with batch monitoring disabled.", "enum": [ "T2H", "P1D", "P7D", "P30D", "P90D", "P180D", "P365D" ], "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` Data drift health status setting. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchCount | integer | false | | Number of recent batches used to compute data drift health status, only applicable to deployments with batch monitoring enabled. | | driftThreshold | number | false | maximum: 1minimum: 0 | The drift metric threshold above which the feature is considered as drifted. | | excludedFeatures | [string] | false | maxItems: 100 | Features that are excluded from data drift status consideration. | | highImportanceFailingCount | integer,null | false | minimum: 1 | Threshold of drifted high importance feature count for failing status. | | highImportanceWarningCount | integer,null | false | minimum: 1 | Threshold of drifted high importance feature count for warning status. | | importanceThreshold | number | false | maximum: 1minimum: 0 | The feature importance threshold above which the feature is considered as important. | | lowImportanceFailingCount | integer,null | false | minimum: 1 | Threshold of drifted low importance feature count for failing status. | | lowImportanceWarningCount | integer,null | false | minimum: 1 | Threshold of drifted low importance feature count for warning status. | | starredFeatures | [string] | false | maxItems: 100 | Features that are deemed to be of high importance, regardless of how their feature importance values compare against the threshold. | | timeInterval | string | false | | Time duration used to compute data drift health status, only applicable to deployments with batch monitoring disabled. | ### Enumerated Values | Property | Value | | --- | --- | | batchCount | [1, 5, 10, 50, 100, 1000, 10000] | | timeInterval | [T2H, P1D, P7D, P30D, P90D, P180D, P365D] | ## DatasetsCredential ``` { "properties": { "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "credentialId": { "description": "ID of the credential store to be used for the given catalog version.", "type": "string" }, "url": { "description": "The URL of the datasource.", "type": [ "string", "null" ] } }, "required": [ "catalogVersionId", "credentialId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogVersionId | string | true | | ID of the catalog version. | | credentialId | string | true | | ID of the credential store to be used for the given catalog version. | | url | string,null | false | | The URL of the datasource. | ## DeploymentSecondaryDataset ``` { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "description": "Policy to use by a dataset while making prediction.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier", "snapshotPolicy" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | ID of the catalog item. | | catalogVersionId | string | true | | ID of the catalog version. | | identifier | string | true | maxLength: 45minLength: 1minLength: 1 | Short name of this table (used directly as part of generated feature names). | | snapshotPolicy | string | true | | Policy to use by a dataset while making prediction. | ### Enumerated Values | Property | Value | | --- | --- | | snapshotPolicy | [specified, latest, dynamic] | ## DeploymentSettingsResponse ``` { "properties": { "associationId": { "description": "The association ID setting for the deployment.", "properties": { "columnNames": { "description": "List of column names used to represent an association ID.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "requiredInPredictionRequests": { "description": "Indicates whether the association ID is required in prediction requests.Note that you may not change an association ID's column names after they have been set and predictions including those columns have been made.", "type": "boolean" } }, "type": "object" }, "automaticActuals": { "description": "Automatic actuals setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether automatic actuals is enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object", "x-versionadded": "v2.25" }, "biasAndFairness": { "description": "Bias and fairness setting for the deployment.", "properties": { "fairnessMetricsSet": { "description": "A set of fairness metrics to use for calculating fairness.", "enum": [ "proportionalParity", "equalParity", "predictionBalance", "trueFavorableAndUnfavorableRateParity", "favorableAndUnfavorablePredictiveValueParity" ], "type": "string" }, "fairnessThreshold": { "description": "Threshold value of the fairness metric.", "maximum": 1, "minimum": 0, "type": "number" }, "preferableTargetValue": { "anyOf": [ { "type": "boolean" }, { "type": "integer" }, { "type": "string" } ], "description": "A target value that should be treated as a positive outcome for the prediction." }, "protectedFeatures": { "description": "A list of features to mark as protected for bias and fairness measurement.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "fairnessMetricsSet", "fairnessThreshold", "preferableTargetValue", "protectedFeatures" ], "type": "object" }, "challengerModels": { "description": "Challenger models setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether challenger models are enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "featureDrift": { "description": "The feature drift setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether feature drift tracking is enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "humility": { "description": "Humility setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether humility rules are enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "predictionIntervals": { "description": "The prediction intervals setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether prediction intervals are enabled for this deployment. ", "type": "boolean" }, "percentiles": { "description": "The percentiles used for this deployment. Currently, we support at most one percentile at a time.", "items": { "type": "integer" }, "type": "array" } }, "required": [ "enabled" ], "type": "object" }, "predictionWarning": { "description": "The prediction warning setting for the deployment.", "properties": { "customBoundaries": { "description": "Null if default boundaries for the model are used", "properties": { "lower": { "description": "All predictions less than provided value are considered anomalous.", "type": "number" }, "upper": { "description": "All predictions greater than provided value are considered anomalous.", "type": "number" } }, "required": [ "lower", "upper" ], "type": "object" }, "enabled": { "description": "Indicates whether prediction warnings are enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "predictionsByForecastDate": { "description": "Forecast date setting for the deployment.", "properties": { "columnName": { "description": "The column name in prediction datasets to be used as forecast date", "type": [ "string", "null" ] }, "datetimeFormat": { "description": "The datetime format of the forecast date column in prediction datasets.\nFor time series deployments using the datetime format `%Y-%m-%d %H:%M:%S.%f`,\nDataRobot automatically populates a `v2` in front of the timestamp format.\nDate/time values submitted in prediction data should not include this `v2` prefix.\nOther timestamp formats are not affected.\n", "type": [ "string", "null" ] }, "enabled": { "description": "Indicates whether predictions by forecast dates is enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object", "x-versionadded": "v2.35" }, "predictionsDataCollection": { "description": "The predictions data collection setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether incoming prediction requests and results should be stored in record-level storage.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "processingLimits": { "description": "Processing limits setting for the deployment.", "properties": { "interval": { "description": "A time interval that is applied when calculating processing limits.", "enum": [ "hour", "day", "week" ], "type": "string" } }, "required": [ "interval" ], "type": "object" }, "segmentAnalysis": { "description": "The segment analysis setting for the deployment.", "properties": { "attributes": { "description": "The segment attributes to be tracked. Note that only categorical columns can be specified as tracked segment attributes. The target column may not be specified", "items": { "type": "string" }, "type": "array" }, "enabled": { "description": "Indicates whether service health, drift, and accuracy are tracked for segments of prediction data.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "targetDrift": { "description": "The target drift setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether target drift tracking is enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } }, "required": [ "associationId", "featureDrift", "predictionIntervals", "targetDrift" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associationId | AssociationID | true | | The association ID setting for the deployment. | | automaticActuals | AutomaticActuals | false | | Automatic actuals setting for the deployment. | | biasAndFairness | BiasAndFairness | false | | Bias and fairness setting for the deployment. | | challengerModels | Challengers | false | | Challenger models setting for the deployment. | | featureDrift | FeatureDriftSetting | true | | The feature drift setting for the deployment. | | humility | Humility | false | | Humility setting for the deployment. | | predictionIntervals | PredictionIntervals | true | | The prediction intervals setting for the deployment. | | predictionWarning | PredictionWarning | false | | The prediction warning setting for the deployment. | | predictionsByForecastDate | PredictionsByForecastDate | false | | Forecast date setting for the deployment. | | predictionsDataCollection | PredictionsDataCollection | false | | The predictions data collection setting for the deployment. | | processingLimits | ProcessingLimits | false | | Processing limits setting for the deployment. | | segmentAnalysis | SegmentAnalysis | false | | The segment analysis setting for the deployment. | | targetDrift | TargetDriftSetting | true | | The target drift setting for the deployment. | ## DeploymentSettingsUpdate ``` { "properties": { "associationId": { "description": "The association ID setting for the deployment.", "properties": { "columnNames": { "description": "List of column names used to represent an association ID.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "requiredInPredictionRequests": { "description": "Indicates whether the association ID is required in prediction requests.Note that you may not change an association ID's column names after they have been set and predictions including those columns have been made.", "type": "boolean" } }, "type": "object" }, "automaticActuals": { "description": "Automatic actuals setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether automatic actuals is enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object", "x-versionadded": "v2.25" }, "biasAndFairness": { "description": "Bias and fairness setting for the deployment.", "properties": { "fairnessMetricsSet": { "description": "A set of fairness metrics to use for calculating fairness.", "enum": [ "proportionalParity", "equalParity", "predictionBalance", "trueFavorableAndUnfavorableRateParity", "favorableAndUnfavorablePredictiveValueParity" ], "type": "string" }, "fairnessThreshold": { "description": "Threshold value of the fairness metric.", "maximum": 1, "minimum": 0, "type": "number" }, "preferableTargetValue": { "anyOf": [ { "type": "boolean" }, { "type": "integer" }, { "type": "string" } ], "description": "A target value that should be treated as a positive outcome for the prediction." }, "protectedFeatures": { "description": "A list of features to mark as protected for bias and fairness measurement.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "fairnessMetricsSet", "fairnessThreshold", "preferableTargetValue", "protectedFeatures" ], "type": "object" }, "challengerModels": { "description": "Challenger models setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether challenger models are enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "featureDrift": { "description": "The feature drift setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether feature drift tracking is enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "humility": { "description": "Humility setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether humility rules are enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "predictionIntervals": { "description": "The prediction intervals setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether prediction intervals are enabled for this deployment. ", "type": "boolean" }, "percentiles": { "description": "The percentiles used for this deployment. Currently, we support at most one percentile at a time.", "items": { "type": "integer" }, "type": "array" } }, "required": [ "enabled" ], "type": "object" }, "predictionWarning": { "description": "The prediction warning setting for the deployment.", "properties": { "customBoundaries": { "description": "Null if default boundaries for the model are used", "properties": { "lower": { "description": "All predictions less than provided value are considered anomalous.", "type": "number" }, "upper": { "description": "All predictions greater than provided value are considered anomalous.", "type": "number" } }, "required": [ "lower", "upper" ], "type": "object" }, "enabled": { "description": "Indicates whether prediction warnings are enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "predictionsByForecastDate": { "description": "Forecast date setting for the deployment.", "properties": { "columnName": { "description": "The column name in prediction datasets to be used as forecast date", "type": [ "string", "null" ] }, "datetimeFormat": { "description": "The datetime format of the forecast date column in prediction datasets.\nFor time series deployments using the datetime format `%Y-%m-%d %H:%M:%S.%f`,\nDataRobot automatically populates a `v2` in front of the timestamp format.\nDate/time values submitted in prediction data should not include this `v2` prefix.\nOther timestamp formats are not affected.\n", "type": [ "string", "null" ] }, "enabled": { "description": "Indicates whether predictions by forecast dates is enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object", "x-versionadded": "v2.35" }, "predictionsDataCollection": { "description": "The predictions data collection setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether incoming prediction requests and results should be stored in record-level storage.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "processingLimits": { "description": "Processing limits setting for the deployment.", "properties": { "interval": { "description": "A time interval that is applied when calculating processing limits.", "enum": [ "hour", "day", "week" ], "type": "string" } }, "required": [ "interval" ], "type": "object" }, "segmentAnalysis": { "description": "The segment analysis setting for the deployment.", "properties": { "attributes": { "description": "The segment attributes to be tracked. Note that only categorical columns can be specified as tracked segment attributes. The target column may not be specified", "items": { "type": "string" }, "type": "array" }, "enabled": { "description": "Indicates whether service health, drift, and accuracy are tracked for segments of prediction data.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" }, "targetDrift": { "description": "The target drift setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether target drift tracking is enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associationId | AssociationID | false | | The association ID setting for the deployment. | | automaticActuals | AutomaticActuals | false | | Automatic actuals setting for the deployment. | | biasAndFairness | BiasAndFairness | false | | Bias and fairness setting for the deployment. | | challengerModels | Challengers | false | | Challenger models setting for the deployment. | | featureDrift | FeatureDriftSetting | false | | The feature drift setting for the deployment. | | humility | Humility | false | | Humility setting for the deployment. | | predictionIntervals | PredictionIntervals | false | | The prediction intervals setting for the deployment. | | predictionWarning | PredictionWarning | false | | The prediction warning setting for the deployment. | | predictionsByForecastDate | PredictionsByForecastDate | false | | Forecast date setting for the deployment. | | predictionsDataCollection | PredictionsDataCollection | false | | The predictions data collection setting for the deployment. | | processingLimits | ProcessingLimits | false | | Processing limits setting for the deployment. | | segmentAnalysis | SegmentAnalysis | false | | The segment analysis setting for the deployment. | | targetDrift | TargetDriftSetting | false | | The target drift setting for the deployment. | ## FairnessHealth ``` { "description": "Fairness health status setting. Only available if deployment supports fairness.", "properties": { "protectedClassFailingCount": { "description": "Number of protected class below fairness threshold for failing status.", "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "protectedClassWarningCount": { "description": "Number of protected class below fairness threshold for warning status.", "minimum": 1, "type": "integer", "x-versionadded": "v2.33" } }, "type": "object" } ``` Fairness health status setting. Only available if deployment supports fairness. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | protectedClassFailingCount | integer | false | minimum: 1 | Number of protected class below fairness threshold for failing status. | | protectedClassWarningCount | integer | false | minimum: 1 | Number of protected class below fairness threshold for warning status. | ## FeatureDriftSetting ``` { "description": "The feature drift setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether feature drift tracking is enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } ``` The feature drift setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | true | | Indicates whether feature drift tracking is enabled for this deployment. | ## HealthSettings ``` { "properties": { "accuracy": { "description": "Accuracy health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute accuracy health status, only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "failingThreshold": { "description": "Threshold for failing status.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "measurement": { "description": "Measurement for calculating accuracy health status.", "enum": [ "percent", "value" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "metric": { "description": "Metric used for calculating accuracy health status.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "warningThreshold": { "description": "Threshold for warning status.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "type": "object" }, "actualsTimeliness": { "description": "Predictions timeliness health setting.", "properties": { "enabled": { "description": "Indicates if timeliness status is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "expectedFrequency": { "description": "Expected frequency for receiving data as an ISO 8601 duration string.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "customMetrics": { "description": "Custom metrics health status setting.", "properties": { "failingConditions": { "description": "Conditions for failing status.", "items": { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.33" }, "warningConditions": { "description": "Conditions for warning status.", "items": { "properties": { "categoryName": { "description": "Category name for the custom metric. Only relevant for categorical metrics.", "maxLength": 256, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "compareOperator": { "description": "Defines how the custom metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string", "x-versionadded": "v2.33" }, "metricId": { "description": "Custom metric ID.", "type": "string", "x-versionadded": "v2.33" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number", "x-versionadded": "v2.33" } }, "required": [ "compareOperator", "metricId", "threshold" ], "type": "object" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.33" } }, "type": "object" }, "dataDrift": { "description": "Data drift health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute data drift health status, only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "driftThreshold": { "description": "The drift metric threshold above which the feature is considered as drifted.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.33" }, "excludedFeatures": { "description": "Features that are excluded from data drift status consideration.", "items": { "maxLength": 256, "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "highImportanceFailingCount": { "description": "Threshold of drifted high importance feature count for failing status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "highImportanceWarningCount": { "description": "Threshold of drifted high importance feature count for warning status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "importanceThreshold": { "description": "The feature importance threshold above which the feature is considered as important.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.33" }, "lowImportanceFailingCount": { "description": "Threshold of drifted low importance feature count for failing status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "lowImportanceWarningCount": { "description": "Threshold of drifted low importance feature count for warning status.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "starredFeatures": { "description": "Features that are deemed to be of high importance, regardless of how their feature importance values compare against the threshold.", "items": { "maxLength": 256, "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "timeInterval": { "description": "Time duration used to compute data drift health status, only applicable to deployments with batch monitoring disabled.", "enum": [ "T2H", "P1D", "P7D", "P30D", "P90D", "P180D", "P365D" ], "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "fairness": { "description": "Fairness health status setting. Only available if deployment supports fairness.", "properties": { "protectedClassFailingCount": { "description": "Number of protected class below fairness threshold for failing status.", "minimum": 1, "type": "integer", "x-versionadded": "v2.33" }, "protectedClassWarningCount": { "description": "Number of protected class below fairness threshold for warning status.", "minimum": 1, "type": "integer", "x-versionadded": "v2.33" } }, "type": "object" }, "predictionsTimeliness": { "description": "Predictions timeliness health setting.", "properties": { "enabled": { "description": "Indicates if timeliness status is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "expectedFrequency": { "description": "Expected frequency for receiving data as an ISO 8601 duration string.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" }, "service": { "description": "Service health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute the service health status. Only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "failingConditions": { "description": "Conditions for \"failing\" status.", "items": { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.41" }, "timeInterval": { "description": "Amount of time used to compute service health status. Only applicable to deployments with batch monitoring disabled.", "enum": [ "T1H", "T2H", "P1D", "P7D", "P30D", "P90D", "P180D", "P365D" ], "type": "string", "x-versionadded": "v2.41" }, "warningConditions": { "description": "Conditions for \"warning\" status.", "items": { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.41" } }, "type": "object" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracy | AccuracyHealth | false | | Accuracy health status setting. | | actualsTimeliness | TimelinessSettings | false | | Predictions timeliness health setting. | | customMetrics | CustomMetricsHealth | false | | Custom metrics health status setting. | | dataDrift | DataDriftHealth | false | | Data drift health status setting. | | fairness | FairnessHealth | false | | Fairness health status setting. Only available if deployment supports fairness. | | predictionsTimeliness | TimelinessSettings | false | | Predictions timeliness health setting. | | service | ServiceHealth | false | | Service health status setting. | ## Humility ``` { "description": "Humility setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether humility rules are enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } ``` Humility setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | true | | Indicates whether humility rules are enabled for the deployment. | ## PredictionIntervals ``` { "description": "The prediction intervals setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether prediction intervals are enabled for this deployment. ", "type": "boolean" }, "percentiles": { "description": "The percentiles used for this deployment. Currently, we support at most one percentile at a time.", "items": { "type": "integer" }, "type": "array" } }, "required": [ "enabled" ], "type": "object" } ``` The prediction intervals setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | true | | Indicates whether prediction intervals are enabled for this deployment. | | percentiles | [integer] | false | | The percentiles used for this deployment. Currently, we support at most one percentile at a time. | ## PredictionWarning ``` { "description": "The prediction warning setting for the deployment.", "properties": { "customBoundaries": { "description": "Null if default boundaries for the model are used", "properties": { "lower": { "description": "All predictions less than provided value are considered anomalous.", "type": "number" }, "upper": { "description": "All predictions greater than provided value are considered anomalous.", "type": "number" } }, "required": [ "lower", "upper" ], "type": "object" }, "enabled": { "description": "Indicates whether prediction warnings are enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } ``` The prediction warning setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customBoundaries | CustomBoundaries | false | | Null if default boundaries for the model are used | | enabled | boolean | true | | Indicates whether prediction warnings are enabled for this deployment. | ## PredictionsByForecastDate ``` { "description": "Forecast date setting for the deployment.", "properties": { "columnName": { "description": "The column name in prediction datasets to be used as forecast date", "type": [ "string", "null" ] }, "datetimeFormat": { "description": "The datetime format of the forecast date column in prediction datasets.\nFor time series deployments using the datetime format `%Y-%m-%d %H:%M:%S.%f`,\nDataRobot automatically populates a `v2` in front of the timestamp format.\nDate/time values submitted in prediction data should not include this `v2` prefix.\nOther timestamp formats are not affected.\n", "type": [ "string", "null" ] }, "enabled": { "description": "Indicates whether predictions by forecast dates is enabled for the deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object", "x-versionadded": "v2.35" } ``` Forecast date setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnName | string,null | false | | The column name in prediction datasets to be used as forecast date | | datetimeFormat | string,null | false | | The datetime format of the forecast date column in prediction datasets.For time series deployments using the datetime format %Y-%m-%d %H:%M:%S.%f,DataRobot automatically populates a v2 in front of the timestamp format.Date/time values submitted in prediction data should not include this v2 prefix.Other timestamp formats are not affected. | | enabled | boolean | true | | Indicates whether predictions by forecast dates is enabled for the deployment. | ## PredictionsDataCollection ``` { "description": "The predictions data collection setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether incoming prediction requests and results should be stored in record-level storage.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } ``` The predictions data collection setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | true | | Indicates whether incoming prediction requests and results should be stored in record-level storage. | ## ProcessingLimits ``` { "description": "Processing limits setting for the deployment.", "properties": { "interval": { "description": "A time interval that is applied when calculating processing limits.", "enum": [ "hour", "day", "week" ], "type": "string" } }, "required": [ "interval" ], "type": "object" } ``` Processing limits setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | interval | string | true | | A time interval that is applied when calculating processing limits. | ### Enumerated Values | Property | Value | | --- | --- | | interval | [hour, day, week] | ## SecondaryDatasetConfigResponse ``` { "properties": { "created": { "description": "DR-formatted datetime, null for legacy (before DR 6.0) db records.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorFullName": { "description": "Full name or email of the user who created this config. Null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorUserId": { "description": "ID of the user who created this config, null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "credentialIds": { "description": "The list of credentials used by the secondary datasets if the datasets used in the configuration are from a datasource.", "items": { "properties": { "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "credentialId": { "description": "ID of the credential store to be used for the given catalog version.", "type": "string" }, "url": { "description": "The URL of the datasource.", "type": [ "string", "null" ] } }, "required": [ "catalogVersionId", "credentialId" ], "type": "object" }, "type": "array" }, "featurelistId": { "description": "ID of the feature list.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "isDefault": { "description": "Whether the secondary datasets config is the default config.", "type": "boolean" }, "isDeleted": { "description": "Whether the secondary datasets config is soft-deleted.", "type": "boolean" }, "name": { "description": "Name of the secondary datasets config.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project.", "type": [ "string", "null" ] }, "secondaryDatasetConfigId": { "description": "ID of the secondary datasets configuration.", "type": "string" }, "secondaryDatasets": { "description": "The list of secondary datasets used in the config.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "description": "Policy to use by a dataset while making prediction.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } }, "required": [ "created", "creatorFullName", "creatorUserId", "featurelistId", "isDefault", "isDeleted", "name", "projectId", "secondaryDatasetConfigId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string,null(date-time) | true | | DR-formatted datetime, null for legacy (before DR 6.0) db records. | | creatorFullName | string,null | true | | Full name or email of the user who created this config. Null for legacy (before DR 6.0) db records. | | creatorUserId | string,null | true | | ID of the user who created this config, null for legacy (before DR 6.0) db records. | | credentialIds | [DatasetsCredential] | false | | The list of credentials used by the secondary datasets if the datasets used in the configuration are from a datasource. | | featurelistId | string,null | true | | ID of the feature list. | | isDefault | boolean | true | | Whether the secondary datasets config is the default config. | | isDeleted | boolean | true | | Whether the secondary datasets config is soft-deleted. | | name | string,null | true | | Name of the secondary datasets config. | | projectId | string,null | true | | ID of the project. | | secondaryDatasetConfigId | string | true | | ID of the secondary datasets configuration. | | secondaryDatasets | [DeploymentSecondaryDataset] | false | | The list of secondary datasets used in the config. | ## SecondaryDatasetConfigUpdate ``` { "properties": { "credentialsIds": { "description": "The list of credentials used by the secondary datasets.", "items": { "properties": { "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "credentialId": { "description": "ID of the credential store to be used for the given catalog version.", "type": "string" }, "url": { "description": "The URL of the datasource.", "type": [ "string", "null" ] } }, "required": [ "catalogVersionId", "credentialId" ], "type": "object" }, "type": "array" }, "secondaryDatasetConfigId": { "description": "ID of the secondary datasets configuration to be used at the time of prediction.", "type": "string" } }, "required": [ "secondaryDatasetConfigId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialsIds | [DatasetsCredential] | false | | The list of credentials used by the secondary datasets. | | secondaryDatasetConfigId | string | true | | ID of the secondary datasets configuration to be used at the time of prediction. | ## SecondaryDatasetsConfig ``` { "properties": { "configId": { "description": "ID of the secondary datasets configuration.", "type": "string" }, "configName": { "description": "The name of the secondary datasets config.", "type": "string" }, "updated": { "description": "Timestamp when configuration was updated on the given deployment.", "type": "string" }, "username": { "description": "The name of the user who made the update.", "type": "string" } }, "required": [ "configId", "configName", "updated", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configId | string | true | | ID of the secondary datasets configuration. | | configName | string | true | | The name of the secondary datasets config. | | updated | string | true | | Timestamp when configuration was updated on the given deployment. | | username | string | true | | The name of the user who made the update. | ## SecondaryDatasetsConfigListResponse ``` { "properties": { "count": { "description": "Number of items.", "type": "integer" }, "data": { "description": "Secondary datasets configuration history.", "items": { "properties": { "configId": { "description": "ID of the secondary datasets configuration.", "type": "string" }, "configName": { "description": "The name of the secondary datasets config.", "type": "string" }, "updated": { "description": "Timestamp when configuration was updated on the given deployment.", "type": "string" }, "username": { "description": "The name of the user who made the update.", "type": "string" } }, "required": [ "configId", "configName", "updated", "username" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Number of items. | | data | [SecondaryDatasetsConfig] | true | | Secondary datasets configuration history. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | ## SegmentAnalysis ``` { "description": "The segment analysis setting for the deployment.", "properties": { "attributes": { "description": "The segment attributes to be tracked. Note that only categorical columns can be specified as tracked segment attributes. The target column may not be specified", "items": { "type": "string" }, "type": "array" }, "enabled": { "description": "Indicates whether service health, drift, and accuracy are tracked for segments of prediction data.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } ``` The segment analysis setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributes | [string] | false | | The segment attributes to be tracked. Note that only categorical columns can be specified as tracked segment attributes. The target column may not be specified | | enabled | boolean | true | | Indicates whether service health, drift, and accuracy are tracked for segments of prediction data. | ## ServiceHealth ``` { "description": "Service health status setting.", "properties": { "batchCount": { "description": "Number of recent batches used to compute the service health status. Only applicable to deployments with batch monitoring enabled.", "enum": [ 1, 5, 10, 50, 100, 1000, 10000 ], "type": "integer", "x-versionadded": "v2.33" }, "failingConditions": { "description": "Conditions for \"failing\" status.", "items": { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.41" }, "timeInterval": { "description": "Amount of time used to compute service health status. Only applicable to deployments with batch monitoring disabled.", "enum": [ "T1H", "T2H", "P1D", "P7D", "P30D", "P90D", "P180D", "P365D" ], "type": "string", "x-versionadded": "v2.41" }, "warningConditions": { "description": "Conditions for \"warning\" status.", "items": { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.41" } }, "type": "object" } ``` Service health status setting. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchCount | integer | false | | Number of recent batches used to compute the service health status. Only applicable to deployments with batch monitoring enabled. | | failingConditions | [ServiceHealthMetricCondition] | false | maxItems: 25 | Conditions for "failing" status. | | timeInterval | string | false | | Amount of time used to compute service health status. Only applicable to deployments with batch monitoring disabled. | | warningConditions | [ServiceHealthMetricCondition] | false | maxItems: 25 | Conditions for "warning" status. | ### Enumerated Values | Property | Value | | --- | --- | | batchCount | [1, 5, 10, 50, 100, 1000, 10000] | | timeInterval | [T1H, T2H, P1D, P7D, P30D, P90D, P180D, P365D] | ## ServiceHealthMetricCondition ``` { "properties": { "compareOperator": { "description": "Defines how the metric value is compared against the threshold.", "enum": [ "lt", "lte", "gt", "gte" ], "type": "string" }, "metric": { "description": "Service health metric to evaluate.", "enum": [ "dataErrorCount", "serverErrorCount", "dataErrorPercent", "serverErrorPercent", "medianResponseTimeMs", "medianExecutionTimeMs" ], "type": "string" }, "threshold": { "description": "Threshold value used to determine if the condition is met.", "type": "number" } }, "required": [ "compareOperator", "metric", "threshold" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | compareOperator | string | true | | Defines how the metric value is compared against the threshold. | | metric | string | true | | Service health metric to evaluate. | | threshold | number | true | | Threshold value used to determine if the condition is met. | ### Enumerated Values | Property | Value | | --- | --- | | compareOperator | [lt, lte, gt, gte] | | metric | [dataErrorCount, serverErrorCount, dataErrorPercent, serverErrorPercent, medianResponseTimeMs, medianExecutionTimeMs] | ## TargetDriftSetting ``` { "description": "The target drift setting for the deployment.", "properties": { "enabled": { "description": "Indicates whether target drift tracking is enabled for this deployment.", "type": "boolean" } }, "required": [ "enabled" ], "type": "object" } ``` The target drift setting for the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | true | | Indicates whether target drift tracking is enabled for this deployment. | ## TimelinessSettings ``` { "description": "Predictions timeliness health setting.", "properties": { "enabled": { "description": "Indicates if timeliness status is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "expectedFrequency": { "description": "Expected frequency for receiving data as an ISO 8601 duration string.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` Predictions timeliness health setting. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | false | | Indicates if timeliness status is enabled. | | expectedFrequency | string | false | | Expected frequency for receiving data as an ISO 8601 duration string. | --- # Deployment management URL: https://docs.datarobot.com/en/docs/api/reference/public-api/deployment_management.html > Use the endpoints described below to manage deployments. # Deployment management Use the endpoints described below to manage deployments. ## List custom model deployments Operation path: `GET /api/v2/customModelDeployments/` Authentication requirements: `BearerAuth` List of model deployments for user sorted by creation time descending. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | customModelIds | query | any | false | List of ID's of the custom model which model deployments will be retrieved. | | environmentIds | query | any | false | List of ID's of the execution environment which model deployments will be retrieved. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom model deployments.", "items": { "properties": { "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelImageId": { "description": "The id of the custom model image associated with this deployment.", "type": "string" }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "deployed": { "description": "ISO-8601 timestamp of when deployment was created.", "type": "string" }, "deployedBy": { "description": "The username of the user that deployed the custom model.", "type": "string" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "imageType": { "description": "The type of the image, either customModelImage if the testing attempt is using a customModelImage as its model or customModelVersion if the testing attempt is using a customModelVersion with dependency management.", "enum": [ "customModelImage", "customModelVersion" ], "type": "string" }, "label": { "description": "User-friendly name of the model deployment.", "type": "string" }, "status": { "description": "Deployment status.", "enum": [ "active", "archived", "errored", "inactive", "launching", "replacingModel", "stopping" ], "type": "string" }, "testingStatus": { "description": "Latest testing status of the deployed custom model image.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "customModel", "customModelImageId", "customModelVersion", "deployed", "deployedBy", "executionEnvironment", "executionEnvironmentVersion", "id", "label", "status", "testingStatus" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomModelDeploymentListResponse | | 400 | Bad Request | Query parameters are invalid. | None | ## Get custom model deployment limits Operation path: `GET /api/v2/customModelDeployments/limitState/` Authentication requirements: `BearerAuth` Retrieve custom model deployment limit state: maximum allowed, current count, and whether the limit has been reached. ### Example responses > 200 Response ``` { "properties": { "customModelDeploymentLimitUsageCount": { "description": "Number of custom model deployments that count toward the limit (current limit usage).", "type": "integer" }, "maxCustomDeploymentsLimit": { "description": "Limit info for the maximum number of custom model deployments.", "properties": { "limitReached": { "description": "Whether the custom model deployment limit has been reached.", "type": "boolean" }, "value": { "description": "The maximum number of custom model deployments allowed (null if unlimited).", "type": [ "integer", "null" ] } }, "required": [ "limitReached" ], "type": "object", "x-versionadded": "v2.42" } }, "required": [ "customModelDeploymentLimitUsageCount", "maxCustomDeploymentsLimit" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK. | CustomModelDeploymentsLimitsResponse | | 404 | Not Found | User not found or user does not have an organization. | None | ## Get custom model log by deployment ID Operation path: `GET /api/v2/customModelDeployments/{deploymentId}/logs/` Authentication requirements: `BearerAuth` Retrieve the logs generated during deployment of the custom model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | The ID of the custom model deployment. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a text file with the contents of the Custom Model deployment log. | None | | 404 | Not Found | No logs found. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename of the log file ("attachment;filename=deployment-.log"). | ## Create logs by id Operation path: `POST /api/v2/customModelDeployments/{deploymentId}/logs/` Authentication requirements: `BearerAuth` Request logs from a custom deployed model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | The ID of the custom model deployment. | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Accepted: request placed to a queue for processing. | CustomModelAsyncOperationResponse | | 404 | Not Found | Deployment not found or model is not a Custom Model. | None | | 422 | Unprocessable Entity | Deployed model is not a Custom Model. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking async job status. | ## Retrieve logs by id Operation path: `GET /api/v2/customModelDeployments/{deploymentId}/logs/{logId}/` Authentication requirements: `BearerAuth` Retrieve the logs generated during predictions on the deployed custom model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | The ID of the custom model deployment. | | logId | path | string | true | The ID of the custom model deployment log. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a text file with the contents of the Custom Model deployment log. | None | | 404 | Not Found | No logs found. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename of the log file ("attachment;filename=deployment-.log"). | ## List deleted deployments Operation path: `GET /api/v2/deletedDeployments/` Authentication requirements: `BearerAuth` List deleted deployments.Only available as part of an enterprise (on-prem) installation. Requires a CAN_DELETE_APP_PROJECTS permission to execute. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of deleted deployments to skip. | | limit | query | integer | true | The number of deleted deployments to return. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of deleted deployments.", "items": { "properties": { "deletedAt": { "description": "The date and time of when the deployment was deleted, in ISO 8601 format.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the deployment.", "type": "string" }, "label": { "description": "Label of the deployment.", "maxLength": 512, "type": "string" } }, "required": [ "deletedAt", "id", "label" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The deleted deployments | DeletedDeploymentListResponse | ## Erase deleted deployments Operation path: `PATCH /api/v2/deletedDeployments/` Authentication requirements: `BearerAuth` Permanently erase data for deleted deployments. Only available as part of an on-premise or private/hybrid cloud deployment. Requires a CAN_DELETE_APP_PROJECTS permission to execute. ### Body parameter ``` { "properties": { "action": { "description": "Action to perform on these deleted deployments.", "enum": [ "PermanentlyErase" ], "type": "string" }, "deploymentIds": { "description": "ID of a list of deleted deployments to perform action on.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "action", "deploymentIds" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DeploymentPermanentDelete | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking deployment permanently erase job status. | ## List deployments Operation path: `GET /api/v2/deployments/` Authentication requirements: `BearerAuth` List deployments a user can view. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of deployments to skip. Defaults to 0. | | limit | query | integer | true | The number of deployments (greater than zero, max 100) to return. Defaults to 20. | | orderBy | query | string | false | The order to sort the deployments.Defaults to order by deployment last prediction timestamp in descending order. | | search | query | string | false | Case insensitive search against deployment's label and description. | | serviceHealth | query | array[string] | false | Filters deployments by their service health status. | | modelHealth | query | array[string] | false | Filters deployments by their model health status. | | accuracyHealth | query | array[string] | false | Filters deployments by their accuracy health status. | | role | query | string | false | Filter deployments to only those that the authenticated user has the specified role for. | | status | query | array[string] | false | Filters deployments by their status | | importance | query | array[string] | false | Filters deployments by their importance | | lastPredictionTimestampStart | query | string(date-time) | false | Only include deployments that have had a prediction request on or after the specified timestamp. | | lastPredictionTimestampEnd | query | string(date-time) | false | Only include deployments that have had a prediction request before the specified timestamp. | | predictionUsageDailyAvgGreaterThan | query | integer | false | only include deployments that have had more than the specified number of predictions per day on average over the past week. | | predictionUsageDailyAvgLessThan | query | integer | false | Only include deployments that have had fewer than the specified number of predictions per day on average over the past week. | | defaultPredictionServerId | query | array[string] | false | Filter deployments to those whose default prediction server has the specified id. | | buildEnvironmentType | query | array[string] | false | Filter deployments based on the type of their current model's build environment type. | | executionEnvironmentType | query | array[string] | false | Filter deployments based on the type of their execution environment. | | predictionEnvironmentPlatform | query | array[string] | false | Filter deployments based on prediction environment platform | | createdByMe | query | string | false | Filter deployments to those created by the current user. | | createdBy | query | string | false | Filters deployments by those created by the given user. | | championModelExecutionType | query | string | false | Filter deployments by the execution type of the champion model. | | championModelTargetType | query | string | false | Filter deployments by the target type of the champion model. Example: Binary | | tagKeys | query | any | false | List of tag keys to filter for. If multiple values are specified, deployments with tags that match any of the values will be returned. | | tagValues | query | any | false | List of tag values to filter for. If multiple values are specified, deployments with tags that match any of the values will be returned. | | isA2AAgent | query | string | false | Filter deployments by whether the deployment is an A2A agent. When true, returns only deployments that are A2A agents. When false, returns only deployments that are not A2A agents. | | predictionEnvironmentTagKeys | query | any | false | List of tag keys. If specified, only deployments with prediction environments that have tags matching any of the keys will be returned. | | predictionEnvironmentTagValues | query | any | false | List of tag values. If specified, only deployments with prediction environments that have tags matching any of the values will be returned. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [label, -label, serviceHealth, -serviceHealth, modelHealth, -modelHealth, accuracyHealth, -accuracyHealth, recentPredictions, -recentPredictions, lastPredictionTimestamp, -lastPredictionTimestamp, currentModelDeployedTimestamp, -currentModelDeployedTimestamp, createdAtTimestamp, -createdAtTimestamp, importance, -importance, fairnessHealth, -fairnessHealth, customMetricsHealth, -customMetricsHealth, actualsTimelinessHealth, -actualsTimelinessHealth, predictionsTimelinessHealth, -predictionsTimelinessHealth] | | serviceHealth | [failing, not_started, passing, unavailable, unknown, warning] | | modelHealth | [failing, not_started, passing, unavailable, unknown, warning] | | accuracyHealth | [failing, not_started, passing, unavailable, unknown, warning] | | role | [OWNER, USER] | | status | [active, archived, errored, inactive, launching, replacingModel, stopping] | | importance | [CRITICAL, HIGH, MODERATE, LOW] | | buildEnvironmentType | [DataRobot, Python, R, Java, Julia, Legacy, Other] | | executionEnvironmentType | [datarobot, external] | | predictionEnvironmentPlatform | [aws, gcp, azure, onPremise, datarobot, datarobotServerless, openShift, other, snowflake, sapAiCore] | | createdByMe | [false, False, true, True] | | championModelExecutionType | [custom_inference_model, external, dedicated] | | isA2AAgent | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of deployments.", "items": { "properties": { "accuracyHealth": { "description": "Accuracy health of the deployment.", "properties": { "endDate": { "description": "End date of accuracy health period.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "A message providing more detail on the status.", "type": "string" }, "startDate": { "description": "Start date of accuracy health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Accuracy health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "approvalStatus": { "description": "Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response.", "enum": [ "PENDING", "APPROVED" ], "type": "string" }, "createdAt": { "description": "The date and time of when the deployment was created, in ISO 8601 format.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "creator": { "description": "Creator of the deployment.", "properties": { "email": { "description": "Email address of the owner.", "type": [ "string", "null" ] }, "firstName": { "description": "First name of the owner.", "type": [ "string", "null" ] }, "id": { "description": "ID of the owner.", "type": "string" }, "lastName": { "description": "Last name of the owner.", "type": [ "string", "null" ] } }, "required": [ "email", "firstName", "id", "lastName" ], "type": "object" }, "defaultPredictionServer": { "description": "The prediction server associated with the deployment.", "properties": { "datarobot-key": { "description": "The `datarobot-key` header used in requests to this prediction server.", "type": "string" }, "id": { "description": "ID of the prediction server for the deployment.", "type": [ "string", "null" ] }, "url": { "description": "URL of the prediction server.", "type": "string" } }, "required": [ "datarobot-key", "id", "url" ], "type": "object" }, "description": { "description": "Description of the deployment.", "type": [ "string", "null" ] }, "governance": { "description": "Deployment governance info.", "properties": { "approvalStatus": { "description": "Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response.", "enum": [ "PENDING", "APPROVED" ], "type": "string" }, "hasOpenedChangeRequests": { "description": "If there are change request related to this deployment with `PENDING` status.", "type": "boolean" }, "reviewers": { "description": "A list of reviewers to approve deployment change requests.", "items": { "properties": { "email": { "description": "The email address of the reviewer.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "id": { "description": "The ID of the reviewer.", "type": "string" }, "status": { "description": "The latest review status.", "enum": [ "APPROVED", "CHANGES_REQUESTED", "COMMENTED" ], "type": [ "string", "null" ] }, "userhash": { "description": "Reviewer's gravatar hash.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "username": { "description": "The username of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "email", "id", "status" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 20, "type": "array", "x-versionadded": "v2.37" } }, "required": [ "approvalStatus", "hasOpenedChangeRequests" ], "type": "object" }, "hasError": { "description": "Whether the deployment is not operational because it failed to start properly.", "type": "boolean", "x-versionadded": "v2.32" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "importance": { "description": "Shows how important this deployment is.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string", "x-versionadded": "v2.21" }, "label": { "description": "Label of the deployment.", "maxLength": 512, "type": "string" }, "model": { "description": "Information related to the current model of the deployment.", "properties": { "buildEnvironmentType": { "description": "Build environment type of the current model.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "customModelImage": { "description": "Information related to the custom model image of the deployment.", "properties": { "customModelId": { "description": "ID of the custom model.", "type": "string" }, "customModelName": { "description": "Name of the custom model.", "type": "string" }, "customModelVersionId": { "description": "ID of the custom model version.", "type": "string" }, "customModelVersionLabel": { "description": "Label of the custom model version.", "type": "string" }, "executionEnvironmentId": { "description": "ID of the execution environment.", "type": "string" }, "executionEnvironmentName": { "description": "Name of the execution environment.", "type": "string" }, "executionEnvironmentVersionId": { "description": "ID of the execution environment version.", "type": "string" }, "executionEnvironmentVersionLabel": { "description": "Label of the execution environment version.", "type": "string" } }, "required": [ "customModelId", "customModelName", "customModelVersionId", "customModelVersionLabel", "executionEnvironmentId", "executionEnvironmentName", "executionEnvironmentVersionId", "executionEnvironmentVersionLabel" ], "type": "object" }, "deployedAt": { "description": "Timestamp of when current model was deployed.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "projectId": { "description": "Project ID of the current model.", "type": [ "string", "null" ] }, "projectName": { "description": "Project name of the current model.", "type": [ "string", "null" ] }, "targetName": { "description": "Target name of the current model.", "type": "string" }, "targetType": { "description": "Target type of the current model.", "type": "string" }, "type": { "description": "Type of the current model.", "type": "string" }, "unstructuredModelKind": { "description": "Whether the current model is an unstructured model.", "type": "boolean", "x-versionadded": "v2.29" }, "unsupervisedMode": { "description": "Whether the current model's project is unsupervised.", "type": "boolean", "x-versionadded": "v2.18" }, "unsupervisedType": { "description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" } }, "required": [ "id", "isDeprecated", "projectName", "targetType", "type", "unstructuredModelKind", "unsupervisedMode" ], "type": "object" }, "modelHealth": { "description": "Model health of the deployment.", "properties": { "endDate": { "description": "End date of model health period.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "A message providing more detail on the status.", "type": "string" }, "startDate": { "description": "Start date of model health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Model health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "modelPackage": { "description": "Information related to the current ModelPackage.", "properties": { "id": { "description": "ID of the ModelPackage.", "type": "string" }, "name": { "description": "Name of the ModelPackage.", "type": "string" }, "registeredModelId": { "description": "The ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name", "registeredModelId" ], "type": "object" }, "modelPackageInitialDownload": { "description": "If a model package has been downloaded for this deployment, then this will tell you when it was first downloaded.", "properties": { "timestamp": { "description": "Timestamp of the first time model package was downloaded.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "timestamp" ], "type": "object" }, "openedChangeRequests": { "description": "An array of the change request IDs related to this deployment that have.", "items": { "type": "string" }, "type": "array" }, "owners": { "description": "Count and preview of owners of the deployment.", "properties": { "count": { "description": "Total count of owners.", "type": "integer" }, "preview": { "description": "A list of owner objects.", "items": { "description": "Creator of the deployment.", "properties": { "email": { "description": "Email address of the owner.", "type": [ "string", "null" ] }, "firstName": { "description": "First name of the owner.", "type": [ "string", "null" ] }, "id": { "description": "ID of the owner.", "type": "string" }, "lastName": { "description": "Last name of the owner.", "type": [ "string", "null" ] } }, "required": [ "email", "firstName", "id", "lastName" ], "type": "object" }, "type": "array" } }, "required": [ "count", "preview" ], "type": "object" }, "permissions": { "description": "Permissions that the user making the request has on the deployment.", "items": { "enum": [ "CAN_ADD_CHALLENGERS", "CAN_APPROVE_REPLACEMENT_MODEL", "CAN_BE_MANAGED_BY_MLOPS_AGENT", "CAN_DELETE_CHALLENGERS", "CAN_DELETE_DEPLOYMENT", "CAN_DOWNLOAD_DOCUMENTATION", "CAN_EDIT_CHALLENGERS", "CAN_EDIT_DEPLOYMENT", "CAN_GENERATE_DOCUMENTATION", "CAN_MAKE_PREDICTIONS", "CAN_REPLACE_MODEL", "CAN_SCORE_CHALLENGERS", "CAN_SHARE", "CAN_SHARE_DEPLOYMENT_OWNERSHIP", "CAN_SUBMIT_ACTUALS", "CAN_UPDATE_DEPLOYMENT_THRESHOLDS", "CAN_VIEW" ], "type": "string" }, "type": "array", "x-versionadded": "v2.18" }, "predictionEnvironment": { "description": "Information related to the current PredictionEnvironment.", "properties": { "id": { "description": "ID of the PredictionEnvironment.", "type": [ "string", "null" ] }, "isManagedByManagementAgent": { "description": "True if PredictionEnvironment is using Management Agent.", "type": "boolean" }, "name": { "description": "Name of the PredictionEnvironment.", "type": "string" }, "platform": { "description": "Platform of the PredictionEnvironment.", "enum": [ "aws", "gcp", "azure", "onPremise", "datarobot", "datarobotServerless", "openShift", "other", "snowflake", "sapAiCore" ], "type": "string" }, "plugin": { "description": "Plugin name of the PredictionEnvironment.", "type": [ "string", "null" ] }, "supportedModelFormats": { "description": "Model formats that the PredictionEnvironment supports.", "items": { "enum": [ "datarobot", "datarobotScoringCode", "customModel", "externalModel" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "id", "isManagedByManagementAgent", "name", "platform" ], "type": "object" }, "predictionUsage": { "description": "Prediction usage of the deployment.", "properties": { "dailyRates": { "description": "Number of predictions made in the last 7 days.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Timestamp of the last prediction request.", "format": "date-time", "type": [ "string", "null" ] }, "serverErrorRates": { "description": "Number of server errors (5xx) in the last 7 days.", "items": { "type": "integer" }, "maxItems": 7, "type": "array", "x-versionadded": "v2.39" }, "userErrorRates": { "description": "Number of user errors (4xx) in the last 7 days.", "items": { "type": "integer" }, "maxItems": 7, "type": "array", "x-versionadded": "v2.39" } }, "required": [ "dailyRates", "lastTimestamp", "serverErrorRates", "userErrorRates" ], "type": "object" }, "scoringCodeInitialDownload": { "description": "If scoring code has been downloaded for this deployment, then this will tell you when it was first downloaded.", "properties": { "timestamp": { "description": "Timestamp of the first time scoring code was downloaded.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "timestamp" ], "type": "object" }, "serviceHealth": { "description": "Service health of the deployment.", "properties": { "endDate": { "description": "End date of model service period.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "Start date of service health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Service health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "startDate", "status" ], "type": "object" }, "settings": { "description": "Settings of the deployment.", "properties": { "batchMonitoringEnabled": { "description": "if batch monitoring is enabled.", "type": "boolean" }, "humbleAiEnabled": { "description": "if humble ai is enabled.", "type": "boolean" }, "predictionIntervalsEnabled": { "description": "If prediction intervals are enabled.", "type": "boolean" }, "predictionWarningEnabled": { "description": "If prediction warning is enabled.", "type": "boolean" } }, "type": "object" }, "status": { "description": "Displays current deployment status.", "enum": [ "active", "archived", "errored", "inactive", "launching", "replacingModel", "stopping" ], "type": "string", "x-versionadded": "v2.22" }, "tags": { "description": "The list of formatted deployment tags.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "maxLength": 50, "type": "string" }, "value": { "description": "The value of the tag.", "maxLength": 50, "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object" }, "type": "array" }, "userProvidedId": { "description": "A user-provided unique ID associated with a deployment definition in a remote git repository.", "maxLength": 100, "type": "string", "x-versionadded": "v2.29" } }, "required": [ "accuracyHealth", "createdAt", "defaultPredictionServer", "description", "id", "label", "model", "modelHealth", "permissions", "predictionUsage", "serviceHealth", "settings", "status", "tags" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The deployments | DeploymentListResponse | ## Create deployment Operation path: `POST /api/v2/deployments/fromLearningModel/` Authentication requirements: `BearerAuth` Create a deployment from a DataRobot model. DEPRECATED in v2.37: Register the Leaderboard model with `POST /api/v2/modelPackages/fromLeaderboard/`. Use the following route to create a deployment `POST /api/v2/deployments/fromModelPackage/`. ### Body parameter ``` { "properties": { "defaultPredictionServerId": { "description": "ID of the default prediction server for deployment. Required for DataRobot Cloud, must be omitted for Enterprise installations.", "type": "string" }, "description": { "default": null, "description": "A description for the deployment.", "maxLength": 10000, "type": [ "string", "null" ] }, "grantInitialDeploymentRolesFromProject": { "default": true, "description": "If True, initial roles on the deployment will be granted to users who have roles on the project that the deployed model comes from. If False, only the creator of the deployment will be given a role on the deployment. Defaults to True.", "type": "boolean", "x-versionadded": "v2.31" }, "importance": { "description": "Importance of the deployment. Defaults to LOW when MLOps is enabled, must not be provided when MLOps disabled.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "A human-readable name for the deployment.", "maxLength": 512, "type": "string" }, "modelId": { "description": "ID of the model to be deployed.", "type": "string" }, "predictionThreshold": { "description": "Prediction threshold used for binary classification. If not specified, model default prediction threshold will be used.", "maximum": 1, "minimum": 0, "type": "number" }, "status": { "default": "active", "description": "Status of the deployment", "enum": [ "active", "inactive" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" } }, "required": [ "description", "label", "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DeploymentCreateFromLearningModel | false | none | ### Example responses > 202 Response ``` { "properties": { "id": { "description": "The ID of the created deployment.", "type": "string" } }, "required": [ "id" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Deployment successfully created. | DeploymentCreateResponse | | 403 | Forbidden | User does not have permission to create a deployment. | None | | 409 | Conflict | User's organization has reached the maximum number of deployments. | None | | 422 | Unprocessable Entity | Unable to process the deployment creation request. | None | ## Create a deployment from a model package Operation path: `POST /api/v2/deployments/fromModelPackage/` Authentication requirements: `BearerAuth` Create a deployment from a model package. ### Body parameter ``` { "properties": { "additionalMetadata": { "description": "Key/Value pair list, with additional metadata", "items": { "properties": { "key": { "description": "The key, a unique identifier for this item of metadata", "maxLength": 50, "type": "string" }, "value": { "description": "The value, the actual content for this item of metadata", "maxLength": 256, "type": "string" } }, "required": [ "key", "value" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 40, "type": "array" }, "defaultPredictionServerId": { "description": "ID of the default prediction server for deployment. Required for DataRobot Cloud for non-external models, must be omitted for Enterprise installations.", "type": "string" }, "description": { "default": null, "description": "A description for the deployment.", "maxLength": 10000, "type": [ "string", "null" ] }, "importance": { "description": "Importance of the deployment. Defaults to LOW when MLOps is enabled, must not be provided when MLOps disabled.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "A human-readable name for the deployment.", "maxLength": 512, "type": "string" }, "modelPackageId": { "description": "ID of the model package to deploy.", "type": "string" }, "predictionEnvironmentId": { "description": "ID of prediction environment where to deploy", "type": "string" }, "runtimeParameterValues": { "description": "Inject values into a model at runtime. The fieldName must match a fieldName defined in the model package. This list is merged with any existing runtime values set through the deployed model package.\n
This property is gated behind the feature flags **`['CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT']`**.\n To enable this feature, you can contact your DataRobot representative or administrator.\n
", "feature_flags": [ { "description": "Enables the ability to edit Custom Model Runtime-Parameters (and replica and resource bundle settings) directly from the Deployment info page. Edited values are local to a given Deployment and do not affect the runtime of any current or future Deployments of the same Model Package.", "enabled_by_default": true, "maturity": "PUBLIC_PREVIEW", "name": "CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT" } ], "public_preview": true, "type": "string", "x-datarobot-public-preview": true, "x-datarobot-required-feature-flags": [ { "description": "Enables the ability to edit Custom Model Runtime-Parameters (and replica and resource bundle settings) directly from the Deployment info page. Edited values are local to a given Deployment and do not affect the runtime of any current or future Deployments of the same Model Package.", "enabled_by_default": true, "maturity": "PUBLIC_PREVIEW", "name": "CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT" } ] }, "status": { "default": "active", "description": "Status of the deployment", "enum": [ "active", "inactive" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "userProvidedId": { "description": "A user-provided unique ID associated with a deployment definition in a remote git repository.", "maxLength": 100, "type": "string", "x-versionadded": "v2.29" } }, "required": [ "description", "label", "modelPackageId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DeploymentCreateFromModelPackage | false | none | ### Example responses > 202 Response ``` { "properties": { "id": { "description": "The ID of the created deployment.", "type": "string" } }, "required": [ "id" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. The URL at the Location header can be used to track when the deployment is ready for predictions. | DeploymentCreateResponse | | 422 | Unprocessable Entity | Unable to process the deployment creation request. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking async job status. | ## Get deployment limits Operation path: `GET /api/v2/deployments/limits/` Authentication requirements: `BearerAuth` Fetch deployment limit information. ### Example responses > 200 Response ``` { "properties": { "maxDeploymentLimit": { "description": "A limitInfo object for prepaid deployment information.", "properties": { "limitReached": { "description": "Whether the limit named has been reached.", "type": "boolean" }, "reason": { "description": "The reason why the limit has been reached.", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "value": { "description": "The limit value of the given limit.", "type": "integer" } }, "required": [ "limitReached", "reason", "value" ], "type": "object", "x-versionadded": "v2.43" }, "prepaidDeploymentLimit": { "description": "A limitInfo object for prepaid deployment information.", "properties": { "limitReached": { "description": "Whether the limit named has been reached.", "type": "boolean" }, "reason": { "description": "The reason why the limit has been reached.", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "value": { "description": "The limit value of the given limit.", "type": "integer" } }, "required": [ "limitReached", "reason", "value" ], "type": "object", "x-versionadded": "v2.43" }, "totalDeploymentCount": { "description": "Total number of deployments counted for limit information.", "type": "integer" } }, "required": [ "maxDeploymentLimit", "prepaidDeploymentLimit", "totalDeploymentCount" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The deployment limits. | DeploymentGetLimitsResponse | ## Update a deployment's prediction environment from dedicated Operation path: `POST /api/v2/deployments/migrateDPStoServerless/` Authentication requirements: `BearerAuth` Update a deployment's prediction environment from dedicated to serverless. ### Body parameter ``` { "properties": { "dpsDeploymentId": { "description": "The ID of the deployment on dedicated prediction environment that will be updated to serverless.", "type": "string" }, "serverlessPredictionEnvironmentId": { "description": "The ID of serverless prediction environment.", "type": "string" } }, "required": [ "dpsDeploymentId", "serverlessPredictionEnvironmentId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DeploymentPredictionEnvironmentUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Deployment successfully updated. | None | ## Delete deployment by deployment ID Operation path: `DELETE /api/v2/deployments/{deploymentId}/` Authentication requirements: `BearerAuth` Delete a deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ignoreManagementAgent | query | string | false | Do not wait for management agent to delete the deployment first. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | ignoreManagementAgent | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Deployment successfully deleted. | None | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Retrieve deployment by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/` Authentication requirements: `BearerAuth` Retrieve a deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "accuracyHealth": { "description": "Accuracy health of the deployment.", "properties": { "endDate": { "description": "End date of accuracy health period.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "A message providing more detail on the status.", "type": "string" }, "startDate": { "description": "Start date of accuracy health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Accuracy health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "approvalStatus": { "description": "Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response.", "enum": [ "PENDING", "APPROVED" ], "type": "string" }, "createdAt": { "description": "The date and time of when the deployment was created, in ISO 8601 format.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "creator": { "description": "Creator of the deployment.", "properties": { "email": { "description": "Email address of the owner.", "type": [ "string", "null" ] }, "firstName": { "description": "First name of the owner.", "type": [ "string", "null" ] }, "id": { "description": "ID of the owner.", "type": "string" }, "lastName": { "description": "Last name of the owner.", "type": [ "string", "null" ] } }, "required": [ "email", "firstName", "id", "lastName" ], "type": "object" }, "defaultPredictionServer": { "description": "The prediction server associated with the deployment.", "properties": { "datarobot-key": { "description": "The `datarobot-key` header used in requests to this prediction server.", "type": "string" }, "id": { "description": "ID of the prediction server for the deployment.", "type": [ "string", "null" ] }, "url": { "description": "URL of the prediction server.", "type": "string" } }, "required": [ "datarobot-key", "id", "url" ], "type": "object" }, "description": { "description": "Description of the deployment.", "type": [ "string", "null" ] }, "governance": { "description": "Deployment governance info.", "properties": { "approvalStatus": { "description": "Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response.", "enum": [ "PENDING", "APPROVED" ], "type": "string" }, "hasOpenedChangeRequests": { "description": "If there are change request related to this deployment with `PENDING` status.", "type": "boolean" }, "reviewers": { "description": "A list of reviewers to approve deployment change requests.", "items": { "properties": { "email": { "description": "The email address of the reviewer.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "id": { "description": "The ID of the reviewer.", "type": "string" }, "status": { "description": "The latest review status.", "enum": [ "APPROVED", "CHANGES_REQUESTED", "COMMENTED" ], "type": [ "string", "null" ] }, "userhash": { "description": "Reviewer's gravatar hash.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "username": { "description": "The username of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "email", "id", "status" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 20, "type": "array", "x-versionadded": "v2.37" } }, "required": [ "approvalStatus", "hasOpenedChangeRequests" ], "type": "object" }, "hasError": { "description": "Whether the deployment is not operational because it failed to start properly.", "type": "boolean", "x-versionadded": "v2.32" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "importance": { "description": "Shows how important this deployment is.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string", "x-versionadded": "v2.21" }, "label": { "description": "Label of the deployment.", "maxLength": 512, "type": "string" }, "model": { "description": "Information related to the current model of the deployment.", "properties": { "buildEnvironmentType": { "description": "Build environment type of the current model.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "customModelImage": { "description": "Information related to the custom model image of the deployment.", "properties": { "customModelId": { "description": "ID of the custom model.", "type": "string" }, "customModelName": { "description": "Name of the custom model.", "type": "string" }, "customModelVersionId": { "description": "ID of the custom model version.", "type": "string" }, "customModelVersionLabel": { "description": "Label of the custom model version.", "type": "string" }, "executionEnvironmentId": { "description": "ID of the execution environment.", "type": "string" }, "executionEnvironmentName": { "description": "Name of the execution environment.", "type": "string" }, "executionEnvironmentVersionId": { "description": "ID of the execution environment version.", "type": "string" }, "executionEnvironmentVersionLabel": { "description": "Label of the execution environment version.", "type": "string" } }, "required": [ "customModelId", "customModelName", "customModelVersionId", "customModelVersionLabel", "executionEnvironmentId", "executionEnvironmentName", "executionEnvironmentVersionId", "executionEnvironmentVersionLabel" ], "type": "object" }, "deployedAt": { "description": "Timestamp of when current model was deployed.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "projectId": { "description": "Project ID of the current model.", "type": [ "string", "null" ] }, "projectName": { "description": "Project name of the current model.", "type": [ "string", "null" ] }, "targetName": { "description": "Target name of the current model.", "type": "string" }, "targetType": { "description": "Target type of the current model.", "type": "string" }, "type": { "description": "Type of the current model.", "type": "string" }, "unstructuredModelKind": { "description": "Whether the current model is an unstructured model.", "type": "boolean", "x-versionadded": "v2.29" }, "unsupervisedMode": { "description": "Whether the current model's project is unsupervised.", "type": "boolean", "x-versionadded": "v2.18" }, "unsupervisedType": { "description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" } }, "required": [ "id", "isDeprecated", "projectName", "targetType", "type", "unstructuredModelKind", "unsupervisedMode" ], "type": "object" }, "modelHealth": { "description": "Model health of the deployment.", "properties": { "endDate": { "description": "End date of model health period.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "A message providing more detail on the status.", "type": "string" }, "startDate": { "description": "Start date of model health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Model health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "modelPackage": { "description": "Information related to the current ModelPackage.", "properties": { "id": { "description": "ID of the ModelPackage.", "type": "string" }, "name": { "description": "Name of the ModelPackage.", "type": "string" }, "registeredModelId": { "description": "The ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name", "registeredModelId" ], "type": "object" }, "modelPackageInitialDownload": { "description": "If a model package has been downloaded for this deployment, then this will tell you when it was first downloaded.", "properties": { "timestamp": { "description": "Timestamp of the first time model package was downloaded.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "timestamp" ], "type": "object" }, "openedChangeRequests": { "description": "An array of the change request IDs related to this deployment that have.", "items": { "type": "string" }, "type": "array" }, "owners": { "description": "Count and preview of owners of the deployment.", "properties": { "count": { "description": "Total count of owners.", "type": "integer" }, "preview": { "description": "A list of owner objects.", "items": { "description": "Creator of the deployment.", "properties": { "email": { "description": "Email address of the owner.", "type": [ "string", "null" ] }, "firstName": { "description": "First name of the owner.", "type": [ "string", "null" ] }, "id": { "description": "ID of the owner.", "type": "string" }, "lastName": { "description": "Last name of the owner.", "type": [ "string", "null" ] } }, "required": [ "email", "firstName", "id", "lastName" ], "type": "object" }, "type": "array" } }, "required": [ "count", "preview" ], "type": "object" }, "permissions": { "description": "Permissions that the user making the request has on the deployment.", "items": { "enum": [ "CAN_ADD_CHALLENGERS", "CAN_APPROVE_REPLACEMENT_MODEL", "CAN_BE_MANAGED_BY_MLOPS_AGENT", "CAN_DELETE_CHALLENGERS", "CAN_DELETE_DEPLOYMENT", "CAN_DOWNLOAD_DOCUMENTATION", "CAN_EDIT_CHALLENGERS", "CAN_EDIT_DEPLOYMENT", "CAN_GENERATE_DOCUMENTATION", "CAN_MAKE_PREDICTIONS", "CAN_REPLACE_MODEL", "CAN_SCORE_CHALLENGERS", "CAN_SHARE", "CAN_SHARE_DEPLOYMENT_OWNERSHIP", "CAN_SUBMIT_ACTUALS", "CAN_UPDATE_DEPLOYMENT_THRESHOLDS", "CAN_VIEW" ], "type": "string" }, "type": "array", "x-versionadded": "v2.18" }, "predictionEnvironment": { "description": "Information related to the current PredictionEnvironment.", "properties": { "id": { "description": "ID of the PredictionEnvironment.", "type": [ "string", "null" ] }, "isManagedByManagementAgent": { "description": "True if PredictionEnvironment is using Management Agent.", "type": "boolean" }, "name": { "description": "Name of the PredictionEnvironment.", "type": "string" }, "platform": { "description": "Platform of the PredictionEnvironment.", "enum": [ "aws", "gcp", "azure", "onPremise", "datarobot", "datarobotServerless", "openShift", "other", "snowflake", "sapAiCore" ], "type": "string" }, "plugin": { "description": "Plugin name of the PredictionEnvironment.", "type": [ "string", "null" ] }, "supportedModelFormats": { "description": "Model formats that the PredictionEnvironment supports.", "items": { "enum": [ "datarobot", "datarobotScoringCode", "customModel", "externalModel" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "id", "isManagedByManagementAgent", "name", "platform" ], "type": "object" }, "predictionUsage": { "description": "Prediction usage of the deployment.", "properties": { "dailyRates": { "description": "Number of predictions made in the last 7 days.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Timestamp of the last prediction request.", "format": "date-time", "type": [ "string", "null" ] }, "serverErrorRates": { "description": "Number of server errors (5xx) in the last 7 days.", "items": { "type": "integer" }, "maxItems": 7, "type": "array", "x-versionadded": "v2.39" }, "userErrorRates": { "description": "Number of user errors (4xx) in the last 7 days.", "items": { "type": "integer" }, "maxItems": 7, "type": "array", "x-versionadded": "v2.39" } }, "required": [ "dailyRates", "lastTimestamp", "serverErrorRates", "userErrorRates" ], "type": "object" }, "scoringCodeInitialDownload": { "description": "If scoring code has been downloaded for this deployment, then this will tell you when it was first downloaded.", "properties": { "timestamp": { "description": "Timestamp of the first time scoring code was downloaded.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "timestamp" ], "type": "object" }, "serviceHealth": { "description": "Service health of the deployment.", "properties": { "endDate": { "description": "End date of model service period.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "Start date of service health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Service health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "startDate", "status" ], "type": "object" }, "settings": { "description": "Settings of the deployment.", "properties": { "batchMonitoringEnabled": { "description": "if batch monitoring is enabled.", "type": "boolean" }, "humbleAiEnabled": { "description": "if humble ai is enabled.", "type": "boolean" }, "predictionIntervalsEnabled": { "description": "If prediction intervals are enabled.", "type": "boolean" }, "predictionWarningEnabled": { "description": "If prediction warning is enabled.", "type": "boolean" } }, "type": "object" }, "status": { "description": "Displays current deployment status.", "enum": [ "active", "archived", "errored", "inactive", "launching", "replacingModel", "stopping" ], "type": "string", "x-versionadded": "v2.22" }, "tags": { "description": "The list of formatted deployment tags.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "maxLength": 50, "type": "string" }, "value": { "description": "The value of the tag.", "maxLength": 50, "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object" }, "type": "array" }, "userProvidedId": { "description": "A user-provided unique ID associated with a deployment definition in a remote git repository.", "maxLength": 100, "type": "string", "x-versionadded": "v2.29" } }, "required": [ "accuracyHealth", "createdAt", "defaultPredictionServer", "description", "id", "label", "model", "modelHealth", "permissions", "predictionUsage", "serviceHealth", "settings", "status", "tags" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The deployment | DeploymentRetrieveResponse | ## Update deployment by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/` Authentication requirements: `BearerAuth` Update a deployment's label and description. ### Body parameter ``` { "properties": { "description": { "description": "A description for the deployment.", "maxLength": 10000, "type": [ "string", "null" ] }, "importance": { "description": "Shows how important this deployment is.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string", "x-versionadded": "v2.21" }, "label": { "description": "A human-readable name for the deployment.", "maxLength": 512, "type": [ "string", "null" ] } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | DeploymentUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Deployment successfully updated | None | ## Delete the agent card by deployment ID Operation path: `DELETE /api/v2/deployments/{deploymentId}/agentCard/` Authentication requirements: `BearerAuth` Delete the agent card associated with an external agent deployment. This operation is idempotent - returns 204 even if no agent card exists. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | The ID of the deployment. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 404 | Not Found | Deployment not found. | None | | 405 | Method Not Allowed | Agent card deletion is only allowed for external deployments. | None | ## Retrieve the agent card by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/agentCard/` Authentication requirements: `BearerAuth` Retrieve the agent card associated with an agent deployment. The agent card is returned by this route as it was returned by the agent during deployment creation or as it was uploaded to DataRobot. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | The ID of the deployment. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The agent card for the deployment. | None | | 404 | Not Found | No agent card found for this deployment. | None | ## Create or replace the agent card by deployment ID Operation path: `PUT /api/v2/deployments/{deploymentId}/agentCard/` Authentication requirements: `BearerAuth` Upload the agent card for an agent deployment. The request body must be the agent card document as a JSON object. If an agent card already exists for this deployment it will be replaced. This operation is only supported for external deployments. For DataRobot hosted deployments, the agent card document is pulled automatically from the deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | The ID of the deployment. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The stored agent card for the deployment. | None | | 413 | Payload Too Large | The agent card exceeds the maximum allowed size. | None | | 422 | Unprocessable Entity | Agent card upload is only supported for external deployments. | None | ## Retrieve capabilities by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/capabilities/` Authentication requirements: `BearerAuth` Retrieve the capabilities for the deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of all capabilities.", "items": { "properties": { "messages": { "description": "Messages explaining why the capability is supported or not supported.", "items": { "type": "string" }, "type": "array" }, "name": { "description": "The name of the capability.", "type": "string" }, "supported": { "description": "Whether the capability is supported.", "type": "boolean" } }, "required": [ "messages", "name", "supported" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | DeploymentCapabilitiesRetrieveResponse | ## Get deployment features by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/features/` Authentication requirements: `BearerAuth` Retrieve features contained within the universe dataset associated with a specific deployment. By default, this returns all raw features required for predictions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of features to skip, defaults to 0. | | limit | query | integer | true | The number of features to return, defaults to 0. | | includeNonPredictionFeatures | query | string | false | When True will return all raw features in the universe dataset associated with the deployment, and when False will return only those raw features used to make predictions on the deployment. | | forSegmentedAnalysis | query | string | false | When True, features returned will be filtered to those usable for segmented analysis. | | search | query | string | false | Case insensitive search against names of the deployment's features. | | orderBy | query | string | false | The sort order to apply to the list of features. Prefix the attribute name with a dash to sort in descending order, e.g., "-name". | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | includeNonPredictionFeatures | [false, False, true, True] | | forSegmentedAnalysis | [false, False, true, True] | | orderBy | [name, -name, importance, -importance] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of features.", "items": { "properties": { "dateFormat": { "description": "The date format string for how this feature was interpreted.", "type": [ "string", "null" ] }, "featureType": { "description": "Feature type.", "type": [ "string", "null" ] }, "importance": { "description": "Numeric measure of the relationship strength between the feature and target (independent of model or other features).", "type": [ "number", "null" ] }, "knownInAdvance": { "description": "Whether the feature was selected as known in advance in a time-series model, false for non-time-series models.", "type": "boolean" }, "name": { "description": "Feature name.", "type": "string" } }, "required": [ "dateFormat", "featureType", "importance", "knownInAdvance", "name" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.35" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The deployment's features | FeatureListResponse | ## Retrieve champion model history of deployment by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/modelHistory/` Authentication requirements: `BearerAuth` Retrieve champion model history of deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "History of deployment's champion models, including the current champion model.", "items": { "properties": { "customModelImage": { "description": "Contains information about the custom model image.", "properties": { "customModelId": { "description": "ID of the custom model.", "type": [ "string", "null" ] }, "customModelName": { "description": "Name of the custom model.", "type": [ "string", "null" ] }, "customModelVersionId": { "description": "ID of the custom model version.", "type": [ "string", "null" ] }, "customModelVersionLabel": { "description": "Label of the custom model version.", "type": [ "string", "null" ] }, "executionEnvironmentId": { "description": "ID of the execution environment.", "type": [ "string", "null" ] }, "executionEnvironmentName": { "description": "Name of the execution environment.", "type": [ "string", "null" ] }, "executionEnvironmentVersionId": { "description": "ID of the execution environment version.", "type": [ "string", "null" ] }, "executionEnvironmentVersionLabel": { "description": "Label of the execution environment version.", "type": [ "string", "null" ] } }, "required": [ "customModelId", "customModelName", "customModelVersionId", "customModelVersionLabel", "executionEnvironmentId", "executionEnvironmentName", "executionEnvironmentVersionId", "executionEnvironmentVersionLabel" ], "type": "object", "x-versionadded": "v2.36" }, "endDate": { "description": "The timestamp when the model is replaced by another model.", "format": "date-time", "type": [ "string", "null" ] }, "forecastEndDate": { "description": "The max timestamp of all predictions made when the model is or was champion of the deployment, if predictions by forecast date is enabled.", "format": "date-time", "type": [ "string", "null" ] }, "forecastStartDate": { "description": "The min timestamp of all predictions made when the model is or was champion of the deployment, if predictions by forecast date is enabled.", "format": "date-time", "type": [ "string", "null" ] }, "modelPackage": { "description": "Contains information about the model package.", "properties": { "id": { "description": "ID of the model package.", "type": [ "string", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "name": { "description": "Name of the model package.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "ID of the project.", "type": "string" }, "registeredModelId": { "description": "ID of the associated registered model.", "type": [ "string", "null" ] } }, "required": [ "id", "modelId", "name", "predictionThreshold", "registeredModelId" ], "type": "object", "x-versionadded": "v2.36" }, "predictionWarningBoundaries": { "description": "Lower and upper boundaries for outlier detection.", "properties": { "lower": { "description": "Lower boundary for outlier detection.", "type": "number" }, "upper": { "description": "Upper boundary for outlier detection.", "type": "number" } }, "required": [ "lower", "upper" ], "type": "object", "x-versionadded": "v2.36" }, "replacementReason": { "description": "Reason for model replacement.", "type": [ "string", "null" ] }, "startDate": { "description": "The timestamp when the model becomes champion of the deployment.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "forecastEndDate", "forecastStartDate", "modelPackage", "replacementReason", "startDate" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of deployment model history. | ModelHistoryListResponse | ## Add report by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/onDemandReports/` Authentication requirements: `BearerAuth` Add report to execution queue. ### Body parameter ``` { "properties": { "id": { "description": "ID of Scheduled report record.", "type": "string" } }, "required": [ "id" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | ScheduledReportOnDemmand | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Scheduled report job was addded to execution. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Location | string | | URL to poll to track report generation has finished. | ## Retrieve deployment consumers by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/quotaConsumers/` Authentication requirements: `BearerAuth` Retrieve list of unique agents/users that have made requests to this deployment. Only available to deployment owners and users with read permissions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Deployment consumer information retrieved. | None | ## Retrieve Scoring Code by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/scoringCode/` Authentication requirements: `BearerAuth` Retrieve Scoring Code of the current deployed model for making predictions outside of a DataRobot prediction server. You need the "Scoring Code" feature enabled to use this route. By default, returns a compiled executable JAR that can be executed locally to calculate model predictions, or it can be used as a library for a Java application. Execute it with the '--help` parameters to learn how to use it as a command-line utility. See model API documentation at https://javadoc.io/doc/com.datarobot/datarobot-prediction/latest/index.html to be able to use it inside an existing Java application. With sourceCode query parameter set to 'true', returns a source code archive that can be used to review internal calculations of the model. This JAR is NOT executable. See "https://docs.datarobot.com/en/docs/predictions/port-pred/scoring-code/index.html" documentation in DataRobot application for more information. Note Cannot retrieve source code if agent is included. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | sourceCode | query | string | false | Whether source code or binary of the Scoring Code will be retrieved | | includeAgent | query | string | false | Whether the Scoring code retrieved will include tracking agent | | includePe | query | string | false | Please use includePredictionExplanations parameter instead | | includePredictionExplanations | query | string | false | Whether the Scoring Code retrieved will include prediction explanations | | includePredictionIntervals | query | string | false | Whether the Scoring Code retrieved will include prediction intervals | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | sourceCode | [false, False, true, True] | | includeAgent | [false, False, true, True] | | includePe | [false, False, true, True] | | includePredictionExplanations | [false, False, true, True] | | includePredictionIntervals | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | JAR file | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Indicating the content is supposed to be downloaded as an attachment | | 200 | Content-Type | string | | application/java-archive | ## Build Java package containing Scoring Code by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/scoringCodeBuilds/` Authentication requirements: `BearerAuth` Build Java package containing Scoring Code with agent integration. ### Body parameter ``` { "properties": { "includeAgent": { "description": "Whether the Scoring Code built will include tracking agent", "type": "boolean" }, "includePredictionExplanations": { "description": "Whether the Scoring Code built will include prediction explanations", "type": "boolean" }, "includePredictionIntervals": { "description": "Whether the Scoring Code built will include prediction intervals", "type": "boolean" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | DeploymentsScoringCodeBuildPayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Retrieve deployment settings checklist by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/settings/checklist/` Authentication requirements: `BearerAuth` Return a checklist of deployment settings indicating which settings are configured and which are not. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of settings for the deployment.", "items": { "properties": { "hint": { "description": "An optional hint for the setting.", "maxLength": 255, "type": [ "string", "null" ] }, "label": { "description": "A descriptive label for the setting.", "maxLength": 80, "type": "string" }, "setting": { "description": "The identifier of the setting.", "enum": [ "deploymentName", "DeploymentName", "DEPLOYMENT_NAME", "settingsDataDrift", "SettingsDataDrift", "SETTINGS_DATA_DRIFT", "settingsAccuracy", "SettingsAccuracy", "SETTINGS_ACCURACY", "settingsFairness", "SettingsFairness", "SETTINGS_FAIRNESS", "settingsCustomMetrics", "SettingsCustomMetrics", "SETTINGS_CUSTOM_METRICS", "settingsHumility", "SettingsHumility", "SETTINGS_HUMILITY", "settingsChallengers", "SettingsChallengers", "SETTINGS_CHALLENGERS", "settingsPredictions", "SettingsPredictions", "SETTINGS_PREDICTIONS", "settingsRetraining", "SettingsRetraining", "SETTINGS_RETRAINING", "settingsUsage", "SettingsUsage", "SETTINGS_USAGE", "settingsDataExploration", "SettingsDataExploration", "SETTINGS_DATA_EXPLORATION", "settingsNotifications", "SettingsNotifications", "SETTINGS_NOTIFICATIONS" ], "type": "string" }, "status": { "description": "Indicates the completion status of the setting.", "enum": [ "notSet", "NotSet", "NOT_SET", "partial", "Partial", "PARTIAL", "configured", "Configured", "CONFIGURED" ], "type": "string" } }, "required": [ "hint", "label", "setting", "status" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 12, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The deployment settings checklist | DeploymentChecklistResponse | ## Change deployment status by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/status/` Authentication requirements: `BearerAuth` Change deployment status. ### Body parameter ``` { "properties": { "status": { "description": "Status that deployment should be transition in.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | DeploymentStatusUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See Location header. | None | | 409 | Conflict | Deployment is already in process of status change or already in requested status. | None | | 422 | Unprocessable Entity | Deployment status change request could not be processed. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking async job status. | ## List resource bundles Operation path: `GET /api/v2/mlops/compute/bundles/` Authentication requirements: `BearerAuth` Retrieve metadata for all resource bundles the user has access to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityId | query | string | false | Identifier used to return recommended resource bundles. Must be used together with entityType parameter. | | entityType | query | string | false | Type of entity for which to get recommended bundles. Must be provided with entityId. | | useCases | query | string | false | If it is specified, only bundles for this use case will be returned. | | offset | query | integer | false | Number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | customModelTemplate | | useCases | [customApplication, customJob, customModel, customTaskFit, modelingMachineWorker, predictionAPI, sapAICore, sparkApplication] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.35" }, "data": { "description": "List of bundles.", "items": { "properties": { "cpuCount": { "description": "Max number of CPUs available.", "type": "number" }, "description": { "description": "A short description of CPU, Memory and other resources.", "type": "string" }, "gpuCount": { "description": "Max number of GPUs available.", "type": [ "number", "null" ] }, "gpuMaker": { "description": "The manufacture of the GPU.", "enum": [ "nvidia", "amd", "intel" ], "type": [ "string", "null" ] }, "gpuMemoryBytes": { "description": "Max amount of GPU memory available.", "type": "integer", "x-versionadded": "v2.36" }, "gpuTypeLabel": { "description": "A label that identifies the GPU type.", "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "hasGpu": { "description": "If this bundle provides at least one GPU resource.", "type": "boolean" }, "id": { "description": "The id of the bundle.", "type": "string" }, "isBillable": { "default": false, "description": "If the bundle has been billable or unbillable.", "type": "boolean", "x-versionadded": "v2.44" }, "isDefault": { "description": "If this should be the default resource choice.", "type": "boolean" }, "isDeleted": { "description": "If the bundle has been deleted and should not be used.", "type": "boolean" }, "memoryBytes": { "description": "Max amount of memory available.", "type": "integer" }, "name": { "description": "A short name for the bundle.", "type": "string" }, "useCases": { "description": "List of use cases this bundle supports.", "items": { "enum": [ "customApplication", "customJob", "customModel", "customTaskFit", "modelingMachineWorker", "predictionAPI", "sapAICore", "sparkApplication" ], "type": "string" }, "type": "array" } }, "required": [ "cpuCount", "description", "gpuMemoryBytes", "hasGpu", "id", "memoryBytes", "name", "useCases" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.35" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of resource bundle metadata. | ResourceRequestBundleListResponse | | 403 | Forbidden | Resource bundles are not enabled. | None | ## Retrieve resource bundle by resource request bundle ID Operation path: `GET /api/v2/mlops/compute/bundles/{resourceRequestBundleId}/` Authentication requirements: `BearerAuth` Retrieve resource bundle. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | resourceRequestBundleId | path | string | true | ID of the bundle. | ### Example responses > 200 Response ``` { "properties": { "cpuCount": { "description": "Max number of CPUs available.", "type": "number" }, "description": { "description": "A short description of CPU, Memory and other resources.", "type": "string" }, "gpuCount": { "description": "Max number of GPUs available.", "type": [ "number", "null" ] }, "gpuMaker": { "description": "The manufacture of the GPU.", "enum": [ "nvidia", "amd", "intel" ], "type": [ "string", "null" ] }, "gpuMemoryBytes": { "description": "Max amount of GPU memory available.", "type": "integer", "x-versionadded": "v2.36" }, "gpuTypeLabel": { "description": "A label that identifies the GPU type.", "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "hasGpu": { "description": "If this bundle provides at least one GPU resource.", "type": "boolean" }, "id": { "description": "The id of the bundle.", "type": "string" }, "isBillable": { "default": false, "description": "If the bundle has been billable or unbillable.", "type": "boolean", "x-versionadded": "v2.44" }, "isDefault": { "description": "If this should be the default resource choice.", "type": "boolean" }, "isDeleted": { "description": "If the bundle has been deleted and should not be used.", "type": "boolean" }, "memoryBytes": { "description": "Max amount of memory available.", "type": "integer" }, "name": { "description": "A short name for the bundle.", "type": "string" }, "useCases": { "description": "List of use cases this bundle supports.", "items": { "enum": [ "customApplication", "customJob", "customModel", "customTaskFit", "modelingMachineWorker", "predictionAPI", "sapAICore", "sparkApplication" ], "type": "string" }, "type": "array" } }, "required": [ "cpuCount", "description", "gpuMemoryBytes", "hasGpu", "id", "memoryBytes", "name", "useCases" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Resource bundle | ResourceRequestBundleResponse | | 403 | Forbidden | Resource bundles are not enabled or the user does not have access to specified bundle. | None | | 404 | Not Found | Resource bundle does not exist. | None | ## Downloads the latest Portable Prediction Server (PPS) Docker image Operation path: `GET /api/v2/mlops/portablePredictionServerImage/` Authentication requirements: `BearerAuth` Fetches the latest Portable Prediction Server (PPS) Docker image. The resulting image can be docker-loaded. Since the image can be quite large (14GB+) consider querying its metadata to check the image size in advance and content hash to verify the downloaded image afterwards. In some environments it can HTTP redirect to some other service (like S3 or GCP) using pre-signed URLs. ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Download the latest available Portable Prediction Server (PPS) Docker image | None | | 302 | Found | Redirect to another service for more efficient content download using pre-signed URL | None | | 404 | Not Found | No PPS images found in the system | None | ## Fetches currently active PPS Docker image metadata Operation path: `GET /api/v2/mlops/portablePredictionServerImage/metadata/` Authentication requirements: `BearerAuth` Fetches currently active PPS Docker image metadata. ### Example responses > 200 Response ``` { "properties": { "baseImageId": { "description": "Internal base image entity id for troubleshooting purposes", "type": "string" }, "created": { "description": "ISO formatted image upload date", "format": "date-time", "type": "string" }, "datarobotRuntimeImageTag": { "description": "For internal use only.", "type": [ "string", "null" ] }, "dockerImageId": { "description": "A Docker image id (immutable, content-based) hash associated with the given image", "type": "string" }, "filename": { "description": "The name of the file when the download requested", "type": "string" }, "hash": { "description": "Hash of the image content, supposed to be used for verifying content after the download. The algorithm used for hashing is specified in `hashAlgorithm` field. Note that hash is calculated over compressed image data.", "type": "string" }, "hashAlgorithm": { "description": "An algorithm name used for calculating content `hash`", "enum": [ "SHA256" ], "type": "string" }, "imageSize": { "description": "Size in bytes of the compressed PPS image data", "type": "integer" }, "shortDockerImageId": { "description": "A 12-chars shortened version of the `dockerImageId` as shown in 'docker images' command line command output", "type": "string" } }, "required": [ "baseImageId", "created", "dockerImageId", "filename", "hash", "hashAlgorithm", "imageSize", "shortDockerImageId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Available Portable Prediction Server (PPS) image metadata | PPSImageMetadataResponse | | 404 | Not Found | No PPS images found in the system | None | ## Get a list of users who have access by environment ID Operation path: `GET /api/v2/executionEnvironments/{environmentId}/accessControl/` Authentication requirements: `BearerAuth` Get a list of users who have access to this execution environment and their roles. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | username | query | string | false | Optional, only return the access control information for a user with this username. | | userId | query | string | false | Optional, only return the access control information for a user with this user ID. | | environmentId | path | string | true | The ID of the environment. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SharingListResponse | | 400 | Bad Request | Bad Request. Both username and userId were specified | None | | 403 | Forbidden | Forbidden. The user does not have permissions to view the execution environment access list. | None | | 404 | Not Found | Execution environment not found. Either the execution environment does not exist or user does not have permission to view it. | None | ## Grant access by environment ID Operation path: `PATCH /api/v2/executionEnvironments/{environmentId}/accessControl/` Authentication requirements: `BearerAuth` Grant access or update roles for users on this execution environment. Up to 100 user roles may be set in a single request. ### Body parameter ``` { "properties": { "data": { "description": "List of sharing roles to update.", "items": { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | The ID of the environment. | | body | body | SharingUpdateOrRemoveWithGrant | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | Roles updated successfully. | None | | 403 | Forbidden | User can view execution environment but does not have permission to grant these roles on the execution environment. | None | | 404 | Not Found | Either the execution environment does not exist or the user does not have permissions to view the execution environment. | None | | 409 | Conflict | The request would leave the execution environment without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid. | None | # Schemas ## AccessControl ``` { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | Whether the recipient can share the role further. | | role | string | true | | The role of the user on this entity. | | userId | string | true | | The identifier of the user that has access to this entity. | | username | string | true | | The username of the user that has access to the entity. | ## CustomModelAsyncOperationResponse ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | ID that can be used with [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid] to poll for the testing job's status | ## CustomModelDeploymentListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of custom model deployments.", "items": { "properties": { "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelImageId": { "description": "The id of the custom model image associated with this deployment.", "type": "string" }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "deployed": { "description": "ISO-8601 timestamp of when deployment was created.", "type": "string" }, "deployedBy": { "description": "The username of the user that deployed the custom model.", "type": "string" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "imageType": { "description": "The type of the image, either customModelImage if the testing attempt is using a customModelImage as its model or customModelVersion if the testing attempt is using a customModelVersion with dependency management.", "enum": [ "customModelImage", "customModelVersion" ], "type": "string" }, "label": { "description": "User-friendly name of the model deployment.", "type": "string" }, "status": { "description": "Deployment status.", "enum": [ "active", "archived", "errored", "inactive", "launching", "replacingModel", "stopping" ], "type": "string" }, "testingStatus": { "description": "Latest testing status of the deployed custom model image.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "customModel", "customModelImageId", "customModelVersion", "deployed", "deployedBy", "executionEnvironment", "executionEnvironmentVersion", "id", "label", "status", "testingStatus" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CustomModelDeploymentResponse] | true | maxItems: 1000 | List of custom model deployments. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CustomModelDeploymentResponse ``` { "properties": { "customModel": { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "customModelImageId": { "description": "The id of the custom model image associated with this deployment.", "type": "string" }, "customModelVersion": { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "deployed": { "description": "ISO-8601 timestamp of when deployment was created.", "type": "string" }, "deployedBy": { "description": "The username of the user that deployed the custom model.", "type": "string" }, "executionEnvironment": { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "executionEnvironmentVersion": { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "imageType": { "description": "The type of the image, either customModelImage if the testing attempt is using a customModelImage as its model or customModelVersion if the testing attempt is using a customModelVersion with dependency management.", "enum": [ "customModelImage", "customModelVersion" ], "type": "string" }, "label": { "description": "User-friendly name of the model deployment.", "type": "string" }, "status": { "description": "Deployment status.", "enum": [ "active", "archived", "errored", "inactive", "launching", "replacingModel", "stopping" ], "type": "string" }, "testingStatus": { "description": "Latest testing status of the deployed custom model image.", "enum": [ "not_tested", "queued", "failed", "canceled", "succeeded", "in_progress", "aborted", "warning", "skipped" ], "type": "string" } }, "required": [ "customModel", "customModelImageId", "customModelVersion", "deployed", "deployedBy", "executionEnvironment", "executionEnvironmentVersion", "id", "label", "status", "testingStatus" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModel | CustomModelShortResponse | true | | Custom model associated with this deployment. | | customModelImageId | string | true | | The id of the custom model image associated with this deployment. | | customModelVersion | CustomModelVersionShortResponse | true | | Custom model version associated with this deployment. | | deployed | string | true | | ISO-8601 timestamp of when deployment was created. | | deployedBy | string | true | | The username of the user that deployed the custom model. | | executionEnvironment | ExecutionEnvironmentShortResponse | true | | Execution environment associated with this deployment. | | executionEnvironmentVersion | ExecutionEnvironmentVersionShortResponse | true | | Execution environment version associated with this deployment. | | id | string | true | | The ID of the deployment. | | imageType | string | false | | The type of the image, either customModelImage if the testing attempt is using a customModelImage as its model or customModelVersion if the testing attempt is using a customModelVersion with dependency management. | | label | string | true | | User-friendly name of the model deployment. | | status | string | true | | Deployment status. | | testingStatus | string | true | | Latest testing status of the deployed custom model image. | ### Enumerated Values | Property | Value | | --- | --- | | imageType | [customModelImage, customModelVersion] | | status | [active, archived, errored, inactive, launching, replacingModel, stopping] | | testingStatus | [not_tested, queued, failed, canceled, succeeded, in_progress, aborted, warning, skipped] | ## CustomModelDeploymentsLimitsLimitInfo ``` { "description": "Limit info for the maximum number of custom model deployments.", "properties": { "limitReached": { "description": "Whether the custom model deployment limit has been reached.", "type": "boolean" }, "value": { "description": "The maximum number of custom model deployments allowed (null if unlimited).", "type": [ "integer", "null" ] } }, "required": [ "limitReached" ], "type": "object", "x-versionadded": "v2.42" } ``` Limit info for the maximum number of custom model deployments. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | limitReached | boolean | true | | Whether the custom model deployment limit has been reached. | | value | integer,null | false | | The maximum number of custom model deployments allowed (null if unlimited). | ## CustomModelDeploymentsLimitsResponse ``` { "properties": { "customModelDeploymentLimitUsageCount": { "description": "Number of custom model deployments that count toward the limit (current limit usage).", "type": "integer" }, "maxCustomDeploymentsLimit": { "description": "Limit info for the maximum number of custom model deployments.", "properties": { "limitReached": { "description": "Whether the custom model deployment limit has been reached.", "type": "boolean" }, "value": { "description": "The maximum number of custom model deployments allowed (null if unlimited).", "type": [ "integer", "null" ] } }, "required": [ "limitReached" ], "type": "object", "x-versionadded": "v2.42" } }, "required": [ "customModelDeploymentLimitUsageCount", "maxCustomDeploymentsLimit" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelDeploymentLimitUsageCount | integer | true | | Number of custom model deployments that count toward the limit (current limit usage). | | maxCustomDeploymentsLimit | CustomModelDeploymentsLimitsLimitInfo | true | | Limit info for the maximum number of custom model deployments. | ## CustomModelImage ``` { "description": "Contains information about the custom model image.", "properties": { "customModelId": { "description": "ID of the custom model.", "type": [ "string", "null" ] }, "customModelName": { "description": "Name of the custom model.", "type": [ "string", "null" ] }, "customModelVersionId": { "description": "ID of the custom model version.", "type": [ "string", "null" ] }, "customModelVersionLabel": { "description": "Label of the custom model version.", "type": [ "string", "null" ] }, "executionEnvironmentId": { "description": "ID of the execution environment.", "type": [ "string", "null" ] }, "executionEnvironmentName": { "description": "Name of the execution environment.", "type": [ "string", "null" ] }, "executionEnvironmentVersionId": { "description": "ID of the execution environment version.", "type": [ "string", "null" ] }, "executionEnvironmentVersionLabel": { "description": "Label of the execution environment version.", "type": [ "string", "null" ] } }, "required": [ "customModelId", "customModelName", "customModelVersionId", "customModelVersionLabel", "executionEnvironmentId", "executionEnvironmentName", "executionEnvironmentVersionId", "executionEnvironmentVersionLabel" ], "type": "object", "x-versionadded": "v2.36" } ``` Contains information about the custom model image. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string,null | true | | ID of the custom model. | | customModelName | string,null | true | | Name of the custom model. | | customModelVersionId | string,null | true | | ID of the custom model version. | | customModelVersionLabel | string,null | true | | Label of the custom model version. | | executionEnvironmentId | string,null | true | | ID of the execution environment. | | executionEnvironmentName | string,null | true | | Name of the execution environment. | | executionEnvironmentVersionId | string,null | true | | ID of the execution environment version. | | executionEnvironmentVersionLabel | string,null | true | | Label of the execution environment version. | ## CustomModelShortResponse ``` { "description": "Custom model associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model.", "type": "string" }, "name": { "description": "User-friendly name of the model.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` Custom model associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model. | | name | string | true | | User-friendly name of the model. | ## CustomModelVersionShortResponse ``` { "description": "Custom model version associated with this deployment.", "properties": { "id": { "description": "The ID of the custom model version.", "type": "string" }, "label": { "description": "User-friendly name of the model version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" } ``` Custom model version associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model version. | | label | string | true | | User-friendly name of the model version. | ## DeletedDeploymentListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of deleted deployments.", "items": { "properties": { "deletedAt": { "description": "The date and time of when the deployment was deleted, in ISO 8601 format.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the deployment.", "type": "string" }, "label": { "description": "Label of the deployment.", "maxLength": 512, "type": "string" } }, "required": [ "deletedAt", "id", "label" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DeletedDeploymentResponse] | true | | List of deleted deployments. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## DeletedDeploymentResponse ``` { "properties": { "deletedAt": { "description": "The date and time of when the deployment was deleted, in ISO 8601 format.", "format": "date-time", "type": "string" }, "id": { "description": "ID of the deployment.", "type": "string" }, "label": { "description": "Label of the deployment.", "maxLength": 512, "type": "string" } }, "required": [ "deletedAt", "id", "label" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deletedAt | string(date-time) | true | | The date and time of when the deployment was deleted, in ISO 8601 format. | | id | string | true | | ID of the deployment. | | label | string | true | maxLength: 512 | Label of the deployment. | ## DeploymentAccuracyHealthResponse ``` { "description": "Accuracy health of the deployment.", "properties": { "endDate": { "description": "End date of accuracy health period.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "A message providing more detail on the status.", "type": "string" }, "startDate": { "description": "Start date of accuracy health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Accuracy health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" } ``` Accuracy health of the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string,null(date-time) | true | | End date of accuracy health period. | | message | string | true | | A message providing more detail on the status. | | startDate | string,null(date-time) | true | | Start date of accuracy health period. | | status | string | true | | Accuracy health status. | ### Enumerated Values | Property | Value | | --- | --- | | status | [failing, notStarted, passing, unavailable, unknown, warning] | ## DeploymentCapabilitiesRetrieveResponse ``` { "properties": { "data": { "description": "The list of all capabilities.", "items": { "properties": { "messages": { "description": "Messages explaining why the capability is supported or not supported.", "items": { "type": "string" }, "type": "array" }, "name": { "description": "The name of the capability.", "type": "string" }, "supported": { "description": "Whether the capability is supported.", "type": "boolean" } }, "required": [ "messages", "name", "supported" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [DeploymentCapability] | true | | The list of all capabilities. | ## DeploymentCapability ``` { "properties": { "messages": { "description": "Messages explaining why the capability is supported or not supported.", "items": { "type": "string" }, "type": "array" }, "name": { "description": "The name of the capability.", "type": "string" }, "supported": { "description": "Whether the capability is supported.", "type": "boolean" } }, "required": [ "messages", "name", "supported" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | messages | [string] | true | | Messages explaining why the capability is supported or not supported. | | name | string | true | | The name of the capability. | | supported | boolean | true | | Whether the capability is supported. | ## DeploymentChecklistResponse ``` { "properties": { "data": { "description": "The list of settings for the deployment.", "items": { "properties": { "hint": { "description": "An optional hint for the setting.", "maxLength": 255, "type": [ "string", "null" ] }, "label": { "description": "A descriptive label for the setting.", "maxLength": 80, "type": "string" }, "setting": { "description": "The identifier of the setting.", "enum": [ "deploymentName", "DeploymentName", "DEPLOYMENT_NAME", "settingsDataDrift", "SettingsDataDrift", "SETTINGS_DATA_DRIFT", "settingsAccuracy", "SettingsAccuracy", "SETTINGS_ACCURACY", "settingsFairness", "SettingsFairness", "SETTINGS_FAIRNESS", "settingsCustomMetrics", "SettingsCustomMetrics", "SETTINGS_CUSTOM_METRICS", "settingsHumility", "SettingsHumility", "SETTINGS_HUMILITY", "settingsChallengers", "SettingsChallengers", "SETTINGS_CHALLENGERS", "settingsPredictions", "SettingsPredictions", "SETTINGS_PREDICTIONS", "settingsRetraining", "SettingsRetraining", "SETTINGS_RETRAINING", "settingsUsage", "SettingsUsage", "SETTINGS_USAGE", "settingsDataExploration", "SettingsDataExploration", "SETTINGS_DATA_EXPLORATION", "settingsNotifications", "SettingsNotifications", "SETTINGS_NOTIFICATIONS" ], "type": "string" }, "status": { "description": "Indicates the completion status of the setting.", "enum": [ "notSet", "NotSet", "NOT_SET", "partial", "Partial", "PARTIAL", "configured", "Configured", "CONFIGURED" ], "type": "string" } }, "required": [ "hint", "label", "setting", "status" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 12, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [SingleDeploymentSetting] | true | maxItems: 12 | The list of settings for the deployment. | ## DeploymentCreateFromLearningModel ``` { "properties": { "defaultPredictionServerId": { "description": "ID of the default prediction server for deployment. Required for DataRobot Cloud, must be omitted for Enterprise installations.", "type": "string" }, "description": { "default": null, "description": "A description for the deployment.", "maxLength": 10000, "type": [ "string", "null" ] }, "grantInitialDeploymentRolesFromProject": { "default": true, "description": "If True, initial roles on the deployment will be granted to users who have roles on the project that the deployed model comes from. If False, only the creator of the deployment will be given a role on the deployment. Defaults to True.", "type": "boolean", "x-versionadded": "v2.31" }, "importance": { "description": "Importance of the deployment. Defaults to LOW when MLOps is enabled, must not be provided when MLOps disabled.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "A human-readable name for the deployment.", "maxLength": 512, "type": "string" }, "modelId": { "description": "ID of the model to be deployed.", "type": "string" }, "predictionThreshold": { "description": "Prediction threshold used for binary classification. If not specified, model default prediction threshold will be used.", "maximum": 1, "minimum": 0, "type": "number" }, "status": { "default": "active", "description": "Status of the deployment", "enum": [ "active", "inactive" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" } }, "required": [ "description", "label", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultPredictionServerId | string | false | | ID of the default prediction server for deployment. Required for DataRobot Cloud, must be omitted for Enterprise installations. | | description | string,null | true | maxLength: 10000 | A description for the deployment. | | grantInitialDeploymentRolesFromProject | boolean | false | | If True, initial roles on the deployment will be granted to users who have roles on the project that the deployed model comes from. If False, only the creator of the deployment will be given a role on the deployment. Defaults to True. | | importance | string,null | false | | Importance of the deployment. Defaults to LOW when MLOps is enabled, must not be provided when MLOps disabled. | | label | string | true | maxLength: 512 | A human-readable name for the deployment. | | modelId | string | true | | ID of the model to be deployed. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Prediction threshold used for binary classification. If not specified, model default prediction threshold will be used. | | status | string,null | false | | Status of the deployment | ### Enumerated Values | Property | Value | | --- | --- | | importance | [CRITICAL, HIGH, MODERATE, LOW] | | status | [active, inactive] | ## DeploymentCreateFromModelPackage ``` { "properties": { "additionalMetadata": { "description": "Key/Value pair list, with additional metadata", "items": { "properties": { "key": { "description": "The key, a unique identifier for this item of metadata", "maxLength": 50, "type": "string" }, "value": { "description": "The value, the actual content for this item of metadata", "maxLength": 256, "type": "string" } }, "required": [ "key", "value" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 40, "type": "array" }, "defaultPredictionServerId": { "description": "ID of the default prediction server for deployment. Required for DataRobot Cloud for non-external models, must be omitted for Enterprise installations.", "type": "string" }, "description": { "default": null, "description": "A description for the deployment.", "maxLength": 10000, "type": [ "string", "null" ] }, "importance": { "description": "Importance of the deployment. Defaults to LOW when MLOps is enabled, must not be provided when MLOps disabled.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "A human-readable name for the deployment.", "maxLength": 512, "type": "string" }, "modelPackageId": { "description": "ID of the model package to deploy.", "type": "string" }, "predictionEnvironmentId": { "description": "ID of prediction environment where to deploy", "type": "string" }, "runtimeParameterValues": { "description": "Inject values into a model at runtime. The fieldName must match a fieldName defined in the model package. This list is merged with any existing runtime values set through the deployed model package.\n
This property is gated behind the feature flags **`['CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT']`**.\n To enable this feature, you can contact your DataRobot representative or administrator.\n
", "feature_flags": [ { "description": "Enables the ability to edit Custom Model Runtime-Parameters (and replica and resource bundle settings) directly from the Deployment info page. Edited values are local to a given Deployment and do not affect the runtime of any current or future Deployments of the same Model Package.", "enabled_by_default": true, "maturity": "PUBLIC_PREVIEW", "name": "CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT" } ], "public_preview": true, "type": "string", "x-datarobot-public-preview": true, "x-datarobot-required-feature-flags": [ { "description": "Enables the ability to edit Custom Model Runtime-Parameters (and replica and resource bundle settings) directly from the Deployment info page. Edited values are local to a given Deployment and do not affect the runtime of any current or future Deployments of the same Model Package.", "enabled_by_default": true, "maturity": "PUBLIC_PREVIEW", "name": "CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT" } ] }, "status": { "default": "active", "description": "Status of the deployment", "enum": [ "active", "inactive" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "userProvidedId": { "description": "A user-provided unique ID associated with a deployment definition in a remote git repository.", "maxLength": 100, "type": "string", "x-versionadded": "v2.29" } }, "required": [ "description", "label", "modelPackageId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | additionalMetadata | [KeyValuePair] | false | maxItems: 40 | Key/Value pair list, with additional metadata | | defaultPredictionServerId | string | false | | ID of the default prediction server for deployment. Required for DataRobot Cloud for non-external models, must be omitted for Enterprise installations. | | description | string,null | true | maxLength: 10000 | A description for the deployment. | | importance | string,null | false | | Importance of the deployment. Defaults to LOW when MLOps is enabled, must not be provided when MLOps disabled. | | label | string | true | maxLength: 512 | A human-readable name for the deployment. | | modelPackageId | string | true | | ID of the model package to deploy. | | predictionEnvironmentId | string | false | | ID of prediction environment where to deploy | | runtimeParameterValues | string | false | | Inject values into a model at runtime. The fieldName must match a fieldName defined in the model package. This list is merged with any existing runtime values set through the deployed model package. This property is gated behind the feature flags ['CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT']. To enable this feature, you can contact your DataRobot representative or administrator. | | status | string,null | false | | Status of the deployment | | userProvidedId | string | false | maxLength: 100 | A user-provided unique ID associated with a deployment definition in a remote git repository. | ### Enumerated Values | Property | Value | | --- | --- | | importance | [CRITICAL, HIGH, MODERATE, LOW] | | status | [active, inactive] | ## DeploymentCreateResponse ``` { "properties": { "id": { "description": "The ID of the created deployment.", "type": "string" } }, "required": [ "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the created deployment. | ## DeploymentDefaultPredictionServerResponse ``` { "description": "The prediction server associated with the deployment.", "properties": { "datarobot-key": { "description": "The `datarobot-key` header used in requests to this prediction server.", "type": "string" }, "id": { "description": "ID of the prediction server for the deployment.", "type": [ "string", "null" ] }, "url": { "description": "URL of the prediction server.", "type": "string" } }, "required": [ "datarobot-key", "id", "url" ], "type": "object" } ``` The prediction server associated with the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datarobot-key | string | true | | The datarobot-key header used in requests to this prediction server. | | id | string,null | true | | ID of the prediction server for the deployment. | | url | string | true | | URL of the prediction server. | ## DeploymentGetLimitsResponse ``` { "properties": { "maxDeploymentLimit": { "description": "A limitInfo object for prepaid deployment information.", "properties": { "limitReached": { "description": "Whether the limit named has been reached.", "type": "boolean" }, "reason": { "description": "The reason why the limit has been reached.", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "value": { "description": "The limit value of the given limit.", "type": "integer" } }, "required": [ "limitReached", "reason", "value" ], "type": "object", "x-versionadded": "v2.43" }, "prepaidDeploymentLimit": { "description": "A limitInfo object for prepaid deployment information.", "properties": { "limitReached": { "description": "Whether the limit named has been reached.", "type": "boolean" }, "reason": { "description": "The reason why the limit has been reached.", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "value": { "description": "The limit value of the given limit.", "type": "integer" } }, "required": [ "limitReached", "reason", "value" ], "type": "object", "x-versionadded": "v2.43" }, "totalDeploymentCount": { "description": "Total number of deployments counted for limit information.", "type": "integer" } }, "required": [ "maxDeploymentLimit", "prepaidDeploymentLimit", "totalDeploymentCount" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxDeploymentLimit | LimitInfo | true | | A limitInfo object for prepaid deployment information. | | prepaidDeploymentLimit | LimitInfo | true | | A limitInfo object for prepaid deployment information. | | totalDeploymentCount | integer | true | | Total number of deployments counted for limit information. | ## DeploymentGovernanceResponse ``` { "description": "Deployment governance info.", "properties": { "approvalStatus": { "description": "Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response.", "enum": [ "PENDING", "APPROVED" ], "type": "string" }, "hasOpenedChangeRequests": { "description": "If there are change request related to this deployment with `PENDING` status.", "type": "boolean" }, "reviewers": { "description": "A list of reviewers to approve deployment change requests.", "items": { "properties": { "email": { "description": "The email address of the reviewer.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "id": { "description": "The ID of the reviewer.", "type": "string" }, "status": { "description": "The latest review status.", "enum": [ "APPROVED", "CHANGES_REQUESTED", "COMMENTED" ], "type": [ "string", "null" ] }, "userhash": { "description": "Reviewer's gravatar hash.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "username": { "description": "The username of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "email", "id", "status" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 20, "type": "array", "x-versionadded": "v2.37" } }, "required": [ "approvalStatus", "hasOpenedChangeRequests" ], "type": "object" } ``` Deployment governance info. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | approvalStatus | string | true | | Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response. | | hasOpenedChangeRequests | boolean | true | | If there are change request related to this deployment with PENDING status. | | reviewers | [DeploymentReviewerResponse] | false | maxItems: 20 | A list of reviewers to approve deployment change requests. | ### Enumerated Values | Property | Value | | --- | --- | | approvalStatus | [PENDING, APPROVED] | ## DeploymentListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of deployments.", "items": { "properties": { "accuracyHealth": { "description": "Accuracy health of the deployment.", "properties": { "endDate": { "description": "End date of accuracy health period.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "A message providing more detail on the status.", "type": "string" }, "startDate": { "description": "Start date of accuracy health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Accuracy health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "approvalStatus": { "description": "Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response.", "enum": [ "PENDING", "APPROVED" ], "type": "string" }, "createdAt": { "description": "The date and time of when the deployment was created, in ISO 8601 format.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "creator": { "description": "Creator of the deployment.", "properties": { "email": { "description": "Email address of the owner.", "type": [ "string", "null" ] }, "firstName": { "description": "First name of the owner.", "type": [ "string", "null" ] }, "id": { "description": "ID of the owner.", "type": "string" }, "lastName": { "description": "Last name of the owner.", "type": [ "string", "null" ] } }, "required": [ "email", "firstName", "id", "lastName" ], "type": "object" }, "defaultPredictionServer": { "description": "The prediction server associated with the deployment.", "properties": { "datarobot-key": { "description": "The `datarobot-key` header used in requests to this prediction server.", "type": "string" }, "id": { "description": "ID of the prediction server for the deployment.", "type": [ "string", "null" ] }, "url": { "description": "URL of the prediction server.", "type": "string" } }, "required": [ "datarobot-key", "id", "url" ], "type": "object" }, "description": { "description": "Description of the deployment.", "type": [ "string", "null" ] }, "governance": { "description": "Deployment governance info.", "properties": { "approvalStatus": { "description": "Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response.", "enum": [ "PENDING", "APPROVED" ], "type": "string" }, "hasOpenedChangeRequests": { "description": "If there are change request related to this deployment with `PENDING` status.", "type": "boolean" }, "reviewers": { "description": "A list of reviewers to approve deployment change requests.", "items": { "properties": { "email": { "description": "The email address of the reviewer.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "id": { "description": "The ID of the reviewer.", "type": "string" }, "status": { "description": "The latest review status.", "enum": [ "APPROVED", "CHANGES_REQUESTED", "COMMENTED" ], "type": [ "string", "null" ] }, "userhash": { "description": "Reviewer's gravatar hash.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "username": { "description": "The username of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "email", "id", "status" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 20, "type": "array", "x-versionadded": "v2.37" } }, "required": [ "approvalStatus", "hasOpenedChangeRequests" ], "type": "object" }, "hasError": { "description": "Whether the deployment is not operational because it failed to start properly.", "type": "boolean", "x-versionadded": "v2.32" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "importance": { "description": "Shows how important this deployment is.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string", "x-versionadded": "v2.21" }, "label": { "description": "Label of the deployment.", "maxLength": 512, "type": "string" }, "model": { "description": "Information related to the current model of the deployment.", "properties": { "buildEnvironmentType": { "description": "Build environment type of the current model.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "customModelImage": { "description": "Information related to the custom model image of the deployment.", "properties": { "customModelId": { "description": "ID of the custom model.", "type": "string" }, "customModelName": { "description": "Name of the custom model.", "type": "string" }, "customModelVersionId": { "description": "ID of the custom model version.", "type": "string" }, "customModelVersionLabel": { "description": "Label of the custom model version.", "type": "string" }, "executionEnvironmentId": { "description": "ID of the execution environment.", "type": "string" }, "executionEnvironmentName": { "description": "Name of the execution environment.", "type": "string" }, "executionEnvironmentVersionId": { "description": "ID of the execution environment version.", "type": "string" }, "executionEnvironmentVersionLabel": { "description": "Label of the execution environment version.", "type": "string" } }, "required": [ "customModelId", "customModelName", "customModelVersionId", "customModelVersionLabel", "executionEnvironmentId", "executionEnvironmentName", "executionEnvironmentVersionId", "executionEnvironmentVersionLabel" ], "type": "object" }, "deployedAt": { "description": "Timestamp of when current model was deployed.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "projectId": { "description": "Project ID of the current model.", "type": [ "string", "null" ] }, "projectName": { "description": "Project name of the current model.", "type": [ "string", "null" ] }, "targetName": { "description": "Target name of the current model.", "type": "string" }, "targetType": { "description": "Target type of the current model.", "type": "string" }, "type": { "description": "Type of the current model.", "type": "string" }, "unstructuredModelKind": { "description": "Whether the current model is an unstructured model.", "type": "boolean", "x-versionadded": "v2.29" }, "unsupervisedMode": { "description": "Whether the current model's project is unsupervised.", "type": "boolean", "x-versionadded": "v2.18" }, "unsupervisedType": { "description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" } }, "required": [ "id", "isDeprecated", "projectName", "targetType", "type", "unstructuredModelKind", "unsupervisedMode" ], "type": "object" }, "modelHealth": { "description": "Model health of the deployment.", "properties": { "endDate": { "description": "End date of model health period.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "A message providing more detail on the status.", "type": "string" }, "startDate": { "description": "Start date of model health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Model health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "modelPackage": { "description": "Information related to the current ModelPackage.", "properties": { "id": { "description": "ID of the ModelPackage.", "type": "string" }, "name": { "description": "Name of the ModelPackage.", "type": "string" }, "registeredModelId": { "description": "The ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name", "registeredModelId" ], "type": "object" }, "modelPackageInitialDownload": { "description": "If a model package has been downloaded for this deployment, then this will tell you when it was first downloaded.", "properties": { "timestamp": { "description": "Timestamp of the first time model package was downloaded.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "timestamp" ], "type": "object" }, "openedChangeRequests": { "description": "An array of the change request IDs related to this deployment that have.", "items": { "type": "string" }, "type": "array" }, "owners": { "description": "Count and preview of owners of the deployment.", "properties": { "count": { "description": "Total count of owners.", "type": "integer" }, "preview": { "description": "A list of owner objects.", "items": { "description": "Creator of the deployment.", "properties": { "email": { "description": "Email address of the owner.", "type": [ "string", "null" ] }, "firstName": { "description": "First name of the owner.", "type": [ "string", "null" ] }, "id": { "description": "ID of the owner.", "type": "string" }, "lastName": { "description": "Last name of the owner.", "type": [ "string", "null" ] } }, "required": [ "email", "firstName", "id", "lastName" ], "type": "object" }, "type": "array" } }, "required": [ "count", "preview" ], "type": "object" }, "permissions": { "description": "Permissions that the user making the request has on the deployment.", "items": { "enum": [ "CAN_ADD_CHALLENGERS", "CAN_APPROVE_REPLACEMENT_MODEL", "CAN_BE_MANAGED_BY_MLOPS_AGENT", "CAN_DELETE_CHALLENGERS", "CAN_DELETE_DEPLOYMENT", "CAN_DOWNLOAD_DOCUMENTATION", "CAN_EDIT_CHALLENGERS", "CAN_EDIT_DEPLOYMENT", "CAN_GENERATE_DOCUMENTATION", "CAN_MAKE_PREDICTIONS", "CAN_REPLACE_MODEL", "CAN_SCORE_CHALLENGERS", "CAN_SHARE", "CAN_SHARE_DEPLOYMENT_OWNERSHIP", "CAN_SUBMIT_ACTUALS", "CAN_UPDATE_DEPLOYMENT_THRESHOLDS", "CAN_VIEW" ], "type": "string" }, "type": "array", "x-versionadded": "v2.18" }, "predictionEnvironment": { "description": "Information related to the current PredictionEnvironment.", "properties": { "id": { "description": "ID of the PredictionEnvironment.", "type": [ "string", "null" ] }, "isManagedByManagementAgent": { "description": "True if PredictionEnvironment is using Management Agent.", "type": "boolean" }, "name": { "description": "Name of the PredictionEnvironment.", "type": "string" }, "platform": { "description": "Platform of the PredictionEnvironment.", "enum": [ "aws", "gcp", "azure", "onPremise", "datarobot", "datarobotServerless", "openShift", "other", "snowflake", "sapAiCore" ], "type": "string" }, "plugin": { "description": "Plugin name of the PredictionEnvironment.", "type": [ "string", "null" ] }, "supportedModelFormats": { "description": "Model formats that the PredictionEnvironment supports.", "items": { "enum": [ "datarobot", "datarobotScoringCode", "customModel", "externalModel" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "id", "isManagedByManagementAgent", "name", "platform" ], "type": "object" }, "predictionUsage": { "description": "Prediction usage of the deployment.", "properties": { "dailyRates": { "description": "Number of predictions made in the last 7 days.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Timestamp of the last prediction request.", "format": "date-time", "type": [ "string", "null" ] }, "serverErrorRates": { "description": "Number of server errors (5xx) in the last 7 days.", "items": { "type": "integer" }, "maxItems": 7, "type": "array", "x-versionadded": "v2.39" }, "userErrorRates": { "description": "Number of user errors (4xx) in the last 7 days.", "items": { "type": "integer" }, "maxItems": 7, "type": "array", "x-versionadded": "v2.39" } }, "required": [ "dailyRates", "lastTimestamp", "serverErrorRates", "userErrorRates" ], "type": "object" }, "scoringCodeInitialDownload": { "description": "If scoring code has been downloaded for this deployment, then this will tell you when it was first downloaded.", "properties": { "timestamp": { "description": "Timestamp of the first time scoring code was downloaded.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "timestamp" ], "type": "object" }, "serviceHealth": { "description": "Service health of the deployment.", "properties": { "endDate": { "description": "End date of model service period.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "Start date of service health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Service health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "startDate", "status" ], "type": "object" }, "settings": { "description": "Settings of the deployment.", "properties": { "batchMonitoringEnabled": { "description": "if batch monitoring is enabled.", "type": "boolean" }, "humbleAiEnabled": { "description": "if humble ai is enabled.", "type": "boolean" }, "predictionIntervalsEnabled": { "description": "If prediction intervals are enabled.", "type": "boolean" }, "predictionWarningEnabled": { "description": "If prediction warning is enabled.", "type": "boolean" } }, "type": "object" }, "status": { "description": "Displays current deployment status.", "enum": [ "active", "archived", "errored", "inactive", "launching", "replacingModel", "stopping" ], "type": "string", "x-versionadded": "v2.22" }, "tags": { "description": "The list of formatted deployment tags.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "maxLength": 50, "type": "string" }, "value": { "description": "The value of the tag.", "maxLength": 50, "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object" }, "type": "array" }, "userProvidedId": { "description": "A user-provided unique ID associated with a deployment definition in a remote git repository.", "maxLength": 100, "type": "string", "x-versionadded": "v2.29" } }, "required": [ "accuracyHealth", "createdAt", "defaultPredictionServer", "description", "id", "label", "model", "modelHealth", "permissions", "predictionUsage", "serviceHealth", "settings", "status", "tags" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DeploymentRetrieveResponse] | true | | List of deployments. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DeploymentModelCustomModelImageResponse ``` { "description": "Information related to the custom model image of the deployment.", "properties": { "customModelId": { "description": "ID of the custom model.", "type": "string" }, "customModelName": { "description": "Name of the custom model.", "type": "string" }, "customModelVersionId": { "description": "ID of the custom model version.", "type": "string" }, "customModelVersionLabel": { "description": "Label of the custom model version.", "type": "string" }, "executionEnvironmentId": { "description": "ID of the execution environment.", "type": "string" }, "executionEnvironmentName": { "description": "Name of the execution environment.", "type": "string" }, "executionEnvironmentVersionId": { "description": "ID of the execution environment version.", "type": "string" }, "executionEnvironmentVersionLabel": { "description": "Label of the execution environment version.", "type": "string" } }, "required": [ "customModelId", "customModelName", "customModelVersionId", "customModelVersionLabel", "executionEnvironmentId", "executionEnvironmentName", "executionEnvironmentVersionId", "executionEnvironmentVersionLabel" ], "type": "object" } ``` Information related to the custom model image of the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string | true | | ID of the custom model. | | customModelName | string | true | | Name of the custom model. | | customModelVersionId | string | true | | ID of the custom model version. | | customModelVersionLabel | string | true | | Label of the custom model version. | | executionEnvironmentId | string | true | | ID of the execution environment. | | executionEnvironmentName | string | true | | Name of the execution environment. | | executionEnvironmentVersionId | string | true | | ID of the execution environment version. | | executionEnvironmentVersionLabel | string | true | | Label of the execution environment version. | ## DeploymentModelHealthResponse ``` { "description": "Model health of the deployment.", "properties": { "endDate": { "description": "End date of model health period.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "A message providing more detail on the status.", "type": "string" }, "startDate": { "description": "Start date of model health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Model health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" } ``` Model health of the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string,null(date-time) | true | | End date of model health period. | | message | string | true | | A message providing more detail on the status. | | startDate | string,null(date-time) | true | | Start date of model health period. | | status | string | true | | Model health status. | ### Enumerated Values | Property | Value | | --- | --- | | status | [failing, notStarted, passing, unavailable, unknown, warning] | ## DeploymentModelPackageInitialDownloadResponse ``` { "description": "If a model package has been downloaded for this deployment, then this will tell you when it was first downloaded.", "properties": { "timestamp": { "description": "Timestamp of the first time model package was downloaded.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "timestamp" ], "type": "object" } ``` If a model package has been downloaded for this deployment, then this will tell you when it was first downloaded. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timestamp | string,null(date-time) | true | | Timestamp of the first time model package was downloaded. | ## DeploymentModelPackageResponse ``` { "description": "Information related to the current ModelPackage.", "properties": { "id": { "description": "ID of the ModelPackage.", "type": "string" }, "name": { "description": "Name of the ModelPackage.", "type": "string" }, "registeredModelId": { "description": "The ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name", "registeredModelId" ], "type": "object" } ``` Information related to the current ModelPackage. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the ModelPackage. | | name | string | true | | Name of the ModelPackage. | | registeredModelId | string,null | true | | The ID of the associated registered model | ## DeploymentModelResponse ``` { "description": "Information related to the current model of the deployment.", "properties": { "buildEnvironmentType": { "description": "Build environment type of the current model.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "customModelImage": { "description": "Information related to the custom model image of the deployment.", "properties": { "customModelId": { "description": "ID of the custom model.", "type": "string" }, "customModelName": { "description": "Name of the custom model.", "type": "string" }, "customModelVersionId": { "description": "ID of the custom model version.", "type": "string" }, "customModelVersionLabel": { "description": "Label of the custom model version.", "type": "string" }, "executionEnvironmentId": { "description": "ID of the execution environment.", "type": "string" }, "executionEnvironmentName": { "description": "Name of the execution environment.", "type": "string" }, "executionEnvironmentVersionId": { "description": "ID of the execution environment version.", "type": "string" }, "executionEnvironmentVersionLabel": { "description": "Label of the execution environment version.", "type": "string" } }, "required": [ "customModelId", "customModelName", "customModelVersionId", "customModelVersionLabel", "executionEnvironmentId", "executionEnvironmentName", "executionEnvironmentVersionId", "executionEnvironmentVersionLabel" ], "type": "object" }, "deployedAt": { "description": "Timestamp of when current model was deployed.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "projectId": { "description": "Project ID of the current model.", "type": [ "string", "null" ] }, "projectName": { "description": "Project name of the current model.", "type": [ "string", "null" ] }, "targetName": { "description": "Target name of the current model.", "type": "string" }, "targetType": { "description": "Target type of the current model.", "type": "string" }, "type": { "description": "Type of the current model.", "type": "string" }, "unstructuredModelKind": { "description": "Whether the current model is an unstructured model.", "type": "boolean", "x-versionadded": "v2.29" }, "unsupervisedMode": { "description": "Whether the current model's project is unsupervised.", "type": "boolean", "x-versionadded": "v2.18" }, "unsupervisedType": { "description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" } }, "required": [ "id", "isDeprecated", "projectName", "targetType", "type", "unstructuredModelKind", "unsupervisedMode" ], "type": "object" } ``` Information related to the current model of the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildEnvironmentType | string,null | false | | Build environment type of the current model. | | customModelImage | DeploymentModelCustomModelImageResponse | false | | Information related to the custom model image of the deployment. | | deployedAt | string,null(date-time) | false | | Timestamp of when current model was deployed. | | id | string | true | | ID of the current model. | | isDeprecated | boolean | true | | Whether the current model is deprecated model. eg. python2 based model. | | projectId | string,null | false | | Project ID of the current model. | | projectName | string,null | true | | Project name of the current model. | | targetName | string | false | | Target name of the current model. | | targetType | string | true | | Target type of the current model. | | type | string | true | | Type of the current model. | | unstructuredModelKind | boolean | true | | Whether the current model is an unstructured model. | | unsupervisedMode | boolean | true | | Whether the current model's project is unsupervised. | | unsupervisedType | string,null | false | | Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'. | ### Enumerated Values | Property | Value | | --- | --- | | unsupervisedType | [anomaly, clustering] | ## DeploymentOwnerResponse ``` { "description": "Creator of the deployment.", "properties": { "email": { "description": "Email address of the owner.", "type": [ "string", "null" ] }, "firstName": { "description": "First name of the owner.", "type": [ "string", "null" ] }, "id": { "description": "ID of the owner.", "type": "string" }, "lastName": { "description": "Last name of the owner.", "type": [ "string", "null" ] } }, "required": [ "email", "firstName", "id", "lastName" ], "type": "object" } ``` Creator of the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string,null | true | | Email address of the owner. | | firstName | string,null | true | | First name of the owner. | | id | string | true | | ID of the owner. | | lastName | string,null | true | | Last name of the owner. | ## DeploymentOwnersResponse ``` { "description": "Count and preview of owners of the deployment.", "properties": { "count": { "description": "Total count of owners.", "type": "integer" }, "preview": { "description": "A list of owner objects.", "items": { "description": "Creator of the deployment.", "properties": { "email": { "description": "Email address of the owner.", "type": [ "string", "null" ] }, "firstName": { "description": "First name of the owner.", "type": [ "string", "null" ] }, "id": { "description": "ID of the owner.", "type": "string" }, "lastName": { "description": "Last name of the owner.", "type": [ "string", "null" ] } }, "required": [ "email", "firstName", "id", "lastName" ], "type": "object" }, "type": "array" } }, "required": [ "count", "preview" ], "type": "object" } ``` Count and preview of owners of the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Total count of owners. | | preview | [DeploymentOwnerResponse] | true | | A list of owner objects. | ## DeploymentPermanentDelete ``` { "properties": { "action": { "description": "Action to perform on these deleted deployments.", "enum": [ "PermanentlyErase" ], "type": "string" }, "deploymentIds": { "description": "ID of a list of deleted deployments to perform action on.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "action", "deploymentIds" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | Action to perform on these deleted deployments. | | deploymentIds | [string] | true | maxItems: 100minItems: 1 | ID of a list of deleted deployments to perform action on. | ### Enumerated Values | Property | Value | | --- | --- | | action | PermanentlyErase | ## DeploymentPredictionEnvironmentResponse ``` { "description": "Information related to the current PredictionEnvironment.", "properties": { "id": { "description": "ID of the PredictionEnvironment.", "type": [ "string", "null" ] }, "isManagedByManagementAgent": { "description": "True if PredictionEnvironment is using Management Agent.", "type": "boolean" }, "name": { "description": "Name of the PredictionEnvironment.", "type": "string" }, "platform": { "description": "Platform of the PredictionEnvironment.", "enum": [ "aws", "gcp", "azure", "onPremise", "datarobot", "datarobotServerless", "openShift", "other", "snowflake", "sapAiCore" ], "type": "string" }, "plugin": { "description": "Plugin name of the PredictionEnvironment.", "type": [ "string", "null" ] }, "supportedModelFormats": { "description": "Model formats that the PredictionEnvironment supports.", "items": { "enum": [ "datarobot", "datarobotScoringCode", "customModel", "externalModel" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "id", "isManagedByManagementAgent", "name", "platform" ], "type": "object" } ``` Information related to the current PredictionEnvironment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string,null | true | | ID of the PredictionEnvironment. | | isManagedByManagementAgent | boolean | true | | True if PredictionEnvironment is using Management Agent. | | name | string | true | | Name of the PredictionEnvironment. | | platform | string | true | | Platform of the PredictionEnvironment. | | plugin | string,null | false | | Plugin name of the PredictionEnvironment. | | supportedModelFormats | [string] | false | maxItems: 4minItems: 1 | Model formats that the PredictionEnvironment supports. | ### Enumerated Values | Property | Value | | --- | --- | | platform | [aws, gcp, azure, onPremise, datarobot, datarobotServerless, openShift, other, snowflake, sapAiCore] | ## DeploymentPredictionEnvironmentUpdate ``` { "properties": { "dpsDeploymentId": { "description": "The ID of the deployment on dedicated prediction environment that will be updated to serverless.", "type": "string" }, "serverlessPredictionEnvironmentId": { "description": "The ID of serverless prediction environment.", "type": "string" } }, "required": [ "dpsDeploymentId", "serverlessPredictionEnvironmentId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dpsDeploymentId | string | true | | The ID of the deployment on dedicated prediction environment that will be updated to serverless. | | serverlessPredictionEnvironmentId | string | true | | The ID of serverless prediction environment. | ## DeploymentPredictionUsageResponse ``` { "description": "Prediction usage of the deployment.", "properties": { "dailyRates": { "description": "Number of predictions made in the last 7 days.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Timestamp of the last prediction request.", "format": "date-time", "type": [ "string", "null" ] }, "serverErrorRates": { "description": "Number of server errors (5xx) in the last 7 days.", "items": { "type": "integer" }, "maxItems": 7, "type": "array", "x-versionadded": "v2.39" }, "userErrorRates": { "description": "Number of user errors (4xx) in the last 7 days.", "items": { "type": "integer" }, "maxItems": 7, "type": "array", "x-versionadded": "v2.39" } }, "required": [ "dailyRates", "lastTimestamp", "serverErrorRates", "userErrorRates" ], "type": "object" } ``` Prediction usage of the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dailyRates | [integer] | true | | Number of predictions made in the last 7 days. | | lastTimestamp | string,null(date-time) | true | | Timestamp of the last prediction request. | | serverErrorRates | [integer] | true | maxItems: 7 | Number of server errors (5xx) in the last 7 days. | | userErrorRates | [integer] | true | maxItems: 7 | Number of user errors (4xx) in the last 7 days. | ## DeploymentRetrieveResponse ``` { "properties": { "accuracyHealth": { "description": "Accuracy health of the deployment.", "properties": { "endDate": { "description": "End date of accuracy health period.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "A message providing more detail on the status.", "type": "string" }, "startDate": { "description": "Start date of accuracy health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Accuracy health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "approvalStatus": { "description": "Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response.", "enum": [ "PENDING", "APPROVED" ], "type": "string" }, "createdAt": { "description": "The date and time of when the deployment was created, in ISO 8601 format.", "format": "date-time", "type": "string", "x-versionadded": "v2.20" }, "creator": { "description": "Creator of the deployment.", "properties": { "email": { "description": "Email address of the owner.", "type": [ "string", "null" ] }, "firstName": { "description": "First name of the owner.", "type": [ "string", "null" ] }, "id": { "description": "ID of the owner.", "type": "string" }, "lastName": { "description": "Last name of the owner.", "type": [ "string", "null" ] } }, "required": [ "email", "firstName", "id", "lastName" ], "type": "object" }, "defaultPredictionServer": { "description": "The prediction server associated with the deployment.", "properties": { "datarobot-key": { "description": "The `datarobot-key` header used in requests to this prediction server.", "type": "string" }, "id": { "description": "ID of the prediction server for the deployment.", "type": [ "string", "null" ] }, "url": { "description": "URL of the prediction server.", "type": "string" } }, "required": [ "datarobot-key", "id", "url" ], "type": "object" }, "description": { "description": "Description of the deployment.", "type": [ "string", "null" ] }, "governance": { "description": "Deployment governance info.", "properties": { "approvalStatus": { "description": "Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response.", "enum": [ "PENDING", "APPROVED" ], "type": "string" }, "hasOpenedChangeRequests": { "description": "If there are change request related to this deployment with `PENDING` status.", "type": "boolean" }, "reviewers": { "description": "A list of reviewers to approve deployment change requests.", "items": { "properties": { "email": { "description": "The email address of the reviewer.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "id": { "description": "The ID of the reviewer.", "type": "string" }, "status": { "description": "The latest review status.", "enum": [ "APPROVED", "CHANGES_REQUESTED", "COMMENTED" ], "type": [ "string", "null" ] }, "userhash": { "description": "Reviewer's gravatar hash.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "username": { "description": "The username of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "email", "id", "status" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 20, "type": "array", "x-versionadded": "v2.37" } }, "required": [ "approvalStatus", "hasOpenedChangeRequests" ], "type": "object" }, "hasError": { "description": "Whether the deployment is not operational because it failed to start properly.", "type": "boolean", "x-versionadded": "v2.32" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "importance": { "description": "Shows how important this deployment is.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string", "x-versionadded": "v2.21" }, "label": { "description": "Label of the deployment.", "maxLength": 512, "type": "string" }, "model": { "description": "Information related to the current model of the deployment.", "properties": { "buildEnvironmentType": { "description": "Build environment type of the current model.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "customModelImage": { "description": "Information related to the custom model image of the deployment.", "properties": { "customModelId": { "description": "ID of the custom model.", "type": "string" }, "customModelName": { "description": "Name of the custom model.", "type": "string" }, "customModelVersionId": { "description": "ID of the custom model version.", "type": "string" }, "customModelVersionLabel": { "description": "Label of the custom model version.", "type": "string" }, "executionEnvironmentId": { "description": "ID of the execution environment.", "type": "string" }, "executionEnvironmentName": { "description": "Name of the execution environment.", "type": "string" }, "executionEnvironmentVersionId": { "description": "ID of the execution environment version.", "type": "string" }, "executionEnvironmentVersionLabel": { "description": "Label of the execution environment version.", "type": "string" } }, "required": [ "customModelId", "customModelName", "customModelVersionId", "customModelVersionLabel", "executionEnvironmentId", "executionEnvironmentName", "executionEnvironmentVersionId", "executionEnvironmentVersionLabel" ], "type": "object" }, "deployedAt": { "description": "Timestamp of when current model was deployed.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "projectId": { "description": "Project ID of the current model.", "type": [ "string", "null" ] }, "projectName": { "description": "Project name of the current model.", "type": [ "string", "null" ] }, "targetName": { "description": "Target name of the current model.", "type": "string" }, "targetType": { "description": "Target type of the current model.", "type": "string" }, "type": { "description": "Type of the current model.", "type": "string" }, "unstructuredModelKind": { "description": "Whether the current model is an unstructured model.", "type": "boolean", "x-versionadded": "v2.29" }, "unsupervisedMode": { "description": "Whether the current model's project is unsupervised.", "type": "boolean", "x-versionadded": "v2.18" }, "unsupervisedType": { "description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" } }, "required": [ "id", "isDeprecated", "projectName", "targetType", "type", "unstructuredModelKind", "unsupervisedMode" ], "type": "object" }, "modelHealth": { "description": "Model health of the deployment.", "properties": { "endDate": { "description": "End date of model health period.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "A message providing more detail on the status.", "type": "string" }, "startDate": { "description": "Start date of model health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Model health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "modelPackage": { "description": "Information related to the current ModelPackage.", "properties": { "id": { "description": "ID of the ModelPackage.", "type": "string" }, "name": { "description": "Name of the ModelPackage.", "type": "string" }, "registeredModelId": { "description": "The ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name", "registeredModelId" ], "type": "object" }, "modelPackageInitialDownload": { "description": "If a model package has been downloaded for this deployment, then this will tell you when it was first downloaded.", "properties": { "timestamp": { "description": "Timestamp of the first time model package was downloaded.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "timestamp" ], "type": "object" }, "openedChangeRequests": { "description": "An array of the change request IDs related to this deployment that have.", "items": { "type": "string" }, "type": "array" }, "owners": { "description": "Count and preview of owners of the deployment.", "properties": { "count": { "description": "Total count of owners.", "type": "integer" }, "preview": { "description": "A list of owner objects.", "items": { "description": "Creator of the deployment.", "properties": { "email": { "description": "Email address of the owner.", "type": [ "string", "null" ] }, "firstName": { "description": "First name of the owner.", "type": [ "string", "null" ] }, "id": { "description": "ID of the owner.", "type": "string" }, "lastName": { "description": "Last name of the owner.", "type": [ "string", "null" ] } }, "required": [ "email", "firstName", "id", "lastName" ], "type": "object" }, "type": "array" } }, "required": [ "count", "preview" ], "type": "object" }, "permissions": { "description": "Permissions that the user making the request has on the deployment.", "items": { "enum": [ "CAN_ADD_CHALLENGERS", "CAN_APPROVE_REPLACEMENT_MODEL", "CAN_BE_MANAGED_BY_MLOPS_AGENT", "CAN_DELETE_CHALLENGERS", "CAN_DELETE_DEPLOYMENT", "CAN_DOWNLOAD_DOCUMENTATION", "CAN_EDIT_CHALLENGERS", "CAN_EDIT_DEPLOYMENT", "CAN_GENERATE_DOCUMENTATION", "CAN_MAKE_PREDICTIONS", "CAN_REPLACE_MODEL", "CAN_SCORE_CHALLENGERS", "CAN_SHARE", "CAN_SHARE_DEPLOYMENT_OWNERSHIP", "CAN_SUBMIT_ACTUALS", "CAN_UPDATE_DEPLOYMENT_THRESHOLDS", "CAN_VIEW" ], "type": "string" }, "type": "array", "x-versionadded": "v2.18" }, "predictionEnvironment": { "description": "Information related to the current PredictionEnvironment.", "properties": { "id": { "description": "ID of the PredictionEnvironment.", "type": [ "string", "null" ] }, "isManagedByManagementAgent": { "description": "True if PredictionEnvironment is using Management Agent.", "type": "boolean" }, "name": { "description": "Name of the PredictionEnvironment.", "type": "string" }, "platform": { "description": "Platform of the PredictionEnvironment.", "enum": [ "aws", "gcp", "azure", "onPremise", "datarobot", "datarobotServerless", "openShift", "other", "snowflake", "sapAiCore" ], "type": "string" }, "plugin": { "description": "Plugin name of the PredictionEnvironment.", "type": [ "string", "null" ] }, "supportedModelFormats": { "description": "Model formats that the PredictionEnvironment supports.", "items": { "enum": [ "datarobot", "datarobotScoringCode", "customModel", "externalModel" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "id", "isManagedByManagementAgent", "name", "platform" ], "type": "object" }, "predictionUsage": { "description": "Prediction usage of the deployment.", "properties": { "dailyRates": { "description": "Number of predictions made in the last 7 days.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Timestamp of the last prediction request.", "format": "date-time", "type": [ "string", "null" ] }, "serverErrorRates": { "description": "Number of server errors (5xx) in the last 7 days.", "items": { "type": "integer" }, "maxItems": 7, "type": "array", "x-versionadded": "v2.39" }, "userErrorRates": { "description": "Number of user errors (4xx) in the last 7 days.", "items": { "type": "integer" }, "maxItems": 7, "type": "array", "x-versionadded": "v2.39" } }, "required": [ "dailyRates", "lastTimestamp", "serverErrorRates", "userErrorRates" ], "type": "object" }, "scoringCodeInitialDownload": { "description": "If scoring code has been downloaded for this deployment, then this will tell you when it was first downloaded.", "properties": { "timestamp": { "description": "Timestamp of the first time scoring code was downloaded.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "timestamp" ], "type": "object" }, "serviceHealth": { "description": "Service health of the deployment.", "properties": { "endDate": { "description": "End date of model service period.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "Start date of service health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Service health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "startDate", "status" ], "type": "object" }, "settings": { "description": "Settings of the deployment.", "properties": { "batchMonitoringEnabled": { "description": "if batch monitoring is enabled.", "type": "boolean" }, "humbleAiEnabled": { "description": "if humble ai is enabled.", "type": "boolean" }, "predictionIntervalsEnabled": { "description": "If prediction intervals are enabled.", "type": "boolean" }, "predictionWarningEnabled": { "description": "If prediction warning is enabled.", "type": "boolean" } }, "type": "object" }, "status": { "description": "Displays current deployment status.", "enum": [ "active", "archived", "errored", "inactive", "launching", "replacingModel", "stopping" ], "type": "string", "x-versionadded": "v2.22" }, "tags": { "description": "The list of formatted deployment tags.", "items": { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "maxLength": 50, "type": "string" }, "value": { "description": "The value of the tag.", "maxLength": 50, "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object" }, "type": "array" }, "userProvidedId": { "description": "A user-provided unique ID associated with a deployment definition in a remote git repository.", "maxLength": 100, "type": "string", "x-versionadded": "v2.29" } }, "required": [ "accuracyHealth", "createdAt", "defaultPredictionServer", "description", "id", "label", "model", "modelHealth", "permissions", "predictionUsage", "serviceHealth", "settings", "status", "tags" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyHealth | DeploymentAccuracyHealthResponse | true | | Accuracy health of the deployment. | | approvalStatus | string | false | | Status to show whether the deployment was approved or not. It also shows up as a part of metadata within the prediction response. | | createdAt | string(date-time) | true | | The date and time of when the deployment was created, in ISO 8601 format. | | creator | DeploymentOwnerResponse | false | | Creator of the deployment. | | defaultPredictionServer | DeploymentDefaultPredictionServerResponse | true | | The prediction server associated with the deployment. | | description | string,null | true | | Description of the deployment. | | governance | DeploymentGovernanceResponse | false | | Deployment governance info. | | hasError | boolean | false | | Whether the deployment is not operational because it failed to start properly. | | id | string | true | | The ID of the deployment. | | importance | string | false | | Shows how important this deployment is. | | label | string | true | maxLength: 512 | Label of the deployment. | | model | DeploymentModelResponse | true | | Information related to the current model of the deployment. | | modelHealth | DeploymentModelHealthResponse | true | | Model health of the deployment. | | modelPackage | DeploymentModelPackageResponse | false | | Information related to the current ModelPackage. | | modelPackageInitialDownload | DeploymentModelPackageInitialDownloadResponse | false | | If a model package has been downloaded for this deployment, then this will tell you when it was first downloaded. | | openedChangeRequests | [string] | false | | An array of the change request IDs related to this deployment that have. | | owners | DeploymentOwnersResponse | false | | Count and preview of owners of the deployment. | | permissions | [string] | true | | Permissions that the user making the request has on the deployment. | | predictionEnvironment | DeploymentPredictionEnvironmentResponse | false | | Information related to the current PredictionEnvironment. | | predictionUsage | DeploymentPredictionUsageResponse | true | | Prediction usage of the deployment. | | scoringCodeInitialDownload | DeploymentScoringCodeInitialDownloadResponse | false | | If scoring code has been downloaded for this deployment, then this will tell you when it was first downloaded. | | serviceHealth | DeploymentServiceHealthResponse | true | | Service health of the deployment. | | settings | DeploymentSettingsInfo | true | | Settings of the deployment. | | status | string | true | | Displays current deployment status. | | tags | [DeploymentTagRetrieveResponse] | true | | The list of formatted deployment tags. | | userProvidedId | string | false | maxLength: 100 | A user-provided unique ID associated with a deployment definition in a remote git repository. | ### Enumerated Values | Property | Value | | --- | --- | | approvalStatus | [PENDING, APPROVED] | | importance | [CRITICAL, HIGH, MODERATE, LOW] | | status | [active, archived, errored, inactive, launching, replacingModel, stopping] | ## DeploymentReviewerResponse ``` { "properties": { "email": { "description": "The email address of the reviewer.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "id": { "description": "The ID of the reviewer.", "type": "string" }, "status": { "description": "The latest review status.", "enum": [ "APPROVED", "CHANGES_REQUESTED", "COMMENTED" ], "type": [ "string", "null" ] }, "userhash": { "description": "Reviewer's gravatar hash.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "username": { "description": "The username of the reviewer.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "email", "id", "status" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string,null | true | | The email address of the reviewer. | | fullName | string,null | false | | The full name of the reviewer. | | id | string | true | | The ID of the reviewer. | | status | string,null | true | | The latest review status. | | userhash | string,null | false | | Reviewer's gravatar hash. | | username | string,null | false | | The username of the reviewer. | ### Enumerated Values | Property | Value | | --- | --- | | status | [APPROVED, CHANGES_REQUESTED, COMMENTED] | ## DeploymentScoringCodeInitialDownloadResponse ``` { "description": "If scoring code has been downloaded for this deployment, then this will tell you when it was first downloaded.", "properties": { "timestamp": { "description": "Timestamp of the first time scoring code was downloaded.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "timestamp" ], "type": "object" } ``` If scoring code has been downloaded for this deployment, then this will tell you when it was first downloaded. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timestamp | string,null(date-time) | true | | Timestamp of the first time scoring code was downloaded. | ## DeploymentServiceHealthResponse ``` { "description": "Service health of the deployment.", "properties": { "endDate": { "description": "End date of model service period.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "Start date of service health period.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Service health status.", "enum": [ "failing", "notStarted", "passing", "unavailable", "unknown", "warning" ], "type": "string" } }, "required": [ "endDate", "startDate", "status" ], "type": "object" } ``` Service health of the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string,null(date-time) | true | | End date of model service period. | | startDate | string,null(date-time) | true | | Start date of service health period. | | status | string | true | | Service health status. | ### Enumerated Values | Property | Value | | --- | --- | | status | [failing, notStarted, passing, unavailable, unknown, warning] | ## DeploymentSettingsInfo ``` { "description": "Settings of the deployment.", "properties": { "batchMonitoringEnabled": { "description": "if batch monitoring is enabled.", "type": "boolean" }, "humbleAiEnabled": { "description": "if humble ai is enabled.", "type": "boolean" }, "predictionIntervalsEnabled": { "description": "If prediction intervals are enabled.", "type": "boolean" }, "predictionWarningEnabled": { "description": "If prediction warning is enabled.", "type": "boolean" } }, "type": "object" } ``` Settings of the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchMonitoringEnabled | boolean | false | | if batch monitoring is enabled. | | humbleAiEnabled | boolean | false | | if humble ai is enabled. | | predictionIntervalsEnabled | boolean | false | | If prediction intervals are enabled. | | predictionWarningEnabled | boolean | false | | If prediction warning is enabled. | ## DeploymentStatusUpdate ``` { "properties": { "status": { "description": "Status that deployment should be transition in.", "enum": [ "active", "inactive" ], "type": "string" } }, "required": [ "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | status | string | true | | Status that deployment should be transition in. | ### Enumerated Values | Property | Value | | --- | --- | | status | [active, inactive] | ## DeploymentTagRetrieveResponse ``` { "properties": { "id": { "description": "The ID of the tag.", "type": "string" }, "name": { "description": "The name of the tag.", "maxLength": 50, "type": "string" }, "value": { "description": "The value of the tag.", "maxLength": 50, "type": "string" } }, "required": [ "id", "name", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the tag. | | name | string | true | maxLength: 50 | The name of the tag. | | value | string | true | maxLength: 50 | The value of the tag. | ## DeploymentUpdate ``` { "properties": { "description": { "description": "A description for the deployment.", "maxLength": 10000, "type": [ "string", "null" ] }, "importance": { "description": "Shows how important this deployment is.", "enum": [ "CRITICAL", "HIGH", "MODERATE", "LOW" ], "type": "string", "x-versionadded": "v2.21" }, "label": { "description": "A human-readable name for the deployment.", "maxLength": 512, "type": [ "string", "null" ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | maxLength: 10000 | A description for the deployment. | | importance | string | false | | Shows how important this deployment is. | | label | string,null | false | maxLength: 512 | A human-readable name for the deployment. | ### Enumerated Values | Property | Value | | --- | --- | | importance | [CRITICAL, HIGH, MODERATE, LOW] | ## DeploymentsScoringCodeBuildPayload ``` { "properties": { "includeAgent": { "description": "Whether the Scoring Code built will include tracking agent", "type": "boolean" }, "includePredictionExplanations": { "description": "Whether the Scoring Code built will include prediction explanations", "type": "boolean" }, "includePredictionIntervals": { "description": "Whether the Scoring Code built will include prediction intervals", "type": "boolean" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | includeAgent | boolean | false | | Whether the Scoring Code built will include tracking agent | | includePredictionExplanations | boolean | false | | Whether the Scoring Code built will include prediction explanations | | includePredictionIntervals | boolean | false | | Whether the Scoring Code built will include prediction intervals | ## ExecutionEnvironmentShortResponse ``` { "description": "Execution environment associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment.", "type": "string" }, "name": { "description": "User-friendly name of the execution environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` Execution environment associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the execution environment. | | name | string | true | | User-friendly name of the execution environment. | ## ExecutionEnvironmentVersionShortResponse ``` { "description": "Execution environment version associated with this deployment.", "properties": { "id": { "description": "The ID of the execution environment version.", "type": "string" }, "label": { "description": "User-friendly name of the execution environment version.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" } ``` Execution environment version associated with this deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the execution environment version. | | label | string | true | | User-friendly name of the execution environment version. | ## Feature ``` { "properties": { "dateFormat": { "description": "The date format string for how this feature was interpreted.", "type": [ "string", "null" ] }, "featureType": { "description": "Feature type.", "type": [ "string", "null" ] }, "importance": { "description": "Numeric measure of the relationship strength between the feature and target (independent of model or other features).", "type": [ "number", "null" ] }, "knownInAdvance": { "description": "Whether the feature was selected as known in advance in a time-series model, false for non-time-series models.", "type": "boolean" }, "name": { "description": "Feature name.", "type": "string" } }, "required": [ "dateFormat", "featureType", "importance", "knownInAdvance", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dateFormat | string,null | true | | The date format string for how this feature was interpreted. | | featureType | string,null | true | | Feature type. | | importance | number,null | true | | Numeric measure of the relationship strength between the feature and target (independent of model or other features). | | knownInAdvance | boolean | true | | Whether the feature was selected as known in advance in a time-series model, false for non-time-series models. | | name | string | true | | Feature name. | ## FeatureListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of features.", "items": { "properties": { "dateFormat": { "description": "The date format string for how this feature was interpreted.", "type": [ "string", "null" ] }, "featureType": { "description": "Feature type.", "type": [ "string", "null" ] }, "importance": { "description": "Numeric measure of the relationship strength between the feature and target (independent of model or other features).", "type": [ "number", "null" ] }, "knownInAdvance": { "description": "Whether the feature was selected as known in advance in a time-series model, false for non-time-series models.", "type": "boolean" }, "name": { "description": "Feature name.", "type": "string" } }, "required": [ "dateFormat", "featureType", "importance", "knownInAdvance", "name" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.35" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [Feature] | true | | An array of features. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## KeyValuePair ``` { "properties": { "key": { "description": "The key, a unique identifier for this item of metadata", "maxLength": 50, "type": "string" }, "value": { "description": "The value, the actual content for this item of metadata", "maxLength": 256, "type": "string" } }, "required": [ "key", "value" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | key | string | true | maxLength: 50 | The key, a unique identifier for this item of metadata | | value | string | true | maxLength: 256 | The value, the actual content for this item of metadata | ## LimitInfo ``` { "description": "A limitInfo object for prepaid deployment information.", "properties": { "limitReached": { "description": "Whether the limit named has been reached.", "type": "boolean" }, "reason": { "description": "The reason why the limit has been reached.", "type": [ "string", "null" ], "x-versionadded": "v2.43" }, "value": { "description": "The limit value of the given limit.", "type": "integer" } }, "required": [ "limitReached", "reason", "value" ], "type": "object", "x-versionadded": "v2.43" } ``` A limitInfo object for prepaid deployment information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | limitReached | boolean | true | | Whether the limit named has been reached. | | reason | string,null | true | | The reason why the limit has been reached. | | value | integer | true | | The limit value of the given limit. | ## ModelHistoryListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "History of deployment's champion models, including the current champion model.", "items": { "properties": { "customModelImage": { "description": "Contains information about the custom model image.", "properties": { "customModelId": { "description": "ID of the custom model.", "type": [ "string", "null" ] }, "customModelName": { "description": "Name of the custom model.", "type": [ "string", "null" ] }, "customModelVersionId": { "description": "ID of the custom model version.", "type": [ "string", "null" ] }, "customModelVersionLabel": { "description": "Label of the custom model version.", "type": [ "string", "null" ] }, "executionEnvironmentId": { "description": "ID of the execution environment.", "type": [ "string", "null" ] }, "executionEnvironmentName": { "description": "Name of the execution environment.", "type": [ "string", "null" ] }, "executionEnvironmentVersionId": { "description": "ID of the execution environment version.", "type": [ "string", "null" ] }, "executionEnvironmentVersionLabel": { "description": "Label of the execution environment version.", "type": [ "string", "null" ] } }, "required": [ "customModelId", "customModelName", "customModelVersionId", "customModelVersionLabel", "executionEnvironmentId", "executionEnvironmentName", "executionEnvironmentVersionId", "executionEnvironmentVersionLabel" ], "type": "object", "x-versionadded": "v2.36" }, "endDate": { "description": "The timestamp when the model is replaced by another model.", "format": "date-time", "type": [ "string", "null" ] }, "forecastEndDate": { "description": "The max timestamp of all predictions made when the model is or was champion of the deployment, if predictions by forecast date is enabled.", "format": "date-time", "type": [ "string", "null" ] }, "forecastStartDate": { "description": "The min timestamp of all predictions made when the model is or was champion of the deployment, if predictions by forecast date is enabled.", "format": "date-time", "type": [ "string", "null" ] }, "modelPackage": { "description": "Contains information about the model package.", "properties": { "id": { "description": "ID of the model package.", "type": [ "string", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "name": { "description": "Name of the model package.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "ID of the project.", "type": "string" }, "registeredModelId": { "description": "ID of the associated registered model.", "type": [ "string", "null" ] } }, "required": [ "id", "modelId", "name", "predictionThreshold", "registeredModelId" ], "type": "object", "x-versionadded": "v2.36" }, "predictionWarningBoundaries": { "description": "Lower and upper boundaries for outlier detection.", "properties": { "lower": { "description": "Lower boundary for outlier detection.", "type": "number" }, "upper": { "description": "Upper boundary for outlier detection.", "type": "number" } }, "required": [ "lower", "upper" ], "type": "object", "x-versionadded": "v2.36" }, "replacementReason": { "description": "Reason for model replacement.", "type": [ "string", "null" ] }, "startDate": { "description": "The timestamp when the model becomes champion of the deployment.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "forecastEndDate", "forecastStartDate", "modelPackage", "replacementReason", "startDate" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ModelHistoryResponse] | true | maxItems: 100 | History of deployment's champion models, including the current champion model. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ModelHistoryResponse ``` { "properties": { "customModelImage": { "description": "Contains information about the custom model image.", "properties": { "customModelId": { "description": "ID of the custom model.", "type": [ "string", "null" ] }, "customModelName": { "description": "Name of the custom model.", "type": [ "string", "null" ] }, "customModelVersionId": { "description": "ID of the custom model version.", "type": [ "string", "null" ] }, "customModelVersionLabel": { "description": "Label of the custom model version.", "type": [ "string", "null" ] }, "executionEnvironmentId": { "description": "ID of the execution environment.", "type": [ "string", "null" ] }, "executionEnvironmentName": { "description": "Name of the execution environment.", "type": [ "string", "null" ] }, "executionEnvironmentVersionId": { "description": "ID of the execution environment version.", "type": [ "string", "null" ] }, "executionEnvironmentVersionLabel": { "description": "Label of the execution environment version.", "type": [ "string", "null" ] } }, "required": [ "customModelId", "customModelName", "customModelVersionId", "customModelVersionLabel", "executionEnvironmentId", "executionEnvironmentName", "executionEnvironmentVersionId", "executionEnvironmentVersionLabel" ], "type": "object", "x-versionadded": "v2.36" }, "endDate": { "description": "The timestamp when the model is replaced by another model.", "format": "date-time", "type": [ "string", "null" ] }, "forecastEndDate": { "description": "The max timestamp of all predictions made when the model is or was champion of the deployment, if predictions by forecast date is enabled.", "format": "date-time", "type": [ "string", "null" ] }, "forecastStartDate": { "description": "The min timestamp of all predictions made when the model is or was champion of the deployment, if predictions by forecast date is enabled.", "format": "date-time", "type": [ "string", "null" ] }, "modelPackage": { "description": "Contains information about the model package.", "properties": { "id": { "description": "ID of the model package.", "type": [ "string", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "name": { "description": "Name of the model package.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "ID of the project.", "type": "string" }, "registeredModelId": { "description": "ID of the associated registered model.", "type": [ "string", "null" ] } }, "required": [ "id", "modelId", "name", "predictionThreshold", "registeredModelId" ], "type": "object", "x-versionadded": "v2.36" }, "predictionWarningBoundaries": { "description": "Lower and upper boundaries for outlier detection.", "properties": { "lower": { "description": "Lower boundary for outlier detection.", "type": "number" }, "upper": { "description": "Upper boundary for outlier detection.", "type": "number" } }, "required": [ "lower", "upper" ], "type": "object", "x-versionadded": "v2.36" }, "replacementReason": { "description": "Reason for model replacement.", "type": [ "string", "null" ] }, "startDate": { "description": "The timestamp when the model becomes champion of the deployment.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "forecastEndDate", "forecastStartDate", "modelPackage", "replacementReason", "startDate" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelImage | CustomModelImage | false | | Contains information about the custom model image. | | endDate | string,null(date-time) | true | | The timestamp when the model is replaced by another model. | | forecastEndDate | string,null(date-time) | true | | The max timestamp of all predictions made when the model is or was champion of the deployment, if predictions by forecast date is enabled. | | forecastStartDate | string,null(date-time) | true | | The min timestamp of all predictions made when the model is or was champion of the deployment, if predictions by forecast date is enabled. | | modelPackage | ModelPackageObject | true | | Contains information about the model package. | | predictionWarningBoundaries | ModelPackagePredictionThresholdWarning | false | | Lower and upper boundaries for outlier detection. | | replacementReason | string,null | true | | Reason for model replacement. | | startDate | string(date-time) | true | | The timestamp when the model becomes champion of the deployment. | ## ModelPackageObject ``` { "description": "Contains information about the model package.", "properties": { "id": { "description": "ID of the model package.", "type": [ "string", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "name": { "description": "Name of the model package.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "ID of the project.", "type": "string" }, "registeredModelId": { "description": "ID of the associated registered model.", "type": [ "string", "null" ] } }, "required": [ "id", "modelId", "name", "predictionThreshold", "registeredModelId" ], "type": "object", "x-versionadded": "v2.36" } ``` Contains information about the model package. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string,null | true | | ID of the model package. | | modelId | string | true | | ID of the model. | | name | string,null | true | | Name of the model package. | | predictionThreshold | number,null | true | | The threshold value used for binary classification prediction. | | projectId | string | false | | ID of the project. | | registeredModelId | string,null | true | | ID of the associated registered model. | ## ModelPackagePredictionThresholdWarning ``` { "description": "Lower and upper boundaries for outlier detection.", "properties": { "lower": { "description": "Lower boundary for outlier detection.", "type": "number" }, "upper": { "description": "Upper boundary for outlier detection.", "type": "number" } }, "required": [ "lower", "upper" ], "type": "object", "x-versionadded": "v2.36" } ``` Lower and upper boundaries for outlier detection. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | lower | number | true | | Lower boundary for outlier detection. | | upper | number | true | | Upper boundary for outlier detection. | ## PPSImageMetadataResponse ``` { "properties": { "baseImageId": { "description": "Internal base image entity id for troubleshooting purposes", "type": "string" }, "created": { "description": "ISO formatted image upload date", "format": "date-time", "type": "string" }, "datarobotRuntimeImageTag": { "description": "For internal use only.", "type": [ "string", "null" ] }, "dockerImageId": { "description": "A Docker image id (immutable, content-based) hash associated with the given image", "type": "string" }, "filename": { "description": "The name of the file when the download requested", "type": "string" }, "hash": { "description": "Hash of the image content, supposed to be used for verifying content after the download. The algorithm used for hashing is specified in `hashAlgorithm` field. Note that hash is calculated over compressed image data.", "type": "string" }, "hashAlgorithm": { "description": "An algorithm name used for calculating content `hash`", "enum": [ "SHA256" ], "type": "string" }, "imageSize": { "description": "Size in bytes of the compressed PPS image data", "type": "integer" }, "shortDockerImageId": { "description": "A 12-chars shortened version of the `dockerImageId` as shown in 'docker images' command line command output", "type": "string" } }, "required": [ "baseImageId", "created", "dockerImageId", "filename", "hash", "hashAlgorithm", "imageSize", "shortDockerImageId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseImageId | string | true | | Internal base image entity id for troubleshooting purposes | | created | string(date-time) | true | | ISO formatted image upload date | | datarobotRuntimeImageTag | string,null | false | | For internal use only. | | dockerImageId | string | true | | A Docker image id (immutable, content-based) hash associated with the given image | | filename | string | true | | The name of the file when the download requested | | hash | string | true | | Hash of the image content, supposed to be used for verifying content after the download. The algorithm used for hashing is specified in hashAlgorithm field. Note that hash is calculated over compressed image data. | | hashAlgorithm | string | true | | An algorithm name used for calculating content hash | | imageSize | integer | true | | Size in bytes of the compressed PPS image data | | shortDockerImageId | string | true | | A 12-chars shortened version of the dockerImageId as shown in 'docker images' command line command output | ### Enumerated Values | Property | Value | | --- | --- | | hashAlgorithm | SHA256 | ## ResourceRequestBundleListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.35" }, "data": { "description": "List of bundles.", "items": { "properties": { "cpuCount": { "description": "Max number of CPUs available.", "type": "number" }, "description": { "description": "A short description of CPU, Memory and other resources.", "type": "string" }, "gpuCount": { "description": "Max number of GPUs available.", "type": [ "number", "null" ] }, "gpuMaker": { "description": "The manufacture of the GPU.", "enum": [ "nvidia", "amd", "intel" ], "type": [ "string", "null" ] }, "gpuMemoryBytes": { "description": "Max amount of GPU memory available.", "type": "integer", "x-versionadded": "v2.36" }, "gpuTypeLabel": { "description": "A label that identifies the GPU type.", "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "hasGpu": { "description": "If this bundle provides at least one GPU resource.", "type": "boolean" }, "id": { "description": "The id of the bundle.", "type": "string" }, "isBillable": { "default": false, "description": "If the bundle has been billable or unbillable.", "type": "boolean", "x-versionadded": "v2.44" }, "isDefault": { "description": "If this should be the default resource choice.", "type": "boolean" }, "isDeleted": { "description": "If the bundle has been deleted and should not be used.", "type": "boolean" }, "memoryBytes": { "description": "Max amount of memory available.", "type": "integer" }, "name": { "description": "A short name for the bundle.", "type": "string" }, "useCases": { "description": "List of use cases this bundle supports.", "items": { "enum": [ "customApplication", "customJob", "customModel", "customTaskFit", "modelingMachineWorker", "predictionAPI", "sapAICore", "sparkApplication" ], "type": "string" }, "type": "array" } }, "required": [ "cpuCount", "description", "gpuMemoryBytes", "hasGpu", "id", "memoryBytes", "name", "useCases" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.35" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ResourceRequestBundleResponse] | true | maxItems: 100 | List of bundles. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ResourceRequestBundleResponse ``` { "properties": { "cpuCount": { "description": "Max number of CPUs available.", "type": "number" }, "description": { "description": "A short description of CPU, Memory and other resources.", "type": "string" }, "gpuCount": { "description": "Max number of GPUs available.", "type": [ "number", "null" ] }, "gpuMaker": { "description": "The manufacture of the GPU.", "enum": [ "nvidia", "amd", "intel" ], "type": [ "string", "null" ] }, "gpuMemoryBytes": { "description": "Max amount of GPU memory available.", "type": "integer", "x-versionadded": "v2.36" }, "gpuTypeLabel": { "description": "A label that identifies the GPU type.", "type": [ "string", "null" ], "x-versionadded": "v2.4" }, "hasGpu": { "description": "If this bundle provides at least one GPU resource.", "type": "boolean" }, "id": { "description": "The id of the bundle.", "type": "string" }, "isBillable": { "default": false, "description": "If the bundle has been billable or unbillable.", "type": "boolean", "x-versionadded": "v2.44" }, "isDefault": { "description": "If this should be the default resource choice.", "type": "boolean" }, "isDeleted": { "description": "If the bundle has been deleted and should not be used.", "type": "boolean" }, "memoryBytes": { "description": "Max amount of memory available.", "type": "integer" }, "name": { "description": "A short name for the bundle.", "type": "string" }, "useCases": { "description": "List of use cases this bundle supports.", "items": { "enum": [ "customApplication", "customJob", "customModel", "customTaskFit", "modelingMachineWorker", "predictionAPI", "sapAICore", "sparkApplication" ], "type": "string" }, "type": "array" } }, "required": [ "cpuCount", "description", "gpuMemoryBytes", "hasGpu", "id", "memoryBytes", "name", "useCases" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cpuCount | number | true | | Max number of CPUs available. | | description | string | true | | A short description of CPU, Memory and other resources. | | gpuCount | number,null | false | | Max number of GPUs available. | | gpuMaker | string,null | false | | The manufacture of the GPU. | | gpuMemoryBytes | integer | true | | Max amount of GPU memory available. | | gpuTypeLabel | string,null | false | | A label that identifies the GPU type. | | hasGpu | boolean | true | | If this bundle provides at least one GPU resource. | | id | string | true | | The id of the bundle. | | isBillable | boolean | false | | If the bundle has been billable or unbillable. | | isDefault | boolean | false | | If this should be the default resource choice. | | isDeleted | boolean | false | | If the bundle has been deleted and should not be used. | | memoryBytes | integer | true | | Max amount of memory available. | | name | string | true | | A short name for the bundle. | | useCases | [string] | true | | List of use cases this bundle supports. | ### Enumerated Values | Property | Value | | --- | --- | | gpuMaker | [nvidia, amd, intel] | ## ScheduledReportOnDemmand ``` { "properties": { "id": { "description": "ID of Scheduled report record.", "type": "string" } }, "required": [ "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of Scheduled report record. | ## SharingListResponse ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControl] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | ## SharingUpdateOrRemoveWithGrant ``` { "properties": { "data": { "description": "List of sharing roles to update.", "items": { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [UserRoleWithGrant] | true | maxItems: 100 | List of sharing roles to update. | ## SingleDeploymentSetting ``` { "properties": { "hint": { "description": "An optional hint for the setting.", "maxLength": 255, "type": [ "string", "null" ] }, "label": { "description": "A descriptive label for the setting.", "maxLength": 80, "type": "string" }, "setting": { "description": "The identifier of the setting.", "enum": [ "deploymentName", "DeploymentName", "DEPLOYMENT_NAME", "settingsDataDrift", "SettingsDataDrift", "SETTINGS_DATA_DRIFT", "settingsAccuracy", "SettingsAccuracy", "SETTINGS_ACCURACY", "settingsFairness", "SettingsFairness", "SETTINGS_FAIRNESS", "settingsCustomMetrics", "SettingsCustomMetrics", "SETTINGS_CUSTOM_METRICS", "settingsHumility", "SettingsHumility", "SETTINGS_HUMILITY", "settingsChallengers", "SettingsChallengers", "SETTINGS_CHALLENGERS", "settingsPredictions", "SettingsPredictions", "SETTINGS_PREDICTIONS", "settingsRetraining", "SettingsRetraining", "SETTINGS_RETRAINING", "settingsUsage", "SettingsUsage", "SETTINGS_USAGE", "settingsDataExploration", "SettingsDataExploration", "SETTINGS_DATA_EXPLORATION", "settingsNotifications", "SettingsNotifications", "SETTINGS_NOTIFICATIONS" ], "type": "string" }, "status": { "description": "Indicates the completion status of the setting.", "enum": [ "notSet", "NotSet", "NOT_SET", "partial", "Partial", "PARTIAL", "configured", "Configured", "CONFIGURED" ], "type": "string" } }, "required": [ "hint", "label", "setting", "status" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | hint | string,null | true | maxLength: 255 | An optional hint for the setting. | | label | string | true | maxLength: 80 | A descriptive label for the setting. | | setting | string | true | | The identifier of the setting. | | status | string | true | | Indicates the completion status of the setting. | ### Enumerated Values | Property | Value | | --- | --- | | setting | [deploymentName, DeploymentName, DEPLOYMENT_NAME, settingsDataDrift, SettingsDataDrift, SETTINGS_DATA_DRIFT, settingsAccuracy, SettingsAccuracy, SETTINGS_ACCURACY, settingsFairness, SettingsFairness, SETTINGS_FAIRNESS, settingsCustomMetrics, SettingsCustomMetrics, SETTINGS_CUSTOM_METRICS, settingsHumility, SettingsHumility, SETTINGS_HUMILITY, settingsChallengers, SettingsChallengers, SETTINGS_CHALLENGERS, settingsPredictions, SettingsPredictions, SETTINGS_PREDICTIONS, settingsRetraining, SettingsRetraining, SETTINGS_RETRAINING, settingsUsage, SettingsUsage, SETTINGS_USAGE, settingsDataExploration, SettingsDataExploration, SETTINGS_DATA_EXPLORATION, settingsNotifications, SettingsNotifications, SETTINGS_NOTIFICATIONS] | | status | [notSet, NotSet, NOT_SET, partial, Partial, PARTIAL, configured, Configured, CONFIGURED] | ## UserRoleWithGrant ``` { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role to set on the entity. When it is None, the role of this user will be removedfrom this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If role is NO_ROLE canShare is ignored. | | role | string,null | true | | The role to set on the entity. When it is None, the role of this user will be removedfrom this entity. | | username | string | true | | The username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | --- # Model replacement URL: https://docs.datarobot.com/en/docs/api/reference/public-api/deployment_model_replacement.html > Use the endpoints described below to manage deployment model replacement. # Model replacement Use the endpoints described below to manage deployment model replacement. ## Model Replacement by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/model/` Authentication requirements: `BearerAuth` Replace the model used to make predictions for the deployment. A validation process will be performed to make sure the new model is eligible as a replacement. If the validation fails, the model replacement will not occur.The Model Replacement Validation endpoint can be used to confirm the new model is eligible as a replacement. ### Body parameter ``` { "properties": { "modelId": { "description": "ID of the model used to replace deployment's champion model. Required if modelPackageId is not provided.", "type": "string", "x-versiondeprecated": "v2.33" }, "modelPackageId": { "description": "ID of the model package used to replace deployment's champion model. Required if modelId is not provided.", "type": "string", "x-versionadded": "v2.21" }, "reason": { "description": "Reason for the model replacement.", "enum": [ "ACCURACY", "DATA_DRIFT", "ERRORS", "SCHEDULED_REFRESH", "SCORING_SPEED", "DEPRECATION", "OTHER" ], "type": "string" }, "runtimeParameterValues": { "description": "Inject values into a model at runtime. The fieldName must match a fieldName defined in the model package. This list is merged with any existing runtime values set through the deployed model package.\n
This property is gated behind the feature flags **`['CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT']`**.\n To enable this feature, you can contact your DataRobot representative or administrator.\n
", "feature_flags": [ { "description": "Enables the ability to edit Custom Model Runtime-Parameters (and replica and resource bundle settings) directly from the Deployment info page. Edited values are local to a given Deployment and do not affect the runtime of any current or future Deployments of the same Model Package.", "enabled_by_default": true, "maturity": "PUBLIC_PREVIEW", "name": "CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT" } ], "public_preview": true, "type": "string", "x-datarobot-public-preview": true, "x-datarobot-required-feature-flags": [ { "description": "Enables the ability to edit Custom Model Runtime-Parameters (and replica and resource bundle settings) directly from the Deployment info page. Edited values are local to a given Deployment and do not affect the runtime of any current or future Deployments of the same Model Package.", "enabled_by_default": true, "maturity": "PUBLIC_PREVIEW", "name": "CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT" } ], "x-versionadded": "v2.35" } }, "required": [ "reason" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | ModelReplacementSubmission | false | none | ### Example responses > 202 Response ``` { "properties": { "checks": { "description": "A more granular explanation of why the replacement model was eligible or ineligible.", "properties": { "combinedModelSegments": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "containsTrackedSegmentAttributes": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "driftTracking": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "featureDataTypes": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "features": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "humilityRules": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "modelCanBeDeployed": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "modelStatus": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "notCurrentModel": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "permission": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "predictionIntervals": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "predictionReady": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "seriesType": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "supported": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "target": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "targetClasses": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "targetType": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "timeSeriesCompatibility": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "validChallenger": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } }, "type": "object" }, "message": { "description": "Message of the overall validation check.", "type": "string" }, "status": { "description": "Status of the overall validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "checks", "message", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See Location header. | ModelReplacementValidationResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Model Replacement Validation by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/model/validation/` Authentication requirements: `BearerAuth` Validate that a model can be used to replace the current model of the deployment. ### Body parameter ``` { "properties": { "modelId": { "description": "ID of the model used to replace deployment's champion model. Required if modelPackageId is not provided.", "type": "string", "x-versiondeprecated": "v2.33" }, "modelPackageId": { "description": "ID of the model package used to replace deployment's champion model. Required if modelId is not provided.", "type": "string", "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | ModelReplacementValidationRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "checks": { "description": "A more granular explanation of why the replacement model was eligible or ineligible.", "properties": { "combinedModelSegments": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "containsTrackedSegmentAttributes": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "driftTracking": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "featureDataTypes": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "features": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "humilityRules": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "modelCanBeDeployed": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "modelStatus": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "notCurrentModel": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "permission": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "predictionIntervals": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "predictionReady": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "seriesType": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "supported": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "target": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "targetClasses": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "targetType": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "timeSeriesCompatibility": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "validChallenger": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } }, "type": "object" }, "message": { "description": "Message of the overall validation check.", "type": "string" }, "status": { "description": "Status of the overall validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "checks", "message", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelReplacementValidationResponse | # Schemas ## ModelReplacementSubmission ``` { "properties": { "modelId": { "description": "ID of the model used to replace deployment's champion model. Required if modelPackageId is not provided.", "type": "string", "x-versiondeprecated": "v2.33" }, "modelPackageId": { "description": "ID of the model package used to replace deployment's champion model. Required if modelId is not provided.", "type": "string", "x-versionadded": "v2.21" }, "reason": { "description": "Reason for the model replacement.", "enum": [ "ACCURACY", "DATA_DRIFT", "ERRORS", "SCHEDULED_REFRESH", "SCORING_SPEED", "DEPRECATION", "OTHER" ], "type": "string" }, "runtimeParameterValues": { "description": "Inject values into a model at runtime. The fieldName must match a fieldName defined in the model package. This list is merged with any existing runtime values set through the deployed model package.\n
This property is gated behind the feature flags **`['CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT']`**.\n To enable this feature, you can contact your DataRobot representative or administrator.\n
", "feature_flags": [ { "description": "Enables the ability to edit Custom Model Runtime-Parameters (and replica and resource bundle settings) directly from the Deployment info page. Edited values are local to a given Deployment and do not affect the runtime of any current or future Deployments of the same Model Package.", "enabled_by_default": true, "maturity": "PUBLIC_PREVIEW", "name": "CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT" } ], "public_preview": true, "type": "string", "x-datarobot-public-preview": true, "x-datarobot-required-feature-flags": [ { "description": "Enables the ability to edit Custom Model Runtime-Parameters (and replica and resource bundle settings) directly from the Deployment info page. Edited values are local to a given Deployment and do not affect the runtime of any current or future Deployments of the same Model Package.", "enabled_by_default": true, "maturity": "PUBLIC_PREVIEW", "name": "CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT" } ], "x-versionadded": "v2.35" } }, "required": [ "reason" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | false | | ID of the model used to replace deployment's champion model. Required if modelPackageId is not provided. | | modelPackageId | string | false | | ID of the model package used to replace deployment's champion model. Required if modelId is not provided. | | reason | string | true | | Reason for the model replacement. | | runtimeParameterValues | string | false | | Inject values into a model at runtime. The fieldName must match a fieldName defined in the model package. This list is merged with any existing runtime values set through the deployed model package. This property is gated behind the feature flags ['CUSTOM_MODEL_EDIT_RUNTIME_PARAMETERS_ON_DEPLOYMENT']. To enable this feature, you can contact your DataRobot representative or administrator. | ### Enumerated Values | Property | Value | | --- | --- | | reason | [ACCURACY, DATA_DRIFT, ERRORS, SCHEDULED_REFRESH, SCORING_SPEED, DEPRECATION, OTHER] | ## ModelReplacementValidationRequest ``` { "properties": { "modelId": { "description": "ID of the model used to replace deployment's champion model. Required if modelPackageId is not provided.", "type": "string", "x-versiondeprecated": "v2.33" }, "modelPackageId": { "description": "ID of the model package used to replace deployment's champion model. Required if modelId is not provided.", "type": "string", "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | false | | ID of the model used to replace deployment's champion model. Required if modelPackageId is not provided. | | modelPackageId | string | false | | ID of the model package used to replace deployment's champion model. Required if modelId is not provided. | ## ModelReplacementValidationResponse ``` { "properties": { "checks": { "description": "A more granular explanation of why the replacement model was eligible or ineligible.", "properties": { "combinedModelSegments": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "containsTrackedSegmentAttributes": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "driftTracking": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "featureDataTypes": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "features": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "humilityRules": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "modelCanBeDeployed": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "modelStatus": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "notCurrentModel": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "permission": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "predictionIntervals": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "predictionReady": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "seriesType": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "supported": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "target": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "targetClasses": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "targetType": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "timeSeriesCompatibility": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "validChallenger": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } }, "type": "object" }, "message": { "description": "Message of the overall validation check.", "type": "string" }, "status": { "description": "Status of the overall validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "checks", "message", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | checks | ValidationChecks | true | | A more granular explanation of why the replacement model was eligible or ineligible. | | message | string | true | | Message of the overall validation check. | | status | string | true | | Status of the overall validation check. | ### Enumerated Values | Property | Value | | --- | --- | | status | [failing, passing, warning] | ## ValidationCheck ``` { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } ``` Whether the replacement model has the same target name as the current model. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | Message of the validation check. | | status | string | true | | Status of the validation check. | ### Enumerated Values | Property | Value | | --- | --- | | status | [failing, passing, warning] | ## ValidationChecks ``` { "description": "A more granular explanation of why the replacement model was eligible or ineligible.", "properties": { "combinedModelSegments": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "containsTrackedSegmentAttributes": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "driftTracking": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "featureDataTypes": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "features": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "humilityRules": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "modelCanBeDeployed": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "modelStatus": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "notCurrentModel": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "permission": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "predictionIntervals": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "predictionReady": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "seriesType": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "supported": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "target": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "targetClasses": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "targetType": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "timeSeriesCompatibility": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" }, "validChallenger": { "description": "Whether the replacement model has the same target name as the current model.", "properties": { "message": { "description": "Message of the validation check.", "type": "string" }, "status": { "description": "Status of the validation check.", "enum": [ "failing", "passing", "warning" ], "type": "string" } }, "required": [ "message", "status" ], "type": "object" } }, "type": "object" } ``` A more granular explanation of why the replacement model was eligible or ineligible. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | combinedModelSegments | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | containsTrackedSegmentAttributes | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | driftTracking | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | featureDataTypes | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | features | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | humilityRules | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | modelCanBeDeployed | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | modelStatus | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | notCurrentModel | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | permission | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | predictionIntervals | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | predictionReady | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | seriesType | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | supported | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | target | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | targetClasses | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | targetType | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | timeSeriesCompatibility | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | | validChallenger | ValidationCheck | false | | Whether the replacement model has the same target name as the current model. | --- # Runtime parameters URL: https://docs.datarobot.com/en/docs/api/reference/public-api/deployment_runtime_parameters.html > Use the endpoints described below to manage deployment runtime parameters for deployed custom models. # Runtime parameters Use the endpoints described below to manage deployment runtime parameters for deployed custom models. ## List runtime parameters by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/runtimeParameters/` Authentication requirements: `BearerAuth` List runtime parameters. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | true | The sort order to apply to the runtime parameters list. Prefix the attribute name with a dash to sort in descending order; for example: '-created_at'. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [createdAt, -createdAt, name, -name] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A unified view of the defined runtime parameters for this object, including the values that are currently set.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ] }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter." }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field." }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ] }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string" }, "keyValueId": { "description": "The ID of the key value associated with a runtime parameter.", "type": [ "string", "null" ] }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ] }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ] }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition." }, "type": { "description": "The value type that the parameter accepts.", "enum": [ "boolean", "credential", "deployment", "numeric", "string" ], "type": "string" } }, "required": [ "fieldName", "type" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RuntimeParameterListResponse | ## Update runtime parameters by deployment ID Operation path: `PUT /api/v2/deployments/{deploymentId}/runtimeParameters/` Authentication requirements: `BearerAuth` Update runtime parameters by replacing existing params with the provided set. Any values not provided will revert back to the values set from the model package. ### Body parameter ``` { "properties": { "runtimeParameterValues": { "description": "Inject values into a model at runtime. The fieldName must match a fieldName defined in the deployed model package. This list is merged with any existing runtime values.", "type": "string" } }, "required": [ "runtimeParameterValues" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | RuntimeParameterUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A unified view of the defined runtime parameters for this object, including the values that are currently set.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ] }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter." }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field." }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ] }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string" }, "keyValueId": { "description": "The ID of the key value associated with a runtime parameter.", "type": [ "string", "null" ] }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ] }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ] }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition." }, "type": { "description": "The value type that the parameter accepts.", "enum": [ "boolean", "credential", "deployment", "numeric", "string" ], "type": "string" } }, "required": [ "fieldName", "type" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RuntimeParameterListResponse | # Schemas ## RuntimeParameterListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A unified view of the defined runtime parameters for this object, including the values that are currently set.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ] }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter." }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field." }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ] }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string" }, "keyValueId": { "description": "The ID of the key value associated with a runtime parameter.", "type": [ "string", "null" ] }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ] }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ] }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition." }, "type": { "description": "The value type that the parameter accepts.", "enum": [ "boolean", "credential", "deployment", "numeric", "string" ], "type": "string" } }, "required": [ "fieldName", "type" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RuntimeParameterResponse] | false | maxItems: 1000 | A unified view of the defined runtime parameters for this object, including the values that are currently set. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RuntimeParameterResponse ``` { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ] }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter." }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field." }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ] }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string" }, "keyValueId": { "description": "The ID of the key value associated with a runtime parameter.", "type": [ "string", "null" ] }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ] }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ] }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition." }, "type": { "description": "The value type that the parameter accepts.", "enum": [ "boolean", "credential", "deployment", "numeric", "string" ], "type": "string" } }, "required": [ "fieldName", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowEmpty | boolean | false | | Indicates whether the param must be set before registration | | credentialType | string,null | false | | The type of credential, required only for credentials parameters. | | currentValue | any | false | | Given the default and the override, this is the actual current value of the parameter. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultValue | any | false | | The default value for the given field. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | | Description how this parameter impacts the running model. | | fieldName | string | true | | The parameter name. This value will be added as an environment variable when running custom models. | | keyValueId | string,null | false | | The ID of the key value associated with a runtime parameter. | | maxValue | number,null | false | | The maximum value for a numeric field. | | minValue | number,null | false | | The minimum value for a numeric field. | | overrideValue | any | false | | Value set by the user that overrides the default set in the definition. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | The value type that the parameter accepts. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | [adls_gen2_oauth, api_token, azure, azure_oauth, azure_service_principal, basic, bearer, box_jwt, client_id_and_secret, databricks_access_token_account, databricks_service_principal_account, external_oauth_provider, gcp, oauth, rsa, s3, sap_oauth, snowflake_key_pair_user_account, snowflake_oauth_user_account, tableau_access_token] | | type | [boolean, credential, deployment, numeric, string] | ## RuntimeParameterUpdate ``` { "properties": { "runtimeParameterValues": { "description": "Inject values into a model at runtime. The fieldName must match a fieldName defined in the deployed model package. This list is merged with any existing runtime values.", "type": "string" } }, "required": [ "runtimeParameterValues" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | runtimeParameterValues | string | true | | Inject values into a model at runtime. The fieldName must match a fieldName defined in the deployed model package. This list is merged with any existing runtime values. | --- # Entitlements URL: https://docs.datarobot.com/en/docs/api/reference/public-api/entitlements.html > Use the endpoints described below to manage entitlements. # Entitlements Use the endpoints described below to manage entitlements. ## Apply entitlement set leases Operation path: `POST /api/v2/entitlements/applyEntitlementSets/` Authentication requirements: `BearerAuth` Request temporary entitlement leases for users within a tenant. ### Body parameter ``` { "properties": { "acceptedTerms": { "description": "Indicates the requesting user has actively accepted the required terms and conditions for starting the trial.", "type": "boolean" }, "durationDays": { "description": "Duration of the lease in days.", "minimum": 1, "type": "integer" }, "entitlementSetId": { "description": "UUID of the entitlement set to lease.", "type": "string" }, "tenantId": { "description": "UUID of the tenant to apply entitlements to.", "type": "string" } }, "required": [ "acceptedTerms", "durationDays", "entitlementSetId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ApplyEntitlementSetLeaseRequest | false | none | ### Example responses > 200 Response ``` { "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Entitlement leases applied successfully. | EntitlementSetLeaseResponse | | 400 | Bad Request | Invalid request data. | None | | 403 | Forbidden | Access denied. | None | | 422 | Unprocessable Entity | Invalid parameters provided. | None | | 500 | Internal Server Error | Service temporarily unavailable. | None | ## Retrieve entitlement set leases Operation path: `GET /api/v2/entitlements/entitlementSetLeases/` Authentication requirements: `BearerAuth` Retrieve entitlement set leases with optional filtering by tenant, entitlement set, and status. ### Body parameter ``` { "properties": { "entitlementSetId": { "description": "UUID of the entitlement set to filter leases by.", "type": "string" }, "limit": { "default": 100, "description": "Pagination limit (max 100).", "maximum": 100, "minimum": 1, "type": "integer" }, "offset": { "default": 0, "description": "Pagination offset.", "minimum": 0, "type": "integer" }, "status": { "description": "Status to filter leases by (e.g., ACTIVE, EXPIRED).", "type": "string" }, "tenantId": { "description": "UUID of the tenant to filter leases by.", "type": "string" } }, "type": "object", "x-versionadded": "v2.38" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | EntitlementSetLeasesRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of items in current page", "type": "integer" }, "data": { "description": "List of entitlement set leases", "items": { "properties": { "createdAt": { "description": "Lease creation timestamp", "type": "string" }, "createdBy": { "description": "Created by user", "type": "string" }, "entitlementSetId": { "description": "UUID of the entitlement set", "type": "string" }, "id": { "description": "UUID of the lease", "type": "string" }, "lastLeaseEndDate": { "description": "Last lease end date timestamp", "type": "string" }, "leaseCount": { "description": "Lease count", "type": "integer" }, "status": { "description": "Status of the lease", "type": "string" }, "tenantId": { "description": "UUID of the tenant", "type": "string" }, "updatedAt": { "description": "Lease last update timestamp", "type": "string" }, "updatedBy": { "description": "Updated by user", "type": "string" }, "validFrom": { "description": "Lease valid from timestamp", "type": "string" }, "validUntil": { "description": "Lease valid until timestamp", "type": "string" } }, "required": [ "createdAt", "entitlementSetId", "id", "status", "tenantId", "updatedAt" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL for next page", "type": "string" }, "previous": { "description": "URL for previous page", "type": "string" }, "totalCount": { "description": "Total number of items", "type": "integer" } }, "required": [ "count", "data", "totalCount" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Entitlement set leases retrieved successfully. | EntitlementSetLeasesResponse | | 400 | Bad Request | Invalid request parameters. | None | | 403 | Forbidden | Access denied. | None | | 500 | Internal Server Error | Service temporarily unavailable. | None | ## Evaluate entitlements Operation path: `POST /api/v2/entitlements/evaluate/` Authentication requirements: `BearerAuth` Evaluate entitlements of the client requesting the API. ### Body parameter ``` { "properties": { "entitlements": { "description": "Entitlements to evaluate", "items": { "properties": { "name": { "description": "Name of the entitlement to evaluate", "type": "string" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" } }, "required": [ "entitlements" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | EvaluateEntitlementsRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "entitlements": { "description": "Results of evaluation", "items": { "properties": { "name": { "description": "Name of the entitlement to evaluate", "type": "string" }, "value": { "description": "The result of an entitlement evaluation.", "oneOf": [ { "type": "boolean" } ] } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" } }, "required": [ "entitlements" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Evaluation succeeded. | EvaluateEntitlementsResponse | # Schemas ## ApplyEntitlementSetLeaseRequest ``` { "properties": { "acceptedTerms": { "description": "Indicates the requesting user has actively accepted the required terms and conditions for starting the trial.", "type": "boolean" }, "durationDays": { "description": "Duration of the lease in days.", "minimum": 1, "type": "integer" }, "entitlementSetId": { "description": "UUID of the entitlement set to lease.", "type": "string" }, "tenantId": { "description": "UUID of the tenant to apply entitlements to.", "type": "string" } }, "required": [ "acceptedTerms", "durationDays", "entitlementSetId" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | acceptedTerms | boolean | true | | Indicates the requesting user has actively accepted the required terms and conditions for starting the trial. | | durationDays | integer | true | minimum: 1 | Duration of the lease in days. | | entitlementSetId | string | true | | UUID of the entitlement set to lease. | | tenantId | string | false | | UUID of the tenant to apply entitlements to. | ## Entitlement ``` { "properties": { "name": { "description": "Name of the entitlement to evaluate", "type": "string" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Name of the entitlement to evaluate | ## EntitlementEvaluateResult ``` { "properties": { "name": { "description": "Name of the entitlement to evaluate", "type": "string" }, "value": { "description": "The result of an entitlement evaluation.", "oneOf": [ { "type": "boolean" } ] } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Name of the entitlement to evaluate | | value | boolean | true | | The result of an entitlement evaluation. | ## EntitlementSetLease ``` { "properties": { "createdAt": { "description": "Lease creation timestamp", "type": "string" }, "createdBy": { "description": "Created by user", "type": "string" }, "entitlementSetId": { "description": "UUID of the entitlement set", "type": "string" }, "id": { "description": "UUID of the lease", "type": "string" }, "lastLeaseEndDate": { "description": "Last lease end date timestamp", "type": "string" }, "leaseCount": { "description": "Lease count", "type": "integer" }, "status": { "description": "Status of the lease", "type": "string" }, "tenantId": { "description": "UUID of the tenant", "type": "string" }, "updatedAt": { "description": "Lease last update timestamp", "type": "string" }, "updatedBy": { "description": "Updated by user", "type": "string" }, "validFrom": { "description": "Lease valid from timestamp", "type": "string" }, "validUntil": { "description": "Lease valid until timestamp", "type": "string" } }, "required": [ "createdAt", "entitlementSetId", "id", "status", "tenantId", "updatedAt" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string | true | | Lease creation timestamp | | createdBy | string | false | | Created by user | | entitlementSetId | string | true | | UUID of the entitlement set | | id | string | true | | UUID of the lease | | lastLeaseEndDate | string | false | | Last lease end date timestamp | | leaseCount | integer | false | | Lease count | | status | string | true | | Status of the lease | | tenantId | string | true | | UUID of the tenant | | updatedAt | string | true | | Lease last update timestamp | | updatedBy | string | false | | Updated by user | | validFrom | string | false | | Lease valid from timestamp | | validUntil | string | false | | Lease valid until timestamp | ## EntitlementSetLeaseResponse ``` { "type": "object", "x-versionadded": "v2.38" } ``` ### Properties None ## EntitlementSetLeasesRequest ``` { "properties": { "entitlementSetId": { "description": "UUID of the entitlement set to filter leases by.", "type": "string" }, "limit": { "default": 100, "description": "Pagination limit (max 100).", "maximum": 100, "minimum": 1, "type": "integer" }, "offset": { "default": 0, "description": "Pagination offset.", "minimum": 0, "type": "integer" }, "status": { "description": "Status to filter leases by (e.g., ACTIVE, EXPIRED).", "type": "string" }, "tenantId": { "description": "UUID of the tenant to filter leases by.", "type": "string" } }, "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entitlementSetId | string | false | | UUID of the entitlement set to filter leases by. | | limit | integer | false | maximum: 100minimum: 1 | Pagination limit (max 100). | | offset | integer | false | minimum: 0 | Pagination offset. | | status | string | false | | Status to filter leases by (e.g., ACTIVE, EXPIRED). | | tenantId | string | false | | UUID of the tenant to filter leases by. | ## EntitlementSetLeasesResponse ``` { "properties": { "count": { "description": "Number of items in current page", "type": "integer" }, "data": { "description": "List of entitlement set leases", "items": { "properties": { "createdAt": { "description": "Lease creation timestamp", "type": "string" }, "createdBy": { "description": "Created by user", "type": "string" }, "entitlementSetId": { "description": "UUID of the entitlement set", "type": "string" }, "id": { "description": "UUID of the lease", "type": "string" }, "lastLeaseEndDate": { "description": "Last lease end date timestamp", "type": "string" }, "leaseCount": { "description": "Lease count", "type": "integer" }, "status": { "description": "Status of the lease", "type": "string" }, "tenantId": { "description": "UUID of the tenant", "type": "string" }, "updatedAt": { "description": "Lease last update timestamp", "type": "string" }, "updatedBy": { "description": "Updated by user", "type": "string" }, "validFrom": { "description": "Lease valid from timestamp", "type": "string" }, "validUntil": { "description": "Lease valid until timestamp", "type": "string" } }, "required": [ "createdAt", "entitlementSetId", "id", "status", "tenantId", "updatedAt" ], "type": "object", "x-versionadded": "v2.38" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL for next page", "type": "string" }, "previous": { "description": "URL for previous page", "type": "string" }, "totalCount": { "description": "Total number of items", "type": "integer" } }, "required": [ "count", "data", "totalCount" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Number of items in current page | | data | [EntitlementSetLease] | true | maxItems: 1000 | List of entitlement set leases | | next | string | false | | URL for next page | | previous | string | false | | URL for previous page | | totalCount | integer | true | | Total number of items | ## EvaluateEntitlementsRequest ``` { "properties": { "entitlements": { "description": "Entitlements to evaluate", "items": { "properties": { "name": { "description": "Name of the entitlement to evaluate", "type": "string" } }, "required": [ "name" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" } }, "required": [ "entitlements" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entitlements | [Entitlement] | true | maxItems: 100 | Entitlements to evaluate | ## EvaluateEntitlementsResponse ``` { "properties": { "entitlements": { "description": "Results of evaluation", "items": { "properties": { "name": { "description": "Name of the entitlement to evaluate", "type": "string" }, "value": { "description": "The result of an entitlement evaluation.", "oneOf": [ { "type": "boolean" } ] } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" } }, "required": [ "entitlements" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entitlements | [EntitlementEvaluateResult] | true | maxItems: 100 | Results of evaluation | --- # Entity Tags URL: https://docs.datarobot.com/en/docs/api/reference/public-api/entity_tags.html > Use the endpoints described below to configure tags for your entities (e.g., use cases). # Entity Tags Use the endpoints described below to configure tags for your entities (e.g., use cases). ## Retrieve the list of entity tags Operation path: `GET /api/v2/entityTags/` Authentication requirements: `BearerAuth` Retrieve the list of entity tags. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | search | query | string | false | Returns only Entity Tags with names that match the given string. | | entityType | query | string | false | Returns only Entity Tags with entityType that match the given type. | | orderBy | query | string | true | The order in which to return entity tags. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | experiment_container | | orderBy | [-entityType, -id, -name, entityType, id, name] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of entity tags that match the query.", "items": { "properties": { "entityType": { "description": "The type of entity provided.", "enum": [ "experiment_container" ], "type": "string" }, "id": { "description": "The ID of the entity tag.", "type": "string" }, "name": { "description": "The name of the entity tag.", "type": "string" } }, "required": [ "entityType", "id", "name" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Entity tags retrieved successfully. | EntityTagListResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## Get entity tag Operation path: `POST /api/v2/entityTags/` Authentication requirements: `BearerAuth` Look up an entity tag. ### Body parameter ``` { "properties": { "entityType": { "description": "The type of entity provided.", "enum": [ "experiment_container" ], "type": "string" }, "name": { "description": "The name of the Tag.", "maxLength": 100, "type": "string" } }, "required": [ "entityType", "name" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | EntityTagCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "entityType": { "description": "The type of entity provided.", "enum": [ "experiment_container" ], "type": "string" }, "id": { "description": "The ID of the entity tag.", "type": "string" }, "name": { "description": "The name of the entity tag.", "type": "string" } }, "required": [ "entityType", "id", "name" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The entity tag. | EntityTagResponse | | 201 | Created | The tag was successfully created. | None | | 422 | Unprocessable Entity | Unprocessable Entity | None | ## Delete an entity tag by entity tag ID Operation path: `DELETE /api/v2/entityTags/{entityTagId}/` Authentication requirements: `BearerAuth` Delete an entity tag. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityTagId | path | string | true | The ID of the entity tag. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | ## Update entity tag by entity tag ID Operation path: `PATCH /api/v2/entityTags/{entityTagId}/` Authentication requirements: `BearerAuth` Update an entity tag. ### Body parameter ``` { "properties": { "name": { "description": "The name of the entity tag.", "maxLength": 100, "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityTagId | path | string | true | The ID of the entity tag. | | body | body | EntityTagUpdate | false | none | ### Example responses > 204 Response ``` { "properties": { "entityType": { "description": "The type of entity provided.", "enum": [ "experiment_container" ], "type": "string" }, "id": { "description": "The ID of the entity tag.", "type": "string" }, "name": { "description": "The name of the entity tag.", "type": "string" } }, "required": [ "entityType", "id", "name" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The entity tag has been successfully updated. | EntityTagResponse | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | Tag not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity | None | # Schemas ## EntityTagCreate ``` { "properties": { "entityType": { "description": "The type of entity provided.", "enum": [ "experiment_container" ], "type": "string" }, "name": { "description": "The name of the Tag.", "maxLength": 100, "type": "string" } }, "required": [ "entityType", "name" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityType | string | true | | The type of entity provided. | | name | string | true | maxLength: 100 | The name of the Tag. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | experiment_container | ## EntityTagListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of entity tags that match the query.", "items": { "properties": { "entityType": { "description": "The type of entity provided.", "enum": [ "experiment_container" ], "type": "string" }, "id": { "description": "The ID of the entity tag.", "type": "string" }, "name": { "description": "The name of the entity tag.", "type": "string" } }, "required": [ "entityType", "id", "name" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [EntityTagResponse] | true | maxItems: 100 | The list of entity tags that match the query. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## EntityTagResponse ``` { "properties": { "entityType": { "description": "The type of entity provided.", "enum": [ "experiment_container" ], "type": "string" }, "id": { "description": "The ID of the entity tag.", "type": "string" }, "name": { "description": "The name of the entity tag.", "type": "string" } }, "required": [ "entityType", "id", "name" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityType | string | true | | The type of entity provided. | | id | string | true | | The ID of the entity tag. | | name | string | true | | The name of the entity tag. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | experiment_container | ## EntityTagUpdate ``` { "properties": { "name": { "description": "The name of the entity tag.", "maxLength": 100, "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string,null | false | maxLength: 100 | The name of the entity tag. | --- # Data management URL: https://docs.datarobot.com/en/docs/api/reference/public-api/features.html > Use the endpoints described below to manage features. # Data management Use the endpoints described below to manage features. ## Retrieve the list of allowed country codes Operation path: `GET /api/v2/calendarCountryCodes/` Authentication requirements: `BearerAuth` Retrieves the list of allowed country codes to request preloaded calendar generation for. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page considering offset and limit values.", "minimum": 0, "type": "integer" }, "data": { "description": "An array of dictionaries which contain country code and full name of the countries corresponding to codes.", "items": { "description": "Each item has the country code and the full name of the corresponding country", "properties": { "code": { "description": "Country code that can be used for calendars generation.", "type": "string" }, "name": { "description": "Full name of the country.", "type": "string" } }, "required": [ "code", "name" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of allowed country codes that have the generated preloaded calendars. | PreloadedCalendarListResponse | ## List all available calendars Operation path: `GET /api/v2/calendars/` Authentication requirements: `BearerAuth` List all the calendars which the user has access to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | query | string | false | Optional, if provided will filter returned calendars to those being used in the specified project. | | offset | query | integer | true | Optional (default: 0), this many results will be skipped. | | limit | query | integer | true | Optional (default: 0), at most this many results will be returned. If 0, all results will be returned. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "An array of calendars, each in the form described under [GET /api/v2/calendars/][get-apiv2calendars].", "items": { "properties": { "created": { "description": "An ISO-8601 string with the time that this calendar was created.", "format": "date-time", "type": "string" }, "datetimeFormat": { "description": "The datetime format detected for the uploaded calendar file.", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": "string", "x-versionadded": "v2.26" }, "earliestEvent": { "description": "An ISO-8601 date string of the earliest event seen in this calendar.", "format": "date-time", "type": "string" }, "id": { "description": "The ID of this calendar.", "type": "string" }, "latestEvent": { "description": "An ISO-8601 date string of the latest event seen in this calendar.", "format": "date-time", "type": "string" }, "multiseriesIdColumns": { "description": "An array of multiseries ID column names in this calendar file. Currently only one multiseries ID column is supported. Will be `null` if this calendar is single-series.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.19" }, "name": { "description": "The name of this calendar. This will be the same as `source` if no name was specified when the calendar was created.", "type": "string" }, "numEventTypes": { "description": "The number of distinct eventTypes in this calendar.", "type": "integer" }, "numEvents": { "description": "The number of dates that are marked as having an event in this calendar.", "type": "integer" }, "projectId": { "description": "The project IDs of projects currently using this calendar.", "items": { "type": "string" }, "type": "array" }, "role": { "description": "The role the requesting user has on this calendar.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "source": { "description": "The name of the source file that was used to create this calendar.", "type": "string" } }, "required": [ "created", "datetimeFormat", "earliestEvent", "id", "latestEvent", "multiseriesIdColumns", "name", "numEventTypes", "numEvents", "projectId", "role", "source" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of calendar objects. | CalendarListResponse | ## Create a calendar Operation path: `POST /api/v2/calendars/fileUpload/` Authentication requirements: `BearerAuth` Create a calendar from a file in a CSV or XLSX format. The calendar file specifies the dates or events in a dataset such that DataRobot automatically derives and creates special features based on the calendar events (e.g., time until the next event, labeling the most recent event). ### Body parameter ``` { "properties": { "file": { "description": "The calendar file used to create a calendar. The calendar file expect to meet the following criteria:\n\nMust be in a csv or xlsx format.\n\nMust have a header row. The names themselves in the header row can be anything.\n\nMust have a single date column, in YYYY-MM-DD format.\n\nMay optionally have a name column as the second column.\n\nMay optionally have one series ID column that states what series each event is applicable for. If present, the name of this column must be specified in the `multiseriesIdColumns` parameter.", "format": "binary", "type": "string" }, "multiseriesIdColumns": { "description": "An array of multiseries ID column names for the calendar file. Currently only one multiseries ID column is supported. If not specified, the calendar is considered to be single series.", "type": "string", "x-versionadded": "v2.19" }, "name": { "description": "The name of the calendar file. If not provided, this will be set to the name of the provided file.", "type": "string" } }, "required": [ "file" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CalendarFileUpload | false | none | ### Example responses > 202 Response ``` { "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Request for calendar generation was submitted. See Location header. | Empty | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Initialize generation of preloaded calendars Operation path: `POST /api/v2/calendars/fromCountryCode/` Authentication requirements: `BearerAuth` Initialize generation of preloaded calendars. Preloaded calendars are available only for time series projects. Preloaded calendars do not support multiseries calendars. ### Body parameter ``` { "properties": { "countryCode": { "description": "Code of the country for which holidays should be generated. Code needs to be uppercase and should belong to the list of countries which can be retrieved via [GET /api/v2/calendarCountryCodes/][get-apiv2calendarcountrycodes]", "enum": [ "AR", "AT", "AU", "AW", "BE", "BG", "BR", "BY", "CA", "CH", "CL", "CO", "CZ", "DE", "DK", "DO", "EE", "ES", "FI", "FRA", "GB", "HK", "HND", "HR", "HU", "IE", "IND", "IS", "IT", "JP", "KE", "LT", "LU", "MX", "NG", "NI", "NL", "NO", "NZ", "PE", "PL", "PT", "RU", "SE", "SE(NS)", "SI", "SK", "TAR", "UA", "UK", "US", "ZA" ], "type": "string" }, "endDate": { "description": "Last date of the range of dates for which holidays are generated.", "format": "date-time", "type": "string" }, "startDate": { "description": "First date of the range of dates for which holidays are generated.", "format": "date-time", "type": "string" } }, "required": [ "countryCode", "endDate", "startDate" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | PreloadedCalendar | false | none | ### Example responses > 202 Response ``` { "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Request for calendar generation was submitted. See Location header. | Empty | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a calendar from a dataset Operation path: `POST /api/v2/calendars/fromDataset/` Authentication requirements: `BearerAuth` Create a calendar from the dataset. ### Body parameter ``` { "properties": { "datasetId": { "description": "The ID of the dataset from which to create the calendar.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version from which to create the calendar.", "type": "string" }, "deleteOnError": { "description": "Whether delete calendar file from Catalog if it's not valid.", "type": "boolean" }, "multiseriesIdColumns": { "description": "Optional multiseries ID columns for the calendar.", "items": { "type": "string" }, "maxItems": 1, "type": "array" }, "name": { "description": "Optional name for catalog.", "type": "string" } }, "required": [ "datasetId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CalendarFromDataset | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/][get-apiv2status] to poll for the testing job's status.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successfully created a calendar from the dataset. | CreatedCalendarDatasetResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Delete a calendar by calendar ID Operation path: `DELETE /api/v2/calendars/{calendarId}/` Authentication requirements: `BearerAuth` Delete a calendar. This can only be done if all projects and deployments using the calendar have been deleted. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | calendarId | path | string | true | The ID of this calendar. | ### Example responses > 204 Response ``` { "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Calendar successfully deleted. | Empty | | 404 | Not Found | Invalid calendarId provided, or user does not have permissions to delete calendar. | None | ## Retrieve information about a calendar by calendar ID Operation path: `GET /api/v2/calendars/{calendarId}/` Authentication requirements: `BearerAuth` List all the information about a calendar such as the total number of event dates, the earliest calendar event date, the IDs of projects currently using this calendar and the others. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | calendarId | path | string | true | The ID of this calendar. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "An ISO-8601 string with the time that this calendar was created.", "format": "date-time", "type": "string" }, "datetimeFormat": { "description": "The datetime format detected for the uploaded calendar file.", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": "string", "x-versionadded": "v2.26" }, "earliestEvent": { "description": "An ISO-8601 date string of the earliest event seen in this calendar.", "format": "date-time", "type": "string" }, "id": { "description": "The ID of this calendar.", "type": "string" }, "latestEvent": { "description": "An ISO-8601 date string of the latest event seen in this calendar.", "format": "date-time", "type": "string" }, "multiseriesIdColumns": { "description": "An array of multiseries ID column names in this calendar file. Currently only one multiseries ID column is supported. Will be `null` if this calendar is single-series.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.19" }, "name": { "description": "The name of this calendar. This will be the same as `source` if no name was specified when the calendar was created.", "type": "string" }, "numEventTypes": { "description": "The number of distinct eventTypes in this calendar.", "type": "integer" }, "numEvents": { "description": "The number of dates that are marked as having an event in this calendar.", "type": "integer" }, "projectId": { "description": "The project IDs of projects currently using this calendar.", "items": { "type": "string" }, "type": "array" }, "role": { "description": "The role the requesting user has on this calendar.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "source": { "description": "The name of the source file that was used to create this calendar.", "type": "string" } }, "required": [ "created", "datetimeFormat", "earliestEvent", "id", "latestEvent", "multiseriesIdColumns", "name", "numEventTypes", "numEvents", "projectId", "role", "source" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Request for a Calendar object was successful. | CalendarRecord | ## Update a calendar's name by calendar ID Operation path: `PATCH /api/v2/calendars/{calendarId}/` Authentication requirements: `BearerAuth` Update a calendar's name. ### Body parameter ``` { "properties": { "name": { "description": "The new name to assign to the calendar.", "type": "string" } }, "required": [ "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | calendarId | path | string | true | The ID of this calendar. | | body | body | CalendarNameUpdate | false | none | ### Example responses > 200 Response ``` { "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Calendar name successfully updated. | Empty | | 404 | Not Found | Invalid calendarId provided, or user does not have permissions to update calendar. | None | ## Get a list of users who have access by calendar ID Operation path: `GET /api/v2/calendars/{calendarId}/accessControl/` Authentication requirements: `BearerAuth` Get a list of users who have access to this calendar and their roles on the calendar. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | username | query | string | false | Optional, only return the access control information for a user with this username. Should not be specified if userId is specified. | | userId | query | string | false | Optional, only return the access control information for a user with this user ID. Should not be specified if username is specified. | | offset | query | integer | false | Optional (default: 0), this many results will be skipped. | | limit | query | integer | false | Optional (default: 0), at most this many results will be returned. If 0, all results will be returned. | | calendarId | path | string | true | The ID of this calendar. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "Records of users and their roles on the calendar.", "items": { "properties": { "canShare": { "description": "Whether this user can share this calendar with other users.", "type": "boolean" }, "role": { "description": "The role of the user on this calendar.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of a user with access to this calendar.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Request for the list of users who have access to this calendar and their roles on the calendar was successful. | CalendarAccessControlListResponse | | 400 | Bad Request | Both username and userId were specified. | None | | 404 | Not Found | Entity not found. Either the calendar does not exist or the user does not have permissions to view the calendar. | None | ## Update the access control by calendar ID Operation path: `PATCH /api/v2/calendars/{calendarId}/accessControl/` Authentication requirements: `BearerAuth` Update the access control for this calendar. See the `entity sharing documentation ` for more information. ### Body parameter ``` { "properties": { "users": { "description": "The list of users and their updated roles used to modify the access for this calendar.", "items": { "description": "Each item in `users` refers to the username record and its newly assigned role.", "properties": { "role": { "description": "The new role to assign to the specified user.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to modify access for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "users" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | calendarId | path | string | true | The ID of this calendar. | | body | body | CalendarAccessControlUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The request to update the access control for this calendar was successful. | None | | 404 | Not Found | Invalid calendarId provided, or user has no access whatsoever on the specified calendar. | None | | 422 | Unprocessable Entity | Invalid username provided to modify access for the specified calendar. | None | ## Retrieve the documents data quality log content by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/documentsDataQualityLog/` Authentication requirements: `BearerAuth` Retrieve the documents data quality log content and log length as JSON. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The content in the form of lines of the documents data quality log", "items": { "description": "Log lines", "type": "string" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Paginated list of lines of the documents data quality log. | DocumentsDataQualityLogLinesResponse | | 404 | Not Found | Documents data quality assessment log not available. | None | ## Retrieve a text file containing the documents data quality log by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/documentsDataQualityLog/file/` Authentication requirements: `BearerAuth` Retrieve a text file containing the documents data quality log. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a text file with the contents of the data quality log. | None | | 404 | Not Found | The data quality assessment log is not available. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | attachment;filename=.txt The suggested filename is dynamically generated | | 200 | Content-Type | string | | The MIME type of the returned data. | ## Retrieve the images data quality log content by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/imagesDataQualityLog/` Authentication requirements: `BearerAuth` Retrieve the images data quality log content and log length as JSON. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The content in the form of lines of the images data quality log", "items": { "description": "Log lines", "type": "string" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Paginated list of lines of the image data quality log | ImagesDataQualityLogLinesResponse | ## Retrieve a text file containing the images data quality log by dataset ID Operation path: `GET /api/v2/datasets/{datasetId}/imagesDataQualityLog/file/` Authentication requirements: `BearerAuth` Retrieve a text file containing the images data quality log. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a text file with the contents of the images data quality log. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | attachment;filename=.txt The suggested filename is dynamically generated | | 200 | Content-Type | string | | MIME type of the returned data | ## Retrieve documents data quality log by id Operation path: `GET /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/documentsDataQualityLog/` Authentication requirements: `BearerAuth` Retrieve the documents data quality log content and log length as JSON. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | datasetId | path | string | true | The ID of the dataset. | | datasetVersionId | path | string | true | The ID of the dataset version. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The content in the form of lines of the documents data quality log", "items": { "description": "Log lines", "type": "string" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Paginated list of lines of the documents data quality log. | DocumentsDataQualityLogLinesResponse | | 404 | Not Found | Documents data quality assessment log not available. | None | ## Retrieve file by id Operation path: `GET /api/v2/datasets/{datasetId}/versions/{datasetVersionId}/documentsDataQualityLog/file/` Authentication requirements: `BearerAuth` Retrieve a text file containing the documents data quality log. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | path | string | true | The ID of the dataset. | | datasetVersionId | path | string | true | The ID of the dataset version. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a text file with the contents of the data quality log. | None | | 404 | Not Found | The data quality assessment log is not available. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | attachment;filename=.txt The suggested filename is dynamically generated | | 200 | Content-Type | string | | The MIME type of the returned data. | ## Retrieve documents by model ID Operation path: `GET /api/v2/models/{modelId}/documentTextExtractionSampleDocuments/` Authentication requirements: `BearerAuth` Retrieve documents with computed document text extraction samples. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | featureName | query | string | true | The name of the feature to retrieve documents for. | | documentTask | query | string | false | The document task to retrieve documents for. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | documentTask | [DOCUMENT_TEXT_EXTRACTOR, TESSERACT_OCR] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of documents.", "items": { "properties": { "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "documentIndex": { "description": "The index of the document within the dataset.", "type": "integer" }, "documentTask": { "description": "The document task this document belongs to.", "enum": [ "DOCUMENT_TEXT_EXTRACTOR", "TESSERACT_OCR" ], "type": "string" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "prediction": { "description": "Object that describes prediction value of the dataset row.", "properties": { "labels": { "description": "List of predicted label names corresponding to values.", "items": { "description": "Predicted label", "type": "string" }, "type": "array" }, "values": { "description": "Predicted value or probability of the class identified by the label.", "items": { "oneOf": [ { "type": "number" }, { "items": { "type": "number" }, "type": "array" } ] }, "type": "array" } }, "required": [ "labels", "values" ], "type": "object" }, "thumbnailHeight": { "description": "The height of the thumbnail in pixels.", "type": "integer" }, "thumbnailId": { "description": "The document page ID of the thumbnail.", "type": "string" }, "thumbnailLink": { "description": "The URL of the thumbnail image.", "format": "uri", "type": "string" }, "thumbnailWidth": { "description": "The width of the thumbnail in pixels.", "type": "integer" } }, "required": [ "actualTargetValue", "documentIndex", "documentTask", "featureName", "prediction", "thumbnailHeight", "thumbnailId", "thumbnailLink", "thumbnailWidth" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "targetBins": { "description": "List of bin objects for regression or null", "items": { "properties": { "targetBinEnd": { "description": "End value for the target bin", "type": "number" }, "targetBinStart": { "description": "Start value for the target bin", "type": "number" } }, "required": [ "targetBinEnd", "targetBinStart" ], "type": "object" }, "type": "array" }, "targetValues": { "description": "List of target values for classification or null", "items": { "description": "Target value", "type": "string" }, "type": "array" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "targetBins", "targetValues", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Documents with computed document text extraction samples. | DocumentTextExtractionSamplesRetrieveDocumentsResponse | ## Retrieve document pages by model ID Operation path: `GET /api/v2/models/{modelId}/documentTextExtractionSamplePages/` Authentication requirements: `BearerAuth` Retrieve document pages with recognized text lines and bounding boxes enclosing the text lines. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | featureName | query | string | true | The name of the feature. | | documentIndex | query | integer | false | The index of the document within the dataset. | | documentTask | query | string | false | The document task to retrieve pages for. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | documentTask | [DOCUMENT_TEXT_EXTRACTOR, TESSERACT_OCR] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of document pages", "items": { "properties": { "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "documentIndex": { "description": "The index of the document within the dataset.", "type": "integer" }, "documentPageHeight": { "description": "The height of the thumbnail in pixels.", "type": "integer" }, "documentPageId": { "description": "The document page ID of the thumbnail.", "type": "string" }, "documentPageLink": { "description": "The URL of the thumbnail image.", "format": "uri", "type": "string" }, "documentPageWidth": { "description": "The width of the thumbnail in pixels.", "type": "integer" }, "documentTask": { "description": "The document task that this page belongs to.", "enum": [ "DOCUMENT_TEXT_EXTRACTOR", "TESSERACT_OCR" ], "type": "string" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "pageIndex": { "description": "The index of this page within the document", "type": "integer" }, "prediction": { "description": "Object that describes prediction value of the dataset row.", "properties": { "labels": { "description": "List of predicted label names corresponding to values.", "items": { "description": "Predicted label", "type": "string" }, "type": "array" }, "values": { "description": "Predicted value or probability of the class identified by the label.", "items": { "oneOf": [ { "type": "number" }, { "items": { "type": "number" }, "type": "array" } ] }, "type": "array" } }, "required": [ "labels", "values" ], "type": "object" }, "textLines": { "description": "The recognized text lines of this document page with bounding box coordinates for each text line.", "items": { "properties": { "bottom": { "description": "Bottom coordinate of the bounding box belonging to this text line in number of pixels from the top image side.", "minimum": 0, "type": "integer" }, "left": { "description": "Left coordinate of the bounding box belonging to this text line in number of pixels from the left image side.", "minimum": 0, "type": "integer" }, "right": { "description": "Right coordinate of the bounding box belonging to this text line in number of pixels from the left image side.", "minimum": 0, "type": "integer" }, "text": { "description": "The text in this text line.", "type": "string" }, "top": { "description": "Top coordinate of the bounding box belonging to this text line in number of pixels from the top image side.", "minimum": 0, "type": "integer" } }, "required": [ "bottom", "left", "right", "text", "top" ], "type": "object" }, "type": "array" } }, "required": [ "actualTargetValue", "documentIndex", "documentPageHeight", "documentPageId", "documentPageLink", "documentPageWidth", "documentTask", "featureName", "pageIndex", "prediction", "textLines" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "targetBins": { "description": "List of bin objects for regression or null", "items": { "properties": { "targetBinEnd": { "description": "End value for the target bin", "type": "number" }, "targetBinStart": { "description": "Start value for the target bin", "type": "number" } }, "required": [ "targetBinEnd", "targetBinStart" ], "type": "object" }, "type": "array" }, "targetValues": { "description": "List of target values for classification or null", "items": { "description": "Target value", "type": "string" }, "type": "array" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "targetBins", "targetValues", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Document pages with recognized text lines and bounding boxes enclosing the text lines. | DocumentTextExtractionSamplesRetrievePagesResponse | ## Requests the computation of document text extraction samples by model ID Operation path: `POST /api/v2/models/{modelId}/documentTextExtractionSamples/` Authentication requirements: `BearerAuth` Requests the computation of document text extraction samples for the specified model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | modelId | path | string | true | The model ID. | ### Example responses > 202 Response ``` { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if the job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "jobType": { "description": "The type of the job.", "enum": [ "compute_document_text_extraction_samples" ], "type": "string" }, "message": { "description": "Error message in case of failure.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The job status.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" }, "url": { "description": "A URL that can be used to request details about the job.", "type": "string" } }, "required": [ "id", "isBlocked", "jobType", "message", "modelId", "projectId", "status", "url" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Document text extraction samples computation has been successfully requested. | DocumentTextExtractionSamplesComputeResponse | | 422 | Unprocessable Entity | Cannot compute document text extraction samples: if the insight was already computed for the model or there was another issue creating this job. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | a url that can be polled to check the status of the job. | ## Retrieve user's OCR job resources Operation path: `GET /api/v2/ocrJobResources/` Authentication requirements: `BearerAuth` Retrieve user's OCR job resources. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of results to skip (for pagination). | | limit | query | integer | true | The max number of results to return. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of records on this page.", "type": "integer" }, "data": { "description": "The list of OCR job resources.", "items": { "properties": { "engineSpecificParameters": { "description": "The OCR engine-specific parameters.", "discriminator": { "propertyName": "engineType" }, "oneOf": [ { "properties": { "engineType": { "description": "The Aryn OCR engine.", "enum": [ "ARYN" ], "type": "string" }, "outputFormat": { "default": "JSON", "description": "The output format of Aryn OCR.", "enum": [ "JSON", "MARKDOWN" ], "type": "string" } }, "required": [ "engineType", "outputFormat" ], "type": "object" }, { "properties": { "engineType": { "description": "The Tesseract OCR engine.", "enum": [ "TESSERACT" ], "type": "string" } }, "required": [ "engineType" ], "type": "object" } ], "x-versionadded": "v2.38" }, "id": { "description": "The OCR job resource ID.", "type": "string" }, "inputCatalogId": { "description": "The OCR input dataset catalog ID.", "type": "string" }, "jobStarted": { "description": "Whether the job has started.", "type": "boolean" }, "language": { "description": "The supported OCR dataset language.", "enum": [ "ENGLISH", "JAPANESE" ], "type": "string" }, "outputCatalogId": { "description": "The OCR output dataset catalog ID.", "type": "string" }, "userId": { "description": "The user ID.", "type": "string" } }, "required": [ "id", "inputCatalogId", "jobStarted", "language", "outputCatalogId", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL to the next page, or `null` if there is no such page.", "type": [ "string", "null" ] }, "previous": { "description": "URL to the previous page, or `null` if there is no such page.", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The OCR job resources. | OCRJobResourceListResponse | ## Create an OCR job resource Operation path: `POST /api/v2/ocrJobResources/` Authentication requirements: `BearerAuth` Create an OCR job resource. This creates several entities, including an empty Dataset for writing the results and associates the input dataset with them. ### Body parameter ``` { "properties": { "datasetId": { "description": "The OCR input dataset ID.", "type": "string" }, "engineSpecificParameters": { "description": "The OCR engine-specific parameters.", "discriminator": { "propertyName": "engineType" }, "oneOf": [ { "properties": { "engineType": { "description": "The Aryn OCR engine.", "enum": [ "ARYN" ], "type": "string" }, "outputFormat": { "default": "JSON", "description": "The output format of Aryn OCR.", "enum": [ "JSON", "MARKDOWN" ], "type": "string" } }, "required": [ "engineType", "outputFormat" ], "type": "object" }, { "properties": { "engineType": { "description": "The Tesseract OCR engine.", "enum": [ "TESSERACT" ], "type": "string" } }, "required": [ "engineType" ], "type": "object" } ], "x-versionadded": "v2.38" }, "language": { "description": "The supported OCR dataset language.", "enum": [ "ENGLISH", "JAPANESE" ], "type": "string" } }, "required": [ "datasetId", "language" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | OCRJobCreationRequest | false | none | ### Example responses > 201 Response ``` { "properties": { "engineSpecificParameters": { "description": "The OCR engine-specific parameters.", "discriminator": { "propertyName": "engineType" }, "oneOf": [ { "properties": { "engineType": { "description": "The Aryn OCR engine.", "enum": [ "ARYN" ], "type": "string" }, "outputFormat": { "default": "JSON", "description": "The output format of Aryn OCR.", "enum": [ "JSON", "MARKDOWN" ], "type": "string" } }, "required": [ "engineType", "outputFormat" ], "type": "object" }, { "properties": { "engineType": { "description": "The Tesseract OCR engine.", "enum": [ "TESSERACT" ], "type": "string" } }, "required": [ "engineType" ], "type": "object" } ], "x-versionadded": "v2.38" }, "id": { "description": "The OCR job resource ID.", "type": "string" }, "inputCatalogId": { "description": "The OCR input dataset catalog ID.", "type": "string" }, "jobStarted": { "description": "Whether the job has started.", "type": "boolean" }, "language": { "description": "The supported OCR dataset language.", "enum": [ "ENGLISH", "JAPANESE" ], "type": "string" }, "outputCatalogId": { "description": "The OCR output dataset catalog ID.", "type": "string" }, "userId": { "description": "The user ID.", "type": "string" } }, "required": [ "id", "inputCatalogId", "jobStarted", "language", "outputCatalogId", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The OCR job resource. | OCRJobResourceResponse | | 202 | Accepted | An OCR job resource is successfully created. | None | | 403 | Forbidden | User doesn't have permission to access the dataset associated with the OCR job resource. | None | | 404 | Not Found | The OCR input dataset was not found. | None | | 422 | Unprocessable Entity | The language parameter in the request is invalid. | None | ## Retrieve an OCR job resource by job resource ID Operation path: `GET /api/v2/ocrJobResources/{jobResourceId}/` Authentication requirements: `BearerAuth` Retrieve an OCR job resource. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobResourceId | path | string | true | The OCR job resource ID. | ### Example responses > 200 Response ``` { "properties": { "engineSpecificParameters": { "description": "The OCR engine-specific parameters.", "discriminator": { "propertyName": "engineType" }, "oneOf": [ { "properties": { "engineType": { "description": "The Aryn OCR engine.", "enum": [ "ARYN" ], "type": "string" }, "outputFormat": { "default": "JSON", "description": "The output format of Aryn OCR.", "enum": [ "JSON", "MARKDOWN" ], "type": "string" } }, "required": [ "engineType", "outputFormat" ], "type": "object" }, { "properties": { "engineType": { "description": "The Tesseract OCR engine.", "enum": [ "TESSERACT" ], "type": "string" } }, "required": [ "engineType" ], "type": "object" } ], "x-versionadded": "v2.38" }, "id": { "description": "The OCR job resource ID.", "type": "string" }, "inputCatalogId": { "description": "The OCR input dataset catalog ID.", "type": "string" }, "jobStarted": { "description": "Whether the job has started.", "type": "boolean" }, "language": { "description": "The supported OCR dataset language.", "enum": [ "ENGLISH", "JAPANESE" ], "type": "string" }, "outputCatalogId": { "description": "The OCR output dataset catalog ID.", "type": "string" }, "userId": { "description": "The user ID.", "type": "string" } }, "required": [ "id", "inputCatalogId", "jobStarted", "language", "outputCatalogId", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The OCR job resource. | OCRJobResourceResponse | | 404 | Not Found | OCR job resource is not found for the requested job resource ID. | None | ## Retrieve the OCR job error report by job resource ID Operation path: `GET /api/v2/ocrJobResources/{jobResourceId}/errorReport/` Authentication requirements: `BearerAuth` Download the OCR job error report. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobResourceId | path | string | true | The OCR job resource ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The OCR job error report. | None | | 404 | Not Found | OCR job error report is not found for the requested OCR job resource ID. | None | ## Save the OCR job error report by job resource ID Operation path: `PUT /api/v2/ocrJobResources/{jobResourceId}/errorReport/` Authentication requirements: `BearerAuth` Upload the job error report to the associated OCR job resource for later downloading. ### Body parameter ``` { "properties": { "file": { "description": "The file to be uploaded", "format": "binary", "type": "string" } }, "required": [ "file" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobResourceId | path | string | true | The OCR job resource ID. | | body | body | OCRJobErrorReportPutRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 403 | Forbidden | User doesn't have permission to save error report. | None | | 404 | Not Found | Dataset with which the error report is associated is not found. | None | ## Retrieve OCR job status by job resource ID Operation path: `GET /api/v2/ocrJobResources/{jobResourceId}/jobStatus/` Authentication requirements: `BearerAuth` Get the status of a running OCR job from the OCR service. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobResourceId | path | string | true | The OCR job resource ID. | ### Example responses > 200 Response ``` { "properties": { "jobStatus": { "description": "The status of general progress of OCR job resource creation.", "enum": [ "executing", "failure", "pending", "stopped", "success", "unknown" ], "type": "string" } }, "required": [ "jobStatus" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The OCR job status. | OCRJobStatusesResponse | | 404 | Not Found | OCR job resource is not found for the requested job resource ID. | None | ## Create/start OCR job by job resource ID Operation path: `POST /api/v2/ocrJobResources/{jobResourceId}/start/` Authentication requirements: `BearerAuth` Start an OCR job from the information in the OCR job resource. Each OCR job may be started exactly once. ### Body parameter ``` { "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobResourceId | path | string | true | The OCR job resource ID. | | body | body | OCRJobPostPayload | false | none | ### Example responses > 201 Response ``` { "properties": { "errorReportLocation": { "description": "The URL to retrieve the OCR job error report.", "format": "uri", "type": "string" }, "jobStatusLocation": { "description": "The URL to retrieve the OCR job status.", "format": "uri", "type": "string" }, "outputLocation": { "description": "The URL to retrieve the OCR job output.", "format": "uri", "type": "string" } }, "required": [ "errorReportLocation", "jobStatusLocation", "outputLocation" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The OCR job info. | OCRJobPostResponse | | 202 | Accepted | An OCR job is successfully created. | None | | 404 | Not Found | The job resource was not found. | None | | 422 | Unprocessable Entity | The OCR job is already started. | None | ## Create multiple new features by changing the type of existing features by project ID Operation path: `POST /api/v2/projects/{projectId}/batchTypeTransformFeatures/` Authentication requirements: `BearerAuth` Create multiple new features by changing the type of existing features. ### Body parameter ``` { "properties": { "parentNames": { "description": "List of feature names that will be transformed into a new variable type.", "items": { "type": "string" }, "maxItems": 500, "minItems": 1, "type": "array" }, "prefix": { "description": "The string that will preface all feature names. Optional if suffix is present. (One or both are required.)", "maxLength": 500, "type": "string" }, "suffix": { "description": "The string that will be appended at the end to all feature names. Optional if prefix is present. (One or both are required.)", "maxLength": 500, "type": "string" }, "variableType": { "description": "The type of the new feature. Must be one of `text`, `categorical` (Deprecated in version v2.21), `numeric`, or `categoricalInt`.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "parentNames", "variableType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project to create the feature in. | | body | body | BatchFeatureTransform | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Creation has successfully started. See the Location header. | None | | 422 | Unprocessable Entity | Unable to process the request | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Location | string | | A url that can be polled to check the status. | ## Retrieve the result of a batch variable type transformation by project ID Operation path: `GET /api/v2/projects/{projectId}/batchTypeTransformFeaturesResult/{jobId}/` Authentication requirements: `BearerAuth` Retrieve the result of a batch variable type transformation. ### Body parameter ``` { "properties": { "parentNames": { "description": "List of feature names that will be transformed into a new variable type.", "items": { "type": "string" }, "maxItems": 500, "minItems": 1, "type": "array" }, "prefix": { "description": "The string that will preface all feature names. Optional if suffix is present. (One or both are required.)", "maxLength": 500, "type": "string" }, "suffix": { "description": "The string that will be appended at the end to all feature names. Optional if prefix is present. (One or both are required.)", "maxLength": 500, "type": "string" }, "variableType": { "description": "The type of the new feature. Must be one of `text`, `categorical` (Deprecated in version v2.21), `numeric`, or `categoricalInt`.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "parentNames", "variableType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project containing transformed features. | | jobId | path | integer | true | The ID of the batch variable type transformation job. | | body | body | BatchFeatureTransform | false | none | ### Example responses > 200 Response ``` { "properties": { "failures": { "description": "An object keyed by original feature names, the values are strings indicating why the transformation failed.", "type": "object" }, "newFeatureNames": { "description": "List of new feature names.", "items": { "type": "string" }, "type": "array" } }, "required": [ "failures", "newFeatureNames" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Names of successfully created features. | BatchFeatureTransformRetrieveResponse | | 404 | Not Found | Could not find specified transformation report | None | ## List available calendar events by project ID Operation path: `GET /api/v2/projects/{projectId}/calendarEvents/` Authentication requirements: `BearerAuth` List available calendar events for the project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | seriesId | query | string | false | The name of the series to retrieve specific series for. If specified, retrieves only series specific to the event and common events. | | startDate | query | string(date-time) | false | The start of the date range to return, inclusive. If not specified, start date for the first calendar event will be used. | | endDate | query | string(date-time) | false | The end of the date range to return, exclusive. If not specified, end date capturing the last calendar event will be used. | | offset | query | integer | false | Optional (default: 0), this many results will be skipped. | | limit | query | integer | false | Optional (default: 1000), at most this many results will be returned. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "An array of calendar events", "items": { "properties": { "date": { "description": "The date of the calendar event.", "format": "date-time", "type": "string" }, "name": { "description": "Name of the calendar event.", "type": "string" }, "seriesId": { "description": "The series ID for the event. If this event does not specify a series ID, then this will be `null`, indicating that the event applies to all series.", "type": [ "string", "null" ] } }, "required": [ "date", "name", "seriesId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of calendar events. | CalendarEventsResponseQuery | ## Validate columns by project ID Operation path: `POST /api/v2/projects/{projectId}/crossSeriesProperties/` Authentication requirements: `BearerAuth` Validate columns for potential use as the group-by column for cross-series functionality. The group-by column is an optional setting that indicates how to further splitseries into related groups. For example, if each series represents sales of an individual product, the group-by column could be the product category, e.g., "clothing" or "sports equipment". ### Body parameter ``` { "properties": { "crossSeriesGroupByColumns": { "description": "If specified, these columns will be validated for usage as the group-by column for creating cross-series features. If not present, then all columns from the dataset will be validated and only the eligible ones returned. To be valid, a column should be categorical or numerical (but not float), not be the series ID or equivalent to the series ID, not split any series, and not consist of only one value.", "items": { "type": "string" }, "type": "array" }, "datetimePartitionColumn": { "description": "The name of the column that will be used as the datetime partitioning column.", "type": "string" }, "multiseriesIdColumn": { "description": "The name of the column that wil be used as the multiseries ID column for this project.", "type": "string" }, "userDefinedSegmentIdColumn": { "description": "The name of the column that wil be used as the user defined segment ID column for this project.", "type": "string", "x-versionadded": "v2.24" } }, "required": [ "datetimePartitionColumn", "multiseriesIdColumn" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | CrossSeriesGroupByColumnValidatePayload | false | none | ### Example responses > 202 Response ``` { "properties": { "message": { "description": "An extended message about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Cross-series group-by column validation job was successfully submitted. See Location header. | CrossSeriesGroupByColumnValidateResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Get discarded features by project ID Operation path: `GET /api/v2/projects/{projectId}/discardedFeatures/` Authentication requirements: `BearerAuth` Get features which were discarded during feature reduction process. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | search | query | string | false | Case insensitive search against discarded feature names. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Discarded features count.", "minimum": 0, "type": "integer" }, "features": { "description": "Discarded features.", "items": { "type": "string" }, "type": "array" }, "remainingRestoreLimit": { "description": "The remaining available number of the features which can be restored in this project.", "minimum": 0, "type": "integer" }, "totalRestoreLimit": { "description": "The total limit indicating how many features can be restored in this project.", "minimum": 0, "type": "integer" } }, "required": [ "count", "features", "remainingRestoreLimit", "totalRestoreLimit" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Discarded features. | DiscardedFeaturesResponse | | 422 | Unprocessable Entity | Unable to process the request | None | ## Returns a file by project ID Operation path: `GET /api/v2/projects/{projectId}/documentPages/{documentPageId}/file/` Authentication requirements: `BearerAuth` Returns a file for a single document page. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | documentPageId | path | string | true | The ID of the document page. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response is an image file (not JSON) that can be saved or displayed. | None | | 404 | Not Found | Document page not found | None | ## Lists metadata on all computed document text extraction samples by project ID Operation path: `GET /api/v2/projects/{projectId}/documentTextExtractionSamples/` Authentication requirements: `BearerAuth` Lists metadata on all computed document text extraction samples in the project across all models. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of Model ID feature name pairs with computed document text extraction samples.", "items": { "properties": { "documentTask": { "description": "The document task that this data belongs to.", "enum": [ "DOCUMENT_TEXT_EXTRACTOR", "TESSERACT_OCR" ], "type": "string" }, "featureName": { "description": "Name of feature", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" } }, "required": [ "documentTask", "featureName", "modelId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Metadata on computed document text extraction samples in the project. | DocumentTextExtractionSamplesListMetadataResponse | ## Lists document thumbnail bins for every target value or range including the metadata for one by project ID Operation path: `GET /api/v2/projects/{projectId}/documentThumbnailBins/` Authentication requirements: `BearerAuth` Lists document thumbnail bins for every target value or range including the metadata for one example thumbnail of the bin. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | featureName | query | string | true | Name of the document feature | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of document thumbnail metadata, as described below", "items": { "properties": { "documentPageId": { "description": "The ID of the document page. The actual document page can be retrieved with [GET /api/v2/projects/{projectId}/documentPages/{documentPageId}/file/][get-apiv2projectsprojectiddocumentpagesdocumentpageidfile].", "type": "string" }, "height": { "description": "The height of the document page in pixels.", "type": "integer" }, "targetBinEnd": { "description": "Target value for bin end for regression, null for classification", "type": [ "integer", "null" ] }, "targetBinRowCount": { "description": "The number of rows in the target bin.", "type": "integer" }, "targetBinStart": { "description": "Target value for bin start for regression, null for classification", "type": [ "integer", "null" ] }, "targetValue": { "description": "Target value", "oneOf": [ { "description": "For regression projects", "type": "number" }, { "description": "For classification projects", "type": "string" }, { "items": { "description": "For multilabel projects", "type": "string" }, "type": "array" } ] }, "width": { "description": "The width of the document page in pixels.", "type": "integer" } }, "required": [ "documentPageId", "height", "targetBinRowCount", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns a list of document thumbnail bins for every target value or range including the metadata for one example thumbnail of the bin. | DocumentThumbnailBinsListResponse | | 422 | Unprocessable Entity | The request cannot be processed | None | ## List all metadata by project ID Operation path: `GET /api/v2/projects/{projectId}/documentThumbnailSamples/` Authentication requirements: `BearerAuth` List all metadata for document thumbnails in the EDA sample. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | featureName | query | string | true | Name of the document feature | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of document thumbnail metadata elements", "items": { "properties": { "documentPageId": { "description": "The ID of the document page. The actual document page can be retrieved with [GET /api/v2/projects/{projectId}/documentPages/{documentPageId}/file/][get-apiv2projectsprojectiddocumentpagesdocumentpageidfile].", "type": "string" }, "height": { "description": "The height of the document page in pixels.", "type": "integer" }, "targetValue": { "description": "Target value", "oneOf": [ { "description": "For regression projects", "type": "number" }, { "description": "For classification projects", "type": "string" }, { "items": { "description": "For multilabel projects", "type": "string" }, "type": "array" } ] }, "width": { "description": "The width of the document page in pixels.", "type": "integer" } }, "required": [ "documentPageId", "height", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of document thumbnail metadata. | DocumentThumbnailMetadataListResponse | | 422 | Unprocessable Entity | The request cannot be processed | None | ## Returns a list of document thumbnail metadata elements by project ID Operation path: `GET /api/v2/projects/{projectId}/documentThumbnails/` Authentication requirements: `BearerAuth` Returns a list of document thumbnail metadata elements. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | featureName | query | string | false | Name of the document feature | | targetValue | query | any | false | For classification projects, when specified, returns only document pages corresponding to this target value. Mutually exclusive with targetBinStart/targetBinEnd. | | targetBinStart | query | any | false | For regression projects, when specified, returns only document pages corresponding to the target values above this value. Mutually exclusive with targetValue. Must be specified with targetBinEnd. | | targetBinEnd | query | any | false | For regression projects, when specified, only document thumbnails corresponding to the target values below this will be returned. Mutually exclusive with targetValue. Must be specified with targetBinStart. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of document thumbnail metadata elements", "items": { "properties": { "documentPageId": { "description": "The ID of the document page. The actual document page can be retrieved with [GET /api/v2/projects/{projectId}/documentPages/{documentPageId}/file/][get-apiv2projectsprojectiddocumentpagesdocumentpageidfile].", "type": "string" }, "height": { "description": "The height of the document page in pixels.", "type": "integer" }, "targetValue": { "description": "Target value", "oneOf": [ { "description": "For regression projects", "type": "number" }, { "description": "For classification projects", "type": "string" }, { "items": { "description": "For multilabel projects", "type": "string" }, "type": "array" } ] }, "width": { "description": "The width of the document page in pixels.", "type": "integer" } }, "required": [ "documentPageId", "height", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns a list of document thumbnail metadata elements. | DocumentThumbnailMetadataListResponse | | 422 | Unprocessable Entity | The request cannot be processed. Possible reasons include: - Cannot supply value for both TargetValue and TargetBin. - Must supply both TargetBinStart and TargetBinEnd. - TargetBin parameters are only valid for regression projects. - TargetValue parameter is only valid for classification projects. | None | ## Retrieve the documents data quality log content by project ID Operation path: `GET /api/v2/projects/{projectId}/documentsDataQualityLog/` Authentication requirements: `BearerAuth` Retrieve the documents data quality log content and log length as JSON. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The content in the form of lines of the documents data quality log", "items": { "description": "Log lines", "type": "string" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of lines of the document data quality log. | DocumentsDataQualityLogLinesResponse | | 422 | Unprocessable Entity | Not a data quality enabled project | None | ## Retrieve a text file containing the documents data quality log by project ID Operation path: `GET /api/v2/projects/{projectId}/documentsDataQualityLog/file/` Authentication requirements: `BearerAuth` Retrieve a text file containing the documents data quality log. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a text file with the contents of the documents data quality log. | None | | 422 | Unprocessable Entity | Not a data quality enabled project | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | attachment;filename=.txt The suggested filename is dynamically generated | | 200 | Content-Type | string | | The MIME type of the returned data. | ## Get a list of duplicate images containing the number of occurrences of each by project ID Operation path: `GET /api/v2/projects/{projectId}/duplicateImages/{column}/` Authentication requirements: `BearerAuth` Get a list of duplicate images containing the number of occurrences of each image. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | projectId | path | string | true | The project ID. | | column | path | string | true | Column parameter to filter the list of duplicate images returned | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of image metadata", "items": { "properties": { "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "rowCount": { "description": "The count of duplicate images i.e. number of times this image is used in column", "type": "integer" } }, "required": [ "imageId", "rowCount" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of image metadata | DuplicateImageTableResponse | ## Download the project dataset by project ID Operation path: `GET /api/v2/projects/{projectId}/featureDiscoveryDatasetDownload/` Authentication requirements: `BearerAuth` Download the project dataset with features added by feature discovery. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | query | string | false | The ID of the dataset to use for the prediction. | | projectId | path | string | true | The project ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Project dataset file. | None | | 404 | Not Found | Data is not found. | None | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Retrieve the feature discovery log content by project ID Operation path: `GET /api/v2/projects/{projectId}/featureDiscoveryLogs/` Authentication requirements: `BearerAuth` Retrieve the feature discovery log content and log length for a feature discovery project. This route is only supported for feature discovery projects that have finished partitioning. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "featureDiscoveryLog": { "description": "List of lines retrieved from the feature discovery log", "items": { "type": "string" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalLogLines": { "description": "The total number of lines in the feature derivation log.", "type": "integer" } }, "required": [ "count", "featureDiscoveryLog", "next", "previous", "totalLogLines" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Feature discovery log data. | FeatureDiscoveryLogListResponse | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Retrieve a text file containing the feature discovery log by project ID Operation path: `GET /api/v2/projects/{projectId}/featureDiscoveryLogs/download/` Authentication requirements: `BearerAuth` Retrieve a text file containing the feature discovery log. This route is only supported for feature discovery projects that have finished partitioning. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Feature discovery log file. | None | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Download the feature discovery SQL recipe by project ID Operation path: `GET /api/v2/projects/{projectId}/featureDiscoveryRecipeSQLs/download/` Authentication requirements: `BearerAuth` Download the feature discovery SQL recipe for a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | modelId | query | string | false | Model ID to export recipe for | | statusOnly | query | string | false | Return status only for availability check | | asText | query | string | false | Determines whether to download the file or just return text. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | statusOnly | [false, False, true, True] | | asText | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Project feature discovery SQL recipe file. | None | | 400 | Bad Request | Unable to process the request | None | | 404 | Not Found | Data not found | None | ## Generate the feature discovery SQL recipe by project ID Operation path: `POST /api/v2/projects/{projectId}/featureDiscoveryRecipeSqlExports/` Authentication requirements: `BearerAuth` Generate the feature discovery SQL recipe for a project. ### Body parameter ``` { "properties": { "modelId": { "description": "Model ID to export recipe for", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | FeatureDiscoveryRecipeSQLsExport | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | None | | 404 | Not Found | Data not found | None | | 422 | Unprocessable Entity | Unable to process the request | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Get the feature histogram by project ID Operation path: `GET /api/v2/projects/{projectId}/featureHistograms/{featureName}/` Authentication requirements: `BearerAuth` Get histogram chart data for a specific feature. Information that can be used to build histogram charts. Plot data returned is based on raw data that is calculated during initial project creation and updated after the project's target variable has been selected. The number of bins in the histogram is no greater than the requested limit. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | binLimit | query | integer | true | The maximum number of bins in the returned plot. | | key | query | string | false | name of the top 50 key for which plot to be retrieved. (Only required for the Summarized categorical feature) | | projectId | path | string | true | The ID of the project. | | featureName | path | string | true | the name of the feature Note: DataRobot renames some features, so the feature name may not be the one from your original data. You can use [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] to list the features and check the name. Note to users with non-ascii features names: The feature name should be utf-8-encoded (before URL-quoting) | ### Example responses > 200 Response ``` { "properties": { "plot": { "description": "plot data based on feature values.", "items": { "properties": { "count": { "description": "number of values in the bin (or weights if project is weighted)", "type": "number" }, "label": { "description": "bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" }, "target": { "description": "Average value of the target feature values for the bin. For regression projects, it will be null, if the feature was deemed as low informative or project target has not been selected yet or AIM processing has not finished yet. You can use [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] endpoint to find more about low informative features. For binary classification, the same conditions apply as above, but the value should be treated as the ratio of total positives in the bin to bin's total size (`count`). For multiclass projects the value is always null.", "type": [ "number", "null" ] } }, "required": [ "count", "label", "target" ], "type": "object" }, "type": "array" } }, "required": [ "plot" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The feature histogram chart data. | FeatureHistogramResponse | | 404 | Not Found | A Histogram is unavailable for this feature because the data contains unsupportedfeature types (e.g., image, location). | None | ## Retrieve the Feature Discovery lineage by project ID Operation path: `GET /api/v2/projects/{projectId}/featureLineages/{featureLineageId}/` Authentication requirements: `BearerAuth` Retrieve the Feature Discovery lineage for a single feature. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project to retrieve a lineage from. | | featureLineageId | path | string | true | id of a feature lineage object to return. You can access the id with ModelingFeatureRetrieveController. | ### Example responses > 200 Response ``` { "properties": { "steps": { "description": "List of steps which were applied to build the feature.", "items": { "properties": { "arguments": { "description": "Generic key-value pairs to describe **action** step additional parameters.", "type": "object" }, "catalogId": { "description": "ID of the catalog for a **data** step.", "type": "string" }, "catalogVersionId": { "description": "id of the catalog version for a **data** step.", "type": "string" }, "description": { "description": "Description of the step.", "type": "string" }, "groupBy": { "description": "List of columns which this **action** step aggregated.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Step id starting with 0.", "minimum": 0, "type": "integer" }, "isTimeAware": { "description": "Indicator of step being time aware. Mandatory only for **action** and **join** steps. **action** step provides additional information about feature derivation window in the `timeInfo` field.", "type": "boolean" }, "joinInfo": { "description": "**join** step details.", "properties": { "joinType": { "description": "Kind of SQL JOIN applied.", "enum": [ "left, right" ], "type": "string" }, "leftTable": { "description": "Information about a dataset which was considered left in a join.", "properties": { "columns": { "description": "List of columns which datasets were joined by.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "datasteps": { "description": "List of *data* steps id which brought the *columns* into the current step dataset.", "items": { "minimum": 1, "type": "integer" }, "type": "array" } }, "required": [ "columns", "datasteps" ], "type": "object" }, "rightTable": { "description": "Information about a dataset which was considered left in a join.", "properties": { "columns": { "description": "List of columns which datasets were joined by.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "datasteps": { "description": "List of *data* steps id which brought the *columns* into the current step dataset.", "items": { "minimum": 1, "type": "integer" }, "type": "array" } }, "required": [ "columns", "datasteps" ], "type": "object" } }, "required": [ "joinType", "leftTable", "rightTable" ], "type": "object" }, "name": { "description": "Name of the step.", "type": "string" }, "parents": { "description": "`id` of steps which use this step output as their input.", "items": { "minimum": 0, "type": "integer" }, "type": "array" }, "stepType": { "description": "One of four types of a step. **data** - source features. **action** - data aggregation or trasformation. **join** - SQL JOIN. **generatedData** - final feature. There is always one **generatedData** step and at least one **data** step.", "enum": [ "data", "action", "join", "generatedData" ], "type": "string" }, "timeInfo": { "description": "Description of a feature derivation window which was applied to this **action** step.", "properties": { "duration": { "description": "End of the feature derivation window applied.", "properties": { "duration": { "description": "Value/size of this time delta.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "Time unit name like 'MINUTE', 'DAY', 'MONTH' etc.", "type": "string" } }, "required": [ "duration", "timeUnit" ], "type": "object" }, "latest": { "description": "End of the feature derivation window applied.", "properties": { "duration": { "description": "Value/size of this time delta.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "Time unit name like 'MINUTE', 'DAY', 'MONTH' etc.", "type": "string" } }, "required": [ "duration", "timeUnit" ], "type": "object" } }, "required": [ "duration", "latest" ], "type": "object" } }, "required": [ "id", "parents", "stepType" ], "type": "object" }, "type": "array" } }, "required": [ "steps" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | FeatureLineageResponse | ## List featurelists by project ID Operation path: `GET /api/v2/projects/{projectId}/featurelists/` Authentication requirements: `BearerAuth` List all featurelists for a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | sortBy | query | string | false | The property to sort featurelists by in the response. | | searchFor | query | string | false | Limit results by specific featurelists. Performs a substring search for the term you provide in featurelist names. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | sortBy | [name, description, features, numModels, created, isUserCreated, -name, -description, -features, -numModels, -created, -isUserCreated] | ### Example responses > 200 Response ``` { "items": { "properties": { "created": { "description": "A :ref:`timestamp ` string specifying when the featurelist was created.", "type": "string", "x-versionadded": "v2.13" }, "description": { "description": "User-friendly description of the featurelist, which can be updated by users.", "type": [ "string", "null" ], "x-versionadded": "v2.13" }, "features": { "description": "Names of features included in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Featurelist ID.", "type": "string" }, "isUserCreated": { "description": "Whether the featurelist was created manually by a user or by DataRobot automation.", "type": "boolean", "x-versionadded": "v2.13" }, "name": { "description": "The name of the featurelist.", "type": "string" }, "numModels": { "description": "The number of models that currently use this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist.", "type": "integer", "x-versionadded": "v2.13" }, "projectId": { "description": "The project ID the featurelist belongs to.", "type": "string" } }, "required": [ "created", "features", "id", "isUserCreated", "name", "numModels", "projectId" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of featurelists. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [FeaturelistResponse] | false | | none | | » created | string | true | | A :ref:timestamp string specifying when the featurelist was created. | | » description | string,null | false | | User-friendly description of the featurelist, which can be updated by users. | | » features | [string] | true | | Names of features included in the featurelist. | | » id | string | true | | Featurelist ID. | | » isUserCreated | boolean | true | | Whether the featurelist was created manually by a user or by DataRobot automation. | | » name | string | true | | The name of the featurelist. | | » numModels | integer | true | | The number of models that currently use this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist. | | » projectId | string | true | | The project ID the featurelist belongs to. | ## Create a new featurelist by project ID Operation path: `POST /api/v2/projects/{projectId}/featurelists/` Authentication requirements: `BearerAuth` Create a new featurelist from list of feature names. ### Body parameter ``` { "properties": { "features": { "description": "List of features for new featurelist.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "name": { "description": "New featurelist name.", "maxLength": 100, "type": "string" }, "skipDatetimePartitionColumn": { "default": false, "description": "Whether featurelist should contain datetime partition column.", "type": "boolean", "x-versionadded": "v2.24" } }, "required": [ "features", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | CreateFeaturelist | false | none | ### Example responses > 201 Response ``` { "properties": { "created": { "description": "A :ref:`timestamp ` string specifying when the featurelist was created.", "type": "string", "x-versionadded": "v2.13" }, "description": { "description": "User-friendly description of the featurelist, which can be updated by users.", "type": [ "string", "null" ], "x-versionadded": "v2.13" }, "features": { "description": "Names of features included in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Featurelist ID.", "type": "string" }, "isUserCreated": { "description": "Whether the featurelist was created manually by a user or by DataRobot automation.", "type": "boolean", "x-versionadded": "v2.13" }, "name": { "description": "The name of the featurelist.", "type": "string" }, "numModels": { "description": "The number of models that currently use this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist.", "type": "integer", "x-versionadded": "v2.13" }, "projectId": { "description": "The project ID the featurelist belongs to.", "type": "string" } }, "required": [ "created", "features", "id", "isUserCreated", "name", "numModels", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The newly created featurelist in the same format as [GET /api/v2/projects/{projectId}/featurelists/{featurelistId}/][get-apiv2projectsprojectidfeaturelistsfeaturelistid]. | FeaturelistResponse | ## Delete a specified featurelist by project ID Operation path: `DELETE /api/v2/projects/{projectId}/featurelists/{featurelistId}/` Authentication requirements: `BearerAuth` Delete a specified featurelist. All models using a featurelist, whether as the training featurelist or as a monotonic constraint featurelist, will also be deleted when the deletion is executed and any queued or running jobs using it will be cancelled. Similarly, predictions made on these models will also be deleted. All the entities that are to be deleted with a featurelist are described as "dependencies" of it. When deleting a featurelist with dependencies, users must pass an additional query parameter `deleteDependencies` to confirm they want to delete the featurelist and all its dependencies. Without that option, only featurelists with no dependencies may be successfully deleted. Featurelists configured into the project as a default featurelist or as a default monotonic constraint featurelist cannot be deleted. Featurelists used in a model deployment cannot be deleted until the model deployment is deleted. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dryRun | query | string | false | Preview the deletion results without actually deleting the featurelist. | | deleteDependencies | query | string | false | Automatically delete all dependencies of a featurelist. If false (default), will only delete the featurelist if it has no dependencies. The value of deleteDependencies will not be used if dryRun is true.If a featurelist has dependencies, deleteDependencies must be true for the request to succeed. | | projectId | path | string | true | The project ID. | | featurelistId | path | string | true | The featurelist ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | dryRun | [false, False, true, True] | | deleteDependencies | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "canDelete": { "description": "Whether the featurelist can be deleted.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "deletionBlockedReason": { "description": "If the featurelist can't be deleted, this explains why.", "type": "string" }, "dryRun": { "description": "Whether this was a dry-run or the featurelist was actually deleted.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "numAffectedJobs": { "description": "Number of incomplete jobs using this featurelist.", "type": "integer" }, "numAffectedModels": { "description": "Number of models using this featurelist.", "type": "integer" } }, "required": [ "canDelete", "deletionBlockedReason", "dryRun", "numAffectedJobs", "numAffectedModels" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | FeaturelistDestroyResponse | ## Retrieve a feature list by project ID Operation path: `GET /api/v2/projects/{projectId}/featurelists/{featurelistId}/` Authentication requirements: `BearerAuth` Retrieve a single known feature list. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | featurelistId | path | string | true | The featurelist ID. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "A :ref:`timestamp ` string specifying when the featurelist was created.", "type": "string", "x-versionadded": "v2.13" }, "description": { "description": "User-friendly description of the featurelist, which can be updated by users.", "type": [ "string", "null" ], "x-versionadded": "v2.13" }, "features": { "description": "Names of features included in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Featurelist ID.", "type": "string" }, "isUserCreated": { "description": "Whether the featurelist was created manually by a user or by DataRobot automation.", "type": "boolean", "x-versionadded": "v2.13" }, "name": { "description": "The name of the featurelist.", "type": "string" }, "numModels": { "description": "The number of models that currently use this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist.", "type": "integer", "x-versionadded": "v2.13" }, "projectId": { "description": "The project ID the featurelist belongs to.", "type": "string" } }, "required": [ "created", "features", "id", "isUserCreated", "name", "numModels", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve a single known feature list. | FeaturelistResponse | ## Update an existing featurelist by project ID Operation path: `PATCH /api/v2/projects/{projectId}/featurelists/{featurelistId}/` Authentication requirements: `BearerAuth` Update an existing featurelist by ID. ### Body parameter ``` { "properties": { "description": { "description": "The new featurelist description.", "maxLength": 1000, "type": "string" }, "name": { "description": "The new featurelist name.", "maxLength": 100, "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | featurelistId | path | string | true | The featurelist ID. | | body | body | UpdateFeaturelist | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The featurelist was successfully updated. | None | | 422 | Unprocessable Entity | Update failed due to an invalid payload. This may be because the name is identical to an existing featurelist name. | None | ## List project features by project ID Operation path: `GET /api/v2/projects/{projectId}/features/` Authentication requirements: `BearerAuth` List the features from a project with descriptive information. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | sortBy | query | string | false | Property to sort features by in the response | | searchFor | query | string | false | Limit results by specific features. Performs a substring search for the term you provide in featurelist names. | | featurelistId | query | string | false | Filter features by a specific featurelist ID. | | forSegmentedAnalysis | query | string | false | When True, features returned will be filtered to those usable for segmented analysis. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | sortBy | [name, id, importance, featureType, uniqueCount, naCount, mean, stdDev, median, min, max, -name, -id, -importance, -featureType, -uniqueCount, -naCount, -mean, -stdDev, -median, -min, -max] | | forSegmentedAnalysis | [false, False, true, True] | ### Example responses > 200 Response ``` { "items": { "properties": { "dataQualities": { "description": "Data Quality Status", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string" }, "dateFormat": { "description": "the date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ], "x-versionadded": "v2.5" }, "featureLineageId": { "description": "The ID of the lineage for automatically generated features.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string" }, "id": { "description": "the feature ID. (Note: Throughout the API, features are specified using their names, not this ID.)", "type": "integer" }, "importance": { "description": "numeric measure of the strength of relationship between the feature and target (independent of any model or other features)", "type": [ "number", "null" ] }, "isRestoredAfterReduction": { "description": "Whether feature is restored after feature reduction", "type": "boolean", "x-versionadded": "v2.26" }, "isZeroInflated": { "description": "Whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ] }, "language": { "description": "Feature's detected language.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "whether feature has too few values to be informative", "type": "boolean" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "max": { "description": "maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "mean": { "description": "arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "median": { "description": "median of the EDA sample of the feature.", "oneOf": [ { "description": "median of the EDA sample of the feature.", "type": "string" }, { "description": "median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "min": { "description": "minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "multilabelInsights": { "description": "Multilabel project specific information", "properties": { "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The response will contain the key for the most recent, finished EDA stage.", "type": "string" } }, "required": [ "multilabelInsightsKey" ], "type": "object" }, "naCount": { "description": "Number of missing values.", "type": "integer" }, "name": { "description": "The feature name.", "type": "string" }, "parentFeatureNames": { "description": "an array of string feature names indicating which features in the input data were used to create this feature if the feature is a transformation.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "The ID of the project the feature belongs to.", "type": "string" }, "stdDev": { "description": "standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "targetLeakage": { "description": "the detected level of risk for target leakage, if any. 'SKIPPED_DETECTION' indicates leakage detection was not run on the feature, 'FALSE' indicates no leakage, 'MODERATE_RISK' indicates a moderate risk of target leakage, and 'HIGH_RISK' indicates a high risk of target leakage.", "enum": [ "FALSE", "HIGH_RISK", "MODERATE_RISK", "SKIPPED_DETECTION" ], "type": "string" }, "targetLeakageReason": { "description": "descriptive sentence explaining the reason for target leakage.", "type": "string", "x-versionadded": "v2.20" }, "timeSeriesEligibilityReason": { "description": "why the feature is ineligible for time series projects, or 'suitable' if it is eligible.", "type": "string", "x-versionadded": "v2.8" }, "timeSeriesEligible": { "description": "whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.", "type": "boolean", "x-versionadded": "v2.8" }, "timeStep": { "description": "The minimum time step that can be used to specify time series windows. The units for this value are the ``timeUnit``. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "timeUnit": { "description": "the unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "uniqueCount": { "description": "number of unique values", "type": "integer" }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "dateFormat", "featureLineageId", "featureType", "id", "importance", "lowInformation", "lowerQuartile", "max", "mean", "median", "min", "naCount", "name", "projectId", "stdDev", "targetLeakage", "targetLeakageReason", "timeSeriesEligibilityReason", "timeSeriesEligible", "timeStep", "timeUnit", "upperQuartile" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of features. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [ProjectFeatureResponse] | false | | none | | » dataQualities | string | false | | Data Quality Status | | » dateFormat | string,null | true | | the date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime . | | » featureLineageId | string,null | true | | The ID of the lineage for automatically generated features. | | » featureType | string | true | | Feature type. | | » id | integer | true | | the feature ID. (Note: Throughout the API, features are specified using their names, not this ID.) | | » importance | number,null | true | | numeric measure of the strength of relationship between the feature and target (independent of any model or other features) | | » isRestoredAfterReduction | boolean | false | | Whether feature is restored after feature reduction | | » isZeroInflated | boolean,null | false | | Whether feature has an excessive number of zeros | | » keySummary | any | false | | Per key summaries for Summarized Categorical or Multicategorical columns | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | FeatureKeySummaryResponseValidatorSummarizedCategorical | false | | For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters) | | »»» key | string | true | | Name of the key. | | »»» summary | FeatureKeySummaryDetailsResponseValidatorSummarizedCategorical | true | | Statistics of the key. | | »»»» dataQualities | string | true | | The indicator of data quality assessment of the feature. | | »»»» max | number | true | | Maximum value of the key. | | »»»» mean | number | true | | Mean value of the key. | | »»»» median | number | true | | Median value of the key. | | »»»» min | number | true | | Minimum value of the key. | | »»»» pctRows | number | true | | Percentage occurrence of key in the EDA sample of the feature. | | »»»» stdDev | number | true | | Standard deviation of the key. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | [FeatureKeySummaryResponseValidatorMultilabel] | false | | For a Multicategorical columns, this will contain statistics for the top classes | | »»» key | string | true | | Name of the key. | | »»» summary | FeatureKeySummaryDetailsResponseValidatorMultilabel | true | | Statistics of the key. | | »»»» max | number | true | | Maximum value of the key. | | »»»» mean | number | true | | Mean value of the key. | | »»»» median | number | true | | Median value of the key. | | »»»» min | number | true | | Minimum value of the key. | | »»»» pctRows | number | true | | Percentage occurrence of key in the EDA sample of the feature. | | »»»» stdDev | number | true | | Standard deviation of the key. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » language | string | false | | Feature's detected language. | | » lowInformation | boolean | true | | whether feature has too few values to be informative | | » lowerQuartile | any | true | | Lower quartile point of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | Lower quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | Lower quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » max | any | true | | maximum value of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | maximum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | maximum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » mean | any | true | | arithmetic mean of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | arithmetic mean of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | arithmetic mean of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » median | any | true | | median of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | median of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | median of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » min | any | true | | minimum value of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | minimum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | minimum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » multilabelInsights | MultilabelInsightsResponse | false | | Multilabel project specific information | | »» multilabelInsightsKey | string | true | | Key for multilabel insights, unique per project, feature, and EDA stage. The response will contain the key for the most recent, finished EDA stage. | | » naCount | integer | true | | Number of missing values. | | » name | string | true | | The feature name. | | » parentFeatureNames | [string] | false | | an array of string feature names indicating which features in the input data were used to create this feature if the feature is a transformation. | | » projectId | string | true | | The ID of the project the feature belongs to. | | » stdDev | any | true | | standard deviation of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | standard deviation of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | standard deviation of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » targetLeakage | string | true | | the detected level of risk for target leakage, if any. 'SKIPPED_DETECTION' indicates leakage detection was not run on the feature, 'FALSE' indicates no leakage, 'MODERATE_RISK' indicates a moderate risk of target leakage, and 'HIGH_RISK' indicates a high risk of target leakage. | | » targetLeakageReason | string | true | | descriptive sentence explaining the reason for target leakage. | | » timeSeriesEligibilityReason | string | true | | why the feature is ineligible for time series projects, or 'suitable' if it is eligible. | | » timeSeriesEligible | boolean | true | | whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements. | | » timeStep | integer,null | true | | The minimum time step that can be used to specify time series windows. The units for this value are the timeUnit. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise. | | » timeUnit | string,null | true | | the unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise. | | » uniqueCount | integer | false | | number of unique values | | » upperQuartile | any | true | | Upper quartile point of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | Upper quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | number | false | | Upper quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | null | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | dataQualities | [ISSUES_FOUND, NOT_ANALYZED, NO_ISSUES_FOUND] | | featureType | [Boolean, Categorical, Currency, Date, Date Duration, Document, Image, Interaction, Length, Location, Multicategorical, Numeric, Percentage, Summarized Categorical, Text, Time] | | dataQualities | [ISSUES_FOUND, NOT_ANALYZED, NO_ISSUES_FOUND] | | targetLeakage | [FALSE, HIGH_RISK, MODERATE_RISK, SKIPPED_DETECTION] | ## List feature metrics by project ID Operation path: `GET /api/v2/projects/{projectId}/features/metrics/` Authentication requirements: `BearerAuth` List the appropriate metrics if a feature were chosen as the target. The metrics listed will include both weighted and unweighted metrics - which are appropriate will depend on whether a weights column is used. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | featureName | query | string | true | The name of the feature to check. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "availableMetrics": { "description": "an array of strings representing the appropriate metrics. If the feature cannot be selected as the target, then this array will be empty.", "items": { "type": "string" }, "type": "array" }, "featureName": { "description": "The name of the feature that was looked up.", "type": "string" }, "metricDetails": { "description": "The list of metricDetails objects.", "items": { "properties": { "ascending": { "description": "Should the metric be sorted in ascending order", "type": "boolean" }, "metricName": { "description": "Name of the metric", "type": "string" }, "supportsBinary": { "description": "Whether this metric is valid for binary classification.", "type": "boolean" }, "supportsMulticlass": { "description": "Whether this metric is valid for multiclass classification.", "type": "boolean" }, "supportsRegression": { "description": "This metric is valid for regression", "type": "boolean" }, "supportsTimeseries": { "description": "This metric is valid for timeseries", "type": "boolean" } }, "required": [ "ascending", "metricName", "supportsBinary", "supportsMulticlass", "supportsRegression", "supportsTimeseries" ], "type": "object" }, "type": "array" } }, "required": [ "availableMetrics", "featureName", "metricDetails" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The feature's metrics. | FeatureMetricsResponse | ## Get a project feature by project ID Operation path: `GET /api/v2/projects/{projectId}/features/{featureName}/` Authentication requirements: `BearerAuth` Retrieve the specified feature with descriptive information. Descriptive information for features also includes summary statistics as of v2.8. These are returned via the fields max, min, mean, median, and stdDev. These fields are formatted according to the original feature type of the feature. For example, the format will be numeric if your feature is numeric, in feet and inches if your feature is length type, in currency if your feature is currency type, in time format if your feature is time type, or in ISO date format if your feature is a date type. Numbers will be rounded so that they have at most two non-zero decimal digits. For projects created prior to v2.8, these descriptive statistics will not be available. Also, some features, like categorical and text features, may not have summary statistics. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The ID of the project. | | featureName | path | string | true | the name of the feature Note: DataRobot renames some features, so the feature name may not be the one from your original data. You can use [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] to list the features and check the name. Note to users with non-ascii features names: The feature name should be utf-8-encoded (before URL-quoting) | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The feature information. | None | ## Retrieve potential multiseries ID columns by project ID Operation path: `GET /api/v2/projects/{projectId}/features/{featureName}/multiseriesProperties/` Authentication requirements: `BearerAuth` Time series projects require that each timestamp have at most one row corresponding to it. However, multiple series of data can be handled within a single project by designating a multiseries ID column that assigns each row to a particular series. See the :ref: `multiseries ` docs on time series projects for more information. Note that detection will have to be triggered via [POST /api/v2/projects/{projectId}/multiseriesProperties/][post-apiv2projectsprojectidmultiseriesproperties] in order for multiseries id columns to appear here. The route will return successfully with an empty array of detected columns if detection hasn't run yet, or hasn't found any valid columns. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID to retrieve multiseries properties from. | | featureName | path | string | true | The feature to be used as the datetime partition column. | ### Example responses > 200 Response ``` { "properties": { "datetimePartitionColumn": { "description": "The datetime partition column name.", "type": "string" }, "detectedMultiseriesIdColumns": { "description": "The list of detected multiseries ID columns along with timeStep and timeUnit information. Note that if no eligible columns have been detected, this list will be empty.", "items": { "description": "The detected multiseries ID columns along with timeStep and timeUnit information.", "properties": { "multiseriesIdColumns": { "description": "The list of one or more names of columns that contain the individual series identifiers if the dataset consists of multiple time series.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "timeStep": { "description": "The detected time step.", "type": "integer" }, "timeUnit": { "description": "The detected time unit (e.g., DAY, HOUR, etc.).", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "multiseriesIdColumns", "timeStep", "timeUnit" ], "type": "object" }, "type": "array" } }, "required": [ "datetimePartitionColumn", "detectedMultiseriesIdColumns" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Request to retrieve the potential multiseries ID columns was successful. | MultiseriesRetrieveResponse | ## List image bins and covers by project ID Operation path: `GET /api/v2/projects/{projectId}/imageBins/` Authentication requirements: `BearerAuth` List image bins and covers for every target value or range. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | featureName | query | string | true | The name of the image feature. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of image metadata, as described below", "items": { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "targetBinEnd": { "description": "Target value for bin end for regression, null for classification", "type": [ "integer", "null" ] }, "targetBinRowCount": { "description": "Number of rows in this target bin", "type": "integer" }, "targetBinStart": { "description": "Target value for bin start for regression, null for classification", "type": [ "integer", "null" ] }, "targetValue": { "description": "Target value", "type": "number" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "targetBinRowCount", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns a list of image metadata | ImageBinsListResponse | | 422 | Unprocessable Entity | The request cannot be processed | None | ## List all Image Embeddings by project ID Operation path: `GET /api/v2/projects/{projectId}/imageEmbeddings/` Authentication requirements: `BearerAuth` List all Image Embeddings for the project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of items to skip over. | | limit | query | integer | false | The number of items to return. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of Image Embeddings", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" } }, "required": [ "featureName", "modelId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Image Embeddings | EmbeddingsListResponse | ## Retrieve image samples by id Operation path: `GET /api/v2/projects/{projectId}/imageSamples/` Authentication requirements: `BearerAuth` List all metadata for images in the EDA sample. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | featureName | query | string | true | The name of the image feature. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of image metadata elements", "items": { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "targetValue": { "description": "Target value", "type": "number" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Paginated list of image metadata | ImageMetadataListResponse | | 422 | Unprocessable Entity | The request cannot be processed | None | ## Returns a list of image metadata elements by project ID Operation path: `GET /api/v2/projects/{projectId}/images/` Authentication requirements: `BearerAuth` Returns a list of image metadata elements. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | column | query | string | false | The name of the column to query. | | targetValue | query | any | false | For classification projects - when specified, only images corresponding to this target value will be returned. Mutually exclusive with targetBinStart/targetBinEnd. | | targetBinStart | query | any | false | For regression projects - when specified, only images corresponding to the target values above this will be returned. Mutually exclusive with targetValue. Must be specified with targetBinEnd. | | targetBinEnd | query | any | false | For regression projects - when specified, only images corresponding to the target values below this will be returned. Mutually exclusive with targetValue. Must be specified with targetBinStart. | | offset | query | integer | true | The number of results to skip. | | limit | query | integer | true | The maximum number of results to return. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of image metadata elements", "items": { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "targetValue": { "description": "Target value", "type": "number" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns a list of image metadata elements. | ImageMetadataListResponse | | 422 | Unprocessable Entity | The request cannot be processed. Possible reasons include: - Cannot supply value for both TargetValue and TargetBin. - Must supply both TargetBinStart and TargetBinEnd. - TargetBin parameters are only valid for regression projects. - TargetValue parameter is only valid for classification projects. | None | ## Returns a single image metadata by project ID Operation path: `GET /api/v2/projects/{projectId}/images/{imageId}/` Authentication requirements: `BearerAuth` Returns a single image metadata. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | imageId | path | string | true | The ID of the image. | ### Example responses > 200 Response ``` { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "targetValue": { "description": "Target value", "type": "number" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "width" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Metadata for a single image | ImageMetadataResponse | | 404 | Not Found | Image not found | None | ## Retrieve file by id Operation path: `GET /api/v2/projects/{projectId}/images/{imageId}/file/` Authentication requirements: `BearerAuth` Returns a file for a single image. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | imageId | path | string | true | The ID of the image. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response is an image file (not JSON) that can be saved or displayed. | None | | 404 | Not Found | Image not found | None | ## Retrieve the images data quality log content by project ID Operation path: `GET /api/v2/projects/{projectId}/imagesDataQualityLog/` Authentication requirements: `BearerAuth` Retrieve the images data quality log content and log length as JSON. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The content in the form of lines of the images data quality log", "items": { "description": "Log lines", "type": "string" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Paginated list of lines of the image data quality log | ImagesDataQualityLogLinesResponse | | 422 | Unprocessable Entity | Not a data quality enabled project | None | ## Retrieve a text file containing the images data quality log by project ID Operation path: `GET /api/v2/projects/{projectId}/imagesDataQualityLog/file/` Authentication requirements: `BearerAuth` Retrieve a text file containing the images data quality log. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a text file with the contents of the images data quality log. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | attachment;filename=.txt The suggested filename is dynamically generated | | 200 | Content-Type | string | | MIME type of the returned data | ## List all modeling featurelists by project ID Operation path: `GET /api/v2/projects/{projectId}/modelingFeaturelists/` Authentication requirements: `BearerAuth` List all modeling featurelists from the project requested by ID. This route will only become available after the target and partitioning options have been set for a project. Modeling featurelists are featurelists of modeling features, and are the correct featurelists to use when creating models or restarting the autopilot. In a time series project, these will differ from those returned from [GET /api/v2/projects/{projectId}/featurelists/][get-apiv2projectsprojectidfeaturelists] while in other projects these will be identical. See the :ref: `documentation ` for more information on the distinction between input and modeling data in time series projects. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | sortBy | query | string | false | The property to sort featurelists by in the response. | | searchFor | query | string | false | Limit results by specific featurelists. Performs a substring search for the term you provide in featurelist names. | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. If 0, all results. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | sortBy | [name, description, features, numModels, created, isUserCreated, -name, -description, -features, -numModels, -created, -isUserCreated] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of modeling features.", "items": { "properties": { "created": { "description": "A :ref:`timestamp ` string specifying when the featurelist was created.", "type": "string", "x-versionadded": "v2.13" }, "description": { "description": "User-friendly description of the featurelist, which can be updated by users.", "type": [ "string", "null" ], "x-versionadded": "v2.13" }, "features": { "description": "Names of features included in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Featurelist ID.", "type": "string" }, "isUserCreated": { "description": "Whether the featurelist was created manually by a user or by DataRobot automation.", "type": "boolean", "x-versionadded": "v2.13" }, "name": { "description": "The name of the featurelist.", "type": "string" }, "numModels": { "description": "The number of models that currently use this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist.", "type": "integer", "x-versionadded": "v2.13" }, "projectId": { "description": "The project ID the featurelist belongs to.", "type": "string" } }, "required": [ "created", "features", "id", "isUserCreated", "name", "numModels", "projectId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of requested project modeling featurelists. | FeaturelistListResponse | ## Create a new modeling featurelist by project ID Operation path: `POST /api/v2/projects/{projectId}/modelingFeaturelists/` Authentication requirements: `BearerAuth` Create new modeling featurelist from list of feature names. Only time series projects differentiate between modeling and input featurelists. On other projects, this route will behave the same as [POST /api/v2/projects/{projectId}/featurelists/][post-apiv2projectsprojectidfeaturelists]. On time series projects, this can be used after the target has been set in order to create a new featurelist on the modeling features, although the previously mentioned route for creating featurelists will be disabled. On time series projects, only modeling features may be passed to this route to create a featurelist. ### Body parameter ``` { "properties": { "features": { "description": "List of features for new featurelist.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "name": { "description": "New featurelist name.", "maxLength": 100, "type": "string" }, "skipDatetimePartitionColumn": { "default": false, "description": "Whether featurelist should contain datetime partition column.", "type": "boolean", "x-versionadded": "v2.24" } }, "required": [ "features", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | CreateFeaturelist | false | none | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "A :ref:`timestamp ` string specifying when the featurelist was created.", "type": "string", "x-versionadded": "v2.13" }, "description": { "description": "User-friendly description of the featurelist, which can be updated by users.", "type": [ "string", "null" ], "x-versionadded": "v2.13" }, "features": { "description": "Names of features included in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Featurelist ID.", "type": "string" }, "isUserCreated": { "description": "Whether the featurelist was created manually by a user or by DataRobot automation.", "type": "boolean", "x-versionadded": "v2.13" }, "name": { "description": "The name of the featurelist.", "type": "string" }, "numModels": { "description": "The number of models that currently use this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist.", "type": "integer", "x-versionadded": "v2.13" }, "projectId": { "description": "The project ID the featurelist belongs to.", "type": "string" } }, "required": [ "created", "features", "id", "isUserCreated", "name", "numModels", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The newly created featurelist in the same format as [GET /api/v2/projects/{projectId}/modelingFeaturelists/{featurelistId}/][get-apiv2projectsprojectidmodelingfeaturelistsfeaturelistid]. | FeaturelistResponse | ## Delete a specified modeling featurelist by project ID Operation path: `DELETE /api/v2/projects/{projectId}/modelingFeaturelists/{featurelistId}/` Authentication requirements: `BearerAuth` Delete a specified modeling featurelist. All models using a featurelist, whether as the training featurelist or as a monotonic constraint featurelist, will also be deleted when the deletion is executed and any queued or running jobs using it will be cancelled. Similarly, predictions made on these models will also be deleted. All the entities that are to be deleted with a featurelist are described as "dependencies" of it. When deleting a featurelist with dependencies, users must pass an additional query parameter `deleteDependencies` to confirm they want to delete the featurelist and all its dependencies. Without that option, only featurelists with no dependencies may be successfully deleted. Featurelists configured into the project as a default featurelist or as a default monotonic constraint featurelist cannot be deleted. Featurelists used in a model deployment cannot be deleted until the model deployment is deleted. Modeling featurelists are featurelists of modeling features, and are the appropriate featurelists to use when creating models or restarting the autopilot. In a time series project, these will be distinct from those returned from [GET /api/v2/projects/{projectId}/featurelists/][get-apiv2projectsprojectidfeaturelists] while in other projects these will be identical. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dryRun | query | string | false | Preview the deletion results without actually deleting the featurelist. | | deleteDependencies | query | string | false | Automatically delete all dependencies of a featurelist. If false (default), will only delete the featurelist if it has no dependencies. The value of deleteDependencies will not be used if dryRun is true.If a featurelist has dependencies, deleteDependencies must be true for the request to succeed. | | projectId | path | string | true | The project ID. | | featurelistId | path | string | true | The featurelist ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | dryRun | [false, False, true, True] | | deleteDependencies | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "canDelete": { "description": "Whether the featurelist can be deleted.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "deletionBlockedReason": { "description": "If the featurelist can't be deleted, this explains why.", "type": "string" }, "dryRun": { "description": "Whether this was a dry-run or the featurelist was actually deleted.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "numAffectedJobs": { "description": "Number of incomplete jobs using this featurelist.", "type": "integer" }, "numAffectedModels": { "description": "Number of models using this featurelist.", "type": "integer" } }, "required": [ "canDelete", "deletionBlockedReason", "dryRun", "numAffectedJobs", "numAffectedModels" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | FeaturelistDestroyResponse | ## Retrieve a single modeling featurelist by ID by project ID Operation path: `GET /api/v2/projects/{projectId}/modelingFeaturelists/{featurelistId}/` Authentication requirements: `BearerAuth` Retrieve a single modeling featurelist by ID. When reporting the number of models that "use" a featurelist, a model is considered to use a featurelist if it is used as to train the model or as a monotonic constraint featurelist, or if the model is a blender with component models that use the featurelist. This route will only become available after the target and partitioning options have been set for a project. Modeling featurelists are featurelists of modeling features, and are the appropriate featurelists to use when creating models or restarting the autopilot. In a time series project, these will be distinct from those returned from [GET /api/v2/projects/{projectId}/featurelists/][get-apiv2projectsprojectidfeaturelists] while in other projects these will be identical. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | featurelistId | path | string | true | The featurelist ID. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "A :ref:`timestamp ` string specifying when the featurelist was created.", "type": "string", "x-versionadded": "v2.13" }, "description": { "description": "User-friendly description of the featurelist, which can be updated by users.", "type": [ "string", "null" ], "x-versionadded": "v2.13" }, "features": { "description": "Names of features included in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Featurelist ID.", "type": "string" }, "isUserCreated": { "description": "Whether the featurelist was created manually by a user or by DataRobot automation.", "type": "boolean", "x-versionadded": "v2.13" }, "name": { "description": "The name of the featurelist.", "type": "string" }, "numModels": { "description": "The number of models that currently use this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist.", "type": "integer", "x-versionadded": "v2.13" }, "projectId": { "description": "The project ID the featurelist belongs to.", "type": "string" } }, "required": [ "created", "features", "id", "isUserCreated", "name", "numModels", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Modeling featurelist with specified ID. | FeaturelistResponse | ## Update an existing modeling featurelist by project ID Operation path: `PATCH /api/v2/projects/{projectId}/modelingFeaturelists/{featurelistId}/` Authentication requirements: `BearerAuth` Update an existing modeling featurelist by ID. In non-time series projects, "modeling featurelists" and "featurelists" routes behave the same, except "modeling featurelists" are only accessible after the project is ready for modeling. In time series projects, "featurelists" contain the input features before feature derivation that are used to derive the time series features, while "modeling featurelists" contain the derived time series features used for modeling. ### Body parameter ``` { "properties": { "description": { "description": "The new featurelist description.", "maxLength": 1000, "type": "string" }, "name": { "description": "The new featurelist name.", "maxLength": 100, "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | featurelistId | path | string | true | The featurelist ID. | | body | body | UpdateFeaturelist | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The modeling featurelist was successfully updated. | None | | 422 | Unprocessable Entity | Update failed due to an invalid payload. This may be because the name is identical to an existing featurelist name. | None | ## List project modeling features by project ID Operation path: `GET /api/v2/projects/{projectId}/modelingFeatures/` Authentication requirements: `BearerAuth` List the features from a project that are used for modeling with descriptive information. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | sortBy | query | string | false | Property to sort features by in the response | | searchFor | query | string | false | Limit results by specific features. Performs a substring search for the term you provide in featurelist names. | | featurelistId | query | string | false | Filter features by a specific featurelist ID. | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. If 0, all results. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | sortBy | [name, id, importance, featureType, uniqueCount, naCount, mean, stdDev, median, min, max, -name, -id, -importance, -featureType, -uniqueCount, -naCount, -mean, -stdDev, -median, -min, -max] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Modeling features data.", "items": { "properties": { "dataQualities": { "description": "Data Quality Status", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string" }, "dateFormat": { "description": "the date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ], "x-versionadded": "v2.5" }, "featureLineageId": { "description": "The ID of the lineage for automatically generated features.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string" }, "importance": { "description": "numeric measure of the strength of relationship between the feature and target (independent of any model or other features)", "type": [ "number", "null" ] }, "isRestoredAfterReduction": { "description": "Whether feature is restored after feature reduction", "type": "boolean", "x-versionadded": "v2.26" }, "isZeroInflated": { "description": "Whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ] }, "language": { "description": "Feature's detected language.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "whether feature has too few values to be informative", "type": "boolean" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "max": { "description": "maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "mean": { "description": "arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "median": { "description": "median of the EDA sample of the feature.", "oneOf": [ { "description": "median of the EDA sample of the feature.", "type": "string" }, { "description": "median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "min": { "description": "minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "multilabelInsights": { "description": "Multilabel project specific information", "properties": { "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The response will contain the key for the most recent, finished EDA stage.", "type": "string" } }, "required": [ "multilabelInsightsKey" ], "type": "object" }, "naCount": { "description": "Number of missing values.", "type": "integer" }, "name": { "description": "The feature name.", "type": "string" }, "parentFeatureNames": { "description": "an array of string feature names indicating which features in the input data were used to create this feature if the feature is a transformation.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "The ID of the project the feature belongs to.", "type": "string" }, "stdDev": { "description": "standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "targetLeakage": { "description": "the detected level of risk for target leakage, if any. 'SKIPPED_DETECTION' indicates leakage detection was not run on the feature, 'FALSE' indicates no leakage, 'MODERATE_RISK' indicates a moderate risk of target leakage, and 'HIGH_RISK' indicates a high risk of target leakage.", "enum": [ "FALSE", "HIGH_RISK", "MODERATE_RISK", "SKIPPED_DETECTION" ], "type": "string" }, "targetLeakageReason": { "description": "descriptive sentence explaining the reason for target leakage.", "type": "string", "x-versionadded": "v2.20" }, "uniqueCount": { "description": "number of unique values", "type": "integer" }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "dateFormat", "featureLineageId", "featureType", "importance", "lowInformation", "lowerQuartile", "max", "mean", "median", "min", "naCount", "name", "projectId", "stdDev", "targetLeakage", "targetLeakageReason", "upperQuartile" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Descriptive information for features. | ModelingFeatureListResponse | | 422 | Unprocessable Entity | Unable to process the request | None | ## Restore discarded time series features by project ID Operation path: `POST /api/v2/projects/{projectId}/modelingFeatures/fromDiscardedFeatures/` Authentication requirements: `BearerAuth` Restore discarded time series features. ### Body parameter ``` { "properties": { "featuresToRestore": { "description": "Discarded features to restore.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "featuresToRestore" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | ModelingFeaturesCreateFromDiscarded | false | none | ### Example responses > 202 Response ``` { "properties": { "featuresToRestore": { "description": "Features to add back to the project.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about features which can not be restored.", "items": { "type": "string" }, "type": "array" } }, "required": [ "featuresToRestore", "warnings" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | ModelingFeaturesCreateFromDiscardedResponse | | 404 | Not Found | No discarded time series features information available. | None | | 422 | Unprocessable Entity | Unable to process the request. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve project modeling feature by project ID Operation path: `GET /api/v2/projects/{projectId}/modelingFeatures/{featureName}/` Authentication requirements: `BearerAuth` Retrieve the specified modeling feature with descriptive information. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The ID of the project. | | featureName | path | string | true | the name of the feature Note: DataRobot renames some features, so the feature name may not be the one from your original data. You can use [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] to list the features and check the name. Note to users with non-ascii features names: The feature name should be utf-8-encoded (before URL-quoting) | ### Example responses > 200 Response ``` { "properties": { "dataQualities": { "description": "Data Quality Status", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string" }, "dateFormat": { "description": "the date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ], "x-versionadded": "v2.5" }, "featureLineageId": { "description": "The ID of the lineage for automatically generated features.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string" }, "importance": { "description": "numeric measure of the strength of relationship between the feature and target (independent of any model or other features)", "type": [ "number", "null" ] }, "isRestoredAfterReduction": { "description": "Whether feature is restored after feature reduction", "type": "boolean", "x-versionadded": "v2.26" }, "isZeroInflated": { "description": "Whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ] }, "language": { "description": "Feature's detected language.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "whether feature has too few values to be informative", "type": "boolean" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "max": { "description": "maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "mean": { "description": "arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "median": { "description": "median of the EDA sample of the feature.", "oneOf": [ { "description": "median of the EDA sample of the feature.", "type": "string" }, { "description": "median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "min": { "description": "minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "multilabelInsights": { "description": "Multilabel project specific information", "properties": { "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The response will contain the key for the most recent, finished EDA stage.", "type": "string" } }, "required": [ "multilabelInsightsKey" ], "type": "object" }, "naCount": { "description": "Number of missing values.", "type": "integer" }, "name": { "description": "The feature name.", "type": "string" }, "parentFeatureNames": { "description": "an array of string feature names indicating which features in the input data were used to create this feature if the feature is a transformation.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "The ID of the project the feature belongs to.", "type": "string" }, "stdDev": { "description": "standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "targetLeakage": { "description": "the detected level of risk for target leakage, if any. 'SKIPPED_DETECTION' indicates leakage detection was not run on the feature, 'FALSE' indicates no leakage, 'MODERATE_RISK' indicates a moderate risk of target leakage, and 'HIGH_RISK' indicates a high risk of target leakage.", "enum": [ "FALSE", "HIGH_RISK", "MODERATE_RISK", "SKIPPED_DETECTION" ], "type": "string" }, "targetLeakageReason": { "description": "descriptive sentence explaining the reason for target leakage.", "type": "string", "x-versionadded": "v2.20" }, "uniqueCount": { "description": "number of unique values", "type": "integer" }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "dateFormat", "featureLineageId", "featureType", "importance", "lowInformation", "lowerQuartile", "max", "mean", "median", "min", "naCount", "name", "projectId", "stdDev", "targetLeakage", "targetLeakageReason", "upperQuartile" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Descriptive information for feature. | ModelingFeatureResponse | | 404 | Not Found | Feature does not exist. | None | | 422 | Unprocessable Entity | Unable to process the request | None | ## Retrieve ImageEmbeddings by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/imageEmbeddings/` Authentication requirements: `BearerAuth` Retrieve ImageEmbeddings for a feature of a model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | featureName | query | string | true | The name of the feature to query. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "embeddings": { "description": "List of Image Embedding objects", "items": { "properties": { "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile].", "type": "string" }, "positionX": { "description": "x coordinate of the image in the embedding space.", "type": "number" }, "positionY": { "description": "y coordinate of the image in the embedding space.", "type": "number" }, "prediction": { "description": "Object that describes prediction value of the dataset row.", "properties": { "labels": { "description": "List of predicted label names corresponding to values.", "items": { "description": "Predicted label", "type": "string" }, "type": "array" }, "values": { "description": "Predicted value or probability of the class identified by the label.", "items": { "oneOf": [ { "type": "number" }, { "items": { "type": "number" }, "type": "array" } ] }, "type": "array" } }, "required": [ "labels", "values" ], "type": "object" } }, "required": [ "actualTargetValue", "imageId", "positionX", "positionY", "prediction" ], "type": "object" }, "type": "array" }, "targetBins": { "description": "List of bin objects for regression or null", "items": { "properties": { "targetBinEnd": { "description": "End value for the target bin", "type": "number" }, "targetBinStart": { "description": "Start value for the target bin", "type": "number" } }, "required": [ "targetBinEnd", "targetBinStart" ], "type": "object" }, "type": "array" }, "targetValues": { "description": "List of target values for classification or null", "items": { "description": "Target value", "type": "string" }, "type": "array" } }, "required": [ "embeddings", "targetBins", "targetValues" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Image Embeddings | EmbeddingsRetrieveResponse | | 422 | Unprocessable Entity | Unable to process request. | None | ## Request the computation of image embeddings by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/imageEmbeddings/` Authentication requirements: `BearerAuth` Request the computation of image embeddings for the specified model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 202 Response ``` { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if the job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "jobType": { "description": "The type of the job.", "enum": [ "compute_image_embeddings" ], "type": "string" }, "message": { "description": "Error message in case of failure.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The job status.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" }, "url": { "description": "A URL that can be used to request details about the job.", "type": "string" } }, "required": [ "id", "isBlocked", "jobType", "message", "modelId", "projectId", "status", "url" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Image embedding computation has been successfully requested | ImageEmbeddingsComputeResponse | | 422 | Unprocessable Entity | Cannot compute image embeddings: if image embeddings were already computed for the model or there was another issue creating this job | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | a url that can be polled to check the status of the job. | ## Submit a relationship quality assessment job by project ID Operation path: `POST /api/v2/projects/{projectId}/relationshipQualityAssessments/` Authentication requirements: `BearerAuth` Submit a job to assess the quality of the relationship configuration within a Feature Discovery project. ### Body parameter ``` { "properties": { "credentials": { "description": "Credentials for dynamic policy secondary datasets.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "credentialId": { "description": "ID of the credentials object in credential store.Can only be used along with catalogVersionId.", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": [ "string", "null" ] } }, "required": [ "credentialId" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. ", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" } ] }, "maxItems": 30, "type": "array" }, "datetimePartitionColumn": { "description": "If a datetime partition column was used, the name of the column.", "type": [ "string", "null" ] }, "featureEngineeringPredictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "type": [ "string", "null" ] }, "relationshipsConfiguration": { "description": "Object describing how secondary datasets are related to the primary dataset", "properties": { "datasetDefinitions": { "description": "The list of datasets.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "maxItems": 30, "minItems": 1, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "Id of the relationship configuration", "type": "string" }, "relationships": { "description": "The list of relationships.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "maxItems": 70, "minItems": 1, "type": "array" }, "snowflakePushDownCompatible": { "description": "Flag indicating if the relationships configuration is compatible with Snowflake push down processing.", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "id", "relationships" ], "type": "object" }, "userId": { "description": "Mongo Id of the User who created the request", "type": "string" } }, "required": [ "relationshipsConfiguration" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | RelationshipQualityAssessmentsCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Relationship quality assessment has successfully started. See the Location header. | None | | 422 | Unprocessable Entity | Unable to process the request | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve relationships configuration by project ID Operation path: `GET /api/v2/projects/{projectId}/relationshipsConfiguration/` Authentication requirements: `BearerAuth` Retrieve relationships configuration for a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | configId | query | string | false | Id of Secondary Dataset Configuration | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "datasetDefinitions": { "description": "The list of dataset definitions.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "dataSource": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "dataSources": { "description": "Data source details for a JDBC dataset", "items": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "type": "array" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": "string" }, "featureLists": { "description": "The list of available feature list IDs for the dataset.", "items": { "type": "string" }, "type": "array" }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "isDeleted": { "description": "Is this dataset deleted?", "type": [ "boolean", "null" ] }, "originalIdentifier": { "description": "Original identifier of the dataset if it was updated to resolve name conflicts.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "featureListId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "type": "array" }, "id": { "description": "ID of relationships configuration.", "type": "string" }, "relationships": { "description": "The list of relationships between datasets specified in datasetDefinitions.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used.", "exclusiveMaximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Can be null, closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present.", "exclusiveMinimum": 0, "maximum": 30, "type": [ "integer", "null" ] }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] } }, "required": [ "dataset1Identifier", "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "type": "array" }, "snowflakePushDownCompatible": { "description": "Is this configuration compatible with pushdown computation on Snowflake?", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "relationships" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Project relationships configuration. | RelationshipsConfigResponse | | 404 | Not Found | Data was not found. | None | ## List all secondary dataset configurations by project ID Operation path: `GET /api/v2/projects/{projectId}/secondaryDatasetsConfigurations/` Authentication requirements: `BearerAuth` List all secondary dataset configurations for a project, optionally filtered by feature list ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | featurelistId | query | string | false | feature list ID of the model | | modelId | query | string | false | ID of the model | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | includeDeleted | query | string | false | Include deleted records. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | includeDeleted | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Secondary dataset configurations.", "items": { "properties": { "config": { "description": "Graph-specific secondary datasets. Deprecated in version v2.23.", "items": { "properties": { "featureEngineeringGraphId": { "description": "Id of the feature engineering graph", "type": "string" }, "secondaryDatasets": { "description": "list of secondary datasets used by the feature engineering graph", "items": { "properties": { "catalogId": { "description": "Id of the catalog item version.", "type": "string" }, "catalogVersionId": { "description": "Id of the catalog item.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "default": "latest", "description": "Type of snapshot policy to use by the dataset.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" } }, "required": [ "featureEngineeringGraphId", "secondaryDatasets" ], "type": "object" }, "type": "array" }, "created": { "description": "DR-formatted datetime, null for legacy (before DR 6.0) db records.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorFullName": { "description": "Fullname or email of the user created this config. null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorUserId": { "description": "ID of the user created this config, null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "credentialIds": { "description": "List of credentials used by the secondary datasets if the datasets used in the configuration are from datasource.", "items": { "properties": { "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "credentialId": { "description": "ID of the credential store to be used for the given catalog version.", "type": "string" }, "url": { "description": "The URL of the datasource.", "type": [ "string", "null" ] } }, "required": [ "catalogVersionId", "credentialId" ], "type": "object" }, "type": "array" }, "featurelistId": { "description": "Id of the feature list.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "id": { "description": "ID of the secondary datasets configuration.", "type": "string" }, "isDefault": { "description": "Secondary datasets config is default config or not.", "type": [ "boolean", "null" ] }, "name": { "description": "Name of the secondary datasets config.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project.", "type": [ "string", "null" ] }, "projectVersion": { "description": "DataRobot project version.", "type": [ "string", "null" ] }, "secondaryDatasets": { "description": "List of secondary datasets used in the config.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "requiredFeatures": { "description": "List of required feature names used by the table.", "items": { "type": "string" }, "type": "array" }, "snapshotPolicy": { "description": "Policy to be used by a dataset while making prediction.", "enum": [ "specified", "latest", "dynamic" ], "type": [ "string", "null" ] } }, "required": [ "catalogId", "catalogVersionId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } }, "required": [ "created", "creatorFullName", "creatorUserId", "id", "isDefault", "name", "projectId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of secondary dataset configurations. | SecondaryDatasetConfigListResponse | | 404 | Not Found | Data is not found. | None | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Create secondary dataset configurations by project ID Operation path: `POST /api/v2/projects/{projectId}/secondaryDatasetsConfigurations/` Authentication requirements: `BearerAuth` Create secondary dataset configurations for a project. ### Body parameter ``` { "properties": { "config": { "description": "Graph-specific secondary datasets. Deprecated in version v2.23", "items": { "properties": { "featureEngineeringGraphId": { "description": "Id of the feature engineering graph", "type": "string" }, "secondaryDatasets": { "description": "list of secondary datasets used by the feature engineering graph", "items": { "properties": { "catalogId": { "description": "Id of the catalog item version.", "type": "string" }, "catalogVersionId": { "description": "Id of the catalog item.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "default": "latest", "description": "Type of snapshot policy to use by the dataset.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" } }, "required": [ "featureEngineeringGraphId", "secondaryDatasets" ], "type": "object" }, "type": "array" }, "featurelistId": { "description": "Feature list ID of the configuration.", "type": "string" }, "modelId": { "description": "ID of the model.", "type": "string" }, "name": { "description": "Name of the configuration.", "type": "string" }, "save": { "default": true, "description": "Determines if the configuration will be saved. If set to False the configuration is validated but not saved. Defaults to True.", "type": "boolean" }, "secondaryDatasets": { "description": "List of secondary dataset definitions to use in the configuration.", "items": { "properties": { "catalogId": { "description": "Id of the catalog item version.", "type": "string" }, "catalogVersionId": { "description": "Id of the catalog item.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "default": "latest", "description": "Type of snapshot policy to use by the dataset.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | SecondaryDatasetCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Secondary dataset configuration created with allowable type mismatches. | None | | 201 | Created | Secondary dataset configuration created with no errors. | None | | 204 | No Content | Validation of secondary dataset configuration is successful. | None | | 422 | Unprocessable Entity | Validation of secondary dataset configuration failed. | None | ## Soft-delete a secondary dataset configuration by project ID Operation path: `DELETE /api/v2/projects/{projectId}/secondaryDatasetsConfigurations/{secondaryDatasetConfigId}/` Authentication requirements: `BearerAuth` Soft-delete a secondary dataset configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | secondaryDatasetConfigId | path | string | true | Secondary dataset configuration ID | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Secondary dataset configuration successfully soft deleted. | None | | 404 | Not Found | Data is not found. | None | | 409 | Conflict | The dataset has already been deleted. | None | ## Retrieve secondary dataset configuration by ID by project ID Operation path: `GET /api/v2/projects/{projectId}/secondaryDatasetsConfigurations/{secondaryDatasetConfigId}/` Authentication requirements: `BearerAuth` Retrieve secondary dataset configuration by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | secondaryDatasetConfigId | path | string | true | Secondary dataset configuration ID | ### Example responses > 200 Response ``` { "properties": { "config": { "description": "Graph-specific secondary datasets. Deprecated in version v2.23.", "items": { "properties": { "featureEngineeringGraphId": { "description": "Id of the feature engineering graph", "type": "string" }, "secondaryDatasets": { "description": "list of secondary datasets used by the feature engineering graph", "items": { "properties": { "catalogId": { "description": "Id of the catalog item version.", "type": "string" }, "catalogVersionId": { "description": "Id of the catalog item.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "default": "latest", "description": "Type of snapshot policy to use by the dataset.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" } }, "required": [ "featureEngineeringGraphId", "secondaryDatasets" ], "type": "object" }, "type": "array" }, "created": { "description": "DR-formatted datetime, null for legacy (before DR 6.0) db records.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorFullName": { "description": "Fullname or email of the user created this config. null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorUserId": { "description": "ID of the user created this config, null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "credentialIds": { "description": "List of credentials used by the secondary datasets if the datasets used in the configuration are from datasource.", "items": { "properties": { "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "credentialId": { "description": "ID of the credential store to be used for the given catalog version.", "type": "string" }, "url": { "description": "The URL of the datasource.", "type": [ "string", "null" ] } }, "required": [ "catalogVersionId", "credentialId" ], "type": "object" }, "type": "array" }, "featurelistId": { "description": "Id of the feature list.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "id": { "description": "ID of the secondary datasets configuration.", "type": "string" }, "isDefault": { "description": "Secondary datasets config is default config or not.", "type": [ "boolean", "null" ] }, "name": { "description": "Name of the secondary datasets config.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project.", "type": [ "string", "null" ] }, "projectVersion": { "description": "DataRobot project version.", "type": [ "string", "null" ] }, "secondaryDatasets": { "description": "List of secondary datasets used in the config.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "requiredFeatures": { "description": "List of required feature names used by the table.", "items": { "type": "string" }, "type": "array" }, "snapshotPolicy": { "description": "Policy to be used by a dataset while making prediction.", "enum": [ "specified", "latest", "dynamic" ], "type": [ "string", "null" ] } }, "required": [ "catalogId", "catalogVersionId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } }, "required": [ "created", "creatorFullName", "creatorUserId", "id", "isDefault", "name", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The secondary dataset configuration. | ProjectSecondaryDatasetConfigResponse | | 404 | Not Found | Data is not found. | None | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Retrieve the feature derivation log content and log length by project ID Operation path: `GET /api/v2/projects/{projectId}/timeSeriesFeatureLog/` Authentication requirements: `BearerAuth` Retrieve the feature derivation log content and log length for a time series project as JSON. The Time Series Feature Log provides details about the feature generation process for a time series project. It includes information about which features are generated and their priority,as well as the detected properties of the time series data such as whether the series is stationary, and periodicities detected. This route is only supported for time series projects that have finished partitioning. The feature derivation log will include information about.: - Detected stationarity of the series, e.g., Series detected as non-stationary - Detected presence of multiplicative trend in the series, e.g., Multiplicative trend detected - Detected periodicities in the series, e.g., Detected periodicities: 7 day - Maximum number of feature to be generated, e.g., Maximum number of feature to be generated is 1440 - Window sizes used in rolling statistics / lag extractors, e.g., The window sizes chosen to be: 2 months - Features that are specified as known-in-advance, e.g., Variables treated as apriori: holiday - Details about features generated as timeseries features, and their priority, e.g., Generating feature "date (actual)" from "date" (priority: 1) - Details about why certain variables are transformed in the input data, e.g., Generating variable "y (log)" from "y" because multiplicative trend is detected . ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "featureLog": { "description": "The content of the feature log.", "type": "string" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalLogLines": { "description": "The total number of lines in the feature derivation log.", "type": "integer" } }, "required": [ "count", "featureLog", "next", "previous", "totalLogLines" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | TimeSeriesFeatureLogListControllerResponse | ## Retrieve a text file containing the time series project feature log by project ID Operation path: `GET /api/v2/projects/{projectId}/timeSeriesFeatureLog/file/` Authentication requirements: `BearerAuth` Retrieve a text file containing the time series project feature log. The Time Series Feature Log provides details about the feature generation process for a time series project. It includes information about which features are generated and their priority,as well as the detected properties of the time series data such as whether the series is stationary, and periodicities detected. This route is only supported for time series projects that have finished partitioning. The feature derivation log will include information about.: - Maximum number of feature to be generated, e.g., Limit on the maximum number of feature in this project is 500 - Number of derived features tested during the feature generation process, e.g., Total number of derived features during the feature generation process is 571 - Number of generated features removed during the feature reduction process e.g. Total number of features removed during the feature reduction process is 472 - Number of remaining features after the combined feature generation and reduction process, e.g., The finalized number of features is 99 - Detected stationarity of the series, e.g., Series detected as non-stationary - Detected presence of multiplicative trend in the series, e.g., Multiplicative trend detected - Detected periodicities in the series, e.g., Detected periodicities: 7 day - Window sizes used in rolling statistics / lag extractors, e.g., The window sizes chosen to be: 2 months (because the time step is 1 month and Feature Derivation Window is 2 months) - Features that are specified as known-in-advance, e.g., Variables treated as apriori: holiday - Details about why certain variables are transformed in the input data, e.g., Generating variable "y (log)" from "y" because multiplicative trend is detected - Details about features generated as time series features, and their priority, e.g., Generating feature "date (actual)" from "date" (priority: 1) . ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | attachment;filename=.txt The suggested filename is dynamically generated | | 200 | Content-Type | string | | MIME type of the returned data | ## Create a new feature by changing the type of an existing one by project ID Operation path: `POST /api/v2/projects/{projectId}/typeTransformFeatures/` Authentication requirements: `BearerAuth` Create a new feature by changing the type of an existing one. ### Body parameter ``` { "properties": { "dateExtraction": { "description": "The value to extract from the date column, of these options: `[year|yearDay|month|monthDay|week|weekDay]`. Required for transformation of a date column. Otherwise must not be provided.", "enum": [ "year", "yearDay", "month", "monthDay", "week", "weekDay" ], "type": "string" }, "name": { "description": "The name of the new feature. Must not be the same as any existing features for this project. Must not contain '/' character.", "type": "string" }, "parentName": { "description": "The name of the parent feature.", "type": "string" }, "replacement": { "anyOf": [ { "type": [ "string", "null" ] }, { "type": [ "boolean", "null" ] }, { "type": [ "number", "null" ] }, { "type": [ "integer", "null" ] } ], "description": "The replacement in case of a failed transformation." }, "variableType": { "description": "The type of the new feature. Must be one of `text`, `categorical` (Deprecated in version v2.21), `numeric`, or `categoricalInt`. See the description of this method for more information.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "name", "parentName", "variableType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project to create the feature in. | | body | body | FeatureTransform | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Creation has successfully started. See the Location header. | None | | 422 | Unprocessable Entity | Unable to process the request | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Location | string | | A url that can be polled to check the status. | ## Create a relationships configuration Operation path: `POST /api/v2/relationshipsConfigurations/` Authentication requirements: `BearerAuth` Create a relationships configuration. ### Body parameter ``` { "properties": { "datasetDefinitions": { "description": "The list of dataset definitions.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": "string" }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process. Applicable when feature_discovery_mode is 'manual'.", "items": { "properties": { "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" }, "relationships": { "description": "The list of relationships between datasets specified in datasetDefinitions.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "type": "array" } }, "required": [ "datasetDefinitions", "relationships" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | RelationshipsConfigCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "datasetDefinitions": { "description": "The list of dataset definitions.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "dataSource": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "dataSources": { "description": "Data source details for a JDBC dataset", "items": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "type": "array" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": "string" }, "featureLists": { "description": "The list of available feature list IDs for the dataset.", "items": { "type": "string" }, "type": "array" }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "isDeleted": { "description": "Is this dataset deleted?", "type": [ "boolean", "null" ] }, "originalIdentifier": { "description": "Original identifier of the dataset if it was updated to resolve name conflicts.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "featureListId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "type": "array" }, "id": { "description": "ID of relationships configuration.", "type": "string" }, "relationships": { "description": "The list of relationships between datasets specified in datasetDefinitions.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used.", "exclusiveMaximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Can be null, closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present.", "exclusiveMinimum": 0, "maximum": 30, "type": [ "integer", "null" ] }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] } }, "required": [ "dataset1Identifier", "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "type": "array" }, "snowflakePushDownCompatible": { "description": "Is this configuration compatible with pushdown computation on Snowflake?", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "relationships" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | RelationshipsConfigResponse | | 422 | Unprocessable Entity | User input fails validation | None | ## Delete a relationships configuration by relationships configuration ID Operation path: `DELETE /api/v2/relationshipsConfigurations/{relationshipsConfigurationId}/` Authentication requirements: `BearerAuth` Delete a relationships configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | relationshipsConfigurationId | path | string | true | Id of the relationships configuration to delete | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 404 | Not Found | Relationships configuration not found | None | ## Retrieve a relationships configuration by relationships configuration ID Operation path: `GET /api/v2/relationshipsConfigurations/{relationshipsConfigurationId}/` Authentication requirements: `BearerAuth` Retrieve a relationships configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | includeRelationshipQuality | query | string | false | Flag indicating whether or not to include relationship quality information in the returned result | | relationshipsConfigurationId | path | string | true | Id of the relationships configuration to delete | ### Enumerated Values | Parameter | Value | | --- | --- | | includeRelationshipQuality | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "datasetDefinitions": { "description": "The list of dataset definitions.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "dataSource": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "dataSources": { "description": "Data source details for a JDBC dataset", "items": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "type": "array" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": "string" }, "featureLists": { "description": "The list of available feature list IDs for the dataset.", "items": { "type": "string" }, "type": "array" }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "isDeleted": { "description": "Is this dataset deleted?", "type": [ "boolean", "null" ] }, "originalIdentifier": { "description": "Original identifier of the dataset if it was updated to resolve name conflicts.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "featureListId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "type": "array" }, "id": { "description": "ID of relationships configuration.", "type": "string" }, "relationships": { "description": "The list of relationships with quality assessment information.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "relationshipQuality": { "description": "Summary of the relationship quality information", "oneOf": [ { "properties": { "detailedReport": { "description": "Detailed report of the relationship quality information", "items": { "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "enrichmentRateValue": { "description": "Percentage of primary table records that can be enriched with a record in this dataset", "type": "number" }, "featureDerivationWindow": { "description": "Feature derivation window.", "type": [ "string", "null" ] }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate", "enrichmentRateValue", "overallCategory" ], "type": "object" }, "maxItems": 3, "type": "array" }, "lastUpdated": { "description": "Last updated timestamp", "format": "date-time", "type": "string" }, "problemCount": { "description": "Total count of problems detected", "type": "integer" }, "samplingFraction": { "description": "Primary dataset sampling fraction used for relationship quality assessment speedup", "type": "number" }, "status": { "description": "Relationship quality assessment status", "enum": [ "Complete", "In progress", "Error" ], "type": "string" }, "statusId": { "description": "The list of status IDs.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" }, "summaryCategory": { "description": "Class of the summary warning of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "summaryMessage": { "description": "Summary warning message about the relationship", "type": "string" } }, "required": [ "detailedReport", "lastUpdated", "problemCount", "samplingFraction", "summaryCategory", "summaryMessage" ], "type": "object" }, { "properties": { "enrichmentRate": { "description": "Percentage of records that can be enriched with a record in the primary table", "type": "number" }, "formattedSummary": { "description": "Relationship quality assessment report associated with the relationship", "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate" ], "type": "object" }, "lastUpdated": { "description": "Last updated timestamp", "format": "date-time", "type": "string" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "status": { "description": "Relationship quality assessment status", "enum": [ "Complete", "In progress", "Error" ], "type": "string" }, "statusId": { "description": "The list of status IDs.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" } }, "required": [ "enrichmentRate", "formattedSummary", "lastUpdated", "overallCategory" ], "type": "object" } ] } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "snowflakePushDownCompatible": { "description": "Is this configuration compatible with pushdown computation on Snowflake?", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "relationships" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ExtendedRelationshipsConfigRetrieve | | 404 | Not Found | Relationships configuration cannot be found | None | ## Replace a relationships configuration by relationships configuration ID Operation path: `PUT /api/v2/relationshipsConfigurations/{relationshipsConfigurationId}/` Authentication requirements: `BearerAuth` Replace a relationships configuration. ### Body parameter ``` { "properties": { "datasetDefinitions": { "description": "The list of dataset definitions.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": "string" }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process. Applicable when feature_discovery_mode is 'manual'.", "items": { "properties": { "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" }, "relationships": { "description": "The list of relationships between datasets specified in datasetDefinitions.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "type": "array" } }, "required": [ "datasetDefinitions", "relationships" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | relationshipsConfigurationId | path | string | true | Id of the relationships configuration to delete | | body | body | RelationshipsConfigCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "datasetDefinitions": { "description": "The list of dataset definitions.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "dataSource": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "dataSources": { "description": "Data source details for a JDBC dataset", "items": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "type": "array" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": "string" }, "featureLists": { "description": "The list of available feature list IDs for the dataset.", "items": { "type": "string" }, "type": "array" }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "isDeleted": { "description": "Is this dataset deleted?", "type": [ "boolean", "null" ] }, "originalIdentifier": { "description": "Original identifier of the dataset if it was updated to resolve name conflicts.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "featureListId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "type": "array" }, "id": { "description": "ID of relationships configuration.", "type": "string" }, "relationships": { "description": "The list of relationships between datasets specified in datasetDefinitions.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used.", "exclusiveMaximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Can be null, closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present.", "exclusiveMinimum": 0, "maximum": 30, "type": [ "integer", "null" ] }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] } }, "required": [ "dataset1Identifier", "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "type": "array" }, "snowflakePushDownCompatible": { "description": "Is this configuration compatible with pushdown computation on Snowflake?", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "relationships" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RelationshipsConfigResponse | | 422 | Unprocessable Entity | User input fails validation | None | ## Retrieve the relationships configuration by relationships configuration ID Operation path: `GET /api/v2/relationshipsConfigurations/{relationshipsConfigurationId}/projects/{projectId}/` Authentication requirements: `BearerAuth` Retrieve the relationships configuration with extended information. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | includeRelationshipQuality | query | string | false | Flag indicating whether or not to include relationship quality information in the returned result | | projectId | path | string | true | The project ID | | relationshipsConfigurationId | path | string | true | The relationships configuration ID | ### Enumerated Values | Parameter | Value | | --- | --- | | includeRelationshipQuality | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "datasetDefinitions": { "description": "The list of dataset definitions.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "dataSource": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "dataSources": { "description": "Data source details for a JDBC dataset", "items": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "type": "array" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": "string" }, "featureLists": { "description": "The list of available feature list IDs for the dataset.", "items": { "type": "string" }, "type": "array" }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "isDeleted": { "description": "Is this dataset deleted?", "type": [ "boolean", "null" ] }, "originalIdentifier": { "description": "Original identifier of the dataset if it was updated to resolve name conflicts.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "featureListId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "type": "array" }, "id": { "description": "ID of relationships configuration.", "type": "string" }, "relationships": { "description": "The list of relationships with quality assessment information.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "relationshipQuality": { "description": "Summary of the relationship quality information", "oneOf": [ { "properties": { "detailedReport": { "description": "Detailed report of the relationship quality information", "items": { "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "enrichmentRateValue": { "description": "Percentage of primary table records that can be enriched with a record in this dataset", "type": "number" }, "featureDerivationWindow": { "description": "Feature derivation window.", "type": [ "string", "null" ] }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate", "enrichmentRateValue", "overallCategory" ], "type": "object" }, "maxItems": 3, "type": "array" }, "lastUpdated": { "description": "Last updated timestamp", "format": "date-time", "type": "string" }, "problemCount": { "description": "Total count of problems detected", "type": "integer" }, "samplingFraction": { "description": "Primary dataset sampling fraction used for relationship quality assessment speedup", "type": "number" }, "status": { "description": "Relationship quality assessment status", "enum": [ "Complete", "In progress", "Error" ], "type": "string" }, "statusId": { "description": "The list of status IDs.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" }, "summaryCategory": { "description": "Class of the summary warning of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "summaryMessage": { "description": "Summary warning message about the relationship", "type": "string" } }, "required": [ "detailedReport", "lastUpdated", "problemCount", "samplingFraction", "summaryCategory", "summaryMessage" ], "type": "object" }, { "properties": { "enrichmentRate": { "description": "Percentage of records that can be enriched with a record in the primary table", "type": "number" }, "formattedSummary": { "description": "Relationship quality assessment report associated with the relationship", "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate" ], "type": "object" }, "lastUpdated": { "description": "Last updated timestamp", "format": "date-time", "type": "string" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "status": { "description": "Relationship quality assessment status", "enum": [ "Complete", "In progress", "Error" ], "type": "string" }, "statusId": { "description": "The list of status IDs.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" } }, "required": [ "enrichmentRate", "formattedSummary", "lastUpdated", "overallCategory" ], "type": "object" } ] } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "snowflakePushDownCompatible": { "description": "Is this configuration compatible with pushdown computation on Snowflake?", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "relationships" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ExtendedRelationshipsConfigRetrieve | # Schemas ## AssessmentNewFormat ``` { "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "enrichmentRateValue": { "description": "Percentage of primary table records that can be enriched with a record in this dataset", "type": "number" }, "featureDerivationWindow": { "description": "Feature derivation window.", "type": [ "string", "null" ] }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate", "enrichmentRateValue", "overallCategory" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enrichmentRate | Warnings | true | | Warning about the enrichment rate | | enrichmentRateValue | number | true | | Percentage of primary table records that can be enriched with a record in this dataset | | featureDerivationWindow | string,null | false | | Feature derivation window. | | mostRecentData | Warnings | false | | Warning about the enrichment rate | | overallCategory | string | true | | Class of the relationship quality | | windowSettings | Warnings | false | | Warning about the enrichment rate | ### Enumerated Values | Property | Value | | --- | --- | | overallCategory | [green, yellow] | ## BatchFeatureTransform ``` { "properties": { "parentNames": { "description": "List of feature names that will be transformed into a new variable type.", "items": { "type": "string" }, "maxItems": 500, "minItems": 1, "type": "array" }, "prefix": { "description": "The string that will preface all feature names. Optional if suffix is present. (One or both are required.)", "maxLength": 500, "type": "string" }, "suffix": { "description": "The string that will be appended at the end to all feature names. Optional if prefix is present. (One or both are required.)", "maxLength": 500, "type": "string" }, "variableType": { "description": "The type of the new feature. Must be one of `text`, `categorical` (Deprecated in version v2.21), `numeric`, or `categoricalInt`.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "parentNames", "variableType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parentNames | [string] | true | maxItems: 500minItems: 1 | List of feature names that will be transformed into a new variable type. | | prefix | string | false | maxLength: 500 | The string that will preface all feature names. Optional if suffix is present. (One or both are required.) | | suffix | string | false | maxLength: 500 | The string that will be appended at the end to all feature names. Optional if prefix is present. (One or both are required.) | | variableType | string | true | | The type of the new feature. Must be one of text, categorical (Deprecated in version v2.21), numeric, or categoricalInt. | ### Enumerated Values | Property | Value | | --- | --- | | variableType | [text, categorical, numeric, categoricalInt] | ## BatchFeatureTransformRetrieveResponse ``` { "properties": { "failures": { "description": "An object keyed by original feature names, the values are strings indicating why the transformation failed.", "type": "object" }, "newFeatureNames": { "description": "List of new feature names.", "items": { "type": "string" }, "type": "array" } }, "required": [ "failures", "newFeatureNames" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | failures | object | true | | An object keyed by original feature names, the values are strings indicating why the transformation failed. | | newFeatureNames | [string] | true | | List of new feature names. | ## CalendarAccessControlListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "Records of users and their roles on the calendar.", "items": { "properties": { "canShare": { "description": "Whether this user can share this calendar with other users.", "type": "boolean" }, "role": { "description": "The role of the user on this calendar.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of a user with access to this calendar.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | The number of items returned on this page. | | data | [CalendarUserRoleRecordResponse] | true | | Records of users and their roles on the calendar. | | next | string,null | true | | A URL pointing to the next page (if null, there is no next page). | | previous | string,null | true | | A URL pointing to the previous page (if null, there is no previous page). | ## CalendarAccessControlUpdate ``` { "properties": { "users": { "description": "The list of users and their updated roles used to modify the access for this calendar.", "items": { "description": "Each item in `users` refers to the username record and its newly assigned role.", "properties": { "role": { "description": "The new role to assign to the specified user.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to modify access for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "users" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | users | [CalendarUsernameRole] | true | maxItems: 100 | The list of users and their updated roles used to modify the access for this calendar. | ## CalendarEvent ``` { "properties": { "date": { "description": "The date of the calendar event.", "format": "date-time", "type": "string" }, "name": { "description": "Name of the calendar event.", "type": "string" }, "seriesId": { "description": "The series ID for the event. If this event does not specify a series ID, then this will be `null`, indicating that the event applies to all series.", "type": [ "string", "null" ] } }, "required": [ "date", "name", "seriesId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | date | string(date-time) | true | | The date of the calendar event. | | name | string | true | | Name of the calendar event. | | seriesId | string,null | true | | The series ID for the event. If this event does not specify a series ID, then this will be null, indicating that the event applies to all series. | ## CalendarEventsResponseQuery ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "An array of calendar events", "items": { "properties": { "date": { "description": "The date of the calendar event.", "format": "date-time", "type": "string" }, "name": { "description": "Name of the calendar event.", "type": "string" }, "seriesId": { "description": "The series ID for the event. If this event does not specify a series ID, then this will be `null`, indicating that the event applies to all series.", "type": [ "string", "null" ] } }, "required": [ "date", "name", "seriesId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | The number of items returned on this page. | | data | [CalendarEvent] | true | maxItems: 1000 | An array of calendar events | | next | string,null | true | | A URL pointing to the next page (if null, there is no next page). | | previous | string,null | true | | A URL pointing to the previous page (if null, there is no previous page). | ## CalendarFileUpload ``` { "properties": { "file": { "description": "The calendar file used to create a calendar. The calendar file expect to meet the following criteria:\n\nMust be in a csv or xlsx format.\n\nMust have a header row. The names themselves in the header row can be anything.\n\nMust have a single date column, in YYYY-MM-DD format.\n\nMay optionally have a name column as the second column.\n\nMay optionally have one series ID column that states what series each event is applicable for. If present, the name of this column must be specified in the `multiseriesIdColumns` parameter.", "format": "binary", "type": "string" }, "multiseriesIdColumns": { "description": "An array of multiseries ID column names for the calendar file. Currently only one multiseries ID column is supported. If not specified, the calendar is considered to be single series.", "type": "string", "x-versionadded": "v2.19" }, "name": { "description": "The name of the calendar file. If not provided, this will be set to the name of the provided file.", "type": "string" } }, "required": [ "file" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | file | string(binary) | true | | The calendar file used to create a calendar. The calendar file expect to meet the following criteria:Must be in a csv or xlsx format.Must have a header row. The names themselves in the header row can be anything.Must have a single date column, in YYYY-MM-DD format.May optionally have a name column as the second column.May optionally have one series ID column that states what series each event is applicable for. If present, the name of this column must be specified in the multiseriesIdColumns parameter. | | multiseriesIdColumns | string | false | | An array of multiseries ID column names for the calendar file. Currently only one multiseries ID column is supported. If not specified, the calendar is considered to be single series. | | name | string | false | | The name of the calendar file. If not provided, this will be set to the name of the provided file. | ## CalendarFromDataset ``` { "properties": { "datasetId": { "description": "The ID of the dataset from which to create the calendar.", "type": "string" }, "datasetVersionId": { "description": "The ID of the dataset version from which to create the calendar.", "type": "string" }, "deleteOnError": { "description": "Whether delete calendar file from Catalog if it's not valid.", "type": "boolean" }, "multiseriesIdColumns": { "description": "Optional multiseries ID columns for the calendar.", "items": { "type": "string" }, "maxItems": 1, "type": "array" }, "name": { "description": "Optional name for catalog.", "type": "string" } }, "required": [ "datasetId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the dataset from which to create the calendar. | | datasetVersionId | string | false | | The ID of the dataset version from which to create the calendar. | | deleteOnError | boolean | false | | Whether delete calendar file from Catalog if it's not valid. | | multiseriesIdColumns | [string] | false | maxItems: 1 | Optional multiseries ID columns for the calendar. | | name | string | false | | Optional name for catalog. | ## CalendarListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "An array of calendars, each in the form described under [GET /api/v2/calendars/][get-apiv2calendars].", "items": { "properties": { "created": { "description": "An ISO-8601 string with the time that this calendar was created.", "format": "date-time", "type": "string" }, "datetimeFormat": { "description": "The datetime format detected for the uploaded calendar file.", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": "string", "x-versionadded": "v2.26" }, "earliestEvent": { "description": "An ISO-8601 date string of the earliest event seen in this calendar.", "format": "date-time", "type": "string" }, "id": { "description": "The ID of this calendar.", "type": "string" }, "latestEvent": { "description": "An ISO-8601 date string of the latest event seen in this calendar.", "format": "date-time", "type": "string" }, "multiseriesIdColumns": { "description": "An array of multiseries ID column names in this calendar file. Currently only one multiseries ID column is supported. Will be `null` if this calendar is single-series.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.19" }, "name": { "description": "The name of this calendar. This will be the same as `source` if no name was specified when the calendar was created.", "type": "string" }, "numEventTypes": { "description": "The number of distinct eventTypes in this calendar.", "type": "integer" }, "numEvents": { "description": "The number of dates that are marked as having an event in this calendar.", "type": "integer" }, "projectId": { "description": "The project IDs of projects currently using this calendar.", "items": { "type": "string" }, "type": "array" }, "role": { "description": "The role the requesting user has on this calendar.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "source": { "description": "The name of the source file that was used to create this calendar.", "type": "string" } }, "required": [ "created", "datetimeFormat", "earliestEvent", "id", "latestEvent", "multiseriesIdColumns", "name", "numEventTypes", "numEvents", "projectId", "role", "source" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | The number of items returned on this page. | | data | [CalendarRecord] | true | | An array of calendars, each in the form described under [GET /api/v2/calendars/][get-apiv2calendars]. | | next | string,null | true | | A URL pointing to the next page (if null, there is no next page). | | previous | string,null | true | | A URL pointing to the previous page (if null, there is no previous page). | ## CalendarNameUpdate ``` { "properties": { "name": { "description": "The new name to assign to the calendar.", "type": "string" } }, "required": [ "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The new name to assign to the calendar. | ## CalendarRecord ``` { "properties": { "created": { "description": "An ISO-8601 string with the time that this calendar was created.", "format": "date-time", "type": "string" }, "datetimeFormat": { "description": "The datetime format detected for the uploaded calendar file.", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": "string", "x-versionadded": "v2.26" }, "earliestEvent": { "description": "An ISO-8601 date string of the earliest event seen in this calendar.", "format": "date-time", "type": "string" }, "id": { "description": "The ID of this calendar.", "type": "string" }, "latestEvent": { "description": "An ISO-8601 date string of the latest event seen in this calendar.", "format": "date-time", "type": "string" }, "multiseriesIdColumns": { "description": "An array of multiseries ID column names in this calendar file. Currently only one multiseries ID column is supported. Will be `null` if this calendar is single-series.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.19" }, "name": { "description": "The name of this calendar. This will be the same as `source` if no name was specified when the calendar was created.", "type": "string" }, "numEventTypes": { "description": "The number of distinct eventTypes in this calendar.", "type": "integer" }, "numEvents": { "description": "The number of dates that are marked as having an event in this calendar.", "type": "integer" }, "projectId": { "description": "The project IDs of projects currently using this calendar.", "items": { "type": "string" }, "type": "array" }, "role": { "description": "The role the requesting user has on this calendar.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "source": { "description": "The name of the source file that was used to create this calendar.", "type": "string" } }, "required": [ "created", "datetimeFormat", "earliestEvent", "id", "latestEvent", "multiseriesIdColumns", "name", "numEventTypes", "numEvents", "projectId", "role", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string(date-time) | true | | An ISO-8601 string with the time that this calendar was created. | | datetimeFormat | string | true | | The datetime format detected for the uploaded calendar file. | | earliestEvent | string(date-time) | true | | An ISO-8601 date string of the earliest event seen in this calendar. | | id | string | true | | The ID of this calendar. | | latestEvent | string(date-time) | true | | An ISO-8601 date string of the latest event seen in this calendar. | | multiseriesIdColumns | [string] | true | maxItems: 1 | An array of multiseries ID column names in this calendar file. Currently only one multiseries ID column is supported. Will be null if this calendar is single-series. | | name | string | true | | The name of this calendar. This will be the same as source if no name was specified when the calendar was created. | | numEventTypes | integer | true | | The number of distinct eventTypes in this calendar. | | numEvents | integer | true | | The number of dates that are marked as having an event in this calendar. | | projectId | [string] | true | | The project IDs of projects currently using this calendar. | | role | string | true | | The role the requesting user has on this calendar. | | source | string | true | | The name of the source file that was used to create this calendar. | ### Enumerated Values | Property | Value | | --- | --- | | datetimeFormat | [%m/%d/%Y, %m/%d/%y, %d/%m/%y, %m-%d-%Y, %m-%d-%y, %Y/%m/%d, %Y-%m-%d, %Y-%m-%d %H:%M:%S, %Y/%m/%d %H:%M:%S, %Y.%m.%d %H:%M:%S, %Y-%m-%d %H:%M, %Y/%m/%d %H:%M, %y/%m/%d, %y-%m-%d, %y-%m-%d %H:%M:%S, %y.%m.%d %H:%M:%S, %y/%m/%d %H:%M:%S, %y-%m-%d %H:%M, %y.%m.%d %H:%M, %y/%m/%d %H:%M, %m/%d/%Y %H:%M, %m/%d/%y %H:%M, %d/%m/%Y %H:%M, %d/%m/%y %H:%M, %m-%d-%Y %H:%M, %m-%d-%y %H:%M, %d-%m-%Y %H:%M, %d-%m-%y %H:%M, %m.%d.%Y %H:%M, %m/%d.%y %H:%M, %d.%m.%Y %H:%M, %d.%m.%y %H:%M, %m/%d/%Y %H:%M:%S, %m/%d/%y %H:%M:%S, %m-%d-%Y %H:%M:%S, %m-%d-%y %H:%M:%S, %m.%d.%Y %H:%M:%S, %m.%d.%y %H:%M:%S, %d/%m/%Y %H:%M:%S, %d/%m/%y %H:%M:%S, %Y-%m-%d %H:%M:%S.%f, %y-%m-%d %H:%M:%S.%f, %Y-%m-%dT%H:%M:%S.%fZ, %y-%m-%dT%H:%M:%S.%fZ, %Y-%m-%dT%H:%M:%S.%f, %y-%m-%dT%H:%M:%S.%f, %Y-%m-%dT%H:%M:%S, %y-%m-%dT%H:%M:%S, %Y-%m-%dT%H:%M:%SZ, %y-%m-%dT%H:%M:%SZ, %Y.%m.%d %H:%M:%S.%f, %y.%m.%d %H:%M:%S.%f, %Y.%m.%dT%H:%M:%S.%fZ, %y.%m.%dT%H:%M:%S.%fZ, %Y.%m.%dT%H:%M:%S.%f, %y.%m.%dT%H:%M:%S.%f, %Y.%m.%dT%H:%M:%S, %y.%m.%dT%H:%M:%S, %Y.%m.%dT%H:%M:%SZ, %y.%m.%dT%H:%M:%SZ, %Y%m%d, %m %d %Y %H %M %S, %m %d %y %H %M %S, %H:%M, %M:%S, %H:%M:%S, %Y %m %d %H %M %S, %y %m %d %H %M %S, %Y %m %d, %y %m %d, %d/%m/%Y, %Y-%d-%m, %y-%d-%m, %Y/%d/%m %H:%M:%S.%f, %Y/%d/%m %H:%M:%S.%fZ, %Y/%m/%d %H:%M:%S.%f, %Y/%m/%d %H:%M:%S.%fZ, %y/%d/%m %H:%M:%S.%f, %y/%d/%m %H:%M:%S.%fZ, %y/%m/%d %H:%M:%S.%f, %y/%m/%d %H:%M:%S.%fZ, %m.%d.%Y, %m.%d.%y, %d.%m.%y, %d.%m.%Y, %Y.%m.%d, %Y.%d.%m, %y.%m.%d, %y.%d.%m, %Y-%m-%d %I:%M:%S %p, %Y/%m/%d %I:%M:%S %p, %Y.%m.%d %I:%M:%S %p, %Y-%m-%d %I:%M %p, %Y/%m/%d %I:%M %p, %y-%m-%d %I:%M:%S %p, %y.%m.%d %I:%M:%S %p, %y/%m/%d %I:%M:%S %p, %y-%m-%d %I:%M %p, %y.%m.%d %I:%M %p, %y/%m/%d %I:%M %p, %m/%d/%Y %I:%M %p, %m/%d/%y %I:%M %p, %d/%m/%Y %I:%M %p, %d/%m/%y %I:%M %p, %m-%d-%Y %I:%M %p, %m-%d-%y %I:%M %p, %d-%m-%Y %I:%M %p, %d-%m-%y %I:%M %p, %m.%d.%Y %I:%M %p, %m/%d.%y %I:%M %p, %d.%m.%Y %I:%M %p, %d.%m.%y %I:%M %p, %m/%d/%Y %I:%M:%S %p, %m/%d/%y %I:%M:%S %p, %m-%d-%Y %I:%M:%S %p, %m-%d-%y %I:%M:%S %p, %m.%d.%Y %I:%M:%S %p, %m.%d.%y %I:%M:%S %p, %d/%m/%Y %I:%M:%S %p, %d/%m/%y %I:%M:%S %p, %Y-%m-%d %I:%M:%S.%f %p, %y-%m-%d %I:%M:%S.%f %p, %Y-%m-%dT%I:%M:%S.%fZ %p, %y-%m-%dT%I:%M:%S.%fZ %p, %Y-%m-%dT%I:%M:%S.%f %p, %y-%m-%dT%I:%M:%S.%f %p, %Y-%m-%dT%I:%M:%S %p, %y-%m-%dT%I:%M:%S %p, %Y-%m-%dT%I:%M:%SZ %p, %y-%m-%dT%I:%M:%SZ %p, %Y.%m.%d %I:%M:%S.%f %p, %y.%m.%d %I:%M:%S.%f %p, %Y.%m.%dT%I:%M:%S.%fZ %p, %y.%m.%dT%I:%M:%S.%fZ %p, %Y.%m.%dT%I:%M:%S.%f %p, %y.%m.%dT%I:%M:%S.%f %p, %Y.%m.%dT%I:%M:%S %p, %y.%m.%dT%I:%M:%S %p, %Y.%m.%dT%I:%M:%SZ %p, %y.%m.%dT%I:%M:%SZ %p, %m %d %Y %I %M %S %p, %m %d %y %I %M %S %p, %I:%M %p, %I:%M:%S %p, %Y %m %d %I %M %S %p, %y %m %d %I %M %S %p, %Y/%d/%m %I:%M:%S.%f %p, %Y/%d/%m %I:%M:%S.%fZ %p, %Y/%m/%d %I:%M:%S.%f %p, %Y/%m/%d %I:%M:%S.%fZ %p, %y/%d/%m %I:%M:%S.%f %p, %y/%d/%m %I:%M:%S.%fZ %p, %y/%m/%d %I:%M:%S.%f %p, %y/%m/%d %I:%M:%S.%fZ %p] | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | ## CalendarUserRoleRecordResponse ``` { "properties": { "canShare": { "description": "Whether this user can share this calendar with other users.", "type": "boolean" }, "role": { "description": "The role of the user on this calendar.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of a user with access to this calendar.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | Whether this user can share this calendar with other users. | | role | string | true | | The role of the user on this calendar. | | userId | string | true | | The ID of the user. | | username | string | true | | The username of a user with access to this calendar. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | ## CalendarUsernameRole ``` { "description": "Each item in `users` refers to the username record and its newly assigned role.", "properties": { "role": { "description": "The new role to assign to the specified user.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to modify access for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" } ``` Each item in `users` refers to the username record and its newly assigned role. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string,null | true | | The new role to assign to the specified user. | | username | string | true | | The username of the user to modify access for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | ## CatalogPasswordCredentials ``` { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. ", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogVersionId | string | false | | Identifier of the catalog version | | password | string | true | | The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. | | url | string | false | | URL that is subject to credentials. | | user | string | true | | The username for database authentication. | ## CreateFeaturelist ``` { "properties": { "features": { "description": "List of features for new featurelist.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "name": { "description": "New featurelist name.", "maxLength": 100, "type": "string" }, "skipDatetimePartitionColumn": { "default": false, "description": "Whether featurelist should contain datetime partition column.", "type": "boolean", "x-versionadded": "v2.24" } }, "required": [ "features", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | features | [string] | true | minItems: 1 | List of features for new featurelist. | | name | string | true | maxLength: 100 | New featurelist name. | | skipDatetimePartitionColumn | boolean | false | | Whether featurelist should contain datetime partition column. | ## CreatedCalendarDatasetResponse ``` { "properties": { "statusId": { "description": "ID that can be used with [GET /api/v2/status/][get-apiv2status] to poll for the testing job's status.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | ID that can be used with [GET /api/v2/status/][get-apiv2status] to poll for the testing job's status. | ## CrossSeriesGroupByColumnValidatePayload ``` { "properties": { "crossSeriesGroupByColumns": { "description": "If specified, these columns will be validated for usage as the group-by column for creating cross-series features. If not present, then all columns from the dataset will be validated and only the eligible ones returned. To be valid, a column should be categorical or numerical (but not float), not be the series ID or equivalent to the series ID, not split any series, and not consist of only one value.", "items": { "type": "string" }, "type": "array" }, "datetimePartitionColumn": { "description": "The name of the column that will be used as the datetime partitioning column.", "type": "string" }, "multiseriesIdColumn": { "description": "The name of the column that wil be used as the multiseries ID column for this project.", "type": "string" }, "userDefinedSegmentIdColumn": { "description": "The name of the column that wil be used as the user defined segment ID column for this project.", "type": "string", "x-versionadded": "v2.24" } }, "required": [ "datetimePartitionColumn", "multiseriesIdColumn" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | crossSeriesGroupByColumns | [string] | false | | If specified, these columns will be validated for usage as the group-by column for creating cross-series features. If not present, then all columns from the dataset will be validated and only the eligible ones returned. To be valid, a column should be categorical or numerical (but not float), not be the series ID or equivalent to the series ID, not split any series, and not consist of only one value. | | datetimePartitionColumn | string | true | | The name of the column that will be used as the datetime partitioning column. | | multiseriesIdColumn | string | true | | The name of the column that wil be used as the multiseries ID column for this project. | | userDefinedSegmentIdColumn | string | false | | The name of the column that wil be used as the user defined segment ID column for this project. | ## CrossSeriesGroupByColumnValidateResponse ``` { "properties": { "message": { "description": "An extended message about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | An extended message about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job was created. | ## DataSource ``` { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" } ``` Data source details for a JDBC dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string,null | false | | Catalog name of the data source. | | dataSourceId | string | false | | ID of the data source. | | dataStoreId | string | true | | ID of the data store. | | dataStoreName | string | true | | Name of the data store. | | dbtable | string | true | | Table name of the data source. | | schema | string,null | true | | Schema name of the data source. | | url | string | true | | URL of the data store. | ## DatasetDefinition ``` { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | ID of the catalog item. | | catalogVersionId | string | true | | ID of the catalog item version. | | featureListId | string,null | false | | ID of the feature list. This decides which columns in the dataset are used for feature generation. | | identifier | string | true | maxLength: 20minLength: 1minLength: 1 | Short name of the dataset (used directly as part of the generated feature names). | | primaryTemporalKey | string,null | false | | Name of the column indicating time of record creation. | | snapshotPolicy | string | false | | Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets). | ### Enumerated Values | Property | Value | | --- | --- | | snapshotPolicy | [specified, latest, dynamic] | ## DatasetDefinitionResponse ``` { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "dataSource": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "dataSources": { "description": "Data source details for a JDBC dataset", "items": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "type": "array" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": "string" }, "featureLists": { "description": "The list of available feature list IDs for the dataset.", "items": { "type": "string" }, "type": "array" }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "isDeleted": { "description": "Is this dataset deleted?", "type": [ "boolean", "null" ] }, "originalIdentifier": { "description": "Original identifier of the dataset if it was updated to resolve name conflicts.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "featureListId", "identifier", "snapshotPolicy" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string,null | true | | ID of the catalog item. | | catalogVersionId | string | true | | ID of the catalog item version. | | dataSource | DataSource | false | | Data source details for a JDBC dataset | | dataSources | [DataSource] | false | | Data source details for a JDBC dataset | | featureListId | string | true | | ID of the feature list. This decides which columns in the dataset are used for feature generation. | | featureLists | [string] | false | | The list of available feature list IDs for the dataset. | | identifier | string | true | maxLength: 20minLength: 1minLength: 1 | Short name of the dataset (used directly as part of the generated feature names). | | isDeleted | boolean,null | false | | Is this dataset deleted? | | originalIdentifier | string,null | false | maxLength: 20minLength: 1minLength: 1 | Original identifier of the dataset if it was updated to resolve name conflicts. | | primaryTemporalKey | string,null | false | | Name of the column indicating time of record creation. | | snapshotPolicy | string | true | | Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets). | ### Enumerated Values | Property | Value | | --- | --- | | snapshotPolicy | [specified, latest, dynamic] | ## DatasetsCredential ``` { "properties": { "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "credentialId": { "description": "ID of the credential store to be used for the given catalog version.", "type": "string" }, "url": { "description": "The URL of the datasource.", "type": [ "string", "null" ] } }, "required": [ "catalogVersionId", "credentialId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogVersionId | string | true | | ID of the catalog version. | | credentialId | string | true | | ID of the credential store to be used for the given catalog version. | | url | string,null | false | | The URL of the datasource. | ## DiscardedFeaturesResponse ``` { "properties": { "count": { "description": "Discarded features count.", "minimum": 0, "type": "integer" }, "features": { "description": "Discarded features.", "items": { "type": "string" }, "type": "array" }, "remainingRestoreLimit": { "description": "The remaining available number of the features which can be restored in this project.", "minimum": 0, "type": "integer" }, "totalRestoreLimit": { "description": "The total limit indicating how many features can be restored in this project.", "minimum": 0, "type": "integer" } }, "required": [ "count", "features", "remainingRestoreLimit", "totalRestoreLimit" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | Discarded features count. | | features | [string] | true | | Discarded features. | | remainingRestoreLimit | integer | true | minimum: 0 | The remaining available number of the features which can be restored in this project. | | totalRestoreLimit | integer | true | minimum: 0 | The total limit indicating how many features can be restored in this project. | ## DocumentTextExtractionDocumentElement ``` { "properties": { "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "documentIndex": { "description": "The index of the document within the dataset.", "type": "integer" }, "documentTask": { "description": "The document task this document belongs to.", "enum": [ "DOCUMENT_TEXT_EXTRACTOR", "TESSERACT_OCR" ], "type": "string" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "prediction": { "description": "Object that describes prediction value of the dataset row.", "properties": { "labels": { "description": "List of predicted label names corresponding to values.", "items": { "description": "Predicted label", "type": "string" }, "type": "array" }, "values": { "description": "Predicted value or probability of the class identified by the label.", "items": { "oneOf": [ { "type": "number" }, { "items": { "type": "number" }, "type": "array" } ] }, "type": "array" } }, "required": [ "labels", "values" ], "type": "object" }, "thumbnailHeight": { "description": "The height of the thumbnail in pixels.", "type": "integer" }, "thumbnailId": { "description": "The document page ID of the thumbnail.", "type": "string" }, "thumbnailLink": { "description": "The URL of the thumbnail image.", "format": "uri", "type": "string" }, "thumbnailWidth": { "description": "The width of the thumbnail in pixels.", "type": "integer" } }, "required": [ "actualTargetValue", "documentIndex", "documentTask", "featureName", "prediction", "thumbnailHeight", "thumbnailId", "thumbnailLink", "thumbnailWidth" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualTargetValue | any | true | | Actual target value of the dataset row | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | documentIndex | integer | true | | The index of the document within the dataset. | | documentTask | string | true | | The document task this document belongs to. | | featureName | string | true | | The name of the feature. | | prediction | InsightsPredictionField | true | | Object that describes prediction value of the dataset row. | | thumbnailHeight | integer | true | | The height of the thumbnail in pixels. | | thumbnailId | string | true | | The document page ID of the thumbnail. | | thumbnailLink | string(uri) | true | | The URL of the thumbnail image. | | thumbnailWidth | integer | true | | The width of the thumbnail in pixels. | ### Enumerated Values | Property | Value | | --- | --- | | documentTask | [DOCUMENT_TEXT_EXTRACTOR, TESSERACT_OCR] | ## DocumentTextExtractionPagesElement ``` { "properties": { "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "documentIndex": { "description": "The index of the document within the dataset.", "type": "integer" }, "documentPageHeight": { "description": "The height of the thumbnail in pixels.", "type": "integer" }, "documentPageId": { "description": "The document page ID of the thumbnail.", "type": "string" }, "documentPageLink": { "description": "The URL of the thumbnail image.", "format": "uri", "type": "string" }, "documentPageWidth": { "description": "The width of the thumbnail in pixels.", "type": "integer" }, "documentTask": { "description": "The document task that this page belongs to.", "enum": [ "DOCUMENT_TEXT_EXTRACTOR", "TESSERACT_OCR" ], "type": "string" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "pageIndex": { "description": "The index of this page within the document", "type": "integer" }, "prediction": { "description": "Object that describes prediction value of the dataset row.", "properties": { "labels": { "description": "List of predicted label names corresponding to values.", "items": { "description": "Predicted label", "type": "string" }, "type": "array" }, "values": { "description": "Predicted value or probability of the class identified by the label.", "items": { "oneOf": [ { "type": "number" }, { "items": { "type": "number" }, "type": "array" } ] }, "type": "array" } }, "required": [ "labels", "values" ], "type": "object" }, "textLines": { "description": "The recognized text lines of this document page with bounding box coordinates for each text line.", "items": { "properties": { "bottom": { "description": "Bottom coordinate of the bounding box belonging to this text line in number of pixels from the top image side.", "minimum": 0, "type": "integer" }, "left": { "description": "Left coordinate of the bounding box belonging to this text line in number of pixels from the left image side.", "minimum": 0, "type": "integer" }, "right": { "description": "Right coordinate of the bounding box belonging to this text line in number of pixels from the left image side.", "minimum": 0, "type": "integer" }, "text": { "description": "The text in this text line.", "type": "string" }, "top": { "description": "Top coordinate of the bounding box belonging to this text line in number of pixels from the top image side.", "minimum": 0, "type": "integer" } }, "required": [ "bottom", "left", "right", "text", "top" ], "type": "object" }, "type": "array" } }, "required": [ "actualTargetValue", "documentIndex", "documentPageHeight", "documentPageId", "documentPageLink", "documentPageWidth", "documentTask", "featureName", "pageIndex", "prediction", "textLines" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualTargetValue | any | true | | Actual target value of the dataset row | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | documentIndex | integer | true | | The index of the document within the dataset. | | documentPageHeight | integer | true | | The height of the thumbnail in pixels. | | documentPageId | string | true | | The document page ID of the thumbnail. | | documentPageLink | string(uri) | true | | The URL of the thumbnail image. | | documentPageWidth | integer | true | | The width of the thumbnail in pixels. | | documentTask | string | true | | The document task that this page belongs to. | | featureName | string | true | | The name of the feature. | | pageIndex | integer | true | | The index of this page within the document | | prediction | InsightsPredictionField | true | | Object that describes prediction value of the dataset row. | | textLines | [TextLine] | true | | The recognized text lines of this document page with bounding box coordinates for each text line. | ### Enumerated Values | Property | Value | | --- | --- | | documentTask | [DOCUMENT_TEXT_EXTRACTOR, TESSERACT_OCR] | ## DocumentTextExtractionSampleMetadataElement ``` { "properties": { "documentTask": { "description": "The document task that this data belongs to.", "enum": [ "DOCUMENT_TEXT_EXTRACTOR", "TESSERACT_OCR" ], "type": "string" }, "featureName": { "description": "Name of feature", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" } }, "required": [ "documentTask", "featureName", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | documentTask | string | true | | The document task that this data belongs to. | | featureName | string | true | | Name of feature | | modelId | string | true | | The model ID of the target model. | ### Enumerated Values | Property | Value | | --- | --- | | documentTask | [DOCUMENT_TEXT_EXTRACTOR, TESSERACT_OCR] | ## DocumentTextExtractionSamplesComputeResponse ``` { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if the job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "jobType": { "description": "The type of the job.", "enum": [ "compute_document_text_extraction_samples" ], "type": "string" }, "message": { "description": "Error message in case of failure.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The job status.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" }, "url": { "description": "A URL that can be used to request details about the job.", "type": "string" } }, "required": [ "id", "isBlocked", "jobType", "message", "modelId", "projectId", "status", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The job ID. | | isBlocked | boolean | true | | True if the job is waiting for its dependencies to be resolved first. | | jobType | string | true | | The type of the job. | | message | string | true | | Error message in case of failure. | | modelId | string | true | | The model ID of the target model. | | projectId | string | true | | The project the job belongs to. | | status | string | true | | The job status. | | url | string | true | | A URL that can be used to request details about the job. | ### Enumerated Values | Property | Value | | --- | --- | | jobType | compute_document_text_extraction_samples | | status | [queue, inprogress, error, ABORTED, COMPLETED] | ## DocumentTextExtractionSamplesListMetadataResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of Model ID feature name pairs with computed document text extraction samples.", "items": { "properties": { "documentTask": { "description": "The document task that this data belongs to.", "enum": [ "DOCUMENT_TEXT_EXTRACTOR", "TESSERACT_OCR" ], "type": "string" }, "featureName": { "description": "Name of feature", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" } }, "required": [ "documentTask", "featureName", "modelId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DocumentTextExtractionSampleMetadataElement] | true | | A list of Model ID feature name pairs with computed document text extraction samples. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DocumentTextExtractionSamplesRetrieveDocumentsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of documents.", "items": { "properties": { "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "documentIndex": { "description": "The index of the document within the dataset.", "type": "integer" }, "documentTask": { "description": "The document task this document belongs to.", "enum": [ "DOCUMENT_TEXT_EXTRACTOR", "TESSERACT_OCR" ], "type": "string" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "prediction": { "description": "Object that describes prediction value of the dataset row.", "properties": { "labels": { "description": "List of predicted label names corresponding to values.", "items": { "description": "Predicted label", "type": "string" }, "type": "array" }, "values": { "description": "Predicted value or probability of the class identified by the label.", "items": { "oneOf": [ { "type": "number" }, { "items": { "type": "number" }, "type": "array" } ] }, "type": "array" } }, "required": [ "labels", "values" ], "type": "object" }, "thumbnailHeight": { "description": "The height of the thumbnail in pixels.", "type": "integer" }, "thumbnailId": { "description": "The document page ID of the thumbnail.", "type": "string" }, "thumbnailLink": { "description": "The URL of the thumbnail image.", "format": "uri", "type": "string" }, "thumbnailWidth": { "description": "The width of the thumbnail in pixels.", "type": "integer" } }, "required": [ "actualTargetValue", "documentIndex", "documentTask", "featureName", "prediction", "thumbnailHeight", "thumbnailId", "thumbnailLink", "thumbnailWidth" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "targetBins": { "description": "List of bin objects for regression or null", "items": { "properties": { "targetBinEnd": { "description": "End value for the target bin", "type": "number" }, "targetBinStart": { "description": "Start value for the target bin", "type": "number" } }, "required": [ "targetBinEnd", "targetBinStart" ], "type": "object" }, "type": "array" }, "targetValues": { "description": "List of target values for classification or null", "items": { "description": "Target value", "type": "string" }, "type": "array" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "targetBins", "targetValues", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DocumentTextExtractionDocumentElement] | true | | A list of documents. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | targetBins | [TargetBin] | true | | List of bin objects for regression or null | | targetValues | [string] | true | | List of target values for classification or null | | totalCount | integer | true | | The total number of items across all pages. | ## DocumentTextExtractionSamplesRetrievePagesResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of document pages", "items": { "properties": { "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "documentIndex": { "description": "The index of the document within the dataset.", "type": "integer" }, "documentPageHeight": { "description": "The height of the thumbnail in pixels.", "type": "integer" }, "documentPageId": { "description": "The document page ID of the thumbnail.", "type": "string" }, "documentPageLink": { "description": "The URL of the thumbnail image.", "format": "uri", "type": "string" }, "documentPageWidth": { "description": "The width of the thumbnail in pixels.", "type": "integer" }, "documentTask": { "description": "The document task that this page belongs to.", "enum": [ "DOCUMENT_TEXT_EXTRACTOR", "TESSERACT_OCR" ], "type": "string" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "pageIndex": { "description": "The index of this page within the document", "type": "integer" }, "prediction": { "description": "Object that describes prediction value of the dataset row.", "properties": { "labels": { "description": "List of predicted label names corresponding to values.", "items": { "description": "Predicted label", "type": "string" }, "type": "array" }, "values": { "description": "Predicted value or probability of the class identified by the label.", "items": { "oneOf": [ { "type": "number" }, { "items": { "type": "number" }, "type": "array" } ] }, "type": "array" } }, "required": [ "labels", "values" ], "type": "object" }, "textLines": { "description": "The recognized text lines of this document page with bounding box coordinates for each text line.", "items": { "properties": { "bottom": { "description": "Bottom coordinate of the bounding box belonging to this text line in number of pixels from the top image side.", "minimum": 0, "type": "integer" }, "left": { "description": "Left coordinate of the bounding box belonging to this text line in number of pixels from the left image side.", "minimum": 0, "type": "integer" }, "right": { "description": "Right coordinate of the bounding box belonging to this text line in number of pixels from the left image side.", "minimum": 0, "type": "integer" }, "text": { "description": "The text in this text line.", "type": "string" }, "top": { "description": "Top coordinate of the bounding box belonging to this text line in number of pixels from the top image side.", "minimum": 0, "type": "integer" } }, "required": [ "bottom", "left", "right", "text", "top" ], "type": "object" }, "type": "array" } }, "required": [ "actualTargetValue", "documentIndex", "documentPageHeight", "documentPageId", "documentPageLink", "documentPageWidth", "documentTask", "featureName", "pageIndex", "prediction", "textLines" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "targetBins": { "description": "List of bin objects for regression or null", "items": { "properties": { "targetBinEnd": { "description": "End value for the target bin", "type": "number" }, "targetBinStart": { "description": "Start value for the target bin", "type": "number" } }, "required": [ "targetBinEnd", "targetBinStart" ], "type": "object" }, "type": "array" }, "targetValues": { "description": "List of target values for classification or null", "items": { "description": "Target value", "type": "string" }, "type": "array" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "targetBins", "targetValues", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DocumentTextExtractionPagesElement] | true | | List of document pages | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | targetBins | [TargetBin] | true | | List of bin objects for regression or null | | targetValues | [string] | true | | List of target values for classification or null | | totalCount | integer | true | | The total number of items across all pages. | ## DocumentThumbnailBinsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of document thumbnail metadata, as described below", "items": { "properties": { "documentPageId": { "description": "The ID of the document page. The actual document page can be retrieved with [GET /api/v2/projects/{projectId}/documentPages/{documentPageId}/file/][get-apiv2projectsprojectiddocumentpagesdocumentpageidfile].", "type": "string" }, "height": { "description": "The height of the document page in pixels.", "type": "integer" }, "targetBinEnd": { "description": "Target value for bin end for regression, null for classification", "type": [ "integer", "null" ] }, "targetBinRowCount": { "description": "The number of rows in the target bin.", "type": "integer" }, "targetBinStart": { "description": "Target value for bin start for regression, null for classification", "type": [ "integer", "null" ] }, "targetValue": { "description": "Target value", "oneOf": [ { "description": "For regression projects", "type": "number" }, { "description": "For classification projects", "type": "string" }, { "items": { "description": "For multilabel projects", "type": "string" }, "type": "array" } ] }, "width": { "description": "The width of the document page in pixels.", "type": "integer" } }, "required": [ "documentPageId", "height", "targetBinRowCount", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DocumentThumbnailMetadataWithBins] | true | | List of document thumbnail metadata, as described below | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DocumentThumbnailMetadataListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of document thumbnail metadata elements", "items": { "properties": { "documentPageId": { "description": "The ID of the document page. The actual document page can be retrieved with [GET /api/v2/projects/{projectId}/documentPages/{documentPageId}/file/][get-apiv2projectsprojectiddocumentpagesdocumentpageidfile].", "type": "string" }, "height": { "description": "The height of the document page in pixels.", "type": "integer" }, "targetValue": { "description": "Target value", "oneOf": [ { "description": "For regression projects", "type": "number" }, { "description": "For classification projects", "type": "string" }, { "items": { "description": "For multilabel projects", "type": "string" }, "type": "array" } ] }, "width": { "description": "The width of the document page in pixels.", "type": "integer" } }, "required": [ "documentPageId", "height", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DocumentThumbnailMetadataResponse] | true | | A list of document thumbnail metadata elements | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DocumentThumbnailMetadataResponse ``` { "properties": { "documentPageId": { "description": "The ID of the document page. The actual document page can be retrieved with [GET /api/v2/projects/{projectId}/documentPages/{documentPageId}/file/][get-apiv2projectsprojectiddocumentpagesdocumentpageidfile].", "type": "string" }, "height": { "description": "The height of the document page in pixels.", "type": "integer" }, "targetValue": { "description": "Target value", "oneOf": [ { "description": "For regression projects", "type": "number" }, { "description": "For classification projects", "type": "string" }, { "items": { "description": "For multilabel projects", "type": "string" }, "type": "array" } ] }, "width": { "description": "The width of the document page in pixels.", "type": "integer" } }, "required": [ "documentPageId", "height", "width" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | documentPageId | string | true | | The ID of the document page. The actual document page can be retrieved with [GET /api/v2/projects/{projectId}/documentPages/{documentPageId}/file/][get-apiv2projectsprojectiddocumentpagesdocumentpageidfile]. | | height | integer | true | | The height of the document page in pixels. | | targetValue | any | false | | Target value | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | For regression projects | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | For classification projects | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | width | integer | true | | The width of the document page in pixels. | ## DocumentThumbnailMetadataWithBins ``` { "properties": { "documentPageId": { "description": "The ID of the document page. The actual document page can be retrieved with [GET /api/v2/projects/{projectId}/documentPages/{documentPageId}/file/][get-apiv2projectsprojectiddocumentpagesdocumentpageidfile].", "type": "string" }, "height": { "description": "The height of the document page in pixels.", "type": "integer" }, "targetBinEnd": { "description": "Target value for bin end for regression, null for classification", "type": [ "integer", "null" ] }, "targetBinRowCount": { "description": "The number of rows in the target bin.", "type": "integer" }, "targetBinStart": { "description": "Target value for bin start for regression, null for classification", "type": [ "integer", "null" ] }, "targetValue": { "description": "Target value", "oneOf": [ { "description": "For regression projects", "type": "number" }, { "description": "For classification projects", "type": "string" }, { "items": { "description": "For multilabel projects", "type": "string" }, "type": "array" } ] }, "width": { "description": "The width of the document page in pixels.", "type": "integer" } }, "required": [ "documentPageId", "height", "targetBinRowCount", "width" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | documentPageId | string | true | | The ID of the document page. The actual document page can be retrieved with [GET /api/v2/projects/{projectId}/documentPages/{documentPageId}/file/][get-apiv2projectsprojectiddocumentpagesdocumentpageidfile]. | | height | integer | true | | The height of the document page in pixels. | | targetBinEnd | integer,null | false | | Target value for bin end for regression, null for classification | | targetBinRowCount | integer | true | | The number of rows in the target bin. | | targetBinStart | integer,null | false | | Target value for bin start for regression, null for classification | | targetValue | any | false | | Target value | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | For regression projects | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | For classification projects | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | width | integer | true | | The width of the document page in pixels. | ## DocumentsDataQualityLogLinesResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The content in the form of lines of the documents data quality log", "items": { "description": "Log lines", "type": "string" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [string] | true | | The content in the form of lines of the documents data quality log | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DuplicateImageItem ``` { "properties": { "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "rowCount": { "description": "The count of duplicate images i.e. number of times this image is used in column", "type": "integer" } }, "required": [ "imageId", "rowCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | imageId | string | true | | Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile] | | rowCount | integer | true | | The count of duplicate images i.e. number of times this image is used in column | ## DuplicateImageTableResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of image metadata", "items": { "properties": { "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "rowCount": { "description": "The count of duplicate images i.e. number of times this image is used in column", "type": "integer" } }, "required": [ "imageId", "rowCount" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DuplicateImageItem] | true | | List of image metadata | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## EmbeddingsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of Image Embeddings", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" } }, "required": [ "featureName", "modelId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ImageInsightsMetadataElement] | true | | List of Image Embeddings | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## EmbeddingsRetrieveResponse ``` { "properties": { "embeddings": { "description": "List of Image Embedding objects", "items": { "properties": { "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile].", "type": "string" }, "positionX": { "description": "x coordinate of the image in the embedding space.", "type": "number" }, "positionY": { "description": "y coordinate of the image in the embedding space.", "type": "number" }, "prediction": { "description": "Object that describes prediction value of the dataset row.", "properties": { "labels": { "description": "List of predicted label names corresponding to values.", "items": { "description": "Predicted label", "type": "string" }, "type": "array" }, "values": { "description": "Predicted value or probability of the class identified by the label.", "items": { "oneOf": [ { "type": "number" }, { "items": { "type": "number" }, "type": "array" } ] }, "type": "array" } }, "required": [ "labels", "values" ], "type": "object" } }, "required": [ "actualTargetValue", "imageId", "positionX", "positionY", "prediction" ], "type": "object" }, "type": "array" }, "targetBins": { "description": "List of bin objects for regression or null", "items": { "properties": { "targetBinEnd": { "description": "End value for the target bin", "type": "number" }, "targetBinStart": { "description": "Start value for the target bin", "type": "number" } }, "required": [ "targetBinEnd", "targetBinStart" ], "type": "object" }, "type": "array" }, "targetValues": { "description": "List of target values for classification or null", "items": { "description": "Target value", "type": "string" }, "type": "array" } }, "required": [ "embeddings", "targetBins", "targetValues" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | embeddings | [ImageEmbedding] | true | | List of Image Embedding objects | | targetBins | [TargetBin] | true | | List of bin objects for regression or null | | targetValues | [string] | true | | List of target values for classification or null | ## Empty ``` { "type": "object" } ``` ### Properties None ## ExtendedRelationship ``` { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "relationshipQuality": { "description": "Summary of the relationship quality information", "oneOf": [ { "properties": { "detailedReport": { "description": "Detailed report of the relationship quality information", "items": { "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "enrichmentRateValue": { "description": "Percentage of primary table records that can be enriched with a record in this dataset", "type": "number" }, "featureDerivationWindow": { "description": "Feature derivation window.", "type": [ "string", "null" ] }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate", "enrichmentRateValue", "overallCategory" ], "type": "object" }, "maxItems": 3, "type": "array" }, "lastUpdated": { "description": "Last updated timestamp", "format": "date-time", "type": "string" }, "problemCount": { "description": "Total count of problems detected", "type": "integer" }, "samplingFraction": { "description": "Primary dataset sampling fraction used for relationship quality assessment speedup", "type": "number" }, "status": { "description": "Relationship quality assessment status", "enum": [ "Complete", "In progress", "Error" ], "type": "string" }, "statusId": { "description": "The list of status IDs.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" }, "summaryCategory": { "description": "Class of the summary warning of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "summaryMessage": { "description": "Summary warning message about the relationship", "type": "string" } }, "required": [ "detailedReport", "lastUpdated", "problemCount", "samplingFraction", "summaryCategory", "summaryMessage" ], "type": "object" }, { "properties": { "enrichmentRate": { "description": "Percentage of records that can be enriched with a record in the primary table", "type": "number" }, "formattedSummary": { "description": "Relationship quality assessment report associated with the relationship", "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate" ], "type": "object" }, "lastUpdated": { "description": "Last updated timestamp", "format": "date-time", "type": "string" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "status": { "description": "Relationship quality assessment status", "enum": [ "Complete", "In progress", "Error" ], "type": "string" }, "statusId": { "description": "The list of status IDs.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" } }, "required": [ "enrichmentRate", "formattedSummary", "lastUpdated", "overallCategory" ], "type": "object" } ] } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataset1Identifier | string,null | false | maxLength: 20minLength: 1minLength: 1 | Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset. | | dataset1Keys | [string] | true | maxItems: 10minItems: 1 | column(s) in the first dataset that are used to join to the second dataset. | | dataset2Identifier | string | true | maxLength: 20minLength: 1minLength: 1 | Identifier of the second dataset in the relationship. | | dataset2Keys | [string] | true | maxItems: 10minItems: 1 | column(s) in the second dataset that are used to join to the first dataset. | | featureDerivationWindowEnd | integer | false | maximum: 0 | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | featureDerivationWindowStart | integer | false | | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | featureDerivationWindowTimeUnit | string | false | | Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | featureDerivationWindows | [FeatureDerivationWindow] | false | maxItems: 3 | The list of feature derivation window definitions that will be used. | | predictionPointRounding | integer | false | maximum: 30 | Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided. | | predictionPointRoundingTimeUnit | string | false | | Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided. | | relationshipQuality | any | false | | Summary of the relationship quality information | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | RelationshipQualitySummaryNewFormat | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | RelationshipQualitySummary | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | featureDerivationWindowTimeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | | predictionPointRoundingTimeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | ## ExtendedRelationshipsConfigRetrieve ``` { "properties": { "datasetDefinitions": { "description": "The list of dataset definitions.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "dataSource": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "dataSources": { "description": "Data source details for a JDBC dataset", "items": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "type": "array" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": "string" }, "featureLists": { "description": "The list of available feature list IDs for the dataset.", "items": { "type": "string" }, "type": "array" }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "isDeleted": { "description": "Is this dataset deleted?", "type": [ "boolean", "null" ] }, "originalIdentifier": { "description": "Original identifier of the dataset if it was updated to resolve name conflicts.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "featureListId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "type": "array" }, "id": { "description": "ID of relationships configuration.", "type": "string" }, "relationships": { "description": "The list of relationships with quality assessment information.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "relationshipQuality": { "description": "Summary of the relationship quality information", "oneOf": [ { "properties": { "detailedReport": { "description": "Detailed report of the relationship quality information", "items": { "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "enrichmentRateValue": { "description": "Percentage of primary table records that can be enriched with a record in this dataset", "type": "number" }, "featureDerivationWindow": { "description": "Feature derivation window.", "type": [ "string", "null" ] }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate", "enrichmentRateValue", "overallCategory" ], "type": "object" }, "maxItems": 3, "type": "array" }, "lastUpdated": { "description": "Last updated timestamp", "format": "date-time", "type": "string" }, "problemCount": { "description": "Total count of problems detected", "type": "integer" }, "samplingFraction": { "description": "Primary dataset sampling fraction used for relationship quality assessment speedup", "type": "number" }, "status": { "description": "Relationship quality assessment status", "enum": [ "Complete", "In progress", "Error" ], "type": "string" }, "statusId": { "description": "The list of status IDs.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" }, "summaryCategory": { "description": "Class of the summary warning of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "summaryMessage": { "description": "Summary warning message about the relationship", "type": "string" } }, "required": [ "detailedReport", "lastUpdated", "problemCount", "samplingFraction", "summaryCategory", "summaryMessage" ], "type": "object" }, { "properties": { "enrichmentRate": { "description": "Percentage of records that can be enriched with a record in the primary table", "type": "number" }, "formattedSummary": { "description": "Relationship quality assessment report associated with the relationship", "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate" ], "type": "object" }, "lastUpdated": { "description": "Last updated timestamp", "format": "date-time", "type": "string" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "status": { "description": "Relationship quality assessment status", "enum": [ "Complete", "In progress", "Error" ], "type": "string" }, "statusId": { "description": "The list of status IDs.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" } }, "required": [ "enrichmentRate", "formattedSummary", "lastUpdated", "overallCategory" ], "type": "object" } ] } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array" }, "snowflakePushDownCompatible": { "description": "Is this configuration compatible with pushdown computation on Snowflake?", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "relationships" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetDefinitions | [DatasetDefinitionResponse] | true | | The list of dataset definitions. | | featureDiscoveryMode | string,null | false | | Mode of feature discovery. Supported values are 'default' and 'manual'. | | featureDiscoverySettings | [FeatureDiscoverySettingResponse] | false | | The list of feature discovery settings used to customize the feature discovery process. | | id | string | false | | ID of relationships configuration. | | relationships | [ExtendedRelationship] | true | maxItems: 100minItems: 1 | The list of relationships with quality assessment information. | | snowflakePushDownCompatible | boolean,null | false | | Is this configuration compatible with pushdown computation on Snowflake? | ### Enumerated Values | Property | Value | | --- | --- | | featureDiscoveryMode | [default, manual] | ## FeatureDerivationWindow ``` { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | integer | true | maximum: 0 | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | start | integer | true | | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | unit | string | true | | Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | ### Enumerated Values | Property | Value | | --- | --- | | unit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | ## FeatureDiscoveryLogListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "featureDiscoveryLog": { "description": "List of lines retrieved from the feature discovery log", "items": { "type": "string" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalLogLines": { "description": "The total number of lines in the feature derivation log.", "type": "integer" } }, "required": [ "count", "featureDiscoveryLog", "next", "previous", "totalLogLines" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | featureDiscoveryLog | [string] | true | | List of lines retrieved from the feature discovery log | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalLogLines | integer | true | | The total number of lines in the feature derivation log. | ## FeatureDiscoveryRecipeSQLsExport ``` { "properties": { "modelId": { "description": "Model ID to export recipe for", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | false | | Model ID to export recipe for | ## FeatureDiscoverySetting ``` { "properties": { "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" } }, "required": [ "name", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 100 | Name of this feature discovery setting | | value | boolean | true | | Value of this feature discovery setting | ## FeatureDiscoverySettingResponse ``` { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | Description of this feature discovery setting | | family | string | true | | Family of this feature discovery setting | | name | string | true | maxLength: 100 | Name of this feature discovery setting | | settingType | string | true | | Type of this feature discovery setting | | value | boolean | true | | Value of this feature discovery setting | | verboseName | string | true | | Human readable name of this feature discovery setting | ## FeatureHistogramPlotResponse ``` { "properties": { "count": { "description": "number of values in the bin (or weights if project is weighted)", "type": "number" }, "label": { "description": "bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" }, "target": { "description": "Average value of the target feature values for the bin. For regression projects, it will be null, if the feature was deemed as low informative or project target has not been selected yet or AIM processing has not finished yet. You can use [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] endpoint to find more about low informative features. For binary classification, the same conditions apply as above, but the value should be treated as the ratio of total positives in the bin to bin's total size (`count`). For multiclass projects the value is always null.", "type": [ "number", "null" ] } }, "required": [ "count", "label", "target" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | number | true | | number of values in the bin (or weights if project is weighted) | | label | string | true | | bin start for numerical/uncapped, or string value for categorical. The bin ==Missing== is created for rows that did not have the feature. | | target | number,null | true | | Average value of the target feature values for the bin. For regression projects, it will be null, if the feature was deemed as low informative or project target has not been selected yet or AIM processing has not finished yet. You can use [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] endpoint to find more about low informative features. For binary classification, the same conditions apply as above, but the value should be treated as the ratio of total positives in the bin to bin's total size (count). For multiclass projects the value is always null. | ## FeatureHistogramResponse ``` { "properties": { "plot": { "description": "plot data based on feature values.", "items": { "properties": { "count": { "description": "number of values in the bin (or weights if project is weighted)", "type": "number" }, "label": { "description": "bin start for numerical/uncapped, or string value for categorical. The bin `==Missing==` is created for rows that did not have the feature.", "type": "string" }, "target": { "description": "Average value of the target feature values for the bin. For regression projects, it will be null, if the feature was deemed as low informative or project target has not been selected yet or AIM processing has not finished yet. You can use [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] endpoint to find more about low informative features. For binary classification, the same conditions apply as above, but the value should be treated as the ratio of total positives in the bin to bin's total size (`count`). For multiclass projects the value is always null.", "type": [ "number", "null" ] } }, "required": [ "count", "label", "target" ], "type": "object" }, "type": "array" } }, "required": [ "plot" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | plot | [FeatureHistogramPlotResponse] | true | | plot data based on feature values. | ## FeatureKeySummaryDetailsResponseValidatorMultilabel ``` { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } ``` Statistics of the key. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | max | number | true | | Maximum value of the key. | | mean | number | true | | Mean value of the key. | | median | number | true | | Median value of the key. | | min | number | true | | Minimum value of the key. | | pctRows | number | true | | Percentage occurrence of key in the EDA sample of the feature. | | stdDev | number | true | | Standard deviation of the key. | ## FeatureKeySummaryDetailsResponseValidatorSummarizedCategorical ``` { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } ``` Statistics of the key. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataQualities | string | true | | The indicator of data quality assessment of the feature. | | max | number | true | | Maximum value of the key. | | mean | number | true | | Mean value of the key. | | median | number | true | | Median value of the key. | | min | number | true | | Minimum value of the key. | | pctRows | number | true | | Percentage occurrence of key in the EDA sample of the feature. | | stdDev | number | true | | Standard deviation of the key. | ### Enumerated Values | Property | Value | | --- | --- | | dataQualities | [ISSUES_FOUND, NOT_ANALYZED, NO_ISSUES_FOUND] | ## FeatureKeySummaryResponseValidatorMultilabel ``` { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | key | string | true | | Name of the key. | | summary | FeatureKeySummaryDetailsResponseValidatorMultilabel | true | | Statistics of the key. | ## FeatureKeySummaryResponseValidatorSummarizedCategorical ``` { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" } ``` For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters) ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | key | string | true | | Name of the key. | | summary | FeatureKeySummaryDetailsResponseValidatorSummarizedCategorical | true | | Statistics of the key. | ## FeatureLineageJoin ``` { "description": "**join** step details.", "properties": { "joinType": { "description": "Kind of SQL JOIN applied.", "enum": [ "left, right" ], "type": "string" }, "leftTable": { "description": "Information about a dataset which was considered left in a join.", "properties": { "columns": { "description": "List of columns which datasets were joined by.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "datasteps": { "description": "List of *data* steps id which brought the *columns* into the current step dataset.", "items": { "minimum": 1, "type": "integer" }, "type": "array" } }, "required": [ "columns", "datasteps" ], "type": "object" }, "rightTable": { "description": "Information about a dataset which was considered left in a join.", "properties": { "columns": { "description": "List of columns which datasets were joined by.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "datasteps": { "description": "List of *data* steps id which brought the *columns* into the current step dataset.", "items": { "minimum": 1, "type": "integer" }, "type": "array" } }, "required": [ "columns", "datasteps" ], "type": "object" } }, "required": [ "joinType", "leftTable", "rightTable" ], "type": "object" } ``` join step details. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | joinType | string | true | | Kind of SQL JOIN applied. | | leftTable | FeatureLineageJoinTable | true | | Information about a dataset which was considered left in a join. | | rightTable | FeatureLineageJoinTable | true | | Information about a dataset which was considered left in a join. | ### Enumerated Values | Property | Value | | --- | --- | | joinType | left, right | ## FeatureLineageJoinTable ``` { "description": "Information about a dataset which was considered left in a join.", "properties": { "columns": { "description": "List of columns which datasets were joined by.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "datasteps": { "description": "List of *data* steps id which brought the *columns* into the current step dataset.", "items": { "minimum": 1, "type": "integer" }, "type": "array" } }, "required": [ "columns", "datasteps" ], "type": "object" } ``` Information about a dataset which was considered left in a join. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columns | [string] | true | minItems: 1 | List of columns which datasets were joined by. | | datasteps | [integer] | true | | List of data steps id which brought the columns into the current step dataset. | ## FeatureLineageResponse ``` { "properties": { "steps": { "description": "List of steps which were applied to build the feature.", "items": { "properties": { "arguments": { "description": "Generic key-value pairs to describe **action** step additional parameters.", "type": "object" }, "catalogId": { "description": "ID of the catalog for a **data** step.", "type": "string" }, "catalogVersionId": { "description": "id of the catalog version for a **data** step.", "type": "string" }, "description": { "description": "Description of the step.", "type": "string" }, "groupBy": { "description": "List of columns which this **action** step aggregated.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Step id starting with 0.", "minimum": 0, "type": "integer" }, "isTimeAware": { "description": "Indicator of step being time aware. Mandatory only for **action** and **join** steps. **action** step provides additional information about feature derivation window in the `timeInfo` field.", "type": "boolean" }, "joinInfo": { "description": "**join** step details.", "properties": { "joinType": { "description": "Kind of SQL JOIN applied.", "enum": [ "left, right" ], "type": "string" }, "leftTable": { "description": "Information about a dataset which was considered left in a join.", "properties": { "columns": { "description": "List of columns which datasets were joined by.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "datasteps": { "description": "List of *data* steps id which brought the *columns* into the current step dataset.", "items": { "minimum": 1, "type": "integer" }, "type": "array" } }, "required": [ "columns", "datasteps" ], "type": "object" }, "rightTable": { "description": "Information about a dataset which was considered left in a join.", "properties": { "columns": { "description": "List of columns which datasets were joined by.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "datasteps": { "description": "List of *data* steps id which brought the *columns* into the current step dataset.", "items": { "minimum": 1, "type": "integer" }, "type": "array" } }, "required": [ "columns", "datasteps" ], "type": "object" } }, "required": [ "joinType", "leftTable", "rightTable" ], "type": "object" }, "name": { "description": "Name of the step.", "type": "string" }, "parents": { "description": "`id` of steps which use this step output as their input.", "items": { "minimum": 0, "type": "integer" }, "type": "array" }, "stepType": { "description": "One of four types of a step. **data** - source features. **action** - data aggregation or trasformation. **join** - SQL JOIN. **generatedData** - final feature. There is always one **generatedData** step and at least one **data** step.", "enum": [ "data", "action", "join", "generatedData" ], "type": "string" }, "timeInfo": { "description": "Description of a feature derivation window which was applied to this **action** step.", "properties": { "duration": { "description": "End of the feature derivation window applied.", "properties": { "duration": { "description": "Value/size of this time delta.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "Time unit name like 'MINUTE', 'DAY', 'MONTH' etc.", "type": "string" } }, "required": [ "duration", "timeUnit" ], "type": "object" }, "latest": { "description": "End of the feature derivation window applied.", "properties": { "duration": { "description": "Value/size of this time delta.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "Time unit name like 'MINUTE', 'DAY', 'MONTH' etc.", "type": "string" } }, "required": [ "duration", "timeUnit" ], "type": "object" } }, "required": [ "duration", "latest" ], "type": "object" } }, "required": [ "id", "parents", "stepType" ], "type": "object" }, "type": "array" } }, "required": [ "steps" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | steps | [FeatureLineageStep] | true | | List of steps which were applied to build the feature. | ## FeatureLineageStep ``` { "properties": { "arguments": { "description": "Generic key-value pairs to describe **action** step additional parameters.", "type": "object" }, "catalogId": { "description": "ID of the catalog for a **data** step.", "type": "string" }, "catalogVersionId": { "description": "id of the catalog version for a **data** step.", "type": "string" }, "description": { "description": "Description of the step.", "type": "string" }, "groupBy": { "description": "List of columns which this **action** step aggregated.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Step id starting with 0.", "minimum": 0, "type": "integer" }, "isTimeAware": { "description": "Indicator of step being time aware. Mandatory only for **action** and **join** steps. **action** step provides additional information about feature derivation window in the `timeInfo` field.", "type": "boolean" }, "joinInfo": { "description": "**join** step details.", "properties": { "joinType": { "description": "Kind of SQL JOIN applied.", "enum": [ "left, right" ], "type": "string" }, "leftTable": { "description": "Information about a dataset which was considered left in a join.", "properties": { "columns": { "description": "List of columns which datasets were joined by.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "datasteps": { "description": "List of *data* steps id which brought the *columns* into the current step dataset.", "items": { "minimum": 1, "type": "integer" }, "type": "array" } }, "required": [ "columns", "datasteps" ], "type": "object" }, "rightTable": { "description": "Information about a dataset which was considered left in a join.", "properties": { "columns": { "description": "List of columns which datasets were joined by.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "datasteps": { "description": "List of *data* steps id which brought the *columns* into the current step dataset.", "items": { "minimum": 1, "type": "integer" }, "type": "array" } }, "required": [ "columns", "datasteps" ], "type": "object" } }, "required": [ "joinType", "leftTable", "rightTable" ], "type": "object" }, "name": { "description": "Name of the step.", "type": "string" }, "parents": { "description": "`id` of steps which use this step output as their input.", "items": { "minimum": 0, "type": "integer" }, "type": "array" }, "stepType": { "description": "One of four types of a step. **data** - source features. **action** - data aggregation or trasformation. **join** - SQL JOIN. **generatedData** - final feature. There is always one **generatedData** step and at least one **data** step.", "enum": [ "data", "action", "join", "generatedData" ], "type": "string" }, "timeInfo": { "description": "Description of a feature derivation window which was applied to this **action** step.", "properties": { "duration": { "description": "End of the feature derivation window applied.", "properties": { "duration": { "description": "Value/size of this time delta.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "Time unit name like 'MINUTE', 'DAY', 'MONTH' etc.", "type": "string" } }, "required": [ "duration", "timeUnit" ], "type": "object" }, "latest": { "description": "End of the feature derivation window applied.", "properties": { "duration": { "description": "Value/size of this time delta.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "Time unit name like 'MINUTE', 'DAY', 'MONTH' etc.", "type": "string" } }, "required": [ "duration", "timeUnit" ], "type": "object" } }, "required": [ "duration", "latest" ], "type": "object" } }, "required": [ "id", "parents", "stepType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | arguments | object | false | | Generic key-value pairs to describe action step additional parameters. | | catalogId | string | false | | ID of the catalog for a data step. | | catalogVersionId | string | false | | id of the catalog version for a data step. | | description | string | false | | Description of the step. | | groupBy | [string] | false | | List of columns which this action step aggregated. | | id | integer | true | minimum: 0 | Step id starting with 0. | | isTimeAware | boolean | false | | Indicator of step being time aware. Mandatory only for action and join steps. action step provides additional information about feature derivation window in the timeInfo field. | | joinInfo | FeatureLineageJoin | false | | join step details. | | name | string | false | | Name of the step. | | parents | [integer] | true | | id of steps which use this step output as their input. | | stepType | string | true | | One of four types of a step. data - source features. action - data aggregation or trasformation. join - SQL JOIN. generatedData - final feature. There is always one generatedData step and at least one data step. | | timeInfo | FeatureLineageTimeInfo | false | | Description of a feature derivation window which was applied to this action step. | ### Enumerated Values | Property | Value | | --- | --- | | stepType | [data, action, join, generatedData] | ## FeatureLineageTimeInfo ``` { "description": "Description of a feature derivation window which was applied to this **action** step.", "properties": { "duration": { "description": "End of the feature derivation window applied.", "properties": { "duration": { "description": "Value/size of this time delta.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "Time unit name like 'MINUTE', 'DAY', 'MONTH' etc.", "type": "string" } }, "required": [ "duration", "timeUnit" ], "type": "object" }, "latest": { "description": "End of the feature derivation window applied.", "properties": { "duration": { "description": "Value/size of this time delta.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "Time unit name like 'MINUTE', 'DAY', 'MONTH' etc.", "type": "string" } }, "required": [ "duration", "timeUnit" ], "type": "object" } }, "required": [ "duration", "latest" ], "type": "object" } ``` Description of a feature derivation window which was applied to this action step. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | duration | TimeDelta | true | | End of the feature derivation window applied. | | latest | TimeDelta | true | | End of the feature derivation window applied. | ## FeatureMetricDetailsResponse ``` { "properties": { "ascending": { "description": "Should the metric be sorted in ascending order", "type": "boolean" }, "metricName": { "description": "Name of the metric", "type": "string" }, "supportsBinary": { "description": "Whether this metric is valid for binary classification.", "type": "boolean" }, "supportsMulticlass": { "description": "Whether this metric is valid for multiclass classification.", "type": "boolean" }, "supportsRegression": { "description": "This metric is valid for regression", "type": "boolean" }, "supportsTimeseries": { "description": "This metric is valid for timeseries", "type": "boolean" } }, "required": [ "ascending", "metricName", "supportsBinary", "supportsMulticlass", "supportsRegression", "supportsTimeseries" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ascending | boolean | true | | Should the metric be sorted in ascending order | | metricName | string | true | | Name of the metric | | supportsBinary | boolean | true | | Whether this metric is valid for binary classification. | | supportsMulticlass | boolean | true | | Whether this metric is valid for multiclass classification. | | supportsRegression | boolean | true | | This metric is valid for regression | | supportsTimeseries | boolean | true | | This metric is valid for timeseries | ## FeatureMetricsResponse ``` { "properties": { "availableMetrics": { "description": "an array of strings representing the appropriate metrics. If the feature cannot be selected as the target, then this array will be empty.", "items": { "type": "string" }, "type": "array" }, "featureName": { "description": "The name of the feature that was looked up.", "type": "string" }, "metricDetails": { "description": "The list of metricDetails objects.", "items": { "properties": { "ascending": { "description": "Should the metric be sorted in ascending order", "type": "boolean" }, "metricName": { "description": "Name of the metric", "type": "string" }, "supportsBinary": { "description": "Whether this metric is valid for binary classification.", "type": "boolean" }, "supportsMulticlass": { "description": "Whether this metric is valid for multiclass classification.", "type": "boolean" }, "supportsRegression": { "description": "This metric is valid for regression", "type": "boolean" }, "supportsTimeseries": { "description": "This metric is valid for timeseries", "type": "boolean" } }, "required": [ "ascending", "metricName", "supportsBinary", "supportsMulticlass", "supportsRegression", "supportsTimeseries" ], "type": "object" }, "type": "array" } }, "required": [ "availableMetrics", "featureName", "metricDetails" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | availableMetrics | [string] | true | | an array of strings representing the appropriate metrics. If the feature cannot be selected as the target, then this array will be empty. | | featureName | string | true | | The name of the feature that was looked up. | | metricDetails | [FeatureMetricDetailsResponse] | true | | The list of metricDetails objects. | ## FeatureTransform ``` { "properties": { "dateExtraction": { "description": "The value to extract from the date column, of these options: `[year|yearDay|month|monthDay|week|weekDay]`. Required for transformation of a date column. Otherwise must not be provided.", "enum": [ "year", "yearDay", "month", "monthDay", "week", "weekDay" ], "type": "string" }, "name": { "description": "The name of the new feature. Must not be the same as any existing features for this project. Must not contain '/' character.", "type": "string" }, "parentName": { "description": "The name of the parent feature.", "type": "string" }, "replacement": { "anyOf": [ { "type": [ "string", "null" ] }, { "type": [ "boolean", "null" ] }, { "type": [ "number", "null" ] }, { "type": [ "integer", "null" ] } ], "description": "The replacement in case of a failed transformation." }, "variableType": { "description": "The type of the new feature. Must be one of `text`, `categorical` (Deprecated in version v2.21), `numeric`, or `categoricalInt`. See the description of this method for more information.", "enum": [ "text", "categorical", "numeric", "categoricalInt" ], "type": "string" } }, "required": [ "name", "parentName", "variableType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dateExtraction | string | false | | The value to extract from the date column, of these options: [year\|yearDay\|month\|monthDay\|week\|weekDay]. Required for transformation of a date column. Otherwise must not be provided. | | name | string | true | | The name of the new feature. Must not be the same as any existing features for this project. Must not contain '/' character. | | parentName | string | true | | The name of the parent feature. | | replacement | any | false | | The replacement in case of a failed transformation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean,null | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number,null | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer,null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | variableType | string | true | | The type of the new feature. Must be one of text, categorical (Deprecated in version v2.21), numeric, or categoricalInt. See the description of this method for more information. | ### Enumerated Values | Property | Value | | --- | --- | | dateExtraction | [year, yearDay, month, monthDay, week, weekDay] | | variableType | [text, categorical, numeric, categoricalInt] | ## FeaturelistDestroyResponse ``` { "properties": { "canDelete": { "description": "Whether the featurelist can be deleted.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "deletionBlockedReason": { "description": "If the featurelist can't be deleted, this explains why.", "type": "string" }, "dryRun": { "description": "Whether this was a dry-run or the featurelist was actually deleted.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "numAffectedJobs": { "description": "Number of incomplete jobs using this featurelist.", "type": "integer" }, "numAffectedModels": { "description": "Number of models using this featurelist.", "type": "integer" } }, "required": [ "canDelete", "deletionBlockedReason", "dryRun", "numAffectedJobs", "numAffectedModels" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canDelete | string | true | | Whether the featurelist can be deleted. | | deletionBlockedReason | string | true | | If the featurelist can't be deleted, this explains why. | | dryRun | string | true | | Whether this was a dry-run or the featurelist was actually deleted. | | numAffectedJobs | integer | true | | Number of incomplete jobs using this featurelist. | | numAffectedModels | integer | true | | Number of models using this featurelist. | ### Enumerated Values | Property | Value | | --- | --- | | canDelete | [false, False, true, True] | | dryRun | [false, False, true, True] | ## FeaturelistListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of modeling features.", "items": { "properties": { "created": { "description": "A :ref:`timestamp ` string specifying when the featurelist was created.", "type": "string", "x-versionadded": "v2.13" }, "description": { "description": "User-friendly description of the featurelist, which can be updated by users.", "type": [ "string", "null" ], "x-versionadded": "v2.13" }, "features": { "description": "Names of features included in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Featurelist ID.", "type": "string" }, "isUserCreated": { "description": "Whether the featurelist was created manually by a user or by DataRobot automation.", "type": "boolean", "x-versionadded": "v2.13" }, "name": { "description": "The name of the featurelist.", "type": "string" }, "numModels": { "description": "The number of models that currently use this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist.", "type": "integer", "x-versionadded": "v2.13" }, "projectId": { "description": "The project ID the featurelist belongs to.", "type": "string" } }, "required": [ "created", "features", "id", "isUserCreated", "name", "numModels", "projectId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [FeaturelistResponse] | true | | An array of modeling features. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## FeaturelistResponse ``` { "properties": { "created": { "description": "A :ref:`timestamp ` string specifying when the featurelist was created.", "type": "string", "x-versionadded": "v2.13" }, "description": { "description": "User-friendly description of the featurelist, which can be updated by users.", "type": [ "string", "null" ], "x-versionadded": "v2.13" }, "features": { "description": "Names of features included in the featurelist.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "Featurelist ID.", "type": "string" }, "isUserCreated": { "description": "Whether the featurelist was created manually by a user or by DataRobot automation.", "type": "boolean", "x-versionadded": "v2.13" }, "name": { "description": "The name of the featurelist.", "type": "string" }, "numModels": { "description": "The number of models that currently use this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist.", "type": "integer", "x-versionadded": "v2.13" }, "projectId": { "description": "The project ID the featurelist belongs to.", "type": "string" } }, "required": [ "created", "features", "id", "isUserCreated", "name", "numModels", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string | true | | A :ref:timestamp string specifying when the featurelist was created. | | description | string,null | false | | User-friendly description of the featurelist, which can be updated by users. | | features | [string] | true | | Names of features included in the featurelist. | | id | string | true | | Featurelist ID. | | isUserCreated | boolean | true | | Whether the featurelist was created manually by a user or by DataRobot automation. | | name | string | true | | The name of the featurelist. | | numModels | integer | true | | The number of models that currently use this featurelist. A model is considered to use a featurelist if it is used to train the model or as a monotonic constraint featurelist, or if the model is a blender with at least one component model using the featurelist. | | projectId | string | true | | The project ID the featurelist belongs to. | ## FormattedSummary ``` { "description": "Relationship quality assessment report associated with the relationship", "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate" ], "type": "object" } ``` Relationship quality assessment report associated with the relationship ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enrichmentRate | Warnings | true | | Warning about the enrichment rate | | mostRecentData | Warnings | false | | Warning about the enrichment rate | | windowSettings | Warnings | false | | Warning about the enrichment rate | ## ImageBinsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of image metadata, as described below", "items": { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "targetBinEnd": { "description": "Target value for bin end for regression, null for classification", "type": [ "integer", "null" ] }, "targetBinRowCount": { "description": "Number of rows in this target bin", "type": "integer" }, "targetBinStart": { "description": "Target value for bin start for regression, null for classification", "type": [ "integer", "null" ] }, "targetValue": { "description": "Target value", "type": "number" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "targetBinRowCount", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ImageMetadataWithBins] | true | | List of image metadata, as described below | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ImageEmbedding ``` { "properties": { "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile].", "type": "string" }, "positionX": { "description": "x coordinate of the image in the embedding space.", "type": "number" }, "positionY": { "description": "y coordinate of the image in the embedding space.", "type": "number" }, "prediction": { "description": "Object that describes prediction value of the dataset row.", "properties": { "labels": { "description": "List of predicted label names corresponding to values.", "items": { "description": "Predicted label", "type": "string" }, "type": "array" }, "values": { "description": "Predicted value or probability of the class identified by the label.", "items": { "oneOf": [ { "type": "number" }, { "items": { "type": "number" }, "type": "array" } ] }, "type": "array" } }, "required": [ "labels", "values" ], "type": "object" } }, "required": [ "actualTargetValue", "imageId", "positionX", "positionY", "prediction" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualTargetValue | any | true | | Actual target value of the dataset row | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | imageId | string | true | | Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]. | | positionX | number | true | | x coordinate of the image in the embedding space. | | positionY | number | true | | y coordinate of the image in the embedding space. | | prediction | InsightsPredictionField | true | | Object that describes prediction value of the dataset row. | ## ImageEmbeddingsComputeResponse ``` { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if the job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "jobType": { "description": "The type of the job.", "enum": [ "compute_image_embeddings" ], "type": "string" }, "message": { "description": "Error message in case of failure.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The job status.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" }, "url": { "description": "A URL that can be used to request details about the job.", "type": "string" } }, "required": [ "id", "isBlocked", "jobType", "message", "modelId", "projectId", "status", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The job ID. | | isBlocked | boolean | true | | True if the job is waiting for its dependencies to be resolved first. | | jobType | string | true | | The type of the job. | | message | string | true | | Error message in case of failure. | | modelId | string | true | | The model ID of the target model. | | projectId | string | true | | The project the job belongs to. | | status | string | true | | The job status. | | url | string | true | | A URL that can be used to request details about the job. | ### Enumerated Values | Property | Value | | --- | --- | | jobType | compute_image_embeddings | | status | [queue, inprogress, error, ABORTED, COMPLETED] | ## ImageInsightsMetadataElement ``` { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" } }, "required": [ "featureName", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | true | | The name of the feature. | | modelId | string | true | | The model ID of the target model. | ## ImageMetadataListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of image metadata elements", "items": { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "targetValue": { "description": "Target value", "type": "number" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "width" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ImageMetadataResponse] | true | | List of image metadata elements | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ImageMetadataResponse ``` { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "targetValue": { "description": "Target value", "type": "number" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "width" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | height | integer | true | | Height of the image in pixels | | imageId | string | true | | Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile] | | targetValue | number | false | | Target value | | width | integer | true | | Width of the image in pixels | ## ImageMetadataWithBins ``` { "properties": { "height": { "description": "Height of the image in pixels", "type": "integer" }, "imageId": { "description": "Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile]", "type": "string" }, "targetBinEnd": { "description": "Target value for bin end for regression, null for classification", "type": [ "integer", "null" ] }, "targetBinRowCount": { "description": "Number of rows in this target bin", "type": "integer" }, "targetBinStart": { "description": "Target value for bin start for regression, null for classification", "type": [ "integer", "null" ] }, "targetValue": { "description": "Target value", "type": "number" }, "width": { "description": "Width of the image in pixels", "type": "integer" } }, "required": [ "height", "imageId", "targetBinRowCount", "width" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | height | integer | true | | Height of the image in pixels | | imageId | string | true | | Id of the image. The actual image file can be retrieved with [GET /api/v2/projects/{projectId}/images/{imageId}/file/][get-apiv2projectsprojectidimagesimageidfile] | | targetBinEnd | integer,null | false | | Target value for bin end for regression, null for classification | | targetBinRowCount | integer | true | | Number of rows in this target bin | | targetBinStart | integer,null | false | | Target value for bin start for regression, null for classification | | targetValue | number | false | | Target value | | width | integer | true | | Width of the image in pixels | ## ImagesDataQualityLogLinesResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The content in the form of lines of the images data quality log", "items": { "description": "Log lines", "type": "string" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [string] | true | | The content in the form of lines of the images data quality log | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## InsightsPredictionField ``` { "description": "Object that describes prediction value of the dataset row.", "properties": { "labels": { "description": "List of predicted label names corresponding to values.", "items": { "description": "Predicted label", "type": "string" }, "type": "array" }, "values": { "description": "Predicted value or probability of the class identified by the label.", "items": { "oneOf": [ { "type": "number" }, { "items": { "type": "number" }, "type": "array" } ] }, "type": "array" } }, "required": [ "labels", "values" ], "type": "object" } ``` Object that describes prediction value of the dataset row. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | labels | [string] | true | | List of predicted label names corresponding to values. | | values | [oneOf] | true | | Predicted value or probability of the class identified by the label. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [number] | false | | none | ## ModelingFeatureListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Modeling features data.", "items": { "properties": { "dataQualities": { "description": "Data Quality Status", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string" }, "dateFormat": { "description": "the date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ], "x-versionadded": "v2.5" }, "featureLineageId": { "description": "The ID of the lineage for automatically generated features.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string" }, "importance": { "description": "numeric measure of the strength of relationship between the feature and target (independent of any model or other features)", "type": [ "number", "null" ] }, "isRestoredAfterReduction": { "description": "Whether feature is restored after feature reduction", "type": "boolean", "x-versionadded": "v2.26" }, "isZeroInflated": { "description": "Whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ] }, "language": { "description": "Feature's detected language.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "whether feature has too few values to be informative", "type": "boolean" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "max": { "description": "maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "mean": { "description": "arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "median": { "description": "median of the EDA sample of the feature.", "oneOf": [ { "description": "median of the EDA sample of the feature.", "type": "string" }, { "description": "median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "min": { "description": "minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "multilabelInsights": { "description": "Multilabel project specific information", "properties": { "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The response will contain the key for the most recent, finished EDA stage.", "type": "string" } }, "required": [ "multilabelInsightsKey" ], "type": "object" }, "naCount": { "description": "Number of missing values.", "type": "integer" }, "name": { "description": "The feature name.", "type": "string" }, "parentFeatureNames": { "description": "an array of string feature names indicating which features in the input data were used to create this feature if the feature is a transformation.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "The ID of the project the feature belongs to.", "type": "string" }, "stdDev": { "description": "standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "targetLeakage": { "description": "the detected level of risk for target leakage, if any. 'SKIPPED_DETECTION' indicates leakage detection was not run on the feature, 'FALSE' indicates no leakage, 'MODERATE_RISK' indicates a moderate risk of target leakage, and 'HIGH_RISK' indicates a high risk of target leakage.", "enum": [ "FALSE", "HIGH_RISK", "MODERATE_RISK", "SKIPPED_DETECTION" ], "type": "string" }, "targetLeakageReason": { "description": "descriptive sentence explaining the reason for target leakage.", "type": "string", "x-versionadded": "v2.20" }, "uniqueCount": { "description": "number of unique values", "type": "integer" }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "dateFormat", "featureLineageId", "featureType", "importance", "lowInformation", "lowerQuartile", "max", "mean", "median", "min", "naCount", "name", "projectId", "stdDev", "targetLeakage", "targetLeakageReason", "upperQuartile" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ModelingFeatureResponse] | true | | Modeling features data. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ModelingFeatureResponse ``` { "properties": { "dataQualities": { "description": "Data Quality Status", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string" }, "dateFormat": { "description": "the date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ], "x-versionadded": "v2.5" }, "featureLineageId": { "description": "The ID of the lineage for automatically generated features.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string" }, "importance": { "description": "numeric measure of the strength of relationship between the feature and target (independent of any model or other features)", "type": [ "number", "null" ] }, "isRestoredAfterReduction": { "description": "Whether feature is restored after feature reduction", "type": "boolean", "x-versionadded": "v2.26" }, "isZeroInflated": { "description": "Whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ] }, "language": { "description": "Feature's detected language.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "whether feature has too few values to be informative", "type": "boolean" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "max": { "description": "maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "mean": { "description": "arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "median": { "description": "median of the EDA sample of the feature.", "oneOf": [ { "description": "median of the EDA sample of the feature.", "type": "string" }, { "description": "median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "min": { "description": "minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "multilabelInsights": { "description": "Multilabel project specific information", "properties": { "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The response will contain the key for the most recent, finished EDA stage.", "type": "string" } }, "required": [ "multilabelInsightsKey" ], "type": "object" }, "naCount": { "description": "Number of missing values.", "type": "integer" }, "name": { "description": "The feature name.", "type": "string" }, "parentFeatureNames": { "description": "an array of string feature names indicating which features in the input data were used to create this feature if the feature is a transformation.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "The ID of the project the feature belongs to.", "type": "string" }, "stdDev": { "description": "standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "targetLeakage": { "description": "the detected level of risk for target leakage, if any. 'SKIPPED_DETECTION' indicates leakage detection was not run on the feature, 'FALSE' indicates no leakage, 'MODERATE_RISK' indicates a moderate risk of target leakage, and 'HIGH_RISK' indicates a high risk of target leakage.", "enum": [ "FALSE", "HIGH_RISK", "MODERATE_RISK", "SKIPPED_DETECTION" ], "type": "string" }, "targetLeakageReason": { "description": "descriptive sentence explaining the reason for target leakage.", "type": "string", "x-versionadded": "v2.20" }, "uniqueCount": { "description": "number of unique values", "type": "integer" }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "dateFormat", "featureLineageId", "featureType", "importance", "lowInformation", "lowerQuartile", "max", "mean", "median", "min", "naCount", "name", "projectId", "stdDev", "targetLeakage", "targetLeakageReason", "upperQuartile" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataQualities | string | false | | Data Quality Status | | dateFormat | string,null | true | | the date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime . | | featureLineageId | string,null | true | | The ID of the lineage for automatically generated features. | | featureType | string | true | | Feature type. | | importance | number,null | true | | numeric measure of the strength of relationship between the feature and target (independent of any model or other features) | | isRestoredAfterReduction | boolean | false | | Whether feature is restored after feature reduction | | isZeroInflated | boolean,null | false | | Whether feature has an excessive number of zeros | | keySummary | any | false | | Per key summaries for Summarized Categorical or Multicategorical columns | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FeatureKeySummaryResponseValidatorSummarizedCategorical | false | | For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [FeatureKeySummaryResponseValidatorMultilabel] | false | | For a Multicategorical columns, this will contain statistics for the top classes | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | language | string | false | | Feature's detected language. | | lowInformation | boolean | true | | whether feature has too few values to be informative | | lowerQuartile | any | true | | Lower quartile point of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Lower quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Lower quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | max | any | true | | maximum value of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | maximum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | maximum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | mean | any | true | | arithmetic mean of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | arithmetic mean of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | arithmetic mean of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | median | any | true | | median of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | median of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | median of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | min | any | true | | minimum value of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | minimum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | minimum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | multilabelInsights | MultilabelInsightsResponse | false | | Multilabel project specific information | | naCount | integer | true | | Number of missing values. | | name | string | true | | The feature name. | | parentFeatureNames | [string] | false | | an array of string feature names indicating which features in the input data were used to create this feature if the feature is a transformation. | | projectId | string | true | | The ID of the project the feature belongs to. | | stdDev | any | true | | standard deviation of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | standard deviation of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | standard deviation of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetLeakage | string | true | | the detected level of risk for target leakage, if any. 'SKIPPED_DETECTION' indicates leakage detection was not run on the feature, 'FALSE' indicates no leakage, 'MODERATE_RISK' indicates a moderate risk of target leakage, and 'HIGH_RISK' indicates a high risk of target leakage. | | targetLeakageReason | string | true | | descriptive sentence explaining the reason for target leakage. | | uniqueCount | integer | false | | number of unique values | | upperQuartile | any | true | | Upper quartile point of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Upper quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Upper quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | dataQualities | [ISSUES_FOUND, NOT_ANALYZED, NO_ISSUES_FOUND] | | featureType | [Boolean, Categorical, Currency, Date, Date Duration, Document, Image, Interaction, Length, Location, Multicategorical, Numeric, Percentage, Summarized Categorical, Text, Time] | | targetLeakage | [FALSE, HIGH_RISK, MODERATE_RISK, SKIPPED_DETECTION] | ## ModelingFeaturesCreateFromDiscarded ``` { "properties": { "featuresToRestore": { "description": "Discarded features to restore.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "featuresToRestore" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featuresToRestore | [string] | true | minItems: 1 | Discarded features to restore. | ## ModelingFeaturesCreateFromDiscardedResponse ``` { "properties": { "featuresToRestore": { "description": "Features to add back to the project.", "items": { "type": "string" }, "type": "array" }, "warnings": { "description": "Warnings about features which can not be restored.", "items": { "type": "string" }, "type": "array" } }, "required": [ "featuresToRestore", "warnings" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featuresToRestore | [string] | true | | Features to add back to the project. | | warnings | [string] | true | | Warnings about features which can not be restored. | ## MultilabelInsightsResponse ``` { "description": "Multilabel project specific information", "properties": { "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The response will contain the key for the most recent, finished EDA stage.", "type": "string" } }, "required": [ "multilabelInsightsKey" ], "type": "object" } ``` Multilabel project specific information ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | multilabelInsightsKey | string | true | | Key for multilabel insights, unique per project, feature, and EDA stage. The response will contain the key for the most recent, finished EDA stage. | ## MultiseriesIdColumnsRecord ``` { "description": "The detected multiseries ID columns along with timeStep and timeUnit information.", "properties": { "multiseriesIdColumns": { "description": "The list of one or more names of columns that contain the individual series identifiers if the dataset consists of multiple time series.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "timeStep": { "description": "The detected time step.", "type": "integer" }, "timeUnit": { "description": "The detected time unit (e.g., DAY, HOUR, etc.).", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "multiseriesIdColumns", "timeStep", "timeUnit" ], "type": "object" } ``` The detected multiseries ID columns along with timeStep and timeUnit information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | multiseriesIdColumns | [string] | true | minItems: 1 | The list of one or more names of columns that contain the individual series identifiers if the dataset consists of multiple time series. | | timeStep | integer | true | | The detected time step. | | timeUnit | string | true | | The detected time unit (e.g., DAY, HOUR, etc.). | ### Enumerated Values | Property | Value | | --- | --- | | timeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] | ## MultiseriesRetrieveResponse ``` { "properties": { "datetimePartitionColumn": { "description": "The datetime partition column name.", "type": "string" }, "detectedMultiseriesIdColumns": { "description": "The list of detected multiseries ID columns along with timeStep and timeUnit information. Note that if no eligible columns have been detected, this list will be empty.", "items": { "description": "The detected multiseries ID columns along with timeStep and timeUnit information.", "properties": { "multiseriesIdColumns": { "description": "The list of one or more names of columns that contain the individual series identifiers if the dataset consists of multiple time series.", "items": { "type": "string" }, "minItems": 1, "type": "array" }, "timeStep": { "description": "The detected time step.", "type": "integer" }, "timeUnit": { "description": "The detected time unit (e.g., DAY, HOUR, etc.).", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "multiseriesIdColumns", "timeStep", "timeUnit" ], "type": "object" }, "type": "array" } }, "required": [ "datetimePartitionColumn", "detectedMultiseriesIdColumns" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimePartitionColumn | string | true | | The datetime partition column name. | | detectedMultiseriesIdColumns | [MultiseriesIdColumnsRecord] | true | | The list of detected multiseries ID columns along with timeStep and timeUnit information. Note that if no eligible columns have been detected, this list will be empty. | ## OCRJobCreationRequest ``` { "properties": { "datasetId": { "description": "The OCR input dataset ID.", "type": "string" }, "engineSpecificParameters": { "description": "The OCR engine-specific parameters.", "discriminator": { "propertyName": "engineType" }, "oneOf": [ { "properties": { "engineType": { "description": "The Aryn OCR engine.", "enum": [ "ARYN" ], "type": "string" }, "outputFormat": { "default": "JSON", "description": "The output format of Aryn OCR.", "enum": [ "JSON", "MARKDOWN" ], "type": "string" } }, "required": [ "engineType", "outputFormat" ], "type": "object" }, { "properties": { "engineType": { "description": "The Tesseract OCR engine.", "enum": [ "TESSERACT" ], "type": "string" } }, "required": [ "engineType" ], "type": "object" } ], "x-versionadded": "v2.38" }, "language": { "description": "The supported OCR dataset language.", "enum": [ "ENGLISH", "JAPANESE" ], "type": "string" } }, "required": [ "datasetId", "language" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The OCR input dataset ID. | | engineSpecificParameters | OneOfEngineSpecificParametersInJobCreationRequest | false | | The OCR engine-specific parameters. | | language | string | true | | The supported OCR dataset language. | ### Enumerated Values | Property | Value | | --- | --- | | language | [ENGLISH, JAPANESE] | ## OCRJobErrorReportPutRequest ``` { "properties": { "file": { "description": "The file to be uploaded", "format": "binary", "type": "string" } }, "required": [ "file" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | file | string(binary) | true | | The file to be uploaded | ## OCRJobPostPayload ``` { "type": "object", "x-versionadded": "v2.35" } ``` ### Properties None ## OCRJobPostResponse ``` { "properties": { "errorReportLocation": { "description": "The URL to retrieve the OCR job error report.", "format": "uri", "type": "string" }, "jobStatusLocation": { "description": "The URL to retrieve the OCR job status.", "format": "uri", "type": "string" }, "outputLocation": { "description": "The URL to retrieve the OCR job output.", "format": "uri", "type": "string" } }, "required": [ "errorReportLocation", "jobStatusLocation", "outputLocation" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorReportLocation | string(uri) | true | | The URL to retrieve the OCR job error report. | | jobStatusLocation | string(uri) | true | | The URL to retrieve the OCR job status. | | outputLocation | string(uri) | true | | The URL to retrieve the OCR job output. | ## OCRJobResourceListResponse ``` { "properties": { "count": { "description": "The number of records on this page.", "type": "integer" }, "data": { "description": "The list of OCR job resources.", "items": { "properties": { "engineSpecificParameters": { "description": "The OCR engine-specific parameters.", "discriminator": { "propertyName": "engineType" }, "oneOf": [ { "properties": { "engineType": { "description": "The Aryn OCR engine.", "enum": [ "ARYN" ], "type": "string" }, "outputFormat": { "default": "JSON", "description": "The output format of Aryn OCR.", "enum": [ "JSON", "MARKDOWN" ], "type": "string" } }, "required": [ "engineType", "outputFormat" ], "type": "object" }, { "properties": { "engineType": { "description": "The Tesseract OCR engine.", "enum": [ "TESSERACT" ], "type": "string" } }, "required": [ "engineType" ], "type": "object" } ], "x-versionadded": "v2.38" }, "id": { "description": "The OCR job resource ID.", "type": "string" }, "inputCatalogId": { "description": "The OCR input dataset catalog ID.", "type": "string" }, "jobStarted": { "description": "Whether the job has started.", "type": "boolean" }, "language": { "description": "The supported OCR dataset language.", "enum": [ "ENGLISH", "JAPANESE" ], "type": "string" }, "outputCatalogId": { "description": "The OCR output dataset catalog ID.", "type": "string" }, "userId": { "description": "The user ID.", "type": "string" } }, "required": [ "id", "inputCatalogId", "jobStarted", "language", "outputCatalogId", "userId" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL to the next page, or `null` if there is no such page.", "type": [ "string", "null" ] }, "previous": { "description": "URL to the previous page, or `null` if there is no such page.", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [OCRJobResourceResponse] | true | maxItems: 1000 | The list of OCR job resources. | | next | string,null | true | | URL to the next page, or null if there is no such page. | | previous | string,null | true | | URL to the previous page, or null if there is no such page. | ## OCRJobResourceResponse ``` { "properties": { "engineSpecificParameters": { "description": "The OCR engine-specific parameters.", "discriminator": { "propertyName": "engineType" }, "oneOf": [ { "properties": { "engineType": { "description": "The Aryn OCR engine.", "enum": [ "ARYN" ], "type": "string" }, "outputFormat": { "default": "JSON", "description": "The output format of Aryn OCR.", "enum": [ "JSON", "MARKDOWN" ], "type": "string" } }, "required": [ "engineType", "outputFormat" ], "type": "object" }, { "properties": { "engineType": { "description": "The Tesseract OCR engine.", "enum": [ "TESSERACT" ], "type": "string" } }, "required": [ "engineType" ], "type": "object" } ], "x-versionadded": "v2.38" }, "id": { "description": "The OCR job resource ID.", "type": "string" }, "inputCatalogId": { "description": "The OCR input dataset catalog ID.", "type": "string" }, "jobStarted": { "description": "Whether the job has started.", "type": "boolean" }, "language": { "description": "The supported OCR dataset language.", "enum": [ "ENGLISH", "JAPANESE" ], "type": "string" }, "outputCatalogId": { "description": "The OCR output dataset catalog ID.", "type": "string" }, "userId": { "description": "The user ID.", "type": "string" } }, "required": [ "id", "inputCatalogId", "jobStarted", "language", "outputCatalogId", "userId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | engineSpecificParameters | OneOfEngineSpecificParametersInJobResourceResponse | false | | The OCR engine-specific parameters. | | id | string | true | | The OCR job resource ID. | | inputCatalogId | string | true | | The OCR input dataset catalog ID. | | jobStarted | boolean | true | | Whether the job has started. | | language | string | true | | The supported OCR dataset language. | | outputCatalogId | string | true | | The OCR output dataset catalog ID. | | userId | string | true | | The user ID. | ### Enumerated Values | Property | Value | | --- | --- | | language | [ENGLISH, JAPANESE] | ## OCRJobStatusesResponse ``` { "properties": { "jobStatus": { "description": "The status of general progress of OCR job resource creation.", "enum": [ "executing", "failure", "pending", "stopped", "success", "unknown" ], "type": "string" } }, "required": [ "jobStatus" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jobStatus | string | true | | The status of general progress of OCR job resource creation. | ### Enumerated Values | Property | Value | | --- | --- | | jobStatus | [executing, failure, pending, stopped, success, unknown] | ## OneOfEngineSpecificParametersInJobCreationRequest ``` { "description": "The OCR engine-specific parameters.", "discriminator": { "propertyName": "engineType" }, "oneOf": [ { "properties": { "engineType": { "description": "The Aryn OCR engine.", "enum": [ "ARYN" ], "type": "string" }, "outputFormat": { "default": "JSON", "description": "The output format of Aryn OCR.", "enum": [ "JSON", "MARKDOWN" ], "type": "string" } }, "required": [ "engineType", "outputFormat" ], "type": "object" }, { "properties": { "engineType": { "description": "The Tesseract OCR engine.", "enum": [ "TESSERACT" ], "type": "string" } }, "required": [ "engineType" ], "type": "object" } ], "x-versionadded": "v2.38" } ``` The OCR engine-specific parameters. ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » engineType | string | true | | The Aryn OCR engine. | | » outputFormat | string | true | | The output format of Aryn OCR. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » engineType | string | true | | The Tesseract OCR engine. | ### Enumerated Values | Property | Value | | --- | --- | | engineType | ARYN | | outputFormat | [JSON, MARKDOWN] | | engineType | TESSERACT | ## OneOfEngineSpecificParametersInJobResourceResponse ``` { "description": "The OCR engine-specific parameters.", "discriminator": { "propertyName": "engineType" }, "oneOf": [ { "properties": { "engineType": { "description": "The Aryn OCR engine.", "enum": [ "ARYN" ], "type": "string" }, "outputFormat": { "default": "JSON", "description": "The output format of Aryn OCR.", "enum": [ "JSON", "MARKDOWN" ], "type": "string" } }, "required": [ "engineType", "outputFormat" ], "type": "object" }, { "properties": { "engineType": { "description": "The Tesseract OCR engine.", "enum": [ "TESSERACT" ], "type": "string" } }, "required": [ "engineType" ], "type": "object" } ], "x-versionadded": "v2.38" } ``` The OCR engine-specific parameters. ### Properties oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » engineType | string | true | | The Aryn OCR engine. | | » outputFormat | string | true | | The output format of Aryn OCR. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | object | false | | none | | » engineType | string | true | | The Tesseract OCR engine. | ### Enumerated Values | Property | Value | | --- | --- | | engineType | ARYN | | outputFormat | [JSON, MARKDOWN] | | engineType | TESSERACT | ## PreloadedCalendar ``` { "properties": { "countryCode": { "description": "Code of the country for which holidays should be generated. Code needs to be uppercase and should belong to the list of countries which can be retrieved via [GET /api/v2/calendarCountryCodes/][get-apiv2calendarcountrycodes]", "enum": [ "AR", "AT", "AU", "AW", "BE", "BG", "BR", "BY", "CA", "CH", "CL", "CO", "CZ", "DE", "DK", "DO", "EE", "ES", "FI", "FRA", "GB", "HK", "HND", "HR", "HU", "IE", "IND", "IS", "IT", "JP", "KE", "LT", "LU", "MX", "NG", "NI", "NL", "NO", "NZ", "PE", "PL", "PT", "RU", "SE", "SE(NS)", "SI", "SK", "TAR", "UA", "UK", "US", "ZA" ], "type": "string" }, "endDate": { "description": "Last date of the range of dates for which holidays are generated.", "format": "date-time", "type": "string" }, "startDate": { "description": "First date of the range of dates for which holidays are generated.", "format": "date-time", "type": "string" } }, "required": [ "countryCode", "endDate", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | countryCode | string | true | | Code of the country for which holidays should be generated. Code needs to be uppercase and should belong to the list of countries which can be retrieved via [GET /api/v2/calendarCountryCodes/][get-apiv2calendarcountrycodes] | | endDate | string(date-time) | true | | Last date of the range of dates for which holidays are generated. | | startDate | string(date-time) | true | | First date of the range of dates for which holidays are generated. | ### Enumerated Values | Property | Value | | --- | --- | | countryCode | [AR, AT, AU, AW, BE, BG, BR, BY, CA, CH, CL, CO, CZ, DE, DK, DO, EE, ES, FI, FRA, GB, HK, HND, HR, HU, IE, IND, IS, IT, JP, KE, LT, LU, MX, NG, NI, NL, NO, NZ, PE, PL, PT, RU, SE, SE(NS), SI, SK, TAR, UA, UK, US, ZA] | ## PreloadedCalendarCountryCodeRecord ``` { "description": "Each item has the country code and the full name of the corresponding country", "properties": { "code": { "description": "Country code that can be used for calendars generation.", "type": "string" }, "name": { "description": "Full name of the country.", "type": "string" } }, "required": [ "code", "name" ], "type": "object" } ``` Each item has the country code and the full name of the corresponding country ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | code | string | true | | Country code that can be used for calendars generation. | | name | string | true | | Full name of the country. | ## PreloadedCalendarListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page considering offset and limit values.", "minimum": 0, "type": "integer" }, "data": { "description": "An array of dictionaries which contain country code and full name of the countries corresponding to codes.", "items": { "description": "Each item has the country code and the full name of the corresponding country", "properties": { "code": { "description": "Country code that can be used for calendars generation.", "type": "string" }, "name": { "description": "Full name of the country.", "type": "string" } }, "required": [ "code", "name" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | The number of items returned on this page considering offset and limit values. | | data | [PreloadedCalendarCountryCodeRecord] | true | | An array of dictionaries which contain country code and full name of the countries corresponding to codes. | | next | string,null | true | | A URL pointing to the next page (if null, there is no next page). | | previous | string,null | true | | A URL pointing to the previous page (if null, there is no previous page). | ## ProjectFeatureResponse ``` { "properties": { "dataQualities": { "description": "Data Quality Status", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string" }, "dateFormat": { "description": "the date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime .", "type": [ "string", "null" ], "x-versionadded": "v2.5" }, "featureLineageId": { "description": "The ID of the lineage for automatically generated features.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "featureType": { "description": "Feature type.", "enum": [ "Boolean", "Categorical", "Currency", "Date", "Date Duration", "Document", "Image", "Interaction", "Length", "Location", "Multicategorical", "Numeric", "Percentage", "Summarized Categorical", "Text", "Time" ], "type": "string" }, "id": { "description": "the feature ID. (Note: Throughout the API, features are specified using their names, not this ID.)", "type": "integer" }, "importance": { "description": "numeric measure of the strength of relationship between the feature and target (independent of any model or other features)", "type": [ "number", "null" ] }, "isRestoredAfterReduction": { "description": "Whether feature is restored after feature reduction", "type": "boolean", "x-versionadded": "v2.26" }, "isZeroInflated": { "description": "Whether feature has an excessive number of zeros", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "keySummary": { "description": "Per key summaries for Summarized Categorical or Multicategorical columns", "oneOf": [ { "description": "For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters)", "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "dataQualities": { "description": "The indicator of data quality assessment of the feature.", "enum": [ "ISSUES_FOUND", "NOT_ANALYZED", "NO_ISSUES_FOUND" ], "type": "string", "x-versionadded": "v2.20" }, "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "dataQualities", "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, { "description": "For a Multicategorical columns, this will contain statistics for the top classes", "items": { "properties": { "key": { "description": "Name of the key.", "type": "string" }, "summary": { "description": "Statistics of the key.", "properties": { "max": { "description": "Maximum value of the key.", "type": "number" }, "mean": { "description": "Mean value of the key.", "type": "number" }, "median": { "description": "Median value of the key.", "type": "number" }, "min": { "description": "Minimum value of the key.", "type": "number" }, "pctRows": { "description": "Percentage occurrence of key in the EDA sample of the feature.", "type": "number" }, "stdDev": { "description": "Standard deviation of the key.", "type": "number" } }, "required": [ "max", "mean", "median", "min", "pctRows", "stdDev" ], "type": "object" } }, "required": [ "key", "summary" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } ] }, "language": { "description": "Feature's detected language.", "type": "string", "x-versionadded": "v2.32" }, "lowInformation": { "description": "whether feature has too few values to be informative", "type": "boolean" }, "lowerQuartile": { "description": "Lower quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Lower quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Lower quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" }, "max": { "description": "maximum value of the EDA sample of the feature.", "oneOf": [ { "description": "maximum value of the EDA sample of the feature.", "type": "string" }, { "description": "maximum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "mean": { "description": "arithmetic mean of the EDA sample of the feature.", "oneOf": [ { "description": "arithmetic mean of the EDA sample of the feature.", "type": "string" }, { "description": "arithmetic mean of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "median": { "description": "median of the EDA sample of the feature.", "oneOf": [ { "description": "median of the EDA sample of the feature.", "type": "string" }, { "description": "median of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "min": { "description": "minimum value of the EDA sample of the feature.", "oneOf": [ { "description": "minimum value of the EDA sample of the feature.", "type": "string" }, { "description": "minimum value of the EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "multilabelInsights": { "description": "Multilabel project specific information", "properties": { "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The response will contain the key for the most recent, finished EDA stage.", "type": "string" } }, "required": [ "multilabelInsightsKey" ], "type": "object" }, "naCount": { "description": "Number of missing values.", "type": "integer" }, "name": { "description": "The feature name.", "type": "string" }, "parentFeatureNames": { "description": "an array of string feature names indicating which features in the input data were used to create this feature if the feature is a transformation.", "items": { "type": "string" }, "type": "array" }, "projectId": { "description": "The ID of the project the feature belongs to.", "type": "string" }, "stdDev": { "description": "standard deviation of EDA sample of the feature.", "oneOf": [ { "description": "standard deviation of EDA sample of the feature.", "type": "string" }, { "description": "standard deviation of EDA sample of the feature.", "type": "number" }, { "type": "null" } ] }, "targetLeakage": { "description": "the detected level of risk for target leakage, if any. 'SKIPPED_DETECTION' indicates leakage detection was not run on the feature, 'FALSE' indicates no leakage, 'MODERATE_RISK' indicates a moderate risk of target leakage, and 'HIGH_RISK' indicates a high risk of target leakage.", "enum": [ "FALSE", "HIGH_RISK", "MODERATE_RISK", "SKIPPED_DETECTION" ], "type": "string" }, "targetLeakageReason": { "description": "descriptive sentence explaining the reason for target leakage.", "type": "string", "x-versionadded": "v2.20" }, "timeSeriesEligibilityReason": { "description": "why the feature is ineligible for time series projects, or 'suitable' if it is eligible.", "type": "string", "x-versionadded": "v2.8" }, "timeSeriesEligible": { "description": "whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.", "type": "boolean", "x-versionadded": "v2.8" }, "timeStep": { "description": "The minimum time step that can be used to specify time series windows. The units for this value are the ``timeUnit``. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.", "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "timeUnit": { "description": "the unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "uniqueCount": { "description": "number of unique values", "type": "integer" }, "upperQuartile": { "description": "Upper quartile point of EDA sample of the feature.", "oneOf": [ { "description": "Upper quartile point of EDA sample of the feature.", "type": "string" }, { "description": "Upper quartile point of EDA sample of the feature.", "type": "number" }, { "type": "null" } ], "x-versionadded": "v2.35" } }, "required": [ "dateFormat", "featureLineageId", "featureType", "id", "importance", "lowInformation", "lowerQuartile", "max", "mean", "median", "min", "naCount", "name", "projectId", "stdDev", "targetLeakage", "targetLeakageReason", "timeSeriesEligibilityReason", "timeSeriesEligible", "timeStep", "timeUnit", "upperQuartile" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataQualities | string | false | | Data Quality Status | | dateFormat | string,null | true | | the date format string for how this feature was interpreted (or null if not a date feature). If not null, it will be compatible with https://docs.python.org/2/library/time.html#time.strftime . | | featureLineageId | string,null | true | | The ID of the lineage for automatically generated features. | | featureType | string | true | | Feature type. | | id | integer | true | | the feature ID. (Note: Throughout the API, features are specified using their names, not this ID.) | | importance | number,null | true | | numeric measure of the strength of relationship between the feature and target (independent of any model or other features) | | isRestoredAfterReduction | boolean | false | | Whether feature is restored after feature reduction | | isZeroInflated | boolean,null | false | | Whether feature has an excessive number of zeros | | keySummary | any | false | | Per key summaries for Summarized Categorical or Multicategorical columns | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FeatureKeySummaryResponseValidatorSummarizedCategorical | false | | For a Summarized Categorical columns, this will contain statistics for the top 50 keys (truncated to 103 characters) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [FeatureKeySummaryResponseValidatorMultilabel] | false | | For a Multicategorical columns, this will contain statistics for the top classes | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | language | string | false | | Feature's detected language. | | lowInformation | boolean | true | | whether feature has too few values to be informative | | lowerQuartile | any | true | | Lower quartile point of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Lower quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Lower quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | max | any | true | | maximum value of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | maximum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | maximum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | mean | any | true | | arithmetic mean of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | arithmetic mean of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | arithmetic mean of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | median | any | true | | median of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | median of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | median of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | min | any | true | | minimum value of the EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | minimum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | minimum value of the EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | multilabelInsights | MultilabelInsightsResponse | false | | Multilabel project specific information | | naCount | integer | true | | Number of missing values. | | name | string | true | | The feature name. | | parentFeatureNames | [string] | false | | an array of string feature names indicating which features in the input data were used to create this feature if the feature is a transformation. | | projectId | string | true | | The ID of the project the feature belongs to. | | stdDev | any | true | | standard deviation of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | standard deviation of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | standard deviation of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetLeakage | string | true | | the detected level of risk for target leakage, if any. 'SKIPPED_DETECTION' indicates leakage detection was not run on the feature, 'FALSE' indicates no leakage, 'MODERATE_RISK' indicates a moderate risk of target leakage, and 'HIGH_RISK' indicates a high risk of target leakage. | | targetLeakageReason | string | true | | descriptive sentence explaining the reason for target leakage. | | timeSeriesEligibilityReason | string | true | | why the feature is ineligible for time series projects, or 'suitable' if it is eligible. | | timeSeriesEligible | boolean | true | | whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements. | | timeStep | integer,null | true | | The minimum time step that can be used to specify time series windows. The units for this value are the timeUnit. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise. | | timeUnit | string,null | true | | the unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise. | | uniqueCount | integer | false | | number of unique values | | upperQuartile | any | true | | Upper quartile point of EDA sample of the feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Upper quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | Upper quartile point of EDA sample of the feature. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | dataQualities | [ISSUES_FOUND, NOT_ANALYZED, NO_ISSUES_FOUND] | | featureType | [Boolean, Categorical, Currency, Date, Date Duration, Document, Image, Interaction, Length, Location, Multicategorical, Numeric, Percentage, Summarized Categorical, Text, Time] | | targetLeakage | [FALSE, HIGH_RISK, MODERATE_RISK, SKIPPED_DETECTION] | ## ProjectSecondaryDatasetConfigResponse ``` { "properties": { "config": { "description": "Graph-specific secondary datasets. Deprecated in version v2.23.", "items": { "properties": { "featureEngineeringGraphId": { "description": "Id of the feature engineering graph", "type": "string" }, "secondaryDatasets": { "description": "list of secondary datasets used by the feature engineering graph", "items": { "properties": { "catalogId": { "description": "Id of the catalog item version.", "type": "string" }, "catalogVersionId": { "description": "Id of the catalog item.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "default": "latest", "description": "Type of snapshot policy to use by the dataset.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" } }, "required": [ "featureEngineeringGraphId", "secondaryDatasets" ], "type": "object" }, "type": "array" }, "created": { "description": "DR-formatted datetime, null for legacy (before DR 6.0) db records.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorFullName": { "description": "Fullname or email of the user created this config. null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorUserId": { "description": "ID of the user created this config, null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "credentialIds": { "description": "List of credentials used by the secondary datasets if the datasets used in the configuration are from datasource.", "items": { "properties": { "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "credentialId": { "description": "ID of the credential store to be used for the given catalog version.", "type": "string" }, "url": { "description": "The URL of the datasource.", "type": [ "string", "null" ] } }, "required": [ "catalogVersionId", "credentialId" ], "type": "object" }, "type": "array" }, "featurelistId": { "description": "Id of the feature list.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "id": { "description": "ID of the secondary datasets configuration.", "type": "string" }, "isDefault": { "description": "Secondary datasets config is default config or not.", "type": [ "boolean", "null" ] }, "name": { "description": "Name of the secondary datasets config.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project.", "type": [ "string", "null" ] }, "projectVersion": { "description": "DataRobot project version.", "type": [ "string", "null" ] }, "secondaryDatasets": { "description": "List of secondary datasets used in the config.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "requiredFeatures": { "description": "List of required feature names used by the table.", "items": { "type": "string" }, "type": "array" }, "snapshotPolicy": { "description": "Policy to be used by a dataset while making prediction.", "enum": [ "specified", "latest", "dynamic" ], "type": [ "string", "null" ] } }, "required": [ "catalogId", "catalogVersionId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } }, "required": [ "created", "creatorFullName", "creatorUserId", "id", "isDefault", "name", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | config | [SecondaryDatasetConfig] | false | | Graph-specific secondary datasets. Deprecated in version v2.23. | | created | string,null(date-time) | true | | DR-formatted datetime, null for legacy (before DR 6.0) db records. | | creatorFullName | string,null | true | | Fullname or email of the user created this config. null for legacy (before DR 6.0) db records. | | creatorUserId | string,null | true | | ID of the user created this config, null for legacy (before DR 6.0) db records. | | credentialIds | [DatasetsCredential] | false | | List of credentials used by the secondary datasets if the datasets used in the configuration are from datasource. | | featurelistId | string,null | false | | Id of the feature list. | | id | string | true | | ID of the secondary datasets configuration. | | isDefault | boolean,null | true | | Secondary datasets config is default config or not. | | name | string,null | true | | Name of the secondary datasets config. | | projectId | string,null | true | | ID of the project. | | projectVersion | string,null | false | | DataRobot project version. | | secondaryDatasets | [SecondaryDatasetResponse] | false | | List of secondary datasets used in the config. | ## Relationship ``` { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataset1Identifier | string,null | false | maxLength: 20minLength: 1minLength: 1 | Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset. | | dataset1Keys | [string] | true | maxItems: 10minItems: 1 | column(s) in the first dataset that are used to join to the second dataset. | | dataset2Identifier | string | true | maxLength: 20minLength: 1minLength: 1 | Identifier of the second dataset in the relationship. | | dataset2Keys | [string] | true | maxItems: 10minItems: 1 | column(s) in the second dataset that are used to join to the first dataset. | | featureDerivationWindowEnd | integer | false | maximum: 0 | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | featureDerivationWindowStart | integer | false | | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | featureDerivationWindowTimeUnit | string | false | | Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided. | | featureDerivationWindows | [FeatureDerivationWindow] | false | maxItems: 3 | The list of feature derivation window definitions that will be used. | | predictionPointRounding | integer | false | maximum: 30 | Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided. | | predictionPointRoundingTimeUnit | string | false | | Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided. | ### Enumerated Values | Property | Value | | --- | --- | | featureDerivationWindowTimeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | | predictionPointRoundingTimeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | ## RelationshipQualityAssessmentsCreate ``` { "properties": { "credentials": { "description": "Credentials for dynamic policy secondary datasets.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "credentialId": { "description": "ID of the credentials object in credential store.Can only be used along with catalogVersionId.", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": [ "string", "null" ] } }, "required": [ "credentialId" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. ", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" } ] }, "maxItems": 30, "type": "array" }, "datetimePartitionColumn": { "description": "If a datetime partition column was used, the name of the column.", "type": [ "string", "null" ] }, "featureEngineeringPredictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "type": [ "string", "null" ] }, "relationshipsConfiguration": { "description": "Object describing how secondary datasets are related to the primary dataset", "properties": { "datasetDefinitions": { "description": "The list of datasets.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "maxItems": 30, "minItems": 1, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "Id of the relationship configuration", "type": "string" }, "relationships": { "description": "The list of relationships.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "maxItems": 70, "minItems": 1, "type": "array" }, "snowflakePushDownCompatible": { "description": "Flag indicating if the relationships configuration is compatible with Snowflake push down processing.", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "id", "relationships" ], "type": "object" }, "userId": { "description": "Mongo Id of the User who created the request", "type": "string" } }, "required": [ "relationshipsConfiguration" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentials | [oneOf] | false | maxItems: 30 | Credentials for dynamic policy secondary datasets. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | StoredCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogPasswordCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimePartitionColumn | string,null | false | | If a datetime partition column was used, the name of the column. | | featureEngineeringPredictionPoint | string,null | false | | The date column to be used as the prediction point for time-based feature engineering. | | relationshipsConfiguration | RelationshipsConfigPayload | true | | Object describing how secondary datasets are related to the primary dataset | | userId | string | false | | Mongo Id of the User who created the request | ## RelationshipQualitySummary ``` { "properties": { "enrichmentRate": { "description": "Percentage of records that can be enriched with a record in the primary table", "type": "number" }, "formattedSummary": { "description": "Relationship quality assessment report associated with the relationship", "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate" ], "type": "object" }, "lastUpdated": { "description": "Last updated timestamp", "format": "date-time", "type": "string" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "status": { "description": "Relationship quality assessment status", "enum": [ "Complete", "In progress", "Error" ], "type": "string" }, "statusId": { "description": "The list of status IDs.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" } }, "required": [ "enrichmentRate", "formattedSummary", "lastUpdated", "overallCategory" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enrichmentRate | number | true | | Percentage of records that can be enriched with a record in the primary table | | formattedSummary | FormattedSummary | true | | Relationship quality assessment report associated with the relationship | | lastUpdated | string(date-time) | true | | Last updated timestamp | | overallCategory | string | true | | Class of the relationship quality | | status | string | false | | Relationship quality assessment status | | statusId | [string] | false | maxItems: 3minItems: 1 | The list of status IDs. | ### Enumerated Values | Property | Value | | --- | --- | | overallCategory | [green, yellow] | | status | [Complete, In progress, Error] | ## RelationshipQualitySummaryNewFormat ``` { "properties": { "detailedReport": { "description": "Detailed report of the relationship quality information", "items": { "properties": { "enrichmentRate": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "enrichmentRateValue": { "description": "Percentage of primary table records that can be enriched with a record in this dataset", "type": "number" }, "featureDerivationWindow": { "description": "Feature derivation window.", "type": [ "string", "null" ] }, "mostRecentData": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" }, "overallCategory": { "description": "Class of the relationship quality", "enum": [ "green", "yellow" ], "type": "string" }, "windowSettings": { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } }, "required": [ "enrichmentRate", "enrichmentRateValue", "overallCategory" ], "type": "object" }, "maxItems": 3, "type": "array" }, "lastUpdated": { "description": "Last updated timestamp", "format": "date-time", "type": "string" }, "problemCount": { "description": "Total count of problems detected", "type": "integer" }, "samplingFraction": { "description": "Primary dataset sampling fraction used for relationship quality assessment speedup", "type": "number" }, "status": { "description": "Relationship quality assessment status", "enum": [ "Complete", "In progress", "Error" ], "type": "string" }, "statusId": { "description": "The list of status IDs.", "items": { "type": "string" }, "maxItems": 3, "minItems": 1, "type": "array" }, "summaryCategory": { "description": "Class of the summary warning of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "summaryMessage": { "description": "Summary warning message about the relationship", "type": "string" } }, "required": [ "detailedReport", "lastUpdated", "problemCount", "samplingFraction", "summaryCategory", "summaryMessage" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | detailedReport | [AssessmentNewFormat] | true | maxItems: 3 | Detailed report of the relationship quality information | | lastUpdated | string(date-time) | true | | Last updated timestamp | | problemCount | integer | true | | Total count of problems detected | | samplingFraction | number | true | | Primary dataset sampling fraction used for relationship quality assessment speedup | | status | string | false | | Relationship quality assessment status | | statusId | [string] | false | maxItems: 3minItems: 1 | The list of status IDs. | | summaryCategory | string | true | | Class of the summary warning of the relationship | | summaryMessage | string | true | | Summary warning message about the relationship | ### Enumerated Values | Property | Value | | --- | --- | | status | [Complete, In progress, Error] | | summaryCategory | [green, yellow] | ## RelationshipResponse ``` { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used.", "exclusiveMaximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Can be null, closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present.", "exclusiveMinimum": 0, "maximum": 30, "type": [ "integer", "null" ] }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] } }, "required": [ "dataset1Identifier", "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataset1Identifier | string,null | true | maxLength: 20minLength: 1minLength: 1 | Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset. | | dataset1Keys | [string] | true | maxItems: 10minItems: 1 | column(s) in the first dataset that are used to join to the second dataset. | | dataset2Identifier | string | true | maxLength: 20minLength: 1minLength: 1 | Identifier of the second dataset in the relationship. | | dataset2Keys | [string] | true | maxItems: 10minItems: 1 | column(s) in the second dataset that are used to join to the first dataset. | | featureDerivationWindowEnd | integer,null | false | maximum: 0 | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. | | featureDerivationWindowStart | integer,null | false | | How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. | | featureDerivationWindowTimeUnit | string,null | false | | Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. | | featureDerivationWindows | [FeatureDerivationWindow] | false | | The list of feature derivation window definitions that will be used. | | predictionPointRounding | integer,null | false | maximum: 30 | Can be null, closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. | | predictionPointRoundingTimeUnit | string,null | false | | Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. | ### Enumerated Values | Property | Value | | --- | --- | | featureDerivationWindowTimeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | | predictionPointRoundingTimeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | ## RelationshipsConfigCreate ``` { "properties": { "datasetDefinitions": { "description": "The list of dataset definitions.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": "string" }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process. Applicable when feature_discovery_mode is 'manual'.", "items": { "properties": { "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" } }, "required": [ "name", "value" ], "type": "object" }, "type": "array" }, "relationships": { "description": "The list of relationships between datasets specified in datasetDefinitions.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "type": "array" } }, "required": [ "datasetDefinitions", "relationships" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetDefinitions | [DatasetDefinition] | true | | The list of dataset definitions. | | featureDiscoveryMode | string | false | | Mode of feature discovery. Supported values are 'default' and 'manual'. | | featureDiscoverySettings | [FeatureDiscoverySetting] | false | | The list of feature discovery settings used to customize the feature discovery process. Applicable when feature_discovery_mode is 'manual'. | | relationships | [Relationship] | true | | The list of relationships between datasets specified in datasetDefinitions. | ### Enumerated Values | Property | Value | | --- | --- | | featureDiscoveryMode | [default, manual] | ## RelationshipsConfigPayload ``` { "description": "Object describing how secondary datasets are related to the primary dataset", "properties": { "datasetDefinitions": { "description": "The list of datasets.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": "string" }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": [ "string", "null" ] }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "maxItems": 30, "minItems": 1, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "Id of the relationship configuration", "type": "string" }, "relationships": { "description": "The list of relationships.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer" }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer" }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "maxItems": 3, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present. Only applicable when table1Identifier is not provided.", "exclusiveMinimum": 0, "maximum": 30, "type": "integer" }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" } }, "required": [ "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "maxItems": 70, "minItems": 1, "type": "array" }, "snowflakePushDownCompatible": { "description": "Flag indicating if the relationships configuration is compatible with Snowflake push down processing.", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "id", "relationships" ], "type": "object" } ``` Object describing how secondary datasets are related to the primary dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetDefinitions | [DatasetDefinition] | true | maxItems: 30minItems: 1 | The list of datasets. | | featureDiscoveryMode | string,null | false | | Mode of feature discovery. Supported values are 'default' and 'manual'. | | featureDiscoverySettings | [FeatureDiscoverySettingResponse] | false | maxItems: 100 | The list of feature discovery settings used to customize the feature discovery process. | | id | string | true | | Id of the relationship configuration | | relationships | [Relationship] | true | maxItems: 70minItems: 1 | The list of relationships. | | snowflakePushDownCompatible | boolean,null | false | | Flag indicating if the relationships configuration is compatible with Snowflake push down processing. | ### Enumerated Values | Property | Value | | --- | --- | | featureDiscoveryMode | [default, manual] | ## RelationshipsConfigResponse ``` { "properties": { "datasetDefinitions": { "description": "The list of dataset definitions.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog item version.", "type": "string" }, "dataSource": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "dataSources": { "description": "Data source details for a JDBC dataset", "items": { "description": "Data source details for a JDBC dataset", "properties": { "catalog": { "description": "Catalog name of the data source.", "type": [ "string", "null" ] }, "dataSourceId": { "description": "ID of the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store.", "type": "string" }, "dataStoreName": { "description": "Name of the data store.", "type": "string" }, "dbtable": { "description": "Table name of the data source.", "type": "string" }, "schema": { "description": "Schema name of the data source.", "type": [ "string", "null" ] }, "url": { "description": "URL of the data store.", "type": "string" } }, "required": [ "dataStoreId", "dataStoreName", "dbtable", "schema", "url" ], "type": "object" }, "type": "array" }, "featureListId": { "description": "ID of the feature list. This decides which columns in the dataset are used for feature generation.", "type": "string" }, "featureLists": { "description": "The list of available feature list IDs for the dataset.", "items": { "type": "string" }, "type": "array" }, "identifier": { "description": "Short name of the dataset (used directly as part of the generated feature names).", "maxLength": 20, "minLength": 1, "type": "string" }, "isDeleted": { "description": "Is this dataset deleted?", "type": [ "boolean", "null" ] }, "originalIdentifier": { "description": "Original identifier of the dataset if it was updated to resolve name conflicts.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "primaryTemporalKey": { "description": "Name of the column indicating time of record creation.", "type": [ "string", "null" ] }, "snapshotPolicy": { "description": "Policy for using dataset snapshots when creating a project or making predictions. Must be one of the following values: 'specified': Use specific snapshot specified by catalogVersionId. 'latest': Use latest snapshot from the same catalog item. 'dynamic': Get data from the source (only applicable for JDBC datasets).", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "featureListId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array" }, "featureDiscoveryMode": { "description": "Mode of feature discovery. Supported values are 'default' and 'manual'.", "enum": [ "default", "manual" ], "type": [ "string", "null" ] }, "featureDiscoverySettings": { "description": "The list of feature discovery settings used to customize the feature discovery process.", "items": { "properties": { "description": { "description": "Description of this feature discovery setting", "type": "string" }, "family": { "description": "Family of this feature discovery setting", "type": "string" }, "name": { "description": "Name of this feature discovery setting", "maxLength": 100, "type": "string" }, "settingType": { "description": "Type of this feature discovery setting", "type": "string" }, "value": { "description": "Value of this feature discovery setting", "type": "boolean" }, "verboseName": { "description": "Human readable name of this feature discovery setting", "type": "string" } }, "required": [ "description", "family", "name", "settingType", "value", "verboseName" ], "type": "object" }, "type": "array" }, "id": { "description": "ID of relationships configuration.", "type": "string" }, "relationships": { "description": "The list of relationships between datasets specified in datasetDefinitions.", "items": { "properties": { "dataset1Identifier": { "description": "Identifier of the first dataset in the relationship. If this is not provided, it represents the primary dataset.", "maxLength": 20, "minLength": 1, "type": [ "string", "null" ] }, "dataset1Keys": { "description": "column(s) in the first dataset that are used to join to the second dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "dataset2Identifier": { "description": "Identifier of the second dataset in the relationship.", "maxLength": 20, "minLength": 1, "type": "string" }, "dataset2Keys": { "description": "column(s) in the second dataset that are used to join to the first dataset.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureDerivationWindowEnd": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used.", "exclusiveMaximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowTimeUnit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "featureDerivationWindows": { "description": "The list of feature derivation window definitions that will be used.", "items": { "properties": { "end": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should end. Will be a non-positive integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "maximum": 0, "type": "integer", "x-versionadded": "2.27" }, "start": { "description": "How many featureDerivationWindowUnits of each dataset's primary temporal key into the past relative to the datetimePartitionColumn the feature derivation window should begin. Will be a negative integer, if present. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "exclusiveMaximum": 0, "type": "integer", "x-versionadded": "2.27" }, "unit": { "description": "Time unit of the feature derivation window. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR. If present, time-aware joins will be used. Only applicable when table1Identifier is not provided.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string", "x-versionadded": "2.27" } }, "required": [ "end", "start", "unit" ], "type": "object" }, "type": "array", "x-versionadded": "2.27" }, "predictionPointRounding": { "description": "Can be null, closest value of predictionPointRoundingTimeUnit to round the prediction point into the past when applying the feature derivation window. Will be a positive integer, if present.", "exclusiveMinimum": 0, "maximum": 30, "type": [ "integer", "null" ] }, "predictionPointRoundingTimeUnit": { "description": "Time unit of the prediction point rounding. Supported values are MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] } }, "required": [ "dataset1Identifier", "dataset1Keys", "dataset2Identifier", "dataset2Keys" ], "type": "object" }, "type": "array" }, "snowflakePushDownCompatible": { "description": "Is this configuration compatible with pushdown computation on Snowflake?", "type": [ "boolean", "null" ] } }, "required": [ "datasetDefinitions", "relationships" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetDefinitions | [DatasetDefinitionResponse] | true | | The list of dataset definitions. | | featureDiscoveryMode | string,null | false | | Mode of feature discovery. Supported values are 'default' and 'manual'. | | featureDiscoverySettings | [FeatureDiscoverySettingResponse] | false | | The list of feature discovery settings used to customize the feature discovery process. | | id | string | false | | ID of relationships configuration. | | relationships | [RelationshipResponse] | true | | The list of relationships between datasets specified in datasetDefinitions. | | snowflakePushDownCompatible | boolean,null | false | | Is this configuration compatible with pushdown computation on Snowflake? | ### Enumerated Values | Property | Value | | --- | --- | | featureDiscoveryMode | [default, manual] | ## SecondaryDataset ``` { "properties": { "catalogId": { "description": "Id of the catalog item version.", "type": "string" }, "catalogVersionId": { "description": "Id of the catalog item.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "default": "latest", "description": "Type of snapshot policy to use by the dataset.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string | true | | Id of the catalog item version. | | catalogVersionId | string | true | | Id of the catalog item. | | identifier | string | true | maxLength: 45minLength: 1minLength: 1 | Short name of this table (used directly as part of generated feature names). | | snapshotPolicy | string | false | | Type of snapshot policy to use by the dataset. | ### Enumerated Values | Property | Value | | --- | --- | | snapshotPolicy | [specified, latest, dynamic] | ## SecondaryDatasetConfig ``` { "properties": { "featureEngineeringGraphId": { "description": "Id of the feature engineering graph", "type": "string" }, "secondaryDatasets": { "description": "list of secondary datasets used by the feature engineering graph", "items": { "properties": { "catalogId": { "description": "Id of the catalog item version.", "type": "string" }, "catalogVersionId": { "description": "Id of the catalog item.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "default": "latest", "description": "Type of snapshot policy to use by the dataset.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" } }, "required": [ "featureEngineeringGraphId", "secondaryDatasets" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureEngineeringGraphId | string | true | | Id of the feature engineering graph | | secondaryDatasets | [SecondaryDataset] | true | | list of secondary datasets used by the feature engineering graph | ## SecondaryDatasetConfigListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Secondary dataset configurations.", "items": { "properties": { "config": { "description": "Graph-specific secondary datasets. Deprecated in version v2.23.", "items": { "properties": { "featureEngineeringGraphId": { "description": "Id of the feature engineering graph", "type": "string" }, "secondaryDatasets": { "description": "list of secondary datasets used by the feature engineering graph", "items": { "properties": { "catalogId": { "description": "Id of the catalog item version.", "type": "string" }, "catalogVersionId": { "description": "Id of the catalog item.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "default": "latest", "description": "Type of snapshot policy to use by the dataset.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" } }, "required": [ "featureEngineeringGraphId", "secondaryDatasets" ], "type": "object" }, "type": "array" }, "created": { "description": "DR-formatted datetime, null for legacy (before DR 6.0) db records.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorFullName": { "description": "Fullname or email of the user created this config. null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "creatorUserId": { "description": "ID of the user created this config, null for legacy (before DR 6.0) db records.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "credentialIds": { "description": "List of credentials used by the secondary datasets if the datasets used in the configuration are from datasource.", "items": { "properties": { "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "credentialId": { "description": "ID of the credential store to be used for the given catalog version.", "type": "string" }, "url": { "description": "The URL of the datasource.", "type": [ "string", "null" ] } }, "required": [ "catalogVersionId", "credentialId" ], "type": "object" }, "type": "array" }, "featurelistId": { "description": "Id of the feature list.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "id": { "description": "ID of the secondary datasets configuration.", "type": "string" }, "isDefault": { "description": "Secondary datasets config is default config or not.", "type": [ "boolean", "null" ] }, "name": { "description": "Name of the secondary datasets config.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project.", "type": [ "string", "null" ] }, "projectVersion": { "description": "DataRobot project version.", "type": [ "string", "null" ] }, "secondaryDatasets": { "description": "List of secondary datasets used in the config.", "items": { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "requiredFeatures": { "description": "List of required feature names used by the table.", "items": { "type": "string" }, "type": "array" }, "snapshotPolicy": { "description": "Policy to be used by a dataset while making prediction.", "enum": [ "specified", "latest", "dynamic" ], "type": [ "string", "null" ] } }, "required": [ "catalogId", "catalogVersionId", "identifier", "snapshotPolicy" ], "type": "object" }, "type": "array", "x-versionadded": "v2.24" } }, "required": [ "created", "creatorFullName", "creatorUserId", "id", "isDefault", "name", "projectId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ProjectSecondaryDatasetConfigResponse] | true | | Secondary dataset configurations. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## SecondaryDatasetCreate ``` { "properties": { "config": { "description": "Graph-specific secondary datasets. Deprecated in version v2.23", "items": { "properties": { "featureEngineeringGraphId": { "description": "Id of the feature engineering graph", "type": "string" }, "secondaryDatasets": { "description": "list of secondary datasets used by the feature engineering graph", "items": { "properties": { "catalogId": { "description": "Id of the catalog item version.", "type": "string" }, "catalogVersionId": { "description": "Id of the catalog item.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "default": "latest", "description": "Type of snapshot policy to use by the dataset.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" } }, "required": [ "featureEngineeringGraphId", "secondaryDatasets" ], "type": "object" }, "type": "array" }, "featurelistId": { "description": "Feature list ID of the configuration.", "type": "string" }, "modelId": { "description": "ID of the model.", "type": "string" }, "name": { "description": "Name of the configuration.", "type": "string" }, "save": { "default": true, "description": "Determines if the configuration will be saved. If set to False the configuration is validated but not saved. Defaults to True.", "type": "boolean" }, "secondaryDatasets": { "description": "List of secondary dataset definitions to use in the configuration.", "items": { "properties": { "catalogId": { "description": "Id of the catalog item version.", "type": "string" }, "catalogVersionId": { "description": "Id of the catalog item.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "snapshotPolicy": { "default": "latest", "description": "Type of snapshot policy to use by the dataset.", "enum": [ "specified", "latest", "dynamic" ], "type": "string" } }, "required": [ "catalogId", "catalogVersionId", "identifier" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | config | [SecondaryDatasetConfig] | false | | Graph-specific secondary datasets. Deprecated in version v2.23 | | featurelistId | string | false | | Feature list ID of the configuration. | | modelId | string | false | | ID of the model. | | name | string | true | | Name of the configuration. | | save | boolean | false | | Determines if the configuration will be saved. If set to False the configuration is validated but not saved. Defaults to True. | | secondaryDatasets | [SecondaryDataset] | false | | List of secondary dataset definitions to use in the configuration. | ## SecondaryDatasetResponse ``` { "properties": { "catalogId": { "description": "ID of the catalog item.", "type": [ "string", "null" ] }, "catalogVersionId": { "description": "ID of the catalog version.", "type": "string" }, "identifier": { "description": "Short name of this table (used directly as part of generated feature names).", "maxLength": 45, "minLength": 1, "type": "string" }, "requiredFeatures": { "description": "List of required feature names used by the table.", "items": { "type": "string" }, "type": "array" }, "snapshotPolicy": { "description": "Policy to be used by a dataset while making prediction.", "enum": [ "specified", "latest", "dynamic" ], "type": [ "string", "null" ] } }, "required": [ "catalogId", "catalogVersionId", "identifier", "snapshotPolicy" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogId | string,null | true | | ID of the catalog item. | | catalogVersionId | string | true | | ID of the catalog version. | | identifier | string | true | maxLength: 45minLength: 1minLength: 1 | Short name of this table (used directly as part of generated feature names). | | requiredFeatures | [string] | false | | List of required feature names used by the table. | | snapshotPolicy | string,null | true | | Policy to be used by a dataset while making prediction. | ### Enumerated Values | Property | Value | | --- | --- | | snapshotPolicy | [specified, latest, dynamic] | ## StoredCredentials ``` { "properties": { "catalogVersionId": { "description": "Identifier of the catalog version", "type": "string" }, "credentialId": { "description": "ID of the credentials object in credential store.Can only be used along with catalogVersionId.", "type": "string" }, "url": { "description": "URL that is subject to credentials.", "type": [ "string", "null" ] } }, "required": [ "credentialId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogVersionId | string | false | | Identifier of the catalog version | | credentialId | string | true | | ID of the credentials object in credential store.Can only be used along with catalogVersionId. | | url | string,null | false | | URL that is subject to credentials. | ## TargetBin ``` { "properties": { "targetBinEnd": { "description": "End value for the target bin", "type": "number" }, "targetBinStart": { "description": "Start value for the target bin", "type": "number" } }, "required": [ "targetBinEnd", "targetBinStart" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetBinEnd | number | true | | End value for the target bin | | targetBinStart | number | true | | Start value for the target bin | ## TextLine ``` { "properties": { "bottom": { "description": "Bottom coordinate of the bounding box belonging to this text line in number of pixels from the top image side.", "minimum": 0, "type": "integer" }, "left": { "description": "Left coordinate of the bounding box belonging to this text line in number of pixels from the left image side.", "minimum": 0, "type": "integer" }, "right": { "description": "Right coordinate of the bounding box belonging to this text line in number of pixels from the left image side.", "minimum": 0, "type": "integer" }, "text": { "description": "The text in this text line.", "type": "string" }, "top": { "description": "Top coordinate of the bounding box belonging to this text line in number of pixels from the top image side.", "minimum": 0, "type": "integer" } }, "required": [ "bottom", "left", "right", "text", "top" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bottom | integer | true | minimum: 0 | Bottom coordinate of the bounding box belonging to this text line in number of pixels from the top image side. | | left | integer | true | minimum: 0 | Left coordinate of the bounding box belonging to this text line in number of pixels from the left image side. | | right | integer | true | minimum: 0 | Right coordinate of the bounding box belonging to this text line in number of pixels from the left image side. | | text | string | true | | The text in this text line. | | top | integer | true | minimum: 0 | Top coordinate of the bounding box belonging to this text line in number of pixels from the top image side. | ## TimeDelta ``` { "description": "End of the feature derivation window applied.", "properties": { "duration": { "description": "Value/size of this time delta.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "Time unit name like 'MINUTE', 'DAY', 'MONTH' etc.", "type": "string" } }, "required": [ "duration", "timeUnit" ], "type": "object" } ``` End of the feature derivation window applied. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | duration | integer | true | minimum: 0 | Value/size of this time delta. | | timeUnit | string | true | | Time unit name like 'MINUTE', 'DAY', 'MONTH' etc. | ## TimeSeriesFeatureLogListControllerResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "featureLog": { "description": "The content of the feature log.", "type": "string" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalLogLines": { "description": "The total number of lines in the feature derivation log.", "type": "integer" } }, "required": [ "count", "featureLog", "next", "previous", "totalLogLines" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | featureLog | string | true | | The content of the feature log. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | | totalLogLines | integer | true | | The total number of lines in the feature derivation log. | ## UpdateFeaturelist ``` { "properties": { "description": { "description": "The new featurelist description.", "maxLength": 1000, "type": "string" }, "name": { "description": "The new featurelist name.", "maxLength": 100, "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 1000 | The new featurelist description. | | name | string | false | maxLength: 100 | The new featurelist name. | ## Warnings ``` { "description": "Warning about the enrichment rate", "properties": { "action": { "description": "Suggested action to fix the relationship", "type": [ "string", "null" ] }, "category": { "description": "Class of the warning about an aspect of the relationship", "enum": [ "green", "yellow" ], "type": "string" }, "message": { "description": "Warning message about an aspect of the relationship", "type": "string" } }, "required": [ "action", "category", "message" ], "type": "object" } ``` Warning about the enrichment rate ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string,null | true | | Suggested action to fix the relationship | | category | string | true | | Class of the warning about an aspect of the relationship | | message | string | true | | Warning message about an aspect of the relationship | ### Enumerated Values | Property | Value | | --- | --- | | category | [green, yellow] | --- # Guardrails URL: https://docs.datarobot.com/en/docs/api/reference/public-api/guardrails.html > Use the endpoints described below to manage guardrails. Model guardrails are a set of rules or machine learning models that ensure the output of a language model is safe, accurate, and ethical. # Guardrails Use the endpoints described below to manage guardrails. Model guardrails are a set of rules or machine learning models that ensure the output of a language model is safe, accurate, and ethical. ## The list of resource tags Operation path: `GET /api/v2/guardConfigurations/` Authentication requirements: `BearerAuth` The list of resource tags. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | entityId | query | string | true | Filter guard configurations by the given entity ID. | | entityType | query | string | true | The entity type of the given entity ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [customModel, customModelVersion, playground] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of guard configurations.", "items": { "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "createdAt": { "description": "When the configuration was created.", "format": "date-time", "type": "string" }, "creatorId": { "description": "The ID of the user who created the guard configuration.", "type": [ "string", "null" ] }, "creatorName": { "description": "The name of the user who created the guard configuration.", "maxLength": 1000, "type": "string" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "The guard configuration description.", "maxLength": 4096, "type": "string" }, "entityId": { "description": "The ID of the custom model or playground for this guard.", "type": [ "string", "null" ] }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "id": { "description": "The guard configuration object ID.", "type": "string" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": false, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "description": "Whether the guard is valid or not.", "type": "boolean" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The guard configuration name.", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "Guard configuration \"NeMo Evaluator\" metric type", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "Guard template \"Out of the Box\" metric type", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "stages": { "description": "The stages where the guard is configured to run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "type": { "description": "The guard configuration type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "createdAt", "description", "entityId", "entityType", "id", "name", "stages", "type" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | GuardConfigurationListResponse | | 400 | Bad Request | Request invalid, refer to messages for detail. | None | | 404 | Not Found | User permissions problem. | None | ## Create a guard configuration Operation path: `POST /api/v2/guardConfigurations/` Authentication requirements: `BearerAuth` Create a guard configuration. ### Body parameter ``` { "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "Guard configuration description", "maxLength": 4096, "type": "string" }, "entityId": { "description": "The ID of the custom model or playground for this guard.", "type": "string" }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": [ "string", "null" ] } }, "required": [ "inputColumnName", "outputColumnName" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Guard configuration name", "maxLength": 255, "type": "string" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "stages": { "description": "The stages where the guard can run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "templateId": { "description": "The ID of the template this guard is based on.", "type": "string" } }, "required": [ "entityId", "entityType", "name", "stages", "templateId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | GuardConfigurationCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "createdAt": { "description": "When the configuration was created.", "format": "date-time", "type": "string" }, "creatorId": { "description": "The ID of the user who created the guard configuration.", "type": [ "string", "null" ] }, "creatorName": { "description": "The name of the user who created the guard configuration.", "maxLength": 1000, "type": "string" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "The guard configuration description.", "maxLength": 4096, "type": "string" }, "entityId": { "description": "The ID of the custom model or playground for this guard.", "type": [ "string", "null" ] }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "id": { "description": "The guard configuration object ID.", "type": "string" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": false, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "description": "Whether the guard is valid or not.", "type": "boolean" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The guard configuration name.", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "Guard configuration \"NeMo Evaluator\" metric type", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "Guard template \"Out of the Box\" metric type", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "stages": { "description": "The stages where the guard is configured to run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "type": { "description": "The guard configuration type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "createdAt", "description", "entityId", "entityType", "id", "name", "stages", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | GuardConfigurationRetrieveResponse | | 404 | Not Found | Either the resource does not exist or the user does not have permission to create the configuration. | None | | 409 | Conflict | The proposed configuration name is already in use for the same entity. | None | ## Show the prediction environments in use Operation path: `GET /api/v2/guardConfigurations/predictionEnvironmentsInUse/` Authentication requirements: `BearerAuth` Show the prediction environments in use for moderation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | customModelVersionId | query | string | true | Show prediction environment information for this custom model version. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "list of prediction environments in use for this custom model version.", "items": { "properties": { "id": { "description": "ID of prediction environment.", "type": "string" }, "name": { "description": "Name of prediction environment.", "type": "string" }, "usedBy": { "description": "Guards using this prediction environment.", "items": { "properties": { "configurationId": { "description": "ID of guard configuration.", "type": "string" }, "deploymentId": { "description": "ID of guard model deployment.", "type": "string" }, "name": { "description": "Name of guard configuration.", "type": "string" } }, "required": [ "configurationId", "deploymentId", "name" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 32, "type": "array" } }, "required": [ "id", "name", "usedBy" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 32, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | GuardConfigurationPredictionEnvironmentsInUseListResponse | | 404 | Not Found | Either the resource does not exist or the user does not have permission to view the configuration. | None | ## Apply moderation configuration Operation path: `POST /api/v2/guardConfigurations/toNewCustomModelVersion/` Authentication requirements: `BearerAuth` Apply moderation configuration to a new custom model version. ### Body parameter ``` { "properties": { "customModelId": { "description": "The ID of the custom model the user is working with.", "type": "string" }, "data": { "description": "The list of complete guard configurations to push.", "items": { "description": "Complete guard configuration to push", "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "Guard configuration description", "maxLength": 4096, "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": false, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "description": "Whether the guard is valid or not.", "type": "boolean" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Guard configuration name", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "Guard configuration \"NeMo Evaluator\" metric type", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "Guard template \"Out of the Box\" metric type", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "parameters": { "description": "Parameter list, not used, deprecated.", "items": { "maxLength": 1, "type": "string" }, "maxItems": 1, "type": "array" }, "stages": { "description": "The stages where the guard is configured to run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "type": { "description": "The guard configuration type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "description", "name", "stages", "type" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "type": "array" }, "overallConfig": { "description": "Overall moderation configuration to push (not specific to one guard)", "properties": { "nemoEvaluatorDeploymentId": { "description": "ID of NeMo Evaluator deployment to use for all NeMo Evaluator guards.", "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "timeoutAction": { "description": "Action to take if timeout occurs", "enum": [ "block", "score" ], "type": "string" }, "timeoutSec": { "description": "Timeout value in seconds for any guard", "minimum": 2, "type": "integer" } }, "required": [ "timeoutAction", "timeoutSec" ], "type": "object", "x-versionadded": "v2.35" } }, "required": [ "customModelId", "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | GuardConfigurationToCustomModelVersion | false | none | ### Example responses > 200 Response ``` { "properties": { "customModelVersionId": { "description": "The ID of the new custom model version created.", "type": "string" } }, "required": [ "customModelVersionId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | GuardConfigurationToCustomModelResponse | | 404 | Not Found | Either the resource does not exist or the user does not have permission to create the configuration. | None | | 409 | Conflict | The destination custom model version is frozen. Create a new version to save configuration. | None | ## Delete a guard config by config ID Operation path: `DELETE /api/v2/guardConfigurations/{configId}/` Authentication requirements: `BearerAuth` Delete a guard config. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | configId | path | string | true | The ID of the configuration. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 404 | Not Found | Either the config does not exist or the user does not have permission to delete it. | None | ## Retrieve info about a guard configuration by config ID Operation path: `GET /api/v2/guardConfigurations/{configId}/` Authentication requirements: `BearerAuth` Retrieve info about a guard configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | configId | path | string | true | The ID of the configuration. | ### Example responses > 200 Response ``` { "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "createdAt": { "description": "When the configuration was created.", "format": "date-time", "type": "string" }, "creatorId": { "description": "The ID of the user who created the guard configuration.", "type": [ "string", "null" ] }, "creatorName": { "description": "The name of the user who created the guard configuration.", "maxLength": 1000, "type": "string" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "The guard configuration description.", "maxLength": 4096, "type": "string" }, "entityId": { "description": "The ID of the custom model or playground for this guard.", "type": [ "string", "null" ] }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "id": { "description": "The guard configuration object ID.", "type": "string" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": false, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "description": "Whether the guard is valid or not.", "type": "boolean" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The guard configuration name.", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "Guard configuration \"NeMo Evaluator\" metric type", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "Guard template \"Out of the Box\" metric type", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "stages": { "description": "The stages where the guard is configured to run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "type": { "description": "The guard configuration type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "createdAt", "description", "entityId", "entityType", "id", "name", "stages", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | GuardConfigurationRetrieveResponse | | 404 | Not Found | Either the config does not exist or the user does not have permission to view it. | None | ## Update a guard config by config ID Operation path: `PATCH /api/v2/guardConfigurations/{configId}/` Authentication requirements: `BearerAuth` Update a guard config. ### Body parameter ``` { "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "Guard configuration description", "maxLength": 4096, "type": "string" }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": [ "string", "null" ] } }, "required": [ "inputColumnName", "outputColumnName" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Guard configuration name", "maxLength": 255, "type": "string" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | configId | path | string | true | The ID of the configuration. | | body | body | GuardConfigurationUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "createdAt": { "description": "When the configuration was created.", "format": "date-time", "type": "string" }, "creatorId": { "description": "The ID of the user who created the guard configuration.", "type": [ "string", "null" ] }, "creatorName": { "description": "The name of the user who created the guard configuration.", "maxLength": 1000, "type": "string" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "The guard configuration description.", "maxLength": 4096, "type": "string" }, "entityId": { "description": "The ID of the custom model or playground for this guard.", "type": [ "string", "null" ] }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "id": { "description": "The guard configuration object ID.", "type": "string" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": false, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "description": "Whether the guard is valid or not.", "type": "boolean" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The guard configuration name.", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "Guard configuration \"NeMo Evaluator\" metric type", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "Guard template \"Out of the Box\" metric type", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "stages": { "description": "The stages where the guard is configured to run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "type": { "description": "The guard configuration type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "createdAt", "description", "entityId", "entityType", "id", "name", "stages", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | GuardConfigurationRetrieveResponse | | 404 | Not Found | Either the resource does not exist or the user does not have permission to create the configuration. | None | | 409 | Conflict | The proposed configuration name is already in use for the same entity. | None | ## List guard templates Operation path: `GET /api/v2/guardTemplates/` Authentication requirements: `BearerAuth` List guard templates. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | includeAgentic | query | string | false | Include agentic and non-agentic templates in results (by default, agentic templates are not included). | | isAgentic | query | string | false | Limit results to agentic templates. If true, this overrides either includeAgentic setting. | | forPlayground | query | string | false | Filter for templates suitable for playground (exclude production-only templates). | | forProduction | query | string | false | Filter for templates suitable for production (exclude playground-only templates). | | name | query | string,null | false | Search for templates by name. | ### Enumerated Values | Parameter | Value | | --- | --- | | includeAgentic | [false, False, true, True] | | isAgentic | [false, False, true, True] | | forPlayground | [false, False, true, True] | | forProduction | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of guard templates.", "items": { "properties": { "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "allowedStages": { "description": "The stages where the guard can run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "createdAt": { "description": "When the template was created.", "format": "date-time", "type": "string" }, "creatorId": { "description": "The ID of the user who created the guard template.", "type": [ "string", "null" ] }, "creatorName": { "description": "The name of the user who created the guard template.", "maxLength": 1000, "type": "string" }, "description": { "description": "The guard template description.", "maxLength": 4096, "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "id": { "description": "The guard template object ID.", "type": "string" }, "intervention": { "description": "The intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "description": "The class name to match.", "maxLength": 128, "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "modifyMessage": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "modifyMessage" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": true, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "default": true, "description": "True if the guard is fully configured and valid.", "type": "boolean" }, "labels": { "description": "The list of short strings to associate with the template.", "items": { "description": "A short string to associate with the template.", "maxLength": 255, "type": "string" }, "maxItems": 16, "type": "array", "x-versionadded": "v2.37" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The guard template name.", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "The guard template \"NeMo Evaluator\" metric type.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "default": "", "description": "The NeMo guardrails actions.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID.", "type": [ "string", "null" ] }, "llmPrompts": { "default": "", "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "The guard template \"Out of the Box\" metric type.", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "orgId": { "description": "Organization ID of the user who created the Guard template.", "type": [ "string", "null" ] }, "playgroundOnly": { "description": "Whether the guard is for playground only, or if it can be used in production and playground.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "productionOnly": { "description": "Whether the guard is for production only, or if it can be used in production and playground.", "type": [ "boolean", "null" ] }, "type": { "description": "The guard template type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "allowedStages", "createdAt", "description", "id", "name", "type" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | GuardTemplateListResponse | | 400 | Bad Request | Request invalid, refer to messages for detail. | None | | 404 | Not Found | Missing feature flag. | None | ## Retrieve information about a guard template by template ID Operation path: `GET /api/v2/guardTemplates/{templateId}/` Authentication requirements: `BearerAuth` Retrieve information about a guard template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | templateId | path | string | true | The ID of the template. | ### Example responses > 200 Response ``` { "properties": { "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "allowedStages": { "description": "The stages where the guard can run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "createdAt": { "description": "When the template was created.", "format": "date-time", "type": "string" }, "creatorId": { "description": "The ID of the user who created the guard template.", "type": [ "string", "null" ] }, "creatorName": { "description": "The name of the user who created the guard template.", "maxLength": 1000, "type": "string" }, "description": { "description": "The guard template description.", "maxLength": 4096, "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "id": { "description": "The guard template object ID.", "type": "string" }, "intervention": { "description": "The intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "description": "The class name to match.", "maxLength": 128, "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "modifyMessage": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "modifyMessage" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": true, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "default": true, "description": "True if the guard is fully configured and valid.", "type": "boolean" }, "labels": { "description": "The list of short strings to associate with the template.", "items": { "description": "A short string to associate with the template.", "maxLength": 255, "type": "string" }, "maxItems": 16, "type": "array", "x-versionadded": "v2.37" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The guard template name.", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "The guard template \"NeMo Evaluator\" metric type.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "default": "", "description": "The NeMo guardrails actions.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID.", "type": [ "string", "null" ] }, "llmPrompts": { "default": "", "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "The guard template \"Out of the Box\" metric type.", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "orgId": { "description": "Organization ID of the user who created the Guard template.", "type": [ "string", "null" ] }, "playgroundOnly": { "description": "Whether the guard is for playground only, or if it can be used in production and playground.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "productionOnly": { "description": "Whether the guard is for production only, or if it can be used in production and playground.", "type": [ "boolean", "null" ] }, "type": { "description": "The guard template type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "allowedStages", "createdAt", "description", "id", "name", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | GuardTemplateRetrieveResponse | | 404 | Not Found | Either the template does not exist or the required feature flag is missing. | None | ## The list of supported LLMs Operation path: `GET /api/v2/moderationSupportedLlms/` Authentication requirements: `BearerAuth` The list of supported LLMs for moderation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of supported LLMs for moderation.", "items": { "properties": { "description": { "description": "The description of this LLM.", "maxLength": 1024, "type": "string" }, "id": { "description": "The ID for this LLM.", "maxLength": 1024, "type": "string" }, "llmType": { "description": "The general category of this LLM.", "maxLength": 1024, "type": "string" }, "model": { "description": "The specific model of this LLM.", "maxLength": 1024, "type": "string" }, "name": { "description": "The display name of this LLM.", "maxLength": 1024, "type": "string" }, "provider": { "description": "The provider of access to this LLM.", "maxLength": 1024, "type": "string" }, "vendor": { "description": "The vendor of this LLM.", "maxLength": 1024, "type": "string" } }, "required": [ "description", "id", "llmType", "model", "name", "provider", "vendor" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SupportedLlmListResponse | | 400 | Bad Request | Request invalid, refer to messages for detail. | None | | 404 | Not Found | Missing feature flag. | None | ## Get the overall moderation configuration Operation path: `GET /api/v2/overallModerationConfiguration/` Authentication requirements: `BearerAuth` Get the overall moderation configuration for an entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityId | query | string | true | Retrieve the overall moderation configuration for the given entity ID. | | entityType | query | string | true | The entity type of the given entity ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [customModel, customModelVersion, playground] | ### Example responses > 200 Response ``` { "properties": { "entityId": { "description": "The ID of the custom model or playground for this configuration.", "type": "string" }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "nemoEvaluatorDeploymentId": { "description": "The ID of the NeMo Evaluator deployment to use for all NeMo Evaluator guards.", "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "timeoutAction": { "description": "The action to take if timeout occurs.", "enum": [ "block", "score" ], "type": "string" }, "timeoutSec": { "description": "The timeout value in seconds for any guard.", "minimum": 2, "type": "integer" }, "updatedAt": { "description": "When the configuration was updated.", "format": "date-time", "type": "string" }, "updaterId": { "description": "The ID of the user who updated the configuration.", "type": [ "string", "null" ] } }, "required": [ "entityId", "entityType", "timeoutAction", "timeoutSec", "updaterId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OverallModerationConfigurationResponse | | 404 | Not Found | User permissions problem. | None | ## Update the overall moderation configuration Operation path: `PATCH /api/v2/overallModerationConfiguration/` Authentication requirements: `BearerAuth` Update the overall moderation configuration for an entity. ### Body parameter ``` { "properties": { "entityId": { "description": "The ID of the custom model or playground for this configuration.", "type": "string" }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "nemoEvaluatorDeploymentId": { "description": "The ID of the NeMo Evaluator deployment to use for all NeMo Evaluator guards.", "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "timeoutAction": { "description": "The action to take if timeout occurs.", "enum": [ "block", "score" ], "type": "string" }, "timeoutSec": { "description": "The timeout value in seconds for any guard.", "minimum": 0, "type": "integer" } }, "required": [ "entityId", "entityType", "timeoutAction", "timeoutSec" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | entityId | path | string | true | Retrieve the overall moderation configuration for the given entity ID. | | entityType | path | string | true | The entity type of the given entity ID. | | body | body | OverallModerationConfigurationUpdate | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [customModel, customModelVersion, playground] | ### Example responses > 200 Response ``` { "properties": { "entityId": { "description": "The ID of the custom model or playground for this configuration.", "type": "string" }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "nemoEvaluatorDeploymentId": { "description": "The ID of the NeMo Evaluator deployment to use for all NeMo Evaluator guards.", "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "timeoutAction": { "description": "The action to take if timeout occurs.", "enum": [ "block", "score" ], "type": "string" }, "timeoutSec": { "description": "The timeout value in seconds for any guard.", "minimum": 2, "type": "integer" }, "updatedAt": { "description": "When the configuration was updated.", "format": "date-time", "type": "string" }, "updaterId": { "description": "The ID of the user who updated the configuration.", "type": [ "string", "null" ] } }, "required": [ "entityId", "entityType", "timeoutAction", "timeoutSec", "updaterId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OverallModerationConfigurationResponse | | 404 | Not Found | Either the resource does not exist or the user does not have permission to create the configuration. | None | # Schemas ## DeploymentAndGuardResponse ``` { "properties": { "configurationId": { "description": "ID of guard configuration.", "type": "string" }, "deploymentId": { "description": "ID of guard model deployment.", "type": "string" }, "name": { "description": "Name of guard configuration.", "type": "string" } }, "required": [ "configurationId", "deploymentId", "name" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configurationId | string | true | | ID of guard configuration. | | deploymentId | string | true | | ID of guard model deployment. | | name | string | true | | Name of guard configuration. | ## GuardAdditionalConfig ``` { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" } ``` Additional configuration for the guard. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | agentGuideline | string,null | false | maxLength: 4096 | How to calculate agent guideline adherence for this guard. | | cost | GuardConfigurationCostInfo | false | | How to calculate cost information for this guard. | | toolCall | GuardToolCallInfo | false | | How to calculate tool call metrics for this guard. | ## GuardConditionResponse ``` { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "description": "The class name to match.", "maxLength": 128, "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" } ``` The condition to trigger intervention. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparand | any | true | | The condition comparand (basis of comparison). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 10 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparator | string | true | | The condition comparator (operator). | ### Enumerated Values | Property | Value | | --- | --- | | comparator | [greaterThan, lessThan, equals, notEquals, is, isNot, matches, doesNotMatch, contains, doesNotContain] | ## GuardConfigurationConditionResponse ``` { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" } ``` The condition to trigger intervention. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparand | any | true | | The condition comparand (basis of comparison). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 10 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparator | string | true | | The condition comparator (operator). | ### Enumerated Values | Property | Value | | --- | --- | | comparator | [greaterThan, lessThan, equals, notEquals, is, isNot, matches, doesNotMatch, contains, doesNotContain] | ## GuardConfigurationCostInfo ``` { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" } ``` How to calculate cost information for this guard. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | currency | string | true | maxLength: 255 | ISO 4217 Currency code for display. | | inputPrice | number | true | minimum: 0 | Cost per unit measure of tokens for input. | | inputUnit | integer | true | minimum: 0 | Number of tokens related to input price. | | outputPrice | number | true | minimum: 0 | Cost per unit measure of tokens for output. | | outputUnit | integer | true | minimum: 0 | Number of tokens related to output price. | ## GuardConfigurationCreate ``` { "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "Guard configuration description", "maxLength": 4096, "type": "string" }, "entityId": { "description": "The ID of the custom model or playground for this guard.", "type": "string" }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": [ "string", "null" ] } }, "required": [ "inputColumnName", "outputColumnName" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Guard configuration name", "maxLength": 255, "type": "string" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "stages": { "description": "The stages where the guard can run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "templateId": { "description": "The ID of the template this guard is based on.", "type": "string" } }, "required": [ "entityId", "entityType", "name", "stages", "templateId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | additionalGuardConfig | GuardAdditionalConfig | false | | Additional configuration for the guard. | | allowedActions | [string] | false | maxItems: 10 | The actions this guard is allowed to take. | | awsAccount | string,null | false | | The ID of the user credential containing an AWS account. | | awsModel | string,null | false | | AWS model. | | awsRegion | string,null | false | maxLength: 255 | AWS model region. | | deploymentId | string,null | false | | The ID of the deployed model for model guards. | | description | string | false | maxLength: 4096 | Guard configuration description | | entityId | string | true | | The ID of the custom model or playground for this guard. | | entityType | string | true | | The type of the associated entity. | | googleModel | string,null | false | | Google model. | | googleRegion | string,null | false | maxLength: 255 | Google model region. | | googleServiceAccount | string,null | false | | The ID of the user credential containing a Google service account. | | intervention | GuardConfigurationInterventionResponse | false | | Intervention configuration for the guard. | | llmGatewayModelId | string,null | false | maxLength: 255 | The LLM Gateway model ID to use as judge. | | llmType | string,null | false | | The type of LLM used by this guard. | | modelInfo | GuardConfigurationPayloadModelInfo | false | | The configuration info for guards using deployed models. | | name | string | true | maxLength: 255 | Guard configuration name | | nemoInfo | GuardConfigurationNemoInfoResponse | false | | The configuration info for NeMo guards. | | nemoLlmJudgeConfig | GuardNemoLlmJudgeConfigResponse | false | | The configuration for the LLM Judge metric. | | nemoResponseRelevancyConfig | GuardNemoResponseRelevancyConfigResponse | false | | The configuration for the Response Relevancy metric. | | nemoTopicAdherenceConfig | GuardNemoTopicAdherenceConfigResponse | false | | The configuration for the Topic Adherence metric. | | openaiApiBase | string,null | false | maxLength: 255 | The Azure OpenAI API Base URL. | | openaiApiKey | string,null | false | maxLength: 255 | Deprecated; use openai_credential instead. | | openaiCredential | string,null | false | | The ID of the user credential containing an OpenAI token. | | openaiDeploymentId | string,null | false | maxLength: 255 | The OpenAPI deployment ID. | | stages | [string] | true | maxItems: 16 | The stages where the guard can run. | | templateId | string | true | | The ID of the template this guard is based on. | ### Enumerated Values | Property | Value | | --- | --- | | awsModel | [amazon-titan, anthropic-claude-2, anthropic-claude-3-haiku, anthropic-claude-3-sonnet, anthropic-claude-3-opus, anthropic-claude-3.5-sonnet-v1, anthropic-claude-3.5-sonnet-v2, amazon-nova-lite, amazon-nova-micro, amazon-nova-pro] | | entityType | [customModel, customModelVersion, playground] | | googleModel | [chat-bison, google-gemini-1.5-flash, google-gemini-1.5-pro] | | llmType | [openAi, azureOpenAi, google, amazon, datarobot, nim, llmGateway] | ## GuardConfigurationFullPost ``` { "description": "Complete guard configuration to push", "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "Guard configuration description", "maxLength": 4096, "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": false, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "description": "Whether the guard is valid or not.", "type": "boolean" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Guard configuration name", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "Guard configuration \"NeMo Evaluator\" metric type", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "Guard template \"Out of the Box\" metric type", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "parameters": { "description": "Parameter list, not used, deprecated.", "items": { "maxLength": 1, "type": "string" }, "maxItems": 1, "type": "array" }, "stages": { "description": "The stages where the guard is configured to run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "type": { "description": "The guard configuration type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "description", "name", "stages", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` Complete guard configuration to push ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | additionalGuardConfig | GuardAdditionalConfig | false | | Additional configuration for the guard. | | allowedActions | [string] | false | maxItems: 10 | The actions this guard is allowed to take. | | awsAccount | string,null | false | | The ID of the user credential containing an AWS account. | | awsModel | string,null | false | | AWS model. | | awsRegion | string,null | false | maxLength: 255 | AWS model region. | | deploymentId | string,null | false | | The ID of the deployed model for model guards. | | description | string | true | maxLength: 4096 | Guard configuration description | | errorMessage | string,null | false | | Error message if the guard configuration is invalid. | | googleModel | string,null | false | | Google model. | | googleRegion | string,null | false | maxLength: 255 | Google model region. | | googleServiceAccount | string,null | false | | The ID of the user credential containing a Google service account. | | intervention | GuardConfigurationInterventionResponse | false | | Intervention configuration for the guard. | | isAgentic | boolean | false | | True if the guard is suitable for agentic workflows only. | | isValid | boolean | false | | Whether the guard is valid or not. | | llmGatewayModelId | string,null | false | maxLength: 255 | The LLM Gateway model ID to use as judge. | | llmType | string,null | false | | The type of LLM used by this guard. | | modelInfo | GuardModelInfoResponse | false | | The configuration info for guards using deployed models. | | name | string | true | maxLength: 255 | Guard configuration name | | nemoEvaluatorType | string,null | false | | Guard configuration "NeMo Evaluator" metric type | | nemoInfo | GuardConfigurationNemoInfoResponse | false | | The configuration info for NeMo guards. | | nemoLlmJudgeConfig | GuardNemoLlmJudgeConfigResponse | false | | The configuration for the LLM Judge metric. | | nemoResponseRelevancyConfig | GuardNemoResponseRelevancyConfigResponse | false | | The configuration for the Response Relevancy metric. | | nemoTopicAdherenceConfig | GuardNemoTopicAdherenceConfigResponse | false | | The configuration for the Topic Adherence metric. | | ootbType | string,null | false | | Guard template "Out of the Box" metric type | | openaiApiBase | string,null | false | maxLength: 255 | The Azure OpenAI API Base URL. | | openaiApiKey | string,null | false | maxLength: 255 | Deprecated; use openai_credential instead. | | openaiCredential | string,null | false | | The ID of the user credential containing an OpenAI token. | | openaiDeploymentId | string,null | false | maxLength: 255 | The OpenAI deployment ID. | | parameters | [string] | false | maxItems: 1 | Parameter list, not used, deprecated. | | stages | [string] | true | maxItems: 16 | The stages where the guard is configured to run. | | type | string | true | | The guard configuration type. | ### Enumerated Values | Property | Value | | --- | --- | | awsModel | [amazon-titan, anthropic-claude-2, anthropic-claude-3-haiku, anthropic-claude-3-sonnet, anthropic-claude-3-opus, anthropic-claude-3.5-sonnet-v1, anthropic-claude-3.5-sonnet-v2, amazon-nova-lite, amazon-nova-micro, amazon-nova-pro] | | googleModel | [chat-bison, google-gemini-1.5-flash, google-gemini-1.5-pro] | | llmType | [openAi, azureOpenAi, google, amazon, datarobot, nim, llmGateway] | | nemoEvaluatorType | [llm_judge, context_relevance, response_groundedness, topic_adherence, agent_goal_accuracy, response_relevancy, faithfulness] | | ootbType | [token_count, faithfulness, rouge_1, agent_goal_accuracy, agent_goal_accuracy_with_reference, cost, task_adherence, tool_call_accuracy, agent_guideline_adherence, agent_latency, agent_tokens, agent_cost, agentGoalAccuracy, agentGoalAccuracyWithReference, taskAdherence, toolCallAccuracy] | | type | [guardModel, nemo, nemoEvaluator, ootb, pii, userModel] | ## GuardConfigurationInterventionResponse ``` { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" } ``` Intervention configuration for the guard. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | The action to take if conditions are met. | | allowedActions | [string] | false | maxItems: 10 | The actions this guard is allowed to take. | | conditionLogic | string | false | | The action to take if conditions are met. | | conditions | [GuardConfigurationConditionResponse] | true | maxItems: 1 | The list of conditions to trigger intervention. | | message | string | true | maxLength: 4096 | The message to use if the prompt or response is blocked. | | sendNotification | boolean | false | | The notification event to create if intervention is triggered. | ### Enumerated Values | Property | Value | | --- | --- | | action | [block, report, replace] | | conditionLogic | any | ## GuardConfigurationListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of guard configurations.", "items": { "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "createdAt": { "description": "When the configuration was created.", "format": "date-time", "type": "string" }, "creatorId": { "description": "The ID of the user who created the guard configuration.", "type": [ "string", "null" ] }, "creatorName": { "description": "The name of the user who created the guard configuration.", "maxLength": 1000, "type": "string" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "The guard configuration description.", "maxLength": 4096, "type": "string" }, "entityId": { "description": "The ID of the custom model or playground for this guard.", "type": [ "string", "null" ] }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "id": { "description": "The guard configuration object ID.", "type": "string" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": false, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "description": "Whether the guard is valid or not.", "type": "boolean" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The guard configuration name.", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "Guard configuration \"NeMo Evaluator\" metric type", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "Guard template \"Out of the Box\" metric type", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "stages": { "description": "The stages where the guard is configured to run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "type": { "description": "The guard configuration type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "createdAt", "description", "entityId", "entityType", "id", "name", "stages", "type" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [GuardConfigurationRetrieveResponse] | true | maxItems: 200 | The list of guard configurations. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## GuardConfigurationNemoInfoResponse ``` { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" } ``` The configuration info for NeMo guards. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actions | string | false | maxLength: 4096 | The NeMo guardrails actions file. | | blockedTerms | string | true | maxLength: 4096 | The NeMo guardrails blocked terms list. | | credentialId | string,null | false | | The NeMo guardrails credential ID (deprecated; use "openai_credential"). | | llmPrompts | string | false | maxLength: 4096 | The NeMo guardrails prompts. | | mainConfig | string | true | maxLength: 4096 | The overall NeMo configuration YAML. | | railsConfig | string | true | maxLength: 4096 | The NeMo guardrails configuration Colang. | ## GuardConfigurationPayloadModelInfo ``` { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": [ "string", "null" ] } }, "required": [ "inputColumnName", "outputColumnName" ], "type": "object", "x-versionadded": "v2.35" } ``` The configuration info for guards using deployed models. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classNames | [string] | false | maxItems: 100 | The list of class names for multiclass models. | | inputColumnName | string | true | maxLength: 255 | The input column name. | | modelId | string,null | false | | The ID of the registered model for model guards. | | modelName | string | false | maxLength: 255 | The ID of the registered model for .model guards. | | outputColumnName | string | true | maxLength: 255 | The output column name. | | replacementTextColumnName | string | false | maxLength: 255 | The name of the output column with replacement text. Required only if intervention.action is replace. | | targetType | string,null | false | | The target type. | ### Enumerated Values | Property | Value | | --- | --- | | targetType | [Binary, Regression, Multiclass, TextGeneration] | ## GuardConfigurationPredictionEnvironmentsInUseListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "list of prediction environments in use for this custom model version.", "items": { "properties": { "id": { "description": "ID of prediction environment.", "type": "string" }, "name": { "description": "Name of prediction environment.", "type": "string" }, "usedBy": { "description": "Guards using this prediction environment.", "items": { "properties": { "configurationId": { "description": "ID of guard configuration.", "type": "string" }, "deploymentId": { "description": "ID of guard model deployment.", "type": "string" }, "name": { "description": "Name of guard configuration.", "type": "string" } }, "required": [ "configurationId", "deploymentId", "name" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 32, "type": "array" } }, "required": [ "id", "name", "usedBy" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 32, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [PredictionEnvironmentInUseResponse] | true | maxItems: 32 | list of prediction environments in use for this custom model version. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## GuardConfigurationRetrieveResponse ``` { "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "createdAt": { "description": "When the configuration was created.", "format": "date-time", "type": "string" }, "creatorId": { "description": "The ID of the user who created the guard configuration.", "type": [ "string", "null" ] }, "creatorName": { "description": "The name of the user who created the guard configuration.", "maxLength": 1000, "type": "string" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "The guard configuration description.", "maxLength": 4096, "type": "string" }, "entityId": { "description": "The ID of the custom model or playground for this guard.", "type": [ "string", "null" ] }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "id": { "description": "The guard configuration object ID.", "type": "string" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": false, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "description": "Whether the guard is valid or not.", "type": "boolean" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The guard configuration name.", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "Guard configuration \"NeMo Evaluator\" metric type", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "Guard template \"Out of the Box\" metric type", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "stages": { "description": "The stages where the guard is configured to run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "type": { "description": "The guard configuration type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "createdAt", "description", "entityId", "entityType", "id", "name", "stages", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | additionalGuardConfig | GuardAdditionalConfig | false | | Additional configuration for the guard. | | allowedActions | [string] | false | maxItems: 10 | The actions this guard is allowed to take. | | awsAccount | string,null | false | | The ID of the user credential containing an AWS account. | | awsModel | string,null | false | | AWS model. | | awsRegion | string,null | false | maxLength: 255 | AWS model region. | | createdAt | string(date-time) | true | | When the configuration was created. | | creatorId | string,null | false | | The ID of the user who created the guard configuration. | | creatorName | string | false | maxLength: 1000 | The name of the user who created the guard configuration. | | deploymentId | string,null | false | | The ID of the deployed model for model guards. | | description | string | true | maxLength: 4096 | The guard configuration description. | | entityId | string,null | true | | The ID of the custom model or playground for this guard. | | entityType | string | true | | The type of the associated entity. | | errorMessage | string,null | false | | Error message if the guard configuration is invalid. | | googleModel | string,null | false | | Google model. | | googleRegion | string,null | false | maxLength: 255 | Google model region. | | googleServiceAccount | string,null | false | | The ID of the user credential containing a Google service account. | | id | string | true | | The guard configuration object ID. | | intervention | GuardConfigurationInterventionResponse | false | | Intervention configuration for the guard. | | isAgentic | boolean | false | | True if the guard is suitable for agentic workflows only. | | isValid | boolean | false | | Whether the guard is valid or not. | | llmGatewayModelId | string,null | false | maxLength: 255 | The LLM Gateway model ID to use as judge. | | llmType | string,null | false | | The type of LLM used by this guard. | | modelInfo | GuardModelInfoResponse | false | | The configuration info for guards using deployed models. | | name | string | true | maxLength: 255 | The guard configuration name. | | nemoEvaluatorType | string,null | false | | Guard configuration "NeMo Evaluator" metric type | | nemoInfo | GuardConfigurationNemoInfoResponse | false | | The configuration info for NeMo guards. | | nemoLlmJudgeConfig | GuardNemoLlmJudgeConfigResponse | false | | The configuration for the LLM Judge metric. | | nemoResponseRelevancyConfig | GuardNemoResponseRelevancyConfigResponse | false | | The configuration for the Response Relevancy metric. | | nemoTopicAdherenceConfig | GuardNemoTopicAdherenceConfigResponse | false | | The configuration for the Topic Adherence metric. | | ootbType | string,null | false | | Guard template "Out of the Box" metric type | | openaiApiBase | string,null | false | maxLength: 255 | The Azure OpenAI API Base URL. | | openaiApiKey | string,null | false | maxLength: 255 | Deprecated; use openai_credential instead. | | openaiCredential | string,null | false | | The ID of the user credential containing an OpenAI token. | | openaiDeploymentId | string,null | false | maxLength: 255 | The OpenAPI deployment ID. | | stages | [string] | true | maxItems: 16 | The stages where the guard is configured to run. | | type | string | true | | The guard configuration type. | ### Enumerated Values | Property | Value | | --- | --- | | awsModel | [amazon-titan, anthropic-claude-2, anthropic-claude-3-haiku, anthropic-claude-3-sonnet, anthropic-claude-3-opus, anthropic-claude-3.5-sonnet-v1, anthropic-claude-3.5-sonnet-v2, amazon-nova-lite, amazon-nova-micro, amazon-nova-pro] | | entityType | [customModel, customModelVersion, playground] | | googleModel | [chat-bison, google-gemini-1.5-flash, google-gemini-1.5-pro] | | llmType | [openAi, azureOpenAi, google, amazon, datarobot, nim, llmGateway] | | nemoEvaluatorType | [llm_judge, context_relevance, response_groundedness, topic_adherence, agent_goal_accuracy, response_relevancy, faithfulness] | | ootbType | [token_count, faithfulness, rouge_1, agent_goal_accuracy, agent_goal_accuracy_with_reference, cost, task_adherence, tool_call_accuracy, agent_guideline_adherence, agent_latency, agent_tokens, agent_cost, agentGoalAccuracy, agentGoalAccuracyWithReference, taskAdherence, toolCallAccuracy] | | type | [guardModel, nemo, nemoEvaluator, ootb, pii, userModel] | ## GuardConfigurationToCustomModelResponse ``` { "properties": { "customModelVersionId": { "description": "The ID of the new custom model version created.", "type": "string" } }, "required": [ "customModelVersionId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelVersionId | string | true | | The ID of the new custom model version created. | ## GuardConfigurationToCustomModelVersion ``` { "properties": { "customModelId": { "description": "The ID of the custom model the user is working with.", "type": "string" }, "data": { "description": "The list of complete guard configurations to push.", "items": { "description": "Complete guard configuration to push", "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "Guard configuration description", "maxLength": 4096, "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": false, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "description": "Whether the guard is valid or not.", "type": "boolean" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Guard configuration name", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "Guard configuration \"NeMo Evaluator\" metric type", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "Guard template \"Out of the Box\" metric type", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "parameters": { "description": "Parameter list, not used, deprecated.", "items": { "maxLength": 1, "type": "string" }, "maxItems": 1, "type": "array" }, "stages": { "description": "The stages where the guard is configured to run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "type": { "description": "The guard configuration type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "description", "name", "stages", "type" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "type": "array" }, "overallConfig": { "description": "Overall moderation configuration to push (not specific to one guard)", "properties": { "nemoEvaluatorDeploymentId": { "description": "ID of NeMo Evaluator deployment to use for all NeMo Evaluator guards.", "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "timeoutAction": { "description": "Action to take if timeout occurs", "enum": [ "block", "score" ], "type": "string" }, "timeoutSec": { "description": "Timeout value in seconds for any guard", "minimum": 2, "type": "integer" } }, "required": [ "timeoutAction", "timeoutSec" ], "type": "object", "x-versionadded": "v2.35" } }, "required": [ "customModelId", "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string | true | | The ID of the custom model the user is working with. | | data | [GuardConfigurationFullPost] | true | maxItems: 200 | The list of complete guard configurations to push. | | overallConfig | OverallConfigUpdate | false | | Overall moderation configuration to push (not specific to one guard) | ## GuardConfigurationUpdate ``` { "properties": { "additionalGuardConfig": { "description": "Additional configuration for the guard.", "properties": { "agentGuideline": { "description": "How to calculate agent guideline adherence for this guard.", "maxLength": 4096, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "cost": { "description": "How to calculate cost information for this guard.", "properties": { "currency": { "description": "ISO 4217 Currency code for display.", "maxLength": 255, "type": "string" }, "inputPrice": { "description": "Cost per unit measure of tokens for input.", "minimum": 0, "type": "number" }, "inputUnit": { "description": "Number of tokens related to input price.", "minimum": 0, "type": "integer" }, "outputPrice": { "description": "Cost per unit measure of tokens for output.", "minimum": 0, "type": "number" }, "outputUnit": { "description": "Number of tokens related to output price.", "minimum": 0, "type": "integer" } }, "required": [ "currency", "inputPrice", "inputUnit", "outputPrice", "outputUnit" ], "type": "object", "x-versionadded": "v2.37" }, "toolCall": { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } }, "type": "object", "x-versionadded": "v2.37" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "awsAccount": { "description": "The ID of the user credential containing an AWS account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "deploymentId": { "description": "The ID of the deployed model for model guards.", "type": [ "string", "null" ] }, "description": { "description": "Guard configuration description", "maxLength": 4096, "type": "string" }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleServiceAccount": { "description": "The ID of the user credential containing a Google service account.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "intervention": { "description": "Intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "message": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "default": false, "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "message" ], "type": "object", "x-versionadded": "v2.35" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": [ "string", "null" ] } }, "required": [ "inputColumnName", "outputColumnName" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "Guard configuration name", "maxLength": 255, "type": "string" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "description": "The NeMo guardrails actions file.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID (deprecated; use \"openai_credential\").", "type": [ "string", "null" ] }, "llmPrompts": { "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiCredential": { "description": "The ID of the user credential containing an OpenAI token.", "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | additionalGuardConfig | GuardAdditionalConfig | false | | Additional configuration for the guard. | | allowedActions | [string] | false | maxItems: 10 | The actions this guard is allowed to take. | | awsAccount | string,null | false | | The ID of the user credential containing an AWS account. | | awsModel | string,null | false | | AWS model. | | awsRegion | string,null | false | maxLength: 255 | AWS model region. | | deploymentId | string,null | false | | The ID of the deployed model for model guards. | | description | string | false | maxLength: 4096 | Guard configuration description | | googleModel | string,null | false | | Google model. | | googleRegion | string,null | false | maxLength: 255 | Google model region. | | googleServiceAccount | string,null | false | | The ID of the user credential containing a Google service account. | | intervention | GuardConfigurationInterventionResponse | false | | Intervention configuration for the guard. | | llmGatewayModelId | string,null | false | maxLength: 255 | The LLM Gateway model ID to use as judge. | | llmType | string,null | false | | The type of LLM used by this guard. | | modelInfo | GuardConfigurationPayloadModelInfo | false | | The configuration info for guards using deployed models. | | name | string | false | maxLength: 255 | Guard configuration name | | nemoInfo | GuardConfigurationNemoInfoResponse | false | | The configuration info for NeMo guards. | | nemoLlmJudgeConfig | GuardNemoLlmJudgeConfigResponse | false | | The configuration for the LLM Judge metric. | | nemoResponseRelevancyConfig | GuardNemoResponseRelevancyConfigResponse | false | | The configuration for the Response Relevancy metric. | | nemoTopicAdherenceConfig | GuardNemoTopicAdherenceConfigResponse | false | | The configuration for the Topic Adherence metric. | | openaiApiBase | string,null | false | maxLength: 255 | The Azure OpenAI API Base URL. | | openaiApiKey | string,null | false | maxLength: 255 | Deprecated; use openai_credential instead. | | openaiCredential | string,null | false | | The ID of the user credential containing an OpenAI token. | | openaiDeploymentId | string,null | false | maxLength: 255 | The OpenAPI deployment ID. | ### Enumerated Values | Property | Value | | --- | --- | | awsModel | [amazon-titan, anthropic-claude-2, anthropic-claude-3-haiku, anthropic-claude-3-sonnet, anthropic-claude-3-opus, anthropic-claude-3.5-sonnet-v1, anthropic-claude-3.5-sonnet-v2, amazon-nova-lite, amazon-nova-micro, amazon-nova-pro] | | googleModel | [chat-bison, google-gemini-1.5-flash, google-gemini-1.5-pro] | | llmType | [openAi, azureOpenAi, google, amazon, datarobot, nim, llmGateway] | ## GuardInterventionResponse ``` { "description": "The intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "description": "The class name to match.", "maxLength": 128, "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "modifyMessage": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "modifyMessage" ], "type": "object", "x-versionadded": "v2.35" } ``` The intervention configuration for the guard. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | The action to take if conditions are met. | | allowedActions | [string] | false | maxItems: 10 | The actions this guard is allowed to take. | | conditionLogic | string | false | | The action to take if conditions are met. | | conditions | [GuardConditionResponse] | true | maxItems: 1 | The list of conditions to trigger intervention. | | modifyMessage | string | true | maxLength: 4096 | The message to use if the prompt or response is blocked. | | sendNotification | boolean | false | | The notification event to create if intervention is triggered. | ### Enumerated Values | Property | Value | | --- | --- | | action | [block, report, replace] | | conditionLogic | any | ## GuardModelInfoResponse ``` { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" } ``` The configuration info for guards using deployed models. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classNames | [string] | false | maxItems: 100 | The list of class names for multiclass models. | | inputColumnName | string | true | maxLength: 255 | The input column name. | | modelId | string,null | false | | The ID of the registered model for model guards. | | modelName | string | false | maxLength: 255 | The ID of the registered model for .model guards. | | outputColumnName | string | true | maxLength: 255 | The output column name. | | replacementTextColumnName | string | false | maxLength: 255 | The name of the output column with replacement text. Required only if intervention.action is replace. | | targetType | string | true | | The target type. | ### Enumerated Values | Property | Value | | --- | --- | | targetType | [Binary, Regression, Multiclass, TextGeneration] | ## GuardNemoInfoResponse ``` { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "default": "", "description": "The NeMo guardrails actions.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID.", "type": [ "string", "null" ] }, "llmPrompts": { "default": "", "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" } ``` The configuration info for NeMo guards. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actions | string | false | maxLength: 4096 | The NeMo guardrails actions. | | blockedTerms | string | true | maxLength: 4096 | The NeMo guardrails blocked terms list. | | credentialId | string,null | false | | The NeMo guardrails credential ID. | | llmPrompts | string | false | maxLength: 4096 | The NeMo guardrails prompts. | | mainConfig | string | true | maxLength: 4096 | The overall NeMo configuration YAML. | | railsConfig | string | true | maxLength: 4096 | The NeMo guardrails configuration Colang. | ## GuardNemoLlmJudgeConfigResponse ``` { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" } ``` The configuration for the LLM Judge metric. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customMetricDirectionality | string | true | | The custom metric directionality for the LLM judge. | | scoreParsingRegex | string | true | maxLength: 1024 | The regex to parse the score from the LLM judge response. | | systemPrompt | string | true | maxLength: 8192 | The system prompt for the LLM judge. | | userPrompt | string | true | maxLength: 8192 | The user prompt template for the LLM judge. | ### Enumerated Values | Property | Value | | --- | --- | | customMetricDirectionality | [higherIsBetter, lowerIsBetter] | ## GuardNemoResponseRelevancyConfigResponse ``` { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" } ``` The configuration for the Response Relevancy metric. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | embeddingDeploymentId | string | true | | The ID of the embedding model deployment. | ## GuardNemoTopicAdherenceConfigResponse ``` { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" } ``` The configuration for the Topic Adherence metric. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metricMode | string | false | | The metric calculation mode. | | referenceTopics | [string] | true | maxItems: 100minItems: 1 | The list of reference topics. | ### Enumerated Values | Property | Value | | --- | --- | | metricMode | [f1, recall, precision] | ## GuardTemplateListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of guard templates.", "items": { "properties": { "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "allowedStages": { "description": "The stages where the guard can run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "createdAt": { "description": "When the template was created.", "format": "date-time", "type": "string" }, "creatorId": { "description": "The ID of the user who created the guard template.", "type": [ "string", "null" ] }, "creatorName": { "description": "The name of the user who created the guard template.", "maxLength": 1000, "type": "string" }, "description": { "description": "The guard template description.", "maxLength": 4096, "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "id": { "description": "The guard template object ID.", "type": "string" }, "intervention": { "description": "The intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "description": "The class name to match.", "maxLength": 128, "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "modifyMessage": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "modifyMessage" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": true, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "default": true, "description": "True if the guard is fully configured and valid.", "type": "boolean" }, "labels": { "description": "The list of short strings to associate with the template.", "items": { "description": "A short string to associate with the template.", "maxLength": 255, "type": "string" }, "maxItems": 16, "type": "array", "x-versionadded": "v2.37" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The guard template name.", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "The guard template \"NeMo Evaluator\" metric type.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "default": "", "description": "The NeMo guardrails actions.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID.", "type": [ "string", "null" ] }, "llmPrompts": { "default": "", "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "The guard template \"Out of the Box\" metric type.", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "orgId": { "description": "Organization ID of the user who created the Guard template.", "type": [ "string", "null" ] }, "playgroundOnly": { "description": "Whether the guard is for playground only, or if it can be used in production and playground.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "productionOnly": { "description": "Whether the guard is for production only, or if it can be used in production and playground.", "type": [ "boolean", "null" ] }, "type": { "description": "The guard template type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "allowedStages", "createdAt", "description", "id", "name", "type" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [GuardTemplateRetrieveResponse] | true | maxItems: 200 | The list of guard templates. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## GuardTemplateRetrieveResponse ``` { "properties": { "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "allowedStages": { "description": "The stages where the guard can run.", "items": { "enum": [ "prompt", "response" ], "type": "string" }, "maxItems": 16, "type": "array" }, "awsModel": { "description": "AWS model.", "enum": [ "amazon-titan", "anthropic-claude-2", "anthropic-claude-3-haiku", "anthropic-claude-3-sonnet", "anthropic-claude-3-opus", "anthropic-claude-3.5-sonnet-v1", "anthropic-claude-3.5-sonnet-v2", "amazon-nova-lite", "amazon-nova-micro", "amazon-nova-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "awsRegion": { "description": "AWS model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "createdAt": { "description": "When the template was created.", "format": "date-time", "type": "string" }, "creatorId": { "description": "The ID of the user who created the guard template.", "type": [ "string", "null" ] }, "creatorName": { "description": "The name of the user who created the guard template.", "maxLength": 1000, "type": "string" }, "description": { "description": "The guard template description.", "maxLength": 4096, "type": "string" }, "errorMessage": { "description": "Error message if the guard configuration is invalid.", "type": [ "string", "null" ] }, "googleModel": { "description": "Google model.", "enum": [ "chat-bison", "google-gemini-1.5-flash", "google-gemini-1.5-pro" ], "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "googleRegion": { "description": "Google model region.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "id": { "description": "The guard template object ID.", "type": "string" }, "intervention": { "description": "The intervention configuration for the guard.", "properties": { "action": { "description": "The action to take if conditions are met.", "enum": [ "block", "report", "replace" ], "type": "string" }, "allowedActions": { "description": "The actions this guard is allowed to take.", "items": { "enum": [ "block", "report", "replace" ], "type": "string" }, "maxItems": 10, "type": "array" }, "conditionLogic": { "default": "any", "description": "The action to take if conditions are met.", "enum": [ "any" ], "type": "string" }, "conditions": { "description": "The list of conditions to trigger intervention.", "items": { "description": "The condition to trigger intervention.", "properties": { "comparand": { "anyOf": [ { "type": "boolean" }, { "type": "number" }, { "type": "string" }, { "items": { "description": "The class name to match.", "maxLength": 128, "type": "string" }, "maxItems": 10, "type": "array" } ], "description": "The condition comparand (basis of comparison)." }, "comparator": { "description": "The condition comparator (operator).", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "type": "string" } }, "required": [ "comparand", "comparator" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1, "type": "array" }, "modifyMessage": { "description": "The message to use if the prompt or response is blocked.", "maxLength": 4096, "type": "string" }, "sendNotification": { "description": "The notification event to create if intervention is triggered.", "type": "boolean" } }, "required": [ "action", "conditions", "modifyMessage" ], "type": "object", "x-versionadded": "v2.35" }, "isAgentic": { "default": true, "description": "True if the guard is suitable for agentic workflows only.", "type": "boolean", "x-versionadded": "v2.37" }, "isValid": { "default": true, "description": "True if the guard is fully configured and valid.", "type": "boolean" }, "labels": { "description": "The list of short strings to associate with the template.", "items": { "description": "A short string to associate with the template.", "maxLength": 255, "type": "string" }, "maxItems": 16, "type": "array", "x-versionadded": "v2.37" }, "llmGatewayModelId": { "description": "The LLM Gateway model ID to use as judge.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "llmType": { "description": "The type of LLM used by this guard.", "enum": [ "openAi", "azureOpenAi", "google", "amazon", "datarobot", "nim", "llmGateway" ], "type": [ "string", "null" ] }, "modelInfo": { "description": "The configuration info for guards using deployed models.", "properties": { "classNames": { "description": "The list of class names for multiclass models.", "items": { "description": "The class name.", "maxLength": 128, "type": "string" }, "maxItems": 100, "type": "array" }, "inputColumnName": { "description": "The input column name.", "maxLength": 255, "type": "string" }, "modelId": { "description": "The ID of the registered model for model guards.", "type": [ "string", "null" ] }, "modelName": { "default": "", "description": "The ID of the registered model for .model guards.", "maxLength": 255, "type": "string" }, "outputColumnName": { "description": "The output column name.", "maxLength": 255, "type": "string" }, "replacementTextColumnName": { "default": "", "description": "The name of the output column with replacement text. Required only if intervention.action is `replace`.", "maxLength": 255, "type": "string" }, "targetType": { "description": "The target type.", "enum": [ "Binary", "Regression", "Multiclass", "TextGeneration" ], "type": "string" } }, "required": [ "inputColumnName", "outputColumnName", "targetType" ], "type": "object", "x-versionadded": "v2.35" }, "name": { "description": "The guard template name.", "maxLength": 255, "type": "string" }, "nemoEvaluatorType": { "description": "The guard template \"NeMo Evaluator\" metric type.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "nemoInfo": { "description": "The configuration info for NeMo guards.", "properties": { "actions": { "default": "", "description": "The NeMo guardrails actions.", "maxLength": 4096, "type": "string" }, "blockedTerms": { "description": "The NeMo guardrails blocked terms list.", "maxLength": 4096, "type": "string" }, "credentialId": { "description": "The NeMo guardrails credential ID.", "type": [ "string", "null" ] }, "llmPrompts": { "default": "", "description": "The NeMo guardrails prompts.", "maxLength": 4096, "type": "string" }, "mainConfig": { "description": "The overall NeMo configuration YAML.", "maxLength": 4096, "type": "string" }, "railsConfig": { "description": "The NeMo guardrails configuration Colang.", "maxLength": 4096, "type": "string" } }, "required": [ "blockedTerms", "mainConfig", "railsConfig" ], "type": "object", "x-versionadded": "v2.35" }, "nemoLlmJudgeConfig": { "description": "The configuration for the LLM Judge metric.", "properties": { "customMetricDirectionality": { "description": "The custom metric directionality for the LLM judge.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string", "x-versionadded": "v2.42" }, "scoreParsingRegex": { "description": "The regex to parse the score from the LLM judge response.", "maxLength": 1024, "type": "string" }, "systemPrompt": { "description": "The system prompt for the LLM judge.", "maxLength": 8192, "type": "string" }, "userPrompt": { "description": "The user prompt template for the LLM judge.", "maxLength": 8192, "type": "string" } }, "required": [ "customMetricDirectionality", "scoreParsingRegex", "systemPrompt", "userPrompt" ], "type": "object", "x-versionadded": "v2.42" }, "nemoResponseRelevancyConfig": { "description": "The configuration for the Response Relevancy metric.", "properties": { "embeddingDeploymentId": { "description": "The ID of the embedding model deployment.", "type": "string" } }, "required": [ "embeddingDeploymentId" ], "type": "object", "x-versionadded": "v2.42" }, "nemoTopicAdherenceConfig": { "description": "The configuration for the Topic Adherence metric.", "properties": { "metricMode": { "default": "f1", "description": "The metric calculation mode.", "enum": [ "f1", "recall", "precision" ], "type": "string" }, "referenceTopics": { "description": "The list of reference topics.", "items": { "description": "The reference topic.", "maxLength": 512, "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "referenceTopics" ], "type": "object", "x-versionadded": "v2.42" }, "ootbType": { "description": "The guard template \"Out of the Box\" metric type.", "enum": [ "token_count", "faithfulness", "rouge_1", "agent_goal_accuracy", "agent_goal_accuracy_with_reference", "cost", "task_adherence", "tool_call_accuracy", "agent_guideline_adherence", "agent_latency", "agent_tokens", "agent_cost", "agentGoalAccuracy", "agentGoalAccuracyWithReference", "taskAdherence", "toolCallAccuracy" ], "type": [ "string", "null" ] }, "openaiApiBase": { "description": "The Azure OpenAI API Base URL.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiApiKey": { "description": "Deprecated; use openai_credential instead.", "maxLength": 255, "type": [ "string", "null" ] }, "openaiDeploymentId": { "description": "The OpenAPI deployment ID.", "maxLength": 255, "type": [ "string", "null" ] }, "orgId": { "description": "Organization ID of the user who created the Guard template.", "type": [ "string", "null" ] }, "playgroundOnly": { "description": "Whether the guard is for playground only, or if it can be used in production and playground.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "productionOnly": { "description": "Whether the guard is for production only, or if it can be used in production and playground.", "type": [ "boolean", "null" ] }, "type": { "description": "The guard template type.", "enum": [ "guardModel", "nemo", "nemoEvaluator", "ootb", "pii", "userModel" ], "type": "string" } }, "required": [ "allowedStages", "createdAt", "description", "id", "name", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowedActions | [string] | false | maxItems: 10 | The actions this guard is allowed to take. | | allowedStages | [string] | true | maxItems: 16 | The stages where the guard can run. | | awsModel | string,null | false | | AWS model. | | awsRegion | string,null | false | maxLength: 255 | AWS model region. | | createdAt | string(date-time) | true | | When the template was created. | | creatorId | string,null | false | | The ID of the user who created the guard template. | | creatorName | string | false | maxLength: 1000 | The name of the user who created the guard template. | | description | string | true | maxLength: 4096 | The guard template description. | | errorMessage | string,null | false | | Error message if the guard configuration is invalid. | | googleModel | string,null | false | | Google model. | | googleRegion | string,null | false | maxLength: 255 | Google model region. | | id | string | true | | The guard template object ID. | | intervention | GuardInterventionResponse | false | | The intervention configuration for the guard. | | isAgentic | boolean | false | | True if the guard is suitable for agentic workflows only. | | isValid | boolean | false | | True if the guard is fully configured and valid. | | labels | [string] | false | maxItems: 16 | The list of short strings to associate with the template. | | llmGatewayModelId | string,null | false | maxLength: 255 | The LLM Gateway model ID to use as judge. | | llmType | string,null | false | | The type of LLM used by this guard. | | modelInfo | GuardModelInfoResponse | false | | The configuration info for guards using deployed models. | | name | string | true | maxLength: 255 | The guard template name. | | nemoEvaluatorType | string,null | false | | The guard template "NeMo Evaluator" metric type. | | nemoInfo | GuardNemoInfoResponse | false | | The configuration info for NeMo guards. | | nemoLlmJudgeConfig | GuardNemoLlmJudgeConfigResponse | false | | The configuration for the LLM Judge metric. | | nemoResponseRelevancyConfig | GuardNemoResponseRelevancyConfigResponse | false | | The configuration for the Response Relevancy metric. | | nemoTopicAdherenceConfig | GuardNemoTopicAdherenceConfigResponse | false | | The configuration for the Topic Adherence metric. | | ootbType | string,null | false | | The guard template "Out of the Box" metric type. | | openaiApiBase | string,null | false | maxLength: 255 | The Azure OpenAI API Base URL. | | openaiApiKey | string,null | false | maxLength: 255 | Deprecated; use openai_credential instead. | | openaiDeploymentId | string,null | false | maxLength: 255 | The OpenAPI deployment ID. | | orgId | string,null | false | | Organization ID of the user who created the Guard template. | | playgroundOnly | boolean,null | false | | Whether the guard is for playground only, or if it can be used in production and playground. | | productionOnly | boolean,null | false | | Whether the guard is for production only, or if it can be used in production and playground. | | type | string | true | | The guard template type. | ### Enumerated Values | Property | Value | | --- | --- | | awsModel | [amazon-titan, anthropic-claude-2, anthropic-claude-3-haiku, anthropic-claude-3-sonnet, anthropic-claude-3-opus, anthropic-claude-3.5-sonnet-v1, anthropic-claude-3.5-sonnet-v2, amazon-nova-lite, amazon-nova-micro, amazon-nova-pro] | | googleModel | [chat-bison, google-gemini-1.5-flash, google-gemini-1.5-pro] | | llmType | [openAi, azureOpenAi, google, amazon, datarobot, nim, llmGateway] | | nemoEvaluatorType | [llm_judge, context_relevance, response_groundedness, topic_adherence, agent_goal_accuracy, response_relevancy, faithfulness] | | ootbType | [token_count, faithfulness, rouge_1, agent_goal_accuracy, agent_goal_accuracy_with_reference, cost, task_adherence, tool_call_accuracy, agent_guideline_adherence, agent_latency, agent_tokens, agent_cost, agentGoalAccuracy, agentGoalAccuracyWithReference, taskAdherence, toolCallAccuracy] | | type | [guardModel, nemo, nemoEvaluator, ootb, pii, userModel] | ## GuardToolCallInfo ``` { "description": "How to calculate tool call metrics for this guard.", "properties": { "comparisonMetric": { "description": "How tool calls should be compared for metrics calculations.", "enum": [ "exactMatch", "doNotCompare" ], "type": "string" } }, "required": [ "comparisonMetric" ], "type": "object", "x-versionadded": "v2.37" } ``` How to calculate tool call metrics for this guard. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparisonMetric | string | true | | How tool calls should be compared for metrics calculations. | ### Enumerated Values | Property | Value | | --- | --- | | comparisonMetric | [exactMatch, doNotCompare] | ## OverallConfigUpdate ``` { "description": "Overall moderation configuration to push (not specific to one guard)", "properties": { "nemoEvaluatorDeploymentId": { "description": "ID of NeMo Evaluator deployment to use for all NeMo Evaluator guards.", "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "timeoutAction": { "description": "Action to take if timeout occurs", "enum": [ "block", "score" ], "type": "string" }, "timeoutSec": { "description": "Timeout value in seconds for any guard", "minimum": 2, "type": "integer" } }, "required": [ "timeoutAction", "timeoutSec" ], "type": "object", "x-versionadded": "v2.35" } ``` Overall moderation configuration to push (not specific to one guard) ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | nemoEvaluatorDeploymentId | string,null | false | | ID of NeMo Evaluator deployment to use for all NeMo Evaluator guards. | | timeoutAction | string | true | | Action to take if timeout occurs | | timeoutSec | integer | true | minimum: 2 | Timeout value in seconds for any guard | ### Enumerated Values | Property | Value | | --- | --- | | timeoutAction | [block, score] | ## OverallModerationConfigurationResponse ``` { "properties": { "entityId": { "description": "The ID of the custom model or playground for this configuration.", "type": "string" }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "nemoEvaluatorDeploymentId": { "description": "The ID of the NeMo Evaluator deployment to use for all NeMo Evaluator guards.", "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "timeoutAction": { "description": "The action to take if timeout occurs.", "enum": [ "block", "score" ], "type": "string" }, "timeoutSec": { "description": "The timeout value in seconds for any guard.", "minimum": 2, "type": "integer" }, "updatedAt": { "description": "When the configuration was updated.", "format": "date-time", "type": "string" }, "updaterId": { "description": "The ID of the user who updated the configuration.", "type": [ "string", "null" ] } }, "required": [ "entityId", "entityType", "timeoutAction", "timeoutSec", "updaterId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityId | string | true | | The ID of the custom model or playground for this configuration. | | entityType | string | true | | The type of the associated entity. | | nemoEvaluatorDeploymentId | string,null | false | | The ID of the NeMo Evaluator deployment to use for all NeMo Evaluator guards. | | timeoutAction | string | true | | The action to take if timeout occurs. | | timeoutSec | integer | true | minimum: 2 | The timeout value in seconds for any guard. | | updatedAt | string(date-time) | false | | When the configuration was updated. | | updaterId | string,null | true | | The ID of the user who updated the configuration. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [customModel, customModelVersion, playground] | | timeoutAction | [block, score] | ## OverallModerationConfigurationUpdate ``` { "properties": { "entityId": { "description": "The ID of the custom model or playground for this configuration.", "type": "string" }, "entityType": { "description": "The type of the associated entity.", "enum": [ "customModel", "customModelVersion", "playground" ], "type": "string" }, "nemoEvaluatorDeploymentId": { "description": "The ID of the NeMo Evaluator deployment to use for all NeMo Evaluator guards.", "type": [ "string", "null" ], "x-versionadded": "v2.42" }, "timeoutAction": { "description": "The action to take if timeout occurs.", "enum": [ "block", "score" ], "type": "string" }, "timeoutSec": { "description": "The timeout value in seconds for any guard.", "minimum": 0, "type": "integer" } }, "required": [ "entityId", "entityType", "timeoutAction", "timeoutSec" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityId | string | true | | The ID of the custom model or playground for this configuration. | | entityType | string | true | | The type of the associated entity. | | nemoEvaluatorDeploymentId | string,null | false | | The ID of the NeMo Evaluator deployment to use for all NeMo Evaluator guards. | | timeoutAction | string | true | | The action to take if timeout occurs. | | timeoutSec | integer | true | minimum: 0 | The timeout value in seconds for any guard. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [customModel, customModelVersion, playground] | | timeoutAction | [block, score] | ## PredictionEnvironmentInUseResponse ``` { "properties": { "id": { "description": "ID of prediction environment.", "type": "string" }, "name": { "description": "Name of prediction environment.", "type": "string" }, "usedBy": { "description": "Guards using this prediction environment.", "items": { "properties": { "configurationId": { "description": "ID of guard configuration.", "type": "string" }, "deploymentId": { "description": "ID of guard model deployment.", "type": "string" }, "name": { "description": "Name of guard configuration.", "type": "string" } }, "required": [ "configurationId", "deploymentId", "name" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 32, "type": "array" } }, "required": [ "id", "name", "usedBy" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of prediction environment. | | name | string | true | | Name of prediction environment. | | usedBy | [DeploymentAndGuardResponse] | true | maxItems: 32 | Guards using this prediction environment. | ## SupportedLlmListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of supported LLMs for moderation.", "items": { "properties": { "description": { "description": "The description of this LLM.", "maxLength": 1024, "type": "string" }, "id": { "description": "The ID for this LLM.", "maxLength": 1024, "type": "string" }, "llmType": { "description": "The general category of this LLM.", "maxLength": 1024, "type": "string" }, "model": { "description": "The specific model of this LLM.", "maxLength": 1024, "type": "string" }, "name": { "description": "The display name of this LLM.", "maxLength": 1024, "type": "string" }, "provider": { "description": "The provider of access to this LLM.", "maxLength": 1024, "type": "string" }, "vendor": { "description": "The vendor of this LLM.", "maxLength": 1024, "type": "string" } }, "required": [ "description", "id", "llmType", "model", "name", "provider", "vendor" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 200, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [SupportedLlmResponse] | true | maxItems: 200 | The list of supported LLMs for moderation. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## SupportedLlmResponse ``` { "properties": { "description": { "description": "The description of this LLM.", "maxLength": 1024, "type": "string" }, "id": { "description": "The ID for this LLM.", "maxLength": 1024, "type": "string" }, "llmType": { "description": "The general category of this LLM.", "maxLength": 1024, "type": "string" }, "model": { "description": "The specific model of this LLM.", "maxLength": 1024, "type": "string" }, "name": { "description": "The display name of this LLM.", "maxLength": 1024, "type": "string" }, "provider": { "description": "The provider of access to this LLM.", "maxLength": 1024, "type": "string" }, "vendor": { "description": "The vendor of this LLM.", "maxLength": 1024, "type": "string" } }, "required": [ "description", "id", "llmType", "model", "name", "provider", "vendor" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | maxLength: 1024 | The description of this LLM. | | id | string | true | maxLength: 1024 | The ID for this LLM. | | llmType | string | true | maxLength: 1024 | The general category of this LLM. | | model | string | true | maxLength: 1024 | The specific model of this LLM. | | name | string | true | maxLength: 1024 | The display name of this LLM. | | provider | string | true | maxLength: 1024 | The provider of access to this LLM. | | vendor | string | true | maxLength: 1024 | The vendor of this LLM. | --- # REST API URL: https://docs.datarobot.com/en/docs/api/reference/public-api/index.html > The DataRobot REST API provides a programmatic alternative to the web interface for creating and managing DataRobot projects. Use the DataRobot API to build highly accurate predictive models and deploy them into production environments. # REST API The DataRobot REST API provides a programmatic alternative to the web interface for creating and managing DataRobot projects. Use the DataRobot API to build highly accurate predictive models and deploy them into production environments. For information about specific endpoints, select one from the table of contents on the left. --- # Infrastructure URL: https://docs.datarobot.com/en/docs/api/reference/public-api/infrastructure.html > Use the endpoints described below to manage cluster licenses. # Infrastructure Use the endpoints described below to manage cluster licenses. ## Retrieve the information about cluster license Operation path: `GET /api/v2/clusterLicense/` Authentication requirements: `BearerAuth` Retrieve the information about the currently deployed cluster license. ### Example responses > 200 Response ``` { "properties": { "licenseInfo": { "description": "The license info object will describe detailed information about the deployed license", "properties": { "agenticPredictiveGovernanceSeatLicensesLimit": { "description": "The number of available Agentic, Predictive and Governance seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.38" }, "concurrentWorkersCount": { "description": "The number of allowed simultaneously running jobs(concurrent workers)", "minimum": 0, "type": "integer" }, "cpuUsageLimit": { "description": "The CPU usage limit for the cluster (0: unlimited, -1: no CPU usage available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.39" }, "expirationTimestamp": { "description": "The time of the license expiration in UTC ISO format,ex. 2020-12-21T23:59:59.000000Z", "type": "string" }, "expired": { "description": "A value indicating whether the license has already expired", "type": "boolean" }, "featureFlags": { "additionalProperties": { "properties": { "uiLabel": { "description": "String representation of the feature flag", "type": "string" }, "uiTooltip": { "description": "Detailed description of the feature flag", "type": "string" }, "value": { "description": "Value of the feature flag", "type": "boolean" } }, "required": [ "value" ], "type": "object" }, "description": "An object containing enforced feature flags. Each key is a name of a feature flag and value is an object described below.", "type": "object" }, "genAiSeatLicensesLimit": { "description": "The number of available GenAI seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.36" }, "gpuUsageLimit": { "description": "The GPU usage limit for the cluster (0: unlimited, -1: no GPU usage available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.39" }, "maxDeploymentLimit": { "description": "The number of maximum deployments limit (0: unlimited)", "minimum": 0, "type": "integer" }, "maximumActiveUsers": { "description": "The number of maximum active users allowed in the system(0: unlimited", "minimum": 0, "type": "integer" }, "nonBuilderUserSeatLicensesLimit": { "description": "The number of available Non-Builder User seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.38" }, "predictiveGovernanceSeatLicensesLimit": { "description": "The number of available Predictive and Governance seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.37" }, "prepaidDeploymentLimit": { "description": "The number of prepaid deployments limit (0: unlimited)", "minimum": 0, "type": "integer" } }, "required": [ "agenticPredictiveGovernanceSeatLicensesLimit", "concurrentWorkersCount", "cpuUsageLimit", "expirationTimestamp", "expired", "featureFlags", "genAiSeatLicensesLimit", "gpuUsageLimit", "maxDeploymentLimit", "maximumActiveUsers", "nonBuilderUserSeatLicensesLimit", "predictiveGovernanceSeatLicensesLimit", "prepaidDeploymentLimit" ], "type": "object" }, "uploadInfo": { "description": "The upload info object will describe detailed information about the user who uploaded the current license", "properties": { "uploadTimestamp": { "description": "the time when the current license was uploaded in UTC ISO format, ex. '2020-12-21T23:59:59.000000Z'", "type": "string" }, "uploaderUserId": { "description": "Id of the user who uploaded the current license", "type": "string" }, "uploaderUsername": { "description": "The username of the user who uploaded the current license", "type": "string" } }, "required": [ "uploadTimestamp", "uploaderUserId", "uploaderUsername" ], "type": "object" } }, "required": [ "licenseInfo", "uploadInfo" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ClusterLicenseRetrieveResponse | ## Create Operation path: `PUT /api/v2/clusterLicense/` Authentication requirements: `BearerAuth` Create or replace the cluster license. ### Body parameter ``` { "properties": { "licenseKey": { "description": "The license key provided by customer support", "type": "string" } }, "required": [ "licenseKey" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ClusterLicenseUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 422 | Unprocessable Entity | License is invalid or has expired. | None | ## Check if a license is valid Operation path: `POST /api/v2/clusterLicenseValidation/` Authentication requirements: `BearerAuth` Check if a cluster license is valid. ### Body parameter ``` { "properties": { "licenseKey": { "description": "The license key provided by customer support", "type": "string" } }, "required": [ "licenseKey" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ClusterLicenseUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "licenseInfo": { "description": "The license info object will describe detailed information about the deployed license", "properties": { "agenticPredictiveGovernanceSeatLicensesLimit": { "description": "The number of available Agentic, Predictive and Governance seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.38" }, "concurrentWorkersCount": { "description": "The number of allowed simultaneously running jobs(concurrent workers)", "minimum": 0, "type": "integer" }, "cpuUsageLimit": { "description": "The CPU usage limit for the cluster (0: unlimited, -1: no CPU usage available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.39" }, "expirationTimestamp": { "description": "The time of the license expiration in UTC ISO format,ex. 2020-12-21T23:59:59.000000Z", "type": "string" }, "expired": { "description": "A value indicating whether the license has already expired", "type": "boolean" }, "featureFlags": { "additionalProperties": { "properties": { "uiLabel": { "description": "String representation of the feature flag", "type": "string" }, "uiTooltip": { "description": "Detailed description of the feature flag", "type": "string" }, "value": { "description": "Value of the feature flag", "type": "boolean" } }, "required": [ "value" ], "type": "object" }, "description": "An object containing enforced feature flags. Each key is a name of a feature flag and value is an object described below.", "type": "object" }, "genAiSeatLicensesLimit": { "description": "The number of available GenAI seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.36" }, "gpuUsageLimit": { "description": "The GPU usage limit for the cluster (0: unlimited, -1: no GPU usage available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.39" }, "maxDeploymentLimit": { "description": "The number of maximum deployments limit (0: unlimited)", "minimum": 0, "type": "integer" }, "maximumActiveUsers": { "description": "The number of maximum active users allowed in the system(0: unlimited", "minimum": 0, "type": "integer" }, "nonBuilderUserSeatLicensesLimit": { "description": "The number of available Non-Builder User seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.38" }, "predictiveGovernanceSeatLicensesLimit": { "description": "The number of available Predictive and Governance seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.37" }, "prepaidDeploymentLimit": { "description": "The number of prepaid deployments limit (0: unlimited)", "minimum": 0, "type": "integer" } }, "required": [ "agenticPredictiveGovernanceSeatLicensesLimit", "concurrentWorkersCount", "cpuUsageLimit", "expirationTimestamp", "expired", "featureFlags", "genAiSeatLicensesLimit", "gpuUsageLimit", "maxDeploymentLimit", "maximumActiveUsers", "nonBuilderUserSeatLicensesLimit", "predictiveGovernanceSeatLicensesLimit", "prepaidDeploymentLimit" ], "type": "object" } }, "required": [ "licenseInfo" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ClusterLicenseValidationResponse | | 422 | Unprocessable Entity | License is invalid or has expired. | None | ## Retrieve version information Operation path: `GET /api/v2/version/` Authentication requirements: `BearerAuth` Provides the version information for the current version of the API. ### Example responses > 200 Response ``` { "properties": { "major": { "description": "The major version number.", "type": "integer" }, "minor": { "description": "The minor version number.", "type": "integer" }, "versionString": { "description": "The full version string.", "type": "string" } }, "required": [ "major", "minor", "versionString" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | VersionRetrieveResponse | # Schemas ## ClusterLicenseRetrieveResponse ``` { "properties": { "licenseInfo": { "description": "The license info object will describe detailed information about the deployed license", "properties": { "agenticPredictiveGovernanceSeatLicensesLimit": { "description": "The number of available Agentic, Predictive and Governance seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.38" }, "concurrentWorkersCount": { "description": "The number of allowed simultaneously running jobs(concurrent workers)", "minimum": 0, "type": "integer" }, "cpuUsageLimit": { "description": "The CPU usage limit for the cluster (0: unlimited, -1: no CPU usage available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.39" }, "expirationTimestamp": { "description": "The time of the license expiration in UTC ISO format,ex. 2020-12-21T23:59:59.000000Z", "type": "string" }, "expired": { "description": "A value indicating whether the license has already expired", "type": "boolean" }, "featureFlags": { "additionalProperties": { "properties": { "uiLabel": { "description": "String representation of the feature flag", "type": "string" }, "uiTooltip": { "description": "Detailed description of the feature flag", "type": "string" }, "value": { "description": "Value of the feature flag", "type": "boolean" } }, "required": [ "value" ], "type": "object" }, "description": "An object containing enforced feature flags. Each key is a name of a feature flag and value is an object described below.", "type": "object" }, "genAiSeatLicensesLimit": { "description": "The number of available GenAI seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.36" }, "gpuUsageLimit": { "description": "The GPU usage limit for the cluster (0: unlimited, -1: no GPU usage available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.39" }, "maxDeploymentLimit": { "description": "The number of maximum deployments limit (0: unlimited)", "minimum": 0, "type": "integer" }, "maximumActiveUsers": { "description": "The number of maximum active users allowed in the system(0: unlimited", "minimum": 0, "type": "integer" }, "nonBuilderUserSeatLicensesLimit": { "description": "The number of available Non-Builder User seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.38" }, "predictiveGovernanceSeatLicensesLimit": { "description": "The number of available Predictive and Governance seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.37" }, "prepaidDeploymentLimit": { "description": "The number of prepaid deployments limit (0: unlimited)", "minimum": 0, "type": "integer" } }, "required": [ "agenticPredictiveGovernanceSeatLicensesLimit", "concurrentWorkersCount", "cpuUsageLimit", "expirationTimestamp", "expired", "featureFlags", "genAiSeatLicensesLimit", "gpuUsageLimit", "maxDeploymentLimit", "maximumActiveUsers", "nonBuilderUserSeatLicensesLimit", "predictiveGovernanceSeatLicensesLimit", "prepaidDeploymentLimit" ], "type": "object" }, "uploadInfo": { "description": "The upload info object will describe detailed information about the user who uploaded the current license", "properties": { "uploadTimestamp": { "description": "the time when the current license was uploaded in UTC ISO format, ex. '2020-12-21T23:59:59.000000Z'", "type": "string" }, "uploaderUserId": { "description": "Id of the user who uploaded the current license", "type": "string" }, "uploaderUsername": { "description": "The username of the user who uploaded the current license", "type": "string" } }, "required": [ "uploadTimestamp", "uploaderUserId", "uploaderUsername" ], "type": "object" } }, "required": [ "licenseInfo", "uploadInfo" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | licenseInfo | ClusterLicenseRetrieveResponseLicense | true | | The license info object will describe detailed information about the deployed license | | uploadInfo | ClusterLicenseRetrieveResponseUploadInfo | true | | The upload info object will describe detailed information about the user who uploaded the current license | ## ClusterLicenseRetrieveResponseLicense ``` { "description": "The license info object will describe detailed information about the deployed license", "properties": { "agenticPredictiveGovernanceSeatLicensesLimit": { "description": "The number of available Agentic, Predictive and Governance seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.38" }, "concurrentWorkersCount": { "description": "The number of allowed simultaneously running jobs(concurrent workers)", "minimum": 0, "type": "integer" }, "cpuUsageLimit": { "description": "The CPU usage limit for the cluster (0: unlimited, -1: no CPU usage available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.39" }, "expirationTimestamp": { "description": "The time of the license expiration in UTC ISO format,ex. 2020-12-21T23:59:59.000000Z", "type": "string" }, "expired": { "description": "A value indicating whether the license has already expired", "type": "boolean" }, "featureFlags": { "additionalProperties": { "properties": { "uiLabel": { "description": "String representation of the feature flag", "type": "string" }, "uiTooltip": { "description": "Detailed description of the feature flag", "type": "string" }, "value": { "description": "Value of the feature flag", "type": "boolean" } }, "required": [ "value" ], "type": "object" }, "description": "An object containing enforced feature flags. Each key is a name of a feature flag and value is an object described below.", "type": "object" }, "genAiSeatLicensesLimit": { "description": "The number of available GenAI seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.36" }, "gpuUsageLimit": { "description": "The GPU usage limit for the cluster (0: unlimited, -1: no GPU usage available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.39" }, "maxDeploymentLimit": { "description": "The number of maximum deployments limit (0: unlimited)", "minimum": 0, "type": "integer" }, "maximumActiveUsers": { "description": "The number of maximum active users allowed in the system(0: unlimited", "minimum": 0, "type": "integer" }, "nonBuilderUserSeatLicensesLimit": { "description": "The number of available Non-Builder User seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.38" }, "predictiveGovernanceSeatLicensesLimit": { "description": "The number of available Predictive and Governance seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.37" }, "prepaidDeploymentLimit": { "description": "The number of prepaid deployments limit (0: unlimited)", "minimum": 0, "type": "integer" } }, "required": [ "agenticPredictiveGovernanceSeatLicensesLimit", "concurrentWorkersCount", "cpuUsageLimit", "expirationTimestamp", "expired", "featureFlags", "genAiSeatLicensesLimit", "gpuUsageLimit", "maxDeploymentLimit", "maximumActiveUsers", "nonBuilderUserSeatLicensesLimit", "predictiveGovernanceSeatLicensesLimit", "prepaidDeploymentLimit" ], "type": "object" } ``` The license info object will describe detailed information about the deployed license ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | agenticPredictiveGovernanceSeatLicensesLimit | integer | true | minimum: -1 | The number of available Agentic, Predictive and Governance seat licenses (0: unlimited, -1: no seats available). | | concurrentWorkersCount | integer | true | minimum: 0 | The number of allowed simultaneously running jobs(concurrent workers) | | cpuUsageLimit | integer | true | minimum: -1 | The CPU usage limit for the cluster (0: unlimited, -1: no CPU usage available). | | expirationTimestamp | string | true | | The time of the license expiration in UTC ISO format,ex. 2020-12-21T23:59:59.000000Z | | expired | boolean | true | | A value indicating whether the license has already expired | | featureFlags | object | true | | An object containing enforced feature flags. Each key is a name of a feature flag and value is an object described below. | | » additionalProperties | FeatureFlagObject | false | | none | | genAiSeatLicensesLimit | integer | true | minimum: -1 | The number of available GenAI seat licenses (0: unlimited, -1: no seats available). | | gpuUsageLimit | integer | true | minimum: -1 | The GPU usage limit for the cluster (0: unlimited, -1: no GPU usage available). | | maxDeploymentLimit | integer | true | minimum: 0 | The number of maximum deployments limit (0: unlimited) | | maximumActiveUsers | integer | true | minimum: 0 | The number of maximum active users allowed in the system(0: unlimited | | nonBuilderUserSeatLicensesLimit | integer | true | minimum: -1 | The number of available Non-Builder User seat licenses (0: unlimited, -1: no seats available). | | predictiveGovernanceSeatLicensesLimit | integer | true | minimum: -1 | The number of available Predictive and Governance seat licenses (0: unlimited, -1: no seats available). | | prepaidDeploymentLimit | integer | true | minimum: 0 | The number of prepaid deployments limit (0: unlimited) | ## ClusterLicenseRetrieveResponseUploadInfo ``` { "description": "The upload info object will describe detailed information about the user who uploaded the current license", "properties": { "uploadTimestamp": { "description": "the time when the current license was uploaded in UTC ISO format, ex. '2020-12-21T23:59:59.000000Z'", "type": "string" }, "uploaderUserId": { "description": "Id of the user who uploaded the current license", "type": "string" }, "uploaderUsername": { "description": "The username of the user who uploaded the current license", "type": "string" } }, "required": [ "uploadTimestamp", "uploaderUserId", "uploaderUsername" ], "type": "object" } ``` The upload info object will describe detailed information about the user who uploaded the current license ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | uploadTimestamp | string | true | | the time when the current license was uploaded in UTC ISO format, ex. '2020-12-21T23:59:59.000000Z' | | uploaderUserId | string | true | | Id of the user who uploaded the current license | | uploaderUsername | string | true | | The username of the user who uploaded the current license | ## ClusterLicenseUpdate ``` { "properties": { "licenseKey": { "description": "The license key provided by customer support", "type": "string" } }, "required": [ "licenseKey" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | licenseKey | string | true | | The license key provided by customer support | ## ClusterLicenseValidationResponse ``` { "properties": { "licenseInfo": { "description": "The license info object will describe detailed information about the deployed license", "properties": { "agenticPredictiveGovernanceSeatLicensesLimit": { "description": "The number of available Agentic, Predictive and Governance seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.38" }, "concurrentWorkersCount": { "description": "The number of allowed simultaneously running jobs(concurrent workers)", "minimum": 0, "type": "integer" }, "cpuUsageLimit": { "description": "The CPU usage limit for the cluster (0: unlimited, -1: no CPU usage available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.39" }, "expirationTimestamp": { "description": "The time of the license expiration in UTC ISO format,ex. 2020-12-21T23:59:59.000000Z", "type": "string" }, "expired": { "description": "A value indicating whether the license has already expired", "type": "boolean" }, "featureFlags": { "additionalProperties": { "properties": { "uiLabel": { "description": "String representation of the feature flag", "type": "string" }, "uiTooltip": { "description": "Detailed description of the feature flag", "type": "string" }, "value": { "description": "Value of the feature flag", "type": "boolean" } }, "required": [ "value" ], "type": "object" }, "description": "An object containing enforced feature flags. Each key is a name of a feature flag and value is an object described below.", "type": "object" }, "genAiSeatLicensesLimit": { "description": "The number of available GenAI seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.36" }, "gpuUsageLimit": { "description": "The GPU usage limit for the cluster (0: unlimited, -1: no GPU usage available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.39" }, "maxDeploymentLimit": { "description": "The number of maximum deployments limit (0: unlimited)", "minimum": 0, "type": "integer" }, "maximumActiveUsers": { "description": "The number of maximum active users allowed in the system(0: unlimited", "minimum": 0, "type": "integer" }, "nonBuilderUserSeatLicensesLimit": { "description": "The number of available Non-Builder User seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.38" }, "predictiveGovernanceSeatLicensesLimit": { "description": "The number of available Predictive and Governance seat licenses (0: unlimited, -1: no seats available).", "minimum": -1, "type": "integer", "x-versionadded": "v2.37" }, "prepaidDeploymentLimit": { "description": "The number of prepaid deployments limit (0: unlimited)", "minimum": 0, "type": "integer" } }, "required": [ "agenticPredictiveGovernanceSeatLicensesLimit", "concurrentWorkersCount", "cpuUsageLimit", "expirationTimestamp", "expired", "featureFlags", "genAiSeatLicensesLimit", "gpuUsageLimit", "maxDeploymentLimit", "maximumActiveUsers", "nonBuilderUserSeatLicensesLimit", "predictiveGovernanceSeatLicensesLimit", "prepaidDeploymentLimit" ], "type": "object" } }, "required": [ "licenseInfo" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | licenseInfo | ClusterLicenseRetrieveResponseLicense | true | | The license info object will describe detailed information about the deployed license | ## FeatureFlagObject ``` { "properties": { "uiLabel": { "description": "String representation of the feature flag", "type": "string" }, "uiTooltip": { "description": "Detailed description of the feature flag", "type": "string" }, "value": { "description": "Value of the feature flag", "type": "boolean" } }, "required": [ "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | uiLabel | string | false | | String representation of the feature flag | | uiTooltip | string | false | | Detailed description of the feature flag | | value | boolean | true | | Value of the feature flag | ## VersionRetrieveResponse ``` { "properties": { "major": { "description": "The major version number.", "type": "integer" }, "minor": { "description": "The minor version number.", "type": "integer" }, "versionString": { "description": "The full version string.", "type": "string" } }, "required": [ "major", "minor", "versionString" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | major | integer | true | | The major version number. | | minor | integer | true | | The minor version number. | | versionString | string | true | | The full version string. | --- # Insights URL: https://docs.datarobot.com/en/docs/api/reference/public-api/insights.html > Use the endpoints described below to generate and interpret insights for specific models. # Insights Use the endpoints described below to generate and interpret insights for specific models. ## Data slices bulk deletion Operation path: `DELETE /api/v2/dataSlices/` Authentication requirements: `BearerAuth` Data slices bulk deletion. ### Body parameter ``` { "properties": { "ids": { "description": "List of data slices to remove.", "items": { "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" } }, "required": [ "ids" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DataSlicesBulkDeleteRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The requested data slice(s) are deleted successfully. | None | ## Request Operation path: `POST /api/v2/dataSlices/` Authentication requirements: `BearerAuth` Request to create a new data slice. ### Body parameter ``` { "properties": { "filters": { "description": "List of filters the data slice is composed of.", "items": { "properties": { "operand": { "description": "Feature to apply operation to.", "type": "string" }, "operator": { "description": "Operator to apply to the named operand in the dataset. The operator 'eq' mean 'equals the single specified value'. The operator 'in' means 'is one of a list of allowed values.'", "enum": [ "eq", "in", "<", ">", "between", "notBetween" ], "type": "string" }, "values": { "description": "Values to filter the operand by with the given operator.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "operand", "operator", "values" ], "type": "object" }, "maxItems": 3, "minItems": 1, "type": "array" }, "name": { "description": "User provided name for the data slice.", "maxLength": 500, "minLength": 1, "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "filters", "name", "projectId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | DataSlicesCreationRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "filters": { "description": "List of filters the data slice is composed of.", "items": { "properties": { "operand": { "description": "Feature to apply operation to.", "type": "string" }, "operator": { "description": "Operator to apply to the named operand in the dataset. The operator 'eq' mean 'equals the single specified value'. The operator 'in' means 'is one of a list of allowed values.'", "enum": [ "eq", "in", "<", ">", "between", "notBetween" ], "type": "string" }, "values": { "description": "Values to filter the operand by with the given operator.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "operand", "operator", "values" ], "type": "object" }, "maxItems": 3, "minItems": 1, "type": "array" }, "id": { "description": "ID of the data slice.", "type": "string" }, "name": { "description": "User provided name for the data slice.", "maxLength": 500, "minLength": 1, "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "filters", "id", "name", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The requested data slice is created successfully. | DataSliceIndividualResponse | ## Delete a data slice by data slice ID Operation path: `DELETE /api/v2/dataSlices/{dataSliceId}/` Authentication requirements: `BearerAuth` Deletes the data slice specified by the data slice ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataSliceId | path | string | true | ID of the data slice. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The specified data slice was deleted. | None | ## Retrieve a Data Slice by data slice ID Operation path: `GET /api/v2/dataSlices/{dataSliceId}/` Authentication requirements: `BearerAuth` Returns details about the specified data slice ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataSliceId | path | string | true | ID of the data slice. | ### Example responses > 200 Response ``` { "properties": { "filters": { "description": "List of filters the data slice is composed of.", "items": { "properties": { "operand": { "description": "Feature to apply operation to.", "type": "string" }, "operator": { "description": "Operator to apply to the named operand in the dataset. The operator 'eq' mean 'equals the single specified value'. The operator 'in' means 'is one of a list of allowed values.'", "enum": [ "eq", "in", "<", ">", "between", "notBetween" ], "type": "string" }, "values": { "description": "Values to filter the operand by with the given operator.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "operand", "operator", "values" ], "type": "object" }, "maxItems": 3, "minItems": 1, "type": "array" }, "id": { "description": "ID of the data slice.", "type": "string" }, "name": { "description": "User provided name for the data slice.", "maxLength": 500, "minLength": 1, "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "filters", "id", "name", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Data slice was found. | DataSliceIndividualResponse | ## Returns the number of rows available after applying a data slice by data slice ID Operation path: `GET /api/v2/dataSlices/{dataSliceId}/sliceSizes/` Authentication requirements: `BearerAuth` Returns the number of rows available after applying a data slice to the specified subset of the dataset. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | query | string | true | The project ID. | | source | query | string | true | The source of data to use to calculate the size. | | externalDatasetId | query | string,null | false | The external dataset ID to use when calculating the size of a slice. Use this parameter only when the source is 'externalTestSet'. | | modelId | query | string,null | false | The model ID whose training dataset should be sliced. Use this parameter only when the source is 'training'. | | dataSliceId | path | string | true | ID of the data slice. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, crossValidation, externalTestSet, holdout, holdout_training, training, validation, vectorDatabase] | ### Example responses > 200 Response ``` { "properties": { "dataSliceId": { "description": "ID of the data slice.", "type": "string" }, "externalDatasetId": { "description": "The external dataset ID to use when calculating the size of a slice. Use this parameter only when the source is 'externalTestSet'.", "type": [ "string", "null" ] }, "messages": { "description": "List of user-relevant messages related to a Data Slice.", "items": { "properties": { "additionalInfo": { "description": "Additional details about this message.", "type": "string" }, "description": { "description": "Short summary description about this message.", "type": "string" }, "level": { "description": "Message level.", "enum": [ "CRITICAL", "INFORMATIONAL", "WARNING" ], "type": "string" } }, "required": [ "additionalInfo", "description", "level" ], "type": "object" }, "maxItems": 100, "type": "array" }, "modelId": { "description": "The model ID whose training dataset should be sliced. Use this parameter only when the source is 'training'.", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "sliceSize": { "description": "Number of rows in the slice for the given source.", "minimum": 0, "type": "integer" }, "source": { "description": "The source of data to use to calculate the size.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "crossValidation", "externalTestSet", "holdout", "holdout_training", "training", "validation", "vectorDatabase" ], "type": "string" } }, "required": [ "dataSliceId", "messages", "projectId", "sliceSize", "source" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The subset slice size was retrieved. | DataSliceRetrieveSubsetSizeResponse | | 204 | No Content | No slice size exists. | None | | 403 | Forbidden | Unathorized access to resource. | None | ## Compute the number of rows available after applying a data slice by data slice ID Operation path: `POST /api/v2/dataSlices/{dataSliceId}/sliceSizes/` Authentication requirements: `BearerAuth` Compute the number of rows available after applying a data slice to the specified subset of the dataset. ### Body parameter ``` { "properties": { "externalDatasetId": { "description": "The external dataset ID to use when calculating the size of a slice. Use this parameter only when the source is 'externalTestSet'.", "type": [ "string", "null" ] }, "modelId": { "description": "The model ID whose training dataset should be sliced. Use this parameter only when the source is 'training'.", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "The source of data to use to calculate the size.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "crossValidation", "externalTestSet", "holdout", "holdout_training", "training", "validation", "vectorDatabase" ], "type": "string" } }, "required": [ "projectId", "source" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | dataSliceId | path | string | true | ID of the data slice. | | body | body | DataSliceComputeSubsetSizeRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested data slices have been successfully validated. | None | ## Request calculation of Confusion Matrix chart Operation path: `POST /api/v2/insights/confusionMatrix/` Authentication requirements: `BearerAuth` Request calculation of insight with an optional data slice. ### Body parameter ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "(-1, -1)", "(-1,-1)", "(0, -1)", "(0,-1)", "CV", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_2", "backtest_20", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "crossValidation", "externalTestSet", "holdout", "validation", "validation_0", "validation_1" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComputeConfusionMatrixRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested insights computation was accepted. | ComputeInsightsResponse | | 422 | Unprocessable Entity | Unsupported project or model type, model not trained, or locked holdout. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## The list of paginated Confusion Matrix chart insights by entity ID Operation path: `GET /api/v2/insights/confusionMatrix/models/{entityId}/` Authentication requirements: `BearerAuth` The list of paginated Confusion Matrix chart insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The number of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | dataSliceId | query | string,null | false | The ID of the data slice. | | source | query | string | false | The subset of data used to compute the insight. | | unslicedOnly | query | string | false | Return only insights without a data_slice_id. | | dataOrderBy | query | string | false | Orders the chart data by the specified attributes.Prefix the attribute name with a dash to sort in descending order (e.g., dataOrderBy='-predictedCount).' | | orientation | query | string | false | Determines whether the values in the rows of the confusion matrix should correspond to the same actual class ('actual') or predicted class ('predicted'). | | rowStart | query | integer | false | The first row (start index) used when applying slicing to the confusion matrix. | | rowEnd | query | integer | false | The last row (end index) used when applying slicing to the confusion matrix. | | colStart | query | integer | false | The first column (start index) used when applying slicing to the confusion matrix. | | colEnd | query | integer | false | The last column (end index) used when applying slicing to the confusion matrix. | | entityId | path | string | true | The ID of the model. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [(-1, -1), (-1,-1), (0, -1), (0,-1), CV, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_2, backtest_20, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, crossValidation, externalTestSet, holdout, validation, validation_0, validation_1] | | unslicedOnly | [false, False, true, True] | | dataOrderBy | [className, -className, actualCount, -actualCount, predictedCount, -predictedCount, f1, -f1, precision, -precision, recall, -recall] | | orientation | [actual, -actual, predicted, -predicted] | | Accept | application/json | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of paginated confusion matrix insights.", "items": { "properties": { "data": { "description": "The confusion matrix chart data in the format specified below.", "properties": { "columns": { "description": "The [colStart, colEnd] column dimension of the confusion matrix in responses.", "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "confusionMatrixData": { "description": "The confusion matrix chart data in the format specified below.", "properties": { "classMetrics": { "description": "The per-class information, including one-vs-all scores, in a format specified below.", "items": { "properties": { "actualCount": { "description": "The number of times this class was seen in the validation data.", "type": "integer" }, "className": { "description": "The name of the class.", "type": "string" }, "confusionMatrixOneVsAll": { "description": "A 2 dimensional array representing a 2x2 one-vs-all matrix. This represents, for each class, the True/False Negative/Positive rates as integers. The data structure is: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``.", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "Precision score", "type": "number" }, "predictedCount": { "description": "The number of times this class was predicted within the validation data.", "type": "integer" }, "recall": { "description": "Recall score", "type": "number" }, "wasActualPercentages": { "description": "The one-vs-all percentage of actuals, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "wasPredictedPercentages": { "description": "The one-vs-all percentages of predicted, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object", "x-versionadded": "v2.42" }, "maxItems": 10000, "type": "array" }, "colClasses": { "description": "Class labels on columns of the confusion matrix.", "items": { "type": "string" }, "maxItems": 10000, "type": "array" }, "confusionMatrix": { "description": "A two-dimensional array of integers representing the confusion matrix, aligned with the `rowClasses` and `colClasses` array. For example, if the orientation is `actual`, then when confusionMatrix[A][B], is `true`,the result is an integer that represents the number of times '`class` with index A was correct, but class with index B was predicted was true. ", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "rowClasses": { "description": "Class labels on rows of the confusion matrix.", "items": { "type": "string" }, "maxItems": 10000, "type": "array" } }, "required": [ "classMetrics", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object", "x-versionadded": "v2.42" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "numberOfClasses": { "description": "The count of classes in the full confusion matrix.", "type": "integer" }, "rows": { "description": "The [rowStart, rowEnd] row dimension of the confusion matrix in responses.", "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "totalMatrixSum": { "description": "The sum of all values in the full confusion matrix.", "type": "integer" } }, "required": [ "columns", "confusionMatrixData", "globalMetrics", "numberOfClasses", "rows", "totalMatrixSum" ], "type": "object", "x-versionadded": "v2.42" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The data partition used to calculate the insight.", "enum": [ "backtest_0", "backtest_0Training", "backtest_1", "backtest_10", "backtest_10Training", "backtest_11", "backtest_11Training", "backtest_12", "backtest_12Training", "backtest_13", "backtest_13Training", "backtest_14", "backtest_14Training", "backtest_15", "backtest_15Training", "backtest_16", "backtest_16Training", "backtest_17", "backtest_17Training", "backtest_18", "backtest_18Training", "backtest_19", "backtest_19Training", "backtest_1Training", "backtest_2", "backtest_20", "backtest_20Training", "backtest_2Training", "backtest_3", "backtest_3Training", "backtest_4", "backtest_4Training", "backtest_5", "backtest_5Training", "backtest_6", "backtest_6Training", "backtest_7", "backtest_7Training", "backtest_8", "backtest_8Training", "backtest_9", "backtest_9Training", "crossValidation", "externalTestSet", "holdout", "holdoutTraining", "training", "validation", "vectorDatabase" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object", "x-versionadded": "v2.42" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a model's Confusion Matrix chart. | RetrieveConfusionMatrixPaginatedResponse | | 404 | Not Found | Requested entity ID or data slice ID not found | None | | 422 | Unprocessable Entity | Unsupported project type, or unsupported insight for model | None | ## Request calculation of Feature Effects Operation path: `POST /api/v2/insights/featureEffects/` Authentication requirements: `BearerAuth` Request calculation of Feature Effects with an optional data slice. ### Body parameter ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "training", "backtest_0", "backtest_1", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20", "holdout", "backtest_0_training", "backtest_1_training", "backtest_2_training", "backtest_3_training", "backtest_4_training", "backtest_5_training", "backtest_6_training", "backtest_7_training", "backtest_8_training", "backtest_9_training", "backtest_10_training", "backtest_11_training", "backtest_12_training", "backtest_13_training", "backtest_14_training", "backtest_15_training", "backtest_16_training", "backtest_17_training", "backtest_18_training", "backtest_19_training", "backtest_20_training", "holdout_training" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComputeFeatureEffectsRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested Feature Effect insights computation was accepted. | ComputeInsightsResponse | | 422 | Unprocessable Entity | Unsupported project or model type, model not trained, or locked holdout | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## The list of paginated Feature Effects insights by entity ID Operation path: `GET /api/v2/insights/featureEffects/models/{entityId}/` Authentication requirements: `BearerAuth` The list of paginated Feature Effects insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The number of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | dataSliceId | query | string,null | false | The ID of the data slice. | | source | query | string | true | The subset of data used to compute the insight. | | unslicedOnly | query | string | false | Return only insights without a data_slice_id. | | entityId | path | string | true | The ID of the model. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [validation, training, backtest_0, backtest_1, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20, holdout, backtest_0_training, backtest_1_training, backtest_2_training, backtest_3_training, backtest_4_training, backtest_5_training, backtest_6_training, backtest_7_training, backtest_8_training, backtest_9_training, backtest_10_training, backtest_11_training, backtest_12_training, backtest_13_training, backtest_14_training, backtest_15_training, backtest_16_training, backtest_17_training, backtest_18_training, backtest_19_training, backtest_20_training, holdout_training] | | unslicedOnly | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of paginated feature effects insights.", "items": { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`.", "type": [ "string", "null" ] }, "data": { "description": "Feature effects data.", "properties": { "featureEffects": { "description": "The Feature Effects computational results for each feature.", "items": { "properties": { "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" } }, "required": [ "featureEffects" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "training", "backtest_0", "backtest_1", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20", "holdout", "backtest_0_training", "backtest_1_training", "backtest_2_training", "backtest_3_training", "backtest_4_training", "backtest_5_training", "backtest_6_training", "backtest_7_training", "backtest_8_training", "backtest_9_training", "backtest_10_training", "backtest_11_training", "backtest_12_training", "backtest_13_training", "backtest_14_training", "backtest_15_training", "backtest_16_training", "backtest_17_training", "backtest_18_training", "backtest_19_training", "backtest_20_training", "holdout_training" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a model's Feature Effects, either for the specified data_slice_id or, if not specified, for all slices in the original data partition. | RetrieveFeatureEffectsPaginatedResponse | | 404 | Not Found | Requested entity ID or data slice ID not found | None | | 422 | Unprocessable Entity | Unsupported project type, or unsupported insight for model | None | ## Request calculation of Feature Impact Operation path: `POST /api/v2/insights/featureImpact/` Authentication requirements: `BearerAuth` Request calculation of insight with an optional data slice. ### Body parameter ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "rowCount": { "description": "The number of rows to use for calculating Feature Impact.", "maximum": 100000, "minimum": 10, "type": "integer" }, "source": { "default": "training", "description": "The subset of data used to compute the insight.", "enum": [ "training", "backtest_2_training", "backtest_3_training", "backtest_4_training", "backtest_5_training", "backtest_6_training", "backtest_7_training", "backtest_8_training", "backtest_9_training", "backtest_10_training", "backtest_11_training", "backtest_12_training", "backtest_13_training", "backtest_14_training", "backtest_15_training", "backtest_16_training", "backtest_17_training", "backtest_18_training", "backtest_19_training", "backtest_20_training", "backtest_1_training", "holdout_training" ], "type": "string" } }, "required": [ "entityId", "entityType", "rowCount", "source" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComputeFeatureImpactRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested insights computation was accepted. | ComputeInsightsResponse | | 422 | Unprocessable Entity | Unsupported project or model type, model not trained, or locked holdout. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## The list of paginated Feature Impact insights by entity ID Operation path: `GET /api/v2/insights/featureImpact/models/{entityId}/` Authentication requirements: `BearerAuth` The list of paginated Feature Impact insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The number of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | dataSliceId | query | string,null | false | The ID of the data slice. | | source | query | string | false | The subset of data used to compute the insight. | | unslicedOnly | query | string | false | Return only insights without a data_slice_id. | | entityId | path | string | true | The ID of the model. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, backtest_2_training, backtest_3_training, backtest_4_training, backtest_5_training, backtest_6_training, backtest_7_training, backtest_8_training, backtest_9_training, backtest_10_training, backtest_11_training, backtest_12_training, backtest_13_training, backtest_14_training, backtest_15_training, backtest_16_training, backtest_17_training, backtest_18_training, backtest_19_training, backtest_20_training, backtest_1_training, holdout_training] | | unslicedOnly | [false, False, true, True] | | Accept | application/json | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of paginated feature impact insights.", "items": { "properties": { "data": { "description": "Feature impact data.", "properties": { "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact.", "type": "boolean" }, "rowCount": { "description": "The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the ``rowCount``, we return ``null`` here.", "type": [ "integer", "null" ] } }, "required": [ "featureImpacts", "ranRedundancyDetection", "rowCount" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "training", "backtest_2Training", "backtest_3Training", "backtest_4Training", "backtest_5Training", "backtest_6Training", "backtest_7Training", "backtest_8Training", "backtest_9Training", "backtest_10Training", "backtest_11Training", "backtest_12Training", "backtest_13Training", "backtest_14Training", "backtest_15Training", "backtest_16Training", "backtest_17Training", "backtest_18Training", "backtest_19Training", "backtest_20Training", "backtest_1Training", "holdoutTraining" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 2, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a model's Feature Impact, either for the specified data_slice_id or, if not specified, for all slices in the original data partition. | RetrieveFeatureImpactPaginatedResponse | | 404 | Not Found | Requested entity ID or data slice ID not found | None | | 422 | Unprocessable Entity | Unsupported project type, or unsupported insight for model | None | ## Request calculation of Lift chart Operation path: `POST /api/v2/insights/liftChart/` Authentication requirements: `BearerAuth` Request calculation of insight with an optional data slice. ### Body parameter ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComputeLiftChartRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested insights computation was accepted. | ComputeInsightsResponse | | 422 | Unprocessable Entity | Unsupported project or model type, model not trained, or locked holdout. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## The list of paginated lift chart insights by entity ID Operation path: `GET /api/v2/insights/liftChart/models/{entityId}/` Authentication requirements: `BearerAuth` The list of paginated lift chart insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The number of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | dataSliceId | query | string,null | false | The ID of the data slice. | | source | query | string | false | The subset of data used to compute the insight. | | unslicedOnly | query | string | false | Return only insights without a data_slice_id. | | externalDatasetId | query | string,null | false | The ID of the external dataset. | | entityId | path | string | true | The ID of the model. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [validation, crossValidation, holdout, externalTestSet, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | | unslicedOnly | [false, False, true, True] | | Accept | application/json | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of paginated lift chart insights.", "items": { "properties": { "data": { "description": "Lift chart data.", "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" } }, "required": [ "bins" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a model's Lift chart, either for the specified data_slice_id or, if not specified, for all slices in the original data partition. | RetrieveLiftChartPaginatedResponse | | 404 | Not Found | Requested entity ID or data slice ID not found | None | | 422 | Unprocessable Entity | Unsupported project type, or unsupported insight for model | None | ## Request calculation of Residuals chart Operation path: `POST /api/v2/insights/residuals/` Authentication requirements: `BearerAuth` Request calculation of insight with an optional data slice. ### Body parameter ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComputeResidualsRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested insights computation was accepted. | ComputeInsightsResponse | | 422 | Unprocessable Entity | Unsupported project or model type, model not trained, or locked holdout. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## The list of paginated Residuals insights by entity ID Operation path: `GET /api/v2/insights/residuals/models/{entityId}/` Authentication requirements: `BearerAuth` The list of paginated Residuals insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The number of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | dataSliceId | query | string,null | false | The ID of the data slice. | | source | query | string | false | The subset of data used to compute the insight. | | unslicedOnly | query | string | false | Return only insights without a data_slice_id. | | externalDatasetId | query | string,null | false | The ID of the external dataset. | | entityId | path | string | true | The ID of the model. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [validation, crossValidation, holdout, externalTestSet] | | unslicedOnly | [false, False, true, True] | | Accept | application/json | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of paginated residuals insights.", "items": { "properties": { "data": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a model's Residuals chart, either for the specified data_slice_id or, if not specified, for all slices in the original data partition. | RetrieveResidualsPaginatedResponse | | 404 | Not Found | Requested entity ID or data slice ID not found | None | | 422 | Unprocessable Entity | Unsupported project type, or unsupported insight for model | None | ## Request calculation of ROC curve Operation path: `POST /api/v2/insights/rocCurve/` Authentication requirements: `BearerAuth` Request calculation of insight with an optional data slice. ### Body parameter ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComputeRocCurveRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested insights computation was accepted. | ComputeInsightsResponse | | 422 | Unprocessable Entity | Unsupported project or model type, model not trained, or locked holdout. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## The list of paginated ROC curve insights by entity ID Operation path: `GET /api/v2/insights/rocCurve/models/{entityId}/` Authentication requirements: `BearerAuth` The list of paginated ROC curve insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The number of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | dataSliceId | query | string,null | false | The ID of the data slice. | | source | query | string | false | The subset of data used to compute the insight. | | unslicedOnly | query | string | false | Return only insights without a data_slice_id. | | externalDatasetId | query | string,null | false | The ID of the external dataset. | | entityId | path | string | true | The ID of the model. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [validation, crossValidation, holdout, externalTestSet, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | | unslicedOnly | [false, False, true, True] | | Accept | application/json | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of paginated ROC curve insights.", "items": { "properties": { "data": { "description": "ROC curve data.", "properties": { "auc": { "description": "AUC value", "type": [ "number", "null" ] }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric value", "type": [ "number", "null" ] }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" } }, "required": [ "auc", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a model's ROC curve, either for the specified data_slice_id or, if not specified, for all slices in the original data partition. | RetrieveRocCurvePaginatedResponse | | 404 | Not Found | Requested entity ID or data slice ID not found | None | | 422 | Unprocessable Entity | Unsupported project type, or unsupported insight for model | None | ## Request calculation of SHAP Distributions Operation path: `POST /api/v2/insights/shapDistributions/` Authentication requirements: `BearerAuth` Request calculation of insight with an optional data slice. ### Body parameter ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "quickCompute": { "default": true, "description": "(Deprecated) Limits the number of rows used from the selected source by default. Cannot be set to False for this insight.", "type": "boolean" }, "rowCount": { "description": "(Deprecated) The number of rows to use for calculating SHAP Impact.", "type": "integer", "x-versiondeprecated": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComputeShapDistributionsRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested insights computation was accepted. | ComputeInsightsResponse | | 422 | Unprocessable Entity | Unsupported project or model type, model not trained, or locked holdout. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## The list of paginated SHAP Distributions insights by entity ID Operation path: `GET /api/v2/insights/shapDistributions/models/{entityId}/` Authentication requirements: `BearerAuth` The list of paginated SHAP Distributions insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The number of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | dataSliceId | query | string,null | false | The ID of the data slice. | | source | query | string | false | The subset of data used to compute the insight. | | unslicedOnly | query | string | false | Return only insights without a data_slice_id. | | externalDatasetId | query | string,null | false | The ID of the external dataset. | | predictionFilterRowCount | query | integer | false | The maximum number of distribution rows to return. | | featureFilterCount | query | integer | false | The maximum number of features to return. | | featureFilterName | query | string | false | The names of the features to return. | | quickCompute | query | boolean | false | When enabled, the default, limits the rows used from the selected subset (training sample or slice). | | seriesId | query | string | false | The series ID used to filter records (for multiseries projects). | | forecastDistance | query | integer | false | The forecast distance used to retrieve insight data. | | featuresOrderBy | query | string | false | Order SHAP distributions by the specified field value. | | entityId | path | string | true | The ID of the model. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, externalTestSet, holdout, holdout_training, training, validation] | | unslicedOnly | [false, False, true, True] | | featuresOrderBy | [featureImpact, -featureImpact, featureName, -featureName] | | Accept | [application/json, text/csv] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of paginated SHAP distributions insights.", "items": { "properties": { "data": { "description": "SHAP distributions data.", "properties": { "features": { "description": "List of SHAP distributions for each requested row.", "items": { "properties": { "feature": { "description": "The feature name in the dataset.", "type": "string" }, "featureType": { "description": "The feature type.", "enum": [ "T", "X", "B", "C", "CI", "N", "D", "DD", "FD", "Q", "CD", "GEO", "MC", "INT", "DOC" ], "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "shapValues": { "description": "The SHAP distributions values for this row.", "items": { "properties": { "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer" }, "featureValue": { "anyOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The value of the feature for this row." }, "predictionValue": { "description": "The prediction value for this row.", "type": "number" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ] }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number" }, "weight": { "description": "The weight of the row in the dataset.", "type": [ "number", "null" ], "x-versionadded": "v2.37" } }, "required": [ "featureRank", "featureValue", "predictionValue", "rowIndex", "shapValue", "weight" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 2500, "type": "array" } }, "required": [ "feature", "featureType", "impactNormalized", "impactUnnormalized", "shapValues" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "totalFeaturesCount": { "description": "The total number of features.", "type": "integer" } }, "required": [ "features", "totalFeaturesCount" ], "type": "object", "x-versionadded": "v2.35" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a model's SHAP Distributions chart, either for the specified data_slice_id or, if not specified, for all slices in the original data partition. | string | | 404 | Not Found | Requested entity ID or data slice ID not found | None | | 422 | Unprocessable Entity | Unsupported project type, or unsupported insight for model | None | ## Request calculation of SHAP Impact Operation path: `POST /api/v2/insights/shapImpact/` Authentication requirements: `BearerAuth` Request calculation of insight with an optional data slice. ### Body parameter ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "quickCompute": { "default": true, "description": "When enabled, limits the number of rows used from the selected source by default. When disabled, all rows are used.", "type": "boolean" }, "rowCount": { "description": "(Deprecated) The number of rows to use for calculating SHAP Impact.", "type": "integer", "x-versionadded": "v2.35", "x-versiondeprecated": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComputeShapInsightsRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested insights computation was accepted. | ComputeInsightsResponse | | 422 | Unprocessable Entity | Unsupported project or model type, model not trained, or locked holdout. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## The list of paginated SHAP Impact insights by entity ID Operation path: `GET /api/v2/insights/shapImpact/models/{entityId}/` Authentication requirements: `BearerAuth` The list of paginated SHAP Impact insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The number of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | dataSliceId | query | string,null | false | The ID of the data slice. | | source | query | string | false | Subset of data used to compute the insight. | | unslicedOnly | query | string | false | Return only insights without a data_slice_id. | | entityId | path | string | true | The ID of the model. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [backtest_0, backtest_0Training, backtest_1, backtest_10, backtest_10Training, backtest_11, backtest_11Training, backtest_12, backtest_12Training, backtest_13, backtest_13Training, backtest_14, backtest_14Training, backtest_15, backtest_15Training, backtest_16, backtest_16Training, backtest_17, backtest_17Training, backtest_18, backtest_18Training, backtest_19, backtest_19Training, backtest_1Training, backtest_2, backtest_20, backtest_20Training, backtest_2Training, backtest_3, backtest_3Training, backtest_4, backtest_4Training, backtest_5, backtest_5Training, backtest_6, backtest_6Training, backtest_7, backtest_7Training, backtest_8, backtest_8Training, backtest_9, backtest_9Training, externalTestSet, holdout, holdoutTraining, training, validation] | | unslicedOnly | [false, False, true, True] | | Accept | application/json | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of paginated SHAP impact insights.", "items": { "properties": { "data": { "description": "SHAP impact data.", "properties": { "baseValue": { "description": "The mean of raw model predictions for the training data.", "items": { "type": "number" }, "type": "array", "x-versionadded": "v2.33" }, "link": { "description": "The link function used to connect the SHAP importance values to the model output.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "quickCompute": { "default": true, "description": "When enabled, limits the rows used from the selected source subset by default. When disabled, all rows are used.", "type": "boolean", "x-versionadded": "v2.35" }, "rowCount": { "description": "(Deprecated) The number of rows used to calculate SHAP impact. If ``rowCount`` is not specified, the value returned is ``null``.", "type": [ "integer", "null" ], "x-versionadded": "v2.33", "x-versiondeprecated": "v2.35" }, "shapImpacts": { "description": "A list that contains SHAP impact scores.", "items": { "properties": { "featureName": { "description": "The feature name in the dataset.", "type": "string" }, "impactNormalized": { "description": "The normalized impact score value.", "type": [ "number", "null" ] }, "impactUnnormalized": { "description": "The raw impact score value.", "type": "number" } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "baseValue", "link", "rowCount", "shapImpacts" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the created insight.", "type": "string", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project.", "type": "string", "x-versionadded": "v2.33" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "Subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a model's SHAP impact chart, either for the specified data_slice_id or, if not specified, for all slices in the original data partition. | RetrieveShapImpactPaginatedResponse | | 404 | Not Found | Requested entity ID or data slice ID not found | None | | 422 | Unprocessable Entity | Unsupported project type, or unsupported insight for model | None | ## Request calculation of SHAP Matrix Operation path: `POST /api/v2/insights/shapMatrix/` Authentication requirements: `BearerAuth` Request calculation of insight with an optional data slice. ### Body parameter ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "quickCompute": { "default": true, "description": "When enabled, limits the number of rows used from the selected source by default. When disabled, all rows are used.", "type": "boolean" }, "rowCount": { "description": "(Deprecated) The number of rows to use for calculating SHAP Impact.", "type": "integer", "x-versionadded": "v2.35", "x-versiondeprecated": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComputeShapInsightsRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested insights computation was accepted. | ComputeInsightsResponse | | 422 | Unprocessable Entity | Unsupported project or model type, model not trained, or locked holdout. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## The list of paginated SHAP Matrix insights by entity ID Operation path: `GET /api/v2/insights/shapMatrix/models/{entityId}/` Authentication requirements: `BearerAuth` The list of paginated SHAP Matrix insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The number of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | dataSliceId | query | string,null | false | The ID of the data slice. | | source | query | string | false | The subset of data used to compute the insight. | | unslicedOnly | query | string | false | Return only insights without a data_slice_id. | | externalDatasetId | query | string,null | false | The ID of the external dataset. | | quickCompute | query | boolean | false | When enabled, limits the rows used from the selected source subset by default. When disabled, all rows are used. | | entityId | path | string | true | The ID of the model. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, externalTestSet, holdout, holdout_training, training, validation] | | unslicedOnly | [false, False, true, True] | | Accept | [application/json, text/csv] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of paginated SHAP matrix insights.", "items": { "properties": { "data": { "description": "SHAP matrix data.", "properties": { "baseValue": { "description": "The mean of the raw model predictions for the training data.", "items": { "type": "number" }, "type": "array", "x-versionadded": "v2.33" }, "colnames": { "description": "The names of each column in the SHAP matrix.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.33" }, "linkFunction": { "description": "The link function used to connect the feature importance values to the model output.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "matrix": { "description": "SHAP matrix values.", "items": { "items": { "type": "number" }, "type": "array" }, "type": "array", "x-versionadded": "v2.33" }, "rowIndex": { "description": "The index of the data row used to compute the SHAP matrix. Not used in time-aware projects.", "items": { "type": "integer" }, "type": "array", "x-versionadded": "v2.33" }, "timeSeriesRowIndex": { "description": "An index composed of the timestamp, series ID, and forecast distance. Only used in time-aware projects.", "items": { "items": { "oneOf": [ { "type": "integer" }, { "type": "string" } ] }, "maxLength": 2, "minLength": 2, "type": "array" }, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "baseValue", "colnames", "linkFunction", "matrix", "rowIndex" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the created insight.", "type": "string", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project.", "type": "string", "x-versionadded": "v2.33" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a model's SHAP Matrix chart, either for the specified data_slice_id or, if not specified, for all slices in the original data partition. | string | | 404 | Not Found | Requested entity ID or data slice ID not found | None | | 422 | Unprocessable Entity | Unsupported project type, or unsupported insight for model | None | ## Request calculation of SHAP Preview Operation path: `POST /api/v2/insights/shapPreview/` Authentication requirements: `BearerAuth` Request calculation of insight with an optional data slice. ### Body parameter ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "quickCompute": { "default": true, "description": "When enabled, limits the number of rows used from the selected source by default. When disabled, all rows are used.", "type": "boolean" }, "rowCount": { "description": "(Deprecated) The number of rows to use for calculating SHAP Impact.", "type": "integer", "x-versionadded": "v2.35", "x-versiondeprecated": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ComputeShapInsightsRequest | false | none | ### Example responses > 202 Response ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The requested insights computation was accepted. | ComputeInsightsResponse | | 422 | Unprocessable Entity | Unsupported project or model type, model not trained, or locked holdout. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## The list of paginated SHAP Preview insights by entity ID Operation path: `GET /api/v2/insights/shapPreview/models/{entityId}/` Authentication requirements: `BearerAuth` The list of paginated SHAP Preview insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The number of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | dataSliceId | query | string,null | false | The ID of the data slice. | | source | query | string | false | The subset of data used to compute the insight. | | unslicedOnly | query | string | false | Return only insights without a data_slice_id. | | externalDatasetId | query | string,null | false | The ID of the external dataset. | | predictionFilterRowCount | query | integer | false | The maximum number of preview rows to return. | | predictionFilterPercentiles | query | integer | false | The number of percentile intervals to select from the total number of rows. This field will supersede predictionFilterRowCount if both are present. | | predictionFilterOperandFirst | query | number | false | The first operand to apply to filtered predictions. | | predictionFilterOperandSecond | query | number | false | The second operand to apply to filtered predictions. | | predictionFilterOperator | query | string | false | The operator to apply to filtered predictions. | | featureFilterCount | query | integer | false | The maximum number of features to return for each preview. | | featureFilterName | query | string | false | The names of specific features to return for each preview. | | quickCompute | query | boolean | false | When enabled, limits the rows used from the selected source subset by default. When disabled, all rows are used. | | seriesId | query | string | false | The series ID used to filter records (for multiseries projects). | | forecastDistance | query | integer | false | The forecast distance used to retrieve insight data. | | entityId | path | string | true | The ID of the model. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, externalTestSet, holdout, holdout_training, training, validation] | | unslicedOnly | [false, False, true, True] | | predictionFilterOperator | [eq, in, <, >, between, notBetween] | | Accept | [application/json, text/csv] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of paginated SHAP preview insights.", "items": { "properties": { "data": { "description": "SHAP preview data.", "properties": { "previews": { "description": "List of SHAP previews for each requested row.", "items": { "properties": { "predictionValue": { "description": "The prediction value for this row.", "type": "number", "x-versionadded": "v2.33" }, "previewValues": { "description": "The SHAP preview values for this row.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string", "x-versionadded": "v2.33" }, "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "featureValue": { "description": "The value of the feature for this row.", "type": "string", "x-versionadded": "v2.33" }, "hasTextExplanations": { "description": "Whether the feature has text explanations available for this row.", "type": "boolean", "x-versionadded": "v2.33" }, "isImage": { "description": "Whether the feature is an image or not.", "type": "boolean", "x-versionadded": "v2.34" }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number", "x-versionadded": "v2.33" }, "textExplanations": { "description": "List of the text explanations for the feature for this row.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "featureName", "featureRank", "featureValue", "hasTextExplanations", "shapValue", "textExplanations" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "timeSeriesRowIndex": { "description": "An index composed of the timestamp, series ID, and forecast distance. This index is used only in time-aware projects.", "items": { "oneOf": [ { "type": "integer" }, { "type": "string" } ] }, "maxLength": 2, "minLength": 2, "type": [ "array", "null" ], "x-versionadded": "v2.36" }, "totalPreviewFeatures": { "description": "The total number of features available after name filters have been applied.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "predictionValue", "previewValues", "rowIndex", "totalPreviewFeatures" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "previewsCount": { "description": "The total number of previews.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "previews", "previewsCount" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the created insight.", "type": "string", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project.", "type": "string", "x-versionadded": "v2.33" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieves a model's SHAP Preview chart, either for the specified data_slice_id or, if not specified, for all slices in the original data partition. | string | | 404 | Not Found | Requested entity ID or data slice ID not found | None | | 422 | Unprocessable Entity | Unsupported project type, or unsupported insight for model | None | ## Delete insights by insightname Operation path: `DELETE /api/v2/insights/{insightName}/models/{entityId}/` Authentication requirements: `BearerAuth` Delete insights for a specific model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | insightName | path | string | true | The name of the insight to be deleted. | | entityId | path | string | true | The ID of the model. | ### Enumerated Values | Parameter | Value | | --- | --- | | insightName | [clusteringBarycentersMetric, clusteringDTW, confusionMatrix, featureEffects, featureImpact, liftChart, residuals, rocCurve, segmentationPreview, shapDistributions, shapImpact, shapMatrix, shapPreview, silhouetteDTW, timeSeriesClusteringBarycenters, umap] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Model insight records deleted. | None | | 404 | Not Found | Data was not found. | None | ## Retrieve multicategorical feature histogram by multilabelinsightskey Operation path: `GET /api/v2/multilabelInsights/{multilabelInsightsKey}/histogram/` Authentication requirements: `BearerAuth` Retrieve multicategorical feature histogram. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | multilabelInsightsKey | path | string | true | Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename] | ### Example responses > 200 Response ``` { "properties": { "featureName": { "description": "Feature name.", "type": "string" }, "histogram": { "description": "Feature histogram.", "items": { "properties": { "label": { "description": "Label name.", "type": "string" }, "plot": { "description": "Relevance histogram for label.", "items": { "properties": { "labelRelevance": { "description": "Label relevance value.", "maximum": 1, "minimum": 0, "type": "integer" }, "rowCount": { "description": "Number of rows for which the label has the given relevance.", "minimum": 0, "type": "integer" }, "rowPct": { "description": "Percentage of rows for which the label has the given relevance.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "labelRelevance", "rowCount", "rowPct" ], "type": "object" }, "maxItems": 2, "minItems": 2, "type": "array" } }, "required": [ "label", "plot" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "featureName", "histogram", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The multicategorical feature histogram. | MulticategoricalHistogram | ## Get all label lists by multilabelinsightskey Operation path: `GET /api/v2/multilabelInsights/{multilabelInsightsKey}/pairwiseManualSelections/` Authentication requirements: `BearerAuth` Get all label lists. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | multilabelInsightsKey | path | string | true | Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of manually selected label sets.", "items": { "properties": { "columnLabels": { "description": "Manually selected column labels.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "id": { "description": "The ID of the manually selected labels set.", "type": "string" }, "name": { "description": "Name for the set of manually selected labels.", "maxLength": 100, "type": "string" }, "rowLabels": { "description": "Manually selected row labels.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "columnLabels", "id", "name", "rowLabels" ], "type": "object" }, "type": "array" }, "featureName": { "description": "The name of the feature the request is related to.", "type": [ "string", "null" ] }, "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename]", "type": "string" }, "projectId": { "description": "The ID of the project the request is related to.", "type": [ "string", "null" ] } }, "required": [ "data", "featureName", "multilabelInsightsKey", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | All label lists. | PairwiseManualSelectionsRetrieveResponse | ## Save a list of manually selected labels by multilabelinsightskey Operation path: `POST /api/v2/multilabelInsights/{multilabelInsightsKey}/pairwiseManualSelections/` Authentication requirements: `BearerAuth` Save a list of manually selected labels for Feature Statistics matrix. ### Body parameter ``` { "properties": { "columnLabels": { "description": "Manually selected column labels.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureName": { "description": "The name of the feature the request is related to.", "type": [ "string", "null" ] }, "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename]", "type": "string" }, "name": { "description": "Name for the set of manually selected labels.", "maxLength": 100, "type": "string" }, "projectId": { "description": "The ID of the project the request is related to.", "type": [ "string", "null" ] }, "rowLabels": { "description": "Manually selected row labels.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "columnLabels", "featureName", "multilabelInsightsKey", "name", "projectId", "rowLabels" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | multilabelInsightsKey | path | string | true | Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename] | | body | body | PairwiseManualSelectionCreatePayload | false | none | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "The ID of the label set.", "type": "string" } }, "required": [ "id" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Whether manually selected labels were saved successfully. | PairwiseManualSelectionCreateResponse | | 422 | Unprocessable Entity | The manual selection name is already taken or another exception occurred. | None | ## Delete the label list by multilabelinsightskey Operation path: `DELETE /api/v2/multilabelInsights/{multilabelInsightsKey}/pairwiseManualSelections/{manualSelectionListId}/` Authentication requirements: `BearerAuth` Delete the label list. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | multilabelInsightsKey | path | string | true | Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename] | | manualSelectionListId | path | string | true | ID of the label set. | ### Example responses > 200 Response ``` { "properties": { "manualSelectionId": { "description": "The ID of the deleted or updated label set.", "type": "string" } }, "required": [ "manualSelectionId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The ID of the deleted label list. | PairwiseManualSelectionResponse | ## Update the label list name by multilabelinsightskey Operation path: `PATCH /api/v2/multilabelInsights/{multilabelInsightsKey}/pairwiseManualSelections/{manualSelectionListId}/` Authentication requirements: `BearerAuth` Update the label list name. ### Body parameter ``` { "properties": { "name": { "description": "Name for the set of manually selected labels.", "maxLength": 100, "type": "string" } }, "required": [ "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | multilabelInsightsKey | path | string | true | Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename] | | manualSelectionListId | path | string | true | ID of the label set. | | body | body | PairwiseManualSelectionUpdateRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "manualSelectionId": { "description": "The ID of the deleted or updated label set.", "type": "string" } }, "required": [ "manualSelectionId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The ID of the updated label list. | PairwiseManualSelectionResponse | ## Retrieve pairwise statistics by multilabelinsightskey Operation path: `GET /api/v2/multilabelInsights/{multilabelInsightsKey}/pairwiseStatistics/` Authentication requirements: `BearerAuth` Retrieve multilabel specific pairwise label statistics for the given multilabel insights key: correlation, joint probability and conditional probability. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statisticType | query | string | true | Type of pairwise statistic. | | multilabelInsightsKey | path | string | true | Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename] | ### Enumerated Values | Parameter | Value | | --- | --- | | statisticType | [conditionalProbability, correlation, jointProbability] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "Statistic values.", "items": { "properties": { "labelConfiguration": { "description": "Configuration of all labels.", "items": { "properties": { "label": { "description": "Label name.", "type": "string" }, "relevance": { "description": "Relevance value of the label.", "maximum": 1, "minimum": 0, "type": "integer" } }, "required": [ "label" ], "type": "object" }, "maxItems": 2, "minItems": 2, "type": "array" }, "statisticValue": { "description": "Statistic value for the given label configuration.", "type": [ "number", "null" ] } }, "required": [ "labelConfiguration", "statisticValue" ], "type": "object" }, "type": "array" }, "featureName": { "description": "Feature name.", "type": "string" }, "projectId": { "description": "The project ID.", "maxLength": 24, "minLength": 24, "type": "string" }, "statisticType": { "description": "Pairwise statistic type.", "enum": [ "conditionalProbability", "correlation", "jointProbability" ], "type": "string" } }, "required": [ "data", "featureName", "projectId", "statisticType" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The pairwise label statistics. | PairwiseStatisticsResponse | ## Retrieve anomaly assessment records by project ID Operation path: `GET /api/v2/projects/{projectId}/anomalyAssessmentRecords/` Authentication requirements: `BearerAuth` Retrieve anomaly assessment records. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | modelId | query | string | false | The model ID to filter records by. | | backtest | query | any | false | The backtest to filter records by. | | source | query | string | false | The source of the data to filter records by. | | seriesId | query | string | false | Can be specified for multiseries projects. The series id to filter records by. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of items in current page.", "type": "integer" }, "data": { "description": "Anomaly assessment record.", "items": { "properties": { "backtest": { "description": "The backtest of the record.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "deleteLocation": { "description": "URL to delete anomaly assessment record.", "format": "uri", "type": [ "string", "null" ] }, "endDate": { "description": "ISO-formatted last timestamp in the subset. For example: ``2019-08-30T00:00:00.000000Z``.", "format": "date-time", "type": [ "string", "null" ] }, "latestExplanationsLocation": { "description": "URL to retrieve the latest predictions with the shap explanations.", "format": "uri", "type": [ "string", "null" ] }, "modelId": { "description": "The model ID of the record.", "type": "string" }, "predictionThreshold": { "description": "The threshold, all rows with anomaly scores greater or equal to it have Shapley explanations computed.", "type": [ "number", "null" ] }, "previewLocation": { "description": "URL to retrieve predictions preview for the record.", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID of the record.", "type": "string" }, "recordId": { "description": "The ID of the anomaly assessment record.", "type": "string" }, "seriesId": { "description": "The series id of the record. Applicable in multiseries projects", "type": [ "string", "null" ] }, "source": { "description": "The source of the record", "enum": [ "training", "validation" ], "type": "string" }, "startDate": { "description": "ISO-formatted first timestamp in the subset. For example: ``2019-08-01T00:00:00.000000Z``.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the anomaly assessment record.", "enum": [ "noData", "notSupported", "completed" ], "type": "string" }, "statusDetails": { "description": "The status details.", "type": "string" } }, "required": [ "backtest", "deleteLocation", "endDate", "latestExplanationsLocation", "modelId", "predictionThreshold", "previewLocation", "projectId", "recordId", "seriesId", "source", "startDate", "status", "statusDetails" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page)", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve anomaly assessment records. | AnomalyAssessmentRecordsResponse | | 404 | Not Found | No data found | None | | 422 | Unprocessable Entity | Input parameters are invalid | None | ## Delete the anomaly assessment record by project ID Operation path: `DELETE /api/v2/projects/{projectId}/anomalyAssessmentRecords/{recordId}/` Authentication requirements: `BearerAuth` Delete the anomaly assessment record. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | recordId | path | string | true | The anomaly assessment record ID | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Anomaly assessment record deleted. | None | | 404 | Not Found | Data was not found. | None | ## Retrieve anomaly assessment record by project ID Operation path: `GET /api/v2/projects/{projectId}/anomalyAssessmentRecords/{recordId}/explanations/` Authentication requirements: `BearerAuth` Retrieve anomaly assessment record. Two out of three parameters: `startDate`, `endDate` or `pointsCount` must be specified. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | startDate | query | string(date-time) | false | The start of the date range to return. Date should be in UTC format. For example: 2019-08-01T00:00:00.000000Z. | | endDate | query | string(date-time) | false | The end of the date range to return, inclusive. Date should be in UTC format. For example: 2020-01-01T00:00:00.000000Z. | | pointsCount | query | integer | false | Count of points to return. | | projectId | path | string | true | The project ID. | | recordId | path | string | true | The anomaly assessment record ID | ### Example responses > 200 Response ``` { "properties": { "backtest": { "description": "The backtest of the record.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "count": { "description": "The count of points.", "type": "integer" }, "data": { "description": "Each is a `DataPoint` corresponding to a row in the specified range.", "items": { "properties": { "prediction": { "description": "The output of the model for this row.", "type": "number" }, "shapExplanation": { "description": "Either ``null`` or an array of up to 10 `ShapleyFeatureContribution` objects. Only rows with the highest anomaly scores have Shapley explanations calculated.", "items": { "properties": { "feature": { "description": "Feature name", "type": "string" }, "featureValue": { "description": "Feature value for this row. First 50 characters are returned.", "type": "string" }, "strength": { "description": "Shapley value for this feature and row.", "type": "number" } }, "required": [ "feature", "featureValue", "strength" ], "type": "object" }, "type": "array" }, "timestamp": { "description": "ISO-formatted timestamp for the row.", "format": "date-time", "type": "string" } }, "required": [ "prediction", "shapExplanation", "timestamp" ], "type": "object" }, "type": "array" }, "endDate": { "description": "ISO-formatted last timestamp in the response. For example: ``2019-08-30T00:00:00.000000Z``.", "format": "date-time", "type": "string" }, "modelId": { "description": "The model ID of the record.", "type": "string" }, "projectId": { "description": "The project ID of the record.", "type": "string" }, "recordId": { "description": "The ID of the anomaly assessment record.", "type": "string" }, "seriesId": { "description": "The series id of the record. Applicable in multiseries projects", "type": [ "string", "null" ] }, "shapBaseValue": { "description": "shap base value", "type": "number" }, "source": { "description": "The source of the record", "enum": [ "training", "validation" ], "type": "string" }, "startDate": { "description": "ISO-formatted first timestamp in the response. For example: ``2019-08-01T00:00:00.000000Z``.", "format": "date-time", "type": "string" } }, "required": [ "backtest", "count", "data", "endDate", "modelId", "projectId", "recordId", "seriesId", "shapBaseValue", "source", "startDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Anomaly assessment record. | AnomalyAssessmentExplanationsResponse | | 404 | Not Found | Data was not found. | None | | 422 | Unprocessable Entity | Insight is not available. | None | ## Retrieve predictions preview by project ID Operation path: `GET /api/v2/projects/{projectId}/anomalyAssessmentRecords/{recordId}/predictionsPreview/` Authentication requirements: `BearerAuth` Retrieve predictions preview for the anomaly assessment record. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | recordId | path | string | true | The anomaly assessment record ID | ### Example responses > 200 Response ``` { "properties": { "backtest": { "description": "The backtest of the record.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "endDate": { "description": "ISO-formatted last timestamp in the subset. For example: ``2019-08-30T00:00:00.000000Z``.", "format": "date-time", "type": "string" }, "modelId": { "description": "The model ID of the record.", "type": "string" }, "previewBins": { "description": "Aggregated predictions for the subset. Bins boundaries may differ from actual start/end dates because this is an aggregation.", "items": { "properties": { "avgPredicted": { "description": "Average prediction of the model in the bin. Null if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "ISO-formatted datetime of the end of the bin (exclusive).", "format": "date-time", "type": "string" }, "frequency": { "description": "Number of the rows in the bin.", "type": "integer" }, "maxPredicted": { "description": "Maximum prediction of the model in the bin. Null if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "ISO-formatted datetime of the start of the bin (inclusive).", "format": "date-time", "type": "string" } }, "required": [ "avgPredicted", "endDate", "frequency", "maxPredicted", "startDate" ], "type": "object" }, "type": "array" }, "projectId": { "description": "The project ID of the record.", "type": "string" }, "recordId": { "description": "The ID of the anomaly assessment record.", "type": "string" }, "seriesId": { "description": "The series id of the record. Applicable in multiseries projects", "type": [ "string", "null" ] }, "source": { "description": "The source of the record", "enum": [ "training", "validation" ], "type": "string" }, "startDate": { "description": "ISO-formatted first timestamp in the subset. For example: ``2019-08-01T00:00:00.000000Z``.", "format": "date-time", "type": "string" } }, "required": [ "backtest", "endDate", "modelId", "previewBins", "projectId", "recordId", "seriesId", "source", "startDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Predictions preview for the anomaly assessment record. | AnomalyAssessmentPreviewResponse | | 404 | Not Found | Record not found. | None | | 422 | Unprocessable Entity | Predictions preview is not available. | None | ## List Bias vs Accuracy insights by project ID Operation path: `GET /api/v2/projects/{projectId}/biasVsAccuracyInsights/` Authentication requirements: `BearerAuth` Retrieve a list of Bias vs Accuracy insights for the model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | accuracyMetric | query | string | false | The metric to return model accuracy scores. Defaults to the optimization metric configured in project options. | | protectedFeature | query | any | false | Name of the protected feature. | | fairnessMetric | query | any | false | The fairness metric used to calculate the fairness scores. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | accuracyMetric | [AUC, Weighted AUC, Area Under PR Curve, Weighted Area Under PR Curve, Kolmogorov-Smirnov, Weighted Kolmogorov-Smirnov, FVE Binomial, Weighted FVE Binomial, Gini Norm, Weighted Gini Norm, LogLoss, Weighted LogLoss, Max MCC, Weighted Max MCC, Rate@Top5%, Weighted Rate@Top5%, Rate@Top10%, Weighted Rate@Top10%, Rate@TopTenth%, RMSE, Weighted RMSE] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "An array of bias vs accuracy insights for the model.", "items": { "properties": { "accuracyMetric": { "description": "The metric to return model accuracy scores. Defaults to the optimization metric configured in project options.", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE" ], "type": "string" }, "fairnessMetric": { "description": "The fairness metric used to calculate the fairness scores.", "oneOf": [ { "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, { "items": { "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "type": "array" } ] }, "fairnessThreshold": { "default": 0.8, "description": "Value of the fairness threshold, defined in project options.", "maximum": 1, "minimum": 0, "type": "number" }, "models": { "description": "An array of models of the insight.", "items": { "properties": { "accuracyValue": { "description": "The model's accuracy score.", "minimum": 0, "type": "number" }, "bp": { "description": "The blueprint number of the model from the leaderboard.", "exclusiveMinimum": 0, "type": "integer" }, "dsName": { "description": "The name of the feature list used for model training.", "type": "string" }, "fairnessValue": { "description": "The model's relative fairness score for the class with the lowest fairness score. In other words, the fairness score of the least privileged class.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": "integer" }, "modelType": { "description": "The type/name of the model.", "type": "string" }, "prime": { "description": "Flag to indicate whether the model is a prime model.", "type": "boolean" }, "samplepct": { "description": "The sample size percentage of the feature list data the model was trained on.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "accuracyValue", "bp", "dsName", "fairnessValue", "modelId", "modelNumber", "modelType", "prime", "samplepct" ], "type": "object" }, "type": "array" }, "protectedFeature": { "description": "Name of the protected feature.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ] } }, "required": [ "fairnessThreshold", "models" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns Bias vs Accuracy results. | BiasVsAccuracyInsightRetrieve | ## List paginated Data Slices by project ID Operation path: `GET /api/v2/projects/{projectId}/dataSlices/` Authentication requirements: `BearerAuth` Returns a paginated list of data slices for the given project ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | true | The numbers of items to return. | | offset | query | integer | true | The number of items to skip before starting to collect the result set. | | searchQuery | query | string | false | Search query for data slices. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of paginated Data Slices.", "items": { "properties": { "filters": { "description": "List of filters the data slice is composed of.", "items": { "properties": { "operand": { "description": "Feature to apply operation to.", "type": "string" }, "operator": { "description": "Operator to apply to the named operand in the dataset. The operator 'eq' mean 'equals the single specified value'. The operator 'in' means 'is one of a list of allowed values.'", "enum": [ "eq", "in", "<", ">", "between", "notBetween" ], "type": "string" }, "values": { "description": "Values to filter the operand by with the given operator.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "operand", "operator", "values" ], "type": "object" }, "maxItems": 3, "minItems": 1, "type": "array" }, "id": { "description": "ID of the data slice.", "type": "string" }, "name": { "description": "User provided name for the data slice.", "maxLength": 500, "minLength": 1, "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "filters", "id", "name", "projectId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of data slices for the project was successfully retrieved. | DataSlicesListAllSlicesResponse | ## Retrieve the data by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/accuracyOverTimePlots/` Authentication requirements: `BearerAuth` Retrieve the data for the Accuracy over Time plots. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | seriesId | query | string | false | The name of the series to retrieve. Only available for time series multiseries projects. If not provided an average plot for the first 1000 series will be retrieved. | | backtest | query | any | false | Retrieve plots for a specific backtest (use the backtest index starting from zero) or holdout. If not specified the first backtest (backtest index 0) will be used. | | source | query | string | false | The source of the data for the backtest/holdout. | | forecastDistance | query | integer | false | Forecast distance to retrieve the data for. If not specified, the first forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects. | | resolution | query | string | false | Specifying at which resolution the data should be binned. If not specified, optimal resolution will be used to build chart data with number of bins <= maxBinSize | | maxBinSize | query | integer | false | Specifies the maximum number of bins for the retrieval. | | startDate | query | string(date-time) | false | The start of the date range to return. If not specified, start date for requested plots will be used. | | endDate | query | string(date-time) | false | The end of the date range to return. If not specified, end date for requested plots will be used. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation] | | resolution | [milliseconds, seconds, minutes, hours, days, weeks, months, quarters, years] | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "actual": { "description": "Average actual value of the target in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "frequency": { "description": "Indicates number of values averaged in bin in case of a resolution change.", "type": [ "integer", "null" ] }, "predicted": { "description": "Average prediction of the model in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "frequency", "predicted", "startDate" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "calendarEvents": { "description": "An array of calendar events for a retrieved plot.", "items": { "properties": { "date": { "description": "The date of the calendar event.", "format": "date-time", "type": "string" }, "name": { "description": "Name of the calendar event.", "type": "string" }, "seriesId": { "description": "The series ID for the event. If this event does not specify a series ID, then this will be `null`, indicating that the event applies to all series.", "type": [ "string", "null" ] } }, "required": [ "date", "name", "seriesId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "resolution": { "description": "The resolution that is used for binning.", "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" }, "statistics": { "description": "Statistics calculated for the chart data.", "properties": { "durbinWatson": { "description": "The Durbin-Watson statistic for the chart data. Value is between 0 and 4. Durbin-Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals (prediction errors) from a regression analysis. More info https://wikipedia.org/wiki/Durbin%E2%80%93Watson_statistic", "maximum": 4, "minimum": 0, "type": [ "number", "null" ] } }, "required": [ "durbinWatson" ], "type": "object" } }, "required": [ "bins", "calendarEvents", "endDate", "resolution", "startDate", "statistics" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Accuracy over Time plots data | AccuracyOverTimePlotsDataResponse | | 404 | Not Found | Accuracy over Time plots data was not found | None | | 422 | Unprocessable Entity | Invalid parameters were submitted | None | ## Retrieve the metadata by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/accuracyOverTimePlots/metadata/` Authentication requirements: `BearerAuth` Retrieve the metadata for the Accuracy over Time insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | forecastDistance | query | integer | false | Forecast distance to retrieve the data for. If not specified, the first forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects. | | seriesId | query | string | false | The name of the series to retrieve. Only available for time series multiseries projects. If not provided a metadata of average plot for the first 1000 series will be retrieved. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | Accept | application/json | ### Example responses > 200 Response ``` { "properties": { "backtestMetadata": { "description": "An array of metadata information for each backtest. The array index of metadata object is the backtest index.", "items": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "backtestStatuses": { "description": "An array of status information for each backtest. The array index of status object is the backtest index.", "items": { "description": "Status for accuracy over time plots.", "properties": { "training": { "description": "The status for the training.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" }, "validation": { "description": "The status for the validation.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "estimatedSeriesLimit": { "description": "Estimated number of series that can be calculated in one request for 1 FD.", "minimum": 1, "type": "integer" }, "forecastDistance": { "description": "The forecast distance for which the data was retrieved. `null` for OTV projects.", "maximum": 1000, "minimum": 0, "type": [ "integer", "null" ] }, "holdoutMetadata": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "holdoutStatuses": { "description": "Status for accuracy over time plots.", "properties": { "training": { "description": "The status for the training.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" }, "validation": { "description": "The status for the validation.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" } }, "required": [ "training", "validation" ], "type": "object" }, "resolutions": { "description": "An array of available time resolutions for which plots can be retrieved.", "items": { "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "maxItems": 9, "minItems": 1, "type": "array" } }, "required": [ "backtestMetadata", "backtestStatuses", "forecastDistance", "holdoutMetadata", "holdoutStatuses", "resolutions" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Accuracy over Time insight metadata. | AccuracyOverTimePlotsMetadataResponse | | 404 | Not Found | Accuracy over Time insight metadata was not found | None | | 422 | Unprocessable Entity | Invalid parameters were submitted. | None | ## Retrieve the preview by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/accuracyOverTimePlots/preview/` Authentication requirements: `BearerAuth` Retrieve the preview for the Accuracy over Time plots. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | seriesId | query | string | false | The name of the series to retrieve. Only available for time series multiseries projects. If not provided an average plot for the first 1000 series will be retrieved. | | backtest | query | any | false | Retrieve plots for a specific backtest (use the backtest index starting from zero) or holdout. If not specified the first backtest (backtest index 0) will be used. | | source | query | string | false | The source of the data for the backtest/holdout. | | forecastDistance | query | integer | false | Forecast distance to retrieve the data for. If not specified, the first forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation] | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "actual": { "description": "Average actual value of the target in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "predicted": { "description": "Average prediction of the model in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "predicted", "startDate" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "bins", "endDate", "startDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Accuracy over Time plots preview | DatetimeTrendPlotsPreviewResponse | | 404 | Not Found | Accuracy over Time plots preview was not found | None | | 422 | Unprocessable Entity | Invalid parameters were submitted | None | ## Retrieve anomaly over time plots by id Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/anomalyOverTimePlots/` Authentication requirements: `BearerAuth` Retrieve the data for the Anomaly over Time plots. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | seriesId | query | string | false | The name of the series to retrieve. Only available for time series multiseries projects. If not provided an average plot for the first 1000 series will be retrieved. | | backtest | query | any | false | Retrieve plots for a specific backtest (use the backtest index starting from zero) or holdout. If not specified the first backtest (backtest index 0) will be used. | | source | query | string | false | The source of the data for the backtest/holdout. | | resolution | query | string | false | Specifying at which resolution the data should be binned. If not specified, optimal resolution will be used to build chart data with number of bins <= maxBinSize | | maxBinSize | query | integer | false | Specifies the maximum number of bins for the retrieval. | | startDate | query | string(date-time) | false | The start of the date range to return. If not specified, start date for requested plots will be used. | | endDate | query | string(date-time) | false | The end of the date range to return. If not specified, end date for requested plots will be used. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation] | | resolution | [milliseconds, seconds, minutes, hours, days, weeks, months, quarters, years] | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "frequency": { "description": "Indicates number of values averaged in bin in case of a resolution change.", "type": [ "integer", "null" ] }, "predicted": { "description": "Average prediction of the model in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "endDate", "frequency", "predicted", "startDate" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "calendarEvents": { "description": "An array of calendar events for a retrieved plot.", "items": { "properties": { "date": { "description": "The date of the calendar event.", "format": "date-time", "type": "string" }, "name": { "description": "Name of the calendar event.", "type": "string" }, "seriesId": { "description": "The series ID for the event. If this event does not specify a series ID, then this will be `null`, indicating that the event applies to all series.", "type": [ "string", "null" ] } }, "required": [ "date", "name", "seriesId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "resolution": { "description": "The resolution that is used for binning.", "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "bins", "calendarEvents", "endDate", "resolution", "startDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Anomaly over Time plots data | AnomalyOverTimePlotsDataResponse | | 404 | Not Found | Anomaly over Time plots data was not found | None | | 422 | Unprocessable Entity | Invalid parameters were submitted | None | ## Retrieve metadata by id Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/anomalyOverTimePlots/metadata/` Authentication requirements: `BearerAuth` Retrieve the metadata for the Anomaly over Time insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | seriesId | query | string | false | The name of the series to retrieve. Only available for time series multiseries projects. If not provided a metadata of average plot for the first 1000 series will be retrieved. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | Accept | application/json | ### Example responses > 200 Response ``` { "properties": { "backtestMetadata": { "description": "An array of metadata information for each backtest. The array index of metadata object is the backtest index.", "items": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "backtestStatuses": { "description": "An array of status information for each backtest. The array index of status object is the backtest index.", "items": { "description": "Status for accuracy over time plots.", "properties": { "training": { "description": "The status for the training.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" }, "validation": { "description": "The status for the validation.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "estimatedSeriesLimit": { "description": "Estimated number of series that can be calculated in one request for 1 FD.", "minimum": 1, "type": "integer" }, "holdoutMetadata": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "holdoutStatuses": { "description": "Status for accuracy over time plots.", "properties": { "training": { "description": "The status for the training.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" }, "validation": { "description": "The status for the validation.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" } }, "required": [ "training", "validation" ], "type": "object" }, "resolutions": { "description": "An array of available time resolutions for which plots can be retrieved.", "items": { "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "maxItems": 9, "minItems": 1, "type": "array" } }, "required": [ "backtestMetadata", "backtestStatuses", "holdoutMetadata", "holdoutStatuses", "resolutions" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Anomaly over Time insight metadata. | AnomalyOverTimePlotsMetadataResponse | | 404 | Not Found | Anomaly over Time insight metadata was not found. | None | | 422 | Unprocessable Entity | Invalid parameters were submitted. | None | ## Retrieve preview by id Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/anomalyOverTimePlots/preview/` Authentication requirements: `BearerAuth` Retrieve the preview for the Anomaly over Time plots. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | seriesId | query | string | false | The name of the series to retrieve. Only available for time series multiseries projects. If not provided an average plot for the first 1000 series will be retrieved. | | backtest | query | any | false | Retrieve plots for a specific backtest (use the backtest index starting from zero) or holdout. If not specified the first backtest (backtest index 0) will be used. | | source | query | string | false | The source of the data for the backtest/holdout. | | predictionThreshold | query | number | false | Only bins with predictions exceeding this threshold will be returned in the response. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation] | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "endDate", "startDate" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "predictionThreshold": { "description": "Only bins with predictions exceeding this threshold are returned in the response.", "exclusiveMinimum": 0, "maximum": 1, "type": "number" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "bins", "endDate", "predictionThreshold", "startDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Anomaly over Time plots preview | AnomalyOverTimePlotsPreviewResponse | | 404 | Not Found | Anomaly over Time plots preview was not found | None | | 422 | Unprocessable Entity | Invalid parameters were submitted | None | ## Retrieve a plot displaying the stability of the datetime model across by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/backtestStabilityPlot/` Authentication requirements: `BearerAuth` Retrieve a plot displaying the stability of the datetime model across different backtests. All durations and datetimes should be specified in accordance with the :ref: `timestamp and duration formatting rules`. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | metricName | query | string | false | The name of the metric to retrieve the scores for. If omitted, the default project metric will be used | | forecastDistance | query | integer | false | The forecast distance to retrieve the plot for. If not specified, the scores for each partition are aggregated across all forecast distances. This parameter is only available for time series models. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "backtestPlotData": { "description": "An array of objects containing the details of the scores for each partition defined for the project.", "items": { "properties": { "backtestIndex": { "description": "An integer representing the index of the backtest, starting from 0. For holdout, this field will be null.", "minimum": 0, "type": [ "integer", "null" ] }, "partition": { "description": "Identifier of the partition. Can either identify a specific backtest (\"backtest0\", \"backtest1\", ...) or the holdout set (\"holdout\").", "type": "string" }, "score": { "description": "Score for this partition. Can be null if the score is unavailable for this partition (e.g. holdout is locked or backtesting has not been run yet).", "type": [ "number", "null" ] }, "scoringEndDate": { "description": "End date of the subset used for scoring.", "format": "date-time", "type": "string" }, "scoringStartDate": { "description": "Start date of the subset used for scoring.", "format": "date-time", "type": "string" } }, "required": [ "backtestIndex", "partition", "score", "scoringEndDate", "scoringStartDate" ], "type": "object" }, "type": "array" }, "endDate": { "description": "End date of the project dataset.", "format": "date-time", "type": "string" }, "metricName": { "description": "Name of the metric used to compute the scores.", "type": "string" }, "startDate": { "description": "Start date of the project dataset.", "format": "date-time", "type": "string" } }, "required": [ "backtestPlotData", "endDate", "metricName", "startDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Backtest stability plot data for datetime partitioned model. | BacktestStabilityPlotResponse | | 422 | Unprocessable Entity | Backtest stability plot data not available. | None | ## Retrieve the Accuracy Over Time (AOT) chart data by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/datasetAccuracyOverTimePlots/{datasetId}/` Authentication requirements: `BearerAuth` Retrieve the Accuracy Over Time (AOT) chart data for an external dataset for a project. Datetimes are specified in accordance with. :ref: `timestamp and duration formatting rules `. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | maxBinSize | query | integer | false | The limit of returned bins. | | startDate | query | string(date-time) | false | The start of the date range to return (UTC string), for example: '2010-05-13T00:00:00.000000Z'. If not specified, the start date for this model and source of the data will be used instead. | | endDate | query | string(date-time) | false | The end of the date range to return (UTC string), for example: '2010-05-13T00:00:00.000000Z'. If not specified, the end date for this model and source of the data will be used instead. | | resolution | query | string | false | Specifies at which resolution the data should be binned. If not specified, optimal resolution will be used to build chart data such that bins <= maxBinSize. | | projectId | path | string | true | The project ID that was used to compute the AOT chart. | | modelId | path | string | true | The model ID that was used to compute the AOT chart. | | datasetId | path | string | true | The dataset ID that was used to compute the AOT chart. | ### Enumerated Values | Parameter | Value | | --- | --- | | resolution | [microseconds, milliseconds, seconds, minutes, hours, days, weeks, months, quarters, years] | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "The datetime chart data for that source.", "items": { "properties": { "actual": { "description": "The average actual value of the target in the bin. ``null`` if there are no entries in the bin or if this is an anomaly detection project.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive).", "format": "date-time", "type": "string" }, "frequency": { "description": "As indicated by the frequencyType in the Metadata, used to determine what the averages mentioned above are taken over. ``null`` if there are no entries in the bin.", "type": [ "number", "null" ] }, "predicted": { "description": "The average prediction of the model in the bin. ``null`` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive).", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "frequency", "predicted", "startDate" ], "type": "object" }, "type": "array" }, "datasetId": { "description": "The dataset ID to which the chart data belongs.", "type": "string" }, "endDate": { "description": "The requested `endDate`, or, if not specified, the end date for this dataset (exclusive). Example: '2010-05-13T00:00:00.000000Z'.", "format": "date-time", "type": "string" }, "modelId": { "description": "The model ID to which the chart data belongs.", "type": "string" }, "projectId": { "description": "The project ID to which the chart data belongs.", "type": "string" }, "resolution": { "description": "The resolution used for binning where a resolution is one of ['milliseconds', 'seconds', 'minutes', 'hours', 'days', 'weeks', 'months', 'years'].", "enum": [ "microseconds", "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "startDate": { "description": "The requested `startDate`, or, if not specified, the start date for this dataset. Example: '2010-05-13T00:00:00.000000Z'.", "format": "date-time", "type": "string" }, "statistics": { "description": "Statistics calculated on the chart data.", "properties": { "durbinWatson": { "description": "The Durbin-Watson statistic for the chart data. Value is between 0 and 4. Returns -1 when the statistic is invalid for the data, e.g. if this is an anomaly detection project.", "type": "number" } }, "required": [ "durbinWatson" ], "type": "object" } }, "required": [ "bins", "datasetId", "modelId", "projectId", "resolution", "statistics" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Accuracy Over Time (AOT) chart data for an external dataset for a project. | AOTChartRetrieveResponse | | 404 | Not Found | No insights found. | None | ## Retrieve the metadata of the Accuracy Over Time (AOT) chart by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/datasetAccuracyOverTimePlots/{datasetId}/metadata/` Authentication requirements: `BearerAuth` Retrieve the metadata of the Accuracy Over Time (AOT) chart for an external dataset. Datetimes are specified in accordance with. :ref: `timestamp and duration formatting rules `. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID that was used to compute the AOT chart. | | modelId | path | string | true | The model ID that was used to compute the AOT chart. | | datasetId | path | string | true | The dataset ID that was used to compute the AOT chart. | ### Example responses > 200 Response ``` { "properties": { "datasetId": { "description": "The dataset ID that was used to compute the AOT chart.", "type": "string" }, "datasetMetadata": { "description": "The dataset metadata.", "properties": { "endDate": { "description": "ISO-8601 formatted end date (max date) in the dataset.", "format": "date-time", "type": "string" }, "startDate": { "description": "ISO-8601 formatted start date (min date) in the dataset.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "startDate" ], "type": "object" }, "frequencyType": { "description": "How to interpret the frequency attribute of each datetimeTrendBin. One of ['rowCount', 'weightedRowCount', 'exposure', 'weightedExposure'].", "enum": [ "rowCount", "weightedRowCount", "exposure", "weightedExposure" ], "type": "string" }, "metricName": { "description": "The metric used to score each bin and the calculate the metric attribute of each datetimeTrendBin.", "type": "string" }, "modelId": { "description": "The model ID that was used to compute the AOT chart.", "type": "string" }, "projectId": { "description": "The project ID that was used to compute the AOT chart.", "type": "string" }, "resolutions": { "description": "Suggested time resolutions where a resolution is one of ['milliseconds', 'seconds', 'minutes', 'hours', 'days', 'weeks', 'months', 'years'].", "items": { "enum": [ "microseconds", "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "type": "array" } }, "required": [ "datasetId", "datasetMetadata", "frequencyType", "metricName", "modelId", "projectId", "resolutions" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Metadata of the Accuracy Over Time (AOT) chart for an external dataset. | AOTChartMetadataResponse | | 404 | Not Found | No insights found. | None | ## Retrieve a preview of the Accuracy Over Time (AOT) chart by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/datasetAccuracyOverTimePlots/{datasetId}/preview/` Authentication requirements: `BearerAuth` Retrieve a preview of the Accuracy Over Time (AOT) chart for an external dataset. Datetimes are specified in accordance with. :ref: `timestamp and duration formatting rules `. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID that was used to compute the AOT chart. | | modelId | path | string | true | The model ID that was used to compute the AOT chart. | | datasetId | path | string | true | The dataset ID that was used to compute the AOT chart. | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "The datetime chart data for that source.", "items": { "properties": { "actual": { "description": "The average actual value of the target in the bin. ``null`` if there are no entries in the bin or if this is an anomaly detection project.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive).", "format": "date-time", "type": "string" }, "frequency": { "description": "As indicated by the frequencyType in the Metadata, used to determine what the averages mentioned above are taken over. ``null`` if there are no entries in the bin.", "type": [ "number", "null" ] }, "predicted": { "description": "The average prediction of the model in the bin. ``null`` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive).", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "frequency", "predicted", "startDate" ], "type": "object" }, "type": "array" }, "datasetId": { "description": "The dataset ID that was used to compute the AOT chart.", "type": "string" }, "modelId": { "description": "The model ID that was used to compute the AOT chart.", "type": "string" }, "projectId": { "description": "The project ID that was used to compute the AOT chart.", "type": "string" } }, "required": [ "bins", "datasetId", "modelId", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Preview of the Accuracy Over Time (AOT) chart for an external dataset. | AOTChartPreviewResponse | | 404 | Not Found | No insights found. | None | ## Computes Datetime Trend plots by project ID Operation path: `POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/datetimeTrendPlots/` Authentication requirements: `BearerAuth` Computes Datetime Trend plots for time series and OTV projects: * For OTV projects, computes Accuracy over Time plots. * For time series supervised projects, computes both Accuracy over Time plots and Forecast vs Actual plots. .. minversion:: v2.25 * For unsupervised time series and OTV models, computes Anomaly Over Time plots. .. note:: For the multiseries time series projects only first 1000 series in alphabetical order and an average plot for them will be computed. .. note:: Maximum 100 forecast distances can be requested for calculation in time series supervised projects. ### Body parameter ``` { "properties": { "backtest": { "default": 0, "description": "Compute plots for a specific backtest (use the backtest index starting from zero) or `holdout`. If not specified the first backtest (backtest index 0) will be used.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "forecastDistanceEnd": { "description": "The end of forecast distance range (forecast window) to compute. If not specified, the last forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects.", "minimum": 0, "type": "integer" }, "forecastDistanceStart": { "description": "The start of forecast distance range (forecast window) to compute. If not specified, the first forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects.", "minimum": 0, "type": "integer" }, "fullAverage": { "default": false, "description": "Whether to compute an average plot for all series. Only available for time series multiseries projects.", "type": "boolean", "x-versionadded": "2.28" }, "seriesIds": { "description": "Only available for time series multiseries projects. Each element should be a name of a single series in a multiseries project. It is possible to compute a maximum of 1000 series per one request. If not specified the first 1000 series in alphabetical order will be computed. It is not possible to specify `fullAverage: true` while also setting `seriesIds`. This parameter can only be specified after first 1000 series in alphabetical order are computed.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "2.28" }, "source": { "default": "validation", "description": "The source of the data for the backtest/holdout.", "enum": [ "training", "validation" ], "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | DatetimeTrendPlotsCreate | false | none | ### Example responses > 202 Response ``` { "properties": { "message": { "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job can be created.", "type": "string" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Datetime Trend plots computation job submitted successfully. | DatetimeTrendPlotsResponse | | 422 | Unprocessable Entity | There were invalid parameters in the submitted request. See the message field for more details. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve Feature Effects by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/featureEffects/` Authentication requirements: `BearerAuth` Retrieve Feature Effects for a model backtest. Feature Effects provides partial dependence and predicted vs actual values for the top 500 features, ordered by feature impact score. The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. If a Feature Effects job was previously submitted for a given backtest, this endpoint will return a response structured as {"message":, "jobId":} wherejobIdis the ID of the job. Retrieve the job with [GET /api/v2/projects/{projectId}/jobs/{jobId}/][get-apiv2projectsprojectidjobsjobid]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | source | query | string | false | The model's data source. | | backtestIndex | query | string | true | The backtest index. For example: 0, 1, ..., 20, holdout, startstop. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation, holdout] | ### Example responses > 200 Response ``` { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "featureEffects": { "description": "The Feature Effects computational results for each feature.", "items": { "properties": { "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "The model's data source.", "type": "string" } }, "required": [ "backtestIndex", "featureEffects", "modelId", "projectId", "source" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | FeatureEffectsDatetimeResponse | | 403 | Forbidden | User does not have permission to view the project. | None | | 404 | Not Found | Project, model, source, backtest index, or computation results do not exist. | None | ## Add a request by project ID Operation path: `POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/featureEffects/` Authentication requirements: `BearerAuth` Add a request to the queue to calculate Feature Effects for a backtest. If the job has been previously submitted, the request fails, returning the `jobId` of the previously submitted job. Use this `jobId` to check status of the previously submitted job. ### Body parameter ``` { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "rowCount": { "description": "The number of rows from dataset to use for Feature Impact calculation.", "maximum": 100000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.21" } }, "required": [ "backtestIndex" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | FeatureEffectsCreateDatetime | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The Feature Effects request for a backtest has been successfully submitted. See Location header. | None | | 403 | Forbidden | User does not have permission to view or submit jobs for the project. | None | | 404 | Not Found | Provided project, model, or backtest index does not exist. | None | | 422 | Unprocessable Entity | Queue submission error. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve Feature Effects metadata for each backtest by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/featureEffectsMetadata/` Authentication requirements: `BearerAuth` Retrieve Feature Effects metadata for each backtest. Response contains status and available sources for each backtest of the model. One of the provided `backtestIndex` indexes used for submitting the compute request and retrieving Feature Effects. * Start/stop models contain a single `backtestIndex` response value of `startstop`. * Other models contain `backtestIndex` of `0`, `1`, ..., `holdout`. One of the provided `source` parameters used for retrieving Feature Effects. * Each backtest source can be, at a minimum, `training` or `validation`. If holdout is configured for the project, `backtestIndex` also includes `holdout` with sources `training` and `holdout`. * Source value of `training` is always available. (versions prior to v2.17 support `validation` only) * When a start/stop model is trained into `validation` or `holdout` without stacked predictions (i.e., no out-of-sample predictions in `validation` or `holdout`), `validation` and `holdout` sources are not available. * Source `holdout` is not available when there is no holdout configured for the project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of objects with status and sources for each backtest.", "items": { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "sources": { "description": "The list of sources available for the model.", "items": { "enum": [ "training", "validation", "holdout" ], "type": "string" }, "type": "array" }, "status": { "description": "The status of the job.", "enum": [ "INPROGRESS", "COMPLETED", "NOT_COMPLETED" ], "type": "string" } }, "required": [ "backtestIndex", "sources", "status" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelXrayMetadataDatetimeResponse | | 403 | Forbidden | User does not have permission to view the project. | None | | 404 | Not Found | The project or model does not exist. | None | | 422 | Unprocessable Entity | The model is not datetime partitioned. | None | ## Retrieve a plot displaying the stability of the time series model across by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/forecastDistanceStabilityPlot/` Authentication requirements: `BearerAuth` Retrieve a plot displaying the stability of the time series model across different forecast distances. .. note:: All durations and datetimes are specified in accordance with the :ref: `timestamp and duration formatting rules `. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | metricName | query | string | false | The name of the metric to retrieve the scores for. If omitted, the default project metric will be used. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "endDate": { "description": "ISO-formatted start date of the project dataset.", "format": "date-time", "type": "string" }, "forecastDistancePlotData": { "description": "An array of objects containing the details of the scores for each forecast distance.", "items": { "properties": { "backtestingScore": { "description": "Backtesting score for this forecast distance. If backtesting has not been run for this model, this score will be `null`.", "type": [ "number", "null" ] }, "forecastDistance": { "description": "The number of time units the scored rows are away from the forecast point.", "type": "integer" }, "holdoutScore": { "description": "Holdout set score for this forecast distance. If holdout is locked for the project, this score will be `null`.", "type": [ "number", "null" ] }, "validationScore": { "description": "Validation set score for this forecast distance.", "type": "number" } }, "required": [ "backtestingScore", "forecastDistance", "holdoutScore", "validationScore" ], "type": "object" }, "type": "array" }, "metricName": { "description": "Name of the metric used to compute the scores.", "type": "string" }, "startDate": { "description": "ISO-formatted start date of the project dataset.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "forecastDistancePlotData", "metricName", "startDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Forecast distance stability plot for datetime partitioned model. | ForecastDistanceStabilityPlotResponse | | 422 | Unprocessable Entity | There was an error while retrieving the plot. | None | ## Retrieve forecast vs actual plots by id Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/forecastVsActualPlots/` Authentication requirements: `BearerAuth` Retrieve the data for the Forecast vs Actual plots. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | seriesId | query | string | false | The name of the series to retrieve. Only available for time series multiseries projects. If not provided an average plot for the first 1000 series will be retrieved. | | backtest | query | any | false | Retrieve plots for a specific backtest (use the backtest index starting from zero) or holdout. If not specified the first backtest (backtest index 0) will be used. | | source | query | string | false | The source of the data for the backtest/holdout. | | resolution | query | string | false | Specifying at which resolution the data should be binned. If not specified, optimal resolution will be used to build chart data with number of bins <= maxBinSize | | forecastDistanceStart | query | integer | false | The start of forecast distance range (forecast window) to retrieve. If not specified, the first forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects. | | forecastDistanceEnd | query | integer | false | The end of forecast distance range (forecast window) to retrieve. If not specified, the last forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects. | | maxBinSize | query | integer | false | Specifies the maximum number of bins for the retrieval. | | startDate | query | string(date-time) | false | The start of the date range to return. If not specified, start date for requested plots will be used. | | endDate | query | string(date-time) | false | The end of the date range to return. If not specified, end date for requested plots will be used. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation] | | resolution | [milliseconds, seconds, minutes, hours, days, weeks, months, quarters, years] | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "actual": { "description": "Average actual value of the target in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "error": { "description": "Average absolute residual value of the bin. `null` if there are no entries in the bin.", "minimum": 0, "type": [ "number", "null" ] }, "forecasts": { "description": "An array of average forecasts for the model for each forecast distance. Empty if there are no forecasts in the bin. Each index in the `forecasts` array maps to `forecastDistances` array index.", "items": { "type": "number" }, "maxItems": 100, "type": "array" }, "frequency": { "description": "Indicates number of values averaged in bin in case of a resolution change.", "type": [ "integer", "null" ] }, "normalizedError": { "description": "Normalized average absolute residual value of the bin. `null` if there are no entries in the bin.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "error", "forecasts", "frequency", "normalizedError", "startDate" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "calendarEvents": { "description": "An array of calendar events for a retrieved plot.", "items": { "properties": { "date": { "description": "The date of the calendar event.", "format": "date-time", "type": "string" }, "name": { "description": "Name of the calendar event.", "type": "string" }, "seriesId": { "description": "The series ID for the event. If this event does not specify a series ID, then this will be `null`, indicating that the event applies to all series.", "type": [ "string", "null" ] } }, "required": [ "date", "name", "seriesId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "forecastDistances": { "description": "An array of forecast distances. Forecast distance specifies the number of time steps between the predicted point and the origin point.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "resolution": { "description": "The resolution that is used for binning.", "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "bins", "calendarEvents", "endDate", "forecastDistances", "resolution", "startDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Forecast vs Actual plots data | ForecastVsActualPlotsDataResponse | | 404 | Not Found | Forecast vs Actual plots data was not found | None | | 422 | Unprocessable Entity | Invalid parameters were submitted | None | ## Retrieve metadata by id Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/forecastVsActualPlots/metadata/` Authentication requirements: `BearerAuth` Retrieve the metadata for the Forecast vs Actual insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | seriesId | query | string | false | The name of the series to retrieve. Only available for time series multiseries projects. If not provided a metadata of average plot for the first 1000 series will be retrieved. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | Accept | header | string | false | Requested MIME type for the returned data. | ### Enumerated Values | Parameter | Value | | --- | --- | | Accept | application/json | ### Example responses > 200 Response ``` { "properties": { "backtestMetadata": { "description": "An array of metadata information for each backtest. The array index of metadata object is the backtest index.", "items": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "backtestStatuses": { "description": "An array of status information for each backtest. The array index of status object is the backtest index.", "items": { "description": "Status for forecast vs actual plots.", "properties": { "training": { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" }, "validation": { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "estimatedSeriesLimit": { "description": "Estimated number of series that can be calculated in one request for 1 FD.", "minimum": 1, "type": "integer" }, "holdoutMetadata": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "holdoutStatuses": { "description": "Status for forecast vs actual plots.", "properties": { "training": { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" }, "validation": { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "resolutions": { "description": "An array of available time resolutions for which plots can be retrieved.", "items": { "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "maxItems": 9, "minItems": 1, "type": "array" } }, "required": [ "backtestMetadata", "backtestStatuses", "holdoutMetadata", "holdoutStatuses", "resolutions" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Forecast vs Actual insights metadata. | ForecastVsActualPlotsMetadataResponse | | 404 | Not Found | Forecast vs Actual insights metadata was not found | None | | 422 | Unprocessable Entity | Invalid parameters were submitted. | None | ## Retrieve preview by id Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/forecastVsActualPlots/preview/` Authentication requirements: `BearerAuth` Retrieve the preview for the Forecast vs Actual plots. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | seriesId | query | string | false | The name of the series to retrieve. Only available for time series multiseries projects. If not provided an average plot for the first 1000 series will be retrieved. | | backtest | query | any | false | Retrieve plots for a specific backtest (use the backtest index starting from zero) or holdout. If not specified the first backtest (backtest index 0) will be used. | | source | query | string | false | The source of the data for the backtest/holdout. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation] | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "actual": { "description": "Average actual value of the target in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "predicted": { "description": "Average prediction of the model in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "predicted", "startDate" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "bins", "endDate", "startDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Forecast vs Actual plots preview | DatetimeTrendPlotsPreviewResponse | | 404 | Not Found | Forecast vs Actual plots preview was not found | None | | 422 | Unprocessable Entity | Invalid parameters were submitted | None | ## Retrieve feature effects by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/multiclassFeatureEffects/` Authentication requirements: `BearerAuth` Retrieve feature effects for each class in a multiclass datetime model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | source | query | string | false | The model's data source. | | backtestIndex | query | string | true | The backtest index. For example: 0, 1, ..., 20, holdout, startstop. | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | class | query | string,null | false | Target class label. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation, holdout] | ### Example responses > 200 Response ``` { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of feature effect scores for each class in a multiclass project.", "items": { "properties": { "class": { "description": "Target class label.", "type": "string" }, "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "class", "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "The model's data source.", "type": "string" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "backtestIndex", "data", "modelId", "next", "previous", "projectId", "source", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | MulticlassDatetimeFeatureEffectsResponse | | 403 | Forbidden | User does not have permission to view the project. | None | | 404 | Not Found | Project, model, source or computation results do not exist. | None | ## Compute feature effects by project ID Operation path: `POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/multiclassFeatureEffects/` Authentication requirements: `BearerAuth` Compute feature effects for a multiclass datetime model. If the job has been previously submitted, the request fails, returning the `jobId` of the previously submitted job. Use this `jobId` to check status of the previously submitted job. NOTE: feature effects are computed for top 100 classes. ### Body parameter ``` { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "features": { "description": "The list of features to use to calculate feature effects.", "items": { "type": "string" }, "maxItems": 20000, "type": "array" }, "rowCount": { "description": "The number of rows from dataset to use for Feature Impact calculation.", "maximum": 100000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "topNFeatures": { "description": "Number of top features (ranked by feature impact) to use to calculate feature effects.", "exclusiveMinimum": 0, "maximum": 1000, "type": [ "integer", "null" ] } }, "required": [ "backtestIndex" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | MulticlassFeatureEffectDatetimeCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The Feature Effects request has been successfully submitted. See Location header. | None | | 403 | Forbidden | User does not have permission to view or submit jobs for the project. | None | | 404 | Not Found | Project, model, source or computation results do not exist. | None | | 422 | Unprocessable Entity | Queue submission error. If the rowCount exceeds the maximum or minimum value for this dataset. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less. If invalid class names are provided in classes.If neither features nor topNFeatures is provided. If invalid backtestIndex is provided. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve the histograms by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/multiseriesHistograms/` Authentication requirements: `BearerAuth` Retrieve the histograms for series insights. Histogram is computed only for first 1000 series (ordered by name). ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | attribute | query | string | true | The series attribute to build a histogram for. | | metric | query | string | false | The name of the metric to retrieve the histogram for attributes "validationScore", "backtestingScore", and"holdoutScore". If omitted, the default project metric will be used. | | bins | query | string | true | The number of bins in a histogram. Can be 10, 20 or 50. The default is 10. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | attribute | [rowCount, duration, startDate, endDate, targetAverage, validationScore, backtestingScore, holdoutScore, rowPercent, clusterCount, clustering] | | bins | [10, 20, 50] | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "List of bins representing histogram.", "items": { "properties": { "count": { "description": "The value count of the bin", "type": "integer" }, "left": { "description": "The inclusive left boundary of the bin.", "oneOf": [ { "type": "number" }, { "type": "string" } ] }, "right": { "description": "The exclusive right boundary of the bin. The last bin has an inclusive right boundary.", "oneOf": [ { "type": "number" }, { "type": "string" } ] } }, "required": [ "count", "left", "right" ], "type": "object" }, "type": "array" } }, "required": [ "bins" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve the histograms for series insights in form of an array of histogram bins. | MultiseriesHistogramsRetrieveResponse | | 403 | Forbidden | User does not have permissions to manage models. | None | | 404 | Not Found | Model with specified modelId doesn't exist, or user does not have access to the project. | None | | 422 | Unprocessable Entity | Metric provided to query is not found. | None | ## List the scores per individual series by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/multiseriesScores/` Authentication requirements: `BearerAuth` List the scores per individual series for the specified multiseries model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | multiseriesValue | query | string | false | Only the series containing the given value in one of the series ID columns will be returned if specified. | | offset | query | integer | true | The number of results to skip. Defaults to 0 if not specified. | | limit | query | integer | true | The maximum number of results to return. Defaults to 100 if not specified. | | metric | query | string | false | The name of the metric to retrieve the scores for.If omitted, the default project metric will be used. | | orderBy | query | string | false | Used for sorting the series. Supported attributes for ordering include: "multiseriesValue", "rowCount", "validationScore", "holdoutScore" and "backtestingScore", "startDate", "endDate", and "targetAverage".Prefix the attribute name with a dash to sort in descending order,e.g. orderBy=-rowCount. If multiple series with equal values of the ordering attributeexist, ties will be broken arbitrarily. | | filterBy | query | string | false | Used to specify on which attribute values to filter the series.Supported attributes for filtering include: "rowCount", "startDate", "endDate", "targetAverage", "validationScore", "holdoutScore", and "backtestingScore".filterByBins and numberOfBins are required if this parameter is used. | | numberOfBins | query | string | false | Used to specify the number of bins in the histogram on which to filter the series.Can be 10, 20 or 50.filterBy and filterByBins are required if this parameter is used. | | filterByBins | query | string | false | Used to specify the multiseries histogram bins on which to filter the series.filterBy and numberOfBins are required if this parameter is used. | | clusterNames | query | string | false | Used to specify the specific cluster on which to filter the series.filterBy is required if this parameter is used.Only valid for unsupervised clustering projects. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [multiseriesValue, -multiseriesValue, rowCount, -rowCount, startDate, -startDate, endDate, -endDate, targetAverage, -targetAverage, validationScore, -validationScore, backtestingScore, -backtestingScore, holdoutScore, -holdoutScore, cluster, -cluster] | | filterBy | [rowCount, startDate, endDate, targetAverage, validationScore, backtestingScore, holdoutScore, cluster] | | numberOfBins | [10, 20, 50] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "An array of available multiseries identifiers and column values.", "items": { "properties": { "backtestingScore": { "description": "The backtesting score for this series. If backtesting has not been run for this model, this score will be null.", "type": [ "number", "null" ] }, "cluster": { "description": "The cluster associated with this series. ", "type": [ "string", "null" ] }, "duration": { "description": "The duration of this series formatted as an ISO 8601 duration string.", "format": "duration", "type": "string" }, "endDate": { "description": "The ISO-formatted end date of this series.", "format": "date-time", "type": [ "string", "null" ] }, "holdoutScore": { "description": "The holdout set score for this series. If holdout is locked for the project, this score will be null.", "type": [ "number", "null" ] }, "multiseriesId": { "description": "A DataRobot-generated ID corresponding to a single series in a multiseries dataset.", "type": "string" }, "multiseriesValues": { "description": "The actual values of series ID columns from the dataset.", "items": { "type": "string" }, "type": "array" }, "rowCount": { "description": "The number of rows available for this series in the input dataset.", "type": "integer" }, "startDate": { "description": "The ISO-formatted start date of this series.", "format": "date-time", "type": [ "string", "null" ] }, "targetAverage": { "description": "For regression projects, this is the average (mean) value of target values for this series.For classification projects, this is the ratio of the positive class in the target for this series.", "oneOf": [ { "type": "string" }, { "type": "number" }, { "type": "null" } ] }, "validationScore": { "description": "The validation set score for this series", "type": [ "number", "null" ] } }, "required": [ "backtestingScore", "duration", "multiseriesId", "multiseriesValues", "rowCount", "validationScore" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if null, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if null, there is no previous page).", "type": [ "string", "null" ] }, "querySeriesCount": { "description": "The total number of series after filtering is applied.", "type": "integer" }, "totalSeriesCount": { "description": "The total number of series in the project dataset.", "type": "integer" } }, "required": [ "data", "querySeriesCount", "totalSeriesCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve the accuracy scores for each series for the specified multiseries model. | SeriesAccuracyRetrieveResponse | | 403 | Forbidden | User does not have permissions to manage models. | None | | 404 | Not Found | Model with specified modelId doesn't exist, or user does not have access to the project. | None | | 422 | Unprocessable Entity | Metric provided to query is not found. | None | ## Request the computation of per-series scores by project ID Operation path: `POST /api/v2/projects/{projectId}/datetimeModels/{modelId}/multiseriesScores/` Authentication requirements: `BearerAuth` Request the computation of per-series scores for a multiseries model. .. note:: Computation uses available partitions only. This endpoint will not compute backtesting scores if no backtesting scores exist prior to this request. ### Body parameter ``` { "properties": { "computeAllSeries": { "default": false, "description": "Indicates whether to calculate accuracy for all series or only first 1000 (sorted by name).", "type": "boolean", "x-versionadded": "2.22" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | SeriesAccuracyCompute | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Multiseries score computation has been successfully requested. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve the CSV file by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimeModels/{modelId}/multiseriesScores/file/` Authentication requirements: `BearerAuth` Retrieve the CSV file for the series accuracy. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | multiseriesValue | query | string | false | If specified, only the series containing the given value in one of the series ID columns will be returned. | | metric | query | string | false | The name of the metric to retrieve the scores for. If omitted, the default project metric will be used. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a file containing the series accuracy data in csv format. | None | | 403 | Forbidden | User does not have permissions to manage models. | None | | 404 | Not Found | Model with specified modelId doesn't exist, or user does not have access to the project. | None | | 422 | Unprocessable Entity | Metric provided to query is not found. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ("attachment;filename=Series accuracy (model:) ().csv"). | | 200 | Content-Type | string | | MIME type of the returned data | ## The list of scores on prediction datasets by project ID Operation path: `GET /api/v2/projects/{projectId}/externalScores/` Authentication requirements: `BearerAuth` The list of scores on prediction datasets for a project with filtering option by dataset, model, or both. A prediction dataset may have scores if it contained a column with actual values and predictions were computed on this dataset. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | datasetId | query | string | false | If provided, will return scores for the dataset with the matching dataset ID. | | modelId | query | string | false | If provided, will return scores for the model with the matching model ID. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of objects containing the following data.", "items": { "properties": { "actualValueColumn": { "description": "The name of the column with actuals that was used to calculate the scores.", "type": "string" }, "datasetId": { "description": "The dataset ID the data comes from.", "type": "string" }, "modelId": { "description": "The model ID for the scores.", "type": "string" }, "projectId": { "description": "The project ID for the scores.", "type": "string" }, "scores": { "description": "A JSON array of the computed scores.", "items": { "properties": { "label": { "description": "The metric name that was used to compute the score.", "type": "string" }, "value": { "description": "The score value.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" } }, "required": [ "actualValueColumn", "datasetId", "modelId", "projectId", "scores" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of scores on prediction datasets. | ExternalScoresListResponse | | 404 | Not Found | The project was not found. | None | ## Compute model scores by project ID Operation path: `POST /api/v2/projects/{projectId}/externalScores/` Authentication requirements: `BearerAuth` Compute model scores for external dataset, first upload your dataset to the project, and then using the corresponding datasetId, compute scores against that dataset. Computing external scores and insights depends on computed prediction, predictions will be computed if they are not available for this dataset. In order to compute scores and insights, uploaded dataset should contain actual value column. This api is not available in time series projects. ### Body parameter ``` { "properties": { "actualValueColumn": { "description": "Actual value column name that contains actual values to be used for computing scores and insights for unsupervised projects only. This value can be set once for a dataset and cannot be changed.", "type": "string" }, "datasetId": { "description": "The dataset to compute predictions for; it must have previously been uploaded.", "type": "string" }, "modelId": { "description": "The model to use to make predictions.", "type": "string" } }, "required": [ "datasetId", "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | ExternalScoresCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | None | | 422 | Unprocessable Entity | The project type does not support or modeling is not finished yet. | None | ## List all featurelists with feature association matrix availability flags by project ID Operation path: `GET /api/v2/projects/{projectId}/featureAssociationFeaturelists/` Authentication requirements: `BearerAuth` List all featurelists with feature association matrix availability flags for a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "featurelists": { "description": "List all featurelists with feature association matrix availability flags.", "items": { "properties": { "featurelistId": { "description": "The featurelist ID.", "type": "string" }, "hasFam": { "description": "Whether Feature Association Matrix is calculated for featurelist.", "type": "boolean" }, "title": { "description": "The name of featurelist.", "type": "string" } }, "required": [ "featurelistId", "hasFam", "title" ], "type": "object" }, "type": "array" } }, "required": [ "featurelists" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List available Feature Association Matrix for a project. | FeatureAssociationListControllerResponse | | 404 | Not Found | Project not found. | None | ## Retrieve pairwise feature association statistics by project ID Operation path: `GET /api/v2/projects/{projectId}/featureAssociationMatrix/` Authentication requirements: `BearerAuth` Retrieval for pairwise feature association statistics. Projects created prior to v2.17 are not supported by this feature. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | type | query | string | true | the type of dependence for the data. Must be either association or correlation. Since v2.19 this is optional and defaults to association. | | metric | query | string | true | the name of a metric to get pairwise data for. Must be one of mutualInfo, cramersV, spearman, pearson, or tau. Since v2.19 this is optional and defaults to mutualInfo. | | featurelistId | query | string | false | the feature list to lookup FAM data for. By default, depending on the type of the project Informative Features or Timeseries Informative Features list will be used. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | type | [association, correlation] | | metric | [mutualInfo, cramersV, spearman, pearson, tau] | ### Example responses > 200 Response ``` { "properties": { "features": { "description": "Metadata for each feature and where it goes in the matrix as structured below.", "items": { "properties": { "alphabeticSortIndex": { "description": "A number representing the alphabetical order of this feature compared to the other features in this dataset.", "type": "integer" }, "clusterId": { "description": "ID of the cluster this feature belongs to.", "type": [ "integer", "null" ] }, "clusterName": { "description": "Name of feature cluster.", "type": "string" }, "clusterSortIndex": { "description": "A number representing the ordering of the feature across all feature clusters. Features in the same cluster always have adjacent indices.", "type": "integer" }, "feature": { "description": "Name of the feature.", "type": "string" }, "importanceSortIndex": { "description": "A number ranking the importance of this feature compared to the other features in this dataset.", "type": "integer" }, "strengthSortIndex": { "description": "A number ranking the strength of this feature compared to the other features in this dataset.", "type": "integer" } }, "required": [ "alphabeticSortIndex", "clusterId", "clusterSortIndex", "feature", "importanceSortIndex", "strengthSortIndex" ], "type": "object" }, "type": "array" }, "strengths": { "description": "Pairwise statistics for the available features as structured below.", "items": { "properties": { "feature1": { "description": "The name of the first feature.", "type": "string" }, "feature2": { "description": "The name of the second feature.", "type": "string" }, "statistic": { "description": "Feature association statistics for `feature1` and `feature2`. For features with no pairwise statistics available the value is `null`.", "type": [ "number", "null" ] } }, "required": [ "feature1", "feature2", "statistic" ], "type": "object" }, "type": "array" } }, "required": [ "features", "strengths" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The feature association matrix for the project. | FeatureAssociationRetrieveControllerResponse | | 404 | Not Found | Wrong query parameters specified or no such projectId exists. | None | | 422 | Unprocessable Entity | The project does not support feature associations. | None | ## Compute the feature association matrix by project ID Operation path: `POST /api/v2/projects/{projectId}/featureAssociationMatrix/` Authentication requirements: `BearerAuth` Compute the feature association matrix for the given featurelist. ### Body parameter ``` { "properties": { "featurelistId": { "description": "A featurelist ID to calculate feature association matrix.", "type": "string" } }, "required": [ "featurelistId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | FeatureAssociationCreatePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | None | | 404 | Not Found | A project with projectId or a featurelist with featurelistId was not found. | None | | 422 | Unprocessable Entity | The feature association matrix calculation is not supported for this project. | None | ## Retrieval by project ID Operation path: `GET /api/v2/projects/{projectId}/featureAssociationMatrixDetails/` Authentication requirements: `BearerAuth` Retrieval for feature association plotting between a pair of features. Projects created prior to v2.17 are not supported by this feature. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | feature1 | query | string | true | The name of a feature. | | feature2 | query | string | true | The name of another feature. | | featurelistId | query | string | false | the feature list to lookup FAM data for. By default, depending on the type of the project Informative Features or Timeseries Informative Features list will be used. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "chartType": { "description": "Which type of plotting the pair of features gets in the UI, e.g. `SCATTER`", "type": "string" }, "features": { "description": "The name of `feature1` and `feature2`.", "items": { "type": "string" }, "type": "array" }, "types": { "description": "The type of `feature1` and `feature2`. Possible values: `CATEGORICAL`, `NUMERIC`.", "items": { "enum": [ "CATEGORICAL", "NUMERIC" ], "type": "string" }, "type": "array" }, "values": { "description": "The data triplets for pairwise plotting.", "items": { "items": { "anyOf": [ { "type": "string" }, { "type": "number" } ] }, "type": "array" }, "type": "array" } }, "required": [ "chartType", "features", "types", "values" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieval for feature association plotting between a pair of features. | FeatureAssociationDetailsRetrieveControllerResponse | | 404 | Not Found | Wrong query parameters specified or no such projectId exists. | None | | 422 | Unprocessable Entity | This project does not support feature associations, (e.g. multilabel, multiseries, time series unsupervised projects.). | None | ## Retrieve the frequent values information by project ID Operation path: `GET /api/v2/projects/{projectId}/features/{featureName}/frequentValues/` Authentication requirements: `BearerAuth` Retrieve the frequent values information for a particular feature. Only valid for numeric features. This route returns information about the frequent values seen for a particular feature, based on the EDA sample of the dataset. Up to 60 values will be returned, and when more values are present, they will be bucketed into a level called "==All Other==" at the end of the response. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | featureName | path | string | true | The name of the feature. | ### Example responses > 200 Response ``` { "properties": { "frequentValues": { "description": "The list of frequent values and data quality information.", "items": { "properties": { "count": { "description": "Count of specified frequent value in the sample, weighted by exposure or weights", "type": "integer" }, "dataQuality": { "description": "Any data quality issue associated with this particularvalue of the feature. Possible data quality types include 'excess_zero', 'inlier', 'disguised_missing_value', and 'no_issues_found' and the relevant statistics.", "type": "string" }, "target": { "description": "Average target value for the specified frequent value if the target is binary or numeric. With weights or exposure, this becomes a weighted average. If the target is not set, it returns None.", "type": [ "number", "null" ] }, "value": { "anyOf": [ { "type": "number" }, { "type": "string" } ], "description": "Specified frequent value, either a float or a string, like `=All Others+`" } }, "required": [ "count", "dataQuality", "target", "value" ], "type": "object" }, "type": "array" }, "name": { "description": "The name of the feature.", "type": "string" }, "numRows": { "description": "Number of rows in the sample used to determine frequent values", "type": "integer" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "frequentValues", "name", "numRows", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve the frequent values information for a particular feature. | FrequentValuesResponse | | 404 | Not Found | If the feature doesn't exist, or no such projectId exists | None | | 422 | Unprocessable Entity | The feature is not numeric. | None | ## Create a map of one location feature by project ID Operation path: `POST /api/v2/projects/{projectId}/geometryFeaturePlots/` Authentication requirements: `BearerAuth` Create a map of one location feature. ### Body parameter ``` { "properties": { "feature": { "description": "Name of a location feature from the dataset to plot on map.", "type": "string" } }, "required": [ "feature" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | Project Id. It is the project to select the location feature from. | | body | body | GeometryFeaturePLotCreatePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Create a map of one location feature | None | | 422 | Unprocessable Entity | Unprocessed Entity | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A URL that can be polled to check the status. | ## Retrieve a map of one location feature by project ID Operation path: `GET /api/v2/projects/{projectId}/geometryFeaturePlots/{featureName}/` Authentication requirements: `BearerAuth` Retrieve a map of one location feature. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | Project Id. It is the project to select the feature from. | | featureName | path | string | true | Name of location feature to plot on map. Must be supplied in order to determine which plot to retrieve. | ### Example responses > 200 Response ``` { "properties": { "feature": { "description": "Name of location feature to plot on map.", "type": "string" }, "plotData": { "description": "Geo feature plot data", "properties": { "aggregation": { "description": "Type of geo aggregation.", "enum": [ "grid", "unique" ], "type": "string" }, "bbox": { "description": "Bounding box of feature map.", "type": "object" }, "features": { "description": "Location features over map", "items": { "properties": { "geometry": { "description": "Geometry.", "properties": { "coordinates": { "description": "Coordinate representative of a geometry.", "items": { "type": "object" }, "type": "array" }, "type": { "description": "Type of geometry.", "enum": [ "Point", "LineString", "Polygon" ], "type": "string" } }, "required": [ "coordinates", "type" ], "type": "object" }, "properties": { "description": "Properties of location features.", "properties": { "count": { "description": "Total num of samples located within this geometry.", "type": "integer" } }, "required": [ "count" ], "type": "object" }, "type": { "description": "With a fixed value of 'Feature'.", "type": "string" } }, "required": [ "geometry", "properties", "type" ], "type": "object" }, "type": "array" }, "summary": { "description": "Summary of feature map.", "properties": { "maxCount": { "description": "Max num of samples located within one geometry.", "type": "integer" }, "minCount": { "description": "Min num of samples located within one geometry.", "type": "integer" }, "totalCount": { "description": "Total num of samples across all geometry objects.", "type": "integer" } }, "required": [ "maxCount", "minCount", "totalCount" ], "type": "object" }, "type": { "description": "GeoJSON FeatureCollection.", "type": "string" }, "valueAggregation": { "description": "Type of feature aggregation.", "enum": [ "geometry" ], "type": "string" } }, "required": [ "aggregation", "bbox", "features", "summary", "type", "valueAggregation" ], "type": "object" }, "projectId": { "description": "The project to select a location feature from.", "type": "string" } }, "required": [ "feature", "plotData", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The map of one location feature. | GeometryFeaturePlotRetrieveResponse | | 404 | Not Found | Map of feature not found | None | ## List all Image Activation Maps by project ID Operation path: `GET /api/v2/projects/{projectId}/imageActivationMaps/` Authentication requirements: `BearerAuth` List all Image Activation Maps for the project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of items to skip over. | | limit | query | integer | false | The number of items to return. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of Image Activation Maps", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" } }, "required": [ "featureName", "modelId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Image Activation Maps | ActivationMapsListResponse | ## Calculate the anomaly assessment insight by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/anomalyAssessmentInitialization/` Authentication requirements: `BearerAuth` Initialize the anomaly assessment insight and calculate Shapley explanations for the most anomalous points in the subset. The insight is available for anomaly detection models in time series unsupervised projects which also support calculation of Shapley values. ### Body parameter ``` { "properties": { "backtest": { "description": "The backtest to compute insight for.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "seriesId": { "description": "Required for multiseries projects. The series id to compute insight for.", "type": "string" }, "source": { "description": "The source to compute insight for.", "enum": [ "training", "validation" ], "type": "string" } }, "required": [ "backtest", "source" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | AnomalyAssessmentInitialize | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve a CSV file of the raw data displayed by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/anomalyInsightsFile/` Authentication requirements: `BearerAuth` Retrieve a CSV file of the raw data displayed with the anomaly score from the specific model. The number of rows included will be set by the expected outlier fraction but up to a maximum of 1000 rows. Only models built from anomaly detection blueprints have those insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | filename | query | string | false | name of the file to generate and return | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve a CSV file of the raw data displayed with the anomaly score from the model. | None | | 404 | Not Found | project Id / model Id does not exist or model doesn't have anomaly insights table. | None | ## Retrieve a table of the raw data displayed by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/anomalyInsightsTable/` Authentication requirements: `BearerAuth` Retrieve a table of the raw data displayed with the anomaly score from the specific model. The number of rows displayed is limited to 100 rows by the ANOMALY_INSIGHT_SAMPLE_ROW_COUNT configuration setting. Additionally, feature column count and the size of data in text fields is also limited. Only models built from anomaly detection blueprints have those insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | anomalyScoreRounding | query | integer | false | number of decimals each element anomalyScore column will be rounded to. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "modelId": { "description": "given model identifier", "type": "string" }, "table": { "description": "anomaly insights table", "items": { "properties": { "columns": { "description": "array of columns that contain columns from training dataset and `anomalyScore` column.", "items": { "type": "string" }, "type": "array" }, "data": { "description": "array of arrays with actual data. Order in each array corresponds to order in columns array.", "items": { "type": "number" }, "type": "array" }, "rowId": { "description": "index 0-based array. Each rowId corresponds to the actual row number of training data", "items": { "type": "integer" }, "type": "array" } }, "required": [ "columns", "data", "rowId" ], "type": "object" }, "type": "array" } }, "required": [ "modelId", "table" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve a table of the raw data displayed with the anomaly score from the specific model. | AnomalyInsightTableRetrieve | | 404 | Not Found | The model doesn't have anomaly insights table. | None | ## Retrieve Cluster Insights by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/clusterInsights/` Authentication requirements: `BearerAuth` Retrieve all computed Cluster Insights for a clustering project model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | true | At most this many results are returned. The default may change without notice. | | orderBy | query | string | false | Order results by the specified field value. | | searchFor | query | string | false | Search for a specific string in a feature name.This search is case insensitive. If not specified, all features will be returned. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [featureImpact, -featureImpact, featureName, -featureName] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of features with clusters insights.", "items": { "anyOf": [ { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "image" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for an image feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "images", "percentageOfMissingImages" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "representativeImages" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "clusterName", "images", "percentageOfMissingImages" ], "type": "object" }, "type": "array" } }, "required": [ "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "geospatialPoint" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a geospatial centroid or point feature.", "items": { "properties": { "insightName": { "description": "Insight name.", "enum": [ "representativeLocations" ], "type": "string" }, "perCluster": { "description": "Statistic for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "representativeLocations": { "description": "A list of latitude and longitude location list", "items": { "description": "Latitude and longitude list.", "items": { "description": "Longitude or latitude, in degrees.", "maximum": 180, "minimum": -180, "type": "number" }, "type": "array" }, "maxItems": 1000, "type": "array" } }, "required": [ "clusterName", "representativeLocations" ], "type": "object" }, "type": "array" } }, "required": [ "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "text" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "importantNgrams" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "numeric" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistic value for all data.", "type": [ "number", "null" ] }, "insightName": { "description": "Insight name.", "enum": [ "min", "max", "median", "avg", "firstQuartile", "thirdQuartile", "missingRowsPercent" ], "type": "string" }, "perCluster": { "description": "Statistic values for for each cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "statistic": { "description": "Statistic value for this cluster.", "type": [ "number", "null" ] } }, "required": [ "clusterName", "statistic" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "categorical" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "categoryLevelFrequencyPercent" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "document" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "importantNgrams" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" } ] }, "maxItems": 100, "type": "array" }, "isCurrentClusterInsightVersion": { "description": "If retrieved insights are current version.", "type": "boolean" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" }, "version": { "description": "Current version of the computed insight.", "minimum": 0, "type": "integer" } }, "required": [ "data", "isCurrentClusterInsightVersion", "next", "previous", "totalCount", "version" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Insights for a clustering project model. | ClusterInsightsPaginatedResponse | | 404 | Not Found | The project or the model was not found or insights have not been computed yet. | None | | 422 | Unprocessable Entity | Feature Impact is required. Please, compute it first. | None | ## Compute Cluster Insights by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/clusterInsights/` Authentication requirements: `BearerAuth` Compute Cluster Insights for a clustering project model.The number of features computed for cluster insights are capped at 100, starting with the features used to train the model sorted by feature impact (high to low), and then the remaining features in the dataset alphabetically. ### Body parameter ``` { "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | Empty | false | none | ### Example responses > 202 Response ``` { "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | A URI of the newly submitted job in the "Location" header. | Empty | | 404 | Not Found | The project or the model was not found or insights have not been computed yet. | None | | 422 | Unprocessable Entity | Feature Impact is already in progress or Cluster Insighst is already in progress, but we were unable to find the previous job. | None | ## Download Cluster Insights result by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/clusterInsights/download/` Authentication requirements: `BearerAuth` Download all computed Cluster Insights for a clustering project model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | format | query | string | false | A format to use. | | featurelistId | query | string,null | false | The ID of the featurelist to download data for. If not specified all columns will be downloaded. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | format | CSV | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A file with insights for a clustering project model. | None | | 404 | Not Found | The project or the model was not found or insights have not been computed yet. | None | | 422 | Unprocessable Entity | Feature Impact is required. Please, compute it first. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ("attachment;filename=cluster_insights__.csv"). | ## Retrieve all available confusion charts by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/confusionCharts/` Authentication requirements: `BearerAuth` Retrieve all available confusion charts for model. The response will include a json array of all available confusion charts, in the same format as the response from [GET /api/v2/projects/{projectId}/models/{modelId}/confusionCharts/{source}/][get-apiv2projectsprojectidmodelsmodelidconfusionchartssource]. .. note:: Available for multiclass projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "charts": { "description": "Chart data from all available sources.", "items": { "properties": { "columns": { "description": "[colStart, colEnd] column dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "data": { "description": "confusion chart data with the format below.", "properties": { "classMetrics": { "description": "per-class information including one vs all scores in a format specified below", "items": { "properties": { "actualCount": { "description": "number of times this class is seen in the validation data", "type": "integer" }, "className": { "description": "name of the class", "type": "string" }, "confusionMatrixOneVsAll": { "description": "2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "precision score", "type": "number" }, "predictedCount": { "description": "number of times this class has been predicted for the validation data", "type": "integer" }, "recall": { "description": "recall score", "type": "number" }, "wasActualPercentages": { "description": "one vs all actual percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" }, "wasPredictedPercentages": { "description": "one vs all predicted percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object" }, "type": "array" }, "classes": { "description": "class labels from the dataset, union of row classes & column classes. This field is deprecated as of v2.13. The rows and columns may have different class labels when using query parameters to retrieve a slice of the matrix; please use 'rowClasses' and 'colClasses' instead.", "items": { "type": "string" }, "type": "array" }, "colClasses": { "description": "class labels on columns of confusion matrix", "items": { "type": "string" }, "type": "array" }, "confusionMatrix": { "description": "2d array of ints representing confusion matrix, aligned with `rowClasses` and 'colClasses'array. For confusionMatrix[A][B] we can get an integer that represents the number of times 'if class with index A was correct we have class with index B predicted' (if the orientation is 'actual').", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "rowClasses": { "description": "class labels on rows of confusion matrix", "items": { "type": "string" }, "type": "array" } }, "required": [ "classMetrics", "classes", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "numberOfClasses": { "description": "count of classes in full confusion matrix.", "type": "integer" }, "rows": { "description": "[rowStart, rowEnd] row dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "source": { "description": "source of the chart", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "totalMatrixSum": { "description": "sum of all values in full confusion matrix", "type": "integer" } }, "required": [ "columns", "data", "globalMetrics", "numberOfClasses", "rows", "source", "totalMatrixSum" ], "type": "object" }, "type": "array" } }, "required": [ "charts" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | All of the available confusion charts for a model. | ModelConfusionChartListResponse | | 404 | Not Found | No confusion chart available. | None | ## Retrieve the confusion chart data by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/confusionCharts/{source}/` Authentication requirements: `BearerAuth` Retrieve the confusion chart data from a single source. A confusion chart consists of the confusion matrix for all classes, classes frequencies and `oneVsAll` metrics for all classes. The confusion matrix can be requested in a particular sort order and orientated by rows or columns. A subset of the confusion matrix can also be requested in part by specifying slicing indices. Throughout the following specification, `C` refers to the total number of classes in the dataset. The full confusion matrix refers to the confusion matrix with `C` classes. ``` .. note:: Available for multiclass projects only. An example on the meaning of wasActualPercentages and wasPredictedPercentages: Let's say we have the following data: .. code-block:: js classMetrics.classA.wasActualPercentages[0].percentage = 0.56 classMetrics.classA.wasPredictedPercentages[0].percentage = 0.62 classA.wasActualPercentages[0].otherClassName = "classB" classA.wasPredictedPercentages[0].otherClassName = "classB" That means: 1) "Given that it was actually classA, it predicted classB 56% of the time". 2) "Given that classA was predicted, it was actually classB 62% of the time". ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | orderBy | query | string | false | Ordering the chart data by following attributes.Prefix the attribute name with a dash to sort in descending order, e.g. orderBy='-predictedCount' | | orientation | query | string | false | Determines whether the values in the rows of the confusion matrix should correspond to the same actual class ('actual') or predicted class ('predicted'). | | rowStart | query | integer | false | start index of row for slicing the confusion matrix. | | rowEnd | query | integer | false | end index of row for slicing the confusion matrix. | | colStart | query | integer | false | start index of column for slicing the confusion matrix. | | colEnd | query | integer | false | end index of column for slicing the confusion matrix. | | projectId | path | string | true | The project ID | | modelId | path | string | true | The model ID | | source | path | string | true | Source of the data | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [className, -className, actualCount, -actualCount, predictedCount, -predictedCount, f1, -f1, precision, -precision, recall, -recall] | | orientation | [actual, -actual, predicted, -predicted] | | source | [validation, crossValidation, holdout, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ### Example responses > 200 Response ``` { "properties": { "columns": { "description": "[colStart, colEnd] column dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "data": { "description": "confusion chart data with the format below.", "properties": { "classMetrics": { "description": "per-class information including one vs all scores in a format specified below", "items": { "properties": { "actualCount": { "description": "number of times this class is seen in the validation data", "type": "integer" }, "className": { "description": "name of the class", "type": "string" }, "confusionMatrixOneVsAll": { "description": "2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "precision score", "type": "number" }, "predictedCount": { "description": "number of times this class has been predicted for the validation data", "type": "integer" }, "recall": { "description": "recall score", "type": "number" }, "wasActualPercentages": { "description": "one vs all actual percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" }, "wasPredictedPercentages": { "description": "one vs all predicted percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object" }, "type": "array" }, "classes": { "description": "class labels from the dataset, union of row classes & column classes. This field is deprecated as of v2.13. The rows and columns may have different class labels when using query parameters to retrieve a slice of the matrix; please use 'rowClasses' and 'colClasses' instead.", "items": { "type": "string" }, "type": "array" }, "colClasses": { "description": "class labels on columns of confusion matrix", "items": { "type": "string" }, "type": "array" }, "confusionMatrix": { "description": "2d array of ints representing confusion matrix, aligned with `rowClasses` and 'colClasses'array. For confusionMatrix[A][B] we can get an integer that represents the number of times 'if class with index A was correct we have class with index B predicted' (if the orientation is 'actual').", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "rowClasses": { "description": "class labels on rows of confusion matrix", "items": { "type": "string" }, "type": "array" } }, "required": [ "classMetrics", "classes", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "numberOfClasses": { "description": "count of classes in full confusion matrix.", "type": "integer" }, "rows": { "description": "[rowStart, rowEnd] row dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "source": { "description": "source of the chart", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "totalMatrixSum": { "description": "sum of all values in full confusion matrix", "type": "integer" } }, "required": [ "columns", "data", "globalMetrics", "numberOfClasses", "rows", "source", "totalMatrixSum" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The confusion chart data from a single source. | ModelConfusionChartRetrieveResponse | | 404 | Not Found | No confusion chart for source. | None | | 422 | Unprocessable Entity | Invalid indices for confusion matrix. | None | ## Calculates and sends frequency of class in distributed among other classes by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/confusionCharts/{source}/classDetails/` Authentication requirements: `BearerAuth` Calculates and sends frequency of class in distributed among other classes for actual and predicted data. A confusion chart class details for given class gives stats of misclassification done by model for given class for actual and predicted data. .. note:: Available for multiclass projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | className | query | string | true | Name of a class for which distribution frequency is requested. | | projectId | path | string | true | The project ID | | modelId | path | string | true | The model ID | | source | path | string | true | Source of the data | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [validation, crossValidation, holdout, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ### Example responses > 200 Response ``` { "properties": { "actualFrequency": { "description": "One vs all actual percentage and count in a format specified below sorted by percentage in decreasing order", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of the times this class was predicted when is was actually `classMetrics.className` (from 0 to 100).", "maximum": 100, "minimum": 0, "type": "number" }, "value": { "description": "The count of the times this class was predicted when is was actually `classMetrics.className`.", "minimum": 0, "type": "integer" } }, "required": [ "otherClassName", "percentage", "value" ], "type": "object" }, "type": "array" }, "className": { "description": "Name of a class for which distribution frequency is requested.", "type": "string" }, "predictedFrequency": { "description": "One vs all predicted percentage and count in a format specified below sorted by percentage in decreasing order", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "the percentage of the times this class was actual when `classMetrics.className` is predicted (from 0 to 100)", "maximum": 100, "minimum": 0, "type": "number" }, "value": { "description": "The count of the times this class was actual `classMetrics.className` when it was predicted", "minimum": 0, "type": "integer" } }, "required": [ "otherClassName", "percentage", "value" ], "type": "object" }, "type": "array" } }, "required": [ "actualFrequency", "className", "predictedFrequency" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The details of the confusion matrix of a model for a specific class. | ModelConfusionChartClassDetailsRetrieveReponseController | | 404 | Not Found | No confusion chart for source. | None | ## Retrieve metadata by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/confusionCharts/{source}/metadata/` Authentication requirements: `BearerAuth` Retrieve metadata for the confusion chart of a model. .. note:: Available for multiclass projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | orderBy | query | string | false | Ordering the chart data by following attributes.Prefix the attribute name with a dash to sort in descending order, e.g. orderBy='-predictedCount' | | orientation | query | string | false | Determines whether the values in the rows of the confusion matrix should correspond to the same actual class ('actual') or predicted class ('predicted'). | | thumbnailCellSize | query | integer | false | Number of classes in a single 'thumbnail' cell. | | projectId | path | string | true | The project ID | | modelId | path | string | true | The model ID | | source | path | string | true | Source of the data | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [className, -className, actualCount, -actualCount, predictedCount, -predictedCount, f1, -f1, precision, -precision, recall, -recall] | | orientation | [actual, -actual, predicted, -predicted] | | source | [validation, crossValidation, holdout, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ### Example responses > 200 Response ``` { "properties": { "classNames": { "description": "List of all class names in the full confusion matrix, sorted by the `orderBy` parameter", "items": { "type": "string" }, "type": "array" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "relevantClassesPositions": { "description": "Matrix to highlight important cell blocks in the confusion chart. Intended to represent a thumbnail view, where the relevantClassesPositions array has a 1 in thumbnail cells that are of interest, and 0 otherwise. The dimensions of the implied thumbnail will not match those of the confusion matrix, e.g. a twenty-class confusion matrix may have a 2x2 thumbnail.", "items": { "items": { "maximum": 1, "minimum": 0, "type": "integer" }, "type": "array" }, "type": "array" }, "source": { "description": "Source of the chart.", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "totalMatrixSum": { "description": "Sum of all values in the full confusion matrix (equal to the number of points considered)", "minimum": 0, "type": "integer" } }, "required": [ "classNames", "globalMetrics", "relevantClassesPositions", "source", "totalMatrixSum" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The metadata for the confusion chart of a model. | ModelConfusionChartMetadataRetrieveResponse | | 404 | Not Found | No confusion chart for source. | None | ## List Cross Class Accuracy scores by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/crossClassAccuracyScores/` Authentication requirements: `BearerAuth` Retrieves a list of Cross Class Accuracy scores for the model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Number of items to skip. Defaults to 0 if not provided. | | limit | query | integer | false | Number of items to return, defaults to 100 if not provided. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of cross-class accuracy scores for the model.", "items": { "properties": { "feature": { "description": "The name of the categorical feature.", "type": "string" }, "modelId": { "description": "ID of the model for the cross-class accuracy scores.", "type": "string" }, "perClassAccuracyScores": { "description": "An array of metric scores for each class of the feature.", "items": { "properties": { "className": { "description": "The name of the class value for the categorical feature.", "type": "string" }, "metrics": { "description": "An array of metric scores.", "items": { "properties": { "metric": { "description": "The name of the metric.", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE", "f1", "accuracy" ], "type": "string" }, "value": { "description": "The calculated score of the metric.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "metric", "value" ], "type": "object" }, "type": "array" } }, "required": [ "className", "metrics" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "Value of the prediction threshold for the model.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "feature", "modelId", "perClassAccuracyScores", "predictionThreshold" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns Cross Class Accuracy scores. | CrossClassAccuracyList | ## Start Cross Class Accuracy calculations by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/crossClassAccuracyScores/` Authentication requirements: `BearerAuth` Submits a job to start Cross Class Accuracy scores calculations for the model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See Location header. | CrossClassAccuracyCreateResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Get Cross Class Data Disparity results by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/dataDisparityInsights/` Authentication requirements: `BearerAuth` Retrieve a list of Cross Class Data Disparity insights for the model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Number of items to skip. Defaults to 0 if not provided. | | limit | query | integer | false | Number of items to return, defaults to 100 if not provided. | | feature | query | string | true | Feature for which insight is computed. | | className1 | query | string | true | One of the compared classes. | | className2 | query | string | true | Another compared class. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Computed data disparity insights if available.", "properties": { "features": { "description": "A mapping of the feature name to the corresponding values on the graph.", "items": { "properties": { "detailsHistogram": { "description": "Histogram details for the specified feature.", "items": { "properties": { "bars": { "description": "Class details for the histogram chart", "items": { "properties": { "label": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Ratio of occurrence of the class.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "bin": { "anyOf": [ { "type": "string" }, { "type": "integer" } ], "description": "Label for the bin grouping" } }, "required": [ "bars", "bin" ], "type": "object" }, "type": "array" }, "disparityScore": { "description": "A number to describe disparity for the feature between the compared classes.", "type": "number" }, "featureImpact": { "description": "A feature importance value.", "type": "number" }, "name": { "description": "Name of the feature.", "type": "string" }, "status": { "description": "A status of the feature.", "enum": [ "Healthy", "At Risk", "Failing" ], "type": "string" } }, "required": [ "detailsHistogram", "disparityScore", "featureImpact", "name", "status" ], "type": "object" }, "type": "array" }, "metric": { "description": "Metric used to calculate the impact of a feature on data disparity.", "type": "string" }, "protectedFeature": { "description": "Feature for which insights were computed.", "type": "string" }, "values": { "description": "Class count details for each class being compared.", "items": { "description": "Number of occurrences of each class being compared.", "properties": { "count": { "description": "Number of times the class was encountered.", "type": "integer" }, "label": { "description": "Name of the class.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array" } }, "type": "object" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns Cross Class Data Disparity results. | DataDisparityRetrieveResponse | ## Start insight calculations by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/dataDisparityInsights/` Authentication requirements: `BearerAuth` Submits a job to start Cross Class Data Disparity insight calculations. ### Body parameter ``` { "properties": { "comparedClassNames": { "description": "An array of classes to calculate data disparity for.", "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "feature": { "description": "Feature for which insight is computed.", "type": "string" } }, "required": [ "comparedClassNames", "feature" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | DataDisparityCreatePayload | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See Location header. | DataDisparityCreateResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## List of Confusion Charts objects on external datasets by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/datasetConfusionCharts/` Authentication requirements: `BearerAuth` List of Confusion Charts objects on external datasets for a project with filtering option by dataset. Prediction dataset may have Confusion Chart for multiclass projects computed if it contained a target with actual values and insights were computed on this dataset. A confusion chart consists of the confusion matrix for all classes, classes frequencies and oneVsAll metrics for all classes. The confusion matrix can be requested in a particular sort order and orientated by rows or columns. Available for multiclass projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | false | Ordering the chart data by following attributes.Prefix the attribute name with a dash to sort in descending order, e.g. orderBy='-predictedCount' | | orientation | query | string | false | Determines whether the values in the rows of the confusion matrix should correspond to the same actual class ('actual') or predicted class ('predicted'). | | rowStart | query | integer | false | start index of row for slicing the confusion matrix. | | rowEnd | query | integer | false | end index of row for slicing the confusion matrix. | | colStart | query | integer | false | start index of column for slicing the confusion matrix. | | colEnd | query | integer | false | end index of column for slicing the confusion matrix. | | datasetId | query | string | false | The dataset ID to retrieve a confusion chart from. | | projectId | path | string | true | The project to retrieve a confusion chart from. | | modelId | path | string | true | The model to retrieve a confusion chart from. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [className, -className, actualCount, -actualCount, predictedCount, -predictedCount, f1, -f1, precision, -precision, recall, -recall] | | orientation | [actual, -actual, predicted, -predicted] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of results returned on this page.", "type": "integer" }, "data": { "description": "Confusion chart data with the in the same format as the response from [GET /api/v2/projects/{projectId}/models/{modelId}/confusionCharts/{source}/][get-apiv2projectsprojectidmodelsmodelidconfusionchartssource] with additional totalCount field.", "items": { "properties": { "columns": { "description": "[colStart, colEnd] column dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "data": { "description": "confusion chart data with the format below.", "properties": { "classMetrics": { "description": "per-class information including one vs all scores in a format specified below", "items": { "properties": { "actualCount": { "description": "number of times this class is seen in the validation data", "type": "integer" }, "className": { "description": "name of the class", "type": "string" }, "confusionMatrixOneVsAll": { "description": "2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "precision score", "type": "number" }, "predictedCount": { "description": "number of times this class has been predicted for the validation data", "type": "integer" }, "recall": { "description": "recall score", "type": "number" }, "wasActualPercentages": { "description": "one vs all actual percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" }, "wasPredictedPercentages": { "description": "one vs all predicted percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object" }, "type": "array" }, "classes": { "description": "class labels from the dataset, union of row classes & column classes. This field is deprecated as of v2.13. The rows and columns may have different class labels when using query parameters to retrieve a slice of the matrix; please use 'rowClasses' and 'colClasses' instead.", "items": { "type": "string" }, "type": "array" }, "colClasses": { "description": "class labels on columns of confusion matrix", "items": { "type": "string" }, "type": "array" }, "confusionMatrix": { "description": "2d array of ints representing confusion matrix, aligned with `rowClasses` and 'colClasses'array. For confusionMatrix[A][B] we can get an integer that represents the number of times 'if class with index A was correct we have class with index B predicted' (if the orientation is 'actual').", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "rowClasses": { "description": "class labels on rows of confusion matrix", "items": { "type": "string" }, "type": "array" } }, "required": [ "classMetrics", "classes", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "numberOfClasses": { "description": "count of classes in full confusion matrix.", "type": "integer" }, "rows": { "description": "[rowStart, rowEnd] row dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "source": { "description": "source of the chart", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "totalMatrixSum": { "description": "sum of all values in full confusion matrix", "type": "integer" } }, "required": [ "columns", "data", "globalMetrics", "numberOfClasses", "rows", "source", "totalMatrixSum" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total count of confusion charts for the model.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of Confusion Charts objects for external datasets. | ConfusionChartForDatasetsListResponse | | 404 | Not Found | No insights found. | None | ## Retrieve Confusion Chart objects on external datasets by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/datasetConfusionCharts/{datasetId}/` Authentication requirements: `BearerAuth` Retrieve Confusion Chart objects on external datasets for a project. Prediction dataset may have Confusion Chart for multiclass projects computed if it contained a target with actual values and insights were computed on this dataset. A confusion chart consists of the confusion matrix for all classes, classes frequencies and oneVsAll metrics for all classes. The confusion matrix can be requested in a particular sort order and oriented by rows or columns (zero-indexed). Available for multiclass projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | orderBy | query | string | false | Ordering the chart data by following attributes.Prefix the attribute name with a dash to sort in descending order, e.g. orderBy='-predictedCount' | | orientation | query | string | false | Determines whether the values in the rows of the confusion matrix should correspond to the same actual class ('actual') or predicted class ('predicted'). | | rowStart | query | integer | false | start index of row for slicing the confusion matrix. | | rowEnd | query | integer | false | end index of row for slicing the confusion matrix. | | colStart | query | integer | false | start index of column for slicing the confusion matrix. | | colEnd | query | integer | false | end index of column for slicing the confusion matrix. | | projectId | path | string | true | The project to retrieve a confusion chart from. | | modelId | path | string | true | The model to retrieve a confusion chart from. | | datasetId | path | string | true | The dataset to retrieve a confusion chart from. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [className, -className, actualCount, -actualCount, predictedCount, -predictedCount, f1, -f1, precision, -precision, recall, -recall] | | orientation | [actual, -actual, predicted, -predicted] | ### Example responses > 200 Response ``` { "properties": { "columns": { "description": "[colStart, colEnd] column dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "data": { "description": "confusion chart data with the format below.", "properties": { "classMetrics": { "description": "per-class information including one vs all scores in a format specified below", "items": { "properties": { "actualCount": { "description": "number of times this class is seen in the validation data", "type": "integer" }, "className": { "description": "name of the class", "type": "string" }, "confusionMatrixOneVsAll": { "description": "2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "precision score", "type": "number" }, "predictedCount": { "description": "number of times this class has been predicted for the validation data", "type": "integer" }, "recall": { "description": "recall score", "type": "number" }, "wasActualPercentages": { "description": "one vs all actual percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" }, "wasPredictedPercentages": { "description": "one vs all predicted percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object" }, "type": "array" }, "classes": { "description": "class labels from the dataset, union of row classes & column classes. This field is deprecated as of v2.13. The rows and columns may have different class labels when using query parameters to retrieve a slice of the matrix; please use 'rowClasses' and 'colClasses' instead.", "items": { "type": "string" }, "type": "array" }, "colClasses": { "description": "class labels on columns of confusion matrix", "items": { "type": "string" }, "type": "array" }, "confusionMatrix": { "description": "2d array of ints representing confusion matrix, aligned with `rowClasses` and 'colClasses'array. For confusionMatrix[A][B] we can get an integer that represents the number of times 'if class with index A was correct we have class with index B predicted' (if the orientation is 'actual').", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "rowClasses": { "description": "class labels on rows of confusion matrix", "items": { "type": "string" }, "type": "array" } }, "required": [ "classMetrics", "classes", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object" }, "datasetId": { "description": "The dataset ID to retrieve a confusion chart from.", "type": "string" }, "numberOfClasses": { "description": "The count of classes in the full confusion matrix.", "type": "integer" }, "rows": { "description": "[rowStart, rowEnd] row dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "totalMatrixSum": { "description": "The sum of all values in the full confusion matrix.", "type": "integer" } }, "required": [ "columns", "data", "datasetId", "numberOfClasses", "rows", "totalMatrixSum" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve Confusion Chart objects on external datasets. | ConfusionChartRetrieveForDatasets | | 404 | Not Found | No insights found. | None | ## Calculate and sends frequency of class in distributed among other classes by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/datasetConfusionCharts/{datasetId}/classDetails/` Authentication requirements: `BearerAuth` Calculate and sends frequency of class in distributed among other classes for actual and predicted data. A confusion chart class details for given class gives stats of misclassification done by model for given class for actual and predicted data. Available for multiclass projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | className | query | string | true | Name of a class for which distribution frequency is requested. | | projectId | path | string | true | The project to retrieve a confusion chart from. | | modelId | path | string | true | The model to retrieve a confusion chart from. | | datasetId | path | string | true | The dataset to retrieve a confusion chart from. | ### Example responses > 200 Response ``` { "properties": { "actualFrequency": { "description": "One vs All actual percentage and count in a format specified below sorted by percentage in decreasing order", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of the times this class was predicted when is was actually `classMetrics.className` (from 0 to 100).", "maximum": 100, "minimum": 0, "type": "number" }, "value": { "description": "The count of the times this class was predicted when is was actually `classMetrics.className`.", "minimum": 0, "type": "integer" } }, "required": [ "otherClassName", "percentage", "value" ], "type": "object" }, "type": "array" }, "className": { "description": "Name of a class for which distribution frequency is requested", "type": "string" }, "datasetId": { "description": "The dataset to retrieve a confusion chart from.", "type": "string" }, "modelId": { "description": "The model to retrieve a confusion chart from.", "type": "string" }, "predictedFrequency": { "description": "One vs All predicted percentage and count in a format specified below sorted by percentage in decreasing order", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "the percentage of the times this class was actual when `classMetrics.className` is predicted (from 0 to 100)", "maximum": 100, "minimum": 0, "type": "number" }, "value": { "description": "The count of the times this class was actual `classMetrics.className` when it was predicted", "minimum": 0, "type": "integer" } }, "required": [ "otherClassName", "percentage", "value" ], "type": "object" }, "type": "array" }, "projectId": { "description": "The project to retrieve a confusion chart from.", "type": "string" } }, "required": [ "actualFrequency", "className", "datasetId", "modelId", "predictedFrequency", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The confusion chart class details for the given class. | ModelConfusionChartClassDetailsForDatasetRetrieve | | 404 | Not Found | No insights found. | None | ## Retrieve metadata by id Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/datasetConfusionCharts/{datasetId}/metadata/` Authentication requirements: `BearerAuth` Retrieve metadata for the confusion chart of a model on external dataset for a project. Available for multiclass projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | orderBy | query | string | false | Ordering the chart data by following attributes.Prefix the attribute name with a dash to sort in descending order, e.g. orderBy='-predictedCount' | | orientation | query | string | false | Determines whether the values in the rows of the confusion matrix should correspond to the same actual class ('actual') or predicted class ('predicted'). | | thumbnailCellSize | query | integer | false | Number of classes in a single 'thumbnail' cell. | | projectId | path | string | true | The project to retrieve a confusion chart from. | | modelId | path | string | true | The model to retrieve a confusion chart from. | | datasetId | path | string | true | The dataset to retrieve a confusion chart from. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [className, -className, actualCount, -actualCount, predictedCount, -predictedCount, f1, -f1, precision, -precision, recall, -recall] | | orientation | [actual, -actual, predicted, -predicted] | ### Example responses > 200 Response ``` { "properties": { "classNames": { "description": "list of all class names in the full confusion matrix, sorted by the `orderBy` parameter.", "items": { "type": "string" }, "type": "array" }, "datasetId": { "description": "The dataset to retrieve a confusion chart from.", "type": "string" }, "modelId": { "description": "The model to retrieve a confusion chart from.", "type": "string" }, "projectId": { "description": "The project to retrieve a confusion chart from.", "type": "string" }, "relevantClassesPositions": { "description": "Matrix to highlight important cell blocks in the confusion chart. Intended to represent a thumbnail view, where the relevantClassesPositions array has a 1 in thumbnail cells that are of interest, and 0 otherwise. The dimensions of the implied thumbnail will not match those of the confusion matrix, e.g. a twenty-class confusion matrix may have a 2x2 thumbnail.", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "totalMatrixSum": { "description": "Sum of all values in the full confusion matrix (equal to the number of points considered).", "type": "integer" } }, "required": [ "classNames", "datasetId", "modelId", "projectId", "relevantClassesPositions", "totalMatrixSum" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve metadata for the Confusion Chart objects on external datasets. | ConfusionChartRetrieveMetadataForDatasets | | 404 | Not Found | No insights found. | None | ## Retrieve List of Lift chart data on prediction datasets by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/datasetLiftCharts/` Authentication requirements: `BearerAuth` List of Lift chart objects on prediction datasets for a project with filtering option by dataset. Prediction dataset may have Lift chart computed if it contained a column with actual values and predictions were computed on this dataset. This controller is not supported for multiclass classification projects. For multiclass, instead use /projects/ /models//datasetMulticlassLiftCharts/. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | datasetId | query | string | false | If provided will return Lift chart for dataset with matching datasetId. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of results returned on this page.", "type": "integer" }, "data": { "description": "Array of lift chart data for dataset, as specified below", "items": { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "datasetId": { "description": "The dataset ID of the dataset that was used to compute the lift chart.", "type": "string" } }, "required": [ "bins", "datasetId" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve List of Lift chart data on prediction datasets. | LiftChartForDatasetsList | | 404 | Not Found | No insights found. | None | ## Retrieve List of Multiclass Lift chart data on prediction datasets by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/datasetMulticlassLiftCharts/` Authentication requirements: `BearerAuth` List of Multiclass Lift chart objects on prediction datasets for a project with filtering option by dataset. Prediction dataset may have Multiclass Lift chart computed if it contained a column with actual values and predictions were computed on this dataset. Multiclass Lift charts are supported for multiclass classification projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | datasetId | query | string | false | If provided will return Lift chart for dataset with matching datasetId. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of results returned on this page.", "type": "integer" }, "data": { "description": "Array of multiclass lift chart data for dataset, as specified below.", "items": { "properties": { "classBins": { "description": "The list of lift chart data for each target class.", "items": { "properties": { "bins": { "description": "The lift chart data for that source and class, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "targetClass": { "description": "The target class for the lift chart.", "type": "string" } }, "required": [ "bins", "targetClass" ], "type": "object" }, "type": "array" }, "datasetId": { "description": "The dataset ID of the dataset that was used to compute the lift chart.", "type": "string" } }, "required": [ "classBins", "datasetId" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID to which the chart data belongs.", "type": "string" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID to which the chart data belongs.", "type": "string" }, "totalCount": { "description": "Total count of multiclass lift charts matching to the query condition.", "type": "integer" } }, "required": [ "count", "data", "modelId", "next", "previous", "projectId", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve List of Multiclass Lift chart data on prediction datasets. | MulticlassLiftChartForDatasetsList | | 404 | Not Found | No insights found. | None | ## The list of residuals chart objects by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/datasetResidualsCharts/` Authentication requirements: `BearerAuth` List of residuals charts objects on prediction datasets for a project with filtering option by dataset. Prediction dataset may have residuals chart computed if it contained a column with actual values and predictions were computed on this dataset. Residuals charts are supported for regression projects only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | datasetId | query | string | false | If provided, will return the residuals chart for the dataset with the matching dataset ID. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of residuals charts for the dataset.", "items": { "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input.", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be `null`.", "items": { "items": { "type": "number" }, "type": "array" }, "type": "array" }, "datasetId": { "description": "The dataset ID.", "type": "string" }, "histogram": { "description": "Data to plot a histogram of residual values. The object contains three keys, intervalStart, intervalEnd, and occurrences, the number of times the predicted value fits within that interval. For all but the last interval, the end value is exclusive.", "items": { "properties": { "intervalEnd": { "description": "The end of the interval.", "type": "number" }, "intervalStart": { "description": "The start of the interval.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within that interval.", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset.", "type": "number" }, "standardDeviation": { "description": "The Standard Deviation value measures variation in the dataset. A low value indicates that the data points tend to be close to the mean; a high value indicates that the data points are spread over a wider range of values.", "type": "number" } }, "required": [ "coefficientOfDetermination", "data", "datasetId", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "modelId", "next", "previous", "projectId", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ResidualsChartForDatasetsList | ## List of ROC curve objects on prediction datasets for a project with filtering option by DEPRECATED by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/datasetRocCurves/` Authentication requirements: `BearerAuth` List of ROC curve objects on prediction datasets for a project with filtering option by dataset. Prediction dataset may have ROC curve computed if it contained a column with actual values and predictions were computed on this dataset. Each ROC curve object includes an array of points showing the performance of the model at different thresholds for classification, and arrays of sample predictions for both the positive and negative classes. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | datasetId | query | string | false | If provided, will return the ROC curve for the dataset with the matching dataset ID. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of ROC curve data for datasets.", "items": { "description": "ROC curve data for datasets.", "properties": { "auc": { "description": "The area under the curve (AUC).", "type": [ "number", "null" ], "x-versionadded": "v2.43" }, "datasetId": { "description": "The ID of the dataset that was used to compute the ROC curve.", "type": "string" }, "kolmogorovSmirnovMetric": { "description": "The Kolmogorv-Smirnov metric.", "type": [ "number", "null" ], "x-versionadded": "v2.43" }, "negativeClassPredictions": { "description": "The list of example predictions for the negative class.", "items": { "type": "number" }, "maxItems": 100000000, "type": "array" }, "positiveClassPredictions": { "description": "The list of example predictions for the negative class.", "items": { "type": "number" }, "maxItems": 100000000, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below. Each point specifies how the model performed for a particular classification threshold.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "maxItems": 10000000, "type": "array" } }, "required": [ "auc", "datasetId", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The objects were returned successfully. No objects is a valid case. | RocCurveForDatasetsList | ## List calculated Per Class Bias insights by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/fairnessInsights/` Authentication requirements: `BearerAuth` Retrieve a list of Per Class Bias insights for the model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Number of items to skip. Defaults to 0 if not provided. | | limit | query | integer | false | Number of items to return, defaults to 100 if not provided. | | fairnessMetricsSet | query | string | false | Metric to use for calculating fairness. Can be one of proportionalParity, equalParity, predictionBalance, trueFavorableAndUnfavorableRateParity or FavorableAndUnfavorablePredictiveValueParity. Used and required only if Bias & Fairness in AutoML feature is enabled. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | fairnessMetricsSet | [proportionalParity, equalParity, predictionBalance, trueFavorableAndUnfavorableRateParity, favorableAndUnfavorablePredictiveValueParity] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of fairness insights for the model.", "items": { "properties": { "fairnessMetric": { "description": "The fairness metric used to calculate the fairness scores.", "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "fairnessThreshold": { "default": 0.8, "description": "Value of the fairness threshold, defined in project options.", "maximum": 1, "minimum": 0, "type": "number" }, "modelId": { "description": "ID of the model fairness was measured for.", "type": "string" }, "perClassFairness": { "description": "An array of calculated fairness scores for each protected feature class.", "items": { "properties": { "absoluteValue": { "description": "Absolute fairness score for the class", "minimum": 0, "type": "number" }, "className": { "description": "Name of the protected class the score is calculated for.", "type": "string" }, "entriesCount": { "description": "The number of entries of the class in the analysed data.", "minimum": 0, "type": "integer" }, "isStatisticallySignificant": { "description": "Flag to tell whether the score can be treated as statistically significant. In other words, whether we are confident enough with the score for this protected class.", "type": "boolean" }, "value": { "description": "The relative fairness score for the class.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "absoluteValue", "className", "entriesCount", "isStatisticallySignificant", "value" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "Model's prediction threshold used when insight was calculated. ``null`` if prediction threshold is not required for the fairness metric calculations.", "maximum": 1, "minimum": 0, "type": "number" }, "protectedFeature": { "description": "Name of the protected feature the fairness calculation is made for.", "type": "string" } }, "required": [ "fairnessMetric", "fairnessThreshold", "modelId", "perClassFairness", "protectedFeature" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Returns Per Class Bias results. | FairnessInsightsListResponse | ## Create fairness insights by id Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/fairnessInsights/` Authentication requirements: `BearerAuth` Submits a job to start Per Class Bias insight calculations for the model. ### Body parameter ``` { "properties": { "fairnessMetricsSet": { "description": "Metric to use for calculating fairness. Can be one of ``proportionalParity``, ``equalParity``, ``predictionBalance``, ``trueFavorableAndUnfavorableRateParity`` or ``FavorableAndUnfavorablePredictiveValueParity``. Used and required only if *Bias & Fairness in AutoML* feature is enabled.", "enum": [ "proportionalParity", "equalParity", "predictionBalance", "trueFavorableAndUnfavorableRateParity", "favorableAndUnfavorablePredictiveValueParity" ], "type": "string", "x-versionadded": "v2.24" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | FairnessInsightsStartCalculationPayload | false | none | ### Example responses > 202 Response ``` { "properties": { "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See Location header. | FairnessInsightsStartCalculationResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve feature effects by id Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/featureEffects/` Authentication requirements: `BearerAuth` Retrieve Feature Effects for the model. Feature Effects provides partial dependence and predicted vs actual values for the top 500 features, ordered by feature impact score. The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. If a Feature Effects job was previously submitted, this endpoint will return a response structured as {"message":, "jobId":} where jobId is the ID of the job. Retrieve the job with [GET /api/v2/projects/{projectId}/jobs/{jobId}/][get-apiv2projectsprojectidjobsjobid]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | source | query | string | false | The model's data source. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation, holdout] | ### Example responses > 200 Response ``` { "properties": { "featureEffects": { "description": "The Feature Effects computational results for each feature.", "items": { "properties": { "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "The model's data source.", "type": "string" } }, "required": [ "featureEffects", "modelId", "projectId", "source" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | FeatureEffectsResponse | | 403 | Forbidden | User does not have permission to view the project. | None | | 404 | Not Found | Project, model, source or computation results do not exist. | None | ## Create feature effects by id Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/featureEffects/` Authentication requirements: `BearerAuth` Add a request to the queue to calculate Feature Effects. If the job has been previously submitted, the request fails, returning the `jobId` of the previously submitted job. Use this `jobId` to check status of the previously submitted job. ### Body parameter ``` { "properties": { "rowCount": { "description": "The number of rows from dataset to use for Feature Impact calculation.", "maximum": 100000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | FeatureEffectCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The Feature Effects request has been successfully submitted. See Location header. | None | | 403 | Forbidden | User does not have permission to view or submit jobs for the project. | None | | 404 | Not Found | The provided project or model does not exist. | None | | 422 | Unprocessable Entity | Queue submission error. | | .. minversion:: v2.21 ``` If the rowCount exceeds the maximum or minimum value for this dataset. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less.|None| ``` ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve Feature Effects metadata by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/featureEffectsMetadata/` Authentication requirements: `BearerAuth` Retrieve Feature Effects metadata. Response contains status and available sources. One of the provided `source` parameters used for retrieving Feature Effects. * Source can be, at a minimum, `training` or `validation`. If holdout is configured for the project, `source` also includes `holdout`. * Source value of `training` is always available. (versions prior to v2.17 support `validation` only) * When a model is trained into `validation` or `holdout` without stacked predictions (i.e., no out-of-sample predictions in `validation` or `holdout`), `validation` and `holdout` sources are not available. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "sources": { "description": "The list of sources available for the model.", "items": { "enum": [ "training", "validation", "holdout" ], "type": "string" }, "type": "array" }, "status": { "description": "The status of the job.", "enum": [ "INPROGRESS", "COMPLETED", "NOT_COMPLETED" ], "type": "string" } }, "required": [ "sources", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelXrayMetadataResponse | | 403 | Forbidden | User does not have permission to view the project. | None | | 404 | Not Found | The project or model does not exist. | None | | 422 | Unprocessable Entity | The model is datetime partitioned. | None | ## Retrieve feature impact scores by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/featureImpact/` Authentication requirements: `BearerAuth` Retrieve feature impact scores for features in a model. Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged), and seeing how the error metric score for the predictions is affected. Elsewhere this technique is sometimes called 'Permutation Importance'. The `impactUnnormalized` is how much worse the error metric score is when making predictions on this modified data. The `impactNormalized` is normalized so that the largest value is 1. In both cases, larger values indicate more important features. If a feature is a redundant feature, i.e. once other features are considered it doesn't contribute much in addition, the `redundantWith` value is the name of feature that has the highest correlation with this feature. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | backtest | query | any | false | The backtest value used for Feature Impact computation. Applicable for datetime aware models. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "backtest": { "description": "The backtest model used to compute Feature Impact.Defined for datetime aware models.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.29" }, "count": { "description": "Number of feature impact records in a given batch.", "type": "integer" }, "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL for the next page of results, or null if there is no next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL for the previous page of results, or null if there is no previous page.", "format": "uri", "type": [ "string", "null" ] }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact.", "type": "boolean" }, "rowCount": { "description": "The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the ``rowCount``, we return ``null`` here.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "shapBased": { "description": "Indicates whether feature impact was calculated using Shapley values. True for anomaly detection models when the project is unsupervised, as permutation approach is not applicable. Note that supervised projects must use an alternative route for SHAP impact: /api/v2/projects/(projectId)/models/(modelId)/shapImpact/", "type": "boolean", "x-versionadded": "v2.18" } }, "required": [ "backtest", "count", "featureImpacts", "next", "previous", "ranRedundancyDetection", "rowCount", "shapBased" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | PermutationFeatureImpactResponse | | 404 | Not Found | No feature impact data found for the given model. | None | ## Create feature impact by id Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/featureImpact/` Authentication requirements: `BearerAuth` Add a request to calculate feature impact to the queue. If the job has been previously submitted, the request will fail and return the `jobId` of previously submitted job. This `jobId` can be used to check status of previously submitted job. ### Body parameter ``` { "properties": { "backtest": { "description": "The backtest value used for Feature Impact computation. Applicable for datetime aware models.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ], "x-versionadded": "v2.28" }, "rowCount": { "description": "The sample size to use for Feature Impact computation. It is possible to re-compute Feature Impact with a different row count.", "maximum": 100000, "minimum": 10, "type": "integer", "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | PermutationFeatureImpactCreatePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was accepted and will be worked on. | None | | 404 | Not Found | If modelId does not exist in project leaderboard | None | | 422 | Unprocessable Entity | If feature impact has already run will return error including jobId property which is the jobId of the previously started feature impact job. | | .. minversion:: v2.21 If the `rowCount` exceeds the maximum or minimum value for this dataset. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less.|None| ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve cluster insights by id Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/featureLists/{datasetId}/clusterInsights/` Authentication requirements: `BearerAuth` Retrieve computed Cluster Insights for a clustering project model on a single featurelist. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | true | At most this many results are returned. The default may change without notice. | | orderBy | query | string | false | Order results by the specified field value. | | searchFor | query | string | false | Search for a specific string in a feature name.This search is case insensitive. If not specified, all features will be returned. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | datasetId | path | string | true | The dataset ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [featureImpact, -featureImpact, featureName, -featureName] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of features with clusters insights.", "items": { "anyOf": [ { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "image" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for an image feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "images", "percentageOfMissingImages" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "representativeImages" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "clusterName", "images", "percentageOfMissingImages" ], "type": "object" }, "type": "array" } }, "required": [ "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "geospatialPoint" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a geospatial centroid or point feature.", "items": { "properties": { "insightName": { "description": "Insight name.", "enum": [ "representativeLocations" ], "type": "string" }, "perCluster": { "description": "Statistic for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "representativeLocations": { "description": "A list of latitude and longitude location list", "items": { "description": "Latitude and longitude list.", "items": { "description": "Longitude or latitude, in degrees.", "maximum": 180, "minimum": -180, "type": "number" }, "type": "array" }, "maxItems": 1000, "type": "array" } }, "required": [ "clusterName", "representativeLocations" ], "type": "object" }, "type": "array" } }, "required": [ "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "text" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "importantNgrams" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "numeric" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistic value for all data.", "type": [ "number", "null" ] }, "insightName": { "description": "Insight name.", "enum": [ "min", "max", "median", "avg", "firstQuartile", "thirdQuartile", "missingRowsPercent" ], "type": "string" }, "perCluster": { "description": "Statistic values for for each cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "statistic": { "description": "Statistic value for this cluster.", "type": [ "number", "null" ] } }, "required": [ "clusterName", "statistic" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "categorical" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "categoryLevelFrequencyPercent" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "document" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "importantNgrams" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" } ] }, "maxItems": 100, "type": "array" }, "isCurrentClusterInsightVersion": { "description": "If retrieved insights are current version.", "type": "boolean" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" }, "version": { "description": "Current version of the computed insight.", "minimum": 0, "type": "integer" } }, "required": [ "data", "isCurrentClusterInsightVersion", "next", "previous", "totalCount", "version" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Insights for a clustering project model on a single featurelist. | ClusterInsightsPaginatedResponse | | 404 | Not Found | The project or the model was not found or insights have not been computed yet. | None | | 422 | Unprocessable Entity | Feature Impact is required. Please, compute it first. | None | ## Retrieve Image Activation Maps by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/imageActivationMaps/` Authentication requirements: `BearerAuth` Retrieve Image Activation Maps for a feature of a model. Image Activation maps are a technique to get the discriminative image regions used by a CNN to identify a specific class in the image. In other words, an image activation map lets us see which regions in the image were relevant to this class. The higher the value in the activation map the greater the effect the region had on the prediction. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | featureName | query | string | true | The name of the feature to query. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "activationMapHeight": { "description": "The height of each activation map (the number of rows in each activationValues matrix).", "type": [ "integer", "null" ] }, "activationMapWidth": { "description": "The width of each activation map (the number of items in each row of each activationValues matrix).", "type": [ "integer", "null" ] }, "activationMaps": { "description": "List of activation map objects", "items": { "properties": { "activationValues": { "description": "A 2D matrix of values (row-major) representing the activation strengths for particular image regions.", "items": { "items": { "maximum": 255, "minimum": 0, "type": "integer" }, "type": "array" }, "type": "array" }, "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "featureName": { "description": "The name of the column containing the image value the activation map is based upon.", "type": "string" }, "imageHeight": { "description": "The height of the original image (in pixels) this activation map has been computed for.", "type": "integer" }, "imageId": { "description": "ID of the original image this activation map has been computed for.", "type": "string" }, "imageWidth": { "description": "The width of the original image (in pixels) this activation map has been computed for.", "type": "integer" }, "links": { "description": "Download URLs.", "properties": { "downloadOriginalImage": { "description": "URL of the original image", "format": "uri", "type": "string" }, "downloadOverlayImage": { "description": "URL of the original image overlaid by the activation heatmap", "format": "uri", "type": "string" } }, "required": [ "downloadOriginalImage", "downloadOverlayImage" ], "type": "object" }, "overlayImageId": { "description": "ID of the image containing the original image overlaid by the activation heatmap.", "type": "string" }, "predictedTargetValue": { "description": "predicted target value of the dataset row containing this image.", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" } ] } }, "required": [ "activationValues", "actualTargetValue", "featureName", "imageHeight", "imageId", "imageWidth", "links", "overlayImageId", "predictedTargetValue" ], "type": "object" }, "type": "array" }, "targetBins": { "description": "List of bin objects for regression or null", "items": { "properties": { "targetBinEnd": { "description": "End value for the target bin", "type": "number" }, "targetBinStart": { "description": "Start value for the target bin", "type": "number" } }, "required": [ "targetBinEnd", "targetBinStart" ], "type": "object" }, "type": "array" }, "targetValues": { "description": "List of target values for classification or null", "items": { "description": "Target value", "type": "string" }, "type": "array" } }, "required": [ "activationMapHeight", "activationMapWidth", "activationMaps", "targetBins", "targetValues" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Image Activation Maps | ActivationMapsRetrieveResponse | | 422 | Unprocessable Entity | Unable to process request. | None | ## Request the computation of image activation maps by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/imageActivationMaps/` Authentication requirements: `BearerAuth` Request the computation of image activation maps for the specified model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 202 Response ``` { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if the job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "jobType": { "description": "The type of the job.", "enum": [ "compute_image_activation_maps" ], "type": "string" }, "message": { "description": "Error message in case of failure.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The job status.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" }, "url": { "description": "A URL that can be used to request details about the job.", "type": "string" } }, "required": [ "id", "isBlocked", "jobType", "message", "modelId", "projectId", "status", "url" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Image activation map computation has been successfully requested | ActivationMapsComputeResponse | | 422 | Unprocessable Entity | Cannot compute image activation maps: if image activation maps were already computed for the model or there was another issue creating this job | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | a url that can be polled to check the status of the job. | ## Retrieve labelwise ROC curves by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/labelwiseRocCurves/{source}/` Authentication requirements: `BearerAuth` Retrieve labelwise ROC curves for model and given source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | labels | query | string | false | Labels for which data is requested. | | searchQuery | query | string | false | Search query for label. | | sortBy | query | string | false | Property to sort labels in the response. | | sortOrder | query | string | false | Sort order. | | threshold | query | number | false | Threshold at which the metric should be sorted. | | offset | query | integer | false | Number of labels to skip. | | limit | query | integer | false | Number of labels to return. | | includeModelAverage | query | boolean | false | Whether model average metrics should be included in the response. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | source | path | string | true | Chart source. | ### Enumerated Values | Parameter | Value | | --- | --- | | sortBy | [accuracy, f1Score, falsePositiveRate, label, matthewsCorrelationCoefficient, negativePredictiveValue, positivePredictiveValue, trueNegativeRate, truePositiveRate] | | sortOrder | [ascending, descending] | | source | [validation, crossValidation, holdout] | ### Example responses > 200 Response ``` { "properties": { "averageModelMetrics": { "description": "All average model metrics from one data source.", "properties": { "metrics": { "description": "Average model metrics for the given thresholds.", "items": { "properties": { "name": { "description": "Metric name.", "enum": [ "accuracy", "f1Score", "falsePositiveRate", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "trueNegativeRate", "truePositiveRate" ], "type": "string" }, "numLabelsUsedInCalculation": { "description": "Number of labels that were taken into account in the calculation of metric averages", "type": "integer" }, "values": { "description": "Metric values at given thresholds.", "items": { "type": "number" }, "maxItems": 100, "minItems": 100, "type": "array" } }, "required": [ "name", "numLabelsUsedInCalculation", "values" ], "type": "object" }, "maxItems": 8, "minItems": 8, "type": "array" }, "source": { "description": "Chart source.", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "thresholds": { "description": "Threshold values for which model metrics are available.", "items": { "maximum": 1, "minimum": 0, "type": "number" }, "maxItems": 100, "minItems": 100, "type": "array" } }, "required": [ "metrics", "source", "thresholds" ], "type": "object" }, "charts": { "description": "ROC data for all labels from one data source.", "items": { "properties": { "auc": { "description": "Area under the curve.", "type": "number" }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric.", "type": "number" }, "label": { "description": "Label name.", "type": "string" }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "type": "number" }, "type": "array" }, "rocPoints": { "description": "ROC characteristics for label.", "items": { "properties": { "accuracy": { "description": "Accuracy.", "maximum": 1, "minimum": 0, "type": "number" }, "f1Score": { "description": "F1 score.", "maximum": 1, "minimum": 0, "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "minimum": 0, "type": [ "integer", "null" ] }, "falsePositiveRate": { "description": "False positive rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "falsePositiveScore": { "description": "False positive score.", "minimum": 0, "type": [ "integer", "null" ] }, "fractionPredictedAsNegative": { "description": "Fraction of negative predictions.", "type": [ "number", "null" ] }, "fractionPredictedAsPositive": { "description": "Fraction of positive predictions.", "type": [ "number", "null" ] }, "liftNegative": { "description": "Negative lift.", "type": [ "number", "null" ] }, "liftPositive": { "description": "Positive lift.", "type": [ "number", "null" ] }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "maximum": 1, "minimum": -1, "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "maximum": 1, "minimum": 0, "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "maximum": 1, "minimum": 0, "type": "number" }, "threshold": { "description": "Threshold.", "maximum": 2, "minimum": 0, "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "trueNegativeScore": { "description": "True negative score.", "minimum": 0, "type": [ "integer", "null" ] }, "truePositiveRate": { "description": "True positive rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "truePositiveScore": { "description": "True positive score.", "minimum": 0, "type": [ "integer", "null" ] } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" }, "source": { "description": "Data source of ROC characteristics.", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" } }, "required": [ "auc", "kolmogorovSmirnovMetric", "label", "negativeClassPredictions", "positiveClassPredictions", "rocPoints", "source" ], "type": "object" }, "type": "array" }, "count": { "description": "Number of labels returned on this page.", "type": "integer" }, "labels": { "description": "All available target labels for this insight.", "items": { "description": "Label name.", "type": "string" }, "maxItems": 100, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "rocType": { "description": "Type of ROC.", "enum": [ "binary", "labelwise" ], "type": "string" }, "totalCount": { "description": "Total number of labels across all pages.", "type": "integer" } }, "required": [ "averageModelMetrics", "charts", "labels", "next", "previous", "rocType", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The labelwise ROC curves for the model and given source. | LabelwiseROC | ## Retrieve all available lift charts by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/liftChart/` Authentication requirements: `BearerAuth` Retrieve all available lift charts for model. The response will include a json list of all available lift charts, in the same format as the response from [GET /api/v2/projects/{projectId}/models/{modelId}/liftChart/{source}/][get-apiv2projectsprojectidmodelsmodelidliftchartsource]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "charts": { "description": "List of lift chart data from all available sources.", "items": { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data.", "enum": [ "validation", "crossValidation", "holdout", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string", "x-enum-versionadded": [ { "value": "backtest_2", "x-versionadded": "v2.23" }, { "value": "backtest_3", "x-versionadded": "v2.23" }, { "value": "backtest_4", "x-versionadded": "v2.23" }, { "value": "backtest_5", "x-versionadded": "v2.23" }, { "value": "backtest_6", "x-versionadded": "v2.23" }, { "value": "backtest_7", "x-versionadded": "v2.23" }, { "value": "backtest_8", "x-versionadded": "v2.23" }, { "value": "backtest_9", "x-versionadded": "v2.23" }, { "value": "backtest_10", "x-versionadded": "v2.23" }, { "value": "backtest_11", "x-versionadded": "v2.23" }, { "value": "backtest_12", "x-versionadded": "v2.23" }, { "value": "backtest_13", "x-versionadded": "v2.23" }, { "value": "backtest_14", "x-versionadded": "v2.23" }, { "value": "backtest_15", "x-versionadded": "v2.23" }, { "value": "backtest_16", "x-versionadded": "v2.23" }, { "value": "backtest_17", "x-versionadded": "v2.23" }, { "value": "backtest_18", "x-versionadded": "v2.23" }, { "value": "backtest_19", "x-versionadded": "v2.23" }, { "value": "backtest_20", "x-versionadded": "v2.23" } ] } }, "required": [ "bins", "source" ], "type": "object" }, "type": "array" } }, "required": [ "charts" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of all of the available lift charts for a model. | ModelLiftChartListResponse | | 403 | Forbidden | Invalid Permissions | None | | 404 | Not Found | Please use multiclass lift route for per-class lift data. | None | | 422 | Unprocessable Entity | Lift chart is not available for unsupervised mode projects. | None | ## Retrieve the lift chart data by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/liftChart/{source}/` Authentication requirements: `BearerAuth` Retrieve the lift chart data from a single source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID | | modelId | path | string | true | The model ID | | source | path | string | true | Source of the data | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [validation, crossValidation, holdout, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ### Example responses > 200 Response ``` { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data.", "enum": [ "validation", "crossValidation", "holdout", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string", "x-enum-versionadded": [ { "value": "backtest_2", "x-versionadded": "v2.23" }, { "value": "backtest_3", "x-versionadded": "v2.23" }, { "value": "backtest_4", "x-versionadded": "v2.23" }, { "value": "backtest_5", "x-versionadded": "v2.23" }, { "value": "backtest_6", "x-versionadded": "v2.23" }, { "value": "backtest_7", "x-versionadded": "v2.23" }, { "value": "backtest_8", "x-versionadded": "v2.23" }, { "value": "backtest_9", "x-versionadded": "v2.23" }, { "value": "backtest_10", "x-versionadded": "v2.23" }, { "value": "backtest_11", "x-versionadded": "v2.23" }, { "value": "backtest_12", "x-versionadded": "v2.23" }, { "value": "backtest_13", "x-versionadded": "v2.23" }, { "value": "backtest_14", "x-versionadded": "v2.23" }, { "value": "backtest_15", "x-versionadded": "v2.23" }, { "value": "backtest_16", "x-versionadded": "v2.23" }, { "value": "backtest_17", "x-versionadded": "v2.23" }, { "value": "backtest_18", "x-versionadded": "v2.23" }, { "value": "backtest_19", "x-versionadded": "v2.23" }, { "value": "backtest_20", "x-versionadded": "v2.23" } ] } }, "required": [ "bins", "source" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Lift chart data from a single source. | ModelLiftChartResponse | ## Retrieve multiclass feature effects by id Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/multiclassFeatureEffects/` Authentication requirements: `BearerAuth` Retrieve feature effects for each class in a multiclass model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | source | query | string | false | The model's data source. | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | class | query | string,null | false | Target class label. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [training, validation, holdout] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of feature effect scores for each class in a multiclass project.", "items": { "properties": { "class": { "description": "Target class label.", "type": "string" }, "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "class", "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "The model's data source.", "type": "string" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "modelId", "next", "previous", "projectId", "source", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | MulticlassFeatureEffectsResponse | | 403 | Forbidden | User does not have permission to view the project. | None | | 404 | Not Found | Project, model, source or computation results do not exist. | None | ## Create multiclass feature effects by id Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/multiclassFeatureEffects/` Authentication requirements: `BearerAuth` Compute feature effects for a multiclass model. If the job has been previously submitted, the request fails, returning the `jobId` of the previously submitted job. Use this `jobId` to check status of the previously submitted job. NOTE: feature effects are computed for top 100 classes. ### Body parameter ``` { "properties": { "features": { "description": "The list of features to use to calculate feature effects.", "items": { "type": "string" }, "maxItems": 20000, "type": "array" }, "rowCount": { "description": "The number of rows from dataset to use for Feature Impact calculation.", "maximum": 100000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "topNFeatures": { "description": "Number of top features (ranked by feature impact) to use to calculate feature effects.", "exclusiveMinimum": 0, "maximum": 1000, "type": [ "integer", "null" ] } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | MulticlassFeatureEffectCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The Feature Effects request has been successfully submitted. See Location header. | None | | 403 | Forbidden | User does not have permission to view or submit jobs for the project. | None | | 404 | Not Found | Project, model, source or computation results do not exist. | None | | 422 | Unprocessable Entity | Queue submission error. If the rowCount exceeds the maximum or minimum value for this dataset. Minimum is 10 rows. Maximum is 100000 rows or the training sample size of the model, whichever is less. If neither features nor topNFeatures is provided. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve multiclass feature impact by id Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/multiclassFeatureImpact/` Authentication requirements: `BearerAuth` Retrieve feature impact scores for each class in a multiclass model. Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged), and seeing how the error metric score for the predictions is affected. Elsewhere this technique is sometimes called 'Permutation Importance'. The `impactUnnormalized` is how much worse the error metric score is when making predictions on this modified data. The `impactNormalized` is normalized so that the largest value is 1. In both cases, larger values indicate more important features. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "classFeatureImpacts": { "description": "A list of feature importance scores for each class in multiclass project.", "items": { "properties": { "class": { "description": "Target class label.", "type": "string" }, "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" } }, "required": [ "class", "featureImpacts" ], "type": "object" }, "type": "array" }, "count": { "description": "Number of feature impact records in a given batch.", "type": "integer" }, "next": { "description": "The URL for the next page of results, or null if there is no next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL for the previous page of results, or null if there is no previous page.", "format": "uri", "type": [ "string", "null" ] }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact. Currently always False, as redundant feature identification isn't supported for multiclass in DataRobot.", "type": "boolean" }, "shapBased": { "description": "Indicates whether feature impact was calculated using Shapley values. Currently always `False`, as SHAP isn't supported for multiclass in DataRobot.", "type": "boolean" } }, "required": [ "classFeatureImpacts", "count", "next", "previous", "ranRedundancyDetection", "shapBased" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | MulticlassFeatureImpactResponse | | 404 | Not Found | If no feature impact data found for a given model. | None | ## Retrieve multiclass lift chart by id Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/multiclassLiftChart/` Authentication requirements: `BearerAuth` Retrieve all available lift charts for multiclass model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "charts": { "description": "List of lift chart data from all available sources.", "items": { "properties": { "classBins": { "description": "List of lift chart data for each target class.", "items": { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "targetClass": { "description": "Target class for the lift chart.", "type": "string" } }, "required": [ "bins", "targetClass" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" } }, "required": [ "classBins", "source" ], "type": "object" }, "type": "array" } }, "required": [ "charts" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Multiclass lift chart data. | AllMulticlassModelLiftChartsResponse | ## Retrieve the multiclass lift chart data by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/multiclassLiftChart/{source}/` Authentication requirements: `BearerAuth` Retrieve the multiclass lift chart data from a single source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | source | path | string | true | The source of the data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [validation, crossValidation, holdout] | ### Example responses > 200 Response ``` { "properties": { "classBins": { "description": "List of lift chart data for each target class.", "items": { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "targetClass": { "description": "Target class for the lift chart.", "type": "string" } }, "required": [ "bins", "targetClass" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" } }, "required": [ "classBins", "source" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Multiclass lift chart data from a single source. | MulticlassModelLiftChartResponse | ## Retrieve labelwise lift charts by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/multilabelLiftCharts/{source}/` Authentication requirements: `BearerAuth` Retrieve labelwise lift charts for model and given source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | labels | query | string | false | Labels for which data is requested. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | source | path | string | true | Chart source. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [validation, crossValidation, holdout] | ### Example responses > 200 Response ``` { "properties": { "labelBins": { "description": "Lift charts for the given data source.", "items": { "properties": { "bins": { "description": "Lift chart data for that label.", "items": { "properties": { "actual": { "description": "Average of actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "For projects with weights, it is the sum of the weights of all rows in the bins. Otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "Average of predicted target values for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "maxItems": 60, "type": "array" }, "label": { "description": "Label name.", "type": "string" } }, "required": [ "bins", "label" ], "type": "object" }, "maxItems": 100, "type": "array" }, "labels": { "description": "All available target labels for this insight.", "items": { "description": "Label name.", "type": "string" }, "maxItems": 100, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "Data source of Lift charts.", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" } }, "required": [ "labelBins", "labels", "modelId", "projectId", "source" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The labelwise lift charts for the model and given source. | LabelwiseLiftChart | ## Retrieve all residuals charts by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/residuals/` Authentication requirements: `BearerAuth` Retrieve all residuals charts for a model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "residuals": { "description": "Residuals chart data from all available sources", "properties": { "crossValidation": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "holdout": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "validation": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" } }, "type": "object" } }, "required": [ "residuals" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelResidualsList | ## Retrieve the residuals chart data by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/residuals/{source}/` Authentication requirements: `BearerAuth` Retrieve the residuals chart data from a single source. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | source | path | string | true | The source of the data. | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [validation, crossValidation, holdout] | ### Example responses > 200 Response ``` { "properties": { "residuals": { "description": "Residuals chart data from all available sources", "properties": { "crossValidation": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "holdout": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "validation": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" } }, "type": "object" } }, "required": [ "residuals" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelResidualsList | ## Retrieve all available ROC curves by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/rocCurve/` Authentication requirements: `BearerAuth` Retrieve all available ROC curves for model. The response will include a json list of all available ROC curves, in the same format as the response from [GET /api/v2/projects/{projectId}/models/{modelId}/rocCurve/{source}/][get-apiv2projectsprojectidmodelsmodelidroccurvesource]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "charts": { "description": "List of ROC curve data from all available sources.", "items": { "properties": { "auc": { "description": "AUC value", "type": [ "number", "null" ] }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric value", "type": [ "number", "null" ] }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data.", "enum": [ "validation", "crossValidation", "holdout", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string", "x-enum-versionadded": [ { "value": "backtest_2", "x-versionadded": "v2.23" }, { "value": "backtest_3", "x-versionadded": "v2.23" }, { "value": "backtest_4", "x-versionadded": "v2.23" }, { "value": "backtest_5", "x-versionadded": "v2.23" }, { "value": "backtest_6", "x-versionadded": "v2.23" }, { "value": "backtest_7", "x-versionadded": "v2.23" }, { "value": "backtest_8", "x-versionadded": "v2.23" }, { "value": "backtest_9", "x-versionadded": "v2.23" }, { "value": "backtest_10", "x-versionadded": "v2.23" }, { "value": "backtest_11", "x-versionadded": "v2.23" }, { "value": "backtest_12", "x-versionadded": "v2.23" }, { "value": "backtest_13", "x-versionadded": "v2.23" }, { "value": "backtest_14", "x-versionadded": "v2.23" }, { "value": "backtest_15", "x-versionadded": "v2.23" }, { "value": "backtest_16", "x-versionadded": "v2.23" }, { "value": "backtest_17", "x-versionadded": "v2.23" }, { "value": "backtest_18", "x-versionadded": "v2.23" }, { "value": "backtest_19", "x-versionadded": "v2.23" }, { "value": "backtest_20", "x-versionadded": "v2.23" } ] } }, "required": [ "auc", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints", "source" ], "type": "object" }, "type": "array" } }, "required": [ "charts" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of all of the available ROC curves for a model. | ModelRocCurveListResponse | | 403 | Forbidden | Invalid Permissions | None | | 404 | Not Found | This resource does not exist. | None | | 422 | Unprocessable Entity | Unsupervised mode projects do not have ROC curves | None | ## Retrieve the ROC curve data by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/rocCurve/{source}/` Authentication requirements: `BearerAuth` Retrieve the ROC curve data from a single source. The response includes an array of pointsshowing the performance of the model at different thresholds for classification, and arrays of sample predictions for both the positive and negative classes. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID | | modelId | path | string | true | The model ID | | source | path | string | true | Source of the data | ### Enumerated Values | Parameter | Value | | --- | --- | | source | [validation, crossValidation, holdout, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ### Example responses > 200 Response ``` { "properties": { "auc": { "description": "AUC value", "type": [ "number", "null" ] }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric value", "type": [ "number", "null" ] }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data.", "enum": [ "validation", "crossValidation", "holdout", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string", "x-enum-versionadded": [ { "value": "backtest_2", "x-versionadded": "v2.23" }, { "value": "backtest_3", "x-versionadded": "v2.23" }, { "value": "backtest_4", "x-versionadded": "v2.23" }, { "value": "backtest_5", "x-versionadded": "v2.23" }, { "value": "backtest_6", "x-versionadded": "v2.23" }, { "value": "backtest_7", "x-versionadded": "v2.23" }, { "value": "backtest_8", "x-versionadded": "v2.23" }, { "value": "backtest_9", "x-versionadded": "v2.23" }, { "value": "backtest_10", "x-versionadded": "v2.23" }, { "value": "backtest_11", "x-versionadded": "v2.23" }, { "value": "backtest_12", "x-versionadded": "v2.23" }, { "value": "backtest_13", "x-versionadded": "v2.23" }, { "value": "backtest_14", "x-versionadded": "v2.23" }, { "value": "backtest_15", "x-versionadded": "v2.23" }, { "value": "backtest_16", "x-versionadded": "v2.23" }, { "value": "backtest_17", "x-versionadded": "v2.23" }, { "value": "backtest_18", "x-versionadded": "v2.23" }, { "value": "backtest_19", "x-versionadded": "v2.23" }, { "value": "backtest_20", "x-versionadded": "v2.23" } ] } }, "required": [ "auc", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints", "source" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | ROC curve data from a single source. | ModelRocCurveResponse | ## Retrieve Feature Impact for a model by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/shapImpact/` Authentication requirements: `BearerAuth` Retrieve Feature Impact for a model. SHAP impact is computed by calculating the shap values on a sample of training data and then taking the mean absolute value for each column. The larger the impact value, the more important the feature. DEPRECATED: Use the componentized route instead: [GET /api/v2/insights/shapImpact/models/{entityId}/][get-apiv2insightsshapimpactmodelsentityid]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of shapImpact objects returned.", "type": "integer" }, "rowCount": { "description": "The number of rows from dataset to use.", "type": "integer" }, "shapImpacts": { "description": "A list which contains shap impact scores for top 1000 features used by a model", "items": { "properties": { "featureName": { "description": "The feature name in the dataset.", "type": "string" }, "impactNormalized": { "description": "The normalized impact score value (largest value is 1)", "type": "number" }, "impactUnnormalized": { "description": "The raw impact score value", "type": "number" } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "count", "shapImpacts" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve Feature Impact for a model. | ShapImpactRetrieveResponse | | 400 | Bad Request | Request for multiclass project. | None | | 404 | Not Found | No Shapley-based impact values calculated for this model | None | ## Create SHAP-based Feature Impact by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/shapImpact/` Authentication requirements: `BearerAuth` Create SHAP-based Feature Impact for the model. DEPRECATED: Use the componentized route instead: [POST /api/v2/insights/shapImpact/][post-apiv2insightsshapimpact]. ### Body parameter ``` { "properties": { "rowCount": { "description": "The sample size to use for Feature Impact computation. It is possible to re-compute Feature Impact with a different row count.", "maximum": 100000, "minimum": 10, "type": "integer", "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | FeatureImpactCreatePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | none | None | ## Retrieve word cloud data by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/wordCloud/` Authentication requirements: `BearerAuth` Retrieve word cloud data for a model. Not all models will have word cloud data available, even when they use text features. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | excludeStopWords | query | string | false | Set to true if you want stopwords excluded from the response. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | excludeStopWords | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "ngrams": { "description": "A list of dictionaries containing information about the most important ngrams.", "items": { "properties": { "class": { "description": "For classification - values of the target class for corresponding word or ngram. For regression - null.", "type": [ "string", "null" ] }, "coefficient": { "description": "Describes effect of this ngram on the target. A large negative value means a strong effect toward the negative class in classification projects and a smaller predicted target value in regression projects. A large positive value means a strong effect toward the positive class and a larger predicted target value respectively.", "maximum": 1, "minimum": -1, "type": "number" }, "count": { "description": "Number of rows in the training sample where this ngram appears.", "type": "integer" }, "frequency": { "description": "Frequency of this ngram relative to the most common ngram.", "exclusiveMinimum": 0, "maximum": 1, "type": "number" }, "isStopword": { "description": "True for ngrams that DataRobot evaluates as stopwords.", "type": "boolean" }, "ngram": { "description": "Word or ngram value.", "type": "string" }, "variable": { "description": "String representation of the ngram source - contains column name and, for some models, preprocessing details. E.g. NGRAM_OCCUR_L2_colname will be for ngram occurrences count using L2 normalization from the colname column.", "type": "string", "x-versionadded": "v2.19" } }, "required": [ "coefficient", "count", "frequency", "isStopword", "ngram", "variable" ], "type": "object" }, "type": "array" } }, "required": [ "ngrams" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | WordCloudRetrieveResponse | ## Retrieve multicategorical data quality log by project ID Operation path: `GET /api/v2/projects/{projectId}/multicategoricalInvalidFormat/` Authentication requirements: `BearerAuth` Retrieve multicategorical data quality log. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The ID of the project this request is associated with. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "Error data.", "properties": { "errors": { "description": "Multicategorical format errors.", "items": { "properties": { "error": { "description": "Error type.", "type": "string" }, "feature": { "description": "Feature name.", "type": "string" }, "rowData": { "description": "Content of the row containing format error.", "type": [ "string", "null" ] }, "rowIndex": { "description": "Row index of the row containing format error.", "type": [ "integer", "null" ] } }, "required": [ "error", "feature", "rowData", "rowIndex" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "errors" ], "type": "object" }, "projectId": { "description": "The ID of the project this request is associated with.", "type": "string" } }, "required": [ "data", "projectId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The data quality log for multicategorical features. | MulticategoricalInvalidFormatResponse | | 422 | Unprocessable Entity | Not a data quality enabled project. | None | ## Get file by project ID Operation path: `GET /api/v2/projects/{projectId}/multicategoricalInvalidFormat/file/` Authentication requirements: `BearerAuth` Get file with format errors of potential multicategorical features. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The ID of the project this request is associated with. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | File with format errors of potential multicategorical features. | None | ## List all payoff matrices by project ID Operation path: `GET /api/v2/projects/{projectId}/payoffMatrices/` Authentication requirements: `BearerAuth` List all payoff matrices for a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of payoff matrices to skip. | | limit | query | integer | true | The number of payoff matrices to return. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items on the current page.", "type": "integer" }, "data": { "description": "The payoff matrices for a project.", "items": { "properties": { "falseNegativeValue": { "description": "Payoff value for false negatives used in profit curve calculation.", "type": "number" }, "falsePositiveValue": { "description": "Payoff value for false positives used in profit curve calculation.", "type": "number" }, "id": { "description": "The ID of the payoff matrix.", "type": "string" }, "name": { "description": "The label for the payoff matrix.", "type": "string" }, "projectId": { "description": "The ID of the project associated with the payoff matrix.", "type": "string" }, "trueNegativeValue": { "description": "Payoff value for true negatives used in profit curve calculation.", "type": "number" }, "truePositiveValue": { "description": "Payoff value for true positives used in profit curve calculation.", "type": "number" } }, "required": [ "falseNegativeValue", "falsePositiveValue", "id", "name", "projectId", "trueNegativeValue", "truePositiveValue" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page)", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of payoff matrices. | PayoffMatricesListResponse | ## Create a payoff matrix by project ID Operation path: `POST /api/v2/projects/{projectId}/payoffMatrices/` Authentication requirements: `BearerAuth` Create a payoff matrix associated with a project. ### Body parameter ``` { "properties": { "falseNegativeValue": { "description": "False negative value to use for profit curve calculation.", "type": "number" }, "falsePositiveValue": { "description": "False positive value to use for profit curve calculation.", "type": "number" }, "name": { "description": "The name of the payoff matrix to be created.", "type": "string" }, "trueNegativeValue": { "description": "True negative value to use for profit curve calculation.", "type": "number" }, "truePositiveValue": { "description": "True positive value to use for profit curve calculation.", "type": "number" } }, "required": [ "falseNegativeValue", "falsePositiveValue", "name", "trueNegativeValue", "truePositiveValue" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | PayoffMatricesCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | None | | 409 | Conflict | Conflict occurred: [Error details from exception] | None | | 422 | Unprocessable Entity | - This route is only allowed for binary classification projects. | | | - Error occurred during processing: [Error details from exception] | None | | | ## Delete a payoff matrix by project ID Operation path: `DELETE /api/v2/projects/{projectId}/payoffMatrices/{payoffMatrixId}/` Authentication requirements: `BearerAuth` Delete a payoff matrix in a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | payoffMatrixId | path | string | true | The ID of the payoff matrix. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Payoff matrix deleted successfully. | None | ## Update a payoff matrix by project ID Operation path: `PUT /api/v2/projects/{projectId}/payoffMatrices/{payoffMatrixId}/` Authentication requirements: `BearerAuth` Update all fields in a payoff matrix, including values and label. ### Body parameter ``` { "properties": { "falseNegativeValue": { "description": "False negative value to use for profit curve calculation.", "type": "number" }, "falsePositiveValue": { "description": "False positive value to use for profit curve calculation.", "type": "number" }, "name": { "description": "The name of the payoff matrix to be created.", "type": "string" }, "trueNegativeValue": { "description": "True negative value to use for profit curve calculation.", "type": "number" }, "truePositiveValue": { "description": "True positive value to use for profit curve calculation.", "type": "number" } }, "required": [ "falseNegativeValue", "falsePositiveValue", "name", "trueNegativeValue", "truePositiveValue" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | payoffMatrixId | path | string | true | The ID of the payoff matrix. | | body | body | PayoffMatricesCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "falseNegativeValue": { "description": "Payoff value for false negatives used in profit curve calculation.", "type": "number" }, "falsePositiveValue": { "description": "Payoff value for false positives used in profit curve calculation.", "type": "number" }, "id": { "description": "The ID of the payoff matrix.", "type": "string" }, "name": { "description": "The label for the payoff matrix.", "type": "string" }, "projectId": { "description": "The ID of the project associated with the payoff matrix.", "type": "string" }, "trueNegativeValue": { "description": "Payoff value for true negatives used in profit curve calculation.", "type": "number" }, "truePositiveValue": { "description": "Payoff value for true positives used in profit curve calculation.", "type": "number" } }, "required": [ "falseNegativeValue", "falsePositiveValue", "id", "name", "projectId", "trueNegativeValue", "truePositiveValue" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The updated payoff matrix values and label. | PayoffMatricesResponse | ## List rating table models by project ID Operation path: `GET /api/v2/projects/{projectId}/ratingTableModels/` Authentication requirements: `BearerAuth` Lists all the models from a project that have rating tables. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | withMetric | query | string | false | If specified, the returned models will only have scores for this metric. If not, all metrics will be included. | | showInSampleScores | query | boolean | false | If specified, will return metric scores for models trained into validation/holdout for projects that do not have stacked predictions. | | name | query | string | false | If specified, filters for models with a model type matching name. | | samplePct | query | number | false | If specified, filters for models with a matching sample percentage. | | isStarred | query | string | false | If specified, filters for models marked as starred. | | orderBy | query | string | false | A comma-separated list of metrics to sort by. If metric is prefixed with a '-', models are sorted by this metric in descending order, otherwise are sorted in ascending order. Valid sorting metrics are metric and samplePct. Use of metric sorts models by metric value selected for this project using the validation score. Use of the prefix accounts for the direction of the metric, so -metric will sort in order of decreasing 'goodness', which may be opposite to the natural numerical order. If not specified, -metric will be used. | | projectId | path | string | true | The project to list models from. | ### Enumerated Values | Parameter | Value | | --- | --- | | isStarred | [false, False, true, True] | | orderBy | [metric, -metric, samplePct, -samplePct] | ### Example responses > 200 Response ``` { "items": { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "ratingTableId": { "description": "The rating table ID", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "ratingTableId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will include a json list of models in the same format as | | | those from GET /api/v2/projects/(projectId)/ratingTableModels/(modelId)/. | Inline | | | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [RatingTableModelDetailsResponse] | false | | none | | » blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. | | » blueprintId | string | true | | The blueprint used to construct the model. | | » dataSelectionMethod | string | false | | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. | | » externalPredictionModel | boolean | true | | If the model is an external prediction model. | | » featurelistId | string,null | true | | The ID of the feature list used by the model. | | » featurelistName | string,null | true | | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. | | » frozenPct | number,null | true | | The training percent used to train the frozen model. | | » hasCodegen | boolean | true | | If the model has a codegen JAR file. | | » hasFinetuners | boolean | false | | Whether a model has fine tuners. | | » icons | integer,null | true | | The icons associated with the model. | | » id | string | true | | The ID of the model. | | » isAugmented | boolean | false | | Whether a model was trained using augmentation. | | » isBlender | boolean | true | | If the model is a blender. | | » isCustom | boolean | true | | If the model contains custom tasks. | | » isFrozen | boolean | true | | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. | | » isNClustersDynamicallyDetermined | boolean | false | | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. | | » isStarred | boolean | true | | Indicates whether the model has been starred. | | » isTrainedIntoHoldout | boolean | true | | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. | | » isTrainedIntoValidation | boolean | true | | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. | | » isTrainedOnGpu | boolean | true | | Whether the model was trained using GPU workers. | | » isTransparent | boolean | true | | If the model is a transparent model with exposed coefficients. | | » isUserModel | boolean | true | | If the model was created with Composable ML. | | » lifecycle | ModelLifecycle | true | | Object returning model lifecycle. | | »» reason | string,null | true | | The reason for the lifecycle stage. None if the model is active. | | »» stage | string | true | | The model lifecycle stage. | | » linkFunction | string,null | true | | The link function the final modeler uses in the blueprint. If no link function exists, returns null. | | » metrics | object | true | | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. | | » modelCategory | string | true | | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. | | » modelFamily | string | true | | The family the model belongs to, e.g., SVM, GBM, etc. | | » modelFamilyFullName | string | true | | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. | | » modelNumber | integer,null | true | | The model number from the Leaderboard. | | » modelType | string | true | | Identifies the model (e.g., Nystroem Kernel SVM Regressor). | | » monotonicDecreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | » monotonicIncreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | » nClusters | integer,null | false | | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | » parentModelId | string,null | true | | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. | | » predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. | | » predictionThresholdReadOnly | boolean | true | | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. | | » processes | [string] | true | maxItems: 100 | The list of processes used by the model. | | » projectId | string | true | | The ID of the project to which the model belongs. | | » ratingTableId | string | true | | The rating table ID | | » samplePct | number,null | true | | The percentage of the dataset used in training the model. | | » samplingMethod | string | false | | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. | | » supportsComposableMl | boolean | true | | indicates whether this model is supported in Composable ML. | | » supportsMonotonicConstraints | boolean | true | | whether this model supports enforcing monotonic constraints | | » timeWindowSamplePct | integer,null | false | | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. | | » trainingDuration | string,null | true | | the duration spanned by the dates in the partition column for the data used to train the model | | » trainingEndDate | string,null(date-time) | true | | the end date of the dates in the partition column for the data used to train the model | | » trainingRowCount | integer,null | true | | The number of rows used to train the model. | | » trainingStartDate | string,null(date-time) | true | | the start date of the dates in the partition column for the data used to train the model | ### Enumerated Values | Property | Value | | --- | --- | | dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] | | stage | [active, deprecated, disabled] | | modelCategory | [model, prime, blend, combined, incrementalLearning] | | samplingMethod | [random, latest] | ## Create new models by project ID Operation path: `POST /api/v2/projects/{projectId}/ratingTableModels/` Authentication requirements: `BearerAuth` Create a new rating table model from a validated rating table record. ### Body parameter ``` { "properties": { "ratingTableId": { "description": "The rating table ID to use to create a new model.", "type": "string" } }, "required": [ "ratingTableId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project that owns this data. | | body | body | CreateRatingTableModel | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | the request was understood and accepted, and is now being worked on | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | a url of an asynchronous operation status object that can be polled to check the status of the job validating the new rating table | ## Retrieve a rating table model by project ID Operation path: `GET /api/v2/projects/{projectId}/ratingTableModels/{modelId}/` Authentication requirements: `BearerAuth` Look up a particular rating table model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project to retrieve the model from. | | modelId | path | string | true | The model to retrieve. | ### Example responses > 200 Response ``` { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "ratingTableId": { "description": "The rating table ID", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "ratingTableId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RatingTableModelDetailsResponse | ## List rating tables by project ID Operation path: `GET /api/v2/projects/{projectId}/ratingTables/` Authentication requirements: `BearerAuth` List rating table objects for a project. These contain metadata about the rating table and the location at which the corresponding rating table file can be retrieved. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | parentModelId | query | string | false | Optional. If specified, only rating tables with this parentModelId will be returned. | | modelId | query | string | false | Optional. If specified, only rating tables with this modelId will be returned. | | offset | query | integer | false | Optional (default: 0). This many results will be skipped. | | limit | query | integer | false | Optional (default: no limit). At most this many results are returned. To specify no limit, use 0. The default may change and a maximum limit may be imposed without notice. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of rating table objects returned.", "type": "integer" }, "data": { "description": "The actual records. Each element of the array has the same schema\n as if retrieving the table individually from\n GET /api/v2/projects/(projectId)/ratingTables/(ratingTableId)/.", "items": { "properties": { "created": { "description": "ISO-8601 timestamp of when the rating table record was created.", "type": "number" }, "id": { "description": "The ID of the rating table record.", "type": "string" }, "modelId": { "description": "The model ID of a model that was created from the rating table.\n May be null if a model has not been created from the rating table.", "type": "string" }, "modelJobId": { "description": "The job ID to create a model from this rating table.\n Can be null if a model has not been created from the rating table.", "type": "integer" }, "originalFilename": { "description": "The filename of the uploaded rating table file.", "type": "string" }, "parentModelId": { "description": "The model ID of the model the rating table was modified from.", "type": "string" }, "projectId": { "description": "The project ID of the rating table record.", "type": "string" }, "ratingTableName": { "description": "The name of the rating table.", "type": "string" }, "validationError": { "description": "Rating table validation error messages. If the rating table\n was validated successfully, it will be an empty string.", "type": "string" }, "validationJobId": { "description": "The job ID of the created job to validate the rating table.\n Can be null if the rating table has not been validated.", "type": "string" }, "validationWarnings": { "description": "Rating table validation warning messages.", "type": "string" } }, "required": [ "created", "id", "modelId", "modelJobId", "originalFilename", "parentModelId", "projectId", "ratingTableName", "validationError", "validationJobId", "validationWarnings" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RatingTableListResponse | ## Upload a modified rating table file by project ID Operation path: `POST /api/v2/projects/{projectId}/ratingTables/` Authentication requirements: `BearerAuth` Create a new rating table from a rating table file This will create a new rating table, regardless of whether the validation succeeds. The rating table object will have a validationError which will be left blank in the case of successful validation. ### Body parameter ``` properties: parentModelId: description: The parent model this rating table file was derived from. type: string ratingTableFile: description: "The rating table file to use for the new rating table. Accepts `Content-Type: multipart/form-data`." format: binary type: string ratingTableName: description: The name of the new rating table to create. type: string required: - parentModelId - ratingTableFile - ratingTableName type: object ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project that owns this data. | | body | body | UploadRatingTable | false | none | ### Example responses > 202 Response ``` { "properties": { "ratingTableId": { "description": "The ID of the newly created rating table.", "type": "string" }, "ratingTableName": { "description": "The name that was used for the rating table. May differ from the ratingTableName in the request, as names are trimmed and a suffix added to ensure all rating tables derived from the same model have unique names", "type": "string" } }, "required": [ "ratingTableId", "ratingTableName" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | the request was understood and accepted, and is now being worked on | RatingTableCreateResponse | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | url | a url of an asynchronous operation status object that can be polled to check the status of the job validating the new rating table | ## Retrieve rating table information by project ID Operation path: `GET /api/v2/projects/{projectId}/ratingTables/{ratingTableId}/` Authentication requirements: `BearerAuth` Retrieves a rating table. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | the project that owns this data | | ratingTableId | path | string | true | The rating table ID to retrieve the source file from. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the rating table record was created.", "type": "number" }, "id": { "description": "The ID of the rating table record.", "type": "string" }, "modelId": { "description": "The model ID of a model that was created from the rating table.\n May be null if a model has not been created from the rating table.", "type": "string" }, "modelJobId": { "description": "The job ID to create a model from this rating table.\n Can be null if a model has not been created from the rating table.", "type": "integer" }, "originalFilename": { "description": "The filename of the uploaded rating table file.", "type": "string" }, "parentModelId": { "description": "The model ID of the model the rating table was modified from.", "type": "string" }, "projectId": { "description": "The project ID of the rating table record.", "type": "string" }, "ratingTableName": { "description": "The name of the rating table.", "type": "string" }, "validationError": { "description": "Rating table validation error messages. If the rating table\n was validated successfully, it will be an empty string.", "type": "string" }, "validationJobId": { "description": "The job ID of the created job to validate the rating table.\n Can be null if the rating table has not been validated.", "type": "string" }, "validationWarnings": { "description": "Rating table validation warning messages.", "type": "string" } }, "required": [ "created", "id", "modelId", "modelJobId", "originalFilename", "parentModelId", "projectId", "ratingTableName", "validationError", "validationJobId", "validationWarnings" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RatingTableRetrieveResponse | ## Update an uploaded rating table by project ID Operation path: `PATCH /api/v2/projects/{projectId}/ratingTables/{ratingTableId}/` Authentication requirements: `BearerAuth` Rating tables may only be updated if they have not yet been used to create a model. ### Body parameter ``` { "properties": { "ratingTableName": { "description": "The name of the new model.", "type": "string" } }, "required": [ "ratingTableName" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | the project that owns this data | | ratingTableId | path | string | true | The rating table ID to retrieve the source file from. | | body | body | RatingTableUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the rating table record was created.", "type": "number" }, "id": { "description": "The ID of the rating table record.", "type": "string" }, "modelId": { "description": "The model ID of a model that was created from the rating table.\n May be null if a model has not been created from the rating table.", "type": "string" }, "modelJobId": { "description": "The job ID to create a model from this rating table.\n Can be null if a model has not been created from the rating table.", "type": "integer" }, "originalFilename": { "description": "The filename of the uploaded rating table file.", "type": "string" }, "parentModelId": { "description": "The model ID of the model the rating table was modified from.", "type": "string" }, "projectId": { "description": "The project ID of the rating table record.", "type": "string" }, "ratingTableName": { "description": "The name of the rating table.", "type": "string" }, "validationError": { "description": "Rating table validation error messages. If the rating table\n was validated successfully, it will be an empty string.", "type": "string" }, "validationJobId": { "description": "The job ID of the created job to validate the rating table.\n Can be null if the rating table has not been validated.", "type": "string" }, "validationWarnings": { "description": "Rating table validation warning messages.", "type": "string" } }, "required": [ "created", "id", "modelId", "modelJobId", "originalFilename", "parentModelId", "projectId", "ratingTableName", "validationError", "validationJobId", "validationWarnings" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | On success, will return the modified rating table record in the same | | | format as GET /api/v2/projects/(projectId)/ratingTables/(ratingTableId)/ | RatingTableRetrieveResponse | | | ## Retrieve the rating table file by project ID Operation path: `GET /api/v2/projects/{projectId}/ratingTables/{ratingTableId}/file/` Authentication requirements: `BearerAuth` Retrieve the CSV file for the rating table. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | the project that owns this data | | ratingTableId | path | string | true | The rating table ID to retrieve the source file from. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The response will contain a file containing the rating table in CSV format. | None | ## List SHAP matrix records by project ID Operation path: `GET /api/v2/projects/{projectId}/shapMatrices/` Authentication requirements: `BearerAuth` Return a list of available SHAP matrix records. DEPRECATED: Use the componentized route instead: [GET /api/v2/insights/shapMatrix/models/{entityId}/][get-apiv2insightsshapmatrixmodelsentityid]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Array of SHAP matrix scores records.", "items": { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "id": { "description": "The ID of the SHAP matrix record.", "type": "string" }, "metadata": { "description": "The metadata containing SHAP matrix calculation details.", "properties": { "maxNormalizedMismatch": { "default": 0, "description": "The maximal relative normalized mismatch value.", "type": "number" }, "mismatchRowCount": { "default": 0, "description": "The count of rows for which additivity check failed.", "type": "integer" } }, "type": "object" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" }, "url": { "description": "The URL at which you can retrieve the SHAP matrix.", "format": "uri", "type": "string" } }, "required": [ "datasetId", "id", "metadata", "modelId", "projectId", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ShapMatrixListResponse | ## Calculate a matrix with SHAP-based prediction explanations scores by project ID Operation path: `POST /api/v2/projects/{projectId}/shapMatrices/` Authentication requirements: `BearerAuth` Submit a request to calculate a matrix with SHAP based prediction explanations scores. DEPRECATED: Use the componentized route instead: [POST /api/v2/insights/shapMatrix/][post-apiv2insightsshapmatrix]. ### Body parameter ``` { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "modelId": { "description": "The model ID.", "type": "string" } }, "required": [ "datasetId", "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | CreateShapMatrixPayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See the Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Return matrix with SHAP-based prediction explanations scores by project ID Operation path: `GET /api/v2/projects/{projectId}/shapMatrices/{shapMatrixId}/` Authentication requirements: `BearerAuth` Return matrix with SHAP-based prediction explanations scores. DEPRECATED: Use the componentized route instead: [GET /api/v2/insights/shapMatrix/models/{entityId}/][get-apiv2insightsshapmatrixmodelsentityid]. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | shapMatrixId | path | string | true | The SHAP matrix ID. | ### Example responses > 200 Response ``` { "properties": { "columnNames": { "description": "The column names for corresponding dataset & their SHAP values.", "items": { "type": "string" }, "type": "array" } }, "required": [ "columnNames" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | string | ## Delete an existing PredictionExplanationsInitialization by project ID Operation path: `DELETE /api/v2/projects/{projectId}/models/{modelId}/predictionExplanationsInitialization/` Authentication requirements: `BearerAuth` Delete an existing PredictionExplanationsInitialization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The deletion was successful. | None | ## Retrieve the current PredictionExplanationsInitialization by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/predictionExplanationsInitialization/` Authentication requirements: `BearerAuth` Retrieve the current PredictionExplanationsInitialization. A PredictionExplanationsInitialization is a pre-requisite for successfully computing prediction explanations using a particular model, and can be used to preview the prediction explanations that would be generated for a complete dataset. ### Body parameter ``` { "properties": { "modelId": { "description": "The model ID.", "type": "string" }, "predictionExplanationsSample": { "description": "Each is a PredictionExplanationsRow. They represent a small sample of prediction explanations that could be generated for a particular dataset. They will have the same schema as the `data` array in the response from [GET /api/v2/projects/{projectId}/predictionExplanations/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationspredictionexplanationsid]. As of v2.21 only difference is that there is no forecastPoint in response for time series projects.", "items": { "properties": { "adjustedPrediction": { "description": "The exposure-adjusted output of the model for this row.", "type": "number", "x-versionadded": "v2.8" }, "adjustedPredictionValues": { "description": "The exposure-adjusted output of the model for this row.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.8" }, "forecastDistance": { "description": "Forecast distance for the row. For time series projects only.", "type": "integer", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "Forecast point for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" }, "prediction": { "description": "The output of the model for this row.", "type": "number" }, "predictionExplanations": { "description": "The list of prediction explanations.", "items": { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "The threshold value used for classification prediction.", "type": [ "number", "null" ] }, "predictionValues": { "description": "The list of prediction values.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row this PredictionExplanationsRow describes.", "type": "integer" }, "seriesId": { "description": "The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "timestamp": { "description": "The timestamp for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "adjustedPrediction", "adjustedPredictionValues", "forecastDistance", "forecastPoint", "prediction", "predictionExplanations", "predictionThreshold", "predictionValues", "rowId", "seriesId", "timestamp" ], "type": "object" }, "type": "array" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "modelId", "predictionExplanationsSample", "projectId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | excludeAdjustedPredictions | query | string | false | Whether to include adjusted predictions in the PredictionExplanationsSample response. | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | PredictionExplanationsInitializationRetrieve | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | excludeAdjustedPredictions | [false, False, true, True] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Create a new prediction explanations initialization by project ID Operation path: `POST /api/v2/projects/{projectId}/models/{modelId}/predictionExplanationsInitialization/` Authentication requirements: `BearerAuth` Create a new prediction explanations initialization. This is a necessary prerequisite for generating prediction explanations. ### Body parameter ``` { "properties": { "maxExplanations": { "default": 3, "description": "The maximum number of prediction explanations to supply per row of the dataset.", "maximum": 10, "minimum": 1, "type": "integer" }, "thresholdHigh": { "default": null, "description": "The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] }, "thresholdLow": { "default": null, "description": "The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | | body | body | PredictionExplanationsInitializationCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was accepted and will be worked on. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Create a new PredictionExplanations object ( by project ID Operation path: `POST /api/v2/projects/{projectId}/predictionExplanations/` Authentication requirements: `BearerAuth` Create a new PredictionExplanations object (and its accompanying PredictionExplanationsRecord). In order to successfully create PredictionExplanations for a particular model and dataset, you must first - Compute feature impact for the model via [POST /api/v2/projects/{projectId}/models/{modelId}/featureImpact/][post-apiv2projectsprojectidmodelsmodelidfeatureimpact] - Compute a PredictionExplanationsInitialization for the model via [POST /api/v2/projects/{projectId}/models/{modelId}/predictionExplanationsInitialization/][post-apiv2projectsprojectidmodelsmodelidpredictionexplanationsinitialization] - Compute predictions for the model and dataset via [POST /api/v2/projects/{projectId}/predictions/][post-apiv2projectsprojectidpredictions]`thresholdHigh` and `thresholdLow` are optional filters applied to speed up computation. When at least one is specified, only the selected outlier rows will have prediction explanations computed. Rows are considered to be outliers if their predicted value (in case of regression projects) or probability of being the positive class (in case of classification projects) isless than `thresholdLow` or greater than `thresholdHigh`. If neither is specified, prediction explanations will be computed for all rows. ### Body parameter ``` { "properties": { "classNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.29" }, "datasetId": { "description": "The dataset ID.", "type": "string" }, "maxExplanations": { "default": 3, "description": "The maximum number of prediction explanations to supply per row of the dataset.", "maximum": 10, "minimum": 0, "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "thresholdHigh": { "default": null, "description": "The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] }, "thresholdLow": { "default": null, "description": "The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | PredictionExplanationsCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was accepted and will be worked on. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve stored Prediction Explanations by project ID Operation path: `GET /api/v2/projects/{projectId}/predictionExplanations/{predictionExplanationsId}/` Authentication requirements: `BearerAuth` Retrieve stored Prediction Explanations. Each PredictionExplanationsRow retrieved corresponds to a row of the prediction dataset, although some rows may not have had prediction explanations computed depending on the thresholds selected. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. The default may change and a new maximum limit may be imposed without notice. | | excludeAdjustedPredictions | query | string | false | Whether to include adjusted predictions in the PredictionExplanationsRow response. | | projectId | path | string | true | The project ID. | | predictionExplanationsId | path | string | true | The ID of the PredictionExplanationsRecord to retrieve. | ### Enumerated Values | Parameter | Value | | --- | --- | | excludeAdjustedPredictions | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "adjustmentMethod": { "description": "'exposureNormalized' (for regression projects with exposure) or 'N/A' (for classification projects) The value of 'exposureNormalized' indicates that prediction outputs are adjusted (or divided) by exposure. The value of 'N/A' indicates that no adjustments are applied to the adjusted predictions and they are identical to the unadjusted predictions.", "type": "string", "x-versionadded": "v2.8" }, "count": { "description": "The number of rows of prediction explanations returned.", "type": "integer" }, "data": { "description": "Each is a PredictionExplanationsRow corresponding to one row of the prediction dataset.", "items": { "properties": { "adjustedPrediction": { "description": "The exposure-adjusted output of the model for this row.", "type": "number", "x-versionadded": "v2.8" }, "adjustedPredictionValues": { "description": "The exposure-adjusted output of the model for this row.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.8" }, "forecastDistance": { "description": "Forecast distance for the row. For time series projects only.", "type": "integer", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "Forecast point for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" }, "prediction": { "description": "The output of the model for this row.", "type": "number" }, "predictionExplanations": { "description": "The list of prediction explanations.", "items": { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "The threshold value used for classification prediction.", "type": [ "number", "null" ] }, "predictionValues": { "description": "The list of prediction values.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row this PredictionExplanationsRow describes.", "type": "integer" }, "seriesId": { "description": "The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "timestamp": { "description": "The timestamp for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "adjustedPrediction", "adjustedPredictionValues", "forecastDistance", "forecastPoint", "prediction", "predictionExplanations", "predictionThreshold", "predictionValues", "rowId", "seriesId", "timestamp" ], "type": "object" }, "type": "array" }, "id": { "description": "The ID of this group of prediction explanations.", "type": "string" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "predictionExplanationsRecordLocation": { "description": "The URL of the PredictionExplanationsRecord associated with these prediction explanations.", "type": "string" }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "adjustmentMethod", "count", "data", "id", "next", "predictionExplanationsRecordLocation", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | PredictionExplanationsRetrieve | ## List PredictionExplanationsRecord objects by project ID Operation path: `GET /api/v2/projects/{projectId}/predictionExplanationsRecords/` Authentication requirements: `BearerAuth` List PredictionExplanationsRecord objects for a project. These contain metadata about the computed prediction explanations and the location at which the PredictionExplanations can be retrieved. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | modelId | query | string | false | If specified, only prediction explanations records computed for this model will be returned. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the prediction explanations individually from [GET /api/v2/projects/{projectId}/predictionExplanationsRecords/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationsrecordspredictionexplanationsid].", "items": { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "finishTime": { "description": "The timestamp referencing when computation for these prediction explanations finished.", "type": "number" }, "id": { "description": "The PredictionExplanationsRecord ID.", "type": "string" }, "maxExplanations": { "description": "The maximum number of codes generated per prediction.", "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numColumns": { "description": "The number of columns prediction explanations were computed for.", "type": "integer" }, "predictionExplanationsLocation": { "description": "Where to retrieve the prediction explanations.", "type": "string" }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "thresholdHigh": { "description": "The prediction explanation high threshold. Predictions must be above this value (or below the thresholdLow value) to have PredictionExplanations computed.", "type": [ "number", "null" ] }, "thresholdLow": { "description": "The prediction explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) to have PredictionExplanations computed.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "finishTime", "id", "maxExplanations", "modelId", "numColumns", "predictionExplanationsLocation", "predictionThreshold", "projectId", "thresholdHigh", "thresholdLow" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The object was found and returned successfully. | PredictionExplanationsRecordList | ## Delete saved Prediction Explanations by project ID Operation path: `DELETE /api/v2/projects/{projectId}/predictionExplanationsRecords/{predictionExplanationsId}/` Authentication requirements: `BearerAuth` Delete saved Prediction Explanations. Deletes both the actual prediction explanations and the corresponding PredictionExplanationsRecord. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | predictionExplanationsId | path | string | true | The ID of the PredictionExplanationsRecord to retrieve. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The object was deleted successfully. | None | ## Retrieve a PredictionExplanationsRecord object by project ID Operation path: `GET /api/v2/projects/{projectId}/predictionExplanationsRecords/{predictionExplanationsId}/` Authentication requirements: `BearerAuth` Retrieve a PredictionExplanationsRecord object. A PredictionExplanationsRecord contains metadata about the computed prediction explanations and the location at which the PredictionExplanations can be retrieved. ### Body parameter ``` { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "finishTime": { "description": "The timestamp referencing when computation for these prediction explanations finished.", "type": "number" }, "id": { "description": "The PredictionExplanationsRecord ID.", "type": "string" }, "maxExplanations": { "description": "The maximum number of codes generated per prediction.", "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numColumns": { "description": "The number of columns prediction explanations were computed for.", "type": "integer" }, "predictionExplanationsLocation": { "description": "Where to retrieve the prediction explanations.", "type": "string" }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "thresholdHigh": { "description": "The prediction explanation high threshold. Predictions must be above this value (or below the thresholdLow value) to have PredictionExplanations computed.", "type": [ "number", "null" ] }, "thresholdLow": { "description": "The prediction explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) to have PredictionExplanations computed.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "finishTime", "id", "maxExplanations", "modelId", "numColumns", "predictionExplanationsLocation", "predictionThreshold", "projectId", "thresholdHigh", "thresholdLow" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | predictionExplanationsId | path | string | true | The ID of the PredictionExplanationsRecord to retrieve. | | body | body | PredictionExplanationsRecord | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The object was found and returned successfully. | None | # Schemas ## AOTChartBins ``` { "properties": { "actual": { "description": "The average actual value of the target in the bin. ``null`` if there are no entries in the bin or if this is an anomaly detection project.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive).", "format": "date-time", "type": "string" }, "frequency": { "description": "As indicated by the frequencyType in the Metadata, used to determine what the averages mentioned above are taken over. ``null`` if there are no entries in the bin.", "type": [ "number", "null" ] }, "predicted": { "description": "The average prediction of the model in the bin. ``null`` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive).", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "frequency", "predicted", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actual | number,null | true | | The average actual value of the target in the bin. null if there are no entries in the bin or if this is an anomaly detection project. | | endDate | string(date-time) | true | | The datetime of the end of the bin (exclusive). | | frequency | number,null | true | | As indicated by the frequencyType in the Metadata, used to determine what the averages mentioned above are taken over. null if there are no entries in the bin. | | predicted | number,null | true | | The average prediction of the model in the bin. null if there are no entries in the bin. | | startDate | string(date-time) | true | | The datetime of the start of the bin (inclusive). | ## AOTChartMetadataDatasetMetadata ``` { "description": "The dataset metadata.", "properties": { "endDate": { "description": "ISO-8601 formatted end date (max date) in the dataset.", "format": "date-time", "type": "string" }, "startDate": { "description": "ISO-8601 formatted start date (min date) in the dataset.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "startDate" ], "type": "object" } ``` The dataset metadata. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string(date-time) | true | | ISO-8601 formatted end date (max date) in the dataset. | | startDate | string(date-time) | true | | ISO-8601 formatted start date (min date) in the dataset. | ## AOTChartMetadataResponse ``` { "properties": { "datasetId": { "description": "The dataset ID that was used to compute the AOT chart.", "type": "string" }, "datasetMetadata": { "description": "The dataset metadata.", "properties": { "endDate": { "description": "ISO-8601 formatted end date (max date) in the dataset.", "format": "date-time", "type": "string" }, "startDate": { "description": "ISO-8601 formatted start date (min date) in the dataset.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "startDate" ], "type": "object" }, "frequencyType": { "description": "How to interpret the frequency attribute of each datetimeTrendBin. One of ['rowCount', 'weightedRowCount', 'exposure', 'weightedExposure'].", "enum": [ "rowCount", "weightedRowCount", "exposure", "weightedExposure" ], "type": "string" }, "metricName": { "description": "The metric used to score each bin and the calculate the metric attribute of each datetimeTrendBin.", "type": "string" }, "modelId": { "description": "The model ID that was used to compute the AOT chart.", "type": "string" }, "projectId": { "description": "The project ID that was used to compute the AOT chart.", "type": "string" }, "resolutions": { "description": "Suggested time resolutions where a resolution is one of ['milliseconds', 'seconds', 'minutes', 'hours', 'days', 'weeks', 'months', 'years'].", "items": { "enum": [ "microseconds", "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "type": "array" } }, "required": [ "datasetId", "datasetMetadata", "frequencyType", "metricName", "modelId", "projectId", "resolutions" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The dataset ID that was used to compute the AOT chart. | | datasetMetadata | AOTChartMetadataDatasetMetadata | true | | The dataset metadata. | | frequencyType | string | true | | How to interpret the frequency attribute of each datetimeTrendBin. One of ['rowCount', 'weightedRowCount', 'exposure', 'weightedExposure']. | | metricName | string | true | | The metric used to score each bin and the calculate the metric attribute of each datetimeTrendBin. | | modelId | string | true | | The model ID that was used to compute the AOT chart. | | projectId | string | true | | The project ID that was used to compute the AOT chart. | | resolutions | [string] | true | | Suggested time resolutions where a resolution is one of ['milliseconds', 'seconds', 'minutes', 'hours', 'days', 'weeks', 'months', 'years']. | ### Enumerated Values | Property | Value | | --- | --- | | frequencyType | [rowCount, weightedRowCount, exposure, weightedExposure] | ## AOTChartPreviewResponse ``` { "properties": { "bins": { "description": "The datetime chart data for that source.", "items": { "properties": { "actual": { "description": "The average actual value of the target in the bin. ``null`` if there are no entries in the bin or if this is an anomaly detection project.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive).", "format": "date-time", "type": "string" }, "frequency": { "description": "As indicated by the frequencyType in the Metadata, used to determine what the averages mentioned above are taken over. ``null`` if there are no entries in the bin.", "type": [ "number", "null" ] }, "predicted": { "description": "The average prediction of the model in the bin. ``null`` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive).", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "frequency", "predicted", "startDate" ], "type": "object" }, "type": "array" }, "datasetId": { "description": "The dataset ID that was used to compute the AOT chart.", "type": "string" }, "modelId": { "description": "The model ID that was used to compute the AOT chart.", "type": "string" }, "projectId": { "description": "The project ID that was used to compute the AOT chart.", "type": "string" } }, "required": [ "bins", "datasetId", "modelId", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [AOTChartBins] | true | | The datetime chart data for that source. | | datasetId | string | true | | The dataset ID that was used to compute the AOT chart. | | modelId | string | true | | The model ID that was used to compute the AOT chart. | | projectId | string | true | | The project ID that was used to compute the AOT chart. | ## AOTChartRetrieveResponse ``` { "properties": { "bins": { "description": "The datetime chart data for that source.", "items": { "properties": { "actual": { "description": "The average actual value of the target in the bin. ``null`` if there are no entries in the bin or if this is an anomaly detection project.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive).", "format": "date-time", "type": "string" }, "frequency": { "description": "As indicated by the frequencyType in the Metadata, used to determine what the averages mentioned above are taken over. ``null`` if there are no entries in the bin.", "type": [ "number", "null" ] }, "predicted": { "description": "The average prediction of the model in the bin. ``null`` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive).", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "frequency", "predicted", "startDate" ], "type": "object" }, "type": "array" }, "datasetId": { "description": "The dataset ID to which the chart data belongs.", "type": "string" }, "endDate": { "description": "The requested `endDate`, or, if not specified, the end date for this dataset (exclusive). Example: '2010-05-13T00:00:00.000000Z'.", "format": "date-time", "type": "string" }, "modelId": { "description": "The model ID to which the chart data belongs.", "type": "string" }, "projectId": { "description": "The project ID to which the chart data belongs.", "type": "string" }, "resolution": { "description": "The resolution used for binning where a resolution is one of ['milliseconds', 'seconds', 'minutes', 'hours', 'days', 'weeks', 'months', 'years'].", "enum": [ "microseconds", "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "startDate": { "description": "The requested `startDate`, or, if not specified, the start date for this dataset. Example: '2010-05-13T00:00:00.000000Z'.", "format": "date-time", "type": "string" }, "statistics": { "description": "Statistics calculated on the chart data.", "properties": { "durbinWatson": { "description": "The Durbin-Watson statistic for the chart data. Value is between 0 and 4. Returns -1 when the statistic is invalid for the data, e.g. if this is an anomaly detection project.", "type": "number" } }, "required": [ "durbinWatson" ], "type": "object" } }, "required": [ "bins", "datasetId", "modelId", "projectId", "resolution", "statistics" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [AOTChartBins] | true | | The datetime chart data for that source. | | datasetId | string | true | | The dataset ID to which the chart data belongs. | | endDate | string(date-time) | false | | The requested endDate, or, if not specified, the end date for this dataset (exclusive). Example: '2010-05-13T00:00:00.000000Z'. | | modelId | string | true | | The model ID to which the chart data belongs. | | projectId | string | true | | The project ID to which the chart data belongs. | | resolution | string | true | | The resolution used for binning where a resolution is one of ['milliseconds', 'seconds', 'minutes', 'hours', 'days', 'weeks', 'months', 'years']. | | startDate | string(date-time) | false | | The requested startDate, or, if not specified, the start date for this dataset. Example: '2010-05-13T00:00:00.000000Z'. | | statistics | AOTChartStatistics | true | | Statistics calculated on the chart data. | ### Enumerated Values | Property | Value | | --- | --- | | resolution | [microseconds, milliseconds, seconds, minutes, hours, days, weeks, months, quarters, years] | ## AOTChartStatistics ``` { "description": "Statistics calculated on the chart data.", "properties": { "durbinWatson": { "description": "The Durbin-Watson statistic for the chart data. Value is between 0 and 4. Returns -1 when the statistic is invalid for the data, e.g. if this is an anomaly detection project.", "type": "number" } }, "required": [ "durbinWatson" ], "type": "object" } ``` Statistics calculated on the chart data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | durbinWatson | number | true | | The Durbin-Watson statistic for the chart data. Value is between 0 and 4. Returns -1 when the statistic is invalid for the data, e.g. if this is an anomaly detection project. | ## AccuracyMetrics ``` { "properties": { "metric": { "description": "The name of the metric.", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE", "f1", "accuracy" ], "type": "string" }, "value": { "description": "The calculated score of the metric.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "metric", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metric | string | true | | The name of the metric. | | value | number | true | maximum: 1minimum: 0 | The calculated score of the metric. | ### Enumerated Values | Property | Value | | --- | --- | | metric | [AUC, Weighted AUC, Area Under PR Curve, Weighted Area Under PR Curve, Kolmogorov-Smirnov, Weighted Kolmogorov-Smirnov, FVE Binomial, Weighted FVE Binomial, Gini Norm, Weighted Gini Norm, LogLoss, Weighted LogLoss, Max MCC, Weighted Max MCC, Rate@Top5%, Weighted Rate@Top5%, Rate@Top10%, Weighted Rate@Top10%, Rate@TopTenth%, RMSE, Weighted RMSE, f1, accuracy] | ## AccuracyOverTimePlotsBins ``` { "properties": { "actual": { "description": "Average actual value of the target in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "frequency": { "description": "Indicates number of values averaged in bin in case of a resolution change.", "type": [ "integer", "null" ] }, "predicted": { "description": "Average prediction of the model in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "frequency", "predicted", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actual | number,null | true | | Average actual value of the target in the bin. null if there are no entries in the bin. | | endDate | string(date-time) | true | | The datetime of the end of the bin (exclusive). | | frequency | integer,null | true | | Indicates number of values averaged in bin in case of a resolution change. | | predicted | number,null | true | | Average prediction of the model in the bin. null if there are no entries in the bin. | | startDate | string(date-time) | true | | The datetime of the start of the bin (inclusive). | ## AccuracyOverTimePlotsDataResponse ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "actual": { "description": "Average actual value of the target in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "frequency": { "description": "Indicates number of values averaged in bin in case of a resolution change.", "type": [ "integer", "null" ] }, "predicted": { "description": "Average prediction of the model in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "frequency", "predicted", "startDate" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "calendarEvents": { "description": "An array of calendar events for a retrieved plot.", "items": { "properties": { "date": { "description": "The date of the calendar event.", "format": "date-time", "type": "string" }, "name": { "description": "Name of the calendar event.", "type": "string" }, "seriesId": { "description": "The series ID for the event. If this event does not specify a series ID, then this will be `null`, indicating that the event applies to all series.", "type": [ "string", "null" ] } }, "required": [ "date", "name", "seriesId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "resolution": { "description": "The resolution that is used for binning.", "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" }, "statistics": { "description": "Statistics calculated for the chart data.", "properties": { "durbinWatson": { "description": "The Durbin-Watson statistic for the chart data. Value is between 0 and 4. Durbin-Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals (prediction errors) from a regression analysis. More info https://wikipedia.org/wiki/Durbin%E2%80%93Watson_statistic", "maximum": 4, "minimum": 0, "type": [ "number", "null" ] } }, "required": [ "durbinWatson" ], "type": "object" } }, "required": [ "bins", "calendarEvents", "endDate", "resolution", "startDate", "statistics" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [AccuracyOverTimePlotsBins] | true | maxItems: 1000minItems: 1 | An array of bins for the retrieved plots. | | calendarEvents | [CalendarEvent] | true | maxItems: 1000 | An array of calendar events for a retrieved plot. | | endDate | string(date-time) | true | | The datetime of the end of the chart data (exclusive). | | resolution | string | true | | The resolution that is used for binning. | | startDate | string(date-time) | true | | The datetime of the start of the chart data (inclusive). | | statistics | AccuracyOverTimePlotsStatistics | true | | Statistics calculated for the chart data. | ### Enumerated Values | Property | Value | | --- | --- | | resolution | [milliseconds, seconds, minutes, hours, days, weeks, months, quarters, years] | ## AccuracyOverTimePlotsMetadataResponse ``` { "properties": { "backtestMetadata": { "description": "An array of metadata information for each backtest. The array index of metadata object is the backtest index.", "items": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "backtestStatuses": { "description": "An array of status information for each backtest. The array index of status object is the backtest index.", "items": { "description": "Status for accuracy over time plots.", "properties": { "training": { "description": "The status for the training.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" }, "validation": { "description": "The status for the validation.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "estimatedSeriesLimit": { "description": "Estimated number of series that can be calculated in one request for 1 FD.", "minimum": 1, "type": "integer" }, "forecastDistance": { "description": "The forecast distance for which the data was retrieved. `null` for OTV projects.", "maximum": 1000, "minimum": 0, "type": [ "integer", "null" ] }, "holdoutMetadata": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "holdoutStatuses": { "description": "Status for accuracy over time plots.", "properties": { "training": { "description": "The status for the training.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" }, "validation": { "description": "The status for the validation.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" } }, "required": [ "training", "validation" ], "type": "object" }, "resolutions": { "description": "An array of available time resolutions for which plots can be retrieved.", "items": { "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "maxItems": 9, "minItems": 1, "type": "array" } }, "required": [ "backtestMetadata", "backtestStatuses", "forecastDistance", "holdoutMetadata", "holdoutStatuses", "resolutions" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestMetadata | [DatetimeTrendPlotsBacktestMetadata] | true | maxItems: 20minItems: 1 | An array of metadata information for each backtest. The array index of metadata object is the backtest index. | | backtestStatuses | [AccuracyOverTimePlotsStatus] | true | maxItems: 20minItems: 1 | An array of status information for each backtest. The array index of status object is the backtest index. | | estimatedSeriesLimit | integer | false | minimum: 1 | Estimated number of series that can be calculated in one request for 1 FD. | | forecastDistance | integer,null | true | maximum: 1000minimum: 0 | The forecast distance for which the data was retrieved. null for OTV projects. | | holdoutMetadata | DatetimeTrendPlotsBacktestMetadata | true | | Metadata for backtest/holdout. | | holdoutStatuses | AccuracyOverTimePlotsStatus | true | | Status for accuracy over time plots. | | resolutions | [string] | true | maxItems: 9minItems: 1 | An array of available time resolutions for which plots can be retrieved. | ## AccuracyOverTimePlotsStatistics ``` { "description": "Statistics calculated for the chart data.", "properties": { "durbinWatson": { "description": "The Durbin-Watson statistic for the chart data. Value is between 0 and 4. Durbin-Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals (prediction errors) from a regression analysis. More info https://wikipedia.org/wiki/Durbin%E2%80%93Watson_statistic", "maximum": 4, "minimum": 0, "type": [ "number", "null" ] } }, "required": [ "durbinWatson" ], "type": "object" } ``` Statistics calculated for the chart data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | durbinWatson | number,null | true | maximum: 4minimum: 0 | The Durbin-Watson statistic for the chart data. Value is between 0 and 4. Durbin-Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 in the residuals (prediction errors) from a regression analysis. More info https://wikipedia.org/wiki/Durbin%E2%80%93Watson_statistic | ## AccuracyOverTimePlotsStatus ``` { "description": "Status for accuracy over time plots.", "properties": { "training": { "description": "The status for the training.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" }, "validation": { "description": "The status for the validation.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" } }, "required": [ "training", "validation" ], "type": "object" } ``` Status for accuracy over time plots. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | training | string | true | | The status for the training. | | validation | string | true | | The status for the validation. | ### Enumerated Values | Property | Value | | --- | --- | | training | [completed, errored, inProgress, insufficientData, notCompleted, notSupported] | | validation | [completed, errored, inProgress, insufficientData, notCompleted, notSupported] | ## ActivationMap ``` { "properties": { "activationValues": { "description": "A 2D matrix of values (row-major) representing the activation strengths for particular image regions.", "items": { "items": { "maximum": 255, "minimum": 0, "type": "integer" }, "type": "array" }, "type": "array" }, "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "featureName": { "description": "The name of the column containing the image value the activation map is based upon.", "type": "string" }, "imageHeight": { "description": "The height of the original image (in pixels) this activation map has been computed for.", "type": "integer" }, "imageId": { "description": "ID of the original image this activation map has been computed for.", "type": "string" }, "imageWidth": { "description": "The width of the original image (in pixels) this activation map has been computed for.", "type": "integer" }, "links": { "description": "Download URLs.", "properties": { "downloadOriginalImage": { "description": "URL of the original image", "format": "uri", "type": "string" }, "downloadOverlayImage": { "description": "URL of the original image overlaid by the activation heatmap", "format": "uri", "type": "string" } }, "required": [ "downloadOriginalImage", "downloadOverlayImage" ], "type": "object" }, "overlayImageId": { "description": "ID of the image containing the original image overlaid by the activation heatmap.", "type": "string" }, "predictedTargetValue": { "description": "predicted target value of the dataset row containing this image.", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" } ] } }, "required": [ "activationValues", "actualTargetValue", "featureName", "imageHeight", "imageId", "imageWidth", "links", "overlayImageId", "predictedTargetValue" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | activationValues | [array] | true | | A 2D matrix of values (row-major) representing the activation strengths for particular image regions. | | actualTargetValue | any | true | | Actual target value of the dataset row | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | true | | The name of the column containing the image value the activation map is based upon. | | imageHeight | integer | true | | The height of the original image (in pixels) this activation map has been computed for. | | imageId | string | true | | ID of the original image this activation map has been computed for. | | imageWidth | integer | true | | The width of the original image (in pixels) this activation map has been computed for. | | links | ActivationMapLinks | true | | Download URLs. | | overlayImageId | string | true | | ID of the image containing the original image overlaid by the activation heatmap. | | predictedTargetValue | any | true | | predicted target value of the dataset row containing this image. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## ActivationMapLinks ``` { "description": "Download URLs.", "properties": { "downloadOriginalImage": { "description": "URL of the original image", "format": "uri", "type": "string" }, "downloadOverlayImage": { "description": "URL of the original image overlaid by the activation heatmap", "format": "uri", "type": "string" } }, "required": [ "downloadOriginalImage", "downloadOverlayImage" ], "type": "object" } ``` Download URLs. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | downloadOriginalImage | string(uri) | true | | URL of the original image | | downloadOverlayImage | string(uri) | true | | URL of the original image overlaid by the activation heatmap | ## ActivationMapsComputeResponse ``` { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if the job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "jobType": { "description": "The type of the job.", "enum": [ "compute_image_activation_maps" ], "type": "string" }, "message": { "description": "Error message in case of failure.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The job status.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" }, "url": { "description": "A URL that can be used to request details about the job.", "type": "string" } }, "required": [ "id", "isBlocked", "jobType", "message", "modelId", "projectId", "status", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The job ID. | | isBlocked | boolean | true | | True if the job is waiting for its dependencies to be resolved first. | | jobType | string | true | | The type of the job. | | message | string | true | | Error message in case of failure. | | modelId | string | true | | The model ID of the target model. | | projectId | string | true | | The project the job belongs to. | | status | string | true | | The job status. | | url | string | true | | A URL that can be used to request details about the job. | ### Enumerated Values | Property | Value | | --- | --- | | jobType | compute_image_activation_maps | | status | [queue, inprogress, error, ABORTED, COMPLETED] | ## ActivationMapsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of Image Activation Maps", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" } }, "required": [ "featureName", "modelId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ImageInsightsMetadataElement] | true | | List of Image Activation Maps | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ActivationMapsRetrieveResponse ``` { "properties": { "activationMapHeight": { "description": "The height of each activation map (the number of rows in each activationValues matrix).", "type": [ "integer", "null" ] }, "activationMapWidth": { "description": "The width of each activation map (the number of items in each row of each activationValues matrix).", "type": [ "integer", "null" ] }, "activationMaps": { "description": "List of activation map objects", "items": { "properties": { "activationValues": { "description": "A 2D matrix of values (row-major) representing the activation strengths for particular image regions.", "items": { "items": { "maximum": 255, "minimum": 0, "type": "integer" }, "type": "array" }, "type": "array" }, "actualTargetValue": { "description": "Actual target value of the dataset row", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ] }, "featureName": { "description": "The name of the column containing the image value the activation map is based upon.", "type": "string" }, "imageHeight": { "description": "The height of the original image (in pixels) this activation map has been computed for.", "type": "integer" }, "imageId": { "description": "ID of the original image this activation map has been computed for.", "type": "string" }, "imageWidth": { "description": "The width of the original image (in pixels) this activation map has been computed for.", "type": "integer" }, "links": { "description": "Download URLs.", "properties": { "downloadOriginalImage": { "description": "URL of the original image", "format": "uri", "type": "string" }, "downloadOverlayImage": { "description": "URL of the original image overlaid by the activation heatmap", "format": "uri", "type": "string" } }, "required": [ "downloadOriginalImage", "downloadOverlayImage" ], "type": "object" }, "overlayImageId": { "description": "ID of the image containing the original image overlaid by the activation heatmap.", "type": "string" }, "predictedTargetValue": { "description": "predicted target value of the dataset row containing this image.", "oneOf": [ { "type": "string" }, { "type": "number" }, { "items": { "type": "string" }, "type": "array" } ] } }, "required": [ "activationValues", "actualTargetValue", "featureName", "imageHeight", "imageId", "imageWidth", "links", "overlayImageId", "predictedTargetValue" ], "type": "object" }, "type": "array" }, "targetBins": { "description": "List of bin objects for regression or null", "items": { "properties": { "targetBinEnd": { "description": "End value for the target bin", "type": "number" }, "targetBinStart": { "description": "Start value for the target bin", "type": "number" } }, "required": [ "targetBinEnd", "targetBinStart" ], "type": "object" }, "type": "array" }, "targetValues": { "description": "List of target values for classification or null", "items": { "description": "Target value", "type": "string" }, "type": "array" } }, "required": [ "activationMapHeight", "activationMapWidth", "activationMaps", "targetBins", "targetValues" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | activationMapHeight | integer,null | true | | The height of each activation map (the number of rows in each activationValues matrix). | | activationMapWidth | integer,null | true | | The width of each activation map (the number of items in each row of each activationValues matrix). | | activationMaps | [ActivationMap] | true | | List of activation map objects | | targetBins | [TargetBin] | true | | List of bin objects for regression or null | | targetValues | [string] | true | | List of target values for classification or null | ## ActualFrequency ``` { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of the times this class was predicted when is was actually `classMetrics.className` (from 0 to 100).", "maximum": 100, "minimum": 0, "type": "number" }, "value": { "description": "The count of the times this class was predicted when is was actually `classMetrics.className`.", "minimum": 0, "type": "integer" } }, "required": [ "otherClassName", "percentage", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | otherClassName | string | true | | The name of the class. | | percentage | number | true | maximum: 100minimum: 0 | The percentage of the times this class was predicted when is was actually classMetrics.className (from 0 to 100). | | value | integer | true | minimum: 0 | The count of the times this class was predicted when is was actually classMetrics.className. | ## ActualPercentages ``` { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | otherClassName | string | true | | The name of the class. | | percentage | number | true | | The percentage of times this class was predicted when the actual class was classMetrics.className. | ## AllDataImage ``` { "description": "Statistics for all data for different feature values.", "properties": { "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "images", "percentageOfMissingImages" ], "type": "object" } ``` Statistics for all data for different feature values. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | images | [string] | true | maxItems: 10minItems: 1 | A list of b64 encoded images. | | percentageOfMissingImages | number | true | maximum: 100minimum: 0 | A percentage of image rows that have a missing value for this feature. | ## AllDataText ``` { "description": "Statistics for all data for different feature values.", "properties": { "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" } ``` Statistics for all data for different feature values. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | missingRowsPercent | number,null | false | maximum: 100minimum: 0 | A percentage of all rows that have a missing value for this feature. | | perValueStatistics | [PerValueStatisticTextListItem] | true | | Statistic value for feature values in all data or a cluster. | ## AllMulticlassModelLiftChartsResponse ``` { "properties": { "charts": { "description": "List of lift chart data from all available sources.", "items": { "properties": { "classBins": { "description": "List of lift chart data for each target class.", "items": { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "targetClass": { "description": "Target class for the lift chart.", "type": "string" } }, "required": [ "bins", "targetClass" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" } }, "required": [ "classBins", "source" ], "type": "object" }, "type": "array" } }, "required": [ "charts" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | charts | [MulticlassModelLiftChartResponse] | true | | List of lift chart data from all available sources. | ## AnalyzedFeature ``` { "properties": { "detailsHistogram": { "description": "Histogram details for the specified feature.", "items": { "properties": { "bars": { "description": "Class details for the histogram chart", "items": { "properties": { "label": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Ratio of occurrence of the class.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "bin": { "anyOf": [ { "type": "string" }, { "type": "integer" } ], "description": "Label for the bin grouping" } }, "required": [ "bars", "bin" ], "type": "object" }, "type": "array" }, "disparityScore": { "description": "A number to describe disparity for the feature between the compared classes.", "type": "number" }, "featureImpact": { "description": "A feature importance value.", "type": "number" }, "name": { "description": "Name of the feature.", "type": "string" }, "status": { "description": "A status of the feature.", "enum": [ "Healthy", "At Risk", "Failing" ], "type": "string" } }, "required": [ "detailsHistogram", "disparityScore", "featureImpact", "name", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | detailsHistogram | [HistogramDetails] | true | | Histogram details for the specified feature. | | disparityScore | number | true | | A number to describe disparity for the feature between the compared classes. | | featureImpact | number | true | | A feature importance value. | | name | string | true | | Name of the feature. | | status | string | true | | A status of the feature. | ### Enumerated Values | Property | Value | | --- | --- | | status | [Healthy, At Risk, Failing] | ## AnomalyAssessmentExplanationsResponse ``` { "properties": { "backtest": { "description": "The backtest of the record.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "count": { "description": "The count of points.", "type": "integer" }, "data": { "description": "Each is a `DataPoint` corresponding to a row in the specified range.", "items": { "properties": { "prediction": { "description": "The output of the model for this row.", "type": "number" }, "shapExplanation": { "description": "Either ``null`` or an array of up to 10 `ShapleyFeatureContribution` objects. Only rows with the highest anomaly scores have Shapley explanations calculated.", "items": { "properties": { "feature": { "description": "Feature name", "type": "string" }, "featureValue": { "description": "Feature value for this row. First 50 characters are returned.", "type": "string" }, "strength": { "description": "Shapley value for this feature and row.", "type": "number" } }, "required": [ "feature", "featureValue", "strength" ], "type": "object" }, "type": "array" }, "timestamp": { "description": "ISO-formatted timestamp for the row.", "format": "date-time", "type": "string" } }, "required": [ "prediction", "shapExplanation", "timestamp" ], "type": "object" }, "type": "array" }, "endDate": { "description": "ISO-formatted last timestamp in the response. For example: ``2019-08-30T00:00:00.000000Z``.", "format": "date-time", "type": "string" }, "modelId": { "description": "The model ID of the record.", "type": "string" }, "projectId": { "description": "The project ID of the record.", "type": "string" }, "recordId": { "description": "The ID of the anomaly assessment record.", "type": "string" }, "seriesId": { "description": "The series id of the record. Applicable in multiseries projects", "type": [ "string", "null" ] }, "shapBaseValue": { "description": "shap base value", "type": "number" }, "source": { "description": "The source of the record", "enum": [ "training", "validation" ], "type": "string" }, "startDate": { "description": "ISO-formatted first timestamp in the response. For example: ``2019-08-01T00:00:00.000000Z``.", "format": "date-time", "type": "string" } }, "required": [ "backtest", "count", "data", "endDate", "modelId", "projectId", "recordId", "seriesId", "shapBaseValue", "source", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtest | any | true | | The backtest of the record. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 19minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The count of points. | | data | [DataPointResponse] | true | | Each is a DataPoint corresponding to a row in the specified range. | | endDate | string(date-time) | true | | ISO-formatted last timestamp in the response. For example: 2019-08-30T00:00:00.000000Z. | | modelId | string | true | | The model ID of the record. | | projectId | string | true | | The project ID of the record. | | recordId | string | true | | The ID of the anomaly assessment record. | | seriesId | string,null | true | | The series id of the record. Applicable in multiseries projects | | shapBaseValue | number | true | | shap base value | | source | string | true | | The source of the record | | startDate | string(date-time) | true | | ISO-formatted first timestamp in the response. For example: 2019-08-01T00:00:00.000000Z. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | holdout | | source | [training, validation] | ## AnomalyAssessmentInitialize ``` { "properties": { "backtest": { "description": "The backtest to compute insight for.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "seriesId": { "description": "Required for multiseries projects. The series id to compute insight for.", "type": "string" }, "source": { "description": "The source to compute insight for.", "enum": [ "training", "validation" ], "type": "string" } }, "required": [ "backtest", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtest | any | true | | The backtest to compute insight for. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 19minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | seriesId | string | false | | Required for multiseries projects. The series id to compute insight for. | | source | string | true | | The source to compute insight for. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | holdout | | source | [training, validation] | ## AnomalyAssessmentPreviewResponse ``` { "properties": { "backtest": { "description": "The backtest of the record.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "endDate": { "description": "ISO-formatted last timestamp in the subset. For example: ``2019-08-30T00:00:00.000000Z``.", "format": "date-time", "type": "string" }, "modelId": { "description": "The model ID of the record.", "type": "string" }, "previewBins": { "description": "Aggregated predictions for the subset. Bins boundaries may differ from actual start/end dates because this is an aggregation.", "items": { "properties": { "avgPredicted": { "description": "Average prediction of the model in the bin. Null if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "ISO-formatted datetime of the end of the bin (exclusive).", "format": "date-time", "type": "string" }, "frequency": { "description": "Number of the rows in the bin.", "type": "integer" }, "maxPredicted": { "description": "Maximum prediction of the model in the bin. Null if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "ISO-formatted datetime of the start of the bin (inclusive).", "format": "date-time", "type": "string" } }, "required": [ "avgPredicted", "endDate", "frequency", "maxPredicted", "startDate" ], "type": "object" }, "type": "array" }, "projectId": { "description": "The project ID of the record.", "type": "string" }, "recordId": { "description": "The ID of the anomaly assessment record.", "type": "string" }, "seriesId": { "description": "The series id of the record. Applicable in multiseries projects", "type": [ "string", "null" ] }, "source": { "description": "The source of the record", "enum": [ "training", "validation" ], "type": "string" }, "startDate": { "description": "ISO-formatted first timestamp in the subset. For example: ``2019-08-01T00:00:00.000000Z``.", "format": "date-time", "type": "string" } }, "required": [ "backtest", "endDate", "modelId", "previewBins", "projectId", "recordId", "seriesId", "source", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtest | any | true | | The backtest of the record. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 19minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string(date-time) | true | | ISO-formatted last timestamp in the subset. For example: 2019-08-30T00:00:00.000000Z. | | modelId | string | true | | The model ID of the record. | | previewBins | [BinResponse] | true | | Aggregated predictions for the subset. Bins boundaries may differ from actual start/end dates because this is an aggregation. | | projectId | string | true | | The project ID of the record. | | recordId | string | true | | The ID of the anomaly assessment record. | | seriesId | string,null | true | | The series id of the record. Applicable in multiseries projects | | source | string | true | | The source of the record | | startDate | string(date-time) | true | | ISO-formatted first timestamp in the subset. For example: 2019-08-01T00:00:00.000000Z. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | holdout | | source | [training, validation] | ## AnomalyAssessmentRecordResponse ``` { "properties": { "backtest": { "description": "The backtest of the record.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "deleteLocation": { "description": "URL to delete anomaly assessment record.", "format": "uri", "type": [ "string", "null" ] }, "endDate": { "description": "ISO-formatted last timestamp in the subset. For example: ``2019-08-30T00:00:00.000000Z``.", "format": "date-time", "type": [ "string", "null" ] }, "latestExplanationsLocation": { "description": "URL to retrieve the latest predictions with the shap explanations.", "format": "uri", "type": [ "string", "null" ] }, "modelId": { "description": "The model ID of the record.", "type": "string" }, "predictionThreshold": { "description": "The threshold, all rows with anomaly scores greater or equal to it have Shapley explanations computed.", "type": [ "number", "null" ] }, "previewLocation": { "description": "URL to retrieve predictions preview for the record.", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID of the record.", "type": "string" }, "recordId": { "description": "The ID of the anomaly assessment record.", "type": "string" }, "seriesId": { "description": "The series id of the record. Applicable in multiseries projects", "type": [ "string", "null" ] }, "source": { "description": "The source of the record", "enum": [ "training", "validation" ], "type": "string" }, "startDate": { "description": "ISO-formatted first timestamp in the subset. For example: ``2019-08-01T00:00:00.000000Z``.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the anomaly assessment record.", "enum": [ "noData", "notSupported", "completed" ], "type": "string" }, "statusDetails": { "description": "The status details.", "type": "string" } }, "required": [ "backtest", "deleteLocation", "endDate", "latestExplanationsLocation", "modelId", "predictionThreshold", "previewLocation", "projectId", "recordId", "seriesId", "source", "startDate", "status", "statusDetails" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtest | any | true | | The backtest of the record. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 19minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deleteLocation | string,null(uri) | true | | URL to delete anomaly assessment record. | | endDate | string,null(date-time) | true | | ISO-formatted last timestamp in the subset. For example: 2019-08-30T00:00:00.000000Z. | | latestExplanationsLocation | string,null(uri) | true | | URL to retrieve the latest predictions with the shap explanations. | | modelId | string | true | | The model ID of the record. | | predictionThreshold | number,null | true | | The threshold, all rows with anomaly scores greater or equal to it have Shapley explanations computed. | | previewLocation | string,null(uri) | true | | URL to retrieve predictions preview for the record. | | projectId | string | true | | The project ID of the record. | | recordId | string | true | | The ID of the anomaly assessment record. | | seriesId | string,null | true | | The series id of the record. Applicable in multiseries projects | | source | string | true | | The source of the record | | startDate | string,null(date-time) | true | | ISO-formatted first timestamp in the subset. For example: 2019-08-01T00:00:00.000000Z. | | status | string | true | | The status of the anomaly assessment record. | | statusDetails | string | true | | The status details. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | holdout | | source | [training, validation] | | status | [noData, notSupported, completed] | ## AnomalyAssessmentRecordsResponse ``` { "properties": { "count": { "description": "Number of items in current page.", "type": "integer" }, "data": { "description": "Anomaly assessment record.", "items": { "properties": { "backtest": { "description": "The backtest of the record.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "deleteLocation": { "description": "URL to delete anomaly assessment record.", "format": "uri", "type": [ "string", "null" ] }, "endDate": { "description": "ISO-formatted last timestamp in the subset. For example: ``2019-08-30T00:00:00.000000Z``.", "format": "date-time", "type": [ "string", "null" ] }, "latestExplanationsLocation": { "description": "URL to retrieve the latest predictions with the shap explanations.", "format": "uri", "type": [ "string", "null" ] }, "modelId": { "description": "The model ID of the record.", "type": "string" }, "predictionThreshold": { "description": "The threshold, all rows with anomaly scores greater or equal to it have Shapley explanations computed.", "type": [ "number", "null" ] }, "previewLocation": { "description": "URL to retrieve predictions preview for the record.", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID of the record.", "type": "string" }, "recordId": { "description": "The ID of the anomaly assessment record.", "type": "string" }, "seriesId": { "description": "The series id of the record. Applicable in multiseries projects", "type": [ "string", "null" ] }, "source": { "description": "The source of the record", "enum": [ "training", "validation" ], "type": "string" }, "startDate": { "description": "ISO-formatted first timestamp in the subset. For example: ``2019-08-01T00:00:00.000000Z``.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the anomaly assessment record.", "enum": [ "noData", "notSupported", "completed" ], "type": "string" }, "statusDetails": { "description": "The status details.", "type": "string" } }, "required": [ "backtest", "deleteLocation", "endDate", "latestExplanationsLocation", "modelId", "predictionThreshold", "previewLocation", "projectId", "recordId", "seriesId", "source", "startDate", "status", "statusDetails" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page)", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Number of items in current page. | | data | [AnomalyAssessmentRecordResponse] | true | | Anomaly assessment record. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page) | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page) | ## AnomalyInsightTableData ``` { "properties": { "columns": { "description": "array of columns that contain columns from training dataset and `anomalyScore` column.", "items": { "type": "string" }, "type": "array" }, "data": { "description": "array of arrays with actual data. Order in each array corresponds to order in columns array.", "items": { "type": "number" }, "type": "array" }, "rowId": { "description": "index 0-based array. Each rowId corresponds to the actual row number of training data", "items": { "type": "integer" }, "type": "array" } }, "required": [ "columns", "data", "rowId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columns | [string] | true | | array of columns that contain columns from training dataset and anomalyScore column. | | data | [number] | true | | array of arrays with actual data. Order in each array corresponds to order in columns array. | | rowId | [integer] | true | | index 0-based array. Each rowId corresponds to the actual row number of training data | ## AnomalyInsightTableRetrieve ``` { "properties": { "modelId": { "description": "given model identifier", "type": "string" }, "table": { "description": "anomaly insights table", "items": { "properties": { "columns": { "description": "array of columns that contain columns from training dataset and `anomalyScore` column.", "items": { "type": "string" }, "type": "array" }, "data": { "description": "array of arrays with actual data. Order in each array corresponds to order in columns array.", "items": { "type": "number" }, "type": "array" }, "rowId": { "description": "index 0-based array. Each rowId corresponds to the actual row number of training data", "items": { "type": "integer" }, "type": "array" } }, "required": [ "columns", "data", "rowId" ], "type": "object" }, "type": "array" } }, "required": [ "modelId", "table" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | given model identifier | | table | [AnomalyInsightTableData] | true | | anomaly insights table | ## AnomalyOverTimePlotsBins ``` { "properties": { "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "frequency": { "description": "Indicates number of values averaged in bin in case of a resolution change.", "type": [ "integer", "null" ] }, "predicted": { "description": "Average prediction of the model in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "endDate", "frequency", "predicted", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string(date-time) | true | | The datetime of the end of the bin (exclusive). | | frequency | integer,null | true | | Indicates number of values averaged in bin in case of a resolution change. | | predicted | number,null | true | | Average prediction of the model in the bin. null if there are no entries in the bin. | | startDate | string(date-time) | true | | The datetime of the start of the bin (inclusive). | ## AnomalyOverTimePlotsDataResponse ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "frequency": { "description": "Indicates number of values averaged in bin in case of a resolution change.", "type": [ "integer", "null" ] }, "predicted": { "description": "Average prediction of the model in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "endDate", "frequency", "predicted", "startDate" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "calendarEvents": { "description": "An array of calendar events for a retrieved plot.", "items": { "properties": { "date": { "description": "The date of the calendar event.", "format": "date-time", "type": "string" }, "name": { "description": "Name of the calendar event.", "type": "string" }, "seriesId": { "description": "The series ID for the event. If this event does not specify a series ID, then this will be `null`, indicating that the event applies to all series.", "type": [ "string", "null" ] } }, "required": [ "date", "name", "seriesId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "resolution": { "description": "The resolution that is used for binning.", "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "bins", "calendarEvents", "endDate", "resolution", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [AnomalyOverTimePlotsBins] | true | maxItems: 1000minItems: 1 | An array of bins for the retrieved plots. | | calendarEvents | [CalendarEvent] | true | maxItems: 1000 | An array of calendar events for a retrieved plot. | | endDate | string(date-time) | true | | The datetime of the end of the chart data (exclusive). | | resolution | string | true | | The resolution that is used for binning. | | startDate | string(date-time) | true | | The datetime of the start of the chart data (inclusive). | ### Enumerated Values | Property | Value | | --- | --- | | resolution | [milliseconds, seconds, minutes, hours, days, weeks, months, quarters, years] | ## AnomalyOverTimePlotsMetadataResponse ``` { "properties": { "backtestMetadata": { "description": "An array of metadata information for each backtest. The array index of metadata object is the backtest index.", "items": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "backtestStatuses": { "description": "An array of status information for each backtest. The array index of status object is the backtest index.", "items": { "description": "Status for accuracy over time plots.", "properties": { "training": { "description": "The status for the training.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" }, "validation": { "description": "The status for the validation.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "estimatedSeriesLimit": { "description": "Estimated number of series that can be calculated in one request for 1 FD.", "minimum": 1, "type": "integer" }, "holdoutMetadata": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "holdoutStatuses": { "description": "Status for accuracy over time plots.", "properties": { "training": { "description": "The status for the training.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" }, "validation": { "description": "The status for the validation.", "enum": [ "completed", "errored", "inProgress", "insufficientData", "notCompleted", "notSupported" ], "type": "string" } }, "required": [ "training", "validation" ], "type": "object" }, "resolutions": { "description": "An array of available time resolutions for which plots can be retrieved.", "items": { "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "maxItems": 9, "minItems": 1, "type": "array" } }, "required": [ "backtestMetadata", "backtestStatuses", "holdoutMetadata", "holdoutStatuses", "resolutions" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestMetadata | [DatetimeTrendPlotsBacktestMetadata] | true | maxItems: 20minItems: 1 | An array of metadata information for each backtest. The array index of metadata object is the backtest index. | | backtestStatuses | [AccuracyOverTimePlotsStatus] | true | maxItems: 20minItems: 1 | An array of status information for each backtest. The array index of status object is the backtest index. | | estimatedSeriesLimit | integer | false | minimum: 1 | Estimated number of series that can be calculated in one request for 1 FD. | | holdoutMetadata | DatetimeTrendPlotsBacktestMetadata | true | | Metadata for backtest/holdout. | | holdoutStatuses | AccuracyOverTimePlotsStatus | true | | Status for accuracy over time plots. | | resolutions | [string] | true | maxItems: 9minItems: 1 | An array of available time resolutions for which plots can be retrieved. | ## AnomalyOverTimePlotsPreviewBins ``` { "properties": { "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "endDate", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string(date-time) | true | | The datetime of the end of the bin (exclusive). | | startDate | string(date-time) | true | | The datetime of the start of the bin (inclusive). | ## AnomalyOverTimePlotsPreviewResponse ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "endDate", "startDate" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "predictionThreshold": { "description": "Only bins with predictions exceeding this threshold are returned in the response.", "exclusiveMinimum": 0, "maximum": 1, "type": "number" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "bins", "endDate", "predictionThreshold", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [AnomalyOverTimePlotsPreviewBins] | true | maxItems: 1000 | An array of bins for the retrieved plots. | | endDate | string(date-time) | true | | The datetime of the end of the chart data (exclusive). | | predictionThreshold | number | true | maximum: 1 | Only bins with predictions exceeding this threshold are returned in the response. | | startDate | string(date-time) | true | | The datetime of the start of the chart data (inclusive). | ## AverageModelMetricsField ``` { "description": "All average model metrics from one data source.", "properties": { "metrics": { "description": "Average model metrics for the given thresholds.", "items": { "properties": { "name": { "description": "Metric name.", "enum": [ "accuracy", "f1Score", "falsePositiveRate", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "trueNegativeRate", "truePositiveRate" ], "type": "string" }, "numLabelsUsedInCalculation": { "description": "Number of labels that were taken into account in the calculation of metric averages", "type": "integer" }, "values": { "description": "Metric values at given thresholds.", "items": { "type": "number" }, "maxItems": 100, "minItems": 100, "type": "array" } }, "required": [ "name", "numLabelsUsedInCalculation", "values" ], "type": "object" }, "maxItems": 8, "minItems": 8, "type": "array" }, "source": { "description": "Chart source.", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "thresholds": { "description": "Threshold values for which model metrics are available.", "items": { "maximum": 1, "minimum": 0, "type": "number" }, "maxItems": 100, "minItems": 100, "type": "array" } }, "required": [ "metrics", "source", "thresholds" ], "type": "object" } ``` All average model metrics from one data source. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metrics | [AverageModelMetricsValues] | true | maxItems: 8minItems: 8 | Average model metrics for the given thresholds. | | source | string | true | | Chart source. | | thresholds | [number] | true | maxItems: 100minItems: 100 | Threshold values for which model metrics are available. | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout] | ## AverageModelMetricsValues ``` { "properties": { "name": { "description": "Metric name.", "enum": [ "accuracy", "f1Score", "falsePositiveRate", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "trueNegativeRate", "truePositiveRate" ], "type": "string" }, "numLabelsUsedInCalculation": { "description": "Number of labels that were taken into account in the calculation of metric averages", "type": "integer" }, "values": { "description": "Metric values at given thresholds.", "items": { "type": "number" }, "maxItems": 100, "minItems": 100, "type": "array" } }, "required": [ "name", "numLabelsUsedInCalculation", "values" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Metric name. | | numLabelsUsedInCalculation | integer | true | | Number of labels that were taken into account in the calculation of metric averages | | values | [number] | true | maxItems: 100minItems: 100 | Metric values at given thresholds. | ### Enumerated Values | Property | Value | | --- | --- | | name | [accuracy, f1Score, falsePositiveRate, matthewsCorrelationCoefficient, negativePredictiveValue, positivePredictiveValue, trueNegativeRate, truePositiveRate] | ## BacktestStabilityPlotData ``` { "properties": { "backtestIndex": { "description": "An integer representing the index of the backtest, starting from 0. For holdout, this field will be null.", "minimum": 0, "type": [ "integer", "null" ] }, "partition": { "description": "Identifier of the partition. Can either identify a specific backtest (\"backtest0\", \"backtest1\", ...) or the holdout set (\"holdout\").", "type": "string" }, "score": { "description": "Score for this partition. Can be null if the score is unavailable for this partition (e.g. holdout is locked or backtesting has not been run yet).", "type": [ "number", "null" ] }, "scoringEndDate": { "description": "End date of the subset used for scoring.", "format": "date-time", "type": "string" }, "scoringStartDate": { "description": "Start date of the subset used for scoring.", "format": "date-time", "type": "string" } }, "required": [ "backtestIndex", "partition", "score", "scoringEndDate", "scoringStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestIndex | integer,null | true | minimum: 0 | An integer representing the index of the backtest, starting from 0. For holdout, this field will be null. | | partition | string | true | | Identifier of the partition. Can either identify a specific backtest ("backtest0", "backtest1", ...) or the holdout set ("holdout"). | | score | number,null | true | | Score for this partition. Can be null if the score is unavailable for this partition (e.g. holdout is locked or backtesting has not been run yet). | | scoringEndDate | string(date-time) | true | | End date of the subset used for scoring. | | scoringStartDate | string(date-time) | true | | Start date of the subset used for scoring. | ## BacktestStabilityPlotResponse ``` { "properties": { "backtestPlotData": { "description": "An array of objects containing the details of the scores for each partition defined for the project.", "items": { "properties": { "backtestIndex": { "description": "An integer representing the index of the backtest, starting from 0. For holdout, this field will be null.", "minimum": 0, "type": [ "integer", "null" ] }, "partition": { "description": "Identifier of the partition. Can either identify a specific backtest (\"backtest0\", \"backtest1\", ...) or the holdout set (\"holdout\").", "type": "string" }, "score": { "description": "Score for this partition. Can be null if the score is unavailable for this partition (e.g. holdout is locked or backtesting has not been run yet).", "type": [ "number", "null" ] }, "scoringEndDate": { "description": "End date of the subset used for scoring.", "format": "date-time", "type": "string" }, "scoringStartDate": { "description": "Start date of the subset used for scoring.", "format": "date-time", "type": "string" } }, "required": [ "backtestIndex", "partition", "score", "scoringEndDate", "scoringStartDate" ], "type": "object" }, "type": "array" }, "endDate": { "description": "End date of the project dataset.", "format": "date-time", "type": "string" }, "metricName": { "description": "Name of the metric used to compute the scores.", "type": "string" }, "startDate": { "description": "Start date of the project dataset.", "format": "date-time", "type": "string" } }, "required": [ "backtestPlotData", "endDate", "metricName", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestPlotData | [BacktestStabilityPlotData] | true | | An array of objects containing the details of the scores for each partition defined for the project. | | endDate | string(date-time) | true | | End date of the project dataset. | | metricName | string | true | | Name of the metric used to compute the scores. | | startDate | string(date-time) | true | | Start date of the project dataset. | ## BiasVsAccuracyInsight ``` { "properties": { "accuracyMetric": { "description": "The metric to return model accuracy scores. Defaults to the optimization metric configured in project options.", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE" ], "type": "string" }, "fairnessMetric": { "description": "The fairness metric used to calculate the fairness scores.", "oneOf": [ { "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, { "items": { "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "type": "array" } ] }, "fairnessThreshold": { "default": 0.8, "description": "Value of the fairness threshold, defined in project options.", "maximum": 1, "minimum": 0, "type": "number" }, "models": { "description": "An array of models of the insight.", "items": { "properties": { "accuracyValue": { "description": "The model's accuracy score.", "minimum": 0, "type": "number" }, "bp": { "description": "The blueprint number of the model from the leaderboard.", "exclusiveMinimum": 0, "type": "integer" }, "dsName": { "description": "The name of the feature list used for model training.", "type": "string" }, "fairnessValue": { "description": "The model's relative fairness score for the class with the lowest fairness score. In other words, the fairness score of the least privileged class.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": "integer" }, "modelType": { "description": "The type/name of the model.", "type": "string" }, "prime": { "description": "Flag to indicate whether the model is a prime model.", "type": "boolean" }, "samplepct": { "description": "The sample size percentage of the feature list data the model was trained on.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "accuracyValue", "bp", "dsName", "fairnessValue", "modelId", "modelNumber", "modelType", "prime", "samplepct" ], "type": "object" }, "type": "array" }, "protectedFeature": { "description": "Name of the protected feature.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ] } }, "required": [ "fairnessThreshold", "models" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyMetric | string | false | | The metric to return model accuracy scores. Defaults to the optimization metric configured in project options. | | fairnessMetric | any | false | | The fairness metric used to calculate the fairness scores. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fairnessThreshold | number | true | maximum: 1minimum: 0 | Value of the fairness threshold, defined in project options. | | models | [BiasVsAccuracyModels] | true | | An array of models of the insight. | | protectedFeature | any | false | | Name of the protected feature. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | accuracyMetric | [AUC, Weighted AUC, Area Under PR Curve, Weighted Area Under PR Curve, Kolmogorov-Smirnov, Weighted Kolmogorov-Smirnov, FVE Binomial, Weighted FVE Binomial, Gini Norm, Weighted Gini Norm, LogLoss, Weighted LogLoss, Max MCC, Weighted Max MCC, Rate@Top5%, Weighted Rate@Top5%, Rate@Top10%, Weighted Rate@Top10%, Rate@TopTenth%, RMSE, Weighted RMSE] | | anonymous | [proportionalParity, equalParity, favorableClassBalance, unfavorableClassBalance, trueUnfavorableRateParity, trueFavorableRateParity, favorablePredictiveValueParity, unfavorablePredictiveValueParity] | ## BiasVsAccuracyInsightRetrieve ``` { "properties": { "data": { "description": "An array of bias vs accuracy insights for the model.", "items": { "properties": { "accuracyMetric": { "description": "The metric to return model accuracy scores. Defaults to the optimization metric configured in project options.", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE" ], "type": "string" }, "fairnessMetric": { "description": "The fairness metric used to calculate the fairness scores.", "oneOf": [ { "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, { "items": { "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "type": "array" } ] }, "fairnessThreshold": { "default": 0.8, "description": "Value of the fairness threshold, defined in project options.", "maximum": 1, "minimum": 0, "type": "number" }, "models": { "description": "An array of models of the insight.", "items": { "properties": { "accuracyValue": { "description": "The model's accuracy score.", "minimum": 0, "type": "number" }, "bp": { "description": "The blueprint number of the model from the leaderboard.", "exclusiveMinimum": 0, "type": "integer" }, "dsName": { "description": "The name of the feature list used for model training.", "type": "string" }, "fairnessValue": { "description": "The model's relative fairness score for the class with the lowest fairness score. In other words, the fairness score of the least privileged class.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": "integer" }, "modelType": { "description": "The type/name of the model.", "type": "string" }, "prime": { "description": "Flag to indicate whether the model is a prime model.", "type": "boolean" }, "samplepct": { "description": "The sample size percentage of the feature list data the model was trained on.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "accuracyValue", "bp", "dsName", "fairnessValue", "modelId", "modelNumber", "modelType", "prime", "samplepct" ], "type": "object" }, "type": "array" }, "protectedFeature": { "description": "Name of the protected feature.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ] } }, "required": [ "fairnessThreshold", "models" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [BiasVsAccuracyInsight] | true | | An array of bias vs accuracy insights for the model. | ## BiasVsAccuracyModels ``` { "properties": { "accuracyValue": { "description": "The model's accuracy score.", "minimum": 0, "type": "number" }, "bp": { "description": "The blueprint number of the model from the leaderboard.", "exclusiveMinimum": 0, "type": "integer" }, "dsName": { "description": "The name of the feature list used for model training.", "type": "string" }, "fairnessValue": { "description": "The model's relative fairness score for the class with the lowest fairness score. In other words, the fairness score of the least privileged class.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": "integer" }, "modelType": { "description": "The type/name of the model.", "type": "string" }, "prime": { "description": "Flag to indicate whether the model is a prime model.", "type": "boolean" }, "samplepct": { "description": "The sample size percentage of the feature list data the model was trained on.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "accuracyValue", "bp", "dsName", "fairnessValue", "modelId", "modelNumber", "modelType", "prime", "samplepct" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyValue | number | true | minimum: 0 | The model's accuracy score. | | bp | integer | true | | The blueprint number of the model from the leaderboard. | | dsName | string | true | | The name of the feature list used for model training. | | fairnessValue | number,null | true | maximum: 1minimum: 0 | The model's relative fairness score for the class with the lowest fairness score. In other words, the fairness score of the least privileged class. | | modelId | string | true | | ID of the model. | | modelNumber | integer | true | | The model number from the Leaderboard. | | modelType | string | true | | The type/name of the model. | | prime | boolean | true | | Flag to indicate whether the model is a prime model. | | samplepct | number | true | maximum: 100minimum: 0 | The sample size percentage of the feature list data the model was trained on. | ## BinResponse ``` { "properties": { "avgPredicted": { "description": "Average prediction of the model in the bin. Null if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "ISO-formatted datetime of the end of the bin (exclusive).", "format": "date-time", "type": "string" }, "frequency": { "description": "Number of the rows in the bin.", "type": "integer" }, "maxPredicted": { "description": "Maximum prediction of the model in the bin. Null if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "ISO-formatted datetime of the start of the bin (inclusive).", "format": "date-time", "type": "string" } }, "required": [ "avgPredicted", "endDate", "frequency", "maxPredicted", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | avgPredicted | number,null | true | | Average prediction of the model in the bin. Null if there are no entries in the bin. | | endDate | string(date-time) | true | | ISO-formatted datetime of the end of the bin (exclusive). | | frequency | integer | true | | Number of the rows in the bin. | | maxPredicted | number,null | true | | Maximum prediction of the model in the bin. Null if there are no entries in the bin. | | startDate | string(date-time) | true | | ISO-formatted datetime of the start of the bin (inclusive). | ## CalendarEvent ``` { "properties": { "date": { "description": "The date of the calendar event.", "format": "date-time", "type": "string" }, "name": { "description": "Name of the calendar event.", "type": "string" }, "seriesId": { "description": "The series ID for the event. If this event does not specify a series ID, then this will be `null`, indicating that the event applies to all series.", "type": [ "string", "null" ] } }, "required": [ "date", "name", "seriesId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | date | string(date-time) | true | | The date of the calendar event. | | name | string | true | | Name of the calendar event. | | seriesId | string,null | true | | The series ID for the event. If this event does not specify a series ID, then this will be null, indicating that the event applies to all series. | ## Categorical ``` { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "categoryLevelFrequencyPercent" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allData | PerValueStatistics | true | | Statistics for all data for different feature values. | | insightName | string | true | | Insight name. | | perCluster | [PerClusterCategorical] | true | | Statistic values for different feature values in this cluster. | ### Enumerated Values | Property | Value | | --- | --- | | insightName | categoryLevelFrequencyPercent | ## CategoricalFeature ``` { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "categorical" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "categoryLevelFrequencyPercent" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureImpact | number,null | false | | Feature Impact score. | | featureName | string | true | | Feature name. | | featureType | string | true | | Feature Type. | | insights | [Categorical] | true | | A list of Cluster Insights for a feature. | ### Enumerated Values | Property | Value | | --- | --- | | featureType | categorical | ## ClusterInsightsPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of features with clusters insights.", "items": { "anyOf": [ { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "image" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for an image feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "images", "percentageOfMissingImages" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "representativeImages" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "clusterName", "images", "percentageOfMissingImages" ], "type": "object" }, "type": "array" } }, "required": [ "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "geospatialPoint" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a geospatial centroid or point feature.", "items": { "properties": { "insightName": { "description": "Insight name.", "enum": [ "representativeLocations" ], "type": "string" }, "perCluster": { "description": "Statistic for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "representativeLocations": { "description": "A list of latitude and longitude location list", "items": { "description": "Latitude and longitude list.", "items": { "description": "Longitude or latitude, in degrees.", "maximum": 180, "minimum": -180, "type": "number" }, "type": "array" }, "maxItems": 1000, "type": "array" } }, "required": [ "clusterName", "representativeLocations" ], "type": "object" }, "type": "array" } }, "required": [ "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "text" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "importantNgrams" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "numeric" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistic value for all data.", "type": [ "number", "null" ] }, "insightName": { "description": "Insight name.", "enum": [ "min", "max", "median", "avg", "firstQuartile", "thirdQuartile", "missingRowsPercent" ], "type": "string" }, "perCluster": { "description": "Statistic values for for each cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "statistic": { "description": "Statistic value for this cluster.", "type": [ "number", "null" ] } }, "required": [ "clusterName", "statistic" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "categorical" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "categoryLevelFrequencyPercent" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" }, { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "document" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "importantNgrams" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" } ] }, "maxItems": 100, "type": "array" }, "isCurrentClusterInsightVersion": { "description": "If retrieved insights are current version.", "type": "boolean" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" }, "version": { "description": "Current version of the computed insight.", "minimum": 0, "type": "integer" } }, "required": [ "data", "isCurrentClusterInsightVersion", "next", "previous", "totalCount", "version" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [anyOf] | true | maxItems: 100 | A list of features with clusters insights. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ImageFeature | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GeospatialFeature | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TextFeature | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | NumericFeature | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CategoricalFeature | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DocumentFeature | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isCurrentClusterInsightVersion | boolean | true | | If retrieved insights are current version. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | | version | integer | true | minimum: 0 | Current version of the computed insight. | ## ComputeConfusionMatrixRequest ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "(-1, -1)", "(-1,-1)", "(0, -1)", "(0,-1)", "CV", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_2", "backtest_20", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "crossValidation", "externalTestSet", "holdout", "validation", "validation_0", "validation_1" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSliceId | string | false | | The ID of the data slice. | | entityId | string | true | | The ID of the entity. | | entityType | string | true | | The type of entity for which insights will be calculated. | | externalDatasetId | string | false | | The ID of the external dataset. | | source | string | true | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [datarobotModel, customModel, vectorDatabase] | | source | [(-1, -1), (-1,-1), (0, -1), (0,-1), CV, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_2, backtest_20, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, crossValidation, externalTestSet, holdout, validation, validation_0, validation_1] | ## ComputeFeatureEffectsRequest ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "training", "backtest_0", "backtest_1", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20", "holdout", "backtest_0_training", "backtest_1_training", "backtest_2_training", "backtest_3_training", "backtest_4_training", "backtest_5_training", "backtest_6_training", "backtest_7_training", "backtest_8_training", "backtest_9_training", "backtest_10_training", "backtest_11_training", "backtest_12_training", "backtest_13_training", "backtest_14_training", "backtest_15_training", "backtest_16_training", "backtest_17_training", "backtest_18_training", "backtest_19_training", "backtest_20_training", "holdout_training" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSliceId | string | false | | The ID of the data slice. | | entityId | string | true | | The ID of the entity. | | entityType | string | true | | The type of entity for which insights will be calculated. | | externalDatasetId | string | false | | The ID of the external dataset. | | source | string | true | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [datarobotModel, customModel, vectorDatabase] | | source | [validation, training, backtest_0, backtest_1, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20, holdout, backtest_0_training, backtest_1_training, backtest_2_training, backtest_3_training, backtest_4_training, backtest_5_training, backtest_6_training, backtest_7_training, backtest_8_training, backtest_9_training, backtest_10_training, backtest_11_training, backtest_12_training, backtest_13_training, backtest_14_training, backtest_15_training, backtest_16_training, backtest_17_training, backtest_18_training, backtest_19_training, backtest_20_training, holdout_training] | ## ComputeFeatureImpactRequest ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "rowCount": { "description": "The number of rows to use for calculating Feature Impact.", "maximum": 100000, "minimum": 10, "type": "integer" }, "source": { "default": "training", "description": "The subset of data used to compute the insight.", "enum": [ "training", "backtest_2_training", "backtest_3_training", "backtest_4_training", "backtest_5_training", "backtest_6_training", "backtest_7_training", "backtest_8_training", "backtest_9_training", "backtest_10_training", "backtest_11_training", "backtest_12_training", "backtest_13_training", "backtest_14_training", "backtest_15_training", "backtest_16_training", "backtest_17_training", "backtest_18_training", "backtest_19_training", "backtest_20_training", "backtest_1_training", "holdout_training" ], "type": "string" } }, "required": [ "entityId", "entityType", "rowCount", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSliceId | string | false | | The ID of the data slice. | | entityId | string | true | | The ID of the entity. | | entityType | string | true | | The type of entity for which insights will be calculated. | | externalDatasetId | string | false | | The ID of the external dataset. | | rowCount | integer | true | maximum: 100000minimum: 10 | The number of rows to use for calculating Feature Impact. | | source | string | true | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [datarobotModel, customModel, vectorDatabase] | | source | [training, backtest_2_training, backtest_3_training, backtest_4_training, backtest_5_training, backtest_6_training, backtest_7_training, backtest_8_training, backtest_9_training, backtest_10_training, backtest_11_training, backtest_12_training, backtest_13_training, backtest_14_training, backtest_15_training, backtest_16_training, backtest_17_training, backtest_18_training, backtest_19_training, backtest_20_training, backtest_1_training, holdout_training] | ## ComputeInsightsResponse ``` { "properties": { "qid": { "description": "The queue ID of the job that computes the insights request.", "type": [ "integer", "null" ] } }, "required": [ "qid" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | qid | integer,null | true | | The queue ID of the job that computes the insights request. | ## ComputeLiftChartRequest ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSliceId | string | false | | The ID of the data slice. | | entityId | string | true | | The ID of the entity. | | entityType | string | true | | The type of entity for which insights will be calculated. | | externalDatasetId | string | false | | The ID of the external dataset. | | source | string | true | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [datarobotModel, customModel, vectorDatabase] | | source | [validation, crossValidation, holdout, externalTestSet, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ## ComputeResidualsRequest ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSliceId | string | false | | The ID of the data slice. | | entityId | string | true | | The ID of the entity. | | entityType | string | true | | The type of entity for which insights will be calculated. | | externalDatasetId | string | false | | The ID of the external dataset. | | source | string | true | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [datarobotModel, customModel, vectorDatabase] | | source | [validation, crossValidation, holdout, externalTestSet] | ## ComputeRocCurveRequest ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSliceId | string | false | | The ID of the data slice. | | entityId | string | true | | The ID of the entity. | | entityType | string | true | | The type of entity for which insights will be calculated. | | externalDatasetId | string | false | | The ID of the external dataset. | | source | string | true | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [datarobotModel, customModel, vectorDatabase] | | source | [validation, crossValidation, holdout, externalTestSet, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ## ComputeShapDistributionsRequest ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "quickCompute": { "default": true, "description": "(Deprecated) Limits the number of rows used from the selected source by default. Cannot be set to False for this insight.", "type": "boolean" }, "rowCount": { "description": "(Deprecated) The number of rows to use for calculating SHAP Impact.", "type": "integer", "x-versiondeprecated": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object", "x-versionadded": "v2.38" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSliceId | string | false | | The ID of the data slice. | | entityId | string | true | | The ID of the entity. | | entityType | string | true | | The type of entity for which insights will be calculated. | | externalDatasetId | string | false | | The ID of the external dataset. | | quickCompute | boolean | false | | (Deprecated) Limits the number of rows used from the selected source by default. Cannot be set to False for this insight. | | rowCount | integer | false | | (Deprecated) The number of rows to use for calculating SHAP Impact. | | source | string | true | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [datarobotModel, customModel, vectorDatabase] | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, externalTestSet, holdout, holdout_training, training, validation] | ## ComputeShapInsightsRequest ``` { "properties": { "dataSliceId": { "description": "The ID of the data slice.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": "string" }, "entityType": { "default": "datarobotModel", "description": "The type of entity for which insights will be calculated.", "enum": [ "datarobotModel", "customModel", "vectorDatabase" ], "type": "string", "x-versionadded": "v2.34" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": "string" }, "quickCompute": { "default": true, "description": "When enabled, limits the number of rows used from the selected source by default. When disabled, all rows are used.", "type": "boolean" }, "rowCount": { "description": "(Deprecated) The number of rows to use for calculating SHAP Impact.", "type": "integer", "x-versionadded": "v2.35", "x-versiondeprecated": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string" } }, "required": [ "entityId", "entityType", "source" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSliceId | string | false | | The ID of the data slice. | | entityId | string | true | | The ID of the entity. | | entityType | string | true | | The type of entity for which insights will be calculated. | | externalDatasetId | string | false | | The ID of the external dataset. | | quickCompute | boolean | false | | When enabled, limits the number of rows used from the selected source by default. When disabled, all rows are used. | | rowCount | integer | false | | (Deprecated) The number of rows to use for calculating SHAP Impact. | | source | string | true | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [datarobotModel, customModel, vectorDatabase] | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, externalTestSet, holdout, holdout_training, training, validation] | ## ConfusionChartClassMatrix ``` { "properties": { "actualCount": { "description": "number of times this class is seen in the validation data", "type": "integer" }, "className": { "description": "name of the class", "type": "string" }, "confusionMatrixOneVsAll": { "description": "2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "precision score", "type": "number" }, "predictedCount": { "description": "number of times this class has been predicted for the validation data", "type": "integer" }, "recall": { "description": "recall score", "type": "number" }, "wasActualPercentages": { "description": "one vs all actual percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" }, "wasPredictedPercentages": { "description": "one vs all predicted percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualCount | integer | true | | number of times this class is seen in the validation data | | className | string | true | | name of the class | | confusionMatrixOneVsAll | [array] | true | | 2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: [ [ True Negative, False Positive ], [ False Negative, True Positive ] ] | | f1 | number | true | | F1 score | | precision | number | true | | precision score | | predictedCount | integer | true | | number of times this class has been predicted for the validation data | | recall | number | true | | recall score | | wasActualPercentages | [ActualPercentages] | true | | one vs all actual percentages in a format specified below | | wasPredictedPercentages | [PredictedPercentages] | true | | one vs all predicted percentages in a format specified below | ## ConfusionChartData ``` { "description": "confusion chart data with the format below.", "properties": { "classMetrics": { "description": "per-class information including one vs all scores in a format specified below", "items": { "properties": { "actualCount": { "description": "number of times this class is seen in the validation data", "type": "integer" }, "className": { "description": "name of the class", "type": "string" }, "confusionMatrixOneVsAll": { "description": "2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "precision score", "type": "number" }, "predictedCount": { "description": "number of times this class has been predicted for the validation data", "type": "integer" }, "recall": { "description": "recall score", "type": "number" }, "wasActualPercentages": { "description": "one vs all actual percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" }, "wasPredictedPercentages": { "description": "one vs all predicted percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object" }, "type": "array" }, "classes": { "description": "class labels from the dataset, union of row classes & column classes. This field is deprecated as of v2.13. The rows and columns may have different class labels when using query parameters to retrieve a slice of the matrix; please use 'rowClasses' and 'colClasses' instead.", "items": { "type": "string" }, "type": "array" }, "colClasses": { "description": "class labels on columns of confusion matrix", "items": { "type": "string" }, "type": "array" }, "confusionMatrix": { "description": "2d array of ints representing confusion matrix, aligned with `rowClasses` and 'colClasses'array. For confusionMatrix[A][B] we can get an integer that represents the number of times 'if class with index A was correct we have class with index B predicted' (if the orientation is 'actual').", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "rowClasses": { "description": "class labels on rows of confusion matrix", "items": { "type": "string" }, "type": "array" } }, "required": [ "classMetrics", "classes", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object" } ``` confusion chart data with the format below. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classMetrics | [ConfusionChartClassMatrix] | true | | per-class information including one vs all scores in a format specified below | | classes | [string] | true | | class labels from the dataset, union of row classes & column classes. This field is deprecated as of v2.13. The rows and columns may have different class labels when using query parameters to retrieve a slice of the matrix; please use 'rowClasses' and 'colClasses' instead. | | colClasses | [string] | true | | class labels on columns of confusion matrix | | confusionMatrix | [array] | true | | 2d array of ints representing confusion matrix, aligned with rowClasses and 'colClasses'array. For confusionMatrix[A][B] we can get an integer that represents the number of times 'if class with index A was correct we have class with index B predicted' (if the orientation is 'actual'). | | rowClasses | [string] | true | | class labels on rows of confusion matrix | ## ConfusionChartForDatasetsListResponse ``` { "properties": { "count": { "description": "The number of results returned on this page.", "type": "integer" }, "data": { "description": "Confusion chart data with the in the same format as the response from [GET /api/v2/projects/{projectId}/models/{modelId}/confusionCharts/{source}/][get-apiv2projectsprojectidmodelsmodelidconfusionchartssource] with additional totalCount field.", "items": { "properties": { "columns": { "description": "[colStart, colEnd] column dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "data": { "description": "confusion chart data with the format below.", "properties": { "classMetrics": { "description": "per-class information including one vs all scores in a format specified below", "items": { "properties": { "actualCount": { "description": "number of times this class is seen in the validation data", "type": "integer" }, "className": { "description": "name of the class", "type": "string" }, "confusionMatrixOneVsAll": { "description": "2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "precision score", "type": "number" }, "predictedCount": { "description": "number of times this class has been predicted for the validation data", "type": "integer" }, "recall": { "description": "recall score", "type": "number" }, "wasActualPercentages": { "description": "one vs all actual percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" }, "wasPredictedPercentages": { "description": "one vs all predicted percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object" }, "type": "array" }, "classes": { "description": "class labels from the dataset, union of row classes & column classes. This field is deprecated as of v2.13. The rows and columns may have different class labels when using query parameters to retrieve a slice of the matrix; please use 'rowClasses' and 'colClasses' instead.", "items": { "type": "string" }, "type": "array" }, "colClasses": { "description": "class labels on columns of confusion matrix", "items": { "type": "string" }, "type": "array" }, "confusionMatrix": { "description": "2d array of ints representing confusion matrix, aligned with `rowClasses` and 'colClasses'array. For confusionMatrix[A][B] we can get an integer that represents the number of times 'if class with index A was correct we have class with index B predicted' (if the orientation is 'actual').", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "rowClasses": { "description": "class labels on rows of confusion matrix", "items": { "type": "string" }, "type": "array" } }, "required": [ "classMetrics", "classes", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "numberOfClasses": { "description": "count of classes in full confusion matrix.", "type": "integer" }, "rows": { "description": "[rowStart, rowEnd] row dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "source": { "description": "source of the chart", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "totalMatrixSum": { "description": "sum of all values in full confusion matrix", "type": "integer" } }, "required": [ "columns", "data", "globalMetrics", "numberOfClasses", "rows", "source", "totalMatrixSum" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total count of confusion charts for the model.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of results returned on this page. | | data | [ModelConfusionChartRetrieveResponse] | true | | Confusion chart data with the in the same format as the response from [GET /api/v2/projects/{projectId}/models/{modelId}/confusionCharts/{source}/][get-apiv2projectsprojectidmodelsmodelidconfusionchartssource] with additional totalCount field. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total count of confusion charts for the model. | ## ConfusionChartRetrieveForDatasets ``` { "properties": { "columns": { "description": "[colStart, colEnd] column dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "data": { "description": "confusion chart data with the format below.", "properties": { "classMetrics": { "description": "per-class information including one vs all scores in a format specified below", "items": { "properties": { "actualCount": { "description": "number of times this class is seen in the validation data", "type": "integer" }, "className": { "description": "name of the class", "type": "string" }, "confusionMatrixOneVsAll": { "description": "2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "precision score", "type": "number" }, "predictedCount": { "description": "number of times this class has been predicted for the validation data", "type": "integer" }, "recall": { "description": "recall score", "type": "number" }, "wasActualPercentages": { "description": "one vs all actual percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" }, "wasPredictedPercentages": { "description": "one vs all predicted percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object" }, "type": "array" }, "classes": { "description": "class labels from the dataset, union of row classes & column classes. This field is deprecated as of v2.13. The rows and columns may have different class labels when using query parameters to retrieve a slice of the matrix; please use 'rowClasses' and 'colClasses' instead.", "items": { "type": "string" }, "type": "array" }, "colClasses": { "description": "class labels on columns of confusion matrix", "items": { "type": "string" }, "type": "array" }, "confusionMatrix": { "description": "2d array of ints representing confusion matrix, aligned with `rowClasses` and 'colClasses'array. For confusionMatrix[A][B] we can get an integer that represents the number of times 'if class with index A was correct we have class with index B predicted' (if the orientation is 'actual').", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "rowClasses": { "description": "class labels on rows of confusion matrix", "items": { "type": "string" }, "type": "array" } }, "required": [ "classMetrics", "classes", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object" }, "datasetId": { "description": "The dataset ID to retrieve a confusion chart from.", "type": "string" }, "numberOfClasses": { "description": "The count of classes in the full confusion matrix.", "type": "integer" }, "rows": { "description": "[rowStart, rowEnd] row dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "totalMatrixSum": { "description": "The sum of all values in the full confusion matrix.", "type": "integer" } }, "required": [ "columns", "data", "datasetId", "numberOfClasses", "rows", "totalMatrixSum" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columns | [integer] | true | | [colStart, colEnd] column dimension of confusion matrix in response | | data | ConfusionChartData | true | | confusion chart data with the format below. | | datasetId | string | true | | The dataset ID to retrieve a confusion chart from. | | numberOfClasses | integer | true | | The count of classes in the full confusion matrix. | | rows | [integer] | true | | [rowStart, rowEnd] row dimension of confusion matrix in response | | totalMatrixSum | integer | true | | The sum of all values in the full confusion matrix. | ## ConfusionChartRetrieveMetadataForDatasets ``` { "properties": { "classNames": { "description": "list of all class names in the full confusion matrix, sorted by the `orderBy` parameter.", "items": { "type": "string" }, "type": "array" }, "datasetId": { "description": "The dataset to retrieve a confusion chart from.", "type": "string" }, "modelId": { "description": "The model to retrieve a confusion chart from.", "type": "string" }, "projectId": { "description": "The project to retrieve a confusion chart from.", "type": "string" }, "relevantClassesPositions": { "description": "Matrix to highlight important cell blocks in the confusion chart. Intended to represent a thumbnail view, where the relevantClassesPositions array has a 1 in thumbnail cells that are of interest, and 0 otherwise. The dimensions of the implied thumbnail will not match those of the confusion matrix, e.g. a twenty-class confusion matrix may have a 2x2 thumbnail.", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "totalMatrixSum": { "description": "Sum of all values in the full confusion matrix (equal to the number of points considered).", "type": "integer" } }, "required": [ "classNames", "datasetId", "modelId", "projectId", "relevantClassesPositions", "totalMatrixSum" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classNames | [string] | true | | list of all class names in the full confusion matrix, sorted by the orderBy parameter. | | datasetId | string | true | | The dataset to retrieve a confusion chart from. | | modelId | string | true | | The model to retrieve a confusion chart from. | | projectId | string | true | | The project to retrieve a confusion chart from. | | relevantClassesPositions | [array] | true | | Matrix to highlight important cell blocks in the confusion chart. Intended to represent a thumbnail view, where the relevantClassesPositions array has a 1 in thumbnail cells that are of interest, and 0 otherwise. The dimensions of the implied thumbnail will not match those of the confusion matrix, e.g. a twenty-class confusion matrix may have a 2x2 thumbnail. | | totalMatrixSum | integer | true | | Sum of all values in the full confusion matrix (equal to the number of points considered). | ## ConfusionMatrixClassMatrix ``` { "properties": { "actualCount": { "description": "The number of times this class was seen in the validation data.", "type": "integer" }, "className": { "description": "The name of the class.", "type": "string" }, "confusionMatrixOneVsAll": { "description": "A 2 dimensional array representing a 2x2 one-vs-all matrix. This represents, for each class, the True/False Negative/Positive rates as integers. The data structure is: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``.", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "Precision score", "type": "number" }, "predictedCount": { "description": "The number of times this class was predicted within the validation data.", "type": "integer" }, "recall": { "description": "Recall score", "type": "number" }, "wasActualPercentages": { "description": "The one-vs-all percentage of actuals, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "wasPredictedPercentages": { "description": "The one-vs-all percentages of predicted, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualCount | integer | true | | The number of times this class was seen in the validation data. | | className | string | true | | The name of the class. | | confusionMatrixOneVsAll | [array] | true | maxItems: 10000 | A 2 dimensional array representing a 2x2 one-vs-all matrix. This represents, for each class, the True/False Negative/Positive rates as integers. The data structure is: [ [ True Negative, False Positive ], [ False Negative, True Positive ] ]. | | f1 | number | true | | F1 score | | precision | number | true | | Precision score | | predictedCount | integer | true | | The number of times this class was predicted within the validation data. | | recall | number | true | | Recall score | | wasActualPercentages | [ActualPercentages] | true | maxItems: 10000 | The one-vs-all percentage of actuals, in a format specified below. | | wasPredictedPercentages | [PredictedPercentages] | true | maxItems: 10000 | The one-vs-all percentages of predicted, in a format specified below. | ## ConfusionMatrixData ``` { "description": "The confusion matrix chart data in the format specified below.", "properties": { "classMetrics": { "description": "The per-class information, including one-vs-all scores, in a format specified below.", "items": { "properties": { "actualCount": { "description": "The number of times this class was seen in the validation data.", "type": "integer" }, "className": { "description": "The name of the class.", "type": "string" }, "confusionMatrixOneVsAll": { "description": "A 2 dimensional array representing a 2x2 one-vs-all matrix. This represents, for each class, the True/False Negative/Positive rates as integers. The data structure is: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``.", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "Precision score", "type": "number" }, "predictedCount": { "description": "The number of times this class was predicted within the validation data.", "type": "integer" }, "recall": { "description": "Recall score", "type": "number" }, "wasActualPercentages": { "description": "The one-vs-all percentage of actuals, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "wasPredictedPercentages": { "description": "The one-vs-all percentages of predicted, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object", "x-versionadded": "v2.42" }, "maxItems": 10000, "type": "array" }, "colClasses": { "description": "Class labels on columns of the confusion matrix.", "items": { "type": "string" }, "maxItems": 10000, "type": "array" }, "confusionMatrix": { "description": "A two-dimensional array of integers representing the confusion matrix, aligned with the `rowClasses` and `colClasses` array. For example, if the orientation is `actual`, then when confusionMatrix[A][B], is `true`,the result is an integer that represents the number of times '`class` with index A was correct, but class with index B was predicted was true. ", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "rowClasses": { "description": "Class labels on rows of the confusion matrix.", "items": { "type": "string" }, "maxItems": 10000, "type": "array" } }, "required": [ "classMetrics", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object", "x-versionadded": "v2.42" } ``` The confusion matrix chart data in the format specified below. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classMetrics | [ConfusionMatrixClassMatrix] | true | maxItems: 10000 | The per-class information, including one-vs-all scores, in a format specified below. | | colClasses | [string] | true | maxItems: 10000 | Class labels on columns of the confusion matrix. | | confusionMatrix | [array] | true | maxItems: 10000 | A two-dimensional array of integers representing the confusion matrix, aligned with the rowClasses and colClasses array. For example, if the orientation is actual, then when confusionMatrix[A][B], is true,the result is an integer that represents the number of times 'class with index A was correct, but class with index B was predicted was true. | | rowClasses | [string] | true | maxItems: 10000 | Class labels on rows of the confusion matrix. | ## ConfusionMatrixInsightsResponse ``` { "description": "The confusion matrix chart data in the format specified below.", "properties": { "columns": { "description": "The [colStart, colEnd] column dimension of the confusion matrix in responses.", "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "confusionMatrixData": { "description": "The confusion matrix chart data in the format specified below.", "properties": { "classMetrics": { "description": "The per-class information, including one-vs-all scores, in a format specified below.", "items": { "properties": { "actualCount": { "description": "The number of times this class was seen in the validation data.", "type": "integer" }, "className": { "description": "The name of the class.", "type": "string" }, "confusionMatrixOneVsAll": { "description": "A 2 dimensional array representing a 2x2 one-vs-all matrix. This represents, for each class, the True/False Negative/Positive rates as integers. The data structure is: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``.", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "Precision score", "type": "number" }, "predictedCount": { "description": "The number of times this class was predicted within the validation data.", "type": "integer" }, "recall": { "description": "Recall score", "type": "number" }, "wasActualPercentages": { "description": "The one-vs-all percentage of actuals, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "wasPredictedPercentages": { "description": "The one-vs-all percentages of predicted, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object", "x-versionadded": "v2.42" }, "maxItems": 10000, "type": "array" }, "colClasses": { "description": "Class labels on columns of the confusion matrix.", "items": { "type": "string" }, "maxItems": 10000, "type": "array" }, "confusionMatrix": { "description": "A two-dimensional array of integers representing the confusion matrix, aligned with the `rowClasses` and `colClasses` array. For example, if the orientation is `actual`, then when confusionMatrix[A][B], is `true`,the result is an integer that represents the number of times '`class` with index A was correct, but class with index B was predicted was true. ", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "rowClasses": { "description": "Class labels on rows of the confusion matrix.", "items": { "type": "string" }, "maxItems": 10000, "type": "array" } }, "required": [ "classMetrics", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object", "x-versionadded": "v2.42" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "numberOfClasses": { "description": "The count of classes in the full confusion matrix.", "type": "integer" }, "rows": { "description": "The [rowStart, rowEnd] row dimension of the confusion matrix in responses.", "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "totalMatrixSum": { "description": "The sum of all values in the full confusion matrix.", "type": "integer" } }, "required": [ "columns", "confusionMatrixData", "globalMetrics", "numberOfClasses", "rows", "totalMatrixSum" ], "type": "object", "x-versionadded": "v2.42" } ``` The confusion matrix chart data in the format specified below. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columns | [integer] | true | maxItems: 10000 | The [colStart, colEnd] column dimension of the confusion matrix in responses. | | confusionMatrixData | ConfusionMatrixData | true | | The confusion matrix chart data in the format specified below. | | globalMetrics | GlobalMetrics | true | | average metrics across all classes | | numberOfClasses | integer | true | | The count of classes in the full confusion matrix. | | rows | [integer] | true | maxItems: 10000 | The [rowStart, rowEnd] row dimension of the confusion matrix in responses. | | totalMatrixSum | integer | true | | The sum of all values in the full confusion matrix. | ## ConfusionMatrixInsightsSingleResponse ``` { "properties": { "data": { "description": "The confusion matrix chart data in the format specified below.", "properties": { "columns": { "description": "The [colStart, colEnd] column dimension of the confusion matrix in responses.", "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "confusionMatrixData": { "description": "The confusion matrix chart data in the format specified below.", "properties": { "classMetrics": { "description": "The per-class information, including one-vs-all scores, in a format specified below.", "items": { "properties": { "actualCount": { "description": "The number of times this class was seen in the validation data.", "type": "integer" }, "className": { "description": "The name of the class.", "type": "string" }, "confusionMatrixOneVsAll": { "description": "A 2 dimensional array representing a 2x2 one-vs-all matrix. This represents, for each class, the True/False Negative/Positive rates as integers. The data structure is: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``.", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "Precision score", "type": "number" }, "predictedCount": { "description": "The number of times this class was predicted within the validation data.", "type": "integer" }, "recall": { "description": "Recall score", "type": "number" }, "wasActualPercentages": { "description": "The one-vs-all percentage of actuals, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "wasPredictedPercentages": { "description": "The one-vs-all percentages of predicted, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object", "x-versionadded": "v2.42" }, "maxItems": 10000, "type": "array" }, "colClasses": { "description": "Class labels on columns of the confusion matrix.", "items": { "type": "string" }, "maxItems": 10000, "type": "array" }, "confusionMatrix": { "description": "A two-dimensional array of integers representing the confusion matrix, aligned with the `rowClasses` and `colClasses` array. For example, if the orientation is `actual`, then when confusionMatrix[A][B], is `true`,the result is an integer that represents the number of times '`class` with index A was correct, but class with index B was predicted was true. ", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "rowClasses": { "description": "Class labels on rows of the confusion matrix.", "items": { "type": "string" }, "maxItems": 10000, "type": "array" } }, "required": [ "classMetrics", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object", "x-versionadded": "v2.42" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "numberOfClasses": { "description": "The count of classes in the full confusion matrix.", "type": "integer" }, "rows": { "description": "The [rowStart, rowEnd] row dimension of the confusion matrix in responses.", "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "totalMatrixSum": { "description": "The sum of all values in the full confusion matrix.", "type": "integer" } }, "required": [ "columns", "confusionMatrixData", "globalMetrics", "numberOfClasses", "rows", "totalMatrixSum" ], "type": "object", "x-versionadded": "v2.42" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The data partition used to calculate the insight.", "enum": [ "backtest_0", "backtest_0Training", "backtest_1", "backtest_10", "backtest_10Training", "backtest_11", "backtest_11Training", "backtest_12", "backtest_12Training", "backtest_13", "backtest_13Training", "backtest_14", "backtest_14Training", "backtest_15", "backtest_15Training", "backtest_16", "backtest_16Training", "backtest_17", "backtest_17Training", "backtest_18", "backtest_18Training", "backtest_19", "backtest_19Training", "backtest_1Training", "backtest_2", "backtest_20", "backtest_20Training", "backtest_2Training", "backtest_3", "backtest_3Training", "backtest_4", "backtest_4Training", "backtest_5", "backtest_5Training", "backtest_6", "backtest_6Training", "backtest_7", "backtest_7Training", "backtest_8", "backtest_8Training", "backtest_9", "backtest_9Training", "crossValidation", "externalTestSet", "holdout", "holdoutTraining", "training", "validation", "vectorDatabase" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | ConfusionMatrixInsightsResponse | true | | The confusion matrix chart data in the format specified below. | | dataSliceId | string,null | false | | The ID of the data slice. | | entityId | string | false | | The ID of the model. | | externalDatasetId | string,null | false | | The ID of the external dataset. | | id | string | true | | The ID of the created insight. | | projectId | string | false | | The ID of the project. | | source | string | false | | The data partition used to calculate the insight. | ### Enumerated Values | Property | Value | | --- | --- | | source | [backtest_0, backtest_0Training, backtest_1, backtest_10, backtest_10Training, backtest_11, backtest_11Training, backtest_12, backtest_12Training, backtest_13, backtest_13Training, backtest_14, backtest_14Training, backtest_15, backtest_15Training, backtest_16, backtest_16Training, backtest_17, backtest_17Training, backtest_18, backtest_18Training, backtest_19, backtest_19Training, backtest_1Training, backtest_2, backtest_20, backtest_20Training, backtest_2Training, backtest_3, backtest_3Training, backtest_4, backtest_4Training, backtest_5, backtest_5Training, backtest_6, backtest_6Training, backtest_7, backtest_7Training, backtest_8, backtest_8Training, backtest_9, backtest_9Training, crossValidation, externalTestSet, holdout, holdoutTraining, training, validation, vectorDatabase] | ## CreateRatingTableModel ``` { "properties": { "ratingTableId": { "description": "The rating table ID to use to create a new model.", "type": "string" } }, "required": [ "ratingTableId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ratingTableId | string | true | | The rating table ID to use to create a new model. | ## CreateShapMatrixPayload ``` { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "modelId": { "description": "The model ID.", "type": "string" } }, "required": [ "datasetId", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The dataset ID. | | modelId | string | true | | The model ID. | ## CrossClassAccuracy ``` { "properties": { "feature": { "description": "The name of the categorical feature.", "type": "string" }, "modelId": { "description": "ID of the model for the cross-class accuracy scores.", "type": "string" }, "perClassAccuracyScores": { "description": "An array of metric scores for each class of the feature.", "items": { "properties": { "className": { "description": "The name of the class value for the categorical feature.", "type": "string" }, "metrics": { "description": "An array of metric scores.", "items": { "properties": { "metric": { "description": "The name of the metric.", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE", "f1", "accuracy" ], "type": "string" }, "value": { "description": "The calculated score of the metric.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "metric", "value" ], "type": "object" }, "type": "array" } }, "required": [ "className", "metrics" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "Value of the prediction threshold for the model.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "feature", "modelId", "perClassAccuracyScores", "predictionThreshold" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature | string | true | | The name of the categorical feature. | | modelId | string | true | | ID of the model for the cross-class accuracy scores. | | perClassAccuracyScores | [PerClassAccuracy] | true | | An array of metric scores for each class of the feature. | | predictionThreshold | number | true | maximum: 1minimum: 0 | Value of the prediction threshold for the model. | ## CrossClassAccuracyCreateResponse ``` { "properties": { "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | The ID of the status object. | ## CrossClassAccuracyList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of cross-class accuracy scores for the model.", "items": { "properties": { "feature": { "description": "The name of the categorical feature.", "type": "string" }, "modelId": { "description": "ID of the model for the cross-class accuracy scores.", "type": "string" }, "perClassAccuracyScores": { "description": "An array of metric scores for each class of the feature.", "items": { "properties": { "className": { "description": "The name of the class value for the categorical feature.", "type": "string" }, "metrics": { "description": "An array of metric scores.", "items": { "properties": { "metric": { "description": "The name of the metric.", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE", "f1", "accuracy" ], "type": "string" }, "value": { "description": "The calculated score of the metric.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "metric", "value" ], "type": "object" }, "type": "array" } }, "required": [ "className", "metrics" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "Value of the prediction threshold for the model.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "feature", "modelId", "perClassAccuracyScores", "predictionThreshold" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CrossClassAccuracy] | true | | An array of cross-class accuracy scores for the model. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DataDisparityCreatePayload ``` { "properties": { "comparedClassNames": { "description": "An array of classes to calculate data disparity for.", "items": { "type": "string" }, "maxItems": 2, "minItems": 2, "type": "array" }, "feature": { "description": "Feature for which insight is computed.", "type": "string" } }, "required": [ "comparedClassNames", "feature" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparedClassNames | [string] | true | maxItems: 2minItems: 2 | An array of classes to calculate data disparity for. | | feature | string | true | | Feature for which insight is computed. | ## DataDisparityCreateResponse ``` { "properties": { "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | The ID of the status object. | ## DataDisparityInsights ``` { "description": "Computed data disparity insights if available.", "properties": { "features": { "description": "A mapping of the feature name to the corresponding values on the graph.", "items": { "properties": { "detailsHistogram": { "description": "Histogram details for the specified feature.", "items": { "properties": { "bars": { "description": "Class details for the histogram chart", "items": { "properties": { "label": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Ratio of occurrence of the class.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "bin": { "anyOf": [ { "type": "string" }, { "type": "integer" } ], "description": "Label for the bin grouping" } }, "required": [ "bars", "bin" ], "type": "object" }, "type": "array" }, "disparityScore": { "description": "A number to describe disparity for the feature between the compared classes.", "type": "number" }, "featureImpact": { "description": "A feature importance value.", "type": "number" }, "name": { "description": "Name of the feature.", "type": "string" }, "status": { "description": "A status of the feature.", "enum": [ "Healthy", "At Risk", "Failing" ], "type": "string" } }, "required": [ "detailsHistogram", "disparityScore", "featureImpact", "name", "status" ], "type": "object" }, "type": "array" }, "metric": { "description": "Metric used to calculate the impact of a feature on data disparity.", "type": "string" }, "protectedFeature": { "description": "Feature for which insights were computed.", "type": "string" }, "values": { "description": "Class count details for each class being compared.", "items": { "description": "Number of occurrences of each class being compared.", "properties": { "count": { "description": "Number of times the class was encountered.", "type": "integer" }, "label": { "description": "Name of the class.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` Computed data disparity insights if available. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | features | [AnalyzedFeature] | false | | A mapping of the feature name to the corresponding values on the graph. | | metric | string | false | | Metric used to calculate the impact of a feature on data disparity. | | protectedFeature | string | false | | Feature for which insights were computed. | | values | [FeatureCounts] | false | | Class count details for each class being compared. | ## DataDisparityRetrieveResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Computed data disparity insights if available.", "properties": { "features": { "description": "A mapping of the feature name to the corresponding values on the graph.", "items": { "properties": { "detailsHistogram": { "description": "Histogram details for the specified feature.", "items": { "properties": { "bars": { "description": "Class details for the histogram chart", "items": { "properties": { "label": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Ratio of occurrence of the class.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "bin": { "anyOf": [ { "type": "string" }, { "type": "integer" } ], "description": "Label for the bin grouping" } }, "required": [ "bars", "bin" ], "type": "object" }, "type": "array" }, "disparityScore": { "description": "A number to describe disparity for the feature between the compared classes.", "type": "number" }, "featureImpact": { "description": "A feature importance value.", "type": "number" }, "name": { "description": "Name of the feature.", "type": "string" }, "status": { "description": "A status of the feature.", "enum": [ "Healthy", "At Risk", "Failing" ], "type": "string" } }, "required": [ "detailsHistogram", "disparityScore", "featureImpact", "name", "status" ], "type": "object" }, "type": "array" }, "metric": { "description": "Metric used to calculate the impact of a feature on data disparity.", "type": "string" }, "protectedFeature": { "description": "Feature for which insights were computed.", "type": "string" }, "values": { "description": "Class count details for each class being compared.", "items": { "description": "Number of occurrences of each class being compared.", "properties": { "count": { "description": "Number of times the class was encountered.", "type": "integer" }, "label": { "description": "Name of the class.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" }, "type": "array" } }, "type": "object" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | DataDisparityInsights | true | | Computed data disparity insights if available. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DataPointResponse ``` { "properties": { "prediction": { "description": "The output of the model for this row.", "type": "number" }, "shapExplanation": { "description": "Either ``null`` or an array of up to 10 `ShapleyFeatureContribution` objects. Only rows with the highest anomaly scores have Shapley explanations calculated.", "items": { "properties": { "feature": { "description": "Feature name", "type": "string" }, "featureValue": { "description": "Feature value for this row. First 50 characters are returned.", "type": "string" }, "strength": { "description": "Shapley value for this feature and row.", "type": "number" } }, "required": [ "feature", "featureValue", "strength" ], "type": "object" }, "type": "array" }, "timestamp": { "description": "ISO-formatted timestamp for the row.", "format": "date-time", "type": "string" } }, "required": [ "prediction", "shapExplanation", "timestamp" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | prediction | number | true | | The output of the model for this row. | | shapExplanation | [ShapExplanationResponse] | true | | Either null or an array of up to 10 ShapleyFeatureContribution objects. Only rows with the highest anomaly scores have Shapley explanations calculated. | | timestamp | string(date-time) | true | | ISO-formatted timestamp for the row. | ## DataSliceComputeSubsetSizeRequest ``` { "properties": { "externalDatasetId": { "description": "The external dataset ID to use when calculating the size of a slice. Use this parameter only when the source is 'externalTestSet'.", "type": [ "string", "null" ] }, "modelId": { "description": "The model ID whose training dataset should be sliced. Use this parameter only when the source is 'training'.", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "The source of data to use to calculate the size.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "crossValidation", "externalTestSet", "holdout", "holdout_training", "training", "validation", "vectorDatabase" ], "type": "string" } }, "required": [ "projectId", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalDatasetId | string,null | false | | The external dataset ID to use when calculating the size of a slice. Use this parameter only when the source is 'externalTestSet'. | | modelId | string,null | false | | The model ID whose training dataset should be sliced. Use this parameter only when the source is 'training'. | | projectId | string | true | | The project ID. | | source | string | true | | The source of data to use to calculate the size. | ### Enumerated Values | Property | Value | | --- | --- | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, crossValidation, externalTestSet, holdout, holdout_training, training, validation, vectorDatabase] | ## DataSliceIndividualResponse ``` { "properties": { "filters": { "description": "List of filters the data slice is composed of.", "items": { "properties": { "operand": { "description": "Feature to apply operation to.", "type": "string" }, "operator": { "description": "Operator to apply to the named operand in the dataset. The operator 'eq' mean 'equals the single specified value'. The operator 'in' means 'is one of a list of allowed values.'", "enum": [ "eq", "in", "<", ">", "between", "notBetween" ], "type": "string" }, "values": { "description": "Values to filter the operand by with the given operator.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "operand", "operator", "values" ], "type": "object" }, "maxItems": 3, "minItems": 1, "type": "array" }, "id": { "description": "ID of the data slice.", "type": "string" }, "name": { "description": "User provided name for the data slice.", "maxLength": 500, "minLength": 1, "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "filters", "id", "name", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | filters | [FilterDataSlices] | true | maxItems: 3minItems: 1 | List of filters the data slice is composed of. | | id | string | true | | ID of the data slice. | | name | string | true | maxLength: 500minLength: 1minLength: 1 | User provided name for the data slice. | | projectId | string | true | | The project ID. | ## DataSliceMessage ``` { "properties": { "additionalInfo": { "description": "Additional details about this message.", "type": "string" }, "description": { "description": "Short summary description about this message.", "type": "string" }, "level": { "description": "Message level.", "enum": [ "CRITICAL", "INFORMATIONAL", "WARNING" ], "type": "string" } }, "required": [ "additionalInfo", "description", "level" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | additionalInfo | string | true | | Additional details about this message. | | description | string | true | | Short summary description about this message. | | level | string | true | | Message level. | ### Enumerated Values | Property | Value | | --- | --- | | level | [CRITICAL, INFORMATIONAL, WARNING] | ## DataSliceRetrieveSubsetSizeResponse ``` { "properties": { "dataSliceId": { "description": "ID of the data slice.", "type": "string" }, "externalDatasetId": { "description": "The external dataset ID to use when calculating the size of a slice. Use this parameter only when the source is 'externalTestSet'.", "type": [ "string", "null" ] }, "messages": { "description": "List of user-relevant messages related to a Data Slice.", "items": { "properties": { "additionalInfo": { "description": "Additional details about this message.", "type": "string" }, "description": { "description": "Short summary description about this message.", "type": "string" }, "level": { "description": "Message level.", "enum": [ "CRITICAL", "INFORMATIONAL", "WARNING" ], "type": "string" } }, "required": [ "additionalInfo", "description", "level" ], "type": "object" }, "maxItems": 100, "type": "array" }, "modelId": { "description": "The model ID whose training dataset should be sliced. Use this parameter only when the source is 'training'.", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "sliceSize": { "description": "Number of rows in the slice for the given source.", "minimum": 0, "type": "integer" }, "source": { "description": "The source of data to use to calculate the size.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "crossValidation", "externalTestSet", "holdout", "holdout_training", "training", "validation", "vectorDatabase" ], "type": "string" } }, "required": [ "dataSliceId", "messages", "projectId", "sliceSize", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSliceId | string | true | | ID of the data slice. | | externalDatasetId | string,null | false | | The external dataset ID to use when calculating the size of a slice. Use this parameter only when the source is 'externalTestSet'. | | messages | [DataSliceMessage] | true | maxItems: 100 | List of user-relevant messages related to a Data Slice. | | modelId | string,null | false | | The model ID whose training dataset should be sliced. Use this parameter only when the source is 'training'. | | projectId | string | true | | The project ID. | | sliceSize | integer | true | minimum: 0 | Number of rows in the slice for the given source. | | source | string | true | | The source of data to use to calculate the size. | ### Enumerated Values | Property | Value | | --- | --- | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, crossValidation, externalTestSet, holdout, holdout_training, training, validation, vectorDatabase] | ## DataSlicesBulkDeleteRequest ``` { "properties": { "ids": { "description": "List of data slices to remove.", "items": { "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" } }, "required": [ "ids" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ids | [string] | true | maxItems: 20minItems: 1 | List of data slices to remove. | ## DataSlicesCreationRequest ``` { "properties": { "filters": { "description": "List of filters the data slice is composed of.", "items": { "properties": { "operand": { "description": "Feature to apply operation to.", "type": "string" }, "operator": { "description": "Operator to apply to the named operand in the dataset. The operator 'eq' mean 'equals the single specified value'. The operator 'in' means 'is one of a list of allowed values.'", "enum": [ "eq", "in", "<", ">", "between", "notBetween" ], "type": "string" }, "values": { "description": "Values to filter the operand by with the given operator.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "operand", "operator", "values" ], "type": "object" }, "maxItems": 3, "minItems": 1, "type": "array" }, "name": { "description": "User provided name for the data slice.", "maxLength": 500, "minLength": 1, "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "filters", "name", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | filters | [FilterDataSlices] | true | maxItems: 3minItems: 1 | List of filters the data slice is composed of. | | name | string | true | maxLength: 500minLength: 1minLength: 1 | User provided name for the data slice. | | projectId | string | true | | The project ID. | ## DataSlicesListAllSlicesResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of paginated Data Slices.", "items": { "properties": { "filters": { "description": "List of filters the data slice is composed of.", "items": { "properties": { "operand": { "description": "Feature to apply operation to.", "type": "string" }, "operator": { "description": "Operator to apply to the named operand in the dataset. The operator 'eq' mean 'equals the single specified value'. The operator 'in' means 'is one of a list of allowed values.'", "enum": [ "eq", "in", "<", ">", "between", "notBetween" ], "type": "string" }, "values": { "description": "Values to filter the operand by with the given operator.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "operand", "operator", "values" ], "type": "object" }, "maxItems": 3, "minItems": 1, "type": "array" }, "id": { "description": "ID of the data slice.", "type": "string" }, "name": { "description": "User provided name for the data slice.", "maxLength": 500, "minLength": 1, "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "filters", "id", "name", "projectId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DataSliceIndividualResponse] | true | maxItems: 100 | List of paginated Data Slices. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## DatetimeTrendPlotsBacktestMetadata ``` { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" } ``` Metadata for backtest/holdout. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | training | DatetimeTrendPlotsMetadataStartEndDates | true | | Start and end dates for the backtest/holdout training. | | validation | DatetimeTrendPlotsMetadataStartEndDates | true | | Start and end dates for the backtest/holdout training. | ## DatetimeTrendPlotsCreate ``` { "properties": { "backtest": { "default": 0, "description": "Compute plots for a specific backtest (use the backtest index starting from zero) or `holdout`. If not specified the first backtest (backtest index 0) will be used.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ] }, "forecastDistanceEnd": { "description": "The end of forecast distance range (forecast window) to compute. If not specified, the last forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects.", "minimum": 0, "type": "integer" }, "forecastDistanceStart": { "description": "The start of forecast distance range (forecast window) to compute. If not specified, the first forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects.", "minimum": 0, "type": "integer" }, "fullAverage": { "default": false, "description": "Whether to compute an average plot for all series. Only available for time series multiseries projects.", "type": "boolean", "x-versionadded": "2.28" }, "seriesIds": { "description": "Only available for time series multiseries projects. Each element should be a name of a single series in a multiseries project. It is possible to compute a maximum of 1000 series per one request. If not specified the first 1000 series in alphabetical order will be computed. It is not possible to specify `fullAverage: true` while also setting `seriesIds`. This parameter can only be specified after first 1000 series in alphabetical order are computed.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array", "x-versionadded": "2.28" }, "source": { "default": "validation", "description": "The source of the data for the backtest/holdout.", "enum": [ "training", "validation" ], "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtest | any | false | | Compute plots for a specific backtest (use the backtest index starting from zero) or holdout. If not specified the first backtest (backtest index 0) will be used. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 19minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forecastDistanceEnd | integer | false | minimum: 0 | The end of forecast distance range (forecast window) to compute. If not specified, the last forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects. | | forecastDistanceStart | integer | false | minimum: 0 | The start of forecast distance range (forecast window) to compute. If not specified, the first forecast distance for this project will be used. Forecast distance specifies the number of time steps between the predicted point and the origin point. Only available for time series supervised projects. | | fullAverage | boolean | false | | Whether to compute an average plot for all series. Only available for time series multiseries projects. | | seriesIds | [string] | false | maxItems: 1000minItems: 1 | Only available for time series multiseries projects. Each element should be a name of a single series in a multiseries project. It is possible to compute a maximum of 1000 series per one request. If not specified the first 1000 series in alphabetical order will be computed. It is not possible to specify fullAverage: true while also setting seriesIds. This parameter can only be specified after first 1000 series in alphabetical order are computed. | | source | string | false | | The source of the data for the backtest/holdout. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | holdout | | source | [training, validation] | ## DatetimeTrendPlotsMetadataStartEndDates ``` { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } ``` Start and end dates for the backtest/holdout training. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string,null(date-time) | true | | The datetime of the end of the chart data (exclusive). Null if chart data is not computed. | | startDate | string,null(date-time) | true | | The datetime of the start of the chart data (inclusive). Null if chart data is not computed. | ## DatetimeTrendPlotsPreviewBins ``` { "properties": { "actual": { "description": "Average actual value of the target in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "predicted": { "description": "Average prediction of the model in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "predicted", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actual | number,null | true | | Average actual value of the target in the bin. null if there are no entries in the bin. | | endDate | string(date-time) | true | | The datetime of the end of the bin (exclusive). | | predicted | number,null | true | | Average prediction of the model in the bin. null if there are no entries in the bin. | | startDate | string(date-time) | true | | The datetime of the start of the bin (inclusive). | ## DatetimeTrendPlotsPreviewResponse ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "actual": { "description": "Average actual value of the target in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "predicted": { "description": "Average prediction of the model in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "predicted", "startDate" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "bins", "endDate", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [DatetimeTrendPlotsPreviewBins] | true | maxItems: 1000minItems: 1 | An array of bins for the retrieved plots. | | endDate | string(date-time) | true | | The datetime of the end of the chart data (exclusive). | | startDate | string(date-time) | true | | The datetime of the start of the chart data (inclusive). | ## DatetimeTrendPlotsResponse ``` { "properties": { "message": { "description": "Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job can be created.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | false | | Any extended message to include about the result. For example, if a job is submitted that is a duplicate of a job that has already been added to the queue, the message will mention that no new job can be created. | ## DocumentFeature ``` { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "document" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "importantNgrams" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureImpact | number,null | false | | Feature Impact score. | | featureName | string | true | | Feature name. | | featureType | string | true | | Feature Type. | | insights | [Text] | true | | A list of Cluster Insights for a feature. | ### Enumerated Values | Property | Value | | --- | --- | | featureType | document | ## Empty ``` { "type": "object" } ``` ### Properties None ## ExternalScoresCreate ``` { "properties": { "actualValueColumn": { "description": "Actual value column name that contains actual values to be used for computing scores and insights for unsupervised projects only. This value can be set once for a dataset and cannot be changed.", "type": "string" }, "datasetId": { "description": "The dataset to compute predictions for; it must have previously been uploaded.", "type": "string" }, "modelId": { "description": "The model to use to make predictions.", "type": "string" } }, "required": [ "datasetId", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string | false | | Actual value column name that contains actual values to be used for computing scores and insights for unsupervised projects only. This value can be set once for a dataset and cannot be changed. | | datasetId | string | true | | The dataset to compute predictions for; it must have previously been uploaded. | | modelId | string | true | | The model to use to make predictions. | ## ExternalScoresListData ``` { "properties": { "actualValueColumn": { "description": "The name of the column with actuals that was used to calculate the scores.", "type": "string" }, "datasetId": { "description": "The dataset ID the data comes from.", "type": "string" }, "modelId": { "description": "The model ID for the scores.", "type": "string" }, "projectId": { "description": "The project ID for the scores.", "type": "string" }, "scores": { "description": "A JSON array of the computed scores.", "items": { "properties": { "label": { "description": "The metric name that was used to compute the score.", "type": "string" }, "value": { "description": "The score value.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" } }, "required": [ "actualValueColumn", "datasetId", "modelId", "projectId", "scores" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string | true | | The name of the column with actuals that was used to calculate the scores. | | datasetId | string | true | | The dataset ID the data comes from. | | modelId | string | true | | The model ID for the scores. | | projectId | string | true | | The project ID for the scores. | | scores | [ExternalScoresListDataScore] | true | | A JSON array of the computed scores. | ## ExternalScoresListDataScore ``` { "properties": { "label": { "description": "The metric name that was used to compute the score.", "type": "string" }, "value": { "description": "The score value.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | string | true | | The metric name that was used to compute the score. | | value | number | true | | The score value. | ## ExternalScoresListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of objects containing the following data.", "items": { "properties": { "actualValueColumn": { "description": "The name of the column with actuals that was used to calculate the scores.", "type": "string" }, "datasetId": { "description": "The dataset ID the data comes from.", "type": "string" }, "modelId": { "description": "The model ID for the scores.", "type": "string" }, "projectId": { "description": "The project ID for the scores.", "type": "string" }, "scores": { "description": "A JSON array of the computed scores.", "items": { "properties": { "label": { "description": "The metric name that was used to compute the score.", "type": "string" }, "value": { "description": "The score value.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" } }, "required": [ "actualValueColumn", "datasetId", "modelId", "projectId", "scores" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ExternalScoresListData] | true | | The list of objects containing the following data. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## FairnessInsight ``` { "properties": { "fairnessMetric": { "description": "The fairness metric used to calculate the fairness scores.", "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "fairnessThreshold": { "default": 0.8, "description": "Value of the fairness threshold, defined in project options.", "maximum": 1, "minimum": 0, "type": "number" }, "modelId": { "description": "ID of the model fairness was measured for.", "type": "string" }, "perClassFairness": { "description": "An array of calculated fairness scores for each protected feature class.", "items": { "properties": { "absoluteValue": { "description": "Absolute fairness score for the class", "minimum": 0, "type": "number" }, "className": { "description": "Name of the protected class the score is calculated for.", "type": "string" }, "entriesCount": { "description": "The number of entries of the class in the analysed data.", "minimum": 0, "type": "integer" }, "isStatisticallySignificant": { "description": "Flag to tell whether the score can be treated as statistically significant. In other words, whether we are confident enough with the score for this protected class.", "type": "boolean" }, "value": { "description": "The relative fairness score for the class.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "absoluteValue", "className", "entriesCount", "isStatisticallySignificant", "value" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "Model's prediction threshold used when insight was calculated. ``null`` if prediction threshold is not required for the fairness metric calculations.", "maximum": 1, "minimum": 0, "type": "number" }, "protectedFeature": { "description": "Name of the protected feature the fairness calculation is made for.", "type": "string" } }, "required": [ "fairnessMetric", "fairnessThreshold", "modelId", "perClassFairness", "protectedFeature" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fairnessMetric | string | true | | The fairness metric used to calculate the fairness scores. | | fairnessThreshold | number | true | maximum: 1minimum: 0 | Value of the fairness threshold, defined in project options. | | modelId | string | true | | ID of the model fairness was measured for. | | perClassFairness | [PerClassFairness] | true | | An array of calculated fairness scores for each protected feature class. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Model's prediction threshold used when insight was calculated. null if prediction threshold is not required for the fairness metric calculations. | | protectedFeature | string | true | | Name of the protected feature the fairness calculation is made for. | ### Enumerated Values | Property | Value | | --- | --- | | fairnessMetric | [proportionalParity, equalParity, favorableClassBalance, unfavorableClassBalance, trueUnfavorableRateParity, trueFavorableRateParity, favorablePredictiveValueParity, unfavorablePredictiveValueParity] | ## FairnessInsightsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of fairness insights for the model.", "items": { "properties": { "fairnessMetric": { "description": "The fairness metric used to calculate the fairness scores.", "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "fairnessThreshold": { "default": 0.8, "description": "Value of the fairness threshold, defined in project options.", "maximum": 1, "minimum": 0, "type": "number" }, "modelId": { "description": "ID of the model fairness was measured for.", "type": "string" }, "perClassFairness": { "description": "An array of calculated fairness scores for each protected feature class.", "items": { "properties": { "absoluteValue": { "description": "Absolute fairness score for the class", "minimum": 0, "type": "number" }, "className": { "description": "Name of the protected class the score is calculated for.", "type": "string" }, "entriesCount": { "description": "The number of entries of the class in the analysed data.", "minimum": 0, "type": "integer" }, "isStatisticallySignificant": { "description": "Flag to tell whether the score can be treated as statistically significant. In other words, whether we are confident enough with the score for this protected class.", "type": "boolean" }, "value": { "description": "The relative fairness score for the class.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "absoluteValue", "className", "entriesCount", "isStatisticallySignificant", "value" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "Model's prediction threshold used when insight was calculated. ``null`` if prediction threshold is not required for the fairness metric calculations.", "maximum": 1, "minimum": 0, "type": "number" }, "protectedFeature": { "description": "Name of the protected feature the fairness calculation is made for.", "type": "string" } }, "required": [ "fairnessMetric", "fairnessThreshold", "modelId", "perClassFairness", "protectedFeature" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [FairnessInsight] | true | | An array of fairness insights for the model. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## FairnessInsightsStartCalculationPayload ``` { "properties": { "fairnessMetricsSet": { "description": "Metric to use for calculating fairness. Can be one of ``proportionalParity``, ``equalParity``, ``predictionBalance``, ``trueFavorableAndUnfavorableRateParity`` or ``FavorableAndUnfavorablePredictiveValueParity``. Used and required only if *Bias & Fairness in AutoML* feature is enabled.", "enum": [ "proportionalParity", "equalParity", "predictionBalance", "trueFavorableAndUnfavorableRateParity", "favorableAndUnfavorablePredictiveValueParity" ], "type": "string", "x-versionadded": "v2.24" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fairnessMetricsSet | string | false | | Metric to use for calculating fairness. Can be one of proportionalParity, equalParity, predictionBalance, trueFavorableAndUnfavorableRateParity or FavorableAndUnfavorablePredictiveValueParity. Used and required only if Bias & Fairness in AutoML feature is enabled. | ### Enumerated Values | Property | Value | | --- | --- | | fairnessMetricsSet | [proportionalParity, equalParity, predictionBalance, trueFavorableAndUnfavorableRateParity, favorableAndUnfavorablePredictiveValueParity] | ## FairnessInsightsStartCalculationResponse ``` { "properties": { "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | statusId | string | true | | The ID of the status object. | ## FeatureAssociationCreatePayload ``` { "properties": { "featurelistId": { "description": "A featurelist ID to calculate feature association matrix.", "type": "string" } }, "required": [ "featurelistId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featurelistId | string | true | | A featurelist ID to calculate feature association matrix. | ## FeatureAssociationDetailsRetrieveControllerResponse ``` { "properties": { "chartType": { "description": "Which type of plotting the pair of features gets in the UI, e.g. `SCATTER`", "type": "string" }, "features": { "description": "The name of `feature1` and `feature2`.", "items": { "type": "string" }, "type": "array" }, "types": { "description": "The type of `feature1` and `feature2`. Possible values: `CATEGORICAL`, `NUMERIC`.", "items": { "enum": [ "CATEGORICAL", "NUMERIC" ], "type": "string" }, "type": "array" }, "values": { "description": "The data triplets for pairwise plotting.", "items": { "items": { "anyOf": [ { "type": "string" }, { "type": "number" } ] }, "type": "array" }, "type": "array" } }, "required": [ "chartType", "features", "types", "values" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chartType | string | true | | Which type of plotting the pair of features gets in the UI, e.g. SCATTER | | features | [string] | true | | The name of feature1 and feature2. | | types | [string] | true | | The type of feature1 and feature2. Possible values: CATEGORICAL, NUMERIC. | | values | [array] | true | | The data triplets for pairwise plotting. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | ## FeatureAssociationList ``` { "properties": { "featurelistId": { "description": "The featurelist ID.", "type": "string" }, "hasFam": { "description": "Whether Feature Association Matrix is calculated for featurelist.", "type": "boolean" }, "title": { "description": "The name of featurelist.", "type": "string" } }, "required": [ "featurelistId", "hasFam", "title" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featurelistId | string | true | | The featurelist ID. | | hasFam | boolean | true | | Whether Feature Association Matrix is calculated for featurelist. | | title | string | true | | The name of featurelist. | ## FeatureAssociationListControllerResponse ``` { "properties": { "featurelists": { "description": "List all featurelists with feature association matrix availability flags.", "items": { "properties": { "featurelistId": { "description": "The featurelist ID.", "type": "string" }, "hasFam": { "description": "Whether Feature Association Matrix is calculated for featurelist.", "type": "boolean" }, "title": { "description": "The name of featurelist.", "type": "string" } }, "required": [ "featurelistId", "hasFam", "title" ], "type": "object" }, "type": "array" } }, "required": [ "featurelists" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featurelists | [FeatureAssociationList] | true | | List all featurelists with feature association matrix availability flags. | ## FeatureAssociationRetrieveControllerResponse ``` { "properties": { "features": { "description": "Metadata for each feature and where it goes in the matrix as structured below.", "items": { "properties": { "alphabeticSortIndex": { "description": "A number representing the alphabetical order of this feature compared to the other features in this dataset.", "type": "integer" }, "clusterId": { "description": "ID of the cluster this feature belongs to.", "type": [ "integer", "null" ] }, "clusterName": { "description": "Name of feature cluster.", "type": "string" }, "clusterSortIndex": { "description": "A number representing the ordering of the feature across all feature clusters. Features in the same cluster always have adjacent indices.", "type": "integer" }, "feature": { "description": "Name of the feature.", "type": "string" }, "importanceSortIndex": { "description": "A number ranking the importance of this feature compared to the other features in this dataset.", "type": "integer" }, "strengthSortIndex": { "description": "A number ranking the strength of this feature compared to the other features in this dataset.", "type": "integer" } }, "required": [ "alphabeticSortIndex", "clusterId", "clusterSortIndex", "feature", "importanceSortIndex", "strengthSortIndex" ], "type": "object" }, "type": "array" }, "strengths": { "description": "Pairwise statistics for the available features as structured below.", "items": { "properties": { "feature1": { "description": "The name of the first feature.", "type": "string" }, "feature2": { "description": "The name of the second feature.", "type": "string" }, "statistic": { "description": "Feature association statistics for `feature1` and `feature2`. For features with no pairwise statistics available the value is `null`.", "type": [ "number", "null" ] } }, "required": [ "feature1", "feature2", "statistic" ], "type": "object" }, "type": "array" } }, "required": [ "features", "strengths" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | features | [FeatureAssociationRetrieveFeatures] | true | | Metadata for each feature and where it goes in the matrix as structured below. | | strengths | [FeatureAssociationRetrieveStrengths] | true | | Pairwise statistics for the available features as structured below. | ## FeatureAssociationRetrieveFeatures ``` { "properties": { "alphabeticSortIndex": { "description": "A number representing the alphabetical order of this feature compared to the other features in this dataset.", "type": "integer" }, "clusterId": { "description": "ID of the cluster this feature belongs to.", "type": [ "integer", "null" ] }, "clusterName": { "description": "Name of feature cluster.", "type": "string" }, "clusterSortIndex": { "description": "A number representing the ordering of the feature across all feature clusters. Features in the same cluster always have adjacent indices.", "type": "integer" }, "feature": { "description": "Name of the feature.", "type": "string" }, "importanceSortIndex": { "description": "A number ranking the importance of this feature compared to the other features in this dataset.", "type": "integer" }, "strengthSortIndex": { "description": "A number ranking the strength of this feature compared to the other features in this dataset.", "type": "integer" } }, "required": [ "alphabeticSortIndex", "clusterId", "clusterSortIndex", "feature", "importanceSortIndex", "strengthSortIndex" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | alphabeticSortIndex | integer | true | | A number representing the alphabetical order of this feature compared to the other features in this dataset. | | clusterId | integer,null | true | | ID of the cluster this feature belongs to. | | clusterName | string | false | | Name of feature cluster. | | clusterSortIndex | integer | true | | A number representing the ordering of the feature across all feature clusters. Features in the same cluster always have adjacent indices. | | feature | string | true | | Name of the feature. | | importanceSortIndex | integer | true | | A number ranking the importance of this feature compared to the other features in this dataset. | | strengthSortIndex | integer | true | | A number ranking the strength of this feature compared to the other features in this dataset. | ## FeatureAssociationRetrieveStrengths ``` { "properties": { "feature1": { "description": "The name of the first feature.", "type": "string" }, "feature2": { "description": "The name of the second feature.", "type": "string" }, "statistic": { "description": "Feature association statistics for `feature1` and `feature2`. For features with no pairwise statistics available the value is `null`.", "type": [ "number", "null" ] } }, "required": [ "feature1", "feature2", "statistic" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature1 | string | true | | The name of the first feature. | | feature2 | string | true | | The name of the second feature. | | statistic | number,null | true | | Feature association statistics for feature1 and feature2. For features with no pairwise statistics available the value is null. | ## FeatureCounts ``` { "description": "Number of occurrences of each class being compared.", "properties": { "count": { "description": "Number of times the class was encountered.", "type": "integer" }, "label": { "description": "Name of the class.", "type": "string" } }, "required": [ "count", "label" ], "type": "object" } ``` Number of occurrences of each class being compared. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Number of times the class was encountered. | | label | string | true | | Name of the class. | ## FeatureEffectCreate ``` { "properties": { "rowCount": { "description": "The number of rows from dataset to use for Feature Impact calculation.", "maximum": 100000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rowCount | integer,null | false | maximum: 100000minimum: 10 | The number of rows from dataset to use for Feature Impact calculation. | ## FeatureEffects ``` { "properties": { "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureImpactScore | number | true | | The feature impact score. | | featureName | string | true | | The name of the feature. | | featureType | string | true | | The feature type, either numeric or categorical. | | isBinnable | boolean | true | | Whether values can be grouped into bins. | | isScalable | boolean,null | true | | Whether numeric feature values can be reported on a log scale. | | partialDependence | PartialDependence | false | | Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight. | | predictedVsActual | PredictedVsActual | false | | Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight. | | weightLabel | string,null | true | | The weight label if a weight was configured for the project. | ## FeatureEffectsCreateDatetime ``` { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "rowCount": { "description": "The number of rows from dataset to use for Feature Impact calculation.", "maximum": 100000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.21" } }, "required": [ "backtestIndex" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestIndex | string | true | | The backtest index. For example: 0, 1, ..., 20, holdout, startstop. | | rowCount | integer,null | false | maximum: 100000minimum: 10 | The number of rows from dataset to use for Feature Impact calculation. | ## FeatureEffectsDatetimeResponse ``` { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "featureEffects": { "description": "The Feature Effects computational results for each feature.", "items": { "properties": { "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "The model's data source.", "type": "string" } }, "required": [ "backtestIndex", "featureEffects", "modelId", "projectId", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestIndex | string | true | | The backtest index. For example: 0, 1, ..., 20, holdout, startstop. | | featureEffects | [FeatureEffects] | true | | The Feature Effects computational results for each feature. | | modelId | string | true | | The model ID. | | projectId | string | true | | The project ID. | | source | string | true | | The model's data source. | ## FeatureEffectsInsightResponse ``` { "description": "Feature effects data.", "properties": { "featureEffects": { "description": "The Feature Effects computational results for each feature.", "items": { "properties": { "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" } }, "required": [ "featureEffects" ], "type": "object" } ``` Feature effects data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureEffects | [FeatureEffects] | true | | The Feature Effects computational results for each feature. | ## FeatureEffectsResponse ``` { "properties": { "featureEffects": { "description": "The Feature Effects computational results for each feature.", "items": { "properties": { "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "The model's data source.", "type": "string" } }, "required": [ "featureEffects", "modelId", "projectId", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureEffects | [FeatureEffects] | true | | The Feature Effects computational results for each feature. | | modelId | string | true | | The model ID. | | projectId | string | true | | The project ID. | | source | string | true | | The model's data source. | ## FeatureImpactCreatePayload ``` { "properties": { "rowCount": { "description": "The sample size to use for Feature Impact computation. It is possible to re-compute Feature Impact with a different row count.", "maximum": 100000, "minimum": 10, "type": "integer", "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rowCount | integer | false | maximum: 100000minimum: 10 | The sample size to use for Feature Impact computation. It is possible to re-compute Feature Impact with a different row count. | ## FeatureImpactInsightResponse ``` { "description": "Feature impact data.", "properties": { "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact.", "type": "boolean" }, "rowCount": { "description": "The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the ``rowCount``, we return ``null`` here.", "type": [ "integer", "null" ] } }, "required": [ "featureImpacts", "ranRedundancyDetection", "rowCount" ], "type": "object" } ``` Feature impact data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureImpacts | [FeatureImpactItem] | true | maxItems: 1000 | A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned. | | ranRedundancyDetection | boolean | true | | Indicates whether redundant feature identification was run while calculating this feature impact. | | rowCount | integer,null | true | | The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the rowCount, we return null here. | ## FeatureImpactItem ``` { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | true | | The name of the feature. | | impactNormalized | number | true | maximum: 1 | The same as impactUnnormalized, but normalized such that the highest value is 1. | | impactUnnormalized | number | true | | How much worse the error metric score is when making predictions on modified data. | | parentFeatureName | string,null | false | | The name of the parent feature. | | redundantWith | string,null | true | | Name of feature that has the highest correlation with this feature. | ## FilterDataSlices ``` { "properties": { "operand": { "description": "Feature to apply operation to.", "type": "string" }, "operator": { "description": "Operator to apply to the named operand in the dataset. The operator 'eq' mean 'equals the single specified value'. The operator 'in' means 'is one of a list of allowed values.'", "enum": [ "eq", "in", "<", ">", "between", "notBetween" ], "type": "string" }, "values": { "description": "Values to filter the operand by with the given operator.", "items": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ] }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "operand", "operator", "values" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operand | string | true | | Feature to apply operation to. | | operator | string | true | | Operator to apply to the named operand in the dataset. The operator 'eq' mean 'equals the single specified value'. The operator 'in' means 'is one of a list of allowed values.' | | values | [anyOf] | true | maxItems: 1000minItems: 1 | Values to filter the operand by with the given operator. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operator | [eq, in, <, >, between, notBetween] | ## ForecastDistancePlotDataEntryResponse ``` { "properties": { "backtestingScore": { "description": "Backtesting score for this forecast distance. If backtesting has not been run for this model, this score will be `null`.", "type": [ "number", "null" ] }, "forecastDistance": { "description": "The number of time units the scored rows are away from the forecast point.", "type": "integer" }, "holdoutScore": { "description": "Holdout set score for this forecast distance. If holdout is locked for the project, this score will be `null`.", "type": [ "number", "null" ] }, "validationScore": { "description": "Validation set score for this forecast distance.", "type": "number" } }, "required": [ "backtestingScore", "forecastDistance", "holdoutScore", "validationScore" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestingScore | number,null | true | | Backtesting score for this forecast distance. If backtesting has not been run for this model, this score will be null. | | forecastDistance | integer | true | | The number of time units the scored rows are away from the forecast point. | | holdoutScore | number,null | true | | Holdout set score for this forecast distance. If holdout is locked for the project, this score will be null. | | validationScore | number | true | | Validation set score for this forecast distance. | ## ForecastDistanceStabilityPlotResponse ``` { "properties": { "endDate": { "description": "ISO-formatted start date of the project dataset.", "format": "date-time", "type": "string" }, "forecastDistancePlotData": { "description": "An array of objects containing the details of the scores for each forecast distance.", "items": { "properties": { "backtestingScore": { "description": "Backtesting score for this forecast distance. If backtesting has not been run for this model, this score will be `null`.", "type": [ "number", "null" ] }, "forecastDistance": { "description": "The number of time units the scored rows are away from the forecast point.", "type": "integer" }, "holdoutScore": { "description": "Holdout set score for this forecast distance. If holdout is locked for the project, this score will be `null`.", "type": [ "number", "null" ] }, "validationScore": { "description": "Validation set score for this forecast distance.", "type": "number" } }, "required": [ "backtestingScore", "forecastDistance", "holdoutScore", "validationScore" ], "type": "object" }, "type": "array" }, "metricName": { "description": "Name of the metric used to compute the scores.", "type": "string" }, "startDate": { "description": "ISO-formatted start date of the project dataset.", "format": "date-time", "type": "string" } }, "required": [ "endDate", "forecastDistancePlotData", "metricName", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string(date-time) | true | | ISO-formatted start date of the project dataset. | | forecastDistancePlotData | [ForecastDistancePlotDataEntryResponse] | true | | An array of objects containing the details of the scores for each forecast distance. | | metricName | string | true | | Name of the metric used to compute the scores. | | startDate | string(date-time) | true | | ISO-formatted start date of the project dataset. | ## ForecastVsActualPlotsBins ``` { "properties": { "actual": { "description": "Average actual value of the target in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "error": { "description": "Average absolute residual value of the bin. `null` if there are no entries in the bin.", "minimum": 0, "type": [ "number", "null" ] }, "forecasts": { "description": "An array of average forecasts for the model for each forecast distance. Empty if there are no forecasts in the bin. Each index in the `forecasts` array maps to `forecastDistances` array index.", "items": { "type": "number" }, "maxItems": 100, "type": "array" }, "frequency": { "description": "Indicates number of values averaged in bin in case of a resolution change.", "type": [ "integer", "null" ] }, "normalizedError": { "description": "Normalized average absolute residual value of the bin. `null` if there are no entries in the bin.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "error", "forecasts", "frequency", "normalizedError", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actual | number,null | true | | Average actual value of the target in the bin. null if there are no entries in the bin. | | endDate | string(date-time) | true | | The datetime of the end of the bin (exclusive). | | error | number,null | true | minimum: 0 | Average absolute residual value of the bin. null if there are no entries in the bin. | | forecasts | [number] | true | maxItems: 100 | An array of average forecasts for the model for each forecast distance. Empty if there are no forecasts in the bin. Each index in the forecasts array maps to forecastDistances array index. | | frequency | integer,null | true | | Indicates number of values averaged in bin in case of a resolution change. | | normalizedError | number,null | true | maximum: 1minimum: 0 | Normalized average absolute residual value of the bin. null if there are no entries in the bin. | | startDate | string(date-time) | true | | The datetime of the start of the bin (inclusive). | ## ForecastVsActualPlotsDataResponse ``` { "properties": { "bins": { "description": "An array of bins for the retrieved plots.", "items": { "properties": { "actual": { "description": "Average actual value of the target in the bin. `null` if there are no entries in the bin.", "type": [ "number", "null" ] }, "endDate": { "description": "The datetime of the end of the bin (exclusive). ", "format": "date-time", "type": "string" }, "error": { "description": "Average absolute residual value of the bin. `null` if there are no entries in the bin.", "minimum": 0, "type": [ "number", "null" ] }, "forecasts": { "description": "An array of average forecasts for the model for each forecast distance. Empty if there are no forecasts in the bin. Each index in the `forecasts` array maps to `forecastDistances` array index.", "items": { "type": "number" }, "maxItems": 100, "type": "array" }, "frequency": { "description": "Indicates number of values averaged in bin in case of a resolution change.", "type": [ "integer", "null" ] }, "normalizedError": { "description": "Normalized average absolute residual value of the bin. `null` if there are no entries in the bin.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "startDate": { "description": "The datetime of the start of the bin (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "actual", "endDate", "error", "forecasts", "frequency", "normalizedError", "startDate" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "calendarEvents": { "description": "An array of calendar events for a retrieved plot.", "items": { "properties": { "date": { "description": "The date of the calendar event.", "format": "date-time", "type": "string" }, "name": { "description": "Name of the calendar event.", "type": "string" }, "seriesId": { "description": "The series ID for the event. If this event does not specify a series ID, then this will be `null`, indicating that the event applies to all series.", "type": [ "string", "null" ] } }, "required": [ "date", "name", "seriesId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "endDate": { "description": "The datetime of the end of the chart data (exclusive). ", "format": "date-time", "type": "string" }, "forecastDistances": { "description": "An array of forecast distances. Forecast distance specifies the number of time steps between the predicted point and the origin point.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 100, "minItems": 1, "type": "array" }, "resolution": { "description": "The resolution that is used for binning.", "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). ", "format": "date-time", "type": "string" } }, "required": [ "bins", "calendarEvents", "endDate", "forecastDistances", "resolution", "startDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [ForecastVsActualPlotsBins] | true | maxItems: 1000minItems: 1 | An array of bins for the retrieved plots. | | calendarEvents | [CalendarEvent] | true | maxItems: 1000 | An array of calendar events for a retrieved plot. | | endDate | string(date-time) | true | | The datetime of the end of the chart data (exclusive). | | forecastDistances | [integer] | true | maxItems: 100minItems: 1 | An array of forecast distances. Forecast distance specifies the number of time steps between the predicted point and the origin point. | | resolution | string | true | | The resolution that is used for binning. | | startDate | string(date-time) | true | | The datetime of the start of the chart data (inclusive). | ### Enumerated Values | Property | Value | | --- | --- | | resolution | [milliseconds, seconds, minutes, hours, days, weeks, months, quarters, years] | ## ForecastVsActualPlotsForecastDistancesStatus ``` { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" } ``` Status for backtest/holdout training. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | completed | [integer] | false | maxItems: 1000minItems: 1 | An array of available forecast distances for the completed status. If there are no forecast distances for this status, it will not appear in the response. | | errored | [integer] | false | maxItems: 1000minItems: 1 | An array of available forecast distances for the errored status. If there are no forecast distances for this status, it will not appear in the response. | | inProgress | [integer] | false | maxItems: 1000minItems: 1 | An array of available forecast distances for the inProgress status. If there are no forecast distances for this status, it will not appear in the response. | | insufficientData | [integer] | false | maxItems: 1000minItems: 1 | An array of available forecast distances for the insufficientData status. If there are no forecast distances for this status, it will not appear in the response. | | notCompleted | [integer] | false | maxItems: 1000minItems: 1 | An array of available forecast distances for the notCompleted status. If there are no forecast distances for this status, it will not appear in the response. | ## ForecastVsActualPlotsMetadataResponse ``` { "properties": { "backtestMetadata": { "description": "An array of metadata information for each backtest. The array index of metadata object is the backtest index.", "items": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "backtestStatuses": { "description": "An array of status information for each backtest. The array index of status object is the backtest index.", "items": { "description": "Status for forecast vs actual plots.", "properties": { "training": { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" }, "validation": { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "estimatedSeriesLimit": { "description": "Estimated number of series that can be calculated in one request for 1 FD.", "minimum": 1, "type": "integer" }, "holdoutMetadata": { "description": "Metadata for backtest/holdout.", "properties": { "training": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" }, "validation": { "description": "Start and end dates for the backtest/holdout training.", "properties": { "endDate": { "description": "The datetime of the end of the chart data (exclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] }, "startDate": { "description": "The datetime of the start of the chart data (inclusive). Null if chart data is not computed.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "endDate", "startDate" ], "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "holdoutStatuses": { "description": "Status for forecast vs actual plots.", "properties": { "training": { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" }, "validation": { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" } }, "required": [ "training", "validation" ], "type": "object" }, "resolutions": { "description": "An array of available time resolutions for which plots can be retrieved.", "items": { "enum": [ "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "quarters", "years" ], "type": "string" }, "maxItems": 9, "minItems": 1, "type": "array" } }, "required": [ "backtestMetadata", "backtestStatuses", "holdoutMetadata", "holdoutStatuses", "resolutions" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestMetadata | [DatetimeTrendPlotsBacktestMetadata] | true | maxItems: 20minItems: 1 | An array of metadata information for each backtest. The array index of metadata object is the backtest index. | | backtestStatuses | [ForecastVsActualPlotsStatus] | true | maxItems: 20minItems: 1 | An array of status information for each backtest. The array index of status object is the backtest index. | | estimatedSeriesLimit | integer | false | minimum: 1 | Estimated number of series that can be calculated in one request for 1 FD. | | holdoutMetadata | DatetimeTrendPlotsBacktestMetadata | true | | Metadata for backtest/holdout. | | holdoutStatuses | ForecastVsActualPlotsStatus | true | | Status for forecast vs actual plots. | | resolutions | [string] | true | maxItems: 9minItems: 1 | An array of available time resolutions for which plots can be retrieved. | ## ForecastVsActualPlotsStatus ``` { "description": "Status for forecast vs actual plots.", "properties": { "training": { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" }, "validation": { "description": "Status for backtest/holdout training.", "properties": { "completed": { "description": "An array of available forecast distances for the `completed` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "errored": { "description": "An array of available forecast distances for the `errored` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "inProgress": { "description": "An array of available forecast distances for the `inProgress` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "insufficientData": { "description": "An array of available forecast distances for the `insufficientData` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "notCompleted": { "description": "An array of available forecast distances for the `notCompleted` status. If there are no forecast distances for this status, it will not appear in the response.", "items": { "maximum": 1000, "minimum": 1, "type": "integer" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "type": "object" } }, "required": [ "training", "validation" ], "type": "object" } ``` Status for forecast vs actual plots. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | training | ForecastVsActualPlotsForecastDistancesStatus | true | | Status for backtest/holdout training. | | validation | ForecastVsActualPlotsForecastDistancesStatus | true | | Status for backtest/holdout training. | ## FrequentValueData ``` { "properties": { "count": { "description": "Count of specified frequent value in the sample, weighted by exposure or weights", "type": "integer" }, "dataQuality": { "description": "Any data quality issue associated with this particularvalue of the feature. Possible data quality types include 'excess_zero', 'inlier', 'disguised_missing_value', and 'no_issues_found' and the relevant statistics.", "type": "string" }, "target": { "description": "Average target value for the specified frequent value if the target is binary or numeric. With weights or exposure, this becomes a weighted average. If the target is not set, it returns None.", "type": [ "number", "null" ] }, "value": { "anyOf": [ { "type": "number" }, { "type": "string" } ], "description": "Specified frequent value, either a float or a string, like `=All Others+`" } }, "required": [ "count", "dataQuality", "target", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Count of specified frequent value in the sample, weighted by exposure or weights | | dataQuality | string | true | | Any data quality issue associated with this particularvalue of the feature. Possible data quality types include 'excess_zero', 'inlier', 'disguised_missing_value', and 'no_issues_found' and the relevant statistics. | | target | number,null | true | | Average target value for the specified frequent value if the target is binary or numeric. With weights or exposure, this becomes a weighted average. If the target is not set, it returns None. | | value | any | true | | Specified frequent value, either a float or a string, like =All Others+ | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | ## FrequentValuesResponse ``` { "properties": { "frequentValues": { "description": "The list of frequent values and data quality information.", "items": { "properties": { "count": { "description": "Count of specified frequent value in the sample, weighted by exposure or weights", "type": "integer" }, "dataQuality": { "description": "Any data quality issue associated with this particularvalue of the feature. Possible data quality types include 'excess_zero', 'inlier', 'disguised_missing_value', and 'no_issues_found' and the relevant statistics.", "type": "string" }, "target": { "description": "Average target value for the specified frequent value if the target is binary or numeric. With weights or exposure, this becomes a weighted average. If the target is not set, it returns None.", "type": [ "number", "null" ] }, "value": { "anyOf": [ { "type": "number" }, { "type": "string" } ], "description": "Specified frequent value, either a float or a string, like `=All Others+`" } }, "required": [ "count", "dataQuality", "target", "value" ], "type": "object" }, "type": "array" }, "name": { "description": "The name of the feature.", "type": "string" }, "numRows": { "description": "Number of rows in the sample used to determine frequent values", "type": "integer" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "frequentValues", "name", "numRows", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | frequentValues | [FrequentValueData] | true | | The list of frequent values and data quality information. | | name | string | true | | The name of the feature. | | numRows | integer | true | | Number of rows in the sample used to determine frequent values | | projectId | string | true | | The project ID. | ## GeoFeaturePlotData ``` { "description": "Geo feature plot data", "properties": { "aggregation": { "description": "Type of geo aggregation.", "enum": [ "grid", "unique" ], "type": "string" }, "bbox": { "description": "Bounding box of feature map.", "type": "object" }, "features": { "description": "Location features over map", "items": { "properties": { "geometry": { "description": "Geometry.", "properties": { "coordinates": { "description": "Coordinate representative of a geometry.", "items": { "type": "object" }, "type": "array" }, "type": { "description": "Type of geometry.", "enum": [ "Point", "LineString", "Polygon" ], "type": "string" } }, "required": [ "coordinates", "type" ], "type": "object" }, "properties": { "description": "Properties of location features.", "properties": { "count": { "description": "Total num of samples located within this geometry.", "type": "integer" } }, "required": [ "count" ], "type": "object" }, "type": { "description": "With a fixed value of 'Feature'.", "type": "string" } }, "required": [ "geometry", "properties", "type" ], "type": "object" }, "type": "array" }, "summary": { "description": "Summary of feature map.", "properties": { "maxCount": { "description": "Max num of samples located within one geometry.", "type": "integer" }, "minCount": { "description": "Min num of samples located within one geometry.", "type": "integer" }, "totalCount": { "description": "Total num of samples across all geometry objects.", "type": "integer" } }, "required": [ "maxCount", "minCount", "totalCount" ], "type": "object" }, "type": { "description": "GeoJSON FeatureCollection.", "type": "string" }, "valueAggregation": { "description": "Type of feature aggregation.", "enum": [ "geometry" ], "type": "string" } }, "required": [ "aggregation", "bbox", "features", "summary", "type", "valueAggregation" ], "type": "object" } ``` Geo feature plot data ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregation | string | true | | Type of geo aggregation. | | bbox | object | true | | Bounding box of feature map. | | features | [GeoFeaturePlotFeature] | true | | Location features over map | | summary | GeoFeaturePlotSummary | true | | Summary of feature map. | | type | string | true | | GeoJSON FeatureCollection. | | valueAggregation | string | true | | Type of feature aggregation. | ### Enumerated Values | Property | Value | | --- | --- | | aggregation | [grid, unique] | | valueAggregation | geometry | ## GeoFeaturePlotFeature ``` { "properties": { "geometry": { "description": "Geometry.", "properties": { "coordinates": { "description": "Coordinate representative of a geometry.", "items": { "type": "object" }, "type": "array" }, "type": { "description": "Type of geometry.", "enum": [ "Point", "LineString", "Polygon" ], "type": "string" } }, "required": [ "coordinates", "type" ], "type": "object" }, "properties": { "description": "Properties of location features.", "properties": { "count": { "description": "Total num of samples located within this geometry.", "type": "integer" } }, "required": [ "count" ], "type": "object" }, "type": { "description": "With a fixed value of 'Feature'.", "type": "string" } }, "required": [ "geometry", "properties", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | geometry | GeoJSON | true | | Geometry. | | properties | GeoFeaturePlotFeatureProperties | true | | Properties of location features. | | type | string | true | | With a fixed value of 'Feature'. | ## GeoFeaturePlotFeatureProperties ``` { "description": "Properties of location features.", "properties": { "count": { "description": "Total num of samples located within this geometry.", "type": "integer" } }, "required": [ "count" ], "type": "object" } ``` Properties of location features. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Total num of samples located within this geometry. | ## GeoFeaturePlotSummary ``` { "description": "Summary of feature map.", "properties": { "maxCount": { "description": "Max num of samples located within one geometry.", "type": "integer" }, "minCount": { "description": "Min num of samples located within one geometry.", "type": "integer" }, "totalCount": { "description": "Total num of samples across all geometry objects.", "type": "integer" } }, "required": [ "maxCount", "minCount", "totalCount" ], "type": "object" } ``` Summary of feature map. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxCount | integer | true | | Max num of samples located within one geometry. | | minCount | integer | true | | Min num of samples located within one geometry. | | totalCount | integer | true | | Total num of samples across all geometry objects. | ## GeoJSON ``` { "description": "Geometry.", "properties": { "coordinates": { "description": "Coordinate representative of a geometry.", "items": { "type": "object" }, "type": "array" }, "type": { "description": "Type of geometry.", "enum": [ "Point", "LineString", "Polygon" ], "type": "string" } }, "required": [ "coordinates", "type" ], "type": "object" } ``` Geometry. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | coordinates | [object] | true | | Coordinate representative of a geometry. | | type | string | true | | Type of geometry. | ### Enumerated Values | Property | Value | | --- | --- | | type | [Point, LineString, Polygon] | ## GeometryFeaturePLotCreatePayload ``` { "properties": { "feature": { "description": "Name of a location feature from the dataset to plot on map.", "type": "string" } }, "required": [ "feature" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature | string | true | | Name of a location feature from the dataset to plot on map. | ## GeometryFeaturePlotRetrieveResponse ``` { "properties": { "feature": { "description": "Name of location feature to plot on map.", "type": "string" }, "plotData": { "description": "Geo feature plot data", "properties": { "aggregation": { "description": "Type of geo aggregation.", "enum": [ "grid", "unique" ], "type": "string" }, "bbox": { "description": "Bounding box of feature map.", "type": "object" }, "features": { "description": "Location features over map", "items": { "properties": { "geometry": { "description": "Geometry.", "properties": { "coordinates": { "description": "Coordinate representative of a geometry.", "items": { "type": "object" }, "type": "array" }, "type": { "description": "Type of geometry.", "enum": [ "Point", "LineString", "Polygon" ], "type": "string" } }, "required": [ "coordinates", "type" ], "type": "object" }, "properties": { "description": "Properties of location features.", "properties": { "count": { "description": "Total num of samples located within this geometry.", "type": "integer" } }, "required": [ "count" ], "type": "object" }, "type": { "description": "With a fixed value of 'Feature'.", "type": "string" } }, "required": [ "geometry", "properties", "type" ], "type": "object" }, "type": "array" }, "summary": { "description": "Summary of feature map.", "properties": { "maxCount": { "description": "Max num of samples located within one geometry.", "type": "integer" }, "minCount": { "description": "Min num of samples located within one geometry.", "type": "integer" }, "totalCount": { "description": "Total num of samples across all geometry objects.", "type": "integer" } }, "required": [ "maxCount", "minCount", "totalCount" ], "type": "object" }, "type": { "description": "GeoJSON FeatureCollection.", "type": "string" }, "valueAggregation": { "description": "Type of feature aggregation.", "enum": [ "geometry" ], "type": "string" } }, "required": [ "aggregation", "bbox", "features", "summary", "type", "valueAggregation" ], "type": "object" }, "projectId": { "description": "The project to select a location feature from.", "type": "string" } }, "required": [ "feature", "plotData", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature | string | true | | Name of location feature to plot on map. | | plotData | GeoFeaturePlotData | true | | Geo feature plot data | | projectId | string | true | | The project to select a location feature from. | ## GeospatialFeature ``` { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "geospatialPoint" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a geospatial centroid or point feature.", "items": { "properties": { "insightName": { "description": "Insight name.", "enum": [ "representativeLocations" ], "type": "string" }, "perCluster": { "description": "Statistic for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "representativeLocations": { "description": "A list of latitude and longitude location list", "items": { "description": "Latitude and longitude list.", "items": { "description": "Longitude or latitude, in degrees.", "maximum": 180, "minimum": -180, "type": "number" }, "type": "array" }, "maxItems": 1000, "type": "array" } }, "required": [ "clusterName", "representativeLocations" ], "type": "object" }, "type": "array" } }, "required": [ "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureImpact | number,null | false | | Feature Impact score. | | featureName | string | true | | Feature name. | | featureType | string | true | | Feature Type. | | insights | [GeospatialInsights] | true | | A list of Cluster Insights for a geospatial centroid or point feature. | ### Enumerated Values | Property | Value | | --- | --- | | featureType | geospatialPoint | ## GeospatialInsights ``` { "properties": { "insightName": { "description": "Insight name.", "enum": [ "representativeLocations" ], "type": "string" }, "perCluster": { "description": "Statistic for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "representativeLocations": { "description": "A list of latitude and longitude location list", "items": { "description": "Latitude and longitude list.", "items": { "description": "Longitude or latitude, in degrees.", "maximum": 180, "minimum": -180, "type": "number" }, "type": "array" }, "maxItems": 1000, "type": "array" } }, "required": [ "clusterName", "representativeLocations" ], "type": "object" }, "type": "array" } }, "required": [ "insightName", "perCluster" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | insightName | string | true | | Insight name. | | perCluster | [PerClusterGeospatial] | true | | Statistic for different feature values in this cluster. | ### Enumerated Values | Property | Value | | --- | --- | | insightName | representativeLocations | ## GlobalMetrics ``` { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" } ``` average metrics across all classes ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | f1 | number | true | | Average F1 score | | precision | number | true | | Average precision score | | recall | number | true | | Average recall score | ## Histogram ``` { "properties": { "intervalEnd": { "description": "The end of the interval.", "type": "number" }, "intervalStart": { "description": "The start of the interval.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within that interval.", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | intervalEnd | number | true | | The end of the interval. | | intervalStart | number | true | | The start of the interval. | | occurrences | integer | true | | The number of times the predicted value fits within that interval. | ## HistogramBarsDetails ``` { "properties": { "label": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Ratio of occurrence of the class.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | string | true | | Name of the class. | | value | number | true | | Ratio of occurrence of the class. | ## HistogramDetails ``` { "properties": { "bars": { "description": "Class details for the histogram chart", "items": { "properties": { "label": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Ratio of occurrence of the class.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "bin": { "anyOf": [ { "type": "string" }, { "type": "integer" } ], "description": "Label for the bin grouping" } }, "required": [ "bars", "bin" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bars | [HistogramBarsDetails] | true | | Class details for the histogram chart | | bin | any | true | | Label for the bin grouping | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | ## ImageFeature ``` { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "image" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for an image feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "images", "percentageOfMissingImages" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "representativeImages" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "clusterName", "images", "percentageOfMissingImages" ], "type": "object" }, "type": "array" } }, "required": [ "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureImpact | number,null | false | | Feature Impact score. | | featureName | string | true | | Feature name. | | featureType | string | true | | Feature Type. | | insights | [ImageInsights] | true | | A list of Cluster Insights for an image feature. | ### Enumerated Values | Property | Value | | --- | --- | | featureType | image | ## ImageInsights ``` { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "images", "percentageOfMissingImages" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "representativeImages" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "clusterName", "images", "percentageOfMissingImages" ], "type": "object" }, "type": "array" } }, "required": [ "insightName", "perCluster" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allData | AllDataImage | false | | Statistics for all data for different feature values. | | insightName | string | true | | Insight name. | | perCluster | [PerClusterImage] | true | | Statistic values for different feature values in this cluster. | ### Enumerated Values | Property | Value | | --- | --- | | insightName | representativeImages | ## ImageInsightsMetadataElement ``` { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "modelId": { "description": "The model ID of the target model.", "type": "string" } }, "required": [ "featureName", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | true | | The name of the feature. | | modelId | string | true | | The model ID of the target model. | ## LabelRelevancePlot ``` { "properties": { "labelRelevance": { "description": "Label relevance value.", "maximum": 1, "minimum": 0, "type": "integer" }, "rowCount": { "description": "Number of rows for which the label has the given relevance.", "minimum": 0, "type": "integer" }, "rowPct": { "description": "Percentage of rows for which the label has the given relevance.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "labelRelevance", "rowCount", "rowPct" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | labelRelevance | integer | true | maximum: 1minimum: 0 | Label relevance value. | | rowCount | integer | true | minimum: 0 | Number of rows for which the label has the given relevance. | | rowPct | number | true | maximum: 100minimum: 0 | Percentage of rows for which the label has the given relevance. | ## LabelwiseLiftChart ``` { "properties": { "labelBins": { "description": "Lift charts for the given data source.", "items": { "properties": { "bins": { "description": "Lift chart data for that label.", "items": { "properties": { "actual": { "description": "Average of actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "For projects with weights, it is the sum of the weights of all rows in the bins. Otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "Average of predicted target values for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "maxItems": 60, "type": "array" }, "label": { "description": "Label name.", "type": "string" } }, "required": [ "bins", "label" ], "type": "object" }, "maxItems": 100, "type": "array" }, "labels": { "description": "All available target labels for this insight.", "items": { "description": "Label name.", "type": "string" }, "maxItems": 100, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "Data source of Lift charts.", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" } }, "required": [ "labelBins", "labels", "modelId", "projectId", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | labelBins | [LabelwiseLiftChartItem] | true | maxItems: 100 | Lift charts for the given data source. | | labels | [string] | true | maxItems: 100 | All available target labels for this insight. | | modelId | string | true | | The model ID. | | projectId | string | true | | The project ID. | | source | string | true | | Data source of Lift charts. | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout] | ## LabelwiseLiftChartBin ``` { "properties": { "actual": { "description": "Average of actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "For projects with weights, it is the sum of the weights of all rows in the bins. Otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "Average of predicted target values for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actual | number | true | | Average of actual target values for the rows in the bin. | | binWeight | number | true | | For projects with weights, it is the sum of the weights of all rows in the bins. Otherwise, it is the number of rows in the bin. | | predicted | number | true | | Average of predicted target values for the rows in the bin. | ## LabelwiseLiftChartItem ``` { "properties": { "bins": { "description": "Lift chart data for that label.", "items": { "properties": { "actual": { "description": "Average of actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "For projects with weights, it is the sum of the weights of all rows in the bins. Otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "Average of predicted target values for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "maxItems": 60, "type": "array" }, "label": { "description": "Label name.", "type": "string" } }, "required": [ "bins", "label" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [LabelwiseLiftChartBin] | true | maxItems: 60 | Lift chart data for that label. | | label | string | true | | Label name. | ## LabelwiseROC ``` { "properties": { "averageModelMetrics": { "description": "All average model metrics from one data source.", "properties": { "metrics": { "description": "Average model metrics for the given thresholds.", "items": { "properties": { "name": { "description": "Metric name.", "enum": [ "accuracy", "f1Score", "falsePositiveRate", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "trueNegativeRate", "truePositiveRate" ], "type": "string" }, "numLabelsUsedInCalculation": { "description": "Number of labels that were taken into account in the calculation of metric averages", "type": "integer" }, "values": { "description": "Metric values at given thresholds.", "items": { "type": "number" }, "maxItems": 100, "minItems": 100, "type": "array" } }, "required": [ "name", "numLabelsUsedInCalculation", "values" ], "type": "object" }, "maxItems": 8, "minItems": 8, "type": "array" }, "source": { "description": "Chart source.", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "thresholds": { "description": "Threshold values for which model metrics are available.", "items": { "maximum": 1, "minimum": 0, "type": "number" }, "maxItems": 100, "minItems": 100, "type": "array" } }, "required": [ "metrics", "source", "thresholds" ], "type": "object" }, "charts": { "description": "ROC data for all labels from one data source.", "items": { "properties": { "auc": { "description": "Area under the curve.", "type": "number" }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric.", "type": "number" }, "label": { "description": "Label name.", "type": "string" }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "type": "number" }, "type": "array" }, "rocPoints": { "description": "ROC characteristics for label.", "items": { "properties": { "accuracy": { "description": "Accuracy.", "maximum": 1, "minimum": 0, "type": "number" }, "f1Score": { "description": "F1 score.", "maximum": 1, "minimum": 0, "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "minimum": 0, "type": [ "integer", "null" ] }, "falsePositiveRate": { "description": "False positive rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "falsePositiveScore": { "description": "False positive score.", "minimum": 0, "type": [ "integer", "null" ] }, "fractionPredictedAsNegative": { "description": "Fraction of negative predictions.", "type": [ "number", "null" ] }, "fractionPredictedAsPositive": { "description": "Fraction of positive predictions.", "type": [ "number", "null" ] }, "liftNegative": { "description": "Negative lift.", "type": [ "number", "null" ] }, "liftPositive": { "description": "Positive lift.", "type": [ "number", "null" ] }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "maximum": 1, "minimum": -1, "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "maximum": 1, "minimum": 0, "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "maximum": 1, "minimum": 0, "type": "number" }, "threshold": { "description": "Threshold.", "maximum": 2, "minimum": 0, "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "trueNegativeScore": { "description": "True negative score.", "minimum": 0, "type": [ "integer", "null" ] }, "truePositiveRate": { "description": "True positive rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "truePositiveScore": { "description": "True positive score.", "minimum": 0, "type": [ "integer", "null" ] } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" }, "source": { "description": "Data source of ROC characteristics.", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" } }, "required": [ "auc", "kolmogorovSmirnovMetric", "label", "negativeClassPredictions", "positiveClassPredictions", "rocPoints", "source" ], "type": "object" }, "type": "array" }, "count": { "description": "Number of labels returned on this page.", "type": "integer" }, "labels": { "description": "All available target labels for this insight.", "items": { "description": "Label name.", "type": "string" }, "maxItems": 100, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "rocType": { "description": "Type of ROC.", "enum": [ "binary", "labelwise" ], "type": "string" }, "totalCount": { "description": "Total number of labels across all pages.", "type": "integer" } }, "required": [ "averageModelMetrics", "charts", "labels", "next", "previous", "rocType", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | averageModelMetrics | AverageModelMetricsField | true | | All average model metrics from one data source. | | charts | [LabelwiseROCItem] | true | | ROC data for all labels from one data source. | | count | integer | false | | Number of labels returned on this page. | | labels | [string] | true | maxItems: 100 | All available target labels for this insight. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | | rocType | string | true | | Type of ROC. | | totalCount | integer | true | | Total number of labels across all pages. | ### Enumerated Values | Property | Value | | --- | --- | | rocType | [binary, labelwise] | ## LabelwiseROCItem ``` { "properties": { "auc": { "description": "Area under the curve.", "type": "number" }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric.", "type": "number" }, "label": { "description": "Label name.", "type": "string" }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "type": "number" }, "type": "array" }, "rocPoints": { "description": "ROC characteristics for label.", "items": { "properties": { "accuracy": { "description": "Accuracy.", "maximum": 1, "minimum": 0, "type": "number" }, "f1Score": { "description": "F1 score.", "maximum": 1, "minimum": 0, "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "minimum": 0, "type": [ "integer", "null" ] }, "falsePositiveRate": { "description": "False positive rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "falsePositiveScore": { "description": "False positive score.", "minimum": 0, "type": [ "integer", "null" ] }, "fractionPredictedAsNegative": { "description": "Fraction of negative predictions.", "type": [ "number", "null" ] }, "fractionPredictedAsPositive": { "description": "Fraction of positive predictions.", "type": [ "number", "null" ] }, "liftNegative": { "description": "Negative lift.", "type": [ "number", "null" ] }, "liftPositive": { "description": "Positive lift.", "type": [ "number", "null" ] }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "maximum": 1, "minimum": -1, "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "maximum": 1, "minimum": 0, "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "maximum": 1, "minimum": 0, "type": "number" }, "threshold": { "description": "Threshold.", "maximum": 2, "minimum": 0, "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "trueNegativeScore": { "description": "True negative score.", "minimum": 0, "type": [ "integer", "null" ] }, "truePositiveRate": { "description": "True positive rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "truePositiveScore": { "description": "True positive score.", "minimum": 0, "type": [ "integer", "null" ] } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" }, "source": { "description": "Data source of ROC characteristics.", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" } }, "required": [ "auc", "kolmogorovSmirnovMetric", "label", "negativeClassPredictions", "positiveClassPredictions", "rocPoints", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | auc | number | true | | Area under the curve. | | kolmogorovSmirnovMetric | number | true | | Kolmogorov-Smirnov metric. | | label | string | true | | Label name. | | negativeClassPredictions | [number] | true | | List of example predictions for the negative class. | | positiveClassPredictions | [number] | true | | List of example predictions for the positive class. | | rocPoints | [LabelwiseROCPoint] | true | | ROC characteristics for label. | | source | string | true | | Data source of ROC characteristics. | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout] | ## LabelwiseROCPoint ``` { "properties": { "accuracy": { "description": "Accuracy.", "maximum": 1, "minimum": 0, "type": "number" }, "f1Score": { "description": "F1 score.", "maximum": 1, "minimum": 0, "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "minimum": 0, "type": [ "integer", "null" ] }, "falsePositiveRate": { "description": "False positive rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "falsePositiveScore": { "description": "False positive score.", "minimum": 0, "type": [ "integer", "null" ] }, "fractionPredictedAsNegative": { "description": "Fraction of negative predictions.", "type": [ "number", "null" ] }, "fractionPredictedAsPositive": { "description": "Fraction of positive predictions.", "type": [ "number", "null" ] }, "liftNegative": { "description": "Negative lift.", "type": [ "number", "null" ] }, "liftPositive": { "description": "Positive lift.", "type": [ "number", "null" ] }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "maximum": 1, "minimum": -1, "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "maximum": 1, "minimum": 0, "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "maximum": 1, "minimum": 0, "type": "number" }, "threshold": { "description": "Threshold.", "maximum": 2, "minimum": 0, "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "trueNegativeScore": { "description": "True negative score.", "minimum": 0, "type": [ "integer", "null" ] }, "truePositiveRate": { "description": "True positive rate.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "truePositiveScore": { "description": "True positive score.", "minimum": 0, "type": [ "integer", "null" ] } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracy | number | true | maximum: 1minimum: 0 | Accuracy. | | f1Score | number | true | maximum: 1minimum: 0 | F1 score. | | falseNegativeScore | integer,null | true | minimum: 0 | False negative score. | | falsePositiveRate | number,null | true | maximum: 1minimum: 0 | False positive rate. | | falsePositiveScore | integer,null | true | minimum: 0 | False positive score. | | fractionPredictedAsNegative | number,null | true | | Fraction of negative predictions. | | fractionPredictedAsPositive | number,null | true | | Fraction of positive predictions. | | liftNegative | number,null | true | | Negative lift. | | liftPositive | number,null | true | | Positive lift. | | matthewsCorrelationCoefficient | number | true | maximum: 1minimum: -1 | Matthews correlation coefficient. | | negativePredictiveValue | number | true | maximum: 1minimum: 0 | Negative predictive value. | | positivePredictiveValue | number | true | maximum: 1minimum: 0 | Positive predictive value. | | threshold | number | true | maximum: 2minimum: 0 | Threshold. | | trueNegativeRate | number,null | true | maximum: 1minimum: 0 | True negative rate. | | trueNegativeScore | integer,null | true | minimum: 0 | True negative score. | | truePositiveRate | number,null | true | maximum: 1minimum: 0 | True positive rate. | | truePositiveScore | integer,null | true | minimum: 0 | True positive score. | ## LiftBinResponse ``` { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actual | number | true | | The average of the actual target values for the rows in the bin. | | binWeight | number | true | | How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin. | | predicted | number | true | | The average of predicted values of the target for the rows in the bin. | ## LiftChartForDatasetsList ``` { "properties": { "count": { "description": "The number of results returned on this page.", "type": "integer" }, "data": { "description": "Array of lift chart data for dataset, as specified below", "items": { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "datasetId": { "description": "The dataset ID of the dataset that was used to compute the lift chart.", "type": "string" } }, "required": [ "bins", "datasetId" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of results returned on this page. | | data | [LiftData] | true | | Array of lift chart data for dataset, as specified below | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | ## LiftChartResponse ``` { "description": "Lift chart data.", "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" } }, "required": [ "bins" ], "type": "object" } ``` Lift chart data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [LiftBinResponse] | true | | The lift chart data for that source, as specified below. | ## LiftChartbins ``` { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actual | number | true | | The average of the actual target values for the rows in the bin. | | binWeight | number | true | | How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin. | | predicted | number | true | | The average of predicted values of the target for the rows in the bin. | ## LiftData ``` { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "datasetId": { "description": "The dataset ID of the dataset that was used to compute the lift chart.", "type": "string" } }, "required": [ "bins", "datasetId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [LiftChartbins] | true | | The lift chart data for that source, as specified below. | | datasetId | string | true | | The dataset ID of the dataset that was used to compute the lift chart. | ## ModelConfusionChartClassDetailsForDatasetRetrieve ``` { "properties": { "actualFrequency": { "description": "One vs All actual percentage and count in a format specified below sorted by percentage in decreasing order", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of the times this class was predicted when is was actually `classMetrics.className` (from 0 to 100).", "maximum": 100, "minimum": 0, "type": "number" }, "value": { "description": "The count of the times this class was predicted when is was actually `classMetrics.className`.", "minimum": 0, "type": "integer" } }, "required": [ "otherClassName", "percentage", "value" ], "type": "object" }, "type": "array" }, "className": { "description": "Name of a class for which distribution frequency is requested", "type": "string" }, "datasetId": { "description": "The dataset to retrieve a confusion chart from.", "type": "string" }, "modelId": { "description": "The model to retrieve a confusion chart from.", "type": "string" }, "predictedFrequency": { "description": "One vs All predicted percentage and count in a format specified below sorted by percentage in decreasing order", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "the percentage of the times this class was actual when `classMetrics.className` is predicted (from 0 to 100)", "maximum": 100, "minimum": 0, "type": "number" }, "value": { "description": "The count of the times this class was actual `classMetrics.className` when it was predicted", "minimum": 0, "type": "integer" } }, "required": [ "otherClassName", "percentage", "value" ], "type": "object" }, "type": "array" }, "projectId": { "description": "The project to retrieve a confusion chart from.", "type": "string" } }, "required": [ "actualFrequency", "className", "datasetId", "modelId", "predictedFrequency", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualFrequency | [ActualFrequency] | true | | One vs All actual percentage and count in a format specified below sorted by percentage in decreasing order | | className | string | true | | Name of a class for which distribution frequency is requested | | datasetId | string | true | | The dataset to retrieve a confusion chart from. | | modelId | string | true | | The model to retrieve a confusion chart from. | | predictedFrequency | [PredictedFrequency] | true | | One vs All predicted percentage and count in a format specified below sorted by percentage in decreasing order | | projectId | string | true | | The project to retrieve a confusion chart from. | ## ModelConfusionChartClassDetailsRetrieveReponseController ``` { "properties": { "actualFrequency": { "description": "One vs all actual percentage and count in a format specified below sorted by percentage in decreasing order", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of the times this class was predicted when is was actually `classMetrics.className` (from 0 to 100).", "maximum": 100, "minimum": 0, "type": "number" }, "value": { "description": "The count of the times this class was predicted when is was actually `classMetrics.className`.", "minimum": 0, "type": "integer" } }, "required": [ "otherClassName", "percentage", "value" ], "type": "object" }, "type": "array" }, "className": { "description": "Name of a class for which distribution frequency is requested.", "type": "string" }, "predictedFrequency": { "description": "One vs all predicted percentage and count in a format specified below sorted by percentage in decreasing order", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "the percentage of the times this class was actual when `classMetrics.className` is predicted (from 0 to 100)", "maximum": 100, "minimum": 0, "type": "number" }, "value": { "description": "The count of the times this class was actual `classMetrics.className` when it was predicted", "minimum": 0, "type": "integer" } }, "required": [ "otherClassName", "percentage", "value" ], "type": "object" }, "type": "array" } }, "required": [ "actualFrequency", "className", "predictedFrequency" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualFrequency | [ActualFrequency] | true | | One vs all actual percentage and count in a format specified below sorted by percentage in decreasing order | | className | string | true | | Name of a class for which distribution frequency is requested. | | predictedFrequency | [PredictedFrequency] | true | | One vs all predicted percentage and count in a format specified below sorted by percentage in decreasing order | ## ModelConfusionChartListResponse ``` { "properties": { "charts": { "description": "Chart data from all available sources.", "items": { "properties": { "columns": { "description": "[colStart, colEnd] column dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "data": { "description": "confusion chart data with the format below.", "properties": { "classMetrics": { "description": "per-class information including one vs all scores in a format specified below", "items": { "properties": { "actualCount": { "description": "number of times this class is seen in the validation data", "type": "integer" }, "className": { "description": "name of the class", "type": "string" }, "confusionMatrixOneVsAll": { "description": "2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "precision score", "type": "number" }, "predictedCount": { "description": "number of times this class has been predicted for the validation data", "type": "integer" }, "recall": { "description": "recall score", "type": "number" }, "wasActualPercentages": { "description": "one vs all actual percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" }, "wasPredictedPercentages": { "description": "one vs all predicted percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object" }, "type": "array" }, "classes": { "description": "class labels from the dataset, union of row classes & column classes. This field is deprecated as of v2.13. The rows and columns may have different class labels when using query parameters to retrieve a slice of the matrix; please use 'rowClasses' and 'colClasses' instead.", "items": { "type": "string" }, "type": "array" }, "colClasses": { "description": "class labels on columns of confusion matrix", "items": { "type": "string" }, "type": "array" }, "confusionMatrix": { "description": "2d array of ints representing confusion matrix, aligned with `rowClasses` and 'colClasses'array. For confusionMatrix[A][B] we can get an integer that represents the number of times 'if class with index A was correct we have class with index B predicted' (if the orientation is 'actual').", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "rowClasses": { "description": "class labels on rows of confusion matrix", "items": { "type": "string" }, "type": "array" } }, "required": [ "classMetrics", "classes", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "numberOfClasses": { "description": "count of classes in full confusion matrix.", "type": "integer" }, "rows": { "description": "[rowStart, rowEnd] row dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "source": { "description": "source of the chart", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "totalMatrixSum": { "description": "sum of all values in full confusion matrix", "type": "integer" } }, "required": [ "columns", "data", "globalMetrics", "numberOfClasses", "rows", "source", "totalMatrixSum" ], "type": "object" }, "type": "array" } }, "required": [ "charts" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | charts | [ModelConfusionChartRetrieveResponse] | true | | Chart data from all available sources. | ## ModelConfusionChartMetadataRetrieveResponse ``` { "properties": { "classNames": { "description": "List of all class names in the full confusion matrix, sorted by the `orderBy` parameter", "items": { "type": "string" }, "type": "array" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "relevantClassesPositions": { "description": "Matrix to highlight important cell blocks in the confusion chart. Intended to represent a thumbnail view, where the relevantClassesPositions array has a 1 in thumbnail cells that are of interest, and 0 otherwise. The dimensions of the implied thumbnail will not match those of the confusion matrix, e.g. a twenty-class confusion matrix may have a 2x2 thumbnail.", "items": { "items": { "maximum": 1, "minimum": 0, "type": "integer" }, "type": "array" }, "type": "array" }, "source": { "description": "Source of the chart.", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "totalMatrixSum": { "description": "Sum of all values in the full confusion matrix (equal to the number of points considered)", "minimum": 0, "type": "integer" } }, "required": [ "classNames", "globalMetrics", "relevantClassesPositions", "source", "totalMatrixSum" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classNames | [string] | true | | List of all class names in the full confusion matrix, sorted by the orderBy parameter | | globalMetrics | GlobalMetrics | true | | average metrics across all classes | | relevantClassesPositions | [array] | true | | Matrix to highlight important cell blocks in the confusion chart. Intended to represent a thumbnail view, where the relevantClassesPositions array has a 1 in thumbnail cells that are of interest, and 0 otherwise. The dimensions of the implied thumbnail will not match those of the confusion matrix, e.g. a twenty-class confusion matrix may have a 2x2 thumbnail. | | source | string | true | | Source of the chart. | | totalMatrixSum | integer | true | minimum: 0 | Sum of all values in the full confusion matrix (equal to the number of points considered) | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout] | ## ModelConfusionChartRetrieveResponse ``` { "properties": { "columns": { "description": "[colStart, colEnd] column dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "data": { "description": "confusion chart data with the format below.", "properties": { "classMetrics": { "description": "per-class information including one vs all scores in a format specified below", "items": { "properties": { "actualCount": { "description": "number of times this class is seen in the validation data", "type": "integer" }, "className": { "description": "name of the class", "type": "string" }, "confusionMatrixOneVsAll": { "description": "2d array representing 2x2 one vs all matrix. This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "precision score", "type": "number" }, "predictedCount": { "description": "number of times this class has been predicted for the validation data", "type": "integer" }, "recall": { "description": "recall score", "type": "number" }, "wasActualPercentages": { "description": "one vs all actual percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" }, "wasPredictedPercentages": { "description": "one vs all predicted percentages in a format specified below", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object" }, "type": "array" }, "classes": { "description": "class labels from the dataset, union of row classes & column classes. This field is deprecated as of v2.13. The rows and columns may have different class labels when using query parameters to retrieve a slice of the matrix; please use 'rowClasses' and 'colClasses' instead.", "items": { "type": "string" }, "type": "array" }, "colClasses": { "description": "class labels on columns of confusion matrix", "items": { "type": "string" }, "type": "array" }, "confusionMatrix": { "description": "2d array of ints representing confusion matrix, aligned with `rowClasses` and 'colClasses'array. For confusionMatrix[A][B] we can get an integer that represents the number of times 'if class with index A was correct we have class with index B predicted' (if the orientation is 'actual').", "items": { "items": { "type": "integer" }, "type": "array" }, "type": "array" }, "rowClasses": { "description": "class labels on rows of confusion matrix", "items": { "type": "string" }, "type": "array" } }, "required": [ "classMetrics", "classes", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "numberOfClasses": { "description": "count of classes in full confusion matrix.", "type": "integer" }, "rows": { "description": "[rowStart, rowEnd] row dimension of confusion matrix in response", "items": { "type": "integer" }, "type": "array" }, "source": { "description": "source of the chart", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" }, "totalMatrixSum": { "description": "sum of all values in full confusion matrix", "type": "integer" } }, "required": [ "columns", "data", "globalMetrics", "numberOfClasses", "rows", "source", "totalMatrixSum" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columns | [integer] | true | | [colStart, colEnd] column dimension of confusion matrix in response | | data | ConfusionChartData | true | | confusion chart data with the format below. | | globalMetrics | GlobalMetrics | true | | average metrics across all classes | | numberOfClasses | integer | true | | count of classes in full confusion matrix. | | rows | [integer] | true | | [rowStart, rowEnd] row dimension of confusion matrix in response | | source | string | true | | source of the chart | | totalMatrixSum | integer | true | | sum of all values in full confusion matrix | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout] | ## ModelLifecycle ``` { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" } ``` Object returning model lifecycle. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | reason | string,null | true | | The reason for the lifecycle stage. None if the model is active. | | stage | string | true | | The model lifecycle stage. | ### Enumerated Values | Property | Value | | --- | --- | | stage | [active, deprecated, disabled] | ## ModelLiftChartListResponse ``` { "properties": { "charts": { "description": "List of lift chart data from all available sources.", "items": { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data.", "enum": [ "validation", "crossValidation", "holdout", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string", "x-enum-versionadded": [ { "value": "backtest_2", "x-versionadded": "v2.23" }, { "value": "backtest_3", "x-versionadded": "v2.23" }, { "value": "backtest_4", "x-versionadded": "v2.23" }, { "value": "backtest_5", "x-versionadded": "v2.23" }, { "value": "backtest_6", "x-versionadded": "v2.23" }, { "value": "backtest_7", "x-versionadded": "v2.23" }, { "value": "backtest_8", "x-versionadded": "v2.23" }, { "value": "backtest_9", "x-versionadded": "v2.23" }, { "value": "backtest_10", "x-versionadded": "v2.23" }, { "value": "backtest_11", "x-versionadded": "v2.23" }, { "value": "backtest_12", "x-versionadded": "v2.23" }, { "value": "backtest_13", "x-versionadded": "v2.23" }, { "value": "backtest_14", "x-versionadded": "v2.23" }, { "value": "backtest_15", "x-versionadded": "v2.23" }, { "value": "backtest_16", "x-versionadded": "v2.23" }, { "value": "backtest_17", "x-versionadded": "v2.23" }, { "value": "backtest_18", "x-versionadded": "v2.23" }, { "value": "backtest_19", "x-versionadded": "v2.23" }, { "value": "backtest_20", "x-versionadded": "v2.23" } ] } }, "required": [ "bins", "source" ], "type": "object" }, "type": "array" } }, "required": [ "charts" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | charts | [ModelLiftChartResponse] | true | | List of lift chart data from all available sources. | ## ModelLiftChartResponse ``` { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data.", "enum": [ "validation", "crossValidation", "holdout", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string", "x-enum-versionadded": [ { "value": "backtest_2", "x-versionadded": "v2.23" }, { "value": "backtest_3", "x-versionadded": "v2.23" }, { "value": "backtest_4", "x-versionadded": "v2.23" }, { "value": "backtest_5", "x-versionadded": "v2.23" }, { "value": "backtest_6", "x-versionadded": "v2.23" }, { "value": "backtest_7", "x-versionadded": "v2.23" }, { "value": "backtest_8", "x-versionadded": "v2.23" }, { "value": "backtest_9", "x-versionadded": "v2.23" }, { "value": "backtest_10", "x-versionadded": "v2.23" }, { "value": "backtest_11", "x-versionadded": "v2.23" }, { "value": "backtest_12", "x-versionadded": "v2.23" }, { "value": "backtest_13", "x-versionadded": "v2.23" }, { "value": "backtest_14", "x-versionadded": "v2.23" }, { "value": "backtest_15", "x-versionadded": "v2.23" }, { "value": "backtest_16", "x-versionadded": "v2.23" }, { "value": "backtest_17", "x-versionadded": "v2.23" }, { "value": "backtest_18", "x-versionadded": "v2.23" }, { "value": "backtest_19", "x-versionadded": "v2.23" }, { "value": "backtest_20", "x-versionadded": "v2.23" } ] } }, "required": [ "bins", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [LiftBinResponse] | true | | The lift chart data for that source, as specified below. | | source | string | true | | Source of the data. | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ## ModelResidualsChartData ``` { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" } ``` Chart data from the validation data source ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | coefficientOfDetermination | number | true | | Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input | | data | [array] | true | | The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. NOTE: In DataRobot v5.2, the row number will not be included. | | histogram | [ModelResidualsHistogram] | true | | Data to plot a histogram of residual values | | residualMean | number | true | | The arithmetic mean of the predicted value minus the actual value over the downsampled dataset | | standardDeviation | number | true | | A measure of deviation from the group as a whole | ## ModelResidualsHistogram ``` { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | intervalEnd | number | true | | The interval end. For all but the last interval, the end value is exclusive. | | intervalStart | number | true | | The interval start. | | occurrences | integer | true | | The number of times the predicted value fits within the interval | ## ModelResidualsList ``` { "properties": { "residuals": { "description": "Residuals chart data from all available sources", "properties": { "crossValidation": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "holdout": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "validation": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" } }, "type": "object" } }, "required": [ "residuals" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | residuals | ModelResidualsSource | true | | Residuals chart data from all available sources | ## ModelResidualsSource ``` { "description": "Residuals chart data from all available sources", "properties": { "crossValidation": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "holdout": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "validation": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" } }, "type": "object" } ``` Residuals chart data from all available sources ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | crossValidation | ModelResidualsChartData | false | | Chart data from the validation data source | | holdout | ModelResidualsChartData | false | | Chart data from the validation data source | | validation | ModelResidualsChartData | false | | Chart data from the validation data source | ## ModelRocCurveListResponse ``` { "properties": { "charts": { "description": "List of ROC curve data from all available sources.", "items": { "properties": { "auc": { "description": "AUC value", "type": [ "number", "null" ] }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric value", "type": [ "number", "null" ] }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data.", "enum": [ "validation", "crossValidation", "holdout", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string", "x-enum-versionadded": [ { "value": "backtest_2", "x-versionadded": "v2.23" }, { "value": "backtest_3", "x-versionadded": "v2.23" }, { "value": "backtest_4", "x-versionadded": "v2.23" }, { "value": "backtest_5", "x-versionadded": "v2.23" }, { "value": "backtest_6", "x-versionadded": "v2.23" }, { "value": "backtest_7", "x-versionadded": "v2.23" }, { "value": "backtest_8", "x-versionadded": "v2.23" }, { "value": "backtest_9", "x-versionadded": "v2.23" }, { "value": "backtest_10", "x-versionadded": "v2.23" }, { "value": "backtest_11", "x-versionadded": "v2.23" }, { "value": "backtest_12", "x-versionadded": "v2.23" }, { "value": "backtest_13", "x-versionadded": "v2.23" }, { "value": "backtest_14", "x-versionadded": "v2.23" }, { "value": "backtest_15", "x-versionadded": "v2.23" }, { "value": "backtest_16", "x-versionadded": "v2.23" }, { "value": "backtest_17", "x-versionadded": "v2.23" }, { "value": "backtest_18", "x-versionadded": "v2.23" }, { "value": "backtest_19", "x-versionadded": "v2.23" }, { "value": "backtest_20", "x-versionadded": "v2.23" } ] } }, "required": [ "auc", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints", "source" ], "type": "object" }, "type": "array" } }, "required": [ "charts" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | charts | [ModelRocCurveResponse] | true | | List of ROC curve data from all available sources. | ## ModelRocCurveResponse ``` { "properties": { "auc": { "description": "AUC value", "type": [ "number", "null" ] }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric value", "type": [ "number", "null" ] }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data.", "enum": [ "validation", "crossValidation", "holdout", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string", "x-enum-versionadded": [ { "value": "backtest_2", "x-versionadded": "v2.23" }, { "value": "backtest_3", "x-versionadded": "v2.23" }, { "value": "backtest_4", "x-versionadded": "v2.23" }, { "value": "backtest_5", "x-versionadded": "v2.23" }, { "value": "backtest_6", "x-versionadded": "v2.23" }, { "value": "backtest_7", "x-versionadded": "v2.23" }, { "value": "backtest_8", "x-versionadded": "v2.23" }, { "value": "backtest_9", "x-versionadded": "v2.23" }, { "value": "backtest_10", "x-versionadded": "v2.23" }, { "value": "backtest_11", "x-versionadded": "v2.23" }, { "value": "backtest_12", "x-versionadded": "v2.23" }, { "value": "backtest_13", "x-versionadded": "v2.23" }, { "value": "backtest_14", "x-versionadded": "v2.23" }, { "value": "backtest_15", "x-versionadded": "v2.23" }, { "value": "backtest_16", "x-versionadded": "v2.23" }, { "value": "backtest_17", "x-versionadded": "v2.23" }, { "value": "backtest_18", "x-versionadded": "v2.23" }, { "value": "backtest_19", "x-versionadded": "v2.23" }, { "value": "backtest_20", "x-versionadded": "v2.23" } ] } }, "required": [ "auc", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | auc | number,null | true | | AUC value | | kolmogorovSmirnovMetric | number,null | true | | Kolmogorov-Smirnov metric value | | negativeClassPredictions | [number] | true | | List of example predictions for the negative class. | | positiveClassPredictions | [number] | true | | List of example predictions for the positive class. | | rocPoints | [RocPointsResponse] | true | | The ROC curve data for that source, as specified below. | | source | string | true | | Source of the data. | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ## ModelXrayMetadataDatetimeDataResponse ``` { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "sources": { "description": "The list of sources available for the model.", "items": { "enum": [ "training", "validation", "holdout" ], "type": "string" }, "type": "array" }, "status": { "description": "The status of the job.", "enum": [ "INPROGRESS", "COMPLETED", "NOT_COMPLETED" ], "type": "string" } }, "required": [ "backtestIndex", "sources", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestIndex | string | true | | The backtest index. For example: 0, 1, ..., 20, holdout, startstop. | | sources | [string] | true | | The list of sources available for the model. | | status | string | true | | The status of the job. | ### Enumerated Values | Property | Value | | --- | --- | | status | [INPROGRESS, COMPLETED, NOT_COMPLETED] | ## ModelXrayMetadataDatetimeResponse ``` { "properties": { "data": { "description": "The list of objects with status and sources for each backtest.", "items": { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "sources": { "description": "The list of sources available for the model.", "items": { "enum": [ "training", "validation", "holdout" ], "type": "string" }, "type": "array" }, "status": { "description": "The status of the job.", "enum": [ "INPROGRESS", "COMPLETED", "NOT_COMPLETED" ], "type": "string" } }, "required": [ "backtestIndex", "sources", "status" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [ModelXrayMetadataDatetimeDataResponse] | true | | The list of objects with status and sources for each backtest. | ## ModelXrayMetadataResponse ``` { "properties": { "sources": { "description": "The list of sources available for the model.", "items": { "enum": [ "training", "validation", "holdout" ], "type": "string" }, "type": "array" }, "status": { "description": "The status of the job.", "enum": [ "INPROGRESS", "COMPLETED", "NOT_COMPLETED" ], "type": "string" } }, "required": [ "sources", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sources | [string] | true | | The list of sources available for the model. | | status | string | true | | The status of the job. | ### Enumerated Values | Property | Value | | --- | --- | | status | [INPROGRESS, COMPLETED, NOT_COMPLETED] | ## MulticategoricalHistogram ``` { "properties": { "featureName": { "description": "Feature name.", "type": "string" }, "histogram": { "description": "Feature histogram.", "items": { "properties": { "label": { "description": "Label name.", "type": "string" }, "plot": { "description": "Relevance histogram for label.", "items": { "properties": { "labelRelevance": { "description": "Label relevance value.", "maximum": 1, "minimum": 0, "type": "integer" }, "rowCount": { "description": "Number of rows for which the label has the given relevance.", "minimum": 0, "type": "integer" }, "rowPct": { "description": "Percentage of rows for which the label has the given relevance.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "labelRelevance", "rowCount", "rowPct" ], "type": "object" }, "maxItems": 2, "minItems": 2, "type": "array" } }, "required": [ "label", "plot" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "featureName", "histogram", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | true | | Feature name. | | histogram | [MulticategoricalHistogramItem] | true | maxItems: 1000minItems: 1 | Feature histogram. | | projectId | string | true | | The project ID. | ## MulticategoricalHistogramItem ``` { "properties": { "label": { "description": "Label name.", "type": "string" }, "plot": { "description": "Relevance histogram for label.", "items": { "properties": { "labelRelevance": { "description": "Label relevance value.", "maximum": 1, "minimum": 0, "type": "integer" }, "rowCount": { "description": "Number of rows for which the label has the given relevance.", "minimum": 0, "type": "integer" }, "rowPct": { "description": "Percentage of rows for which the label has the given relevance.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "labelRelevance", "rowCount", "rowPct" ], "type": "object" }, "maxItems": 2, "minItems": 2, "type": "array" } }, "required": [ "label", "plot" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | string | true | | Label name. | | plot | [LabelRelevancePlot] | true | maxItems: 2minItems: 2 | Relevance histogram for label. | ## MulticategoricalInvalidFormatErrorData ``` { "description": "Error data.", "properties": { "errors": { "description": "Multicategorical format errors.", "items": { "properties": { "error": { "description": "Error type.", "type": "string" }, "feature": { "description": "Feature name.", "type": "string" }, "rowData": { "description": "Content of the row containing format error.", "type": [ "string", "null" ] }, "rowIndex": { "description": "Row index of the row containing format error.", "type": [ "integer", "null" ] } }, "required": [ "error", "feature", "rowData", "rowIndex" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "errors" ], "type": "object" } ``` Error data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errors | [MulticategoricalInvalidFormatErrorList] | true | maxItems: 100 | Multicategorical format errors. | ## MulticategoricalInvalidFormatErrorList ``` { "properties": { "error": { "description": "Error type.", "type": "string" }, "feature": { "description": "Feature name.", "type": "string" }, "rowData": { "description": "Content of the row containing format error.", "type": [ "string", "null" ] }, "rowIndex": { "description": "Row index of the row containing format error.", "type": [ "integer", "null" ] } }, "required": [ "error", "feature", "rowData", "rowIndex" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | error | string | true | | Error type. | | feature | string | true | | Feature name. | | rowData | string,null | true | | Content of the row containing format error. | | rowIndex | integer,null | true | | Row index of the row containing format error. | ## MulticategoricalInvalidFormatResponse ``` { "properties": { "data": { "description": "Error data.", "properties": { "errors": { "description": "Multicategorical format errors.", "items": { "properties": { "error": { "description": "Error type.", "type": "string" }, "feature": { "description": "Feature name.", "type": "string" }, "rowData": { "description": "Content of the row containing format error.", "type": [ "string", "null" ] }, "rowIndex": { "description": "Row index of the row containing format error.", "type": [ "integer", "null" ] } }, "required": [ "error", "feature", "rowData", "rowIndex" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "errors" ], "type": "object" }, "projectId": { "description": "The ID of the project this request is associated with.", "type": "string" } }, "required": [ "data", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | MulticategoricalInvalidFormatErrorData | true | | Error data. | | projectId | string | true | | The ID of the project this request is associated with. | ## MulticlassDatetimeFeatureEffectsResponse ``` { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of feature effect scores for each class in a multiclass project.", "items": { "properties": { "class": { "description": "Target class label.", "type": "string" }, "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "class", "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "The model's data source.", "type": "string" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "backtestIndex", "data", "modelId", "next", "previous", "projectId", "source", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestIndex | string | true | | The backtest index. For example: 0, 1, ..., 20, holdout, startstop. | | count | integer | false | | The number of items returned on this page. | | data | [MulticlassFeatureEffects] | true | | The list of feature effect scores for each class in a multiclass project. | | modelId | string | true | | The model ID. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | projectId | string | true | | The project ID. | | source | string | true | | The model's data source. | | totalCount | integer | true | | The total number of items across all pages. | ## MulticlassFeatureEffectCreate ``` { "properties": { "features": { "description": "The list of features to use to calculate feature effects.", "items": { "type": "string" }, "maxItems": 20000, "type": "array" }, "rowCount": { "description": "The number of rows from dataset to use for Feature Impact calculation.", "maximum": 100000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "topNFeatures": { "description": "Number of top features (ranked by feature impact) to use to calculate feature effects.", "exclusiveMinimum": 0, "maximum": 1000, "type": [ "integer", "null" ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | features | [string] | false | maxItems: 20000 | The list of features to use to calculate feature effects. | | rowCount | integer,null | false | maximum: 100000minimum: 10 | The number of rows from dataset to use for Feature Impact calculation. | | topNFeatures | integer,null | false | maximum: 1000 | Number of top features (ranked by feature impact) to use to calculate feature effects. | ## MulticlassFeatureEffectDatetimeCreate ``` { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`, `startstop`.", "type": "string" }, "features": { "description": "The list of features to use to calculate feature effects.", "items": { "type": "string" }, "maxItems": 20000, "type": "array" }, "rowCount": { "description": "The number of rows from dataset to use for Feature Impact calculation.", "maximum": 100000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "topNFeatures": { "description": "Number of top features (ranked by feature impact) to use to calculate feature effects.", "exclusiveMinimum": 0, "maximum": 1000, "type": [ "integer", "null" ] } }, "required": [ "backtestIndex" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestIndex | string | true | | The backtest index. For example: 0, 1, ..., 20, holdout, startstop. | | features | [string] | false | maxItems: 20000 | The list of features to use to calculate feature effects. | | rowCount | integer,null | false | maximum: 100000minimum: 10 | The number of rows from dataset to use for Feature Impact calculation. | | topNFeatures | integer,null | false | maximum: 1000 | Number of top features (ranked by feature impact) to use to calculate feature effects. | ## MulticlassFeatureEffects ``` { "properties": { "class": { "description": "Target class label.", "type": "string" }, "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "class", "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | class | string | true | | Target class label. | | featureImpactScore | number | true | | The feature impact score. | | featureName | string | true | | The name of the feature. | | featureType | string | true | | The feature type, either numeric or categorical. | | isBinnable | boolean | true | | Whether values can be grouped into bins. | | isScalable | boolean,null | true | | Whether numeric feature values can be reported on a log scale. | | partialDependence | PartialDependence | false | | Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight. | | predictedVsActual | PredictedVsActual | false | | Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight. | | weightLabel | string,null | true | | The weight label if a weight was configured for the project. | ## MulticlassFeatureEffectsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of feature effect scores for each class in a multiclass project.", "items": { "properties": { "class": { "description": "Target class label.", "type": "string" }, "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "class", "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "source": { "description": "The model's data source.", "type": "string" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "modelId", "next", "previous", "projectId", "source", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [MulticlassFeatureEffects] | true | | The list of feature effect scores for each class in a multiclass project. | | modelId | string | true | | The model ID. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | projectId | string | true | | The project ID. | | source | string | true | | The model's data source. | | totalCount | integer | true | | The total number of items across all pages. | ## MulticlassFeatureImpact ``` { "properties": { "class": { "description": "Target class label.", "type": "string" }, "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" } }, "required": [ "class", "featureImpacts" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | class | string | true | | Target class label. | | featureImpacts | [FeatureImpactItem] | true | maxItems: 1000 | A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned. | ## MulticlassFeatureImpactResponse ``` { "properties": { "classFeatureImpacts": { "description": "A list of feature importance scores for each class in multiclass project.", "items": { "properties": { "class": { "description": "Target class label.", "type": "string" }, "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" } }, "required": [ "class", "featureImpacts" ], "type": "object" }, "type": "array" }, "count": { "description": "Number of feature impact records in a given batch.", "type": "integer" }, "next": { "description": "The URL for the next page of results, or null if there is no next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL for the previous page of results, or null if there is no previous page.", "format": "uri", "type": [ "string", "null" ] }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact. Currently always False, as redundant feature identification isn't supported for multiclass in DataRobot.", "type": "boolean" }, "shapBased": { "description": "Indicates whether feature impact was calculated using Shapley values. Currently always `False`, as SHAP isn't supported for multiclass in DataRobot.", "type": "boolean" } }, "required": [ "classFeatureImpacts", "count", "next", "previous", "ranRedundancyDetection", "shapBased" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classFeatureImpacts | [MulticlassFeatureImpact] | true | | A list of feature importance scores for each class in multiclass project. | | count | integer | true | | Number of feature impact records in a given batch. | | next | string,null(uri) | true | | The URL for the next page of results, or null if there is no next page. | | previous | string,null(uri) | true | | The URL for the previous page of results, or null if there is no previous page. | | ranRedundancyDetection | boolean | true | | Indicates whether redundant feature identification was run while calculating this feature impact. Currently always False, as redundant feature identification isn't supported for multiclass in DataRobot. | | shapBased | boolean | true | | Indicates whether feature impact was calculated using Shapley values. Currently always False, as SHAP isn't supported for multiclass in DataRobot. | ## MulticlassLiftBinResponse ``` { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "targetClass": { "description": "Target class for the lift chart.", "type": "string" } }, "required": [ "bins", "targetClass" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [LiftBinResponse] | true | | The lift chart data for that source, as specified below. | | targetClass | string | true | | Target class for the lift chart. | ## MulticlassLiftChartForDatasetsList ``` { "properties": { "count": { "description": "The number of results returned on this page.", "type": "integer" }, "data": { "description": "Array of multiclass lift chart data for dataset, as specified below.", "items": { "properties": { "classBins": { "description": "The list of lift chart data for each target class.", "items": { "properties": { "bins": { "description": "The lift chart data for that source and class, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "targetClass": { "description": "The target class for the lift chart.", "type": "string" } }, "required": [ "bins", "targetClass" ], "type": "object" }, "type": "array" }, "datasetId": { "description": "The dataset ID of the dataset that was used to compute the lift chart.", "type": "string" } }, "required": [ "classBins", "datasetId" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID to which the chart data belongs.", "type": "string" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID to which the chart data belongs.", "type": "string" }, "totalCount": { "description": "Total count of multiclass lift charts matching to the query condition.", "type": "integer" } }, "required": [ "count", "data", "modelId", "next", "previous", "projectId", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of results returned on this page. | | data | [MulticlassLiftData] | true | | Array of multiclass lift chart data for dataset, as specified below. | | modelId | string | true | | The model ID to which the chart data belongs. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | | projectId | string | true | | The project ID to which the chart data belongs. | | totalCount | integer | true | | Total count of multiclass lift charts matching to the query condition. | ## MulticlassLiftData ``` { "properties": { "classBins": { "description": "The list of lift chart data for each target class.", "items": { "properties": { "bins": { "description": "The lift chart data for that source and class, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "targetClass": { "description": "The target class for the lift chart.", "type": "string" } }, "required": [ "bins", "targetClass" ], "type": "object" }, "type": "array" }, "datasetId": { "description": "The dataset ID of the dataset that was used to compute the lift chart.", "type": "string" } }, "required": [ "classBins", "datasetId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classBins | [MulticlassLiftDataClassBins] | true | | The list of lift chart data for each target class. | | datasetId | string | true | | The dataset ID of the dataset that was used to compute the lift chart. | ## MulticlassLiftDataClassBins ``` { "properties": { "bins": { "description": "The lift chart data for that source and class, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "targetClass": { "description": "The target class for the lift chart.", "type": "string" } }, "required": [ "bins", "targetClass" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [LiftChartbins] | true | | The lift chart data for that source and class, as specified below. | | targetClass | string | true | | The target class for the lift chart. | ## MulticlassModelLiftChartResponse ``` { "properties": { "classBins": { "description": "List of lift chart data for each target class.", "items": { "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" }, "targetClass": { "description": "Target class for the lift chart.", "type": "string" } }, "required": [ "bins", "targetClass" ], "type": "object" }, "type": "array" }, "source": { "description": "Source of the data", "enum": [ "validation", "crossValidation", "holdout" ], "type": "string" } }, "required": [ "classBins", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classBins | [MulticlassLiftBinResponse] | true | | List of lift chart data for each target class. | | source | string | true | | Source of the data | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout] | ## MultiseriesHistogramsBin ``` { "properties": { "count": { "description": "The value count of the bin", "type": "integer" }, "left": { "description": "The inclusive left boundary of the bin.", "oneOf": [ { "type": "number" }, { "type": "string" } ] }, "right": { "description": "The exclusive right boundary of the bin. The last bin has an inclusive right boundary.", "oneOf": [ { "type": "number" }, { "type": "string" } ] } }, "required": [ "count", "left", "right" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The value count of the bin | | left | any | true | | The inclusive left boundary of the bin. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | right | any | true | | The exclusive right boundary of the bin. The last bin has an inclusive right boundary. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | ## MultiseriesHistogramsRetrieveResponse ``` { "properties": { "bins": { "description": "List of bins representing histogram.", "items": { "properties": { "count": { "description": "The value count of the bin", "type": "integer" }, "left": { "description": "The inclusive left boundary of the bin.", "oneOf": [ { "type": "number" }, { "type": "string" } ] }, "right": { "description": "The exclusive right boundary of the bin. The last bin has an inclusive right boundary.", "oneOf": [ { "type": "number" }, { "type": "string" } ] } }, "required": [ "count", "left", "right" ], "type": "object" }, "type": "array" } }, "required": [ "bins" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bins | [MultiseriesHistogramsBin] | true | | List of bins representing histogram. | ## Numeric ``` { "properties": { "allData": { "description": "Statistic value for all data.", "type": [ "number", "null" ] }, "insightName": { "description": "Insight name.", "enum": [ "min", "max", "median", "avg", "firstQuartile", "thirdQuartile", "missingRowsPercent" ], "type": "string" }, "perCluster": { "description": "Statistic values for for each cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "statistic": { "description": "Statistic value for this cluster.", "type": [ "number", "null" ] } }, "required": [ "clusterName", "statistic" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allData | number,null | true | | Statistic value for all data. | | insightName | string | true | | Insight name. | | perCluster | [PerClusterNumeric] | true | | Statistic values for for each cluster. | ### Enumerated Values | Property | Value | | --- | --- | | insightName | [min, max, median, avg, firstQuartile, thirdQuartile, missingRowsPercent] | ## NumericFeature ``` { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "numeric" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistic value for all data.", "type": [ "number", "null" ] }, "insightName": { "description": "Insight name.", "enum": [ "min", "max", "median", "avg", "firstQuartile", "thirdQuartile", "missingRowsPercent" ], "type": "string" }, "perCluster": { "description": "Statistic values for for each cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "statistic": { "description": "Statistic value for this cluster.", "type": [ "number", "null" ] } }, "required": [ "clusterName", "statistic" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureImpact | number,null | false | | Feature Impact score. | | featureName | string | true | | Feature name. | | featureType | string | true | | Feature Type. | | insights | [Numeric] | true | | A list of Cluster Insights for a feature. | ### Enumerated Values | Property | Value | | --- | --- | | featureType | numeric | ## PairwiseManualSelectionCreatePayload ``` { "properties": { "columnLabels": { "description": "Manually selected column labels.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "featureName": { "description": "The name of the feature the request is related to.", "type": [ "string", "null" ] }, "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename]", "type": "string" }, "name": { "description": "Name for the set of manually selected labels.", "maxLength": 100, "type": "string" }, "projectId": { "description": "The ID of the project the request is related to.", "type": [ "string", "null" ] }, "rowLabels": { "description": "Manually selected row labels.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "columnLabels", "featureName", "multilabelInsightsKey", "name", "projectId", "rowLabels" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnLabels | [string] | true | maxItems: 10minItems: 1 | Manually selected column labels. | | featureName | string,null | true | | The name of the feature the request is related to. | | multilabelInsightsKey | string | true | | Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename] | | name | string | true | maxLength: 100 | Name for the set of manually selected labels. | | projectId | string,null | true | | The ID of the project the request is related to. | | rowLabels | [string] | true | maxItems: 10minItems: 1 | Manually selected row labels. | ## PairwiseManualSelectionCreateResponse ``` { "properties": { "id": { "description": "The ID of the label set.", "type": "string" } }, "required": [ "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the label set. | ## PairwiseManualSelectionCreatedItem ``` { "properties": { "columnLabels": { "description": "Manually selected column labels.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "id": { "description": "The ID of the manually selected labels set.", "type": "string" }, "name": { "description": "Name for the set of manually selected labels.", "maxLength": 100, "type": "string" }, "rowLabels": { "description": "Manually selected row labels.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "columnLabels", "id", "name", "rowLabels" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnLabels | [string] | true | maxItems: 10minItems: 1 | Manually selected column labels. | | id | string | true | | The ID of the manually selected labels set. | | name | string | true | maxLength: 100 | Name for the set of manually selected labels. | | rowLabels | [string] | true | maxItems: 10minItems: 1 | Manually selected row labels. | ## PairwiseManualSelectionResponse ``` { "properties": { "manualSelectionId": { "description": "The ID of the deleted or updated label set.", "type": "string" } }, "required": [ "manualSelectionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | manualSelectionId | string | true | | The ID of the deleted or updated label set. | ## PairwiseManualSelectionUpdateRequest ``` { "properties": { "name": { "description": "Name for the set of manually selected labels.", "maxLength": 100, "type": "string" } }, "required": [ "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 100 | Name for the set of manually selected labels. | ## PairwiseManualSelectionsRetrieveResponse ``` { "properties": { "data": { "description": "The list of manually selected label sets.", "items": { "properties": { "columnLabels": { "description": "Manually selected column labels.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" }, "id": { "description": "The ID of the manually selected labels set.", "type": "string" }, "name": { "description": "Name for the set of manually selected labels.", "maxLength": 100, "type": "string" }, "rowLabels": { "description": "Manually selected row labels.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array" } }, "required": [ "columnLabels", "id", "name", "rowLabels" ], "type": "object" }, "type": "array" }, "featureName": { "description": "The name of the feature the request is related to.", "type": [ "string", "null" ] }, "multilabelInsightsKey": { "description": "Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename]", "type": "string" }, "projectId": { "description": "The ID of the project the request is related to.", "type": [ "string", "null" ] } }, "required": [ "data", "featureName", "multilabelInsightsKey", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [PairwiseManualSelectionCreatedItem] | true | | The list of manually selected label sets. | | featureName | string,null | true | | The name of the feature the request is related to. | | multilabelInsightsKey | string | true | | Key for multilabel insights, unique per project, feature, and EDA stage. The most recent key can be retrieved via [GET /api/v2/projects/{projectId}/features/][get-apiv2projectsprojectidfeatures] or [GET /api/v2/projects/{projectId}/features/{featurename:featureName}/][get-apiv2projectsprojectidfeaturesfeaturenamefeaturename] | | projectId | string,null | true | | The ID of the project the request is related to. | ## PairwiseStatisticsItem ``` { "properties": { "labelConfiguration": { "description": "Configuration of all labels.", "items": { "properties": { "label": { "description": "Label name.", "type": "string" }, "relevance": { "description": "Relevance value of the label.", "maximum": 1, "minimum": 0, "type": "integer" } }, "required": [ "label" ], "type": "object" }, "maxItems": 2, "minItems": 2, "type": "array" }, "statisticValue": { "description": "Statistic value for the given label configuration.", "type": [ "number", "null" ] } }, "required": [ "labelConfiguration", "statisticValue" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | labelConfiguration | [PairwiseStatisticsLabelConfiguration] | true | maxItems: 2minItems: 2 | Configuration of all labels. | | statisticValue | number,null | true | | Statistic value for the given label configuration. | ## PairwiseStatisticsLabelConfiguration ``` { "properties": { "label": { "description": "Label name.", "type": "string" }, "relevance": { "description": "Relevance value of the label.", "maximum": 1, "minimum": 0, "type": "integer" } }, "required": [ "label" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | string | true | | Label name. | | relevance | integer | false | maximum: 1minimum: 0 | Relevance value of the label. | ## PairwiseStatisticsResponse ``` { "properties": { "data": { "description": "Statistic values.", "items": { "properties": { "labelConfiguration": { "description": "Configuration of all labels.", "items": { "properties": { "label": { "description": "Label name.", "type": "string" }, "relevance": { "description": "Relevance value of the label.", "maximum": 1, "minimum": 0, "type": "integer" } }, "required": [ "label" ], "type": "object" }, "maxItems": 2, "minItems": 2, "type": "array" }, "statisticValue": { "description": "Statistic value for the given label configuration.", "type": [ "number", "null" ] } }, "required": [ "labelConfiguration", "statisticValue" ], "type": "object" }, "type": "array" }, "featureName": { "description": "Feature name.", "type": "string" }, "projectId": { "description": "The project ID.", "maxLength": 24, "minLength": 24, "type": "string" }, "statisticType": { "description": "Pairwise statistic type.", "enum": [ "conditionalProbability", "correlation", "jointProbability" ], "type": "string" } }, "required": [ "data", "featureName", "projectId", "statisticType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [PairwiseStatisticsItem] | true | | Statistic values. | | featureName | string | true | | Feature name. | | projectId | string | true | maxLength: 24minLength: 24minLength: 24 | The project ID. | | statisticType | string | true | | Pairwise statistic type. | ### Enumerated Values | Property | Value | | --- | --- | | statisticType | [conditionalProbability, correlation, jointProbability] | ## PartialDependence ``` { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" } ``` Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [PartialDependenceData] | true | | The partial dependence results. | | isCapped | boolean | true | | Indicates whether the data for computation is capped. | ## PartialDependenceData ``` { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dependence | number | true | | The value of partial dependence. | | label | string | true | | Contains the label for categorical and numeric features as a string. | ## PayoffMatricesCreate ``` { "properties": { "falseNegativeValue": { "description": "False negative value to use for profit curve calculation.", "type": "number" }, "falsePositiveValue": { "description": "False positive value to use for profit curve calculation.", "type": "number" }, "name": { "description": "The name of the payoff matrix to be created.", "type": "string" }, "trueNegativeValue": { "description": "True negative value to use for profit curve calculation.", "type": "number" }, "truePositiveValue": { "description": "True positive value to use for profit curve calculation.", "type": "number" } }, "required": [ "falseNegativeValue", "falsePositiveValue", "name", "trueNegativeValue", "truePositiveValue" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | falseNegativeValue | number | true | | False negative value to use for profit curve calculation. | | falsePositiveValue | number | true | | False positive value to use for profit curve calculation. | | name | string | true | | The name of the payoff matrix to be created. | | trueNegativeValue | number | true | | True negative value to use for profit curve calculation. | | truePositiveValue | number | true | | True positive value to use for profit curve calculation. | ## PayoffMatricesListResponse ``` { "properties": { "count": { "description": "The number of items on the current page.", "type": "integer" }, "data": { "description": "The payoff matrices for a project.", "items": { "properties": { "falseNegativeValue": { "description": "Payoff value for false negatives used in profit curve calculation.", "type": "number" }, "falsePositiveValue": { "description": "Payoff value for false positives used in profit curve calculation.", "type": "number" }, "id": { "description": "The ID of the payoff matrix.", "type": "string" }, "name": { "description": "The label for the payoff matrix.", "type": "string" }, "projectId": { "description": "The ID of the project associated with the payoff matrix.", "type": "string" }, "trueNegativeValue": { "description": "Payoff value for true negatives used in profit curve calculation.", "type": "number" }, "truePositiveValue": { "description": "Payoff value for true positives used in profit curve calculation.", "type": "number" } }, "required": [ "falseNegativeValue", "falsePositiveValue", "id", "name", "projectId", "trueNegativeValue", "truePositiveValue" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page)", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items on the current page. | | data | [PayoffMatricesResponse] | true | | The payoff matrices for a project. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page) | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page) | | totalCount | integer | true | | The total number of items. | ## PayoffMatricesResponse ``` { "properties": { "falseNegativeValue": { "description": "Payoff value for false negatives used in profit curve calculation.", "type": "number" }, "falsePositiveValue": { "description": "Payoff value for false positives used in profit curve calculation.", "type": "number" }, "id": { "description": "The ID of the payoff matrix.", "type": "string" }, "name": { "description": "The label for the payoff matrix.", "type": "string" }, "projectId": { "description": "The ID of the project associated with the payoff matrix.", "type": "string" }, "trueNegativeValue": { "description": "Payoff value for true negatives used in profit curve calculation.", "type": "number" }, "truePositiveValue": { "description": "Payoff value for true positives used in profit curve calculation.", "type": "number" } }, "required": [ "falseNegativeValue", "falsePositiveValue", "id", "name", "projectId", "trueNegativeValue", "truePositiveValue" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | falseNegativeValue | number | true | | Payoff value for false negatives used in profit curve calculation. | | falsePositiveValue | number | true | | Payoff value for false positives used in profit curve calculation. | | id | string | true | | The ID of the payoff matrix. | | name | string | true | | The label for the payoff matrix. | | projectId | string | true | | The ID of the project associated with the payoff matrix. | | trueNegativeValue | number | true | | Payoff value for true negatives used in profit curve calculation. | | truePositiveValue | number | true | | Payoff value for true positives used in profit curve calculation. | ## PerClassAccuracy ``` { "properties": { "className": { "description": "The name of the class value for the categorical feature.", "type": "string" }, "metrics": { "description": "An array of metric scores.", "items": { "properties": { "metric": { "description": "The name of the metric.", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE", "f1", "accuracy" ], "type": "string" }, "value": { "description": "The calculated score of the metric.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "metric", "value" ], "type": "object" }, "type": "array" } }, "required": [ "className", "metrics" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | className | string | true | | The name of the class value for the categorical feature. | | metrics | [AccuracyMetrics] | true | | An array of metric scores. | ## PerClassFairness ``` { "properties": { "absoluteValue": { "description": "Absolute fairness score for the class", "minimum": 0, "type": "number" }, "className": { "description": "Name of the protected class the score is calculated for.", "type": "string" }, "entriesCount": { "description": "The number of entries of the class in the analysed data.", "minimum": 0, "type": "integer" }, "isStatisticallySignificant": { "description": "Flag to tell whether the score can be treated as statistically significant. In other words, whether we are confident enough with the score for this protected class.", "type": "boolean" }, "value": { "description": "The relative fairness score for the class.", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "absoluteValue", "className", "entriesCount", "isStatisticallySignificant", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | absoluteValue | number | true | minimum: 0 | Absolute fairness score for the class | | className | string | true | | Name of the protected class the score is calculated for. | | entriesCount | integer | true | minimum: 0 | The number of entries of the class in the analysed data. | | isStatisticallySignificant | boolean | true | | Flag to tell whether the score can be treated as statistically significant. In other words, whether we are confident enough with the score for this protected class. | | value | number | true | maximum: 1minimum: 0 | The relative fairness score for the class. | ## PerClusterCategorical ``` { "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allOther | number,null | false | maximum: 100minimum: 0 | A percentage of rows that do not have any of these values or categories. | | clusterName | string | true | | Cluster name. | | missingRowsPercent | number,null | false | maximum: 100minimum: 0 | A percentage of all rows that have a missing value for this feature. | | perValueStatistics | [PerValueStatisticsListItem] | true | | Statistic value for feature values in all data or a cluster. | ## PerClusterGeospatial ``` { "properties": { "clusterName": { "description": "Cluster name.", "maxLength": 50, "minLength": 1, "type": "string" }, "representativeLocations": { "description": "A list of latitude and longitude location list", "items": { "description": "Latitude and longitude list.", "items": { "description": "Longitude or latitude, in degrees.", "maximum": 180, "minimum": -180, "type": "number" }, "type": "array" }, "maxItems": 1000, "type": "array" } }, "required": [ "clusterName", "representativeLocations" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clusterName | string | true | maxLength: 50minLength: 1minLength: 1 | Cluster name. | | representativeLocations | [array] | true | maxItems: 1000 | A list of latitude and longitude location list | ## PerClusterImage ``` { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "images": { "description": "A list of b64 encoded images.", "items": { "description": "b64 encoded image", "type": "string" }, "type": "array" }, "percentageOfMissingImages": { "description": "A percentage of image rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": "number" } }, "required": [ "clusterName", "images", "percentageOfMissingImages" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clusterName | string | true | | Cluster name. | | images | [string] | true | | A list of b64 encoded images. | | percentageOfMissingImages | number | true | maximum: 100minimum: 0 | A percentage of image rows that have a missing value for this feature. | ## PerClusterNumeric ``` { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "statistic": { "description": "Statistic value for this cluster.", "type": [ "number", "null" ] } }, "required": [ "clusterName", "statistic" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clusterName | string | true | | Cluster name. | | statistic | number,null | true | | Statistic value for this cluster. | ## PerClusterText ``` { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clusterName | string | true | | Cluster name. | | missingRowsPercent | number,null | false | maximum: 100minimum: 0 | A percentage of all rows that have a missing value for this feature. | | perValueStatistics | [PerValueStatisticTextListItem] | true | | Statistic value for feature values in all data or a cluster. | ## PerNgramTextExplanations ``` { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isUnknown | boolean | true | | Whether the ngram is identifiable by the blueprint or not. | | ngrams | [PredictionExplanationsPredictionValues] | true | maxItems: 1000 | The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information. | | qualitativateStrength | string | true | | A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-'). | | strength | number | true | | The amount these ngrams affected the prediction. | ## PerValueStatisticTextListItem ``` { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | contextualExtracts | [string] | true | | Contextual extracts that show context for the n-gram. | | importance | number | true | | Importance value for this n-gram. | | ngram | string | true | | An n-gram. | ## PerValueStatistics ``` { "description": "Statistics for all data for different feature values.", "properties": { "allOther": { "description": "A percentage of rows that do not have any of these values or categories.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" } ``` Statistics for all data for different feature values. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allOther | number,null | false | maximum: 100minimum: 0 | A percentage of rows that do not have any of these values or categories. | | missingRowsPercent | number,null | false | maximum: 100minimum: 0 | A percentage of all rows that have a missing value for this feature. | | perValueStatistics | [PerValueStatisticsListItem] | true | | Statistic value for feature values in all data or a cluster. | ## PerValueStatisticsListItem ``` { "properties": { "categoryLevel": { "description": "A category level.", "type": "string" }, "frequency": { "description": "Statistic value for this cluster.", "type": "number" } }, "required": [ "categoryLevel", "frequency" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categoryLevel | string | true | | A category level. | | frequency | number | true | | Statistic value for this cluster. | ## PermutationFeatureImpactCreatePayload ``` { "properties": { "backtest": { "description": "The backtest value used for Feature Impact computation. Applicable for datetime aware models.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" } ], "x-versionadded": "v2.28" }, "rowCount": { "description": "The sample size to use for Feature Impact computation. It is possible to re-compute Feature Impact with a different row count.", "maximum": 100000, "minimum": 10, "type": "integer", "x-versionadded": "v2.21" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtest | any | false | | The backtest value used for Feature Impact computation. Applicable for datetime aware models. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 19minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rowCount | integer | false | maximum: 100000minimum: 10 | The sample size to use for Feature Impact computation. It is possible to re-compute Feature Impact with a different row count. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | holdout | ## PermutationFeatureImpactResponse ``` { "properties": { "backtest": { "description": "The backtest model used to compute Feature Impact.Defined for datetime aware models.", "oneOf": [ { "maximum": 19, "minimum": 0, "type": "integer" }, { "enum": [ "holdout" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.29" }, "count": { "description": "Number of feature impact records in a given batch.", "type": "integer" }, "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL for the next page of results, or null if there is no next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL for the previous page of results, or null if there is no previous page.", "format": "uri", "type": [ "string", "null" ] }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact.", "type": "boolean" }, "rowCount": { "description": "The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the ``rowCount``, we return ``null`` here.", "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "shapBased": { "description": "Indicates whether feature impact was calculated using Shapley values. True for anomaly detection models when the project is unsupervised, as permutation approach is not applicable. Note that supervised projects must use an alternative route for SHAP impact: /api/v2/projects/(projectId)/models/(modelId)/shapImpact/", "type": "boolean", "x-versionadded": "v2.18" } }, "required": [ "backtest", "count", "featureImpacts", "next", "previous", "ranRedundancyDetection", "rowCount", "shapBased" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtest | any | true | | The backtest model used to compute Feature Impact.Defined for datetime aware models. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 19minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | Number of feature impact records in a given batch. | | featureImpacts | [FeatureImpactItem] | true | maxItems: 1000 | A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned. | | next | string,null(uri) | true | | The URL for the next page of results, or null if there is no next page. | | previous | string,null(uri) | true | | The URL for the previous page of results, or null if there is no previous page. | | ranRedundancyDetection | boolean | true | | Indicates whether redundant feature identification was run while calculating this feature impact. | | rowCount | integer,null | true | | The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the rowCount, we return null here. | | shapBased | boolean | true | | Indicates whether feature impact was calculated using Shapley values. True for anomaly detection models when the project is unsupervised, as permutation approach is not applicable. Note that supervised projects must use an alternative route for SHAP impact: /api/v2/projects/(projectId)/models/(modelId)/shapImpact/ | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | holdout | ## PredictedFrequency ``` { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "the percentage of the times this class was actual when `classMetrics.className` is predicted (from 0 to 100)", "maximum": 100, "minimum": 0, "type": "number" }, "value": { "description": "The count of the times this class was actual `classMetrics.className` when it was predicted", "minimum": 0, "type": "integer" } }, "required": [ "otherClassName", "percentage", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | otherClassName | string | true | | The name of the class. | | percentage | number | true | maximum: 100minimum: 0 | the percentage of the times this class was actual when classMetrics.className is predicted (from 0 to 100) | | value | integer | true | minimum: 0 | The count of the times this class was actual classMetrics.className when it was predicted | ## PredictedPercentages ``` { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | otherClassName | string | true | | The name of the class. | | percentage | number | true | | The percentage of times this was the actual class but classMetrics.className was predicted | ## PredictedVsActual ``` { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" } ``` Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [PredictedVsActualData] | true | | The predicted versus actual results. | | isCapped | boolean | true | | Indicates whether the data for computation is capped. | | logScaledData | [PredictedVsActualData] | true | | The predicted versus actual results on a log scale. | ## PredictedVsActualData ``` { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actual | number,null | true | | The actual value. null for 0-row bins and unsupervised time series models. | | bin | [string] | false | | The labels for the left and right bin limits for numeric features. | | label | string | true | | Contains label for categorical features; For numeric features contains range or numeric value. | | predicted | number,null | true | | The predicted value. null for 0-row bins. | | rowCount | integer | true | | The number of rows for the label and bin. | ## PredictionExplanation ``` { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature | string | true | | The name of the feature contributing to the prediction. | | featureValue | string | true | | The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21). | | imageExplanationUrl | string,null | true | | For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null. | | label | string | true | | Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score. | | perNgramTextExplanations | [PerNgramTextExplanations] | false | maxItems: 10000 | For text features, an array of JSON object containing the per ngram based text prediction explanations. | | qualitativateStrength | string | true | | A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+, medium', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'. | | strength | number | true | | The amount this feature's value affected the prediction. | ## PredictionExplanationsCreate ``` { "properties": { "classNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.29" }, "datasetId": { "description": "The dataset ID.", "type": "string" }, "maxExplanations": { "default": 3, "description": "The maximum number of prediction explanations to supply per row of the dataset.", "maximum": 10, "minimum": 0, "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "thresholdHigh": { "default": null, "description": "The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] }, "thresholdLow": { "default": null, "description": "The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classNames | [string] | false | maxItems: 100 | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | | datasetId | string | true | | The dataset ID. | | maxExplanations | integer | false | maximum: 10minimum: 0 | The maximum number of prediction explanations to supply per row of the dataset. | | modelId | string | true | | The model ID. | | numTopClasses | integer | false | maximum: 100minimum: 1 | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | | thresholdHigh | number,null | false | | The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. | | thresholdLow | number,null | false | | The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. | ## PredictionExplanationsInitializationCreate ``` { "properties": { "maxExplanations": { "default": 3, "description": "The maximum number of prediction explanations to supply per row of the dataset.", "maximum": 10, "minimum": 1, "type": "integer" }, "thresholdHigh": { "default": null, "description": "The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] }, "thresholdLow": { "default": null, "description": "The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows.", "type": [ "number", "null" ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | false | maximum: 10minimum: 1 | The maximum number of prediction explanations to supply per row of the dataset. | | thresholdHigh | number,null | false | | The high threshold, above which a prediction must score in order for prediction explanations to be computed. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. | | thresholdLow | number,null | false | | The lower threshold, below which a prediction must score in order for prediction explanations to be computed for a row in the dataset. If neither thresholdHigh nor thresholdLow is specified, prediction explanations will be computed for all rows. | ## PredictionExplanationsInitializationRetrieve ``` { "properties": { "modelId": { "description": "The model ID.", "type": "string" }, "predictionExplanationsSample": { "description": "Each is a PredictionExplanationsRow. They represent a small sample of prediction explanations that could be generated for a particular dataset. They will have the same schema as the `data` array in the response from [GET /api/v2/projects/{projectId}/predictionExplanations/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationspredictionexplanationsid]. As of v2.21 only difference is that there is no forecastPoint in response for time series projects.", "items": { "properties": { "adjustedPrediction": { "description": "The exposure-adjusted output of the model for this row.", "type": "number", "x-versionadded": "v2.8" }, "adjustedPredictionValues": { "description": "The exposure-adjusted output of the model for this row.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.8" }, "forecastDistance": { "description": "Forecast distance for the row. For time series projects only.", "type": "integer", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "Forecast point for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" }, "prediction": { "description": "The output of the model for this row.", "type": "number" }, "predictionExplanations": { "description": "The list of prediction explanations.", "items": { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "The threshold value used for classification prediction.", "type": [ "number", "null" ] }, "predictionValues": { "description": "The list of prediction values.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row this PredictionExplanationsRow describes.", "type": "integer" }, "seriesId": { "description": "The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "timestamp": { "description": "The timestamp for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "adjustedPrediction", "adjustedPredictionValues", "forecastDistance", "forecastPoint", "prediction", "predictionExplanations", "predictionThreshold", "predictionValues", "rowId", "seriesId", "timestamp" ], "type": "object" }, "type": "array" }, "projectId": { "description": "The project ID.", "type": "string" } }, "required": [ "modelId", "predictionExplanationsSample", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | The model ID. | | predictionExplanationsSample | [PredictionExplanationsRow] | true | | Each is a PredictionExplanationsRow. They represent a small sample of prediction explanations that could be generated for a particular dataset. They will have the same schema as the data array in the response from [GET /api/v2/projects/{projectId}/predictionExplanations/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationspredictionexplanationsid]. As of v2.21 only difference is that there is no forecastPoint in response for time series projects. | | projectId | string | true | | The project ID. | ## PredictionExplanationsPredictionValues ``` { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | label | string | true | | Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score. | | value | number | true | | The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label. | ## PredictionExplanationsRecord ``` { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "finishTime": { "description": "The timestamp referencing when computation for these prediction explanations finished.", "type": "number" }, "id": { "description": "The PredictionExplanationsRecord ID.", "type": "string" }, "maxExplanations": { "description": "The maximum number of codes generated per prediction.", "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numColumns": { "description": "The number of columns prediction explanations were computed for.", "type": "integer" }, "predictionExplanationsLocation": { "description": "Where to retrieve the prediction explanations.", "type": "string" }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "thresholdHigh": { "description": "The prediction explanation high threshold. Predictions must be above this value (or below the thresholdLow value) to have PredictionExplanations computed.", "type": [ "number", "null" ] }, "thresholdLow": { "description": "The prediction explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) to have PredictionExplanations computed.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "finishTime", "id", "maxExplanations", "modelId", "numColumns", "predictionExplanationsLocation", "predictionThreshold", "projectId", "thresholdHigh", "thresholdLow" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The dataset ID. | | finishTime | number | true | | The timestamp referencing when computation for these prediction explanations finished. | | id | string | true | | The PredictionExplanationsRecord ID. | | maxExplanations | integer | true | | The maximum number of codes generated per prediction. | | modelId | string | true | | The model ID. | | numColumns | integer | true | | The number of columns prediction explanations were computed for. | | predictionExplanationsLocation | string | true | | Where to retrieve the prediction explanations. | | predictionThreshold | number,null | true | | The threshold value used for binary classification prediction. | | projectId | string | true | | The project ID. | | thresholdHigh | number,null | true | | The prediction explanation high threshold. Predictions must be above this value (or below the thresholdLow value) to have PredictionExplanations computed. | | thresholdLow | number,null | true | | The prediction explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) to have PredictionExplanations computed. | ## PredictionExplanationsRecordList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "minimum": 0, "type": "integer" }, "data": { "description": "Each has the same schema as if retrieving the prediction explanations individually from [GET /api/v2/projects/{projectId}/predictionExplanationsRecords/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationsrecordspredictionexplanationsid].", "items": { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "finishTime": { "description": "The timestamp referencing when computation for these prediction explanations finished.", "type": "number" }, "id": { "description": "The PredictionExplanationsRecord ID.", "type": "string" }, "maxExplanations": { "description": "The maximum number of codes generated per prediction.", "type": "integer" }, "modelId": { "description": "The model ID.", "type": "string" }, "numColumns": { "description": "The number of columns prediction explanations were computed for.", "type": "integer" }, "predictionExplanationsLocation": { "description": "Where to retrieve the prediction explanations.", "type": "string" }, "predictionThreshold": { "description": "The threshold value used for binary classification prediction.", "type": [ "number", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "thresholdHigh": { "description": "The prediction explanation high threshold. Predictions must be above this value (or below the thresholdLow value) to have PredictionExplanations computed.", "type": [ "number", "null" ] }, "thresholdLow": { "description": "The prediction explanation low threshold. Predictions must be below this value (or above the thresholdHigh value) to have PredictionExplanations computed.", "type": [ "number", "null" ] } }, "required": [ "datasetId", "finishTime", "id", "maxExplanations", "modelId", "numColumns", "predictionExplanationsLocation", "predictionThreshold", "projectId", "thresholdHigh", "thresholdLow" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | The number of items returned on this page. | | data | [PredictionExplanationsRecord] | true | | Each has the same schema as if retrieving the prediction explanations individually from [GET /api/v2/projects/{projectId}/predictionExplanationsRecords/{predictionExplanationsId}/][get-apiv2projectsprojectidpredictionexplanationsrecordspredictionexplanationsid]. | | next | string,null | true | | A URL pointing to the next page (if null, there is no next page). | | previous | string,null | true | | A URL pointing to the previous page (if null, there is no previous page). | ## PredictionExplanationsRetrieve ``` { "properties": { "adjustmentMethod": { "description": "'exposureNormalized' (for regression projects with exposure) or 'N/A' (for classification projects) The value of 'exposureNormalized' indicates that prediction outputs are adjusted (or divided) by exposure. The value of 'N/A' indicates that no adjustments are applied to the adjusted predictions and they are identical to the unadjusted predictions.", "type": "string", "x-versionadded": "v2.8" }, "count": { "description": "The number of rows of prediction explanations returned.", "type": "integer" }, "data": { "description": "Each is a PredictionExplanationsRow corresponding to one row of the prediction dataset.", "items": { "properties": { "adjustedPrediction": { "description": "The exposure-adjusted output of the model for this row.", "type": "number", "x-versionadded": "v2.8" }, "adjustedPredictionValues": { "description": "The exposure-adjusted output of the model for this row.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.8" }, "forecastDistance": { "description": "Forecast distance for the row. For time series projects only.", "type": "integer", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "Forecast point for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" }, "prediction": { "description": "The output of the model for this row.", "type": "number" }, "predictionExplanations": { "description": "The list of prediction explanations.", "items": { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "The threshold value used for classification prediction.", "type": [ "number", "null" ] }, "predictionValues": { "description": "The list of prediction values.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row this PredictionExplanationsRow describes.", "type": "integer" }, "seriesId": { "description": "The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "timestamp": { "description": "The timestamp for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "adjustedPrediction", "adjustedPredictionValues", "forecastDistance", "forecastPoint", "prediction", "predictionExplanations", "predictionThreshold", "predictionValues", "rowId", "seriesId", "timestamp" ], "type": "object" }, "type": "array" }, "id": { "description": "The ID of this group of prediction explanations.", "type": "string" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "predictionExplanationsRecordLocation": { "description": "The URL of the PredictionExplanationsRecord associated with these prediction explanations.", "type": "string" }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "adjustmentMethod", "count", "data", "id", "next", "predictionExplanationsRecordLocation", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | adjustmentMethod | string | true | | 'exposureNormalized' (for regression projects with exposure) or 'N/A' (for classification projects) The value of 'exposureNormalized' indicates that prediction outputs are adjusted (or divided) by exposure. The value of 'N/A' indicates that no adjustments are applied to the adjusted predictions and they are identical to the unadjusted predictions. | | count | integer | true | | The number of rows of prediction explanations returned. | | data | [PredictionExplanationsRow] | true | | Each is a PredictionExplanationsRow corresponding to one row of the prediction dataset. | | id | string | true | | The ID of this group of prediction explanations. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | predictionExplanationsRecordLocation | string | true | | The URL of the PredictionExplanationsRecord associated with these prediction explanations. | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | ## PredictionExplanationsRow ``` { "properties": { "adjustedPrediction": { "description": "The exposure-adjusted output of the model for this row.", "type": "number", "x-versionadded": "v2.8" }, "adjustedPredictionValues": { "description": "The exposure-adjusted output of the model for this row.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.8" }, "forecastDistance": { "description": "Forecast distance for the row. For time series projects only.", "type": "integer", "x-versionadded": "v2.21" }, "forecastPoint": { "description": "Forecast point for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" }, "prediction": { "description": "The output of the model for this row.", "type": "number" }, "predictionExplanations": { "description": "The list of prediction explanations.", "items": { "properties": { "feature": { "description": "The name of the feature contributing to the prediction.", "type": "string" }, "featureValue": { "description": "The value the feature took on for this row. For image features, this value is the URL of the input image (New in v2.21).", "type": "string" }, "imageExplanationUrl": { "description": "For image features, the URL of the image containing the input image overlaid by the activation heatmap. For non-image features, this field is null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "perNgramTextExplanations": { "description": "For text features, an array of JSON object containing the per ngram based text prediction explanations.", "items": { "properties": { "isUnknown": { "description": "Whether the ngram is identifiable by the blueprint or not.", "type": "boolean", "x-versionadded": "v2.28" }, "ngrams": { "description": "The list of JSON objects with the ngram starting index, ngram ending index and unknown ngram information.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly these ngrams affected the prediction (e.g., '+++', '--', '+', '<+', '<-').", "type": "string", "x-versionadded": "v2.28" }, "strength": { "description": "The amount these ngrams affected the prediction.", "type": "number", "x-versionadded": "v2.28" } }, "required": [ "isUnknown", "ngrams", "qualitativateStrength", "strength" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.28" }, "qualitativateStrength": { "description": "A human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted '+++', medium '++', small '+', very small '<+'. A large negative effect is denoted '---', medium '--', small '-', very small '<-'.", "type": "string" }, "strength": { "description": "The amount this feature's value affected the prediction.", "type": "number" } }, "required": [ "feature", "featureValue", "imageExplanationUrl", "label", "qualitativateStrength", "strength" ], "type": "object" }, "type": "array" }, "predictionThreshold": { "description": "The threshold value used for classification prediction.", "type": [ "number", "null" ] }, "predictionValues": { "description": "The list of prediction values.", "items": { "properties": { "label": { "description": "Describes what this model output corresponds to. For regression projects, it is the name of the target feature. For classification projects, it is a level from the target feature. For Anomaly Detection models it is an Anomaly Score.", "type": "string" }, "value": { "description": "The output of the prediction. For regression projects, it is the predicted value of the target. For classification projects, it is the predicted probability the row belongs to the class identified by the label.", "type": "number" } }, "required": [ "label", "value" ], "type": "object" }, "type": "array" }, "rowId": { "description": "The row this PredictionExplanationsRow describes.", "type": "integer" }, "seriesId": { "description": "The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "timestamp": { "description": "The timestamp for the row. For time series projects only.", "type": "string", "x-versionadded": "v2.21" } }, "required": [ "adjustedPrediction", "adjustedPredictionValues", "forecastDistance", "forecastPoint", "prediction", "predictionExplanations", "predictionThreshold", "predictionValues", "rowId", "seriesId", "timestamp" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | adjustedPrediction | number | true | | The exposure-adjusted output of the model for this row. | | adjustedPredictionValues | [PredictionExplanationsPredictionValues] | true | | The exposure-adjusted output of the model for this row. | | forecastDistance | integer | true | | Forecast distance for the row. For time series projects only. | | forecastPoint | string | true | | Forecast point for the row. For time series projects only. | | prediction | number | true | | The output of the model for this row. | | predictionExplanations | [PredictionExplanation] | true | | The list of prediction explanations. | | predictionThreshold | number,null | true | | The threshold value used for classification prediction. | | predictionValues | [PredictionExplanationsPredictionValues] | true | | The list of prediction values. | | rowId | integer | true | | The row this PredictionExplanationsRow describes. | | seriesId | string,null | true | | The ID of the series value for the row in a multiseries project. For a single series project this will be null. For time series projects only. | | timestamp | string | true | | The timestamp for the row. For time series projects only. | ## RatingTableCreateResponse ``` { "properties": { "ratingTableId": { "description": "The ID of the newly created rating table.", "type": "string" }, "ratingTableName": { "description": "The name that was used for the rating table. May differ from the ratingTableName in the request, as names are trimmed and a suffix added to ensure all rating tables derived from the same model have unique names", "type": "string" } }, "required": [ "ratingTableId", "ratingTableName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ratingTableId | string | true | | The ID of the newly created rating table. | | ratingTableName | string | true | | The name that was used for the rating table. May differ from the ratingTableName in the request, as names are trimmed and a suffix added to ensure all rating tables derived from the same model have unique names | ## RatingTableListResponse ``` { "properties": { "count": { "description": "The number of rating table objects returned.", "type": "integer" }, "data": { "description": "The actual records. Each element of the array has the same schema\n as if retrieving the table individually from\n GET /api/v2/projects/(projectId)/ratingTables/(ratingTableId)/.", "items": { "properties": { "created": { "description": "ISO-8601 timestamp of when the rating table record was created.", "type": "number" }, "id": { "description": "The ID of the rating table record.", "type": "string" }, "modelId": { "description": "The model ID of a model that was created from the rating table.\n May be null if a model has not been created from the rating table.", "type": "string" }, "modelJobId": { "description": "The job ID to create a model from this rating table.\n Can be null if a model has not been created from the rating table.", "type": "integer" }, "originalFilename": { "description": "The filename of the uploaded rating table file.", "type": "string" }, "parentModelId": { "description": "The model ID of the model the rating table was modified from.", "type": "string" }, "projectId": { "description": "The project ID of the rating table record.", "type": "string" }, "ratingTableName": { "description": "The name of the rating table.", "type": "string" }, "validationError": { "description": "Rating table validation error messages. If the rating table\n was validated successfully, it will be an empty string.", "type": "string" }, "validationJobId": { "description": "The job ID of the created job to validate the rating table.\n Can be null if the rating table has not been validated.", "type": "string" }, "validationWarnings": { "description": "Rating table validation warning messages.", "type": "string" } }, "required": [ "created", "id", "modelId", "modelJobId", "originalFilename", "parentModelId", "projectId", "ratingTableName", "validationError", "validationJobId", "validationWarnings" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of rating table objects returned. | | data | [RatingTableRetrieveResponse] | true | | The actual records. Each element of the array has the same schema as if retrieving the table individually from GET /api/v2/projects/(projectId)/ratingTables/(ratingTableId)/. | | next | string,null(uri) | true | | The URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL pointing to the previous page (if null, there is no previous page). | ## RatingTableModelDetailsResponse ``` { "properties": { "blenderModels": { "description": "Models that are in the blender.", "items": { "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintId": { "description": "The blueprint used to construct the model.", "type": "string" }, "dataSelectionMethod": { "description": "Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models.", "enum": [ "duration", "rowCount", "selectedDateRange", "useProjectSettings" ], "type": "string" }, "externalPredictionModel": { "description": "If the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "featurelistId": { "description": "The ID of the feature list used by the model.", "type": [ "string", "null" ] }, "featurelistName": { "description": "The name of the feature list used by the model. If null, themodel was trained on multiple feature lists.", "type": [ "string", "null" ] }, "frozenPct": { "description": "The training percent used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "If the model has a codegen JAR file.", "type": "boolean", "x-versionadded": "v2.36" }, "hasFinetuners": { "description": "Whether a model has fine tuners.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the model.", "type": "string" }, "isAugmented": { "description": "Whether a model was trained using augmentation.", "type": "boolean" }, "isBlender": { "description": "If the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "If the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model.", "type": "boolean" }, "isNClustersDynamicallyDetermined": { "description": "Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Whether the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.33" }, "isTransparent": { "description": "If the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "If the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "lifecycle": { "description": "Object returning model lifecycle.", "properties": { "reason": { "description": "The reason for the lifecycle stage. None if the model is active.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "stage": { "description": "The model lifecycle stage.", "enum": [ "active", "deprecated", "disabled" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "reason", "stage" ], "type": "object" }, "linkFunction": { "description": "The link function the final modeler uses in the blueprint. If no link function exists, returns null.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "metrics": { "description": "The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed.", "type": "object" }, "modelCategory": { "description": "Indicates the type of model. Returns `prime` for DataRobot Prime models, `blend` for blender models, `combined` for combined models, and `model` for all other models.", "enum": [ "model", "prime", "blend", "combined", "incrementalLearning" ], "type": "string" }, "modelFamily": { "description": "The family the model belongs to, e.g., SVM, GBM, etc.", "type": "string", "x-versionadded": "v2.21" }, "modelFamilyFullName": { "description": "The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc.", "type": "string", "x-versionadded": "v2.31" }, "modelNumber": { "description": "The model number from the Leaderboard.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "modelType": { "description": "Identifies the model (e.g., `Nystroem Kernel SVM Regressor`).", "type": "string" }, "monotonicDecreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "monotonicIncreasingFeaturelistId": { "description": "the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "nClusters": { "description": "The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects.", "type": [ "integer", "null" ] }, "parentModelId": { "description": "The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise.", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "threshold used for binary classification in predictions.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.13" }, "predictionThresholdReadOnly": { "description": "indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed.", "type": "boolean", "x-versionadded": "v2.13" }, "processes": { "description": "The list of processes used by the model.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "projectId": { "description": "The ID of the project to which the model belongs.", "type": "string" }, "ratingTableId": { "description": "The rating table ID", "type": "string" }, "samplePct": { "description": "The percentage of the dataset used in training the model.", "exclusiveMinimum": 0, "type": [ "number", "null" ] }, "samplingMethod": { "description": "indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window.", "enum": [ "random", "latest" ], "type": "string" }, "supportsComposableMl": { "description": "indicates whether this model is supported in Composable ML.", "type": "boolean", "x-versionadded": "2.26" }, "supportsMonotonicConstraints": { "description": "whether this model supports enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.21" }, "timeWindowSamplePct": { "description": "An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models.", "exclusiveMaximum": 100, "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingDuration": { "description": "the duration spanned by the dates in the partition column for the data used to train the model", "type": [ "string", "null" ] }, "trainingEndDate": { "description": "the end date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] }, "trainingRowCount": { "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": [ "integer", "null" ] }, "trainingStartDate": { "description": "the start date of the dates in the partition column for the data used to train the model", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "blenderModels", "blueprintId", "externalPredictionModel", "featurelistId", "featurelistName", "frozenPct", "hasCodegen", "icons", "id", "isBlender", "isCustom", "isFrozen", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "lifecycle", "linkFunction", "metrics", "modelCategory", "modelFamily", "modelFamilyFullName", "modelNumber", "modelType", "monotonicDecreasingFeaturelistId", "monotonicIncreasingFeaturelistId", "parentModelId", "predictionThreshold", "predictionThresholdReadOnly", "processes", "projectId", "ratingTableId", "samplePct", "supportsComposableMl", "supportsMonotonicConstraints", "trainingDuration", "trainingEndDate", "trainingRowCount", "trainingStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blenderModels | [integer] | true | maxItems: 100 | Models that are in the blender. | | blueprintId | string | true | | The blueprint used to construct the model. | | dataSelectionMethod | string | false | | Identifies which setting defines the training size of the model when making predictions and scoring. Only used by datetime models. | | externalPredictionModel | boolean | true | | If the model is an external prediction model. | | featurelistId | string,null | true | | The ID of the feature list used by the model. | | featurelistName | string,null | true | | The name of the feature list used by the model. If null, themodel was trained on multiple feature lists. | | frozenPct | number,null | true | | The training percent used to train the frozen model. | | hasCodegen | boolean | true | | If the model has a codegen JAR file. | | hasFinetuners | boolean | false | | Whether a model has fine tuners. | | icons | integer,null | true | | The icons associated with the model. | | id | string | true | | The ID of the model. | | isAugmented | boolean | false | | Whether a model was trained using augmentation. | | isBlender | boolean | true | | If the model is a blender. | | isCustom | boolean | true | | If the model contains custom tasks. | | isFrozen | boolean | true | | Indicates whether the model is frozen, i.e., uses tuning parameters from a parent model. | | isNClustersDynamicallyDetermined | boolean | false | | Whether number of clusters is dynamically determined. Only valid in unsupervised clustering projects. | | isStarred | boolean | true | | Indicates whether the model has been starred. | | isTrainedIntoHoldout | boolean | true | | Indicates if model used holdout data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed validation size. | | isTrainedIntoValidation | boolean | true | | Indicates if model used validation data for training. This can happen for time-aware models using trainingStartDate/trainingEndDate parameters or when the model's training row count was greater than the max allowed training size. | | isTrainedOnGpu | boolean | true | | Whether the model was trained using GPU workers. | | isTransparent | boolean | true | | If the model is a transparent model with exposed coefficients. | | isUserModel | boolean | true | | If the model was created with Composable ML. | | lifecycle | ModelLifecycle | true | | Object returning model lifecycle. | | linkFunction | string,null | true | | The link function the final modeler uses in the blueprint. If no link function exists, returns null. | | metrics | object | true | | The performance of the model according to various metrics, where each metric has validation, crossValidation, holdout, and training scores reported, or null if they have not been computed. | | modelCategory | string | true | | Indicates the type of model. Returns prime for DataRobot Prime models, blend for blender models, combined for combined models, and model for all other models. | | modelFamily | string | true | | The family the model belongs to, e.g., SVM, GBM, etc. | | modelFamilyFullName | string | true | | The full name of the family that the model belongs to. For e.g., Support Vector Machine, Gradient Boosting Machine, etc. | | modelNumber | integer,null | true | | The model number from the Leaderboard. | | modelType | string | true | | Identifies the model (e.g., Nystroem Kernel SVM Regressor). | | monotonicDecreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. | | monotonicIncreasingFeaturelistId | string,null | true | | the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. | | nClusters | integer,null | false | | The number of clusters to use in the specified unsupervised clustering model. Only valid in unsupervised clustering projects. | | parentModelId | string,null | true | | The ID of the parent model if the model is frozen or a result of incremental learning. Null otherwise. | | predictionThreshold | number | true | maximum: 1minimum: 0 | threshold used for binary classification in predictions. | | predictionThresholdReadOnly | boolean | true | | indicates whether modification of a predictions threshold is forbidden. Since v2.22 threshold modification is allowed. | | processes | [string] | true | maxItems: 100 | The list of processes used by the model. | | projectId | string | true | | The ID of the project to which the model belongs. | | ratingTableId | string | true | | The rating table ID | | samplePct | number,null | true | | The percentage of the dataset used in training the model. | | samplingMethod | string | false | | indicates sampling method used to select training data in datetime models. For row-based project this is the way how requested number of rows are selected.For other projects (duration-based, start/end, project settings) - how specified percent of rows (timeWindowSamplePct) is selected from specified time window. | | supportsComposableMl | boolean | true | | indicates whether this model is supported in Composable ML. | | supportsMonotonicConstraints | boolean | true | | whether this model supports enforcing monotonic constraints | | timeWindowSamplePct | integer,null | false | | An integer between 1 and 99, indicating the percentage of sampling within the time window. The points kept are determined by samplingMethod option. Will be null if no sampling was specified. Only used by datetime models. | | trainingDuration | string,null | true | | the duration spanned by the dates in the partition column for the data used to train the model | | trainingEndDate | string,null(date-time) | true | | the end date of the dates in the partition column for the data used to train the model | | trainingRowCount | integer,null | true | | The number of rows used to train the model. | | trainingStartDate | string,null(date-time) | true | | the start date of the dates in the partition column for the data used to train the model | ### Enumerated Values | Property | Value | | --- | --- | | dataSelectionMethod | [duration, rowCount, selectedDateRange, useProjectSettings] | | modelCategory | [model, prime, blend, combined, incrementalLearning] | | samplingMethod | [random, latest] | ## RatingTableRetrieveResponse ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the rating table record was created.", "type": "number" }, "id": { "description": "The ID of the rating table record.", "type": "string" }, "modelId": { "description": "The model ID of a model that was created from the rating table.\n May be null if a model has not been created from the rating table.", "type": "string" }, "modelJobId": { "description": "The job ID to create a model from this rating table.\n Can be null if a model has not been created from the rating table.", "type": "integer" }, "originalFilename": { "description": "The filename of the uploaded rating table file.", "type": "string" }, "parentModelId": { "description": "The model ID of the model the rating table was modified from.", "type": "string" }, "projectId": { "description": "The project ID of the rating table record.", "type": "string" }, "ratingTableName": { "description": "The name of the rating table.", "type": "string" }, "validationError": { "description": "Rating table validation error messages. If the rating table\n was validated successfully, it will be an empty string.", "type": "string" }, "validationJobId": { "description": "The job ID of the created job to validate the rating table.\n Can be null if the rating table has not been validated.", "type": "string" }, "validationWarnings": { "description": "Rating table validation warning messages.", "type": "string" } }, "required": [ "created", "id", "modelId", "modelJobId", "originalFilename", "parentModelId", "projectId", "ratingTableName", "validationError", "validationJobId", "validationWarnings" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | number | true | | ISO-8601 timestamp of when the rating table record was created. | | id | string | true | | The ID of the rating table record. | | modelId | string | true | | The model ID of a model that was created from the rating table. May be null if a model has not been created from the rating table. | | modelJobId | integer | true | | The job ID to create a model from this rating table. Can be null if a model has not been created from the rating table. | | originalFilename | string | true | | The filename of the uploaded rating table file. | | parentModelId | string | true | | The model ID of the model the rating table was modified from. | | projectId | string | true | | The project ID of the rating table record. | | ratingTableName | string | true | | The name of the rating table. | | validationError | string | true | | Rating table validation error messages. If the rating table was validated successfully, it will be an empty string. | | validationJobId | string | true | | The job ID of the created job to validate the rating table. Can be null if the rating table has not been validated. | | validationWarnings | string | true | | Rating table validation warning messages. | ## RatingTableUpdate ``` { "properties": { "ratingTableName": { "description": "The name of the new model.", "type": "string" } }, "required": [ "ratingTableName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ratingTableName | string | true | | The name of the new model. | ## ResidualsChartForDatasets ``` { "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input.", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be `null`.", "items": { "items": { "type": "number" }, "type": "array" }, "type": "array" }, "datasetId": { "description": "The dataset ID.", "type": "string" }, "histogram": { "description": "Data to plot a histogram of residual values. The object contains three keys, intervalStart, intervalEnd, and occurrences, the number of times the predicted value fits within that interval. For all but the last interval, the end value is exclusive.", "items": { "properties": { "intervalEnd": { "description": "The end of the interval.", "type": "number" }, "intervalStart": { "description": "The start of the interval.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within that interval.", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset.", "type": "number" }, "standardDeviation": { "description": "The Standard Deviation value measures variation in the dataset. A low value indicates that the data points tend to be close to the mean; a high value indicates that the data points are spread over a wider range of values.", "type": "number" } }, "required": [ "coefficientOfDetermination", "data", "datasetId", "histogram", "residualMean", "standardDeviation" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | coefficientOfDetermination | number | true | | Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input. | | data | [array] | true | | The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. | | datasetId | string | true | | The dataset ID. | | histogram | [Histogram] | true | | Data to plot a histogram of residual values. The object contains three keys, intervalStart, intervalEnd, and occurrences, the number of times the predicted value fits within that interval. For all but the last interval, the end value is exclusive. | | residualMean | number | true | | The arithmetic mean of the predicted value minus the actual value over the downsampled dataset. | | standardDeviation | number | true | | The Standard Deviation value measures variation in the dataset. A low value indicates that the data points tend to be close to the mean; a high value indicates that the data points are spread over a wider range of values. | ## ResidualsChartForDatasetsList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of residuals charts for the dataset.", "items": { "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input.", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be `null`.", "items": { "items": { "type": "number" }, "type": "array" }, "type": "array" }, "datasetId": { "description": "The dataset ID.", "type": "string" }, "histogram": { "description": "Data to plot a histogram of residual values. The object contains three keys, intervalStart, intervalEnd, and occurrences, the number of times the predicted value fits within that interval. For all but the last interval, the end value is exclusive.", "items": { "properties": { "intervalEnd": { "description": "The end of the interval.", "type": "number" }, "intervalStart": { "description": "The start of the interval.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within that interval.", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset.", "type": "number" }, "standardDeviation": { "description": "The Standard Deviation value measures variation in the dataset. A low value indicates that the data points tend to be close to the mean; a high value indicates that the data points are spread over a wider range of values.", "type": "number" } }, "required": [ "coefficientOfDetermination", "data", "datasetId", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The model ID.", "type": "string" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "projectId": { "description": "The project ID.", "type": "string" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "modelId", "next", "previous", "projectId", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ResidualsChartForDatasets] | true | | The list of residuals charts for the dataset. | | modelId | string | true | | The model ID. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | projectId | string | true | | The project ID. | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveConfusionMatrixPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of paginated confusion matrix insights.", "items": { "properties": { "data": { "description": "The confusion matrix chart data in the format specified below.", "properties": { "columns": { "description": "The [colStart, colEnd] column dimension of the confusion matrix in responses.", "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "confusionMatrixData": { "description": "The confusion matrix chart data in the format specified below.", "properties": { "classMetrics": { "description": "The per-class information, including one-vs-all scores, in a format specified below.", "items": { "properties": { "actualCount": { "description": "The number of times this class was seen in the validation data.", "type": "integer" }, "className": { "description": "The name of the class.", "type": "string" }, "confusionMatrixOneVsAll": { "description": "A 2 dimensional array representing a 2x2 one-vs-all matrix. This represents, for each class, the True/False Negative/Positive rates as integers. The data structure is: ``[ [ True Negative, False Positive ], [ False Negative, True Positive ] ]``.", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "f1": { "description": "F1 score", "type": "number" }, "precision": { "description": "Precision score", "type": "number" }, "predictedCount": { "description": "The number of times this class was predicted within the validation data.", "type": "integer" }, "recall": { "description": "Recall score", "type": "number" }, "wasActualPercentages": { "description": "The one-vs-all percentage of actuals, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this class was predicted when the actual class was `classMetrics.className`.", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "wasPredictedPercentages": { "description": "The one-vs-all percentages of predicted, in a format specified below.", "items": { "properties": { "otherClassName": { "description": "The name of the class.", "type": "string" }, "percentage": { "description": "The percentage of times this was the actual class but `classMetrics.className` was predicted", "type": "number" } }, "required": [ "otherClassName", "percentage" ], "type": "object" }, "maxItems": 10000, "type": "array" } }, "required": [ "actualCount", "className", "confusionMatrixOneVsAll", "f1", "precision", "predictedCount", "recall", "wasActualPercentages", "wasPredictedPercentages" ], "type": "object", "x-versionadded": "v2.42" }, "maxItems": 10000, "type": "array" }, "colClasses": { "description": "Class labels on columns of the confusion matrix.", "items": { "type": "string" }, "maxItems": 10000, "type": "array" }, "confusionMatrix": { "description": "A two-dimensional array of integers representing the confusion matrix, aligned with the `rowClasses` and `colClasses` array. For example, if the orientation is `actual`, then when confusionMatrix[A][B], is `true`,the result is an integer that represents the number of times '`class` with index A was correct, but class with index B was predicted was true. ", "items": { "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "maxItems": 10000, "type": "array" }, "rowClasses": { "description": "Class labels on rows of the confusion matrix.", "items": { "type": "string" }, "maxItems": 10000, "type": "array" } }, "required": [ "classMetrics", "colClasses", "confusionMatrix", "rowClasses" ], "type": "object", "x-versionadded": "v2.42" }, "globalMetrics": { "description": "average metrics across all classes", "properties": { "f1": { "description": "Average F1 score", "type": "number" }, "precision": { "description": "Average precision score", "type": "number" }, "recall": { "description": "Average recall score", "type": "number" } }, "required": [ "f1", "precision", "recall" ], "type": "object" }, "numberOfClasses": { "description": "The count of classes in the full confusion matrix.", "type": "integer" }, "rows": { "description": "The [rowStart, rowEnd] row dimension of the confusion matrix in responses.", "items": { "type": "integer" }, "maxItems": 10000, "type": "array" }, "totalMatrixSum": { "description": "The sum of all values in the full confusion matrix.", "type": "integer" } }, "required": [ "columns", "confusionMatrixData", "globalMetrics", "numberOfClasses", "rows", "totalMatrixSum" ], "type": "object", "x-versionadded": "v2.42" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The data partition used to calculate the insight.", "enum": [ "backtest_0", "backtest_0Training", "backtest_1", "backtest_10", "backtest_10Training", "backtest_11", "backtest_11Training", "backtest_12", "backtest_12Training", "backtest_13", "backtest_13Training", "backtest_14", "backtest_14Training", "backtest_15", "backtest_15Training", "backtest_16", "backtest_16Training", "backtest_17", "backtest_17Training", "backtest_18", "backtest_18Training", "backtest_19", "backtest_19Training", "backtest_1Training", "backtest_2", "backtest_20", "backtest_20Training", "backtest_2Training", "backtest_3", "backtest_3Training", "backtest_4", "backtest_4Training", "backtest_5", "backtest_5Training", "backtest_6", "backtest_6Training", "backtest_7", "backtest_7Training", "backtest_8", "backtest_8Training", "backtest_9", "backtest_9Training", "crossValidation", "externalTestSet", "holdout", "holdoutTraining", "training", "validation", "vectorDatabase" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object", "x-versionadded": "v2.42" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.42" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ConfusionMatrixInsightsSingleResponse] | true | maxItems: 10 | A list of paginated confusion matrix insights. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveFeatureEffectsIndividualResponse ``` { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`.", "type": [ "string", "null" ] }, "data": { "description": "Feature effects data.", "properties": { "featureEffects": { "description": "The Feature Effects computational results for each feature.", "items": { "properties": { "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" } }, "required": [ "featureEffects" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "training", "backtest_0", "backtest_1", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20", "holdout", "backtest_0_training", "backtest_1_training", "backtest_2_training", "backtest_3_training", "backtest_4_training", "backtest_5_training", "backtest_6_training", "backtest_7_training", "backtest_8_training", "backtest_9_training", "backtest_10_training", "backtest_11_training", "backtest_12_training", "backtest_13_training", "backtest_14_training", "backtest_15_training", "backtest_16_training", "backtest_17_training", "backtest_18_training", "backtest_19_training", "backtest_20_training", "holdout_training" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestIndex | string,null | false | | The backtest index. For example: 0, 1, ..., 20, holdout. | | data | FeatureEffectsInsightResponse | true | | Feature effects data. | | dataSliceId | string,null | false | | The ID of the data slice. | | entityId | string | false | | The ID of the model. | | id | string | true | | The ID of the created insight. | | projectId | string | false | | The ID of the project. | | source | string | false | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, training, backtest_0, backtest_1, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20, holdout, backtest_0_training, backtest_1_training, backtest_2_training, backtest_3_training, backtest_4_training, backtest_5_training, backtest_6_training, backtest_7_training, backtest_8_training, backtest_9_training, backtest_10_training, backtest_11_training, backtest_12_training, backtest_13_training, backtest_14_training, backtest_15_training, backtest_16_training, backtest_17_training, backtest_18_training, backtest_19_training, backtest_20_training, holdout_training] | ## RetrieveFeatureEffectsPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of paginated feature effects insights.", "items": { "properties": { "backtestIndex": { "description": "The backtest index. For example: `0`, `1`, ..., `20`, `holdout`.", "type": [ "string", "null" ] }, "data": { "description": "Feature effects data.", "properties": { "featureEffects": { "description": "The Feature Effects computational results for each feature.", "items": { "properties": { "featureImpactScore": { "description": "The feature impact score.", "type": "number" }, "featureName": { "description": "The name of the feature.", "type": "string" }, "featureType": { "description": "The feature type, either numeric or categorical.", "type": "string" }, "isBinnable": { "description": "Whether values can be grouped into bins.", "type": "boolean" }, "isScalable": { "description": "Whether numeric feature values can be reported on a log scale.", "type": [ "boolean", "null" ] }, "partialDependence": { "description": "Partial dependence results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The partial dependence results.", "items": { "properties": { "dependence": { "description": "The value of partial dependence.", "type": "number" }, "label": { "description": "Contains the label for categorical and numeric features as a string.", "type": "string" } }, "required": [ "dependence", "label" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" } }, "required": [ "data", "isCapped" ], "type": "object" }, "predictedVsActual": { "description": "Predicted versus actual results. Can be missing if no data for the feature was qualified to generate the insight.", "properties": { "data": { "description": "The predicted versus actual results.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" }, "isCapped": { "description": "Indicates whether the data for computation is capped.", "type": "boolean" }, "logScaledData": { "description": "The predicted versus actual results on a log scale.", "items": { "properties": { "actual": { "description": "The actual value. `null` for 0-row bins and unsupervised time series models.", "type": [ "number", "null" ] }, "bin": { "description": "The labels for the left and right bin limits for numeric features.", "items": { "type": "string" }, "type": "array" }, "label": { "description": "Contains label for categorical features; For numeric features contains range or numeric value.", "type": "string" }, "predicted": { "description": "The predicted value. `null` for 0-row bins.", "type": [ "number", "null" ] }, "rowCount": { "description": "The number of rows for the label and bin.", "type": "integer" } }, "required": [ "actual", "label", "predicted", "rowCount" ], "type": "object" }, "type": "array" } }, "required": [ "data", "isCapped", "logScaledData" ], "type": "object" }, "weightLabel": { "description": "The weight label if a weight was configured for the project.", "type": [ "string", "null" ] } }, "required": [ "featureImpactScore", "featureName", "featureType", "isBinnable", "isScalable", "weightLabel" ], "type": "object" }, "type": "array" } }, "required": [ "featureEffects" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "training", "backtest_0", "backtest_1", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20", "holdout", "backtest_0_training", "backtest_1_training", "backtest_2_training", "backtest_3_training", "backtest_4_training", "backtest_5_training", "backtest_6_training", "backtest_7_training", "backtest_8_training", "backtest_9_training", "backtest_10_training", "backtest_11_training", "backtest_12_training", "backtest_13_training", "backtest_14_training", "backtest_15_training", "backtest_16_training", "backtest_17_training", "backtest_18_training", "backtest_19_training", "backtest_20_training", "holdout_training" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrieveFeatureEffectsIndividualResponse] | true | maxItems: 10 | The list of paginated feature effects insights. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveFeatureImpactIndividualResponse ``` { "properties": { "data": { "description": "Feature impact data.", "properties": { "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact.", "type": "boolean" }, "rowCount": { "description": "The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the ``rowCount``, we return ``null`` here.", "type": [ "integer", "null" ] } }, "required": [ "featureImpacts", "ranRedundancyDetection", "rowCount" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "training", "backtest_2Training", "backtest_3Training", "backtest_4Training", "backtest_5Training", "backtest_6Training", "backtest_7Training", "backtest_8Training", "backtest_9Training", "backtest_10Training", "backtest_11Training", "backtest_12Training", "backtest_13Training", "backtest_14Training", "backtest_15Training", "backtest_16Training", "backtest_17Training", "backtest_18Training", "backtest_19Training", "backtest_20Training", "backtest_1Training", "holdoutTraining" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | FeatureImpactInsightResponse | true | | Feature impact data. | | dataSliceId | string,null | false | | The ID of the data slice. | | entityId | string | false | | The ID of the model. | | id | string | true | | The ID of the created insight. | | projectId | string | false | | The ID of the project. | | source | string | false | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | source | [training, backtest_2Training, backtest_3Training, backtest_4Training, backtest_5Training, backtest_6Training, backtest_7Training, backtest_8Training, backtest_9Training, backtest_10Training, backtest_11Training, backtest_12Training, backtest_13Training, backtest_14Training, backtest_15Training, backtest_16Training, backtest_17Training, backtest_18Training, backtest_19Training, backtest_20Training, backtest_1Training, holdoutTraining] | ## RetrieveFeatureImpactPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of paginated feature impact insights.", "items": { "properties": { "data": { "description": "Feature impact data.", "properties": { "featureImpacts": { "description": "A list which contains feature impact scores for each feature used by a model. If the model has more than 1000 features, the most important 1000 features are returned.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "parentFeatureName": { "description": "The name of the parent feature.", "type": [ "string", "null" ] }, "redundantWith": { "description": "Name of feature that has the highest correlation with this feature.", "type": [ "string", "null" ] } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized", "redundantWith" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "ranRedundancyDetection": { "description": "Indicates whether redundant feature identification was run while calculating this feature impact.", "type": "boolean" }, "rowCount": { "description": "The number of rows that was used to calculate feature impact. For the feature impact calculated with the default logic, without specifying the ``rowCount``, we return ``null`` here.", "type": [ "integer", "null" ] } }, "required": [ "featureImpacts", "ranRedundancyDetection", "rowCount" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "training", "backtest_2Training", "backtest_3Training", "backtest_4Training", "backtest_5Training", "backtest_6Training", "backtest_7Training", "backtest_8Training", "backtest_9Training", "backtest_10Training", "backtest_11Training", "backtest_12Training", "backtest_13Training", "backtest_14Training", "backtest_15Training", "backtest_16Training", "backtest_17Training", "backtest_18Training", "backtest_19Training", "backtest_20Training", "backtest_1Training", "holdoutTraining" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 2, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrieveFeatureImpactIndividualResponse] | true | maxItems: 2 | The list of paginated feature impact insights. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveLiftChartIndividualResponse ``` { "properties": { "data": { "description": "Lift chart data.", "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" } }, "required": [ "bins" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | LiftChartResponse | true | | Lift chart data. | | dataSliceId | string,null | false | | The ID of the data slice. | | entityId | string | false | | The ID of the model. | | externalDatasetId | string,null | false | | The ID of the external dataset. | | id | string | true | | The ID of the created insight. | | projectId | string | false | | The ID of the project. | | source | string | false | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout, externalTestSet, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ## RetrieveLiftChartPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of paginated lift chart insights.", "items": { "properties": { "data": { "description": "Lift chart data.", "properties": { "bins": { "description": "The lift chart data for that source, as specified below.", "items": { "properties": { "actual": { "description": "The average of the actual target values for the rows in the bin.", "type": "number" }, "binWeight": { "description": "How much data is in the bin. For projects with weights, it is the sum of the weights of all rows in the bins; otherwise, it is the number of rows in the bin.", "type": "number" }, "predicted": { "description": "The average of predicted values of the target for the rows in the bin.", "type": "number" } }, "required": [ "actual", "binWeight", "predicted" ], "type": "object" }, "type": "array" } }, "required": [ "bins" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrieveLiftChartIndividualResponse] | true | maxItems: 10 | The list of paginated lift chart insights. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveResidualsIndividualResponse ``` { "properties": { "data": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | ModelResidualsChartData | true | | Chart data from the validation data source | | dataSliceId | string,null | false | | The ID of the data slice. | | entityId | string | false | | The ID of the model. | | externalDatasetId | string,null | false | | The ID of the external dataset. | | id | string | true | | The ID of the created insight. | | projectId | string | false | | The ID of the project. | | source | string | false | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout, externalTestSet] | ## RetrieveResidualsPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of paginated residuals insights.", "items": { "properties": { "data": { "description": "Chart data from the validation data source", "properties": { "coefficientOfDetermination": { "description": "Also known as the r-squared value. This value is calculated over the downsampled dataset, not the full input", "type": "number" }, "data": { "description": "The rows of chart data in [actual, predicted, residual, row number] form. If the row number was not available at the time of model creation, the row number will be null. **NOTE**: In DataRobot v5.2, the row number will not be included.", "items": { "items": { "type": "number" }, "maxItems": 4, "type": "array" }, "type": "array" }, "histogram": { "description": "Data to plot a histogram of residual values", "items": { "properties": { "intervalEnd": { "description": "The interval end. For all but the last interval, the end value is exclusive.", "type": "number" }, "intervalStart": { "description": "The interval start.", "type": "number" }, "occurrences": { "description": "The number of times the predicted value fits within the interval", "type": "integer" } }, "required": [ "intervalEnd", "intervalStart", "occurrences" ], "type": "object" }, "type": "array", "x-versionadded": "v2.19" }, "residualMean": { "description": "The arithmetic mean of the predicted value minus the actual value over the downsampled dataset", "type": "number" }, "standardDeviation": { "description": "A measure of deviation from the group as a whole", "type": "number", "x-versionadded": "v2.19" } }, "required": [ "coefficientOfDetermination", "data", "histogram", "residualMean", "standardDeviation" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrieveResidualsIndividualResponse] | true | maxItems: 10 | The list of paginated residuals insights. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveRocCurveIndividualResponse ``` { "properties": { "data": { "description": "ROC curve data.", "properties": { "auc": { "description": "AUC value", "type": [ "number", "null" ] }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric value", "type": [ "number", "null" ] }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" } }, "required": [ "auc", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | RocCurveResponse | true | | ROC curve data. | | dataSliceId | string,null | false | | The ID of the data slice. | | entityId | string | false | | The ID of the model. | | externalDatasetId | string,null | false | | The ID of the external dataset. | | id | string | true | | The ID of the created insight. | | projectId | string | false | | The ID of the project. | | source | string | false | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | source | [validation, crossValidation, holdout, externalTestSet, backtest_2, backtest_3, backtest_4, backtest_5, backtest_6, backtest_7, backtest_8, backtest_9, backtest_10, backtest_11, backtest_12, backtest_13, backtest_14, backtest_15, backtest_16, backtest_17, backtest_18, backtest_19, backtest_20] | ## RetrieveRocCurvePaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of paginated ROC curve insights.", "items": { "properties": { "data": { "description": "ROC curve data.", "properties": { "auc": { "description": "AUC value", "type": [ "number", "null" ] }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric value", "type": [ "number", "null" ] }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" } }, "required": [ "auc", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "validation", "crossValidation", "holdout", "externalTestSet", "backtest_2", "backtest_3", "backtest_4", "backtest_5", "backtest_6", "backtest_7", "backtest_8", "backtest_9", "backtest_10", "backtest_11", "backtest_12", "backtest_13", "backtest_14", "backtest_15", "backtest_16", "backtest_17", "backtest_18", "backtest_19", "backtest_20" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrieveRocCurveIndividualResponse] | true | maxItems: 10 | The list of paginated ROC curve insights. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveShapDistributionsIndividualResponse ``` { "properties": { "data": { "description": "SHAP distributions data.", "properties": { "features": { "description": "List of SHAP distributions for each requested row.", "items": { "properties": { "feature": { "description": "The feature name in the dataset.", "type": "string" }, "featureType": { "description": "The feature type.", "enum": [ "T", "X", "B", "C", "CI", "N", "D", "DD", "FD", "Q", "CD", "GEO", "MC", "INT", "DOC" ], "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "shapValues": { "description": "The SHAP distributions values for this row.", "items": { "properties": { "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer" }, "featureValue": { "anyOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The value of the feature for this row." }, "predictionValue": { "description": "The prediction value for this row.", "type": "number" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ] }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number" }, "weight": { "description": "The weight of the row in the dataset.", "type": [ "number", "null" ], "x-versionadded": "v2.37" } }, "required": [ "featureRank", "featureValue", "predictionValue", "rowIndex", "shapValue", "weight" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 2500, "type": "array" } }, "required": [ "feature", "featureType", "impactNormalized", "impactUnnormalized", "shapValues" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "totalFeaturesCount": { "description": "The total number of features.", "type": "integer" } }, "required": [ "features", "totalFeaturesCount" ], "type": "object", "x-versionadded": "v2.35" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | ShapDistributionsData | true | | SHAP distributions data. | | dataSliceId | string,null | false | | The ID of the data slice. | | entityId | string | false | | The ID of the model. | | externalDatasetId | string,null | false | | The ID of the external dataset. | | id | string | true | | The ID of the created insight. | | projectId | string | false | | The ID of the project. | | quickCompute | boolean | false | | Whether the insight was computed using the quickCompute setting. | | source | string | false | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, externalTestSet, holdout, holdout_training, training, validation] | ## RetrieveShapDistributionsPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of paginated SHAP distributions insights.", "items": { "properties": { "data": { "description": "SHAP distributions data.", "properties": { "features": { "description": "List of SHAP distributions for each requested row.", "items": { "properties": { "feature": { "description": "The feature name in the dataset.", "type": "string" }, "featureType": { "description": "The feature type.", "enum": [ "T", "X", "B", "C", "CI", "N", "D", "DD", "FD", "Q", "CD", "GEO", "MC", "INT", "DOC" ], "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "shapValues": { "description": "The SHAP distributions values for this row.", "items": { "properties": { "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer" }, "featureValue": { "anyOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The value of the feature for this row." }, "predictionValue": { "description": "The prediction value for this row.", "type": "number" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ] }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number" }, "weight": { "description": "The weight of the row in the dataset.", "type": [ "number", "null" ], "x-versionadded": "v2.37" } }, "required": [ "featureRank", "featureValue", "predictionValue", "rowIndex", "shapValue", "weight" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 2500, "type": "array" } }, "required": [ "feature", "featureType", "impactNormalized", "impactUnnormalized", "shapValues" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "totalFeaturesCount": { "description": "The total number of features.", "type": "integer" } }, "required": [ "features", "totalFeaturesCount" ], "type": "object", "x-versionadded": "v2.35" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ] }, "entityId": { "description": "The ID of the model.", "type": "string" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the created insight.", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string" } }, "required": [ "data", "id" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrieveShapDistributionsIndividualResponse] | true | maxItems: 10 | List of paginated SHAP distributions insights. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveShapImpactIndividualResponse ``` { "properties": { "data": { "description": "SHAP impact data.", "properties": { "baseValue": { "description": "The mean of raw model predictions for the training data.", "items": { "type": "number" }, "type": "array", "x-versionadded": "v2.33" }, "link": { "description": "The link function used to connect the SHAP importance values to the model output.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "quickCompute": { "default": true, "description": "When enabled, limits the rows used from the selected source subset by default. When disabled, all rows are used.", "type": "boolean", "x-versionadded": "v2.35" }, "rowCount": { "description": "(Deprecated) The number of rows used to calculate SHAP impact. If ``rowCount`` is not specified, the value returned is ``null``.", "type": [ "integer", "null" ], "x-versionadded": "v2.33", "x-versiondeprecated": "v2.35" }, "shapImpacts": { "description": "A list that contains SHAP impact scores.", "items": { "properties": { "featureName": { "description": "The feature name in the dataset.", "type": "string" }, "impactNormalized": { "description": "The normalized impact score value.", "type": [ "number", "null" ] }, "impactUnnormalized": { "description": "The raw impact score value.", "type": "number" } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "baseValue", "link", "rowCount", "shapImpacts" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the created insight.", "type": "string", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project.", "type": "string", "x-versionadded": "v2.33" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "Subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "data", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | ShapImpactData | true | | SHAP impact data. | | dataSliceId | string,null | false | | The ID of the data slice. | | entityId | string | false | | The ID of the model. | | externalDatasetId | string,null | false | | The ID of the external dataset. | | id | string | true | | The ID of the created insight. | | projectId | string | false | | The ID of the project. | | quickCompute | boolean | false | | Whether the insight was computed using the quickCompute setting. | | source | string | false | | Subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, externalTestSet, holdout, holdout_training, training, validation] | ## RetrieveShapImpactPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of paginated SHAP impact insights.", "items": { "properties": { "data": { "description": "SHAP impact data.", "properties": { "baseValue": { "description": "The mean of raw model predictions for the training data.", "items": { "type": "number" }, "type": "array", "x-versionadded": "v2.33" }, "link": { "description": "The link function used to connect the SHAP importance values to the model output.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "quickCompute": { "default": true, "description": "When enabled, limits the rows used from the selected source subset by default. When disabled, all rows are used.", "type": "boolean", "x-versionadded": "v2.35" }, "rowCount": { "description": "(Deprecated) The number of rows used to calculate SHAP impact. If ``rowCount`` is not specified, the value returned is ``null``.", "type": [ "integer", "null" ], "x-versionadded": "v2.33", "x-versiondeprecated": "v2.35" }, "shapImpacts": { "description": "A list that contains SHAP impact scores.", "items": { "properties": { "featureName": { "description": "The feature name in the dataset.", "type": "string" }, "impactNormalized": { "description": "The normalized impact score value.", "type": [ "number", "null" ] }, "impactUnnormalized": { "description": "The raw impact score value.", "type": "number" } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "baseValue", "link", "rowCount", "shapImpacts" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the created insight.", "type": "string", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project.", "type": "string", "x-versionadded": "v2.33" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "Subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrieveShapImpactIndividualResponse] | true | maxItems: 10 | List of paginated SHAP impact insights. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveShapMatrixIndividualResponse ``` { "properties": { "data": { "description": "SHAP matrix data.", "properties": { "baseValue": { "description": "The mean of the raw model predictions for the training data.", "items": { "type": "number" }, "type": "array", "x-versionadded": "v2.33" }, "colnames": { "description": "The names of each column in the SHAP matrix.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.33" }, "linkFunction": { "description": "The link function used to connect the feature importance values to the model output.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "matrix": { "description": "SHAP matrix values.", "items": { "items": { "type": "number" }, "type": "array" }, "type": "array", "x-versionadded": "v2.33" }, "rowIndex": { "description": "The index of the data row used to compute the SHAP matrix. Not used in time-aware projects.", "items": { "type": "integer" }, "type": "array", "x-versionadded": "v2.33" }, "timeSeriesRowIndex": { "description": "An index composed of the timestamp, series ID, and forecast distance. Only used in time-aware projects.", "items": { "items": { "oneOf": [ { "type": "integer" }, { "type": "string" } ] }, "maxLength": 2, "minLength": 2, "type": "array" }, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "baseValue", "colnames", "linkFunction", "matrix", "rowIndex" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the created insight.", "type": "string", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project.", "type": "string", "x-versionadded": "v2.33" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "data", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | ShapMatrixData | true | | SHAP matrix data. | | dataSliceId | string,null | false | | The ID of the data slice. | | entityId | string | false | | The ID of the model. | | externalDatasetId | string,null | false | | The ID of the external dataset. | | id | string | true | | The ID of the created insight. | | projectId | string | false | | The ID of the project. | | quickCompute | boolean | false | | Whether the insight was computed using the quickCompute setting. | | source | string | false | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, externalTestSet, holdout, holdout_training, training, validation] | ## RetrieveShapMatrixPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of paginated SHAP matrix insights.", "items": { "properties": { "data": { "description": "SHAP matrix data.", "properties": { "baseValue": { "description": "The mean of the raw model predictions for the training data.", "items": { "type": "number" }, "type": "array", "x-versionadded": "v2.33" }, "colnames": { "description": "The names of each column in the SHAP matrix.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.33" }, "linkFunction": { "description": "The link function used to connect the feature importance values to the model output.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "matrix": { "description": "SHAP matrix values.", "items": { "items": { "type": "number" }, "type": "array" }, "type": "array", "x-versionadded": "v2.33" }, "rowIndex": { "description": "The index of the data row used to compute the SHAP matrix. Not used in time-aware projects.", "items": { "type": "integer" }, "type": "array", "x-versionadded": "v2.33" }, "timeSeriesRowIndex": { "description": "An index composed of the timestamp, series ID, and forecast distance. Only used in time-aware projects.", "items": { "items": { "oneOf": [ { "type": "integer" }, { "type": "string" } ] }, "maxLength": 2, "minLength": 2, "type": "array" }, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "baseValue", "colnames", "linkFunction", "matrix", "rowIndex" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the created insight.", "type": "string", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project.", "type": "string", "x-versionadded": "v2.33" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrieveShapMatrixIndividualResponse] | true | maxItems: 10 | List of paginated SHAP matrix insights. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrieveShapPreviewIndividualResponse ``` { "properties": { "data": { "description": "SHAP preview data.", "properties": { "previews": { "description": "List of SHAP previews for each requested row.", "items": { "properties": { "predictionValue": { "description": "The prediction value for this row.", "type": "number", "x-versionadded": "v2.33" }, "previewValues": { "description": "The SHAP preview values for this row.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string", "x-versionadded": "v2.33" }, "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "featureValue": { "description": "The value of the feature for this row.", "type": "string", "x-versionadded": "v2.33" }, "hasTextExplanations": { "description": "Whether the feature has text explanations available for this row.", "type": "boolean", "x-versionadded": "v2.33" }, "isImage": { "description": "Whether the feature is an image or not.", "type": "boolean", "x-versionadded": "v2.34" }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number", "x-versionadded": "v2.33" }, "textExplanations": { "description": "List of the text explanations for the feature for this row.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "featureName", "featureRank", "featureValue", "hasTextExplanations", "shapValue", "textExplanations" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "timeSeriesRowIndex": { "description": "An index composed of the timestamp, series ID, and forecast distance. This index is used only in time-aware projects.", "items": { "oneOf": [ { "type": "integer" }, { "type": "string" } ] }, "maxLength": 2, "minLength": 2, "type": [ "array", "null" ], "x-versionadded": "v2.36" }, "totalPreviewFeatures": { "description": "The total number of features available after name filters have been applied.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "predictionValue", "previewValues", "rowIndex", "totalPreviewFeatures" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "previewsCount": { "description": "The total number of previews.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "previews", "previewsCount" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the created insight.", "type": "string", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project.", "type": "string", "x-versionadded": "v2.33" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "data", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | ShapPreviewData | true | | SHAP preview data. | | dataSliceId | string,null | false | | The ID of the data slice. | | entityId | string | false | | The ID of the model. | | externalDatasetId | string,null | false | | The ID of the external dataset. | | id | string | true | | The ID of the created insight. | | projectId | string | false | | The ID of the project. | | quickCompute | boolean | false | | Whether the insight was computed using the quickCompute setting. | | source | string | false | | The subset of data used to compute the insight. | ### Enumerated Values | Property | Value | | --- | --- | | source | [backtest_0, backtest_0_training, backtest_1, backtest_10, backtest_10_training, backtest_11, backtest_11_training, backtest_12, backtest_12_training, backtest_13, backtest_13_training, backtest_14, backtest_14_training, backtest_15, backtest_15_training, backtest_16, backtest_16_training, backtest_17, backtest_17_training, backtest_18, backtest_18_training, backtest_19, backtest_19_training, backtest_1_training, backtest_2, backtest_20, backtest_20_training, backtest_2_training, backtest_3, backtest_3_training, backtest_4, backtest_4_training, backtest_5, backtest_5_training, backtest_6, backtest_6_training, backtest_7, backtest_7_training, backtest_8, backtest_8_training, backtest_9, backtest_9_training, externalTestSet, holdout, holdout_training, training, validation] | ## RetrieveShapPreviewPaginatedResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of paginated SHAP preview insights.", "items": { "properties": { "data": { "description": "SHAP preview data.", "properties": { "previews": { "description": "List of SHAP previews for each requested row.", "items": { "properties": { "predictionValue": { "description": "The prediction value for this row.", "type": "number", "x-versionadded": "v2.33" }, "previewValues": { "description": "The SHAP preview values for this row.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string", "x-versionadded": "v2.33" }, "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "featureValue": { "description": "The value of the feature for this row.", "type": "string", "x-versionadded": "v2.33" }, "hasTextExplanations": { "description": "Whether the feature has text explanations available for this row.", "type": "boolean", "x-versionadded": "v2.33" }, "isImage": { "description": "Whether the feature is an image or not.", "type": "boolean", "x-versionadded": "v2.34" }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number", "x-versionadded": "v2.33" }, "textExplanations": { "description": "List of the text explanations for the feature for this row.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "featureName", "featureRank", "featureValue", "hasTextExplanations", "shapValue", "textExplanations" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "timeSeriesRowIndex": { "description": "An index composed of the timestamp, series ID, and forecast distance. This index is used only in time-aware projects.", "items": { "oneOf": [ { "type": "integer" }, { "type": "string" } ] }, "maxLength": 2, "minLength": 2, "type": [ "array", "null" ], "x-versionadded": "v2.36" }, "totalPreviewFeatures": { "description": "The total number of features available after name filters have been applied.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "predictionValue", "previewValues", "rowIndex", "totalPreviewFeatures" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "previewsCount": { "description": "The total number of previews.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "previews", "previewsCount" ], "type": "object" }, "dataSliceId": { "description": "The ID of the data slice.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "externalDatasetId": { "description": "The ID of the external dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the created insight.", "type": "string", "x-versionadded": "v2.33" }, "projectId": { "description": "The ID of the project.", "type": "string", "x-versionadded": "v2.33" }, "quickCompute": { "description": "Whether the insight was computed using the quickCompute setting.", "type": "boolean", "x-versionadded": "v2.35" }, "source": { "description": "The subset of data used to compute the insight.", "enum": [ "backtest_0", "backtest_0_training", "backtest_1", "backtest_10", "backtest_10_training", "backtest_11", "backtest_11_training", "backtest_12", "backtest_12_training", "backtest_13", "backtest_13_training", "backtest_14", "backtest_14_training", "backtest_15", "backtest_15_training", "backtest_16", "backtest_16_training", "backtest_17", "backtest_17_training", "backtest_18", "backtest_18_training", "backtest_19", "backtest_19_training", "backtest_1_training", "backtest_2", "backtest_20", "backtest_20_training", "backtest_2_training", "backtest_3", "backtest_3_training", "backtest_4", "backtest_4_training", "backtest_5", "backtest_5_training", "backtest_6", "backtest_6_training", "backtest_7", "backtest_7_training", "backtest_8", "backtest_8_training", "backtest_9", "backtest_9_training", "externalTestSet", "holdout", "holdout_training", "training", "validation" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "data", "id" ], "type": "object" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrieveShapPreviewIndividualResponse] | true | maxItems: 10 | List of paginated SHAP preview insights. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RocCurveForDatasets ``` { "description": "ROC curve data for datasets.", "properties": { "auc": { "description": "The area under the curve (AUC).", "type": [ "number", "null" ], "x-versionadded": "v2.43" }, "datasetId": { "description": "The ID of the dataset that was used to compute the ROC curve.", "type": "string" }, "kolmogorovSmirnovMetric": { "description": "The Kolmogorv-Smirnov metric.", "type": [ "number", "null" ], "x-versionadded": "v2.43" }, "negativeClassPredictions": { "description": "The list of example predictions for the negative class.", "items": { "type": "number" }, "maxItems": 100000000, "type": "array" }, "positiveClassPredictions": { "description": "The list of example predictions for the negative class.", "items": { "type": "number" }, "maxItems": 100000000, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below. Each point specifies how the model performed for a particular classification threshold.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "maxItems": 10000000, "type": "array" } }, "required": [ "auc", "datasetId", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints" ], "type": "object" } ``` ROC curve data for datasets. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | auc | number,null | true | | The area under the curve (AUC). | | datasetId | string | true | | The ID of the dataset that was used to compute the ROC curve. | | kolmogorovSmirnovMetric | number,null | true | | The Kolmogorv-Smirnov metric. | | negativeClassPredictions | [number] | true | maxItems: 100000000 | The list of example predictions for the negative class. | | positiveClassPredictions | [number] | true | maxItems: 100000000 | The list of example predictions for the negative class. | | rocPoints | [RocPointsResponse] | true | maxItems: 10000000 | The ROC curve data for that source, as specified below. Each point specifies how the model performed for a particular classification threshold. | ## RocCurveForDatasetsList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of ROC curve data for datasets.", "items": { "description": "ROC curve data for datasets.", "properties": { "auc": { "description": "The area under the curve (AUC).", "type": [ "number", "null" ], "x-versionadded": "v2.43" }, "datasetId": { "description": "The ID of the dataset that was used to compute the ROC curve.", "type": "string" }, "kolmogorovSmirnovMetric": { "description": "The Kolmogorv-Smirnov metric.", "type": [ "number", "null" ], "x-versionadded": "v2.43" }, "negativeClassPredictions": { "description": "The list of example predictions for the negative class.", "items": { "type": "number" }, "maxItems": 100000000, "type": "array" }, "positiveClassPredictions": { "description": "The list of example predictions for the negative class.", "items": { "type": "number" }, "maxItems": 100000000, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below. Each point specifies how the model performed for a particular classification threshold.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "maxItems": 10000000, "type": "array" } }, "required": [ "auc", "datasetId", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RocCurveForDatasets] | true | | The list of ROC curve data for datasets. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## RocCurveResponse ``` { "description": "ROC curve data.", "properties": { "auc": { "description": "AUC value", "type": [ "number", "null" ] }, "kolmogorovSmirnovMetric": { "description": "Kolmogorov-Smirnov metric value", "type": [ "number", "null" ] }, "negativeClassPredictions": { "description": "List of example predictions for the negative class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "positiveClassPredictions": { "description": "List of example predictions for the positive class.", "items": { "description": "An example prediction.", "type": "number" }, "type": "array" }, "rocPoints": { "description": "The ROC curve data for that source, as specified below.", "items": { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" }, "type": "array" } }, "required": [ "auc", "kolmogorovSmirnovMetric", "negativeClassPredictions", "positiveClassPredictions", "rocPoints" ], "type": "object" } ``` ROC curve data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | auc | number,null | true | | AUC value | | kolmogorovSmirnovMetric | number,null | true | | Kolmogorov-Smirnov metric value | | negativeClassPredictions | [number] | true | | List of example predictions for the negative class. | | positiveClassPredictions | [number] | true | | List of example predictions for the positive class. | | rocPoints | [RocPointsResponse] | true | | The ROC curve data for that source, as specified below. | ## RocPointsResponse ``` { "description": "ROC curve data for a single source.", "properties": { "accuracy": { "description": "Accuracy for given threshold.", "type": "number" }, "f1Score": { "description": "F1 score.", "type": "number" }, "falseNegativeScore": { "description": "False negative score.", "type": "integer" }, "falsePositiveRate": { "description": "False positive rate.", "type": "number" }, "falsePositiveScore": { "description": "False positive score.", "type": "integer" }, "fractionPredictedAsNegative": { "description": "Fraction of data that will be predicted as negative.", "type": "number" }, "fractionPredictedAsPositive": { "description": "Fraction of data that will be predicted as positive.", "type": "number" }, "liftNegative": { "description": "Lift for the negative class.", "type": "number" }, "liftPositive": { "description": "Lift for the positive class.", "type": "number" }, "matthewsCorrelationCoefficient": { "description": "Matthews correlation coefficient.", "type": "number" }, "negativePredictiveValue": { "description": "Negative predictive value.", "type": "number" }, "positivePredictiveValue": { "description": "Positive predictive value.", "type": "number" }, "threshold": { "description": "Value of threshold for this ROC point.", "type": "number" }, "trueNegativeRate": { "description": "True negative rate.", "type": "number" }, "trueNegativeScore": { "description": "True negative score.", "type": "integer" }, "truePositiveRate": { "description": "True positive rate.", "type": "number" }, "truePositiveScore": { "description": "True positive score.", "type": "integer" } }, "required": [ "accuracy", "f1Score", "falseNegativeScore", "falsePositiveRate", "falsePositiveScore", "fractionPredictedAsNegative", "fractionPredictedAsPositive", "liftNegative", "liftPositive", "matthewsCorrelationCoefficient", "negativePredictiveValue", "positivePredictiveValue", "threshold", "trueNegativeRate", "trueNegativeScore", "truePositiveRate", "truePositiveScore" ], "type": "object" } ``` ROC curve data for a single source. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracy | number | true | | Accuracy for given threshold. | | f1Score | number | true | | F1 score. | | falseNegativeScore | integer | true | | False negative score. | | falsePositiveRate | number | true | | False positive rate. | | falsePositiveScore | integer | true | | False positive score. | | fractionPredictedAsNegative | number | true | | Fraction of data that will be predicted as negative. | | fractionPredictedAsPositive | number | true | | Fraction of data that will be predicted as positive. | | liftNegative | number | true | | Lift for the negative class. | | liftPositive | number | true | | Lift for the positive class. | | matthewsCorrelationCoefficient | number | true | | Matthews correlation coefficient. | | negativePredictiveValue | number | true | | Negative predictive value. | | positivePredictiveValue | number | true | | Positive predictive value. | | threshold | number | true | | Value of threshold for this ROC point. | | trueNegativeRate | number | true | | True negative rate. | | trueNegativeScore | integer | true | | True negative score. | | truePositiveRate | number | true | | True positive rate. | | truePositiveScore | integer | true | | True positive score. | ## SeriesAccuracyCompute ``` { "properties": { "computeAllSeries": { "default": false, "description": "Indicates whether to calculate accuracy for all series or only first 1000 (sorted by name).", "type": "boolean", "x-versionadded": "2.22" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | computeAllSeries | boolean | false | | Indicates whether to calculate accuracy for all series or only first 1000 (sorted by name). | ## SeriesAccuracyRetrieveDataResponse ``` { "properties": { "backtestingScore": { "description": "The backtesting score for this series. If backtesting has not been run for this model, this score will be null.", "type": [ "number", "null" ] }, "cluster": { "description": "The cluster associated with this series. ", "type": [ "string", "null" ] }, "duration": { "description": "The duration of this series formatted as an ISO 8601 duration string.", "format": "duration", "type": "string" }, "endDate": { "description": "The ISO-formatted end date of this series.", "format": "date-time", "type": [ "string", "null" ] }, "holdoutScore": { "description": "The holdout set score for this series. If holdout is locked for the project, this score will be null.", "type": [ "number", "null" ] }, "multiseriesId": { "description": "A DataRobot-generated ID corresponding to a single series in a multiseries dataset.", "type": "string" }, "multiseriesValues": { "description": "The actual values of series ID columns from the dataset.", "items": { "type": "string" }, "type": "array" }, "rowCount": { "description": "The number of rows available for this series in the input dataset.", "type": "integer" }, "startDate": { "description": "The ISO-formatted start date of this series.", "format": "date-time", "type": [ "string", "null" ] }, "targetAverage": { "description": "For regression projects, this is the average (mean) value of target values for this series.For classification projects, this is the ratio of the positive class in the target for this series.", "oneOf": [ { "type": "string" }, { "type": "number" }, { "type": "null" } ] }, "validationScore": { "description": "The validation set score for this series", "type": [ "number", "null" ] } }, "required": [ "backtestingScore", "duration", "multiseriesId", "multiseriesValues", "rowCount", "validationScore" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestingScore | number,null | true | | The backtesting score for this series. If backtesting has not been run for this model, this score will be null. | | cluster | string,null | false | | The cluster associated with this series. | | duration | string(duration) | true | | The duration of this series formatted as an ISO 8601 duration string. | | endDate | string,null(date-time) | false | | The ISO-formatted end date of this series. | | holdoutScore | number,null | false | | The holdout set score for this series. If holdout is locked for the project, this score will be null. | | multiseriesId | string | true | | A DataRobot-generated ID corresponding to a single series in a multiseries dataset. | | multiseriesValues | [string] | true | | The actual values of series ID columns from the dataset. | | rowCount | integer | true | | The number of rows available for this series in the input dataset. | | startDate | string,null(date-time) | false | | The ISO-formatted start date of this series. | | targetAverage | any | false | | For regression projects, this is the average (mean) value of target values for this series.For classification projects, this is the ratio of the positive class in the target for this series. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationScore | number,null | true | | The validation set score for this series | ## SeriesAccuracyRetrieveResponse ``` { "properties": { "data": { "description": "An array of available multiseries identifiers and column values.", "items": { "properties": { "backtestingScore": { "description": "The backtesting score for this series. If backtesting has not been run for this model, this score will be null.", "type": [ "number", "null" ] }, "cluster": { "description": "The cluster associated with this series. ", "type": [ "string", "null" ] }, "duration": { "description": "The duration of this series formatted as an ISO 8601 duration string.", "format": "duration", "type": "string" }, "endDate": { "description": "The ISO-formatted end date of this series.", "format": "date-time", "type": [ "string", "null" ] }, "holdoutScore": { "description": "The holdout set score for this series. If holdout is locked for the project, this score will be null.", "type": [ "number", "null" ] }, "multiseriesId": { "description": "A DataRobot-generated ID corresponding to a single series in a multiseries dataset.", "type": "string" }, "multiseriesValues": { "description": "The actual values of series ID columns from the dataset.", "items": { "type": "string" }, "type": "array" }, "rowCount": { "description": "The number of rows available for this series in the input dataset.", "type": "integer" }, "startDate": { "description": "The ISO-formatted start date of this series.", "format": "date-time", "type": [ "string", "null" ] }, "targetAverage": { "description": "For regression projects, this is the average (mean) value of target values for this series.For classification projects, this is the ratio of the positive class in the target for this series.", "oneOf": [ { "type": "string" }, { "type": "number" }, { "type": "null" } ] }, "validationScore": { "description": "The validation set score for this series", "type": [ "number", "null" ] } }, "required": [ "backtestingScore", "duration", "multiseriesId", "multiseriesValues", "rowCount", "validationScore" ], "type": "object" }, "type": "array" }, "next": { "description": "A URL pointing to the next page (if null, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if null, there is no previous page).", "type": [ "string", "null" ] }, "querySeriesCount": { "description": "The total number of series after filtering is applied.", "type": "integer" }, "totalSeriesCount": { "description": "The total number of series in the project dataset.", "type": "integer" } }, "required": [ "data", "querySeriesCount", "totalSeriesCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [SeriesAccuracyRetrieveDataResponse] | true | | An array of available multiseries identifiers and column values. | | next | string,null | false | | A URL pointing to the next page (if null, there is no next page). | | previous | string,null | false | | A URL pointing to the previous page (if null, there is no previous page). | | querySeriesCount | integer | true | | The total number of series after filtering is applied. | | totalSeriesCount | integer | true | | The total number of series in the project dataset. | ## ShapDistributionsData ``` { "description": "SHAP distributions data.", "properties": { "features": { "description": "List of SHAP distributions for each requested row.", "items": { "properties": { "feature": { "description": "The feature name in the dataset.", "type": "string" }, "featureType": { "description": "The feature type.", "enum": [ "T", "X", "B", "C", "CI", "N", "D", "DD", "FD", "Q", "CD", "GEO", "MC", "INT", "DOC" ], "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "shapValues": { "description": "The SHAP distributions values for this row.", "items": { "properties": { "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer" }, "featureValue": { "anyOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The value of the feature for this row." }, "predictionValue": { "description": "The prediction value for this row.", "type": "number" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ] }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number" }, "weight": { "description": "The weight of the row in the dataset.", "type": [ "number", "null" ], "x-versionadded": "v2.37" } }, "required": [ "featureRank", "featureValue", "predictionValue", "rowIndex", "shapValue", "weight" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 2500, "type": "array" } }, "required": [ "feature", "featureType", "impactNormalized", "impactUnnormalized", "shapValues" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "totalFeaturesCount": { "description": "The total number of features.", "type": "integer" } }, "required": [ "features", "totalFeaturesCount" ], "type": "object", "x-versionadded": "v2.35" } ``` SHAP distributions data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | features | [ShapDistributionsRow] | true | maxItems: 100 | List of SHAP distributions for each requested row. | | totalFeaturesCount | integer | true | | The total number of features. | ## ShapDistributionsRow ``` { "properties": { "feature": { "description": "The feature name in the dataset.", "type": "string" }, "featureType": { "description": "The feature type.", "enum": [ "T", "X", "B", "C", "CI", "N", "D", "DD", "FD", "Q", "CD", "GEO", "MC", "INT", "DOC" ], "type": "string" }, "impactNormalized": { "description": "The same as `impactUnnormalized`, but normalized such that the highest value is `1`.", "maximum": 1, "type": "number" }, "impactUnnormalized": { "description": "How much worse the error metric score is when making predictions on modified data.", "type": "number" }, "shapValues": { "description": "The SHAP distributions values for this row.", "items": { "properties": { "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer" }, "featureValue": { "anyOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The value of the feature for this row." }, "predictionValue": { "description": "The prediction value for this row.", "type": "number" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ] }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number" }, "weight": { "description": "The weight of the row in the dataset.", "type": [ "number", "null" ], "x-versionadded": "v2.37" } }, "required": [ "featureRank", "featureValue", "predictionValue", "rowIndex", "shapValue", "weight" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 2500, "type": "array" } }, "required": [ "feature", "featureType", "impactNormalized", "impactUnnormalized", "shapValues" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature | string | true | | The feature name in the dataset. | | featureType | string | true | | The feature type. | | impactNormalized | number | true | maximum: 1 | The same as impactUnnormalized, but normalized such that the highest value is 1. | | impactUnnormalized | number | true | | How much worse the error metric score is when making predictions on modified data. | | shapValues | [ShapDistributionsValue] | true | maxItems: 2500 | The SHAP distributions values for this row. | ### Enumerated Values | Property | Value | | --- | --- | | featureType | [T, X, B, C, CI, N, D, DD, FD, Q, CD, GEO, MC, INT, DOC] | ## ShapDistributionsValue ``` { "properties": { "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer" }, "featureValue": { "anyOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The value of the feature for this row." }, "predictionValue": { "description": "The prediction value for this row.", "type": "number" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ] }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number" }, "weight": { "description": "The weight of the row in the dataset.", "type": [ "number", "null" ], "x-versionadded": "v2.37" } }, "required": [ "featureRank", "featureValue", "predictionValue", "rowIndex", "shapValue", "weight" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureRank | integer | true | | The SHAP value rank of the feature for this row. | | featureValue | any | true | | The value of the feature for this row. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionValue | number | true | | The prediction value for this row. | | rowIndex | integer,null | true | minimum: 0 | The index of this row. | | shapValue | number | true | | The SHAP value of the feature for this row. | | weight | number,null | true | | The weight of the row in the dataset. | ## ShapExplanationResponse ``` { "properties": { "feature": { "description": "Feature name", "type": "string" }, "featureValue": { "description": "Feature value for this row. First 50 characters are returned.", "type": "string" }, "strength": { "description": "Shapley value for this feature and row.", "type": "number" } }, "required": [ "feature", "featureValue", "strength" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feature | string | true | | Feature name | | featureValue | string | true | | Feature value for this row. First 50 characters are returned. | | strength | number | true | | Shapley value for this feature and row. | ## ShapImpactData ``` { "description": "SHAP impact data.", "properties": { "baseValue": { "description": "The mean of raw model predictions for the training data.", "items": { "type": "number" }, "type": "array", "x-versionadded": "v2.33" }, "link": { "description": "The link function used to connect the SHAP importance values to the model output.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "quickCompute": { "default": true, "description": "When enabled, limits the rows used from the selected source subset by default. When disabled, all rows are used.", "type": "boolean", "x-versionadded": "v2.35" }, "rowCount": { "description": "(Deprecated) The number of rows used to calculate SHAP impact. If ``rowCount`` is not specified, the value returned is ``null``.", "type": [ "integer", "null" ], "x-versionadded": "v2.33", "x-versiondeprecated": "v2.35" }, "shapImpacts": { "description": "A list that contains SHAP impact scores.", "items": { "properties": { "featureName": { "description": "The feature name in the dataset.", "type": "string" }, "impactNormalized": { "description": "The normalized impact score value.", "type": [ "number", "null" ] }, "impactUnnormalized": { "description": "The raw impact score value.", "type": "number" } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "baseValue", "link", "rowCount", "shapImpacts" ], "type": "object" } ``` SHAP impact data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseValue | [number] | true | | The mean of raw model predictions for the training data. | | link | string,null | true | | The link function used to connect the SHAP importance values to the model output. | | quickCompute | boolean | false | | When enabled, limits the rows used from the selected source subset by default. When disabled, all rows are used. | | rowCount | integer,null | true | | (Deprecated) The number of rows used to calculate SHAP impact. If rowCount is not specified, the value returned is null. | | shapImpacts | [ShapImpactEntity] | true | | A list that contains SHAP impact scores. | ## ShapImpactEntity ``` { "properties": { "featureName": { "description": "The feature name in the dataset.", "type": "string" }, "impactNormalized": { "description": "The normalized impact score value.", "type": [ "number", "null" ] }, "impactUnnormalized": { "description": "The raw impact score value.", "type": "number" } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | true | | The feature name in the dataset. | | impactNormalized | number,null | true | | The normalized impact score value. | | impactUnnormalized | number | true | | The raw impact score value. | ## ShapImpactRetrieveResponse ``` { "properties": { "count": { "description": "The number of shapImpact objects returned.", "type": "integer" }, "rowCount": { "description": "The number of rows from dataset to use.", "type": "integer" }, "shapImpacts": { "description": "A list which contains shap impact scores for top 1000 features used by a model", "items": { "properties": { "featureName": { "description": "The feature name in the dataset.", "type": "string" }, "impactNormalized": { "description": "The normalized impact score value (largest value is 1)", "type": "number" }, "impactUnnormalized": { "description": "The raw impact score value", "type": "number" } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" } }, "required": [ "count", "shapImpacts" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of shapImpact objects returned. | | rowCount | integer | false | | The number of rows from dataset to use. | | shapImpacts | [ShapImpactsResponse] | true | maxItems: 1000minItems: 1 | A list which contains shap impact scores for top 1000 features used by a model | ## ShapImpactsResponse ``` { "properties": { "featureName": { "description": "The feature name in the dataset.", "type": "string" }, "impactNormalized": { "description": "The normalized impact score value (largest value is 1)", "type": "number" }, "impactUnnormalized": { "description": "The raw impact score value", "type": "number" } }, "required": [ "featureName", "impactNormalized", "impactUnnormalized" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | true | | The feature name in the dataset. | | impactNormalized | number | true | | The normalized impact score value (largest value is 1) | | impactUnnormalized | number | true | | The raw impact score value | ## ShapMatrixData ``` { "description": "SHAP matrix data.", "properties": { "baseValue": { "description": "The mean of the raw model predictions for the training data.", "items": { "type": "number" }, "type": "array", "x-versionadded": "v2.33" }, "colnames": { "description": "The names of each column in the SHAP matrix.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.33" }, "linkFunction": { "description": "The link function used to connect the feature importance values to the model output.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "matrix": { "description": "SHAP matrix values.", "items": { "items": { "type": "number" }, "type": "array" }, "type": "array", "x-versionadded": "v2.33" }, "rowIndex": { "description": "The index of the data row used to compute the SHAP matrix. Not used in time-aware projects.", "items": { "type": "integer" }, "type": "array", "x-versionadded": "v2.33" }, "timeSeriesRowIndex": { "description": "An index composed of the timestamp, series ID, and forecast distance. Only used in time-aware projects.", "items": { "items": { "oneOf": [ { "type": "integer" }, { "type": "string" } ] }, "maxLength": 2, "minLength": 2, "type": "array" }, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "baseValue", "colnames", "linkFunction", "matrix", "rowIndex" ], "type": "object" } ``` SHAP matrix data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseValue | [number] | true | | The mean of the raw model predictions for the training data. | | colnames | [string] | true | | The names of each column in the SHAP matrix. | | linkFunction | string,null | true | | The link function used to connect the feature importance values to the model output. | | matrix | [array] | true | | SHAP matrix values. | | rowIndex | [integer] | true | | The index of the data row used to compute the SHAP matrix. Not used in time-aware projects. | | timeSeriesRowIndex | [array] | false | | An index composed of the timestamp, series ID, and forecast distance. Only used in time-aware projects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | ## ShapMatrixListDataField ``` { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "id": { "description": "The ID of the SHAP matrix record.", "type": "string" }, "metadata": { "description": "The metadata containing SHAP matrix calculation details.", "properties": { "maxNormalizedMismatch": { "default": 0, "description": "The maximal relative normalized mismatch value.", "type": "number" }, "mismatchRowCount": { "default": 0, "description": "The count of rows for which additivity check failed.", "type": "integer" } }, "type": "object" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" }, "url": { "description": "The URL at which you can retrieve the SHAP matrix.", "format": "uri", "type": "string" } }, "required": [ "datasetId", "id", "metadata", "modelId", "projectId", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The dataset ID. | | id | string | true | | The ID of the SHAP matrix record. | | metadata | ShapMatrixMetadataField | true | | The metadata containing SHAP matrix calculation details. | | modelId | string | true | | The model ID. | | projectId | string | true | | The project ID. | | url | string(uri) | true | | The URL at which you can retrieve the SHAP matrix. | ## ShapMatrixListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Array of SHAP matrix scores records.", "items": { "properties": { "datasetId": { "description": "The dataset ID.", "type": "string" }, "id": { "description": "The ID of the SHAP matrix record.", "type": "string" }, "metadata": { "description": "The metadata containing SHAP matrix calculation details.", "properties": { "maxNormalizedMismatch": { "default": 0, "description": "The maximal relative normalized mismatch value.", "type": "number" }, "mismatchRowCount": { "default": 0, "description": "The count of rows for which additivity check failed.", "type": "integer" } }, "type": "object" }, "modelId": { "description": "The model ID.", "type": "string" }, "projectId": { "description": "The project ID.", "type": "string" }, "url": { "description": "The URL at which you can retrieve the SHAP matrix.", "format": "uri", "type": "string" } }, "required": [ "datasetId", "id", "metadata", "modelId", "projectId", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ShapMatrixListDataField] | true | | Array of SHAP matrix scores records. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## ShapMatrixMetadataField ``` { "description": "The metadata containing SHAP matrix calculation details.", "properties": { "maxNormalizedMismatch": { "default": 0, "description": "The maximal relative normalized mismatch value.", "type": "number" }, "mismatchRowCount": { "default": 0, "description": "The count of rows for which additivity check failed.", "type": "integer" } }, "type": "object" } ``` The metadata containing SHAP matrix calculation details. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxNormalizedMismatch | number | false | | The maximal relative normalized mismatch value. | | mismatchRowCount | integer | false | | The count of rows for which additivity check failed. | ## ShapMatrixRetrieveResponse ``` { "properties": { "columnNames": { "description": "The column names for corresponding dataset & their SHAP values.", "items": { "type": "string" }, "type": "array" } }, "required": [ "columnNames" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNames | [string] | true | | The column names for corresponding dataset & their SHAP values. | ## ShapPreviewData ``` { "description": "SHAP preview data.", "properties": { "previews": { "description": "List of SHAP previews for each requested row.", "items": { "properties": { "predictionValue": { "description": "The prediction value for this row.", "type": "number", "x-versionadded": "v2.33" }, "previewValues": { "description": "The SHAP preview values for this row.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string", "x-versionadded": "v2.33" }, "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "featureValue": { "description": "The value of the feature for this row.", "type": "string", "x-versionadded": "v2.33" }, "hasTextExplanations": { "description": "Whether the feature has text explanations available for this row.", "type": "boolean", "x-versionadded": "v2.33" }, "isImage": { "description": "Whether the feature is an image or not.", "type": "boolean", "x-versionadded": "v2.34" }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number", "x-versionadded": "v2.33" }, "textExplanations": { "description": "List of the text explanations for the feature for this row.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "featureName", "featureRank", "featureValue", "hasTextExplanations", "shapValue", "textExplanations" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "timeSeriesRowIndex": { "description": "An index composed of the timestamp, series ID, and forecast distance. This index is used only in time-aware projects.", "items": { "oneOf": [ { "type": "integer" }, { "type": "string" } ] }, "maxLength": 2, "minLength": 2, "type": [ "array", "null" ], "x-versionadded": "v2.36" }, "totalPreviewFeatures": { "description": "The total number of features available after name filters have been applied.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "predictionValue", "previewValues", "rowIndex", "totalPreviewFeatures" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "previewsCount": { "description": "The total number of previews.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "previews", "previewsCount" ], "type": "object" } ``` SHAP preview data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previews | [ShapPreviewRow] | true | | List of SHAP previews for each requested row. | | previewsCount | integer | true | | The total number of previews. | ## ShapPreviewRow ``` { "properties": { "predictionValue": { "description": "The prediction value for this row.", "type": "number", "x-versionadded": "v2.33" }, "previewValues": { "description": "The SHAP preview values for this row.", "items": { "properties": { "featureName": { "description": "The name of the feature.", "type": "string", "x-versionadded": "v2.33" }, "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "featureValue": { "description": "The value of the feature for this row.", "type": "string", "x-versionadded": "v2.33" }, "hasTextExplanations": { "description": "Whether the feature has text explanations available for this row.", "type": "boolean", "x-versionadded": "v2.33" }, "isImage": { "description": "Whether the feature is an image or not.", "type": "boolean", "x-versionadded": "v2.34" }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number", "x-versionadded": "v2.33" }, "textExplanations": { "description": "List of the text explanations for the feature for this row.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "featureName", "featureRank", "featureValue", "hasTextExplanations", "shapValue", "textExplanations" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "rowIndex": { "description": "The index of this row.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "timeSeriesRowIndex": { "description": "An index composed of the timestamp, series ID, and forecast distance. This index is used only in time-aware projects.", "items": { "oneOf": [ { "type": "integer" }, { "type": "string" } ] }, "maxLength": 2, "minLength": 2, "type": [ "array", "null" ], "x-versionadded": "v2.36" }, "totalPreviewFeatures": { "description": "The total number of features available after name filters have been applied.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "predictionValue", "previewValues", "rowIndex", "totalPreviewFeatures" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionValue | number | true | | The prediction value for this row. | | previewValues | [ShapPreviewValue] | true | | The SHAP preview values for this row. | | rowIndex | integer,null | true | minimum: 0 | The index of this row. | | timeSeriesRowIndex | array,null | false | maxLength: 2minLength: 2minLength: 2 | An index composed of the timestamp, series ID, and forecast distance. This index is used only in time-aware projects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalPreviewFeatures | integer | true | minimum: 0 | The total number of features available after name filters have been applied. | ## ShapPreviewValue ``` { "properties": { "featureName": { "description": "The name of the feature.", "type": "string", "x-versionadded": "v2.33" }, "featureRank": { "description": "The SHAP value rank of the feature for this row.", "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "featureValue": { "description": "The value of the feature for this row.", "type": "string", "x-versionadded": "v2.33" }, "hasTextExplanations": { "description": "Whether the feature has text explanations available for this row.", "type": "boolean", "x-versionadded": "v2.33" }, "isImage": { "description": "Whether the feature is an image or not.", "type": "boolean", "x-versionadded": "v2.34" }, "shapValue": { "description": "The SHAP value of the feature for this row.", "type": "number", "x-versionadded": "v2.33" }, "textExplanations": { "description": "List of the text explanations for the feature for this row.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "featureName", "featureRank", "featureValue", "hasTextExplanations", "shapValue", "textExplanations" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | true | | The name of the feature. | | featureRank | integer | true | | The SHAP value rank of the feature for this row. | | featureValue | string | true | | The value of the feature for this row. | | hasTextExplanations | boolean | true | | Whether the feature has text explanations available for this row. | | isImage | boolean | false | | Whether the feature is an image or not. | | shapValue | number | true | | The SHAP value of the feature for this row. | | textExplanations | [string] | true | | List of the text explanations for the feature for this row. | ## TargetBin ``` { "properties": { "targetBinEnd": { "description": "End value for the target bin", "type": "number" }, "targetBinStart": { "description": "Start value for the target bin", "type": "number" } }, "required": [ "targetBinEnd", "targetBinStart" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetBinEnd | number | true | | End value for the target bin | | targetBinStart | number | true | | Start value for the target bin | ## Text ``` { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "importantNgrams" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allData | AllDataText | true | | Statistics for all data for different feature values. | | insightName | string | true | | Insight name. | | perCluster | [PerClusterText] | true | | Statistic values for different feature values in this cluster. | ### Enumerated Values | Property | Value | | --- | --- | | insightName | importantNgrams | ## TextFeature ``` { "properties": { "featureImpact": { "description": "Feature Impact score.", "type": [ "number", "null" ] }, "featureName": { "description": "Feature name.", "type": "string" }, "featureType": { "description": "Feature Type.", "enum": [ "text" ], "type": "string" }, "insights": { "description": "A list of Cluster Insights for a feature.", "items": { "properties": { "allData": { "description": "Statistics for all data for different feature values.", "properties": { "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "perValueStatistics" ], "type": "object" }, "insightName": { "description": "Insight name.", "enum": [ "importantNgrams" ], "type": "string" }, "perCluster": { "description": "Statistic values for different feature values in this cluster.", "items": { "properties": { "clusterName": { "description": "Cluster name.", "type": "string" }, "missingRowsPercent": { "description": "A percentage of all rows that have a missing value for this feature.", "maximum": 100, "minimum": 0, "type": [ "number", "null" ] }, "perValueStatistics": { "description": "Statistic value for feature values in all data or a cluster.", "items": { "properties": { "contextualExtracts": { "description": "Contextual extracts that show context for the n-gram.", "items": { "type": "string" }, "type": "array" }, "importance": { "description": "Importance value for this n-gram.", "type": "number" }, "ngram": { "description": "An n-gram.", "type": "string" } }, "required": [ "contextualExtracts", "importance", "ngram" ], "type": "object" }, "type": "array" } }, "required": [ "clusterName", "perValueStatistics" ], "type": "object" }, "type": "array" } }, "required": [ "allData", "insightName", "perCluster" ], "type": "object" }, "type": "array" } }, "required": [ "featureName", "featureType", "insights" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureImpact | number,null | false | | Feature Impact score. | | featureName | string | true | | Feature name. | | featureType | string | true | | Feature Type. | | insights | [Text] | true | | A list of Cluster Insights for a feature. | ### Enumerated Values | Property | Value | | --- | --- | | featureType | text | ## UploadRatingTable ``` { "properties": { "parentModelId": { "description": "The parent model this rating table file was derived from.", "type": "string" }, "ratingTableFile": { "description": "The rating table file to use for the new rating table. Accepts `Content-Type: multipart/form-data`.", "format": "binary", "type": "string" }, "ratingTableName": { "description": "The name of the new rating table to create.", "type": "string" } }, "required": [ "parentModelId", "ratingTableFile", "ratingTableName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parentModelId | string | true | | The parent model this rating table file was derived from. | | ratingTableFile | string(binary) | true | | The rating table file to use for the new rating table. Accepts Content-Type: multipart/form-data. | | ratingTableName | string | true | | The name of the new rating table to create. | ## WordCloudNgram ``` { "properties": { "class": { "description": "For classification - values of the target class for corresponding word or ngram. For regression - null.", "type": [ "string", "null" ] }, "coefficient": { "description": "Describes effect of this ngram on the target. A large negative value means a strong effect toward the negative class in classification projects and a smaller predicted target value in regression projects. A large positive value means a strong effect toward the positive class and a larger predicted target value respectively.", "maximum": 1, "minimum": -1, "type": "number" }, "count": { "description": "Number of rows in the training sample where this ngram appears.", "type": "integer" }, "frequency": { "description": "Frequency of this ngram relative to the most common ngram.", "exclusiveMinimum": 0, "maximum": 1, "type": "number" }, "isStopword": { "description": "True for ngrams that DataRobot evaluates as stopwords.", "type": "boolean" }, "ngram": { "description": "Word or ngram value.", "type": "string" }, "variable": { "description": "String representation of the ngram source - contains column name and, for some models, preprocessing details. E.g. NGRAM_OCCUR_L2_colname will be for ngram occurrences count using L2 normalization from the colname column.", "type": "string", "x-versionadded": "v2.19" } }, "required": [ "coefficient", "count", "frequency", "isStopword", "ngram", "variable" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | class | string,null | false | | For classification - values of the target class for corresponding word or ngram. For regression - null. | | coefficient | number | true | maximum: 1minimum: -1 | Describes effect of this ngram on the target. A large negative value means a strong effect toward the negative class in classification projects and a smaller predicted target value in regression projects. A large positive value means a strong effect toward the positive class and a larger predicted target value respectively. | | count | integer | true | | Number of rows in the training sample where this ngram appears. | | frequency | number | true | maximum: 1 | Frequency of this ngram relative to the most common ngram. | | isStopword | boolean | true | | True for ngrams that DataRobot evaluates as stopwords. | | ngram | string | true | | Word or ngram value. | | variable | string | true | | String representation of the ngram source - contains column name and, for some models, preprocessing details. E.g. NGRAM_OCCUR_L2_colname will be for ngram occurrences count using L2 normalization from the colname column. | ## WordCloudRetrieveResponse ``` { "properties": { "ngrams": { "description": "A list of dictionaries containing information about the most important ngrams.", "items": { "properties": { "class": { "description": "For classification - values of the target class for corresponding word or ngram. For regression - null.", "type": [ "string", "null" ] }, "coefficient": { "description": "Describes effect of this ngram on the target. A large negative value means a strong effect toward the negative class in classification projects and a smaller predicted target value in regression projects. A large positive value means a strong effect toward the positive class and a larger predicted target value respectively.", "maximum": 1, "minimum": -1, "type": "number" }, "count": { "description": "Number of rows in the training sample where this ngram appears.", "type": "integer" }, "frequency": { "description": "Frequency of this ngram relative to the most common ngram.", "exclusiveMinimum": 0, "maximum": 1, "type": "number" }, "isStopword": { "description": "True for ngrams that DataRobot evaluates as stopwords.", "type": "boolean" }, "ngram": { "description": "Word or ngram value.", "type": "string" }, "variable": { "description": "String representation of the ngram source - contains column name and, for some models, preprocessing details. E.g. NGRAM_OCCUR_L2_colname will be for ngram occurrences count using L2 normalization from the colname column.", "type": "string", "x-versionadded": "v2.19" } }, "required": [ "coefficient", "count", "frequency", "isStopword", "ngram", "variable" ], "type": "object" }, "type": "array" } }, "required": [ "ngrams" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ngrams | [WordCloudNgram] | true | | A list of dictionaries containing information about the most important ngrams. | --- # Jobs URL: https://docs.datarobot.com/en/docs/api/reference/public-api/jobs.html > This page outlines the endpoints that create and manage DataRobot model evaluation jobs. # Jobs This page outlines the endpoints that create and manage DataRobot model evaluation jobs. ## List tasks Operation path: `GET /api/v2/status/` Authentication requirements: `BearerAuth` List the async tasks that are currently running for your account. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. If 0, all results. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of currently running async status jobs.", "items": { "properties": { "code": { "description": "If no error occurred, 0. Otherwise, may contain a status code reflecting the error.", "type": "integer" }, "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "description": { "description": "A description of the task being performed, if applicable", "type": [ "string", "null" ] }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The status of the task.", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" }, "statusType": { "description": "The type of thing being created by the Task, if applicable.", "type": [ "string", "null" ] }, "type": { "description": "The type of thing being created by the Task, if applicable.", "enum": [ "CustomModelBlueprintBuild", "CustomModelFeatureImpact", "Testing", "customModelWaitForServer", "ManagedImageBuild", "Dataset", "LongRunningServiceControllerOperation", "ProjectPermanentDeletion" ], "type": [ "string", "null" ] } }, "required": [ "code", "created", "description", "message", "status", "statusId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of statuses | StatusListResponse | ## Delete a task by status ID Operation path: `DELETE /api/v2/status/{statusId}/` Authentication requirements: `BearerAuth` Destroy an async status object. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string | true | The ID of the status object. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully deleted | None | ## Get task status by status ID Operation path: `GET /api/v2/status/{statusId}/` Authentication requirements: `BearerAuth` Check the status of an asynchronous task such as project creation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string | true | The ID of the status object. | ### Example responses > 200 Response ``` { "properties": { "code": { "description": "If no error occurred, 0. Otherwise, may contain a status code reflecting the error.", "type": "integer" }, "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "description": { "description": "A description of the task being performed, if applicable", "type": [ "string", "null" ] }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The status of the task.", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" }, "statusType": { "description": "The type of thing being created by the Task, if applicable.", "type": [ "string", "null" ] }, "type": { "description": "The type of thing being created by the Task, if applicable.", "enum": [ "CustomModelBlueprintBuild", "CustomModelFeatureImpact", "Testing", "customModelWaitForServer", "ManagedImageBuild", "Dataset", "LongRunningServiceControllerOperation", "ProjectPermanentDeletion" ], "type": [ "string", "null" ] } }, "required": [ "code", "created", "description", "message", "status", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The status of the asynchronous task | StatusRetrieveResponse | | 303 | See Other | Task is completed, see Location header for the location of a new resource | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Location | string | | A url that can be polled to check the status. | # Schemas ## StatusListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of currently running async status jobs.", "items": { "properties": { "code": { "description": "If no error occurred, 0. Otherwise, may contain a status code reflecting the error.", "type": "integer" }, "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "description": { "description": "A description of the task being performed, if applicable", "type": [ "string", "null" ] }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The status of the task.", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" }, "statusType": { "description": "The type of thing being created by the Task, if applicable.", "type": [ "string", "null" ] }, "type": { "description": "The type of thing being created by the Task, if applicable.", "enum": [ "CustomModelBlueprintBuild", "CustomModelFeatureImpact", "Testing", "customModelWaitForServer", "ManagedImageBuild", "Dataset", "LongRunningServiceControllerOperation", "ProjectPermanentDeletion" ], "type": [ "string", "null" ] } }, "required": [ "code", "created", "description", "message", "status", "statusId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [StatusRetrieveResponse] | true | | An array of currently running async status jobs. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## StatusRetrieveResponse ``` { "properties": { "code": { "description": "If no error occurred, 0. Otherwise, may contain a status code reflecting the error.", "type": "integer" }, "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "description": { "description": "A description of the task being performed, if applicable", "type": [ "string", "null" ] }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The status of the task.", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" }, "statusType": { "description": "The type of thing being created by the Task, if applicable.", "type": [ "string", "null" ] }, "type": { "description": "The type of thing being created by the Task, if applicable.", "enum": [ "CustomModelBlueprintBuild", "CustomModelFeatureImpact", "Testing", "customModelWaitForServer", "ManagedImageBuild", "Dataset", "LongRunningServiceControllerOperation", "ProjectPermanentDeletion" ], "type": [ "string", "null" ] } }, "required": [ "code", "created", "description", "message", "status", "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | code | integer | true | | If no error occurred, 0. Otherwise, may contain a status code reflecting the error. | | created | string(date-time) | true | | The time the status record was created. | | description | string,null | true | | A description of the task being performed, if applicable | | message | string,null | true | | May contain further information about the status. | | status | string | true | | The status of the task. | | statusId | string | true | | The ID of the status object. | | statusType | string,null | false | | The type of thing being created by the Task, if applicable. | | type | string,null | false | | The type of thing being created by the Task, if applicable. | ### Enumerated Values | Property | Value | | --- | --- | | status | [ABORTED, COMPLETED, ERROR, EXPIRED, INITIALIZED, RUNNING] | | type | [CustomModelBlueprintBuild, CustomModelFeatureImpact, Testing, customModelWaitForServer, ManagedImageBuild, Dataset, LongRunningServiceControllerOperation, ProjectPermanentDeletion] | --- # LLM blueprints URL: https://docs.datarobot.com/en/docs/api/reference/public-api/llm_generation.html > The following endpoints outline how to manage LLM generation. # LLM blueprints The following endpoints outline how to manage LLM generation. ## List custom model LLM validations Operation path: `GET /api/v2/genai/customModelLLMValidations/` Authentication requirements: `BearerAuth` List custom model LLM validations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | any | false | Only retrieve the custom model LLM validations associated with these use case IDs. | | playgroundId | query | any | false | Only retrieve the custom model LLM validations associated with this playground ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | search | query | any | false | Only retrieve the custom model LLM validations matching the search query. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name", "deploymentName", "userName", "creationDate". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | | completedOnly | query | boolean | false | If true, only retrieve the completed custom model LLM validations. The default is false. | | deploymentId | query | any | false | Only retrieve the custom model LLM validations associated with this deployment ID. | | modelId | query | any | false | Only retrieve the custom model LLM validations associated with this model ID. | | promptColumnName | query | any | false | Only retrieve the custom model LLM validations where the custom model uses this column name for prompt input. | | targetColumnName | query | any | false | Only retrieve the custom model LLM validations where the custom model uses this column name for prediction output. | ### Example responses > 200 Response ``` { "description": "Paginated list of custom model LLM validations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single custom model LLM validation.", "properties": { "chatModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment.", "title": "chatModelId" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelLLMValidationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListCustomModelLLMValidationsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model LLM validations successfully retrieved. | ListCustomModelLLMValidationsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Validate custom model LLM Operation path: `POST /api/v2/genai/customModelLLMValidations/` Authentication requirements: `BearerAuth` Validate an LLM hosted in a custom model deployment for use in the playground. ### Body parameter ``` { "description": "The body of the \"Validate custom model\" request.", "properties": { "chatModelId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API.", "title": "chatModelId" }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model used in the deployment.", "title": "modelId" }, "name": { "default": "Untitled", "description": "The name to use for the validated custom model.", "maxLength": 5000, "title": "name", "type": "string" }, "predictionTimeout": { "default": 300, "description": "The timeout in seconds for the prediction when validating a custom model. Defaults to 300.", "maximum": 600, "minimum": 1, "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input. This value is used to call the Prediction API of the deployment. For LLM deployments that support the OpenAI chat completion API, it is recommended to specify `chatModelId` instead.", "title": "promptColumnName" }, "targetColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output. This value is used to call the Prediction API of the deployment. For LLM deployments that support the OpenAI chat completion API, it is recommended to specify `chatModelId` instead.", "title": "targetColumnName" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case to associate with the validated custom model.", "title": "useCaseId" } }, "required": [ "deploymentId" ], "title": "CreateCustomModelLLMValidationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateCustomModelLLMValidationRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for a single custom model LLM validation.", "properties": { "chatModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment.", "title": "chatModelId" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelLLMValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Custom model LLM validation job successfully accepted. Follow the Location header to poll for job execution status. | CustomModelLLMValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete custom model LLM validation by validation ID Operation path: `DELETE /api/v2/genai/customModelLLMValidations/{validationId}/` Authentication requirements: `BearerAuth` Delete an existing custom model LLM validation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model LLM validation to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Custom model LLM validation successfully deleted. | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve custom model LLM validation status by validation ID Operation path: `GET /api/v2/genai/customModelLLMValidations/{validationId}/` Authentication requirements: `BearerAuth` Retrieve the status of validating a custom model LLM. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model LLM validation to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single custom model LLM validation.", "properties": { "chatModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment.", "title": "chatModelId" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelLLMValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model LLM validation status successfully retrieved. | CustomModelLLMValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit custom model LLM validation by validation ID Operation path: `PATCH /api/v2/genai/customModelLLMValidations/{validationId}/` Authentication requirements: `BearerAuth` Edit an existing custom model LLM validation. ### Body parameter ``` { "description": "The body of the \"Edit custom model validation\" request.", "properties": { "chatModelId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API.", "title": "chatModelId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the deployment associated with this custom model validation.", "title": "deploymentId" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the model associated with this custom model validation.", "title": "modelId" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the custom model validation to this value.", "title": "name" }, "predictionTimeout": { "anyOf": [ { "maximum": 600, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "If specified, sets the timeout in seconds for the prediction when validating a custom model.", "title": "predictionTimeout" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment.", "title": "promptColumnName" }, "targetColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment.", "title": "targetColumnName" } }, "title": "EditCustomModelValidationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model LLM validation to edit. | | body | body | EditCustomModelValidationRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single custom model LLM validation.", "properties": { "chatModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment.", "title": "chatModelId" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelLLMValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model LLM validation successfully updated. | CustomModelLLMValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Revalidate custom model LLM by validation ID Operation path: `POST /api/v2/genai/customModelLLMValidations/{validationId}/revalidate/` Authentication requirements: `BearerAuth` Revalidate an existing custom model LLM validation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model LLM validation to revalidate. | ### Example responses > 200 Response ``` { "description": "API response object for a single custom model LLM validation.", "properties": { "chatModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment.", "title": "chatModelId" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelLLMValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model LLM successfully revalidated. | CustomModelLLMValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create custom model version Operation path: `POST /api/v2/genai/customModelVersions/` Authentication requirements: `BearerAuth` Export the specified LLM blueprint as a custom model version in Model Registry. ### Body parameter ``` { "description": "The body of the \"Create custom model version\" request.", "properties": { "defaultPredictionServerId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of a prediction server for the new deployment to use. Only used if this LLM blueprint's vector database is not already deployed. Cannot be used with predictionEnvironmentId.", "title": "defaultPredictionServerId" }, "insightsConfiguration": { "default": [], "description": "The configuration of insights to transfer to production.", "items": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "minItems": 0, "title": "insightsConfiguration", "type": "array" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint to use for the custom model version.", "title": "llmBlueprintId", "type": "string" }, "llmTestConfigurationIds": { "default": [], "description": "The IDs of the LLM test configurations to execute.", "items": { "type": "string" }, "maxItems": 100, "title": "llmTestConfigurationIds", "type": "array" }, "predictionEnvironmentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the prediction environment for a new vector database deployment to use. Only used if this LLM blueprint's vector database is not already deployed. Cannot be used with defaultPredictionServerId.", "title": "predictionEnvironmentId" }, "promptColumnName": { "default": "promptText", "description": "The name of the column to use for prompt text input in the custom model.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "targetColumnName": { "default": "resultText", "description": "The name of the column to use for prediction output in the custom model.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "vectorDatabaseResources": { "anyOf": [ { "description": "The structure that describes resource settings for a custom model created from buzok.", "properties": { "maximumMemory": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum memory that can be allocated to the custom model.", "title": "maximumMemory" }, "networkEgressPolicy": { "default": "Public", "description": "Network egress policy for the custom model. Can be either Public or None.", "maxLength": 5000, "title": "networkEgressPolicy", "type": "string" }, "replicas": { "default": 1, "description": "A fixed number of replicas that will be created for the custom model.", "title": "replicas", "type": "integer" }, "resourceBundleId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "An identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "title": "resourceBundleId" } }, "title": "CustomModelResourcesRequest", "type": "object" }, { "type": "null" } ], "description": "The resources that the vector database custom model will be provisioned with. Only used if this LLM blueprint's vector database is not already deployed." } }, "required": [ "llmBlueprintId" ], "title": "CreateCustomModelVersionRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateCustomModelVersionRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for the \"Create custom model version\" request.", "properties": { "customModelId": { "description": "The ID of the created custom model.", "title": "customModelId", "type": "string" } }, "required": [ "customModelId" ], "title": "CreateCustomModelVersionResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successful Response | CreateCustomModelVersionResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List LLM blueprints Operation path: `GET /api/v2/genai/llmBlueprints/` Authentication requirements: `BearerAuth` List LLM blueprints. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | query | any | false | Playground ID. | | llmIds | query | any | false | Retrieve only the LLM blueprints that use the specified LLM IDs. | | vectorDatabaseIds | query | any | false | Retrieve only the LLM blueprints linked to the specified vector database IDs. | | isSaved | query | any | false | Retrieve only the LLM blueprints that have the specified draft/saved status. | | isStarred | query | any | false | Retrieve only the LLM blueprints that have the specified starred status. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name", "description", "creationDate", "lastUpdateDate", "llmId", "vectorDatabaseId". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | | creationUserIds | query | any | false | Retrieve only the LLM blueprints created by these users. | ### Example responses > 200 Response ``` { "description": "Paginated list of LLM blueprints.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single LLM blueprint.", "properties": { "creationDate": { "description": "The creation date of the LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this LLM blueprint.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM blueprint.", "title": "creationUserName", "type": "string" }, "customModelLLMErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorMessage" }, "customModelLLMErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorResolution" }, "customModelLLMValidationStatus": { "anyOf": [ { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" }, { "type": "null" } ], "description": "The validation status of the custom model LLM (if using a custom model LLM)." }, "description": { "description": "The description of the LLM blueprint.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM blueprint.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM specified in this blueprint is active in the current environment.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM specified in this blueprint is deprecated.", "title": "isDeprecated", "type": "boolean" }, "isSaved": { "description": "If false, then this LLM blueprint is a draft and its settings can be changed. If true, then its settings are frozen and cannot be changed anymore.", "title": "isSaved", "type": "boolean" }, "isStarred": { "description": "Specifies whether this LLM blueprint is starred.", "title": "isStarred", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this LLM blueprint.", "title": "lastUpdateUserId", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM used by this LLM blueprint.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLM blueprint.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground this LLM blueprint belongs to.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "vectorDatabaseErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the vector database associated with this LLM.", "title": "vectorDatabaseErrorMessage" }, "vectorDatabaseErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database associated with this LLM blueprint.", "title": "vectorDatabaseErrorResolution" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family associated with this LLM blueprint.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the vector database associated with this LLM blueprint.", "title": "vectorDatabaseName" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." }, "vectorDatabaseStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The creation status of the vector database associated with this LLM blueprint." } }, "required": [ "id", "name", "description", "isSaved", "isStarred", "promptType", "playgroundId", "llmName", "creationDate", "creationUserId", "creationUserName", "lastUpdateDate", "lastUpdateUserId", "vectorDatabaseName", "vectorDatabaseStatus", "vectorDatabaseErrorMessage", "vectorDatabaseErrorResolution", "customModelLLMValidationStatus", "customModelLLMErrorMessage", "customModelLLMErrorResolution", "vectorDatabaseFamilyId", "isActive", "isDeprecated" ], "title": "LLMBlueprintResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMBlueprintsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListLLMBlueprintsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create LLM blueprint Operation path: `POST /api/v2/genai/llmBlueprints/` Authentication requirements: `BearerAuth` Create a new LLM blueprint. ### Body parameter ``` { "description": "The body of the Create LLM Blueprint request.", "properties": { "description": { "default": "", "description": "The description of the LLM blueprint.", "maxLength": 5000, "title": "description", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLM blueprint.", "maxLength": 5000, "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground to link this LLM blueprint to.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Specifies the vector database retrieval settings in LLM blueprint API requests.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 128, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettingsRequest", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "playgroundId", "name" ], "title": "CreateLLMBlueprintRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateLLMBlueprintRequest | true | none | ### Example responses > 201 Response ``` { "description": "API response object for a single LLM blueprint.", "properties": { "creationDate": { "description": "The creation date of the LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this LLM blueprint.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM blueprint.", "title": "creationUserName", "type": "string" }, "customModelLLMErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorMessage" }, "customModelLLMErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorResolution" }, "customModelLLMValidationStatus": { "anyOf": [ { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" }, { "type": "null" } ], "description": "The validation status of the custom model LLM (if using a custom model LLM)." }, "description": { "description": "The description of the LLM blueprint.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM blueprint.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM specified in this blueprint is active in the current environment.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM specified in this blueprint is deprecated.", "title": "isDeprecated", "type": "boolean" }, "isSaved": { "description": "If false, then this LLM blueprint is a draft and its settings can be changed. If true, then its settings are frozen and cannot be changed anymore.", "title": "isSaved", "type": "boolean" }, "isStarred": { "description": "Specifies whether this LLM blueprint is starred.", "title": "isStarred", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this LLM blueprint.", "title": "lastUpdateUserId", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM used by this LLM blueprint.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLM blueprint.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground this LLM blueprint belongs to.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "vectorDatabaseErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the vector database associated with this LLM.", "title": "vectorDatabaseErrorMessage" }, "vectorDatabaseErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database associated with this LLM blueprint.", "title": "vectorDatabaseErrorResolution" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family associated with this LLM blueprint.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the vector database associated with this LLM blueprint.", "title": "vectorDatabaseName" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." }, "vectorDatabaseStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The creation status of the vector database associated with this LLM blueprint." } }, "required": [ "id", "name", "description", "isSaved", "isStarred", "promptType", "playgroundId", "llmName", "creationDate", "creationUserId", "creationUserName", "lastUpdateDate", "lastUpdateUserId", "vectorDatabaseName", "vectorDatabaseStatus", "vectorDatabaseErrorMessage", "vectorDatabaseErrorResolution", "customModelLLMValidationStatus", "customModelLLMErrorMessage", "customModelLLMErrorResolution", "vectorDatabaseFamilyId", "isActive", "isDeprecated" ], "title": "LLMBlueprintResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successful Response | LLMBlueprintResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create a LLM blueprints from a chat prompt Operation path: `POST /api/v2/genai/llmBlueprints/fromChatPrompt/` Authentication requirements: `BearerAuth` Create a new LLM blueprint using the LLM and vector database settings currently used by the specified existing chat prompt. ### Body parameter ``` { "description": "The body of the Create LLM Blueprint from a ChatPrompt request.", "properties": { "chatPromptId": { "description": "The ID of an existing chat prompt to copy the LLM and vector database settings from.", "title": "chatPromptId", "type": "string" }, "description": { "default": "", "description": "The description of the new LLM blueprint.", "maxLength": 5000, "title": "description", "type": "string" }, "name": { "description": "The name of the new LLM blueprint.", "maxLength": 5000, "title": "name", "type": "string" } }, "required": [ "name", "chatPromptId" ], "title": "CreateFromChatPromptRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateFromChatPromptRequest | true | none | ### Example responses > 201 Response ``` { "description": "API response object for a single LLM blueprint.", "properties": { "creationDate": { "description": "The creation date of the LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this LLM blueprint.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM blueprint.", "title": "creationUserName", "type": "string" }, "customModelLLMErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorMessage" }, "customModelLLMErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorResolution" }, "customModelLLMValidationStatus": { "anyOf": [ { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" }, { "type": "null" } ], "description": "The validation status of the custom model LLM (if using a custom model LLM)." }, "description": { "description": "The description of the LLM blueprint.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM blueprint.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM specified in this blueprint is active in the current environment.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM specified in this blueprint is deprecated.", "title": "isDeprecated", "type": "boolean" }, "isSaved": { "description": "If false, then this LLM blueprint is a draft and its settings can be changed. If true, then its settings are frozen and cannot be changed anymore.", "title": "isSaved", "type": "boolean" }, "isStarred": { "description": "Specifies whether this LLM blueprint is starred.", "title": "isStarred", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this LLM blueprint.", "title": "lastUpdateUserId", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM used by this LLM blueprint.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLM blueprint.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground this LLM blueprint belongs to.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "vectorDatabaseErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the vector database associated with this LLM.", "title": "vectorDatabaseErrorMessage" }, "vectorDatabaseErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database associated with this LLM blueprint.", "title": "vectorDatabaseErrorResolution" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family associated with this LLM blueprint.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the vector database associated with this LLM blueprint.", "title": "vectorDatabaseName" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." }, "vectorDatabaseStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The creation status of the vector database associated with this LLM blueprint." } }, "required": [ "id", "name", "description", "isSaved", "isStarred", "promptType", "playgroundId", "llmName", "creationDate", "creationUserId", "creationUserName", "lastUpdateDate", "lastUpdateUserId", "vectorDatabaseName", "vectorDatabaseStatus", "vectorDatabaseErrorMessage", "vectorDatabaseErrorResolution", "customModelLLMValidationStatus", "customModelLLMErrorMessage", "customModelLLMErrorResolution", "vectorDatabaseFamilyId", "isActive", "isDeprecated" ], "title": "LLMBlueprintResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successful Response | LLMBlueprintResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Duplicate LLM blueprint Operation path: `POST /api/v2/genai/llmBlueprints/fromLLMBlueprint/` Authentication requirements: `BearerAuth` Create a new LLM blueprint using the LLM and vector database settings currently used by the specified existing LLM blueprint. ### Body parameter ``` { "description": "The body of the Create LLM Blueprint from an LLM Blueprint request.", "properties": { "description": { "default": "", "description": "The description of the new LLM blueprint.", "maxLength": 5000, "title": "description", "type": "string" }, "llmBlueprintId": { "description": "The ID of an existing LLM blueprint to copy the LLM and vector database settings from.", "title": "llmBlueprintId", "type": "string" }, "name": { "description": "The name of the new LLM blueprint.", "maxLength": 5000, "title": "name", "type": "string" } }, "required": [ "name", "llmBlueprintId" ], "title": "CreateFromLLMBlueprintRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateFromLLMBlueprintRequest | true | none | ### Example responses > 201 Response ``` { "description": "API response object for a single LLM blueprint.", "properties": { "creationDate": { "description": "The creation date of the LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this LLM blueprint.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM blueprint.", "title": "creationUserName", "type": "string" }, "customModelLLMErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorMessage" }, "customModelLLMErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorResolution" }, "customModelLLMValidationStatus": { "anyOf": [ { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" }, { "type": "null" } ], "description": "The validation status of the custom model LLM (if using a custom model LLM)." }, "description": { "description": "The description of the LLM blueprint.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM blueprint.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM specified in this blueprint is active in the current environment.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM specified in this blueprint is deprecated.", "title": "isDeprecated", "type": "boolean" }, "isSaved": { "description": "If false, then this LLM blueprint is a draft and its settings can be changed. If true, then its settings are frozen and cannot be changed anymore.", "title": "isSaved", "type": "boolean" }, "isStarred": { "description": "Specifies whether this LLM blueprint is starred.", "title": "isStarred", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this LLM blueprint.", "title": "lastUpdateUserId", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM used by this LLM blueprint.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLM blueprint.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground this LLM blueprint belongs to.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "vectorDatabaseErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the vector database associated with this LLM.", "title": "vectorDatabaseErrorMessage" }, "vectorDatabaseErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database associated with this LLM blueprint.", "title": "vectorDatabaseErrorResolution" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family associated with this LLM blueprint.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the vector database associated with this LLM blueprint.", "title": "vectorDatabaseName" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." }, "vectorDatabaseStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The creation status of the vector database associated with this LLM blueprint." } }, "required": [ "id", "name", "description", "isSaved", "isStarred", "promptType", "playgroundId", "llmName", "creationDate", "creationUserId", "creationUserName", "lastUpdateDate", "lastUpdateUserId", "vectorDatabaseName", "vectorDatabaseStatus", "vectorDatabaseErrorMessage", "vectorDatabaseErrorResolution", "customModelLLMValidationStatus", "customModelLLMErrorMessage", "customModelLLMErrorResolution", "vectorDatabaseFamilyId", "isActive", "isDeprecated" ], "title": "LLMBlueprintResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successful Response | LLMBlueprintResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete LLM blueprint by LLM blueprint ID Operation path: `DELETE /api/v2/genai/llmBlueprints/{llmBlueprintId}/` Authentication requirements: `BearerAuth` Delete an existing LLM blueprint. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmBlueprintId | path | string | true | The ID of the LLM blueprint to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve LLM blueprint by LLM blueprint ID Operation path: `GET /api/v2/genai/llmBlueprints/{llmBlueprintId}/` Authentication requirements: `BearerAuth` Retrieve an existing LLM blueprint. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmBlueprintId | path | string | true | The ID of the LLM blueprint to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single LLM blueprint.", "properties": { "creationDate": { "description": "The creation date of the LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this LLM blueprint.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM blueprint.", "title": "creationUserName", "type": "string" }, "customModelLLMErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorMessage" }, "customModelLLMErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorResolution" }, "customModelLLMValidationStatus": { "anyOf": [ { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" }, { "type": "null" } ], "description": "The validation status of the custom model LLM (if using a custom model LLM)." }, "description": { "description": "The description of the LLM blueprint.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM blueprint.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM specified in this blueprint is active in the current environment.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM specified in this blueprint is deprecated.", "title": "isDeprecated", "type": "boolean" }, "isSaved": { "description": "If false, then this LLM blueprint is a draft and its settings can be changed. If true, then its settings are frozen and cannot be changed anymore.", "title": "isSaved", "type": "boolean" }, "isStarred": { "description": "Specifies whether this LLM blueprint is starred.", "title": "isStarred", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this LLM blueprint.", "title": "lastUpdateUserId", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM used by this LLM blueprint.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLM blueprint.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground this LLM blueprint belongs to.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "vectorDatabaseErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the vector database associated with this LLM.", "title": "vectorDatabaseErrorMessage" }, "vectorDatabaseErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database associated with this LLM blueprint.", "title": "vectorDatabaseErrorResolution" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family associated with this LLM blueprint.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the vector database associated with this LLM blueprint.", "title": "vectorDatabaseName" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." }, "vectorDatabaseStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The creation status of the vector database associated with this LLM blueprint." } }, "required": [ "id", "name", "description", "isSaved", "isStarred", "promptType", "playgroundId", "llmName", "creationDate", "creationUserId", "creationUserName", "lastUpdateDate", "lastUpdateUserId", "vectorDatabaseName", "vectorDatabaseStatus", "vectorDatabaseErrorMessage", "vectorDatabaseErrorResolution", "customModelLLMValidationStatus", "customModelLLMErrorMessage", "customModelLLMErrorResolution", "vectorDatabaseFamilyId", "isActive", "isDeprecated" ], "title": "LLMBlueprintResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | LLMBlueprintResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit LLM blueprint by LLM blueprint ID Operation path: `PATCH /api/v2/genai/llmBlueprints/{llmBlueprintId}/` Authentication requirements: `BearerAuth` Edit an existing LLM blueprint. ### Body parameter ``` { "description": "The body of the Update LLM Blueprint request.", "properties": { "description": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, updates the LLM blueprint description to this value.", "title": "description" }, "isSaved": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "If specified, updates the saved status of the LLM blueprint to this value.", "title": "isSaved" }, "isStarred": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "If specified, updates the starred status of the LLM blueprint to this value.", "title": "isStarred" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the LLM used by the LLM blueprint.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "If specified, updates the LLM settings to these values.", "title": "llmSettings" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the LLM blueprint to this value.", "title": "name" }, "promptType": { "anyOf": [ { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, { "type": "null" } ], "description": "If specified, updates the chat context behavior of the LLM blueprint to this value." }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the vector database used by the LLM blueprint. If the specified value is `null`, unlinks the vector database from the LLM blueprint. If omitted, the currently used vector database remains in use.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Specifies the vector database retrieval settings in LLM blueprint API requests.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 128, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettingsRequest", "type": "object" }, { "type": "null" } ], "description": "If specified, updates the vector database retrieval settings to these values." } }, "title": "UpdateLLMBlueprintRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmBlueprintId | path | string | true | The ID of the LLM blueprint to edit. | | body | body | UpdateLLMBlueprintRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single LLM blueprint.", "properties": { "creationDate": { "description": "The creation date of the LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this LLM blueprint.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM blueprint.", "title": "creationUserName", "type": "string" }, "customModelLLMErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorMessage" }, "customModelLLMErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorResolution" }, "customModelLLMValidationStatus": { "anyOf": [ { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" }, { "type": "null" } ], "description": "The validation status of the custom model LLM (if using a custom model LLM)." }, "description": { "description": "The description of the LLM blueprint.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM blueprint.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM specified in this blueprint is active in the current environment.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM specified in this blueprint is deprecated.", "title": "isDeprecated", "type": "boolean" }, "isSaved": { "description": "If false, then this LLM blueprint is a draft and its settings can be changed. If true, then its settings are frozen and cannot be changed anymore.", "title": "isSaved", "type": "boolean" }, "isStarred": { "description": "Specifies whether this LLM blueprint is starred.", "title": "isStarred", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this LLM blueprint.", "title": "lastUpdateUserId", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM used by this LLM blueprint.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLM blueprint.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground this LLM blueprint belongs to.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "vectorDatabaseErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the vector database associated with this LLM.", "title": "vectorDatabaseErrorMessage" }, "vectorDatabaseErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database associated with this LLM blueprint.", "title": "vectorDatabaseErrorResolution" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family associated with this LLM blueprint.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the vector database associated with this LLM blueprint.", "title": "vectorDatabaseName" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." }, "vectorDatabaseStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The creation status of the vector database associated with this LLM blueprint." } }, "required": [ "id", "name", "description", "isSaved", "isStarred", "promptType", "playgroundId", "llmName", "creationDate", "creationUserId", "creationUserName", "lastUpdateDate", "lastUpdateUserId", "vectorDatabaseName", "vectorDatabaseStatus", "vectorDatabaseErrorMessage", "vectorDatabaseErrorResolution", "customModelLLMValidationStatus", "customModelLLMErrorMessage", "customModelLLMErrorResolution", "vectorDatabaseFamilyId", "isActive", "isDeprecated" ], "title": "LLMBlueprintResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | LLMBlueprintResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List LLMs Operation path: `GET /api/v2/genai/llms/` Authentication requirements: `BearerAuth` List the large language models (LLMs) available in the DataRobot platform for the current user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | useCaseId | query | any | false | If specified, include custom model LLMs available for this use case. | | activeOnly | query | boolean | false | Whether to include only active models. | | moderationSupportedOnly | query | boolean | false | Whether to include only the models available for moderations. | | chatCompletionsSupportedOnly | query | boolean | false | Whether to include only the models available for chat completions. | ### Example responses > 200 Response ``` { "description": "Paginated list of LLMs.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "additionalProperties": true, "description": "The metadata that defines an LLM.", "properties": { "availableLitellmEndpoints": { "description": "The supported endpoints for the LLM.", "properties": { "supportsChatCompletions": { "description": "Whether the chat completions endpoint is supported.", "title": "supportsChatCompletions", "type": "boolean" }, "supportsResponses": { "description": "Whether the responses endpoint is supported.", "title": "supportsResponses", "type": "boolean" } }, "required": [ "supportsChatCompletions", "supportsResponses" ], "title": "AvailableLiteLLMEndpoints", "type": "object" }, "contextSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the LLM's context, measured in tokens. `null` if unknown.", "title": "contextSize" }, "creator": { "description": "The company that originally created the LLM.", "title": "creator", "type": "string" }, "dateAdded": { "description": "The date the LLM was added to the GenAI playground.", "format": "date", "title": "dateAdded", "type": "string" }, "description": { "description": "The details about the LLM.", "title": "description", "type": "string" }, "documentationLink": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The link to the vendor documentation for the LLM.", "title": "documentationLink" }, "id": { "description": "The ID of the LLM.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM is active.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "isDeprecated", "type": "boolean" }, "isMetered": { "default": true, "description": "Whether the LLM usage is metered.", "title": "isMetered", "type": "boolean" }, "isSupportedForModeration": { "description": "Whether the LLM is supported for moderation.", "title": "isSupportedForModeration", "type": "boolean" }, "license": { "description": "The usage license information for the LLM.", "title": "license", "type": "string" }, "name": { "description": "The name of the LLM.", "title": "name", "type": "string" }, "provider": { "description": "The party that provides access to the LLM.", "title": "provider", "type": "string" }, "referenceLinks": { "description": "The references for the LLM.", "items": { "description": "A reference link for an LLM.", "properties": { "name": { "description": "Description of the reference document.", "title": "name", "type": "string" }, "url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "URL of the reference document.", "title": "url" } }, "required": [ "name", "url" ], "title": "LLMReference", "type": "object" }, "title": "referenceLinks", "type": "array" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "settings": { "description": "The settings supported by the LLM.", "items": { "description": "Metadata describing a single setting.", "properties": { "constraints": { "anyOf": [ { "discriminator": { "mapping": { "boolean": "#/components/schemas/BooleanSettingConstraints", "float": "#/components/schemas/FloatSettingConstraints", "integer": "#/components/schemas/IntegerSettingConstraints", "list": "#/components/schemas/ListParameterConstraints", "object_id": "#/components/schemas/ObjectIdSettingConstraints", "string": "#/components/schemas/StringSettingConstraints" }, "propertyName": "type" }, "oneOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "description": "Available constraints for list parameters.", "properties": { "elementConstraints": { "anyOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "type": "null" } ], "description": "Constraints to apply to each element.", "title": "elementConstraints" }, "elementType": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the list (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the list (inclusive).", "title": "minLength" }, "type": { "const": "list", "default": "list", "description": "The data type of the parameter.", "title": "type", "type": "string" } }, "required": [ "elementType" ], "title": "ListParameterConstraints", "type": "object" } ] }, { "type": "null" } ], "description": "The constraints for the LLM setting values.", "title": "constraints" }, "defaultValue": { "anyOf": [ { "type": "boolean" }, { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The default value of the LLM setting.", "title": "defaultValue" }, "description": { "description": "The description of the LLM setting.", "title": "description", "type": "string" }, "format": { "anyOf": [ { "description": "Supported formats for settings.", "enum": [ "multiline" ], "title": "SettingFormat", "type": "string" }, { "type": "null" } ], "description": "The expected format of the value of the LLM setting." }, "id": { "description": "The ID of the LLM setting.", "title": "id", "type": "string" }, "isNullable": { "default": true, "description": "Whether the setting allows null values (default: true).", "title": "isNullable", "type": "boolean" }, "name": { "description": "The name of the LLM setting.", "title": "name", "type": "string" }, "type": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" } }, "required": [ "id", "name", "description", "type", "constraints", "defaultValue" ], "title": "LLMSettingDefinition", "type": "object" }, "title": "settings", "type": "array" }, "suggestedReplacement": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM suggested as a replacement for this one when it is retired.", "title": "suggestedReplacement" }, "supportedCustomModelLLMValidations": { "anyOf": [ { "items": { "description": "The metadata describing a validated custom model LLM.", "properties": { "id": { "description": "The ID of the custom model LLM validation.", "title": "id", "type": "string" }, "name": { "description": "The name of the custom model LLM validation.", "title": "name", "type": "string" } }, "required": [ "id", "name" ], "title": "SupportedCustomModelLLMValidation", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The supported custom model validations if applicable for this LLM ID.", "title": "supportedCustomModelLLMValidations" }, "supportedLanguages": { "description": "The languages supported by the LLM.", "title": "supportedLanguages", "type": "string" }, "vendor": { "description": "The vendor of the LLM.", "title": "vendor", "type": "string" } }, "required": [ "id", "name", "description", "vendor", "provider", "creator", "license", "supportedLanguages", "settings", "documentationLink", "referenceLinks", "dateAdded", "isDeprecated", "isActive", "isSupportedForModeration", "availableLitellmEndpoints" ], "title": "LanguageModelDefinitionResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListLLMsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Get LLM by LLM ID Operation path: `GET /api/v2/genai/llms/{llmId}/` Authentication requirements: `BearerAuth` Get the large language model (LLM) by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmId | path | string | true | The ID of the LLM to retrieve. | | useCaseId | query | any | false | If specified, include custom model LLMs available for this use case. | ### Example responses > 200 Response ``` { "additionalProperties": true, "description": "The metadata that defines an LLM.", "properties": { "availableLitellmEndpoints": { "description": "The supported endpoints for the LLM.", "properties": { "supportsChatCompletions": { "description": "Whether the chat completions endpoint is supported.", "title": "supportsChatCompletions", "type": "boolean" }, "supportsResponses": { "description": "Whether the responses endpoint is supported.", "title": "supportsResponses", "type": "boolean" } }, "required": [ "supportsChatCompletions", "supportsResponses" ], "title": "AvailableLiteLLMEndpoints", "type": "object" }, "contextSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the LLM's context, measured in tokens. `null` if unknown.", "title": "contextSize" }, "creator": { "description": "The company that originally created the LLM.", "title": "creator", "type": "string" }, "dateAdded": { "description": "The date the LLM was added to the GenAI playground.", "format": "date", "title": "dateAdded", "type": "string" }, "description": { "description": "The details about the LLM.", "title": "description", "type": "string" }, "documentationLink": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The link to the vendor documentation for the LLM.", "title": "documentationLink" }, "id": { "description": "The ID of the LLM.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM is active.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "isDeprecated", "type": "boolean" }, "isMetered": { "default": true, "description": "Whether the LLM usage is metered.", "title": "isMetered", "type": "boolean" }, "isSupportedForModeration": { "description": "Whether the LLM is supported for moderation.", "title": "isSupportedForModeration", "type": "boolean" }, "license": { "description": "The usage license information for the LLM.", "title": "license", "type": "string" }, "name": { "description": "The name of the LLM.", "title": "name", "type": "string" }, "provider": { "description": "The party that provides access to the LLM.", "title": "provider", "type": "string" }, "referenceLinks": { "description": "The references for the LLM.", "items": { "description": "A reference link for an LLM.", "properties": { "name": { "description": "Description of the reference document.", "title": "name", "type": "string" }, "url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "URL of the reference document.", "title": "url" } }, "required": [ "name", "url" ], "title": "LLMReference", "type": "object" }, "title": "referenceLinks", "type": "array" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "settings": { "description": "The settings supported by the LLM.", "items": { "description": "Metadata describing a single setting.", "properties": { "constraints": { "anyOf": [ { "discriminator": { "mapping": { "boolean": "#/components/schemas/BooleanSettingConstraints", "float": "#/components/schemas/FloatSettingConstraints", "integer": "#/components/schemas/IntegerSettingConstraints", "list": "#/components/schemas/ListParameterConstraints", "object_id": "#/components/schemas/ObjectIdSettingConstraints", "string": "#/components/schemas/StringSettingConstraints" }, "propertyName": "type" }, "oneOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "description": "Available constraints for list parameters.", "properties": { "elementConstraints": { "anyOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "type": "null" } ], "description": "Constraints to apply to each element.", "title": "elementConstraints" }, "elementType": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the list (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the list (inclusive).", "title": "minLength" }, "type": { "const": "list", "default": "list", "description": "The data type of the parameter.", "title": "type", "type": "string" } }, "required": [ "elementType" ], "title": "ListParameterConstraints", "type": "object" } ] }, { "type": "null" } ], "description": "The constraints for the LLM setting values.", "title": "constraints" }, "defaultValue": { "anyOf": [ { "type": "boolean" }, { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The default value of the LLM setting.", "title": "defaultValue" }, "description": { "description": "The description of the LLM setting.", "title": "description", "type": "string" }, "format": { "anyOf": [ { "description": "Supported formats for settings.", "enum": [ "multiline" ], "title": "SettingFormat", "type": "string" }, { "type": "null" } ], "description": "The expected format of the value of the LLM setting." }, "id": { "description": "The ID of the LLM setting.", "title": "id", "type": "string" }, "isNullable": { "default": true, "description": "Whether the setting allows null values (default: true).", "title": "isNullable", "type": "boolean" }, "name": { "description": "The name of the LLM setting.", "title": "name", "type": "string" }, "type": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" } }, "required": [ "id", "name", "description", "type", "constraints", "defaultValue" ], "title": "LLMSettingDefinition", "type": "object" }, "title": "settings", "type": "array" }, "suggestedReplacement": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM suggested as a replacement for this one when it is retired.", "title": "suggestedReplacement" }, "supportedCustomModelLLMValidations": { "anyOf": [ { "items": { "description": "The metadata describing a validated custom model LLM.", "properties": { "id": { "description": "The ID of the custom model LLM validation.", "title": "id", "type": "string" }, "name": { "description": "The name of the custom model LLM validation.", "title": "name", "type": "string" } }, "required": [ "id", "name" ], "title": "SupportedCustomModelLLMValidation", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The supported custom model validations if applicable for this LLM ID.", "title": "supportedCustomModelLLMValidations" }, "supportedLanguages": { "description": "The languages supported by the LLM.", "title": "supportedLanguages", "type": "string" }, "vendor": { "description": "The vendor of the LLM.", "title": "vendor", "type": "string" } }, "required": [ "id", "name", "description", "vendor", "provider", "creator", "license", "supportedLanguages", "settings", "documentationLink", "referenceLinks", "dateAdded", "isDeprecated", "isActive", "isSupportedForModeration", "availableLitellmEndpoints" ], "title": "LanguageModelDefinitionResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | LanguageModelDefinitionResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List playgrounds Operation path: `GET /api/v2/genai/playgrounds/` Authentication requirements: `BearerAuth` List playgrounds. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | any | false | Only retrieve the playgrounds linked to the specified use cases. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | search | query | any | false | Only retrieve the playgrounds with names matching the search query. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name", "description", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "savedLLMBlueprintsCount". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | ### Example responses > 200 Response ``` { "description": "Paginated list of playgrounds.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single playground.", "properties": { "creationDate": { "description": "The creation date of the playground (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this playground.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the playground.", "title": "description", "type": "string" }, "id": { "description": "The ID of the playground.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this playground.", "title": "lastUpdateUserId", "type": "string" }, "llmBlueprintsCount": { "description": "The number of LLM blueprints in this playground.", "title": "llmBlueprintsCount", "type": "integer" }, "name": { "description": "The name of the playground.", "title": "name", "type": "string" }, "playgroundType": { "description": "Playground type.", "enum": [ "rag", "agentic" ], "title": "PlaygroundType", "type": "string" }, "savedLLMBlueprintsCount": { "description": "The number of saved LLM blueprints in this playground.", "title": "savedLLMBlueprintsCount", "type": "integer" }, "useCaseId": { "description": "The ID of the use case this playground belongs to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this playground.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "savedLLMBlueprintsCount", "llmBlueprintsCount", "userName" ], "title": "PlaygroundResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListPlaygroundsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListPlaygroundsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create playground Operation path: `POST /api/v2/genai/playgrounds/` Authentication requirements: `BearerAuth` Create a new playground. ### Body parameter ``` { "description": "The body of the Create Playground request.", "properties": { "copyInsights": { "anyOf": [ { "description": "The body of the Copy Insights request.", "properties": { "sourcePlaygroundId": { "description": "The ID of the existing playground from where to copy insights.", "title": "sourcePlaygroundId", "type": "string" }, "withEvaluationDatasets": { "default": false, "description": "If `true` also copies source playground evaluation datasets to target playground.", "title": "withEvaluationDatasets", "type": "boolean" } }, "required": [ "sourcePlaygroundId" ], "title": "CopyInsightsRequest", "type": "object" }, { "type": "null" } ], "description": "If present, copy insights from source playground to the created playground." }, "description": { "description": "The description of the playground.", "maxLength": 5000, "title": "description", "type": "string" }, "name": { "description": "The name of the playground.", "maxLength": 5000, "title": "name", "type": "string" }, "playgroundType": { "description": "Playground type.", "enum": [ "rag", "agentic" ], "title": "PlaygroundType", "type": "string" }, "useCaseId": { "description": "The ID of the use case to link the playground to.", "title": "useCaseId", "type": "string" } }, "required": [ "useCaseId", "name", "description" ], "title": "CreatePlaygroundRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreatePlaygroundRequest | true | none | ### Example responses > 201 Response ``` { "description": "API response object for a single playground.", "properties": { "creationDate": { "description": "The creation date of the playground (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this playground.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the playground.", "title": "description", "type": "string" }, "id": { "description": "The ID of the playground.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this playground.", "title": "lastUpdateUserId", "type": "string" }, "llmBlueprintsCount": { "description": "The number of LLM blueprints in this playground.", "title": "llmBlueprintsCount", "type": "integer" }, "name": { "description": "The name of the playground.", "title": "name", "type": "string" }, "playgroundType": { "description": "Playground type.", "enum": [ "rag", "agentic" ], "title": "PlaygroundType", "type": "string" }, "savedLLMBlueprintsCount": { "description": "The number of saved LLM blueprints in this playground.", "title": "savedLLMBlueprintsCount", "type": "integer" }, "useCaseId": { "description": "The ID of the use case this playground belongs to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this playground.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "savedLLMBlueprintsCount", "llmBlueprintsCount", "userName" ], "title": "PlaygroundResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successful Response | PlaygroundResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete playground by playground ID Operation path: `DELETE /api/v2/genai/playgrounds/{playgroundId}/` Authentication requirements: `BearerAuth` Delete an existing playground. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve playground by playground ID Operation path: `GET /api/v2/genai/playgrounds/{playgroundId}/` Authentication requirements: `BearerAuth` Retrieve an existing playground. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single playground.", "properties": { "creationDate": { "description": "The creation date of the playground (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this playground.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the playground.", "title": "description", "type": "string" }, "id": { "description": "The ID of the playground.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this playground.", "title": "lastUpdateUserId", "type": "string" }, "llmBlueprintsCount": { "description": "The number of LLM blueprints in this playground.", "title": "llmBlueprintsCount", "type": "integer" }, "name": { "description": "The name of the playground.", "title": "name", "type": "string" }, "playgroundType": { "description": "Playground type.", "enum": [ "rag", "agentic" ], "title": "PlaygroundType", "type": "string" }, "savedLLMBlueprintsCount": { "description": "The number of saved LLM blueprints in this playground.", "title": "savedLLMBlueprintsCount", "type": "integer" }, "useCaseId": { "description": "The ID of the use case this playground belongs to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this playground.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "savedLLMBlueprintsCount", "llmBlueprintsCount", "userName" ], "title": "PlaygroundResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | PlaygroundResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit playground by playground ID Operation path: `PATCH /api/v2/genai/playgrounds/{playgroundId}/` Authentication requirements: `BearerAuth` Edit an existing playground. ### Body parameter ``` { "description": "The body of the Edit Playground request.", "properties": { "description": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, updates the playground description to this value.", "title": "description" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the playground to this value.", "title": "name" } }, "title": "EditPlaygroundRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to edit. | | body | body | EditPlaygroundRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single playground.", "properties": { "creationDate": { "description": "The creation date of the playground (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this playground.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the playground.", "title": "description", "type": "string" }, "id": { "description": "The ID of the playground.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this playground.", "title": "lastUpdateUserId", "type": "string" }, "llmBlueprintsCount": { "description": "The number of LLM blueprints in this playground.", "title": "llmBlueprintsCount", "type": "integer" }, "name": { "description": "The name of the playground.", "title": "name", "type": "string" }, "playgroundType": { "description": "Playground type.", "enum": [ "rag", "agentic" ], "title": "PlaygroundType", "type": "string" }, "savedLLMBlueprintsCount": { "description": "The number of saved LLM blueprints in this playground.", "title": "savedLLMBlueprintsCount", "type": "integer" }, "useCaseId": { "description": "The ID of the use case this playground belongs to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this playground.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "savedLLMBlueprintsCount", "llmBlueprintsCount", "userName" ], "title": "PlaygroundResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | PlaygroundResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete the NeMo configuration by playground ID Operation path: `DELETE /api/v2/genai/playgrounds/{playgroundId}/nemoConfiguration/` Authentication requirements: `BearerAuth` Delete the NeMo configuration for the playground. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to retrieve NeMo configuration for. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrive the NeMo configuration by playground ID Operation path: `GET /api/v2/genai/playgrounds/{playgroundId}/nemoConfiguration/` Authentication requirements: `BearerAuth` Retrieve the NeMo configuration for the playground. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to retrieve NeMo configuration for. | ### Example responses > 200 Response ``` { "description": "API response object for a playground Nemo configuration.", "properties": { "blockedTermsFileContents": { "description": "The contents of the blocked terms file.", "title": "blockedTermsFileContents", "type": "string" }, "promptLlmConfiguration": { "anyOf": [ { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" }, { "type": "null" } ], "description": "The LLM configuration for the prompt pipeline." }, "promptModerationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration for the prompt pipeline." }, "promptPipelineFiles": { "anyOf": [ { "description": "Files used to setup a NeMo pipeline.", "properties": { "actionsFileContents": { "description": "Contents of actions.py file.", "title": "actionsFileContents", "type": "string" }, "configYamlFileContents": { "description": "Contents of config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "Contents of flows.co file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "Contents of prompts.yaml file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "actionsFileContents", "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPipelineFiles", "type": "object" }, { "type": "null" } ], "description": "The prompt pipeline files for this configuration." }, "promptPipelineMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name for the NeMo Prompt pipeline metric.", "title": "promptPipelineMetricName" }, "promptPipelineTemplateId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Guard template to be used for the prompt pipeline, defines actions.py.", "title": "promptPipelineTemplateId" }, "responseLlmConfiguration": { "anyOf": [ { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" }, { "type": "null" } ], "description": "The LLM configuration for the response pipeline." }, "responseModerationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration for the response pipeline." }, "responsePipelineFiles": { "anyOf": [ { "description": "Files used to setup a NeMo pipeline.", "properties": { "actionsFileContents": { "description": "Contents of actions.py file.", "title": "actionsFileContents", "type": "string" }, "configYamlFileContents": { "description": "Contents of config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "Contents of flows.co file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "Contents of prompts.yaml file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "actionsFileContents", "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPipelineFiles", "type": "object" }, { "type": "null" } ], "description": "The response pipeline files for this configuration." }, "responsePipelineMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name for the NeMo Response pipeline metric.", "title": "responsePipelineMetricName" }, "responsePipelineTemplateId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Guard template to be used for the response pipeline, defines actions.py.", "title": "responsePipelineTemplateId" } }, "required": [ "promptPipelineMetricName", "promptPipelineFiles", "promptPipelineTemplateId", "promptLlmConfiguration", "responsePipelineMetricName", "responsePipelineFiles", "responsePipelineTemplateId", "responseLlmConfiguration", "blockedTermsFileContents" ], "title": "NemoConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | NemoConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Update/insert the NeMo configuration by playground ID Operation path: `POST /api/v2/genai/playgrounds/{playgroundId}/nemoConfiguration/` Authentication requirements: `BearerAuth` Update/insert the NeMo configuration for the playground. ### Body parameter ``` { "description": "The body of the Nemo Configuration upsert request.", "properties": { "blockedTermsFileContents": { "description": "The contents of the blocked terms file.", "title": "blockedTermsFileContents", "type": "string" }, "promptLlmConfiguration": { "anyOf": [ { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" }, { "type": "null" } ], "description": "The LLM configuration for the prompt pipeline." }, "promptModerationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration for the prompt pipeline." }, "promptPipelineFiles": { "anyOf": [ { "description": "All of the files except for the actions.py file.", "properties": { "configYamlFileContents": { "description": "The contents of the config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "The contents of the flow definition file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "The contents of the prompts file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPiplineFilesRequest", "type": "object" }, { "type": "null" } ], "description": "The prompt pipeline files for this configuration." }, "promptPipelineMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name for the NeMo Prompt pipeline metric.", "title": "promptPipelineMetricName" }, "promptPipelineTemplateId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Guard template to be used for the prompt pipeline, defines actions.py.", "title": "promptPipelineTemplateId" }, "responseLlmConfiguration": { "anyOf": [ { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" }, { "type": "null" } ], "description": "The LLM configuration for the response pipeline." }, "responseModerationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration for the response pipeline." }, "responsePipelineFiles": { "anyOf": [ { "description": "All of the files except for the actions.py file.", "properties": { "configYamlFileContents": { "description": "The contents of the config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "The contents of the flow definition file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "The contents of the prompts file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPiplineFilesRequest", "type": "object" }, { "type": "null" } ], "description": "The response pipeline files for this configuration." }, "responsePipelineMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name for the NeMo Response pipeline metric.", "title": "responsePipelineMetricName" }, "responsePipelineTemplateId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Guard template to be used for the response pipeline, defines actions.py.", "title": "responsePipelineTemplateId" } }, "required": [ "blockedTermsFileContents" ], "title": "NemoConfigurationUpsertRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to retrieve NeMo configuration for. | | body | body | NemoConfigurationUpsertRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for a playground Nemo configuration.", "properties": { "blockedTermsFileContents": { "description": "The contents of the blocked terms file.", "title": "blockedTermsFileContents", "type": "string" }, "promptLlmConfiguration": { "anyOf": [ { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" }, { "type": "null" } ], "description": "The LLM configuration for the prompt pipeline." }, "promptModerationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration for the prompt pipeline." }, "promptPipelineFiles": { "anyOf": [ { "description": "Files used to setup a NeMo pipeline.", "properties": { "actionsFileContents": { "description": "Contents of actions.py file.", "title": "actionsFileContents", "type": "string" }, "configYamlFileContents": { "description": "Contents of config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "Contents of flows.co file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "Contents of prompts.yaml file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "actionsFileContents", "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPipelineFiles", "type": "object" }, { "type": "null" } ], "description": "The prompt pipeline files for this configuration." }, "promptPipelineMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name for the NeMo Prompt pipeline metric.", "title": "promptPipelineMetricName" }, "promptPipelineTemplateId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Guard template to be used for the prompt pipeline, defines actions.py.", "title": "promptPipelineTemplateId" }, "responseLlmConfiguration": { "anyOf": [ { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" }, { "type": "null" } ], "description": "The LLM configuration for the response pipeline." }, "responseModerationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration for the response pipeline." }, "responsePipelineFiles": { "anyOf": [ { "description": "Files used to setup a NeMo pipeline.", "properties": { "actionsFileContents": { "description": "Contents of actions.py file.", "title": "actionsFileContents", "type": "string" }, "configYamlFileContents": { "description": "Contents of config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "Contents of flows.co file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "Contents of prompts.yaml file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "actionsFileContents", "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPipelineFiles", "type": "object" }, { "type": "null" } ], "description": "The response pipeline files for this configuration." }, "responsePipelineMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name for the NeMo Response pipeline metric.", "title": "responsePipelineMetricName" }, "responsePipelineTemplateId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Guard template to be used for the response pipeline, defines actions.py.", "title": "responsePipelineTemplateId" } }, "required": [ "promptPipelineMetricName", "promptPipelineFiles", "promptPipelineTemplateId", "promptLlmConfiguration", "responsePipelineMetricName", "responsePipelineFiles", "responsePipelineTemplateId", "responseLlmConfiguration", "blockedTermsFileContents" ], "title": "NemoConfigurationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successful Response | NemoConfigurationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete a NeMo metric by playground ID Operation path: `DELETE /api/v2/genai/playgrounds/{playgroundId}/nemoConfiguration/{metricId}/` Authentication requirements: `BearerAuth` Delete a NeMo metric for the playground. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to retrieve NeMo configuration for. | | metricId | path | string | true | The ID of the metric to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List OOTB metric configurations by playground ID Operation path: `GET /api/v2/genai/playgrounds/{playgroundId}/ootbMetricConfigurations/` Authentication requirements: `BearerAuth` List OOTB metric configurations for the selected playground. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground. | ### Example responses > 200 Response ``` { "description": "The response for the \"OOTB metric configurations\" retrieve request.", "properties": { "ootbMetricConfigurations": { "description": "The list of OOTB metric configurations to use.", "items": { "description": "API response object for a single OOTB metric.", "properties": { "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "customOotbMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The custom OOTB metric name to be associated with the OOTB metric.", "title": "customOotbMetricName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the OOTB metric configuration.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "isAgentic": { "default": false, "description": "Whether the OOTB metric configuration is specific to agentic workflows.", "title": "isAgentic", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM to use for `correctness` and `faithfulness` metrics.", "title": "llmId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the OOTB metric." }, "ootbMetricConfigurationId": { "description": "The ID of OOTB metric.", "title": "ootbMetricConfigurationId", "type": "string" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ], "description": "The name of the OOTB metric.", "title": "ootbMetricName" } }, "required": [ "ootbMetricName", "ootbMetricConfigurationId", "executionStatus" ], "title": "OOTBMetricConfigurationResponse", "type": "object" }, "title": "ootbMetricConfigurations", "type": "array" } }, "required": [ "ootbMetricConfigurations" ], "title": "OOTBMetricConfigurationsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OOTB metric configurations list | OOTBMetricConfigurationsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create OOTB metric configuration by playground ID Operation path: `POST /api/v2/genai/playgrounds/{playgroundId}/ootbMetricConfigurations/` Authentication requirements: `BearerAuth` Create a new OOTB metric configuration. ### Body parameter ``` { "description": "The body of the \"Create OOTB metric configurations\" request.", "properties": { "ootbMetricConfigurations": { "description": "The list of OOTB metrics to use.", "items": { "description": "API request object for a single OOTB metric configuration.", "properties": { "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "customOotbMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The custom OOTB metric name to be associated with the OOTB metric. Will default to OOTB metric name if not defined.", "title": "customOotbMetricName" }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM to use for `correctness` and `faithfulness` metrics.", "title": "llmId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the OOTB metric." }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" } }, "required": [ "ootbMetricName" ], "title": "OOTBMetricConfigurationRequest", "type": "object" }, "title": "ootbMetricConfigurations", "type": "array" } }, "required": [ "ootbMetricConfigurations" ], "title": "CreateOOTBMetricConfigurationsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground associated with the OOTB metric configuration. | | body | body | CreateOOTBMetricConfigurationsRequest | true | none | ### Example responses > 201 Response ``` { "description": "The response for the \"OOTB metric configurations\" retrieve request.", "properties": { "ootbMetricConfigurations": { "description": "The list of OOTB metric configurations to use.", "items": { "description": "API response object for a single OOTB metric.", "properties": { "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "customOotbMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The custom OOTB metric name to be associated with the OOTB metric.", "title": "customOotbMetricName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the OOTB metric configuration.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "isAgentic": { "default": false, "description": "Whether the OOTB metric configuration is specific to agentic workflows.", "title": "isAgentic", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM to use for `correctness` and `faithfulness` metrics.", "title": "llmId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the OOTB metric." }, "ootbMetricConfigurationId": { "description": "The ID of OOTB metric.", "title": "ootbMetricConfigurationId", "type": "string" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ], "description": "The name of the OOTB metric.", "title": "ootbMetricName" } }, "required": [ "ootbMetricName", "ootbMetricConfigurationId", "executionStatus" ], "title": "OOTBMetricConfigurationResponse", "type": "object" }, "title": "ootbMetricConfigurations", "type": "array" } }, "required": [ "ootbMetricConfigurations" ], "title": "OOTBMetricConfigurationsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | OOTB metric configuration created successfully | OOTBMetricConfigurationsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List supported insights by playground ID Operation path: `GET /api/v2/genai/playgrounds/{playgroundId}/supportedInsights/` Authentication requirements: `BearerAuth` List the supported insights. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to retrieve supported insights from. | | withAggregationTypesOnly | query | boolean | false | If true, only include the insights containing aggregation types. The default is false. | | productionOnly | query | boolean | false | If true, only include the insights that can be transferred to production. The default is false. | | completedOnly | query | boolean | false | If true, only include the insights that are completed. The default is false. | | llmBlueprintIds | query | any | false | The IDs of the LLM blueprints to check for VDB related metrics support. | ### Example responses > 200 Response ``` { "description": "Response model for supported insights.", "properties": { "insightsConfiguration": { "description": "The list of supported insights configurations.", "items": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "title": "insightsConfiguration", "type": "array" } }, "required": [ "insightsConfiguration" ], "title": "SupportedInsightsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Supported insights configuration successfully retrieved. | SupportedInsightsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve playground prompt traces by playground ID Operation path: `GET /api/v2/genai/playgrounds/{playgroundId}/trace/` Authentication requirements: `BearerAuth` Retrieve the playground prompt traces for an existing playground. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to retrieve prompt traces for. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | ### Example responses > 200 Response ``` { "description": "Paginated list of playground prompt traces.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single playground prompt trace.", "properties": { "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat associated with the trace.", "title": "chatId" }, "chatName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the chat associated with the trace.", "title": "chatName" }, "chatPromptId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat prompt associated with the trace.", "title": "chatPromptId" }, "citations": { "anyOf": [ { "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The list of relevant vector database citations (in case of using a vector database).", "title": "citations" }, "comparisonPromptId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the comparison prompts associated with the trace.", "title": "comparisonPromptId" }, "confidenceScores": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion for the prompt associated with the trace.", "title": "confidenceScores" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset associated with the trace.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the trace.", "title": "llmBlueprintId", "type": "string" }, "llmBlueprintName": { "description": "The name of the LLM blueprint associated with the trace.", "title": "llmBlueprintName", "type": "string" }, "llmLicense": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The usage license information for the LLM associated with the trace.", "title": "llmLicense" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM associated with the trace.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "The settings of the LLM associated with the trace.", "title": "llmSettings" }, "llmVendor": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The vendor of the LLM associated with the trace.", "title": "llmVendor" }, "promptType": { "anyOf": [ { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, { "type": "null" } ], "description": "The type of prompt, chat history awair or one time prompt." }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the prompt associated with the trace." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The completion text of the prompt associated with the trace.", "title": "resultText" }, "text": { "description": "The text of the prompt associated with the trace.", "title": "text", "type": "string" }, "timestamp": { "description": "The timestamp of the trace (ISO 8601 formatted).", "format": "date-time", "title": "timestamp", "type": "string" }, "useCaseId": { "description": "The ID of the use case associated with the trace.", "title": "useCaseId", "type": "string" }, "user": { "description": "DataRobot application user.", "properties": { "id": { "description": "The ID of the user.", "title": "id", "type": "string" }, "name": { "description": "The name of the user.", "title": "name", "type": "string" }, "userhash": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Gravatar hash for user avatar.", "title": "userhash" } }, "required": [ "id", "name" ], "title": "DataRobotUser", "type": "object" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database associated with the trace.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "The settings of the vector database associated with the trace." }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The warning message for the prompt associated with the trace.", "title": "warning" } }, "required": [ "timestamp", "user", "chatPromptId", "comparisonPromptId", "useCaseId", "llmBlueprintId", "llmBlueprintName", "llmName", "llmVendor", "llmLicense", "llmSettings", "chatName", "chatId", "vectorDatabaseId", "vectorDatabaseSettings", "citations", "resultMetadata", "resultText", "confidenceScores", "text", "executionStatus", "promptType", "evaluationDatasetConfigurationId", "warning" ], "title": "PromptTraceResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListTracesResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListTracesResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve playground prompt traces metadata by playground ID Operation path: `GET /api/v2/genai/playgrounds/{playgroundId}/trace/metadata/` Authentication requirements: `BearerAuth` Retrieve the prompt traces metadata for an existing playground. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to retrieve prompt traces for. | ### Example responses > 200 Response ``` { "description": "API response for prompt trace metadata retrieval request.", "properties": { "chats": { "description": "The list of combined chat and comparison prompt chats available in trace.", "items": { "description": "Reference to a chat or comparison chat available in a trace.", "properties": { "id": { "description": "The ID of the chat associated with the trace.", "title": "id", "type": "string" }, "name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name of the chat associated with the trace.", "title": "name" } }, "required": [ "id", "name" ], "title": "TraceChat", "type": "object" }, "title": "chats", "type": "array" }, "users": { "description": "The list of unique users available in the trace response.", "items": { "description": "DataRobot application user.", "properties": { "id": { "description": "The ID of the user.", "title": "id", "type": "string" }, "name": { "description": "The name of the user.", "title": "name", "type": "string" }, "userhash": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Gravatar hash for user avatar.", "title": "userhash" } }, "required": [ "id", "name" ], "title": "DataRobotUser", "type": "object" }, "title": "users", "type": "array" } }, "required": [ "users", "chats" ], "title": "TraceMetadataResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | TraceMetadataResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create playground prompt trace dataset by playground ID Operation path: `POST /api/v2/genai/playgrounds/{playgroundId}/traceDatasets/` Authentication requirements: `BearerAuth` Export prompt traces for an existing playground as a Data Registry dataset. ### Body parameter ``` { "description": "The body of the Create Playground request.", "properties": { "excludeTracesWithWarnings": { "default": true, "description": "Whether to exclude traces with warnings.", "title": "excludeTracesWithWarnings", "type": "boolean" } }, "title": "CreateTraceDatasetRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | path | string | true | The ID of the playground to export the prompt traces for. | | body | body | CreateTraceDatasetRequest | false | none | ### Example responses > 202 Response ``` { "description": "API response for prompt traces export request.", "properties": { "aiCatalogId": { "description": "The Data Registry dataset ID.", "title": "aiCatalogId", "type": "string" } }, "required": [ "aiCatalogId" ], "title": "TraceDatasetResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successful Response | TraceDatasetResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Copy supported insights by target playground ID Operation path: `PUT /api/v2/genai/playgrounds/{targetPlaygroundId}/supportedInsights/{sourcePlaygroundId}/` Authentication requirements: `BearerAuth` Copy supported insights between playgrounds. ### Body parameter ``` { "description": "The body of the \"Copy supported insights\" request.", "properties": { "addToExisting": { "default": true, "description": "If `true`, adds copied insights to existing insights in the target playground. If `false`, replaces insights in the target playground with copied insights.", "title": "addToExisting", "type": "boolean" }, "withEvaluationDatasets": { "default": false, "description": "If `true` also copies source playground evaluation datasets to target playground.", "title": "withEvaluationDatasets", "type": "boolean" } }, "title": "CopySupportedInsightsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | sourcePlaygroundId | path | string | true | The ID of the source playground to copy supported insights from. | | targetPlaygroundId | path | string | true | The ID of the target playground to copy supported insights to. | | body | body | CopySupportedInsightsRequest | true | none | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Supported insights configuration successfully copied. | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve job status by status ID Operation path: `GET /api/v2/genai/status/{statusId}/` Authentication requirements: `BearerAuth` Retrieve the execution status of a GenAI worker job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string(uuid) | true | The ID of the job to retrieve the status for. | ### Example responses > 200 Response ``` { "anyOf": [ { "description": "API response object for job execution status.", "properties": { "code": { "description": "Possible job error codes. This enum exists for consistency with the DataRobot Status API.", "enum": [ 0, 1 ], "title": "JobErrorCode", "type": "integer" }, "created": { "description": "The creation date of the job (ISO 8601 formatted).", "format": "date-time", "title": "created", "type": "string" }, "description": { "default": "", "description": "The description associated with the job.", "title": "description", "type": "string" }, "message": { "default": "", "description": "The error message associated with the job.", "title": "message", "type": "string" }, "status": { "description": "Possible job states. Values match the DataRobot Status API.", "enum": [ "INITIALIZED", "RUNNING", "COMPLETED", "ERROR", "ABORTED", "EXPIRED" ], "title": "JobExecutionState", "type": "string" }, "statusId": { "description": "The ID of the job.", "format": "uuid", "title": "statusId", "type": "string" }, "statusType": { "default": "", "description": "The type of the status object.", "title": "statusType", "type": "string" } }, "required": [ "statusId", "status", "created", "code" ], "title": "StatusResponse", "type": "object" }, { "type": "string" } ], "title": "Response Get Status Status Statusid Get" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | Inline | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ### Response Schema Status Code 200 Response Get Status Status Statusid Get | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | Response Get Status Status Statusid Get | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | StatusResponse | false | | API response object for job execution status. | | »» code | JobErrorCode | true | | Possible job error codes. This enum exists for consistency with the DataRobot Status API. | | »» created | string(date-time) | true | | The creation date of the job (ISO 8601 formatted). | | »» description | string | false | | The description associated with the job. | | »» message | string | false | | The error message associated with the job. | | »» status | JobExecutionState | true | | Possible job states. Values match the DataRobot Status API. | | »» statusId | string(uuid) | true | | The ID of the job. | | »» statusType | string | false | | The type of the status object. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | code | [0, 1] | | status | [INITIALIZED, RUNNING, COMPLETED, ERROR, ABORTED, EXPIRED] | ## [DEPRECATED] Retrieve LLM API call count Operation path: `GET /api/v2/genai/userLimits/llmApiCalls/` Authentication requirements: `BearerAuth` DEPRECATED: Retrieve count of LLM Api calls made by user. This endpoint is deprecated and will be removed in future releases. Please use the appropriate LLM Gateway stats endpoint instead: /api/v2/genai/llmgw/stats/llmApiCalls/ . ### Example responses > 200 Response ``` { "description": "API response object for retrieving user limit counters.", "properties": { "counter": { "description": "The number of completed operations which count towards the usage limit.", "title": "counter", "type": "integer" } }, "required": [ "counter" ], "title": "RetrieveUserLimitCounterResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | RetrieveUserLimitCounterResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve vector database creation count Operation path: `GET /api/v2/genai/userLimits/vectorDatabases/` Authentication requirements: `BearerAuth` Retrieve the number of vector databases the user has created which count towards the usage limit. ### Example responses > 200 Response ``` { "description": "API response object for retrieving user limit counters.", "properties": { "counter": { "description": "The number of completed operations which count towards the usage limit.", "title": "counter", "type": "integer" } }, "required": [ "counter" ], "title": "RetrieveUserLimitCounterResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | RetrieveUserLimitCounterResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | # Schemas ## AggregationType ``` { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" } ``` AggregationType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | AggregationType | string | false | | The type of the metric aggregation. | ### Enumerated Values | Property | Value | | --- | --- | | AggregationType | [average, percentYes, classPercentCoverage, ngramImportance, guardConditionPercentYes] | ## ArgumentMatchMode ``` { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } ``` ArgumentMatchMode ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ArgumentMatchMode | string | false | | The different modes for comparing the arguments of tool calls. | ### Enumerated Values | Property | Value | | --- | --- | | ArgumentMatchMode | [exact_match, ignore_arguments] | ## AvailableLiteLLMEndpoints ``` { "description": "The supported endpoints for the LLM.", "properties": { "supportsChatCompletions": { "description": "Whether the chat completions endpoint is supported.", "title": "supportsChatCompletions", "type": "boolean" }, "supportsResponses": { "description": "Whether the responses endpoint is supported.", "title": "supportsResponses", "type": "boolean" } }, "required": [ "supportsChatCompletions", "supportsResponses" ], "title": "AvailableLiteLLMEndpoints", "type": "object" } ``` AvailableLiteLLMEndpoints ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | supportsChatCompletions | boolean | true | | Whether the chat completions endpoint is supported. | | supportsResponses | boolean | true | | Whether the responses endpoint is supported. | ## BooleanSettingConstraints ``` { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" } ``` BooleanSettingConstraints ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | false | | The data type of the setting. | ## Citation ``` { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" } ``` Citation ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkId | any | false | | The ID of the chunk in the vector database index. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metadata | any | false | | LangChain Document metadata information holder. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | page | any | false | | The source page number where the citation was found. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | similarityScore | any | false | | The similarity score between the citation and the user prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | source | any | true | | The source of the citation (e.g., a filename in the original dataset). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | startIndex | any | false | | The chunk's start character index in the source document. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | text | string | true | | The text of the citation. | ## CommonLLMSettings ``` { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" } ``` CommonLLMSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxCompletionLength | any | false | | Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | systemPrompt | any | false | | System prompt guides the style of the LLM response. It is a "universal" prompt, prepended to all individual prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CopyInsightsRequest ``` { "description": "The body of the Copy Insights request.", "properties": { "sourcePlaygroundId": { "description": "The ID of the existing playground from where to copy insights.", "title": "sourcePlaygroundId", "type": "string" }, "withEvaluationDatasets": { "default": false, "description": "If `true` also copies source playground evaluation datasets to target playground.", "title": "withEvaluationDatasets", "type": "boolean" } }, "required": [ "sourcePlaygroundId" ], "title": "CopyInsightsRequest", "type": "object" } ``` CopyInsightsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sourcePlaygroundId | string | true | | The ID of the existing playground from where to copy insights. | | withEvaluationDatasets | boolean | false | | If true also copies source playground evaluation datasets to target playground. | ## CopySupportedInsightsRequest ``` { "description": "The body of the \"Copy supported insights\" request.", "properties": { "addToExisting": { "default": true, "description": "If `true`, adds copied insights to existing insights in the target playground. If `false`, replaces insights in the target playground with copied insights.", "title": "addToExisting", "type": "boolean" }, "withEvaluationDatasets": { "default": false, "description": "If `true` also copies source playground evaluation datasets to target playground.", "title": "withEvaluationDatasets", "type": "boolean" } }, "title": "CopySupportedInsightsRequest", "type": "object" } ``` CopySupportedInsightsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addToExisting | boolean | false | | If true, adds copied insights to existing insights in the target playground. If false, replaces insights in the target playground with copied insights. | | withEvaluationDatasets | boolean | false | | If true also copies source playground evaluation datasets to target playground. | ## CreateCustomModelLLMValidationRequest ``` { "description": "The body of the \"Validate custom model\" request.", "properties": { "chatModelId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API.", "title": "chatModelId" }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model used in the deployment.", "title": "modelId" }, "name": { "default": "Untitled", "description": "The name to use for the validated custom model.", "maxLength": 5000, "title": "name", "type": "string" }, "predictionTimeout": { "default": 300, "description": "The timeout in seconds for the prediction when validating a custom model. Defaults to 300.", "maximum": 600, "minimum": 1, "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input. This value is used to call the Prediction API of the deployment. For LLM deployments that support the OpenAI chat completion API, it is recommended to specify `chatModelId` instead.", "title": "promptColumnName" }, "targetColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output. This value is used to call the Prediction API of the deployment. For LLM deployments that support the OpenAI chat completion API, it is recommended to specify `chatModelId` instead.", "title": "targetColumnName" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case to associate with the validated custom model.", "title": "useCaseId" } }, "required": [ "deploymentId" ], "title": "CreateCustomModelLLMValidationRequest", "type": "object" } ``` CreateCustomModelLLMValidationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatModelId | any | false | | The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the custom model deployment. | | modelId | any | false | | The ID of the model used in the deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | maxLength: 5000 | The name to use for the validated custom model. | | predictionTimeout | integer | false | maximum: 600minimum: 1 | The timeout in seconds for the prediction when validating a custom model. Defaults to 300. | | promptColumnName | any | false | | The name of the column the custom model uses for prompt text input. This value is used to call the Prediction API of the deployment. For LLM deployments that support the OpenAI chat completion API, it is recommended to specify chatModelId instead. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetColumnName | any | false | | The name of the column the custom model uses for prediction output. This value is used to call the Prediction API of the deployment. For LLM deployments that support the OpenAI chat completion API, it is recommended to specify chatModelId instead. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseId | any | false | | The ID of the use case to associate with the validated custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CreateCustomModelVersionRequest ``` { "description": "The body of the \"Create custom model version\" request.", "properties": { "defaultPredictionServerId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of a prediction server for the new deployment to use. Only used if this LLM blueprint's vector database is not already deployed. Cannot be used with predictionEnvironmentId.", "title": "defaultPredictionServerId" }, "insightsConfiguration": { "default": [], "description": "The configuration of insights to transfer to production.", "items": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "minItems": 0, "title": "insightsConfiguration", "type": "array" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint to use for the custom model version.", "title": "llmBlueprintId", "type": "string" }, "llmTestConfigurationIds": { "default": [], "description": "The IDs of the LLM test configurations to execute.", "items": { "type": "string" }, "maxItems": 100, "title": "llmTestConfigurationIds", "type": "array" }, "predictionEnvironmentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the prediction environment for a new vector database deployment to use. Only used if this LLM blueprint's vector database is not already deployed. Cannot be used with defaultPredictionServerId.", "title": "predictionEnvironmentId" }, "promptColumnName": { "default": "promptText", "description": "The name of the column to use for prompt text input in the custom model.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "targetColumnName": { "default": "resultText", "description": "The name of the column to use for prediction output in the custom model.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "vectorDatabaseResources": { "anyOf": [ { "description": "The structure that describes resource settings for a custom model created from buzok.", "properties": { "maximumMemory": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum memory that can be allocated to the custom model.", "title": "maximumMemory" }, "networkEgressPolicy": { "default": "Public", "description": "Network egress policy for the custom model. Can be either Public or None.", "maxLength": 5000, "title": "networkEgressPolicy", "type": "string" }, "replicas": { "default": 1, "description": "A fixed number of replicas that will be created for the custom model.", "title": "replicas", "type": "integer" }, "resourceBundleId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "An identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "title": "resourceBundleId" } }, "title": "CustomModelResourcesRequest", "type": "object" }, { "type": "null" } ], "description": "The resources that the vector database custom model will be provisioned with. Only used if this LLM blueprint's vector database is not already deployed." } }, "required": [ "llmBlueprintId" ], "title": "CreateCustomModelVersionRequest", "type": "object" } ``` CreateCustomModelVersionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultPredictionServerId | any | false | | The ID of a prediction server for the new deployment to use. Only used if this LLM blueprint's vector database is not already deployed. Cannot be used with predictionEnvironmentId. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | insightsConfiguration | [InsightsConfigurationWithAdditionalData] | false | | The configuration of insights to transfer to production. | | llmBlueprintId | string | true | | The ID of the LLM blueprint to use for the custom model version. | | llmTestConfigurationIds | [string] | false | maxItems: 100 | The IDs of the LLM test configurations to execute. | | predictionEnvironmentId | any | false | | The ID of the prediction environment for a new vector database deployment to use. Only used if this LLM blueprint's vector database is not already deployed. Cannot be used with defaultPredictionServerId. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | string | false | maxLength: 5000 | The name of the column to use for prompt text input in the custom model. | | targetColumnName | string | false | maxLength: 5000 | The name of the column to use for prediction output in the custom model. | | vectorDatabaseResources | any | false | | The resources that the vector database custom model will be provisioned with. Only used if this LLM blueprint's vector database is not already deployed. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelResourcesRequest | false | | The structure that describes resource settings for a custom model created from buzok. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CreateCustomModelVersionResponse ``` { "description": "API response object for the \"Create custom model version\" request.", "properties": { "customModelId": { "description": "The ID of the created custom model.", "title": "customModelId", "type": "string" } }, "required": [ "customModelId" ], "title": "CreateCustomModelVersionResponse", "type": "object" } ``` CreateCustomModelVersionResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string | true | | The ID of the created custom model. | ## CreateFromChatPromptRequest ``` { "description": "The body of the Create LLM Blueprint from a ChatPrompt request.", "properties": { "chatPromptId": { "description": "The ID of an existing chat prompt to copy the LLM and vector database settings from.", "title": "chatPromptId", "type": "string" }, "description": { "default": "", "description": "The description of the new LLM blueprint.", "maxLength": 5000, "title": "description", "type": "string" }, "name": { "description": "The name of the new LLM blueprint.", "maxLength": 5000, "title": "name", "type": "string" } }, "required": [ "name", "chatPromptId" ], "title": "CreateFromChatPromptRequest", "type": "object" } ``` CreateFromChatPromptRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatPromptId | string | true | | The ID of an existing chat prompt to copy the LLM and vector database settings from. | | description | string | false | maxLength: 5000 | The description of the new LLM blueprint. | | name | string | true | maxLength: 5000 | The name of the new LLM blueprint. | ## CreateFromLLMBlueprintRequest ``` { "description": "The body of the Create LLM Blueprint from an LLM Blueprint request.", "properties": { "description": { "default": "", "description": "The description of the new LLM blueprint.", "maxLength": 5000, "title": "description", "type": "string" }, "llmBlueprintId": { "description": "The ID of an existing LLM blueprint to copy the LLM and vector database settings from.", "title": "llmBlueprintId", "type": "string" }, "name": { "description": "The name of the new LLM blueprint.", "maxLength": 5000, "title": "name", "type": "string" } }, "required": [ "name", "llmBlueprintId" ], "title": "CreateFromLLMBlueprintRequest", "type": "object" } ``` CreateFromLLMBlueprintRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 5000 | The description of the new LLM blueprint. | | llmBlueprintId | string | true | | The ID of an existing LLM blueprint to copy the LLM and vector database settings from. | | name | string | true | maxLength: 5000 | The name of the new LLM blueprint. | ## CreateLLMBlueprintRequest ``` { "description": "The body of the Create LLM Blueprint request.", "properties": { "description": { "default": "", "description": "The description of the LLM blueprint.", "maxLength": 5000, "title": "description", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLM blueprint.", "maxLength": 5000, "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground to link this LLM blueprint to.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Specifies the vector database retrieval settings in LLM blueprint API requests.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 128, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettingsRequest", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "playgroundId", "name" ], "title": "CreateLLMBlueprintRequest", "type": "object" } ``` CreateLLMBlueprintRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 5000 | The description of the LLM blueprint. | | llmId | any | false | | The ID of the LLM selected for this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmSettings | any | false | | A key/value dictionary of LLM settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CommonLLMSettings | false | | The settings that are available for all non-custom LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelLLMSettings | false | | The settings that are available for custom model LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelChatLLMSettings | false | | The settings that are available for custom model LLMs used via chat completion interface. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 5000 | The name of the LLM blueprint. | | playgroundId | string | true | | The ID of the playground to link this LLM blueprint to. | | promptType | PromptType | false | | Specifies whether chat history is submitted as context to the user prompt. | | vectorDatabaseId | any | false | | The ID of the vector database linked to this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseSettings | any | false | | A key/value dictionary of vector database settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseSettingsRequest | false | | Specifies the vector database retrieval settings in LLM blueprint API requests. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CreateOOTBMetricConfigurationsRequest ``` { "description": "The body of the \"Create OOTB metric configurations\" request.", "properties": { "ootbMetricConfigurations": { "description": "The list of OOTB metrics to use.", "items": { "description": "API request object for a single OOTB metric configuration.", "properties": { "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "customOotbMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The custom OOTB metric name to be associated with the OOTB metric. Will default to OOTB metric name if not defined.", "title": "customOotbMetricName" }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM to use for `correctness` and `faithfulness` metrics.", "title": "llmId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the OOTB metric." }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" } }, "required": [ "ootbMetricName" ], "title": "OOTBMetricConfigurationRequest", "type": "object" }, "title": "ootbMetricConfigurations", "type": "array" } }, "required": [ "ootbMetricConfigurations" ], "title": "CreateOOTBMetricConfigurationsRequest", "type": "object" } ``` CreateOOTBMetricConfigurationsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricConfigurations | [OOTBMetricConfigurationRequest] | true | | The list of OOTB metrics to use. | ## CreatePlaygroundRequest ``` { "description": "The body of the Create Playground request.", "properties": { "copyInsights": { "anyOf": [ { "description": "The body of the Copy Insights request.", "properties": { "sourcePlaygroundId": { "description": "The ID of the existing playground from where to copy insights.", "title": "sourcePlaygroundId", "type": "string" }, "withEvaluationDatasets": { "default": false, "description": "If `true` also copies source playground evaluation datasets to target playground.", "title": "withEvaluationDatasets", "type": "boolean" } }, "required": [ "sourcePlaygroundId" ], "title": "CopyInsightsRequest", "type": "object" }, { "type": "null" } ], "description": "If present, copy insights from source playground to the created playground." }, "description": { "description": "The description of the playground.", "maxLength": 5000, "title": "description", "type": "string" }, "name": { "description": "The name of the playground.", "maxLength": 5000, "title": "name", "type": "string" }, "playgroundType": { "description": "Playground type.", "enum": [ "rag", "agentic" ], "title": "PlaygroundType", "type": "string" }, "useCaseId": { "description": "The ID of the use case to link the playground to.", "title": "useCaseId", "type": "string" } }, "required": [ "useCaseId", "name", "description" ], "title": "CreatePlaygroundRequest", "type": "object" } ``` CreatePlaygroundRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | copyInsights | any | false | | If present, copy insights from source playground to the created playground. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CopyInsightsRequest | false | | The body of the Copy Insights request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | maxLength: 5000 | The description of the playground. | | name | string | true | maxLength: 5000 | The name of the playground. | | playgroundType | PlaygroundType | false | | The type of the playground. | | useCaseId | string | true | | The ID of the use case to link the playground to. | ## CreateTraceDatasetRequest ``` { "description": "The body of the Create Playground request.", "properties": { "excludeTracesWithWarnings": { "default": true, "description": "Whether to exclude traces with warnings.", "title": "excludeTracesWithWarnings", "type": "boolean" } }, "title": "CreateTraceDatasetRequest", "type": "object" } ``` CreateTraceDatasetRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | excludeTracesWithWarnings | boolean | false | | Whether to exclude traces with warnings. | ## CustomModelChatLLMSettings ``` { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" } ``` CustomModelChatLLMSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string | true | | The ID of the custom model used via chat completion interface. | | customModelVersionId | any | false | | The ID of the custom model version used via chat completion interface. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | systemPrompt | any | false | | System prompt guides the style of the LLM response. It is a "universal" prompt, prepended to all individual prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CustomModelGuard ``` { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" } ``` CustomModelGuard ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the guard. | | nemoEvaluatorType | any | false | | NeMo Evaluator type of the guard. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelGuardNemoEvaluatorType | false | | NeMo evaluator type as used in the moderation_config.yaml file of the custom model. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbType | any | false | | Out of the box type of the guard. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelGuardOOTBType | false | | OOTB type as used in the moderation_config.yaml file of the custom model. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | CustomModelGuardStage | true | | Stage on which the guard gets applied. | | type | CustomModelGuardType | true | | Type of the guard. | ## CustomModelGuardNemoEvaluatorType ``` { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" } ``` CustomModelGuardNemoEvaluatorType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelGuardNemoEvaluatorType | string | false | | NeMo evaluator type as used in the moderation_config.yaml file of the custom model. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelGuardNemoEvaluatorType | [llm_judge, context_relevance, response_groundedness, topic_adherence, agent_goal_accuracy, response_relevancy, faithfulness] | ## CustomModelGuardOOTBType ``` { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" } ``` CustomModelGuardOOTBType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelGuardOOTBType | string | false | | OOTB type as used in the moderation_config.yaml file of the custom model. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelGuardOOTBType | [token_count, rouge_1, faithfulness, agent_goal_accuracy, custom_metric, cost, task_adherence] | ## CustomModelGuardStage ``` { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" } ``` CustomModelGuardStage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelGuardStage | string | false | | Guard stage as used in the moderation_config.yaml file of the custom model. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelGuardStage | [prompt, response] | ## CustomModelGuardType ``` { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } ``` CustomModelGuardType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelGuardType | string | false | | Guard type as used in the moderation_config.yaml file of the custom model. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelGuardType | [ootb, model, nemo_guardrails, nemo_evaluator] | ## CustomModelLLMSettings ``` { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" } ``` CustomModelLLMSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalLlmContextSize | any | false | | The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 128000minimum: 128 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | systemPrompt | any | false | | System prompt guides the style of the LLM response. It is a "universal" prompt, prepended to all individual prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationId | any | false | | The validation ID of the custom model LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CustomModelLLMValidationResponse ``` { "description": "API response object for a single custom model LLM validation.", "properties": { "chatModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment.", "title": "chatModelId" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelLLMValidationResponse", "type": "object" } ``` CustomModelLLMValidationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatModelId | any | false | | The model ID to specify when calling the OpenAI chat completion API of the deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the custom model validation (ISO 8601 formatted). | | deploymentAccessData | any | true | | The parameters used for accessing the deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentAccessData | false | | Add authorization_header to avoid breaking change to API. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the custom model deployment. | | deploymentName | any | false | | The name of the custom model deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the validation error (if the validation failed). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model validation. | | modelId | string | true | | The ID of the model used in the deployment. | | name | string | true | | The name of the validated custom model. | | predictionTimeout | integer | true | | The timeout in seconds for the prediction API used in this custom model validation. | | promptColumnName | string | true | | The name of the column the custom model uses for prompt text input. | | targetColumnName | string | true | | The name of the column the custom model uses for prediction output. | | tenantId | string(uuid4) | true | | The ID of the tenant the custom model validation belongs to. | | useCaseId | any | true | | The ID of the use case associated with the validated custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | userId | string | true | | The ID of the user that created this custom model validation. | | userName | any | false | | The name of the user that created this custom model validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationStatus | CustomModelValidationStatus | true | | The status of the custom model validation. | ## CustomModelResourcesRequest ``` { "description": "The structure that describes resource settings for a custom model created from buzok.", "properties": { "maximumMemory": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum memory that can be allocated to the custom model.", "title": "maximumMemory" }, "networkEgressPolicy": { "default": "Public", "description": "Network egress policy for the custom model. Can be either Public or None.", "maxLength": 5000, "title": "networkEgressPolicy", "type": "string" }, "replicas": { "default": 1, "description": "A fixed number of replicas that will be created for the custom model.", "title": "replicas", "type": "integer" }, "resourceBundleId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "An identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "title": "resourceBundleId" } }, "title": "CustomModelResourcesRequest", "type": "object" } ``` CustomModelResourcesRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maximumMemory | any | false | | The maximum memory that can be allocated to the custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | networkEgressPolicy | string | false | maxLength: 5000 | Network egress policy for the custom model. Can be either Public or None. | | replicas | integer | false | | A fixed number of replicas that will be created for the custom model. | | resourceBundleId | any | false | | An identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CustomModelValidationStatus ``` { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } ``` CustomModelValidationStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelValidationStatus | string | false | | Status of custom model validation. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelValidationStatus | [TESTING, PASSED, FAILED] | ## DataRobotUser ``` { "description": "DataRobot application user.", "properties": { "id": { "description": "The ID of the user.", "title": "id", "type": "string" }, "name": { "description": "The name of the user.", "title": "name", "type": "string" }, "userhash": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Gravatar hash for user avatar.", "title": "userhash" } }, "required": [ "id", "name" ], "title": "DataRobotUser", "type": "object" } ``` DataRobotUser ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the user. | | name | string | true | | The name of the user. | | userhash | any | false | | Gravatar hash for user avatar. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## DeploymentAccessData ``` { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" } ``` DeploymentAccessData ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authorizationHeader | string | false | | The Authorization header to use for the deployment. | | chatApiUrl | any | false | | The URL of the deployment's chat API. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datarobotKey | any | true | | The server key associated with the prediction API. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputType | DeploymentInputType | true | | The format of the input data. | | modelType | SupportedDeploymentType | true | | The type of the target output the deployment produces. | | predictionApiUrl | string | true | | The URL of the deployment's prediction API. | ## DeploymentInputType ``` { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" } ``` DeploymentInputType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | DeploymentInputType | string | false | | The format of the input data submitted to a DataRobot deployment. | ### Enumerated Values | Property | Value | | --- | --- | | DeploymentInputType | [CSV, JSON] | ## EditCustomModelValidationRequest ``` { "description": "The body of the \"Edit custom model validation\" request.", "properties": { "chatModelId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API.", "title": "chatModelId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the deployment associated with this custom model validation.", "title": "deploymentId" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the model associated with this custom model validation.", "title": "modelId" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the custom model validation to this value.", "title": "name" }, "predictionTimeout": { "anyOf": [ { "maximum": 600, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "If specified, sets the timeout in seconds for the prediction when validating a custom model.", "title": "predictionTimeout" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment.", "title": "promptColumnName" }, "targetColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment.", "title": "targetColumnName" } }, "title": "EditCustomModelValidationRequest", "type": "object" } ``` EditCustomModelValidationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatModelId | any | false | | The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | any | false | | If specified, changes the ID of the deployment associated with this custom model validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | any | false | | If specified, changes the ID of the model associated with this custom model validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | any | false | | If specified, renames the custom model validation to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionTimeout | any | false | | If specified, sets the timeout in seconds for the prediction when validating a custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 600minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | any | false | | If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetColumnName | any | false | | If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## EditPlaygroundRequest ``` { "description": "The body of the Edit Playground request.", "properties": { "description": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, updates the playground description to this value.", "title": "description" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the playground to this value.", "title": "name" } }, "title": "EditPlaygroundRequest", "type": "object" } ``` EditPlaygroundRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | any | false | | If specified, updates the playground description to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | any | false | | If specified, renames the playground to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ExecutionStatus ``` { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" } ``` ExecutionStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ExecutionStatus | string | false | | Job and entity execution status. | ### Enumerated Values | Property | Value | | --- | --- | | ExecutionStatus | [NEW, RUNNING, COMPLETED, REQUIRES_USER_INPUT, SKIPPED, ERROR] | ## ExtraMetricSettings ``` { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" } ``` ExtraMetricSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | toolCallAccuracy | any | false | | Extra settings for the tool call accuracy metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ToolCallAccuracySettings | false | | Additional arguments for the tool call accuracy metric. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## FeedbackResult ``` { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" } ``` FeedbackResult ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | negativeUserIds | [string] | false | | The list of user IDs whose feedback is negative. | | positiveUserIds | [string] | false | | The list of user IDs whose feedback is positive. | ## FloatSettingConstraints ``` { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" } ``` FloatSettingConstraints ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxValue | any | false | | The maximum value of the setting (inclusive). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | minValue | any | false | | The minimum value of the setting (inclusive). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | false | | The data type of the setting. | ## GuardCondition ``` { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" } ``` GuardCondition ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparand | any | true | | The comparand(s) used in the guard condition. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparator | GuardConditionComparator | true | | The comparator used in the guard condition. | ## GuardConditionComparator ``` { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } ``` GuardConditionComparator ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | GuardConditionComparator | string | false | | The comparator used in a guard condition. | ### Enumerated Values | Property | Value | | --- | --- | | GuardConditionComparator | [greaterThan, lessThan, equals, notEquals, is, isNot, matches, doesNotMatch, contains, doesNotContain] | ## HTTPValidationErrorResponse ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` HTTPValidationErrorResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | detail | [ValidationError] | false | | none | ## InsightErrorResolution ``` { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" } ``` InsightErrorResolution ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | InsightErrorResolution | string | false | | Error type linking directly to the field name that is related to the error. | ### Enumerated Values | Property | Value | | --- | --- | | InsightErrorResolution | [ootbMetricName, intervention, guardCondition, sidecarOverall, sidecarRevalidate, sidecarDeploymentId, sidecarInputColumnName, sidecarOutputColumnName, promptPipelineFiles, promptPipelineTemplateId, responsePipelineFiles, responsePipelineTemplateId] | ## InsightTypes ``` { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" } ``` InsightTypes ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | InsightTypes | string | false | | The type of insight. | ### Enumerated Values | Property | Value | | --- | --- | | InsightTypes | [Reference, Quality metric, Operational metric, Evaluation deployment, Custom metric, Nemo] | ## InsightsConfigurationWithAdditionalData ``` { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" } ``` InsightsConfigurationWithAdditionalData ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregationTypes | any | true | | The aggregation types used in the insights configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [AggregationType] | false | | [The type of the metric aggregation.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | costConfigurationId | any | false | | The ID of the cost configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customMetricId | any | false | | The ID of the custom metric (if using a custom metric). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelGuard | any | false | | Guard as configured in the custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelGuard | false | | Details of a guard as defined for the custom model. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelLLMValidationId | any | false | | The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | any | false | | The ID of the custom model deployment associated with the insight. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorResolution | any | false | | The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | any | false | | The ID of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | any | false | | The execution status of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExecutionStatus | false | | Job and entity execution status. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | extraMetricSettings | any | false | | Extra settings for the metric that do not reference other entities. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExtraMetricSettings | false | | Extra settings for the metric that do not reference other entities. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | insightName | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the insight. | | insightType | any | false | | The type of the insight. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | InsightTypes | false | | The type of insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isTransferable | boolean | false | | Indicates if insight can be transferred to production. | | llmId | any | false | | The LLM ID for OOTB metrics that use LLMs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmIsActive | any | false | | Whether the LLM is active. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmIsDeprecated | any | false | | Whether the LLM is deprecated and will be removed in a future release. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | any | false | | The ID of the model associated with deploymentId. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelPackageRegisteredModelId | any | false | | The ID of the registered model package associated with deploymentId. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | moderationConfiguration | any | false | | The moderation configuration associated with the insight configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | nemoMetricId | any | false | | The ID of the Nemo configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricId | any | false | | The ID of the ootb metric (if using an ootb metric). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricName | any | false | | The OOTB metric name. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBMetricInsightNames | false | | The Out-Of-The-Box metric name that can be used in the playground. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBAgenticMetricInsightNames | false | | The Out-Of-The-Box metric name that can be used in an Agentic playground. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OTELMetricInsightNames | false | | Metrics that can only be calculated using OTEL Trace/metric data. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | resultUnit | any | false | | The unit of measurement associated with the insight result. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | MetricUnit | false | | The unit of measurement associated with a metric. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sidecarModelMetricMetadata | any | false | | The metadata of the sidecar model metric (if using a sidecar model metric). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SidecarModelMetricMetadata | false | | The metadata of a sidecar model metric. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sidecarModelMetricValidationId | any | false | | The ID of the sidecar model metric validation (if using a sidecar model metric). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | any | false | | The stage (prompt or response) where insight is calculated at. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PipelineStage | false | | Enum that describes at which stage the metric may be calculated. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## IntegerSettingConstraints ``` { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" } ``` IntegerSettingConstraints ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxValue | any | false | | The maximum value of the setting (inclusive). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | minValue | any | false | | The minimum value of the setting (inclusive). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | false | | The data type of the setting. | ## Intervention ``` { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } ``` Intervention ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | ModerationAction | true | | The intervention strategy. | | message | string | true | minLength: 1minLength: 1 | The intervention message to replace the prediction when a guard condition is satisfied. | ## JobErrorCode ``` { "description": "Possible job error codes. This enum exists for consistency with the DataRobot Status API.", "enum": [ 0, 1 ], "title": "JobErrorCode", "type": "integer" } ``` JobErrorCode ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | JobErrorCode | integer | false | | Possible job error codes. This enum exists for consistency with the DataRobot Status API. | ### Enumerated Values | Property | Value | | --- | --- | | JobErrorCode | [0, 1] | ## JobExecutionState ``` { "description": "Possible job states. Values match the DataRobot Status API.", "enum": [ "INITIALIZED", "RUNNING", "COMPLETED", "ERROR", "ABORTED", "EXPIRED" ], "title": "JobExecutionState", "type": "string" } ``` JobExecutionState ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | JobExecutionState | string | false | | Possible job states. Values match the DataRobot Status API. | ### Enumerated Values | Property | Value | | --- | --- | | JobExecutionState | [INITIALIZED, RUNNING, COMPLETED, ERROR, ABORTED, EXPIRED] | ## LLMBlueprintResponse ``` { "description": "API response object for a single LLM blueprint.", "properties": { "creationDate": { "description": "The creation date of the LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this LLM blueprint.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM blueprint.", "title": "creationUserName", "type": "string" }, "customModelLLMErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorMessage" }, "customModelLLMErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorResolution" }, "customModelLLMValidationStatus": { "anyOf": [ { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" }, { "type": "null" } ], "description": "The validation status of the custom model LLM (if using a custom model LLM)." }, "description": { "description": "The description of the LLM blueprint.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM blueprint.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM specified in this blueprint is active in the current environment.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM specified in this blueprint is deprecated.", "title": "isDeprecated", "type": "boolean" }, "isSaved": { "description": "If false, then this LLM blueprint is a draft and its settings can be changed. If true, then its settings are frozen and cannot be changed anymore.", "title": "isSaved", "type": "boolean" }, "isStarred": { "description": "Specifies whether this LLM blueprint is starred.", "title": "isStarred", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this LLM blueprint.", "title": "lastUpdateUserId", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM used by this LLM blueprint.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLM blueprint.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground this LLM blueprint belongs to.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "vectorDatabaseErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the vector database associated with this LLM.", "title": "vectorDatabaseErrorMessage" }, "vectorDatabaseErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database associated with this LLM blueprint.", "title": "vectorDatabaseErrorResolution" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family associated with this LLM blueprint.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the vector database associated with this LLM blueprint.", "title": "vectorDatabaseName" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." }, "vectorDatabaseStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The creation status of the vector database associated with this LLM blueprint." } }, "required": [ "id", "name", "description", "isSaved", "isStarred", "promptType", "playgroundId", "llmName", "creationDate", "creationUserId", "creationUserName", "lastUpdateDate", "lastUpdateUserId", "vectorDatabaseName", "vectorDatabaseStatus", "vectorDatabaseErrorMessage", "vectorDatabaseErrorResolution", "customModelLLMValidationStatus", "customModelLLMErrorMessage", "customModelLLMErrorResolution", "vectorDatabaseFamilyId", "isActive", "isDeprecated" ], "title": "LLMBlueprintResponse", "type": "object" } ``` LLMBlueprintResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the LLM blueprint (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created this LLM blueprint. | | creationUserName | string | true | | The name of the user who created this LLM blueprint. | | customModelLLMErrorMessage | any | true | | The error message of the custom model LLM (if using a custom model LLM). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelLLMErrorResolution | any | true | | The suggested error resolution for the custom model LLM (if using a custom model LLM). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelLLMValidationStatus | any | true | | The validation status of the custom model LLM (if using a custom model LLM). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelValidationStatus | false | | Status of custom model validation. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | The description of the LLM blueprint. | | id | string | true | | The ID of the LLM blueprint. | | isActive | boolean | true | | Whether the LLM specified in this blueprint is active in the current environment. | | isDeprecated | boolean | true | | Whether the LLM specified in this blueprint is deprecated. | | isSaved | boolean | true | | If false, then this LLM blueprint is a draft and its settings can be changed. If true, then its settings are frozen and cannot be changed anymore. | | isStarred | boolean | true | | Specifies whether this LLM blueprint is starred. | | lastUpdateDate | string(date-time) | true | | The date of the most recent update of this LLM blueprint (ISO 8601 formatted). | | lastUpdateUserId | string | true | | The ID of the user that made the most recent update to this LLM blueprint. | | llmId | any | false | | The ID of the LLM selected for this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmName | any | true | | The name of the LLM used by this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmSettings | any | false | | A key/value dictionary of LLM settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CommonLLMSettings | false | | The settings that are available for all non-custom LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelLLMSettings | false | | The settings that are available for custom model LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelChatLLMSettings | false | | The settings that are available for custom model LLMs used via chat completion interface. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the LLM blueprint. | | playgroundId | string | true | | The ID of the playground this LLM blueprint belongs to. | | promptType | PromptType | true | | Specifies whether the chat history is submitted as context to the user prompt. | | retirementDate | any | false | | When the LLM is expected to be retired and no longer available for submitting new prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string(date) | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseErrorMessage | any | true | | The error message of the vector database associated with this LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseErrorResolution | any | true | | The suggested error resolution for the vector database associated with this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseFamilyId | any | true | | The ID of the vector database family associated with this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | any | false | | The ID of the vector database linked to this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseName | any | true | | The name of the vector database associated with this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseSettings | any | false | | A key/value dictionary of vector database settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseSettings | false | | Vector database retrieval settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseStatus | any | true | | The creation status of the vector database associated with this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExecutionStatus | false | | Job and entity execution status. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## LLMReference ``` { "description": "A reference link for an LLM.", "properties": { "name": { "description": "Description of the reference document.", "title": "name", "type": "string" }, "url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "URL of the reference document.", "title": "url" } }, "required": [ "name", "url" ], "title": "LLMReference", "type": "object" } ``` LLMReference ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Description of the reference document. | | url | any | true | | URL of the reference document. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## LLMSettingDefinition ``` { "description": "Metadata describing a single setting.", "properties": { "constraints": { "anyOf": [ { "discriminator": { "mapping": { "boolean": "#/components/schemas/BooleanSettingConstraints", "float": "#/components/schemas/FloatSettingConstraints", "integer": "#/components/schemas/IntegerSettingConstraints", "list": "#/components/schemas/ListParameterConstraints", "object_id": "#/components/schemas/ObjectIdSettingConstraints", "string": "#/components/schemas/StringSettingConstraints" }, "propertyName": "type" }, "oneOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "description": "Available constraints for list parameters.", "properties": { "elementConstraints": { "anyOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "type": "null" } ], "description": "Constraints to apply to each element.", "title": "elementConstraints" }, "elementType": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the list (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the list (inclusive).", "title": "minLength" }, "type": { "const": "list", "default": "list", "description": "The data type of the parameter.", "title": "type", "type": "string" } }, "required": [ "elementType" ], "title": "ListParameterConstraints", "type": "object" } ] }, { "type": "null" } ], "description": "The constraints for the LLM setting values.", "title": "constraints" }, "defaultValue": { "anyOf": [ { "type": "boolean" }, { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The default value of the LLM setting.", "title": "defaultValue" }, "description": { "description": "The description of the LLM setting.", "title": "description", "type": "string" }, "format": { "anyOf": [ { "description": "Supported formats for settings.", "enum": [ "multiline" ], "title": "SettingFormat", "type": "string" }, { "type": "null" } ], "description": "The expected format of the value of the LLM setting." }, "id": { "description": "The ID of the LLM setting.", "title": "id", "type": "string" }, "isNullable": { "default": true, "description": "Whether the setting allows null values (default: true).", "title": "isNullable", "type": "boolean" }, "name": { "description": "The name of the LLM setting.", "title": "name", "type": "string" }, "type": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" } }, "required": [ "id", "name", "description", "type", "constraints", "defaultValue" ], "title": "LLMSettingDefinition", "type": "object" } ``` LLMSettingDefinition ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | constraints | any | true | | The constraints for the LLM setting values. | anyOf - discriminator: type | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | IntegerSettingConstraints | false | | Available constraints for integer settings. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | FloatSettingConstraints | false | | Available constraints for float settings. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | StringSettingConstraints | false | | Available constraints for string settings. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | BooleanSettingConstraints | false | | Available constraints for boolean settings. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | ObjectIdSettingConstraints | false | | Available constraints for ObjectId settings. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | ListParameterConstraints | false | | Available constraints for list parameters. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultValue | any | true | | The default value of the LLM setting. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | The description of the LLM setting. | | format | any | false | | The expected format of the value of the LLM setting. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SettingFormat | false | | Supported formats for settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the LLM setting. | | isNullable | boolean | false | | Whether the setting allows null values (default: true). | | name | string | true | | The name of the LLM setting. | | type | SettingType | true | | The data type of the LLM setting. | ## LLMType ``` { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" } ``` LLMType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | LLMType | string | false | | LLM provider type. | ### Enumerated Values | Property | Value | | --- | --- | | LLMType | [openAi, azureOpenAi] | ## LanguageModelDefinitionResponse ``` { "additionalProperties": true, "description": "The metadata that defines an LLM.", "properties": { "availableLitellmEndpoints": { "description": "The supported endpoints for the LLM.", "properties": { "supportsChatCompletions": { "description": "Whether the chat completions endpoint is supported.", "title": "supportsChatCompletions", "type": "boolean" }, "supportsResponses": { "description": "Whether the responses endpoint is supported.", "title": "supportsResponses", "type": "boolean" } }, "required": [ "supportsChatCompletions", "supportsResponses" ], "title": "AvailableLiteLLMEndpoints", "type": "object" }, "contextSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the LLM's context, measured in tokens. `null` if unknown.", "title": "contextSize" }, "creator": { "description": "The company that originally created the LLM.", "title": "creator", "type": "string" }, "dateAdded": { "description": "The date the LLM was added to the GenAI playground.", "format": "date", "title": "dateAdded", "type": "string" }, "description": { "description": "The details about the LLM.", "title": "description", "type": "string" }, "documentationLink": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The link to the vendor documentation for the LLM.", "title": "documentationLink" }, "id": { "description": "The ID of the LLM.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM is active.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "isDeprecated", "type": "boolean" }, "isMetered": { "default": true, "description": "Whether the LLM usage is metered.", "title": "isMetered", "type": "boolean" }, "isSupportedForModeration": { "description": "Whether the LLM is supported for moderation.", "title": "isSupportedForModeration", "type": "boolean" }, "license": { "description": "The usage license information for the LLM.", "title": "license", "type": "string" }, "name": { "description": "The name of the LLM.", "title": "name", "type": "string" }, "provider": { "description": "The party that provides access to the LLM.", "title": "provider", "type": "string" }, "referenceLinks": { "description": "The references for the LLM.", "items": { "description": "A reference link for an LLM.", "properties": { "name": { "description": "Description of the reference document.", "title": "name", "type": "string" }, "url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "URL of the reference document.", "title": "url" } }, "required": [ "name", "url" ], "title": "LLMReference", "type": "object" }, "title": "referenceLinks", "type": "array" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "settings": { "description": "The settings supported by the LLM.", "items": { "description": "Metadata describing a single setting.", "properties": { "constraints": { "anyOf": [ { "discriminator": { "mapping": { "boolean": "#/components/schemas/BooleanSettingConstraints", "float": "#/components/schemas/FloatSettingConstraints", "integer": "#/components/schemas/IntegerSettingConstraints", "list": "#/components/schemas/ListParameterConstraints", "object_id": "#/components/schemas/ObjectIdSettingConstraints", "string": "#/components/schemas/StringSettingConstraints" }, "propertyName": "type" }, "oneOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "description": "Available constraints for list parameters.", "properties": { "elementConstraints": { "anyOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "type": "null" } ], "description": "Constraints to apply to each element.", "title": "elementConstraints" }, "elementType": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the list (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the list (inclusive).", "title": "minLength" }, "type": { "const": "list", "default": "list", "description": "The data type of the parameter.", "title": "type", "type": "string" } }, "required": [ "elementType" ], "title": "ListParameterConstraints", "type": "object" } ] }, { "type": "null" } ], "description": "The constraints for the LLM setting values.", "title": "constraints" }, "defaultValue": { "anyOf": [ { "type": "boolean" }, { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The default value of the LLM setting.", "title": "defaultValue" }, "description": { "description": "The description of the LLM setting.", "title": "description", "type": "string" }, "format": { "anyOf": [ { "description": "Supported formats for settings.", "enum": [ "multiline" ], "title": "SettingFormat", "type": "string" }, { "type": "null" } ], "description": "The expected format of the value of the LLM setting." }, "id": { "description": "The ID of the LLM setting.", "title": "id", "type": "string" }, "isNullable": { "default": true, "description": "Whether the setting allows null values (default: true).", "title": "isNullable", "type": "boolean" }, "name": { "description": "The name of the LLM setting.", "title": "name", "type": "string" }, "type": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" } }, "required": [ "id", "name", "description", "type", "constraints", "defaultValue" ], "title": "LLMSettingDefinition", "type": "object" }, "title": "settings", "type": "array" }, "suggestedReplacement": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM suggested as a replacement for this one when it is retired.", "title": "suggestedReplacement" }, "supportedCustomModelLLMValidations": { "anyOf": [ { "items": { "description": "The metadata describing a validated custom model LLM.", "properties": { "id": { "description": "The ID of the custom model LLM validation.", "title": "id", "type": "string" }, "name": { "description": "The name of the custom model LLM validation.", "title": "name", "type": "string" } }, "required": [ "id", "name" ], "title": "SupportedCustomModelLLMValidation", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The supported custom model validations if applicable for this LLM ID.", "title": "supportedCustomModelLLMValidations" }, "supportedLanguages": { "description": "The languages supported by the LLM.", "title": "supportedLanguages", "type": "string" }, "vendor": { "description": "The vendor of the LLM.", "title": "vendor", "type": "string" } }, "required": [ "id", "name", "description", "vendor", "provider", "creator", "license", "supportedLanguages", "settings", "documentationLink", "referenceLinks", "dateAdded", "isDeprecated", "isActive", "isSupportedForModeration", "availableLitellmEndpoints" ], "title": "LanguageModelDefinitionResponse", "type": "object" } ``` LanguageModelDefinitionResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | availableLitellmEndpoints | AvailableLiteLLMEndpoints | true | | Supported endpoints for this LLM (i.e. usable LiteLLM endpoints). Includes supports_chat_completions and supports_responses keys. | | contextSize | any | false | | The size of the LLM's context, measured in tokens. null if unknown. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creator | string | true | | The company that originally created the LLM. | | dateAdded | string(date) | true | | The date the LLM was added to the GenAI playground. | | description | string | true | | The details about the LLM. | | documentationLink | any | true | | The link to the vendor documentation for the LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the LLM. | | isActive | boolean | true | | Whether the LLM is active. | | isDeprecated | boolean | true | | Whether the LLM is deprecated and will be removed in a future release. | | isMetered | boolean | false | | Whether the LLM usage is metered. | | isSupportedForModeration | boolean | true | | Whether the LLM is supported for moderation. | | license | string | true | | The usage license information for the LLM. | | name | string | true | | The name of the LLM. | | provider | string | true | | The party that provides access to the LLM. | | referenceLinks | [LLMReference] | true | | The references for the LLM. | | retirementDate | any | false | | When the LLM is expected to be retired and no longer available for submitting new prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string(date) | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | settings | [LLMSettingDefinition] | true | | The settings supported by the LLM. | | suggestedReplacement | any | false | | The ID of the LLM suggested as a replacement for this one when it is retired. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | supportedCustomModelLLMValidations | any | false | | The supported custom model validations if applicable for this LLM ID. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [SupportedCustomModelLLMValidation] | false | | [The metadata describing a validated custom model LLM.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | supportedLanguages | string | true | | The languages supported by the LLM. | | vendor | string | true | | The vendor of the LLM. | ## ListCustomModelLLMValidationsResponse ``` { "description": "Paginated list of custom model LLM validations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single custom model LLM validation.", "properties": { "chatModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment.", "title": "chatModelId" }, "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelLLMValidationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListCustomModelLLMValidationsResponse", "type": "object" } ``` ListCustomModelLLMValidationsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [CustomModelLLMValidationResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListCustomModelValidationSortQueryParam ``` { "description": "Sort order values for listing custom model validations.", "enum": [ "name", "-name", "deploymentName", "-deploymentName", "userName", "-userName", "creationDate", "-creationDate" ], "title": "ListCustomModelValidationSortQueryParam", "type": "string" } ``` ListCustomModelValidationSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ListCustomModelValidationSortQueryParam | string | false | | Sort order values for listing custom model validations. | ### Enumerated Values | Property | Value | | --- | --- | | ListCustomModelValidationSortQueryParam | [name, -name, deploymentName, -deploymentName, userName, -userName, creationDate, -creationDate] | ## ListLLMBlueprintSortQueryParam ``` { "description": "Sort order values for listing LLM blueprints.", "enum": [ "name", "-name", "description", "-description", "creationUserId", "-creationUserId", "creationDate", "-creationDate", "lastUpdateUserId", "-lastUpdateUserId", "lastUpdateDate", "-lastUpdateDate", "llmId", "-llmId", "vectorDatabaseId", "-vectorDatabaseId" ], "title": "ListLLMBlueprintSortQueryParam", "type": "string" } ``` ListLLMBlueprintSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ListLLMBlueprintSortQueryParam | string | false | | Sort order values for listing LLM blueprints. | ### Enumerated Values | Property | Value | | --- | --- | | ListLLMBlueprintSortQueryParam | [name, -name, description, -description, creationUserId, -creationUserId, creationDate, -creationDate, lastUpdateUserId, -lastUpdateUserId, lastUpdateDate, -lastUpdateDate, llmId, -llmId, vectorDatabaseId, -vectorDatabaseId] | ## ListLLMBlueprintsResponse ``` { "description": "Paginated list of LLM blueprints.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single LLM blueprint.", "properties": { "creationDate": { "description": "The creation date of the LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this LLM blueprint.", "title": "creationUserId", "type": "string" }, "creationUserName": { "description": "The name of the user who created this LLM blueprint.", "title": "creationUserName", "type": "string" }, "customModelLLMErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorMessage" }, "customModelLLMErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the custom model LLM (if using a custom model LLM).", "title": "customModelLLMErrorResolution" }, "customModelLLMValidationStatus": { "anyOf": [ { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" }, { "type": "null" } ], "description": "The validation status of the custom model LLM (if using a custom model LLM)." }, "description": { "description": "The description of the LLM blueprint.", "title": "description", "type": "string" }, "id": { "description": "The ID of the LLM blueprint.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM specified in this blueprint is active in the current environment.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM specified in this blueprint is deprecated.", "title": "isDeprecated", "type": "boolean" }, "isSaved": { "description": "If false, then this LLM blueprint is a draft and its settings can be changed. If true, then its settings are frozen and cannot be changed anymore.", "title": "isSaved", "type": "boolean" }, "isStarred": { "description": "Specifies whether this LLM blueprint is starred.", "title": "isStarred", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this LLM blueprint (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this LLM blueprint.", "title": "lastUpdateUserId", "type": "string" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM selected for this LLM blueprint.", "title": "llmId" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM used by this LLM blueprint.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "name": { "description": "The name of the LLM blueprint.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground this LLM blueprint belongs to.", "title": "playgroundId", "type": "string" }, "promptType": { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "vectorDatabaseErrorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message of the vector database associated with this LLM.", "title": "vectorDatabaseErrorMessage" }, "vectorDatabaseErrorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database associated with this LLM blueprint.", "title": "vectorDatabaseErrorResolution" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family associated with this LLM blueprint.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the vector database associated with this LLM blueprint.", "title": "vectorDatabaseName" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." }, "vectorDatabaseStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The creation status of the vector database associated with this LLM blueprint." } }, "required": [ "id", "name", "description", "isSaved", "isStarred", "promptType", "playgroundId", "llmName", "creationDate", "creationUserId", "creationUserName", "lastUpdateDate", "lastUpdateUserId", "vectorDatabaseName", "vectorDatabaseStatus", "vectorDatabaseErrorMessage", "vectorDatabaseErrorResolution", "customModelLLMValidationStatus", "customModelLLMErrorMessage", "customModelLLMErrorResolution", "vectorDatabaseFamilyId", "isActive", "isDeprecated" ], "title": "LLMBlueprintResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMBlueprintsResponse", "type": "object" } ``` ListLLMBlueprintsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [LLMBlueprintResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListLLMsResponse ``` { "description": "Paginated list of LLMs.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "additionalProperties": true, "description": "The metadata that defines an LLM.", "properties": { "availableLitellmEndpoints": { "description": "The supported endpoints for the LLM.", "properties": { "supportsChatCompletions": { "description": "Whether the chat completions endpoint is supported.", "title": "supportsChatCompletions", "type": "boolean" }, "supportsResponses": { "description": "Whether the responses endpoint is supported.", "title": "supportsResponses", "type": "boolean" } }, "required": [ "supportsChatCompletions", "supportsResponses" ], "title": "AvailableLiteLLMEndpoints", "type": "object" }, "contextSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the LLM's context, measured in tokens. `null` if unknown.", "title": "contextSize" }, "creator": { "description": "The company that originally created the LLM.", "title": "creator", "type": "string" }, "dateAdded": { "description": "The date the LLM was added to the GenAI playground.", "format": "date", "title": "dateAdded", "type": "string" }, "description": { "description": "The details about the LLM.", "title": "description", "type": "string" }, "documentationLink": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The link to the vendor documentation for the LLM.", "title": "documentationLink" }, "id": { "description": "The ID of the LLM.", "title": "id", "type": "string" }, "isActive": { "description": "Whether the LLM is active.", "title": "isActive", "type": "boolean" }, "isDeprecated": { "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "isDeprecated", "type": "boolean" }, "isMetered": { "default": true, "description": "Whether the LLM usage is metered.", "title": "isMetered", "type": "boolean" }, "isSupportedForModeration": { "description": "Whether the LLM is supported for moderation.", "title": "isSupportedForModeration", "type": "boolean" }, "license": { "description": "The usage license information for the LLM.", "title": "license", "type": "string" }, "name": { "description": "The name of the LLM.", "title": "name", "type": "string" }, "provider": { "description": "The party that provides access to the LLM.", "title": "provider", "type": "string" }, "referenceLinks": { "description": "The references for the LLM.", "items": { "description": "A reference link for an LLM.", "properties": { "name": { "description": "Description of the reference document.", "title": "name", "type": "string" }, "url": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "URL of the reference document.", "title": "url" } }, "required": [ "name", "url" ], "title": "LLMReference", "type": "object" }, "title": "referenceLinks", "type": "array" }, "retirementDate": { "anyOf": [ { "format": "date", "type": "string" }, { "type": "null" } ], "description": "When the LLM is expected to be retired and no longer available for submitting new prompts.", "title": "retirementDate" }, "settings": { "description": "The settings supported by the LLM.", "items": { "description": "Metadata describing a single setting.", "properties": { "constraints": { "anyOf": [ { "discriminator": { "mapping": { "boolean": "#/components/schemas/BooleanSettingConstraints", "float": "#/components/schemas/FloatSettingConstraints", "integer": "#/components/schemas/IntegerSettingConstraints", "list": "#/components/schemas/ListParameterConstraints", "object_id": "#/components/schemas/ObjectIdSettingConstraints", "string": "#/components/schemas/StringSettingConstraints" }, "propertyName": "type" }, "oneOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "description": "Available constraints for list parameters.", "properties": { "elementConstraints": { "anyOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "type": "null" } ], "description": "Constraints to apply to each element.", "title": "elementConstraints" }, "elementType": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the list (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the list (inclusive).", "title": "minLength" }, "type": { "const": "list", "default": "list", "description": "The data type of the parameter.", "title": "type", "type": "string" } }, "required": [ "elementType" ], "title": "ListParameterConstraints", "type": "object" } ] }, { "type": "null" } ], "description": "The constraints for the LLM setting values.", "title": "constraints" }, "defaultValue": { "anyOf": [ { "type": "boolean" }, { "type": "integer" }, { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "The default value of the LLM setting.", "title": "defaultValue" }, "description": { "description": "The description of the LLM setting.", "title": "description", "type": "string" }, "format": { "anyOf": [ { "description": "Supported formats for settings.", "enum": [ "multiline" ], "title": "SettingFormat", "type": "string" }, { "type": "null" } ], "description": "The expected format of the value of the LLM setting." }, "id": { "description": "The ID of the LLM setting.", "title": "id", "type": "string" }, "isNullable": { "default": true, "description": "Whether the setting allows null values (default: true).", "title": "isNullable", "type": "boolean" }, "name": { "description": "The name of the LLM setting.", "title": "name", "type": "string" }, "type": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" } }, "required": [ "id", "name", "description", "type", "constraints", "defaultValue" ], "title": "LLMSettingDefinition", "type": "object" }, "title": "settings", "type": "array" }, "suggestedReplacement": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM suggested as a replacement for this one when it is retired.", "title": "suggestedReplacement" }, "supportedCustomModelLLMValidations": { "anyOf": [ { "items": { "description": "The metadata describing a validated custom model LLM.", "properties": { "id": { "description": "The ID of the custom model LLM validation.", "title": "id", "type": "string" }, "name": { "description": "The name of the custom model LLM validation.", "title": "name", "type": "string" } }, "required": [ "id", "name" ], "title": "SupportedCustomModelLLMValidation", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The supported custom model validations if applicable for this LLM ID.", "title": "supportedCustomModelLLMValidations" }, "supportedLanguages": { "description": "The languages supported by the LLM.", "title": "supportedLanguages", "type": "string" }, "vendor": { "description": "The vendor of the LLM.", "title": "vendor", "type": "string" } }, "required": [ "id", "name", "description", "vendor", "provider", "creator", "license", "supportedLanguages", "settings", "documentationLink", "referenceLinks", "dateAdded", "isDeprecated", "isActive", "isSupportedForModeration", "availableLitellmEndpoints" ], "title": "LanguageModelDefinitionResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListLLMsResponse", "type": "object" } ``` ListLLMsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [LanguageModelDefinitionResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListParameterConstraints ``` { "description": "Available constraints for list parameters.", "properties": { "elementConstraints": { "anyOf": [ { "description": "Available constraints for integer settings.", "properties": { "maxValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "integer", "default": "integer", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "IntegerSettingConstraints", "type": "object" }, { "description": "Available constraints for float settings.", "properties": { "maxValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The maximum value of the setting (inclusive).", "title": "maxValue" }, "minValue": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The minimum value of the setting (inclusive).", "title": "minValue" }, "type": { "const": "float", "default": "float", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "FloatSettingConstraints", "type": "object" }, { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" }, { "description": "Available constraints for boolean settings.", "properties": { "type": { "const": "boolean", "default": "boolean", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "BooleanSettingConstraints", "type": "object" }, { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" }, { "type": "null" } ], "description": "Constraints to apply to each element.", "title": "elementConstraints" }, "elementType": { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the list (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the list (inclusive).", "title": "minLength" }, "type": { "const": "list", "default": "list", "description": "The data type of the parameter.", "title": "type", "type": "string" } }, "required": [ "elementType" ], "title": "ListParameterConstraints", "type": "object" } ``` ListParameterConstraints ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | elementConstraints | any | false | | Constraints to apply to each element. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | IntegerSettingConstraints | false | | Available constraints for integer settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FloatSettingConstraints | false | | Available constraints for float settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | StringSettingConstraints | false | | Available constraints for string settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BooleanSettingConstraints | false | | Available constraints for boolean settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ObjectIdSettingConstraints | false | | Available constraints for ObjectId settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | elementType | SettingType | true | | The type of elements in the list. | | maxLength | any | false | | The maximum length of the list (inclusive). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | minLength | any | false | | The minimum length of the list (inclusive). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | false | | The data type of the parameter. | ## ListPlaygroundSortQueryParam ``` { "description": "Sort order values for listing playgrounds.", "enum": [ "name", "-name", "description", "-description", "creationUserId", "-creationUserId", "creationDate", "-creationDate", "lastUpdateUserId", "-lastUpdateUserId", "lastUpdateDate", "-lastUpdateDate", "savedLLMBlueprintsCount", "-savedLLMBlueprintsCount", "llmBlueprintsCount", "-llmBlueprintsCount" ], "title": "ListPlaygroundSortQueryParam", "type": "string" } ``` ListPlaygroundSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ListPlaygroundSortQueryParam | string | false | | Sort order values for listing playgrounds. | ### Enumerated Values | Property | Value | | --- | --- | | ListPlaygroundSortQueryParam | [name, -name, description, -description, creationUserId, -creationUserId, creationDate, -creationDate, lastUpdateUserId, -lastUpdateUserId, lastUpdateDate, -lastUpdateDate, savedLLMBlueprintsCount, -savedLLMBlueprintsCount, llmBlueprintsCount, -llmBlueprintsCount] | ## ListPlaygroundsResponse ``` { "description": "Paginated list of playgrounds.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single playground.", "properties": { "creationDate": { "description": "The creation date of the playground (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this playground.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the playground.", "title": "description", "type": "string" }, "id": { "description": "The ID of the playground.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this playground.", "title": "lastUpdateUserId", "type": "string" }, "llmBlueprintsCount": { "description": "The number of LLM blueprints in this playground.", "title": "llmBlueprintsCount", "type": "integer" }, "name": { "description": "The name of the playground.", "title": "name", "type": "string" }, "playgroundType": { "description": "Playground type.", "enum": [ "rag", "agentic" ], "title": "PlaygroundType", "type": "string" }, "savedLLMBlueprintsCount": { "description": "The number of saved LLM blueprints in this playground.", "title": "savedLLMBlueprintsCount", "type": "integer" }, "useCaseId": { "description": "The ID of the use case this playground belongs to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this playground.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "savedLLMBlueprintsCount", "llmBlueprintsCount", "userName" ], "title": "PlaygroundResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListPlaygroundsResponse", "type": "object" } ``` ListPlaygroundsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [PlaygroundResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListTracesResponse ``` { "description": "Paginated list of playground prompt traces.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single playground prompt trace.", "properties": { "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat associated with the trace.", "title": "chatId" }, "chatName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the chat associated with the trace.", "title": "chatName" }, "chatPromptId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat prompt associated with the trace.", "title": "chatPromptId" }, "citations": { "anyOf": [ { "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The list of relevant vector database citations (in case of using a vector database).", "title": "citations" }, "comparisonPromptId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the comparison prompts associated with the trace.", "title": "comparisonPromptId" }, "confidenceScores": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion for the prompt associated with the trace.", "title": "confidenceScores" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset associated with the trace.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the trace.", "title": "llmBlueprintId", "type": "string" }, "llmBlueprintName": { "description": "The name of the LLM blueprint associated with the trace.", "title": "llmBlueprintName", "type": "string" }, "llmLicense": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The usage license information for the LLM associated with the trace.", "title": "llmLicense" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM associated with the trace.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "The settings of the LLM associated with the trace.", "title": "llmSettings" }, "llmVendor": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The vendor of the LLM associated with the trace.", "title": "llmVendor" }, "promptType": { "anyOf": [ { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, { "type": "null" } ], "description": "The type of prompt, chat history awair or one time prompt." }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the prompt associated with the trace." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The completion text of the prompt associated with the trace.", "title": "resultText" }, "text": { "description": "The text of the prompt associated with the trace.", "title": "text", "type": "string" }, "timestamp": { "description": "The timestamp of the trace (ISO 8601 formatted).", "format": "date-time", "title": "timestamp", "type": "string" }, "useCaseId": { "description": "The ID of the use case associated with the trace.", "title": "useCaseId", "type": "string" }, "user": { "description": "DataRobot application user.", "properties": { "id": { "description": "The ID of the user.", "title": "id", "type": "string" }, "name": { "description": "The name of the user.", "title": "name", "type": "string" }, "userhash": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Gravatar hash for user avatar.", "title": "userhash" } }, "required": [ "id", "name" ], "title": "DataRobotUser", "type": "object" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database associated with the trace.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "The settings of the vector database associated with the trace." }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The warning message for the prompt associated with the trace.", "title": "warning" } }, "required": [ "timestamp", "user", "chatPromptId", "comparisonPromptId", "useCaseId", "llmBlueprintId", "llmBlueprintName", "llmName", "llmVendor", "llmLicense", "llmSettings", "chatName", "chatId", "vectorDatabaseId", "vectorDatabaseSettings", "citations", "resultMetadata", "resultText", "confidenceScores", "text", "executionStatus", "promptType", "evaluationDatasetConfigurationId", "warning" ], "title": "PromptTraceResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListTracesResponse", "type": "object" } ``` ListTracesResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [PromptTraceResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## MetricMetadata ``` { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" } ``` MetricMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | costConfigurationId | any | false | | The ID of the cost configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelGuardId | any | false | | Id of the custom model guard. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | any | false | | The ID of the custom model used for the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the metric computation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | any | false | | The ID of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | any | false | | The computation status of the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExecutionStatus | false | | Job and entity execution status. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | formattedName | any | false | | The formatted name of the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | formattedValue | any | false | | The formatted value of the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmIsDeprecated | any | false | | Whether the LLM is deprecated and will be removed in a future release. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the metric. | | nemoMetricId | any | false | | The id of the NeMo Pipeline configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricId | any | false | | The id of the OOTB metric configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sidecarModelMetricValidationId | any | false | | The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | any | false | | The stage (prompt or response) that the metric applies to. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PipelineStage | false | | Enum that describes at which stage the metric may be calculated. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | value | any | true | | The value of the metric. | ## MetricUnit ``` { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" } ``` MetricUnit ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | MetricUnit | string | false | | The unit of measurement associated with a metric. | ### Enumerated Values | Property | Value | | --- | --- | | MetricUnit | [s, ms, %] | ## ModerationAction ``` { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" } ``` ModerationAction ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ModerationAction | string | false | | The moderation strategy. | ### Enumerated Values | Property | Value | | --- | --- | | ModerationAction | [block, report, reportAndBlock] | ## ModerationConfigurationWithID ``` { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" } ``` ModerationConfigurationWithID ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | guardConditions | [GuardCondition] | true | maxItems: 1minItems: 1 | The guard conditions associated with a metric. | | intervention | Intervention | true | | The intervention specific moderation configuration. | ## ModerationConfigurationWithoutID ``` { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" } ``` ModerationConfigurationWithoutID ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | guardConditions | [GuardCondition] | true | maxItems: 1minItems: 1 | The guard conditions associated with a metric. | | intervention | Intervention | true | | The intervention specific moderation configuration. | ## NemoConfigurationResponse ``` { "description": "API response object for a playground Nemo configuration.", "properties": { "blockedTermsFileContents": { "description": "The contents of the blocked terms file.", "title": "blockedTermsFileContents", "type": "string" }, "promptLlmConfiguration": { "anyOf": [ { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" }, { "type": "null" } ], "description": "The LLM configuration for the prompt pipeline." }, "promptModerationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration for the prompt pipeline." }, "promptPipelineFiles": { "anyOf": [ { "description": "Files used to setup a NeMo pipeline.", "properties": { "actionsFileContents": { "description": "Contents of actions.py file.", "title": "actionsFileContents", "type": "string" }, "configYamlFileContents": { "description": "Contents of config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "Contents of flows.co file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "Contents of prompts.yaml file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "actionsFileContents", "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPipelineFiles", "type": "object" }, { "type": "null" } ], "description": "The prompt pipeline files for this configuration." }, "promptPipelineMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name for the NeMo Prompt pipeline metric.", "title": "promptPipelineMetricName" }, "promptPipelineTemplateId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Guard template to be used for the prompt pipeline, defines actions.py.", "title": "promptPipelineTemplateId" }, "responseLlmConfiguration": { "anyOf": [ { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" }, { "type": "null" } ], "description": "The LLM configuration for the response pipeline." }, "responseModerationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration for the response pipeline." }, "responsePipelineFiles": { "anyOf": [ { "description": "Files used to setup a NeMo pipeline.", "properties": { "actionsFileContents": { "description": "Contents of actions.py file.", "title": "actionsFileContents", "type": "string" }, "configYamlFileContents": { "description": "Contents of config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "Contents of flows.co file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "Contents of prompts.yaml file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "actionsFileContents", "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPipelineFiles", "type": "object" }, { "type": "null" } ], "description": "The response pipeline files for this configuration." }, "responsePipelineMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name for the NeMo Response pipeline metric.", "title": "responsePipelineMetricName" }, "responsePipelineTemplateId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Guard template to be used for the response pipeline, defines actions.py.", "title": "responsePipelineTemplateId" } }, "required": [ "promptPipelineMetricName", "promptPipelineFiles", "promptPipelineTemplateId", "promptLlmConfiguration", "responsePipelineMetricName", "responsePipelineFiles", "responsePipelineTemplateId", "responseLlmConfiguration", "blockedTermsFileContents" ], "title": "NemoConfigurationResponse", "type": "object" } ``` NemoConfigurationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blockedTermsFileContents | string | true | | The contents of the blocked terms file. | | promptLlmConfiguration | any | true | | The LLM configuration for the prompt pipeline. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | NemoLLMConfiguration | false | | Configuration of LLM used for NeMo guardrails. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptModerationConfiguration | any | false | | The moderation configuration for the prompt pipeline. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptPipelineFiles | any | true | | The prompt pipeline files for this configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | NemoPipelineFiles | false | | Files used to setup a NeMo pipeline. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptPipelineMetricName | any | true | | Name for the NeMo Prompt pipeline metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptPipelineTemplateId | any | true | | Guard template to be used for the prompt pipeline, defines actions.py. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responseLlmConfiguration | any | true | | The LLM configuration for the response pipeline. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | NemoLLMConfiguration | false | | Configuration of LLM used for NeMo guardrails. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responseModerationConfiguration | any | false | | The moderation configuration for the response pipeline. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responsePipelineFiles | any | true | | The response pipeline files for this configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | NemoPipelineFiles | false | | Files used to setup a NeMo pipeline. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responsePipelineMetricName | any | true | | Name for the NeMo Response pipeline metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responsePipelineTemplateId | any | true | | Guard template to be used for the response pipeline, defines actions.py. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## NemoConfigurationUpsertRequest ``` { "description": "The body of the Nemo Configuration upsert request.", "properties": { "blockedTermsFileContents": { "description": "The contents of the blocked terms file.", "title": "blockedTermsFileContents", "type": "string" }, "promptLlmConfiguration": { "anyOf": [ { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" }, { "type": "null" } ], "description": "The LLM configuration for the prompt pipeline." }, "promptModerationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration for the prompt pipeline." }, "promptPipelineFiles": { "anyOf": [ { "description": "All of the files except for the actions.py file.", "properties": { "configYamlFileContents": { "description": "The contents of the config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "The contents of the flow definition file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "The contents of the prompts file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPiplineFilesRequest", "type": "object" }, { "type": "null" } ], "description": "The prompt pipeline files for this configuration." }, "promptPipelineMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name for the NeMo Prompt pipeline metric.", "title": "promptPipelineMetricName" }, "promptPipelineTemplateId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Guard template to be used for the prompt pipeline, defines actions.py.", "title": "promptPipelineTemplateId" }, "responseLlmConfiguration": { "anyOf": [ { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" }, { "type": "null" } ], "description": "The LLM configuration for the response pipeline." }, "responseModerationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration for the response pipeline." }, "responsePipelineFiles": { "anyOf": [ { "description": "All of the files except for the actions.py file.", "properties": { "configYamlFileContents": { "description": "The contents of the config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "The contents of the flow definition file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "The contents of the prompts file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPiplineFilesRequest", "type": "object" }, { "type": "null" } ], "description": "The response pipeline files for this configuration." }, "responsePipelineMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name for the NeMo Response pipeline metric.", "title": "responsePipelineMetricName" }, "responsePipelineTemplateId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Guard template to be used for the response pipeline, defines actions.py.", "title": "responsePipelineTemplateId" } }, "required": [ "blockedTermsFileContents" ], "title": "NemoConfigurationUpsertRequest", "type": "object" } ``` NemoConfigurationUpsertRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blockedTermsFileContents | string | true | | The contents of the blocked terms file. | | promptLlmConfiguration | any | false | | The LLM configuration for the prompt pipeline. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | NemoLLMConfiguration | false | | Configuration of LLM used for NeMo guardrails. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptModerationConfiguration | any | false | | The moderation configuration for the prompt pipeline. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptPipelineFiles | any | false | | The prompt pipeline files for this configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | NemoPiplineFilesRequest | false | | All of the files except for the actions.py file. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptPipelineMetricName | any | false | | Name for the NeMo Prompt pipeline metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptPipelineTemplateId | any | false | | Guard template to be used for the prompt pipeline, defines actions.py. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responseLlmConfiguration | any | false | | The LLM configuration for the response pipeline. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | NemoLLMConfiguration | false | | Configuration of LLM used for NeMo guardrails. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responseModerationConfiguration | any | false | | The moderation configuration for the response pipeline. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responsePipelineFiles | any | false | | The response pipeline files for this configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | NemoPiplineFilesRequest | false | | All of the files except for the actions.py file. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responsePipelineMetricName | any | false | | Name for the NeMo Response pipeline metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responsePipelineTemplateId | any | false | | Guard template to be used for the response pipeline, defines actions.py. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## NemoLLMConfiguration ``` { "description": "Configuration of LLM used for NeMo guardrails.", "properties": { "llmType": { "description": "LLM provider type.", "enum": [ "openAi", "azureOpenAi" ], "title": "LLMType", "type": "string" }, "openaiApiBase": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API base.", "title": "openaiApiBase" }, "openaiApiDeploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Azure OpenAI API deployment ID.", "title": "openaiApiDeploymentId" }, "openaiApiKeyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "OpenAI API Key.", "title": "openaiApiKeyId" } }, "required": [ "llmType" ], "title": "NemoLLMConfiguration", "type": "object" } ``` NemoLLMConfiguration ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmType | LLMType | true | | LLM provider type. | | openaiApiBase | any | false | | Azure OpenAI API base. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | openaiApiDeploymentId | any | false | | Azure OpenAI API deployment ID. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | openaiApiKeyId | any | false | | OpenAI API Key. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## NemoPipelineFiles ``` { "description": "Files used to setup a NeMo pipeline.", "properties": { "actionsFileContents": { "description": "Contents of actions.py file.", "title": "actionsFileContents", "type": "string" }, "configYamlFileContents": { "description": "Contents of config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "Contents of flows.co file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "Contents of prompts.yaml file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "actionsFileContents", "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPipelineFiles", "type": "object" } ``` NemoPipelineFiles ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actionsFileContents | string | true | | Contents of actions.py file. | | configYamlFileContents | string | true | | Contents of config.yaml file. | | flowDefinitionFileContents | string | true | | Contents of flows.co file. | | promptsFileContents | string | true | | Contents of prompts.yaml file. | ## NemoPiplineFilesRequest ``` { "description": "All of the files except for the actions.py file.", "properties": { "configYamlFileContents": { "description": "The contents of the config.yaml file.", "title": "configYamlFileContents", "type": "string" }, "flowDefinitionFileContents": { "description": "The contents of the flow definition file.", "title": "flowDefinitionFileContents", "type": "string" }, "promptsFileContents": { "description": "The contents of the prompts file.", "title": "promptsFileContents", "type": "string" } }, "required": [ "configYamlFileContents", "flowDefinitionFileContents", "promptsFileContents" ], "title": "NemoPiplineFilesRequest", "type": "object" } ``` NemoPiplineFilesRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configYamlFileContents | string | true | | The contents of the config.yaml file. | | flowDefinitionFileContents | string | true | | The contents of the flow definition file. | | promptsFileContents | string | true | | The contents of the prompts file. | ## OOTBAgenticMetricInsightNames ``` { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" } ``` OOTBAgenticMetricInsightNames ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OOTBAgenticMetricInsightNames | string | false | | The Out-Of-The-Box metric name that can be used in an Agentic playground. | ### Enumerated Values | Property | Value | | --- | --- | | OOTBAgenticMetricInsightNames | [tool_call_accuracy, agent_goal_accuracy_with_reference] | ## OOTBMetricConfigurationRequest ``` { "description": "API request object for a single OOTB metric configuration.", "properties": { "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "customOotbMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The custom OOTB metric name to be associated with the OOTB metric. Will default to OOTB metric name if not defined.", "title": "customOotbMetricName" }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM to use for `correctness` and `faithfulness` metrics.", "title": "llmId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the OOTB metric." }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" } }, "required": [ "ootbMetricName" ], "title": "OOTBMetricConfigurationRequest", "type": "object" } ``` OOTBMetricConfigurationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelLLMValidationId | any | false | | The ID of the custom model LLM validation (if using a custom model LLM). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customOotbMetricName | any | false | | The custom OOTB metric name to be associated with the OOTB metric. Will default to OOTB metric name if not defined. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | extraMetricSettings | any | false | | Extra settings for the metric that do not reference other entities. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExtraMetricSettings | false | | Extra settings for the metric that do not reference other entities. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmId | any | false | | The ID of the LLM to use for correctness and faithfulness metrics. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | moderationConfiguration | any | false | | The moderation configuration to be associated with the OOTB metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricName | any | true | | The OOTB metric name. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBMetricInsightNames | false | | The Out-Of-The-Box metric name that can be used in the playground. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBAgenticMetricInsightNames | false | | The Out-Of-The-Box metric name that can be used in an Agentic playground. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OTELMetricInsightNames | false | | Metrics that can only be calculated using OTEL Trace/metric data. | ## OOTBMetricConfigurationResponse ``` { "description": "API response object for a single OOTB metric.", "properties": { "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "customOotbMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The custom OOTB metric name to be associated with the OOTB metric.", "title": "customOotbMetricName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the OOTB metric configuration.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "isAgentic": { "default": false, "description": "Whether the OOTB metric configuration is specific to agentic workflows.", "title": "isAgentic", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM to use for `correctness` and `faithfulness` metrics.", "title": "llmId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the OOTB metric." }, "ootbMetricConfigurationId": { "description": "The ID of OOTB metric.", "title": "ootbMetricConfigurationId", "type": "string" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ], "description": "The name of the OOTB metric.", "title": "ootbMetricName" } }, "required": [ "ootbMetricName", "ootbMetricConfigurationId", "executionStatus" ], "title": "OOTBMetricConfigurationResponse", "type": "object" } ``` OOTBMetricConfigurationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelLLMValidationId | any | false | | The ID of the custom model LLM validation (if using a custom model LLM). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customOotbMetricName | any | false | | The custom OOTB metric name to be associated with the OOTB metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the OOTB metric configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorResolution | any | false | | The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [InsightErrorResolution] | false | | [Error type linking directly to the field name that is related to the error.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | ExecutionStatus | true | | The execution status of the OOTB metric configuration. | | extraMetricSettings | any | false | | Extra settings for the metric that do not reference other entities. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExtraMetricSettings | false | | Extra settings for the metric that do not reference other entities. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isAgentic | boolean | false | | Whether the OOTB metric configuration is specific to agentic workflows. | | llmId | any | false | | The ID of the LLM to use for correctness and faithfulness metrics. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | moderationConfiguration | any | false | | The moderation configuration to be associated with the OOTB metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModerationConfigurationWithoutID | false | | Moderation Configuration associated with an insight. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricConfigurationId | string | true | | The ID of OOTB metric. | | ootbMetricName | any | true | | The name of the OOTB metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBMetricInsightNames | false | | The Out-Of-The-Box metric name that can be used in the playground. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OOTBAgenticMetricInsightNames | false | | The Out-Of-The-Box metric name that can be used in an Agentic playground. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OTELMetricInsightNames | false | | Metrics that can only be calculated using OTEL Trace/metric data. | ## OOTBMetricConfigurationsResponse ``` { "description": "The response for the \"OOTB metric configurations\" retrieve request.", "properties": { "ootbMetricConfigurations": { "description": "The list of OOTB metric configurations to use.", "items": { "description": "API response object for a single OOTB metric.", "properties": { "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation (if using a custom model LLM).", "title": "customModelLLMValidationId" }, "customOotbMetricName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The custom OOTB metric name to be associated with the OOTB metric.", "title": "customOotbMetricName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the OOTB metric configuration.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "description": "Error type linking directly to the field name that is related to the error.", "enum": [ "ootbMetricName", "intervention", "guardCondition", "sidecarOverall", "sidecarRevalidate", "sidecarDeploymentId", "sidecarInputColumnName", "sidecarOutputColumnName", "promptPipelineFiles", "promptPipelineTemplateId", "responsePipelineFiles", "responsePipelineTemplateId" ], "title": "InsightErrorResolution", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "isAgentic": { "default": false, "description": "Whether the OOTB metric configuration is specific to agentic workflows.", "title": "isAgentic", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM to use for `correctness` and `faithfulness` metrics.", "title": "llmId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration to be associated with the OOTB metric." }, "ootbMetricConfigurationId": { "description": "The ID of OOTB metric.", "title": "ootbMetricConfigurationId", "type": "string" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ], "description": "The name of the OOTB metric.", "title": "ootbMetricName" } }, "required": [ "ootbMetricName", "ootbMetricConfigurationId", "executionStatus" ], "title": "OOTBMetricConfigurationResponse", "type": "object" }, "title": "ootbMetricConfigurations", "type": "array" } }, "required": [ "ootbMetricConfigurations" ], "title": "OOTBMetricConfigurationsResponse", "type": "object" } ``` OOTBMetricConfigurationsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricConfigurations | [OOTBMetricConfigurationResponse] | true | | The list of OOTB metric configurations to use. | ## OOTBMetricInsightNames ``` { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" } ``` OOTBMetricInsightNames ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OOTBMetricInsightNames | string | false | | The Out-Of-The-Box metric name that can be used in the playground. | ### Enumerated Values | Property | Value | | --- | --- | | OOTBMetricInsightNames | [latency, citations, rouge_1, faithfulness, correctness, prompt_tokens, response_tokens, document_tokens, all_tokens, jailbreak_violation, toxicity_violation, pii_violation, exact_match, starts_with, contains] | ## OTELMetricInsightNames ``` { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" } ``` OTELMetricInsightNames ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OTELMetricInsightNames | string | false | | Metrics that can only be calculated using OTEL Trace/metric data. | ### Enumerated Values | Property | Value | | --- | --- | | OTELMetricInsightNames | [agent_latency, agent_tokens, agent_cost] | ## ObjectIdSettingConstraints ``` { "description": "Available constraints for ObjectId settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "type": { "const": "object_id", "default": "object_id", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "ObjectIdSettingConstraints", "type": "object" } ``` ObjectIdSettingConstraints ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowedChoices | any | false | | The allowed values for the setting. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | false | | The data type of the setting. | ## PipelineStage ``` { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" } ``` PipelineStage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | PipelineStage | string | false | | Enum that describes at which stage the metric may be calculated. | ### Enumerated Values | Property | Value | | --- | --- | | PipelineStage | [prompt_pipeline, response_pipeline] | ## PlaygroundResponse ``` { "description": "API response object for a single playground.", "properties": { "creationDate": { "description": "The creation date of the playground (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created this playground.", "title": "creationUserId", "type": "string" }, "description": { "description": "The description of the playground.", "title": "description", "type": "string" }, "id": { "description": "The ID of the playground.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "The ID of the user that made the most recent update to this playground.", "title": "lastUpdateUserId", "type": "string" }, "llmBlueprintsCount": { "description": "The number of LLM blueprints in this playground.", "title": "llmBlueprintsCount", "type": "integer" }, "name": { "description": "The name of the playground.", "title": "name", "type": "string" }, "playgroundType": { "description": "Playground type.", "enum": [ "rag", "agentic" ], "title": "PlaygroundType", "type": "string" }, "savedLLMBlueprintsCount": { "description": "The number of saved LLM blueprints in this playground.", "title": "savedLLMBlueprintsCount", "type": "integer" }, "useCaseId": { "description": "The ID of the use case this playground belongs to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this playground.", "title": "userName", "type": "string" } }, "required": [ "id", "name", "description", "useCaseId", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "savedLLMBlueprintsCount", "llmBlueprintsCount", "userName" ], "title": "PlaygroundResponse", "type": "object" } ``` PlaygroundResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the playground (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created this playground. | | description | string | true | | The description of the playground. | | id | string | true | | The ID of the playground. | | lastUpdateDate | string(date-time) | true | | The date of the most recent update of this playground (ISO 8601 formatted). | | lastUpdateUserId | string | true | | The ID of the user that made the most recent update to this playground. | | llmBlueprintsCount | integer | true | | The number of LLM blueprints in this playground. | | name | string | true | | The name of the playground. | | playgroundType | PlaygroundType | false | | The type of the playground. | | savedLLMBlueprintsCount | integer | true | | The number of saved LLM blueprints in this playground. | | useCaseId | string | true | | The ID of the use case this playground belongs to. | | userName | string | true | | The name of the user that created this playground. | ## PlaygroundType ``` { "description": "Playground type.", "enum": [ "rag", "agentic" ], "title": "PlaygroundType", "type": "string" } ``` PlaygroundType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | PlaygroundType | string | false | | Playground type. | ### Enumerated Values | Property | Value | | --- | --- | | PlaygroundType | [rag, agentic] | ## PromptTraceResponse ``` { "description": "API response object for a single playground prompt trace.", "properties": { "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat associated with the trace.", "title": "chatId" }, "chatName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the chat associated with the trace.", "title": "chatName" }, "chatPromptId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat prompt associated with the trace.", "title": "chatPromptId" }, "citations": { "anyOf": [ { "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The list of relevant vector database citations (in case of using a vector database).", "title": "citations" }, "comparisonPromptId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the comparison prompts associated with the trace.", "title": "comparisonPromptId" }, "confidenceScores": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion for the prompt associated with the trace.", "title": "confidenceScores" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset associated with the trace.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the trace.", "title": "llmBlueprintId", "type": "string" }, "llmBlueprintName": { "description": "The name of the LLM blueprint associated with the trace.", "title": "llmBlueprintName", "type": "string" }, "llmLicense": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The usage license information for the LLM associated with the trace.", "title": "llmLicense" }, "llmName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the LLM associated with the trace.", "title": "llmName" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "The settings of the LLM associated with the trace.", "title": "llmSettings" }, "llmVendor": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The vendor of the LLM associated with the trace.", "title": "llmVendor" }, "promptType": { "anyOf": [ { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, { "type": "null" } ], "description": "The type of prompt, chat history awair or one time prompt." }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the prompt associated with the trace." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The completion text of the prompt associated with the trace.", "title": "resultText" }, "text": { "description": "The text of the prompt associated with the trace.", "title": "text", "type": "string" }, "timestamp": { "description": "The timestamp of the trace (ISO 8601 formatted).", "format": "date-time", "title": "timestamp", "type": "string" }, "useCaseId": { "description": "The ID of the use case associated with the trace.", "title": "useCaseId", "type": "string" }, "user": { "description": "DataRobot application user.", "properties": { "id": { "description": "The ID of the user.", "title": "id", "type": "string" }, "name": { "description": "The name of the user.", "title": "name", "type": "string" }, "userhash": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Gravatar hash for user avatar.", "title": "userhash" } }, "required": [ "id", "name" ], "title": "DataRobotUser", "type": "object" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database associated with the trace.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "The settings of the vector database associated with the trace." }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The warning message for the prompt associated with the trace.", "title": "warning" } }, "required": [ "timestamp", "user", "chatPromptId", "comparisonPromptId", "useCaseId", "llmBlueprintId", "llmBlueprintName", "llmName", "llmVendor", "llmLicense", "llmSettings", "chatName", "chatId", "vectorDatabaseId", "vectorDatabaseSettings", "citations", "resultMetadata", "resultText", "confidenceScores", "text", "executionStatus", "promptType", "evaluationDatasetConfigurationId", "warning" ], "title": "PromptTraceResponse", "type": "object" } ``` PromptTraceResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatId | any | true | | The ID of the chat associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatName | any | true | | The name of the chat associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatPromptId | any | true | | The ID of the chat prompt associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | citations | any | true | | The list of relevant vector database citations (in case of using a vector database). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [Citation] | false | | [API response object for a single vector database citation.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparisonPromptId | any | true | | The ID of the comparison prompts associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | confidenceScores | any | true | | The confidence scores that measure the similarity between the prompt context and the prompt completion for the prompt associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | any | true | | The ID of the evaluation dataset associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | ExecutionStatus | true | | The execution status of the prompt associated with the trace. | | llmBlueprintId | string | true | | The ID of the LLM blueprint associated with the trace. | | llmBlueprintName | string | true | | The name of the LLM blueprint associated with the trace. | | llmLicense | any | true | | The usage license information for the LLM associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmName | any | true | | The name of the LLM associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmSettings | any | true | | The settings of the LLM associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CommonLLMSettings | false | | The settings that are available for all non-custom LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelLLMSettings | false | | The settings that are available for custom model LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelChatLLMSettings | false | | The settings that are available for custom model LLMs used via chat completion interface. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmVendor | any | true | | The vendor of the LLM associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptType | any | true | | The type of prompt, chat history awair or one time prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PromptType | false | | Determines whether chat history is submitted as context to the user prompt. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | resultMetadata | any | true | | The additional information about the prompt associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ResultMetadata | false | | The additional information about prompt execution results. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | resultText | any | true | | The completion text of the prompt associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | text | string | true | | The text of the prompt associated with the trace. | | timestamp | string(date-time) | true | | The timestamp of the trace (ISO 8601 formatted). | | useCaseId | string | true | | The ID of the use case associated with the trace. | | user | DataRobotUser | true | | The user associated with the trace. | | vectorDatabaseId | any | true | | The ID of the vector database associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseSettings | any | true | | The settings of the vector database associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseSettings | false | | Vector database retrieval settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | warning | any | true | | The warning message for the prompt associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## PromptType ``` { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" } ``` PromptType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | PromptType | string | false | | Determines whether chat history is submitted as context to the user prompt. | ### Enumerated Values | Property | Value | | --- | --- | | PromptType | [CHAT_HISTORY_AWARE, ONE_TIME_PROMPT] | ## ProviderGuardStage ``` { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" } ``` ProviderGuardStage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ProviderGuardStage | string | false | | The data stage where the provider guard metric is acting upon. | ### Enumerated Values | Property | Value | | --- | --- | | ProviderGuardStage | [prompt, response] | ## ProviderGuardsMetadata ``` { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" } ``` ProviderGuardsMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the provider guard metric. | | satisfyCriteria | boolean | true | | Whether the configured provider guard metric satisfied its hidden internal guard criteria. | | stage | ProviderGuardStage | true | | The data stage where the provider guard metric is acting upon. | | value | any | true | | The value of the provider guard metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ResultMetadata ``` { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" } ``` ResultMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blockedResultText | any | false | | The message to replace the result text if it is non empty, which represents a blocked response. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cost | any | false | | The estimated cost of executing the prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message for the prompt (in case of an errored prompt). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | estimatedDocsTokenCount | integer | false | | The estimated number of tokens in the documents retrieved from the vector database. | | feedbackResult | FeedbackResult | false | | The user feedback associated with the prompt. | | finalPrompt | any | false | | The final representation of the prompt that was submitted to the LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [object] | false | | none | | »» additionalProperties | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | [object] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | null | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [object] | false | | none | | »» additionalProperties | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | [object] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | none | | »» additionalProperties | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | [object] | false | | none | | »»»» additionalProperties | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputTokenCount | integer | false | | The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database). | | latencyMilliseconds | integer | true | | The latency of the LLM response (in milliseconds). | | metrics | [MetricMetadata] | false | | The evaluation metrics for the prompt. | | outputTokenCount | integer | false | | The number of tokens in the LLM output. | | providerLLMGuards | any | false | | The provider llm guards metadata. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [ProviderGuardsMetadata] | false | | [Info on the provider guard metrics.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalTokenCount | integer | false | | The combined number of tokens in the LLM input and output. | ## RetrievalMode ``` { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" } ``` RetrievalMode ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | RetrievalMode | string | false | | Retrieval modes for vector databases. | ### Enumerated Values | Property | Value | | --- | --- | | RetrievalMode | [similarity, maximal_marginal_relevance] | ## RetrieveUserLimitCounterResponse ``` { "description": "API response object for retrieving user limit counters.", "properties": { "counter": { "description": "The number of completed operations which count towards the usage limit.", "title": "counter", "type": "integer" } }, "required": [ "counter" ], "title": "RetrieveUserLimitCounterResponse", "type": "object" } ``` RetrieveUserLimitCounterResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | counter | integer | true | | The number of completed operations which count towards the usage limit. | ## SettingFormat ``` { "description": "Supported formats for settings.", "enum": [ "multiline" ], "title": "SettingFormat", "type": "string" } ``` SettingFormat ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | SettingFormat | string | false | | Supported formats for settings. | ### Enumerated Values | Property | Value | | --- | --- | | SettingFormat | multiline | ## SettingType ``` { "description": "Supported data types for settings.", "enum": [ "integer", "float", "string", "boolean", "object_id", "list" ], "title": "SettingType", "type": "string" } ``` SettingType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | SettingType | string | false | | Supported data types for settings. | ### Enumerated Values | Property | Value | | --- | --- | | SettingType | [integer, float, string, boolean, object_id, list] | ## SidecarModelMetricMetadata ``` { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" } ``` SidecarModelMetricMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | expectedResponseColumnName | any | false | | The name of the column the custom model uses for expected response text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | any | false | | The name of the column the custom model uses for prompt text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | responseColumnName | any | false | | The name of the column the custom model uses for response text input. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetColumnName | any | true | | The name of the column the custom model uses for prediction output. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## StatusResponse ``` { "description": "API response object for job execution status.", "properties": { "code": { "description": "Possible job error codes. This enum exists for consistency with the DataRobot Status API.", "enum": [ 0, 1 ], "title": "JobErrorCode", "type": "integer" }, "created": { "description": "The creation date of the job (ISO 8601 formatted).", "format": "date-time", "title": "created", "type": "string" }, "description": { "default": "", "description": "The description associated with the job.", "title": "description", "type": "string" }, "message": { "default": "", "description": "The error message associated with the job.", "title": "message", "type": "string" }, "status": { "description": "Possible job states. Values match the DataRobot Status API.", "enum": [ "INITIALIZED", "RUNNING", "COMPLETED", "ERROR", "ABORTED", "EXPIRED" ], "title": "JobExecutionState", "type": "string" }, "statusId": { "description": "The ID of the job.", "format": "uuid", "title": "statusId", "type": "string" }, "statusType": { "default": "", "description": "The type of the status object.", "title": "statusType", "type": "string" } }, "required": [ "statusId", "status", "created", "code" ], "title": "StatusResponse", "type": "object" } ``` StatusResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | code | JobErrorCode | true | | The error code associated with the job. | | created | string(date-time) | true | | The creation date of the job (ISO 8601 formatted). | | description | string | false | | The description associated with the job. | | message | string | false | | The error message associated with the job. | | status | JobExecutionState | true | | The execution status of the job. | | statusId | string(uuid) | true | | The ID of the job. | | statusType | string | false | | The type of the status object. | ## StringSettingConstraints ``` { "description": "Available constraints for string settings.", "properties": { "allowedChoices": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The allowed values for the setting.", "title": "allowedChoices" }, "maxLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum length of the value (inclusive).", "title": "maxLength" }, "minLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum length of the value (inclusive).", "title": "minLength" }, "type": { "const": "string", "default": "string", "description": "The data type of the setting.", "title": "type", "type": "string" } }, "title": "StringSettingConstraints", "type": "object" } ``` StringSettingConstraints ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowedChoices | any | false | | The allowed values for the setting. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxLength | any | false | | The maximum length of the value (inclusive). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | minLength | any | false | | The minimum length of the value (inclusive). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | false | | The data type of the setting. | ## SupportedCustomModelLLMValidation ``` { "description": "The metadata describing a validated custom model LLM.", "properties": { "id": { "description": "The ID of the custom model LLM validation.", "title": "id", "type": "string" }, "name": { "description": "The name of the custom model LLM validation.", "title": "name", "type": "string" } }, "required": [ "id", "name" ], "title": "SupportedCustomModelLLMValidation", "type": "object" } ``` SupportedCustomModelLLMValidation ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model LLM validation. | | name | string | true | | The name of the custom model LLM validation. | ## SupportedDeploymentType ``` { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" } ``` SupportedDeploymentType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | SupportedDeploymentType | string | false | | The type of the target output a DataRobot deployment produces. | ### Enumerated Values | Property | Value | | --- | --- | | SupportedDeploymentType | [TEXT_GENERATION, VECTOR_DATABASE, UNSTRUCTURED, REGRESSION, MULTICLASS, BINARY, NOT_SUPPORTED] | ## SupportedInsightsResponse ``` { "description": "Response model for supported insights.", "properties": { "insightsConfiguration": { "description": "The list of supported insights configurations.", "items": { "description": "The configuration of insights with extra data.", "properties": { "aggregationTypes": { "anyOf": [ { "items": { "description": "The type of the metric aggregation.", "enum": [ "average", "percentYes", "classPercentCoverage", "ngramImportance", "guardConditionPercentYes" ], "title": "AggregationType", "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The aggregation types used in the insights configuration.", "title": "aggregationTypes" }, "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom metric (if using a custom metric).", "title": "customMetricId" }, "customModelGuard": { "anyOf": [ { "description": "Details of a guard as defined for the custom model.", "properties": { "name": { "description": "The name of the guard.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "nemoEvaluatorType": { "anyOf": [ { "description": "NeMo evaluator type as used in the moderation_config.yaml file of the custom model.", "enum": [ "llm_judge", "context_relevance", "response_groundedness", "topic_adherence", "agent_goal_accuracy", "response_relevancy", "faithfulness" ], "title": "CustomModelGuardNemoEvaluatorType", "type": "string" }, { "type": "null" } ], "description": "NeMo Evaluator type of the guard." }, "ootbType": { "anyOf": [ { "description": "OOTB type as used in the moderation_config.yaml file of the custom model.", "enum": [ "token_count", "rouge_1", "faithfulness", "agent_goal_accuracy", "custom_metric", "cost", "task_adherence" ], "title": "CustomModelGuardOOTBType", "type": "string" }, { "type": "null" } ], "description": "Out of the box type of the guard." }, "stage": { "description": "Guard stage as used in the moderation_config.yaml file of the custom model.", "enum": [ "prompt", "response" ], "title": "CustomModelGuardStage", "type": "string" }, "type": { "description": "Guard type as used in the moderation_config.yaml file of the custom model.", "enum": [ "ootb", "model", "nemo_guardrails", "nemo_evaluator" ], "title": "CustomModelGuardType", "type": "string" } }, "required": [ "type", "stage", "name" ], "title": "CustomModelGuard", "type": "object" }, { "type": "null" } ], "description": "Guard as configured in the custom model." }, "customModelLLMValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model LLM validation if using a custom model LLM for OOTB metrics.", "title": "customModelLLMValidationId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model deployment associated with the insight.", "title": "deploymentId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the evaluation dataset configuration or sidecar model metric validation or OOTB metric.", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The error type associated with the insight error status and error message as an indicator of what fields needs to be edited if any.", "title": "errorResolution" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The execution status of the evaluation dataset configuration." }, "extraMetricSettings": { "anyOf": [ { "description": "Extra settings for the metric that do not reference other entities.", "properties": { "toolCallAccuracy": { "anyOf": [ { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the tool call accuracy metric." } }, "title": "ExtraMetricSettings", "type": "object" }, { "type": "null" } ], "description": "Extra settings for the metric that do not reference other entities." }, "insightName": { "description": "The name of the insight.", "maxLength": 5000, "minLength": 1, "title": "insightName", "type": "string" }, "insightType": { "anyOf": [ { "description": "The type of insight.", "enum": [ "Reference", "Quality metric", "Operational metric", "Evaluation deployment", "Custom metric", "Nemo" ], "title": "InsightTypes", "type": "string" }, { "type": "null" } ], "description": "The type of the insight." }, "isTransferable": { "default": false, "description": "Indicates if insight can be transferred to production.", "title": "isTransferable", "type": "boolean" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The LLM ID for OOTB metrics that use LLMs.", "title": "llmId" }, "llmIsActive": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is active.", "title": "llmIsActive" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model associated with `deploymentId`.", "title": "modelId" }, "modelPackageRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the registered model package associated with `deploymentId`.", "title": "modelPackageRegisteredModelId" }, "moderationConfiguration": { "anyOf": [ { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithID", "type": "object" }, { "description": "Moderation Configuration associated with an insight.", "properties": { "guardConditions": { "description": "The guard conditions associated with a metric.", "items": { "description": "The guard condition for a metric.", "properties": { "comparand": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "boolean" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The comparand(s) used in the guard condition.", "title": "comparand" }, "comparator": { "description": "The comparator used in a guard condition.", "enum": [ "greaterThan", "lessThan", "equals", "notEquals", "is", "isNot", "matches", "doesNotMatch", "contains", "doesNotContain" ], "title": "GuardConditionComparator", "type": "string" } }, "required": [ "comparator", "comparand" ], "title": "GuardCondition", "type": "object" }, "maxItems": 1, "minItems": 1, "title": "guardConditions", "type": "array" }, "intervention": { "description": "The intervention configuration for a metric.", "properties": { "action": { "description": "The moderation strategy.", "enum": [ "block", "report", "reportAndBlock" ], "title": "ModerationAction", "type": "string" }, "message": { "description": "The intervention message to replace the prediction when a guard condition is satisfied.", "minLength": 1, "title": "message", "type": "string" } }, "required": [ "action", "message" ], "title": "Intervention", "type": "object" } }, "required": [ "guardConditions", "intervention" ], "title": "ModerationConfigurationWithoutID", "type": "object" }, { "type": "null" } ], "description": "The moderation configuration associated with the insight configuration.", "title": "moderationConfiguration" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the Nemo configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the ootb metric (if using an ootb metric).", "title": "ootbMetricId" }, "ootbMetricName": { "anyOf": [ { "description": "The Out-Of-The-Box metric name that can be used in the playground.", "enum": [ "latency", "citations", "rouge_1", "faithfulness", "correctness", "prompt_tokens", "response_tokens", "document_tokens", "all_tokens", "jailbreak_violation", "toxicity_violation", "pii_violation", "exact_match", "starts_with", "contains" ], "title": "OOTBMetricInsightNames", "type": "string" }, { "description": "The Out-Of-The-Box metric name that can be used in an Agentic playground.", "enum": [ "tool_call_accuracy", "agent_goal_accuracy_with_reference" ], "title": "OOTBAgenticMetricInsightNames", "type": "string" }, { "description": "Metrics that can only be calculated using OTEL Trace/metric data.", "enum": [ "agent_latency", "agent_tokens", "agent_cost" ], "title": "OTELMetricInsightNames", "type": "string" }, { "type": "null" } ], "description": "The OOTB metric name.", "title": "ootbMetricName" }, "resultUnit": { "anyOf": [ { "description": "The unit of measurement associated with a metric.", "enum": [ "s", "ms", "%" ], "title": "MetricUnit", "type": "string" }, { "type": "null" } ], "description": "The unit of measurement associated with the insight result." }, "sidecarModelMetricMetadata": { "anyOf": [ { "description": "The metadata of a sidecar model metric.", "properties": { "expectedResponseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for expected response text input.", "title": "expectedResponseColumnName" }, "promptColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName" }, "responseColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for response text input.", "title": "responseColumnName" }, "targetColumnName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName" } }, "required": [ "targetColumnName" ], "title": "SidecarModelMetricMetadata", "type": "object" }, { "type": "null" } ], "description": "The metadata of the sidecar model metric (if using a sidecar model metric)." }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the sidecar model metric validation (if using a sidecar model metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) where insight is calculated at." } }, "required": [ "insightName", "aggregationTypes" ], "title": "InsightsConfigurationWithAdditionalData", "type": "object" }, "title": "insightsConfiguration", "type": "array" } }, "required": [ "insightsConfiguration" ], "title": "SupportedInsightsResponse", "type": "object" } ``` SupportedInsightsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | insightsConfiguration | [InsightsConfigurationWithAdditionalData] | true | | The list of supported insights configurations. | ## ToolCallAccuracySettings ``` { "description": "Additional arguments for the tool call accuracy metric.", "properties": { "argumentComparison": { "description": "The different modes for comparing the arguments of tool calls.", "enum": [ "exact_match", "ignore_arguments" ], "title": "ArgumentMatchMode", "type": "string" } }, "required": [ "argumentComparison" ], "title": "ToolCallAccuracySettings", "type": "object" } ``` ToolCallAccuracySettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | argumentComparison | ArgumentMatchMode | true | | Setting defining how arguments of tool calls should be compared during the metric computation. | ## TraceChat ``` { "description": "Reference to a chat or comparison chat available in a trace.", "properties": { "id": { "description": "The ID of the chat associated with the trace.", "title": "id", "type": "string" }, "name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name of the chat associated with the trace.", "title": "name" } }, "required": [ "id", "name" ], "title": "TraceChat", "type": "object" } ``` TraceChat ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the chat associated with the trace. | | name | any | true | | Name of the chat associated with the trace. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## TraceDatasetResponse ``` { "description": "API response for prompt traces export request.", "properties": { "aiCatalogId": { "description": "The Data Registry dataset ID.", "title": "aiCatalogId", "type": "string" } }, "required": [ "aiCatalogId" ], "title": "TraceDatasetResponse", "type": "object" } ``` TraceDatasetResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aiCatalogId | string | true | | The Data Registry dataset ID. | ## TraceMetadataResponse ``` { "description": "API response for prompt trace metadata retrieval request.", "properties": { "chats": { "description": "The list of combined chat and comparison prompt chats available in trace.", "items": { "description": "Reference to a chat or comparison chat available in a trace.", "properties": { "id": { "description": "The ID of the chat associated with the trace.", "title": "id", "type": "string" }, "name": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Name of the chat associated with the trace.", "title": "name" } }, "required": [ "id", "name" ], "title": "TraceChat", "type": "object" }, "title": "chats", "type": "array" }, "users": { "description": "The list of unique users available in the trace response.", "items": { "description": "DataRobot application user.", "properties": { "id": { "description": "The ID of the user.", "title": "id", "type": "string" }, "name": { "description": "The name of the user.", "title": "name", "type": "string" }, "userhash": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Gravatar hash for user avatar.", "title": "userhash" } }, "required": [ "id", "name" ], "title": "DataRobotUser", "type": "object" }, "title": "users", "type": "array" } }, "required": [ "users", "chats" ], "title": "TraceMetadataResponse", "type": "object" } ``` TraceMetadataResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chats | [TraceChat] | true | | The list of combined chat and comparison prompt chats available in trace. | | users | [DataRobotUser] | true | | The list of unique users available in the trace response. | ## UpdateLLMBlueprintRequest ``` { "description": "The body of the Update LLM Blueprint request.", "properties": { "description": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, updates the LLM blueprint description to this value.", "title": "description" }, "isSaved": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "If specified, updates the saved status of the LLM blueprint to this value.", "title": "isSaved" }, "isStarred": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "If specified, updates the starred status of the LLM blueprint to this value.", "title": "isStarred" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the LLM used by the LLM blueprint.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "If specified, updates the LLM settings to these values.", "title": "llmSettings" }, "name": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the LLM blueprint to this value.", "title": "name" }, "promptType": { "anyOf": [ { "description": "Determines whether chat history is submitted as context to the user prompt.", "enum": [ "CHAT_HISTORY_AWARE", "ONE_TIME_PROMPT" ], "title": "PromptType", "type": "string" }, { "type": "null" } ], "description": "If specified, updates the chat context behavior of the LLM blueprint to this value." }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the vector database used by the LLM blueprint. If the specified value is `null`, unlinks the vector database from the LLM blueprint. If omitted, the currently used vector database remains in use.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Specifies the vector database retrieval settings in LLM blueprint API requests.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 128, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettingsRequest", "type": "object" }, { "type": "null" } ], "description": "If specified, updates the vector database retrieval settings to these values." } }, "title": "UpdateLLMBlueprintRequest", "type": "object" } ``` UpdateLLMBlueprintRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | any | false | | If specified, updates the LLM blueprint description to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isSaved | any | false | | If specified, updates the saved status of the LLM blueprint to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isStarred | any | false | | If specified, updates the starred status of the LLM blueprint to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmId | any | false | | If specified, changes the LLM used by the LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmSettings | any | false | | If specified, updates the LLM settings to these values. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CommonLLMSettings | false | | The settings that are available for all non-custom LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelLLMSettings | false | | The settings that are available for custom model LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelChatLLMSettings | false | | The settings that are available for custom model LLMs used via chat completion interface. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | any | false | | If specified, renames the LLM blueprint to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptType | any | false | | If specified, updates the chat context behavior of the LLM blueprint to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PromptType | false | | Determines whether chat history is submitted as context to the user prompt. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | any | false | | If specified, changes the vector database used by the LLM blueprint. If the specified value is null, unlinks the vector database from the LLM blueprint. If omitted, the currently used vector database remains in use. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseSettings | any | false | | If specified, updates the vector database retrieval settings to these values. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseSettingsRequest | false | | Specifies the vector database retrieval settings in LLM blueprint API requests. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ValidationError ``` { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" } ``` ValidationError ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | loc | [anyOf] | true | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | msg | string | true | | none | | type | string | true | | none | ## VectorDatabaseRetrievers ``` { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } ``` VectorDatabaseRetrievers ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | VectorDatabaseRetrievers | string | false | | The method used to retrieve relevant chunks from the vector database. | ### Enumerated Values | Property | Value | | --- | --- | | VectorDatabaseRetrievers | [SINGLE_LOOKUP_RETRIEVER, CONVERSATIONAL_RETRIEVER, MULTI_STEP_RETRIEVER] | ## VectorDatabaseSettings ``` { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" } ``` VectorDatabaseSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addNeighborChunks | boolean | false | | Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1. | | maxDocumentsRetrievedPerPrompt | any | false | | The maximum number of chunks to retrieve from the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 10minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxTokens | any | false | | The maximum number of tokens to retrieve from the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 51200minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maximalMarginalRelevanceLambda | number | false | maximum: 1minimum: 0 | Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0). | | retrievalMode | RetrievalMode | false | | The retrieval mode to use. Similarity search or maximal marginal relevance. | | retriever | VectorDatabaseRetrievers | false | | The method used to retrieve relevant chunks from the vector database. | ## VectorDatabaseSettingsRequest ``` { "description": "Specifies the vector database retrieval settings in LLM blueprint API requests.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 128, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettingsRequest", "type": "object" } ``` VectorDatabaseSettingsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addNeighborChunks | boolean | false | | Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1. | | maxDocumentsRetrievedPerPrompt | any | false | | The maximum number of chunks to retrieve from the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 10minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxTokens | any | false | | The maximum number of tokens to retrieve from the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 51200minimum: 128 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maximalMarginalRelevanceLambda | number | false | maximum: 1minimum: 0 | Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0). | | retrievalMode | RetrievalMode | false | | The retrieval mode to use. Similarity search or maximal marginal relevance. | | retriever | VectorDatabaseRetrievers | false | | The method used to retrieve relevant chunks from the vector database. | --- # Challenger models URL: https://docs.datarobot.com/en/docs/api/reference/public-api/mitigation_challenger_models.html > Use the endpoints described below to manage challenger models. # Challenger models Use the endpoints described below to manage challenger models. ## Score challenger models by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/challengerPredictions/` Authentication requirements: `BearerAuth` Score main model prediction requests against challenger model requests. ### Body parameter ``` { "properties": { "timestamp": { "description": "The date and time in ISO8601 format, challenger models will be scored on data starting from deployment creation until timestamp. If not specified UTC current time is used.", "format": "date-time", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | ChallengerScore | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See Location header. | None | | 422 | Unprocessable Entity | Unable to process the challenger scoring request. | None | | 429 | Too Many Requests | Another challenger scoring job is running. See the Location header to track the running job. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL to poll to track challenger scoring progress. | ## Retrieve challenger replay settings by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/challengerReplaySettings/` Authentication requirements: `BearerAuth` Retrieve challenger replay settings. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "enabled": { "description": "Identifies whether scheduled replay is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Challenger replay settings. | ChallengersReplaySettings | ## Update challenger replay settings by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/challengerReplaySettings/` Authentication requirements: `BearerAuth` Update challenger replay settings. ### Body parameter ``` { "properties": { "enabled": { "description": "Identifies whether scheduled replay is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | ChallengersReplaySettings | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Challenger replay settings updated. | None | ## List challenger models by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/challengers/` Authentication requirements: `BearerAuth` List challenger models for deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "List of challengers.", "items": { "properties": { "id": { "description": "ID of the challenger.", "type": "string" }, "model": { "description": "Model of the challenger.", "properties": { "datasetName": { "description": "Name of dataset used to train challenger model", "type": "string" }, "description": { "description": "Description of the model.", "type": "string" }, "executionType": { "description": "Type of the current model.", "type": "string" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "name": { "description": "Name of the model.", "type": "string" }, "projectId": { "description": "Project ID of the current model.", "type": "string" }, "projectName": { "description": "Project name of the current model.", "type": "string" } }, "required": [ "datasetName", "description", "executionType", "id", "isDeprecated", "name", "projectId", "projectName" ], "type": "object" }, "modelPackage": { "description": "modelPackage of the challenger.", "properties": { "id": { "description": "ID of the model package.", "type": "string" }, "name": { "description": "Type of the current model.", "type": "string" }, "registeredModelId": { "description": "ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "name": { "description": "Name of the challenger.", "type": "string" }, "predictionEnvironment": { "description": "Prediction environment used by the challenger", "properties": { "id": { "description": "ID of the prediction environment.", "type": "string" }, "name": { "description": "Name of the prediction environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "id", "model", "modelPackage", "name" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The challenger models | ChallengerListResponse | ## Create challenger model by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/challengers/` Authentication requirements: `BearerAuth` Create new challenger model. ### Body parameter ``` { "properties": { "modelPackageId": { "description": "ID of the model package to add as a challenger.", "type": "string" }, "name": { "description": "Human-readable name for the challenger.", "maxLength": 512, "type": "string" }, "predictionEnvironmentId": { "description": "ID of the Prediction Environment the challenger should use. If prediction environments are enabled, this is required", "type": "string" } }, "required": [ "modelPackageId", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | ChallengerCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted successfully. See Location header. | None | | 422 | Unprocessable Entity | Unable to process the challenger creation request. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL to poll to track challenger creation has finished. | ## Delete challenger model by deployment ID Operation path: `DELETE /api/v2/deployments/{deploymentId}/challengers/{challengerId}/` Authentication requirements: `BearerAuth` Delete challenger model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | challengerId | path | string | true | Unique identifier of the challenger. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Model successfully deleted. | None | ## Get challenger model by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/challengers/{challengerId}/` Authentication requirements: `BearerAuth` Retrieve challenger model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | challengerId | path | string | true | Unique identifier of the challenger. | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "ID of the challenger.", "type": "string" }, "model": { "description": "Model of the challenger.", "properties": { "datasetName": { "description": "Name of dataset used to train challenger model", "type": "string" }, "description": { "description": "Description of the model.", "type": "string" }, "executionType": { "description": "Type of the current model.", "type": "string" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "name": { "description": "Name of the model.", "type": "string" }, "projectId": { "description": "Project ID of the current model.", "type": "string" }, "projectName": { "description": "Project name of the current model.", "type": "string" } }, "required": [ "datasetName", "description", "executionType", "id", "isDeprecated", "name", "projectId", "projectName" ], "type": "object" }, "modelPackage": { "description": "modelPackage of the challenger.", "properties": { "id": { "description": "ID of the model package.", "type": "string" }, "name": { "description": "Type of the current model.", "type": "string" }, "registeredModelId": { "description": "ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "name": { "description": "Name of the challenger.", "type": "string" }, "predictionEnvironment": { "description": "Prediction environment used by the challenger", "properties": { "id": { "description": "ID of the prediction environment.", "type": "string" }, "name": { "description": "Name of the prediction environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "id", "model", "modelPackage", "name" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The challenger model | ChallengerResponse | ## Update challenger model by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/challengers/{challengerId}/` Authentication requirements: `BearerAuth` Update challenger model. ### Body parameter ``` { "properties": { "name": { "description": "Human-readable name for the challenger.", "maxLength": 512, "type": "string" }, "predictionEnvironmentId": { "description": "ID of the Prediction Environment the challenger should use.", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | challengerId | path | string | true | Unique identifier of the challenger. | | body | body | ChallengerUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "ID of the challenger.", "type": "string" }, "model": { "description": "Model of the challenger.", "properties": { "datasetName": { "description": "Name of dataset used to train challenger model", "type": "string" }, "description": { "description": "Description of the model.", "type": "string" }, "executionType": { "description": "Type of the current model.", "type": "string" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "name": { "description": "Name of the model.", "type": "string" }, "projectId": { "description": "Project ID of the current model.", "type": "string" }, "projectName": { "description": "Project name of the current model.", "type": "string" } }, "required": [ "datasetName", "description", "executionType", "id", "isDeprecated", "name", "projectId", "projectName" ], "type": "object" }, "modelPackage": { "description": "modelPackage of the challenger.", "properties": { "id": { "description": "ID of the model package.", "type": "string" }, "name": { "description": "Type of the current model.", "type": "string" }, "registeredModelId": { "description": "ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "name": { "description": "Name of the challenger.", "type": "string" }, "predictionEnvironment": { "description": "Prediction environment used by the challenger", "properties": { "id": { "description": "ID of the prediction environment.", "type": "string" }, "name": { "description": "Name of the prediction environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "id", "model", "modelPackage", "name" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Model successfully updated. | ChallengerResponse | ## Retrieve information about the champion model package by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/championModelPackage/` Authentication requirements: `BearerAuth` Retrieve information about the champion model package. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string", "x-versionadded": "v2.33" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean", "x-versionadded": "v2.33" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string", "x-versionadded": "v2.33" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Information retrieved successfully. | ModelPackageRetrieveResponseBase | # Schemas ## ChallengerCreate ``` { "properties": { "modelPackageId": { "description": "ID of the model package to add as a challenger.", "type": "string" }, "name": { "description": "Human-readable name for the challenger.", "maxLength": 512, "type": "string" }, "predictionEnvironmentId": { "description": "ID of the Prediction Environment the challenger should use. If prediction environments are enabled, this is required", "type": "string" } }, "required": [ "modelPackageId", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelPackageId | string | true | | ID of the model package to add as a challenger. | | name | string | true | maxLength: 512 | Human-readable name for the challenger. | | predictionEnvironmentId | string | false | | ID of the Prediction Environment the challenger should use. If prediction environments are enabled, this is required | ## ChallengerListResponse ``` { "properties": { "data": { "description": "List of challengers.", "items": { "properties": { "id": { "description": "ID of the challenger.", "type": "string" }, "model": { "description": "Model of the challenger.", "properties": { "datasetName": { "description": "Name of dataset used to train challenger model", "type": "string" }, "description": { "description": "Description of the model.", "type": "string" }, "executionType": { "description": "Type of the current model.", "type": "string" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "name": { "description": "Name of the model.", "type": "string" }, "projectId": { "description": "Project ID of the current model.", "type": "string" }, "projectName": { "description": "Project name of the current model.", "type": "string" } }, "required": [ "datasetName", "description", "executionType", "id", "isDeprecated", "name", "projectId", "projectName" ], "type": "object" }, "modelPackage": { "description": "modelPackage of the challenger.", "properties": { "id": { "description": "ID of the model package.", "type": "string" }, "name": { "description": "Type of the current model.", "type": "string" }, "registeredModelId": { "description": "ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "name": { "description": "Name of the challenger.", "type": "string" }, "predictionEnvironment": { "description": "Prediction environment used by the challenger", "properties": { "id": { "description": "ID of the prediction environment.", "type": "string" }, "name": { "description": "Name of the prediction environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "id", "model", "modelPackage", "name" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [ChallengerResponse] | true | | List of challengers. | ## ChallengerResponse ``` { "properties": { "id": { "description": "ID of the challenger.", "type": "string" }, "model": { "description": "Model of the challenger.", "properties": { "datasetName": { "description": "Name of dataset used to train challenger model", "type": "string" }, "description": { "description": "Description of the model.", "type": "string" }, "executionType": { "description": "Type of the current model.", "type": "string" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "name": { "description": "Name of the model.", "type": "string" }, "projectId": { "description": "Project ID of the current model.", "type": "string" }, "projectName": { "description": "Project name of the current model.", "type": "string" } }, "required": [ "datasetName", "description", "executionType", "id", "isDeprecated", "name", "projectId", "projectName" ], "type": "object" }, "modelPackage": { "description": "modelPackage of the challenger.", "properties": { "id": { "description": "ID of the model package.", "type": "string" }, "name": { "description": "Type of the current model.", "type": "string" }, "registeredModelId": { "description": "ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "name": { "description": "Name of the challenger.", "type": "string" }, "predictionEnvironment": { "description": "Prediction environment used by the challenger", "properties": { "id": { "description": "ID of the prediction environment.", "type": "string" }, "name": { "description": "Name of the prediction environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "id", "model", "modelPackage", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the challenger. | | model | ModelResponse | true | | Model of the challenger. | | modelPackage | ModelPackageResponse | true | | modelPackage of the challenger. | | name | string | true | | Name of the challenger. | | predictionEnvironment | PredictionEnvironmentResponse | false | | Prediction environment used by the challenger | ## ChallengerScore ``` { "properties": { "timestamp": { "description": "The date and time in ISO8601 format, challenger models will be scored on data starting from deployment creation until timestamp. If not specified UTC current time is used.", "format": "date-time", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timestamp | string(date-time) | false | | The date and time in ISO8601 format, challenger models will be scored on data starting from deployment creation until timestamp. If not specified UTC current time is used. | ## ChallengerUpdate ``` { "properties": { "name": { "description": "Human-readable name for the challenger.", "maxLength": 512, "type": "string" }, "predictionEnvironmentId": { "description": "ID of the Prediction Environment the challenger should use.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | maxLength: 512 | Human-readable name for the challenger. | | predictionEnvironmentId | string | false | | ID of the Prediction Environment the challenger should use. | ## ChallengersReplaySettings ``` { "properties": { "enabled": { "description": "Identifies whether scheduled replay is enabled.", "type": "boolean", "x-versionadded": "v2.33" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | false | | Identifies whether scheduled replay is enabled. | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | ## CustomModelDetails ``` { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } ``` Details of the custom model associated to this registered model version ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string | true | | The time when the custom model was created. | | creatorEmail | string,null | false | | The email of the user who created the custom model. | | creatorId | string | true | | The ID of the creator of the custom model. | | creatorName | string,null | false | | The name of the user who created the custom model. | | id | string | true | | The ID of the associated custom model. | | versionLabel | string,null | false | | The label of the associated custom model version. | ## MlpkgFileContents ``` { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" } ``` Information about the content of .mlpkg artifact ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allTimeSeriesPredictionIntervals | boolean,null | false | | Whether .mlpkg contains TS prediction intervals computed for all percentiles | ## ModelPackageCapabilities ``` { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" } ``` Capabilities of the current model package. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | supportsAutomaticActuals | boolean | false | | Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package. | | supportsChallengerModels | boolean | true | | Whether Challenger Models are supported by this model package. | | supportsFeatureDriftTracking | boolean | true | | Whether Feature Drift is supported by this model package. | | supportsHumilityRecommendedRules | boolean | true | | Whether calculating values for recommended Humility Rules is supported by this model package. | | supportsHumilityRules | boolean | true | | Whether Humility Rules are supported by this model package. | | supportsHumilityRulesDefaultCalculations | boolean | true | | Whether calculating default values for Humility Rules is supported by this model package. | | supportsPredictionWarning | boolean | true | | Whether Prediction Warnings are supported by this model package. | | supportsRetraining | boolean | false | | Whether deployment supports retraining. | | supportsScoringCodeDownload | boolean | false | | Whether scoring code download is supported by this model package. | | supportsSecondaryDatasets | boolean | true | | If the deployments supports secondary datasets. | | supportsSegmentedAnalysisDriftAndAccuracy | boolean | true | | Whether tracking features in training and predictions data for segmented analysis is supported by this model package. | | supportsShapBasedPredictionExplanations | boolean | true | | Whether shap-based prediction explanations are supported by this model package. | | supportsTargetDriftTracking | boolean | true | | Whether Target Drift is supported by this model package. | ## ModelPackageDatasets ``` { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" } ``` dataset information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineSegmentedBy | [string] | true | | Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value. | | datasetName | string,null | true | | Name of dataset used to train the model | | holdoutDataCatalogId | string,null | true | | ID for holdout data (returned from uploading a data set) | | holdoutDataCatalogVersionId | string,null | true | | Version ID for holdout data (returned from uploading a data set) | | holdoutDataCreatedAt | string,null | false | | Time when the holdout data item was created | | holdoutDataCreatorEmail | string,null | false | | Email of the user who created the holdout data item | | holdoutDataCreatorId | string,null | false | | ID of the creator of the holdout data item | | holdoutDataCreatorName | string,null | false | | Name of the user who created the holdout data item | | holdoutDatasetName | string,null | true | | Name of dataset used for model holdout | | targetHistogramBaseline | string | false | | Values used to establish the training baseline | | trainingDataCatalogId | string,null | true | | ID for training data (returned from uploading a data set) | | trainingDataCatalogVersionId | string,null | true | | Version ID for training data (returned from uploading a data set) | | trainingDataCreatedAt | string,null | false | | Time when the training data item was created | | trainingDataCreatorEmail | string,null | false | | Email of the user who created the training data item | | trainingDataCreatorId | string,null | false | | ID of the creator of the training data item | | trainingDataCreatorName | string,null | false | | Name of the user who created the training data item | | trainingDataSize | integer | false | | Number of rows in training data (used by DR models) | ### Enumerated Values | Property | Value | | --- | --- | | targetHistogramBaseline | [predictions, actuals] | ## ModelPackageImportMeta ``` { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" } ``` Information from when this Model Package was first saved ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | containsFearPipeline | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with fear pipeline. | | containsFeaturelists | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with featurelists. | | containsLeaderboardMeta | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with leaderboard meta. | | containsProjectMeta | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with project meta. | | creatorFullName | string,null | true | | The full name of the person who created this model package. | | creatorId | string | true | | The user ID of the person who created this model package. | | creatorUsername | string | true | | The username of the person who created this model package. | | dateCreated | string | true | | When this model package was created. | | originalFileName | string,null | true | | Exists for imported models only, the original file name that was uploaded | ## ModelPackageModelDescription ``` { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" } ``` model description information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildEnvironmentType | string | true | | build environment type of the model | | description | string,null | true | | a description of the model | | location | string,null | true | | location of the model | | modelCreatedAt | string,null | false | | time when the model was created | | modelCreatorEmail | string,null | false | | email of the user who created the model | | modelCreatorId | string,null | false | | ID of the creator of the model | | modelCreatorName | string,null | false | | name of the user who created the model | | modelName | string | false | | model name | ### Enumerated Values | Property | Value | | --- | --- | | buildEnvironmentType | [DataRobot, Python, R, Java, Other] | ## ModelPackageModelKind ``` { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" } ``` Model attribute information ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isAnomalyDetectionModel | boolean | true | | true if this is an anomaly detection model | | isCombinedModel | boolean | true | | true if model is a combined model | | isFeatureDiscovery | boolean | true | | true if this model uses the Feature Discovery feature | | isMultiseries | boolean | true | | true if model is multiseries | | isTimeSeries | boolean | true | | true if model is time series | | isUnsupervisedLearning | boolean | true | | true if model used unsupervised learning | ## ModelPackageResponse ``` { "description": "modelPackage of the challenger.", "properties": { "id": { "description": "ID of the model package.", "type": "string" }, "name": { "description": "Type of the current model.", "type": "string" }, "registeredModelId": { "description": "ID of the associated registered model", "type": [ "string", "null" ] } }, "required": [ "id", "name" ], "type": "object" } ``` modelPackage of the challenger. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the model package. | | name | string | true | | Type of the current model. | | registeredModelId | string,null | false | | ID of the associated registered model | ## ModelPackageRetrieveResponseBase ``` { "properties": { "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string", "x-versionadded": "v2.33" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean", "x-versionadded": "v2.33" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string", "x-versionadded": "v2.33" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildStatus | string,null | false | | Model package build status | | capabilities | ModelPackageCapabilities | true | | Capabilities of the current model package. | | datasets | ModelPackageDatasets | true | | dataset information for the model package | | id | string | true | | The ID of the model package. | | importMeta | ModelPackageImportMeta | true | | Information from when this Model Package was first saved | | isArchived | boolean | true | | Whether the model package is permanently archived (cannot be used in deployment or replacement) | | isDeprecated | boolean | true | | Whether the model package is deprecated. eg. python2 models are deprecated. | | mlpkgFileContents | MlpkgFileContents | false | | Information about the content of .mlpkg artifact | | modelDescription | ModelPackageModelDescription | true | | model description information for the model package | | modelExecutionType | string | true | | Type of model package. dedicated (native DataRobot models) and custom_inference_model (user added inference models) both execute on DataRobot prediction servers, external do not | | modelId | string | true | | The ID of the model. | | modelKind | ModelPackageModelKind | true | | Model attribute information | | name | string | true | | The model package name. | | sourceMeta | ModelPackageSourceMeta | true | | Meta information from where this model was generated | | target | ModelPackageTarget | true | | target information for the model package | | timeseries | ModelPackageTimeseries | true | | time series information for the model package | | updatedBy | UserMetadata | true | | Information on the user who last modified the registered model | | userProvidedId | string | false | | A user-provided unique ID associated with the given custom inference model. | ### Enumerated Values | Property | Value | | --- | --- | | buildStatus | [inProgress, complete, failed] | | modelExecutionType | [dedicated, custom_inference_model, external] | ## ModelPackageScoringCodeMeta ``` { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" } ``` If available, information about the model's scoring code ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataRobotPredictionVersion | string,null | true | | The DataRobot prediction API version for the scoring code. | | location | string,null | true | | The location of the scoring code. | ### Enumerated Values | Property | Value | | --- | --- | | location | [local_leaderboard, mlpkg] | ## ModelPackageSourceMeta ``` { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" } ``` Meta information from where this model was generated ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelDetails | CustomModelDetails | false | | Details of the custom model associated to this registered model version | | environmentUrl | string,null(uri) | true | | If available, URL of the source model | | fips_140_2Enabled | boolean | false | | true if the model was built with FIPS-140-2 | | projectCreatedAt | string,null | false | | If available, the time when the project was created. | | projectCreatorEmail | string,null | false | | If available, the email of the user who created the project. | | projectCreatorId | string,null | false | | If available, the ID of the creator of the project. | | projectCreatorName | string,null | false | | If available, the name of the user who created the project. | | projectId | string,null | true | | If available, the project ID used for this model. | | projectName | string,null | true | | If available, the project name for this model. | | scoringCode | ModelPackageScoringCodeMeta | true | | If available, information about the model's scoring code | | useCaseDetails | UseCaseDetails | false | | Details of the use-case associated to this registered model version | ## ModelPackageTarget ``` { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" } ``` target information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classCount | integer,null | true | minimum: 0 | Number of classes for classification models. | | classNames | [string] | true | maxItems: 100 | Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass. | | name | string,null | true | | Name of the target column | | predictionProbabilitiesColumn | string,null | true | | Field or column name containing prediction probabilities | | predictionThreshold | number,null | true | maximum: 1minimum: 0 | Prediction threshold used for binary classification models | | type | string | true | | Target type of the model. | ### Enumerated Values | Property | Value | | --- | --- | | type | [Binary, Regression, Multiclass, Multilabel, TextGeneration, GeoPoint, AgenticWorkflow, MCP] | ## ModelPackageTimeseries ``` { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" } ``` time series information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimeColumnFormat | string,null | true | | Date format for forecast date and forecast point column | | datetimeColumnName | string,null | true | | Name of the forecast date column | | effectiveFeatureDerivationWindowEnd | integer,null | true | maximum: 0 | Same concept as featureDerivationWindowEnd which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the "real" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. | | effectiveFeatureDerivationWindowStart | integer,null | true | maximum: 0 | Same concept as featureDerivationWindowStart which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the "real" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. | | featureDerivationWindowEnd | integer,null | true | maximum: 0 | Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end. | | featureDerivationWindowStart | integer,null | true | maximum: 0 | Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin. | | forecastDistanceColumnName | string,null | true | | Name of the forecast distance column | | forecastDistances | [integer] | true | | List of integer forecast distances | | forecastDistancesTimeUnit | string,null | true | | The time unit of forecast distances | | forecastPointColumnName | string,null | true | | Name of the forecast point column | | isCrossSeries | boolean,null | true | | true if the model is cross-series. | | isNewSeriesSupport | boolean,null | true | | true if the model is optimized to support new series. | | isTraditionalTimeSeries | boolean,null | true | | true if the model is traditional time series. | | seriesColumnName | string,null | true | | Name of the series column in case of multi-series date | ### Enumerated Values | Property | Value | | --- | --- | | forecastDistancesTimeUnit | [MICROSECOND, MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | ## ModelResponse ``` { "description": "Model of the challenger.", "properties": { "datasetName": { "description": "Name of dataset used to train challenger model", "type": "string" }, "description": { "description": "Description of the model.", "type": "string" }, "executionType": { "description": "Type of the current model.", "type": "string" }, "id": { "description": "ID of the current model.", "type": "string" }, "isDeprecated": { "description": "Whether the current model is deprecated model. eg. python2 based model.", "type": "boolean", "x-versionadded": "v2.29" }, "name": { "description": "Name of the model.", "type": "string" }, "projectId": { "description": "Project ID of the current model.", "type": "string" }, "projectName": { "description": "Project name of the current model.", "type": "string" } }, "required": [ "datasetName", "description", "executionType", "id", "isDeprecated", "name", "projectId", "projectName" ], "type": "object" } ``` Model of the challenger. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetName | string | true | | Name of dataset used to train challenger model | | description | string | true | | Description of the model. | | executionType | string | true | | Type of the current model. | | id | string | true | | ID of the current model. | | isDeprecated | boolean | true | | Whether the current model is deprecated model. eg. python2 based model. | | name | string | true | | Name of the model. | | projectId | string | true | | Project ID of the current model. | | projectName | string | true | | Project name of the current model. | ## PredictionEnvironmentResponse ``` { "description": "Prediction environment used by the challenger", "properties": { "id": { "description": "ID of the prediction environment.", "type": "string" }, "name": { "description": "Name of the prediction environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` Prediction environment used by the challenger ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the prediction environment. | | name | string | true | | Name of the prediction environment. | ## Schedule ``` { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" } ``` The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dayOfMonth | [number,string] | true | maxItems: 31 | The date(s) of the month that the job will run. Allowed values are either [1 ... 31] or ["*"] for all days of the month. This field is additive with dayOfWeek, meaning the job will run both on the date(s) defined in this field and the day specified by dayOfWeek (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If dayOfMonth is set to ["*"] and dayOfWeek is defined, the scheduler will trigger on every day of the month that matches dayOfWeek (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored. | | dayOfWeek | [number,string] | true | maxItems: 7 | The day(s) of the week that the job will run. Allowed values are [0 .. 6], where (Sunday=0), or ["*"], for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., "sunday", "Sunday", "sun", or "Sun", all map to [0]. This field is additive with dayOfMonth, meaning the job will run both on the date specified by dayOfMonth and the day defined in this field. | | hour | [number,string] | true | maxItems: 24 | The hour(s) of the day that the job will run. Allowed values are either ["*"] meaning every hour of the day or [0 ... 23]. | | minute | [number,string] | true | maxItems: 60 | The minute(s) of the day that the job will run. Allowed values are either ["*"] meaning every minute of the day or[0 ... 59]. | | month | [number,string] | true | maxItems: 12 | The month(s) of the year that the job will run. Allowed values are either [1 ... 12] or ["*"] for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., "jan" or "october"). Months that are not compatible with dayOfMonth are ignored, for example {"dayOfMonth": [31], "month":["feb"]}. | ## UseCaseDetails ``` { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } ``` Details of the use-case associated to this registered model version ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string | true | | The time when the use case was created. | | creatorEmail | string,null | false | | The email of the user who created the use case. | | creatorId | string | true | | The ID of the creator of the use case. | | creatorName | string,null | false | | The name of the user who created the use case. | | id | string | true | | The ID of the associated use case. | | name | string,null | false | | The name of the use case at the moment of creation. | ## UserMetadata ``` { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" } ``` Information on the user who last modified the registered model ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string,null | true | | The email of the user. | | id | string | true | | The ID of the user. | | name | string,null | true | | The full name of the user. | --- # Humility URL: https://docs.datarobot.com/en/docs/api/reference/public-api/mitigation_humility.html > Use the endpoints described below to manage humility. # Humility Use the endpoints described below to manage humility. ## Retrieve humility stats by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/humilityStats/` Authentication requirements: `BearerAuth` Retrieve humility rule service triggers statistics overview. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | bucketSize | query | string(duration) | false | The time duration of a bucket. Needs to be multiple of one hour. Can not be longer than the total length of the period. If not set, a default value will be calculated based on the start and end time. | | modelId | query | string | false | The id of the model for which metrics are being retrieved. | | segmentAttribute | query | string | false | The name of a segment attribute used for segment analysis. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | segmentAttribute | [DataRobot-Consumer, DataRobot-Remote-IP, DataRobot-Host-IP] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "metrics rules", "items": { "properties": { "ruleId": { "description": "Id of the humility rule.", "type": "string" }, "ruleName": { "description": "Name of the rule.", "type": "string" }, "value": { "description": "Number of times the rule was triggered.", "type": "integer" } }, "required": [ "ruleId", "ruleName", "value" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "data", "period" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Humility service health statistics overview retrieved. | HumilityStatsResponse | | 403 | Forbidden | Model Deployments and/or Monitoring are not enabled. | None | | 404 | Not Found | Either the deployment does not exist or user does not have permission to view the deployment. | None | ## Retrieve humility stats over time by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/humilityStatsOverTime/` Authentication requirements: `BearerAuth` Retrieve humility service statistics over time. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | bucketSize | query | string(duration) | false | The time duration of a bucket. Needs to be multiple of one hour. Can not be longer than the total length of the period. If not set, a default value will be calculated based on the start and end time. | | modelId | query | string | false | The id of the model for which metrics are being retrieved. | | segmentAttribute | query | string | false | The name of a segment attribute used for segment analysis. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | segmentAttribute | [DataRobot-Consumer, DataRobot-Remote-IP, DataRobot-Host-IP] | ### Example responses > 200 Response ``` { "properties": { "buckets": { "description": "An array of `bucket` objects, representing service health stats of the deployment over time", "items": { "description": "A `bucket` object covering whole `start`/`end` time range", "properties": { "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "values": { "description": "Rules response objects.", "items": { "properties": { "ruleId": { "description": "Id of the humility rule.", "type": "string" }, "ruleName": { "description": "Name of the rule.", "type": "string" }, "value": { "description": "Number of times the rule was triggered.", "type": "integer" } }, "required": [ "ruleId", "ruleName", "value" ], "type": "object" }, "type": "array" } }, "required": [ "period", "values" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "summary": { "description": "A `bucket` object covering whole `start`/`end` time range", "properties": { "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "values": { "description": "Rules response objects.", "items": { "properties": { "ruleId": { "description": "Id of the humility rule.", "type": "string" }, "ruleName": { "description": "Name of the rule.", "type": "string" }, "value": { "description": "Number of times the rule was triggered.", "type": "integer" } }, "required": [ "ruleId", "ruleName", "value" ], "type": "object" }, "type": "array" } }, "required": [ "period", "values" ], "type": "object" } }, "required": [ "buckets", "summary" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Humility statistics for deployment retrieved. | HumilityStatsOverTimeResponse | | 403 | Forbidden | Model Deployments and/or Monitoring are not enabled. | None | | 404 | Not Found | Either the deployment does not exist or user does not have permission to view the deployment. | None | # Schemas ## HumilityStatsBucket ``` { "description": "A `bucket` object covering whole `start`/`end` time range", "properties": { "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "values": { "description": "Rules response objects.", "items": { "properties": { "ruleId": { "description": "Id of the humility rule.", "type": "string" }, "ruleName": { "description": "Name of the rule.", "type": "string" }, "value": { "description": "Number of times the rule was triggered.", "type": "integer" } }, "required": [ "ruleId", "ruleName", "value" ], "type": "object" }, "type": "array" } }, "required": [ "period", "values" ], "type": "object" } ``` A `bucket` object covering whole `start` / `end` time range ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | period | TimeRange | true | | An object with the keys "start" and "end" defining the period. | | values | [HumilityStatsRule] | true | | Rules response objects. | ## HumilityStatsOverTimeResponse ``` { "properties": { "buckets": { "description": "An array of `bucket` objects, representing service health stats of the deployment over time", "items": { "description": "A `bucket` object covering whole `start`/`end` time range", "properties": { "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "values": { "description": "Rules response objects.", "items": { "properties": { "ruleId": { "description": "Id of the humility rule.", "type": "string" }, "ruleName": { "description": "Name of the rule.", "type": "string" }, "value": { "description": "Number of times the rule was triggered.", "type": "integer" } }, "required": [ "ruleId", "ruleName", "value" ], "type": "object" }, "type": "array" } }, "required": [ "period", "values" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "summary": { "description": "A `bucket` object covering whole `start`/`end` time range", "properties": { "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "values": { "description": "Rules response objects.", "items": { "properties": { "ruleId": { "description": "Id of the humility rule.", "type": "string" }, "ruleName": { "description": "Name of the rule.", "type": "string" }, "value": { "description": "Number of times the rule was triggered.", "type": "integer" } }, "required": [ "ruleId", "ruleName", "value" ], "type": "object" }, "type": "array" } }, "required": [ "period", "values" ], "type": "object" } }, "required": [ "buckets", "summary" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [HumilityStatsBucket] | true | | An array of bucket objects, representing service health stats of the deployment over time | | modelId | string | false | | The id of the model for which metrics are being retrieved. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | | summary | HumilityStatsBucket | true | | A bucket object covering whole start/end time range | ## HumilityStatsResponse ``` { "properties": { "data": { "description": "metrics rules", "items": { "properties": { "ruleId": { "description": "Id of the humility rule.", "type": "string" }, "ruleName": { "description": "Name of the rule.", "type": "string" }, "value": { "description": "Number of times the rule was triggered.", "type": "integer" } }, "required": [ "ruleId", "ruleName", "value" ], "type": "object" }, "type": "array" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "data", "period" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [HumilityStatsRule] | true | | metrics rules | | modelId | string | false | | The id of the model for which metrics are being retrieved. | | period | TimeRange | true | | An object with the keys "start" and "end" defining the period. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | ## HumilityStatsRule ``` { "properties": { "ruleId": { "description": "Id of the humility rule.", "type": "string" }, "ruleName": { "description": "Name of the rule.", "type": "string" }, "value": { "description": "Number of times the rule was triggered.", "type": "integer" } }, "required": [ "ruleId", "ruleName", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ruleId | string | true | | Id of the humility rule. | | ruleName | string | true | | Name of the rule. | | value | integer | true | | Number of times the rule was triggered. | ## TimeRange ``` { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } ``` An object with the keys "start" and "end" defining the period. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string,null(date-time) | false | | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | start | string,null(date-time) | false | | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | --- # Retraining URL: https://docs.datarobot.com/en/docs/api/reference/public-api/mitigation_retraining.html > Use the endpoints described below to manage retraining. To maintain model performance after deployment without extensive manual work, DataRobot provides an automatic retraining capability for deployments. Upon providing a retraining dataset registered in the Data Registry, you can define up to five retraining policies on each deployment. Each policy consists of a trigger, a modeling strategy, modeling settings, and a replacement action. When triggered, retraining will produce a new model based on these settings and notify you to consider promoting it. # Retraining Use the endpoints described below to manage retraining. To maintain model performance after deployment without extensive manual work, DataRobot provides an automatic retraining capability for deployments. Upon providing a retraining dataset registered in the Data Registry, you can define up to five retraining policies on each deployment. Each policy consists of a trigger, a modeling strategy, modeling settings, and a replacement action. When triggered, retraining will produce a new model based on these settings and notify you to consider promoting it. ## Endpoint by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/retrainingPolicies/` Authentication requirements: `BearerAuth` Retrieve a list of deployment retraining policies. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of retraining policies.", "items": { "properties": { "action": { "description": "Configure the action to take on the resultant new model.", "enum": [ "create_challenger", "create_model_package", "model_replacement" ], "type": "string" }, "autopilotOptions": { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" }, "customJob": { "description": "The ID, schedule, and last run for the custom job.", "properties": { "id": { "description": "The ID of the custom job associated with the policy.", "maxLength": 24, "type": "string" }, "lastRun": { "description": "The ISO-8601 timestamp of when the custom job was last run.", "maxLength": 256, "type": [ "string", "null" ] }, "status": { "description": "Status of the custom job run.", "type": [ "string", "null" ] } }, "required": [ "lastRun", "status" ], "type": "object", "x-versionadded": "v2.37" }, "description": { "description": "Description of the retraining policy.", "type": [ "string", "null" ] }, "featureListStrategy": { "description": "Configure the feature list strategy used for modeling.", "enum": [ "informative_features", "same_as_champion" ], "type": "string" }, "id": { "description": "ID of the retraining policy.", "type": "string" }, "latestRun": { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" }, "modelSelectionStrategy": { "description": "Configure how new model is selected when the retraining policy runs.", "enum": [ "autopilot_recommended", "same_blueprint", "same_hyperparameters", "custom_job" ], "type": "string" }, "name": { "description": "Name of the retraining policy.", "type": "string" }, "projectOptions": { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" }, "projectOptionsStrategy": { "description": "Configure the project option strategy used for modeling.", "enum": [ "same_as_champion", "override_champion", "custom" ], "type": "string" }, "timeSeriesOptions": { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "useCase": { "description": "The use case to link the retrained model to.", "properties": { "id": { "description": "ID of the use case.", "type": "string" }, "name": { "description": "Name of the use case.", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "action", "autopilotOptions", "description", "id", "latestRun", "modelSelectionStrategy", "name", "projectOptions", "trigger" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve a list of deployment retraining policies. | RetrainingPolicyListResponse | | 404 | Not Found | Deployment not found. | None | ## Create retraining policies by id Operation path: `POST /api/v2/deployments/{deploymentId}/retrainingPolicies/` Authentication requirements: `BearerAuth` Create a deployment retraining policy. ### Body parameter ``` { "properties": { "action": { "description": "Configure the action to take on the resultant new model.", "enum": [ "create_challenger", "create_model_package", "model_replacement" ], "type": "string" }, "autopilotOptions": { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" }, "customJobId": { "description": "The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "description": { "default": null, "description": "Description of the retraining policy.", "maxLength": 10000, "type": [ "string", "null" ] }, "featureListStrategy": { "description": "Configure the feature list strategy used for modeling.", "enum": [ "informative_features", "same_as_champion" ], "type": "string" }, "modelSelectionStrategy": { "description": "Configure how new model is selected when the retraining policy runs.", "enum": [ "autopilot_recommended", "same_blueprint", "same_hyperparameters", "custom_job" ], "type": "string" }, "name": { "description": "Name of the retraining policy.", "maxLength": 512, "type": "string" }, "projectOptions": { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" }, "projectOptionsStrategy": { "description": "Configure the project option strategy used for modeling.", "enum": [ "same_as_champion", "override_champion", "custom" ], "type": "string" }, "timeSeriesOptions": { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "useCaseId": { "description": "The ID of the use case to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "description", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | RetrainingPolicyCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "action": { "description": "Configure the action to take on the resultant new model.", "enum": [ "create_challenger", "create_model_package", "model_replacement" ], "type": "string" }, "autopilotOptions": { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" }, "customJob": { "description": "The ID, schedule, and last run for the custom job.", "properties": { "id": { "description": "The ID of the custom job associated with the policy.", "maxLength": 24, "type": "string" }, "lastRun": { "description": "The ISO-8601 timestamp of when the custom job was last run.", "maxLength": 256, "type": [ "string", "null" ] }, "status": { "description": "Status of the custom job run.", "type": [ "string", "null" ] } }, "required": [ "lastRun", "status" ], "type": "object", "x-versionadded": "v2.37" }, "description": { "description": "Description of the retraining policy.", "type": [ "string", "null" ] }, "featureListStrategy": { "description": "Configure the feature list strategy used for modeling.", "enum": [ "informative_features", "same_as_champion" ], "type": "string" }, "id": { "description": "ID of the retraining policy.", "type": "string" }, "latestRun": { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" }, "modelSelectionStrategy": { "description": "Configure how new model is selected when the retraining policy runs.", "enum": [ "autopilot_recommended", "same_blueprint", "same_hyperparameters", "custom_job" ], "type": "string" }, "name": { "description": "Name of the retraining policy.", "type": "string" }, "projectOptions": { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" }, "projectOptionsStrategy": { "description": "Configure the project option strategy used for modeling.", "enum": [ "same_as_champion", "override_champion", "custom" ], "type": "string" }, "timeSeriesOptions": { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "useCase": { "description": "The use case to link the retrained model to.", "properties": { "id": { "description": "ID of the use case.", "type": "string" }, "name": { "description": "Name of the use case.", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "action", "autopilotOptions", "description", "id", "latestRun", "modelSelectionStrategy", "name", "projectOptions", "trigger" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RetrainingPolicyRetrieve | | 404 | Not Found | Deployment not found. | None | ## Delete retraining policies by id Operation path: `DELETE /api/v2/deployments/{deploymentId}/retrainingPolicies/{retrainingPolicyId}/` Authentication requirements: `BearerAuth` Delete a deployment retraining policy. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | retrainingPolicyId | path | string | true | ID of the retraining policy. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Delete a deployment retraining policy. | None | | 404 | Not Found | Deployment or retraining policy not found. | None | ## Retrieve retraining policies by id Operation path: `GET /api/v2/deployments/{deploymentId}/retrainingPolicies/{retrainingPolicyId}/` Authentication requirements: `BearerAuth` Retrieve a deployment retraining policy. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | retrainingPolicyId | path | string | true | ID of the retraining policy. | ### Example responses > 200 Response ``` { "properties": { "action": { "description": "Configure the action to take on the resultant new model.", "enum": [ "create_challenger", "create_model_package", "model_replacement" ], "type": "string" }, "autopilotOptions": { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" }, "customJob": { "description": "The ID, schedule, and last run for the custom job.", "properties": { "id": { "description": "The ID of the custom job associated with the policy.", "maxLength": 24, "type": "string" }, "lastRun": { "description": "The ISO-8601 timestamp of when the custom job was last run.", "maxLength": 256, "type": [ "string", "null" ] }, "status": { "description": "Status of the custom job run.", "type": [ "string", "null" ] } }, "required": [ "lastRun", "status" ], "type": "object", "x-versionadded": "v2.37" }, "description": { "description": "Description of the retraining policy.", "type": [ "string", "null" ] }, "featureListStrategy": { "description": "Configure the feature list strategy used for modeling.", "enum": [ "informative_features", "same_as_champion" ], "type": "string" }, "id": { "description": "ID of the retraining policy.", "type": "string" }, "latestRun": { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" }, "modelSelectionStrategy": { "description": "Configure how new model is selected when the retraining policy runs.", "enum": [ "autopilot_recommended", "same_blueprint", "same_hyperparameters", "custom_job" ], "type": "string" }, "name": { "description": "Name of the retraining policy.", "type": "string" }, "projectOptions": { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" }, "projectOptionsStrategy": { "description": "Configure the project option strategy used for modeling.", "enum": [ "same_as_champion", "override_champion", "custom" ], "type": "string" }, "timeSeriesOptions": { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "useCase": { "description": "The use case to link the retrained model to.", "properties": { "id": { "description": "ID of the use case.", "type": "string" }, "name": { "description": "Name of the use case.", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "action", "autopilotOptions", "description", "id", "latestRun", "modelSelectionStrategy", "name", "projectOptions", "trigger" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve a deployment retraining policy. | RetrainingPolicyRetrieve | | 404 | Not Found | Deployment or retraining policy not found. | None | ## Modify retraining policies by id Operation path: `PATCH /api/v2/deployments/{deploymentId}/retrainingPolicies/{retrainingPolicyId}/` Authentication requirements: `BearerAuth` Update a deployment retraining policy. ### Body parameter ``` { "properties": { "action": { "description": "Configure the action to take on the resultant new model.", "enum": [ "create_challenger", "create_model_package", "model_replacement" ], "type": "string" }, "autopilotOptions": { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" }, "customJobId": { "description": "The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "description": { "description": "Description of the retraining policy.", "maxLength": 10000, "type": [ "string", "null" ] }, "featureListStrategy": { "description": "Configure the feature list strategy used for modeling.", "enum": [ "informative_features", "same_as_champion" ], "type": "string" }, "modelSelectionStrategy": { "description": "Configure how new model is selected when the retraining policy runs.", "enum": [ "autopilot_recommended", "same_blueprint", "same_hyperparameters", "custom_job" ], "type": "string" }, "name": { "description": "Name of the retraining policy.", "maxLength": 512, "type": "string" }, "projectOptions": { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" }, "projectOptionsStrategy": { "description": "Configure the project option strategy used for modeling.", "enum": [ "same_as_champion", "override_champion", "custom" ], "type": "string" }, "timeSeriesOptions": { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "useCaseId": { "description": "The ID of the use case to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | retrainingPolicyId | path | string | true | ID of the retraining policy. | | body | body | RetrainingPolicyUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "action": { "description": "Configure the action to take on the resultant new model.", "enum": [ "create_challenger", "create_model_package", "model_replacement" ], "type": "string" }, "autopilotOptions": { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" }, "customJob": { "description": "The ID, schedule, and last run for the custom job.", "properties": { "id": { "description": "The ID of the custom job associated with the policy.", "maxLength": 24, "type": "string" }, "lastRun": { "description": "The ISO-8601 timestamp of when the custom job was last run.", "maxLength": 256, "type": [ "string", "null" ] }, "status": { "description": "Status of the custom job run.", "type": [ "string", "null" ] } }, "required": [ "lastRun", "status" ], "type": "object", "x-versionadded": "v2.37" }, "description": { "description": "Description of the retraining policy.", "type": [ "string", "null" ] }, "featureListStrategy": { "description": "Configure the feature list strategy used for modeling.", "enum": [ "informative_features", "same_as_champion" ], "type": "string" }, "id": { "description": "ID of the retraining policy.", "type": "string" }, "latestRun": { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" }, "modelSelectionStrategy": { "description": "Configure how new model is selected when the retraining policy runs.", "enum": [ "autopilot_recommended", "same_blueprint", "same_hyperparameters", "custom_job" ], "type": "string" }, "name": { "description": "Name of the retraining policy.", "type": "string" }, "projectOptions": { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" }, "projectOptionsStrategy": { "description": "Configure the project option strategy used for modeling.", "enum": [ "same_as_champion", "override_champion", "custom" ], "type": "string" }, "timeSeriesOptions": { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "useCase": { "description": "The use case to link the retrained model to.", "properties": { "id": { "description": "ID of the use case.", "type": "string" }, "name": { "description": "Name of the use case.", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "action", "autopilotOptions", "description", "id", "latestRun", "modelSelectionStrategy", "name", "projectOptions", "trigger" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RetrainingPolicyRetrieve | | 404 | Not Found | Deployment or retraining policy not found. | None | ## Retrieve runs by id Operation path: `GET /api/v2/deployments/{deploymentId}/retrainingPolicies/{retrainingPolicyId}/runs/` Authentication requirements: `BearerAuth` Retrieve a list of deployment retraining policy runs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | deploymentId | path | string | true | Unique identifier of the deployment. | | retrainingPolicyId | path | string | true | ID of the retraining policy. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of retraining policy runs.", "items": { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve a list of deployment retraining policy runs. | RetrainingPolicyRunListResponse | | 404 | Not Found | Deployment or retraining policy not found. | None | ## Create runs by id Operation path: `POST /api/v2/deployments/{deploymentId}/retrainingPolicies/{retrainingPolicyId}/runs/` Authentication requirements: `BearerAuth` Initiate a deployment retraining policy run. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | retrainingPolicyId | path | string | true | ID of the retraining policy. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 404 | Not Found | Deployment or retraining policy not found. | None | ## Retrieve runs by id Operation path: `GET /api/v2/deployments/{deploymentId}/retrainingPolicies/{retrainingPolicyId}/runs/{runId}/` Authentication requirements: `BearerAuth` Retrieve a single deployment retraining policy run. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | retrainingPolicyId | path | string | true | ID of the retraining policy. | | runId | path | string | true | ID of the retraining policy run. | ### Example responses > 200 Response ``` { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve a single deployment retraining policy run. | RetrainingPolicyRunRetrieve | | 404 | Not Found | Deployment or retraining policy not found. | None | ## Modify runs by id Operation path: `PATCH /api/v2/deployments/{deploymentId}/retrainingPolicies/{retrainingPolicyId}/runs/{runId}/` Authentication requirements: `BearerAuth` Update a single deployment retraining policy run. ### Body parameter ``` { "properties": { "status": { "description": "New status of the retraining policy.", "enum": [ "cancelled" ], "type": "string" } }, "required": [ "status" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | retrainingPolicyId | path | string | true | ID of the retraining policy. | | runId | path | string | true | ID of the retraining policy run. | | body | body | RetrainingPolicyRunUpdate | false | none | ### Example responses > 200 Response ``` { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Update a single deployment retraining policy run. | RetrainingPolicyRunRetrieve | | 404 | Not Found | Deployment or retraining policy not found. | None | ## Retrieve retraining settings by id Operation path: `GET /api/v2/deployments/{deploymentId}/retrainingSettings/` Authentication requirements: `BearerAuth` Retrieve deployment retraining settings. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "dataset": { "description": "The dataset that will be used as retraining data.", "properties": { "id": { "description": "ID of the retraining dataset.", "type": "string" }, "name": { "description": "Name of the retraining dataset.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "predictionEnvironment": { "description": "The prediction environment that will be associated with the challengers created by retraining policies.", "properties": { "id": { "description": "ID of the prediction environment.", "type": "string" }, "name": { "description": "Name of the prediction environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "retrainingUser": { "description": "The user scheduled retraining will be performed on behalf of.", "properties": { "id": { "description": "ID of the scheduled retraining user.", "type": "string" }, "username": { "description": "Username of the scheduled retraining user.", "type": "string" } }, "required": [ "id", "username" ], "type": "object" } }, "required": [ "dataset", "predictionEnvironment", "retrainingUser" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve deployment retraining settings. | RetrainingSettingsRetrieve | | 404 | Not Found | Deployment not found. | None | ## Modify retraining settings by id Operation path: `PATCH /api/v2/deployments/{deploymentId}/retrainingSettings/` Authentication requirements: `BearerAuth` Update deployment retraining settings. ### Body parameter ``` { "properties": { "credentialId": { "description": "ID of the credential used to refresh retraining dataset.", "type": [ "string", "null" ] }, "datasetId": { "description": "ID of the retraining dataset.", "type": [ "string", "null" ] }, "predictionEnvironmentId": { "description": "ID of the prediction environment to associate with the challengers created by retraining policies.", "type": [ "string", "null" ] }, "retrainingUserId": { "description": "ID of the retraining user.", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | RetrainingSettingsUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Update deployment retraining settings. | None | | 404 | Not Found | Deployment not found. | None | # Schemas ## AutopilotOptions ``` { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" } ``` Options for projects used to build new models. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blendBestModels | boolean | false | | Blend best models during Autopilot run. This option is not supported in SHAP-only mode. | | mode | string | false | | The autopilot mode. | | runLeakageRemovedFeatureList | boolean | false | | Run Autopilot on Leakage Removed feature list (if exists). | | scoringCodeOnly | boolean | false | | Keep only models that can be converted to scorable java code during Autopilot run. | | shapOnlyMode | boolean | false | | Include only models with SHAP value support. | ### Enumerated Values | Property | Value | | --- | --- | | mode | [auto, comprehensive, quick] | ## CustomRetrainingJob ``` { "description": "The ID, schedule, and last run for the custom job.", "properties": { "id": { "description": "The ID of the custom job associated with the policy.", "maxLength": 24, "type": "string" }, "lastRun": { "description": "The ISO-8601 timestamp of when the custom job was last run.", "maxLength": 256, "type": [ "string", "null" ] }, "status": { "description": "Status of the custom job run.", "type": [ "string", "null" ] } }, "required": [ "lastRun", "status" ], "type": "object", "x-versionadded": "v2.37" } ``` The ID, schedule, and last run for the custom job. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | false | maxLength: 24 | The ID of the custom job associated with the policy. | | lastRun | string,null | true | maxLength: 256 | The ISO-8601 timestamp of when the custom job was last run. | | status | string,null | true | | Status of the custom job run. | ## Dataset ``` { "description": "The dataset that will be used as retraining data.", "properties": { "id": { "description": "ID of the retraining dataset.", "type": "string" }, "name": { "description": "Name of the retraining dataset.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` The dataset that will be used as retraining data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the retraining dataset. | | name | string | true | | Name of the retraining dataset. | ## Periodicity ``` { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timeSteps | integer | true | minimum: 0 | The number of time steps. | | timeUnit | string | true | | The time unit or ROW if windowsBasisUnit is ROW | ### Enumerated Values | Property | Value | | --- | --- | | timeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] | ## PredictionEnvironment ``` { "description": "The prediction environment that will be associated with the challengers created by retraining policies.", "properties": { "id": { "description": "ID of the prediction environment.", "type": "string" }, "name": { "description": "Name of the prediction environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` The prediction environment that will be associated with the challengers created by retraining policies. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the prediction environment. | | name | string | true | | Name of the prediction environment. | ## ProjectOptions ``` { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" } ``` Options for projects used to build new models. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cvMethod | string | false | | The partitioning method for projects used to build new models. | | holdoutPct | number,null | false | maximum: 98minimum: 0 | The percentage of dataset to assign to holdout set in projects used to build new models. | | metric | string,null | false | | The model selection metric in projects used to build new models. | | reps | integer,null | false | maximum: 50minimum: 2 | The number of cross validation folds to use for projects used to build new models. | | validationPct | number,null | false | maximum: 99minimum: 1 | The percentage of dataset to assign to validation set in projects used to build new models. | | validationType | string | false | | The validation type for projects used to build new models. | ### Enumerated Values | Property | Value | | --- | --- | | cvMethod | [RandomCV, StratifiedCV] | | metric | [Accuracy, AUC, Balanced Accuracy, FVE Binomial, Gini Norm, Kolmogorov-Smirnov, LogLoss, Rate@Top5%, Rate@Top10%, TPR, FPR, TNR, PPV, NPV, F1, MCC, FVE Gamma, FVE Poisson, FVE Tweedie, Gamma Deviance, MAE, MAPE, Poisson Deviance, R Squared, RMSE, RMSLE, Tweedie Deviance, null] | | validationType | [CV, TVH] | ## RetrainingPolicyCreate ``` { "properties": { "action": { "description": "Configure the action to take on the resultant new model.", "enum": [ "create_challenger", "create_model_package", "model_replacement" ], "type": "string" }, "autopilotOptions": { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" }, "customJobId": { "description": "The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "description": { "default": null, "description": "Description of the retraining policy.", "maxLength": 10000, "type": [ "string", "null" ] }, "featureListStrategy": { "description": "Configure the feature list strategy used for modeling.", "enum": [ "informative_features", "same_as_champion" ], "type": "string" }, "modelSelectionStrategy": { "description": "Configure how new model is selected when the retraining policy runs.", "enum": [ "autopilot_recommended", "same_blueprint", "same_hyperparameters", "custom_job" ], "type": "string" }, "name": { "description": "Name of the retraining policy.", "maxLength": 512, "type": "string" }, "projectOptions": { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" }, "projectOptionsStrategy": { "description": "Configure the project option strategy used for modeling.", "enum": [ "same_as_champion", "override_champion", "custom" ], "type": "string" }, "timeSeriesOptions": { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "useCaseId": { "description": "The ID of the use case to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "required": [ "description", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | false | | Configure the action to take on the resultant new model. | | autopilotOptions | AutopilotOptions | false | | Options for projects used to build new models. | | customJobId | string,null | false | | The ID of the custom job to be used in this policy. | | description | string,null | true | maxLength: 10000 | Description of the retraining policy. | | featureListStrategy | string | false | | Configure the feature list strategy used for modeling. | | modelSelectionStrategy | string | false | | Configure how new model is selected when the retraining policy runs. | | name | string | true | maxLength: 512 | Name of the retraining policy. | | projectOptions | ProjectOptions | false | | Options for projects used to build new models. | | projectOptionsStrategy | string | false | | Configure the project option strategy used for modeling. | | timeSeriesOptions | TimeSeriesOptions | false | | Time Series project option used to build new models. | | trigger | Trigger | false | | Retraining policy trigger. | | useCaseId | string,null | false | | The ID of the use case to be used in this policy. | ### Enumerated Values | Property | Value | | --- | --- | | action | [create_challenger, create_model_package, model_replacement] | | featureListStrategy | [informative_features, same_as_champion] | | modelSelectionStrategy | [autopilot_recommended, same_blueprint, same_hyperparameters, custom_job] | | projectOptionsStrategy | [same_as_champion, override_champion, custom] | ## RetrainingPolicyListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of retraining policies.", "items": { "properties": { "action": { "description": "Configure the action to take on the resultant new model.", "enum": [ "create_challenger", "create_model_package", "model_replacement" ], "type": "string" }, "autopilotOptions": { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" }, "customJob": { "description": "The ID, schedule, and last run for the custom job.", "properties": { "id": { "description": "The ID of the custom job associated with the policy.", "maxLength": 24, "type": "string" }, "lastRun": { "description": "The ISO-8601 timestamp of when the custom job was last run.", "maxLength": 256, "type": [ "string", "null" ] }, "status": { "description": "Status of the custom job run.", "type": [ "string", "null" ] } }, "required": [ "lastRun", "status" ], "type": "object", "x-versionadded": "v2.37" }, "description": { "description": "Description of the retraining policy.", "type": [ "string", "null" ] }, "featureListStrategy": { "description": "Configure the feature list strategy used for modeling.", "enum": [ "informative_features", "same_as_champion" ], "type": "string" }, "id": { "description": "ID of the retraining policy.", "type": "string" }, "latestRun": { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" }, "modelSelectionStrategy": { "description": "Configure how new model is selected when the retraining policy runs.", "enum": [ "autopilot_recommended", "same_blueprint", "same_hyperparameters", "custom_job" ], "type": "string" }, "name": { "description": "Name of the retraining policy.", "type": "string" }, "projectOptions": { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" }, "projectOptionsStrategy": { "description": "Configure the project option strategy used for modeling.", "enum": [ "same_as_champion", "override_champion", "custom" ], "type": "string" }, "timeSeriesOptions": { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "useCase": { "description": "The use case to link the retrained model to.", "properties": { "id": { "description": "ID of the use case.", "type": "string" }, "name": { "description": "Name of the use case.", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "action", "autopilotOptions", "description", "id", "latestRun", "modelSelectionStrategy", "name", "projectOptions", "trigger" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrainingPolicyRetrieve] | true | | List of retraining policies. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrainingPolicyRetrieve ``` { "properties": { "action": { "description": "Configure the action to take on the resultant new model.", "enum": [ "create_challenger", "create_model_package", "model_replacement" ], "type": "string" }, "autopilotOptions": { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" }, "customJob": { "description": "The ID, schedule, and last run for the custom job.", "properties": { "id": { "description": "The ID of the custom job associated with the policy.", "maxLength": 24, "type": "string" }, "lastRun": { "description": "The ISO-8601 timestamp of when the custom job was last run.", "maxLength": 256, "type": [ "string", "null" ] }, "status": { "description": "Status of the custom job run.", "type": [ "string", "null" ] } }, "required": [ "lastRun", "status" ], "type": "object", "x-versionadded": "v2.37" }, "description": { "description": "Description of the retraining policy.", "type": [ "string", "null" ] }, "featureListStrategy": { "description": "Configure the feature list strategy used for modeling.", "enum": [ "informative_features", "same_as_champion" ], "type": "string" }, "id": { "description": "ID of the retraining policy.", "type": "string" }, "latestRun": { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" }, "modelSelectionStrategy": { "description": "Configure how new model is selected when the retraining policy runs.", "enum": [ "autopilot_recommended", "same_blueprint", "same_hyperparameters", "custom_job" ], "type": "string" }, "name": { "description": "Name of the retraining policy.", "type": "string" }, "projectOptions": { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" }, "projectOptionsStrategy": { "description": "Configure the project option strategy used for modeling.", "enum": [ "same_as_champion", "override_champion", "custom" ], "type": "string" }, "timeSeriesOptions": { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "useCase": { "description": "The use case to link the retrained model to.", "properties": { "id": { "description": "ID of the use case.", "type": "string" }, "name": { "description": "Name of the use case.", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "action", "autopilotOptions", "description", "id", "latestRun", "modelSelectionStrategy", "name", "projectOptions", "trigger" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | Configure the action to take on the resultant new model. | | autopilotOptions | AutopilotOptions | true | | Options for projects used to build new models. | | customJob | CustomRetrainingJob | false | | The ID, schedule, and last run for the custom job. | | description | string,null | true | | Description of the retraining policy. | | featureListStrategy | string | false | | Configure the feature list strategy used for modeling. | | id | string | true | | ID of the retraining policy. | | latestRun | RetrainingPolicyRunRetrieve | true | | Latest run of the retraining policy. | | modelSelectionStrategy | string | true | | Configure how new model is selected when the retraining policy runs. | | name | string | true | | Name of the retraining policy. | | projectOptions | ProjectOptions | true | | Options for projects used to build new models. | | projectOptionsStrategy | string | false | | Configure the project option strategy used for modeling. | | timeSeriesOptions | TimeSeriesOptions | false | | Time Series project option used to build new models. | | trigger | Trigger | true | | Retraining policy trigger. | | useCase | RetrainingUseCase | false | | The use case to link the retrained model to. | ### Enumerated Values | Property | Value | | --- | --- | | action | [create_challenger, create_model_package, model_replacement] | | featureListStrategy | [informative_features, same_as_champion] | | modelSelectionStrategy | [autopilot_recommended, same_blueprint, same_hyperparameters, custom_job] | | projectOptionsStrategy | [same_as_champion, override_champion, custom] | ## RetrainingPolicyRunListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of retraining policy runs.", "items": { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RetrainingPolicyRunRetrieve] | true | | List of retraining policy runs. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RetrainingPolicyRunRetrieve ``` { "description": "Latest run of the retraining policy.", "properties": { "challengerId": { "description": "ID of the challenger created from this retraining policy run.", "type": [ "string", "null" ] }, "errorMessage": { "description": "Error message of the retraining policy run.", "type": [ "string", "null" ] }, "finishTime": { "description": "Finish time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "id": { "description": "ID of the retraining policy run.", "type": [ "string", "null" ] }, "modelPackageId": { "description": "ID of the model package created from this retraining policy run.", "type": [ "string", "null" ] }, "projectId": { "description": "ID of the project created from this retraining policy run.", "type": [ "string", "null" ] }, "registeredModelId": { "description": "ID of the registered model associated with model package created from this retraining policy run", "type": [ "string", "null" ] }, "startTime": { "description": "Start time of the retraining policy run.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "Status of the retraining policy run.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case associated with the model package created from this retraining policy run.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "challengerId", "errorMessage", "finishTime", "id", "modelPackageId", "projectId", "registeredModelId", "startTime", "status" ], "type": "object" } ``` Latest run of the retraining policy. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | challengerId | string,null | true | | ID of the challenger created from this retraining policy run. | | errorMessage | string,null | true | | Error message of the retraining policy run. | | finishTime | string,null(date-time) | true | | Finish time of the retraining policy run. | | id | string,null | true | | ID of the retraining policy run. | | modelPackageId | string,null | true | | ID of the model package created from this retraining policy run. | | projectId | string,null | true | | ID of the project created from this retraining policy run. | | registeredModelId | string,null | true | | ID of the registered model associated with model package created from this retraining policy run | | startTime | string,null(date-time) | true | | Start time of the retraining policy run. | | status | string,null | true | | Status of the retraining policy run. | | useCaseId | string,null | false | | The ID of the Use Case associated with the model package created from this retraining policy run. | ## RetrainingPolicyRunUpdate ``` { "properties": { "status": { "description": "New status of the retraining policy.", "enum": [ "cancelled" ], "type": "string" } }, "required": [ "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | status | string | true | | New status of the retraining policy. | ### Enumerated Values | Property | Value | | --- | --- | | status | cancelled | ## RetrainingPolicyUpdate ``` { "properties": { "action": { "description": "Configure the action to take on the resultant new model.", "enum": [ "create_challenger", "create_model_package", "model_replacement" ], "type": "string" }, "autopilotOptions": { "description": "Options for projects used to build new models.", "properties": { "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode.", "type": "boolean" }, "mode": { "description": "The autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean" }, "shapOnlyMode": { "description": "Include only models with SHAP value support.", "type": "boolean" } }, "type": "object" }, "customJobId": { "description": "The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "description": { "description": "Description of the retraining policy.", "maxLength": 10000, "type": [ "string", "null" ] }, "featureListStrategy": { "description": "Configure the feature list strategy used for modeling.", "enum": [ "informative_features", "same_as_champion" ], "type": "string" }, "modelSelectionStrategy": { "description": "Configure how new model is selected when the retraining policy runs.", "enum": [ "autopilot_recommended", "same_blueprint", "same_hyperparameters", "custom_job" ], "type": "string" }, "name": { "description": "Name of the retraining policy.", "maxLength": 512, "type": "string" }, "projectOptions": { "description": "Options for projects used to build new models.", "properties": { "cvMethod": { "description": "The partitioning method for projects used to build new models.", "enum": [ "RandomCV", "StratifiedCV" ], "type": "string" }, "holdoutPct": { "default": null, "description": "The percentage of dataset to assign to holdout set in projects used to build new models.", "maximum": 98, "minimum": 0, "type": [ "number", "null" ] }, "metric": { "default": null, "description": "The model selection metric in projects used to build new models.", "enum": [ "Accuracy", "AUC", "Balanced Accuracy", "FVE Binomial", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "Rate@Top5%", "Rate@Top10%", "TPR", "FPR", "TNR", "PPV", "NPV", "F1", "MCC", "FVE Gamma", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "MAE", "MAPE", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Tweedie Deviance", null ], "type": [ "string", "null" ] }, "reps": { "default": null, "description": "The number of cross validation folds to use for projects used to build new models.", "maximum": 50, "minimum": 2, "type": [ "integer", "null" ] }, "validationPct": { "default": null, "description": "The percentage of dataset to assign to validation set in projects used to build new models.", "maximum": 99, "minimum": 1, "type": [ "number", "null" ] }, "validationType": { "description": "The validation type for projects used to build new models.", "enum": [ "CV", "TVH" ], "type": "string" } }, "type": "object" }, "projectOptionsStrategy": { "description": "Configure the project option strategy used for modeling.", "enum": [ "same_as_champion", "override_champion", "custom" ], "type": "string" }, "timeSeriesOptions": { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" }, "trigger": { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "useCaseId": { "description": "The ID of the use case to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | false | | Configure the action to take on the resultant new model. | | autopilotOptions | AutopilotOptions | false | | Options for projects used to build new models. | | customJobId | string,null | false | | The ID of the custom job to be used in this policy. | | description | string,null | false | maxLength: 10000 | Description of the retraining policy. | | featureListStrategy | string | false | | Configure the feature list strategy used for modeling. | | modelSelectionStrategy | string | false | | Configure how new model is selected when the retraining policy runs. | | name | string | false | maxLength: 512 | Name of the retraining policy. | | projectOptions | ProjectOptions | false | | Options for projects used to build new models. | | projectOptionsStrategy | string | false | | Configure the project option strategy used for modeling. | | timeSeriesOptions | TimeSeriesOptions | false | | Time Series project option used to build new models. | | trigger | Trigger | false | | Retraining policy trigger. | | useCaseId | string,null | false | | The ID of the use case to be used in this policy. | ### Enumerated Values | Property | Value | | --- | --- | | action | [create_challenger, create_model_package, model_replacement] | | featureListStrategy | [informative_features, same_as_champion] | | modelSelectionStrategy | [autopilot_recommended, same_blueprint, same_hyperparameters, custom_job] | | projectOptionsStrategy | [same_as_champion, override_champion, custom] | ## RetrainingSettingsRetrieve ``` { "properties": { "dataset": { "description": "The dataset that will be used as retraining data.", "properties": { "id": { "description": "ID of the retraining dataset.", "type": "string" }, "name": { "description": "Name of the retraining dataset.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "predictionEnvironment": { "description": "The prediction environment that will be associated with the challengers created by retraining policies.", "properties": { "id": { "description": "ID of the prediction environment.", "type": "string" }, "name": { "description": "Name of the prediction environment.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "retrainingUser": { "description": "The user scheduled retraining will be performed on behalf of.", "properties": { "id": { "description": "ID of the scheduled retraining user.", "type": "string" }, "username": { "description": "Username of the scheduled retraining user.", "type": "string" } }, "required": [ "id", "username" ], "type": "object" } }, "required": [ "dataset", "predictionEnvironment", "retrainingUser" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataset | Dataset | true | | The dataset that will be used as retraining data. | | predictionEnvironment | PredictionEnvironment | true | | The prediction environment that will be associated with the challengers created by retraining policies. | | retrainingUser | RetrainingUser | true | | The user scheduled retraining will be performed on behalf of. | ## RetrainingSettingsUpdate ``` { "properties": { "credentialId": { "description": "ID of the credential used to refresh retraining dataset.", "type": [ "string", "null" ] }, "datasetId": { "description": "ID of the retraining dataset.", "type": [ "string", "null" ] }, "predictionEnvironmentId": { "description": "ID of the prediction environment to associate with the challengers created by retraining policies.", "type": [ "string", "null" ] }, "retrainingUserId": { "description": "ID of the retraining user.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | ID of the credential used to refresh retraining dataset. | | datasetId | string,null | false | | ID of the retraining dataset. | | predictionEnvironmentId | string,null | false | | ID of the prediction environment to associate with the challengers created by retraining policies. | | retrainingUserId | string | false | | ID of the retraining user. | ## RetrainingUseCase ``` { "description": "The use case to link the retrained model to.", "properties": { "id": { "description": "ID of the use case.", "type": "string" }, "name": { "description": "Name of the use case.", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" } ``` The use case to link the retrained model to. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the use case. | | name | string | true | | Name of the use case. | ## RetrainingUser ``` { "description": "The user scheduled retraining will be performed on behalf of.", "properties": { "id": { "description": "ID of the scheduled retraining user.", "type": "string" }, "username": { "description": "Username of the scheduled retraining user.", "type": "string" } }, "required": [ "id", "username" ], "type": "object" } ``` The user scheduled retraining will be performed on behalf of. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the scheduled retraining user. | | username | string | true | | Username of the scheduled retraining user. | ## Schedule ``` { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" } ``` The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dayOfMonth | [number,string] | true | maxItems: 31 | The date(s) of the month that the job will run. Allowed values are either [1 ... 31] or ["*"] for all days of the month. This field is additive with dayOfWeek, meaning the job will run both on the date(s) defined in this field and the day specified by dayOfWeek (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If dayOfMonth is set to ["*"] and dayOfWeek is defined, the scheduler will trigger on every day of the month that matches dayOfWeek (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored. | | dayOfWeek | [number,string] | true | maxItems: 7 | The day(s) of the week that the job will run. Allowed values are [0 .. 6], where (Sunday=0), or ["*"], for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., "sunday", "Sunday", "sun", or "Sun", all map to [0]. This field is additive with dayOfMonth, meaning the job will run both on the date specified by dayOfMonth and the day defined in this field. | | hour | [number,string] | true | maxItems: 24 | The hour(s) of the day that the job will run. Allowed values are either ["*"] meaning every hour of the day or [0 ... 23]. | | minute | [number,string] | true | maxItems: 60 | The minute(s) of the day that the job will run. Allowed values are either ["*"] meaning every minute of the day or[0 ... 59]. | | month | [number,string] | true | maxItems: 12 | The month(s) of the year that the job will run. Allowed values are either [1 ... 12] or ["*"] for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., "jan" or "october"). Months that are not compatible with dayOfMonth are ignored, for example {"dayOfMonth": [31], "month":["feb"]}. | ## TimeSeriesOptions ``` { "description": "Time Series project option used to build new models.", "properties": { "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ] }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ] }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "exclusiveMinimum": 0, "maximum": 1, "type": [ "number", "null" ] }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ] } }, "type": "object" } ``` Time Series project option used to build new models. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | calendarId | string,null | false | | The ID of the calendar to be used in this project. | | differencingMethod | string,null | false | | For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems simple and seasonal are not allowed. Parameter periodicities must be specified if seasonal is chosen. Defaults to auto. | | exponentiallyWeightedMovingAlpha | number,null | false | maximum: 1 | Discount factor (alpha) used for exponentially weighted moving features | | periodicities | [Periodicity] | false | | A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'. | | treatAsExponential | string,null | false | | For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems always is not allowed. Defaults to auto. | ### Enumerated Values | Property | Value | | --- | --- | | differencingMethod | [auto, none, simple, seasonal] | | treatAsExponential | [auto, never, always] | ## Trigger ``` { "description": "Retraining policy trigger.", "properties": { "customJobId": { "description": "Deprecated - The ID of the custom job to be used in this policy.", "type": [ "string", "null" ], "x-versionadded": "v2.37", "x-versiondeprecated": "v2.38" }, "minIntervalBetweenRuns": { "description": "Minimal interval between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "statusDeclinesToFailing": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing.", "type": "boolean" }, "statusDeclinesToWarning": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning.", "type": "boolean" }, "statusStillInDecline": { "description": "Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline.", "type": [ "boolean", "null" ] }, "type": { "description": "Type of retraining policy trigger.", "enum": [ "schedule", "data_drift_decline", "accuracy_decline", null ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` Retraining policy trigger. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customJobId | string,null | false | | Deprecated - The ID of the custom job to be used in this policy. | | minIntervalBetweenRuns | string,null | false | | Minimal interval between policy runs in ISO 8601 duration string. | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | statusDeclinesToFailing | boolean | false | | Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to failing. | | statusDeclinesToWarning | boolean | false | | Identifies when trigger type is based on deployment a health status, whether the policy will run when health status declines to warning. | | statusStillInDecline | boolean,null | false | | Identifies when trigger type is based on deployment a health status, whether the policy will run when health status still in decline. | | type | string | true | | Type of retraining policy trigger. | ### Enumerated Values | Property | Value | | --- | --- | | type | [schedule, data_drift_decline, accuracy_decline, null] | --- # Model management URL: https://docs.datarobot.com/en/docs/api/reference/public-api/model_management.html > Use the endpoints described below to for model management. # Model management Use the endpoints described below to for model management. ## List model packages Operation path: `GET /api/v2/modelPackages/` Authentication requirements: `BearerAuth` Retrieve the list of model packages a user has access to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | modelId | query | string | false | If specified, limit results to model packages for the model with the specified ID. | | similarTo | query | string | false | Return model packages similar to a given model package ID. If used, will only return model packages that match target.name, target.type, target.classNames (for classification models), modelKind.isTimeSeries, and modelKind.isMultiseries of the specified model package. | | forChallenger | query | boolean | false | Can be used with similarTo to request similar model packages with the intent to use them as challenger models; for external model packages, instead of returning similar external model packages, similar DataRobot and Custom model packages will be retrieved. | | search | query | string | false | Provide a term to search for in package name, model name, or description | | predictionThreshold | query | number | false | Prediction threshold used for binary classification models | | imported | query | boolean | false | If specified, filter for either imported (true) or non-imported (false) model packages | | predictionEnvironmentId | query | string | false | Can be used to filter packages by what is supported by the prediction environment | | modelKind | query | any | false | Return models from the registry that match a specific format. | | buildStatus | query | string | false | If specified, filter model packages by the build status. | ### Enumerated Values | Parameter | Value | | --- | --- | | buildStatus | [inProgress, complete, failed] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "list of formatted model packages", "items": { "properties": { "activeDeploymentCount": { "description": "Number of deployments currently using this model package", "type": "integer" }, "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ] }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string" }, "permissions": { "description": "List of action permissions the user making the request has on the model package", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string" } }, "required": [ "activeDeploymentCount", "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "permissions", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelPackageListResponse | | 400 | Bad Request | Request invalid, refer to messages for detail. | None | | 403 | Forbidden | Either MMM Model Packages or New Model Registry are not enabled. | None | | 404 | Not Found | User permissions problem. | None | ## Create model package Operation path: `POST /api/v2/modelPackages/fromLeaderboard/` Authentication requirements: `BearerAuth` Create model package from a Leaderboard model. ### Body parameter ``` { "properties": { "computeAllTsIntervals": { "default": null, "description": "Whether to compute all Time Series prediction intervals (1-100 percentiles)", "type": [ "boolean", "null" ] }, "description": { "default": "", "description": "Description of the model package.", "maxLength": 2048, "type": [ "string", "null" ] }, "distributionPredictionModelId": { "default": null, "description": "ID of the DataRobot distribution prediction model trained on predictions from the DataRobot model.", "type": [ "string", "null" ] }, "modelId": { "description": "ID of the DataRobot model.", "type": "string" }, "name": { "default": null, "description": "Name of the model package.", "maxLength": 1024, "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ModelPackageCreateFromLeaderboard | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | A job for building model package file was successfully submitted. | None | | 422 | Unprocessable Entity | Unable to process the Model Package creation request. | None | ## Create a model packages from a learning model Operation path: `POST /api/v2/modelPackages/fromLearningModel/` Authentication requirements: `BearerAuth` Create model package from DataRobot model. .. minversion:: v2.31 DEPRECATED: please use the following route instead: [POST /api/v2/modelPackages/fromLeaderboard/][post-apiv2modelpackagesfromleaderboard]. ### Body parameter ``` { "properties": { "description": { "description": "Description of the model package.", "maxLength": 2048, "type": [ "string", "null" ] }, "distributionPredictionModelId": { "default": null, "description": "ID of the DataRobot distribution prediction model trained on predictions from the DataRobot model.", "type": [ "string", "null" ] }, "modelId": { "description": "ID of the DataRobot model.", "type": "string" }, "name": { "default": null, "description": "Name of the model package.", "maxLength": 1024, "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ModelPackageCreateFromLearningModel | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | None | | 403 | Forbidden | The user does not have permission to create a Model Package. | None | | 404 | Not Found | Either the model_id not exist or the user does not have permission to view the model and project. | None | | 422 | Unprocessable Entity | Unable to process the Model Package creation request. | None | ## Retrieve info about a model package by model package ID Operation path: `GET /api/v2/modelPackages/{modelPackageId}/` Authentication requirements: `BearerAuth` Retrieve info about a model package. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | modelPackageId | path | string | true | The ID of the model package. | ### Example responses > 200 Response ``` { "properties": { "activeDeploymentCount": { "description": "Number of deployments currently using this model package", "type": "integer" }, "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ] }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string" }, "permissions": { "description": "List of action permissions the user making the request has on the model package", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string" } }, "required": [ "activeDeploymentCount", "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "permissions", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelPackageRetrieveResponse | | 404 | Not Found | Either the model package does not exist or the user does not have permission to view the model package. | None | ## Archive a model package by model package ID Operation path: `POST /api/v2/modelPackages/{modelPackageId}/archive/` Authentication requirements: `BearerAuth` (Deprecated in v2.32) Permanently archive a model package. It will no longer be able to be used in new deployments or replacement. It will not be accessible in the model package list api. It will only be accessible at the model package retrieve route for this model package. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | modelPackageId | path | string | true | The ID of the model package. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | ## Retrieve capabilities by model package ID Operation path: `GET /api/v2/modelPackages/{modelPackageId}/capabilities/` Authentication requirements: `BearerAuth` Retrieve the capabilities for the model package. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | modelPackageId | path | string | true | The ID of the model package. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "List of all capabilities.", "items": { "properties": { "messages": { "description": "Messages explaining why the capability is supported or not supported.", "items": { "type": "string" }, "type": "array" }, "name": { "description": "The name of the capability.", "type": "string" }, "supported": { "description": "If the capability is supported.", "type": "boolean" } }, "required": [ "messages", "name", "supported" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelPackageCapabilitiesRetrieveResponse | ## Retrieve feature list by model package ID Operation path: `GET /api/v2/modelPackages/{modelPackageId}/features/` Authentication requirements: `BearerAuth` Retrieve the feature list for given model package. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of features to skip, defaults to 0. | | limit | query | integer | true | The number of features to return, defaults to 0. | | includeNonPredictionFeatures | query | string | false | When True will return all raw features in the universe dataset associated with the deployment, and when False will return only those raw features used to make predictions on the deployment. | | forSegmentedAnalysis | query | string | false | When True, features returned will be filtered to those usable for segmented analysis. | | search | query | string | false | Case insensitive search against names of the deployment's features. | | orderBy | query | string | false | The sort order to apply to the list of features. Prefix the attribute name with a dash to sort in descending order, e.g., "-name". | | modelPackageId | path | string | true | The ID of the model package. | ### Enumerated Values | Parameter | Value | | --- | --- | | includeNonPredictionFeatures | [false, False, true, True] | | forSegmentedAnalysis | [false, False, true, True] | | orderBy | [name, importance, -name, -importance] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of features.", "items": { "properties": { "dateFormat": { "description": "The date format string for how this feature was interpreted.", "type": [ "string", "null" ] }, "featureType": { "description": "Feature type.", "type": [ "string", "null" ] }, "importance": { "description": "Numeric measure of the relationship strength between the feature and target (independent of model or other features).", "type": [ "number", "null" ] }, "knownInAdvance": { "description": "Whether the feature was selected as known in advance in a time-series model, false for non-time-series models.", "type": "boolean" }, "name": { "description": "Feature name.", "type": "string" } }, "required": [ "dateFormat", "featureType", "importance", "knownInAdvance", "name" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.35" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Model package feature list. | FeatureListResponse | ## The list of the model package's model logs by model package ID Operation path: `GET /api/v2/modelPackages/{modelPackageId}/modelLogs/` Authentication requirements: `BearerAuth` The list of the model package's model logs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results that will be skipped. | | limit | query | integer | false | The maximum number of results to return. | | modelPackageId | path | string | true | The ID of the model package. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The log entries.", "items": { "properties": { "level": { "description": "The name of the level of the logging event.", "enum": [ "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL" ], "type": "string" }, "message": { "description": "The message of the logging event.", "type": "string" }, "time": { "description": "The POSIX time of the logging event.", "type": "number" } }, "required": [ "level", "message", "time" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelPackageModelLogsListResponse | ## Get the model package's access control list by model package ID Operation path: `GET /api/v2/modelPackages/{modelPackageId}/sharedRoles/` Authentication requirements: `BearerAuth` (Deprecated in v2.32) instead.Get a list of users, groups and organizations who have access to this model package and their roles on the model package. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | modelPackageId | path | string | true | The ID of the model package. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The model package's access control list. | SharingListV2Response | | 404 | Not Found | Either the Model Package does not exist or the user does not have permissions to view the Model Package. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## List registered models Operation path: `GET /api/v2/registeredModels/` Authentication requirements: `BearerAuth` List registered models. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | search | query | string | false | A term to search for in registered model name | | createdAtStartTs | query | string(date-time) | false | Registered models created on or after this timestamp | | createdAtEndTs | query | string(date-time) | false | Registered models created before this timestamp. Defaults to the current time | | modifiedAtStartTs | query | string(date-time) | false | Registered models modified on or after this timestamp | | modifiedAtEndTs | query | string(date-time) | false | Registered models modified before this timestamp. Defaults to the current time | | targetName | query | string | false | The name of the target used for filtering. | | targetType | query | any | false | The type of target(s) to filter by. | | createdBy | query | string | false | Email of the user that created registered model to filter by | | sortKey | query | string | false | The key to order results by. | | sortDirection | query | string | false | Sort direction | | compatibleWithLeaderboardModelId | query | string | false | If specified, limit results to registered models containing versions (model packages) for the leaderboard model with the specified ID. | | compatibleWithModelPackageId | query | string | false | Return registered models that have versions (model packages) compatible with given model package ID. If used, will only return registered models which have versions that match target.name, target.type, target.classNames (for classification models), modelKind.isTimeSeries, and modelKind.isMultiseries of the specified model package. | | forChallenger | query | boolean | false | Can be used with compatibleWithModelPackageId to request similar registered models that contain versions (model packages)that can be used as challenger models; for external model packages, instead of returning similar external model packages, similar DataRobot and Custom model packages will be retrieved. | | predictionThreshold | query | number | false | If specified, return any registered models containing one or more versions matching the prediction threshold used for binary classification models | | imported | query | boolean | false | If specified, return any registered models that contain either imported (true) or non-imported (false) versions (model packages) | | predictionEnvironmentId | query | string | false | Can be used to filter registered models by what is supported by the prediction environment | | modelKind | query | any | false | Return models that contain versions matching a specific format | | buildStatus | query | string | false | If specified, only return models that have versions with specified build status | | stage | query | string | false | If specified, only returns models that have versions in the specified stage. | | isGlobal | query | boolean | false | Return only global (accessible to all users in the organization) registered models or local(accessible only to the owner and the users with whom it has been explicitly shared) | | tagKeys | query | string | false | List of tag keys to filter by. If multiple tag keys are provided, registered models matching any of the tag keys are returned. | | tagValues | query | string | false | List of tag values to filter by. If multiple tag values are provided, registered models matching any of the tag values are returned. | | tagFilters | query | string | false | Comma separated tag pairs (e.g., key1%3Dvalue2,key2%3Dvalue2). Only registered models that match exactly (case sensitive) are returned.If specified, this overrides the tagKey and tagValues query params.If multiple tag filters (up to 10) are provided, registered models matching any of the tag filters are selected. These registered models will be further filtered by other query params such as targetType. | ### Enumerated Values | Parameter | Value | | --- | --- | | sortKey | [createdAt, modifiedAt, name] | | sortDirection | [asc, desc] | | buildStatus | [inProgress, complete, failed] | | stage | [Registered, Development, Staging, Production, Archived] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of formatted registered models.", "items": { "properties": { "createdAt": { "description": "The date when the registered model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "Information on the creator of the registered model.", "properties": { "email": { "description": "The email of the user who created the registered model.", "type": "string" }, "id": { "description": "The ID of the user who created the registered model.", "type": "string" }, "name": { "description": "The full name of the user who created the registered model.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "description": { "description": "The description of the registered model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the registered model.", "type": "string" }, "isArchived": { "description": "Whether the model is archived.", "type": "boolean" }, "isGlobal": { "description": "Whether the registered model is global (accessible to all users in the organization) or local(accessible only to the owner and the users with whom it has been explicitly shared)", "type": "boolean" }, "lastVersionNum": { "description": "The latest version associated with this registered model.", "type": "integer" }, "modifiedAt": { "description": "The date when the registered model was last modified.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "name": { "description": "The name of the registered model.", "type": "string" }, "target": { "description": "Information on the target variable.", "properties": { "name": { "description": "The name of the target variable.", "type": [ "string", "null" ] }, "type": { "description": "The type of the target variable.", "type": [ "string", "null" ] } }, "required": [ "name", "type" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isArchived", "lastVersionNum", "modifiedAt", "modifiedBy", "name", "target" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RegisteredModelListResponse | ## Archive a registered model by registered model ID Operation path: `DELETE /api/v2/registeredModels/{registeredModelId}/` Authentication requirements: `BearerAuth` Permanently archive a registered model and all of its versions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | registeredModelId | path | string | true | The ID of the registered model. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | ## Retrieve information about a registered model by registered model ID Operation path: `GET /api/v2/registeredModels/{registeredModelId}/` Authentication requirements: `BearerAuth` Retrieve information about a registered model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | registeredModelId | path | string | true | The ID of the registered model. | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "The date when the registered model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "Information on the creator of the registered model.", "properties": { "email": { "description": "The email of the user who created the registered model.", "type": "string" }, "id": { "description": "The ID of the user who created the registered model.", "type": "string" }, "name": { "description": "The full name of the user who created the registered model.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "description": { "description": "The description of the registered model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the registered model.", "type": "string" }, "isArchived": { "description": "Whether the model is archived.", "type": "boolean" }, "isGlobal": { "description": "Whether the registered model is global (accessible to all users in the organization) or local(accessible only to the owner and the users with whom it has been explicitly shared)", "type": "boolean" }, "lastVersionNum": { "description": "The latest version associated with this registered model.", "type": "integer" }, "modifiedAt": { "description": "The date when the registered model was last modified.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "name": { "description": "The name of the registered model.", "type": "string" }, "target": { "description": "Information on the target variable.", "properties": { "name": { "description": "The name of the target variable.", "type": [ "string", "null" ] }, "type": { "description": "The type of the target variable.", "type": [ "string", "null" ] } }, "required": [ "name", "type" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isArchived", "lastVersionNum", "modifiedAt", "modifiedBy", "name", "target" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RegisteredModelResponse | | 404 | Not Found | The registered model does not exist or the user does not have permission to view the model package. | None | ## Update a registered model by registered model ID Operation path: `PATCH /api/v2/registeredModels/{registeredModelId}/` Authentication requirements: `BearerAuth` Update a registered model. ### Body parameter ``` { "properties": { "description": { "description": "The description of the registered model.", "maxLength": 2048, "type": "string" }, "isGlobal": { "description": "Make registered model global (accessible to all users in the organization) or local(accessible only to the owner and the users with whom it has been explicitly shared)", "type": "boolean" }, "name": { "description": "The name of the registered model.", "maxLength": 1024, "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | registeredModelId | path | string | true | The ID of the registered model. | | body | body | RegisteredModelUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 422 | Unprocessable Entity | Unable to process the Registered Model update request | None | ## List deployments associated by registered model ID Operation path: `GET /api/v2/registeredModels/{registeredModelId}/deployments/` Authentication requirements: `BearerAuth` List deployments associated with the given registered model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | search | query | string | false | Filter deployments with name matching search term | | sortKey | query | string | false | The key to order results by. | | sortDirection | query | string | false | Sort direction | | registeredModelId | path | string | true | The ID of the registered model. | ### Enumerated Values | Parameter | Value | | --- | --- | | sortKey | [createdAt, label] | | sortDirection | [asc, desc] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of formatted deployments.", "items": { "properties": { "createdAt": { "description": "Deployment creation date", "type": "string" }, "createdBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "currentlyDeployed": { "description": "Whether version of this registered model is currently deployed", "type": "boolean" }, "firstDeployedAt": { "description": "When version of this registered model was first deployed", "type": [ "string", "null" ] }, "firstDeployedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "isChallenger": { "description": "True if given version is a challenger in a given deployment", "type": "boolean" }, "label": { "description": "Label of the deployment", "type": [ "string", "null" ] }, "predictionEnvironment": { "description": "Information related to the current PredictionEnvironment.", "properties": { "id": { "description": "ID of the PredictionEnvironment.", "type": [ "string", "null" ] }, "isManagedByManagementAgent": { "description": "True if PredictionEnvironment is using Management Agent.", "type": "boolean" }, "name": { "description": "Name of the PredictionEnvironment.", "type": "string" }, "platform": { "description": "Platform of the PredictionEnvironment.", "enum": [ "aws", "gcp", "azure", "onPremise", "datarobot", "datarobotServerless", "openShift", "other", "snowflake", "sapAiCore" ], "type": "string" }, "plugin": { "description": "Plugin name of the PredictionEnvironment.", "type": [ "string", "null" ] }, "supportedModelFormats": { "description": "Model formats that the PredictionEnvironment supports.", "items": { "enum": [ "datarobot", "datarobotScoringCode", "customModel", "externalModel" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "id", "isManagedByManagementAgent", "name", "platform" ], "type": "object" }, "registeredModelVersion": { "description": "Version of the registered model", "type": "integer" }, "status": { "description": "Status of the deployment", "type": "string" } }, "required": [ "createdAt", "createdBy", "currentlyDeployed", "firstDeployedAt", "firstDeployedBy", "id", "isChallenger", "label", "registeredModelVersion", "status" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RegisteredModelDeploymentsListResponse | ## Get the registered model access control list by registered model ID Operation path: `GET /api/v2/registeredModels/{registeredModelId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations who have access to this registered model and their roles on the registered model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | registeredModelId | path | string | true | The ID of the registered model. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The registered model access control list. | SharingListV2Response | | 404 | Not Found | Either the Registered Model does not exist or the user does not have permissions to view the Registered Model. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## Update the registered model controls by registered model ID Operation path: `PATCH /api/v2/registeredModels/{registeredModelId}/sharedRoles/` Authentication requirements: `BearerAuth` Set roles for users on this registered model. ### Body parameter ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | registeredModelId | path | string | true | The ID of the registered model. | | body | body | SharedRolesUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The roles were updated successfully. | None | | 409 | Conflict | The request would leave the registered model without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid | None | ## List the registered model's versions by registered model ID Operation path: `GET /api/v2/registeredModels/{registeredModelId}/versions/` Authentication requirements: `BearerAuth` List the registered model's versions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | sortKey | query | string | false | The key to order results by. | | sortDirection | query | string | false | Sort direction | | targetName | query | string | false | Name of the target to filter by | | targetType | query | string | false | Type of the target to filter by | | search | query | string | false | A term to search for in version name, model name, or description | | compatibleWithLeaderboardModelId | query | string | false | If specified, limit results to versions (model packages) of the leaderboard model with the specified ID. | | compatibleWithModelPackageId | query | string | false | Return versions compatible with given model package ID. If used, will only return versions that match target.name, target.type, target.classNames (for classification models), modelKind.isTimeSeries, and modelKind.isMultiseries of the specified model package | | forChallenger | query | boolean | false | Can be used with compatibleWithModelPackageId to request similar versions that can be used as challenger models; for external model packages, instead of returning similar external model packages, similar DataRobot and Custom model packages will be retrieved | | predictionThreshold | query | number | false | Return versions with the specified prediction threshold used for binary classification models | | imported | query | boolean | false | If specified, return either imported (true) or non-imported (false) versions (model packages) | | predictionEnvironmentId | query | string | false | Can be used to filter versions (model packages) by what is supported by the prediction environment | | modelKind | query | any | false | Return versions that match a specific format. | | buildStatus | query | string | false | If specified, filter versions by the build status. | | stage | query | string | false | If specified, filter versions by the stage. | | useCaseId | query | string | false | If specified, filter versions by use-case ID. | | createdBy | query | string | false | Email of the user that created registered model version to filter by | | registeredModelId | path | string | true | The ID of the registered model. | ### Enumerated Values | Parameter | Value | | --- | --- | | sortKey | [version, modelType, status, createdAt, updatedAt] | | sortDirection | [asc, desc] | | buildStatus | [inProgress, complete, failed] | | stage | [Registered, Development, Staging, Production, Archived] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of formatted registered model versions.", "items": { "properties": { "activeDeploymentCount": { "description": "Number of deployments currently using this model package", "type": "integer" }, "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ] }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string" }, "permissions": { "description": "List of action permissions the user making the request has on the model package", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string" } }, "required": [ "activeDeploymentCount", "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "permissions", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RegisteredModelVersionsListResponse | ## Get the registered model's version by registered model ID Operation path: `GET /api/v2/registeredModels/{registeredModelId}/versions/{versionId}/` Authentication requirements: `BearerAuth` Get the registered model's version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | registeredModelId | path | string | true | The ID of the registered model. | | versionId | path | string | true | The ID of the registered model's version. | ### Example responses > 200 Response ``` { "properties": { "activeDeploymentCount": { "description": "Number of deployments currently using this model package", "type": "integer" }, "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ] }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string" }, "permissions": { "description": "List of action permissions the user making the request has on the model package", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string" } }, "required": [ "activeDeploymentCount", "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "permissions", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelPackageRetrieveResponse | ## List all deployments associated by registered model ID Operation path: `GET /api/v2/registeredModels/{registeredModelId}/versions/{versionId}/deployments/` Authentication requirements: `BearerAuth` List all deployments associated with registered model version. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | search | query | string | false | Filter deployments with name matching search term | | sortKey | query | string | false | The key to order results by. | | sortDirection | query | string | false | Sort direction | | registeredModelId | path | string | true | The ID of the registered model. | | versionId | path | string | true | The ID of the registered model's version. | ### Enumerated Values | Parameter | Value | | --- | --- | | sortKey | [createdAt, label] | | sortDirection | [asc, desc] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of formatted deployments.", "items": { "properties": { "createdAt": { "description": "Deployment creation date", "type": "string" }, "createdBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "currentlyDeployed": { "description": "Whether version of this registered model is currently deployed", "type": "boolean" }, "firstDeployedAt": { "description": "When version of this registered model was first deployed", "type": [ "string", "null" ] }, "firstDeployedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "isChallenger": { "description": "True if given version is a challenger in a given deployment", "type": "boolean" }, "label": { "description": "Label of the deployment", "type": [ "string", "null" ] }, "predictionEnvironment": { "description": "Information related to the current PredictionEnvironment.", "properties": { "id": { "description": "ID of the PredictionEnvironment.", "type": [ "string", "null" ] }, "isManagedByManagementAgent": { "description": "True if PredictionEnvironment is using Management Agent.", "type": "boolean" }, "name": { "description": "Name of the PredictionEnvironment.", "type": "string" }, "platform": { "description": "Platform of the PredictionEnvironment.", "enum": [ "aws", "gcp", "azure", "onPremise", "datarobot", "datarobotServerless", "openShift", "other", "snowflake", "sapAiCore" ], "type": "string" }, "plugin": { "description": "Plugin name of the PredictionEnvironment.", "type": [ "string", "null" ] }, "supportedModelFormats": { "description": "Model formats that the PredictionEnvironment supports.", "items": { "enum": [ "datarobot", "datarobotScoringCode", "customModel", "externalModel" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "id", "isManagedByManagementAgent", "name", "platform" ], "type": "object" }, "registeredModelVersion": { "description": "Version of the registered model", "type": "integer" }, "status": { "description": "Status of the deployment", "type": "string" } }, "required": [ "createdAt", "createdBy", "currentlyDeployed", "firstDeployedAt", "firstDeployedBy", "id", "isChallenger", "label", "registeredModelVersion", "status" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | RegisteredModelDeploymentsListResponse | # Schemas ## AccessControlV2 ``` { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the recipient. | | name | string | true | | The name of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | The type of the recipient. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## CustomModelDetails ``` { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } ``` Details of the custom model associated to this registered model version ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string | true | | The time when the custom model was created. | | creatorEmail | string,null | false | | The email of the user who created the custom model. | | creatorId | string | true | | The ID of the creator of the custom model. | | creatorName | string,null | false | | The name of the user who created the custom model. | | id | string | true | | The ID of the associated custom model. | | versionLabel | string,null | false | | The label of the associated custom model version. | ## DeploymentPredictionEnvironmentResponse ``` { "description": "Information related to the current PredictionEnvironment.", "properties": { "id": { "description": "ID of the PredictionEnvironment.", "type": [ "string", "null" ] }, "isManagedByManagementAgent": { "description": "True if PredictionEnvironment is using Management Agent.", "type": "boolean" }, "name": { "description": "Name of the PredictionEnvironment.", "type": "string" }, "platform": { "description": "Platform of the PredictionEnvironment.", "enum": [ "aws", "gcp", "azure", "onPremise", "datarobot", "datarobotServerless", "openShift", "other", "snowflake", "sapAiCore" ], "type": "string" }, "plugin": { "description": "Plugin name of the PredictionEnvironment.", "type": [ "string", "null" ] }, "supportedModelFormats": { "description": "Model formats that the PredictionEnvironment supports.", "items": { "enum": [ "datarobot", "datarobotScoringCode", "customModel", "externalModel" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "id", "isManagedByManagementAgent", "name", "platform" ], "type": "object" } ``` Information related to the current PredictionEnvironment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string,null | true | | ID of the PredictionEnvironment. | | isManagedByManagementAgent | boolean | true | | True if PredictionEnvironment is using Management Agent. | | name | string | true | | Name of the PredictionEnvironment. | | platform | string | true | | Platform of the PredictionEnvironment. | | plugin | string,null | false | | Plugin name of the PredictionEnvironment. | | supportedModelFormats | [string] | false | maxItems: 4minItems: 1 | Model formats that the PredictionEnvironment supports. | ### Enumerated Values | Property | Value | | --- | --- | | platform | [aws, gcp, azure, onPremise, datarobot, datarobotServerless, openShift, other, snowflake, sapAiCore] | ## Feature ``` { "properties": { "dateFormat": { "description": "The date format string for how this feature was interpreted.", "type": [ "string", "null" ] }, "featureType": { "description": "Feature type.", "type": [ "string", "null" ] }, "importance": { "description": "Numeric measure of the relationship strength between the feature and target (independent of model or other features).", "type": [ "number", "null" ] }, "knownInAdvance": { "description": "Whether the feature was selected as known in advance in a time-series model, false for non-time-series models.", "type": "boolean" }, "name": { "description": "Feature name.", "type": "string" } }, "required": [ "dateFormat", "featureType", "importance", "knownInAdvance", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dateFormat | string,null | true | | The date format string for how this feature was interpreted. | | featureType | string,null | true | | Feature type. | | importance | number,null | true | | Numeric measure of the relationship strength between the feature and target (independent of model or other features). | | knownInAdvance | boolean | true | | Whether the feature was selected as known in advance in a time-series model, false for non-time-series models. | | name | string | true | | Feature name. | ## FeatureListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of features.", "items": { "properties": { "dateFormat": { "description": "The date format string for how this feature was interpreted.", "type": [ "string", "null" ] }, "featureType": { "description": "Feature type.", "type": [ "string", "null" ] }, "importance": { "description": "Numeric measure of the relationship strength between the feature and target (independent of model or other features).", "type": [ "number", "null" ] }, "knownInAdvance": { "description": "Whether the feature was selected as known in advance in a time-series model, false for non-time-series models.", "type": "boolean" }, "name": { "description": "Feature name.", "type": "string" } }, "required": [ "dateFormat", "featureType", "importance", "knownInAdvance", "name" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.35" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [Feature] | true | | An array of features. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## GrantAccessControlWithId ``` { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## GrantAccessControlWithUsername ``` { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | Username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## MlpkgFileContents ``` { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" } ``` Information about the content of .mlpkg artifact ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allTimeSeriesPredictionIntervals | boolean,null | false | | Whether .mlpkg contains TS prediction intervals computed for all percentiles | ## ModelPackageCapabilities ``` { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" } ``` Capabilities of the current model package. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | supportsAutomaticActuals | boolean | false | | Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package. | | supportsChallengerModels | boolean | true | | Whether Challenger Models are supported by this model package. | | supportsFeatureDriftTracking | boolean | true | | Whether Feature Drift is supported by this model package. | | supportsHumilityRecommendedRules | boolean | true | | Whether calculating values for recommended Humility Rules is supported by this model package. | | supportsHumilityRules | boolean | true | | Whether Humility Rules are supported by this model package. | | supportsHumilityRulesDefaultCalculations | boolean | true | | Whether calculating default values for Humility Rules is supported by this model package. | | supportsPredictionWarning | boolean | true | | Whether Prediction Warnings are supported by this model package. | | supportsRetraining | boolean | false | | Whether deployment supports retraining. | | supportsScoringCodeDownload | boolean | false | | Whether scoring code download is supported by this model package. | | supportsSecondaryDatasets | boolean | true | | If the deployments supports secondary datasets. | | supportsSegmentedAnalysisDriftAndAccuracy | boolean | true | | Whether tracking features in training and predictions data for segmented analysis is supported by this model package. | | supportsShapBasedPredictionExplanations | boolean | true | | Whether shap-based prediction explanations are supported by this model package. | | supportsTargetDriftTracking | boolean | true | | Whether Target Drift is supported by this model package. | ## ModelPackageCapabilitiesRetrieveResponse ``` { "properties": { "data": { "description": "List of all capabilities.", "items": { "properties": { "messages": { "description": "Messages explaining why the capability is supported or not supported.", "items": { "type": "string" }, "type": "array" }, "name": { "description": "The name of the capability.", "type": "string" }, "supported": { "description": "If the capability is supported.", "type": "boolean" } }, "required": [ "messages", "name", "supported" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [ModelPackageCapability] | true | | List of all capabilities. | ## ModelPackageCapability ``` { "properties": { "messages": { "description": "Messages explaining why the capability is supported or not supported.", "items": { "type": "string" }, "type": "array" }, "name": { "description": "The name of the capability.", "type": "string" }, "supported": { "description": "If the capability is supported.", "type": "boolean" } }, "required": [ "messages", "name", "supported" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | messages | [string] | true | | Messages explaining why the capability is supported or not supported. | | name | string | true | | The name of the capability. | | supported | boolean | true | | If the capability is supported. | ## ModelPackageCreateFromLeaderboard ``` { "properties": { "computeAllTsIntervals": { "default": null, "description": "Whether to compute all Time Series prediction intervals (1-100 percentiles)", "type": [ "boolean", "null" ] }, "description": { "default": "", "description": "Description of the model package.", "maxLength": 2048, "type": [ "string", "null" ] }, "distributionPredictionModelId": { "default": null, "description": "ID of the DataRobot distribution prediction model trained on predictions from the DataRobot model.", "type": [ "string", "null" ] }, "modelId": { "description": "ID of the DataRobot model.", "type": "string" }, "name": { "default": null, "description": "Name of the model package.", "maxLength": 1024, "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | computeAllTsIntervals | boolean,null | false | | Whether to compute all Time Series prediction intervals (1-100 percentiles) | | description | string,null | false | maxLength: 2048 | Description of the model package. | | distributionPredictionModelId | string,null | false | | ID of the DataRobot distribution prediction model trained on predictions from the DataRobot model. | | modelId | string | true | | ID of the DataRobot model. | | name | string,null | false | maxLength: 1024 | Name of the model package. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold used for binary classification in predictions | ## ModelPackageCreateFromLearningModel ``` { "properties": { "description": { "description": "Description of the model package.", "maxLength": 2048, "type": [ "string", "null" ] }, "distributionPredictionModelId": { "default": null, "description": "ID of the DataRobot distribution prediction model trained on predictions from the DataRobot model.", "type": [ "string", "null" ] }, "modelId": { "description": "ID of the DataRobot model.", "type": "string" }, "name": { "default": null, "description": "Name of the model package.", "maxLength": 1024, "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions", "maximum": 1, "minimum": 0, "type": "number" } }, "required": [ "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | maxLength: 2048 | Description of the model package. | | distributionPredictionModelId | string,null | false | | ID of the DataRobot distribution prediction model trained on predictions from the DataRobot model. | | modelId | string | true | | ID of the DataRobot model. | | name | string,null | false | maxLength: 1024 | Name of the model package. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold used for binary classification in predictions | ## ModelPackageDatasets ``` { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" } ``` dataset information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineSegmentedBy | [string] | true | | Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value. | | datasetName | string,null | true | | Name of dataset used to train the model | | holdoutDataCatalogId | string,null | true | | ID for holdout data (returned from uploading a data set) | | holdoutDataCatalogVersionId | string,null | true | | Version ID for holdout data (returned from uploading a data set) | | holdoutDataCreatedAt | string,null | false | | Time when the holdout data item was created | | holdoutDataCreatorEmail | string,null | false | | Email of the user who created the holdout data item | | holdoutDataCreatorId | string,null | false | | ID of the creator of the holdout data item | | holdoutDataCreatorName | string,null | false | | Name of the user who created the holdout data item | | holdoutDatasetName | string,null | true | | Name of dataset used for model holdout | | targetHistogramBaseline | string | false | | Values used to establish the training baseline | | trainingDataCatalogId | string,null | true | | ID for training data (returned from uploading a data set) | | trainingDataCatalogVersionId | string,null | true | | Version ID for training data (returned from uploading a data set) | | trainingDataCreatedAt | string,null | false | | Time when the training data item was created | | trainingDataCreatorEmail | string,null | false | | Email of the user who created the training data item | | trainingDataCreatorId | string,null | false | | ID of the creator of the training data item | | trainingDataCreatorName | string,null | false | | Name of the user who created the training data item | | trainingDataSize | integer | false | | Number of rows in training data (used by DR models) | ### Enumerated Values | Property | Value | | --- | --- | | targetHistogramBaseline | [predictions, actuals] | ## ModelPackageImportMeta ``` { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" } ``` Information from when this Model Package was first saved ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | containsFearPipeline | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with fear pipeline. | | containsFeaturelists | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with featurelists. | | containsLeaderboardMeta | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with leaderboard meta. | | containsProjectMeta | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with project meta. | | creatorFullName | string,null | true | | The full name of the person who created this model package. | | creatorId | string | true | | The user ID of the person who created this model package. | | creatorUsername | string | true | | The username of the person who created this model package. | | dateCreated | string | true | | When this model package was created. | | originalFileName | string,null | true | | Exists for imported models only, the original file name that was uploaded | ## ModelPackageListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "list of formatted model packages", "items": { "properties": { "activeDeploymentCount": { "description": "Number of deployments currently using this model package", "type": "integer" }, "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ] }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string" }, "permissions": { "description": "List of action permissions the user making the request has on the model package", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string" } }, "required": [ "activeDeploymentCount", "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "permissions", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ModelPackageRetrieveResponse] | true | | list of formatted model packages | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ModelPackageModelDescription ``` { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" } ``` model description information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildEnvironmentType | string | true | | build environment type of the model | | description | string,null | true | | a description of the model | | location | string,null | true | | location of the model | | modelCreatedAt | string,null | false | | time when the model was created | | modelCreatorEmail | string,null | false | | email of the user who created the model | | modelCreatorId | string,null | false | | ID of the creator of the model | | modelCreatorName | string,null | false | | name of the user who created the model | | modelName | string | false | | model name | ### Enumerated Values | Property | Value | | --- | --- | | buildEnvironmentType | [DataRobot, Python, R, Java, Other] | ## ModelPackageModelKind ``` { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" } ``` Model attribute information ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isAnomalyDetectionModel | boolean | true | | true if this is an anomaly detection model | | isCombinedModel | boolean | true | | true if model is a combined model | | isFeatureDiscovery | boolean | true | | true if this model uses the Feature Discovery feature | | isMultiseries | boolean | true | | true if model is multiseries | | isTimeSeries | boolean | true | | true if model is time series | | isUnsupervisedLearning | boolean | true | | true if model used unsupervised learning | ## ModelPackageModelLogsEntry ``` { "properties": { "level": { "description": "The name of the level of the logging event.", "enum": [ "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL" ], "type": "string" }, "message": { "description": "The message of the logging event.", "type": "string" }, "time": { "description": "The POSIX time of the logging event.", "type": "number" } }, "required": [ "level", "message", "time" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | level | string | true | | The name of the level of the logging event. | | message | string | true | | The message of the logging event. | | time | number | true | | The POSIX time of the logging event. | ### Enumerated Values | Property | Value | | --- | --- | | level | [DEBUG, INFO, WARNING, ERROR, CRITICAL] | ## ModelPackageModelLogsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The log entries.", "items": { "properties": { "level": { "description": "The name of the level of the logging event.", "enum": [ "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL" ], "type": "string" }, "message": { "description": "The message of the logging event.", "type": "string" }, "time": { "description": "The POSIX time of the logging event.", "type": "number" } }, "required": [ "level", "message", "time" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ModelPackageModelLogsEntry] | true | maxItems: 100 | The log entries. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ModelPackageRetrieveResponse ``` { "properties": { "activeDeploymentCount": { "description": "Number of deployments currently using this model package", "type": "integer" }, "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ] }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string" }, "permissions": { "description": "List of action permissions the user making the request has on the model package", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string" } }, "required": [ "activeDeploymentCount", "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "permissions", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | activeDeploymentCount | integer | true | | Number of deployments currently using this model package | | buildStatus | string,null | false | | Model package build status | | capabilities | ModelPackageCapabilities | true | | Capabilities of the current model package. | | datasets | ModelPackageDatasets | true | | dataset information for the model package | | id | string | true | | The ID of the model package. | | importMeta | ModelPackageImportMeta | true | | Information from when this Model Package was first saved | | isArchived | boolean | true | | Whether the model package is permanently archived (cannot be used in deployment or replacement) | | isDeprecated | boolean | true | | Whether the model package is deprecated. eg. python2 models are deprecated. | | mlpkgFileContents | MlpkgFileContents | false | | Information about the content of .mlpkg artifact | | modelDescription | ModelPackageModelDescription | true | | model description information for the model package | | modelExecutionType | string | true | | Type of model package. dedicated (native DataRobot models) and custom_inference_model (user added inference models) both execute on DataRobot prediction servers, external do not | | modelId | string | true | | The ID of the model. | | modelKind | ModelPackageModelKind | true | | Model attribute information | | name | string | true | | The model package name. | | permissions | [string] | true | | List of action permissions the user making the request has on the model package | | sourceMeta | ModelPackageSourceMeta | true | | Meta information from where this model was generated | | target | ModelPackageTarget | true | | target information for the model package | | timeseries | ModelPackageTimeseries | true | | time series information for the model package | | updatedBy | UserMetadata | true | | Information on the user who last modified the registered model | | userProvidedId | string | false | | A user-provided unique ID associated with the given custom inference model. | ### Enumerated Values | Property | Value | | --- | --- | | buildStatus | [inProgress, complete, failed] | | modelExecutionType | [dedicated, custom_inference_model, external] | ## ModelPackageScoringCodeMeta ``` { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" } ``` If available, information about the model's scoring code ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataRobotPredictionVersion | string,null | true | | The DataRobot prediction API version for the scoring code. | | location | string,null | true | | The location of the scoring code. | ### Enumerated Values | Property | Value | | --- | --- | | location | [local_leaderboard, mlpkg] | ## ModelPackageSourceMeta ``` { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" } ``` Meta information from where this model was generated ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelDetails | CustomModelDetails | false | | Details of the custom model associated to this registered model version | | environmentUrl | string,null(uri) | true | | If available, URL of the source model | | fips_140_2Enabled | boolean | false | | true if the model was built with FIPS-140-2 | | projectCreatedAt | string,null | false | | If available, the time when the project was created. | | projectCreatorEmail | string,null | false | | If available, the email of the user who created the project. | | projectCreatorId | string,null | false | | If available, the ID of the creator of the project. | | projectCreatorName | string,null | false | | If available, the name of the user who created the project. | | projectId | string,null | true | | If available, the project ID used for this model. | | projectName | string,null | true | | If available, the project name for this model. | | scoringCode | ModelPackageScoringCodeMeta | true | | If available, information about the model's scoring code | | useCaseDetails | UseCaseDetails | false | | Details of the use-case associated to this registered model version | ## ModelPackageTarget ``` { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" } ``` target information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classCount | integer,null | true | minimum: 0 | Number of classes for classification models. | | classNames | [string] | true | maxItems: 100 | Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass. | | name | string,null | true | | Name of the target column | | predictionProbabilitiesColumn | string,null | true | | Field or column name containing prediction probabilities | | predictionThreshold | number,null | true | maximum: 1minimum: 0 | Prediction threshold used for binary classification models | | type | string | true | | Target type of the model. | ### Enumerated Values | Property | Value | | --- | --- | | type | [Binary, Regression, Multiclass, Multilabel, TextGeneration, GeoPoint, AgenticWorkflow, MCP] | ## ModelPackageTimeseries ``` { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" } ``` time series information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimeColumnFormat | string,null | true | | Date format for forecast date and forecast point column | | datetimeColumnName | string,null | true | | Name of the forecast date column | | effectiveFeatureDerivationWindowEnd | integer,null | true | maximum: 0 | Same concept as featureDerivationWindowEnd which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the "real" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. | | effectiveFeatureDerivationWindowStart | integer,null | true | maximum: 0 | Same concept as featureDerivationWindowStart which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the "real" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. | | featureDerivationWindowEnd | integer,null | true | maximum: 0 | Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end. | | featureDerivationWindowStart | integer,null | true | maximum: 0 | Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin. | | forecastDistanceColumnName | string,null | true | | Name of the forecast distance column | | forecastDistances | [integer] | true | | List of integer forecast distances | | forecastDistancesTimeUnit | string,null | true | | The time unit of forecast distances | | forecastPointColumnName | string,null | true | | Name of the forecast point column | | isCrossSeries | boolean,null | true | | true if the model is cross-series. | | isNewSeriesSupport | boolean,null | true | | true if the model is optimized to support new series. | | isTraditionalTimeSeries | boolean,null | true | | true if the model is traditional time series. | | seriesColumnName | string,null | true | | Name of the series column in case of multi-series date | ### Enumerated Values | Property | Value | | --- | --- | | forecastDistancesTimeUnit | [MICROSECOND, MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | ## RegisteredModelCreatedBy ``` { "description": "Information on the creator of the registered model.", "properties": { "email": { "description": "The email of the user who created the registered model.", "type": "string" }, "id": { "description": "The ID of the user who created the registered model.", "type": "string" }, "name": { "description": "The full name of the user who created the registered model.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" } ``` Information on the creator of the registered model. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string | true | | The email of the user who created the registered model. | | id | string | true | | The ID of the user who created the registered model. | | name | string,null | true | | The full name of the user who created the registered model. | ## RegisteredModelDeploymentResponse ``` { "properties": { "createdAt": { "description": "Deployment creation date", "type": "string" }, "createdBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "currentlyDeployed": { "description": "Whether version of this registered model is currently deployed", "type": "boolean" }, "firstDeployedAt": { "description": "When version of this registered model was first deployed", "type": [ "string", "null" ] }, "firstDeployedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "isChallenger": { "description": "True if given version is a challenger in a given deployment", "type": "boolean" }, "label": { "description": "Label of the deployment", "type": [ "string", "null" ] }, "predictionEnvironment": { "description": "Information related to the current PredictionEnvironment.", "properties": { "id": { "description": "ID of the PredictionEnvironment.", "type": [ "string", "null" ] }, "isManagedByManagementAgent": { "description": "True if PredictionEnvironment is using Management Agent.", "type": "boolean" }, "name": { "description": "Name of the PredictionEnvironment.", "type": "string" }, "platform": { "description": "Platform of the PredictionEnvironment.", "enum": [ "aws", "gcp", "azure", "onPremise", "datarobot", "datarobotServerless", "openShift", "other", "snowflake", "sapAiCore" ], "type": "string" }, "plugin": { "description": "Plugin name of the PredictionEnvironment.", "type": [ "string", "null" ] }, "supportedModelFormats": { "description": "Model formats that the PredictionEnvironment supports.", "items": { "enum": [ "datarobot", "datarobotScoringCode", "customModel", "externalModel" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "id", "isManagedByManagementAgent", "name", "platform" ], "type": "object" }, "registeredModelVersion": { "description": "Version of the registered model", "type": "integer" }, "status": { "description": "Status of the deployment", "type": "string" } }, "required": [ "createdAt", "createdBy", "currentlyDeployed", "firstDeployedAt", "firstDeployedBy", "id", "isChallenger", "label", "registeredModelVersion", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string | true | | Deployment creation date | | createdBy | UserMetadata | true | | Information on the user who last modified the registered model | | currentlyDeployed | boolean | true | | Whether version of this registered model is currently deployed | | firstDeployedAt | string,null | true | | When version of this registered model was first deployed | | firstDeployedBy | UserMetadata | true | | Information on the user who last modified the registered model | | id | string | true | | The ID of the deployment. | | isChallenger | boolean | true | | True if given version is a challenger in a given deployment | | label | string,null | true | | Label of the deployment | | predictionEnvironment | DeploymentPredictionEnvironmentResponse | false | | Information related to the current PredictionEnvironment. | | registeredModelVersion | integer | true | | Version of the registered model | | status | string | true | | Status of the deployment | ## RegisteredModelDeploymentsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of formatted deployments.", "items": { "properties": { "createdAt": { "description": "Deployment creation date", "type": "string" }, "createdBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "currentlyDeployed": { "description": "Whether version of this registered model is currently deployed", "type": "boolean" }, "firstDeployedAt": { "description": "When version of this registered model was first deployed", "type": [ "string", "null" ] }, "firstDeployedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "id": { "description": "The ID of the deployment.", "type": "string" }, "isChallenger": { "description": "True if given version is a challenger in a given deployment", "type": "boolean" }, "label": { "description": "Label of the deployment", "type": [ "string", "null" ] }, "predictionEnvironment": { "description": "Information related to the current PredictionEnvironment.", "properties": { "id": { "description": "ID of the PredictionEnvironment.", "type": [ "string", "null" ] }, "isManagedByManagementAgent": { "description": "True if PredictionEnvironment is using Management Agent.", "type": "boolean" }, "name": { "description": "Name of the PredictionEnvironment.", "type": "string" }, "platform": { "description": "Platform of the PredictionEnvironment.", "enum": [ "aws", "gcp", "azure", "onPremise", "datarobot", "datarobotServerless", "openShift", "other", "snowflake", "sapAiCore" ], "type": "string" }, "plugin": { "description": "Plugin name of the PredictionEnvironment.", "type": [ "string", "null" ] }, "supportedModelFormats": { "description": "Model formats that the PredictionEnvironment supports.", "items": { "enum": [ "datarobot", "datarobotScoringCode", "customModel", "externalModel" ], "type": "string" }, "maxItems": 4, "minItems": 1, "type": "array" } }, "required": [ "id", "isManagedByManagementAgent", "name", "platform" ], "type": "object" }, "registeredModelVersion": { "description": "Version of the registered model", "type": "integer" }, "status": { "description": "Status of the deployment", "type": "string" } }, "required": [ "createdAt", "createdBy", "currentlyDeployed", "firstDeployedAt", "firstDeployedBy", "id", "isChallenger", "label", "registeredModelVersion", "status" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RegisteredModelDeploymentResponse] | true | | The list of formatted deployments. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RegisteredModelListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of formatted registered models.", "items": { "properties": { "createdAt": { "description": "The date when the registered model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "Information on the creator of the registered model.", "properties": { "email": { "description": "The email of the user who created the registered model.", "type": "string" }, "id": { "description": "The ID of the user who created the registered model.", "type": "string" }, "name": { "description": "The full name of the user who created the registered model.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "description": { "description": "The description of the registered model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the registered model.", "type": "string" }, "isArchived": { "description": "Whether the model is archived.", "type": "boolean" }, "isGlobal": { "description": "Whether the registered model is global (accessible to all users in the organization) or local(accessible only to the owner and the users with whom it has been explicitly shared)", "type": "boolean" }, "lastVersionNum": { "description": "The latest version associated with this registered model.", "type": "integer" }, "modifiedAt": { "description": "The date when the registered model was last modified.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "name": { "description": "The name of the registered model.", "type": "string" }, "target": { "description": "Information on the target variable.", "properties": { "name": { "description": "The name of the target variable.", "type": [ "string", "null" ] }, "type": { "description": "The type of the target variable.", "type": [ "string", "null" ] } }, "required": [ "name", "type" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isArchived", "lastVersionNum", "modifiedAt", "modifiedBy", "name", "target" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [RegisteredModelResponse] | true | | The list of formatted registered models. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## RegisteredModelResponse ``` { "properties": { "createdAt": { "description": "The date when the registered model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "Information on the creator of the registered model.", "properties": { "email": { "description": "The email of the user who created the registered model.", "type": "string" }, "id": { "description": "The ID of the user who created the registered model.", "type": "string" }, "name": { "description": "The full name of the user who created the registered model.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "description": { "description": "The description of the registered model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the registered model.", "type": "string" }, "isArchived": { "description": "Whether the model is archived.", "type": "boolean" }, "isGlobal": { "description": "Whether the registered model is global (accessible to all users in the organization) or local(accessible only to the owner and the users with whom it has been explicitly shared)", "type": "boolean" }, "lastVersionNum": { "description": "The latest version associated with this registered model.", "type": "integer" }, "modifiedAt": { "description": "The date when the registered model was last modified.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "name": { "description": "The name of the registered model.", "type": "string" }, "target": { "description": "Information on the target variable.", "properties": { "name": { "description": "The name of the target variable.", "type": [ "string", "null" ] }, "type": { "description": "The type of the target variable.", "type": [ "string", "null" ] } }, "required": [ "name", "type" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isArchived", "lastVersionNum", "modifiedAt", "modifiedBy", "name", "target" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The date when the registered model was created. | | createdBy | RegisteredModelCreatedBy | true | | Information on the creator of the registered model. | | description | string,null | false | | The description of the registered model. | | id | string | true | | The ID of the registered model. | | isArchived | boolean | true | | Whether the model is archived. | | isGlobal | boolean | false | | Whether the registered model is global (accessible to all users in the organization) or local(accessible only to the owner and the users with whom it has been explicitly shared) | | lastVersionNum | integer | true | | The latest version associated with this registered model. | | modifiedAt | string(date-time) | true | | The date when the registered model was last modified. | | modifiedBy | UserMetadata | true | | Information on the user who last modified the registered model | | name | string | true | | The name of the registered model. | | target | RegisteredModelTarget | true | | Information on the target variable. | ## RegisteredModelTarget ``` { "description": "Information on the target variable.", "properties": { "name": { "description": "The name of the target variable.", "type": [ "string", "null" ] }, "type": { "description": "The type of the target variable.", "type": [ "string", "null" ] } }, "required": [ "name", "type" ], "type": "object" } ``` Information on the target variable. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string,null | true | | The name of the target variable. | | type | string,null | true | | The type of the target variable. | ## RegisteredModelUpdate ``` { "properties": { "description": { "description": "The description of the registered model.", "maxLength": 2048, "type": "string" }, "isGlobal": { "description": "Make registered model global (accessible to all users in the organization) or local(accessible only to the owner and the users with whom it has been explicitly shared)", "type": "boolean" }, "name": { "description": "The name of the registered model.", "maxLength": 1024, "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 2048 | The description of the registered model. | | isGlobal | boolean | false | | Make registered model global (accessible to all users in the organization) or local(accessible only to the owner and the users with whom it has been explicitly shared) | | name | string | false | maxLength: 1024 | The name of the registered model. | ## RegisteredModelVersionsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of formatted registered model versions.", "items": { "properties": { "activeDeploymentCount": { "description": "Number of deployments currently using this model package", "type": "integer" }, "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ] }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string" }, "permissions": { "description": "List of action permissions the user making the request has on the model package", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string" } }, "required": [ "activeDeploymentCount", "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "permissions", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ModelPackageRetrieveResponse] | true | | The list of formatted registered model versions. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## SharedRolesUpdate ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | Name of the action being taken. The only operation is 'updateRoles'. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | Array of GrantAccessControl objects., up to maximum 100 objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithUsername | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithId | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## SharingListV2Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControlV2] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of items matching the condition. | ## UseCaseDetails ``` { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } ``` Details of the use-case associated to this registered model version ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string | true | | The time when the use case was created. | | creatorEmail | string,null | false | | The email of the user who created the use case. | | creatorId | string | true | | The ID of the creator of the use case. | | creatorName | string,null | false | | The name of the user who created the use case. | | id | string | true | | The ID of the associated use case. | | name | string,null | false | | The name of the use case at the moment of creation. | ## UserMetadata ``` { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" } ``` Information on the user who last modified the registered model ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string,null | true | | The email of the user. | | id | string | true | | The ID of the user. | | name | string,null | true | | The full name of the user. | --- # Models URL: https://docs.datarobot.com/en/docs/api/reference/public-api/models.html > Use the endpoints described below to create and manage DataRobot models. # Models Use the endpoints described below to create and manage DataRobot models. ## Create a model package Operation path: `POST /api/v2/modelPackages/fromJSON/` Authentication requirements: `BearerAuth` Create a model package from json. ### Body parameter ``` { "properties": { "datasets": { "description": "The dataset information for the model package.", "properties": { "holdoutDataCatalogId": { "description": "The ID for Holdout data (returned from uploading a dataset).", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "The version ID for Holdout data (returned from uploading a dataset).", "type": [ "string", "null" ] }, "trainingDataCatalogId": { "description": "The ID for training data (returned from uploading a dataset).", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "The version ID for training data (returned from uploading a dataset).", "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.37" }, "geospatialMonitoring": { "description": "Geospatial monitoring information for the model package", "properties": { "primaryLocationColumn": { "description": "The name of the geo-analysis column,", "type": [ "string", "null" ] } }, "required": [ "primaryLocationColumn" ], "type": "object", "x-versionadded": "v2.37" }, "modelDescription": { "description": "The model description information for the model package.", "properties": { "buildEnvironmentType": { "description": "The build environment type of the model.", "enum": [ "DataRobot", "Python", "R", "Java", "Julia", "Legacy", "Other" ], "type": "string" }, "description": { "description": "A description of the model.", "maxLength": 2048, "type": [ "string", "null" ] }, "location": { "description": "The location of the model.", "maxLength": 2048, "type": [ "string", "null" ] }, "modelName": { "description": "The model name.", "maxLength": 512, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "name": { "description": "The model package name.", "maxLength": 1024, "type": "string" }, "registeredModelName": { "description": "The registered model name.", "maxLength": 1024, "type": "string", "x-versionadded": "v2.39" }, "target": { "description": "The target information for the model package.", "properties": { "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class.", "items": { "maxLength": 128, "type": "string" }, "maxItems": 1000, "type": "array" }, "name": { "description": "Name of the target column", "maxLength": 128, "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "maxLength": 128, "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "type" ], "type": "object", "x-versionadded": "v2.37" }, "textGeneration": { "description": "Text generation information for the model package", "properties": { "prompt": { "description": "Name of the prompt column", "type": [ "string", "null" ] } }, "required": [ "prompt" ], "type": "object", "x-versionadded": "v2.37" }, "timeseries": { "description": "Time series information for the model package.", "properties": { "datetimeColumnFormat": { "description": "The date format for the forecast date and forecast point column.", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "The name of the forecast date column.", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "A negative number or zero describing the end of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.37" }, "effectiveFeatureDerivationWindowStart": { "description": "A negative number or zero describing the start of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.37" }, "featureDerivationWindowEnd": { "description": "A negative number or zero defining the end point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -7 days would mean the feature derivation would be done with data ending at 7 days ago.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "A negative number or zero defining the start point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -28 days would means the feature derivation would be done with data starting from 28 days ago.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "The name of the forecast distance column.", "type": [ "string", "null" ] }, "forecastDistances": { "description": "A list of integer forecast distances.", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances.", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "forecastPointColumnName": { "description": "The name of the forecast point column.", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "default": false, "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ] }, "isTraditionalTimeSeries": { "default": false, "description": "Determines if the model is a traditional time series model.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "The name of the series column in the case of a multi-series date.", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "forecastDistanceColumnName", "forecastDistancesTimeUnit", "forecastPointColumnName" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "name", "target" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ModelPackageCreateExternal | false | none | ### Example responses > 200 Response ``` { "properties": { "activeDeploymentCount": { "description": "Number of deployments currently using this model package", "type": "integer" }, "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ] }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string" }, "permissions": { "description": "List of action permissions the user making the request has on the model package", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string" } }, "required": [ "activeDeploymentCount", "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "permissions", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ModelPackageRetrieveResponse | ## Retrieve an archive (tar by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/logs/` Authentication requirements: `BearerAuth` Retrieve an archive (tar.gz) of the logs produced and persisted by a model. Note that only blueprints with custom tasks create persistent logs - this will not work with any other type of model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "An archive (tar.gz) of the logs produced and persisted by a model.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | An archive (tar.gz) of the logs produced and persisted by a model. | PersistentLogsForModelWithCustomTasksRetrieveResponse | | 403 | Forbidden | User does not have permissions to fetch model logs. | None | | 404 | Not Found | Logs for this model could not be found. | None | ## Retrieve training artifact by id by project ID Operation path: `GET /api/v2/projects/{projectId}/models/{modelId}/trainingArtifact/` Authentication requirements: `BearerAuth` Retrieve an archive (tar.gz) of the artifacts produced and persisted by a model. Note that only blueprints with custom tasks create these artifacts - this will not work with any other type of model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | modelId | path | string | true | The model ID. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "An archive (tar.gz) of the artifacts produced and persisted by a model.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | An archive (tar.gz) of the artifacts produced by this model. | ArtifactsForModelWithCustomTasksRetrieveResponse | | 403 | Forbidden | User does not have permissions to fetch this artifact. | None | | 404 | Not Found | The model with this modelID does not have any artifacts. | None | # Schemas ## ArtifactsForModelWithCustomTasksRetrieveResponse ``` { "properties": { "data": { "description": "An archive (tar.gz) of the artifacts produced and persisted by a model.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | string(binary) | true | | An archive (tar.gz) of the artifacts produced and persisted by a model. | ## CustomModelDetails ``` { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } ``` Details of the custom model associated to this registered model version ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string | true | | The time when the custom model was created. | | creatorEmail | string,null | false | | The email of the user who created the custom model. | | creatorId | string | true | | The ID of the creator of the custom model. | | creatorName | string,null | false | | The name of the user who created the custom model. | | id | string | true | | The ID of the associated custom model. | | versionLabel | string,null | false | | The label of the associated custom model version. | ## MlpkgFileContents ``` { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" } ``` Information about the content of .mlpkg artifact ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allTimeSeriesPredictionIntervals | boolean,null | false | | Whether .mlpkg contains TS prediction intervals computed for all percentiles | ## ModelPackageCapabilities ``` { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" } ``` Capabilities of the current model package. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | supportsAutomaticActuals | boolean | false | | Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package. | | supportsChallengerModels | boolean | true | | Whether Challenger Models are supported by this model package. | | supportsFeatureDriftTracking | boolean | true | | Whether Feature Drift is supported by this model package. | | supportsHumilityRecommendedRules | boolean | true | | Whether calculating values for recommended Humility Rules is supported by this model package. | | supportsHumilityRules | boolean | true | | Whether Humility Rules are supported by this model package. | | supportsHumilityRulesDefaultCalculations | boolean | true | | Whether calculating default values for Humility Rules is supported by this model package. | | supportsPredictionWarning | boolean | true | | Whether Prediction Warnings are supported by this model package. | | supportsRetraining | boolean | false | | Whether deployment supports retraining. | | supportsScoringCodeDownload | boolean | false | | Whether scoring code download is supported by this model package. | | supportsSecondaryDatasets | boolean | true | | If the deployments supports secondary datasets. | | supportsSegmentedAnalysisDriftAndAccuracy | boolean | true | | Whether tracking features in training and predictions data for segmented analysis is supported by this model package. | | supportsShapBasedPredictionExplanations | boolean | true | | Whether shap-based prediction explanations are supported by this model package. | | supportsTargetDriftTracking | boolean | true | | Whether Target Drift is supported by this model package. | ## ModelPackageCreateExternal ``` { "properties": { "datasets": { "description": "The dataset information for the model package.", "properties": { "holdoutDataCatalogId": { "description": "The ID for Holdout data (returned from uploading a dataset).", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "The version ID for Holdout data (returned from uploading a dataset).", "type": [ "string", "null" ] }, "trainingDataCatalogId": { "description": "The ID for training data (returned from uploading a dataset).", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "The version ID for training data (returned from uploading a dataset).", "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.37" }, "geospatialMonitoring": { "description": "Geospatial monitoring information for the model package", "properties": { "primaryLocationColumn": { "description": "The name of the geo-analysis column,", "type": [ "string", "null" ] } }, "required": [ "primaryLocationColumn" ], "type": "object", "x-versionadded": "v2.37" }, "modelDescription": { "description": "The model description information for the model package.", "properties": { "buildEnvironmentType": { "description": "The build environment type of the model.", "enum": [ "DataRobot", "Python", "R", "Java", "Julia", "Legacy", "Other" ], "type": "string" }, "description": { "description": "A description of the model.", "maxLength": 2048, "type": [ "string", "null" ] }, "location": { "description": "The location of the model.", "maxLength": 2048, "type": [ "string", "null" ] }, "modelName": { "description": "The model name.", "maxLength": 512, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "name": { "description": "The model package name.", "maxLength": 1024, "type": "string" }, "registeredModelName": { "description": "The registered model name.", "maxLength": 1024, "type": "string", "x-versionadded": "v2.39" }, "target": { "description": "The target information for the model package.", "properties": { "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class.", "items": { "maxLength": 128, "type": "string" }, "maxItems": 1000, "type": "array" }, "name": { "description": "Name of the target column", "maxLength": 128, "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "maxLength": 128, "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "type" ], "type": "object", "x-versionadded": "v2.37" }, "textGeneration": { "description": "Text generation information for the model package", "properties": { "prompt": { "description": "Name of the prompt column", "type": [ "string", "null" ] } }, "required": [ "prompt" ], "type": "object", "x-versionadded": "v2.37" }, "timeseries": { "description": "Time series information for the model package.", "properties": { "datetimeColumnFormat": { "description": "The date format for the forecast date and forecast point column.", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "The name of the forecast date column.", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "A negative number or zero describing the end of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.37" }, "effectiveFeatureDerivationWindowStart": { "description": "A negative number or zero describing the start of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.37" }, "featureDerivationWindowEnd": { "description": "A negative number or zero defining the end point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -7 days would mean the feature derivation would be done with data ending at 7 days ago.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "A negative number or zero defining the start point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -28 days would means the feature derivation would be done with data starting from 28 days ago.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "The name of the forecast distance column.", "type": [ "string", "null" ] }, "forecastDistances": { "description": "A list of integer forecast distances.", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances.", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "forecastPointColumnName": { "description": "The name of the forecast point column.", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "default": false, "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ] }, "isTraditionalTimeSeries": { "default": false, "description": "Determines if the model is a traditional time series model.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "The name of the series column in the case of a multi-series date.", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "forecastDistanceColumnName", "forecastDistancesTimeUnit", "forecastPointColumnName" ], "type": "object", "x-versionadded": "v2.37" } }, "required": [ "name", "target" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasets | ModelPackageDatasetsCreate | false | | The dataset information for the model package. | | geospatialMonitoring | ModelPackageExternalGeospatialMonitoring | false | | Geospatial monitoring information for the model package | | modelDescription | ModelPackageModelDescriptionCreate | false | | The model description information for the model package. | | modelId | string | false | | The ID of the model. | | name | string | true | maxLength: 1024 | The model package name. | | registeredModelName | string | false | maxLength: 1024 | The registered model name. | | target | ModelPackageTargetCreate | true | | The target information for the model package. | | textGeneration | ModelPackageTextGeneration | false | | Text generation information for the model package | | timeseries | ModelPackageTimeseriesCreate | false | | Time series information for the model package. | ## ModelPackageDatasets ``` { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" } ``` dataset information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineSegmentedBy | [string] | true | | Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value. | | datasetName | string,null | true | | Name of dataset used to train the model | | holdoutDataCatalogId | string,null | true | | ID for holdout data (returned from uploading a data set) | | holdoutDataCatalogVersionId | string,null | true | | Version ID for holdout data (returned from uploading a data set) | | holdoutDataCreatedAt | string,null | false | | Time when the holdout data item was created | | holdoutDataCreatorEmail | string,null | false | | Email of the user who created the holdout data item | | holdoutDataCreatorId | string,null | false | | ID of the creator of the holdout data item | | holdoutDataCreatorName | string,null | false | | Name of the user who created the holdout data item | | holdoutDatasetName | string,null | true | | Name of dataset used for model holdout | | targetHistogramBaseline | string | false | | Values used to establish the training baseline | | trainingDataCatalogId | string,null | true | | ID for training data (returned from uploading a data set) | | trainingDataCatalogVersionId | string,null | true | | Version ID for training data (returned from uploading a data set) | | trainingDataCreatedAt | string,null | false | | Time when the training data item was created | | trainingDataCreatorEmail | string,null | false | | Email of the user who created the training data item | | trainingDataCreatorId | string,null | false | | ID of the creator of the training data item | | trainingDataCreatorName | string,null | false | | Name of the user who created the training data item | | trainingDataSize | integer | false | | Number of rows in training data (used by DR models) | ### Enumerated Values | Property | Value | | --- | --- | | targetHistogramBaseline | [predictions, actuals] | ## ModelPackageDatasetsCreate ``` { "description": "The dataset information for the model package.", "properties": { "holdoutDataCatalogId": { "description": "The ID for Holdout data (returned from uploading a dataset).", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "The version ID for Holdout data (returned from uploading a dataset).", "type": [ "string", "null" ] }, "trainingDataCatalogId": { "description": "The ID for training data (returned from uploading a dataset).", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "The version ID for training data (returned from uploading a dataset).", "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.37" } ``` The dataset information for the model package. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | holdoutDataCatalogId | string,null | false | | The ID for Holdout data (returned from uploading a dataset). | | holdoutDataCatalogVersionId | string,null | false | | The version ID for Holdout data (returned from uploading a dataset). | | trainingDataCatalogId | string,null | false | | The ID for training data (returned from uploading a dataset). | | trainingDataCatalogVersionId | string,null | false | | The version ID for training data (returned from uploading a dataset). | ## ModelPackageExternalGeospatialMonitoring ``` { "description": "Geospatial monitoring information for the model package", "properties": { "primaryLocationColumn": { "description": "The name of the geo-analysis column,", "type": [ "string", "null" ] } }, "required": [ "primaryLocationColumn" ], "type": "object", "x-versionadded": "v2.37" } ``` Geospatial monitoring information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | primaryLocationColumn | string,null | true | | The name of the geo-analysis column, | ## ModelPackageImportMeta ``` { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" } ``` Information from when this Model Package was first saved ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | containsFearPipeline | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with fear pipeline. | | containsFeaturelists | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with featurelists. | | containsLeaderboardMeta | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with leaderboard meta. | | containsProjectMeta | boolean,null | false | | Exists for imported models only, indicates thatmodel package contains file with project meta. | | creatorFullName | string,null | true | | The full name of the person who created this model package. | | creatorId | string | true | | The user ID of the person who created this model package. | | creatorUsername | string | true | | The username of the person who created this model package. | | dateCreated | string | true | | When this model package was created. | | originalFileName | string,null | true | | Exists for imported models only, the original file name that was uploaded | ## ModelPackageModelDescription ``` { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" } ``` model description information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildEnvironmentType | string | true | | build environment type of the model | | description | string,null | true | | a description of the model | | location | string,null | true | | location of the model | | modelCreatedAt | string,null | false | | time when the model was created | | modelCreatorEmail | string,null | false | | email of the user who created the model | | modelCreatorId | string,null | false | | ID of the creator of the model | | modelCreatorName | string,null | false | | name of the user who created the model | | modelName | string | false | | model name | ### Enumerated Values | Property | Value | | --- | --- | | buildEnvironmentType | [DataRobot, Python, R, Java, Other] | ## ModelPackageModelDescriptionCreate ``` { "description": "The model description information for the model package.", "properties": { "buildEnvironmentType": { "description": "The build environment type of the model.", "enum": [ "DataRobot", "Python", "R", "Java", "Julia", "Legacy", "Other" ], "type": "string" }, "description": { "description": "A description of the model.", "maxLength": 2048, "type": [ "string", "null" ] }, "location": { "description": "The location of the model.", "maxLength": 2048, "type": [ "string", "null" ] }, "modelName": { "description": "The model name.", "maxLength": 512, "type": "string" } }, "type": "object", "x-versionadded": "v2.37" } ``` The model description information for the model package. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildEnvironmentType | string | false | | The build environment type of the model. | | description | string,null | false | maxLength: 2048 | A description of the model. | | location | string,null | false | maxLength: 2048 | The location of the model. | | modelName | string | false | maxLength: 512 | The model name. | ### Enumerated Values | Property | Value | | --- | --- | | buildEnvironmentType | [DataRobot, Python, R, Java, Julia, Legacy, Other] | ## ModelPackageModelKind ``` { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" } ``` Model attribute information ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isAnomalyDetectionModel | boolean | true | | true if this is an anomaly detection model | | isCombinedModel | boolean | true | | true if model is a combined model | | isFeatureDiscovery | boolean | true | | true if this model uses the Feature Discovery feature | | isMultiseries | boolean | true | | true if model is multiseries | | isTimeSeries | boolean | true | | true if model is time series | | isUnsupervisedLearning | boolean | true | | true if model used unsupervised learning | ## ModelPackageRetrieveResponse ``` { "properties": { "activeDeploymentCount": { "description": "Number of deployments currently using this model package", "type": "integer" }, "buildStatus": { "description": "Model package build status", "enum": [ "inProgress", "complete", "failed" ], "type": [ "string", "null" ] }, "capabilities": { "description": "Capabilities of the current model package.", "properties": { "supportsAutomaticActuals": { "description": "Whether inferring actual values from time series history data and automatically feeding them back for accuracy estimation is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsChallengerModels": { "description": "Whether Challenger Models are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsFeatureDriftTracking": { "description": "Whether Feature Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRecommendedRules": { "description": "Whether calculating values for recommended Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRules": { "description": "Whether Humility Rules are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsHumilityRulesDefaultCalculations": { "description": "Whether calculating default values for Humility Rules is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2" }, "supportsPredictionWarning": { "description": "Whether Prediction Warnings are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsRetraining": { "description": "Whether deployment supports retraining.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsScoringCodeDownload": { "description": "Whether scoring code download is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsSecondaryDatasets": { "description": "If the deployments supports secondary datasets.", "type": "boolean", "x-versionadded": "v2.28", "x-versiondeprecated": "v2.29" }, "supportsSegmentedAnalysisDriftAndAccuracy": { "description": "Whether tracking features in training and predictions data for segmented analysis is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsShapBasedPredictionExplanations": { "description": "Whether shap-based prediction explanations are supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" }, "supportsTargetDriftTracking": { "description": "Whether Target Drift is supported by this model package.", "type": "boolean", "x-versionadded": "v2.25.2", "x-versiondeprecated": "v2.29" } }, "required": [ "supportsChallengerModels", "supportsFeatureDriftTracking", "supportsHumilityRecommendedRules", "supportsHumilityRules", "supportsHumilityRulesDefaultCalculations", "supportsPredictionWarning", "supportsSecondaryDatasets", "supportsSegmentedAnalysisDriftAndAccuracy", "supportsShapBasedPredictionExplanations", "supportsTargetDriftTracking" ], "type": "object" }, "datasets": { "description": "dataset information for the model package", "properties": { "baselineSegmentedBy": { "description": "Names of categorical features by which the training baseline was segmented. This allows for deployment prediction requests to be segmented by those same features. Segmenting the training baseline by these features allows for users to perform segmented analysis of Data Drift and Accuracy, and to compare the same subset of training and scoring data based on the selected segment attribute and segment value.", "items": { "type": "string" }, "type": "array" }, "datasetName": { "description": "Name of dataset used to train the model", "type": [ "string", "null" ] }, "holdoutDataCatalogId": { "description": "ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCatalogVersionId": { "description": "Version ID for holdout data (returned from uploading a data set)", "type": [ "string", "null" ] }, "holdoutDataCreatedAt": { "description": "Time when the holdout data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorEmail": { "description": "Email of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorId": { "default": null, "description": "ID of the creator of the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDataCreatorName": { "description": "Name of the user who created the holdout data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "holdoutDatasetName": { "description": "Name of dataset used for model holdout", "type": [ "string", "null" ] }, "targetHistogramBaseline": { "description": "Values used to establish the training baseline", "enum": [ "predictions", "actuals" ], "type": "string" }, "trainingDataCatalogId": { "description": "ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCatalogVersionId": { "description": "Version ID for training data (returned from uploading a data set)", "type": [ "string", "null" ] }, "trainingDataCreatedAt": { "description": "Time when the training data item was created", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorEmail": { "description": "Email of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorId": { "default": null, "description": "ID of the creator of the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataCreatorName": { "description": "Name of the user who created the training data item", "type": [ "string", "null" ], "x-versionadded": "v2.34" }, "trainingDataSize": { "description": "Number of rows in training data (used by DR models)", "type": "integer" } }, "required": [ "baselineSegmentedBy", "datasetName", "holdoutDataCatalogId", "holdoutDataCatalogVersionId", "holdoutDatasetName", "trainingDataCatalogId", "trainingDataCatalogVersionId" ], "type": "object" }, "id": { "description": "The ID of the model package.", "type": "string" }, "importMeta": { "description": "Information from when this Model Package was first saved", "properties": { "containsFearPipeline": { "description": "Exists for imported models only, indicates thatmodel package contains file with fear pipeline.", "type": [ "boolean", "null" ] }, "containsFeaturelists": { "description": "Exists for imported models only, indicates thatmodel package contains file with featurelists.", "type": [ "boolean", "null" ] }, "containsLeaderboardMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with leaderboard meta.", "type": [ "boolean", "null" ] }, "containsProjectMeta": { "description": "Exists for imported models only, indicates thatmodel package contains file with project meta.", "type": [ "boolean", "null" ] }, "creatorFullName": { "description": "The full name of the person who created this model package.", "type": [ "string", "null" ] }, "creatorId": { "description": "The user ID of the person who created this model package.", "type": "string" }, "creatorUsername": { "description": "The username of the person who created this model package.", "type": "string" }, "dateCreated": { "description": "When this model package was created.", "type": "string" }, "originalFileName": { "description": "Exists for imported models only, the original file name that was uploaded", "type": [ "string", "null" ] } }, "required": [ "creatorFullName", "creatorId", "creatorUsername", "dateCreated", "originalFileName" ], "type": "object" }, "isArchived": { "description": "Whether the model package is permanently archived (cannot be used in deployment or replacement)", "type": "boolean" }, "isDeprecated": { "description": "Whether the model package is deprecated. eg. python2 models are deprecated.", "type": "boolean", "x-versionadded": "v2.29" }, "mlpkgFileContents": { "description": "Information about the content of .mlpkg artifact", "properties": { "allTimeSeriesPredictionIntervals": { "description": "Whether .mlpkg contains TS prediction intervals computed for all percentiles", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "type": "object" }, "modelDescription": { "description": "model description information for the model package", "properties": { "buildEnvironmentType": { "description": "build environment type of the model", "enum": [ "DataRobot", "Python", "R", "Java", "Other" ], "type": "string" }, "description": { "description": "a description of the model", "type": [ "string", "null" ] }, "location": { "description": "location of the model", "type": [ "string", "null" ] }, "modelCreatedAt": { "description": "time when the model was created", "type": [ "string", "null" ] }, "modelCreatorEmail": { "description": "email of the user who created the model", "type": [ "string", "null" ] }, "modelCreatorId": { "default": null, "description": "ID of the creator of the model", "type": [ "string", "null" ] }, "modelCreatorName": { "description": "name of the user who created the model", "type": [ "string", "null" ] }, "modelName": { "description": "model name", "type": "string" } }, "required": [ "buildEnvironmentType", "description", "location" ], "type": "object" }, "modelExecutionType": { "description": "Type of model package. `dedicated` (native DataRobot models) and `custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, `external` do not", "enum": [ "dedicated", "custom_inference_model", "external" ], "type": "string" }, "modelId": { "description": "The ID of the model.", "type": "string" }, "modelKind": { "description": "Model attribute information", "properties": { "isAnomalyDetectionModel": { "description": "true if this is an anomaly detection model", "type": "boolean" }, "isCombinedModel": { "description": "true if model is a combined model", "type": "boolean", "x-versionadded": "v2.27" }, "isFeatureDiscovery": { "description": "true if this model uses the Feature Discovery feature", "type": "boolean" }, "isMultiseries": { "description": "true if model is multiseries", "type": "boolean" }, "isTimeSeries": { "description": "true if model is time series", "type": "boolean" }, "isUnsupervisedLearning": { "description": "true if model used unsupervised learning", "type": "boolean" } }, "required": [ "isAnomalyDetectionModel", "isCombinedModel", "isFeatureDiscovery", "isMultiseries", "isTimeSeries", "isUnsupervisedLearning" ], "type": "object" }, "name": { "description": "The model package name.", "type": "string" }, "permissions": { "description": "List of action permissions the user making the request has on the model package", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.20" }, "sourceMeta": { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" }, "target": { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" }, "timeseries": { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" }, "updatedBy": { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" }, "userProvidedId": { "description": "A user-provided unique ID associated with the given custom inference model.", "type": "string" } }, "required": [ "activeDeploymentCount", "capabilities", "datasets", "id", "importMeta", "isArchived", "isDeprecated", "modelDescription", "modelExecutionType", "modelId", "modelKind", "name", "permissions", "sourceMeta", "target", "timeseries", "updatedBy" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | activeDeploymentCount | integer | true | | Number of deployments currently using this model package | | buildStatus | string,null | false | | Model package build status | | capabilities | ModelPackageCapabilities | true | | Capabilities of the current model package. | | datasets | ModelPackageDatasets | true | | dataset information for the model package | | id | string | true | | The ID of the model package. | | importMeta | ModelPackageImportMeta | true | | Information from when this Model Package was first saved | | isArchived | boolean | true | | Whether the model package is permanently archived (cannot be used in deployment or replacement) | | isDeprecated | boolean | true | | Whether the model package is deprecated. eg. python2 models are deprecated. | | mlpkgFileContents | MlpkgFileContents | false | | Information about the content of .mlpkg artifact | | modelDescription | ModelPackageModelDescription | true | | model description information for the model package | | modelExecutionType | string | true | | Type of model package. dedicated (native DataRobot models) and custom_inference_model (user added inference models) both execute on DataRobot prediction servers, external do not | | modelId | string | true | | The ID of the model. | | modelKind | ModelPackageModelKind | true | | Model attribute information | | name | string | true | | The model package name. | | permissions | [string] | true | | List of action permissions the user making the request has on the model package | | sourceMeta | ModelPackageSourceMeta | true | | Meta information from where this model was generated | | target | ModelPackageTarget | true | | target information for the model package | | timeseries | ModelPackageTimeseries | true | | time series information for the model package | | updatedBy | UserMetadata | true | | Information on the user who last modified the registered model | | userProvidedId | string | false | | A user-provided unique ID associated with the given custom inference model. | ### Enumerated Values | Property | Value | | --- | --- | | buildStatus | [inProgress, complete, failed] | | modelExecutionType | [dedicated, custom_inference_model, external] | ## ModelPackageScoringCodeMeta ``` { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" } ``` If available, information about the model's scoring code ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataRobotPredictionVersion | string,null | true | | The DataRobot prediction API version for the scoring code. | | location | string,null | true | | The location of the scoring code. | ### Enumerated Values | Property | Value | | --- | --- | | location | [local_leaderboard, mlpkg] | ## ModelPackageSourceMeta ``` { "description": "Meta information from where this model was generated", "properties": { "customModelDetails": { "description": "Details of the custom model associated to this registered model version", "properties": { "createdAt": { "description": "The time when the custom model was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the custom model.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the custom model.", "type": "string" }, "creatorName": { "description": "The name of the user who created the custom model.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated custom model.", "type": "string" }, "versionLabel": { "description": "The label of the associated custom model version.", "type": [ "string", "null" ], "x-versionadded": "v2.34" } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" }, "environmentUrl": { "description": "If available, URL of the source model", "format": "uri", "type": [ "string", "null" ] }, "fips_140_2Enabled": { "description": "true if the model was built with FIPS-140-2", "type": "boolean" }, "projectCreatedAt": { "description": "If available, the time when the project was created.", "type": [ "string", "null" ] }, "projectCreatorEmail": { "description": "If available, the email of the user who created the project.", "type": [ "string", "null" ] }, "projectCreatorId": { "default": null, "description": "If available, the ID of the creator of the project.", "type": [ "string", "null" ] }, "projectCreatorName": { "description": "If available, the name of the user who created the project.", "type": [ "string", "null" ] }, "projectId": { "description": "If available, the project ID used for this model.", "type": [ "string", "null" ] }, "projectName": { "description": "If available, the project name for this model.", "type": [ "string", "null" ] }, "scoringCode": { "description": "If available, information about the model's scoring code", "properties": { "dataRobotPredictionVersion": { "description": "The DataRobot prediction API version for the scoring code.", "type": [ "string", "null" ] }, "location": { "description": "The location of the scoring code.", "enum": [ "local_leaderboard", "mlpkg" ], "type": [ "string", "null" ] } }, "required": [ "dataRobotPredictionVersion", "location" ], "type": "object" }, "useCaseDetails": { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } }, "required": [ "environmentUrl", "projectId", "projectName", "scoringCode" ], "type": "object" } ``` Meta information from where this model was generated ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelDetails | CustomModelDetails | false | | Details of the custom model associated to this registered model version | | environmentUrl | string,null(uri) | true | | If available, URL of the source model | | fips_140_2Enabled | boolean | false | | true if the model was built with FIPS-140-2 | | projectCreatedAt | string,null | false | | If available, the time when the project was created. | | projectCreatorEmail | string,null | false | | If available, the email of the user who created the project. | | projectCreatorId | string,null | false | | If available, the ID of the creator of the project. | | projectCreatorName | string,null | false | | If available, the name of the user who created the project. | | projectId | string,null | true | | If available, the project ID used for this model. | | projectName | string,null | true | | If available, the project name for this model. | | scoringCode | ModelPackageScoringCodeMeta | true | | If available, information about the model's scoring code | | useCaseDetails | UseCaseDetails | false | | Details of the use-case associated to this registered model version | ## ModelPackageTarget ``` { "description": "target information for the model package", "properties": { "classCount": { "description": "Number of classes for classification models.", "minimum": 0, "type": [ "integer", "null" ] }, "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "name": { "description": "Name of the target column", "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "classCount", "classNames", "name", "predictionProbabilitiesColumn", "predictionThreshold", "type" ], "type": "object" } ``` target information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classCount | integer,null | true | minimum: 0 | Number of classes for classification models. | | classNames | [string] | true | maxItems: 100 | Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. Limited to 100 returned for Multiclass. | | name | string,null | true | | Name of the target column | | predictionProbabilitiesColumn | string,null | true | | Field or column name containing prediction probabilities | | predictionThreshold | number,null | true | maximum: 1minimum: 0 | Prediction threshold used for binary classification models | | type | string | true | | Target type of the model. | ### Enumerated Values | Property | Value | | --- | --- | | type | [Binary, Regression, Multiclass, Multilabel, TextGeneration, GeoPoint, AgenticWorkflow, MCP] | ## ModelPackageTargetCreate ``` { "description": "The target information for the model package.", "properties": { "classNames": { "description": "Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class.", "items": { "maxLength": 128, "type": "string" }, "maxItems": 1000, "type": "array" }, "name": { "description": "Name of the target column", "maxLength": 128, "type": [ "string", "null" ] }, "predictionProbabilitiesColumn": { "description": "Field or column name containing prediction probabilities", "maxLength": 128, "type": [ "string", "null" ] }, "predictionThreshold": { "description": "Prediction threshold used for binary classification models", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] }, "type": { "description": "Target type of the model.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel", "TextGeneration", "GeoPoint", "AgenticWorkflow", "MCP" ], "type": "string" } }, "required": [ "type" ], "type": "object", "x-versionadded": "v2.37" } ``` The target information for the model package. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classNames | [string] | false | maxItems: 1000 | Class names for prediction results. When target type is Binary, two class names are returned. The first element is the minority (positive) class and the second element is the majority (negative) class. | | name | string,null | false | maxLength: 128 | Name of the target column | | predictionProbabilitiesColumn | string,null | false | maxLength: 128 | Field or column name containing prediction probabilities | | predictionThreshold | number,null | false | maximum: 1minimum: 0 | Prediction threshold used for binary classification models | | type | string | true | | Target type of the model. | ### Enumerated Values | Property | Value | | --- | --- | | type | [Binary, Regression, Multiclass, Multilabel, TextGeneration, GeoPoint, AgenticWorkflow, MCP] | ## ModelPackageTextGeneration ``` { "description": "Text generation information for the model package", "properties": { "prompt": { "description": "Name of the prompt column", "type": [ "string", "null" ] } }, "required": [ "prompt" ], "type": "object", "x-versionadded": "v2.37" } ``` Text generation information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | prompt | string,null | true | | Name of the prompt column | ## ModelPackageTimeseries ``` { "description": "time series information for the model package", "properties": { "datetimeColumnFormat": { "description": "Date format for forecast date and forecast point column", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "Name of the forecast date column", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "Same concept as `featureDerivationWindowEnd` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "effectiveFeatureDerivationWindowStart": { "description": "Same concept as `featureDerivationWindowStart` which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the \"real\" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.25" }, "featureDerivationWindowEnd": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "Name of the forecast distance column", "type": [ "string", "null" ] }, "forecastDistances": { "description": "List of integer forecast distances", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": [ "string", "null" ] }, "forecastPointColumnName": { "description": "Name of the forecast point column", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ], "x-versionadded": "v2.25" }, "isTraditionalTimeSeries": { "description": "true if the model is traditional time series.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "Name of the series column in case of multi-series date", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "effectiveFeatureDerivationWindowEnd", "effectiveFeatureDerivationWindowStart", "featureDerivationWindowEnd", "featureDerivationWindowStart", "forecastDistanceColumnName", "forecastDistances", "forecastDistancesTimeUnit", "forecastPointColumnName", "isCrossSeries", "isNewSeriesSupport", "isTraditionalTimeSeries", "seriesColumnName" ], "type": "object" } ``` time series information for the model package ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimeColumnFormat | string,null | true | | Date format for forecast date and forecast point column | | datetimeColumnName | string,null | true | | Name of the forecast date column | | effectiveFeatureDerivationWindowEnd | integer,null | true | maximum: 0 | Same concept as featureDerivationWindowEnd which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the "real" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. | | effectiveFeatureDerivationWindowStart | integer,null | true | maximum: 0 | Same concept as featureDerivationWindowStart which is chosen by the user and based on the initial sampled data from the eda sample. When the dataset goes through aim, the pipeline reads the full dataset and figures out the "real" FDW (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. | | featureDerivationWindowEnd | integer,null | true | maximum: 0 | Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should end. | | featureDerivationWindowStart | integer,null | true | maximum: 0 | Negative number or zero defining how many time units of the forecast distances time unit into the past relative to the forecast point the feature derivation window should begin. | | forecastDistanceColumnName | string,null | true | | Name of the forecast distance column | | forecastDistances | [integer] | true | | List of integer forecast distances | | forecastDistancesTimeUnit | string,null | true | | The time unit of forecast distances | | forecastPointColumnName | string,null | true | | Name of the forecast point column | | isCrossSeries | boolean,null | true | | true if the model is cross-series. | | isNewSeriesSupport | boolean,null | true | | true if the model is optimized to support new series. | | isTraditionalTimeSeries | boolean,null | true | | true if the model is traditional time series. | | seriesColumnName | string,null | true | | Name of the series column in case of multi-series date | ### Enumerated Values | Property | Value | | --- | --- | | forecastDistancesTimeUnit | [MICROSECOND, MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | ## ModelPackageTimeseriesCreate ``` { "description": "Time series information for the model package.", "properties": { "datetimeColumnFormat": { "description": "The date format for the forecast date and forecast point column.", "type": [ "string", "null" ] }, "datetimeColumnName": { "description": "The name of the forecast date column.", "type": [ "string", "null" ] }, "effectiveFeatureDerivationWindowEnd": { "description": "A negative number or zero describing the end of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.37" }, "effectiveFeatureDerivationWindowStart": { "description": "A negative number or zero describing the start of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the \"real\" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.37" }, "featureDerivationWindowEnd": { "description": "A negative number or zero defining the end point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -7 days would mean the feature derivation would be done with data ending at 7 days ago.", "maximum": 0, "type": [ "integer", "null" ] }, "featureDerivationWindowStart": { "description": "A negative number or zero defining the start point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -28 days would means the feature derivation would be done with data starting from 28 days ago.", "maximum": 0, "type": [ "integer", "null" ] }, "forecastDistanceColumnName": { "description": "The name of the forecast distance column.", "type": [ "string", "null" ] }, "forecastDistances": { "description": "A list of integer forecast distances.", "items": { "type": "integer" }, "type": "array" }, "forecastDistancesTimeUnit": { "description": "The time unit of forecast distances.", "enum": [ "MICROSECOND", "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR" ], "type": "string" }, "forecastPointColumnName": { "description": "The name of the forecast point column.", "type": [ "string", "null" ] }, "isCrossSeries": { "description": "true if the model is cross-series.", "type": [ "boolean", "null" ] }, "isNewSeriesSupport": { "default": false, "description": "true if the model is optimized to support new series.", "type": [ "boolean", "null" ] }, "isTraditionalTimeSeries": { "default": false, "description": "Determines if the model is a traditional time series model.", "type": [ "boolean", "null" ] }, "seriesColumnName": { "description": "The name of the series column in the case of a multi-series date.", "type": [ "string", "null" ] } }, "required": [ "datetimeColumnFormat", "datetimeColumnName", "forecastDistanceColumnName", "forecastDistancesTimeUnit", "forecastPointColumnName" ], "type": "object", "x-versionadded": "v2.37" } ``` Time series information for the model package. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimeColumnFormat | string,null | true | | The date format for the forecast date and forecast point column. | | datetimeColumnName | string,null | true | | The name of the forecast date column. | | effectiveFeatureDerivationWindowEnd | integer,null | false | maximum: 0 | A negative number or zero describing the end of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the "real" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. | | effectiveFeatureDerivationWindowStart | integer,null | false | maximum: 0 | A negative number or zero describing the start of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. When the dataset goes through aim, the pipeline reads the full dataset and calculates the "real" window (i.e., the effective FDW). For most models, eFDW is approximately the same as the FDW. | | featureDerivationWindowEnd | integer,null | false | maximum: 0 | A negative number or zero defining the end point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -7 days would mean the feature derivation would be done with data ending at 7 days ago. | | featureDerivationWindowStart | integer,null | false | maximum: 0 | A negative number or zero defining the start point of the rolling window used to derive new features for the modeling dataset. This is relative to the forecast point, and the units are the forecast distances time units. For example, -28 days would means the feature derivation would be done with data starting from 28 days ago. | | forecastDistanceColumnName | string,null | true | | The name of the forecast distance column. | | forecastDistances | [integer] | false | | A list of integer forecast distances. | | forecastDistancesTimeUnit | string | true | | The time unit of forecast distances. | | forecastPointColumnName | string,null | true | | The name of the forecast point column. | | isCrossSeries | boolean,null | false | | true if the model is cross-series. | | isNewSeriesSupport | boolean,null | false | | true if the model is optimized to support new series. | | isTraditionalTimeSeries | boolean,null | false | | Determines if the model is a traditional time series model. | | seriesColumnName | string,null | false | | The name of the series column in the case of a multi-series date. | ### Enumerated Values | Property | Value | | --- | --- | | forecastDistancesTimeUnit | [MICROSECOND, MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR] | ## PersistentLogsForModelWithCustomTasksRetrieveResponse ``` { "properties": { "data": { "description": "An archive (tar.gz) of the logs produced and persisted by a model.", "format": "binary", "type": "string" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | string(binary) | true | | An archive (tar.gz) of the logs produced and persisted by a model. | ## UseCaseDetails ``` { "description": "Details of the use-case associated to this registered model version", "properties": { "createdAt": { "description": "The time when the use case was created.", "type": "string" }, "creatorEmail": { "description": "The email of the user who created the use case.", "type": [ "string", "null" ] }, "creatorId": { "description": "The ID of the creator of the use case.", "type": "string" }, "creatorName": { "description": "The name of the user who created the use case.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the associated use case.", "type": "string" }, "name": { "description": "The name of the use case at the moment of creation.", "type": [ "string", "null" ] } }, "required": [ "createdAt", "creatorId", "id" ], "type": "object" } ``` Details of the use-case associated to this registered model version ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string | true | | The time when the use case was created. | | creatorEmail | string,null | false | | The email of the user who created the use case. | | creatorId | string | true | | The ID of the creator of the use case. | | creatorName | string,null | false | | The name of the user who created the use case. | | id | string | true | | The ID of the associated use case. | | name | string,null | false | | The name of the use case at the moment of creation. | ## UserMetadata ``` { "description": "Information on the user who last modified the registered model", "properties": { "email": { "description": "The email of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "name": { "description": "The full name of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id", "name" ], "type": "object" } ``` Information on the user who last modified the registered model ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string,null | true | | The email of the user. | | id | string | true | | The ID of the user. | | name | string,null | true | | The full name of the user. | --- # Monitoring jobs URL: https://docs.datarobot.com/en/docs/api/reference/public-api/monitoring_jobs.html > Use the endpoints described below to manage monitoring jobs. To integrate more closely with external data sources, monitoring job definitions allow DataRobot to monitor deployments running and storing feature data and predictions outside of DataRobot. For example, you can create a monitoring job to connect to Snowflake, fetch raw data from the relevant Snowflake tables, and send the data to DataRobot for monitoring purposes. # Monitoring jobs Use the endpoints described below to manage monitoring jobs. To integrate more closely with external data sources, monitoring job definitions allow DataRobot to monitor deployments running and storing feature data and predictions outside of DataRobot. For example, you can create a monitoring job to connect to Snowflake, fetch raw data from the relevant Snowflake tables, and send the data to DataRobot for monitoring purposes. ## Creates a new Batch Monitoring job Operation path: `POST /api/v2/batchMonitoring/` Authentication requirements: `BearerAuth` Submit the configuration for the job and it will be submitted to the queue. ### Body parameter ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "monitoring", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | BatchMonitoringJobCreate | false | none | ### Example responses > 202 Response ``` { "properties": { "batchMonitoringJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "id": { "description": "The ID of the Batch job", "type": "string", "x-versionadded": "v2.30" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object", "x-versionadded": "v2.35" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.30" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.30" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.30" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.30" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.30" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job details for the created Batch Monitoring job | BatchJobResponse | ## List Batch Monitoring job definitions Operation path: `GET /api/v2/batchMonitoringJobDefinitions/` Authentication requirements: `BearerAuth` List all Batch Monitoring jobs definitions available. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | searchName | query | string | false | A human-readable name for the definition, must be unique across organisations. | | deploymentId | query | string | false | Includes only definitions for this particular deployment | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of scheduled jobs", "items": { "properties": { "batchMonitoringJob": { "description": "The Batch Monitoring Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "default": 0, "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchMonitoringJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of all available jobs | BatchMonitoringJobDefinitionsListResponse | | 422 | Unprocessable Entity | Your input data or query arguments did not work together | None | ## Creates a new Batch Monitoring job definition Operation path: `POST /api/v2/batchMonitoringJobDefinitions/` Authentication requirements: `BearerAuth` Create a Batch Monitoring Job definition. A configuration for a Batch Monitoring job which can either be executed manually upon request or on scheduled intervals, if enabled. The API payload is the same as for `/batchJobs` along with optional `enabled` and `schedule` items. ### Body parameter ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "monitoring", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment that the monitoring jobs is associated with.", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "enabled": { "description": "If this job definition is enabled as a scheduled job. Optional if no schedule is supplied.", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you.", "maxLength": 100, "minLength": 1, "type": "string" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "deploymentId", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | BatchMonitoringJobDefinitionsCreate | false | none | ### Example responses > 202 Response ``` { "properties": { "batchMonitoringJob": { "description": "The Batch Monitoring Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "default": 0, "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchMonitoringJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job details for the created Batch Monitoring job definition | BatchMonitoringJobDefinitionsResponse | | 403 | Forbidden | You are not authorized to create a job definition on this deployment due to your permissions role | None | | 422 | Unprocessable Entity | You tried to create a job definition with incompatible or missing parameters to create a fully functioning job definition | None | ## Delete Batch Prediction job definition by job definition ID Operation path: `DELETE /api/v2/batchMonitoringJobDefinitions/{jobDefinitionId}/` Authentication requirements: `BearerAuth` Delete a Batch Prediction job definition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobDefinitionId | path | string | true | ID of the Batch Prediction job definition | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | You are not authorized to delete this job definition due to your permissions role | None | | 404 | Not Found | Job was deleted, never existed or you do not have access to it | None | | 409 | Conflict | Job could not be deleted, as there are currently running jobs in the queue. | None | ## Retrieve Batch Monitoring job definition by job definition ID Operation path: `GET /api/v2/batchMonitoringJobDefinitions/{jobDefinitionId}/` Authentication requirements: `BearerAuth` Retrieve a Batch Monitoring job definition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobDefinitionId | path | string | true | ID of the Batch Prediction job definition | ### Example responses > 200 Response ``` { "properties": { "batchMonitoringJob": { "description": "The Batch Monitoring Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "default": 0, "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchMonitoringJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Job details for the requested Batch Monitoring job definition | BatchMonitoringJobDefinitionsResponse | | 404 | Not Found | Job was deleted, never existed or you do not have access to it | None | ## Update Batch Monitoring job definition by job definition ID Operation path: `PATCH /api/v2/batchMonitoringJobDefinitions/{jobDefinitionId}/` Authentication requirements: `BearerAuth` Update a Batch Monitoring job definition. ### Body parameter ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "enabled": { "description": "If this job definition is enabled as a scheduled job. Optional if no schedule is supplied.", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you.", "maxLength": 100, "minLength": 1, "type": "string" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobDefinitionId | path | string | true | ID of the Batch Prediction job definition | | body | body | BatchMonitoringJobDefinitionsUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "batchMonitoringJob": { "description": "The Batch Monitoring Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "default": 0, "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchMonitoringJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Job details for the updated Batch Monitoring job definition | BatchMonitoringJobDefinitionsResponse | | 403 | Forbidden | You are not authorized to alter the contents of this monitoring job due to your permissions role | None | | 404 | Not Found | Job was deleted, never existed or you do not have access to it | None | | 409 | Conflict | You chose a name of your monitoring job that was already existing within your organization | None | | 422 | Unprocessable Entity | Could not update the monitoring job. Possible reasons: {} | None | # Schemas ## AzureDataStreamer ``` { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Azure ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | azure | ## AzureIntake ``` { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Azure ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | azure | ## AzureOutput ``` { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Save CSV data chunks to Azure Blob Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | format | string | false | | Type of output file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the file or directory | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | azure | ## AzureOutputAdaptor ``` { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Save CSV data chunks to Azure Blob Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | format | string | false | | Type of output file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the file or directory | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | azure | ## BatchJobCSVSettings ``` { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" } ``` The CSV settings used for this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | delimiter | any | true | | CSV fields are delimited by this character. Use the string "tab" to denote TSV (TAB separated values). | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 1minLength: 1minLength: 1 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | encoding | string | true | | The encoding to be used for intake and output. For example (but not limited to): "shift_jis", "latin_1" or "mskanji". | | quotechar | string | true | maxLength: 1minLength: 1minLength: 1 | Fields containing the delimiter or newlines must be quoted using this character. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | tab | ## BatchJobCreatedBy ``` { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } ``` Who created this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fullName | string,null | true | | The full name of the user who created this job (if defined by the user) | | userId | string | true | | The User ID of the user who created this job | | username | string | true | | The username (e-mail address) of the user who created this job | ## BatchJobDefinitionResponse ``` { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" } ``` The Batch Prediction Job Definition linking to this job, if any. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | string | true | | The ID of creator of this job definition | | id | string | true | | The ID of the Batch Prediction job definition | | name | string | true | | A human-readable name for the definition, must be unique across organisations | ## BatchJobDefinitionsSpecResponse ``` { "description": "The Batch Monitoring Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "default": 0, "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" } ``` The Batch Monitoring Job specification to be put on the queue in intervals ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | true | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchJobCSVSettings | true | | The CSV settings used for this job | | deploymentId | string | false | | ID of deployment which is used in job for processing predictions dataset | | disableRowLevelErrorHandling | boolean | true | | Skip row by row error handling | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 10minItems: 1 | List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1 | | explanationNumTopClasses | integer | false | maximum: 10minimum: 1 | Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1 | | includePredictionStatus | boolean | true | | Include prediction status column in the output | | includeProbabilities | boolean | true | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | true | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | true | | The response option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureDataStreamer | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageDataStreamer | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogDataStreamer | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPDataStreamer | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryDataStreamer | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3DataStreamer | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeDataStreamer | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseDataStreamer | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSSDataStreamer | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemDataStreamer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPDataStreamer | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCDataStreamer | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileDataStreamer | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksDataStreamer | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereDataStreamer | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoDataStreamer | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | true | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | maxNgramExplanations | any | false | | The maximum number of text ngram explanations to supply per row of the dataset. The default recommended maxNgramExplanations is all (no limit) | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringAggregation | MonitoringAggregation | false | | Defines the aggregation policy for monitoring jobs. | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | monitoringColumns | MonitoringColumnsMapping | false | | Column names mapping for monitoring | | monitoringOutputSettings | MonitoringOutputSettings | false | | Output settings for monitoring jobs | | numConcurrent | integer | false | minimum: 0 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The response option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutputAdaptor | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutputAdaptor | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutputAdaptor | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3OutputAdaptor | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutputAdaptor | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutputAdaptor | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutputAdaptor | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HttpOutputAdaptor | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JdbcOutputAdaptor | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutputAdaptor | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutputAdaptor | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutputAdaptor | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutputAdaptor | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | redactedFields | [string] | true | | A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId | | skipDriftTracking | boolean | true | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsHistorical | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | anonymous | all | | passthroughColumnsSet | all | ## BatchJobLinks ``` { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object", "x-versionadded": "v2.35" } ``` Links useful for this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvUpload | string(url) | false | | The URL used to upload the dataset for this job. Only available for localFile intake. | | download | string,null | false | | The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available. | | self | string(url) | true | | The URL used access this job. | ## BatchJobPredictionInstance ``` { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" } ``` Override the default prediction instance from the deployment when scoring this job. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | apiKey | string | false | | By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users. | | datarobotKey | string | false | | If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key. | | hostName | string | true | | Override the default host name of the deployment with this. | | sslEnabled | boolean | true | | Use SSL (HTTPS) when communicating with the overriden prediction server. | ## BatchJobRemapping ``` { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputName | string | true | | Rename column with this name | | outputName | string,null | true | | Rename column to this name (leave as null to remove from the output) | ## BatchJobResponse ``` { "properties": { "batchMonitoringJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "batchPredictionJobDefinition": { "description": "The Batch Prediction Job Definition linking to this job, if any.", "properties": { "createdBy": { "description": "The ID of creator of this job definition", "type": "string" }, "id": { "description": "The ID of the Batch Prediction job definition", "type": "string" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" } }, "required": [ "createdBy", "id", "name" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "elapsedTimeSec": { "description": "Number of seconds the job has been processing for", "minimum": 0, "type": "integer" }, "failedRows": { "description": "Number of rows that have failed scoring", "minimum": 0, "type": "integer" }, "hidden": { "description": "When was this job was hidden last, blank if visible", "format": "date-time", "type": "string", "x-versionadded": "v2.30" }, "id": { "description": "The ID of the Batch job", "type": "string", "x-versionadded": "v2.30" }, "intakeDatasetDisplayName": { "description": "If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.30" }, "jobIntakeSize": { "description": "Number of bytes in the intake dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobOutputSize": { "description": "Number of bytes in the output dataset for this job", "minimum": 0, "type": [ "integer", "null" ] }, "jobSpec": { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "links": { "description": "Links useful for this job", "properties": { "csvUpload": { "description": "The URL used to upload the dataset for this job. Only available for localFile intake.", "format": "url", "type": "string" }, "download": { "description": "The URL used to download the results from this job. Only available for localFile outputs. Will be null if the download is not yet available.", "type": [ "string", "null" ] }, "self": { "description": "The URL used access this job.", "format": "url", "type": "string" } }, "required": [ "self" ], "type": "object", "x-versionadded": "v2.35" }, "logs": { "description": "The job log.", "items": { "description": "A log line from the job log.", "type": "string" }, "type": "array" }, "monitoringBatchId": { "description": "Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "percentageCompleted": { "description": "Indicates job progress which is based on number of already processed rows in dataset", "maximum": 100, "minimum": 0, "type": "number" }, "queuePosition": { "description": "To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.30" }, "queued": { "description": "The job has been put on the queue for execution.", "type": "boolean", "x-versionadded": "v2.30" }, "resultsDeleted": { "description": "Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage)", "type": "boolean", "x-versionadded": "v2.30" }, "scoredRows": { "description": "Number of rows that have been used in prediction computation", "minimum": 0, "type": "integer" }, "skippedRows": { "description": "Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows.", "minimum": 0, "type": "integer", "x-versionadded": "v2.30" }, "source": { "description": "Source from which batch job was started", "type": "string", "x-versionadded": "v2.30" }, "status": { "description": "The current job status", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": "string" }, "statusDetails": { "description": "Explanation for current status", "type": "string" } }, "required": [ "created", "createdBy", "elapsedTimeSec", "failedRows", "id", "jobIntakeSize", "jobOutputSize", "jobSpec", "links", "logs", "monitoringBatchId", "percentageCompleted", "queued", "scoredRows", "skippedRows", "status", "statusDetails" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchMonitoringJobDefinition | BatchJobDefinitionResponse | false | | The Batch Prediction Job Definition linking to this job, if any. | | batchPredictionJobDefinition | BatchJobDefinitionResponse | false | | The Batch Prediction Job Definition linking to this job, if any. | | created | string(date-time) | true | | When was this job created | | createdBy | BatchJobCreatedBy | true | | Who created this job | | elapsedTimeSec | integer | true | minimum: 0 | Number of seconds the job has been processing for | | failedRows | integer | true | minimum: 0 | Number of rows that have failed scoring | | hidden | string(date-time) | false | | When was this job was hidden last, blank if visible | | id | string | true | | The ID of the Batch job | | intakeDatasetDisplayName | string,null | false | | If applicable (e.g. for AI catalog), will contain the dataset name used for the intake dataset. | | jobIntakeSize | integer,null | true | minimum: 0 | Number of bytes in the intake dataset for this job | | jobOutputSize | integer,null | true | minimum: 0 | Number of bytes in the output dataset for this job | | jobSpec | BatchJobSpecResponse | true | | The job configuration used to create this job | | links | BatchJobLinks | true | | Links useful for this job | | logs | [string] | true | | The job log. | | monitoringBatchId | string,null | true | | Id of the monitoring batch created by this job. Only present if the job runs on a deployment with batch monitoring enabled. | | percentageCompleted | number | true | maximum: 100minimum: 0 | Indicates job progress which is based on number of already processed rows in dataset | | queuePosition | integer,null | false | minimum: 0 | To ensure a dedicated prediction instance is not overloaded, only one job will be run against it at a time. This is the number of jobs that are awaiting processing before this job start running. May not be available in all environments. | | queued | boolean | true | | The job has been put on the queue for execution. | | resultsDeleted | boolean | false | | Indicates if the job was subject to garbage collection and had its artifacts deleted (output files, if any, and scoring data on local storage) | | scoredRows | integer | true | minimum: 0 | Number of rows that have been used in prediction computation | | skippedRows | integer | true | minimum: 0 | Number of rows that have been skipped during scoring. May contain non-zero value only in time-series predictions case if provided dataset contains more than required historical rows. | | source | string | false | | Source from which batch job was started | | status | string | true | | The current job status | | statusDetails | string | true | | Explanation for current status | ### Enumerated Values | Property | Value | | --- | --- | | status | [INITIALIZING, RUNNING, COMPLETED, ABORTED, FAILED] | ## BatchJobSpecResponse ``` { "description": "The job configuration used to create this job", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" } ``` The job configuration used to create this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | true | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchJobCSVSettings | true | | The CSV settings used for this job | | deploymentId | string | false | | ID of deployment which is used in job for processing predictions dataset | | disableRowLevelErrorHandling | boolean | true | | Skip row by row error handling | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 10minItems: 1 | List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1 | | explanationNumTopClasses | integer | false | maximum: 10minimum: 1 | Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1 | | includePredictionStatus | boolean | true | | Include prediction status column in the output | | includeProbabilities | boolean | true | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | true | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | true | | The response option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureDataStreamer | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageDataStreamer | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CatalogDataStreamer | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPDataStreamer | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryDataStreamer | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3DataStreamer | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeDataStreamer | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseDataStreamer | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSSDataStreamer | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemDataStreamer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPDataStreamer | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCDataStreamer | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileDataStreamer | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksDataStreamer | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereDataStreamer | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoDataStreamer | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | true | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | maxNgramExplanations | any | false | | The maximum number of text ngram explanations to supply per row of the dataset. The default recommended maxNgramExplanations is all (no limit) | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | minimum: 0 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringAggregation | MonitoringAggregation | false | | Defines the aggregation policy for monitoring jobs. | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | monitoringColumns | MonitoringColumnsMapping | false | | Column names mapping for monitoring | | monitoringOutputSettings | MonitoringOutputSettings | false | | Output settings for monitoring jobs | | numConcurrent | integer | false | minimum: 1 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The response option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutputAdaptor | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutputAdaptor | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutputAdaptor | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3OutputAdaptor | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutputAdaptor | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutputAdaptor | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutputAdaptor | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HttpOutputAdaptor | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JdbcOutputAdaptor | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutputAdaptor | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutputAdaptor | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutputAdaptor | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutputAdaptor | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | redactedFields | [string] | true | | A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId | | skipDriftTracking | boolean | true | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchJobTimeSeriesSettingsHistorical | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | anonymous | all | | passthroughColumnsSet | all | ## BatchJobTimeSeriesSettingsForecast ``` { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forecastPoint | string(date-time) | false | | Used for forecast predictions in order to override the inferred forecast point from the dataset. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Forecast mode makes predictions using forecastPoint or rows in the dataset without target. | ### Enumerated Values | Property | Value | | --- | --- | | type | forecast | ## BatchJobTimeSeriesSettingsHistorical ``` { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionsEndDate | string(date-time) | false | | Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset. | | predictionsStartDate | string(date-time) | false | | Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range. | ### Enumerated Values | Property | Value | | --- | --- | | type | historical | ## BatchMonitoringJobCreate ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "monitoring", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | true | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchPredictionJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchPredictionJobCSVSettings | true | | The CSV settings used for this job | | deploymentId | string | false | | ID of deployment which is used in job for processing predictions dataset | | disableRowLevelErrorHandling | boolean | true | | Skip row by row error handling | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 100minItems: 1 | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | | explanationNumTopClasses | integer | false | maximum: 100minimum: 1 | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | | includePredictionStatus | boolean | true | | Include prediction status column in the output | | includeProbabilities | boolean | true | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | true | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | true | | The intake option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureIntake | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryIntake | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageIntake | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksIntake | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | Catalog | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereIntake | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSS | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemIntake | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPIntake | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPIntake | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCIntake | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileIntake | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Intake | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeIntake | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseIntake | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoIntake | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | true | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringAggregation | MonitoringAggregation | false | | Defines the aggregation policy for monitoring jobs. | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | monitoringColumns | MonitoringColumnsMapping | false | | Column names mapping for monitoring | | monitoringOutputSettings | MonitoringOutputSettings | false | | Output settings for monitoring jobs | | numConcurrent | integer | false | minimum: 1 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The output option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutput | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutput | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutput | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutput | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutput | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutput | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPOutput | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCOutput | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutput | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Output | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutput | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutput | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutput | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchPredictionJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | secondaryDatasetsConfigId | string | false | | Configuration id for secondary datasets to use when making a prediction. | | skipDriftTracking | boolean | true | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsHistorical | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsTraining | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | passthroughColumnsSet | all | ## BatchMonitoringJobDefinitionsCreate ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "monitoring", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment that the monitoring jobs is associated with.", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "enabled": { "description": "If this job definition is enabled as a scheduled job. Optional if no schedule is supplied.", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you.", "maxLength": 100, "minLength": 1, "type": "string" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "required": [ "abortOnError", "csvSettings", "deploymentId", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | true | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchPredictionJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchPredictionJobCSVSettings | true | | The CSV settings used for this job | | deploymentId | string | true | | ID of deployment that the monitoring jobs is associated with. | | disableRowLevelErrorHandling | boolean | true | | Skip row by row error handling | | enabled | boolean | false | | If this job definition is enabled as a scheduled job. Optional if no schedule is supplied. | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 100minItems: 1 | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | | explanationNumTopClasses | integer | false | maximum: 100minimum: 1 | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | | includePredictionStatus | boolean | true | | Include prediction status column in the output | | includeProbabilities | boolean | true | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | true | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | true | | The intake option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureIntake | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryIntake | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageIntake | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksIntake | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | Catalog | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereIntake | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSS | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemIntake | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPIntake | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPIntake | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCIntake | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileIntake | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Intake | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeIntake | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseIntake | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoIntake | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | true | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringAggregation | MonitoringAggregation | false | | Defines the aggregation policy for monitoring jobs. | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | monitoringColumns | MonitoringColumnsMapping | false | | Column names mapping for monitoring | | monitoringOutputSettings | MonitoringOutputSettings | false | | Output settings for monitoring jobs | | name | string | false | maxLength: 100minLength: 1minLength: 1 | A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you. | | numConcurrent | integer | false | minimum: 1 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The output option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutput | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutput | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutput | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutput | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutput | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutput | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPOutput | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCOutput | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutput | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Output | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutput | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutput | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutput | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchPredictionJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | secondaryDatasetsConfigId | string | false | | Configuration id for secondary datasets to use when making a prediction. | | skipDriftTracking | boolean | true | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsHistorical | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsTraining | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | passthroughColumnsSet | all | ## BatchMonitoringJobDefinitionsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of scheduled jobs", "items": { "properties": { "batchMonitoringJob": { "description": "The Batch Monitoring Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "default": 0, "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchMonitoringJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [BatchMonitoringJobDefinitionsResponse] | true | maxItems: 10000 | An array of scheduled jobs | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## BatchMonitoringJobDefinitionsResponse ``` { "properties": { "batchMonitoringJob": { "description": "The Batch Monitoring Job specification to be put on the queue in intervals", "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string", "x-versionadded": "v2.30" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "List of class names that will be explained for each row for multiclass. Mutually exclusive with explanationNumTopClasses. If neither specified - we assume explanationNumTopClasses=1", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.30" }, "explanationNumTopClasses": { "description": "Number of top predicted classes for each row that will be explained for multiclass. Mutually exclusive with explanationClassNames. If neither specified - we assume explanationNumTopClasses=1", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.30" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The response option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "maxNgramExplanations": { "description": "The maximum number of text ngram explanations to supply per row of the dataset. The default recommended `maxNgramExplanations` is `all` (no limit)", "oneOf": [ { "minimum": 0, "type": "integer" }, { "enum": [ "all" ], "type": "string" }, { "type": "null" } ], "x-versionadded": "v2.30" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.30" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "numConcurrent": { "default": 0, "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 0, "type": "integer" }, "outputSettings": { "description": "The response option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "redactedFields": { "description": "A list of qualified field names from intake- and/or outputSettings that was redacted due to permissions and sharing settings. For example: intakeSettings.dataStoreId", "items": { "description": "Field names that are potentially redacted", "type": "string" }, "type": "array", "x-versionadded": "v2.30" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.30" } }, "required": [ "abortOnError", "csvSettings", "disableRowLevelErrorHandling", "includePredictionStatus", "includeProbabilities", "includeProbabilitiesClasses", "intakeSettings", "maxExplanations", "redactedFields", "skipDriftTracking" ], "type": "object", "x-versionadded": "v2.35" }, "created": { "description": "When was this job created", "format": "date-time", "type": "string" }, "createdBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" }, "enabled": { "default": false, "description": "If this job definition is enabled as a scheduled job.", "type": "boolean" }, "id": { "description": "The ID of the Batch job definition", "type": "string" }, "lastFailedRunTime": { "description": "Last time this job had a failed run", "format": "date-time", "type": [ "string", "null" ] }, "lastScheduledRunTime": { "description": "Last time this job was scheduled to run (though not guaranteed it actually ran at that time)", "format": "date-time", "type": [ "string", "null" ] }, "lastStartedJobStatus": { "description": "The status of the latest job launched to the queue (if any).", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED" ], "type": [ "string", "null" ] }, "lastStartedJobTime": { "description": "The last time (if any) a job was launched.", "format": "date-time", "type": [ "string", "null" ] }, "lastSuccessfulRunTime": { "description": "Last time this job had a successful run", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "A human-readable name for the definition, must be unique across organisations", "type": "string" }, "nextScheduledRunTime": { "description": "Next time this job is scheduled to run", "format": "date-time", "type": [ "string", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "updated": { "description": "When was this job last updated", "format": "date-time", "type": "string" }, "updatedBy": { "description": "Who created this job", "properties": { "fullName": { "description": "The full name of the user who created this job (if defined by the user)", "type": [ "string", "null" ] }, "userId": { "description": "The User ID of the user who created this job", "type": "string" }, "username": { "description": "The username (e-mail address) of the user who created this job", "type": "string" } }, "required": [ "fullName", "userId", "username" ], "type": "object" } }, "required": [ "batchMonitoringJob", "created", "createdBy", "enabled", "id", "lastStartedJobStatus", "lastStartedJobTime", "name", "updated", "updatedBy" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchMonitoringJob | BatchJobDefinitionsSpecResponse | true | | The Batch Monitoring Job specification to be put on the queue in intervals | | created | string(date-time) | true | | When was this job created | | createdBy | BatchJobCreatedBy | true | | Who created this job | | enabled | boolean | true | | If this job definition is enabled as a scheduled job. | | id | string | true | | The ID of the Batch job definition | | lastFailedRunTime | string,null(date-time) | false | | Last time this job had a failed run | | lastScheduledRunTime | string,null(date-time) | false | | Last time this job was scheduled to run (though not guaranteed it actually ran at that time) | | lastStartedJobStatus | string,null | true | | The status of the latest job launched to the queue (if any). | | lastStartedJobTime | string,null(date-time) | true | | The last time (if any) a job was launched. | | lastSuccessfulRunTime | string,null(date-time) | false | | Last time this job had a successful run | | name | string | true | | A human-readable name for the definition, must be unique across organisations | | nextScheduledRunTime | string,null(date-time) | false | | Next time this job is scheduled to run | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | updated | string(date-time) | true | | When was this job last updated | | updatedBy | BatchJobCreatedBy | true | | Who created this job | ### Enumerated Values | Property | Value | | --- | --- | | lastStartedJobStatus | [INITIALIZING, RUNNING, COMPLETED, ABORTED, FAILED] | ## BatchMonitoringJobDefinitionsUpdate ``` { "properties": { "abortOnError": { "default": true, "description": "Should this job abort if too many errors are encountered", "type": "boolean" }, "batchJobType": { "default": "prediction", "description": "Batch job type.", "enum": [ "monitoring", "prediction" ], "type": "string" }, "chunkSize": { "default": "auto", "description": "Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.", "oneOf": [ { "enum": [ "auto", "fixed", "dynamic" ], "type": "string" }, { "maximum": 41943040, "minimum": 20, "type": "integer" } ] }, "columnNamesRemapping": { "description": "Remap (rename or remove columns from) the output from this job", "oneOf": [ { "description": "Provide a dictionary with key/value pairs to remap (deprecated)", "type": "object" }, { "description": "Provide a list of items to remap", "items": { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" }, "maxItems": 1000, "type": "array" }, { "type": "null" } ] }, "csvSettings": { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" }, "deploymentId": { "description": "ID of deployment which is used in job for processing predictions dataset", "type": "string" }, "disableRowLevelErrorHandling": { "default": false, "description": "Skip row by row error handling", "type": "boolean" }, "enabled": { "description": "If this job definition is enabled as a scheduled job. Optional if no schedule is supplied.", "type": "boolean" }, "explanationAlgorithm": { "description": "Which algorithm will be used to calculate prediction explanations", "enum": [ "shap", "xemp" ], "type": "string" }, "explanationClassNames": { "description": "Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1.", "items": { "description": "Class name to explain", "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.29" }, "explanationNumTopClasses": { "description": "Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1.", "maximum": 100, "minimum": 1, "type": "integer", "x-versionadded": "v2.29" }, "includePredictionStatus": { "default": false, "description": "Include prediction status column in the output", "type": "boolean" }, "includeProbabilities": { "default": true, "description": "Include probabilities for all classes", "type": "boolean" }, "includeProbabilitiesClasses": { "default": [], "description": "Include only probabilities for these specific class names.", "items": { "description": "Include probability for this class name", "type": "string" }, "maxItems": 100, "type": "array" }, "intakeSettings": { "default": { "type": "localFile" }, "description": "The intake option configured for this job", "oneOf": [ { "description": "Stream CSV data chunks from Azure", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" }, { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" }, { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ] }, "maxExplanations": { "default": 0, "description": "Number of explanations requested. Will be ordered by strength.", "maximum": 100, "minimum": 0, "type": "integer" }, "modelId": { "description": "ID of leaderboard model which is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "modelPackageId": { "description": "ID of model package from registry is used in job for processing predictions dataset", "type": "string", "x-versionadded": "v2.28" }, "monitoringAggregation": { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" }, "monitoringBatchPrefix": { "description": "Name of the batch to create with this job", "type": [ "string", "null" ] }, "monitoringColumns": { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" }, "monitoringOutputSettings": { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" }, "name": { "description": "A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you.", "maxLength": 100, "minLength": 1, "type": "string" }, "numConcurrent": { "description": "Number of simultaneous requests to run against the prediction instance", "minimum": 1, "type": "integer" }, "outputSettings": { "description": "The output option configured for this job", "oneOf": [ { "description": "Save CSV data chunks to Azure Blob Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "azure" ], "type": "string" }, "url": { "description": "URL for the file or directory", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" }, { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" }, { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" }, { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" }, { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" }, { "type": "null" } ] }, "passthroughColumns": { "description": "Pass through columns from the original dataset", "items": { "description": "A column name from the original dataset to pass through to the resulting predictions", "maxLength": 50, "minLength": 1, "type": "string" }, "maxItems": 100, "type": "array" }, "passthroughColumnsSet": { "description": "Pass through all columns from the original dataset", "enum": [ "all" ], "type": "string" }, "pinnedModelId": { "description": "Specify a model ID used for scoring", "type": "string" }, "predictionInstance": { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" }, "predictionThreshold": { "description": "Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0.", "maximum": 1, "minimum": 0, "type": "number" }, "predictionWarningEnabled": { "description": "Enable prediction warnings.", "type": [ "boolean", "null" ] }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "secondaryDatasetsConfigId": { "description": "Configuration id for secondary datasets to use when making a prediction.", "type": "string", "x-versionadded": "v2.33" }, "skipDriftTracking": { "default": false, "description": "Skip drift tracking for this job.", "type": "boolean" }, "thresholdHigh": { "description": "Compute explanations for predictions above this threshold", "type": "number" }, "thresholdLow": { "description": "Compute explanations for predictions below this threshold", "type": "number" }, "timeseriesSettings": { "description": "Time Series settings included of this job is a Time Series job.", "oneOf": [ { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ], "x-versionadded": "v2.20" } }, "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | abortOnError | boolean | false | | Should this job abort if too many errors are encountered | | batchJobType | string | false | | Batch job type. | | chunkSize | any | false | | Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 41943040minimum: 20 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnNamesRemapping | any | false | | Remap (rename or remove columns from) the output from this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | Provide a dictionary with key/value pairs to remap (deprecated) | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [BatchPredictionJobRemapping] | false | maxItems: 1000 | Provide a list of items to remap | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | csvSettings | BatchPredictionJobCSVSettings | false | | The CSV settings used for this job | | deploymentId | string | false | | ID of deployment which is used in job for processing predictions dataset | | disableRowLevelErrorHandling | boolean | false | | Skip row by row error handling | | enabled | boolean | false | | If this job definition is enabled as a scheduled job. Optional if no schedule is supplied. | | explanationAlgorithm | string | false | | Which algorithm will be used to calculate prediction explanations | | explanationClassNames | [string] | false | maxItems: 100minItems: 1 | Sets a list of selected class names for which corresponding explanations are returned in each row. This setting is mutually exclusive with numTopClasses. If neither parameter is specified, the default setting is numTopClasses=1. | | explanationNumTopClasses | integer | false | maximum: 100minimum: 1 | Sets the number of most probable (top predicted) classes for which corresponding explanations are returned in each row. This setting is mutually exclusive with classNames. If neither parameter is specified, the default setting is numTopClasses=1. | | includePredictionStatus | boolean | false | | Include prediction status column in the output | | includeProbabilities | boolean | false | | Include probabilities for all classes | | includeProbabilitiesClasses | [string] | false | maxItems: 100 | Include only probabilities for these specific class names. | | intakeSettings | any | false | | The intake option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureIntake | false | | Stream CSV data chunks from Azure | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryIntake | false | | Stream CSV data chunks from Big Query using GCS | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DataStageIntake | false | | Stream CSV data chunks from data stage storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksIntake | false | | Stream CSV data chunks from Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | Catalog | false | | Stream CSV data chunks from AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereIntake | false | | Stream CSV data chunks from Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DSS | false | | Stream CSV data chunks from DSS dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemIntake | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPIntake | false | | Stream CSV data chunks from Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPIntake | false | | Stream CSV data chunks from HTTP | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCIntake | false | | Stream CSV data chunks from JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileIntake | false | | Stream CSV data chunks from local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Intake | false | | Stream CSV data chunks from Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeIntake | false | | Stream CSV data chunks from Snowflake | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseIntake | false | | Stream CSV data chunks from Azure Synapse | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoIntake | false | | Stream CSV data chunks from Trino using browser-trino | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxExplanations | integer | false | maximum: 100minimum: 0 | Number of explanations requested. Will be ordered by strength. | | modelId | string | false | | ID of leaderboard model which is used in job for processing predictions dataset | | modelPackageId | string | false | | ID of model package from registry is used in job for processing predictions dataset | | monitoringAggregation | MonitoringAggregation | false | | Defines the aggregation policy for monitoring jobs. | | monitoringBatchPrefix | string,null | false | | Name of the batch to create with this job | | monitoringColumns | MonitoringColumnsMapping | false | | Column names mapping for monitoring | | monitoringOutputSettings | MonitoringOutputSettings | false | | Output settings for monitoring jobs | | name | string | false | maxLength: 100minLength: 1minLength: 1 | A human-readable name for the definition, must be unique across organisations, if left out the backend will generate one for you. | | numConcurrent | integer | false | minimum: 1 | Number of simultaneous requests to run against the prediction instance | | outputSettings | any | false | | The output option configured for this job | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureOutput | false | | Save CSV data chunks to Azure Blob Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BigQueryOutput | false | | Save CSV data chunks to Google BigQuery in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksOutput | false | | Saves CSV data chunks to Databricks using browser-databricks | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatasphereOutput | false | | Saves CSV data chunks to Datasphere using browser-datasphere | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FileSystemOutput | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GCPOutput | false | | Save CSV data chunks to Google Storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | HTTPOutput | false | | Save CSV data chunks to HTTP data endpoint | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | JDBCOutput | false | | Save CSV data chunks via JDBC | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | LocalFileOutput | false | | Save CSV data chunks to local file storage | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Output | false | | Saves CSV data chunks to Amazon Cloud Storage S3 | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeOutput | false | | Save CSV data chunks to Snowflake in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SynapseOutput | false | | Save CSV data chunks to Azure Synapse in bulk | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | TrinoOutput | false | | Saves CSV data chunks to Trino using browser-trino | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | passthroughColumns | [string] | false | maxItems: 100 | Pass through columns from the original dataset | | passthroughColumnsSet | string | false | | Pass through all columns from the original dataset | | pinnedModelId | string | false | | Specify a model ID used for scoring | | predictionInstance | BatchPredictionJobPredictionInstance | false | | Override the default prediction instance from the deployment when scoring this job. | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold is the point that sets the class boundary for a predicted value. The model classifies an observation below the threshold as FALSE, and an observation above the threshold as TRUE. In other words, DataRobot automatically assigns the positive class label to any prediction exceeding the threshold. This value can be set between 0.0 and 1.0. | | predictionWarningEnabled | boolean,null | false | | Enable prediction warnings. | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | secondaryDatasetsConfigId | string | false | | Configuration id for secondary datasets to use when making a prediction. | | skipDriftTracking | boolean | false | | Skip drift tracking for this job. | | thresholdHigh | number | false | | Compute explanations for predictions above this threshold | | thresholdLow | number | false | | Compute explanations for predictions below this threshold | | timeseriesSettings | any | false | | Time Series settings included of this job is a Time Series job. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsForecast | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsHistorical | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BatchPredictionJobTimeSeriesSettingsTraining | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | batchJobType | [monitoring, prediction] | | anonymous | [auto, fixed, dynamic] | | explanationAlgorithm | [shap, xemp] | | passthroughColumnsSet | all | ## BatchPredictionJobCSVSettings ``` { "description": "The CSV settings used for this job", "properties": { "delimiter": { "default": ",", "description": "CSV fields are delimited by this character. Use the string \"tab\" to denote TSV (TAB separated values).", "oneOf": [ { "enum": [ "tab" ], "type": "string" }, { "maxLength": 1, "minLength": 1, "type": "string" } ] }, "encoding": { "default": "utf-8", "description": "The encoding to be used for intake and output. For example (but not limited to): \"shift_jis\", \"latin_1\" or \"mskanji\".", "type": "string" }, "quotechar": { "default": "\"", "description": "Fields containing the delimiter or newlines must be quoted using this character.", "maxLength": 1, "minLength": 1, "type": "string" } }, "required": [ "delimiter", "encoding", "quotechar" ], "type": "object" } ``` The CSV settings used for this job ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | delimiter | any | true | | CSV fields are delimited by this character. Use the string "tab" to denote TSV (TAB separated values). | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 1minLength: 1minLength: 1 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | encoding | string | true | | The encoding to be used for intake and output. For example (but not limited to): "shift_jis", "latin_1" or "mskanji". | | quotechar | string | true | maxLength: 1minLength: 1minLength: 1 | Fields containing the delimiter or newlines must be quoted using this character. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | tab | ## BatchPredictionJobPredictionInstance ``` { "description": "Override the default prediction instance from the deployment when scoring this job.", "properties": { "apiKey": { "description": "By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.", "type": "string" }, "datarobotKey": { "description": "If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.", "type": "string" }, "hostName": { "description": "Override the default host name of the deployment with this.", "type": "string" }, "sslEnabled": { "default": true, "description": "Use SSL (HTTPS) when communicating with the overriden prediction server.", "type": "boolean" } }, "required": [ "hostName", "sslEnabled" ], "type": "object" } ``` Override the default prediction instance from the deployment when scoring this job. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | apiKey | string | false | | By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users. | | datarobotKey | string | false | | If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key. | | hostName | string | true | | Override the default host name of the deployment with this. | | sslEnabled | boolean | true | | Use SSL (HTTPS) when communicating with the overriden prediction server. | ## BatchPredictionJobRemapping ``` { "properties": { "inputName": { "description": "Rename column with this name", "type": "string" }, "outputName": { "description": "Rename column to this name (leave as null to remove from the output)", "type": [ "string", "null" ] } }, "required": [ "inputName", "outputName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputName | string | true | | Rename column with this name | | outputName | string,null | true | | Rename column to this name (leave as null to remove from the output) | ## BatchPredictionJobTimeSeriesSettingsForecast ``` { "properties": { "forecastPoint": { "description": "Used for forecast predictions in order to override the inferred forecast point from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode makes predictions using forecastPoint or rows in the dataset without target.", "enum": [ "forecast" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forecastPoint | string(date-time) | false | | Used for forecast predictions in order to override the inferred forecast point from the dataset. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Forecast mode makes predictions using forecastPoint or rows in the dataset without target. | ### Enumerated Values | Property | Value | | --- | --- | | type | forecast | ## BatchPredictionJobTimeSeriesSettingsHistorical ``` { "properties": { "predictionsEndDate": { "description": "Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "predictionsStartDate": { "description": "Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset.", "format": "date-time", "type": "string" }, "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range.", "enum": [ "historical" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionsEndDate | string(date-time) | false | | Used for historical predictions in order to override date to which predictions should be calculated. By default value will be inferred automatically from the dataset. | | predictionsStartDate | string(date-time) | false | | Used for historical predictions in order to override date from which predictions should be calculated. By default value will be inferred automatically from the dataset. | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Historical mode enables bulk predictions which calculates predictions for all possible forecast points and forecast distances in the dataset within the predictionsStartDate/predictionsEndDate range. | ### Enumerated Values | Property | Value | | --- | --- | | type | historical | ## BatchPredictionJobTimeSeriesSettingsTraining ``` { "properties": { "relaxKnownInAdvanceFeaturesCheck": { "default": false, "description": "If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed.", "type": "boolean" }, "type": { "description": "Forecast mode used for making predictions on subsets of training data.", "enum": [ "training" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | relaxKnownInAdvanceFeaturesCheck | boolean | false | | If activated, missing values in the known in advance features are allowed in the forecast window at prediction time. If omitted or false, missing values are not allowed. | | type | string | true | | Forecast mode used for making predictions on subsets of training data. | ### Enumerated Values | Property | Value | | --- | --- | | type | training | ## BigQueryDataStreamer ``` { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" } ``` Stream CSV data chunks from Big Query using GCS ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bucket | string | true | | The name of gcs bucket for data export | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the GCP credentials | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataset | string | true | | The name of the specified big query dataset to read input data from | | table | string | true | | The name of the specified big query table to read input data from | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | type | bigquery | ## BigQueryIntake ``` { "description": "Stream CSV data chunks from Big Query using GCS", "properties": { "bucket": { "description": "The name of gcs bucket for data export", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to read input data from", "type": "string" }, "table": { "description": "The name of the specified big query table to read input data from", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" } ``` Stream CSV data chunks from Big Query using GCS ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bucket | string | true | | The name of gcs bucket for data export | | credentialId | string | true | | The ID of the GCP credentials | | dataset | string | true | | The name of the specified big query dataset to read input data from | | table | string | true | | The name of the specified big query table to read input data from | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | bigquery | ## BigQueryOutput ``` { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "The ID of the GCP credentials", "type": "string" }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Google BigQuery in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bucket | string | true | | The name of gcs bucket for data loading | | credentialId | string | true | | The ID of the GCP credentials | | dataset | string | true | | The name of the specified big query dataset to write data back | | table | string | true | | The name of the specified big query table to write data back | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | bigquery | ## BigQueryOutputAdaptor ``` { "description": "Save CSV data chunks to Google BigQuery in bulk", "properties": { "bucket": { "description": "The name of gcs bucket for data loading", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the GCP credentials", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataset": { "description": "The name of the specified big query dataset to write data back", "type": "string" }, "table": { "description": "The name of the specified big query table to write data back", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "bigquery" ], "type": "string" } }, "required": [ "bucket", "credentialId", "dataset", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Google BigQuery in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bucket | string | true | | The name of gcs bucket for data loading | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the GCP credentials | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataset | string | true | | The name of the specified big query dataset to write data back | | table | string | true | | The name of the specified big query table to write data back | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | type | bigquery | ## Catalog ``` { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "The ID of the AI catalog dataset", "type": "string" }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" } ``` Stream CSV data chunks from AI catalog dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the AI catalog dataset | | datasetVersionId | string | false | | The ID of the AI catalog dataset version | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | dataset | ## CatalogDataStreamer ``` { "description": "Stream CSV data chunks from AI catalog dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the AI catalog dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "datasetVersionId": { "description": "The ID of the AI catalog dataset version", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataset" ], "type": "string" } }, "required": [ "datasetId", "type" ], "type": "object" } ``` Stream CSV data chunks from AI catalog dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the AI catalog dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetVersionId | string | false | | The ID of the AI catalog dataset version | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | type | dataset | ## DSS ``` { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "The ID of the dataset", "type": "string" }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" } ``` Stream CSV data chunks from DSS dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | false | | The ID of the dataset | | partition | string,null | false | | Partition used to predict | | projectId | string | true | | The ID of the project | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | partition | [holdout, validation, allBacktests, null] | | type | dss | ## DSSDataStreamer ``` { "description": "Stream CSV data chunks from DSS dataset", "properties": { "datasetId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the dataset", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "partition": { "default": null, "description": "Partition used to predict", "enum": [ "holdout", "validation", "allBacktests", null ], "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the project", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dss" ], "type": "string" } }, "required": [ "projectId", "type" ], "type": "object" } ``` Stream CSV data chunks from DSS dataset ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the dataset | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | partition | string,null | false | | Partition used to predict | | projectId | string | true | | The ID of the project | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | partition | [holdout, validation, allBacktests, null] | | type | dss | ## DataStageDataStreamer ``` { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" } ``` Stream CSV data chunks from data stage storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStageId | string | true | | The ID of the data stage | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | dataStage | ## DataStageIntake ``` { "description": "Stream CSV data chunks from data stage storage", "properties": { "dataStageId": { "description": "The ID of the data stage", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "dataStage" ], "type": "string" } }, "required": [ "dataStageId", "type" ], "type": "object" } ``` Stream CSV data chunks from data stage storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStageId | string | true | | The ID of the data stage | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | dataStage | ## DatabricksDataStreamer ``` { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" } ``` Stream CSV data chunks from Databricks using browser-databricks ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with read access to the data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | databricks | ## DatabricksIntake ``` { "description": "Stream CSV data chunks from Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" } ``` Stream CSV data chunks from Databricks using browser-databricks ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | string | true | | The ID of the credential holding information about a user with read access to the data source. | | dataStoreId | string | true | | ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | databricks | ## DatabricksOutput ``` { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" } ``` Saves CSV data chunks to Databricks using browser-databricks ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | credentialId | string | true | | The ID of the credential holding information about a user with write access to the data destination. | | dataStoreId | string | true | | The ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | databricks | ## DatabricksOutputAdaptor ``` { "description": "Saves CSV data chunks to Databricks using browser-databricks", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "databricks" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.43" } ``` Saves CSV data chunks to Databricks using browser-databricks ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with write access to the data destination. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | databricks | ## DatasphereDataStreamer ``` { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` Stream CSV data chunks from Datasphere using browser-datasphere ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with read access to the data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | datasphere | ## DatasphereIntake ``` { "description": "Stream CSV data chunks from Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` Stream CSV data chunks from Datasphere using browser-datasphere ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | string | true | | The ID of the credential holding information about a user with read access to the data source. | | dataStoreId | string | true | | ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | datasphere | ## DatasphereOutput ``` { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.35" } ``` Saves CSV data chunks to Datasphere using browser-datasphere ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | credentialId | string | true | | The ID of the credential holding information about a user with write access to the data destination. | | dataStoreId | string | true | | The ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | datasphere | ## DatasphereOutputAdaptor ``` { "description": "Saves CSV data chunks to Datasphere using browser-datasphere", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "datasphere" ], "type": "string" } }, "required": [ "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.42" } ``` Saves CSV data chunks to Datasphere using browser-datasphere ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with write access to the data destination. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | datasphere | ## FileSystemDataStreamer ``` { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to data on host filesystem | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | filesystem | ## FileSystemIntake ``` { "properties": { "path": { "description": "Path to data on host filesystem", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to data on host filesystem | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | filesystem | ## FileSystemOutput ``` { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to results on host filesystem | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | filesystem | ## FileSystemOutputAdaptor ``` { "properties": { "path": { "description": "Path to results on host filesystem", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "filesystem" ], "type": "string" } }, "required": [ "path", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to results on host filesystem | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | filesystem | ## GCPDataStreamer ``` { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Google Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | gcp | ## GCPIntake ``` { "description": "Stream CSV data chunks from Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Google Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | gcp | ## GCPOutput ``` { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Save CSV data chunks to Google Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | format | string | false | | Type of input file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | gcp | ## GCPOutputAdaptor ``` { "description": "Save CSV data chunks to Google Storage", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "type": { "description": "Type name for this output type", "enum": [ "gcp" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Save CSV data chunks to Google Storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | format | string | false | | Type of input file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | gcp | ## HTTPDataStreamer ``` { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from HTTP ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | type | http | ## HTTPIntake ``` { "description": "Stream CSV data chunks from HTTP", "properties": { "type": { "description": "Type name for this intake type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from HTTP ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | type | http | ## HTTPOutput ``` { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" } ``` Save CSV data chunks to HTTP data endpoint ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | headers | object | false | | Extra headers to send with the request | | method | string | true | | Method to use when saving the CSV file | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | method | [POST, PUT] | | type | http | ## HttpOutputAdaptor ``` { "description": "Save CSV data chunks to HTTP data endpoint", "properties": { "headers": { "description": "Extra headers to send with the request", "type": "object" }, "method": { "description": "Method to use when saving the CSV file", "enum": [ "POST", "PUT" ], "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "http" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "method", "type", "url" ], "type": "object" } ``` Save CSV data chunks to HTTP data endpoint ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | headers | object | false | | Extra headers to send with the request | | method | string | true | | Method to use when saving the CSV file | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | method | [POST, PUT] | | type | http | ## JDBCDataStreamer ``` { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" } ``` Stream CSV data chunks from JDBC ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with read access to the JDBC data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fetchSize | integer | false | maximum: 1000000minimum: 1 | A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21. | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | jdbc | ## JDBCIntake ``` { "description": "Stream CSV data chunks from JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.22" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "fetchSize": { "description": "A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21.", "maximum": 1000000, "minimum": 1, "type": "integer" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" } }, "required": [ "dataStoreId", "type" ], "type": "object" } ``` Stream CSV data chunks from JDBC ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | credentialId | string,null | false | | The ID of the credential holding information about a user with read access to the JDBC data source. | | dataStoreId | string | true | | ID of the data store to connect to | | fetchSize | integer | false | maximum: 1000000minimum: 1 | A user specified fetch size. Changing it can be used to balance throughput and memory usage. Deprecated and ignored since v2.21. | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | jdbc | ## JDBCOutput ``` { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks via JDBC ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | commitInterval | integer | false | maximum: 86400minimum: 0 | Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing. | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | string,null | false | | The ID of the credential holding information about a user with write access to the JDBC data source. | | dataStoreId | string | true | | ID of the data store to connect to | | schema | string | false | | The name of the specified database schema to write the results to. | | statementType | string | true | | The statement type to use when writing the results. Deprecation Warning: Use of create_table is now discouraged. Use one of the other possibilities along with the parameter createTableIfNotExists set to true. | | table | string | true | | The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | type | string | true | | Type name for this intake type | | updateColumns | [string] | false | maxItems: 100 | The column names to be updated if statementType is set to either update or upsert. | | whereColumns | [string] | false | maxItems: 100 | The column names to be used in the where clause if statementType is set to update or upsert. | ### Enumerated Values | Property | Value | | --- | --- | | statementType | [createTable, create_table, insert, insertUpdate, insert_update, update] | | type | jdbc | ## JdbcOutputAdaptor ``` { "description": "Save CSV data chunks via JDBC", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.22" }, "commitInterval": { "default": 600, "description": "Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing.", "maximum": 86400, "minimum": 0, "type": "integer", "x-versionadded": "v2.21" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.24" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write the results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results. Deprecation Warning: Use of `create_table` is now discouraged. Use one of the other possibilities along with the parameter `createTableIfNotExists` set to `true`.", "enum": [ "createTable", "create_table", "insert", "insertUpdate", "insert_update", "update" ], "type": "string" }, "table": { "description": "The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "jdbc" ], "type": "string" }, "updateColumns": { "description": "The column names to be updated if statementType is set to either update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "whereColumns": { "description": "The column names to be used in the where clause if statementType is set to update or upsert.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "dataStoreId", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks via JDBC ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | commitInterval | integer | false | maximum: 86400minimum: 0 | Defines a time interval in seconds between each commit is done to the JDBC source. If set to 0, the batch prediction operation will write the entire job before committing. | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with write access to the JDBC data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | false | | The name of the specified database schema to write the results to. | | statementType | string | true | | The statement type to use when writing the results. Deprecation Warning: Use of create_table is now discouraged. Use one of the other possibilities along with the parameter createTableIfNotExists set to true. | | table | string | true | | The name of the specified database table to write the results to.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | type | string | true | | Type name for this intake type | | updateColumns | [string] | false | maxItems: 100 | The column names to be updated if statementType is set to either update or upsert. | | whereColumns | [string] | false | maxItems: 100 | The column names to be used in the where clause if statementType is set to update or upsert. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | statementType | [createTable, create_table, insert, insertUpdate, insert_update, update] | | type | jdbc | ## LocalFileDataStreamer ``` { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` Stream CSV data chunks from local file storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | async | boolean,null | false | | The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished. | | multipart | boolean | false | | specify if the data will be uploaded in multiple parts instead of a single file | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | [local_file, localFile] | ## LocalFileIntake ``` { "description": "Stream CSV data chunks from local file storage", "properties": { "async": { "description": "The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished.", "type": [ "boolean", "null" ], "x-versionadded": "v2.28" }, "multipart": { "description": "specify if the data will be uploaded in multiple parts instead of a single file", "type": "boolean", "x-versionadded": "v2.27" }, "type": { "description": "Type name for this intake type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` Stream CSV data chunks from local file storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | async | boolean,null | false | | The default behavior (async: true) will still submit the job to the queue and start processing as soon as the upload is started.Setting it to false will postpone submitting the job to the queue until all data has been uploaded.This is helpful if the user is on a bad connection and bottlednecked by the upload speed. Instead of blocking the queue this will allow others to submit to the queue until the upload has finished. | | multipart | boolean | false | | specify if the data will be uploaded in multiple parts instead of a single file | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | [local_file, localFile] | ## LocalFileOutput ``` { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` Save CSV data chunks to local file storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | [local_file, localFile] | ## LocalFileOutputAdaptor ``` { "description": "Save CSV data chunks to local file storage", "properties": { "type": { "description": "Type name for this output type", "enum": [ "local_file", "localFile" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` Save CSV data chunks to local file storage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | [local_file, localFile] | ## MonitoringAggregation ``` { "description": "Defines the aggregation policy for monitoring jobs.", "properties": { "retentionPolicy": { "default": "percentage", "description": "Monitoring jobs retention policy for aggregation.", "enum": [ "samples", "percentage" ], "type": "string" }, "retentionValue": { "default": 0, "description": "Amount/percentage of samples to retain.", "type": "integer" } }, "type": "object" } ``` Defines the aggregation policy for monitoring jobs. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | retentionPolicy | string | false | | Monitoring jobs retention policy for aggregation. | | retentionValue | integer | false | | Amount/percentage of samples to retain. | ### Enumerated Values | Property | Value | | --- | --- | | retentionPolicy | [samples, percentage] | ## MonitoringColumnsMapping ``` { "description": "Column names mapping for monitoring", "properties": { "actedUponColumn": { "description": "Name of column that contains value for acted_on.", "type": "string" }, "actualsTimestampColumn": { "description": "Name of column that contains actual timestamps.", "type": "string" }, "actualsValueColumn": { "description": "Name of column that contains actuals value.", "type": "string" }, "associationIdColumn": { "description": "Name of column that contains association Id.", "type": "string" }, "customMetricId": { "description": "Id of custom metric to process values for.", "type": "string" }, "customMetricTimestampColumn": { "description": "Name of column that contains custom metric values timestamps.", "type": "string" }, "customMetricTimestampFormat": { "description": "Format of timestamps from customMetricTimestampColumn.", "type": "string" }, "customMetricValueColumn": { "description": "Name of column that contains values for custom metric.", "type": "string" }, "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "predictionsColumns": { "description": "Name of the column(s) which contain prediction values.", "oneOf": [ { "description": "Map containing column name(s) and class name(s) for multiclass problem.", "items": { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" }, "maxItems": 100, "type": "array" }, { "description": "Column name that contains the prediction for regressions problem.", "type": "string" } ] }, "reportDrift": { "description": "True to report drift, False otherwise.", "type": "boolean" }, "reportPredictions": { "description": "True to report prediction, False otherwise.", "type": "boolean" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "type": "object" } ``` Column names mapping for monitoring ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actedUponColumn | string | false | | Name of column that contains value for acted_on. | | actualsTimestampColumn | string | false | | Name of column that contains actual timestamps. | | actualsValueColumn | string | false | | Name of column that contains actuals value. | | associationIdColumn | string | false | | Name of column that contains association Id. | | customMetricId | string | false | | Id of custom metric to process values for. | | customMetricTimestampColumn | string | false | | Name of column that contains custom metric values timestamps. | | customMetricTimestampFormat | string | false | | Format of timestamps from customMetricTimestampColumn. | | customMetricValueColumn | string | false | | Name of column that contains values for custom metric. | | monitoredStatusColumn | string | false | | Column name used to mark monitored rows. | | predictionsColumns | any | false | | Name of the column(s) which contain prediction values. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [PredictionColumMap] | false | maxItems: 100 | Map containing column name(s) and class name(s) for multiclass problem. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | Column name that contains the prediction for regressions problem. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | reportDrift | boolean | false | | True to report drift, False otherwise. | | reportPredictions | boolean | false | | True to report prediction, False otherwise. | | uniqueRowIdentifierColumns | [string] | false | maxItems: 100 | Column(s) name of unique row identifiers. | ## MonitoringOutputSettings ``` { "description": "Output settings for monitoring jobs", "properties": { "monitoredStatusColumn": { "description": "Column name used to mark monitored rows.", "type": "string" }, "uniqueRowIdentifierColumns": { "description": "Column(s) name of unique row identifiers.", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "monitoredStatusColumn", "uniqueRowIdentifierColumns" ], "type": "object" } ``` Output settings for monitoring jobs ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | monitoredStatusColumn | string | true | | Column name used to mark monitored rows. | | uniqueRowIdentifierColumns | [string] | true | maxItems: 100 | Column(s) name of unique row identifiers. | ## PredictionColumMap ``` { "properties": { "className": { "description": "Class name.", "type": "string" }, "columnName": { "description": "Column name that contains the prediction for a specific class.", "type": "string" } }, "required": [ "className", "columnName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | className | string | true | | Class name. | | columnName | string | true | | Column name that contains the prediction for a specific class. | ## S3DataStreamer ``` { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Amazon Cloud Storage S3 ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endpointUrl | string(url) | false | | Endpoint URL for the S3 connection (omit to use the default) | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | s3 | ## S3Intake ``` { "description": "Stream CSV data chunks from Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of input file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "type": { "description": "Type name for this intake type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Stream CSV data chunks from Amazon Cloud Storage S3 ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | endpointUrl | string(url) | false | | Endpoint URL for the S3 connection (omit to use the default) | | format | string | false | | Type of input file format | | type | string | true | | Type name for this intake type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | s3 | ## S3Output ``` { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Saves CSV data chunks to Amazon Cloud Storage S3 ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string,null | false | | Use the specified credential to access the url | | endpointUrl | string(url) | false | | Endpoint URL for the S3 connection (omit to use the default) | | format | string | false | | Type of output file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | serverSideEncryption | ServerSideEncryption | false | | Configure Server-Side Encryption for S3 output | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | format | [csv, parquet] | | type | s3 | ## S3OutputAdaptor ``` { "description": "Saves CSV data chunks to Amazon Cloud Storage S3", "properties": { "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "Use the specified credential to access the url", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "endpointUrl": { "description": "Endpoint URL for the S3 connection (omit to use the default)", "format": "url", "type": "string", "x-versionadded": "v2.29" }, "format": { "default": "csv", "description": "Type of output file format", "enum": [ "csv", "parquet" ], "type": "string", "x-versionadded": "v2.25" }, "partitionColumns": { "description": "For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash (\"/\").", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.26" }, "serverSideEncryption": { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" }, "type": { "description": "Type name for this output type", "enum": [ "s3" ], "type": "string" }, "url": { "description": "URL for the CSV file", "format": "url", "type": "string" } }, "required": [ "type", "url" ], "type": "object" } ``` Saves CSV data chunks to Amazon Cloud Storage S3 ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | Use the specified credential to access the url | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endpointUrl | string(url) | false | | Endpoint URL for the S3 connection (omit to use the default) | | format | string | false | | Type of output file format | | partitionColumns | [string] | false | maxItems: 100 | For Parquet directory-scoring only. The column names of the intake data of which to partition the dataset. Columns are partitioned in the order they are given. At least one value is required if scoring to a directory (meaning the output url ends with a slash ("/"). | | serverSideEncryption | ServerSideEncryption | false | | Configure Server-Side Encryption for S3 output | | type | string | true | | Type name for this output type | | url | string(url) | true | | URL for the CSV file | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | format | [csv, parquet] | | type | s3 | ## Schedule ``` { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" } ``` The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dayOfMonth | [number,string] | true | maxItems: 31 | The date(s) of the month that the job will run. Allowed values are either [1 ... 31] or ["*"] for all days of the month. This field is additive with dayOfWeek, meaning the job will run both on the date(s) defined in this field and the day specified by dayOfWeek (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If dayOfMonth is set to ["*"] and dayOfWeek is defined, the scheduler will trigger on every day of the month that matches dayOfWeek (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored. | | dayOfWeek | [number,string] | true | maxItems: 7 | The day(s) of the week that the job will run. Allowed values are [0 .. 6], where (Sunday=0), or ["*"], for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., "sunday", "Sunday", "sun", or "Sun", all map to [0]. This field is additive with dayOfMonth, meaning the job will run both on the date specified by dayOfMonth and the day defined in this field. | | hour | [number,string] | true | maxItems: 24 | The hour(s) of the day that the job will run. Allowed values are either ["*"] meaning every hour of the day or [0 ... 23]. | | minute | [number,string] | true | maxItems: 60 | The minute(s) of the day that the job will run. Allowed values are either ["*"] meaning every minute of the day or[0 ... 59]. | | month | [number,string] | true | maxItems: 12 | The month(s) of the year that the job will run. Allowed values are either [1 ... 12] or ["*"] for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., "jan" or "october"). Months that are not compatible with dayOfMonth are ignored, for example {"dayOfMonth": [31], "month":["feb"]}. | ## ServerSideEncryption ``` { "description": "Configure Server-Side Encryption for S3 output", "properties": { "algorithm": { "description": "The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).", "type": "string" }, "customerAlgorithm": { "description": "Specifies the algorithm to use to when encrypting the object (for example, AES256).", "type": "string" }, "customerKey": { "description": "Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string.", "type": "string" }, "kmsEncryptionContext": { "description": "Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.", "type": "string" }, "kmsKeyId": { "description": "Specifies the ID of the symmetric customer managed key to use for object encryption.", "type": "string" } }, "type": "object" } ``` Configure Server-Side Encryption for S3 output ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | algorithm | string | false | | The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms). | | customerAlgorithm | string | false | | Specifies the algorithm to use to when encrypting the object (for example, AES256). | | customerKey | string | false | | Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in customerAlgorithm. The key must be sent as an base64 encoded string. | | kmsEncryptionContext | string | false | | Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs. | | kmsKeyId | string | false | | Specifies the ID of the symmetric customer managed key to use for object encryption. | ## SnowflakeDataStreamer ``` { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" } ``` Stream CSV data chunks from Snowflake ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | cloudStorageCredentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with read access to the cloud storage. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageType | string | false | | Type name for cloud storage | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with read access to the Snowflake data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalStage | string | true | | External storage | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | cloudStorageType | [azure, gcp, s3] | | anonymous | [redacted] | | anonymous | [redacted] | | type | snowflake | ## SnowflakeIntake ``` { "description": "Stream CSV data chunks from Snowflake", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "type" ], "type": "object" } ``` Stream CSV data chunks from Snowflake ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to read input data from. | | cloudStorageCredentialId | string,null | false | | The ID of the credential holding information about a user with read access to the cloud storage. | | cloudStorageType | string | false | | Type name for cloud storage | | credentialId | string,null | false | | The ID of the credential holding information about a user with read access to the Snowflake data source. | | dataStoreId | string | true | | ID of the data store to connect to | | externalStage | string | true | | External storage | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | cloudStorageType | [azure, gcp, s3] | | type | snowflake | ## SnowflakeOutput ``` { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Snowflake in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | cloudStorageCredentialId | string,null | false | | The ID of the credential holding information about a user with write access to the cloud storage. | | cloudStorageType | string | false | | Type name for cloud storage | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | string,null | false | | The ID of the credential holding information about a user with write access to the Snowflake data source. | | dataStoreId | string | true | | ID of the data store to connect to | | externalStage | string | true | | External storage | | schema | string | false | | The name of the specified database schema to write results to. | | statementType | string | true | | The statement type to use when writing the results. | | table | string | true | | The name of the specified database table to write results to. | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | cloudStorageType | [azure, gcp, s3] | | statementType | [insert, create_table, createTable] | | type | snowflake | ## SnowflakeOutputAdaptor ``` { "description": "Save CSV data chunks to Snowflake in bulk", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string", "x-versionadded": "v2.28" }, "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "cloudStorageType": { "default": "s3", "description": "Type name for cloud storage", "enum": [ "azure", "gcp", "s3" ], "type": "string" }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the Snowflake data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalStage": { "description": "External storage", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "snowflake" ], "type": "string" } }, "required": [ "dataStoreId", "externalStage", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Snowflake in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | false | | The name of the specified database catalog to write output data to. | | cloudStorageCredentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with write access to the cloud storage. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageType | string | false | | Type name for cloud storage | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with write access to the Snowflake data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalStage | string | true | | External storage | | schema | string | false | | The name of the specified database schema to write results to. | | statementType | string | true | | The statement type to use when writing the results. | | table | string | true | | The name of the specified database table to write results to. | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | cloudStorageType | [azure, gcp, s3] | | anonymous | [redacted] | | anonymous | [redacted] | | statementType | [insert, create_table, createTable] | | type | snowflake | ## SynapseDataStreamer ``` { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" } ``` Stream CSV data chunks from Azure Synapse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageCredentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the Azure credential holding information about a user with read access to the cloud storage. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with read access to the JDBC data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalDataSource | string | true | | External datasource name | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | anonymous | [redacted] | | type | synapse | ## SynapseIntake ``` { "description": "Stream CSV data chunks from Azure Synapse", "properties": { "cloudStorageCredentialId": { "description": "The ID of the Azure credential holding information about a user with read access to the cloud storage.", "type": [ "string", "null" ] }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External datasource name", "type": "string" }, "query": { "description": "A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of \"table\" and/or \"schema\" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }}", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "type" ], "type": "object" } ``` Stream CSV data chunks from Azure Synapse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageCredentialId | string,null | false | | The ID of the Azure credential holding information about a user with read access to the cloud storage. | | credentialId | string,null | false | | The ID of the credential holding information about a user with read access to the JDBC data source. | | dataStoreId | string | true | | ID of the data store to connect to | | externalDataSource | string | true | | External datasource name | | query | string | false | | A self-supplied SELECT statement of the dataset you wish to score. Helpful for supplying a more fine-grained selection of data not achievable through specification of "table" and/or "schema" parameters exclusively.If this job is executed with a job definition, then template variables are available which will be substituted for timestamps: {{ current_run_timestamp }}, {{ last_completed_run_time }}, {{ last_scheduled_run_time }}, {{ next_scheduled_run_time }}, {{ current_run_time }} | | schema | string | false | | The name of the specified database schema to read input data from. | | table | string | false | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | synapse | ## SynapseOutput ``` { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Azure Synapse in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageCredentialId | string,null | false | | The ID of the credential holding information about a user with write access to the cloud storage. | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | string,null | false | | The ID of the credential holding information about a user with write access to the JDBC data source. | | dataStoreId | string | true | | ID of the data store to connect to | | externalDataSource | string | true | | External data source name | | schema | string | false | | The name of the specified database schema to write results to. | | statementType | string | true | | The statement type to use when writing the results. | | table | string | true | | The name of the specified database table to write results to. | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | statementType | [insert, create_table, createTable] | | type | synapse | ## SynapseOutputAdaptor ``` { "description": "Save CSV data chunks to Azure Synapse in bulk", "properties": { "cloudStorageCredentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the cloud storage.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "createTableIfNotExists": { "default": false, "description": "Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the `statementType` parameter.", "type": "boolean", "x-versionadded": "v2.25" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the JDBC data source.", "type": [ "string", "null" ] }, { "enum": [ "[redacted]" ], "type": "string" }, { "type": "null" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "externalDataSource": { "description": "External data source name", "type": "string" }, "schema": { "description": "The name of the specified database schema to write results to.", "type": "string" }, "statementType": { "description": "The statement type to use when writing the results.", "enum": [ "insert", "create_table", "createTable" ], "type": "string" }, "table": { "description": "The name of the specified database table to write results to.", "type": "string" }, "type": { "description": "Type name for this output type", "enum": [ "synapse" ], "type": "string" } }, "required": [ "dataStoreId", "externalDataSource", "statementType", "table", "type" ], "type": "object" } ``` Save CSV data chunks to Azure Synapse in bulk ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudStorageCredentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with write access to the cloud storage. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createTableIfNotExists | boolean | false | | Attempt to create the table first if no existing one is detected, before writing data with the strategy defined in the statementType parameter. | | credentialId | any | false | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string,null | false | | The ID of the credential holding information about a user with write access to the JDBC data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalDataSource | string | true | | External data source name | | schema | string | false | | The name of the specified database schema to write results to. | | statementType | string | true | | The statement type to use when writing the results. | | table | string | true | | The name of the specified database table to write results to. | | type | string | true | | Type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | anonymous | [redacted] | | statementType | [insert, create_table, createTable] | | type | synapse | ## TrinoDataStreamer ``` { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ``` Stream CSV data chunks from Trino using browser-trino ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | The name of the specified database catalog to read input data from. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with read access to the data source. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | trino | ## TrinoIntake ``` { "description": "Stream CSV data chunks from Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to read input data from.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with read access to the data source.", "type": "string" }, "dataStoreId": { "description": "ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to read input data from.", "type": "string" }, "table": { "description": "The name of the specified database table to read input data from.", "type": "string" }, "type": { "description": "Type name for this intake type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ``` Stream CSV data chunks from Trino using browser-trino ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | The name of the specified database catalog to read input data from. | | credentialId | string | true | | The ID of the credential holding information about a user with read access to the data source. | | dataStoreId | string | true | | ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to read input data from. | | table | string | true | | The name of the specified database table to read input data from. | | type | string | true | | Type name for this intake type | ### Enumerated Values | Property | Value | | --- | --- | | type | trino | ## TrinoOutput ``` { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, "dataStoreId": { "description": "The ID of the data store to connect to", "type": "string" }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ``` Saves CSV data chunks to Trino using browser-trino ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | The name of the specified database catalog to write output data to. | | credentialId | string | true | | The ID of the credential holding information about a user with write access to the data destination. | | dataStoreId | string | true | | The ID of the data store to connect to | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | type | trino | ## TrinoOutputAdaptor ``` { "description": "Saves CSV data chunks to Trino using browser-trino", "properties": { "catalog": { "description": "The name of the specified database catalog to write output data to.", "type": "string" }, "credentialId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the credential holding information about a user with write access to the data destination.", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "dataStoreId": { "description": "Either the populated value of the field or [redacted] due to permission settings", "oneOf": [ { "description": "The ID of the data store to connect to", "type": "string" }, { "enum": [ "[redacted]" ], "type": "string" } ] }, "schema": { "description": "The name of the specified database schema to write output data to.", "type": "string" }, "table": { "description": "The name of the specified database table to write output data to.", "type": "string" }, "type": { "description": "The type name for this output type", "enum": [ "trino" ], "type": "string" } }, "required": [ "catalog", "credentialId", "dataStoreId", "schema", "table", "type" ], "type": "object", "x-versionadded": "v2.41" } ``` Saves CSV data chunks to Trino using browser-trino ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalog | string | true | | The name of the specified database catalog to write output data to. | | credentialId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the credential holding information about a user with write access to the data destination. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataStoreId | any | true | | Either the populated value of the field or [redacted] due to permission settings | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | The ID of the data store to connect to | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | schema | string | true | | The name of the specified database schema to write output data to. | | table | string | true | | The name of the specified database table to write output data to. | | type | string | true | | The type name for this output type | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [redacted] | | anonymous | [redacted] | | type | trino | --- # No-code applications URL: https://docs.datarobot.com/en/docs/api/reference/public-api/no_code_applications.html > Read below to learn about DataRobot's endpoints for no-code applications. Reference the [AI App Builder](app-builder/index) documentation for more information. # No-code applications Read below to learn about DataRobot's endpoints for no-code applications. Reference the [AI App Builder](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html) documentation for more information. ## Paginated list of applications created by the currently authenticated user Operation path: `GET /api/v2/applications/` Authentication requirements: `BearerAuth` Paginated list of applications created by the currently authenticated user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. If 0, all results. | | lid | query | string | false | The ID of the application | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of Application objects", "items": { "properties": { "applicationTemplateType": { "description": "Application template type, purpose", "type": [ "string", "null" ] }, "applicationTypeId": { "description": "The ID of the type of the application", "type": "string" }, "cloudProvider": { "description": "The host of this application", "type": "string" }, "createdAt": { "description": "The timestamp when the application was created", "type": "string" }, "createdBy": { "description": "The username of who created the application.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "Application creator first name", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Application creator last name", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "Application creator userhash", "type": [ "string", "null" ] }, "datasets": { "description": "The list of datasets IDs associated with the application", "items": { "type": "string" }, "type": "array" }, "deactivationStatusId": { "description": "The ID of the status object to track the asynchronous app deactivation process status. Will be null if the app was never deactivated.", "type": [ "string", "null" ] }, "deploymentIds": { "description": "A list of deployment IDs for this app", "items": { "description": "The ID of one deployment", "type": "string" }, "type": "array" }, "deploymentName": { "description": "Name of the deployment", "type": [ "string", "null" ] }, "deploymentStatusId": { "description": " The ID of the status object to track the asynchronous deployment process status", "type": "string" }, "deployments": { "description": "A list of deployment details", "items": { "properties": { "deploymentId": { "description": "The ID of the deployment", "type": "string" }, "referenceName": { "description": "The reference name of the deployment", "type": "string" } }, "required": [ "deploymentId", "referenceName" ], "type": "object" }, "type": "array" }, "description": { "description": "A description of the application.", "type": "string" }, "hasCustomLogo": { "description": "Whether the app has a custom logo", "type": "boolean" }, "id": { "description": "The ID of the created application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the associated model deployment", "type": "string" }, "name": { "description": "The name of the application", "type": "string" }, "orgId": { "description": "ID of the app's organization", "type": "string" }, "orgName": { "description": "Name of the app's organization", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_DELETE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "type": "array" }, "poolUsed": { "description": "Whether the pool where used for last app deployment", "type": "boolean" }, "realtimePredictionsSupport": { "description": "Sets whether you can do realtime predictions in the app.", "type": "boolean", "x-versionadded": "v2.34" }, "relatedEntities": { "description": "IDs of entities, related to app for easy search", "properties": { "isFromExperimentContainer": { "description": "[DEPRECATED - replaced with is_from_use_case] Whether the app was created from an experiment container", "type": "boolean" }, "isFromUseCase": { "description": "Whether the app was created from an Use Case", "type": "boolean" }, "isTrialOrganization": { "description": "Whether the app was created from by trial customer", "type": "boolean" }, "modelId": { "description": "The ID of the associated model", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": "string" } }, "type": "object" }, "updatedAt": { "description": "The timestamp when the application was updated", "type": "string" }, "userId": { "description": "The ID of the user which created the application", "type": "string" } }, "required": [ "applicationTemplateType", "applicationTypeId", "cloudProvider", "createdAt", "createdBy", "creatorFirstName", "creatorLastName", "creatorUserhash", "datasets", "deactivationStatusId", "deploymentIds", "deploymentName", "deploymentStatusId", "deployments", "hasCustomLogo", "id", "modelDeploymentId", "name", "orgId", "permissions", "poolUsed", "realtimePredictionsSupport", "updatedAt", "userId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ApplicationList | ## Create an application Operation path: `POST /api/v2/applications/` Authentication requirements: `BearerAuth` Create an application. Note that the number of active applications users can have at the same time is limited. ### Body parameter ``` { "properties": { "applicationTypeId": { "description": "The ID of the of application to be created.", "type": "string" }, "authenticationType": { "default": "invitedUsersOnly", "description": "Authentication type", "enum": [ "invitedUsersOnly", "token" ], "type": [ "string", "null" ] }, "cloudProvider": { "default": "drcloud", "description": "The optional cloud provider", "enum": [ "drcloud", "heroku" ], "type": "string" }, "description": { "description": "The description of the application", "maxLength": 512, "type": [ "string", "null" ] }, "experimentContainerId": { "description": "[DEPRECATED - replaced with use_case_id] The ID of the experiment container associated with the application.", "type": "string" }, "modelDeploymentId": { "description": "The ID of the model deployment. The deployed application will use this deployment to make predictions.", "type": "string" }, "name": { "description": "The name of the app", "maxLength": 512, "minLength": 1, "type": "string" }, "purpose": { "description": "An optional field to describe the purpose of the application.", "type": [ "string", "null" ] }, "sources": { "description": "The sources for this application", "items": { "properties": { "info": { "description": "Information about the Deployment or the Model", "oneOf": [ { "properties": { "modelDeploymentId": { "description": "The ID of the model deployment", "type": "string" } }, "required": [ "modelDeploymentId" ], "type": "object" }, { "properties": { "modelId": { "description": "The ID of the model", "type": "string" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions", "maximum": 1, "minimum": 0, "type": "number" }, "projectId": { "description": "The ID of the project", "type": "string" } }, "required": [ "modelId", "projectId" ], "type": "object" } ] }, "name": { "description": "The name of this source.", "type": "string" }, "source": { "description": "Information about the source for this application.", "enum": [ "deployment", "model" ], "type": "string" } }, "required": [ "info", "source" ], "type": "object" }, "type": "array" }, "useCaseId": { "description": "The ID of the Use Case associated with the application.", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ApplicationCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "applicationTemplateType": { "description": "Application template type, purpose", "type": [ "string", "null" ] }, "applicationTypeId": { "description": "The ID of the type of the application", "type": "string" }, "cloudProvider": { "description": "The host of this application", "type": "string" }, "createdAt": { "description": "The timestamp when the application was created", "type": "string" }, "createdBy": { "description": "The username of who created the application.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "Application creator first name", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Application creator last name", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "Application creator userhash", "type": [ "string", "null" ] }, "datasets": { "description": "The list of datasets IDs associated with the application", "items": { "type": "string" }, "type": "array" }, "deactivationStatusId": { "description": "The ID of the status object to track the asynchronous app deactivation process status. Will be null if the app was never deactivated.", "type": [ "string", "null" ] }, "deploymentIds": { "description": "A list of deployment IDs for this app", "items": { "description": "The ID of one deployment", "type": "string" }, "type": "array" }, "deploymentName": { "description": "Name of the deployment", "type": [ "string", "null" ] }, "deploymentStatusId": { "description": " The ID of the status object to track the asynchronous deployment process status", "type": "string" }, "deployments": { "description": "A list of deployment details", "items": { "properties": { "deploymentId": { "description": "The ID of the deployment", "type": "string" }, "referenceName": { "description": "The reference name of the deployment", "type": "string" } }, "required": [ "deploymentId", "referenceName" ], "type": "object" }, "type": "array" }, "description": { "description": "A description of the application.", "type": "string" }, "hasCustomLogo": { "description": "Whether the app has a custom logo", "type": "boolean" }, "id": { "description": "The ID of the created application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the associated model deployment", "type": "string" }, "name": { "description": "The name of the application", "type": "string" }, "orgId": { "description": "ID of the app's organization", "type": "string" }, "orgName": { "description": "Name of the app's organization", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_DELETE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "type": "array" }, "poolUsed": { "description": "Whether the pool where used for last app deployment", "type": "boolean" }, "realtimePredictionsSupport": { "description": "Sets whether you can do realtime predictions in the app.", "type": "boolean", "x-versionadded": "v2.34" }, "relatedEntities": { "description": "IDs of entities, related to app for easy search", "properties": { "isFromExperimentContainer": { "description": "[DEPRECATED - replaced with is_from_use_case] Whether the app was created from an experiment container", "type": "boolean" }, "isFromUseCase": { "description": "Whether the app was created from an Use Case", "type": "boolean" }, "isTrialOrganization": { "description": "Whether the app was created from by trial customer", "type": "boolean" }, "modelId": { "description": "The ID of the associated model", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": "string" } }, "type": "object" }, "updatedAt": { "description": "The timestamp when the application was updated", "type": "string" }, "userId": { "description": "The ID of the user which created the application", "type": "string" } }, "required": [ "applicationTemplateType", "applicationTypeId", "cloudProvider", "createdAt", "createdBy", "creatorFirstName", "creatorLastName", "creatorUserhash", "datasets", "deactivationStatusId", "deploymentIds", "deploymentName", "deploymentStatusId", "deployments", "hasCustomLogo", "id", "modelDeploymentId", "name", "orgId", "permissions", "poolUsed", "realtimePredictionsSupport", "updatedAt", "userId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | Application | | 202 | Accepted | Creation has successfully started. See the Location header. | None | | 403 | Forbidden | User does not have permission to launch application of provided type. | None | | 404 | Not Found | No app type matching the specified identifier found or user does not have permissions to access to this app type. | None | | 422 | Unprocessable Entity | Application could not be created with the given input. | None | ## Verify ability Operation path: `POST /api/v2/applications/verify/` Authentication requirements: `BearerAuth` Verifies a user can create an app. ### Body parameter ``` { "properties": { "experimentContainerId": { "description": "The ID of the experiment container (for apps from an experiment container", "type": "string" }, "projectId": { "description": "The ID of the project (for apps from leaderboard)", "type": "string" }, "sourceId": { "description": "The ID of the source", "type": "string" }, "sourceType": { "description": "Whether the app is from a deployment or a project", "enum": [ "deployment", "model" ], "type": "string" } }, "required": [ "sourceId", "sourceType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ApplicationCanCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | App may be created | None | | 403 | Forbidden | User does not have permission to launch application of provided type. | None | | 404 | Not Found | No app type matching the specified identifier found or user does not have permissions to access to this app type. | None | | 422 | Unprocessable Entity | Application can not be created with the given input. | None | ## Delete an application by application ID Operation path: `DELETE /api/v2/applications/{applicationId}/` Authentication requirements: `BearerAuth` Delete an application. ### Body parameter ``` { "properties": { "applicationId": { "description": "The ID of the application", "type": "string" } }, "required": [ "applicationId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | hard | query | boolean | false | Determines whether or not DataRobot deletes the underlying entity, or just marks it as deleted. | | applicationId | path | string | true | The ID of the application | | body | body | ApplicationParam | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | The application has been deleted. | None | ## Retrieve an application by application ID Operation path: `GET /api/v2/applications/{applicationId}/` Authentication requirements: `BearerAuth` Retrieve an application. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationId | path | string | true | The ID of the application | ### Example responses > 200 Response ``` { "properties": { "applicationTemplateType": { "description": "Application template type, purpose", "type": [ "string", "null" ] }, "applicationTypeId": { "description": "The ID of the type of the application", "type": "string" }, "cloudProvider": { "description": "The host of this application", "type": "string" }, "createdAt": { "description": "The timestamp when the application was created", "type": "string" }, "createdBy": { "description": "The username of who created the application.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "Application creator first name", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Application creator last name", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "Application creator userhash", "type": [ "string", "null" ] }, "datasets": { "description": "The list of datasets IDs associated with the application", "items": { "type": "string" }, "type": "array" }, "deactivationStatusId": { "description": "The ID of the status object to track the asynchronous app deactivation process status. Will be null if the app was never deactivated.", "type": [ "string", "null" ] }, "deploymentIds": { "description": "A list of deployment IDs for this app", "items": { "description": "The ID of one deployment", "type": "string" }, "type": "array" }, "deploymentName": { "description": "Name of the deployment", "type": [ "string", "null" ] }, "deploymentStatusId": { "description": " The ID of the status object to track the asynchronous deployment process status", "type": "string" }, "deployments": { "description": "A list of deployment details", "items": { "properties": { "deploymentId": { "description": "The ID of the deployment", "type": "string" }, "referenceName": { "description": "The reference name of the deployment", "type": "string" } }, "required": [ "deploymentId", "referenceName" ], "type": "object" }, "type": "array" }, "description": { "description": "A description of the application.", "type": "string" }, "hasCustomLogo": { "description": "Whether the app has a custom logo", "type": "boolean" }, "id": { "description": "The ID of the created application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the model deployment. The deployed application will use this deployment to make predictions.", "type": [ "string", "null" ] }, "name": { "description": "The name of the application", "type": "string" }, "orgId": { "description": "ID of the app's organization", "type": "string" }, "orgName": { "description": "Name of the app's organization", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_DELETE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "type": "array" }, "poolUsed": { "description": "Whether the pool where used for last app deployment", "type": "boolean" }, "realtimePredictionsSupport": { "description": "Sets whether you can do realtime predictions in the app.", "type": "boolean", "x-versionadded": "v2.34" }, "relatedEntities": { "description": "IDs of entities, related to app for easy search", "properties": { "isFromExperimentContainer": { "description": "[DEPRECATED - replaced with is_from_use_case] Whether the app was created from an experiment container", "type": "boolean" }, "isFromUseCase": { "description": "Whether the app was created from an Use Case", "type": "boolean" }, "isTrialOrganization": { "description": "Whether the app was created from by trial customer", "type": "boolean" }, "modelId": { "description": "The ID of the associated model", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": "string" } }, "type": "object" }, "updatedAt": { "description": "The timestamp when the application was updated", "type": "string" }, "userId": { "description": "The ID of the user which created the application", "type": "string" } }, "required": [ "applicationTemplateType", "applicationTypeId", "cloudProvider", "createdAt", "createdBy", "creatorFirstName", "creatorLastName", "creatorUserhash", "datasets", "deactivationStatusId", "deploymentIds", "deploymentName", "deploymentStatusId", "deployments", "hasCustomLogo", "id", "name", "orgId", "permissions", "poolUsed", "realtimePredictionsSupport", "updatedAt", "userId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ApplicationRetrieve | ## Update an application's name and/ by application ID Operation path: `PATCH /api/v2/applications/{applicationId}/` Authentication requirements: `BearerAuth` Update an application's name and/or description. ### Body parameter ``` { "properties": { "description": { "description": "The description of the application", "maxLength": 512, "type": [ "string", "null" ] }, "name": { "description": "The name of the app", "maxLength": 512, "minLength": 1, "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationId | path | string | true | The ID of the application | | body | body | ApplicationNameAndDescription | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | The application has been updated. | None | ## A list of users with access by application ID Operation path: `GET /api/v2/applications/{applicationId}/accessControl/` Authentication requirements: `BearerAuth` A list of users who have access to this application and their roles. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | username | query | string | false | Optional, only return the access control information for a user with this username. | | userId | query | string | false | Optional, only return the access control information for a user with this user ID. | | applicationId | path | string | true | The ID of the application | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of AccessControlData objects", "items": { "properties": { "canShare": { "description": "Whether this user can share with other users", "type": "boolean" }, "role": { "description": "The role of the user on this application", "type": "string" }, "userId": { "description": "The ID of the user", "type": "string" }, "username": { "description": "Username of a user with access to this application", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ApplicationAccessControlList | | 400 | Bad Request | Bad Request, both username and userId were specified | None | | 404 | Not Found | Entity not found. Either the application does not exist or the user does not have permissions to view the application. | None | ## Update access control by application ID Operation path: `PATCH /api/v2/applications/{applicationId}/accessControl/` Authentication requirements: `BearerAuth` Update access control for this application. Request is processed only if updates can be performed on all entries. ### Body parameter ``` { "properties": { "data": { "description": "An array of AccessControlPermissionValidator objects", "items": { "properties": { "role": { "description": "The role to grant to the user, or \"\" (empty string) to remove the users access", "enum": [ "CONSUMER", "EDITOR", "OWNER", "" ], "type": "string" }, "username": { "description": "The username of the user to modify access for", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "type": "array" }, "permissions": { "description": "The list of permission objects describing which users to modify access for.", "items": { "properties": { "role": { "description": "The role to grant to the user, or \"\" (empty string) to remove the users access", "enum": [ "CONSUMER", "EDITOR", "OWNER", "" ], "type": "string" }, "username": { "description": "The username of the user to modify access for", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationId | path | string | true | The ID of the application | | body | body | ApplicationAccessControlUpdateRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 403 | Forbidden | User does not have appropriate privileges | None | | 404 | Not Found | Invalid applicationId provided or invalid username provided to modify access for | None | ## Links a deployment by application ID Operation path: `POST /api/v2/applications/{applicationId}/deployments/` Authentication requirements: `BearerAuth` If application creates deployment during its lifetime, we want to have an API to link deployment with application. ### Body parameter ``` { "properties": { "linkName": { "description": "Internal name of deployment to match in the application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the model deployment to link to the application.", "type": "string" } }, "required": [ "modelDeploymentId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationId | path | string | true | The ID of the application | | body | body | AddDeploymentToApplication | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Delete link between application by application ID Operation path: `DELETE /api/v2/applications/{applicationId}/deployments/{modelDeploymentId}/` Authentication requirements: `BearerAuth` Delete link between application and deployment. ### Body parameter ``` { "properties": { "applicationId": { "description": "The ID of the application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the model deployment", "type": "string" } }, "required": [ "applicationId", "modelDeploymentId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationId | path | string | true | The ID of the application | | modelDeploymentId | path | string | true | The ID of the model deployment | | body | body | ApplicationModelDeploymentParam | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | The link has been deleted. | None | ## Create a duplicate of the application by application ID Operation path: `POST /api/v2/applications/{applicationId}/duplicate/` Authentication requirements: `BearerAuth` Create a copy of App Builder application. Note that the number of active applications users can have at the same time is limited. ### Body parameter ``` { "properties": { "authenticationType": { "default": "invitedUsersOnly", "description": "Authentication type", "enum": [ "invitedUsersOnly", "token" ], "type": [ "string", "null" ] }, "description": { "description": "The description of the application", "maxLength": 512, "type": [ "string", "null" ] }, "duplicatePredictions": { "default": false, "description": "Import all predictions from the source application", "type": "boolean" }, "name": { "description": "The name of the app", "maxLength": 512, "minLength": 1, "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationId | path | string | true | The ID of the application | | body | body | ApplicationDuplicate | false | none | ### Example responses > 200 Response ``` { "properties": { "applicationTemplateType": { "description": "Application template type, purpose", "type": [ "string", "null" ] }, "applicationTypeId": { "description": "The ID of the type of the application", "type": "string" }, "cloudProvider": { "description": "The host of this application", "type": "string" }, "createdAt": { "description": "The timestamp when the application was created", "type": "string" }, "createdBy": { "description": "The username of who created the application.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "Application creator first name", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Application creator last name", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "Application creator userhash", "type": [ "string", "null" ] }, "datasets": { "description": "The list of datasets IDs associated with the application", "items": { "type": "string" }, "type": "array" }, "deactivationStatusId": { "description": "The ID of the status object to track the asynchronous app deactivation process status. Will be null if the app was never deactivated.", "type": [ "string", "null" ] }, "deploymentIds": { "description": "A list of deployment IDs for this app", "items": { "description": "The ID of one deployment", "type": "string" }, "type": "array" }, "deploymentName": { "description": "Name of the deployment", "type": [ "string", "null" ] }, "deploymentStatusId": { "description": " The ID of the status object to track the asynchronous deployment process status", "type": "string" }, "deployments": { "description": "A list of deployment details", "items": { "properties": { "deploymentId": { "description": "The ID of the deployment", "type": "string" }, "referenceName": { "description": "The reference name of the deployment", "type": "string" } }, "required": [ "deploymentId", "referenceName" ], "type": "object" }, "type": "array" }, "description": { "description": "A description of the application.", "type": "string" }, "hasCustomLogo": { "description": "Whether the app has a custom logo", "type": "boolean" }, "id": { "description": "The ID of the created application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the associated model deployment", "type": "string" }, "name": { "description": "The name of the application", "type": "string" }, "orgId": { "description": "ID of the app's organization", "type": "string" }, "orgName": { "description": "Name of the app's organization", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_DELETE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "type": "array" }, "poolUsed": { "description": "Whether the pool where used for last app deployment", "type": "boolean" }, "realtimePredictionsSupport": { "description": "Sets whether you can do realtime predictions in the app.", "type": "boolean", "x-versionadded": "v2.34" }, "relatedEntities": { "description": "IDs of entities, related to app for easy search", "properties": { "isFromExperimentContainer": { "description": "[DEPRECATED - replaced with is_from_use_case] Whether the app was created from an experiment container", "type": "boolean" }, "isFromUseCase": { "description": "Whether the app was created from an Use Case", "type": "boolean" }, "isTrialOrganization": { "description": "Whether the app was created from by trial customer", "type": "boolean" }, "modelId": { "description": "The ID of the associated model", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": "string" } }, "type": "object" }, "updatedAt": { "description": "The timestamp when the application was updated", "type": "string" }, "userId": { "description": "The ID of the user which created the application", "type": "string" } }, "required": [ "applicationTemplateType", "applicationTypeId", "cloudProvider", "createdAt", "createdBy", "creatorFirstName", "creatorLastName", "creatorUserhash", "datasets", "deactivationStatusId", "deploymentIds", "deploymentName", "deploymentStatusId", "deployments", "hasCustomLogo", "id", "modelDeploymentId", "name", "orgId", "permissions", "poolUsed", "realtimePredictionsSupport", "updatedAt", "userId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | Application | | 202 | Accepted | Dulication has successfully started. See the Location header. | None | | 403 | Forbidden | User does not have permission to launch application of provided type. | None | | 404 | Not Found | App for duplication was not found | None | | 422 | Unprocessable Entity | Application could not be created with the given input. | None | ## Get a list of users, groups and organizations that have an access by application ID Operation path: `GET /api/v2/applications/{applicationId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations that have an access to this application. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | limit | query | integer | false | The number of records to return in the range of 1 to 100 | | offset | query | integer | false | The number of records to skip over. Default 0. | | name | query | string | false | Only return roles for a user, group or organization with this name | | id | query | string | false | Only return roles for a user, group or organization with this id | | shareRecipientType | query | string | false | Specify the recipient type, one of 'user', 'group', 'organization' | | applicationId | path | string | true | The ID of the application | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Details about the Shared Role entries", "items": { "properties": { "id": { "description": "The id of the recipient", "type": "string" }, "name": { "description": "The name of the user, group, or organization", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "USER", "CONSUMER", "EDITOR" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "Number of items matching to the query condition", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ApplicationSharedRolesList | ## Share an application by application ID Operation path: `PATCH /api/v2/applications/{applicationId}/sharedRoles/` Authentication requirements: `BearerAuth` Share an application with a user, group, or organization. ### Body parameter ``` { "properties": { "note": { "default": "", "description": "A note to go with the project share", "type": "string" }, "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" }, "sendNotification": { "default": false, "description": "Send a notification?", "type": "boolean" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | sendNotification | query | boolean | true | Send a notification | | note | query | string | true | A note to go with the project share | | operation | query | string | true | Name of the action being taken, only 'updateRoles' is supported | | roles | query | array[object] | true | Role objects, may contain up to 100 per request | | applicationId | path | string | true | The ID of the application | | body | body | ApplicationSharingUpdateOrRemove | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | The roles updated successfully | None | | 422 | Unprocessable Entity | The request was formatted improperly. | None | ## Get application user role by application ID Operation path: `GET /api/v2/applications/{applicationId}/userRole/` Authentication requirements: `BearerAuth` Get application user role. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | applicationId | path | string | true | The ID of the application | ### Example responses > 200 Response ``` { "properties": { "role": { "description": "The role of the user on this entity.", "type": "string" } }, "required": [ "role" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | user's role on application entity, taking into account RBAC, groups and organization. | ApplicationUserRoleResponse | ## Retrieve available code snippets Operation path: `GET /api/v2/codeSnippets/` Authentication requirements: `BearerAuth` Retrieve a list of available code snippets for the given parameters. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | templateType | query | string | true | The selected template type the generated snippet or notebook should be for (i.e. dataset, model, etc.). | | language | query | string | true | The selected language the generated snippet or notebook should be written in. | | filters | query | string | false | Optional comma separated list of sub filters to limit the returned notebooks. | ### Enumerated Values | Parameter | Value | | --- | --- | | templateType | [model, prediction, workload] | | language | [curl, powershell, python, qlik] | ### Example responses > 200 Response ``` { "properties": { "codeSnippets": { "description": "A list of the available snippets for a given language and template type.", "items": { "properties": { "description": { "description": "The descriptive text to be displayed in the UI.", "maxLength": 255, "minLength": 1, "type": "string" }, "snippetId": { "description": "The ID of this snippet.", "maxLength": 255, "minLength": 1, "type": "string" }, "templating": { "description": "A list of templating variables that will be used in the snippet.", "items": { "type": "string" }, "maxItems": 255, "type": "array" }, "title": { "description": "The title of the snippet.", "maxLength": 255, "minLength": 1, "type": "string" } }, "required": [ "description", "snippetId", "templating", "title" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 22, "type": "array" } }, "required": [ "codeSnippets" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of snippets with descriptions and ID tags. | CodeSnippetListResponse | ## Retrieve a code snippet Operation path: `POST /api/v2/codeSnippets/` Authentication requirements: `BearerAuth` Retrieve a code snippet to be displayed in the UI. ### Body parameter ``` { "properties": { "config": { "description": "Template type specific configuration used to generate a snippet or notebook.", "oneOf": [ { "properties": { "modelId": { "description": "The selected model ID.", "type": "string" }, "projectId": { "description": "The selected project ID.", "type": "string" }, "showSecrets": { "default": "False", "description": "If true, the DATAROBOT_KEY and DATAROBOT_API_KEY will be available in the context.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "modelId", "projectId" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "cliScript": { "default": true, "description": "When combined with is_standalone, a true value returns an example CLI run script for a snippet, while a false value returns an example executable script.", "type": "boolean", "x-versionadded": "v2.35" }, "deploymentId": { "description": "The selected deployment ID.", "type": "string" }, "isBatchPrediction": { "default": true, "description": "If true, returns snippet that can be used to make batch predictions. Not valid with time series projects.", "type": "boolean" }, "isLowLatencyPrediction": { "default": false, "description": "If true, returns snippet that can be used to make low latency predictions.Valid for Feature Discovery projects.", "type": "boolean" }, "isStandalone": { "default": false, "description": "If true, returns an example script for a snippet.", "type": "boolean" }, "showSecrets": { "default": false, "description": "If true, the DATAROBOT_KEY AND DATROBOT_API_KEY will be available in the context.", "type": "boolean" }, "testMode": { "default": false, "description": "Generate a snippet with mocked information.", "type": "boolean" }, "withApiClient": { "default": true, "description": "Instead of raw Python code in the example, show a snippet using the DataRobot Python API client.", "type": "boolean" } }, "required": [ "deploymentId" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "showSecrets": { "default": "False", "description": "If true, the DATAROBOT_KEY and DATAROBOT_API_KEY will be available in the context.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "workloadId": { "description": "The selected workload ID.", "type": "string" } }, "required": [ "workloadId" ], "type": "object", "x-versionadded": "v2.41" } ] }, "language": { "description": "The selected language the generated snippet or notebook should be written in.", "enum": [ "curl", "powershell", "python", "qlik" ], "type": "string" }, "snippetId": { "description": "The selected snippet to be returned to the user. This field is optional for Prediction snippets.", "maxLength": 255, "minLength": 1, "type": "string" }, "templateType": { "description": "The selected template type the generated snippet or notebook should be for (i.e. dataset, model, etc.).", "enum": [ "model", "prediction", "workload" ], "type": "string" } }, "required": [ "config", "language", "templateType" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CodeSnippetCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "codeSnippet": { "description": "A UTF-8 encoded code snippet generated for the user.", "type": "string" }, "snippetId": { "description": "The selected snippet to be returned to the user.", "type": "string" } }, "required": [ "codeSnippet", "snippetId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The generated code snippet. | CodeSnippetResponse | ## Create download (no_code_applications) Operation path: `POST /api/v2/codeSnippets/download/` Authentication requirements: `BearerAuth` Retrieve a code snippet to be displayed in the UI. ### Body parameter ``` { "properties": { "config": { "description": "Template type specific configuration used to generate a snippet or notebook.", "oneOf": [ { "properties": { "modelId": { "description": "The selected model ID.", "type": "string" }, "projectId": { "description": "The selected project ID.", "type": "string" }, "showSecrets": { "default": "False", "description": "If true, the DATAROBOT_KEY and DATAROBOT_API_KEY will be available in the context.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "modelId", "projectId" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "cliScript": { "default": true, "description": "When combined with is_standalone, a true value returns an example CLI run script for a snippet, while a false value returns an example executable script.", "type": "boolean", "x-versionadded": "v2.35" }, "deploymentId": { "description": "The selected deployment ID.", "type": "string" }, "isBatchPrediction": { "default": true, "description": "If true, returns snippet that can be used to make batch predictions. Not valid with time series projects.", "type": "boolean" }, "isLowLatencyPrediction": { "default": false, "description": "If true, returns snippet that can be used to make low latency predictions.Valid for Feature Discovery projects.", "type": "boolean" }, "isStandalone": { "default": false, "description": "If true, returns an example script for a snippet.", "type": "boolean" }, "showSecrets": { "default": false, "description": "If true, the DATAROBOT_KEY AND DATROBOT_API_KEY will be available in the context.", "type": "boolean" }, "testMode": { "default": false, "description": "Generate a snippet with mocked information.", "type": "boolean" }, "withApiClient": { "default": true, "description": "Instead of raw Python code in the example, show a snippet using the DataRobot Python API client.", "type": "boolean" } }, "required": [ "deploymentId" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "showSecrets": { "default": "False", "description": "If true, the DATAROBOT_KEY and DATAROBOT_API_KEY will be available in the context.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "workloadId": { "description": "The selected workload ID.", "type": "string" } }, "required": [ "workloadId" ], "type": "object", "x-versionadded": "v2.41" } ] }, "language": { "description": "The selected language the generated snippet or notebook should be written in.", "enum": [ "curl", "powershell", "python", "qlik" ], "type": "string" }, "snippetId": { "description": "The selected snippet to be returned to the user. This field is optional for Prediction snippets.", "maxLength": 255, "minLength": 1, "type": "string" }, "templateType": { "description": "The selected template type the generated snippet or notebook should be for (i.e. dataset, model, etc.).", "enum": [ "model", "prediction", "workload" ], "type": "string" } }, "required": [ "config", "language", "templateType" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CodeSnippetCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The code snippet file of the selected code snippet. | None | # Schemas ## AddDeploymentToApplication ``` { "properties": { "linkName": { "description": "Internal name of deployment to match in the application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the model deployment to link to the application.", "type": "string" } }, "required": [ "modelDeploymentId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | linkName | string | false | | Internal name of deployment to match in the application | | modelDeploymentId | string | true | | The ID of the model deployment to link to the application. | ## Application ``` { "properties": { "applicationTemplateType": { "description": "Application template type, purpose", "type": [ "string", "null" ] }, "applicationTypeId": { "description": "The ID of the type of the application", "type": "string" }, "cloudProvider": { "description": "The host of this application", "type": "string" }, "createdAt": { "description": "The timestamp when the application was created", "type": "string" }, "createdBy": { "description": "The username of who created the application.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "Application creator first name", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Application creator last name", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "Application creator userhash", "type": [ "string", "null" ] }, "datasets": { "description": "The list of datasets IDs associated with the application", "items": { "type": "string" }, "type": "array" }, "deactivationStatusId": { "description": "The ID of the status object to track the asynchronous app deactivation process status. Will be null if the app was never deactivated.", "type": [ "string", "null" ] }, "deploymentIds": { "description": "A list of deployment IDs for this app", "items": { "description": "The ID of one deployment", "type": "string" }, "type": "array" }, "deploymentName": { "description": "Name of the deployment", "type": [ "string", "null" ] }, "deploymentStatusId": { "description": " The ID of the status object to track the asynchronous deployment process status", "type": "string" }, "deployments": { "description": "A list of deployment details", "items": { "properties": { "deploymentId": { "description": "The ID of the deployment", "type": "string" }, "referenceName": { "description": "The reference name of the deployment", "type": "string" } }, "required": [ "deploymentId", "referenceName" ], "type": "object" }, "type": "array" }, "description": { "description": "A description of the application.", "type": "string" }, "hasCustomLogo": { "description": "Whether the app has a custom logo", "type": "boolean" }, "id": { "description": "The ID of the created application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the associated model deployment", "type": "string" }, "name": { "description": "The name of the application", "type": "string" }, "orgId": { "description": "ID of the app's organization", "type": "string" }, "orgName": { "description": "Name of the app's organization", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_DELETE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "type": "array" }, "poolUsed": { "description": "Whether the pool where used for last app deployment", "type": "boolean" }, "realtimePredictionsSupport": { "description": "Sets whether you can do realtime predictions in the app.", "type": "boolean", "x-versionadded": "v2.34" }, "relatedEntities": { "description": "IDs of entities, related to app for easy search", "properties": { "isFromExperimentContainer": { "description": "[DEPRECATED - replaced with is_from_use_case] Whether the app was created from an experiment container", "type": "boolean" }, "isFromUseCase": { "description": "Whether the app was created from an Use Case", "type": "boolean" }, "isTrialOrganization": { "description": "Whether the app was created from by trial customer", "type": "boolean" }, "modelId": { "description": "The ID of the associated model", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": "string" } }, "type": "object" }, "updatedAt": { "description": "The timestamp when the application was updated", "type": "string" }, "userId": { "description": "The ID of the user which created the application", "type": "string" } }, "required": [ "applicationTemplateType", "applicationTypeId", "cloudProvider", "createdAt", "createdBy", "creatorFirstName", "creatorLastName", "creatorUserhash", "datasets", "deactivationStatusId", "deploymentIds", "deploymentName", "deploymentStatusId", "deployments", "hasCustomLogo", "id", "modelDeploymentId", "name", "orgId", "permissions", "poolUsed", "realtimePredictionsSupport", "updatedAt", "userId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | applicationTemplateType | string,null | true | | Application template type, purpose | | applicationTypeId | string | true | | The ID of the type of the application | | cloudProvider | string | true | | The host of this application | | createdAt | string | true | | The timestamp when the application was created | | createdBy | string,null | true | | The username of who created the application. | | creatorFirstName | string,null | true | | Application creator first name | | creatorLastName | string,null | true | | Application creator last name | | creatorUserhash | string,null | true | | Application creator userhash | | datasets | [string] | true | | The list of datasets IDs associated with the application | | deactivationStatusId | string,null | true | | The ID of the status object to track the asynchronous app deactivation process status. Will be null if the app was never deactivated. | | deploymentIds | [string] | true | | A list of deployment IDs for this app | | deploymentName | string,null | true | | Name of the deployment | | deploymentStatusId | string | true | | The ID of the status object to track the asynchronous deployment process status | | deployments | [ApplicationDeployment] | true | | A list of deployment details | | description | string | false | | A description of the application. | | hasCustomLogo | boolean | true | | Whether the app has a custom logo | | id | string | true | | The ID of the created application | | modelDeploymentId | string | true | | The ID of the associated model deployment | | name | string | true | | The name of the application | | orgId | string | true | | ID of the app's organization | | orgName | string,null | false | | Name of the app's organization | | permissions | [string] | true | | The list of permitted actions, which the authenticated user can perform on this application. | | poolUsed | boolean | true | | Whether the pool where used for last app deployment | | realtimePredictionsSupport | boolean | true | | Sets whether you can do realtime predictions in the app. | | relatedEntities | ApplicationRelatedEntities | false | | IDs of entities, related to app for easy search | | updatedAt | string | true | | The timestamp when the application was updated | | userId | string | true | | The ID of the user which created the application | ## ApplicationAccessControlData ``` { "properties": { "canShare": { "description": "Whether this user can share with other users", "type": "boolean" }, "role": { "description": "The role of the user on this application", "type": "string" }, "userId": { "description": "The ID of the user", "type": "string" }, "username": { "description": "Username of a user with access to this application", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | Whether this user can share with other users | | role | string | true | | The role of the user on this application | | userId | string | true | | The ID of the user | | username | string | true | | Username of a user with access to this application | ## ApplicationAccessControlList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of AccessControlData objects", "items": { "properties": { "canShare": { "description": "Whether this user can share with other users", "type": "boolean" }, "role": { "description": "The role of the user on this application", "type": "string" }, "userId": { "description": "The ID of the user", "type": "string" }, "username": { "description": "Username of a user with access to this application", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ApplicationAccessControlData] | true | | An array of AccessControlData objects | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## ApplicationAccessControlUpdateRequest ``` { "properties": { "data": { "description": "An array of AccessControlPermissionValidator objects", "items": { "properties": { "role": { "description": "The role to grant to the user, or \"\" (empty string) to remove the users access", "enum": [ "CONSUMER", "EDITOR", "OWNER", "" ], "type": "string" }, "username": { "description": "The username of the user to modify access for", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "type": "array" }, "permissions": { "description": "The list of permission objects describing which users to modify access for.", "items": { "properties": { "role": { "description": "The role to grant to the user, or \"\" (empty string) to remove the users access", "enum": [ "CONSUMER", "EDITOR", "OWNER", "" ], "type": "string" }, "username": { "description": "The username of the user to modify access for", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "type": "array" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [ApplicationAccessPermission] | true | | An array of AccessControlPermissionValidator objects | | permissions | [ApplicationAccessPermission] | false | | The list of permission objects describing which users to modify access for. | ## ApplicationAccessPermission ``` { "properties": { "role": { "description": "The role to grant to the user, or \"\" (empty string) to remove the users access", "enum": [ "CONSUMER", "EDITOR", "OWNER", "" ], "type": "string" }, "username": { "description": "The username of the user to modify access for", "type": "string" } }, "required": [ "role", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role to grant to the user, or "" (empty string) to remove the users access | | username | string | true | | The username of the user to modify access for | ### Enumerated Values | Property | Value | | --- | --- | | role | [CONSUMER, EDITOR, OWNER, ``] | ## ApplicationCanCreate ``` { "properties": { "experimentContainerId": { "description": "The ID of the experiment container (for apps from an experiment container", "type": "string" }, "projectId": { "description": "The ID of the project (for apps from leaderboard)", "type": "string" }, "sourceId": { "description": "The ID of the source", "type": "string" }, "sourceType": { "description": "Whether the app is from a deployment or a project", "enum": [ "deployment", "model" ], "type": "string" } }, "required": [ "sourceId", "sourceType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | experimentContainerId | string | false | | The ID of the experiment container (for apps from an experiment container | | projectId | string | false | | The ID of the project (for apps from leaderboard) | | sourceId | string | true | | The ID of the source | | sourceType | string | true | | Whether the app is from a deployment or a project | ### Enumerated Values | Property | Value | | --- | --- | | sourceType | [deployment, model] | ## ApplicationCreate ``` { "properties": { "applicationTypeId": { "description": "The ID of the of application to be created.", "type": "string" }, "authenticationType": { "default": "invitedUsersOnly", "description": "Authentication type", "enum": [ "invitedUsersOnly", "token" ], "type": [ "string", "null" ] }, "cloudProvider": { "default": "drcloud", "description": "The optional cloud provider", "enum": [ "drcloud", "heroku" ], "type": "string" }, "description": { "description": "The description of the application", "maxLength": 512, "type": [ "string", "null" ] }, "experimentContainerId": { "description": "[DEPRECATED - replaced with use_case_id] The ID of the experiment container associated with the application.", "type": "string" }, "modelDeploymentId": { "description": "The ID of the model deployment. The deployed application will use this deployment to make predictions.", "type": "string" }, "name": { "description": "The name of the app", "maxLength": 512, "minLength": 1, "type": "string" }, "purpose": { "description": "An optional field to describe the purpose of the application.", "type": [ "string", "null" ] }, "sources": { "description": "The sources for this application", "items": { "properties": { "info": { "description": "Information about the Deployment or the Model", "oneOf": [ { "properties": { "modelDeploymentId": { "description": "The ID of the model deployment", "type": "string" } }, "required": [ "modelDeploymentId" ], "type": "object" }, { "properties": { "modelId": { "description": "The ID of the model", "type": "string" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions", "maximum": 1, "minimum": 0, "type": "number" }, "projectId": { "description": "The ID of the project", "type": "string" } }, "required": [ "modelId", "projectId" ], "type": "object" } ] }, "name": { "description": "The name of this source.", "type": "string" }, "source": { "description": "Information about the source for this application.", "enum": [ "deployment", "model" ], "type": "string" } }, "required": [ "info", "source" ], "type": "object" }, "type": "array" }, "useCaseId": { "description": "The ID of the Use Case associated with the application.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | applicationTypeId | string | false | | The ID of the of application to be created. | | authenticationType | string,null | false | | Authentication type | | cloudProvider | string | false | | The optional cloud provider | | description | string,null | false | maxLength: 512 | The description of the application | | experimentContainerId | string | false | | [DEPRECATED - replaced with use_case_id] The ID of the experiment container associated with the application. | | modelDeploymentId | string | false | | The ID of the model deployment. The deployed application will use this deployment to make predictions. | | name | string | false | maxLength: 512minLength: 1minLength: 1 | The name of the app | | purpose | string,null | false | | An optional field to describe the purpose of the application. | | sources | [ApplicationCreateSources] | false | | The sources for this application | | useCaseId | string | false | | The ID of the Use Case associated with the application. | ### Enumerated Values | Property | Value | | --- | --- | | authenticationType | [invitedUsersOnly, token] | | cloudProvider | [drcloud, heroku] | ## ApplicationCreateSources ``` { "properties": { "info": { "description": "Information about the Deployment or the Model", "oneOf": [ { "properties": { "modelDeploymentId": { "description": "The ID of the model deployment", "type": "string" } }, "required": [ "modelDeploymentId" ], "type": "object" }, { "properties": { "modelId": { "description": "The ID of the model", "type": "string" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions", "maximum": 1, "minimum": 0, "type": "number" }, "projectId": { "description": "The ID of the project", "type": "string" } }, "required": [ "modelId", "projectId" ], "type": "object" } ] }, "name": { "description": "The name of this source.", "type": "string" }, "source": { "description": "Information about the source for this application.", "enum": [ "deployment", "model" ], "type": "string" } }, "required": [ "info", "source" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | info | any | true | | Information about the Deployment or the Model | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ApplicationDeploymentSource | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ApplicationModelSource | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | | The name of this source. | | source | string | true | | Information about the source for this application. | ### Enumerated Values | Property | Value | | --- | --- | | source | [deployment, model] | ## ApplicationDeployment ``` { "properties": { "deploymentId": { "description": "The ID of the deployment", "type": "string" }, "referenceName": { "description": "The reference name of the deployment", "type": "string" } }, "required": [ "deploymentId", "referenceName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the deployment | | referenceName | string | true | | The reference name of the deployment | ## ApplicationDeploymentSource ``` { "properties": { "modelDeploymentId": { "description": "The ID of the model deployment", "type": "string" } }, "required": [ "modelDeploymentId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelDeploymentId | string | true | | The ID of the model deployment | ## ApplicationDuplicate ``` { "properties": { "authenticationType": { "default": "invitedUsersOnly", "description": "Authentication type", "enum": [ "invitedUsersOnly", "token" ], "type": [ "string", "null" ] }, "description": { "description": "The description of the application", "maxLength": 512, "type": [ "string", "null" ] }, "duplicatePredictions": { "default": false, "description": "Import all predictions from the source application", "type": "boolean" }, "name": { "description": "The name of the app", "maxLength": 512, "minLength": 1, "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authenticationType | string,null | false | | Authentication type | | description | string,null | false | maxLength: 512 | The description of the application | | duplicatePredictions | boolean | false | | Import all predictions from the source application | | name | string | false | maxLength: 512minLength: 1minLength: 1 | The name of the app | ### Enumerated Values | Property | Value | | --- | --- | | authenticationType | [invitedUsersOnly, token] | ## ApplicationList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of Application objects", "items": { "properties": { "applicationTemplateType": { "description": "Application template type, purpose", "type": [ "string", "null" ] }, "applicationTypeId": { "description": "The ID of the type of the application", "type": "string" }, "cloudProvider": { "description": "The host of this application", "type": "string" }, "createdAt": { "description": "The timestamp when the application was created", "type": "string" }, "createdBy": { "description": "The username of who created the application.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "Application creator first name", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Application creator last name", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "Application creator userhash", "type": [ "string", "null" ] }, "datasets": { "description": "The list of datasets IDs associated with the application", "items": { "type": "string" }, "type": "array" }, "deactivationStatusId": { "description": "The ID of the status object to track the asynchronous app deactivation process status. Will be null if the app was never deactivated.", "type": [ "string", "null" ] }, "deploymentIds": { "description": "A list of deployment IDs for this app", "items": { "description": "The ID of one deployment", "type": "string" }, "type": "array" }, "deploymentName": { "description": "Name of the deployment", "type": [ "string", "null" ] }, "deploymentStatusId": { "description": " The ID of the status object to track the asynchronous deployment process status", "type": "string" }, "deployments": { "description": "A list of deployment details", "items": { "properties": { "deploymentId": { "description": "The ID of the deployment", "type": "string" }, "referenceName": { "description": "The reference name of the deployment", "type": "string" } }, "required": [ "deploymentId", "referenceName" ], "type": "object" }, "type": "array" }, "description": { "description": "A description of the application.", "type": "string" }, "hasCustomLogo": { "description": "Whether the app has a custom logo", "type": "boolean" }, "id": { "description": "The ID of the created application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the associated model deployment", "type": "string" }, "name": { "description": "The name of the application", "type": "string" }, "orgId": { "description": "ID of the app's organization", "type": "string" }, "orgName": { "description": "Name of the app's organization", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_DELETE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "type": "array" }, "poolUsed": { "description": "Whether the pool where used for last app deployment", "type": "boolean" }, "realtimePredictionsSupport": { "description": "Sets whether you can do realtime predictions in the app.", "type": "boolean", "x-versionadded": "v2.34" }, "relatedEntities": { "description": "IDs of entities, related to app for easy search", "properties": { "isFromExperimentContainer": { "description": "[DEPRECATED - replaced with is_from_use_case] Whether the app was created from an experiment container", "type": "boolean" }, "isFromUseCase": { "description": "Whether the app was created from an Use Case", "type": "boolean" }, "isTrialOrganization": { "description": "Whether the app was created from by trial customer", "type": "boolean" }, "modelId": { "description": "The ID of the associated model", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": "string" } }, "type": "object" }, "updatedAt": { "description": "The timestamp when the application was updated", "type": "string" }, "userId": { "description": "The ID of the user which created the application", "type": "string" } }, "required": [ "applicationTemplateType", "applicationTypeId", "cloudProvider", "createdAt", "createdBy", "creatorFirstName", "creatorLastName", "creatorUserhash", "datasets", "deactivationStatusId", "deploymentIds", "deploymentName", "deploymentStatusId", "deployments", "hasCustomLogo", "id", "modelDeploymentId", "name", "orgId", "permissions", "poolUsed", "realtimePredictionsSupport", "updatedAt", "userId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [Application] | true | | An array of Application objects | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## ApplicationModelDeploymentParam ``` { "properties": { "applicationId": { "description": "The ID of the application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the model deployment", "type": "string" } }, "required": [ "applicationId", "modelDeploymentId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | applicationId | string | true | | The ID of the application | | modelDeploymentId | string | true | | The ID of the model deployment | ## ApplicationModelSource ``` { "properties": { "modelId": { "description": "The ID of the model", "type": "string" }, "predictionThreshold": { "description": "Threshold used for binary classification in predictions", "maximum": 1, "minimum": 0, "type": "number" }, "projectId": { "description": "The ID of the project", "type": "string" } }, "required": [ "modelId", "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | The ID of the model | | predictionThreshold | number | false | maximum: 1minimum: 0 | Threshold used for binary classification in predictions | | projectId | string | true | | The ID of the project | ## ApplicationNameAndDescription ``` { "properties": { "description": { "description": "The description of the application", "maxLength": 512, "type": [ "string", "null" ] }, "name": { "description": "The name of the app", "maxLength": 512, "minLength": 1, "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | maxLength: 512 | The description of the application | | name | string | false | maxLength: 512minLength: 1minLength: 1 | The name of the app | ## ApplicationParam ``` { "properties": { "applicationId": { "description": "The ID of the application", "type": "string" } }, "required": [ "applicationId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | applicationId | string | true | | The ID of the application | ## ApplicationRelatedEntities ``` { "description": "IDs of entities, related to app for easy search", "properties": { "isFromExperimentContainer": { "description": "[DEPRECATED - replaced with is_from_use_case] Whether the app was created from an experiment container", "type": "boolean" }, "isFromUseCase": { "description": "Whether the app was created from an Use Case", "type": "boolean" }, "isTrialOrganization": { "description": "Whether the app was created from by trial customer", "type": "boolean" }, "modelId": { "description": "The ID of the associated model", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": "string" } }, "type": "object" } ``` IDs of entities, related to app for easy search ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isFromExperimentContainer | boolean | false | | [DEPRECATED - replaced with is_from_use_case] Whether the app was created from an experiment container | | isFromUseCase | boolean | false | | Whether the app was created from an Use Case | | isTrialOrganization | boolean | false | | Whether the app was created from by trial customer | | modelId | string | false | | The ID of the associated model | | projectId | string | false | | The ID of the associated project | ## ApplicationRetrieve ``` { "properties": { "applicationTemplateType": { "description": "Application template type, purpose", "type": [ "string", "null" ] }, "applicationTypeId": { "description": "The ID of the type of the application", "type": "string" }, "cloudProvider": { "description": "The host of this application", "type": "string" }, "createdAt": { "description": "The timestamp when the application was created", "type": "string" }, "createdBy": { "description": "The username of who created the application.", "type": [ "string", "null" ] }, "creatorFirstName": { "description": "Application creator first name", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Application creator last name", "type": [ "string", "null" ] }, "creatorUserhash": { "description": "Application creator userhash", "type": [ "string", "null" ] }, "datasets": { "description": "The list of datasets IDs associated with the application", "items": { "type": "string" }, "type": "array" }, "deactivationStatusId": { "description": "The ID of the status object to track the asynchronous app deactivation process status. Will be null if the app was never deactivated.", "type": [ "string", "null" ] }, "deploymentIds": { "description": "A list of deployment IDs for this app", "items": { "description": "The ID of one deployment", "type": "string" }, "type": "array" }, "deploymentName": { "description": "Name of the deployment", "type": [ "string", "null" ] }, "deploymentStatusId": { "description": " The ID of the status object to track the asynchronous deployment process status", "type": "string" }, "deployments": { "description": "A list of deployment details", "items": { "properties": { "deploymentId": { "description": "The ID of the deployment", "type": "string" }, "referenceName": { "description": "The reference name of the deployment", "type": "string" } }, "required": [ "deploymentId", "referenceName" ], "type": "object" }, "type": "array" }, "description": { "description": "A description of the application.", "type": "string" }, "hasCustomLogo": { "description": "Whether the app has a custom logo", "type": "boolean" }, "id": { "description": "The ID of the created application", "type": "string" }, "modelDeploymentId": { "description": "The ID of the model deployment. The deployed application will use this deployment to make predictions.", "type": [ "string", "null" ] }, "name": { "description": "The name of the application", "type": "string" }, "orgId": { "description": "ID of the app's organization", "type": "string" }, "orgName": { "description": "Name of the app's organization", "type": [ "string", "null" ] }, "permissions": { "description": "The list of permitted actions, which the authenticated user can perform on this application.", "items": { "enum": [ "CAN_DELETE", "CAN_SHARE", "CAN_UPDATE", "CAN_VIEW" ], "type": "string" }, "type": "array" }, "poolUsed": { "description": "Whether the pool where used for last app deployment", "type": "boolean" }, "realtimePredictionsSupport": { "description": "Sets whether you can do realtime predictions in the app.", "type": "boolean", "x-versionadded": "v2.34" }, "relatedEntities": { "description": "IDs of entities, related to app for easy search", "properties": { "isFromExperimentContainer": { "description": "[DEPRECATED - replaced with is_from_use_case] Whether the app was created from an experiment container", "type": "boolean" }, "isFromUseCase": { "description": "Whether the app was created from an Use Case", "type": "boolean" }, "isTrialOrganization": { "description": "Whether the app was created from by trial customer", "type": "boolean" }, "modelId": { "description": "The ID of the associated model", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": "string" } }, "type": "object" }, "updatedAt": { "description": "The timestamp when the application was updated", "type": "string" }, "userId": { "description": "The ID of the user which created the application", "type": "string" } }, "required": [ "applicationTemplateType", "applicationTypeId", "cloudProvider", "createdAt", "createdBy", "creatorFirstName", "creatorLastName", "creatorUserhash", "datasets", "deactivationStatusId", "deploymentIds", "deploymentName", "deploymentStatusId", "deployments", "hasCustomLogo", "id", "name", "orgId", "permissions", "poolUsed", "realtimePredictionsSupport", "updatedAt", "userId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | applicationTemplateType | string,null | true | | Application template type, purpose | | applicationTypeId | string | true | | The ID of the type of the application | | cloudProvider | string | true | | The host of this application | | createdAt | string | true | | The timestamp when the application was created | | createdBy | string,null | true | | The username of who created the application. | | creatorFirstName | string,null | true | | Application creator first name | | creatorLastName | string,null | true | | Application creator last name | | creatorUserhash | string,null | true | | Application creator userhash | | datasets | [string] | true | | The list of datasets IDs associated with the application | | deactivationStatusId | string,null | true | | The ID of the status object to track the asynchronous app deactivation process status. Will be null if the app was never deactivated. | | deploymentIds | [string] | true | | A list of deployment IDs for this app | | deploymentName | string,null | true | | Name of the deployment | | deploymentStatusId | string | true | | The ID of the status object to track the asynchronous deployment process status | | deployments | [ApplicationDeployment] | true | | A list of deployment details | | description | string | false | | A description of the application. | | hasCustomLogo | boolean | true | | Whether the app has a custom logo | | id | string | true | | The ID of the created application | | modelDeploymentId | string,null | false | | The ID of the model deployment. The deployed application will use this deployment to make predictions. | | name | string | true | | The name of the application | | orgId | string | true | | ID of the app's organization | | orgName | string,null | false | | Name of the app's organization | | permissions | [string] | true | | The list of permitted actions, which the authenticated user can perform on this application. | | poolUsed | boolean | true | | Whether the pool where used for last app deployment | | realtimePredictionsSupport | boolean | true | | Sets whether you can do realtime predictions in the app. | | relatedEntities | ApplicationRelatedEntities | false | | IDs of entities, related to app for easy search | | updatedAt | string | true | | The timestamp when the application was updated | | userId | string | true | | The ID of the user which created the application | ## ApplicationSharedRolesEntry ``` { "properties": { "id": { "description": "The id of the recipient", "type": "string" }, "name": { "description": "The name of the user, group, or organization", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "USER", "CONSUMER", "EDITOR" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The id of the recipient | | name | string | true | | The name of the user, group, or organization | | role | string | true | | The assigned role | | shareRecipientType | string | true | | The recipient type | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, USER, CONSUMER, EDITOR] | | shareRecipientType | [user, group, organization] | ## ApplicationSharedRolesEntryUpdate ``` { "properties": { "id": { "description": "The id of the recipient", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "USER", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The id of the recipient | | role | string | true | | The assigned role | | shareRecipientType | string | true | | The recipient type | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, USER, CONSUMER, NO_ROLE] | | shareRecipientType | [user, group, organization] | ## ApplicationSharedRolesList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Details about the Shared Role entries", "items": { "properties": { "id": { "description": "The id of the recipient", "type": "string" }, "name": { "description": "The name of the user, group, or organization", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "USER", "CONSUMER", "EDITOR" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "Number of items matching to the query condition", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ApplicationSharedRolesEntry] | true | | Details about the Shared Role entries | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | Number of items matching to the query condition | ## ApplicationSharingUpdateOrRemove ``` { "properties": { "note": { "default": "", "description": "A note to go with the project share", "type": "string" }, "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" }, "sendNotification": { "default": false, "description": "Send a notification?", "type": "boolean" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | note | string | false | | A note to go with the project share | | operation | string | true | | Name of the action being taken. The only operation is 'updateRoles'. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | Array of GrantAccessControl objects., up to maximum 100 objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithUsername | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithId | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sendNotification | boolean | false | | Send a notification? | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## ApplicationUserRoleResponse ``` { "properties": { "role": { "description": "The role of the user on this entity.", "type": "string" } }, "required": [ "role" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the user on this entity. | ## CodeSnippetCreate ``` { "properties": { "config": { "description": "Template type specific configuration used to generate a snippet or notebook.", "oneOf": [ { "properties": { "modelId": { "description": "The selected model ID.", "type": "string" }, "projectId": { "description": "The selected project ID.", "type": "string" }, "showSecrets": { "default": "False", "description": "If true, the DATAROBOT_KEY and DATAROBOT_API_KEY will be available in the context.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "modelId", "projectId" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "cliScript": { "default": true, "description": "When combined with is_standalone, a true value returns an example CLI run script for a snippet, while a false value returns an example executable script.", "type": "boolean", "x-versionadded": "v2.35" }, "deploymentId": { "description": "The selected deployment ID.", "type": "string" }, "isBatchPrediction": { "default": true, "description": "If true, returns snippet that can be used to make batch predictions. Not valid with time series projects.", "type": "boolean" }, "isLowLatencyPrediction": { "default": false, "description": "If true, returns snippet that can be used to make low latency predictions.Valid for Feature Discovery projects.", "type": "boolean" }, "isStandalone": { "default": false, "description": "If true, returns an example script for a snippet.", "type": "boolean" }, "showSecrets": { "default": false, "description": "If true, the DATAROBOT_KEY AND DATROBOT_API_KEY will be available in the context.", "type": "boolean" }, "testMode": { "default": false, "description": "Generate a snippet with mocked information.", "type": "boolean" }, "withApiClient": { "default": true, "description": "Instead of raw Python code in the example, show a snippet using the DataRobot Python API client.", "type": "boolean" } }, "required": [ "deploymentId" ], "type": "object", "x-versionadded": "v2.35" }, { "properties": { "showSecrets": { "default": "False", "description": "If true, the DATAROBOT_KEY and DATAROBOT_API_KEY will be available in the context.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "workloadId": { "description": "The selected workload ID.", "type": "string" } }, "required": [ "workloadId" ], "type": "object", "x-versionadded": "v2.41" } ] }, "language": { "description": "The selected language the generated snippet or notebook should be written in.", "enum": [ "curl", "powershell", "python", "qlik" ], "type": "string" }, "snippetId": { "description": "The selected snippet to be returned to the user. This field is optional for Prediction snippets.", "maxLength": 255, "minLength": 1, "type": "string" }, "templateType": { "description": "The selected template type the generated snippet or notebook should be for (i.e. dataset, model, etc.).", "enum": [ "model", "prediction", "workload" ], "type": "string" } }, "required": [ "config", "language", "templateType" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | config | any | true | | Template type specific configuration used to generate a snippet or notebook. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ModelSnippetModel | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PredictionSnippetModel | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | WorkloadSnippet | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | language | string | true | | The selected language the generated snippet or notebook should be written in. | | snippetId | string | false | maxLength: 255minLength: 1minLength: 1 | The selected snippet to be returned to the user. This field is optional for Prediction snippets. | | templateType | string | true | | The selected template type the generated snippet or notebook should be for (i.e. dataset, model, etc.). | ### Enumerated Values | Property | Value | | --- | --- | | language | [curl, powershell, python, qlik] | | templateType | [model, prediction, workload] | ## CodeSnippetItem ``` { "properties": { "description": { "description": "The descriptive text to be displayed in the UI.", "maxLength": 255, "minLength": 1, "type": "string" }, "snippetId": { "description": "The ID of this snippet.", "maxLength": 255, "minLength": 1, "type": "string" }, "templating": { "description": "A list of templating variables that will be used in the snippet.", "items": { "type": "string" }, "maxItems": 255, "type": "array" }, "title": { "description": "The title of the snippet.", "maxLength": 255, "minLength": 1, "type": "string" } }, "required": [ "description", "snippetId", "templating", "title" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | maxLength: 255minLength: 1minLength: 1 | The descriptive text to be displayed in the UI. | | snippetId | string | true | maxLength: 255minLength: 1minLength: 1 | The ID of this snippet. | | templating | [string] | true | maxItems: 255 | A list of templating variables that will be used in the snippet. | | title | string | true | maxLength: 255minLength: 1minLength: 1 | The title of the snippet. | ## CodeSnippetListResponse ``` { "properties": { "codeSnippets": { "description": "A list of the available snippets for a given language and template type.", "items": { "properties": { "description": { "description": "The descriptive text to be displayed in the UI.", "maxLength": 255, "minLength": 1, "type": "string" }, "snippetId": { "description": "The ID of this snippet.", "maxLength": 255, "minLength": 1, "type": "string" }, "templating": { "description": "A list of templating variables that will be used in the snippet.", "items": { "type": "string" }, "maxItems": 255, "type": "array" }, "title": { "description": "The title of the snippet.", "maxLength": 255, "minLength": 1, "type": "string" } }, "required": [ "description", "snippetId", "templating", "title" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 22, "type": "array" } }, "required": [ "codeSnippets" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | codeSnippets | [CodeSnippetItem] | true | maxItems: 22 | A list of the available snippets for a given language and template type. | ## CodeSnippetResponse ``` { "properties": { "codeSnippet": { "description": "A UTF-8 encoded code snippet generated for the user.", "type": "string" }, "snippetId": { "description": "The selected snippet to be returned to the user.", "type": "string" } }, "required": [ "codeSnippet", "snippetId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | codeSnippet | string | true | | A UTF-8 encoded code snippet generated for the user. | | snippetId | string | true | | The selected snippet to be returned to the user. | ## GrantAccessControlWithId ``` { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## GrantAccessControlWithUsername ``` { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | Username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## ModelSnippetModel ``` { "properties": { "modelId": { "description": "The selected model ID.", "type": "string" }, "projectId": { "description": "The selected project ID.", "type": "string" }, "showSecrets": { "default": "False", "description": "If true, the DATAROBOT_KEY and DATAROBOT_API_KEY will be available in the context.", "enum": [ "false", "False", "true", "True" ], "type": "string" } }, "required": [ "modelId", "projectId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | The selected model ID. | | projectId | string | true | | The selected project ID. | | showSecrets | string | false | | If true, the DATAROBOT_KEY and DATAROBOT_API_KEY will be available in the context. | ### Enumerated Values | Property | Value | | --- | --- | | showSecrets | [false, False, true, True] | ## PredictionSnippetModel ``` { "properties": { "cliScript": { "default": true, "description": "When combined with is_standalone, a true value returns an example CLI run script for a snippet, while a false value returns an example executable script.", "type": "boolean", "x-versionadded": "v2.35" }, "deploymentId": { "description": "The selected deployment ID.", "type": "string" }, "isBatchPrediction": { "default": true, "description": "If true, returns snippet that can be used to make batch predictions. Not valid with time series projects.", "type": "boolean" }, "isLowLatencyPrediction": { "default": false, "description": "If true, returns snippet that can be used to make low latency predictions.Valid for Feature Discovery projects.", "type": "boolean" }, "isStandalone": { "default": false, "description": "If true, returns an example script for a snippet.", "type": "boolean" }, "showSecrets": { "default": false, "description": "If true, the DATAROBOT_KEY AND DATROBOT_API_KEY will be available in the context.", "type": "boolean" }, "testMode": { "default": false, "description": "Generate a snippet with mocked information.", "type": "boolean" }, "withApiClient": { "default": true, "description": "Instead of raw Python code in the example, show a snippet using the DataRobot Python API client.", "type": "boolean" } }, "required": [ "deploymentId" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cliScript | boolean | false | | When combined with is_standalone, a true value returns an example CLI run script for a snippet, while a false value returns an example executable script. | | deploymentId | string | true | | The selected deployment ID. | | isBatchPrediction | boolean | false | | If true, returns snippet that can be used to make batch predictions. Not valid with time series projects. | | isLowLatencyPrediction | boolean | false | | If true, returns snippet that can be used to make low latency predictions.Valid for Feature Discovery projects. | | isStandalone | boolean | false | | If true, returns an example script for a snippet. | | showSecrets | boolean | false | | If true, the DATAROBOT_KEY AND DATROBOT_API_KEY will be available in the context. | | testMode | boolean | false | | Generate a snippet with mocked information. | | withApiClient | boolean | false | | Instead of raw Python code in the example, show a snippet using the DataRobot Python API client. | ## WorkloadSnippet ``` { "properties": { "showSecrets": { "default": "False", "description": "If true, the DATAROBOT_KEY and DATAROBOT_API_KEY will be available in the context.", "enum": [ "false", "False", "true", "True" ], "type": "string" }, "workloadId": { "description": "The selected workload ID.", "type": "string" } }, "required": [ "workloadId" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | showSecrets | string | false | | If true, the DATAROBOT_KEY and DATAROBOT_API_KEY will be available in the context. | | workloadId | string | true | | The selected workload ID. | ### Enumerated Values | Property | Value | | --- | --- | | showSecrets | [false, False, true, True] | --- # Notebooks URL: https://docs.datarobot.com/en/docs/api/reference/public-api/notebooks.html > Endpoints for notebooks which offer an in-browser editor to create and execute code for data science analysis and modeling. # Notebooks Endpoints for notebooks which offer an in-browser editor to create and execute code for data science analysis and modeling. ## Retrieve Notebook Code Snippets Operation path: `GET /api/v2/notebookCodeSnippets/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ListSnippetsQuerySchema | query | ListSnippetsQuerySchema | false | Query schema for searching code nuggets. | ### Example responses > 200 Response ``` { "description": "Response schema for the list of code snippets.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "A list of code snippets.", "items": { "description": "CodeSnippet represents a code snippet.", "properties": { "code": { "description": "The code snippet code content.", "title": "Code", "type": "string" }, "description": { "description": "The description of the code snippet.", "title": "Description", "type": "string" }, "id": { "description": "The ID of the code snippet.", "title": "Id", "type": "string" }, "language": { "description": "The programming language of the code snippet.", "title": "Language", "type": "string" }, "languageVersion": { "description": "The programming language version of the code snippet.", "title": "Languageversion", "type": "string" }, "locale": { "description": "The language locale of the code snippet.", "title": "Locale", "type": "string" }, "tags": { "description": "A comma separated list of snippet tags.", "title": "Tags", "type": "string" }, "title": { "description": "The title of the code snippet.", "title": "Title", "type": "string" } }, "required": [ "id", "locale", "title", "description", "code", "language", "languageVersion" ], "title": "CodeSnippet", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ListCodeSnippetsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for the list of code snippets. | ListCodeSnippetsResponse | ## Retrieve Tags Operation path: `GET /api/v2/notebookCodeSnippets/tags/` Authentication requirements: `BearerAuth` ### Example responses > 200 Response ``` { "description": "Response schema for the list of code snippets tags.", "properties": { "tags": { "description": "A list of tags for the code snippets.", "items": { "type": "string" }, "title": "Tags", "type": "array" } }, "title": "ListCodeSnippetsTagsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for the list of code snippets tags. | ListCodeSnippetsTagsResponse | ## Retrieve Notebook Code Snippets by snippet ID Operation path: `GET /api/v2/notebookCodeSnippets/{snippetId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | snippetId | path | string | true | The ID of a code snippet to retrieve. | ### Example responses > 200 Response ``` { "description": "CodeSnippet represents a code snippet.", "properties": { "code": { "description": "The code snippet code content.", "title": "Code", "type": "string" }, "description": { "description": "The description of the code snippet.", "title": "Description", "type": "string" }, "id": { "description": "The ID of the code snippet.", "title": "Id", "type": "string" }, "language": { "description": "The programming language of the code snippet.", "title": "Language", "type": "string" }, "languageVersion": { "description": "The programming language version of the code snippet.", "title": "Languageversion", "type": "string" }, "locale": { "description": "The language locale of the code snippet.", "title": "Locale", "type": "string" }, "tags": { "description": "A comma separated list of snippet tags.", "title": "Tags", "type": "string" }, "title": { "description": "The title of the code snippet.", "title": "Title", "type": "string" } }, "required": [ "id", "locale", "title", "description", "code", "language", "languageVersion" ], "title": "CodeSnippet", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | CodeSnippet represents a code snippet. | CodeSnippet | ## Delete Notebook Environment Variables by notebook ID Operation path: `DELETE /api/v2/notebookEnvironmentVariables/{notebookId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to delete all environment variables for. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Retrieve Notebook Environment Variables by notebook ID Operation path: `GET /api/v2/notebookEnvironmentVariables/{notebookId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to list environment variables for. | ### Example responses > 200 Response ``` { "description": "List environment variables response schema.", "properties": { "data": { "description": "List of environment variables", "items": { "description": "Environment variable schema.", "properties": { "assignedResourceId": { "description": "The resource ID to which the environment variable is assigned.", "title": "Assignedresourceid", "type": "string" }, "assignedResourceType": { "allOf": [ { "description": "An enumeration.", "enum": [ "notebook" ], "title": "EnvironmentVariableResourceType", "type": "string" } ], "default": "notebook", "description": "The resource type to which the environment variable is assigned." }, "createdAt": { "description": "The date and time when the environment variable was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "createdBy": { "description": "The ID of the user who created the environment variable.", "title": "Createdby", "type": "string" }, "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "id": { "description": "The environment variable ID.", "title": "Id", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "title": "Name", "type": "string" }, "updatedAt": { "description": "The date and time when the environment variable was last updated.", "format": "date-time", "title": "Updatedat", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated the environment variable.", "title": "Updatedby", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value", "id", "assignedResourceId", "createdBy", "createdAt" ], "title": "EnvironmentVariableSchema", "type": "object" }, "title": "Data", "type": "array" } }, "title": "EnvironmentVariablesResponseSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List environment variables response schema. | EnvironmentVariablesResponseSchema | ## Create Notebook Environment Variables by notebook ID Operation path: `POST /api/v2/notebookEnvironmentVariables/{notebookId}/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Create environment variables request schema.", "properties": { "data": { "description": "List of environment variables to create.", "items": { "description": "Schema for updating environment variables.", "properties": { "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "pattern": "^[a-zA-Z_$][\\w$]*$", "title": "Name", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "NewEnvironmentVariableSchema", "type": "object" }, "title": "Data", "type": "array" } }, "title": "CreateEnvironmentVariablesRequestSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to create environment variables for. | | body | body | CreateEnvironmentVariablesRequestSchema | false | none | ### Example responses > 201 Response ``` { "description": "List environment variables response schema.", "properties": { "data": { "description": "List of environment variables", "items": { "description": "Environment variable schema.", "properties": { "assignedResourceId": { "description": "The resource ID to which the environment variable is assigned.", "title": "Assignedresourceid", "type": "string" }, "assignedResourceType": { "allOf": [ { "description": "An enumeration.", "enum": [ "notebook" ], "title": "EnvironmentVariableResourceType", "type": "string" } ], "default": "notebook", "description": "The resource type to which the environment variable is assigned." }, "createdAt": { "description": "The date and time when the environment variable was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "createdBy": { "description": "The ID of the user who created the environment variable.", "title": "Createdby", "type": "string" }, "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "id": { "description": "The environment variable ID.", "title": "Id", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "title": "Name", "type": "string" }, "updatedAt": { "description": "The date and time when the environment variable was last updated.", "format": "date-time", "title": "Updatedat", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated the environment variable.", "title": "Updatedby", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value", "id", "assignedResourceId", "createdBy", "createdAt" ], "title": "EnvironmentVariableSchema", "type": "object" }, "title": "Data", "type": "array" } }, "title": "EnvironmentVariablesResponseSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | List environment variables response schema. | EnvironmentVariablesResponseSchema | ## Delete notebook environment variables by id Operation path: `DELETE /api/v2/notebookEnvironmentVariables/{notebookId}/{envVarId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID that the environment variable belongs to. | | envVarId | path | string | true | The ID of the environment variable to delete. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Modify Notebook Environment Variables by notebook ID Operation path: `PATCH /api/v2/notebookEnvironmentVariables/{notebookId}/{envVarId}/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Schema for updating environment variables.", "properties": { "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "pattern": "^[a-zA-Z_$][\\w$]*$", "title": "Name", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "NewEnvironmentVariableSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID that the environment variable belongs to. | | envVarId | path | string | true | The ID of the environment variable to update. | | body | body | NewEnvironmentVariableSchema | false | none | ### Example responses > 200 Response ``` { "description": "Environment variable schema.", "properties": { "assignedResourceId": { "description": "The resource ID to which the environment variable is assigned.", "title": "Assignedresourceid", "type": "string" }, "assignedResourceType": { "allOf": [ { "description": "An enumeration.", "enum": [ "notebook" ], "title": "EnvironmentVariableResourceType", "type": "string" } ], "default": "notebook", "description": "The resource type to which the environment variable is assigned." }, "createdAt": { "description": "The date and time when the environment variable was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "createdBy": { "description": "The ID of the user who created the environment variable.", "title": "Createdby", "type": "string" }, "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "id": { "description": "The environment variable ID.", "title": "Id", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "title": "Name", "type": "string" }, "updatedAt": { "description": "The date and time when the environment variable was last updated.", "format": "date-time", "title": "Updatedat", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated the environment variable.", "title": "Updatedby", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value", "id", "assignedResourceId", "createdBy", "createdAt" ], "title": "EnvironmentVariableSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Environment variable schema. | EnvironmentVariableSchema | ## Retrieve Notebook Execution Environments Operation path: `GET /api/v2/notebookExecutionEnvironments/` Authentication requirements: `BearerAuth` ### Example responses > 200 Response ``` { "description": "Response schema for listing execution environments.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of execution environments.", "items": { "properties": { "created": { "description": "The time the environment was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Execution environment description.", "title": "Description", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the environment is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Execution environment ID.", "title": "Id", "type": "string" }, "isBuiltIn": { "default": false, "description": "Whether the environment is a built-in environment supplied by DataRobot.", "title": "Isbuiltin", "type": "boolean" }, "isDownloadable": { "description": "Whether the environment is downloadable.", "title": "Isdownloadable", "type": "boolean" }, "isPublic": { "description": "Whether the environment is public.", "title": "Ispublic", "type": "boolean" }, "latestSuccessfulVersion": { "description": "Execution environment version schema.", "properties": { "buildStatus": { "description": "Build status of the environment version. Possible values: 'success', 'failed'.", "title": "Buildstatus", "type": "string" }, "created": { "description": "The time the environment version was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Description of the environment version.", "title": "Description", "type": "string" }, "dockerContext": { "description": "URL for downloading the Docker context of the environment version.", "title": "Dockercontext", "type": "string" }, "dockerContextSize": { "description": "Size of the Docker context in bytes.", "title": "Dockercontextsize", "type": "integer" }, "dockerImage": { "description": "URL for downloading the Docker image of the environment version.", "title": "Dockerimage", "type": "string" }, "dockerImageSize": { "description": "Size of the Docker image in bytes.", "title": "Dockerimagesize", "type": "integer" }, "environmentId": { "description": "Execution environment ID.", "title": "Environmentid", "type": "string" }, "id": { "description": "Execution environment version ID.", "title": "Id", "type": "string" }, "imageId": { "description": "Image ID of the environment version.", "title": "Imageid", "type": "string" }, "label": { "description": "Label of the environment version.", "title": "Label", "type": "string" }, "libraries": { "description": "List of libraries in the environment version.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "sourceDockerImageUri": { "description": "The URI that the image environment version is based on. Basing off of the image URI is different from docker context or uploaded image-based environment versions.", "title": "Sourcedockerimageuri", "type": "string" } }, "required": [ "id", "environmentId", "buildStatus", "created" ], "title": "ExecutionEnvironmentVersionSchema", "type": "object" }, "latestVersion": { "description": "Execution environment version schema.", "properties": { "buildStatus": { "description": "Build status of the environment version. Possible values: 'success', 'failed'.", "title": "Buildstatus", "type": "string" }, "created": { "description": "The time the environment version was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Description of the environment version.", "title": "Description", "type": "string" }, "dockerContext": { "description": "URL for downloading the Docker context of the environment version.", "title": "Dockercontext", "type": "string" }, "dockerContextSize": { "description": "Size of the Docker context in bytes.", "title": "Dockercontextsize", "type": "integer" }, "dockerImage": { "description": "URL for downloading the Docker image of the environment version.", "title": "Dockerimage", "type": "string" }, "dockerImageSize": { "description": "Size of the Docker image in bytes.", "title": "Dockerimagesize", "type": "integer" }, "environmentId": { "description": "Execution environment ID.", "title": "Environmentid", "type": "string" }, "id": { "description": "Execution environment version ID.", "title": "Id", "type": "string" }, "imageId": { "description": "Image ID of the environment version.", "title": "Imageid", "type": "string" }, "label": { "description": "Label of the environment version.", "title": "Label", "type": "string" }, "libraries": { "description": "List of libraries in the environment version.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "sourceDockerImageUri": { "description": "The URI that the image environment version is based on. Basing off of the image URI is different from docker context or uploaded image-based environment versions.", "title": "Sourcedockerimageuri", "type": "string" } }, "required": [ "id", "environmentId", "buildStatus", "created" ], "title": "ExecutionEnvironmentVersionSchema", "type": "object" }, "name": { "description": "Execution environment name.", "title": "Name", "type": "string" }, "programmingLanguage": { "description": "Programming language of the environment.", "title": "Programminglanguage", "type": "string" }, "requiredMetadataKeys": { "description": "Required metadata keys.", "items": { "description": "Define additional parameters required to assemble a model. Model versions using this environment must define values\nfor each fieldName in the requiredMetadata.", "properties": { "displayName": { "description": "A human readable name for the required field.", "title": "Displayname", "type": "string" }, "fieldName": { "description": "The required field key. This value is added as an environment variable when running custom models.", "title": "Fieldname", "type": "string" } }, "required": [ "fieldName", "displayName" ], "title": "RequiredMetadataKey", "type": "object" }, "title": "Requiredmetadatakeys", "type": "array" }, "useCases": { "description": "List of use cases for the environment. This includes 'notebooks', at a minimum .", "items": { "type": "string" }, "title": "Usecases", "type": "array" }, "username": { "description": "The username of the user who created the environment.", "title": "Username", "type": "string" } }, "required": [ "id", "isDownloadable", "isPublic", "name", "programmingLanguage", "created", "latestVersion", "latestSuccessfulVersion", "username" ], "title": "ExecutionEnvironmentSchema", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ExecutionEnvironmentsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for listing execution environments. | ExecutionEnvironmentsResponse | ## Retrieve Machines Operation path: `GET /api/v2/notebookExecutionEnvironments/machines/` Authentication requirements: `BearerAuth` Returns all available environment machine options for which to run the notebook. ### Example responses > 200 Response ``` { "description": "Represents a list of machine types in the system.", "properties": { "machines": { "description": "List of machine types.", "items": { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" }, "title": "Machines", "type": "array" } }, "title": "MachinesPublic", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Represents a list of machine types in the system. | MachinesPublic | ## Retrieve Notebooks by environment ID Operation path: `GET /api/v2/notebookExecutionEnvironments/{environmentId}/notebooks/` Authentication requirements: `BearerAuth` Return list of notebooks that are using environment by environment ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | Environment ID to get notebooks for. | | ExecutionEnvironmentUsageByNotebooksQuery | query | ExecutionEnvironmentUsageByNotebooksQuery | false | Query parameters for listing notebooks using a specific execution environment. | ### Example responses > 200 Response ``` { "description": "Response schema for listing notebooks using a specific execution environment.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of notebooks using the environment.", "items": { "description": "Notebook using a specific execution environment.", "properties": { "createdAt": { "description": "The time the notebook was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "createdBy": { "description": "The username of the user who created the notebook.", "title": "Createdby", "type": "string" }, "environmentVersion": { "description": "The version of the environment used by the notebook.", "title": "Environmentversion", "type": "string" }, "notebookId": { "description": "Notebook ID.", "title": "Notebookid", "type": "string" }, "notebookLastSession": { "description": "The last time the notebook was started.", "format": "date-time", "title": "Notebooklastsession", "type": "string" }, "notebookName": { "description": "Notebook name.", "title": "Notebookname", "type": "string" }, "useCaseId": { "description": "The use case ID of the notebook.", "title": "Usecaseid", "type": "string" } }, "required": [ "notebookId", "notebookName", "createdAt", "environmentVersion" ], "title": "ExecutionEnvironmentUsageByNotebooks", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ExecutionEnvironmentUsageByNotebooksResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for listing notebooks using a specific execution environment. | ExecutionEnvironmentUsageByNotebooksResponse | ## List Notebook Execution Environments versions by environment ID Operation path: `GET /api/v2/notebookExecutionEnvironments/{environmentId}/versions/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | environmentId | path | string | true | Environment ID to get versions for. | ### Example responses > 200 Response ``` { "description": "Response schema for listing execution environment versions.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of environment versions.", "items": { "description": "Execution environment version schema.", "properties": { "buildStatus": { "description": "Build status of the environment version. Possible values: 'success', 'failed'.", "title": "Buildstatus", "type": "string" }, "created": { "description": "The time the environment version was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Description of the environment version.", "title": "Description", "type": "string" }, "dockerContext": { "description": "URL for downloading the Docker context of the environment version.", "title": "Dockercontext", "type": "string" }, "dockerContextSize": { "description": "Size of the Docker context in bytes.", "title": "Dockercontextsize", "type": "integer" }, "dockerImage": { "description": "URL for downloading the Docker image of the environment version.", "title": "Dockerimage", "type": "string" }, "dockerImageSize": { "description": "Size of the Docker image in bytes.", "title": "Dockerimagesize", "type": "integer" }, "environmentId": { "description": "Execution environment ID.", "title": "Environmentid", "type": "string" }, "id": { "description": "Execution environment version ID.", "title": "Id", "type": "string" }, "imageId": { "description": "Image ID of the environment version.", "title": "Imageid", "type": "string" }, "label": { "description": "Label of the environment version.", "title": "Label", "type": "string" }, "libraries": { "description": "List of libraries in the environment version.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "sourceDockerImageUri": { "description": "The URI that the image environment version is based on. Basing off of the image URI is different from docker context or uploaded image-based environment versions.", "title": "Sourcedockerimageuri", "type": "string" } }, "required": [ "id", "environmentId", "buildStatus", "created" ], "title": "ExecutionEnvironmentVersionSchema", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ExecutionEnvironmentVersionsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for listing execution environment versions. | ExecutionEnvironmentVersionsResponse | ## Retrieve Notebook Execution Environments by notebook ID Operation path: `GET /api/v2/notebookExecutionEnvironments/{notebookId}/` Authentication requirements: `BearerAuth` Return assigned execution environment as an EnvironmentPublic entity. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID to get execution environment for. | ### Example responses > 200 Response ``` { "description": "The public representation of an execution environment.", "properties": { "image": { "allOf": [ { "description": "This class is used to represent the public information of an image.", "properties": { "default": { "default": false, "description": "Whether the image is the default image.", "title": "Default", "type": "boolean" }, "description": { "description": "Image description.", "title": "Description", "type": "string" }, "environmentId": { "description": "Environment ID.", "title": "Environmentid", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the image is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Image ID.", "title": "Id", "type": "string" }, "label": { "description": "Image label.", "title": "Label", "type": "string" }, "language": { "description": "Image programming language.", "title": "Language", "type": "string" }, "languageVersion": { "description": "Image programming language version.", "title": "Languageversion", "type": "string" }, "libraries": { "description": "The preinstalled libraries in the image.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "name": { "description": "Image name.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "description", "language", "languageVersion" ], "title": "ImagePublic", "type": "object" } ], "description": "The image of the environment.", "title": "Image" }, "machine": { "allOf": [ { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" } ], "description": "The machine of the environment.", "title": "Machine" }, "timeToLive": { "description": "The inactivity timeout of the environment.", "title": "Timetolive", "type": "integer" } }, "required": [ "image", "machine" ], "title": "EnvironmentPublic", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The public representation of an execution environment. | EnvironmentPublic | ## Modify Notebook Execution Environments by notebook ID Operation path: `PATCH /api/v2/notebookExecutionEnvironments/{notebookId}/` Authentication requirements: `BearerAuth` Sets the environment configuration for the notebook. ### Body parameter ``` { "description": "Request schema for assigning an execution environment to a notebook.", "properties": { "environmentId": { "description": "The execution environment ID.", "title": "Environmentid", "type": "string" }, "environmentSlug": { "description": "The execution environment slug.", "title": "Environmentslug", "type": "string" }, "language": { "description": "The programming language of the environment.", "title": "Language", "type": "string" }, "languageVersion": { "description": "The programming language version.", "title": "Languageversion", "type": "string" }, "machineId": { "description": "The machine ID.", "title": "Machineid", "type": "string" }, "machineSlug": { "description": "The machine slug.", "title": "Machineslug", "type": "string" }, "timeToLive": { "description": "Inactivity timeout limit.", "maximum": 525600, "minimum": 3, "title": "Timetolive", "type": "integer" }, "versionId": { "description": "The execution environment version ID.", "title": "Versionid", "type": "string" } }, "title": "ExecutionEnvironmentAssignRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID to assign execution environment to. | | body | body | ExecutionEnvironmentAssignRequest | false | none | ### Example responses > 200 Response ``` { "description": "The public representation of an execution environment.", "properties": { "image": { "allOf": [ { "description": "This class is used to represent the public information of an image.", "properties": { "default": { "default": false, "description": "Whether the image is the default image.", "title": "Default", "type": "boolean" }, "description": { "description": "Image description.", "title": "Description", "type": "string" }, "environmentId": { "description": "Environment ID.", "title": "Environmentid", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the image is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Image ID.", "title": "Id", "type": "string" }, "label": { "description": "Image label.", "title": "Label", "type": "string" }, "language": { "description": "Image programming language.", "title": "Language", "type": "string" }, "languageVersion": { "description": "Image programming language version.", "title": "Languageversion", "type": "string" }, "libraries": { "description": "The preinstalled libraries in the image.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "name": { "description": "Image name.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "description", "language", "languageVersion" ], "title": "ImagePublic", "type": "object" } ], "description": "The image of the environment.", "title": "Image" }, "machine": { "allOf": [ { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" } ], "description": "The machine of the environment.", "title": "Machine" }, "timeToLive": { "description": "The inactivity timeout of the environment.", "title": "Timetolive", "type": "integer" } }, "required": [ "image", "machine" ], "title": "EnvironmentPublic", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The public representation of an execution environment. | EnvironmentPublic | ## Delete Ports by notebook ID Operation path: `DELETE /api/v2/notebookExecutionEnvironments/{notebookId}/ports/` Authentication requirements: `BearerAuth` Remove all exposed port for a given notebook. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID to delete all exposed ports for. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Retrieve Ports by notebook ID Operation path: `GET /api/v2/notebookExecutionEnvironments/{notebookId}/ports/` Authentication requirements: `BearerAuth` List all exposed ports for a given notebook. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID to get exposed ports for. | ### Example responses > 200 Response ``` { "description": "List of exposed ports for a notebook.", "properties": { "data": { "description": "List of exposed ports.", "items": { "description": "Exposed port schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "title": "Description", "type": "string" }, "id": { "description": "Exposed port ID.", "title": "Id", "type": "string" }, "notebookId": { "description": "Notebook ID the exposed port belongs to.", "title": "Notebookid", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" }, "url": { "description": "URL to access the exposed port.", "title": "Url", "type": "string" } }, "required": [ "id", "notebookId", "port", "url" ], "title": "ExposedPortSchema", "type": "object" }, "title": "Data", "type": "array" } }, "title": "ExposedPortListSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of exposed ports for a notebook. | ExposedPortListSchema | ## Create Ports by notebook ID Operation path: `POST /api/v2/notebookExecutionEnvironments/{notebookId}/ports/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Port creation schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "maxLength": 500, "title": "Description", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" } }, "required": [ "port" ], "title": "ExposePortSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID to create exposed port for. | | body | body | ExposePortSchema | false | none | ### Example responses > 201 Response ``` { "description": "Exposed port schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "title": "Description", "type": "string" }, "id": { "description": "Exposed port ID.", "title": "Id", "type": "string" }, "notebookId": { "description": "Notebook ID the exposed port belongs to.", "title": "Notebookid", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" }, "url": { "description": "URL to access the exposed port.", "title": "Url", "type": "string" } }, "required": [ "id", "notebookId", "port", "url" ], "title": "ExposedPortSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Exposed port schema for a notebook. | ExposedPortSchema | ## Delete ports by id Operation path: `DELETE /api/v2/notebookExecutionEnvironments/{notebookId}/ports/{portId}/` Authentication requirements: `BearerAuth` Remove previously exposed port for a given notebook. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID for exposed port that is being deleted. | | portId | path | string | true | Port ID that is being deleted. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Modify Ports by notebook ID Operation path: `PATCH /api/v2/notebookExecutionEnvironments/{notebookId}/ports/{portId}/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Port update schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "maxLength": 500, "title": "Description", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" } }, "title": "UpdateExposedPortSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID for exposed port that is being updated. | | portId | path | string | true | Port ID that is being updated. | | body | body | UpdateExposedPortSchema | false | none | ### Example responses > 200 Response ``` { "description": "Exposed port schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "title": "Description", "type": "string" }, "id": { "description": "Exposed port ID.", "title": "Id", "type": "string" }, "notebookId": { "description": "Notebook ID the exposed port belongs to.", "title": "Notebookid", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" }, "url": { "description": "URL to access the exposed port.", "title": "Url", "type": "string" } }, "required": [ "id", "notebookId", "port", "url" ], "title": "ExposedPortSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Exposed port schema for a notebook. | ExposedPortSchema | ## Retrieve Notebook jobs Operation path: `GET /api/v2/notebookJobs/` Authentication requirements: `BearerAuth` Get scheduled jobs list. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ListScheduledJobQuery | query | ListScheduledJobQuery | false | Query parameters for listing scheduled jobs. | ### Example responses > 200 Response ``` { "description": "Paginated response for notebook schedule jobs.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of notebook schedule jobs.", "items": { "description": "Notebook schedule definition.", "properties": { "createdBy": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who created the job.", "title": "Createdby" }, "enabled": { "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "id": { "description": "The ID of the scheduled job.", "title": "Id", "type": "string" }, "isNotebookRunning": { "default": false, "description": "Whether or not the notebook is currently running (including manual runs). This accounts for notebook_path in Codespaces.", "title": "Isnotebookrunning", "type": "boolean" }, "jobPayload": { "allOf": [ { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ], "description": "The payload for the job.", "title": "Jobpayload" }, "lastFailedRun": { "description": "Last failed run time of the job.", "format": "date-time", "title": "Lastfailedrun", "type": "string" }, "lastRunTime": { "description": "Calculated last run time (if it has run) by considering both failed and successful.", "format": "date-time", "title": "Lastruntime", "type": "string" }, "lastSuccessfulRun": { "description": "Last successful run time of the job.", "format": "date-time", "title": "Lastsuccessfulrun", "type": "string" }, "nextRunTime": { "description": "Next run time of the job.", "format": "date-time", "title": "Nextruntime", "type": "string" }, "notebook": { "allOf": [ { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ], "description": "Notebook metadata.", "title": "Notebook" }, "notebookHasEnabledSchedule": { "default": false, "description": "Whether or not the notebook for this schedule has an enabled schedule - includes other schedules.", "title": "Notebookhasenabledschedule", "type": "boolean" }, "notebookType": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "permissions": { "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "runType": { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" }, "schedule": { "description": "Cron-like string to define how frequently job should be run.", "title": "Schedule", "type": "string" }, "scheduleLocalized": { "description": "Human-readable string calculated from the cron string that is translated and localized.", "title": "Schedulelocalized", "type": "string" }, "title": { "description": "Human readable name for the job that a user can create and update.", "title": "Title", "type": "string" }, "userId": { "description": "The ID of the user who created the job.", "title": "Userid", "type": "string" } }, "required": [ "id", "enabled", "userId", "jobPayload" ], "title": "NotebookScheduleDefinition", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" }, "totalEnabledCount": { "description": "Total number of enabled schedule jobs.", "title": "Totalenabledcount", "type": "integer" } }, "required": [ "totalEnabledCount" ], "title": "NotebookScheduleJobsPaginated", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Paginated response for notebook schedule jobs. | NotebookScheduleJobsPaginated | ## Create Notebook jobs Operation path: `POST /api/v2/notebookJobs/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request to create a new scheduled job.", "properties": { "enabled": { "default": true, "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "notebookId": { "description": "The ID of the notebook to schedule.", "title": "Notebookid", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule for the job.", "title": "Schedule" }, "title": { "description": "The title of the scheduled job.", "maxLength": 100, "minLength": 1, "title": "Title", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" } }, "required": [ "useCaseId", "notebookId", "schedule" ], "title": "CreateScheduledJobRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateScheduledJobRequest | false | none | ### Example responses > 201 Response ``` { "description": "Notebook schedule definition.", "properties": { "createdBy": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who created the job.", "title": "Createdby" }, "enabled": { "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "id": { "description": "The ID of the scheduled job.", "title": "Id", "type": "string" }, "isNotebookRunning": { "default": false, "description": "Whether or not the notebook is currently running (including manual runs). This accounts for notebook_path in Codespaces.", "title": "Isnotebookrunning", "type": "boolean" }, "jobPayload": { "allOf": [ { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ], "description": "The payload for the job.", "title": "Jobpayload" }, "lastFailedRun": { "description": "Last failed run time of the job.", "format": "date-time", "title": "Lastfailedrun", "type": "string" }, "lastRunTime": { "description": "Calculated last run time (if it has run) by considering both failed and successful.", "format": "date-time", "title": "Lastruntime", "type": "string" }, "lastSuccessfulRun": { "description": "Last successful run time of the job.", "format": "date-time", "title": "Lastsuccessfulrun", "type": "string" }, "nextRunTime": { "description": "Next run time of the job.", "format": "date-time", "title": "Nextruntime", "type": "string" }, "notebook": { "allOf": [ { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ], "description": "Notebook metadata.", "title": "Notebook" }, "notebookHasEnabledSchedule": { "default": false, "description": "Whether or not the notebook for this schedule has an enabled schedule - includes other schedules.", "title": "Notebookhasenabledschedule", "type": "boolean" }, "notebookType": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "permissions": { "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "runType": { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" }, "schedule": { "description": "Cron-like string to define how frequently job should be run.", "title": "Schedule", "type": "string" }, "scheduleLocalized": { "description": "Human-readable string calculated from the cron string that is translated and localized.", "title": "Schedulelocalized", "type": "string" }, "title": { "description": "Human readable name for the job that a user can create and update.", "title": "Title", "type": "string" }, "userId": { "description": "The ID of the user who created the job.", "title": "Userid", "type": "string" } }, "required": [ "id", "enabled", "userId", "jobPayload" ], "title": "NotebookScheduleDefinition", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Notebook schedule definition. | NotebookScheduleDefinition | ## Create Manual Run Operation path: `POST /api/v2/notebookJobs/manualRun/` Authentication requirements: `BearerAuth` Create a manual run. ### Body parameter ``` { "description": "Request to create a manual run for a scheduled job.", "properties": { "manualRunType": { "allOf": [ { "description": "Intentionally a subset of RunTypes above - to be used in API schemas", "enum": [ "manual", "pipeline" ], "title": "ManualRunTypes", "type": "string" } ], "default": "manual", "description": "The type of manual run. Possible values are 'manual' or 'pipeline'." }, "notebookId": { "description": "The ID of the notebook to schedule.", "title": "Notebookid", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "title": { "description": "The title of the scheduled job.", "maxLength": 100, "minLength": 1, "title": "Title", "type": "string" } }, "required": [ "notebookId" ], "title": "CreateManualRunRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateManualRunRequest | false | none | ### Example responses > 201 Response ``` { "description": "Notebook schedule definition.", "properties": { "createdBy": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who created the job.", "title": "Createdby" }, "enabled": { "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "id": { "description": "The ID of the scheduled job.", "title": "Id", "type": "string" }, "isNotebookRunning": { "default": false, "description": "Whether or not the notebook is currently running (including manual runs). This accounts for notebook_path in Codespaces.", "title": "Isnotebookrunning", "type": "boolean" }, "jobPayload": { "allOf": [ { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ], "description": "The payload for the job.", "title": "Jobpayload" }, "lastFailedRun": { "description": "Last failed run time of the job.", "format": "date-time", "title": "Lastfailedrun", "type": "string" }, "lastRunTime": { "description": "Calculated last run time (if it has run) by considering both failed and successful.", "format": "date-time", "title": "Lastruntime", "type": "string" }, "lastSuccessfulRun": { "description": "Last successful run time of the job.", "format": "date-time", "title": "Lastsuccessfulrun", "type": "string" }, "nextRunTime": { "description": "Next run time of the job.", "format": "date-time", "title": "Nextruntime", "type": "string" }, "notebook": { "allOf": [ { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ], "description": "Notebook metadata.", "title": "Notebook" }, "notebookHasEnabledSchedule": { "default": false, "description": "Whether or not the notebook for this schedule has an enabled schedule - includes other schedules.", "title": "Notebookhasenabledschedule", "type": "boolean" }, "notebookType": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "permissions": { "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "runType": { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" }, "schedule": { "description": "Cron-like string to define how frequently job should be run.", "title": "Schedule", "type": "string" }, "scheduleLocalized": { "description": "Human-readable string calculated from the cron string that is translated and localized.", "title": "Schedulelocalized", "type": "string" }, "title": { "description": "Human readable name for the job that a user can create and update.", "title": "Title", "type": "string" }, "userId": { "description": "The ID of the user who created the job.", "title": "Userid", "type": "string" } }, "required": [ "id", "enabled", "userId", "jobPayload" ], "title": "NotebookScheduleDefinition", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Notebook schedule definition. | NotebookScheduleDefinition | ## Retrieve Run history Operation path: `GET /api/v2/notebookJobs/runHistory/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ListScheduledRunsHistoryQuery | query | ListScheduledRunsHistoryQuery | false | Query parameters for listing scheduled runs history. | ### Example responses > 200 Response ``` { "description": "Paginated response for notebook scheduled runs history.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of notebook scheduled runs history.", "items": { "description": "Notebook scheduled run.", "properties": { "createdAt": { "description": "The time the job was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "duration": { "description": "The duration of the job in seconds.", "title": "Duration", "type": "integer" }, "endTime": { "description": "The time the job ended.", "format": "date-time", "title": "Endtime", "type": "string" }, "endTimeTs": { "description": "The time the job ended.", "format": "date-time", "title": "Endtimets", "type": "string" }, "environment": { "allOf": [ { "description": "Supplemental metadata for an environment.", "properties": { "id": { "description": "The ID of the environment.", "title": "Id", "type": "string" }, "label": { "description": "The label of the environment.", "title": "Label", "type": "string" }, "name": { "description": "The name of the environment.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "label" ], "title": "EnvironmentSupplementalMetadata", "type": "object" } ], "description": "Environment metadata.", "title": "Environment" }, "id": { "description": "The ID of the job run.", "title": "Id", "type": "string" }, "jobAbortedTs": { "description": "The time the job was aborted.", "format": "date-time", "title": "Jobabortedts", "type": "string" }, "jobCompletedTs": { "description": "The time the job completed.", "format": "date-time", "title": "Jobcompletedts", "type": "string" }, "jobCreatedBy": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who created the job.", "title": "Jobcreatedby" }, "jobErrorTs": { "description": "The time the job errored.", "format": "date-time", "title": "Joberrorts", "type": "string" }, "jobStartedTs": { "description": "The time the job started.", "format": "date-time", "title": "Jobstartedts", "type": "string" }, "notebook": { "allOf": [ { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ], "description": "Notebook metadata.", "title": "Notebook" }, "notebookDeleted": { "default": false, "description": "Whether the notebook was deleted.", "title": "Notebookdeleted", "type": "boolean" }, "notebookEnvironmentId": { "description": "The ID of the notebook environment.", "title": "Notebookenvironmentid", "type": "string" }, "notebookEnvironmentImageId": { "description": "The ID of the notebook environment image.", "title": "Notebookenvironmentimageid", "type": "string" }, "notebookEnvironmentLabel": { "description": "The label of the notebook environment.", "title": "Notebookenvironmentlabel", "type": "string" }, "notebookEnvironmentName": { "description": "The name of the notebook environment.", "title": "Notebookenvironmentname", "type": "string" }, "notebookHadErrors": { "default": false, "description": "Whether the notebook had errors.", "title": "Notebookhaderrors", "type": "boolean" }, "notebookId": { "description": "The ID of the notebook associated with the run.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the run.", "title": "Notebookname", "type": "string" }, "notebookRevisionId": { "description": "The ID of the notebook revision.", "title": "Notebookrevisionid", "type": "string" }, "notebookRevisionName": { "description": "The name of the notebook revision.", "title": "Notebookrevisionname", "type": "string" }, "notebookRunType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "payload": { "allOf": [ { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ], "description": "The payload for the job.", "title": "Payload" }, "revision": { "allOf": [ { "description": "Supplemental metadata for a revision.", "properties": { "id": { "description": "The ID of the revision.", "title": "Id", "type": "string" }, "name": { "description": "The name of the revision.", "title": "Name", "type": "string" } }, "required": [ "id", "name" ], "title": "RevisionSupplementalMetadata", "type": "object" } ], "description": "Revision metadata.", "title": "Revision" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "scheduledJobId": { "description": "The ID of the scheduled job.", "title": "Scheduledjobid", "type": "string" }, "startTime": { "description": "The time the job started.", "format": "date-time", "title": "Starttime", "type": "string" }, "status": { "description": "The status of the job.", "title": "Status", "type": "string" }, "title": { "description": "The name of the schedule.", "title": "Title", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" }, "userId": { "description": "The ID of the user who created the job.", "title": "Userid", "type": "string" } }, "required": [ "id", "createdAt", "useCaseId", "userId", "orgId", "notebookId", "scheduledJobId", "title", "status", "payload", "notebookName", "notebookRunType" ], "title": "NotebookScheduledRun", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "NotebookScheduledRunsHistoryPaginated", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Paginated response for notebook scheduled runs history. | NotebookScheduledRunsHistoryPaginated | ## Delete Notebook jobs by notebook schedule ID Operation path: `DELETE /api/v2/notebookJobs/{notebookScheduleId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookScheduleId | path | string | true | Notebook Schedule ID to delete. | | DeleteScheduledJobQuery | query | DeleteScheduledJobQuery | false | Query parameters for deleting a scheduled job. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Retrieve Notebook jobs by notebook schedule ID Operation path: `GET /api/v2/notebookJobs/{notebookScheduleId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookScheduleId | path | string | true | Notebook Schedule ID to fetch. | | ScheduledJobQuery | query | ScheduledJobQuery | false | Query parameters for a scheduled job. | ### Example responses > 200 Response ``` { "description": "Notebook schedule definition.", "properties": { "createdBy": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who created the job.", "title": "Createdby" }, "enabled": { "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "id": { "description": "The ID of the scheduled job.", "title": "Id", "type": "string" }, "isNotebookRunning": { "default": false, "description": "Whether or not the notebook is currently running (including manual runs). This accounts for notebook_path in Codespaces.", "title": "Isnotebookrunning", "type": "boolean" }, "jobPayload": { "allOf": [ { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ], "description": "The payload for the job.", "title": "Jobpayload" }, "lastFailedRun": { "description": "Last failed run time of the job.", "format": "date-time", "title": "Lastfailedrun", "type": "string" }, "lastRunTime": { "description": "Calculated last run time (if it has run) by considering both failed and successful.", "format": "date-time", "title": "Lastruntime", "type": "string" }, "lastSuccessfulRun": { "description": "Last successful run time of the job.", "format": "date-time", "title": "Lastsuccessfulrun", "type": "string" }, "nextRunTime": { "description": "Next run time of the job.", "format": "date-time", "title": "Nextruntime", "type": "string" }, "notebook": { "allOf": [ { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ], "description": "Notebook metadata.", "title": "Notebook" }, "notebookHasEnabledSchedule": { "default": false, "description": "Whether or not the notebook for this schedule has an enabled schedule - includes other schedules.", "title": "Notebookhasenabledschedule", "type": "boolean" }, "notebookType": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "permissions": { "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "runType": { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" }, "schedule": { "description": "Cron-like string to define how frequently job should be run.", "title": "Schedule", "type": "string" }, "scheduleLocalized": { "description": "Human-readable string calculated from the cron string that is translated and localized.", "title": "Schedulelocalized", "type": "string" }, "title": { "description": "Human readable name for the job that a user can create and update.", "title": "Title", "type": "string" }, "userId": { "description": "The ID of the user who created the job.", "title": "Userid", "type": "string" } }, "required": [ "id", "enabled", "userId", "jobPayload" ], "title": "NotebookScheduleDefinition", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Notebook schedule definition. | NotebookScheduleDefinition | ## Modify Notebook jobs by notebook schedule ID Operation path: `PATCH /api/v2/notebookJobs/{notebookScheduleId}/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request to update an existing scheduled job.", "properties": { "enabled": { "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule for the job.", "title": "Schedule" }, "title": { "description": "The title of the scheduled job.", "maxLength": 100, "minLength": 1, "title": "Title", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" } }, "required": [ "useCaseId" ], "title": "UpdateScheduledJobRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookScheduleId | path | string | true | Notebook Schedule ID to update. | | body | body | UpdateScheduledJobRequest | false | none | ### Example responses > 200 Response ``` { "description": "Notebook schedule definition.", "properties": { "createdBy": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who created the job.", "title": "Createdby" }, "enabled": { "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "id": { "description": "The ID of the scheduled job.", "title": "Id", "type": "string" }, "isNotebookRunning": { "default": false, "description": "Whether or not the notebook is currently running (including manual runs). This accounts for notebook_path in Codespaces.", "title": "Isnotebookrunning", "type": "boolean" }, "jobPayload": { "allOf": [ { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ], "description": "The payload for the job.", "title": "Jobpayload" }, "lastFailedRun": { "description": "Last failed run time of the job.", "format": "date-time", "title": "Lastfailedrun", "type": "string" }, "lastRunTime": { "description": "Calculated last run time (if it has run) by considering both failed and successful.", "format": "date-time", "title": "Lastruntime", "type": "string" }, "lastSuccessfulRun": { "description": "Last successful run time of the job.", "format": "date-time", "title": "Lastsuccessfulrun", "type": "string" }, "nextRunTime": { "description": "Next run time of the job.", "format": "date-time", "title": "Nextruntime", "type": "string" }, "notebook": { "allOf": [ { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ], "description": "Notebook metadata.", "title": "Notebook" }, "notebookHasEnabledSchedule": { "default": false, "description": "Whether or not the notebook for this schedule has an enabled schedule - includes other schedules.", "title": "Notebookhasenabledschedule", "type": "boolean" }, "notebookType": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "permissions": { "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "runType": { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" }, "schedule": { "description": "Cron-like string to define how frequently job should be run.", "title": "Schedule", "type": "string" }, "scheduleLocalized": { "description": "Human-readable string calculated from the cron string that is translated and localized.", "title": "Schedulelocalized", "type": "string" }, "title": { "description": "Human readable name for the job that a user can create and update.", "title": "Title", "type": "string" }, "userId": { "description": "The ID of the user who created the job.", "title": "Userid", "type": "string" } }, "required": [ "id", "enabled", "userId", "jobPayload" ], "title": "NotebookScheduleDefinition", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Notebook schedule definition. | NotebookScheduleDefinition | ## Cancel Notebook jobs by notebook schedule ID Operation path: `POST /api/v2/notebookJobs/{notebookScheduleId}/cancel/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookScheduleId | path | string | true | Notebook Schedule ID to cancel running jobs for. | | CancelScheduledJobsQuery | query | CancelScheduledJobsQuery | false | Query parameters for canceling scheduled jobs. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Create Clone by notebook ID Operation path: `POST /api/v2/notebookRevisions/fromRevision/{notebookId}/{revisionId}/clone/` Authentication requirements: `BearerAuth` Clone a notebook from a revision. ### Body parameter ``` { "description": "Request to clone a notebook from a revision.", "properties": { "isAuto": { "default": false, "description": "Whether the revision is autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Notebook revision name.", "minLength": 1, "title": "Name", "type": "string" }, "notebookPath": { "description": "Path to the notebook file, if using Codespaces.", "title": "Notebookpath", "type": "string" } }, "title": "CloneNotebookFromRevisionRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID associated with revision. | | revisionId | path | string | true | Revision ID to clone as notebook. | | body | body | CloneNotebookFromRevisionRequest | false | none | ### Example responses > 201 Response ``` { "description": "Response schema for cloning a notebook from a revision.", "properties": { "notebookId": { "description": "Newly cloned notebook ID.", "title": "Notebookid", "type": "string" } }, "required": [ "notebookId" ], "title": "ClonedNotebookResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Response schema for cloning a notebook from a revision. | ClonedNotebookResponse | ## Create Restore by notebook ID Operation path: `POST /api/v2/notebookRevisions/fromRevision/{notebookId}/{revisionId}/restore/` Authentication requirements: `BearerAuth` Restore a notebook from a revision. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID associated with revision. | | revisionId | path | string | true | Revision ID to restore notebook contents to. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | No content. | None | ## Delete Notebook Revisions by notebook ID Operation path: `DELETE /api/v2/notebookRevisions/{notebookId}/` Authentication requirements: `BearerAuth` Delete all revisions for the given notebook. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID to delete revisions for. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Retrieve Notebook Revisions by notebook ID Operation path: `GET /api/v2/notebookRevisions/{notebookId}/` Authentication requirements: `BearerAuth` List all revisions for the given notebook. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID to list revisions for. | | ListNotebookRevisionsQuerySchema | query | ListNotebookRevisionsQuerySchema | false | Query schema for listing notebook revisions. | ### Example responses > 200 Response ``` { "description": "Response schema for listing notebook revisions.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of notebook revisions.", "items": { "description": "Notebook revision schema.", "properties": { "created": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision creation action information.", "title": "Created" }, "isAuto": { "description": "Whether the revision was autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Revision name.", "title": "Name", "type": "string" }, "notebookId": { "description": "Notebook ID this revision belongs to.", "title": "Notebookid", "type": "string" }, "revisionId": { "description": "Revision ID.", "title": "Revisionid", "type": "string" }, "updated": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision update action information.", "title": "Updated" } }, "required": [ "revisionId", "notebookId", "name", "isAuto", "created" ], "title": "NotebookRevisionSchema", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ListNotebookRevisionsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for listing notebook revisions. | ListNotebookRevisionsResponse | ## Create Notebook Revisions by notebook ID Operation path: `POST /api/v2/notebookRevisions/{notebookId}/` Authentication requirements: `BearerAuth` Create a new revision from the given notebook's current state. ### Body parameter ``` { "description": "Request to create a new notebook revision.", "properties": { "isAuto": { "default": false, "description": "Whether the revision is autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Notebook revision name.", "minLength": 1, "title": "Name", "type": "string" }, "notebookPath": { "description": "Path to the notebook file, if using Codespaces.", "title": "Notebookpath", "type": "string" } }, "title": "CreateNotebookRevisionRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID to create a revision for. | | body | body | CreateNotebookRevisionRequest | false | none | ### Example responses > 201 Response ``` { "description": "Notebook revision schema.", "properties": { "created": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision creation action information.", "title": "Created" }, "isAuto": { "description": "Whether the revision was autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Revision name.", "title": "Name", "type": "string" }, "notebookId": { "description": "Notebook ID this revision belongs to.", "title": "Notebookid", "type": "string" }, "revisionId": { "description": "Revision ID.", "title": "Revisionid", "type": "string" }, "updated": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision update action information.", "title": "Updated" } }, "required": [ "revisionId", "notebookId", "name", "isAuto", "created" ], "title": "NotebookRevisionSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Notebook revision schema. | NotebookRevisionSchema | ## Delete notebook revisions by id Operation path: `DELETE /api/v2/notebookRevisions/{notebookId}/{revisionId}/` Authentication requirements: `BearerAuth` Delete single revision. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID associated with revision. | | revisionId | path | string | true | Revision ID to delete. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Retrieve notebook revisions by id Operation path: `GET /api/v2/notebookRevisions/{notebookId}/{revisionId}/` Authentication requirements: `BearerAuth` Get a specific revision for the given notebook. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID associated with revision. | | revisionId | path | string | true | Revision ID to fetch. | ### Example responses > 200 Response ``` { "description": "Versioned notebook schema.", "properties": { "created": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision creation action information.", "title": "Created" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "isAuto": { "description": "Whether the revision was autosaved.", "title": "Isauto", "type": "boolean" }, "lastViewed": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision last viewed action information.", "title": "Lastviewed" }, "name": { "description": "Revision name.", "title": "Name", "type": "string" }, "orgId": { "description": "Organization ID.", "title": "Orgid", "type": "string" }, "permissions": { "description": "Notebook permissions.", "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "revisionId": { "description": "Revision ID.", "title": "Revisionid", "type": "string" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "Notebook settings.", "title": "Settings" }, "tags": { "description": "Notebook tags.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "updated": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision update action information.", "title": "Updated" }, "useCaseId": { "description": "Use case ID.", "title": "Usecaseid", "type": "string" } }, "required": [ "id", "revisionId", "name", "isAuto", "created", "lastViewed" ], "title": "VersionedNotebookSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Versioned notebook schema. | VersionedNotebookSchema | ## Modify Notebook Revisions by notebook ID Operation path: `PATCH /api/v2/notebookRevisions/{notebookId}/{revisionId}/` Authentication requirements: `BearerAuth` Update a revision's name. ### Body parameter ``` { "description": "Request", "properties": { "isAuto": { "default": false, "description": "Whether the revision is autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Notebook revision name.", "minLength": 1, "title": "Name", "type": "string" }, "notebookPath": { "description": "Path to the notebook file, if using Codespaces.", "title": "Notebookpath", "type": "string" } }, "title": "UpdateNotebookRevisionRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID associated with revision. | | revisionId | path | string | true | Revision ID to update. | | body | body | UpdateNotebookRevisionRequest | false | none | ### Example responses > 200 Response ``` { "description": "Notebook revision schema.", "properties": { "created": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision creation action information.", "title": "Created" }, "isAuto": { "description": "Whether the revision was autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Revision name.", "title": "Name", "type": "string" }, "notebookId": { "description": "Notebook ID this revision belongs to.", "title": "Notebookid", "type": "string" }, "revisionId": { "description": "Revision ID.", "title": "Revisionid", "type": "string" }, "updated": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision update action information.", "title": "Updated" } }, "required": [ "revisionId", "notebookId", "name", "isAuto", "created" ], "title": "NotebookRevisionSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Notebook revision schema. | NotebookRevisionSchema | ## Retrieve Cells by notebook ID Operation path: `GET /api/v2/notebookRevisions/{notebookId}/{revisionId}/cells/` Authentication requirements: `BearerAuth` Get cells for a specific revision version of the given notebook. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID associated with revision. | | revisionId | path | string | true | Revision ID to fetch cells for. | ### Example responses > 200 Response ``` { "items": { "description": "Schema for notebook cell.", "properties": { "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata" ], "title": "NotebookCellCommonSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook cell. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [NotebookCellCommonSchema] | false | | [Schema for notebook cell.] | | » NotebookCellCommonSchema | NotebookCellCommonSchema | false | | Schema for notebook cell. | | »» executed | NotebookTimestampInfo | false | | The timestamp of when the cell was executed. | | »»» at | string(date-time) | true | | Timestamp of the action. | | »»» by | UserInfo | true | | User info of the actor who caused the action to occur. | | »»»» activated | boolean | false | | Whether the user is activated. | | »»»» firstName | string | false | | The first name of the user. | | »»»» gravatarHash | string | false | | The gravatar hash of the user. | | »»»» id | string | true | | The ID of the user. | | »»»» lastName | string | false | | The last name of the user. | | »»»» orgId | string | false | | The ID of the organization the user belongs to. | | »»»» tenantPhase | string | false | | The tenant phase of the user. | | »»»» username | string | false | | The username of the user. | | »» executionCount | integer | false | | The execution count of the cell relative to other cells in the current session. | | »» executionTimeMillis | integer | false | | The execution time of the cell in milliseconds. | | »» metadata | NotebookCellMetadata | true | | The metadata of the cell. | | »»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» aggregation | string | false | | Aggregation function for the axis. | | »»»»»» color | string | false | | Color for the axis. | | »»»»»» hideGrid | boolean | false | | Whether to hide the grid lines on the axis. | | »»»»»» hideInTooltip | boolean | false | | Whether to hide the axis in the tooltip. | | »»»»»» hideLabel | boolean | false | | Whether to hide the axis label. | | »»»»»» key | string | false | | Key for the axis. | | »»»»»» label | string | false | | Label for the axis. | | »»»»»» position | string | false | | Position of the axis. | | »»»»»» showPointMarkers | boolean | false | | Whether to show point markers on the axis. | | »»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»» data | object | false | | The data associated with the cell chart. | | »»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»» chartType | string | false | | Type of the chart. | | »»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»» title | string | false | | Title of the chart. | | »»» collapsed | boolean | false | | Whether the cell's output is collapsed/expanded. | | »»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»» metricName | string | true | | The name of the custom LLM metric. | | »»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»» metricId | string | true | | The ID of the custom metric. | | »»»» metricName | string | false | | The name of the custom metric. | | »»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» month | [anyOf] | true | | The month(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» dataframeViewOptions | object | false | | Dataframe cell view options and metadata. | | »»» datarobot | NotebookCellDataRobotMetadata | false | | Metadata specific to DataRobot's notebooks and notebook environment. | | »»»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»» data | object | false | | The data associated with the cell chart. | | »»»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»»» chartType | string | false | | Type of the chart. | | »»»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»»» title | string | false | | Title of the chart. | | »»»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»»» metricName | string | true | | The name of the custom LLM metric. | | »»»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»»» metricId | string | true | | The ID of the custom metric. | | »»»»» metricName | string | false | | The name of the custom metric. | | »»»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | | »»»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | | »»»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | | »»»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | | »»»»»» month | [anyOf] | true | | The month(s) to run the schedule. | | »»»» dataframeViewOptions | object | false | | DataFrame view options and metadata. | | »»»» disableRun | boolean | false | | Whether to disable the run button in the cell. | | »»»» executionTimeMillis | integer | false | | Execution time of the cell in milliseconds. | | »»»» hideCode | boolean | false | | Whether to hide the code in the cell. | | »»»» hideResults | boolean | false | | Whether to hide the results in the cell. | | »»»» language | Language | false | | An enumeration. | | »»» disableRun | boolean | false | | Whether or not the cell is disabled in the UI. | | »»» hideCode | boolean | false | | Whether or not code is hidden in the UI. | | »»» hideResults | boolean | false | | Whether or not results are hidden in the UI. | | »»» jupyter | JupyterCellMetadata | false | | Jupyter metadata. | | »»»» outputsHidden | boolean | false | | Whether the cell's outputs are hidden. | | »»»» sourceHidden | boolean | false | | Whether the cell's source is hidden. | | »»» name | string | false | | Name of the notebook cell. | | »»» scrolled | any | false | | Whether the cell's output is scrolled, unscrolled, or autoscrolled. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» outputType | OutputStorageType | false | | The type of storage used for the cell output. | | »» outputs | [anyOf] | false | | The cell outputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellStreamOutput | false | | Cell stream output. | | »»»» name | string | true | | The name of the stream. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» text | any | true | | The text of the stream. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellInputRequest | false | | Cell input request. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» password | boolean | true | | Whether the input request is for a password. | | »»»» prompt | string | true | | The prompt for the input request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellErrorOutput | false | | Cell error output. | | »»»» ename | string | true | | The name of the error. | | »»»» evalue | string | true | | The value of the error. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» traceback | [string] | true | | The traceback of the error. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellExecuteResultOutput | false | | Cell execute results output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» executionCount | integer | false | | A result's prompt number. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellDisplayDataOutput | false | | Cell display data output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» source | string | true | | The contents of the cell, represented as a string. | ### Enumerated Values | Property | Value | | --- | --- | | language | [dataframe, markdown, python, r, shell, scala, sas, custommetric] | | anonymous | auto | | outputType | RAW_OUTPUT | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | ## Retrieve To File by notebook ID Operation path: `GET /api/v2/notebookRevisions/{notebookId}/{revisionId}/toFile/` Authentication requirements: `BearerAuth` Export Notebook Revision to file. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | Notebook ID associated with revision. | | revisionId | path | string | true | Revision ID to export as file. | ### Example responses > 200 Response ``` { "type": "string" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Export notebook revision to file JSON response. | string | ## Create And Start Codespace Operation path: `POST /api/v2/notebookRuntimes/notebooks/createAndStartCodespace/` Authentication requirements: `BearerAuth` Creates the Codespace and starts a session. ### Body parameter ``` { "description": "Schema for creating and starting a codespace.", "properties": { "cloneRepository": { "allOf": [ { "description": "Schema for cloning a repository.", "properties": { "checkoutRef": { "description": "The branch or commit to checkout.", "title": "Checkoutref", "type": "string" }, "url": { "description": "The URL of the repository to clone.", "title": "Url", "type": "string" } }, "required": [ "url" ], "title": "CloneRepositorySchema", "type": "object" } ], "description": "The repository to clone for the codespace.", "title": "Clonerepository" }, "description": { "description": "The description of the codespace.", "title": "Description", "type": "string" }, "environment": { "allOf": [ { "description": "Request schema for assigning an execution environment to a notebook.", "properties": { "environmentId": { "description": "The execution environment ID.", "title": "Environmentid", "type": "string" }, "environmentSlug": { "description": "The execution environment slug.", "title": "Environmentslug", "type": "string" }, "language": { "description": "The programming language of the environment.", "title": "Language", "type": "string" }, "languageVersion": { "description": "The programming language version.", "title": "Languageversion", "type": "string" }, "machineId": { "description": "The machine ID.", "title": "Machineid", "type": "string" }, "machineSlug": { "description": "The machine slug.", "title": "Machineslug", "type": "string" }, "timeToLive": { "description": "Inactivity timeout limit.", "maximum": 525600, "minimum": 3, "title": "Timetolive", "type": "integer" }, "versionId": { "description": "The execution environment version ID.", "title": "Versionid", "type": "string" } }, "title": "ExecutionEnvironmentAssignRequest", "type": "object" } ], "description": "The environment for the codespace.", "title": "Environment" }, "environmentVariables": { "description": "The environment variables for the codespace.", "items": { "description": "Schema for updating environment variables.", "properties": { "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "pattern": "^[a-zA-Z_$][\\w$]*$", "title": "Name", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "NewEnvironmentVariableSchema", "type": "object" }, "title": "Environmentvariables", "type": "array" }, "exposedPorts": { "description": "The exposed ports for the codespace.", "items": { "description": "Port creation schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "maxLength": 500, "title": "Description", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" } }, "required": [ "port" ], "title": "ExposePortSchema", "type": "object" }, "title": "Exposedports", "type": "array" }, "name": { "default": "Untitled Codespace", "description": "The name of the codespace.", "title": "Name", "type": "string" }, "openFilePaths": { "description": "The file paths to open in the codespace.", "items": { "type": "string" }, "title": "Openfilepaths", "type": "array" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "description": "The settings for the codespace.", "title": "Settings" }, "tags": { "description": "The tags associated with the codespace.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "useCaseId": { "description": "The ID of the use case associated with the codespace.", "title": "Usecaseid", "type": "string" } }, "required": [ "useCaseId" ], "title": "CreateAndStartCodespaceSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateAndStartCodespaceSchema | false | none | ### Example responses > 200 Response ``` { "description": "The schema for the notebook session.", "properties": { "environment": { "allOf": [ { "description": "The public representation of an execution environment.", "properties": { "image": { "allOf": [ { "description": "This class is used to represent the public information of an image.", "properties": { "default": { "default": false, "description": "Whether the image is the default image.", "title": "Default", "type": "boolean" }, "description": { "description": "Image description.", "title": "Description", "type": "string" }, "environmentId": { "description": "Environment ID.", "title": "Environmentid", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the image is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Image ID.", "title": "Id", "type": "string" }, "label": { "description": "Image label.", "title": "Label", "type": "string" }, "language": { "description": "Image programming language.", "title": "Language", "type": "string" }, "languageVersion": { "description": "Image programming language version.", "title": "Languageversion", "type": "string" }, "libraries": { "description": "The preinstalled libraries in the image.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "name": { "description": "Image name.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "description", "language", "languageVersion" ], "title": "ImagePublic", "type": "object" } ], "description": "The image of the environment.", "title": "Image" }, "machine": { "allOf": [ { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" } ], "description": "The machine of the environment.", "title": "Machine" }, "timeToLive": { "description": "The inactivity timeout of the environment.", "title": "Timetolive", "type": "integer" } }, "required": [ "image", "machine" ], "title": "EnvironmentPublic", "type": "object" } ], "description": "The environment of the notebook session.", "title": "Environment" }, "ephemeralSessionKey": { "allOf": [ { "description": "Key for an ephemeral session.", "properties": { "entityId": { "description": "The ID of the entity.", "title": "Entityid", "type": "string" }, "entityType": { "allOf": [ { "description": "Types of entities that can be associated with an ephemeral session.", "enum": [ "CUSTOM_APP", "CUSTOM_JOB", "CUSTOM_MODEL", "CUSTOM_METRIC", "CODE_SNIPPET" ], "title": "EphemeralSessionEntityType", "type": "string" } ], "description": "The type of the entity." } }, "required": [ "entityType", "entityId" ], "title": "EphemeralSessionKey", "type": "object" } ], "description": "The key of the ephemeral session. None if not an ephemeral session.", "title": "Ephemeralsessionkey" }, "executionCount": { "default": 0, "description": "The execution count of the notebook session.", "title": "Executioncount", "type": "integer" }, "machineStatus": { "allOf": [ { "description": "This enum represents possible overall state of the machine(s) of all components of the notebook.", "enum": [ "not_started", "allocated", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "MachineStatuses", "type": "string" } ], "description": "The status of the machine running the notebook session." }, "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "parameters": { "description": "Parameters to use as environment variables.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runnerStatus": { "allOf": [ { "description": "Runner is the same as kernel since it will manage multiple kernels in the future,\nWe can't consider kernel running if runner is not functioning and therefor this enum represents\npossible statuses of the kernel and runner sidecar functionality states.\nIn the future this will likely be renamed to kernel.", "enum": [ "not_started", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "RunnerStatuses", "type": "string" } ], "description": "The status of the runner for the notebook session." }, "sessionId": { "description": "The ID of the notebook session.", "title": "Sessionid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "default": "interactive", "description": "The type of the notebook session. Possible values are interactive and triggered." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "startedBy": { "allOf": [ { "description": "Schema for notebook user.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "permissions": { "allOf": [ { "description": "User feature flags.", "properties": { "DISABLE_CODESPACE_SCHEDULING": { "description": "Whether codespace scheduling is disabled for the user.", "title": "Disable Codespace Scheduling", "type": "boolean" }, "DISABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Disable Dummy Feature Flag For Testing", "type": "boolean" }, "DISABLE_NOTEBOOKS_CODESPACES": { "description": "Whether codespaces are disabled for the user.", "title": "Disable Notebooks Codespaces", "type": "boolean" }, "DISABLE_NOTEBOOKS_SCHEDULING": { "description": "Whether scheduling is disabled for the user.", "title": "Disable Notebooks Scheduling", "type": "boolean" }, "DISABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": false, "description": "Whether session port forwarding is disabled for the user.", "title": "Disable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Enable Dummy Feature Flag For Testing", "type": "boolean" }, "ENABLE_MMM_HOSTED_CUSTOM_METRICS": { "description": "Whether custom metrics are enabled for the user.", "title": "Enable Mmm Hosted Custom Metrics", "type": "boolean" }, "ENABLE_NOTEBOOKS": { "description": "Whether notebooks are enabled for the user.", "title": "Enable Notebooks", "type": "boolean" }, "ENABLE_NOTEBOOKS_CUSTOM_ENVIRONMENTS": { "default": true, "description": "Whether custom environments are enabled for the user.", "title": "Enable Notebooks Custom Environments", "type": "boolean" }, "ENABLE_NOTEBOOKS_FILESYSTEM_MANAGEMENT": { "description": "Whether filesystem management is enabled for the user.", "title": "Enable Notebooks Filesystem Management", "type": "boolean" }, "ENABLE_NOTEBOOKS_GPU": { "description": "Whether GPU is enabled for the user.", "title": "Enable Notebooks Gpu", "type": "boolean" }, "ENABLE_NOTEBOOKS_OPEN_AI": { "description": "Whether OpenAI is enabled for the user.", "title": "Enable Notebooks Open Ai", "type": "boolean" }, "ENABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": true, "description": "Whether session port forwarding is enabled for the user.", "title": "Enable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_NOTEBOOKS_TERMINAL": { "description": "Whether terminals are enabled for the user.", "title": "Enable Notebooks Terminal", "type": "boolean" } }, "title": "UserFeatureFlags", "type": "object" } ], "description": "The feature flags of the user.", "title": "Permissions" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "NotebookUserSchema", "type": "object" } ], "description": "The user who started the notebook session.", "title": "Startedby" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" }, "withNetworkPolicy": { "description": "Whether the session is created with network policies.", "title": "Withnetworkpolicy", "type": "boolean" } }, "required": [ "status", "notebookId", "sessionId", "environment", "machineStatus", "runnerStatus" ], "title": "NotebookSessionSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the notebook session. | NotebookSessionSchema | ## Retrieve Notebooks by notebook ID Operation path: `GET /api/v2/notebookRuntimes/notebooks/{notebookId}/` Authentication requirements: `BearerAuth` Retrieves the notebook session or returns the one in the default state. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to get session data for. | ### Example responses > 200 Response ``` { "description": "The schema for the notebook session.", "properties": { "environment": { "allOf": [ { "description": "The public representation of an execution environment.", "properties": { "image": { "allOf": [ { "description": "This class is used to represent the public information of an image.", "properties": { "default": { "default": false, "description": "Whether the image is the default image.", "title": "Default", "type": "boolean" }, "description": { "description": "Image description.", "title": "Description", "type": "string" }, "environmentId": { "description": "Environment ID.", "title": "Environmentid", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the image is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Image ID.", "title": "Id", "type": "string" }, "label": { "description": "Image label.", "title": "Label", "type": "string" }, "language": { "description": "Image programming language.", "title": "Language", "type": "string" }, "languageVersion": { "description": "Image programming language version.", "title": "Languageversion", "type": "string" }, "libraries": { "description": "The preinstalled libraries in the image.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "name": { "description": "Image name.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "description", "language", "languageVersion" ], "title": "ImagePublic", "type": "object" } ], "description": "The image of the environment.", "title": "Image" }, "machine": { "allOf": [ { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" } ], "description": "The machine of the environment.", "title": "Machine" }, "timeToLive": { "description": "The inactivity timeout of the environment.", "title": "Timetolive", "type": "integer" } }, "required": [ "image", "machine" ], "title": "EnvironmentPublic", "type": "object" } ], "description": "The environment of the notebook session.", "title": "Environment" }, "ephemeralSessionKey": { "allOf": [ { "description": "Key for an ephemeral session.", "properties": { "entityId": { "description": "The ID of the entity.", "title": "Entityid", "type": "string" }, "entityType": { "allOf": [ { "description": "Types of entities that can be associated with an ephemeral session.", "enum": [ "CUSTOM_APP", "CUSTOM_JOB", "CUSTOM_MODEL", "CUSTOM_METRIC", "CODE_SNIPPET" ], "title": "EphemeralSessionEntityType", "type": "string" } ], "description": "The type of the entity." } }, "required": [ "entityType", "entityId" ], "title": "EphemeralSessionKey", "type": "object" } ], "description": "The key of the ephemeral session. None if not an ephemeral session.", "title": "Ephemeralsessionkey" }, "executionCount": { "default": 0, "description": "The execution count of the notebook session.", "title": "Executioncount", "type": "integer" }, "machineStatus": { "allOf": [ { "description": "This enum represents possible overall state of the machine(s) of all components of the notebook.", "enum": [ "not_started", "allocated", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "MachineStatuses", "type": "string" } ], "description": "The status of the machine running the notebook session." }, "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "parameters": { "description": "Parameters to use as environment variables.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runnerStatus": { "allOf": [ { "description": "Runner is the same as kernel since it will manage multiple kernels in the future,\nWe can't consider kernel running if runner is not functioning and therefor this enum represents\npossible statuses of the kernel and runner sidecar functionality states.\nIn the future this will likely be renamed to kernel.", "enum": [ "not_started", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "RunnerStatuses", "type": "string" } ], "description": "The status of the runner for the notebook session." }, "sessionId": { "description": "The ID of the notebook session.", "title": "Sessionid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "default": "interactive", "description": "The type of the notebook session. Possible values are interactive and triggered." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "startedBy": { "allOf": [ { "description": "Schema for notebook user.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "permissions": { "allOf": [ { "description": "User feature flags.", "properties": { "DISABLE_CODESPACE_SCHEDULING": { "description": "Whether codespace scheduling is disabled for the user.", "title": "Disable Codespace Scheduling", "type": "boolean" }, "DISABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Disable Dummy Feature Flag For Testing", "type": "boolean" }, "DISABLE_NOTEBOOKS_CODESPACES": { "description": "Whether codespaces are disabled for the user.", "title": "Disable Notebooks Codespaces", "type": "boolean" }, "DISABLE_NOTEBOOKS_SCHEDULING": { "description": "Whether scheduling is disabled for the user.", "title": "Disable Notebooks Scheduling", "type": "boolean" }, "DISABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": false, "description": "Whether session port forwarding is disabled for the user.", "title": "Disable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Enable Dummy Feature Flag For Testing", "type": "boolean" }, "ENABLE_MMM_HOSTED_CUSTOM_METRICS": { "description": "Whether custom metrics are enabled for the user.", "title": "Enable Mmm Hosted Custom Metrics", "type": "boolean" }, "ENABLE_NOTEBOOKS": { "description": "Whether notebooks are enabled for the user.", "title": "Enable Notebooks", "type": "boolean" }, "ENABLE_NOTEBOOKS_CUSTOM_ENVIRONMENTS": { "default": true, "description": "Whether custom environments are enabled for the user.", "title": "Enable Notebooks Custom Environments", "type": "boolean" }, "ENABLE_NOTEBOOKS_FILESYSTEM_MANAGEMENT": { "description": "Whether filesystem management is enabled for the user.", "title": "Enable Notebooks Filesystem Management", "type": "boolean" }, "ENABLE_NOTEBOOKS_GPU": { "description": "Whether GPU is enabled for the user.", "title": "Enable Notebooks Gpu", "type": "boolean" }, "ENABLE_NOTEBOOKS_OPEN_AI": { "description": "Whether OpenAI is enabled for the user.", "title": "Enable Notebooks Open Ai", "type": "boolean" }, "ENABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": true, "description": "Whether session port forwarding is enabled for the user.", "title": "Enable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_NOTEBOOKS_TERMINAL": { "description": "Whether terminals are enabled for the user.", "title": "Enable Notebooks Terminal", "type": "boolean" } }, "title": "UserFeatureFlags", "type": "object" } ], "description": "The feature flags of the user.", "title": "Permissions" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "NotebookUserSchema", "type": "object" } ], "description": "The user who started the notebook session.", "title": "Startedby" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" }, "withNetworkPolicy": { "description": "Whether the session is created with network policies.", "title": "Withnetworkpolicy", "type": "boolean" } }, "required": [ "status", "notebookId", "sessionId", "environment", "machineStatus", "runnerStatus" ], "title": "NotebookSessionSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the notebook session. | NotebookSessionSchema | ## Create Autocomplete by notebook ID Operation path: `POST /api/v2/notebookRuntimes/notebooks/{notebookId}/autocomplete/` Authentication requirements: `BearerAuth` Autocompletes code. ### Body parameter ``` { "description": "The schema for the code completion request.", "properties": { "code": { "description": "The code to complete.", "title": "Code", "type": "string" }, "cursorPosition": { "description": "The position of the cursor in the code.", "title": "Cursorposition", "type": "integer" } }, "required": [ "code", "cursorPosition" ], "title": "CodeCompletionSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to run autocomplete for. | | body | body | CodeCompletionSchema | false | none | ### Example responses > 200 Response ``` { "description": "The schema for the code completion result.", "properties": { "cursorEnd": { "description": "The end position of the cursor in the code.", "title": "Cursorend", "type": "integer" }, "cursorStart": { "description": "The start position of the cursor in the code.", "title": "Cursorstart", "type": "integer" }, "matches": { "description": "The list of code completions.", "items": { "type": "string" }, "title": "Matches", "type": "array" } }, "required": [ "matches", "cursorStart", "cursorEnd" ], "title": "CodeCompletionResultSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the code completion result. | CodeCompletionResultSchema | ## Cancel Notebooks by notebook ID Operation path: `POST /api/v2/notebookRuntimes/notebooks/{notebookId}/cancel/` Authentication requirements: `BearerAuth` Cancels execution. ### Body parameter ``` { "description": "Request schema for canceling the execution of cells in a notebook session.", "properties": { "cellIds": { "description": "List of cell IDs to cancel execution.", "items": { "type": "string" }, "title": "Cellids", "type": "array" } }, "title": "CancelExecutionRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to cancel execution for. | | body | body | CancelExecutionRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | No content. | None | ## Retrieve Dataframe by notebook ID Operation path: `GET /api/v2/notebookRuntimes/notebooks/{notebookId}/cells/{cellId}/dataframe/{referenceId}/` Authentication requirements: `BearerAuth` Gets a dataframe from a cell execution. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID the dataframe belongs to. | | cellId | path | string | true | The cell ID the dataframe belongs to. | | referenceId | path | integer | true | The reference ID for the dataframe. | | SortedPaginationQuerySchema | query | SortedPaginationQuerySchema | false | Schema for query parameters for paginated requests. | ### Example responses > 200 Response ``` { "description": "Response schema for getting a dataframe from a notebook session.", "properties": { "dataframe": { "description": "The requested dataframe in the notebook session.", "title": "Dataframe", "type": "string" } }, "required": [ "dataframe" ], "title": "GetDataframeResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for getting a dataframe from a notebook session. | GetDataframeResponse | ## Create Client Activity by notebook ID Operation path: `POST /api/v2/notebookRuntimes/notebooks/{notebookId}/clientActivity/` Authentication requirements: `BearerAuth` Reset session TTL from client, when there is a user activity. ### Body parameter ``` { "description": "Empty payload used when updating the client activity for a notebook session.", "properties": {}, "title": "UpdateClientActivityRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID for which client activity is being acknowledged. | | body | body | UpdateClientActivityRequest | false | none | ### Example responses > 202 Response ``` { "description": "Response schema indicating success of an operation.", "properties": { "success": { "description": "Indicates if the operation was successful.", "title": "Success", "type": "boolean" } }, "required": [ "success" ], "title": "SuccessResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Response schema indicating success of an operation. | SuccessResponse | ## Retrieve Dataframe List by notebook ID Operation path: `GET /api/v2/notebookRuntimes/notebooks/{notebookId}/dataframeList/` Authentication requirements: `BearerAuth` Lists all dataframes for a notebook. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to list dataframes for. | ### Example responses > 200 Response ``` { "description": "Response schema for listing DataFrames in a notebook session.", "properties": { "dataframes": { "description": "List of DataFrames in the notebook session.", "items": { "description": "Interactive variables schema. For use with results from IPython's magic `%who_ls` command.", "properties": { "name": { "description": "The name of the variable.", "title": "Name", "type": "string" }, "type": { "description": "The type of the variable.", "title": "Type", "type": "string" } }, "required": [ "name", "type" ], "title": "InteractiveVariablesSchema", "type": "object" }, "title": "Dataframes", "type": "array" } }, "title": "ListDataframesResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for listing DataFrames in a notebook session. | ListDataframesResponse | ## Execute Notebooks by notebook ID Operation path: `POST /api/v2/notebookRuntimes/notebooks/{notebookId}/execute/` Authentication requirements: `BearerAuth` Executes specific cells or the entire notebook. ### Body parameter ``` { "description": "Request payload values for executing notebook cells.", "properties": { "cellIds": { "description": "List of cell IDs to execute.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "cells": { "description": "List of cells to execute.", "items": { "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType", "source" ], "title": "NotebookCellExecData", "type": "object" }, "title": "Cells", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path", "generation", "cells" ], "title": "ExecuteCellsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to execute cells for. | | body | body | ExecuteCellsRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | No content. | None | ## Retrieve Execution Status by notebook ID Operation path: `GET /api/v2/notebookRuntimes/notebooks/{notebookId}/executionStatus/` Authentication requirements: `BearerAuth` Gets execution status. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to get execution status for. | ### Example responses > 200 Response ``` { "description": "Schema for the execution status of a notebook session.", "properties": { "cellId": { "description": "The ID of the cell currently being executed.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Info related to when the execution was completed.", "title": "Executed" }, "inputRequest": { "allOf": [ { "description": "AwaitingInputState represents the state of a cell that is awaiting input from the user.", "properties": { "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" }, "requestedAt": { "description": "The time the input was requested.", "format": "date-time", "title": "Requestedat", "type": "string" } }, "required": [ "requestedAt", "prompt", "password" ], "title": "AwaitingInputState", "type": "object" } ], "description": "The input request state of the cell.", "title": "Inputrequest" }, "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "queuedCellIds": { "description": "The IDs of the cells that are queued for execution.", "items": { "type": "string" }, "title": "Queuedcellids", "type": "array" }, "status": { "allOf": [ { "description": "Kernel execution status.", "enum": [ "busy", "idle" ], "title": "KernelExecutionStatus", "type": "string" } ], "description": "The status of the kernel execution. Possible values are 'busy' or 'idle'." }, "submitted": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Info related to when the execution request was submitted.", "title": "Submitted" } }, "required": [ "status", "notebookId" ], "title": "ExecutionStatusSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for the execution status of a notebook session. | ExecutionStatusSchema | ## Create Input Reply by notebook ID Operation path: `POST /api/v2/notebookRuntimes/notebooks/{notebookId}/inputReply/` Authentication requirements: `BearerAuth` Endpoint used in response to when the kernel requests input from the user. ### Body parameter ``` { "description": "The schema for the input reply request.", "properties": { "value": { "description": "The value to send as a reply after kernel has requested input.", "title": "Value", "type": "string" } }, "required": [ "value" ], "title": "InputReplySchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID receiving a user's reply to a corresponding request for input. | | body | body | InputReplySchema | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | No content. | None | ## Create Inspect by notebook ID Operation path: `POST /api/v2/notebookRuntimes/notebooks/{notebookId}/inspect/` Authentication requirements: `BearerAuth` Inspects code. ### Body parameter ``` { "description": "The schema for the code inspection request.", "properties": { "code": { "description": "The code to inspect.", "title": "Code", "type": "string" }, "cursorPosition": { "description": "The position of the cursor in the code.", "title": "Cursorposition", "type": "integer" }, "detailLevel": { "description": "The detail level of the inspection. Possible values are 0 and 1.", "title": "Detaillevel", "type": "integer" } }, "required": [ "code", "cursorPosition", "detailLevel" ], "title": "CodeInspectionSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to run code inspection for. | | body | body | CodeInspectionSchema | false | none | ### Example responses > 200 Response ``` { "description": "The schema for the code inspection result.", "properties": { "data": { "additionalProperties": { "type": "string" }, "description": "The inspection result.", "title": "Data", "type": "object" }, "found": { "description": "True if an object was found, false otherwise.", "title": "Found", "type": "boolean" } }, "required": [ "data", "found" ], "title": "CodeInspectionResultSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the code inspection result. | CodeInspectionResultSchema | ## Create Restart Kernel by notebook ID Operation path: `POST /api/v2/notebookRuntimes/notebooks/{notebookId}/restartKernel/` Authentication requirements: `BearerAuth` Restarts kernel. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to restart kernel for. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | No content. | None | ## Start Notebooks by notebook ID Operation path: `POST /api/v2/notebookRuntimes/notebooks/{notebookId}/start/` Authentication requirements: `BearerAuth` Starts a new notebook session. ### Body parameter ``` { "description": "Schema for starting a notebook session.", "properties": { "cloneRepository": { "allOf": [ { "description": "Schema for cloning a repository.", "properties": { "checkoutRef": { "description": "The branch or commit to checkout.", "title": "Checkoutref", "type": "string" }, "url": { "description": "The URL of the repository to clone.", "title": "Url", "type": "string" } }, "required": [ "url" ], "title": "CloneRepositorySchema", "type": "object" } ], "description": "Automatically tells the runner to clone remote repository if it's supported as part of its environment setup flow.", "title": "Clonerepository" }, "isTriggeredRun": { "default": false, "description": "Indicates if the session is a triggered run versus an interactive run.", "title": "Istriggeredrun", "type": "boolean" }, "openFilePaths": { "description": "List of file paths to open in the notebook session.", "items": { "type": "string" }, "title": "Openfilepaths", "type": "array" }, "parameters": { "description": "Parameters to use as environment variables.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" } }, "title": "StartNotebookSessionSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to start a session for. | | body | body | StartNotebookSessionSchema | false | none | ### Example responses > 200 Response ``` { "description": "The schema for the notebook session.", "properties": { "environment": { "allOf": [ { "description": "The public representation of an execution environment.", "properties": { "image": { "allOf": [ { "description": "This class is used to represent the public information of an image.", "properties": { "default": { "default": false, "description": "Whether the image is the default image.", "title": "Default", "type": "boolean" }, "description": { "description": "Image description.", "title": "Description", "type": "string" }, "environmentId": { "description": "Environment ID.", "title": "Environmentid", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the image is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Image ID.", "title": "Id", "type": "string" }, "label": { "description": "Image label.", "title": "Label", "type": "string" }, "language": { "description": "Image programming language.", "title": "Language", "type": "string" }, "languageVersion": { "description": "Image programming language version.", "title": "Languageversion", "type": "string" }, "libraries": { "description": "The preinstalled libraries in the image.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "name": { "description": "Image name.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "description", "language", "languageVersion" ], "title": "ImagePublic", "type": "object" } ], "description": "The image of the environment.", "title": "Image" }, "machine": { "allOf": [ { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" } ], "description": "The machine of the environment.", "title": "Machine" }, "timeToLive": { "description": "The inactivity timeout of the environment.", "title": "Timetolive", "type": "integer" } }, "required": [ "image", "machine" ], "title": "EnvironmentPublic", "type": "object" } ], "description": "The environment of the notebook session.", "title": "Environment" }, "ephemeralSessionKey": { "allOf": [ { "description": "Key for an ephemeral session.", "properties": { "entityId": { "description": "The ID of the entity.", "title": "Entityid", "type": "string" }, "entityType": { "allOf": [ { "description": "Types of entities that can be associated with an ephemeral session.", "enum": [ "CUSTOM_APP", "CUSTOM_JOB", "CUSTOM_MODEL", "CUSTOM_METRIC", "CODE_SNIPPET" ], "title": "EphemeralSessionEntityType", "type": "string" } ], "description": "The type of the entity." } }, "required": [ "entityType", "entityId" ], "title": "EphemeralSessionKey", "type": "object" } ], "description": "The key of the ephemeral session. None if not an ephemeral session.", "title": "Ephemeralsessionkey" }, "executionCount": { "default": 0, "description": "The execution count of the notebook session.", "title": "Executioncount", "type": "integer" }, "machineStatus": { "allOf": [ { "description": "This enum represents possible overall state of the machine(s) of all components of the notebook.", "enum": [ "not_started", "allocated", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "MachineStatuses", "type": "string" } ], "description": "The status of the machine running the notebook session." }, "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "parameters": { "description": "Parameters to use as environment variables.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runnerStatus": { "allOf": [ { "description": "Runner is the same as kernel since it will manage multiple kernels in the future,\nWe can't consider kernel running if runner is not functioning and therefor this enum represents\npossible statuses of the kernel and runner sidecar functionality states.\nIn the future this will likely be renamed to kernel.", "enum": [ "not_started", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "RunnerStatuses", "type": "string" } ], "description": "The status of the runner for the notebook session." }, "sessionId": { "description": "The ID of the notebook session.", "title": "Sessionid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "default": "interactive", "description": "The type of the notebook session. Possible values are interactive and triggered." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "startedBy": { "allOf": [ { "description": "Schema for notebook user.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "permissions": { "allOf": [ { "description": "User feature flags.", "properties": { "DISABLE_CODESPACE_SCHEDULING": { "description": "Whether codespace scheduling is disabled for the user.", "title": "Disable Codespace Scheduling", "type": "boolean" }, "DISABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Disable Dummy Feature Flag For Testing", "type": "boolean" }, "DISABLE_NOTEBOOKS_CODESPACES": { "description": "Whether codespaces are disabled for the user.", "title": "Disable Notebooks Codespaces", "type": "boolean" }, "DISABLE_NOTEBOOKS_SCHEDULING": { "description": "Whether scheduling is disabled for the user.", "title": "Disable Notebooks Scheduling", "type": "boolean" }, "DISABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": false, "description": "Whether session port forwarding is disabled for the user.", "title": "Disable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Enable Dummy Feature Flag For Testing", "type": "boolean" }, "ENABLE_MMM_HOSTED_CUSTOM_METRICS": { "description": "Whether custom metrics are enabled for the user.", "title": "Enable Mmm Hosted Custom Metrics", "type": "boolean" }, "ENABLE_NOTEBOOKS": { "description": "Whether notebooks are enabled for the user.", "title": "Enable Notebooks", "type": "boolean" }, "ENABLE_NOTEBOOKS_CUSTOM_ENVIRONMENTS": { "default": true, "description": "Whether custom environments are enabled for the user.", "title": "Enable Notebooks Custom Environments", "type": "boolean" }, "ENABLE_NOTEBOOKS_FILESYSTEM_MANAGEMENT": { "description": "Whether filesystem management is enabled for the user.", "title": "Enable Notebooks Filesystem Management", "type": "boolean" }, "ENABLE_NOTEBOOKS_GPU": { "description": "Whether GPU is enabled for the user.", "title": "Enable Notebooks Gpu", "type": "boolean" }, "ENABLE_NOTEBOOKS_OPEN_AI": { "description": "Whether OpenAI is enabled for the user.", "title": "Enable Notebooks Open Ai", "type": "boolean" }, "ENABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": true, "description": "Whether session port forwarding is enabled for the user.", "title": "Enable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_NOTEBOOKS_TERMINAL": { "description": "Whether terminals are enabled for the user.", "title": "Enable Notebooks Terminal", "type": "boolean" } }, "title": "UserFeatureFlags", "type": "object" } ], "description": "The feature flags of the user.", "title": "Permissions" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "NotebookUserSchema", "type": "object" } ], "description": "The user who started the notebook session.", "title": "Startedby" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" }, "withNetworkPolicy": { "description": "Whether the session is created with network policies.", "title": "Withnetworkpolicy", "type": "boolean" } }, "required": [ "status", "notebookId", "sessionId", "environment", "machineStatus", "runnerStatus" ], "title": "NotebookSessionSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the notebook session. | NotebookSessionSchema | ## Stop Notebooks by notebook ID Operation path: `POST /api/v2/notebookRuntimes/notebooks/{notebookId}/stop/` Authentication requirements: `BearerAuth` Stops the notebook session. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to stop session for. | ### Example responses > 200 Response ``` { "description": "The schema for the notebook session.", "properties": { "environment": { "allOf": [ { "description": "The public representation of an execution environment.", "properties": { "image": { "allOf": [ { "description": "This class is used to represent the public information of an image.", "properties": { "default": { "default": false, "description": "Whether the image is the default image.", "title": "Default", "type": "boolean" }, "description": { "description": "Image description.", "title": "Description", "type": "string" }, "environmentId": { "description": "Environment ID.", "title": "Environmentid", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the image is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Image ID.", "title": "Id", "type": "string" }, "label": { "description": "Image label.", "title": "Label", "type": "string" }, "language": { "description": "Image programming language.", "title": "Language", "type": "string" }, "languageVersion": { "description": "Image programming language version.", "title": "Languageversion", "type": "string" }, "libraries": { "description": "The preinstalled libraries in the image.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "name": { "description": "Image name.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "description", "language", "languageVersion" ], "title": "ImagePublic", "type": "object" } ], "description": "The image of the environment.", "title": "Image" }, "machine": { "allOf": [ { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" } ], "description": "The machine of the environment.", "title": "Machine" }, "timeToLive": { "description": "The inactivity timeout of the environment.", "title": "Timetolive", "type": "integer" } }, "required": [ "image", "machine" ], "title": "EnvironmentPublic", "type": "object" } ], "description": "The environment of the notebook session.", "title": "Environment" }, "ephemeralSessionKey": { "allOf": [ { "description": "Key for an ephemeral session.", "properties": { "entityId": { "description": "The ID of the entity.", "title": "Entityid", "type": "string" }, "entityType": { "allOf": [ { "description": "Types of entities that can be associated with an ephemeral session.", "enum": [ "CUSTOM_APP", "CUSTOM_JOB", "CUSTOM_MODEL", "CUSTOM_METRIC", "CODE_SNIPPET" ], "title": "EphemeralSessionEntityType", "type": "string" } ], "description": "The type of the entity." } }, "required": [ "entityType", "entityId" ], "title": "EphemeralSessionKey", "type": "object" } ], "description": "The key of the ephemeral session. None if not an ephemeral session.", "title": "Ephemeralsessionkey" }, "executionCount": { "default": 0, "description": "The execution count of the notebook session.", "title": "Executioncount", "type": "integer" }, "machineStatus": { "allOf": [ { "description": "This enum represents possible overall state of the machine(s) of all components of the notebook.", "enum": [ "not_started", "allocated", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "MachineStatuses", "type": "string" } ], "description": "The status of the machine running the notebook session." }, "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "parameters": { "description": "Parameters to use as environment variables.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runnerStatus": { "allOf": [ { "description": "Runner is the same as kernel since it will manage multiple kernels in the future,\nWe can't consider kernel running if runner is not functioning and therefor this enum represents\npossible statuses of the kernel and runner sidecar functionality states.\nIn the future this will likely be renamed to kernel.", "enum": [ "not_started", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "RunnerStatuses", "type": "string" } ], "description": "The status of the runner for the notebook session." }, "sessionId": { "description": "The ID of the notebook session.", "title": "Sessionid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "default": "interactive", "description": "The type of the notebook session. Possible values are interactive and triggered." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "startedBy": { "allOf": [ { "description": "Schema for notebook user.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "permissions": { "allOf": [ { "description": "User feature flags.", "properties": { "DISABLE_CODESPACE_SCHEDULING": { "description": "Whether codespace scheduling is disabled for the user.", "title": "Disable Codespace Scheduling", "type": "boolean" }, "DISABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Disable Dummy Feature Flag For Testing", "type": "boolean" }, "DISABLE_NOTEBOOKS_CODESPACES": { "description": "Whether codespaces are disabled for the user.", "title": "Disable Notebooks Codespaces", "type": "boolean" }, "DISABLE_NOTEBOOKS_SCHEDULING": { "description": "Whether scheduling is disabled for the user.", "title": "Disable Notebooks Scheduling", "type": "boolean" }, "DISABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": false, "description": "Whether session port forwarding is disabled for the user.", "title": "Disable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Enable Dummy Feature Flag For Testing", "type": "boolean" }, "ENABLE_MMM_HOSTED_CUSTOM_METRICS": { "description": "Whether custom metrics are enabled for the user.", "title": "Enable Mmm Hosted Custom Metrics", "type": "boolean" }, "ENABLE_NOTEBOOKS": { "description": "Whether notebooks are enabled for the user.", "title": "Enable Notebooks", "type": "boolean" }, "ENABLE_NOTEBOOKS_CUSTOM_ENVIRONMENTS": { "default": true, "description": "Whether custom environments are enabled for the user.", "title": "Enable Notebooks Custom Environments", "type": "boolean" }, "ENABLE_NOTEBOOKS_FILESYSTEM_MANAGEMENT": { "description": "Whether filesystem management is enabled for the user.", "title": "Enable Notebooks Filesystem Management", "type": "boolean" }, "ENABLE_NOTEBOOKS_GPU": { "description": "Whether GPU is enabled for the user.", "title": "Enable Notebooks Gpu", "type": "boolean" }, "ENABLE_NOTEBOOKS_OPEN_AI": { "description": "Whether OpenAI is enabled for the user.", "title": "Enable Notebooks Open Ai", "type": "boolean" }, "ENABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": true, "description": "Whether session port forwarding is enabled for the user.", "title": "Enable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_NOTEBOOKS_TERMINAL": { "description": "Whether terminals are enabled for the user.", "title": "Enable Notebooks Terminal", "type": "boolean" } }, "title": "UserFeatureFlags", "type": "object" } ], "description": "The feature flags of the user.", "title": "Permissions" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "NotebookUserSchema", "type": "object" } ], "description": "The user who started the notebook session.", "title": "Startedby" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" }, "withNetworkPolicy": { "description": "Whether the session is created with network policies.", "title": "Withnetworkpolicy", "type": "boolean" } }, "required": [ "status", "notebookId", "sessionId", "environment", "machineStatus", "runnerStatus" ], "title": "NotebookSessionSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the notebook session. | NotebookSessionSchema | ## Retrieve Download by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/filesystem/download/` Authentication requirements: `BearerAuth` Download entire file system as an archive. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | ### Example responses > 200 Response ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Download entire file system as an archive. | string | ## Retrieve Objects by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/filesystem/objects/` Authentication requirements: `BearerAuth` Get paginated list of objects for a given path in the filesystem. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | ListFilesystemObjectsRequest | query | ListFilesystemObjectsRequest | false | We have set the limit max value to one thousand | ### Example responses > 200 Response ``` { "description": "Response schema for listing filesystem objects.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of filesystem objects.", "items": { "description": "This schema is used for filesystem objects (files and directories) in the filesystem.", "properties": { "lastUpdatedAt": { "description": "Last updated time of the filesystem object.", "format": "date-time", "title": "Lastupdatedat", "type": "string" }, "mediaType": { "description": "Media type of the filesystem object.", "title": "Mediatype", "type": "string" }, "name": { "description": "Name of the filesystem object.", "title": "Name", "type": "string" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" }, "sizeInBytes": { "description": "Size of the filesystem object in bytes.", "title": "Sizeinbytes", "type": "integer" }, "type": { "allOf": [ { "description": "Type of the filesystem object.", "enum": [ "dir", "file" ], "title": "FilesystemObjectType", "type": "string" } ], "description": "Type of the filesystem object. Possible values include 'dir', 'file'." } }, "required": [ "path", "type", "name" ], "title": "FilesystemObjectSchema", "type": "object" }, "title": "Data", "type": "array" }, "datarobotMetadata": { "additionalProperties": { "description": "Metadata for DataRobot files.", "properties": { "commandArgs": { "description": "Command arguments for the file.", "title": "Commandargs", "type": "string" } }, "title": "DRFileMetadata", "type": "object" }, "description": "Metadata for DataRobot files.", "title": "Datarobotmetadata", "type": "object" }, "directoryHierarchy": { "description": "List of directories respective names and paths.", "items": { "description": "These are only directories\n\nThe name property can be any string or the constant/sentinel \"root\"", "properties": { "name": { "anyOf": [ { "type": "string" }, { "enum": [ "root" ], "type": "string" } ], "description": "Name of the directory.", "title": "Name" }, "path": { "description": "Path to the directory.", "title": "Path", "type": "string" } }, "required": [ "name", "path" ], "title": "DirHierarchyObjectSchema", "type": "object" }, "title": "Directoryhierarchy", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "required": [ "data", "directoryHierarchy", "datarobotMetadata" ], "title": "ListFilesystemObjectsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for listing filesystem objects. | ListFilesystemObjectsResponse | ## Create Copy by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/filesystem/objects/copy/` Authentication requirements: `BearerAuth` Copy a filesystem object at a given path to a specific destination. ### Body parameter ``` { "description": "Copy filesystem object request schema.", "properties": { "operations": { "description": "List of source-destination pairs.", "items": { "description": "Source and destination schema for filesystem object operations.", "properties": { "destination": { "description": "Destination path.", "title": "Destination", "type": "string" }, "source": { "description": "Source path.", "title": "Source", "type": "string" } }, "required": [ "source", "destination" ], "title": "SourceDestinationSchema", "type": "object" }, "title": "Operations", "type": "array" }, "overrideExisting": { "default": false, "description": "Whether to override existing files or directories.", "title": "Overrideexisting", "type": "boolean" } }, "title": "CopyFilesystemObjectRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | CopyFilesystemObjectRequest | false | none | ### Example responses > 200 Response ``` { "items": { "description": "This schema is used for filesystem objects (files and directories) in the filesystem.", "properties": { "lastUpdatedAt": { "description": "Last updated time of the filesystem object.", "format": "date-time", "title": "Lastupdatedat", "type": "string" }, "mediaType": { "description": "Media type of the filesystem object.", "title": "Mediatype", "type": "string" }, "name": { "description": "Name of the filesystem object.", "title": "Name", "type": "string" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" }, "sizeInBytes": { "description": "Size of the filesystem object in bytes.", "title": "Sizeinbytes", "type": "integer" }, "type": { "allOf": [ { "description": "Type of the filesystem object.", "enum": [ "dir", "file" ], "title": "FilesystemObjectType", "type": "string" } ], "description": "Type of the filesystem object. Possible values include 'dir', 'file'." } }, "required": [ "path", "type", "name" ], "title": "FilesystemObjectSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | This schema is used for filesystem objects (files and directories) in the filesystem. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [FilesystemObjectSchema] | false | | [This schema is used for filesystem objects (files and directories) in the filesystem.] | | » FilesystemObjectSchema | FilesystemObjectSchema | false | | This schema is used for filesystem objects (files and directories) in the filesystem. | | »» lastUpdatedAt | string(date-time) | false | | Last updated time of the filesystem object. | | »» mediaType | string | false | | Media type of the filesystem object. | | »» name | string | true | | Name of the filesystem object. | | »» path | string | true | | Path to the filesystem object. | | »» sizeInBytes | integer | false | | Size of the filesystem object in bytes. | | »» type | FilesystemObjectType | true | | Type of the filesystem object. Possible values include 'dir', 'file'. | ### Enumerated Values | Property | Value | | --- | --- | | type | [dir, file] | ## Delete Delete by notebook ID Operation path: `DELETE /api/v2/notebookSessions/{notebookId}/filesystem/objects/delete/` Authentication requirements: `BearerAuth` Delete a filesystem object(s) from a list of paths. ### Body parameter ``` { "description": "Delete filesystem object request schema.", "properties": { "paths": { "description": "List of paths to delete.", "items": { "type": "string" }, "title": "Paths", "type": "array" } }, "title": "DeleteFilesystemObjectsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | DeleteFilesystemObjectsRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Retrieve download by id Operation path: `GET /api/v2/notebookSessions/{notebookId}/filesystem/objects/download/` Authentication requirements: `BearerAuth` Download many files or directories from the notebook session. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | DownloadFilesystemObjectsQuery | query | DownloadFilesystemObjectsQuery | false | Query schema for downloading filesystem objects. | ### Example responses > 200 Response ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Download many files or directories from the notebook session. | string | ## Create Download by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/filesystem/objects/download/` Authentication requirements: `BearerAuth` Download a file or directory from the notebook session. ### Body parameter ``` { "description": "Query schema for downloading filesystem objects.", "properties": { "paths": { "description": "List of paths to download.", "items": { "type": "string" }, "title": "Paths", "type": "array" } }, "title": "DownloadFilesystemObjectsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | DownloadFilesystemObjectsRequest | false | none | ### Example responses > 200 Response ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Download a file or directory from the notebook session. | string | ## Retrieve Metadata by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/filesystem/objects/metadata/` Authentication requirements: `BearerAuth` Get the filesystem's metadata. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | GetFilesystemObjectMetadataRequest | query | GetFilesystemObjectMetadataRequest | false | Request payload values for getting filesystem object metadata. | ### Example responses > 200 Response ``` { "description": "Response schema for DataRobot filesystem metadata.", "properties": { "datarobotMetadata": { "additionalProperties": { "description": "Metadata for DataRobot files.", "properties": { "commandArgs": { "description": "Command arguments for the file.", "title": "Commandargs", "type": "string" } }, "title": "DRFileMetadata", "type": "object" }, "description": "Metadata for the files.", "title": "Datarobotmetadata", "type": "object" } }, "required": [ "datarobotMetadata" ], "title": "DRFilesystemMetadataResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for DataRobot filesystem metadata. | DRFilesystemMetadataResponse | ## Modify Metadata by notebook ID Operation path: `PATCH /api/v2/notebookSessions/{notebookId}/filesystem/objects/metadata/` Authentication requirements: `BearerAuth` Update a filesystem object's metadata. ### Body parameter ``` { "description": "Request payload values for updating filesystem object metadata.", "properties": { "updates": { "description": "List of updates to apply.", "items": { "description": "Update filesystem object metadata path and values.", "properties": { "metadata": { "allOf": [ { "description": "Metadata for DataRobot files.", "properties": { "commandArgs": { "description": "Command arguments for the file.", "title": "Commandargs", "type": "string" } }, "title": "DRFileMetadata", "type": "object" } ], "description": "Metadata to update.", "title": "Metadata" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" } }, "required": [ "path", "metadata" ], "title": "UpdateFilesystemObjectMetadata", "type": "object" }, "title": "Updates", "type": "array" } }, "title": "UpdateFilesystemObjectsMetadataRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | UpdateFilesystemObjectsMetadataRequest | false | none | ### Example responses > 200 Response ``` { "items": { "description": "Metadata for DataRobot files.", "properties": { "commandArgs": { "description": "Command arguments for the file.", "title": "Commandargs", "type": "string" } }, "title": "DRFileMetadata", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Metadata for DataRobot files. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [DRFileMetadata] | false | | [Metadata for DataRobot files.] | | » DRFileMetadata | DRFileMetadata | false | | Metadata for DataRobot files. | | »» commandArgs | string | false | | Command arguments for the file. | ## Create Move by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/filesystem/objects/move/` Authentication requirements: `BearerAuth` Move a filesystem object at a given path. ### Body parameter ``` { "description": "Move filesystem object request schema.", "properties": { "operations": { "description": "List of source-destination pairs.", "items": { "description": "Source and destination schema for filesystem object operations.", "properties": { "destination": { "description": "Destination path.", "title": "Destination", "type": "string" }, "source": { "description": "Source path.", "title": "Source", "type": "string" } }, "required": [ "source", "destination" ], "title": "SourceDestinationSchema", "type": "object" }, "title": "Operations", "type": "array" } }, "title": "MoveFilesystemObjectRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | MoveFilesystemObjectRequest | false | none | ### Example responses > 200 Response ``` { "items": { "description": "This schema is used for filesystem objects (files and directories) in the filesystem.", "properties": { "lastUpdatedAt": { "description": "Last updated time of the filesystem object.", "format": "date-time", "title": "Lastupdatedat", "type": "string" }, "mediaType": { "description": "Media type of the filesystem object.", "title": "Mediatype", "type": "string" }, "name": { "description": "Name of the filesystem object.", "title": "Name", "type": "string" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" }, "sizeInBytes": { "description": "Size of the filesystem object in bytes.", "title": "Sizeinbytes", "type": "integer" }, "type": { "allOf": [ { "description": "Type of the filesystem object.", "enum": [ "dir", "file" ], "title": "FilesystemObjectType", "type": "string" } ], "description": "Type of the filesystem object. Possible values include 'dir', 'file'." } }, "required": [ "path", "type", "name" ], "title": "FilesystemObjectSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | This schema is used for filesystem objects (files and directories) in the filesystem. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [FilesystemObjectSchema] | false | | [This schema is used for filesystem objects (files and directories) in the filesystem.] | | » FilesystemObjectSchema | FilesystemObjectSchema | false | | This schema is used for filesystem objects (files and directories) in the filesystem. | | »» lastUpdatedAt | string(date-time) | false | | Last updated time of the filesystem object. | | »» mediaType | string | false | | Media type of the filesystem object. | | »» name | string | true | | Name of the filesystem object. | | »» path | string | true | | Path to the filesystem object. | | »» sizeInBytes | integer | false | | Size of the filesystem object in bytes. | | »» type | FilesystemObjectType | true | | Type of the filesystem object. Possible values include 'dir', 'file'. | ### Enumerated Values | Property | Value | | --- | --- | | type | [dir, file] | ## New by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/filesystem/objects/new/` Authentication requirements: `BearerAuth` Create a new filesystem object (file or directory) at the given path. ### Body parameter ``` { "description": "Create a new filesystem object request schema.", "properties": { "isDirectory": { "description": "Whether the filesystem object is a directory or not.", "title": "Isdirectory", "type": "boolean" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" } }, "required": [ "path", "isDirectory" ], "title": "CreateFilesystemObjectRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | CreateFilesystemObjectRequest | false | none | ### Example responses > 201 Response ``` { "description": "This schema is used for filesystem objects (files and directories) in the filesystem.", "properties": { "lastUpdatedAt": { "description": "Last updated time of the filesystem object.", "format": "date-time", "title": "Lastupdatedat", "type": "string" }, "mediaType": { "description": "Media type of the filesystem object.", "title": "Mediatype", "type": "string" }, "name": { "description": "Name of the filesystem object.", "title": "Name", "type": "string" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" }, "sizeInBytes": { "description": "Size of the filesystem object in bytes.", "title": "Sizeinbytes", "type": "integer" }, "type": { "allOf": [ { "description": "Type of the filesystem object.", "enum": [ "dir", "file" ], "title": "FilesystemObjectType", "type": "string" } ], "description": "Type of the filesystem object. Possible values include 'dir', 'file'." } }, "required": [ "path", "type", "name" ], "title": "FilesystemObjectSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | This schema is used for filesystem objects (files and directories) in the filesystem. | FilesystemObjectSchema | ## Create Upload by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/filesystem/objects/upload/` Authentication requirements: `BearerAuth` Upload a file or directory into the notebook session and save it under given path. If file exists by given path, it will be overridden. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | ### Example responses > 200 Response ``` { "items": { "description": "This schema is used for filesystem objects (files and directories) in the filesystem.", "properties": { "lastUpdatedAt": { "description": "Last updated time of the filesystem object.", "format": "date-time", "title": "Lastupdatedat", "type": "string" }, "mediaType": { "description": "Media type of the filesystem object.", "title": "Mediatype", "type": "string" }, "name": { "description": "Name of the filesystem object.", "title": "Name", "type": "string" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" }, "sizeInBytes": { "description": "Size of the filesystem object in bytes.", "title": "Sizeinbytes", "type": "integer" }, "type": { "allOf": [ { "description": "Type of the filesystem object.", "enum": [ "dir", "file" ], "title": "FilesystemObjectType", "type": "string" } ], "description": "Type of the filesystem object. Possible values include 'dir', 'file'." } }, "required": [ "path", "type", "name" ], "title": "FilesystemObjectSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | This schema is used for filesystem objects (files and directories) in the filesystem. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [FilesystemObjectSchema] | false | | [This schema is used for filesystem objects (files and directories) in the filesystem.] | | » FilesystemObjectSchema | FilesystemObjectSchema | false | | This schema is used for filesystem objects (files and directories) in the filesystem. | | »» lastUpdatedAt | string(date-time) | false | | Last updated time of the filesystem object. | | »» mediaType | string | false | | Media type of the filesystem object. | | »» name | string | true | | Name of the filesystem object. | | »» path | string | true | | Path to the filesystem object. | | »» sizeInBytes | integer | false | | Size of the filesystem object in bytes. | | »» type | FilesystemObjectType | true | | Type of the filesystem object. Possible values include 'dir', 'file'. | ### Enumerated Values | Property | Value | | --- | --- | | type | [dir, file] | ## Retrieve objects by id Operation path: `GET /api/v2/notebookSessions/{notebookId}/filesystem/objects/{subpath}/{path}/` Authentication requirements: `BearerAuth` Preview a file. The file path is passed as a parameter. For example: filesystem/objects/home/usr/storage/img.png/. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | subpath | path | string | true | See method level description for details. | | path | path | string | true | See method level description for details. | ### Example responses > 200 Response ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Preview a file. | string | ## Create Clone by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/git/clone/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for cloning a Git repository.", "properties": { "url": { "description": "URL of the Git repository to clone.", "format": "uri", "maxLength": 65536, "minLength": 1, "title": "Url", "type": "string" } }, "required": [ "url" ], "title": "GitCloneRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | GitCloneRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | No content. | None | ## Cancel Clone by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/git/clone/cancel/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | No content. | None | ## Retrieve Status by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/git/clone/status/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | ### Example responses > 200 Response ``` { "description": "Response schema for the status of a Git clone operation.", "properties": { "errorMsg": { "description": "Error message if the clone operation failed.", "title": "Errormsg", "type": "string" }, "status": { "allOf": [ { "description": "Status of the VCS command.", "enum": [ "not_inited", "running", "finished", "error" ], "title": "VCSCommandStatus", "type": "string" } ], "description": "Status of the Git clone operation." } }, "required": [ "status" ], "title": "GitCloneStatusResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for the status of a Git clone operation. | GitCloneStatusResponse | ## Retrieve Kernels by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/kernels/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | ### Example responses > 200 Response ``` { "items": { "description": "The schema for the notebook kernel.", "properties": { "executionState": { "allOf": [ { "description": "Event Sequences on Various Workflows:\n- On kernel created: CONNECTED -> BUSY -> IDLE\n- On kernel restarted: RESTARTING -> STARTING -> BUSY -> IDLE\n- On regular execution: IDLE -> BUSY -> IDLE\n- On execution interrupted: IDLE -> BUSY -> INTERRUPTING -> IDLE\n- On execution with error: IDLE -> BUSY -> IDLE -> INTERRUPTING -> IDLE\n- On kernel shut down via calling the stop kernel endpoint:\n DISCONNECTED (can be sent a few times) -> NOT_RUNNING (after 5s)", "enum": [ "connecting", "disconnected", "connected", "starting", "idle", "busy", "interrupting", "restarting", "not_running" ], "title": "KernelState", "type": "string" } ], "description": "The execution state of the kernel." }, "id": { "description": "The ID of the kernel.", "title": "Id", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "The programming language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "The name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" }, "running": { "default": false, "description": "Whether the kernel is running.", "title": "Running", "type": "boolean" } }, "required": [ "id", "name", "language", "executionState" ], "title": "KernelSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the notebook kernel. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [KernelSchema] | false | | [The schema for the notebook kernel.] | | » KernelSchema | KernelSchema | false | | The schema for the notebook kernel. | | »» executionState | KernelState | true | | The execution state of the kernel. | | »» id | string | true | | The ID of the kernel. | | »» language | RuntimeLanguage | true | | The programming language of the kernel. Possible values include 'python', 'r'. | | »» name | string | true | | The name of the kernel. Possible values include 'python3', 'ir'. | | »» running | boolean | false | | Whether the kernel is running. | ### Enumerated Values | Property | Value | | --- | --- | | executionState | [connecting, disconnected, connected, starting, idle, busy, interrupting, restarting, not_running] | | language | [python, r, shell, markdown] | ## Create Kernels by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/kernels/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request payload values for starting a kernel.", "properties": { "spec": { "description": "Name of the kernel to start. Possible values include 'python3', 'ir'.", "title": "Spec", "type": "string" } }, "required": [ "spec" ], "title": "StartKernelRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | StartKernelRequest | false | none | ### Example responses > 201 Response ``` { "description": "The schema for the notebook kernel.", "properties": { "executionState": { "allOf": [ { "description": "Event Sequences on Various Workflows:\n- On kernel created: CONNECTED -> BUSY -> IDLE\n- On kernel restarted: RESTARTING -> STARTING -> BUSY -> IDLE\n- On regular execution: IDLE -> BUSY -> IDLE\n- On execution interrupted: IDLE -> BUSY -> INTERRUPTING -> IDLE\n- On execution with error: IDLE -> BUSY -> IDLE -> INTERRUPTING -> IDLE\n- On kernel shut down via calling the stop kernel endpoint:\n DISCONNECTED (can be sent a few times) -> NOT_RUNNING (after 5s)", "enum": [ "connecting", "disconnected", "connected", "starting", "idle", "busy", "interrupting", "restarting", "not_running" ], "title": "KernelState", "type": "string" } ], "description": "The execution state of the kernel." }, "id": { "description": "The ID of the kernel.", "title": "Id", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "The programming language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "The name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" }, "running": { "default": false, "description": "Whether the kernel is running.", "title": "Running", "type": "boolean" } }, "required": [ "id", "name", "language", "executionState" ], "title": "KernelSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The schema for the notebook kernel. | KernelSchema | ## Delete Kernels by notebook ID Operation path: `DELETE /api/v2/notebookSessions/{notebookId}/kernels/{kernelId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | kernelId | path | string | true | The kernel ID to stop. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Retrieve kernels by id Operation path: `GET /api/v2/notebookSessions/{notebookId}/kernels/{kernelId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | kernelId | path | string | true | The kernel ID to retrieve. | ### Example responses > 200 Response ``` { "description": "The schema for the notebook kernel.", "properties": { "executionState": { "allOf": [ { "description": "Event Sequences on Various Workflows:\n- On kernel created: CONNECTED -> BUSY -> IDLE\n- On kernel restarted: RESTARTING -> STARTING -> BUSY -> IDLE\n- On regular execution: IDLE -> BUSY -> IDLE\n- On execution interrupted: IDLE -> BUSY -> INTERRUPTING -> IDLE\n- On execution with error: IDLE -> BUSY -> IDLE -> INTERRUPTING -> IDLE\n- On kernel shut down via calling the stop kernel endpoint:\n DISCONNECTED (can be sent a few times) -> NOT_RUNNING (after 5s)", "enum": [ "connecting", "disconnected", "connected", "starting", "idle", "busy", "interrupting", "restarting", "not_running" ], "title": "KernelState", "type": "string" } ], "description": "The execution state of the kernel." }, "id": { "description": "The ID of the kernel.", "title": "Id", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "The programming language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "The name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" }, "running": { "default": false, "description": "Whether the kernel is running.", "title": "Running", "type": "boolean" } }, "required": [ "id", "name", "language", "executionState" ], "title": "KernelSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the notebook kernel. | KernelSchema | ## Create Completion by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/kernels/{kernelId}/completion/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "The schema for the code completion request.", "properties": { "code": { "description": "The code to complete.", "title": "Code", "type": "string" }, "cursorPosition": { "description": "The position of the cursor in the code.", "title": "Cursorposition", "type": "integer" } }, "required": [ "code", "cursorPosition" ], "title": "CodeCompletionSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | kernelId | path | string | true | The kernel ID to use when requesting code completion. | | body | body | CodeCompletionSchema | false | none | ### Example responses > 200 Response ``` { "description": "The schema for the code completion result.", "properties": { "cursorEnd": { "description": "The end position of the cursor in the code.", "title": "Cursorend", "type": "integer" }, "cursorStart": { "description": "The start position of the cursor in the code.", "title": "Cursorstart", "type": "integer" }, "matches": { "description": "The list of code completions.", "items": { "type": "string" }, "title": "Matches", "type": "array" } }, "required": [ "matches", "cursorStart", "cursorEnd" ], "title": "CodeCompletionResultSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the code completion result. | CodeCompletionResultSchema | ## Execute Kernels by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/kernels/{kernelId}/execute/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "properties": { "code": { "description": "The code to execute.", "title": "Code", "type": "string" } }, "required": [ "code" ], "title": "ExecuteCodeRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | kernelId | path | string | true | The kernel ID to use when executing code. | | body | body | ExecuteCodeRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | No content. | None | ## Create Input Reply by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/kernels/{kernelId}/inputReply/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "The schema for the input reply request.", "properties": { "value": { "description": "The value to send as a reply after kernel has requested input.", "title": "Value", "type": "string" } }, "required": [ "value" ], "title": "InputReplySchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | kernelId | path | string | true | The kernel ID to send input request reply to. | | body | body | InputReplySchema | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | No content. | None | ## Create Inspect by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/kernels/{kernelId}/inspect/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "The schema for the code inspection request.", "properties": { "code": { "description": "The code to inspect.", "title": "Code", "type": "string" }, "cursorPosition": { "description": "The position of the cursor in the code.", "title": "Cursorposition", "type": "integer" }, "detailLevel": { "description": "The detail level of the inspection. Possible values are 0 and 1.", "title": "Detaillevel", "type": "integer" } }, "required": [ "code", "cursorPosition", "detailLevel" ], "title": "CodeInspectionSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | kernelId | path | string | true | The kernel ID to inspect. | | body | body | CodeInspectionSchema | false | none | ### Example responses > 200 Response ``` { "description": "The schema for the code inspection result.", "properties": { "data": { "additionalProperties": { "type": "string" }, "description": "The inspection result.", "title": "Data", "type": "object" }, "found": { "description": "True if an object was found, false otherwise.", "title": "Found", "type": "boolean" } }, "required": [ "data", "found" ], "title": "CodeInspectionResultSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the code inspection result. | CodeInspectionResultSchema | ## Create Interrupt by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/kernels/{kernelId}/interrupt/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | kernelId | path | string | true | The kernel ID to interrupt. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | No content. | None | ## Create Restart by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/kernels/{kernelId}/restart/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | kernelId | path | string | true | The kernel ID to restart. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | No content. | None | ## Retrieve Kernelspecs by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/kernelspecs/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | ### Example responses > 200 Response ``` { "description": "Schema for the Codespace's kernel information.", "properties": { "default": { "description": "The default kernel. Possible values include 'python3', 'ir'.", "title": "Default", "type": "string" }, "kernels": { "description": "List of available kernels.", "items": { "properties": { "displayName": { "description": "Display name of the kernel. Possible values include 'R', 'Python 3 (ipykernel)'.", "title": "Displayname", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "Runtime language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "Name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" } }, "required": [ "name", "displayName", "language" ], "title": "KernelSpecSchema", "type": "object" }, "title": "Kernels", "type": "array" } }, "required": [ "default" ], "title": "KernelSpecsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for the Codespace's kernel information. | KernelSpecsResponse | ## Retrieve Notebook by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/notebook/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | NotebookOperationQuery | query | NotebookOperationQuery | false | Base query schema for notebook operations. | ### Example responses > 200 Response ``` { "description": "The schema for a .ipynb notebook as part of a Codespace.", "properties": { "cells": { "description": "List of cells in the notebook.", "items": { "anyOf": [ { "description": "The schema for a code cell in the notebook.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "code", "description": "Type of the cell." }, "executionCount": { "default": 0, "description": "Execution count of the cell.", "title": "Executioncount", "type": "integer" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "outputs": { "description": "Outputs of the cell.", "items": { "anyOf": [ { "description": "The schema for the execute result output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "executionCount": { "default": 0, "description": "Execution count of the output.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "execute_result", "default": "execute_result", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "ExecuteResultOutputSchema", "type": "object" }, { "description": "The schema for the display data output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "display_data", "default": "display_data", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "DisplayDataOutputSchema", "type": "object" }, { "description": "The schema for the stream output in a notebook cell.", "properties": { "name": { "description": "Name of the stream.", "title": "Name", "type": "string" }, "outputType": { "const": "stream", "default": "stream", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Text of the stream.", "title": "Text" } }, "required": [ "name", "text" ], "title": "StreamOutputSchema", "type": "object" }, { "description": "The schema for the error output in a notebook cell.", "properties": { "ename": { "description": "Error name.", "title": "Ename", "type": "string" }, "evalue": { "description": "Error value.", "title": "Evalue", "type": "string" }, "outputType": { "const": "error", "default": "error", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "traceback": { "description": "Traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "ename", "evalue" ], "title": "ErrorOutputSchema", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "CodeCellSchema", "type": "object" }, { "description": "The schema for a markdown cell in the notebook.", "properties": { "attachments": { "description": "Attachments of the cell.", "title": "Attachments", "type": "object" }, "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "markdown", "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "MarkdownCellSchema", "type": "object" }, { "description": "The schema for cells in a notebook that are not code or markdown.", "properties": { "cellType": { "description": "Type of the cell.", "title": "Celltype", "type": "string" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType" ], "title": "AnyCellSchema", "type": "object" } ] }, "title": "Cells", "type": "array" }, "executionState": { "allOf": [ { "description": "Notebook execution state model", "properties": { "executingCellId": { "description": "The ID of the cell currently being executed.", "title": "Executingcellid", "type": "string" }, "executionFinishedAt": { "description": "The time the execution finished. This is based on the finish time of the last cell.", "format": "date-time", "title": "Executionfinishedat", "type": "string" }, "executionStartedAt": { "description": "The time the execution started.", "format": "date-time", "title": "Executionstartedat", "type": "string" }, "inputRequest": { "allOf": [ { "description": "AwaitingInputState represents the state of a cell that is awaiting input from the user.", "properties": { "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" }, "requestedAt": { "description": "The time the input was requested.", "format": "date-time", "title": "Requestedat", "type": "string" } }, "required": [ "requestedAt", "prompt", "password" ], "title": "AwaitingInputState", "type": "object" } ], "description": "The input request state of the cell.", "title": "Inputrequest" }, "kernelId": { "description": "The ID of the kernel used for execution.", "title": "Kernelid", "type": "string" }, "queuedCellIds": { "description": "The IDs of the cells that are queued for execution.", "items": { "type": "string" }, "title": "Queuedcellids", "type": "array" } }, "title": "NotebookExecutionState", "type": "object" } ], "description": "Execution state of the notebook.", "title": "Executionstate" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "kernelId": { "description": "Kernel ID assigned to the notebook.", "title": "Kernelid", "type": "string" }, "lastExecuted": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Last executed information.", "title": "Lastexecuted" }, "metadata": { "description": "Metadata for the notebook.", "title": "Metadata", "type": "object" }, "name": { "description": "Name of the notebook.", "title": "Name", "type": "string" }, "nbformat": { "description": "The notebook format version.", "title": "Nbformat", "type": "integer" }, "nbformatMinor": { "description": "The notebook format minor version.", "title": "Nbformatminor", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "name", "path", "generation", "nbformat", "nbformatMinor", "metadata" ], "title": "NotebookFileSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for a .ipynb notebook as part of a Codespace. | NotebookFileSchema | ## Create Notebook by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/notebook/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Base schema for notebook operations.", "properties": { "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path" ], "title": "NotebookOperationSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | NotebookOperationSchema | false | none | ### Example responses > 201 Response ``` { "description": "The schema for the newly created notebook.", "properties": { "cells": { "description": "List of cells in the notebook.", "items": { "anyOf": [ { "description": "The schema for a code cell in the notebook.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "code", "description": "Type of the cell." }, "executionCount": { "default": 0, "description": "Execution count of the cell.", "title": "Executioncount", "type": "integer" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "outputs": { "description": "Outputs of the cell.", "items": { "anyOf": [ { "description": "The schema for the execute result output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "executionCount": { "default": 0, "description": "Execution count of the output.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "execute_result", "default": "execute_result", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "ExecuteResultOutputSchema", "type": "object" }, { "description": "The schema for the display data output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "display_data", "default": "display_data", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "DisplayDataOutputSchema", "type": "object" }, { "description": "The schema for the stream output in a notebook cell.", "properties": { "name": { "description": "Name of the stream.", "title": "Name", "type": "string" }, "outputType": { "const": "stream", "default": "stream", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Text of the stream.", "title": "Text" } }, "required": [ "name", "text" ], "title": "StreamOutputSchema", "type": "object" }, { "description": "The schema for the error output in a notebook cell.", "properties": { "ename": { "description": "Error name.", "title": "Ename", "type": "string" }, "evalue": { "description": "Error value.", "title": "Evalue", "type": "string" }, "outputType": { "const": "error", "default": "error", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "traceback": { "description": "Traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "ename", "evalue" ], "title": "ErrorOutputSchema", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "CodeCellSchema", "type": "object" }, { "description": "The schema for a markdown cell in the notebook.", "properties": { "attachments": { "description": "Attachments of the cell.", "title": "Attachments", "type": "object" }, "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "markdown", "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "MarkdownCellSchema", "type": "object" }, { "description": "The schema for cells in a notebook that are not code or markdown.", "properties": { "cellType": { "description": "Type of the cell.", "title": "Celltype", "type": "string" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType" ], "title": "AnyCellSchema", "type": "object" } ] }, "title": "Cells", "type": "array" }, "metadata": { "description": "Metadata for the notebook.", "title": "Metadata", "type": "object" }, "name": { "description": "Name of the notebook.", "title": "Name", "type": "string" }, "nbformat": { "description": "The notebook format version.", "title": "Nbformat", "type": "integer" }, "nbformatMinor": { "description": "The notebook format minor version.", "title": "Nbformatminor", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "name", "path", "nbformat", "nbformatMinor", "metadata" ], "title": "NewNotebookFileSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The schema for the newly created notebook. | NewNotebookFileSchema | ## Create Activity by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/notebook/activity/` Authentication requirements: `BearerAuth` Reports that the client is still working with these notebooks. UI uses it to report notebooks that are open and potentially viewed by the user. Knowing this information helps to shut down inactive notebook reconcilers/executors. ### Body parameter ``` { "description": "Request payload values for updating notebook activity.", "properties": { "paths": { "description": "List of paths to update activity for.", "items": { "type": "string" }, "title": "Paths", "type": "array" } }, "title": "NotebookActivityRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | NotebookActivityRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Delete Cells by notebook ID Operation path: `DELETE /api/v2/notebookSessions/{notebookId}/notebook/cells/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | BatchCellQuery | query | BatchCellQuery | false | Base schema for batch cell queries. | ### Example responses > 202 Response ``` { "description": "Base schema for action created responses.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "title": "Actionid", "type": "string" } }, "required": [ "actionId" ], "title": "ActionCreatedResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Base schema for action created responses. | ActionCreatedResponse | ## Retrieve Cells by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/notebook/cells/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | GetCellsQuery | query | GetCellsQuery | false | Base query schema for getting notebook cells. | ### Example responses > 200 Response ``` { "description": "The schema for the notebook cells.", "properties": { "cells": { "description": "List of cells in the notebook.", "items": { "anyOf": [ { "description": "The schema for a code cell in the notebook.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "code", "description": "Type of the cell." }, "executionCount": { "default": 0, "description": "Execution count of the cell.", "title": "Executioncount", "type": "integer" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "outputs": { "description": "Outputs of the cell.", "items": { "anyOf": [ { "description": "The schema for the execute result output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "executionCount": { "default": 0, "description": "Execution count of the output.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "execute_result", "default": "execute_result", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "ExecuteResultOutputSchema", "type": "object" }, { "description": "The schema for the display data output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "display_data", "default": "display_data", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "DisplayDataOutputSchema", "type": "object" }, { "description": "The schema for the stream output in a notebook cell.", "properties": { "name": { "description": "Name of the stream.", "title": "Name", "type": "string" }, "outputType": { "const": "stream", "default": "stream", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Text of the stream.", "title": "Text" } }, "required": [ "name", "text" ], "title": "StreamOutputSchema", "type": "object" }, { "description": "The schema for the error output in a notebook cell.", "properties": { "ename": { "description": "Error name.", "title": "Ename", "type": "string" }, "evalue": { "description": "Error value.", "title": "Evalue", "type": "string" }, "outputType": { "const": "error", "default": "error", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "traceback": { "description": "Traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "ename", "evalue" ], "title": "ErrorOutputSchema", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "CodeCellSchema", "type": "object" }, { "description": "The schema for a markdown cell in the notebook.", "properties": { "attachments": { "description": "Attachments of the cell.", "title": "Attachments", "type": "object" }, "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "markdown", "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "MarkdownCellSchema", "type": "object" }, { "description": "The schema for cells in a notebook that are not code or markdown.", "properties": { "cellType": { "description": "Type of the cell.", "title": "Celltype", "type": "string" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType" ], "title": "AnyCellSchema", "type": "object" } ] }, "title": "Cells", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" } }, "required": [ "generation", "cells" ], "title": "NotebookCellsSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the notebook cells. | NotebookCellsSchema | ## Modify Cells by notebook ID Operation path: `PATCH /api/v2/notebookSessions/{notebookId}/notebook/cells/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request payload values for updating notebook cells.", "properties": { "actionId": { "description": "Action ID of notebook update request.", "maxLength": 64, "title": "Actionid", "type": "string" }, "cells": { "description": "List of updated notebook cells.", "items": { "description": "The schema for the updated notebook cell.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "UpdateNotebookCellSchema", "type": "object" }, "title": "Cells", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "cells" ], "title": "UpdateCellsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | UpdateCellsRequest | false | none | ### Example responses > 202 Response ``` { "description": "Base schema for action created responses.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "title": "Actionid", "type": "string" } }, "required": [ "actionId" ], "title": "ActionCreatedResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Base schema for action created responses. | ActionCreatedResponse | ## Create Cells by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/notebook/cells/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request payload values for creating notebook cells.", "properties": { "actionId": { "description": "Action ID of notebook update request.", "maxLength": 64, "title": "Actionid", "type": "string" }, "cells": { "description": "List of cells to create.", "items": { "description": "The schema for the cells to be inserted into a notebook.", "properties": { "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "ID of the cell after which to insert the new cell.", "title": "Aftercellid" }, "data": { "allOf": [ { "description": "The schema for the notebook cell to be created.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell to create." }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "cellType" ], "title": "CreateNotebookCellSchema", "type": "object" } ], "description": "The cell data to insert.", "title": "Data" } }, "required": [ "afterCellId", "data" ], "title": "InsertCellsSchema", "type": "object" }, "title": "Cells", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "cells" ], "title": "CreateCellsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | CreateCellsRequest | false | none | ### Example responses > 202 Response ``` { "description": "Response schema for creating notebook cells.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "title": "Actionid", "type": "string" }, "cellIds": { "description": "List of cell IDs of the created cells.", "items": { "type": "string" }, "title": "Cellids", "type": "array" } }, "required": [ "actionId", "cellIds" ], "title": "CreateCellsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Response schema for creating notebook cells. | CreateCellsResponse | ## Cancel Cells by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/notebook/cells/cancel/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request payload values for canceling notebook cells execution.", "properties": { "cellIds": { "description": "List of cell IDs to cancel execution for.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "cellIds" ], "title": "CancelCellsExecutionRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | CancelCellsExecutionRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | No content. | None | ## Delete Output by notebook ID Operation path: `DELETE /api/v2/notebookSessions/{notebookId}/notebook/cells/output/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | BatchCellQuery | query | BatchCellQuery | false | Base schema for batch cell queries. | ### Example responses > 202 Response ``` { "description": "Base schema for action created responses.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "title": "Actionid", "type": "string" } }, "required": [ "actionId" ], "title": "ActionCreatedResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Base schema for action created responses. | ActionCreatedResponse | ## Create Reorder by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/notebook/cells/reorder/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request payload values for reordering notebook cells.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "maxLength": 64, "title": "Actionid", "type": "string" }, "cellIds": { "description": "List of cell IDs to reorder.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "cellIds" ], "title": "ReorderCellsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | ReorderCellsRequest | false | none | ### Example responses > 202 Response ``` { "description": "Base schema for action created responses.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "title": "Actionid", "type": "string" } }, "required": [ "actionId" ], "title": "ActionCreatedResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Base schema for action created responses. | ActionCreatedResponse | ## Create move by id Operation path: `POST /api/v2/notebookSessions/{notebookId}/notebook/cells/{cellId}/move/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request payload values for moving a notebook cell.", "properties": { "actionId": { "description": "Action ID of notebook update request.", "maxLength": 64, "title": "Actionid", "type": "string" }, "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "ID of the cell after which to move the cell.", "title": "Aftercellid" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "afterCellId" ], "title": "MoveCellRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | cellId | path | string | true | The cell ID to move. | | body | body | MoveCellRequest | false | none | ### Example responses > 202 Response ``` { "description": "Base schema for action created responses.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "title": "Actionid", "type": "string" } }, "required": [ "actionId" ], "title": "ActionCreatedResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Base schema for action created responses. | ActionCreatedResponse | ## Retrieve Dataframes by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/notebook/dataframes/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | NotebookOperationQuery | query | NotebookOperationQuery | false | Base query schema for notebook operations. | ### Example responses > 200 Response ``` { "description": "Schema for the list of DataFrames in a notebook.", "properties": { "dataframes": { "description": "List of DataFrames information.", "items": { "description": "Schema for a single DataFrame entry.", "properties": { "name": { "description": "Name of the DataFrame.", "title": "Name", "type": "string" }, "type": { "description": "Type of the DataFrame.", "title": "Type", "type": "string" } }, "required": [ "name", "type" ], "title": "DataFrameEntrySchema", "type": "object" }, "title": "Dataframes", "type": "array" } }, "title": "ListDataFrameResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for the list of DataFrames in a notebook. | ListDataFrameResponse | ## Retrieve dataframes by id Operation path: `GET /api/v2/notebookSessions/{notebookId}/notebook/dataframes/{referenceId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | referenceId | path | string | true | The reference ID for the DataFrame. | | PaginatedDataframeQuery | query | PaginatedDataframeQuery | false | Query schema for paginated dataframe requests. | ### Example responses > 200 Response ``` { "description": "Schema for the DataFrame response.", "properties": { "dataframe": { "description": "The DataFrame as a string.", "title": "Dataframe", "type": "string" } }, "required": [ "dataframe" ], "title": "DataFrameResponseSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for the DataFrame response. | DataFrameResponseSchema | ## Execute Notebook by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/notebook/execute/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request payload values for executing notebook cells.", "properties": { "cellIds": { "description": "List of cell IDs to execute.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "cells": { "description": "List of cells to execute.", "items": { "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType", "source" ], "title": "NotebookCellExecData", "type": "object" }, "title": "Cells", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path", "generation", "cells" ], "title": "ExecuteCellsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | ExecuteCellsRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | No content. | None | ## Retrieve Execution by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/notebook/execution/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | MultiNotebookOperationQuery | query | MultiNotebookOperationQuery | false | Base query schema for multiple notebook operations. | ### Example responses > 200 Response ``` { "description": "Response schema for the notebooks execution state.", "properties": { "states": { "description": "List of notebooks execution states.", "items": { "description": "The schema for the notebook execution state.", "properties": { "executionState": { "allOf": [ { "description": "Notebook execution state model", "properties": { "executingCellId": { "description": "The ID of the cell currently being executed.", "title": "Executingcellid", "type": "string" }, "executionFinishedAt": { "description": "The time the execution finished. This is based on the finish time of the last cell.", "format": "date-time", "title": "Executionfinishedat", "type": "string" }, "executionStartedAt": { "description": "The time the execution started.", "format": "date-time", "title": "Executionstartedat", "type": "string" }, "inputRequest": { "allOf": [ { "description": "AwaitingInputState represents the state of a cell that is awaiting input from the user.", "properties": { "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" }, "requestedAt": { "description": "The time the input was requested.", "format": "date-time", "title": "Requestedat", "type": "string" } }, "required": [ "requestedAt", "prompt", "password" ], "title": "AwaitingInputState", "type": "object" } ], "description": "The input request state of the cell.", "title": "Inputrequest" }, "kernelId": { "description": "The ID of the kernel used for execution.", "title": "Kernelid", "type": "string" }, "queuedCellIds": { "description": "The IDs of the cells that are queued for execution.", "items": { "type": "string" }, "title": "Queuedcellids", "type": "array" } }, "title": "NotebookExecutionState", "type": "object" } ], "description": "Execution state of the notebook.", "title": "Executionstate" }, "kernel": { "allOf": [ { "description": "The schema for the notebook kernel.", "properties": { "executionState": { "allOf": [ { "description": "Event Sequences on Various Workflows:\n- On kernel created: CONNECTED -> BUSY -> IDLE\n- On kernel restarted: RESTARTING -> STARTING -> BUSY -> IDLE\n- On regular execution: IDLE -> BUSY -> IDLE\n- On execution interrupted: IDLE -> BUSY -> INTERRUPTING -> IDLE\n- On execution with error: IDLE -> BUSY -> IDLE -> INTERRUPTING -> IDLE\n- On kernel shut down via calling the stop kernel endpoint:\n DISCONNECTED (can be sent a few times) -> NOT_RUNNING (after 5s)", "enum": [ "connecting", "disconnected", "connected", "starting", "idle", "busy", "interrupting", "restarting", "not_running" ], "title": "KernelState", "type": "string" } ], "description": "The execution state of the kernel." }, "id": { "description": "The ID of the kernel.", "title": "Id", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "The programming language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "The name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" }, "running": { "default": false, "description": "Whether the kernel is running.", "title": "Running", "type": "boolean" } }, "required": [ "id", "name", "language", "executionState" ], "title": "KernelSchema", "type": "object" } ], "description": "Kernel assigned to the notebook.", "title": "Kernel" }, "kernelId": { "description": "Kernel ID assigned to the notebook.", "title": "Kernelid", "type": "string" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path" ], "title": "NotebookExecutionStateSchema", "type": "object" }, "title": "States", "type": "array" } }, "required": [ "states" ], "title": "NotebooksExecutionStateResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for the notebooks execution state. | NotebooksExecutionStateResponse | ## Create Kernel by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/notebook/kernel/` Authentication requirements: `BearerAuth` Sets kernel for a given notebook. ### Body parameter ``` { "description": "Request payload values for assigning a kernel to a notebook.", "properties": { "kernelId": { "description": "ID of the kernel to assign to the notebook.", "title": "Kernelid", "type": "string" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path" ], "title": "AssignNotebookKernelRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | AssignNotebookKernelRequest | false | none | ### Example responses > 200 Response ``` { "description": "The schema for the notebook kernel.", "properties": { "executionState": { "allOf": [ { "description": "Event Sequences on Various Workflows:\n- On kernel created: CONNECTED -> BUSY -> IDLE\n- On kernel restarted: RESTARTING -> STARTING -> BUSY -> IDLE\n- On regular execution: IDLE -> BUSY -> IDLE\n- On execution interrupted: IDLE -> BUSY -> INTERRUPTING -> IDLE\n- On execution with error: IDLE -> BUSY -> IDLE -> INTERRUPTING -> IDLE\n- On kernel shut down via calling the stop kernel endpoint:\n DISCONNECTED (can be sent a few times) -> NOT_RUNNING (after 5s)", "enum": [ "connecting", "disconnected", "connected", "starting", "idle", "busy", "interrupting", "restarting", "not_running" ], "title": "KernelState", "type": "string" } ], "description": "The execution state of the kernel." }, "id": { "description": "The ID of the kernel.", "title": "Id", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "The programming language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "The name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" }, "running": { "default": false, "description": "Whether the kernel is running.", "title": "Running", "type": "boolean" } }, "required": [ "id", "name", "language", "executionState" ], "title": "KernelSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The schema for the notebook kernel. | KernelSchema | ## Create Trigger Execution by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/notebook/triggerExecution/` Authentication requirements: `BearerAuth` A variation of execute_notebook that automatically prepares, assigns kernel and executes the notebook in its entirety. ### Body parameter ``` { "description": "Request payload values for executing a notebook with parameters.", "properties": { "parameters": { "description": "List of parameters to use as environment variables during execution.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path" ], "title": "TriggeredNotebookExecutionRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | TriggeredNotebookExecutionRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | No content. | None | ## Execute scripts by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/scripts/execute/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request payload values for running a file.", "properties": { "commandArgs": { "description": "Arguments and/or flags to pass to a file execution command. For example: '--filename foo.txt -r'", "title": "Commandargs", "type": "string" }, "commandType": { "allOf": [ { "description": "These are the command types/languages we support running files for.", "enum": [ "python", "bash" ], "title": "RunnableCommandType", "type": "string" } ], "description": "The type of command to be run. For example 'python'" }, "filePath": { "description": "Path to the file to execute.", "title": "Filepath", "type": "string" } }, "required": [ "filePath" ], "title": "RunFileRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | RunFileRequest | false | none | ### Example responses > 200 Response ``` { "description": "Response schema for executing a file.", "properties": { "kernelId": { "description": "ID of the kernel assigned to the file.", "title": "Kernelid", "type": "string" } }, "required": [ "kernelId" ], "title": "ExecuteFileResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for executing a file. | ExecuteFileResponse | ## Retrieve Executing by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/scripts/executing/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | ### Example responses > 200 Response ``` { "description": "Response schema for listing executing files.", "properties": { "executingFiles": { "description": "List of executing files with their kernel IDs.", "items": { "description": "Schema for executing file information.", "properties": { "filePath": { "description": "Path to the file being executed.", "title": "Filepath", "type": "string" }, "kernelId": { "description": "ID of the kernel assigned to the file.", "title": "Kernelid", "type": "string" } }, "required": [ "kernelId", "filePath" ], "title": "ExecutingFileData", "type": "object" }, "title": "Executingfiles", "type": "array" } }, "required": [ "executingFiles" ], "title": "ExecutingFilesResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for listing executing files. | ExecutingFilesResponse | ## Retrieve Terminals by notebook ID Operation path: `GET /api/v2/notebookSessions/{notebookId}/terminals/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | ### Example responses > 200 Response ``` { "description": "Schema for the list of terminals in a notebook.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of terminals in a notebook.", "items": { "description": "Schema for a terminal.", "properties": { "createdAt": { "description": "Creation time of the terminal.", "format": "date-time", "title": "Createdat", "type": "string" }, "name": { "description": "Name of the terminal.", "title": "Name", "type": "string" }, "terminalId": { "description": "ID of the terminal.", "title": "Terminalid", "type": "string" } }, "required": [ "terminalId", "name", "createdAt" ], "title": "TerminalSchema", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "TerminalsResponseSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for the list of terminals in a notebook. | TerminalsResponseSchema | ## Create Terminals by notebook ID Operation path: `POST /api/v2/notebookSessions/{notebookId}/terminals/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Schema for creating a terminal.", "properties": { "name": { "description": "Name of the terminal.", "title": "Name", "type": "string" } }, "required": [ "name" ], "title": "CreateTerminalSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | body | body | CreateTerminalSchema | false | none | ### Example responses > 201 Response ``` { "description": "Schema for a terminal.", "properties": { "createdAt": { "description": "Creation time of the terminal.", "format": "date-time", "title": "Createdat", "type": "string" }, "name": { "description": "Name of the terminal.", "title": "Name", "type": "string" }, "terminalId": { "description": "ID of the terminal.", "title": "Terminalid", "type": "string" } }, "required": [ "terminalId", "name", "createdAt" ], "title": "TerminalSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Schema for a terminal. | TerminalSchema | ## Delete Terminals by notebook ID Operation path: `DELETE /api/v2/notebookSessions/{notebookId}/terminals/{terminalId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | terminalId | path | string | true | The terminal ID to delete. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Modify Terminals by notebook ID Operation path: `PATCH /api/v2/notebookSessions/{notebookId}/terminals/{terminalId}/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Schema for updating a terminal.", "properties": { "name": { "description": "Name to update the terminal with.", "title": "Name", "type": "string" } }, "required": [ "name" ], "title": "UpdateTerminalSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that the session belongs to. | | terminalId | path | string | true | The terminal ID to update. | | body | body | UpdateTerminalSchema | false | none | ### Example responses > 200 Response ``` { "description": "Schema for a terminal.", "properties": { "createdAt": { "description": "Creation time of the terminal.", "format": "date-time", "title": "Createdat", "type": "string" }, "name": { "description": "Name of the terminal.", "title": "Name", "type": "string" }, "terminalId": { "description": "ID of the terminal.", "title": "Terminalid", "type": "string" } }, "required": [ "terminalId", "name", "createdAt" ], "title": "TerminalSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for a terminal. | TerminalSchema | ## Retrieve Notebooks Operation path: `GET /api/v2/notebooks/` Authentication requirements: `BearerAuth` Return list of notebooks. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ListNotebooksQuery | query | ListNotebooksQuery | false | Query options for listing notebooks in the notebooks directory. | ### Example responses > 200 Response ``` { "description": "Response schema for listing notebooks.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "A list of notebooks.", "items": { "description": "Schema for notebook metadata with additional fields.", "properties": { "created": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the creation of the notebook.", "title": "Created" }, "description": { "description": "The description of the notebook.", "title": "Description", "type": "string" }, "hasEnabledSchedule": { "description": "Whether the notebook has an enabled schedule.", "title": "Hasenabledschedule", "type": "boolean" }, "hasSchedule": { "description": "Whether the notebook has a schedule.", "title": "Hasschedule", "type": "boolean" }, "id": { "description": "The ID of the notebook.", "title": "Id", "type": "string" }, "lastViewed": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last viewed time of the notebook.", "title": "Lastviewed" }, "name": { "description": "The name of the notebook.", "title": "Name", "type": "string" }, "orgId": { "description": "The ID of the organization associated with the notebook.", "title": "Orgid", "type": "string" }, "permissions": { "description": "The permissions associated with the notebook.", "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "session": { "allOf": [ { "description": "The schema for the notebook session.", "properties": { "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "The type of the notebook session." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" } }, "required": [ "status", "notebookId" ], "title": "NotebookSessionSharedSchema", "type": "object" } ], "description": "Information about the session associated with the notebook.", "title": "Session" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "The settings of the notebook.", "title": "Settings" }, "tags": { "description": "The tags of the notebook.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "tenantId": { "description": "The tenant ID associated with the notebook.", "title": "Tenantid", "type": "string" }, "type": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of the notebook." }, "typeTransition": { "allOf": [ { "description": "An enumeration.", "enum": [ "initiated_to_codespace", "completed" ], "title": "NotebookTypeTransition", "type": "string" } ], "description": "The type transition of the notebook." }, "updated": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last update of the notebook.", "title": "Updated" }, "useCaseId": { "description": "The ID of the Use Case associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "name", "id", "created", "lastViewed" ], "title": "NotebookSchema", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ListNotebooksResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for listing notebooks. | ListNotebooksResponse | ## Create Notebooks Operation path: `POST /api/v2/notebooks/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for creating a notebook.", "properties": { "createInitialCell": { "default": true, "description": "Whether to create an initial cell.", "title": "Createinitialcell", "type": "boolean" }, "description": { "description": "The description of the notebook.", "title": "Description", "type": "string" }, "name": { "default": "Untitled Notebook", "description": "The name of the notebook.", "title": "Name", "type": "string" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "The settings of the notebook.", "title": "Settings" }, "tags": { "description": "The tags of the notebook.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "type": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of the notebook." }, "typeTransition": { "allOf": [ { "description": "An enumeration.", "enum": [ "initiated_to_codespace", "completed" ], "title": "NotebookTypeTransition", "type": "string" } ], "description": "The type transition of the notebook." }, "useCaseId": { "description": "The ID of the Use Case associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case associated with the notebook.", "title": "Usecasename", "type": "string" } }, "title": "CreateNotebookRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateNotebookRequest | false | none | ### Example responses > 201 Response ``` { "description": "Schema for notebook metadata with additional fields.", "properties": { "created": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the creation of the notebook.", "title": "Created" }, "description": { "description": "The description of the notebook.", "title": "Description", "type": "string" }, "hasEnabledSchedule": { "description": "Whether the notebook has an enabled schedule.", "title": "Hasenabledschedule", "type": "boolean" }, "hasSchedule": { "description": "Whether the notebook has a schedule.", "title": "Hasschedule", "type": "boolean" }, "id": { "description": "The ID of the notebook.", "title": "Id", "type": "string" }, "lastViewed": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last viewed time of the notebook.", "title": "Lastviewed" }, "name": { "description": "The name of the notebook.", "title": "Name", "type": "string" }, "orgId": { "description": "The ID of the organization associated with the notebook.", "title": "Orgid", "type": "string" }, "permissions": { "description": "The permissions associated with the notebook.", "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "session": { "allOf": [ { "description": "The schema for the notebook session.", "properties": { "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "The type of the notebook session." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" } }, "required": [ "status", "notebookId" ], "title": "NotebookSessionSharedSchema", "type": "object" } ], "description": "Information about the session associated with the notebook.", "title": "Session" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "The settings of the notebook.", "title": "Settings" }, "tags": { "description": "The tags of the notebook.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "tenantId": { "description": "The tenant ID associated with the notebook.", "title": "Tenantid", "type": "string" }, "type": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of the notebook." }, "typeTransition": { "allOf": [ { "description": "An enumeration.", "enum": [ "initiated_to_codespace", "completed" ], "title": "NotebookTypeTransition", "type": "string" } ], "description": "The type transition of the notebook." }, "updated": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last update of the notebook.", "title": "Updated" }, "useCaseId": { "description": "The ID of the Use Case associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "name", "id", "created", "lastViewed" ], "title": "NotebookSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Schema for notebook metadata with additional fields. | NotebookSchema | ## Create Bulk Link Use Case Operation path: `POST /api/v2/notebooks/bulkLinkUseCase/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request payload schema for bulk linking notebooks to a use case.", "properties": { "notebookIds": { "description": "List of notebook IDs to link.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "title": "Notebookids", "type": "array" }, "source": { "allOf": [ { "description": "The source of the entity linking. Does not affect the operation of the API call. Only used for analytics.", "enum": [ "classic", "nextGen", "api" ], "title": "EntityLinkingSources", "type": "string" } ], "description": "The source of the entity linking." }, "useCaseId": { "description": "The ID of the Use Case to link the notebooks to.", "title": "Usecaseid", "type": "string" }, "workflow": { "allOf": [ { "description": "The workflow that is attaching this entity. Does not affect the operation of the API call. Only used for analytics.", "enum": [ "migration", "creation", "unspecified" ], "title": "EntityLinkingWorkflows", "type": "string" } ], "description": "The workflow responsible for attaching the notebook to the Use Case." } }, "required": [ "notebookIds", "useCaseId" ], "title": "BulkLinkNotebooksToUseCaseRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | BulkLinkNotebooksToUseCaseRequest | false | none | ### Example responses > 200 Response ``` { "description": "Response schema indicating success of an operation.", "properties": { "success": { "description": "Indicates if the operation was successful.", "title": "Success", "type": "boolean" } }, "required": [ "success" ], "title": "SuccessResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema indicating successful or failed operation. | SuccessResponse | ## Retrieve Filter Options Operation path: `GET /api/v2/notebooks/filterOptions/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | FilterOptionsQuery | query | FilterOptionsQuery | false | Query options for filtering notebooks in the notebooks directory. | ### Example responses > 200 Response ``` { "description": "Response schema for filter options in the notebooks directory.", "properties": { "owners": { "description": "A list of possible owners to filter notebooks by.", "items": { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" }, "title": "Owners", "type": "array" }, "tags": { "description": "A list of possible tags to filter notebooks by.", "items": { "type": "string" }, "title": "Tags", "type": "array" } }, "title": "FilterOptionsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Response schema for filter options in the notebooks directory. | FilterOptionsResponse | ## Create From File Operation path: `POST /api/v2/notebooks/fromFile/` Authentication requirements: `BearerAuth` Import Notebook from file endpoint. ### Example responses > 201 Response ``` { "description": "Imported notebook information.", "properties": { "id": { "description": "The ID of the created notebook.", "title": "Id", "type": "string" }, "name": { "description": "The name of the created notebook.", "title": "Name", "type": "string" } }, "required": [ "id" ], "title": "ImportNotebookResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Imported notebook information. | ImportNotebookResponse | ## Create From URL Operation path: `POST /api/v2/notebooks/fromUrl/` Authentication requirements: `BearerAuth` Import the notebook from the URL endpoint. ### Body parameter ``` { "description": "Request payload values for importing a notebook from a URL.", "properties": { "includeOutput": { "default": true, "description": "Whether to include the cell output of the notebook in the import.", "title": "Includeoutput", "type": "boolean" }, "name": { "description": "The name of the notebook to be created upon import.", "title": "Name", "type": "string" }, "uri": { "description": "The URL of the notebook to import.", "title": "Uri", "type": "string" }, "useCaseId": { "description": "The ID of the Use Case to import the notebook into.", "title": "Usecaseid", "type": "string" } }, "required": [ "uri" ], "title": "ImportNotebookFromUrlRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ImportNotebookFromUrlRequest | false | none | ### Example responses > 201 Response ``` { "description": "Imported notebook information.", "properties": { "id": { "description": "The ID of the created notebook.", "title": "Id", "type": "string" }, "name": { "description": "The name of the created notebook.", "title": "Name", "type": "string" } }, "required": [ "id" ], "title": "ImportNotebookResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Imported notebook information. | ImportNotebookResponse | ## Get access control lists Operation path: `GET /api/v2/notebooks/sharedRoles/` Authentication requirements: `BearerAuth` Get a lists of users who have access to each notebook and their roles on each respective notebook. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookIds | query | array[string] | true | Comma separated notebook IDs to get info of. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "Roles data for multiple notebooks.", "items": { "properties": { "notebookId": { "description": "The ID of the notebook.", "type": "string" }, "roles": { "description": "Individual roles data for the notebook.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.35" }, "type": "array" } }, "required": [ "notebookId", "roles" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Access control list for each notebook keyed by notebook ID. | NotebooksSharedRolesListResponse | ## Delete Notebooks by notebook ID Operation path: `DELETE /api/v2/notebooks/{notebookId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to delete. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Retrieve Notebooks by notebook ID Operation path: `GET /api/v2/notebooks/{notebookId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to retrieve. | ### Example responses > 200 Response ``` { "description": "Schema for notebook metadata with additional fields.", "properties": { "created": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the creation of the notebook.", "title": "Created" }, "description": { "description": "The description of the notebook.", "title": "Description", "type": "string" }, "hasEnabledSchedule": { "description": "Whether the notebook has an enabled schedule.", "title": "Hasenabledschedule", "type": "boolean" }, "hasSchedule": { "description": "Whether the notebook has a schedule.", "title": "Hasschedule", "type": "boolean" }, "id": { "description": "The ID of the notebook.", "title": "Id", "type": "string" }, "lastViewed": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last viewed time of the notebook.", "title": "Lastviewed" }, "name": { "description": "The name of the notebook.", "title": "Name", "type": "string" }, "orgId": { "description": "The ID of the organization associated with the notebook.", "title": "Orgid", "type": "string" }, "permissions": { "description": "The permissions associated with the notebook.", "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "session": { "allOf": [ { "description": "The schema for the notebook session.", "properties": { "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "The type of the notebook session." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" } }, "required": [ "status", "notebookId" ], "title": "NotebookSessionSharedSchema", "type": "object" } ], "description": "Information about the session associated with the notebook.", "title": "Session" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "The settings of the notebook.", "title": "Settings" }, "tags": { "description": "The tags of the notebook.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "tenantId": { "description": "The tenant ID associated with the notebook.", "title": "Tenantid", "type": "string" }, "type": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of the notebook." }, "typeTransition": { "allOf": [ { "description": "An enumeration.", "enum": [ "initiated_to_codespace", "completed" ], "title": "NotebookTypeTransition", "type": "string" } ], "description": "The type transition of the notebook." }, "updated": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last update of the notebook.", "title": "Updated" }, "useCaseId": { "description": "The ID of the Use Case associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "name", "id", "created", "lastViewed" ], "title": "NotebookSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook metadata with additional fields. | NotebookSchema | ## Modify Notebooks by notebook ID Operation path: `PATCH /api/v2/notebooks/{notebookId}/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for updating a notebook.", "properties": { "description": { "description": "The value to update the description of the notebook to.", "title": "Description", "type": "string" }, "name": { "description": "The value to update the name of the notebook to.", "title": "Name", "type": "string" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "description": "The value to update the settings of the notebook to.", "title": "Settings" }, "tags": { "description": "The value to update the tags of the notebook to.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "useCaseId": { "description": "The value to update the Use Case ID of the notebook to.", "title": "Usecaseid", "type": "string" } }, "title": "UpdateNotebookRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to update. | | body | body | UpdateNotebookRequest | false | none | ### Example responses > 200 Response ``` { "description": "Schema for notebook metadata with additional fields.", "properties": { "created": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the creation of the notebook.", "title": "Created" }, "description": { "description": "The description of the notebook.", "title": "Description", "type": "string" }, "hasEnabledSchedule": { "description": "Whether the notebook has an enabled schedule.", "title": "Hasenabledschedule", "type": "boolean" }, "hasSchedule": { "description": "Whether the notebook has a schedule.", "title": "Hasschedule", "type": "boolean" }, "id": { "description": "The ID of the notebook.", "title": "Id", "type": "string" }, "lastViewed": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last viewed time of the notebook.", "title": "Lastviewed" }, "name": { "description": "The name of the notebook.", "title": "Name", "type": "string" }, "orgId": { "description": "The ID of the organization associated with the notebook.", "title": "Orgid", "type": "string" }, "permissions": { "description": "The permissions associated with the notebook.", "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "session": { "allOf": [ { "description": "The schema for the notebook session.", "properties": { "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "The type of the notebook session." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" } }, "required": [ "status", "notebookId" ], "title": "NotebookSessionSharedSchema", "type": "object" } ], "description": "Information about the session associated with the notebook.", "title": "Session" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "The settings of the notebook.", "title": "Settings" }, "tags": { "description": "The tags of the notebook.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "tenantId": { "description": "The tenant ID associated with the notebook.", "title": "Tenantid", "type": "string" }, "type": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of the notebook." }, "typeTransition": { "allOf": [ { "description": "An enumeration.", "enum": [ "initiated_to_codespace", "completed" ], "title": "NotebookTypeTransition", "type": "string" } ], "description": "The type transition of the notebook." }, "updated": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last update of the notebook.", "title": "Updated" }, "useCaseId": { "description": "The ID of the Use Case associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "name", "id", "created", "lastViewed" ], "title": "NotebookSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook metadata with additional fields. | NotebookSchema | ## Modify Batch Clear Cells Execution Count by notebook ID Operation path: `PATCH /api/v2/notebooks/{notebookId}/batchClearCellsExecutionCount/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to batch clear cell execution count for. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Retrieve Cells by notebook ID Operation path: `GET /api/v2/notebooks/{notebookId}/cells/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to retrieve cells for. | ### Example responses > 200 Response ``` { "items": { "description": "Schema for notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionId": { "description": "The ID of the execution.", "title": "Executionid", "type": "string" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata", "cellId", "md5" ], "title": "CellSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook cell. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [CellSchema] | false | | [Schema for notebook cell.] | | » CellSchema | CellSchema | false | | Schema for notebook cell. | | »» attachments | object | false | | Cell attachments. | | »» cellId | string | true | | The ID of the cell. | | »» executed | NotebookTimestampInfo | false | | The timestamp of when the cell was executed. | | »»» at | string(date-time) | true | | Timestamp of the action. | | »»» by | UserInfo | true | | User info of the actor who caused the action to occur. | | »»»» activated | boolean | false | | Whether the user is activated. | | »»»» firstName | string | false | | The first name of the user. | | »»»» gravatarHash | string | false | | The gravatar hash of the user. | | »»»» id | string | true | | The ID of the user. | | »»»» lastName | string | false | | The last name of the user. | | »»»» orgId | string | false | | The ID of the organization the user belongs to. | | »»»» tenantPhase | string | false | | The tenant phase of the user. | | »»»» username | string | false | | The username of the user. | | »» executionCount | integer | false | | The execution count of the cell relative to other cells in the current session. | | »» executionId | string | false | | The ID of the execution. | | »» executionTimeMillis | integer | false | | The execution time of the cell in milliseconds. | | »» md5 | string | true | | The MD5 hash of the cell. | | »» metadata | NotebookCellMetadata | true | | The metadata of the cell. | | »»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» aggregation | string | false | | Aggregation function for the axis. | | »»»»»» color | string | false | | Color for the axis. | | »»»»»» hideGrid | boolean | false | | Whether to hide the grid lines on the axis. | | »»»»»» hideInTooltip | boolean | false | | Whether to hide the axis in the tooltip. | | »»»»»» hideLabel | boolean | false | | Whether to hide the axis label. | | »»»»»» key | string | false | | Key for the axis. | | »»»»»» label | string | false | | Label for the axis. | | »»»»»» position | string | false | | Position of the axis. | | »»»»»» showPointMarkers | boolean | false | | Whether to show point markers on the axis. | | »»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»» data | object | false | | The data associated with the cell chart. | | »»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»» chartType | string | false | | Type of the chart. | | »»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»» title | string | false | | Title of the chart. | | »»» collapsed | boolean | false | | Whether the cell's output is collapsed/expanded. | | »»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»» metricName | string | true | | The name of the custom LLM metric. | | »»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»» metricId | string | true | | The ID of the custom metric. | | »»»» metricName | string | false | | The name of the custom metric. | | »»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» month | [anyOf] | true | | The month(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» dataframeViewOptions | object | false | | Dataframe cell view options and metadata. | | »»» datarobot | NotebookCellDataRobotMetadata | false | | Metadata specific to DataRobot's notebooks and notebook environment. | | »»»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»» data | object | false | | The data associated with the cell chart. | | »»»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»»» chartType | string | false | | Type of the chart. | | »»»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»»» title | string | false | | Title of the chart. | | »»»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»»» metricName | string | true | | The name of the custom LLM metric. | | »»»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»»» metricId | string | true | | The ID of the custom metric. | | »»»»» metricName | string | false | | The name of the custom metric. | | »»»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | | »»»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | | »»»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | | »»»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | | »»»»»» month | [anyOf] | true | | The month(s) to run the schedule. | | »»»» dataframeViewOptions | object | false | | DataFrame view options and metadata. | | »»»» disableRun | boolean | false | | Whether to disable the run button in the cell. | | »»»» executionTimeMillis | integer | false | | Execution time of the cell in milliseconds. | | »»»» hideCode | boolean | false | | Whether to hide the code in the cell. | | »»»» hideResults | boolean | false | | Whether to hide the results in the cell. | | »»»» language | Language | false | | An enumeration. | | »»» disableRun | boolean | false | | Whether or not the cell is disabled in the UI. | | »»» hideCode | boolean | false | | Whether or not code is hidden in the UI. | | »»» hideResults | boolean | false | | Whether or not results are hidden in the UI. | | »»» jupyter | JupyterCellMetadata | false | | Jupyter metadata. | | »»»» outputsHidden | boolean | false | | Whether the cell's outputs are hidden. | | »»»» sourceHidden | boolean | false | | Whether the cell's source is hidden. | | »»» name | string | false | | Name of the notebook cell. | | »»» scrolled | any | false | | Whether the cell's output is scrolled, unscrolled, or autoscrolled. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» outputType | OutputStorageType | false | | The type of storage used for the cell output. | | »» outputs | [anyOf] | false | | The cell outputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellStreamOutput | false | | Cell stream output. | | »»»» name | string | true | | The name of the stream. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» text | any | true | | The text of the stream. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellInputRequest | false | | Cell input request. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» password | boolean | true | | Whether the input request is for a password. | | »»»» prompt | string | true | | The prompt for the input request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellErrorOutput | false | | Cell error output. | | »»»» ename | string | true | | The name of the error. | | »»»» evalue | string | true | | The value of the error. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» traceback | [string] | true | | The traceback of the error. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellExecuteResultOutput | false | | Cell execute results output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» executionCount | integer | false | | A result's prompt number. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellDisplayDataOutput | false | | Cell display data output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» source | string | true | | The contents of the cell, represented as a string. | ### Enumerated Values | Property | Value | | --- | --- | | language | [dataframe, markdown, python, r, shell, scala, sas, custommetric] | | anonymous | auto | | outputType | RAW_OUTPUT | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | ## Create Cells by notebook ID Operation path: `POST /api/v2/notebooks/{notebookId}/cells/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for creating a notebook cell.", "properties": { "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "The ID of the cell after which to create the new cell.", "title": "Aftercellid" }, "attachments": { "description": "The attachments associated with the cell.", "title": "Attachments", "type": "object" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata associated with the cell.", "title": "Metadata" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "afterCellId", "source" ], "title": "CreateCellRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to create cells for. | | body | body | CreateCellRequest | false | none | ### Example responses > 200 Response ``` { "description": "Schema for notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionId": { "description": "The ID of the execution.", "title": "Executionid", "type": "string" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata", "cellId", "md5" ], "title": "CellSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook cell. | CellSchema | ## Modify Batch Clear Output by notebook ID Operation path: `PATCH /api/v2/notebooks/{notebookId}/cells/batchClearOutput/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for batch clearing the output of notebook cells.", "properties": { "cellIds": { "description": "The IDs of the cells to clear the output of.", "items": { "type": "string" }, "title": "Cellids", "type": "array" } }, "required": [ "cellIds" ], "title": "BatchClearCellsOutputRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to clear all cell output of. | | body | body | BatchClearCellsOutputRequest | false | none | ### Example responses > 200 Response ``` { "items": { "description": "Schema for notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionId": { "description": "The ID of the execution.", "title": "Executionid", "type": "string" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata", "cellId", "md5" ], "title": "CellSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook cell. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [CellSchema] | false | | [Schema for notebook cell.] | | » CellSchema | CellSchema | false | | Schema for notebook cell. | | »» attachments | object | false | | Cell attachments. | | »» cellId | string | true | | The ID of the cell. | | »» executed | NotebookTimestampInfo | false | | The timestamp of when the cell was executed. | | »»» at | string(date-time) | true | | Timestamp of the action. | | »»» by | UserInfo | true | | User info of the actor who caused the action to occur. | | »»»» activated | boolean | false | | Whether the user is activated. | | »»»» firstName | string | false | | The first name of the user. | | »»»» gravatarHash | string | false | | The gravatar hash of the user. | | »»»» id | string | true | | The ID of the user. | | »»»» lastName | string | false | | The last name of the user. | | »»»» orgId | string | false | | The ID of the organization the user belongs to. | | »»»» tenantPhase | string | false | | The tenant phase of the user. | | »»»» username | string | false | | The username of the user. | | »» executionCount | integer | false | | The execution count of the cell relative to other cells in the current session. | | »» executionId | string | false | | The ID of the execution. | | »» executionTimeMillis | integer | false | | The execution time of the cell in milliseconds. | | »» md5 | string | true | | The MD5 hash of the cell. | | »» metadata | NotebookCellMetadata | true | | The metadata of the cell. | | »»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» aggregation | string | false | | Aggregation function for the axis. | | »»»»»» color | string | false | | Color for the axis. | | »»»»»» hideGrid | boolean | false | | Whether to hide the grid lines on the axis. | | »»»»»» hideInTooltip | boolean | false | | Whether to hide the axis in the tooltip. | | »»»»»» hideLabel | boolean | false | | Whether to hide the axis label. | | »»»»»» key | string | false | | Key for the axis. | | »»»»»» label | string | false | | Label for the axis. | | »»»»»» position | string | false | | Position of the axis. | | »»»»»» showPointMarkers | boolean | false | | Whether to show point markers on the axis. | | »»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»» data | object | false | | The data associated with the cell chart. | | »»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»» chartType | string | false | | Type of the chart. | | »»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»» title | string | false | | Title of the chart. | | »»» collapsed | boolean | false | | Whether the cell's output is collapsed/expanded. | | »»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»» metricName | string | true | | The name of the custom LLM metric. | | »»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»» metricId | string | true | | The ID of the custom metric. | | »»»» metricName | string | false | | The name of the custom metric. | | »»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» month | [anyOf] | true | | The month(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» dataframeViewOptions | object | false | | Dataframe cell view options and metadata. | | »»» datarobot | NotebookCellDataRobotMetadata | false | | Metadata specific to DataRobot's notebooks and notebook environment. | | »»»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»» data | object | false | | The data associated with the cell chart. | | »»»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»»» chartType | string | false | | Type of the chart. | | »»»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»»» title | string | false | | Title of the chart. | | »»»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»»» metricName | string | true | | The name of the custom LLM metric. | | »»»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»»» metricId | string | true | | The ID of the custom metric. | | »»»»» metricName | string | false | | The name of the custom metric. | | »»»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | | »»»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | | »»»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | | »»»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | | »»»»»» month | [anyOf] | true | | The month(s) to run the schedule. | | »»»» dataframeViewOptions | object | false | | DataFrame view options and metadata. | | »»»» disableRun | boolean | false | | Whether to disable the run button in the cell. | | »»»» executionTimeMillis | integer | false | | Execution time of the cell in milliseconds. | | »»»» hideCode | boolean | false | | Whether to hide the code in the cell. | | »»»» hideResults | boolean | false | | Whether to hide the results in the cell. | | »»»» language | Language | false | | An enumeration. | | »»» disableRun | boolean | false | | Whether or not the cell is disabled in the UI. | | »»» hideCode | boolean | false | | Whether or not code is hidden in the UI. | | »»» hideResults | boolean | false | | Whether or not results are hidden in the UI. | | »»» jupyter | JupyterCellMetadata | false | | Jupyter metadata. | | »»»» outputsHidden | boolean | false | | Whether the cell's outputs are hidden. | | »»»» sourceHidden | boolean | false | | Whether the cell's source is hidden. | | »»» name | string | false | | Name of the notebook cell. | | »»» scrolled | any | false | | Whether the cell's output is scrolled, unscrolled, or autoscrolled. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» outputType | OutputStorageType | false | | The type of storage used for the cell output. | | »» outputs | [anyOf] | false | | The cell outputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellStreamOutput | false | | Cell stream output. | | »»»» name | string | true | | The name of the stream. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» text | any | true | | The text of the stream. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellInputRequest | false | | Cell input request. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» password | boolean | true | | Whether the input request is for a password. | | »»»» prompt | string | true | | The prompt for the input request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellErrorOutput | false | | Cell error output. | | »»»» ename | string | true | | The name of the error. | | »»»» evalue | string | true | | The value of the error. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» traceback | [string] | true | | The traceback of the error. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellExecuteResultOutput | false | | Cell execute results output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» executionCount | integer | false | | A result's prompt number. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellDisplayDataOutput | false | | Cell display data output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» source | string | true | | The contents of the cell, represented as a string. | ### Enumerated Values | Property | Value | | --- | --- | | language | [dataframe, markdown, python, r, shell, scala, sas, custommetric] | | anonymous | auto | | outputType | RAW_OUTPUT | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | ## Create Batch Create by notebook ID Operation path: `POST /api/v2/notebooks/{notebookId}/cells/batchCreate/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for batch creating notebook cells.", "properties": { "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "The ID of the cell after which to create the new cells.", "title": "Aftercellid" }, "cells": { "description": "The list of cells to create.", "items": { "description": "Metadata for the source of a notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Cell metadata.", "title": "Metadata" }, "outputs": { "description": "Cell outputs that conform to the nbformat-based NBX schema.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "Contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source" ], "title": "NotebookCellSourceMetadata", "type": "object" }, "title": "Cells", "type": "array" } }, "required": [ "afterCellId" ], "title": "BatchCreateCellsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to create cells for. | | body | body | BatchCreateCellsRequest | false | none | ### Example responses > 201 Response ``` { "items": { "description": "Schema for notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionId": { "description": "The ID of the execution.", "title": "Executionid", "type": "string" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata", "cellId", "md5" ], "title": "CellSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Schema for notebook cell. | Inline | ### Response Schema Status Code 201 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [CellSchema] | false | | [Schema for notebook cell.] | | » CellSchema | CellSchema | false | | Schema for notebook cell. | | »» attachments | object | false | | Cell attachments. | | »» cellId | string | true | | The ID of the cell. | | »» executed | NotebookTimestampInfo | false | | The timestamp of when the cell was executed. | | »»» at | string(date-time) | true | | Timestamp of the action. | | »»» by | UserInfo | true | | User info of the actor who caused the action to occur. | | »»»» activated | boolean | false | | Whether the user is activated. | | »»»» firstName | string | false | | The first name of the user. | | »»»» gravatarHash | string | false | | The gravatar hash of the user. | | »»»» id | string | true | | The ID of the user. | | »»»» lastName | string | false | | The last name of the user. | | »»»» orgId | string | false | | The ID of the organization the user belongs to. | | »»»» tenantPhase | string | false | | The tenant phase of the user. | | »»»» username | string | false | | The username of the user. | | »» executionCount | integer | false | | The execution count of the cell relative to other cells in the current session. | | »» executionId | string | false | | The ID of the execution. | | »» executionTimeMillis | integer | false | | The execution time of the cell in milliseconds. | | »» md5 | string | true | | The MD5 hash of the cell. | | »» metadata | NotebookCellMetadata | true | | The metadata of the cell. | | »»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» aggregation | string | false | | Aggregation function for the axis. | | »»»»»» color | string | false | | Color for the axis. | | »»»»»» hideGrid | boolean | false | | Whether to hide the grid lines on the axis. | | »»»»»» hideInTooltip | boolean | false | | Whether to hide the axis in the tooltip. | | »»»»»» hideLabel | boolean | false | | Whether to hide the axis label. | | »»»»»» key | string | false | | Key for the axis. | | »»»»»» label | string | false | | Label for the axis. | | »»»»»» position | string | false | | Position of the axis. | | »»»»»» showPointMarkers | boolean | false | | Whether to show point markers on the axis. | | »»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»» data | object | false | | The data associated with the cell chart. | | »»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»» chartType | string | false | | Type of the chart. | | »»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»» title | string | false | | Title of the chart. | | »»» collapsed | boolean | false | | Whether the cell's output is collapsed/expanded. | | »»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»» metricName | string | true | | The name of the custom LLM metric. | | »»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»» metricId | string | true | | The ID of the custom metric. | | »»»» metricName | string | false | | The name of the custom metric. | | »»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» month | [anyOf] | true | | The month(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» dataframeViewOptions | object | false | | Dataframe cell view options and metadata. | | »»» datarobot | NotebookCellDataRobotMetadata | false | | Metadata specific to DataRobot's notebooks and notebook environment. | | »»»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»» data | object | false | | The data associated with the cell chart. | | »»»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»»» chartType | string | false | | Type of the chart. | | »»»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»»» title | string | false | | Title of the chart. | | »»»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»»» metricName | string | true | | The name of the custom LLM metric. | | »»»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»»» metricId | string | true | | The ID of the custom metric. | | »»»»» metricName | string | false | | The name of the custom metric. | | »»»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | | »»»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | | »»»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | | »»»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | | »»»»»» month | [anyOf] | true | | The month(s) to run the schedule. | | »»»» dataframeViewOptions | object | false | | DataFrame view options and metadata. | | »»»» disableRun | boolean | false | | Whether to disable the run button in the cell. | | »»»» executionTimeMillis | integer | false | | Execution time of the cell in milliseconds. | | »»»» hideCode | boolean | false | | Whether to hide the code in the cell. | | »»»» hideResults | boolean | false | | Whether to hide the results in the cell. | | »»»» language | Language | false | | An enumeration. | | »»» disableRun | boolean | false | | Whether or not the cell is disabled in the UI. | | »»» hideCode | boolean | false | | Whether or not code is hidden in the UI. | | »»» hideResults | boolean | false | | Whether or not results are hidden in the UI. | | »»» jupyter | JupyterCellMetadata | false | | Jupyter metadata. | | »»»» outputsHidden | boolean | false | | Whether the cell's outputs are hidden. | | »»»» sourceHidden | boolean | false | | Whether the cell's source is hidden. | | »»» name | string | false | | Name of the notebook cell. | | »»» scrolled | any | false | | Whether the cell's output is scrolled, unscrolled, or autoscrolled. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» outputType | OutputStorageType | false | | The type of storage used for the cell output. | | »» outputs | [anyOf] | false | | The cell outputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellStreamOutput | false | | Cell stream output. | | »»»» name | string | true | | The name of the stream. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» text | any | true | | The text of the stream. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellInputRequest | false | | Cell input request. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» password | boolean | true | | Whether the input request is for a password. | | »»»» prompt | string | true | | The prompt for the input request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellErrorOutput | false | | Cell error output. | | »»»» ename | string | true | | The name of the error. | | »»»» evalue | string | true | | The value of the error. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» traceback | [string] | true | | The traceback of the error. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellExecuteResultOutput | false | | Cell execute results output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» executionCount | integer | false | | A result's prompt number. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellDisplayDataOutput | false | | Cell display data output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» source | string | true | | The contents of the cell, represented as a string. | ### Enumerated Values | Property | Value | | --- | --- | | language | [dataframe, markdown, python, r, shell, scala, sas, custommetric] | | anonymous | auto | | outputType | RAW_OUTPUT | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | ## Create Batch Delete by notebook ID Operation path: `POST /api/v2/notebooks/{notebookId}/cells/batchDelete/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for batch deleting notebook cells.", "properties": { "cellIds": { "description": "The IDs of the cells to delete.", "items": { "type": "string" }, "title": "Cellids", "type": "array" } }, "required": [ "cellIds" ], "title": "BatchDeleteCellsRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to batch delete cells for. | | body | body | BatchDeleteCellsRequest | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Modify Batch Update Metadata by notebook ID Operation path: `PATCH /api/v2/notebooks/{notebookId}/cells/batchUpdateMetadata/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for batch updating notebook cells metadata.", "properties": { "cellIds": { "description": "The IDs of the cells to update.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata to update the cells with.", "title": "Metadata" } }, "required": [ "cellIds", "metadata" ], "title": "BatchUpdateCellsMetadataRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to batch update cell metadata for. | | body | body | BatchUpdateCellsMetadataRequest | false | none | ### Example responses > 200 Response ``` { "items": { "description": "Schema for notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionId": { "description": "The ID of the execution.", "title": "Executionid", "type": "string" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata", "cellId", "md5" ], "title": "CellSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook cell. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [CellSchema] | false | | [Schema for notebook cell.] | | » CellSchema | CellSchema | false | | Schema for notebook cell. | | »» attachments | object | false | | Cell attachments. | | »» cellId | string | true | | The ID of the cell. | | »» executed | NotebookTimestampInfo | false | | The timestamp of when the cell was executed. | | »»» at | string(date-time) | true | | Timestamp of the action. | | »»» by | UserInfo | true | | User info of the actor who caused the action to occur. | | »»»» activated | boolean | false | | Whether the user is activated. | | »»»» firstName | string | false | | The first name of the user. | | »»»» gravatarHash | string | false | | The gravatar hash of the user. | | »»»» id | string | true | | The ID of the user. | | »»»» lastName | string | false | | The last name of the user. | | »»»» orgId | string | false | | The ID of the organization the user belongs to. | | »»»» tenantPhase | string | false | | The tenant phase of the user. | | »»»» username | string | false | | The username of the user. | | »» executionCount | integer | false | | The execution count of the cell relative to other cells in the current session. | | »» executionId | string | false | | The ID of the execution. | | »» executionTimeMillis | integer | false | | The execution time of the cell in milliseconds. | | »» md5 | string | true | | The MD5 hash of the cell. | | »» metadata | NotebookCellMetadata | true | | The metadata of the cell. | | »»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» aggregation | string | false | | Aggregation function for the axis. | | »»»»»» color | string | false | | Color for the axis. | | »»»»»» hideGrid | boolean | false | | Whether to hide the grid lines on the axis. | | »»»»»» hideInTooltip | boolean | false | | Whether to hide the axis in the tooltip. | | »»»»»» hideLabel | boolean | false | | Whether to hide the axis label. | | »»»»»» key | string | false | | Key for the axis. | | »»»»»» label | string | false | | Label for the axis. | | »»»»»» position | string | false | | Position of the axis. | | »»»»»» showPointMarkers | boolean | false | | Whether to show point markers on the axis. | | »»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»» data | object | false | | The data associated with the cell chart. | | »»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»» chartType | string | false | | Type of the chart. | | »»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»» title | string | false | | Title of the chart. | | »»» collapsed | boolean | false | | Whether the cell's output is collapsed/expanded. | | »»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»» metricName | string | true | | The name of the custom LLM metric. | | »»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»» metricId | string | true | | The ID of the custom metric. | | »»»» metricName | string | false | | The name of the custom metric. | | »»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» month | [anyOf] | true | | The month(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» dataframeViewOptions | object | false | | Dataframe cell view options and metadata. | | »»» datarobot | NotebookCellDataRobotMetadata | false | | Metadata specific to DataRobot's notebooks and notebook environment. | | »»»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»» data | object | false | | The data associated with the cell chart. | | »»»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»»» chartType | string | false | | Type of the chart. | | »»»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»»» title | string | false | | Title of the chart. | | »»»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»»» metricName | string | true | | The name of the custom LLM metric. | | »»»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»»» metricId | string | true | | The ID of the custom metric. | | »»»»» metricName | string | false | | The name of the custom metric. | | »»»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | | »»»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | | »»»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | | »»»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | | »»»»»» month | [anyOf] | true | | The month(s) to run the schedule. | | »»»» dataframeViewOptions | object | false | | DataFrame view options and metadata. | | »»»» disableRun | boolean | false | | Whether to disable the run button in the cell. | | »»»» executionTimeMillis | integer | false | | Execution time of the cell in milliseconds. | | »»»» hideCode | boolean | false | | Whether to hide the code in the cell. | | »»»» hideResults | boolean | false | | Whether to hide the results in the cell. | | »»»» language | Language | false | | An enumeration. | | »»» disableRun | boolean | false | | Whether or not the cell is disabled in the UI. | | »»» hideCode | boolean | false | | Whether or not code is hidden in the UI. | | »»» hideResults | boolean | false | | Whether or not results are hidden in the UI. | | »»» jupyter | JupyterCellMetadata | false | | Jupyter metadata. | | »»»» outputsHidden | boolean | false | | Whether the cell's outputs are hidden. | | »»»» sourceHidden | boolean | false | | Whether the cell's source is hidden. | | »»» name | string | false | | Name of the notebook cell. | | »»» scrolled | any | false | | Whether the cell's output is scrolled, unscrolled, or autoscrolled. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» outputType | OutputStorageType | false | | The type of storage used for the cell output. | | »» outputs | [anyOf] | false | | The cell outputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellStreamOutput | false | | Cell stream output. | | »»»» name | string | true | | The name of the stream. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» text | any | true | | The text of the stream. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellInputRequest | false | | Cell input request. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» password | boolean | true | | Whether the input request is for a password. | | »»»» prompt | string | true | | The prompt for the input request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellErrorOutput | false | | Cell error output. | | »»»» ename | string | true | | The name of the error. | | »»»» evalue | string | true | | The value of the error. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» traceback | [string] | true | | The traceback of the error. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellExecuteResultOutput | false | | Cell execute results output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» executionCount | integer | false | | A result's prompt number. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellDisplayDataOutput | false | | Cell display data output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» source | string | true | | The contents of the cell, represented as a string. | ### Enumerated Values | Property | Value | | --- | --- | | language | [dataframe, markdown, python, r, shell, scala, sas, custommetric] | | anonymous | auto | | outputType | RAW_OUTPUT | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | ## Modify Batch Update Sources by notebook ID Operation path: `PATCH /api/v2/notebooks/{notebookId}/cells/batchUpdateSources/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for batch updating notebook cells sources.", "properties": { "cells": { "description": "The list of cells to update.", "items": { "description": "Notebook cell source model.", "properties": { "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "source": { "description": "Contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "cellId", "source", "md5" ], "title": "NotebookCellSource", "type": "object" }, "title": "Cells", "type": "array" } }, "title": "BatchUpdateCellsSourcesRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to batch update cell sources for. | | body | body | BatchUpdateCellsSourcesRequest | false | none | ### Example responses > 200 Response ``` { "items": { "description": "Schema for notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionId": { "description": "The ID of the execution.", "title": "Executionid", "type": "string" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata", "cellId", "md5" ], "title": "CellSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook cell. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [CellSchema] | false | | [Schema for notebook cell.] | | » CellSchema | CellSchema | false | | Schema for notebook cell. | | »» attachments | object | false | | Cell attachments. | | »» cellId | string | true | | The ID of the cell. | | »» executed | NotebookTimestampInfo | false | | The timestamp of when the cell was executed. | | »»» at | string(date-time) | true | | Timestamp of the action. | | »»» by | UserInfo | true | | User info of the actor who caused the action to occur. | | »»»» activated | boolean | false | | Whether the user is activated. | | »»»» firstName | string | false | | The first name of the user. | | »»»» gravatarHash | string | false | | The gravatar hash of the user. | | »»»» id | string | true | | The ID of the user. | | »»»» lastName | string | false | | The last name of the user. | | »»»» orgId | string | false | | The ID of the organization the user belongs to. | | »»»» tenantPhase | string | false | | The tenant phase of the user. | | »»»» username | string | false | | The username of the user. | | »» executionCount | integer | false | | The execution count of the cell relative to other cells in the current session. | | »» executionId | string | false | | The ID of the execution. | | »» executionTimeMillis | integer | false | | The execution time of the cell in milliseconds. | | »» md5 | string | true | | The MD5 hash of the cell. | | »» metadata | NotebookCellMetadata | true | | The metadata of the cell. | | »»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» aggregation | string | false | | Aggregation function for the axis. | | »»»»»» color | string | false | | Color for the axis. | | »»»»»» hideGrid | boolean | false | | Whether to hide the grid lines on the axis. | | »»»»»» hideInTooltip | boolean | false | | Whether to hide the axis in the tooltip. | | »»»»»» hideLabel | boolean | false | | Whether to hide the axis label. | | »»»»»» key | string | false | | Key for the axis. | | »»»»»» label | string | false | | Label for the axis. | | »»»»»» position | string | false | | Position of the axis. | | »»»»»» showPointMarkers | boolean | false | | Whether to show point markers on the axis. | | »»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»» data | object | false | | The data associated with the cell chart. | | »»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»» chartType | string | false | | Type of the chart. | | »»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»» title | string | false | | Title of the chart. | | »»» collapsed | boolean | false | | Whether the cell's output is collapsed/expanded. | | »»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»» metricName | string | true | | The name of the custom LLM metric. | | »»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»» metricId | string | true | | The ID of the custom metric. | | »»»» metricName | string | false | | The name of the custom metric. | | »»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» month | [anyOf] | true | | The month(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» dataframeViewOptions | object | false | | Dataframe cell view options and metadata. | | »»» datarobot | NotebookCellDataRobotMetadata | false | | Metadata specific to DataRobot's notebooks and notebook environment. | | »»»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»» data | object | false | | The data associated with the cell chart. | | »»»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»»» chartType | string | false | | Type of the chart. | | »»»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»»» title | string | false | | Title of the chart. | | »»»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»»» metricName | string | true | | The name of the custom LLM metric. | | »»»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»»» metricId | string | true | | The ID of the custom metric. | | »»»»» metricName | string | false | | The name of the custom metric. | | »»»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | | »»»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | | »»»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | | »»»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | | »»»»»» month | [anyOf] | true | | The month(s) to run the schedule. | | »»»» dataframeViewOptions | object | false | | DataFrame view options and metadata. | | »»»» disableRun | boolean | false | | Whether to disable the run button in the cell. | | »»»» executionTimeMillis | integer | false | | Execution time of the cell in milliseconds. | | »»»» hideCode | boolean | false | | Whether to hide the code in the cell. | | »»»» hideResults | boolean | false | | Whether to hide the results in the cell. | | »»»» language | Language | false | | An enumeration. | | »»» disableRun | boolean | false | | Whether or not the cell is disabled in the UI. | | »»» hideCode | boolean | false | | Whether or not code is hidden in the UI. | | »»» hideResults | boolean | false | | Whether or not results are hidden in the UI. | | »»» jupyter | JupyterCellMetadata | false | | Jupyter metadata. | | »»»» outputsHidden | boolean | false | | Whether the cell's outputs are hidden. | | »»»» sourceHidden | boolean | false | | Whether the cell's source is hidden. | | »»» name | string | false | | Name of the notebook cell. | | »»» scrolled | any | false | | Whether the cell's output is scrolled, unscrolled, or autoscrolled. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» outputType | OutputStorageType | false | | The type of storage used for the cell output. | | »» outputs | [anyOf] | false | | The cell outputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellStreamOutput | false | | Cell stream output. | | »»»» name | string | true | | The name of the stream. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» text | any | true | | The text of the stream. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellInputRequest | false | | Cell input request. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» password | boolean | true | | Whether the input request is for a password. | | »»»» prompt | string | true | | The prompt for the input request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellErrorOutput | false | | Cell error output. | | »»»» ename | string | true | | The name of the error. | | »»»» evalue | string | true | | The value of the error. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» traceback | [string] | true | | The traceback of the error. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellExecuteResultOutput | false | | Cell execute results output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» executionCount | integer | false | | A result's prompt number. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellDisplayDataOutput | false | | Cell display data output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» source | string | true | | The contents of the cell, represented as a string. | ### Enumerated Values | Property | Value | | --- | --- | | language | [dataframe, markdown, python, r, shell, scala, sas, custommetric] | | anonymous | auto | | outputType | RAW_OUTPUT | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | ## Delete Cells by notebook ID Operation path: `DELETE /api/v2/notebooks/{notebookId}/cells/{cellId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook the cell belongs to. | | cellId | path | string | true | The ID of the cell to delete. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No content. | None | ## Modify Cells by notebook ID Operation path: `PATCH /api/v2/notebooks/{notebookId}/cells/{cellId}/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for updating a notebook cell.", "properties": { "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "The ID of the cell after which to create the new cell.", "title": "Aftercellid" }, "attachments": { "description": "The attachments associated with the cell.", "title": "Attachments", "type": "object" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata associated with the cell.", "title": "Metadata" }, "source": { "description": "Contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "md5" ], "title": "UpdateCellRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook the cell belongs to. | | cellId | path | string | true | The ID of the cell to update. | | body | body | UpdateCellRequest | false | none | ### Example responses > 200 Response ``` { "description": "Schema for notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionId": { "description": "The ID of the execution.", "title": "Executionid", "type": "string" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata", "cellId", "md5" ], "title": "CellSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook cell. | CellSchema | ## Modify Output by notebook ID Operation path: `PATCH /api/v2/notebooks/{notebookId}/cells/{cellId}/output/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for clearing the output of a notebook cell.", "properties": { "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" } }, "required": [ "md5" ], "title": "ClearCellOutputRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook the cell belongs to. | | cellId | path | string | true | The ID of the cell to clear output for. | | body | body | ClearCellOutputRequest | false | none | ### Example responses > 200 Response ``` { "description": "Schema for notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionId": { "description": "The ID of the execution.", "title": "Executionid", "type": "string" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata", "cellId", "md5" ], "title": "CellSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook cell. | CellSchema | ## Modify Reorder Cells by notebook ID Operation path: `PATCH /api/v2/notebooks/{notebookId}/reorderCells/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Schema for taking an action on a notebook cell.", "properties": { "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" } }, "required": [ "md5", "cellId" ], "title": "CellVersionActionSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to reorder cells for. | | body | body | CellVersionActionSchema | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | No content. | None | ## Get the notebook access control list by notebook ID Operation path: `GET /api/v2/notebooks/{notebookId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users and their roles who have access to this notebook. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. | | limit | query | integer | false | The number of records to return. | | notebookId | path | string | true | The ID of the notebook. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The access control list for a notebook. | SharingListV2Response | ## Modify State by notebook ID Operation path: `PATCH /api/v2/notebooks/{notebookId}/state/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Schema for taking an action on a notebook cell.", "properties": { "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" } }, "required": [ "md5", "cellId" ], "title": "CellVersionActionSchema", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook that cells belong to. | | body | body | CellVersionActionSchema | false | none | ### Example responses > 200 Response ``` { "items": { "description": "Schema for notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionId": { "description": "The ID of the execution.", "title": "Executionid", "type": "string" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata", "cellId", "md5" ], "title": "CellSchema", "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Schema for notebook cell. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [CellSchema] | false | | [Schema for notebook cell.] | | » CellSchema | CellSchema | false | | Schema for notebook cell. | | »» attachments | object | false | | Cell attachments. | | »» cellId | string | true | | The ID of the cell. | | »» executed | NotebookTimestampInfo | false | | The timestamp of when the cell was executed. | | »»» at | string(date-time) | true | | Timestamp of the action. | | »»» by | UserInfo | true | | User info of the actor who caused the action to occur. | | »»»» activated | boolean | false | | Whether the user is activated. | | »»»» firstName | string | false | | The first name of the user. | | »»»» gravatarHash | string | false | | The gravatar hash of the user. | | »»»» id | string | true | | The ID of the user. | | »»»» lastName | string | false | | The last name of the user. | | »»»» orgId | string | false | | The ID of the organization the user belongs to. | | »»»» tenantPhase | string | false | | The tenant phase of the user. | | »»»» username | string | false | | The username of the user. | | »» executionCount | integer | false | | The execution count of the cell relative to other cells in the current session. | | »» executionId | string | false | | The ID of the execution. | | »» executionTimeMillis | integer | false | | The execution time of the cell in milliseconds. | | »» md5 | string | true | | The MD5 hash of the cell. | | »» metadata | NotebookCellMetadata | true | | The metadata of the cell. | | »»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» aggregation | string | false | | Aggregation function for the axis. | | »»»»»» color | string | false | | Color for the axis. | | »»»»»» hideGrid | boolean | false | | Whether to hide the grid lines on the axis. | | »»»»»» hideInTooltip | boolean | false | | Whether to hide the axis in the tooltip. | | »»»»»» hideLabel | boolean | false | | Whether to hide the axis label. | | »»»»»» key | string | false | | Key for the axis. | | »»»»»» label | string | false | | Label for the axis. | | »»»»»» position | string | false | | Position of the axis. | | »»»»»» showPointMarkers | boolean | false | | Whether to show point markers on the axis. | | »»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»» data | object | false | | The data associated with the cell chart. | | »»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»» chartType | string | false | | Type of the chart. | | »»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»» title | string | false | | Title of the chart. | | »»» collapsed | boolean | false | | Whether the cell's output is collapsed/expanded. | | »»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»» metricName | string | true | | The name of the custom LLM metric. | | »»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»» metricId | string | true | | The ID of the custom metric. | | »»»» metricName | string | false | | The name of the custom metric. | | »»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» month | [anyOf] | true | | The month(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» dataframeViewOptions | object | false | | Dataframe cell view options and metadata. | | »»» datarobot | NotebookCellDataRobotMetadata | false | | Metadata specific to DataRobot's notebooks and notebook environment. | | »»»» chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | »»»»» axis | NotebookChartCellAxis | false | | Axis settings. | | »»»»»» x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»»» y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | »»»»» data | object | false | | The data associated with the cell chart. | | »»»»» dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | »»»»» viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | | »»»»»» chartType | string | false | | Type of the chart. | | »»»»»» showLegend | boolean | false | | Whether to show the chart legend. | | »»»»»» showTitle | boolean | false | | Whether to show the chart title. | | »»»»»» showTooltip | boolean | false | | Whether to show the chart tooltip. | | »»»»»» title | string | false | | Title of the chart. | | »»»» customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | »»»»» metricId | string | true | | The ID of the custom LLM metric. | | »»»»» metricName | string | true | | The name of the custom LLM metric. | | »»»»» playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | | »»»» customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | »»»»» deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | »»»»» metricId | string | true | | The ID of the custom metric. | | »»»»» metricName | string | false | | The name of the custom metric. | | »»»»» schedule | Schedule | false | | The schedule associated with the custom metric. | | »»»»»» dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | | »»»»»» dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | | »»»»»» hour | [anyOf] | true | | The hour(s) to run the schedule. | | »»»»»» minute | [anyOf] | true | | The minute(s) to run the schedule. | | »»»»»» month | [anyOf] | true | | The month(s) to run the schedule. | | »»»» dataframeViewOptions | object | false | | DataFrame view options and metadata. | | »»»» disableRun | boolean | false | | Whether to disable the run button in the cell. | | »»»» executionTimeMillis | integer | false | | Execution time of the cell in milliseconds. | | »»»» hideCode | boolean | false | | Whether to hide the code in the cell. | | »»»» hideResults | boolean | false | | Whether to hide the results in the cell. | | »»»» language | Language | false | | An enumeration. | | »»» disableRun | boolean | false | | Whether or not the cell is disabled in the UI. | | »»» hideCode | boolean | false | | Whether or not code is hidden in the UI. | | »»» hideResults | boolean | false | | Whether or not results are hidden in the UI. | | »»» jupyter | JupyterCellMetadata | false | | Jupyter metadata. | | »»»» outputsHidden | boolean | false | | Whether the cell's outputs are hidden. | | »»»» sourceHidden | boolean | false | | Whether the cell's source is hidden. | | »»» name | string | false | | Name of the notebook cell. | | »»» scrolled | any | false | | Whether the cell's output is scrolled, unscrolled, or autoscrolled. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»» anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» outputType | OutputStorageType | false | | The type of storage used for the cell output. | | »» outputs | [anyOf] | false | | The cell outputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellStreamOutput | false | | Cell stream output. | | »»»» name | string | true | | The name of the stream. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» text | any | true | | The text of the stream. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»»»» anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellInputRequest | false | | Cell input request. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» password | boolean | true | | Whether the input request is for a password. | | »»»» prompt | string | true | | The prompt for the input request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellErrorOutput | false | | Cell error output. | | »»»» ename | string | true | | The name of the error. | | »»»» evalue | string | true | | The value of the error. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | »»»» traceback | [string] | true | | The traceback of the error. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellExecuteResultOutput | false | | Cell execute results output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» executionCount | integer | false | | A result's prompt number. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | APINotebookCellDisplayDataOutput | false | | Cell display data output. | | »»»» data | object | true | | A mime-type keyed dictionary of data. | | »»»» metadata | object | true | | Cell output metadata. | | »»»» outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» source | string | true | | The contents of the cell, represented as a string. | ### Enumerated Values | Property | Value | | --- | --- | | language | [dataframe, markdown, python, r, shell, scala, sas, custommetric] | | anonymous | auto | | outputType | RAW_OUTPUT | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | | outputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | ## Create To Codespace by notebook ID Operation path: `POST /api/v2/notebooks/{notebookId}/toCodespace/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "description": "Request schema for converting a notebook to a codespace.", "properties": { "useCaseDescription": { "description": "The description of the Use Case.", "maxLength": 500, "title": "Usecasedescription", "type": "string" }, "useCaseId": { "description": "The ID of the Use Case to associate the notebook with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case to associate the notebook with.", "title": "Usecasename", "type": "string" } }, "title": "ConvertToCodespaceRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The ID of the notebook to convert to a codespace. | | body | body | ConvertToCodespaceRequest | false | none | ### Example responses > 201 Response ``` { "description": "Schema for notebook metadata with additional fields.", "properties": { "created": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the creation of the notebook.", "title": "Created" }, "description": { "description": "The description of the notebook.", "title": "Description", "type": "string" }, "hasEnabledSchedule": { "description": "Whether the notebook has an enabled schedule.", "title": "Hasenabledschedule", "type": "boolean" }, "hasSchedule": { "description": "Whether the notebook has a schedule.", "title": "Hasschedule", "type": "boolean" }, "id": { "description": "The ID of the notebook.", "title": "Id", "type": "string" }, "lastViewed": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last viewed time of the notebook.", "title": "Lastviewed" }, "name": { "description": "The name of the notebook.", "title": "Name", "type": "string" }, "orgId": { "description": "The ID of the organization associated with the notebook.", "title": "Orgid", "type": "string" }, "permissions": { "description": "The permissions associated with the notebook.", "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "session": { "allOf": [ { "description": "The schema for the notebook session.", "properties": { "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "The type of the notebook session." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" } }, "required": [ "status", "notebookId" ], "title": "NotebookSessionSharedSchema", "type": "object" } ], "description": "Information about the session associated with the notebook.", "title": "Session" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "The settings of the notebook.", "title": "Settings" }, "tags": { "description": "The tags of the notebook.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "tenantId": { "description": "The tenant ID associated with the notebook.", "title": "Tenantid", "type": "string" }, "type": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of the notebook." }, "typeTransition": { "allOf": [ { "description": "An enumeration.", "enum": [ "initiated_to_codespace", "completed" ], "title": "NotebookTypeTransition", "type": "string" } ], "description": "The type transition of the notebook." }, "updated": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last update of the notebook.", "title": "Updated" }, "useCaseId": { "description": "The ID of the Use Case associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "name", "id", "created", "lastViewed" ], "title": "NotebookSchema", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Schema for notebook metadata with additional fields. | NotebookSchema | ## Retrieve To File by notebook ID Operation path: `GET /api/v2/notebooks/{notebookId}/toFile/` Authentication requirements: `BearerAuth` Export Notebook to file endpoint. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notebookId | path | string | true | The notebook ID to return as a file. | | ExportNotebookQuerySchema | query | ExportNotebookQuerySchema | false | Query parameters for exporting a notebook. | ### Example responses > 200 Response ``` { "type": "string" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Export notebook to file JSON response. | string | ## Retrieve Providers Operation path: `GET /api/v2/vcs/git/providers/` Authentication requirements: `BearerAuth` List authorized Git OAuth Providers. ### Example responses > 200 Response ``` { "description": "A list of authorized Git OAuth providers available for the user.", "properties": { "providers": { "description": "A list of authorized Git OAuth providers.", "items": { "description": "The Git OAuth Provider details.", "properties": { "hostname": { "description": "The hostname of the Git provider.", "title": "Hostname", "type": "string" }, "id": { "description": "The authorized Git OAuth Provider ID.", "title": "Id", "type": "string" }, "name": { "description": "The name of the Git provider.", "title": "Name", "type": "string" }, "settingsUrl": { "description": "The URL to manage the Git provider settings.", "title": "Settingsurl", "type": "string" }, "status": { "description": "The status of the OAuth authorization.", "title": "Status", "type": "string" }, "type": { "description": "The type of the Git provider.", "title": "Type", "type": "string" } }, "required": [ "id", "status", "type", "name", "hostname" ], "title": "GitProviderSchema", "type": "object" }, "maxItems": 1000, "minItems": 0, "title": "Providers", "type": "array" } }, "required": [ "providers" ], "title": "ListGitProvidersResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of authorized Git OAuth providers available for the user. | ListGitProvidersResponse | ## Retrieve Repositories by authorization ID Operation path: `GET /api/v2/vcs/git/providers/{authorizationId}/repositories/` Authentication requirements: `BearerAuth` Get or search repositories for a given authorized Git provider. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | authorizationId | path | string | true | The authorization ID of the Git provider. | | ListGitRepositoryQuery | query | ListGitRepositoryQuery | false | The possible query params to use when listing git repositories. | ### Example responses > 200 Response ``` { "description": "A list of Git repositories available for the authorized Git provider and the related params used for filtering\n(if provided).", "properties": { "data": { "description": "A list of Git repositories.", "items": { "properties": { "createdAt": { "description": "The creation date of the Git repository.", "format": "date-time", "title": "Createdat", "type": "string" }, "fullName": { "description": "The full name of Git repository, e.g., datarobot/notebooks.", "title": "Fullname", "type": "string" }, "httpUrl": { "description": "The HTTP URL of the Git repository, e.g., https://github.com/datarobot/notebooks.", "title": "Httpurl", "type": "string" }, "isPrivate": { "description": "Determines if the Git repository is private.", "title": "Isprivate", "type": "boolean" }, "name": { "description": "The name of Git repository, e.g., \"notebooks\".", "title": "Name", "type": "string" }, "owner": { "description": "The owner account of the Git repository.", "title": "Owner", "type": "string" }, "pushedAt": { "description": "The last push date of the Git repository.", "format": "date-time", "title": "Pushedat", "type": "string" }, "updatedAt": { "description": "The last update date of the Git repository.", "format": "date-time", "title": "Updatedat", "type": "string" } }, "required": [ "name", "fullName", "httpUrl", "owner" ], "title": "GitRepositorySchema", "type": "object" }, "title": "Data", "type": "array" }, "params": { "additionalProperties": { "type": "string" }, "description": "The params used when listing Git repositories.", "title": "Params", "type": "object" } }, "required": [ "data" ], "title": "ListGitRepositoriesResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of Git repositories available for the authorized Git provider and the related params used for filtering | | | (if provided). | ListGitRepositoriesResponse | | | # Schemas ## APINotebookCellDisplayDataOutput ``` { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ``` APINotebookCellDisplayDataOutput ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | object | true | | A mime-type keyed dictionary of data. | | metadata | object | true | | Cell output metadata. | | outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | ## APINotebookCellErrorOutput ``` { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" } ``` APINotebookCellErrorOutput ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ename | string | true | | The name of the error. | | evalue | string | true | | The value of the error. | | outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | traceback | [string] | true | | The traceback of the error. | ## APINotebookCellExecuteResultOutput ``` { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" } ``` APINotebookCellExecuteResultOutput ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | object | true | | A mime-type keyed dictionary of data. | | executionCount | integer | false | | A result's prompt number. | | metadata | object | true | | Cell output metadata. | | outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | ## APINotebookCellInputRequest ``` { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" } ``` APINotebookCellInputRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | password | boolean | true | | Whether the input request is for a password. | | prompt | string | true | | The prompt for the input request. | ## APINotebookCellStreamOutput ``` { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" } ``` APINotebookCellStreamOutput ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the stream. | | outputType | OutputType | true | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | | text | any | true | | The text of the stream. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## AccessControlV2 ``` { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the recipient. | | name | string | true | | The name of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | The type of the recipient. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## ActionCreatedResponse ``` { "description": "Base schema for action created responses.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "title": "Actionid", "type": "string" } }, "required": [ "actionId" ], "title": "ActionCreatedResponse", "type": "object" } ``` ActionCreatedResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actionId | string | true | | Action ID of the notebook update request. | ## AnyCellSchema ``` { "description": "The schema for cells in a notebook that are not code or markdown.", "properties": { "cellType": { "description": "Type of the cell.", "title": "Celltype", "type": "string" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType" ], "title": "AnyCellSchema", "type": "object" } ``` AnyCellSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellType | string | true | | Type of the cell. | | id | string | true | | ID of the cell. | | metadata | NotebookCellMetadata | false | | Metadata of the cell. | | source | any | false | | Contents of the cell, represented as a string. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## AssignNotebookKernelRequest ``` { "description": "Request payload values for assigning a kernel to a notebook.", "properties": { "kernelId": { "description": "ID of the kernel to assign to the notebook.", "title": "Kernelid", "type": "string" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path" ], "title": "AssignNotebookKernelRequest", "type": "object" } ``` AssignNotebookKernelRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | kernelId | string | false | | ID of the kernel to assign to the notebook. | | path | string | true | | Path to the notebook. | ## AwaitingInputState ``` { "description": "AwaitingInputState represents the state of a cell that is awaiting input from the user.", "properties": { "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" }, "requestedAt": { "description": "The time the input was requested.", "format": "date-time", "title": "Requestedat", "type": "string" } }, "required": [ "requestedAt", "prompt", "password" ], "title": "AwaitingInputState", "type": "object" } ``` AwaitingInputState ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | password | boolean | true | | Whether the input request is for a password. | | prompt | string | true | | The prompt for the input request. | | requestedAt | string(date-time) | true | | The time the input was requested. | ## BatchCellQuery ``` { "description": "Base schema for batch cell queries.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "maxLength": 64, "title": "Actionid", "type": "string" }, "cellIds": { "description": "List of cell IDs to operate on.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "cellIds" ], "title": "BatchCellQuery", "type": "object" } ``` BatchCellQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actionId | string | false | maxLength: 64 | Action ID of the notebook update request. | | cellIds | [string] | true | | List of cell IDs to operate on. | | generation | integer | true | | Integer representing the generation of the notebook. | | path | string | true | | Path to the notebook. | ## BatchClearCellsOutputRequest ``` { "description": "Request schema for batch clearing the output of notebook cells.", "properties": { "cellIds": { "description": "The IDs of the cells to clear the output of.", "items": { "type": "string" }, "title": "Cellids", "type": "array" } }, "required": [ "cellIds" ], "title": "BatchClearCellsOutputRequest", "type": "object" } ``` BatchClearCellsOutputRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellIds | [string] | true | | The IDs of the cells to clear the output of. | ## BatchCreateCellsRequest ``` { "description": "Request schema for batch creating notebook cells.", "properties": { "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "The ID of the cell after which to create the new cells.", "title": "Aftercellid" }, "cells": { "description": "The list of cells to create.", "items": { "description": "Metadata for the source of a notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Cell metadata.", "title": "Metadata" }, "outputs": { "description": "Cell outputs that conform to the nbformat-based NBX schema.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "Contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source" ], "title": "NotebookCellSourceMetadata", "type": "object" }, "title": "Cells", "type": "array" } }, "required": [ "afterCellId" ], "title": "BatchCreateCellsRequest", "type": "object" } ``` BatchCreateCellsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | afterCellId | any | true | | The ID of the cell after which to create the new cells. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cells | [NotebookCellSourceMetadata] | false | | The list of cells to create. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | FIRST | ## BatchDeleteCellsRequest ``` { "description": "Request schema for batch deleting notebook cells.", "properties": { "cellIds": { "description": "The IDs of the cells to delete.", "items": { "type": "string" }, "title": "Cellids", "type": "array" } }, "required": [ "cellIds" ], "title": "BatchDeleteCellsRequest", "type": "object" } ``` BatchDeleteCellsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellIds | [string] | true | | The IDs of the cells to delete. | ## BatchUpdateCellsMetadataRequest ``` { "description": "Request schema for batch updating notebook cells metadata.", "properties": { "cellIds": { "description": "The IDs of the cells to update.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata to update the cells with.", "title": "Metadata" } }, "required": [ "cellIds", "metadata" ], "title": "BatchUpdateCellsMetadataRequest", "type": "object" } ``` BatchUpdateCellsMetadataRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellIds | [string] | true | | The IDs of the cells to update. | | metadata | NotebookCellMetadata | true | | The metadata to update the cells with. | ## BatchUpdateCellsSourcesRequest ``` { "description": "Request schema for batch updating notebook cells sources.", "properties": { "cells": { "description": "The list of cells to update.", "items": { "description": "Notebook cell source model.", "properties": { "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "source": { "description": "Contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "cellId", "source", "md5" ], "title": "NotebookCellSource", "type": "object" }, "title": "Cells", "type": "array" } }, "title": "BatchUpdateCellsSourcesRequest", "type": "object" } ``` BatchUpdateCellsSourcesRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cells | [NotebookCellSource] | false | | The list of cells to update. | ## BulkLinkNotebooksToUseCaseRequest ``` { "description": "Request payload schema for bulk linking notebooks to a use case.", "properties": { "notebookIds": { "description": "List of notebook IDs to link.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "title": "Notebookids", "type": "array" }, "source": { "allOf": [ { "description": "The source of the entity linking. Does not affect the operation of the API call. Only used for analytics.", "enum": [ "classic", "nextGen", "api" ], "title": "EntityLinkingSources", "type": "string" } ], "description": "The source of the entity linking." }, "useCaseId": { "description": "The ID of the Use Case to link the notebooks to.", "title": "Usecaseid", "type": "string" }, "workflow": { "allOf": [ { "description": "The workflow that is attaching this entity. Does not affect the operation of the API call. Only used for analytics.", "enum": [ "migration", "creation", "unspecified" ], "title": "EntityLinkingWorkflows", "type": "string" } ], "description": "The workflow responsible for attaching the notebook to the Use Case." } }, "required": [ "notebookIds", "useCaseId" ], "title": "BulkLinkNotebooksToUseCaseRequest", "type": "object" } ``` BulkLinkNotebooksToUseCaseRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notebookIds | [string] | true | maxItems: 100minItems: 1 | List of notebook IDs to link. | | source | EntityLinkingSources | false | | The source of the entity linking. | | useCaseId | string | true | | The ID of the Use Case to link the notebooks to. | | workflow | EntityLinkingWorkflows | false | | The workflow responsible for attaching the notebook to the Use Case. | ## CancelCellsExecutionRequest ``` { "description": "Request payload values for canceling notebook cells execution.", "properties": { "cellIds": { "description": "List of cell IDs to cancel execution for.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "cellIds" ], "title": "CancelCellsExecutionRequest", "type": "object" } ``` CancelCellsExecutionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellIds | [string] | true | | List of cell IDs to cancel execution for. | | generation | integer | true | | Integer representing the generation of the notebook. | | path | string | true | | Path to the notebook. | ## CancelExecutionRequest ``` { "description": "Request schema for canceling the execution of cells in a notebook session.", "properties": { "cellIds": { "description": "List of cell IDs to cancel execution.", "items": { "type": "string" }, "title": "Cellids", "type": "array" } }, "title": "CancelExecutionRequest", "type": "object" } ``` CancelExecutionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellIds | [string] | false | | List of cell IDs to cancel execution. | ## CancelScheduledJobsQuery ``` { "description": "Query parameters for canceling scheduled jobs.", "properties": { "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" } }, "required": [ "useCaseId" ], "title": "CancelScheduledJobsQuery", "type": "object" } ``` CancelScheduledJobsQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseId | string | true | | The ID of the use case this notebook is associated with. | ## CellSchema ``` { "description": "Schema for notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionId": { "description": "The ID of the execution.", "title": "Executionid", "type": "string" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata", "cellId", "md5" ], "title": "CellSchema", "type": "object" } ``` CellSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attachments | object | false | | Cell attachments. | | cellId | string | true | | The ID of the cell. | | executed | NotebookTimestampInfo | false | | The timestamp of when the cell was executed. | | executionCount | integer | false | | The execution count of the cell relative to other cells in the current session. | | executionId | string | false | | The ID of the execution. | | executionTimeMillis | integer | false | | The execution time of the cell in milliseconds. | | md5 | string | true | | The MD5 hash of the cell. | | metadata | NotebookCellMetadata | true | | The metadata of the cell. | | outputType | OutputStorageType | false | | The type of storage used for the cell output. | | outputs | [anyOf] | false | | The cell outputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellStreamOutput | false | | Cell stream output. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellInputRequest | false | | Cell input request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellErrorOutput | false | | Cell error output. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellExecuteResultOutput | false | | Cell execute results output. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellDisplayDataOutput | false | | Cell display data output. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | source | string | true | | The contents of the cell, represented as a string. | ## CellVersionActionSchema ``` { "description": "Schema for taking an action on a notebook cell.", "properties": { "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" } }, "required": [ "md5", "cellId" ], "title": "CellVersionActionSchema", "type": "object" } ``` CellVersionActionSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellId | string | true | | The ID of the cell. | | md5 | string | true | | The MD5 hash of the cell. | ## ClearCellOutputRequest ``` { "description": "Request schema for clearing the output of a notebook cell.", "properties": { "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" } }, "required": [ "md5" ], "title": "ClearCellOutputRequest", "type": "object" } ``` ClearCellOutputRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | md5 | string | true | | The MD5 hash of the cell. | ## CloneNotebookFromRevisionRequest ``` { "description": "Request to clone a notebook from a revision.", "properties": { "isAuto": { "default": false, "description": "Whether the revision is autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Notebook revision name.", "minLength": 1, "title": "Name", "type": "string" }, "notebookPath": { "description": "Path to the notebook file, if using Codespaces.", "title": "Notebookpath", "type": "string" } }, "title": "CloneNotebookFromRevisionRequest", "type": "object" } ``` CloneNotebookFromRevisionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isAuto | boolean | false | | Whether the revision is autosaved. | | name | string | false | minLength: 1minLength: 1 | Notebook revision name. | | notebookPath | string | false | | Path to the notebook file, if using Codespaces. | ## CloneRepositorySchema ``` { "description": "Schema for cloning a repository.", "properties": { "checkoutRef": { "description": "The branch or commit to checkout.", "title": "Checkoutref", "type": "string" }, "url": { "description": "The URL of the repository to clone.", "title": "Url", "type": "string" } }, "required": [ "url" ], "title": "CloneRepositorySchema", "type": "object" } ``` CloneRepositorySchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | checkoutRef | string | false | | The branch or commit to checkout. | | url | string | true | | The URL of the repository to clone. | ## ClonedNotebookResponse ``` { "description": "Response schema for cloning a notebook from a revision.", "properties": { "notebookId": { "description": "Newly cloned notebook ID.", "title": "Notebookid", "type": "string" } }, "required": [ "notebookId" ], "title": "ClonedNotebookResponse", "type": "object" } ``` ClonedNotebookResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notebookId | string | true | | Newly cloned notebook ID. | ## CodeCellSchema ``` { "description": "The schema for a code cell in the notebook.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "code", "description": "Type of the cell." }, "executionCount": { "default": 0, "description": "Execution count of the cell.", "title": "Executioncount", "type": "integer" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "outputs": { "description": "Outputs of the cell.", "items": { "anyOf": [ { "description": "The schema for the execute result output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "executionCount": { "default": 0, "description": "Execution count of the output.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "execute_result", "default": "execute_result", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "ExecuteResultOutputSchema", "type": "object" }, { "description": "The schema for the display data output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "display_data", "default": "display_data", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "DisplayDataOutputSchema", "type": "object" }, { "description": "The schema for the stream output in a notebook cell.", "properties": { "name": { "description": "Name of the stream.", "title": "Name", "type": "string" }, "outputType": { "const": "stream", "default": "stream", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Text of the stream.", "title": "Text" } }, "required": [ "name", "text" ], "title": "StreamOutputSchema", "type": "object" }, { "description": "The schema for the error output in a notebook cell.", "properties": { "ename": { "description": "Error name.", "title": "Ename", "type": "string" }, "evalue": { "description": "Error value.", "title": "Evalue", "type": "string" }, "outputType": { "const": "error", "default": "error", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "traceback": { "description": "Traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "ename", "evalue" ], "title": "ErrorOutputSchema", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "CodeCellSchema", "type": "object" } ``` CodeCellSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellType | SupportedCellTypes | false | | Type of the cell. | | executionCount | integer | false | | Execution count of the cell. | | id | string | true | | ID of the cell. | | metadata | NotebookCellMetadata | false | | Metadata of the cell. | | outputs | [anyOf] | false | | Outputs of the cell. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExecuteResultOutputSchema | false | | The schema for the execute result output in a notebook cell. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DisplayDataOutputSchema | false | | The schema for the display data output in a notebook cell. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | StreamOutputSchema | false | | The schema for the stream output in a notebook cell. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ErrorOutputSchema | false | | The schema for the error output in a notebook cell. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | source | any | false | | Contents of the cell, represented as a string. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## CodeCompletionResultSchema ``` { "description": "The schema for the code completion result.", "properties": { "cursorEnd": { "description": "The end position of the cursor in the code.", "title": "Cursorend", "type": "integer" }, "cursorStart": { "description": "The start position of the cursor in the code.", "title": "Cursorstart", "type": "integer" }, "matches": { "description": "The list of code completions.", "items": { "type": "string" }, "title": "Matches", "type": "array" } }, "required": [ "matches", "cursorStart", "cursorEnd" ], "title": "CodeCompletionResultSchema", "type": "object" } ``` CodeCompletionResultSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cursorEnd | integer | true | | The end position of the cursor in the code. | | cursorStart | integer | true | | The start position of the cursor in the code. | | matches | [string] | true | | The list of code completions. | ## CodeCompletionSchema ``` { "description": "The schema for the code completion request.", "properties": { "code": { "description": "The code to complete.", "title": "Code", "type": "string" }, "cursorPosition": { "description": "The position of the cursor in the code.", "title": "Cursorposition", "type": "integer" } }, "required": [ "code", "cursorPosition" ], "title": "CodeCompletionSchema", "type": "object" } ``` CodeCompletionSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | code | string | true | | The code to complete. | | cursorPosition | integer | true | | The position of the cursor in the code. | ## CodeInspectionResultSchema ``` { "description": "The schema for the code inspection result.", "properties": { "data": { "additionalProperties": { "type": "string" }, "description": "The inspection result.", "title": "Data", "type": "object" }, "found": { "description": "True if an object was found, false otherwise.", "title": "Found", "type": "boolean" } }, "required": [ "data", "found" ], "title": "CodeInspectionResultSchema", "type": "object" } ``` CodeInspectionResultSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | object | true | | The inspection result. | | » additionalProperties | string | false | | none | | found | boolean | true | | True if an object was found, false otherwise. | ## CodeInspectionSchema ``` { "description": "The schema for the code inspection request.", "properties": { "code": { "description": "The code to inspect.", "title": "Code", "type": "string" }, "cursorPosition": { "description": "The position of the cursor in the code.", "title": "Cursorposition", "type": "integer" }, "detailLevel": { "description": "The detail level of the inspection. Possible values are 0 and 1.", "title": "Detaillevel", "type": "integer" } }, "required": [ "code", "cursorPosition", "detailLevel" ], "title": "CodeInspectionSchema", "type": "object" } ``` CodeInspectionSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | code | string | true | | The code to inspect. | | cursorPosition | integer | true | | The position of the cursor in the code. | | detailLevel | integer | true | | The detail level of the inspection. Possible values are 0 and 1. | ## CodeSnippet ``` { "description": "CodeSnippet represents a code snippet.", "properties": { "code": { "description": "The code snippet code content.", "title": "Code", "type": "string" }, "description": { "description": "The description of the code snippet.", "title": "Description", "type": "string" }, "id": { "description": "The ID of the code snippet.", "title": "Id", "type": "string" }, "language": { "description": "The programming language of the code snippet.", "title": "Language", "type": "string" }, "languageVersion": { "description": "The programming language version of the code snippet.", "title": "Languageversion", "type": "string" }, "locale": { "description": "The language locale of the code snippet.", "title": "Locale", "type": "string" }, "tags": { "description": "A comma separated list of snippet tags.", "title": "Tags", "type": "string" }, "title": { "description": "The title of the code snippet.", "title": "Title", "type": "string" } }, "required": [ "id", "locale", "title", "description", "code", "language", "languageVersion" ], "title": "CodeSnippet", "type": "object" } ``` CodeSnippet ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | code | string | true | | The code snippet code content. | | description | string | true | | The description of the code snippet. | | id | string | true | | The ID of the code snippet. | | language | string | true | | The programming language of the code snippet. | | languageVersion | string | true | | The programming language version of the code snippet. | | locale | string | true | | The language locale of the code snippet. | | tags | string | false | | A comma separated list of snippet tags. | | title | string | true | | The title of the code snippet. | ## ConvertToCodespaceRequest ``` { "description": "Request schema for converting a notebook to a codespace.", "properties": { "useCaseDescription": { "description": "The description of the Use Case.", "maxLength": 500, "title": "Usecasedescription", "type": "string" }, "useCaseId": { "description": "The ID of the Use Case to associate the notebook with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case to associate the notebook with.", "title": "Usecasename", "type": "string" } }, "title": "ConvertToCodespaceRequest", "type": "object" } ``` ConvertToCodespaceRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseDescription | string | false | maxLength: 500 | The description of the Use Case. | | useCaseId | string | false | | The ID of the Use Case to associate the notebook with. | | useCaseName | string | false | | The name of the Use Case to associate the notebook with. | ## CopyFilesystemObjectRequest ``` { "description": "Copy filesystem object request schema.", "properties": { "operations": { "description": "List of source-destination pairs.", "items": { "description": "Source and destination schema for filesystem object operations.", "properties": { "destination": { "description": "Destination path.", "title": "Destination", "type": "string" }, "source": { "description": "Source path.", "title": "Source", "type": "string" } }, "required": [ "source", "destination" ], "title": "SourceDestinationSchema", "type": "object" }, "title": "Operations", "type": "array" }, "overrideExisting": { "default": false, "description": "Whether to override existing files or directories.", "title": "Overrideexisting", "type": "boolean" } }, "title": "CopyFilesystemObjectRequest", "type": "object" } ``` CopyFilesystemObjectRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operations | [SourceDestinationSchema] | false | | List of source-destination pairs. | | overrideExisting | boolean | false | | Whether to override existing files or directories. | ## CreateAndStartCodespaceSchema ``` { "description": "Schema for creating and starting a codespace.", "properties": { "cloneRepository": { "allOf": [ { "description": "Schema for cloning a repository.", "properties": { "checkoutRef": { "description": "The branch or commit to checkout.", "title": "Checkoutref", "type": "string" }, "url": { "description": "The URL of the repository to clone.", "title": "Url", "type": "string" } }, "required": [ "url" ], "title": "CloneRepositorySchema", "type": "object" } ], "description": "The repository to clone for the codespace.", "title": "Clonerepository" }, "description": { "description": "The description of the codespace.", "title": "Description", "type": "string" }, "environment": { "allOf": [ { "description": "Request schema for assigning an execution environment to a notebook.", "properties": { "environmentId": { "description": "The execution environment ID.", "title": "Environmentid", "type": "string" }, "environmentSlug": { "description": "The execution environment slug.", "title": "Environmentslug", "type": "string" }, "language": { "description": "The programming language of the environment.", "title": "Language", "type": "string" }, "languageVersion": { "description": "The programming language version.", "title": "Languageversion", "type": "string" }, "machineId": { "description": "The machine ID.", "title": "Machineid", "type": "string" }, "machineSlug": { "description": "The machine slug.", "title": "Machineslug", "type": "string" }, "timeToLive": { "description": "Inactivity timeout limit.", "maximum": 525600, "minimum": 3, "title": "Timetolive", "type": "integer" }, "versionId": { "description": "The execution environment version ID.", "title": "Versionid", "type": "string" } }, "title": "ExecutionEnvironmentAssignRequest", "type": "object" } ], "description": "The environment for the codespace.", "title": "Environment" }, "environmentVariables": { "description": "The environment variables for the codespace.", "items": { "description": "Schema for updating environment variables.", "properties": { "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "pattern": "^[a-zA-Z_$][\\w$]*$", "title": "Name", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "NewEnvironmentVariableSchema", "type": "object" }, "title": "Environmentvariables", "type": "array" }, "exposedPorts": { "description": "The exposed ports for the codespace.", "items": { "description": "Port creation schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "maxLength": 500, "title": "Description", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" } }, "required": [ "port" ], "title": "ExposePortSchema", "type": "object" }, "title": "Exposedports", "type": "array" }, "name": { "default": "Untitled Codespace", "description": "The name of the codespace.", "title": "Name", "type": "string" }, "openFilePaths": { "description": "The file paths to open in the codespace.", "items": { "type": "string" }, "title": "Openfilepaths", "type": "array" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "description": "The settings for the codespace.", "title": "Settings" }, "tags": { "description": "The tags associated with the codespace.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "useCaseId": { "description": "The ID of the use case associated with the codespace.", "title": "Usecaseid", "type": "string" } }, "required": [ "useCaseId" ], "title": "CreateAndStartCodespaceSchema", "type": "object" } ``` CreateAndStartCodespaceSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloneRepository | CloneRepositorySchema | false | | The repository to clone for the codespace. | | description | string | false | | The description of the codespace. | | environment | ExecutionEnvironmentAssignRequest | false | | The environment for the codespace. | | environmentVariables | [NewEnvironmentVariableSchema] | false | | The environment variables for the codespace. | | exposedPorts | [ExposePortSchema] | false | | The exposed ports for the codespace. | | name | string | false | | The name of the codespace. | | openFilePaths | [string] | false | | The file paths to open in the codespace. | | settings | NotebookSettings | false | | The settings for the codespace. | | tags | [string] | false | | The tags associated with the codespace. | | useCaseId | string | true | | The ID of the use case associated with the codespace. | ## CreateCellRequest ``` { "description": "Request schema for creating a notebook cell.", "properties": { "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "The ID of the cell after which to create the new cell.", "title": "Aftercellid" }, "attachments": { "description": "The attachments associated with the cell.", "title": "Attachments", "type": "object" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata associated with the cell.", "title": "Metadata" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "afterCellId", "source" ], "title": "CreateCellRequest", "type": "object" } ``` CreateCellRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | afterCellId | any | true | | The ID of the cell after which to create the new cell. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attachments | object | false | | The attachments associated with the cell. | | metadata | NotebookCellMetadata | false | | The metadata associated with the cell. | | source | string | true | | The contents of the cell, represented as a string. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | FIRST | ## CreateCellsRequest ``` { "description": "Request payload values for creating notebook cells.", "properties": { "actionId": { "description": "Action ID of notebook update request.", "maxLength": 64, "title": "Actionid", "type": "string" }, "cells": { "description": "List of cells to create.", "items": { "description": "The schema for the cells to be inserted into a notebook.", "properties": { "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "ID of the cell after which to insert the new cell.", "title": "Aftercellid" }, "data": { "allOf": [ { "description": "The schema for the notebook cell to be created.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell to create." }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "cellType" ], "title": "CreateNotebookCellSchema", "type": "object" } ], "description": "The cell data to insert.", "title": "Data" } }, "required": [ "afterCellId", "data" ], "title": "InsertCellsSchema", "type": "object" }, "title": "Cells", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "cells" ], "title": "CreateCellsRequest", "type": "object" } ``` CreateCellsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actionId | string | false | maxLength: 64 | Action ID of notebook update request. | | cells | [InsertCellsSchema] | true | | List of cells to create. | | generation | integer | true | | Integer representing the generation of the notebook. | | path | string | true | | Path to the notebook. | ## CreateCellsResponse ``` { "description": "Response schema for creating notebook cells.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "title": "Actionid", "type": "string" }, "cellIds": { "description": "List of cell IDs of the created cells.", "items": { "type": "string" }, "title": "Cellids", "type": "array" } }, "required": [ "actionId", "cellIds" ], "title": "CreateCellsResponse", "type": "object" } ``` CreateCellsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actionId | string | true | | Action ID of the notebook update request. | | cellIds | [string] | true | | List of cell IDs of the created cells. | ## CreateEnvironmentVariablesRequestSchema ``` { "description": "Create environment variables request schema.", "properties": { "data": { "description": "List of environment variables to create.", "items": { "description": "Schema for updating environment variables.", "properties": { "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "pattern": "^[a-zA-Z_$][\\w$]*$", "title": "Name", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "NewEnvironmentVariableSchema", "type": "object" }, "title": "Data", "type": "array" } }, "title": "CreateEnvironmentVariablesRequestSchema", "type": "object" } ``` CreateEnvironmentVariablesRequestSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [NewEnvironmentVariableSchema] | false | | List of environment variables to create. | ## CreateFilesystemObjectRequest ``` { "description": "Create a new filesystem object request schema.", "properties": { "isDirectory": { "description": "Whether the filesystem object is a directory or not.", "title": "Isdirectory", "type": "boolean" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" } }, "required": [ "path", "isDirectory" ], "title": "CreateFilesystemObjectRequest", "type": "object" } ``` CreateFilesystemObjectRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isDirectory | boolean | true | | Whether the filesystem object is a directory or not. | | path | string | true | | Path to the filesystem object. | ## CreateManualRunRequest ``` { "description": "Request to create a manual run for a scheduled job.", "properties": { "manualRunType": { "allOf": [ { "description": "Intentionally a subset of RunTypes above - to be used in API schemas", "enum": [ "manual", "pipeline" ], "title": "ManualRunTypes", "type": "string" } ], "default": "manual", "description": "The type of manual run. Possible values are 'manual' or 'pipeline'." }, "notebookId": { "description": "The ID of the notebook to schedule.", "title": "Notebookid", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "title": { "description": "The title of the scheduled job.", "maxLength": 100, "minLength": 1, "title": "Title", "type": "string" } }, "required": [ "notebookId" ], "title": "CreateManualRunRequest", "type": "object" } ``` CreateManualRunRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | manualRunType | ManualRunTypes | false | | The type of manual run. Possible values are 'manual' or 'pipeline'. | | notebookId | string | true | | The ID of the notebook to schedule. | | notebookPath | string | false | | The path to the notebook in the file system if a Codespace is being used. | | parameters | [ScheduledJobParam] | false | | The parameters to pass to the notebook when it runs. | | title | string | false | maxLength: 100minLength: 1minLength: 1 | The title of the scheduled job. | ## CreateNotebookCellSchema ``` { "description": "The schema for the notebook cell to be created.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell to create." }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "cellType" ], "title": "CreateNotebookCellSchema", "type": "object" } ``` CreateNotebookCellSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellType | SupportedCellTypes | true | | Type of the cell to create. | | metadata | NotebookCellMetadata | false | | Metadata of the cell. | | source | any | false | | Contents of the cell, represented as a string. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## CreateNotebookRequest ``` { "description": "Request schema for creating a notebook.", "properties": { "createInitialCell": { "default": true, "description": "Whether to create an initial cell.", "title": "Createinitialcell", "type": "boolean" }, "description": { "description": "The description of the notebook.", "title": "Description", "type": "string" }, "name": { "default": "Untitled Notebook", "description": "The name of the notebook.", "title": "Name", "type": "string" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "The settings of the notebook.", "title": "Settings" }, "tags": { "description": "The tags of the notebook.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "type": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of the notebook." }, "typeTransition": { "allOf": [ { "description": "An enumeration.", "enum": [ "initiated_to_codespace", "completed" ], "title": "NotebookTypeTransition", "type": "string" } ], "description": "The type transition of the notebook." }, "useCaseId": { "description": "The ID of the Use Case associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case associated with the notebook.", "title": "Usecasename", "type": "string" } }, "title": "CreateNotebookRequest", "type": "object" } ``` CreateNotebookRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createInitialCell | boolean | false | | Whether to create an initial cell. | | description | string | false | | The description of the notebook. | | name | string | false | | The name of the notebook. | | settings | NotebookSettings | false | | The settings of the notebook. | | tags | [string] | false | | The tags of the notebook. | | type | NotebookType | false | | The type of the notebook. | | typeTransition | NotebookTypeTransition | false | | The type transition of the notebook. | | useCaseId | string | false | | The ID of the Use Case associated with the notebook. | | useCaseName | string | false | | The name of the Use Case associated with the notebook. | ## CreateNotebookRevisionRequest ``` { "description": "Request to create a new notebook revision.", "properties": { "isAuto": { "default": false, "description": "Whether the revision is autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Notebook revision name.", "minLength": 1, "title": "Name", "type": "string" }, "notebookPath": { "description": "Path to the notebook file, if using Codespaces.", "title": "Notebookpath", "type": "string" } }, "title": "CreateNotebookRevisionRequest", "type": "object" } ``` CreateNotebookRevisionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isAuto | boolean | false | | Whether the revision is autosaved. | | name | string | false | minLength: 1minLength: 1 | Notebook revision name. | | notebookPath | string | false | | Path to the notebook file, if using Codespaces. | ## CreateScheduledJobRequest ``` { "description": "Request to create a new scheduled job.", "properties": { "enabled": { "default": true, "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "notebookId": { "description": "The ID of the notebook to schedule.", "title": "Notebookid", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule for the job.", "title": "Schedule" }, "title": { "description": "The title of the scheduled job.", "maxLength": 100, "minLength": 1, "title": "Title", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" } }, "required": [ "useCaseId", "notebookId", "schedule" ], "title": "CreateScheduledJobRequest", "type": "object" } ``` CreateScheduledJobRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | false | | Whether the job is enabled. | | notebookId | string | true | | The ID of the notebook to schedule. | | notebookPath | string | false | | The path to the notebook in the file system if a Codespace is being used. | | parameters | [ScheduledJobParam] | false | | The parameters to pass to the notebook when it runs. | | schedule | Schedule | true | | The schedule for the job. | | title | string | false | maxLength: 100minLength: 1minLength: 1 | The title of the scheduled job. | | useCaseId | string | true | | The ID of the use case this notebook is associated with. | ## CreateTerminalSchema ``` { "description": "Schema for creating a terminal.", "properties": { "name": { "description": "Name of the terminal.", "title": "Name", "type": "string" } }, "required": [ "name" ], "title": "CreateTerminalSchema", "type": "object" } ``` CreateTerminalSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Name of the terminal. | ## DRFileMetadata ``` { "description": "Metadata for DataRobot files.", "properties": { "commandArgs": { "description": "Command arguments for the file.", "title": "Commandargs", "type": "string" } }, "title": "DRFileMetadata", "type": "object" } ``` DRFileMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commandArgs | string | false | | Command arguments for the file. | ## DRFilesystemMetadataResponse ``` { "description": "Response schema for DataRobot filesystem metadata.", "properties": { "datarobotMetadata": { "additionalProperties": { "description": "Metadata for DataRobot files.", "properties": { "commandArgs": { "description": "Command arguments for the file.", "title": "Commandargs", "type": "string" } }, "title": "DRFileMetadata", "type": "object" }, "description": "Metadata for the files.", "title": "Datarobotmetadata", "type": "object" } }, "required": [ "datarobotMetadata" ], "title": "DRFilesystemMetadataResponse", "type": "object" } ``` DRFilesystemMetadataResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datarobotMetadata | object | true | | Metadata for the files. | | » additionalProperties | DRFileMetadata | false | | Metadata for DataRobot files. | ## DataFrameEntrySchema ``` { "description": "Schema for a single DataFrame entry.", "properties": { "name": { "description": "Name of the DataFrame.", "title": "Name", "type": "string" }, "type": { "description": "Type of the DataFrame.", "title": "Type", "type": "string" } }, "required": [ "name", "type" ], "title": "DataFrameEntrySchema", "type": "object" } ``` DataFrameEntrySchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Name of the DataFrame. | | type | string | true | | Type of the DataFrame. | ## DataFrameResponseSchema ``` { "description": "Schema for the DataFrame response.", "properties": { "dataframe": { "description": "The DataFrame as a string.", "title": "Dataframe", "type": "string" } }, "required": [ "dataframe" ], "title": "DataFrameResponseSchema", "type": "object" } ``` DataFrameResponseSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataframe | string | true | | The DataFrame as a string. | ## DeleteFilesystemObjectsRequest ``` { "description": "Delete filesystem object request schema.", "properties": { "paths": { "description": "List of paths to delete.", "items": { "type": "string" }, "title": "Paths", "type": "array" } }, "title": "DeleteFilesystemObjectsRequest", "type": "object" } ``` DeleteFilesystemObjectsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | paths | [string] | false | | List of paths to delete. | ## DeleteScheduledJobQuery ``` { "description": "Query parameters for deleting a scheduled job.", "properties": { "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" } }, "required": [ "useCaseId" ], "title": "DeleteScheduledJobQuery", "type": "object" } ``` DeleteScheduledJobQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseId | string | true | | The ID of the use case this notebook is associated with. | ## DirHierarchyObjectSchema ``` { "description": "These are only directories\n\nThe name property can be any string or the constant/sentinel \"root\"", "properties": { "name": { "anyOf": [ { "type": "string" }, { "enum": [ "root" ], "type": "string" } ], "description": "Name of the directory.", "title": "Name" }, "path": { "description": "Path to the directory.", "title": "Path", "type": "string" } }, "required": [ "name", "path" ], "title": "DirHierarchyObjectSchema", "type": "object" } ``` DirHierarchyObjectSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | any | true | | Name of the directory. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to the directory. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | root | ## DisplayDataOutputSchema ``` { "description": "The schema for the display data output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "display_data", "default": "display_data", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "DisplayDataOutputSchema", "type": "object" } ``` DisplayDataOutputSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | object | false | | Data of the output. | | » additionalProperties | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | any | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metadata | object | false | | Metadata of the output. | | outputType | string | false | | Type of the output. | ## DownloadFilesystemObjectsQuery ``` { "description": "Query schema for downloading filesystem objects.", "properties": { "paths": { "description": "List of paths to download.", "items": { "type": "string" }, "title": "Paths", "type": "array" } }, "title": "DownloadFilesystemObjectsQuery", "type": "object" } ``` DownloadFilesystemObjectsQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | paths | [string] | false | | List of paths to download. | ## DownloadFilesystemObjectsRequest ``` { "description": "Query schema for downloading filesystem objects.", "properties": { "paths": { "description": "List of paths to download.", "items": { "type": "string" }, "title": "Paths", "type": "array" } }, "title": "DownloadFilesystemObjectsRequest", "type": "object" } ``` DownloadFilesystemObjectsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | paths | [string] | false | | List of paths to download. | ## EntityLinkingSources ``` { "description": "The source of the entity linking. Does not affect the operation of the API call. Only used for analytics.", "enum": [ "classic", "nextGen", "api" ], "title": "EntityLinkingSources", "type": "string" } ``` EntityLinkingSources ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | EntityLinkingSources | string | false | | The source of the entity linking. Does not affect the operation of the API call. Only used for analytics. | ### Enumerated Values | Property | Value | | --- | --- | | EntityLinkingSources | [classic, nextGen, api] | ## EntityLinkingWorkflows ``` { "description": "The workflow that is attaching this entity. Does not affect the operation of the API call. Only used for analytics.", "enum": [ "migration", "creation", "unspecified" ], "title": "EntityLinkingWorkflows", "type": "string" } ``` EntityLinkingWorkflows ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | EntityLinkingWorkflows | string | false | | The workflow that is attaching this entity. Does not affect the operation of the API call. Only used for analytics. | ### Enumerated Values | Property | Value | | --- | --- | | EntityLinkingWorkflows | [migration, creation, unspecified] | ## EnvironmentPublic ``` { "description": "The public representation of an execution environment.", "properties": { "image": { "allOf": [ { "description": "This class is used to represent the public information of an image.", "properties": { "default": { "default": false, "description": "Whether the image is the default image.", "title": "Default", "type": "boolean" }, "description": { "description": "Image description.", "title": "Description", "type": "string" }, "environmentId": { "description": "Environment ID.", "title": "Environmentid", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the image is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Image ID.", "title": "Id", "type": "string" }, "label": { "description": "Image label.", "title": "Label", "type": "string" }, "language": { "description": "Image programming language.", "title": "Language", "type": "string" }, "languageVersion": { "description": "Image programming language version.", "title": "Languageversion", "type": "string" }, "libraries": { "description": "The preinstalled libraries in the image.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "name": { "description": "Image name.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "description", "language", "languageVersion" ], "title": "ImagePublic", "type": "object" } ], "description": "The image of the environment.", "title": "Image" }, "machine": { "allOf": [ { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" } ], "description": "The machine of the environment.", "title": "Machine" }, "timeToLive": { "description": "The inactivity timeout of the environment.", "title": "Timetolive", "type": "integer" } }, "required": [ "image", "machine" ], "title": "EnvironmentPublic", "type": "object" } ``` EnvironmentPublic ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | image | ImagePublic | true | | The image of the environment. | | machine | Machine | true | | The machine of the environment. | | timeToLive | integer | false | | The inactivity timeout of the environment. | ## EnvironmentSupplementalMetadata ``` { "description": "Supplemental metadata for an environment.", "properties": { "id": { "description": "The ID of the environment.", "title": "Id", "type": "string" }, "label": { "description": "The label of the environment.", "title": "Label", "type": "string" }, "name": { "description": "The name of the environment.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "label" ], "title": "EnvironmentSupplementalMetadata", "type": "object" } ``` EnvironmentSupplementalMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the environment. | | label | string | true | | The label of the environment. | | name | string | true | | The name of the environment. | ## EnvironmentVariableResourceType ``` { "description": "An enumeration.", "enum": [ "notebook" ], "title": "EnvironmentVariableResourceType", "type": "string" } ``` EnvironmentVariableResourceType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | EnvironmentVariableResourceType | string | false | | An enumeration. | ### Enumerated Values | Property | Value | | --- | --- | | EnvironmentVariableResourceType | notebook | ## EnvironmentVariableSchema ``` { "description": "Environment variable schema.", "properties": { "assignedResourceId": { "description": "The resource ID to which the environment variable is assigned.", "title": "Assignedresourceid", "type": "string" }, "assignedResourceType": { "allOf": [ { "description": "An enumeration.", "enum": [ "notebook" ], "title": "EnvironmentVariableResourceType", "type": "string" } ], "default": "notebook", "description": "The resource type to which the environment variable is assigned." }, "createdAt": { "description": "The date and time when the environment variable was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "createdBy": { "description": "The ID of the user who created the environment variable.", "title": "Createdby", "type": "string" }, "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "id": { "description": "The environment variable ID.", "title": "Id", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "title": "Name", "type": "string" }, "updatedAt": { "description": "The date and time when the environment variable was last updated.", "format": "date-time", "title": "Updatedat", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated the environment variable.", "title": "Updatedby", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value", "id", "assignedResourceId", "createdBy", "createdAt" ], "title": "EnvironmentVariableSchema", "type": "object" } ``` EnvironmentVariableSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | assignedResourceId | string | true | | The resource ID to which the environment variable is assigned. | | assignedResourceType | EnvironmentVariableResourceType | false | | The resource type to which the environment variable is assigned. | | createdAt | string(date-time) | true | | The date and time when the environment variable was created. | | createdBy | string | true | | The ID of the user who created the environment variable. | | description | string | false | maxLength: 500 | The description of the environment variable. | | id | string | true | | The environment variable ID. | | name | string | true | maxLength: 253 | The name of the environment variable. | | updatedAt | string(date-time) | false | | The date and time when the environment variable was last updated. | | updatedBy | string | false | | The ID of the user who last updated the environment variable. | | value | string | true | maxLength: 131072 | The value of the environment variable. | ## EnvironmentVariablesResponseSchema ``` { "description": "List environment variables response schema.", "properties": { "data": { "description": "List of environment variables", "items": { "description": "Environment variable schema.", "properties": { "assignedResourceId": { "description": "The resource ID to which the environment variable is assigned.", "title": "Assignedresourceid", "type": "string" }, "assignedResourceType": { "allOf": [ { "description": "An enumeration.", "enum": [ "notebook" ], "title": "EnvironmentVariableResourceType", "type": "string" } ], "default": "notebook", "description": "The resource type to which the environment variable is assigned." }, "createdAt": { "description": "The date and time when the environment variable was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "createdBy": { "description": "The ID of the user who created the environment variable.", "title": "Createdby", "type": "string" }, "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "id": { "description": "The environment variable ID.", "title": "Id", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "title": "Name", "type": "string" }, "updatedAt": { "description": "The date and time when the environment variable was last updated.", "format": "date-time", "title": "Updatedat", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated the environment variable.", "title": "Updatedby", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value", "id", "assignedResourceId", "createdBy", "createdAt" ], "title": "EnvironmentVariableSchema", "type": "object" }, "title": "Data", "type": "array" } }, "title": "EnvironmentVariablesResponseSchema", "type": "object" } ``` EnvironmentVariablesResponseSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [EnvironmentVariableSchema] | false | | List of environment variables | ## EphemeralSessionEntityType ``` { "description": "Types of entities that can be associated with an ephemeral session.", "enum": [ "CUSTOM_APP", "CUSTOM_JOB", "CUSTOM_MODEL", "CUSTOM_METRIC", "CODE_SNIPPET" ], "title": "EphemeralSessionEntityType", "type": "string" } ``` EphemeralSessionEntityType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | EphemeralSessionEntityType | string | false | | Types of entities that can be associated with an ephemeral session. | ### Enumerated Values | Property | Value | | --- | --- | | EphemeralSessionEntityType | [CUSTOM_APP, CUSTOM_JOB, CUSTOM_MODEL, CUSTOM_METRIC, CODE_SNIPPET] | ## EphemeralSessionKey ``` { "description": "Key for an ephemeral session.", "properties": { "entityId": { "description": "The ID of the entity.", "title": "Entityid", "type": "string" }, "entityType": { "allOf": [ { "description": "Types of entities that can be associated with an ephemeral session.", "enum": [ "CUSTOM_APP", "CUSTOM_JOB", "CUSTOM_MODEL", "CUSTOM_METRIC", "CODE_SNIPPET" ], "title": "EphemeralSessionEntityType", "type": "string" } ], "description": "The type of the entity." } }, "required": [ "entityType", "entityId" ], "title": "EphemeralSessionKey", "type": "object" } ``` EphemeralSessionKey ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityId | string | true | | The ID of the entity. | | entityType | EphemeralSessionEntityType | true | | The type of the entity. | ## ErrorOutputSchema ``` { "description": "The schema for the error output in a notebook cell.", "properties": { "ename": { "description": "Error name.", "title": "Ename", "type": "string" }, "evalue": { "description": "Error value.", "title": "Evalue", "type": "string" }, "outputType": { "const": "error", "default": "error", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "traceback": { "description": "Traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "ename", "evalue" ], "title": "ErrorOutputSchema", "type": "object" } ``` ErrorOutputSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ename | string | true | | Error name. | | evalue | string | true | | Error value. | | outputType | string | false | | Type of the output. | | traceback | [string] | false | | Traceback of the error. | ## ExecuteCellsRequest ``` { "description": "Request payload values for executing notebook cells.", "properties": { "cellIds": { "description": "List of cell IDs to execute.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "cells": { "description": "List of cells to execute.", "items": { "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType", "source" ], "title": "NotebookCellExecData", "type": "object" }, "title": "Cells", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path", "generation", "cells" ], "title": "ExecuteCellsRequest", "type": "object" } ``` ExecuteCellsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellIds | [string] | false | | List of cell IDs to execute. | | cells | [NotebookCellExecData] | true | | List of cells to execute. | | generation | integer | true | | Integer representing the generation of the notebook. | | path | string | true | | Path to the notebook. | ## ExecuteCodeRequest ``` { "properties": { "code": { "description": "The code to execute.", "title": "Code", "type": "string" } }, "required": [ "code" ], "title": "ExecuteCodeRequest", "type": "object" } ``` ExecuteCodeRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | code | string | true | | The code to execute. | ## ExecuteFileResponse ``` { "description": "Response schema for executing a file.", "properties": { "kernelId": { "description": "ID of the kernel assigned to the file.", "title": "Kernelid", "type": "string" } }, "required": [ "kernelId" ], "title": "ExecuteFileResponse", "type": "object" } ``` ExecuteFileResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | kernelId | string | true | | ID of the kernel assigned to the file. | ## ExecuteResultOutputSchema ``` { "description": "The schema for the execute result output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "executionCount": { "default": 0, "description": "Execution count of the output.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "execute_result", "default": "execute_result", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "ExecuteResultOutputSchema", "type": "object" } ``` ExecuteResultOutputSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | object | false | | Data of the output. | | » additionalProperties | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | any | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionCount | integer | false | | Execution count of the output. | | metadata | object | false | | Metadata of the output. | | outputType | string | false | | Type of the output. | ## ExecutingFileData ``` { "description": "Schema for executing file information.", "properties": { "filePath": { "description": "Path to the file being executed.", "title": "Filepath", "type": "string" }, "kernelId": { "description": "ID of the kernel assigned to the file.", "title": "Kernelid", "type": "string" } }, "required": [ "kernelId", "filePath" ], "title": "ExecutingFileData", "type": "object" } ``` ExecutingFileData ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | filePath | string | true | | Path to the file being executed. | | kernelId | string | true | | ID of the kernel assigned to the file. | ## ExecutingFilesResponse ``` { "description": "Response schema for listing executing files.", "properties": { "executingFiles": { "description": "List of executing files with their kernel IDs.", "items": { "description": "Schema for executing file information.", "properties": { "filePath": { "description": "Path to the file being executed.", "title": "Filepath", "type": "string" }, "kernelId": { "description": "ID of the kernel assigned to the file.", "title": "Kernelid", "type": "string" } }, "required": [ "kernelId", "filePath" ], "title": "ExecutingFileData", "type": "object" }, "title": "Executingfiles", "type": "array" } }, "required": [ "executingFiles" ], "title": "ExecutingFilesResponse", "type": "object" } ``` ExecutingFilesResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executingFiles | [ExecutingFileData] | true | | List of executing files with their kernel IDs. | ## ExecutionEnvironmentAssignRequest ``` { "description": "Request schema for assigning an execution environment to a notebook.", "properties": { "environmentId": { "description": "The execution environment ID.", "title": "Environmentid", "type": "string" }, "environmentSlug": { "description": "The execution environment slug.", "title": "Environmentslug", "type": "string" }, "language": { "description": "The programming language of the environment.", "title": "Language", "type": "string" }, "languageVersion": { "description": "The programming language version.", "title": "Languageversion", "type": "string" }, "machineId": { "description": "The machine ID.", "title": "Machineid", "type": "string" }, "machineSlug": { "description": "The machine slug.", "title": "Machineslug", "type": "string" }, "timeToLive": { "description": "Inactivity timeout limit.", "maximum": 525600, "minimum": 3, "title": "Timetolive", "type": "integer" }, "versionId": { "description": "The execution environment version ID.", "title": "Versionid", "type": "string" } }, "title": "ExecutionEnvironmentAssignRequest", "type": "object" } ``` ExecutionEnvironmentAssignRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | environmentId | string | false | | The execution environment ID. | | environmentSlug | string | false | | The execution environment slug. | | language | string | false | | The programming language of the environment. | | languageVersion | string | false | | The programming language version. | | machineId | string | false | | The machine ID. | | machineSlug | string | false | | The machine slug. | | timeToLive | integer | false | maximum: 525600minimum: 3 | Inactivity timeout limit. | | versionId | string | false | | The execution environment version ID. | ## ExecutionEnvironmentSchema ``` { "properties": { "created": { "description": "The time the environment was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Execution environment description.", "title": "Description", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the environment is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Execution environment ID.", "title": "Id", "type": "string" }, "isBuiltIn": { "default": false, "description": "Whether the environment is a built-in environment supplied by DataRobot.", "title": "Isbuiltin", "type": "boolean" }, "isDownloadable": { "description": "Whether the environment is downloadable.", "title": "Isdownloadable", "type": "boolean" }, "isPublic": { "description": "Whether the environment is public.", "title": "Ispublic", "type": "boolean" }, "latestSuccessfulVersion": { "description": "Execution environment version schema.", "properties": { "buildStatus": { "description": "Build status of the environment version. Possible values: 'success', 'failed'.", "title": "Buildstatus", "type": "string" }, "created": { "description": "The time the environment version was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Description of the environment version.", "title": "Description", "type": "string" }, "dockerContext": { "description": "URL for downloading the Docker context of the environment version.", "title": "Dockercontext", "type": "string" }, "dockerContextSize": { "description": "Size of the Docker context in bytes.", "title": "Dockercontextsize", "type": "integer" }, "dockerImage": { "description": "URL for downloading the Docker image of the environment version.", "title": "Dockerimage", "type": "string" }, "dockerImageSize": { "description": "Size of the Docker image in bytes.", "title": "Dockerimagesize", "type": "integer" }, "environmentId": { "description": "Execution environment ID.", "title": "Environmentid", "type": "string" }, "id": { "description": "Execution environment version ID.", "title": "Id", "type": "string" }, "imageId": { "description": "Image ID of the environment version.", "title": "Imageid", "type": "string" }, "label": { "description": "Label of the environment version.", "title": "Label", "type": "string" }, "libraries": { "description": "List of libraries in the environment version.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "sourceDockerImageUri": { "description": "The URI that the image environment version is based on. Basing off of the image URI is different from docker context or uploaded image-based environment versions.", "title": "Sourcedockerimageuri", "type": "string" } }, "required": [ "id", "environmentId", "buildStatus", "created" ], "title": "ExecutionEnvironmentVersionSchema", "type": "object" }, "latestVersion": { "description": "Execution environment version schema.", "properties": { "buildStatus": { "description": "Build status of the environment version. Possible values: 'success', 'failed'.", "title": "Buildstatus", "type": "string" }, "created": { "description": "The time the environment version was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Description of the environment version.", "title": "Description", "type": "string" }, "dockerContext": { "description": "URL for downloading the Docker context of the environment version.", "title": "Dockercontext", "type": "string" }, "dockerContextSize": { "description": "Size of the Docker context in bytes.", "title": "Dockercontextsize", "type": "integer" }, "dockerImage": { "description": "URL for downloading the Docker image of the environment version.", "title": "Dockerimage", "type": "string" }, "dockerImageSize": { "description": "Size of the Docker image in bytes.", "title": "Dockerimagesize", "type": "integer" }, "environmentId": { "description": "Execution environment ID.", "title": "Environmentid", "type": "string" }, "id": { "description": "Execution environment version ID.", "title": "Id", "type": "string" }, "imageId": { "description": "Image ID of the environment version.", "title": "Imageid", "type": "string" }, "label": { "description": "Label of the environment version.", "title": "Label", "type": "string" }, "libraries": { "description": "List of libraries in the environment version.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "sourceDockerImageUri": { "description": "The URI that the image environment version is based on. Basing off of the image URI is different from docker context or uploaded image-based environment versions.", "title": "Sourcedockerimageuri", "type": "string" } }, "required": [ "id", "environmentId", "buildStatus", "created" ], "title": "ExecutionEnvironmentVersionSchema", "type": "object" }, "name": { "description": "Execution environment name.", "title": "Name", "type": "string" }, "programmingLanguage": { "description": "Programming language of the environment.", "title": "Programminglanguage", "type": "string" }, "requiredMetadataKeys": { "description": "Required metadata keys.", "items": { "description": "Define additional parameters required to assemble a model. Model versions using this environment must define values\nfor each fieldName in the requiredMetadata.", "properties": { "displayName": { "description": "A human readable name for the required field.", "title": "Displayname", "type": "string" }, "fieldName": { "description": "The required field key. This value is added as an environment variable when running custom models.", "title": "Fieldname", "type": "string" } }, "required": [ "fieldName", "displayName" ], "title": "RequiredMetadataKey", "type": "object" }, "title": "Requiredmetadatakeys", "type": "array" }, "useCases": { "description": "List of use cases for the environment. This includes 'notebooks', at a minimum .", "items": { "type": "string" }, "title": "Usecases", "type": "array" }, "username": { "description": "The username of the user who created the environment.", "title": "Username", "type": "string" } }, "required": [ "id", "isDownloadable", "isPublic", "name", "programmingLanguage", "created", "latestVersion", "latestSuccessfulVersion", "username" ], "title": "ExecutionEnvironmentSchema", "type": "object" } ``` ExecutionEnvironmentSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string(date-time) | true | | The time the environment was created. | | description | string | false | | Execution environment description. | | gpuOptimized | boolean | false | | Whether the environment is GPU optimized. | | id | string | true | | Execution environment ID. | | isBuiltIn | boolean | false | | Whether the environment is a built-in environment supplied by DataRobot. | | isDownloadable | boolean | true | | Whether the environment is downloadable. | | isPublic | boolean | true | | Whether the environment is public. | | latestSuccessfulVersion | ExecutionEnvironmentVersionSchema | true | | Execution environment version schema. | | latestVersion | ExecutionEnvironmentVersionSchema | true | | Execution environment version schema. | | name | string | true | | Execution environment name. | | programmingLanguage | string | true | | Programming language of the environment. | | requiredMetadataKeys | [RequiredMetadataKey] | false | | Required metadata keys. | | useCases | [string] | false | | List of use cases for the environment. This includes 'notebooks', at a minimum . | | username | string | true | | The username of the user who created the environment. | ## ExecutionEnvironmentUsageByNotebooks ``` { "description": "Notebook using a specific execution environment.", "properties": { "createdAt": { "description": "The time the notebook was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "createdBy": { "description": "The username of the user who created the notebook.", "title": "Createdby", "type": "string" }, "environmentVersion": { "description": "The version of the environment used by the notebook.", "title": "Environmentversion", "type": "string" }, "notebookId": { "description": "Notebook ID.", "title": "Notebookid", "type": "string" }, "notebookLastSession": { "description": "The last time the notebook was started.", "format": "date-time", "title": "Notebooklastsession", "type": "string" }, "notebookName": { "description": "Notebook name.", "title": "Notebookname", "type": "string" }, "useCaseId": { "description": "The use case ID of the notebook.", "title": "Usecaseid", "type": "string" } }, "required": [ "notebookId", "notebookName", "createdAt", "environmentVersion" ], "title": "ExecutionEnvironmentUsageByNotebooks", "type": "object" } ``` ExecutionEnvironmentUsageByNotebooks ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The time the notebook was created. | | createdBy | string | false | | The username of the user who created the notebook. | | environmentVersion | string | true | | The version of the environment used by the notebook. | | notebookId | string | true | | Notebook ID. | | notebookLastSession | string(date-time) | false | | The last time the notebook was started. | | notebookName | string | true | | Notebook name. | | useCaseId | string | false | | The use case ID of the notebook. | ## ExecutionEnvironmentUsageByNotebooksQuery ``` { "description": "Query parameters for listing notebooks using a specific execution environment.", "properties": { "limit": { "default": 10, "description": "The limit or results to return.", "exclusiveMinimum": 0, "maximum": 100, "title": "Limit", "type": "integer" }, "offset": { "default": 0, "description": "The offset to use when querying paginated results.", "minimum": 0, "title": "Offset", "type": "integer" } }, "title": "ExecutionEnvironmentUsageByNotebooksQuery", "type": "object" } ``` ExecutionEnvironmentUsageByNotebooksQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | limit | integer | false | maximum: 100 | The limit or results to return. | | offset | integer | false | minimum: 0 | The offset to use when querying paginated results. | ## ExecutionEnvironmentUsageByNotebooksResponse ``` { "description": "Response schema for listing notebooks using a specific execution environment.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of notebooks using the environment.", "items": { "description": "Notebook using a specific execution environment.", "properties": { "createdAt": { "description": "The time the notebook was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "createdBy": { "description": "The username of the user who created the notebook.", "title": "Createdby", "type": "string" }, "environmentVersion": { "description": "The version of the environment used by the notebook.", "title": "Environmentversion", "type": "string" }, "notebookId": { "description": "Notebook ID.", "title": "Notebookid", "type": "string" }, "notebookLastSession": { "description": "The last time the notebook was started.", "format": "date-time", "title": "Notebooklastsession", "type": "string" }, "notebookName": { "description": "Notebook name.", "title": "Notebookname", "type": "string" }, "useCaseId": { "description": "The use case ID of the notebook.", "title": "Usecaseid", "type": "string" } }, "required": [ "notebookId", "notebookName", "createdAt", "environmentVersion" ], "title": "ExecutionEnvironmentUsageByNotebooks", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ExecutionEnvironmentUsageByNotebooksResponse", "type": "object" } ``` ExecutionEnvironmentUsageByNotebooksResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The total of paginated results. | | data | [ExecutionEnvironmentUsageByNotebooks] | false | | List of notebooks using the environment. | | next | string | false | | The URL to fetch the next batch of results. | | previous | string | false | | The URL to fetch the previous batch of results. | | totalCount | integer | false | | The total of all results. | ## ExecutionEnvironmentVersionSchema ``` { "description": "Execution environment version schema.", "properties": { "buildStatus": { "description": "Build status of the environment version. Possible values: 'success', 'failed'.", "title": "Buildstatus", "type": "string" }, "created": { "description": "The time the environment version was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Description of the environment version.", "title": "Description", "type": "string" }, "dockerContext": { "description": "URL for downloading the Docker context of the environment version.", "title": "Dockercontext", "type": "string" }, "dockerContextSize": { "description": "Size of the Docker context in bytes.", "title": "Dockercontextsize", "type": "integer" }, "dockerImage": { "description": "URL for downloading the Docker image of the environment version.", "title": "Dockerimage", "type": "string" }, "dockerImageSize": { "description": "Size of the Docker image in bytes.", "title": "Dockerimagesize", "type": "integer" }, "environmentId": { "description": "Execution environment ID.", "title": "Environmentid", "type": "string" }, "id": { "description": "Execution environment version ID.", "title": "Id", "type": "string" }, "imageId": { "description": "Image ID of the environment version.", "title": "Imageid", "type": "string" }, "label": { "description": "Label of the environment version.", "title": "Label", "type": "string" }, "libraries": { "description": "List of libraries in the environment version.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "sourceDockerImageUri": { "description": "The URI that the image environment version is based on. Basing off of the image URI is different from docker context or uploaded image-based environment versions.", "title": "Sourcedockerimageuri", "type": "string" } }, "required": [ "id", "environmentId", "buildStatus", "created" ], "title": "ExecutionEnvironmentVersionSchema", "type": "object" } ``` ExecutionEnvironmentVersionSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buildStatus | string | true | | Build status of the environment version. Possible values: 'success', 'failed'. | | created | string(date-time) | true | | The time the environment version was created. | | description | string | false | | Description of the environment version. | | dockerContext | string | false | | URL for downloading the Docker context of the environment version. | | dockerContextSize | integer | false | | Size of the Docker context in bytes. | | dockerImage | string | false | | URL for downloading the Docker image of the environment version. | | dockerImageSize | integer | false | | Size of the Docker image in bytes. | | environmentId | string | true | | Execution environment ID. | | id | string | true | | Execution environment version ID. | | imageId | string | false | | Image ID of the environment version. | | label | string | false | | Label of the environment version. | | libraries | [string] | false | | List of libraries in the environment version. | | sourceDockerImageUri | string | false | | The URI that the image environment version is based on. Basing off of the image URI is different from docker context or uploaded image-based environment versions. | ## ExecutionEnvironmentVersionsResponse ``` { "description": "Response schema for listing execution environment versions.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of environment versions.", "items": { "description": "Execution environment version schema.", "properties": { "buildStatus": { "description": "Build status of the environment version. Possible values: 'success', 'failed'.", "title": "Buildstatus", "type": "string" }, "created": { "description": "The time the environment version was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Description of the environment version.", "title": "Description", "type": "string" }, "dockerContext": { "description": "URL for downloading the Docker context of the environment version.", "title": "Dockercontext", "type": "string" }, "dockerContextSize": { "description": "Size of the Docker context in bytes.", "title": "Dockercontextsize", "type": "integer" }, "dockerImage": { "description": "URL for downloading the Docker image of the environment version.", "title": "Dockerimage", "type": "string" }, "dockerImageSize": { "description": "Size of the Docker image in bytes.", "title": "Dockerimagesize", "type": "integer" }, "environmentId": { "description": "Execution environment ID.", "title": "Environmentid", "type": "string" }, "id": { "description": "Execution environment version ID.", "title": "Id", "type": "string" }, "imageId": { "description": "Image ID of the environment version.", "title": "Imageid", "type": "string" }, "label": { "description": "Label of the environment version.", "title": "Label", "type": "string" }, "libraries": { "description": "List of libraries in the environment version.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "sourceDockerImageUri": { "description": "The URI that the image environment version is based on. Basing off of the image URI is different from docker context or uploaded image-based environment versions.", "title": "Sourcedockerimageuri", "type": "string" } }, "required": [ "id", "environmentId", "buildStatus", "created" ], "title": "ExecutionEnvironmentVersionSchema", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ExecutionEnvironmentVersionsResponse", "type": "object" } ``` ExecutionEnvironmentVersionsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The total of paginated results. | | data | [ExecutionEnvironmentVersionSchema] | false | | List of environment versions. | | next | string | false | | The URL to fetch the next batch of results. | | previous | string | false | | The URL to fetch the previous batch of results. | | totalCount | integer | false | | The total of all results. | ## ExecutionEnvironmentsResponse ``` { "description": "Response schema for listing execution environments.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of execution environments.", "items": { "properties": { "created": { "description": "The time the environment was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Execution environment description.", "title": "Description", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the environment is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Execution environment ID.", "title": "Id", "type": "string" }, "isBuiltIn": { "default": false, "description": "Whether the environment is a built-in environment supplied by DataRobot.", "title": "Isbuiltin", "type": "boolean" }, "isDownloadable": { "description": "Whether the environment is downloadable.", "title": "Isdownloadable", "type": "boolean" }, "isPublic": { "description": "Whether the environment is public.", "title": "Ispublic", "type": "boolean" }, "latestSuccessfulVersion": { "description": "Execution environment version schema.", "properties": { "buildStatus": { "description": "Build status of the environment version. Possible values: 'success', 'failed'.", "title": "Buildstatus", "type": "string" }, "created": { "description": "The time the environment version was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Description of the environment version.", "title": "Description", "type": "string" }, "dockerContext": { "description": "URL for downloading the Docker context of the environment version.", "title": "Dockercontext", "type": "string" }, "dockerContextSize": { "description": "Size of the Docker context in bytes.", "title": "Dockercontextsize", "type": "integer" }, "dockerImage": { "description": "URL for downloading the Docker image of the environment version.", "title": "Dockerimage", "type": "string" }, "dockerImageSize": { "description": "Size of the Docker image in bytes.", "title": "Dockerimagesize", "type": "integer" }, "environmentId": { "description": "Execution environment ID.", "title": "Environmentid", "type": "string" }, "id": { "description": "Execution environment version ID.", "title": "Id", "type": "string" }, "imageId": { "description": "Image ID of the environment version.", "title": "Imageid", "type": "string" }, "label": { "description": "Label of the environment version.", "title": "Label", "type": "string" }, "libraries": { "description": "List of libraries in the environment version.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "sourceDockerImageUri": { "description": "The URI that the image environment version is based on. Basing off of the image URI is different from docker context or uploaded image-based environment versions.", "title": "Sourcedockerimageuri", "type": "string" } }, "required": [ "id", "environmentId", "buildStatus", "created" ], "title": "ExecutionEnvironmentVersionSchema", "type": "object" }, "latestVersion": { "description": "Execution environment version schema.", "properties": { "buildStatus": { "description": "Build status of the environment version. Possible values: 'success', 'failed'.", "title": "Buildstatus", "type": "string" }, "created": { "description": "The time the environment version was created.", "format": "date-time", "title": "Created", "type": "string" }, "description": { "description": "Description of the environment version.", "title": "Description", "type": "string" }, "dockerContext": { "description": "URL for downloading the Docker context of the environment version.", "title": "Dockercontext", "type": "string" }, "dockerContextSize": { "description": "Size of the Docker context in bytes.", "title": "Dockercontextsize", "type": "integer" }, "dockerImage": { "description": "URL for downloading the Docker image of the environment version.", "title": "Dockerimage", "type": "string" }, "dockerImageSize": { "description": "Size of the Docker image in bytes.", "title": "Dockerimagesize", "type": "integer" }, "environmentId": { "description": "Execution environment ID.", "title": "Environmentid", "type": "string" }, "id": { "description": "Execution environment version ID.", "title": "Id", "type": "string" }, "imageId": { "description": "Image ID of the environment version.", "title": "Imageid", "type": "string" }, "label": { "description": "Label of the environment version.", "title": "Label", "type": "string" }, "libraries": { "description": "List of libraries in the environment version.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "sourceDockerImageUri": { "description": "The URI that the image environment version is based on. Basing off of the image URI is different from docker context or uploaded image-based environment versions.", "title": "Sourcedockerimageuri", "type": "string" } }, "required": [ "id", "environmentId", "buildStatus", "created" ], "title": "ExecutionEnvironmentVersionSchema", "type": "object" }, "name": { "description": "Execution environment name.", "title": "Name", "type": "string" }, "programmingLanguage": { "description": "Programming language of the environment.", "title": "Programminglanguage", "type": "string" }, "requiredMetadataKeys": { "description": "Required metadata keys.", "items": { "description": "Define additional parameters required to assemble a model. Model versions using this environment must define values\nfor each fieldName in the requiredMetadata.", "properties": { "displayName": { "description": "A human readable name for the required field.", "title": "Displayname", "type": "string" }, "fieldName": { "description": "The required field key. This value is added as an environment variable when running custom models.", "title": "Fieldname", "type": "string" } }, "required": [ "fieldName", "displayName" ], "title": "RequiredMetadataKey", "type": "object" }, "title": "Requiredmetadatakeys", "type": "array" }, "useCases": { "description": "List of use cases for the environment. This includes 'notebooks', at a minimum .", "items": { "type": "string" }, "title": "Usecases", "type": "array" }, "username": { "description": "The username of the user who created the environment.", "title": "Username", "type": "string" } }, "required": [ "id", "isDownloadable", "isPublic", "name", "programmingLanguage", "created", "latestVersion", "latestSuccessfulVersion", "username" ], "title": "ExecutionEnvironmentSchema", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ExecutionEnvironmentsResponse", "type": "object" } ``` ExecutionEnvironmentsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The total of paginated results. | | data | [ExecutionEnvironmentSchema] | false | | List of execution environments. | | next | string | false | | The URL to fetch the next batch of results. | | previous | string | false | | The URL to fetch the previous batch of results. | | totalCount | integer | false | | The total of all results. | ## ExecutionStatusSchema ``` { "description": "Schema for the execution status of a notebook session.", "properties": { "cellId": { "description": "The ID of the cell currently being executed.", "title": "Cellid", "type": "string" }, "executed": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Info related to when the execution was completed.", "title": "Executed" }, "inputRequest": { "allOf": [ { "description": "AwaitingInputState represents the state of a cell that is awaiting input from the user.", "properties": { "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" }, "requestedAt": { "description": "The time the input was requested.", "format": "date-time", "title": "Requestedat", "type": "string" } }, "required": [ "requestedAt", "prompt", "password" ], "title": "AwaitingInputState", "type": "object" } ], "description": "The input request state of the cell.", "title": "Inputrequest" }, "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "queuedCellIds": { "description": "The IDs of the cells that are queued for execution.", "items": { "type": "string" }, "title": "Queuedcellids", "type": "array" }, "status": { "allOf": [ { "description": "Kernel execution status.", "enum": [ "busy", "idle" ], "title": "KernelExecutionStatus", "type": "string" } ], "description": "The status of the kernel execution. Possible values are 'busy' or 'idle'." }, "submitted": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Info related to when the execution request was submitted.", "title": "Submitted" } }, "required": [ "status", "notebookId" ], "title": "ExecutionStatusSchema", "type": "object" } ``` ExecutionStatusSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellId | string | false | | The ID of the cell currently being executed. | | executed | NotebookActionSignature | false | | Info related to when the execution was completed. | | inputRequest | AwaitingInputState | false | | The input request state of the cell. | | notebookId | string | true | | The ID of the notebook. | | queuedCellIds | [string] | false | | The IDs of the cells that are queued for execution. | | status | KernelExecutionStatus | true | | The status of the kernel execution. Possible values are 'busy' or 'idle'. | | submitted | NotebookActionSignature | false | | Info related to when the execution request was submitted. | ## ExportNotebookQuerySchema ``` { "description": "Query parameters for exporting a notebook.", "properties": { "includeOutput": { "default": true, "description": "Whether to include the cell output of the notebook in the export.", "title": "Includeoutput", "type": "boolean" } }, "title": "ExportNotebookQuerySchema", "type": "object" } ``` ExportNotebookQuerySchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | includeOutput | boolean | false | | Whether to include the cell output of the notebook in the export. | ## ExposePortSchema ``` { "description": "Port creation schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "maxLength": 500, "title": "Description", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" } }, "required": [ "port" ], "title": "ExposePortSchema", "type": "object" } ``` ExposePortSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 500 | Description of the exposed port. | | port | integer | true | | Exposed port number. | ## ExposedPortListSchema ``` { "description": "List of exposed ports for a notebook.", "properties": { "data": { "description": "List of exposed ports.", "items": { "description": "Exposed port schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "title": "Description", "type": "string" }, "id": { "description": "Exposed port ID.", "title": "Id", "type": "string" }, "notebookId": { "description": "Notebook ID the exposed port belongs to.", "title": "Notebookid", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" }, "url": { "description": "URL to access the exposed port.", "title": "Url", "type": "string" } }, "required": [ "id", "notebookId", "port", "url" ], "title": "ExposedPortSchema", "type": "object" }, "title": "Data", "type": "array" } }, "title": "ExposedPortListSchema", "type": "object" } ``` ExposedPortListSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [ExposedPortSchema] | false | | List of exposed ports. | ## ExposedPortSchema ``` { "description": "Exposed port schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "title": "Description", "type": "string" }, "id": { "description": "Exposed port ID.", "title": "Id", "type": "string" }, "notebookId": { "description": "Notebook ID the exposed port belongs to.", "title": "Notebookid", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" }, "url": { "description": "URL to access the exposed port.", "title": "Url", "type": "string" } }, "required": [ "id", "notebookId", "port", "url" ], "title": "ExposedPortSchema", "type": "object" } ``` ExposedPortSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | | Description of the exposed port. | | id | string | true | | Exposed port ID. | | notebookId | string | true | | Notebook ID the exposed port belongs to. | | port | integer | true | | Exposed port number. | | url | string | true | | URL to access the exposed port. | ## FilesystemObjectSchema ``` { "description": "This schema is used for filesystem objects (files and directories) in the filesystem.", "properties": { "lastUpdatedAt": { "description": "Last updated time of the filesystem object.", "format": "date-time", "title": "Lastupdatedat", "type": "string" }, "mediaType": { "description": "Media type of the filesystem object.", "title": "Mediatype", "type": "string" }, "name": { "description": "Name of the filesystem object.", "title": "Name", "type": "string" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" }, "sizeInBytes": { "description": "Size of the filesystem object in bytes.", "title": "Sizeinbytes", "type": "integer" }, "type": { "allOf": [ { "description": "Type of the filesystem object.", "enum": [ "dir", "file" ], "title": "FilesystemObjectType", "type": "string" } ], "description": "Type of the filesystem object. Possible values include 'dir', 'file'." } }, "required": [ "path", "type", "name" ], "title": "FilesystemObjectSchema", "type": "object" } ``` FilesystemObjectSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | lastUpdatedAt | string(date-time) | false | | Last updated time of the filesystem object. | | mediaType | string | false | | Media type of the filesystem object. | | name | string | true | | Name of the filesystem object. | | path | string | true | | Path to the filesystem object. | | sizeInBytes | integer | false | | Size of the filesystem object in bytes. | | type | FilesystemObjectType | true | | Type of the filesystem object. Possible values include 'dir', 'file'. | ## FilesystemObjectType ``` { "description": "Type of the filesystem object.", "enum": [ "dir", "file" ], "title": "FilesystemObjectType", "type": "string" } ``` FilesystemObjectType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | FilesystemObjectType | string | false | | Type of the filesystem object. | ### Enumerated Values | Property | Value | | --- | --- | | FilesystemObjectType | [dir, file] | ## FilesystemSortBy ``` { "description": "An enumeration.", "enum": [ "name", "-name", "updated_at", "-updated_at" ], "title": "FilesystemSortBy", "type": "string" } ``` FilesystemSortBy ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | FilesystemSortBy | string | false | | An enumeration. | ### Enumerated Values | Property | Value | | --- | --- | | FilesystemSortBy | [name, -name, updated_at, -updated_at] | ## FilterOptionsQuery ``` { "description": "Query options for filtering notebooks in the notebooks directory.", "properties": { "allUseCases": { "default": false, "description": "Whether to include all use cases.", "title": "Allusecases", "type": "boolean" }, "useCaseId": { "description": "The ID of a usecase to filter by.", "title": "Usecaseid", "type": "string" } }, "title": "FilterOptionsQuery", "type": "object" } ``` FilterOptionsQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allUseCases | boolean | false | | Whether to include all use cases. | | useCaseId | string | false | | The ID of a usecase to filter by. | ## FilterOptionsResponse ``` { "description": "Response schema for filter options in the notebooks directory.", "properties": { "owners": { "description": "A list of possible owners to filter notebooks by.", "items": { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" }, "title": "Owners", "type": "array" }, "tags": { "description": "A list of possible tags to filter notebooks by.", "items": { "type": "string" }, "title": "Tags", "type": "array" } }, "title": "FilterOptionsResponse", "type": "object" } ``` FilterOptionsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | owners | [UserInfo] | false | | A list of possible owners to filter notebooks by. | | tags | [string] | false | | A list of possible tags to filter notebooks by. | ## GetCellsQuery ``` { "description": "Base query schema for getting notebook cells.", "properties": { "cellIds": { "description": "List of cell IDs to get.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path", "cellIds" ], "title": "GetCellsQuery", "type": "object" } ``` GetCellsQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellIds | [string] | true | | List of cell IDs to get. | | path | string | true | | Path to the notebook. | ## GetDataframeResponse ``` { "description": "Response schema for getting a dataframe from a notebook session.", "properties": { "dataframe": { "description": "The requested dataframe in the notebook session.", "title": "Dataframe", "type": "string" } }, "required": [ "dataframe" ], "title": "GetDataframeResponse", "type": "object" } ``` GetDataframeResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataframe | string | true | | The requested dataframe in the notebook session. | ## GetFilesystemObjectMetadataRequest ``` { "description": "Request payload values for getting filesystem object metadata.", "properties": { "path": { "description": "List of paths to get metadata for.", "items": { "type": "string" }, "title": "Path", "type": "array" } }, "title": "GetFilesystemObjectMetadataRequest", "type": "object" } ``` GetFilesystemObjectMetadataRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | [string] | false | | List of paths to get metadata for. | ## GitCloneRequest ``` { "description": "Request schema for cloning a Git repository.", "properties": { "url": { "description": "URL of the Git repository to clone.", "format": "uri", "maxLength": 65536, "minLength": 1, "title": "Url", "type": "string" } }, "required": [ "url" ], "title": "GitCloneRequest", "type": "object" } ``` GitCloneRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | url | string(uri) | true | maxLength: 65536minLength: 1minLength: 1 | URL of the Git repository to clone. | ## GitCloneStatusResponse ``` { "description": "Response schema for the status of a Git clone operation.", "properties": { "errorMsg": { "description": "Error message if the clone operation failed.", "title": "Errormsg", "type": "string" }, "status": { "allOf": [ { "description": "Status of the VCS command.", "enum": [ "not_inited", "running", "finished", "error" ], "title": "VCSCommandStatus", "type": "string" } ], "description": "Status of the Git clone operation." } }, "required": [ "status" ], "title": "GitCloneStatusResponse", "type": "object" } ``` GitCloneStatusResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMsg | string | false | | Error message if the clone operation failed. | | status | VCSCommandStatus | true | | Status of the Git clone operation. | ## GitProviderSchema ``` { "description": "The Git OAuth Provider details.", "properties": { "hostname": { "description": "The hostname of the Git provider.", "title": "Hostname", "type": "string" }, "id": { "description": "The authorized Git OAuth Provider ID.", "title": "Id", "type": "string" }, "name": { "description": "The name of the Git provider.", "title": "Name", "type": "string" }, "settingsUrl": { "description": "The URL to manage the Git provider settings.", "title": "Settingsurl", "type": "string" }, "status": { "description": "The status of the OAuth authorization.", "title": "Status", "type": "string" }, "type": { "description": "The type of the Git provider.", "title": "Type", "type": "string" } }, "required": [ "id", "status", "type", "name", "hostname" ], "title": "GitProviderSchema", "type": "object" } ``` GitProviderSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | hostname | string | true | | The hostname of the Git provider. | | id | string | true | | The authorized Git OAuth Provider ID. | | name | string | true | | The name of the Git provider. | | settingsUrl | string | false | | The URL to manage the Git provider settings. | | status | string | true | | The status of the OAuth authorization. | | type | string | true | | The type of the Git provider. | ## GitRepositorySchema ``` { "properties": { "createdAt": { "description": "The creation date of the Git repository.", "format": "date-time", "title": "Createdat", "type": "string" }, "fullName": { "description": "The full name of Git repository, e.g., datarobot/notebooks.", "title": "Fullname", "type": "string" }, "httpUrl": { "description": "The HTTP URL of the Git repository, e.g., https://github.com/datarobot/notebooks.", "title": "Httpurl", "type": "string" }, "isPrivate": { "description": "Determines if the Git repository is private.", "title": "Isprivate", "type": "boolean" }, "name": { "description": "The name of Git repository, e.g., \"notebooks\".", "title": "Name", "type": "string" }, "owner": { "description": "The owner account of the Git repository.", "title": "Owner", "type": "string" }, "pushedAt": { "description": "The last push date of the Git repository.", "format": "date-time", "title": "Pushedat", "type": "string" }, "updatedAt": { "description": "The last update date of the Git repository.", "format": "date-time", "title": "Updatedat", "type": "string" } }, "required": [ "name", "fullName", "httpUrl", "owner" ], "title": "GitRepositorySchema", "type": "object" } ``` GitRepositorySchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | false | | The creation date of the Git repository. | | fullName | string | true | | The full name of Git repository, e.g., datarobot/notebooks. | | httpUrl | string | true | | The HTTP URL of the Git repository, e.g., https://github.com/datarobot/notebooks. | | isPrivate | boolean | false | | Determines if the Git repository is private. | | name | string | true | | The name of Git repository, e.g., "notebooks". | | owner | string | true | | The owner account of the Git repository. | | pushedAt | string(date-time) | false | | The last push date of the Git repository. | | updatedAt | string(date-time) | false | | The last update date of the Git repository. | ## ImagePublic ``` { "description": "This class is used to represent the public information of an image.", "properties": { "default": { "default": false, "description": "Whether the image is the default image.", "title": "Default", "type": "boolean" }, "description": { "description": "Image description.", "title": "Description", "type": "string" }, "environmentId": { "description": "Environment ID.", "title": "Environmentid", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the image is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Image ID.", "title": "Id", "type": "string" }, "label": { "description": "Image label.", "title": "Label", "type": "string" }, "language": { "description": "Image programming language.", "title": "Language", "type": "string" }, "languageVersion": { "description": "Image programming language version.", "title": "Languageversion", "type": "string" }, "libraries": { "description": "The preinstalled libraries in the image.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "name": { "description": "Image name.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "description", "language", "languageVersion" ], "title": "ImagePublic", "type": "object" } ``` ImagePublic ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | default | boolean | false | | Whether the image is the default image. | | description | string | true | | Image description. | | environmentId | string | false | | Environment ID. | | gpuOptimized | boolean | false | | Whether the image is GPU optimized. | | id | string | true | | Image ID. | | label | string | false | | Image label. | | language | string | true | | Image programming language. | | languageVersion | string | true | | Image programming language version. | | libraries | [string] | false | | The preinstalled libraries in the image. | | name | string | true | | Image name. | ## ImportNotebookFromUrlRequest ``` { "description": "Request payload values for importing a notebook from a URL.", "properties": { "includeOutput": { "default": true, "description": "Whether to include the cell output of the notebook in the import.", "title": "Includeoutput", "type": "boolean" }, "name": { "description": "The name of the notebook to be created upon import.", "title": "Name", "type": "string" }, "uri": { "description": "The URL of the notebook to import.", "title": "Uri", "type": "string" }, "useCaseId": { "description": "The ID of the Use Case to import the notebook into.", "title": "Usecaseid", "type": "string" } }, "required": [ "uri" ], "title": "ImportNotebookFromUrlRequest", "type": "object" } ``` ImportNotebookFromUrlRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | includeOutput | boolean | false | | Whether to include the cell output of the notebook in the import. | | name | string | false | | The name of the notebook to be created upon import. | | uri | string | true | | The URL of the notebook to import. | | useCaseId | string | false | | The ID of the Use Case to import the notebook into. | ## ImportNotebookResponse ``` { "description": "Imported notebook information.", "properties": { "id": { "description": "The ID of the created notebook.", "title": "Id", "type": "string" }, "name": { "description": "The name of the created notebook.", "title": "Name", "type": "string" } }, "required": [ "id" ], "title": "ImportNotebookResponse", "type": "object" } ``` ImportNotebookResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the created notebook. | | name | string | false | | The name of the created notebook. | ## InputReplySchema ``` { "description": "The schema for the input reply request.", "properties": { "value": { "description": "The value to send as a reply after kernel has requested input.", "title": "Value", "type": "string" } }, "required": [ "value" ], "title": "InputReplySchema", "type": "object" } ``` InputReplySchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | value | string | true | | The value to send as a reply after kernel has requested input. | ## InsertCellsSchema ``` { "description": "The schema for the cells to be inserted into a notebook.", "properties": { "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "ID of the cell after which to insert the new cell.", "title": "Aftercellid" }, "data": { "allOf": [ { "description": "The schema for the notebook cell to be created.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell to create." }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "cellType" ], "title": "CreateNotebookCellSchema", "type": "object" } ], "description": "The cell data to insert.", "title": "Data" } }, "required": [ "afterCellId", "data" ], "title": "InsertCellsSchema", "type": "object" } ``` InsertCellsSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | afterCellId | any | true | | ID of the cell after which to insert the new cell. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | CreateNotebookCellSchema | true | | The cell data to insert. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | FIRST | ## InteractiveVariablesSchema ``` { "description": "Interactive variables schema. For use with results from IPython's magic `%who_ls` command.", "properties": { "name": { "description": "The name of the variable.", "title": "Name", "type": "string" }, "type": { "description": "The type of the variable.", "title": "Type", "type": "string" } }, "required": [ "name", "type" ], "title": "InteractiveVariablesSchema", "type": "object" } ``` InteractiveVariablesSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the variable. | | type | string | true | | The type of the variable. | ## JupyterCellMetadata ``` { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ``` JupyterCellMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | outputsHidden | boolean | false | | Whether the cell's outputs are hidden. | | sourceHidden | boolean | false | | Whether the cell's source is hidden. | ## KernelExecutionStatus ``` { "description": "Kernel execution status.", "enum": [ "busy", "idle" ], "title": "KernelExecutionStatus", "type": "string" } ``` KernelExecutionStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | KernelExecutionStatus | string | false | | Kernel execution status. | ### Enumerated Values | Property | Value | | --- | --- | | KernelExecutionStatus | [busy, idle] | ## KernelSchema ``` { "description": "The schema for the notebook kernel.", "properties": { "executionState": { "allOf": [ { "description": "Event Sequences on Various Workflows:\n- On kernel created: CONNECTED -> BUSY -> IDLE\n- On kernel restarted: RESTARTING -> STARTING -> BUSY -> IDLE\n- On regular execution: IDLE -> BUSY -> IDLE\n- On execution interrupted: IDLE -> BUSY -> INTERRUPTING -> IDLE\n- On execution with error: IDLE -> BUSY -> IDLE -> INTERRUPTING -> IDLE\n- On kernel shut down via calling the stop kernel endpoint:\n DISCONNECTED (can be sent a few times) -> NOT_RUNNING (after 5s)", "enum": [ "connecting", "disconnected", "connected", "starting", "idle", "busy", "interrupting", "restarting", "not_running" ], "title": "KernelState", "type": "string" } ], "description": "The execution state of the kernel." }, "id": { "description": "The ID of the kernel.", "title": "Id", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "The programming language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "The name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" }, "running": { "default": false, "description": "Whether the kernel is running.", "title": "Running", "type": "boolean" } }, "required": [ "id", "name", "language", "executionState" ], "title": "KernelSchema", "type": "object" } ``` KernelSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionState | KernelState | true | | The execution state of the kernel. | | id | string | true | | The ID of the kernel. | | language | RuntimeLanguage | true | | The programming language of the kernel. Possible values include 'python', 'r'. | | name | string | true | | The name of the kernel. Possible values include 'python3', 'ir'. | | running | boolean | false | | Whether the kernel is running. | ## KernelSpecSchema ``` { "properties": { "displayName": { "description": "Display name of the kernel. Possible values include 'R', 'Python 3 (ipykernel)'.", "title": "Displayname", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "Runtime language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "Name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" } }, "required": [ "name", "displayName", "language" ], "title": "KernelSpecSchema", "type": "object" } ``` KernelSpecSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | displayName | string | true | | Display name of the kernel. Possible values include 'R', 'Python 3 (ipykernel)'. | | language | RuntimeLanguage | true | | Runtime language of the kernel. Possible values include 'python', 'r'. | | name | string | true | | Name of the kernel. Possible values include 'python3', 'ir'. | ## KernelSpecsResponse ``` { "description": "Schema for the Codespace's kernel information.", "properties": { "default": { "description": "The default kernel. Possible values include 'python3', 'ir'.", "title": "Default", "type": "string" }, "kernels": { "description": "List of available kernels.", "items": { "properties": { "displayName": { "description": "Display name of the kernel. Possible values include 'R', 'Python 3 (ipykernel)'.", "title": "Displayname", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "Runtime language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "Name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" } }, "required": [ "name", "displayName", "language" ], "title": "KernelSpecSchema", "type": "object" }, "title": "Kernels", "type": "array" } }, "required": [ "default" ], "title": "KernelSpecsResponse", "type": "object" } ``` KernelSpecsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | default | string | true | | The default kernel. Possible values include 'python3', 'ir'. | | kernels | [KernelSpecSchema] | false | | List of available kernels. | ## KernelState ``` { "description": "Event Sequences on Various Workflows:\n- On kernel created: CONNECTED -> BUSY -> IDLE\n- On kernel restarted: RESTARTING -> STARTING -> BUSY -> IDLE\n- On regular execution: IDLE -> BUSY -> IDLE\n- On execution interrupted: IDLE -> BUSY -> INTERRUPTING -> IDLE\n- On execution with error: IDLE -> BUSY -> IDLE -> INTERRUPTING -> IDLE\n- On kernel shut down via calling the stop kernel endpoint:\n DISCONNECTED (can be sent a few times) -> NOT_RUNNING (after 5s)", "enum": [ "connecting", "disconnected", "connected", "starting", "idle", "busy", "interrupting", "restarting", "not_running" ], "title": "KernelState", "type": "string" } ``` KernelState ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | KernelState | string | false | | Event Sequences on Various Workflows:- On kernel created: CONNECTED -> BUSY -> IDLE- On kernel restarted: RESTARTING -> STARTING -> BUSY -> IDLE- On regular execution: IDLE -> BUSY -> IDLE- On execution interrupted: IDLE -> BUSY -> INTERRUPTING -> IDLE- On execution with error: IDLE -> BUSY -> IDLE -> INTERRUPTING -> IDLE- On kernel shut down via calling the stop kernel endpoint: DISCONNECTED (can be sent a few times) -> NOT_RUNNING (after 5s) | ### Enumerated Values | Property | Value | | --- | --- | | KernelState | [connecting, disconnected, connected, starting, idle, busy, interrupting, restarting, not_running] | ## Language ``` { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } ``` Language ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | Language | string | false | | An enumeration. | ### Enumerated Values | Property | Value | | --- | --- | | Language | [dataframe, markdown, python, r, shell, scala, sas, custommetric] | ## ListCodeSnippetsResponse ``` { "description": "Response schema for the list of code snippets.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "A list of code snippets.", "items": { "description": "CodeSnippet represents a code snippet.", "properties": { "code": { "description": "The code snippet code content.", "title": "Code", "type": "string" }, "description": { "description": "The description of the code snippet.", "title": "Description", "type": "string" }, "id": { "description": "The ID of the code snippet.", "title": "Id", "type": "string" }, "language": { "description": "The programming language of the code snippet.", "title": "Language", "type": "string" }, "languageVersion": { "description": "The programming language version of the code snippet.", "title": "Languageversion", "type": "string" }, "locale": { "description": "The language locale of the code snippet.", "title": "Locale", "type": "string" }, "tags": { "description": "A comma separated list of snippet tags.", "title": "Tags", "type": "string" }, "title": { "description": "The title of the code snippet.", "title": "Title", "type": "string" } }, "required": [ "id", "locale", "title", "description", "code", "language", "languageVersion" ], "title": "CodeSnippet", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ListCodeSnippetsResponse", "type": "object" } ``` ListCodeSnippetsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The total of paginated results. | | data | [CodeSnippet] | false | | A list of code snippets. | | next | string | false | | The URL to fetch the next batch of results. | | previous | string | false | | The URL to fetch the previous batch of results. | | totalCount | integer | false | | The total of all results. | ## ListCodeSnippetsTagsResponse ``` { "description": "Response schema for the list of code snippets tags.", "properties": { "tags": { "description": "A list of tags for the code snippets.", "items": { "type": "string" }, "title": "Tags", "type": "array" } }, "title": "ListCodeSnippetsTagsResponse", "type": "object" } ``` ListCodeSnippetsTagsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | tags | [string] | false | | A list of tags for the code snippets. | ## ListDataFrameResponse ``` { "description": "Schema for the list of DataFrames in a notebook.", "properties": { "dataframes": { "description": "List of DataFrames information.", "items": { "description": "Schema for a single DataFrame entry.", "properties": { "name": { "description": "Name of the DataFrame.", "title": "Name", "type": "string" }, "type": { "description": "Type of the DataFrame.", "title": "Type", "type": "string" } }, "required": [ "name", "type" ], "title": "DataFrameEntrySchema", "type": "object" }, "title": "Dataframes", "type": "array" } }, "title": "ListDataFrameResponse", "type": "object" } ``` ListDataFrameResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataframes | [DataFrameEntrySchema] | false | | List of DataFrames information. | ## ListDataframesResponse ``` { "description": "Response schema for listing DataFrames in a notebook session.", "properties": { "dataframes": { "description": "List of DataFrames in the notebook session.", "items": { "description": "Interactive variables schema. For use with results from IPython's magic `%who_ls` command.", "properties": { "name": { "description": "The name of the variable.", "title": "Name", "type": "string" }, "type": { "description": "The type of the variable.", "title": "Type", "type": "string" } }, "required": [ "name", "type" ], "title": "InteractiveVariablesSchema", "type": "object" }, "title": "Dataframes", "type": "array" } }, "title": "ListDataframesResponse", "type": "object" } ``` ListDataframesResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataframes | [InteractiveVariablesSchema] | false | | List of DataFrames in the notebook session. | ## ListFilesystemObjectsRequest ``` { "description": "We have set the limit max value to one thousand", "properties": { "limit": { "default": 100, "description": "Maximum number of objects to return.", "exclusiveMinimum": 0, "maximum": 1000, "title": "Limit", "type": "integer" }, "offset": { "default": 0, "description": "Offset for pagination.", "minimum": 0, "title": "Offset", "type": "integer" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" }, "sortBy": { "allOf": [ { "description": "An enumeration.", "enum": [ "name", "-name", "updated_at", "-updated_at" ], "title": "FilesystemSortBy", "type": "string" } ], "default": "name", "description": "Sort results by this field." } }, "required": [ "path" ], "title": "ListFilesystemObjectsRequest", "type": "object" } ``` ListFilesystemObjectsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | limit | integer | false | maximum: 1000 | Maximum number of objects to return. | | offset | integer | false | minimum: 0 | Offset for pagination. | | path | string | true | | Path to the filesystem object. | | sortBy | FilesystemSortBy | false | | Sort results by this field. | ## ListFilesystemObjectsResponse ``` { "description": "Response schema for listing filesystem objects.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of filesystem objects.", "items": { "description": "This schema is used for filesystem objects (files and directories) in the filesystem.", "properties": { "lastUpdatedAt": { "description": "Last updated time of the filesystem object.", "format": "date-time", "title": "Lastupdatedat", "type": "string" }, "mediaType": { "description": "Media type of the filesystem object.", "title": "Mediatype", "type": "string" }, "name": { "description": "Name of the filesystem object.", "title": "Name", "type": "string" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" }, "sizeInBytes": { "description": "Size of the filesystem object in bytes.", "title": "Sizeinbytes", "type": "integer" }, "type": { "allOf": [ { "description": "Type of the filesystem object.", "enum": [ "dir", "file" ], "title": "FilesystemObjectType", "type": "string" } ], "description": "Type of the filesystem object. Possible values include 'dir', 'file'." } }, "required": [ "path", "type", "name" ], "title": "FilesystemObjectSchema", "type": "object" }, "title": "Data", "type": "array" }, "datarobotMetadata": { "additionalProperties": { "description": "Metadata for DataRobot files.", "properties": { "commandArgs": { "description": "Command arguments for the file.", "title": "Commandargs", "type": "string" } }, "title": "DRFileMetadata", "type": "object" }, "description": "Metadata for DataRobot files.", "title": "Datarobotmetadata", "type": "object" }, "directoryHierarchy": { "description": "List of directories respective names and paths.", "items": { "description": "These are only directories\n\nThe name property can be any string or the constant/sentinel \"root\"", "properties": { "name": { "anyOf": [ { "type": "string" }, { "enum": [ "root" ], "type": "string" } ], "description": "Name of the directory.", "title": "Name" }, "path": { "description": "Path to the directory.", "title": "Path", "type": "string" } }, "required": [ "name", "path" ], "title": "DirHierarchyObjectSchema", "type": "object" }, "title": "Directoryhierarchy", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "required": [ "data", "directoryHierarchy", "datarobotMetadata" ], "title": "ListFilesystemObjectsResponse", "type": "object" } ``` ListFilesystemObjectsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The total of paginated results. | | data | [FilesystemObjectSchema] | true | | List of filesystem objects. | | datarobotMetadata | object | true | | Metadata for DataRobot files. | | » additionalProperties | DRFileMetadata | false | | Metadata for DataRobot files. | | directoryHierarchy | [DirHierarchyObjectSchema] | true | | List of directories respective names and paths. | | next | string | false | | The URL to fetch the next batch of results. | | previous | string | false | | The URL to fetch the previous batch of results. | | totalCount | integer | false | | The total of all results. | ## ListGitProvidersResponse ``` { "description": "A list of authorized Git OAuth providers available for the user.", "properties": { "providers": { "description": "A list of authorized Git OAuth providers.", "items": { "description": "The Git OAuth Provider details.", "properties": { "hostname": { "description": "The hostname of the Git provider.", "title": "Hostname", "type": "string" }, "id": { "description": "The authorized Git OAuth Provider ID.", "title": "Id", "type": "string" }, "name": { "description": "The name of the Git provider.", "title": "Name", "type": "string" }, "settingsUrl": { "description": "The URL to manage the Git provider settings.", "title": "Settingsurl", "type": "string" }, "status": { "description": "The status of the OAuth authorization.", "title": "Status", "type": "string" }, "type": { "description": "The type of the Git provider.", "title": "Type", "type": "string" } }, "required": [ "id", "status", "type", "name", "hostname" ], "title": "GitProviderSchema", "type": "object" }, "maxItems": 1000, "minItems": 0, "title": "Providers", "type": "array" } }, "required": [ "providers" ], "title": "ListGitProvidersResponse", "type": "object" } ``` ListGitProvidersResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | providers | [GitProviderSchema] | true | maxItems: 1000 | A list of authorized Git OAuth providers. | ## ListGitRepositoriesResponse ``` { "description": "A list of Git repositories available for the authorized Git provider and the related params used for filtering\n(if provided).", "properties": { "data": { "description": "A list of Git repositories.", "items": { "properties": { "createdAt": { "description": "The creation date of the Git repository.", "format": "date-time", "title": "Createdat", "type": "string" }, "fullName": { "description": "The full name of Git repository, e.g., datarobot/notebooks.", "title": "Fullname", "type": "string" }, "httpUrl": { "description": "The HTTP URL of the Git repository, e.g., https://github.com/datarobot/notebooks.", "title": "Httpurl", "type": "string" }, "isPrivate": { "description": "Determines if the Git repository is private.", "title": "Isprivate", "type": "boolean" }, "name": { "description": "The name of Git repository, e.g., \"notebooks\".", "title": "Name", "type": "string" }, "owner": { "description": "The owner account of the Git repository.", "title": "Owner", "type": "string" }, "pushedAt": { "description": "The last push date of the Git repository.", "format": "date-time", "title": "Pushedat", "type": "string" }, "updatedAt": { "description": "The last update date of the Git repository.", "format": "date-time", "title": "Updatedat", "type": "string" } }, "required": [ "name", "fullName", "httpUrl", "owner" ], "title": "GitRepositorySchema", "type": "object" }, "title": "Data", "type": "array" }, "params": { "additionalProperties": { "type": "string" }, "description": "The params used when listing Git repositories.", "title": "Params", "type": "object" } }, "required": [ "data" ], "title": "ListGitRepositoriesResponse", "type": "object" } ``` ListGitRepositoriesResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [GitRepositorySchema] | true | | A list of Git repositories. | | params | object | false | | The params used when listing Git repositories. | | » additionalProperties | string | false | | none | ## ListGitRepositoryQuery ``` { "description": "The possible query params to use when listing git repositories.", "properties": { "limit": { "default": 30, "description": "The number of repositories to return.", "exclusiveMinimum": 1, "maximum": 100, "title": "Limit", "type": "integer" }, "query": { "description": "A search query for repository names.", "title": "Query", "type": "string" } }, "title": "ListGitRepositoryQuery", "type": "object" } ``` ListGitRepositoryQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | limit | integer | false | maximum: 100 | The number of repositories to return. | | query | string | false | | A search query for repository names. | ## ListNotebookRevisionsQuerySchema ``` { "description": "Query schema for listing notebook revisions.", "properties": { "autosaved": { "description": "Whether to include autosaved revisions.", "title": "Autosaved", "type": "boolean" }, "createdAfter": { "description": "Filter revisions created after this date.", "format": "date-time", "title": "Createdafter", "type": "string" }, "limit": { "default": 10, "description": "The limit or results to return.", "exclusiveMinimum": 0, "maximum": 100, "title": "Limit", "type": "integer" }, "offset": { "default": 0, "description": "The offset to use when querying paginated results.", "minimum": 0, "title": "Offset", "type": "integer" }, "orderBy": { "allOf": [ { "description": "Enum for ordering notebooks when querying.", "enum": [ "name", "-name", "created", "-created", "updated", "-updated", "tags", "-tags", "lastViewed", "-lastViewed", "useCaseName", "-useCaseName", "sessionStatus", "-sessionStatus" ], "title": "OrderBy", "type": "string" } ], "default": "created", "description": "Order by field." } }, "title": "ListNotebookRevisionsQuerySchema", "type": "object" } ``` ListNotebookRevisionsQuerySchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | autosaved | boolean | false | | Whether to include autosaved revisions. | | createdAfter | string(date-time) | false | | Filter revisions created after this date. | | limit | integer | false | maximum: 100 | The limit or results to return. | | offset | integer | false | minimum: 0 | The offset to use when querying paginated results. | | orderBy | OrderBy | false | | Order by field. | ## ListNotebookRevisionsResponse ``` { "description": "Response schema for listing notebook revisions.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of notebook revisions.", "items": { "description": "Notebook revision schema.", "properties": { "created": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision creation action information.", "title": "Created" }, "isAuto": { "description": "Whether the revision was autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Revision name.", "title": "Name", "type": "string" }, "notebookId": { "description": "Notebook ID this revision belongs to.", "title": "Notebookid", "type": "string" }, "revisionId": { "description": "Revision ID.", "title": "Revisionid", "type": "string" }, "updated": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision update action information.", "title": "Updated" } }, "required": [ "revisionId", "notebookId", "name", "isAuto", "created" ], "title": "NotebookRevisionSchema", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ListNotebookRevisionsResponse", "type": "object" } ``` ListNotebookRevisionsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The total of paginated results. | | data | [NotebookRevisionSchema] | false | | List of notebook revisions. | | next | string | false | | The URL to fetch the next batch of results. | | previous | string | false | | The URL to fetch the previous batch of results. | | totalCount | integer | false | | The total of all results. | ## ListNotebooksQuery ``` { "description": "Query options for listing notebooks in the notebooks directory.", "properties": { "allUseCases": { "default": false, "description": "Whether to include all Use Cases.", "title": "Allusecases", "type": "boolean" }, "createdAfter": { "description": "Filter notebooks created after this date.", "format": "date-time", "title": "Createdafter", "type": "string" }, "createdBefore": { "description": "Filter notebooks created before this date.", "format": "date-time", "title": "Createdbefore", "type": "string" }, "limit": { "default": 10, "description": "The maximum number of notebooks to return.", "exclusiveMinimum": 0, "maximum": 1000, "title": "Limit", "type": "integer" }, "name": { "default": "", "description": "A name to filter notebooks by.", "title": "Name", "type": "string" }, "offset": { "default": 0, "description": "The offset to use when querying paginated results.", "minimum": 0, "title": "Offset", "type": "integer" }, "orderBy": { "allOf": [ { "description": "Enum for ordering notebooks when querying.", "enum": [ "name", "-name", "created", "-created", "updated", "-updated", "tags", "-tags", "lastViewed", "-lastViewed", "useCaseName", "-useCaseName", "sessionStatus", "-sessionStatus" ], "title": "OrderBy", "type": "string" } ], "default": "-updated", "description": "The order in which to sort the notebooks." }, "owners": { "description": "A list of user IDs to filter notebooks by.", "items": { "type": "string" }, "title": "Owners", "type": "array" }, "query": { "description": "A query to filter notebooks by name and description.", "title": "Query", "type": "string" }, "tags": { "description": "A list of tags to filter notebooks by.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "type": { "description": "The notebook type to filter by.", "items": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "type": "array" }, "types": { "description": "The notebook type to filter by.", "items": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "type": "array" }, "useCaseId": { "description": "The ID of a Use Case to filter notebooks by.", "title": "Usecaseid", "type": "string" } }, "title": "ListNotebooksQuery", "type": "object" } ``` ListNotebooksQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allUseCases | boolean | false | | Whether to include all Use Cases. | | createdAfter | string(date-time) | false | | Filter notebooks created after this date. | | createdBefore | string(date-time) | false | | Filter notebooks created before this date. | | limit | integer | false | maximum: 1000 | The maximum number of notebooks to return. | | name | string | false | | A name to filter notebooks by. | | offset | integer | false | minimum: 0 | The offset to use when querying paginated results. | | orderBy | OrderBy | false | | The order in which to sort the notebooks. | | owners | [string] | false | | A list of user IDs to filter notebooks by. | | query | string | false | | A query to filter notebooks by name and description. | | tags | [string] | false | | A list of tags to filter notebooks by. | | type | [NotebookType] | false | | The notebook type to filter by. | | types | [NotebookType] | false | | The notebook type to filter by. | | useCaseId | string | false | | The ID of a Use Case to filter notebooks by. | ## ListNotebooksResponse ``` { "description": "Response schema for listing notebooks.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "A list of notebooks.", "items": { "description": "Schema for notebook metadata with additional fields.", "properties": { "created": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the creation of the notebook.", "title": "Created" }, "description": { "description": "The description of the notebook.", "title": "Description", "type": "string" }, "hasEnabledSchedule": { "description": "Whether the notebook has an enabled schedule.", "title": "Hasenabledschedule", "type": "boolean" }, "hasSchedule": { "description": "Whether the notebook has a schedule.", "title": "Hasschedule", "type": "boolean" }, "id": { "description": "The ID of the notebook.", "title": "Id", "type": "string" }, "lastViewed": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last viewed time of the notebook.", "title": "Lastviewed" }, "name": { "description": "The name of the notebook.", "title": "Name", "type": "string" }, "orgId": { "description": "The ID of the organization associated with the notebook.", "title": "Orgid", "type": "string" }, "permissions": { "description": "The permissions associated with the notebook.", "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "session": { "allOf": [ { "description": "The schema for the notebook session.", "properties": { "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "The type of the notebook session." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" } }, "required": [ "status", "notebookId" ], "title": "NotebookSessionSharedSchema", "type": "object" } ], "description": "Information about the session associated with the notebook.", "title": "Session" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "The settings of the notebook.", "title": "Settings" }, "tags": { "description": "The tags of the notebook.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "tenantId": { "description": "The tenant ID associated with the notebook.", "title": "Tenantid", "type": "string" }, "type": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of the notebook." }, "typeTransition": { "allOf": [ { "description": "An enumeration.", "enum": [ "initiated_to_codespace", "completed" ], "title": "NotebookTypeTransition", "type": "string" } ], "description": "The type transition of the notebook." }, "updated": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last update of the notebook.", "title": "Updated" }, "useCaseId": { "description": "The ID of the Use Case associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "name", "id", "created", "lastViewed" ], "title": "NotebookSchema", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "ListNotebooksResponse", "type": "object" } ``` ListNotebooksResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The total of paginated results. | | data | [NotebookSchema] | false | | A list of notebooks. | | next | string | false | | The URL to fetch the next batch of results. | | previous | string | false | | The URL to fetch the previous batch of results. | | totalCount | integer | false | | The total of all results. | ## ListScheduledJobQuery ``` { "description": "Query parameters for listing scheduled jobs.", "properties": { "createdByIds": { "description": "The IDs of the users who created the scheduled jobs.", "items": { "type": "string" }, "title": "Createdbyids", "type": "array" }, "lastRunEnd": { "description": "The end time of the last run to filter the scheduled jobs by.", "format": "date-time", "title": "Lastrunend", "type": "string" }, "lastRunStart": { "description": "The start time of the last run to filter the scheduled jobs by.", "format": "date-time", "title": "Lastrunstart", "type": "string" }, "limit": { "default": 10, "description": "The limit or results to return.", "exclusiveMinimum": 0, "maximum": 100, "title": "Limit", "type": "integer" }, "nextRunEnd": { "description": "The end time of the next run to filter the scheduled jobs by.", "format": "date-time", "title": "Nextrunend", "type": "string" }, "nextRunStart": { "description": "The start time of the next run to filter the scheduled jobs by.", "format": "date-time", "title": "Nextrunstart", "type": "string" }, "notebookIds": { "description": "The IDs of the notebooks to filter the scheduled jobs by.", "items": { "type": "string" }, "title": "Notebookids", "type": "array" }, "notebookTypes": { "description": "The types of notebooks to filter the scheduled jobs by.", "items": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "type": "array" }, "offset": { "default": 0, "description": "The offset to use when querying paginated results.", "minimum": 0, "title": "Offset", "type": "integer" }, "orderBy": { "allOf": [ { "description": "Fields to order the list of scheduled jobs by.", "enum": [ "title", "-title", "username", "-username", "notebookName", "-notebookName", "useCaseName", "-useCaseName", "status", "-status", "schedule", "-schedule", "lastRun", "-lastRun", "nextRun", "-nextRun" ], "title": "ScheduledJobListOrderBy", "type": "string" } ], "description": "The field to order the results by." }, "search": { "description": "The search term to filter the scheduled jobs.", "title": "Search", "type": "string" }, "statuses": { "description": "The statuses to filter the scheduled jobs by.", "items": { "enum": [ "enabled", "disabled" ], "type": "string" }, "title": "Statuses", "type": "array" }, "titles": { "description": "The titles to filter the scheduled jobs by.", "items": { "type": "string" }, "title": "Titles", "type": "array" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" } }, "title": "ListScheduledJobQuery", "type": "object" } ``` ListScheduledJobQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdByIds | [string] | false | | The IDs of the users who created the scheduled jobs. | | lastRunEnd | string(date-time) | false | | The end time of the last run to filter the scheduled jobs by. | | lastRunStart | string(date-time) | false | | The start time of the last run to filter the scheduled jobs by. | | limit | integer | false | maximum: 100 | The limit or results to return. | | nextRunEnd | string(date-time) | false | | The end time of the next run to filter the scheduled jobs by. | | nextRunStart | string(date-time) | false | | The start time of the next run to filter the scheduled jobs by. | | notebookIds | [string] | false | | The IDs of the notebooks to filter the scheduled jobs by. | | notebookTypes | [NotebookType] | false | | The types of notebooks to filter the scheduled jobs by. | | offset | integer | false | minimum: 0 | The offset to use when querying paginated results. | | orderBy | ScheduledJobListOrderBy | false | | The field to order the results by. | | search | string | false | | The search term to filter the scheduled jobs. | | statuses | [string] | false | | The statuses to filter the scheduled jobs by. | | titles | [string] | false | | The titles to filter the scheduled jobs by. | | useCaseId | string | false | | The ID of the use case this notebook is associated with. | ## ListScheduledRunsHistoryQuery ``` { "description": "Query parameters for listing scheduled runs history.", "properties": { "createdByIds": { "description": "The IDs of the users who created the scheduled runs history.", "items": { "type": "string" }, "title": "Createdbyids", "type": "array" }, "endTimeEnd": { "description": "The end time of the run to filter the scheduled runs history by.", "format": "date-time", "title": "Endtimeend", "type": "string" }, "endTimeStart": { "description": "The start time of the run to filter the scheduled runs history by.", "format": "date-time", "title": "Endtimestart", "type": "string" }, "envIds": { "description": "The IDs of the environments to filter the scheduled runs history by.", "items": { "type": "string" }, "title": "Envids", "type": "array" }, "jobIds": { "description": "The IDs of the jobs to filter the scheduled runs history by.", "items": { "type": "string" }, "title": "Jobids", "type": "array" }, "limit": { "default": 10, "description": "The limit or results to return.", "exclusiveMinimum": 0, "maximum": 100, "title": "Limit", "type": "integer" }, "notebookIds": { "description": "The IDs of the notebooks to filter the scheduled runs history by.", "items": { "type": "string" }, "title": "Notebookids", "type": "array" }, "notebookTypes": { "description": "The types of notebooks to filter the scheduled runs history by.", "items": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "type": "array" }, "offset": { "default": 0, "description": "The offset to use when querying paginated results.", "minimum": 0, "title": "Offset", "type": "integer" }, "orderBy": { "allOf": [ { "description": "An enumeration.", "enum": [ "created", "-created", "title", "-title", "notebookName", "-notebookName", "runType", "-runType", "status", "-status", "startTime", "-startTime", "endTime", "-endTime", "duration", "-duration", "revisionName", "-revisionName", "environmentName", "-environmentName", "useCaseName", "-useCaseName" ], "title": "ScheduledHistoryListOrderBy", "type": "string" } ], "default": "-created", "description": "The field to order the results by." }, "runTypes": { "description": "The types of runs to filter the scheduled runs history by.", "items": { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" }, "type": "array" }, "search": { "description": "The search term to filter the scheduled runs history.", "title": "Search", "type": "string" }, "startTimeEnd": { "description": "The end time of the run to filter the scheduled runs history by.", "format": "date-time", "title": "Starttimeend", "type": "string" }, "startTimeStart": { "description": "The start time of the run to filter the scheduled runs history by.", "format": "date-time", "title": "Starttimestart", "type": "string" }, "statuses": { "description": "The statuses to filter the scheduled runs history by.", "items": { "type": "string" }, "title": "Statuses", "type": "array" }, "titles": { "description": "The titles to filter the scheduled runs history by.", "items": { "type": "string" }, "title": "Titles", "type": "array" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" } }, "title": "ListScheduledRunsHistoryQuery", "type": "object" } ``` ListScheduledRunsHistoryQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdByIds | [string] | false | | The IDs of the users who created the scheduled runs history. | | endTimeEnd | string(date-time) | false | | The end time of the run to filter the scheduled runs history by. | | endTimeStart | string(date-time) | false | | The start time of the run to filter the scheduled runs history by. | | envIds | [string] | false | | The IDs of the environments to filter the scheduled runs history by. | | jobIds | [string] | false | | The IDs of the jobs to filter the scheduled runs history by. | | limit | integer | false | maximum: 100 | The limit or results to return. | | notebookIds | [string] | false | | The IDs of the notebooks to filter the scheduled runs history by. | | notebookTypes | [NotebookType] | false | | The types of notebooks to filter the scheduled runs history by. | | offset | integer | false | minimum: 0 | The offset to use when querying paginated results. | | orderBy | ScheduledHistoryListOrderBy | false | | The field to order the results by. | | runTypes | [RunTypes] | false | | The types of runs to filter the scheduled runs history by. | | search | string | false | | The search term to filter the scheduled runs history. | | startTimeEnd | string(date-time) | false | | The end time of the run to filter the scheduled runs history by. | | startTimeStart | string(date-time) | false | | The start time of the run to filter the scheduled runs history by. | | statuses | [string] | false | | The statuses to filter the scheduled runs history by. | | titles | [string] | false | | The titles to filter the scheduled runs history by. | | useCaseId | string | false | | The ID of the use case this notebook is associated with. | ## ListSnippetsQuerySchema ``` { "description": "Query schema for searching code nuggets.", "properties": { "language": { "description": "The programming language of the code snippet to query for.", "title": "Language", "type": "string" }, "languageVersion": { "description": "The programming language version of the code snippet to query for.", "title": "Languageversion", "type": "string" }, "limit": { "default": 10, "description": "The limit or results to return.", "exclusiveMinimum": 0, "maximum": 100, "title": "Limit", "type": "integer" }, "offset": { "default": 0, "description": "The offset to use when querying paginated results.", "minimum": 0, "title": "Offset", "type": "integer" }, "q": { "description": "A search query to filter the code snippets.", "title": "Q", "type": "string" }, "tags": { "description": "A comma separated list of tags to filter the code snippets.", "title": "Tags", "type": "string" } }, "title": "ListSnippetsQuerySchema", "type": "object" } ``` ListSnippetsQuerySchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | language | string | false | | The programming language of the code snippet to query for. | | languageVersion | string | false | | The programming language version of the code snippet to query for. | | limit | integer | false | maximum: 100 | The limit or results to return. | | offset | integer | false | minimum: 0 | The offset to use when querying paginated results. | | q | string | false | | A search query to filter the code snippets. | | tags | string | false | | A comma separated list of tags to filter the code snippets. | ## Machine ``` { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" } ``` Machine ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bundleId | string | false | | Bundle ID. | | cpu | string | false | | Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1). | | cpuCores | number | false | | CPU cores. | | default | boolean | false | | Is this machine type default for the environment. | | ephemeralStorage | string | false | | Ephemeral storage size. | | gpu | string | false | | GPU cores. | | hasGpu | boolean | false | | Whether or not this machine type has a GPU. | | id | string | true | | Machine ID. | | memory | string | false | | Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G). | | name | string | true | | Machine name. | | ramGb | integer | false | | RAM in GB. | ## MachineStatuses ``` { "description": "This enum represents possible overall state of the machine(s) of all components of the notebook.", "enum": [ "not_started", "allocated", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "MachineStatuses", "type": "string" } ``` MachineStatuses ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | MachineStatuses | string | false | | This enum represents possible overall state of the machine(s) of all components of the notebook. | ### Enumerated Values | Property | Value | | --- | --- | | MachineStatuses | [not_started, allocated, starting, running, restarting, stopping, stopped, dead, deleted] | ## MachinesPublic ``` { "description": "Represents a list of machine types in the system.", "properties": { "machines": { "description": "List of machine types.", "items": { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" }, "title": "Machines", "type": "array" } }, "title": "MachinesPublic", "type": "object" } ``` MachinesPublic ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | machines | [Machine] | false | | List of machine types. | ## ManualRunTypes ``` { "description": "Intentionally a subset of RunTypes above - to be used in API schemas", "enum": [ "manual", "pipeline" ], "title": "ManualRunTypes", "type": "string" } ``` ManualRunTypes ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ManualRunTypes | string | false | | Intentionally a subset of RunTypes above - to be used in API schemas | ### Enumerated Values | Property | Value | | --- | --- | | ManualRunTypes | [manual, pipeline] | ## MarkdownCellSchema ``` { "description": "The schema for a markdown cell in the notebook.", "properties": { "attachments": { "description": "Attachments of the cell.", "title": "Attachments", "type": "object" }, "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "markdown", "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "MarkdownCellSchema", "type": "object" } ``` MarkdownCellSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attachments | object | false | | Attachments of the cell. | | cellType | SupportedCellTypes | false | | Type of the cell. | | id | string | true | | ID of the cell. | | metadata | NotebookCellMetadata | false | | Metadata of the cell. | | source | any | false | | Contents of the cell, represented as a string. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## MoveCellRequest ``` { "description": "Request payload values for moving a notebook cell.", "properties": { "actionId": { "description": "Action ID of notebook update request.", "maxLength": 64, "title": "Actionid", "type": "string" }, "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "ID of the cell after which to move the cell.", "title": "Aftercellid" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "afterCellId" ], "title": "MoveCellRequest", "type": "object" } ``` MoveCellRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actionId | string | false | maxLength: 64 | Action ID of notebook update request. | | afterCellId | any | true | | ID of the cell after which to move the cell. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | generation | integer | true | | Integer representing the generation of the notebook. | | path | string | true | | Path to the notebook. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | FIRST | ## MoveFilesystemObjectRequest ``` { "description": "Move filesystem object request schema.", "properties": { "operations": { "description": "List of source-destination pairs.", "items": { "description": "Source and destination schema for filesystem object operations.", "properties": { "destination": { "description": "Destination path.", "title": "Destination", "type": "string" }, "source": { "description": "Source path.", "title": "Source", "type": "string" } }, "required": [ "source", "destination" ], "title": "SourceDestinationSchema", "type": "object" }, "title": "Operations", "type": "array" } }, "title": "MoveFilesystemObjectRequest", "type": "object" } ``` MoveFilesystemObjectRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operations | [SourceDestinationSchema] | false | | List of source-destination pairs. | ## MultiNotebookOperationQuery ``` { "description": "Base query schema for multiple notebook operations.", "properties": { "path": { "description": "List of paths to the notebooks.", "items": { "type": "string" }, "title": "Path", "type": "array" } }, "title": "MultiNotebookOperationQuery", "type": "object" } ``` MultiNotebookOperationQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | [string] | false | | List of paths to the notebooks. | ## MultipleNotebooksSharedRole ``` { "properties": { "notebookId": { "description": "The ID of the notebook.", "type": "string" }, "roles": { "description": "Individual roles data for the notebook.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.35" }, "type": "array" } }, "required": [ "notebookId", "roles" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notebookId | string | true | | The ID of the notebook. | | roles | [NotebookSharedRole] | true | | Individual roles data for the notebook. | ## NewEnvironmentVariableSchema ``` { "description": "Schema for updating environment variables.", "properties": { "description": { "description": "The description of the environment variable.", "maxLength": 500, "title": "Description", "type": "string" }, "name": { "description": "The name of the environment variable.", "maxLength": 253, "pattern": "^[a-zA-Z_$][\\w$]*$", "title": "Name", "type": "string" }, "value": { "description": "The value of the environment variable.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "NewEnvironmentVariableSchema", "type": "object" } ``` NewEnvironmentVariableSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 500 | The description of the environment variable. | | name | string | true | maxLength: 253 | The name of the environment variable. | | value | string | true | maxLength: 131072 | The value of the environment variable. | ## NewNotebookFileSchema ``` { "description": "The schema for the newly created notebook.", "properties": { "cells": { "description": "List of cells in the notebook.", "items": { "anyOf": [ { "description": "The schema for a code cell in the notebook.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "code", "description": "Type of the cell." }, "executionCount": { "default": 0, "description": "Execution count of the cell.", "title": "Executioncount", "type": "integer" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "outputs": { "description": "Outputs of the cell.", "items": { "anyOf": [ { "description": "The schema for the execute result output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "executionCount": { "default": 0, "description": "Execution count of the output.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "execute_result", "default": "execute_result", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "ExecuteResultOutputSchema", "type": "object" }, { "description": "The schema for the display data output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "display_data", "default": "display_data", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "DisplayDataOutputSchema", "type": "object" }, { "description": "The schema for the stream output in a notebook cell.", "properties": { "name": { "description": "Name of the stream.", "title": "Name", "type": "string" }, "outputType": { "const": "stream", "default": "stream", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Text of the stream.", "title": "Text" } }, "required": [ "name", "text" ], "title": "StreamOutputSchema", "type": "object" }, { "description": "The schema for the error output in a notebook cell.", "properties": { "ename": { "description": "Error name.", "title": "Ename", "type": "string" }, "evalue": { "description": "Error value.", "title": "Evalue", "type": "string" }, "outputType": { "const": "error", "default": "error", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "traceback": { "description": "Traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "ename", "evalue" ], "title": "ErrorOutputSchema", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "CodeCellSchema", "type": "object" }, { "description": "The schema for a markdown cell in the notebook.", "properties": { "attachments": { "description": "Attachments of the cell.", "title": "Attachments", "type": "object" }, "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "markdown", "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "MarkdownCellSchema", "type": "object" }, { "description": "The schema for cells in a notebook that are not code or markdown.", "properties": { "cellType": { "description": "Type of the cell.", "title": "Celltype", "type": "string" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType" ], "title": "AnyCellSchema", "type": "object" } ] }, "title": "Cells", "type": "array" }, "metadata": { "description": "Metadata for the notebook.", "title": "Metadata", "type": "object" }, "name": { "description": "Name of the notebook.", "title": "Name", "type": "string" }, "nbformat": { "description": "The notebook format version.", "title": "Nbformat", "type": "integer" }, "nbformatMinor": { "description": "The notebook format minor version.", "title": "Nbformatminor", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "name", "path", "nbformat", "nbformatMinor", "metadata" ], "title": "NewNotebookFileSchema", "type": "object" } ``` NewNotebookFileSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cells | [anyOf] | false | | List of cells in the notebook. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CodeCellSchema | false | | The schema for a code cell in the notebook. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | MarkdownCellSchema | false | | The schema for a markdown cell in the notebook. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AnyCellSchema | false | | The schema for cells in a notebook that are not code or markdown. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metadata | object | true | | Metadata for the notebook. | | name | string | true | | Name of the notebook. | | nbformat | integer | true | | The notebook format version. | | nbformatMinor | integer | true | | The notebook format minor version. | | path | string | true | | Path to the notebook. | ## NotebookActionSignature ``` { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ``` NotebookActionSignature ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | at | string(date-time) | true | | Timestamp of the action. | | by | UserInfo | true | | User info of the actor who performed the action. | ## NotebookActivityRequest ``` { "description": "Request payload values for updating notebook activity.", "properties": { "paths": { "description": "List of paths to update activity for.", "items": { "type": "string" }, "title": "Paths", "type": "array" } }, "title": "NotebookActivityRequest", "type": "object" } ``` NotebookActivityRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | paths | [string] | false | | List of paths to update activity for. | ## NotebookCellCommonSchema ``` { "description": "Schema for notebook cell.", "properties": { "executed": { "allOf": [ { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ], "description": "The timestamp of when the cell was executed.", "title": "Executed" }, "executionCount": { "description": "The execution count of the cell relative to other cells in the current session.", "title": "Executioncount", "type": "integer" }, "executionTimeMillis": { "description": "The execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata of the cell.", "title": "Metadata" }, "outputType": { "allOf": [ { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ], "default": "RAW_OUTPUT", "description": "The type of storage used for the cell output." }, "outputs": { "description": "The cell outputs.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "The contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source", "metadata" ], "title": "NotebookCellCommonSchema", "type": "object" } ``` NotebookCellCommonSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executed | NotebookTimestampInfo | false | | The timestamp of when the cell was executed. | | executionCount | integer | false | | The execution count of the cell relative to other cells in the current session. | | executionTimeMillis | integer | false | | The execution time of the cell in milliseconds. | | metadata | NotebookCellMetadata | true | | The metadata of the cell. | | outputType | OutputStorageType | false | | The type of storage used for the cell output. | | outputs | [anyOf] | false | | The cell outputs. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellStreamOutput | false | | Cell stream output. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellInputRequest | false | | Cell input request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellErrorOutput | false | | Cell error output. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellExecuteResultOutput | false | | Cell execute results output. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellDisplayDataOutput | false | | Cell display data output. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | source | string | true | | The contents of the cell, represented as a string. | ## NotebookCellDataRobotMetadata ``` { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ``` NotebookCellDataRobotMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | dataframeViewOptions | object | false | | DataFrame view options and metadata. | | disableRun | boolean | false | | Whether to disable the run button in the cell. | | executionTimeMillis | integer | false | | Execution time of the cell in milliseconds. | | hideCode | boolean | false | | Whether to hide the code in the cell. | | hideResults | boolean | false | | Whether to hide the results in the cell. | | language | Language | false | | Programming language of the notebook cell. | ## NotebookCellExecData ``` { "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType", "source" ], "title": "NotebookCellExecData", "type": "object" } ``` NotebookCellExecData ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellType | SupportedCellTypes | true | | Type of the cell. | | id | string | true | | ID of the cell. | | source | any | true | | Contents of the cell, represented as a string. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## NotebookCellMetadata ``` { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ``` NotebookCellMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chartSettings | NotebookChartCellMetadata | false | | Chart cell view options and metadata. | | collapsed | boolean | false | | Whether the cell's output is collapsed/expanded. | | customLlmMetricSettings | NotebookCustomLlmMetricCellMetadata | false | | Custom LLM metric cell metadata. | | customMetricSettings | NotebookCustomMetricCellMetadata | false | | Custom metric cell metadata. | | dataframeViewOptions | object | false | | Dataframe cell view options and metadata. | | datarobot | NotebookCellDataRobotMetadata | false | | Metadata specific to DataRobot's notebooks and notebook environment. | | disableRun | boolean | false | | Whether or not the cell is disabled in the UI. | | hideCode | boolean | false | | Whether or not code is hidden in the UI. | | hideResults | boolean | false | | Whether or not results are hidden in the UI. | | jupyter | JupyterCellMetadata | false | | Jupyter metadata. | | name | string | false | | Name of the notebook cell. | | scrolled | any | false | | Whether the cell's output is scrolled, unscrolled, or autoscrolled. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | auto | ## NotebookCellSource ``` { "description": "Notebook cell source model.", "properties": { "cellId": { "description": "The ID of the cell.", "title": "Cellid", "type": "string" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "source": { "description": "Contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "cellId", "source", "md5" ], "title": "NotebookCellSource", "type": "object" } ``` NotebookCellSource ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellId | string | true | | The ID of the cell. | | md5 | string | true | | The MD5 hash of the cell. | | source | string | true | | Contents of the cell, represented as a string. | ## NotebookCellSourceMetadata ``` { "description": "Metadata for the source of a notebook cell.", "properties": { "attachments": { "description": "Cell attachments.", "title": "Attachments", "type": "object" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Cell metadata.", "title": "Metadata" }, "outputs": { "description": "Cell outputs that conform to the nbformat-based NBX schema.", "items": { "anyOf": [ { "description": "Cell stream output.", "properties": { "name": { "description": "The name of the stream.", "title": "Name", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "The text of the stream.", "title": "Text" } }, "required": [ "outputType", "name", "text" ], "title": "APINotebookCellStreamOutput", "type": "object" }, { "description": "Cell input request.", "properties": { "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" } }, "required": [ "outputType", "prompt", "password" ], "title": "APINotebookCellInputRequest", "type": "object" }, { "description": "Cell error output.", "properties": { "ename": { "description": "The name of the error.", "title": "Ename", "type": "string" }, "evalue": { "description": "The value of the error.", "title": "Evalue", "type": "string" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" }, "traceback": { "description": "The traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "outputType", "ename", "evalue", "traceback" ], "title": "APINotebookCellErrorOutput", "type": "object" }, { "description": "Cell execute results output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "executionCount": { "description": "A result's prompt number.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellExecuteResultOutput", "type": "object" }, { "description": "Cell display data output.", "properties": { "data": { "description": "A mime-type keyed dictionary of data.", "title": "Data", "type": "object" }, "metadata": { "description": "Cell output metadata.", "title": "Metadata", "type": "object" }, "outputType": { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } }, "required": [ "outputType", "data", "metadata" ], "title": "APINotebookCellDisplayDataOutput", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "description": "Contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "source" ], "title": "NotebookCellSourceMetadata", "type": "object" } ``` NotebookCellSourceMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attachments | object | false | | Cell attachments. | | metadata | NotebookCellMetadata | false | | Cell metadata. | | outputs | [anyOf] | false | | Cell outputs that conform to the nbformat-based NBX schema. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellStreamOutput | false | | Cell stream output. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellInputRequest | false | | Cell input request. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellErrorOutput | false | | Cell error output. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellExecuteResultOutput | false | | Cell execute results output. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | APINotebookCellDisplayDataOutput | false | | Cell display data output. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | source | string | true | | Contents of the cell, represented as a string. | ## NotebookCellsSchema ``` { "description": "The schema for the notebook cells.", "properties": { "cells": { "description": "List of cells in the notebook.", "items": { "anyOf": [ { "description": "The schema for a code cell in the notebook.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "code", "description": "Type of the cell." }, "executionCount": { "default": 0, "description": "Execution count of the cell.", "title": "Executioncount", "type": "integer" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "outputs": { "description": "Outputs of the cell.", "items": { "anyOf": [ { "description": "The schema for the execute result output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "executionCount": { "default": 0, "description": "Execution count of the output.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "execute_result", "default": "execute_result", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "ExecuteResultOutputSchema", "type": "object" }, { "description": "The schema for the display data output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "display_data", "default": "display_data", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "DisplayDataOutputSchema", "type": "object" }, { "description": "The schema for the stream output in a notebook cell.", "properties": { "name": { "description": "Name of the stream.", "title": "Name", "type": "string" }, "outputType": { "const": "stream", "default": "stream", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Text of the stream.", "title": "Text" } }, "required": [ "name", "text" ], "title": "StreamOutputSchema", "type": "object" }, { "description": "The schema for the error output in a notebook cell.", "properties": { "ename": { "description": "Error name.", "title": "Ename", "type": "string" }, "evalue": { "description": "Error value.", "title": "Evalue", "type": "string" }, "outputType": { "const": "error", "default": "error", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "traceback": { "description": "Traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "ename", "evalue" ], "title": "ErrorOutputSchema", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "CodeCellSchema", "type": "object" }, { "description": "The schema for a markdown cell in the notebook.", "properties": { "attachments": { "description": "Attachments of the cell.", "title": "Attachments", "type": "object" }, "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "markdown", "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "MarkdownCellSchema", "type": "object" }, { "description": "The schema for cells in a notebook that are not code or markdown.", "properties": { "cellType": { "description": "Type of the cell.", "title": "Celltype", "type": "string" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType" ], "title": "AnyCellSchema", "type": "object" } ] }, "title": "Cells", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" } }, "required": [ "generation", "cells" ], "title": "NotebookCellsSchema", "type": "object" } ``` NotebookCellsSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cells | [anyOf] | true | | List of cells in the notebook. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CodeCellSchema | false | | The schema for a code cell in the notebook. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | MarkdownCellSchema | false | | The schema for a markdown cell in the notebook. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AnyCellSchema | false | | The schema for cells in a notebook that are not code or markdown. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | generation | integer | true | | Integer representing the generation of the notebook. | ## NotebookChartCellAxis ``` { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ``` NotebookChartCellAxis ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | x | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | | y | NotebookChartCellAxisSettings | false | | Chart cell axis settings. | ## NotebookChartCellAxisSettings ``` { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } ``` NotebookChartCellAxisSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregation | string | false | | Aggregation function for the axis. | | color | string | false | | Color for the axis. | | hideGrid | boolean | false | | Whether to hide the grid lines on the axis. | | hideInTooltip | boolean | false | | Whether to hide the axis in the tooltip. | | hideLabel | boolean | false | | Whether to hide the axis label. | | key | string | false | | Key for the axis. | | label | string | false | | Label for the axis. | | position | string | false | | Position of the axis. | | showPointMarkers | boolean | false | | Whether to show point markers on the axis. | ## NotebookChartCellMetadata ``` { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ``` NotebookChartCellMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | axis | NotebookChartCellAxis | false | | Axis settings. | | data | object | false | | The data associated with the cell chart. | | dataframeId | string | false | | The ID of the dataframe associated with the cell chart. | | viewOptions | NotebookChartCellViewOptions | false | | Chart cell view options. | ## NotebookChartCellViewOptions ``` { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ``` NotebookChartCellViewOptions ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chartType | string | false | | Type of the chart. | | showLegend | boolean | false | | Whether to show the chart legend. | | showTitle | boolean | false | | Whether to show the chart title. | | showTooltip | boolean | false | | Whether to show the chart tooltip. | | title | string | false | | Title of the chart. | ## NotebookCustomLlmMetricCellMetadata ``` { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ``` NotebookCustomLlmMetricCellMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metricId | string | true | | The ID of the custom LLM metric. | | metricName | string | true | | The name of the custom LLM metric. | | playgroundId | string | true | | The ID of the playground associated with the custom LLM metric. | ## NotebookCustomMetricCellMetadata ``` { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ``` NotebookCustomMetricCellMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the deployment associated with the custom metric. | | metricId | string | true | | The ID of the custom metric. | | metricName | string | false | | The name of the custom metric. | | schedule | Schedule | false | | The schedule associated with the custom metric. | ## NotebookExecutionState ``` { "description": "Notebook execution state model", "properties": { "executingCellId": { "description": "The ID of the cell currently being executed.", "title": "Executingcellid", "type": "string" }, "executionFinishedAt": { "description": "The time the execution finished. This is based on the finish time of the last cell.", "format": "date-time", "title": "Executionfinishedat", "type": "string" }, "executionStartedAt": { "description": "The time the execution started.", "format": "date-time", "title": "Executionstartedat", "type": "string" }, "inputRequest": { "allOf": [ { "description": "AwaitingInputState represents the state of a cell that is awaiting input from the user.", "properties": { "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" }, "requestedAt": { "description": "The time the input was requested.", "format": "date-time", "title": "Requestedat", "type": "string" } }, "required": [ "requestedAt", "prompt", "password" ], "title": "AwaitingInputState", "type": "object" } ], "description": "The input request state of the cell.", "title": "Inputrequest" }, "kernelId": { "description": "The ID of the kernel used for execution.", "title": "Kernelid", "type": "string" }, "queuedCellIds": { "description": "The IDs of the cells that are queued for execution.", "items": { "type": "string" }, "title": "Queuedcellids", "type": "array" } }, "title": "NotebookExecutionState", "type": "object" } ``` NotebookExecutionState ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executingCellId | string | false | | The ID of the cell currently being executed. | | executionFinishedAt | string(date-time) | false | | The time the execution finished. This is based on the finish time of the last cell. | | executionStartedAt | string(date-time) | false | | The time the execution started. | | inputRequest | AwaitingInputState | false | | The input request state of the cell. | | kernelId | string | false | | The ID of the kernel used for execution. | | queuedCellIds | [string] | false | | The IDs of the cells that are queued for execution. | ## NotebookExecutionStateSchema ``` { "description": "The schema for the notebook execution state.", "properties": { "executionState": { "allOf": [ { "description": "Notebook execution state model", "properties": { "executingCellId": { "description": "The ID of the cell currently being executed.", "title": "Executingcellid", "type": "string" }, "executionFinishedAt": { "description": "The time the execution finished. This is based on the finish time of the last cell.", "format": "date-time", "title": "Executionfinishedat", "type": "string" }, "executionStartedAt": { "description": "The time the execution started.", "format": "date-time", "title": "Executionstartedat", "type": "string" }, "inputRequest": { "allOf": [ { "description": "AwaitingInputState represents the state of a cell that is awaiting input from the user.", "properties": { "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" }, "requestedAt": { "description": "The time the input was requested.", "format": "date-time", "title": "Requestedat", "type": "string" } }, "required": [ "requestedAt", "prompt", "password" ], "title": "AwaitingInputState", "type": "object" } ], "description": "The input request state of the cell.", "title": "Inputrequest" }, "kernelId": { "description": "The ID of the kernel used for execution.", "title": "Kernelid", "type": "string" }, "queuedCellIds": { "description": "The IDs of the cells that are queued for execution.", "items": { "type": "string" }, "title": "Queuedcellids", "type": "array" } }, "title": "NotebookExecutionState", "type": "object" } ], "description": "Execution state of the notebook.", "title": "Executionstate" }, "kernel": { "allOf": [ { "description": "The schema for the notebook kernel.", "properties": { "executionState": { "allOf": [ { "description": "Event Sequences on Various Workflows:\n- On kernel created: CONNECTED -> BUSY -> IDLE\n- On kernel restarted: RESTARTING -> STARTING -> BUSY -> IDLE\n- On regular execution: IDLE -> BUSY -> IDLE\n- On execution interrupted: IDLE -> BUSY -> INTERRUPTING -> IDLE\n- On execution with error: IDLE -> BUSY -> IDLE -> INTERRUPTING -> IDLE\n- On kernel shut down via calling the stop kernel endpoint:\n DISCONNECTED (can be sent a few times) -> NOT_RUNNING (after 5s)", "enum": [ "connecting", "disconnected", "connected", "starting", "idle", "busy", "interrupting", "restarting", "not_running" ], "title": "KernelState", "type": "string" } ], "description": "The execution state of the kernel." }, "id": { "description": "The ID of the kernel.", "title": "Id", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "The programming language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "The name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" }, "running": { "default": false, "description": "Whether the kernel is running.", "title": "Running", "type": "boolean" } }, "required": [ "id", "name", "language", "executionState" ], "title": "KernelSchema", "type": "object" } ], "description": "Kernel assigned to the notebook.", "title": "Kernel" }, "kernelId": { "description": "Kernel ID assigned to the notebook.", "title": "Kernelid", "type": "string" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path" ], "title": "NotebookExecutionStateSchema", "type": "object" } ``` NotebookExecutionStateSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionState | NotebookExecutionState | false | | Execution state of the notebook. | | kernel | KernelSchema | false | | Kernel assigned to the notebook. | | kernelId | string | false | | Kernel ID assigned to the notebook. | | path | string | true | | Path to the notebook. | ## NotebookFileSchema ``` { "description": "The schema for a .ipynb notebook as part of a Codespace.", "properties": { "cells": { "description": "List of cells in the notebook.", "items": { "anyOf": [ { "description": "The schema for a code cell in the notebook.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "code", "description": "Type of the cell." }, "executionCount": { "default": 0, "description": "Execution count of the cell.", "title": "Executioncount", "type": "integer" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "outputs": { "description": "Outputs of the cell.", "items": { "anyOf": [ { "description": "The schema for the execute result output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "executionCount": { "default": 0, "description": "Execution count of the output.", "title": "Executioncount", "type": "integer" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "execute_result", "default": "execute_result", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "ExecuteResultOutputSchema", "type": "object" }, { "description": "The schema for the display data output in a notebook cell.", "properties": { "data": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" }, {} ] }, "description": "Data of the output.", "title": "Data", "type": "object" }, "metadata": { "description": "Metadata of the output.", "title": "Metadata", "type": "object" }, "outputType": { "const": "display_data", "default": "display_data", "description": "Type of the output.", "title": "Outputtype", "type": "string" } }, "title": "DisplayDataOutputSchema", "type": "object" }, { "description": "The schema for the stream output in a notebook cell.", "properties": { "name": { "description": "Name of the stream.", "title": "Name", "type": "string" }, "outputType": { "const": "stream", "default": "stream", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Text of the stream.", "title": "Text" } }, "required": [ "name", "text" ], "title": "StreamOutputSchema", "type": "object" }, { "description": "The schema for the error output in a notebook cell.", "properties": { "ename": { "description": "Error name.", "title": "Ename", "type": "string" }, "evalue": { "description": "Error value.", "title": "Evalue", "type": "string" }, "outputType": { "const": "error", "default": "error", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "traceback": { "description": "Traceback of the error.", "items": { "type": "string" }, "title": "Traceback", "type": "array" } }, "required": [ "ename", "evalue" ], "title": "ErrorOutputSchema", "type": "object" } ] }, "title": "Outputs", "type": "array" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "CodeCellSchema", "type": "object" }, { "description": "The schema for a markdown cell in the notebook.", "properties": { "attachments": { "description": "Attachments of the cell.", "title": "Attachments", "type": "object" }, "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "default": "markdown", "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "MarkdownCellSchema", "type": "object" }, { "description": "The schema for cells in a notebook that are not code or markdown.", "properties": { "cellType": { "description": "Type of the cell.", "title": "Celltype", "type": "string" }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "default": "", "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id", "cellType" ], "title": "AnyCellSchema", "type": "object" } ] }, "title": "Cells", "type": "array" }, "executionState": { "allOf": [ { "description": "Notebook execution state model", "properties": { "executingCellId": { "description": "The ID of the cell currently being executed.", "title": "Executingcellid", "type": "string" }, "executionFinishedAt": { "description": "The time the execution finished. This is based on the finish time of the last cell.", "format": "date-time", "title": "Executionfinishedat", "type": "string" }, "executionStartedAt": { "description": "The time the execution started.", "format": "date-time", "title": "Executionstartedat", "type": "string" }, "inputRequest": { "allOf": [ { "description": "AwaitingInputState represents the state of a cell that is awaiting input from the user.", "properties": { "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" }, "requestedAt": { "description": "The time the input was requested.", "format": "date-time", "title": "Requestedat", "type": "string" } }, "required": [ "requestedAt", "prompt", "password" ], "title": "AwaitingInputState", "type": "object" } ], "description": "The input request state of the cell.", "title": "Inputrequest" }, "kernelId": { "description": "The ID of the kernel used for execution.", "title": "Kernelid", "type": "string" }, "queuedCellIds": { "description": "The IDs of the cells that are queued for execution.", "items": { "type": "string" }, "title": "Queuedcellids", "type": "array" } }, "title": "NotebookExecutionState", "type": "object" } ], "description": "Execution state of the notebook.", "title": "Executionstate" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "kernelId": { "description": "Kernel ID assigned to the notebook.", "title": "Kernelid", "type": "string" }, "lastExecuted": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Last executed information.", "title": "Lastexecuted" }, "metadata": { "description": "Metadata for the notebook.", "title": "Metadata", "type": "object" }, "name": { "description": "Name of the notebook.", "title": "Name", "type": "string" }, "nbformat": { "description": "The notebook format version.", "title": "Nbformat", "type": "integer" }, "nbformatMinor": { "description": "The notebook format minor version.", "title": "Nbformatminor", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "name", "path", "generation", "nbformat", "nbformatMinor", "metadata" ], "title": "NotebookFileSchema", "type": "object" } ``` NotebookFileSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cells | [anyOf] | false | | List of cells in the notebook. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CodeCellSchema | false | | The schema for a code cell in the notebook. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | MarkdownCellSchema | false | | The schema for a markdown cell in the notebook. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AnyCellSchema | false | | The schema for cells in a notebook that are not code or markdown. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionState | NotebookExecutionState | false | | Execution state of the notebook. | | generation | integer | true | | Integer representing the generation of the notebook. | | kernelId | string | false | | Kernel ID assigned to the notebook. | | lastExecuted | NotebookActionSignature | false | | Last executed information. | | metadata | object | true | | Metadata for the notebook. | | name | string | true | | Name of the notebook. | | nbformat | integer | true | | The notebook format version. | | nbformatMinor | integer | true | | The notebook format minor version. | | path | string | true | | Path to the notebook. | ## NotebookOperationQuery ``` { "description": "Base query schema for notebook operations.", "properties": { "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path" ], "title": "NotebookOperationQuery", "type": "object" } ``` NotebookOperationQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to the notebook. | ## NotebookOperationSchema ``` { "description": "Base schema for notebook operations.", "properties": { "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path" ], "title": "NotebookOperationSchema", "type": "object" } ``` NotebookOperationSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | path | string | true | | Path to the notebook. | ## NotebookPermission ``` { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" } ``` NotebookPermission ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | NotebookPermission | string | false | | The possible allowed actions for the current user for a given Notebook. | ### Enumerated Values | Property | Value | | --- | --- | | NotebookPermission | [CAN_READ, CAN_UPDATE, CAN_DELETE, CAN_SHARE, CAN_COPY, CAN_EXECUTE] | ## NotebookRevisionSchema ``` { "description": "Notebook revision schema.", "properties": { "created": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision creation action information.", "title": "Created" }, "isAuto": { "description": "Whether the revision was autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Revision name.", "title": "Name", "type": "string" }, "notebookId": { "description": "Notebook ID this revision belongs to.", "title": "Notebookid", "type": "string" }, "revisionId": { "description": "Revision ID.", "title": "Revisionid", "type": "string" }, "updated": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision update action information.", "title": "Updated" } }, "required": [ "revisionId", "notebookId", "name", "isAuto", "created" ], "title": "NotebookRevisionSchema", "type": "object" } ``` NotebookRevisionSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | RevisionActionSchema | true | | Revision creation action information. | | isAuto | boolean | true | | Whether the revision was autosaved. | | name | string | true | | Revision name. | | notebookId | string | true | | Notebook ID this revision belongs to. | | revisionId | string | true | | Revision ID. | | updated | RevisionActionSchema | false | | Revision update action information. | ## NotebookScheduleDefinition ``` { "description": "Notebook schedule definition.", "properties": { "createdBy": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who created the job.", "title": "Createdby" }, "enabled": { "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "id": { "description": "The ID of the scheduled job.", "title": "Id", "type": "string" }, "isNotebookRunning": { "default": false, "description": "Whether or not the notebook is currently running (including manual runs). This accounts for notebook_path in Codespaces.", "title": "Isnotebookrunning", "type": "boolean" }, "jobPayload": { "allOf": [ { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ], "description": "The payload for the job.", "title": "Jobpayload" }, "lastFailedRun": { "description": "Last failed run time of the job.", "format": "date-time", "title": "Lastfailedrun", "type": "string" }, "lastRunTime": { "description": "Calculated last run time (if it has run) by considering both failed and successful.", "format": "date-time", "title": "Lastruntime", "type": "string" }, "lastSuccessfulRun": { "description": "Last successful run time of the job.", "format": "date-time", "title": "Lastsuccessfulrun", "type": "string" }, "nextRunTime": { "description": "Next run time of the job.", "format": "date-time", "title": "Nextruntime", "type": "string" }, "notebook": { "allOf": [ { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ], "description": "Notebook metadata.", "title": "Notebook" }, "notebookHasEnabledSchedule": { "default": false, "description": "Whether or not the notebook for this schedule has an enabled schedule - includes other schedules.", "title": "Notebookhasenabledschedule", "type": "boolean" }, "notebookType": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "permissions": { "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "runType": { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" }, "schedule": { "description": "Cron-like string to define how frequently job should be run.", "title": "Schedule", "type": "string" }, "scheduleLocalized": { "description": "Human-readable string calculated from the cron string that is translated and localized.", "title": "Schedulelocalized", "type": "string" }, "title": { "description": "Human readable name for the job that a user can create and update.", "title": "Title", "type": "string" }, "userId": { "description": "The ID of the user who created the job.", "title": "Userid", "type": "string" } }, "required": [ "id", "enabled", "userId", "jobPayload" ], "title": "NotebookScheduleDefinition", "type": "object" } ``` NotebookScheduleDefinition ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | UserInfo | false | | User who created the job. | | enabled | boolean | true | | Whether the job is enabled. | | id | string | true | | The ID of the scheduled job. | | isNotebookRunning | boolean | false | | Whether or not the notebook is currently running (including manual runs). This accounts for notebook_path in Codespaces. | | jobPayload | ScheduledJobPayload | true | | The payload for the job. | | lastFailedRun | string(date-time) | false | | Last failed run time of the job. | | lastRunTime | string(date-time) | false | | Calculated last run time (if it has run) by considering both failed and successful. | | lastSuccessfulRun | string(date-time) | false | | Last successful run time of the job. | | nextRunTime | string(date-time) | false | | Next run time of the job. | | notebook | NotebookSupplementalMetadata | false | | Notebook metadata. | | notebookHasEnabledSchedule | boolean | false | | Whether or not the notebook for this schedule has an enabled schedule - includes other schedules. | | notebookType | NotebookType | false | | Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks thatare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after sessionshutdown. | | permissions | [NotebookPermission] | false | | [The possible allowed actions for the current user for a given Notebook.] | | runType | RunTypes | false | | Types of runs that can be scheduled. | | schedule | string | false | | Cron-like string to define how frequently job should be run. | | scheduleLocalized | string | false | | Human-readable string calculated from the cron string that is translated and localized. | | title | string | false | | Human readable name for the job that a user can create and update. | | userId | string | true | | The ID of the user who created the job. | ## NotebookScheduleJobsPaginated ``` { "description": "Paginated response for notebook schedule jobs.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of notebook schedule jobs.", "items": { "description": "Notebook schedule definition.", "properties": { "createdBy": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who created the job.", "title": "Createdby" }, "enabled": { "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "id": { "description": "The ID of the scheduled job.", "title": "Id", "type": "string" }, "isNotebookRunning": { "default": false, "description": "Whether or not the notebook is currently running (including manual runs). This accounts for notebook_path in Codespaces.", "title": "Isnotebookrunning", "type": "boolean" }, "jobPayload": { "allOf": [ { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ], "description": "The payload for the job.", "title": "Jobpayload" }, "lastFailedRun": { "description": "Last failed run time of the job.", "format": "date-time", "title": "Lastfailedrun", "type": "string" }, "lastRunTime": { "description": "Calculated last run time (if it has run) by considering both failed and successful.", "format": "date-time", "title": "Lastruntime", "type": "string" }, "lastSuccessfulRun": { "description": "Last successful run time of the job.", "format": "date-time", "title": "Lastsuccessfulrun", "type": "string" }, "nextRunTime": { "description": "Next run time of the job.", "format": "date-time", "title": "Nextruntime", "type": "string" }, "notebook": { "allOf": [ { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ], "description": "Notebook metadata.", "title": "Notebook" }, "notebookHasEnabledSchedule": { "default": false, "description": "Whether or not the notebook for this schedule has an enabled schedule - includes other schedules.", "title": "Notebookhasenabledschedule", "type": "boolean" }, "notebookType": { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" }, "permissions": { "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "runType": { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" }, "schedule": { "description": "Cron-like string to define how frequently job should be run.", "title": "Schedule", "type": "string" }, "scheduleLocalized": { "description": "Human-readable string calculated from the cron string that is translated and localized.", "title": "Schedulelocalized", "type": "string" }, "title": { "description": "Human readable name for the job that a user can create and update.", "title": "Title", "type": "string" }, "userId": { "description": "The ID of the user who created the job.", "title": "Userid", "type": "string" } }, "required": [ "id", "enabled", "userId", "jobPayload" ], "title": "NotebookScheduleDefinition", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" }, "totalEnabledCount": { "description": "Total number of enabled schedule jobs.", "title": "Totalenabledcount", "type": "integer" } }, "required": [ "totalEnabledCount" ], "title": "NotebookScheduleJobsPaginated", "type": "object" } ``` NotebookScheduleJobsPaginated ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The total of paginated results. | | data | [NotebookScheduleDefinition] | false | | List of notebook schedule jobs. | | next | string | false | | The URL to fetch the next batch of results. | | previous | string | false | | The URL to fetch the previous batch of results. | | totalCount | integer | false | | The total of all results. | | totalEnabledCount | integer | true | | Total number of enabled schedule jobs. | ## NotebookScheduledRun ``` { "description": "Notebook scheduled run.", "properties": { "createdAt": { "description": "The time the job was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "duration": { "description": "The duration of the job in seconds.", "title": "Duration", "type": "integer" }, "endTime": { "description": "The time the job ended.", "format": "date-time", "title": "Endtime", "type": "string" }, "endTimeTs": { "description": "The time the job ended.", "format": "date-time", "title": "Endtimets", "type": "string" }, "environment": { "allOf": [ { "description": "Supplemental metadata for an environment.", "properties": { "id": { "description": "The ID of the environment.", "title": "Id", "type": "string" }, "label": { "description": "The label of the environment.", "title": "Label", "type": "string" }, "name": { "description": "The name of the environment.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "label" ], "title": "EnvironmentSupplementalMetadata", "type": "object" } ], "description": "Environment metadata.", "title": "Environment" }, "id": { "description": "The ID of the job run.", "title": "Id", "type": "string" }, "jobAbortedTs": { "description": "The time the job was aborted.", "format": "date-time", "title": "Jobabortedts", "type": "string" }, "jobCompletedTs": { "description": "The time the job completed.", "format": "date-time", "title": "Jobcompletedts", "type": "string" }, "jobCreatedBy": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who created the job.", "title": "Jobcreatedby" }, "jobErrorTs": { "description": "The time the job errored.", "format": "date-time", "title": "Joberrorts", "type": "string" }, "jobStartedTs": { "description": "The time the job started.", "format": "date-time", "title": "Jobstartedts", "type": "string" }, "notebook": { "allOf": [ { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ], "description": "Notebook metadata.", "title": "Notebook" }, "notebookDeleted": { "default": false, "description": "Whether the notebook was deleted.", "title": "Notebookdeleted", "type": "boolean" }, "notebookEnvironmentId": { "description": "The ID of the notebook environment.", "title": "Notebookenvironmentid", "type": "string" }, "notebookEnvironmentImageId": { "description": "The ID of the notebook environment image.", "title": "Notebookenvironmentimageid", "type": "string" }, "notebookEnvironmentLabel": { "description": "The label of the notebook environment.", "title": "Notebookenvironmentlabel", "type": "string" }, "notebookEnvironmentName": { "description": "The name of the notebook environment.", "title": "Notebookenvironmentname", "type": "string" }, "notebookHadErrors": { "default": false, "description": "Whether the notebook had errors.", "title": "Notebookhaderrors", "type": "boolean" }, "notebookId": { "description": "The ID of the notebook associated with the run.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the run.", "title": "Notebookname", "type": "string" }, "notebookRevisionId": { "description": "The ID of the notebook revision.", "title": "Notebookrevisionid", "type": "string" }, "notebookRevisionName": { "description": "The name of the notebook revision.", "title": "Notebookrevisionname", "type": "string" }, "notebookRunType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "payload": { "allOf": [ { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ], "description": "The payload for the job.", "title": "Payload" }, "revision": { "allOf": [ { "description": "Supplemental metadata for a revision.", "properties": { "id": { "description": "The ID of the revision.", "title": "Id", "type": "string" }, "name": { "description": "The name of the revision.", "title": "Name", "type": "string" } }, "required": [ "id", "name" ], "title": "RevisionSupplementalMetadata", "type": "object" } ], "description": "Revision metadata.", "title": "Revision" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "scheduledJobId": { "description": "The ID of the scheduled job.", "title": "Scheduledjobid", "type": "string" }, "startTime": { "description": "The time the job started.", "format": "date-time", "title": "Starttime", "type": "string" }, "status": { "description": "The status of the job.", "title": "Status", "type": "string" }, "title": { "description": "The name of the schedule.", "title": "Title", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" }, "userId": { "description": "The ID of the user who created the job.", "title": "Userid", "type": "string" } }, "required": [ "id", "createdAt", "useCaseId", "userId", "orgId", "notebookId", "scheduledJobId", "title", "status", "payload", "notebookName", "notebookRunType" ], "title": "NotebookScheduledRun", "type": "object" } ``` NotebookScheduledRun ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The time the job was created. | | duration | integer | false | | The duration of the job in seconds. | | endTime | string(date-time) | false | | The time the job ended. | | endTimeTs | string(date-time) | false | | The time the job ended. | | environment | EnvironmentSupplementalMetadata | false | | Environment metadata. | | id | string | true | | The ID of the job run. | | jobAbortedTs | string(date-time) | false | | The time the job was aborted. | | jobCompletedTs | string(date-time) | false | | The time the job completed. | | jobCreatedBy | UserInfo | false | | User who created the job. | | jobErrorTs | string(date-time) | false | | The time the job errored. | | jobStartedTs | string(date-time) | false | | The time the job started. | | notebook | NotebookSupplementalMetadata | false | | Notebook metadata. | | notebookDeleted | boolean | false | | Whether the notebook was deleted. | | notebookEnvironmentId | string | false | | The ID of the notebook environment. | | notebookEnvironmentImageId | string | false | | The ID of the notebook environment image. | | notebookEnvironmentLabel | string | false | | The label of the notebook environment. | | notebookEnvironmentName | string | false | | The name of the notebook environment. | | notebookHadErrors | boolean | false | | Whether the notebook had errors. | | notebookId | string | true | | The ID of the notebook associated with the run. | | notebookName | string | true | | The name of the notebook associated with the run. | | notebookRevisionId | string | false | | The ID of the notebook revision. | | notebookRevisionName | string | false | | The name of the notebook revision. | | notebookRunType | RunTypes | true | | The run type of the notebook. | | orgId | string | true | | The ID of the organization the job is associated with. | | payload | ScheduledJobPayload | true | | The payload for the job. | | revision | RevisionSupplementalMetadata | false | | Revision metadata. | | runType | RunTypes | false | | The run type of the job. | | scheduledJobId | string | true | | The ID of the scheduled job. | | startTime | string(date-time) | false | | The time the job started. | | status | string | true | | The status of the job. | | title | string | true | | The name of the schedule. | | useCaseId | string | true | | The ID of the use case this notebook is associated with. | | useCaseName | string | false | | The name of the use case this notebook is associated with. | | userId | string | true | | The ID of the user who created the job. | ## NotebookScheduledRunsHistoryPaginated ``` { "description": "Paginated response for notebook scheduled runs history.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of notebook scheduled runs history.", "items": { "description": "Notebook scheduled run.", "properties": { "createdAt": { "description": "The time the job was created.", "format": "date-time", "title": "Createdat", "type": "string" }, "duration": { "description": "The duration of the job in seconds.", "title": "Duration", "type": "integer" }, "endTime": { "description": "The time the job ended.", "format": "date-time", "title": "Endtime", "type": "string" }, "endTimeTs": { "description": "The time the job ended.", "format": "date-time", "title": "Endtimets", "type": "string" }, "environment": { "allOf": [ { "description": "Supplemental metadata for an environment.", "properties": { "id": { "description": "The ID of the environment.", "title": "Id", "type": "string" }, "label": { "description": "The label of the environment.", "title": "Label", "type": "string" }, "name": { "description": "The name of the environment.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "label" ], "title": "EnvironmentSupplementalMetadata", "type": "object" } ], "description": "Environment metadata.", "title": "Environment" }, "id": { "description": "The ID of the job run.", "title": "Id", "type": "string" }, "jobAbortedTs": { "description": "The time the job was aborted.", "format": "date-time", "title": "Jobabortedts", "type": "string" }, "jobCompletedTs": { "description": "The time the job completed.", "format": "date-time", "title": "Jobcompletedts", "type": "string" }, "jobCreatedBy": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who created the job.", "title": "Jobcreatedby" }, "jobErrorTs": { "description": "The time the job errored.", "format": "date-time", "title": "Joberrorts", "type": "string" }, "jobStartedTs": { "description": "The time the job started.", "format": "date-time", "title": "Jobstartedts", "type": "string" }, "notebook": { "allOf": [ { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ], "description": "Notebook metadata.", "title": "Notebook" }, "notebookDeleted": { "default": false, "description": "Whether the notebook was deleted.", "title": "Notebookdeleted", "type": "boolean" }, "notebookEnvironmentId": { "description": "The ID of the notebook environment.", "title": "Notebookenvironmentid", "type": "string" }, "notebookEnvironmentImageId": { "description": "The ID of the notebook environment image.", "title": "Notebookenvironmentimageid", "type": "string" }, "notebookEnvironmentLabel": { "description": "The label of the notebook environment.", "title": "Notebookenvironmentlabel", "type": "string" }, "notebookEnvironmentName": { "description": "The name of the notebook environment.", "title": "Notebookenvironmentname", "type": "string" }, "notebookHadErrors": { "default": false, "description": "Whether the notebook had errors.", "title": "Notebookhaderrors", "type": "boolean" }, "notebookId": { "description": "The ID of the notebook associated with the run.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the run.", "title": "Notebookname", "type": "string" }, "notebookRevisionId": { "description": "The ID of the notebook revision.", "title": "Notebookrevisionid", "type": "string" }, "notebookRevisionName": { "description": "The name of the notebook revision.", "title": "Notebookrevisionname", "type": "string" }, "notebookRunType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "payload": { "allOf": [ { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ], "description": "The payload for the job.", "title": "Payload" }, "revision": { "allOf": [ { "description": "Supplemental metadata for a revision.", "properties": { "id": { "description": "The ID of the revision.", "title": "Id", "type": "string" }, "name": { "description": "The name of the revision.", "title": "Name", "type": "string" } }, "required": [ "id", "name" ], "title": "RevisionSupplementalMetadata", "type": "object" } ], "description": "Revision metadata.", "title": "Revision" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "scheduledJobId": { "description": "The ID of the scheduled job.", "title": "Scheduledjobid", "type": "string" }, "startTime": { "description": "The time the job started.", "format": "date-time", "title": "Starttime", "type": "string" }, "status": { "description": "The status of the job.", "title": "Status", "type": "string" }, "title": { "description": "The name of the schedule.", "title": "Title", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" }, "userId": { "description": "The ID of the user who created the job.", "title": "Userid", "type": "string" } }, "required": [ "id", "createdAt", "useCaseId", "userId", "orgId", "notebookId", "scheduledJobId", "title", "status", "payload", "notebookName", "notebookRunType" ], "title": "NotebookScheduledRun", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "NotebookScheduledRunsHistoryPaginated", "type": "object" } ``` NotebookScheduledRunsHistoryPaginated ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The total of paginated results. | | data | [NotebookScheduledRun] | false | | List of notebook scheduled runs history. | | next | string | false | | The URL to fetch the next batch of results. | | previous | string | false | | The URL to fetch the previous batch of results. | | totalCount | integer | false | | The total of all results. | ## NotebookSchema ``` { "description": "Schema for notebook metadata with additional fields.", "properties": { "created": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the creation of the notebook.", "title": "Created" }, "description": { "description": "The description of the notebook.", "title": "Description", "type": "string" }, "hasEnabledSchedule": { "description": "Whether the notebook has an enabled schedule.", "title": "Hasenabledschedule", "type": "boolean" }, "hasSchedule": { "description": "Whether the notebook has a schedule.", "title": "Hasschedule", "type": "boolean" }, "id": { "description": "The ID of the notebook.", "title": "Id", "type": "string" }, "lastViewed": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last viewed time of the notebook.", "title": "Lastviewed" }, "name": { "description": "The name of the notebook.", "title": "Name", "type": "string" }, "orgId": { "description": "The ID of the organization associated with the notebook.", "title": "Orgid", "type": "string" }, "permissions": { "description": "The permissions associated with the notebook.", "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "session": { "allOf": [ { "description": "The schema for the notebook session.", "properties": { "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "The type of the notebook session." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" } }, "required": [ "status", "notebookId" ], "title": "NotebookSessionSharedSchema", "type": "object" } ], "description": "Information about the session associated with the notebook.", "title": "Session" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "The settings of the notebook.", "title": "Settings" }, "tags": { "description": "The tags of the notebook.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "tenantId": { "description": "The tenant ID associated with the notebook.", "title": "Tenantid", "type": "string" }, "type": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of the notebook." }, "typeTransition": { "allOf": [ { "description": "An enumeration.", "enum": [ "initiated_to_codespace", "completed" ], "title": "NotebookTypeTransition", "type": "string" } ], "description": "The type transition of the notebook." }, "updated": { "allOf": [ { "description": "Notebook action signature model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who performed the action.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookActionSignature", "type": "object" } ], "description": "Information about the last update of the notebook.", "title": "Updated" }, "useCaseId": { "description": "The ID of the Use Case associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the Use Case associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "name", "id", "created", "lastViewed" ], "title": "NotebookSchema", "type": "object" } ``` NotebookSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | NotebookActionSignature | true | | Information about the creation of the notebook. | | description | string | false | | The description of the notebook. | | hasEnabledSchedule | boolean | false | | Whether the notebook has an enabled schedule. | | hasSchedule | boolean | false | | Whether the notebook has a schedule. | | id | string | true | | The ID of the notebook. | | lastViewed | NotebookActionSignature | true | | Information about the last viewed time of the notebook. | | name | string | true | | The name of the notebook. | | orgId | string | false | | The ID of the organization associated with the notebook. | | permissions | [NotebookPermission] | false | | The permissions associated with the notebook. | | session | NotebookSessionSharedSchema | false | | Information about the session associated with the notebook. | | settings | NotebookSettings | false | | The settings of the notebook. | | tags | [string] | false | | The tags of the notebook. | | tenantId | string | false | | The tenant ID associated with the notebook. | | type | NotebookType | false | | The type of the notebook. | | typeTransition | NotebookTypeTransition | false | | The type transition of the notebook. | | updated | NotebookActionSignature | false | | Information about the last update of the notebook. | | useCaseId | string | false | | The ID of the Use Case associated with the notebook. | | useCaseName | string | false | | The name of the Use Case associated with the notebook. | ## NotebookSessionSchema ``` { "description": "The schema for the notebook session.", "properties": { "environment": { "allOf": [ { "description": "The public representation of an execution environment.", "properties": { "image": { "allOf": [ { "description": "This class is used to represent the public information of an image.", "properties": { "default": { "default": false, "description": "Whether the image is the default image.", "title": "Default", "type": "boolean" }, "description": { "description": "Image description.", "title": "Description", "type": "string" }, "environmentId": { "description": "Environment ID.", "title": "Environmentid", "type": "string" }, "gpuOptimized": { "default": false, "description": "Whether the image is GPU optimized.", "title": "Gpuoptimized", "type": "boolean" }, "id": { "description": "Image ID.", "title": "Id", "type": "string" }, "label": { "description": "Image label.", "title": "Label", "type": "string" }, "language": { "description": "Image programming language.", "title": "Language", "type": "string" }, "languageVersion": { "description": "Image programming language version.", "title": "Languageversion", "type": "string" }, "libraries": { "description": "The preinstalled libraries in the image.", "items": { "type": "string" }, "title": "Libraries", "type": "array" }, "name": { "description": "Image name.", "title": "Name", "type": "string" } }, "required": [ "id", "name", "description", "language", "languageVersion" ], "title": "ImagePublic", "type": "object" } ], "description": "The image of the environment.", "title": "Image" }, "machine": { "allOf": [ { "description": "Machine is a class that represents a machine type in the system.", "properties": { "bundleId": { "description": "Bundle ID.", "title": "Bundleid", "type": "string" }, "cpu": { "description": "Number of CPU cores. Can be in millicores (e.g., 1000m) or cores (e.g. ,1).", "title": "Cpu", "type": "string" }, "cpuCores": { "default": 0, "description": "CPU cores.", "title": "Cpucores", "type": "number" }, "default": { "default": false, "description": "Is this machine type default for the environment.", "title": "Default", "type": "boolean" }, "ephemeralStorage": { "default": "10Gi", "description": "Ephemeral storage size.", "title": "Ephemeralstorage", "type": "string" }, "gpu": { "description": "GPU cores.", "title": "Gpu", "type": "string" }, "hasGpu": { "default": false, "description": "Whether or not this machine type has a GPU.", "title": "Hasgpu", "type": "boolean" }, "id": { "description": "Machine ID.", "title": "Id", "type": "string" }, "memory": { "description": "Memory size. Can be in GiB (e.g., 4Gi) or bytes (e.g., 4G).", "title": "Memory", "type": "string" }, "name": { "description": "Machine name.", "title": "Name", "type": "string" }, "ramGb": { "default": 0, "description": "RAM in GB.", "title": "Ramgb", "type": "integer" } }, "required": [ "id", "name" ], "title": "Machine", "type": "object" } ], "description": "The machine of the environment.", "title": "Machine" }, "timeToLive": { "description": "The inactivity timeout of the environment.", "title": "Timetolive", "type": "integer" } }, "required": [ "image", "machine" ], "title": "EnvironmentPublic", "type": "object" } ], "description": "The environment of the notebook session.", "title": "Environment" }, "ephemeralSessionKey": { "allOf": [ { "description": "Key for an ephemeral session.", "properties": { "entityId": { "description": "The ID of the entity.", "title": "Entityid", "type": "string" }, "entityType": { "allOf": [ { "description": "Types of entities that can be associated with an ephemeral session.", "enum": [ "CUSTOM_APP", "CUSTOM_JOB", "CUSTOM_MODEL", "CUSTOM_METRIC", "CODE_SNIPPET" ], "title": "EphemeralSessionEntityType", "type": "string" } ], "description": "The type of the entity." } }, "required": [ "entityType", "entityId" ], "title": "EphemeralSessionKey", "type": "object" } ], "description": "The key of the ephemeral session. None if not an ephemeral session.", "title": "Ephemeralsessionkey" }, "executionCount": { "default": 0, "description": "The execution count of the notebook session.", "title": "Executioncount", "type": "integer" }, "machineStatus": { "allOf": [ { "description": "This enum represents possible overall state of the machine(s) of all components of the notebook.", "enum": [ "not_started", "allocated", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "MachineStatuses", "type": "string" } ], "description": "The status of the machine running the notebook session." }, "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "parameters": { "description": "Parameters to use as environment variables.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runnerStatus": { "allOf": [ { "description": "Runner is the same as kernel since it will manage multiple kernels in the future,\nWe can't consider kernel running if runner is not functioning and therefor this enum represents\npossible statuses of the kernel and runner sidecar functionality states.\nIn the future this will likely be renamed to kernel.", "enum": [ "not_started", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "RunnerStatuses", "type": "string" } ], "description": "The status of the runner for the notebook session." }, "sessionId": { "description": "The ID of the notebook session.", "title": "Sessionid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "default": "interactive", "description": "The type of the notebook session. Possible values are interactive and triggered." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "startedBy": { "allOf": [ { "description": "Schema for notebook user.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "permissions": { "allOf": [ { "description": "User feature flags.", "properties": { "DISABLE_CODESPACE_SCHEDULING": { "description": "Whether codespace scheduling is disabled for the user.", "title": "Disable Codespace Scheduling", "type": "boolean" }, "DISABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Disable Dummy Feature Flag For Testing", "type": "boolean" }, "DISABLE_NOTEBOOKS_CODESPACES": { "description": "Whether codespaces are disabled for the user.", "title": "Disable Notebooks Codespaces", "type": "boolean" }, "DISABLE_NOTEBOOKS_SCHEDULING": { "description": "Whether scheduling is disabled for the user.", "title": "Disable Notebooks Scheduling", "type": "boolean" }, "DISABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": false, "description": "Whether session port forwarding is disabled for the user.", "title": "Disable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Enable Dummy Feature Flag For Testing", "type": "boolean" }, "ENABLE_MMM_HOSTED_CUSTOM_METRICS": { "description": "Whether custom metrics are enabled for the user.", "title": "Enable Mmm Hosted Custom Metrics", "type": "boolean" }, "ENABLE_NOTEBOOKS": { "description": "Whether notebooks are enabled for the user.", "title": "Enable Notebooks", "type": "boolean" }, "ENABLE_NOTEBOOKS_CUSTOM_ENVIRONMENTS": { "default": true, "description": "Whether custom environments are enabled for the user.", "title": "Enable Notebooks Custom Environments", "type": "boolean" }, "ENABLE_NOTEBOOKS_FILESYSTEM_MANAGEMENT": { "description": "Whether filesystem management is enabled for the user.", "title": "Enable Notebooks Filesystem Management", "type": "boolean" }, "ENABLE_NOTEBOOKS_GPU": { "description": "Whether GPU is enabled for the user.", "title": "Enable Notebooks Gpu", "type": "boolean" }, "ENABLE_NOTEBOOKS_OPEN_AI": { "description": "Whether OpenAI is enabled for the user.", "title": "Enable Notebooks Open Ai", "type": "boolean" }, "ENABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": true, "description": "Whether session port forwarding is enabled for the user.", "title": "Enable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_NOTEBOOKS_TERMINAL": { "description": "Whether terminals are enabled for the user.", "title": "Enable Notebooks Terminal", "type": "boolean" } }, "title": "UserFeatureFlags", "type": "object" } ], "description": "The feature flags of the user.", "title": "Permissions" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "NotebookUserSchema", "type": "object" } ], "description": "The user who started the notebook session.", "title": "Startedby" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" }, "withNetworkPolicy": { "description": "Whether the session is created with network policies.", "title": "Withnetworkpolicy", "type": "boolean" } }, "required": [ "status", "notebookId", "sessionId", "environment", "machineStatus", "runnerStatus" ], "title": "NotebookSessionSchema", "type": "object" } ``` NotebookSessionSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | environment | EnvironmentPublic | true | | The environment of the notebook session. | | ephemeralSessionKey | EphemeralSessionKey | false | | The key of the ephemeral session. None if not an ephemeral session. | | executionCount | integer | false | | The execution count of the notebook session. | | machineStatus | MachineStatuses | true | | The status of the machine running the notebook session. | | notebookId | string | true | | The ID of the notebook. | | parameters | [ScheduledJobParam] | false | | Parameters to use as environment variables. | | runnerStatus | RunnerStatuses | true | | The status of the runner for the notebook session. | | sessionId | string | true | | The ID of the notebook session. | | sessionType | SessionType | false | | The type of the notebook session. Possible values are interactive and triggered. | | startedAt | string(date-time) | false | | The time the notebook session was started. | | startedBy | NotebookUserSchema | false | | The user who started the notebook session. | | status | NotebookSessionStatus | true | | The status of the notebook session. | | userId | string | false | | The ID of the user associated with the notebook session. | | withNetworkPolicy | boolean | false | | Whether the session is created with network policies. | ## NotebookSessionSharedSchema ``` { "description": "The schema for the notebook session.", "properties": { "notebookId": { "description": "The ID of the notebook.", "title": "Notebookid", "type": "string" }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "The type of the notebook session." }, "startedAt": { "description": "The time the notebook session was started.", "format": "date-time", "title": "Startedat", "type": "string" }, "status": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "The status of the notebook session." }, "userId": { "description": "The ID of the user associated with the notebook session.", "title": "Userid", "type": "string" } }, "required": [ "status", "notebookId" ], "title": "NotebookSessionSharedSchema", "type": "object" } ``` NotebookSessionSharedSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notebookId | string | true | | The ID of the notebook. | | sessionType | SessionType | false | | The type of the notebook session. | | startedAt | string(date-time) | false | | The time the notebook session was started. | | status | NotebookSessionStatus | true | | The status of the notebook session. | | userId | string | false | | The ID of the user associated with the notebook session. | ## NotebookSessionStatus ``` { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ``` NotebookSessionStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | NotebookSessionStatus | string | false | | Possible overall states of a notebook session. | ### Enumerated Values | Property | Value | | --- | --- | | NotebookSessionStatus | [stopping, stopped, starting, running, restarting, dead, deleted] | ## NotebookSettings ``` { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ``` NotebookSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | hideCellFooters | boolean | false | | Whether or not cell footers are hidden in the UI. | | hideCellOutputs | boolean | false | | Whether or not cell outputs are hidden in the UI. | | hideCellTitles | boolean | false | | Whether or not cell titles are hidden in the UI. | | highlightWhitespace | boolean | false | | Whether or whitespace is highlighted in the UI. | | showLineNumbers | boolean | false | | Whether or not line numbers are shown in the UI. | | showScrollers | boolean | false | | Whether or not scroll bars are shown in the UI. | ## NotebookSharedRole ``` { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the recipient. | | name | string | true | | The name of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | The recipient type. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | user | ## NotebookSupplementalMetadata ``` { "description": "Subset of metadata that is useful for display purposes.", "properties": { "deleted": { "default": false, "description": "Whether the notebook is deleted.", "title": "Deleted", "type": "boolean" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "name": { "description": "Notebook name.", "title": "Name", "type": "string" }, "sessionStatus": { "allOf": [ { "description": "Possible overall states of a notebook session.", "enum": [ "stopping", "stopped", "starting", "running", "restarting", "dead", "deleted" ], "title": "NotebookSessionStatus", "type": "string" } ], "description": "Status of the notebook session." }, "sessionType": { "allOf": [ { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ], "description": "Type of the notebook session." }, "useCaseId": { "description": "Use case ID associated with the notebook.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "Use case name associated with the notebook.", "title": "Usecasename", "type": "string" } }, "required": [ "id", "name" ], "title": "NotebookSupplementalMetadata", "type": "object" } ``` NotebookSupplementalMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deleted | boolean | false | | Whether the notebook is deleted. | | id | string | true | | Notebook ID. | | name | string | true | | Notebook name. | | sessionStatus | NotebookSessionStatus | false | | Status of the notebook session. | | sessionType | SessionType | false | | Type of the notebook session. | | useCaseId | string | false | | Use case ID associated with the notebook. | | useCaseName | string | false | | Use case name associated with the notebook. | ## NotebookTimestampInfo ``` { "description": "Notebook usage info model that holds the information about when and who performed action on a notebook.", "properties": { "at": { "description": "Timestamp of the action.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User info of the actor who caused the action to occur.", "title": "By" } }, "required": [ "at", "by" ], "title": "NotebookTimestampInfo", "type": "object" } ``` NotebookTimestampInfo ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | at | string(date-time) | true | | Timestamp of the action. | | by | UserInfo | true | | User info of the actor who caused the action to occur. | ## NotebookType ``` { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ``` NotebookType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | NotebookType | string | false | | Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks thatare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after sessionshutdown. | ### Enumerated Values | Property | Value | | --- | --- | | NotebookType | [plain, codespace, ephemeral] | ## NotebookTypeTransition ``` { "description": "An enumeration.", "enum": [ "initiated_to_codespace", "completed" ], "title": "NotebookTypeTransition", "type": "string" } ``` NotebookTypeTransition ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | NotebookTypeTransition | string | false | | An enumeration. | ### Enumerated Values | Property | Value | | --- | --- | | NotebookTypeTransition | [initiated_to_codespace, completed] | ## NotebookUserSchema ``` { "description": "Schema for notebook user.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "permissions": { "allOf": [ { "description": "User feature flags.", "properties": { "DISABLE_CODESPACE_SCHEDULING": { "description": "Whether codespace scheduling is disabled for the user.", "title": "Disable Codespace Scheduling", "type": "boolean" }, "DISABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Disable Dummy Feature Flag For Testing", "type": "boolean" }, "DISABLE_NOTEBOOKS_CODESPACES": { "description": "Whether codespaces are disabled for the user.", "title": "Disable Notebooks Codespaces", "type": "boolean" }, "DISABLE_NOTEBOOKS_SCHEDULING": { "description": "Whether scheduling is disabled for the user.", "title": "Disable Notebooks Scheduling", "type": "boolean" }, "DISABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": false, "description": "Whether session port forwarding is disabled for the user.", "title": "Disable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Enable Dummy Feature Flag For Testing", "type": "boolean" }, "ENABLE_MMM_HOSTED_CUSTOM_METRICS": { "description": "Whether custom metrics are enabled for the user.", "title": "Enable Mmm Hosted Custom Metrics", "type": "boolean" }, "ENABLE_NOTEBOOKS": { "description": "Whether notebooks are enabled for the user.", "title": "Enable Notebooks", "type": "boolean" }, "ENABLE_NOTEBOOKS_CUSTOM_ENVIRONMENTS": { "default": true, "description": "Whether custom environments are enabled for the user.", "title": "Enable Notebooks Custom Environments", "type": "boolean" }, "ENABLE_NOTEBOOKS_FILESYSTEM_MANAGEMENT": { "description": "Whether filesystem management is enabled for the user.", "title": "Enable Notebooks Filesystem Management", "type": "boolean" }, "ENABLE_NOTEBOOKS_GPU": { "description": "Whether GPU is enabled for the user.", "title": "Enable Notebooks Gpu", "type": "boolean" }, "ENABLE_NOTEBOOKS_OPEN_AI": { "description": "Whether OpenAI is enabled for the user.", "title": "Enable Notebooks Open Ai", "type": "boolean" }, "ENABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": true, "description": "Whether session port forwarding is enabled for the user.", "title": "Enable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_NOTEBOOKS_TERMINAL": { "description": "Whether terminals are enabled for the user.", "title": "Enable Notebooks Terminal", "type": "boolean" } }, "title": "UserFeatureFlags", "type": "object" } ], "description": "The feature flags of the user.", "title": "Permissions" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "NotebookUserSchema", "type": "object" } ``` NotebookUserSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | activated | boolean | false | | Whether the user is activated. | | firstName | string | false | | The first name of the user. | | gravatarHash | string | false | | The gravatar hash of the user. | | id | string | true | | The ID of the user. | | lastName | string | false | | The last name of the user. | | orgId | string | false | | The ID of the organization the user belongs to. | | permissions | UserFeatureFlags | false | | The feature flags of the user. | | tenantPhase | string | false | | The tenant phase of the user. | | username | string | false | | The username of the user. | ## NotebooksExecutionStateResponse ``` { "description": "Response schema for the notebooks execution state.", "properties": { "states": { "description": "List of notebooks execution states.", "items": { "description": "The schema for the notebook execution state.", "properties": { "executionState": { "allOf": [ { "description": "Notebook execution state model", "properties": { "executingCellId": { "description": "The ID of the cell currently being executed.", "title": "Executingcellid", "type": "string" }, "executionFinishedAt": { "description": "The time the execution finished. This is based on the finish time of the last cell.", "format": "date-time", "title": "Executionfinishedat", "type": "string" }, "executionStartedAt": { "description": "The time the execution started.", "format": "date-time", "title": "Executionstartedat", "type": "string" }, "inputRequest": { "allOf": [ { "description": "AwaitingInputState represents the state of a cell that is awaiting input from the user.", "properties": { "password": { "description": "Whether the input request is for a password.", "title": "Password", "type": "boolean" }, "prompt": { "description": "The prompt for the input request.", "title": "Prompt", "type": "string" }, "requestedAt": { "description": "The time the input was requested.", "format": "date-time", "title": "Requestedat", "type": "string" } }, "required": [ "requestedAt", "prompt", "password" ], "title": "AwaitingInputState", "type": "object" } ], "description": "The input request state of the cell.", "title": "Inputrequest" }, "kernelId": { "description": "The ID of the kernel used for execution.", "title": "Kernelid", "type": "string" }, "queuedCellIds": { "description": "The IDs of the cells that are queued for execution.", "items": { "type": "string" }, "title": "Queuedcellids", "type": "array" } }, "title": "NotebookExecutionState", "type": "object" } ], "description": "Execution state of the notebook.", "title": "Executionstate" }, "kernel": { "allOf": [ { "description": "The schema for the notebook kernel.", "properties": { "executionState": { "allOf": [ { "description": "Event Sequences on Various Workflows:\n- On kernel created: CONNECTED -> BUSY -> IDLE\n- On kernel restarted: RESTARTING -> STARTING -> BUSY -> IDLE\n- On regular execution: IDLE -> BUSY -> IDLE\n- On execution interrupted: IDLE -> BUSY -> INTERRUPTING -> IDLE\n- On execution with error: IDLE -> BUSY -> IDLE -> INTERRUPTING -> IDLE\n- On kernel shut down via calling the stop kernel endpoint:\n DISCONNECTED (can be sent a few times) -> NOT_RUNNING (after 5s)", "enum": [ "connecting", "disconnected", "connected", "starting", "idle", "busy", "interrupting", "restarting", "not_running" ], "title": "KernelState", "type": "string" } ], "description": "The execution state of the kernel." }, "id": { "description": "The ID of the kernel.", "title": "Id", "type": "string" }, "language": { "allOf": [ { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ], "description": "The programming language of the kernel. Possible values include 'python', 'r'." }, "name": { "description": "The name of the kernel. Possible values include 'python3', 'ir'.", "title": "Name", "type": "string" }, "running": { "default": false, "description": "Whether the kernel is running.", "title": "Running", "type": "boolean" } }, "required": [ "id", "name", "language", "executionState" ], "title": "KernelSchema", "type": "object" } ], "description": "Kernel assigned to the notebook.", "title": "Kernel" }, "kernelId": { "description": "Kernel ID assigned to the notebook.", "title": "Kernelid", "type": "string" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path" ], "title": "NotebookExecutionStateSchema", "type": "object" }, "title": "States", "type": "array" } }, "required": [ "states" ], "title": "NotebooksExecutionStateResponse", "type": "object" } ``` NotebooksExecutionStateResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | states | [NotebookExecutionStateSchema] | true | | List of notebooks execution states. | ## NotebooksSharedRolesListResponse ``` { "properties": { "data": { "description": "Roles data for multiple notebooks.", "items": { "properties": { "notebookId": { "description": "The ID of the notebook.", "type": "string" }, "roles": { "description": "Individual roles data for the notebook.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.35" }, "type": "array" } }, "required": [ "notebookId", "roles" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [MultipleNotebooksSharedRole] | true | maxItems: 100 | Roles data for multiple notebooks. | ## OrderBy ``` { "description": "Enum for ordering notebooks when querying.", "enum": [ "name", "-name", "created", "-created", "updated", "-updated", "tags", "-tags", "lastViewed", "-lastViewed", "useCaseName", "-useCaseName", "sessionStatus", "-sessionStatus" ], "title": "OrderBy", "type": "string" } ``` OrderBy ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OrderBy | string | false | | Enum for ordering notebooks when querying. | ### Enumerated Values | Property | Value | | --- | --- | | OrderBy | [name, -name, created, -created, updated, -updated, tags, -tags, lastViewed, -lastViewed, useCaseName, -useCaseName, sessionStatus, -sessionStatus] | ## OutputStorageType ``` { "description": "The possible allowed values for where/how notebook cell output is stored.", "enum": [ "RAW_OUTPUT" ], "title": "OutputStorageType", "type": "string" } ``` OutputStorageType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OutputStorageType | string | false | | The possible allowed values for where/how notebook cell output is stored. | ### Enumerated Values | Property | Value | | --- | --- | | OutputStorageType | RAW_OUTPUT | ## OutputType ``` { "description": "Type of cell output. This is a superset of the permitted values for \"output_type\" in the nbformat v4 schema.\n\nReferences:\n- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs\n- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406\n- services.runner.kernel.KernelMessageType", "enum": [ "execute_result", "stream", "display_data", "error", "pyout", "pyerr", "input_request" ], "title": "OutputType", "type": "string" } ``` OutputType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | OutputType | string | false | | Type of cell output. This is a superset of the permitted values for "output_type" in the nbformat v4 schema.References:- https://nbformat.readthedocs.io/en/latest/format_description.html#code-cell-outputs- https://github.com/jupyter/nbformat/blob/5.3.0/nbformat/v4/nbformat.v4.schema.json#L301-L406- services.runner.kernel.KernelMessageType | ### Enumerated Values | Property | Value | | --- | --- | | OutputType | [execute_result, stream, display_data, error, pyout, pyerr, input_request] | ## PaginatedDataframeQuery ``` { "description": "Query schema for paginated dataframe requests.", "properties": { "cellId": { "description": "ID of the cell.", "title": "Cellid", "type": "string" }, "limit": { "default": 10, "description": "Maximum number of objects to return.", "exclusiveMinimum": 0, "maximum": 1000, "title": "Limit", "type": "integer" }, "offset": { "default": 0, "description": "Offset for pagination.", "minimum": 0, "title": "Offset", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" }, "sortBy": { "description": "Sort results by this field.", "title": "Sortby", "type": "string" } }, "required": [ "path", "cellId" ], "title": "PaginatedDataframeQuery", "type": "object" } ``` PaginatedDataframeQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellId | string | true | | ID of the cell. | | limit | integer | false | maximum: 1000 | Maximum number of objects to return. | | offset | integer | false | minimum: 0 | Offset for pagination. | | path | string | true | | Path to the notebook. | | sortBy | string | false | | Sort results by this field. | ## ReorderCellsRequest ``` { "description": "Request payload values for reordering notebook cells.", "properties": { "actionId": { "description": "Action ID of the notebook update request.", "maxLength": 64, "title": "Actionid", "type": "string" }, "cellIds": { "description": "List of cell IDs to reorder.", "items": { "type": "string" }, "title": "Cellids", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "cellIds" ], "title": "ReorderCellsRequest", "type": "object" } ``` ReorderCellsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actionId | string | false | maxLength: 64 | Action ID of the notebook update request. | | cellIds | [string] | true | | List of cell IDs to reorder. | | generation | integer | true | | Integer representing the generation of the notebook. | | path | string | true | | Path to the notebook. | ## RequiredMetadataKey ``` { "description": "Define additional parameters required to assemble a model. Model versions using this environment must define values\nfor each fieldName in the requiredMetadata.", "properties": { "displayName": { "description": "A human readable name for the required field.", "title": "Displayname", "type": "string" }, "fieldName": { "description": "The required field key. This value is added as an environment variable when running custom models.", "title": "Fieldname", "type": "string" } }, "required": [ "fieldName", "displayName" ], "title": "RequiredMetadataKey", "type": "object" } ``` RequiredMetadataKey ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | displayName | string | true | | A human readable name for the required field. | | fieldName | string | true | | The required field key. This value is added as an environment variable when running custom models. | ## RevisionActionSchema ``` { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ``` RevisionActionSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | at | string(date-time) | true | | Action timestamp. | | by | UserInfo | false | | User who performed the action. | ## RevisionSupplementalMetadata ``` { "description": "Supplemental metadata for a revision.", "properties": { "id": { "description": "The ID of the revision.", "title": "Id", "type": "string" }, "name": { "description": "The name of the revision.", "title": "Name", "type": "string" } }, "required": [ "id", "name" ], "title": "RevisionSupplementalMetadata", "type": "object" } ``` RevisionSupplementalMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the revision. | | name | string | true | | The name of the revision. | ## RunFileRequest ``` { "description": "Request payload values for running a file.", "properties": { "commandArgs": { "description": "Arguments and/or flags to pass to a file execution command. For example: '--filename foo.txt -r'", "title": "Commandargs", "type": "string" }, "commandType": { "allOf": [ { "description": "These are the command types/languages we support running files for.", "enum": [ "python", "bash" ], "title": "RunnableCommandType", "type": "string" } ], "description": "The type of command to be run. For example 'python'" }, "filePath": { "description": "Path to the file to execute.", "title": "Filepath", "type": "string" } }, "required": [ "filePath" ], "title": "RunFileRequest", "type": "object" } ``` RunFileRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commandArgs | string | false | | Arguments and/or flags to pass to a file execution command. For example: '--filename foo.txt -r' | | commandType | RunnableCommandType | false | | The type of command to be run. For example 'python' | | filePath | string | true | | Path to the file to execute. | ## RunTypes ``` { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ``` RunTypes ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | RunTypes | string | false | | Types of runs that can be scheduled. | ### Enumerated Values | Property | Value | | --- | --- | | RunTypes | [scheduled, manual, pipeline] | ## RunnableCommandType ``` { "description": "These are the command types/languages we support running files for.", "enum": [ "python", "bash" ], "title": "RunnableCommandType", "type": "string" } ``` RunnableCommandType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | RunnableCommandType | string | false | | These are the command types/languages we support running files for. | ### Enumerated Values | Property | Value | | --- | --- | | RunnableCommandType | [python, bash] | ## RunnerStatuses ``` { "description": "Runner is the same as kernel since it will manage multiple kernels in the future,\nWe can't consider kernel running if runner is not functioning and therefor this enum represents\npossible statuses of the kernel and runner sidecar functionality states.\nIn the future this will likely be renamed to kernel.", "enum": [ "not_started", "starting", "running", "restarting", "stopping", "stopped", "dead", "deleted" ], "title": "RunnerStatuses", "type": "string" } ``` RunnerStatuses ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | RunnerStatuses | string | false | | Runner is the same as kernel since it will manage multiple kernels in the future,We can't consider kernel running if runner is not functioning and therefor this enum representspossible statuses of the kernel and runner sidecar functionality states.In the future this will likely be renamed to kernel. | ### Enumerated Values | Property | Value | | --- | --- | | RunnerStatuses | [not_started, starting, running, restarting, stopping, stopped, dead, deleted] | ## RuntimeLanguage ``` { "description": "Runtime language for notebook execution in the kernel.", "enum": [ "python", "r", "shell", "markdown" ], "title": "RuntimeLanguage", "type": "string" } ``` RuntimeLanguage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | RuntimeLanguage | string | false | | Runtime language for notebook execution in the kernel. | ### Enumerated Values | Property | Value | | --- | --- | | RuntimeLanguage | [python, r, shell, markdown] | ## Schedule ``` { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ``` Schedule ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dayOfMonth | [anyOf] | true | | The day(s) of the month to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dayOfWeek | [anyOf] | true | | The day(s) of the week to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | hour | [anyOf] | true | | The hour(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | minute | [anyOf] | true | | The minute(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | month | [anyOf] | true | | The month(s) to run the schedule. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | ## ScheduledHistoryListOrderBy ``` { "description": "An enumeration.", "enum": [ "created", "-created", "title", "-title", "notebookName", "-notebookName", "runType", "-runType", "status", "-status", "startTime", "-startTime", "endTime", "-endTime", "duration", "-duration", "revisionName", "-revisionName", "environmentName", "-environmentName", "useCaseName", "-useCaseName" ], "title": "ScheduledHistoryListOrderBy", "type": "string" } ``` ScheduledHistoryListOrderBy ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ScheduledHistoryListOrderBy | string | false | | An enumeration. | ### Enumerated Values | Property | Value | | --- | --- | | ScheduledHistoryListOrderBy | [created, -created, title, -title, notebookName, -notebookName, runType, -runType, status, -status, startTime, -startTime, endTime, -endTime, duration, -duration, revisionName, -revisionName, environmentName, -environmentName, useCaseName, -useCaseName] | ## ScheduledJobListOrderBy ``` { "description": "Fields to order the list of scheduled jobs by.", "enum": [ "title", "-title", "username", "-username", "notebookName", "-notebookName", "useCaseName", "-useCaseName", "status", "-status", "schedule", "-schedule", "lastRun", "-lastRun", "nextRun", "-nextRun" ], "title": "ScheduledJobListOrderBy", "type": "string" } ``` ScheduledJobListOrderBy ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ScheduledJobListOrderBy | string | false | | Fields to order the list of scheduled jobs by. | ### Enumerated Values | Property | Value | | --- | --- | | ScheduledJobListOrderBy | [title, -title, username, -username, notebookName, -notebookName, useCaseName, -useCaseName, status, -status, schedule, -schedule, lastRun, -lastRun, nextRun, -nextRun] | ## ScheduledJobParam ``` { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" } ``` ScheduledJobParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 256 | Environment variable name. | | value | string | true | maxLength: 131072 | Environment variable value. | ## ScheduledJobPayload ``` { "description": "Payload for the scheduled job.", "properties": { "notebookId": { "description": "The ID of the notebook associated with the schedule.", "title": "Notebookid", "type": "string" }, "notebookName": { "description": "The name of the notebook associated with the schedule.", "title": "Notebookname", "type": "string" }, "notebookPath": { "description": "The path to the notebook in the file system if a Codespace is being used.", "title": "Notebookpath", "type": "string" }, "notebookType": { "allOf": [ { "description": "Notebook type can be 'plain', meaning a notebook that is not part of a codespace or 'codespace' for notebooks that\nare part of a codespace. 'Ephemeral' notebooks are created for a codespace but do not persist after session\nshutdown.", "enum": [ "plain", "codespace", "ephemeral" ], "title": "NotebookType", "type": "string" } ], "default": "plain", "description": "The type of notebook." }, "orgId": { "description": "The ID of the organization the job is associated with.", "title": "Orgid", "type": "string" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "runType": { "allOf": [ { "description": "Types of runs that can be scheduled.", "enum": [ "scheduled", "manual", "pipeline" ], "title": "RunTypes", "type": "string" } ], "description": "The run type of the job." }, "uid": { "description": "The ID of the user who created the job.", "title": "Uid", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" }, "useCaseName": { "description": "The name of the use case this notebook is associated with.", "title": "Usecasename", "type": "string" } }, "required": [ "uid", "orgId", "useCaseId", "notebookId", "notebookName" ], "title": "ScheduledJobPayload", "type": "object" } ``` ScheduledJobPayload ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notebookId | string | true | | The ID of the notebook associated with the schedule. | | notebookName | string | true | | The name of the notebook associated with the schedule. | | notebookPath | string | false | | The path to the notebook in the file system if a Codespace is being used. | | notebookType | NotebookType | false | | The type of notebook. | | orgId | string | true | | The ID of the organization the job is associated with. | | parameters | [ScheduledJobParam] | false | | The parameters to pass to the notebook when it runs. | | runType | RunTypes | false | | The run type of the job. | | uid | string | true | | The ID of the user who created the job. | | useCaseId | string | true | | The ID of the use case this notebook is associated with. | | useCaseName | string | false | | The name of the use case this notebook is associated with. | ## ScheduledJobQuery ``` { "description": "Query parameters for a scheduled job.", "properties": { "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" } }, "required": [ "useCaseId" ], "title": "ScheduledJobQuery", "type": "object" } ``` ScheduledJobQuery ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useCaseId | string | true | | The ID of the use case this notebook is associated with. | ## SessionType ``` { "description": "Session type is either 'interactive', meaning a user has started the session via their own interactions or\n'triggered' for when a session is started programmatically.", "enum": [ "interactive", "triggered" ], "title": "SessionType", "type": "string" } ``` SessionType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | SessionType | string | false | | Session type is either 'interactive', meaning a user has started the session via their own interactions or'triggered' for when a session is started programmatically. | ### Enumerated Values | Property | Value | | --- | --- | | SessionType | [interactive, triggered] | ## SharingListV2Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControlV2] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of items matching the condition. | ## SortedPaginationQuerySchema ``` { "description": "Schema for query parameters for paginated requests.", "properties": { "limit": { "default": 10, "description": "The limit or results to return.", "exclusiveMinimum": 0, "maximum": 100, "title": "Limit", "type": "integer" }, "offset": { "default": 0, "description": "The offset to use when querying paginated results.", "minimum": 0, "title": "Offset", "type": "integer" }, "sortBy": { "description": "Field to sort by.", "title": "Sortby", "type": "string" } }, "title": "SortedPaginationQuerySchema", "type": "object" } ``` SortedPaginationQuerySchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | limit | integer | false | maximum: 100 | The limit or results to return. | | offset | integer | false | minimum: 0 | The offset to use when querying paginated results. | | sortBy | string | false | | Field to sort by. | ## SourceDestinationSchema ``` { "description": "Source and destination schema for filesystem object operations.", "properties": { "destination": { "description": "Destination path.", "title": "Destination", "type": "string" }, "source": { "description": "Source path.", "title": "Source", "type": "string" } }, "required": [ "source", "destination" ], "title": "SourceDestinationSchema", "type": "object" } ``` SourceDestinationSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | destination | string | true | | Destination path. | | source | string | true | | Source path. | ## StartKernelRequest ``` { "description": "Request payload values for starting a kernel.", "properties": { "spec": { "description": "Name of the kernel to start. Possible values include 'python3', 'ir'.", "title": "Spec", "type": "string" } }, "required": [ "spec" ], "title": "StartKernelRequest", "type": "object" } ``` StartKernelRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | spec | string | true | | Name of the kernel to start. Possible values include 'python3', 'ir'. | ## StartNotebookSessionSchema ``` { "description": "Schema for starting a notebook session.", "properties": { "cloneRepository": { "allOf": [ { "description": "Schema for cloning a repository.", "properties": { "checkoutRef": { "description": "The branch or commit to checkout.", "title": "Checkoutref", "type": "string" }, "url": { "description": "The URL of the repository to clone.", "title": "Url", "type": "string" } }, "required": [ "url" ], "title": "CloneRepositorySchema", "type": "object" } ], "description": "Automatically tells the runner to clone remote repository if it's supported as part of its environment setup flow.", "title": "Clonerepository" }, "isTriggeredRun": { "default": false, "description": "Indicates if the session is a triggered run versus an interactive run.", "title": "Istriggeredrun", "type": "boolean" }, "openFilePaths": { "description": "List of file paths to open in the notebook session.", "items": { "type": "string" }, "title": "Openfilepaths", "type": "array" }, "parameters": { "description": "Parameters to use as environment variables.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" } }, "title": "StartNotebookSessionSchema", "type": "object" } ``` StartNotebookSessionSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloneRepository | CloneRepositorySchema | false | | Automatically tells the runner to clone remote repository if it's supported as part of its environment setup flow. | | isTriggeredRun | boolean | false | | Indicates if the session is a triggered run versus an interactive run. | | openFilePaths | [string] | false | | List of file paths to open in the notebook session. | | parameters | [ScheduledJobParam] | false | | Parameters to use as environment variables. | ## StreamOutputSchema ``` { "description": "The schema for the stream output in a notebook cell.", "properties": { "name": { "description": "Name of the stream.", "title": "Name", "type": "string" }, "outputType": { "const": "stream", "default": "stream", "description": "Type of the output.", "title": "Outputtype", "type": "string" }, "text": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Text of the stream.", "title": "Text" } }, "required": [ "name", "text" ], "title": "StreamOutputSchema", "type": "object" } ``` StreamOutputSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Name of the stream. | | outputType | string | false | | Type of the output. | | text | any | true | | Text of the stream. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## SuccessResponse ``` { "description": "Response schema indicating success of an operation.", "properties": { "success": { "description": "Indicates if the operation was successful.", "title": "Success", "type": "boolean" } }, "required": [ "success" ], "title": "SuccessResponse", "type": "object" } ``` SuccessResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | success | boolean | true | | Indicates if the operation was successful. | ## SupportedCellTypes ``` { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ``` SupportedCellTypes ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | SupportedCellTypes | string | false | | Supported cell types for notebooks. | ### Enumerated Values | Property | Value | | --- | --- | | SupportedCellTypes | [code, markdown] | ## TerminalSchema ``` { "description": "Schema for a terminal.", "properties": { "createdAt": { "description": "Creation time of the terminal.", "format": "date-time", "title": "Createdat", "type": "string" }, "name": { "description": "Name of the terminal.", "title": "Name", "type": "string" }, "terminalId": { "description": "ID of the terminal.", "title": "Terminalid", "type": "string" } }, "required": [ "terminalId", "name", "createdAt" ], "title": "TerminalSchema", "type": "object" } ``` TerminalSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | Creation time of the terminal. | | name | string | true | | Name of the terminal. | | terminalId | string | true | | ID of the terminal. | ## TerminalsResponseSchema ``` { "description": "Schema for the list of terminals in a notebook.", "properties": { "count": { "default": 0, "description": "The total of paginated results.", "title": "Count", "type": "integer" }, "data": { "description": "List of terminals in a notebook.", "items": { "description": "Schema for a terminal.", "properties": { "createdAt": { "description": "Creation time of the terminal.", "format": "date-time", "title": "Createdat", "type": "string" }, "name": { "description": "Name of the terminal.", "title": "Name", "type": "string" }, "terminalId": { "description": "ID of the terminal.", "title": "Terminalid", "type": "string" } }, "required": [ "terminalId", "name", "createdAt" ], "title": "TerminalSchema", "type": "object" }, "title": "Data", "type": "array" }, "next": { "description": "The URL to fetch the next batch of results.", "title": "Next", "type": "string" }, "previous": { "description": "The URL to fetch the previous batch of results.", "title": "Previous", "type": "string" }, "totalCount": { "default": 0, "description": "The total of all results.", "title": "Totalcount", "type": "integer" } }, "title": "TerminalsResponseSchema", "type": "object" } ``` TerminalsResponseSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The total of paginated results. | | data | [TerminalSchema] | false | | List of terminals in a notebook. | | next | string | false | | The URL to fetch the next batch of results. | | previous | string | false | | The URL to fetch the previous batch of results. | | totalCount | integer | false | | The total of all results. | ## TriggeredNotebookExecutionRequest ``` { "description": "Request payload values for executing a notebook with parameters.", "properties": { "parameters": { "description": "List of parameters to use as environment variables during execution.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "path" ], "title": "TriggeredNotebookExecutionRequest", "type": "object" } ``` TriggeredNotebookExecutionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parameters | [ScheduledJobParam] | false | | List of parameters to use as environment variables during execution. | | path | string | true | | Path to the notebook. | ## UpdateCellRequest ``` { "description": "Request schema for updating a notebook cell.", "properties": { "afterCellId": { "anyOf": [ { "type": "string" }, { "enum": [ "FIRST" ], "type": "string" } ], "description": "The ID of the cell after which to create the new cell.", "title": "Aftercellid" }, "attachments": { "description": "The attachments associated with the cell.", "title": "Attachments", "type": "object" }, "md5": { "description": "The MD5 hash of the cell.", "title": "Md5", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "The metadata associated with the cell.", "title": "Metadata" }, "source": { "description": "Contents of the cell, represented as a string.", "title": "Source", "type": "string" } }, "required": [ "md5" ], "title": "UpdateCellRequest", "type": "object" } ``` UpdateCellRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | afterCellId | any | false | | The ID of the cell after which to create the new cell. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attachments | object | false | | The attachments associated with the cell. | | md5 | string | true | | The MD5 hash of the cell. | | metadata | NotebookCellMetadata | false | | The metadata associated with the cell. | | source | string | false | | Contents of the cell, represented as a string. | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | FIRST | ## UpdateCellsRequest ``` { "description": "Request payload values for updating notebook cells.", "properties": { "actionId": { "description": "Action ID of notebook update request.", "maxLength": 64, "title": "Actionid", "type": "string" }, "cells": { "description": "List of updated notebook cells.", "items": { "description": "The schema for the updated notebook cell.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "UpdateNotebookCellSchema", "type": "object" }, "title": "Cells", "type": "array" }, "generation": { "description": "Integer representing the generation of the notebook.", "title": "Generation", "type": "integer" }, "path": { "description": "Path to the notebook.", "title": "Path", "type": "string" } }, "required": [ "generation", "path", "cells" ], "title": "UpdateCellsRequest", "type": "object" } ``` UpdateCellsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actionId | string | false | maxLength: 64 | Action ID of notebook update request. | | cells | [UpdateNotebookCellSchema] | true | | List of updated notebook cells. | | generation | integer | true | | Integer representing the generation of the notebook. | | path | string | true | | Path to the notebook. | ## UpdateClientActivityRequest ``` { "description": "Empty payload used when updating the client activity for a notebook session.", "properties": {}, "title": "UpdateClientActivityRequest", "type": "object" } ``` UpdateClientActivityRequest ### Properties None ## UpdateExposedPortSchema ``` { "description": "Port update schema for a notebook.", "properties": { "description": { "description": "Description of the exposed port.", "maxLength": 500, "title": "Description", "type": "string" }, "port": { "description": "Exposed port number.", "title": "Port", "type": "integer" } }, "title": "UpdateExposedPortSchema", "type": "object" } ``` UpdateExposedPortSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 500 | Description of the exposed port. | | port | integer | false | | Exposed port number. | ## UpdateFilesystemObjectMetadata ``` { "description": "Update filesystem object metadata path and values.", "properties": { "metadata": { "allOf": [ { "description": "Metadata for DataRobot files.", "properties": { "commandArgs": { "description": "Command arguments for the file.", "title": "Commandargs", "type": "string" } }, "title": "DRFileMetadata", "type": "object" } ], "description": "Metadata to update.", "title": "Metadata" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" } }, "required": [ "path", "metadata" ], "title": "UpdateFilesystemObjectMetadata", "type": "object" } ``` UpdateFilesystemObjectMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metadata | DRFileMetadata | true | | Metadata to update. | | path | string | true | | Path to the filesystem object. | ## UpdateFilesystemObjectsMetadataRequest ``` { "description": "Request payload values for updating filesystem object metadata.", "properties": { "updates": { "description": "List of updates to apply.", "items": { "description": "Update filesystem object metadata path and values.", "properties": { "metadata": { "allOf": [ { "description": "Metadata for DataRobot files.", "properties": { "commandArgs": { "description": "Command arguments for the file.", "title": "Commandargs", "type": "string" } }, "title": "DRFileMetadata", "type": "object" } ], "description": "Metadata to update.", "title": "Metadata" }, "path": { "description": "Path to the filesystem object.", "title": "Path", "type": "string" } }, "required": [ "path", "metadata" ], "title": "UpdateFilesystemObjectMetadata", "type": "object" }, "title": "Updates", "type": "array" } }, "title": "UpdateFilesystemObjectsMetadataRequest", "type": "object" } ``` UpdateFilesystemObjectsMetadataRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | updates | [UpdateFilesystemObjectMetadata] | false | | List of updates to apply. | ## UpdateNotebookCellSchema ``` { "description": "The schema for the updated notebook cell.", "properties": { "cellType": { "allOf": [ { "description": "Supported cell types for notebooks.", "enum": [ "code", "markdown" ], "title": "SupportedCellTypes", "type": "string" } ], "description": "Type of the cell." }, "id": { "description": "ID of the cell.", "title": "Id", "type": "string" }, "metadata": { "allOf": [ { "description": "The schema for the notebook cell metadata.", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "collapsed": { "default": false, "description": "Whether the cell's output is collapsed/expanded.", "title": "Collapsed", "type": "boolean" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "Dataframe cell view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "datarobot": { "allOf": [ { "description": "A custom namespaces for all DataRobot-specific information", "properties": { "chartSettings": { "allOf": [ { "description": "Chart cell metadata.", "properties": { "axis": { "allOf": [ { "description": "Chart cell axis settings per axis.", "properties": { "x": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" }, "y": { "description": "Chart cell axis settings.", "properties": { "aggregation": { "description": "Aggregation function for the axis.", "title": "Aggregation", "type": "string" }, "color": { "description": "Color for the axis.", "title": "Color", "type": "string" }, "hideGrid": { "default": false, "description": "Whether to hide the grid lines on the axis.", "title": "Hidegrid", "type": "boolean" }, "hideInTooltip": { "default": false, "description": "Whether to hide the axis in the tooltip.", "title": "Hideintooltip", "type": "boolean" }, "hideLabel": { "default": false, "description": "Whether to hide the axis label.", "title": "Hidelabel", "type": "boolean" }, "key": { "description": "Key for the axis.", "title": "Key", "type": "string" }, "label": { "description": "Label for the axis.", "title": "Label", "type": "string" }, "position": { "description": "Position of the axis.", "title": "Position", "type": "string" }, "showPointMarkers": { "default": false, "description": "Whether to show point markers on the axis.", "title": "Showpointmarkers", "type": "boolean" } }, "title": "NotebookChartCellAxisSettings", "type": "object" } }, "title": "NotebookChartCellAxis", "type": "object" } ], "description": "Axis settings.", "title": "Axis" }, "data": { "description": "The data associated with the cell chart.", "title": "Data", "type": "object" }, "dataframeId": { "description": "The ID of the dataframe associated with the cell chart.", "title": "Dataframeid", "type": "string" }, "viewOptions": { "allOf": [ { "description": "Chart cell view options.", "properties": { "chartType": { "description": "Type of the chart.", "title": "Charttype", "type": "string" }, "showLegend": { "default": false, "description": "Whether to show the chart legend.", "title": "Showlegend", "type": "boolean" }, "showTitle": { "default": false, "description": "Whether to show the chart title.", "title": "Showtitle", "type": "boolean" }, "showTooltip": { "default": false, "description": "Whether to show the chart tooltip.", "title": "Showtooltip", "type": "boolean" }, "title": { "description": "Title of the chart.", "title": "Title", "type": "string" } }, "title": "NotebookChartCellViewOptions", "type": "object" } ], "description": "Chart cell view options.", "title": "Viewoptions" } }, "title": "NotebookChartCellMetadata", "type": "object" } ], "description": "Chart cell view options and metadata.", "title": "Chartsettings" }, "customLlmMetricSettings": { "allOf": [ { "description": "Custom LLM metric cell metadata.", "properties": { "metricId": { "description": "The ID of the custom LLM metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom LLM metric.", "title": "Metricname", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the custom LLM metric.", "title": "Playgroundid", "type": "string" } }, "required": [ "metricId", "playgroundId", "metricName" ], "title": "NotebookCustomLlmMetricCellMetadata", "type": "object" } ], "description": "Custom LLM metric cell metadata.", "title": "Customllmmetricsettings" }, "customMetricSettings": { "allOf": [ { "description": "Custom metric cell metadata.", "properties": { "deploymentId": { "description": "The ID of the deployment associated with the custom metric.", "title": "Deploymentid", "type": "string" }, "metricId": { "description": "The ID of the custom metric.", "title": "Metricid", "type": "string" }, "metricName": { "description": "The name of the custom metric.", "title": "Metricname", "type": "string" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule associated with the custom metric.", "title": "Schedule" } }, "required": [ "metricId", "deploymentId" ], "title": "NotebookCustomMetricCellMetadata", "type": "object" } ], "description": "Custom metric cell metadata.", "title": "Custommetricsettings" }, "dataframeViewOptions": { "description": "DataFrame view options and metadata.", "title": "Dataframeviewoptions", "type": "object" }, "disableRun": { "default": false, "description": "Whether to disable the run button in the cell.", "title": "Disablerun", "type": "boolean" }, "executionTimeMillis": { "description": "Execution time of the cell in milliseconds.", "title": "Executiontimemillis", "type": "integer" }, "hideCode": { "default": false, "description": "Whether to hide the code in the cell.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether to hide the results in the cell.", "title": "Hideresults", "type": "boolean" }, "language": { "description": "An enumeration.", "enum": [ "dataframe", "markdown", "python", "r", "shell", "scala", "sas", "custommetric" ], "title": "Language", "type": "string" } }, "title": "NotebookCellDataRobotMetadata", "type": "object" } ], "description": "Metadata specific to DataRobot's notebooks and notebook environment.", "title": "Datarobot" }, "disableRun": { "default": false, "description": "Whether or not the cell is disabled in the UI.", "title": "Disablerun", "type": "boolean" }, "hideCode": { "default": false, "description": "Whether or not code is hidden in the UI.", "title": "Hidecode", "type": "boolean" }, "hideResults": { "default": false, "description": "Whether or not results are hidden in the UI.", "title": "Hideresults", "type": "boolean" }, "jupyter": { "allOf": [ { "description": "The schema for the Jupyter cell metadata.", "properties": { "outputsHidden": { "default": false, "description": "Whether the cell's outputs are hidden.", "title": "Outputshidden", "type": "boolean" }, "sourceHidden": { "default": false, "description": "Whether the cell's source is hidden.", "title": "Sourcehidden", "type": "boolean" } }, "title": "JupyterCellMetadata", "type": "object" } ], "description": "Jupyter metadata.", "title": "Jupyter" }, "name": { "description": "Name of the notebook cell.", "title": "Name", "type": "string" }, "scrolled": { "anyOf": [ { "type": "boolean" }, { "enum": [ "auto" ], "type": "string" } ], "default": "auto", "description": "Whether the cell's output is scrolled, unscrolled, or autoscrolled.", "title": "Scrolled" } }, "title": "NotebookCellMetadata", "type": "object" } ], "description": "Metadata of the cell.", "title": "Metadata" }, "source": { "anyOf": [ { "type": "string" }, { "items": { "type": "string" }, "type": "array" } ], "description": "Contents of the cell, represented as a string.", "title": "Source" } }, "required": [ "id" ], "title": "UpdateNotebookCellSchema", "type": "object" } ``` UpdateNotebookCellSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cellType | SupportedCellTypes | false | | Type of the cell. | | id | string | true | | ID of the cell. | | metadata | NotebookCellMetadata | false | | Metadata of the cell. | | source | any | false | | Contents of the cell, represented as a string. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | ## UpdateNotebookRequest ``` { "description": "Request schema for updating a notebook.", "properties": { "description": { "description": "The value to update the description of the notebook to.", "title": "Description", "type": "string" }, "name": { "description": "The value to update the name of the notebook to.", "title": "Name", "type": "string" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "description": "The value to update the settings of the notebook to.", "title": "Settings" }, "tags": { "description": "The value to update the tags of the notebook to.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "useCaseId": { "description": "The value to update the Use Case ID of the notebook to.", "title": "Usecaseid", "type": "string" } }, "title": "UpdateNotebookRequest", "type": "object" } ``` UpdateNotebookRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | | The value to update the description of the notebook to. | | name | string | false | | The value to update the name of the notebook to. | | settings | NotebookSettings | false | | The value to update the settings of the notebook to. | | tags | [string] | false | | The value to update the tags of the notebook to. | | useCaseId | string | false | | The value to update the Use Case ID of the notebook to. | ## UpdateNotebookRevisionRequest ``` { "description": "Request", "properties": { "isAuto": { "default": false, "description": "Whether the revision is autosaved.", "title": "Isauto", "type": "boolean" }, "name": { "description": "Notebook revision name.", "minLength": 1, "title": "Name", "type": "string" }, "notebookPath": { "description": "Path to the notebook file, if using Codespaces.", "title": "Notebookpath", "type": "string" } }, "title": "UpdateNotebookRevisionRequest", "type": "object" } ``` UpdateNotebookRevisionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isAuto | boolean | false | | Whether the revision is autosaved. | | name | string | false | minLength: 1minLength: 1 | Notebook revision name. | | notebookPath | string | false | | Path to the notebook file, if using Codespaces. | ## UpdateScheduledJobRequest ``` { "description": "Request to update an existing scheduled job.", "properties": { "enabled": { "description": "Whether the job is enabled.", "title": "Enabled", "type": "boolean" }, "parameters": { "description": "The parameters to pass to the notebook when it runs.", "items": { "description": "Parameter that is passed to the notebook when it is run as an environment variable.", "properties": { "name": { "description": "Environment variable name.", "maxLength": 256, "pattern": "^[a-z-A-Z0-9_]+$", "title": "Name", "type": "string" }, "value": { "description": "Environment variable value.", "maxLength": 131072, "title": "Value", "type": "string" } }, "required": [ "name", "value" ], "title": "ScheduledJobParam", "type": "object" }, "title": "Parameters", "type": "array" }, "schedule": { "allOf": [ { "description": "Data class that represents a cron schedule.", "properties": { "dayOfMonth": { "description": "The day(s) of the month to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofmonth", "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Dayofweek", "type": "array" }, "hour": { "description": "The hour(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Hour", "type": "array" }, "minute": { "description": "The minute(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Minute", "type": "array" }, "month": { "description": "The month(s) to run the schedule.", "items": { "anyOf": [ { "type": "integer" }, { "type": "string" } ] }, "title": "Month", "type": "array" } }, "required": [ "minute", "hour", "dayOfMonth", "month", "dayOfWeek" ], "title": "Schedule", "type": "object" } ], "description": "The schedule for the job.", "title": "Schedule" }, "title": { "description": "The title of the scheduled job.", "maxLength": 100, "minLength": 1, "title": "Title", "type": "string" }, "useCaseId": { "description": "The ID of the use case this notebook is associated with.", "title": "Usecaseid", "type": "string" } }, "required": [ "useCaseId" ], "title": "UpdateScheduledJobRequest", "type": "object" } ``` UpdateScheduledJobRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | enabled | boolean | false | | Whether the job is enabled. | | parameters | [ScheduledJobParam] | false | | The parameters to pass to the notebook when it runs. | | schedule | Schedule | false | | The schedule for the job. | | title | string | false | maxLength: 100minLength: 1minLength: 1 | The title of the scheduled job. | | useCaseId | string | true | | The ID of the use case this notebook is associated with. | ## UpdateTerminalSchema ``` { "description": "Schema for updating a terminal.", "properties": { "name": { "description": "Name to update the terminal with.", "title": "Name", "type": "string" } }, "required": [ "name" ], "title": "UpdateTerminalSchema", "type": "object" } ``` UpdateTerminalSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Name to update the terminal with. | ## UserFeatureFlags ``` { "description": "User feature flags.", "properties": { "DISABLE_CODESPACE_SCHEDULING": { "description": "Whether codespace scheduling is disabled for the user.", "title": "Disable Codespace Scheduling", "type": "boolean" }, "DISABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Disable Dummy Feature Flag For Testing", "type": "boolean" }, "DISABLE_NOTEBOOKS_CODESPACES": { "description": "Whether codespaces are disabled for the user.", "title": "Disable Notebooks Codespaces", "type": "boolean" }, "DISABLE_NOTEBOOKS_SCHEDULING": { "description": "Whether scheduling is disabled for the user.", "title": "Disable Notebooks Scheduling", "type": "boolean" }, "DISABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": false, "description": "Whether session port forwarding is disabled for the user.", "title": "Disable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_DUMMY_FEATURE_FLAG_FOR_TESTING": { "description": "Dummy feature flag used for testing.", "title": "Enable Dummy Feature Flag For Testing", "type": "boolean" }, "ENABLE_MMM_HOSTED_CUSTOM_METRICS": { "description": "Whether custom metrics are enabled for the user.", "title": "Enable Mmm Hosted Custom Metrics", "type": "boolean" }, "ENABLE_NOTEBOOKS": { "description": "Whether notebooks are enabled for the user.", "title": "Enable Notebooks", "type": "boolean" }, "ENABLE_NOTEBOOKS_CUSTOM_ENVIRONMENTS": { "default": true, "description": "Whether custom environments are enabled for the user.", "title": "Enable Notebooks Custom Environments", "type": "boolean" }, "ENABLE_NOTEBOOKS_FILESYSTEM_MANAGEMENT": { "description": "Whether filesystem management is enabled for the user.", "title": "Enable Notebooks Filesystem Management", "type": "boolean" }, "ENABLE_NOTEBOOKS_GPU": { "description": "Whether GPU is enabled for the user.", "title": "Enable Notebooks Gpu", "type": "boolean" }, "ENABLE_NOTEBOOKS_OPEN_AI": { "description": "Whether OpenAI is enabled for the user.", "title": "Enable Notebooks Open Ai", "type": "boolean" }, "ENABLE_NOTEBOOKS_SESSION_PORT_FORWARDING": { "default": true, "description": "Whether session port forwarding is enabled for the user.", "title": "Enable Notebooks Session Port Forwarding", "type": "boolean" }, "ENABLE_NOTEBOOKS_TERMINAL": { "description": "Whether terminals are enabled for the user.", "title": "Enable Notebooks Terminal", "type": "boolean" } }, "title": "UserFeatureFlags", "type": "object" } ``` UserFeatureFlags ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | DISABLE_CODESPACE_SCHEDULING | boolean | false | | Whether codespace scheduling is disabled for the user. | | DISABLE_DUMMY_FEATURE_FLAG_FOR_TESTING | boolean | false | | Dummy feature flag used for testing. | | DISABLE_NOTEBOOKS_CODESPACES | boolean | false | | Whether codespaces are disabled for the user. | | DISABLE_NOTEBOOKS_SCHEDULING | boolean | false | | Whether scheduling is disabled for the user. | | DISABLE_NOTEBOOKS_SESSION_PORT_FORWARDING | boolean | false | | Whether session port forwarding is disabled for the user. | | ENABLE_DUMMY_FEATURE_FLAG_FOR_TESTING | boolean | false | | Dummy feature flag used for testing. | | ENABLE_MMM_HOSTED_CUSTOM_METRICS | boolean | false | | Whether custom metrics are enabled for the user. | | ENABLE_NOTEBOOKS | boolean | false | | Whether notebooks are enabled for the user. | | ENABLE_NOTEBOOKS_CUSTOM_ENVIRONMENTS | boolean | false | | Whether custom environments are enabled for the user. | | ENABLE_NOTEBOOKS_FILESYSTEM_MANAGEMENT | boolean | false | | Whether filesystem management is enabled for the user. | | ENABLE_NOTEBOOKS_GPU | boolean | false | | Whether GPU is enabled for the user. | | ENABLE_NOTEBOOKS_OPEN_AI | boolean | false | | Whether OpenAI is enabled for the user. | | ENABLE_NOTEBOOKS_SESSION_PORT_FORWARDING | boolean | false | | Whether session port forwarding is enabled for the user. | | ENABLE_NOTEBOOKS_TERMINAL | boolean | false | | Whether terminals are enabled for the user. | ## UserInfo ``` { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ``` UserInfo ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | activated | boolean | false | | Whether the user is activated. | | firstName | string | false | | The first name of the user. | | gravatarHash | string | false | | The gravatar hash of the user. | | id | string | true | | The ID of the user. | | lastName | string | false | | The last name of the user. | | orgId | string | false | | The ID of the organization the user belongs to. | | tenantPhase | string | false | | The tenant phase of the user. | | username | string | false | | The username of the user. | ## VCSCommandStatus ``` { "description": "Status of the VCS command.", "enum": [ "not_inited", "running", "finished", "error" ], "title": "VCSCommandStatus", "type": "string" } ``` VCSCommandStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | VCSCommandStatus | string | false | | Status of the VCS command. | ### Enumerated Values | Property | Value | | --- | --- | | VCSCommandStatus | [not_inited, running, finished, error] | ## VersionedNotebookSchema ``` { "description": "Versioned notebook schema.", "properties": { "created": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision creation action information.", "title": "Created" }, "id": { "description": "Notebook ID.", "title": "Id", "type": "string" }, "isAuto": { "description": "Whether the revision was autosaved.", "title": "Isauto", "type": "boolean" }, "lastViewed": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision last viewed action information.", "title": "Lastviewed" }, "name": { "description": "Revision name.", "title": "Name", "type": "string" }, "orgId": { "description": "Organization ID.", "title": "Orgid", "type": "string" }, "permissions": { "description": "Notebook permissions.", "items": { "description": "The possible allowed actions for the current user for a given Notebook.", "enum": [ "CAN_READ", "CAN_UPDATE", "CAN_DELETE", "CAN_SHARE", "CAN_COPY", "CAN_EXECUTE" ], "title": "NotebookPermission", "type": "string" }, "type": "array" }, "revisionId": { "description": "Revision ID.", "title": "Revisionid", "type": "string" }, "settings": { "allOf": [ { "description": "Notebook UI settings.", "properties": { "hideCellFooters": { "default": false, "description": "Whether or not cell footers are hidden in the UI.", "title": "Hidecellfooters", "type": "boolean" }, "hideCellOutputs": { "default": false, "description": "Whether or not cell outputs are hidden in the UI.", "title": "Hidecelloutputs", "type": "boolean" }, "hideCellTitles": { "default": false, "description": "Whether or not cell titles are hidden in the UI.", "title": "Hidecelltitles", "type": "boolean" }, "highlightWhitespace": { "default": false, "description": "Whether or whitespace is highlighted in the UI.", "title": "Highlightwhitespace", "type": "boolean" }, "showLineNumbers": { "default": false, "description": "Whether or not line numbers are shown in the UI.", "title": "Showlinenumbers", "type": "boolean" }, "showScrollers": { "default": false, "description": "Whether or not scroll bars are shown in the UI.", "title": "Showscrollers", "type": "boolean" } }, "title": "NotebookSettings", "type": "object" } ], "default": { "hide_cell_footers": false, "hide_cell_outputs": false, "hide_cell_titles": false, "highlight_whitespace": false, "show_line_numbers": false, "show_scrollers": false }, "description": "Notebook settings.", "title": "Settings" }, "tags": { "description": "Notebook tags.", "items": { "type": "string" }, "title": "Tags", "type": "array" }, "updated": { "allOf": [ { "description": "Revision action information schema.", "properties": { "at": { "description": "Action timestamp.", "format": "date-time", "title": "At", "type": "string" }, "by": { "allOf": [ { "description": "User information.", "properties": { "activated": { "default": true, "description": "Whether the user is activated.", "title": "Activated", "type": "boolean" }, "firstName": { "description": "The first name of the user.", "title": "Firstname", "type": "string" }, "gravatarHash": { "description": "The gravatar hash of the user.", "title": "Gravatarhash", "type": "string" }, "id": { "description": "The ID of the user.", "title": "Id", "type": "string" }, "lastName": { "description": "The last name of the user.", "title": "Lastname", "type": "string" }, "orgId": { "description": "The ID of the organization the user belongs to.", "title": "Orgid", "type": "string" }, "tenantPhase": { "description": "The tenant phase of the user.", "title": "Tenantphase", "type": "string" }, "username": { "description": "The username of the user.", "title": "Username", "type": "string" } }, "required": [ "id" ], "title": "UserInfo", "type": "object" } ], "description": "User who performed the action.", "title": "By" } }, "required": [ "at" ], "title": "RevisionActionSchema", "type": "object" } ], "description": "Revision update action information.", "title": "Updated" }, "useCaseId": { "description": "Use case ID.", "title": "Usecaseid", "type": "string" } }, "required": [ "id", "revisionId", "name", "isAuto", "created", "lastViewed" ], "title": "VersionedNotebookSchema", "type": "object" } ``` VersionedNotebookSchema ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | RevisionActionSchema | true | | Revision creation action information. | | id | string | true | | Notebook ID. | | isAuto | boolean | true | | Whether the revision was autosaved. | | lastViewed | RevisionActionSchema | true | | Revision last viewed action information. | | name | string | true | | Revision name. | | orgId | string | false | | Organization ID. | | permissions | [NotebookPermission] | false | | Notebook permissions. | | revisionId | string | true | | Revision ID. | | settings | NotebookSettings | false | | Notebook settings. | | tags | [string] | false | | Notebook tags. | | updated | RevisionActionSchema | false | | Revision update action information. | | useCaseId | string | false | | Use case ID. | --- # Notifications URL: https://docs.datarobot.com/en/docs/api/reference/public-api/notifications.html > This page outlines how to manage notifications received by users. # Notifications This page outlines how to manage notifications received by users. ## Create an entity notification channel Operation path: `POST /api/v2/entityNotificationChannels/` Authentication requirements: `BearerAuth` Create a new entity notification channel. ### Body parameter ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": "string", "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The ID of the related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean", "x-versionadded": "v2.33" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "channelType", "name", "relatedEntityId", "relatedEntityType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | EntityNotificationChannelCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The ID of the related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "relatedEntityId", "relatedEntityType", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The entity notification channel was created successfully. | EntityNotificationChannelResponse | ## List notification channels related by relatedentitytype Operation path: `GET /api/v2/entityNotificationChannels/{relatedEntityType}/{relatedEntityId}/` Authentication requirements: `BearerAuth` List the notification channels related to the entity according to the query. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of notification channels to skip. | | limit | query | integer | true | The maximum number of notification channels to return. | | namePart | query | string | false | Only return the notification channels whose names contain the given substring. | | relatedEntityId | path | string | true | The ID of the related entity. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of channels returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The notification entity channels.", "items": { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The ID of the related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "relatedEntityId", "relatedEntityType", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of channels that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The entity notification channels were listed successfully. | EntityNotificationChannelsListResponse | ## Delete an entity notification channel by relatedentitytype Operation path: `DELETE /api/v2/entityNotificationChannels/{relatedEntityType}/{relatedEntityId}/{channelId}/` Authentication requirements: `BearerAuth` Delete the entity notification channel. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | channelId | path | string | true | The ID of the entity notification channel. | | relatedEntityId | path | string | true | The ID of the related entity. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The entity notification channel was deleted successfully. | None | ## Retrieve an entity notification channel by relatedentitytype Operation path: `GET /api/v2/entityNotificationChannels/{relatedEntityType}/{relatedEntityId}/{channelId}/` Authentication requirements: `BearerAuth` Retrieve the entity notification channel. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | channelId | path | string | true | The ID of the entity notification channel. | | relatedEntityId | path | string | true | The ID of the related entity. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Example responses > 200 Response ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The ID of the related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "relatedEntityId", "relatedEntityType", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Entity notification channel retrieved successfully. | EntityNotificationChannelResponse | ## Update an entity notification channel by relatedentitytype Operation path: `PUT /api/v2/entityNotificationChannels/{relatedEntityType}/{relatedEntityId}/{channelId}/` Authentication requirements: `BearerAuth` Update the entity notification channel. ### Body parameter ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean", "x-versionadded": "v2.33" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | channelId | path | string | true | The ID of the entity notification channel. | | relatedEntityId | path | string | true | The ID of the related entity. | | relatedEntityType | path | string | true | The type of the related entity. | | body | body | NotificationChannelWithDREntityUpdate | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The entity notification channel was updated successfully. | None | | 400 | Bad Request | Email verification code is invalid. | None | ## Create an entity notification policy Operation path: `POST /api/v2/entityNotificationPolicies/` Authentication requirements: `BearerAuth` Create a new entity notification policy. ### Body parameter ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string", "x-versionadded": "v2.33" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string", "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification policy.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "channelId", "channelScope", "name", "relatedEntityId", "relatedEntityType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | EntityNotificationPolicyCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "templateId": { "description": "The id of template.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "channelScope", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityId", "relatedEntityType", "templateId", "uid", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Entity notification policy created successfully. | EntityNotificationPolicyResponse | ## List entity notification policies by relatedentitytype Operation path: `GET /api/v2/entityNotificationPolicies/{relatedEntityType}/{relatedEntityId}/` Authentication requirements: `BearerAuth` List the entity notification policies that satisfy the query condition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | How many notification channels to skip. | | limit | query | integer | true | At most this many notification channels to return. | | channelId | query | string | false | Return policies with this channel. | | namePart | query | string | false | Only return the notification channels whose names contain the given substring. | | eventGroup | query | string | false | Return policies with this event group. | | channelScope | query | string | false | Scope of the channel. | | relatedEntityId | path | string | true | The id of related entity. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | eventGroup | [secure_config.all, dataset.all, file.all, comment.all, invite_job.all, deployment_prediction_explanations_computation.all, model_deployments.critical_health, model_deployments.critical_frequent_health_change, model_deployments.frequent_health_change, model_deployments.health, model_deployments.retraining_policy, inference_endpoints.health, model_deployments.management_agent, model_deployments.management_agent_health, prediction_request.all, challenger_management.all, challenger_replay.all, model_deployments.all, project.all, perma_delete_project.all, users_delete.all, applications.all, model_version.stage_transitions, model_version.all, use_case.all, batch_predictions.all, change_requests.all, custom_job_run.all, custom_job_run.unsuccessful, insights_computation.all, notebook_schedule.all, monitoring.all] | | channelScope | [organization, Organization, ORGANIZATION, entity, Entity, ENTITY, template, Template, TEMPLATE] | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of entity policies returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The entity notification policies.", "items": { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "templateId": { "description": "The id of template.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "channelScope", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityId", "relatedEntityType", "templateId", "uid", "updatedAt" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of entity policies that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Entity notification policies listed successfully. | EntityNotificationPoliciesListResponse | ## Delete an entity notification policy by relatedentitytype Operation path: `DELETE /api/v2/entityNotificationPolicies/{relatedEntityType}/{relatedEntityId}/{policyId}/` Authentication requirements: `BearerAuth` Delete the entity notification policy. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | policyId | path | string | true | The ID of the notification policy. | | relatedEntityId | path | string | true | The id of related entity. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Entity notification policy deleted successfully. | None | ## Retrieve an entity notification policy by relatedentitytype Operation path: `GET /api/v2/entityNotificationPolicies/{relatedEntityType}/{relatedEntityId}/{policyId}/` Authentication requirements: `BearerAuth` Retrieve the entity notification policy. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | policyId | path | string | true | The ID of the notification policy. | | relatedEntityId | path | string | true | The id of related entity. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Example responses > 200 Response ``` { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "templateId": { "description": "The id of template.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "channelScope", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityId", "relatedEntityType", "templateId", "uid", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Entity notification policy retrieved successfully. | EntityNotificationPolicyResponse | ## Update an entity notification policy by relatedentitytype Operation path: `PUT /api/v2/entityNotificationPolicies/{relatedEntityType}/{relatedEntityId}/{policyId}/` Authentication requirements: `BearerAuth` Update the entity notification policy. ### Body parameter ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": "string", "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string", "x-versionadded": "v2.33" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string", "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | policyId | path | string | true | The ID of the notification policy. | | relatedEntityId | path | string | true | The id of related entity. | | relatedEntityType | path | string | true | The type of the related entity. | | body | body | EntityNotificationPolicyUpdate | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Entity notification policy updated successfully. | None | ## Create an entity notification policy Operation path: `POST /api/v2/entityNotificationPoliciesFromTemplate/` Authentication requirements: `BearerAuth` Create a new entity notification policy from template. ### Body parameter ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification policy.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "templateId": { "description": "The id of template.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "name", "relatedEntityId", "relatedEntityType", "templateId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | EntityNotificationPolicyCreateFromTemplate | false | none | ### Example responses > 201 Response ``` { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "templateId": { "description": "The id of template.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "channelScope", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityId", "relatedEntityType", "templateId", "uid", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Entity notification policy created successfully. | EntityNotificationPolicyResponse | ## Create an entity notification policy template Operation path: `POST /api/v2/entityNotificationPolicyTemplates/` Authentication requirements: `BearerAuth` Create a new entity notification policy template. ### Body parameter ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string", "x-versionadded": "v2.33" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string", "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification policy.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "channelId", "name", "relatedEntityType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | EntityNotificationPolicyTemplateCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": "string", "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "User that created template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "relatedPoliciesCount": { "description": "The total number of entity policies that are using this template.", "type": "integer", "x-versionadded": "v2.33" }, "role": { "description": "User role on entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "created", "createdBy", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityType", "relatedPoliciesCount", "role", "uid", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Entity notification policy template created successfully. | EntityNotificationPolicyTemplateResponse | ## List entity notification policy templates by relatedentitytype Operation path: `GET /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/` Authentication requirements: `BearerAuth` List the entity notification policies templates that satisfy the query condition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | How many notification channels to skip. | | limit | query | integer | true | At most this many notification channels to return. | | channelId | query | string | false | Return policies with this channel. | | namePart | query | string | false | Only return the notification channels whose names contain the given substring. | | eventGroup | query | string | false | Return policies with this event group. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | eventGroup | [secure_config.all, dataset.all, file.all, comment.all, invite_job.all, deployment_prediction_explanations_computation.all, model_deployments.critical_health, model_deployments.critical_frequent_health_change, model_deployments.frequent_health_change, model_deployments.health, model_deployments.retraining_policy, inference_endpoints.health, model_deployments.management_agent, model_deployments.management_agent_health, prediction_request.all, challenger_management.all, challenger_replay.all, model_deployments.all, project.all, perma_delete_project.all, users_delete.all, applications.all, model_version.stage_transitions, model_version.all, use_case.all, batch_predictions.all, change_requests.all, custom_job_run.all, custom_job_run.unsuccessful, insights_computation.all, notebook_schedule.all, monitoring.all] | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of entity policy templates returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The entity notification policy templates.", "items": { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": "string", "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "User that created template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "relatedPoliciesCount": { "description": "The total number of entity policies that are using this template.", "type": "integer", "x-versionadded": "v2.33" }, "role": { "description": "User role on entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "created", "createdBy", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityType", "relatedPoliciesCount", "role", "uid", "updatedAt" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of entity policies templates that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Entity notification policies templates listed successfully. | EntityNotificationPoliciesTemplatesListResponse | ## Delete an entity notification policy template by relatedentitytype Operation path: `DELETE /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/` Authentication requirements: `BearerAuth` Delete the entity notification policy template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | policyId | path | string | true | The ID of the notification policy template. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Entity notification policy template deleted successfully. | None | ## Retrieve an entity notification policy template by relatedentitytype Operation path: `GET /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/` Authentication requirements: `BearerAuth` Retrieve the entity notification policy template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | policyId | path | string | true | The ID of the notification policy template. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Example responses > 200 Response ``` { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": "string", "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "User that created template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "relatedPoliciesCount": { "description": "The total number of entity policies that are using this template.", "type": "integer", "x-versionadded": "v2.33" }, "role": { "description": "User role on entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "created", "createdBy", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityType", "relatedPoliciesCount", "role", "uid", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Entity notification policy template retrieved successfully. | EntityNotificationPolicyTemplateResponse | ## Update an entity notification policy template by relatedentitytype Operation path: `PUT /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/` Authentication requirements: `BearerAuth` Update the entity notification policy template. ### Body parameter ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string", "x-versionadded": "v2.33" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string", "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | policyId | path | string | true | The ID of the notification policy template. | | relatedEntityType | path | string | true | The type of the related entity. | | body | body | EntityNotificationPolicyTemplateUpdate | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Entity notification policy template updated successfully. | None | ## Retrieve list of all policies that are created from this template and are visible by relatedentitytype Operation path: `GET /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/relatedPolicies/` Authentication requirements: `BearerAuth` Retrieve list of all policies that are created from this template and are visible for user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | How many notification channels to skip. | | limit | query | integer | true | At most this many notification channels to return. | | policyId | path | string | true | The ID of the notification policy template. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of entity policies returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The entity notification policies.", "items": { "properties": { "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityName": { "description": "The name of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name", "relatedEntityId", "relatedEntityName", "relatedEntityType" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of entity policies that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of all policies that are created from this template and are visible for user | EntityNotificationPolicyTemplateRelatedPoliciesListResponse | ## Get the registered model access control list by relatedentitytype Operation path: `GET /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations who have access to this registered model and their roles on the registered model. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | policyId | path | string | true | The ID of the notification policy template. | | relatedEntityType | path | string | true | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The registered model access control list. | SharingListV2Response | | 404 | Not Found | Either the Registered Model does not exist or the user does not have permissions to view the Registered Model. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## Update the registered model controls by relatedentitytype Operation path: `PATCH /api/v2/entityNotificationPolicyTemplates/{relatedEntityType}/{policyId}/sharedRoles/` Authentication requirements: `BearerAuth` Set roles for users on this registered model. ### Body parameter ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | policyId | path | string | true | The ID of the notification policy template. | | relatedEntityType | path | string | true | The type of the related entity. | | body | body | SharedRolesUpdate | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The roles were updated successfully. | None | | 409 | Conflict | The request would leave the registered model without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid | None | ## List notification channel templates Operation path: `GET /api/v2/notificationChannelTemplates/` Authentication requirements: `BearerAuth` List the notification channel template according to the query. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of notification channels to skip. | | limit | query | integer | true | The maximum number of notification channels to return. | | namePart | query | string | false | Only return the notification channels whose names contain the given substring. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of channel templates returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The notification channel templates.", "items": { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user who created the template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "policyTemplatesCount": { "description": "The count of policy templates assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "role": { "description": "User role on entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "createdBy", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "policyTemplatesCount", "role", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of channel templates that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Notification channel templates listed successfully. | NotificationChannelTemplatesListResponse | ## Create a notification channel template Operation path: `POST /api/v2/notificationChannelTemplates/` Authentication requirements: `BearerAuth` Create a new notification channel template. ### Body parameter ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": "string", "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean", "x-versionadded": "v2.33" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "channelType", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | NotificationChannelTemplateCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user who created the template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "policyTemplatesCount": { "description": "The count of policy templates assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "role": { "description": "User role on entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "createdBy", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "policyTemplatesCount", "role", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Notification channel template created successfully. | NotificationChannelTemplateResponse | ## Delete a notification channel template by channel ID Operation path: `DELETE /api/v2/notificationChannelTemplates/{channelId}/` Authentication requirements: `BearerAuth` Delete the notification channel template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | channelId | path | string | true | The ID of the notification channel. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Notification channel template deleted successfully. | None | ## Retrieve a notification channel template by channel ID Operation path: `GET /api/v2/notificationChannelTemplates/{channelId}/` Authentication requirements: `BearerAuth` Retrieve the notification channel template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | channelId | path | string | true | The ID of the notification channel. | ### Example responses > 200 Response ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user who created the template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "policyTemplatesCount": { "description": "The count of policy templates assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "role": { "description": "User role on entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "createdBy", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "policyTemplatesCount", "role", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Notification channel template retrieved successfully. | NotificationChannelTemplateResponse | ## Update a notification channel template by channel ID Operation path: `PUT /api/v2/notificationChannelTemplates/{channelId}/` Authentication requirements: `BearerAuth` Update the notification channel template. ### Body parameter ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean", "x-versionadded": "v2.33" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | channelId | path | string | true | The ID of the notification channel. | | body | body | NotificationChannelWithDREntityUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Notification channel template updated successfully. | None | | 400 | Bad Request | Email verification code is invalid. | None | ## Retrieve list of all policy templates that are using this channel and are visible by channel ID Operation path: `GET /api/v2/notificationChannelTemplates/{channelId}/policyTemplates/` Authentication requirements: `BearerAuth` Retrieve list of all policy templates that are using this channel and are visible for user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of notification channels to skip. | | limit | query | integer | true | The maximum number of notification channels to return. | | channelId | path | string | true | The ID of the notification channel. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of entity policies returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The entity notification policy template.", "items": { "properties": { "id": { "description": "The ID of the policy template.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy template.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity template.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name", "relatedEntityType" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of entity policies that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of all policy templates that are using this channel and are visible for user | NotificationChannelTemplateRelatedPolicyTemplateListResponse | ## Retrieve list of all policies that are created from this template and are visible by channel ID Operation path: `GET /api/v2/notificationChannelTemplates/{channelId}/relatedPolicies/` Authentication requirements: `BearerAuth` Retrieve list of all policies that are created from this template and are visible for user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of notification channels to skip. | | limit | query | integer | true | The maximum number of notification channels to return. | | channelId | path | string | true | The ID of the notification channel. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of entity policies returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The entity notification policies.", "items": { "properties": { "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityName": { "description": "The name of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name", "relatedEntityId", "relatedEntityName", "relatedEntityType" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of entity policies that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of all policies that are created from this template and are visible for user | NotificationChannelTemplateRelatedPoliciesListResponse | ## Get the channel template access control list by channel ID Operation path: `GET /api/v2/notificationChannelTemplates/{channelId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations who have access to this channel template and their roles on the channel template. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | channelId | path | string | true | The ID of the notification channel. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The channel template access control list. | SharingListV2Response | | 404 | Not Found | Either the channel template does not exist or the user does not have permissions to view the channel template. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## Update the channel template controls by channel ID Operation path: `PATCH /api/v2/notificationChannelTemplates/{channelId}/sharedRoles/` Authentication requirements: `BearerAuth` Set roles for users on this channel template. ### Body parameter ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | channelId | path | string | true | The ID of the notification channel. | | body | body | SharedRolesUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The roles were updated successfully. | None | | 409 | Conflict | The request would leave the channel template without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid | None | ## List notification channels Operation path: `GET /api/v2/notificationChannels/` Authentication requirements: `BearerAuth` List the notification channels according to the query. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of notification channels to skip. | | limit | query | integer | true | The maximum number of notification channels to return. | | namePart | query | string | false | Only return the notification channels whose names contain the given substring. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of channels returned.", "type": "integer" }, "data": { "description": "The notification channels.", "items": { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of channels that satisfy the query.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The notification channels were listed successfully. | NotificationChannelsListResponse | ## Create a notification channel Operation path: `POST /api/v2/notificationChannels/` Authentication requirements: `BearerAuth` Create a new notification channel. ### Body parameter ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": "string" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string" } }, "required": [ "channelType", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | NotificationChannelCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The notification channel was created successfully. | NotificationChannelResponse | ## Delete a notification channel by channel ID Operation path: `DELETE /api/v2/notificationChannels/{channelId}/` Authentication requirements: `BearerAuth` Delete the notification channel. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | channelId | path | string | true | The ID of the notification channel. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The notification channel was deleted successfully. | None | ## Retrieve a notification channel by channel ID Operation path: `GET /api/v2/notificationChannels/{channelId}/` Authentication requirements: `BearerAuth` Retrieve the notification channel. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | channelId | path | string | true | The ID of the notification channel. | ### Example responses > 200 Response ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The notification channel was retrieved successfully. | NotificationChannelResponse | ## Update a notification channel by channel ID Operation path: `PUT /api/v2/notificationChannels/{channelId}/` Authentication requirements: `BearerAuth` Update the notification channel. ### Body parameter ``` { "properties": { "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | channelId | path | string | true | The ID of the notification channel. | | body | body | NotificationChannelUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The notification channel was updated successfully. | None | | 400 | Bad Request | Email verification code is invalid. | None | ## Send a 6-digit verification code Operation path: `POST /api/v2/notificationEmailChannelVerification/` Authentication requirements: `BearerAuth` Send a 6-digit verification code to the user's email. ### Body parameter ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "type": "string" }, "emailAddress": { "description": "The email address of the recipient.", "type": "string" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": "string" } }, "required": [ "channelType", "emailAddress", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | NotificationEmailChannelVerification | false | none | ### Example responses > 200 Response ``` { "properties": { "notificationId": { "description": "The response body is the JSON object with notification_id in it.", "type": "string" } }, "required": [ "notificationId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | NotificationEmailChannelVerificationResponse | ## Retrieve the status of whether the admin entered the code correctly Operation path: `POST /api/v2/notificationEmailChannelVerificationStatus/` Authentication requirements: `BearerAuth` Retrieve the status of whether the admin entered the code correctly. ### Body parameter ``` { "properties": { "emailAddress": { "description": "The email address of the recipient.", "type": "string" }, "verificationCode": { "description": "The user-entered verification code.", "type": "string" } }, "required": [ "emailAddress", "verificationCode" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | NotificationEmailChannelVerificationStatus | false | none | ### Example responses > 200 Response ``` { "properties": { "status": { "description": "The status shows whether the admin entered the correct 6-digit verification code.", "type": "boolean" } }, "required": [ "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | NotificationEmailChannelVerificationStatusResponse | ## The list of event types and groups the user can include Operation path: `GET /api/v2/notificationEvents/` Authentication requirements: `BearerAuth` The list of event types and groups the user can include in notification policies. Events and groups are filtered by user permissions and event properties. It is not a complete list of all defined events; instead, it is the list of the events available to the user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | relatedEntityType | query | string | false | The type of the related entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ### Example responses > 200 Response ``` { "properties": { "eventGroups": { "description": "The selectable event groups.", "items": { "properties": { "events": { "description": "The event types belonging to the group.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "The group ID.", "type": "string" }, "label": { "description": "The group name for display.", "type": "string" }, "requireMaxFrequency": { "description": "Indicates if a group requires max frequency setting.", "type": "boolean" } }, "required": [ "events", "id", "label", "requireMaxFrequency" ], "type": "object" }, "type": "array" }, "events": { "description": "The selectable individual events.", "items": { "properties": { "id": { "description": "The event type as an ID.", "type": "string" }, "label": { "description": "The event type for display.", "type": "string" }, "requireMaxFrequency": { "description": "Indicates if an event requires max frequency setting.", "type": "boolean" } }, "required": [ "id", "label", "requireMaxFrequency" ], "type": "object" }, "type": "array" } }, "required": [ "eventGroups", "events" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The event types and groups selectable for notification policies. | NotificationEventListResponse | ## List the notification logs Operation path: `GET /api/v2/notificationLogs/` Authentication requirements: `BearerAuth` List the notification logs that correspond to provided conditions. Default ordering is desc by notification log timestamp. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of records to skip over. Default 0. | | limit | query | integer | true | The number of records to return. Default 100, minimum 1, maximum 1000. | | policyId | query | string | false | The ID of the policy to filter notification logs. | | channelId | query | string | false | The ID of the channel to filter notification logs. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The array of notification logs.", "items": { "properties": { "channelId": { "description": "The ID of the channel that was used to send the notification.", "type": "string" }, "channelScope": { "description": "The scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ] }, "emailSubject": { "description": "The email subject.", "type": "string" }, "id": { "description": "The ID of the notification log.", "type": "string" }, "parentNotificationId": { "description": "The ID of the parent notification", "type": [ "string", "null" ] }, "policyId": { "description": "The ID of the policy that was used to send the notification.", "type": "string" }, "request": { "description": "The request that was sent in the notification.", "properties": { "body": { "description": "The body of the request.", "type": "string" }, "headers": { "description": "The headers of the request.", "type": "string" }, "url": { "description": "The URL of the request.", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "body", "headers", "url" ], "type": "object" }, "response": { "description": "The response that was received after sending the notification.", "properties": { "body": { "description": "The body of the response.", "type": "string" }, "duration": { "description": "The duration.", "type": "integer" }, "headers": { "description": "The headers of the response.", "type": "string" }, "statusCode": { "description": "The status code of the response.", "type": "string" } }, "required": [ "body", "duration", "headers", "statusCode" ], "type": "object" }, "retryCount": { "description": "The count of retries while sending the notification.", "type": "integer" }, "status": { "description": "The status of the notification.", "type": [ "string", "null" ] }, "timestamp": { "description": "The date and time when the notification was sent.", "format": "date-time", "type": "string" } }, "required": [ "channelId", "channelScope", "emailSubject", "id", "parentNotificationId", "policyId", "request", "response", "retryCount", "status", "timestamp" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of notifications.", "type": "integer" } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | NotificationLogListResponse | ## List notification policies Operation path: `GET /api/v2/notificationPolicies/` Authentication requirements: `BearerAuth` List the notification policies that satisfy the query condition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | How many notification channels to skip. | | limit | query | integer | true | At most this many notification channels to return. | | channelId | query | string | false | Return policies with this channel. | | namePart | query | string | false | Only return the notification channels whose names contain the given substring. | | eventGroup | query | string | false | Return policies with this event group. | ### Enumerated Values | Parameter | Value | | --- | --- | | eventGroup | [secure_config.all, dataset.all, file.all, comment.all, invite_job.all, deployment_prediction_explanations_computation.all, model_deployments.critical_health, model_deployments.critical_frequent_health_change, model_deployments.frequent_health_change, model_deployments.health, model_deployments.retraining_policy, inference_endpoints.health, model_deployments.management_agent, model_deployments.management_agent_health, prediction_request.all, challenger_management.all, challenger_replay.all, model_deployments.all, project.all, perma_delete_project.all, users_delete.all, applications.all, model_version.stage_transitions, model_version.all, use_case.all, batch_predictions.all, change_requests.all, custom_job_run.all, custom_job_run.unsuccessful, insights_computation.all, notebook_schedule.all, monitoring.all] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of policies returned.", "type": "integer" }, "data": { "description": "The notification policies.", "items": { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ] }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "The name of the notification policy.", "type": "string" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "active", "channel", "channelId", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "uid", "updatedAt" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of policies that satisfy the query.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Notification policies listed successfully. | NotificationPoliciesListResponse | ## Create a notification policy Operation path: `POST /api/v2/notificationPolicies/` Authentication requirements: `BearerAuth` Create a new notification policy. ### Body parameter ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string" }, "name": { "description": "The name of the new notification policy.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": "string" } }, "required": [ "channelId", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | NotificationPolicyCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ] }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "The name of the notification policy.", "type": "string" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "active", "channel", "channelId", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "uid", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Notification policy created successfully. | NotificationPolicyResponse | ## Delete a notification policy by policy ID Operation path: `DELETE /api/v2/notificationPolicies/{policyId}/` Authentication requirements: `BearerAuth` Delete the notification policy. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | policyId | path | string | true | The ID of the notification policy. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Notification policy deleted successfully. | None | ## Retrieve a notification policy by policy ID Operation path: `GET /api/v2/notificationPolicies/{policyId}/` Authentication requirements: `BearerAuth` Retrieve the notification policy. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | policyId | path | string | true | The ID of the notification policy. | ### Example responses > 200 Response ``` { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ] }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "The name of the notification policy.", "type": "string" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "active", "channel", "channelId", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "uid", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Notification policy retrieved successfully. | NotificationPolicyResponse | ## Update a notification policy by policy ID Operation path: `PUT /api/v2/notificationPolicies/{policyId}/` Authentication requirements: `BearerAuth` Update the notification policy. ### Body parameter ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string" }, "name": { "description": "The name of the notification policy.", "maxLength": 100, "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | policyId | path | string | true | The ID of the notification policy. | | body | body | NotificationPolicyUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Notification policy updated successfully. | None | ## The list of the ignored notifications filtered by org ID if provided Operation path: `GET /api/v2/notificationPolicyMutes/` Authentication requirements: `BearerAuth` The list of the ignored notifications filtered by org ID if provided. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | The number of records to skip over. Default 0. | | limit | query | integer | true | The number of records to return. Default 100, minimum 1, maximum 1000. | | entityId | query | string | false | The ID of the entity to filter. | | orgId | query | string | false | The ID of the organization that ignored notifications relate to. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of notification policy mutes.", "items": { "properties": { "entityId": { "description": "The ID of the entity to mute the notification for.", "type": "string" }, "id": { "description": "The ID of the notification policy mute.", "type": "string" }, "policyId": { "description": "The ID of the policy to mute notification for.", "type": "string" }, "uid": { "description": "The UID of the notification policy mute.", "type": "string" } }, "required": [ "entityId", "id", "policyId", "uid" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items returned by the query.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | NotificationPolicyMuteListResponse | ## Create a new ignored notification Operation path: `POST /api/v2/notificationPolicyMutes/` Authentication requirements: `BearerAuth` Create a new ignored notification. ### Body parameter ``` { "properties": { "entityId": { "description": "The ID of the entity to mute the notification for.", "type": "string" }, "policyId": { "description": "The ID of the policy to mute notification for.", "type": "string" } }, "required": [ "entityId", "policyId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | NotificationPolicyMuteCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "entityId": { "description": "The ID of the entity to mute the notification for.", "type": "string" }, "id": { "description": "The ID of the notification policy mute.", "type": "string" }, "policyId": { "description": "The ID of the policy to mute notification for.", "type": "string" }, "uid": { "description": "The UID of the notification policy mute.", "type": "string" } }, "required": [ "entityId", "id", "policyId", "uid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | NotificationPolicyMuteResponse | ## Delete the existing notification policy mute by mute ID Operation path: `DELETE /api/v2/notificationPolicyMutes/{muteId}/` Authentication requirements: `BearerAuth` Delete the existing notification policy mute. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | muteId | path | string | true | The ID of the notification policy mute to delete. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Test the webhook notification channel Operation path: `POST /api/v2/notificationWebhookChannelTests/` Authentication requirements: `BearerAuth` Test the webhook notification channel. ### Body parameter ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The identifier of the organization that notification channel belongs to.", "type": "string" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string" }, "validateSsl": { "description": "Whether SSL will be validated in the notification channel.", "type": "boolean" } }, "required": [ "channelType", "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | NotificationWebhookChannelTestCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "notificationId": { "description": "The ID of the notification.", "type": "string" } }, "required": [ "notificationId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The test for the webhook notification channel was created. | NotificationWebhookChannelTestId | ## Retrieve the status of the notification channel test by notification ID Operation path: `GET /api/v2/notificationWebhookChannelTests/{notificationId}/` Authentication requirements: `BearerAuth` Retrieve the status of the notification channel test. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | notificationId | path | string | true | The ID of the notification. | ### Example responses > 200 Response ``` { "properties": { "notificationLog": { "description": "The notification log record.", "properties": { "channelId": { "description": "The ID of the channel that was used to send the notification.", "type": "string" }, "channelScope": { "description": "The scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ] }, "emailSubject": { "description": "The email subject of the notification.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification log.", "type": "string" }, "parentNotificationId": { "description": "The ID of the parent notification.", "type": [ "string", "null" ] }, "policyId": { "description": "The ID of the policy that was used to send the notification.", "type": "string" }, "request": { "description": "The request that was sent in the notification.", "type": [ "string", "null" ] }, "response": { "description": "The response that was received after sending the notification.", "type": [ "string", "null" ] }, "retryCount": { "description": "The number of attempts to send the notification.", "type": [ "integer", "null" ] }, "status": { "description": "The status of the notification.", "type": "string" }, "timestamp": { "description": "The date when the notification was sent.", "format": "date-time", "type": "string" } }, "required": [ "channelId", "channelScope", "emailSubject", "id", "parentNotificationId", "policyId", "request", "response", "retryCount", "status", "timestamp" ], "type": "object" }, "status": { "description": "The status of the test notification.", "type": "string" } }, "required": [ "notificationLog", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The notification status was retrieved. | NotificationWebhookChannelStatusResponse | ## Resend the notification Operation path: `POST /api/v2/notifications/` Authentication requirements: `BearerAuth` Resend the notification. ### Body parameter ``` { "properties": { "notificationId": { "description": "The ID of the notification to resend.", "type": "string" } }, "required": [ "notificationId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | NotificationResend | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | None | ## Post a remote deployment event Operation path: `POST /api/v2/remoteEvents/` Authentication requirements: `BearerAuth` Post an event from a remote deployment. ### Body parameter ``` { "properties": { "data": { "description": "Event payload.", "properties": { "newModelId": { "description": "The identifier of the model after replacement.", "type": "string" }, "oldModelId": { "description": "The identifier of the model before replacement.", "type": "string" }, "reason": { "description": "The explanation on why the model has been replaced.", "type": "string" } }, "type": "object" }, "deploymentId": { "description": "The identifier of the deployment associated with the event.", "type": "string" }, "eventType": { "description": "The type of the event. Labels in all_lower_case are deprecated.", "enum": [ "deploymentInfo", "externalNaNPredictions", "management.deploymentInfo", "model_deployments.accuracy_green", "model_deployments.accuracy_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.data_drift_green", "model_deployments.data_drift_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.model_replacement", "model_deployments.service_health_green", "model_deployments.service_health_red", "model_deployments.service_health_yellow_from_green", "moderationMetricCreationError", "moderationMetricReportingError", "moderationModelConfigError", "moderationModelModerationCompleted", "moderationModelModerationStarted", "moderationModelPostScorePhaseCompleted", "moderationModelPostScorePhaseStarted", "moderationModelPreScorePhaseCompleted", "moderationModelPreScorePhaseStarted", "moderationModelRuntimeError", "moderationModelScoringCompleted", "moderationModelScoringError", "moderationModelScoringStarted", "monitoring.external_model_nan_predictions", "monitoring.spooler_channel_green", "monitoring.spooler_channel_red", "predictionRequestFailed", "prediction_request.failed", "serviceHealthChangeGreen", "serviceHealthChangeRed", "serviceHealthChangeYellowFromGreen", "spoolerChannelGreen", "spoolerChannelRed" ], "type": "string" }, "externalNanPredictionsData": { "description": "The external NaN predictions event payload.", "properties": { "count": { "description": "The number of NaN predictions by the external model.", "exclusiveMinimum": 0, "type": "integer" }, "modelId": { "default": null, "description": "The identifier of the model.", "type": [ "string", "null" ] } }, "required": [ "count", "modelId" ], "type": "object" }, "message": { "description": "Descriptive message for health events.", "maxLength": 16384, "type": "string" }, "moderationData": { "description": "The moderation event information.", "properties": { "guardName": { "description": "Name or label of the guard.", "maxLength": 255, "type": "string" }, "metricName": { "description": "Name or label of the metric.", "maxLength": 255, "type": "string" } }, "required": [ "guardName", "metricName" ], "type": "object", "x-versionadded": "v2.35" }, "orgId": { "description": "The identifier of the organization associated with the event.", "type": [ "string", "null" ] }, "predictionEnvironmentId": { "description": "The identifier of the prediction environment associated with the event.", "type": "string" }, "predictionRequestData": { "description": "Prediction event payload.", "properties": { "error_code": { "description": "The error code if any.", "type": [ "string", "null" ] }, "model_id": { "description": "The identifier of the model.", "type": [ "string", "null" ] }, "response_body": { "description": "The response body message of the prediction event.", "type": "string" }, "status_code": { "description": "The response status code of the prediction event.", "type": "integer" }, "user_id": { "description": "The identifier of the user that accesses the deployment.", "type": [ "string", "null" ] } }, "required": [ "response_body", "status_code" ], "type": "object" }, "spoolerChannelData": { "description": "Spooler channel event payload.", "properties": { "name": { "description": "Name or label of the spooler channel.", "maxLength": 512, "type": "string" }, "type": { "description": "Type of the spooler channel.", "enum": [ "asyncMemory", "filesystem", "kafka", "memory", "pubSub", "rabbitMQ", "sqs" ], "type": "string" } }, "required": [ "name", "type" ], "type": "object" }, "timestamp": { "description": "The time when the event occurred.", "format": "date-time", "type": "string" }, "title": { "description": "The title of the event.", "maxLength": 512, "type": "string" } }, "required": [ "eventType", "timestamp" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | RemoteEventCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "message": { "description": "The descriptive message about the event creation.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The event was created. | CreateRemoteEventResponse | | 404 | Not Found | The deployment was not found. | None | | 422 | Unprocessable Entity | Unable to process the request instructions or failed to post the event. | None | ## Delete all user notifications Operation path: `DELETE /api/v2/userNotifications/` Authentication requirements: `BearerAuth` Delete all notifications associated with the user. ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Notifications were deleted. | None | ## The list of user notifications Operation path: `GET /api/v2/userNotifications/` Authentication requirements: `BearerAuth` The list of the user's notifications from latest to oldest. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. | | limit | query | integer | false | The number of records to return. | | isRead | query | boolean | false | When provided, returns only read or unread notifications. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the user's notifications.", "items": { "properties": { "callerUser": { "description": "Details about the user who triggered the notification.", "properties": { "fullName": { "description": "User's full name.", "type": [ "string", "null" ] }, "gravatarHash": { "description": "User's gravatar hash.", "type": "string" }, "inactive": { "description": "True if the user was deleted.", "type": "boolean" }, "uid": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "uid" ], "type": "object" }, "created": { "description": "The ISO 8601 formatted date and time when the notification was created.", "format": "date-time", "type": "string" }, "data": { "description": "Notification type-specific metadata.", "oneOf": [ { "properties": { "failedInvites": { "description": "Failed invites.", "items": { "description": "Details about why the invite failed.", "properties": { "email": { "description": "The email address of the invited user.", "type": "string" }, "errorMessage": { "description": "The error message explaining why the invite failed.", "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" } }, "required": [ "email", "errorMessage", "userId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 20, "type": "array" }, "jobId": { "description": "The ID of the invite job.", "type": [ "string", "null" ] }, "statusId": { "description": "The ID of the invite job status.", "type": "string", "x-versionadded": "v2.40" }, "successfulInvites": { "description": "Successful invites.", "items": { "description": "Details about the successful invite.", "properties": { "email": { "description": "The email address of the invited user.", "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" } }, "required": [ "email", "userId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 20, "type": "array" } }, "required": [ "failedInvites", "jobId", "statusId", "successfulInvites" ], "type": "object", "x-versionadded": "v2.39" }, { "type": "null" } ], "x-versionadded": "v2.39" }, "description": { "description": "The notification description.", "type": [ "string", "null" ] }, "eventType": { "description": "The type of the notification.", "enum": [ "autopilot.complete", "project.shared", "comment.created", "comment.updated", "model_deployments.service_health_red", "model_deployments.data_drift_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.red", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "misc.asset_access_request", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "perma_delete_project.failure", "perma_delete_project.success", "secure_config.shared", "entity_notification_policy_template.shared", "notification_channel_template.shared", "invite_job.completed" ], "type": "string" }, "isRead": { "description": "True if the notification is already read.", "type": "boolean" }, "link": { "description": "The call-to-action link for the notification.", "type": "string" }, "pushNotificationSent": { "description": "True if the notification was also sent via push notifications.", "type": "boolean" }, "relatedComment": { "description": "Details about the comment related to the notification.", "properties": { "commentId": { "description": "The ID of the comment.", "type": "string" }, "commentLink": { "description": "The link to the comment.", "type": "string" }, "entityId": { "description": "The ID of the commented entity.", "type": "string" }, "entityType": { "description": "The type of the commented entity.", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "inactive": { "description": "True if the comment was deleted.", "type": "boolean" } }, "required": [ "commentId", "entityId" ], "type": "object" }, "relatedDeployment": { "description": "Details about the deployment related to the notification.", "properties": { "deploymentId": { "description": "The ID of the deployment.", "type": "string" }, "deploymentName": { "description": "The deployment label.", "type": [ "string", "null" ] }, "deploymentUrl": { "description": "The link to the deployment.", "type": "string" }, "inactive": { "description": "True if the deployment was deleted.", "type": "boolean" }, "modelId": { "description": "The ID of the related model.", "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the related project.", "type": [ "string", "null" ] }, "userId": { "description": "The ID of the related user.", "type": [ "string", "null" ] } }, "required": [ "deploymentId" ], "type": "object" }, "relatedProject": { "description": "Details about the project related to the notification.", "properties": { "inactive": { "description": "True if the project was deleted.", "type": "boolean" }, "pid": { "description": "The ID of the project.", "type": "string" }, "projectLink": { "description": "The link to the project.", "type": "string" }, "projectName": { "description": "The project name.", "type": [ "string", "null" ] } }, "required": [ "pid" ], "type": "object" }, "relatedSecureConfig": { "description": "Details about the secure config related to the notification.", "properties": { "secureConfigLink": { "description": "The link to the secure config.", "type": "string" }, "secureConfigName": { "description": "The name of the secure config.", "type": "string" }, "secureConfigSchemaName": { "description": "The type UUID of the secure config.", "type": "string" }, "secureConfigSchemaUuid": { "description": "The type name of the secure config.", "type": "string" }, "secureConfigUuid": { "description": "The ID of the secure config.", "type": "string" } }, "required": [ "secureConfigLink", "secureConfigName", "secureConfigSchemaName", "secureConfigSchemaUuid", "secureConfigUuid" ], "type": "object" }, "relatedUsersDelete": { "description": "Details about the users permanent delete.", "properties": { "reportId": { "description": "The ID of the user's permanent delete report.", "type": "string" }, "statusId": { "description": "The ID of the user's delete status.", "type": "string" }, "usersToDeleteCount": { "description": "The number of users that will be deleted.", "type": "string" } }, "required": [ "reportId", "statusId", "usersToDeleteCount" ], "type": "object" }, "sharedUsers": { "description": "The list of the user details a resource was shared with.", "items": { "description": "Details about the user who triggered the notification.", "properties": { "fullName": { "description": "User's full name.", "type": [ "string", "null" ] }, "gravatarHash": { "description": "User's gravatar hash.", "type": "string" }, "inactive": { "description": "True if the user was deleted.", "type": "boolean" }, "uid": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "uid" ], "type": "object" }, "type": "array" }, "statusId": { "description": "The asynchronous job status ID.", "type": "string" }, "title": { "description": "The notification title.", "type": [ "string", "null" ] }, "tooltip": { "description": "The notification tooltip.", "type": [ "string", "null" ] }, "updated": { "description": "The ISO 8601 formatted date and time when the notification was updated.", "format": "date-time", "type": "string" }, "userNotificationId": { "description": "The ID of the notification.", "type": "string" } }, "required": [ "callerUser", "created", "description", "eventType", "isRead", "link", "pushNotificationSent", "title", "tooltip", "updated", "userNotificationId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A paginated list of notifications. | UserNotificationListResponse | ## Mark all as read Operation path: `PATCH /api/v2/userNotifications/` Authentication requirements: `BearerAuth` Mark all associated notifications with the user as read. ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | All notifications were marked as read. | None | ## Delete user notification by user notification ID Operation path: `DELETE /api/v2/userNotifications/{userNotificationId}/` Authentication requirements: `BearerAuth` Delete one notification associated with the user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userNotificationId | path | string | true | Unique identifier of the notification. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Notification was deleted. | None | ## Mark as read by user notification ID Operation path: `PATCH /api/v2/userNotifications/{userNotificationId}/` Authentication requirements: `BearerAuth` Mark one associated notification with the user as read. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userNotificationId | path | string | true | Unique identifier of the notification. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Notification was marked as read. | None | # Schemas ## AccessControlV2 ``` { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the recipient. | | name | string | true | | The name of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | The type of the recipient. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## ChannelCreatedByResponse ``` { "description": "The user who created the template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" } ``` The user who created the template. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | firstName | string,null | true | | First Name. | | lastName | string,null | true | | Last Name. | | username | string | true | | Username. | ## ChannelDREntity ``` { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The id of DataRobot entity. | | name | string | true | | The name of the entity. | ## CreateRemoteEventResponse ``` { "properties": { "message": { "description": "The descriptive message about the event creation.", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | The descriptive message about the event creation. | ## CreatedByResponse ``` { "description": "User that created template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" } ``` User that created template. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | firstName | string,null | true | | First Name. | | lastName | string,null | true | | Last Name. | | username | string | true | | Username. | ## CustomerHeader ``` { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the header. | | value | string | true | | The value of the header. | ## DREntity ``` { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the DataRobot entity. | | name | string | true | | The name of the entity. | ## EntityNotificationChannel ``` { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` The notification channel information used to send the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | true | | The type of the new notification channel. | | contentType | string,null | true | | The content type of the messages of the new notification channel. | | createdAt | string(date-time) | true | | The date of the notification channel creation. | | customHeaders | [CustomerHeader] | true | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | drEntities | [ChannelDREntity] | false | maxItems: 100minItems: 1 | The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types. | | emailAddress | string,null | true | | The email address to be used in the new notification channel. | | id | string | true | | The ID of the notification channel. | | languageCode | string,null | true | | The preferred language code. | | name | string | true | | The name of the new notification channel. | | orgId | string,null | true | | The ID of the organization that the notification channel belongs to. | | payloadUrl | string,null(uri) | true | | The payload URL of the new notification channel. | | relatedEntityId | string,null | false | | The id of related entity. | | relatedEntityType | string,null | false | | Type of related entity. | | secretToken | string,null | true | | Secret token to be used for new notification channel. | | uid | string,null | true | | The identifier of the user who created the channel. | | updatedAt | string,null(date-time) | true | | The date when the channel was updated. | | validateSsl | boolean,null | true | | Whether to validate SSL for the notification channel. | ### Enumerated Values | Property | Value | | --- | --- | | channelType | [DataRobotCustomJob, DataRobotGroup, DataRobotUser, Database, Email, InApp, InsightsComputations, MSTeams, Slack, Webhook] | | contentType | [application/json, application/x-www-form-urlencoded] | | languageCode | [en, es_419, fr, ja, ko, ptBR] | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## EntityNotificationChannelCreate ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": "string", "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The ID of the related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean", "x-versionadded": "v2.33" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "channelType", "name", "relatedEntityId", "relatedEntityType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | true | | The type of the new notification channel. | | contentType | string | false | | The content type of the messages of the new notification channel. | | customHeaders | [CustomerHeader] | false | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | drEntities | [DREntity] | false | maxItems: 100minItems: 1 | The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types. | | emailAddress | string | false | | The email address to be used in the new notification channel. | | languageCode | string | false | | The preferred language code. | | name | string | true | maxLength: 100 | The name of the new notification channel. | | orgId | string | false | | The id of organization that notification channel belongs to. | | payloadUrl | string(uri) | false | | The payload URL of the new notification channel. | | relatedEntityId | string | true | | The ID of the related entity. | | relatedEntityType | string | true | | The type of the related entity. | | secretToken | string | false | | Secret token to be used for new notification channel. | | validateSsl | boolean | false | | Defines if validate ssl or not in the notification channel. | | verificationCode | string | false | | Required if the channel type is Email. | ### Enumerated Values | Property | Value | | --- | --- | | channelType | [DataRobotCustomJob, DataRobotGroup, DataRobotUser, Database, Email, InApp, InsightsComputations, MSTeams, Slack, Webhook] | | contentType | [application/json, application/x-www-form-urlencoded] | | languageCode | [en, es_419, fr, ja, ko, ptBR] | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## EntityNotificationChannelResponse ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The ID of the related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "relatedEntityId", "relatedEntityType", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | true | | The type of the new notification channel. | | contentType | string,null | true | | The content type of the messages of the new notification channel. | | createdAt | string(date-time) | true | | The date of the notification channel creation. | | customHeaders | [CustomerHeader] | true | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | drEntities | [DREntity] | false | maxItems: 100minItems: 1 | The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types. | | emailAddress | string,null | true | | The email address to be used in the new notification channel. | | id | string | true | | The ID of the notification channel. | | languageCode | string,null | true | | The preferred language code. | | lastNotificationAt | string,null(date-time) | true | | The timestamp of the last notification sent to the channel. | | name | string | true | | The name of the new notification channel. | | orgId | string,null | true | | The id of organization that notification channel belongs to. | | payloadUrl | string,null(uri) | true | | The payload URL of the new notification channel. | | policyCount | integer | true | | The count of policies assigned to the channel. | | relatedEntityId | string | true | | The ID of the related entity. | | relatedEntityType | string | true | | The type of the related entity. | | secretToken | string,null | true | | Secret token to be used for new notification channel. | | uid | string,null | true | | The identifier of the user who created the channel. | | updatedAt | string,null(date-time) | true | | The date when the channel was updated. | | validateSsl | boolean,null | true | | Defines if validate ssl or not in the notification channel. | ### Enumerated Values | Property | Value | | --- | --- | | channelType | [DataRobotCustomJob, DataRobotGroup, DataRobotUser, Database, Email, InApp, InsightsComputations, MSTeams, Slack, Webhook] | | contentType | [application/json, application/x-www-form-urlencoded] | | languageCode | [en, es_419, fr, ja, ko, ptBR] | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## EntityNotificationChannelsListResponse ``` { "properties": { "count": { "description": "The number of channels returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The notification entity channels.", "items": { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The ID of the related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "relatedEntityId", "relatedEntityType", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of channels that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of channels returned. | | data | [EntityNotificationChannelResponse] | true | maxItems: 1000 | The notification entity channels. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of channels that satisfy the query. | ## EntityNotificationPoliciesListResponse ``` { "properties": { "count": { "description": "The number of entity policies returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The entity notification policies.", "items": { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "templateId": { "description": "The id of template.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "channelScope", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityId", "relatedEntityType", "templateId", "uid", "updatedAt" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of entity policies that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of entity policies returned. | | data | [EntityNotificationPolicyResponse] | true | maxItems: 1000 | The entity notification policies. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of entity policies that satisfy the query. | ## EntityNotificationPoliciesTemplatesListResponse ``` { "properties": { "count": { "description": "The number of entity policy templates returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The entity notification policy templates.", "items": { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": "string", "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "User that created template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "relatedPoliciesCount": { "description": "The total number of entity policies that are using this template.", "type": "integer", "x-versionadded": "v2.33" }, "role": { "description": "User role on entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "created", "createdBy", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityType", "relatedPoliciesCount", "role", "uid", "updatedAt" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of entity policies templates that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of entity policy templates returned. | | data | [EntityNotificationPolicyTemplateResponse] | true | maxItems: 1000 | The entity notification policy templates. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of entity policies templates that satisfy the query. | ## EntityNotificationPolicyCreate ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string", "x-versionadded": "v2.33" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string", "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification policy.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "channelId", "channelScope", "name", "relatedEntityId", "relatedEntityType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | false | | Whether the notification policy is active or not. | | channelId | string | true | | The ID of the notification channel to be used to send the notification. | | channelScope | string,null | true | | Scope of the channel. | | eventGroup | string | false | | The group of the event that triggers the notification. | | eventType | string | false | | The type of the event that triggers the notification. | | maximalFrequency | string,null | false | | Maximal frequency between policy runs in ISO 8601 duration string. | | name | string | true | maxLength: 100 | The name of the new notification policy. | | orgId | string | false | | The ID of the organization that owns the notification policy. | | relatedEntityId | string | true | | The id of related entity. | | relatedEntityType | string | true | | The type of the related entity. | ### Enumerated Values | Property | Value | | --- | --- | | channelScope | [organization, Organization, ORGANIZATION, entity, Entity, ENTITY, template, Template, TEMPLATE] | | eventGroup | [secure_config.all, dataset.all, file.all, comment.all, invite_job.all, deployment_prediction_explanations_computation.all, model_deployments.critical_health, model_deployments.critical_frequent_health_change, model_deployments.frequent_health_change, model_deployments.health, model_deployments.retraining_policy, inference_endpoints.health, model_deployments.management_agent, model_deployments.management_agent_health, prediction_request.all, challenger_management.all, challenger_replay.all, model_deployments.all, project.all, perma_delete_project.all, users_delete.all, applications.all, model_version.stage_transitions, model_version.all, use_case.all, batch_predictions.all, change_requests.all, custom_job_run.all, custom_job_run.unsuccessful, insights_computation.all, notebook_schedule.all, monitoring.all] | | eventType | [secure_config.created, secure_config.deleted, secure_config.shared, dataset.created, dataset.registered, dataset.deleted, datasets.deleted, datasetrelationship.created, dataset.shared, datasets.shared, file.created, file.registered, file.deleted, file.shared, comment.created, comment.updated, invite_job.completed, misc.asset_access_request, misc.webhook_connection_test, misc.webhook_resend, misc.email_verification, monitoring.spooler_channel_base, monitoring.spooler_channel_red, monitoring.spooler_channel_green, monitoring.external_model_nan_predictions, management.deploymentInfo, model_deployments.None, model_deployments.deployment_sharing, model_deployments.model_replacement, prediction_request.None, prediction_request.failed, model_deployments.humility_rule, model_deployments.model_replacement_lifecycle, model_deployments.model_replacement_started, model_deployments.model_replacement_succeeded, model_deployments.model_replacement_failed, model_deployments.model_replacement_validation_warning, model_deployments.deployment_creation, model_deployments.deployment_deletion, model_deployments.service_health_yellow_from_green, model_deployments.service_health_yellow_from_red, model_deployments.service_health_red, model_deployments.data_drift_yellow_from_green, model_deployments.data_drift_yellow_from_red, model_deployments.data_drift_red, model_deployments.accuracy_yellow_from_green, model_deployments.accuracy_yellow_from_red, model_deployments.accuracy_red, model_deployments.health.fairness_health.green_to_yellow, model_deployments.health.fairness_health.red_to_yellow, model_deployments.health.fairness_health.red, model_deployments.health.custom_metrics_health.green_to_yellow, model_deployments.health.custom_metrics_health.red_to_yellow, model_deployments.health.custom_metrics_health.red, model_deployments.health.base.green, model_deployments.service_health_green, model_deployments.data_drift_green, model_deployments.accuracy_green, model_deployments.health.fairness_health.green, model_deployments.health.custom_metrics_health.green, model_deployments.retraining_policy_run_started, model_deployments.retraining_policy_run_succeeded, model_deployments.retraining_policy_run_failed, model_deployments.challenger_scoring_success, model_deployments.challenger_scoring_data_warning, model_deployments.challenger_scoring_failure, model_deployments.challenger_scoring_started, model_deployments.challenger_model_validation_warning, model_deployments.challenger_model_created, model_deployments.challenger_model_deleted, model_deployments.actuals_upload_failed, model_deployments.actuals_upload_warning, model_deployments.training_data_baseline_calculation_started, model_deployments.training_data_baseline_calculation_completed, model_deployments.training_data_baseline_failed, model_deployments.custom_model_deployment_creation_started, model_deployments.custom_model_deployment_creation_completed, model_deployments.custom_model_deployment_creation_failed, model_deployments.custom_model_deployment_secure_config_failure, model_deployments.deployment_prediction_explanations_preview_job_submitted, model_deployments.deployment_prediction_explanations_preview_job_completed, model_deployments.deployment_prediction_explanations_preview_job_failed, model_deployments.custom_model_deployment_activated, model_deployments.custom_model_deployment_activation_failed, model_deployments.custom_model_deployment_deactivated, model_deployments.custom_model_deployment_deactivation_failed, model_deployments.prediction_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_warning, model_deployments.actuals_processing_rate_limit_reached, model_deployments.actuals_processing_rate_limit_warning, model_deployments.deployment_monitoring_data_cleared, model_deployments.deployment_launch_started, model_deployments.deployment_launch_succeeded, model_deployments.deployment_launch_failed, model_deployments.deployment_shutdown_started, model_deployments.deployment_shutdown_succeeded, model_deployments.deployment_shutdown_failed, model_deployments.endpoint_update_started, model_deployments.endpoint_update_succeeded, model_deployments.endpoint_update_failed, model_deployments.management_agent_service_health_green, model_deployments.management_agent_service_health_yellow, model_deployments.management_agent_service_health_red, model_deployments.management_agent_service_health_unknown, model_deployments.predictions_missing_association_id, model_deployments.prediction_result_rows_cleand_up, model_deployments.batch_deleted, model_deployments.batch_creation_limit_reached, model_deployments.batch_creation_limit_exceeded, model_deployments.batch_not_found, model_deployments.predictions_encountered_for_locked_batch, model_deployments.predictions_encountered_for_deleted_batch, model_deployments.scheduled_report_generated, model_deployments.predictions_timeliness_health_red, model_deployments.actuals_timeliness_health_red, model_deployments.service_health_still_red, model_deployments.data_drift_still_red, model_deployments.accuracy_still_red, model_deployments.health.fairness_health.still_red, model_deployments.health.custom_metrics_health.still_red, model_deployments.predictions_timeliness_health_still_red, model_deployments.actuals_timeliness_health_still_red, model_deployments.service_health_still_yellow, model_deployments.data_drift_still_yellow, model_deployments.accuracy_still_yellow, model_deployments.health.fairness_health.still_yellow, model_deployments.health.custom_metrics_health.still_yellow, model_deployments.prediction_payload_parsing_failure, model_deployments.deployment_inference_server_creation_started, model_deployments.deployment_inference_server_creation_failed, model_deployments.deployment_inference_server_creation_completed, model_deployments.deployment_inference_server_deletion, model_deployments.deployment_inference_server_idle_stopped, model_deployments.deployment_inference_server_maintenance_started, entity_notification_policy_template.shared, notification_channel_template.shared, project.created, project.deleted, project.shared, autopilot.complete, autopilot.started, autostart.failure, perma_delete_project.success, perma_delete_project.failure, users_delete.preview_started, users_delete.preview_completed, users_delete.preview_failed, users_delete.started, users_delete.completed, users_delete.failed, application.created, application.shared, model_version.added, model_version.stage_transition_from_registered_to_development, model_version.stage_transition_from_registered_to_staging, model_version.stage_transition_from_registered_to_production, model_version.stage_transition_from_registered_to_archived, model_version.stage_transition_from_development_to_registered, model_version.stage_transition_from_development_to_staging, model_version.stage_transition_from_development_to_production, model_version.stage_transition_from_development_to_archived, model_version.stage_transition_from_staging_to_registered, model_version.stage_transition_from_staging_to_development, model_version.stage_transition_from_staging_to_production, model_version.stage_transition_from_staging_to_archived, model_version.stage_transition_from_production_to_registered, model_version.stage_transition_from_production_to_development, model_version.stage_transition_from_production_to_staging, model_version.stage_transition_from_production_to_archived, model_version.stage_transition_from_archived_to_registered, model_version.stage_transition_from_archived_to_development, model_version.stage_transition_from_archived_to_production, model_version.stage_transition_from_archived_to_staging, use_case.shared, batch_predictions.success, batch_predictions.failed, batch_predictions.scheduler.auto_disabled, change_request.cancelled, change_request.created, change_request.deployment_approval_requested, change_request.resolved, change_request.proposed_changes_updated, change_request.pending, change_request.commenting_review_added, change_request.approving_review_added, change_request.changes_requesting_review_added, custom_job_run.success, custom_job_run.failed, custom_job_run.interrupted, custom_job_run.cancelled, prediction_explanations_computation.None, prediction_explanations_computation.prediction_explanations_preview_job_submitted, prediction_explanations_computation.prediction_explanations_preview_job_completed, prediction_explanations_computation.prediction_explanations_preview_job_failed, monitoring.rate_limit_enforced, notebook_schedule.created, notebook_schedule.failure, notebook_schedule.completed, abstract, moderation.metric.creation_error, moderation.metric.reporting_error, moderation.model.moderation_started, moderation.model.moderation_completed, moderation.model.pre_score_phase_started, moderation.model.pre_score_phase_completed, moderation.model.post_score_phase_started, moderation.model.post_score_phase_completed, moderation.model.config_error, moderation.model.runtime_error, moderation.model.scoring_started, moderation.model.scoring_completed, moderation.model.scoring_error] | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## EntityNotificationPolicyCreateFromTemplate ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification policy.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "templateId": { "description": "The id of template.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "name", "relatedEntityId", "relatedEntityType", "templateId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | false | | Whether the notification policy is active or not. | | name | string | true | maxLength: 100 | The name of the new notification policy. | | relatedEntityId | string | true | | The id of related entity. | | relatedEntityType | string | true | | The type of the related entity. | | templateId | string | true | | The id of template. | ### Enumerated Values | Property | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## EntityNotificationPolicyResponse ``` { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "templateId": { "description": "The id of template.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "channelScope", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityId", "relatedEntityType", "templateId", "uid", "updatedAt" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | true | | Defines if the notification policy is active or not. | | channel | EntityNotificationChannel | true | | The notification channel information used to send the notification. | | channelId | string | true | | The ID of the notification channel to be used to send the notification. | | channelScope | string,null | true | | Scope of the channel. | | created | string,null(date-time) | true | | The date when the policy was created. | | eventGroup | EventGroup | true | | The group of the event that triggers the notification. | | eventType | EventType | true | | The type of the event that triggers the notification. | | id | string | true | | The ID of the policy. | | lastTriggered | string,null(date-time) | true | | The date when the last notification with the policy was triggered. | | maximalFrequency | string,null | false | | Maximal frequency between policy runs in ISO 8601 duration string. | | name | string | true | | The name of the notification policy. | | orgId | string,null | true | | The ID of the organization that owns the notification policy. | | relatedEntityId | string | true | | The id of related entity. | | relatedEntityType | string | true | | The type of the related entity. | | templateId | string,null | true | | The id of template. | | uid | string | true | | The identifier of the user who created the policy. | | updatedAt | string,null(date-time) | true | | The date when the policy was updated. | ### Enumerated Values | Property | Value | | --- | --- | | channelScope | [organization, Organization, ORGANIZATION, entity, Entity, ENTITY, template, Template, TEMPLATE] | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## EntityNotificationPolicyTemplateCreate ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string", "x-versionadded": "v2.33" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string", "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification policy.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "channelId", "name", "relatedEntityType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | false | | Whether the notification policy is active or not. | | channelId | string | true | | The ID of the notification channel to be used to send the notification. | | eventGroup | string | false | | The group of the event that triggers the notification. | | eventType | string | false | | The type of the event that triggers the notification. | | maximalFrequency | string,null | false | | Maximal frequency between policy runs in ISO 8601 duration string. | | name | string | true | maxLength: 100 | The name of the new notification policy. | | orgId | string | false | | The ID of the organization that owns the notification policy. | | relatedEntityType | string | true | | The type of the related entity. | ### Enumerated Values | Property | Value | | --- | --- | | eventGroup | [secure_config.all, dataset.all, file.all, comment.all, invite_job.all, deployment_prediction_explanations_computation.all, model_deployments.critical_health, model_deployments.critical_frequent_health_change, model_deployments.frequent_health_change, model_deployments.health, model_deployments.retraining_policy, inference_endpoints.health, model_deployments.management_agent, model_deployments.management_agent_health, prediction_request.all, challenger_management.all, challenger_replay.all, model_deployments.all, project.all, perma_delete_project.all, users_delete.all, applications.all, model_version.stage_transitions, model_version.all, use_case.all, batch_predictions.all, change_requests.all, custom_job_run.all, custom_job_run.unsuccessful, insights_computation.all, notebook_schedule.all, monitoring.all] | | eventType | [secure_config.created, secure_config.deleted, secure_config.shared, dataset.created, dataset.registered, dataset.deleted, datasets.deleted, datasetrelationship.created, dataset.shared, datasets.shared, file.created, file.registered, file.deleted, file.shared, comment.created, comment.updated, invite_job.completed, misc.asset_access_request, misc.webhook_connection_test, misc.webhook_resend, misc.email_verification, monitoring.spooler_channel_base, monitoring.spooler_channel_red, monitoring.spooler_channel_green, monitoring.external_model_nan_predictions, management.deploymentInfo, model_deployments.None, model_deployments.deployment_sharing, model_deployments.model_replacement, prediction_request.None, prediction_request.failed, model_deployments.humility_rule, model_deployments.model_replacement_lifecycle, model_deployments.model_replacement_started, model_deployments.model_replacement_succeeded, model_deployments.model_replacement_failed, model_deployments.model_replacement_validation_warning, model_deployments.deployment_creation, model_deployments.deployment_deletion, model_deployments.service_health_yellow_from_green, model_deployments.service_health_yellow_from_red, model_deployments.service_health_red, model_deployments.data_drift_yellow_from_green, model_deployments.data_drift_yellow_from_red, model_deployments.data_drift_red, model_deployments.accuracy_yellow_from_green, model_deployments.accuracy_yellow_from_red, model_deployments.accuracy_red, model_deployments.health.fairness_health.green_to_yellow, model_deployments.health.fairness_health.red_to_yellow, model_deployments.health.fairness_health.red, model_deployments.health.custom_metrics_health.green_to_yellow, model_deployments.health.custom_metrics_health.red_to_yellow, model_deployments.health.custom_metrics_health.red, model_deployments.health.base.green, model_deployments.service_health_green, model_deployments.data_drift_green, model_deployments.accuracy_green, model_deployments.health.fairness_health.green, model_deployments.health.custom_metrics_health.green, model_deployments.retraining_policy_run_started, model_deployments.retraining_policy_run_succeeded, model_deployments.retraining_policy_run_failed, model_deployments.challenger_scoring_success, model_deployments.challenger_scoring_data_warning, model_deployments.challenger_scoring_failure, model_deployments.challenger_scoring_started, model_deployments.challenger_model_validation_warning, model_deployments.challenger_model_created, model_deployments.challenger_model_deleted, model_deployments.actuals_upload_failed, model_deployments.actuals_upload_warning, model_deployments.training_data_baseline_calculation_started, model_deployments.training_data_baseline_calculation_completed, model_deployments.training_data_baseline_failed, model_deployments.custom_model_deployment_creation_started, model_deployments.custom_model_deployment_creation_completed, model_deployments.custom_model_deployment_creation_failed, model_deployments.custom_model_deployment_secure_config_failure, model_deployments.deployment_prediction_explanations_preview_job_submitted, model_deployments.deployment_prediction_explanations_preview_job_completed, model_deployments.deployment_prediction_explanations_preview_job_failed, model_deployments.custom_model_deployment_activated, model_deployments.custom_model_deployment_activation_failed, model_deployments.custom_model_deployment_deactivated, model_deployments.custom_model_deployment_deactivation_failed, model_deployments.prediction_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_warning, model_deployments.actuals_processing_rate_limit_reached, model_deployments.actuals_processing_rate_limit_warning, model_deployments.deployment_monitoring_data_cleared, model_deployments.deployment_launch_started, model_deployments.deployment_launch_succeeded, model_deployments.deployment_launch_failed, model_deployments.deployment_shutdown_started, model_deployments.deployment_shutdown_succeeded, model_deployments.deployment_shutdown_failed, model_deployments.endpoint_update_started, model_deployments.endpoint_update_succeeded, model_deployments.endpoint_update_failed, model_deployments.management_agent_service_health_green, model_deployments.management_agent_service_health_yellow, model_deployments.management_agent_service_health_red, model_deployments.management_agent_service_health_unknown, model_deployments.predictions_missing_association_id, model_deployments.prediction_result_rows_cleand_up, model_deployments.batch_deleted, model_deployments.batch_creation_limit_reached, model_deployments.batch_creation_limit_exceeded, model_deployments.batch_not_found, model_deployments.predictions_encountered_for_locked_batch, model_deployments.predictions_encountered_for_deleted_batch, model_deployments.scheduled_report_generated, model_deployments.predictions_timeliness_health_red, model_deployments.actuals_timeliness_health_red, model_deployments.service_health_still_red, model_deployments.data_drift_still_red, model_deployments.accuracy_still_red, model_deployments.health.fairness_health.still_red, model_deployments.health.custom_metrics_health.still_red, model_deployments.predictions_timeliness_health_still_red, model_deployments.actuals_timeliness_health_still_red, model_deployments.service_health_still_yellow, model_deployments.data_drift_still_yellow, model_deployments.accuracy_still_yellow, model_deployments.health.fairness_health.still_yellow, model_deployments.health.custom_metrics_health.still_yellow, model_deployments.prediction_payload_parsing_failure, model_deployments.deployment_inference_server_creation_started, model_deployments.deployment_inference_server_creation_failed, model_deployments.deployment_inference_server_creation_completed, model_deployments.deployment_inference_server_deletion, model_deployments.deployment_inference_server_idle_stopped, model_deployments.deployment_inference_server_maintenance_started, entity_notification_policy_template.shared, notification_channel_template.shared, project.created, project.deleted, project.shared, autopilot.complete, autopilot.started, autostart.failure, perma_delete_project.success, perma_delete_project.failure, users_delete.preview_started, users_delete.preview_completed, users_delete.preview_failed, users_delete.started, users_delete.completed, users_delete.failed, application.created, application.shared, model_version.added, model_version.stage_transition_from_registered_to_development, model_version.stage_transition_from_registered_to_staging, model_version.stage_transition_from_registered_to_production, model_version.stage_transition_from_registered_to_archived, model_version.stage_transition_from_development_to_registered, model_version.stage_transition_from_development_to_staging, model_version.stage_transition_from_development_to_production, model_version.stage_transition_from_development_to_archived, model_version.stage_transition_from_staging_to_registered, model_version.stage_transition_from_staging_to_development, model_version.stage_transition_from_staging_to_production, model_version.stage_transition_from_staging_to_archived, model_version.stage_transition_from_production_to_registered, model_version.stage_transition_from_production_to_development, model_version.stage_transition_from_production_to_staging, model_version.stage_transition_from_production_to_archived, model_version.stage_transition_from_archived_to_registered, model_version.stage_transition_from_archived_to_development, model_version.stage_transition_from_archived_to_production, model_version.stage_transition_from_archived_to_staging, use_case.shared, batch_predictions.success, batch_predictions.failed, batch_predictions.scheduler.auto_disabled, change_request.cancelled, change_request.created, change_request.deployment_approval_requested, change_request.resolved, change_request.proposed_changes_updated, change_request.pending, change_request.commenting_review_added, change_request.approving_review_added, change_request.changes_requesting_review_added, custom_job_run.success, custom_job_run.failed, custom_job_run.interrupted, custom_job_run.cancelled, prediction_explanations_computation.None, prediction_explanations_computation.prediction_explanations_preview_job_submitted, prediction_explanations_computation.prediction_explanations_preview_job_completed, prediction_explanations_computation.prediction_explanations_preview_job_failed, monitoring.rate_limit_enforced, notebook_schedule.created, notebook_schedule.failure, notebook_schedule.completed, abstract, moderation.metric.creation_error, moderation.metric.reporting_error, moderation.model.moderation_started, moderation.model.moderation_completed, moderation.model.pre_score_phase_started, moderation.model.pre_score_phase_completed, moderation.model.post_score_phase_started, moderation.model.post_score_phase_completed, moderation.model.config_error, moderation.model.runtime_error, moderation.model.scoring_started, moderation.model.scoring_completed, moderation.model.scoring_error] | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## EntityNotificationPolicyTemplateRelatedPoliciesListResponse ``` { "properties": { "count": { "description": "The number of entity policies returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The entity notification policies.", "items": { "properties": { "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityName": { "description": "The name of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name", "relatedEntityId", "relatedEntityName", "relatedEntityType" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of entity policies that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of entity policies returned. | | data | [EntityNotificationPolicyTemplateRelatedPolicyResponse] | true | maxItems: 1000 | The entity notification policies. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of entity policies that satisfy the query. | ## EntityNotificationPolicyTemplateRelatedPolicyResponse ``` { "properties": { "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityName": { "description": "The name of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name", "relatedEntityId", "relatedEntityName", "relatedEntityType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the policy. | | name | string | true | | The name of the notification policy. | | relatedEntityId | string | true | | The id of related entity. | | relatedEntityName | string | true | | The name of related entity. | | relatedEntityType | string | true | | The type of the related entity. | ### Enumerated Values | Property | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## EntityNotificationPolicyTemplateResponse ``` { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The ids of DataRobot Users or Group for DataRobotUser or DataRobotGroup channel types.", "items": { "properties": { "id": { "description": "The id of DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "Type of related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": "string", "x-versionadded": "v2.33" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "User that created template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" }, "relatedPoliciesCount": { "description": "The total number of entity policies that are using this template.", "type": "integer", "x-versionadded": "v2.33" }, "role": { "description": "User role on entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string", "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "active", "channel", "channelId", "created", "createdBy", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "relatedEntityType", "relatedPoliciesCount", "role", "uid", "updatedAt" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | true | | Defines if the notification policy is active or not. | | channel | EntityNotificationChannel | true | | The notification channel information used to send the notification. | | channelId | string | true | | The ID of the notification channel to be used to send the notification. | | channelScope | string | false | | Scope of the channel. | | created | string,null(date-time) | true | | The date when the policy was created. | | createdBy | CreatedByResponse | true | | User that created template. | | eventGroup | EventGroup | true | | The group of the event that triggers the notification. | | eventType | EventType | true | | The type of the event that triggers the notification. | | id | string | true | | The ID of the policy. | | lastTriggered | string,null(date-time) | true | | The date when the last notification with the policy was triggered. | | maximalFrequency | string,null | false | | Maximal frequency between policy runs in ISO 8601 duration string. | | name | string | true | | The name of the notification policy. | | orgId | string,null | true | | The ID of the organization that owns the notification policy. | | relatedEntityType | string | true | | The type of the related entity. | | relatedPoliciesCount | integer | true | | The total number of entity policies that are using this template. | | role | string,null | true | | User role on entity. | | uid | string | true | | The identifier of the user who created the policy. | | updatedAt | string,null(date-time) | true | | The date when the policy was updated. | ### Enumerated Values | Property | Value | | --- | --- | | channelScope | [organization, Organization, ORGANIZATION, entity, Entity, ENTITY, template, Template, TEMPLATE] | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## EntityNotificationPolicyTemplateUpdate ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string", "x-versionadded": "v2.33" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string", "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | false | | Whether the notification policy is active or not. | | channelId | string | false | | The ID of the notification channel to be used to send the notification. | | eventGroup | string | false | | The group of the event that triggers the notification. | | eventType | string | false | | The type of the event that triggers the notification. | | maximalFrequency | string,null | false | | Maximal frequency between policy runs in ISO 8601 duration string. | | name | string | false | maxLength: 100 | The name of the notification policy. | ### Enumerated Values | Property | Value | | --- | --- | | eventGroup | [secure_config.all, dataset.all, file.all, comment.all, invite_job.all, deployment_prediction_explanations_computation.all, model_deployments.critical_health, model_deployments.critical_frequent_health_change, model_deployments.frequent_health_change, model_deployments.health, model_deployments.retraining_policy, inference_endpoints.health, model_deployments.management_agent, model_deployments.management_agent_health, prediction_request.all, challenger_management.all, challenger_replay.all, model_deployments.all, project.all, perma_delete_project.all, users_delete.all, applications.all, model_version.stage_transitions, model_version.all, use_case.all, batch_predictions.all, change_requests.all, custom_job_run.all, custom_job_run.unsuccessful, insights_computation.all, notebook_schedule.all, monitoring.all] | | eventType | [secure_config.created, secure_config.deleted, secure_config.shared, dataset.created, dataset.registered, dataset.deleted, datasets.deleted, datasetrelationship.created, dataset.shared, datasets.shared, file.created, file.registered, file.deleted, file.shared, comment.created, comment.updated, invite_job.completed, misc.asset_access_request, misc.webhook_connection_test, misc.webhook_resend, misc.email_verification, monitoring.spooler_channel_base, monitoring.spooler_channel_red, monitoring.spooler_channel_green, monitoring.external_model_nan_predictions, management.deploymentInfo, model_deployments.None, model_deployments.deployment_sharing, model_deployments.model_replacement, prediction_request.None, prediction_request.failed, model_deployments.humility_rule, model_deployments.model_replacement_lifecycle, model_deployments.model_replacement_started, model_deployments.model_replacement_succeeded, model_deployments.model_replacement_failed, model_deployments.model_replacement_validation_warning, model_deployments.deployment_creation, model_deployments.deployment_deletion, model_deployments.service_health_yellow_from_green, model_deployments.service_health_yellow_from_red, model_deployments.service_health_red, model_deployments.data_drift_yellow_from_green, model_deployments.data_drift_yellow_from_red, model_deployments.data_drift_red, model_deployments.accuracy_yellow_from_green, model_deployments.accuracy_yellow_from_red, model_deployments.accuracy_red, model_deployments.health.fairness_health.green_to_yellow, model_deployments.health.fairness_health.red_to_yellow, model_deployments.health.fairness_health.red, model_deployments.health.custom_metrics_health.green_to_yellow, model_deployments.health.custom_metrics_health.red_to_yellow, model_deployments.health.custom_metrics_health.red, model_deployments.health.base.green, model_deployments.service_health_green, model_deployments.data_drift_green, model_deployments.accuracy_green, model_deployments.health.fairness_health.green, model_deployments.health.custom_metrics_health.green, model_deployments.retraining_policy_run_started, model_deployments.retraining_policy_run_succeeded, model_deployments.retraining_policy_run_failed, model_deployments.challenger_scoring_success, model_deployments.challenger_scoring_data_warning, model_deployments.challenger_scoring_failure, model_deployments.challenger_scoring_started, model_deployments.challenger_model_validation_warning, model_deployments.challenger_model_created, model_deployments.challenger_model_deleted, model_deployments.actuals_upload_failed, model_deployments.actuals_upload_warning, model_deployments.training_data_baseline_calculation_started, model_deployments.training_data_baseline_calculation_completed, model_deployments.training_data_baseline_failed, model_deployments.custom_model_deployment_creation_started, model_deployments.custom_model_deployment_creation_completed, model_deployments.custom_model_deployment_creation_failed, model_deployments.custom_model_deployment_secure_config_failure, model_deployments.deployment_prediction_explanations_preview_job_submitted, model_deployments.deployment_prediction_explanations_preview_job_completed, model_deployments.deployment_prediction_explanations_preview_job_failed, model_deployments.custom_model_deployment_activated, model_deployments.custom_model_deployment_activation_failed, model_deployments.custom_model_deployment_deactivated, model_deployments.custom_model_deployment_deactivation_failed, model_deployments.prediction_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_warning, model_deployments.actuals_processing_rate_limit_reached, model_deployments.actuals_processing_rate_limit_warning, model_deployments.deployment_monitoring_data_cleared, model_deployments.deployment_launch_started, model_deployments.deployment_launch_succeeded, model_deployments.deployment_launch_failed, model_deployments.deployment_shutdown_started, model_deployments.deployment_shutdown_succeeded, model_deployments.deployment_shutdown_failed, model_deployments.endpoint_update_started, model_deployments.endpoint_update_succeeded, model_deployments.endpoint_update_failed, model_deployments.management_agent_service_health_green, model_deployments.management_agent_service_health_yellow, model_deployments.management_agent_service_health_red, model_deployments.management_agent_service_health_unknown, model_deployments.predictions_missing_association_id, model_deployments.prediction_result_rows_cleand_up, model_deployments.batch_deleted, model_deployments.batch_creation_limit_reached, model_deployments.batch_creation_limit_exceeded, model_deployments.batch_not_found, model_deployments.predictions_encountered_for_locked_batch, model_deployments.predictions_encountered_for_deleted_batch, model_deployments.scheduled_report_generated, model_deployments.predictions_timeliness_health_red, model_deployments.actuals_timeliness_health_red, model_deployments.service_health_still_red, model_deployments.data_drift_still_red, model_deployments.accuracy_still_red, model_deployments.health.fairness_health.still_red, model_deployments.health.custom_metrics_health.still_red, model_deployments.predictions_timeliness_health_still_red, model_deployments.actuals_timeliness_health_still_red, model_deployments.service_health_still_yellow, model_deployments.data_drift_still_yellow, model_deployments.accuracy_still_yellow, model_deployments.health.fairness_health.still_yellow, model_deployments.health.custom_metrics_health.still_yellow, model_deployments.prediction_payload_parsing_failure, model_deployments.deployment_inference_server_creation_started, model_deployments.deployment_inference_server_creation_failed, model_deployments.deployment_inference_server_creation_completed, model_deployments.deployment_inference_server_deletion, model_deployments.deployment_inference_server_idle_stopped, model_deployments.deployment_inference_server_maintenance_started, entity_notification_policy_template.shared, notification_channel_template.shared, project.created, project.deleted, project.shared, autopilot.complete, autopilot.started, autostart.failure, perma_delete_project.success, perma_delete_project.failure, users_delete.preview_started, users_delete.preview_completed, users_delete.preview_failed, users_delete.started, users_delete.completed, users_delete.failed, application.created, application.shared, model_version.added, model_version.stage_transition_from_registered_to_development, model_version.stage_transition_from_registered_to_staging, model_version.stage_transition_from_registered_to_production, model_version.stage_transition_from_registered_to_archived, model_version.stage_transition_from_development_to_registered, model_version.stage_transition_from_development_to_staging, model_version.stage_transition_from_development_to_production, model_version.stage_transition_from_development_to_archived, model_version.stage_transition_from_staging_to_registered, model_version.stage_transition_from_staging_to_development, model_version.stage_transition_from_staging_to_production, model_version.stage_transition_from_staging_to_archived, model_version.stage_transition_from_production_to_registered, model_version.stage_transition_from_production_to_development, model_version.stage_transition_from_production_to_staging, model_version.stage_transition_from_production_to_archived, model_version.stage_transition_from_archived_to_registered, model_version.stage_transition_from_archived_to_development, model_version.stage_transition_from_archived_to_production, model_version.stage_transition_from_archived_to_staging, use_case.shared, batch_predictions.success, batch_predictions.failed, batch_predictions.scheduler.auto_disabled, change_request.cancelled, change_request.created, change_request.deployment_approval_requested, change_request.resolved, change_request.proposed_changes_updated, change_request.pending, change_request.commenting_review_added, change_request.approving_review_added, change_request.changes_requesting_review_added, custom_job_run.success, custom_job_run.failed, custom_job_run.interrupted, custom_job_run.cancelled, prediction_explanations_computation.None, prediction_explanations_computation.prediction_explanations_preview_job_submitted, prediction_explanations_computation.prediction_explanations_preview_job_completed, prediction_explanations_computation.prediction_explanations_preview_job_failed, monitoring.rate_limit_enforced, notebook_schedule.created, notebook_schedule.failure, notebook_schedule.completed, abstract, moderation.metric.creation_error, moderation.metric.reporting_error, moderation.model.moderation_started, moderation.model.moderation_completed, moderation.model.pre_score_phase_started, moderation.model.pre_score_phase_completed, moderation.model.post_score_phase_started, moderation.model.post_score_phase_completed, moderation.model.config_error, moderation.model.runtime_error, moderation.model.scoring_started, moderation.model.scoring_completed, moderation.model.scoring_error] | ## EntityNotificationPolicyUpdate ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean", "x-versionadded": "v2.33" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string", "x-versionadded": "v2.33" }, "channelScope": { "description": "Scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": "string", "x-versionadded": "v2.33" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string", "x-versionadded": "v2.33" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string", "x-versionadded": "v2.33" }, "maximalFrequency": { "description": "Maximal frequency between policy runs in ISO 8601 duration string.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | false | | Whether the notification policy is active or not. | | channelId | string | false | | The ID of the notification channel to be used to send the notification. | | channelScope | string | false | | Scope of the channel. | | eventGroup | string | false | | The group of the event that triggers the notification. | | eventType | string | false | | The type of the event that triggers the notification. | | maximalFrequency | string,null | false | | Maximal frequency between policy runs in ISO 8601 duration string. | | name | string | false | maxLength: 100 | The name of the notification policy. | ### Enumerated Values | Property | Value | | --- | --- | | channelScope | [organization, Organization, ORGANIZATION, entity, Entity, ENTITY, template, Template, TEMPLATE] | | eventGroup | [secure_config.all, dataset.all, file.all, comment.all, invite_job.all, deployment_prediction_explanations_computation.all, model_deployments.critical_health, model_deployments.critical_frequent_health_change, model_deployments.frequent_health_change, model_deployments.health, model_deployments.retraining_policy, inference_endpoints.health, model_deployments.management_agent, model_deployments.management_agent_health, prediction_request.all, challenger_management.all, challenger_replay.all, model_deployments.all, project.all, perma_delete_project.all, users_delete.all, applications.all, model_version.stage_transitions, model_version.all, use_case.all, batch_predictions.all, change_requests.all, custom_job_run.all, custom_job_run.unsuccessful, insights_computation.all, notebook_schedule.all, monitoring.all] | | eventType | [secure_config.created, secure_config.deleted, secure_config.shared, dataset.created, dataset.registered, dataset.deleted, datasets.deleted, datasetrelationship.created, dataset.shared, datasets.shared, file.created, file.registered, file.deleted, file.shared, comment.created, comment.updated, invite_job.completed, misc.asset_access_request, misc.webhook_connection_test, misc.webhook_resend, misc.email_verification, monitoring.spooler_channel_base, monitoring.spooler_channel_red, monitoring.spooler_channel_green, monitoring.external_model_nan_predictions, management.deploymentInfo, model_deployments.None, model_deployments.deployment_sharing, model_deployments.model_replacement, prediction_request.None, prediction_request.failed, model_deployments.humility_rule, model_deployments.model_replacement_lifecycle, model_deployments.model_replacement_started, model_deployments.model_replacement_succeeded, model_deployments.model_replacement_failed, model_deployments.model_replacement_validation_warning, model_deployments.deployment_creation, model_deployments.deployment_deletion, model_deployments.service_health_yellow_from_green, model_deployments.service_health_yellow_from_red, model_deployments.service_health_red, model_deployments.data_drift_yellow_from_green, model_deployments.data_drift_yellow_from_red, model_deployments.data_drift_red, model_deployments.accuracy_yellow_from_green, model_deployments.accuracy_yellow_from_red, model_deployments.accuracy_red, model_deployments.health.fairness_health.green_to_yellow, model_deployments.health.fairness_health.red_to_yellow, model_deployments.health.fairness_health.red, model_deployments.health.custom_metrics_health.green_to_yellow, model_deployments.health.custom_metrics_health.red_to_yellow, model_deployments.health.custom_metrics_health.red, model_deployments.health.base.green, model_deployments.service_health_green, model_deployments.data_drift_green, model_deployments.accuracy_green, model_deployments.health.fairness_health.green, model_deployments.health.custom_metrics_health.green, model_deployments.retraining_policy_run_started, model_deployments.retraining_policy_run_succeeded, model_deployments.retraining_policy_run_failed, model_deployments.challenger_scoring_success, model_deployments.challenger_scoring_data_warning, model_deployments.challenger_scoring_failure, model_deployments.challenger_scoring_started, model_deployments.challenger_model_validation_warning, model_deployments.challenger_model_created, model_deployments.challenger_model_deleted, model_deployments.actuals_upload_failed, model_deployments.actuals_upload_warning, model_deployments.training_data_baseline_calculation_started, model_deployments.training_data_baseline_calculation_completed, model_deployments.training_data_baseline_failed, model_deployments.custom_model_deployment_creation_started, model_deployments.custom_model_deployment_creation_completed, model_deployments.custom_model_deployment_creation_failed, model_deployments.custom_model_deployment_secure_config_failure, model_deployments.deployment_prediction_explanations_preview_job_submitted, model_deployments.deployment_prediction_explanations_preview_job_completed, model_deployments.deployment_prediction_explanations_preview_job_failed, model_deployments.custom_model_deployment_activated, model_deployments.custom_model_deployment_activation_failed, model_deployments.custom_model_deployment_deactivated, model_deployments.custom_model_deployment_deactivation_failed, model_deployments.prediction_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_warning, model_deployments.actuals_processing_rate_limit_reached, model_deployments.actuals_processing_rate_limit_warning, model_deployments.deployment_monitoring_data_cleared, model_deployments.deployment_launch_started, model_deployments.deployment_launch_succeeded, model_deployments.deployment_launch_failed, model_deployments.deployment_shutdown_started, model_deployments.deployment_shutdown_succeeded, model_deployments.deployment_shutdown_failed, model_deployments.endpoint_update_started, model_deployments.endpoint_update_succeeded, model_deployments.endpoint_update_failed, model_deployments.management_agent_service_health_green, model_deployments.management_agent_service_health_yellow, model_deployments.management_agent_service_health_red, model_deployments.management_agent_service_health_unknown, model_deployments.predictions_missing_association_id, model_deployments.prediction_result_rows_cleand_up, model_deployments.batch_deleted, model_deployments.batch_creation_limit_reached, model_deployments.batch_creation_limit_exceeded, model_deployments.batch_not_found, model_deployments.predictions_encountered_for_locked_batch, model_deployments.predictions_encountered_for_deleted_batch, model_deployments.scheduled_report_generated, model_deployments.predictions_timeliness_health_red, model_deployments.actuals_timeliness_health_red, model_deployments.service_health_still_red, model_deployments.data_drift_still_red, model_deployments.accuracy_still_red, model_deployments.health.fairness_health.still_red, model_deployments.health.custom_metrics_health.still_red, model_deployments.predictions_timeliness_health_still_red, model_deployments.actuals_timeliness_health_still_red, model_deployments.service_health_still_yellow, model_deployments.data_drift_still_yellow, model_deployments.accuracy_still_yellow, model_deployments.health.fairness_health.still_yellow, model_deployments.health.custom_metrics_health.still_yellow, model_deployments.prediction_payload_parsing_failure, model_deployments.deployment_inference_server_creation_started, model_deployments.deployment_inference_server_creation_failed, model_deployments.deployment_inference_server_creation_completed, model_deployments.deployment_inference_server_deletion, model_deployments.deployment_inference_server_idle_stopped, model_deployments.deployment_inference_server_maintenance_started, entity_notification_policy_template.shared, notification_channel_template.shared, project.created, project.deleted, project.shared, autopilot.complete, autopilot.started, autostart.failure, perma_delete_project.success, perma_delete_project.failure, users_delete.preview_started, users_delete.preview_completed, users_delete.preview_failed, users_delete.started, users_delete.completed, users_delete.failed, application.created, application.shared, model_version.added, model_version.stage_transition_from_registered_to_development, model_version.stage_transition_from_registered_to_staging, model_version.stage_transition_from_registered_to_production, model_version.stage_transition_from_registered_to_archived, model_version.stage_transition_from_development_to_registered, model_version.stage_transition_from_development_to_staging, model_version.stage_transition_from_development_to_production, model_version.stage_transition_from_development_to_archived, model_version.stage_transition_from_staging_to_registered, model_version.stage_transition_from_staging_to_development, model_version.stage_transition_from_staging_to_production, model_version.stage_transition_from_staging_to_archived, model_version.stage_transition_from_production_to_registered, model_version.stage_transition_from_production_to_development, model_version.stage_transition_from_production_to_staging, model_version.stage_transition_from_production_to_archived, model_version.stage_transition_from_archived_to_registered, model_version.stage_transition_from_archived_to_development, model_version.stage_transition_from_archived_to_production, model_version.stage_transition_from_archived_to_staging, use_case.shared, batch_predictions.success, batch_predictions.failed, batch_predictions.scheduler.auto_disabled, change_request.cancelled, change_request.created, change_request.deployment_approval_requested, change_request.resolved, change_request.proposed_changes_updated, change_request.pending, change_request.commenting_review_added, change_request.approving_review_added, change_request.changes_requesting_review_added, custom_job_run.success, custom_job_run.failed, custom_job_run.interrupted, custom_job_run.cancelled, prediction_explanations_computation.None, prediction_explanations_computation.prediction_explanations_preview_job_submitted, prediction_explanations_computation.prediction_explanations_preview_job_completed, prediction_explanations_computation.prediction_explanations_preview_job_failed, monitoring.rate_limit_enforced, notebook_schedule.created, notebook_schedule.failure, notebook_schedule.completed, abstract, moderation.metric.creation_error, moderation.metric.reporting_error, moderation.model.moderation_started, moderation.model.moderation_completed, moderation.model.pre_score_phase_started, moderation.model.pre_score_phase_completed, moderation.model.post_score_phase_started, moderation.model.post_score_phase_completed, moderation.model.config_error, moderation.model.runtime_error, moderation.model.scoring_started, moderation.model.scoring_completed, moderation.model.scoring_error] | ## EventGroup ``` { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" } ``` The group of the event that triggers the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | events | [EventType] | true | maxItems: 1000 | The events included in this group. | | id | string | true | | The ID of the event group. | | label | string | true | | The display name of the event group. | ## EventGroupResponse ``` { "properties": { "events": { "description": "The event types belonging to the group.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "The group ID.", "type": "string" }, "label": { "description": "The group name for display.", "type": "string" }, "requireMaxFrequency": { "description": "Indicates if a group requires max frequency setting.", "type": "boolean" } }, "required": [ "events", "id", "label", "requireMaxFrequency" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | events | [string] | true | | The event types belonging to the group. | | id | string | true | | The group ID. | | label | string | true | | The group name for display. | | requireMaxFrequency | boolean | true | | Indicates if a group requires max frequency setting. | ## EventResponse ``` { "properties": { "id": { "description": "The event type as an ID.", "type": "string" }, "label": { "description": "The event type for display.", "type": "string" }, "requireMaxFrequency": { "description": "Indicates if an event requires max frequency setting.", "type": "boolean" } }, "required": [ "id", "label", "requireMaxFrequency" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The event type as an ID. | | label | string | true | | The event type for display. | | requireMaxFrequency | boolean | true | | Indicates if an event requires max frequency setting. | ## EventType ``` { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" } ``` The type of the event that triggers the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the event type. | | label | string | true | | The display name of the event type. | ## ExternalNaNPredictionsEventData ``` { "description": "The external NaN predictions event payload.", "properties": { "count": { "description": "The number of NaN predictions by the external model.", "exclusiveMinimum": 0, "type": "integer" }, "modelId": { "default": null, "description": "The identifier of the model.", "type": [ "string", "null" ] } }, "required": [ "count", "modelId" ], "type": "object" } ``` The external NaN predictions event payload. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of NaN predictions by the external model. | | modelId | string,null | true | | The identifier of the model. | ## FailedInviteData ``` { "description": "Details about why the invite failed.", "properties": { "email": { "description": "The email address of the invited user.", "type": "string" }, "errorMessage": { "description": "The error message explaining why the invite failed.", "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" } }, "required": [ "email", "errorMessage", "userId" ], "type": "object", "x-versionadded": "v2.39" } ``` Details about why the invite failed. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string | true | | The email address of the invited user. | | errorMessage | string | true | | The error message explaining why the invite failed. | | userId | string | true | | The ID of the user. | ## GrantAccessControlWithId ``` { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## GrantAccessControlWithUsername ``` { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | Username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## InviteJobNotificationData ``` { "properties": { "failedInvites": { "description": "Failed invites.", "items": { "description": "Details about why the invite failed.", "properties": { "email": { "description": "The email address of the invited user.", "type": "string" }, "errorMessage": { "description": "The error message explaining why the invite failed.", "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" } }, "required": [ "email", "errorMessage", "userId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 20, "type": "array" }, "jobId": { "description": "The ID of the invite job.", "type": [ "string", "null" ] }, "statusId": { "description": "The ID of the invite job status.", "type": "string", "x-versionadded": "v2.40" }, "successfulInvites": { "description": "Successful invites.", "items": { "description": "Details about the successful invite.", "properties": { "email": { "description": "The email address of the invited user.", "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" } }, "required": [ "email", "userId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 20, "type": "array" } }, "required": [ "failedInvites", "jobId", "statusId", "successfulInvites" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | failedInvites | [FailedInviteData] | true | maxItems: 20 | Failed invites. | | jobId | string,null | true | | The ID of the invite job. | | statusId | string | true | | The ID of the invite job status. | | successfulInvites | [SuccessfulInviteData] | true | maxItems: 20 | Successful invites. | ## ModerationData ``` { "description": "The moderation event information.", "properties": { "guardName": { "description": "Name or label of the guard.", "maxLength": 255, "type": "string" }, "metricName": { "description": "Name or label of the metric.", "maxLength": 255, "type": "string" } }, "required": [ "guardName", "metricName" ], "type": "object", "x-versionadded": "v2.35" } ``` The moderation event information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | guardName | string | true | maxLength: 255 | Name or label of the guard. | | metricName | string | true | maxLength: 255 | Name or label of the metric. | ## NotificationChannel ``` { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` The notification channel information used to send the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | true | | The type of the new notification channel. | | contentType | string,null | true | | The content type of the messages of the new notification channel. | | createdAt | string(date-time) | true | | The date of the notification channel creation. | | customHeaders | [CustomerHeader] | true | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | emailAddress | string,null | true | | The email address to be used in the new notification channel. | | id | string | true | | The ID of the notification channel. | | languageCode | string,null | true | | The preferred language code. | | name | string | true | | The name of the new notification channel. | | orgId | string,null | true | | The ID of the organization that the notification channel belongs to. | | payloadUrl | string,null(uri) | true | | The payload URL of the new notification channel. | | secretToken | string,null | true | | Secret token to be used for new notification channel. | | uid | string,null | true | | The identifier of the user who created the channel. | | updatedAt | string,null(date-time) | true | | The date when the channel was updated. | | validateSsl | boolean,null | true | | Whether to validate SSL for the notification channel. | ### Enumerated Values | Property | Value | | --- | --- | | channelType | [Database, Email, InApp, InsightsComputations, MSTeams, Slack, Webhook] | | contentType | [application/json, application/x-www-form-urlencoded] | | languageCode | [en, es_419, fr, ja, ko, ptBR] | ## NotificationChannelCreate ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": "string" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string" } }, "required": [ "channelType", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | true | | The type of the new notification channel. | | contentType | string | false | | The content type of the messages of the new notification channel. | | customHeaders | [CustomerHeader] | false | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | emailAddress | string | false | | The email address to be used in the new notification channel. | | languageCode | string | false | | The preferred language code. | | name | string | true | maxLength: 100 | The name of the new notification channel. | | orgId | string | false | | The id of organization that notification channel belongs to. | | payloadUrl | string(uri) | false | | The payload URL of the new notification channel. | | secretToken | string | false | | Secret token to be used for new notification channel. | | validateSsl | boolean | false | | Defines if validate ssl or not in the notification channel. | | verificationCode | string | false | | Required if the channel type is Email. | ### Enumerated Values | Property | Value | | --- | --- | | channelType | [Database, Email, InApp, InsightsComputations, MSTeams, Slack, Webhook] | | contentType | [application/json, application/x-www-form-urlencoded] | | languageCode | [en, es_419, fr, ja, ko, ptBR] | ## NotificationChannelResponse ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | true | | The type of the new notification channel. | | contentType | string,null | true | | The content type of the messages of the new notification channel. | | createdAt | string(date-time) | true | | The date of the notification channel creation. | | customHeaders | [CustomerHeader] | true | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | emailAddress | string,null | true | | The email address to be used in the new notification channel. | | id | string | true | | The ID of the notification channel. | | languageCode | string,null | true | | The preferred language code. | | lastNotificationAt | string,null(date-time) | true | | The timestamp of the last notification sent to the channel. | | name | string | true | | The name of the new notification channel. | | orgId | string,null | true | | The id of organization that notification channel belongs to. | | payloadUrl | string,null(uri) | true | | The payload URL of the new notification channel. | | policyCount | integer | true | | The count of policies assigned to the channel. | | secretToken | string,null | true | | Secret token to be used for new notification channel. | | uid | string,null | true | | The identifier of the user who created the channel. | | updatedAt | string,null(date-time) | true | | The date when the channel was updated. | | validateSsl | boolean,null | true | | Defines if validate ssl or not in the notification channel. | ### Enumerated Values | Property | Value | | --- | --- | | channelType | [Database, Email, InApp, InsightsComputations, MSTeams, Slack, Webhook] | | contentType | [application/json, application/x-www-form-urlencoded] | | languageCode | [en, es_419, fr, ja, ko, ptBR] | ## NotificationChannelTempPolicyTemplateResponse ``` { "properties": { "id": { "description": "The ID of the policy template.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy template.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity template.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name", "relatedEntityType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the policy template. | | name | string | true | | The name of the notification policy template. | | relatedEntityType | string | true | | The type of the related entity template. | ### Enumerated Values | Property | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## NotificationChannelTemplateCreate ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": "string", "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean", "x-versionadded": "v2.33" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "channelType", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | true | | The type of the new notification channel. | | contentType | string | false | | The content type of the messages of the new notification channel. | | customHeaders | [CustomerHeader] | false | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | drEntities | [DREntity] | false | maxItems: 100minItems: 1 | The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types. | | emailAddress | string | false | | The email address to be used in the new notification channel. | | languageCode | string | false | | The preferred language code. | | name | string | true | maxLength: 100 | The name of the new notification channel. | | orgId | string | false | | The id of organization that notification channel belongs to. | | payloadUrl | string(uri) | false | | The payload URL of the new notification channel. | | secretToken | string | false | | Secret token to be used for new notification channel. | | validateSsl | boolean | false | | Defines if validate ssl or not in the notification channel. | | verificationCode | string | false | | Required if the channel type is Email. | ### Enumerated Values | Property | Value | | --- | --- | | channelType | [DataRobotCustomJob, DataRobotGroup, DataRobotUser, Database, Email, InApp, InsightsComputations, MSTeams, Slack, Webhook] | | contentType | [application/json, application/x-www-form-urlencoded] | | languageCode | [en, es_419, fr, ja, ko, ptBR] | ## NotificationChannelTemplateRelatedPoliciesListResponse ``` { "properties": { "count": { "description": "The number of entity policies returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The entity notification policies.", "items": { "properties": { "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityName": { "description": "The name of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name", "relatedEntityId", "relatedEntityName", "relatedEntityType" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of entity policies that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of entity policies returned. | | data | [NotificationChannelTemplateRelatedPolicyResponse] | true | maxItems: 1000 | The entity notification policies. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of entity policies that satisfy the query. | ## NotificationChannelTemplateRelatedPolicyResponse ``` { "properties": { "id": { "description": "The ID of the policy.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityId": { "description": "The id of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityName": { "description": "The name of related entity.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name", "relatedEntityId", "relatedEntityName", "relatedEntityType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the policy. | | name | string | true | | The name of the notification policy. | | relatedEntityId | string | true | | The id of related entity. | | relatedEntityName | string | true | | The name of related entity. | | relatedEntityType | string | true | | The type of the related entity. | ### Enumerated Values | Property | Value | | --- | --- | | relatedEntityType | [deployment, Deployment, DEPLOYMENT, customjob, Customjob, CUSTOMJOB] | ## NotificationChannelTemplateRelatedPolicyTemplateListResponse ``` { "properties": { "count": { "description": "The number of entity policies returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The entity notification policy template.", "items": { "properties": { "id": { "description": "The ID of the policy template.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the notification policy template.", "type": "string", "x-versionadded": "v2.33" }, "relatedEntityType": { "description": "The type of the related entity template.", "enum": [ "deployment", "Deployment", "DEPLOYMENT", "customjob", "Customjob", "CUSTOMJOB" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name", "relatedEntityType" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of entity policies that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of entity policies returned. | | data | [NotificationChannelTempPolicyTemplateResponse] | true | maxItems: 1000 | The entity notification policy template. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of entity policies that satisfy the query. | ## NotificationChannelTemplateResponse ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user who created the template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "policyTemplatesCount": { "description": "The count of policy templates assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "role": { "description": "User role on entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "createdBy", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "policyTemplatesCount", "role", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | true | | The type of the new notification channel. | | contentType | string,null | true | | The content type of the messages of the new notification channel. | | createdAt | string(date-time) | true | | The date of the notification channel creation. | | createdBy | ChannelCreatedByResponse | true | | The user who created the template. | | customHeaders | [CustomerHeader] | true | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | drEntities | [DREntity] | false | maxItems: 100minItems: 1 | The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types. | | emailAddress | string,null | true | | The email address to be used in the new notification channel. | | id | string | true | | The ID of the notification channel. | | languageCode | string,null | true | | The preferred language code. | | lastNotificationAt | string,null(date-time) | true | | The timestamp of the last notification sent to the channel. | | name | string | true | | The name of the new notification channel. | | orgId | string,null | true | | The id of organization that notification channel belongs to. | | payloadUrl | string,null(uri) | true | | The payload URL of the new notification channel. | | policyCount | integer | true | | The count of policies assigned to the channel. | | policyTemplatesCount | integer | true | | The count of policy templates assigned to the channel. | | role | string,null | true | | User role on entity. | | secretToken | string,null | true | | Secret token to be used for new notification channel. | | uid | string,null | true | | The identifier of the user who created the channel. | | updatedAt | string,null(date-time) | true | | The date when the channel was updated. | | validateSsl | boolean,null | true | | Defines if validate ssl or not in the notification channel. | ### Enumerated Values | Property | Value | | --- | --- | | channelType | [DataRobotCustomJob, DataRobotGroup, DataRobotUser, Database, Email, InApp, InsightsComputations, MSTeams, Slack, Webhook] | | contentType | [application/json, application/x-www-form-urlencoded] | | languageCode | [en, es_419, fr, ja, ko, ptBR] | ## NotificationChannelTemplatesListResponse ``` { "properties": { "count": { "description": "The number of channel templates returned.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The notification channel templates.", "items": { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user who created the template.", "properties": { "firstName": { "description": "First Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastName": { "description": "Last Name.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "username": { "description": "Username.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "firstName", "lastName", "username" ], "type": "object" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "policyTemplatesCount": { "description": "The count of policy templates assigned to the channel.", "type": "integer", "x-versionadded": "v2.33" }, "role": { "description": "User role on entity.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "required": [ "channelType", "contentType", "createdAt", "createdBy", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "policyTemplatesCount", "role", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of channel templates that satisfy the query.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of channel templates returned. | | data | [NotificationChannelTemplateResponse] | true | maxItems: 1000 | The notification channel templates. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of channel templates that satisfy the query. | ## NotificationChannelUpdate ``` { "properties": { "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | contentType | string | false | | The content type of the messages of the new notification channel. | | customHeaders | [CustomerHeader] | false | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | emailAddress | string | false | | The email address to be used in the new notification channel. | | languageCode | string | false | | The preferred language code. | | name | string | false | maxLength: 100 | The name of the new notification channel. | | payloadUrl | string(uri) | false | | The payload URL of the new notification channel. | | secretToken | string | false | | Secret token to be used for new notification channel. | | validateSsl | boolean | false | | Defines if validate ssl or not in the notification channel. | | verificationCode | string | false | | Required if the channel type is Email. | ### Enumerated Values | Property | Value | | --- | --- | | contentType | [application/json, application/x-www-form-urlencoded] | | languageCode | [en, es_419, fr, ja, ko, ptBR] | ## NotificationChannelWithDREntityUpdate ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "DataRobotCustomJob", "DataRobotGroup", "DataRobotUser", "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string", "x-versionadded": "v2.33" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string", "x-versionadded": "v2.33" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "drEntities": { "description": "The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types.", "items": { "properties": { "id": { "description": "The ID of the DataRobot entity.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the entity.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string", "x-versionadded": "v2.33" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string", "x-versionadded": "v2.33" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string", "x-versionadded": "v2.33" }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": "boolean", "x-versionadded": "v2.33" }, "verificationCode": { "description": "Required if the channel type is Email.", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | false | | The type of the new notification channel. | | contentType | string | false | | The content type of the messages of the new notification channel. | | customHeaders | [CustomerHeader] | false | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | drEntities | [DREntity] | false | maxItems: 100minItems: 1 | The IDs of the DataRobot Users, Group or Custom Job associated with the DataRobotUser, DataRobotGroup or DataRobotCustomJob channel types. | | emailAddress | string | false | | The email address to be used in the new notification channel. | | languageCode | string | false | | The preferred language code. | | name | string | false | maxLength: 100 | The name of the new notification channel. | | payloadUrl | string(uri) | false | | The payload URL of the new notification channel. | | secretToken | string | false | | Secret token to be used for new notification channel. | | validateSsl | boolean | false | | Defines if validate ssl or not in the notification channel. | | verificationCode | string | false | | Required if the channel type is Email. | ### Enumerated Values | Property | Value | | --- | --- | | channelType | [DataRobotCustomJob, DataRobotGroup, DataRobotUser, Database, Email, InApp, InsightsComputations, MSTeams, Slack, Webhook] | | contentType | [application/json, application/x-www-form-urlencoded] | | languageCode | [en, es_419, fr, ja, ko, ptBR] | ## NotificationChannelsListResponse ``` { "properties": { "count": { "description": "The number of channels returned.", "type": "integer" }, "data": { "description": "The notification channels.", "items": { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "lastNotificationAt": { "description": "The timestamp of the last notification sent to the channel.", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The id of organization that notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "policyCount": { "description": "The count of policies assigned to the channel.", "type": "integer" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Defines if validate ssl or not in the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "lastNotificationAt", "name", "orgId", "payloadUrl", "policyCount", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of channels that satisfy the query.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of channels returned. | | data | [NotificationChannelResponse] | true | maxItems: 1000 | The notification channels. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of channels that satisfy the query. | ## NotificationEmailChannelVerification ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "type": "string" }, "emailAddress": { "description": "The email address of the recipient.", "type": "string" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": "string" } }, "required": [ "channelType", "emailAddress", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | true | | The type of the new notification channel. | | emailAddress | string | true | | The email address of the recipient. | | name | string | true | maxLength: 100 | The name of the new notification channel. | | orgId | string | false | | The ID of the organization that the notification channel belongs to. | ## NotificationEmailChannelVerificationResponse ``` { "properties": { "notificationId": { "description": "The response body is the JSON object with notification_id in it.", "type": "string" } }, "required": [ "notificationId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notificationId | string | true | | The response body is the JSON object with notification_id in it. | ## NotificationEmailChannelVerificationStatus ``` { "properties": { "emailAddress": { "description": "The email address of the recipient.", "type": "string" }, "verificationCode": { "description": "The user-entered verification code.", "type": "string" } }, "required": [ "emailAddress", "verificationCode" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | emailAddress | string | true | | The email address of the recipient. | | verificationCode | string | true | | The user-entered verification code. | ## NotificationEmailChannelVerificationStatusResponse ``` { "properties": { "status": { "description": "The status shows whether the admin entered the correct 6-digit verification code.", "type": "boolean" } }, "required": [ "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | status | boolean | true | | The status shows whether the admin entered the correct 6-digit verification code. | ## NotificationEventListResponse ``` { "properties": { "eventGroups": { "description": "The selectable event groups.", "items": { "properties": { "events": { "description": "The event types belonging to the group.", "items": { "type": "string" }, "type": "array" }, "id": { "description": "The group ID.", "type": "string" }, "label": { "description": "The group name for display.", "type": "string" }, "requireMaxFrequency": { "description": "Indicates if a group requires max frequency setting.", "type": "boolean" } }, "required": [ "events", "id", "label", "requireMaxFrequency" ], "type": "object" }, "type": "array" }, "events": { "description": "The selectable individual events.", "items": { "properties": { "id": { "description": "The event type as an ID.", "type": "string" }, "label": { "description": "The event type for display.", "type": "string" }, "requireMaxFrequency": { "description": "Indicates if an event requires max frequency setting.", "type": "boolean" } }, "required": [ "id", "label", "requireMaxFrequency" ], "type": "object" }, "type": "array" } }, "required": [ "eventGroups", "events" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | eventGroups | [EventGroupResponse] | true | | The selectable event groups. | | events | [EventResponse] | true | | The selectable individual events. | ## NotificationLog ``` { "description": "The notification log record.", "properties": { "channelId": { "description": "The ID of the channel that was used to send the notification.", "type": "string" }, "channelScope": { "description": "The scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ] }, "emailSubject": { "description": "The email subject of the notification.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification log.", "type": "string" }, "parentNotificationId": { "description": "The ID of the parent notification.", "type": [ "string", "null" ] }, "policyId": { "description": "The ID of the policy that was used to send the notification.", "type": "string" }, "request": { "description": "The request that was sent in the notification.", "type": [ "string", "null" ] }, "response": { "description": "The response that was received after sending the notification.", "type": [ "string", "null" ] }, "retryCount": { "description": "The number of attempts to send the notification.", "type": [ "integer", "null" ] }, "status": { "description": "The status of the notification.", "type": "string" }, "timestamp": { "description": "The date when the notification was sent.", "format": "date-time", "type": "string" } }, "required": [ "channelId", "channelScope", "emailSubject", "id", "parentNotificationId", "policyId", "request", "response", "retryCount", "status", "timestamp" ], "type": "object" } ``` The notification log record. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelId | string | true | | The ID of the channel that was used to send the notification. | | channelScope | string,null | true | | The scope of the channel. | | emailSubject | string,null | true | | The email subject of the notification. | | id | string | true | | The ID of the notification log. | | parentNotificationId | string,null | true | | The ID of the parent notification. | | policyId | string | true | | The ID of the policy that was used to send the notification. | | request | string,null | true | | The request that was sent in the notification. | | response | string,null | true | | The response that was received after sending the notification. | | retryCount | integer,null | true | | The number of attempts to send the notification. | | status | string | true | | The status of the notification. | | timestamp | string(date-time) | true | | The date when the notification was sent. | ### Enumerated Values | Property | Value | | --- | --- | | channelScope | [organization, Organization, ORGANIZATION, entity, Entity, ENTITY, template, Template, TEMPLATE] | ## NotificationLogListDataResponse ``` { "properties": { "channelId": { "description": "The ID of the channel that was used to send the notification.", "type": "string" }, "channelScope": { "description": "The scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ] }, "emailSubject": { "description": "The email subject.", "type": "string" }, "id": { "description": "The ID of the notification log.", "type": "string" }, "parentNotificationId": { "description": "The ID of the parent notification", "type": [ "string", "null" ] }, "policyId": { "description": "The ID of the policy that was used to send the notification.", "type": "string" }, "request": { "description": "The request that was sent in the notification.", "properties": { "body": { "description": "The body of the request.", "type": "string" }, "headers": { "description": "The headers of the request.", "type": "string" }, "url": { "description": "The URL of the request.", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "body", "headers", "url" ], "type": "object" }, "response": { "description": "The response that was received after sending the notification.", "properties": { "body": { "description": "The body of the response.", "type": "string" }, "duration": { "description": "The duration.", "type": "integer" }, "headers": { "description": "The headers of the response.", "type": "string" }, "statusCode": { "description": "The status code of the response.", "type": "string" } }, "required": [ "body", "duration", "headers", "statusCode" ], "type": "object" }, "retryCount": { "description": "The count of retries while sending the notification.", "type": "integer" }, "status": { "description": "The status of the notification.", "type": [ "string", "null" ] }, "timestamp": { "description": "The date and time when the notification was sent.", "format": "date-time", "type": "string" } }, "required": [ "channelId", "channelScope", "emailSubject", "id", "parentNotificationId", "policyId", "request", "response", "retryCount", "status", "timestamp" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelId | string | true | | The ID of the channel that was used to send the notification. | | channelScope | string,null | true | | The scope of the channel. | | emailSubject | string | true | | The email subject. | | id | string | true | | The ID of the notification log. | | parentNotificationId | string,null | true | | The ID of the parent notification | | policyId | string | true | | The ID of the policy that was used to send the notification. | | request | RequestNotification | true | | The request that was sent in the notification. | | response | ResponseNotification | true | | The response that was received after sending the notification. | | retryCount | integer | true | | The count of retries while sending the notification. | | status | string,null | true | | The status of the notification. | | timestamp | string(date-time) | true | | The date and time when the notification was sent. | ### Enumerated Values | Property | Value | | --- | --- | | channelScope | [organization, Organization, ORGANIZATION, entity, Entity, ENTITY, template, Template, TEMPLATE] | ## NotificationLogListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The array of notification logs.", "items": { "properties": { "channelId": { "description": "The ID of the channel that was used to send the notification.", "type": "string" }, "channelScope": { "description": "The scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ] }, "emailSubject": { "description": "The email subject.", "type": "string" }, "id": { "description": "The ID of the notification log.", "type": "string" }, "parentNotificationId": { "description": "The ID of the parent notification", "type": [ "string", "null" ] }, "policyId": { "description": "The ID of the policy that was used to send the notification.", "type": "string" }, "request": { "description": "The request that was sent in the notification.", "properties": { "body": { "description": "The body of the request.", "type": "string" }, "headers": { "description": "The headers of the request.", "type": "string" }, "url": { "description": "The URL of the request.", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "body", "headers", "url" ], "type": "object" }, "response": { "description": "The response that was received after sending the notification.", "properties": { "body": { "description": "The body of the response.", "type": "string" }, "duration": { "description": "The duration.", "type": "integer" }, "headers": { "description": "The headers of the response.", "type": "string" }, "statusCode": { "description": "The status code of the response.", "type": "string" } }, "required": [ "body", "duration", "headers", "statusCode" ], "type": "object" }, "retryCount": { "description": "The count of retries while sending the notification.", "type": "integer" }, "status": { "description": "The status of the notification.", "type": [ "string", "null" ] }, "timestamp": { "description": "The date and time when the notification was sent.", "format": "date-time", "type": "string" } }, "required": [ "channelId", "channelScope", "emailSubject", "id", "parentNotificationId", "policyId", "request", "response", "retryCount", "status", "timestamp" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of notifications.", "type": "integer" } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [NotificationLogListDataResponse] | true | | The array of notification logs. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | false | | The total number of notifications. | ## NotificationPoliciesListResponse ``` { "properties": { "count": { "description": "The number of policies returned.", "type": "integer" }, "data": { "description": "The notification policies.", "items": { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ] }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "The name of the notification policy.", "type": "string" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "active", "channel", "channelId", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "uid", "updatedAt" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of policies that satisfy the query.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of policies returned. | | data | [NotificationPolicyResponse] | true | maxItems: 1000 | The notification policies. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of policies that satisfy the query. | ## NotificationPolicyCreate ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string" }, "name": { "description": "The name of the new notification policy.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": "string" } }, "required": [ "channelId", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | false | | Whether the notification policy is active or not. | | channelId | string | true | | The ID of the notification channel to be used to send the notification. | | eventGroup | string | false | | The group of the event that triggers the notification. | | eventType | string | false | | The type of the event that triggers the notification. | | name | string | true | maxLength: 100 | The name of the new notification policy. | | orgId | string | false | | The ID of the organization that owns the notification policy. | ### Enumerated Values | Property | Value | | --- | --- | | eventGroup | [secure_config.all, dataset.all, file.all, comment.all, invite_job.all, deployment_prediction_explanations_computation.all, model_deployments.critical_health, model_deployments.critical_frequent_health_change, model_deployments.frequent_health_change, model_deployments.health, model_deployments.retraining_policy, inference_endpoints.health, model_deployments.management_agent, model_deployments.management_agent_health, prediction_request.all, challenger_management.all, challenger_replay.all, model_deployments.all, project.all, perma_delete_project.all, users_delete.all, applications.all, model_version.stage_transitions, model_version.all, use_case.all, batch_predictions.all, change_requests.all, custom_job_run.all, custom_job_run.unsuccessful, insights_computation.all, notebook_schedule.all, monitoring.all] | | eventType | [secure_config.created, secure_config.deleted, secure_config.shared, dataset.created, dataset.registered, dataset.deleted, datasets.deleted, datasetrelationship.created, dataset.shared, datasets.shared, file.created, file.registered, file.deleted, file.shared, comment.created, comment.updated, invite_job.completed, misc.asset_access_request, misc.webhook_connection_test, misc.webhook_resend, misc.email_verification, monitoring.spooler_channel_base, monitoring.spooler_channel_red, monitoring.spooler_channel_green, monitoring.external_model_nan_predictions, management.deploymentInfo, model_deployments.None, model_deployments.deployment_sharing, model_deployments.model_replacement, prediction_request.None, prediction_request.failed, model_deployments.humility_rule, model_deployments.model_replacement_lifecycle, model_deployments.model_replacement_started, model_deployments.model_replacement_succeeded, model_deployments.model_replacement_failed, model_deployments.model_replacement_validation_warning, model_deployments.deployment_creation, model_deployments.deployment_deletion, model_deployments.service_health_yellow_from_green, model_deployments.service_health_yellow_from_red, model_deployments.service_health_red, model_deployments.data_drift_yellow_from_green, model_deployments.data_drift_yellow_from_red, model_deployments.data_drift_red, model_deployments.accuracy_yellow_from_green, model_deployments.accuracy_yellow_from_red, model_deployments.accuracy_red, model_deployments.health.fairness_health.green_to_yellow, model_deployments.health.fairness_health.red_to_yellow, model_deployments.health.fairness_health.red, model_deployments.health.custom_metrics_health.green_to_yellow, model_deployments.health.custom_metrics_health.red_to_yellow, model_deployments.health.custom_metrics_health.red, model_deployments.health.base.green, model_deployments.service_health_green, model_deployments.data_drift_green, model_deployments.accuracy_green, model_deployments.health.fairness_health.green, model_deployments.health.custom_metrics_health.green, model_deployments.retraining_policy_run_started, model_deployments.retraining_policy_run_succeeded, model_deployments.retraining_policy_run_failed, model_deployments.challenger_scoring_success, model_deployments.challenger_scoring_data_warning, model_deployments.challenger_scoring_failure, model_deployments.challenger_scoring_started, model_deployments.challenger_model_validation_warning, model_deployments.challenger_model_created, model_deployments.challenger_model_deleted, model_deployments.actuals_upload_failed, model_deployments.actuals_upload_warning, model_deployments.training_data_baseline_calculation_started, model_deployments.training_data_baseline_calculation_completed, model_deployments.training_data_baseline_failed, model_deployments.custom_model_deployment_creation_started, model_deployments.custom_model_deployment_creation_completed, model_deployments.custom_model_deployment_creation_failed, model_deployments.custom_model_deployment_secure_config_failure, model_deployments.deployment_prediction_explanations_preview_job_submitted, model_deployments.deployment_prediction_explanations_preview_job_completed, model_deployments.deployment_prediction_explanations_preview_job_failed, model_deployments.custom_model_deployment_activated, model_deployments.custom_model_deployment_activation_failed, model_deployments.custom_model_deployment_deactivated, model_deployments.custom_model_deployment_deactivation_failed, model_deployments.prediction_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_warning, model_deployments.actuals_processing_rate_limit_reached, model_deployments.actuals_processing_rate_limit_warning, model_deployments.deployment_monitoring_data_cleared, model_deployments.deployment_launch_started, model_deployments.deployment_launch_succeeded, model_deployments.deployment_launch_failed, model_deployments.deployment_shutdown_started, model_deployments.deployment_shutdown_succeeded, model_deployments.deployment_shutdown_failed, model_deployments.endpoint_update_started, model_deployments.endpoint_update_succeeded, model_deployments.endpoint_update_failed, model_deployments.management_agent_service_health_green, model_deployments.management_agent_service_health_yellow, model_deployments.management_agent_service_health_red, model_deployments.management_agent_service_health_unknown, model_deployments.predictions_missing_association_id, model_deployments.prediction_result_rows_cleand_up, model_deployments.batch_deleted, model_deployments.batch_creation_limit_reached, model_deployments.batch_creation_limit_exceeded, model_deployments.batch_not_found, model_deployments.predictions_encountered_for_locked_batch, model_deployments.predictions_encountered_for_deleted_batch, model_deployments.scheduled_report_generated, model_deployments.predictions_timeliness_health_red, model_deployments.actuals_timeliness_health_red, model_deployments.service_health_still_red, model_deployments.data_drift_still_red, model_deployments.accuracy_still_red, model_deployments.health.fairness_health.still_red, model_deployments.health.custom_metrics_health.still_red, model_deployments.predictions_timeliness_health_still_red, model_deployments.actuals_timeliness_health_still_red, model_deployments.service_health_still_yellow, model_deployments.data_drift_still_yellow, model_deployments.accuracy_still_yellow, model_deployments.health.fairness_health.still_yellow, model_deployments.health.custom_metrics_health.still_yellow, model_deployments.prediction_payload_parsing_failure, model_deployments.deployment_inference_server_creation_started, model_deployments.deployment_inference_server_creation_failed, model_deployments.deployment_inference_server_creation_completed, model_deployments.deployment_inference_server_deletion, model_deployments.deployment_inference_server_idle_stopped, model_deployments.deployment_inference_server_maintenance_started, entity_notification_policy_template.shared, notification_channel_template.shared, project.created, project.deleted, project.shared, autopilot.complete, autopilot.started, autostart.failure, perma_delete_project.success, perma_delete_project.failure, users_delete.preview_started, users_delete.preview_completed, users_delete.preview_failed, users_delete.started, users_delete.completed, users_delete.failed, application.created, application.shared, model_version.added, model_version.stage_transition_from_registered_to_development, model_version.stage_transition_from_registered_to_staging, model_version.stage_transition_from_registered_to_production, model_version.stage_transition_from_registered_to_archived, model_version.stage_transition_from_development_to_registered, model_version.stage_transition_from_development_to_staging, model_version.stage_transition_from_development_to_production, model_version.stage_transition_from_development_to_archived, model_version.stage_transition_from_staging_to_registered, model_version.stage_transition_from_staging_to_development, model_version.stage_transition_from_staging_to_production, model_version.stage_transition_from_staging_to_archived, model_version.stage_transition_from_production_to_registered, model_version.stage_transition_from_production_to_development, model_version.stage_transition_from_production_to_staging, model_version.stage_transition_from_production_to_archived, model_version.stage_transition_from_archived_to_registered, model_version.stage_transition_from_archived_to_development, model_version.stage_transition_from_archived_to_production, model_version.stage_transition_from_archived_to_staging, use_case.shared, batch_predictions.success, batch_predictions.failed, batch_predictions.scheduler.auto_disabled, change_request.cancelled, change_request.created, change_request.deployment_approval_requested, change_request.resolved, change_request.proposed_changes_updated, change_request.pending, change_request.commenting_review_added, change_request.approving_review_added, change_request.changes_requesting_review_added, custom_job_run.success, custom_job_run.failed, custom_job_run.interrupted, custom_job_run.cancelled, prediction_explanations_computation.None, prediction_explanations_computation.prediction_explanations_preview_job_submitted, prediction_explanations_computation.prediction_explanations_preview_job_completed, prediction_explanations_computation.prediction_explanations_preview_job_failed, monitoring.rate_limit_enforced, notebook_schedule.created, notebook_schedule.failure, notebook_schedule.completed, abstract, moderation.metric.creation_error, moderation.metric.reporting_error, moderation.model.moderation_started, moderation.model.moderation_completed, moderation.model.pre_score_phase_started, moderation.model.pre_score_phase_completed, moderation.model.post_score_phase_started, moderation.model.post_score_phase_completed, moderation.model.config_error, moderation.model.runtime_error, moderation.model.scoring_started, moderation.model.scoring_completed, moderation.model.scoring_error] | ## NotificationPolicyMuteCreate ``` { "properties": { "entityId": { "description": "The ID of the entity to mute the notification for.", "type": "string" }, "policyId": { "description": "The ID of the policy to mute notification for.", "type": "string" } }, "required": [ "entityId", "policyId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityId | string | true | | The ID of the entity to mute the notification for. | | policyId | string | true | | The ID of the policy to mute notification for. | ## NotificationPolicyMuteListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of notification policy mutes.", "items": { "properties": { "entityId": { "description": "The ID of the entity to mute the notification for.", "type": "string" }, "id": { "description": "The ID of the notification policy mute.", "type": "string" }, "policyId": { "description": "The ID of the policy to mute notification for.", "type": "string" }, "uid": { "description": "The UID of the notification policy mute.", "type": "string" } }, "required": [ "entityId", "id", "policyId", "uid" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items returned by the query.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [NotificationPolicyMuteResponse] | true | maxItems: 100 | The list of notification policy mutes. | | next | string,null(uri) | true | | The URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL pointing to the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items returned by the query. | ## NotificationPolicyMuteResponse ``` { "properties": { "entityId": { "description": "The ID of the entity to mute the notification for.", "type": "string" }, "id": { "description": "The ID of the notification policy mute.", "type": "string" }, "policyId": { "description": "The ID of the policy to mute notification for.", "type": "string" }, "uid": { "description": "The UID of the notification policy mute.", "type": "string" } }, "required": [ "entityId", "id", "policyId", "uid" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityId | string | true | | The ID of the entity to mute the notification for. | | id | string | true | | The ID of the notification policy mute. | | policyId | string | true | | The ID of the policy to mute notification for. | | uid | string | true | | The UID of the notification policy mute. | ## NotificationPolicyResponse ``` { "properties": { "active": { "description": "Defines if the notification policy is active or not.", "type": "boolean" }, "channel": { "description": "The notification channel information used to send the notification.", "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": [ "string", "null" ] }, "createdAt": { "description": "The date of the notification channel creation.", "format": "date-time", "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification channel.", "type": "string" }, "languageCode": { "description": "The preferred language code.", "enum": [ "en", "es_419", "fr", "ja", "ko", "ptBR" ], "type": [ "string", "null" ] }, "name": { "description": "The name of the new notification channel.", "type": "string" }, "orgId": { "description": "The ID of the organization that the notification channel belongs to.", "type": [ "string", "null" ] }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": [ "string", "null" ] }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the channel.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The date when the channel was updated.", "format": "date-time", "type": [ "string", "null" ] }, "validateSsl": { "description": "Whether to validate SSL for the notification channel.", "type": [ "boolean", "null" ] } }, "required": [ "channelType", "contentType", "createdAt", "customHeaders", "emailAddress", "id", "languageCode", "name", "orgId", "payloadUrl", "secretToken", "uid", "updatedAt", "validateSsl" ], "type": "object" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string" }, "created": { "description": "The date when the policy was created.", "format": "date-time", "type": [ "string", "null" ] }, "eventGroup": { "description": "The group of the event that triggers the notification.", "properties": { "events": { "description": "The events included in this group.", "items": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "id": { "description": "The ID of the event group.", "type": "string" }, "label": { "description": "The display name of the event group.", "type": "string" } }, "required": [ "events", "id", "label" ], "type": "object" }, "eventType": { "description": "The type of the event that triggers the notification.", "properties": { "id": { "description": "The ID of the event type.", "type": "string" }, "label": { "description": "The display name of the event type.", "type": "string" } }, "required": [ "id", "label" ], "type": "object" }, "id": { "description": "The ID of the policy.", "type": "string" }, "lastTriggered": { "description": "The date when the last notification with the policy was triggered.", "format": "date-time", "type": [ "string", "null" ] }, "name": { "description": "The name of the notification policy.", "type": "string" }, "orgId": { "description": "The ID of the organization that owns the notification policy.", "type": [ "string", "null" ] }, "uid": { "description": "The identifier of the user who created the policy.", "type": "string" }, "updatedAt": { "description": "The date when the policy was updated.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "active", "channel", "channelId", "created", "eventGroup", "eventType", "id", "lastTriggered", "name", "orgId", "uid", "updatedAt" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | true | | Defines if the notification policy is active or not. | | channel | NotificationChannel | true | | The notification channel information used to send the notification. | | channelId | string | true | | The ID of the notification channel to be used to send the notification. | | created | string,null(date-time) | true | | The date when the policy was created. | | eventGroup | EventGroup | true | | The group of the event that triggers the notification. | | eventType | EventType | true | | The type of the event that triggers the notification. | | id | string | true | | The ID of the policy. | | lastTriggered | string,null(date-time) | true | | The date when the last notification with the policy was triggered. | | name | string | true | | The name of the notification policy. | | orgId | string,null | true | | The ID of the organization that owns the notification policy. | | uid | string | true | | The identifier of the user who created the policy. | | updatedAt | string,null(date-time) | true | | The date when the policy was updated. | ## NotificationPolicyUpdate ``` { "properties": { "active": { "description": "Whether the notification policy is active or not.", "type": "boolean" }, "channelId": { "description": "The ID of the notification channel to be used to send the notification.", "type": "string" }, "eventGroup": { "description": "The group of the event that triggers the notification.", "enum": [ "secure_config.all", "dataset.all", "file.all", "comment.all", "invite_job.all", "deployment_prediction_explanations_computation.all", "model_deployments.critical_health", "model_deployments.critical_frequent_health_change", "model_deployments.frequent_health_change", "model_deployments.health", "model_deployments.retraining_policy", "inference_endpoints.health", "model_deployments.management_agent", "model_deployments.management_agent_health", "prediction_request.all", "challenger_management.all", "challenger_replay.all", "model_deployments.all", "project.all", "perma_delete_project.all", "users_delete.all", "applications.all", "model_version.stage_transitions", "model_version.all", "use_case.all", "batch_predictions.all", "change_requests.all", "custom_job_run.all", "custom_job_run.unsuccessful", "insights_computation.all", "notebook_schedule.all", "monitoring.all" ], "type": "string" }, "eventType": { "description": "The type of the event that triggers the notification.", "enum": [ "secure_config.created", "secure_config.deleted", "secure_config.shared", "dataset.created", "dataset.registered", "dataset.deleted", "datasets.deleted", "datasetrelationship.created", "dataset.shared", "datasets.shared", "file.created", "file.registered", "file.deleted", "file.shared", "comment.created", "comment.updated", "invite_job.completed", "misc.asset_access_request", "misc.webhook_connection_test", "misc.webhook_resend", "misc.email_verification", "monitoring.spooler_channel_base", "monitoring.spooler_channel_red", "monitoring.spooler_channel_green", "monitoring.external_model_nan_predictions", "management.deploymentInfo", "model_deployments.None", "model_deployments.deployment_sharing", "model_deployments.model_replacement", "prediction_request.None", "prediction_request.failed", "model_deployments.humility_rule", "model_deployments.model_replacement_lifecycle", "model_deployments.model_replacement_started", "model_deployments.model_replacement_succeeded", "model_deployments.model_replacement_failed", "model_deployments.model_replacement_validation_warning", "model_deployments.deployment_creation", "model_deployments.deployment_deletion", "model_deployments.service_health_yellow_from_green", "model_deployments.service_health_yellow_from_red", "model_deployments.service_health_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.data_drift_yellow_from_red", "model_deployments.data_drift_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.accuracy_yellow_from_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.green_to_yellow", "model_deployments.health.fairness_health.red_to_yellow", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.green_to_yellow", "model_deployments.health.custom_metrics_health.red_to_yellow", "model_deployments.health.custom_metrics_health.red", "model_deployments.health.base.green", "model_deployments.service_health_green", "model_deployments.data_drift_green", "model_deployments.accuracy_green", "model_deployments.health.fairness_health.green", "model_deployments.health.custom_metrics_health.green", "model_deployments.retraining_policy_run_started", "model_deployments.retraining_policy_run_succeeded", "model_deployments.retraining_policy_run_failed", "model_deployments.challenger_scoring_success", "model_deployments.challenger_scoring_data_warning", "model_deployments.challenger_scoring_failure", "model_deployments.challenger_scoring_started", "model_deployments.challenger_model_validation_warning", "model_deployments.challenger_model_created", "model_deployments.challenger_model_deleted", "model_deployments.actuals_upload_failed", "model_deployments.actuals_upload_warning", "model_deployments.training_data_baseline_calculation_started", "model_deployments.training_data_baseline_calculation_completed", "model_deployments.training_data_baseline_failed", "model_deployments.custom_model_deployment_creation_started", "model_deployments.custom_model_deployment_creation_completed", "model_deployments.custom_model_deployment_creation_failed", "model_deployments.custom_model_deployment_secure_config_failure", "model_deployments.deployment_prediction_explanations_preview_job_submitted", "model_deployments.deployment_prediction_explanations_preview_job_completed", "model_deployments.deployment_prediction_explanations_preview_job_failed", "model_deployments.custom_model_deployment_activated", "model_deployments.custom_model_deployment_activation_failed", "model_deployments.custom_model_deployment_deactivated", "model_deployments.custom_model_deployment_deactivation_failed", "model_deployments.prediction_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_reached", "model_deployments.prediction_data_processing_rate_limit_warning", "model_deployments.actuals_processing_rate_limit_reached", "model_deployments.actuals_processing_rate_limit_warning", "model_deployments.deployment_monitoring_data_cleared", "model_deployments.deployment_launch_started", "model_deployments.deployment_launch_succeeded", "model_deployments.deployment_launch_failed", "model_deployments.deployment_shutdown_started", "model_deployments.deployment_shutdown_succeeded", "model_deployments.deployment_shutdown_failed", "model_deployments.endpoint_update_started", "model_deployments.endpoint_update_succeeded", "model_deployments.endpoint_update_failed", "model_deployments.management_agent_service_health_green", "model_deployments.management_agent_service_health_yellow", "model_deployments.management_agent_service_health_red", "model_deployments.management_agent_service_health_unknown", "model_deployments.predictions_missing_association_id", "model_deployments.prediction_result_rows_cleand_up", "model_deployments.batch_deleted", "model_deployments.batch_creation_limit_reached", "model_deployments.batch_creation_limit_exceeded", "model_deployments.batch_not_found", "model_deployments.predictions_encountered_for_locked_batch", "model_deployments.predictions_encountered_for_deleted_batch", "model_deployments.scheduled_report_generated", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "model_deployments.service_health_still_red", "model_deployments.data_drift_still_red", "model_deployments.accuracy_still_red", "model_deployments.health.fairness_health.still_red", "model_deployments.health.custom_metrics_health.still_red", "model_deployments.predictions_timeliness_health_still_red", "model_deployments.actuals_timeliness_health_still_red", "model_deployments.service_health_still_yellow", "model_deployments.data_drift_still_yellow", "model_deployments.accuracy_still_yellow", "model_deployments.health.fairness_health.still_yellow", "model_deployments.health.custom_metrics_health.still_yellow", "model_deployments.prediction_payload_parsing_failure", "model_deployments.deployment_inference_server_creation_started", "model_deployments.deployment_inference_server_creation_failed", "model_deployments.deployment_inference_server_creation_completed", "model_deployments.deployment_inference_server_deletion", "model_deployments.deployment_inference_server_idle_stopped", "model_deployments.deployment_inference_server_maintenance_started", "entity_notification_policy_template.shared", "notification_channel_template.shared", "project.created", "project.deleted", "project.shared", "autopilot.complete", "autopilot.started", "autostart.failure", "perma_delete_project.success", "perma_delete_project.failure", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "users_delete.started", "users_delete.completed", "users_delete.failed", "application.created", "application.shared", "model_version.added", "model_version.stage_transition_from_registered_to_development", "model_version.stage_transition_from_registered_to_staging", "model_version.stage_transition_from_registered_to_production", "model_version.stage_transition_from_registered_to_archived", "model_version.stage_transition_from_development_to_registered", "model_version.stage_transition_from_development_to_staging", "model_version.stage_transition_from_development_to_production", "model_version.stage_transition_from_development_to_archived", "model_version.stage_transition_from_staging_to_registered", "model_version.stage_transition_from_staging_to_development", "model_version.stage_transition_from_staging_to_production", "model_version.stage_transition_from_staging_to_archived", "model_version.stage_transition_from_production_to_registered", "model_version.stage_transition_from_production_to_development", "model_version.stage_transition_from_production_to_staging", "model_version.stage_transition_from_production_to_archived", "model_version.stage_transition_from_archived_to_registered", "model_version.stage_transition_from_archived_to_development", "model_version.stage_transition_from_archived_to_production", "model_version.stage_transition_from_archived_to_staging", "use_case.shared", "batch_predictions.success", "batch_predictions.failed", "batch_predictions.scheduler.auto_disabled", "change_request.cancelled", "change_request.created", "change_request.deployment_approval_requested", "change_request.resolved", "change_request.proposed_changes_updated", "change_request.pending", "change_request.commenting_review_added", "change_request.approving_review_added", "change_request.changes_requesting_review_added", "custom_job_run.success", "custom_job_run.failed", "custom_job_run.interrupted", "custom_job_run.cancelled", "prediction_explanations_computation.None", "prediction_explanations_computation.prediction_explanations_preview_job_submitted", "prediction_explanations_computation.prediction_explanations_preview_job_completed", "prediction_explanations_computation.prediction_explanations_preview_job_failed", "monitoring.rate_limit_enforced", "notebook_schedule.created", "notebook_schedule.failure", "notebook_schedule.completed", "abstract", "moderation.metric.creation_error", "moderation.metric.reporting_error", "moderation.model.moderation_started", "moderation.model.moderation_completed", "moderation.model.pre_score_phase_started", "moderation.model.pre_score_phase_completed", "moderation.model.post_score_phase_started", "moderation.model.post_score_phase_completed", "moderation.model.config_error", "moderation.model.runtime_error", "moderation.model.scoring_started", "moderation.model.scoring_completed", "moderation.model.scoring_error" ], "type": "string" }, "name": { "description": "The name of the notification policy.", "maxLength": 100, "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | false | | Whether the notification policy is active or not. | | channelId | string | false | | The ID of the notification channel to be used to send the notification. | | eventGroup | string | false | | The group of the event that triggers the notification. | | eventType | string | false | | The type of the event that triggers the notification. | | name | string | false | maxLength: 100 | The name of the notification policy. | ### Enumerated Values | Property | Value | | --- | --- | | eventGroup | [secure_config.all, dataset.all, file.all, comment.all, invite_job.all, deployment_prediction_explanations_computation.all, model_deployments.critical_health, model_deployments.critical_frequent_health_change, model_deployments.frequent_health_change, model_deployments.health, model_deployments.retraining_policy, inference_endpoints.health, model_deployments.management_agent, model_deployments.management_agent_health, prediction_request.all, challenger_management.all, challenger_replay.all, model_deployments.all, project.all, perma_delete_project.all, users_delete.all, applications.all, model_version.stage_transitions, model_version.all, use_case.all, batch_predictions.all, change_requests.all, custom_job_run.all, custom_job_run.unsuccessful, insights_computation.all, notebook_schedule.all, monitoring.all] | | eventType | [secure_config.created, secure_config.deleted, secure_config.shared, dataset.created, dataset.registered, dataset.deleted, datasets.deleted, datasetrelationship.created, dataset.shared, datasets.shared, file.created, file.registered, file.deleted, file.shared, comment.created, comment.updated, invite_job.completed, misc.asset_access_request, misc.webhook_connection_test, misc.webhook_resend, misc.email_verification, monitoring.spooler_channel_base, monitoring.spooler_channel_red, monitoring.spooler_channel_green, monitoring.external_model_nan_predictions, management.deploymentInfo, model_deployments.None, model_deployments.deployment_sharing, model_deployments.model_replacement, prediction_request.None, prediction_request.failed, model_deployments.humility_rule, model_deployments.model_replacement_lifecycle, model_deployments.model_replacement_started, model_deployments.model_replacement_succeeded, model_deployments.model_replacement_failed, model_deployments.model_replacement_validation_warning, model_deployments.deployment_creation, model_deployments.deployment_deletion, model_deployments.service_health_yellow_from_green, model_deployments.service_health_yellow_from_red, model_deployments.service_health_red, model_deployments.data_drift_yellow_from_green, model_deployments.data_drift_yellow_from_red, model_deployments.data_drift_red, model_deployments.accuracy_yellow_from_green, model_deployments.accuracy_yellow_from_red, model_deployments.accuracy_red, model_deployments.health.fairness_health.green_to_yellow, model_deployments.health.fairness_health.red_to_yellow, model_deployments.health.fairness_health.red, model_deployments.health.custom_metrics_health.green_to_yellow, model_deployments.health.custom_metrics_health.red_to_yellow, model_deployments.health.custom_metrics_health.red, model_deployments.health.base.green, model_deployments.service_health_green, model_deployments.data_drift_green, model_deployments.accuracy_green, model_deployments.health.fairness_health.green, model_deployments.health.custom_metrics_health.green, model_deployments.retraining_policy_run_started, model_deployments.retraining_policy_run_succeeded, model_deployments.retraining_policy_run_failed, model_deployments.challenger_scoring_success, model_deployments.challenger_scoring_data_warning, model_deployments.challenger_scoring_failure, model_deployments.challenger_scoring_started, model_deployments.challenger_model_validation_warning, model_deployments.challenger_model_created, model_deployments.challenger_model_deleted, model_deployments.actuals_upload_failed, model_deployments.actuals_upload_warning, model_deployments.training_data_baseline_calculation_started, model_deployments.training_data_baseline_calculation_completed, model_deployments.training_data_baseline_failed, model_deployments.custom_model_deployment_creation_started, model_deployments.custom_model_deployment_creation_completed, model_deployments.custom_model_deployment_creation_failed, model_deployments.custom_model_deployment_secure_config_failure, model_deployments.deployment_prediction_explanations_preview_job_submitted, model_deployments.deployment_prediction_explanations_preview_job_completed, model_deployments.deployment_prediction_explanations_preview_job_failed, model_deployments.custom_model_deployment_activated, model_deployments.custom_model_deployment_activation_failed, model_deployments.custom_model_deployment_deactivated, model_deployments.custom_model_deployment_deactivation_failed, model_deployments.prediction_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_reached, model_deployments.prediction_data_processing_rate_limit_warning, model_deployments.actuals_processing_rate_limit_reached, model_deployments.actuals_processing_rate_limit_warning, model_deployments.deployment_monitoring_data_cleared, model_deployments.deployment_launch_started, model_deployments.deployment_launch_succeeded, model_deployments.deployment_launch_failed, model_deployments.deployment_shutdown_started, model_deployments.deployment_shutdown_succeeded, model_deployments.deployment_shutdown_failed, model_deployments.endpoint_update_started, model_deployments.endpoint_update_succeeded, model_deployments.endpoint_update_failed, model_deployments.management_agent_service_health_green, model_deployments.management_agent_service_health_yellow, model_deployments.management_agent_service_health_red, model_deployments.management_agent_service_health_unknown, model_deployments.predictions_missing_association_id, model_deployments.prediction_result_rows_cleand_up, model_deployments.batch_deleted, model_deployments.batch_creation_limit_reached, model_deployments.batch_creation_limit_exceeded, model_deployments.batch_not_found, model_deployments.predictions_encountered_for_locked_batch, model_deployments.predictions_encountered_for_deleted_batch, model_deployments.scheduled_report_generated, model_deployments.predictions_timeliness_health_red, model_deployments.actuals_timeliness_health_red, model_deployments.service_health_still_red, model_deployments.data_drift_still_red, model_deployments.accuracy_still_red, model_deployments.health.fairness_health.still_red, model_deployments.health.custom_metrics_health.still_red, model_deployments.predictions_timeliness_health_still_red, model_deployments.actuals_timeliness_health_still_red, model_deployments.service_health_still_yellow, model_deployments.data_drift_still_yellow, model_deployments.accuracy_still_yellow, model_deployments.health.fairness_health.still_yellow, model_deployments.health.custom_metrics_health.still_yellow, model_deployments.prediction_payload_parsing_failure, model_deployments.deployment_inference_server_creation_started, model_deployments.deployment_inference_server_creation_failed, model_deployments.deployment_inference_server_creation_completed, model_deployments.deployment_inference_server_deletion, model_deployments.deployment_inference_server_idle_stopped, model_deployments.deployment_inference_server_maintenance_started, entity_notification_policy_template.shared, notification_channel_template.shared, project.created, project.deleted, project.shared, autopilot.complete, autopilot.started, autostart.failure, perma_delete_project.success, perma_delete_project.failure, users_delete.preview_started, users_delete.preview_completed, users_delete.preview_failed, users_delete.started, users_delete.completed, users_delete.failed, application.created, application.shared, model_version.added, model_version.stage_transition_from_registered_to_development, model_version.stage_transition_from_registered_to_staging, model_version.stage_transition_from_registered_to_production, model_version.stage_transition_from_registered_to_archived, model_version.stage_transition_from_development_to_registered, model_version.stage_transition_from_development_to_staging, model_version.stage_transition_from_development_to_production, model_version.stage_transition_from_development_to_archived, model_version.stage_transition_from_staging_to_registered, model_version.stage_transition_from_staging_to_development, model_version.stage_transition_from_staging_to_production, model_version.stage_transition_from_staging_to_archived, model_version.stage_transition_from_production_to_registered, model_version.stage_transition_from_production_to_development, model_version.stage_transition_from_production_to_staging, model_version.stage_transition_from_production_to_archived, model_version.stage_transition_from_archived_to_registered, model_version.stage_transition_from_archived_to_development, model_version.stage_transition_from_archived_to_production, model_version.stage_transition_from_archived_to_staging, use_case.shared, batch_predictions.success, batch_predictions.failed, batch_predictions.scheduler.auto_disabled, change_request.cancelled, change_request.created, change_request.deployment_approval_requested, change_request.resolved, change_request.proposed_changes_updated, change_request.pending, change_request.commenting_review_added, change_request.approving_review_added, change_request.changes_requesting_review_added, custom_job_run.success, custom_job_run.failed, custom_job_run.interrupted, custom_job_run.cancelled, prediction_explanations_computation.None, prediction_explanations_computation.prediction_explanations_preview_job_submitted, prediction_explanations_computation.prediction_explanations_preview_job_completed, prediction_explanations_computation.prediction_explanations_preview_job_failed, monitoring.rate_limit_enforced, notebook_schedule.created, notebook_schedule.failure, notebook_schedule.completed, abstract, moderation.metric.creation_error, moderation.metric.reporting_error, moderation.model.moderation_started, moderation.model.moderation_completed, moderation.model.pre_score_phase_started, moderation.model.pre_score_phase_completed, moderation.model.post_score_phase_started, moderation.model.post_score_phase_completed, moderation.model.config_error, moderation.model.runtime_error, moderation.model.scoring_started, moderation.model.scoring_completed, moderation.model.scoring_error] | ## NotificationResend ``` { "properties": { "notificationId": { "description": "The ID of the notification to resend.", "type": "string" } }, "required": [ "notificationId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notificationId | string | true | | The ID of the notification to resend. | ## NotificationWebhookChannelStatusResponse ``` { "properties": { "notificationLog": { "description": "The notification log record.", "properties": { "channelId": { "description": "The ID of the channel that was used to send the notification.", "type": "string" }, "channelScope": { "description": "The scope of the channel.", "enum": [ "organization", "Organization", "ORGANIZATION", "entity", "Entity", "ENTITY", "template", "Template", "TEMPLATE" ], "type": [ "string", "null" ] }, "emailSubject": { "description": "The email subject of the notification.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the notification log.", "type": "string" }, "parentNotificationId": { "description": "The ID of the parent notification.", "type": [ "string", "null" ] }, "policyId": { "description": "The ID of the policy that was used to send the notification.", "type": "string" }, "request": { "description": "The request that was sent in the notification.", "type": [ "string", "null" ] }, "response": { "description": "The response that was received after sending the notification.", "type": [ "string", "null" ] }, "retryCount": { "description": "The number of attempts to send the notification.", "type": [ "integer", "null" ] }, "status": { "description": "The status of the notification.", "type": "string" }, "timestamp": { "description": "The date when the notification was sent.", "format": "date-time", "type": "string" } }, "required": [ "channelId", "channelScope", "emailSubject", "id", "parentNotificationId", "policyId", "request", "response", "retryCount", "status", "timestamp" ], "type": "object" }, "status": { "description": "The status of the test notification.", "type": "string" } }, "required": [ "notificationLog", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notificationLog | NotificationLog | true | | The notification log record. | | status | string | true | | The status of the test notification. | ## NotificationWebhookChannelTestCreate ``` { "properties": { "channelType": { "description": "The type of the new notification channel.", "enum": [ "Database", "Email", "InApp", "InsightsComputations", "MSTeams", "Slack", "Webhook" ], "type": "string" }, "contentType": { "description": "The content type of the messages of the new notification channel.", "enum": [ "application/json", "application/x-www-form-urlencoded" ], "type": "string" }, "customHeaders": { "description": "Custom headers and their values to be sent in the new notification channel.", "items": { "properties": { "name": { "description": "The name of the header.", "type": "string" }, "value": { "description": "The value of the header.", "type": "string" } }, "required": [ "name", "value" ], "type": "object" }, "maxItems": 100, "type": "array" }, "emailAddress": { "description": "The email address to be used in the new notification channel.", "type": "string" }, "name": { "description": "The name of the new notification channel.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The identifier of the organization that notification channel belongs to.", "type": "string" }, "payloadUrl": { "description": "The payload URL of the new notification channel.", "format": "uri", "type": "string" }, "secretToken": { "description": "Secret token to be used for new notification channel.", "type": "string" }, "validateSsl": { "description": "Whether SSL will be validated in the notification channel.", "type": "boolean" } }, "required": [ "channelType", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | channelType | string | true | | The type of the new notification channel. | | contentType | string | false | | The content type of the messages of the new notification channel. | | customHeaders | [CustomerHeader] | false | maxItems: 100 | Custom headers and their values to be sent in the new notification channel. | | emailAddress | string | false | | The email address to be used in the new notification channel. | | name | string | true | maxLength: 100 | The name of the new notification channel. | | orgId | string | false | | The identifier of the organization that notification channel belongs to. | | payloadUrl | string(uri) | false | | The payload URL of the new notification channel. | | secretToken | string | false | | Secret token to be used for new notification channel. | | validateSsl | boolean | false | | Whether SSL will be validated in the notification channel. | ### Enumerated Values | Property | Value | | --- | --- | | channelType | [Database, Email, InApp, InsightsComputations, MSTeams, Slack, Webhook] | | contentType | [application/json, application/x-www-form-urlencoded] | ## NotificationWebhookChannelTestId ``` { "properties": { "notificationId": { "description": "The ID of the notification.", "type": "string" } }, "required": [ "notificationId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notificationId | string | true | | The ID of the notification. | ## PredictionRemoteEventData ``` { "description": "Prediction event payload.", "properties": { "error_code": { "description": "The error code if any.", "type": [ "string", "null" ] }, "model_id": { "description": "The identifier of the model.", "type": [ "string", "null" ] }, "response_body": { "description": "The response body message of the prediction event.", "type": "string" }, "status_code": { "description": "The response status code of the prediction event.", "type": "integer" }, "user_id": { "description": "The identifier of the user that accesses the deployment.", "type": [ "string", "null" ] } }, "required": [ "response_body", "status_code" ], "type": "object" } ``` Prediction event payload. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | error_code | string,null | false | | The error code if any. | | model_id | string,null | false | | The identifier of the model. | | response_body | string | true | | The response body message of the prediction event. | | status_code | integer | true | | The response status code of the prediction event. | | user_id | string,null | false | | The identifier of the user that accesses the deployment. | ## RemoteEventCreate ``` { "properties": { "data": { "description": "Event payload.", "properties": { "newModelId": { "description": "The identifier of the model after replacement.", "type": "string" }, "oldModelId": { "description": "The identifier of the model before replacement.", "type": "string" }, "reason": { "description": "The explanation on why the model has been replaced.", "type": "string" } }, "type": "object" }, "deploymentId": { "description": "The identifier of the deployment associated with the event.", "type": "string" }, "eventType": { "description": "The type of the event. Labels in all_lower_case are deprecated.", "enum": [ "deploymentInfo", "externalNaNPredictions", "management.deploymentInfo", "model_deployments.accuracy_green", "model_deployments.accuracy_red", "model_deployments.accuracy_yellow_from_green", "model_deployments.data_drift_green", "model_deployments.data_drift_red", "model_deployments.data_drift_yellow_from_green", "model_deployments.model_replacement", "model_deployments.service_health_green", "model_deployments.service_health_red", "model_deployments.service_health_yellow_from_green", "moderationMetricCreationError", "moderationMetricReportingError", "moderationModelConfigError", "moderationModelModerationCompleted", "moderationModelModerationStarted", "moderationModelPostScorePhaseCompleted", "moderationModelPostScorePhaseStarted", "moderationModelPreScorePhaseCompleted", "moderationModelPreScorePhaseStarted", "moderationModelRuntimeError", "moderationModelScoringCompleted", "moderationModelScoringError", "moderationModelScoringStarted", "monitoring.external_model_nan_predictions", "monitoring.spooler_channel_green", "monitoring.spooler_channel_red", "predictionRequestFailed", "prediction_request.failed", "serviceHealthChangeGreen", "serviceHealthChangeRed", "serviceHealthChangeYellowFromGreen", "spoolerChannelGreen", "spoolerChannelRed" ], "type": "string" }, "externalNanPredictionsData": { "description": "The external NaN predictions event payload.", "properties": { "count": { "description": "The number of NaN predictions by the external model.", "exclusiveMinimum": 0, "type": "integer" }, "modelId": { "default": null, "description": "The identifier of the model.", "type": [ "string", "null" ] } }, "required": [ "count", "modelId" ], "type": "object" }, "message": { "description": "Descriptive message for health events.", "maxLength": 16384, "type": "string" }, "moderationData": { "description": "The moderation event information.", "properties": { "guardName": { "description": "Name or label of the guard.", "maxLength": 255, "type": "string" }, "metricName": { "description": "Name or label of the metric.", "maxLength": 255, "type": "string" } }, "required": [ "guardName", "metricName" ], "type": "object", "x-versionadded": "v2.35" }, "orgId": { "description": "The identifier of the organization associated with the event.", "type": [ "string", "null" ] }, "predictionEnvironmentId": { "description": "The identifier of the prediction environment associated with the event.", "type": "string" }, "predictionRequestData": { "description": "Prediction event payload.", "properties": { "error_code": { "description": "The error code if any.", "type": [ "string", "null" ] }, "model_id": { "description": "The identifier of the model.", "type": [ "string", "null" ] }, "response_body": { "description": "The response body message of the prediction event.", "type": "string" }, "status_code": { "description": "The response status code of the prediction event.", "type": "integer" }, "user_id": { "description": "The identifier of the user that accesses the deployment.", "type": [ "string", "null" ] } }, "required": [ "response_body", "status_code" ], "type": "object" }, "spoolerChannelData": { "description": "Spooler channel event payload.", "properties": { "name": { "description": "Name or label of the spooler channel.", "maxLength": 512, "type": "string" }, "type": { "description": "Type of the spooler channel.", "enum": [ "asyncMemory", "filesystem", "kafka", "memory", "pubSub", "rabbitMQ", "sqs" ], "type": "string" } }, "required": [ "name", "type" ], "type": "object" }, "timestamp": { "description": "The time when the event occurred.", "format": "date-time", "type": "string" }, "title": { "description": "The title of the event.", "maxLength": 512, "type": "string" } }, "required": [ "eventType", "timestamp" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | RemoteEventData | false | | Event payload. | | deploymentId | string | false | | The identifier of the deployment associated with the event. | | eventType | string | true | | The type of the event. Labels in all_lower_case are deprecated. | | externalNanPredictionsData | ExternalNaNPredictionsEventData | false | | The external NaN predictions event payload. | | message | string | false | maxLength: 16384 | Descriptive message for health events. | | moderationData | ModerationData | false | | The moderation event information. | | orgId | string,null | false | | The identifier of the organization associated with the event. | | predictionEnvironmentId | string | false | | The identifier of the prediction environment associated with the event. | | predictionRequestData | PredictionRemoteEventData | false | | Prediction event payload. | | spoolerChannelData | SpoolerChannelEventData | false | | Spooler channel event payload. | | timestamp | string(date-time) | true | | The time when the event occurred. | | title | string | false | maxLength: 512 | The title of the event. | ### Enumerated Values | Property | Value | | --- | --- | | eventType | [deploymentInfo, externalNaNPredictions, management.deploymentInfo, model_deployments.accuracy_green, model_deployments.accuracy_red, model_deployments.accuracy_yellow_from_green, model_deployments.data_drift_green, model_deployments.data_drift_red, model_deployments.data_drift_yellow_from_green, model_deployments.model_replacement, model_deployments.service_health_green, model_deployments.service_health_red, model_deployments.service_health_yellow_from_green, moderationMetricCreationError, moderationMetricReportingError, moderationModelConfigError, moderationModelModerationCompleted, moderationModelModerationStarted, moderationModelPostScorePhaseCompleted, moderationModelPostScorePhaseStarted, moderationModelPreScorePhaseCompleted, moderationModelPreScorePhaseStarted, moderationModelRuntimeError, moderationModelScoringCompleted, moderationModelScoringError, moderationModelScoringStarted, monitoring.external_model_nan_predictions, monitoring.spooler_channel_green, monitoring.spooler_channel_red, predictionRequestFailed, prediction_request.failed, serviceHealthChangeGreen, serviceHealthChangeRed, serviceHealthChangeYellowFromGreen, spoolerChannelGreen, spoolerChannelRed] | ## RemoteEventData ``` { "description": "Event payload.", "properties": { "newModelId": { "description": "The identifier of the model after replacement.", "type": "string" }, "oldModelId": { "description": "The identifier of the model before replacement.", "type": "string" }, "reason": { "description": "The explanation on why the model has been replaced.", "type": "string" } }, "type": "object" } ``` Event payload. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | newModelId | string | false | | The identifier of the model after replacement. | | oldModelId | string | false | | The identifier of the model before replacement. | | reason | string | false | | The explanation on why the model has been replaced. | ## RequestNotification ``` { "description": "The request that was sent in the notification.", "properties": { "body": { "description": "The body of the request.", "type": "string" }, "headers": { "description": "The headers of the request.", "type": "string" }, "url": { "description": "The URL of the request.", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "body", "headers", "url" ], "type": "object" } ``` The request that was sent in the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | body | string | true | | The body of the request. | | headers | string | true | | The headers of the request. | | url | string,null(uri) | true | | The URL of the request. | ## ResponseNotification ``` { "description": "The response that was received after sending the notification.", "properties": { "body": { "description": "The body of the response.", "type": "string" }, "duration": { "description": "The duration.", "type": "integer" }, "headers": { "description": "The headers of the response.", "type": "string" }, "statusCode": { "description": "The status code of the response.", "type": "string" } }, "required": [ "body", "duration", "headers", "statusCode" ], "type": "object" } ``` The response that was received after sending the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | body | string | true | | The body of the response. | | duration | integer | true | | The duration. | | headers | string | true | | The headers of the response. | | statusCode | string | true | | The status code of the response. | ## SharedRolesUpdate ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "Array of GrantAccessControl objects., up to maximum 100 objects.", "items": { "oneOf": [ { "properties": { "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | Name of the action being taken. The only operation is 'updateRoles'. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | Array of GrantAccessControl objects., up to maximum 100 objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithUsername | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithId | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## SharingListV2Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControlV2] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of items matching the condition. | ## SpoolerChannelEventData ``` { "description": "Spooler channel event payload.", "properties": { "name": { "description": "Name or label of the spooler channel.", "maxLength": 512, "type": "string" }, "type": { "description": "Type of the spooler channel.", "enum": [ "asyncMemory", "filesystem", "kafka", "memory", "pubSub", "rabbitMQ", "sqs" ], "type": "string" } }, "required": [ "name", "type" ], "type": "object" } ``` Spooler channel event payload. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 512 | Name or label of the spooler channel. | | type | string | true | | Type of the spooler channel. | ### Enumerated Values | Property | Value | | --- | --- | | type | [asyncMemory, filesystem, kafka, memory, pubSub, rabbitMQ, sqs] | ## SuccessfulInviteData ``` { "description": "Details about the successful invite.", "properties": { "email": { "description": "The email address of the invited user.", "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" } }, "required": [ "email", "userId" ], "type": "object", "x-versionadded": "v2.39" } ``` Details about the successful invite. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string | true | | The email address of the invited user. | | userId | string | true | | The ID of the user. | ## UserNotificationListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the user's notifications.", "items": { "properties": { "callerUser": { "description": "Details about the user who triggered the notification.", "properties": { "fullName": { "description": "User's full name.", "type": [ "string", "null" ] }, "gravatarHash": { "description": "User's gravatar hash.", "type": "string" }, "inactive": { "description": "True if the user was deleted.", "type": "boolean" }, "uid": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "uid" ], "type": "object" }, "created": { "description": "The ISO 8601 formatted date and time when the notification was created.", "format": "date-time", "type": "string" }, "data": { "description": "Notification type-specific metadata.", "oneOf": [ { "properties": { "failedInvites": { "description": "Failed invites.", "items": { "description": "Details about why the invite failed.", "properties": { "email": { "description": "The email address of the invited user.", "type": "string" }, "errorMessage": { "description": "The error message explaining why the invite failed.", "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" } }, "required": [ "email", "errorMessage", "userId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 20, "type": "array" }, "jobId": { "description": "The ID of the invite job.", "type": [ "string", "null" ] }, "statusId": { "description": "The ID of the invite job status.", "type": "string", "x-versionadded": "v2.40" }, "successfulInvites": { "description": "Successful invites.", "items": { "description": "Details about the successful invite.", "properties": { "email": { "description": "The email address of the invited user.", "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" } }, "required": [ "email", "userId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 20, "type": "array" } }, "required": [ "failedInvites", "jobId", "statusId", "successfulInvites" ], "type": "object", "x-versionadded": "v2.39" }, { "type": "null" } ], "x-versionadded": "v2.39" }, "description": { "description": "The notification description.", "type": [ "string", "null" ] }, "eventType": { "description": "The type of the notification.", "enum": [ "autopilot.complete", "project.shared", "comment.created", "comment.updated", "model_deployments.service_health_red", "model_deployments.data_drift_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.red", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "misc.asset_access_request", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "perma_delete_project.failure", "perma_delete_project.success", "secure_config.shared", "entity_notification_policy_template.shared", "notification_channel_template.shared", "invite_job.completed" ], "type": "string" }, "isRead": { "description": "True if the notification is already read.", "type": "boolean" }, "link": { "description": "The call-to-action link for the notification.", "type": "string" }, "pushNotificationSent": { "description": "True if the notification was also sent via push notifications.", "type": "boolean" }, "relatedComment": { "description": "Details about the comment related to the notification.", "properties": { "commentId": { "description": "The ID of the comment.", "type": "string" }, "commentLink": { "description": "The link to the comment.", "type": "string" }, "entityId": { "description": "The ID of the commented entity.", "type": "string" }, "entityType": { "description": "The type of the commented entity.", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "inactive": { "description": "True if the comment was deleted.", "type": "boolean" } }, "required": [ "commentId", "entityId" ], "type": "object" }, "relatedDeployment": { "description": "Details about the deployment related to the notification.", "properties": { "deploymentId": { "description": "The ID of the deployment.", "type": "string" }, "deploymentName": { "description": "The deployment label.", "type": [ "string", "null" ] }, "deploymentUrl": { "description": "The link to the deployment.", "type": "string" }, "inactive": { "description": "True if the deployment was deleted.", "type": "boolean" }, "modelId": { "description": "The ID of the related model.", "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the related project.", "type": [ "string", "null" ] }, "userId": { "description": "The ID of the related user.", "type": [ "string", "null" ] } }, "required": [ "deploymentId" ], "type": "object" }, "relatedProject": { "description": "Details about the project related to the notification.", "properties": { "inactive": { "description": "True if the project was deleted.", "type": "boolean" }, "pid": { "description": "The ID of the project.", "type": "string" }, "projectLink": { "description": "The link to the project.", "type": "string" }, "projectName": { "description": "The project name.", "type": [ "string", "null" ] } }, "required": [ "pid" ], "type": "object" }, "relatedSecureConfig": { "description": "Details about the secure config related to the notification.", "properties": { "secureConfigLink": { "description": "The link to the secure config.", "type": "string" }, "secureConfigName": { "description": "The name of the secure config.", "type": "string" }, "secureConfigSchemaName": { "description": "The type UUID of the secure config.", "type": "string" }, "secureConfigSchemaUuid": { "description": "The type name of the secure config.", "type": "string" }, "secureConfigUuid": { "description": "The ID of the secure config.", "type": "string" } }, "required": [ "secureConfigLink", "secureConfigName", "secureConfigSchemaName", "secureConfigSchemaUuid", "secureConfigUuid" ], "type": "object" }, "relatedUsersDelete": { "description": "Details about the users permanent delete.", "properties": { "reportId": { "description": "The ID of the user's permanent delete report.", "type": "string" }, "statusId": { "description": "The ID of the user's delete status.", "type": "string" }, "usersToDeleteCount": { "description": "The number of users that will be deleted.", "type": "string" } }, "required": [ "reportId", "statusId", "usersToDeleteCount" ], "type": "object" }, "sharedUsers": { "description": "The list of the user details a resource was shared with.", "items": { "description": "Details about the user who triggered the notification.", "properties": { "fullName": { "description": "User's full name.", "type": [ "string", "null" ] }, "gravatarHash": { "description": "User's gravatar hash.", "type": "string" }, "inactive": { "description": "True if the user was deleted.", "type": "boolean" }, "uid": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "uid" ], "type": "object" }, "type": "array" }, "statusId": { "description": "The asynchronous job status ID.", "type": "string" }, "title": { "description": "The notification title.", "type": [ "string", "null" ] }, "tooltip": { "description": "The notification tooltip.", "type": [ "string", "null" ] }, "updated": { "description": "The ISO 8601 formatted date and time when the notification was updated.", "format": "date-time", "type": "string" }, "userNotificationId": { "description": "The ID of the notification.", "type": "string" } }, "required": [ "callerUser", "created", "description", "eventType", "isRead", "link", "pushNotificationSent", "title", "tooltip", "updated", "userNotificationId" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UserNotificationResponse] | true | maxItems: 1000 | The list of the user's notifications. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UserNotificationRelatedCommentResponse ``` { "description": "Details about the comment related to the notification.", "properties": { "commentId": { "description": "The ID of the comment.", "type": "string" }, "commentLink": { "description": "The link to the comment.", "type": "string" }, "entityId": { "description": "The ID of the commented entity.", "type": "string" }, "entityType": { "description": "The type of the commented entity.", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "inactive": { "description": "True if the comment was deleted.", "type": "boolean" } }, "required": [ "commentId", "entityId" ], "type": "object" } ``` Details about the comment related to the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commentId | string | true | | The ID of the comment. | | commentLink | string | false | | The link to the comment. | | entityId | string | true | | The ID of the commented entity. | | entityType | string | false | | The type of the commented entity. | | inactive | boolean | false | | True if the comment was deleted. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [useCase, model, catalog, experimentContainer, deployment, workloadDeployment, workload] | ## UserNotificationRelatedDeploymentResponse ``` { "description": "Details about the deployment related to the notification.", "properties": { "deploymentId": { "description": "The ID of the deployment.", "type": "string" }, "deploymentName": { "description": "The deployment label.", "type": [ "string", "null" ] }, "deploymentUrl": { "description": "The link to the deployment.", "type": "string" }, "inactive": { "description": "True if the deployment was deleted.", "type": "boolean" }, "modelId": { "description": "The ID of the related model.", "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the related project.", "type": [ "string", "null" ] }, "userId": { "description": "The ID of the related user.", "type": [ "string", "null" ] } }, "required": [ "deploymentId" ], "type": "object" } ``` Details about the deployment related to the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the deployment. | | deploymentName | string,null | false | | The deployment label. | | deploymentUrl | string | false | | The link to the deployment. | | inactive | boolean | false | | True if the deployment was deleted. | | modelId | string,null | false | | The ID of the related model. | | projectId | string,null | false | | The ID of the related project. | | userId | string,null | false | | The ID of the related user. | ## UserNotificationRelatedProjectResponse ``` { "description": "Details about the project related to the notification.", "properties": { "inactive": { "description": "True if the project was deleted.", "type": "boolean" }, "pid": { "description": "The ID of the project.", "type": "string" }, "projectLink": { "description": "The link to the project.", "type": "string" }, "projectName": { "description": "The project name.", "type": [ "string", "null" ] } }, "required": [ "pid" ], "type": "object" } ``` Details about the project related to the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inactive | boolean | false | | True if the project was deleted. | | pid | string | true | | The ID of the project. | | projectLink | string | false | | The link to the project. | | projectName | string,null | false | | The project name. | ## UserNotificationRelatedSecureConfigResponse ``` { "description": "Details about the secure config related to the notification.", "properties": { "secureConfigLink": { "description": "The link to the secure config.", "type": "string" }, "secureConfigName": { "description": "The name of the secure config.", "type": "string" }, "secureConfigSchemaName": { "description": "The type UUID of the secure config.", "type": "string" }, "secureConfigSchemaUuid": { "description": "The type name of the secure config.", "type": "string" }, "secureConfigUuid": { "description": "The ID of the secure config.", "type": "string" } }, "required": [ "secureConfigLink", "secureConfigName", "secureConfigSchemaName", "secureConfigSchemaUuid", "secureConfigUuid" ], "type": "object" } ``` Details about the secure config related to the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | secureConfigLink | string | true | | The link to the secure config. | | secureConfigName | string | true | | The name of the secure config. | | secureConfigSchemaName | string | true | | The type UUID of the secure config. | | secureConfigSchemaUuid | string | true | | The type name of the secure config. | | secureConfigUuid | string | true | | The ID of the secure config. | ## UserNotificationRelatedUserResponse ``` { "description": "Details about the user who triggered the notification.", "properties": { "fullName": { "description": "User's full name.", "type": [ "string", "null" ] }, "gravatarHash": { "description": "User's gravatar hash.", "type": "string" }, "inactive": { "description": "True if the user was deleted.", "type": "boolean" }, "uid": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "uid" ], "type": "object" } ``` Details about the user who triggered the notification. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fullName | string,null | false | | User's full name. | | gravatarHash | string | false | | User's gravatar hash. | | inactive | boolean | false | | True if the user was deleted. | | uid | string | true | | The ID of the user. | | username | string | false | | The username of the user. | ## UserNotificationRelatedUsersDeleteResponse ``` { "description": "Details about the users permanent delete.", "properties": { "reportId": { "description": "The ID of the user's permanent delete report.", "type": "string" }, "statusId": { "description": "The ID of the user's delete status.", "type": "string" }, "usersToDeleteCount": { "description": "The number of users that will be deleted.", "type": "string" } }, "required": [ "reportId", "statusId", "usersToDeleteCount" ], "type": "object" } ``` Details about the users permanent delete. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | reportId | string | true | | The ID of the user's permanent delete report. | | statusId | string | true | | The ID of the user's delete status. | | usersToDeleteCount | string | true | | The number of users that will be deleted. | ## UserNotificationResponse ``` { "properties": { "callerUser": { "description": "Details about the user who triggered the notification.", "properties": { "fullName": { "description": "User's full name.", "type": [ "string", "null" ] }, "gravatarHash": { "description": "User's gravatar hash.", "type": "string" }, "inactive": { "description": "True if the user was deleted.", "type": "boolean" }, "uid": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "uid" ], "type": "object" }, "created": { "description": "The ISO 8601 formatted date and time when the notification was created.", "format": "date-time", "type": "string" }, "data": { "description": "Notification type-specific metadata.", "oneOf": [ { "properties": { "failedInvites": { "description": "Failed invites.", "items": { "description": "Details about why the invite failed.", "properties": { "email": { "description": "The email address of the invited user.", "type": "string" }, "errorMessage": { "description": "The error message explaining why the invite failed.", "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" } }, "required": [ "email", "errorMessage", "userId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 20, "type": "array" }, "jobId": { "description": "The ID of the invite job.", "type": [ "string", "null" ] }, "statusId": { "description": "The ID of the invite job status.", "type": "string", "x-versionadded": "v2.40" }, "successfulInvites": { "description": "Successful invites.", "items": { "description": "Details about the successful invite.", "properties": { "email": { "description": "The email address of the invited user.", "type": "string" }, "userId": { "description": "The ID of the user.", "type": "string" } }, "required": [ "email", "userId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 20, "type": "array" } }, "required": [ "failedInvites", "jobId", "statusId", "successfulInvites" ], "type": "object", "x-versionadded": "v2.39" }, { "type": "null" } ], "x-versionadded": "v2.39" }, "description": { "description": "The notification description.", "type": [ "string", "null" ] }, "eventType": { "description": "The type of the notification.", "enum": [ "autopilot.complete", "project.shared", "comment.created", "comment.updated", "model_deployments.service_health_red", "model_deployments.data_drift_red", "model_deployments.accuracy_red", "model_deployments.health.fairness_health.red", "model_deployments.health.custom_metrics_health.red", "model_deployments.predictions_timeliness_health_red", "model_deployments.actuals_timeliness_health_red", "misc.asset_access_request", "users_delete.preview_started", "users_delete.preview_completed", "users_delete.preview_failed", "perma_delete_project.failure", "perma_delete_project.success", "secure_config.shared", "entity_notification_policy_template.shared", "notification_channel_template.shared", "invite_job.completed" ], "type": "string" }, "isRead": { "description": "True if the notification is already read.", "type": "boolean" }, "link": { "description": "The call-to-action link for the notification.", "type": "string" }, "pushNotificationSent": { "description": "True if the notification was also sent via push notifications.", "type": "boolean" }, "relatedComment": { "description": "Details about the comment related to the notification.", "properties": { "commentId": { "description": "The ID of the comment.", "type": "string" }, "commentLink": { "description": "The link to the comment.", "type": "string" }, "entityId": { "description": "The ID of the commented entity.", "type": "string" }, "entityType": { "description": "The type of the commented entity.", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "inactive": { "description": "True if the comment was deleted.", "type": "boolean" } }, "required": [ "commentId", "entityId" ], "type": "object" }, "relatedDeployment": { "description": "Details about the deployment related to the notification.", "properties": { "deploymentId": { "description": "The ID of the deployment.", "type": "string" }, "deploymentName": { "description": "The deployment label.", "type": [ "string", "null" ] }, "deploymentUrl": { "description": "The link to the deployment.", "type": "string" }, "inactive": { "description": "True if the deployment was deleted.", "type": "boolean" }, "modelId": { "description": "The ID of the related model.", "type": [ "string", "null" ] }, "projectId": { "description": "The ID of the related project.", "type": [ "string", "null" ] }, "userId": { "description": "The ID of the related user.", "type": [ "string", "null" ] } }, "required": [ "deploymentId" ], "type": "object" }, "relatedProject": { "description": "Details about the project related to the notification.", "properties": { "inactive": { "description": "True if the project was deleted.", "type": "boolean" }, "pid": { "description": "The ID of the project.", "type": "string" }, "projectLink": { "description": "The link to the project.", "type": "string" }, "projectName": { "description": "The project name.", "type": [ "string", "null" ] } }, "required": [ "pid" ], "type": "object" }, "relatedSecureConfig": { "description": "Details about the secure config related to the notification.", "properties": { "secureConfigLink": { "description": "The link to the secure config.", "type": "string" }, "secureConfigName": { "description": "The name of the secure config.", "type": "string" }, "secureConfigSchemaName": { "description": "The type UUID of the secure config.", "type": "string" }, "secureConfigSchemaUuid": { "description": "The type name of the secure config.", "type": "string" }, "secureConfigUuid": { "description": "The ID of the secure config.", "type": "string" } }, "required": [ "secureConfigLink", "secureConfigName", "secureConfigSchemaName", "secureConfigSchemaUuid", "secureConfigUuid" ], "type": "object" }, "relatedUsersDelete": { "description": "Details about the users permanent delete.", "properties": { "reportId": { "description": "The ID of the user's permanent delete report.", "type": "string" }, "statusId": { "description": "The ID of the user's delete status.", "type": "string" }, "usersToDeleteCount": { "description": "The number of users that will be deleted.", "type": "string" } }, "required": [ "reportId", "statusId", "usersToDeleteCount" ], "type": "object" }, "sharedUsers": { "description": "The list of the user details a resource was shared with.", "items": { "description": "Details about the user who triggered the notification.", "properties": { "fullName": { "description": "User's full name.", "type": [ "string", "null" ] }, "gravatarHash": { "description": "User's gravatar hash.", "type": "string" }, "inactive": { "description": "True if the user was deleted.", "type": "boolean" }, "uid": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "uid" ], "type": "object" }, "type": "array" }, "statusId": { "description": "The asynchronous job status ID.", "type": "string" }, "title": { "description": "The notification title.", "type": [ "string", "null" ] }, "tooltip": { "description": "The notification tooltip.", "type": [ "string", "null" ] }, "updated": { "description": "The ISO 8601 formatted date and time when the notification was updated.", "format": "date-time", "type": "string" }, "userNotificationId": { "description": "The ID of the notification.", "type": "string" } }, "required": [ "callerUser", "created", "description", "eventType", "isRead", "link", "pushNotificationSent", "title", "tooltip", "updated", "userNotificationId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | callerUser | UserNotificationRelatedUserResponse | true | | Details about the user who triggered the notification. | | created | string(date-time) | true | | The ISO 8601 formatted date and time when the notification was created. | | data | any | false | | Notification type-specific metadata. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | InviteJobNotificationData | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | true | | The notification description. | | eventType | string | true | | The type of the notification. | | isRead | boolean | true | | True if the notification is already read. | | link | string | true | | The call-to-action link for the notification. | | pushNotificationSent | boolean | true | | True if the notification was also sent via push notifications. | | relatedComment | UserNotificationRelatedCommentResponse | false | | Details about the comment related to the notification. | | relatedDeployment | UserNotificationRelatedDeploymentResponse | false | | Details about the deployment related to the notification. | | relatedProject | UserNotificationRelatedProjectResponse | false | | Details about the project related to the notification. | | relatedSecureConfig | UserNotificationRelatedSecureConfigResponse | false | | Details about the secure config related to the notification. | | relatedUsersDelete | UserNotificationRelatedUsersDeleteResponse | false | | Details about the users permanent delete. | | sharedUsers | [UserNotificationRelatedUserResponse] | false | | The list of the user details a resource was shared with. | | statusId | string | false | | The asynchronous job status ID. | | title | string,null | true | | The notification title. | | tooltip | string,null | true | | The notification tooltip. | | updated | string(date-time) | true | | The ISO 8601 formatted date and time when the notification was updated. | | userNotificationId | string | true | | The ID of the notification. | ### Enumerated Values | Property | Value | | --- | --- | | eventType | [autopilot.complete, project.shared, comment.created, comment.updated, model_deployments.service_health_red, model_deployments.data_drift_red, model_deployments.accuracy_red, model_deployments.health.fairness_health.red, model_deployments.health.custom_metrics_health.red, model_deployments.predictions_timeliness_health_red, model_deployments.actuals_timeliness_health_red, misc.asset_access_request, users_delete.preview_started, users_delete.preview_completed, users_delete.preview_failed, perma_delete_project.failure, perma_delete_project.success, secure_config.shared, entity_notification_policy_template.shared, notification_channel_template.shared, invite_job.completed] | --- # OAuth providers URL: https://docs.datarobot.com/en/docs/api/reference/public-api/oauth_providers.html > OAuth Providers Service API enables management of external OAuth providers allowing DataRobot users to connect their workflows with external provider resources. # OAuth providers OAuth Providers Service API enables management of external OAuth providers allowing DataRobot users to connect their workflows with external provider resources. ## Delete OAuth Provider Authorization by authorized provider ID Operation path: `DELETE /api/v2/externalOAuth/authorizedProviders/{authorizedProviderId}/` Revoke an OAuth Provider Authorization by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | authorizationID | path | string | true | OAuth Provider Authorization ID | ### Example responses > 404 Response ``` { "properties": { "errorFields": { "items": { "properties": { "message": { "type": "string" }, "name": { "type": "string" } }, "type": "object" }, "type": "array", "uniqueItems": false }, "message": { "type": "string" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | No Content | None | | 404 | Not Found | OAuth Provider Authorization not found | httpres.ErrorResponse | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | ## Acquire OAuth Provider Authorization's Access Token by authorized provider ID Operation path: `POST /api/v2/externalOAuth/authorizedProviders/{authorizedProviderId}/token/` Acquire an access token for the OAuth Provider Authorization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | authorizationID | path | string | true | OAuth Provider Authorization ID | ### Example responses > 201 Response ``` { "properties": { "accessToken": { "type": "string" }, "authorizedOauthProvider": { "properties": { "createdAt": { "format": "date-time", "type": "string" }, "credentialId": { "type": "string" }, "id": { "type": "string" }, "oauthClientId": { "type": "string" }, "orgId": { "type": "string" }, "provider": { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" }, "refreshTokenExpiresAt": { "format": "date-time", "type": "string" }, "status": { "enum": [ "active", "expired", "invalidOAuthClient", "invalidProviderState" ], "type": "string", "x-enum-varnames": [ "AuthzStatusActive", "AuthzStatusExpired", "AuthzStatusInvalidOAuthClient", "AuthzInactiveProviderState" ] }, "userId": { "type": "string" } }, "type": "object" }, "expiresAt": { "type": "string" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Created | handler.GetAccessTokenResponse | | 403 | Forbidden | Forbidden operation for the given OAuth Provider | httpres.ErrorResponse | | 404 | Not Found | OAuth Provider Authorization not found | httpres.ErrorResponse | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | ## Get User Information by authorized provider ID Operation path: `GET /api/v2/externalOAuth/authorizedProviders/{authorizedProviderId}/userinfo/` Retrieve user information from the OAuth Provider using the previously obtained authorization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | authorizationID | path | string | true | OAuth Provider Authorization ID | ### Example responses > 200 Response ``` { "properties": { "description": { "type": "string" }, "email": { "type": "string" }, "familyName": { "type": "string" }, "givenName": { "type": "string" }, "locale": { "type": "string" }, "name": { "type": "string" }, "nickName": { "type": "string" }, "picture": { "type": "string" }, "raw": { "additionalProperties": {}, "type": "object" }, "sub": { "type": "string" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | User information | oauth.User | | 403 | Forbidden | Forbidden operation for the given OAuth Provider | httpres.ErrorResponse | | 404 | Not Found | OAuth Provider Authorization not found | httpres.ErrorResponse | | 410 | Gone | Authorization expired | httpres.ErrorResponse | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | ## Get OAuth Provider Job by job ID Operation path: `GET /api/v2/externalOAuth/jobs/{jobId}/` Retrieve the status of an OAuth Provider Job by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | jobID | path | string | true | Job ID | ### Example responses > 200 Response ``` { "properties": { "details": { "description": "Details is a slice of key-value pairs containing additional information about the job.\nFor example, for a delete provider job, it may include the provider ID and connected authorization IDs.\nExample usage:\nDetails = []struct {\n Name: \"provider_id\",\n Value: \"1234\",\n},\n{\n Name: \"authorization_ids\",\n Value: []string{\"1234\", \"5678\"},\n}", "items": { "properties": { "name": { "type": "string" }, "value": { "type": "string" } }, "type": "object" }, "type": "array", "uniqueItems": false }, "finishedAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "startedAt": { "format": "date-time", "type": "string" }, "status": { "enum": [ "PENDING", "RUNNING", "COMPLETED", "ERROR" ], "type": "string", "x-enum-varnames": [ "JobStatusPending", "JobStatusRunning", "JobStatusCompleted", "JobStatusError" ] }, "type": { "enum": [ "DELETE_PROVIDER", "STATS" ], "type": "string", "x-enum-varnames": [ "JobTypeDeleteProvider", "JobTypeStats" ] } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK | handler.jobStatusResponse | | 404 | Not Found | Job not found | httpres.ErrorResponse | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | ## List OAuth Providers Operation path: `GET /api/v2/externalOAuth/providers/` Returns a list of all available OAuth providers. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ids | query | array[string] | false | Filter by provider IDs | | types | query | array[string] | false | Filter by provider types | | host | query | array[string] | false | Filter by host | | orderBy | query | string | false | Order results by field | ### Enumerated Values | Parameter | Value | | --- | --- | | types | [github, gitlab, bitbucket, google, box, microsoft, sharepoint, jira, confluence] | | orderBy | [createdAt, -createdAt] | ### Example responses > 200 Response ``` { "properties": { "data": { "items": { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" }, "type": "array", "uniqueItems": false } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK | handler.ListOAuthProvidersResponse | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | ## Create OAuth Provider Operation path: `POST /api/v2/externalOAuth/providers/` Creates a new OAuth provider. ### Body parameter ``` { "properties": { "clientId": { "type": "string" }, "clientSecret": { "type": "string" }, "name": { "type": "string" }, "skipConsent": { "type": "boolean" }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] } }, "required": [ "clientId", "clientSecret", "name", "type" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | provider.CreateProviderRequest | true | Create provider request body | ### Example responses > 201 Response ``` { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Created | models.OAuthProvider | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | ## OAuth Provider Callback Operation path: `POST /api/v2/externalOAuth/providers/callback/` Handles the OAuth2 callback, exchanges authorization code for an access/refresh tokens, and creates a new. ### Body parameter ``` { "properties": { "code": { "type": "string" }, "providerId": { "type": "string" }, "state": { "type": "string" } }, "required": [ "code", "providerId", "state" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | handler.OAuthCallbackRequest | true | OAuth callback request body | ### Example responses > 200 Response ``` { "properties": { "authorizedProvider": { "properties": { "createdAt": { "format": "date-time", "type": "string" }, "credentialId": { "type": "string" }, "id": { "type": "string" }, "oauthClientId": { "type": "string" }, "orgId": { "type": "string" }, "provider": { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" }, "refreshTokenExpiresAt": { "format": "date-time", "type": "string" }, "status": { "enum": [ "active", "expired", "invalidOAuthClient", "invalidProviderState" ], "type": "string", "x-enum-varnames": [ "AuthzStatusActive", "AuthzStatusExpired", "AuthzStatusInvalidOAuthClient", "AuthzInactiveProviderState" ] }, "userId": { "type": "string" } }, "type": "object" }, "userInfo": { "properties": { "description": { "type": "string" }, "email": { "type": "string" }, "familyName": { "type": "string" }, "givenName": { "type": "string" }, "locale": { "type": "string" }, "name": { "type": "string" }, "nickName": { "type": "string" }, "picture": { "type": "string" }, "raw": { "additionalProperties": {}, "type": "object" }, "sub": { "type": "string" } }, "type": "object" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK | handler.CallbackResponse | | 400 | Bad Request | Invalid authorization data was provided | httpres.ErrorResponse | | 404 | Not Found | Provider not found | httpres.ErrorResponse | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | ## Delete OAuth Provider by provider ID Operation path: `DELETE /api/v2/externalOAuth/providers/{providerID}/` Initiates a job to delete an OAuth provider by its ID, along with all associated resources. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | providerId | path | string | true | OAuth Provider ID | ### Example responses > 202 Response ``` { "properties": { "location": { "type": "string" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Location of the job that will perform deletion | handler.DeleteOAuthProviderResponse | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | ## Get OAuth Provider by provider ID Operation path: `GET /api/v2/externalOAuth/providers/{providerID}/` Retrieves an OAuth provider by its ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | providerId | path | string | true | OAuth Provider ID | ### Example responses > 200 Response ``` { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK | models.OAuthProvider | | 404 | Not Found | Provider not found | httpres.ErrorResponse | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | ## Update OAuth Provider by provider ID Operation path: `PATCH /api/v2/externalOAuth/providers/{providerID}/` Updates an existing OAuth provider by its ID. ### Body parameter ``` { "properties": { "clientSecret": { "type": "string" }, "name": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | providerId | path | string | true | OAuth Provider ID | | body | body | provider.UpdateProviderRequest | true | Update provider request body | ### Example responses > 200 Response ``` { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK | models.OAuthProvider | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | ## Authorize OAuth Provider by provider ID Operation path: `POST /api/v2/externalOAuth/providers/{providerId}/authorize/` Returns a redirect URL to the OAuth2 provider's authorization page to get user's consent for accessing their data. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | providerId | path | string | true | OAuth Provider ID | | state | query | string | false | Client-supplied state to correlate the authorization request with the callback | | redirect_uri | query | string | false | Custom redirect URI which overrides the default callback URL | ### Example responses > 200 Response ``` { "properties": { "redirectUrl": { "type": "string" }, "state": { "type": "string" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | OK | handler.AuthorizeProviderResponse | | 404 | Not Found | Provider not found | httpres.ErrorResponse | | 409 | Conflict | Provider is in a conflicting state | httpres.ErrorResponse | | 422 | Unprocessable Entity | Validation error | httpres.ErrorResponse | # Schemas ## handler.AuthorizeProviderResponse ``` { "properties": { "redirectUrl": { "type": "string" }, "state": { "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | redirectUrl | string | false | | none | | state | string | false | | none | ## handler.CallbackResponse ``` { "properties": { "authorizedProvider": { "properties": { "createdAt": { "format": "date-time", "type": "string" }, "credentialId": { "type": "string" }, "id": { "type": "string" }, "oauthClientId": { "type": "string" }, "orgId": { "type": "string" }, "provider": { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" }, "refreshTokenExpiresAt": { "format": "date-time", "type": "string" }, "status": { "enum": [ "active", "expired", "invalidOAuthClient", "invalidProviderState" ], "type": "string", "x-enum-varnames": [ "AuthzStatusActive", "AuthzStatusExpired", "AuthzStatusInvalidOAuthClient", "AuthzInactiveProviderState" ] }, "userId": { "type": "string" } }, "type": "object" }, "userInfo": { "properties": { "description": { "type": "string" }, "email": { "type": "string" }, "familyName": { "type": "string" }, "givenName": { "type": "string" }, "locale": { "type": "string" }, "name": { "type": "string" }, "nickName": { "type": "string" }, "picture": { "type": "string" }, "raw": { "additionalProperties": {}, "type": "object" }, "sub": { "type": "string" } }, "type": "object" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authorizedProvider | models.AuthorizedProvider | false | | none | | userInfo | oauth.User | false | | none | ## handler.DeleteOAuthProviderResponse ``` { "properties": { "location": { "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | location | string | false | | none | ## handler.GetAccessTokenResponse ``` { "properties": { "accessToken": { "type": "string" }, "authorizedOauthProvider": { "properties": { "createdAt": { "format": "date-time", "type": "string" }, "credentialId": { "type": "string" }, "id": { "type": "string" }, "oauthClientId": { "type": "string" }, "orgId": { "type": "string" }, "provider": { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" }, "refreshTokenExpiresAt": { "format": "date-time", "type": "string" }, "status": { "enum": [ "active", "expired", "invalidOAuthClient", "invalidProviderState" ], "type": "string", "x-enum-varnames": [ "AuthzStatusActive", "AuthzStatusExpired", "AuthzStatusInvalidOAuthClient", "AuthzInactiveProviderState" ] }, "userId": { "type": "string" } }, "type": "object" }, "expiresAt": { "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accessToken | string | false | | none | | authorizedOauthProvider | models.AuthorizedProvider | false | | none | | expiresAt | string | false | | none | ## handler.ListOAuthProvidersResponse ``` { "properties": { "data": { "items": { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" }, "type": "array", "uniqueItems": false } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [models.OAuthProvider] | false | | none | ## handler.OAuthCallbackRequest ``` { "properties": { "code": { "type": "string" }, "providerId": { "type": "string" }, "state": { "type": "string" } }, "required": [ "code", "providerId", "state" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | code | string | true | | none | | providerId | string | true | | none | | state | string | true | | none | ## handler.jobStatusResponse ``` { "properties": { "details": { "description": "Details is a slice of key-value pairs containing additional information about the job.\nFor example, for a delete provider job, it may include the provider ID and connected authorization IDs.\nExample usage:\nDetails = []struct {\n Name: \"provider_id\",\n Value: \"1234\",\n},\n{\n Name: \"authorization_ids\",\n Value: []string{\"1234\", \"5678\"},\n}", "items": { "properties": { "name": { "type": "string" }, "value": { "type": "string" } }, "type": "object" }, "type": "array", "uniqueItems": false }, "finishedAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "startedAt": { "format": "date-time", "type": "string" }, "status": { "enum": [ "PENDING", "RUNNING", "COMPLETED", "ERROR" ], "type": "string", "x-enum-varnames": [ "JobStatusPending", "JobStatusRunning", "JobStatusCompleted", "JobStatusError" ] }, "type": { "enum": [ "DELETE_PROVIDER", "STATS" ], "type": "string", "x-enum-varnames": [ "JobTypeDeleteProvider", "JobTypeStats" ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | details | [handler.keyValue] | false | | Details is a slice of key-value pairs containing additional information about the job.For example, for a delete provider job, it may include the provider ID and connected authorization IDs.Example usage:Details = []struct { Name: "provider_id", Value: "1234",},{ Name: "authorization_ids", Value: []string{"1234", "5678"},} | | finishedAt | string(date-time) | false | | none | | id | string | false | | none | | name | string | false | | none | | orgId | string | false | | none | | startedAt | string(date-time) | false | | none | | status | models.JobStatus | false | | none | | type | models.JobType | false | | none | ## handler.keyValue ``` { "properties": { "name": { "type": "string" }, "value": { "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | | none | | value | string | false | | none | ## httpres.ErrorField ``` { "properties": { "message": { "type": "string" }, "name": { "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | false | | none | | name | string | false | | none | ## httpres.ErrorResponse ``` { "properties": { "errorFields": { "items": { "properties": { "message": { "type": "string" }, "name": { "type": "string" } }, "type": "object" }, "type": "array", "uniqueItems": false }, "message": { "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorFields | [httpres.ErrorField] | false | | none | | message | string | false | | none | ## models.AuthorizedProvider ``` { "properties": { "createdAt": { "format": "date-time", "type": "string" }, "credentialId": { "type": "string" }, "id": { "type": "string" }, "oauthClientId": { "type": "string" }, "orgId": { "type": "string" }, "provider": { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" }, "refreshTokenExpiresAt": { "format": "date-time", "type": "string" }, "status": { "enum": [ "active", "expired", "invalidOAuthClient", "invalidProviderState" ], "type": "string", "x-enum-varnames": [ "AuthzStatusActive", "AuthzStatusExpired", "AuthzStatusInvalidOAuthClient", "AuthzInactiveProviderState" ] }, "userId": { "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | false | | none | | credentialId | string | false | | none | | id | string | false | | none | | oauthClientId | string | false | | none | | orgId | string | false | | none | | provider | models.OAuthProvider | false | | none | | refreshTokenExpiresAt | string(date-time) | false | | none | | status | models.AuthzStatus | false | | none | | userId | string | false | | none | ## models.AuthzStatus ``` { "enum": [ "active", "expired", "invalidOAuthClient", "invalidProviderState" ], "type": "string", "x-enum-varnames": [ "AuthzStatusActive", "AuthzStatusExpired", "AuthzStatusInvalidOAuthClient", "AuthzInactiveProviderState" ] } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | string | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [active, expired, invalidOAuthClient, invalidProviderState] | ## models.JobStatus ``` { "enum": [ "PENDING", "RUNNING", "COMPLETED", "ERROR" ], "type": "string", "x-enum-varnames": [ "JobStatusPending", "JobStatusRunning", "JobStatusCompleted", "JobStatusError" ] } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | string | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [PENDING, RUNNING, COMPLETED, ERROR] | ## models.JobType ``` { "enum": [ "DELETE_PROVIDER", "STATS" ], "type": "string", "x-enum-varnames": [ "JobTypeDeleteProvider", "JobTypeStats" ] } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | string | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [DELETE_PROVIDER, STATS] | ## models.OAuthProvider ``` { "properties": { "clientId": { "type": "string" }, "createdAt": { "format": "date-time", "type": "string" }, "id": { "type": "string" }, "metadata": { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" }, "name": { "type": "string" }, "orgId": { "type": "string" }, "secureConfigId": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] }, "updatedAt": { "format": "date-time", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clientId | string | false | | none | | createdAt | string(date-time) | false | | none | | id | string | false | | none | | metadata | models.ProviderMetadata | false | | none | | name | string | false | | none | | orgId | string | false | | none | | secureConfigId | string | false | | none | | skipConsent | boolean | false | | none | | status | oauthprovider.Status | false | | none | | type | oauthprovider.Type | false | | none | | updatedAt | string(date-time) | false | | none | ## models.ProviderMetadata ``` { "properties": { "baseUrl": { "type": "string" }, "host": { "type": "string" }, "settingsUrl": { "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseUrl | string | false | | none | | host | string | false | | none | | settingsUrl | string | false | | none | ## oauth.User ``` { "properties": { "description": { "type": "string" }, "email": { "type": "string" }, "familyName": { "type": "string" }, "givenName": { "type": "string" }, "locale": { "type": "string" }, "name": { "type": "string" }, "nickName": { "type": "string" }, "picture": { "type": "string" }, "raw": { "additionalProperties": {}, "type": "object" }, "sub": { "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | | none | | email | string | false | | none | | familyName | string | false | | none | | givenName | string | false | | none | | locale | string | false | | none | | name | string | false | | none | | nickName | string | false | | none | | picture | string | false | | none | | raw | object | false | | none | | » additionalProperties | any | false | | none | | sub | string | false | | none | ## oauthprovider.Status ``` { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | string | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [active, invalid, deleting, deletion_failed] | ## oauthprovider.Type ``` { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | string | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | anonymous | [github, gitlab, bitbucket, google, box, microsoft, sharepoint, jira, confluence] | ## provider.CreateProviderRequest ``` { "properties": { "clientId": { "type": "string" }, "clientSecret": { "type": "string" }, "name": { "type": "string" }, "skipConsent": { "type": "boolean" }, "type": { "enum": [ "github", "gitlab", "bitbucket", "google", "box", "microsoft", "sharepoint", "jira", "confluence" ], "type": "string", "x-enum-varnames": [ "TypeGithub", "TypeGitlab", "TypeBitbucket", "TypeGoogle", "TypeBox", "TypeMicrosoft", "TypeSharepoint", "TypeJira", "TypeConfluence" ] } }, "required": [ "clientId", "clientSecret", "name", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clientId | string | true | | none | | clientSecret | string | true | | none | | name | string | true | | none | | skipConsent | boolean | false | | none | | type | oauthprovider.Type | true | | none | ## provider.UpdateProviderRequest ``` { "properties": { "clientSecret": { "type": "string" }, "name": { "type": "string" }, "skipConsent": { "type": "boolean" }, "status": { "enum": [ "active", "invalid", "deleting", "deletion_failed" ], "type": "string", "x-enum-varnames": [ "StatusActive", "StatusInvalid", "StatusDeleting", "StatusDeletionFailed" ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clientSecret | string | false | | none | | name | string | false | | none | | skipConsent | boolean | false | | none | | status | oauthprovider.Status | false | | none | --- # Accuracy URL: https://docs.datarobot.com/en/docs/api/reference/public-api/observability_accuracy.html > Use the endpoints described below to manage accuracy. Accuracy allows you to analyze the performance of model deployments over time using standard statistical measures and exportable visualizations. Use this tool to determine whether a model's quality is decaying and if you should consider replacing it. # Accuracy Use the endpoints described below to manage accuracy. Accuracy allows you to analyze the performance of model deployments over time using standard statistical measures and exportable visualizations. Use this tool to determine whether a model's quality is decaying and if you should consider replacing it. ## Retrieve accuracy metric by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/accuracy/` Authentication requirements: `BearerAuth` Retrieve accuracy metric for a certain time period. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | modelId | query | any | false | The ID of the models for which metrics are being retrieved. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | segmentAttribute | query | string,null | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | targetClass | query | any | false | Target class to filter out results. | | metric | query | string | false | Name of the metric to retrieve. Must be provided when using multiple modelId query params. | | baselineModelId | query | string | false | ID of the model whose metric value to use as baseline. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | metric | [AUC, Accuracy, Balanced Accuracy, F1, FPR, FVE Binomial, FVE Gamma, FVE Multinomial, FVE Poisson, FVE Tweedie, Gamma Deviance, Gini Norm, Kolmogorov-Smirnov, LogLoss, MAE, MAPE, MCC, NPV, PPV, Poisson Deviance, R Squared, RMSE, RMSLE, Rate@Top10%, Rate@Top5%, TNR, TPR, Tweedie Deviance, WGS84 MAE, WGS84 RMSE] | ### Example responses > 200 Response ``` { "properties": { "batchIds": { "description": "ID of the batches used to calculate accuracy metrics.", "items": { "type": "string" }, "type": "array" }, "data": { "description": "Accuracy metric data.", "items": { "properties": { "baselineSampleSize": { "description": "Number of predictions used to calculate the baseline metric value.", "type": "integer", "x-versionadded": "v2.37" }, "baselineValue": { "description": "Baseline value of the metric.", "type": [ "number", "null" ] }, "metricName": { "description": "Name of the metric.", "type": "string" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "percentChange": { "description": "Percent of change by comparing metric value to baseline, with metric direction taken into account.", "type": [ "number", "null" ] }, "sampleSize": { "description": "Number of predictions used to calculate the metric value.", "type": "integer", "x-versionadded": "v2.37" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "baselineSampleSize", "baselineValue", "metricName", "percentChange", "sampleSize", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "metrics": { "description": "Accuracy metrics of the deployment. Deprecated and to be removed in v2.40; use data objects.", "example": "\n {\"metrics\": {\n \"LogLoss\": {\n \"baselineValue\": 0.454221484838069,\n \"value\": 0.880778024500618,\n \"percentChange\": -93.91\n },\n \"AUC\": {\n \"baselineValue\": 0.8690358459556535,\n \"value\": 0.5294117647058824,\n \"percentChange\": -39.08\n },\n \"Kolmogorov-Smirnov\": {\n \"baselineValue\": 0.5753202944706626,\n \"value\": 0.4117647058823529,\n \"percentChange\": -28.43\n },\n \"Rate@Top10%\": {\n \"baselineValue\": 0.9603223806571606,\n \"value\": 1.0,\n \"percentChange\": 4.13\n },\n \"Gini Norm\": {\n \"baselineValue\": 0.7380716919113071,\n \"value\": 0.05882352941176472,\n \"percentChange\": -92.03\n }\n }\n ", "type": "object", "x-versiondeprecated": "v2.36" }, "modelId": { "description": "The id of the model for which metrics are being retrieved. Deprecated and to be removed in v2.40; use modelId in each data object.", "type": "string", "x-versiondeprecated": "v2.36" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Deployment accuracy metrics are retrieved. | AccuracyResponse | ## Endpoint by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/accuracyMetrics/` Authentication requirements: `BearerAuth` Retrieve information about which accuracy metrics will be displayed and in what order. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "List of Accuracy Metrics.", "items": { "description": "Name of the accuracy metric.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": "string" }, "maxItems": 15, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Information on which accuracy metrics will be displayed and in what order. | AccuracyMetricList | | 404 | Not Found | Deployment not found. | None | ## Update deployment accuracy metrics settings by deployment ID Operation path: `PUT /api/v2/deployments/{deploymentId}/accuracyMetrics/` Authentication requirements: `BearerAuth` Update accuracy metrics being returned in accuracy endpoint. ### Body parameter ``` { "properties": { "data": { "description": "List of Accuracy Metrics.", "items": { "description": "Name of the accuracy metric.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": "string" }, "maxItems": 15, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | AccuracyMetricUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "List of Accuracy Metrics.", "items": { "description": "Name of the accuracy metric.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": "string" }, "maxItems": 15, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The updated accuracy metrics list. | AccuracyMetricList | | 404 | Not Found | Deployment not found. | None | ## Accuracy over batch by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/accuracyOverBatch/` Authentication requirements: `BearerAuth` Retrieve accuracy metric baseline, and metric value calculated for each batch. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | segmentAttribute | query | string,null | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | modelId | query | string | false | The id of the model for which metrics are being retrieved. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | metric | query | string | false | Accuracy metric being requested. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | metric | [AUC, Accuracy, Balanced Accuracy, F1, FPR, FVE Binomial, FVE Gamma, FVE Poisson, FVE Tweedie, Gamma Deviance, Gini Norm, Kolmogorov-Smirnov, LogLoss, MAE, MAPE, MCC, NPV, PPV, Poisson Deviance, R Squared, RMSE, RMSLE, Rate@Top10%, Rate@Top5%, TNR, TPR, Tweedie Deviance] | ### Example responses > 200 Response ``` { "properties": { "baselines": { "description": "Accuracy metric training baseline.", "items": { "properties": { "metric": { "description": "Name of the metric.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.33" }, "perClass": { "description": "Accuracy metric for selected classes.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer", "x-versionadded": "v2.33" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "metric", "sampleSize", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "buckets": { "description": "Accuracy metric for batches that are requested, with non-existing batches omitted.", "items": { "properties": { "batchId": { "description": "ID of the batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "Name of the batch.", "type": "string", "x-versionadded": "v2.33" }, "metric": { "description": "Name of the metric.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.33" }, "perClass": { "description": "Accuracy metric for selected classes.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "period": { "description": "Time period of the batch.", "properties": { "end": { "description": "End time of the bucket", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start time of the bucket", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "end", "start" ], "type": "object" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer", "x-versionadded": "v2.33" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName", "metric", "period", "sampleSize", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "baselines", "buckets" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Retrieve accuracy metrics over batches for a deployment. | AccuracyOverBatchResponse | ## Retrieve accuracy over space by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/accuracyOverSpace/` Authentication requirements: `BearerAuth` Retrieve accuracy metric baseline, and metric value calculated for each geospatial h3 hexagon. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | modelId | query | string | false | The ID of the model for which metrics are being retrieved. | | metric | query | string | false | Name of the metric. | | geoFeatureName | query | string | false | The name of the geospatial feature. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | metric | [AUC, Accuracy, Balanced Accuracy, F1, FPR, FVE Binomial, FVE Gamma, FVE Multinomial, FVE Poisson, FVE Tweedie, Gamma Deviance, Gini Norm, Kolmogorov-Smirnov, LogLoss, MAD, MAE, MAPE, MCC, NPV, PPV, Poisson Deviance, R Squared, RMSE, RMSLE, Rate@Top10%, Rate@Top5%, TNR, TPR, Tweedie Deviance, WGS84 MAE, WGS84 RMSE] | ### Example responses > 200 Response ``` { "properties": { "baselines": { "description": "Baseline accuracy per geospatial hexagon.", "items": { "properties": { "hexagon": { "description": "h3 hexagon.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 125, "type": "array" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] } }, "required": [ "hexagon", "sampleSize", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "buckets": { "description": "Accuracy per geospatial hexagon.", "items": { "properties": { "hexagon": { "description": "h3 hexagon.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 125, "type": "array" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] } }, "required": [ "hexagon", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "geoFeatureName": { "description": "The name of the geospatial feature. Segmented analysis must be enabled for the feature specified.", "type": "string" }, "metric": { "description": "The metric being retrieved.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": "string" }, "modelId": { "description": "The ID of the model for which metrics are being retrieved. ", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } }, "required": [ "baselines", "buckets", "geoFeatureName", "metric" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Accuracy over space | AccuracyOverSpaceResponse | ## Retrieve accuracy over time by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/accuracyOverTime/` Authentication requirements: `BearerAuth` Retrieve accuracy metric baseline, and metric value calculated for each time bucket dividing a longer time period. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | bucketSize | query | string(duration) | false | The time duration of a bucket. Needs to be multiple of one hour. Can not be longer than the total length of the period. If not set, a default value will be calculated based on the start and end time. | | modelId | query | any | false | The ID of the models for which metrics are being retrieved. | | metric | query | string | false | Name of the metric. | | segmentAttribute | query | string,null | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | targetClass | query | any | false | Target class to filter out results. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | metric | [AUC, Accuracy, Balanced Accuracy, F1, FPR, FVE Binomial, FVE Gamma, FVE Multinomial, FVE Poisson, FVE Tweedie, Gamma Deviance, Gini Norm, Kolmogorov-Smirnov, LogLoss, MAD, MAE, MAPE, MCC, NPV, PPV, Poisson Deviance, R Squared, RMSE, RMSLE, Rate@Top10%, Rate@Top5%, TNR, TPR, Tweedie Deviance, WGS84 MAE, WGS84 RMSE] | ### Example responses > 200 Response ``` { "properties": { "baseline": { "description": "A bucket object containing metric info calculated. Deprecated and to be removed in v2.35, use summaries field with includeSummaries query param to get accuracy metrics computed over the whole time range.", "properties": { "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "Number of predictions used to calculate the metric.", "type": [ "integer", "null" ] }, "value": { "description": "Value of the metric, null if no value.", "type": [ "number", "null" ] }, "valuePerClass": { "description": "A dict keyed by class names with metric calculated for specific classes as values, if targetClass is set. Deprecated and to be removed in v2.35, use perClass instead.", "type": [ "object", "null" ] } }, "required": [ "period", "sampleSize", "value" ], "type": "object" }, "baselines": { "description": "Accuracy metric training baseline.", "items": { "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] } }, "required": [ "sampleSize", "value" ], "type": "object" }, "type": "array" }, "buckets": { "description": "Accuracy metric for requested models and time buckets.", "items": { "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] }, "valuePerClass": { "description": "A dict keyed by class names with metric calculated for specific classes as values, if targetClass is set. Deprecated and to be removed in v2.35, use perClass instead.", "type": [ "object", "null" ], "x-versiondeprecated": "v2.33" } }, "required": [ "period", "sampleSize", "value" ], "type": "object" }, "type": "array" }, "metric": { "description": "The metric being retrieved.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": "string" }, "modelId": { "description": "The id of the model for which metrics are being retrieved. Deprecated and to be removed in v2.35, use modelId in each baseline or bucket object.", "type": "string", "x-versiondeprecated": "v2.33" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "summary": { "description": "A bucket object containing metric info calculated. Deprecated and to be removed in v2.35, use summaries field with includeSummaries query param to get accuracy metrics computed over the whole time range.", "properties": { "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "Number of predictions used to calculate the metric.", "type": [ "integer", "null" ] }, "value": { "description": "Value of the metric, null if no value.", "type": [ "number", "null" ] }, "valuePerClass": { "description": "A dict keyed by class names with metric calculated for specific classes as values, if targetClass is set. Deprecated and to be removed in v2.35, use perClass instead.", "type": [ "object", "null" ] } }, "required": [ "period", "sampleSize", "value" ], "type": "object" } }, "required": [ "baseline", "baselines", "buckets", "metric", "summary" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Accuracy over time. | AccuracyOverTimeResponse | ## Retrieve metrics about predictions and actuals, such as mean predicted & actual value, predicted & by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/predictionsVsActualsOverBatch/` Authentication requirements: `BearerAuth` Retrieve metrics about predictions and actuals, such as mean predicted & actual value, predicted & actual class distribution, over a specific set of batches. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | segmentAttribute | query | string,null | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | modelId | query | string | false | The id of the model for which metrics are being retrieved. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | targetClass | query | any | false | Target class to filter out results. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "baselines": { "description": "Predictions vs actuals baselines.", "items": { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer" } }, "required": [ "modelId", "rowCountTotal", "rowCountWithActual" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "buckets": { "description": "Predictions vs actuals buckets.", "items": { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "batchId": { "description": "ID of the batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "Name of the batch.", "type": "string", "x-versionadded": "v2.33" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "modelId": { "description": "ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "period": { "description": "Time period of the batch.", "properties": { "end": { "description": "End time of the bucket", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start time of the bucket", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "end", "start" ], "type": "object" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer", "x-versionadded": "v2.33" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName", "modelId", "period", "rowCountTotal", "rowCountWithActual" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "baselines", "buckets" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Predictions vs actuals over batch info. | PredictionsVsActualsOverBatchResponse | ## Retrieve predictions vs by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/predictionsVsActualsOverSpace/` Authentication requirements: `BearerAuth` Retrieve predictions vs. actuals over space. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | modelId | query | string | false | The ID of the model for which metrics are being retrieved. | | geoFeatureName | query | string | false | The name of the geospatial feature. | | targetClass | query | any | false | Target class to filter out results. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "baselines": { "description": "The predictions vs. actuals over space baselines.", "items": { "properties": { "actualClassDistribution": { "description": "For classification deployments, the class distribution for all actuals in the bucket.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 125, "type": "array" }, "hexagon": { "description": "The H3 geospatial indexing hexagon.", "type": "string" }, "meanActualValue": { "description": "For regression deployments, the mean actual value of all rows in the bucket.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "For regression deployments, the mean predicted value of all rows in the bucket.", "type": [ "number", "null" ] }, "predictedClassDistribution": { "description": "For classification deployments, the class distribution for all prediction rows in the bucket with associated actuals.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 125, "type": "array" }, "rowCountTotal": { "description": "The number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "The number of prediction rows in the bucket with associated actuals.", "type": "integer" } }, "required": [ "hexagon", "rowCountTotal", "rowCountWithActual" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "buckets": { "description": "The predictions vs. actuals over space buckets.", "items": { "properties": { "actualClassDistribution": { "description": "For classification deployments, the class distribution for all actuals in the bucket.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 125, "type": "array" }, "hexagon": { "description": "The H3 geospatial indexing hexagon.", "type": "string" }, "meanActualValue": { "description": "For regression deployments, the mean actual value of all rows in the bucket.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "For regression deployments, the mean predicted value of all rows in the bucket.", "type": [ "number", "null" ] }, "predictedClassDistribution": { "description": "For classification deployments, the class distribution for all prediction rows in the bucket with associated actuals.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 125, "type": "array" }, "rowCountTotal": { "description": "The number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "The number of prediction rows in the bucket with associated actuals.", "type": "integer" } }, "required": [ "hexagon", "rowCountTotal", "rowCountWithActual" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "geoFeatureName": { "description": "The name of the geospatial feature.", "type": "string" }, "modelId": { "description": "The ID of the model for which metrics are being retrieved. ", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "summary": { "description": "Predictions vs actuals summary.", "properties": { "rowCountTotal": { "description": "Number of rows for all buckets.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual for all buckets.", "type": "integer" } }, "required": [ "rowCountTotal", "rowCountWithActual" ], "type": "object" } }, "required": [ "baselines", "buckets", "geoFeatureName", "summary" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Predictions vs. actuals over space. | PredictionsVsActualsOverSpaceResponse | ## Retrieve predictions vs actuals over time info by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/predictionsVsActualsOverTime/` Authentication requirements: `BearerAuth` Retrieve metrics about predictions and actuals, such as mean predicted & actual value, predicted & actual class distribution, over a specific time range. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | bucketSize | query | string | false | Time duration of buckets | | segmentAttribute | query | string,null | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | modelId | query | any | false | The ID of the models for which metrics are being retrieved. | | targetClass | query | any | false | Target class to filter out results. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | bucketSize | [PT1H, P1D, P7D, P1M] | ### Example responses > 200 Response ``` { "properties": { "baselines": { "description": "Predictions vs actuals baselines.", "items": { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer" } }, "required": [ "modelId", "rowCountTotal", "rowCountWithActual" ], "type": "object" }, "type": "array" }, "buckets": { "description": "Predictions vs actuals buckets.", "items": { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "period": { "description": "Time period of the batch.", "properties": { "end": { "description": "End time of the bucket", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start time of the bucket", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "end", "start" ], "type": "object" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer" } }, "required": [ "modelId", "period", "rowCountTotal", "rowCountWithActual" ], "type": "object" }, "type": "array" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "summary": { "description": "Predictions vs actuals summary.", "properties": { "rowCountTotal": { "description": "Number of rows for all buckets.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual for all buckets.", "type": "integer" } }, "required": [ "rowCountTotal", "rowCountWithActual" ], "type": "object" } }, "required": [ "baselines", "buckets", "summary" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Predictions vs actuals over time info. | PredictionsVsActualsOverTimeResponse | # Schemas ## AccuracyBatchBaselineBucket ``` { "properties": { "metric": { "description": "Name of the metric.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.33" }, "perClass": { "description": "Accuracy metric for selected classes.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer", "x-versionadded": "v2.33" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "metric", "sampleSize", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metric | string | true | | Name of the metric. | | modelId | string | false | | The id of the model for which metrics are being retrieved. | | perClass | [AccuracyPerClass] | false | | Accuracy metric for selected classes. | | sampleSize | integer | true | | Number of rows used to calculate the metric. | | value | number,null | true | | Accuracy metric value. | ## AccuracyBatchBucket ``` { "properties": { "batchId": { "description": "ID of the batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "Name of the batch.", "type": "string", "x-versionadded": "v2.33" }, "metric": { "description": "Name of the metric.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.33" }, "perClass": { "description": "Accuracy metric for selected classes.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "period": { "description": "Time period of the batch.", "properties": { "end": { "description": "End time of the bucket", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start time of the bucket", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "end", "start" ], "type": "object" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer", "x-versionadded": "v2.33" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName", "metric", "period", "sampleSize", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchId | string | true | | ID of the batch. | | batchName | string | true | | Name of the batch. | | metric | string | true | | Name of the metric. | | modelId | string | false | | The id of the model for which metrics are being retrieved. | | perClass | [AccuracyPerClass] | false | | Accuracy metric for selected classes. | | period | BatchPeriod | true | | Time period of the batch. | | sampleSize | integer | true | | Number of rows used to calculate the metric. | | value | number,null | true | | Accuracy metric value. | ## AccuracyLegacyTimeBucket ``` { "description": "A bucket object containing metric info calculated. Deprecated and to be removed in v2.35, use summaries field with includeSummaries query param to get accuracy metrics computed over the whole time range.", "properties": { "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "Number of predictions used to calculate the metric.", "type": [ "integer", "null" ] }, "value": { "description": "Value of the metric, null if no value.", "type": [ "number", "null" ] }, "valuePerClass": { "description": "A dict keyed by class names with metric calculated for specific classes as values, if targetClass is set. Deprecated and to be removed in v2.35, use perClass instead.", "type": [ "object", "null" ] } }, "required": [ "period", "sampleSize", "value" ], "type": "object" } ``` A bucket object containing metric info calculated. Deprecated and to be removed in v2.35, use summaries field with includeSummaries query param to get accuracy metrics computed over the whole time range. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | period | TimeRange | true | | An object with the keys "start" and "end" defining the period. | | sampleSize | integer,null | true | | Number of predictions used to calculate the metric. | | value | number,null | true | | Value of the metric, null if no value. | | valuePerClass | object,null | false | | A dict keyed by class names with metric calculated for specific classes as values, if targetClass is set. Deprecated and to be removed in v2.35, use perClass instead. | ## AccuracyMetric ``` { "properties": { "baselineSampleSize": { "description": "Number of predictions used to calculate the baseline metric value.", "type": "integer", "x-versionadded": "v2.37" }, "baselineValue": { "description": "Baseline value of the metric.", "type": [ "number", "null" ] }, "metricName": { "description": "Name of the metric.", "type": "string" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "percentChange": { "description": "Percent of change by comparing metric value to baseline, with metric direction taken into account.", "type": [ "number", "null" ] }, "sampleSize": { "description": "Number of predictions used to calculate the metric value.", "type": "integer", "x-versionadded": "v2.37" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "baselineSampleSize", "baselineValue", "metricName", "percentChange", "sampleSize", "value" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineSampleSize | integer | true | | Number of predictions used to calculate the baseline metric value. | | baselineValue | number,null | true | | Baseline value of the metric. | | metricName | string | true | | Name of the metric. | | modelId | string | false | | The id of the model for which metrics are being retrieved. | | percentChange | number,null | true | | Percent of change by comparing metric value to baseline, with metric direction taken into account. | | sampleSize | integer | true | | Number of predictions used to calculate the metric value. | | value | number,null | true | | Value of the metric. | ## AccuracyMetricList ``` { "properties": { "data": { "description": "List of Accuracy Metrics.", "items": { "description": "Name of the accuracy metric.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": "string" }, "maxItems": 15, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [string] | true | maxItems: 15 | List of Accuracy Metrics. | ## AccuracyMetricUpdate ``` { "properties": { "data": { "description": "List of Accuracy Metrics.", "items": { "description": "Name of the accuracy metric.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": "string" }, "maxItems": 15, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [string] | true | maxItems: 15 | List of Accuracy Metrics. | ## AccuracyOverBatchResponse ``` { "properties": { "baselines": { "description": "Accuracy metric training baseline.", "items": { "properties": { "metric": { "description": "Name of the metric.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.33" }, "perClass": { "description": "Accuracy metric for selected classes.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer", "x-versionadded": "v2.33" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "metric", "sampleSize", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "buckets": { "description": "Accuracy metric for batches that are requested, with non-existing batches omitted.", "items": { "properties": { "batchId": { "description": "ID of the batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "Name of the batch.", "type": "string", "x-versionadded": "v2.33" }, "metric": { "description": "Name of the metric.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.33" }, "perClass": { "description": "Accuracy metric for selected classes.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "period": { "description": "Time period of the batch.", "properties": { "end": { "description": "End time of the bucket", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start time of the bucket", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "end", "start" ], "type": "object" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer", "x-versionadded": "v2.33" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName", "metric", "period", "sampleSize", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "baselines", "buckets" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselines | [AccuracyBatchBaselineBucket] | true | | Accuracy metric training baseline. | | buckets | [AccuracyBatchBucket] | true | | Accuracy metric for batches that are requested, with non-existing batches omitted. | ## AccuracyOverSpaceBaselineBucket ``` { "properties": { "hexagon": { "description": "h3 hexagon.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 125, "type": "array" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] } }, "required": [ "hexagon", "sampleSize", "value" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | hexagon | string | true | | h3 hexagon. | | perClass | [AccuracyPerClass] | false | maxItems: 125 | Accuracy metric values for selected classes, only available for multiclass deployments. | | sampleSize | integer | true | | Number of rows used to calculate the metric. | | value | number,null | true | | Accuracy metric value. | ## AccuracyOverSpaceBucket ``` { "properties": { "hexagon": { "description": "h3 hexagon.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 125, "type": "array" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] } }, "required": [ "hexagon", "value" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | hexagon | string | true | | h3 hexagon. | | perClass | [AccuracyPerClass] | false | maxItems: 125 | Accuracy metric values for selected classes, only available for multiclass deployments. | | value | number,null | true | | Accuracy metric value. | ## AccuracyOverSpaceResponse ``` { "properties": { "baselines": { "description": "Baseline accuracy per geospatial hexagon.", "items": { "properties": { "hexagon": { "description": "h3 hexagon.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 125, "type": "array" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] } }, "required": [ "hexagon", "sampleSize", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "buckets": { "description": "Accuracy per geospatial hexagon.", "items": { "properties": { "hexagon": { "description": "h3 hexagon.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 125, "type": "array" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] } }, "required": [ "hexagon", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "geoFeatureName": { "description": "The name of the geospatial feature. Segmented analysis must be enabled for the feature specified.", "type": "string" }, "metric": { "description": "The metric being retrieved.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": "string" }, "modelId": { "description": "The ID of the model for which metrics are being retrieved. ", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } }, "required": [ "baselines", "buckets", "geoFeatureName", "metric" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselines | [AccuracyOverSpaceBaselineBucket] | true | maxItems: 1000 | Baseline accuracy per geospatial hexagon. | | buckets | [AccuracyOverSpaceBucket] | true | maxItems: 1000 | Accuracy per geospatial hexagon. | | geoFeatureName | string | true | | The name of the geospatial feature. Segmented analysis must be enabled for the feature specified. | | metric | string | true | | The metric being retrieved. | | modelId | string | false | | The ID of the model for which metrics are being retrieved. | | period | TimeRange | false | | An object with the keys "start" and "end" defining the period. | ### Enumerated Values | Property | Value | | --- | --- | | metric | [AUC, Accuracy, Balanced Accuracy, F1, FPR, FVE Binomial, FVE Gamma, FVE Multinomial, FVE Poisson, FVE Tweedie, Gamma Deviance, Gini Norm, Kolmogorov-Smirnov, LogLoss, MAE, MAPE, MCC, NPV, PPV, Poisson Deviance, R Squared, RMSE, RMSLE, Rate@Top10%, Rate@Top5%, TNR, TPR, Tweedie Deviance, WGS84 MAE, WGS84 RMSE] | ## AccuracyOverTimeResponse ``` { "properties": { "baseline": { "description": "A bucket object containing metric info calculated. Deprecated and to be removed in v2.35, use summaries field with includeSummaries query param to get accuracy metrics computed over the whole time range.", "properties": { "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "Number of predictions used to calculate the metric.", "type": [ "integer", "null" ] }, "value": { "description": "Value of the metric, null if no value.", "type": [ "number", "null" ] }, "valuePerClass": { "description": "A dict keyed by class names with metric calculated for specific classes as values, if targetClass is set. Deprecated and to be removed in v2.35, use perClass instead.", "type": [ "object", "null" ] } }, "required": [ "period", "sampleSize", "value" ], "type": "object" }, "baselines": { "description": "Accuracy metric training baseline.", "items": { "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] } }, "required": [ "sampleSize", "value" ], "type": "object" }, "type": "array" }, "buckets": { "description": "Accuracy metric for requested models and time buckets.", "items": { "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] }, "valuePerClass": { "description": "A dict keyed by class names with metric calculated for specific classes as values, if targetClass is set. Deprecated and to be removed in v2.35, use perClass instead.", "type": [ "object", "null" ], "x-versiondeprecated": "v2.33" } }, "required": [ "period", "sampleSize", "value" ], "type": "object" }, "type": "array" }, "metric": { "description": "The metric being retrieved.", "enum": [ "AUC", "Accuracy", "Balanced Accuracy", "F1", "FPR", "FVE Binomial", "FVE Gamma", "FVE Multinomial", "FVE Poisson", "FVE Tweedie", "Gamma Deviance", "Gini Norm", "Kolmogorov-Smirnov", "LogLoss", "MAE", "MAPE", "MCC", "NPV", "PPV", "Poisson Deviance", "R Squared", "RMSE", "RMSLE", "Rate@Top10%", "Rate@Top5%", "TNR", "TPR", "Tweedie Deviance", "WGS84 MAE", "WGS84 RMSE" ], "type": "string" }, "modelId": { "description": "The id of the model for which metrics are being retrieved. Deprecated and to be removed in v2.35, use modelId in each baseline or bucket object.", "type": "string", "x-versiondeprecated": "v2.33" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "summary": { "description": "A bucket object containing metric info calculated. Deprecated and to be removed in v2.35, use summaries field with includeSummaries query param to get accuracy metrics computed over the whole time range.", "properties": { "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "Number of predictions used to calculate the metric.", "type": [ "integer", "null" ] }, "value": { "description": "Value of the metric, null if no value.", "type": [ "number", "null" ] }, "valuePerClass": { "description": "A dict keyed by class names with metric calculated for specific classes as values, if targetClass is set. Deprecated and to be removed in v2.35, use perClass instead.", "type": [ "object", "null" ] } }, "required": [ "period", "sampleSize", "value" ], "type": "object" } }, "required": [ "baseline", "baselines", "buckets", "metric", "summary" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baseline | AccuracyLegacyTimeBucket | true | | A bucket object containing metric info calculated. Deprecated and to be removed in v2.35, use summaries field with includeSummaries query param to get accuracy metrics computed over the whole time range. | | baselines | [AccuracyTimeBaselineBucket] | true | | Accuracy metric training baseline. | | buckets | [AccuracyTimeBucket] | true | | Accuracy metric for requested models and time buckets. | | metric | string | true | | The metric being retrieved. | | modelId | string | false | | The id of the model for which metrics are being retrieved. Deprecated and to be removed in v2.35, use modelId in each baseline or bucket object. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | | summary | AccuracyLegacyTimeBucket | true | | A bucket object containing metric info calculated. Deprecated and to be removed in v2.35, use summaries field with includeSummaries query param to get accuracy metrics computed over the whole time range. | ### Enumerated Values | Property | Value | | --- | --- | | metric | [AUC, Accuracy, Balanced Accuracy, F1, FPR, FVE Binomial, FVE Gamma, FVE Multinomial, FVE Poisson, FVE Tweedie, Gamma Deviance, Gini Norm, Kolmogorov-Smirnov, LogLoss, MAE, MAPE, MCC, NPV, PPV, Poisson Deviance, R Squared, RMSE, RMSLE, Rate@Top10%, Rate@Top5%, TNR, TPR, Tweedie Deviance, WGS84 MAE, WGS84 RMSE] | ## AccuracyPerClass ``` { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | className | string | true | | Name of the class. | | value | number,null | true | | Value of the metric. | ## AccuracyResponse ``` { "properties": { "batchIds": { "description": "ID of the batches used to calculate accuracy metrics.", "items": { "type": "string" }, "type": "array" }, "data": { "description": "Accuracy metric data.", "items": { "properties": { "baselineSampleSize": { "description": "Number of predictions used to calculate the baseline metric value.", "type": "integer", "x-versionadded": "v2.37" }, "baselineValue": { "description": "Baseline value of the metric.", "type": [ "number", "null" ] }, "metricName": { "description": "Name of the metric.", "type": "string" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "percentChange": { "description": "Percent of change by comparing metric value to baseline, with metric direction taken into account.", "type": [ "number", "null" ] }, "sampleSize": { "description": "Number of predictions used to calculate the metric value.", "type": "integer", "x-versionadded": "v2.37" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "baselineSampleSize", "baselineValue", "metricName", "percentChange", "sampleSize", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "metrics": { "description": "Accuracy metrics of the deployment. Deprecated and to be removed in v2.40; use data objects.", "example": "\n {\"metrics\": {\n \"LogLoss\": {\n \"baselineValue\": 0.454221484838069,\n \"value\": 0.880778024500618,\n \"percentChange\": -93.91\n },\n \"AUC\": {\n \"baselineValue\": 0.8690358459556535,\n \"value\": 0.5294117647058824,\n \"percentChange\": -39.08\n },\n \"Kolmogorov-Smirnov\": {\n \"baselineValue\": 0.5753202944706626,\n \"value\": 0.4117647058823529,\n \"percentChange\": -28.43\n },\n \"Rate@Top10%\": {\n \"baselineValue\": 0.9603223806571606,\n \"value\": 1.0,\n \"percentChange\": 4.13\n },\n \"Gini Norm\": {\n \"baselineValue\": 0.7380716919113071,\n \"value\": 0.05882352941176472,\n \"percentChange\": -92.03\n }\n }\n ", "type": "object", "x-versiondeprecated": "v2.36" }, "modelId": { "description": "The id of the model for which metrics are being retrieved. Deprecated and to be removed in v2.40; use modelId in each data object.", "type": "string", "x-versiondeprecated": "v2.36" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchIds | [string] | false | | ID of the batches used to calculate accuracy metrics. | | data | [AccuracyMetric] | true | maxItems: 100 | Accuracy metric data. | | metrics | object | false | | Accuracy metrics of the deployment. Deprecated and to be removed in v2.40; use data objects. | | modelId | string | false | | The id of the model for which metrics are being retrieved. Deprecated and to be removed in v2.40; use modelId in each data object. | | period | TimeRange | false | | An object with the keys "start" and "end" defining the period. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | ## AccuracyTimeBaselineBucket ``` { "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] } }, "required": [ "sampleSize", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | false | | The id of the model for which metrics are being retrieved. | | perClass | [AccuracyPerClass] | false | | Accuracy metric values for selected classes, only available for multiclass deployments. | | sampleSize | integer | true | | Number of rows used to calculate the metric. | | value | number,null | true | | Accuracy metric value. | ## AccuracyTimeBucket ``` { "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "perClass": { "description": "Accuracy metric values for selected classes, only available for multiclass deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "value": { "description": "Value of the metric.", "type": [ "number", "null" ] } }, "required": [ "className", "value" ], "type": "object" }, "type": "array" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "Number of rows used to calculate the metric.", "type": "integer" }, "value": { "description": "Accuracy metric value.", "type": [ "number", "null" ] }, "valuePerClass": { "description": "A dict keyed by class names with metric calculated for specific classes as values, if targetClass is set. Deprecated and to be removed in v2.35, use perClass instead.", "type": [ "object", "null" ], "x-versiondeprecated": "v2.33" } }, "required": [ "period", "sampleSize", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | false | | The id of the model for which metrics are being retrieved. | | perClass | [AccuracyPerClass] | false | | Accuracy metric values for selected classes, only available for multiclass deployments. | | period | TimeRange | true | | An object with the keys "start" and "end" defining the period. | | sampleSize | integer | true | | Number of rows used to calculate the metric. | | value | number,null | true | | Accuracy metric value. | | valuePerClass | object,null | false | | A dict keyed by class names with metric calculated for specific classes as values, if targetClass is set. Deprecated and to be removed in v2.35, use perClass instead. | ## ActualClassDistribution ``` { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | className | string | true | | Name of the class. | | count | integer | true | | Count of actual rows labeled with a class in the bucket. | | percent | number | true | | Percent of actual rows labeled with a class in the bucket. | ## BatchPeriod ``` { "description": "Time period of the batch.", "properties": { "end": { "description": "End time of the bucket", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start time of the bucket", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "end", "start" ], "type": "object" } ``` Time period of the batch. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string,null(date-time) | true | | End time of the bucket | | start | string,null(date-time) | true | | Start time of the bucket | ## PredictedClassDistribution ``` { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | className | string | true | | Name of the class. | | count | integer | true | | Count of prediction rows labeled with a class in the bucket. | | percent | number | true | | Percent of prediction rows labeled with a class in the bucket. | ## PredictionsVsActualsBaseline ``` { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer" } }, "required": [ "modelId", "rowCountTotal", "rowCountWithActual" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualClassDistribution | [ActualClassDistribution] | false | | Class distribution for all actuals in the bucket, only for classification deployments. | | meanActualValue | number,null | false | | Mean actual value for all rows in the bucket, only for regression deployments. | | meanPredictedValue | number,null | false | | Mean predicted value for all rows in the bucket, only for regression deployments. | | modelId | string | true | | ID of the model. | | predictedClassDistribution | [PredictedClassDistribution] | false | | Class distribution for all rows with actual in the bucket, only for classification deployments. | | rowCountTotal | integer | true | | Number of rows in the bucket. | | rowCountWithActual | integer | true | | Number of rows with actual in the bucket. | ## PredictionsVsActualsBatchBucket ``` { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "batchId": { "description": "ID of the batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "Name of the batch.", "type": "string", "x-versionadded": "v2.33" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "modelId": { "description": "ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "period": { "description": "Time period of the batch.", "properties": { "end": { "description": "End time of the bucket", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start time of the bucket", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "end", "start" ], "type": "object" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer", "x-versionadded": "v2.33" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName", "modelId", "period", "rowCountTotal", "rowCountWithActual" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualClassDistribution | [ActualClassDistribution] | false | | Class distribution for all actuals in the bucket, only for classification deployments. | | batchId | string | true | | ID of the batch. | | batchName | string | true | | Name of the batch. | | meanActualValue | number,null | false | | Mean actual value for all rows in the bucket, only for regression deployments. | | meanPredictedValue | number,null | false | | Mean predicted value for all rows in the bucket, only for regression deployments. | | modelId | string | true | | ID of the model. | | period | BatchPeriod | true | | Time period of the batch. | | predictedClassDistribution | [PredictedClassDistribution] | false | | Class distribution for all rows with actual in the bucket, only for classification deployments. | | rowCountTotal | integer | true | | Number of rows in the bucket. | | rowCountWithActual | integer | true | | Number of rows with actual in the bucket. | ## PredictionsVsActualsOverBatchResponse ``` { "properties": { "baselines": { "description": "Predictions vs actuals baselines.", "items": { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer" } }, "required": [ "modelId", "rowCountTotal", "rowCountWithActual" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "buckets": { "description": "Predictions vs actuals buckets.", "items": { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "batchId": { "description": "ID of the batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "Name of the batch.", "type": "string", "x-versionadded": "v2.33" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "modelId": { "description": "ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "period": { "description": "Time period of the batch.", "properties": { "end": { "description": "End time of the bucket", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start time of the bucket", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "end", "start" ], "type": "object" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer", "x-versionadded": "v2.33" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName", "modelId", "period", "rowCountTotal", "rowCountWithActual" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "baselines", "buckets" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselines | [PredictionsVsActualsBaseline] | true | | Predictions vs actuals baselines. | | buckets | [PredictionsVsActualsBatchBucket] | true | | Predictions vs actuals buckets. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | ## PredictionsVsActualsOverSpaceBucket ``` { "properties": { "actualClassDistribution": { "description": "For classification deployments, the class distribution for all actuals in the bucket.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 125, "type": "array" }, "hexagon": { "description": "The H3 geospatial indexing hexagon.", "type": "string" }, "meanActualValue": { "description": "For regression deployments, the mean actual value of all rows in the bucket.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "For regression deployments, the mean predicted value of all rows in the bucket.", "type": [ "number", "null" ] }, "predictedClassDistribution": { "description": "For classification deployments, the class distribution for all prediction rows in the bucket with associated actuals.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 125, "type": "array" }, "rowCountTotal": { "description": "The number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "The number of prediction rows in the bucket with associated actuals.", "type": "integer" } }, "required": [ "hexagon", "rowCountTotal", "rowCountWithActual" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualClassDistribution | [ActualClassDistribution] | false | maxItems: 125 | For classification deployments, the class distribution for all actuals in the bucket. | | hexagon | string | true | | The H3 geospatial indexing hexagon. | | meanActualValue | number,null | false | | For regression deployments, the mean actual value of all rows in the bucket. | | meanPredictedValue | number,null | false | | For regression deployments, the mean predicted value of all rows in the bucket. | | predictedClassDistribution | [PredictedClassDistribution] | false | maxItems: 125 | For classification deployments, the class distribution for all prediction rows in the bucket with associated actuals. | | rowCountTotal | integer | true | | The number of rows in the bucket. | | rowCountWithActual | integer | true | | The number of prediction rows in the bucket with associated actuals. | ## PredictionsVsActualsOverSpaceResponse ``` { "properties": { "baselines": { "description": "The predictions vs. actuals over space baselines.", "items": { "properties": { "actualClassDistribution": { "description": "For classification deployments, the class distribution for all actuals in the bucket.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 125, "type": "array" }, "hexagon": { "description": "The H3 geospatial indexing hexagon.", "type": "string" }, "meanActualValue": { "description": "For regression deployments, the mean actual value of all rows in the bucket.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "For regression deployments, the mean predicted value of all rows in the bucket.", "type": [ "number", "null" ] }, "predictedClassDistribution": { "description": "For classification deployments, the class distribution for all prediction rows in the bucket with associated actuals.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 125, "type": "array" }, "rowCountTotal": { "description": "The number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "The number of prediction rows in the bucket with associated actuals.", "type": "integer" } }, "required": [ "hexagon", "rowCountTotal", "rowCountWithActual" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "buckets": { "description": "The predictions vs. actuals over space buckets.", "items": { "properties": { "actualClassDistribution": { "description": "For classification deployments, the class distribution for all actuals in the bucket.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 125, "type": "array" }, "hexagon": { "description": "The H3 geospatial indexing hexagon.", "type": "string" }, "meanActualValue": { "description": "For regression deployments, the mean actual value of all rows in the bucket.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "For regression deployments, the mean predicted value of all rows in the bucket.", "type": [ "number", "null" ] }, "predictedClassDistribution": { "description": "For classification deployments, the class distribution for all prediction rows in the bucket with associated actuals.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 125, "type": "array" }, "rowCountTotal": { "description": "The number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "The number of prediction rows in the bucket with associated actuals.", "type": "integer" } }, "required": [ "hexagon", "rowCountTotal", "rowCountWithActual" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" }, "geoFeatureName": { "description": "The name of the geospatial feature.", "type": "string" }, "modelId": { "description": "The ID of the model for which metrics are being retrieved. ", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "summary": { "description": "Predictions vs actuals summary.", "properties": { "rowCountTotal": { "description": "Number of rows for all buckets.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual for all buckets.", "type": "integer" } }, "required": [ "rowCountTotal", "rowCountWithActual" ], "type": "object" } }, "required": [ "baselines", "buckets", "geoFeatureName", "summary" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselines | [PredictionsVsActualsOverSpaceBucket] | true | maxItems: 1000 | The predictions vs. actuals over space baselines. | | buckets | [PredictionsVsActualsOverSpaceBucket] | true | maxItems: 1000 | The predictions vs. actuals over space buckets. | | geoFeatureName | string | true | | The name of the geospatial feature. | | modelId | string | false | | The ID of the model for which metrics are being retrieved. | | period | TimeRange | false | | An object with the keys "start" and "end" defining the period. | | summary | PredictionsVsActualsSummaryBucket | true | | Predictions vs actuals summary. | ## PredictionsVsActualsOverTimeResponse ``` { "properties": { "baselines": { "description": "Predictions vs actuals baselines.", "items": { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer" } }, "required": [ "modelId", "rowCountTotal", "rowCountWithActual" ], "type": "object" }, "type": "array" }, "buckets": { "description": "Predictions vs actuals buckets.", "items": { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "period": { "description": "Time period of the batch.", "properties": { "end": { "description": "End time of the bucket", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start time of the bucket", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "end", "start" ], "type": "object" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer" } }, "required": [ "modelId", "period", "rowCountTotal", "rowCountWithActual" ], "type": "object" }, "type": "array" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "summary": { "description": "Predictions vs actuals summary.", "properties": { "rowCountTotal": { "description": "Number of rows for all buckets.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual for all buckets.", "type": "integer" } }, "required": [ "rowCountTotal", "rowCountWithActual" ], "type": "object" } }, "required": [ "baselines", "buckets", "summary" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselines | [PredictionsVsActualsBaseline] | true | | Predictions vs actuals baselines. | | buckets | [PredictionsVsActualsTimeBucket] | true | | Predictions vs actuals buckets. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | | summary | PredictionsVsActualsSummaryBucket | true | | Predictions vs actuals summary. | ## PredictionsVsActualsSummaryBucket ``` { "description": "Predictions vs actuals summary.", "properties": { "rowCountTotal": { "description": "Number of rows for all buckets.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual for all buckets.", "type": "integer" } }, "required": [ "rowCountTotal", "rowCountWithActual" ], "type": "object" } ``` Predictions vs actuals summary. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | rowCountTotal | integer | true | | Number of rows for all buckets. | | rowCountWithActual | integer | true | | Number of rows with actual for all buckets. | ## PredictionsVsActualsTimeBucket ``` { "properties": { "actualClassDistribution": { "description": "Class distribution for all actuals in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of actual rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of actual rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "meanActualValue": { "description": "Mean actual value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "modelId": { "description": "ID of the model.", "type": "string" }, "period": { "description": "Time period of the batch.", "properties": { "end": { "description": "End time of the bucket", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start time of the bucket", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "end", "start" ], "type": "object" }, "predictedClassDistribution": { "description": "Class distribution for all rows with actual in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class.", "type": "string" }, "count": { "description": "Count of prediction rows labeled with a class in the bucket.", "type": "integer" }, "percent": { "description": "Percent of prediction rows labeled with a class in the bucket.", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "type": "array" }, "rowCountTotal": { "description": "Number of rows in the bucket.", "type": "integer" }, "rowCountWithActual": { "description": "Number of rows with actual in the bucket.", "type": "integer" } }, "required": [ "modelId", "period", "rowCountTotal", "rowCountWithActual" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualClassDistribution | [ActualClassDistribution] | false | | Class distribution for all actuals in the bucket, only for classification deployments. | | meanActualValue | number,null | false | | Mean actual value for all rows in the bucket, only for regression deployments. | | meanPredictedValue | number,null | false | | Mean predicted value for all rows in the bucket, only for regression deployments. | | modelId | string | true | | ID of the model. | | period | BatchPeriod | true | | Time period of the batch. | | predictedClassDistribution | [PredictedClassDistribution] | false | | Class distribution for all rows with actual in the bucket, only for classification deployments. | | rowCountTotal | integer | true | | Number of rows in the bucket. | | rowCountWithActual | integer | true | | Number of rows with actual in the bucket. | ## TimeRange ``` { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } ``` An object with the keys "start" and "end" defining the period. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string,null(date-time) | false | | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | start | string,null(date-time) | false | | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | --- # Batch monitoring URL: https://docs.datarobot.com/en/docs/api/reference/public-api/observability_batch_monitoring.html > Use the endpoints described below to manage batch monitoring. You can view monitoring statistics organized by batch, instead of by time, with batch-enabled deployments. To do this, you can create batches, add predictions to those batches, and view service health, data drift, and accuracy statistics for the batches in your deployment. # Batch monitoring Use the endpoints described below to manage batch monitoring. You can view monitoring statistics organized by batch, instead of by time, with batch-enabled deployments. To do this, you can create batches, add predictions to those batches, and view service health, data drift, and accuracy statistics for the batches in your deployment. ## Get the limits related by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/monitoringBatchLimits/` Authentication requirements: `BearerAuth` Get the limits related to monitoring batches. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## List monitoring batches by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/monitoringBatches/` Authentication requirements: `BearerAuth` List monitoring batches in a deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | createdBy | query | string | false | ID of the user who created a batch. | | search | query | string | false | Search by matching batch name in a case-insensitive manner or exact match of batch ID. | | orderBy | query | string | false | Order of the returning batches. | | createdAfter | query | string(date-time) | false | Filter for batches created after the given time | | createdBefore | query | string(date-time) | false | Filter for batches created before the given time | | startAfter | query | string(date-time) | false | Filter for batches with a start time after the given time | | endBefore | query | string(date-time) | false | Filter for batches with an end time before the given time | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [name, -name, createdAt, -createdAt, earliestPredictionTimestamp, -earliestPredictionTimestamp, latestPredictionTimestamp, -latestPredictionTimestamp] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of monitoring batches.", "items": { "properties": { "batchPredictionJobId": { "description": "ID of the batch prediction job associated with the batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batchPredictionJobStatus": { "default": null, "description": "Status of the batch prediction job associated with the batch", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED", null ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "Creation timestamp of the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "Creator of the monitoring batch.", "properties": { "id": { "description": "ID of the user who created the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the user who created monitoring batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "description": { "description": "Description of the monitoring batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "earliestPredictionTimestamp": { "description": "Earliest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "externalContextUrl": { "description": "External URL associated with the batch.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "ID of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "isLocked": { "description": "Whether or not predictions can be added to the batch", "type": "boolean", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "predictionsCount": { "description": "Count of predictions rows in the monitoring batch.", "type": "integer", "x-versionadded": "v2.33" }, "serviceHealth": { "description": "Service health of the monitoring batch.", "properties": { "message": { "description": "Monitoring batch service health message.", "type": "string", "x-versionadded": "v2.33" }, "status": { "description": "Monitoring batch service health status.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message", "status" ], "type": "object" } }, "required": [ "batchPredictionJobId", "createdAt", "createdBy", "description", "earliestPredictionTimestamp", "externalContextUrl", "id", "isLocked", "latestPredictionTimestamp", "name", "predictionsCount", "serviceHealth" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | MonitoringBatchListResponse | ## Create a monitoring batch by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/monitoringBatches/` Authentication requirements: `BearerAuth` Create a monitoring batch for a deployment. ### Body parameter ``` { "properties": { "batchName": { "description": "Name of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "Description of the monitoring batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "externalContextUrl": { "description": "External URL associated with the batch.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "isLocked": { "description": "Whether or not predictions can be added to the batch", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | MonitoringBatchCreateUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "batchPredictionJobId": { "description": "ID of the batch prediction job associated with the batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batchPredictionJobStatus": { "default": null, "description": "Status of the batch prediction job associated with the batch", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED", null ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "Creation timestamp of the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "Creator of the monitoring batch.", "properties": { "id": { "description": "ID of the user who created the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the user who created monitoring batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "description": { "description": "Description of the monitoring batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "earliestPredictionTimestamp": { "description": "Earliest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "externalContextUrl": { "description": "External URL associated with the batch.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "ID of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "isLocked": { "description": "Whether or not predictions can be added to the batch", "type": "boolean", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "predictionsCount": { "description": "Count of predictions rows in the monitoring batch.", "type": "integer", "x-versionadded": "v2.33" }, "serviceHealth": { "description": "Service health of the monitoring batch.", "properties": { "message": { "description": "Monitoring batch service health message.", "type": "string", "x-versionadded": "v2.33" }, "status": { "description": "Monitoring batch service health status.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message", "status" ], "type": "object" } }, "required": [ "batchPredictionJobId", "createdAt", "createdBy", "description", "earliestPredictionTimestamp", "externalContextUrl", "id", "isLocked", "latestPredictionTimestamp", "name", "predictionsCount", "serviceHealth" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | MonitoringBatch | ## Delete a monitoring batch by deployment ID Operation path: `DELETE /api/v2/deployments/{deploymentId}/monitoringBatches/{monitoringBatchId}/` Authentication requirements: `BearerAuth` Delete a monitoring batch in a deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | monitoringBatchId | path | string | true | ID of the monitoring batch. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Retrieve a monitoring batch by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/monitoringBatches/{monitoringBatchId}/` Authentication requirements: `BearerAuth` Retrieve a monitoring batch in a deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | monitoringBatchId | path | string | true | ID of the monitoring batch. | ### Example responses > 200 Response ``` { "properties": { "batchPredictionJobId": { "description": "ID of the batch prediction job associated with the batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batchPredictionJobStatus": { "default": null, "description": "Status of the batch prediction job associated with the batch", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED", null ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "Creation timestamp of the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "Creator of the monitoring batch.", "properties": { "id": { "description": "ID of the user who created the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the user who created monitoring batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "description": { "description": "Description of the monitoring batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "earliestPredictionTimestamp": { "description": "Earliest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "externalContextUrl": { "description": "External URL associated with the batch.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "ID of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "isLocked": { "description": "Whether or not predictions can be added to the batch", "type": "boolean", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "predictionsCount": { "description": "Count of predictions rows in the monitoring batch.", "type": "integer", "x-versionadded": "v2.33" }, "serviceHealth": { "description": "Service health of the monitoring batch.", "properties": { "message": { "description": "Monitoring batch service health message.", "type": "string", "x-versionadded": "v2.33" }, "status": { "description": "Monitoring batch service health status.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message", "status" ], "type": "object" } }, "required": [ "batchPredictionJobId", "createdAt", "createdBy", "description", "earliestPredictionTimestamp", "externalContextUrl", "id", "isLocked", "latestPredictionTimestamp", "name", "predictionsCount", "serviceHealth" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | MonitoringBatch | ## Update a monitoring batch by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/monitoringBatches/{monitoringBatchId}/` Authentication requirements: `BearerAuth` Update a monitoring batch in a deployment. ### Body parameter ``` { "properties": { "batchName": { "description": "Name of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "Description of the monitoring batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "externalContextUrl": { "description": "External URL associated with the batch.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "isLocked": { "description": "Whether or not predictions can be added to the batch", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | monitoringBatchId | path | string | true | ID of the monitoring batch. | | body | body | MonitoringBatchCreateUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "batchPredictionJobId": { "description": "ID of the batch prediction job associated with the batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batchPredictionJobStatus": { "default": null, "description": "Status of the batch prediction job associated with the batch", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED", null ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "Creation timestamp of the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "Creator of the monitoring batch.", "properties": { "id": { "description": "ID of the user who created the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the user who created monitoring batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "description": { "description": "Description of the monitoring batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "earliestPredictionTimestamp": { "description": "Earliest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "externalContextUrl": { "description": "External URL associated with the batch.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "ID of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "isLocked": { "description": "Whether or not predictions can be added to the batch", "type": "boolean", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "predictionsCount": { "description": "Count of predictions rows in the monitoring batch.", "type": "integer", "x-versionadded": "v2.33" }, "serviceHealth": { "description": "Service health of the monitoring batch.", "properties": { "message": { "description": "Monitoring batch service health message.", "type": "string", "x-versionadded": "v2.33" }, "status": { "description": "Monitoring batch service health status.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message", "status" ], "type": "object" } }, "required": [ "batchPredictionJobId", "createdAt", "createdBy", "description", "earliestPredictionTimestamp", "externalContextUrl", "id", "isLocked", "latestPredictionTimestamp", "name", "predictionsCount", "serviceHealth" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | MonitoringBatch | ## List information about models that have data by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/monitoringBatches/{monitoringBatchId}/models/` Authentication requirements: `BearerAuth` List information about models that have data in a monitoring batch. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | modelId | query | string | false | ID of the model associated with a batch. | | deploymentId | path | string | true | Unique identifier of the deployment. | | monitoringBatchId | path | string | true | ID of the monitoring batch. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of models with data on a monitoring batch.", "items": { "properties": { "batchId": { "description": "ID of this monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "endTime": { "description": "Latest time of a prediction on this model in this batch", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "ID of a model with data on this batch", "type": "string", "x-versionadded": "v2.33" }, "predictionsCount": { "description": "Count of predictions rows on this model in this batch", "type": "integer", "x-versionadded": "v2.33" }, "startTime": { "description": "Earliest time of a prediction on this model in this batch", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "endTime", "modelId", "predictionsCount", "startTime" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | MonitoringBatchModelListResponse | ## Get information about a model that has data by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/monitoringBatches/{monitoringBatchId}/models/{modelId}/` Authentication requirements: `BearerAuth` Get information about a model that has data in a monitoring batch. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | monitoringBatchId | path | string | true | ID of the monitoring batch. | | modelId | path | string | true | ID of the model. | ### Example responses > 200 Response ``` { "properties": { "batchId": { "description": "ID of this monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "endTime": { "description": "Latest time of a prediction on this model in this batch", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "ID of a model with data on this batch", "type": "string", "x-versionadded": "v2.33" }, "predictionsCount": { "description": "Count of predictions rows on this model in this batch", "type": "integer", "x-versionadded": "v2.33" }, "startTime": { "description": "Earliest time of a prediction on this model in this batch", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "endTime", "modelId", "predictionsCount", "startTime" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | MonitoringBatchModel | ## Update information about model data by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/monitoringBatches/{monitoringBatchId}/models/{modelId}/` Authentication requirements: `BearerAuth` Update information about model data in a batch. ### Body parameter ``` { "properties": { "endTime": { "description": "Latest time of predictions on this model in this batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "startTime": { "description": "Earliest time of predictions on this model in this batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | monitoringBatchId | path | string | true | ID of the monitoring batch. | | modelId | path | string | true | ID of the model. | | body | body | MonitoringBatchModelUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | # Schemas ## MonitoringBatch ``` { "properties": { "batchPredictionJobId": { "description": "ID of the batch prediction job associated with the batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batchPredictionJobStatus": { "default": null, "description": "Status of the batch prediction job associated with the batch", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED", null ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "Creation timestamp of the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "Creator of the monitoring batch.", "properties": { "id": { "description": "ID of the user who created the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the user who created monitoring batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "description": { "description": "Description of the monitoring batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "earliestPredictionTimestamp": { "description": "Earliest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "externalContextUrl": { "description": "External URL associated with the batch.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "ID of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "isLocked": { "description": "Whether or not predictions can be added to the batch", "type": "boolean", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "predictionsCount": { "description": "Count of predictions rows in the monitoring batch.", "type": "integer", "x-versionadded": "v2.33" }, "serviceHealth": { "description": "Service health of the monitoring batch.", "properties": { "message": { "description": "Monitoring batch service health message.", "type": "string", "x-versionadded": "v2.33" }, "status": { "description": "Monitoring batch service health status.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message", "status" ], "type": "object" } }, "required": [ "batchPredictionJobId", "createdAt", "createdBy", "description", "earliestPredictionTimestamp", "externalContextUrl", "id", "isLocked", "latestPredictionTimestamp", "name", "predictionsCount", "serviceHealth" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchPredictionJobId | string,null | true | | ID of the batch prediction job associated with the batch. | | batchPredictionJobStatus | string,null | false | | Status of the batch prediction job associated with the batch | | createdAt | string(date-time) | true | | Creation timestamp of the monitoring batch. | | createdBy | MonitoringBatchCreator | true | | Creator of the monitoring batch. | | description | string,null | true | | Description of the monitoring batch. | | earliestPredictionTimestamp | string(date-time) | true | | Earliest prediction request timestamp in the monitoring batch. | | externalContextUrl | string,null(uri) | true | | External URL associated with the batch. | | id | string | true | | ID of the monitoring batch. | | isLocked | boolean | true | | Whether or not predictions can be added to the batch | | latestPredictionTimestamp | string(date-time) | true | | Latest prediction request timestamp in the monitoring batch. | | name | string | true | | Name of the monitoring batch. | | predictionsCount | integer | true | | Count of predictions rows in the monitoring batch. | | serviceHealth | MonitoringBatchServiceHealth | true | | Service health of the monitoring batch. | ### Enumerated Values | Property | Value | | --- | --- | | batchPredictionJobStatus | [INITIALIZING, RUNNING, COMPLETED, ABORTED, FAILED, null] | ## MonitoringBatchCreateUpdate ``` { "properties": { "batchName": { "description": "Name of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "Description of the monitoring batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "externalContextUrl": { "description": "External URL associated with the batch.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "isLocked": { "description": "Whether or not predictions can be added to the batch", "type": [ "boolean", "null" ], "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchName | string | false | | Name of the monitoring batch. | | description | string,null | false | | Description of the monitoring batch. | | externalContextUrl | string,null(uri) | false | | External URL associated with the batch. | | isLocked | boolean,null | false | | Whether or not predictions can be added to the batch | ## MonitoringBatchCreator ``` { "description": "Creator of the monitoring batch.", "properties": { "id": { "description": "ID of the user who created the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the user who created monitoring batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" } ``` Creator of the monitoring batch. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the user who created the monitoring batch. | | name | string | true | | Name of the user who created monitoring batch. | ## MonitoringBatchListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of monitoring batches.", "items": { "properties": { "batchPredictionJobId": { "description": "ID of the batch prediction job associated with the batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batchPredictionJobStatus": { "default": null, "description": "Status of the batch prediction job associated with the batch", "enum": [ "INITIALIZING", "RUNNING", "COMPLETED", "ABORTED", "FAILED", null ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "createdAt": { "description": "Creation timestamp of the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "Creator of the monitoring batch.", "properties": { "id": { "description": "ID of the user who created the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the user who created monitoring batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "description": { "description": "Description of the monitoring batch.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "earliestPredictionTimestamp": { "description": "Earliest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "externalContextUrl": { "description": "External URL associated with the batch.", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "ID of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "isLocked": { "description": "Whether or not predictions can be added to the batch", "type": "boolean", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction request timestamp in the monitoring batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "Name of the monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "predictionsCount": { "description": "Count of predictions rows in the monitoring batch.", "type": "integer", "x-versionadded": "v2.33" }, "serviceHealth": { "description": "Service health of the monitoring batch.", "properties": { "message": { "description": "Monitoring batch service health message.", "type": "string", "x-versionadded": "v2.33" }, "status": { "description": "Monitoring batch service health status.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message", "status" ], "type": "object" } }, "required": [ "batchPredictionJobId", "createdAt", "createdBy", "description", "earliestPredictionTimestamp", "externalContextUrl", "id", "isLocked", "latestPredictionTimestamp", "name", "predictionsCount", "serviceHealth" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [MonitoringBatch] | true | | List of monitoring batches. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## MonitoringBatchModel ``` { "properties": { "batchId": { "description": "ID of this monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "endTime": { "description": "Latest time of a prediction on this model in this batch", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "ID of a model with data on this batch", "type": "string", "x-versionadded": "v2.33" }, "predictionsCount": { "description": "Count of predictions rows on this model in this batch", "type": "integer", "x-versionadded": "v2.33" }, "startTime": { "description": "Earliest time of a prediction on this model in this batch", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "endTime", "modelId", "predictionsCount", "startTime" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchId | string | true | | ID of this monitoring batch. | | endTime | string(date-time) | true | | Latest time of a prediction on this model in this batch | | modelId | string | true | | ID of a model with data on this batch | | predictionsCount | integer | true | | Count of predictions rows on this model in this batch | | startTime | string(date-time) | true | | Earliest time of a prediction on this model in this batch | ## MonitoringBatchModelListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "List of models with data on a monitoring batch.", "items": { "properties": { "batchId": { "description": "ID of this monitoring batch.", "type": "string", "x-versionadded": "v2.33" }, "endTime": { "description": "Latest time of a prediction on this model in this batch", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "ID of a model with data on this batch", "type": "string", "x-versionadded": "v2.33" }, "predictionsCount": { "description": "Count of predictions rows on this model in this batch", "type": "integer", "x-versionadded": "v2.33" }, "startTime": { "description": "Earliest time of a prediction on this model in this batch", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "endTime", "modelId", "predictionsCount", "startTime" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [MonitoringBatchModel] | true | | List of models with data on a monitoring batch. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## MonitoringBatchModelUpdate ``` { "properties": { "endTime": { "description": "Latest time of predictions on this model in this batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "startTime": { "description": "Earliest time of predictions on this model in this batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endTime | string(date-time) | false | | Latest time of predictions on this model in this batch. | | startTime | string(date-time) | false | | Earliest time of predictions on this model in this batch. | ## MonitoringBatchServiceHealth ``` { "description": "Service health of the monitoring batch.", "properties": { "message": { "description": "Monitoring batch service health message.", "type": "string", "x-versionadded": "v2.33" }, "status": { "description": "Monitoring batch service health status.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message", "status" ], "type": "object" } ``` Service health of the monitoring batch. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | Monitoring batch service health message. | | status | string | true | | Monitoring batch service health status. | --- # Bias and fairness URL: https://docs.datarobot.com/en/docs/api/reference/public-api/observability_bias_fairness.html > Use the endpoints described below to manage Bias and Fairness. Bias and Fairness testing provides methods to calculate fairness for a binary classification model and attempt to identify any biases in the model's predictive behavior. In DataRobot, bias represents the difference between a model's predictions for different populations (or groups) while fairness is the measure of the model's bias. Select protected features in the dataset and choose fairness metrics and mitigation techniques either [before model building](fairness-metrics#configure-metrics-and-mitigation-pre-autopilot) or [from the Leaderboard](fairness-metrics#configure-metrics-and-mitigation-post-autopilot) once models are built. [Bias and Fairness insights](bias/index) help identify bias in a model and visualize the root-cause analysis, explaining why the model is learning bias from the training data and from where. # Bias and fairness Use the endpoints described below to manage Bias and Fairness. Bias and Fairness testing provides methods to calculate fairness for a binary classification model and attempt to identify any biases in the model's predictive behavior. In DataRobot, bias represents the difference between a model's predictions for different populations (or groups) while fairness is the measure of the model's bias. Select protected features in the dataset and choose fairness metrics and mitigation techniques either [before model building](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html#configure-metrics-and-mitigation-pre-autopilot) or [from the Leaderboard](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html#configure-metrics-and-mitigation-post-autopilot) once models are built.[Bias and Fairness insights](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/index.html) help identify bias in a model and visualize the root-cause analysis, explaining why the model is learning bias from the training data and from where. ## Retrieve fairness over time info of the deployment by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/fairnessScoresOverTime/` Authentication requirements: `BearerAuth` Retrieve fairness scores for all features, or for different classes of a single feature changes over a specific time period. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | bucketSize | query | string | false | Time duration of buckets | | modelId | query | string | false | The ID of the model to retrieve the statistics for. | | fairnessMetric | query | string,null | false | The name of the fairness metric to compute fairness over time. | | protectedFeature | query | string | false | Name of the protected feature. When present, will return per class fairness score of the protected feature, otherwise returning fairness score of all protected features. | | orderBy | query | string | false | The type of FairnessScore to order the response by. | | includePrivilegedClass | query | string | false | Always include privileged class in response. | | onlyStatisticallySignificant | query | string | false | Include only statistically significant attributes. | | limit | query | integer | false | Max number of features in response. | | offset | query | integer | false | Number of features to offset in response. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | bucketSize | [PT1H, P1D, P7D, P1M] | | fairnessMetric | [proportionalParity, equalParity, favorableClassBalance, unfavorableClassBalance, trueUnfavorableRateParity, trueFavorableRateParity, favorablePredictiveValueParity, unfavorablePredictiveValueParity] | | orderBy | [absoluteFairnessScore, relativeFairnessScore, -absoluteFairnessScore, -relativeFairnessScore] | | includePrivilegedClass | [false, False, true, True] | | onlyStatisticallySignificant | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "buckets": { "description": "Per bucket summary.", "items": { "description": "Summary of per feature fairness.", "properties": { "metricName": { "description": "Name of the metric.", "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "scores": { "description": "List of per-feature fairness details.", "items": { "properties": { "absoluteValue": { "description": "Absolute fairness score.", "type": "number" }, "classesCount": { "description": "Count of occurences of the class within the data the fairness is calculated on.", "type": "integer" }, "healthyClassesCount": { "description": "Count of statistically significant classes above fairness threshold.", "type": "integer" }, "healthyCount": { "description": "Total number of classes with fairness score above the threshold.", "minimum": 0, "type": "integer" }, "isStatisticallySignificant": { "description": "Class is statistically significant.", "type": "boolean" }, "label": { "description": "Name of the feature.", "type": "string" }, "message": { "description": "Explanation message.", "type": "string" }, "privilegedClass": { "description": "Name of the privileged class (the one with the highest fairness score) within the feature.", "type": "string" }, "sampleSize": { "description": "Sample size used for fairness status calculation", "exclusiveMinimum": 0, "type": "integer" }, "totalCount": { "description": "Total number of classes.", "minimum": 1, "type": "integer" }, "value": { "description": "Fairness score in relation to the privileged class fairness score.", "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" }, "type": "array" } }, "required": [ "metricName", "period", "scores" ], "type": "object" }, "type": "array" }, "fairnessThreshold": { "description": "Threshold used to compute fairness results.", "maximum": 1, "minimum": 0, "type": "number" }, "favorableTargetOutcome": { "description": "Preferable class of target.", "oneOf": [ { "type": "boolean" }, { "type": "string" }, { "type": "integer" } ] }, "modelId": { "description": "Model Id for which fairness is computed.", "type": "string" }, "modelPackageId": { "description": "Model package Id.", "type": "string" }, "protectedFeature": { "description": "Name of the protected feature.", "type": "string" }, "summary": { "description": "Summary of per feature fairness.", "properties": { "metricName": { "description": "Name of the metric.", "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "scores": { "description": "List of per-feature fairness details.", "items": { "properties": { "absoluteValue": { "description": "Absolute fairness score.", "type": "number" }, "classesCount": { "description": "Count of occurences of the class within the data the fairness is calculated on.", "type": "integer" }, "healthyClassesCount": { "description": "Count of statistically significant classes above fairness threshold.", "type": "integer" }, "healthyCount": { "description": "Total number of classes with fairness score above the threshold.", "minimum": 0, "type": "integer" }, "isStatisticallySignificant": { "description": "Class is statistically significant.", "type": "boolean" }, "label": { "description": "Name of the feature.", "type": "string" }, "message": { "description": "Explanation message.", "type": "string" }, "privilegedClass": { "description": "Name of the privileged class (the one with the highest fairness score) within the feature.", "type": "string" }, "sampleSize": { "description": "Sample size used for fairness status calculation", "exclusiveMinimum": 0, "type": "integer" }, "totalCount": { "description": "Total number of classes.", "minimum": 1, "type": "integer" }, "value": { "description": "Fairness score in relation to the privileged class fairness score.", "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" }, "type": "array" } }, "required": [ "metricName", "period", "scores" ], "type": "object" } }, "required": [ "buckets", "fairnessThreshold", "favorableTargetOutcome", "modelId", "modelPackageId", "summary" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Fairness summary and over time. | FairnessOverTimeResponse | # Schemas ## FairnessOverTimeBucket ``` { "description": "Summary of per feature fairness.", "properties": { "metricName": { "description": "Name of the metric.", "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "scores": { "description": "List of per-feature fairness details.", "items": { "properties": { "absoluteValue": { "description": "Absolute fairness score.", "type": "number" }, "classesCount": { "description": "Count of occurences of the class within the data the fairness is calculated on.", "type": "integer" }, "healthyClassesCount": { "description": "Count of statistically significant classes above fairness threshold.", "type": "integer" }, "healthyCount": { "description": "Total number of classes with fairness score above the threshold.", "minimum": 0, "type": "integer" }, "isStatisticallySignificant": { "description": "Class is statistically significant.", "type": "boolean" }, "label": { "description": "Name of the feature.", "type": "string" }, "message": { "description": "Explanation message.", "type": "string" }, "privilegedClass": { "description": "Name of the privileged class (the one with the highest fairness score) within the feature.", "type": "string" }, "sampleSize": { "description": "Sample size used for fairness status calculation", "exclusiveMinimum": 0, "type": "integer" }, "totalCount": { "description": "Total number of classes.", "minimum": 1, "type": "integer" }, "value": { "description": "Fairness score in relation to the privileged class fairness score.", "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" }, "type": "array" } }, "required": [ "metricName", "period", "scores" ], "type": "object" } ``` Summary of per feature fairness. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metricName | string | true | | Name of the metric. | | period | TimeRange | true | | An object with the keys "start" and "end" defining the period. | | scores | [FairnessOverTimeScore] | true | | List of per-feature fairness details. | ### Enumerated Values | Property | Value | | --- | --- | | metricName | [proportionalParity, equalParity, favorableClassBalance, unfavorableClassBalance, trueUnfavorableRateParity, trueFavorableRateParity, favorablePredictiveValueParity, unfavorablePredictiveValueParity] | ## FairnessOverTimeResponse ``` { "properties": { "buckets": { "description": "Per bucket summary.", "items": { "description": "Summary of per feature fairness.", "properties": { "metricName": { "description": "Name of the metric.", "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "scores": { "description": "List of per-feature fairness details.", "items": { "properties": { "absoluteValue": { "description": "Absolute fairness score.", "type": "number" }, "classesCount": { "description": "Count of occurences of the class within the data the fairness is calculated on.", "type": "integer" }, "healthyClassesCount": { "description": "Count of statistically significant classes above fairness threshold.", "type": "integer" }, "healthyCount": { "description": "Total number of classes with fairness score above the threshold.", "minimum": 0, "type": "integer" }, "isStatisticallySignificant": { "description": "Class is statistically significant.", "type": "boolean" }, "label": { "description": "Name of the feature.", "type": "string" }, "message": { "description": "Explanation message.", "type": "string" }, "privilegedClass": { "description": "Name of the privileged class (the one with the highest fairness score) within the feature.", "type": "string" }, "sampleSize": { "description": "Sample size used for fairness status calculation", "exclusiveMinimum": 0, "type": "integer" }, "totalCount": { "description": "Total number of classes.", "minimum": 1, "type": "integer" }, "value": { "description": "Fairness score in relation to the privileged class fairness score.", "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" }, "type": "array" } }, "required": [ "metricName", "period", "scores" ], "type": "object" }, "type": "array" }, "fairnessThreshold": { "description": "Threshold used to compute fairness results.", "maximum": 1, "minimum": 0, "type": "number" }, "favorableTargetOutcome": { "description": "Preferable class of target.", "oneOf": [ { "type": "boolean" }, { "type": "string" }, { "type": "integer" } ] }, "modelId": { "description": "Model Id for which fairness is computed.", "type": "string" }, "modelPackageId": { "description": "Model package Id.", "type": "string" }, "protectedFeature": { "description": "Name of the protected feature.", "type": "string" }, "summary": { "description": "Summary of per feature fairness.", "properties": { "metricName": { "description": "Name of the metric.", "enum": [ "proportionalParity", "equalParity", "favorableClassBalance", "unfavorableClassBalance", "trueUnfavorableRateParity", "trueFavorableRateParity", "favorablePredictiveValueParity", "unfavorablePredictiveValueParity" ], "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "scores": { "description": "List of per-feature fairness details.", "items": { "properties": { "absoluteValue": { "description": "Absolute fairness score.", "type": "number" }, "classesCount": { "description": "Count of occurences of the class within the data the fairness is calculated on.", "type": "integer" }, "healthyClassesCount": { "description": "Count of statistically significant classes above fairness threshold.", "type": "integer" }, "healthyCount": { "description": "Total number of classes with fairness score above the threshold.", "minimum": 0, "type": "integer" }, "isStatisticallySignificant": { "description": "Class is statistically significant.", "type": "boolean" }, "label": { "description": "Name of the feature.", "type": "string" }, "message": { "description": "Explanation message.", "type": "string" }, "privilegedClass": { "description": "Name of the privileged class (the one with the highest fairness score) within the feature.", "type": "string" }, "sampleSize": { "description": "Sample size used for fairness status calculation", "exclusiveMinimum": 0, "type": "integer" }, "totalCount": { "description": "Total number of classes.", "minimum": 1, "type": "integer" }, "value": { "description": "Fairness score in relation to the privileged class fairness score.", "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" }, "type": "array" } }, "required": [ "metricName", "period", "scores" ], "type": "object" } }, "required": [ "buckets", "fairnessThreshold", "favorableTargetOutcome", "modelId", "modelPackageId", "summary" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [FairnessOverTimeBucket] | true | | Per bucket summary. | | fairnessThreshold | number | true | maximum: 1minimum: 0 | Threshold used to compute fairness results. | | favorableTargetOutcome | any | true | | Preferable class of target. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | true | | Model Id for which fairness is computed. | | modelPackageId | string | true | | Model package Id. | | protectedFeature | string | false | | Name of the protected feature. | | summary | FairnessOverTimeBucket | true | | Summary of per feature fairness. | ## FairnessOverTimeScore ``` { "properties": { "absoluteValue": { "description": "Absolute fairness score.", "type": "number" }, "classesCount": { "description": "Count of occurences of the class within the data the fairness is calculated on.", "type": "integer" }, "healthyClassesCount": { "description": "Count of statistically significant classes above fairness threshold.", "type": "integer" }, "healthyCount": { "description": "Total number of classes with fairness score above the threshold.", "minimum": 0, "type": "integer" }, "isStatisticallySignificant": { "description": "Class is statistically significant.", "type": "boolean" }, "label": { "description": "Name of the feature.", "type": "string" }, "message": { "description": "Explanation message.", "type": "string" }, "privilegedClass": { "description": "Name of the privileged class (the one with the highest fairness score) within the feature.", "type": "string" }, "sampleSize": { "description": "Sample size used for fairness status calculation", "exclusiveMinimum": 0, "type": "integer" }, "totalCount": { "description": "Total number of classes.", "minimum": 1, "type": "integer" }, "value": { "description": "Fairness score in relation to the privileged class fairness score.", "maximum": 1, "minimum": 0, "type": "number" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | absoluteValue | number | false | | Absolute fairness score. | | classesCount | integer | false | | Count of occurences of the class within the data the fairness is calculated on. | | healthyClassesCount | integer | false | | Count of statistically significant classes above fairness threshold. | | healthyCount | integer | false | minimum: 0 | Total number of classes with fairness score above the threshold. | | isStatisticallySignificant | boolean | false | | Class is statistically significant. | | label | string | false | | Name of the feature. | | message | string | false | | Explanation message. | | privilegedClass | string | false | | Name of the privileged class (the one with the highest fairness score) within the feature. | | sampleSize | integer | false | | Sample size used for fairness status calculation | | totalCount | integer | false | minimum: 1 | Total number of classes. | | value | number | false | maximum: 1minimum: 0 | Fairness score in relation to the privileged class fairness score. | ## TimeRange ``` { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } ``` An object with the keys "start" and "end" defining the period. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string,null(date-time) | false | | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | start | string,null(date-time) | false | | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | --- # Actuals configuration URL: https://docs.datarobot.com/en/docs/api/reference/public-api/observability_configuration.html > Use the endpoints described below to manage deployment configuration. Configure capabilities of your current deployment based on the data provided, for example, training data, prediction data, or actuals. After you create and configure a deployment, you can use the deployment settings to add or update deployment functionality that wasn't configured during deployment creation. # Actuals configuration Use the endpoints described below to manage deployment configuration. Configure capabilities of your current deployment based on the data provided, for example, training data, prediction data, or actuals. After you create and configure a deployment, you can use the deployment settings to add or update deployment functionality that wasn't configured during deployment creation. ## Submit actuals values by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/actuals/fromDataset/` Authentication requirements: `BearerAuth` Submit actuals values for processing using catalog item. Submission of actuals is limited to 10,000,000 actuals per hour. For time series deployments, total actuals = number of actuals * number of forecast distances. For example, submitting 10 actuals for a deployment with 50 forecast distances = 500 total actuals. For multiclass deployments, a similar calculation is made where total actuals = number of actuals * number of classes. For example, submitting 10 actuals for a deployment with 20 classes = 200 actuals. ### Body parameter ``` { "properties": { "actualValueColumn": { "description": "Column name that contains actual values.", "type": "string" }, "associationIdColumn": { "description": "Column name that contains unique identifiers used with a predicted rows.", "type": "string" }, "datasetId": { "description": "The ID of dataset from catalog.", "type": "string" }, "datasetVersionId": { "description": "Version of dataset to retrieve.", "type": [ "string", "null" ] }, "keepActualsWithoutPredictions": { "default": true, "description": "Indicates if actual without predictions are kept.", "type": "boolean" }, "password": { "description": "The password for database authentication.", "type": "string" }, "timestampColumn": { "description": "Column name that contain datetime when actual values were obtained.", "type": "string", "x-versiondeprecated": "v2.32" }, "user": { "description": "The username for database authentication.", "type": "string" }, "wasActedOnColumn": { "description": "Column name that contains boolean values if any action was made based on predictions data.", "type": "string", "x-versiondeprecated": "v2.32" } }, "required": [ "actualValueColumn", "associationIdColumn", "datasetId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | DeploymentDatasetCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Submitted successfully. See Location header. | None | | 422 | Unprocessable Entity | Unable to process the Actuals submission request. | None | | 429 | Too Many Requests | The number of actuals uploaded this hour exceeds the limit of 10000000 rows. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking async job status. | ## Create a actuals from a JSON Operation path: `POST /api/v2/deployments/{deploymentId}/actuals/fromJSON/` Authentication requirements: `BearerAuth` Submit actuals values for processing. Values are not processed immediately and may take some time to propagate through deployment systems. Submission of actuals is limited to 10,000,000 actuals per hour. For time series deployments, total actuals = number of actuals * number of forecast distances. For example, submitting 10 actuals for a deployment with 50 forecast distances = 500 total actuals. For multiclass deployments, a similar calculation is made where total actuals = number of actuals * number of classes. For example, submitting 10 actuals for a deployment with 20 classes = 200 actuals. ### Body parameter ``` { "properties": { "data": { "description": "A list of `actual` objects that describes actual values. Minimum size of the list is 1 and maximum size is 10000 items. An `actual` object has the following schema.", "items": { "properties": { "actualValue": { "anyOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" } ], "description": "The actual value of a prediction. Will be numeric for regression models and a string label for classification models." }, "associationId": { "description": "A unique identifier used with a predicted row.", "maxLength": 128, "type": "string" }, "timestamp": { "description": "The datetime when actual values were obtained, formatted according to RFC3339.", "format": "date-time", "type": [ "string", "null" ], "x-versiondeprecated": "v2.32" }, "wasActedOn": { "description": "Indicates if the prediction was acted on in a way that could have affected the actual outcome. For example, if a hospital patient is predicted to be readmitted in 30 days, extra procedures or new medication might be given to mitigate this problem, influencing the actual outcome of the prediction.", "type": [ "boolean", "null" ], "x-versiondeprecated": "v2.32" } }, "required": [ "actualValue", "associationId" ], "type": "object" }, "minItems": 1, "type": "array" }, "keepActualsWithoutPredictions": { "default": true, "description": "Indicates if actual without predictions are kept.", "type": "boolean" } }, "required": [ "data" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | DeploymentActuals | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Submitted successfully. See Location header. | None | | 422 | Unprocessable Entity | Unable to process the Actuals submission request. | None | | 429 | Too Many Requests | The number of actuals uploaded this hour exceeds the limit of 10000000 rows. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL for tracking async job status. | ## Endpoint by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/monitoringDataDeletions/` Authentication requirements: `BearerAuth` Delete deployment monitoring data. ### Body parameter ``` { "properties": { "end": { "description": "End of the period to delete monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "description": "The id of the model for which monitoring data are being deleted.", "type": "string" }, "start": { "description": "Start of the period to delete monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "modelId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | MonitoringDataDeletePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 404 | Not Found | Deployment or model not found. | None | ## Retrieve Segment attributes by id Operation path: `GET /api/v2/deployments/{deploymentId}/segmentAttributes/` Authentication requirements: `BearerAuth` Retrieve deployment segment attributes. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | monitoringType | query | string | false | The monitoring type for which segment attributes are being retrieved. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | monitoringType | [serviceHealth, dataDrift, accuracy, humility, customMetrics, geospatial] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The segment attributes that were retrieved.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" }, "monitoringType": { "description": "The monitoring type for which segment attributes are being retrieved.", "enum": [ "serviceHealth", "dataDrift", "accuracy", "humility", "customMetrics", "geospatial" ], "type": "string" } }, "required": [ "data", "monitoringType" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SegmentAttributesRetrieveResponse | ## Retrieve Segment values by id Operation path: `GET /api/v2/deployments/{deploymentId}/segmentValues/` Authentication requirements: `BearerAuth` Retrieve deployment segment values. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | segmentAttribute | query | string | false | The name of the segment attribute whose values the user wants to retrieve. | | search | query | string | false | The search query to filter the list of segment values. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of segment values.", "items": { "properties": { "segmentAttribute": { "description": "Name of the segment attribute.", "type": "string" }, "value": { "description": "The segment value.", "type": "string", "x-versionadded": "v2.35" } }, "required": [ "segmentAttribute", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SegmentValuesRetrieveResponse | # Schemas ## Actual ``` { "properties": { "actualValue": { "anyOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" } ], "description": "The actual value of a prediction. Will be numeric for regression models and a string label for classification models." }, "associationId": { "description": "A unique identifier used with a predicted row.", "maxLength": 128, "type": "string" }, "timestamp": { "description": "The datetime when actual values were obtained, formatted according to RFC3339.", "format": "date-time", "type": [ "string", "null" ], "x-versiondeprecated": "v2.32" }, "wasActedOn": { "description": "Indicates if the prediction was acted on in a way that could have affected the actual outcome. For example, if a hospital patient is predicted to be readmitted in 30 days, extra procedures or new medication might be given to mitigate this problem, influencing the actual outcome of the prediction.", "type": [ "boolean", "null" ], "x-versiondeprecated": "v2.32" } }, "required": [ "actualValue", "associationId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValue | any | true | | The actual value of a prediction. Will be numeric for regression models and a string label for classification models. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associationId | string | true | maxLength: 128 | A unique identifier used with a predicted row. | | timestamp | string,null(date-time) | false | | The datetime when actual values were obtained, formatted according to RFC3339. | | wasActedOn | boolean,null | false | | Indicates if the prediction was acted on in a way that could have affected the actual outcome. For example, if a hospital patient is predicted to be readmitted in 30 days, extra procedures or new medication might be given to mitigate this problem, influencing the actual outcome of the prediction. | ## DeploymentActuals ``` { "properties": { "data": { "description": "A list of `actual` objects that describes actual values. Minimum size of the list is 1 and maximum size is 10000 items. An `actual` object has the following schema.", "items": { "properties": { "actualValue": { "anyOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" } ], "description": "The actual value of a prediction. Will be numeric for regression models and a string label for classification models." }, "associationId": { "description": "A unique identifier used with a predicted row.", "maxLength": 128, "type": "string" }, "timestamp": { "description": "The datetime when actual values were obtained, formatted according to RFC3339.", "format": "date-time", "type": [ "string", "null" ], "x-versiondeprecated": "v2.32" }, "wasActedOn": { "description": "Indicates if the prediction was acted on in a way that could have affected the actual outcome. For example, if a hospital patient is predicted to be readmitted in 30 days, extra procedures or new medication might be given to mitigate this problem, influencing the actual outcome of the prediction.", "type": [ "boolean", "null" ], "x-versiondeprecated": "v2.32" } }, "required": [ "actualValue", "associationId" ], "type": "object" }, "minItems": 1, "type": "array" }, "keepActualsWithoutPredictions": { "default": true, "description": "Indicates if actual without predictions are kept.", "type": "boolean" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [Actual] | true | minItems: 1 | A list of actual objects that describes actual values. Minimum size of the list is 1 and maximum size is 10000 items. An actual object has the following schema. | | keepActualsWithoutPredictions | boolean | false | | Indicates if actual without predictions are kept. | ## DeploymentDatasetCreate ``` { "properties": { "actualValueColumn": { "description": "Column name that contains actual values.", "type": "string" }, "associationIdColumn": { "description": "Column name that contains unique identifiers used with a predicted rows.", "type": "string" }, "datasetId": { "description": "The ID of dataset from catalog.", "type": "string" }, "datasetVersionId": { "description": "Version of dataset to retrieve.", "type": [ "string", "null" ] }, "keepActualsWithoutPredictions": { "default": true, "description": "Indicates if actual without predictions are kept.", "type": "boolean" }, "password": { "description": "The password for database authentication.", "type": "string" }, "timestampColumn": { "description": "Column name that contain datetime when actual values were obtained.", "type": "string", "x-versiondeprecated": "v2.32" }, "user": { "description": "The username for database authentication.", "type": "string" }, "wasActedOnColumn": { "description": "Column name that contains boolean values if any action was made based on predictions data.", "type": "string", "x-versiondeprecated": "v2.32" } }, "required": [ "actualValueColumn", "associationIdColumn", "datasetId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValueColumn | string | true | | Column name that contains actual values. | | associationIdColumn | string | true | | Column name that contains unique identifiers used with a predicted rows. | | datasetId | string | true | | The ID of dataset from catalog. | | datasetVersionId | string,null | false | | Version of dataset to retrieve. | | keepActualsWithoutPredictions | boolean | false | | Indicates if actual without predictions are kept. | | password | string | false | | The password for database authentication. | | timestampColumn | string | false | | Column name that contain datetime when actual values were obtained. | | user | string | false | | The username for database authentication. | | wasActedOnColumn | string | false | | Column name that contains boolean values if any action was made based on predictions data. | ## MonitoringDataDeletePayload ``` { "properties": { "end": { "description": "End of the period to delete monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "modelId": { "description": "The id of the model for which monitoring data are being deleted.", "type": "string" }, "start": { "description": "Start of the period to delete monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string,null(date-time) | false | | End of the period to delete monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | modelId | string | true | | The id of the model for which monitoring data are being deleted. | | start | string,null(date-time) | false | | Start of the period to delete monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | ## SegmentAttributesRetrieveResponse ``` { "properties": { "data": { "description": "The segment attributes that were retrieved.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" }, "monitoringType": { "description": "The monitoring type for which segment attributes are being retrieved.", "enum": [ "serviceHealth", "dataDrift", "accuracy", "humility", "customMetrics", "geospatial" ], "type": "string" } }, "required": [ "data", "monitoringType" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [string] | true | maxItems: 100minItems: 1 | The segment attributes that were retrieved. | | monitoringType | string | true | | The monitoring type for which segment attributes are being retrieved. | ### Enumerated Values | Property | Value | | --- | --- | | monitoringType | [serviceHealth, dataDrift, accuracy, humility, customMetrics, geospatial] | ## SegmentValue ``` { "properties": { "segmentAttribute": { "description": "Name of the segment attribute.", "type": "string" }, "value": { "description": "The segment value.", "type": "string", "x-versionadded": "v2.35" } }, "required": [ "segmentAttribute", "value" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | segmentAttribute | string | true | | Name of the segment attribute. | | value | string | true | | The segment value. | ## SegmentValuesRetrieveResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of segment values.", "items": { "properties": { "segmentAttribute": { "description": "Name of the segment attribute.", "type": "string" }, "value": { "description": "The segment value.", "type": "string", "x-versionadded": "v2.35" } }, "required": [ "segmentAttribute", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [SegmentValue] | true | maxItems: 100 | List of segment values. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | --- # Custom metrics URL: https://docs.datarobot.com/en/docs/api/reference/public-api/observability_custom_metrics.html > Use the endpoints described below to manage custom metrics. Use the data you collect from data exploration (or data calculated through other custom metrics) to compute and monitor custom business or performance metrics. This feature allows you to implement your organization's specialized metrics, expanding on the insights provided by DataRobot's built-in service health, data drift, and accuracy metrics. # Custom metrics Use the endpoints described below to manage custom metrics. Use the data you collect from data exploration (or data calculated through other custom metrics) to compute and monitor custom business or performance metrics. This feature allows you to implement your organization's specialized metrics, expanding on the insights provided by DataRobot's built-in service health, data drift, and accuracy metrics. ## Creates a custom job Operation path: `POST /api/v2/customJobs/fromHostedCustomMetricGalleryTemplate/` Authentication requirements: `BearerAuth` Creates a custom job from the hosted custom metric template. ### Body parameter ``` { "properties": { "description": { "default": "", "description": "Description of the hosted custom metric job.", "maxLength": 1000, "type": "string" }, "name": { "description": "Name of the hosted custom metric job.", "maxLength": 255, "type": "string" }, "sidecarDeploymentId": { "description": "Sidecar deployment ID.", "type": "string" }, "templateId": { "description": "Custom Metric Template ID.", "type": "string" } }, "required": [ "name", "templateId" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CustomJobFromGalleryTemplateCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the custom job was created.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobType": { "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "lastRun": { "description": "The last custom job run.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "updated": { "description": "ISO-8601 timestamp of when custom job was last updated.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "environmentId", "environmentVersionId", "id", "items", "jobType", "lastRun", "name", "resources", "updated" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Custom metric test job creation started. | CustomJobResponse | | 403 | Forbidden | User does not have permission to create a custom job. | None | ## List all of the custom metrics associated by custom job ID Operation path: `GET /api/v2/customJobs/{customJobId}/customMetrics/` Authentication requirements: `BearerAuth` List all of the custom metrics associated with a custom job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | customJobId | path | string | true | ID of the custom job. | ### Example responses > 201 Response ``` { "properties": { "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Hosted custom metric creation timestamp.", "format": "date-time", "type": "string" }, "deployment": { "description": "The deployment associated to the custom metric.", "properties": { "createdAt": { "description": "Timestamp when the deployment was created.", "format": "date-time", "type": "string" }, "creatorFirstName": { "description": "First name of the deployment creator.", "type": [ "string", "null" ] }, "creatorGravatarHash": { "description": "Gravatar hash of the deployment creator.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Last name of the deployment creator.", "type": [ "string", "null" ] }, "creatorUsername": { "description": "Username of the deployment creator.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the deployment.", "type": "string" }, "name": { "description": "Name of the deployment.", "type": "string" } }, "required": [ "createdAt", "creatorFirstName", "creatorGravatarHash", "creatorLastName", "creatorUsername", "id", "name" ], "type": "object", "x-versionadded": "v2.34" }, "description": { "default": "", "description": "Description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "geospatialSegmentAttribute": { "description": "Name of the column that contains geospatial values.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "name": { "description": "Name of the custom metric.", "maxLength": 255, "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "deployment", "directionality", "id", "isModelSpecific", "name", "timeStep", "type", "units" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Custom metrics associated with custom job. | MetricCreateFromCustomJobResponse | | 403 | Forbidden | User does not have permission to custom jobs. | None | ## Delete a custom metric associated by custom job ID Operation path: `DELETE /api/v2/customJobs/{customJobId}/customMetrics/{customMetricId}/` Authentication requirements: `BearerAuth` Delete a custom metric associated with a custom job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | customMetricId | path | string | true | ID of the custom metric. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | User does not have permission to custom jobs. | None | ## Update custom metric associated by custom job ID Operation path: `PATCH /api/v2/customJobs/{customJobId}/customMetrics/{customMetricId}/` Authentication requirements: `BearerAuth` Update custom metric associated with a custom job. ### Body parameter ``` { "properties": { "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "batch": { "description": "A custom metric batch ID source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "categories": { "description": "Category values. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": "string" }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "type": "object", "x-versionadded": "v2.34" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | customMetricId | path | string | true | ID of the custom metric. | | body | body | CustomJobCustomMetricUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Hosted custom metric creation timestamp.", "format": "date-time", "type": "string" }, "deployment": { "description": "The deployment associated to the custom metric.", "properties": { "createdAt": { "description": "Timestamp when the deployment was created.", "format": "date-time", "type": "string" }, "creatorFirstName": { "description": "First name of the deployment creator.", "type": [ "string", "null" ] }, "creatorGravatarHash": { "description": "Gravatar hash of the deployment creator.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Last name of the deployment creator.", "type": [ "string", "null" ] }, "creatorUsername": { "description": "Username of the deployment creator.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the deployment.", "type": "string" }, "name": { "description": "Name of the deployment.", "type": "string" } }, "required": [ "createdAt", "creatorFirstName", "creatorGravatarHash", "creatorLastName", "creatorUsername", "id", "name" ], "type": "object", "x-versionadded": "v2.34" }, "description": { "default": "", "description": "Description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "geospatialSegmentAttribute": { "description": "Name of the column that contains geospatial values.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "name": { "description": "Name of the custom metric.", "maxLength": 255, "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "deployment", "directionality", "id", "isModelSpecific", "name", "timeStep", "type", "units" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Updated custom metric. | MetricCreateFromCustomJobResponse | | 403 | Forbidden | User does not have permission to custom jobs. | None | ## Retrieve a template by custom job ID Operation path: `GET /api/v2/customJobs/{customJobId}/hostedCustomMetricTemplate/` Authentication requirements: `BearerAuth` Retrieve a template for hosted custom metric job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | ### Example responses > 200 Response ``` { "properties": { "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Hosted custom metric template creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "ID of the user who created the hosted custom metric template.", "type": "string" }, "customJobId": { "description": "ID of the associatedCustom job.", "type": "string" }, "directionality": { "description": "Directionality of the custom metric. This field is required for numeric custom metrics.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "id": { "description": "ID of the hosted custom metric template.", "type": "string" }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics.", "type": [ "string", "null" ] }, "updatedAt": { "description": "Hosted custom metric template update timestamp.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "ID of the user who updated the hosted custom metric template.", "type": "string" } }, "required": [ "createdAt", "createdBy", "customJobId", "id", "isModelSpecific", "timeStep", "type", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Hosted custom metric template retrieved. | HostedCustomMetricTemplateResponse | | 403 | Forbidden | User does not have permission to retrieve a custom metric test job. | None | | 404 | Not Found | Custom job does not exist or user does not have permission to access it. | None | ## Updates a template by custom job ID Operation path: `PATCH /api/v2/customJobs/{customJobId}/hostedCustomMetricTemplate/` Authentication requirements: `BearerAuth` Updates a template for hosted custom metric job. ### Body parameter ``` { "properties": { "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": "string" } }, "type": "object", "x-versionadded": "v2.34" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | body | body | HostedCustomMetricTemplateUpdate | false | none | ### Example responses > 201 Response ``` { "properties": { "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Hosted custom metric template creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "ID of the user who created the hosted custom metric template.", "type": "string" }, "customJobId": { "description": "ID of the associatedCustom job.", "type": "string" }, "directionality": { "description": "Directionality of the custom metric. This field is required for numeric custom metrics.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "id": { "description": "ID of the hosted custom metric template.", "type": "string" }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics.", "type": [ "string", "null" ] }, "updatedAt": { "description": "Hosted custom metric template update timestamp.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "ID of the user who updated the hosted custom metric template.", "type": "string" } }, "required": [ "createdAt", "createdBy", "customJobId", "id", "isModelSpecific", "timeStep", "type", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Hosted custom metric template updated. | HostedCustomMetricTemplateResponse | | 403 | Forbidden | User does not have permission to update a custom metric test job. | None | | 404 | Not Found | Custom job or template does not exist or user does not have permission to access it. | None | | 409 | Conflict | Custom job have at least 1 associated deployment. | None | ## Creates a template by custom job ID Operation path: `POST /api/v2/customJobs/{customJobId}/hostedCustomMetricTemplate/` Authentication requirements: `BearerAuth` Creates a template for hosted custom metric job. ### Body parameter ``` { "properties": { "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "directionality": { "description": "Directionality of the custom metric. This field is required for numeric custom metrics.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics.", "type": [ "string", "null" ] } }, "required": [ "isModelSpecific", "timeStep", "type" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | customJobId | path | string | true | ID of the custom job. | | body | body | HostedCustomMetricTemplateCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Hosted custom metric template creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "ID of the user who created the hosted custom metric template.", "type": "string" }, "customJobId": { "description": "ID of the associatedCustom job.", "type": "string" }, "directionality": { "description": "Directionality of the custom metric. This field is required for numeric custom metrics.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "id": { "description": "ID of the hosted custom metric template.", "type": "string" }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics.", "type": [ "string", "null" ] }, "updatedAt": { "description": "Hosted custom metric template update timestamp.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "ID of the user who updated the hosted custom metric template.", "type": "string" } }, "required": [ "createdAt", "createdBy", "customJobId", "id", "isModelSpecific", "timeStep", "type", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Hosted custom metric template created. | HostedCustomMetricTemplateResponse | | 403 | Forbidden | User does not have permission to create a custom metric test job. | None | | 404 | Not Found | Custom job does not exist or user does not have permission to access it. | None | | 409 | Conflict | Hosted custom metric template already exists. | None | ## Retrieve a list of custom metrics by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/customMetrics/` Authentication requirements: `BearerAuth` Retrieve a list of custom metrics. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Offset this number of objects to retrieve | | limit | query | integer | false | At most this number of objects to retrieve | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of paginated entries.", "minimum": 0, "type": "integer" }, "data": { "description": "A list of custom metrics.", "items": { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" }, "type": "array" }, "next": { "description": "URL to the next page, or null if there is no such page", "type": [ "string", "null" ] }, "previous": { "description": "URL to the previous page, or null if there is no such page", "type": [ "string", "null" ] }, "totalCount": { "description": "Total number of entries.", "minimum": 0, "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Success | MetricListResponse | | 403 | Forbidden | User does not have permission to access a particular deployment or create a custom metric. | None | ## Create a deployment custom metric by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/customMetrics/` Authentication requirements: `BearerAuth` Create a deployment custom metric. ### Body parameter ``` { "properties": { "baselineValues": { "description": "Baseline values. This field is required for numeric custom metrics.", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric. This field is required for numeric custom metrics.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "isModelSpecific", "name", "timeStep", "type" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | MetricCreatePayload | false | none | ### Example responses > 201 Response ``` { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Custom metric is successfully created. | MetricEntity | | 403 | Forbidden | User does not have permission to create a custom metric. | None | ## Bulk upload custom metric values by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/customMetrics/bulkUpload/` Authentication requirements: `BearerAuth` Bulk upload custom metric values from JSON. ### Body parameter ``` { "properties": { "buckets": { "description": "A list of timestamped buckets with custom metric values.", "items": { "properties": { "associationId": { "default": null, "description": "Identifies prediction row corresponding to value.", "type": [ "string", "null" ] }, "customMetricId": { "description": "ID of the custom metric.", "type": "string" }, "metadata": { "default": null, "description": "A read-only metadata association with a custom metric value.", "maxLength": 128, "type": [ "string", "null" ] }, "sampleSize": { "default": 1, "description": "Custom metric value sample size.", "type": "integer" }, "segments": { "description": "A list of segments for a custom metric used in segmented analysis.", "items": { "properties": { "name": { "description": "Name of the segment on which segment analysis is being performed.", "type": "string" }, "value": { "description": "Value of the segment attribute to segment on.", "type": "string" } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 10, "type": "array" }, "timestamp": { "description": "Value timestamp.", "format": "date-time", "type": [ "string", "null" ] }, "value": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "Custom metric value to ingest." } }, "required": [ "customMetricId", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "modelId": { "default": null, "description": "For a model metric, the ID of the model of related champion/challenger to update the metric values. For a deployment metric, the ID of the model is not needed.", "type": [ "string", "null" ] }, "modelPackageId": { "default": null, "description": "For a model metric, the ID of the model package of related champion/challenger to update the metric values. For a deployment metric, the ID of the model package is not needed.", "type": [ "string", "null" ] } }, "required": [ "buckets" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | BulkMetricValuesPayload | false | none | ### Example responses > 202 Response ``` { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Custom metric values import started | MetricEntity | | 403 | Forbidden | User does not have permission to import custom metric values. | None | ## Create a custom metrics from a custom job Operation path: `POST /api/v2/deployments/{deploymentId}/customMetrics/fromCustomJob/` Authentication requirements: `BearerAuth` Create a deployment custom metric from an existing custom job. ### Body parameter ``` { "properties": { "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "customJobId": { "description": "Custom Job ID.", "type": "string" }, "description": { "default": "", "description": "Description of the custom metric.", "maxLength": 1000, "type": "string" }, "geospatialSegmentAttribute": { "description": "Name of the column that contains geospatial values.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "maxLength": 255, "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "customJobId", "name" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | MetricCreateFromCustomJob | false | none | ### Example responses > 201 Response ``` { "properties": { "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Hosted custom metric creation timestamp.", "format": "date-time", "type": "string" }, "deployment": { "description": "The deployment associated to the custom metric.", "properties": { "createdAt": { "description": "Timestamp when the deployment was created.", "format": "date-time", "type": "string" }, "creatorFirstName": { "description": "First name of the deployment creator.", "type": [ "string", "null" ] }, "creatorGravatarHash": { "description": "Gravatar hash of the deployment creator.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Last name of the deployment creator.", "type": [ "string", "null" ] }, "creatorUsername": { "description": "Username of the deployment creator.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the deployment.", "type": "string" }, "name": { "description": "Name of the deployment.", "type": "string" } }, "required": [ "createdAt", "creatorFirstName", "creatorGravatarHash", "creatorLastName", "creatorUsername", "id", "name" ], "type": "object", "x-versionadded": "v2.34" }, "description": { "default": "", "description": "Description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "geospatialSegmentAttribute": { "description": "Name of the column that contains geospatial values.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "name": { "description": "Name of the custom metric.", "maxLength": 255, "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "deployment", "directionality", "id", "isModelSpecific", "name", "timeStep", "type", "units" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Custom metric is successfully created. | MetricCreateFromCustomJobResponse | | 403 | Forbidden | User does not have permission to create a custom metric. | None | ## Delete a custom metric by deployment ID Operation path: `DELETE /api/v2/deployments/{deploymentId}/customMetrics/{customMetricId}/` Authentication requirements: `BearerAuth` Delete a custom metric. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | ID of the deployment | | customMetricId | path | string | true | ID of the custom metric | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | A custom metric was successfully deleted. | None | | 403 | Forbidden | User does not have permission to delete a custom metric. | None | | 404 | Not Found | Custom metric was not found. | None | ## Retrieve a single custom metric metadata by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/customMetrics/{customMetricId}/` Authentication requirements: `BearerAuth` Retrieve a single custom metric metadata. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | ID of the deployment | | customMetricId | path | string | true | ID of the custom metric | ### Example responses > 200 Response ``` { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A given custom metric metadata. | MetricEntity | | 403 | Forbidden | User does not have permission to access a particular deployment or custom metric metadata. | None | ## Update given custom metric settings by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/customMetrics/{customMetricId}/` Authentication requirements: `BearerAuth` Update given custom metric settings. ### Body parameter ``` { "properties": { "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "batch": { "description": "A custom metric batch ID source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "categories": { "description": "Category values. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": "string" }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | ID of the deployment | | customMetricId | path | string | true | ID of the custom metric | | body | body | MetricUpdatePayload | false | none | ### Example responses > 200 Response ``` { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom metric settings updated. | MetricEntity | | 403 | Forbidden | User does not have permission to update the custom metric. | None | ## Retrieve the summary of deployment batch custom metric by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/customMetrics/{customMetricId}/batchSummary/` Authentication requirements: `BearerAuth` Retrieve the summary of deployment batch custom metric. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date-time) | false | Start of the period to retrieve monitoring stats, defaults to 7 days ago from the end of the period. | | end | query | string(date-time) | false | End of the period to retrieve monitoring stats, defaults to the next top of the hour from now. | | modelPackageId | query | string,null | false | The model package ID of related champion/challenger to retrieve custom metric values for. | | modelId | query | string,null | false | The model ID of related champion/challenger to retrieve custom metric values for. | | segmentAttribute | query | string | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string | false | The value of the segmentAttribute to segment on. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | deploymentId | path | string | true | ID of the deployment | | customMetricId | path | string | true | ID of the custom metric | ### Example responses > 200 Response ``` { "properties": { "metric": { "description": "Summary of the custom metric.", "properties": { "baselineValue": { "description": "Baseline value.", "type": [ "number", "null" ] }, "categories": { "description": "Category values.", "items": { "properties": { "baselineValue": { "description": "A reference value for the current category count.", "type": [ "number", "null" ] }, "categoryName": { "description": "Category value", "type": "string" }, "percentChange": { "description": "Percent change when compared with the reference value.", "type": [ "number", "null" ] }, "value": { "description": "A value for the current category count.", "type": [ "number", "null" ] } }, "required": [ "categoryName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "percentChange": { "description": "Percentage change of the baseline over its baseline.", "type": [ "number", "null" ] }, "sampleCount": { "description": "Number of samples used to calculate the aggregated value.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "Count of unknown categories for categorical metrics.", "type": [ "number", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated value of the custom metric.", "type": [ "number", "null" ] } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "metric" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Summary retrieved successfully. | CustomMetricBatchSummary | | 403 | Forbidden | User does not have permission to read the custom metric summary. | None | ## Upload custom metric values by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/customMetrics/{customMetricId}/fromDataset/` Authentication requirements: `BearerAuth` Upload custom metric values from dataset. ### Body parameter ``` { "properties": { "associationId": { "description": "A custom metric batch ID source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "batch": { "description": "A custom metric batch ID source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "datasetId": { "description": "Dataset ID to process.", "type": "string" }, "geospatial": { "description": "A custom metric geospatial coordinate source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "modelId": { "default": null, "description": "For a model metric, the ID of the model of related champion/challenger to update the metric values. For a deployment metric, the ID of the model is not needed.", "type": [ "string", "null" ] }, "modelPackageId": { "default": null, "description": "For a model metric, the ID of the model package of related champion/challenger to update the metric values. For a deployment metric, the ID of the model package is not needed.", "type": [ "string", "null" ] }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "segments": { "description": "A list of segments for a custom metric used in segmented analysis. Cannot be used with geospatial custom metrics.", "items": { "properties": { "column": { "description": "Name of the column that contains segment values.", "type": "string" }, "name": { "description": "Name of the segment on which segment analysis is being performed.", "type": "string" } }, "required": [ "column", "name" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "datasetId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | ID of the deployment | | customMetricId | path | string | true | ID of the custom metric | | body | body | MetricValuesFromDatasetPayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Custom metric values import started | None | | 403 | Forbidden | User does not have permission to import custom metric values. | None | ## Create a custom metrics from a JSON Operation path: `POST /api/v2/deployments/{deploymentId}/customMetrics/{customMetricId}/fromJSON/` Authentication requirements: `BearerAuth` Upload custom metric values from JSON. ### Body parameter ``` { "properties": { "buckets": { "description": "A list of timestamped buckets with custom metric values.", "items": { "properties": { "associationId": { "default": null, "description": "Identifies prediction row corresponding to value.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "geospatialCoordinate": { "description": "A geospatial object in WKB or WKT format. Only required for geospatial custom metrics.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.36" }, "metadata": { "default": null, "description": "A read-only metadata association with a custom metric value.", "maxLength": 128, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "sampleSize": { "default": 1, "description": "Custom metric value sample size.", "type": "integer" }, "segments": { "description": "A list of segments for a custom metric used in segmented analysis. Segments cannot be provided for geospatial metrics.", "items": { "properties": { "name": { "description": "Name of the segment on which segment analysis is being performed.", "type": "string" }, "value": { "description": "Value of the segment attribute to segment on.", "type": "string" } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "timestamp": { "description": "Value timestamp.", "format": "date-time", "type": [ "string", "null" ] }, "value": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "Custom metric value to ingest." } }, "required": [ "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "dryRun": { "default": false, "description": "Determines if the custom metric data calculated for this run is saved to the database.", "type": "boolean" }, "modelId": { "default": null, "description": "For a model metric, the ID of the model of related champion/challenger to update the metric values. For a deployment metric, the ID of the model is not needed.", "type": [ "string", "null" ] }, "modelPackageId": { "default": null, "description": "For a model metric, the ID of the model package of related champion/challenger to update the metric values. For a deployment metric, the ID of the model package is not needed.", "type": [ "string", "null" ] } }, "required": [ "buckets", "dryRun" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | ID of the deployment | | customMetricId | path | string | true | ID of the custom metric | | body | body | MetricValuesFromJSONPayload | false | none | ### Example responses > 202 Response ``` { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Custom metric values import started | MetricEntity | | 403 | Forbidden | User does not have permission to import custom metric values. | None | ## Retrieve the summary of deployment custom metric by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/customMetrics/{customMetricId}/summary/` Authentication requirements: `BearerAuth` Retrieve the summary of deployment custom metric. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date-time) | false | Start of the period to retrieve monitoring stats, defaults to 7 days ago from the end of the period. | | end | query | string(date-time) | false | End of the period to retrieve monitoring stats, defaults to the next top of the hour from now. | | modelPackageId | query | string,null | false | The model package ID of related champion/challenger to retrieve custom metric values for. | | modelId | query | string,null | false | The model ID of related champion/challenger to retrieve custom metric values for. | | segmentAttribute | query | string | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string | false | The value of the segmentAttribute to segment on. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | deploymentId | path | string | true | ID of the deployment | | customMetricId | path | string | true | ID of the custom metric | ### Example responses > 200 Response ``` { "properties": { "metric": { "description": "Summary of the custom metric.", "properties": { "baselineValue": { "description": "Baseline value.", "type": [ "number", "null" ] }, "categories": { "description": "Category values.", "items": { "properties": { "baselineValue": { "description": "A reference value for the current category count.", "type": [ "number", "null" ] }, "categoryName": { "description": "Category value", "type": "string" }, "percentChange": { "description": "Percent change when compared with the reference value.", "type": [ "number", "null" ] }, "value": { "description": "A value for the current category count.", "type": [ "number", "null" ] } }, "required": [ "categoryName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "percentChange": { "description": "Percentage change of the baseline over its baseline.", "type": [ "number", "null" ] }, "sampleCount": { "description": "Number of samples used to calculate the aggregated value.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "Count of unknown categories for categorical metrics.", "type": [ "number", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated value of the custom metric.", "type": [ "number", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "period": { "description": "A time period defined by a start and end time", "properties": { "end": { "description": "End of the bucket.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the bucket.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } }, "required": [ "metric", "period" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Summary retrieved successfully. | CustomMetricSummary | | 403 | Forbidden | User does not have permission to read the custom metric summary. | None | ## Retrieve custom metric values over batch by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/customMetrics/{customMetricId}/valuesOverBatch/` Authentication requirements: `BearerAuth` Retrieve custom metric values over batch. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | modelPackageId | query | string,null | false | The model package ID of related champion/challenger to retrieve batch custom metric values for. | | modelId | query | string,null | false | The model ID of related champion/challenger to retrieve batch custom metric values for. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | segmentAttribute | query | string | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string | false | The value of the segmentAttribute to segment on. | | deploymentId | path | string | true | ID of the deployment | | customMetricId | path | string | true | ID of the custom metric | ### Example responses > 200 Response ``` { "properties": { "buckets": { "description": "A list of bucketed batches and the custom metric values aggregated over that batches.", "items": { "properties": { "batch": { "description": "Describes a batch associated with the bucket.", "properties": { "createdAt": { "description": "Timestamp when the batch was created.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "DataRobot assigned ID of the batch.", "type": "string", "x-versionadded": "v2.33" }, "lastPredictionTimestamp": { "description": "Timestamp when the latest prediction request of the batch was made", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "User provided name of the batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "id", "name" ], "type": "object" }, "categories": { "description": "Aggregated custom metric categories in the bucket.", "items": { "properties": { "categoryName": { "description": "Category value.", "type": "string" }, "count": { "description": "Count of the categories in the bucket.", "type": "integer" } }, "required": [ "categoryName", "count" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.35" }, "sampleSize": { "description": "Total number of values aggregated in the bucket.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "unknownCategoriesCount": { "description": "The count of the values that do not correspond to the any of the known categories.", "type": [ "integer", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated custom metric value in the bucket.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batch" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "metric": { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "segmentValue": { "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "buckets", "metric" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Data retrieved successfully | MetricValuesOverBatchResponse | | 403 | Forbidden | User does not have permission to read custom metric values. | None | ## Retrieve custom metric values over space by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/customMetrics/{customMetricId}/valuesOverSpace/` Authentication requirements: `BearerAuth` Retrieve custom metric values over space. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date-time) | false | Start of the period to retrieve monitoring stats, defaults to 7 days ago from the end of the period. | | end | query | string(date-time) | false | End of the period to retrieve monitoring stats, defaults to the next top of the hour from now. | | modelPackageId | query | string,null | false | The model package ID of related champion/challenger to retrieve custom metric values for. | | modelId | query | string,null | false | The model ID of related champion/challenger to retrieve custom metric values for. | | deploymentId | path | string | true | ID of the deployment | | customMetricId | path | string | true | ID of the custom metric | ### Example responses > 200 Response ``` { "properties": { "buckets": { "description": "A list of buckets containing aggregated custom metric values.", "items": { "properties": { "categories": { "description": "Aggregated custom metric categories in the bucket.", "items": { "properties": { "categoryName": { "description": "Category value.", "type": "string" }, "count": { "description": "Count of the categories in the bucket.", "type": "integer" } }, "required": [ "categoryName", "count" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 25, "type": "array" }, "hexagon": { "description": "h3 hexagon.", "type": "string" }, "sampleSize": { "description": "Total number of values aggregated in the bucket.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "The count of the values that do not correspond to the any of the known categories.", "type": [ "integer", "null" ] }, "value": { "description": "Aggregated custom metric value in the bucket.", "type": [ "number", "null" ] } }, "required": [ "hexagon" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "metric": { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" }, "modelId": { "description": "The ID of the model for which metrics are being retrieved. ", "type": "string" }, "modelPackageId": { "description": "The ID of the model package for which metrics are being retrieved.", "type": [ "string", "null" ] }, "summary": { "description": "Summary of values over time retrieval.", "properties": { "end": { "description": "End of the retrieval range.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the retrieval range.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } }, "required": [ "buckets", "metric", "summary" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom metric values over space. | MetricValuesOverSpaceResponse | | 403 | Forbidden | User does not have permission to read custom metric values. | None | ## Retrieve custom metric values over time by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/customMetrics/{customMetricId}/valuesOverTime/` Authentication requirements: `BearerAuth` Retrieve custom metric values over time. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date-time) | false | Start of the period to retrieve monitoring stats, defaults to 7 days ago from the end of the period. | | end | query | string(date-time) | false | End of the period to retrieve monitoring stats, defaults to the next top of the hour from now. | | modelPackageId | query | string,null | false | The model package ID of related champion/challenger to retrieve custom metric values for. | | modelId | query | string,null | false | The model ID of related champion/challenger to retrieve custom metric values for. | | bucketSize | query | string(duration) | false | Time duration of a bucket, default to seven days. | | segmentAttribute | query | string | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string | false | The value of the segmentAttribute to segment on. | | deploymentId | path | string | true | ID of the deployment | | customMetricId | path | string | true | ID of the custom metric | ### Example responses > 200 Response ``` { "properties": { "buckets": { "description": "A list of bucketed time periods and the custom metric values aggregated over that period.", "items": { "properties": { "categories": { "description": "Aggregated custom metric categories in the bucket.", "items": { "properties": { "categoryName": { "description": "Category value.", "type": "string" }, "count": { "description": "Count of the categories in the bucket.", "type": "integer" } }, "required": [ "categoryName", "count" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.35" }, "period": { "description": "A time period defined by a start and end time", "properties": { "end": { "description": "End of the bucket.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the bucket.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "sampleSize": { "description": "Total number of values aggregated in the bucket.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "The count of the values that do not correspond to the any of the known categories.", "type": [ "integer", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated custom metric value in the bucket.", "type": [ "number", "null" ] } }, "required": [ "period" ], "type": "object" }, "maxItems": 100, "type": "array" }, "metric": { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "segmentValue": { "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "summary": { "description": "Summary of values over time retrieval.", "properties": { "end": { "description": "End of the retrieval range.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the retrieval range.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } }, "required": [ "buckets", "metric", "summary" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Data retrieved successfully | MetricValuesOverTimeResponse | | 403 | Forbidden | User does not have permission to read custom metric values. | None | ## Retrieve the bulk summary of deployment batch custom metrics by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/customMetricsBatchSummary/` Authentication requirements: `BearerAuth` Retrieve the bulk summary of deployment batch custom metrics. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date-time) | false | Start of the period to retrieve monitoring stats, defaults to 7 days ago from the end of the period. | | end | query | string(date-time) | false | End of the period to retrieve monitoring stats, defaults to the next top of the hour from now. | | modelPackageId | query | string,null | false | The model package ID of related champion/challenger to retrieve custom metric values for. | | modelId | query | string,null | false | The model ID of related champion/challenger to retrieve custom metric values for. | | segmentAttribute | query | string | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string | false | The value of the segmentAttribute to segment on. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "metrics": { "default": [], "description": "Custom metrics summaries.", "items": { "description": "Summary of the custom metric.", "properties": { "baselineValue": { "description": "Baseline value.", "type": [ "number", "null" ] }, "categories": { "description": "Category values.", "items": { "properties": { "baselineValue": { "description": "A reference value for the current category count.", "type": [ "number", "null" ] }, "categoryName": { "description": "Category value", "type": "string" }, "percentChange": { "description": "Percent change when compared with the reference value.", "type": [ "number", "null" ] }, "value": { "description": "A value for the current category count.", "type": [ "number", "null" ] } }, "required": [ "categoryName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "percentChange": { "description": "Percentage change of the baseline over its baseline.", "type": [ "number", "null" ] }, "sampleCount": { "description": "Number of samples used to calculate the aggregated value.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "Count of unknown categories for categorical metrics.", "type": [ "number", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated value of the custom metric.", "type": [ "number", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 50, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "metrics" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Bulk summary retrieved successfully. | CustomMetricsBulkBatchSummary | | 403 | Forbidden | User does not have permission to read the custom metrics summary. | None | ## Retrieve the bulk summary of deployment custom metrics by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/customMetricsSummary/` Authentication requirements: `BearerAuth` Retrieve the bulk summary of deployment custom metrics. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date-time) | false | Start of the period to retrieve monitoring stats, defaults to 7 days ago from the end of the period. | | end | query | string(date-time) | false | End of the period to retrieve monitoring stats, defaults to the next top of the hour from now. | | modelPackageId | query | string,null | false | The model package ID of related champion/challenger to retrieve custom metric values for. | | modelId | query | string,null | false | The model ID of related champion/challenger to retrieve custom metric values for. | | segmentAttribute | query | string | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string | false | The value of the segmentAttribute to segment on. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "details": { "description": "Additional information related to custom metrics.", "properties": { "earliestDate": { "description": "The earliest date of the custom metrics values.", "format": "date-time", "type": [ "string", "null" ] }, "latestDate": { "description": "The latest date of the custom metrics values.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "earliestDate", "latestDate" ], "type": "object" }, "metrics": { "default": [], "description": "Custom metrics summaries.", "items": { "description": "Summary of the custom metric.", "properties": { "baselineValue": { "description": "Baseline value.", "type": [ "number", "null" ] }, "categories": { "description": "Category values.", "items": { "properties": { "baselineValue": { "description": "A reference value for the current category count.", "type": [ "number", "null" ] }, "categoryName": { "description": "Category value", "type": "string" }, "percentChange": { "description": "Percent change when compared with the reference value.", "type": [ "number", "null" ] }, "value": { "description": "A value for the current category count.", "type": [ "number", "null" ] } }, "required": [ "categoryName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "percentChange": { "description": "Percentage change of the baseline over its baseline.", "type": [ "number", "null" ] }, "sampleCount": { "description": "Number of samples used to calculate the aggregated value.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "Count of unknown categories for categorical metrics.", "type": [ "number", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated value of the custom metric.", "type": [ "number", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 50, "type": "array" }, "period": { "description": "A time period defined by a start and end time", "properties": { "end": { "description": "End of the bucket.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the bucket.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } }, "required": [ "details", "metrics", "period" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Bulk summary retrieved successfully. | CustomMetricsBulkSummary | | 403 | Forbidden | User does not have permission to read the custom metrics summary. | None | # Schemas ## AssociationIdField ``` { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" } ``` A custom metric associationId source when reading values from columnar dataset like a file. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnName | string,null | true | | Column name | ## BatchField ``` { "description": "A custom metric batch ID source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } ``` A custom metric batch ID source when reading values from columnar dataset like a file. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnName | string,null | true | | Column name | ## BucketBatch ``` { "description": "Describes a batch associated with the bucket.", "properties": { "createdAt": { "description": "Timestamp when the batch was created.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "DataRobot assigned ID of the batch.", "type": "string", "x-versionadded": "v2.33" }, "lastPredictionTimestamp": { "description": "Timestamp when the latest prediction request of the batch was made", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "User provided name of the batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "id", "name" ], "type": "object" } ``` Describes a batch associated with the bucket. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | Timestamp when the batch was created. | | id | string | true | | DataRobot assigned ID of the batch. | | lastPredictionTimestamp | string(date-time) | false | | Timestamp when the latest prediction request of the batch was made | | name | string | true | | User provided name of the batch. | ## BulkMetricInputValueBucket ``` { "properties": { "associationId": { "default": null, "description": "Identifies prediction row corresponding to value.", "type": [ "string", "null" ] }, "customMetricId": { "description": "ID of the custom metric.", "type": "string" }, "metadata": { "default": null, "description": "A read-only metadata association with a custom metric value.", "maxLength": 128, "type": [ "string", "null" ] }, "sampleSize": { "default": 1, "description": "Custom metric value sample size.", "type": "integer" }, "segments": { "description": "A list of segments for a custom metric used in segmented analysis.", "items": { "properties": { "name": { "description": "Name of the segment on which segment analysis is being performed.", "type": "string" }, "value": { "description": "Value of the segment attribute to segment on.", "type": "string" } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 10, "type": "array" }, "timestamp": { "description": "Value timestamp.", "format": "date-time", "type": [ "string", "null" ] }, "value": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "Custom metric value to ingest." } }, "required": [ "customMetricId", "value" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associationId | string,null | false | | Identifies prediction row corresponding to value. | | customMetricId | string | true | | ID of the custom metric. | | metadata | string,null | false | maxLength: 128 | A read-only metadata association with a custom metric value. | | sampleSize | integer | false | | Custom metric value sample size. | | segments | [CustomMetricSegment] | false | maxItems: 10 | A list of segments for a custom metric used in segmented analysis. | | timestamp | string,null(date-time) | false | | Value timestamp. | | value | any | true | | Custom metric value to ingest. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## BulkMetricValuesPayload ``` { "properties": { "buckets": { "description": "A list of timestamped buckets with custom metric values.", "items": { "properties": { "associationId": { "default": null, "description": "Identifies prediction row corresponding to value.", "type": [ "string", "null" ] }, "customMetricId": { "description": "ID of the custom metric.", "type": "string" }, "metadata": { "default": null, "description": "A read-only metadata association with a custom metric value.", "maxLength": 128, "type": [ "string", "null" ] }, "sampleSize": { "default": 1, "description": "Custom metric value sample size.", "type": "integer" }, "segments": { "description": "A list of segments for a custom metric used in segmented analysis.", "items": { "properties": { "name": { "description": "Name of the segment on which segment analysis is being performed.", "type": "string" }, "value": { "description": "Value of the segment attribute to segment on.", "type": "string" } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 10, "type": "array" }, "timestamp": { "description": "Value timestamp.", "format": "date-time", "type": [ "string", "null" ] }, "value": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "Custom metric value to ingest." } }, "required": [ "customMetricId", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "minItems": 1, "type": "array" }, "modelId": { "default": null, "description": "For a model metric, the ID of the model of related champion/challenger to update the metric values. For a deployment metric, the ID of the model is not needed.", "type": [ "string", "null" ] }, "modelPackageId": { "default": null, "description": "For a model metric, the ID of the model package of related champion/challenger to update the metric values. For a deployment metric, the ID of the model package is not needed.", "type": [ "string", "null" ] } }, "required": [ "buckets" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [BulkMetricInputValueBucket] | true | maxItems: 10000minItems: 1 | A list of timestamped buckets with custom metric values. | | modelId | string,null | false | | For a model metric, the ID of the model of related champion/challenger to update the metric values. For a deployment metric, the ID of the model is not needed. | | modelPackageId | string,null | false | | For a model metric, the ID of the model package of related champion/challenger to update the metric values. For a deployment metric, the ID of the model package is not needed. | ## CustomJobCustomMetricUpdate ``` { "properties": { "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "batch": { "description": "A custom metric batch ID source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "categories": { "description": "Category values. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": "string" }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineValues | [MetricBaselineValue] | false | maxItems: 5 | Baseline values | | batch | BatchField | false | | A custom metric batch ID source when reading values from columnar dataset like a file. | | categories | [CustomMetricCategory] | false | maxItems: 25 | Category values. This field is required for categorical custom metrics. | | description | string | false | maxLength: 1000 | A description of the custom metric. | | directionality | string | false | | Directionality of the custom metric. | | name | string | false | | Name of the custom metric. | | sampleCount | SampleCountField | false | | Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets. | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | timestamp | MetricTimestampSpoofing | false | | A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour. | | type | string | false | | Type (and aggregation character) of a metric. | | units | string | false | | Units or Y Label of given custom metric. | | value | ValueField | false | | A custom metric value source when reading values from columnar dataset like a file. | ### Enumerated Values | Property | Value | | --- | --- | | directionality | [higherIsBetter, lowerIsBetter] | | type | [average, categorical, gauge, sum] | ## CustomJobFromGalleryTemplateCreate ``` { "properties": { "description": { "default": "", "description": "Description of the hosted custom metric job.", "maxLength": 1000, "type": "string" }, "name": { "description": "Name of the hosted custom metric job.", "maxLength": 255, "type": "string" }, "sidecarDeploymentId": { "description": "Sidecar deployment ID.", "type": "string" }, "templateId": { "description": "Custom Metric Template ID.", "type": "string" } }, "required": [ "name", "templateId" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | false | maxLength: 1000 | Description of the hosted custom metric job. | | name | string | true | maxLength: 255 | Name of the hosted custom metric job. | | sidecarDeploymentId | string | false | | Sidecar deployment ID. | | templateId | string | true | | Custom Metric Template ID. | ## CustomJobResources ``` { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" } ``` The custom job resources that will be applied in the k8s cluster. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | egressNetworkPolicy | string | true | | Egress network policy. | | resourceBundleId | string,null | false | | A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | ### Enumerated Values | Property | Value | | --- | --- | | egressNetworkPolicy | [none, public] | ## CustomJobResponse ``` { "properties": { "created": { "description": "ISO-8601 timestamp of when the custom job was created.", "type": "string", "x-versionadded": "v2.33" }, "description": { "description": "The description of the custom job.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.33" }, "entryPoint": { "description": "The ID of the entry point file to use.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentId": { "description": "The ID of the execution environment used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "environmentVersionId": { "description": "The ID of the execution environment version used for this custom job.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "id": { "description": "The ID of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "items": { "description": "List of file items.", "items": { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.33" }, "jobType": { "description": "Type of the custom job.", "enum": [ "default", "hostedCustomMetric", "notification", "retraining" ], "type": "string", "x-versionadded": "v2.33" }, "lastRun": { "description": "The last custom job run.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the custom job.", "type": "string", "x-versionadded": "v2.33" }, "resources": { "description": "The custom job resources that will be applied in the k8s cluster.", "properties": { "egressNetworkPolicy": { "description": "Egress network policy.", "enum": [ "none", "public" ], "type": "string", "x-versionadded": "v2.33" }, "resourceBundleId": { "description": "A single identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "required": [ "egressNetworkPolicy" ], "type": "object" }, "runtimeParameters": { "description": "Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition.", "items": { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "updated": { "description": "ISO-8601 timestamp of when custom job was last updated.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "created", "environmentId", "environmentVersionId", "id", "items", "jobType", "lastRun", "name", "resources", "updated" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string | true | | ISO-8601 timestamp of when the custom job was created. | | description | string | false | maxLength: 10000 | The description of the custom job. | | entryPoint | string,null | false | | The ID of the entry point file to use. | | environmentId | string,null | true | | The ID of the execution environment used for this custom job. | | environmentVersionId | string,null | true | | The ID of the execution environment version used for this custom job. | | id | string | true | | The ID of the custom job. | | items | [WorkspaceItemResponse] | true | maxItems: 1000 | List of file items. | | jobType | string | true | | Type of the custom job. | | lastRun | string,null | true | | The last custom job run. | | name | string | true | | The name of the custom job. | | resources | CustomJobResources | true | | The custom job resources that will be applied in the k8s cluster. | | runtimeParameters | [RuntimeParameterUnified] | false | maxItems: 100 | Unified view of the defined runtime parameters for this custom job along with any values that are currently set that override the default value from their definition. | | updated | string | true | | ISO-8601 timestamp of when custom job was last updated. | ### Enumerated Values | Property | Value | | --- | --- | | jobType | [default, hostedCustomMetric, notification, retraining] | ## CustomMetricBatchSummary ``` { "properties": { "metric": { "description": "Summary of the custom metric.", "properties": { "baselineValue": { "description": "Baseline value.", "type": [ "number", "null" ] }, "categories": { "description": "Category values.", "items": { "properties": { "baselineValue": { "description": "A reference value for the current category count.", "type": [ "number", "null" ] }, "categoryName": { "description": "Category value", "type": "string" }, "percentChange": { "description": "Percent change when compared with the reference value.", "type": [ "number", "null" ] }, "value": { "description": "A value for the current category count.", "type": [ "number", "null" ] } }, "required": [ "categoryName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "percentChange": { "description": "Percentage change of the baseline over its baseline.", "type": [ "number", "null" ] }, "sampleCount": { "description": "Number of samples used to calculate the aggregated value.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "Count of unknown categories for categorical metrics.", "type": [ "number", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated value of the custom metric.", "type": [ "number", "null" ] } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "metric" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metric | MetricSummary | true | | Summary of the custom metric. | ## CustomMetricCategory ``` { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineCount | integer,null | false | | A reference value for the current category count. | | directionality | string | true | | Directionality of the custom metric category. | | value | string | true | | Category value | ### Enumerated Values | Property | Value | | --- | --- | | directionality | [higherIsBetter, lowerIsBetter] | ## CustomMetricCategorySummary ``` { "properties": { "baselineValue": { "description": "A reference value for the current category count.", "type": [ "number", "null" ] }, "categoryName": { "description": "Category value", "type": "string" }, "percentChange": { "description": "Percent change when compared with the reference value.", "type": [ "number", "null" ] }, "value": { "description": "A value for the current category count.", "type": [ "number", "null" ] } }, "required": [ "categoryName" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineValue | number,null | false | | A reference value for the current category count. | | categoryName | string | true | | Category value | | percentChange | number,null | false | | Percent change when compared with the reference value. | | value | number,null | false | | A value for the current category count. | ## CustomMetricDetails ``` { "description": "Additional information related to custom metrics.", "properties": { "earliestDate": { "description": "The earliest date of the custom metrics values.", "format": "date-time", "type": [ "string", "null" ] }, "latestDate": { "description": "The latest date of the custom metrics values.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "earliestDate", "latestDate" ], "type": "object" } ``` Additional information related to custom metrics. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | earliestDate | string,null(date-time) | true | | The earliest date of the custom metrics values. | | latestDate | string,null(date-time) | true | | The latest date of the custom metrics values. | ## CustomMetricOverSpaceBucket ``` { "properties": { "categories": { "description": "Aggregated custom metric categories in the bucket.", "items": { "properties": { "categoryName": { "description": "Category value.", "type": "string" }, "count": { "description": "Count of the categories in the bucket.", "type": "integer" } }, "required": [ "categoryName", "count" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 25, "type": "array" }, "hexagon": { "description": "h3 hexagon.", "type": "string" }, "sampleSize": { "description": "Total number of values aggregated in the bucket.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "The count of the values that do not correspond to the any of the known categories.", "type": [ "integer", "null" ] }, "value": { "description": "Aggregated custom metric value in the bucket.", "type": [ "number", "null" ] } }, "required": [ "hexagon" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [MetricCategory] | false | maxItems: 25 | Aggregated custom metric categories in the bucket. | | hexagon | string | true | | h3 hexagon. | | sampleSize | integer,null | false | | Total number of values aggregated in the bucket. | | unknownCategoriesCount | integer,null | false | | The count of the values that do not correspond to the any of the known categories. | | value | number,null | false | | Aggregated custom metric value in the bucket. | ## CustomMetricSegment ``` { "properties": { "name": { "description": "Name of the segment on which segment analysis is being performed.", "type": "string" }, "value": { "description": "Value of the segment attribute to segment on.", "type": "string" } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | Name of the segment on which segment analysis is being performed. | | value | string | true | | Value of the segment attribute to segment on. | ## CustomMetricSegmentDataset ``` { "properties": { "column": { "description": "Name of the column that contains segment values.", "type": "string" }, "name": { "description": "Name of the segment on which segment analysis is being performed.", "type": "string" } }, "required": [ "column", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | column | string | true | | Name of the column that contains segment values. | | name | string | true | | Name of the segment on which segment analysis is being performed. | ## CustomMetricSummary ``` { "properties": { "metric": { "description": "Summary of the custom metric.", "properties": { "baselineValue": { "description": "Baseline value.", "type": [ "number", "null" ] }, "categories": { "description": "Category values.", "items": { "properties": { "baselineValue": { "description": "A reference value for the current category count.", "type": [ "number", "null" ] }, "categoryName": { "description": "Category value", "type": "string" }, "percentChange": { "description": "Percent change when compared with the reference value.", "type": [ "number", "null" ] }, "value": { "description": "A value for the current category count.", "type": [ "number", "null" ] } }, "required": [ "categoryName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "percentChange": { "description": "Percentage change of the baseline over its baseline.", "type": [ "number", "null" ] }, "sampleCount": { "description": "Number of samples used to calculate the aggregated value.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "Count of unknown categories for categorical metrics.", "type": [ "number", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated value of the custom metric.", "type": [ "number", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "period": { "description": "A time period defined by a start and end time", "properties": { "end": { "description": "End of the bucket.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the bucket.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } }, "required": [ "metric", "period" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metric | MetricSummary | true | | Summary of the custom metric. | | period | MetricPeriodBucket | true | | A time period defined by a start and end time | ## CustomMetricsBulkBatchSummary ``` { "properties": { "metrics": { "default": [], "description": "Custom metrics summaries.", "items": { "description": "Summary of the custom metric.", "properties": { "baselineValue": { "description": "Baseline value.", "type": [ "number", "null" ] }, "categories": { "description": "Category values.", "items": { "properties": { "baselineValue": { "description": "A reference value for the current category count.", "type": [ "number", "null" ] }, "categoryName": { "description": "Category value", "type": "string" }, "percentChange": { "description": "Percent change when compared with the reference value.", "type": [ "number", "null" ] }, "value": { "description": "A value for the current category count.", "type": [ "number", "null" ] } }, "required": [ "categoryName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "percentChange": { "description": "Percentage change of the baseline over its baseline.", "type": [ "number", "null" ] }, "sampleCount": { "description": "Number of samples used to calculate the aggregated value.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "Count of unknown categories for categorical metrics.", "type": [ "number", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated value of the custom metric.", "type": [ "number", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 50, "type": "array", "x-versionadded": "v2.33" } }, "required": [ "metrics" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metrics | [MetricSummary] | true | maxItems: 50 | Custom metrics summaries. | ## CustomMetricsBulkSummary ``` { "properties": { "details": { "description": "Additional information related to custom metrics.", "properties": { "earliestDate": { "description": "The earliest date of the custom metrics values.", "format": "date-time", "type": [ "string", "null" ] }, "latestDate": { "description": "The latest date of the custom metrics values.", "format": "date-time", "type": [ "string", "null" ] } }, "required": [ "earliestDate", "latestDate" ], "type": "object" }, "metrics": { "default": [], "description": "Custom metrics summaries.", "items": { "description": "Summary of the custom metric.", "properties": { "baselineValue": { "description": "Baseline value.", "type": [ "number", "null" ] }, "categories": { "description": "Category values.", "items": { "properties": { "baselineValue": { "description": "A reference value for the current category count.", "type": [ "number", "null" ] }, "categoryName": { "description": "Category value", "type": "string" }, "percentChange": { "description": "Percent change when compared with the reference value.", "type": [ "number", "null" ] }, "value": { "description": "A value for the current category count.", "type": [ "number", "null" ] } }, "required": [ "categoryName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "percentChange": { "description": "Percentage change of the baseline over its baseline.", "type": [ "number", "null" ] }, "sampleCount": { "description": "Number of samples used to calculate the aggregated value.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "Count of unknown categories for categorical metrics.", "type": [ "number", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated value of the custom metric.", "type": [ "number", "null" ] } }, "required": [ "id", "name" ], "type": "object" }, "maxItems": 50, "type": "array" }, "period": { "description": "A time period defined by a start and end time", "properties": { "end": { "description": "End of the bucket.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the bucket.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } }, "required": [ "details", "metrics", "period" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | details | CustomMetricDetails | true | | Additional information related to custom metrics. | | metrics | [MetricSummary] | true | maxItems: 50 | Custom metrics summaries. | | period | MetricPeriodBucket | true | | A time period defined by a start and end time | ## Deployment ``` { "description": "The deployment associated to the custom metric.", "properties": { "createdAt": { "description": "Timestamp when the deployment was created.", "format": "date-time", "type": "string" }, "creatorFirstName": { "description": "First name of the deployment creator.", "type": [ "string", "null" ] }, "creatorGravatarHash": { "description": "Gravatar hash of the deployment creator.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Last name of the deployment creator.", "type": [ "string", "null" ] }, "creatorUsername": { "description": "Username of the deployment creator.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the deployment.", "type": "string" }, "name": { "description": "Name of the deployment.", "type": "string" } }, "required": [ "createdAt", "creatorFirstName", "creatorGravatarHash", "creatorLastName", "creatorUsername", "id", "name" ], "type": "object", "x-versionadded": "v2.34" } ``` The deployment associated to the custom metric. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | Timestamp when the deployment was created. | | creatorFirstName | string,null | true | | First name of the deployment creator. | | creatorGravatarHash | string,null | true | | Gravatar hash of the deployment creator. | | creatorLastName | string,null | true | | Last name of the deployment creator. | | creatorUsername | string,null | true | | Username of the deployment creator. | | id | string | true | | The ID of the deployment. | | name | string | true | | Name of the deployment. | ## GeospatialCoordinateField ``` { "description": "A custom metric geospatial coordinate source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" } ``` A custom metric geospatial coordinate source when reading values from columnar dataset like a file. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnName | string,null | true | | Column name | ## HostedCustomMetricTemplateCreate ``` { "properties": { "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "directionality": { "description": "Directionality of the custom metric. This field is required for numeric custom metrics.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics.", "type": [ "string", "null" ] } }, "required": [ "isModelSpecific", "timeStep", "type" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [CustomMetricCategory] | false | maxItems: 25 | Values of the categories. This field is required for categorical custom metrics. | | directionality | string,null | false | | Directionality of the custom metric. This field is required for numeric custom metrics. | | isGeospatial | boolean | false | | Determines whether the metric is geospatial. | | isModelSpecific | boolean | true | | Determines whether the metric is related to the model or deployment. | | timeStep | string | true | | Custom metric time bucket size. | | type | string | true | | Type (and aggregation character) of a metric. | | units | string,null | false | | Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics. | ### Enumerated Values | Property | Value | | --- | --- | | directionality | [higherIsBetter, lowerIsBetter] | | timeStep | hour | | type | [average, categorical, gauge, sum] | ## HostedCustomMetricTemplateResponse ``` { "properties": { "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Hosted custom metric template creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "ID of the user who created the hosted custom metric template.", "type": "string" }, "customJobId": { "description": "ID of the associatedCustom job.", "type": "string" }, "directionality": { "description": "Directionality of the custom metric. This field is required for numeric custom metrics.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "id": { "description": "ID of the hosted custom metric template.", "type": "string" }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics.", "type": [ "string", "null" ] }, "updatedAt": { "description": "Hosted custom metric template update timestamp.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "ID of the user who updated the hosted custom metric template.", "type": "string" } }, "required": [ "createdAt", "createdBy", "customJobId", "id", "isModelSpecific", "timeStep", "type", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [CustomMetricCategory] | false | maxItems: 25 | Values of the categories. This field is required for categorical custom metrics. | | createdAt | string(date-time) | true | | Hosted custom metric template creation timestamp. | | createdBy | string | true | | ID of the user who created the hosted custom metric template. | | customJobId | string | true | | ID of the associatedCustom job. | | directionality | string,null | false | | Directionality of the custom metric. This field is required for numeric custom metrics. | | id | string | true | | ID of the hosted custom metric template. | | isGeospatial | boolean | false | | Determines whether the metric is geospatial. | | isModelSpecific | boolean | true | | Determines whether the metric is related to the model or deployment. | | timeStep | string | true | | Custom metric time bucket size. | | type | string | true | | Type (and aggregation character) of a metric. | | units | string,null | false | | Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics. | | updatedAt | string(date-time) | true | | Hosted custom metric template update timestamp. | | updatedBy | string | true | | ID of the user who updated the hosted custom metric template. | ### Enumerated Values | Property | Value | | --- | --- | | directionality | [higherIsBetter, lowerIsBetter] | | timeStep | hour | | type | [average, categorical, gauge, sum] | ## HostedCustomMetricTemplateUpdate ``` { "properties": { "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": "string" } }, "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [CustomMetricCategory] | false | maxItems: 25 | Values of the categories. This field is required for categorical custom metrics. | | directionality | string | false | | Directionality of the custom metric. | | isGeospatial | boolean | false | | Determines whether the metric is geospatial. | | isModelSpecific | boolean | false | | Determines whether the metric is related to the model or deployment. | | timeStep | string | false | | Custom metric time bucket size. | | type | string | false | | Type (and aggregation character) of a metric. | | units | string | false | | Units or Y Label of given custom metric. | ### Enumerated Values | Property | Value | | --- | --- | | directionality | [higherIsBetter, lowerIsBetter] | | timeStep | hour | | type | [average, categorical, gauge, sum] | ## MetadataField ``` { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" } ``` A custom metric metadata source when reading values from columnar datasets like a file. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnName | string,null | true | maxLength: 128 | Column name | ## MetricBaselineValue ``` { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | value | number | true | | A reference value in given metric units. | ## MetricCategory ``` { "properties": { "categoryName": { "description": "Category value.", "type": "string" }, "count": { "description": "Count of the categories in the bucket.", "type": "integer" } }, "required": [ "categoryName", "count" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categoryName | string | true | | Category value. | | count | integer | true | | Count of the categories in the bucket. | ## MetricCreateFromCustomJob ``` { "properties": { "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "customJobId": { "description": "Custom Job ID.", "type": "string" }, "description": { "default": "", "description": "Description of the custom metric.", "maxLength": 1000, "type": "string" }, "geospatialSegmentAttribute": { "description": "Name of the column that contains geospatial values.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "maxLength": 255, "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "customJobId", "name" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineValues | [MetricBaselineValue] | false | maxItems: 5 | Baseline values | | customJobId | string | true | | Custom Job ID. | | description | string | false | maxLength: 1000 | Description of the custom metric. | | geospatialSegmentAttribute | string,null | false | | Name of the column that contains geospatial values. | | name | string | true | maxLength: 255 | Name of the custom metric. | | sampleCount | SampleCountField | false | | Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets. | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | timestamp | MetricTimestampSpoofing | false | | A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour. | | value | ValueField | false | | A custom metric value source when reading values from columnar dataset like a file. | ## MetricCreateFromCustomJobResponse ``` { "properties": { "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Hosted custom metric creation timestamp.", "format": "date-time", "type": "string" }, "deployment": { "description": "The deployment associated to the custom metric.", "properties": { "createdAt": { "description": "Timestamp when the deployment was created.", "format": "date-time", "type": "string" }, "creatorFirstName": { "description": "First name of the deployment creator.", "type": [ "string", "null" ] }, "creatorGravatarHash": { "description": "Gravatar hash of the deployment creator.", "type": [ "string", "null" ] }, "creatorLastName": { "description": "Last name of the deployment creator.", "type": [ "string", "null" ] }, "creatorUsername": { "description": "Username of the deployment creator.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the deployment.", "type": "string" }, "name": { "description": "Name of the deployment.", "type": "string" } }, "required": [ "createdAt", "creatorFirstName", "creatorGravatarHash", "creatorLastName", "creatorUsername", "id", "name" ], "type": "object", "x-versionadded": "v2.34" }, "description": { "default": "", "description": "Description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "geospatialSegmentAttribute": { "description": "Name of the column that contains geospatial values.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isGeospatial": { "description": "Determines whether the metric is geospatial.", "type": "boolean", "x-versionadded": "v2.36" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "name": { "description": "Name of the custom metric.", "maxLength": 255, "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "schedule": { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "deployment", "directionality", "id", "isModelSpecific", "name", "timeStep", "type", "units" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineValues | [MetricBaselineValue] | false | maxItems: 5 | Baseline values | | categories | [CustomMetricCategory] | false | maxItems: 25 | Values of the categories. This field is required for categorical custom metrics. | | createdAt | string(date-time) | true | | Hosted custom metric creation timestamp. | | deployment | Deployment | true | | The deployment associated to the custom metric. | | description | string | false | maxLength: 1000 | Description of the custom metric. | | directionality | string,null | true | | Directionality of the custom metric. | | geospatialSegmentAttribute | string,null | false | | Name of the column that contains geospatial values. | | id | string | true | | The ID of the custom metric. | | isGeospatial | boolean | false | | Determines whether the metric is geospatial. | | isModelSpecific | boolean | true | | Determines whether the metric is related to the model or deployment. | | name | string | true | maxLength: 255 | Name of the custom metric. | | sampleCount | SampleCountField | false | | Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets. | | schedule | Schedule | false | | The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. | | timeStep | string | true | | Custom metric time bucket size. | | timestamp | MetricTimestampSpoofing | false | | A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour. | | type | string | true | | Type (and aggregation character) of a metric. | | units | string,null | true | | Units or the Y-axis label of the given custom metric. | | value | ValueField | false | | A custom metric value source when reading values from columnar dataset like a file. | ### Enumerated Values | Property | Value | | --- | --- | | directionality | [higherIsBetter, lowerIsBetter] | | timeStep | hour | | type | [average, categorical, gauge, sum] | ## MetricCreatePayload ``` { "properties": { "baselineValues": { "description": "Baseline values. This field is required for numeric custom metrics.", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Values of the categories. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric. This field is required for numeric custom metrics.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "default": "hour", "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "isModelSpecific", "name", "timeStep", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineValues | [MetricBaselineValue] | false | maxItems: 5 | Baseline values. This field is required for numeric custom metrics. | | categories | [CustomMetricCategory] | false | maxItems: 25 | Values of the categories. This field is required for categorical custom metrics. | | description | string | false | maxLength: 1000 | A description of the custom metric. | | directionality | string,null | false | | Directionality of the custom metric. This field is required for numeric custom metrics. | | isModelSpecific | boolean | true | | Determines whether the metric is related to the model or deployment. | | name | string | true | | Name of the custom metric. | | sampleCount | SampleCountField | false | | Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets. | | timeStep | string | true | | Custom metric time bucket size. | | timestamp | MetricTimestampSpoofing | false | | A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour. | | type | string | true | | Type (and aggregation character) of a metric. | | units | string,null | false | | Units or the Y-axis label of the given custom metric. This field is required for numeric custom metrics. | | value | ValueField | false | | A custom metric value source when reading values from columnar dataset like a file. | ### Enumerated Values | Property | Value | | --- | --- | | directionality | [higherIsBetter, lowerIsBetter] | | timeStep | hour | | type | [average, categorical, gauge, sum] | ## MetricCreatedBy ``` { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" } ``` The user that created custom metric. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of user who created custom metric. | ## MetricEntity ``` { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" } ``` A custom metric definition. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associationId | AssociationIdField | false | | A custom metric associationId source when reading values from columnar dataset like a file. | | baselineValues | [MetricBaselineValue] | false | maxItems: 5 | Baseline values | | categories | [CustomMetricCategory] | false | maxItems: 25 | Category values | | createdAt | string(date-time) | true | | Custom metric creation timestamp. | | createdBy | MetricCreatedBy | true | | The user that created custom metric. | | description | string | false | maxLength: 1000 | A description of the custom metric. | | directionality | string,null | false | | Directionality of the custom metric. | | displayChart | boolean | false | | Indicates if UI should show chart by default. | | id | string | true | | The ID of the custom metric. | | isModelSpecific | boolean | true | | Determines whether the metric is related to the model or deployment. | | metadata | MetadataField | false | | A custom metric metadata source when reading values from columnar datasets like a file. | | name | string | true | | Name of the custom metric. | | sampleCount | SampleCountField | false | | Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets. | | timeStep | string | true | | Custom metric time bucket size. | | timestamp | MetricTimestampSpoofing | false | | A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour. | | type | string | true | | Type (and aggregation character) of a metric. | | units | string,null | false | | Units or Y Label of given custom metric. | | value | ValueField | false | | A custom metric value source when reading values from columnar dataset like a file. | ### Enumerated Values | Property | Value | | --- | --- | | directionality | [higherIsBetter, lowerIsBetter] | | timeStep | hour | | type | [average, categorical, gauge, sum] | ## MetricInputValueBucket ``` { "properties": { "associationId": { "default": null, "description": "Identifies prediction row corresponding to value.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "geospatialCoordinate": { "description": "A geospatial object in WKB or WKT format. Only required for geospatial custom metrics.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.36" }, "metadata": { "default": null, "description": "A read-only metadata association with a custom metric value.", "maxLength": 128, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "sampleSize": { "default": 1, "description": "Custom metric value sample size.", "type": "integer" }, "segments": { "description": "A list of segments for a custom metric used in segmented analysis. Segments cannot be provided for geospatial metrics.", "items": { "properties": { "name": { "description": "Name of the segment on which segment analysis is being performed.", "type": "string" }, "value": { "description": "Value of the segment attribute to segment on.", "type": "string" } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "timestamp": { "description": "Value timestamp.", "format": "date-time", "type": [ "string", "null" ] }, "value": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "Custom metric value to ingest." } }, "required": [ "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associationId | string,null | false | | Identifies prediction row corresponding to value. | | geospatialCoordinate | string | false | maxLength: 10000 | A geospatial object in WKB or WKT format. Only required for geospatial custom metrics. | | metadata | string,null | false | maxLength: 128 | A read-only metadata association with a custom metric value. | | sampleSize | integer | false | | Custom metric value sample size. | | segments | [CustomMetricSegment] | false | maxItems: 10 | A list of segments for a custom metric used in segmented analysis. Segments cannot be provided for geospatial metrics. | | timestamp | string,null(date-time) | false | | Value timestamp. | | value | any | true | | Custom metric value to ingest. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## MetricListResponse ``` { "properties": { "count": { "description": "Number of paginated entries.", "minimum": 0, "type": "integer" }, "data": { "description": "A list of custom metrics.", "items": { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" }, "type": "array" }, "next": { "description": "URL to the next page, or null if there is no such page", "type": [ "string", "null" ] }, "previous": { "description": "URL to the previous page, or null if there is no such page", "type": [ "string", "null" ] }, "totalCount": { "description": "Total number of entries.", "minimum": 0, "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | Number of paginated entries. | | data | [MetricEntity] | true | | A list of custom metrics. | | next | string,null | true | | URL to the next page, or null if there is no such page | | previous | string,null | true | | URL to the previous page, or null if there is no such page | | totalCount | integer | true | minimum: 0 | Total number of entries. | ## MetricPeriodBucket ``` { "description": "A time period defined by a start and end time", "properties": { "end": { "description": "End of the bucket.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the bucket.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } ``` A time period defined by a start and end time ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string(date-time) | true | | End of the bucket. | | start | string(date-time) | true | | Start of the bucket. | ## MetricSummary ``` { "description": "Summary of the custom metric.", "properties": { "baselineValue": { "description": "Baseline value.", "type": [ "number", "null" ] }, "categories": { "description": "Category values.", "items": { "properties": { "baselineValue": { "description": "A reference value for the current category count.", "type": [ "number", "null" ] }, "categoryName": { "description": "Category value", "type": "string" }, "percentChange": { "description": "Percent change when compared with the reference value.", "type": [ "number", "null" ] }, "value": { "description": "A value for the current category count.", "type": [ "number", "null" ] } }, "required": [ "categoryName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "percentChange": { "description": "Percentage change of the baseline over its baseline.", "type": [ "number", "null" ] }, "sampleCount": { "description": "Number of samples used to calculate the aggregated value.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "Count of unknown categories for categorical metrics.", "type": [ "number", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated value of the custom metric.", "type": [ "number", "null" ] } }, "required": [ "id", "name" ], "type": "object" } ``` Summary of the custom metric. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineValue | number,null | false | | Baseline value. | | categories | [CustomMetricCategorySummary] | false | maxItems: 25 | Category values. | | id | string | true | | The ID of the custom metric. | | name | string | true | | Name of the custom metric. | | percentChange | number,null | false | | Percentage change of the baseline over its baseline. | | sampleCount | integer,null | false | | Number of samples used to calculate the aggregated value. | | unknownCategoriesCount | number,null | false | | Count of unknown categories for categorical metrics. | | value | number,null | false | | Aggregated value of the custom metric. | ## MetricTimestampSpoofing ``` { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" } ``` A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnName | string,null | false | | Column name | | timeFormat | string,null | false | | Format | ### Enumerated Values | Property | Value | | --- | --- | | timeFormat | [%m/%d/%Y, %m/%d/%y, %d/%m/%y, %m-%d-%Y, %m-%d-%y, %Y/%m/%d, %Y-%m-%d, %Y-%m-%d %H:%M:%S, %Y/%m/%d %H:%M:%S, %Y.%m.%d %H:%M:%S, %Y-%m-%d %H:%M, %Y/%m/%d %H:%M, %y/%m/%d, %y-%m-%d, %y-%m-%d %H:%M:%S, %y.%m.%d %H:%M:%S, %y/%m/%d %H:%M:%S, %y-%m-%d %H:%M, %y.%m.%d %H:%M, %y/%m/%d %H:%M, %m/%d/%Y %H:%M, %m/%d/%y %H:%M, %d/%m/%Y %H:%M, %d/%m/%y %H:%M, %m-%d-%Y %H:%M, %m-%d-%y %H:%M, %d-%m-%Y %H:%M, %d-%m-%y %H:%M, %m.%d.%Y %H:%M, %m/%d.%y %H:%M, %d.%m.%Y %H:%M, %d.%m.%y %H:%M, %m/%d/%Y %H:%M:%S, %m/%d/%y %H:%M:%S, %m-%d-%Y %H:%M:%S, %m-%d-%y %H:%M:%S, %m.%d.%Y %H:%M:%S, %m.%d.%y %H:%M:%S, %d/%m/%Y %H:%M:%S, %d/%m/%y %H:%M:%S, %Y-%m-%d %H:%M:%S.%f, %y-%m-%d %H:%M:%S.%f, %Y-%m-%dT%H:%M:%S.%fZ, %y-%m-%dT%H:%M:%S.%fZ, %Y-%m-%dT%H:%M:%S.%f, %y-%m-%dT%H:%M:%S.%f, %Y-%m-%dT%H:%M:%S, %y-%m-%dT%H:%M:%S, %Y-%m-%dT%H:%M:%SZ, %y-%m-%dT%H:%M:%SZ, %Y.%m.%d %H:%M:%S.%f, %y.%m.%d %H:%M:%S.%f, %Y.%m.%dT%H:%M:%S.%fZ, %y.%m.%dT%H:%M:%S.%fZ, %Y.%m.%dT%H:%M:%S.%f, %y.%m.%dT%H:%M:%S.%f, %Y.%m.%dT%H:%M:%S, %y.%m.%dT%H:%M:%S, %Y.%m.%dT%H:%M:%SZ, %y.%m.%dT%H:%M:%SZ, %Y%m%d, %m %d %Y %H %M %S, %m %d %y %H %M %S, %H:%M, %M:%S, %H:%M:%S, %Y %m %d %H %M %S, %y %m %d %H %M %S, %Y %m %d, %y %m %d, %d/%m/%Y, %Y-%d-%m, %y-%d-%m, %Y/%d/%m %H:%M:%S.%f, %Y/%d/%m %H:%M:%S.%fZ, %Y/%m/%d %H:%M:%S.%f, %Y/%m/%d %H:%M:%S.%fZ, %y/%d/%m %H:%M:%S.%f, %y/%d/%m %H:%M:%S.%fZ, %y/%m/%d %H:%M:%S.%f, %y/%m/%d %H:%M:%S.%fZ, %m.%d.%Y, %m.%d.%y, %d.%m.%y, %d.%m.%Y, %Y.%m.%d, %Y.%d.%m, %y.%m.%d, %y.%d.%m, %Y-%m-%d %I:%M:%S %p, %Y/%m/%d %I:%M:%S %p, %Y.%m.%d %I:%M:%S %p, %Y-%m-%d %I:%M %p, %Y/%m/%d %I:%M %p, %y-%m-%d %I:%M:%S %p, %y.%m.%d %I:%M:%S %p, %y/%m/%d %I:%M:%S %p, %y-%m-%d %I:%M %p, %y.%m.%d %I:%M %p, %y/%m/%d %I:%M %p, %m/%d/%Y %I:%M %p, %m/%d/%y %I:%M %p, %d/%m/%Y %I:%M %p, %d/%m/%y %I:%M %p, %m-%d-%Y %I:%M %p, %m-%d-%y %I:%M %p, %d-%m-%Y %I:%M %p, %d-%m-%y %I:%M %p, %m.%d.%Y %I:%M %p, %m/%d.%y %I:%M %p, %d.%m.%Y %I:%M %p, %d.%m.%y %I:%M %p, %m/%d/%Y %I:%M:%S %p, %m/%d/%y %I:%M:%S %p, %m-%d-%Y %I:%M:%S %p, %m-%d-%y %I:%M:%S %p, %m.%d.%Y %I:%M:%S %p, %m.%d.%y %I:%M:%S %p, %d/%m/%Y %I:%M:%S %p, %d/%m/%y %I:%M:%S %p, %Y-%m-%d %I:%M:%S.%f %p, %y-%m-%d %I:%M:%S.%f %p, %Y-%m-%dT%I:%M:%S.%fZ %p, %y-%m-%dT%I:%M:%S.%fZ %p, %Y-%m-%dT%I:%M:%S.%f %p, %y-%m-%dT%I:%M:%S.%f %p, %Y-%m-%dT%I:%M:%S %p, %y-%m-%dT%I:%M:%S %p, %Y-%m-%dT%I:%M:%SZ %p, %y-%m-%dT%I:%M:%SZ %p, %Y.%m.%d %I:%M:%S.%f %p, %y.%m.%d %I:%M:%S.%f %p, %Y.%m.%dT%I:%M:%S.%fZ %p, %y.%m.%dT%I:%M:%S.%fZ %p, %Y.%m.%dT%I:%M:%S.%f %p, %y.%m.%dT%I:%M:%S.%f %p, %Y.%m.%dT%I:%M:%S %p, %y.%m.%dT%I:%M:%S %p, %Y.%m.%dT%I:%M:%SZ %p, %y.%m.%dT%I:%M:%SZ %p, %m %d %Y %I %M %S %p, %m %d %y %I %M %S %p, %I:%M %p, %I:%M:%S %p, %Y %m %d %I %M %S %p, %y %m %d %I %M %S %p, %Y/%d/%m %I:%M:%S.%f %p, %Y/%d/%m %I:%M:%S.%fZ %p, %Y/%m/%d %I:%M:%S.%f %p, %Y/%m/%d %I:%M:%S.%fZ %p, %y/%d/%m %I:%M:%S.%f %p, %y/%d/%m %I:%M:%S.%fZ %p, %y/%m/%d %I:%M:%S.%f %p, %y/%m/%d %I:%M:%S.%fZ %p] | ## MetricUpdatePayload ``` { "properties": { "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "batch": { "description": "A custom metric batch ID source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "categories": { "description": "Category values. This field is required for categorical custom metrics.", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": "string" }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineValues | [MetricBaselineValue] | false | maxItems: 5 | Baseline values | | batch | BatchField | false | | A custom metric batch ID source when reading values from columnar dataset like a file. | | categories | [CustomMetricCategory] | false | maxItems: 25 | Category values. This field is required for categorical custom metrics. | | description | string | false | maxLength: 1000 | A description of the custom metric. | | directionality | string | false | | Directionality of the custom metric. | | name | string | false | | Name of the custom metric. | | sampleCount | SampleCountField | false | | Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets. | | timestamp | MetricTimestampSpoofing | false | | A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour. | | type | string | false | | Type (and aggregation character) of a metric. | | units | string | false | | Units or Y Label of given custom metric. | | value | ValueField | false | | A custom metric value source when reading values from columnar dataset like a file. | ### Enumerated Values | Property | Value | | --- | --- | | directionality | [higherIsBetter, lowerIsBetter] | | type | [average, categorical, gauge, sum] | ## MetricValuesFromDatasetPayload ``` { "properties": { "associationId": { "description": "A custom metric batch ID source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "batch": { "description": "A custom metric batch ID source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "datasetId": { "description": "Dataset ID to process.", "type": "string" }, "geospatial": { "description": "A custom metric geospatial coordinate source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "modelId": { "default": null, "description": "For a model metric, the ID of the model of related champion/challenger to update the metric values. For a deployment metric, the ID of the model is not needed.", "type": [ "string", "null" ] }, "modelPackageId": { "default": null, "description": "For a model metric, the ID of the model package of related champion/challenger to update the metric values. For a deployment metric, the ID of the model package is not needed.", "type": [ "string", "null" ] }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "segments": { "description": "A list of segments for a custom metric used in segmented analysis. Cannot be used with geospatial custom metrics.", "items": { "properties": { "column": { "description": "Name of the column that contains segment values.", "type": "string" }, "name": { "description": "Name of the segment on which segment analysis is being performed.", "type": "string" } }, "required": [ "column", "name" ], "type": "object" }, "maxItems": 10, "minItems": 1, "type": "array" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "datasetId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associationId | BatchField | false | | A custom metric batch ID source when reading values from columnar dataset like a file. | | batch | BatchField | false | | A custom metric batch ID source when reading values from columnar dataset like a file. | | datasetId | string | true | | Dataset ID to process. | | geospatial | GeospatialCoordinateField | false | | A custom metric geospatial coordinate source when reading values from columnar dataset like a file. | | modelId | string,null | false | | For a model metric, the ID of the model of related champion/challenger to update the metric values. For a deployment metric, the ID of the model is not needed. | | modelPackageId | string,null | false | | For a model metric, the ID of the model package of related champion/challenger to update the metric values. For a deployment metric, the ID of the model package is not needed. | | sampleCount | SampleCountField | false | | Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets. | | segments | [CustomMetricSegmentDataset] | false | maxItems: 10minItems: 1 | A list of segments for a custom metric used in segmented analysis. Cannot be used with geospatial custom metrics. | | timestamp | MetricTimestampSpoofing | false | | A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour. | | value | ValueField | false | | A custom metric value source when reading values from columnar dataset like a file. | ## MetricValuesFromJSONPayload ``` { "properties": { "buckets": { "description": "A list of timestamped buckets with custom metric values.", "items": { "properties": { "associationId": { "default": null, "description": "Identifies prediction row corresponding to value.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "geospatialCoordinate": { "description": "A geospatial object in WKB or WKT format. Only required for geospatial custom metrics.", "maxLength": 10000, "type": "string", "x-versionadded": "v2.36" }, "metadata": { "default": null, "description": "A read-only metadata association with a custom metric value.", "maxLength": 128, "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "sampleSize": { "default": 1, "description": "Custom metric value sample size.", "type": "integer" }, "segments": { "description": "A list of segments for a custom metric used in segmented analysis. Segments cannot be provided for geospatial metrics.", "items": { "properties": { "name": { "description": "Name of the segment on which segment analysis is being performed.", "type": "string" }, "value": { "description": "Value of the segment attribute to segment on.", "type": "string" } }, "required": [ "name", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.36" }, "timestamp": { "description": "Value timestamp.", "format": "date-time", "type": [ "string", "null" ] }, "value": { "anyOf": [ { "type": "number" }, { "type": "string" }, { "type": "null" } ], "description": "Custom metric value to ingest." } }, "required": [ "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "dryRun": { "default": false, "description": "Determines if the custom metric data calculated for this run is saved to the database.", "type": "boolean" }, "modelId": { "default": null, "description": "For a model metric, the ID of the model of related champion/challenger to update the metric values. For a deployment metric, the ID of the model is not needed.", "type": [ "string", "null" ] }, "modelPackageId": { "default": null, "description": "For a model metric, the ID of the model package of related champion/challenger to update the metric values. For a deployment metric, the ID of the model package is not needed.", "type": [ "string", "null" ] } }, "required": [ "buckets", "dryRun" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [MetricInputValueBucket] | true | maxItems: 10000 | A list of timestamped buckets with custom metric values. | | dryRun | boolean | true | | Determines if the custom metric data calculated for this run is saved to the database. | | modelId | string,null | false | | For a model metric, the ID of the model of related champion/challenger to update the metric values. For a deployment metric, the ID of the model is not needed. | | modelPackageId | string,null | false | | For a model metric, the ID of the model package of related champion/challenger to update the metric values. For a deployment metric, the ID of the model package is not needed. | ## MetricValuesOverBatchBucket ``` { "properties": { "batch": { "description": "Describes a batch associated with the bucket.", "properties": { "createdAt": { "description": "Timestamp when the batch was created.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "DataRobot assigned ID of the batch.", "type": "string", "x-versionadded": "v2.33" }, "lastPredictionTimestamp": { "description": "Timestamp when the latest prediction request of the batch was made", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "User provided name of the batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "id", "name" ], "type": "object" }, "categories": { "description": "Aggregated custom metric categories in the bucket.", "items": { "properties": { "categoryName": { "description": "Category value.", "type": "string" }, "count": { "description": "Count of the categories in the bucket.", "type": "integer" } }, "required": [ "categoryName", "count" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.35" }, "sampleSize": { "description": "Total number of values aggregated in the bucket.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "unknownCategoriesCount": { "description": "The count of the values that do not correspond to the any of the known categories.", "type": [ "integer", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated custom metric value in the bucket.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batch" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batch | BucketBatch | true | | Describes a batch associated with the bucket. | | categories | [MetricCategory] | false | maxItems: 25 | Aggregated custom metric categories in the bucket. | | sampleSize | integer,null | false | | Total number of values aggregated in the bucket. | | unknownCategoriesCount | integer,null | false | | The count of the values that do not correspond to the any of the known categories. | | value | number,null | false | | Aggregated custom metric value in the bucket. | ## MetricValuesOverBatchResponse ``` { "properties": { "buckets": { "description": "A list of bucketed batches and the custom metric values aggregated over that batches.", "items": { "properties": { "batch": { "description": "Describes a batch associated with the bucket.", "properties": { "createdAt": { "description": "Timestamp when the batch was created.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "DataRobot assigned ID of the batch.", "type": "string", "x-versionadded": "v2.33" }, "lastPredictionTimestamp": { "description": "Timestamp when the latest prediction request of the batch was made", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "User provided name of the batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "id", "name" ], "type": "object" }, "categories": { "description": "Aggregated custom metric categories in the bucket.", "items": { "properties": { "categoryName": { "description": "Category value.", "type": "string" }, "count": { "description": "Count of the categories in the bucket.", "type": "integer" } }, "required": [ "categoryName", "count" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.35" }, "sampleSize": { "description": "Total number of values aggregated in the bucket.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "unknownCategoriesCount": { "description": "The count of the values that do not correspond to the any of the known categories.", "type": [ "integer", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated custom metric value in the bucket.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batch" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "metric": { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "segmentValue": { "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "buckets", "metric" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [MetricValuesOverBatchBucket] | true | maxItems: 100 | A list of bucketed batches and the custom metric values aggregated over that batches. | | metric | MetricEntity | true | | A custom metric definition. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | ## MetricValuesOverSpaceResponse ``` { "properties": { "buckets": { "description": "A list of buckets containing aggregated custom metric values.", "items": { "properties": { "categories": { "description": "Aggregated custom metric categories in the bucket.", "items": { "properties": { "categoryName": { "description": "Category value.", "type": "string" }, "count": { "description": "Count of the categories in the bucket.", "type": "integer" } }, "required": [ "categoryName", "count" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 25, "type": "array" }, "hexagon": { "description": "h3 hexagon.", "type": "string" }, "sampleSize": { "description": "Total number of values aggregated in the bucket.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "The count of the values that do not correspond to the any of the known categories.", "type": [ "integer", "null" ] }, "value": { "description": "Aggregated custom metric value in the bucket.", "type": [ "number", "null" ] } }, "required": [ "hexagon" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "metric": { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" }, "modelId": { "description": "The ID of the model for which metrics are being retrieved. ", "type": "string" }, "modelPackageId": { "description": "The ID of the model package for which metrics are being retrieved.", "type": [ "string", "null" ] }, "summary": { "description": "Summary of values over time retrieval.", "properties": { "end": { "description": "End of the retrieval range.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the retrieval range.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } }, "required": [ "buckets", "metric", "summary" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [CustomMetricOverSpaceBucket] | true | maxItems: 1000 | A list of buckets containing aggregated custom metric values. | | metric | MetricEntity | true | | A custom metric definition. | | modelId | string | false | | The ID of the model for which metrics are being retrieved. | | modelPackageId | string,null | false | | The ID of the model package for which metrics are being retrieved. | | summary | MetricValuesOverTimeSummary | true | | Summary of values over time retrieval. | ## MetricValuesOverTimeBucket ``` { "properties": { "categories": { "description": "Aggregated custom metric categories in the bucket.", "items": { "properties": { "categoryName": { "description": "Category value.", "type": "string" }, "count": { "description": "Count of the categories in the bucket.", "type": "integer" } }, "required": [ "categoryName", "count" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.35" }, "period": { "description": "A time period defined by a start and end time", "properties": { "end": { "description": "End of the bucket.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the bucket.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "sampleSize": { "description": "Total number of values aggregated in the bucket.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "The count of the values that do not correspond to the any of the known categories.", "type": [ "integer", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated custom metric value in the bucket.", "type": [ "number", "null" ] } }, "required": [ "period" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [MetricCategory] | false | maxItems: 25 | Aggregated custom metric categories in the bucket. | | period | MetricPeriodBucket | true | | A time period defined by a start and end time | | sampleSize | integer,null | false | | Total number of values aggregated in the bucket. | | unknownCategoriesCount | integer,null | false | | The count of the values that do not correspond to the any of the known categories. | | value | number,null | false | | Aggregated custom metric value in the bucket. | ## MetricValuesOverTimeResponse ``` { "properties": { "buckets": { "description": "A list of bucketed time periods and the custom metric values aggregated over that period.", "items": { "properties": { "categories": { "description": "Aggregated custom metric categories in the bucket.", "items": { "properties": { "categoryName": { "description": "Category value.", "type": "string" }, "count": { "description": "Count of the categories in the bucket.", "type": "integer" } }, "required": [ "categoryName", "count" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.35" }, "period": { "description": "A time period defined by a start and end time", "properties": { "end": { "description": "End of the bucket.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the bucket.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "sampleSize": { "description": "Total number of values aggregated in the bucket.", "type": [ "integer", "null" ] }, "unknownCategoriesCount": { "description": "The count of the values that do not correspond to the any of the known categories.", "type": [ "integer", "null" ], "x-versionadded": "v2.35" }, "value": { "description": "Aggregated custom metric value in the bucket.", "type": [ "number", "null" ] } }, "required": [ "period" ], "type": "object" }, "maxItems": 100, "type": "array" }, "metric": { "description": "A custom metric definition.", "properties": { "associationId": { "description": "A custom metric associationId source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "baselineValues": { "description": "Baseline values", "items": { "properties": { "value": { "description": "A reference value in given metric units.", "type": "number" } }, "required": [ "value" ], "type": "object" }, "maxItems": 5, "type": "array" }, "categories": { "description": "Category values", "items": { "properties": { "baselineCount": { "description": "A reference value for the current category count.", "type": [ "integer", "null" ] }, "directionality": { "description": "Directionality of the custom metric category.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": "string" }, "value": { "description": "Category value", "type": "string" } }, "required": [ "directionality", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 25, "type": "array", "x-versionadded": "v2.36" }, "createdAt": { "description": "Custom metric creation timestamp.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The user that created custom metric.", "properties": { "id": { "description": "The ID of user who created custom metric.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "description": { "description": "A description of the custom metric.", "maxLength": 1000, "type": "string" }, "directionality": { "description": "Directionality of the custom metric.", "enum": [ "higherIsBetter", "lowerIsBetter" ], "type": [ "string", "null" ] }, "displayChart": { "description": "Indicates if UI should show chart by default.", "type": "boolean" }, "id": { "description": "The ID of the custom metric.", "type": "string" }, "isModelSpecific": { "description": "Determines whether the metric is related to the model or deployment.", "type": "boolean" }, "metadata": { "description": "A custom metric metadata source when reading values from columnar datasets like a file.", "properties": { "columnName": { "description": "Column name", "maxLength": 128, "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "Name of the custom metric.", "type": "string" }, "sampleCount": { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" }, "timeStep": { "description": "Custom metric time bucket size.", "enum": [ "hour" ], "type": "string" }, "timestamp": { "description": "A custom metric timestamp spoofing when reading values from file, like dataset. By default, we replicate pd.to_datetime formatting behaviour.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] }, "timeFormat": { "description": "Format", "enum": [ "%m/%d/%Y", "%m/%d/%y", "%d/%m/%y", "%m-%d-%Y", "%m-%d-%y", "%Y/%m/%d", "%Y-%m-%d", "%Y-%m-%d %H:%M:%S", "%Y/%m/%d %H:%M:%S", "%Y.%m.%d %H:%M:%S", "%Y-%m-%d %H:%M", "%Y/%m/%d %H:%M", "%y/%m/%d", "%y-%m-%d", "%y-%m-%d %H:%M:%S", "%y.%m.%d %H:%M:%S", "%y/%m/%d %H:%M:%S", "%y-%m-%d %H:%M", "%y.%m.%d %H:%M", "%y/%m/%d %H:%M", "%m/%d/%Y %H:%M", "%m/%d/%y %H:%M", "%d/%m/%Y %H:%M", "%d/%m/%y %H:%M", "%m-%d-%Y %H:%M", "%m-%d-%y %H:%M", "%d-%m-%Y %H:%M", "%d-%m-%y %H:%M", "%m.%d.%Y %H:%M", "%m/%d.%y %H:%M", "%d.%m.%Y %H:%M", "%d.%m.%y %H:%M", "%m/%d/%Y %H:%M:%S", "%m/%d/%y %H:%M:%S", "%m-%d-%Y %H:%M:%S", "%m-%d-%y %H:%M:%S", "%m.%d.%Y %H:%M:%S", "%m.%d.%y %H:%M:%S", "%d/%m/%Y %H:%M:%S", "%d/%m/%y %H:%M:%S", "%Y-%m-%d %H:%M:%S.%f", "%y-%m-%d %H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S.%fZ", "%y-%m-%dT%H:%M:%S.%fZ", "%Y-%m-%dT%H:%M:%S.%f", "%y-%m-%dT%H:%M:%S.%f", "%Y-%m-%dT%H:%M:%S", "%y-%m-%dT%H:%M:%S", "%Y-%m-%dT%H:%M:%SZ", "%y-%m-%dT%H:%M:%SZ", "%Y.%m.%d %H:%M:%S.%f", "%y.%m.%d %H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S.%fZ", "%y.%m.%dT%H:%M:%S.%fZ", "%Y.%m.%dT%H:%M:%S.%f", "%y.%m.%dT%H:%M:%S.%f", "%Y.%m.%dT%H:%M:%S", "%y.%m.%dT%H:%M:%S", "%Y.%m.%dT%H:%M:%SZ", "%y.%m.%dT%H:%M:%SZ", "%Y%m%d", "%m %d %Y %H %M %S", "%m %d %y %H %M %S", "%H:%M", "%M:%S", "%H:%M:%S", "%Y %m %d %H %M %S", "%y %m %d %H %M %S", "%Y %m %d", "%y %m %d", "%d/%m/%Y", "%Y-%d-%m", "%y-%d-%m", "%Y/%d/%m %H:%M:%S.%f", "%Y/%d/%m %H:%M:%S.%fZ", "%Y/%m/%d %H:%M:%S.%f", "%Y/%m/%d %H:%M:%S.%fZ", "%y/%d/%m %H:%M:%S.%f", "%y/%d/%m %H:%M:%S.%fZ", "%y/%m/%d %H:%M:%S.%f", "%y/%m/%d %H:%M:%S.%fZ", "%m.%d.%Y", "%m.%d.%y", "%d.%m.%y", "%d.%m.%Y", "%Y.%m.%d", "%Y.%d.%m", "%y.%m.%d", "%y.%d.%m", "%Y-%m-%d %I:%M:%S %p", "%Y/%m/%d %I:%M:%S %p", "%Y.%m.%d %I:%M:%S %p", "%Y-%m-%d %I:%M %p", "%Y/%m/%d %I:%M %p", "%y-%m-%d %I:%M:%S %p", "%y.%m.%d %I:%M:%S %p", "%y/%m/%d %I:%M:%S %p", "%y-%m-%d %I:%M %p", "%y.%m.%d %I:%M %p", "%y/%m/%d %I:%M %p", "%m/%d/%Y %I:%M %p", "%m/%d/%y %I:%M %p", "%d/%m/%Y %I:%M %p", "%d/%m/%y %I:%M %p", "%m-%d-%Y %I:%M %p", "%m-%d-%y %I:%M %p", "%d-%m-%Y %I:%M %p", "%d-%m-%y %I:%M %p", "%m.%d.%Y %I:%M %p", "%m/%d.%y %I:%M %p", "%d.%m.%Y %I:%M %p", "%d.%m.%y %I:%M %p", "%m/%d/%Y %I:%M:%S %p", "%m/%d/%y %I:%M:%S %p", "%m-%d-%Y %I:%M:%S %p", "%m-%d-%y %I:%M:%S %p", "%m.%d.%Y %I:%M:%S %p", "%m.%d.%y %I:%M:%S %p", "%d/%m/%Y %I:%M:%S %p", "%d/%m/%y %I:%M:%S %p", "%Y-%m-%d %I:%M:%S.%f %p", "%y-%m-%d %I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S.%fZ %p", "%y-%m-%dT%I:%M:%S.%fZ %p", "%Y-%m-%dT%I:%M:%S.%f %p", "%y-%m-%dT%I:%M:%S.%f %p", "%Y-%m-%dT%I:%M:%S %p", "%y-%m-%dT%I:%M:%S %p", "%Y-%m-%dT%I:%M:%SZ %p", "%y-%m-%dT%I:%M:%SZ %p", "%Y.%m.%d %I:%M:%S.%f %p", "%y.%m.%d %I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S.%fZ %p", "%y.%m.%dT%I:%M:%S.%fZ %p", "%Y.%m.%dT%I:%M:%S.%f %p", "%y.%m.%dT%I:%M:%S.%f %p", "%Y.%m.%dT%I:%M:%S %p", "%y.%m.%dT%I:%M:%S %p", "%Y.%m.%dT%I:%M:%SZ %p", "%y.%m.%dT%I:%M:%SZ %p", "%m %d %Y %I %M %S %p", "%m %d %y %I %M %S %p", "%I:%M %p", "%I:%M:%S %p", "%Y %m %d %I %M %S %p", "%y %m %d %I %M %S %p", "%Y/%d/%m %I:%M:%S.%f %p", "%Y/%d/%m %I:%M:%S.%fZ %p", "%Y/%m/%d %I:%M:%S.%f %p", "%Y/%m/%d %I:%M:%S.%fZ %p", "%y/%d/%m %I:%M:%S.%f %p", "%y/%d/%m %I:%M:%S.%fZ %p", "%y/%m/%d %I:%M:%S.%f %p", "%y/%m/%d %I:%M:%S.%fZ %p" ], "type": [ "string", "null" ] } }, "type": "object" }, "type": { "description": "Type (and aggregation character) of a metric.", "enum": [ "average", "categorical", "gauge", "sum" ], "type": "string" }, "units": { "description": "Units or Y Label of given custom metric.", "type": [ "string", "null" ] }, "value": { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "isModelSpecific", "name", "timeStep", "type" ], "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "segmentValue": { "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "summary": { "description": "Summary of values over time retrieval.", "properties": { "end": { "description": "End of the retrieval range.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the retrieval range.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } }, "required": [ "buckets", "metric", "summary" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [MetricValuesOverTimeBucket] | true | maxItems: 100 | A list of bucketed time periods and the custom metric values aggregated over that period. | | metric | MetricEntity | true | | A custom metric definition. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | | summary | MetricValuesOverTimeSummary | true | | Summary of values over time retrieval. | ## MetricValuesOverTimeSummary ``` { "description": "Summary of values over time retrieval.", "properties": { "end": { "description": "End of the retrieval range.", "format": "date-time", "type": "string" }, "start": { "description": "Start of the retrieval range.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } ``` Summary of values over time retrieval. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string(date-time) | true | | End of the retrieval range. | | start | string(date-time) | true | | Start of the retrieval range. | ## RuntimeParameterUnified ``` { "properties": { "allowEmpty": { "default": true, "description": "Indicates whether the param must be set before registration", "type": "boolean", "x-versionadded": "v2.33" }, "credentialType": { "description": "The type of credential, required only for credentials parameters.", "enum": [ "adls_gen2_oauth", "api_token", "azure", "azure_oauth", "azure_service_principal", "basic", "bearer", "box_jwt", "client_id_and_secret", "databricks_access_token_account", "databricks_service_principal_account", "external_oauth_provider", "gcp", "oauth", "rsa", "s3", "sap_oauth", "snowflake_key_pair_user_account", "snowflake_oauth_user_account", "tableau_access_token" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "currentValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Given the default and the override, this is the actual current value of the parameter.", "x-versionadded": "v2.33" }, "defaultValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "The default value for the given field.", "x-versionadded": "v2.33" }, "description": { "description": "Description how this parameter impacts the running model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "fieldName": { "description": "The parameter name. This value will be added as an environment variable when running custom models.", "type": "string", "x-versionadded": "v2.33" }, "keyValueId": { "description": "The ID of the key-value entry storing this parameter value.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "maxValue": { "description": "The maximum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "minValue": { "description": "The minimum value for a numeric field.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "overrideValue": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "boolean" }, { "type": "null" } ], "description": "Value set by the user that overrides the default set in the definition.", "x-versionadded": "v2.33" }, "type": { "description": "The type of this value.", "enum": [ "boolean", "credential", "customMetric", "deployment", "modelPackage", "numeric", "string" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "fieldName", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowEmpty | boolean | false | | Indicates whether the param must be set before registration | | credentialType | string,null | false | | The type of credential, required only for credentials parameters. | | currentValue | any | false | | Given the default and the override, this is the actual current value of the parameter. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultValue | any | false | | The default value for the given field. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | | Description how this parameter impacts the running model. | | fieldName | string | true | | The parameter name. This value will be added as an environment variable when running custom models. | | keyValueId | string,null | false | | The ID of the key-value entry storing this parameter value. | | maxValue | number,null | false | | The maximum value for a numeric field. | | minValue | number,null | false | | The minimum value for a numeric field. | | overrideValue | any | false | | Value set by the user that overrides the default set in the definition. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | The type of this value. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | [adls_gen2_oauth, api_token, azure, azure_oauth, azure_service_principal, basic, bearer, box_jwt, client_id_and_secret, databricks_access_token_account, databricks_service_principal_account, external_oauth_provider, gcp, oauth, rsa, s3, sap_oauth, snowflake_key_pair_user_account, snowflake_oauth_user_account, tableau_access_token] | | type | [boolean, credential, customMetric, deployment, modelPackage, numeric, string] | ## SampleCountField ``` { "description": "Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } ``` Points to a weight column if users provide pre-aggregated metric values. Used with columnar datasets. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnName | string,null | true | | Column name | ## Schedule ``` { "description": "The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False.", "properties": { "dayOfMonth": { "description": "The date(s) of the month that the job will run. Allowed values are either ``[1 ... 31]`` or ``[\"*\"]`` for all days of the month. This field is additive with ``dayOfWeek``, meaning the job will run both on the date(s) defined in this field and the day specified by ``dayOfWeek`` (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If ``dayOfMonth`` is set to ``[\"*\"]`` and ``dayOfWeek`` is defined, the scheduler will trigger on every day of the month that matches ``dayOfWeek`` (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 ], "type": [ "number", "string" ] }, "maxItems": 31, "type": "array" }, "dayOfWeek": { "description": "The day(s) of the week that the job will run. Allowed values are ``[0 .. 6]``, where (Sunday=0), or ``[\"*\"]``, for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., \"sunday\", \"Sunday\", \"sun\", or \"Sun\", all map to ``[0]``. This field is additive with ``dayOfMonth``, meaning the job will run both on the date specified by ``dayOfMonth`` and the day defined in this field.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, "sunday", "SUNDAY", "Sunday", "monday", "MONDAY", "Monday", "tuesday", "TUESDAY", "Tuesday", "wednesday", "WEDNESDAY", "Wednesday", "thursday", "THURSDAY", "Thursday", "friday", "FRIDAY", "Friday", "saturday", "SATURDAY", "Saturday", "sun", "SUN", "Sun", "mon", "MON", "Mon", "tue", "TUE", "Tue", "wed", "WED", "Wed", "thu", "THU", "Thu", "fri", "FRI", "Fri", "sat", "SAT", "Sat" ], "type": [ "number", "string" ] }, "maxItems": 7, "type": "array" }, "hour": { "description": "The hour(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every hour of the day or ``[0 ... 23]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ], "type": [ "number", "string" ] }, "maxItems": 24, "type": "array" }, "minute": { "description": "The minute(s) of the day that the job will run. Allowed values are either ``[\"*\"]`` meaning every minute of the day or``[0 ... 59]``.", "items": { "enum": [ "*", 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 ], "type": [ "number", "string" ] }, "maxItems": 60, "type": "array" }, "month": { "description": "The month(s) of the year that the job will run. Allowed values are either ``[1 ... 12]`` or ``[\"*\"]`` for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., \"jan\" or \"october\"). Months that are not compatible with ``dayOfMonth`` are ignored, for example ``{\"dayOfMonth\": [31], \"month\":[\"feb\"]}``.", "items": { "enum": [ "*", 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, "january", "JANUARY", "January", "february", "FEBRUARY", "February", "march", "MARCH", "March", "april", "APRIL", "April", "may", "MAY", "May", "june", "JUNE", "June", "july", "JULY", "July", "august", "AUGUST", "August", "september", "SEPTEMBER", "September", "october", "OCTOBER", "October", "november", "NOVEMBER", "November", "december", "DECEMBER", "December", "jan", "JAN", "Jan", "feb", "FEB", "Feb", "mar", "MAR", "Mar", "apr", "APR", "Apr", "jun", "JUN", "Jun", "jul", "JUL", "Jul", "aug", "AUG", "Aug", "sep", "SEP", "Sep", "oct", "OCT", "Oct", "nov", "NOV", "Nov", "dec", "DEC", "Dec" ], "type": [ "number", "string" ] }, "maxItems": 12, "type": "array" } }, "required": [ "dayOfMonth", "dayOfWeek", "hour", "minute", "month" ], "type": "object" } ``` The scheduling information defining how often and when to execute this job to the Job Scheduling service. Optional if enabled = False. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dayOfMonth | [number,string] | true | maxItems: 31 | The date(s) of the month that the job will run. Allowed values are either [1 ... 31] or ["*"] for all days of the month. This field is additive with dayOfWeek, meaning the job will run both on the date(s) defined in this field and the day specified by dayOfWeek (for example, dates 1st, 2nd, 3rd, plus every Tuesday). If dayOfMonth is set to ["*"] and dayOfWeek is defined, the scheduler will trigger on every day of the month that matches dayOfWeek (for example, Tuesday the 2nd, 9th, 16th, 23rd, 30th). Invalid dates such as February 31st are ignored. | | dayOfWeek | [number,string] | true | maxItems: 7 | The day(s) of the week that the job will run. Allowed values are [0 .. 6], where (Sunday=0), or ["*"], for all days of the week. Strings, either 3-letter abbreviations or the full name of the day, can be used interchangeably (e.g., "sunday", "Sunday", "sun", or "Sun", all map to [0]. This field is additive with dayOfMonth, meaning the job will run both on the date specified by dayOfMonth and the day defined in this field. | | hour | [number,string] | true | maxItems: 24 | The hour(s) of the day that the job will run. Allowed values are either ["*"] meaning every hour of the day or [0 ... 23]. | | minute | [number,string] | true | maxItems: 60 | The minute(s) of the day that the job will run. Allowed values are either ["*"] meaning every minute of the day or[0 ... 59]. | | month | [number,string] | true | maxItems: 12 | The month(s) of the year that the job will run. Allowed values are either [1 ... 12] or ["*"] for all months of the year. Strings, either 3-letter abbreviations or the full name of the month, can be used interchangeably (e.g., "jan" or "october"). Months that are not compatible with dayOfMonth are ignored, for example {"dayOfMonth": [31], "month":["feb"]}. | ## ValueField ``` { "description": "A custom metric value source when reading values from columnar dataset like a file.", "properties": { "columnName": { "description": "Column name", "type": [ "string", "null" ] } }, "required": [ "columnName" ], "type": "object" } ``` A custom metric value source when reading values from columnar dataset like a file. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnName | string,null | true | | Column name | ## WorkspaceItemResponse ``` { "properties": { "commitSha": { "description": "SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories).", "type": [ "string", "null" ] }, "created": { "description": "ISO-8601 timestamp of when the file item was created.", "type": "string" }, "fileName": { "description": "The name of the file item.", "type": "string" }, "filePath": { "description": "The path of the file item.", "type": "string" }, "fileSource": { "description": "The source of the file item.", "type": "string" }, "id": { "description": "ID of the file item.", "type": "string" }, "ref": { "description": "Remote reference (branch, commit, tag). Branch \"master\", if not specified.", "type": [ "string", "null" ] }, "repositoryFilePath": { "description": "Full path to the file in the remote repository.", "type": [ "string", "null" ] }, "repositoryLocation": { "description": "URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name).", "type": [ "string", "null" ] }, "repositoryName": { "description": "Name of the repository from which the file was pulled.", "type": [ "string", "null" ] } }, "required": [ "created", "fileName", "filePath", "fileSource", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commitSha | string,null | false | | SHA1 hash pointing to the original file revision (set only for files pulled from Git-like repositories). | | created | string | true | | ISO-8601 timestamp of when the file item was created. | | fileName | string | true | | The name of the file item. | | filePath | string | true | | The path of the file item. | | fileSource | string | true | | The source of the file item. | | id | string | true | | ID of the file item. | | ref | string,null | false | | Remote reference (branch, commit, tag). Branch "master", if not specified. | | repositoryFilePath | string,null | false | | Full path to the file in the remote repository. | | repositoryLocation | string,null | false | | URL to remote repository from which the file was pulled (e.g. Git server or S3 Bucket name). | | repositoryName | string,null | false | | Name of the repository from which the file was pulled. | --- # Data exports URL: https://docs.datarobot.com/en/docs/api/reference/public-api/observability_data_exploration.html > Use the endpoints described below to manage data exploration. Use the data you collect from data exploration tab (or data calculated through other custom metrics) to compute and monitor custom business or performance metrics. This feature allows you to implement your organization's specialized metrics, expanding on the insights provided by DataRobot's built-in service health, data drift, and accuracy metrics. # Data exports Use the endpoints described below to manage data exploration. Use the data you collect from data exploration tab (or data calculated through other custom metrics) to compute and monitor custom business or performance metrics. This feature allows you to implement your organization's specialized metrics, expanding on the insights provided by DataRobot's built-in service health, data drift, and accuracy metrics. ## Retrieve a list of asynchronous actuals data exports by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/actualsDataExports/` Authentication requirements: `BearerAuth` Retrieve a list of asynchronous actuals data exports. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Offset this number of objects to retrieve. | | limit | query | integer | false | At most this number of objects to retrieve. | | status | query | string,null | false | An actuals data export state overwrite value. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | status | [CANCELLED, CREATED, FAILED, SCHEDULED, SUCCEEDED, null] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of paginated entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "A list of asynchronous actuals data exports.", "items": { "properties": { "createdAt": { "description": "Actuals data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created actuals data export.", "properties": { "id": { "description": "The ID of user who created actuals data export.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id" ], "type": "object" }, "data": { "description": "An actuals data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of an entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "A name of an entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when actuals data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the actuals data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "onlyMatchedPredictions": { "default": true, "description": "If true, exports actuals with matching predictions only.", "type": "boolean", "x-versionadded": "v2.33" }, "period": { "description": "An actuals data time range definition.", "properties": { "end": { "description": "End of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "An actuals data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "URL to the next page, or null if there is no such page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "URL to the previous page, or null if there is no such page", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "Total number of entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Success | ActualsExportListResponse | | 403 | Forbidden | User does not have permission to access a particular deployment. | None | | 404 | Not Found | Resource not found. | None | | 422 | Unprocessable Entity | Unable to process the actuals data exports list retrieval due to invalid parameter values. | None | ## Create a deployment actuals data export by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/actualsDataExports/` Authentication requirements: `BearerAuth` Create a deployment actuals data export. ### Body parameter ``` { "properties": { "end": { "description": "End of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "onlyMatchedPredictions": { "default": true, "description": "If true, exports actuals with matching predictions only.", "type": "boolean", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | ActualsExportCreatePayload | false | none | ### Example responses > 202 Response ``` { "properties": { "createdAt": { "description": "Actuals data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created actuals data export.", "properties": { "id": { "description": "The ID of user who created actuals data export.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id" ], "type": "object" }, "data": { "description": "An actuals data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of an entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "A name of an entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when actuals data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the actuals data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "onlyMatchedPredictions": { "default": true, "description": "If true, exports actuals with matching predictions only.", "type": "boolean", "x-versionadded": "v2.33" }, "period": { "description": "An actuals data time range definition.", "properties": { "end": { "description": "End of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "An actuals data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Actuals data export successfully created. | ActualsDataExportEntity | | 403 | Forbidden | User does not have permission to create an actuals data export. | None | | 404 | Not Found | Resource not found. | None | | 422 | Unprocessable Entity | Unable to process the actuals data export creation request. | None | ## Delete an actual export by deployment ID Operation path: `DELETE /api/v2/deployments/{deploymentId}/actualsDataExports/{exportId}/` Authentication requirements: `BearerAuth` Delete an actual export. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | ID of the deployment. | | exportId | path | string | true | ID of the actuals data export job. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | An actual export was successfully deleted. | None | | 403 | Forbidden | User does not have permission to delete an actual export. | None | | 404 | Not Found | Actual export was not found. | None | ## Retrieve a single actuals data export by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/actualsDataExports/{exportId}/` Authentication requirements: `BearerAuth` Retrieve a single actuals data export. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | ID of the deployment. | | exportId | path | string | true | ID of the actuals data export job. | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "Actuals data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created actuals data export.", "properties": { "id": { "description": "The ID of user who created actuals data export.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id" ], "type": "object" }, "data": { "description": "An actuals data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of an entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "A name of an entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when actuals data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the actuals data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "onlyMatchedPredictions": { "default": true, "description": "If true, exports actuals with matching predictions only.", "type": "boolean", "x-versionadded": "v2.33" }, "period": { "description": "An actuals data time range definition.", "properties": { "end": { "description": "End of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "An actuals data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A given actuals data export user can view is retrieved. | ActualsDataExportEntity | | 403 | Forbidden | User does not have permission to access a particular deployment or actuals data export. | None | | 404 | Not Found | Resource not found. | None | ## Update actuals data export by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/actualsDataExports/{exportId}/` Authentication requirements: `BearerAuth` Update actuals data export. ### Body parameter ``` { "properties": { "status": { "description": "An actuals data export state overwrite value.", "enum": [ "CANCELLED" ], "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | ID of the deployment. | | exportId | path | string | true | ID of the actuals data export job. | | body | body | ActualsDataUpdate | false | none | ### Example responses > 204 Response ``` { "properties": { "createdAt": { "description": "Actuals data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created actuals data export.", "properties": { "id": { "description": "The ID of user who created actuals data export.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id" ], "type": "object" }, "data": { "description": "An actuals data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of an entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "A name of an entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when actuals data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the actuals data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "onlyMatchedPredictions": { "default": true, "description": "If true, exports actuals with matching predictions only.", "type": "boolean", "x-versionadded": "v2.33" }, "period": { "description": "An actuals data time range definition.", "properties": { "end": { "description": "End of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "An actuals data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Actuals data export updated. | ActualsDataExportEntity | | 403 | Forbidden | User does not have permission to update the actuals data export. | None | | 404 | Not Found | Requested resource does not exist or the user does not have permission to view it. | None | | 422 | Unprocessable Entity | Unable to process the actuals data export update. | None | ## Retrieve metadata of the data by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/dataQualityView/` Authentication requirements: `BearerAuth` Retrieve metadata of the data to be exported. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Specifies the number of rows to skip before starting to return rows from the query. | | limit | query | integer | false | Specifies the number of rows to return after the offset. | | start | query | string(date-time) | false | Start of the period of prediction data to collect. Defaults to a week before the end time. | | end | query | string(date-time) | false | End of the period of prediction data to collect. Defaults to now, or a week after the start time. | | modelId | query | string | false | The ID of the model. | | predictionPattern | query | string,null | false | The keywords to search in a predicted value, for text generation target types. | | promptPattern | query | string,null | false | The keywords to search in a prompt value, for text generation target types. | | actualPattern | query | string,null | false | The keywords to search in an actual value, for text generation target types. | | orderBy | query | string,null | false | Field by which to order the text generation target types. | | orderMetric | query | string,null | false | Metric name or ID used for metric ordering. | | filterMetric | query | string,null | false | Metric name or ID used for metric filtering. | | filterValue | query | string,null | false | Metric value required to match value with filter metric name. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [timestamp, associationId, customMetrics, -timestamp, -associationId, -customMetrics] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of paginated entries.", "minimum": 0, "type": "integer" }, "data": { "description": "A list of prediction data exports.", "items": { "properties": { "actualValue": { "description": "Actual value", "type": [ "string", "null" ] }, "associationId": { "description": "Association ID of prediction.", "type": "string" }, "context": { "description": "A list of contexts used for text generation response.", "items": { "properties": { "context": { "description": "Text generation context.", "type": "string" }, "link": { "description": "Text generation context link.", "type": [ "string", "null" ] } }, "required": [ "context" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10, "type": "array" }, "metrics": { "description": "A list of collected metrics associated with the prediction instance.", "items": { "properties": { "id": { "description": "The ID of the custom metric.", "type": "string" }, "metadata": { "default": null, "description": "Custom metric value metadata.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "name": { "description": "Custom metric name.", "type": "string" }, "value": { "description": "Custom metric value.", "oneOf": [ { "type": "number" }, { "type": "string" }, { "type": "null" } ] } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "predictedValue": { "description": "Predicted value(s)", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" }, { "type": "null" } ] }, "prompt": { "description": "Prompt for text generation target types.", "type": [ "string", "null" ] }, "timestamp": { "description": "Record creation timestamp.", "format": "date-time", "type": "string" } }, "required": [ "actualValue", "associationId", "context", "metrics", "predictedValue", "prompt", "timestamp" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL to the next page, or null if there is no such page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL to the previous page, or null if there is no such page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of entries.", "minimum": 0, "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | DataQualityResponse | ## A list prediction data exports by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/predictionDataExports/` Authentication requirements: `BearerAuth` A list of asynchronous prediction data exports. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Specifies the number of rows to skip before starting to return rows from the query. | | limit | query | integer | false | Specifies the number of rows to return after the offset. | | status | query | string,null | false | A prediction data export processing state. | | modelId | query | string,null | false | Id of model used for prediction data export. | | batch | query | string | false | If true, returns just the exports associated with batches. If false, just the real-time ones. If not provided, returns both real-time and batch exports. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | status | [CANCELLED, CREATED, FAILED, SCHEDULED, SUCCEEDED, null] | | batch | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of paginated entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "A list of prediction data exports.", "items": { "properties": { "associationIds": { "description": "Association IDs of rows to export", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.35" }, "augmentationType": { "default": "NO_AUGMENTATION", "description": "Indicates if prediction data is augmented with actuals or metrics", "enum": [ "NO_AUGMENTATION", "ACTUALS_AND_METRICS" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batches": { "description": "Metadata associated with exported batch.", "items": { "properties": { "batchId": { "description": "An ID of a batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "A name of a batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "createdAt": { "description": "Prediction data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "data": { "description": "A prediction data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when prediction data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "period": { "description": "A prediction data time range definition.", "properties": { "end": { "description": "End of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "A prediction data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "URL to the next page, or null if there is no such page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "URL to the previous page, or null if there is no such page", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "Total number of entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The prediction data exports. | PredictionExportListResponse | | 403 | Forbidden | User does not have permission to access a particular deployment. | None | | 404 | Not Found | Resource not found. | None | | 422 | Unprocessable Entity | Unable to process the prediction data exports list retrieval due to invalid parameter values. | None | ## Create a deployment prediction data export by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/predictionDataExports/` Authentication requirements: `BearerAuth` Create a deployment prediction data export. ### Body parameter ``` { "properties": { "augmentationType": { "default": "NO_AUGMENTATION", "description": "Indicates if prediction data is augmented with actuals or metrics", "enum": [ "NO_AUGMENTATION", "ACTUALS_AND_METRICS" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batchIds": { "description": "IDs of batches to export. Null for real-time data exports.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "end": { "description": "End of the period of prediction data to collect. Defaults to now, or a week after the start time.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of prediction data to collect. Defaults to a week before the end time.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | ExportCreatePayload | false | none | ### Example responses > 202 Response ``` { "properties": { "associationIds": { "description": "Association IDs of rows to export", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.35" }, "augmentationType": { "default": "NO_AUGMENTATION", "description": "Indicates if prediction data is augmented with actuals or metrics", "enum": [ "NO_AUGMENTATION", "ACTUALS_AND_METRICS" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batches": { "description": "Metadata associated with exported batch.", "items": { "properties": { "batchId": { "description": "An ID of a batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "A name of a batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "createdAt": { "description": "Prediction data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "data": { "description": "A prediction data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when prediction data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "period": { "description": "A prediction data time range definition.", "properties": { "end": { "description": "End of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "A prediction data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Prediction data export successfully created. | ExportEntity | | 403 | Forbidden | User does not have permission to create a prediction data export. | None | | 404 | Not Found | Resource not found. | None | | 422 | Unprocessable Entity | Unable to process the prediction data export creation request. | None | ## Retrieve a single prediction data export by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/predictionDataExports/{exportId}/` Authentication requirements: `BearerAuth` Retrieve a single prediction data export. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | exportId | path | string | true | Unique identifier of the export. | ### Example responses > 200 Response ``` { "properties": { "associationIds": { "description": "Association IDs of rows to export", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.35" }, "augmentationType": { "default": "NO_AUGMENTATION", "description": "Indicates if prediction data is augmented with actuals or metrics", "enum": [ "NO_AUGMENTATION", "ACTUALS_AND_METRICS" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batches": { "description": "Metadata associated with exported batch.", "items": { "properties": { "batchId": { "description": "An ID of a batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "A name of a batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "createdAt": { "description": "Prediction data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "data": { "description": "A prediction data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when prediction data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "period": { "description": "A prediction data time range definition.", "properties": { "end": { "description": "End of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "A prediction data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A given prediction data export user can view is retrieved. | ExportEntity | | 403 | Forbidden | User does not have permission to access a particular deployment or prediction data export. | None | | 404 | Not Found | Resource not found. | None | ## Update prediction data export by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/predictionDataExports/{exportId}/` Authentication requirements: `BearerAuth` Update prediction data export. ### Body parameter ``` { "properties": { "status": { "description": "A prediction data export processing state.", "enum": [ "CANCELLED" ], "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | exportId | path | string | true | Unique identifier of the export. | | body | body | PredictionDataUpdate | false | none | ### Example responses > 204 Response ``` { "properties": { "associationIds": { "description": "Association IDs of rows to export", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.35" }, "augmentationType": { "default": "NO_AUGMENTATION", "description": "Indicates if prediction data is augmented with actuals or metrics", "enum": [ "NO_AUGMENTATION", "ACTUALS_AND_METRICS" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batches": { "description": "Metadata associated with exported batch.", "items": { "properties": { "batchId": { "description": "An ID of a batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "A name of a batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "createdAt": { "description": "Prediction data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "data": { "description": "A prediction data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when prediction data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "period": { "description": "A prediction data time range definition.", "properties": { "end": { "description": "End of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "A prediction data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Prediction data export successfully updated. | ExportEntity | | 403 | Forbidden | User does not have permission to update a prediction data export. | None | | 404 | Not Found | Requested resource does not exist or the user does not have permission to view it. | None | | 422 | Unprocessable Entity | Unable to process the prediction data export update. | None | ## Retrieve predictions results by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/predictionResults/` Authentication requirements: `BearerAuth` Retrieve predictions results of the deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | modelId | query | string | false | The id of the model for which prediction results are being retrieved. | | start | query | string(date-time) | false | Start of the period for which prediction results are being retrieved. | | end | query | string(date-time) | false | End of the period for which prediction results are being retrieved. | | batchId | query | any | false | The id of the batch for which prediction results are being retrieved. | | actualsPresent | query | boolean | false | Filters predictions results to only those who have actuals present or with missing actuals. | | deploymentId | path | string | true | Unique identifier of the deployment. | | Accept | header | string | false | Requested MIME type for the returned data | ### Enumerated Values | Parameter | Value | | --- | --- | | Accept | [application/json, text/csv] | ### Example responses > 200 Response ``` { "properties": { "associationIdColumnNames": { "description": "List of column names used to represent an association id, which is used to unique identify individual prediction rows.", "items": { "type": "string" }, "type": "array" }, "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of prediction requests.", "items": { "properties": { "actual": { "description": "Actual value of the prediction", "oneOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" } ] }, "associationId": { "description": "Association id of the prediction, which is used to associate a prediction with its actual value and calculate accuracy.", "type": "string" }, "forecastDistance": { "description": "The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column. For time series models only.", "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "modelId": { "description": "ID of the model used to make these predictions", "type": "string" }, "predicted": { "description": "Predicted value of the prediction", "oneOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" } ] }, "timestamp": { "description": "When the prediction was made.", "format": "date-time", "type": "string" } }, "required": [ "actual", "associationId", "modelId", "predicted", "timestamp" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "associationIdColumnNames", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | string | ## The list of training data exports by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/trainingDataExports/` Authentication requirements: `BearerAuth` A list of successful training data exports. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Specifies the number of rows to skip before starting to return rows from the query. | | limit | query | integer | false | Specifies the number of rows to return after the offset. | | modelId | query | string,null | false | ID of model used for training data export. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of paginated entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The list of training data exports.", "items": { "properties": { "createdAt": { "description": "Training data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user who created the training data export.", "type": "string", "x-versionadded": "v2.33" }, "data": { "description": "A training data entry.", "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "id": { "description": "The ID of the training data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "modelPackageId": { "description": "The ID of the related model package.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "data", "id", "modelId", "modelPackageId" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "URL to the next page, or null if there is no such page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "URL to the previous page, or null if there is no such page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "Total number of entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The training data exports. | TrainingDataExportListResponse | | 403 | Forbidden | User does not have permission to access a particular deployment. | None | | 404 | Not Found | Resource not found. | None | | 422 | Unprocessable Entity | Unable to process the training data exports list retrieval due to invalid parameter values. | None | ## Create a deployment training data export by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/trainingDataExports/` Authentication requirements: `BearerAuth` Create a deployment training data export. ### Body parameter ``` { "properties": { "modelId": { "default": null, "description": "(Optional) The ID of the model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | TrainingDataExportCreatePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Training data export successfully enqueued. | None | | 403 | Forbidden | User does not have permission to create a training data export. | None | | 404 | Not Found | Resource not found. | None | | 422 | Unprocessable Entity | Unable to process the training data export request. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | URL to poll to get status of job that waits for the training data export to finish | ## Retrieve details by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/trainingDataExports/{exportId}/` Authentication requirements: `BearerAuth` Retrieve details for a single training data export. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | exportId | path | string | true | Unique identifier of the export. | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "Training data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user who created the training data export.", "type": "string", "x-versionadded": "v2.33" }, "data": { "description": "A training data entry.", "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "id": { "description": "The ID of the training data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "modelPackageId": { "description": "The ID of the related model package.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "data", "id", "modelId", "modelPackageId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A given training data export user can view is retrieved. | TrainingDataExportEntity | | 403 | Forbidden | User does not have permission to access a particular deployment or training data export. | None | | 404 | Not Found | Resource not found. | None | # Schemas ## ActualsCreatedBy ``` { "description": "The user that created actuals data export.", "properties": { "id": { "description": "The ID of user who created actuals data export.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id" ], "type": "object" } ``` The user that created actuals data export. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of user who created actuals data export. | ## ActualsDataEntry ``` { "properties": { "id": { "description": "The ID of an entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "A name of an entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of an entry. | | name | string | true | | A name of an entry. | ## ActualsDataExportEntity ``` { "properties": { "createdAt": { "description": "Actuals data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created actuals data export.", "properties": { "id": { "description": "The ID of user who created actuals data export.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id" ], "type": "object" }, "data": { "description": "An actuals data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of an entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "A name of an entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when actuals data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the actuals data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "onlyMatchedPredictions": { "default": true, "description": "If true, exports actuals with matching predictions only.", "type": "boolean", "x-versionadded": "v2.33" }, "period": { "description": "An actuals data time range definition.", "properties": { "end": { "description": "End of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "An actuals data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | Actuals data export creation timestamp. | | createdBy | ActualsCreatedBy | true | | The user that created actuals data export. | | data | [ActualsDataEntry] | false | | An actuals data export collected data entries. Available only when status is SUCCEEDED. | | error | ActualsError | false | | Error description. Appears when actuals data export job failed (status is FAILED). | | id | string | true | | The ID of the actuals data export. | | modelId | string | true | | The ID of the model (or null if not specified). | | onlyMatchedPredictions | boolean | false | | If true, exports actuals with matching predictions only. | | period | ActualsDataTimeRange | true | | An actuals data time range definition. | | status | string | true | | An actuals data export processing state. | ### Enumerated Values | Property | Value | | --- | --- | | status | [CANCELLED, CREATED, FAILED, SCHEDULED, SUCCEEDED] | ## ActualsDataTimeRange ``` { "description": "An actuals data time range definition.", "properties": { "end": { "description": "End of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" } ``` An actuals data time range definition. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string(date-time) | true | | End of the period of actuals data to collect. | | start | string(date-time) | true | | Start of the period of actuals data to collect. | ## ActualsDataUpdate ``` { "properties": { "status": { "description": "An actuals data export state overwrite value.", "enum": [ "CANCELLED" ], "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | status | string | false | | An actuals data export state overwrite value. | ### Enumerated Values | Property | Value | | --- | --- | | status | CANCELLED | ## ActualsError ``` { "description": "Error description. Appears when actuals data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" } ``` Error description. Appears when actuals data export job failed (status is FAILED). ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | A human readable error message describing failure cause. | ## ActualsExportCreatePayload ``` { "properties": { "end": { "description": "End of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "onlyMatchedPredictions": { "default": true, "description": "If true, exports actuals with matching predictions only.", "type": "boolean", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string(date-time) | true | | End of the period of actuals data to collect. | | modelId | string | false | | The ID of the model. | | onlyMatchedPredictions | boolean | false | | If true, exports actuals with matching predictions only. | | start | string(date-time) | true | | Start of the period of actuals data to collect. | ## ActualsExportListResponse ``` { "properties": { "count": { "description": "Number of paginated entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "A list of asynchronous actuals data exports.", "items": { "properties": { "createdAt": { "description": "Actuals data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created actuals data export.", "properties": { "id": { "description": "The ID of user who created actuals data export.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id" ], "type": "object" }, "data": { "description": "An actuals data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of an entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "A name of an entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when actuals data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the actuals data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "onlyMatchedPredictions": { "default": true, "description": "If true, exports actuals with matching predictions only.", "type": "boolean", "x-versionadded": "v2.33" }, "period": { "description": "An actuals data time range definition.", "properties": { "end": { "description": "End of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of actuals data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "An actuals data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "URL to the next page, or null if there is no such page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "URL to the previous page, or null if there is no such page", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "Total number of entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | Number of paginated entries. | | data | [ActualsDataExportEntity] | true | | A list of asynchronous actuals data exports. | | next | string,null | true | | URL to the next page, or null if there is no such page. | | previous | string,null | true | | URL to the previous page, or null if there is no such page | | totalCount | integer | true | minimum: 0 | Total number of entries. | ## ContextItem ``` { "properties": { "context": { "description": "Text generation context.", "type": "string" }, "link": { "description": "Text generation context link.", "type": [ "string", "null" ] } }, "required": [ "context" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | context | string | true | | Text generation context. | | link | string,null | false | | Text generation context link. | ## DataQualityItem ``` { "properties": { "actualValue": { "description": "Actual value", "type": [ "string", "null" ] }, "associationId": { "description": "Association ID of prediction.", "type": "string" }, "context": { "description": "A list of contexts used for text generation response.", "items": { "properties": { "context": { "description": "Text generation context.", "type": "string" }, "link": { "description": "Text generation context link.", "type": [ "string", "null" ] } }, "required": [ "context" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10, "type": "array" }, "metrics": { "description": "A list of collected metrics associated with the prediction instance.", "items": { "properties": { "id": { "description": "The ID of the custom metric.", "type": "string" }, "metadata": { "default": null, "description": "Custom metric value metadata.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "name": { "description": "Custom metric name.", "type": "string" }, "value": { "description": "Custom metric value.", "oneOf": [ { "type": "number" }, { "type": "string" }, { "type": "null" } ] } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "predictedValue": { "description": "Predicted value(s)", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" }, { "type": "null" } ] }, "prompt": { "description": "Prompt for text generation target types.", "type": [ "string", "null" ] }, "timestamp": { "description": "Record creation timestamp.", "format": "date-time", "type": "string" } }, "required": [ "actualValue", "associationId", "context", "metrics", "predictedValue", "prompt", "timestamp" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actualValue | string,null | true | | Actual value | | associationId | string | true | | Association ID of prediction. | | context | [ContextItem] | true | maxItems: 10 | A list of contexts used for text generation response. | | metrics | [MetricValue] | true | maxItems: 100 | A list of collected metrics associated with the prediction instance. | | predictedValue | any | true | | Predicted value(s) | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 100 | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | prompt | string,null | true | | Prompt for text generation target types. | | timestamp | string(date-time) | true | | Record creation timestamp. | ## DataQualityResponse ``` { "properties": { "count": { "description": "Number of paginated entries.", "minimum": 0, "type": "integer" }, "data": { "description": "A list of prediction data exports.", "items": { "properties": { "actualValue": { "description": "Actual value", "type": [ "string", "null" ] }, "associationId": { "description": "Association ID of prediction.", "type": "string" }, "context": { "description": "A list of contexts used for text generation response.", "items": { "properties": { "context": { "description": "Text generation context.", "type": "string" }, "link": { "description": "Text generation context link.", "type": [ "string", "null" ] } }, "required": [ "context" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10, "type": "array" }, "metrics": { "description": "A list of collected metrics associated with the prediction instance.", "items": { "properties": { "id": { "description": "The ID of the custom metric.", "type": "string" }, "metadata": { "default": null, "description": "Custom metric value metadata.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "name": { "description": "Custom metric name.", "type": "string" }, "value": { "description": "Custom metric value.", "oneOf": [ { "type": "number" }, { "type": "string" }, { "type": "null" } ] } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "predictedValue": { "description": "Predicted value(s)", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 100, "type": "array" }, { "type": "null" } ] }, "prompt": { "description": "Prompt for text generation target types.", "type": [ "string", "null" ] }, "timestamp": { "description": "Record creation timestamp.", "format": "date-time", "type": "string" } }, "required": [ "actualValue", "associationId", "context", "metrics", "predictedValue", "prompt", "timestamp" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL to the next page, or null if there is no such page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL to the previous page, or null if there is no such page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of entries.", "minimum": 0, "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | Number of paginated entries. | | data | [DataQualityItem] | true | maxItems: 100 | A list of prediction data exports. | | next | string,null | true | | The URL to the next page, or null if there is no such page. | | previous | string,null | true | | The URL to the previous page, or null if there is no such page. | | totalCount | integer | true | minimum: 0 | The total number of entries. | ## ExportCreatePayload ``` { "properties": { "augmentationType": { "default": "NO_AUGMENTATION", "description": "Indicates if prediction data is augmented with actuals or metrics", "enum": [ "NO_AUGMENTATION", "ACTUALS_AND_METRICS" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batchIds": { "description": "IDs of batches to export. Null for real-time data exports.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "end": { "description": "End of the period of prediction data to collect. Defaults to now, or a week after the start time.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of prediction data to collect. Defaults to a week before the end time.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | augmentationType | string,null | false | | Indicates if prediction data is augmented with actuals or metrics | | batchIds | [string] | false | maxItems: 100minItems: 1 | IDs of batches to export. Null for real-time data exports. | | end | string(date-time) | false | | End of the period of prediction data to collect. Defaults to now, or a week after the start time. | | modelId | string | false | | The ID of the model. | | start | string(date-time) | false | | Start of the period of prediction data to collect. Defaults to a week before the end time. | ### Enumerated Values | Property | Value | | --- | --- | | augmentationType | [NO_AUGMENTATION, ACTUALS_AND_METRICS] | ## ExportEntity ``` { "properties": { "associationIds": { "description": "Association IDs of rows to export", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.35" }, "augmentationType": { "default": "NO_AUGMENTATION", "description": "Indicates if prediction data is augmented with actuals or metrics", "enum": [ "NO_AUGMENTATION", "ACTUALS_AND_METRICS" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batches": { "description": "Metadata associated with exported batch.", "items": { "properties": { "batchId": { "description": "An ID of a batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "A name of a batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "createdAt": { "description": "Prediction data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "data": { "description": "A prediction data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when prediction data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "period": { "description": "A prediction data time range definition.", "properties": { "end": { "description": "End of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "A prediction data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associationIds | [string] | false | maxItems: 100minItems: 1 | Association IDs of rows to export | | augmentationType | string,null | false | | Indicates if prediction data is augmented with actuals or metrics | | batches | [PredictionDataBatch] | false | maxItems: 100minItems: 1 | Metadata associated with exported batch. | | createdAt | string(date-time) | true | | Prediction data export creation timestamp. | | createdBy | string | true | | The user that created prediction data export. | | data | [PredictionDataEntry] | false | | A prediction data export collected data entries. Available only when status is SUCCEEDED. | | error | ExportError | false | | Error description. Appears when prediction data export job failed (status is FAILED). | | id | string | true | | The ID of the prediction data export. | | modelId | string | true | | The ID of the model (or null if not specified). | | period | PredictionDataTimeRange | true | | A prediction data time range definition. | | status | string | true | | A prediction data export processing state. | ### Enumerated Values | Property | Value | | --- | --- | | augmentationType | [NO_AUGMENTATION, ACTUALS_AND_METRICS] | | status | [CANCELLED, CREATED, FAILED, SCHEDULED, SUCCEEDED] | ## ExportError ``` { "description": "Error description. Appears when prediction data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" } ``` Error description. Appears when prediction data export job failed (status is FAILED). ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | A human readable error message describing failure cause. | ## MetricValue ``` { "properties": { "id": { "description": "The ID of the custom metric.", "type": "string" }, "metadata": { "default": null, "description": "Custom metric value metadata.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "name": { "description": "Custom metric name.", "type": "string" }, "value": { "description": "Custom metric value.", "oneOf": [ { "type": "number" }, { "type": "string" }, { "type": "null" } ] } }, "required": [ "id", "name", "value" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom metric. | | metadata | string,null | false | | Custom metric value metadata. | | name | string | true | | Custom metric name. | | value | any | true | | Custom metric value. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## PredictionDataBatch ``` { "properties": { "batchId": { "description": "An ID of a batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "A name of a batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchId | string | true | | An ID of a batch. | | batchName | string | true | | A name of a batch. | ## PredictionDataEntry ``` { "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the AI Catalog entry. | | name | string | true | | The name of the AI Catalog entry. | ## PredictionDataTimeRange ``` { "description": "A prediction data time range definition.", "properties": { "end": { "description": "End of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" } ``` A prediction data time range definition. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string(date-time) | true | | End of the period of prediction data to collect. | | start | string(date-time) | true | | Start of the period of prediction data to collect. | ## PredictionDataUpdate ``` { "properties": { "status": { "description": "A prediction data export processing state.", "enum": [ "CANCELLED" ], "type": "string", "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | status | string | false | | A prediction data export processing state. | ### Enumerated Values | Property | Value | | --- | --- | | status | CANCELLED | ## PredictionExportListResponse ``` { "properties": { "count": { "description": "Number of paginated entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "A list of prediction data exports.", "items": { "properties": { "associationIds": { "description": "Association IDs of rows to export", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.35" }, "augmentationType": { "default": "NO_AUGMENTATION", "description": "Indicates if prediction data is augmented with actuals or metrics", "enum": [ "NO_AUGMENTATION", "ACTUALS_AND_METRICS" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "batches": { "description": "Metadata associated with exported batch.", "items": { "properties": { "batchId": { "description": "An ID of a batch.", "type": "string", "x-versionadded": "v2.33" }, "batchName": { "description": "A name of a batch.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "batchId", "batchName" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "createdAt": { "description": "Prediction data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user that created prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "data": { "description": "A prediction data export collected data entries. Available only when status is SUCCEEDED.", "items": { "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "error": { "description": "Error description. Appears when prediction data export job failed (status is FAILED).", "properties": { "message": { "description": "A human readable error message describing failure cause.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "message" ], "type": "object" }, "id": { "description": "The ID of the prediction data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model (or null if not specified).", "type": "string", "x-versionadded": "v2.33" }, "period": { "description": "A prediction data time range definition.", "properties": { "end": { "description": "End of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "start": { "description": "Start of the period of prediction data to collect.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "end", "start" ], "type": "object" }, "status": { "description": "A prediction data export processing state.", "enum": [ "CANCELLED", "CREATED", "FAILED", "SCHEDULED", "SUCCEEDED" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "id", "modelId", "period", "status" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "URL to the next page, or null if there is no such page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "URL to the previous page, or null if there is no such page", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "Total number of entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | Number of paginated entries. | | data | [ExportEntity] | true | | A list of prediction data exports. | | next | string,null | true | | URL to the next page, or null if there is no such page. | | previous | string,null | true | | URL to the previous page, or null if there is no such page | | totalCount | integer | true | minimum: 0 | Total number of entries. | ## PredictionResultResponse ``` { "properties": { "actual": { "description": "Actual value of the prediction", "oneOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" } ] }, "associationId": { "description": "Association id of the prediction, which is used to associate a prediction with its actual value and calculate accuracy.", "type": "string" }, "forecastDistance": { "description": "The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column. For time series models only.", "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "modelId": { "description": "ID of the model used to make these predictions", "type": "string" }, "predicted": { "description": "Predicted value of the prediction", "oneOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" } ] }, "timestamp": { "description": "When the prediction was made.", "format": "date-time", "type": "string" } }, "required": [ "actual", "associationId", "modelId", "predicted", "timestamp" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | actual | any | true | | Actual value of the prediction | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associationId | string | true | | Association id of the prediction, which is used to associate a prediction with its actual value and calculate accuracy. | | forecastDistance | integer,null | false | | The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column. For time series models only. | | modelId | string | true | | ID of the model used to make these predictions | | predicted | any | true | | Predicted value of the prediction | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timestamp | string(date-time) | true | | When the prediction was made. | ## PredictionResultsListResponse ``` { "properties": { "associationIdColumnNames": { "description": "List of column names used to represent an association id, which is used to unique identify individual prediction rows.", "items": { "type": "string" }, "type": "array" }, "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array of prediction requests.", "items": { "properties": { "actual": { "description": "Actual value of the prediction", "oneOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" } ] }, "associationId": { "description": "Association id of the prediction, which is used to associate a prediction with its actual value and calculate accuracy.", "type": "string" }, "forecastDistance": { "description": "The number of time units this prediction is away from the forecastPoint. The unit of time is determined by the timeUnit of the datetime partition column. For time series models only.", "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "modelId": { "description": "ID of the model used to make these predictions", "type": "string" }, "predicted": { "description": "Predicted value of the prediction", "oneOf": [ { "type": "integer" }, { "type": "number" }, { "type": "string" } ] }, "timestamp": { "description": "When the prediction was made.", "format": "date-time", "type": "string" } }, "required": [ "actual", "associationId", "modelId", "predicted", "timestamp" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "associationIdColumnNames", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | associationIdColumnNames | [string] | true | | List of column names used to represent an association id, which is used to unique identify individual prediction rows. | | count | integer | false | | The number of items returned on this page. | | data | [PredictionResultResponse] | true | | An array of prediction requests. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## TrainingDataEntry ``` { "description": "A training data entry.", "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" } ``` A training data entry. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the AI Catalog entry. | | name | string | true | | The name of the AI Catalog entry. | ## TrainingDataExportCreatePayload ``` { "properties": { "modelId": { "default": null, "description": "(Optional) The ID of the model.", "type": [ "string", "null" ], "x-versionadded": "v2.33" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string,null | false | | (Optional) The ID of the model. | ## TrainingDataExportEntity ``` { "properties": { "createdAt": { "description": "Training data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user who created the training data export.", "type": "string", "x-versionadded": "v2.33" }, "data": { "description": "A training data entry.", "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "id": { "description": "The ID of the training data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "modelPackageId": { "description": "The ID of the related model package.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "data", "id", "modelId", "modelPackageId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | Training data export creation timestamp. | | createdBy | string | true | | The user who created the training data export. | | data | TrainingDataEntry | true | | A training data entry. | | id | string | true | | The ID of the training data export. | | modelId | string | true | | The ID of the model. | | modelPackageId | string | true | | The ID of the related model package. | ## TrainingDataExportListResponse ``` { "properties": { "count": { "description": "Number of paginated entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The list of training data exports.", "items": { "properties": { "createdAt": { "description": "Training data export creation timestamp.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "createdBy": { "description": "The user who created the training data export.", "type": "string", "x-versionadded": "v2.33" }, "data": { "description": "A training data entry.", "properties": { "id": { "description": "The ID of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" }, "name": { "description": "The name of the AI Catalog entry.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "id", "name" ], "type": "object" }, "id": { "description": "The ID of the training data export.", "type": "string", "x-versionadded": "v2.33" }, "modelId": { "description": "The ID of the model.", "type": "string", "x-versionadded": "v2.33" }, "modelPackageId": { "description": "The ID of the related model package.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "createdAt", "createdBy", "data", "id", "modelId", "modelPackageId" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "URL to the next page, or null if there is no such page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "URL to the previous page, or null if there is no such page.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "Total number of entries.", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | Number of paginated entries. | | data | [TrainingDataExportEntity] | true | | The list of training data exports. | | next | string,null | true | | URL to the next page, or null if there is no such page. | | previous | string,null | true | | URL to the previous page, or null if there is no such page. | | totalCount | integer | true | minimum: 0 | Total number of entries. | --- # Data drift URL: https://docs.datarobot.com/en/docs/api/reference/public-api/observability_drift.html > Use the endpoints described below to manage drift. When deploying a model, there is a chance that the dataset used for training and validation differs from the prediction data. DataRobot monitors both target and feature drift information. # Data drift Use the endpoints described below to manage drift. When deploying a model, there is a chance that the dataset used for training and validation differs from the prediction data. DataRobot monitors both target and feature drift information. ## Retrieve feature drift scores by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/featureDrift/` Authentication requirements: `BearerAuth` Retrieve drift scores for features of the deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | modelId | query | string | false | ID of the model in the deployment. If not set, defaults to the deployment current model. | | metric | query | string | false | Name of the metric used to calculate the drift. Can be one of psi, kl_divergence, dissimilarity, hellinger, and js_divergence. Defaults to psi. | | offset | query | integer | false | The number of features to skip, defaults to 0. | | limit | query | integer | false | The number of features to return, defaults to 25. | | segmentAttribute | query | string | false | The name of a segment attribute used for segment analysis. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | metric | [psi, kl_divergence, dissimilarity, hellinger, js_divergence] | ### Example responses > 200 Response ``` { "properties": { "batchId": { "default": [], "description": "The id of the batch for which metrics are being retrieved.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 25, "type": "array" } ] }, "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array [DriftObject], each in the form described below", "items": { "properties": { "baselineSampleSize": { "description": "The sample size of the training data.", "type": "integer" }, "driftScore": { "description": "The drift score for this feature.", "type": [ "number", "null" ] }, "featureImpact": { "description": "The feature impact score for this feature.", "type": [ "number", "null" ] }, "name": { "description": "The name of the feature.", "type": "string" }, "sampleSize": { "description": "The number of predictions used to compute the drift score.", "type": "integer" }, "type": { "description": "Type of the feature.", "enum": [ "numeric", "categorical", "text" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "baselineSampleSize", "driftScore", "featureImpact", "name", "sampleSize", "type" ], "type": "object" }, "type": "array" }, "metric": { "description": "Metric used to calculate drift score.", "enum": [ "psi", "kl_divergence", "dissimilarity", "hellinger", "js_divergence" ], "type": "string" }, "modelId": { "description": "The id of the model for which the features drift is being retrieved.", "type": "string" }, "next": { "description": "A URL pointing to the next page (if null, there is no next page)", "type": [ "string", "null" ] }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "previous": { "description": "A URL pointing to the previous page (if null, there is no previous page)", "type": [ "string", "null" ] }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ] }, "segmentValue": { "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ] } }, "required": [ "data", "modelId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Features drift over specified time period retrieved. | FeatureDriftResponse | ## Retrieve drift over batch info by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/featureDriftOverBatch/` Authentication requirements: `BearerAuth` Retrieve drift over batch info for a feature of the deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | segmentAttribute | query | string,null | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | featureNames | query | any | true | List of feature names, limited to two per request. | | driftMetric | query | string,null | false | The metric used to calculate data drift scores. | | modelId | query | string | false | The id of the model for which metrics are being retrieved. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | driftMetric | [psi, kl_divergence, dissimilarity, hellinger, js_divergence] | ### Example responses > 200 Response ``` { "properties": { "buckets": { "description": "A list of buckets to display feature drift over batch.", "items": { "properties": { "baselineSampleSize": { "description": "The sample size in the baseline used to calculate drift score.", "type": "integer", "x-versionadded": "v2.33" }, "batch": { "description": "Info of the batch associated with the bucket.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "driftScore": { "description": "Drift score of the feature.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "featureImpact": { "description": "The feature impact score of the feature.", "type": "number", "x-versionadded": "v2.33" }, "featureName": { "description": "Feature name.", "type": "string", "x-versionadded": "v2.33" }, "sampleSize": { "description": "The sample size in the batch used to calculate drift score.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "baselineSampleSize", "batch", "driftScore", "featureImpact", "featureName", "sampleSize" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "driftMetric": { "description": "The metric used to calculate data drift scores.", "enum": [ "psi", "kl_divergence", "dissimilarity", "hellinger", "js_divergence" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "buckets" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Drift over batch info for a feature of the deployment. | FeatureDriftOverBatchResponse | ## Retrieve feature drift scores over space through geospatial monitoring by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/featureDriftOverSpace/` Authentication requirements: `BearerAuth` Retrieve drift scores for a feature of the deployment over the geospatial feature data. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | geoFeatureName | query | string | false | The name of the geospatial feature. Segmented analysis must be enabled for the feature specified. | | featureName | query | string | false | The name of the feature to retrieve drift scores for. Defaults to the target or, if there is no target, the most important feature. | | modelId | query | string | false | The ID of the model that feature drift is being retrieved from. | | metric | query | string | false | The metric used to calculate drift score. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | metric | [psi, klDivergence, dissimilarity, hellinger, jsDivergence] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "An array [DriftObject], each in the form described below", "items": { "properties": { "baselineSampleSize": { "description": "The baseline sample size for the hexagon.", "type": "integer" }, "driftScore": { "description": "The drift score for this feature.", "type": "number" }, "hexagon": { "description": "h3 hexagon.", "type": "string" }, "sampleSize": { "description": "The sample size for the hexagon.", "type": "integer" } }, "required": [ "baselineSampleSize", "driftScore", "hexagon", "sampleSize" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" }, "featureName": { "description": "The name of the feature to retrieve drift scores for. Defaults to the target or, if there is no target, the most important feature.", "type": "string" }, "geoFeatureName": { "description": "The name of the geospatial feature. Segmented analysis must be enabled for the feature specified.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } }, "required": [ "data", "featureName", "geoFeatureName" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The retrieved drift scores for the specified feature over the specified geospatial feature data. | FeatureDriftOverSpaceResponse | ## Retrieve drift over time info by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/featureDriftOverTime/` Authentication requirements: `BearerAuth` Retrieve drift over time info for a feature of the deployment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | bucketSize | query | string(duration) | false | The time duration of a bucket. Needs to be multiple of one hour. Can not be longer than the total length of the period. If not set, a default value will be calculated based on the start and end time. | | modelId | query | string | false | The id of the model for which the features drift is being retrieved. | | featureNames | query | any | true | List of feature names, limited to two per request. | | metric | query | string | false | Name of the metric used to calculate the drift. Can be one of psi, kl_divergence, dissimilarity, hellinger, and js_divergence. Defaults to psi. | | segmentAttribute | query | string,null | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | metric | [psi, kl_divergence, dissimilarity, hellinger, js_divergence] | ### Example responses > 200 Response ``` { "properties": { "buckets": { "description": "A list of aggregated drift scores by feature over a given period.", "items": { "properties": { "baselineSampleSize": { "description": "The sample size of the training data used during model creation", "type": "integer" }, "driftScore": { "description": "The aggregated drift score for the target.", "type": [ "number", "null" ] }, "featureImpact": { "description": "The feature impact score of the feature.", "type": "number" }, "featureName": { "description": "Name of the feature.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "The sample size in the query period used to calculate drift score.", "type": "integer" } }, "required": [ "baselineSampleSize", "driftScore", "featureImpact", "featureName", "period", "sampleSize" ], "type": "object" }, "type": "array" }, "features": { "description": "A list of the requested features and their feature type.", "items": { "properties": { "featureName": { "description": "Name of the requested feature.", "type": "string" }, "featureType": { "description": "Data type of the requested feature.", "enum": [ "numeric", "categorical", "text" ], "type": "string" } }, "required": [ "featureName", "featureType" ], "type": "object" }, "type": "array" }, "metric": { "description": "Name of requested metric.", "type": "string" }, "summaries": { "description": "A list of aggregated drift scores by feature over a given period.", "items": { "properties": { "featureImpact": { "description": "The feature impact score of the feature.", "type": "number" }, "featureName": { "description": "Name of the feature.", "type": "string" } }, "required": [ "featureImpact", "featureName" ], "type": "object" }, "type": "array" } }, "required": [ "buckets", "features", "metric", "summaries" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Feature drift over time info of the deployment retrieved. | FeatureDriftOverTimeResponse | | 400 | Bad Request | Request invalid, refer to messages for detail. | None | | 403 | Forbidden | Model Deployments and/or Monitoring are not enabled. | None | | 404 | Not Found | Either the deployment does not exist or user does not have permission to view the deployment. | None | ## Retrieve prediction metadata over batches by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/predictionsOverBatch/` Authentication requirements: `BearerAuth` Retrieve metrics about predictions, such as row count, mean predicted value, mean probabilities, and class distribution, over batches. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | segmentAttribute | query | string,null | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | modelId | query | string | false | The id of the model for which metrics are being retrieved. | | includePercentiles | query | string | false | Include percentiles in the response, only applicable to deployments with binary classification, location and regression target. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | includePercentiles | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "baselines": { "description": "Target baselines", "items": { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "modelId": { "description": "ID of the model", "type": "string" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "modelId", "rowCount" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "buckets": { "description": "Predictions over batch buckets", "items": { "properties": { "batch": { "description": "Info of the batch associated with the bucket.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.33" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.33" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.33" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batch", "rowCount" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "baselines", "buckets" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Predictions over batch info retrieved. | PredictionsOverBatchResponse | ## Retrieve predictions stats over space through geospatial monitoring by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/predictionsOverSpace/` Authentication requirements: `BearerAuth` Retrieve predictions stats for the deployment over the geospatial feature data. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | geoFeatureName | query | string | false | The name of the geospatial feature. Segmented analysis must be enabled for the feature specified. | | modelId | query | string | false | The ID of the model that feature drift is being retrieved from. | | targetClass | query | any | false | Target class to filter out results. | | includePercentiles | query | string | false | Include percentiles in the response, only applicable to deployments with binary classification, location and regression target. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | includePercentiles | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "baselines": { "description": "Baseline predictions per geospatial hexagon.", "items": { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "hexagon": { "description": "h3 hexagon.", "type": [ "string", "null" ] }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "rowCount" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" }, "buckets": { "description": "Predictions per geospatial hexagon.", "items": { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "hexagon": { "description": "h3 hexagon.", "type": [ "string", "null" ] }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "rowCount" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" }, "geoFeatureName": { "description": "The name of the geospatial feature. Segmented analysis must be enabled for the feature specified.", "type": "string" }, "modelId": { "description": "The ID of the model that predictions are being retrieved from.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } }, "required": [ "baselines", "buckets", "geoFeatureName" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The retrieved prediction stats for the specified feature over the specified geospatial feature data. | PredictionsOverSpaceResponse | ## Retrieve metrics about predictions over time by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/predictionsOverTime/` Authentication requirements: `BearerAuth` Retrieve metrics about predictions, such as row count, mean predicted value, mean probabilities, and class distribution, over a specific time range. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | bucketSize | query | string | false | Time duration of buckets | | segmentAttribute | query | string,null | false | The name of the segment on which segment analysis is being performed. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | modelId | query | any | false | The ID of the models for which metrics are being retrieved. | | targetClass | query | any | false | Target class to filter out results. | | includePercentiles | query | string | false | Include percentiles in the response, only applicable to deployments with binary classification, location and regression target. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | bucketSize | [PT1H, P1D, P7D, P1M] | | includePercentiles | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "baselines": { "description": "Target baselines", "items": { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "modelId": { "description": "ID of the model", "type": "string" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "modelId", "rowCount" ], "type": "object" }, "type": "array" }, "buckets": { "description": "Predictions over time buckets", "items": { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "modelId": { "description": "ID of the model", "type": "string" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "modelId", "period", "rowCount" ], "type": "object" }, "type": "array" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "baselines", "buckets" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Predictions over time info retrieved. | PredictionsOverTimeResponse | ## Retrieve target drift by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/targetDrift/` Authentication requirements: `BearerAuth` Retrieve target drift data. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | modelId | query | string | false | An ID of the model in the deployment. If not set, defaults to the deployment current model. | | metric | query | string | false | Metric used to calculate drift score. | | segmentAttribute | query | string | false | The name of a segment attribute used for segment analysis. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | metric | [psi, kl_divergence, dissimilarity, hellinger, js_divergence] | ### Example responses > 200 Response ``` { "properties": { "baselineSampleSize": { "description": "sample size of the training data.", "type": "integer" }, "batchId": { "default": [], "description": "The id of the batch for which metrics are being retrieved.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 25, "type": "array" } ] }, "driftScore": { "description": "drift score for the target.", "type": [ "number", "null" ] }, "metric": { "description": "Metric used to calculate drift score.", "enum": [ "psi", "kl_divergence", "dissimilarity", "hellinger", "js_divergence" ], "type": "string" }, "modelId": { "description": "id of the model for which data drift is being retrieved.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "number of predictions used to compute the drift score.", "type": "integer" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ] }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "targetName": { "description": "name of the target feature.", "type": "string" }, "type": { "description": "Type of the feature.", "enum": [ "numeric", "categorical", "text" ], "type": "string" } }, "required": [ "baselineSampleSize", "driftScore", "modelId", "sampleSize", "targetName", "type" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Target drift over specified time period retrieved. | TargetDriftResponse | # Schemas ## ClassDistribution ``` { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | className | string | true | | Name of the class | | count | integer | true | | Count of rows labeled with a class in the bucket | | percent | number | true | | Percent of rows labeled with a class in the bucket | ## DriftBatch ``` { "description": "Info of the batch associated with the bucket.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" } ``` Info of the batch associated with the bucket. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | earliestPredictionTimestamp | string(date-time) | true | | Earliest prediction timestamp of a batch. | | id | string | true | | Batch ID. | | latestPredictionTimestamp | string,null(date-time) | true | | Latest prediction timestamp of a batch. | | name | string | true | | Batch name. | ## FeatureDrift ``` { "properties": { "baselineSampleSize": { "description": "The sample size of the training data.", "type": "integer" }, "driftScore": { "description": "The drift score for this feature.", "type": [ "number", "null" ] }, "featureImpact": { "description": "The feature impact score for this feature.", "type": [ "number", "null" ] }, "name": { "description": "The name of the feature.", "type": "string" }, "sampleSize": { "description": "The number of predictions used to compute the drift score.", "type": "integer" }, "type": { "description": "Type of the feature.", "enum": [ "numeric", "categorical", "text" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "baselineSampleSize", "driftScore", "featureImpact", "name", "sampleSize", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineSampleSize | integer | true | | The sample size of the training data. | | driftScore | number,null | true | | The drift score for this feature. | | featureImpact | number,null | true | | The feature impact score for this feature. | | name | string | true | | The name of the feature. | | sampleSize | integer | true | | The number of predictions used to compute the drift score. | | type | string | true | | Type of the feature. | ### Enumerated Values | Property | Value | | --- | --- | | type | [numeric, categorical, text] | ## FeatureDriftOverBatchBucket ``` { "properties": { "baselineSampleSize": { "description": "The sample size in the baseline used to calculate drift score.", "type": "integer", "x-versionadded": "v2.33" }, "batch": { "description": "Info of the batch associated with the bucket.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "driftScore": { "description": "Drift score of the feature.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "featureImpact": { "description": "The feature impact score of the feature.", "type": "number", "x-versionadded": "v2.33" }, "featureName": { "description": "Feature name.", "type": "string", "x-versionadded": "v2.33" }, "sampleSize": { "description": "The sample size in the batch used to calculate drift score.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "baselineSampleSize", "batch", "driftScore", "featureImpact", "featureName", "sampleSize" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineSampleSize | integer | true | | The sample size in the baseline used to calculate drift score. | | batch | DriftBatch | true | | Info of the batch associated with the bucket. | | driftScore | number,null | true | | Drift score of the feature. | | featureImpact | number | true | | The feature impact score of the feature. | | featureName | string | true | | Feature name. | | sampleSize | integer | true | | The sample size in the batch used to calculate drift score. | ## FeatureDriftOverBatchResponse ``` { "properties": { "buckets": { "description": "A list of buckets to display feature drift over batch.", "items": { "properties": { "baselineSampleSize": { "description": "The sample size in the baseline used to calculate drift score.", "type": "integer", "x-versionadded": "v2.33" }, "batch": { "description": "Info of the batch associated with the bucket.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "driftScore": { "description": "Drift score of the feature.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "featureImpact": { "description": "The feature impact score of the feature.", "type": "number", "x-versionadded": "v2.33" }, "featureName": { "description": "Feature name.", "type": "string", "x-versionadded": "v2.33" }, "sampleSize": { "description": "The sample size in the batch used to calculate drift score.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "baselineSampleSize", "batch", "driftScore", "featureImpact", "featureName", "sampleSize" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "driftMetric": { "description": "The metric used to calculate data drift scores.", "enum": [ "psi", "kl_divergence", "dissimilarity", "hellinger", "js_divergence" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "buckets" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [FeatureDriftOverBatchBucket] | true | | A list of buckets to display feature drift over batch. | | driftMetric | string | false | | The metric used to calculate data drift scores. | ### Enumerated Values | Property | Value | | --- | --- | | driftMetric | [psi, kl_divergence, dissimilarity, hellinger, js_divergence] | ## FeatureDriftOverSpaceBucket ``` { "properties": { "baselineSampleSize": { "description": "The baseline sample size for the hexagon.", "type": "integer" }, "driftScore": { "description": "The drift score for this feature.", "type": "number" }, "hexagon": { "description": "h3 hexagon.", "type": "string" }, "sampleSize": { "description": "The sample size for the hexagon.", "type": "integer" } }, "required": [ "baselineSampleSize", "driftScore", "hexagon", "sampleSize" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineSampleSize | integer | true | | The baseline sample size for the hexagon. | | driftScore | number | true | | The drift score for this feature. | | hexagon | string | true | | h3 hexagon. | | sampleSize | integer | true | | The sample size for the hexagon. | ## FeatureDriftOverSpaceResponse ``` { "properties": { "data": { "description": "An array [DriftObject], each in the form described below", "items": { "properties": { "baselineSampleSize": { "description": "The baseline sample size for the hexagon.", "type": "integer" }, "driftScore": { "description": "The drift score for this feature.", "type": "number" }, "hexagon": { "description": "h3 hexagon.", "type": "string" }, "sampleSize": { "description": "The sample size for the hexagon.", "type": "integer" } }, "required": [ "baselineSampleSize", "driftScore", "hexagon", "sampleSize" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" }, "featureName": { "description": "The name of the feature to retrieve drift scores for. Defaults to the target or, if there is no target, the most important feature.", "type": "string" }, "geoFeatureName": { "description": "The name of the geospatial feature. Segmented analysis must be enabled for the feature specified.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } }, "required": [ "data", "featureName", "geoFeatureName" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [FeatureDriftOverSpaceBucket] | true | maxItems: 10000 | An array [DriftObject], each in the form described below | | featureName | string | true | | The name of the feature to retrieve drift scores for. Defaults to the target or, if there is no target, the most important feature. | | geoFeatureName | string | true | | The name of the geospatial feature. Segmented analysis must be enabled for the feature specified. | | period | TimeRange | false | | An object with the keys "start" and "end" defining the period. | ## FeatureDriftOverTimeBucket ``` { "properties": { "baselineSampleSize": { "description": "The sample size of the training data used during model creation", "type": "integer" }, "driftScore": { "description": "The aggregated drift score for the target.", "type": [ "number", "null" ] }, "featureImpact": { "description": "The feature impact score of the feature.", "type": "number" }, "featureName": { "description": "Name of the feature.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "The sample size in the query period used to calculate drift score.", "type": "integer" } }, "required": [ "baselineSampleSize", "driftScore", "featureImpact", "featureName", "period", "sampleSize" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineSampleSize | integer | true | | The sample size of the training data used during model creation | | driftScore | number,null | true | | The aggregated drift score for the target. | | featureImpact | number | true | | The feature impact score of the feature. | | featureName | string | true | | Name of the feature. | | period | TimeRange | true | | An object with the keys "start" and "end" defining the period. | | sampleSize | integer | true | | The sample size in the query period used to calculate drift score. | ## FeatureDriftOverTimeFeature ``` { "properties": { "featureName": { "description": "Name of the requested feature.", "type": "string" }, "featureType": { "description": "Data type of the requested feature.", "enum": [ "numeric", "categorical", "text" ], "type": "string" } }, "required": [ "featureName", "featureType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureName | string | true | | Name of the requested feature. | | featureType | string | true | | Data type of the requested feature. | ### Enumerated Values | Property | Value | | --- | --- | | featureType | [numeric, categorical, text] | ## FeatureDriftOverTimeResponse ``` { "properties": { "buckets": { "description": "A list of aggregated drift scores by feature over a given period.", "items": { "properties": { "baselineSampleSize": { "description": "The sample size of the training data used during model creation", "type": "integer" }, "driftScore": { "description": "The aggregated drift score for the target.", "type": [ "number", "null" ] }, "featureImpact": { "description": "The feature impact score of the feature.", "type": "number" }, "featureName": { "description": "Name of the feature.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "The sample size in the query period used to calculate drift score.", "type": "integer" } }, "required": [ "baselineSampleSize", "driftScore", "featureImpact", "featureName", "period", "sampleSize" ], "type": "object" }, "type": "array" }, "features": { "description": "A list of the requested features and their feature type.", "items": { "properties": { "featureName": { "description": "Name of the requested feature.", "type": "string" }, "featureType": { "description": "Data type of the requested feature.", "enum": [ "numeric", "categorical", "text" ], "type": "string" } }, "required": [ "featureName", "featureType" ], "type": "object" }, "type": "array" }, "metric": { "description": "Name of requested metric.", "type": "string" }, "summaries": { "description": "A list of aggregated drift scores by feature over a given period.", "items": { "properties": { "featureImpact": { "description": "The feature impact score of the feature.", "type": "number" }, "featureName": { "description": "Name of the feature.", "type": "string" } }, "required": [ "featureImpact", "featureName" ], "type": "object" }, "type": "array" } }, "required": [ "buckets", "features", "metric", "summaries" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [FeatureDriftOverTimeBucket] | true | | A list of aggregated drift scores by feature over a given period. | | features | [FeatureDriftOverTimeFeature] | true | | A list of the requested features and their feature type. | | metric | string | true | | Name of requested metric. | | summaries | [FeatureDriftOverTimeSummary] | true | | A list of aggregated drift scores by feature over a given period. | ## FeatureDriftOverTimeSummary ``` { "properties": { "featureImpact": { "description": "The feature impact score of the feature.", "type": "number" }, "featureName": { "description": "Name of the feature.", "type": "string" } }, "required": [ "featureImpact", "featureName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | featureImpact | number | true | | The feature impact score of the feature. | | featureName | string | true | | Name of the feature. | ## FeatureDriftResponse ``` { "properties": { "batchId": { "default": [], "description": "The id of the batch for which metrics are being retrieved.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 25, "type": "array" } ] }, "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "An array [DriftObject], each in the form described below", "items": { "properties": { "baselineSampleSize": { "description": "The sample size of the training data.", "type": "integer" }, "driftScore": { "description": "The drift score for this feature.", "type": [ "number", "null" ] }, "featureImpact": { "description": "The feature impact score for this feature.", "type": [ "number", "null" ] }, "name": { "description": "The name of the feature.", "type": "string" }, "sampleSize": { "description": "The number of predictions used to compute the drift score.", "type": "integer" }, "type": { "description": "Type of the feature.", "enum": [ "numeric", "categorical", "text" ], "type": "string", "x-versionadded": "v2.30" } }, "required": [ "baselineSampleSize", "driftScore", "featureImpact", "name", "sampleSize", "type" ], "type": "object" }, "type": "array" }, "metric": { "description": "Metric used to calculate drift score.", "enum": [ "psi", "kl_divergence", "dissimilarity", "hellinger", "js_divergence" ], "type": "string" }, "modelId": { "description": "The id of the model for which the features drift is being retrieved.", "type": "string" }, "next": { "description": "A URL pointing to the next page (if null, there is no next page)", "type": [ "string", "null" ] }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "previous": { "description": "A URL pointing to the previous page (if null, there is no previous page)", "type": [ "string", "null" ] }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ] }, "segmentValue": { "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ] } }, "required": [ "data", "modelId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchId | any | false | | The id of the batch for which metrics are being retrieved. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 25 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [FeatureDrift] | true | | An array [DriftObject], each in the form described below | | metric | string | false | | Metric used to calculate drift score. | | modelId | string | true | | The id of the model for which the features drift is being retrieved. | | next | string,null | false | | A URL pointing to the next page (if null, there is no next page) | | period | TimeRange | false | | An object with the keys "start" and "end" defining the period. | | previous | string,null | false | | A URL pointing to the previous page (if null, there is no previous page) | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | ### Enumerated Values | Property | Value | | --- | --- | | metric | [psi, kl_divergence, dissimilarity, hellinger, js_divergence] | ## GeoPoint ``` { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" } ``` Geo centroid. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | latitude | number | true | | Latitude. | | longitude | number | true | | Longitude. | ## MeanProbability ``` { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | className | string | true | | Name of the class | | value | number | true | | Mean predicted probability for a class for all rows in the bucket | ## Percentile ``` { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | geoCentroid | GeoPoint | false | | Geo centroid. | | percent | number | true | | Percent of the percentile | | value | number,null | false | | Predicted value or probability at a percentile | ## PredictionsOverBatchBucket ``` { "properties": { "batch": { "description": "Info of the batch associated with the bucket.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.33" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.33" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.33" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batch", "rowCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batch | DriftBatch | true | | Info of the batch associated with the bucket. | | classDistribution | [ClassDistribution] | false | maxItems: 10000 | Class distribution for all classes in the bucket, only for classification deployments. | | meanGeoCentroid | GeoPoint | false | | Geo centroid. | | meanPredictedValue | number,null | false | | Mean predicted value for all rows in the bucket, only for regression deployments. | | meanProbabilities | [MeanProbability] | false | maxItems: 10000 | Mean predicted probabilities for all classes in the bucket, only for classification deployments | | percentiles | [Percentile] | false | maxItems: 10 | Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments. | | predictionsWarningCount | integer,null | false | | The number of predictions with warning in the bucket | | rowCount | integer,null | true | | Number of rows in the bucket. | ## PredictionsOverBatchResponse ``` { "properties": { "baselines": { "description": "Target baselines", "items": { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "modelId": { "description": "ID of the model", "type": "string" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "modelId", "rowCount" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "buckets": { "description": "Predictions over batch buckets", "items": { "properties": { "batch": { "description": "Info of the batch associated with the bucket.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.33" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array", "x-versionadded": "v2.33" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.33" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batch", "rowCount" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "baselines", "buckets" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselines | [TargetBaseline] | true | | Target baselines | | buckets | [PredictionsOverBatchBucket] | true | | Predictions over batch buckets | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | ## PredictionsOverSpaceBucket ``` { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "hexagon": { "description": "h3 hexagon.", "type": [ "string", "null" ] }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "rowCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classDistribution | [ClassDistribution] | false | maxItems: 10000 | Class distribution for all classes in the bucket, only for classification deployments. | | hexagon | string,null | false | | h3 hexagon. | | meanGeoCentroid | GeoPoint | false | | Geo centroid. | | meanPredictedValue | number,null | false | | Mean predicted value for all rows in the bucket, only for regression deployments. | | meanProbabilities | [MeanProbability] | false | maxItems: 10000 | Mean predicted probabilities for all classes in the bucket, only for classification deployments | | percentiles | [Percentile] | false | maxItems: 10 | Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments. | | predictionsWarningCount | integer,null | false | | The number of predictions with warning in the bucket | | rowCount | integer,null | true | | Number of rows in the bucket. | ## PredictionsOverSpaceResponse ``` { "properties": { "baselines": { "description": "Baseline predictions per geospatial hexagon.", "items": { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "hexagon": { "description": "h3 hexagon.", "type": [ "string", "null" ] }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "rowCount" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" }, "buckets": { "description": "Predictions per geospatial hexagon.", "items": { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "hexagon": { "description": "h3 hexagon.", "type": [ "string", "null" ] }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "rowCount" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" }, "geoFeatureName": { "description": "The name of the geospatial feature. Segmented analysis must be enabled for the feature specified.", "type": "string" }, "modelId": { "description": "The ID of the model that predictions are being retrieved from.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } }, "required": [ "baselines", "buckets", "geoFeatureName" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselines | [PredictionsOverSpaceBucket] | true | maxItems: 10000 | Baseline predictions per geospatial hexagon. | | buckets | [PredictionsOverSpaceBucket] | true | maxItems: 10000 | Predictions per geospatial hexagon. | | geoFeatureName | string | true | | The name of the geospatial feature. Segmented analysis must be enabled for the feature specified. | | modelId | string | false | | The ID of the model that predictions are being retrieved from. | | period | TimeRange | false | | An object with the keys "start" and "end" defining the period. | ## PredictionsOverTimeBucket ``` { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "modelId": { "description": "ID of the model", "type": "string" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "modelId", "period", "rowCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classDistribution | [ClassDistribution] | false | maxItems: 10000 | Class distribution for all classes in the bucket, only for classification deployments. | | meanGeoCentroid | GeoPoint | false | | Geo centroid. | | meanPredictedValue | number,null | false | | Mean predicted value for all rows in the bucket, only for regression deployments. | | meanProbabilities | [MeanProbability] | false | maxItems: 10000 | Mean predicted probabilities for all classes in the bucket, only for classification deployments | | modelId | string | true | | ID of the model | | percentiles | [Percentile] | false | maxItems: 10 | Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments. | | period | TimeRange | true | | An object with the keys "start" and "end" defining the period. | | predictionsWarningCount | integer,null | false | | The number of predictions with warning in the bucket | | rowCount | integer,null | true | | Number of rows in the bucket. | ## PredictionsOverTimeResponse ``` { "properties": { "baselines": { "description": "Target baselines", "items": { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "modelId": { "description": "ID of the model", "type": "string" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "modelId", "rowCount" ], "type": "object" }, "type": "array" }, "buckets": { "description": "Predictions over time buckets", "items": { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "modelId": { "description": "ID of the model", "type": "string" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "modelId", "period", "rowCount" ], "type": "object" }, "type": "array" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "baselines", "buckets" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselines | [TargetBaseline] | true | | Target baselines | | buckets | [PredictionsOverTimeBucket] | true | | Predictions over time buckets | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | ## TargetBaseline ``` { "properties": { "classDistribution": { "description": "Class distribution for all classes in the bucket, only for classification deployments.", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "count": { "description": "Count of rows labeled with a class in the bucket", "type": "integer" }, "percent": { "description": "Percent of rows labeled with a class in the bucket", "type": "number" } }, "required": [ "className", "count", "percent" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "meanGeoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "meanPredictedValue": { "description": "Mean predicted value for all rows in the bucket, only for regression deployments.", "type": [ "number", "null" ] }, "meanProbabilities": { "description": "Mean predicted probabilities for all classes in the bucket, only for classification deployments", "items": { "properties": { "className": { "description": "Name of the class", "type": "string" }, "value": { "description": "Mean predicted probability for a class for all rows in the bucket", "type": "number" } }, "required": [ "className", "value" ], "type": "object" }, "maxItems": 10000, "type": "array" }, "modelId": { "description": "ID of the model", "type": "string" }, "percentiles": { "description": "Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments.", "items": { "properties": { "geoCentroid": { "description": "Geo centroid.", "properties": { "latitude": { "description": "Latitude.", "type": "number" }, "longitude": { "description": "Longitude.", "type": "number" } }, "required": [ "latitude", "longitude" ], "type": "object", "x-versionadded": "v2.36" }, "percent": { "description": "Percent of the percentile", "type": "number" }, "value": { "description": "Predicted value or probability at a percentile", "type": [ "number", "null" ] } }, "required": [ "percent" ], "type": "object" }, "maxItems": 10, "type": "array" }, "predictionsWarningCount": { "description": "The number of predictions with warning in the bucket", "type": [ "integer", "null" ] }, "rowCount": { "description": "Number of rows in the bucket.", "type": [ "integer", "null" ] } }, "required": [ "modelId", "rowCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | classDistribution | [ClassDistribution] | false | maxItems: 10000 | Class distribution for all classes in the bucket, only for classification deployments. | | meanGeoCentroid | GeoPoint | false | | Geo centroid. | | meanPredictedValue | number,null | false | | Mean predicted value for all rows in the bucket, only for regression deployments. | | meanProbabilities | [MeanProbability] | false | maxItems: 10000 | Mean predicted probabilities for all classes in the bucket, only for classification deployments | | modelId | string | true | | ID of the model | | percentiles | [Percentile] | false | maxItems: 10 | Predicted value or positive class predicted probability at specific percentiles in the bucket, only for regression and binary classification deployments. | | predictionsWarningCount | integer,null | false | | The number of predictions with warning in the bucket | | rowCount | integer,null | true | | Number of rows in the bucket. | ## TargetDriftResponse ``` { "properties": { "baselineSampleSize": { "description": "sample size of the training data.", "type": "integer" }, "batchId": { "default": [], "description": "The id of the batch for which metrics are being retrieved.", "oneOf": [ { "type": "string" }, { "items": { "type": "string" }, "maxItems": 25, "type": "array" } ] }, "driftScore": { "description": "drift score for the target.", "type": [ "number", "null" ] }, "metric": { "description": "Metric used to calculate drift score.", "enum": [ "psi", "kl_divergence", "dissimilarity", "hellinger", "js_divergence" ], "type": "string" }, "modelId": { "description": "id of the model for which data drift is being retrieved.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "sampleSize": { "description": "number of predictions used to compute the drift score.", "type": "integer" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ] }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "targetName": { "description": "name of the target feature.", "type": "string" }, "type": { "description": "Type of the feature.", "enum": [ "numeric", "categorical", "text" ], "type": "string" } }, "required": [ "baselineSampleSize", "driftScore", "modelId", "sampleSize", "targetName", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | baselineSampleSize | integer | true | | sample size of the training data. | | batchId | any | false | | The id of the batch for which metrics are being retrieved. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 25 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | driftScore | number,null | true | | drift score for the target. | | metric | string | false | | Metric used to calculate drift score. | | modelId | string | true | | id of the model for which data drift is being retrieved. | | period | TimeRange | false | | An object with the keys "start" and "end" defining the period. | | sampleSize | integer | true | | number of predictions used to compute the drift score. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | | targetName | string | true | | name of the target feature. | | type | string | true | | Type of the feature. | ### Enumerated Values | Property | Value | | --- | --- | | metric | [psi, kl_divergence, dissimilarity, hellinger, js_divergence] | | type | [numeric, categorical, text] | ## TimeRange ``` { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } ``` An object with the keys "start" and "end" defining the period. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string,null(date-time) | false | | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | start | string,null(date-time) | false | | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | --- # Service health URL: https://docs.datarobot.com/en/docs/api/reference/public-api/observability_service_health.html > Use the endpoints described below to manage service health. Service health tracks metrics about a deployment's ability to respond to prediction requests quickly and reliably. This helps identify bottlenecks and assess capacity, which is critical to proper provisioning. # Service health Use the endpoints described below to manage service health. Service health tracks metrics about a deployment's ability to respond to prediction requests quickly and reliably. This helps identify bottlenecks and assess capacity, which is critical to proper provisioning. ## Retrieve service health metrics by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/batchServiceStats/` Authentication requirements: `BearerAuth` Retrieve all deployment service health metrics over a set of batches. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | executionTimeQuantile | query | number | false | Quantile for executionTime metric. | | responseTimeQuantile | query | number | false | Quantile for responseTime metric. | | slowRequestsThreshold | query | integer | false | Threshold for slowRequests metric. | | segmentAttribute | query | string | false | The name of a segment attribute used for segment analysis. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | modelId | query | string | false | The id of the model for which metrics are being retrieved. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | segmentAttribute | [DataRobot-Consumer, DataRobot-Remote-IP, DataRobot-Host-IP] | ### Example responses > 200 Response ``` { "properties": { "batches": { "description": "Info of the batches the metric is aggregated on.", "items": { "description": "Batch info.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "metrics": { "description": "Service health metrics of the deployment", "properties": { "cacheHitRatio": { "description": "Number of cache hits.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "executionTime": { "description": "Request execution time at executionTimeQuantile (in milliseconds).", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "numConsumers": { "description": "Number of unique users performing requests.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "responseTime": { "description": "Request response time at responseTimeQuantile (in milliseconds).", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "serverErrorRate": { "description": "Ratio of server errors to the total number of requests.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "slowRequests": { "description": "Number of requests with response time greater than slowRequestsThreshold", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "totalPredictions": { "description": "Total number of prediction rows.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "totalRequests": { "description": "Total number of prediction requests performed.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "userErrorRate": { "description": "Ratio of user errors to the total number of requests.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "cacheHitRatio", "executionTime", "numConsumers", "responseTime", "serverErrorRate", "slowRequests", "totalPredictions", "totalRequests", "userErrorRate" ], "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "batches", "metrics" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Service health metric data retrieved. | ServiceStatsForBatchRetrieveResponse | ## Retrieve service stats by id Operation path: `GET /api/v2/deployments/{deploymentId}/serviceStats/` Authentication requirements: `BearerAuth` Retrieve all deployment service health metrics over a single period of time. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | executionTimeQuantile | query | number | false | Quantile for executionTime metric. | | responseTimeQuantile | query | number | false | Quantile for responseTime metric. | | slowRequestsThreshold | query | integer | false | Threshold for slowRequests metric. | | segmentAttribute | query | string | false | The name of a segment attribute used for segment analysis. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | modelId | query | string | false | The id of the model for which metrics are being retrieved. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | segmentAttribute | [DataRobot-Consumer, DataRobot-Remote-IP, DataRobot-Host-IP] | ### Example responses > 200 Response ``` { "properties": { "metrics": { "description": "Service health metrics of the deployment", "properties": { "cacheHitRatio": { "description": "Number of cache hits.", "type": [ "number", "null" ] }, "executionTime": { "description": "Request execution time at executionTimeQuantile (in milliseconds).", "type": [ "number", "null" ] }, "medianLoad": { "description": "Median of the request rate (in requests per minute).", "type": [ "number", "null" ] }, "numConsumers": { "description": "Number of unique users performing requests.", "type": [ "integer", "null" ] }, "peakLoad": { "description": "Maximum of the request rate (in requests per minute).", "type": [ "number", "null" ] }, "responseTime": { "description": "Request response time at responseTimeQuantile (in milliseconds).", "type": [ "number", "null" ] }, "serverErrorRate": { "description": "Ratio of server errors to the total number of requests.", "type": [ "number", "null" ] }, "slowRequests": { "description": "Number of requests with response time greater than slowRequestsThreshold", "type": [ "integer", "null" ] }, "totalPredictions": { "description": "Total number of prediction rows.", "type": [ "integer", "null" ] }, "totalRequests": { "description": "Total number of prediction requests performed.", "type": [ "integer", "null" ] }, "userErrorRate": { "description": "Ratio of user errors to the total number of requests.", "type": [ "number", "null" ] } }, "required": [ "cacheHitRatio", "executionTime", "medianLoad", "numConsumers", "peakLoad", "responseTime", "serverErrorRate", "slowRequests", "totalPredictions", "totalRequests", "userErrorRate" ], "type": "object" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "metrics", "period" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Service health metric data retrieved. | ServiceStatsForTimeRangeResponse | ## Retrieve service health metric over batch by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/serviceStatsOverBatch/` Authentication requirements: `BearerAuth` Retrieve values for one single deployment service health metric over batch. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | segmentAttribute | query | string | false | The name of a segment attribute used for segment analysis. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | batchId | query | any | false | The id of the batch for which metrics are being retrieved. | | modelId | query | string | false | The id of the model for which metrics are being retrieved. | | metric | query | string | false | A service health metric. | | quantile | query | number | false | Quantile for executionTime and responseTime metrics | | threshold | query | integer | false | Threshold for slowQueries metric. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | segmentAttribute | [DataRobot-Consumer, DataRobot-Remote-IP, DataRobot-Host-IP] | | metric | [totalPredictions, totalRequests, slowRequests, executionTime, responseTime, userErrorRate, serverErrorRate, numConsumers, cacheHitRatio] | ### Example responses > 200 Response ``` { "properties": { "buckets": { "description": "An array of buckets, representing service health stats of the deployment over selected batches.", "items": { "description": "Service health stats of the deployment over a batch.", "properties": { "batch": { "description": "Batch info.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "value": { "description": "Value of the metric in the bucket.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batch", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "metric": { "description": "Name of the metric requested.", "type": "string", "x-versionadded": "v2.33" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "buckets", "metric" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Service health metric data retrieved. | ServiceStatsOverBatchResponse | ## Retrieve service health metric over time by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/serviceStatsOverTime/` Authentication requirements: `BearerAuth` Retrieve values for one single deployment service health metric over time. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | bucketSize | query | string(duration) | false | The time duration of a bucket. Needs to be multiple of one hour. Can not be longer than the total length of the period. If not set, a default value will be calculated based on the start and end time. | | segmentAttribute | query | string | false | The name of a segment attribute used for segment analysis. | | segmentValue | query | string,null | false | The value of the segmentAttribute to segment on. | | modelId | query | any | false | The ID of the models for which metrics are being retrieved. | | metric | query | string | false | Name of the metric. See below for a list of supported metrics. | | quantile | query | number | false | A quantile for resulting data, used if metric is executionTime or responseTime, defaults to 0.5. | | threshold | query | integer | false | A threshold for filtering results, used if metric is slowQueries, defaults to 1000. | | deploymentId | path | string | true | Unique identifier of the deployment. | ### Enumerated Values | Parameter | Value | | --- | --- | | segmentAttribute | [DataRobot-Consumer, DataRobot-Remote-IP, DataRobot-Host-IP] | | metric | [totalPredictions, totalRequests, slowRequests, executionTime, responseTime, userErrorRate, serverErrorRate, numConsumers, cacheHitRatio, medianLoad, peakLoad] | ### Example responses > 200 Response ``` { "properties": { "buckets": { "description": "An array of bucket, representing service health stats of the deployment over time.", "items": { "description": "Service health stats of the deployment over a time range.", "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.37" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "value": { "description": "Value of the metric in the bucket. Null if no value", "type": [ "number", "null" ] } }, "required": [ "period", "value" ], "type": "object" }, "type": "array" }, "metric": { "description": "Name of the metric requested.", "type": "string" }, "modelId": { "description": "The id of the model for which metrics are being retrieved. Deprecated, use modelId in each bucket instead.", "type": "string", "x-versiondeprecated": "v2.37" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "summary": { "description": "Service health stats of the deployment over a time range.", "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.37" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "value": { "description": "Value of the metric in the bucket. Null if no value", "type": [ "number", "null" ] } }, "required": [ "period", "value" ], "type": "object" } }, "required": [ "buckets", "metric" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Service health metric data retrieved. | ServiceStatsOverTimeResponse | # Schemas ## Batch ``` { "description": "Batch info.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" } ``` Batch info. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | earliestPredictionTimestamp | string(date-time) | true | | Earliest prediction timestamp of a batch. | | id | string | true | | Batch ID. | | latestPredictionTimestamp | string,null(date-time) | true | | Latest prediction timestamp of a batch. | | name | string | true | | Batch name. | ## ServiceStatsForBatchRetrieveResponse ``` { "properties": { "batches": { "description": "Info of the batches the metric is aggregated on.", "items": { "description": "Batch info.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "metrics": { "description": "Service health metrics of the deployment", "properties": { "cacheHitRatio": { "description": "Number of cache hits.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "executionTime": { "description": "Request execution time at executionTimeQuantile (in milliseconds).", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "numConsumers": { "description": "Number of unique users performing requests.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "responseTime": { "description": "Request response time at responseTimeQuantile (in milliseconds).", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "serverErrorRate": { "description": "Ratio of server errors to the total number of requests.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "slowRequests": { "description": "Number of requests with response time greater than slowRequestsThreshold", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "totalPredictions": { "description": "Total number of prediction rows.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "totalRequests": { "description": "Total number of prediction requests performed.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "userErrorRate": { "description": "Ratio of user errors to the total number of requests.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "cacheHitRatio", "executionTime", "numConsumers", "responseTime", "serverErrorRate", "slowRequests", "totalPredictions", "totalRequests", "userErrorRate" ], "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "batches", "metrics" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batches | [Batch] | true | | Info of the batches the metric is aggregated on. | | metrics | ServiceStatsMetricsForBatch | true | | Service health metrics of the deployment | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | ## ServiceStatsForTimeRangeResponse ``` { "properties": { "metrics": { "description": "Service health metrics of the deployment", "properties": { "cacheHitRatio": { "description": "Number of cache hits.", "type": [ "number", "null" ] }, "executionTime": { "description": "Request execution time at executionTimeQuantile (in milliseconds).", "type": [ "number", "null" ] }, "medianLoad": { "description": "Median of the request rate (in requests per minute).", "type": [ "number", "null" ] }, "numConsumers": { "description": "Number of unique users performing requests.", "type": [ "integer", "null" ] }, "peakLoad": { "description": "Maximum of the request rate (in requests per minute).", "type": [ "number", "null" ] }, "responseTime": { "description": "Request response time at responseTimeQuantile (in milliseconds).", "type": [ "number", "null" ] }, "serverErrorRate": { "description": "Ratio of server errors to the total number of requests.", "type": [ "number", "null" ] }, "slowRequests": { "description": "Number of requests with response time greater than slowRequestsThreshold", "type": [ "integer", "null" ] }, "totalPredictions": { "description": "Total number of prediction rows.", "type": [ "integer", "null" ] }, "totalRequests": { "description": "Total number of prediction requests performed.", "type": [ "integer", "null" ] }, "userErrorRate": { "description": "Ratio of user errors to the total number of requests.", "type": [ "number", "null" ] } }, "required": [ "cacheHitRatio", "executionTime", "medianLoad", "numConsumers", "peakLoad", "responseTime", "serverErrorRate", "slowRequests", "totalPredictions", "totalRequests", "userErrorRate" ], "type": "object" }, "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "metrics", "period" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metrics | ServiceStatsMetricsForTimeRange | true | | Service health metrics of the deployment | | modelId | string | false | | The id of the model for which metrics are being retrieved. | | period | TimeRange | true | | An object with the keys "start" and "end" defining the period. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | ## ServiceStatsMetricsForBatch ``` { "description": "Service health metrics of the deployment", "properties": { "cacheHitRatio": { "description": "Number of cache hits.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "executionTime": { "description": "Request execution time at executionTimeQuantile (in milliseconds).", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "numConsumers": { "description": "Number of unique users performing requests.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "responseTime": { "description": "Request response time at responseTimeQuantile (in milliseconds).", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "serverErrorRate": { "description": "Ratio of server errors to the total number of requests.", "type": [ "number", "null" ], "x-versionadded": "v2.33" }, "slowRequests": { "description": "Number of requests with response time greater than slowRequestsThreshold", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "totalPredictions": { "description": "Total number of prediction rows.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "totalRequests": { "description": "Total number of prediction requests performed.", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "userErrorRate": { "description": "Ratio of user errors to the total number of requests.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "cacheHitRatio", "executionTime", "numConsumers", "responseTime", "serverErrorRate", "slowRequests", "totalPredictions", "totalRequests", "userErrorRate" ], "type": "object" } ``` Service health metrics of the deployment ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cacheHitRatio | number,null | true | | Number of cache hits. | | executionTime | number,null | true | | Request execution time at executionTimeQuantile (in milliseconds). | | numConsumers | integer,null | true | | Number of unique users performing requests. | | responseTime | number,null | true | | Request response time at responseTimeQuantile (in milliseconds). | | serverErrorRate | number,null | true | | Ratio of server errors to the total number of requests. | | slowRequests | integer,null | true | | Number of requests with response time greater than slowRequestsThreshold | | totalPredictions | integer,null | true | | Total number of prediction rows. | | totalRequests | integer,null | true | | Total number of prediction requests performed. | | userErrorRate | number,null | true | | Ratio of user errors to the total number of requests. | ## ServiceStatsMetricsForTimeRange ``` { "description": "Service health metrics of the deployment", "properties": { "cacheHitRatio": { "description": "Number of cache hits.", "type": [ "number", "null" ] }, "executionTime": { "description": "Request execution time at executionTimeQuantile (in milliseconds).", "type": [ "number", "null" ] }, "medianLoad": { "description": "Median of the request rate (in requests per minute).", "type": [ "number", "null" ] }, "numConsumers": { "description": "Number of unique users performing requests.", "type": [ "integer", "null" ] }, "peakLoad": { "description": "Maximum of the request rate (in requests per minute).", "type": [ "number", "null" ] }, "responseTime": { "description": "Request response time at responseTimeQuantile (in milliseconds).", "type": [ "number", "null" ] }, "serverErrorRate": { "description": "Ratio of server errors to the total number of requests.", "type": [ "number", "null" ] }, "slowRequests": { "description": "Number of requests with response time greater than slowRequestsThreshold", "type": [ "integer", "null" ] }, "totalPredictions": { "description": "Total number of prediction rows.", "type": [ "integer", "null" ] }, "totalRequests": { "description": "Total number of prediction requests performed.", "type": [ "integer", "null" ] }, "userErrorRate": { "description": "Ratio of user errors to the total number of requests.", "type": [ "number", "null" ] } }, "required": [ "cacheHitRatio", "executionTime", "medianLoad", "numConsumers", "peakLoad", "responseTime", "serverErrorRate", "slowRequests", "totalPredictions", "totalRequests", "userErrorRate" ], "type": "object" } ``` Service health metrics of the deployment ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cacheHitRatio | number,null | true | | Number of cache hits. | | executionTime | number,null | true | | Request execution time at executionTimeQuantile (in milliseconds). | | medianLoad | number,null | true | | Median of the request rate (in requests per minute). | | numConsumers | integer,null | true | | Number of unique users performing requests. | | peakLoad | number,null | true | | Maximum of the request rate (in requests per minute). | | responseTime | number,null | true | | Request response time at responseTimeQuantile (in milliseconds). | | serverErrorRate | number,null | true | | Ratio of server errors to the total number of requests. | | slowRequests | integer,null | true | | Number of requests with response time greater than slowRequestsThreshold | | totalPredictions | integer,null | true | | Total number of prediction rows. | | totalRequests | integer,null | true | | Total number of prediction requests performed. | | userErrorRate | number,null | true | | Ratio of user errors to the total number of requests. | ## ServiceStatsOverBatchBucket ``` { "description": "Service health stats of the deployment over a batch.", "properties": { "batch": { "description": "Batch info.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "value": { "description": "Value of the metric in the bucket.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batch", "value" ], "type": "object" } ``` Service health stats of the deployment over a batch. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batch | Batch | true | | Batch info. | | value | number,null | true | | Value of the metric in the bucket. | ## ServiceStatsOverBatchResponse ``` { "properties": { "buckets": { "description": "An array of buckets, representing service health stats of the deployment over selected batches.", "items": { "description": "Service health stats of the deployment over a batch.", "properties": { "batch": { "description": "Batch info.", "properties": { "earliestPredictionTimestamp": { "description": "Earliest prediction timestamp of a batch.", "format": "date-time", "type": "string", "x-versionadded": "v2.33" }, "id": { "description": "Batch ID.", "type": "string", "x-versionadded": "v2.33" }, "latestPredictionTimestamp": { "description": "Latest prediction timestamp of a batch.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "Batch name.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "earliestPredictionTimestamp", "id", "latestPredictionTimestamp", "name" ], "type": "object" }, "value": { "description": "Value of the metric in the bucket.", "type": [ "number", "null" ], "x-versionadded": "v2.33" } }, "required": [ "batch", "value" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "metric": { "description": "Name of the metric requested.", "type": "string", "x-versionadded": "v2.33" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" } }, "required": [ "buckets", "metric" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [ServiceStatsOverBatchBucket] | true | | An array of buckets, representing service health stats of the deployment over selected batches. | | metric | string | true | | Name of the metric requested. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | ## ServiceStatsOverTimeBucket ``` { "description": "Service health stats of the deployment over a time range.", "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.37" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "value": { "description": "Value of the metric in the bucket. Null if no value", "type": [ "number", "null" ] } }, "required": [ "period", "value" ], "type": "object" } ``` Service health stats of the deployment over a time range. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | string | false | | The id of the model for which metrics are being retrieved. | | period | TimeRange | true | | An object with the keys "start" and "end" defining the period. | | value | number,null | true | | Value of the metric in the bucket. Null if no value | ## ServiceStatsOverTimeResponse ``` { "properties": { "buckets": { "description": "An array of bucket, representing service health stats of the deployment over time.", "items": { "description": "Service health stats of the deployment over a time range.", "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.37" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "value": { "description": "Value of the metric in the bucket. Null if no value", "type": [ "number", "null" ] } }, "required": [ "period", "value" ], "type": "object" }, "type": "array" }, "metric": { "description": "Name of the metric requested.", "type": "string" }, "modelId": { "description": "The id of the model for which metrics are being retrieved. Deprecated, use modelId in each bucket instead.", "type": "string", "x-versiondeprecated": "v2.37" }, "segmentAttribute": { "description": "The name of the segment on which segment analysis is being performed.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "segmentValue": { "default": "", "description": "The value of the `segmentAttribute` to segment on.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "summary": { "description": "Service health stats of the deployment over a time range.", "properties": { "modelId": { "description": "The id of the model for which metrics are being retrieved.", "type": "string", "x-versionadded": "v2.37" }, "period": { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" }, "value": { "description": "Value of the metric in the bucket. Null if no value", "type": [ "number", "null" ] } }, "required": [ "period", "value" ], "type": "object" } }, "required": [ "buckets", "metric" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [ServiceStatsOverTimeBucket] | true | | An array of bucket, representing service health stats of the deployment over time. | | metric | string | true | | Name of the metric requested. | | modelId | string | false | | The id of the model for which metrics are being retrieved. Deprecated, use modelId in each bucket instead. | | segmentAttribute | string,null | false | | The name of the segment on which segment analysis is being performed. | | segmentValue | string,null | false | | The value of the segmentAttribute to segment on. | | summary | ServiceStatsOverTimeBucket | false | | Service health stats of the deployment over a time range. | ## TimeRange ``` { "description": "An object with the keys \"start\" and \"end\" defining the period.", "properties": { "end": { "description": "End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] }, "start": { "description": "Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: ``2019-08-01T00:00:00Z``.", "format": "date-time", "type": [ "string", "null" ] } }, "type": "object" } ``` An object with the keys "start" and "end" defining the period. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string,null(date-time) | false | | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | start | string,null(date-time) | false | | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | --- # Real-time predictions URL: https://docs.datarobot.com/en/docs/api/reference/public-api/predictions_info.html > Use the endpoints described below to manage predictions information. # Real-time predictions Use the endpoints described below to manage predictions information. ## List prediction servers Operation path: `GET /api/v2/predictionServers/` Authentication requirements: `BearerAuth` The list of prediction servers available for a user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of prediction servers.", "items": { "properties": { "batchPredictions": { "description": "The batch prediction status for this instance.", "properties": { "processing": { "description": "The number of jobs processing.", "minimum": 0, "type": "integer" }, "queued": { "description": "The number of jobs queued.", "minimum": 0, "type": "integer" } }, "required": [ "processing", "queued" ], "type": "object" }, "datarobot-key": { "description": "The `datarobot-key` header used in requests to this prediction server.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the prediction server.", "type": [ "string", "null" ] }, "url": { "description": "The URL of the prediction server.", "type": "string" } }, "required": [ "batchPredictions", "datarobot-key", "id", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | PredictionServerListResponse | # Schemas ## BatchPredictionStatus ``` { "description": "The batch prediction status for this instance.", "properties": { "processing": { "description": "The number of jobs processing.", "minimum": 0, "type": "integer" }, "queued": { "description": "The number of jobs queued.", "minimum": 0, "type": "integer" } }, "required": [ "processing", "queued" ], "type": "object" } ``` The batch prediction status for this instance. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | processing | integer | true | minimum: 0 | The number of jobs processing. | | queued | integer | true | minimum: 0 | The number of jobs queued. | ## PredictionServerListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of prediction servers.", "items": { "properties": { "batchPredictions": { "description": "The batch prediction status for this instance.", "properties": { "processing": { "description": "The number of jobs processing.", "minimum": 0, "type": "integer" }, "queued": { "description": "The number of jobs queued.", "minimum": 0, "type": "integer" } }, "required": [ "processing", "queued" ], "type": "object" }, "datarobot-key": { "description": "The `datarobot-key` header used in requests to this prediction server.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the prediction server.", "type": [ "string", "null" ] }, "url": { "description": "The URL of the prediction server.", "type": "string" } }, "required": [ "batchPredictions", "datarobot-key", "id", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [PredictionServerResponse] | true | | The list of prediction servers. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## PredictionServerResponse ``` { "properties": { "batchPredictions": { "description": "The batch prediction status for this instance.", "properties": { "processing": { "description": "The number of jobs processing.", "minimum": 0, "type": "integer" }, "queued": { "description": "The number of jobs queued.", "minimum": 0, "type": "integer" } }, "required": [ "processing", "queued" ], "type": "object" }, "datarobot-key": { "description": "The `datarobot-key` header used in requests to this prediction server.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the prediction server.", "type": [ "string", "null" ] }, "url": { "description": "The URL of the prediction server.", "type": "string" } }, "required": [ "batchPredictions", "datarobot-key", "id", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | batchPredictions | BatchPredictionStatus | true | | The batch prediction status for this instance. | | datarobot-key | string,null | true | | The datarobot-key header used in requests to this prediction server. | | id | string,null | true | | The ID of the prediction server. | | url | string | true | | The URL of the prediction server. | --- # Projects URL: https://docs.datarobot.com/en/docs/api/reference/public-api/projects.html > Use the endpoints described below to manage projects and their properties. # Projects Use the endpoints described below to manage projects and their properties. ## Retrieve the list of soft-deleted projects Operation path: `GET /api/v2/deletedProjects/` Authentication requirements: `BearerAuth` Retrieve a list of soft-deleted projects matching search criteria. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | searchFor | query | string | false | Project or dataset name to filter by. | | creator | query | string | false | Creator ID to filter projects by | | organization | query | string | false | ID of organization that projects should belong to. Given project belongs to the organization the user who created the project is part of that organization.If there are no users in organization, then no projects will match the query. | | deletedBefore | query | string(date-time) | false | ISO-8601 formatted date projects were deleted before | | deletedAfter | query | string(date-time) | false | ISO-8601 formatted date projects were deleted after | | projectId | query | string | false | Project ID to search | | limit | query | integer | false | At most this many results are returned. | | offset | query | integer | false | This many results will be skipped. | | orderBy | query | string | false | Order deleted projects by | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [projectId, projectName, datasetName, deletedOn, deletedBy, creator, -projectId, -projectName, -datasetName, -deletedOn, -deletedBy, -creator] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of deleted projects", "items": { "properties": { "createdBy": { "description": "The user who created the project", "properties": { "email": { "description": "Email of the user", "type": "string" }, "id": { "description": "ID of the user", "type": "string" } }, "required": [ "email", "id" ], "type": "object" }, "deletedBy": { "description": "The user who created the project", "properties": { "email": { "description": "Email of the user", "type": "string" }, "id": { "description": "ID of the user", "type": "string" } }, "required": [ "email", "id" ], "type": "object" }, "deletionTime": { "description": "ISO-8601 formatted date when project was deleted", "format": "date-time", "type": [ "string", "null" ] }, "fileName": { "description": "The name of the file uploaded for the project dataset", "type": [ "string", "null" ] }, "id": { "description": "The ID of the project", "type": "string" }, "organization": { "description": "The organization the project belongs to", "properties": { "id": { "description": "ID of the organization the project belongs to", "type": "string" }, "name": { "description": "Name of the organization the project belongs to", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "projectName": { "default": "Untitled Project", "description": "The name of the project", "type": "string" }, "scheduledForDeletion": { "description": "Whether project permanent deletion has already been scheduled", "type": "boolean" } }, "required": [ "createdBy", "deletedBy", "deletionTime", "fileName", "id", "organization", "projectName", "scheduledForDeletion" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of soft-deleted projects. | DeletedProjectListResponse | ## Recover soft-deleted project by project ID Operation path: `PATCH /api/v2/deletedProjects/{projectId}/` Authentication requirements: `BearerAuth` Recover (undelete) soft-deleted project. ### Body parameter ``` { "properties": { "action": { "description": "Action to perform on a project", "enum": [ "undelete" ], "type": "string" } }, "required": [ "action" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | ProjectRecover | false | none | ### Example responses > 200 Response ``` { "properties": { "message": { "description": "Operation result description", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The recovery operation result description. | ProjectRecoverResponse | ## Count soft-deleted projects Operation path: `GET /api/v2/deletedProjectsCount/` Authentication requirements: `BearerAuth` Get current number of deleted projects matching search criteria. Value is limited by DELETED_PROJECTS_BATCH_LIMIT system setting. That means that the actual amount of deleted projects can be greater than the limit, but counting will stop when reaching it. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | searchFor | query | string | false | Project or dataset name to filter by. | | creator | query | string | false | Creator ID to filter projects by | | organization | query | string | false | ID of organization that projects should belong to. Given project belongs to the organization the user who created the project is part of that organization.If there are no users in organization, then no projects will match the query. | | deletedBefore | query | string(date-time) | false | ISO-8601 formatted date projects were deleted before | | deletedAfter | query | string(date-time) | false | ISO-8601 formatted date projects were deleted after | | projectId | query | string | false | Project ID to search | | limit | query | integer | false | Count deleted projects until specified value reached. | ### Example responses > 200 Response ``` { "properties": { "deletedProjectsCount": { "description": "Amount of soft-deleted projects. The value is limited by projectCountLimit", "minimum": 0, "type": "integer" }, "projectCountLimit": { "description": "Deleted projects counting limit value. Stop counting above this limit", "minimum": 0, "type": "integer" }, "valueExceedsLimit": { "description": "If an actual number of soft-deleted projects exceeds counting limit", "type": "boolean" } }, "required": [ "deletedProjectsCount", "projectCountLimit", "valueExceedsLimit" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Soft-deleted projects amount, current counting limit value and boolean flag to notify if an actual amount of soft-deleted projects in the system exceeds the limit value. | DeletedProjectCountResponse | ## Create a project Operation path: `POST /api/v2/hdfsProjects/` Authentication requirements: `BearerAuth` Create a project from an HDFS file via WebHDFS API. Represent the file using URL, optionally, port, and optionally, user/password credentials. For example, `{"url": "hdfs:///path/to/file.csv", "port": "50070"}`. ### Body parameter ``` { "properties": { "password": { "description": "Password for authenticating to HDFS using Kerberos. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "port": { "description": "Port of the WebHDFS Namenode server. If not specified, defaults to HDFS default port 50070.", "type": "integer" }, "projectName": { "description": "Name of the project to be created. If not specified, project name will be based on the file name.", "type": "string" }, "url": { "description": "URL of the WebHDFS resource. Represent the file using the `hdfs://` protocol marker (for example, `hdfs:///tmp/somedataset.csv`).", "format": "uri", "type": "string" }, "user": { "description": "Username for authenticating to HDFS using Kerberos", "type": "string" } }, "required": [ "url" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | HdfsProjectCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Retrieve project permadelete job status Operation path: `GET /api/v2/projectCleanupJobs/` Authentication requirements: `BearerAuth` Get async status of the project permadelete job. ### Example responses > 200 Response ``` { "properties": { "jobs": { "description": "List of active permadelete jobs with their statuses.", "items": { "properties": { "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "data": { "description": "List of projects and associated statuses.", "items": { "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "projectId": { "description": "Project ID", "type": "string" }, "status": { "description": "The processing state of project cleanup task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "projectId", "status" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The processing state of the cleanup job.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "created", "data", "message", "status", "statusId" ], "type": "object" }, "type": "array" } }, "required": [ "jobs" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The permadelete job status with details per project. | ProjectNukeJobListStatus | ## Schedule project permadelete job Operation path: `POST /api/v2/projectCleanupJobs/` Authentication requirements: `BearerAuth` Add list of projects to permadelete and returns async status. ### Body parameter ``` { "properties": { "creator": { "description": "Creator ID to filter projects by", "type": "string" }, "deletedAfter": { "description": "ISO-8601 formatted date projects were deleted after", "format": "date-time", "type": "string" }, "deletedBefore": { "description": "ISO-8601 formatted date projects were deleted before", "format": "date-time", "type": "string" }, "limit": { "default": 1000, "description": "At most this many projects are deleted.", "maximum": 1000, "minimum": 1, "type": "integer" }, "offset": { "default": 0, "description": "This many projects will be skipped.", "minimum": 0, "type": "integer" }, "organization": { "description": "ID of organization that projects should belong to", "type": "string" }, "projectIds": { "description": "The list of project IDs to delete permanently.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "searchFor": { "description": "Project or dataset name to filter by.", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ProjectNuke | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Location URL to check permadelete status per project | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Cancel scheduled project permadelete job by status ID Operation path: `DELETE /api/v2/projectCleanupJobs/{statusId}/` Authentication requirements: `BearerAuth` Stop permadelete job, if possible. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string | true | The ID of the status object. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Retrieve project permadelete job status by status ID Operation path: `GET /api/v2/projectCleanupJobs/{statusId}/` Authentication requirements: `BearerAuth` Get async status of the project permadelete job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string | true | The ID of the status object. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "data": { "description": "List of projects and associated statuses.", "items": { "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "projectId": { "description": "Project ID", "type": "string" }, "status": { "description": "The processing state of project cleanup task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "projectId", "status" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The processing state of the cleanup job.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "created", "data", "message", "status", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The permadelete job status with details per project. | ProjectNukeJobStatus | ## Download a projects permadeletion report by status ID Operation path: `GET /api/v2/projectCleanupJobs/{statusId}/download/` Authentication requirements: `BearerAuth` Get a file containing a per-project report of permanent deletion. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string | true | The ID of the status object. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "data": { "description": "List of projects and associated statuses.", "items": { "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "projectId": { "description": "Project ID", "type": "string" }, "status": { "description": "The processing state of project cleanup task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "projectId", "status" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The processing state of the cleanup job.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "created", "data", "message", "status", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | JSON-formatted project permadeletion report. | ProjectNukeJobStatus | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | Contains an auto generated filename for this download ('attachment;filename="project_permadeletion_.json"'). | ## Get a projects cleanup jobs summary by status ID Operation path: `GET /api/v2/projectCleanupJobs/{statusId}/summary/` Authentication requirements: `BearerAuth` Get number of projects whose deletion finished in particular state. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string | true | The ID of the status object. | ### Example responses > 200 Response ``` { "properties": { "jobId": { "description": "The ID of the permadeletion multi-job.", "type": "string" }, "summary": { "description": "Project permanent deletion status to count mapping.", "properties": { "aborted": { "description": "Number of project permadelete jobs with Aborted status.", "minimum": 0, "type": "integer" }, "completed": { "description": "Number of project permadelete jobs with Completed status.", "minimum": 0, "type": "integer" }, "error": { "description": "Number of project permadelete jobs with Error status.", "minimum": 0, "type": "integer" }, "expired": { "description": "Number of project permadelete jobs with Expired status.", "minimum": 0, "type": "integer" } }, "required": [ "aborted", "completed", "error", "expired" ], "type": "object" } }, "required": [ "jobId", "summary" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Project permanent deletion job status to occurrence count | ProjectNukeJobStatusSummary | ## Clone a project Operation path: `POST /api/v2/projectClones/` Authentication requirements: `BearerAuth` Create a clone of an existing project. ``` The resultant project will begin the initial exploratory data analysis and will be ready to set the target of the new project shortly. ``` ### Body parameter ``` { "properties": { "copyOptions": { "default": false, "description": "Whether all project options should be copied to the cloned project.", "type": "boolean" }, "projectId": { "description": "The ID of the project to clone.", "type": "string" }, "projectName": { "description": "The name of the project to be created.", "type": "string" } }, "required": [ "projectId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ProjectClone | false | none | ### Example responses > 200 Response ``` { "properties": { "pid": { "description": "The project ID.", "type": "string" } }, "required": [ "pid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Project cloning has successfully started. See the Location header. | ProjectCreateResponse | ## List projects Operation path: `GET /api/v2/projects/` Authentication requirements: `BearerAuth` List all available projects. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectName | query | string | false | if provided will filter returned projects for projects with matching names | | projectId | query | any | false | if provided will filter returned projects with matching project IDs | | orderBy | query | string | false | if provided will order the results by this field | | featureDiscovery | query | string | false | Return only feature discovery projects | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [projectName, -projectName] | | featureDiscovery | [false, False, true, True] | ### Example responses > 200 Response ``` { "items": { "properties": { "advancedOptions": { "description": "Information related to the current model of the deployment.", "properties": { "allowedPairwiseInteractionGroups": { "description": "For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [[\"A\", \"B\", \"C\"], [\"C\", \"D\"]] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.", "items": { "items": { "type": "string" }, "type": "array" }, "type": "array", "x-versionadded": "v2.19" }, "blendBestModels": { "description": "blend best models during Autopilot run [DEPRECATED]", "type": "boolean", "x-versionadded": "v2.19", "x-versiondeprecated": "2.30.0" }, "blueprintThreshold": { "description": "an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs", "type": [ "integer", "null" ] }, "considerBlendersInRecommendation": { "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED]", "type": "boolean", "x-versionadded": "v2.21", "x-versiondeprecated": "2.30.0" }, "defaultMonotonicDecreasingFeaturelistId": { "description": "null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "defaultMonotonicIncreasingFeaturelistId": { "description": "null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "downsampledMajorityRows": { "description": "the total number of the majority rows available for modeling, or null for projects without smart downsampling", "type": [ "integer", "null" ], "x-versionadded": "v2.5" }, "downsampledMinorityRows": { "description": "the total number of the minority rows available for modeling, or null for projects without smart downsampling", "type": [ "integer", "null" ], "x-versionadded": "v2.5" }, "eventsCount": { "description": "the name of the event count column, if specified, otherwise null.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "exposure": { "description": "the name of the exposure column, if specified.", "type": [ "string", "null" ], "x-versionadded": "v2.6" }, "majorityDownsamplingRate": { "description": "the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling", "type": [ "number", "null" ], "x-versionadded": "v2.5" }, "minSecondaryValidationModelCount": { "description": "Compute \"All backtest\" scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.", "type": "boolean", "x-versionadded": "v2.19" }, "offset": { "description": "the list of names of the offset columns, if specified, otherwise null.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.6" }, "onlyIncludeMonotonicBlueprints": { "default": false, "description": "whether the project only includes blueprints support enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.11" }, "prepareModelForDeployment": { "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning \"RECOMMENDED FOR DEPLOYMENT\" label.", "type": [ "boolean", "null" ], "x-versionadded": "v2.19" }, "responseCap": { "description": "defaults to False, if specified used to cap the maximum response of a model", "oneOf": [ { "type": "boolean" }, { "maximum": 1, "minimum": 0.5, "type": "number" } ] }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean", "x-versionadded": "v2.21" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean", "x-versionadded": "v2.19" }, "seed": { "description": "defaults to null, the random seed to be used if specified", "type": [ "string", "null" ] }, "shapOnlyMode": { "description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always ``null``.", "type": [ "boolean", "null" ], "x-versionadded": "v2.21" }, "smartDownsampled": { "description": "whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).", "type": "boolean", "x-versionadded": "v2.5" }, "weights": { "description": "the name of the weight column, if specified, otherwise null.", "type": [ "string", "null" ] } }, "required": [ "blendBestModels", "blueprintThreshold", "defaultMonotonicDecreasingFeaturelistId", "defaultMonotonicIncreasingFeaturelistId", "downsampledMajorityRows", "downsampledMinorityRows", "majorityDownsamplingRate", "onlyIncludeMonotonicBlueprints", "prepareModelForDeployment", "responseCap", "seed", "shapOnlyMode", "smartDownsampled", "weights" ], "type": "object" }, "autopilotClusterList": { "description": "Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotMode": { "description": "The current autopilot mode, 0 for full autopilot, 2 for manual mode, 3 for quick mode, 4 for comprehensive mode", "type": "integer" }, "created": { "description": "The time of project creation.", "format": "date-time", "type": "string" }, "featureEngineeringPredictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "fileName": { "description": "The name of the dataset used to create the project.", "type": "string" }, "holdoutUnlocked": { "description": "whether the holdout has been unlocked", "type": "boolean" }, "id": { "description": "The ID of a project.", "type": "string" }, "maxClusters": { "description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `minClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100.", "maximum": 100, "minimum": 2, "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "maxTrainPct": { "description": "the maximum percentage of the dataset that can be used to successfully train a model without going into the validation data.", "type": "number" }, "maxTrainRows": { "description": "the maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data", "type": "integer" }, "metric": { "description": "the metric used to select the best-performing models.", "type": "string" }, "minClusters": { "description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `maxClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2.", "maximum": 100, "minimum": 2, "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "partition": { "description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose `cvMethod` is `\"datetime\"`, full specification of the partitioning method can be found at [GET /api/v2/projects/{projectId}/datetimePartitioning/][get-apiv2projectsprojectiddatetimepartitioning].", "properties": { "cvHoldoutLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with cross validation, the value assigned to the holdout set" }, "cvMethod": { "description": "the partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.", "enum": [ "random", "user", "stratified", "group", "datetime" ], "type": "string" }, "datetimeCol": { "description": "if a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "if a datetime partition column was used, the name of the column", "type": "string" }, "holdoutLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with train-validation-holdout split, the value assigned to the holdout set" }, "holdoutPct": { "description": "the percentage of the dataset reserved for the holdout set", "type": "number" }, "partitionKeyCols": { "description": "An array containing a single string - the name of the group partition column", "items": { "type": "string" }, "type": "array" }, "reps": { "description": "if cross validation was used, the number of folds to use", "type": [ "number", "null" ] }, "trainingLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with train-validation-holdout split, the value assigned to the training set" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project was created as opposed to a regular project using datetime partitioning.", "type": [ "boolean", "null" ], "x-versionadded": "v2.9" }, "userPartitionCol": { "description": "if a user partition column was used, the name of the column", "type": [ "string", "null" ] }, "validationLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with train-validation-holdout split, the value assigned to the validation set" }, "validationPct": { "description": "if train-validation-holdout split was used, the percentage of the dataset used for the validation set", "type": [ "number", "null" ] }, "validationType": { "description": "either CV for cross-validation or TVH for train-validation-holdout split", "enum": [ "CV", "TVH" ], "type": "string" } }, "required": [ "cvHoldoutLevel", "cvMethod", "datetimeCol", "holdoutLevel", "holdoutPct", "partitionKeyCols", "reps", "trainingLevel", "useTimeSeries", "userPartitionCol", "validationLevel", "validationPct", "validationType" ], "type": "object" }, "positiveClass": { "description": "if the project uses binary classification, the class designated to be the positive class. Otherwise, null.", "type": [ "number", "null" ] }, "projectName": { "description": "The name of a project.", "type": "string" }, "stage": { "description": "the stage of the project - if modeling, then the target is successfully set, and modeling or predictions can proceed.", "type": "string" }, "target": { "description": "the target of the project, null if project is unsupervised.", "type": "string" }, "targetType": { "description": "The target type of the project.", "enum": [ "Binary", "Regression", "Multiclass", "minInflated", "Multilabel", "TextGeneration", "GeoPoint", "VectorDatabase" ], "type": [ "string", "null" ] }, "unsupervisedMode": { "description": "indicates whether a project is unsupervised.", "type": "boolean", "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCase": { "description": "The information of the use case associated with the project.", "properties": { "id": { "description": "The ID of the use case.", "type": [ "string", "null" ] }, "name": { "description": "The name of the use case.", "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.36" }, "useFeatureDiscovery": { "description": "A boolean value indicating whether a feature discovery project was created as opposed to a regular project.", "type": "boolean", "x-versionadded": "v2.21" } }, "required": [ "advancedOptions", "autopilotMode", "created", "fileName", "holdoutUnlocked", "id", "maxTrainPct", "maxTrainRows", "metric", "partition", "positiveClass", "projectName", "stage", "target", "targetType", "unsupervisedMode", "useFeatureDiscovery" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of projects. | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [ProjectDetailsResponse] | false | | none | | » advancedOptions | ProjectAdvancedOptionsResponse | true | | Information related to the current model of the deployment. | | »» allowedPairwiseInteractionGroups | [array] | false | | For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [["A", "B", "C"], ["C", "D"]] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model. | | »» blendBestModels | boolean | true | | blend best models during Autopilot run [DEPRECATED] | | »» blueprintThreshold | integer,null | true | | an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs | | »» considerBlendersInRecommendation | boolean | false | | Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED] | | »» defaultMonotonicDecreasingFeaturelistId | string,null | true | | null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time. | | »» defaultMonotonicIncreasingFeaturelistId | string,null | true | | null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time. | | »» downsampledMajorityRows | integer,null | true | | the total number of the majority rows available for modeling, or null for projects without smart downsampling | | »» downsampledMinorityRows | integer,null | true | | the total number of the minority rows available for modeling, or null for projects without smart downsampling | | »» eventsCount | string,null | false | | the name of the event count column, if specified, otherwise null. | | »» exposure | string,null | false | | the name of the exposure column, if specified. | | »» majorityDownsamplingRate | number,null | true | | the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling | | »» minSecondaryValidationModelCount | boolean | false | | Compute "All backtest" scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default. | | »» offset | [string] | false | | the list of names of the offset columns, if specified, otherwise null. | | »» onlyIncludeMonotonicBlueprints | boolean | true | | whether the project only includes blueprints support enforcing monotonic constraints | | »» prepareModelForDeployment | boolean,null | true | | Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning "RECOMMENDED FOR DEPLOYMENT" label. | | »» responseCap | any | true | | defaults to False, if specified used to cap the maximum response of a model | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | boolean | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | number | false | maximum: 1minimum: 0.5 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» runLeakageRemovedFeatureList | boolean | false | | Run Autopilot on Leakage Removed feature list (if exists). | | »» scoringCodeOnly | boolean | false | | Keep only models that can be converted to scorable java code during Autopilot run. | | »» seed | string,null | true | | defaults to null, the random seed to be used if specified | | »» shapOnlyMode | boolean,null | true | | Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always null. | | »» smartDownsampled | boolean | true | | whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows). | | »» weights | string,null | true | | the name of the weight column, if specified, otherwise null. | | » autopilotClusterList | [integer] | false | maxItems: 10 | Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'. | | » autopilotMode | integer | true | | The current autopilot mode, 0 for full autopilot, 2 for manual mode, 3 for quick mode, 4 for comprehensive mode | | » created | string(date-time) | true | | The time of project creation. | | » featureEngineeringPredictionPoint | string,null | false | | The date column to be used as the prediction point for time-based feature engineering. | | » fileName | string | true | | The name of the dataset used to create the project. | | » holdoutUnlocked | boolean | true | | whether the holdout has been unlocked | | » id | string | true | | The ID of a project. | | » maxClusters | integer,null | false | maximum: 100minimum: 2 | Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to minClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100. | | » maxTrainPct | number | true | | the maximum percentage of the dataset that can be used to successfully train a model without going into the validation data. | | » maxTrainRows | integer | true | | the maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data | | » metric | string | true | | the metric used to select the best-performing models. | | » minClusters | integer,null | false | maximum: 100minimum: 2 | Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to maxClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2. | | » partition | ProjectPartitionResponse | true | | The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose cvMethod is "datetime", full specification of the partitioning method can be found at [GET /api/v2/projects/{projectId}/datetimePartitioning/][get-apiv2projectsprojectiddatetimepartitioning]. | | »» cvHoldoutLevel | any | true | | if a user partition column was used with cross validation, the value assigned to the holdout set | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» cvMethod | string | true | | the partitioning method used. Note that "date" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0. | | »» datetimeCol | string,null | true | | if a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0. | | »» datetimePartitionColumn | string | false | | if a datetime partition column was used, the name of the column | | »» holdoutLevel | any | true | | if a user partition column was used with train-validation-holdout split, the value assigned to the holdout set | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» holdoutPct | number | true | | the percentage of the dataset reserved for the holdout set | | »» partitionKeyCols | [string] | true | | An array containing a single string - the name of the group partition column | | »» reps | number,null | true | | if cross validation was used, the number of folds to use | | »» trainingLevel | any | true | | if a user partition column was used with train-validation-holdout split, the value assigned to the training set | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» useTimeSeries | boolean,null | true | | A boolean value indicating whether a time series project was created as opposed to a regular project using datetime partitioning. | | »» userPartitionCol | string,null | true | | if a user partition column was used, the name of the column | | »» validationLevel | any | true | | if a user partition column was used with train-validation-holdout split, the value assigned to the validation set | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» validationPct | number,null | true | | if train-validation-holdout split was used, the percentage of the dataset used for the validation set | | »» validationType | string | true | | either CV for cross-validation or TVH for train-validation-holdout split | | » positiveClass | number,null | true | | if the project uses binary classification, the class designated to be the positive class. Otherwise, null. | | » projectName | string | true | | The name of a project. | | » stage | string | true | | the stage of the project - if modeling, then the target is successfully set, and modeling or predictions can proceed. | | » target | string | true | | the target of the project, null if project is unsupervised. | | » targetType | string,null | true | | The target type of the project. | | » unsupervisedMode | boolean | true | | indicates whether a project is unsupervised. | | » unsupervisedType | string,null | false | | Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'. | | » useCase | UseCaseIdName | false | | The information of the use case associated with the project. | | »» id | string,null | false | | The ID of the use case. | | »» name | string,null | false | | The name of the use case. | | » useFeatureDiscovery | boolean | true | | A boolean value indicating whether a feature discovery project was created as opposed to a regular project. | ### Enumerated Values | Property | Value | | --- | --- | | cvMethod | [random, user, stratified, group, datetime] | | validationType | [CV, TVH] | | targetType | [Binary, Regression, Multiclass, minInflated, Multilabel, TextGeneration, GeoPoint, VectorDatabase] | | unsupervisedType | [anomaly, clustering] | ## Create a project Operation path: `POST /api/v2/projects/` Authentication requirements: `BearerAuth` Create a new project. ### Body parameter ``` { "properties": { "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID. Can only be used along with datasetId or dataSourceId.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database. Can only be used along with datasetId or dataSourceId.", "type": "string", "x-versionadded": "v2.19" }, "dataSourceId": { "description": "Identifier for the data source to retrieve.", "type": "string" }, "datasetId": { "description": "The ID of the dataset entry to use for the project dataset.", "type": "string" }, "datasetVersionId": { "description": "Only used when also providing a datasetId, and specifies the the ID of the dataset version to use for the project dataset. If not specified, the latest version associated with the dataset ID is used.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "projectName": { "description": "The name of the project to be created. If not specified, 'Untitled Project' will be used for database connections and file name will be used as the project name.", "type": "string" }, "recipeId": { "description": "The ID of the wrangling recipe that will be used for project creation.", "type": "string" }, "url": { "description": "The URL to download the dataset used to create the project.", "format": "url", "type": "string" }, "useKerberos": { "description": "If true, use Kerberos authentication for database authentication. Default is false. Can only be used along with datasetId or dataSourceId.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for database authentication. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ProjectCreate | false | none | ### Example responses > 202 Response ``` { "properties": { "pid": { "description": "The project ID.", "type": "string" } }, "required": [ "pid" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Creation has successfully started. See the Location header. | ProjectCreateResponse | | 403 | Forbidden | User does not have permission to use specified dataset item for project. | None | | 404 | Not Found | The dataset item with the given ID or version ID is not found. | None | | 422 | Unprocessable Entity | Ingest not yet completed. | None | ## Delete a project by project ID Operation path: `DELETE /api/v2/projects/{projectId}/` Authentication requirements: `BearerAuth` Delete a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The project has been successfully deleted. | None | | 409 | Conflict | The project is in use and cannot be deleted. | None | ## Get project by project ID Operation path: `GET /api/v2/projects/{projectId}/` Authentication requirements: `BearerAuth` Look up a particular project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "advancedOptions": { "description": "Information related to the current model of the deployment.", "properties": { "allowedPairwiseInteractionGroups": { "description": "For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [[\"A\", \"B\", \"C\"], [\"C\", \"D\"]] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.", "items": { "items": { "type": "string" }, "type": "array" }, "type": "array", "x-versionadded": "v2.19" }, "blendBestModels": { "description": "blend best models during Autopilot run [DEPRECATED]", "type": "boolean", "x-versionadded": "v2.19", "x-versiondeprecated": "2.30.0" }, "blueprintThreshold": { "description": "an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs", "type": [ "integer", "null" ] }, "considerBlendersInRecommendation": { "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED]", "type": "boolean", "x-versionadded": "v2.21", "x-versiondeprecated": "2.30.0" }, "defaultMonotonicDecreasingFeaturelistId": { "description": "null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "defaultMonotonicIncreasingFeaturelistId": { "description": "null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "downsampledMajorityRows": { "description": "the total number of the majority rows available for modeling, or null for projects without smart downsampling", "type": [ "integer", "null" ], "x-versionadded": "v2.5" }, "downsampledMinorityRows": { "description": "the total number of the minority rows available for modeling, or null for projects without smart downsampling", "type": [ "integer", "null" ], "x-versionadded": "v2.5" }, "eventsCount": { "description": "the name of the event count column, if specified, otherwise null.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "exposure": { "description": "the name of the exposure column, if specified.", "type": [ "string", "null" ], "x-versionadded": "v2.6" }, "majorityDownsamplingRate": { "description": "the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling", "type": [ "number", "null" ], "x-versionadded": "v2.5" }, "minSecondaryValidationModelCount": { "description": "Compute \"All backtest\" scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.", "type": "boolean", "x-versionadded": "v2.19" }, "offset": { "description": "the list of names of the offset columns, if specified, otherwise null.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.6" }, "onlyIncludeMonotonicBlueprints": { "default": false, "description": "whether the project only includes blueprints support enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.11" }, "prepareModelForDeployment": { "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning \"RECOMMENDED FOR DEPLOYMENT\" label.", "type": [ "boolean", "null" ], "x-versionadded": "v2.19" }, "responseCap": { "description": "defaults to False, if specified used to cap the maximum response of a model", "oneOf": [ { "type": "boolean" }, { "maximum": 1, "minimum": 0.5, "type": "number" } ] }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean", "x-versionadded": "v2.21" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean", "x-versionadded": "v2.19" }, "seed": { "description": "defaults to null, the random seed to be used if specified", "type": [ "string", "null" ] }, "shapOnlyMode": { "description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always ``null``.", "type": [ "boolean", "null" ], "x-versionadded": "v2.21" }, "smartDownsampled": { "description": "whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).", "type": "boolean", "x-versionadded": "v2.5" }, "weights": { "description": "the name of the weight column, if specified, otherwise null.", "type": [ "string", "null" ] } }, "required": [ "blendBestModels", "blueprintThreshold", "defaultMonotonicDecreasingFeaturelistId", "defaultMonotonicIncreasingFeaturelistId", "downsampledMajorityRows", "downsampledMinorityRows", "majorityDownsamplingRate", "onlyIncludeMonotonicBlueprints", "prepareModelForDeployment", "responseCap", "seed", "shapOnlyMode", "smartDownsampled", "weights" ], "type": "object" }, "autopilotClusterList": { "description": "Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotMode": { "description": "The current autopilot mode. 0: Full Autopilot. 2: Manual Mode. 3: Quick Mode. 4: Comprehensive Autopilot. null: Mode not set.", "enum": [ "0", "2", "3", "4" ], "type": [ "string", "null" ] }, "catalogId": { "description": "The ID of the AI catalog entry used to create the project, or null if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "catalogVersionId": { "description": "The ID of the AI catalog version used to create the project, or null if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "created": { "description": "The time of project creation.", "format": "date-time", "type": "string" }, "externalTimeSeriesBaselineDatasetMetadata": { "description": "The id of the catalog item that is being used as the external baseline data", "properties": { "datasetId": { "description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.", "type": [ "string", "null" ] }, "datasetName": { "description": "The name of the timeseries baseline dataset for the project", "type": [ "string", "null" ] } }, "required": [ "datasetId", "datasetName" ], "type": "object" }, "featureEngineeringPredictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "fileName": { "description": "The name of the dataset used to create the project.", "type": "string" }, "holdoutUnlocked": { "description": "Whether the holdout has been unlocked.", "type": "boolean" }, "id": { "description": "The ID of the project.", "type": "string" }, "isScoringAvailableForModelsTrainedIntoValidationHoldout": { "description": "Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass.", "type": "boolean", "x-versionadded": "v2.31" }, "maxClusters": { "description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `minClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100.", "maximum": 100, "minimum": 2, "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "maxTrainPct": { "description": "The maximum percentage of the dataset that can be used to successfully train a model without going into the validation data.", "type": "number" }, "maxTrainRows": { "description": "The maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data.", "type": "integer" }, "metric": { "description": "The metric used to select the best-performing models.", "type": "string" }, "minClusters": { "description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `maxClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2.", "maximum": 100, "minimum": 2, "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "partition": { "description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null.", "properties": { "cvHoldoutLevel": { "description": "If a user partition column was used with cross validation, the value assigned to the holdout set", "type": [ "string", "null" ] }, "cvMethod": { "description": "The partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.", "enum": [ "random", "stratified", "datetime", "user", "group", "date" ], "type": "string" }, "datetimeCol": { "description": "If a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "If a datetime partition column was used, the name of the column.", "type": [ "string", "null" ] }, "holdoutLevel": { "description": "If a user partition column was used with train-validation-holdout split, the value assigned to the holdout set.", "type": [ "string", "null" ] }, "holdoutPct": { "description": "The percentage of the dataset reserved for the holdout set.", "type": [ "number", "null" ] }, "partitionKeyCols": { "description": "An array containing a single string - the name of the group partition column", "items": { "type": "string" }, "type": "array" }, "reps": { "description": "If cross validation was used, the number of folds to use.", "type": [ "integer", "null" ] }, "trainingLevel": { "description": "If a user partition column was used with train-validation-holdout split, the value assigned to the training set.", "type": [ "string", "null" ] }, "useTimeSeries": { "description": "Indicates whether a time series project was created as opposed to a regular project using datetime partitioning.", "type": [ "boolean", "null" ], "x-versionadded": "v2.9" }, "userPartitionCol": { "description": "If a user partition column was used, the name of the column.", "type": [ "string", "null" ] }, "validationLevel": { "description": "If a user partition column was used with train-validation-holdout split, the value assigned to the validation set.", "type": [ "string", "null" ] }, "validationPct": { "description": "If train-validation-holdout split was used, the percentage of the dataset used for the validation set.", "type": [ "number", "null" ] }, "validationType": { "description": "The type of validation used. Either CV (cross validation) or TVH (train-validation-holdout split).", "enum": [ "CV", "TVH" ], "type": "string" } }, "required": [ "cvHoldoutLevel", "cvMethod", "datetimeCol", "holdoutLevel", "holdoutPct", "partitionKeyCols", "reps", "trainingLevel", "userPartitionCol", "validationLevel", "validationPct", "validationType" ], "type": "object" }, "positiveClass": { "description": "If the project uses binary classification, the class designated to be the positive class.", "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "null" } ] }, "primaryLocationColumn": { "description": "Primary location column name", "type": "string", "x-versionadded": "v2.27" }, "projectName": { "description": "The name of the project.", "type": "string" }, "queryGeneratorId": { "description": "The ID of the time series data prep query generator associated with the project, or null if there is no associated query generator.", "type": [ "string", "null" ], "x-versionadded": "v2.27" }, "quickrun": { "description": "If the Autopilot mode is set to quick. DEPRECATED: look at autopilot_mode instead.", "type": "boolean", "x-versionadded": "v2.22", "x-versiondeprecated": "v2.30" }, "relationshipsConfigurationId": { "description": "Relationships configuration id to be used for Feature Discovery projects.", "type": [ "string", "null" ], "x-versionadded": "v2.27" }, "segmentation": { "description": "Segmentation info for the project.", "properties": { "parentProjectId": { "description": "ID of the original project.", "type": [ "string", "null" ] }, "segment": { "description": "Segment value.", "type": [ "string", "null" ] }, "segmentationTaskId": { "description": "ID of the Segmentation Task.", "type": "string" } }, "required": [ "segmentationTaskId" ], "type": "object" }, "stage": { "description": "The stage of the project. If modeling, then the target is successfully set and modeling or predictions can proceed.", "enum": [ "modeling", "aim", "fasteda", "eda", "eda2", "empty" ], "type": "string" }, "target": { "description": "The target of the project, null if project is unsupervised.", "type": "string" }, "targetType": { "description": "The type of the selected target. Null if the project is unsupervised.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel" ], "type": [ "string", "null" ] }, "unsupervisedMode": { "description": "Indicates whether a project is unsupervised.", "type": [ "boolean", "null" ], "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCase": { "description": "The information of the use case associated with the project.", "properties": { "id": { "description": "The ID of the use case.", "type": [ "string", "null" ] }, "name": { "description": "The name of the use case.", "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.36" }, "useFeatureDiscovery": { "description": "Indicates whether a feature discovery project was created as opposed to a regular project", "type": "boolean", "x-versionadded": "v2.21" }, "useGpu": { "description": "Indicates whether project should use GPU workers", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "required": [ "advancedOptions", "autopilotMode", "catalogId", "catalogVersionId", "created", "fileName", "holdoutUnlocked", "id", "isScoringAvailableForModelsTrainedIntoValidationHoldout", "maxTrainPct", "maxTrainRows", "metric", "partition", "positiveClass", "projectName", "queryGeneratorId", "quickrun", "stage", "target", "targetType", "unsupervisedMode", "useFeatureDiscovery" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The project. | ProjectRetrieveResponse | ## Update a project by project ID Operation path: `PATCH /api/v2/projects/{projectId}/` Authentication requirements: `BearerAuth` Change project name, worker count, or unlock the holdout. If any of the optional json arguments are not provided, that aspect of the project will not be altered. ### Body parameter ``` { "properties": { "gpuWorkerCount": { "description": "The desired new number of gpu workers if the\n number of gpu workers should be changed.\n Must not exceed the number of gpu workers available to the user. `0` is allowed.\n `-1` requests the maximum number available to the user.", "type": "integer" }, "holdoutUnlocked": { "description": "If specified, the holdout will be unlocked;\n note that the holdout cannot be relocked after unlocking", "enum": [ "True" ], "type": "string" }, "projectDescription": { "description": "The new description of the project, if the description should be updated.", "maxLength": 500, "type": "string", "x-versionadded": "v2.20" }, "projectName": { "description": "The new name of the project, if it should be renamed.", "maxLength": 100, "type": "string" }, "workerCount": { "description": "The desired new number of workers if the\n number of workers should be changed.\n Must not exceed the number of workers available to the user. `0` is allowed.\n (New in version v2.14) `-1` requests the maximum number available to the user.", "type": "integer" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | ProjectUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The project was successfully updated. | None | ## Get the project access control list by project ID Operation path: `GET /api/v2/projects/{projectId}/accessControl/` Authentication requirements: `BearerAuth` Get a list of users who have access to this project and their roles on the project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | username | query | string | false | Optional, only return the access control information for a user with this username. | | userId | query | string | false | Optional, only return the access control information for a user with this user ID. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The project's access control list. | SharingListResponse | | 404 | Not Found | Either the project does not exist or the user does not have permissions to view the project. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## Update the project's access controls by project ID Operation path: `PATCH /api/v2/projects/{projectId}/accessControl/` Authentication requirements: `BearerAuth` Set roles for users on this project. ### Body parameter ``` { "properties": { "data": { "description": "The role to set for the user.", "items": { "properties": { "role": { "description": "The role to set for the user.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to set the role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" }, "includeFeatureDiscoveryEntities": { "default": false, "description": "Whether to share all the related entities.", "type": "boolean", "x-versionadded": "v2.21" }, "sendNotification": { "default": true, "description": "Send an email notification.", "type": "boolean", "x-versionadded": "v2.20" } }, "required": [ "data" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | SharingUpdateOrRemove | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | None | | 409 | Conflict | The request would leave the project without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid | None | ## Start modeling by project ID Operation path: `PATCH /api/v2/projects/{projectId}/aim/` Authentication requirements: `BearerAuth` Start the data modeling process. ### Body parameter ``` { "properties": { "accuracyOptimizedMb": { "description": "Include additional, longer-running models that will be run by the autopilot and available to run manually.", "type": "boolean" }, "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": "string", "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": "boolean", "x-versionadded": "v2.24" }, "allowedPairwiseInteractionGroups": { "description": "For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [['A', 'B', 'C'], ['C', 'D']] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.", "items": { "items": { "type": "string" }, "maxItems": 10, "minItems": 2, "type": "array" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.20" }, "allowedPairwiseInteractionGroupsFilename": { "description": "Filename that was used to upload allowed_pairwise_interaction_groups. Necessary for persistence of UI/UX when you specify that parameter via file.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "autopilotClusterList": { "description": "A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotDataSamplingMethod": { "description": "Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "default": "duration", "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "autopilotWithFeatureDiscovery": { "description": "If true, autopilot will run on a feature list that includes features found via search for interactions.", "type": "boolean" }, "backtests": { "description": "An array specifying the format of the backtests.", "items": { "properties": { "gapDuration": { "description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.", "format": "duration", "type": "string" }, "index": { "description": "The index from zero of the backtest specified by this object.", "type": "integer" }, "primaryTrainingEndDate": { "description": "A datetime string representing the end date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "primaryTrainingStartDate": { "description": "A datetime string representing the start date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationDuration": { "description": "A duration string representing the duration of the validation data for this backtest.", "format": "duration", "type": "string" }, "validationEndDate": { "description": "A datetime string representing the end date of the validation data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "index", "validationStartDate" ], "type": "object" }, "maxItems": 50, "minItems": 1, "type": "array" }, "biasMitigationFeatureName": { "description": "The name of the protected feature used to mitigate bias on models.", "minLength": 1, "type": "string", "x-versionadded": "v2.27" }, "biasMitigationTechnique": { "description": "Method applied to perform bias mitigation.", "enum": [ "preprocessingReweighing", "postProcessingRejectionOptionBasedClassification" ], "type": "string", "x-versionadded": "v2.27" }, "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects.", "type": "boolean", "x-versionadded": "v2.19" }, "blueprintThreshold": { "description": "The runtime (in hours) which if exceeded will exclude a model from autopilot runs.", "maximum": 1440, "minimum": 1, "type": [ "integer", "null" ] }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": "string", "x-versionadded": "v2.15" }, "chunkDefinitionId": { "description": "Chunk definition id for incremental learning using chunking service", "type": "string", "x-versionadded": "v2.33" }, "classMappingAggregationSettings": { "description": "Class mapping aggregation settings.", "properties": { "aggregationClassName": { "description": "The name of the class that will be assigned to all rows with aggregated classes. Should not match any excluded_from_aggregation or we will have 2 classes with the same name and no way to distinguish between them. This option is only available formulticlass projects. By default 'DR_RARE_TARGET_VALUES' is used.", "type": "string" }, "excludedFromAggregation": { "default": [], "description": "List of target values that should be guaranteed to kept as is, regardless of other settings.", "items": { "type": "string" }, "type": "array" }, "maxUnaggregatedClassValues": { "default": 1000, "description": "The maximum number of unique labels before aggregation kicks in. Should be at least len(excludedFromAggregation) + 1 for multiclass and at least len(excludedFromAggregation) for multilabel.", "maximum": 1000, "minimum": 3, "type": "integer" }, "minClassSupport": { "default": 1, "description": "Minimum number of instances necessary for each target value in the dataset. All values with fewer instances than this value will be aggregated", "type": "integer" } }, "type": "object" }, "considerBlendersInRecommendation": { "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects.", "type": "boolean", "x-versionadded": "v2.21" }, "credentials": { "description": "List of credentials for the secondary datasets used in feature discovery project.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" } }, "required": [ "credentialId" ], "type": "object" } ] }, "maxItems": 30, "type": "array", "x-versionadded": "v2.19" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "customMetricsLossesInfo": { "description": "Returns custom metrics information. This field is required for custom metrics projects.", "properties": { "id": { "description": "The ID of an existing custom_metrics_loss_function document for this project.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.44" }, "cvHoldoutLevel": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "null" } ], "description": "The value of the partition column indicating a row is part of the holdout set. This level is optional - if not specified or if provided as ``null``, then no holdout will be used in the project. The rest of the levels indicate which cross validation fold each row should fall into." }, "cvMethod": { "description": "The partitioning method to be applied to the training data.", "enum": [ "random", "user", "stratified", "group", "datetime" ], "type": "string" }, "dateRemoval": { "description": "If true, enable creating additional feature lists without dates (does not apply to time-aware projects).", "type": "boolean", "x-versionadded": "v2.21" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of a datetime partitioning to use for the project.When datetime_partitioning_id is specified, no other datetime partitioning related field is allowed to be specified, as these fields get loaded from the already created partitioning.", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": "string", "x-versionadded": "v2.9" }, "disableHoldout": { "default": false, "description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.", "type": "boolean", "x-versionadded": "v2.8" }, "eventsCount": { "description": "The name of a column specifying events count. The data in this column must be pure numeric and non negative without missing values", "type": "string" }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "maximum": 1, "minimum": 0, "type": "number" }, "exposure": { "description": "The name of a column specifying row exposure.The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values", "type": "string" }, "externalPredictions": { "description": "List of external prediction columns from the dataset.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.26" }, "externalTimeSeriesBaselineDatasetId": { "description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.", "type": "string", "x-versionadded": "v2.24" }, "externalTimeSeriesBaselineDatasetName": { "description": "The name of the time series baseline dataset for the project.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "fairnessMetricsSet": { "description": "Metric to use for calculating fairness. Can be one of ``proportionalParity``, ``equalParity``, ``predictionBalance``, ``trueFavorableAndUnfavorableRateParity`` or ``FavorableAndUnfavorablePredictiveValueParity``. Used and required only if *Bias & Fairness in AutoML* feature is enabled.", "enum": [ "proportionalParity", "equalParity", "predictionBalance", "trueFavorableAndUnfavorableRateParity", "favorableAndUnfavorablePredictiveValueParity" ], "type": "string", "x-versionadded": "v2.24" }, "fairnessThreshold": { "description": "The threshold value of the fairness metric. The valid range is [0:1]; the default fairness metric value is 0.8. This metric is only applicable if the *Bias & Fairness in AutoML* feature is enabled.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.24" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureDiscoverySupervisedFeatureReduction": { "description": "Run supervised feature reduction for feature discovery projects.", "type": "boolean", "x-versionadded": "v2.21" }, "featureEngineeringPredictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array" }, "featurelistId": { "description": "The ID of a featurelist to use for autopilot.", "type": "string" }, "forecastDistance": { "description": "The name of a column specifying the forecast distance to which each row of the dataset belongs. Column unique values are used to subset the modeling data and build a separate model for each unique column value. Similar to time series this column is well suited to be used as forecast distance.", "type": "string", "x-versionadded": "v2.35" }, "forecastOffsets": { "description": "An array of strings with names of a columns specifying row offsets. Columns values are used as offset or predictions to boost for models. The data in this column must be pure numeric (e.g. not currency, date, length, etc.).", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.35" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).", "format": "duration", "type": "string" }, "holdoutDuration": { "description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "holdoutLevel": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ], "description": "The value of the partition column indicating a row is part of the holdout set. This level is optional - if not specified or if provided as ``null``, then no holdout will be used in the project. However, the column must have exactly 2 values in order for this option to be valid" }, "holdoutPct": { "description": "The percentage of the dataset to assign to the holdout set", "maximum": 98, "minimum": 0, "type": "number" }, "holdoutStartDate": { "description": "The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "includeBiasMitigationFeatureAsPredictorVariable": { "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.", "type": "boolean", "x-versionadded": "v2.27" }, "incrementalLearningEarlyStoppingRounds": { "description": "Early stopping rounds for the auto incremental learning service", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "incrementalLearningOnBestModel": { "description": "Automatically run incremental learning on the best model during Autopilot run.", "type": "boolean", "x-versionadded": "v2.33" }, "incrementalLearningOnlyMode": { "description": "Keep only models that support incremental learning during Autopilot run.", "type": "boolean", "x-versionadded": "v2.33" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "majorityDownsamplingRate": { "description": "The percentage between 0 and 100 of the majority rows that should be kept. Must be specified only if using smart downsampling. If not specified, a default will be selected based on the dataset distribution. The chosen rate may not cause the majority class to become smaller than the minority class.", "type": "number" }, "metric": { "description": "The metric to use to select the best models. See `/api/v2/projects/(projectId)/features/metrics/` for the metrics that may be valid for a potential target. Note that weighted metrics must be used with a weights column.", "type": "string" }, "minSecondaryValidationModelCount": { "description": "Compute 'All backtest' scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.", "maximum": 10, "minimum": 0, "type": "integer", "x-versionadded": "v2.19" }, "mode": { "description": "The autopilot mode to use. Either 'quick', 'auto', 'manual', or 'comprehensive'.", "enum": [ "0", "2", "4", "3", "auto", "manual", "comprehensive", "quick" ], "type": "string" }, "modelSplits": { "default": 5, "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "monotonicDecreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired.", "type": [ "string", "null" ] }, "monotonicIncreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired.", "type": [ "string", "null" ] }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "type": "integer" }, "numberOfIncrementalLearningIterationsBeforeBestModelSelection": { "description": "Number of incremental_learning iterations before best model selection.", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.35" }, "offset": { "description": "An array of strings with names of a columns specifying row offsets.The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values", "items": { "type": "string" }, "type": "array" }, "onlyIncludeMonotonicBlueprints": { "default": false, "description": "When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot.", "type": "boolean" }, "partitionKeyCols": { "description": "An array containing a single string - the name of the group partition column", "items": { "type": "string" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "positiveClass": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ], "description": "A value from the target column to use for the positive class. May only be specified for projects doing binary classification.If not specified, a positive class is selected automatically." }, "preferableTargetValue": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ], "description": "A target value that should be treated as a positive outcome for the prediction. For example if we want to check gender discrimination for giving a loan and our target named ``is_bad``, then the positive outcome for the prediction would be ``No``, which means that the loan is good and that's what we treat as a preferable result for the loaner. Used and required only if *Bias & Fairness in AutoML* feature is enabled.", "x-versionadded": "v2.24" }, "prepareModelForDeployment": { "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning 'RECOMMENDED FOR DEPLOYMENT' label.", "type": "boolean", "x-versionadded": "v2.19" }, "primaryLocationColumn": { "description": "Primary geospatial location column.", "type": [ "string", "null" ] }, "protectedFeatures": { "description": "A list of project feature to mark as protected for Bias metric calculation and Fairness correction. Used and required only if *Bias & Fairness in AutoML* feature is enabled.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.24" }, "quantileLevel": { "description": "The quantile level between 0.01 and 0.99 for specifying the Quantile metric.", "exclusiveMaximum": 1, "exclusiveMinimum": 0, "type": "number", "x-versionadded": "v2.22" }, "quickrun": { "description": "(Deprecated): 'quick' should be used in the `mode` parameter instead of using this parameter. If set to `true`, autopilot mode will be set to 'quick'.Cannot be set to `true` when `mode` is set to 'comprehensive' or 'manual'.", "type": "boolean", "x-versiondeprecated": "v2.21" }, "rateTopPctThreshold": { "description": "The percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric.", "maximum": 100, "minimum": 0, "type": "number" }, "relationshipsConfigurationId": { "description": "Relationships configuration id to be used for Feature Discovery projects.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "reps": { "description": "The number of cross validation folds to use.", "maximum": 999999, "minimum": 2, "type": "integer" }, "responseCap": { "description": "Used to cap the maximum response of a model", "maximum": 1, "minimum": 0.5, "type": "number" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean", "x-versionadded": "v2.21" }, "sampleStepPct": { "description": "A float between 0 and 100 indicating the desired percentage of data to sample when training models in comprehensive Autopilot. Note: this only supported for comprehensive Autopilot and the specified value may be lowered in order to be compatible with the project's dataset and partition settings.", "exclusiveMinimum": 0, "maximum": 100, "type": "number", "x-versionadded": "v2.20" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean", "x-versionadded": "v2.19" }, "seed": { "description": "A seed to use for randomization.", "maximum": 999999999, "minimum": 0, "type": "integer" }, "segmentationTaskId": { "description": "Specifies the SegmentationTask that will be used for dividing the project up into multiple segmented projects.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "seriesId": { "description": "The name of a column specifying the series ID to which each row of the dataset belongs. Typically the series was used to derive the additional features, that are independent from each other. Column unique values are used to subset the modeling data and build a separate model for each unique column value. Similar to time series this column is well suited to be used as multi-series ID column.", "type": "string", "x-versionadded": "v2.35" }, "shapOnlyMode": { "description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible.", "type": "boolean", "x-versionadded": "v2.21" }, "smartDownsampled": { "description": "Whether to use smart downsampling to throw away excess rows of the majority class. Only applicable to classification and zero-boosted regression projects.", "type": "boolean" }, "stopWords": { "description": "A list of stop words to be used for text blueprints. Note: ``stop_words=True`` must be set in the blueprint preprocessing parameters for this list of stop words to actually be used during preprocessing.", "items": { "maxLength": 100, "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.21" }, "target": { "description": "The name of the target feature.", "type": "string" }, "targetType": { "description": "Used to specify the targetType to use for a project when it is ambiguous, i.e. a numeric target with a few unique values that could be used for either regression or multiclass.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel" ], "type": "string" }, "trainingLevel": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ], "description": "The value of the partition column indicating a row is part of the training set." }, "treatAsExponential": { "default": "auto", "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed.", "enum": [ "auto", "never", "always" ], "type": "string" }, "unsupervisedMode": { "default": false, "description": "If True, unsupervised project (without target) will be created. ``target`` cannot be specified if ``unsupervisedMode`` is True.", "type": "boolean", "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCrossSeriesFeatures": { "description": "Indicating if user wants to use cross-series features.", "type": "boolean" }, "useGpu": { "description": "Indicates whether project should use GPU workers", "type": "boolean", "x-versionadded": "v2.31" }, "useProjectSettings": { "description": "Specifies whether datetime-partitioned project should use project settings (i.e. backtests configuration has been modified by the user).", "type": "boolean", "x-versionadded": "v2.25" }, "useSupervisedFeatureReduction": { "default": true, "description": "When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.", "type": "boolean" }, "useTimeSeries": { "default": false, "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "userPartitionCol": { "description": "The name of the column containing the partition assignments.", "type": "string" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.", "format": "duration", "type": "string" }, "validationLevel": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ], "description": "The value of the partition column indicating a row is part of the validation set." }, "validationPct": { "description": "The percentage of the dataset to assign to the validation set", "maximum": 99, "minimum": 0, "type": "number" }, "validationType": { "description": "The validation method to be used. CV for cross validation or TVH for train-validation-holdout split.", "enum": [ "CV", "TVH" ], "type": "string" }, "weights": { "description": "The name of a column specifying row weights. The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values", "type": "string" }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.14" } }, "required": [ "autopilotDataSelectionMethod", "onlyIncludeMonotonicBlueprints" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | Aim | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Autopilot has successfully started. See the Location header. | None | ## Pause by project ID Operation path: `POST /api/v2/projects/{projectId}/autopilot/` Authentication requirements: `BearerAuth` Pause or unpause the autopilot for a project. ### Body parameter ``` { "properties": { "command": { "description": "If `start`, will unpause the autopilot and run queued jobs if workers are available. If `stop`, will pause the autopilot so no new jobs will be started.", "enum": [ "start", "stop" ], "type": "string" } }, "required": [ "command" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | Autopilot | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The request was received. | None | ## Start autopilot by project ID Operation path: `POST /api/v2/projects/{projectId}/autopilots/` Authentication requirements: `BearerAuth` Start autopilot on provided featurelist. ### Body parameter ``` { "properties": { "autopilotClusterList": { "description": "Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects.", "type": "boolean", "x-versionadded": "v2.21" }, "considerBlendersInRecommendation": { "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects.", "type": "boolean", "x-versionadded": "v2.21" }, "featurelistId": { "description": "The ID of a featurelist that should be used for autopilot.", "type": "string" }, "mode": { "default": "auto", "description": "The Autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "prepareModelForDeployment": { "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning \"RECOMMENDED FOR DEPLOYMENT\" label.", "type": "boolean", "x-versionadded": "v2.21" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean", "x-versionadded": "v2.21" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean", "x-versionadded": "v2.21" }, "useGpu": { "description": "Use GPU workers for Autopilot run.", "type": "boolean", "x-versionadded": "v2.33" } }, "required": [ "featurelistId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | AutopilotStart | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successfully started. | None | | 422 | Unprocessable Entity | Autopilot on this featurelist has already completed or is already in progress. This status code is also returned if target was not selected for specified project. | None | ## Retrieve datetime partitioning configuration by project ID Operation path: `GET /api/v2/projects/{projectId}/datetimePartitioning/` Authentication requirements: `BearerAuth` Retrieve datetime partitioning configuration The datetime partition object in the response describes the full partitioning parameters. Since it becomes available after the target has been fully specified and the project is ready for modeling, there are some additional fields available compared to the response from [POST /api/v2/projects/{projectId}/datetimePartitioning/][post-apiv2projectsprojectiddatetimepartitioning]. The available training data corresponds to all the data available for training, while the primary training data corresponds to the data that can be used to train while ensuring that all backtests are available. If a model is trained with more data than is available in the primary training data, then all backtests may not have scores available. All durations and datetimes will be specified in accordance with the :ref: `timestamp and duration formatting rules`. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "autopilotDataSamplingMethod": { "description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "availableHoldoutEndDate": { "description": "The maximum valid date of holdout scoring data.", "format": "date-time", "type": [ "string", "null" ] }, "availableTrainingDuration": { "description": "The duration of available training duration for scoring the holdout.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "availableTrainingRowCount": { "description": "The number of rows in the available training data for scoring the holdout partition", "type": "integer" }, "availableTrainingStartDate": { "description": "The start date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingRowCount": { "description": "The number of rows in the available training data for this backtest.", "type": "integer" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapRowCount": { "description": "The number of rows in the gap between the training and the validation scoring data for this backtest.", "type": "integer" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingRowCount": { "description": "The number of rows in the primary training data for this backtest.", "type": "integer" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "totalRowCount": { "description": "The total number of rows in this backtest", "type": "integer" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationRowCount": { "description": "The number of rows in the validation scoring data for this backtest.", "type": "integer" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingRowCount", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapRowCount", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingRowCount", "primaryTrainingStartDate", "totalRowCount", "validationDuration", "validationEndDate", "validationRowCount", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.15" }, "calendarName": { "description": "The name of the calendar used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.17" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "dateFormat": { "description": "The date format of the partition column.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of the current optimized datetime partitioning", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "disableHoldout": { "description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array", "x-versionadded": "v2.9" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between the training and holdout scoring data.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "gapRowCount": { "description": "The number of rows in the gap between the training and holdout scoring data", "type": "integer" }, "gapStartDate": { "description": "The start date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutDuration": { "description": "The duration of the holdout scoring data.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutRowCount": { "description": "The number of rows in the holdout scoring data", "type": "integer" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "numberOfDoNotDeriveFeatures": { "description": "Number of features that are marked as \"do not derive\".", "type": "integer", "x-versionadded": "v2.17" }, "numberOfKnownInAdvanceFeatures": { "description": "Number of features that are marked as \"known in advance\".", "type": "integer", "x-versionadded": "v2.14" }, "partitioningExtendedWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "partitioningWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "type": "string" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "primaryTrainingDuration": { "description": "The duration of primary training duration for scoring the holdout.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "primaryTrainingRowCount": { "description": "The number of rows in the primary training data for scoring the holdout", "type": "integer" }, "primaryTrainingStartDate": { "description": "The start date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "totalRowCount": { "description": "The total number of rows in the project dataset", "type": "integer" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "unsupervisedMode": { "description": "A boolean value indicating whether an unsupervised project should be created.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "unsupervisedType": { "description": "The type of unsupervised project.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.31" }, "useCrossSeriesFeatures": { "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": [ "boolean", "null" ], "x-versionadded": "v2.14" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.", "format": "duration", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" } }, "required": [ "aggregationType", "autopilotDataSamplingMethod", "autopilotDataSelectionMethod", "availableHoldoutEndDate", "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingRowCount", "availableTrainingStartDate", "backtests", "calendarId", "calendarName", "crossSeriesGroupByColumns", "dateFormat", "datetimePartitionColumn", "defaultToAPriori", "defaultToDoNotDerive", "defaultToKnownInAdvance", "differencingMethod", "disableHoldout", "featureDerivationWindowEnd", "featureDerivationWindowStart", "featureSettings", "forecastWindowEnd", "forecastWindowStart", "gapDuration", "gapEndDate", "gapRowCount", "gapStartDate", "holdoutDuration", "holdoutEndDate", "holdoutRowCount", "holdoutStartDate", "modelSplits", "multiseriesIdColumns", "numberOfBacktests", "numberOfDoNotDeriveFeatures", "numberOfKnownInAdvanceFeatures", "partitioningWarnings", "periodicities", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingRowCount", "primaryTrainingStartDate", "projectId", "totalRowCount", "treatAsExponential", "useCrossSeriesFeatures", "useTimeSeries", "validationDuration", "windowsBasisUnit" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Generated datetime partitioning. | FinalDatetimePartitioningResponse | | 422 | Unprocessable Entity | Partitioning has not been set. | None | ## Preview the fully specified datetime partitioning generated by the requested configuration by project ID Operation path: `POST /api/v2/projects/{projectId}/datetimePartitioning/` Authentication requirements: `BearerAuth` Preview the fully specified datetime partitioning generated by the requested configuration. DEPRECATED: Please use the optimized partitioning route instead: [POST /api/v2/projects/{projectId}/optimizedDatetimePartitionings/][post-apiv2projectsprojectidoptimizeddatetimepartitionings] Populates the full datetime partitioning that would be used if the same arguments were passed to [PATCH /api/v2/projects/{projectId}/aim/][patch-apiv2projectsprojectidaim] based on the requested configuration, generating defaults for all non-specified values, so that potential configurations can be tested prior to setting the target and applying a configuration. `useTimeSeries` controls whether a time series project should be created or a normal project that uses datetime partitioning. See :ref: `Time-Series Projects` for more detail on the differences between time series projects and datetime partitioned projects. Time-series projects are only available to some users and use the additional settings of `featureDerivationWindowStart` and `featureDerivationWindowEnd` to establish feature derivation window and `forecastWindowStart` and `forecastWindowEnd` to establish a forecast window. The overview referenced above provides more information about using feature derivation and forecast windows. When specifying a feature derivation window of a forecast window, the number of units it spans (end - start) must be an integer multiple of the timeStep of the datetimePartitionColumn. All durations and datetimes should be specified in accordance with the :ref: `timestamp and duration formatting rules`. ### Body parameter ``` { "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": "string", "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "default": false, "description": "Specifies whether the time series predictions can use partial historical data.", "type": "boolean", "x-versionadded": "v2.24" }, "autopilotClusterList": { "description": "A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotDataSamplingMethod": { "description": "Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "backtests": { "description": "An array specifying individual backtests.", "items": { "oneOf": [ { "description": "Method 1 - pass validation and gap durations", "properties": { "gapDuration": { "description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.", "format": "duration", "type": "string" }, "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "validationDuration": { "description": "A duration string representing the duration of the validation data for this backtest.", "format": "duration", "type": "string" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "gapDuration", "index", "validationDuration", "validationStartDate" ], "type": "object" }, { "description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.", "properties": { "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "primaryTrainingEndDate": { "description": "A datetime string representing the end date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "primaryTrainingStartDate": { "description": "A datetime string representing the start date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationEndDate": { "description": "A datetime string representing the end date of the validation data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "index", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationEndDate", "validationStartDate" ], "type": "object" } ] }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": "string", "x-versionadded": "v2.15" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": "string", "x-versionadded": "v2.9" }, "disableHoldout": { "default": false, "description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).", "format": "duration", "type": "string" }, "holdoutDuration": { "description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "default": 5, "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": "string", "x-versionadded": "v2.9" }, "unsupervisedMode": { "default": false, "description": "A boolean value indicating whether an unsupervised project should be created.", "type": "boolean", "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCrossSeriesFeatures": { "default": false, "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": "boolean", "x-versionadded": "v2.14" }, "useSupervisedFeatureReduction": { "default": true, "description": "When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.", "type": "boolean" }, "useTimeSeries": { "default": false, "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.", "format": "duration", "type": "string" }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.14" } }, "required": [ "datetimePartitionColumn" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | DatetimePartitioningDataForOpenApi | false | none | ### Example responses > 200 Response ``` { "description": "Partitioning information for a datetime project.", "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "autopilotDataSamplingMethod": { "description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "availableHoldoutEndDate": { "description": "The maximum valid date of holdout scoring data.", "format": "date-time", "type": [ "string", "null" ] }, "availableTrainingDuration": { "description": "The duration of available training duration for scoring the holdout.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationDuration", "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.15" }, "calendarName": { "description": "The name of the calendar used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.17" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "dateFormat": { "description": "The date format of the partition column.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of the current optimized datetime partitioning", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "disableHoldout": { "description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array", "x-versionadded": "v2.9" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between the training and holdout scoring data.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutDuration": { "description": "The duration of the holdout scoring data.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "numberOfDoNotDeriveFeatures": { "description": "Number of features that are marked as \"do not derive\".", "type": "integer", "x-versionadded": "v2.17" }, "numberOfKnownInAdvanceFeatures": { "description": "Number of features that are marked as \"known in advance\".", "type": "integer", "x-versionadded": "v2.14" }, "partitioningExtendedWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "partitioningWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "type": "string" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "primaryTrainingDuration": { "description": "The duration of primary training duration for scoring the holdout.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "unsupervisedMode": { "description": "A boolean value indicating whether an unsupervised project should be created.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "unsupervisedType": { "description": "The type of unsupervised project.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.31" }, "useCrossSeriesFeatures": { "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": [ "boolean", "null" ], "x-versionadded": "v2.14" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.", "format": "duration", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" } }, "required": [ "autopilotDataSamplingMethod", "autopilotDataSelectionMethod", "availableHoldoutEndDate", "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "backtests", "dateFormat", "datetimePartitionColumn", "defaultToAPriori", "defaultToDoNotDerive", "defaultToKnownInAdvance", "differencingMethod", "disableHoldout", "featureDerivationWindowEnd", "featureDerivationWindowStart", "featureSettings", "forecastWindowEnd", "forecastWindowStart", "gapDuration", "gapEndDate", "gapStartDate", "holdoutDuration", "holdoutEndDate", "holdoutStartDate", "multiseriesIdColumns", "numberOfBacktests", "numberOfDoNotDeriveFeatures", "numberOfKnownInAdvanceFeatures", "partitioningWarnings", "periodicities", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "projectId", "treatAsExponential", "useTimeSeries", "validationDuration", "windowsBasisUnit" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The generated datetime partitioning. | DatetimePartitioningResponse | | 404 | Not Found | Requested feature was not found. | None | | 422 | Unprocessable Entity | Partitioning generation failed. | None | ## Validate baseline data by project ID Operation path: `POST /api/v2/projects/{projectId}/externalTimeSeriesBaselineDataValidationJobs/` Authentication requirements: `BearerAuth` This route validates if a provided catalog version id can be used as baseline for calculating metrics. This functionality is available only for time series projects.For a baseline dataset to be valid, the number of unique date amd multiseries_id columnrows must match the unique number of date and multiseries_id column rows in the uploadedtraining dataset. This functionality is limited to one forecast distance. Additionally, the catalog must be a snapshot. ### Body parameter ``` { "properties": { "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "catalogVersionId": { "description": "The version ID of the external baseline data item in the AI catalog.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as the datetime partition column for the specified project.", "type": "string" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "multiseriesIdColumns": { "description": "An array of column names identifying the multiseries ID column(s)to use to identify series within the data. Must match the multiseries ID column(s) for the specified project. Currently, only one multiseries ID column may be specified.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "target": { "description": "The selected target of the specified project.", "type": "string" } }, "required": [ "catalogVersionId", "datetimePartitionColumn", "forecastWindowEnd", "forecastWindowStart", "target" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | ExternalTSBaselinePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Validate baseline data that is provided in the form of a catalog version id. We willconfirm that the dataset contains the proper date, target column, and multiseries ID column. If the provided dataset meets the criteria, the job will be successful. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 422 | Unprocessable Entity | Unable to process the external time series baseline validation job. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve the baseline validation job by project ID Operation path: `GET /api/v2/projects/{projectId}/externalTimeSeriesBaselineDataValidationJobs/{baselineValidationJobId}/` Authentication requirements: `BearerAuth` Retrieve information to confirm if the validation job triggered via /api/v2/projects/(projectId)/externalTimeSeriesBaselineDataValidationJobs/ is valid. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project to retrieve the validation job information from. | | baselineValidationJobId | path | string | true | The ID for the validation job. | ### Example responses > 200 Response ``` { "properties": { "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "baselineValidationJobId": { "description": "The ID of the validation job.", "type": "string" }, "catalogVersionId": { "description": "The version ID of the external baseline data item in the AI catalog.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as the datetime partition column for the specified project.", "type": "string" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isExternalBaselineDatasetValid": { "description": "Indicates whether the external dataset has passed the validation check or not.", "type": "boolean" }, "message": { "description": "A message providing more detail on the validation result.", "type": [ "string", "null" ] }, "multiseriesIdColumns": { "description": "An array of column names identifying the multiseries ID column(s)to use to identify series within the data. Must match the multiseries ID column(s) for the specified project. Currently, only one multiseries ID column may be specified.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "projectId": { "description": "The project ID of the external baseline data item.", "type": "string" }, "target": { "description": "The selected target of the specified project.", "type": "string" } }, "required": [ "baselineValidationJobId", "catalogVersionId", "datetimePartitionColumn", "isExternalBaselineDatasetValid", "message", "projectId", "target" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ExternalTSBaselineResponse | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | External time series validation job not found. | None | ## List project jobs by project ID Operation path: `GET /api/v2/projects/{projectId}/jobs/` Authentication requirements: `BearerAuth` List the project's jobs. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | status | query | string | false | If provided, only jobs with the same status will be included in the results; otherwise, queued and inprogress jobs (but not errored jobs) will be returned. | | projectId | path | string | true | The project ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | status | [queue, inprogress, error] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of jobs returned.", "type": "integer" }, "jobs": { "description": "A JSON array of jobs.", "items": { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if the job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "jobType": { "description": "The job type.", "enum": [ "model", "predict", "trainingPredictions", "featureImpact", "featureEffects", "shapImpact", "anomalyAssessment", "shapExplanations", "shapMatrix", "reasonCodesInitialization", "reasonCodes", "predictionExplanations", "predictionExplanationsInitialization", "primeDownloadValidation", "ruleFitDownloadValidation", "primeRulesets", "primeModel", "modelExport", "usageData", "modelXRay", "accuracyOverTime", "seriesAccuracy", "validateRatingTable", "generateComplianceDocumentation", "automatedDocumentation", "eda", "pipeline", "calculatePredictionIntervals", "calculatePredictionIntervalBoundUsingOnlineConformal", "batchVarTypeTransform", "computeImageActivationMaps", "computeImageAugmentations", "computeImageEmbeddings", "computeDocumentTextExtractionSamples", "externalDatasetInsights", "startDatetimePartitioning", "runSegmentationTasks", "piiDetection", "computeBiasAndFairness", "sensitivityTesting", "clusterInsights", "onnxExport", "scoringCodeSegmentedModeling", "insights", "distributionPredictionModel", "batchScoringAvailableForecastPoints", "notebooksScheduling", "uncategorized" ], "type": "string" }, "message": { "description": "Error message in case of failure.", "type": "string" }, "modelId": { "description": "The model this job is associated with.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The job status.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" }, "url": { "description": "A URL that can be used to request details about the job.", "type": "string" } }, "required": [ "id", "isBlocked", "jobType", "message", "modelId", "projectId", "status", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page (if null, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page (if null, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "jobs", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of the project's jobs. | JobListResponse | ## Cancel a job by project ID Operation path: `DELETE /api/v2/projects/{projectId}/jobs/{jobId}/` Authentication requirements: `BearerAuth` Cancel a pending job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | jobId | path | string | true | The job ID. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The job has been canceled. | None | ## Get a job by project ID Operation path: `GET /api/v2/projects/{projectId}/jobs/{jobId}/` Authentication requirements: `BearerAuth` Retrieve details for a job that has been started but has not yet completed. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | jobId | path | string | true | The job ID. | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if the job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "jobType": { "description": "The job type.", "enum": [ "model", "predict", "trainingPredictions", "featureImpact", "featureEffects", "shapImpact", "anomalyAssessment", "shapExplanations", "shapMatrix", "reasonCodesInitialization", "reasonCodes", "predictionExplanations", "predictionExplanationsInitialization", "primeDownloadValidation", "ruleFitDownloadValidation", "primeRulesets", "primeModel", "modelExport", "usageData", "modelXRay", "accuracyOverTime", "seriesAccuracy", "validateRatingTable", "generateComplianceDocumentation", "automatedDocumentation", "eda", "pipeline", "calculatePredictionIntervals", "calculatePredictionIntervalBoundUsingOnlineConformal", "batchVarTypeTransform", "computeImageActivationMaps", "computeImageAugmentations", "computeImageEmbeddings", "computeDocumentTextExtractionSamples", "externalDatasetInsights", "startDatetimePartitioning", "runSegmentationTasks", "piiDetection", "computeBiasAndFairness", "sensitivityTesting", "clusterInsights", "onnxExport", "scoringCodeSegmentedModeling", "insights", "distributionPredictionModel", "batchScoringAvailableForecastPoints", "notebooksScheduling", "uncategorized" ], "type": "string" }, "message": { "description": "Error message in case of failure.", "type": "string" }, "modelId": { "description": "The model this job is associated with.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The job status.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" }, "url": { "description": "A URL that can be used to request details about the job.", "type": "string" } }, "required": [ "id", "isBlocked", "jobType", "message", "modelId", "projectId", "status", "url" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The job details. | JobDetailsResponse | | 303 | See Other | The requested job has already finished. See the Location header for the job details. | None | ## Retrieve eligible cross-series group-by columns by project ID Operation path: `GET /api/v2/projects/{projectId}/multiseriesIds/{multiseriesId}/crossSeriesProperties/` Authentication requirements: `BearerAuth` Retrieve eligible cross-series group-by columns. Note that validation will have to have been triggered via [POST /api/v2/projects/{projectId}/crossSeriesProperties/][post-apiv2projectsprojectidcrossseriesproperties] in order for results to appear here. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | crossSeriesGroupByColumns | query | any | false | The names of the columns to retrieve the validation status for. If not specified, all eligible columns will be returned. | | projectId | path | string | true | The project to retrieve cross-series group-by columns for. | | multiseriesId | path | string | true | The name of the column to be used as the multiseries ID column. | ### Example responses > 200 Response ``` { "properties": { "crossSeriesGroupByColumns": { "description": "A list of columns with information about each column's eligibility as a cross-series group-by column.", "items": { "properties": { "eligibility": { "description": "Information about the column's eligibility. If the column is not eligible, this will include the reason why.", "type": "string" }, "isEligible": { "description": "Indicates whether this column can be used as a group-by column.", "type": "boolean" }, "name": { "description": "The name of the column.", "type": "string" } }, "required": [ "eligibility", "isEligible", "name" ], "type": "object" }, "type": "array" }, "multiseriesId": { "description": "The name of the multiseries ID column.", "type": "string" } }, "required": [ "crossSeriesGroupByColumns", "multiseriesId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Request was successful. | CrossSeriesGroupByColumnRetrieveResponse | ## List the names of a multiseries project by project ID Operation path: `GET /api/v2/projects/{projectId}/multiseriesNames/` Authentication requirements: `BearerAuth` List the individual series names of a multiseries project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The total number of series items in the response.", "type": "integer" }, "data": { "description": "The data fields of the multiseries names.", "properties": { "items": { "description": "The list of series names.", "items": { "description": "Name of one series item", "type": "string" }, "type": "array" } }, "required": [ "items" ], "type": "object" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] }, "totalSeriesCount": { "description": "The total number of series items.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalSeriesCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | MultiseriesNamesControllerResponse | ## Detect multiseries properties by project ID Operation path: `POST /api/v2/projects/{projectId}/multiseriesProperties/` Authentication requirements: `BearerAuth` Analyze relationships between potential partition and multiseries ID columns. Time series projects require that each timestamp have at most one row corresponding to it. However, multiple series of data can be handled within a single project by designating a multiseries ID column that assigns each row to a particular series. See the :ref: `multiseries ` docs on time series projects for more information. A detection job analyzing the relationship between the multiseries ID column and the datetime partition column must be ran before it can be used. If the desired multiseries ID column(s) are known, it can be specified to limit the analysis to only those columns. ### Body parameter ``` { "properties": { "datetimePartitionColumn": { "description": "The date column that will be used to perform detection and validation for.", "type": "string" }, "multiseriesIdColumns": { "description": "The list of one or more names of potential multiseries ID columns. If not provided, all numerical and categorical columns are used.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | MultiseriesPayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Request to analyze relationships between potential partition and multiseries ID columns was submitted. See Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Lists all created optimized datetime partitioning configurations by project ID Operation path: `GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/` Authentication requirements: `BearerAuth` Lists all created optimized datetime partitioning configurations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of datetime partitionings returned in order of creation.", "items": { "description": "Datetime partition information.", "properties": { "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "id": { "description": "The ID of the datetime partitioning.", "type": "string" }, "partitionData": { "description": "Partitioning information for a datetime project.", "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "autopilotDataSamplingMethod": { "description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "availableHoldoutEndDate": { "description": "The maximum valid date of holdout scoring data.", "format": "date-time", "type": [ "string", "null" ] }, "availableTrainingDuration": { "description": "The duration of available training duration for scoring the holdout.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationDuration", "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.15" }, "calendarName": { "description": "The name of the calendar used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.17" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "dateFormat": { "description": "The date format of the partition column.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of the current optimized datetime partitioning", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "disableHoldout": { "description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array", "x-versionadded": "v2.9" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between the training and holdout scoring data.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutDuration": { "description": "The duration of the holdout scoring data.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "numberOfDoNotDeriveFeatures": { "description": "Number of features that are marked as \"do not derive\".", "type": "integer", "x-versionadded": "v2.17" }, "numberOfKnownInAdvanceFeatures": { "description": "Number of features that are marked as \"known in advance\".", "type": "integer", "x-versionadded": "v2.14" }, "partitioningExtendedWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "partitioningWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "type": "string" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "primaryTrainingDuration": { "description": "The duration of primary training duration for scoring the holdout.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "unsupervisedMode": { "description": "A boolean value indicating whether an unsupervised project should be created.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "unsupervisedType": { "description": "The type of unsupervised project.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.31" }, "useCrossSeriesFeatures": { "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": [ "boolean", "null" ], "x-versionadded": "v2.14" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.", "format": "duration", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" } }, "required": [ "autopilotDataSamplingMethod", "autopilotDataSelectionMethod", "availableHoldoutEndDate", "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "backtests", "dateFormat", "datetimePartitionColumn", "defaultToAPriori", "defaultToDoNotDerive", "defaultToKnownInAdvance", "differencingMethod", "disableHoldout", "featureDerivationWindowEnd", "featureDerivationWindowStart", "featureSettings", "forecastWindowEnd", "forecastWindowStart", "gapDuration", "gapEndDate", "gapStartDate", "holdoutDuration", "holdoutEndDate", "holdoutStartDate", "multiseriesIdColumns", "numberOfBacktests", "numberOfDoNotDeriveFeatures", "numberOfKnownInAdvanceFeatures", "partitioningWarnings", "periodicities", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "projectId", "treatAsExponential", "useTimeSeries", "validationDuration", "windowsBasisUnit" ], "type": "object" }, "target": { "description": "The name of the target column.", "type": "string" } }, "required": [ "datetimePartitionColumn", "id", "partitionData", "target" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of optimized datetime partitionings for the project. | OptimizedDatetimePartitioningListResponse | ## Create an optimized datetime partitioning configuration using the target by project ID Operation path: `POST /api/v2/projects/{projectId}/optimizedDatetimePartitionings/` Authentication requirements: `BearerAuth` Create an optimized datetime partitioning configuration using the target. Initializes a job to construct an optimized datetime partitioning using the date and target information to ensure that backtests sufficiently cover regions of interest in the target. This is an asynchronous job. The results of the asynchronous job (backtests and other parameters can be used in the synchronous version. `useTimeSeries` controls whether a time series project should be created or a normal project that uses datetime partitioning. See :ref: `Time-Series Projects` for more detail on the differences between time series projects and datetime partitioned projects. Time-series projects are only available to some users and use the additional settings of `featureDerivationWindowStart` and `featureDerivationWindowEnd` to establish feature derivation window and `forecastWindowStart` and `forecastWindowEnd` to establish a forecast window. The overview referenced above provides more information about using feature derivation and forecast windows. When specifying a feature derivation window of a forecast window, the number of units it spans (end - start) must be an integer multiple of the timeStep of the datetimePartitionColumn. All durations and datetimes should be specified in accordance with the :ref: `timestamp and duration formatting rules`. ### Body parameter ``` { "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": "string", "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "default": false, "description": "Specifies whether the time series predictions can use partial historical data.", "type": "boolean", "x-versionadded": "v2.24" }, "autopilotClusterList": { "description": "A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotDataSamplingMethod": { "description": "Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "backtests": { "description": "An array specifying individual backtests.", "items": { "oneOf": [ { "description": "Method 1 - pass validation and gap durations", "properties": { "gapDuration": { "description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.", "format": "duration", "type": "string" }, "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "validationDuration": { "description": "A duration string representing the duration of the validation data for this backtest.", "format": "duration", "type": "string" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "gapDuration", "index", "validationDuration", "validationStartDate" ], "type": "object" }, { "description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.", "properties": { "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "primaryTrainingEndDate": { "description": "A datetime string representing the end date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "primaryTrainingStartDate": { "description": "A datetime string representing the start date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationEndDate": { "description": "A datetime string representing the end date of the validation data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "index", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationEndDate", "validationStartDate" ], "type": "object" } ] }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": "string", "x-versionadded": "v2.15" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": "string", "x-versionadded": "v2.9" }, "disableHoldout": { "default": false, "description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).", "format": "duration", "type": "string" }, "holdoutDuration": { "description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "default": 5, "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "target": { "description": "The name of the target column.", "type": "string", "x-versionadded": "v2.22" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": "string", "x-versionadded": "v2.9" }, "unsupervisedMode": { "default": false, "description": "A boolean value indicating whether an unsupervised project should be created.", "type": "boolean", "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCrossSeriesFeatures": { "default": false, "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": "boolean", "x-versionadded": "v2.14" }, "useSupervisedFeatureReduction": { "default": true, "description": "When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.", "type": "boolean" }, "useTimeSeries": { "default": false, "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.", "format": "duration", "type": "string" }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.14" } }, "required": [ "datetimePartitionColumn" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | OptimizedDatetimePartitioningData | false | none | ### Example responses > 200 Response ``` { "description": "Partitioning information for a datetime project.", "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "autopilotDataSamplingMethod": { "description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "availableHoldoutEndDate": { "description": "The maximum valid date of holdout scoring data.", "format": "date-time", "type": [ "string", "null" ] }, "availableTrainingDuration": { "description": "The duration of available training duration for scoring the holdout.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationDuration", "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.15" }, "calendarName": { "description": "The name of the calendar used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.17" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "dateFormat": { "description": "The date format of the partition column.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of the current optimized datetime partitioning", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "disableHoldout": { "description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array", "x-versionadded": "v2.9" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between the training and holdout scoring data.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutDuration": { "description": "The duration of the holdout scoring data.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "numberOfDoNotDeriveFeatures": { "description": "Number of features that are marked as \"do not derive\".", "type": "integer", "x-versionadded": "v2.17" }, "numberOfKnownInAdvanceFeatures": { "description": "Number of features that are marked as \"known in advance\".", "type": "integer", "x-versionadded": "v2.14" }, "partitioningExtendedWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "partitioningWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "type": "string" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "primaryTrainingDuration": { "description": "The duration of primary training duration for scoring the holdout.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "unsupervisedMode": { "description": "A boolean value indicating whether an unsupervised project should be created.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "unsupervisedType": { "description": "The type of unsupervised project.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.31" }, "useCrossSeriesFeatures": { "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": [ "boolean", "null" ], "x-versionadded": "v2.14" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.", "format": "duration", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" } }, "required": [ "autopilotDataSamplingMethod", "autopilotDataSelectionMethod", "availableHoldoutEndDate", "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "backtests", "dateFormat", "datetimePartitionColumn", "defaultToAPriori", "defaultToDoNotDerive", "defaultToKnownInAdvance", "differencingMethod", "disableHoldout", "featureDerivationWindowEnd", "featureDerivationWindowStart", "featureSettings", "forecastWindowEnd", "forecastWindowStart", "gapDuration", "gapEndDate", "gapStartDate", "holdoutDuration", "holdoutEndDate", "holdoutStartDate", "multiseriesIdColumns", "numberOfBacktests", "numberOfDoNotDeriveFeatures", "numberOfKnownInAdvanceFeatures", "partitioningWarnings", "periodicities", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "projectId", "treatAsExponential", "useTimeSeries", "validationDuration", "windowsBasisUnit" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The generated datetime partitioning. | DatetimePartitioningResponse | ## Retrieve the optimized datetime partitioning configuration by project ID Operation path: `GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/{datetimePartitioningId}/` Authentication requirements: `BearerAuth` Retrieve optimized datetime partitioning configuration The optimized datetime partition objects are structurally identical to the original datetime partition objects, however they are retrieved from a mongo database after creation as opposed to being calculated synchronously. The datetime partition object in the response describes the full partitioning parameters. The available training data corresponds to all the data available for training, while the primary training data corresponds to the data that can be used to train while ensuring that all backtests are available. If a model is trained with more data than is available in the primary training data, then all backtests may not have scores available. .. note:: All durations and datetimes should be specified in accordance with the :ref: `timestamp and duration formatting rules `. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | datetimePartitioningId | path | string | true | The ID of the datetime partitioning to retrieve. | ### Example responses > 200 Response ``` { "description": "Partitioning information for a datetime project.", "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "autopilotDataSamplingMethod": { "description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "availableHoldoutEndDate": { "description": "The maximum valid date of holdout scoring data.", "format": "date-time", "type": [ "string", "null" ] }, "availableTrainingDuration": { "description": "The duration of available training duration for scoring the holdout.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationDuration", "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.15" }, "calendarName": { "description": "The name of the calendar used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.17" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "dateFormat": { "description": "The date format of the partition column.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of the current optimized datetime partitioning", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "disableHoldout": { "description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array", "x-versionadded": "v2.9" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between the training and holdout scoring data.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutDuration": { "description": "The duration of the holdout scoring data.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "numberOfDoNotDeriveFeatures": { "description": "Number of features that are marked as \"do not derive\".", "type": "integer", "x-versionadded": "v2.17" }, "numberOfKnownInAdvanceFeatures": { "description": "Number of features that are marked as \"known in advance\".", "type": "integer", "x-versionadded": "v2.14" }, "partitioningExtendedWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "partitioningWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "type": "string" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "primaryTrainingDuration": { "description": "The duration of primary training duration for scoring the holdout.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "unsupervisedMode": { "description": "A boolean value indicating whether an unsupervised project should be created.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "unsupervisedType": { "description": "The type of unsupervised project.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.31" }, "useCrossSeriesFeatures": { "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": [ "boolean", "null" ], "x-versionadded": "v2.14" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.", "format": "duration", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" } }, "required": [ "autopilotDataSamplingMethod", "autopilotDataSelectionMethod", "availableHoldoutEndDate", "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "backtests", "dateFormat", "datetimePartitionColumn", "defaultToAPriori", "defaultToDoNotDerive", "defaultToKnownInAdvance", "differencingMethod", "disableHoldout", "featureDerivationWindowEnd", "featureDerivationWindowStart", "featureSettings", "forecastWindowEnd", "forecastWindowStart", "gapDuration", "gapEndDate", "gapStartDate", "holdoutDuration", "holdoutEndDate", "holdoutStartDate", "multiseriesIdColumns", "numberOfBacktests", "numberOfDoNotDeriveFeatures", "numberOfKnownInAdvanceFeatures", "partitioningWarnings", "periodicities", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "projectId", "treatAsExponential", "useTimeSeries", "validationDuration", "windowsBasisUnit" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The optimized datetime partitioning configuration. | DatetimePartitioningResponse | ## Retrieve the optimized datetime partitioning input by project ID Operation path: `GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/{datetimePartitioningId}/datetimePartitioningInput/` Authentication requirements: `BearerAuth` Retrieve optimized datetime partitioning input The datetime partition object in the response describes the inputs used to create the full partitioning object. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | datetimePartitioningId | path | string | true | The ID of the datetime partitioning to retrieve. | ### Example responses > 200 Response ``` { "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": "string", "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "default": false, "description": "Specifies whether the time series predictions can use partial historical data.", "type": "boolean", "x-versionadded": "v2.24" }, "autopilotClusterList": { "description": "A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotDataSamplingMethod": { "description": "Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "backtests": { "description": "An array specifying individual backtests.", "items": { "oneOf": [ { "description": "Method 1 - pass validation and gap durations", "properties": { "gapDuration": { "description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.", "format": "duration", "type": "string" }, "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "validationDuration": { "description": "A duration string representing the duration of the validation data for this backtest.", "format": "duration", "type": "string" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "gapDuration", "index", "validationDuration", "validationStartDate" ], "type": "object" }, { "description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.", "properties": { "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "primaryTrainingEndDate": { "description": "A datetime string representing the end date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "primaryTrainingStartDate": { "description": "A datetime string representing the start date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationEndDate": { "description": "A datetime string representing the end date of the validation data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "index", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationEndDate", "validationStartDate" ], "type": "object" } ] }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": "string", "x-versionadded": "v2.15" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": "string", "x-versionadded": "v2.9" }, "disableHoldout": { "default": false, "description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).", "format": "duration", "type": "string" }, "holdoutDuration": { "description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "default": 5, "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "target": { "description": "The name of the target column.", "type": "string", "x-versionadded": "v2.22" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": "string", "x-versionadded": "v2.9" }, "unsupervisedMode": { "default": false, "description": "A boolean value indicating whether an unsupervised project should be created.", "type": "boolean", "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCrossSeriesFeatures": { "default": false, "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": "boolean", "x-versionadded": "v2.14" }, "useSupervisedFeatureReduction": { "default": true, "description": "When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.", "type": "boolean" }, "useTimeSeries": { "default": false, "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.", "format": "duration", "type": "string" }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.14" } }, "required": [ "datetimePartitionColumn" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The optimized datetime partitioning input. | OptimizedDatetimePartitioningData | ## Retrieve the datetime partitioning log by project ID Operation path: `GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/{datetimePartitioningId}/datetimePartitioningLog/` Authentication requirements: `BearerAuth` Retrieve the datetime partitioning log content and log length for an optimized datetime partitioning as JSON. The Date/Time Partitioning Log provides details about the partitioning process for an OTV or Time Series project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | projectId | path | string | true | The project ID. | | datetimePartitioningId | path | string | true | The ID of the datetime partitioning to retrieve. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "datetimePartitioningLog": { "description": "The content of the date/time partitioning log.", "type": "string" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalLogLines": { "description": "The total number of lines in feature derivation log.", "type": "integer" } }, "required": [ "count", "datetimePartitioningLog", "next", "previous", "totalLogLines" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | DatetimePartitioningLogListControllerResponse | ## Retrieve a text file containing the datetime partitioning log by project ID Operation path: `GET /api/v2/projects/{projectId}/optimizedDatetimePartitionings/{datetimePartitioningId}/datetimePartitioningLog/file/` Authentication requirements: `BearerAuth` Retrieve a text file containing the datetime partitioning log. The Date/Time Partitioning Log provides details about the partitioning process for an OTV or Time Series project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | datetimePartitioningId | path | string | true | The ID of the datetime partitioning to retrieve. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | attachment;filename=.txt The suggested filename is dynamically generated | | 200 | Content-Type | string | | MIME type of the returned data | ## Retrieve segmentation task statuses by project ID Operation path: `GET /api/v2/projects/{projectId}/segmentationTaskJobResults/{segmentationTaskId}/` Authentication requirements: `BearerAuth` Retrieve the statuses of segmentation task jobs associated with the ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | segmentationTaskId | path | string | true | The ID of the segmentation task to check the status of. | ### Example responses > 200 Response ``` { "properties": { "completedJobs": { "description": "The list of completed segmentation tasks.", "items": { "properties": { "name": { "description": "The name of the segmentation task job.", "type": "string" }, "segmentationTaskId": { "description": "The ID of the completed segmentation task.", "type": "string" }, "segmentsCount": { "description": "The number of segments produced by the task.", "type": "integer" }, "segmentsEda": { "description": "The array of segments EDA information.", "items": { "properties": { "maxDate": { "description": "The latest date in the segment.", "format": "date-time", "type": "string" }, "minDate": { "description": "The earliest date in the segment.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the segment.", "type": "string" }, "numberOfRows": { "description": "The number of rows in the segment.", "exclusiveMinimum": 0, "type": "integer" }, "sizeInBytes": { "description": "The size of the segment in bytes.", "exclusiveMinimum": 0, "type": "integer" } }, "required": [ "maxDate", "minDate", "name", "numberOfRows", "sizeInBytes" ], "type": "object" }, "type": "array" }, "url": { "description": "The URL to retrieve detailed information about the segmentation task.", "type": "string" } }, "required": [ "name", "segmentationTaskId", "segmentsCount", "segmentsEda", "url" ], "type": "object" }, "type": "array" }, "failedJobs": { "description": "The list of failed segmentation tasks.", "items": { "properties": { "message": { "description": "The response containing the error message from the segmentation task.", "type": "string" }, "name": { "description": "Name of the segmentation task job", "type": "string" }, "parameters": { "description": "The parameters submitted by the user to the failed job.", "type": "object" } }, "required": [ "message", "name", "parameters" ], "type": "object" }, "type": "array" }, "numberOfJobs": { "description": "The total number of completed and failed jobs processed.", "type": "integer" } }, "required": [ "completedJobs", "failedJobs", "numberOfJobs" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SegmentationResultsResponse | ## List segmentation tasks by project ID Operation path: `GET /api/v2/projects/{projectId}/segmentationTasks/` Authentication requirements: `BearerAuth` List all segmentation tasks created for the project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the segmentation tasks that are associated with the project ID.", "items": { "properties": { "created": { "description": "The date and time when the segmentation task was originally created.", "format": "date-time", "type": "string" }, "data": { "description": "The data for the segmentation task.", "properties": { "clusteringModelId": { "description": "The ID of the model used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "clusteringModelName": { "description": "The name of the model used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "clusteringProjectId": { "description": "The ID of the project used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "The name of the datetime partitioning column used by the segmentation task.", "type": "string" }, "modelPackageId": { "description": "The external model package ID used by the segmentation task for automated segmentation.", "type": "string" }, "multiseriesIdColumns": { "description": "The multiseries ID columns used by the segmentation task.", "items": { "type": "string" }, "type": "array" }, "userDefinedSegmentIdColumns": { "description": "The user-defined segmentation columns used by the segmentation task.", "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "metadata": { "description": "The metadata for the segmentation task.", "properties": { "useAutomatedSegmentation": { "description": "Whether the segmentation task uses automated segmentation.", "type": "boolean" }, "useMultiseriesIdColumns": { "description": "Whether the segmentation task uses a multiseries column.", "type": "boolean" }, "useTimeSeries": { "description": "Whether the segmentation task is a time series task.", "type": "boolean" } }, "required": [ "useAutomatedSegmentation", "useMultiseriesIdColumns", "useTimeSeries" ], "type": "object" }, "name": { "description": "The name of the segmentation task.", "type": "string" }, "projectId": { "description": "The ID of the parent project associated with the segmentation task.", "type": "string" }, "segmentationTaskId": { "description": "Id of the segmentation task", "type": "string" }, "segments": { "description": "The names of the unique segments generated by the segmentation task.", "items": { "type": "string" }, "type": "array" }, "segmentsCount": { "description": "The number of segments generated by the segmentation task.", "type": "integer" }, "segmentsEda": { "description": "The array of segments EDA information.", "items": { "properties": { "maxDate": { "description": "The latest date in the segment.", "format": "date-time", "type": "string" }, "minDate": { "description": "The earliest date in the segment.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the segment.", "type": "string" }, "numberOfRows": { "description": "The number of rows in the segment.", "exclusiveMinimum": 0, "type": "integer" }, "sizeInBytes": { "description": "The size of the segment in bytes.", "exclusiveMinimum": 0, "type": "integer" } }, "required": [ "maxDate", "minDate", "name", "numberOfRows", "sizeInBytes" ], "type": "object" }, "type": "array" }, "type": { "description": "The type of segmentation task (e.g., AutoML, AutoTS).", "type": "string" } }, "required": [ "created", "data", "metadata", "name", "projectId", "segmentationTaskId", "segments", "segmentsCount", "segmentsEda", "type" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SegmentationTaskListResponse | ## Create segmentation tasks by project ID Operation path: `POST /api/v2/projects/{projectId}/segmentationTasks/` Authentication requirements: `BearerAuth` Create segmentation tasks for the dataset used in the project. ### Body parameter ``` { "properties": { "datetimePartitionColumn": { "description": "The date column that will be used to identify the date in time series segmentation.", "type": "string" }, "modelPackageId": { "description": "The model package ID for using an external model registry package.", "type": "string" }, "multiseriesIdColumns": { "description": "The list of one or more names of multiseries ID columns.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "target": { "description": "The target for the dataset.", "type": "string" }, "useAutomatedSegmentation": { "default": false, "description": "Enable the use of automated segmentation tasks.", "type": "boolean" }, "useTimeSeries": { "default": false, "description": "Enable time series-based segmentation tasks.", "type": "boolean" }, "userDefinedSegmentIdColumns": { "description": "The list of one or more names of columns to be used for user-defined business rule segmentations.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "target" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | body | body | SegmentationTaskCreate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Job submitted. See Location header. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Retrieve segmentation task by project ID Operation path: `GET /api/v2/projects/{projectId}/segmentationTasks/{segmentationTaskId}/` Authentication requirements: `BearerAuth` Retrieve information about a segmentation task. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | segmentationTaskId | path | string | true | The ID of the segmentation task. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "The date and time when the segmentation task was originally created.", "format": "date-time", "type": "string" }, "data": { "description": "The data for the segmentation task.", "properties": { "clusteringModelId": { "description": "The ID of the model used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "clusteringModelName": { "description": "The name of the model used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "clusteringProjectId": { "description": "The ID of the project used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "The name of the datetime partitioning column used by the segmentation task.", "type": "string" }, "modelPackageId": { "description": "The external model package ID used by the segmentation task for automated segmentation.", "type": "string" }, "multiseriesIdColumns": { "description": "The multiseries ID columns used by the segmentation task.", "items": { "type": "string" }, "type": "array" }, "userDefinedSegmentIdColumns": { "description": "The user-defined segmentation columns used by the segmentation task.", "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "metadata": { "description": "The metadata for the segmentation task.", "properties": { "useAutomatedSegmentation": { "description": "Whether the segmentation task uses automated segmentation.", "type": "boolean" }, "useMultiseriesIdColumns": { "description": "Whether the segmentation task uses a multiseries column.", "type": "boolean" }, "useTimeSeries": { "description": "Whether the segmentation task is a time series task.", "type": "boolean" } }, "required": [ "useAutomatedSegmentation", "useMultiseriesIdColumns", "useTimeSeries" ], "type": "object" }, "name": { "description": "The name of the segmentation task.", "type": "string" }, "projectId": { "description": "The ID of the parent project associated with the segmentation task.", "type": "string" }, "segmentationTaskId": { "description": "Id of the segmentation task", "type": "string" }, "segments": { "description": "The names of the unique segments generated by the segmentation task.", "items": { "type": "string" }, "type": "array" }, "segmentsCount": { "description": "The number of segments generated by the segmentation task.", "type": "integer" }, "segmentsEda": { "description": "The array of segments EDA information.", "items": { "properties": { "maxDate": { "description": "The latest date in the segment.", "format": "date-time", "type": "string" }, "minDate": { "description": "The earliest date in the segment.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the segment.", "type": "string" }, "numberOfRows": { "description": "The number of rows in the segment.", "exclusiveMinimum": 0, "type": "integer" }, "sizeInBytes": { "description": "The size of the segment in bytes.", "exclusiveMinimum": 0, "type": "integer" } }, "required": [ "maxDate", "minDate", "name", "numberOfRows", "sizeInBytes" ], "type": "object" }, "type": "array" }, "type": { "description": "The type of segmentation task (e.g., AutoML, AutoTS).", "type": "string" } }, "required": [ "created", "data", "metadata", "name", "projectId", "segmentationTaskId", "segments", "segmentsCount", "segmentsEda", "type" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SegmentationTaskResponse | ## Retrieve series ID by project ID Operation path: `GET /api/v2/projects/{projectId}/segmentationTasks/{segmentationTaskId}/mappings/` Authentication requirements: `BearerAuth` Retrieve the series ID to segment ID mappings for a segmentation task. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | projectId | path | string | true | The project ID. | | segmentationTaskId | path | string | true | The ID of the segmentation task. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The array of segmentation mappings.", "items": { "properties": { "segment": { "description": "The segment name associated with the multiseries ID column by the segmentation task.", "type": "string" }, "seriesId": { "description": "The multiseries ID column used to identify series for segmentation.", "type": "string" } }, "required": [ "segment", "seriesId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SegmentationTaskSegmentMappingsResponse | ## Update child segment project by project ID Operation path: `PATCH /api/v2/projects/{projectId}/segments/{segmentId}/` Authentication requirements: `BearerAuth` The only supported operation right now is segment restart, which removes existing child segment project and starts another child project instead for the given segment. Should be only used for child segments which are stuck during project startup or upload. ### Body parameter ``` { "properties": { "operation": { "default": "restart", "description": "The name of the operation to perform on the project segment.", "enum": [ "restart" ], "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | | segmentId | path | string | true | The name of the segment. | | body | body | ProjectSegmentUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "projectId": { "description": "The new project ID of the restarted segment.", "type": "string" }, "segmentId": { "description": "The name of the restarted segment.", "type": "string" } }, "required": [ "projectId", "segmentId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The segment is updated. | ProjectSegmentUpdateResponse | ## Check project status by project ID Operation path: `GET /api/v2/projects/{projectId}/status/` Authentication requirements: `BearerAuth` Check the status of a project. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | projectId | path | string | true | The project ID. | ### Example responses > 200 Response ``` { "properties": { "autopilotDone": { "description": "whether the current autopilot run has finished", "type": "boolean" }, "stage": { "description": "the current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed", "enum": [ "modeling", "aim", "fasteda", "eda", "eda2", "empty" ], "type": "string" }, "stageDescription": { "description": "a description of the current stage of the project", "type": "string" } }, "required": [ "autopilotDone", "stage", "stageDescription" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The project status. | ProjectStatusResponse | # Schemas ## AccessControl ``` { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | Whether the recipient can share the role further. | | role | string | true | | The role of the user on this entity. | | userId | string | true | | The identifier of the user that has access to this entity. | | username | string | true | | The username of the user that has access to the entity. | ## Aim ``` { "properties": { "accuracyOptimizedMb": { "description": "Include additional, longer-running models that will be run by the autopilot and available to run manually.", "type": "boolean" }, "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": "string", "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": "boolean", "x-versionadded": "v2.24" }, "allowedPairwiseInteractionGroups": { "description": "For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [['A', 'B', 'C'], ['C', 'D']] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.", "items": { "items": { "type": "string" }, "maxItems": 10, "minItems": 2, "type": "array" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.20" }, "allowedPairwiseInteractionGroupsFilename": { "description": "Filename that was used to upload allowed_pairwise_interaction_groups. Necessary for persistence of UI/UX when you specify that parameter via file.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "autopilotClusterList": { "description": "A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotDataSamplingMethod": { "description": "Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "default": "duration", "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "autopilotWithFeatureDiscovery": { "description": "If true, autopilot will run on a feature list that includes features found via search for interactions.", "type": "boolean" }, "backtests": { "description": "An array specifying the format of the backtests.", "items": { "properties": { "gapDuration": { "description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.", "format": "duration", "type": "string" }, "index": { "description": "The index from zero of the backtest specified by this object.", "type": "integer" }, "primaryTrainingEndDate": { "description": "A datetime string representing the end date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "primaryTrainingStartDate": { "description": "A datetime string representing the start date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationDuration": { "description": "A duration string representing the duration of the validation data for this backtest.", "format": "duration", "type": "string" }, "validationEndDate": { "description": "A datetime string representing the end date of the validation data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "index", "validationStartDate" ], "type": "object" }, "maxItems": 50, "minItems": 1, "type": "array" }, "biasMitigationFeatureName": { "description": "The name of the protected feature used to mitigate bias on models.", "minLength": 1, "type": "string", "x-versionadded": "v2.27" }, "biasMitigationTechnique": { "description": "Method applied to perform bias mitigation.", "enum": [ "preprocessingReweighing", "postProcessingRejectionOptionBasedClassification" ], "type": "string", "x-versionadded": "v2.27" }, "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects.", "type": "boolean", "x-versionadded": "v2.19" }, "blueprintThreshold": { "description": "The runtime (in hours) which if exceeded will exclude a model from autopilot runs.", "maximum": 1440, "minimum": 1, "type": [ "integer", "null" ] }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": "string", "x-versionadded": "v2.15" }, "chunkDefinitionId": { "description": "Chunk definition id for incremental learning using chunking service", "type": "string", "x-versionadded": "v2.33" }, "classMappingAggregationSettings": { "description": "Class mapping aggregation settings.", "properties": { "aggregationClassName": { "description": "The name of the class that will be assigned to all rows with aggregated classes. Should not match any excluded_from_aggregation or we will have 2 classes with the same name and no way to distinguish between them. This option is only available formulticlass projects. By default 'DR_RARE_TARGET_VALUES' is used.", "type": "string" }, "excludedFromAggregation": { "default": [], "description": "List of target values that should be guaranteed to kept as is, regardless of other settings.", "items": { "type": "string" }, "type": "array" }, "maxUnaggregatedClassValues": { "default": 1000, "description": "The maximum number of unique labels before aggregation kicks in. Should be at least len(excludedFromAggregation) + 1 for multiclass and at least len(excludedFromAggregation) for multilabel.", "maximum": 1000, "minimum": 3, "type": "integer" }, "minClassSupport": { "default": 1, "description": "Minimum number of instances necessary for each target value in the dataset. All values with fewer instances than this value will be aggregated", "type": "integer" } }, "type": "object" }, "considerBlendersInRecommendation": { "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects.", "type": "boolean", "x-versionadded": "v2.21" }, "credentials": { "description": "List of credentials for the secondary datasets used in feature discovery project.", "items": { "oneOf": [ { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" }, { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" } }, "required": [ "credentialId" ], "type": "object" } ] }, "maxItems": 30, "type": "array", "x-versionadded": "v2.19" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "customMetricsLossesInfo": { "description": "Returns custom metrics information. This field is required for custom metrics projects.", "properties": { "id": { "description": "The ID of an existing custom_metrics_loss_function document for this project.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.44" }, "cvHoldoutLevel": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" }, { "type": "null" } ], "description": "The value of the partition column indicating a row is part of the holdout set. This level is optional - if not specified or if provided as ``null``, then no holdout will be used in the project. The rest of the levels indicate which cross validation fold each row should fall into." }, "cvMethod": { "description": "The partitioning method to be applied to the training data.", "enum": [ "random", "user", "stratified", "group", "datetime" ], "type": "string" }, "dateRemoval": { "description": "If true, enable creating additional feature lists without dates (does not apply to time-aware projects).", "type": "boolean", "x-versionadded": "v2.21" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of a datetime partitioning to use for the project.When datetime_partitioning_id is specified, no other datetime partitioning related field is allowed to be specified, as these fields get loaded from the already created partitioning.", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": "string", "x-versionadded": "v2.9" }, "disableHoldout": { "default": false, "description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.", "type": "boolean", "x-versionadded": "v2.8" }, "eventsCount": { "description": "The name of a column specifying events count. The data in this column must be pure numeric and non negative without missing values", "type": "string" }, "exponentiallyWeightedMovingAlpha": { "description": "Discount factor (alpha) used for exponentially weighted moving features", "maximum": 1, "minimum": 0, "type": "number" }, "exposure": { "description": "The name of a column specifying row exposure.The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values", "type": "string" }, "externalPredictions": { "description": "List of external prediction columns from the dataset.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.26" }, "externalTimeSeriesBaselineDatasetId": { "description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.", "type": "string", "x-versionadded": "v2.24" }, "externalTimeSeriesBaselineDatasetName": { "description": "The name of the time series baseline dataset for the project.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "fairnessMetricsSet": { "description": "Metric to use for calculating fairness. Can be one of ``proportionalParity``, ``equalParity``, ``predictionBalance``, ``trueFavorableAndUnfavorableRateParity`` or ``FavorableAndUnfavorablePredictiveValueParity``. Used and required only if *Bias & Fairness in AutoML* feature is enabled.", "enum": [ "proportionalParity", "equalParity", "predictionBalance", "trueFavorableAndUnfavorableRateParity", "favorableAndUnfavorablePredictiveValueParity" ], "type": "string", "x-versionadded": "v2.24" }, "fairnessThreshold": { "description": "The threshold value of the fairness metric. The valid range is [0:1]; the default fairness metric value is 0.8. This metric is only applicable if the *Bias & Fairness in AutoML* feature is enabled.", "maximum": 1, "minimum": 0, "type": "number", "x-versionadded": "v2.24" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureDiscoverySupervisedFeatureReduction": { "description": "Run supervised feature reduction for feature discovery projects.", "type": "boolean", "x-versionadded": "v2.21" }, "featureEngineeringPredictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "type": [ "string", "null" ], "x-versionadded": "v2.20" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array" }, "featurelistId": { "description": "The ID of a featurelist to use for autopilot.", "type": "string" }, "forecastDistance": { "description": "The name of a column specifying the forecast distance to which each row of the dataset belongs. Column unique values are used to subset the modeling data and build a separate model for each unique column value. Similar to time series this column is well suited to be used as forecast distance.", "type": "string", "x-versionadded": "v2.35" }, "forecastOffsets": { "description": "An array of strings with names of a columns specifying row offsets. Columns values are used as offset or predictions to boost for models. The data in this column must be pure numeric (e.g. not currency, date, length, etc.).", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.35" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).", "format": "duration", "type": "string" }, "holdoutDuration": { "description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "holdoutLevel": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ], "description": "The value of the partition column indicating a row is part of the holdout set. This level is optional - if not specified or if provided as ``null``, then no holdout will be used in the project. However, the column must have exactly 2 values in order for this option to be valid" }, "holdoutPct": { "description": "The percentage of the dataset to assign to the holdout set", "maximum": 98, "minimum": 0, "type": "number" }, "holdoutStartDate": { "description": "The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "includeBiasMitigationFeatureAsPredictorVariable": { "description": "Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task.", "type": "boolean", "x-versionadded": "v2.27" }, "incrementalLearningEarlyStoppingRounds": { "description": "Early stopping rounds for the auto incremental learning service", "minimum": 0, "type": "integer", "x-versionadded": "v2.33" }, "incrementalLearningOnBestModel": { "description": "Automatically run incremental learning on the best model during Autopilot run.", "type": "boolean", "x-versionadded": "v2.33" }, "incrementalLearningOnlyMode": { "description": "Keep only models that support incremental learning during Autopilot run.", "type": "boolean", "x-versionadded": "v2.33" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "majorityDownsamplingRate": { "description": "The percentage between 0 and 100 of the majority rows that should be kept. Must be specified only if using smart downsampling. If not specified, a default will be selected based on the dataset distribution. The chosen rate may not cause the majority class to become smaller than the minority class.", "type": "number" }, "metric": { "description": "The metric to use to select the best models. See `/api/v2/projects/(projectId)/features/metrics/` for the metrics that may be valid for a potential target. Note that weighted metrics must be used with a weights column.", "type": "string" }, "minSecondaryValidationModelCount": { "description": "Compute 'All backtest' scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.", "maximum": 10, "minimum": 0, "type": "integer", "x-versionadded": "v2.19" }, "mode": { "description": "The autopilot mode to use. Either 'quick', 'auto', 'manual', or 'comprehensive'.", "enum": [ "0", "2", "4", "3", "auto", "manual", "comprehensive", "quick" ], "type": "string" }, "modelSplits": { "default": 5, "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "monotonicDecreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired.", "type": [ "string", "null" ] }, "monotonicIncreasingFeaturelistId": { "description": "The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired.", "type": [ "string", "null" ] }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "type": "integer" }, "numberOfIncrementalLearningIterationsBeforeBestModelSelection": { "description": "Number of incremental_learning iterations before best model selection.", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.35" }, "offset": { "description": "An array of strings with names of a columns specifying row offsets.The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values", "items": { "type": "string" }, "type": "array" }, "onlyIncludeMonotonicBlueprints": { "default": false, "description": "When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot.", "type": "boolean" }, "partitionKeyCols": { "description": "An array containing a single string - the name of the group partition column", "items": { "type": "string" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "positiveClass": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ], "description": "A value from the target column to use for the positive class. May only be specified for projects doing binary classification.If not specified, a positive class is selected automatically." }, "preferableTargetValue": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ], "description": "A target value that should be treated as a positive outcome for the prediction. For example if we want to check gender discrimination for giving a loan and our target named ``is_bad``, then the positive outcome for the prediction would be ``No``, which means that the loan is good and that's what we treat as a preferable result for the loaner. Used and required only if *Bias & Fairness in AutoML* feature is enabled.", "x-versionadded": "v2.24" }, "prepareModelForDeployment": { "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning 'RECOMMENDED FOR DEPLOYMENT' label.", "type": "boolean", "x-versionadded": "v2.19" }, "primaryLocationColumn": { "description": "Primary geospatial location column.", "type": [ "string", "null" ] }, "protectedFeatures": { "description": "A list of project feature to mark as protected for Bias metric calculation and Fairness correction. Used and required only if *Bias & Fairness in AutoML* feature is enabled.", "items": { "type": "string" }, "maxItems": 10, "minItems": 1, "type": "array", "x-versionadded": "v2.24" }, "quantileLevel": { "description": "The quantile level between 0.01 and 0.99 for specifying the Quantile metric.", "exclusiveMaximum": 1, "exclusiveMinimum": 0, "type": "number", "x-versionadded": "v2.22" }, "quickrun": { "description": "(Deprecated): 'quick' should be used in the `mode` parameter instead of using this parameter. If set to `true`, autopilot mode will be set to 'quick'.Cannot be set to `true` when `mode` is set to 'comprehensive' or 'manual'.", "type": "boolean", "x-versiondeprecated": "v2.21" }, "rateTopPctThreshold": { "description": "The percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric.", "maximum": 100, "minimum": 0, "type": "number" }, "relationshipsConfigurationId": { "description": "Relationships configuration id to be used for Feature Discovery projects.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "reps": { "description": "The number of cross validation folds to use.", "maximum": 999999, "minimum": 2, "type": "integer" }, "responseCap": { "description": "Used to cap the maximum response of a model", "maximum": 1, "minimum": 0.5, "type": "number" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean", "x-versionadded": "v2.21" }, "sampleStepPct": { "description": "A float between 0 and 100 indicating the desired percentage of data to sample when training models in comprehensive Autopilot. Note: this only supported for comprehensive Autopilot and the specified value may be lowered in order to be compatible with the project's dataset and partition settings.", "exclusiveMinimum": 0, "maximum": 100, "type": "number", "x-versionadded": "v2.20" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean", "x-versionadded": "v2.19" }, "seed": { "description": "A seed to use for randomization.", "maximum": 999999999, "minimum": 0, "type": "integer" }, "segmentationTaskId": { "description": "Specifies the SegmentationTask that will be used for dividing the project up into multiple segmented projects.", "type": [ "string", "null" ], "x-versionadded": "v2.24" }, "seriesId": { "description": "The name of a column specifying the series ID to which each row of the dataset belongs. Typically the series was used to derive the additional features, that are independent from each other. Column unique values are used to subset the modeling data and build a separate model for each unique column value. Similar to time series this column is well suited to be used as multi-series ID column.", "type": "string", "x-versionadded": "v2.35" }, "shapOnlyMode": { "description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible.", "type": "boolean", "x-versionadded": "v2.21" }, "smartDownsampled": { "description": "Whether to use smart downsampling to throw away excess rows of the majority class. Only applicable to classification and zero-boosted regression projects.", "type": "boolean" }, "stopWords": { "description": "A list of stop words to be used for text blueprints. Note: ``stop_words=True`` must be set in the blueprint preprocessing parameters for this list of stop words to actually be used during preprocessing.", "items": { "maxLength": 100, "type": "string" }, "maxItems": 1000, "type": "array", "x-versionadded": "v2.21" }, "target": { "description": "The name of the target feature.", "type": "string" }, "targetType": { "description": "Used to specify the targetType to use for a project when it is ambiguous, i.e. a numeric target with a few unique values that could be used for either regression or multiclass.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel" ], "type": "string" }, "trainingLevel": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ], "description": "The value of the partition column indicating a row is part of the training set." }, "treatAsExponential": { "default": "auto", "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed.", "enum": [ "auto", "never", "always" ], "type": "string" }, "unsupervisedMode": { "default": false, "description": "If True, unsupervised project (without target) will be created. ``target`` cannot be specified if ``unsupervisedMode`` is True.", "type": "boolean", "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCrossSeriesFeatures": { "description": "Indicating if user wants to use cross-series features.", "type": "boolean" }, "useGpu": { "description": "Indicates whether project should use GPU workers", "type": "boolean", "x-versionadded": "v2.31" }, "useProjectSettings": { "description": "Specifies whether datetime-partitioned project should use project settings (i.e. backtests configuration has been modified by the user).", "type": "boolean", "x-versionadded": "v2.25" }, "useSupervisedFeatureReduction": { "default": true, "description": "When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.", "type": "boolean" }, "useTimeSeries": { "default": false, "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "userPartitionCol": { "description": "The name of the column containing the partition assignments.", "type": "string" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.", "format": "duration", "type": "string" }, "validationLevel": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "type": "number" } ], "description": "The value of the partition column indicating a row is part of the validation set." }, "validationPct": { "description": "The percentage of the dataset to assign to the validation set", "maximum": 99, "minimum": 0, "type": "number" }, "validationType": { "description": "The validation method to be used. CV for cross validation or TVH for train-validation-holdout split.", "enum": [ "CV", "TVH" ], "type": "string" }, "weights": { "description": "The name of a column specifying row weights. The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values", "type": "string" }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.14" } }, "required": [ "autopilotDataSelectionMethod", "onlyIncludeMonotonicBlueprints" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyOptimizedMb | boolean | false | | Include additional, longer-running models that will be run by the autopilot and available to run manually. | | aggregationType | string | false | | For multiseries projects only. The aggregation type to apply when creating cross-series features. | | allowPartialHistoryTimeSeriesPredictions | boolean | false | | Specifies whether the time series predictions can use partial historical data. | | allowedPairwiseInteractionGroups | [array] | false | maxItems: 100 | For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [['A', 'B', 'C'], ['C', 'D']] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model. | | allowedPairwiseInteractionGroupsFilename | string,null | false | | Filename that was used to upload allowed_pairwise_interaction_groups. Necessary for persistence of UI/UX when you specify that parameter via file. | | autopilotClusterList | [integer] | false | maxItems: 10 | A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to clustering. | | autopilotDataSamplingMethod | string | false | | Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'. | | autopilotDataSelectionMethod | string | true | | The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets. | | autopilotWithFeatureDiscovery | boolean | false | | If true, autopilot will run on a feature list that includes features found via search for interactions. | | backtests | [Backtest] | false | maxItems: 50minItems: 1 | An array specifying the format of the backtests. | | biasMitigationFeatureName | string | false | minLength: 1minLength: 1 | The name of the protected feature used to mitigate bias on models. | | biasMitigationTechnique | string | false | | Method applied to perform bias mitigation. | | blendBestModels | boolean | false | | Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects. | | blueprintThreshold | integer,null | false | maximum: 1440minimum: 1 | The runtime (in hours) which if exceeded will exclude a model from autopilot runs. | | calendarId | string | false | | The ID of the calendar to be used in this project. | | chunkDefinitionId | string | false | | Chunk definition id for incremental learning using chunking service | | classMappingAggregationSettings | ClassMappingAggregationSettings | false | | Class mapping aggregation settings. | | considerBlendersInRecommendation | boolean | false | | Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects. | | credentials | [oneOf] | false | maxItems: 30 | List of credentials for the secondary datasets used in feature discovery project. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PasswordCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CredentialId | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | crossSeriesGroupByColumns | [string] | false | maxItems: 1 | For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like "men's clothing", "sports equipment", etc. | | customMetricsLossesInfo | CustomMetricsLossesInfo | false | | Returns custom metrics information. This field is required for custom metrics projects. | | cvHoldoutLevel | any | false | | The value of the partition column indicating a row is part of the holdout set. This level is optional - if not specified or if provided as null, then no holdout will be used in the project. The rest of the levels indicate which cross validation fold each row should fall into. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cvMethod | string | false | | The partitioning method to be applied to the training data. | | dateRemoval | boolean | false | | If true, enable creating additional feature lists without dates (does not apply to time-aware projects). | | datetimePartitionColumn | string | false | | The date column that will be used as a datetime partition column. | | datetimePartitioningId | string | false | | The ID of a datetime partitioning to use for the project.When datetime_partitioning_id is specified, no other datetime partitioning related field is allowed to be specified, as these fields get loaded from the already created partitioning. | | defaultToAPriori | boolean | false | | Renamed to defaultToKnownInAdvance. | | defaultToDoNotDerive | boolean | false | | For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter. | | defaultToKnownInAdvance | boolean | false | | For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the :ref:Time Series Overview for more context. | | differencingMethod | string | false | | For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems simple and seasonal are not allowed. Parameter periodicities must be specified if seasonal is chosen. Defaults to auto. | | disableHoldout | boolean | false | | Whether to suppress allocating a holdout fold. If disableHoldout is set to true, holdoutStartDate and holdoutDuration must not be set. | | eventsCount | string | false | | The name of a column specifying events count. The data in this column must be pure numeric and non negative without missing values | | exponentiallyWeightedMovingAlpha | number | false | maximum: 1minimum: 0 | Discount factor (alpha) used for exponentially weighted moving features | | exposure | string | false | | The name of a column specifying row exposure.The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values | | externalPredictions | [string] | false | maxItems: 100minItems: 1 | List of external prediction columns from the dataset. | | externalTimeSeriesBaselineDatasetId | string | false | | Catalog version id for external prediction data that can be used as a baseline to calculate new metrics. | | externalTimeSeriesBaselineDatasetName | string,null | false | | The name of the time series baseline dataset for the project. | | fairnessMetricsSet | string | false | | Metric to use for calculating fairness. Can be one of proportionalParity, equalParity, predictionBalance, trueFavorableAndUnfavorableRateParity or FavorableAndUnfavorablePredictiveValueParity. Used and required only if Bias & Fairness in AutoML feature is enabled. | | fairnessThreshold | number | false | maximum: 1minimum: 0 | The threshold value of the fairness metric. The valid range is [0:1]; the default fairness metric value is 0.8. This metric is only applicable if the Bias & Fairness in AutoML feature is enabled. | | featureDerivationWindowEnd | integer | false | maximum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end. | | featureDerivationWindowStart | integer | false | maximum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin. | | featureDiscoverySupervisedFeatureReduction | boolean | false | | Run supervised feature reduction for feature discovery projects. | | featureEngineeringPredictionPoint | string,null | false | | The date column to be used as the prediction point for time-based feature engineering. | | featureSettings | [FeatureSetting] | false | | An array specifying per feature settings. Features can be left unspecified. | | featurelistId | string | false | | The ID of a featurelist to use for autopilot. | | forecastDistance | string | false | | The name of a column specifying the forecast distance to which each row of the dataset belongs. Column unique values are used to subset the modeling data and build a separate model for each unique column value. Similar to time series this column is well suited to be used as forecast distance. | | forecastOffsets | [string] | false | maxItems: 100minItems: 1 | An array of strings with names of a columns specifying row offsets. Columns values are used as offset or predictions to boost for models. The data in this column must be pure numeric (e.g. not currency, date, length, etc.). | | forecastWindowEnd | integer | false | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end. | | forecastWindowStart | integer | false | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. | | gapDuration | string(duration) | false | | The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D). | | holdoutDuration | string(duration) | false | | The duration of holdout scoring data. When specifying holdoutDuration, holdoutStartDate must also be specified. This attribute cannot be specified when disableHoldout is true. | | holdoutEndDate | string(date-time) | false | | The end date of holdout scoring data. When specifying holdoutEndDate, holdoutStartDate must also be specified. This attribute cannot be specified when disableHoldout is true. | | holdoutLevel | any | false | | The value of the partition column indicating a row is part of the holdout set. This level is optional - if not specified or if provided as null, then no holdout will be used in the project. However, the column must have exactly 2 values in order for this option to be valid | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | holdoutPct | number | false | maximum: 98minimum: 0 | The percentage of the dataset to assign to the holdout set | | holdoutStartDate | string(date-time) | false | | The start date of holdout scoring data. When specifying holdoutStartDate, one of holdoutEndDate or holdoutDuration must also be specified. This attribute cannot be specified when disableHoldout is true. | | includeBiasMitigationFeatureAsPredictorVariable | boolean | false | | Specifies whether the mitigation feature will be used as a predictor variable (i.e., treated like other categorical features in the input to train the modeler), in addition to being used for bias mitigation. If false, the mitigation feature will be used only for bias mitigation, and not for training the modeler task. | | incrementalLearningEarlyStoppingRounds | integer | false | minimum: 0 | Early stopping rounds for the auto incremental learning service | | incrementalLearningOnBestModel | boolean | false | | Automatically run incremental learning on the best model during Autopilot run. | | incrementalLearningOnlyMode | boolean | false | | Keep only models that support incremental learning during Autopilot run. | | isHoldoutModified | boolean | false | | A boolean value indicating whether holdout settings (start/end dates) have been modified by user. | | majorityDownsamplingRate | number | false | | The percentage between 0 and 100 of the majority rows that should be kept. Must be specified only if using smart downsampling. If not specified, a default will be selected based on the dataset distribution. The chosen rate may not cause the majority class to become smaller than the minority class. | | metric | string | false | | The metric to use to select the best models. See /api/v2/projects/(projectId)/features/metrics/ for the metrics that may be valid for a potential target. Note that weighted metrics must be used with a weights column. | | minSecondaryValidationModelCount | integer | false | maximum: 10minimum: 0 | Compute 'All backtest' scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default. | | mode | string | false | | The autopilot mode to use. Either 'quick', 'auto', 'manual', or 'comprehensive'. | | modelSplits | integer | false | maximum: 10minimum: 1 | Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. | | monotonicDecreasingFeaturelistId | string,null | false | | The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired. | | monotonicIncreasingFeaturelistId | string,null | false | | The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overriden at model submission time if desired. | | multiseriesIdColumns | [string] | false | minItems: 1 | May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:multiseries section of the time series documentation for more context. | | numberOfBacktests | integer | false | | The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations. | | numberOfIncrementalLearningIterationsBeforeBestModelSelection | integer | false | maximum: 10minimum: 1 | Number of incremental_learning iterations before best model selection. | | offset | [string] | false | | An array of strings with names of a columns specifying row offsets.The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values | | onlyIncludeMonotonicBlueprints | boolean | true | | When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot. | | partitionKeyCols | [string] | false | | An array containing a single string - the name of the group partition column | | periodicities | [Periodicity] | false | | A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'. | | positiveClass | any | false | | A value from the target column to use for the positive class. May only be specified for projects doing binary classification.If not specified, a positive class is selected automatically. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | preferableTargetValue | any | false | | A target value that should be treated as a positive outcome for the prediction. For example if we want to check gender discrimination for giving a loan and our target named is_bad, then the positive outcome for the prediction would be No, which means that the loan is good and that's what we treat as a preferable result for the loaner. Used and required only if Bias & Fairness in AutoML feature is enabled. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | prepareModelForDeployment | boolean | false | | Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning 'RECOMMENDED FOR DEPLOYMENT' label. | | primaryLocationColumn | string,null | false | | Primary geospatial location column. | | protectedFeatures | [string] | false | maxItems: 10minItems: 1 | A list of project feature to mark as protected for Bias metric calculation and Fairness correction. Used and required only if Bias & Fairness in AutoML feature is enabled. | | quantileLevel | number | false | | The quantile level between 0.01 and 0.99 for specifying the Quantile metric. | | quickrun | boolean | false | | (Deprecated): 'quick' should be used in the mode parameter instead of using this parameter. If set to true, autopilot mode will be set to 'quick'.Cannot be set to true when mode is set to 'comprehensive' or 'manual'. | | rateTopPctThreshold | number | false | maximum: 100minimum: 0 | The percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric. | | relationshipsConfigurationId | string,null | false | | Relationships configuration id to be used for Feature Discovery projects. | | reps | integer | false | maximum: 999999minimum: 2 | The number of cross validation folds to use. | | responseCap | number | false | maximum: 1minimum: 0.5 | Used to cap the maximum response of a model | | runLeakageRemovedFeatureList | boolean | false | | Run Autopilot on Leakage Removed feature list (if exists). | | sampleStepPct | number | false | maximum: 100 | A float between 0 and 100 indicating the desired percentage of data to sample when training models in comprehensive Autopilot. Note: this only supported for comprehensive Autopilot and the specified value may be lowered in order to be compatible with the project's dataset and partition settings. | | scoringCodeOnly | boolean | false | | Keep only models that can be converted to scorable java code during Autopilot run. | | seed | integer | false | maximum: 999999999minimum: 0 | A seed to use for randomization. | | segmentationTaskId | string,null | false | | Specifies the SegmentationTask that will be used for dividing the project up into multiple segmented projects. | | seriesId | string | false | | The name of a column specifying the series ID to which each row of the dataset belongs. Typically the series was used to derive the additional features, that are independent from each other. Column unique values are used to subset the modeling data and build a separate model for each unique column value. Similar to time series this column is well suited to be used as multi-series ID column. | | shapOnlyMode | boolean | false | | Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. | | smartDownsampled | boolean | false | | Whether to use smart downsampling to throw away excess rows of the majority class. Only applicable to classification and zero-boosted regression projects. | | stopWords | [string] | false | maxItems: 1000 | A list of stop words to be used for text blueprints. Note: stop_words=True must be set in the blueprint preprocessing parameters for this list of stop words to actually be used during preprocessing. | | target | string | false | | The name of the target feature. | | targetType | string | false | | Used to specify the targetType to use for a project when it is ambiguous, i.e. a numeric target with a few unique values that could be used for either regression or multiclass. | | trainingLevel | any | false | | The value of the partition column indicating a row is part of the training set. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | treatAsExponential | string | false | | For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems always is not allowed. | | unsupervisedMode | boolean | false | | If True, unsupervised project (without target) will be created. target cannot be specified if unsupervisedMode is True. | | unsupervisedType | string,null | false | | The type of unsupervised project. Only valid when unsupervisedMode is true. If unsupervisedMode, defaults to anomaly. | | useCrossSeriesFeatures | boolean | false | | Indicating if user wants to use cross-series features. | | useGpu | boolean | false | | Indicates whether project should use GPU workers | | useProjectSettings | boolean | false | | Specifies whether datetime-partitioned project should use project settings (i.e. backtests configuration has been modified by the user). | | useSupervisedFeatureReduction | boolean | false | | When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features. | | useTimeSeries | boolean | false | | A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning. | | userPartitionCol | string | false | | The name of the column containing the partition assignments. | | validationDuration | string(duration) | false | | The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular. | | validationLevel | any | false | | The value of the partition column indicating a row is part of the validation set. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationPct | number | false | maximum: 99minimum: 0 | The percentage of the dataset to assign to the validation set | | validationType | string | false | | The validation method to be used. CV for cross validation or TVH for train-validation-holdout split. | | weights | string | false | | The name of a column specifying row weights. The data in this column must be pure numeric (e.g. not currency, date, length, etc.) and without missing values | | windowsBasisUnit | string | false | | For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or ROW. If omitted, the default value is detected time unit. | ### Enumerated Values | Property | Value | | --- | --- | | aggregationType | [total, average] | | autopilotDataSamplingMethod | [random, latest] | | autopilotDataSelectionMethod | [duration, rowCount] | | biasMitigationTechnique | [preprocessingReweighing, postProcessingRejectionOptionBasedClassification] | | cvMethod | [random, user, stratified, group, datetime] | | differencingMethod | [auto, none, simple, seasonal] | | fairnessMetricsSet | [proportionalParity, equalParity, predictionBalance, trueFavorableAndUnfavorableRateParity, favorableAndUnfavorablePredictiveValueParity] | | mode | [0, 2, 4, 3, auto, manual, comprehensive, quick] | | targetType | [Binary, Regression, Multiclass, Multilabel] | | treatAsExponential | [auto, never, always] | | unsupervisedType | [anomaly, clustering] | | validationType | [CV, TVH] | | windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] | ## AllowExtra ``` { "description": "The parameters submitted by the user to the failed job.", "type": "object" } ``` The parameters submitted by the user to the failed job. ### Properties None ## Autopilot ``` { "properties": { "command": { "description": "If `start`, will unpause the autopilot and run queued jobs if workers are available. If `stop`, will pause the autopilot so no new jobs will be started.", "enum": [ "start", "stop" ], "type": "string" } }, "required": [ "command" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | command | string | true | | If start, will unpause the autopilot and run queued jobs if workers are available. If stop, will pause the autopilot so no new jobs will be started. | ### Enumerated Values | Property | Value | | --- | --- | | command | [start, stop] | ## AutopilotStart ``` { "properties": { "autopilotClusterList": { "description": "Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "blendBestModels": { "description": "Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects.", "type": "boolean", "x-versionadded": "v2.21" }, "considerBlendersInRecommendation": { "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects.", "type": "boolean", "x-versionadded": "v2.21" }, "featurelistId": { "description": "The ID of a featurelist that should be used for autopilot.", "type": "string" }, "mode": { "default": "auto", "description": "The Autopilot mode.", "enum": [ "auto", "comprehensive", "quick" ], "type": "string" }, "prepareModelForDeployment": { "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning \"RECOMMENDED FOR DEPLOYMENT\" label.", "type": "boolean", "x-versionadded": "v2.21" }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean", "x-versionadded": "v2.21" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean", "x-versionadded": "v2.21" }, "useGpu": { "description": "Use GPU workers for Autopilot run.", "type": "boolean", "x-versionadded": "v2.33" } }, "required": [ "featurelistId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | autopilotClusterList | [integer] | false | maxItems: 10 | Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'. | | blendBestModels | boolean | false | | Blend best models during Autopilot run. This option is not supported in SHAP-only mode or for multilabel projects. | | considerBlendersInRecommendation | boolean | false | | Include blenders when selecting a model to prepare for deployment in an Autopilot Run. This option is not supported in SHAP-only mode or for multilabel projects. | | featurelistId | string | true | | The ID of a featurelist that should be used for autopilot. | | mode | string | false | | The Autopilot mode. | | prepareModelForDeployment | boolean | false | | Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning "RECOMMENDED FOR DEPLOYMENT" label. | | runLeakageRemovedFeatureList | boolean | false | | Run Autopilot on Leakage Removed feature list (if exists). | | scoringCodeOnly | boolean | false | | Keep only models that can be converted to scorable java code during Autopilot run. | | useGpu | boolean | false | | Use GPU workers for Autopilot run. | ### Enumerated Values | Property | Value | | --- | --- | | mode | [auto, comprehensive, quick] | ## AzureServicePrincipalCredentials ``` { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | azureTenantId | string | false | | Tenant ID of the Azure AD service principal. | | clientId | string | false | | Client ID of the Azure AD service principal. | | clientSecret | string | false | | Client Secret of the Azure AD service principal. | | configId | string | false | | The ID of secure configurations of credentials shared by admin. | | credentialType | string | true | | The type of these credentials, 'azure_service_principal' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | azure_service_principal | ## Backtest ``` { "properties": { "gapDuration": { "description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.", "format": "duration", "type": "string" }, "index": { "description": "The index from zero of the backtest specified by this object.", "type": "integer" }, "primaryTrainingEndDate": { "description": "A datetime string representing the end date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "primaryTrainingStartDate": { "description": "A datetime string representing the start date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationDuration": { "description": "A duration string representing the duration of the validation data for this backtest.", "format": "duration", "type": "string" }, "validationEndDate": { "description": "A datetime string representing the end date of the validation data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "index", "validationStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | gapDuration | string(duration) | false | | A duration string representing the duration of the gap between the training and the validation data for this backtest. | | index | integer | true | | The index from zero of the backtest specified by this object. | | primaryTrainingEndDate | string(date-time) | false | | A datetime string representing the end date of the primary training data for this backtest. | | primaryTrainingStartDate | string(date-time) | false | | A datetime string representing the start date of the primary training data for this backtest. | | validationDuration | string(duration) | false | | A duration string representing the duration of the validation data for this backtest. | | validationEndDate | string(date-time) | false | | A datetime string representing the end date of the validation data for this backtest. | | validationStartDate | string(date-time) | true | | A datetime string representing the start date of the validation data for this backtest. | ## BacktestNewMethodForOpenApi ``` { "description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.", "properties": { "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "primaryTrainingEndDate": { "description": "A datetime string representing the end date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "primaryTrainingStartDate": { "description": "A datetime string representing the start date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationEndDate": { "description": "A datetime string representing the end date of the validation data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "index", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationEndDate", "validationStartDate" ], "type": "object" } ``` Method 2 - directly configure the start and end dates of each partition, including the training partition. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | index | integer | true | | The index from zero of the backtest. | | primaryTrainingEndDate | string(date-time) | true | | A datetime string representing the end date of the primary training data for this backtest. | | primaryTrainingStartDate | string(date-time) | true | | A datetime string representing the start date of the primary training data for this backtest. | | validationEndDate | string(date-time) | true | | A datetime string representing the end date of the validation data for this backtest. | | validationStartDate | string(date-time) | true | | A datetime string representing the start date of the validation data for this backtest. | ## BacktestOldMethodForOpenApi ``` { "description": "Method 1 - pass validation and gap durations", "properties": { "gapDuration": { "description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.", "format": "duration", "type": "string" }, "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "validationDuration": { "description": "A duration string representing the duration of the validation data for this backtest.", "format": "duration", "type": "string" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "gapDuration", "index", "validationDuration", "validationStartDate" ], "type": "object" } ``` Method 1 - pass validation and gap durations ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | gapDuration | string(duration) | true | | A duration string representing the duration of the gap between the training and the validation data for this backtest. | | index | integer | true | | The index from zero of the backtest. | | validationDuration | string(duration) | true | | A duration string representing the duration of the validation data for this backtest. | | validationStartDate | string(date-time) | true | | A datetime string representing the start date of the validation data for this backtest. | ## BacktestResponse ``` { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationDuration", "validationEndDate", "validationStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | availableTrainingDuration | string(duration) | true | | The duration of the available training data for this backtest. | | availableTrainingEndDate | string(date-time) | true | | The end date of the available training data for this backtest. | | availableTrainingStartDate | string(date-time) | true | | The start date of the available training data for this backtest. | | gapDuration | string(duration) | true | | The duration of the gap between the training and the validation scoring data for this backtest. | | gapEndDate | string(date-time) | true | | The end date of the gap between the training and validation scoring data for this backtest. | | gapStartDate | string(date-time) | true | | The start date of the gap between the training and validation scoring data for this backtest. | | index | integer | true | | The index from zero of this backtest. | | primaryTrainingDuration | string(duration) | true | | The duration of the primary training data for this backtest. | | primaryTrainingEndDate | string(date-time) | true | | The end date of the primary training data for this backtest. | | primaryTrainingStartDate | string(date-time) | true | | The start date of the primary training data for this backtest. | | validationDuration | string | true | | The duration of the validation scoring data for this backtest. | | validationEndDate | string(date-time) | true | | The end date of the validation scoring data for this backtest. | | validationStartDate | string(date-time) | true | | The start date of the validation scoring data for this backtest. | ## Backtests ``` { "properties": { "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "validationEndDate", "validationStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationEndDate | string(date-time) | true | | The end date of the validation scoring data for this backtest. | | validationStartDate | string(date-time) | true | | The start date of the validation scoring data for this backtest. | ## BasicCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'basic' here. | | password | string | true | | The password for database authentication. The password is encrypted at rest and never saved or stored. | | user | string | true | | The username for database authentication. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | basic | ## ClassMappingAggregationSettings ``` { "description": "Class mapping aggregation settings.", "properties": { "aggregationClassName": { "description": "The name of the class that will be assigned to all rows with aggregated classes. Should not match any excluded_from_aggregation or we will have 2 classes with the same name and no way to distinguish between them. This option is only available formulticlass projects. By default 'DR_RARE_TARGET_VALUES' is used.", "type": "string" }, "excludedFromAggregation": { "default": [], "description": "List of target values that should be guaranteed to kept as is, regardless of other settings.", "items": { "type": "string" }, "type": "array" }, "maxUnaggregatedClassValues": { "default": 1000, "description": "The maximum number of unique labels before aggregation kicks in. Should be at least len(excludedFromAggregation) + 1 for multiclass and at least len(excludedFromAggregation) for multilabel.", "maximum": 1000, "minimum": 3, "type": "integer" }, "minClassSupport": { "default": 1, "description": "Minimum number of instances necessary for each target value in the dataset. All values with fewer instances than this value will be aggregated", "type": "integer" } }, "type": "object" } ``` Class mapping aggregation settings. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregationClassName | string | false | | The name of the class that will be assigned to all rows with aggregated classes. Should not match any excluded_from_aggregation or we will have 2 classes with the same name and no way to distinguish between them. This option is only available formulticlass projects. By default 'DR_RARE_TARGET_VALUES' is used. | | excludedFromAggregation | [string] | false | | List of target values that should be guaranteed to kept as is, regardless of other settings. | | maxUnaggregatedClassValues | integer | false | maximum: 1000minimum: 3 | The maximum number of unique labels before aggregation kicks in. Should be at least len(excludedFromAggregation) + 1 for multiclass and at least len(excludedFromAggregation) for multilabel. | | minClassSupport | integer | false | | Minimum number of instances necessary for each target value in the dataset. All values with fewer instances than this value will be aggregated | ## CredentialId ``` { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "credentialId": { "description": "The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" } }, "required": [ "credentialId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogVersionId | string | false | | The ID of the latest version of the catalog entry. | | credentialId | string | true | | The ID of the set of credentials to use instead of user and password. Note that with this change, username and password will become optional. | | url | string | false | | The link to retrieve more detailed information about the entity that uses this catalog dataset. | ## CrossSeriesGroupByColumnRetrieveResponse ``` { "properties": { "crossSeriesGroupByColumns": { "description": "A list of columns with information about each column's eligibility as a cross-series group-by column.", "items": { "properties": { "eligibility": { "description": "Information about the column's eligibility. If the column is not eligible, this will include the reason why.", "type": "string" }, "isEligible": { "description": "Indicates whether this column can be used as a group-by column.", "type": "boolean" }, "name": { "description": "The name of the column.", "type": "string" } }, "required": [ "eligibility", "isEligible", "name" ], "type": "object" }, "type": "array" }, "multiseriesId": { "description": "The name of the multiseries ID column.", "type": "string" } }, "required": [ "crossSeriesGroupByColumns", "multiseriesId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | crossSeriesGroupByColumns | [CrossSeriesGroupByColumnsListItem] | true | | A list of columns with information about each column's eligibility as a cross-series group-by column. | | multiseriesId | string | true | | The name of the multiseries ID column. | ## CrossSeriesGroupByColumnsListItem ``` { "properties": { "eligibility": { "description": "Information about the column's eligibility. If the column is not eligible, this will include the reason why.", "type": "string" }, "isEligible": { "description": "Indicates whether this column can be used as a group-by column.", "type": "boolean" }, "name": { "description": "The name of the column.", "type": "string" } }, "required": [ "eligibility", "isEligible", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | eligibility | string | true | | Information about the column's eligibility. If the column is not eligible, this will include the reason why. | | isEligible | boolean | true | | Indicates whether this column can be used as a group-by column. | | name | string | true | | The name of the column. | ## CustomMetricsLossesInfo ``` { "description": "Returns custom metrics information. This field is required for custom metrics projects.", "properties": { "id": { "description": "The ID of an existing custom_metrics_loss_function document for this project.", "type": "string" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.44" } ``` Returns custom metrics information. This field is required for custom metrics projects. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of an existing custom_metrics_loss_function document for this project. | ## DatabricksAccessTokenCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'databricks_access_token_account' here. | | databricksAccessToken | string | true | minLength: 1minLength: 1 | Databricks personal access token. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | databricks_access_token_account | ## DatabricksServicePrincipalCredentials ``` { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clientId | string | false | minLength: 1minLength: 1 | Client ID for Databricks service principal. | | clientSecret | string | false | minLength: 1minLength: 1 | Client secret for Databricks service principal. | | configId | string | false | | The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret. | | credentialType | string | true | | The type of these credentials, 'databricks_service_principal_account' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | databricks_service_principal_account | ## DatetimePartitioningDataForOpenApi ``` { "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": "string", "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "default": false, "description": "Specifies whether the time series predictions can use partial historical data.", "type": "boolean", "x-versionadded": "v2.24" }, "autopilotClusterList": { "description": "A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotDataSamplingMethod": { "description": "Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "backtests": { "description": "An array specifying individual backtests.", "items": { "oneOf": [ { "description": "Method 1 - pass validation and gap durations", "properties": { "gapDuration": { "description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.", "format": "duration", "type": "string" }, "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "validationDuration": { "description": "A duration string representing the duration of the validation data for this backtest.", "format": "duration", "type": "string" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "gapDuration", "index", "validationDuration", "validationStartDate" ], "type": "object" }, { "description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.", "properties": { "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "primaryTrainingEndDate": { "description": "A datetime string representing the end date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "primaryTrainingStartDate": { "description": "A datetime string representing the start date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationEndDate": { "description": "A datetime string representing the end date of the validation data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "index", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationEndDate", "validationStartDate" ], "type": "object" } ] }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": "string", "x-versionadded": "v2.15" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": "string", "x-versionadded": "v2.9" }, "disableHoldout": { "default": false, "description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).", "format": "duration", "type": "string" }, "holdoutDuration": { "description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "default": 5, "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": "string", "x-versionadded": "v2.9" }, "unsupervisedMode": { "default": false, "description": "A boolean value indicating whether an unsupervised project should be created.", "type": "boolean", "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCrossSeriesFeatures": { "default": false, "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": "boolean", "x-versionadded": "v2.14" }, "useSupervisedFeatureReduction": { "default": true, "description": "When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.", "type": "boolean" }, "useTimeSeries": { "default": false, "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.", "format": "duration", "type": "string" }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.14" } }, "required": [ "datetimePartitionColumn" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregationType | string | false | | For multiseries projects only. The aggregation type to apply when creating cross-series features. | | allowPartialHistoryTimeSeriesPredictions | boolean | false | | Specifies whether the time series predictions can use partial historical data. | | autopilotClusterList | [integer] | false | maxItems: 10 | A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to clustering. | | autopilotDataSamplingMethod | string | false | | Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'. | | autopilotDataSelectionMethod | string | false | | The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets. | | backtests | [oneOf] | false | maxItems: 20minItems: 1 | An array specifying individual backtests. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BacktestOldMethodForOpenApi | false | | Method 1 - pass validation and gap durations | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BacktestNewMethodForOpenApi | false | | Method 2 - directly configure the start and end dates of each partition, including the training partition. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | calendarId | string | false | | The ID of the calendar to be used in this project. | | clusteringBufferDisabled | boolean | false | | A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project. | | crossSeriesGroupByColumns | [string] | false | maxItems: 1 | For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like "men's clothing", "sports equipment", etc. | | datetimePartitionColumn | string | true | | The date column that will be used as a datetime partition column. | | defaultToAPriori | boolean | false | | Renamed to defaultToKnownInAdvance. | | defaultToDoNotDerive | boolean | false | | For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter. | | defaultToKnownInAdvance | boolean | false | | For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the :ref:Time Series Overview for more context. | | differencingMethod | string | false | | For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems simple and seasonal are not allowed. Parameter periodicities must be specified if seasonal is chosen. Defaults to auto. | | disableHoldout | boolean | false | | Whether to suppress allocating a holdout fold. If disableHoldout is set to true, holdoutStartDate and holdoutDuration must not be set. | | featureDerivationWindowEnd | integer | false | maximum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end. | | featureDerivationWindowStart | integer | false | maximum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin. | | featureSettings | [FeatureSetting] | false | | An array specifying per feature settings. Features can be left unspecified. | | forecastWindowEnd | integer | false | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end. | | forecastWindowStart | integer | false | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. | | gapDuration | string(duration) | false | | The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D). | | holdoutDuration | string(duration) | false | | The duration of holdout scoring data. When specifying holdoutDuration, holdoutStartDate must also be specified. This attribute cannot be specified when disableHoldout is true. | | holdoutEndDate | string(date-time) | false | | The end date of holdout scoring data. When specifying holdoutEndDate, holdoutStartDate must also be specified. This attribute cannot be specified when disableHoldout is true. | | holdoutStartDate | string(date-time) | false | | The start date of holdout scoring data. When specifying holdoutStartDate, one of holdoutEndDate or holdoutDuration must also be specified. This attribute cannot be specified when disableHoldout is true. | | isHoldoutModified | boolean | false | | A boolean value indicating whether holdout settings (start/end dates) have been modified by user. | | modelSplits | integer | false | maximum: 10minimum: 1 | Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. | | multiseriesIdColumns | [string] | false | minItems: 1 | May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:multiseries section of the time series documentation for more context. | | numberOfBacktests | integer | false | maximum: 20minimum: 1 | The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations. | | periodicities | [Periodicity] | false | | A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'. | | treatAsExponential | string | false | | For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems always is not allowed. Defaults to auto. | | unsupervisedMode | boolean | false | | A boolean value indicating whether an unsupervised project should be created. | | unsupervisedType | string,null | false | | The type of unsupervised project. Only valid when unsupervisedMode is true. If unsupervisedMode, defaults to anomaly. | | useCrossSeriesFeatures | boolean | false | | For multiseries projects only. Indicating whether to use cross-series features. | | useSupervisedFeatureReduction | boolean | false | | When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features. | | useTimeSeries | boolean | false | | A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning. | | validationDuration | string(duration) | false | | The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular. | | windowsBasisUnit | string | false | | For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or ROW. If omitted, the default value is detected time unit. | ### Enumerated Values | Property | Value | | --- | --- | | aggregationType | [total, average] | | autopilotDataSamplingMethod | [random, latest] | | autopilotDataSelectionMethod | [duration, rowCount] | | differencingMethod | [auto, none, simple, seasonal] | | treatAsExponential | [auto, never, always] | | unsupervisedType | [anomaly, clustering] | | windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] | ## DatetimePartitioningLogListControllerResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "datetimePartitioningLog": { "description": "The content of the date/time partitioning log.", "type": "string" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalLogLines": { "description": "The total number of lines in feature derivation log.", "type": "integer" } }, "required": [ "count", "datetimePartitioningLog", "next", "previous", "totalLogLines" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | datetimePartitioningLog | string | true | | The content of the date/time partitioning log. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | | totalLogLines | integer | true | | The total number of lines in feature derivation log. | ## DatetimePartitioningResponse ``` { "description": "Partitioning information for a datetime project.", "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "autopilotDataSamplingMethod": { "description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "availableHoldoutEndDate": { "description": "The maximum valid date of holdout scoring data.", "format": "date-time", "type": [ "string", "null" ] }, "availableTrainingDuration": { "description": "The duration of available training duration for scoring the holdout.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationDuration", "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.15" }, "calendarName": { "description": "The name of the calendar used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.17" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "dateFormat": { "description": "The date format of the partition column.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of the current optimized datetime partitioning", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "disableHoldout": { "description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array", "x-versionadded": "v2.9" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between the training and holdout scoring data.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutDuration": { "description": "The duration of the holdout scoring data.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "numberOfDoNotDeriveFeatures": { "description": "Number of features that are marked as \"do not derive\".", "type": "integer", "x-versionadded": "v2.17" }, "numberOfKnownInAdvanceFeatures": { "description": "Number of features that are marked as \"known in advance\".", "type": "integer", "x-versionadded": "v2.14" }, "partitioningExtendedWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "partitioningWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "type": "string" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "primaryTrainingDuration": { "description": "The duration of primary training duration for scoring the holdout.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "unsupervisedMode": { "description": "A boolean value indicating whether an unsupervised project should be created.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "unsupervisedType": { "description": "The type of unsupervised project.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.31" }, "useCrossSeriesFeatures": { "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": [ "boolean", "null" ], "x-versionadded": "v2.14" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.", "format": "duration", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" } }, "required": [ "autopilotDataSamplingMethod", "autopilotDataSelectionMethod", "availableHoldoutEndDate", "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "backtests", "dateFormat", "datetimePartitionColumn", "defaultToAPriori", "defaultToDoNotDerive", "defaultToKnownInAdvance", "differencingMethod", "disableHoldout", "featureDerivationWindowEnd", "featureDerivationWindowStart", "featureSettings", "forecastWindowEnd", "forecastWindowStart", "gapDuration", "gapEndDate", "gapStartDate", "holdoutDuration", "holdoutEndDate", "holdoutStartDate", "multiseriesIdColumns", "numberOfBacktests", "numberOfDoNotDeriveFeatures", "numberOfKnownInAdvanceFeatures", "partitioningWarnings", "periodicities", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "projectId", "treatAsExponential", "useTimeSeries", "validationDuration", "windowsBasisUnit" ], "type": "object" } ``` Partitioning information for a datetime project. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregationType | string,null | false | | For multiseries projects only. The aggregation type to apply when creating cross-series features. | | allowPartialHistoryTimeSeriesPredictions | boolean,null | false | | Specifies whether the time series predictions can use partial historical data. | | autopilotDataSamplingMethod | string | true | | The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets. | | autopilotDataSelectionMethod | string | true | | The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets. | | availableHoldoutEndDate | string,null(date-time) | true | | The maximum valid date of holdout scoring data. | | availableTrainingDuration | string(duration) | true | | The duration of available training duration for scoring the holdout. | | availableTrainingEndDate | string(date-time) | true | | The end date of available training data for scoring the holdout. | | availableTrainingStartDate | string(date-time) | true | | The start date of available training data for scoring the holdout. | | backtests | [BacktestResponse] | true | maxItems: 20minItems: 1 | An array of the configured backtests. | | calendarId | string,null | false | | The ID of the calendar to be used in this project. | | calendarName | string,null | false | | The name of the calendar used in this project. | | clusteringBufferDisabled | boolean | false | | A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project. | | crossSeriesGroupByColumns | [string] | false | maxItems: 1 | For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like "men's clothing", "sports equipment", etc. | | dateFormat | string | true | | The date format of the partition column. | | datetimePartitionColumn | string | true | | The date column that will be used as a datetime partition column. | | datetimePartitioningId | string | false | | The ID of the current optimized datetime partitioning | | defaultToAPriori | boolean | true | | Renamed to defaultToKnownInAdvance. | | defaultToDoNotDerive | boolean | true | | For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter. | | defaultToKnownInAdvance | boolean | true | | For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the :ref:Time Series Overview for more context. | | differencingMethod | string,null | true | | For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems simple and seasonal are not allowed. Parameter periodicities must be specified if seasonal is chosen. Defaults to auto. | | disableHoldout | boolean | true | | A boolean value indicating whether date partitioning skipped allocating a holdout fold. | | featureDerivationWindowEnd | integer,null | true | maximum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end. | | featureDerivationWindowStart | integer,null | true | maximum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin. | | featureSettings | [FeatureSetting] | true | | An array specifying per feature settings. Features can be left unspecified. | | forecastWindowEnd | integer,null | true | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end. | | forecastWindowStart | integer,null | true | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. | | gapDuration | string(duration) | true | | The duration of the gap between the training and holdout scoring data. | | gapEndDate | string(date-time) | true | | The end date of the gap between the training and holdout scoring data. | | gapStartDate | string(date-time) | true | | The start date of the gap between the training and holdout scoring data. | | holdoutDuration | string(duration) | true | | The duration of the holdout scoring data. | | holdoutEndDate | string(date-time) | true | | The end date of holdout scoring data. | | holdoutStartDate | string(date-time) | true | | The start date of holdout scoring data. | | isHoldoutModified | boolean | false | | A boolean value indicating whether holdout settings (start/end dates) have been modified by user. | | modelSplits | integer | false | maximum: 10minimum: 1 | Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. | | multiseriesIdColumns | [string] | true | minItems: 1 | May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:multiseries section of the time series documentation for more context. | | numberOfBacktests | integer | true | maximum: 20minimum: 1 | The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations. | | numberOfDoNotDeriveFeatures | integer | true | | Number of features that are marked as "do not derive". | | numberOfKnownInAdvanceFeatures | integer | true | | Number of features that are marked as "known in advance". | | partitioningExtendedWarnings | [PartitioningExtendedWarning] | false | | An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted. | | partitioningWarnings | [PartitioningWarning] | true | | An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted. | | periodicities | [Periodicity] | true | | A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'. | | primaryTrainingDuration | string(duration) | true | | The duration of primary training duration for scoring the holdout. | | primaryTrainingEndDate | string(date-time) | true | | The end date of primary training data for scoring the holdout. | | primaryTrainingStartDate | string(date-time) | true | | The start date of primary training data for scoring the holdout. | | projectId | string | true | | The ID of the project. | | treatAsExponential | string,null | true | | For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems always is not allowed. Defaults to auto. | | unsupervisedMode | boolean,null | false | | A boolean value indicating whether an unsupervised project should be created. | | unsupervisedType | string,null | false | | The type of unsupervised project. | | useCrossSeriesFeatures | boolean,null | false | | For multiseries projects only. Indicating whether to use cross-series features. | | useTimeSeries | boolean | true | | A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning. | | validationDuration | string,null(duration) | true | | The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. | | windowsBasisUnit | string,null | true | | For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or ROW. If omitted, the default value is detected time unit. | ### Enumerated Values | Property | Value | | --- | --- | | aggregationType | [total, average] | | autopilotDataSamplingMethod | [random, latest] | | autopilotDataSelectionMethod | [duration, rowCount] | | differencingMethod | [auto, none, simple, seasonal] | | treatAsExponential | [auto, never, always] | | unsupervisedType | [anomaly, clustering] | | windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] | ## DeletedProjectCountResponse ``` { "properties": { "deletedProjectsCount": { "description": "Amount of soft-deleted projects. The value is limited by projectCountLimit", "minimum": 0, "type": "integer" }, "projectCountLimit": { "description": "Deleted projects counting limit value. Stop counting above this limit", "minimum": 0, "type": "integer" }, "valueExceedsLimit": { "description": "If an actual number of soft-deleted projects exceeds counting limit", "type": "boolean" } }, "required": [ "deletedProjectsCount", "projectCountLimit", "valueExceedsLimit" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deletedProjectsCount | integer | true | minimum: 0 | Amount of soft-deleted projects. The value is limited by projectCountLimit | | projectCountLimit | integer | true | minimum: 0 | Deleted projects counting limit value. Stop counting above this limit | | valueExceedsLimit | boolean | true | | If an actual number of soft-deleted projects exceeds counting limit | ## DeletedProjectListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of deleted projects", "items": { "properties": { "createdBy": { "description": "The user who created the project", "properties": { "email": { "description": "Email of the user", "type": "string" }, "id": { "description": "ID of the user", "type": "string" } }, "required": [ "email", "id" ], "type": "object" }, "deletedBy": { "description": "The user who created the project", "properties": { "email": { "description": "Email of the user", "type": "string" }, "id": { "description": "ID of the user", "type": "string" } }, "required": [ "email", "id" ], "type": "object" }, "deletionTime": { "description": "ISO-8601 formatted date when project was deleted", "format": "date-time", "type": [ "string", "null" ] }, "fileName": { "description": "The name of the file uploaded for the project dataset", "type": [ "string", "null" ] }, "id": { "description": "The ID of the project", "type": "string" }, "organization": { "description": "The organization the project belongs to", "properties": { "id": { "description": "ID of the organization the project belongs to", "type": "string" }, "name": { "description": "Name of the organization the project belongs to", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "projectName": { "default": "Untitled Project", "description": "The name of the project", "type": "string" }, "scheduledForDeletion": { "description": "Whether project permanent deletion has already been scheduled", "type": "boolean" } }, "required": [ "createdBy", "deletedBy", "deletionTime", "fileName", "id", "organization", "projectName", "scheduledForDeletion" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [DeletedProjectResponse] | true | | List of deleted projects | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## DeletedProjectOrganization ``` { "description": "The organization the project belongs to", "properties": { "id": { "description": "ID of the organization the project belongs to", "type": "string" }, "name": { "description": "Name of the organization the project belongs to", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` The organization the project belongs to ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | ID of the organization the project belongs to | | name | string | true | | Name of the organization the project belongs to | ## DeletedProjectResponse ``` { "properties": { "createdBy": { "description": "The user who created the project", "properties": { "email": { "description": "Email of the user", "type": "string" }, "id": { "description": "ID of the user", "type": "string" } }, "required": [ "email", "id" ], "type": "object" }, "deletedBy": { "description": "The user who created the project", "properties": { "email": { "description": "Email of the user", "type": "string" }, "id": { "description": "ID of the user", "type": "string" } }, "required": [ "email", "id" ], "type": "object" }, "deletionTime": { "description": "ISO-8601 formatted date when project was deleted", "format": "date-time", "type": [ "string", "null" ] }, "fileName": { "description": "The name of the file uploaded for the project dataset", "type": [ "string", "null" ] }, "id": { "description": "The ID of the project", "type": "string" }, "organization": { "description": "The organization the project belongs to", "properties": { "id": { "description": "ID of the organization the project belongs to", "type": "string" }, "name": { "description": "Name of the organization the project belongs to", "type": "string" } }, "required": [ "id", "name" ], "type": "object" }, "projectName": { "default": "Untitled Project", "description": "The name of the project", "type": "string" }, "scheduledForDeletion": { "description": "Whether project permanent deletion has already been scheduled", "type": "boolean" } }, "required": [ "createdBy", "deletedBy", "deletionTime", "fileName", "id", "organization", "projectName", "scheduledForDeletion" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | DeletedProjectUser | true | | The user who created the project | | deletedBy | DeletedProjectUser | true | | The user who created the project | | deletionTime | string,null(date-time) | true | | ISO-8601 formatted date when project was deleted | | fileName | string,null | true | | The name of the file uploaded for the project dataset | | id | string | true | | The ID of the project | | organization | DeletedProjectOrganization | true | | The organization the project belongs to | | projectName | string | true | | The name of the project | | scheduledForDeletion | boolean | true | | Whether project permanent deletion has already been scheduled | ## DeletedProjectUser ``` { "description": "The user who created the project", "properties": { "email": { "description": "Email of the user", "type": "string" }, "id": { "description": "ID of the user", "type": "string" } }, "required": [ "email", "id" ], "type": "object" } ``` The user who created the project ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string | true | | Email of the user | | id | string | true | | ID of the user | ## ExternalTSBaselineMetadata ``` { "description": "The id of the catalog item that is being used as the external baseline data", "properties": { "datasetId": { "description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.", "type": [ "string", "null" ] }, "datasetName": { "description": "The name of the timeseries baseline dataset for the project", "type": [ "string", "null" ] } }, "required": [ "datasetId", "datasetName" ], "type": "object" } ``` The id of the catalog item that is being used as the external baseline data ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string,null | true | | Catalog version id for external prediction data that can be used as a baseline to calculate new metrics. | | datasetName | string,null | true | | The name of the timeseries baseline dataset for the project | ## ExternalTSBaselinePayload ``` { "properties": { "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "catalogVersionId": { "description": "The version ID of the external baseline data item in the AI catalog.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as the datetime partition column for the specified project.", "type": "string" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "multiseriesIdColumns": { "description": "An array of column names identifying the multiseries ID column(s)to use to identify series within the data. Must match the multiseries ID column(s) for the specified project. Currently, only one multiseries ID column may be specified.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "target": { "description": "The selected target of the specified project.", "type": "string" } }, "required": [ "catalogVersionId", "datetimePartitionColumn", "forecastWindowEnd", "forecastWindowStart", "target" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtests | [Backtests] | false | maxItems: 20minItems: 1 | An array of the configured backtests. | | catalogVersionId | string | true | | The version ID of the external baseline data item in the AI catalog. | | datetimePartitionColumn | string | true | | The date column that will be used as the datetime partition column for the specified project. | | forecastWindowEnd | integer | true | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end. | | forecastWindowStart | integer | true | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. | | holdoutEndDate | string(date-time) | false | | The end date of holdout scoring data. | | holdoutStartDate | string(date-time) | false | | The start date of holdout scoring data. | | multiseriesIdColumns | [string] | false | maxItems: 1minItems: 1 | An array of column names identifying the multiseries ID column(s)to use to identify series within the data. Must match the multiseries ID column(s) for the specified project. Currently, only one multiseries ID column may be specified. | | target | string | true | | The selected target of the specified project. | ## ExternalTSBaselineResponse ``` { "properties": { "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "baselineValidationJobId": { "description": "The ID of the validation job.", "type": "string" }, "catalogVersionId": { "description": "The version ID of the external baseline data item in the AI catalog.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as the datetime partition column for the specified project.", "type": "string" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isExternalBaselineDatasetValid": { "description": "Indicates whether the external dataset has passed the validation check or not.", "type": "boolean" }, "message": { "description": "A message providing more detail on the validation result.", "type": [ "string", "null" ] }, "multiseriesIdColumns": { "description": "An array of column names identifying the multiseries ID column(s)to use to identify series within the data. Must match the multiseries ID column(s) for the specified project. Currently, only one multiseries ID column may be specified.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "projectId": { "description": "The project ID of the external baseline data item.", "type": "string" }, "target": { "description": "The selected target of the specified project.", "type": "string" } }, "required": [ "baselineValidationJobId", "catalogVersionId", "datetimePartitionColumn", "isExternalBaselineDatasetValid", "message", "projectId", "target" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtests | [Backtests] | false | maxItems: 20minItems: 1 | An array of the configured backtests. | | baselineValidationJobId | string | true | | The ID of the validation job. | | catalogVersionId | string | true | | The version ID of the external baseline data item in the AI catalog. | | datetimePartitionColumn | string | true | | The date column that will be used as the datetime partition column for the specified project. | | forecastWindowEnd | integer | false | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end. | | forecastWindowStart | integer | false | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. | | holdoutEndDate | string(date-time) | false | | The end date of holdout scoring data. | | holdoutStartDate | string(date-time) | false | | The start date of holdout scoring data. | | isExternalBaselineDatasetValid | boolean | true | | Indicates whether the external dataset has passed the validation check or not. | | message | string,null | true | | A message providing more detail on the validation result. | | multiseriesIdColumns | [string] | false | maxItems: 1minItems: 1 | An array of column names identifying the multiseries ID column(s)to use to identify series within the data. Must match the multiseries ID column(s) for the specified project. Currently, only one multiseries ID column may be specified. | | projectId | string | true | | The project ID of the external baseline data item. | | target | string | true | | The selected target of the specified project. | ## FeatureSetting ``` { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aPriori | boolean | false | | Renamed to knownInAdvance. | | doNotDerive | boolean | false | | For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the defaultToDoNotDerive flag. | | featureName | string | true | | The name of the feature being specified. | | knownInAdvance | boolean | false | | For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the defaultToKnownInAdvance flag. | ## FinalBacktestResponse ``` { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingRowCount": { "description": "The number of rows in the available training data for this backtest.", "type": "integer" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapRowCount": { "description": "The number of rows in the gap between the training and the validation scoring data for this backtest.", "type": "integer" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingRowCount": { "description": "The number of rows in the primary training data for this backtest.", "type": "integer" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "totalRowCount": { "description": "The total number of rows in this backtest", "type": "integer" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationRowCount": { "description": "The number of rows in the validation scoring data for this backtest.", "type": "integer" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingRowCount", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapRowCount", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingRowCount", "primaryTrainingStartDate", "totalRowCount", "validationDuration", "validationEndDate", "validationRowCount", "validationStartDate" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | availableTrainingDuration | string(duration) | true | | The duration of the available training data for this backtest. | | availableTrainingEndDate | string(date-time) | true | | The end date of the available training data for this backtest. | | availableTrainingRowCount | integer | true | | The number of rows in the available training data for this backtest. | | availableTrainingStartDate | string(date-time) | true | | The start date of the available training data for this backtest. | | gapDuration | string(duration) | true | | The duration of the gap between the training and the validation scoring data for this backtest. | | gapEndDate | string(date-time) | true | | The end date of the gap between the training and validation scoring data for this backtest. | | gapRowCount | integer | true | | The number of rows in the gap between the training and the validation scoring data for this backtest. | | gapStartDate | string(date-time) | true | | The start date of the gap between the training and validation scoring data for this backtest. | | index | integer | true | | The index from zero of this backtest. | | primaryTrainingDuration | string(duration) | true | | The duration of the primary training data for this backtest. | | primaryTrainingEndDate | string(date-time) | true | | The end date of the primary training data for this backtest. | | primaryTrainingRowCount | integer | true | | The number of rows in the primary training data for this backtest. | | primaryTrainingStartDate | string(date-time) | true | | The start date of the primary training data for this backtest. | | totalRowCount | integer | true | | The total number of rows in this backtest | | validationDuration | string | true | | The duration of the validation scoring data for this backtest. | | validationEndDate | string(date-time) | true | | The end date of the validation scoring data for this backtest. | | validationRowCount | integer | true | | The number of rows in the validation scoring data for this backtest. | | validationStartDate | string(date-time) | true | | The start date of the validation scoring data for this backtest. | ## FinalDatetimePartitioningResponse ``` { "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "autopilotDataSamplingMethod": { "description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "availableHoldoutEndDate": { "description": "The maximum valid date of holdout scoring data.", "format": "date-time", "type": [ "string", "null" ] }, "availableTrainingDuration": { "description": "The duration of available training duration for scoring the holdout.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "availableTrainingRowCount": { "description": "The number of rows in the available training data for scoring the holdout partition", "type": "integer" }, "availableTrainingStartDate": { "description": "The start date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingRowCount": { "description": "The number of rows in the available training data for this backtest.", "type": "integer" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapRowCount": { "description": "The number of rows in the gap between the training and the validation scoring data for this backtest.", "type": "integer" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingRowCount": { "description": "The number of rows in the primary training data for this backtest.", "type": "integer" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "totalRowCount": { "description": "The total number of rows in this backtest", "type": "integer" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationRowCount": { "description": "The number of rows in the validation scoring data for this backtest.", "type": "integer" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingRowCount", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapRowCount", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingRowCount", "primaryTrainingStartDate", "totalRowCount", "validationDuration", "validationEndDate", "validationRowCount", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.15" }, "calendarName": { "description": "The name of the calendar used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.17" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "dateFormat": { "description": "The date format of the partition column.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of the current optimized datetime partitioning", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "disableHoldout": { "description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array", "x-versionadded": "v2.9" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between the training and holdout scoring data.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "gapRowCount": { "description": "The number of rows in the gap between the training and holdout scoring data", "type": "integer" }, "gapStartDate": { "description": "The start date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutDuration": { "description": "The duration of the holdout scoring data.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutRowCount": { "description": "The number of rows in the holdout scoring data", "type": "integer" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "numberOfDoNotDeriveFeatures": { "description": "Number of features that are marked as \"do not derive\".", "type": "integer", "x-versionadded": "v2.17" }, "numberOfKnownInAdvanceFeatures": { "description": "Number of features that are marked as \"known in advance\".", "type": "integer", "x-versionadded": "v2.14" }, "partitioningExtendedWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "partitioningWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "type": "string" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "primaryTrainingDuration": { "description": "The duration of primary training duration for scoring the holdout.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "primaryTrainingRowCount": { "description": "The number of rows in the primary training data for scoring the holdout", "type": "integer" }, "primaryTrainingStartDate": { "description": "The start date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "totalRowCount": { "description": "The total number of rows in the project dataset", "type": "integer" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "unsupervisedMode": { "description": "A boolean value indicating whether an unsupervised project should be created.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "unsupervisedType": { "description": "The type of unsupervised project.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.31" }, "useCrossSeriesFeatures": { "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": [ "boolean", "null" ], "x-versionadded": "v2.14" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.", "format": "duration", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" } }, "required": [ "aggregationType", "autopilotDataSamplingMethod", "autopilotDataSelectionMethod", "availableHoldoutEndDate", "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingRowCount", "availableTrainingStartDate", "backtests", "calendarId", "calendarName", "crossSeriesGroupByColumns", "dateFormat", "datetimePartitionColumn", "defaultToAPriori", "defaultToDoNotDerive", "defaultToKnownInAdvance", "differencingMethod", "disableHoldout", "featureDerivationWindowEnd", "featureDerivationWindowStart", "featureSettings", "forecastWindowEnd", "forecastWindowStart", "gapDuration", "gapEndDate", "gapRowCount", "gapStartDate", "holdoutDuration", "holdoutEndDate", "holdoutRowCount", "holdoutStartDate", "modelSplits", "multiseriesIdColumns", "numberOfBacktests", "numberOfDoNotDeriveFeatures", "numberOfKnownInAdvanceFeatures", "partitioningWarnings", "periodicities", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingRowCount", "primaryTrainingStartDate", "projectId", "totalRowCount", "treatAsExponential", "useCrossSeriesFeatures", "useTimeSeries", "validationDuration", "windowsBasisUnit" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregationType | string,null | true | | For multiseries projects only. The aggregation type to apply when creating cross-series features. | | allowPartialHistoryTimeSeriesPredictions | boolean,null | false | | Specifies whether the time series predictions can use partial historical data. | | autopilotDataSamplingMethod | string | true | | The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets. | | autopilotDataSelectionMethod | string | true | | The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets. | | availableHoldoutEndDate | string,null(date-time) | true | | The maximum valid date of holdout scoring data. | | availableTrainingDuration | string(duration) | true | | The duration of available training duration for scoring the holdout. | | availableTrainingEndDate | string(date-time) | true | | The end date of available training data for scoring the holdout. | | availableTrainingRowCount | integer | true | | The number of rows in the available training data for scoring the holdout partition | | availableTrainingStartDate | string(date-time) | true | | The start date of available training data for scoring the holdout. | | backtests | [FinalBacktestResponse] | true | maxItems: 20minItems: 1 | An array of the configured backtests. | | calendarId | string,null | true | | The ID of the calendar to be used in this project. | | calendarName | string,null | true | | The name of the calendar used in this project. | | crossSeriesGroupByColumns | [string] | true | maxItems: 1 | For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like "men's clothing", "sports equipment", etc. | | dateFormat | string | true | | The date format of the partition column. | | datetimePartitionColumn | string | true | | The date column that will be used as a datetime partition column. | | datetimePartitioningId | string | false | | The ID of the current optimized datetime partitioning | | defaultToAPriori | boolean | true | | Renamed to defaultToKnownInAdvance. | | defaultToDoNotDerive | boolean | true | | For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter. | | defaultToKnownInAdvance | boolean | true | | For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the :ref:Time Series Overview for more context. | | differencingMethod | string,null | true | | For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems simple and seasonal are not allowed. Parameter periodicities must be specified if seasonal is chosen. Defaults to auto. | | disableHoldout | boolean | true | | A boolean value indicating whether date partitioning skipped allocating a holdout fold. | | featureDerivationWindowEnd | integer,null | true | maximum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end. | | featureDerivationWindowStart | integer,null | true | maximum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin. | | featureSettings | [FeatureSetting] | true | | An array specifying per feature settings. Features can be left unspecified. | | forecastWindowEnd | integer,null | true | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end. | | forecastWindowStart | integer,null | true | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. | | gapDuration | string(duration) | true | | The duration of the gap between the training and holdout scoring data. | | gapEndDate | string(date-time) | true | | The end date of the gap between the training and holdout scoring data. | | gapRowCount | integer | true | | The number of rows in the gap between the training and holdout scoring data | | gapStartDate | string(date-time) | true | | The start date of the gap between the training and holdout scoring data. | | holdoutDuration | string(duration) | true | | The duration of the holdout scoring data. | | holdoutEndDate | string(date-time) | true | | The end date of holdout scoring data. | | holdoutRowCount | integer | true | | The number of rows in the holdout scoring data | | holdoutStartDate | string(date-time) | true | | The start date of holdout scoring data. | | isHoldoutModified | boolean | false | | A boolean value indicating whether holdout settings (start/end dates) have been modified by user. | | modelSplits | integer,null | true | maximum: 10minimum: 1 | Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. | | multiseriesIdColumns | [string] | true | minItems: 1 | May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:multiseries section of the time series documentation for more context. | | numberOfBacktests | integer | true | maximum: 20minimum: 1 | The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations. | | numberOfDoNotDeriveFeatures | integer | true | | Number of features that are marked as "do not derive". | | numberOfKnownInAdvanceFeatures | integer | true | | Number of features that are marked as "known in advance". | | partitioningExtendedWarnings | [PartitioningExtendedWarning] | false | | An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted. | | partitioningWarnings | [PartitioningWarning] | true | | An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted. | | periodicities | [Periodicity] | true | | A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'. | | primaryTrainingDuration | string(duration) | true | | The duration of primary training duration for scoring the holdout. | | primaryTrainingEndDate | string(date-time) | true | | The end date of primary training data for scoring the holdout. | | primaryTrainingRowCount | integer | true | | The number of rows in the primary training data for scoring the holdout | | primaryTrainingStartDate | string(date-time) | true | | The start date of primary training data for scoring the holdout. | | projectId | string | true | | The ID of the project. | | totalRowCount | integer | true | | The total number of rows in the project dataset | | treatAsExponential | string,null | true | | For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems always is not allowed. Defaults to auto. | | unsupervisedMode | boolean,null | false | | A boolean value indicating whether an unsupervised project should be created. | | unsupervisedType | string,null | false | | The type of unsupervised project. | | useCrossSeriesFeatures | boolean,null | true | | For multiseries projects only. Indicating whether to use cross-series features. | | useTimeSeries | boolean | true | | A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning. | | validationDuration | string,null(duration) | true | | The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. | | windowsBasisUnit | string,null | true | | For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or ROW. If omitted, the default value is detected time unit. | ### Enumerated Values | Property | Value | | --- | --- | | aggregationType | [total, average] | | autopilotDataSamplingMethod | [random, latest] | | autopilotDataSelectionMethod | [duration, rowCount] | | differencingMethod | [auto, none, simple, seasonal] | | treatAsExponential | [auto, never, always] | | unsupervisedType | [anomaly, clustering] | | windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] | ## GCPKey ``` { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" } ``` The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authProviderX509CertUrl | string(uri) | false | | Auth provider X509 certificate URL. | | authUri | string(uri) | false | | Auth URI. | | clientEmail | string | false | | Client email address. | | clientId | string | false | | Client ID. | | clientX509CertUrl | string(uri) | false | | Client X509 certificate URL. | | privateKey | string | false | | Private key. | | privateKeyId | string | false | | Private key ID | | projectId | string | false | | Project ID. | | tokenUri | string(uri) | false | | Token URI. | | type | string | true | | GCP account type. | ### Enumerated Values | Property | Value | | --- | --- | | type | service_account | ## GoogleServiceAccountCredentials ``` { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configId | string | false | | The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey. | | credentialType | string | true | | The type of these credentials, 'gcp' here. | | gcpKey | GCPKey | false | | The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account User Managed Key (in the IAM & admin > Service accounts section of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified. | | googleConfigId | string | false | | The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | gcp | ## HdfsProjectCreate ``` { "properties": { "password": { "description": "Password for authenticating to HDFS using Kerberos. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "port": { "description": "Port of the WebHDFS Namenode server. If not specified, defaults to HDFS default port 50070.", "type": "integer" }, "projectName": { "description": "Name of the project to be created. If not specified, project name will be based on the file name.", "type": "string" }, "url": { "description": "URL of the WebHDFS resource. Represent the file using the `hdfs://` protocol marker (for example, `hdfs:///tmp/somedataset.csv`).", "format": "uri", "type": "string" }, "user": { "description": "Username for authenticating to HDFS using Kerberos", "type": "string" } }, "required": [ "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | password | string | false | | Password for authenticating to HDFS using Kerberos. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. | | port | integer | false | | Port of the WebHDFS Namenode server. If not specified, defaults to HDFS default port 50070. | | projectName | string | false | | Name of the project to be created. If not specified, project name will be based on the file name. | | url | string(uri) | true | | URL of the WebHDFS resource. Represent the file using the hdfs:// protocol marker (for example, hdfs:///tmp/somedataset.csv). | | user | string | false | | Username for authenticating to HDFS using Kerberos | ## JobDetailsResponse ``` { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if the job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "jobType": { "description": "The job type.", "enum": [ "model", "predict", "trainingPredictions", "featureImpact", "featureEffects", "shapImpact", "anomalyAssessment", "shapExplanations", "shapMatrix", "reasonCodesInitialization", "reasonCodes", "predictionExplanations", "predictionExplanationsInitialization", "primeDownloadValidation", "ruleFitDownloadValidation", "primeRulesets", "primeModel", "modelExport", "usageData", "modelXRay", "accuracyOverTime", "seriesAccuracy", "validateRatingTable", "generateComplianceDocumentation", "automatedDocumentation", "eda", "pipeline", "calculatePredictionIntervals", "calculatePredictionIntervalBoundUsingOnlineConformal", "batchVarTypeTransform", "computeImageActivationMaps", "computeImageAugmentations", "computeImageEmbeddings", "computeDocumentTextExtractionSamples", "externalDatasetInsights", "startDatetimePartitioning", "runSegmentationTasks", "piiDetection", "computeBiasAndFairness", "sensitivityTesting", "clusterInsights", "onnxExport", "scoringCodeSegmentedModeling", "insights", "distributionPredictionModel", "batchScoringAvailableForecastPoints", "notebooksScheduling", "uncategorized" ], "type": "string" }, "message": { "description": "Error message in case of failure.", "type": "string" }, "modelId": { "description": "The model this job is associated with.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The job status.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" }, "url": { "description": "A URL that can be used to request details about the job.", "type": "string" } }, "required": [ "id", "isBlocked", "jobType", "message", "modelId", "projectId", "status", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The job ID. | | isBlocked | boolean | true | | True if the job is waiting for its dependencies to be resolved first. | | jobType | string | true | | The job type. | | message | string | true | | Error message in case of failure. | | modelId | string | true | | The model this job is associated with. | | projectId | string | true | | The project the job belongs to. | | status | string | true | | The job status. | | url | string | true | | A URL that can be used to request details about the job. | ### Enumerated Values | Property | Value | | --- | --- | | jobType | [model, predict, trainingPredictions, featureImpact, featureEffects, shapImpact, anomalyAssessment, shapExplanations, shapMatrix, reasonCodesInitialization, reasonCodes, predictionExplanations, predictionExplanationsInitialization, primeDownloadValidation, ruleFitDownloadValidation, primeRulesets, primeModel, modelExport, usageData, modelXRay, accuracyOverTime, seriesAccuracy, validateRatingTable, generateComplianceDocumentation, automatedDocumentation, eda, pipeline, calculatePredictionIntervals, calculatePredictionIntervalBoundUsingOnlineConformal, batchVarTypeTransform, computeImageActivationMaps, computeImageAugmentations, computeImageEmbeddings, computeDocumentTextExtractionSamples, externalDatasetInsights, startDatetimePartitioning, runSegmentationTasks, piiDetection, computeBiasAndFairness, sensitivityTesting, clusterInsights, onnxExport, scoringCodeSegmentedModeling, insights, distributionPredictionModel, batchScoringAvailableForecastPoints, notebooksScheduling, uncategorized] | | status | [queue, inprogress, error, ABORTED, COMPLETED] | ## JobListResponse ``` { "properties": { "count": { "description": "The number of jobs returned.", "type": "integer" }, "jobs": { "description": "A JSON array of jobs.", "items": { "properties": { "id": { "description": "The job ID.", "type": "string" }, "isBlocked": { "description": "True if the job is waiting for its dependencies to be resolved first.", "type": "boolean" }, "jobType": { "description": "The job type.", "enum": [ "model", "predict", "trainingPredictions", "featureImpact", "featureEffects", "shapImpact", "anomalyAssessment", "shapExplanations", "shapMatrix", "reasonCodesInitialization", "reasonCodes", "predictionExplanations", "predictionExplanationsInitialization", "primeDownloadValidation", "ruleFitDownloadValidation", "primeRulesets", "primeModel", "modelExport", "usageData", "modelXRay", "accuracyOverTime", "seriesAccuracy", "validateRatingTable", "generateComplianceDocumentation", "automatedDocumentation", "eda", "pipeline", "calculatePredictionIntervals", "calculatePredictionIntervalBoundUsingOnlineConformal", "batchVarTypeTransform", "computeImageActivationMaps", "computeImageAugmentations", "computeImageEmbeddings", "computeDocumentTextExtractionSamples", "externalDatasetInsights", "startDatetimePartitioning", "runSegmentationTasks", "piiDetection", "computeBiasAndFairness", "sensitivityTesting", "clusterInsights", "onnxExport", "scoringCodeSegmentedModeling", "insights", "distributionPredictionModel", "batchScoringAvailableForecastPoints", "notebooksScheduling", "uncategorized" ], "type": "string" }, "message": { "description": "Error message in case of failure.", "type": "string" }, "modelId": { "description": "The model this job is associated with.", "type": "string" }, "projectId": { "description": "The project the job belongs to.", "type": "string" }, "status": { "description": "The job status.", "enum": [ "queue", "inprogress", "error", "ABORTED", "COMPLETED" ], "type": "string" }, "url": { "description": "A URL that can be used to request details about the job.", "type": "string" } }, "required": [ "id", "isBlocked", "jobType", "message", "modelId", "projectId", "status", "url" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page (if null, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page (if null, there is no previous page).", "type": [ "string", "null" ] } }, "required": [ "count", "jobs", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of jobs returned. | | jobs | [JobDetailsResponse] | true | | A JSON array of jobs. | | next | string,null | true | | The URL pointing to the next page (if null, there is no next page). | | previous | string,null | true | | The URL pointing to the previous page (if null, there is no previous page). | ## MultiseriesNamesControllerDataRecord ``` { "description": "The data fields of the multiseries names.", "properties": { "items": { "description": "The list of series names.", "items": { "description": "Name of one series item", "type": "string" }, "type": "array" } }, "required": [ "items" ], "type": "object" } ``` The data fields of the multiseries names. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | items | [string] | true | | The list of series names. | ## MultiseriesNamesControllerResponse ``` { "properties": { "count": { "description": "The total number of series items in the response.", "type": "integer" }, "data": { "description": "The data fields of the multiseries names.", "properties": { "items": { "description": "The list of series names.", "items": { "description": "Name of one series item", "type": "string" }, "type": "array" } }, "required": [ "items" ], "type": "object" }, "next": { "description": "A URL pointing to the next page (if `null`, there is no next page).", "type": [ "string", "null" ] }, "previous": { "description": "A URL pointing to the previous page (if `null`, there is no previous page).", "type": [ "string", "null" ] }, "totalSeriesCount": { "description": "The total number of series items.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalSeriesCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The total number of series items in the response. | | data | MultiseriesNamesControllerDataRecord | true | | The data fields of the multiseries names. | | next | string,null | true | | A URL pointing to the next page (if null, there is no next page). | | previous | string,null | true | | A URL pointing to the previous page (if null, there is no previous page). | | totalSeriesCount | integer | true | | The total number of series items. | ## MultiseriesPayload ``` { "properties": { "datetimePartitionColumn": { "description": "The date column that will be used to perform detection and validation for.", "type": "string" }, "multiseriesIdColumns": { "description": "The list of one or more names of potential multiseries ID columns. If not provided, all numerical and categorical columns are used.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "datetimePartitionColumn" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimePartitionColumn | string | true | | The date column that will be used to perform detection and validation for. | | multiseriesIdColumns | [string] | false | minItems: 1 | The list of one or more names of potential multiseries ID columns. If not provided, all numerical and categorical columns are used. | ## OAuthCredentials ``` { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialType | string | true | | The type of these credentials, 'oauth' here. | | oauthAccessToken | string,null | false | | The OAuth access token. | | oauthClientId | string,null | false | | The OAuth client ID. | | oauthClientSecret | string,null | false | | The OAuth client secret. | | oauthRefreshToken | string | true | | The OAuth refresh token. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | oauth | ## OptimizedDatetimePartitioningData ``` { "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": "string", "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "default": false, "description": "Specifies whether the time series predictions can use partial historical data.", "type": "boolean", "x-versionadded": "v2.24" }, "autopilotClusterList": { "description": "A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless `unsupervisedMode` is true and `unsupervisedType` is set to `clustering`.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotDataSamplingMethod": { "description": "Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "backtests": { "description": "An array specifying individual backtests.", "items": { "oneOf": [ { "description": "Method 1 - pass validation and gap durations", "properties": { "gapDuration": { "description": "A duration string representing the duration of the gap between the training and the validation data for this backtest.", "format": "duration", "type": "string" }, "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "validationDuration": { "description": "A duration string representing the duration of the validation data for this backtest.", "format": "duration", "type": "string" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "gapDuration", "index", "validationDuration", "validationStartDate" ], "type": "object" }, { "description": "Method 2 - directly configure the start and end dates of each partition, including the training partition.", "properties": { "index": { "description": "The index from zero of the backtest.", "type": "integer" }, "primaryTrainingEndDate": { "description": "A datetime string representing the end date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "primaryTrainingStartDate": { "description": "A datetime string representing the start date of the primary training data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationEndDate": { "description": "A datetime string representing the end date of the validation data for this backtest.", "format": "date-time", "type": "string", "x-versionadded": "v2.19" }, "validationStartDate": { "description": "A datetime string representing the start date of the validation data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "index", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationEndDate", "validationStartDate" ], "type": "object" } ] }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": "string", "x-versionadded": "v2.15" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": "string", "x-versionadded": "v2.9" }, "disableHoldout": { "default": false, "description": "Whether to suppress allocating a holdout fold. If `disableHoldout` is set to true, `holdoutStartDate` and `holdoutDuration` must not be set.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": "integer", "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": "integer", "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D).", "format": "duration", "type": "string" }, "holdoutDuration": { "description": "The duration of holdout scoring data. When specifying `holdoutDuration`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data. When specifying `holdoutEndDate`, `holdoutStartDate` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data. When specifying `holdoutStartDate`, one of `holdoutEndDate` or `holdoutDuration` must also be specified. This attribute cannot be specified when `disableHoldout` is true.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "default": 5, "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "target": { "description": "The name of the target column.", "type": "string", "x-versionadded": "v2.22" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": "string", "x-versionadded": "v2.9" }, "unsupervisedMode": { "default": false, "description": "A boolean value indicating whether an unsupervised project should be created.", "type": "boolean", "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "The type of unsupervised project. Only valid when `unsupervisedMode` is true. If `unsupervisedMode`, defaults to `anomaly`.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCrossSeriesFeatures": { "default": false, "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": "boolean", "x-versionadded": "v2.14" }, "useSupervisedFeatureReduction": { "default": true, "description": "When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features.", "type": "boolean" }, "useTimeSeries": { "default": false, "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular.", "format": "duration", "type": "string" }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string", "x-versionadded": "v2.14" } }, "required": [ "datetimePartitionColumn" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aggregationType | string | false | | For multiseries projects only. The aggregation type to apply when creating cross-series features. | | allowPartialHistoryTimeSeriesPredictions | boolean | false | | Specifies whether the time series predictions can use partial historical data. | | autopilotClusterList | [integer] | false | maxItems: 10 | A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to clustering. | | autopilotDataSamplingMethod | string | false | | Defines how autopilot will select subsample from training dataset in OTV/TS projects. Defaults to 'latest' for 'rowCount' dataSelectionMethod and to 'random' for 'duration'. | | autopilotDataSelectionMethod | string | false | | The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets. | | backtests | [oneOf] | false | maxItems: 20minItems: 1 | An array specifying individual backtests. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BacktestOldMethodForOpenApi | false | | Method 1 - pass validation and gap durations | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BacktestNewMethodForOpenApi | false | | Method 2 - directly configure the start and end dates of each partition, including the training partition. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | calendarId | string | false | | The ID of the calendar to be used in this project. | | clusteringBufferDisabled | boolean | false | | A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project. | | crossSeriesGroupByColumns | [string] | false | maxItems: 1 | For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like "men's clothing", "sports equipment", etc. | | datetimePartitionColumn | string | true | | The date column that will be used as a datetime partition column. | | defaultToAPriori | boolean | false | | Renamed to defaultToKnownInAdvance. | | defaultToDoNotDerive | boolean | false | | For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the featureSettings parameter. | | defaultToKnownInAdvance | boolean | false | | For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the featureSettings parameter. See the :ref:Time Series Overview for more context. | | differencingMethod | string | false | | For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems simple and seasonal are not allowed. Parameter periodicities must be specified if seasonal is chosen. Defaults to auto. | | disableHoldout | boolean | false | | Whether to suppress allocating a holdout fold. If disableHoldout is set to true, holdoutStartDate and holdoutDuration must not be set. | | featureDerivationWindowEnd | integer | false | maximum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end. | | featureDerivationWindowStart | integer | false | maximum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin. | | featureSettings | [FeatureSetting] | false | | An array specifying per feature settings. Features can be left unspecified. | | forecastWindowEnd | integer | false | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end. | | forecastWindowStart | integer | false | minimum: 0 | For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start. | | gapDuration | string(duration) | false | | The duration of the gap between holdout training and holdout scoring data. For time series projects, defaults to the duration of the gap between the end of the feature derivation window and the beginning of the forecast window. For OTV projects, defaults to a zero duration (P0Y0M0D). | | holdoutDuration | string(duration) | false | | The duration of holdout scoring data. When specifying holdoutDuration, holdoutStartDate must also be specified. This attribute cannot be specified when disableHoldout is true. | | holdoutEndDate | string(date-time) | false | | The end date of holdout scoring data. When specifying holdoutEndDate, holdoutStartDate must also be specified. This attribute cannot be specified when disableHoldout is true. | | holdoutStartDate | string(date-time) | false | | The start date of holdout scoring data. When specifying holdoutStartDate, one of holdoutEndDate or holdoutDuration must also be specified. This attribute cannot be specified when disableHoldout is true. | | isHoldoutModified | boolean | false | | A boolean value indicating whether holdout settings (start/end dates) have been modified by user. | | modelSplits | integer | false | maximum: 10minimum: 1 | Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. | | multiseriesIdColumns | [string] | false | minItems: 1 | May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:multiseries section of the time series documentation for more context. | | numberOfBacktests | integer | false | maximum: 20minimum: 1 | The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations. | | periodicities | [Periodicity] | false | | A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'. | | target | string | false | | The name of the target column. | | treatAsExponential | string | false | | For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems always is not allowed. Defaults to auto. | | unsupervisedMode | boolean | false | | A boolean value indicating whether an unsupervised project should be created. | | unsupervisedType | string,null | false | | The type of unsupervised project. Only valid when unsupervisedMode is true. If unsupervisedMode, defaults to anomaly. | | useCrossSeriesFeatures | boolean | false | | For multiseries projects only. Indicating whether to use cross-series features. | | useSupervisedFeatureReduction | boolean | false | | When true, during feature generation DataRobot runs a supervised algorithm that identifies those features with predictive impact on the target and builds feature lists using only qualifying features. Setting false can severely impact autopilot duration, especially for datasets with many features. | | useTimeSeries | boolean | false | | A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning. | | validationDuration | string(duration) | false | | The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually. For an OTV project setting the validation duration will always use regular partitioning. Omitting it will use irregular partitioning if the date/time feature is irregular. | | windowsBasisUnit | string | false | | For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or ROW. If omitted, the default value is detected time unit. | ### Enumerated Values | Property | Value | | --- | --- | | aggregationType | [total, average] | | autopilotDataSamplingMethod | [random, latest] | | autopilotDataSelectionMethod | [duration, rowCount] | | differencingMethod | [auto, none, simple, seasonal] | | treatAsExponential | [auto, never, always] | | unsupervisedType | [anomaly, clustering] | | windowsBasisUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] | ## OptimizedDatetimePartitioningList ``` { "description": "Datetime partition information.", "properties": { "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "id": { "description": "The ID of the datetime partitioning.", "type": "string" }, "partitionData": { "description": "Partitioning information for a datetime project.", "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "autopilotDataSamplingMethod": { "description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "availableHoldoutEndDate": { "description": "The maximum valid date of holdout scoring data.", "format": "date-time", "type": [ "string", "null" ] }, "availableTrainingDuration": { "description": "The duration of available training duration for scoring the holdout.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationDuration", "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.15" }, "calendarName": { "description": "The name of the calendar used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.17" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "dateFormat": { "description": "The date format of the partition column.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of the current optimized datetime partitioning", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "disableHoldout": { "description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array", "x-versionadded": "v2.9" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between the training and holdout scoring data.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutDuration": { "description": "The duration of the holdout scoring data.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "numberOfDoNotDeriveFeatures": { "description": "Number of features that are marked as \"do not derive\".", "type": "integer", "x-versionadded": "v2.17" }, "numberOfKnownInAdvanceFeatures": { "description": "Number of features that are marked as \"known in advance\".", "type": "integer", "x-versionadded": "v2.14" }, "partitioningExtendedWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "partitioningWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "type": "string" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "primaryTrainingDuration": { "description": "The duration of primary training duration for scoring the holdout.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "unsupervisedMode": { "description": "A boolean value indicating whether an unsupervised project should be created.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "unsupervisedType": { "description": "The type of unsupervised project.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.31" }, "useCrossSeriesFeatures": { "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": [ "boolean", "null" ], "x-versionadded": "v2.14" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.", "format": "duration", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" } }, "required": [ "autopilotDataSamplingMethod", "autopilotDataSelectionMethod", "availableHoldoutEndDate", "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "backtests", "dateFormat", "datetimePartitionColumn", "defaultToAPriori", "defaultToDoNotDerive", "defaultToKnownInAdvance", "differencingMethod", "disableHoldout", "featureDerivationWindowEnd", "featureDerivationWindowStart", "featureSettings", "forecastWindowEnd", "forecastWindowStart", "gapDuration", "gapEndDate", "gapStartDate", "holdoutDuration", "holdoutEndDate", "holdoutStartDate", "multiseriesIdColumns", "numberOfBacktests", "numberOfDoNotDeriveFeatures", "numberOfKnownInAdvanceFeatures", "partitioningWarnings", "periodicities", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "projectId", "treatAsExponential", "useTimeSeries", "validationDuration", "windowsBasisUnit" ], "type": "object" }, "target": { "description": "The name of the target column.", "type": "string" } }, "required": [ "datetimePartitionColumn", "id", "partitionData", "target" ], "type": "object" } ``` Datetime partition information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimePartitionColumn | string | true | | The date column that will be used as a datetime partition column. | | id | string | true | | The ID of the datetime partitioning. | | partitionData | DatetimePartitioningResponse | true | | Partitioning information for a datetime project. | | target | string | true | | The name of the target column. | ## OptimizedDatetimePartitioningListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of datetime partitionings returned in order of creation.", "items": { "description": "Datetime partition information.", "properties": { "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "id": { "description": "The ID of the datetime partitioning.", "type": "string" }, "partitionData": { "description": "Partitioning information for a datetime project.", "properties": { "aggregationType": { "description": "For multiseries projects only. The aggregation type to apply when creating cross-series features.", "enum": [ "total", "average" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" }, "allowPartialHistoryTimeSeriesPredictions": { "description": "Specifies whether the time series predictions can use partial historical data.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "autopilotDataSamplingMethod": { "description": "The Data Sampling method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "random", "latest" ], "type": "string" }, "autopilotDataSelectionMethod": { "description": "The Data Selection method to be used by autopilot when creating models for datetime-partitioned datasets.", "enum": [ "duration", "rowCount" ], "type": "string" }, "availableHoldoutEndDate": { "description": "The maximum valid date of holdout scoring data.", "format": "date-time", "type": [ "string", "null" ] }, "availableTrainingDuration": { "description": "The duration of available training duration for scoring the holdout.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of available training data for scoring the holdout.", "format": "date-time", "type": "string" }, "backtests": { "description": "An array of the configured backtests.", "items": { "properties": { "availableTrainingDuration": { "description": "The duration of the available training data for this backtest.", "format": "duration", "type": "string" }, "availableTrainingEndDate": { "description": "The end date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "availableTrainingStartDate": { "description": "The start date of the available training data for this backtest.", "format": "date-time", "type": "string" }, "gapDuration": { "description": "The duration of the gap between the training and the validation scoring data for this backtest.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "index": { "description": "The index from zero of this backtest.", "type": "integer" }, "primaryTrainingDuration": { "description": "The duration of the primary training data for this backtest.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of the primary training data for this backtest.", "format": "date-time", "type": "string" }, "validationDuration": { "description": "The duration of the validation scoring data for this backtest.", "type": "string" }, "validationEndDate": { "description": "The end date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" }, "validationStartDate": { "description": "The start date of the validation scoring data for this backtest.", "format": "date-time", "type": "string" } }, "required": [ "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "gapDuration", "gapEndDate", "gapStartDate", "index", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "validationDuration", "validationEndDate", "validationStartDate" ], "type": "object" }, "maxItems": 20, "minItems": 1, "type": "array" }, "calendarId": { "description": "The ID of the calendar to be used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.15" }, "calendarName": { "description": "The name of the calendar used in this project.", "type": [ "string", "null" ], "x-versionadded": "v2.17" }, "clusteringBufferDisabled": { "default": false, "description": "A boolean value indicating whether an clustering buffer creation should be disabled for unsupervised time series clustering project.", "type": "boolean", "x-versionadded": "v2.30" }, "crossSeriesGroupByColumns": { "description": "For multiseries projects with cross-series features enabled only. List of columns (currently of length 1). Setting that indicates how to further split series into related groups. For example, if every series is sales of an individual product, the series group-by could be the product category with values like \"men's clothing\", \"sports equipment\", etc.", "items": { "type": "string" }, "maxItems": 1, "type": "array", "x-versionadded": "v2.15" }, "dateFormat": { "description": "The date format of the partition column.", "type": "string" }, "datetimePartitionColumn": { "description": "The date column that will be used as a datetime partition column.", "type": "string" }, "datetimePartitioningId": { "description": "The ID of the current optimized datetime partitioning", "type": "string", "x-versionadded": "v2.30" }, "defaultToAPriori": { "description": "Renamed to `defaultToKnownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "defaultToDoNotDerive": { "description": "For time series projects only. Sets whether all features default to being treated as do-not-derive features, excluding them from feature derivation. Individual features can be set to a value different than the default by using the `featureSettings` parameter.", "type": "boolean", "x-versionadded": "v2.17" }, "defaultToKnownInAdvance": { "description": "For time series projects only. Sets whether all features default to being treated as known in advance features, which are features that are known into the future. Features marked as known in advance must be specified into the future when making predictions. The default is false, all features are not known in advance. Individual features can be set to a value different than the default using the `featureSettings` parameter. See the :ref:`Time Series Overview ` for more context.", "type": "boolean", "x-versionadded": "v2.11" }, "differencingMethod": { "description": "For time series projects only. Used to specify which differencing method to apply if the data is stationary. For classification problems `simple` and `seasonal` are not allowed. Parameter `periodicities` must be specified if `seasonal` is chosen. Defaults to `auto`.", "enum": [ "auto", "none", "simple", "seasonal" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "disableHoldout": { "description": "A boolean value indicating whether date partitioning skipped allocating a holdout fold.", "type": "boolean", "x-versionadded": "v2.8" }, "featureDerivationWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should end.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureDerivationWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the past relative to the forecast point the feature derivation window should begin.", "maximum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "featureSettings": { "description": "An array specifying per feature settings. Features can be left unspecified.", "items": { "properties": { "aPriori": { "description": "Renamed to `knownInAdvance`.", "type": "boolean", "x-versiondeprecated": "v2.11" }, "doNotDerive": { "description": "For time series projects only. Sets whether the feature is do-not-derive, i.e., is excluded from feature derivation. If not specified, the feature uses the value from the `defaultToDoNotDerive` flag.", "type": "boolean" }, "featureName": { "description": "The name of the feature being specified.", "type": "string" }, "knownInAdvance": { "description": "For time series projects only. Sets whether the feature is known in advance, i.e., values for future dates are known at prediction time. If not specified, the feature uses the value from the `defaultToKnownInAdvance` flag.", "type": "boolean" } }, "required": [ "featureName" ], "type": "object" }, "type": "array", "x-versionadded": "v2.9" }, "forecastWindowEnd": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should end.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "forecastWindowStart": { "description": "For time series projects only. How many timeUnits of the datetimePartitionColumn into the future relative to the forecast point the forecast window should start.", "minimum": 0, "type": [ "integer", "null" ], "x-versionadded": "v2.8" }, "gapDuration": { "description": "The duration of the gap between the training and holdout scoring data.", "format": "duration", "type": "string" }, "gapEndDate": { "description": "The end date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "gapStartDate": { "description": "The start date of the gap between the training and holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutDuration": { "description": "The duration of the holdout scoring data.", "format": "duration", "type": "string" }, "holdoutEndDate": { "description": "The end date of holdout scoring data.", "format": "date-time", "type": "string" }, "holdoutStartDate": { "description": "The start date of holdout scoring data.", "format": "date-time", "type": "string" }, "isHoldoutModified": { "description": "A boolean value indicating whether holdout settings (start/end dates) have been modified by user.", "type": "boolean" }, "modelSplits": { "description": "Sets the cap on the number of jobs per model used when building models to control number of jobs in the queue. Higher number of modelSplits will allow for less downsampling leading to the use of more post-processed data. ", "maximum": 10, "minimum": 1, "type": "integer", "x-versionadded": "v2.21" }, "multiseriesIdColumns": { "description": "May be used only with time series projects. An array of the column names identifying the series to which each row of the dataset belongs. Currently only one multiseries ID column is supported. See the :ref:`multiseries ` section of the time series documentation for more context.", "items": { "type": "string" }, "minItems": 1, "type": "array", "x-versionadded": "v2.11" }, "numberOfBacktests": { "description": "The number of backtests to use. If omitted, defaults to a positive value selected by the server based on the validation and gap durations.", "maximum": 20, "minimum": 1, "type": "integer" }, "numberOfDoNotDeriveFeatures": { "description": "Number of features that are marked as \"do not derive\".", "type": "integer", "x-versionadded": "v2.17" }, "numberOfKnownInAdvanceFeatures": { "description": "Number of features that are marked as \"known in advance\".", "type": "integer", "x-versionadded": "v2.14" }, "partitioningExtendedWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "partitioningWarnings": { "description": "An array of available warnings about potential problems with the chosen partitioning that could cause issues during modeling, although the partitioning may be successfully submitted.", "items": { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "type": "string" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" }, "type": "array" }, "periodicities": { "description": "A list of periodicities for time series projects only. For classification problems periodicities are not allowed. If this is provided, parameter 'differencing_method' will default to 'seasonal' if not provided or 'auto'.", "items": { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" }, "type": "array" }, "primaryTrainingDuration": { "description": "The duration of primary training duration for scoring the holdout.", "format": "duration", "type": "string" }, "primaryTrainingEndDate": { "description": "The end date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "primaryTrainingStartDate": { "description": "The start date of primary training data for scoring the holdout.", "format": "date-time", "type": "string" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "treatAsExponential": { "description": "For time series projects only. Used to specify whether to treat data as exponential trend and apply transformations like log-transform. For classification problems `always` is not allowed. Defaults to `auto`.", "enum": [ "auto", "never", "always" ], "type": [ "string", "null" ], "x-versionadded": "v2.9" }, "unsupervisedMode": { "description": "A boolean value indicating whether an unsupervised project should be created.", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" }, "unsupervisedType": { "description": "The type of unsupervised project.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.31" }, "useCrossSeriesFeatures": { "description": "For multiseries projects only. Indicating whether to use cross-series features.", "type": [ "boolean", "null" ], "x-versionadded": "v2.14" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.", "type": "boolean", "x-versionadded": "v2.8" }, "validationDuration": { "description": "The default validation duration for all backtests. If the primary date/time feature in a time series project is irregular, you cannot set a default validation length. Instead, set each duration individually.", "format": "duration", "type": [ "string", "null" ] }, "windowsBasisUnit": { "description": "For time series projects only. Indicates which unit is basis for feature derivation window and forecast window. Valid options are detected time unit or `ROW`. If omitted, the default value is detected time unit.", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": [ "string", "null" ], "x-versionadded": "v2.14" } }, "required": [ "autopilotDataSamplingMethod", "autopilotDataSelectionMethod", "availableHoldoutEndDate", "availableTrainingDuration", "availableTrainingEndDate", "availableTrainingStartDate", "backtests", "dateFormat", "datetimePartitionColumn", "defaultToAPriori", "defaultToDoNotDerive", "defaultToKnownInAdvance", "differencingMethod", "disableHoldout", "featureDerivationWindowEnd", "featureDerivationWindowStart", "featureSettings", "forecastWindowEnd", "forecastWindowStart", "gapDuration", "gapEndDate", "gapStartDate", "holdoutDuration", "holdoutEndDate", "holdoutStartDate", "multiseriesIdColumns", "numberOfBacktests", "numberOfDoNotDeriveFeatures", "numberOfKnownInAdvanceFeatures", "partitioningWarnings", "periodicities", "primaryTrainingDuration", "primaryTrainingEndDate", "primaryTrainingStartDate", "projectId", "treatAsExponential", "useTimeSeries", "validationDuration", "windowsBasisUnit" ], "type": "object" }, "target": { "description": "The name of the target column.", "type": "string" } }, "required": [ "datetimePartitionColumn", "id", "partitionData", "target" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [OptimizedDatetimePartitioningList] | true | | A list of datetime partitionings returned in order of creation. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## Partition ``` { "description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null.", "properties": { "cvHoldoutLevel": { "description": "If a user partition column was used with cross validation, the value assigned to the holdout set", "type": [ "string", "null" ] }, "cvMethod": { "description": "The partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.", "enum": [ "random", "stratified", "datetime", "user", "group", "date" ], "type": "string" }, "datetimeCol": { "description": "If a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "If a datetime partition column was used, the name of the column.", "type": [ "string", "null" ] }, "holdoutLevel": { "description": "If a user partition column was used with train-validation-holdout split, the value assigned to the holdout set.", "type": [ "string", "null" ] }, "holdoutPct": { "description": "The percentage of the dataset reserved for the holdout set.", "type": [ "number", "null" ] }, "partitionKeyCols": { "description": "An array containing a single string - the name of the group partition column", "items": { "type": "string" }, "type": "array" }, "reps": { "description": "If cross validation was used, the number of folds to use.", "type": [ "integer", "null" ] }, "trainingLevel": { "description": "If a user partition column was used with train-validation-holdout split, the value assigned to the training set.", "type": [ "string", "null" ] }, "useTimeSeries": { "description": "Indicates whether a time series project was created as opposed to a regular project using datetime partitioning.", "type": [ "boolean", "null" ], "x-versionadded": "v2.9" }, "userPartitionCol": { "description": "If a user partition column was used, the name of the column.", "type": [ "string", "null" ] }, "validationLevel": { "description": "If a user partition column was used with train-validation-holdout split, the value assigned to the validation set.", "type": [ "string", "null" ] }, "validationPct": { "description": "If train-validation-holdout split was used, the percentage of the dataset used for the validation set.", "type": [ "number", "null" ] }, "validationType": { "description": "The type of validation used. Either CV (cross validation) or TVH (train-validation-holdout split).", "enum": [ "CV", "TVH" ], "type": "string" } }, "required": [ "cvHoldoutLevel", "cvMethod", "datetimeCol", "holdoutLevel", "holdoutPct", "partitionKeyCols", "reps", "trainingLevel", "userPartitionCol", "validationLevel", "validationPct", "validationType" ], "type": "object" } ``` The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cvHoldoutLevel | string,null | true | | If a user partition column was used with cross validation, the value assigned to the holdout set | | cvMethod | string | true | | The partitioning method used. Note that "date" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0. | | datetimeCol | string,null | true | | If a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0. | | datetimePartitionColumn | string,null | false | | If a datetime partition column was used, the name of the column. | | holdoutLevel | string,null | true | | If a user partition column was used with train-validation-holdout split, the value assigned to the holdout set. | | holdoutPct | number,null | true | | The percentage of the dataset reserved for the holdout set. | | partitionKeyCols | [string] | true | | An array containing a single string - the name of the group partition column | | reps | integer,null | true | | If cross validation was used, the number of folds to use. | | trainingLevel | string,null | true | | If a user partition column was used with train-validation-holdout split, the value assigned to the training set. | | useTimeSeries | boolean,null | false | | Indicates whether a time series project was created as opposed to a regular project using datetime partitioning. | | userPartitionCol | string,null | true | | If a user partition column was used, the name of the column. | | validationLevel | string,null | true | | If a user partition column was used with train-validation-holdout split, the value assigned to the validation set. | | validationPct | number,null | true | | If train-validation-holdout split was used, the percentage of the dataset used for the validation set. | | validationType | string | true | | The type of validation used. Either CV (cross validation) or TVH (train-validation-holdout split). | ### Enumerated Values | Property | Value | | --- | --- | | cvMethod | [random, stratified, datetime, user, group, date] | | validationType | [CV, TVH] | ## PartitioningExtendedWarning ``` { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestIndex | integer,null | true | | Backtest index. Null if the warning does not correspond to a single backtest. | | partition | string | true | | The partition name. | | warnings | [PartitioningSingleExtendedWarning] | true | | A list of strings representing warnings for the specified partition | ## PartitioningSingleExtendedWarning ``` { "properties": { "message": { "description": "The warning message.", "type": "string" }, "title": { "description": "The warning short title.", "type": "string" }, "type": { "description": "The warning severity type.", "type": "string" } }, "required": [ "message", "title", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | The warning message. | | title | string | true | | The warning short title. | | type | string | true | | The warning severity type. | ## PartitioningWarning ``` { "properties": { "backtestIndex": { "description": "Backtest index. Null if the warning does not correspond to a single backtest.", "type": [ "integer", "null" ] }, "partition": { "description": "The partition name.", "type": "string" }, "warnings": { "description": "A list of strings representing warnings for the specified partition", "items": { "type": "string" }, "type": "array" } }, "required": [ "backtestIndex", "partition", "warnings" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | backtestIndex | integer,null | true | | Backtest index. Null if the warning does not correspond to a single backtest. | | partition | string | true | | The partition name. | | warnings | [string] | true | | A list of strings representing warnings for the specified partition | ## PasswordCredentials ``` { "properties": { "catalogVersionId": { "description": "The ID of the latest version of the catalog entry.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored.", "type": "string" }, "url": { "description": "The link to retrieve more detailed information about the entity that uses this catalog dataset.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "password", "user" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | catalogVersionId | string | false | | The ID of the latest version of the catalog entry. | | password | string | true | | The password (in cleartext) for database authentication. The password will be encrypted on the server side in scope of HTTP request and never saved or stored. | | url | string | false | | The link to retrieve more detailed information about the entity that uses this catalog dataset. | | user | string | true | | The username for database authentication. | ## Periodicity ``` { "properties": { "timeSteps": { "description": "The number of time steps.", "minimum": 0, "type": "integer" }, "timeUnit": { "description": "The time unit or `ROW` if windowsBasisUnit is `ROW`", "enum": [ "MILLISECOND", "SECOND", "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "QUARTER", "YEAR", "ROW" ], "type": "string" } }, "required": [ "timeSteps", "timeUnit" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timeSteps | integer | true | minimum: 0 | The number of time steps. | | timeUnit | string | true | | The time unit or ROW if windowsBasisUnit is ROW | ### Enumerated Values | Property | Value | | --- | --- | | timeUnit | [MILLISECOND, SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR, ROW] | ## ProjectAdvancedOptionsResponse ``` { "description": "Information related to the current model of the deployment.", "properties": { "allowedPairwiseInteractionGroups": { "description": "For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [[\"A\", \"B\", \"C\"], [\"C\", \"D\"]] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.", "items": { "items": { "type": "string" }, "type": "array" }, "type": "array", "x-versionadded": "v2.19" }, "blendBestModels": { "description": "blend best models during Autopilot run [DEPRECATED]", "type": "boolean", "x-versionadded": "v2.19", "x-versiondeprecated": "2.30.0" }, "blueprintThreshold": { "description": "an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs", "type": [ "integer", "null" ] }, "considerBlendersInRecommendation": { "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED]", "type": "boolean", "x-versionadded": "v2.21", "x-versiondeprecated": "2.30.0" }, "defaultMonotonicDecreasingFeaturelistId": { "description": "null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "defaultMonotonicIncreasingFeaturelistId": { "description": "null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "downsampledMajorityRows": { "description": "the total number of the majority rows available for modeling, or null for projects without smart downsampling", "type": [ "integer", "null" ], "x-versionadded": "v2.5" }, "downsampledMinorityRows": { "description": "the total number of the minority rows available for modeling, or null for projects without smart downsampling", "type": [ "integer", "null" ], "x-versionadded": "v2.5" }, "eventsCount": { "description": "the name of the event count column, if specified, otherwise null.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "exposure": { "description": "the name of the exposure column, if specified.", "type": [ "string", "null" ], "x-versionadded": "v2.6" }, "majorityDownsamplingRate": { "description": "the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling", "type": [ "number", "null" ], "x-versionadded": "v2.5" }, "minSecondaryValidationModelCount": { "description": "Compute \"All backtest\" scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.", "type": "boolean", "x-versionadded": "v2.19" }, "offset": { "description": "the list of names of the offset columns, if specified, otherwise null.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.6" }, "onlyIncludeMonotonicBlueprints": { "default": false, "description": "whether the project only includes blueprints support enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.11" }, "prepareModelForDeployment": { "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning \"RECOMMENDED FOR DEPLOYMENT\" label.", "type": [ "boolean", "null" ], "x-versionadded": "v2.19" }, "responseCap": { "description": "defaults to False, if specified used to cap the maximum response of a model", "oneOf": [ { "type": "boolean" }, { "maximum": 1, "minimum": 0.5, "type": "number" } ] }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean", "x-versionadded": "v2.21" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean", "x-versionadded": "v2.19" }, "seed": { "description": "defaults to null, the random seed to be used if specified", "type": [ "string", "null" ] }, "shapOnlyMode": { "description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always ``null``.", "type": [ "boolean", "null" ], "x-versionadded": "v2.21" }, "smartDownsampled": { "description": "whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).", "type": "boolean", "x-versionadded": "v2.5" }, "weights": { "description": "the name of the weight column, if specified, otherwise null.", "type": [ "string", "null" ] } }, "required": [ "blendBestModels", "blueprintThreshold", "defaultMonotonicDecreasingFeaturelistId", "defaultMonotonicIncreasingFeaturelistId", "downsampledMajorityRows", "downsampledMinorityRows", "majorityDownsamplingRate", "onlyIncludeMonotonicBlueprints", "prepareModelForDeployment", "responseCap", "seed", "shapOnlyMode", "smartDownsampled", "weights" ], "type": "object" } ``` Information related to the current model of the deployment. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowedPairwiseInteractionGroups | [array] | false | | For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [["A", "B", "C"], ["C", "D"]] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model. | | blendBestModels | boolean | true | | blend best models during Autopilot run [DEPRECATED] | | blueprintThreshold | integer,null | true | | an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs | | considerBlendersInRecommendation | boolean | false | | Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED] | | defaultMonotonicDecreasingFeaturelistId | string,null | true | | null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time. | | defaultMonotonicIncreasingFeaturelistId | string,null | true | | null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time. | | downsampledMajorityRows | integer,null | true | | the total number of the majority rows available for modeling, or null for projects without smart downsampling | | downsampledMinorityRows | integer,null | true | | the total number of the minority rows available for modeling, or null for projects without smart downsampling | | eventsCount | string,null | false | | the name of the event count column, if specified, otherwise null. | | exposure | string,null | false | | the name of the exposure column, if specified. | | majorityDownsamplingRate | number,null | true | | the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling | | minSecondaryValidationModelCount | boolean | false | | Compute "All backtest" scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default. | | offset | [string] | false | | the list of names of the offset columns, if specified, otherwise null. | | onlyIncludeMonotonicBlueprints | boolean | true | | whether the project only includes blueprints support enforcing monotonic constraints | | prepareModelForDeployment | boolean,null | true | | Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning "RECOMMENDED FOR DEPLOYMENT" label. | | responseCap | any | true | | defaults to False, if specified used to cap the maximum response of a model | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | maximum: 1minimum: 0.5 | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | runLeakageRemovedFeatureList | boolean | false | | Run Autopilot on Leakage Removed feature list (if exists). | | scoringCodeOnly | boolean | false | | Keep only models that can be converted to scorable java code during Autopilot run. | | seed | string,null | true | | defaults to null, the random seed to be used if specified | | shapOnlyMode | boolean,null | true | | Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always null. | | smartDownsampled | boolean | true | | whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows). | | weights | string,null | true | | the name of the weight column, if specified, otherwise null. | ## ProjectClone ``` { "properties": { "copyOptions": { "default": false, "description": "Whether all project options should be copied to the cloned project.", "type": "boolean" }, "projectId": { "description": "The ID of the project to clone.", "type": "string" }, "projectName": { "description": "The name of the project to be created.", "type": "string" } }, "required": [ "projectId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | copyOptions | boolean | false | | Whether all project options should be copied to the cloned project. | | projectId | string | true | | The ID of the project to clone. | | projectName | string | false | | The name of the project to be created. | ## ProjectCreate ``` { "properties": { "credentialData": { "description": "The credentials to authenticate with the database, to be used instead of credential ID. Can only be used along with datasetId or dataSourceId.", "oneOf": [ { "properties": { "credentialType": { "description": "The type of these credentials, 'basic' here.", "enum": [ "basic" ], "type": "string" }, "password": { "description": "The password for database authentication. The password is encrypted at rest and never saved or stored.", "type": "string" }, "user": { "description": "The username for database authentication.", "type": "string" } }, "required": [ "credentialType", "password", "user" ], "type": "object" }, { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'oauth' here.", "enum": [ "oauth" ], "type": "string" }, "oauthAccessToken": { "default": null, "description": "The OAuth access token.", "type": [ "string", "null" ] }, "oauthClientId": { "default": null, "description": "The OAuth client ID.", "type": [ "string", "null" ] }, "oauthClientSecret": { "default": null, "description": "The OAuth client secret.", "type": [ "string", "null" ] }, "oauthRefreshToken": { "description": "The OAuth refresh token.", "type": "string" } }, "required": [ "credentialType", "oauthRefreshToken" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "configId": { "description": "The ID of secure configurations shared by admin. Alternative to googleConfigId (deprecated). If specified, cannot include gcpKey.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'gcp' here.", "enum": [ "gcp" ], "type": "string" }, "gcpKey": { "description": "The Google Cloud Platform (GCP) key. Output is the downloaded JSON resulting from creating a service account *User Managed Key* (in the *IAM & admin > Service accounts section* of GCP).Required if googleConfigId/configId is not specified.Cannot include this parameter if googleConfigId/configId is specified.", "properties": { "authProviderX509CertUrl": { "description": "Auth provider X509 certificate URL.", "format": "uri", "type": "string" }, "authUri": { "description": "Auth URI.", "format": "uri", "type": "string" }, "clientEmail": { "description": "Client email address.", "type": "string" }, "clientId": { "description": "Client ID.", "type": "string" }, "clientX509CertUrl": { "description": "Client X509 certificate URL.", "format": "uri", "type": "string" }, "privateKey": { "description": "Private key.", "type": "string" }, "privateKeyId": { "description": "Private key ID", "type": "string" }, "projectId": { "description": "Project ID.", "type": "string" }, "tokenUri": { "description": "Token URI.", "format": "uri", "type": "string" }, "type": { "description": "GCP account type.", "enum": [ "service_account" ], "type": "string" } }, "required": [ "type" ], "type": "object" }, "googleConfigId": { "description": "The ID of secure configurations shared by admin. This is deprecated. Please use configId instead. If specified, cannot include gcpKey.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "credentialType": { "description": "The type of these credentials, 'databricks_access_token_account' here.", "enum": [ "databricks_access_token_account" ], "type": "string" }, "databricksAccessToken": { "description": "Databricks personal access token.", "minLength": 1, "type": "string" } }, "required": [ "credentialType", "databricksAccessToken" ], "type": "object" }, { "properties": { "clientId": { "description": "Client ID for Databricks service principal.", "minLength": 1, "type": "string" }, "clientSecret": { "description": "Client secret for Databricks service principal.", "minLength": 1, "type": "string" }, "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include clientId and clientSecret.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'databricks_service_principal_account' here.", "enum": [ "databricks_service_principal_account" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" }, { "properties": { "azureTenantId": { "description": "Tenant ID of the Azure AD service principal.", "type": "string" }, "clientId": { "description": "Client ID of the Azure AD service principal.", "type": "string" }, "clientSecret": { "description": "Client Secret of the Azure AD service principal.", "type": "string" }, "configId": { "description": "The ID of secure configurations of credentials shared by admin.", "type": "string", "x-versionadded": "v2.35" }, "credentialType": { "description": "The type of these credentials, 'azure_service_principal' here.", "enum": [ "azure_service_principal" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ], "x-versionadded": "v2.23" }, "credentialId": { "description": "The ID of the set of credentials to authenticate with the database. Can only be used along with datasetId or dataSourceId.", "type": "string", "x-versionadded": "v2.19" }, "dataSourceId": { "description": "Identifier for the data source to retrieve.", "type": "string" }, "datasetId": { "description": "The ID of the dataset entry to use for the project dataset.", "type": "string" }, "datasetVersionId": { "description": "Only used when also providing a datasetId, and specifies the the ID of the dataset version to use for the project dataset. If not specified, the latest version associated with the dataset ID is used.", "type": "string" }, "password": { "description": "The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" }, "projectName": { "description": "The name of the project to be created. If not specified, 'Untitled Project' will be used for database connections and file name will be used as the project name.", "type": "string" }, "recipeId": { "description": "The ID of the wrangling recipe that will be used for project creation.", "type": "string" }, "url": { "description": "The URL to download the dataset used to create the project.", "format": "url", "type": "string" }, "useKerberos": { "description": "If true, use Kerberos authentication for database authentication. Default is false. Can only be used along with datasetId or dataSourceId.", "type": "boolean", "x-versionadded": "v2.19" }, "user": { "description": "The username for database authentication. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead.", "type": "string", "x-versiondeprecated": "v2.23" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialData | any | false | | The credentials to authenticate with the database, to be used instead of credential ID. Can only be used along with datasetId or dataSourceId. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | BasicCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | S3Credentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | OAuthCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SnowflakeKeyPairCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GoogleServiceAccountCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksAccessTokenCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DatabricksServicePrincipalCredentials | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | AzureServicePrincipalCredentials | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | false | | The ID of the set of credentials to authenticate with the database. Can only be used along with datasetId or dataSourceId. | | dataSourceId | string | false | | Identifier for the data source to retrieve. | | datasetId | string | false | | The ID of the dataset entry to use for the project dataset. | | datasetVersionId | string | false | | Only used when also providing a datasetId, and specifies the the ID of the dataset version to use for the project dataset. If not specified, the latest version associated with the dataset ID is used. | | password | string | false | | The password (in cleartext) for database authentication. The password will be encrypted on the server side as part of the HTTP request and never saved or stored. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead. | | projectName | string | false | | The name of the project to be created. If not specified, 'Untitled Project' will be used for database connections and file name will be used as the project name. | | recipeId | string | false | | The ID of the wrangling recipe that will be used for project creation. | | url | string(url) | false | | The URL to download the dataset used to create the project. | | useKerberos | boolean | false | | If true, use Kerberos authentication for database authentication. Default is false. Can only be used along with datasetId or dataSourceId. | | user | string | false | | The username for database authentication. Can only be used along with datasetId or dataSourceId. DEPRECATED: please use credentialId or credentialData instead. | ## ProjectCreateResponse ``` { "properties": { "pid": { "description": "The project ID.", "type": "string" } }, "required": [ "pid" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | pid | string | true | | The project ID. | ## ProjectDetailsResponse ``` { "properties": { "advancedOptions": { "description": "Information related to the current model of the deployment.", "properties": { "allowedPairwiseInteractionGroups": { "description": "For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [[\"A\", \"B\", \"C\"], [\"C\", \"D\"]] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.", "items": { "items": { "type": "string" }, "type": "array" }, "type": "array", "x-versionadded": "v2.19" }, "blendBestModels": { "description": "blend best models during Autopilot run [DEPRECATED]", "type": "boolean", "x-versionadded": "v2.19", "x-versiondeprecated": "2.30.0" }, "blueprintThreshold": { "description": "an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs", "type": [ "integer", "null" ] }, "considerBlendersInRecommendation": { "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED]", "type": "boolean", "x-versionadded": "v2.21", "x-versiondeprecated": "2.30.0" }, "defaultMonotonicDecreasingFeaturelistId": { "description": "null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "defaultMonotonicIncreasingFeaturelistId": { "description": "null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "downsampledMajorityRows": { "description": "the total number of the majority rows available for modeling, or null for projects without smart downsampling", "type": [ "integer", "null" ], "x-versionadded": "v2.5" }, "downsampledMinorityRows": { "description": "the total number of the minority rows available for modeling, or null for projects without smart downsampling", "type": [ "integer", "null" ], "x-versionadded": "v2.5" }, "eventsCount": { "description": "the name of the event count column, if specified, otherwise null.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "exposure": { "description": "the name of the exposure column, if specified.", "type": [ "string", "null" ], "x-versionadded": "v2.6" }, "majorityDownsamplingRate": { "description": "the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling", "type": [ "number", "null" ], "x-versionadded": "v2.5" }, "minSecondaryValidationModelCount": { "description": "Compute \"All backtest\" scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.", "type": "boolean", "x-versionadded": "v2.19" }, "offset": { "description": "the list of names of the offset columns, if specified, otherwise null.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.6" }, "onlyIncludeMonotonicBlueprints": { "default": false, "description": "whether the project only includes blueprints support enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.11" }, "prepareModelForDeployment": { "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning \"RECOMMENDED FOR DEPLOYMENT\" label.", "type": [ "boolean", "null" ], "x-versionadded": "v2.19" }, "responseCap": { "description": "defaults to False, if specified used to cap the maximum response of a model", "oneOf": [ { "type": "boolean" }, { "maximum": 1, "minimum": 0.5, "type": "number" } ] }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean", "x-versionadded": "v2.21" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean", "x-versionadded": "v2.19" }, "seed": { "description": "defaults to null, the random seed to be used if specified", "type": [ "string", "null" ] }, "shapOnlyMode": { "description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always ``null``.", "type": [ "boolean", "null" ], "x-versionadded": "v2.21" }, "smartDownsampled": { "description": "whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).", "type": "boolean", "x-versionadded": "v2.5" }, "weights": { "description": "the name of the weight column, if specified, otherwise null.", "type": [ "string", "null" ] } }, "required": [ "blendBestModels", "blueprintThreshold", "defaultMonotonicDecreasingFeaturelistId", "defaultMonotonicIncreasingFeaturelistId", "downsampledMajorityRows", "downsampledMinorityRows", "majorityDownsamplingRate", "onlyIncludeMonotonicBlueprints", "prepareModelForDeployment", "responseCap", "seed", "shapOnlyMode", "smartDownsampled", "weights" ], "type": "object" }, "autopilotClusterList": { "description": "Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotMode": { "description": "The current autopilot mode, 0 for full autopilot, 2 for manual mode, 3 for quick mode, 4 for comprehensive mode", "type": "integer" }, "created": { "description": "The time of project creation.", "format": "date-time", "type": "string" }, "featureEngineeringPredictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "fileName": { "description": "The name of the dataset used to create the project.", "type": "string" }, "holdoutUnlocked": { "description": "whether the holdout has been unlocked", "type": "boolean" }, "id": { "description": "The ID of a project.", "type": "string" }, "maxClusters": { "description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `minClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100.", "maximum": 100, "minimum": 2, "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "maxTrainPct": { "description": "the maximum percentage of the dataset that can be used to successfully train a model without going into the validation data.", "type": "number" }, "maxTrainRows": { "description": "the maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data", "type": "integer" }, "metric": { "description": "the metric used to select the best-performing models.", "type": "string" }, "minClusters": { "description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `maxClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2.", "maximum": 100, "minimum": 2, "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "partition": { "description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose `cvMethod` is `\"datetime\"`, full specification of the partitioning method can be found at [GET /api/v2/projects/{projectId}/datetimePartitioning/][get-apiv2projectsprojectiddatetimepartitioning].", "properties": { "cvHoldoutLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with cross validation, the value assigned to the holdout set" }, "cvMethod": { "description": "the partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.", "enum": [ "random", "user", "stratified", "group", "datetime" ], "type": "string" }, "datetimeCol": { "description": "if a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "if a datetime partition column was used, the name of the column", "type": "string" }, "holdoutLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with train-validation-holdout split, the value assigned to the holdout set" }, "holdoutPct": { "description": "the percentage of the dataset reserved for the holdout set", "type": "number" }, "partitionKeyCols": { "description": "An array containing a single string - the name of the group partition column", "items": { "type": "string" }, "type": "array" }, "reps": { "description": "if cross validation was used, the number of folds to use", "type": [ "number", "null" ] }, "trainingLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with train-validation-holdout split, the value assigned to the training set" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project was created as opposed to a regular project using datetime partitioning.", "type": [ "boolean", "null" ], "x-versionadded": "v2.9" }, "userPartitionCol": { "description": "if a user partition column was used, the name of the column", "type": [ "string", "null" ] }, "validationLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with train-validation-holdout split, the value assigned to the validation set" }, "validationPct": { "description": "if train-validation-holdout split was used, the percentage of the dataset used for the validation set", "type": [ "number", "null" ] }, "validationType": { "description": "either CV for cross-validation or TVH for train-validation-holdout split", "enum": [ "CV", "TVH" ], "type": "string" } }, "required": [ "cvHoldoutLevel", "cvMethod", "datetimeCol", "holdoutLevel", "holdoutPct", "partitionKeyCols", "reps", "trainingLevel", "useTimeSeries", "userPartitionCol", "validationLevel", "validationPct", "validationType" ], "type": "object" }, "positiveClass": { "description": "if the project uses binary classification, the class designated to be the positive class. Otherwise, null.", "type": [ "number", "null" ] }, "projectName": { "description": "The name of a project.", "type": "string" }, "stage": { "description": "the stage of the project - if modeling, then the target is successfully set, and modeling or predictions can proceed.", "type": "string" }, "target": { "description": "the target of the project, null if project is unsupervised.", "type": "string" }, "targetType": { "description": "The target type of the project.", "enum": [ "Binary", "Regression", "Multiclass", "minInflated", "Multilabel", "TextGeneration", "GeoPoint", "VectorDatabase" ], "type": [ "string", "null" ] }, "unsupervisedMode": { "description": "indicates whether a project is unsupervised.", "type": "boolean", "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCase": { "description": "The information of the use case associated with the project.", "properties": { "id": { "description": "The ID of the use case.", "type": [ "string", "null" ] }, "name": { "description": "The name of the use case.", "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.36" }, "useFeatureDiscovery": { "description": "A boolean value indicating whether a feature discovery project was created as opposed to a regular project.", "type": "boolean", "x-versionadded": "v2.21" } }, "required": [ "advancedOptions", "autopilotMode", "created", "fileName", "holdoutUnlocked", "id", "maxTrainPct", "maxTrainRows", "metric", "partition", "positiveClass", "projectName", "stage", "target", "targetType", "unsupervisedMode", "useFeatureDiscovery" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | advancedOptions | ProjectAdvancedOptionsResponse | true | | Information related to the current model of the deployment. | | autopilotClusterList | [integer] | false | maxItems: 10 | Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'. | | autopilotMode | integer | true | | The current autopilot mode, 0 for full autopilot, 2 for manual mode, 3 for quick mode, 4 for comprehensive mode | | created | string(date-time) | true | | The time of project creation. | | featureEngineeringPredictionPoint | string,null | false | | The date column to be used as the prediction point for time-based feature engineering. | | fileName | string | true | | The name of the dataset used to create the project. | | holdoutUnlocked | boolean | true | | whether the holdout has been unlocked | | id | string | true | | The ID of a project. | | maxClusters | integer,null | false | maximum: 100minimum: 2 | Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to minClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100. | | maxTrainPct | number | true | | the maximum percentage of the dataset that can be used to successfully train a model without going into the validation data. | | maxTrainRows | integer | true | | the maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data | | metric | string | true | | the metric used to select the best-performing models. | | minClusters | integer,null | false | maximum: 100minimum: 2 | Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to maxClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2. | | partition | ProjectPartitionResponse | true | | The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose cvMethod is "datetime", full specification of the partitioning method can be found at [GET /api/v2/projects/{projectId}/datetimePartitioning/][get-apiv2projectsprojectiddatetimepartitioning]. | | positiveClass | number,null | true | | if the project uses binary classification, the class designated to be the positive class. Otherwise, null. | | projectName | string | true | | The name of a project. | | stage | string | true | | the stage of the project - if modeling, then the target is successfully set, and modeling or predictions can proceed. | | target | string | true | | the target of the project, null if project is unsupervised. | | targetType | string,null | true | | The target type of the project. | | unsupervisedMode | boolean | true | | indicates whether a project is unsupervised. | | unsupervisedType | string,null | false | | Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'. | | useCase | UseCaseIdName | false | | The information of the use case associated with the project. | | useFeatureDiscovery | boolean | true | | A boolean value indicating whether a feature discovery project was created as opposed to a regular project. | ### Enumerated Values | Property | Value | | --- | --- | | targetType | [Binary, Regression, Multiclass, minInflated, Multilabel, TextGeneration, GeoPoint, VectorDatabase] | | unsupervisedType | [anomaly, clustering] | ## ProjectNuke ``` { "properties": { "creator": { "description": "Creator ID to filter projects by", "type": "string" }, "deletedAfter": { "description": "ISO-8601 formatted date projects were deleted after", "format": "date-time", "type": "string" }, "deletedBefore": { "description": "ISO-8601 formatted date projects were deleted before", "format": "date-time", "type": "string" }, "limit": { "default": 1000, "description": "At most this many projects are deleted.", "maximum": 1000, "minimum": 1, "type": "integer" }, "offset": { "default": 0, "description": "This many projects will be skipped.", "minimum": 0, "type": "integer" }, "organization": { "description": "ID of organization that projects should belong to", "type": "string" }, "projectIds": { "description": "The list of project IDs to delete permanently.", "items": { "type": "string" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "searchFor": { "description": "Project or dataset name to filter by.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creator | string | false | | Creator ID to filter projects by | | deletedAfter | string(date-time) | false | | ISO-8601 formatted date projects were deleted after | | deletedBefore | string(date-time) | false | | ISO-8601 formatted date projects were deleted before | | limit | integer | false | maximum: 1000minimum: 1 | At most this many projects are deleted. | | offset | integer | false | minimum: 0 | This many projects will be skipped. | | organization | string | false | | ID of organization that projects should belong to | | projectIds | [string] | false | maxItems: 1000minItems: 1 | The list of project IDs to delete permanently. | | searchFor | string | false | | Project or dataset name to filter by. | ## ProjectNukeJobListStatus ``` { "properties": { "jobs": { "description": "List of active permadelete jobs with their statuses.", "items": { "properties": { "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "data": { "description": "List of projects and associated statuses.", "items": { "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "projectId": { "description": "Project ID", "type": "string" }, "status": { "description": "The processing state of project cleanup task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "projectId", "status" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The processing state of the cleanup job.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "created", "data", "message", "status", "statusId" ], "type": "object" }, "type": "array" } }, "required": [ "jobs" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jobs | [ProjectNukeJobStatus] | true | | List of active permadelete jobs with their statuses. | ## ProjectNukeJobStatus ``` { "properties": { "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "data": { "description": "List of projects and associated statuses.", "items": { "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "projectId": { "description": "Project ID", "type": "string" }, "status": { "description": "The processing state of project cleanup task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "projectId", "status" ], "type": "object" }, "maxItems": 1000, "minItems": 1, "type": "array" }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The processing state of the cleanup job.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "created", "data", "message", "status", "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string(date-time) | true | | The time the status record was created. | | data | [ProjectPermadeleteStatus] | true | maxItems: 1000minItems: 1 | List of projects and associated statuses. | | message | string,null | true | | May contain further information about the status. | | status | string | true | | The processing state of the cleanup job. | | statusId | string | true | | The ID of the status object. | ### Enumerated Values | Property | Value | | --- | --- | | status | [ABORTED, BLOCKED, COMPLETED, CREATED, ERROR, EXPIRED, INCOMPLETE, INITIALIZED, PAUSED, RUNNING] | ## ProjectNukeJobStatusSummary ``` { "properties": { "jobId": { "description": "The ID of the permadeletion multi-job.", "type": "string" }, "summary": { "description": "Project permanent deletion status to count mapping.", "properties": { "aborted": { "description": "Number of project permadelete jobs with Aborted status.", "minimum": 0, "type": "integer" }, "completed": { "description": "Number of project permadelete jobs with Completed status.", "minimum": 0, "type": "integer" }, "error": { "description": "Number of project permadelete jobs with Error status.", "minimum": 0, "type": "integer" }, "expired": { "description": "Number of project permadelete jobs with Expired status.", "minimum": 0, "type": "integer" } }, "required": [ "aborted", "completed", "error", "expired" ], "type": "object" } }, "required": [ "jobId", "summary" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jobId | string | true | | The ID of the permadeletion multi-job. | | summary | ProjectNukeJobStatusSummaryObject | true | | Project permanent deletion status to count mapping. | ## ProjectNukeJobStatusSummaryObject ``` { "description": "Project permanent deletion status to count mapping.", "properties": { "aborted": { "description": "Number of project permadelete jobs with Aborted status.", "minimum": 0, "type": "integer" }, "completed": { "description": "Number of project permadelete jobs with Completed status.", "minimum": 0, "type": "integer" }, "error": { "description": "Number of project permadelete jobs with Error status.", "minimum": 0, "type": "integer" }, "expired": { "description": "Number of project permadelete jobs with Expired status.", "minimum": 0, "type": "integer" } }, "required": [ "aborted", "completed", "error", "expired" ], "type": "object" } ``` Project permanent deletion status to count mapping. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | aborted | integer | true | minimum: 0 | Number of project permadelete jobs with Aborted status. | | completed | integer | true | minimum: 0 | Number of project permadelete jobs with Completed status. | | error | integer | true | minimum: 0 | Number of project permadelete jobs with Error status. | | expired | integer | true | minimum: 0 | Number of project permadelete jobs with Expired status. | ## ProjectPartitionResponse ``` { "description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose `cvMethod` is `\"datetime\"`, full specification of the partitioning method can be found at [GET /api/v2/projects/{projectId}/datetimePartitioning/][get-apiv2projectsprojectiddatetimepartitioning].", "properties": { "cvHoldoutLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with cross validation, the value assigned to the holdout set" }, "cvMethod": { "description": "the partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.", "enum": [ "random", "user", "stratified", "group", "datetime" ], "type": "string" }, "datetimeCol": { "description": "if a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "if a datetime partition column was used, the name of the column", "type": "string" }, "holdoutLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with train-validation-holdout split, the value assigned to the holdout set" }, "holdoutPct": { "description": "the percentage of the dataset reserved for the holdout set", "type": "number" }, "partitionKeyCols": { "description": "An array containing a single string - the name of the group partition column", "items": { "type": "string" }, "type": "array" }, "reps": { "description": "if cross validation was used, the number of folds to use", "type": [ "number", "null" ] }, "trainingLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with train-validation-holdout split, the value assigned to the training set" }, "useTimeSeries": { "description": "A boolean value indicating whether a time series project was created as opposed to a regular project using datetime partitioning.", "type": [ "boolean", "null" ], "x-versionadded": "v2.9" }, "userPartitionCol": { "description": "if a user partition column was used, the name of the column", "type": [ "string", "null" ] }, "validationLevel": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "if a user partition column was used with train-validation-holdout split, the value assigned to the validation set" }, "validationPct": { "description": "if train-validation-holdout split was used, the percentage of the dataset used for the validation set", "type": [ "number", "null" ] }, "validationType": { "description": "either CV for cross-validation or TVH for train-validation-holdout split", "enum": [ "CV", "TVH" ], "type": "string" } }, "required": [ "cvHoldoutLevel", "cvMethod", "datetimeCol", "holdoutLevel", "holdoutPct", "partitionKeyCols", "reps", "trainingLevel", "useTimeSeries", "userPartitionCol", "validationLevel", "validationPct", "validationType" ], "type": "object" } ``` The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. Note that for projects whose `cvMethod` is `"datetime"`, full specification of the partitioning method can be found at [GET /api/v2/projects/{projectId}/datetimePartitioning/][get-apiv2projectsprojectiddatetimepartitioning]. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cvHoldoutLevel | any | true | | if a user partition column was used with cross validation, the value assigned to the holdout set | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cvMethod | string | true | | the partitioning method used. Note that "date" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0. | | datetimeCol | string,null | true | | if a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0. | | datetimePartitionColumn | string | false | | if a datetime partition column was used, the name of the column | | holdoutLevel | any | true | | if a user partition column was used with train-validation-holdout split, the value assigned to the holdout set | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | holdoutPct | number | true | | the percentage of the dataset reserved for the holdout set | | partitionKeyCols | [string] | true | | An array containing a single string - the name of the group partition column | | reps | number,null | true | | if cross validation was used, the number of folds to use | | trainingLevel | any | true | | if a user partition column was used with train-validation-holdout split, the value assigned to the training set | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useTimeSeries | boolean,null | true | | A boolean value indicating whether a time series project was created as opposed to a regular project using datetime partitioning. | | userPartitionCol | string,null | true | | if a user partition column was used, the name of the column | | validationLevel | any | true | | if a user partition column was used with train-validation-holdout split, the value assigned to the validation set | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationPct | number,null | true | | if train-validation-holdout split was used, the percentage of the dataset used for the validation set | | validationType | string | true | | either CV for cross-validation or TVH for train-validation-holdout split | ### Enumerated Values | Property | Value | | --- | --- | | cvMethod | [random, user, stratified, group, datetime] | | validationType | [CV, TVH] | ## ProjectPermadeleteStatus ``` { "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "projectId": { "description": "Project ID", "type": "string" }, "status": { "description": "The processing state of project cleanup task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "projectId", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string,null | true | | May contain further information about the status. | | projectId | string | true | | Project ID | | status | string | true | | The processing state of project cleanup task. | ### Enumerated Values | Property | Value | | --- | --- | | status | [ABORTED, BLOCKED, COMPLETED, CREATED, ERROR, EXPIRED, INCOMPLETE, INITIALIZED, PAUSED, RUNNING] | ## ProjectRecover ``` { "properties": { "action": { "description": "Action to perform on a project", "enum": [ "undelete" ], "type": "string" } }, "required": [ "action" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | action | string | true | | Action to perform on a project | ### Enumerated Values | Property | Value | | --- | --- | | action | undelete | ## ProjectRecoverResponse ``` { "properties": { "message": { "description": "Operation result description", "type": "string" } }, "required": [ "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | Operation result description | ## ProjectRetrieveResponse ``` { "properties": { "advancedOptions": { "description": "Information related to the current model of the deployment.", "properties": { "allowedPairwiseInteractionGroups": { "description": "For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to [[\"A\", \"B\", \"C\"], [\"C\", \"D\"]] then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.", "items": { "items": { "type": "string" }, "type": "array" }, "type": "array", "x-versionadded": "v2.19" }, "blendBestModels": { "description": "blend best models during Autopilot run [DEPRECATED]", "type": "boolean", "x-versionadded": "v2.19", "x-versiondeprecated": "2.30.0" }, "blueprintThreshold": { "description": "an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs", "type": [ "integer", "null" ] }, "considerBlendersInRecommendation": { "description": "Include blenders when selecting a model to prepare for deployment in an Autopilot Run.[DEPRECATED]", "type": "boolean", "x-versionadded": "v2.21", "x-versiondeprecated": "2.30.0" }, "defaultMonotonicDecreasingFeaturelistId": { "description": "null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "defaultMonotonicIncreasingFeaturelistId": { "description": "null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.", "type": [ "string", "null" ], "x-versionadded": "v2.11" }, "downsampledMajorityRows": { "description": "the total number of the majority rows available for modeling, or null for projects without smart downsampling", "type": [ "integer", "null" ], "x-versionadded": "v2.5" }, "downsampledMinorityRows": { "description": "the total number of the minority rows available for modeling, or null for projects without smart downsampling", "type": [ "integer", "null" ], "x-versionadded": "v2.5" }, "eventsCount": { "description": "the name of the event count column, if specified, otherwise null.", "type": [ "string", "null" ], "x-versionadded": "v2.8" }, "exposure": { "description": "the name of the exposure column, if specified.", "type": [ "string", "null" ], "x-versionadded": "v2.6" }, "majorityDownsamplingRate": { "description": "the percentage between 0 and 100 of the majority rows that are kept, or null for projects without smart downsampling", "type": [ "number", "null" ], "x-versionadded": "v2.5" }, "minSecondaryValidationModelCount": { "description": "Compute \"All backtest\" scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.", "type": "boolean", "x-versionadded": "v2.19" }, "offset": { "description": "the list of names of the offset columns, if specified, otherwise null.", "items": { "type": "string" }, "type": "array", "x-versionadded": "v2.6" }, "onlyIncludeMonotonicBlueprints": { "default": false, "description": "whether the project only includes blueprints support enforcing monotonic constraints", "type": "boolean", "x-versionadded": "v2.11" }, "prepareModelForDeployment": { "description": "Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning \"RECOMMENDED FOR DEPLOYMENT\" label.", "type": [ "boolean", "null" ], "x-versionadded": "v2.19" }, "responseCap": { "description": "defaults to False, if specified used to cap the maximum response of a model", "oneOf": [ { "type": "boolean" }, { "maximum": 1, "minimum": 0.5, "type": "number" } ] }, "runLeakageRemovedFeatureList": { "description": "Run Autopilot on Leakage Removed feature list (if exists).", "type": "boolean", "x-versionadded": "v2.21" }, "scoringCodeOnly": { "description": "Keep only models that can be converted to scorable java code during Autopilot run.", "type": "boolean", "x-versionadded": "v2.19" }, "seed": { "description": "defaults to null, the random seed to be used if specified", "type": [ "string", "null" ] }, "shapOnlyMode": { "description": "Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible. For pre SHAP-only mode projects this is always ``null``.", "type": [ "boolean", "null" ], "x-versionadded": "v2.21" }, "smartDownsampled": { "description": "whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).", "type": "boolean", "x-versionadded": "v2.5" }, "weights": { "description": "the name of the weight column, if specified, otherwise null.", "type": [ "string", "null" ] } }, "required": [ "blendBestModels", "blueprintThreshold", "defaultMonotonicDecreasingFeaturelistId", "defaultMonotonicIncreasingFeaturelistId", "downsampledMajorityRows", "downsampledMinorityRows", "majorityDownsamplingRate", "onlyIncludeMonotonicBlueprints", "prepareModelForDeployment", "responseCap", "seed", "shapOnlyMode", "smartDownsampled", "weights" ], "type": "object" }, "autopilotClusterList": { "description": "Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'.", "items": { "maximum": 100, "minimum": 2, "type": "integer" }, "maxItems": 10, "type": "array", "x-versionadded": "v2.25" }, "autopilotMode": { "description": "The current autopilot mode. 0: Full Autopilot. 2: Manual Mode. 3: Quick Mode. 4: Comprehensive Autopilot. null: Mode not set.", "enum": [ "0", "2", "3", "4" ], "type": [ "string", "null" ] }, "catalogId": { "description": "The ID of the AI catalog entry used to create the project, or null if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "catalogVersionId": { "description": "The ID of the AI catalog version used to create the project, or null if not created from the AI catalog.", "type": [ "string", "null" ], "x-versionadded": "v2.21" }, "created": { "description": "The time of project creation.", "format": "date-time", "type": "string" }, "externalTimeSeriesBaselineDatasetMetadata": { "description": "The id of the catalog item that is being used as the external baseline data", "properties": { "datasetId": { "description": "Catalog version id for external prediction data that can be used as a baseline to calculate new metrics.", "type": [ "string", "null" ] }, "datasetName": { "description": "The name of the timeseries baseline dataset for the project", "type": [ "string", "null" ] } }, "required": [ "datasetId", "datasetName" ], "type": "object" }, "featureEngineeringPredictionPoint": { "description": "The date column to be used as the prediction point for time-based feature engineering.", "type": [ "string", "null" ], "x-versionadded": "v2.22" }, "fileName": { "description": "The name of the dataset used to create the project.", "type": "string" }, "holdoutUnlocked": { "description": "Whether the holdout has been unlocked.", "type": "boolean" }, "id": { "description": "The ID of the project.", "type": "string" }, "isScoringAvailableForModelsTrainedIntoValidationHoldout": { "description": "Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass.", "type": "boolean", "x-versionadded": "v2.31" }, "maxClusters": { "description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `minClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100.", "maximum": 100, "minimum": 2, "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "maxTrainPct": { "description": "The maximum percentage of the dataset that can be used to successfully train a model without going into the validation data.", "type": "number" }, "maxTrainRows": { "description": "The maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data.", "type": "integer" }, "metric": { "description": "The metric used to select the best-performing models.", "type": "string" }, "minClusters": { "description": "Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to `maxClusters`. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2.", "maximum": 100, "minimum": 2, "type": [ "integer", "null" ], "x-versionadded": "v2.23" }, "partition": { "description": "The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null.", "properties": { "cvHoldoutLevel": { "description": "If a user partition column was used with cross validation, the value assigned to the holdout set", "type": [ "string", "null" ] }, "cvMethod": { "description": "The partitioning method used. Note that \"date\" partitioning is an old partitioning method no longer supported for new projects, as of API version v2.0.", "enum": [ "random", "stratified", "datetime", "user", "group", "date" ], "type": "string" }, "datetimeCol": { "description": "If a date partition column was used, the name of the column. Note that datetimeCol applies to an old partitioning method no longer supported for new projects, as of API version v2.0.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "If a datetime partition column was used, the name of the column.", "type": [ "string", "null" ] }, "holdoutLevel": { "description": "If a user partition column was used with train-validation-holdout split, the value assigned to the holdout set.", "type": [ "string", "null" ] }, "holdoutPct": { "description": "The percentage of the dataset reserved for the holdout set.", "type": [ "number", "null" ] }, "partitionKeyCols": { "description": "An array containing a single string - the name of the group partition column", "items": { "type": "string" }, "type": "array" }, "reps": { "description": "If cross validation was used, the number of folds to use.", "type": [ "integer", "null" ] }, "trainingLevel": { "description": "If a user partition column was used with train-validation-holdout split, the value assigned to the training set.", "type": [ "string", "null" ] }, "useTimeSeries": { "description": "Indicates whether a time series project was created as opposed to a regular project using datetime partitioning.", "type": [ "boolean", "null" ], "x-versionadded": "v2.9" }, "userPartitionCol": { "description": "If a user partition column was used, the name of the column.", "type": [ "string", "null" ] }, "validationLevel": { "description": "If a user partition column was used with train-validation-holdout split, the value assigned to the validation set.", "type": [ "string", "null" ] }, "validationPct": { "description": "If train-validation-holdout split was used, the percentage of the dataset used for the validation set.", "type": [ "number", "null" ] }, "validationType": { "description": "The type of validation used. Either CV (cross validation) or TVH (train-validation-holdout split).", "enum": [ "CV", "TVH" ], "type": "string" } }, "required": [ "cvHoldoutLevel", "cvMethod", "datetimeCol", "holdoutLevel", "holdoutPct", "partitionKeyCols", "reps", "trainingLevel", "userPartitionCol", "validationLevel", "validationPct", "validationType" ], "type": "object" }, "positiveClass": { "description": "If the project uses binary classification, the class designated to be the positive class.", "oneOf": [ { "type": "string" }, { "type": "integer" }, { "type": "null" } ] }, "primaryLocationColumn": { "description": "Primary location column name", "type": "string", "x-versionadded": "v2.27" }, "projectName": { "description": "The name of the project.", "type": "string" }, "queryGeneratorId": { "description": "The ID of the time series data prep query generator associated with the project, or null if there is no associated query generator.", "type": [ "string", "null" ], "x-versionadded": "v2.27" }, "quickrun": { "description": "If the Autopilot mode is set to quick. DEPRECATED: look at autopilot_mode instead.", "type": "boolean", "x-versionadded": "v2.22", "x-versiondeprecated": "v2.30" }, "relationshipsConfigurationId": { "description": "Relationships configuration id to be used for Feature Discovery projects.", "type": [ "string", "null" ], "x-versionadded": "v2.27" }, "segmentation": { "description": "Segmentation info for the project.", "properties": { "parentProjectId": { "description": "ID of the original project.", "type": [ "string", "null" ] }, "segment": { "description": "Segment value.", "type": [ "string", "null" ] }, "segmentationTaskId": { "description": "ID of the Segmentation Task.", "type": "string" } }, "required": [ "segmentationTaskId" ], "type": "object" }, "stage": { "description": "The stage of the project. If modeling, then the target is successfully set and modeling or predictions can proceed.", "enum": [ "modeling", "aim", "fasteda", "eda", "eda2", "empty" ], "type": "string" }, "target": { "description": "The target of the project, null if project is unsupervised.", "type": "string" }, "targetType": { "description": "The type of the selected target. Null if the project is unsupervised.", "enum": [ "Binary", "Regression", "Multiclass", "Multilabel" ], "type": [ "string", "null" ] }, "unsupervisedMode": { "description": "Indicates whether a project is unsupervised.", "type": [ "boolean", "null" ], "x-versionadded": "v2.20" }, "unsupervisedType": { "description": "Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'.", "enum": [ "anomaly", "clustering" ], "type": [ "string", "null" ], "x-versionadded": "v2.23" }, "useCase": { "description": "The information of the use case associated with the project.", "properties": { "id": { "description": "The ID of the use case.", "type": [ "string", "null" ] }, "name": { "description": "The name of the use case.", "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.36" }, "useFeatureDiscovery": { "description": "Indicates whether a feature discovery project was created as opposed to a regular project", "type": "boolean", "x-versionadded": "v2.21" }, "useGpu": { "description": "Indicates whether project should use GPU workers", "type": [ "boolean", "null" ], "x-versionadded": "v2.31" } }, "required": [ "advancedOptions", "autopilotMode", "catalogId", "catalogVersionId", "created", "fileName", "holdoutUnlocked", "id", "isScoringAvailableForModelsTrainedIntoValidationHoldout", "maxTrainPct", "maxTrainRows", "metric", "partition", "positiveClass", "projectName", "queryGeneratorId", "quickrun", "stage", "target", "targetType", "unsupervisedMode", "useFeatureDiscovery" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | advancedOptions | ProjectAdvancedOptionsResponse | true | | Information related to the current model of the deployment. | | autopilotClusterList | [integer] | false | maxItems: 10 | Optional. A list of integers where each value will be used as the number of clusters in Autopilot model(s) for unsupervised clustering projects. Cannot be specified unless unsupervisedMode is true and unsupervisedType is set to 'clustering'. | | autopilotMode | string,null | true | | The current autopilot mode. 0: Full Autopilot. 2: Manual Mode. 3: Quick Mode. 4: Comprehensive Autopilot. null: Mode not set. | | catalogId | string,null | true | | The ID of the AI catalog entry used to create the project, or null if not created from the AI catalog. | | catalogVersionId | string,null | true | | The ID of the AI catalog version used to create the project, or null if not created from the AI catalog. | | created | string(date-time) | true | | The time of project creation. | | externalTimeSeriesBaselineDatasetMetadata | ExternalTSBaselineMetadata | false | | The id of the catalog item that is being used as the external baseline data | | featureEngineeringPredictionPoint | string,null | false | | The date column to be used as the prediction point for time-based feature engineering. | | fileName | string | true | | The name of the dataset used to create the project. | | holdoutUnlocked | boolean | true | | Whether the holdout has been unlocked. | | id | string | true | | The ID of the project. | | isScoringAvailableForModelsTrainedIntoValidationHoldout | boolean | true | | Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass. | | maxClusters | integer,null | false | maximum: 100minimum: 2 | Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The maximum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to minClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to the number of rows in the project's dataset divided by 50 or 100 if that number of greater than 100. | | maxTrainPct | number | true | | The maximum percentage of the dataset that can be used to successfully train a model without going into the validation data. | | maxTrainRows | integer | true | | The maximum number of rows of the dataset that can be used to successfully train a model without going into the validation data. | | metric | string | true | | The metric used to select the best-performing models. | | minClusters | integer,null | false | maximum: 100minimum: 2 | Only valid when unsupervisedMode is True and unsupervisedType is 'clustering'. The minimum number of clusters allowed when training clustering models. If specified cannot be exceed the number of rows in a project's dataset divided by 50 and must be less than or equal to maxClusters. If unsupervisedMode is True and unsupervisedType is 'clustering' then defaults to 2. | | partition | Partition | true | | The partition object of a project indicates the settings used for partitioning. Depending on the partitioning selected, many of the options will be null. | | positiveClass | any | true | | If the project uses binary classification, the class designated to be the positive class. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | primaryLocationColumn | string | false | | Primary location column name | | projectName | string | true | | The name of the project. | | queryGeneratorId | string,null | true | | The ID of the time series data prep query generator associated with the project, or null if there is no associated query generator. | | quickrun | boolean | true | | If the Autopilot mode is set to quick. DEPRECATED: look at autopilot_mode instead. | | relationshipsConfigurationId | string,null | false | | Relationships configuration id to be used for Feature Discovery projects. | | segmentation | ProjectSegmentationInfoResponse | false | | Segmentation info for the project. | | stage | string | true | | The stage of the project. If modeling, then the target is successfully set and modeling or predictions can proceed. | | target | string | true | | The target of the project, null if project is unsupervised. | | targetType | string,null | true | | The type of the selected target. Null if the project is unsupervised. | | unsupervisedMode | boolean,null | true | | Indicates whether a project is unsupervised. | | unsupervisedType | string,null | false | | Only valid when unsupervisedMode is True. The type of unsupervised project, anomaly or clustering. If unsupervisedMode, defaults to 'anomaly'. | | useCase | UseCaseIdName | false | | The information of the use case associated with the project. | | useFeatureDiscovery | boolean | true | | Indicates whether a feature discovery project was created as opposed to a regular project | | useGpu | boolean,null | false | | Indicates whether project should use GPU workers | ### Enumerated Values | Property | Value | | --- | --- | | autopilotMode | [0, 2, 3, 4] | | stage | [modeling, aim, fasteda, eda, eda2, empty] | | targetType | [Binary, Regression, Multiclass, Multilabel] | | unsupervisedType | [anomaly, clustering] | ## ProjectSegmentUpdate ``` { "properties": { "operation": { "default": "restart", "description": "The name of the operation to perform on the project segment.", "enum": [ "restart" ], "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | false | | The name of the operation to perform on the project segment. | ### Enumerated Values | Property | Value | | --- | --- | | operation | restart | ## ProjectSegmentUpdateResponse ``` { "properties": { "projectId": { "description": "The new project ID of the restarted segment.", "type": "string" }, "segmentId": { "description": "The name of the restarted segment.", "type": "string" } }, "required": [ "projectId", "segmentId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | projectId | string | true | | The new project ID of the restarted segment. | | segmentId | string | true | | The name of the restarted segment. | ## ProjectSegmentationInfoResponse ``` { "description": "Segmentation info for the project.", "properties": { "parentProjectId": { "description": "ID of the original project.", "type": [ "string", "null" ] }, "segment": { "description": "Segment value.", "type": [ "string", "null" ] }, "segmentationTaskId": { "description": "ID of the Segmentation Task.", "type": "string" } }, "required": [ "segmentationTaskId" ], "type": "object" } ``` Segmentation info for the project. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parentProjectId | string,null | false | | ID of the original project. | | segment | string,null | false | | Segment value. | | segmentationTaskId | string | true | | ID of the Segmentation Task. | ## ProjectStatusResponse ``` { "properties": { "autopilotDone": { "description": "whether the current autopilot run has finished", "type": "boolean" }, "stage": { "description": "the current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed", "enum": [ "modeling", "aim", "fasteda", "eda", "eda2", "empty" ], "type": "string" }, "stageDescription": { "description": "a description of the current stage of the project", "type": "string" } }, "required": [ "autopilotDone", "stage", "stageDescription" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | autopilotDone | boolean | true | | whether the current autopilot run has finished | | stage | string | true | | the current stage of the project, where modeling indicates that the target has been successfully set and modeling and predictions may proceed | | stageDescription | string | true | | a description of the current stage of the project | ### Enumerated Values | Property | Value | | --- | --- | | stage | [modeling, aim, fasteda, eda, eda2, empty] | ## ProjectUpdate ``` { "properties": { "gpuWorkerCount": { "description": "The desired new number of gpu workers if the\n number of gpu workers should be changed.\n Must not exceed the number of gpu workers available to the user. `0` is allowed.\n `-1` requests the maximum number available to the user.", "type": "integer" }, "holdoutUnlocked": { "description": "If specified, the holdout will be unlocked;\n note that the holdout cannot be relocked after unlocking", "enum": [ "True" ], "type": "string" }, "projectDescription": { "description": "The new description of the project, if the description should be updated.", "maxLength": 500, "type": "string", "x-versionadded": "v2.20" }, "projectName": { "description": "The new name of the project, if it should be renamed.", "maxLength": 100, "type": "string" }, "workerCount": { "description": "The desired new number of workers if the\n number of workers should be changed.\n Must not exceed the number of workers available to the user. `0` is allowed.\n (New in version v2.14) `-1` requests the maximum number available to the user.", "type": "integer" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | gpuWorkerCount | integer | false | | The desired new number of gpu workers if the number of gpu workers should be changed. Must not exceed the number of gpu workers available to the user. 0 is allowed. -1 requests the maximum number available to the user. | | holdoutUnlocked | string | false | | If specified, the holdout will be unlocked; note that the holdout cannot be relocked after unlocking | | projectDescription | string | false | maxLength: 500 | The new description of the project, if the description should be updated. | | projectName | string | false | maxLength: 100 | The new name of the project, if it should be renamed. | | workerCount | integer | false | | The desired new number of workers if the number of workers should be changed. Must not exceed the number of workers available to the user. 0 is allowed. (New in version v2.14) -1 requests the maximum number available to the user. | ### Enumerated Values | Property | Value | | --- | --- | | holdoutUnlocked | True | ## S3Credentials ``` { "properties": { "awsAccessKeyId": { "description": "The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSecretAccessKey": { "description": "The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified.", "type": "string" }, "awsSessionToken": { "default": null, "description": "The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified.", "type": [ "string", "null" ] }, "configId": { "description": "The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 's3' here.", "enum": [ "s3" ], "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | awsAccessKeyId | string | false | | The S3 AWS access key ID. Required if configId is not specified.Cannot include this parameter if configId is specified. | | awsSecretAccessKey | string | false | | The S3 AWS secret access key. Required if configId is not specified.Cannot include this parameter if configId is specified. | | awsSessionToken | string,null | false | | The S3 AWS session token for AWS temporary credentials.Cannot include this parameter if configId is specified. | | configId | string | false | | The ID of secure configurations of credentials shared by admin. If specified, cannot include awsAccessKeyId, awsSecretAccessKey or awsSessionToken. | | credentialType | string | true | | The type of these credentials, 's3' here. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | s3 | ## SegmentationDataMappingResponse ``` { "properties": { "segment": { "description": "The segment name associated with the multiseries ID column by the segmentation task.", "type": "string" }, "seriesId": { "description": "The multiseries ID column used to identify series for segmentation.", "type": "string" } }, "required": [ "segment", "seriesId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | segment | string | true | | The segment name associated with the multiseries ID column by the segmentation task. | | seriesId | string | true | | The multiseries ID column used to identify series for segmentation. | ## SegmentationEDACompletedResponse ``` { "properties": { "maxDate": { "description": "The latest date in the segment.", "format": "date-time", "type": "string" }, "minDate": { "description": "The earliest date in the segment.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the segment.", "type": "string" }, "numberOfRows": { "description": "The number of rows in the segment.", "exclusiveMinimum": 0, "type": "integer" }, "sizeInBytes": { "description": "The size of the segment in bytes.", "exclusiveMinimum": 0, "type": "integer" } }, "required": [ "maxDate", "minDate", "name", "numberOfRows", "sizeInBytes" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxDate | string(date-time) | true | | The latest date in the segment. | | minDate | string(date-time) | true | | The earliest date in the segment. | | name | string | true | | The name of the segment. | | numberOfRows | integer | true | | The number of rows in the segment. | | sizeInBytes | integer | true | | The size of the segment in bytes. | ## SegmentationResultsCompletedResponse ``` { "properties": { "name": { "description": "The name of the segmentation task job.", "type": "string" }, "segmentationTaskId": { "description": "The ID of the completed segmentation task.", "type": "string" }, "segmentsCount": { "description": "The number of segments produced by the task.", "type": "integer" }, "segmentsEda": { "description": "The array of segments EDA information.", "items": { "properties": { "maxDate": { "description": "The latest date in the segment.", "format": "date-time", "type": "string" }, "minDate": { "description": "The earliest date in the segment.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the segment.", "type": "string" }, "numberOfRows": { "description": "The number of rows in the segment.", "exclusiveMinimum": 0, "type": "integer" }, "sizeInBytes": { "description": "The size of the segment in bytes.", "exclusiveMinimum": 0, "type": "integer" } }, "required": [ "maxDate", "minDate", "name", "numberOfRows", "sizeInBytes" ], "type": "object" }, "type": "array" }, "url": { "description": "The URL to retrieve detailed information about the segmentation task.", "type": "string" } }, "required": [ "name", "segmentationTaskId", "segmentsCount", "segmentsEda", "url" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the segmentation task job. | | segmentationTaskId | string | true | | The ID of the completed segmentation task. | | segmentsCount | integer | true | | The number of segments produced by the task. | | segmentsEda | [SegmentationEDACompletedResponse] | true | | The array of segments EDA information. | | url | string | true | | The URL to retrieve detailed information about the segmentation task. | ## SegmentationResultsFailedResponse ``` { "properties": { "message": { "description": "The response containing the error message from the segmentation task.", "type": "string" }, "name": { "description": "Name of the segmentation task job", "type": "string" }, "parameters": { "description": "The parameters submitted by the user to the failed job.", "type": "object" } }, "required": [ "message", "name", "parameters" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | true | | The response containing the error message from the segmentation task. | | name | string | true | | Name of the segmentation task job | | parameters | AllowExtra | true | | The parameters submitted by the user to the failed job. | ## SegmentationResultsResponse ``` { "properties": { "completedJobs": { "description": "The list of completed segmentation tasks.", "items": { "properties": { "name": { "description": "The name of the segmentation task job.", "type": "string" }, "segmentationTaskId": { "description": "The ID of the completed segmentation task.", "type": "string" }, "segmentsCount": { "description": "The number of segments produced by the task.", "type": "integer" }, "segmentsEda": { "description": "The array of segments EDA information.", "items": { "properties": { "maxDate": { "description": "The latest date in the segment.", "format": "date-time", "type": "string" }, "minDate": { "description": "The earliest date in the segment.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the segment.", "type": "string" }, "numberOfRows": { "description": "The number of rows in the segment.", "exclusiveMinimum": 0, "type": "integer" }, "sizeInBytes": { "description": "The size of the segment in bytes.", "exclusiveMinimum": 0, "type": "integer" } }, "required": [ "maxDate", "minDate", "name", "numberOfRows", "sizeInBytes" ], "type": "object" }, "type": "array" }, "url": { "description": "The URL to retrieve detailed information about the segmentation task.", "type": "string" } }, "required": [ "name", "segmentationTaskId", "segmentsCount", "segmentsEda", "url" ], "type": "object" }, "type": "array" }, "failedJobs": { "description": "The list of failed segmentation tasks.", "items": { "properties": { "message": { "description": "The response containing the error message from the segmentation task.", "type": "string" }, "name": { "description": "Name of the segmentation task job", "type": "string" }, "parameters": { "description": "The parameters submitted by the user to the failed job.", "type": "object" } }, "required": [ "message", "name", "parameters" ], "type": "object" }, "type": "array" }, "numberOfJobs": { "description": "The total number of completed and failed jobs processed.", "type": "integer" } }, "required": [ "completedJobs", "failedJobs", "numberOfJobs" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | completedJobs | [SegmentationResultsCompletedResponse] | true | | The list of completed segmentation tasks. | | failedJobs | [SegmentationResultsFailedResponse] | true | | The list of failed segmentation tasks. | | numberOfJobs | integer | true | | The total number of completed and failed jobs processed. | ## SegmentationTaskCreate ``` { "properties": { "datetimePartitionColumn": { "description": "The date column that will be used to identify the date in time series segmentation.", "type": "string" }, "modelPackageId": { "description": "The model package ID for using an external model registry package.", "type": "string" }, "multiseriesIdColumns": { "description": "The list of one or more names of multiseries ID columns.", "items": { "type": "string" }, "maxItems": 1, "minItems": 1, "type": "array" }, "target": { "description": "The target for the dataset.", "type": "string" }, "useAutomatedSegmentation": { "default": false, "description": "Enable the use of automated segmentation tasks.", "type": "boolean" }, "useTimeSeries": { "default": false, "description": "Enable time series-based segmentation tasks.", "type": "boolean" }, "userDefinedSegmentIdColumns": { "description": "The list of one or more names of columns to be used for user-defined business rule segmentations.", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "target" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datetimePartitionColumn | string | false | | The date column that will be used to identify the date in time series segmentation. | | modelPackageId | string | false | | The model package ID for using an external model registry package. | | multiseriesIdColumns | [string] | false | maxItems: 1minItems: 1 | The list of one or more names of multiseries ID columns. | | target | string | true | | The target for the dataset. | | useAutomatedSegmentation | boolean | false | | Enable the use of automated segmentation tasks. | | useTimeSeries | boolean | false | | Enable time series-based segmentation tasks. | | userDefinedSegmentIdColumns | [string] | false | minItems: 1 | The list of one or more names of columns to be used for user-defined business rule segmentations. | ## SegmentationTaskDataResponse ``` { "description": "The data for the segmentation task.", "properties": { "clusteringModelId": { "description": "The ID of the model used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "clusteringModelName": { "description": "The name of the model used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "clusteringProjectId": { "description": "The ID of the project used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "The name of the datetime partitioning column used by the segmentation task.", "type": "string" }, "modelPackageId": { "description": "The external model package ID used by the segmentation task for automated segmentation.", "type": "string" }, "multiseriesIdColumns": { "description": "The multiseries ID columns used by the segmentation task.", "items": { "type": "string" }, "type": "array" }, "userDefinedSegmentIdColumns": { "description": "The user-defined segmentation columns used by the segmentation task.", "items": { "type": "string" }, "type": "array" } }, "type": "object" } ``` The data for the segmentation task. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | clusteringModelId | string,null | false | | The ID of the model used by the segmentation task for automated segmentation. | | clusteringModelName | string,null | false | | The name of the model used by the segmentation task for automated segmentation. | | clusteringProjectId | string,null | false | | The ID of the project used by the segmentation task for automated segmentation. | | datetimePartitionColumn | string | false | | The name of the datetime partitioning column used by the segmentation task. | | modelPackageId | string | false | | The external model package ID used by the segmentation task for automated segmentation. | | multiseriesIdColumns | [string] | false | | The multiseries ID columns used by the segmentation task. | | userDefinedSegmentIdColumns | [string] | false | | The user-defined segmentation columns used by the segmentation task. | ## SegmentationTaskListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the segmentation tasks that are associated with the project ID.", "items": { "properties": { "created": { "description": "The date and time when the segmentation task was originally created.", "format": "date-time", "type": "string" }, "data": { "description": "The data for the segmentation task.", "properties": { "clusteringModelId": { "description": "The ID of the model used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "clusteringModelName": { "description": "The name of the model used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "clusteringProjectId": { "description": "The ID of the project used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "The name of the datetime partitioning column used by the segmentation task.", "type": "string" }, "modelPackageId": { "description": "The external model package ID used by the segmentation task for automated segmentation.", "type": "string" }, "multiseriesIdColumns": { "description": "The multiseries ID columns used by the segmentation task.", "items": { "type": "string" }, "type": "array" }, "userDefinedSegmentIdColumns": { "description": "The user-defined segmentation columns used by the segmentation task.", "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "metadata": { "description": "The metadata for the segmentation task.", "properties": { "useAutomatedSegmentation": { "description": "Whether the segmentation task uses automated segmentation.", "type": "boolean" }, "useMultiseriesIdColumns": { "description": "Whether the segmentation task uses a multiseries column.", "type": "boolean" }, "useTimeSeries": { "description": "Whether the segmentation task is a time series task.", "type": "boolean" } }, "required": [ "useAutomatedSegmentation", "useMultiseriesIdColumns", "useTimeSeries" ], "type": "object" }, "name": { "description": "The name of the segmentation task.", "type": "string" }, "projectId": { "description": "The ID of the parent project associated with the segmentation task.", "type": "string" }, "segmentationTaskId": { "description": "Id of the segmentation task", "type": "string" }, "segments": { "description": "The names of the unique segments generated by the segmentation task.", "items": { "type": "string" }, "type": "array" }, "segmentsCount": { "description": "The number of segments generated by the segmentation task.", "type": "integer" }, "segmentsEda": { "description": "The array of segments EDA information.", "items": { "properties": { "maxDate": { "description": "The latest date in the segment.", "format": "date-time", "type": "string" }, "minDate": { "description": "The earliest date in the segment.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the segment.", "type": "string" }, "numberOfRows": { "description": "The number of rows in the segment.", "exclusiveMinimum": 0, "type": "integer" }, "sizeInBytes": { "description": "The size of the segment in bytes.", "exclusiveMinimum": 0, "type": "integer" } }, "required": [ "maxDate", "minDate", "name", "numberOfRows", "sizeInBytes" ], "type": "object" }, "type": "array" }, "type": { "description": "The type of segmentation task (e.g., AutoML, AutoTS).", "type": "string" } }, "required": [ "created", "data", "metadata", "name", "projectId", "segmentationTaskId", "segments", "segmentsCount", "segmentsEda", "type" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [SegmentationTaskResponse] | true | | The list of the segmentation tasks that are associated with the project ID. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## SegmentationTaskMetadataResponse ``` { "description": "The metadata for the segmentation task.", "properties": { "useAutomatedSegmentation": { "description": "Whether the segmentation task uses automated segmentation.", "type": "boolean" }, "useMultiseriesIdColumns": { "description": "Whether the segmentation task uses a multiseries column.", "type": "boolean" }, "useTimeSeries": { "description": "Whether the segmentation task is a time series task.", "type": "boolean" } }, "required": [ "useAutomatedSegmentation", "useMultiseriesIdColumns", "useTimeSeries" ], "type": "object" } ``` The metadata for the segmentation task. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | useAutomatedSegmentation | boolean | true | | Whether the segmentation task uses automated segmentation. | | useMultiseriesIdColumns | boolean | true | | Whether the segmentation task uses a multiseries column. | | useTimeSeries | boolean | true | | Whether the segmentation task is a time series task. | ## SegmentationTaskResponse ``` { "properties": { "created": { "description": "The date and time when the segmentation task was originally created.", "format": "date-time", "type": "string" }, "data": { "description": "The data for the segmentation task.", "properties": { "clusteringModelId": { "description": "The ID of the model used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "clusteringModelName": { "description": "The name of the model used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "clusteringProjectId": { "description": "The ID of the project used by the segmentation task for automated segmentation.", "type": [ "string", "null" ] }, "datetimePartitionColumn": { "description": "The name of the datetime partitioning column used by the segmentation task.", "type": "string" }, "modelPackageId": { "description": "The external model package ID used by the segmentation task for automated segmentation.", "type": "string" }, "multiseriesIdColumns": { "description": "The multiseries ID columns used by the segmentation task.", "items": { "type": "string" }, "type": "array" }, "userDefinedSegmentIdColumns": { "description": "The user-defined segmentation columns used by the segmentation task.", "items": { "type": "string" }, "type": "array" } }, "type": "object" }, "metadata": { "description": "The metadata for the segmentation task.", "properties": { "useAutomatedSegmentation": { "description": "Whether the segmentation task uses automated segmentation.", "type": "boolean" }, "useMultiseriesIdColumns": { "description": "Whether the segmentation task uses a multiseries column.", "type": "boolean" }, "useTimeSeries": { "description": "Whether the segmentation task is a time series task.", "type": "boolean" } }, "required": [ "useAutomatedSegmentation", "useMultiseriesIdColumns", "useTimeSeries" ], "type": "object" }, "name": { "description": "The name of the segmentation task.", "type": "string" }, "projectId": { "description": "The ID of the parent project associated with the segmentation task.", "type": "string" }, "segmentationTaskId": { "description": "Id of the segmentation task", "type": "string" }, "segments": { "description": "The names of the unique segments generated by the segmentation task.", "items": { "type": "string" }, "type": "array" }, "segmentsCount": { "description": "The number of segments generated by the segmentation task.", "type": "integer" }, "segmentsEda": { "description": "The array of segments EDA information.", "items": { "properties": { "maxDate": { "description": "The latest date in the segment.", "format": "date-time", "type": "string" }, "minDate": { "description": "The earliest date in the segment.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the segment.", "type": "string" }, "numberOfRows": { "description": "The number of rows in the segment.", "exclusiveMinimum": 0, "type": "integer" }, "sizeInBytes": { "description": "The size of the segment in bytes.", "exclusiveMinimum": 0, "type": "integer" } }, "required": [ "maxDate", "minDate", "name", "numberOfRows", "sizeInBytes" ], "type": "object" }, "type": "array" }, "type": { "description": "The type of segmentation task (e.g., AutoML, AutoTS).", "type": "string" } }, "required": [ "created", "data", "metadata", "name", "projectId", "segmentationTaskId", "segments", "segmentsCount", "segmentsEda", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string(date-time) | true | | The date and time when the segmentation task was originally created. | | data | SegmentationTaskDataResponse | true | | The data for the segmentation task. | | metadata | SegmentationTaskMetadataResponse | true | | The metadata for the segmentation task. | | name | string | true | | The name of the segmentation task. | | projectId | string | true | | The ID of the parent project associated with the segmentation task. | | segmentationTaskId | string | true | | Id of the segmentation task | | segments | [string] | true | | The names of the unique segments generated by the segmentation task. | | segmentsCount | integer | true | | The number of segments generated by the segmentation task. | | segmentsEda | [SegmentationEDACompletedResponse] | true | | The array of segments EDA information. | | type | string | true | | The type of segmentation task (e.g., AutoML, AutoTS). | ## SegmentationTaskSegmentMappingsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The array of segmentation mappings.", "items": { "properties": { "segment": { "description": "The segment name associated with the multiseries ID column by the segmentation task.", "type": "string" }, "seriesId": { "description": "The multiseries ID column used to identify series for segmentation.", "type": "string" } }, "required": [ "segment", "seriesId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [SegmentationDataMappingResponse] | true | | The array of segmentation mappings. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## SharingListResponse ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "role": { "description": "The role of the user on this entity.", "type": "string" }, "userId": { "description": "The identifier of the user that has access to this entity.", "type": "string" }, "username": { "description": "The username of the user that has access to the entity.", "type": "string" } }, "required": [ "canShare", "role", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControl] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | ## SharingUpdateOrRemove ``` { "properties": { "data": { "description": "The role to set for the user.", "items": { "properties": { "role": { "description": "The role to set for the user.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to set the role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" }, "maxItems": 100, "type": "array" }, "includeFeatureDiscoveryEntities": { "default": false, "description": "Whether to share all the related entities.", "type": "boolean", "x-versionadded": "v2.21" }, "sendNotification": { "default": true, "description": "Send an email notification.", "type": "boolean", "x-versionadded": "v2.20" } }, "required": [ "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [UpdateAccessControl] | true | maxItems: 100 | The role to set for the user. | | includeFeatureDiscoveryEntities | boolean | false | | Whether to share all the related entities. | | sendNotification | boolean | false | | Send an email notification. | ## SnowflakeKeyPairCredentials ``` { "properties": { "configId": { "description": "The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase.", "type": "string" }, "credentialType": { "description": "The type of these credentials, 'snowflake_key_pair_user_account' here.", "enum": [ "snowflake_key_pair_user_account" ], "type": "string" }, "passphrase": { "description": "Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified.", "type": "string" }, "privateKeyStr": { "description": "Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" }, "user": { "description": "Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified.", "type": "string" } }, "required": [ "credentialType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | configId | string | false | | The ID of the saved shared credentials. If specified, cannot include user, privateKeyStr, or passphrase. | | credentialType | string | true | | The type of these credentials, 'snowflake_key_pair_user_account' here. | | passphrase | string | false | | Optional passphrase to decrypt private key. Cannot include this parameter if configId is specified. | | privateKeyStr | string | false | | Private key for key pair authentication. Required if configId is not specified. Cannot include this parameter if configId is specified. | | user | string | false | | Username for this credential. Required if configId is not specified. Cannot include this parameter if configId is specified. | ### Enumerated Values | Property | Value | | --- | --- | | credentialType | snowflake_key_pair_user_account | ## UpdateAccessControl ``` { "properties": { "role": { "description": "The role to set for the user.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "username": { "description": "The username of the user to set the role for.", "type": "string" } }, "required": [ "role", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string,null | true | | The role to set for the user. | | username | string | true | | The username of the user to set the role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | ## UseCaseIdName ``` { "description": "The information of the use case associated with the project.", "properties": { "id": { "description": "The ID of the use case.", "type": [ "string", "null" ] }, "name": { "description": "The name of the use case.", "type": [ "string", "null" ] } }, "type": "object", "x-versionadded": "v2.36" } ``` The information of the use case associated with the project. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string,null | false | | The ID of the use case. | | name | string,null | false | | The name of the use case. | --- # Prompting URL: https://docs.datarobot.com/en/docs/api/reference/public-api/prompting.html > The following endpoints outline how to manage prompts. # Prompting The following endpoints outline how to manage prompts. ## List chat prompts Operation path: `GET /api/v2/genai/chatPrompts/` Authentication requirements: `BearerAuth` List chat prompts. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | query | any | false | Only retrieve the chat prompts associated with this playground ID. | | llmBlueprintId | query | any | false | Only retrieve the chat prompts associated with this LLM blueprint ID. If specified, will retrieve the chat prompts for the oldest chat in this LLM blueprint. | | chatId | query | any | false | Only retrieve the chat prompts associated with this chat ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | ### Example responses > 200 Response ``` { "description": "Paginated list of chat prompts.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single chat prompt.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat this chat prompt belongs to.", "title": "chatId" }, "chatPromptIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the chat prompts included in this prompt's history.", "title": "chatPromptIdsIncludedInHistory" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "creationDate": { "description": "The creation date of the chat prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the chat prompt.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint the chat prompt belongs to.", "title": "llmBlueprintId", "type": "string" }, "llmId": { "description": "The ID of the LLM used by the chat prompt.", "title": "llmId", "type": "string" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata dictionary defining the filters that documents must match in order to be retrieved.", "title": "metadataFilter" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the chat prompt results." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the chat prompt.", "title": "userName", "type": "string" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family this chat prompt belongs to.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "llmId", "id", "text", "llmBlueprintId", "creationDate", "creationUserId", "userName", "resultMetadata", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ChatPromptResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListChatPromptsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListChatPromptsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create chat prompt Operation path: `POST /api/v2/genai/chatPrompts/` Authentication requirements: `BearerAuth` Request the execution of a new prompt within a chat or an LLM blueprint. ### Body parameter ``` { "description": "The body of the \"Create chat prompt\" request.", "properties": { "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat this prompt belongs to. If LLM and vector database settings are not specified in the request, then the prompt will use the current settings of the chat.", "title": "chatId" }, "llmBlueprintId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM blueprint this prompt belongs to. If LLM and vector database settings are not specified in the request, then the prompt will use the current settings of the LLM blueprint.", "title": "llmBlueprintId" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, uses this LLM ID for the prompt and updates the settings of the corresponding chat or LLM blueprint to use this LLM ID.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "If specified, uses these LLM settings for the prompt and updates the settings of the corresponding chat or LLM blueprint to use these LLM settings.", "title": "llmSettings" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata fields to add to the chat prompt.", "title": "metadataFilter" }, "text": { "description": "The text of the user prompt.", "maxLength": 5000000, "title": "text", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, uses this vector database ID for the prompt and updates the settings of the corresponding chat or LLM blueprint to use this vector database ID.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "If specified, uses these vector database settings for the prompt and updates the settings of the corresponding chat or LLM blueprint to use these vector database settings." } }, "required": [ "text" ], "title": "CreateChatPromptRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateChatPromptRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for a single chat prompt.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat this chat prompt belongs to.", "title": "chatId" }, "chatPromptIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the chat prompts included in this prompt's history.", "title": "chatPromptIdsIncludedInHistory" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "creationDate": { "description": "The creation date of the chat prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the chat prompt.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint the chat prompt belongs to.", "title": "llmBlueprintId", "type": "string" }, "llmId": { "description": "The ID of the LLM used by the chat prompt.", "title": "llmId", "type": "string" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata dictionary defining the filters that documents must match in order to be retrieved.", "title": "metadataFilter" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the chat prompt results." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the chat prompt.", "title": "userName", "type": "string" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family this chat prompt belongs to.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "llmId", "id", "text", "llmBlueprintId", "creationDate", "creationUserId", "userName", "resultMetadata", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ChatPromptResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successful Response | ChatPromptResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete chat prompt by chat prompt ID Operation path: `DELETE /api/v2/genai/chatPrompts/{chatPromptId}/` Authentication requirements: `BearerAuth` Delete an existing chat prompt. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | chatPromptId | path | string | true | The ID of the chat prompt to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve chat prompt by chat prompt ID Operation path: `GET /api/v2/genai/chatPrompts/{chatPromptId}/` Authentication requirements: `BearerAuth` Retrieve an existing chat prompt. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | chatPromptId | path | string | true | The ID of the chat prompt to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single chat prompt.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat this chat prompt belongs to.", "title": "chatId" }, "chatPromptIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the chat prompts included in this prompt's history.", "title": "chatPromptIdsIncludedInHistory" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "creationDate": { "description": "The creation date of the chat prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the chat prompt.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint the chat prompt belongs to.", "title": "llmBlueprintId", "type": "string" }, "llmId": { "description": "The ID of the LLM used by the chat prompt.", "title": "llmId", "type": "string" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata dictionary defining the filters that documents must match in order to be retrieved.", "title": "metadataFilter" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the chat prompt results." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the chat prompt.", "title": "userName", "type": "string" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family this chat prompt belongs to.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "llmId", "id", "text", "llmBlueprintId", "creationDate", "creationUserId", "userName", "resultMetadata", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ChatPromptResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ChatPromptResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit chat prompt by chat prompt ID Operation path: `PATCH /api/v2/genai/chatPrompts/{chatPromptId}/` Authentication requirements: `BearerAuth` Edit an existing chat prompt. ### Body parameter ``` { "description": "The body of the \"Update chat prompt\" request.", "properties": { "customMetrics": { "anyOf": [ { "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The list of metric results to add to the chat prompt.", "title": "customMetrics" }, "feedbackMetadata": { "anyOf": [ { "description": "Prompt feedback metadata.", "properties": { "feedback": { "anyOf": [ { "description": "The sentiment of the feedback.", "enum": [ "1", "0" ], "title": "FeedbackSentiment", "type": "string" }, { "type": "null" } ], "description": "The sentiment of the feedback." } }, "required": [ "feedback" ], "title": "FeedbackMetadata", "type": "object" }, { "type": "null" } ], "description": "The feedback metadata to add to the chat prompt." } }, "title": "EditChatPromptRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | chatPromptId | path | string | true | The ID of the chat prompt to edit. | | body | body | EditChatPromptRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single chat prompt.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat this chat prompt belongs to.", "title": "chatId" }, "chatPromptIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the chat prompts included in this prompt's history.", "title": "chatPromptIdsIncludedInHistory" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "creationDate": { "description": "The creation date of the chat prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the chat prompt.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint the chat prompt belongs to.", "title": "llmBlueprintId", "type": "string" }, "llmId": { "description": "The ID of the LLM used by the chat prompt.", "title": "llmId", "type": "string" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata dictionary defining the filters that documents must match in order to be retrieved.", "title": "metadataFilter" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the chat prompt results." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the chat prompt.", "title": "userName", "type": "string" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family this chat prompt belongs to.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "llmId", "id", "text", "llmBlueprintId", "creationDate", "creationUserId", "userName", "resultMetadata", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ChatPromptResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ChatPromptResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List chats Operation path: `GET /api/v2/genai/chats/` Authentication requirements: `BearerAuth` List chats. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmBlueprintId | query | any | false | Only retrieve the chats associated with this LLM blueprint ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name" and "creationDate". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | ### Example responses > 200 Response ``` { "description": "Paginated list of chats.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "Chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the chat.", "title": "id", "type": "string" }, "isFrozen": { "description": "Whether the chat is frozen (e.g., an evaluation chat). If the chat is frozen, it does not accept new prompts.", "title": "isFrozen", "type": "boolean" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the chat.", "title": "llmBlueprintId", "type": "string" }, "name": { "description": "The name of the chat.", "title": "name", "type": "string" }, "promptsCount": { "description": "The number of chat prompts in the chat.", "title": "promptsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the contents of the chat.", "title": "warning" } }, "required": [ "id", "name", "llmBlueprintId", "isFrozen", "warning", "creationDate", "creationUserId", "promptsCount" ], "title": "ChatResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListChatsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListChatsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create chat Operation path: `POST /api/v2/genai/chats/` Authentication requirements: `BearerAuth` Create a new chat. ### Body parameter ``` { "description": "The body of the \"Create chat\" request.", "properties": { "llmBlueprintId": { "description": "The ID of the LLM blueprint to associate with the chat.", "title": "llmBlueprintId", "type": "string" }, "name": { "description": "The name of the chat.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" } }, "required": [ "name", "llmBlueprintId" ], "title": "CreateChatRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateChatRequest | true | none | ### Example responses > 201 Response ``` { "description": "Chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the chat.", "title": "id", "type": "string" }, "isFrozen": { "description": "Whether the chat is frozen (e.g., an evaluation chat). If the chat is frozen, it does not accept new prompts.", "title": "isFrozen", "type": "boolean" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the chat.", "title": "llmBlueprintId", "type": "string" }, "name": { "description": "The name of the chat.", "title": "name", "type": "string" }, "promptsCount": { "description": "The number of chat prompts in the chat.", "title": "promptsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the contents of the chat.", "title": "warning" } }, "required": [ "id", "name", "llmBlueprintId", "isFrozen", "warning", "creationDate", "creationUserId", "promptsCount" ], "title": "ChatResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successful Response | ChatResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete chat by chat ID Operation path: `DELETE /api/v2/genai/chats/{chatId}/` Authentication requirements: `BearerAuth` Delete an existing chat. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | chatId | path | string | true | The ID of the chat to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve chat by chat ID Operation path: `GET /api/v2/genai/chats/{chatId}/` Authentication requirements: `BearerAuth` Retrieve an existing chat. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | chatId | path | string | true | The ID of the chat to retrieve. | ### Example responses > 200 Response ``` { "description": "Chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the chat.", "title": "id", "type": "string" }, "isFrozen": { "description": "Whether the chat is frozen (e.g., an evaluation chat). If the chat is frozen, it does not accept new prompts.", "title": "isFrozen", "type": "boolean" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the chat.", "title": "llmBlueprintId", "type": "string" }, "name": { "description": "The name of the chat.", "title": "name", "type": "string" }, "promptsCount": { "description": "The number of chat prompts in the chat.", "title": "promptsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the contents of the chat.", "title": "warning" } }, "required": [ "id", "name", "llmBlueprintId", "isFrozen", "warning", "creationDate", "creationUserId", "promptsCount" ], "title": "ChatResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ChatResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit chat by chat ID Operation path: `PATCH /api/v2/genai/chats/{chatId}/` Authentication requirements: `BearerAuth` Edit an existing chat. ### Body parameter ``` { "description": "The body of the \"Edit chat\" request.", "properties": { "name": { "description": "The new name of the chat.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" } }, "required": [ "name" ], "title": "EditChatRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | chatId | path | string | true | The ID of the chat to edit. | | body | body | EditChatRequest | true | none | ### Example responses > 200 Response ``` { "description": "Chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the chat.", "title": "id", "type": "string" }, "isFrozen": { "description": "Whether the chat is frozen (e.g., an evaluation chat). If the chat is frozen, it does not accept new prompts.", "title": "isFrozen", "type": "boolean" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the chat.", "title": "llmBlueprintId", "type": "string" }, "name": { "description": "The name of the chat.", "title": "name", "type": "string" }, "promptsCount": { "description": "The number of chat prompts in the chat.", "title": "promptsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the contents of the chat.", "title": "warning" } }, "required": [ "id", "name", "llmBlueprintId", "isFrozen", "warning", "creationDate", "creationUserId", "promptsCount" ], "title": "ChatResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ChatResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List comparison chats Operation path: `GET /api/v2/genai/comparisonChats/` Authentication requirements: `BearerAuth` List comparison chats. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | playgroundId | query | any | false | Only retrieve the comparison chats associated with this playground ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name" and "creationDate". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | ### Example responses > 200 Response ``` { "description": "Paginated list of comparison chats.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "Comparison chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the comparison chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the comparison chat.", "title": "id", "type": "string" }, "name": { "description": "The name of the comparison chat.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the comparison chat.", "title": "playgroundId", "type": "string" }, "promptsCount": { "description": "The number of comparison prompts in the comparison chat.", "title": "promptsCount", "type": "integer" } }, "required": [ "id", "name", "playgroundId", "creationDate", "creationUserId", "promptsCount" ], "title": "ComparisonChatResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListComparisonChatsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListComparisonChatsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create comparison chat Operation path: `POST /api/v2/genai/comparisonChats/` Authentication requirements: `BearerAuth` Create a new comparison chat. ### Body parameter ``` { "description": "The body of the \"Create comparison chat\" request.", "properties": { "name": { "description": "The name of the comparison chat.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground to associate with the comparison chat.", "title": "playgroundId", "type": "string" } }, "required": [ "name", "playgroundId" ], "title": "CreateComparisonChatRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateComparisonChatRequest | true | none | ### Example responses > 201 Response ``` { "description": "Comparison chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the comparison chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the comparison chat.", "title": "id", "type": "string" }, "name": { "description": "The name of the comparison chat.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the comparison chat.", "title": "playgroundId", "type": "string" }, "promptsCount": { "description": "The number of comparison prompts in the comparison chat.", "title": "promptsCount", "type": "integer" } }, "required": [ "id", "name", "playgroundId", "creationDate", "creationUserId", "promptsCount" ], "title": "ComparisonChatResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successful Response | ComparisonChatResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete comparison chat by comparison chat ID Operation path: `DELETE /api/v2/genai/comparisonChats/{comparisonChatId}/` Authentication requirements: `BearerAuth` Delete an existing comparison chat. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | comparisonChatId | path | string | true | The ID of the comparison chat to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve comparison chat by comparison chat ID Operation path: `GET /api/v2/genai/comparisonChats/{comparisonChatId}/` Authentication requirements: `BearerAuth` Retrieve an existing comparison chat. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | comparisonChatId | path | string | true | The ID of the comparison chat to retrieve. | ### Example responses > 200 Response ``` { "description": "Comparison chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the comparison chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the comparison chat.", "title": "id", "type": "string" }, "name": { "description": "The name of the comparison chat.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the comparison chat.", "title": "playgroundId", "type": "string" }, "promptsCount": { "description": "The number of comparison prompts in the comparison chat.", "title": "promptsCount", "type": "integer" } }, "required": [ "id", "name", "playgroundId", "creationDate", "creationUserId", "promptsCount" ], "title": "ComparisonChatResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ComparisonChatResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit comparison chat by comparison chat ID Operation path: `PATCH /api/v2/genai/comparisonChats/{comparisonChatId}/` Authentication requirements: `BearerAuth` Edit an existing comparison chat. ### Body parameter ``` { "description": "The body of the \"Edit comparison chat\" request.", "properties": { "name": { "description": "The new name of the comparison chat.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" } }, "required": [ "name" ], "title": "EditComparisonChatRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | comparisonChatId | path | string | true | The ID of the comparison chat to edit. | | body | body | EditComparisonChatRequest | true | none | ### Example responses > 200 Response ``` { "description": "Comparison chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the comparison chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the comparison chat.", "title": "id", "type": "string" }, "name": { "description": "The name of the comparison chat.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the comparison chat.", "title": "playgroundId", "type": "string" }, "promptsCount": { "description": "The number of comparison prompts in the comparison chat.", "title": "promptsCount", "type": "integer" } }, "required": [ "id", "name", "playgroundId", "creationDate", "creationUserId", "promptsCount" ], "title": "ComparisonChatResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ComparisonChatResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List comparison prompts Operation path: `GET /api/v2/genai/comparisonPrompts/` Authentication requirements: `BearerAuth` List the comparison prompts associated with a comparison chat or a set of LLM blueprints. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | llmBlueprintIds | query | any | false | Only retrieve the comparison prompts associated with the specified LLM blueprint IDs. Either this parameter or comparisonChatId must be specified, but not both. | | comparisonChatId | query | any | false | Only retrieve the comparison prompts associated with the specified comparison chat ID. Either this parameter or llmBlueprintIds must be specified, but not both. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | ### Example responses > 200 Response ``` { "description": "Paginated list of comparison prompts.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "ComparisonPrompt object formatted for API output.", "properties": { "comparisonChatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the comparison chat associated with the comparison prompt.", "title": "comparisonChatId" }, "creationDate": { "description": "The creation date of the comparison prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt.", "title": "id", "type": "string" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata filters applied to the comparison prompt.", "title": "metadataFilter" }, "results": { "description": "The list of comparison prompt results.", "items": { "description": "API response object for a single comparison prompt result.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "comparisonPromptResultIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the comparison prompt results included in this prompt's history.", "title": "comparisonPromptResultIdsIncludedInHistory" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt result.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint that produced the result.", "title": "llmBlueprintId", "type": "string" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the prompt result." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" } }, "required": [ "id", "llmBlueprintId", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ComparisonPromptResult", "type": "object" }, "title": "results", "type": "array" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the comparison prompt.", "title": "userName", "type": "string" } }, "required": [ "id", "text", "results", "creationDate", "creationUserId", "userName", "executionStatus" ], "title": "ComparisonPromptResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListComparisonPromptsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListComparisonPromptsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create comparison prompt Operation path: `POST /api/v2/genai/comparisonPrompts/` Authentication requirements: `BearerAuth` Request the execution of a new comparison prompt. ### Body parameter ``` { "description": "The body of the \"Create comparison prompt\" request.", "properties": { "comparisonChatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the comparison chat to associate the comparison prompt with.", "title": "comparisonChatId" }, "llmBlueprintIds": { "description": "The list of LLM blueprint IDs that should execute the comparison prompt.", "items": { "type": "string" }, "maxItems": 10, "title": "llmBlueprintIds", "type": "array" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata dict that defines filters that the retrieved documents need to match.", "title": "metadataFilter" }, "text": { "description": "The text of the user prompt.", "maxLength": 5000000, "title": "text", "type": "string" } }, "required": [ "llmBlueprintIds", "text" ], "title": "CreateComparisonPromptRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateComparisonPromptRequest | true | none | ### Example responses > 202 Response ``` { "description": "ComparisonPrompt object formatted for API output.", "properties": { "comparisonChatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the comparison chat associated with the comparison prompt.", "title": "comparisonChatId" }, "creationDate": { "description": "The creation date of the comparison prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt.", "title": "id", "type": "string" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata filters applied to the comparison prompt.", "title": "metadataFilter" }, "results": { "description": "The list of comparison prompt results.", "items": { "description": "API response object for a single comparison prompt result.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "comparisonPromptResultIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the comparison prompt results included in this prompt's history.", "title": "comparisonPromptResultIdsIncludedInHistory" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt result.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint that produced the result.", "title": "llmBlueprintId", "type": "string" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the prompt result." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" } }, "required": [ "id", "llmBlueprintId", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ComparisonPromptResult", "type": "object" }, "title": "results", "type": "array" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the comparison prompt.", "title": "userName", "type": "string" } }, "required": [ "id", "text", "results", "creationDate", "creationUserId", "userName", "executionStatus" ], "title": "ComparisonPromptResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successful Response | ComparisonPromptResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete comparison prompt by comparison prompt ID Operation path: `DELETE /api/v2/genai/comparisonPrompts/{comparisonPromptId}/` Authentication requirements: `BearerAuth` Delete an existing comparison prompt. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | comparisonPromptId | path | string | true | The ID of the comparison prompt to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successful Response | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve comparison prompt by comparison prompt ID Operation path: `GET /api/v2/genai/comparisonPrompts/{comparisonPromptId}/` Authentication requirements: `BearerAuth` Retrieve an existing comparison prompt. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | comparisonPromptId | path | string | true | The ID of the comparison prompt to retrieve. | ### Example responses > 200 Response ``` { "description": "ComparisonPrompt object formatted for API output.", "properties": { "comparisonChatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the comparison chat associated with the comparison prompt.", "title": "comparisonChatId" }, "creationDate": { "description": "The creation date of the comparison prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt.", "title": "id", "type": "string" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata filters applied to the comparison prompt.", "title": "metadataFilter" }, "results": { "description": "The list of comparison prompt results.", "items": { "description": "API response object for a single comparison prompt result.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "comparisonPromptResultIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the comparison prompt results included in this prompt's history.", "title": "comparisonPromptResultIdsIncludedInHistory" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt result.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint that produced the result.", "title": "llmBlueprintId", "type": "string" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the prompt result." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" } }, "required": [ "id", "llmBlueprintId", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ComparisonPromptResult", "type": "object" }, "title": "results", "type": "array" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the comparison prompt.", "title": "userName", "type": "string" } }, "required": [ "id", "text", "results", "creationDate", "creationUserId", "userName", "executionStatus" ], "title": "ComparisonPromptResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ComparisonPromptResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit comparison prompt by comparison prompt ID Operation path: `PATCH /api/v2/genai/comparisonPrompts/{comparisonPromptId}/` Authentication requirements: `BearerAuth` Edit an existing comparison prompt. Editing may involve adding new prompt result metadata or executing this comparison prompt on new LLM blueprints. ### Body parameter ``` { "description": "The body of the \"Edit comparison prompt\" request.", "properties": { "additionalLLMBlueprintIds": { "default": [], "description": "The list of additional LLM blueprint IDs that should execute this comparison prompt.", "items": { "type": "string" }, "maxItems": 10, "title": "additionalLLMBlueprintIds", "type": "array" }, "feedbackResult": { "anyOf": [ { "description": "Feedback metadata for a comparison prompt result.", "properties": { "comparisonPromptResultId": { "description": "The ID of the comparison prompt result associated with this feedback.", "title": "comparisonPromptResultId", "type": "string" }, "feedbackMetadata": { "description": "Prompt feedback metadata.", "properties": { "feedback": { "anyOf": [ { "description": "The sentiment of the feedback.", "enum": [ "1", "0" ], "title": "FeedbackSentiment", "type": "string" }, { "type": "null" } ], "description": "The sentiment of the feedback." } }, "required": [ "feedback" ], "title": "FeedbackMetadata", "type": "object" } }, "required": [ "comparisonPromptResultId", "feedbackMetadata" ], "title": "ComparisonPromptFeedbackResult", "type": "object" }, { "type": "null" } ], "description": "The feedback information to add to the comparison prompt." } }, "title": "EditComparisonPromptRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | comparisonPromptId | path | string | true | The ID of the comparison prompt to edit. | | body | body | EditComparisonPromptRequest | true | none | ### Example responses > 202 Response ``` { "title": "Response Update Comparison Prompt Comparisonprompts Comparisonpromptid Patch", "type": "string" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successful Response | string | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List prompt templates Operation path: `GET /api/v2/genai/promptTemplates/` Authentication requirements: `BearerAuth` List prompt templates for the user with optional filtering and sorting. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name", "description", "creationDate", "lastUpdateDate". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | | search | query | any | false | Only retrieve prompt templates with names matching the search query. | | creationUserIds | query | any | false | Retrieve only the prompt templates created by these users. | ### Example responses > 200 Response ``` { "description": "Paginated list of prompt templates.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single prompt template.", "properties": { "creationDate": { "description": "Prompt template creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template.", "title": "creationUserId", "type": "string" }, "description": { "description": "Prompt template description.", "title": "description", "type": "string" }, "id": { "description": "Prompt template ID.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "Date of the most recent update of this prompt template (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "ID of the user that made the most recent update to this prompt template.", "title": "lastUpdateUserId", "type": "string" }, "name": { "description": "Prompt template name.", "title": "name", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template.", "title": "userName", "type": "string" }, "versionCount": { "description": "The number of versions associated with this prompt template.", "title": "versionCount", "type": "integer" } }, "required": [ "id", "name", "description", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "userName", "versionCount" ], "title": "PromptTemplateResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListPromptTemplatesResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListPromptTemplatesResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create prompt template Operation path: `POST /api/v2/genai/promptTemplates/` Authentication requirements: `BearerAuth` Create a new prompt template. ### Body parameter ``` { "description": "The body of the Create PromptTemplate request.", "properties": { "description": { "description": "New prompt template description.", "maxLength": 5000, "title": "description", "type": "string" }, "name": { "description": "New prompt template name.", "maxLength": 5000, "title": "name", "type": "string" } }, "required": [ "name", "description" ], "title": "CreatePromptTemplateRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreatePromptTemplateRequest | true | none | ### Example responses > 201 Response ``` { "description": "API response object for a single prompt template.", "properties": { "creationDate": { "description": "Prompt template creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template.", "title": "creationUserId", "type": "string" }, "description": { "description": "Prompt template description.", "title": "description", "type": "string" }, "id": { "description": "Prompt template ID.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "Date of the most recent update of this prompt template (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "ID of the user that made the most recent update to this prompt template.", "title": "lastUpdateUserId", "type": "string" }, "name": { "description": "Prompt template name.", "title": "name", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template.", "title": "userName", "type": "string" }, "versionCount": { "description": "The number of versions associated with this prompt template.", "title": "versionCount", "type": "integer" } }, "required": [ "id", "name", "description", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "userName", "versionCount" ], "title": "PromptTemplateResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successful Response | PromptTemplateResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List prompt templates versions Operation path: `GET /api/v2/genai/promptTemplates/versions/` Authentication requirements: `BearerAuth` List prompt templates versions for the user with optional filtering. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | promptTemplatesIds | query | any | false | Retrieve only versions with matching prompt templates ids. | ### Example responses > 200 Response ``` { "description": "Paginated list of prompt template versions.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single prompt template version.", "properties": { "commitComment": { "description": "Description of changes for this prompt template version.", "title": "commitComment", "type": "string" }, "creationDate": { "description": "Prompt template version creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template version.", "title": "creationUserId", "type": "string" }, "id": { "description": "Prompt template version ID.", "title": "id", "type": "string" }, "promptTemplateId": { "description": "Prompt template ID.", "title": "promptTemplateId", "type": "string" }, "promptText": { "description": "The text of the prompt with variables enclosed in double curly brackets.", "title": "promptText", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template version.", "title": "userName", "type": "string" }, "variables": { "description": "List of variables associated with this prompt template version.", "items": { "description": "Variable used in prompt template version.", "properties": { "description": { "description": "Description of the variable. This is exposed to MCP clients.", "title": "description", "type": "string" }, "name": { "description": "Name of the variable.", "title": "name", "type": "string" }, "type": { "default": "str", "description": "Type of the variable.", "title": "type", "type": "string" } }, "required": [ "name", "description" ], "title": "Variable", "type": "object" }, "title": "variables", "type": "array" }, "version": { "description": "Version of this prompt template version.", "title": "version", "type": "integer" } }, "required": [ "id", "promptTemplateId", "promptText", "commitComment", "version", "variables", "creationDate", "creationUserId", "userName" ], "title": "PromptTemplateVersionResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListPromptTemplateVersionsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListPromptTemplateVersionsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Get prompt template by prompt template ID Operation path: `GET /api/v2/genai/promptTemplates/{promptTemplateId}/` Authentication requirements: `BearerAuth` Get prompt template by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | promptTemplateId | path | string | true | The ID of the prompt template to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single prompt template.", "properties": { "creationDate": { "description": "Prompt template creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template.", "title": "creationUserId", "type": "string" }, "description": { "description": "Prompt template description.", "title": "description", "type": "string" }, "id": { "description": "Prompt template ID.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "Date of the most recent update of this prompt template (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "ID of the user that made the most recent update to this prompt template.", "title": "lastUpdateUserId", "type": "string" }, "name": { "description": "Prompt template name.", "title": "name", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template.", "title": "userName", "type": "string" }, "versionCount": { "description": "The number of versions associated with this prompt template.", "title": "versionCount", "type": "integer" } }, "required": [ "id", "name", "description", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "userName", "versionCount" ], "title": "PromptTemplateResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | PromptTemplateResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List prompt template versions by prompt template ID Operation path: `GET /api/v2/genai/promptTemplates/{promptTemplateId}/versions/` Authentication requirements: `BearerAuth` List versions for a prompt template with pagination. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | promptTemplateId | path | string | true | The ID of the prompt template to retrieve. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | ### Example responses > 200 Response ``` { "description": "Paginated list of prompt template versions.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single prompt template version.", "properties": { "commitComment": { "description": "Description of changes for this prompt template version.", "title": "commitComment", "type": "string" }, "creationDate": { "description": "Prompt template version creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template version.", "title": "creationUserId", "type": "string" }, "id": { "description": "Prompt template version ID.", "title": "id", "type": "string" }, "promptTemplateId": { "description": "Prompt template ID.", "title": "promptTemplateId", "type": "string" }, "promptText": { "description": "The text of the prompt with variables enclosed in double curly brackets.", "title": "promptText", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template version.", "title": "userName", "type": "string" }, "variables": { "description": "List of variables associated with this prompt template version.", "items": { "description": "Variable used in prompt template version.", "properties": { "description": { "description": "Description of the variable. This is exposed to MCP clients.", "title": "description", "type": "string" }, "name": { "description": "Name of the variable.", "title": "name", "type": "string" }, "type": { "default": "str", "description": "Type of the variable.", "title": "type", "type": "string" } }, "required": [ "name", "description" ], "title": "Variable", "type": "object" }, "title": "variables", "type": "array" }, "version": { "description": "Version of this prompt template version.", "title": "version", "type": "integer" } }, "required": [ "id", "promptTemplateId", "promptText", "commitComment", "version", "variables", "creationDate", "creationUserId", "userName" ], "title": "PromptTemplateVersionResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListPromptTemplateVersionsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | ListPromptTemplateVersionsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create prompt template version by prompt template ID Operation path: `POST /api/v2/genai/promptTemplates/{promptTemplateId}/versions/` Authentication requirements: `BearerAuth` Create a new prompt template version. ### Body parameter ``` { "description": "The body of the Create PromptTemplateVersion request.", "properties": { "commitComment": { "description": "Description of changes for this prompt template version.", "maxLength": 5000, "title": "commitComment", "type": "string" }, "promptText": { "description": "The text of the prompt with variables enclosed in double curly brackets.", "maxLength": 5000000, "title": "promptText", "type": "string" }, "variables": { "description": "Variables for this prompt.", "items": { "description": "Variable used in prompt template version.", "properties": { "description": { "description": "Description of the variable. This is exposed to MCP clients.", "title": "description", "type": "string" }, "name": { "description": "Name of the variable.", "title": "name", "type": "string" }, "type": { "default": "str", "description": "Type of the variable.", "title": "type", "type": "string" } }, "required": [ "name", "description" ], "title": "Variable", "type": "object" }, "maxItems": 100, "title": "variables", "type": "array" } }, "required": [ "promptText", "commitComment", "variables" ], "title": "CreatePromptTemplateVersionRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | promptTemplateId | path | string | true | The ID of the prompt template to create a version for. | | body | body | CreatePromptTemplateVersionRequest | true | none | ### Example responses > 201 Response ``` { "description": "API response object for a single prompt template version.", "properties": { "commitComment": { "description": "Description of changes for this prompt template version.", "title": "commitComment", "type": "string" }, "creationDate": { "description": "Prompt template version creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template version.", "title": "creationUserId", "type": "string" }, "id": { "description": "Prompt template version ID.", "title": "id", "type": "string" }, "promptTemplateId": { "description": "Prompt template ID.", "title": "promptTemplateId", "type": "string" }, "promptText": { "description": "The text of the prompt with variables enclosed in double curly brackets.", "title": "promptText", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template version.", "title": "userName", "type": "string" }, "variables": { "description": "List of variables associated with this prompt template version.", "items": { "description": "Variable used in prompt template version.", "properties": { "description": { "description": "Description of the variable. This is exposed to MCP clients.", "title": "description", "type": "string" }, "name": { "description": "Name of the variable.", "title": "name", "type": "string" }, "type": { "default": "str", "description": "Type of the variable.", "title": "type", "type": "string" } }, "required": [ "name", "description" ], "title": "Variable", "type": "object" }, "title": "variables", "type": "array" }, "version": { "description": "Version of this prompt template version.", "title": "version", "type": "integer" } }, "required": [ "id", "promptTemplateId", "promptText", "commitComment", "version", "variables", "creationDate", "creationUserId", "userName" ], "title": "PromptTemplateVersionResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Successful Response | PromptTemplateVersionResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Get prompt template version by prompt template ID Operation path: `GET /api/v2/genai/promptTemplates/{promptTemplateId}/versions/{promptTemplateVersionId}/` Authentication requirements: `BearerAuth` Get prompt template version by ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | promptTemplateVersionId | path | string | true | The ID of the prompt template version to retrieve. | | promptTemplateId | path | string | true | The ID of the prompt template to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single prompt template version.", "properties": { "commitComment": { "description": "Description of changes for this prompt template version.", "title": "commitComment", "type": "string" }, "creationDate": { "description": "Prompt template version creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template version.", "title": "creationUserId", "type": "string" }, "id": { "description": "Prompt template version ID.", "title": "id", "type": "string" }, "promptTemplateId": { "description": "Prompt template ID.", "title": "promptTemplateId", "type": "string" }, "promptText": { "description": "The text of the prompt with variables enclosed in double curly brackets.", "title": "promptText", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template version.", "title": "userName", "type": "string" }, "variables": { "description": "List of variables associated with this prompt template version.", "items": { "description": "Variable used in prompt template version.", "properties": { "description": { "description": "Description of the variable. This is exposed to MCP clients.", "title": "description", "type": "string" }, "name": { "description": "Name of the variable.", "title": "name", "type": "string" }, "type": { "default": "str", "description": "Type of the variable.", "title": "type", "type": "string" } }, "required": [ "name", "description" ], "title": "Variable", "type": "object" }, "title": "variables", "type": "array" }, "version": { "description": "Version of this prompt template version.", "title": "version", "type": "integer" } }, "required": [ "id", "promptTemplateId", "promptText", "commitComment", "version", "variables", "creationDate", "creationUserId", "userName" ], "title": "PromptTemplateVersionResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Successful Response | PromptTemplateVersionResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | # Schemas ## ChatPromptResponse ``` { "description": "API response object for a single chat prompt.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat this chat prompt belongs to.", "title": "chatId" }, "chatPromptIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the chat prompts included in this prompt's history.", "title": "chatPromptIdsIncludedInHistory" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "creationDate": { "description": "The creation date of the chat prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the chat prompt.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint the chat prompt belongs to.", "title": "llmBlueprintId", "type": "string" }, "llmId": { "description": "The ID of the LLM used by the chat prompt.", "title": "llmId", "type": "string" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata dictionary defining the filters that documents must match in order to be retrieved.", "title": "metadataFilter" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the chat prompt results." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the chat prompt.", "title": "userName", "type": "string" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family this chat prompt belongs to.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "llmId", "id", "text", "llmBlueprintId", "creationDate", "creationUserId", "userName", "resultMetadata", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ChatPromptResponse", "type": "object" } ``` ChatPromptResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatContextId | any | false | | The ID of the chat context for this prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatId | any | false | | The ID of the chat this chat prompt belongs to. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatPromptIdsIncludedInHistory | any | false | | The list of IDs of the chat prompts included in this prompt's history. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | citations | [Citation] | true | | The list of relevant vector database citations (in case of using a vector database). | | confidenceScores | any | true | | The confidence scores that measure the similarity between the prompt context and the prompt completion. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ConfidenceScores | false | | API response object for confidence scores. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the chat prompt (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created the chat prompt. | | executionStatus | ExecutionStatus | true | | The execution status of the chat prompt. | | id | string | true | | The ID of the chat prompt. | | llmBlueprintId | string | true | | The ID of the LLM blueprint the chat prompt belongs to. | | llmId | string | true | | The ID of the LLM used by the chat prompt. | | llmSettings | any | false | | A key/value dictionary of LLM settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CommonLLMSettings | false | | The settings that are available for all non-custom LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelLLMSettings | false | | The settings that are available for custom model LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelChatLLMSettings | false | | The settings that are available for custom model LLMs used via chat completion interface. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metadataFilter | any | false | | The metadata dictionary defining the filters that documents must match in order to be retrieved. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | resultMetadata | any | true | | The additional information about the chat prompt results. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ResultMetadata | false | | The additional information about prompt execution results. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | resultText | any | true | | The text of the prompt completion. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | text | string | true | | The text of the user prompt. | | userName | string | true | | The name of the user that created the chat prompt. | | vectorDatabaseFamilyId | any | false | | The ID of the vector database family this chat prompt belongs to. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | any | false | | The ID of the vector database linked to this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseSettings | any | false | | A key/value dictionary of vector database settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseSettings | false | | Vector database retrieval settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ChatResponse ``` { "description": "Chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the chat.", "title": "id", "type": "string" }, "isFrozen": { "description": "Whether the chat is frozen (e.g., an evaluation chat). If the chat is frozen, it does not accept new prompts.", "title": "isFrozen", "type": "boolean" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the chat.", "title": "llmBlueprintId", "type": "string" }, "name": { "description": "The name of the chat.", "title": "name", "type": "string" }, "promptsCount": { "description": "The number of chat prompts in the chat.", "title": "promptsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the contents of the chat.", "title": "warning" } }, "required": [ "id", "name", "llmBlueprintId", "isFrozen", "warning", "creationDate", "creationUserId", "promptsCount" ], "title": "ChatResponse", "type": "object" } ``` ChatResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the chat (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created the chat. | | id | string | true | | The ID of the chat. | | isFrozen | boolean | true | | Whether the chat is frozen (e.g., an evaluation chat). If the chat is frozen, it does not accept new prompts. | | llmBlueprintId | string | true | | The ID of the LLM blueprint associated with the chat. | | name | string | true | | The name of the chat. | | promptsCount | integer | true | | The number of chat prompts in the chat. | | warning | any | true | | Warning about the contents of the chat. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ChatsSortQueryParam ``` { "description": "Sort order values for listing chats.", "enum": [ "name", "-name", "creationDate", "-creationDate" ], "title": "ChatsSortQueryParam", "type": "string" } ``` ChatsSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ChatsSortQueryParam | string | false | | Sort order values for listing chats. | ### Enumerated Values | Property | Value | | --- | --- | | ChatsSortQueryParam | [name, -name, creationDate, -creationDate] | ## Citation ``` { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" } ``` Citation ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkId | any | false | | The ID of the chunk in the vector database index. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metadata | any | false | | LangChain Document metadata information holder. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | page | any | false | | The source page number where the citation was found. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | similarityScore | any | false | | The similarity score between the citation and the user prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | source | any | true | | The source of the citation (e.g., a filename in the original dataset). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | startIndex | any | false | | The chunk's start character index in the source document. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | text | string | true | | The text of the citation. | ## CommonLLMSettings ``` { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" } ``` CommonLLMSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxCompletionLength | any | false | | Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | systemPrompt | any | false | | System prompt guides the style of the LLM response. It is a "universal" prompt, prepended to all individual prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ComparisonChatResponse ``` { "description": "Comparison chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the comparison chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the comparison chat.", "title": "id", "type": "string" }, "name": { "description": "The name of the comparison chat.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the comparison chat.", "title": "playgroundId", "type": "string" }, "promptsCount": { "description": "The number of comparison prompts in the comparison chat.", "title": "promptsCount", "type": "integer" } }, "required": [ "id", "name", "playgroundId", "creationDate", "creationUserId", "promptsCount" ], "title": "ComparisonChatResponse", "type": "object" } ``` ComparisonChatResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the comparison chat (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created the comparison chat. | | id | string | true | | The ID of the comparison chat. | | name | string | true | | The name of the comparison chat. | | playgroundId | string | true | | The ID of the playground associated with the comparison chat. | | promptsCount | integer | true | | The number of comparison prompts in the comparison chat. | ## ComparisonChatsSortQueryParam ``` { "description": "Sort order values for listing comparison chats.", "enum": [ "name", "-name", "creationDate", "-creationDate" ], "title": "ComparisonChatsSortQueryParam", "type": "string" } ``` ComparisonChatsSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ComparisonChatsSortQueryParam | string | false | | Sort order values for listing comparison chats. | ### Enumerated Values | Property | Value | | --- | --- | | ComparisonChatsSortQueryParam | [name, -name, creationDate, -creationDate] | ## ComparisonPromptFeedbackResult ``` { "description": "Feedback metadata for a comparison prompt result.", "properties": { "comparisonPromptResultId": { "description": "The ID of the comparison prompt result associated with this feedback.", "title": "comparisonPromptResultId", "type": "string" }, "feedbackMetadata": { "description": "Prompt feedback metadata.", "properties": { "feedback": { "anyOf": [ { "description": "The sentiment of the feedback.", "enum": [ "1", "0" ], "title": "FeedbackSentiment", "type": "string" }, { "type": "null" } ], "description": "The sentiment of the feedback." } }, "required": [ "feedback" ], "title": "FeedbackMetadata", "type": "object" } }, "required": [ "comparisonPromptResultId", "feedbackMetadata" ], "title": "ComparisonPromptFeedbackResult", "type": "object" } ``` ComparisonPromptFeedbackResult ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparisonPromptResultId | string | true | | The ID of the comparison prompt result associated with this feedback. | | feedbackMetadata | FeedbackMetadata | true | | The feedback metadata for the comparison prompt result. | ## ComparisonPromptResponse ``` { "description": "ComparisonPrompt object formatted for API output.", "properties": { "comparisonChatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the comparison chat associated with the comparison prompt.", "title": "comparisonChatId" }, "creationDate": { "description": "The creation date of the comparison prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt.", "title": "id", "type": "string" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata filters applied to the comparison prompt.", "title": "metadataFilter" }, "results": { "description": "The list of comparison prompt results.", "items": { "description": "API response object for a single comparison prompt result.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "comparisonPromptResultIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the comparison prompt results included in this prompt's history.", "title": "comparisonPromptResultIdsIncludedInHistory" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt result.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint that produced the result.", "title": "llmBlueprintId", "type": "string" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the prompt result." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" } }, "required": [ "id", "llmBlueprintId", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ComparisonPromptResult", "type": "object" }, "title": "results", "type": "array" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the comparison prompt.", "title": "userName", "type": "string" } }, "required": [ "id", "text", "results", "creationDate", "creationUserId", "userName", "executionStatus" ], "title": "ComparisonPromptResponse", "type": "object" } ``` ComparisonPromptResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparisonChatId | any | false | | The ID of the comparison chat associated with the comparison prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the comparison prompt (ISO 8601 formatted). | | creationUserId | string | true | | The ID of the user that created the comparison prompt. | | executionStatus | ExecutionStatus | true | | The execution status of the entire comparison prompt. | | id | string | true | | The ID of the comparison prompt. | | metadataFilter | any | false | | The metadata filters applied to the comparison prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | results | [ComparisonPromptResult] | true | | The list of comparison prompt results. | | text | string | true | | The text of the user prompt. | | userName | string | true | | The name of the user that created the comparison prompt. | ## ComparisonPromptResult ``` { "description": "API response object for a single comparison prompt result.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "comparisonPromptResultIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the comparison prompt results included in this prompt's history.", "title": "comparisonPromptResultIdsIncludedInHistory" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt result.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint that produced the result.", "title": "llmBlueprintId", "type": "string" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the prompt result." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" } }, "required": [ "id", "llmBlueprintId", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ComparisonPromptResult", "type": "object" } ``` ComparisonPromptResult ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatContextId | any | false | | The ID of the chat context for this prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | citations | [Citation] | true | | The list of relevant vector database citations (in case of using a vector database). | | comparisonPromptResultIdsIncludedInHistory | any | false | | The list of IDs of the comparison prompt results included in this prompt's history. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | confidenceScores | any | true | | The confidence scores that measure the similarity between the prompt context and the prompt completion. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ConfidenceScores | false | | API response object for confidence scores. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | ExecutionStatus | true | | The execution status of the comparison prompt by this LLM blueprint. | | id | string | true | | The ID of the comparison prompt result. | | llmBlueprintId | string | true | | The ID of the LLM blueprint that produced the result. | | resultMetadata | any | false | | The additional information about the prompt result. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ResultMetadata | false | | The additional information about prompt execution results. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | resultText | any | true | | The text of the prompt completion. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ConfidenceScores ``` { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" } ``` ConfidenceScores ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | bleu | number | true | | BLEU score. | | meteor | number | true | | METEOR score. | | rouge | number | true | | ROUGE score. | ## CreateChatPromptRequest ``` { "description": "The body of the \"Create chat prompt\" request.", "properties": { "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat this prompt belongs to. If LLM and vector database settings are not specified in the request, then the prompt will use the current settings of the chat.", "title": "chatId" }, "llmBlueprintId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the LLM blueprint this prompt belongs to. If LLM and vector database settings are not specified in the request, then the prompt will use the current settings of the LLM blueprint.", "title": "llmBlueprintId" }, "llmId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, uses this LLM ID for the prompt and updates the settings of the corresponding chat or LLM blueprint to use this LLM ID.", "title": "llmId" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "If specified, uses these LLM settings for the prompt and updates the settings of the corresponding chat or LLM blueprint to use these LLM settings.", "title": "llmSettings" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata fields to add to the chat prompt.", "title": "metadataFilter" }, "text": { "description": "The text of the user prompt.", "maxLength": 5000000, "title": "text", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, uses this vector database ID for the prompt and updates the settings of the corresponding chat or LLM blueprint to use this vector database ID.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "If specified, uses these vector database settings for the prompt and updates the settings of the corresponding chat or LLM blueprint to use these vector database settings." } }, "required": [ "text" ], "title": "CreateChatPromptRequest", "type": "object" } ``` CreateChatPromptRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatId | any | false | | The ID of the chat this prompt belongs to. If LLM and vector database settings are not specified in the request, then the prompt will use the current settings of the chat. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmBlueprintId | any | false | | The ID of the LLM blueprint this prompt belongs to. If LLM and vector database settings are not specified in the request, then the prompt will use the current settings of the LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmId | any | false | | If specified, uses this LLM ID for the prompt and updates the settings of the corresponding chat or LLM blueprint to use this LLM ID. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmSettings | any | false | | If specified, uses these LLM settings for the prompt and updates the settings of the corresponding chat or LLM blueprint to use these LLM settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CommonLLMSettings | false | | The settings that are available for all non-custom LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelLLMSettings | false | | The settings that are available for custom model LLMs. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelChatLLMSettings | false | | The settings that are available for custom model LLMs used via chat completion interface. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metadataFilter | any | false | | The metadata fields to add to the chat prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | text | string | true | maxLength: 5000000 | The text of the user prompt. | | vectorDatabaseId | any | false | | If specified, uses this vector database ID for the prompt and updates the settings of the corresponding chat or LLM blueprint to use this vector database ID. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseSettings | any | false | | If specified, uses these vector database settings for the prompt and updates the settings of the corresponding chat or LLM blueprint to use these vector database settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseSettings | false | | Vector database retrieval settings. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CreateChatRequest ``` { "description": "The body of the \"Create chat\" request.", "properties": { "llmBlueprintId": { "description": "The ID of the LLM blueprint to associate with the chat.", "title": "llmBlueprintId", "type": "string" }, "name": { "description": "The name of the chat.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" } }, "required": [ "name", "llmBlueprintId" ], "title": "CreateChatRequest", "type": "object" } ``` CreateChatRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmBlueprintId | string | true | | The ID of the LLM blueprint to associate with the chat. | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the chat. | ## CreateComparisonChatRequest ``` { "description": "The body of the \"Create comparison chat\" request.", "properties": { "name": { "description": "The name of the comparison chat.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground to associate with the comparison chat.", "title": "playgroundId", "type": "string" } }, "required": [ "name", "playgroundId" ], "title": "CreateComparisonChatRequest", "type": "object" } ``` CreateComparisonChatRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the comparison chat. | | playgroundId | string | true | | The ID of the playground to associate with the comparison chat. | ## CreateComparisonPromptRequest ``` { "description": "The body of the \"Create comparison prompt\" request.", "properties": { "comparisonChatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the comparison chat to associate the comparison prompt with.", "title": "comparisonChatId" }, "llmBlueprintIds": { "description": "The list of LLM blueprint IDs that should execute the comparison prompt.", "items": { "type": "string" }, "maxItems": 10, "title": "llmBlueprintIds", "type": "array" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata dict that defines filters that the retrieved documents need to match.", "title": "metadataFilter" }, "text": { "description": "The text of the user prompt.", "maxLength": 5000000, "title": "text", "type": "string" } }, "required": [ "llmBlueprintIds", "text" ], "title": "CreateComparisonPromptRequest", "type": "object" } ``` CreateComparisonPromptRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | comparisonChatId | any | false | | The ID of the comparison chat to associate the comparison prompt with. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmBlueprintIds | [string] | true | maxItems: 10 | The list of LLM blueprint IDs that should execute the comparison prompt. | | metadataFilter | any | false | | The metadata dict that defines filters that the retrieved documents need to match. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | text | string | true | maxLength: 5000000 | The text of the user prompt. | ## CreatePromptTemplateRequest ``` { "description": "The body of the Create PromptTemplate request.", "properties": { "description": { "description": "New prompt template description.", "maxLength": 5000, "title": "description", "type": "string" }, "name": { "description": "New prompt template name.", "maxLength": 5000, "title": "name", "type": "string" } }, "required": [ "name", "description" ], "title": "CreatePromptTemplateRequest", "type": "object" } ``` CreatePromptTemplateRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | maxLength: 5000 | New prompt template description. | | name | string | true | maxLength: 5000 | New prompt template name. | ## CreatePromptTemplateVersionRequest ``` { "description": "The body of the Create PromptTemplateVersion request.", "properties": { "commitComment": { "description": "Description of changes for this prompt template version.", "maxLength": 5000, "title": "commitComment", "type": "string" }, "promptText": { "description": "The text of the prompt with variables enclosed in double curly brackets.", "maxLength": 5000000, "title": "promptText", "type": "string" }, "variables": { "description": "Variables for this prompt.", "items": { "description": "Variable used in prompt template version.", "properties": { "description": { "description": "Description of the variable. This is exposed to MCP clients.", "title": "description", "type": "string" }, "name": { "description": "Name of the variable.", "title": "name", "type": "string" }, "type": { "default": "str", "description": "Type of the variable.", "title": "type", "type": "string" } }, "required": [ "name", "description" ], "title": "Variable", "type": "object" }, "maxItems": 100, "title": "variables", "type": "array" } }, "required": [ "promptText", "commitComment", "variables" ], "title": "CreatePromptTemplateVersionRequest", "type": "object" } ``` CreatePromptTemplateVersionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commitComment | string | true | maxLength: 5000 | Description of changes for this prompt template version. | | promptText | string | true | maxLength: 5000000 | The text of the prompt with variables enclosed in double curly brackets. | | variables | [Variable] | true | maxItems: 100 | Variables for this prompt. | ## CustomModelChatLLMSettings ``` { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" } ``` CustomModelChatLLMSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string | true | | The ID of the custom model used via chat completion interface. | | customModelVersionId | any | false | | The ID of the custom model version used via chat completion interface. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | systemPrompt | any | false | | System prompt guides the style of the LLM response. It is a "universal" prompt, prepended to all individual prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CustomModelLLMSettings ``` { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" } ``` CustomModelLLMSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | externalLlmContextSize | any | false | | The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 128000minimum: 128 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | systemPrompt | any | false | | System prompt guides the style of the LLM response. It is a "universal" prompt, prepended to all individual prompts. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationId | any | false | | The validation ID of the custom model LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## EditChatPromptRequest ``` { "description": "The body of the \"Update chat prompt\" request.", "properties": { "customMetrics": { "anyOf": [ { "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The list of metric results to add to the chat prompt.", "title": "customMetrics" }, "feedbackMetadata": { "anyOf": [ { "description": "Prompt feedback metadata.", "properties": { "feedback": { "anyOf": [ { "description": "The sentiment of the feedback.", "enum": [ "1", "0" ], "title": "FeedbackSentiment", "type": "string" }, { "type": "null" } ], "description": "The sentiment of the feedback." } }, "required": [ "feedback" ], "title": "FeedbackMetadata", "type": "object" }, { "type": "null" } ], "description": "The feedback metadata to add to the chat prompt." } }, "title": "EditChatPromptRequest", "type": "object" } ``` EditChatPromptRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customMetrics | any | false | | The list of metric results to add to the chat prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [MetricMetadata] | false | | [Prompt metric metadata.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feedbackMetadata | any | false | | The feedback metadata to add to the chat prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FeedbackMetadata | false | | Prompt feedback metadata. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## EditChatRequest ``` { "description": "The body of the \"Edit chat\" request.", "properties": { "name": { "description": "The new name of the chat.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" } }, "required": [ "name" ], "title": "EditChatRequest", "type": "object" } ``` EditChatRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | The new name of the chat. | ## EditComparisonChatRequest ``` { "description": "The body of the \"Edit comparison chat\" request.", "properties": { "name": { "description": "The new name of the comparison chat.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" } }, "required": [ "name" ], "title": "EditComparisonChatRequest", "type": "object" } ``` EditComparisonChatRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | The new name of the comparison chat. | ## EditComparisonPromptRequest ``` { "description": "The body of the \"Edit comparison prompt\" request.", "properties": { "additionalLLMBlueprintIds": { "default": [], "description": "The list of additional LLM blueprint IDs that should execute this comparison prompt.", "items": { "type": "string" }, "maxItems": 10, "title": "additionalLLMBlueprintIds", "type": "array" }, "feedbackResult": { "anyOf": [ { "description": "Feedback metadata for a comparison prompt result.", "properties": { "comparisonPromptResultId": { "description": "The ID of the comparison prompt result associated with this feedback.", "title": "comparisonPromptResultId", "type": "string" }, "feedbackMetadata": { "description": "Prompt feedback metadata.", "properties": { "feedback": { "anyOf": [ { "description": "The sentiment of the feedback.", "enum": [ "1", "0" ], "title": "FeedbackSentiment", "type": "string" }, { "type": "null" } ], "description": "The sentiment of the feedback." } }, "required": [ "feedback" ], "title": "FeedbackMetadata", "type": "object" } }, "required": [ "comparisonPromptResultId", "feedbackMetadata" ], "title": "ComparisonPromptFeedbackResult", "type": "object" }, { "type": "null" } ], "description": "The feedback information to add to the comparison prompt." } }, "title": "EditComparisonPromptRequest", "type": "object" } ``` EditComparisonPromptRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | additionalLLMBlueprintIds | [string] | false | maxItems: 10 | The list of additional LLM blueprint IDs that should execute this comparison prompt. | | feedbackResult | any | false | | The feedback information to add to the comparison prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ComparisonPromptFeedbackResult | false | | Feedback metadata for a comparison prompt result. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ExecutionStatus ``` { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" } ``` ExecutionStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ExecutionStatus | string | false | | Job and entity execution status. | ### Enumerated Values | Property | Value | | --- | --- | | ExecutionStatus | [NEW, RUNNING, COMPLETED, REQUIRES_USER_INPUT, SKIPPED, ERROR] | ## FeedbackMetadata ``` { "description": "Prompt feedback metadata.", "properties": { "feedback": { "anyOf": [ { "description": "The sentiment of the feedback.", "enum": [ "1", "0" ], "title": "FeedbackSentiment", "type": "string" }, { "type": "null" } ], "description": "The sentiment of the feedback." } }, "required": [ "feedback" ], "title": "FeedbackMetadata", "type": "object" } ``` FeedbackMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | feedback | any | true | | The sentiment of the feedback. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | FeedbackSentiment | false | | The sentiment of the feedback. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## FeedbackResult ``` { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" } ``` FeedbackResult ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | negativeUserIds | [string] | false | | The list of user IDs whose feedback is negative. | | positiveUserIds | [string] | false | | The list of user IDs whose feedback is positive. | ## FeedbackSentiment ``` { "description": "The sentiment of the feedback.", "enum": [ "1", "0" ], "title": "FeedbackSentiment", "type": "string" } ``` FeedbackSentiment ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | FeedbackSentiment | string | false | | The sentiment of the feedback. | ### Enumerated Values | Property | Value | | --- | --- | | FeedbackSentiment | [1, 0] | ## HTTPValidationErrorResponse ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` HTTPValidationErrorResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | detail | [ValidationError] | false | | none | ## ListChatPromptsResponse ``` { "description": "Paginated list of chat prompts.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single chat prompt.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "chatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat this chat prompt belongs to.", "title": "chatId" }, "chatPromptIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the chat prompts included in this prompt's history.", "title": "chatPromptIdsIncludedInHistory" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "creationDate": { "description": "The creation date of the chat prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the chat prompt.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint the chat prompt belongs to.", "title": "llmBlueprintId", "type": "string" }, "llmId": { "description": "The ID of the LLM used by the chat prompt.", "title": "llmId", "type": "string" }, "llmSettings": { "anyOf": [ { "additionalProperties": true, "description": "The settings that are available for all non-custom LLMs.", "properties": { "maxCompletionLength": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "Maximum number of tokens allowed in the chat completion. Use this value to, for example, control costs on token-based charges or manage response length for chat text limits.", "title": "maxCompletionLength" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "title": "CommonLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs.", "properties": { "externalLlmContextSize": { "anyOf": [ { "maximum": 128000, "minimum": 128, "type": "integer" }, { "type": "null" } ], "default": 4096, "description": "The external LLM's context size, in tokens. This value is only used for pruning documents supplied to the LLM when a vector database is associated with the LLM blueprint. It does not affect the external LLM's actual context size in any way and is not supplied to the LLM.", "title": "externalLlmContextSize" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model LLM.", "title": "validationId" } }, "title": "CustomModelLLMSettings", "type": "object" }, { "additionalProperties": false, "description": "The settings that are available for custom model LLMs used via chat completion interface.", "properties": { "customModelId": { "description": "The ID of the custom model used via chat completion interface.", "title": "customModelId", "type": "string" }, "customModelVersionId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model version used via chat completion interface.", "title": "customModelVersionId" }, "systemPrompt": { "anyOf": [ { "maxLength": 5000000, "type": "string" }, { "type": "null" } ], "description": "System prompt guides the style of the LLM response. It is a \"universal\" prompt, prepended to all individual prompts.", "title": "systemPrompt" } }, "required": [ "customModelId" ], "title": "CustomModelChatLLMSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of LLM settings.", "title": "llmSettings" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata dictionary defining the filters that documents must match in order to be retrieved.", "title": "metadataFilter" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the chat prompt results." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the chat prompt.", "title": "userName", "type": "string" }, "vectorDatabaseFamilyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database family this chat prompt belongs to.", "title": "vectorDatabaseFamilyId" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "required": [ "llmId", "id", "text", "llmBlueprintId", "creationDate", "creationUserId", "userName", "resultMetadata", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ChatPromptResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListChatPromptsResponse", "type": "object" } ``` ListChatPromptsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [ChatPromptResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListChatsResponse ``` { "description": "Paginated list of chats.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "Chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the chat.", "title": "id", "type": "string" }, "isFrozen": { "description": "Whether the chat is frozen (e.g., an evaluation chat). If the chat is frozen, it does not accept new prompts.", "title": "isFrozen", "type": "boolean" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint associated with the chat.", "title": "llmBlueprintId", "type": "string" }, "name": { "description": "The name of the chat.", "title": "name", "type": "string" }, "promptsCount": { "description": "The number of chat prompts in the chat.", "title": "promptsCount", "type": "integer" }, "warning": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Warning about the contents of the chat.", "title": "warning" } }, "required": [ "id", "name", "llmBlueprintId", "isFrozen", "warning", "creationDate", "creationUserId", "promptsCount" ], "title": "ChatResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListChatsResponse", "type": "object" } ``` ListChatsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [ChatResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListComparisonChatsResponse ``` { "description": "Paginated list of comparison chats.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "Comparison chat object formatted for API output.", "properties": { "creationDate": { "description": "The creation date of the comparison chat (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison chat.", "title": "creationUserId", "type": "string" }, "id": { "description": "The ID of the comparison chat.", "title": "id", "type": "string" }, "name": { "description": "The name of the comparison chat.", "title": "name", "type": "string" }, "playgroundId": { "description": "The ID of the playground associated with the comparison chat.", "title": "playgroundId", "type": "string" }, "promptsCount": { "description": "The number of comparison prompts in the comparison chat.", "title": "promptsCount", "type": "integer" } }, "required": [ "id", "name", "playgroundId", "creationDate", "creationUserId", "promptsCount" ], "title": "ComparisonChatResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListComparisonChatsResponse", "type": "object" } ``` ListComparisonChatsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [ComparisonChatResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListComparisonPromptsResponse ``` { "description": "Paginated list of comparison prompts.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "ComparisonPrompt object formatted for API output.", "properties": { "comparisonChatId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the comparison chat associated with the comparison prompt.", "title": "comparisonChatId" }, "creationDate": { "description": "The creation date of the comparison prompt (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "The ID of the user that created the comparison prompt.", "title": "creationUserId", "type": "string" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt.", "title": "id", "type": "string" }, "metadataFilter": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "The metadata filters applied to the comparison prompt.", "title": "metadataFilter" }, "results": { "description": "The list of comparison prompt results.", "items": { "description": "API response object for a single comparison prompt result.", "properties": { "chatContextId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the chat context for this prompt.", "title": "chatContextId" }, "citations": { "description": "The list of relevant vector database citations (in case of using a vector database).", "items": { "description": "API response object for a single vector database citation.", "properties": { "chunkId": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The ID of the chunk in the vector database index.", "title": "chunkId" }, "metadata": { "anyOf": [ { "additionalProperties": true, "type": "object" }, { "type": "null" } ], "description": "LangChain Document metadata information holder.", "title": "metadata" }, "page": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The source page number where the citation was found.", "title": "page" }, "similarityScore": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The similarity score between the citation and the user prompt.", "title": "similarityScore" }, "source": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The source of the citation (e.g., a filename in the original dataset).", "title": "source" }, "startIndex": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk's start character index in the source document.", "title": "startIndex" }, "text": { "description": "The text of the citation.", "title": "text", "type": "string" } }, "required": [ "text", "source" ], "title": "Citation", "type": "object" }, "title": "citations", "type": "array" }, "comparisonPromptResultIdsIncludedInHistory": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of IDs of the comparison prompt results included in this prompt's history.", "title": "comparisonPromptResultIdsIncludedInHistory" }, "confidenceScores": { "anyOf": [ { "description": "API response object for confidence scores.", "properties": { "bleu": { "description": "BLEU score.", "title": "bleu", "type": "number" }, "meteor": { "description": "METEOR score.", "title": "meteor", "type": "number" }, "rouge": { "description": "ROUGE score.", "title": "rouge", "type": "number" } }, "required": [ "rouge", "meteor", "bleu" ], "title": "ConfidenceScores", "type": "object" }, { "type": "null" } ], "description": "The confidence scores that measure the similarity between the prompt context and the prompt completion." }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "id": { "description": "The ID of the comparison prompt result.", "title": "id", "type": "string" }, "llmBlueprintId": { "description": "The ID of the LLM blueprint that produced the result.", "title": "llmBlueprintId", "type": "string" }, "resultMetadata": { "anyOf": [ { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" }, { "type": "null" } ], "description": "The additional information about the prompt result." }, "resultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The text of the prompt completion.", "title": "resultText" } }, "required": [ "id", "llmBlueprintId", "resultText", "confidenceScores", "citations", "executionStatus" ], "title": "ComparisonPromptResult", "type": "object" }, "title": "results", "type": "array" }, "text": { "description": "The text of the user prompt.", "title": "text", "type": "string" }, "userName": { "description": "The name of the user that created the comparison prompt.", "title": "userName", "type": "string" } }, "required": [ "id", "text", "results", "creationDate", "creationUserId", "userName", "executionStatus" ], "title": "ComparisonPromptResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListComparisonPromptsResponse", "type": "object" } ``` ListComparisonPromptsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [ComparisonPromptResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListPromptTemplateVersionsResponse ``` { "description": "Paginated list of prompt template versions.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single prompt template version.", "properties": { "commitComment": { "description": "Description of changes for this prompt template version.", "title": "commitComment", "type": "string" }, "creationDate": { "description": "Prompt template version creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template version.", "title": "creationUserId", "type": "string" }, "id": { "description": "Prompt template version ID.", "title": "id", "type": "string" }, "promptTemplateId": { "description": "Prompt template ID.", "title": "promptTemplateId", "type": "string" }, "promptText": { "description": "The text of the prompt with variables enclosed in double curly brackets.", "title": "promptText", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template version.", "title": "userName", "type": "string" }, "variables": { "description": "List of variables associated with this prompt template version.", "items": { "description": "Variable used in prompt template version.", "properties": { "description": { "description": "Description of the variable. This is exposed to MCP clients.", "title": "description", "type": "string" }, "name": { "description": "Name of the variable.", "title": "name", "type": "string" }, "type": { "default": "str", "description": "Type of the variable.", "title": "type", "type": "string" } }, "required": [ "name", "description" ], "title": "Variable", "type": "object" }, "title": "variables", "type": "array" }, "version": { "description": "Version of this prompt template version.", "title": "version", "type": "integer" } }, "required": [ "id", "promptTemplateId", "promptText", "commitComment", "version", "variables", "creationDate", "creationUserId", "userName" ], "title": "PromptTemplateVersionResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListPromptTemplateVersionsResponse", "type": "object" } ``` ListPromptTemplateVersionsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [PromptTemplateVersionResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListPromptTemplatesResponse ``` { "description": "Paginated list of prompt templates.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single prompt template.", "properties": { "creationDate": { "description": "Prompt template creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template.", "title": "creationUserId", "type": "string" }, "description": { "description": "Prompt template description.", "title": "description", "type": "string" }, "id": { "description": "Prompt template ID.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "Date of the most recent update of this prompt template (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "ID of the user that made the most recent update to this prompt template.", "title": "lastUpdateUserId", "type": "string" }, "name": { "description": "Prompt template name.", "title": "name", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template.", "title": "userName", "type": "string" }, "versionCount": { "description": "The number of versions associated with this prompt template.", "title": "versionCount", "type": "integer" } }, "required": [ "id", "name", "description", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "userName", "versionCount" ], "title": "PromptTemplateResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListPromptTemplatesResponse", "type": "object" } ``` ListPromptTemplatesResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [PromptTemplateResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## MetricMetadata ``` { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" } ``` MetricMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | costConfigurationId | any | false | | The ID of the cost configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelGuardId | any | false | | Id of the custom model guard. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | any | false | | The ID of the custom model used for the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the metric computation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | evaluationDatasetConfigurationId | any | false | | The ID of the evaluation dataset configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | any | false | | The computation status of the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExecutionStatus | false | | Job and entity execution status. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | formattedName | any | false | | The formatted name of the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | formattedValue | any | false | | The formatted value of the metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | llmIsDeprecated | any | false | | Whether the LLM is deprecated and will be removed in a future release. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the metric. | | nemoMetricId | any | false | | The id of the NeMo Pipeline configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ootbMetricId | any | false | | The id of the OOTB metric configuration. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | sidecarModelMetricValidationId | any | false | | The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | any | false | | The stage (prompt or response) that the metric applies to. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | PipelineStage | false | | Enum that describes at which stage the metric may be calculated. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | value | any | true | | The value of the metric. | ## PipelineStage ``` { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" } ``` PipelineStage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | PipelineStage | string | false | | Enum that describes at which stage the metric may be calculated. | ### Enumerated Values | Property | Value | | --- | --- | | PipelineStage | [prompt_pipeline, response_pipeline] | ## PromptTemplateResponse ``` { "description": "API response object for a single prompt template.", "properties": { "creationDate": { "description": "Prompt template creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template.", "title": "creationUserId", "type": "string" }, "description": { "description": "Prompt template description.", "title": "description", "type": "string" }, "id": { "description": "Prompt template ID.", "title": "id", "type": "string" }, "lastUpdateDate": { "description": "Date of the most recent update of this prompt template (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "lastUpdateUserId": { "description": "ID of the user that made the most recent update to this prompt template.", "title": "lastUpdateUserId", "type": "string" }, "name": { "description": "Prompt template name.", "title": "name", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template.", "title": "userName", "type": "string" }, "versionCount": { "description": "The number of versions associated with this prompt template.", "title": "versionCount", "type": "integer" } }, "required": [ "id", "name", "description", "creationDate", "creationUserId", "lastUpdateDate", "lastUpdateUserId", "userName", "versionCount" ], "title": "PromptTemplateResponse", "type": "object" } ``` PromptTemplateResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | Prompt template creation date (ISO 8601 formatted). | | creationUserId | string | true | | ID of the user who created this prompt template. | | description | string | true | | Prompt template description. | | id | string | true | | Prompt template ID. | | lastUpdateDate | string(date-time) | true | | Date of the most recent update of this prompt template (ISO 8601 formatted). | | lastUpdateUserId | string | true | | ID of the user that made the most recent update to this prompt template. | | name | string | true | | Prompt template name. | | userName | string | true | | Name of the user who created this prompt template. | | versionCount | integer | true | | The number of versions associated with this prompt template. | ## PromptTemplateVersionResponse ``` { "description": "API response object for a single prompt template version.", "properties": { "commitComment": { "description": "Description of changes for this prompt template version.", "title": "commitComment", "type": "string" }, "creationDate": { "description": "Prompt template version creation date (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationUserId": { "description": "ID of the user who created this prompt template version.", "title": "creationUserId", "type": "string" }, "id": { "description": "Prompt template version ID.", "title": "id", "type": "string" }, "promptTemplateId": { "description": "Prompt template ID.", "title": "promptTemplateId", "type": "string" }, "promptText": { "description": "The text of the prompt with variables enclosed in double curly brackets.", "title": "promptText", "type": "string" }, "userName": { "description": "Name of the user who created this prompt template version.", "title": "userName", "type": "string" }, "variables": { "description": "List of variables associated with this prompt template version.", "items": { "description": "Variable used in prompt template version.", "properties": { "description": { "description": "Description of the variable. This is exposed to MCP clients.", "title": "description", "type": "string" }, "name": { "description": "Name of the variable.", "title": "name", "type": "string" }, "type": { "default": "str", "description": "Type of the variable.", "title": "type", "type": "string" } }, "required": [ "name", "description" ], "title": "Variable", "type": "object" }, "title": "variables", "type": "array" }, "version": { "description": "Version of this prompt template version.", "title": "version", "type": "integer" } }, "required": [ "id", "promptTemplateId", "promptText", "commitComment", "version", "variables", "creationDate", "creationUserId", "userName" ], "title": "PromptTemplateVersionResponse", "type": "object" } ``` PromptTemplateVersionResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | commitComment | string | true | | Description of changes for this prompt template version. | | creationDate | string(date-time) | true | | Prompt template version creation date (ISO 8601 formatted). | | creationUserId | string | true | | ID of the user who created this prompt template version. | | id | string | true | | Prompt template version ID. | | promptTemplateId | string | true | | Prompt template ID. | | promptText | string | true | | The text of the prompt with variables enclosed in double curly brackets. | | userName | string | true | | Name of the user who created this prompt template version. | | variables | [Variable] | true | | List of variables associated with this prompt template version. | | version | integer | true | | Version of this prompt template version. | ## PromptTemplatesSortQueryParam ``` { "description": "Sort order values for listing prompt templates.", "enum": [ "name", "-name", "description", "-description", "creationDate", "-creationDate", "lastUpdateDate", "-lastUpdateDate" ], "title": "PromptTemplatesSortQueryParam", "type": "string" } ``` PromptTemplatesSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | PromptTemplatesSortQueryParam | string | false | | Sort order values for listing prompt templates. | ### Enumerated Values | Property | Value | | --- | --- | | PromptTemplatesSortQueryParam | [name, -name, description, -description, creationDate, -creationDate, lastUpdateDate, -lastUpdateDate] | ## ProviderGuardStage ``` { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" } ``` ProviderGuardStage ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ProviderGuardStage | string | false | | The data stage where the provider guard metric is acting upon. | ### Enumerated Values | Property | Value | | --- | --- | | ProviderGuardStage | [prompt, response] | ## ProviderGuardsMetadata ``` { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" } ``` ProviderGuardsMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the provider guard metric. | | satisfyCriteria | boolean | true | | Whether the configured provider guard metric satisfied its hidden internal guard criteria. | | stage | ProviderGuardStage | true | | The data stage where the provider guard metric is acting upon. | | value | any | true | | The value of the provider guard metric. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ResultMetadata ``` { "description": "The additional information about prompt execution results.", "properties": { "blockedResultText": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The message to replace the result text if it is non empty, which represents a blocked response.", "title": "blockedResultText" }, "cost": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The estimated cost of executing the prompt.", "title": "cost" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message for the prompt (in case of an errored prompt).", "title": "errorMessage" }, "estimatedDocsTokenCount": { "default": 0, "description": "The estimated number of tokens in the documents retrieved from the vector database.", "title": "estimatedDocsTokenCount", "type": "integer" }, "feedbackResult": { "description": "Prompt feedback included in the result metadata.", "properties": { "negativeUserIds": { "default": [], "description": "The list of user IDs whose feedback is negative.", "items": { "type": "string" }, "title": "negativeUserIds", "type": "array" }, "positiveUserIds": { "default": [], "description": "The list of user IDs whose feedback is positive.", "items": { "type": "string" }, "title": "positiveUserIds", "type": "array" } }, "title": "FeedbackResult", "type": "object" }, "finalPrompt": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" }, { "type": "null" } ] }, "type": "object" }, "type": "array" }, { "items": { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": true, "type": "object" }, "type": "array" } ] }, "type": "object" }, "type": "array" }, { "additionalProperties": { "anyOf": [ { "type": "string" }, { "items": { "additionalProperties": { "type": "string" }, "type": "object" }, "type": "array" } ] }, "type": "object" }, { "type": "null" } ], "description": "The final representation of the prompt that was submitted to the LLM.", "title": "finalPrompt" }, "inputTokenCount": { "default": 0, "description": "The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database).", "title": "inputTokenCount", "type": "integer" }, "latencyMilliseconds": { "description": "The latency of the LLM response (in milliseconds).", "title": "latencyMilliseconds", "type": "integer" }, "metrics": { "default": [], "description": "The evaluation metrics for the prompt.", "items": { "description": "Prompt metric metadata.", "properties": { "costConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the cost configuration.", "title": "costConfigurationId" }, "customModelGuardId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "Id of the custom model guard.", "title": "customModelGuardId" }, "customModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the custom model used for the metric.", "title": "customModelId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the metric computation.", "title": "errorMessage" }, "evaluationDatasetConfigurationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the evaluation dataset configuration.", "title": "evaluationDatasetConfigurationId" }, "executionStatus": { "anyOf": [ { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, { "type": "null" } ], "description": "The computation status of the metric." }, "formattedName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted name of the metric.", "title": "formattedName" }, "formattedValue": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The formatted value of the metric.", "title": "formattedValue" }, "llmIsDeprecated": { "anyOf": [ { "type": "boolean" }, { "type": "null" } ], "description": "Whether the LLM is deprecated and will be removed in a future release.", "title": "llmIsDeprecated" }, "name": { "description": "The name of the metric.", "title": "name", "type": "string" }, "nemoMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the NeMo Pipeline configuration.", "title": "nemoMetricId" }, "ootbMetricId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The id of the OOTB metric configuration.", "title": "ootbMetricId" }, "sidecarModelMetricValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the sidecar model validation(in case of using a sidecar model deployment for the metric).", "title": "sidecarModelMetricValidationId" }, "stage": { "anyOf": [ { "description": "Enum that describes at which stage the metric may be calculated.", "enum": [ "prompt_pipeline", "response_pipeline" ], "title": "PipelineStage", "type": "string" }, { "type": "null" } ], "description": "The stage (prompt or response) that the metric applies to." }, "value": { "description": "The value of the metric.", "title": "value" } }, "required": [ "name", "value" ], "title": "MetricMetadata", "type": "object" }, "title": "metrics", "type": "array" }, "outputTokenCount": { "default": 0, "description": "The number of tokens in the LLM output.", "title": "outputTokenCount", "type": "integer" }, "providerLLMGuards": { "anyOf": [ { "items": { "description": "Info on the provider guard metrics.", "properties": { "name": { "description": "The name of the provider guard metric.", "title": "name", "type": "string" }, "satisfyCriteria": { "description": "Whether the configured provider guard metric satisfied its hidden internal guard criteria.", "title": "satisfyCriteria", "type": "boolean" }, "stage": { "description": "The data stage where the provider guard metric is acting upon.", "enum": [ "prompt", "response" ], "title": "ProviderGuardStage", "type": "string" }, "value": { "anyOf": [ { "type": "string" }, { "type": "number" }, { "type": "integer" }, { "type": "null" } ], "description": "The value of the provider guard metric.", "title": "value" } }, "required": [ "satisfyCriteria", "name", "value", "stage" ], "title": "ProviderGuardsMetadata", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "The provider llm guards metadata.", "title": "providerLLMGuards" }, "totalTokenCount": { "default": 0, "description": "The combined number of tokens in the LLM input and output.", "title": "totalTokenCount", "type": "integer" } }, "required": [ "latencyMilliseconds" ], "title": "ResultMetadata", "type": "object" } ``` ResultMetadata ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | blockedResultText | any | false | | The message to replace the result text if it is non empty, which represents a blocked response. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cost | any | false | | The estimated cost of executing the prompt. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message for the prompt (in case of an errored prompt). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | estimatedDocsTokenCount | integer | false | | The estimated number of tokens in the documents retrieved from the vector database. | | feedbackResult | FeedbackResult | false | | The user feedback associated with the prompt. | | finalPrompt | any | false | | The final representation of the prompt that was submitted to the LLM. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [object] | false | | none | | »» additionalProperties | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | [object] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | null | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [object] | false | | none | | »» additionalProperties | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | [object] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | object | false | | none | | »» additionalProperties | any | false | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »»» anonymous | [object] | false | | none | | »»»» additionalProperties | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputTokenCount | integer | false | | The number of tokens in the LLM input. This number includes the tokens in the system prompt, the user prompt, the chat history (for history-aware chats) and the documents retrieved from the vector database (in case of using a vector database). | | latencyMilliseconds | integer | true | | The latency of the LLM response (in milliseconds). | | metrics | [MetricMetadata] | false | | The evaluation metrics for the prompt. | | outputTokenCount | integer | false | | The number of tokens in the LLM output. | | providerLLMGuards | any | false | | The provider llm guards metadata. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [ProviderGuardsMetadata] | false | | [Info on the provider guard metrics.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalTokenCount | integer | false | | The combined number of tokens in the LLM input and output. | ## RetrievalMode ``` { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" } ``` RetrievalMode ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | RetrievalMode | string | false | | Retrieval modes for vector databases. | ### Enumerated Values | Property | Value | | --- | --- | | RetrievalMode | [similarity, maximal_marginal_relevance] | ## ValidationError ``` { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" } ``` ValidationError ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | loc | [anyOf] | true | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | msg | string | true | | none | | type | string | true | | none | ## Variable ``` { "description": "Variable used in prompt template version.", "properties": { "description": { "description": "Description of the variable. This is exposed to MCP clients.", "title": "description", "type": "string" }, "name": { "description": "Name of the variable.", "title": "name", "type": "string" }, "type": { "default": "str", "description": "Type of the variable.", "title": "type", "type": "string" } }, "required": [ "name", "description" ], "title": "Variable", "type": "object" } ``` Variable ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | Description of the variable. This is exposed to MCP clients. | | name | string | true | | Name of the variable. | | type | string | false | | Type of the variable. | ## VectorDatabaseRetrievers ``` { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } ``` VectorDatabaseRetrievers ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | VectorDatabaseRetrievers | string | false | | The method used to retrieve relevant chunks from the vector database. | ### Enumerated Values | Property | Value | | --- | --- | | VectorDatabaseRetrievers | [SINGLE_LOOKUP_RETRIEVER, CONVERSATIONAL_RETRIEVER, MULTI_STEP_RETRIEVER] | ## VectorDatabaseSettings ``` { "description": "Vector database retrieval settings.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettings", "type": "object" } ``` VectorDatabaseSettings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addNeighborChunks | boolean | false | | Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1. | | maxDocumentsRetrievedPerPrompt | any | false | | The maximum number of chunks to retrieve from the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 10minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxTokens | any | false | | The maximum number of tokens to retrieve from the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 51200minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maximalMarginalRelevanceLambda | number | false | maximum: 1minimum: 0 | Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0). | | retrievalMode | RetrievalMode | false | | The retrieval mode to use. Similarity search or maximal marginal relevance. | | retriever | VectorDatabaseRetrievers | false | | The method used to retrieve relevant chunks from the vector database. | --- # Quotas URL: https://docs.datarobot.com/en/docs/api/reference/public-api/quotas.html > Use the endpoints described below to manage resource quotas. Quotas provide limits on resource consumption for deployments, models, and other DataRobot resources to ensure fair usage and prevent resource exhaustion. # Quotas Use the endpoints described below to manage resource quotas. Quotas provide limits on resource consumption for deployments, models, and other DataRobot resources to ensure fair usage and prevent resource exhaustion. ## Retrieve Quota Templates Operation path: `GET /api/v2/quotaTemplates/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of quota templates", "items": { "properties": { "id": { "description": "Unique identifier for the quota template", "type": "string" }, "name": { "description": "Name of the quota template", "type": "string" }, "rules": { "description": "List of quota rules in this template", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "id", "name", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | QuotaTemplateListResponse | ## Retrieve Quota Templates by quota template ID Operation path: `GET /api/v2/quotaTemplates/{quotaTemplateId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | quotaTemplateId | path | string | true | Specific quota template ID | ### Example responses > 200 Response ``` { "properties": { "id": { "description": "Unique identifier for the quota template", "type": "string" }, "name": { "description": "Name of the quota template", "type": "string" }, "rules": { "description": "List of quota rules in this template", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "id", "name", "rules" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | QuotaTemplateResponse | ## Retrieve Quotas Operation path: `GET /api/v2/quotas/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | resourceId | query | string | false | Resource ID for which quota is configured | | resourceType | query | string | false | Resource Type for which quota is configured | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of quotas", "items": { "properties": { "capacity": { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" }, "defaultRules": { "description": "List of default quota rules", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "id": { "description": "Unique identifier for the quota", "type": "string" }, "policies": { "description": "List of quota policies to overwrite quota per specific consumer", "items": { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "resourceId": { "description": "ID of the specific resource", "type": "string" }, "resourceType": { "description": "Type of resource the quota applies to", "enum": [ "deployment", "workload" ], "type": "string" }, "saturationThreshold": { "description": "Saturation threshold to enable guaranteed quotas", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "userId": { "description": "ID of the user associated with the quota", "type": "string" } }, "required": [ "defaultRules", "id", "policies", "resourceId", "resourceType", "userId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | QuotaListResponse | ## Create Quotas Operation path: `POST /api/v2/quotas/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "properties": { "capacity": { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" }, "defaultRules": { "description": "List of default quota rules.", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "policies": { "description": "List of quota policies to overwrite quota, per specific consumer.", "items": { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "resourceId": { "description": "The ID of the resource.", "type": "string" }, "resourceType": { "description": "Type of resource the quota applies to.", "enum": [ "deployment", "workload" ], "type": "string" }, "saturationThreshold": { "description": "Saturation threshold.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] } }, "required": [ "defaultRules", "resourceId", "resourceType" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | QuotaCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "capacity": { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" }, "defaultRules": { "description": "List of default quota rules", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "id": { "description": "Unique identifier for the quota", "type": "string" }, "policies": { "description": "List of quota policies to overwrite quota per specific consumer", "items": { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "resourceId": { "description": "ID of the specific resource", "type": "string" }, "resourceType": { "description": "Type of resource the quota applies to", "enum": [ "deployment", "workload" ], "type": "string" }, "saturationThreshold": { "description": "Saturation threshold to enable guaranteed quotas", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "userId": { "description": "ID of the user associated with the quota", "type": "string" } }, "required": [ "defaultRules", "id", "policies", "resourceId", "resourceType", "userId" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | QuotaResponse | ## Delete Quotas by quota ID Operation path: `DELETE /api/v2/quotas/{quotaId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | quotaId | path | string | true | Specific quota ID | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | The quota has been deleted. | None | ## Retrieve Quotas by quota ID Operation path: `GET /api/v2/quotas/{quotaId}/` Authentication requirements: `BearerAuth` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | quotaId | path | string | true | Specific quota ID | ### Example responses > 200 Response ``` { "properties": { "capacity": { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" }, "defaultRules": { "description": "List of default quota rules", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "id": { "description": "Unique identifier for the quota", "type": "string" }, "policies": { "description": "List of quota policies to overwrite quota per specific consumer", "items": { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "resourceId": { "description": "ID of the specific resource", "type": "string" }, "resourceType": { "description": "Type of resource the quota applies to", "enum": [ "deployment", "workload" ], "type": "string" }, "saturationThreshold": { "description": "Saturation threshold to enable guaranteed quotas", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "userId": { "description": "ID of the user associated with the quota", "type": "string" } }, "required": [ "defaultRules", "id", "policies", "resourceId", "resourceType", "userId" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | QuotaResponse | ## Modify Quotas by quota ID Operation path: `PATCH /api/v2/quotas/{quotaId}/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "properties": { "capacity": { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" }, "defaultRules": { "description": "List of default quota rules", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "policies": { "description": "List of quota policies to overwrite quota per specific consumer", "items": { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "saturationThreshold": { "description": "Saturation threshold", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" } }, "type": "object", "x-versionadded": "v2.39" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | quotaId | path | string | true | Specific quota ID | | body | body | QuotaUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "capacity": { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" }, "defaultRules": { "description": "List of default quota rules", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "id": { "description": "Unique identifier for the quota", "type": "string" }, "policies": { "description": "List of quota policies to overwrite quota per specific consumer", "items": { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "resourceId": { "description": "ID of the specific resource", "type": "string" }, "resourceType": { "description": "Type of resource the quota applies to", "enum": [ "deployment", "workload" ], "type": "string" }, "saturationThreshold": { "description": "Saturation threshold to enable guaranteed quotas", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "userId": { "description": "ID of the user associated with the quota", "type": "string" } }, "required": [ "defaultRules", "id", "policies", "resourceId", "resourceType", "userId" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | QuotaResponse | # Schemas ## QuotaCapacity ``` { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" } ``` Resource capacity ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | capacity | integer | true | maximum: 2147483647minimum: 0 | Capacity value | | units | string | true | | Capacity type | ### Enumerated Values | Property | Value | | --- | --- | | units | [requests, token] | ## QuotaCreate ``` { "properties": { "capacity": { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" }, "defaultRules": { "description": "List of default quota rules.", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "policies": { "description": "List of quota policies to overwrite quota, per specific consumer.", "items": { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "resourceId": { "description": "The ID of the resource.", "type": "string" }, "resourceType": { "description": "Type of resource the quota applies to.", "enum": [ "deployment", "workload" ], "type": "string" }, "saturationThreshold": { "description": "Saturation threshold.", "maximum": 1, "minimum": 0, "type": [ "number", "null" ] } }, "required": [ "defaultRules", "resourceId", "resourceType" ], "type": "object", "x-versionadded": "v2.43" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | capacity | QuotaCapacity | false | | Resource capacity | | defaultRules | [QuotaRule] | true | maxItems: 100 | List of default quota rules. | | policies | [QuotaPolicy] | false | maxItems: 10000 | List of quota policies to overwrite quota, per specific consumer. | | resourceId | string | true | | The ID of the resource. | | resourceType | string | true | | Type of resource the quota applies to. | | saturationThreshold | number,null | false | maximum: 1minimum: 0 | Saturation threshold. | ### Enumerated Values | Property | Value | | --- | --- | | resourceType | [deployment, workload] | ## QuotaListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of quotas", "items": { "properties": { "capacity": { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" }, "defaultRules": { "description": "List of default quota rules", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "id": { "description": "Unique identifier for the quota", "type": "string" }, "policies": { "description": "List of quota policies to overwrite quota per specific consumer", "items": { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "resourceId": { "description": "ID of the specific resource", "type": "string" }, "resourceType": { "description": "Type of resource the quota applies to", "enum": [ "deployment", "workload" ], "type": "string" }, "saturationThreshold": { "description": "Saturation threshold to enable guaranteed quotas", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "userId": { "description": "ID of the user associated with the quota", "type": "string" } }, "required": [ "defaultRules", "id", "policies", "resourceId", "resourceType", "userId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [QuotaResponse] | true | maxItems: 10000 | List of quotas | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## QuotaPolicy ``` { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | consumerId | string | true | | ID of the consumer | | consumerName | string,null | false | | Consumer name | | consumerType | string | true | | Type of consumer | | reserved | number,null | false | maximum: 1minimum: 0 | Reserved resource percentage | | rules | [QuotaRule] | true | maxItems: 100 | List of quota rules for this policy | ### Enumerated Values | Property | Value | | --- | --- | | consumerType | [deployment, user, usergroup] | ## QuotaResponse ``` { "properties": { "capacity": { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" }, "defaultRules": { "description": "List of default quota rules", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "id": { "description": "Unique identifier for the quota", "type": "string" }, "policies": { "description": "List of quota policies to overwrite quota per specific consumer", "items": { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "resourceId": { "description": "ID of the specific resource", "type": "string" }, "resourceType": { "description": "Type of resource the quota applies to", "enum": [ "deployment", "workload" ], "type": "string" }, "saturationThreshold": { "description": "Saturation threshold to enable guaranteed quotas", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "userId": { "description": "ID of the user associated with the quota", "type": "string" } }, "required": [ "defaultRules", "id", "policies", "resourceId", "resourceType", "userId" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | capacity | QuotaCapacity | false | | Resource capacity | | defaultRules | [QuotaRule] | true | maxItems: 100 | List of default quota rules | | id | string | true | | Unique identifier for the quota | | policies | [QuotaPolicy] | true | maxItems: 10000 | List of quota policies to overwrite quota per specific consumer | | resourceId | string | true | | ID of the specific resource | | resourceType | string | true | | Type of resource the quota applies to | | saturationThreshold | number,null | false | maximum: 1minimum: 0 | Saturation threshold to enable guaranteed quotas | | userId | string | true | | ID of the user associated with the quota | ### Enumerated Values | Property | Value | | --- | --- | | resourceType | [deployment, workload] | ## QuotaRule ``` { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | limit | integer | true | | Maximum limit for the quota rule | | rule | string | true | | Type of quota rule | | window | string | false | | Time window for the quota | ### Enumerated Values | Property | Value | | --- | --- | | rule | [concurrentRequests, inputSequenceLength, requests, token] | | window | [day, hour, min] | ## QuotaTemplateListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of quota templates", "items": { "properties": { "id": { "description": "Unique identifier for the quota template", "type": "string" }, "name": { "description": "Name of the quota template", "type": "string" }, "rules": { "description": "List of quota rules in this template", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "id", "name", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [QuotaTemplateResponse] | true | maxItems: 100 | List of quota templates | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## QuotaTemplateResponse ``` { "properties": { "id": { "description": "Unique identifier for the quota template", "type": "string" }, "name": { "description": "Name of the quota template", "type": "string" }, "rules": { "description": "List of quota rules in this template", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "id", "name", "rules" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | Unique identifier for the quota template | | name | string | true | | Name of the quota template | | rules | [QuotaRule] | true | maxItems: 100 | List of quota rules in this template | ## QuotaUpdate ``` { "properties": { "capacity": { "description": "Resource capacity", "properties": { "capacity": { "description": "Capacity value", "maximum": 2147483647, "minimum": 0, "type": "integer" }, "units": { "description": "Capacity type", "enum": [ "requests", "token" ], "type": "string" } }, "required": [ "capacity", "units" ], "type": "object", "x-versionadded": "v2.42" }, "defaultRules": { "description": "List of default quota rules", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "policies": { "description": "List of quota policies to overwrite quota per specific consumer", "items": { "properties": { "consumerId": { "description": "ID of the consumer", "type": "string" }, "consumerName": { "description": "Consumer name", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "consumerType": { "description": "Type of consumer", "enum": [ "deployment", "user", "usergroup" ], "type": "string" }, "reserved": { "description": "Reserved resource percentage", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" }, "rules": { "description": "List of quota rules for this policy", "items": { "properties": { "limit": { "description": "Maximum limit for the quota rule", "exclusiveMaximum": 9223372036854776000, "exclusiveMinimum": 0, "type": "integer" }, "rule": { "description": "Type of quota rule", "enum": [ "concurrentRequests", "inputSequenceLength", "requests", "token" ], "type": "string" }, "window": { "default": "min", "description": "Time window for the quota", "enum": [ "day", "hour", "min" ], "type": "string" } }, "required": [ "limit", "rule" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" } }, "required": [ "consumerId", "consumerType", "rules" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "saturationThreshold": { "description": "Saturation threshold", "maximum": 1, "minimum": 0, "type": [ "number", "null" ], "x-versionadded": "v2.42" } }, "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | capacity | QuotaCapacity | false | | Resource capacity | | defaultRules | [QuotaRule] | false | maxItems: 100 | List of default quota rules | | policies | [QuotaPolicy] | false | maxItems: 10000 | List of quota policies to overwrite quota per specific consumer | | saturationThreshold | number,null | false | maximum: 1minimum: 0 | Saturation threshold | --- # Seat Licenses URL: https://docs.datarobot.com/en/docs/api/reference/public-api/seat_licenses.html > Manage seat licenses using the endpoints described below. # Seat Licenses Manage seat licenses using the endpoints described below. ## List seat license allocations Operation path: `GET /api/v2/seatLicenseAllocations/` Authentication requirements: `BearerAuth` List seat license allocations. Org admins can view seat licenses that were allocated to their organization. System admins can view allocations across all organizations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | ids | query | any | false | List seat license allocations by the specified IDs. | | orgId | query | string | false | The ID of the organization the seat licenses have been allocated to. | | seatLicenseIds | query | any | false | List seat license allocations by the seat license IDs. | | subjectIds | query | any | false | The subject IDs that should be part of the seat license allocations | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The queried seat license allocations.", "items": { "description": "The seat license allocation", "properties": { "createdAt": { "description": "The creation date of the seat license allocation.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The current number of seat license grants within the organization, corresponding to the number of subjects.", "type": [ "integer", "null" ] }, "id": { "description": "The ID of the seat license allocation.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit of seat license grants available for the organization.", "type": "integer" }, "orgId": { "description": "The ID of the organization the seat license has been allocated to.", "type": "string" }, "seatLicense": { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" }, "subjects": { "description": "The subjects that have been granted the seat license", "items": { "description": "The subject", "properties": { "createdAt": { "description": "The date of the seat license allocation to the subject", "format": "date-time", "type": "string" }, "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "createdAt", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "updatedAt": { "description": "The date when the seat license allocation was last updated.", "format": "date-time", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "id", "limit", "orgId", "seatLicense", "subjects", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The listed seat license allocations. | SeatLicenseAllocationsListResponse | ## Allocate seat licenses Operation path: `POST /api/v2/seatLicenseAllocations/` Authentication requirements: `BearerAuth` Allocate the seat license to the organization, given that the total number of allocated seats in the cluster, including the new organization's limit, does not exceed the cluster-level limit applied through the license key. Available to system admins only. ### Body parameter ``` { "properties": { "limit": { "description": "The total number of seat license grants available for the organization.", "exclusiveMinimum": 0, "type": "integer" }, "orgId": { "description": "The ID of the organization to allocate the seat license to.", "type": "string" }, "seatLicenseId": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "subjects": { "description": "The subjects to grant the seat license to.", "items": { "description": "The subject", "properties": { "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" } }, "required": [ "limit", "orgId", "seatLicenseId" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | SeatLicenseAllocationCreate | false | none | ### Example responses > 201 Response ``` { "description": "The seat license allocation", "properties": { "createdAt": { "description": "The creation date of the seat license allocation.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The current number of seat license grants within the organization, corresponding to the number of subjects.", "type": [ "integer", "null" ] }, "id": { "description": "The ID of the seat license allocation.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit of seat license grants available for the organization.", "type": "integer" }, "orgId": { "description": "The ID of the organization the seat license has been allocated to.", "type": "string" }, "seatLicense": { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" }, "subjects": { "description": "The subjects that have been granted the seat license", "items": { "description": "The subject", "properties": { "createdAt": { "description": "The date of the seat license allocation to the subject", "format": "date-time", "type": "string" }, "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "createdAt", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "updatedAt": { "description": "The date when the seat license allocation was last updated.", "format": "date-time", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "id", "limit", "orgId", "seatLicense", "subjects", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The allocated seat license for the organization. | SeatLicenseAllocation | ## Evaluate the seat license Operation path: `POST /api/v2/seatLicenseAllocations/evaluate/` Authentication requirements: `BearerAuth` Evaluate whether the subject has been authorized to use the seat license. ### Body parameter ``` { "properties": { "orgId": { "description": "The ID of the organization the seat license was allocated to. Must correspond to the subject's organization.", "type": "string" }, "seatName": { "description": "The name of the seat license.", "type": "string" }, "subjectId": { "description": "The ID of the subject.", "type": "string" }, "subjectType": { "description": "The type of the subject", "enum": [ "user" ], "type": "string" } }, "required": [ "orgId", "seatName", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | SeatLicenseEvaluateRequest | false | none | ### Example responses > 200 Response ``` { "properties": { "allowed": { "description": "The result of the evaluation: true if the subject is allowed to use the seat license, false otherwise.", "type": "boolean" }, "seatLicenseAllocation": { "description": "The seat license allocation", "properties": { "createdAt": { "description": "The creation date of the seat license allocation.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The current number of seat license grants within the organization, corresponding to the number of subjects.", "type": [ "integer", "null" ] }, "id": { "description": "The ID of the seat license allocation.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit of seat license grants available for the organization.", "type": "integer" }, "orgId": { "description": "The ID of the organization the seat license has been allocated to.", "type": "string" }, "seatLicense": { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" }, "subjects": { "description": "The subjects that have been granted the seat license", "items": { "description": "The subject", "properties": { "createdAt": { "description": "The date of the seat license allocation to the subject", "format": "date-time", "type": "string" }, "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "createdAt", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "updatedAt": { "description": "The date when the seat license allocation was last updated.", "format": "date-time", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "id", "limit", "orgId", "seatLicense", "subjects", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" } }, "required": [ "allowed", "seatLicenseAllocation" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The evaluation was performed successfully. | SeatLicenseEvaluateResponse | ## Delete a seat license allocation by allocation ID Operation path: `DELETE /api/v2/seatLicenseAllocations/{allocationId}/` Authentication requirements: `BearerAuth` Delete the seat license allocation. Only system admins can execute this operation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | allocationId | path | string | true | The ID of the seat license allocation. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The seat license allocation was successfully deleted. | None | ## Retrieve a seat license allocation by allocation ID Operation path: `GET /api/v2/seatLicenseAllocations/{allocationId}/` Authentication requirements: `BearerAuth` Retrieve an allocated seat license by ID. Org admins can view seat licenses that were allocated to their organization. System admins can view allocations across all organizations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | allocationId | path | string | true | The ID of the seat license allocation. | ### Example responses > 200 Response ``` { "description": "The seat license allocation", "properties": { "createdAt": { "description": "The creation date of the seat license allocation.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The current number of seat license grants within the organization, corresponding to the number of subjects.", "type": [ "integer", "null" ] }, "id": { "description": "The ID of the seat license allocation.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit of seat license grants available for the organization.", "type": "integer" }, "orgId": { "description": "The ID of the organization the seat license has been allocated to.", "type": "string" }, "seatLicense": { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" }, "subjects": { "description": "The subjects that have been granted the seat license", "items": { "description": "The subject", "properties": { "createdAt": { "description": "The date of the seat license allocation to the subject", "format": "date-time", "type": "string" }, "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "createdAt", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "updatedAt": { "description": "The date when the seat license allocation was last updated.", "format": "date-time", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "id", "limit", "orgId", "seatLicense", "subjects", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The seat license allocation was successfully retrieved. | SeatLicenseAllocation | ## Update a seat license allocation by allocation ID Operation path: `PATCH /api/v2/seatLicenseAllocations/{allocationId}/` Authentication requirements: `BearerAuth` Update Seat License Allocation. ### Body parameter ``` { "properties": { "addSubjects": { "description": "The subjects to grant the seat license to.", "items": { "description": "The subject", "properties": { "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "limit": { "description": "The total number of seat license grants available for the organization.", "exclusiveMinimum": 0, "type": "integer" }, "removeSubjects": { "description": "The subjects to remove the seat license from.", "items": { "description": "The subject", "properties": { "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | allocationId | path | string | true | The ID of the seat license allocation. | | body | body | SeatLicenseAllocationUpdate | false | none | ### Example responses > 200 Response ``` { "description": "The seat license allocation", "properties": { "createdAt": { "description": "The creation date of the seat license allocation.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The current number of seat license grants within the organization, corresponding to the number of subjects.", "type": [ "integer", "null" ] }, "id": { "description": "The ID of the seat license allocation.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit of seat license grants available for the organization.", "type": "integer" }, "orgId": { "description": "The ID of the organization the seat license has been allocated to.", "type": "string" }, "seatLicense": { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" }, "subjects": { "description": "The subjects that have been granted the seat license", "items": { "description": "The subject", "properties": { "createdAt": { "description": "The date of the seat license allocation to the subject", "format": "date-time", "type": "string" }, "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "createdAt", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "updatedAt": { "description": "The date when the seat license allocation was last updated.", "format": "date-time", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "id", "limit", "orgId", "seatLicense", "subjects", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The updated seat license allocation for the organization. | SeatLicenseAllocation | ## List seat licenses Operation path: `GET /api/v2/seatLicenses/` Authentication requirements: `BearerAuth` List seat licenses in the current installation. Seat licenses may become available only when applied through the cluster license. Accessible to system admins only. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | ids | query | any | false | List seat licenses by the specified IDs. | | names | query | any | false | List seat licenses by the specified names. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The queried seat licenses.", "items": { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The listed seat licenses. | SeatLicensesListResponse | # Schemas ## SeatLicense ``` { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" } ``` The seat license ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The creation date of the seat license. | | currentAllocation | integer,null | true | | The total number of seat license allocations; null if the limit is null (i.e., unlimited). | | description | string | true | | The description of the seat license. | | id | string | true | maxLength: 36minLength: 32minLength: 32 | The ID of the seat license. | | limit | integer,null | true | | The total limit seat licenses available in the cluster, null if unlimited | | name | string | true | | The name of the seat license. | ## SeatLicenseAllocation ``` { "description": "The seat license allocation", "properties": { "createdAt": { "description": "The creation date of the seat license allocation.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The current number of seat license grants within the organization, corresponding to the number of subjects.", "type": [ "integer", "null" ] }, "id": { "description": "The ID of the seat license allocation.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit of seat license grants available for the organization.", "type": "integer" }, "orgId": { "description": "The ID of the organization the seat license has been allocated to.", "type": "string" }, "seatLicense": { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" }, "subjects": { "description": "The subjects that have been granted the seat license", "items": { "description": "The subject", "properties": { "createdAt": { "description": "The date of the seat license allocation to the subject", "format": "date-time", "type": "string" }, "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "createdAt", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "updatedAt": { "description": "The date when the seat license allocation was last updated.", "format": "date-time", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "id", "limit", "orgId", "seatLicense", "subjects", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" } ``` The seat license allocation ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The creation date of the seat license allocation. | | currentAllocation | integer,null | true | | The current number of seat license grants within the organization, corresponding to the number of subjects. | | id | string | true | maxLength: 36minLength: 32minLength: 32 | The ID of the seat license allocation. | | limit | integer | true | | The total limit of seat license grants available for the organization. | | orgId | string | true | | The ID of the organization the seat license has been allocated to. | | seatLicense | SeatLicense | true | | The seat license | | subjects | [Subject] | true | maxItems: 1000 | The subjects that have been granted the seat license | | updatedAt | string(date-time) | true | | The date when the seat license allocation was last updated. | ## SeatLicenseAllocationCreate ``` { "properties": { "limit": { "description": "The total number of seat license grants available for the organization.", "exclusiveMinimum": 0, "type": "integer" }, "orgId": { "description": "The ID of the organization to allocate the seat license to.", "type": "string" }, "seatLicenseId": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "subjects": { "description": "The subjects to grant the seat license to.", "items": { "description": "The subject", "properties": { "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" } }, "required": [ "limit", "orgId", "seatLicenseId" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | limit | integer | true | | The total number of seat license grants available for the organization. | | orgId | string | true | | The ID of the organization to allocate the seat license to. | | seatLicenseId | string | true | maxLength: 36minLength: 32minLength: 32 | The ID of the seat license. | | subjects | [SubjectCreate] | false | maxItems: 1000 | The subjects to grant the seat license to. | ## SeatLicenseAllocationUpdate ``` { "properties": { "addSubjects": { "description": "The subjects to grant the seat license to.", "items": { "description": "The subject", "properties": { "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "limit": { "description": "The total number of seat license grants available for the organization.", "exclusiveMinimum": 0, "type": "integer" }, "removeSubjects": { "description": "The subjects to remove the seat license from.", "items": { "description": "The subject", "properties": { "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addSubjects | [SubjectCreate] | false | maxItems: 100 | The subjects to grant the seat license to. | | limit | integer | false | | The total number of seat license grants available for the organization. | | removeSubjects | [SubjectCreate] | false | maxItems: 100 | The subjects to remove the seat license from. | ## SeatLicenseAllocationsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The queried seat license allocations.", "items": { "description": "The seat license allocation", "properties": { "createdAt": { "description": "The creation date of the seat license allocation.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The current number of seat license grants within the organization, corresponding to the number of subjects.", "type": [ "integer", "null" ] }, "id": { "description": "The ID of the seat license allocation.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit of seat license grants available for the organization.", "type": "integer" }, "orgId": { "description": "The ID of the organization the seat license has been allocated to.", "type": "string" }, "seatLicense": { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" }, "subjects": { "description": "The subjects that have been granted the seat license", "items": { "description": "The subject", "properties": { "createdAt": { "description": "The date of the seat license allocation to the subject", "format": "date-time", "type": "string" }, "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "createdAt", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "updatedAt": { "description": "The date when the seat license allocation was last updated.", "format": "date-time", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "id", "limit", "orgId", "seatLicense", "subjects", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [SeatLicenseAllocation] | true | maxItems: 100 | The queried seat license allocations. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## SeatLicenseEvaluateRequest ``` { "properties": { "orgId": { "description": "The ID of the organization the seat license was allocated to. Must correspond to the subject's organization.", "type": "string" }, "seatName": { "description": "The name of the seat license.", "type": "string" }, "subjectId": { "description": "The ID of the subject.", "type": "string" }, "subjectType": { "description": "The type of the subject", "enum": [ "user" ], "type": "string" } }, "required": [ "orgId", "seatName", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | orgId | string | true | | The ID of the organization the seat license was allocated to. Must correspond to the subject's organization. | | seatName | string | true | | The name of the seat license. | | subjectId | string | true | | The ID of the subject. | | subjectType | string | true | | The type of the subject | ### Enumerated Values | Property | Value | | --- | --- | | subjectType | user | ## SeatLicenseEvaluateResponse ``` { "properties": { "allowed": { "description": "The result of the evaluation: true if the subject is allowed to use the seat license, false otherwise.", "type": "boolean" }, "seatLicenseAllocation": { "description": "The seat license allocation", "properties": { "createdAt": { "description": "The creation date of the seat license allocation.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The current number of seat license grants within the organization, corresponding to the number of subjects.", "type": [ "integer", "null" ] }, "id": { "description": "The ID of the seat license allocation.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit of seat license grants available for the organization.", "type": "integer" }, "orgId": { "description": "The ID of the organization the seat license has been allocated to.", "type": "string" }, "seatLicense": { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" }, "subjects": { "description": "The subjects that have been granted the seat license", "items": { "description": "The subject", "properties": { "createdAt": { "description": "The date of the seat license allocation to the subject", "format": "date-time", "type": "string" }, "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "createdAt", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 1000, "type": "array" }, "updatedAt": { "description": "The date when the seat license allocation was last updated.", "format": "date-time", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "id", "limit", "orgId", "seatLicense", "subjects", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" } }, "required": [ "allowed", "seatLicenseAllocation" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowed | boolean | true | | The result of the evaluation: true if the subject is allowed to use the seat license, false otherwise. | | seatLicenseAllocation | SeatLicenseAllocation | true | | The seat license allocation | ## SeatLicensesListResponse ``` { "properties": { "data": { "description": "The queried seat licenses.", "items": { "description": "The seat license", "properties": { "createdAt": { "description": "The creation date of the seat license.", "format": "date-time", "type": "string" }, "currentAllocation": { "description": "The total number of seat license allocations; null if the limit is null (i.e., unlimited).", "type": [ "integer", "null" ] }, "description": { "description": "The description of the seat license.", "type": "string" }, "id": { "description": "The ID of the seat license.", "maxLength": 36, "minLength": 32, "type": "string" }, "limit": { "description": "The total limit seat licenses available in the cluster, null if unlimited", "type": [ "integer", "null" ] }, "name": { "description": "The name of the seat license.", "type": "string" } }, "required": [ "createdAt", "currentAllocation", "description", "id", "limit", "name" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [SeatLicense] | true | maxItems: 100 | The queried seat licenses. | ## Subject ``` { "description": "The subject", "properties": { "createdAt": { "description": "The date of the seat license allocation to the subject", "format": "date-time", "type": "string" }, "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "createdAt", "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" } ``` The subject ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The date of the seat license allocation to the subject | | subjectId | string | true | | The subject ID. | | subjectType | string | true | | The subject type. | ### Enumerated Values | Property | Value | | --- | --- | | subjectType | user | ## SubjectCreate ``` { "description": "The subject", "properties": { "subjectId": { "description": "The subject ID.", "type": "string" }, "subjectType": { "description": "The subject type.", "enum": [ "user" ], "type": "string" } }, "required": [ "subjectId", "subjectType" ], "type": "object", "x-versionadded": "v2.36" } ``` The subject ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | subjectId | string | true | | The subject ID. | | subjectType | string | true | | The subject type. | ### Enumerated Values | Property | Value | | --- | --- | | subjectType | user | --- # Secure Config URL: https://docs.datarobot.com/en/docs/api/reference/public-api/secure_config.html > Use the endpoints described below to configure and manage Secure Configuration objects. This abstraction allows system and organization administrations to securely store and manage credentials. Users with lesser permissions can have the credentials shared with them, but be unable to view or edit their contents. # Secure Config Use the endpoints described below to configure and manage Secure Configuration objects. This abstraction allows system and organization administrations to securely store and manage credentials. Users with lesser permissions can have the credentials shared with them, but be unable to view or edit their contents. ## Retrieve a list of secure configuration schemas Operation path: `GET /api/v2/secureConfigSchemas/` Authentication requirements: `BearerAuth` Retrieve all secure configuration schemas. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | orderBy | query | string | false | The order to sort the secure configuration schemas. Defaults to the order by the name in descending order. | | name | query | string | false | Filter for a specific secure configuration schema name. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [name, -name] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of secure configuration schemas.", "items": { "properties": { "id": { "description": "The ID of the secure configuration.", "maxLength": 36, "minLength": 32, "type": "string" }, "name": { "description": "The name of the secure configuration.", "type": "string" }, "schemaDefinition": { "description": "Secure config schema definition.", "properties": { "$id": { "description": "Secure config schema identifier.", "format": "uri", "type": "string" }, "$schema": { "default": "http://json-schema.org/draft-07/schema#", "description": "The JSON schema meta schema version.", "enum": [ "http://json-schema.org/draft-07/schema#" ], "type": "string" }, "additionalProperties": { "default": false, "description": "Allows properties other than schema defined in corresponding secure configs.", "type": "boolean" }, "description": { "description": "Secure config schema description.", "type": "string" }, "properties": { "additionalProperties": { "properties": { "description": { "description": "Property description.", "type": "string" }, "format": { "description": "Property value format.", "enum": [ "uri" ], "type": "string" }, "title": { "description": "Property title.", "type": "string" }, "type": { "description": "Property type.", "enum": [ "string", "array" ], "type": "string" } }, "required": [ "description", "title", "type" ], "type": "object", "x-versionadded": "v2.36" }, "description": "Secure config properties' definitions.", "type": "object" }, "required": { "description": "The list of required properties.", "items": { "type": "string" }, "type": "array" }, "title": { "description": "Secure config schema title.", "type": "string" }, "type": { "default": "object", "description": "Secure config definition type.", "enum": [ "object" ], "type": "string" } }, "required": [ "$id", "$schema", "additionalProperties", "description", "properties", "required", "title" ], "type": "object", "x-versionadded": "v2.36" } }, "required": [ "id", "name", "schemaDefinition" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of secure configuration schemas. | SecureConfigSchemaListResponse | ## Retrieve a secure configuration schema by secure config schema ID Operation path: `GET /api/v2/secureConfigSchemas/{secureConfigSchemaId}/` Authentication requirements: `BearerAuth` Retrieve a secure configuration schema. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | secureConfigSchemaId | path | string | true | The ID of the secure configuration schema. | ### Example responses > 200 Response ``` { "description": "Secure config schema definition.", "properties": { "$id": { "description": "Secure config schema identifier.", "format": "uri", "type": "string" }, "$schema": { "default": "http://json-schema.org/draft-07/schema#", "description": "The JSON schema meta schema version.", "enum": [ "http://json-schema.org/draft-07/schema#" ], "type": "string" }, "additionalProperties": { "default": false, "description": "Allows properties other than schema defined in corresponding secure configs.", "type": "boolean" }, "description": { "description": "Secure config schema description.", "type": "string" }, "properties": { "additionalProperties": { "properties": { "description": { "description": "Property description.", "type": "string" }, "format": { "description": "Property value format.", "enum": [ "uri" ], "type": "string" }, "title": { "description": "Property title.", "type": "string" }, "type": { "description": "Property type.", "enum": [ "string", "array" ], "type": "string" } }, "required": [ "description", "title", "type" ], "type": "object", "x-versionadded": "v2.36" }, "description": "Secure config properties' definitions.", "type": "object" }, "required": { "description": "The list of required properties.", "items": { "type": "string" }, "type": "array" }, "title": { "description": "Secure config schema title.", "type": "string" }, "type": { "default": "object", "description": "Secure config definition type.", "enum": [ "object" ], "type": "string" } }, "required": [ "$id", "$schema", "additionalProperties", "description", "properties", "required", "title" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The configuration schema. | SecureConfigSchema | ## Retrieve a list of secure configurations Operation path: `GET /api/v2/secureConfigs/` Authentication requirements: `BearerAuth` Retrieve all secure configurations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | orderBy | query | string | false | The order to sort the secure configurations. Defaults to the order by the name in descending order. | | name | query | string | false | Filter for a specific secure configuration name. | | namePart | query | string | false | Filter for a secure configuration name. | | schemas | query | string | false | A comma-separated list of schema names to filter on. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [name, -name, createdAt, -createdAt] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of secure configurations.", "items": { "properties": { "createdAt": { "description": "When this secure configuration was created.", "format": "date-time", "type": "string" }, "id": { "description": "The ID of the secure configuration.", "maxLength": 36, "minLength": 32, "type": "string" }, "name": { "description": "The name of the secure configuration.", "type": "string" }, "ownerId": { "description": "The ID of the secure configuration owner.", "type": "string" }, "schemaName": { "description": "The name of the schema used for the secure configuration.", "type": "string" }, "valuesHref": { "description": "Relative URI that points to the values for this secure configuration.", "type": "string" } }, "required": [ "createdAt", "id", "name", "ownerId", "schemaName", "valuesHref" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of secure configurations. | SecureConfigListResponse | ## Create a secure configuration Operation path: `POST /api/v2/secureConfigs/` Authentication requirements: `BearerAuth` Create a secure configuration. ### Body parameter ``` { "properties": { "name": { "description": "The name of the secure configuration.", "type": "string" }, "schemaName": { "description": "The name of the schema used for validating the secure configuration.", "type": "string" }, "values": { "description": "The values to associate with the secure configuration.", "items": { "properties": { "key": { "description": "The name of the key for the secure configuration value.", "type": "string" }, "value": { "description": "The value of the secure configuration.", "type": "string" } }, "required": [ "key", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "required": [ "name", "schemaName", "values" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | SecureConfigCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "createdAt": { "description": "When this secure configuration was created.", "format": "date-time", "type": "string" }, "id": { "description": "The ID of the secure configuration.", "maxLength": 36, "minLength": 32, "type": "string" }, "name": { "description": "The name of the secure configuration.", "type": "string" }, "ownerId": { "description": "The ID of the secure configuration owner.", "type": "string" }, "schemaName": { "description": "The name of the schema used for the secure configuration.", "type": "string" }, "valuesHref": { "description": "Relative URI that points to the values for this secure configuration.", "type": "string" } }, "required": [ "createdAt", "id", "name", "ownerId", "schemaName", "valuesHref" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The created secure configuration. | SecureConfigResponse | ## Delete secure configuration by secure config ID Operation path: `DELETE /api/v2/secureConfigs/{secureConfigId}/` Authentication requirements: `BearerAuth` Delete secure configuration and its values. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | secureConfigId | path | string | true | The ID of the secure configuration. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Secure configuration deleted successfully. | None | ## Retrieve a secure configuration by secure config ID Operation path: `GET /api/v2/secureConfigs/{secureConfigId}/` Authentication requirements: `BearerAuth` Retrieve a secure configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | secureConfigId | path | string | true | The ID of the secure configuration. | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "When this secure configuration was created.", "format": "date-time", "type": "string" }, "id": { "description": "The ID of the secure configuration.", "maxLength": 36, "minLength": 32, "type": "string" }, "name": { "description": "The name of the secure configuration.", "type": "string" }, "ownerId": { "description": "The ID of the secure configuration owner.", "type": "string" }, "schemaName": { "description": "The name of the schema used for the secure configuration.", "type": "string" }, "valuesHref": { "description": "Relative URI that points to the values for this secure configuration.", "type": "string" } }, "required": [ "createdAt", "id", "name", "ownerId", "schemaName", "valuesHref" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The secure configuration. | SecureConfigResponse | ## Update a secure configuration by secure config ID Operation path: `PATCH /api/v2/secureConfigs/{secureConfigId}/` Authentication requirements: `BearerAuth` Update a secure configuration. ### Body parameter ``` { "properties": { "name": { "description": "The name of the secure configuration.", "type": "string" }, "schemaName": { "description": "The name of the schema used for validating the secure configuration.", "type": [ "string", "null" ] }, "values": { "description": "The values to associate with the secure configuration.", "items": { "properties": { "key": { "description": "The name of the key for the secure configuration value.", "type": "string" }, "value": { "description": "The value of the secure configuration.", "type": "string" } }, "required": [ "key", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | secureConfigId | path | string | true | The ID of the secure configuration. | | body | body | SecureConfigUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "createdAt": { "description": "When this secure configuration was created.", "format": "date-time", "type": "string" }, "id": { "description": "The ID of the secure configuration.", "maxLength": 36, "minLength": 32, "type": "string" }, "name": { "description": "The name of the secure configuration.", "type": "string" }, "ownerId": { "description": "The ID of the secure configuration owner.", "type": "string" }, "schemaName": { "description": "The name of the schema used for the secure configuration.", "type": "string" }, "valuesHref": { "description": "Relative URI that points to the values for this secure configuration.", "type": "string" } }, "required": [ "createdAt", "id", "name", "ownerId", "schemaName", "valuesHref" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The updated secure configuration. | SecureConfigResponse | ## Get a list of users, groups, and organizations that have access by secure config ID Operation path: `GET /api/v2/secureConfigs/{secureConfigId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups, and organizations that have access to this secure configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | At most this many results are returned. | | name | query | string | false | Only return roles for a user, group, or organization with this name. | | id | query | string | false | Only return roles for a user, group, or organization with this ID. | | shareRecipientType | query | string | false | The recipient type: one of 'user', 'group', or 'organization'. | | secureConfigId | path | string | true | The ID of the secure configuration. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Details about the Shared Role entries.", "items": { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "name": { "description": "The name of the user, group, or organization.", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SecureConfigSharedRolesList | ## Share a secure configuration by secure config ID Operation path: `PATCH /api/v2/secureConfigs/{secureConfigId}/sharedRoles/` Authentication requirements: `BearerAuth` Share a secure configuration with a user, group or organization. ### Body parameter ``` { "properties": { "roles": { "description": "An array of RoleRequest objects. May contain at most 100 such objects.", "items": { "oneOf": [ { "properties": { "canShare": { "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role of the recipient on this entity. One of OWNER, EDITOR, CONSUMER.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object", "x-versionadded": "v2.36" }, { "properties": { "canShare": { "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity. One of OWNER, EDITOR, CONSUMER.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.36" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "roles" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | secureConfigId | path | string | true | The ID of the secure configuration. | | body | body | SecureConfigSharingUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The roles were updated successfully. | None | | 422 | Unprocessable Entity | The request was formatted improperly. | None | ## Retrieve a list of values by secure config ID Operation path: `GET /api/v2/secureConfigs/{secureConfigId}/values/` Authentication requirements: `BearerAuth` Retrieve all values created for the secure configuration. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | consumer | query | boolean | false | Consumer access to the Secure Config values. | | secureConfigId | path | string | true | The ID of the secure configuration. | ### Example responses > 200 Response ``` { "properties": { "secureConfigId": { "description": "The ID of the SecureConfig.", "maxLength": 36, "minLength": 32, "type": "string" }, "values": { "description": "The secure configuration values.", "items": { "properties": { "key": { "description": "The name of the key for the secure configuration value.", "type": "string" }, "value": { "description": "The value of the secure configuration.", "type": "string" } }, "required": [ "key", "value" ], "type": "object", "x-versionadded": "v2.36" }, "type": "array" } }, "required": [ "secureConfigId", "values" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | A list of values for the secure configuration. | SecureConfigListValuesResponse | # Schemas ## SecureConfigCreate ``` { "properties": { "name": { "description": "The name of the secure configuration.", "type": "string" }, "schemaName": { "description": "The name of the schema used for validating the secure configuration.", "type": "string" }, "values": { "description": "The values to associate with the secure configuration.", "items": { "properties": { "key": { "description": "The name of the key for the secure configuration value.", "type": "string" }, "value": { "description": "The value of the secure configuration.", "type": "string" } }, "required": [ "key", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "required": [ "name", "schemaName", "values" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the secure configuration. | | schemaName | string | true | | The name of the schema used for validating the secure configuration. | | values | [SecureConfigKeyValue] | true | maxItems: 100 | The values to associate with the secure configuration. | ## SecureConfigGrantAccessControlWithIdWithGrantValidators ``` { "properties": { "canShare": { "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity. One of OWNER, EDITOR, CONSUMER.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If role is NO_ROLE canShare is ignored. | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. One of OWNER, EDITOR, CONSUMER. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, EDITOR, CONSUMER, NO_ROLE] | | shareRecipientType | [user, group, organization] | ## SecureConfigGrantAccessControlWithUsernameWithGrantValidators ``` { "properties": { "canShare": { "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role of the recipient on this entity. One of OWNER, EDITOR, CONSUMER.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If role is NO_ROLE canShare is ignored. | | role | string | true | | The role of the recipient on this entity. One of OWNER, EDITOR, CONSUMER. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | Username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, EDITOR, CONSUMER, NO_ROLE] | | shareRecipientType | [user, group, organization] | ## SecureConfigKeyValue ``` { "properties": { "key": { "description": "The name of the key for the secure configuration value.", "type": "string" }, "value": { "description": "The value of the secure configuration.", "type": "string" } }, "required": [ "key", "value" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | key | string | true | | The name of the key for the secure configuration value. | | value | string | true | | The value of the secure configuration. | ## SecureConfigListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of secure configurations.", "items": { "properties": { "createdAt": { "description": "When this secure configuration was created.", "format": "date-time", "type": "string" }, "id": { "description": "The ID of the secure configuration.", "maxLength": 36, "minLength": 32, "type": "string" }, "name": { "description": "The name of the secure configuration.", "type": "string" }, "ownerId": { "description": "The ID of the secure configuration owner.", "type": "string" }, "schemaName": { "description": "The name of the schema used for the secure configuration.", "type": "string" }, "valuesHref": { "description": "Relative URI that points to the values for this secure configuration.", "type": "string" } }, "required": [ "createdAt", "id", "name", "ownerId", "schemaName", "valuesHref" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [SecureConfigResponse] | true | maxItems: 100 | The list of secure configurations. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## SecureConfigListValuesResponse ``` { "properties": { "secureConfigId": { "description": "The ID of the SecureConfig.", "maxLength": 36, "minLength": 32, "type": "string" }, "values": { "description": "The secure configuration values.", "items": { "properties": { "key": { "description": "The name of the key for the secure configuration value.", "type": "string" }, "value": { "description": "The value of the secure configuration.", "type": "string" } }, "required": [ "key", "value" ], "type": "object", "x-versionadded": "v2.36" }, "type": "array" } }, "required": [ "secureConfigId", "values" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | secureConfigId | string | true | maxLength: 36minLength: 32minLength: 32 | The ID of the SecureConfig. | | values | [SecureConfigKeyValue] | true | | The secure configuration values. | ## SecureConfigResponse ``` { "properties": { "createdAt": { "description": "When this secure configuration was created.", "format": "date-time", "type": "string" }, "id": { "description": "The ID of the secure configuration.", "maxLength": 36, "minLength": 32, "type": "string" }, "name": { "description": "The name of the secure configuration.", "type": "string" }, "ownerId": { "description": "The ID of the secure configuration owner.", "type": "string" }, "schemaName": { "description": "The name of the schema used for the secure configuration.", "type": "string" }, "valuesHref": { "description": "Relative URI that points to the values for this secure configuration.", "type": "string" } }, "required": [ "createdAt", "id", "name", "ownerId", "schemaName", "valuesHref" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | When this secure configuration was created. | | id | string | true | maxLength: 36minLength: 32minLength: 32 | The ID of the secure configuration. | | name | string | true | | The name of the secure configuration. | | ownerId | string | true | | The ID of the secure configuration owner. | | schemaName | string | true | | The name of the schema used for the secure configuration. | | valuesHref | string | true | | Relative URI that points to the values for this secure configuration. | ## SecureConfigSchema ``` { "description": "Secure config schema definition.", "properties": { "$id": { "description": "Secure config schema identifier.", "format": "uri", "type": "string" }, "$schema": { "default": "http://json-schema.org/draft-07/schema#", "description": "The JSON schema meta schema version.", "enum": [ "http://json-schema.org/draft-07/schema#" ], "type": "string" }, "additionalProperties": { "default": false, "description": "Allows properties other than schema defined in corresponding secure configs.", "type": "boolean" }, "description": { "description": "Secure config schema description.", "type": "string" }, "properties": { "additionalProperties": { "properties": { "description": { "description": "Property description.", "type": "string" }, "format": { "description": "Property value format.", "enum": [ "uri" ], "type": "string" }, "title": { "description": "Property title.", "type": "string" }, "type": { "description": "Property type.", "enum": [ "string", "array" ], "type": "string" } }, "required": [ "description", "title", "type" ], "type": "object", "x-versionadded": "v2.36" }, "description": "Secure config properties' definitions.", "type": "object" }, "required": { "description": "The list of required properties.", "items": { "type": "string" }, "type": "array" }, "title": { "description": "Secure config schema title.", "type": "string" }, "type": { "default": "object", "description": "Secure config definition type.", "enum": [ "object" ], "type": "string" } }, "required": [ "$id", "$schema", "additionalProperties", "description", "properties", "required", "title" ], "type": "object", "x-versionadded": "v2.36" } ``` Secure config schema definition. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | $id | string(uri) | true | | Secure config schema identifier. | | $schema | string | true | | The JSON schema meta schema version. | | additionalProperties | boolean | true | | Allows properties other than schema defined in corresponding secure configs. | | description | string | true | | Secure config schema description. | | properties | object | true | | Secure config properties' definitions. | | » additionalProperties | SecureConfigSchemaProperty | false | | none | | required | [string] | true | | The list of required properties. | | title | string | true | | Secure config schema title. | | type | string | false | | Secure config definition type. | ### Enumerated Values | Property | Value | | --- | --- | | $schema | http://json-schema.org/draft-07/schema# | | type | object | ## SecureConfigSchemaListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of secure configuration schemas.", "items": { "properties": { "id": { "description": "The ID of the secure configuration.", "maxLength": 36, "minLength": 32, "type": "string" }, "name": { "description": "The name of the secure configuration.", "type": "string" }, "schemaDefinition": { "description": "Secure config schema definition.", "properties": { "$id": { "description": "Secure config schema identifier.", "format": "uri", "type": "string" }, "$schema": { "default": "http://json-schema.org/draft-07/schema#", "description": "The JSON schema meta schema version.", "enum": [ "http://json-schema.org/draft-07/schema#" ], "type": "string" }, "additionalProperties": { "default": false, "description": "Allows properties other than schema defined in corresponding secure configs.", "type": "boolean" }, "description": { "description": "Secure config schema description.", "type": "string" }, "properties": { "additionalProperties": { "properties": { "description": { "description": "Property description.", "type": "string" }, "format": { "description": "Property value format.", "enum": [ "uri" ], "type": "string" }, "title": { "description": "Property title.", "type": "string" }, "type": { "description": "Property type.", "enum": [ "string", "array" ], "type": "string" } }, "required": [ "description", "title", "type" ], "type": "object", "x-versionadded": "v2.36" }, "description": "Secure config properties' definitions.", "type": "object" }, "required": { "description": "The list of required properties.", "items": { "type": "string" }, "type": "array" }, "title": { "description": "Secure config schema title.", "type": "string" }, "type": { "default": "object", "description": "Secure config definition type.", "enum": [ "object" ], "type": "string" } }, "required": [ "$id", "$schema", "additionalProperties", "description", "properties", "required", "title" ], "type": "object", "x-versionadded": "v2.36" } }, "required": [ "id", "name", "schemaDefinition" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [SecureConfigSchemaResponse] | true | maxItems: 100 | The list of secure configuration schemas. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## SecureConfigSchemaProperty ``` { "properties": { "description": { "description": "Property description.", "type": "string" }, "format": { "description": "Property value format.", "enum": [ "uri" ], "type": "string" }, "title": { "description": "Property title.", "type": "string" }, "type": { "description": "Property type.", "enum": [ "string", "array" ], "type": "string" } }, "required": [ "description", "title", "type" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | Property description. | | format | string | false | | Property value format. | | title | string | true | | Property title. | | type | string | true | | Property type. | ### Enumerated Values | Property | Value | | --- | --- | | format | uri | | type | [string, array] | ## SecureConfigSchemaResponse ``` { "properties": { "id": { "description": "The ID of the secure configuration.", "maxLength": 36, "minLength": 32, "type": "string" }, "name": { "description": "The name of the secure configuration.", "type": "string" }, "schemaDefinition": { "description": "Secure config schema definition.", "properties": { "$id": { "description": "Secure config schema identifier.", "format": "uri", "type": "string" }, "$schema": { "default": "http://json-schema.org/draft-07/schema#", "description": "The JSON schema meta schema version.", "enum": [ "http://json-schema.org/draft-07/schema#" ], "type": "string" }, "additionalProperties": { "default": false, "description": "Allows properties other than schema defined in corresponding secure configs.", "type": "boolean" }, "description": { "description": "Secure config schema description.", "type": "string" }, "properties": { "additionalProperties": { "properties": { "description": { "description": "Property description.", "type": "string" }, "format": { "description": "Property value format.", "enum": [ "uri" ], "type": "string" }, "title": { "description": "Property title.", "type": "string" }, "type": { "description": "Property type.", "enum": [ "string", "array" ], "type": "string" } }, "required": [ "description", "title", "type" ], "type": "object", "x-versionadded": "v2.36" }, "description": "Secure config properties' definitions.", "type": "object" }, "required": { "description": "The list of required properties.", "items": { "type": "string" }, "type": "array" }, "title": { "description": "Secure config schema title.", "type": "string" }, "type": { "default": "object", "description": "Secure config definition type.", "enum": [ "object" ], "type": "string" } }, "required": [ "$id", "$schema", "additionalProperties", "description", "properties", "required", "title" ], "type": "object", "x-versionadded": "v2.36" } }, "required": [ "id", "name", "schemaDefinition" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | maxLength: 36minLength: 32minLength: 32 | The ID of the secure configuration. | | name | string | true | | The name of the secure configuration. | | schemaDefinition | SecureConfigSchema | true | | Secure config schema definition. | ## SecureConfigSharedRolesEntry ``` { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "name": { "description": "The name of the user, group, or organization.", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient. | | name | string | true | | The name of the user, group, or organization. | | role | string | true | | The assigned role | | shareRecipientType | string | true | | The recipient type. | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, EDITOR, CONSUMER] | | shareRecipientType | [user, group, organization] | ## SecureConfigSharedRolesList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Details about the Shared Role entries.", "items": { "properties": { "id": { "description": "The ID of the recipient.", "type": "string" }, "name": { "description": "The name of the user, group, or organization.", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [SecureConfigSharedRolesEntry] | true | maxItems: 100 | Details about the Shared Role entries. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## SecureConfigSharingUpdate ``` { "properties": { "roles": { "description": "An array of RoleRequest objects. May contain at most 100 such objects.", "items": { "oneOf": [ { "properties": { "canShare": { "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role of the recipient on this entity. One of OWNER, EDITOR, CONSUMER.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "Username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object", "x-versionadded": "v2.36" }, { "properties": { "canShare": { "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity. One of OWNER, EDITOR, CONSUMER.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object", "x-versionadded": "v2.36" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "roles" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | roles | [oneOf] | true | maxItems: 100minItems: 1 | An array of RoleRequest objects. May contain at most 100 such objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SecureConfigGrantAccessControlWithUsernameWithGrantValidators | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | SecureConfigGrantAccessControlWithIdWithGrantValidators | false | | none | ## SecureConfigUpdate ``` { "properties": { "name": { "description": "The name of the secure configuration.", "type": "string" }, "schemaName": { "description": "The name of the schema used for validating the secure configuration.", "type": [ "string", "null" ] }, "values": { "description": "The values to associate with the secure configuration.", "items": { "properties": { "key": { "description": "The name of the key for the secure configuration value.", "type": "string" }, "value": { "description": "The value of the secure configuration.", "type": "string" } }, "required": [ "key", "value" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | | The name of the secure configuration. | | schemaName | string,null | false | | The name of the schema used for validating the secure configuration. | | values | [SecureConfigKeyValue] | false | maxItems: 100 | The values to associate with the secure configuration. | --- # SSO configuration URL: https://docs.datarobot.com/en/docs/api/reference/public-api/sso_configuration.html > Use the endpoints described below to configure single sign-on for your organization. # SSO configuration Use the endpoints described below to configure single sign-on for your organization. ## List sso configurations Operation path: `GET /api/v2/ssoConfigurations/` Authentication requirements: `BearerAuth` List the sso configurations that correspond to provided conditions. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. | | limit | query | integer | false | The number of records to return. | | orgId | query | string | false | The ID of the organization. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of SSO configurations returned.", "minimum": 0, "type": "integer" }, "data": { "description": "SSO configuration.", "items": { "properties": { "attributeMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "autoGenerateUsers": { "description": "Determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application.", "type": "boolean" }, "certificate": { "description": "Certificate to be used by IdP.", "properties": { "fileName": { "description": "Path to certificate file.", "type": "string" }, "value": { "description": "Certificate content.", "type": "string" } }, "required": [ "value" ], "type": "object" }, "configurationType": { "description": "The type of the SSO configuration, defines the source of SSO metadata.\n It can be one of the following: `METADATA` - when IDP metadata is provided in the\n config, `METADATA_URL` - when an URL for metadata retrieval is provided in the config and\n `MANUAL` - when IDP sign-on/sign-out URLs and certificate are provided.", "enum": [ "MANUAL", "METADATA", "METADATA_URL" ], "type": "string" }, "enableSso": { "description": "Defines if SSO is enabled.", "type": "boolean" }, "enforceSso": { "description": "Defines if SSO is enforced.", "type": "boolean" }, "entityId": { "description": "The globally unique identifier of the entity. Provided by IdP service.", "type": "string" }, "groupDelimiter": { "description": "A delimiter used to split IdP provided Group assertions if provided as a singledelimiter-separated list.", "type": "string" }, "groupMapping": { "description": "The list of DataRobot group to identity provider group maps.", "items": { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "datarobotGroupName": { "description": "DataRobot group name.", "type": "string" }, "idpGroupId": { "description": "A name of the identity provider group.", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "SSO configuration ID.", "type": "string" }, "idpMetadata": { "description": "XML document, IdP SSO descriptor. Provided by IdP service.", "properties": { "fileName": { "description": "Path to IdP metadata file.", "type": "string" }, "value": { "description": "IdP metadata.", "type": "string" } }, "required": [ "fileName", "value" ], "type": "object" }, "idpMetadataHttpsVerify": { "description": "When idp_metadata_url uses HTTPS, require the server to have a trusted certificate.\n To avoid security vulnerabilities, only set to False when a trusted server has a\n self-signed certificate.", "type": "boolean" }, "idpMetadataUrl": { "description": "URL to the IdP SSO descriptor. Provided by IdP service.", "format": "uri", "type": "string" }, "idpResponseMethod": { "default": "POST", "description": "Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side.", "enum": [ "POST", "REDIRECT" ], "type": "string" }, "issuer": { "description": "Optional Issuer field that may be required by IdP.", "type": [ "string", "null" ] }, "name": { "description": "The name of the SSO configuration.", "type": "string" }, "organizationId": { "description": "The organization ID to which the SSO config belongs.", "type": "string" }, "organizationMapping": { "description": "The list of DataRobot organization to identity provider organization maps.", "items": { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "datarobotOrganizationName": { "description": "DataRobot organization name.", "type": "string" }, "idpOrganizationId": { "description": "A name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.37" }, "roleDelimiter": { "description": "A delimiter used to split IdP provided Role assertions if provided as a singledelimiter-separated list.", "type": "string" }, "roleMapping": { "description": "The list of DataRobot access role to identity provider role maps.", "items": { "properties": { "datarobotRoleId": { "description": "DataRobot access role ID.", "type": "string" }, "idpRoleId": { "description": "Name of the identity provider role.", "type": "string" } }, "required": [ "datarobotRoleId", "idpRoleId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "securityParameters": { "description": "The object that contains SAML specific directives.", "properties": { "allowUnsolicited": { "description": "Allow unsolicited.", "type": "boolean" }, "authnRequestsSigned": { "description": "Sign auth requests.", "type": "boolean" }, "logoutRequestsSigned": { "description": "Sign logout requests.", "type": "boolean" }, "wantAssertionsSigned": { "description": "Sign assertions.", "type": "boolean" }, "wantResponseSigned": { "description": "Sign response.", "type": "boolean" } }, "type": "object" }, "sessionLengthSeconds": { "default": 604800, "description": "Time window for the authentication session via IDP", "exclusiveMinimum": 0, "type": "integer" }, "signOnUrl": { "description": "URL to sign on via SSO.", "format": "uri", "type": "string" }, "signOutUrl": { "description": "URL to sign out via SSO.", "format": "uri", "type": "string" }, "spRequestMethod": { "default": "REDIRECT", "description": "Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form.", "enum": [ "POST", "REDIRECT" ], "type": "string" } }, "required": [ "configurationType", "enableSso", "enforceSso", "entityId", "id", "idpResponseMethod", "name", "sessionLengthSeconds", "spRequestMethod" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "Link to the next page of the SSO configurations.", "type": [ "string", "null" ] }, "previous": { "description": "Link to the previous page of the SSO configurations.", "type": [ "string", "null" ] }, "totalCount": { "description": "Total number of SSO configurations.", "minimum": 0, "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of sso configurations. | ListSsoConfigurationResponse | ## Create an SSO configuration Operation path: `POST /api/v2/ssoConfigurations/` Authentication requirements: `BearerAuth` Create an SSO configuration for a specific organization. ### Body parameter ``` { "properties": { "attributeMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "autoGenerateUsers": { "description": "Determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application.", "type": "boolean" }, "certificate": { "description": "Certificate to be used by IdP.", "properties": { "fileName": { "description": "Path to certificate file.", "type": "string" }, "value": { "description": "Certificate content.", "type": "string" } }, "required": [ "value" ], "type": "object" }, "configurationType": { "description": "The type of the SSO configuration, defines the source of SSO metadata.\n It can be one of the following: `METADATA` - when IDP metadata is provided in the\n config, `METADATA_URL` - when an URL for metadata retrieval is provided in the config and\n `MANUAL` - when IDP sign-on/sign-out URLs and certificate are provided.", "enum": [ "MANUAL", "METADATA", "METADATA_URL" ], "type": "string" }, "enableSso": { "description": "Defines if SSO is enabled.", "type": "boolean" }, "enforceSso": { "description": "Defines if SSO is enforced.", "type": "boolean" }, "entityId": { "description": "The globally unique identifier of the entity. Provided by IdP service.", "type": "string" }, "groupMapping": { "description": "The list of DataRobot group to identity provider group maps.", "items": { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "idpGroupId": { "description": "Name of the identity provider group", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "idpMetadata": { "description": "XML document, IdP SSO descriptor. Provided by IdP service.", "properties": { "fileName": { "description": "Path to IdP metadata file.", "type": "string" }, "value": { "description": "IdP metadata.", "type": "string" } }, "required": [ "fileName", "value" ], "type": "object" }, "idpMetadataHttpsVerify": { "description": "When idp_metadata_url uses HTTPS, require the server to have a trusted certificate.\n To avoid security vulnerabilities, only set to False when a trusted server has a\n self-signed certificate.", "type": "boolean" }, "idpMetadataUrl": { "description": "URL to the IdP SSO descriptor. Provided by IdP service.", "format": "uri", "type": "string" }, "idpResponseMethod": { "default": "POST", "description": "Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side.", "enum": [ "POST", "REDIRECT" ], "type": "string" }, "issuer": { "description": "Optional Issuer field that may be required by IdP.", "type": "string" }, "name": { "description": "The name of the SSO configuration.", "type": "string" }, "organizationId": { "description": "The organization ID to which the SSO config belongs.", "type": "string" }, "organizationMapping": { "description": "The list of DataRobot organization to identity provider organization maps.", "items": { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "idpOrganizationId": { "description": "Name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.37" }, "roleMapping": { "description": "The list of DataRobot access role to identity provider role maps.", "items": { "properties": { "datarobotRoleId": { "description": "DataRobot access role ID.", "type": "string" }, "idpRoleId": { "description": "Name of the identity provider role.", "type": "string" } }, "required": [ "datarobotRoleId", "idpRoleId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "securityParameters": { "description": "The object that contains SAML specific directives.", "properties": { "allowUnsolicited": { "description": "Allow unsolicited.", "type": "boolean" }, "authnRequestsSigned": { "description": "Sign auth requests.", "type": "boolean" }, "logoutRequestsSigned": { "description": "Sign logout requests.", "type": "boolean" }, "wantAssertionsSigned": { "description": "Sign assertions.", "type": "boolean" }, "wantResponseSigned": { "description": "Sign response.", "type": "boolean" } }, "type": "object" }, "sessionLengthSeconds": { "default": 604800, "description": "Time window for the authentication session via IDP", "exclusiveMinimum": 0, "type": "integer" }, "signOnUrl": { "description": "URL to sign on via SSO.", "format": "uri", "type": "string" }, "signOutUrl": { "description": "URL to sign out via SSO.", "format": "uri", "type": "string" }, "spRequestMethod": { "default": "REDIRECT", "description": "Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form.", "enum": [ "POST", "REDIRECT" ], "type": "string" } }, "required": [ "configurationType", "enableSso", "enforceSso", "entityId", "idpResponseMethod", "name", "sessionLengthSeconds", "spRequestMethod" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateSsoConfiguration | false | none | ### Example responses > 200 Response ``` { "properties": { "attributeMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "autoGenerateUsers": { "description": "Determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application.", "type": "boolean" }, "certificate": { "description": "Certificate to be used by IdP.", "properties": { "fileName": { "description": "Path to certificate file.", "type": "string" }, "value": { "description": "Certificate content.", "type": "string" } }, "required": [ "value" ], "type": "object" }, "configurationType": { "description": "The type of the SSO configuration, defines the source of SSO metadata.\n It can be one of the following: `METADATA` - when IDP metadata is provided in the\n config, `METADATA_URL` - when an URL for metadata retrieval is provided in the config and\n `MANUAL` - when IDP sign-on/sign-out URLs and certificate are provided.", "enum": [ "MANUAL", "METADATA", "METADATA_URL" ], "type": "string" }, "enableSso": { "description": "Defines if SSO is enabled.", "type": "boolean" }, "enforceSso": { "description": "Defines if SSO is enforced.", "type": "boolean" }, "entityId": { "description": "The globally unique identifier of the entity. Provided by IdP service.", "type": "string" }, "groupDelimiter": { "description": "A delimiter used to split IdP provided Group assertions if provided as a singledelimiter-separated list.", "type": "string" }, "groupMapping": { "description": "The list of DataRobot group to identity provider group maps.", "items": { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "datarobotGroupName": { "description": "DataRobot group name.", "type": "string" }, "idpGroupId": { "description": "A name of the identity provider group.", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "SSO configuration ID.", "type": "string" }, "idpMetadata": { "description": "XML document, IdP SSO descriptor. Provided by IdP service.", "properties": { "fileName": { "description": "Path to IdP metadata file.", "type": "string" }, "value": { "description": "IdP metadata.", "type": "string" } }, "required": [ "fileName", "value" ], "type": "object" }, "idpMetadataHttpsVerify": { "description": "When idp_metadata_url uses HTTPS, require the server to have a trusted certificate.\n To avoid security vulnerabilities, only set to False when a trusted server has a\n self-signed certificate.", "type": "boolean" }, "idpMetadataUrl": { "description": "URL to the IdP SSO descriptor. Provided by IdP service.", "format": "uri", "type": "string" }, "idpResponseMethod": { "default": "POST", "description": "Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side.", "enum": [ "POST", "REDIRECT" ], "type": "string" }, "issuer": { "description": "Optional Issuer field that may be required by IdP.", "type": [ "string", "null" ] }, "name": { "description": "The name of the SSO configuration.", "type": "string" }, "organizationId": { "description": "The organization ID to which the SSO config belongs.", "type": "string" }, "organizationMapping": { "description": "The list of DataRobot organization to identity provider organization maps.", "items": { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "datarobotOrganizationName": { "description": "DataRobot organization name.", "type": "string" }, "idpOrganizationId": { "description": "A name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.37" }, "roleDelimiter": { "description": "A delimiter used to split IdP provided Role assertions if provided as a singledelimiter-separated list.", "type": "string" }, "roleMapping": { "description": "The list of DataRobot access role to identity provider role maps.", "items": { "properties": { "datarobotRoleId": { "description": "DataRobot access role ID.", "type": "string" }, "idpRoleId": { "description": "Name of the identity provider role.", "type": "string" } }, "required": [ "datarobotRoleId", "idpRoleId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "securityParameters": { "description": "The object that contains SAML specific directives.", "properties": { "allowUnsolicited": { "description": "Allow unsolicited.", "type": "boolean" }, "authnRequestsSigned": { "description": "Sign auth requests.", "type": "boolean" }, "logoutRequestsSigned": { "description": "Sign logout requests.", "type": "boolean" }, "wantAssertionsSigned": { "description": "Sign assertions.", "type": "boolean" }, "wantResponseSigned": { "description": "Sign response.", "type": "boolean" } }, "type": "object" }, "sessionLengthSeconds": { "default": 604800, "description": "Time window for the authentication session via IDP", "exclusiveMinimum": 0, "type": "integer" }, "signOnUrl": { "description": "URL to sign on via SSO.", "format": "uri", "type": "string" }, "signOutUrl": { "description": "URL to sign out via SSO.", "format": "uri", "type": "string" }, "spRequestMethod": { "default": "REDIRECT", "description": "Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form.", "enum": [ "POST", "REDIRECT" ], "type": "string" } }, "required": [ "configurationType", "enableSso", "enforceSso", "entityId", "id", "idpResponseMethod", "name", "sessionLengthSeconds", "spRequestMethod" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Configuration created successfully | EnhancedSsoConfigurationResponse | ## Retrieve SSO configuration of a specific organization by configuration ID Operation path: `GET /api/v2/ssoConfigurations/{configurationId}/` Authentication requirements: `BearerAuth` Retrieve SSO configuration of a specific organization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | configurationId | path | string | true | The ID of the organization to retrieve SSO config for. | ### Example responses > 200 Response ``` { "properties": { "attributeMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "autoGenerateUsers": { "description": "Determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application.", "type": "boolean" }, "certificate": { "description": "Certificate to be used by IdP.", "properties": { "fileName": { "description": "Path to certificate file.", "type": "string" }, "value": { "description": "Certificate content.", "type": "string" } }, "required": [ "value" ], "type": "object" }, "configurationType": { "description": "The type of the SSO configuration, defines the source of SSO metadata.\n It can be one of the following: `METADATA` - when IDP metadata is provided in the\n config, `METADATA_URL` - when an URL for metadata retrieval is provided in the config and\n `MANUAL` - when IDP sign-on/sign-out URLs and certificate are provided.", "enum": [ "MANUAL", "METADATA", "METADATA_URL" ], "type": "string" }, "enableSso": { "description": "Defines if SSO is enabled.", "type": "boolean" }, "enforceSso": { "description": "Defines if SSO is enforced.", "type": "boolean" }, "entityId": { "description": "The globally unique identifier of the entity. Provided by IdP service.", "type": "string" }, "groupDelimiter": { "description": "A delimiter used to split IdP provided Group assertions if provided as a singledelimiter-separated list.", "type": "string" }, "groupMapping": { "description": "The list of DataRobot group to identity provider group maps.", "items": { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "datarobotGroupName": { "description": "DataRobot group name.", "type": "string" }, "idpGroupId": { "description": "A name of the identity provider group.", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "SSO configuration ID.", "type": "string" }, "idpMetadata": { "description": "XML document, IdP SSO descriptor. Provided by IdP service.", "properties": { "fileName": { "description": "Path to IdP metadata file.", "type": "string" }, "value": { "description": "IdP metadata.", "type": "string" } }, "required": [ "fileName", "value" ], "type": "object" }, "idpMetadataHttpsVerify": { "description": "When idp_metadata_url uses HTTPS, require the server to have a trusted certificate.\n To avoid security vulnerabilities, only set to False when a trusted server has a\n self-signed certificate.", "type": "boolean" }, "idpMetadataUrl": { "description": "URL to the IdP SSO descriptor. Provided by IdP service.", "format": "uri", "type": "string" }, "idpResponseMethod": { "default": "POST", "description": "Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side.", "enum": [ "POST", "REDIRECT" ], "type": "string" }, "issuer": { "description": "Optional Issuer field that may be required by IdP.", "type": [ "string", "null" ] }, "name": { "description": "The name of the SSO configuration.", "type": "string" }, "organizationId": { "description": "The organization ID to which the SSO config belongs.", "type": "string" }, "organizationMapping": { "description": "The list of DataRobot organization to identity provider organization maps.", "items": { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "datarobotOrganizationName": { "description": "DataRobot organization name.", "type": "string" }, "idpOrganizationId": { "description": "A name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.37" }, "roleDelimiter": { "description": "A delimiter used to split IdP provided Role assertions if provided as a singledelimiter-separated list.", "type": "string" }, "roleMapping": { "description": "The list of DataRobot access role to identity provider role maps.", "items": { "properties": { "datarobotRoleId": { "description": "DataRobot access role ID.", "type": "string" }, "idpRoleId": { "description": "Name of the identity provider role.", "type": "string" } }, "required": [ "datarobotRoleId", "idpRoleId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "securityParameters": { "description": "The object that contains SAML specific directives.", "properties": { "allowUnsolicited": { "description": "Allow unsolicited.", "type": "boolean" }, "authnRequestsSigned": { "description": "Sign auth requests.", "type": "boolean" }, "logoutRequestsSigned": { "description": "Sign logout requests.", "type": "boolean" }, "wantAssertionsSigned": { "description": "Sign assertions.", "type": "boolean" }, "wantResponseSigned": { "description": "Sign response.", "type": "boolean" } }, "type": "object" }, "sessionLengthSeconds": { "default": 604800, "description": "Time window for the authentication session via IDP", "exclusiveMinimum": 0, "type": "integer" }, "signOnUrl": { "description": "URL to sign on via SSO.", "format": "uri", "type": "string" }, "signOutUrl": { "description": "URL to sign out via SSO.", "format": "uri", "type": "string" }, "spRequestMethod": { "default": "REDIRECT", "description": "Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form.", "enum": [ "POST", "REDIRECT" ], "type": "string" } }, "required": [ "configurationType", "enableSso", "enforceSso", "entityId", "id", "idpResponseMethod", "name", "sessionLengthSeconds", "spRequestMethod" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | SSO configuration. | EnhancedSsoConfigurationResponse | ## Update an SSO configuration by configuration ID Operation path: `PATCH /api/v2/ssoConfigurations/{configurationId}/` Authentication requirements: `BearerAuth` Update an SSO configuration for a specific organization. ### Body parameter ``` { "properties": { "advancedConfiguration": { "description": "An object containing SSO client advanced parameters.", "properties": { "digestAlgorithm": { "description": "Algorithm for calculating digest.", "enum": [ "DIGEST_RIPEMD160", "DIGEST_SHA1", "DIGEST_SHA224", "DIGEST_SHA256", "DIGEST_SHA384", "DIGEST_SHA512" ], "type": "string" }, "samlAttributesMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "samlClientConfiguration": { "description": "Encryption related parameters.", "properties": { "cert_file": { "description": "Path to the pem file with a single certificate.", "type": "string" }, "cert_file_value": { "description": "A single certificate pem file content as a single string. Has priority over cert_file.", "type": "string" }, "encryption_keypairs": { "description": "Indicates which certificates will be used for encryption capabilities.", "items": { "properties": { "cert_file": { "description": "Path to the pem file with a single certificate.", "type": "string" }, "cert_file_value": { "description": "A single certificate pem file content as a single string. Has priority over cert_file.", "type": "string" }, "key_file": { "description": "Path to the private key pem file.", "type": "string" }, "key_file_value": { "description": "The private key pem file content as a single string. Has priority over key_file.", "type": "string" } }, "type": "object" }, "maxItems": 100, "type": "array" }, "id_attr_name": { "description": "Attribute is required to be set to 'Id' value when Okta encrypted assertions are used", "type": "string" }, "id_attr_name_crypto": { "description": "Attribute is required to be set to 'Id' value when Okta encrypted assertions are used", "type": "string" }, "key_file": { "description": "Path to the private key pem file.", "type": "string" }, "key_file_value": { "description": "The private key pem file content as a single string. Has priority over key_file.", "type": "string" } }, "type": "object" }, "signatureAlgorithm": { "description": "Algorithm for calculating signature.", "enum": [ "SIG_RSA_SHA1", "SIG_RSA_SHA224", "SIG_RSA_SHA256", "SIG_RSA_SHA384", "SIG_RSA_SHA512" ], "type": "string" } }, "required": [ "samlAttributesMapping", "samlClientConfiguration" ], "type": "object" }, "attributeMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "autoGenerateUsers": { "description": "determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application.", "type": "boolean" }, "certificate": { "description": "Certificate to be used by IdP.", "properties": { "fileName": { "description": "Path to certificate file.", "type": "string" }, "value": { "description": "Certificate content.", "type": "string" } }, "required": [ "value" ], "type": "object" }, "configurationType": { "description": "The type of the SSO configuration, defines the source of SSO metadata. It can be one of the following: `METADATA` - when IDP metadata is provided in the config, `METADATA_URL` - when an URL for metadata retrieval is provided in the config and `MANUAL` - when IDP sign-on/sign-out URLs and certificate are provided.", "enum": [ "MANUAL", "METADATA", "METADATA_URL" ], "type": "string" }, "enableSso": { "description": "Defines if SSO is enabled.", "type": "boolean" }, "enforceSso": { "description": "Defines if SSO is enforced.", "type": "boolean" }, "entityId": { "description": "The globally unique identifier of the entity. Provided by IdP service.", "type": "string" }, "groupMapping": { "description": "The list of DataRobot group to identity provider group maps.", "items": { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "idpGroupId": { "description": "Name of the identity provider group", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "idpMetadata": { "description": "XML document, IdP SSO descriptor. Provided by IdP service.", "properties": { "fileName": { "description": "Path to IdP metadata file.", "type": "string" }, "value": { "description": "IdP metadata.", "type": "string" } }, "required": [ "fileName", "value" ], "type": "object" }, "idpMetadataHttpsVerify": { "description": "When idp_metadata_url uses HTTPS, require the server to have a trusted certificate. To avoid security vulnerabilities, only set to False when a trusted server has a self-signed certificate.", "type": "boolean" }, "idpMetadataUrl": { "description": "URL to the IdP SSO descriptor. Provided by IdP service.", "format": "uri", "type": "string" }, "idpResponseMethod": { "description": "Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side.", "enum": [ "POST", "REDIRECT" ], "type": "string" }, "issuer": { "description": "Optional Issuer field that may be required by IdP.", "type": "string" }, "name": { "description": "The name of the SSO configuration.", "type": "string" }, "organizationId": { "description": "The organization ID to which the SSO config belongs.", "type": "string" }, "organizationMapping": { "description": "The list of DataRobot organization to identity provider organization maps.", "items": { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "idpOrganizationId": { "description": "Name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.37" }, "roleMapping": { "description": "The list of DataRobot access role to identity provider role maps.", "items": { "properties": { "datarobotRoleId": { "description": "DataRobot access role ID.", "type": "string" }, "idpRoleId": { "description": "Name of the identity provider role.", "type": "string" } }, "required": [ "datarobotRoleId", "idpRoleId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "securityParameters": { "description": "The object that contains SAML specific directives.", "properties": { "allowUnsolicited": { "description": "Allow unsolicited.", "type": "boolean" }, "authnRequestsSigned": { "description": "Sign auth requests.", "type": "boolean" }, "logoutRequestsSigned": { "description": "Sign logout requests.", "type": "boolean" }, "wantAssertionsSigned": { "description": "Sign assertions.", "type": "boolean" }, "wantResponseSigned": { "description": "Sign response.", "type": "boolean" } }, "type": "object" }, "sessionLengthSeconds": { "description": "Time window for the authentication session via IdP.", "type": "integer" }, "signOnUrl": { "description": "URL to sign on via SSO.", "format": "uri", "type": "string" }, "signOutUrl": { "description": "URL to sign out via SSO.", "format": "uri", "type": "string" }, "spRequestMethod": { "description": "Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form.", "enum": [ "POST", "REDIRECT" ], "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | configurationId | path | string | true | The ID of the organization to retrieve SSO config for. | | body | body | UpdateSsoConfiguration | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | # Schemas ## CreateSsoConfiguration ``` { "properties": { "attributeMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "autoGenerateUsers": { "description": "Determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application.", "type": "boolean" }, "certificate": { "description": "Certificate to be used by IdP.", "properties": { "fileName": { "description": "Path to certificate file.", "type": "string" }, "value": { "description": "Certificate content.", "type": "string" } }, "required": [ "value" ], "type": "object" }, "configurationType": { "description": "The type of the SSO configuration, defines the source of SSO metadata.\n It can be one of the following: `METADATA` - when IDP metadata is provided in the\n config, `METADATA_URL` - when an URL for metadata retrieval is provided in the config and\n `MANUAL` - when IDP sign-on/sign-out URLs and certificate are provided.", "enum": [ "MANUAL", "METADATA", "METADATA_URL" ], "type": "string" }, "enableSso": { "description": "Defines if SSO is enabled.", "type": "boolean" }, "enforceSso": { "description": "Defines if SSO is enforced.", "type": "boolean" }, "entityId": { "description": "The globally unique identifier of the entity. Provided by IdP service.", "type": "string" }, "groupMapping": { "description": "The list of DataRobot group to identity provider group maps.", "items": { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "idpGroupId": { "description": "Name of the identity provider group", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "idpMetadata": { "description": "XML document, IdP SSO descriptor. Provided by IdP service.", "properties": { "fileName": { "description": "Path to IdP metadata file.", "type": "string" }, "value": { "description": "IdP metadata.", "type": "string" } }, "required": [ "fileName", "value" ], "type": "object" }, "idpMetadataHttpsVerify": { "description": "When idp_metadata_url uses HTTPS, require the server to have a trusted certificate.\n To avoid security vulnerabilities, only set to False when a trusted server has a\n self-signed certificate.", "type": "boolean" }, "idpMetadataUrl": { "description": "URL to the IdP SSO descriptor. Provided by IdP service.", "format": "uri", "type": "string" }, "idpResponseMethod": { "default": "POST", "description": "Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side.", "enum": [ "POST", "REDIRECT" ], "type": "string" }, "issuer": { "description": "Optional Issuer field that may be required by IdP.", "type": "string" }, "name": { "description": "The name of the SSO configuration.", "type": "string" }, "organizationId": { "description": "The organization ID to which the SSO config belongs.", "type": "string" }, "organizationMapping": { "description": "The list of DataRobot organization to identity provider organization maps.", "items": { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "idpOrganizationId": { "description": "Name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.37" }, "roleMapping": { "description": "The list of DataRobot access role to identity provider role maps.", "items": { "properties": { "datarobotRoleId": { "description": "DataRobot access role ID.", "type": "string" }, "idpRoleId": { "description": "Name of the identity provider role.", "type": "string" } }, "required": [ "datarobotRoleId", "idpRoleId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "securityParameters": { "description": "The object that contains SAML specific directives.", "properties": { "allowUnsolicited": { "description": "Allow unsolicited.", "type": "boolean" }, "authnRequestsSigned": { "description": "Sign auth requests.", "type": "boolean" }, "logoutRequestsSigned": { "description": "Sign logout requests.", "type": "boolean" }, "wantAssertionsSigned": { "description": "Sign assertions.", "type": "boolean" }, "wantResponseSigned": { "description": "Sign response.", "type": "boolean" } }, "type": "object" }, "sessionLengthSeconds": { "default": 604800, "description": "Time window for the authentication session via IDP", "exclusiveMinimum": 0, "type": "integer" }, "signOnUrl": { "description": "URL to sign on via SSO.", "format": "uri", "type": "string" }, "signOutUrl": { "description": "URL to sign out via SSO.", "format": "uri", "type": "string" }, "spRequestMethod": { "default": "REDIRECT", "description": "Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form.", "enum": [ "POST", "REDIRECT" ], "type": "string" } }, "required": [ "configurationType", "enableSso", "enforceSso", "entityId", "idpResponseMethod", "name", "sessionLengthSeconds", "spRequestMethod" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributeMapping | EnhancedSamlAttributeMapping | false | | Attribute mapping between DataRobot and IdP. | | autoGenerateUsers | boolean | false | | Determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application. | | certificate | SamlCertificate | false | | Certificate to be used by IdP. | | configurationType | string | true | | The type of the SSO configuration, defines the source of SSO metadata. It can be one of the following: METADATA - when IDP metadata is provided in the config, METADATA_URL - when an URL for metadata retrieval is provided in the config and MANUAL - when IDP sign-on/sign-out URLs and certificate are provided. | | enableSso | boolean | true | | Defines if SSO is enabled. | | enforceSso | boolean | true | | Defines if SSO is enforced. | | entityId | string | true | | The globally unique identifier of the entity. Provided by IdP service. | | groupMapping | [EnhancedSamlGroupMapping] | false | maxItems: 100 | The list of DataRobot group to identity provider group maps. | | idpMetadata | SamlMetadataFile | false | | XML document, IdP SSO descriptor. Provided by IdP service. | | idpMetadataHttpsVerify | boolean | false | | When idp_metadata_url uses HTTPS, require the server to have a trusted certificate. To avoid security vulnerabilities, only set to False when a trusted server has a self-signed certificate. | | idpMetadataUrl | string(uri) | false | | URL to the IdP SSO descriptor. Provided by IdP service. | | idpResponseMethod | string | true | | Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side. | | issuer | string | false | | Optional Issuer field that may be required by IdP. | | name | string | true | | The name of the SSO configuration. | | organizationId | string | false | | The organization ID to which the SSO config belongs. | | organizationMapping | [EnhancedSamlOrganizationMapping] | false | maxItems: 100 | The list of DataRobot organization to identity provider organization maps. | | roleMapping | [EnhancedSamlRoleMapping] | false | maxItems: 100 | The list of DataRobot access role to identity provider role maps. | | securityParameters | SamlSecurityParameters | false | | The object that contains SAML specific directives. | | sessionLengthSeconds | integer | true | | Time window for the authentication session via IDP | | signOnUrl | string(uri) | false | | URL to sign on via SSO. | | signOutUrl | string(uri) | false | | URL to sign out via SSO. | | spRequestMethod | string | true | | Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form. | ### Enumerated Values | Property | Value | | --- | --- | | configurationType | [MANUAL, METADATA, METADATA_URL] | | idpResponseMethod | [POST, REDIRECT] | | spRequestMethod | [POST, REDIRECT] | ## EnhancedEncryptionKeypairs ``` { "properties": { "cert_file": { "description": "Path to the pem file with a single certificate.", "type": "string" }, "cert_file_value": { "description": "A single certificate pem file content as a single string. Has priority over cert_file.", "type": "string" }, "key_file": { "description": "Path to the private key pem file.", "type": "string" }, "key_file_value": { "description": "The private key pem file content as a single string. Has priority over key_file.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cert_file | string | false | | Path to the pem file with a single certificate. | | cert_file_value | string | false | | A single certificate pem file content as a single string. Has priority over cert_file. | | key_file | string | false | | Path to the private key pem file. | | key_file_value | string | false | | The private key pem file content as a single string. Has priority over key_file. | ## EnhancedSamlAttributeMapping ``` { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" } ``` Attribute mapping between DataRobot and IdP. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | displayName | string | false | | Display name. | | email | string | false | | Email. | | firstName | string | false | | First name. | | group | string | false | | Group. | | impersonationUser | string | false | | Impersonation user. | | lastName | string | false | | Last name. | | organization | string | false | | Organization. | | role | string | false | | Role. | | username | string | false | | Username. | ## EnhancedSamlClientConfig ``` { "description": "Encryption related parameters.", "properties": { "cert_file": { "description": "Path to the pem file with a single certificate.", "type": "string" }, "cert_file_value": { "description": "A single certificate pem file content as a single string. Has priority over cert_file.", "type": "string" }, "encryption_keypairs": { "description": "Indicates which certificates will be used for encryption capabilities.", "items": { "properties": { "cert_file": { "description": "Path to the pem file with a single certificate.", "type": "string" }, "cert_file_value": { "description": "A single certificate pem file content as a single string. Has priority over cert_file.", "type": "string" }, "key_file": { "description": "Path to the private key pem file.", "type": "string" }, "key_file_value": { "description": "The private key pem file content as a single string. Has priority over key_file.", "type": "string" } }, "type": "object" }, "maxItems": 100, "type": "array" }, "id_attr_name": { "description": "Attribute is required to be set to 'Id' value when Okta encrypted assertions are used", "type": "string" }, "id_attr_name_crypto": { "description": "Attribute is required to be set to 'Id' value when Okta encrypted assertions are used", "type": "string" }, "key_file": { "description": "Path to the private key pem file.", "type": "string" }, "key_file_value": { "description": "The private key pem file content as a single string. Has priority over key_file.", "type": "string" } }, "type": "object" } ``` Encryption related parameters. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cert_file | string | false | | Path to the pem file with a single certificate. | | cert_file_value | string | false | | A single certificate pem file content as a single string. Has priority over cert_file. | | encryption_keypairs | [EnhancedEncryptionKeypairs] | false | maxItems: 100 | Indicates which certificates will be used for encryption capabilities. | | id_attr_name | string | false | | Attribute is required to be set to 'Id' value when Okta encrypted assertions are used | | id_attr_name_crypto | string | false | | Attribute is required to be set to 'Id' value when Okta encrypted assertions are used | | key_file | string | false | | Path to the private key pem file. | | key_file_value | string | false | | The private key pem file content as a single string. Has priority over key_file. | ## EnhancedSamlGroupMapping ``` { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "idpGroupId": { "description": "Name of the identity provider group", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datarobotGroupId | string | true | | DataRobot group ID. | | idpGroupId | string | true | | Name of the identity provider group | ## EnhancedSamlOrganizationMapping ``` { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "idpOrganizationId": { "description": "Name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datarobotOrganizationId | string | true | | DataRobot organization ID. | | idpOrganizationId | string | true | | Name of the identity provider organization. | ## EnhancedSamlRoleMapping ``` { "properties": { "datarobotRoleId": { "description": "DataRobot access role ID.", "type": "string" }, "idpRoleId": { "description": "Name of the identity provider role.", "type": "string" } }, "required": [ "datarobotRoleId", "idpRoleId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datarobotRoleId | string | true | | DataRobot access role ID. | | idpRoleId | string | true | | Name of the identity provider role. | ## EnhancedSsoConfigurationResponse ``` { "properties": { "attributeMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "autoGenerateUsers": { "description": "Determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application.", "type": "boolean" }, "certificate": { "description": "Certificate to be used by IdP.", "properties": { "fileName": { "description": "Path to certificate file.", "type": "string" }, "value": { "description": "Certificate content.", "type": "string" } }, "required": [ "value" ], "type": "object" }, "configurationType": { "description": "The type of the SSO configuration, defines the source of SSO metadata.\n It can be one of the following: `METADATA` - when IDP metadata is provided in the\n config, `METADATA_URL` - when an URL for metadata retrieval is provided in the config and\n `MANUAL` - when IDP sign-on/sign-out URLs and certificate are provided.", "enum": [ "MANUAL", "METADATA", "METADATA_URL" ], "type": "string" }, "enableSso": { "description": "Defines if SSO is enabled.", "type": "boolean" }, "enforceSso": { "description": "Defines if SSO is enforced.", "type": "boolean" }, "entityId": { "description": "The globally unique identifier of the entity. Provided by IdP service.", "type": "string" }, "groupDelimiter": { "description": "A delimiter used to split IdP provided Group assertions if provided as a singledelimiter-separated list.", "type": "string" }, "groupMapping": { "description": "The list of DataRobot group to identity provider group maps.", "items": { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "datarobotGroupName": { "description": "DataRobot group name.", "type": "string" }, "idpGroupId": { "description": "A name of the identity provider group.", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "SSO configuration ID.", "type": "string" }, "idpMetadata": { "description": "XML document, IdP SSO descriptor. Provided by IdP service.", "properties": { "fileName": { "description": "Path to IdP metadata file.", "type": "string" }, "value": { "description": "IdP metadata.", "type": "string" } }, "required": [ "fileName", "value" ], "type": "object" }, "idpMetadataHttpsVerify": { "description": "When idp_metadata_url uses HTTPS, require the server to have a trusted certificate.\n To avoid security vulnerabilities, only set to False when a trusted server has a\n self-signed certificate.", "type": "boolean" }, "idpMetadataUrl": { "description": "URL to the IdP SSO descriptor. Provided by IdP service.", "format": "uri", "type": "string" }, "idpResponseMethod": { "default": "POST", "description": "Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side.", "enum": [ "POST", "REDIRECT" ], "type": "string" }, "issuer": { "description": "Optional Issuer field that may be required by IdP.", "type": [ "string", "null" ] }, "name": { "description": "The name of the SSO configuration.", "type": "string" }, "organizationId": { "description": "The organization ID to which the SSO config belongs.", "type": "string" }, "organizationMapping": { "description": "The list of DataRobot organization to identity provider organization maps.", "items": { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "datarobotOrganizationName": { "description": "DataRobot organization name.", "type": "string" }, "idpOrganizationId": { "description": "A name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.37" }, "roleDelimiter": { "description": "A delimiter used to split IdP provided Role assertions if provided as a singledelimiter-separated list.", "type": "string" }, "roleMapping": { "description": "The list of DataRobot access role to identity provider role maps.", "items": { "properties": { "datarobotRoleId": { "description": "DataRobot access role ID.", "type": "string" }, "idpRoleId": { "description": "Name of the identity provider role.", "type": "string" } }, "required": [ "datarobotRoleId", "idpRoleId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "securityParameters": { "description": "The object that contains SAML specific directives.", "properties": { "allowUnsolicited": { "description": "Allow unsolicited.", "type": "boolean" }, "authnRequestsSigned": { "description": "Sign auth requests.", "type": "boolean" }, "logoutRequestsSigned": { "description": "Sign logout requests.", "type": "boolean" }, "wantAssertionsSigned": { "description": "Sign assertions.", "type": "boolean" }, "wantResponseSigned": { "description": "Sign response.", "type": "boolean" } }, "type": "object" }, "sessionLengthSeconds": { "default": 604800, "description": "Time window for the authentication session via IDP", "exclusiveMinimum": 0, "type": "integer" }, "signOnUrl": { "description": "URL to sign on via SSO.", "format": "uri", "type": "string" }, "signOutUrl": { "description": "URL to sign out via SSO.", "format": "uri", "type": "string" }, "spRequestMethod": { "default": "REDIRECT", "description": "Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form.", "enum": [ "POST", "REDIRECT" ], "type": "string" } }, "required": [ "configurationType", "enableSso", "enforceSso", "entityId", "id", "idpResponseMethod", "name", "sessionLengthSeconds", "spRequestMethod" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributeMapping | EnhancedSamlAttributeMapping | false | | Attribute mapping between DataRobot and IdP. | | autoGenerateUsers | boolean | false | | Determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application. | | certificate | SamlCertificate | false | | Certificate to be used by IdP. | | configurationType | string | true | | The type of the SSO configuration, defines the source of SSO metadata. It can be one of the following: METADATA - when IDP metadata is provided in the config, METADATA_URL - when an URL for metadata retrieval is provided in the config and MANUAL - when IDP sign-on/sign-out URLs and certificate are provided. | | enableSso | boolean | true | | Defines if SSO is enabled. | | enforceSso | boolean | true | | Defines if SSO is enforced. | | entityId | string | true | | The globally unique identifier of the entity. Provided by IdP service. | | groupDelimiter | string | false | | A delimiter used to split IdP provided Group assertions if provided as a singledelimiter-separated list. | | groupMapping | [SamlGroupMappingResponse] | false | maxItems: 100 | The list of DataRobot group to identity provider group maps. | | id | string | true | | SSO configuration ID. | | idpMetadata | SamlMetadataFile | false | | XML document, IdP SSO descriptor. Provided by IdP service. | | idpMetadataHttpsVerify | boolean | false | | When idp_metadata_url uses HTTPS, require the server to have a trusted certificate. To avoid security vulnerabilities, only set to False when a trusted server has a self-signed certificate. | | idpMetadataUrl | string(uri) | false | | URL to the IdP SSO descriptor. Provided by IdP service. | | idpResponseMethod | string | true | | Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side. | | issuer | string,null | false | | Optional Issuer field that may be required by IdP. | | name | string | true | | The name of the SSO configuration. | | organizationId | string | false | | The organization ID to which the SSO config belongs. | | organizationMapping | [SamlOrganizationMappingResponse] | false | maxItems: 100 | The list of DataRobot organization to identity provider organization maps. | | roleDelimiter | string | false | | A delimiter used to split IdP provided Role assertions if provided as a singledelimiter-separated list. | | roleMapping | [EnhancedSamlRoleMapping] | false | maxItems: 100 | The list of DataRobot access role to identity provider role maps. | | securityParameters | SamlSecurityParameters | false | | The object that contains SAML specific directives. | | sessionLengthSeconds | integer | true | | Time window for the authentication session via IDP | | signOnUrl | string(uri) | false | | URL to sign on via SSO. | | signOutUrl | string(uri) | false | | URL to sign out via SSO. | | spRequestMethod | string | true | | Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form. | ### Enumerated Values | Property | Value | | --- | --- | | configurationType | [MANUAL, METADATA, METADATA_URL] | | idpResponseMethod | [POST, REDIRECT] | | spRequestMethod | [POST, REDIRECT] | ## ListSsoConfigurationResponse ``` { "properties": { "count": { "description": "Number of SSO configurations returned.", "minimum": 0, "type": "integer" }, "data": { "description": "SSO configuration.", "items": { "properties": { "attributeMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "autoGenerateUsers": { "description": "Determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application.", "type": "boolean" }, "certificate": { "description": "Certificate to be used by IdP.", "properties": { "fileName": { "description": "Path to certificate file.", "type": "string" }, "value": { "description": "Certificate content.", "type": "string" } }, "required": [ "value" ], "type": "object" }, "configurationType": { "description": "The type of the SSO configuration, defines the source of SSO metadata.\n It can be one of the following: `METADATA` - when IDP metadata is provided in the\n config, `METADATA_URL` - when an URL for metadata retrieval is provided in the config and\n `MANUAL` - when IDP sign-on/sign-out URLs and certificate are provided.", "enum": [ "MANUAL", "METADATA", "METADATA_URL" ], "type": "string" }, "enableSso": { "description": "Defines if SSO is enabled.", "type": "boolean" }, "enforceSso": { "description": "Defines if SSO is enforced.", "type": "boolean" }, "entityId": { "description": "The globally unique identifier of the entity. Provided by IdP service.", "type": "string" }, "groupDelimiter": { "description": "A delimiter used to split IdP provided Group assertions if provided as a singledelimiter-separated list.", "type": "string" }, "groupMapping": { "description": "The list of DataRobot group to identity provider group maps.", "items": { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "datarobotGroupName": { "description": "DataRobot group name.", "type": "string" }, "idpGroupId": { "description": "A name of the identity provider group.", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "id": { "description": "SSO configuration ID.", "type": "string" }, "idpMetadata": { "description": "XML document, IdP SSO descriptor. Provided by IdP service.", "properties": { "fileName": { "description": "Path to IdP metadata file.", "type": "string" }, "value": { "description": "IdP metadata.", "type": "string" } }, "required": [ "fileName", "value" ], "type": "object" }, "idpMetadataHttpsVerify": { "description": "When idp_metadata_url uses HTTPS, require the server to have a trusted certificate.\n To avoid security vulnerabilities, only set to False when a trusted server has a\n self-signed certificate.", "type": "boolean" }, "idpMetadataUrl": { "description": "URL to the IdP SSO descriptor. Provided by IdP service.", "format": "uri", "type": "string" }, "idpResponseMethod": { "default": "POST", "description": "Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side.", "enum": [ "POST", "REDIRECT" ], "type": "string" }, "issuer": { "description": "Optional Issuer field that may be required by IdP.", "type": [ "string", "null" ] }, "name": { "description": "The name of the SSO configuration.", "type": "string" }, "organizationId": { "description": "The organization ID to which the SSO config belongs.", "type": "string" }, "organizationMapping": { "description": "The list of DataRobot organization to identity provider organization maps.", "items": { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "datarobotOrganizationName": { "description": "DataRobot organization name.", "type": "string" }, "idpOrganizationId": { "description": "A name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.37" }, "roleDelimiter": { "description": "A delimiter used to split IdP provided Role assertions if provided as a singledelimiter-separated list.", "type": "string" }, "roleMapping": { "description": "The list of DataRobot access role to identity provider role maps.", "items": { "properties": { "datarobotRoleId": { "description": "DataRobot access role ID.", "type": "string" }, "idpRoleId": { "description": "Name of the identity provider role.", "type": "string" } }, "required": [ "datarobotRoleId", "idpRoleId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "securityParameters": { "description": "The object that contains SAML specific directives.", "properties": { "allowUnsolicited": { "description": "Allow unsolicited.", "type": "boolean" }, "authnRequestsSigned": { "description": "Sign auth requests.", "type": "boolean" }, "logoutRequestsSigned": { "description": "Sign logout requests.", "type": "boolean" }, "wantAssertionsSigned": { "description": "Sign assertions.", "type": "boolean" }, "wantResponseSigned": { "description": "Sign response.", "type": "boolean" } }, "type": "object" }, "sessionLengthSeconds": { "default": 604800, "description": "Time window for the authentication session via IDP", "exclusiveMinimum": 0, "type": "integer" }, "signOnUrl": { "description": "URL to sign on via SSO.", "format": "uri", "type": "string" }, "signOutUrl": { "description": "URL to sign out via SSO.", "format": "uri", "type": "string" }, "spRequestMethod": { "default": "REDIRECT", "description": "Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form.", "enum": [ "POST", "REDIRECT" ], "type": "string" } }, "required": [ "configurationType", "enableSso", "enforceSso", "entityId", "id", "idpResponseMethod", "name", "sessionLengthSeconds", "spRequestMethod" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "Link to the next page of the SSO configurations.", "type": [ "string", "null" ] }, "previous": { "description": "Link to the previous page of the SSO configurations.", "type": [ "string", "null" ] }, "totalCount": { "description": "Total number of SSO configurations.", "minimum": 0, "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | Number of SSO configurations returned. | | data | [EnhancedSsoConfigurationResponse] | true | maxItems: 1000 | SSO configuration. | | next | string,null | true | | Link to the next page of the SSO configurations. | | previous | string,null | true | | Link to the previous page of the SSO configurations. | | totalCount | integer | true | minimum: 0 | Total number of SSO configurations. | ## SamlAdvancedConfiguration ``` { "description": "An object containing SSO client advanced parameters.", "properties": { "digestAlgorithm": { "description": "Algorithm for calculating digest.", "enum": [ "DIGEST_RIPEMD160", "DIGEST_SHA1", "DIGEST_SHA224", "DIGEST_SHA256", "DIGEST_SHA384", "DIGEST_SHA512" ], "type": "string" }, "samlAttributesMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "samlClientConfiguration": { "description": "Encryption related parameters.", "properties": { "cert_file": { "description": "Path to the pem file with a single certificate.", "type": "string" }, "cert_file_value": { "description": "A single certificate pem file content as a single string. Has priority over cert_file.", "type": "string" }, "encryption_keypairs": { "description": "Indicates which certificates will be used for encryption capabilities.", "items": { "properties": { "cert_file": { "description": "Path to the pem file with a single certificate.", "type": "string" }, "cert_file_value": { "description": "A single certificate pem file content as a single string. Has priority over cert_file.", "type": "string" }, "key_file": { "description": "Path to the private key pem file.", "type": "string" }, "key_file_value": { "description": "The private key pem file content as a single string. Has priority over key_file.", "type": "string" } }, "type": "object" }, "maxItems": 100, "type": "array" }, "id_attr_name": { "description": "Attribute is required to be set to 'Id' value when Okta encrypted assertions are used", "type": "string" }, "id_attr_name_crypto": { "description": "Attribute is required to be set to 'Id' value when Okta encrypted assertions are used", "type": "string" }, "key_file": { "description": "Path to the private key pem file.", "type": "string" }, "key_file_value": { "description": "The private key pem file content as a single string. Has priority over key_file.", "type": "string" } }, "type": "object" }, "signatureAlgorithm": { "description": "Algorithm for calculating signature.", "enum": [ "SIG_RSA_SHA1", "SIG_RSA_SHA224", "SIG_RSA_SHA256", "SIG_RSA_SHA384", "SIG_RSA_SHA512" ], "type": "string" } }, "required": [ "samlAttributesMapping", "samlClientConfiguration" ], "type": "object" } ``` An object containing SSO client advanced parameters. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | digestAlgorithm | string | false | | Algorithm for calculating digest. | | samlAttributesMapping | EnhancedSamlAttributeMapping | true | | Attribute mapping between DataRobot and IdP. | | samlClientConfiguration | EnhancedSamlClientConfig | true | | Encryption related parameters. | | signatureAlgorithm | string | false | | Algorithm for calculating signature. | ### Enumerated Values | Property | Value | | --- | --- | | digestAlgorithm | [DIGEST_RIPEMD160, DIGEST_SHA1, DIGEST_SHA224, DIGEST_SHA256, DIGEST_SHA384, DIGEST_SHA512] | | signatureAlgorithm | [SIG_RSA_SHA1, SIG_RSA_SHA224, SIG_RSA_SHA256, SIG_RSA_SHA384, SIG_RSA_SHA512] | ## SamlCertificate ``` { "description": "Certificate to be used by IdP.", "properties": { "fileName": { "description": "Path to certificate file.", "type": "string" }, "value": { "description": "Certificate content.", "type": "string" } }, "required": [ "value" ], "type": "object" } ``` Certificate to be used by IdP. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fileName | string | false | | Path to certificate file. | | value | string | true | | Certificate content. | ## SamlGroupMappingResponse ``` { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "datarobotGroupName": { "description": "DataRobot group name.", "type": "string" }, "idpGroupId": { "description": "A name of the identity provider group.", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datarobotGroupId | string | true | | DataRobot group ID. | | datarobotGroupName | string | false | | DataRobot group name. | | idpGroupId | string | true | | A name of the identity provider group. | ## SamlMetadataFile ``` { "description": "XML document, IdP SSO descriptor. Provided by IdP service.", "properties": { "fileName": { "description": "Path to IdP metadata file.", "type": "string" }, "value": { "description": "IdP metadata.", "type": "string" } }, "required": [ "fileName", "value" ], "type": "object" } ``` XML document, IdP SSO descriptor. Provided by IdP service. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fileName | string | true | | Path to IdP metadata file. | | value | string | true | | IdP metadata. | ## SamlOrganizationMappingResponse ``` { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "datarobotOrganizationName": { "description": "DataRobot organization name.", "type": "string" }, "idpOrganizationId": { "description": "A name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datarobotOrganizationId | string | true | | DataRobot organization ID. | | datarobotOrganizationName | string | false | | DataRobot organization name. | | idpOrganizationId | string | true | | A name of the identity provider organization. | ## SamlSecurityParameters ``` { "description": "The object that contains SAML specific directives.", "properties": { "allowUnsolicited": { "description": "Allow unsolicited.", "type": "boolean" }, "authnRequestsSigned": { "description": "Sign auth requests.", "type": "boolean" }, "logoutRequestsSigned": { "description": "Sign logout requests.", "type": "boolean" }, "wantAssertionsSigned": { "description": "Sign assertions.", "type": "boolean" }, "wantResponseSigned": { "description": "Sign response.", "type": "boolean" } }, "type": "object" } ``` The object that contains SAML specific directives. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | allowUnsolicited | boolean | false | | Allow unsolicited. | | authnRequestsSigned | boolean | false | | Sign auth requests. | | logoutRequestsSigned | boolean | false | | Sign logout requests. | | wantAssertionsSigned | boolean | false | | Sign assertions. | | wantResponseSigned | boolean | false | | Sign response. | ## UpdateSsoConfiguration ``` { "properties": { "advancedConfiguration": { "description": "An object containing SSO client advanced parameters.", "properties": { "digestAlgorithm": { "description": "Algorithm for calculating digest.", "enum": [ "DIGEST_RIPEMD160", "DIGEST_SHA1", "DIGEST_SHA224", "DIGEST_SHA256", "DIGEST_SHA384", "DIGEST_SHA512" ], "type": "string" }, "samlAttributesMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "samlClientConfiguration": { "description": "Encryption related parameters.", "properties": { "cert_file": { "description": "Path to the pem file with a single certificate.", "type": "string" }, "cert_file_value": { "description": "A single certificate pem file content as a single string. Has priority over cert_file.", "type": "string" }, "encryption_keypairs": { "description": "Indicates which certificates will be used for encryption capabilities.", "items": { "properties": { "cert_file": { "description": "Path to the pem file with a single certificate.", "type": "string" }, "cert_file_value": { "description": "A single certificate pem file content as a single string. Has priority over cert_file.", "type": "string" }, "key_file": { "description": "Path to the private key pem file.", "type": "string" }, "key_file_value": { "description": "The private key pem file content as a single string. Has priority over key_file.", "type": "string" } }, "type": "object" }, "maxItems": 100, "type": "array" }, "id_attr_name": { "description": "Attribute is required to be set to 'Id' value when Okta encrypted assertions are used", "type": "string" }, "id_attr_name_crypto": { "description": "Attribute is required to be set to 'Id' value when Okta encrypted assertions are used", "type": "string" }, "key_file": { "description": "Path to the private key pem file.", "type": "string" }, "key_file_value": { "description": "The private key pem file content as a single string. Has priority over key_file.", "type": "string" } }, "type": "object" }, "signatureAlgorithm": { "description": "Algorithm for calculating signature.", "enum": [ "SIG_RSA_SHA1", "SIG_RSA_SHA224", "SIG_RSA_SHA256", "SIG_RSA_SHA384", "SIG_RSA_SHA512" ], "type": "string" } }, "required": [ "samlAttributesMapping", "samlClientConfiguration" ], "type": "object" }, "attributeMapping": { "description": "Attribute mapping between DataRobot and IdP.", "properties": { "displayName": { "description": "Display name.", "type": "string" }, "email": { "description": "Email.", "type": "string" }, "firstName": { "description": "First name.", "type": "string" }, "group": { "description": "Group.", "type": "string" }, "impersonationUser": { "description": "Impersonation user.", "type": "string" }, "lastName": { "description": "Last name.", "type": "string" }, "organization": { "description": "Organization.", "type": "string", "x-versionadded": "v2.37" }, "role": { "description": "Role.", "type": "string" }, "username": { "description": "Username.", "type": "string" } }, "type": "object" }, "autoGenerateUsers": { "description": "determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application.", "type": "boolean" }, "certificate": { "description": "Certificate to be used by IdP.", "properties": { "fileName": { "description": "Path to certificate file.", "type": "string" }, "value": { "description": "Certificate content.", "type": "string" } }, "required": [ "value" ], "type": "object" }, "configurationType": { "description": "The type of the SSO configuration, defines the source of SSO metadata. It can be one of the following: `METADATA` - when IDP metadata is provided in the config, `METADATA_URL` - when an URL for metadata retrieval is provided in the config and `MANUAL` - when IDP sign-on/sign-out URLs and certificate are provided.", "enum": [ "MANUAL", "METADATA", "METADATA_URL" ], "type": "string" }, "enableSso": { "description": "Defines if SSO is enabled.", "type": "boolean" }, "enforceSso": { "description": "Defines if SSO is enforced.", "type": "boolean" }, "entityId": { "description": "The globally unique identifier of the entity. Provided by IdP service.", "type": "string" }, "groupMapping": { "description": "The list of DataRobot group to identity provider group maps.", "items": { "properties": { "datarobotGroupId": { "description": "DataRobot group ID.", "type": "string" }, "idpGroupId": { "description": "Name of the identity provider group", "type": "string" } }, "required": [ "datarobotGroupId", "idpGroupId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "idpMetadata": { "description": "XML document, IdP SSO descriptor. Provided by IdP service.", "properties": { "fileName": { "description": "Path to IdP metadata file.", "type": "string" }, "value": { "description": "IdP metadata.", "type": "string" } }, "required": [ "fileName", "value" ], "type": "object" }, "idpMetadataHttpsVerify": { "description": "When idp_metadata_url uses HTTPS, require the server to have a trusted certificate. To avoid security vulnerabilities, only set to False when a trusted server has a self-signed certificate.", "type": "boolean" }, "idpMetadataUrl": { "description": "URL to the IdP SSO descriptor. Provided by IdP service.", "format": "uri", "type": "string" }, "idpResponseMethod": { "description": "Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side.", "enum": [ "POST", "REDIRECT" ], "type": "string" }, "issuer": { "description": "Optional Issuer field that may be required by IdP.", "type": "string" }, "name": { "description": "The name of the SSO configuration.", "type": "string" }, "organizationId": { "description": "The organization ID to which the SSO config belongs.", "type": "string" }, "organizationMapping": { "description": "The list of DataRobot organization to identity provider organization maps.", "items": { "properties": { "datarobotOrganizationId": { "description": "DataRobot organization ID.", "type": "string" }, "idpOrganizationId": { "description": "Name of the identity provider organization.", "type": "string" } }, "required": [ "datarobotOrganizationId", "idpOrganizationId" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.37" }, "roleMapping": { "description": "The list of DataRobot access role to identity provider role maps.", "items": { "properties": { "datarobotRoleId": { "description": "DataRobot access role ID.", "type": "string" }, "idpRoleId": { "description": "Name of the identity provider role.", "type": "string" } }, "required": [ "datarobotRoleId", "idpRoleId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "securityParameters": { "description": "The object that contains SAML specific directives.", "properties": { "allowUnsolicited": { "description": "Allow unsolicited.", "type": "boolean" }, "authnRequestsSigned": { "description": "Sign auth requests.", "type": "boolean" }, "logoutRequestsSigned": { "description": "Sign logout requests.", "type": "boolean" }, "wantAssertionsSigned": { "description": "Sign assertions.", "type": "boolean" }, "wantResponseSigned": { "description": "Sign response.", "type": "boolean" } }, "type": "object" }, "sessionLengthSeconds": { "description": "Time window for the authentication session via IdP.", "type": "integer" }, "signOnUrl": { "description": "URL to sign on via SSO.", "format": "uri", "type": "string" }, "signOutUrl": { "description": "URL to sign out via SSO.", "format": "uri", "type": "string" }, "spRequestMethod": { "description": "Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form.", "enum": [ "POST", "REDIRECT" ], "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | advancedConfiguration | SamlAdvancedConfiguration | false | | An object containing SSO client advanced parameters. | | attributeMapping | EnhancedSamlAttributeMapping | false | | Attribute mapping between DataRobot and IdP. | | autoGenerateUsers | boolean | false | | determines if DataRobot automatically creates an account on first successful login via IdP if the user doesn't exist in the DataRobot application. | | certificate | SamlCertificate | false | | Certificate to be used by IdP. | | configurationType | string | false | | The type of the SSO configuration, defines the source of SSO metadata. It can be one of the following: METADATA - when IDP metadata is provided in the config, METADATA_URL - when an URL for metadata retrieval is provided in the config and MANUAL - when IDP sign-on/sign-out URLs and certificate are provided. | | enableSso | boolean | false | | Defines if SSO is enabled. | | enforceSso | boolean | false | | Defines if SSO is enforced. | | entityId | string | false | | The globally unique identifier of the entity. Provided by IdP service. | | groupMapping | [EnhancedSamlGroupMapping] | false | maxItems: 100 | The list of DataRobot group to identity provider group maps. | | idpMetadata | SamlMetadataFile | false | | XML document, IdP SSO descriptor. Provided by IdP service. | | idpMetadataHttpsVerify | boolean | false | | When idp_metadata_url uses HTTPS, require the server to have a trusted certificate. To avoid security vulnerabilities, only set to False when a trusted server has a self-signed certificate. | | idpMetadataUrl | string(uri) | false | | URL to the IdP SSO descriptor. Provided by IdP service. | | idpResponseMethod | string | false | | Identity provider response method, used to move user from IdP's authentication form back to the DataRobot side. | | issuer | string | false | | Optional Issuer field that may be required by IdP. | | name | string | false | | The name of the SSO configuration. | | organizationId | string | false | | The organization ID to which the SSO config belongs. | | organizationMapping | [EnhancedSamlOrganizationMapping] | false | maxItems: 100 | The list of DataRobot organization to identity provider organization maps. | | roleMapping | [EnhancedSamlRoleMapping] | false | maxItems: 100 | The list of DataRobot access role to identity provider role maps. | | securityParameters | SamlSecurityParameters | false | | The object that contains SAML specific directives. | | sessionLengthSeconds | integer | false | | Time window for the authentication session via IdP. | | signOnUrl | string(uri) | false | | URL to sign on via SSO. | | signOutUrl | string(uri) | false | | URL to sign out via SSO. | | spRequestMethod | string | false | | Service provider (DataRobot application) request method, is used to move user to the IdP's authentication form. | ### Enumerated Values | Property | Value | | --- | --- | | configurationType | [MANUAL, METADATA, METADATA_URL] | | idpResponseMethod | [POST, REDIRECT] | | spRequestMethod | [POST, REDIRECT] | --- # Administration URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-admin.html # Administration These endpoints cover the requisite functionality for administrators to manage their organization’s DataRobot deployment and application. These workflows include authentication, user management and RBAC controls, resource management and monitoring, and notifications integration. --- # Applications URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-applications.html # Applications DataRobot offers various approaches for building applications that allow you to share machine learning projects. No-code applications are used to visualize, optimize, or otherwise interact with a machine learning model. They enable core DataRobot services without having to build and evaluate models. Use application templates to launch an end-to-end DataRobot experience. This can include aspects of importing data, model building, deployment, and monitoring. Custom applications are a simple method for building and running custom code within the DataRobot infrastructure. You can customize your application to fit whatever your organization’s needs might be—from something simple, such as tailoring the application UI to a specific design, or something more robust. --- # Collaboration URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-collaboration.html # Collaboration Teams can efficiently organize and deliver on their machine learning project goals with DataRobot’s collaborative capabilities, which enable business and engineering stakeholders to come together to solve specific business problems. DataRobot Use Cases offer a folder-like experience for organizing and sharing assets related to a specific ML project for rapid experimentation and streamlined productionization. --- # Custom models URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-custom-models.html # Custom models --- # Data preparation URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-data-prep.html # Data preparation DataRobot’s data preparation offerings give you the ability to analyze, ingest, and transform your data for modeling and inference workflows. These endpoints include the functionality for creating connections to external data sources, ingesting and managing static and dynamic data assets in the Data Registry, and applying data transformations in preparation for modeling. --- # Deliver URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-deliver.html # Deliver Deliver endpoints focus on the ways you can bring predictive and generative AI into production. This includes common deployment, monitoring, and model management functionality used to observe and mitigate the performance of models. --- # Deployments URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-deployment-management.html # Deployments This section contains endpoints that are the central hub for deployment management activity. It serves as a coordination point for all stakeholders involved in bringing models into production, including the information you supplied when creating the deployment and any model replacement activity. --- # Developer tools URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-dev-tools.html # Developer tools DataRobot offers native tools for accelerating code iteration and development for data scientists and app developers. DataRobot Notebooks and Codespaces provide a fully managed, hosted platform working with notebooks and other code assets. These endpoints include the functionality for creating and managing notebook and codespace assets. --- # Develop URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-develop.html # Develop Develop endpoints focus on the various ways you can build models of various types in DataRobot. These include LLM generation, end-to-end project development for machine learning models, and building user-defined custom models. --- # Generative AI URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-genai.html # Generative AI DataRobot’s Generative AI capabilities allow you to build enterprise-grade generative API applications using state-of-the-art LLMs and comprehensive evaluation tooling. These endpoints include the functionality for creating and managing vector databases, building and evaluating LLM blueprints, and prompt engineering and chat comparisons. --- # Secure and govern URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-govern.html # Secure and govern DataRobot offers a rich governance framework for organizations to ensure quality and regulatory compliance for their predictive and generative AI models in production. These endpoints control governance capabilities across model deployment approval workflows, compliance documentation, model lineage and traceability, and humility and fairness monitoring. --- # Modeling URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-machine-learning.html # Modeling Use the endpoints in this section to manage the process to build predictive AI models with DataRobot. --- # Mitigation URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-mitigation.html # Mitigation Machine learning models in production environments have a complex lifecycle; maintaining the predictive value of these models requires a robust and repeatable process to manage that lifecycle. Without proper management, models that reach production may deliver inaccurate data, poor performance, or unexpected results that can damage your business’s reputation for AI trustworthiness. Lifecycle management is essential for creating a machine learning operations system that allows you to scale many models in production. --- # Monitoring URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-observability.html # Monitoring To trust a model to power mission-critical operations, you must have confidence in all aspects of model deployment. By closely tracking the performance of models in production, you can identify potential issues before they impact business operations. Monitoring ranges from whether the service is reliably providing predictions in a timely manner and without errors to ensuring the predictions themselves are reliable. The predictive performance of a model typically starts to diminish as soon as it’s deployed. For example, someone might be making live predictions on a dataset with customer data, but the customer’s behavioral patterns might have changed due to an economic crisis, market volatility, natural disaster, or even the weather. Models trained on older data that no longer represents the current reality might not just be inaccurate, but irrelevant, leaving the prediction results meaningless or even harmful. Without dedicated production model monitoring, the user cannot know or detect when this happens. If model accuracy starts to decline without detection, the results can impact a business, expose it to risk, and destroy user trust. The endpoints in this section cover the many aspects of model monitoring. --- # Predictions URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tag-predictions.html # Predictions These endpoints include the functionality to make predictions on your trained model to assess model performance prior to deployment on training data or external test data, as well as create and manage batch predictions for your deployed models. For more information on making real-time predictions with your deployed models, view the documentation on [real-time predictions with the API](https://docs.datarobot.com/en/docs/api/reference/predapi/index.html). --- # Tracking Agent URL: https://docs.datarobot.com/en/docs/api/reference/public-api/tracking_agent.html > Use the endpoints described below to manage tracking agent. When you enable the monitoring agent feature, you have access to the agent installation and MLOps components, all packaged within a single tarball. # Tracking Agent Use the endpoints described below to manage tracking agent. When you enable the monitoring agent feature, you have access to the agent installation and MLOps components, all packaged within a single tarball. ## Submit external deployment prediction data by deployment ID Operation path: `POST /api/v2/deployments/{deploymentId}/predictionInputs/fromDataset/` Authentication requirements: `BearerAuth` Assigns prediction dataset to the external deployment to enable the analysis of historical model performance. Multiple datasets containing historical predictions for the external deployment can be uploaded. This requires one request for each dataset. For a regression deployment, predictions can be either an int or float. For a classification (binary/multiclass) deployment, predictions must be lists with each list containing probabilities for each class. ### Body parameter ``` { "properties": { "datasetId": { "description": "the ID of the dataset", "type": "string" }, "datasetVersionId": { "description": "the ID of the dataset version", "type": "string" } }, "required": [ "datasetId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | Unique identifier of the deployment. | | body | body | PredictionDatasetAssignment | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Submitted successfully. | None | | 405 | Method Not Allowed | Data can only be uploaded to an external deployment. | None | | 422 | Unprocessable Entity | Unable to process predictions upload. | None | # Schemas ## PredictionDatasetAssignment ``` { "properties": { "datasetId": { "description": "the ID of the dataset", "type": "string" }, "datasetVersionId": { "description": "the ID of the dataset version", "type": "string" } }, "required": [ "datasetId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | the ID of the dataset | | datasetVersionId | string | false | | the ID of the dataset version | --- # Use Cases URL: https://docs.datarobot.com/en/docs/api/reference/public-api/use_cases.html > The endpoints below outline how to create and manage Workbench Use Cases. They are not associated with the Value Tracker, which was formerly called Use Cases. # Use Cases The endpoints below outline how to create and manage Workbench Use Cases. They are not associated with the Value Tracker, which was formerly called Use Cases. ## Retrieve Pinned Usecases Operation path: `GET /api/v2/pinnedUsecases/` Authentication requirements: `BearerAuth` ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The list of the pinned use cases.", "items": { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "applicationsCount": { "description": "The number of applications in a use case.", "type": "integer" }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "customApplicationsCount": { "description": "The number of custom applications referenced in a use case.", "type": "integer" }, "customJobsCount": { "description": "The number of custom jobs referenced in a use case.", "type": "integer" }, "customModelVersionsCount": { "description": "The number of custom models referenced in a use case.", "type": "integer" }, "datasetsCount": { "description": "The number of datasets in a use case.", "type": "integer" }, "deploymentsCount": { "description": "The number of deployments referenced in a use case.", "type": "integer" }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "filesCount": { "description": "The number of files in a use case.", "type": "integer", "x-versionadded": "v2.37" }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "modelsCount": { "description": "[DEPRECATED] The number of models in a Use Case.", "type": "integer", "x-versiondeprecated": "v2.34" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "notebooksCount": { "description": "The number of notebooks in a use case.", "type": "integer" }, "projectsCount": { "description": "The number of projects in a use case.", "type": "integer" }, "recipesCount": { "description": "The number of recipes in a Use Case.", "type": "integer" }, "registeredModelVersionsCount": { "description": "The number of registered models referenced in a use case.", "type": "integer" }, "riskAssessments": { "description": "The ID List of the Risk Assessments.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "role": { "description": "The requesting user's role on this Use Case.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string", "x-versionadded": "v2.37" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ] } }, "required": [ "applicationsCount", "created", "createdAt", "customApplicationsCount", "customJobsCount", "customModelVersionsCount", "datasetsCount", "deploymentsCount", "description", "filesCount", "id", "name", "notebooksCount", "projectsCount", "recipesCount", "registeredModelVersionsCount", "role", "tenantId", "updated", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 8, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Pinned Use Cases retrieved successfully. | PinnedUsecasesResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## Modify Pinned Usecases Operation path: `PATCH /api/v2/pinnedUsecases/` Authentication requirements: `BearerAuth` ### Body parameter ``` { "properties": { "operation": { "description": "Pinned Use Case possible operations.", "enum": [ "add", "remove" ], "type": "string" }, "pinnedUseCasesIds": { "description": "The list of the pinned Use Case IDs.", "items": { "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" } }, "required": [ "operation", "pinnedUseCasesIds" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | PinnedUsecaseUpdatePayload | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Pinned Use Cases retrieved successfully. | None | | 403 | Forbidden | User does not have access to this functionality. | None | ## Retrieve the list of use cases Operation path: `GET /api/v2/useCases/` Authentication requirements: `BearerAuth` Retrieve the list of use cases. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | search | query | string | false | Returns only Use Cases with names that match the given string. | | projectId | query | string | false | Only return experiment containers associated with the given project ID. | | applicationId | query | string | false | Only return experiment containers associated with the given app. | | entityId | query | string | false | The id of the entity type that is linked with the Use Case. | | entityType | query | string | false | The entity type that is linked to the use case. | | sort | query | string | true | [DEPRECATED - replaced with order_by] The order in which Use Cases and return Use Cases. | | orderBy | query | string | true | The order in which use cases are returned. | | usecaseType | query | string | true | A filter to return use cases by type. | | riskLevel | query | any | false | Only return Use Cases associated with the given risk level. | | stage | query | any | false | Only return use cases in the given stage. | | createdBy | query | string | false | Filter Use Cases to return only those created by the selected user. | | showOrgUseCases | query | boolean | false | Defines if the Use Cases available on Organization level should be shown. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [project, dataset, file, notebook, application, recipe, playground, vectorDatabase, syftrSearchInstance, customModelVersion, registeredModelVersion, deployment, customApplication, customJob] | | sort | [-applicationsCount, -createdAt, -createdBy, -customApplicationsCount, -datasetsCount, -description, -filesCount, -id, -name, -notebooksCount, -playgroundsCount, -potentialValue, -projectsCount, -riskLevel, -stage, -updatedAt, -updatedBy, -vectorDatabasesCount, applicationsCount, createdAt, createdBy, customApplicationsCount, datasetsCount, description, filesCount, id, name, notebooksCount, playgroundsCount, potentialValue, projectsCount, riskLevel, stage, updatedAt, updatedBy, vectorDatabasesCount] | | orderBy | [-applicationsCount, -createdAt, -createdBy, -customApplicationsCount, -datasetsCount, -description, -filesCount, -id, -name, -notebooksCount, -playgroundsCount, -potentialValue, -projectsCount, -riskLevel, -stage, -updatedAt, -updatedBy, -vectorDatabasesCount, applicationsCount, createdAt, createdBy, customApplicationsCount, datasetsCount, description, filesCount, id, name, notebooksCount, playgroundsCount, potentialValue, projectsCount, riskLevel, stage, updatedAt, updatedBy, vectorDatabasesCount] | | usecaseType | [all, general, walkthrough] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of use cases that match the query.", "items": { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "applicationsCount": { "description": "The number of applications in a Use Case.", "type": "integer" }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "customApplicationsCount": { "description": "The number of custom applications referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customJobsCount": { "description": "The number of custom jobs referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customModelVersionsCount": { "description": "The number of custom models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "datasetsCount": { "description": "The number of datasets in a Use Case.", "type": "integer" }, "deploymentsCount": { "description": "The number of deployments referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "filesCount": { "description": "The number of files in a Use Case.", "type": "integer", "x-versionadded": "v2.37" }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "members": { "description": "The list of use case members.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "modelsCount": { "description": "[DEPRECATED] The number of models in a Use Case.", "type": "integer", "x-versiondeprecated": "v2.34" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "notebooksCount": { "description": "The number of notebooks in a Use Case.", "type": "integer" }, "owners": { "description": "The list of owners of a use case.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "primaryRiskAssessment": { "description": "Risk assessment defined as primary the current Use Case.", "properties": { "createdAt": { "description": "The creation date of the risk assessment.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created the risk assessment.", "type": "string" }, "description": { "description": "The description of the risk assessment.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "entityType": { "description": "The type of entity this assessment belongs to.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "evidence": { "description": "The evidence for the risk assessment.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the risk assessment.", "type": "string" }, "isPrimary": { "default": false, "description": "Determines if the risk assessment is primary.", "type": "boolean" }, "mitigationPlan": { "description": "The mitigation plan for the risk assessment.", "type": [ "string", "null" ] }, "name": { "description": "The name of the risk assessment.", "type": "string" }, "policyId": { "description": "The ID of the risk policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskDescription": { "description": "The risk description for this assessment.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskLevel": { "description": "The name of the risk assessment level.", "type": "string" }, "riskLevelId": { "description": "The ID of the risk level within the policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskManagementPlan": { "description": "The risk management plan.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "score": { "description": "The assessment score.", "type": [ "number", "null" ], "x-versionadded": "v2.44" }, "tenantId": { "description": "The tenant ID related to the risk assessment.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The last updated date of the risk assessment.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who updated the risk assessment.", "type": "string" } }, "required": [ "createdAt", "createdBy", "description", "entityId", "entityType", "evidence", "id", "isPrimary", "mitigationPlan", "name", "policyId", "riskDescription", "riskLevelId", "riskManagementPlan", "score", "tenantId", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "projectsCount": { "description": "The number of projects in a Use Case.", "type": "integer" }, "recipesCount": { "description": "The number of recipes in a Use Case.", "type": "integer", "x-versionadded": "v2.33" }, "registeredModelVersionsCount": { "description": "The number of registered models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "riskAssessments": { "description": "The ID List of the Risk Assessments.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "role": { "description": "The requesting user's role on this Use Case.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "valueTracker": { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "applicationsCount", "created", "createdAt", "customApplicationsCount", "customJobsCount", "customModelVersionsCount", "datasetsCount", "deploymentsCount", "description", "filesCount", "id", "members", "name", "notebooksCount", "projectsCount", "recipesCount", "registeredModelVersionsCount", "role", "tenantId", "updated", "updatedAt" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The use cases were retrieved successfully. | UseCaseListResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## Get a use case Operation path: `POST /api/v2/useCases/` Authentication requirements: `BearerAuth` Look up a use case. ### Body parameter ``` { "properties": { "advancedTour": { "description": "Advanced tour key.", "enum": [ "flightDelays", "hospital" ], "type": [ "string", "null" ] }, "description": { "default": null, "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "name": { "default": null, "description": "The name of the Use Case.", "maxLength": 100, "type": [ "string", "null" ] } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UseCaseCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "applicationsCount": { "description": "The number of applications in a Use Case.", "type": "integer" }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "customApplicationsCount": { "description": "The number of custom applications referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customJobsCount": { "description": "The number of custom jobs referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customModelVersionsCount": { "description": "The number of custom models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "datasetsCount": { "description": "The number of datasets in a Use Case.", "type": "integer" }, "deploymentsCount": { "description": "The number of deployments referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "filesCount": { "description": "The number of files in a Use Case.", "type": "integer", "x-versionadded": "v2.37" }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "members": { "description": "The list of use case members.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "modelsCount": { "description": "[DEPRECATED] The number of models in a Use Case.", "type": "integer", "x-versiondeprecated": "v2.34" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "notebooksCount": { "description": "The number of notebooks in a Use Case.", "type": "integer" }, "owners": { "description": "The list of owners of a use case.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "primaryRiskAssessment": { "description": "Risk assessment defined as primary the current Use Case.", "properties": { "createdAt": { "description": "The creation date of the risk assessment.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created the risk assessment.", "type": "string" }, "description": { "description": "The description of the risk assessment.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "entityType": { "description": "The type of entity this assessment belongs to.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "evidence": { "description": "The evidence for the risk assessment.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the risk assessment.", "type": "string" }, "isPrimary": { "default": false, "description": "Determines if the risk assessment is primary.", "type": "boolean" }, "mitigationPlan": { "description": "The mitigation plan for the risk assessment.", "type": [ "string", "null" ] }, "name": { "description": "The name of the risk assessment.", "type": "string" }, "policyId": { "description": "The ID of the risk policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskDescription": { "description": "The risk description for this assessment.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskLevel": { "description": "The name of the risk assessment level.", "type": "string" }, "riskLevelId": { "description": "The ID of the risk level within the policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskManagementPlan": { "description": "The risk management plan.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "score": { "description": "The assessment score.", "type": [ "number", "null" ], "x-versionadded": "v2.44" }, "tenantId": { "description": "The tenant ID related to the risk assessment.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The last updated date of the risk assessment.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who updated the risk assessment.", "type": "string" } }, "required": [ "createdAt", "createdBy", "description", "entityId", "entityType", "evidence", "id", "isPrimary", "mitigationPlan", "name", "policyId", "riskDescription", "riskLevelId", "riskManagementPlan", "score", "tenantId", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "projectsCount": { "description": "The number of projects in a Use Case.", "type": "integer" }, "recipesCount": { "description": "The number of recipes in a Use Case.", "type": "integer", "x-versionadded": "v2.33" }, "registeredModelVersionsCount": { "description": "The number of registered models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "riskAssessments": { "description": "The ID List of the Risk Assessments.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "role": { "description": "The requesting user's role on this Use Case.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "valueTracker": { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "applicationsCount", "created", "createdAt", "customApplicationsCount", "customJobsCount", "customModelVersionsCount", "datasetsCount", "deploymentsCount", "description", "filesCount", "id", "members", "name", "notebooksCount", "projectsCount", "recipesCount", "registeredModelVersionsCount", "role", "tenantId", "updated", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The experiment container. | UseCaseResponse | | 422 | Unprocessable Entity | Unprocessable Entity | None | ## Get the list of the references associated Operation path: `GET /api/v2/useCases/allResources/` Authentication requirements: `BearerAuth` Get a list of the all used assets associated with a Use Case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | sort | query | string | true | [DEPRECATED - replaced with order_by] The order to sort the Use Case references. | | orderBy | query | string | true | The order to sort the Use Case references. | | daysSinceLastActivity | query | integer | false | Only retrieve resources that had activity within the specified number of days. | | recipeStatus | query | any | false | The recipe status used for filtering recipes. | ### Enumerated Values | Parameter | Value | | --- | --- | | sort | [-entityType, -lastActivity, -name, -updatedAt, -updatedBy, entityType, lastActivity, name, updatedAt, updatedBy] | | orderBy | [-entityType, -lastActivity, -name, -updatedAt, -updatedBy, entityType, lastActivity, name, updatedAt, updatedBy] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The list of the use case references that match the query.", "items": { "properties": { "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary ID of the entity.", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity ID.", "type": "string" }, "id": { "description": "The ID of the Use Case reference.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "metadata": { "description": "The reference metadata for the use case.", "oneOf": [ { "properties": { "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "The experiment failure explanation.", "type": "string" } }, "required": [ "isDraft", "isErrored", "isWorkbenchEligible", "stage", "statusErrorMessage" ], "type": "object" }, { "properties": { "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "datasetSize": { "description": "The size of the dataset in bytes.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "datasetSourceType": { "description": "The source type of the dataset", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] } }, "required": [ "dataSourceType", "datasetSize", "datasetSourceType", "isSnapshot", "isWranglingEligible", "latestRecipeId" ], "type": "object" }, { "properties": { "dataType": { "description": "The type of the recipe (wrangling or feature discovery)", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": [ "string", "null" ] } }, "required": [ "dataType" ], "type": "object" }, { "properties": { "description": { "description": "The description of the playground.", "type": "string" }, "playgroundType": { "description": "The type of the playground.", "type": "string", "x-versionadded": "v2.37" } }, "required": [ "description" ], "type": "object" }, { "properties": { "errorMessage": { "description": "The error message, if any, for the vector database.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "source": { "description": "The source of the vector database", "type": "string" } }, "required": [ "errorMessage", "source" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "dataSourceType": { "description": "The data source type used to create the file if relevant.", "type": [ "string", "null" ] }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "numFiles": { "description": "The number of files in the catalog item.", "type": [ "integer", "null" ] } }, "required": [ "dataSourceType", "fileSourceType", "numFiles" ], "type": "object", "x-versionadded": "v2.37" } ] }, "name": { "description": "The name of the Use Case reference.", "type": "string" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the linked use case.", "type": "string" }, "useCaseName": { "description": "The name of the linked use case.", "type": [ "string", "null" ] }, "userHasAccess": { "description": "Identifies if a user has access to the entity.", "type": [ "boolean", "null" ], "x-versionadded": "v2.36" }, "versions": { "description": "The list of entity versions.", "items": { "properties": { "createdAt": { "description": "Time when this entity was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The id of the entity version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the entity version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the entity version.", "type": "integer" }, "updatedAt": { "description": "Time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "entityId", "entityType", "id", "name", "updatedAt", "useCaseId" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseRecentAssetsListResponse | ## Get the list of the notebooks Operation path: `GET /api/v2/useCases/notebooks/` Authentication requirements: `BearerAuth` Get the list of the notebooks from all use cases. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | includeName | query | boolean | false | Include use case name. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the notebooks in this use case.", "items": { "properties": { "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at notebook creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity (same as ID of the notebook).", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "enum": [ "notebook" ], "type": "string" }, "experimentContainerId": { "description": "[DEPRECATED - replaced with use_case_id] The ID of the Use Case.", "type": "string", "x-versiondeprecated": "v2.32" }, "id": { "description": "The ID of the notebook.", "type": "string" }, "isDeleted": { "description": "Soft deletion flag for notebooks", "type": "boolean" }, "referenceId": { "description": "Original ID from DB", "type": "string" }, "tenantId": { "description": "The id of the tenant the notebook belongs to.", "type": [ "string", "null" ] }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case.", "type": "string" }, "useCaseName": { "description": "Use Case name", "type": "string" } }, "required": [ "created", "createdAt", "entityId", "entityType", "experimentContainerId", "id", "isDeleted", "referenceId", "tenantId", "useCaseId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCasesNotebooksListResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## Delete a use case by use case ID Operation path: `DELETE /api/v2/useCases/{useCaseId}/` Authentication requirements: `BearerAuth` Delete a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | path | string | true | The ID of the use case to update. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | ## Get a use case by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/` Authentication requirements: `BearerAuth` Retrieve a single Use Case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | path | string | true | The ID of the use case to update. | ### Example responses > 200 Response ``` { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "applicationsCount": { "description": "The number of applications in a Use Case.", "type": "integer" }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "customApplicationsCount": { "description": "The number of custom applications referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customJobsCount": { "description": "The number of custom jobs referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customModelVersionsCount": { "description": "The number of custom models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "datasetsCount": { "description": "The number of datasets in a Use Case.", "type": "integer" }, "deploymentsCount": { "description": "The number of deployments referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "filesCount": { "description": "The number of files in a Use Case.", "type": "integer", "x-versionadded": "v2.37" }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "members": { "description": "The list of use case members.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "modelsCount": { "description": "[DEPRECATED] The number of models in a Use Case.", "type": "integer", "x-versiondeprecated": "v2.34" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "notebooksCount": { "description": "The number of notebooks in a Use Case.", "type": "integer" }, "owners": { "description": "The list of owners of a use case.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "primaryRiskAssessment": { "description": "Risk assessment defined as primary the current Use Case.", "properties": { "createdAt": { "description": "The creation date of the risk assessment.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created the risk assessment.", "type": "string" }, "description": { "description": "The description of the risk assessment.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "entityType": { "description": "The type of entity this assessment belongs to.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "evidence": { "description": "The evidence for the risk assessment.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the risk assessment.", "type": "string" }, "isPrimary": { "default": false, "description": "Determines if the risk assessment is primary.", "type": "boolean" }, "mitigationPlan": { "description": "The mitigation plan for the risk assessment.", "type": [ "string", "null" ] }, "name": { "description": "The name of the risk assessment.", "type": "string" }, "policyId": { "description": "The ID of the risk policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskDescription": { "description": "The risk description for this assessment.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskLevel": { "description": "The name of the risk assessment level.", "type": "string" }, "riskLevelId": { "description": "The ID of the risk level within the policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskManagementPlan": { "description": "The risk management plan.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "score": { "description": "The assessment score.", "type": [ "number", "null" ], "x-versionadded": "v2.44" }, "tenantId": { "description": "The tenant ID related to the risk assessment.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The last updated date of the risk assessment.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who updated the risk assessment.", "type": "string" } }, "required": [ "createdAt", "createdBy", "description", "entityId", "entityType", "evidence", "id", "isPrimary", "mitigationPlan", "name", "policyId", "riskDescription", "riskLevelId", "riskManagementPlan", "score", "tenantId", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "projectsCount": { "description": "The number of projects in a Use Case.", "type": "integer" }, "recipesCount": { "description": "The number of recipes in a Use Case.", "type": "integer", "x-versionadded": "v2.33" }, "registeredModelVersionsCount": { "description": "The number of registered models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "riskAssessments": { "description": "The ID List of the Risk Assessments.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "role": { "description": "The requesting user's role on this Use Case.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "valueTracker": { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "applicationsCount", "created", "createdAt", "customApplicationsCount", "customJobsCount", "customModelVersionsCount", "datasetsCount", "deploymentsCount", "description", "filesCount", "id", "members", "name", "notebooksCount", "projectsCount", "recipesCount", "registeredModelVersionsCount", "role", "tenantId", "updated", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseResponse | ## Update a Use Case by use case ID Operation path: `PATCH /api/v2/useCases/{useCaseId}/` Authentication requirements: `BearerAuth` Update a Use Case. ### Body parameter ``` { "properties": { "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "name": { "description": "The name of the Use Case.", "maxLength": 100, "type": [ "string", "null" ] } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | path | string | true | The ID of the use case to update. | | body | body | UseCaseUpdate | false | none | ### Example responses > 204 Response ``` { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "applicationsCount": { "description": "The number of applications in a Use Case.", "type": "integer" }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "customApplicationsCount": { "description": "The number of custom applications referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customJobsCount": { "description": "The number of custom jobs referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customModelVersionsCount": { "description": "The number of custom models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "datasetsCount": { "description": "The number of datasets in a Use Case.", "type": "integer" }, "deploymentsCount": { "description": "The number of deployments referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "filesCount": { "description": "The number of files in a Use Case.", "type": "integer", "x-versionadded": "v2.37" }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "members": { "description": "The list of use case members.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "modelsCount": { "description": "[DEPRECATED] The number of models in a Use Case.", "type": "integer", "x-versiondeprecated": "v2.34" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "notebooksCount": { "description": "The number of notebooks in a Use Case.", "type": "integer" }, "owners": { "description": "The list of owners of a use case.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "primaryRiskAssessment": { "description": "Risk assessment defined as primary the current Use Case.", "properties": { "createdAt": { "description": "The creation date of the risk assessment.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created the risk assessment.", "type": "string" }, "description": { "description": "The description of the risk assessment.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "entityType": { "description": "The type of entity this assessment belongs to.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "evidence": { "description": "The evidence for the risk assessment.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the risk assessment.", "type": "string" }, "isPrimary": { "default": false, "description": "Determines if the risk assessment is primary.", "type": "boolean" }, "mitigationPlan": { "description": "The mitigation plan for the risk assessment.", "type": [ "string", "null" ] }, "name": { "description": "The name of the risk assessment.", "type": "string" }, "policyId": { "description": "The ID of the risk policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskDescription": { "description": "The risk description for this assessment.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskLevel": { "description": "The name of the risk assessment level.", "type": "string" }, "riskLevelId": { "description": "The ID of the risk level within the policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskManagementPlan": { "description": "The risk management plan.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "score": { "description": "The assessment score.", "type": [ "number", "null" ], "x-versionadded": "v2.44" }, "tenantId": { "description": "The tenant ID related to the risk assessment.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The last updated date of the risk assessment.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who updated the risk assessment.", "type": "string" } }, "required": [ "createdAt", "createdBy", "description", "entityId", "entityType", "evidence", "id", "isPrimary", "mitigationPlan", "name", "policyId", "riskDescription", "riskLevelId", "riskManagementPlan", "score", "tenantId", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "projectsCount": { "description": "The number of projects in a Use Case.", "type": "integer" }, "recipesCount": { "description": "The number of recipes in a Use Case.", "type": "integer", "x-versionadded": "v2.33" }, "registeredModelVersionsCount": { "description": "The number of registered models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "riskAssessments": { "description": "The ID List of the Risk Assessments.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "role": { "description": "The requesting user's role on this Use Case.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "valueTracker": { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "applicationsCount", "created", "createdAt", "customApplicationsCount", "customJobsCount", "customModelVersionsCount", "datasetsCount", "deploymentsCount", "description", "filesCount", "id", "members", "name", "notebooksCount", "projectsCount", "recipesCount", "registeredModelVersionsCount", "role", "tenantId", "updated", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | The Use Case has been successfully updated. | UseCaseResponse | | 403 | Forbidden | User does not have access to this functionality. | None | | 404 | Not Found | The use case was not found. | None | | 422 | Unprocessable Entity | Unprocessable Entity | None | ## Get the list of the applications associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/applications/` Authentication requirements: `BearerAuth` Get the list of the applications associated with a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | search | query | string | false | Only return applications with names that match the given string. | | sort | query | string | true | [DEPRECATED - replaced with order_by] The order to sort applications. | | orderBy | query | string | true | The order to sort applications. | | useCaseId | path | string | true | The ID of the use case to update. | ### Enumerated Values | Parameter | Value | | --- | --- | | sort | [-applicationTemplateType, -createdAt, -lastActivity, -name, -source, -updatedAt, -userId, applicationTemplateType, createdAt, lastActivity, name, source, updatedAt, userId] | | orderBy | [-applicationTemplateType, -createdAt, -lastActivity, -name, -source, -updatedAt, -userId, applicationTemplateType, createdAt, lastActivity, name, source, updatedAt, userId] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the applications in this use case.", "items": { "properties": { "applicationId": { "description": "The application id of the application.", "type": "string" }, "applicationTemplateType": { "description": "The type of the application.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp generated at application creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The description of the application.", "type": [ "string", "null" ] }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the application.", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": [ "string", "null" ] }, "source": { "description": "The source used to create the application.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp generated at application modification.", "format": "date-time", "type": "string" } }, "required": [ "applicationId", "applicationTemplateType", "createdAt", "createdBy", "description", "lastActivity", "name", "projectId", "source", "updatedAt" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCasesApplicationsListResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## The list of the custom applications referenced by a use case by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/customApplications/` Authentication requirements: `BearerAuth` The list of the custom applications referenced by a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | useCaseId | path | string | true | The ID of the use case to update. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of custom applications.", "items": { "properties": { "applicationUrl": { "description": "The reachable URL of the custom application.", "type": "string", "x-versionadded": "v2.35" }, "createdAt": { "description": "The time when this model was created.", "format": "date-time", "type": "string", "x-versionadded": "v2.35" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "customApplicationSourceId": { "description": "The ID of the custom application source.", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "id": { "description": "The ID of the custom application.", "type": "string" }, "lastActivity": { "description": "The type of the last activity. Can be \"Added\" or \"Modified\".", "enum": [ "created", "Created", "CREATED", "modified", "Modified", "MODIFIED" ], "type": "string" }, "name": { "description": "The name of the custom application.", "type": "string" }, "permissions": { "description": "The list of permissions available for the user.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.39" }, "status": { "description": "The status of the custom application.", "type": "string", "x-versionadded": "v2.35" }, "updatedAt": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "userHasAccess": { "description": "Indicates if a user has access to this custom application.", "type": "boolean", "x-versionadded": "v2.35" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseCustomApplicationsResponse | ## List datasets by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/data/` Authentication requirements: `BearerAuth` Get a list of the data (datasets and recipes) associated with a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | true | Sorting order which will be applied to data list. | | search | query | string | false | Only return datasets or recipes from use case with names that match the given string. | | dataType | query | any | false | Data types used for filtering. | | dataSourceType | query | any | false | The driver class type of the recipe wrangling engine. | | recipeStatus | query | any | false | The recipe status used for filtering recipes. | | creatorUserId | query | any | false | Filter results to display only those created by user(s) identified by the specified ID | | creatorUsername | query | any | false | Filter results to display only those created by user(s) identified by the specified username | | useCaseId | path | string | true | The ID of the use case. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [name, -name, description, -description, createdBy, -createdBy, modifiedAt, -modifiedAt, dataType, -dataType, dataSourceType, -dataSourceType, rowCount, -rowCount, columnCount, -columnCount, datasetSize, -datasetSize] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The list of the datasets in this use case.", "items": { "properties": { "columnCount": { "description": "The number of columns in a dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "dataSourceType": { "description": "The driver class type used to create the dataset if relevant.", "enum": [ "s3", "native-s3", "native-adls", "adlsgen2", "oracle", "iris", "exasol", "sap", "databricks-v1", "native-databricks", "bigquery-v1", "bigquery1", "bigquery2", "athena2", "athena1", "kdb", "treasuredata", "elasticsearch", "snowflake", "mysql", "mssql", "postgres", "palantirfoundry", "teradata", "redshift", "datasphere-v1", "trino-v1", "native-gdrive", "native-sharepoint", "native-confluence", "native-jira", "native-box", "native-onedrive", "native-salesforce", "unknown" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataType": { "description": "The type of data entity.", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": "string", "x-versionadded": "v2.33" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "description": { "description": "The description of the dataset or recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The primary ID of the entity.", "type": "string", "x-versionadded": "v2.33" }, "entityType": { "description": "The type of entity provided by the entity ID.", "enum": [ "recipe", "Recipe", "RECIPE", "dataset", "Dataset", "DATASET", "project", "Project", "PROJECT", "application", "Application", "APPLICATION" ], "type": "string", "x-versionadded": "v2.33" }, "featureDiscoveryProjectId": { "description": "Related feature discovery project if this is a feature discovery dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean", "x-versionadded": "v2.33" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "modifiedAt": { "description": "The timestamp generated at dataset modification.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the dataset or recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rowCount": { "description": "The number of data rows in the dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ], "x-versionadded": "v2.33" } }, "required": [ "columnCount", "createdBy", "dataSourceType", "dataType", "datasetSize", "description", "entityId", "entityType", "featureDiscoveryProjectId", "isWranglingEligible", "latestRecipeId", "modifiedAt", "name", "processingState", "rowCount" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseDataListResponse | ## Get the list of the datasets associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/datasets/` Authentication requirements: `BearerAuth` Get the list of the datasets associated with a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | sort | query | string | true | [DEPRECATED - replaced with order_by] The order to sort the Use Case datasets. | | orderBy | query | string | true | The order to sort the Use Case datasets. | | search | query | string | false | Only return datasets from Use Case with names that match the given string. | | useCaseId | path | string | true | The ID of the use case to update. | ### Enumerated Values | Parameter | Value | | --- | --- | | sort | [-columnCount, -createdAt, -createdBy, -dataSourceType, -datasetSize, -datasetSourceType, -lastActivity, -modifiedAt, -modifiedBy, -name, -rowCount, columnCount, createdAt, createdBy, dataSourceType, datasetSize, datasetSourceType, lastActivity, modifiedAt, modifiedBy, name, rowCount] | | orderBy | [-columnCount, -createdAt, -createdBy, -dataSourceType, -datasetSize, -datasetSourceType, -lastActivity, -modifiedAt, -modifiedBy, -name, -rowCount, columnCount, createdAt, createdBy, dataSourceType, datasetSize, datasetSourceType, lastActivity, modifiedAt, modifiedBy, name, rowCount] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of the datasets in the Use Case", "items": { "properties": { "columnCount": { "description": "The number of columns in a dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ] }, "createdAt": { "description": "The timestamp generated at dataset creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "dataType": { "description": "The type of data entity.", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": "string", "x-versionadded": "v2.33" }, "datasetId": { "description": "The dataset ID of the dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ] }, "datasetSourceType": { "description": "The source type of the dataset.", "type": [ "string", "null" ] }, "description": { "description": "The description of the dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoveryProjectId": { "description": "Related feature discovery project if this is a feature discovery dataset.", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] }, "modifiedAt": { "description": "The timestamp generated at dataset modification.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "name": { "description": "The name of the dataset.", "type": "string" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "referenceMetadata": { "description": "Metadata about the reference of the dataset in the Use Case.", "properties": { "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" } }, "required": [ "createdAt", "lastActivity" ], "type": "object" }, "rowCount": { "description": "The number of data rows in the dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ] }, "versionId": { "description": "The dataset version id of the latest version of the dataset.", "type": "string" } }, "required": [ "columnCount", "createdAt", "createdBy", "dataPersisted", "dataSourceType", "dataType", "datasetId", "datasetSize", "datasetSourceType", "description", "isSnapshot", "isWranglingEligible", "latestRecipeId", "modifiedAt", "modifiedBy", "name", "processingState", "referenceMetadata", "rowCount", "versionId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseDatasetsListResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## Get the dataset details by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/datasets/{datasetId}/` Authentication requirements: `BearerAuth` Retrieves the details of the dataset with given ID for given use case ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | path | string | true | The ID linking the use case with the entity type. | | datasetId | path | string | true | The ID of the dataset. | ### Example responses > 200 Response ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "columnCount": { "description": "The number of columns in the dataset.", "type": "integer", "x-versionadded": "v2.30" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataEngineQueryId": { "description": "The ID of the source data engine query.", "type": [ "string", "null" ] }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "dataSourceId": { "description": "The ID of the datasource used as the source of the dataset.", "type": [ "string", "null" ] }, "dataSourceType": { "description": "The type of the datasource that was used as the source of the dataset.", "type": "string" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes.", "type": "integer", "x-versionadded": "v2.21" }, "description": { "description": "The description of the dataset.", "type": [ "string", "null" ] }, "eda1ModificationDate": { "description": "The ISO 8601 formatted date and time when the EDA1 for the dataset was updated.", "format": "date-time", "type": "string" }, "eda1ModifierFullName": { "description": "The user who was the last to update EDA1 for the dataset.", "type": "string" }, "entityCountByType": { "description": "Number of different type entities that use the dataset.", "properties": { "numCalendars": { "description": "The number of calendars that use the dataset", "type": "integer" }, "numExperimentContainer": { "description": "The number of experiment containers that use the dataset.", "type": "integer", "x-versionadded": "v2.37" }, "numExternalModelPackages": { "description": "The number of external model packages that use the dataset", "type": "integer" }, "numFeatureDiscoveryConfigs": { "description": "The number of feature discovery configs that use the dataset", "type": "integer" }, "numPredictionDatasets": { "description": "The number of prediction datasets that use the dataset", "type": "integer" }, "numProjects": { "description": "The number of projects that use the dataset", "type": "integer" }, "numSparkSqlQueries": { "description": "The number of spark sql queries that use the dataset", "type": "integer" } }, "required": [ "numCalendars", "numExperimentContainer", "numExternalModelPackages", "numFeatureDiscoveryConfigs", "numPredictionDatasets", "numProjects", "numSparkSqlQueries" ], "type": "object" }, "error": { "description": "Details of exception raised during ingestion process, if any.", "type": "string" }, "featureCount": { "description": "Total number of features in the dataset.", "type": "integer" }, "featureCountByType": { "description": "Number of features in the dataset grouped by feature type.", "items": { "properties": { "count": { "description": "The number of features of this type in the dataset", "type": "integer" }, "featureType": { "description": "The data type grouped in this count", "type": "string" } }, "required": [ "count", "featureType" ], "type": "object" }, "type": "array" }, "featureDiscoveryProjectId": { "description": "Feature Discovery project ID used to create the dataset.", "type": "string", "x-versionadded": "v2.35" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean", "x-versionadded": "2.30.0" }, "lastModificationDate": { "description": "The ISO 8601 formatted date and time when the dataset was last modified.", "format": "date-time", "type": "string" }, "lastModifierFullName": { "description": "Full name of user who was the last to modify the dataset.", "type": "string" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "recipeId": { "description": "The ID of the source recipe.", "type": [ "string", "null" ] }, "rowCount": { "description": "The number of rows in the dataset.", "type": "integer", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "tags": { "description": "List of tags attached to the item.", "items": { "type": "string" }, "type": "array" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "uri": { "description": "The URI to datasource. For example, `file_name.csv`, or `jdbc:DATA_SOURCE_GIVEN_NAME/SCHEMA.TABLE_NAME`, or `jdbc:DATA_SOURCE_GIVEN_NAME/` for `query` based datasources, or`https://s3.amazonaws.com/dr-pr-tst-data/kickcars-sample-200.csv`, etc.", "type": "string" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "columnCount", "createdBy", "creationDate", "dataEngineQueryId", "dataPersisted", "dataSourceId", "dataSourceType", "datasetId", "datasetSize", "description", "eda1ModificationDate", "eda1ModifierFullName", "error", "featureCount", "featureCountByType", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "isWranglingEligible", "lastModificationDate", "lastModifierFullName", "name", "processingState", "recipeId", "rowCount", "tags", "timeSeriesProperties", "uri", "versionId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The dataset details. | FullDatasetDetailsResponse | ## Get the deployments associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/deployments/` Authentication requirements: `BearerAuth` The list of deployments associated with the use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | true | The order to sort the use case deployments. | | search | query | string | false | Only return deployments from the use case with names that match the given string. | | useCaseId | path | string | true | The ID of the use case. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [-createdAt, -createdBy, -lastActivity, -name, -updatedAt, -updatedBy, createdAt, createdBy, lastActivity, name, updatedAt, updatedBy] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of deployments linked to a use case.", "items": { "properties": { "createdAt": { "description": "The created date time.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The deployment description.", "type": "string" }, "id": { "description": "Unique identifier of the deployment.", "type": "string" }, "label": { "description": "The deployment label.", "type": "string" }, "lastActivity": { "description": "The type of the last activity. Can be \"Added\" or \"Modified\".", "enum": [ "created", "Created", "CREATED", "modified", "Modified", "MODIFIED" ], "type": "string", "x-versionadded": "v2.35" }, "type": { "description": "The deployment type.", "type": "string" }, "updatedAt": { "description": "The last modified date time.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "userHasAccess": { "description": "Indicates if a user has access to this deployment.", "type": "boolean", "x-versionadded": "v2.35" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of deployments. | DeploymentPagination | ## Get the list of the catalog files associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/files/` Authentication requirements: `BearerAuth` Get the list of the catalog files associated with a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | true | The order to sort the Use Case files. | | search | query | string | false | Only return files from Use Cases with names that match the given string. | | useCaseId | path | string | true | The ID of the use case to update. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [-createdAt, -createdBy, -dataSourceType, -fileSourceType, -lastActivity, -modifiedAt, -modifiedBy, -name, -numFiles, createdAt, createdBy, dataSourceType, fileSourceType, lastActivity, modifiedAt, modifiedBy, name, numFiles] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of the files in the Use Case.", "items": { "properties": { "createdAt": { "description": "The timestamp generated at file creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "dataSourceType": { "description": "The type of the data source used to create the file (if relevant).", "type": [ "string", "null" ] }, "description": { "description": "The description of the file.", "type": [ "string", "null" ] }, "fileId": { "description": "The file ID.", "type": "string" }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "modifiedAt": { "description": "The timestamp generated at file modification.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "name": { "description": "The name of the file.", "type": "string" }, "numFiles": { "description": "The number of files in catalog item. ``null`` might be returned in cases where the file is in a running or errored state.", "type": [ "integer", "null" ] }, "processingState": { "description": "Current ingestion process state of file.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "referenceMetadata": { "description": "Metadata about the reference of the dataset in the Use Case.", "properties": { "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" } }, "required": [ "createdAt", "lastActivity" ], "type": "object" }, "versionId": { "description": "The file version ID for the file's latest version.", "type": "string" } }, "required": [ "createdAt", "createdBy", "dataSourceType", "description", "fileId", "fileSourceType", "modifiedAt", "modifiedBy", "name", "numFiles", "processingState", "referenceMetadata", "versionId" ], "type": "object", "x-versionadded": "v2.37" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseFilesListResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## Get the file details by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/files/{fileId}/` Authentication requirements: `BearerAuth` Retrieves the details of the file with given ID for a given Use Case ID. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | path | string | true | The ID linking the use case with the entity type. | | fileId | path | string | true | The ID of the file. | ### Example responses > 200 Response ``` { "properties": { "createdBy": { "description": "Username of the user who created the file.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the file was created.", "format": "date-time", "type": "string" }, "dataSourceId": { "description": "ID of the data source used as the source of the file.", "type": [ "string", "null" ] }, "dataSourceType": { "description": "The type of the data source that was used as the source of the file.", "type": "string" }, "description": { "description": "The description of the file.", "type": [ "string", "null" ] }, "entityCountByType": { "description": "Number of different type entities that use the file.", "properties": { "numExperimentContainer": { "description": "The number of experiment containers that use the file.", "type": "integer" } }, "required": [ "numExperimentContainer" ], "type": "object", "x-versionadded": "v2.37" }, "error": { "description": "Details of exception raised during ingestion process, if any.", "type": "string" }, "fileId": { "description": "The ID of this file.", "type": "string" }, "isLatestVersion": { "description": "Whether this file version is the latest version of this file.", "type": "boolean", "x-versionadded": "v2.37" }, "lastModificationDate": { "description": "The ISO 8601 formatted date and time when the file was last modified.", "format": "date-time", "type": "string" }, "lastModifierFullName": { "description": "Full name of user who was the last to modify the file.", "type": "string" }, "name": { "description": "The name of this file in the catalog.", "type": "string" }, "numFiles": { "description": "The number of files in the file entity.", "type": "integer" }, "processingState": { "description": "Current ingestion process state of file.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "tags": { "description": "List of tags attached to the item.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "uri": { "description": "The URI to the data source.", "type": "string" }, "versionId": { "description": "The object ID of the catalog_version the file belongs to.", "type": "string" } }, "required": [ "createdBy", "creationDate", "dataSourceId", "dataSourceType", "description", "entityCountByType", "error", "fileId", "isLatestVersion", "lastModificationDate", "lastModifierFullName", "name", "numFiles", "processingState", "tags", "uri", "versionId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The file details. | FullFileDetailsResponse | ## Get filtering metadata information from Use Cases associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/filterMetadata/` Authentication requirements: `BearerAuth` Get filtering metadata information from Use Cases associated with a Use Case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | path | string | true | The ID of the use case. | ### Example responses > 200 Response ``` { "properties": { "metrics": { "description": "Model performance evaluation metrics (shorthand abbreviations)", "properties": { "binary": { "description": "Binary metrics associated with the models.", "items": { "description": "Binary metric names", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE", "F1 Score", "Weighted F1 Score", "Precision", "Weighted Precision", "Recall", "Weighted Recall" ], "type": "string" }, "type": "array" }, "regression": { "description": "Regression metrics associated with the models", "items": { "description": "Regression metric names", "enum": [ "FVE Poisson", "Weighted FVE Poisson", "FVE Gamma", "Weighted FVE Gamma", "FVE Tweedie", "Weighted FVE Tweedie", "Gamma Deviance", "Weighted Gamma Deviance", "Gini Norm", "Weighted Gini Norm", "MAE", "Weighted MAE", "MAPE", "Weighted MAPE", "SMAPE", "Weighted SMAPE", "Poisson Deviance", "Weighted Poisson Deviance", "RMSLE", "RMSE", "Weighted RMSLE", "Weighted RMSE", "R Squared", "Weighted R Squared", "Tweedie Deviance", "Weighted Tweedie Deviance" ], "type": "string" }, "type": "array" } }, "type": "object" }, "modelFamilies": { "description": "Model families associated with the models", "items": { "properties": { "fullName": { "description": "Full name of the model family", "type": "string" }, "key": { "description": "Abbreviated form of model family name", "enum": [ "AB", "AD", "BLENDER", "CAL", "CLUSTER", "COUNT_DICT", "CUSTOM", "DOCUMENT", "DT", "DUMMY", "EP", "EQ", "EQ_TS", "FM", "GAM", "GBM", "GLM", "GLMNET", "IMAGE", "KNN", "NB", "NN", "OTHER", "RF", "RI", "SEGMENTED", "SVM", "TEXT", "TS", "TS_NN", "TTS", "VW" ], "type": "string" } }, "required": [ "fullName", "key" ], "type": "object" }, "maxItems": 32, "type": "array" }, "samplePcts": { "description": "Model training sample sizes (in percentage)", "items": { "exclusiveMinimum": 0, "maximum": 100, "type": "number" }, "maxItems": 7, "type": "array" } }, "required": [ "metrics", "modelFamilies", "samplePcts" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ExperimentContainerFilterMetadataRetrieveResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## Get a list of models from projects associated with a Use Case by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/modelsForComparison/` Authentication requirements: `BearerAuth` Get a list of models from projects associated with a Use Case for direct comparison. NOTE: datetime partitioned models are not supported currently. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | orderBy | query | string | false | Sort by creation date of the project associated with the model, default is descending order | | binarySortMetric | query | string | false | The binary classification sort metric. | | binarySortPartition | query | string | false | Retrieve Validation, Cross-Validation, or Holdout metric scores for list of models, only sort by descending order (i.e. most accurate to least accurate) | | regressionSortMetric | query | string | false | The regression sort metric. | | regressionSortPartition | query | string | false | Retrieve Validation, Cross-Validation, or Holdout metric scores for list of models, only sort by descending order (i.e. most accurate to least accurate) | | numberTopModels | query | integer | false | Filter to limited number of top scoring models, where default value is 1. A value of 0 means no top scoring models will be returned. | | samplePct | query | any | false | Filter to models trained at the specified sample size percentage(s) | | modelFamily | query | any | false | Filter to models that match the specified model family/families | | includeAllStarredModels | query | boolean | false | Whether to include all starred models in filtering output. This means starred models will be included in addition to top-scoring models. | | trainingDatasetId | query | any | false | Filter to models from projects built using specified training dataset ID(s) | | targetFeature | query | any | false | Filter to models from projects built using specified target feature(s) | | scoringCodeOnly | query | boolean | false | Whether to include only models that can be converted to scorable java code | | useCaseId | path | string | true | The ID of the use case. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [-createdAt, createdAt] | | binarySortMetric | [AUC, Weighted AUC, Area Under PR Curve, Weighted Area Under PR Curve, Kolmogorov-Smirnov, Weighted Kolmogorov-Smirnov, FVE Binomial, Weighted FVE Binomial, Gini Norm, Weighted Gini Norm, LogLoss, Weighted LogLoss, Max MCC, Weighted Max MCC, Rate@Top5%, Weighted Rate@Top5%, Rate@Top10%, Weighted Rate@Top10%, Rate@TopTenth%, RMSE, Weighted RMSE, F1 Score, Weighted F1 Score, Precision, Weighted Precision, Recall, Weighted Recall] | | binarySortPartition | [validation, holdout, crossValidation] | | regressionSortMetric | [FVE Poisson, Weighted FVE Poisson, FVE Gamma, Weighted FVE Gamma, FVE Tweedie, Weighted FVE Tweedie, Gamma Deviance, Weighted Gamma Deviance, Gini Norm, Weighted Gini Norm, MAE, Weighted MAE, MAPE, Weighted MAPE, SMAPE, Weighted SMAPE, Poisson Deviance, Weighted Poisson Deviance, RMSLE, RMSE, Weighted RMSLE, Weighted RMSE, R Squared, Weighted R Squared, Tweedie Deviance, Weighted Tweedie Deviance] | | regressionSortPartition | [validation, holdout, crossValidation] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Models from multiple projects in the Use Case", "items": { "properties": { "autopilotDataSelectionMethod": { "description": "The Data Selection method of the datetime-partitioned model. `null` if model is not datetime-partitioned.", "enum": [ "duration", "rowCount" ], "type": [ "string", "null" ] }, "blenderInputModelNumbers": { "description": "List of model ID numbers used in the blender.", "items": { "description": "Model ID numbers used in the blender.", "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintNumber": { "description": "The blueprint number associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "createdAt": { "description": "Timestamp generated at model's project creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "datasetName": { "description": "The name of the dataset used to build the model in the associated project.", "type": "string" }, "featurelistName": { "description": "The name of the feature list associated with the model.", "type": "string", "x-versionadded": "v2.36" }, "frozenPct": { "description": "The percentage used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "A boolean setting whether the model can be converted to scorable Java code.", "type": "boolean" }, "hasHoldout": { "description": "Whether the model has holdout.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "isBlender": { "description": "Indicates if the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "Indicates if the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isDatetimePartitioned": { "description": "Indicates whether the model is a datetime-partitioned model.", "type": "boolean" }, "isExternalPredictionModel": { "description": "Indicates if the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates if the model is a frozen model.", "type": "boolean", "x-versionadded": "v2.36" }, "isMaseBaselineModel": { "description": "Indicates if the model is a baseline model with MASE score '1.000'.", "type": "boolean", "x-versionadded": "v2.36" }, "isReferenceModel": { "description": "Indicates if the model is a reference model.", "type": "boolean", "x-versionadded": "v2.36" }, "isScoringAvailableForModelsTrainedIntoValidationHoldout": { "description": "Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred for easier identification.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Whether the model used holdout data for training.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Whether the model used validation data for training.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Indicates if the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.36" }, "isTransparent": { "description": "Indicates if the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "Indicates if the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "isUsingCrossValidation": { "default": true, "description": "Indicates whether cross-validation is the partitioning strategy used for the project associated with the model.", "type": "boolean", "x-versionadded": "v2.36" }, "metric": { "description": "Model performance information by the specified filtered evaluation metric", "type": "object" }, "modelFamily": { "description": "Model family associated with the model", "enum": [ "AB", "AD", "BLENDER", "CAL", "CLUSTER", "COUNT_DICT", "CUSTOM", "DOCUMENT", "DT", "DUMMY", "EP", "EQ", "EQ_TS", "FM", "GAM", "GBM", "GLM", "GLMNET", "IMAGE", "KNN", "NB", "NN", "OTHER", "RF", "RI", "SEGMENTED", "SVM", "TEXT", "TS", "TS_NN", "TTS", "VW" ], "type": "string" }, "modelId": { "description": "ID of the model", "type": "string" }, "modelNumber": { "description": "The model number from the single experiment leaderboard.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "name": { "description": "Name of the model", "type": "string" }, "projectId": { "description": "ID of the project associated with the model", "type": "string" }, "projectName": { "description": "Name of the project associated with the model", "type": "string" }, "samplePct": { "description": "Percentage of the dataset to use with the model", "exclusiveMinimum": 0, "maximum": 100, "type": [ "number", "null" ] }, "supportsMonotonicConstraints": { "description": "Indicates if the model supports enforcing monotonic constraints.", "type": "boolean", "x-versionadded": "v2.36" }, "supportsNewSeries": { "description": "Indicates if the model supports new series (time series only).", "type": "boolean", "x-versionadded": "v2.36" }, "targetName": { "description": "Name of modeling target", "type": "string" }, "targetType": { "description": "The type of modeling target", "enum": [ "Binary", "Regression" ], "type": "string" }, "trainingRowCount": { "default": 1, "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.36" } }, "required": [ "autopilotDataSelectionMethod", "blueprintNumber", "createdAt", "createdBy", "datasetName", "hasCodegen", "hasHoldout", "icons", "isBlender", "isCustom", "isDatetimePartitioned", "isExternalPredictionModel", "isFrozen", "isMaseBaselineModel", "isReferenceModel", "isScoringAvailableForModelsTrainedIntoValidationHoldout", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "isUsingCrossValidation", "metric", "modelFamily", "modelId", "name", "projectId", "projectName", "samplePct", "supportsMonotonicConstraints", "supportsNewSeries", "targetName", "targetType", "trainingRowCount" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseModelsForComparisonListResponse | | 400 | Bad Request | Bad request. | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 422 | Unprocessable Entity | Unable to retrieve models for the Use Case. | None | ## Link multiple entities by use case ID Operation path: `POST /api/v2/useCases/{useCaseId}/multilink/` Authentication requirements: `BearerAuth` Link multiple entities to a use case. ### Body parameter ``` { "properties": { "entitiesList": { "description": "Entities to link to this Use Case", "items": { "properties": { "entityId": { "description": "The primary id of the entity to link.", "type": "string", "x-versionadded": "v2.33" }, "entityType": { "description": "The type of entity to link.", "enum": [ "project", "dataset", "file", "notebook", "application", "recipe", "syftrSearchInstance", "customModelVersion", "registeredModelVersion", "deployment", "customApplication", "customJob" ], "type": "string", "x-versionadded": "v2.33" }, "includeDataset": { "default": true, "description": "Include dataset migration when an experiment is migrated", "type": "boolean", "x-versionadded": "v2.34" } }, "required": [ "entityId", "entityType" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "workflow": { "default": "unspecified", "description": "The workflow that is attaching this entity. Does not affect the operation of the API call. Only used for analytics.", "enum": [ "migration", "creation", "move", "unspecified" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "entitiesList", "workflow" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | path | string | true | The ID of the use case. | | body | body | UseCaseReferenceMultilink | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Get the list of the notebooks associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/notebooks/` Authentication requirements: `BearerAuth` Get the list of the notebooks associated with a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | useCaseId | path | string | true | The ID of the use case to update. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the notebooks in this use case.", "items": { "properties": { "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at notebook creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity (same as ID of the notebook).", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "enum": [ "notebook" ], "type": "string" }, "experimentContainerId": { "description": "[DEPRECATED - replaced with use_case_id] The ID of the Use Case.", "type": "string", "x-versiondeprecated": "v2.32" }, "id": { "description": "The ID of the notebook.", "type": "string" }, "isDeleted": { "description": "Soft deletion flag for notebooks", "type": "boolean" }, "referenceId": { "description": "Original ID from DB", "type": "string" }, "tenantId": { "description": "The id of the tenant the notebook belongs to.", "type": [ "string", "null" ] }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case.", "type": "string" }, "useCaseName": { "description": "Use Case name", "type": "string" } }, "required": [ "created", "createdAt", "entityId", "entityType", "experimentContainerId", "id", "isDeleted", "referenceId", "tenantId", "useCaseId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCasesNotebooksListResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## Get the list of the playgrounds associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/playgrounds/` Authentication requirements: `BearerAuth` Get the list of the playgrounds associated with a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | useCaseId | path | string | true | The ID of the use case to update. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the playgrounds in this use case.", "items": { "properties": { "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at playground creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity (same as ID of the playground).", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "enum": [ "playground" ], "type": "string" }, "id": { "description": "The ID of the playground.", "type": "string" }, "isDeleted": { "description": "Soft deletion flag for playgrounds", "type": "boolean" }, "referenceId": { "description": "Original ID from DB", "type": "string" }, "tenantId": { "description": "The id of the tenant the playground belongs to.", "type": [ "string", "null" ] }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] } }, "required": [ "created", "createdAt", "entityId", "entityType", "id", "isDeleted", "referenceId", "tenantId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCasesPlaygroundsListResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## Get the list of the projects associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/projects/` Authentication requirements: `BearerAuth` Get the list of the projects associated with a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | search | query | string | false | Returns only Use Cases with names that match the given string. | | sort | query | string | true | [DEPRECATED - replaced with order_by] The order to sort the Use Case projects. | | orderBy | query | string | true | The order to sort the Use Case projects. | | useCaseId | path | string | true | The ID of the use case to update. | ### Enumerated Values | Parameter | Value | | --- | --- | | sort | [-createdAt, -createdBy, -dataset, -featureCount, -fullName, -lastActivity, -models, -name, -projectId, -rowCount, -target, -targetType, -timeAware, -updatedAt, -updatedBy, createdAt, createdBy, dataset, featureCount, fullName, lastActivity, models, name, projectId, rowCount, target, targetType, timeAware, updatedAt, updatedBy] | | orderBy | [-createdAt, -createdBy, -dataset, -featureCount, -fullName, -lastActivity, -models, -name, -projectId, -rowCount, -target, -targetType, -timeAware, -updatedAt, -updatedBy, createdAt, createdBy, dataset, featureCount, fullName, lastActivity, models, name, projectId, rowCount, target, targetType, timeAware, updatedAt, updatedBy] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of the Use Case projects that match the query", "items": { "properties": { "autopilotDataSelectionMethod": { "description": "The Data Selection method of the datetime-partitioned project. `null` if project is not datetime-partitioned.", "enum": [ "duration", "rowCount" ], "type": [ "string", "null" ] }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at project creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "dataset": { "description": "Name of the dataset in the registry used to build the project associated with the project", "type": "string" }, "datasetId": { "description": "The dataset ID of the dataset in the registry.", "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The dataset version ID of the dataset in the registry.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "hasHoldout": { "description": "Whether the project has holdout.", "type": "boolean" }, "isDatasetLinkedToUseCase": { "description": "Indicates whether the dataset that this project was created from is attached to this Use Case.", "type": "boolean" }, "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isScoringAvailableForModelsTrainedIntoValidationHoldout": { "description": "Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "metricDetail": { "description": "Project metrics", "items": { "properties": { "ascending": { "description": "Should the metric be sorted in ascending order", "type": "boolean" }, "name": { "description": "Name of the metric", "type": "string" } }, "required": [ "ascending", "name" ], "type": "object" }, "type": "array" }, "models": { "description": "The number of models in an use case.", "type": "integer" }, "name": { "description": "The name of the project.", "type": "string" }, "numberOfBacktests": { "description": "The number of backtests of the datetime-partitioned project. 0 if project is not datetime-partitioned.", "minimum": 0, "type": "integer" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "projectOptions": { "description": "Project options currently saved for a project. Can be changed while project is in draft status.", "properties": { "target": { "description": "Name of the target", "type": [ "string", "null" ] }, "targetType": { "description": "The target type of the project.", "enum": [ "Binary", "Regression", "Multiclass", "minInflated", "Multilabel", "TextGeneration", "GeoPoint", "VectorDatabase" ], "type": [ "string", "null" ] }, "validationType": { "description": "The validation type of the partitioning strategy for the project, either CV for cross-validation or TVH for train-validation-holdout split.", "enum": [ "CV", "TVH" ], "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "weight": { "description": "Name of the weight (if configured)", "type": [ "string", "null" ] } }, "type": "object" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "Experiment failure explanation.", "type": "string" }, "target": { "description": "Name of the target", "type": [ "string", "null" ] }, "targetType": { "description": "The type of modeling target.", "enum": [ "Binary", "Regression", "Multiclass", "minInflated", "Multilabel", "TextGeneration", "GeoPoint", "VectorDatabase" ], "type": [ "string", "null" ] }, "timeAware": { "description": "Shows if project uses time series", "type": "boolean" }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated at project modification.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] } }, "required": [ "autopilotDataSelectionMethod", "created", "createdAt", "dataset", "datasetId", "datasetVersionId", "hasHoldout", "isDatasetLinkedToUseCase", "isDraft", "isErrored", "isScoringAvailableForModelsTrainedIntoValidationHoldout", "isWorkbenchEligible", "lastActivity", "metricDetail", "models", "name", "numberOfBacktests", "projectId", "projectOptions", "stage", "statusErrorMessage", "timeAware", "updated", "updatedAt" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseProjectsListResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## The list of the registered models referenced by a use case by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/registeredModels/` Authentication requirements: `BearerAuth` The list of the registered models referenced by a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 10. | | useCaseId | path | string | true | The ID of the use case to update. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of registered models.", "items": { "properties": { "createdAt": { "description": "The time when this model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The ID of the registered model.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the registered model.", "type": "string" }, "updatedAt": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "userHasAccess": { "description": "Indicates if a user has access to this registered model.", "type": "boolean", "x-versionadded": "v2.35" }, "versions": { "description": "The list of registered model versions.", "items": { "properties": { "createdAt": { "description": "The time when this model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The ID of the registered model version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the registered model version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the registered model version.", "type": "integer", "x-versionadded": "v2.35" }, "updatedAt": { "description": "The time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" } }, "required": [ "id", "userHasAccess" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseRegisteredModelsResponse | ## Get the list of the references associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/resources/` Authentication requirements: `BearerAuth` Get the list of the references associated with a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | sort | query | string | true | [DEPRECATED - replaced with order_by] The order to sort the Use Case references. | | orderBy | query | string | true | The order to sort the Use Case references. | | daysSinceLastActivity | query | integer | false | Only retrieve resources that had activity within the specified number of days. | | recipeStatus | query | any | false | The recipe status used for filtering recipes. | | useCaseId | path | string | true | The ID of the use case to update. | ### Enumerated Values | Parameter | Value | | --- | --- | | sort | [-entityType, -lastActivity, -name, -updatedAt, -updatedBy, entityType, lastActivity, name, updatedAt, updatedBy] | | orderBy | [-entityType, -lastActivity, -name, -updatedAt, -updatedBy, entityType, lastActivity, name, updatedAt, updatedBy] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the use case references that match the query.", "items": { "properties": { "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary ID of the entity.", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity ID.", "type": "string" }, "id": { "description": "The ID of the Use Case reference.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "metadata": { "description": "The reference metadata for the use case.", "oneOf": [ { "properties": { "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "The experiment failure explanation.", "type": "string" } }, "required": [ "isDraft", "isErrored", "isWorkbenchEligible", "stage", "statusErrorMessage" ], "type": "object" }, { "properties": { "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "datasetSize": { "description": "The size of the dataset in bytes.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "datasetSourceType": { "description": "The source type of the dataset", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] } }, "required": [ "dataSourceType", "datasetSize", "datasetSourceType", "isSnapshot", "isWranglingEligible", "latestRecipeId" ], "type": "object" }, { "properties": { "dataType": { "description": "The type of the recipe (wrangling or feature discovery)", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": [ "string", "null" ] } }, "required": [ "dataType" ], "type": "object" }, { "properties": { "description": { "description": "The description of the playground.", "type": "string" }, "playgroundType": { "description": "The type of the playground.", "type": "string", "x-versionadded": "v2.37" } }, "required": [ "description" ], "type": "object" }, { "properties": { "errorMessage": { "description": "The error message, if any, for the vector database.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "source": { "description": "The source of the vector database", "type": "string" } }, "required": [ "errorMessage", "source" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "dataSourceType": { "description": "The data source type used to create the file if relevant.", "type": [ "string", "null" ] }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "numFiles": { "description": "The number of files in the catalog item.", "type": [ "integer", "null" ] } }, "required": [ "dataSourceType", "fileSourceType", "numFiles" ], "type": "object", "x-versionadded": "v2.37" } ] }, "name": { "description": "The name of the Use Case reference.", "type": "string" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the linked use case.", "type": "string" }, "useCaseName": { "description": "The name of the linked use case.", "type": [ "string", "null" ] }, "userHasAccess": { "description": "Identifies if a user has access to the entity.", "type": [ "boolean", "null" ], "x-versionadded": "v2.36" }, "versions": { "description": "The list of entity versions.", "items": { "properties": { "createdAt": { "description": "Time when this entity was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The id of the entity version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the entity version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the entity version.", "type": "integer" }, "updatedAt": { "description": "Time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "entityId", "entityType", "id", "name", "updatedAt", "useCaseId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseReferenceListResponse | ## Get the use case's access control list by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users who have access to this Use Case and their roles on the Use Case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | id | query | string | false | Optional, only return the access control information for a user with this user ID. | | useCaseId | path | string | true | The ID of the use case to update. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" }, "userFullName": { "description": "The full name of the recipient user.", "type": "string" } }, "required": [ "canShare", "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SharedRolesWithGrantListResponse | | 404 | Not Found | Either the Use Case does not exist or the user does not have permissions to view the data store. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## Update the use case's access control list by use case ID Operation path: `PATCH /api/v2/useCases/{useCaseId}/sharedRoles/` Authentication requirements: `BearerAuth` Update the list of users who have access to this Use Case and their roles on the Use Case. ### Body parameter ``` { "properties": { "operation": { "description": "The name of the action being taken. Only 'updateRoles' is supported.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "A list of sharing role objects", "items": { "oneOf": [ { "properties": { "role": { "description": "The assigned role.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string", "x-versionadded": "v2.33" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user" ], "type": "string", "x-versionadded": "v2.33" }, "username": { "description": "The username of the user to update the access role for. If included with a name, the username is used.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "name": { "description": "The name of the user to update the access role for. If included with a username, the username is used.", "type": "string" }, "role": { "description": "The assigned role.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user" ], "type": "string" } }, "required": [ "name", "role", "shareRecipientType" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient", "type": "string" }, "role": { "description": "The assigned role.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | path | string | true | The ID of the use case to update. | | body | body | ExperimentContainerSharedRolesUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Get the list of the vector databases associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/vectorDatabases/` Authentication requirements: `BearerAuth` Get a list of the vector databases associated with a Use Case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | useCaseId | path | string | true | The ID of the use case to update. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the vector databases in this use case.", "items": { "properties": { "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at vector database creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity (same as ID of the vector database).", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "enum": [ "vector_database" ], "type": "string" }, "id": { "description": "The ID of the vector database.", "type": "string" }, "isDeleted": { "description": "Soft deletion flag for vector databases", "type": "boolean" }, "referenceId": { "description": "Original ID from DB", "type": "string" }, "tenantId": { "description": "The id of the tenant the vector database belongs to.", "type": [ "string", "null" ] }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] } }, "required": [ "created", "createdAt", "entityId", "entityType", "id", "isDeleted", "referenceId", "tenantId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCasesVectorDatabasesListResponse | | 403 | Forbidden | User does not have access to this functionality. | None | ## Get a list of custom models that are associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/vectorDatabases/{vectorDatabaseId}/relatedCustomModels/` Authentication requirements: `BearerAuth` Get a list of custom models that are associated with this vector database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | useCaseId | path | string | true | The ID of the use case this vector database belongs to. | | vectorDatabaseId | path | string | true | The ID of the vector database. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of custom model versions created from this vector database.", "items": { "properties": { "createdAt": { "description": "The time that this custom model version was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who created this custom model version.", "type": "string" }, "customModelId": { "description": "The ID of this custom model.", "type": "string" }, "customModelVersionId": { "description": "The ID of this version of this custom model.", "type": "string" }, "name": { "description": "The name of the custom model created from this vector database.", "type": "string" }, "role": { "description": "The role that the requesting user has on this custom model version", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "versionMajor": { "description": "The major version number of this custom model version.", "type": "integer" }, "versionMinor": { "description": "The minor version number of this custom model version.", "type": "integer" } }, "required": [ "createdAt", "createdBy", "customModelId", "customModelVersionId", "name", "role", "versionMajor", "versionMinor" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseVectorDatabaseRelatedCustomModelList | ## Get a list of deployments that are associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/vectorDatabases/{vectorDatabaseId}/relatedDeployments/` Authentication requirements: `BearerAuth` Get a list of deployments that are associated with this vector database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. | | targetType | query | string | false | Filters related deployments by target type. | | status | query | string | false | Filters related deployments by deployment status. | | includeModelInfo | query | boolean | true | Indicates if IDs of champion registered model and champion custom model for each deployment should be returned | | useCaseId | path | string | true | The ID of the use case this vector database belongs to. | | vectorDatabaseId | path | string | true | The ID of the vector database. | ### Enumerated Values | Parameter | Value | | --- | --- | | targetType | [Binary, Regression, Multiclass, Anomaly, Transform, TextGeneration, GeoPoint, Unstructured, VectorDatabase, AgenticWorkflow, MCP] | | status | [active, archived, errored, inactive, launching, replacingModel, stopping] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of deployments that this vector database is currently deployed on.", "items": { "properties": { "createdAt": { "description": "The time that this deployment was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who created this deployment.", "type": "string" }, "customModelId": { "description": "ID of the custom model currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "customModelVersionId": { "description": "ID of the custom model version currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "id": { "description": "The ID of the deployment with this vector database deployed.", "type": "string" }, "name": { "description": "The name of the deployment with this vector database deployed.", "type": "string" }, "registeredModelId": { "description": "ID of the registered model currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "registeredModelVersionId": { "description": "ID of the registered model version currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "role": { "description": "The role that the requesting user has on this deployment", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] } }, "required": [ "createdAt", "createdBy", "id", "name", "role" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseVectorDatabaseRelatedDeploymentList | ## Get a list of registered models that are associated by use case ID Operation path: `GET /api/v2/useCases/{useCaseId}/vectorDatabases/{vectorDatabaseId}/relatedRegisteredModels/` Authentication requirements: `BearerAuth` Get a list of registered models that are associated with this vector database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | useCaseId | path | string | true | The ID of the use case this vector database belongs to. | | vectorDatabaseId | path | string | true | The ID of the vector database. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of registered model versions created from this vector database.", "items": { "properties": { "createdAt": { "description": "The time that this registered model version was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who created this registered model version.", "type": "string" }, "registeredModelId": { "description": "The ID of this registered model.", "type": "string" }, "registeredModelName": { "description": "The name of the registered model created from this vector database.", "type": "string" }, "registeredModelVersionId": { "description": "The ID of this version of this registered model.", "type": "string" }, "registeredModelVersionName": { "description": "The name of the registered model version created from this vector database.", "type": "string" }, "role": { "description": "The role that the requesting user has on this registered model version", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "version": { "description": "The version number of this registered model version.", "type": "integer" } }, "required": [ "createdAt", "createdBy", "registeredModelId", "registeredModelName", "registeredModelVersionId", "registeredModelVersionName", "role", "version" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UseCaseVectorDatabaseRelatedRegisteredModelList | ## Remove a related entity by use case ID Operation path: `DELETE /api/v2/useCases/{useCaseId}/{referenceCollectionType}/{entityId}/` Authentication requirements: `BearerAuth` Remove a related entity from a use case. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deleteResource | query | boolean | true | If true, delete the linked resource. | | useCaseId | path | string | true | The ID linking the use case with the entity type. | | referenceCollectionType | path | string | true | The reference collection type. | | entityId | path | string | true | The primary ID of the entity. | ### Enumerated Values | Parameter | Value | | --- | --- | | referenceCollectionType | [projects, datasets, files, notebooks, applications, recipes, syftrSearchInstances, customModelVersions, registeredModelVersions, deployments, customApplications, customJobs] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | The relationship has been removed successfully | None | | 404 | Not Found | The use case reference was not found. | None | ## Move entity from one Use Case by use case ID Operation path: `PATCH /api/v2/useCases/{useCaseId}/{referenceCollectionType}/{entityId}/` Authentication requirements: `BearerAuth` Move entity from one Use Case to another. ### Body parameter ``` { "properties": { "includeDataset": { "default": true, "description": "Include dataset migration when project is migrated.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.34" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | path | string | true | The ID linking the use case with the entity type. | | referenceCollectionType | path | string | true | The reference collection type. | | entityId | path | string | true | The primary ID of the entity. | | body | body | UseCasesProjectMigrationParam | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | referenceCollectionType | [projects, datasets, files, notebooks, applications, recipes, syftrSearchInstances, customModelVersions, registeredModelVersions, deployments, customApplications, customJobs] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | ## Link entity by use case ID Operation path: `POST /api/v2/useCases/{useCaseId}/{referenceCollectionType}/{entityId}/` Authentication requirements: `BearerAuth` Link entity to a single use case. ### Body parameter ``` { "properties": { "workflow": { "default": "unspecified", "description": "The workflow that is attaching this entity. Does not affect the operation of the API call. Only used for analytics.", "enum": [ "migration", "creation", "move", "unspecified" ], "type": "string" } }, "required": [ "workflow" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | path | string | true | The ID linking the use case with the entity type. | | referenceCollectionType | path | string | true | The reference collection type. | | entityId | path | string | true | The primary ID of the entity. | | body | body | UseCaseReferenceLink | false | none | ### Enumerated Values | Parameter | Value | | --- | --- | | referenceCollectionType | [projects, datasets, files, notebooks, applications, recipes, syftrSearchInstances, customModelVersions, registeredModelVersions, deployments, customApplications, customJobs] | ### Example responses > 200 Response ``` { "properties": { "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity.", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "type": "string" }, "id": { "description": "The ID of the experiment container reference.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "metadata": { "description": "Reference metadata for the experiment container", "oneOf": [ { "properties": { "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "Experiment failure explanation.", "type": "string" } }, "required": [ "isDraft", "isErrored", "isWorkbenchEligible", "stage", "statusErrorMessage" ], "type": "object" }, { "properties": { "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "datasetSize": { "description": "Size of the dataset in bytes", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "datasetSourceType": { "description": "The source type of the dataset.", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] } }, "required": [ "dataSourceType", "datasetSize", "datasetSourceType", "isSnapshot", "isWranglingEligible", "latestRecipeId" ], "type": "object" }, { "properties": { "dataType": { "description": "The type of the recipe (wrangling or feature discovery)", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": [ "string", "null" ] } }, "required": [ "dataType" ], "type": "object" }, { "properties": { "description": { "description": "Description of the playground", "type": "string" }, "playgroundType": { "description": "The type of the playground", "type": "string", "x-versionadded": "v2.37" } }, "required": [ "description" ], "type": "object" }, { "properties": { "errorMessage": { "description": "The error message, if any, for the vector database.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "source": { "description": "The source of the vector database", "type": "string" } }, "required": [ "errorMessage", "source" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "dataSourceType": { "description": "The data source type used to create the file if relevant.", "type": [ "string", "null" ] }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "numFiles": { "description": "The number of files in the file.", "type": [ "integer", "null" ] } }, "required": [ "dataSourceType", "fileSourceType", "numFiles" ], "type": "object", "x-versionadded": "v2.37" } ] }, "name": { "description": "The name of the experiment container reference.", "type": "string" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID linking the Use Case with the entity type.", "type": "string" }, "userHasAccess": { "description": "Identifies if a user has access to the entity.", "type": "boolean", "x-versionadded": "v2.36" }, "versions": { "description": "A list of entity versions.", "items": { "properties": { "createdAt": { "description": "Time when this entity was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The id of the entity version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the entity version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the entity version.", "type": "integer" }, "updatedAt": { "description": "Time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "entityId", "entityType", "id", "name", "updatedAt", "useCaseId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ExperimentContainerReferenceRetrieve | | 201 | Created | The entity has been linked successfully | None | | 403 | Forbidden | User does not have access to this functionality. | None | | 409 | Conflict | The entity has already been linked | None | | 422 | Unprocessable Entity | Unprocessable Entity | None | ## Use an endpoint Operation path: `GET /api/v2/useCasesWithShortenedInfo/` Authentication requirements: `BearerAuth` Retrieves the list of use cases using a new endpoint that abbreviates the display metadata for faster retrieval. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | search | query | string | false | Returns only Use Cases with names that match the given string. | | projectId | query | string | false | Only return experiment containers associated with the given project ID. | | applicationId | query | string | false | Only return experiment containers associated with the given app. | | entityId | query | string | false | The id of the entity type that is linked with the Use Case. | | entityType | query | string | false | The entity type that is linked to the use case. | | sort | query | string | true | [DEPRECATED - replaced with order_by] The order in which Use Cases and return Use Cases. | | orderBy | query | string | true | The order in which use cases are returned. | | usecaseType | query | string | true | A filter to return use cases by type. | | riskLevel | query | any | false | Only return Use Cases associated with the given risk level. | | stage | query | any | false | Only return use cases in the given stage. | | createdBy | query | string | false | Filter Use Cases to return only those created by the selected user. | | showOrgUseCases | query | boolean | false | Defines if the Use Cases available on Organization level should be shown. | ### Enumerated Values | Parameter | Value | | --- | --- | | entityType | [project, dataset, file, notebook, application, recipe, playground, vectorDatabase, syftrSearchInstance, customModelVersion, registeredModelVersion, deployment, customApplication, customJob] | | sort | [-applicationsCount, -createdAt, -createdBy, -customApplicationsCount, -datasetsCount, -description, -filesCount, -id, -name, -notebooksCount, -playgroundsCount, -potentialValue, -projectsCount, -riskLevel, -stage, -updatedAt, -updatedBy, -vectorDatabasesCount, applicationsCount, createdAt, createdBy, customApplicationsCount, datasetsCount, description, filesCount, id, name, notebooksCount, playgroundsCount, potentialValue, projectsCount, riskLevel, stage, updatedAt, updatedBy, vectorDatabasesCount] | | orderBy | [-applicationsCount, -createdAt, -createdBy, -customApplicationsCount, -datasetsCount, -description, -filesCount, -id, -name, -notebooksCount, -playgroundsCount, -potentialValue, -projectsCount, -riskLevel, -stage, -updatedAt, -updatedBy, -vectorDatabasesCount, applicationsCount, createdAt, createdBy, customApplicationsCount, datasetsCount, description, filesCount, id, name, notebooksCount, playgroundsCount, potentialValue, projectsCount, riskLevel, stage, updatedAt, updatedBy, vectorDatabasesCount] | | usecaseType | [all, general, walkthrough] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of Use Cases with shortened info that match the query", "items": { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created the Use Case.", "type": [ "string", "null" ] }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated the Use Case.", "type": [ "string", "null" ] }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "createdAt", "description", "formattedDescription", "id", "name", "tenantId", "updatedAt" ], "type": "object", "x-versionadded": "v2.34" }, "maxItems": 100, "minItems": 1, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The use cases with abbreviated metadata were retrieved successfully. | UseCaseListWithShortenedInfoResponse | | 403 | Forbidden | User does not have access to this functionality. | None | # Schemas ## AccessControlWithGrant ``` { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" }, "userFullName": { "description": "The full name of the recipient user.", "type": "string" } }, "required": [ "canShare", "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | true | | Whether the recipient can share the role further. | | id | string | true | | The identifier of the recipient. | | name | string | true | | The name of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | The type of the recipient. | | userFullName | string | false | | The full name of the recipient user. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## DeploymentPagination ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of deployments linked to a use case.", "items": { "properties": { "createdAt": { "description": "The created date time.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The deployment description.", "type": "string" }, "id": { "description": "Unique identifier of the deployment.", "type": "string" }, "label": { "description": "The deployment label.", "type": "string" }, "lastActivity": { "description": "The type of the last activity. Can be \"Added\" or \"Modified\".", "enum": [ "created", "Created", "CREATED", "modified", "Modified", "MODIFIED" ], "type": "string", "x-versionadded": "v2.35" }, "type": { "description": "The deployment type.", "type": "string" }, "updatedAt": { "description": "The last modified date time.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "userHasAccess": { "description": "Indicates if a user has access to this deployment.", "type": "boolean", "x-versionadded": "v2.35" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [FormattedResponseDeployment] | true | maxItems: 100 | The list of deployments linked to a use case. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## EntityCountByTypeResponse ``` { "description": "Number of different type entities that use the dataset.", "properties": { "numCalendars": { "description": "The number of calendars that use the dataset", "type": "integer" }, "numExperimentContainer": { "description": "The number of experiment containers that use the dataset.", "type": "integer", "x-versionadded": "v2.37" }, "numExternalModelPackages": { "description": "The number of external model packages that use the dataset", "type": "integer" }, "numFeatureDiscoveryConfigs": { "description": "The number of feature discovery configs that use the dataset", "type": "integer" }, "numPredictionDatasets": { "description": "The number of prediction datasets that use the dataset", "type": "integer" }, "numProjects": { "description": "The number of projects that use the dataset", "type": "integer" }, "numSparkSqlQueries": { "description": "The number of spark sql queries that use the dataset", "type": "integer" } }, "required": [ "numCalendars", "numExperimentContainer", "numExternalModelPackages", "numFeatureDiscoveryConfigs", "numPredictionDatasets", "numProjects", "numSparkSqlQueries" ], "type": "object" } ``` Number of different type entities that use the dataset. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | numCalendars | integer | true | | The number of calendars that use the dataset | | numExperimentContainer | integer | true | | The number of experiment containers that use the dataset. | | numExternalModelPackages | integer | true | | The number of external model packages that use the dataset | | numFeatureDiscoveryConfigs | integer | true | | The number of feature discovery configs that use the dataset | | numPredictionDatasets | integer | true | | The number of prediction datasets that use the dataset | | numProjects | integer | true | | The number of projects that use the dataset | | numSparkSqlQueries | integer | true | | The number of spark sql queries that use the dataset | ## ExperimentContainerApplicationResponse ``` { "properties": { "applicationId": { "description": "The application id of the application.", "type": "string" }, "applicationTemplateType": { "description": "The type of the application.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp generated at application creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The description of the application.", "type": [ "string", "null" ] }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the application.", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": [ "string", "null" ] }, "source": { "description": "The source used to create the application.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp generated at application modification.", "format": "date-time", "type": "string" } }, "required": [ "applicationId", "applicationTemplateType", "createdAt", "createdBy", "description", "lastActivity", "name", "projectId", "source", "updatedAt" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | applicationId | string | true | | The application id of the application. | | applicationTemplateType | string,null | true | | The type of the application. | | createdAt | string(date-time) | true | | The timestamp generated at application creation. | | createdBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | | description | string,null | true | | The description of the application. | | lastActivity | ExperimentContainerLastActivity | true | | The last activity details. | | name | string | true | | The name of the application. | | projectId | string,null | true | | The ID of the associated project | | source | string,null | true | | The source used to create the application. | | updatedAt | string(date-time) | true | | The timestamp generated at application modification. | ## ExperimentContainerFilterMetadataMetricsObjectResponse ``` { "description": "Model performance evaluation metrics (shorthand abbreviations)", "properties": { "binary": { "description": "Binary metrics associated with the models.", "items": { "description": "Binary metric names", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE", "F1 Score", "Weighted F1 Score", "Precision", "Weighted Precision", "Recall", "Weighted Recall" ], "type": "string" }, "type": "array" }, "regression": { "description": "Regression metrics associated with the models", "items": { "description": "Regression metric names", "enum": [ "FVE Poisson", "Weighted FVE Poisson", "FVE Gamma", "Weighted FVE Gamma", "FVE Tweedie", "Weighted FVE Tweedie", "Gamma Deviance", "Weighted Gamma Deviance", "Gini Norm", "Weighted Gini Norm", "MAE", "Weighted MAE", "MAPE", "Weighted MAPE", "SMAPE", "Weighted SMAPE", "Poisson Deviance", "Weighted Poisson Deviance", "RMSLE", "RMSE", "Weighted RMSLE", "Weighted RMSE", "R Squared", "Weighted R Squared", "Tweedie Deviance", "Weighted Tweedie Deviance" ], "type": "string" }, "type": "array" } }, "type": "object" } ``` Model performance evaluation metrics (shorthand abbreviations) ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | binary | [string] | false | | Binary metrics associated with the models. | | regression | [string] | false | | Regression metrics associated with the models | ## ExperimentContainerFilterMetadataModelFamiliesObjectResponse ``` { "properties": { "fullName": { "description": "Full name of the model family", "type": "string" }, "key": { "description": "Abbreviated form of model family name", "enum": [ "AB", "AD", "BLENDER", "CAL", "CLUSTER", "COUNT_DICT", "CUSTOM", "DOCUMENT", "DT", "DUMMY", "EP", "EQ", "EQ_TS", "FM", "GAM", "GBM", "GLM", "GLMNET", "IMAGE", "KNN", "NB", "NN", "OTHER", "RF", "RI", "SEGMENTED", "SVM", "TEXT", "TS", "TS_NN", "TTS", "VW" ], "type": "string" } }, "required": [ "fullName", "key" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fullName | string | true | | Full name of the model family | | key | string | true | | Abbreviated form of model family name | ### Enumerated Values | Property | Value | | --- | --- | | key | [AB, AD, BLENDER, CAL, CLUSTER, COUNT_DICT, CUSTOM, DOCUMENT, DT, DUMMY, EP, EQ, EQ_TS, FM, GAM, GBM, GLM, GLMNET, IMAGE, KNN, NB, NN, OTHER, RF, RI, SEGMENTED, SVM, TEXT, TS, TS_NN, TTS, VW] | ## ExperimentContainerFilterMetadataRetrieveResponse ``` { "properties": { "metrics": { "description": "Model performance evaluation metrics (shorthand abbreviations)", "properties": { "binary": { "description": "Binary metrics associated with the models.", "items": { "description": "Binary metric names", "enum": [ "AUC", "Weighted AUC", "Area Under PR Curve", "Weighted Area Under PR Curve", "Kolmogorov-Smirnov", "Weighted Kolmogorov-Smirnov", "FVE Binomial", "Weighted FVE Binomial", "Gini Norm", "Weighted Gini Norm", "LogLoss", "Weighted LogLoss", "Max MCC", "Weighted Max MCC", "Rate@Top5%", "Weighted Rate@Top5%", "Rate@Top10%", "Weighted Rate@Top10%", "Rate@TopTenth%", "RMSE", "Weighted RMSE", "F1 Score", "Weighted F1 Score", "Precision", "Weighted Precision", "Recall", "Weighted Recall" ], "type": "string" }, "type": "array" }, "regression": { "description": "Regression metrics associated with the models", "items": { "description": "Regression metric names", "enum": [ "FVE Poisson", "Weighted FVE Poisson", "FVE Gamma", "Weighted FVE Gamma", "FVE Tweedie", "Weighted FVE Tweedie", "Gamma Deviance", "Weighted Gamma Deviance", "Gini Norm", "Weighted Gini Norm", "MAE", "Weighted MAE", "MAPE", "Weighted MAPE", "SMAPE", "Weighted SMAPE", "Poisson Deviance", "Weighted Poisson Deviance", "RMSLE", "RMSE", "Weighted RMSLE", "Weighted RMSE", "R Squared", "Weighted R Squared", "Tweedie Deviance", "Weighted Tweedie Deviance" ], "type": "string" }, "type": "array" } }, "type": "object" }, "modelFamilies": { "description": "Model families associated with the models", "items": { "properties": { "fullName": { "description": "Full name of the model family", "type": "string" }, "key": { "description": "Abbreviated form of model family name", "enum": [ "AB", "AD", "BLENDER", "CAL", "CLUSTER", "COUNT_DICT", "CUSTOM", "DOCUMENT", "DT", "DUMMY", "EP", "EQ", "EQ_TS", "FM", "GAM", "GBM", "GLM", "GLMNET", "IMAGE", "KNN", "NB", "NN", "OTHER", "RF", "RI", "SEGMENTED", "SVM", "TEXT", "TS", "TS_NN", "TTS", "VW" ], "type": "string" } }, "required": [ "fullName", "key" ], "type": "object" }, "maxItems": 32, "type": "array" }, "samplePcts": { "description": "Model training sample sizes (in percentage)", "items": { "exclusiveMinimum": 0, "maximum": 100, "type": "number" }, "maxItems": 7, "type": "array" } }, "required": [ "metrics", "modelFamilies", "samplePcts" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metrics | ExperimentContainerFilterMetadataMetricsObjectResponse | true | | Model performance evaluation metrics (shorthand abbreviations) | | modelFamilies | [ExperimentContainerFilterMetadataModelFamiliesObjectResponse] | true | maxItems: 32 | Model families associated with the models | | samplePcts | [number] | true | maxItems: 7 | Model training sample sizes (in percentage) | ## ExperimentContainerLastActivity ``` { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" } ``` The last activity details. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timestamp | string(date-time) | true | | The time when this activity occurred. | | type | string | true | | The type of activity. Can be "Added" or "Modified". | ## ExperimentContainerModelsForComparisonModelResponse ``` { "properties": { "autopilotDataSelectionMethod": { "description": "The Data Selection method of the datetime-partitioned model. `null` if model is not datetime-partitioned.", "enum": [ "duration", "rowCount" ], "type": [ "string", "null" ] }, "blenderInputModelNumbers": { "description": "List of model ID numbers used in the blender.", "items": { "description": "Model ID numbers used in the blender.", "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintNumber": { "description": "The blueprint number associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "createdAt": { "description": "Timestamp generated at model's project creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "datasetName": { "description": "The name of the dataset used to build the model in the associated project.", "type": "string" }, "featurelistName": { "description": "The name of the feature list associated with the model.", "type": "string", "x-versionadded": "v2.36" }, "frozenPct": { "description": "The percentage used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "A boolean setting whether the model can be converted to scorable Java code.", "type": "boolean" }, "hasHoldout": { "description": "Whether the model has holdout.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "isBlender": { "description": "Indicates if the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "Indicates if the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isDatetimePartitioned": { "description": "Indicates whether the model is a datetime-partitioned model.", "type": "boolean" }, "isExternalPredictionModel": { "description": "Indicates if the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates if the model is a frozen model.", "type": "boolean", "x-versionadded": "v2.36" }, "isMaseBaselineModel": { "description": "Indicates if the model is a baseline model with MASE score '1.000'.", "type": "boolean", "x-versionadded": "v2.36" }, "isReferenceModel": { "description": "Indicates if the model is a reference model.", "type": "boolean", "x-versionadded": "v2.36" }, "isScoringAvailableForModelsTrainedIntoValidationHoldout": { "description": "Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred for easier identification.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Whether the model used holdout data for training.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Whether the model used validation data for training.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Indicates if the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.36" }, "isTransparent": { "description": "Indicates if the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "Indicates if the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "isUsingCrossValidation": { "default": true, "description": "Indicates whether cross-validation is the partitioning strategy used for the project associated with the model.", "type": "boolean", "x-versionadded": "v2.36" }, "metric": { "description": "Model performance information by the specified filtered evaluation metric", "type": "object" }, "modelFamily": { "description": "Model family associated with the model", "enum": [ "AB", "AD", "BLENDER", "CAL", "CLUSTER", "COUNT_DICT", "CUSTOM", "DOCUMENT", "DT", "DUMMY", "EP", "EQ", "EQ_TS", "FM", "GAM", "GBM", "GLM", "GLMNET", "IMAGE", "KNN", "NB", "NN", "OTHER", "RF", "RI", "SEGMENTED", "SVM", "TEXT", "TS", "TS_NN", "TTS", "VW" ], "type": "string" }, "modelId": { "description": "ID of the model", "type": "string" }, "modelNumber": { "description": "The model number from the single experiment leaderboard.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "name": { "description": "Name of the model", "type": "string" }, "projectId": { "description": "ID of the project associated with the model", "type": "string" }, "projectName": { "description": "Name of the project associated with the model", "type": "string" }, "samplePct": { "description": "Percentage of the dataset to use with the model", "exclusiveMinimum": 0, "maximum": 100, "type": [ "number", "null" ] }, "supportsMonotonicConstraints": { "description": "Indicates if the model supports enforcing monotonic constraints.", "type": "boolean", "x-versionadded": "v2.36" }, "supportsNewSeries": { "description": "Indicates if the model supports new series (time series only).", "type": "boolean", "x-versionadded": "v2.36" }, "targetName": { "description": "Name of modeling target", "type": "string" }, "targetType": { "description": "The type of modeling target", "enum": [ "Binary", "Regression" ], "type": "string" }, "trainingRowCount": { "default": 1, "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.36" } }, "required": [ "autopilotDataSelectionMethod", "blueprintNumber", "createdAt", "createdBy", "datasetName", "hasCodegen", "hasHoldout", "icons", "isBlender", "isCustom", "isDatetimePartitioned", "isExternalPredictionModel", "isFrozen", "isMaseBaselineModel", "isReferenceModel", "isScoringAvailableForModelsTrainedIntoValidationHoldout", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "isUsingCrossValidation", "metric", "modelFamily", "modelId", "name", "projectId", "projectName", "samplePct", "supportsMonotonicConstraints", "supportsNewSeries", "targetName", "targetType", "trainingRowCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | autopilotDataSelectionMethod | string,null | true | | The Data Selection method of the datetime-partitioned model. null if model is not datetime-partitioned. | | blenderInputModelNumbers | [integer] | false | maxItems: 100 | List of model ID numbers used in the blender. | | blueprintNumber | integer,null | true | | The blueprint number associated with the model. | | createdAt | string(date-time) | true | | Timestamp generated at model's project creation. | | createdBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | | datasetName | string | true | | The name of the dataset used to build the model in the associated project. | | featurelistName | string | false | | The name of the feature list associated with the model. | | frozenPct | number,null | false | | The percentage used to train the frozen model. | | hasCodegen | boolean | true | | A boolean setting whether the model can be converted to scorable Java code. | | hasHoldout | boolean | true | | Whether the model has holdout. | | icons | integer,null | true | | The icons associated with the model. | | isBlender | boolean | true | | Indicates if the model is a blender. | | isCustom | boolean | true | | Indicates if the model contains custom tasks. | | isDatetimePartitioned | boolean | true | | Indicates whether the model is a datetime-partitioned model. | | isExternalPredictionModel | boolean | true | | Indicates if the model is an external prediction model. | | isFrozen | boolean | true | | Indicates if the model is a frozen model. | | isMaseBaselineModel | boolean | true | | Indicates if the model is a baseline model with MASE score '1.000'. | | isReferenceModel | boolean | true | | Indicates if the model is a reference model. | | isScoringAvailableForModelsTrainedIntoValidationHoldout | boolean | true | | Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass. | | isStarred | boolean | true | | Indicates whether the model has been starred for easier identification. | | isTrainedIntoHoldout | boolean | true | | Whether the model used holdout data for training. | | isTrainedIntoValidation | boolean | true | | Whether the model used validation data for training. | | isTrainedOnGpu | boolean | true | | Indicates if the model was trained using GPU workers. | | isTransparent | boolean | true | | Indicates if the model is a transparent model with exposed coefficients. | | isUserModel | boolean | true | | Indicates if the model was created with Composable ML. | | isUsingCrossValidation | boolean | true | | Indicates whether cross-validation is the partitioning strategy used for the project associated with the model. | | metric | object | true | | Model performance information by the specified filtered evaluation metric | | modelFamily | string | true | | Model family associated with the model | | modelId | string | true | | ID of the model | | modelNumber | integer,null | false | | The model number from the single experiment leaderboard. | | name | string | true | | Name of the model | | projectId | string | true | | ID of the project associated with the model | | projectName | string | true | | Name of the project associated with the model | | samplePct | number,null | true | maximum: 100 | Percentage of the dataset to use with the model | | supportsMonotonicConstraints | boolean | true | | Indicates if the model supports enforcing monotonic constraints. | | supportsNewSeries | boolean | true | | Indicates if the model supports new series (time series only). | | targetName | string | true | | Name of modeling target | | targetType | string | true | | The type of modeling target | | trainingRowCount | integer | true | | The number of rows used to train the model. | ### Enumerated Values | Property | Value | | --- | --- | | autopilotDataSelectionMethod | [duration, rowCount] | | modelFamily | [AB, AD, BLENDER, CAL, CLUSTER, COUNT_DICT, CUSTOM, DOCUMENT, DT, DUMMY, EP, EQ, EQ_TS, FM, GAM, GBM, GLM, GLMNET, IMAGE, KNN, NB, NN, OTHER, RF, RI, SEGMENTED, SVM, TEXT, TS, TS_NN, TTS, VW] | | targetType | [Binary, Regression] | ## ExperimentContainerNotebookResponse ``` { "properties": { "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at notebook creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity (same as ID of the notebook).", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "enum": [ "notebook" ], "type": "string" }, "experimentContainerId": { "description": "[DEPRECATED - replaced with use_case_id] The ID of the Use Case.", "type": "string", "x-versiondeprecated": "v2.32" }, "id": { "description": "The ID of the notebook.", "type": "string" }, "isDeleted": { "description": "Soft deletion flag for notebooks", "type": "boolean" }, "referenceId": { "description": "Original ID from DB", "type": "string" }, "tenantId": { "description": "The id of the tenant the notebook belongs to.", "type": [ "string", "null" ] }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case.", "type": "string" }, "useCaseName": { "description": "Use Case name", "type": "string" } }, "required": [ "created", "createdAt", "entityId", "entityType", "experimentContainerId", "id", "isDeleted", "referenceId", "tenantId", "useCaseId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | ExperimentContainerUserResponse | true | | A user associated with a use case. | | createdAt | string(date-time) | true | | The timestamp generated at notebook creation. | | createdBy | string,null | false | | The ID of the user who created. | | entityId | string | true | | The primary id of the entity (same as ID of the notebook). | | entityType | string | true | | The type of entity provided by the entity id. | | experimentContainerId | string | true | | [DEPRECATED - replaced with use_case_id] The ID of the Use Case. | | id | string | true | | The ID of the notebook. | | isDeleted | boolean | true | | Soft deletion flag for notebooks | | referenceId | string | true | | Original ID from DB | | tenantId | string,null | true | | The id of the tenant the notebook belongs to. | | updatedBy | string,null | false | | The ID of the user who last updated. | | useCaseId | string | true | | The ID of the Use Case. | | useCaseName | string | false | | Use Case name | ### Enumerated Values | Property | Value | | --- | --- | | entityType | notebook | ## ExperimentContainerPlaygroundResponse ``` { "properties": { "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at playground creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity (same as ID of the playground).", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "enum": [ "playground" ], "type": "string" }, "id": { "description": "The ID of the playground.", "type": "string" }, "isDeleted": { "description": "Soft deletion flag for playgrounds", "type": "boolean" }, "referenceId": { "description": "Original ID from DB", "type": "string" }, "tenantId": { "description": "The id of the tenant the playground belongs to.", "type": [ "string", "null" ] }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] } }, "required": [ "created", "createdAt", "entityId", "entityType", "id", "isDeleted", "referenceId", "tenantId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | ExperimentContainerUserResponse | true | | A user associated with a use case. | | createdAt | string(date-time) | true | | The timestamp generated at playground creation. | | createdBy | string,null | false | | The ID of the user who created. | | entityId | string | true | | The primary id of the entity (same as ID of the playground). | | entityType | string | true | | The type of entity provided by the entity id. | | id | string | true | | The ID of the playground. | | isDeleted | boolean | true | | Soft deletion flag for playgrounds | | referenceId | string | true | | Original ID from DB | | tenantId | string,null | true | | The id of the tenant the playground belongs to. | | updatedBy | string,null | false | | The ID of the user who last updated. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | playground | ## ExperimentContainerReferenceDatasetMetadata ``` { "properties": { "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "datasetSize": { "description": "Size of the dataset in bytes", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "datasetSourceType": { "description": "The source type of the dataset.", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] } }, "required": [ "dataSourceType", "datasetSize", "datasetSourceType", "isSnapshot", "isWranglingEligible", "latestRecipeId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSourceType | string,null | true | | The type of the data source used to create the dataset if relevant. | | datasetSize | integer,null | true | | Size of the dataset in bytes | | datasetSourceType | string,null | true | | The source type of the dataset. | | isSnapshot | boolean | true | | Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot. | | isWranglingEligible | boolean | true | | Whether the source of the dataset can support wrangling. | | latestRecipeId | string,null | true | | The latest recipe ID linked to the dataset. | ## ExperimentContainerReferenceDatasetResponse ``` { "description": "Metadata about the reference of the dataset in the Use Case.", "properties": { "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" } }, "required": [ "createdAt", "lastActivity" ], "type": "object" } ``` Metadata about the reference of the dataset in the Use Case. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The timestamp generated at record creation. | | createdBy | ExperimentContainerUserResponse | false | | A user associated with a use case. | | lastActivity | ExperimentContainerLastActivity | true | | The last activity details. | ## ExperimentContainerReferenceFileMetadata ``` { "properties": { "dataSourceType": { "description": "The data source type used to create the file if relevant.", "type": [ "string", "null" ] }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "numFiles": { "description": "The number of files in the file.", "type": [ "integer", "null" ] } }, "required": [ "dataSourceType", "fileSourceType", "numFiles" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSourceType | string,null | true | | The data source type used to create the file if relevant. | | fileSourceType | string,null | true | | The source type of the file. | | numFiles | integer,null | true | | The number of files in the file. | ## ExperimentContainerReferencePlaygroundMetadata ``` { "properties": { "description": { "description": "Description of the playground", "type": "string" }, "playgroundType": { "description": "The type of the playground", "type": "string", "x-versionadded": "v2.37" } }, "required": [ "description" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | Description of the playground | | playgroundType | string | false | | The type of the playground | ## ExperimentContainerReferenceProjectMetadata ``` { "properties": { "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "Experiment failure explanation.", "type": "string" } }, "required": [ "isDraft", "isErrored", "isWorkbenchEligible", "stage", "statusErrorMessage" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isDraft | boolean | true | | Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True). | | isErrored | boolean | true | | Indicates whether the experiment failed. | | isWorkbenchEligible | boolean | true | | Indicates whether the experiment is Workbench-compatible. | | stage | string | true | | Stage of the experiment. | | statusErrorMessage | string | true | | Experiment failure explanation. | ## ExperimentContainerReferenceRecipeMetadata ``` { "properties": { "dataType": { "description": "The type of the recipe (wrangling or feature discovery)", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": [ "string", "null" ] } }, "required": [ "dataType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataType | string,null | true | | The type of the recipe (wrangling or feature discovery) | ### Enumerated Values | Property | Value | | --- | --- | | dataType | [static, Static, STATIC, snapshot, Snapshot, SNAPSHOT, dynamic, Dynamic, DYNAMIC, sqlRecipe, SqlRecipe, SQL_RECIPE, wranglingRecipe, WranglingRecipe, WRANGLING_RECIPE, featureDiscoveryRecipe, FeatureDiscoveryRecipe, FEATURE_DISCOVERY_RECIPE] | ## ExperimentContainerReferenceRetrieve ``` { "properties": { "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity.", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "type": "string" }, "id": { "description": "The ID of the experiment container reference.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "metadata": { "description": "Reference metadata for the experiment container", "oneOf": [ { "properties": { "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "Experiment failure explanation.", "type": "string" } }, "required": [ "isDraft", "isErrored", "isWorkbenchEligible", "stage", "statusErrorMessage" ], "type": "object" }, { "properties": { "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "datasetSize": { "description": "Size of the dataset in bytes", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "datasetSourceType": { "description": "The source type of the dataset.", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] } }, "required": [ "dataSourceType", "datasetSize", "datasetSourceType", "isSnapshot", "isWranglingEligible", "latestRecipeId" ], "type": "object" }, { "properties": { "dataType": { "description": "The type of the recipe (wrangling or feature discovery)", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": [ "string", "null" ] } }, "required": [ "dataType" ], "type": "object" }, { "properties": { "description": { "description": "Description of the playground", "type": "string" }, "playgroundType": { "description": "The type of the playground", "type": "string", "x-versionadded": "v2.37" } }, "required": [ "description" ], "type": "object" }, { "properties": { "errorMessage": { "description": "The error message, if any, for the vector database.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "source": { "description": "The source of the vector database", "type": "string" } }, "required": [ "errorMessage", "source" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "dataSourceType": { "description": "The data source type used to create the file if relevant.", "type": [ "string", "null" ] }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "numFiles": { "description": "The number of files in the file.", "type": [ "integer", "null" ] } }, "required": [ "dataSourceType", "fileSourceType", "numFiles" ], "type": "object", "x-versionadded": "v2.37" } ] }, "name": { "description": "The name of the experiment container reference.", "type": "string" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID linking the Use Case with the entity type.", "type": "string" }, "userHasAccess": { "description": "Identifies if a user has access to the entity.", "type": "boolean", "x-versionadded": "v2.36" }, "versions": { "description": "A list of entity versions.", "items": { "properties": { "createdAt": { "description": "Time when this entity was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The id of the entity version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the entity version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the entity version.", "type": "integer" }, "updatedAt": { "description": "Time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "entityId", "entityType", "id", "name", "updatedAt", "useCaseId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | string,null | false | | The ID of the user who created. | | entityId | string | true | | The primary id of the entity. | | entityType | string | true | | The type of entity provided by the entity id. | | id | string | true | | The ID of the experiment container reference. | | lastActivity | ExperimentContainerLastActivity | false | | The last activity details. | | metadata | any | false | | Reference metadata for the experiment container | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExperimentContainerReferenceProjectMetadata | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExperimentContainerReferenceDatasetMetadata | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExperimentContainerReferenceRecipeMetadata | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExperimentContainerReferencePlaygroundMetadata | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExperimentContainerReferenceVectorDatabasesMetadata | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExperimentContainerReferenceFileMetadata | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the experiment container reference. | | processingState | string | false | | The current ingestion process state of the dataset. | | updated | ExperimentContainerUserResponse | false | | A user associated with a use case. | | updatedAt | string(date-time) | true | | The timestamp generated at record creation. | | updatedBy | string,null | false | | The ID of the user who last updated. | | useCaseId | string | true | | The ID linking the Use Case with the entity type. | | userHasAccess | boolean | false | | Identifies if a user has access to the entity. | | versions | [UseCaseEntityVersion] | false | maxItems: 100 | A list of entity versions. | ### Enumerated Values | Property | Value | | --- | --- | | processingState | [COMPLETED, ERROR, RUNNING] | ## ExperimentContainerReferenceVectorDatabasesMetadata ``` { "properties": { "errorMessage": { "description": "The error message, if any, for the vector database.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "source": { "description": "The source of the vector database", "type": "string" } }, "required": [ "errorMessage", "source" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | string,null | true | | The error message, if any, for the vector database. | | source | string | true | | The source of the vector database | ## ExperimentContainerSharedRolesUpdate ``` { "properties": { "operation": { "description": "The name of the action being taken. Only 'updateRoles' is supported.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "A list of sharing role objects", "items": { "oneOf": [ { "properties": { "role": { "description": "The assigned role.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string", "x-versionadded": "v2.33" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user" ], "type": "string", "x-versionadded": "v2.33" }, "username": { "description": "The username of the user to update the access role for. If included with a name, the username is used.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "name": { "description": "The name of the user to update the access role for. If included with a username, the username is used.", "type": "string" }, "role": { "description": "The assigned role.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user" ], "type": "string" } }, "required": [ "name", "role", "shareRecipientType" ], "type": "object" }, { "properties": { "id": { "description": "The ID of the recipient", "type": "string" }, "role": { "description": "The assigned role.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | The name of the action being taken. Only 'updateRoles' is supported. | | roles | [oneOf] | true | minItems: 1 | A list of sharing role objects | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExperimentContainerSharingRoleUpdateDataWithUsername | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExperimentContainerSharingRoleUpdateDataWithName | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ExperimentContainerSharingRoleUpdateDataWithId | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## ExperimentContainerSharingRoleUpdateDataWithId ``` { "properties": { "id": { "description": "The ID of the recipient", "type": "string" }, "role": { "description": "The assigned role.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient | | role | string | true | | The assigned role. | | shareRecipientType | string | true | | The recipient type. | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, EDITOR, CONSUMER, NO_ROLE] | | shareRecipientType | [user, group, organization] | ## ExperimentContainerSharingRoleUpdateDataWithName ``` { "properties": { "name": { "description": "The name of the user to update the access role for. If included with a username, the username is used.", "type": "string" }, "role": { "description": "The assigned role.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user" ], "type": "string" } }, "required": [ "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the user to update the access role for. If included with a username, the username is used. | | role | string | true | | The assigned role. | | shareRecipientType | string | true | | The recipient type. | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, EDITOR, CONSUMER, NO_ROLE] | | shareRecipientType | user | ## ExperimentContainerSharingRoleUpdateDataWithUsername ``` { "properties": { "role": { "description": "The assigned role.", "enum": [ "OWNER", "EDITOR", "CONSUMER", "NO_ROLE" ], "type": "string", "x-versionadded": "v2.33" }, "shareRecipientType": { "description": "The recipient type.", "enum": [ "user" ], "type": "string", "x-versionadded": "v2.33" }, "username": { "description": "The username of the user to update the access role for. If included with a name, the username is used.", "type": "string", "x-versionadded": "v2.33" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | role | string | true | | The assigned role. | | shareRecipientType | string | true | | The recipient type. | | username | string | true | | The username of the user to update the access role for. If included with a name, the username is used. | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, EDITOR, CONSUMER, NO_ROLE] | | shareRecipientType | user | ## ExperimentContainerUserResponse ``` { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } ``` A user associated with a use case. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string,null | true | | The email address of the user. | | fullName | string,null | false | | The full name of the user. | | id | string | true | | The ID of the user. | | userhash | string,null | false | | The user's gravatar hash. | | username | string,null | false | | The username of the user. | ## ExperimentContainerVectorDatabaseResponse ``` { "properties": { "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at vector database creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity (same as ID of the vector database).", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "enum": [ "vector_database" ], "type": "string" }, "id": { "description": "The ID of the vector database.", "type": "string" }, "isDeleted": { "description": "Soft deletion flag for vector databases", "type": "boolean" }, "referenceId": { "description": "Original ID from DB", "type": "string" }, "tenantId": { "description": "The id of the tenant the vector database belongs to.", "type": [ "string", "null" ] }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] } }, "required": [ "created", "createdAt", "entityId", "entityType", "id", "isDeleted", "referenceId", "tenantId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | ExperimentContainerUserResponse | true | | A user associated with a use case. | | createdAt | string(date-time) | true | | The timestamp generated at vector database creation. | | createdBy | string,null | false | | The ID of the user who created. | | entityId | string | true | | The primary id of the entity (same as ID of the vector database). | | entityType | string | true | | The type of entity provided by the entity id. | | id | string | true | | The ID of the vector database. | | isDeleted | boolean | true | | Soft deletion flag for vector databases | | referenceId | string | true | | Original ID from DB | | tenantId | string,null | true | | The id of the tenant the vector database belongs to. | | updatedBy | string,null | false | | The ID of the user who last updated. | ### Enumerated Values | Property | Value | | --- | --- | | entityType | vector_database | ## FeatureCountByTypeResponse ``` { "properties": { "count": { "description": "The number of features of this type in the dataset", "type": "integer" }, "featureType": { "description": "The data type grouped in this count", "type": "string" } }, "required": [ "count", "featureType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of features of this type in the dataset | | featureType | string | true | | The data type grouped in this count | ## FilesEntityCountByTypeResponse ``` { "description": "Number of different type entities that use the file.", "properties": { "numExperimentContainer": { "description": "The number of experiment containers that use the file.", "type": "integer" } }, "required": [ "numExperimentContainer" ], "type": "object", "x-versionadded": "v2.37" } ``` Number of different type entities that use the file. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | numExperimentContainer | integer | true | | The number of experiment containers that use the file. | ## FormattedResponseDeployment ``` { "properties": { "createdAt": { "description": "The created date time.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The deployment description.", "type": "string" }, "id": { "description": "Unique identifier of the deployment.", "type": "string" }, "label": { "description": "The deployment label.", "type": "string" }, "lastActivity": { "description": "The type of the last activity. Can be \"Added\" or \"Modified\".", "enum": [ "created", "Created", "CREATED", "modified", "Modified", "MODIFIED" ], "type": "string", "x-versionadded": "v2.35" }, "type": { "description": "The deployment type.", "type": "string" }, "updatedAt": { "description": "The last modified date time.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "userHasAccess": { "description": "Indicates if a user has access to this deployment.", "type": "boolean", "x-versionadded": "v2.35" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | false | | The created date time. | | createdBy | ExperimentContainerUserResponse | false | | A user associated with a use case. | | description | string | false | | The deployment description. | | id | string | true | | Unique identifier of the deployment. | | label | string | false | | The deployment label. | | lastActivity | string | false | | The type of the last activity. Can be "Added" or "Modified". | | type | string | false | | The deployment type. | | updatedAt | string(date-time) | false | | The last modified date time. | | updatedBy | ExperimentContainerUserResponse | false | | A user associated with a use case. | | userHasAccess | boolean | false | | Indicates if a user has access to this deployment. | ### Enumerated Values | Property | Value | | --- | --- | | lastActivity | [created, Created, CREATED, modified, Modified, MODIFIED] | ## FullDatasetDetailsResponse ``` { "properties": { "categories": { "description": "An array of strings describing the intended use of the dataset.", "items": { "description": "The dataset category.", "enum": [ "BATCH_PREDICTIONS", "CUSTOM_MODEL_TESTING", "MULTI_SERIES_CALENDAR", "PREDICTION", "SAMPLE", "SINGLE_SERIES_CALENDAR", "TRAINING" ], "type": "string" }, "type": "array" }, "columnCount": { "description": "The number of columns in the dataset.", "type": "integer", "x-versionadded": "v2.30" }, "createdBy": { "description": "Username of the user who created the dataset.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the dataset was created.", "format": "date-time", "type": "string" }, "dataEngineQueryId": { "description": "The ID of the source data engine query.", "type": [ "string", "null" ] }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "dataSourceId": { "description": "The ID of the datasource used as the source of the dataset.", "type": [ "string", "null" ] }, "dataSourceType": { "description": "The type of the datasource that was used as the source of the dataset.", "type": "string" }, "datasetId": { "description": "The ID of this dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes.", "type": "integer", "x-versionadded": "v2.21" }, "description": { "description": "The description of the dataset.", "type": [ "string", "null" ] }, "eda1ModificationDate": { "description": "The ISO 8601 formatted date and time when the EDA1 for the dataset was updated.", "format": "date-time", "type": "string" }, "eda1ModifierFullName": { "description": "The user who was the last to update EDA1 for the dataset.", "type": "string" }, "entityCountByType": { "description": "Number of different type entities that use the dataset.", "properties": { "numCalendars": { "description": "The number of calendars that use the dataset", "type": "integer" }, "numExperimentContainer": { "description": "The number of experiment containers that use the dataset.", "type": "integer", "x-versionadded": "v2.37" }, "numExternalModelPackages": { "description": "The number of external model packages that use the dataset", "type": "integer" }, "numFeatureDiscoveryConfigs": { "description": "The number of feature discovery configs that use the dataset", "type": "integer" }, "numPredictionDatasets": { "description": "The number of prediction datasets that use the dataset", "type": "integer" }, "numProjects": { "description": "The number of projects that use the dataset", "type": "integer" }, "numSparkSqlQueries": { "description": "The number of spark sql queries that use the dataset", "type": "integer" } }, "required": [ "numCalendars", "numExperimentContainer", "numExternalModelPackages", "numFeatureDiscoveryConfigs", "numPredictionDatasets", "numProjects", "numSparkSqlQueries" ], "type": "object" }, "error": { "description": "Details of exception raised during ingestion process, if any.", "type": "string" }, "featureCount": { "description": "Total number of features in the dataset.", "type": "integer" }, "featureCountByType": { "description": "Number of features in the dataset grouped by feature type.", "items": { "properties": { "count": { "description": "The number of features of this type in the dataset", "type": "integer" }, "featureType": { "description": "The data type grouped in this count", "type": "string" } }, "required": [ "count", "featureType" ], "type": "object" }, "type": "array" }, "featureDiscoveryProjectId": { "description": "Feature Discovery project ID used to create the dataset.", "type": "string", "x-versionadded": "v2.35" }, "isDataEngineEligible": { "description": "Whether this dataset can be a data source of a data engine query.", "type": "boolean", "x-versionadded": "v2.20" }, "isLatestVersion": { "description": "Whether this dataset version is the latest version of this dataset.", "type": "boolean" }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean", "x-versionadded": "2.30.0" }, "lastModificationDate": { "description": "The ISO 8601 formatted date and time when the dataset was last modified.", "format": "date-time", "type": "string" }, "lastModifierFullName": { "description": "Full name of user who was the last to modify the dataset.", "type": "string" }, "name": { "description": "The name of this dataset in the catalog.", "type": "string" }, "processingState": { "description": "Current ingestion process state of dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string", "x-versionadded": "v2.21" }, "recipeId": { "description": "The ID of the source recipe.", "type": [ "string", "null" ] }, "rowCount": { "description": "The number of rows in the dataset.", "type": "integer", "x-versionadded": "v2.21" }, "sampleSize": { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "tags": { "description": "List of tags attached to the item.", "items": { "type": "string" }, "type": "array" }, "timeSeriesProperties": { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" }, "uri": { "description": "The URI to datasource. For example, `file_name.csv`, or `jdbc:DATA_SOURCE_GIVEN_NAME/SCHEMA.TABLE_NAME`, or `jdbc:DATA_SOURCE_GIVEN_NAME/` for `query` based datasources, or`https://s3.amazonaws.com/dr-pr-tst-data/kickcars-sample-200.csv`, etc.", "type": "string" }, "versionId": { "description": "The object ID of the catalog_version the dataset belongs to.", "type": "string" } }, "required": [ "categories", "columnCount", "createdBy", "creationDate", "dataEngineQueryId", "dataPersisted", "dataSourceId", "dataSourceType", "datasetId", "datasetSize", "description", "eda1ModificationDate", "eda1ModifierFullName", "error", "featureCount", "featureCountByType", "isDataEngineEligible", "isLatestVersion", "isSnapshot", "isWranglingEligible", "lastModificationDate", "lastModifierFullName", "name", "processingState", "recipeId", "rowCount", "tags", "timeSeriesProperties", "uri", "versionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | categories | [string] | true | | An array of strings describing the intended use of the dataset. | | columnCount | integer | true | | The number of columns in the dataset. | | createdBy | string,null | true | | Username of the user who created the dataset. | | creationDate | string(date-time) | true | | The date when the dataset was created. | | dataEngineQueryId | string,null | true | | The ID of the source data engine query. | | dataPersisted | boolean | true | | If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available. | | dataSourceId | string,null | true | | The ID of the datasource used as the source of the dataset. | | dataSourceType | string | true | | The type of the datasource that was used as the source of the dataset. | | datasetId | string | true | | The ID of this dataset. | | datasetSize | integer | true | | The size of the dataset as a CSV in bytes. | | description | string,null | true | | The description of the dataset. | | eda1ModificationDate | string(date-time) | true | | The ISO 8601 formatted date and time when the EDA1 for the dataset was updated. | | eda1ModifierFullName | string | true | | The user who was the last to update EDA1 for the dataset. | | entityCountByType | EntityCountByTypeResponse | false | | Number of different type entities that use the dataset. | | error | string | true | | Details of exception raised during ingestion process, if any. | | featureCount | integer | true | | Total number of features in the dataset. | | featureCountByType | [FeatureCountByTypeResponse] | true | | Number of features in the dataset grouped by feature type. | | featureDiscoveryProjectId | string | false | | Feature Discovery project ID used to create the dataset. | | isDataEngineEligible | boolean | true | | Whether this dataset can be a data source of a data engine query. | | isLatestVersion | boolean | true | | Whether this dataset version is the latest version of this dataset. | | isSnapshot | boolean | true | | Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot. | | isWranglingEligible | boolean | true | | Whether the source of the dataset can support wrangling. | | lastModificationDate | string(date-time) | true | | The ISO 8601 formatted date and time when the dataset was last modified. | | lastModifierFullName | string | true | | Full name of user who was the last to modify the dataset. | | name | string | true | | The name of this dataset in the catalog. | | processingState | string | true | | Current ingestion process state of dataset. | | recipeId | string,null | true | | The ID of the source recipe. | | rowCount | integer | true | | The number of rows in the dataset. | | sampleSize | SampleSize | false | | Ingest size to use during dataset registration. Default behavior is to ingest full dataset. | | tags | [string] | true | | List of tags attached to the item. | | timeSeriesProperties | TimeSeriesProperties | true | | Properties related to time series data prep. | | uri | string | true | | The URI to datasource. For example, file_name.csv, or jdbc:DATA_SOURCE_GIVEN_NAME/SCHEMA.TABLE_NAME, or jdbc:DATA_SOURCE_GIVEN_NAME/ for query based datasources, orhttps://s3.amazonaws.com/dr-pr-tst-data/kickcars-sample-200.csv, etc. | | versionId | string | true | | The object ID of the catalog_version the dataset belongs to. | ### Enumerated Values | Property | Value | | --- | --- | | processingState | [COMPLETED, ERROR, RUNNING] | ## FullFileDetailsResponse ``` { "properties": { "createdBy": { "description": "Username of the user who created the file.", "type": [ "string", "null" ] }, "creationDate": { "description": "The date when the file was created.", "format": "date-time", "type": "string" }, "dataSourceId": { "description": "ID of the data source used as the source of the file.", "type": [ "string", "null" ] }, "dataSourceType": { "description": "The type of the data source that was used as the source of the file.", "type": "string" }, "description": { "description": "The description of the file.", "type": [ "string", "null" ] }, "entityCountByType": { "description": "Number of different type entities that use the file.", "properties": { "numExperimentContainer": { "description": "The number of experiment containers that use the file.", "type": "integer" } }, "required": [ "numExperimentContainer" ], "type": "object", "x-versionadded": "v2.37" }, "error": { "description": "Details of exception raised during ingestion process, if any.", "type": "string" }, "fileId": { "description": "The ID of this file.", "type": "string" }, "isLatestVersion": { "description": "Whether this file version is the latest version of this file.", "type": "boolean", "x-versionadded": "v2.37" }, "lastModificationDate": { "description": "The ISO 8601 formatted date and time when the file was last modified.", "format": "date-time", "type": "string" }, "lastModifierFullName": { "description": "Full name of user who was the last to modify the file.", "type": "string" }, "name": { "description": "The name of this file in the catalog.", "type": "string" }, "numFiles": { "description": "The number of files in the file entity.", "type": "integer" }, "processingState": { "description": "Current ingestion process state of file.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "tags": { "description": "List of tags attached to the item.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "uri": { "description": "The URI to the data source.", "type": "string" }, "versionId": { "description": "The object ID of the catalog_version the file belongs to.", "type": "string" } }, "required": [ "createdBy", "creationDate", "dataSourceId", "dataSourceType", "description", "entityCountByType", "error", "fileId", "isLatestVersion", "lastModificationDate", "lastModifierFullName", "name", "numFiles", "processingState", "tags", "uri", "versionId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | string,null | true | | Username of the user who created the file. | | creationDate | string(date-time) | true | | The date when the file was created. | | dataSourceId | string,null | true | | ID of the data source used as the source of the file. | | dataSourceType | string | true | | The type of the data source that was used as the source of the file. | | description | string,null | true | | The description of the file. | | entityCountByType | FilesEntityCountByTypeResponse | true | | Number of different type entities that use the file. | | error | string | true | | Details of exception raised during ingestion process, if any. | | fileId | string | true | | The ID of this file. | | isLatestVersion | boolean | true | | Whether this file version is the latest version of this file. | | lastModificationDate | string(date-time) | true | | The ISO 8601 formatted date and time when the file was last modified. | | lastModifierFullName | string | true | | Full name of user who was the last to modify the file. | | name | string | true | | The name of this file in the catalog. | | numFiles | integer | true | | The number of files in the file entity. | | processingState | string | true | | Current ingestion process state of file. | | tags | [string] | true | maxItems: 100 | List of tags attached to the item. | | uri | string | true | | The URI to the data source. | | versionId | string | true | | The object ID of the catalog_version the file belongs to. | ### Enumerated Values | Property | Value | | --- | --- | | processingState | [COMPLETED, ERROR, RUNNING] | ## MetricDetail ``` { "properties": { "ascending": { "description": "Should the metric be sorted in ascending order", "type": "boolean" }, "name": { "description": "Name of the metric", "type": "string" } }, "required": [ "ascending", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ascending | boolean | true | | Should the metric be sorted in ascending order | | name | string | true | | Name of the metric | ## PinnedUsecaseResponse ``` { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "applicationsCount": { "description": "The number of applications in a use case.", "type": "integer" }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "customApplicationsCount": { "description": "The number of custom applications referenced in a use case.", "type": "integer" }, "customJobsCount": { "description": "The number of custom jobs referenced in a use case.", "type": "integer" }, "customModelVersionsCount": { "description": "The number of custom models referenced in a use case.", "type": "integer" }, "datasetsCount": { "description": "The number of datasets in a use case.", "type": "integer" }, "deploymentsCount": { "description": "The number of deployments referenced in a use case.", "type": "integer" }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "filesCount": { "description": "The number of files in a use case.", "type": "integer", "x-versionadded": "v2.37" }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "modelsCount": { "description": "[DEPRECATED] The number of models in a Use Case.", "type": "integer", "x-versiondeprecated": "v2.34" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "notebooksCount": { "description": "The number of notebooks in a use case.", "type": "integer" }, "projectsCount": { "description": "The number of projects in a use case.", "type": "integer" }, "recipesCount": { "description": "The number of recipes in a Use Case.", "type": "integer" }, "registeredModelVersionsCount": { "description": "The number of registered models referenced in a use case.", "type": "integer" }, "riskAssessments": { "description": "The ID List of the Risk Assessments.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "role": { "description": "The requesting user's role on this Use Case.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string", "x-versionadded": "v2.37" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ] } }, "required": [ "applicationsCount", "created", "createdAt", "customApplicationsCount", "customJobsCount", "customModelVersionsCount", "datasetsCount", "deploymentsCount", "description", "filesCount", "id", "name", "notebooksCount", "projectsCount", "recipesCount", "registeredModelVersionsCount", "role", "tenantId", "updated", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | advancedTour | string,null | false | | Advanced tour key. | | applicationsCount | integer | true | | The number of applications in a use case. | | created | ExperimentContainerUserResponse | true | | A user associated with a use case. | | createdAt | string(date-time) | true | | The timestamp generated at record creation. | | createdBy | string,null | false | | The ID of the user who created. | | customApplicationsCount | integer | true | | The number of custom applications referenced in a use case. | | customJobsCount | integer | true | | The number of custom jobs referenced in a use case. | | customModelVersionsCount | integer | true | | The number of custom models referenced in a use case. | | datasetsCount | integer | true | | The number of datasets in a use case. | | deploymentsCount | integer | true | | The number of deployments referenced in a use case. | | description | string,null | true | | The description of the Use Case. | | filesCount | integer | true | | The number of files in a use case. | | formattedDescription | string,null | false | | The formatted description of the experiment container used as styled description. | | id | string | true | | The ID of the Use Case. | | modelsCount | integer | false | | [DEPRECATED] The number of models in a Use Case. | | name | string | true | | The name of the Use Case. | | notebooksCount | integer | true | | The number of notebooks in a use case. | | projectsCount | integer | true | | The number of projects in a use case. | | recipesCount | integer | true | | The number of recipes in a Use Case. | | registeredModelVersionsCount | integer | true | | The number of registered models referenced in a use case. | | riskAssessments | [string] | false | maxItems: 100 | The ID List of the Risk Assessments. | | role | string | true | | The requesting user's role on this Use Case. | | tenantId | string,null | true | | The ID of the tenant to associate this organization with. | | updated | ExperimentContainerUserResponse | true | | A user associated with a use case. | | updatedAt | string(date-time) | true | | The timestamp generated when the record was last updated. | | updatedBy | string,null | false | | The ID of the user who last updated. | | valueTrackerId | string,null | false | | The ID of the Value Tracker. | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, EDITOR, CONSUMER] | ## PinnedUsecaseUpdatePayload ``` { "properties": { "operation": { "description": "Pinned Use Case possible operations.", "enum": [ "add", "remove" ], "type": "string" }, "pinnedUseCasesIds": { "description": "The list of the pinned Use Case IDs.", "items": { "type": "string" }, "maxItems": 8, "minItems": 1, "type": "array" } }, "required": [ "operation", "pinnedUseCasesIds" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | Pinned Use Case possible operations. | | pinnedUseCasesIds | [string] | true | maxItems: 8minItems: 1 | The list of the pinned Use Case IDs. | ### Enumerated Values | Property | Value | | --- | --- | | operation | [add, remove] | ## PinnedUsecasesResponse ``` { "properties": { "data": { "description": "The list of the pinned use cases.", "items": { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "applicationsCount": { "description": "The number of applications in a use case.", "type": "integer" }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "customApplicationsCount": { "description": "The number of custom applications referenced in a use case.", "type": "integer" }, "customJobsCount": { "description": "The number of custom jobs referenced in a use case.", "type": "integer" }, "customModelVersionsCount": { "description": "The number of custom models referenced in a use case.", "type": "integer" }, "datasetsCount": { "description": "The number of datasets in a use case.", "type": "integer" }, "deploymentsCount": { "description": "The number of deployments referenced in a use case.", "type": "integer" }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "filesCount": { "description": "The number of files in a use case.", "type": "integer", "x-versionadded": "v2.37" }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "modelsCount": { "description": "[DEPRECATED] The number of models in a Use Case.", "type": "integer", "x-versiondeprecated": "v2.34" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "notebooksCount": { "description": "The number of notebooks in a use case.", "type": "integer" }, "projectsCount": { "description": "The number of projects in a use case.", "type": "integer" }, "recipesCount": { "description": "The number of recipes in a Use Case.", "type": "integer" }, "registeredModelVersionsCount": { "description": "The number of registered models referenced in a use case.", "type": "integer" }, "riskAssessments": { "description": "The ID List of the Risk Assessments.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "role": { "description": "The requesting user's role on this Use Case.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string", "x-versionadded": "v2.37" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ] } }, "required": [ "applicationsCount", "created", "createdAt", "customApplicationsCount", "customJobsCount", "customModelVersionsCount", "datasetsCount", "deploymentsCount", "description", "filesCount", "id", "name", "notebooksCount", "projectsCount", "recipesCount", "registeredModelVersionsCount", "role", "tenantId", "updated", "updatedAt" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 8, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [PinnedUsecaseResponse] | true | maxItems: 8 | The list of the pinned use cases. | ## RiskAssessmentComplete ``` { "description": "Risk assessment defined as primary the current Use Case.", "properties": { "createdAt": { "description": "The creation date of the risk assessment.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created the risk assessment.", "type": "string" }, "description": { "description": "The description of the risk assessment.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "entityType": { "description": "The type of entity this assessment belongs to.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "evidence": { "description": "The evidence for the risk assessment.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the risk assessment.", "type": "string" }, "isPrimary": { "default": false, "description": "Determines if the risk assessment is primary.", "type": "boolean" }, "mitigationPlan": { "description": "The mitigation plan for the risk assessment.", "type": [ "string", "null" ] }, "name": { "description": "The name of the risk assessment.", "type": "string" }, "policyId": { "description": "The ID of the risk policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskDescription": { "description": "The risk description for this assessment.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskLevel": { "description": "The name of the risk assessment level.", "type": "string" }, "riskLevelId": { "description": "The ID of the risk level within the policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskManagementPlan": { "description": "The risk management plan.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "score": { "description": "The assessment score.", "type": [ "number", "null" ], "x-versionadded": "v2.44" }, "tenantId": { "description": "The tenant ID related to the risk assessment.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The last updated date of the risk assessment.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who updated the risk assessment.", "type": "string" } }, "required": [ "createdAt", "createdBy", "description", "entityId", "entityType", "evidence", "id", "isPrimary", "mitigationPlan", "name", "policyId", "riskDescription", "riskLevelId", "riskManagementPlan", "score", "tenantId", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" } ``` Risk assessment defined as primary the current Use Case. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The creation date of the risk assessment. | | createdBy | string | true | | The ID of the user who created the risk assessment. | | description | string | true | | The description of the risk assessment. | | entityId | string,null | true | | The ID of the entity. | | entityType | string,null | true | maxLength: 255 | The type of entity this assessment belongs to. | | evidence | string,null | true | | The evidence for the risk assessment. | | id | string | true | | The ID of the risk assessment. | | isPrimary | boolean | true | | Determines if the risk assessment is primary. | | mitigationPlan | string,null | true | | The mitigation plan for the risk assessment. | | name | string | true | | The name of the risk assessment. | | policyId | string,null | true | | The ID of the risk policy. | | riskDescription | string,null | true | maxLength: 10000 | The risk description for this assessment. | | riskLevel | string | false | | The name of the risk assessment level. | | riskLevelId | string,null | true | | The ID of the risk level within the policy. | | riskManagementPlan | string,null | true | maxLength: 10000 | The risk management plan. | | score | number,null | true | | The assessment score. | | tenantId | string,null | true | | The tenant ID related to the risk assessment. | | updatedAt | string(date-time) | true | | The last updated date of the risk assessment. | | updatedBy | string | true | | The ID of the user who updated the risk assessment. | ## SampleSize ``` { "description": "Ingest size to use during dataset registration. Default behavior is to ingest full dataset.", "properties": { "type": { "description": "The sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" } ``` Ingest size to use during dataset registration. Default behavior is to ingest full dataset. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | The sample size can be specified only as a number of rows for now. | | value | integer | true | maximum: 1000000 | Number of rows to ingest during dataset registration. | ### Enumerated Values | Property | Value | | --- | --- | | type | rows | ## SharedRolesWithGrantListResponse ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "canShare": { "description": "Whether the recipient can share the role further.", "type": "boolean" }, "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" }, "userFullName": { "description": "The full name of the recipient user.", "type": "string" } }, "required": [ "canShare", "id", "name", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControlWithGrant] | true | | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of items matching the condition. | ## TimeSeriesProperties ``` { "description": "Properties related to time series data prep.", "properties": { "isMostlyImputed": { "default": null, "description": "Whether more than half of the rows are imputed.", "type": [ "boolean", "null" ], "x-versionadded": "v2.26" } }, "required": [ "isMostlyImputed" ], "type": "object" } ``` Properties related to time series data prep. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isMostlyImputed | boolean,null | true | | Whether more than half of the rows are imputed. | ## UseCaseCreate ``` { "properties": { "advancedTour": { "description": "Advanced tour key.", "enum": [ "flightDelays", "hospital" ], "type": [ "string", "null" ] }, "description": { "default": null, "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "name": { "default": null, "description": "The name of the Use Case.", "maxLength": 100, "type": [ "string", "null" ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | advancedTour | string,null | false | | Advanced tour key. | | description | string,null | false | | The description of the Use Case. | | name | string,null | false | maxLength: 100 | The name of the Use Case. | ### Enumerated Values | Property | Value | | --- | --- | | advancedTour | [flightDelays, hospital] | ## UseCaseCustomApplication ``` { "properties": { "applicationUrl": { "description": "The reachable URL of the custom application.", "type": "string", "x-versionadded": "v2.35" }, "createdAt": { "description": "The time when this model was created.", "format": "date-time", "type": "string", "x-versionadded": "v2.35" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "customApplicationSourceId": { "description": "The ID of the custom application source.", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "id": { "description": "The ID of the custom application.", "type": "string" }, "lastActivity": { "description": "The type of the last activity. Can be \"Added\" or \"Modified\".", "enum": [ "created", "Created", "CREATED", "modified", "Modified", "MODIFIED" ], "type": "string" }, "name": { "description": "The name of the custom application.", "type": "string" }, "permissions": { "description": "The list of permissions available for the user.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.39" }, "status": { "description": "The status of the custom application.", "type": "string", "x-versionadded": "v2.35" }, "updatedAt": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "userHasAccess": { "description": "Indicates if a user has access to this custom application.", "type": "boolean", "x-versionadded": "v2.35" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | applicationUrl | string | false | | The reachable URL of the custom application. | | createdAt | string(date-time) | false | | The time when this model was created. | | createdBy | ExperimentContainerUserResponse | false | | A user associated with a use case. | | customApplicationSourceId | string,null | false | | The ID of the custom application source. | | id | string | true | | The ID of the custom application. | | lastActivity | string | false | | The type of the last activity. Can be "Added" or "Modified". | | name | string | false | | The name of the custom application. | | permissions | [string] | false | maxItems: 100 | The list of permissions available for the user. | | status | string | false | | The status of the custom application. | | updatedAt | string(date-time) | false | | The time when this activity occurred. | | updatedBy | ExperimentContainerUserResponse | false | | A user associated with a use case. | | userHasAccess | boolean | false | | Indicates if a user has access to this custom application. | ### Enumerated Values | Property | Value | | --- | --- | | lastActivity | [created, Created, CREATED, modified, Modified, MODIFIED] | ## UseCaseCustomApplicationsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of custom applications.", "items": { "properties": { "applicationUrl": { "description": "The reachable URL of the custom application.", "type": "string", "x-versionadded": "v2.35" }, "createdAt": { "description": "The time when this model was created.", "format": "date-time", "type": "string", "x-versionadded": "v2.35" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "customApplicationSourceId": { "description": "The ID of the custom application source.", "type": [ "string", "null" ], "x-versionadded": "v2.39" }, "id": { "description": "The ID of the custom application.", "type": "string" }, "lastActivity": { "description": "The type of the last activity. Can be \"Added\" or \"Modified\".", "enum": [ "created", "Created", "CREATED", "modified", "Modified", "MODIFIED" ], "type": "string" }, "name": { "description": "The name of the custom application.", "type": "string" }, "permissions": { "description": "The list of permissions available for the user.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.39" }, "status": { "description": "The status of the custom application.", "type": "string", "x-versionadded": "v2.35" }, "updatedAt": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "userHasAccess": { "description": "Indicates if a user has access to this custom application.", "type": "boolean", "x-versionadded": "v2.35" } }, "required": [ "id" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseCustomApplication] | true | maxItems: 100 | The list of custom applications. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseDataListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The list of the datasets in this use case.", "items": { "properties": { "columnCount": { "description": "The number of columns in a dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "dataSourceType": { "description": "The driver class type used to create the dataset if relevant.", "enum": [ "s3", "native-s3", "native-adls", "adlsgen2", "oracle", "iris", "exasol", "sap", "databricks-v1", "native-databricks", "bigquery-v1", "bigquery1", "bigquery2", "athena2", "athena1", "kdb", "treasuredata", "elasticsearch", "snowflake", "mysql", "mssql", "postgres", "palantirfoundry", "teradata", "redshift", "datasphere-v1", "trino-v1", "native-gdrive", "native-sharepoint", "native-confluence", "native-jira", "native-box", "native-onedrive", "native-salesforce", "unknown" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataType": { "description": "The type of data entity.", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": "string", "x-versionadded": "v2.33" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "description": { "description": "The description of the dataset or recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The primary ID of the entity.", "type": "string", "x-versionadded": "v2.33" }, "entityType": { "description": "The type of entity provided by the entity ID.", "enum": [ "recipe", "Recipe", "RECIPE", "dataset", "Dataset", "DATASET", "project", "Project", "PROJECT", "application", "Application", "APPLICATION" ], "type": "string", "x-versionadded": "v2.33" }, "featureDiscoveryProjectId": { "description": "Related feature discovery project if this is a feature discovery dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean", "x-versionadded": "v2.33" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "modifiedAt": { "description": "The timestamp generated at dataset modification.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the dataset or recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rowCount": { "description": "The number of data rows in the dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ], "x-versionadded": "v2.33" } }, "required": [ "columnCount", "createdBy", "dataSourceType", "dataType", "datasetSize", "description", "entityId", "entityType", "featureDiscoveryProjectId", "isWranglingEligible", "latestRecipeId", "modifiedAt", "name", "processingState", "rowCount" ], "type": "object" }, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseDataResponse] | true | | The list of the datasets in this use case. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseDataResponse ``` { "properties": { "columnCount": { "description": "The number of columns in a dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "dataSourceType": { "description": "The driver class type used to create the dataset if relevant.", "enum": [ "s3", "native-s3", "native-adls", "adlsgen2", "oracle", "iris", "exasol", "sap", "databricks-v1", "native-databricks", "bigquery-v1", "bigquery1", "bigquery2", "athena2", "athena1", "kdb", "treasuredata", "elasticsearch", "snowflake", "mysql", "mssql", "postgres", "palantirfoundry", "teradata", "redshift", "datasphere-v1", "trino-v1", "native-gdrive", "native-sharepoint", "native-confluence", "native-jira", "native-box", "native-onedrive", "native-salesforce", "unknown" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "dataType": { "description": "The type of data entity.", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": "string", "x-versionadded": "v2.33" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ], "x-versionadded": "v2.33" }, "description": { "description": "The description of the dataset or recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "entityId": { "description": "The primary ID of the entity.", "type": "string", "x-versionadded": "v2.33" }, "entityType": { "description": "The type of entity provided by the entity ID.", "enum": [ "recipe", "Recipe", "RECIPE", "dataset", "Dataset", "DATASET", "project", "Project", "PROJECT", "application", "Application", "APPLICATION" ], "type": "string", "x-versionadded": "v2.33" }, "featureDiscoveryProjectId": { "description": "Related feature discovery project if this is a feature discovery dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean", "x-versionadded": "v2.33" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "modifiedAt": { "description": "The timestamp generated at dataset modification.", "format": "date-time", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "name": { "description": "The name of the dataset or recipe.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "rowCount": { "description": "The number of data rows in the dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ], "x-versionadded": "v2.33" } }, "required": [ "columnCount", "createdBy", "dataSourceType", "dataType", "datasetSize", "description", "entityId", "entityType", "featureDiscoveryProjectId", "isWranglingEligible", "latestRecipeId", "modifiedAt", "name", "processingState", "rowCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnCount | integer,null | true | | The number of columns in a dataset. null might be returned in case dataset is in running or errored state | | createdBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | | dataSourceType | string,null | true | | The driver class type used to create the dataset if relevant. | | dataType | string | true | | The type of data entity. | | datasetSize | integer,null | true | | The size of the dataset as a CSV in bytes. null might be returned in case dataset is in running or errored state | | description | string,null | true | | The description of the dataset or recipe. | | entityId | string | true | | The primary ID of the entity. | | entityType | string | true | | The type of entity provided by the entity ID. | | featureDiscoveryProjectId | string,null | true | | Related feature discovery project if this is a feature discovery dataset. | | isWranglingEligible | boolean | true | | Whether the source of the dataset can support wrangling. | | latestRecipeId | string,null | true | | The latest recipe ID linked to the dataset. | | modifiedAt | string,null(date-time) | true | | The timestamp generated at dataset modification. | | name | string,null | true | | The name of the dataset or recipe. | | processingState | string,null | true | | The current ingestion process state of the dataset. | | rowCount | integer,null | true | | The number of data rows in the dataset. null might be returned in case dataset is in running or errored state | ### Enumerated Values | Property | Value | | --- | --- | | dataSourceType | [s3, native-s3, native-adls, adlsgen2, oracle, iris, exasol, sap, databricks-v1, native-databricks, bigquery-v1, bigquery1, bigquery2, athena2, athena1, kdb, treasuredata, elasticsearch, snowflake, mysql, mssql, postgres, palantirfoundry, teradata, redshift, datasphere-v1, trino-v1, native-gdrive, native-sharepoint, native-confluence, native-jira, native-box, native-onedrive, native-salesforce, unknown] | | dataType | [static, Static, STATIC, snapshot, Snapshot, SNAPSHOT, dynamic, Dynamic, DYNAMIC, sqlRecipe, SqlRecipe, SQL_RECIPE, wranglingRecipe, WranglingRecipe, WRANGLING_RECIPE, featureDiscoveryRecipe, FeatureDiscoveryRecipe, FEATURE_DISCOVERY_RECIPE] | | entityType | [recipe, Recipe, RECIPE, dataset, Dataset, DATASET, project, Project, PROJECT, application, Application, APPLICATION] | | processingState | [COMPLETED, ERROR, RUNNING] | ## UseCaseDatasetResponse ``` { "properties": { "columnCount": { "description": "The number of columns in a dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ] }, "createdAt": { "description": "The timestamp generated at dataset creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "dataType": { "description": "The type of data entity.", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": "string", "x-versionadded": "v2.33" }, "datasetId": { "description": "The dataset ID of the dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ] }, "datasetSourceType": { "description": "The source type of the dataset.", "type": [ "string", "null" ] }, "description": { "description": "The description of the dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoveryProjectId": { "description": "Related feature discovery project if this is a feature discovery dataset.", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] }, "modifiedAt": { "description": "The timestamp generated at dataset modification.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "name": { "description": "The name of the dataset.", "type": "string" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "referenceMetadata": { "description": "Metadata about the reference of the dataset in the Use Case.", "properties": { "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" } }, "required": [ "createdAt", "lastActivity" ], "type": "object" }, "rowCount": { "description": "The number of data rows in the dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ] }, "versionId": { "description": "The dataset version id of the latest version of the dataset.", "type": "string" } }, "required": [ "columnCount", "createdAt", "createdBy", "dataPersisted", "dataSourceType", "dataType", "datasetId", "datasetSize", "datasetSourceType", "description", "isSnapshot", "isWranglingEligible", "latestRecipeId", "modifiedAt", "modifiedBy", "name", "processingState", "referenceMetadata", "rowCount", "versionId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | columnCount | integer,null | true | | The number of columns in a dataset. null might be returned in case dataset is in running or errored state | | createdAt | string(date-time) | true | | The timestamp generated at dataset creation. | | createdBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | | dataPersisted | boolean | true | | If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available. | | dataSourceType | string,null | true | | The type of the data source used to create the dataset if relevant. | | dataType | string | true | | The type of data entity. | | datasetId | string | true | | The dataset ID of the dataset. | | datasetSize | integer,null | true | | The size of the dataset as a CSV in bytes. null might be returned in case dataset is in running or errored state | | datasetSourceType | string,null | true | | The source type of the dataset. | | description | string,null | true | | The description of the dataset. | | featureDiscoveryProjectId | string,null | false | | Related feature discovery project if this is a feature discovery dataset. | | isSnapshot | boolean | true | | Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot. | | isWranglingEligible | boolean | true | | Whether the source of the dataset can support wrangling. | | latestRecipeId | string,null | true | | The latest recipe ID linked to the dataset. | | modifiedAt | string(date-time) | true | | The timestamp generated at dataset modification. | | modifiedBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | | name | string | true | | The name of the dataset. | | processingState | string | true | | The current ingestion process state of the dataset. | | referenceMetadata | ExperimentContainerReferenceDatasetResponse | true | | Metadata about the reference of the dataset in the Use Case. | | rowCount | integer,null | true | | The number of data rows in the dataset. null might be returned in case dataset is in running or errored state | | versionId | string | true | | The dataset version id of the latest version of the dataset. | ### Enumerated Values | Property | Value | | --- | --- | | dataType | [static, Static, STATIC, snapshot, Snapshot, SNAPSHOT, dynamic, Dynamic, DYNAMIC, sqlRecipe, SqlRecipe, SQL_RECIPE, wranglingRecipe, WranglingRecipe, WRANGLING_RECIPE, featureDiscoveryRecipe, FeatureDiscoveryRecipe, FEATURE_DISCOVERY_RECIPE] | | processingState | [COMPLETED, ERROR, RUNNING] | ## UseCaseDatasetsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of the datasets in the Use Case", "items": { "properties": { "columnCount": { "description": "The number of columns in a dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ] }, "createdAt": { "description": "The timestamp generated at dataset creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "dataPersisted": { "description": "If true, user is allowed to view extended data profile (which includes data statistics like min/max/median/mean, histogram, etc.) and download data. If false, download is not allowed and only the data schema (feature names and types) will be available.", "type": "boolean" }, "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "dataType": { "description": "The type of data entity.", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": "string", "x-versionadded": "v2.33" }, "datasetId": { "description": "The dataset ID of the dataset.", "type": "string" }, "datasetSize": { "description": "The size of the dataset as a CSV in bytes. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ] }, "datasetSourceType": { "description": "The source type of the dataset.", "type": [ "string", "null" ] }, "description": { "description": "The description of the dataset.", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "featureDiscoveryProjectId": { "description": "Related feature discovery project if this is a feature discovery dataset.", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] }, "modifiedAt": { "description": "The timestamp generated at dataset modification.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "name": { "description": "The name of the dataset.", "type": "string" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "referenceMetadata": { "description": "Metadata about the reference of the dataset in the Use Case.", "properties": { "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" } }, "required": [ "createdAt", "lastActivity" ], "type": "object" }, "rowCount": { "description": "The number of data rows in the dataset. ``null`` might be returned in case dataset is in running or errored state", "type": [ "integer", "null" ] }, "versionId": { "description": "The dataset version id of the latest version of the dataset.", "type": "string" } }, "required": [ "columnCount", "createdAt", "createdBy", "dataPersisted", "dataSourceType", "dataType", "datasetId", "datasetSize", "datasetSourceType", "description", "isSnapshot", "isWranglingEligible", "latestRecipeId", "modifiedAt", "modifiedBy", "name", "processingState", "referenceMetadata", "rowCount", "versionId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseDatasetResponse] | true | | A list of the datasets in the Use Case | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseEntityVersion ``` { "properties": { "createdAt": { "description": "Time when this entity was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The id of the entity version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the entity version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the entity version.", "type": "integer" }, "updatedAt": { "description": "Time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | Time when this entity was created. | | createdBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | | id | string | true | | The id of the entity version. | | lastActivity | ExperimentContainerLastActivity | true | | The last activity details. | | name | string | true | | The name of the entity version. | | registeredModelVersion | integer | true | | The version number of the entity version. | | updatedAt | string(date-time) | true | | Time when the last update occurred. | | updatedBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | ## UseCaseFileResponse ``` { "properties": { "createdAt": { "description": "The timestamp generated at file creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "dataSourceType": { "description": "The type of the data source used to create the file (if relevant).", "type": [ "string", "null" ] }, "description": { "description": "The description of the file.", "type": [ "string", "null" ] }, "fileId": { "description": "The file ID.", "type": "string" }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "modifiedAt": { "description": "The timestamp generated at file modification.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "name": { "description": "The name of the file.", "type": "string" }, "numFiles": { "description": "The number of files in catalog item. ``null`` might be returned in cases where the file is in a running or errored state.", "type": [ "integer", "null" ] }, "processingState": { "description": "Current ingestion process state of file.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "referenceMetadata": { "description": "Metadata about the reference of the dataset in the Use Case.", "properties": { "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" } }, "required": [ "createdAt", "lastActivity" ], "type": "object" }, "versionId": { "description": "The file version ID for the file's latest version.", "type": "string" } }, "required": [ "createdAt", "createdBy", "dataSourceType", "description", "fileId", "fileSourceType", "modifiedAt", "modifiedBy", "name", "numFiles", "processingState", "referenceMetadata", "versionId" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The timestamp generated at file creation. | | createdBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | | dataSourceType | string,null | true | | The type of the data source used to create the file (if relevant). | | description | string,null | true | | The description of the file. | | fileId | string | true | | The file ID. | | fileSourceType | string,null | true | | The source type of the file. | | modifiedAt | string(date-time) | true | | The timestamp generated at file modification. | | modifiedBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | | name | string | true | | The name of the file. | | numFiles | integer,null | true | | The number of files in catalog item. null might be returned in cases where the file is in a running or errored state. | | processingState | string | true | | Current ingestion process state of file. | | referenceMetadata | ExperimentContainerReferenceDatasetResponse | true | | Metadata about the reference of the dataset in the Use Case. | | versionId | string | true | | The file version ID for the file's latest version. | ### Enumerated Values | Property | Value | | --- | --- | | processingState | [COMPLETED, ERROR, RUNNING] | ## UseCaseFilesListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of the files in the Use Case.", "items": { "properties": { "createdAt": { "description": "The timestamp generated at file creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "dataSourceType": { "description": "The type of the data source used to create the file (if relevant).", "type": [ "string", "null" ] }, "description": { "description": "The description of the file.", "type": [ "string", "null" ] }, "fileId": { "description": "The file ID.", "type": "string" }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "modifiedAt": { "description": "The timestamp generated at file modification.", "format": "date-time", "type": "string" }, "modifiedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "name": { "description": "The name of the file.", "type": "string" }, "numFiles": { "description": "The number of files in catalog item. ``null`` might be returned in cases where the file is in a running or errored state.", "type": [ "integer", "null" ] }, "processingState": { "description": "Current ingestion process state of file.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "referenceMetadata": { "description": "Metadata about the reference of the dataset in the Use Case.", "properties": { "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" } }, "required": [ "createdAt", "lastActivity" ], "type": "object" }, "versionId": { "description": "The file version ID for the file's latest version.", "type": "string" } }, "required": [ "createdAt", "createdBy", "dataSourceType", "description", "fileId", "fileSourceType", "modifiedAt", "modifiedBy", "name", "numFiles", "processingState", "referenceMetadata", "versionId" ], "type": "object", "x-versionadded": "v2.37" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseFileResponse] | true | | A list of the files in the Use Case. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseLastActivity ``` { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" } ``` The last activity details. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timestamp | string(date-time) | true | | The time when this activity occurred. | | type | string | true | | The type of activity. Can be "Added" or "Modified". | ## UseCaseListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of use cases that match the query.", "items": { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "applicationsCount": { "description": "The number of applications in a Use Case.", "type": "integer" }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "customApplicationsCount": { "description": "The number of custom applications referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customJobsCount": { "description": "The number of custom jobs referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customModelVersionsCount": { "description": "The number of custom models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "datasetsCount": { "description": "The number of datasets in a Use Case.", "type": "integer" }, "deploymentsCount": { "description": "The number of deployments referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "filesCount": { "description": "The number of files in a Use Case.", "type": "integer", "x-versionadded": "v2.37" }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "members": { "description": "The list of use case members.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "modelsCount": { "description": "[DEPRECATED] The number of models in a Use Case.", "type": "integer", "x-versiondeprecated": "v2.34" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "notebooksCount": { "description": "The number of notebooks in a Use Case.", "type": "integer" }, "owners": { "description": "The list of owners of a use case.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "primaryRiskAssessment": { "description": "Risk assessment defined as primary the current Use Case.", "properties": { "createdAt": { "description": "The creation date of the risk assessment.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created the risk assessment.", "type": "string" }, "description": { "description": "The description of the risk assessment.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "entityType": { "description": "The type of entity this assessment belongs to.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "evidence": { "description": "The evidence for the risk assessment.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the risk assessment.", "type": "string" }, "isPrimary": { "default": false, "description": "Determines if the risk assessment is primary.", "type": "boolean" }, "mitigationPlan": { "description": "The mitigation plan for the risk assessment.", "type": [ "string", "null" ] }, "name": { "description": "The name of the risk assessment.", "type": "string" }, "policyId": { "description": "The ID of the risk policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskDescription": { "description": "The risk description for this assessment.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskLevel": { "description": "The name of the risk assessment level.", "type": "string" }, "riskLevelId": { "description": "The ID of the risk level within the policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskManagementPlan": { "description": "The risk management plan.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "score": { "description": "The assessment score.", "type": [ "number", "null" ], "x-versionadded": "v2.44" }, "tenantId": { "description": "The tenant ID related to the risk assessment.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The last updated date of the risk assessment.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who updated the risk assessment.", "type": "string" } }, "required": [ "createdAt", "createdBy", "description", "entityId", "entityType", "evidence", "id", "isPrimary", "mitigationPlan", "name", "policyId", "riskDescription", "riskLevelId", "riskManagementPlan", "score", "tenantId", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "projectsCount": { "description": "The number of projects in a Use Case.", "type": "integer" }, "recipesCount": { "description": "The number of recipes in a Use Case.", "type": "integer", "x-versionadded": "v2.33" }, "registeredModelVersionsCount": { "description": "The number of registered models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "riskAssessments": { "description": "The ID List of the Risk Assessments.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "role": { "description": "The requesting user's role on this Use Case.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "valueTracker": { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "applicationsCount", "created", "createdAt", "customApplicationsCount", "customJobsCount", "customModelVersionsCount", "datasetsCount", "deploymentsCount", "description", "filesCount", "id", "members", "name", "notebooksCount", "projectsCount", "recipesCount", "registeredModelVersionsCount", "role", "tenantId", "updated", "updatedAt" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseResponse] | true | maxItems: 100 | The list of use cases that match the query. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseListWithShortenedInfoResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of Use Cases with shortened info that match the query", "items": { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created the Use Case.", "type": [ "string", "null" ] }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated the Use Case.", "type": [ "string", "null" ] }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "createdAt", "description", "formattedDescription", "id", "name", "tenantId", "updatedAt" ], "type": "object", "x-versionadded": "v2.34" }, "maxItems": 100, "minItems": 1, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseWithShortenedInfoResponse] | true | maxItems: 100minItems: 1 | A list of Use Cases with shortened info that match the query | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseMemberResponse ``` { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | email | string,null | true | | The email address of the member. | | fullName | string,null | false | | The full name of the member. | | id | string | true | | The ID of the member. | | isOrganization | boolean,null | false | | Whether the member is an organization. | | userhash | string,null | false | | The member's gravatar hash. | | username | string,null | false | | The username of the member. | ## UseCaseModelsForComparisonListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Models from multiple projects in the Use Case", "items": { "properties": { "autopilotDataSelectionMethod": { "description": "The Data Selection method of the datetime-partitioned model. `null` if model is not datetime-partitioned.", "enum": [ "duration", "rowCount" ], "type": [ "string", "null" ] }, "blenderInputModelNumbers": { "description": "List of model ID numbers used in the blender.", "items": { "description": "Model ID numbers used in the blender.", "type": "integer" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "blueprintNumber": { "description": "The blueprint number associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "createdAt": { "description": "Timestamp generated at model's project creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "datasetName": { "description": "The name of the dataset used to build the model in the associated project.", "type": "string" }, "featurelistName": { "description": "The name of the feature list associated with the model.", "type": "string", "x-versionadded": "v2.36" }, "frozenPct": { "description": "The percentage used to train the frozen model.", "type": [ "number", "null" ], "x-versionadded": "v2.36" }, "hasCodegen": { "description": "A boolean setting whether the model can be converted to scorable Java code.", "type": "boolean" }, "hasHoldout": { "description": "Whether the model has holdout.", "type": "boolean" }, "icons": { "description": "The icons associated with the model.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "isBlender": { "description": "Indicates if the model is a blender.", "type": "boolean", "x-versionadded": "v2.36" }, "isCustom": { "description": "Indicates if the model contains custom tasks.", "type": "boolean", "x-versionadded": "v2.36" }, "isDatetimePartitioned": { "description": "Indicates whether the model is a datetime-partitioned model.", "type": "boolean" }, "isExternalPredictionModel": { "description": "Indicates if the model is an external prediction model.", "type": "boolean", "x-versionadded": "v2.36" }, "isFrozen": { "description": "Indicates if the model is a frozen model.", "type": "boolean", "x-versionadded": "v2.36" }, "isMaseBaselineModel": { "description": "Indicates if the model is a baseline model with MASE score '1.000'.", "type": "boolean", "x-versionadded": "v2.36" }, "isReferenceModel": { "description": "Indicates if the model is a reference model.", "type": "boolean", "x-versionadded": "v2.36" }, "isScoringAvailableForModelsTrainedIntoValidationHoldout": { "description": "Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass.", "type": "boolean" }, "isStarred": { "description": "Indicates whether the model has been starred for easier identification.", "type": "boolean" }, "isTrainedIntoHoldout": { "description": "Whether the model used holdout data for training.", "type": "boolean" }, "isTrainedIntoValidation": { "description": "Whether the model used validation data for training.", "type": "boolean" }, "isTrainedOnGpu": { "description": "Indicates if the model was trained using GPU workers.", "type": "boolean", "x-versionadded": "v2.36" }, "isTransparent": { "description": "Indicates if the model is a transparent model with exposed coefficients.", "type": "boolean", "x-versionadded": "v2.36" }, "isUserModel": { "description": "Indicates if the model was created with Composable ML.", "type": "boolean", "x-versionadded": "v2.36" }, "isUsingCrossValidation": { "default": true, "description": "Indicates whether cross-validation is the partitioning strategy used for the project associated with the model.", "type": "boolean", "x-versionadded": "v2.36" }, "metric": { "description": "Model performance information by the specified filtered evaluation metric", "type": "object" }, "modelFamily": { "description": "Model family associated with the model", "enum": [ "AB", "AD", "BLENDER", "CAL", "CLUSTER", "COUNT_DICT", "CUSTOM", "DOCUMENT", "DT", "DUMMY", "EP", "EQ", "EQ_TS", "FM", "GAM", "GBM", "GLM", "GLMNET", "IMAGE", "KNN", "NB", "NN", "OTHER", "RF", "RI", "SEGMENTED", "SVM", "TEXT", "TS", "TS_NN", "TTS", "VW" ], "type": "string" }, "modelId": { "description": "ID of the model", "type": "string" }, "modelNumber": { "description": "The model number from the single experiment leaderboard.", "type": [ "integer", "null" ], "x-versionadded": "v2.36" }, "name": { "description": "Name of the model", "type": "string" }, "projectId": { "description": "ID of the project associated with the model", "type": "string" }, "projectName": { "description": "Name of the project associated with the model", "type": "string" }, "samplePct": { "description": "Percentage of the dataset to use with the model", "exclusiveMinimum": 0, "maximum": 100, "type": [ "number", "null" ] }, "supportsMonotonicConstraints": { "description": "Indicates if the model supports enforcing monotonic constraints.", "type": "boolean", "x-versionadded": "v2.36" }, "supportsNewSeries": { "description": "Indicates if the model supports new series (time series only).", "type": "boolean", "x-versionadded": "v2.36" }, "targetName": { "description": "Name of modeling target", "type": "string" }, "targetType": { "description": "The type of modeling target", "enum": [ "Binary", "Regression" ], "type": "string" }, "trainingRowCount": { "default": 1, "description": "The number of rows used to train the model.", "exclusiveMinimum": 0, "type": "integer", "x-versionadded": "v2.36" } }, "required": [ "autopilotDataSelectionMethod", "blueprintNumber", "createdAt", "createdBy", "datasetName", "hasCodegen", "hasHoldout", "icons", "isBlender", "isCustom", "isDatetimePartitioned", "isExternalPredictionModel", "isFrozen", "isMaseBaselineModel", "isReferenceModel", "isScoringAvailableForModelsTrainedIntoValidationHoldout", "isStarred", "isTrainedIntoHoldout", "isTrainedIntoValidation", "isTrainedOnGpu", "isTransparent", "isUserModel", "isUsingCrossValidation", "metric", "modelFamily", "modelId", "name", "projectId", "projectName", "samplePct", "supportsMonotonicConstraints", "supportsNewSeries", "targetName", "targetType", "trainingRowCount" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ExperimentContainerModelsForComparisonModelResponse] | true | | Models from multiple projects in the Use Case | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseProjectOptionsResponse ``` { "description": "Project options currently saved for a project. Can be changed while project is in draft status.", "properties": { "target": { "description": "Name of the target", "type": [ "string", "null" ] }, "targetType": { "description": "The target type of the project.", "enum": [ "Binary", "Regression", "Multiclass", "minInflated", "Multilabel", "TextGeneration", "GeoPoint", "VectorDatabase" ], "type": [ "string", "null" ] }, "validationType": { "description": "The validation type of the partitioning strategy for the project, either CV for cross-validation or TVH for train-validation-holdout split.", "enum": [ "CV", "TVH" ], "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "weight": { "description": "Name of the weight (if configured)", "type": [ "string", "null" ] } }, "type": "object" } ``` Project options currently saved for a project. Can be changed while project is in draft status. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | target | string,null | false | | Name of the target | | targetType | string,null | false | | The target type of the project. | | validationType | string,null | false | | The validation type of the partitioning strategy for the project, either CV for cross-validation or TVH for train-validation-holdout split. | | weight | string,null | false | | Name of the weight (if configured) | ### Enumerated Values | Property | Value | | --- | --- | | targetType | [Binary, Regression, Multiclass, minInflated, Multilabel, TextGeneration, GeoPoint, VectorDatabase] | | validationType | [CV, TVH] | ## UseCaseProjectResponse ``` { "properties": { "autopilotDataSelectionMethod": { "description": "The Data Selection method of the datetime-partitioned project. `null` if project is not datetime-partitioned.", "enum": [ "duration", "rowCount" ], "type": [ "string", "null" ] }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at project creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "dataset": { "description": "Name of the dataset in the registry used to build the project associated with the project", "type": "string" }, "datasetId": { "description": "The dataset ID of the dataset in the registry.", "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The dataset version ID of the dataset in the registry.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "hasHoldout": { "description": "Whether the project has holdout.", "type": "boolean" }, "isDatasetLinkedToUseCase": { "description": "Indicates whether the dataset that this project was created from is attached to this Use Case.", "type": "boolean" }, "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isScoringAvailableForModelsTrainedIntoValidationHoldout": { "description": "Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "metricDetail": { "description": "Project metrics", "items": { "properties": { "ascending": { "description": "Should the metric be sorted in ascending order", "type": "boolean" }, "name": { "description": "Name of the metric", "type": "string" } }, "required": [ "ascending", "name" ], "type": "object" }, "type": "array" }, "models": { "description": "The number of models in an use case.", "type": "integer" }, "name": { "description": "The name of the project.", "type": "string" }, "numberOfBacktests": { "description": "The number of backtests of the datetime-partitioned project. 0 if project is not datetime-partitioned.", "minimum": 0, "type": "integer" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "projectOptions": { "description": "Project options currently saved for a project. Can be changed while project is in draft status.", "properties": { "target": { "description": "Name of the target", "type": [ "string", "null" ] }, "targetType": { "description": "The target type of the project.", "enum": [ "Binary", "Regression", "Multiclass", "minInflated", "Multilabel", "TextGeneration", "GeoPoint", "VectorDatabase" ], "type": [ "string", "null" ] }, "validationType": { "description": "The validation type of the partitioning strategy for the project, either CV for cross-validation or TVH for train-validation-holdout split.", "enum": [ "CV", "TVH" ], "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "weight": { "description": "Name of the weight (if configured)", "type": [ "string", "null" ] } }, "type": "object" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "Experiment failure explanation.", "type": "string" }, "target": { "description": "Name of the target", "type": [ "string", "null" ] }, "targetType": { "description": "The type of modeling target.", "enum": [ "Binary", "Regression", "Multiclass", "minInflated", "Multilabel", "TextGeneration", "GeoPoint", "VectorDatabase" ], "type": [ "string", "null" ] }, "timeAware": { "description": "Shows if project uses time series", "type": "boolean" }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated at project modification.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] } }, "required": [ "autopilotDataSelectionMethod", "created", "createdAt", "dataset", "datasetId", "datasetVersionId", "hasHoldout", "isDatasetLinkedToUseCase", "isDraft", "isErrored", "isScoringAvailableForModelsTrainedIntoValidationHoldout", "isWorkbenchEligible", "lastActivity", "metricDetail", "models", "name", "numberOfBacktests", "projectId", "projectOptions", "stage", "statusErrorMessage", "timeAware", "updated", "updatedAt" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | autopilotDataSelectionMethod | string,null | true | | The Data Selection method of the datetime-partitioned project. null if project is not datetime-partitioned. | | created | ExperimentContainerUserResponse | true | | A user associated with a use case. | | createdAt | string(date-time) | true | | The timestamp generated at project creation. | | createdBy | string,null | false | | The ID of the user who created. | | dataset | string | true | | Name of the dataset in the registry used to build the project associated with the project | | datasetId | string,null | true | | The dataset ID of the dataset in the registry. | | datasetVersionId | string,null | true | | The dataset version ID of the dataset in the registry. | | hasHoldout | boolean | true | | Whether the project has holdout. | | isDatasetLinkedToUseCase | boolean | true | | Indicates whether the dataset that this project was created from is attached to this Use Case. | | isDraft | boolean | true | | Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True). | | isErrored | boolean | true | | Indicates whether the experiment failed. | | isScoringAvailableForModelsTrainedIntoValidationHoldout | boolean | true | | Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass. | | isWorkbenchEligible | boolean | true | | Indicates whether the experiment is Workbench-compatible. | | lastActivity | ExperimentContainerLastActivity | true | | The last activity details. | | metricDetail | [MetricDetail] | true | | Project metrics | | models | integer | true | | The number of models in an use case. | | name | string | true | | The name of the project. | | numberOfBacktests | integer | true | minimum: 0 | The number of backtests of the datetime-partitioned project. 0 if project is not datetime-partitioned. | | projectId | string | true | | The ID of the project. | | projectOptions | UseCaseProjectOptionsResponse | true | | Project options currently saved for a project. Can be changed while project is in draft status. | | stage | string | true | | Stage of the experiment. | | statusErrorMessage | string | true | | Experiment failure explanation. | | target | string,null | false | | Name of the target | | targetType | string,null | false | | The type of modeling target. | | timeAware | boolean | true | | Shows if project uses time series | | updated | ExperimentContainerUserResponse | true | | A user associated with a use case. | | updatedAt | string(date-time) | true | | The timestamp generated at project modification. | | updatedBy | string,null | false | | The ID of the user who last updated. | ### Enumerated Values | Property | Value | | --- | --- | | autopilotDataSelectionMethod | [duration, rowCount] | | targetType | [Binary, Regression, Multiclass, minInflated, Multilabel, TextGeneration, GeoPoint, VectorDatabase] | ## UseCaseProjectsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of the Use Case projects that match the query", "items": { "properties": { "autopilotDataSelectionMethod": { "description": "The Data Selection method of the datetime-partitioned project. `null` if project is not datetime-partitioned.", "enum": [ "duration", "rowCount" ], "type": [ "string", "null" ] }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at project creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "dataset": { "description": "Name of the dataset in the registry used to build the project associated with the project", "type": "string" }, "datasetId": { "description": "The dataset ID of the dataset in the registry.", "type": [ "string", "null" ] }, "datasetVersionId": { "description": "The dataset version ID of the dataset in the registry.", "type": [ "string", "null" ], "x-versionadded": "v2.41" }, "hasHoldout": { "description": "Whether the project has holdout.", "type": "boolean" }, "isDatasetLinkedToUseCase": { "description": "Indicates whether the dataset that this project was created from is attached to this Use Case.", "type": "boolean" }, "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isScoringAvailableForModelsTrainedIntoValidationHoldout": { "description": "Indicates whether validation scores are available. A result of 'N/A' in the UI indicates that a model was trained into validation or holdout and also either does not have stacked predictions or uses extended multiclass.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "metricDetail": { "description": "Project metrics", "items": { "properties": { "ascending": { "description": "Should the metric be sorted in ascending order", "type": "boolean" }, "name": { "description": "Name of the metric", "type": "string" } }, "required": [ "ascending", "name" ], "type": "object" }, "type": "array" }, "models": { "description": "The number of models in an use case.", "type": "integer" }, "name": { "description": "The name of the project.", "type": "string" }, "numberOfBacktests": { "description": "The number of backtests of the datetime-partitioned project. 0 if project is not datetime-partitioned.", "minimum": 0, "type": "integer" }, "projectId": { "description": "The ID of the project.", "type": "string" }, "projectOptions": { "description": "Project options currently saved for a project. Can be changed while project is in draft status.", "properties": { "target": { "description": "Name of the target", "type": [ "string", "null" ] }, "targetType": { "description": "The target type of the project.", "enum": [ "Binary", "Regression", "Multiclass", "minInflated", "Multilabel", "TextGeneration", "GeoPoint", "VectorDatabase" ], "type": [ "string", "null" ] }, "validationType": { "description": "The validation type of the partitioning strategy for the project, either CV for cross-validation or TVH for train-validation-holdout split.", "enum": [ "CV", "TVH" ], "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "weight": { "description": "Name of the weight (if configured)", "type": [ "string", "null" ] } }, "type": "object" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "Experiment failure explanation.", "type": "string" }, "target": { "description": "Name of the target", "type": [ "string", "null" ] }, "targetType": { "description": "The type of modeling target.", "enum": [ "Binary", "Regression", "Multiclass", "minInflated", "Multilabel", "TextGeneration", "GeoPoint", "VectorDatabase" ], "type": [ "string", "null" ] }, "timeAware": { "description": "Shows if project uses time series", "type": "boolean" }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated at project modification.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] } }, "required": [ "autopilotDataSelectionMethod", "created", "createdAt", "dataset", "datasetId", "datasetVersionId", "hasHoldout", "isDatasetLinkedToUseCase", "isDraft", "isErrored", "isScoringAvailableForModelsTrainedIntoValidationHoldout", "isWorkbenchEligible", "lastActivity", "metricDetail", "models", "name", "numberOfBacktests", "projectId", "projectOptions", "stage", "statusErrorMessage", "timeAware", "updated", "updatedAt" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseProjectResponse] | true | | A list of the Use Case projects that match the query | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseRecentAssetsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer", "x-versionadded": "v2.33" }, "data": { "description": "The list of the use case references that match the query.", "items": { "properties": { "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary ID of the entity.", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity ID.", "type": "string" }, "id": { "description": "The ID of the Use Case reference.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "metadata": { "description": "The reference metadata for the use case.", "oneOf": [ { "properties": { "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "The experiment failure explanation.", "type": "string" } }, "required": [ "isDraft", "isErrored", "isWorkbenchEligible", "stage", "statusErrorMessage" ], "type": "object" }, { "properties": { "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "datasetSize": { "description": "The size of the dataset in bytes.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "datasetSourceType": { "description": "The source type of the dataset", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] } }, "required": [ "dataSourceType", "datasetSize", "datasetSourceType", "isSnapshot", "isWranglingEligible", "latestRecipeId" ], "type": "object" }, { "properties": { "dataType": { "description": "The type of the recipe (wrangling or feature discovery)", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": [ "string", "null" ] } }, "required": [ "dataType" ], "type": "object" }, { "properties": { "description": { "description": "The description of the playground.", "type": "string" }, "playgroundType": { "description": "The type of the playground.", "type": "string", "x-versionadded": "v2.37" } }, "required": [ "description" ], "type": "object" }, { "properties": { "errorMessage": { "description": "The error message, if any, for the vector database.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "source": { "description": "The source of the vector database", "type": "string" } }, "required": [ "errorMessage", "source" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "dataSourceType": { "description": "The data source type used to create the file if relevant.", "type": [ "string", "null" ] }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "numFiles": { "description": "The number of files in the catalog item.", "type": [ "integer", "null" ] } }, "required": [ "dataSourceType", "fileSourceType", "numFiles" ], "type": "object", "x-versionadded": "v2.37" } ] }, "name": { "description": "The name of the Use Case reference.", "type": "string" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the linked use case.", "type": "string" }, "useCaseName": { "description": "The name of the linked use case.", "type": [ "string", "null" ] }, "userHasAccess": { "description": "Identifies if a user has access to the entity.", "type": [ "boolean", "null" ], "x-versionadded": "v2.36" }, "versions": { "description": "The list of entity versions.", "items": { "properties": { "createdAt": { "description": "Time when this entity was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The id of the entity version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the entity version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the entity version.", "type": "integer" }, "updatedAt": { "description": "Time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "entityId", "entityType", "id", "name", "updatedAt", "useCaseId" ], "type": "object" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.33" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ], "x-versionadded": "v2.33" }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer", "x-versionadded": "v2.33" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseReferenceRetrieve] | true | maxItems: 100 | The list of the use case references that match the query. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseReferenceDatasetMetadata ``` { "properties": { "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "datasetSize": { "description": "The size of the dataset in bytes.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "datasetSourceType": { "description": "The source type of the dataset", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] } }, "required": [ "dataSourceType", "datasetSize", "datasetSourceType", "isSnapshot", "isWranglingEligible", "latestRecipeId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSourceType | string,null | true | | The type of the data source used to create the dataset if relevant. | | datasetSize | integer,null | true | | The size of the dataset in bytes. | | datasetSourceType | string,null | true | | The source type of the dataset | | isSnapshot | boolean | true | | Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot. | | isWranglingEligible | boolean | true | | Whether the source of the dataset can support wrangling. | | latestRecipeId | string,null | true | | The latest recipe ID linked to the dataset. | ## UseCaseReferenceFileMetadata ``` { "properties": { "dataSourceType": { "description": "The data source type used to create the file if relevant.", "type": [ "string", "null" ] }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "numFiles": { "description": "The number of files in the catalog item.", "type": [ "integer", "null" ] } }, "required": [ "dataSourceType", "fileSourceType", "numFiles" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataSourceType | string,null | true | | The data source type used to create the file if relevant. | | fileSourceType | string,null | true | | The source type of the file. | | numFiles | integer,null | true | | The number of files in the catalog item. | ## UseCaseReferenceLink ``` { "properties": { "workflow": { "default": "unspecified", "description": "The workflow that is attaching this entity. Does not affect the operation of the API call. Only used for analytics.", "enum": [ "migration", "creation", "move", "unspecified" ], "type": "string" } }, "required": [ "workflow" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | workflow | string | true | | The workflow that is attaching this entity. Does not affect the operation of the API call. Only used for analytics. | ### Enumerated Values | Property | Value | | --- | --- | | workflow | [migration, creation, move, unspecified] | ## UseCaseReferenceListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the use case references that match the query.", "items": { "properties": { "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary ID of the entity.", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity ID.", "type": "string" }, "id": { "description": "The ID of the Use Case reference.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "metadata": { "description": "The reference metadata for the use case.", "oneOf": [ { "properties": { "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "The experiment failure explanation.", "type": "string" } }, "required": [ "isDraft", "isErrored", "isWorkbenchEligible", "stage", "statusErrorMessage" ], "type": "object" }, { "properties": { "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "datasetSize": { "description": "The size of the dataset in bytes.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "datasetSourceType": { "description": "The source type of the dataset", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] } }, "required": [ "dataSourceType", "datasetSize", "datasetSourceType", "isSnapshot", "isWranglingEligible", "latestRecipeId" ], "type": "object" }, { "properties": { "dataType": { "description": "The type of the recipe (wrangling or feature discovery)", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": [ "string", "null" ] } }, "required": [ "dataType" ], "type": "object" }, { "properties": { "description": { "description": "The description of the playground.", "type": "string" }, "playgroundType": { "description": "The type of the playground.", "type": "string", "x-versionadded": "v2.37" } }, "required": [ "description" ], "type": "object" }, { "properties": { "errorMessage": { "description": "The error message, if any, for the vector database.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "source": { "description": "The source of the vector database", "type": "string" } }, "required": [ "errorMessage", "source" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "dataSourceType": { "description": "The data source type used to create the file if relevant.", "type": [ "string", "null" ] }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "numFiles": { "description": "The number of files in the catalog item.", "type": [ "integer", "null" ] } }, "required": [ "dataSourceType", "fileSourceType", "numFiles" ], "type": "object", "x-versionadded": "v2.37" } ] }, "name": { "description": "The name of the Use Case reference.", "type": "string" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the linked use case.", "type": "string" }, "useCaseName": { "description": "The name of the linked use case.", "type": [ "string", "null" ] }, "userHasAccess": { "description": "Identifies if a user has access to the entity.", "type": [ "boolean", "null" ], "x-versionadded": "v2.36" }, "versions": { "description": "The list of entity versions.", "items": { "properties": { "createdAt": { "description": "Time when this entity was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The id of the entity version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the entity version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the entity version.", "type": "integer" }, "updatedAt": { "description": "Time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "entityId", "entityType", "id", "name", "updatedAt", "useCaseId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseReferenceRetrieve] | true | | The list of the use case references that match the query. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseReferenceMultilink ``` { "properties": { "entitiesList": { "description": "Entities to link to this Use Case", "items": { "properties": { "entityId": { "description": "The primary id of the entity to link.", "type": "string", "x-versionadded": "v2.33" }, "entityType": { "description": "The type of entity to link.", "enum": [ "project", "dataset", "file", "notebook", "application", "recipe", "syftrSearchInstance", "customModelVersion", "registeredModelVersion", "deployment", "customApplication", "customJob" ], "type": "string", "x-versionadded": "v2.33" }, "includeDataset": { "default": true, "description": "Include dataset migration when an experiment is migrated", "type": "boolean", "x-versionadded": "v2.34" } }, "required": [ "entityId", "entityType" ], "type": "object" }, "maxItems": 100, "minItems": 1, "type": "array", "x-versionadded": "v2.33" }, "workflow": { "default": "unspecified", "description": "The workflow that is attaching this entity. Does not affect the operation of the API call. Only used for analytics.", "enum": [ "migration", "creation", "move", "unspecified" ], "type": "string", "x-versionadded": "v2.33" } }, "required": [ "entitiesList", "workflow" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entitiesList | [UseCaseReferenceMultilinkEntity] | true | maxItems: 100minItems: 1 | Entities to link to this Use Case | | workflow | string | true | | The workflow that is attaching this entity. Does not affect the operation of the API call. Only used for analytics. | ### Enumerated Values | Property | Value | | --- | --- | | workflow | [migration, creation, move, unspecified] | ## UseCaseReferenceMultilinkEntity ``` { "properties": { "entityId": { "description": "The primary id of the entity to link.", "type": "string", "x-versionadded": "v2.33" }, "entityType": { "description": "The type of entity to link.", "enum": [ "project", "dataset", "file", "notebook", "application", "recipe", "syftrSearchInstance", "customModelVersion", "registeredModelVersion", "deployment", "customApplication", "customJob" ], "type": "string", "x-versionadded": "v2.33" }, "includeDataset": { "default": true, "description": "Include dataset migration when an experiment is migrated", "type": "boolean", "x-versionadded": "v2.34" } }, "required": [ "entityId", "entityType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | entityId | string | true | | The primary id of the entity to link. | | entityType | string | true | | The type of entity to link. | | includeDataset | boolean | false | | Include dataset migration when an experiment is migrated | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [project, dataset, file, notebook, application, recipe, syftrSearchInstance, customModelVersion, registeredModelVersion, deployment, customApplication, customJob] | ## UseCaseReferencePlaygroundMetadata ``` { "properties": { "description": { "description": "The description of the playground.", "type": "string" }, "playgroundType": { "description": "The type of the playground.", "type": "string", "x-versionadded": "v2.37" } }, "required": [ "description" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | The description of the playground. | | playgroundType | string | false | | The type of the playground. | ## UseCaseReferenceProjectMetadata ``` { "properties": { "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "The experiment failure explanation.", "type": "string" } }, "required": [ "isDraft", "isErrored", "isWorkbenchEligible", "stage", "statusErrorMessage" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isDraft | boolean | true | | Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True). | | isErrored | boolean | true | | Indicates whether the experiment failed. | | isWorkbenchEligible | boolean | true | | Indicates whether the experiment is Workbench-compatible. | | stage | string | true | | Stage of the experiment. | | statusErrorMessage | string | true | | The experiment failure explanation. | ## UseCaseReferenceRecipeMetadata ``` { "properties": { "dataType": { "description": "The type of the recipe (wrangling or feature discovery)", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": [ "string", "null" ] } }, "required": [ "dataType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dataType | string,null | true | | The type of the recipe (wrangling or feature discovery) | ### Enumerated Values | Property | Value | | --- | --- | | dataType | [static, Static, STATIC, snapshot, Snapshot, SNAPSHOT, dynamic, Dynamic, DYNAMIC, sqlRecipe, SqlRecipe, SQL_RECIPE, wranglingRecipe, WranglingRecipe, WRANGLING_RECIPE, featureDiscoveryRecipe, FeatureDiscoveryRecipe, FEATURE_DISCOVERY_RECIPE] | ## UseCaseReferenceRetrieve ``` { "properties": { "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary ID of the entity.", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity ID.", "type": "string" }, "id": { "description": "The ID of the Use Case reference.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "metadata": { "description": "The reference metadata for the use case.", "oneOf": [ { "properties": { "isDraft": { "description": "Indicates whether the experiment configuration has been used for modeling and is therefore locked (False) or whether the setup has not been applied , and as a draft, can still be modified and used in training (True).", "type": "boolean" }, "isErrored": { "description": "Indicates whether the experiment failed.", "type": "boolean" }, "isWorkbenchEligible": { "description": "Indicates whether the experiment is Workbench-compatible.", "type": "boolean" }, "stage": { "description": "Stage of the experiment.", "type": "string", "x-versionadded": "v2.34" }, "statusErrorMessage": { "description": "The experiment failure explanation.", "type": "string" } }, "required": [ "isDraft", "isErrored", "isWorkbenchEligible", "stage", "statusErrorMessage" ], "type": "object" }, { "properties": { "dataSourceType": { "description": "The type of the data source used to create the dataset if relevant.", "type": [ "string", "null" ] }, "datasetSize": { "description": "The size of the dataset in bytes.", "type": [ "integer", "null" ], "x-versionadded": "v2.34" }, "datasetSourceType": { "description": "The source type of the dataset", "type": [ "string", "null" ] }, "isSnapshot": { "description": "Whether the dataset is an immutable snapshot of data which has previously been retrieved and saved to DataRobot.", "type": "boolean" }, "isWranglingEligible": { "description": "Whether the source of the dataset can support wrangling.", "type": "boolean" }, "latestRecipeId": { "description": "The latest recipe ID linked to the dataset.", "type": [ "string", "null" ] } }, "required": [ "dataSourceType", "datasetSize", "datasetSourceType", "isSnapshot", "isWranglingEligible", "latestRecipeId" ], "type": "object" }, { "properties": { "dataType": { "description": "The type of the recipe (wrangling or feature discovery)", "enum": [ "static", "Static", "STATIC", "snapshot", "Snapshot", "SNAPSHOT", "dynamic", "Dynamic", "DYNAMIC", "sqlRecipe", "SqlRecipe", "SQL_RECIPE", "wranglingRecipe", "WranglingRecipe", "WRANGLING_RECIPE", "featureDiscoveryRecipe", "FeatureDiscoveryRecipe", "FEATURE_DISCOVERY_RECIPE" ], "type": [ "string", "null" ] } }, "required": [ "dataType" ], "type": "object" }, { "properties": { "description": { "description": "The description of the playground.", "type": "string" }, "playgroundType": { "description": "The type of the playground.", "type": "string", "x-versionadded": "v2.37" } }, "required": [ "description" ], "type": "object" }, { "properties": { "errorMessage": { "description": "The error message, if any, for the vector database.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "source": { "description": "The source of the vector database", "type": "string" } }, "required": [ "errorMessage", "source" ], "type": "object", "x-versionadded": "v2.37" }, { "properties": { "dataSourceType": { "description": "The data source type used to create the file if relevant.", "type": [ "string", "null" ] }, "fileSourceType": { "description": "The source type of the file.", "type": [ "string", "null" ] }, "numFiles": { "description": "The number of files in the catalog item.", "type": [ "integer", "null" ] } }, "required": [ "dataSourceType", "fileSourceType", "numFiles" ], "type": "object", "x-versionadded": "v2.37" } ] }, "name": { "description": "The name of the Use Case reference.", "type": "string" }, "processingState": { "description": "The current ingestion process state of the dataset.", "enum": [ "COMPLETED", "ERROR", "RUNNING" ], "type": "string" }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the linked use case.", "type": "string" }, "useCaseName": { "description": "The name of the linked use case.", "type": [ "string", "null" ] }, "userHasAccess": { "description": "Identifies if a user has access to the entity.", "type": [ "boolean", "null" ], "x-versionadded": "v2.36" }, "versions": { "description": "The list of entity versions.", "items": { "properties": { "createdAt": { "description": "Time when this entity was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The id of the entity version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the entity version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the entity version.", "type": "integer" }, "updatedAt": { "description": "Time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" } }, "required": [ "entityId", "entityType", "id", "name", "updatedAt", "useCaseId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdBy | string,null | false | | The ID of the user who created. | | entityId | string | true | | The primary ID of the entity. | | entityType | string | true | | The type of entity provided by the entity ID. | | id | string | true | | The ID of the Use Case reference. | | lastActivity | UseCaseLastActivity | false | | The last activity details. | | metadata | any | false | | The reference metadata for the use case. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | UseCaseReferenceProjectMetadata | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | UseCaseReferenceDatasetMetadata | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | UseCaseReferenceRecipeMetadata | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | UseCaseReferencePlaygroundMetadata | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | UseCaseReferenceVectorDatabasesMetadata | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | UseCaseReferenceFileMetadata | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the Use Case reference. | | processingState | string | false | | The current ingestion process state of the dataset. | | updated | ExperimentContainerUserResponse | false | | A user associated with a use case. | | updatedAt | string(date-time) | true | | The timestamp generated at record creation. | | updatedBy | string,null | false | | The ID of the user who last updated. | | useCaseId | string | true | | The ID of the linked use case. | | useCaseName | string,null | false | | The name of the linked use case. | | userHasAccess | boolean,null | false | | Identifies if a user has access to the entity. | | versions | [UseCaseEntityVersion] | false | maxItems: 100 | The list of entity versions. | ### Enumerated Values | Property | Value | | --- | --- | | processingState | [COMPLETED, ERROR, RUNNING] | ## UseCaseReferenceVectorDatabasesMetadata ``` { "properties": { "errorMessage": { "description": "The error message, if any, for the vector database.", "type": [ "string", "null" ], "x-versionadded": "v2.38" }, "source": { "description": "The source of the vector database", "type": "string" } }, "required": [ "errorMessage", "source" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | string,null | true | | The error message, if any, for the vector database. | | source | string | true | | The source of the vector database | ## UseCaseRegisteredModel ``` { "properties": { "createdAt": { "description": "The time when this model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The ID of the registered model.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the registered model.", "type": "string" }, "updatedAt": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "userHasAccess": { "description": "Indicates if a user has access to this registered model.", "type": "boolean", "x-versionadded": "v2.35" }, "versions": { "description": "The list of registered model versions.", "items": { "properties": { "createdAt": { "description": "The time when this model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The ID of the registered model version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the registered model version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the registered model version.", "type": "integer", "x-versionadded": "v2.35" }, "updatedAt": { "description": "The time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" } }, "required": [ "id", "userHasAccess" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | false | | The time when this model was created. | | createdBy | ExperimentContainerUserResponse | false | | A user associated with a use case. | | id | string | true | | The ID of the registered model. | | lastActivity | UseCaseRegisteredModelLastActivity | false | | The last activity details. | | name | string | false | | The name of the registered model. | | updatedAt | string(date-time) | false | | The time when this activity occurred. | | updatedBy | ExperimentContainerUserResponse | false | | A user associated with a use case. | | userHasAccess | boolean | true | | Indicates if a user has access to this registered model. | | versions | [UseCaseRegisteredModelVersion] | false | maxItems: 100 | The list of registered model versions. | ## UseCaseRegisteredModelLastActivity ``` { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object", "x-versionadded": "v2.36" } ``` The last activity details. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | timestamp | string(date-time) | true | | The time when this activity occurred. | | type | string | true | | The type of activity. Can be "Added" or "Modified". | ## UseCaseRegisteredModelVersion ``` { "properties": { "createdAt": { "description": "The time when this model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The ID of the registered model version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the registered model version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the registered model version.", "type": "integer", "x-versionadded": "v2.35" }, "updatedAt": { "description": "The time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The time when this model was created. | | createdBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | | id | string | true | | The ID of the registered model version. | | lastActivity | UseCaseRegisteredModelLastActivity | true | | The last activity details. | | name | string | true | | The name of the registered model version. | | registeredModelVersion | integer | true | | The version number of the registered model version. | | updatedAt | string(date-time) | true | | The time when the last update occurred. | | updatedBy | ExperimentContainerUserResponse | true | | A user associated with a use case. | ## UseCaseRegisteredModelsResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of registered models.", "items": { "properties": { "createdAt": { "description": "The time when this model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The ID of the registered model.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the registered model.", "type": "string" }, "updatedAt": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "userHasAccess": { "description": "Indicates if a user has access to this registered model.", "type": "boolean", "x-versionadded": "v2.35" }, "versions": { "description": "The list of registered model versions.", "items": { "properties": { "createdAt": { "description": "The time when this model was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "id": { "description": "The ID of the registered model version.", "type": "string" }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object", "x-versionadded": "v2.36" }, "name": { "description": "The name of the registered model version.", "type": "string" }, "registeredModelVersion": { "description": "The version number of the registered model version.", "type": "integer", "x-versionadded": "v2.35" }, "updatedAt": { "description": "The time when the last update occurred.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" } }, "required": [ "createdAt", "createdBy", "id", "lastActivity", "name", "registeredModelVersion", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" } }, "required": [ "id", "userHasAccess" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseRegisteredModel] | true | maxItems: 100 | The list of registered models. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCaseResponse ``` { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "applicationsCount": { "description": "The number of applications in a Use Case.", "type": "integer" }, "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "customApplicationsCount": { "description": "The number of custom applications referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customJobsCount": { "description": "The number of custom jobs referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "customModelVersionsCount": { "description": "The number of custom models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "datasetsCount": { "description": "The number of datasets in a Use Case.", "type": "integer" }, "deploymentsCount": { "description": "The number of deployments referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "filesCount": { "description": "The number of files in a Use Case.", "type": "integer", "x-versionadded": "v2.37" }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "members": { "description": "The list of use case members.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "modelsCount": { "description": "[DEPRECATED] The number of models in a Use Case.", "type": "integer", "x-versiondeprecated": "v2.34" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "notebooksCount": { "description": "The number of notebooks in a Use Case.", "type": "integer" }, "owners": { "description": "The list of owners of a use case.", "items": { "properties": { "email": { "description": "The email address of the member.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the member.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the member.", "type": "string" }, "isOrganization": { "description": "Whether the member is an organization.", "type": [ "boolean", "null" ] }, "userhash": { "description": "The member's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the member.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "type": "array" }, "primaryRiskAssessment": { "description": "Risk assessment defined as primary the current Use Case.", "properties": { "createdAt": { "description": "The creation date of the risk assessment.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created the risk assessment.", "type": "string" }, "description": { "description": "The description of the risk assessment.", "type": "string" }, "entityId": { "description": "The ID of the entity.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "entityType": { "description": "The type of entity this assessment belongs to.", "maxLength": 255, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "evidence": { "description": "The evidence for the risk assessment.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the risk assessment.", "type": "string" }, "isPrimary": { "default": false, "description": "Determines if the risk assessment is primary.", "type": "boolean" }, "mitigationPlan": { "description": "The mitigation plan for the risk assessment.", "type": [ "string", "null" ] }, "name": { "description": "The name of the risk assessment.", "type": "string" }, "policyId": { "description": "The ID of the risk policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskDescription": { "description": "The risk description for this assessment.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskLevel": { "description": "The name of the risk assessment level.", "type": "string" }, "riskLevelId": { "description": "The ID of the risk level within the policy.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "riskManagementPlan": { "description": "The risk management plan.", "maxLength": 10000, "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "score": { "description": "The assessment score.", "type": [ "number", "null" ], "x-versionadded": "v2.44" }, "tenantId": { "description": "The tenant ID related to the risk assessment.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The last updated date of the risk assessment.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who updated the risk assessment.", "type": "string" } }, "required": [ "createdAt", "createdBy", "description", "entityId", "entityType", "evidence", "id", "isPrimary", "mitigationPlan", "name", "policyId", "riskDescription", "riskLevelId", "riskManagementPlan", "score", "tenantId", "updatedAt", "updatedBy" ], "type": "object", "x-versionadded": "v2.36" }, "projectsCount": { "description": "The number of projects in a Use Case.", "type": "integer" }, "recipesCount": { "description": "The number of recipes in a Use Case.", "type": "integer", "x-versionadded": "v2.33" }, "registeredModelVersionsCount": { "description": "The number of registered models referenced in a Use Case.", "type": "integer", "x-versionadded": "v2.35" }, "riskAssessments": { "description": "The ID List of the Risk Assessments.", "items": { "type": "string" }, "maxItems": 100, "type": "array", "x-versionadded": "v2.36" }, "role": { "description": "The requesting user's role on this Use Case.", "enum": [ "OWNER", "EDITOR", "CONSUMER" ], "type": "string" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updated": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "valueTracker": { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "applicationsCount", "created", "createdAt", "customApplicationsCount", "customJobsCount", "customModelVersionsCount", "datasetsCount", "deploymentsCount", "description", "filesCount", "id", "members", "name", "notebooksCount", "projectsCount", "recipesCount", "registeredModelVersionsCount", "role", "tenantId", "updated", "updatedAt" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | advancedTour | string,null | false | | Advanced tour key. | | applicationsCount | integer | true | | The number of applications in a Use Case. | | created | ExperimentContainerUserResponse | true | | A user associated with a use case. | | createdAt | string(date-time) | true | | The timestamp generated at record creation. | | createdBy | string,null | false | | The ID of the user who created. | | customApplicationsCount | integer | true | | The number of custom applications referenced in a Use Case. | | customJobsCount | integer | true | | The number of custom jobs referenced in a Use Case. | | customModelVersionsCount | integer | true | | The number of custom models referenced in a Use Case. | | datasetsCount | integer | true | | The number of datasets in a Use Case. | | deploymentsCount | integer | true | | The number of deployments referenced in a Use Case. | | description | string,null | true | | The description of the Use Case. | | filesCount | integer | true | | The number of files in a Use Case. | | formattedDescription | string,null | false | | The formatted description of the experiment container used as styled description. | | id | string | true | | The ID of the Use Case. | | members | [UseCaseMemberResponse] | true | | The list of use case members. | | modelsCount | integer | false | | [DEPRECATED] The number of models in a Use Case. | | name | string | true | | The name of the Use Case. | | notebooksCount | integer | true | | The number of notebooks in a Use Case. | | owners | [UseCaseMemberResponse] | false | | The list of owners of a use case. | | primaryRiskAssessment | RiskAssessmentComplete | false | | Risk assessment defined as primary the current Use Case. | | projectsCount | integer | true | | The number of projects in a Use Case. | | recipesCount | integer | true | | The number of recipes in a Use Case. | | registeredModelVersionsCount | integer | true | | The number of registered models referenced in a Use Case. | | riskAssessments | [string] | false | maxItems: 100 | The ID List of the Risk Assessments. | | role | string | true | | The requesting user's role on this Use Case. | | tenantId | string,null | true | | The ID of the tenant to associate this organization with. | | updated | ExperimentContainerUserResponse | true | | A user associated with a use case. | | updatedAt | string(date-time) | true | | The timestamp generated when the record was last updated. | | updatedBy | string,null | false | | The ID of the user who last updated. | | valueTracker | ValueTrackerResponse | false | | The value tracker information. | | valueTrackerId | string,null | false | | The ID of the Value Tracker. | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, EDITOR, CONSUMER] | ## UseCaseUpdate ``` { "properties": { "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "name": { "description": "The name of the Use Case.", "maxLength": 100, "type": [ "string", "null" ] } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string,null | false | | The description of the Use Case. | | name | string,null | false | maxLength: 100 | The name of the Use Case. | ## UseCaseVectorDatabaseRelatedCustomModel ``` { "properties": { "createdAt": { "description": "The time that this custom model version was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who created this custom model version.", "type": "string" }, "customModelId": { "description": "The ID of this custom model.", "type": "string" }, "customModelVersionId": { "description": "The ID of this version of this custom model.", "type": "string" }, "name": { "description": "The name of the custom model created from this vector database.", "type": "string" }, "role": { "description": "The role that the requesting user has on this custom model version", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "versionMajor": { "description": "The major version number of this custom model version.", "type": "integer" }, "versionMinor": { "description": "The minor version number of this custom model version.", "type": "integer" } }, "required": [ "createdAt", "createdBy", "customModelId", "customModelVersionId", "name", "role", "versionMajor", "versionMinor" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The time that this custom model version was created. | | createdBy | string | true | | The username of the user who created this custom model version. | | customModelId | string | true | | The ID of this custom model. | | customModelVersionId | string | true | | The ID of this version of this custom model. | | name | string | true | | The name of the custom model created from this vector database. | | role | string,null | true | | The role that the requesting user has on this custom model version | | versionMajor | integer | true | | The major version number of this custom model version. | | versionMinor | integer | true | | The minor version number of this custom model version. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | ## UseCaseVectorDatabaseRelatedCustomModelList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of custom model versions created from this vector database.", "items": { "properties": { "createdAt": { "description": "The time that this custom model version was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who created this custom model version.", "type": "string" }, "customModelId": { "description": "The ID of this custom model.", "type": "string" }, "customModelVersionId": { "description": "The ID of this version of this custom model.", "type": "string" }, "name": { "description": "The name of the custom model created from this vector database.", "type": "string" }, "role": { "description": "The role that the requesting user has on this custom model version", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "versionMajor": { "description": "The major version number of this custom model version.", "type": "integer" }, "versionMinor": { "description": "The minor version number of this custom model version.", "type": "integer" } }, "required": [ "createdAt", "createdBy", "customModelId", "customModelVersionId", "name", "role", "versionMajor", "versionMinor" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseVectorDatabaseRelatedCustomModel] | true | maxItems: 100 | The list of custom model versions created from this vector database. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## UseCaseVectorDatabaseRelatedDeployment ``` { "properties": { "createdAt": { "description": "The time that this deployment was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who created this deployment.", "type": "string" }, "customModelId": { "description": "ID of the custom model currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "customModelVersionId": { "description": "ID of the custom model version currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "id": { "description": "The ID of the deployment with this vector database deployed.", "type": "string" }, "name": { "description": "The name of the deployment with this vector database deployed.", "type": "string" }, "registeredModelId": { "description": "ID of the registered model currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "registeredModelVersionId": { "description": "ID of the registered model version currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "role": { "description": "The role that the requesting user has on this deployment", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] } }, "required": [ "createdAt", "createdBy", "id", "name", "role" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The time that this deployment was created. | | createdBy | string | true | | The username of the user who created this deployment. | | customModelId | string,null | false | | ID of the custom model currently deployed to this deployment | | customModelVersionId | string,null | false | | ID of the custom model version currently deployed to this deployment | | id | string | true | | The ID of the deployment with this vector database deployed. | | name | string | true | | The name of the deployment with this vector database deployed. | | registeredModelId | string,null | false | | ID of the registered model currently deployed to this deployment | | registeredModelVersionId | string,null | false | | ID of the registered model version currently deployed to this deployment | | role | string,null | true | | The role that the requesting user has on this deployment | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | ## UseCaseVectorDatabaseRelatedDeploymentList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of deployments that this vector database is currently deployed on.", "items": { "properties": { "createdAt": { "description": "The time that this deployment was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who created this deployment.", "type": "string" }, "customModelId": { "description": "ID of the custom model currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "customModelVersionId": { "description": "ID of the custom model version currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "id": { "description": "The ID of the deployment with this vector database deployed.", "type": "string" }, "name": { "description": "The name of the deployment with this vector database deployed.", "type": "string" }, "registeredModelId": { "description": "ID of the registered model currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "registeredModelVersionId": { "description": "ID of the registered model version currently deployed to this deployment", "type": [ "string", "null" ], "x-versionadded": "v2.37" }, "role": { "description": "The role that the requesting user has on this deployment", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] } }, "required": [ "createdAt", "createdBy", "id", "name", "role" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseVectorDatabaseRelatedDeployment] | true | maxItems: 100 | The list of deployments that this vector database is currently deployed on. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## UseCaseVectorDatabaseRelatedRegisteredModel ``` { "properties": { "createdAt": { "description": "The time that this registered model version was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who created this registered model version.", "type": "string" }, "registeredModelId": { "description": "The ID of this registered model.", "type": "string" }, "registeredModelName": { "description": "The name of the registered model created from this vector database.", "type": "string" }, "registeredModelVersionId": { "description": "The ID of this version of this registered model.", "type": "string" }, "registeredModelVersionName": { "description": "The name of the registered model version created from this vector database.", "type": "string" }, "role": { "description": "The role that the requesting user has on this registered model version", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "version": { "description": "The version number of this registered model version.", "type": "integer" } }, "required": [ "createdAt", "createdBy", "registeredModelId", "registeredModelName", "registeredModelVersionId", "registeredModelVersionName", "role", "version" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The time that this registered model version was created. | | createdBy | string | true | | The username of the user who created this registered model version. | | registeredModelId | string | true | | The ID of this registered model. | | registeredModelName | string | true | | The name of the registered model created from this vector database. | | registeredModelVersionId | string | true | | The ID of this version of this registered model. | | registeredModelVersionName | string | true | | The name of the registered model version created from this vector database. | | role | string,null | true | | The role that the requesting user has on this registered model version | | version | integer | true | | The version number of this registered model version. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | ## UseCaseVectorDatabaseRelatedRegisteredModelList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of registered model versions created from this vector database.", "items": { "properties": { "createdAt": { "description": "The time that this registered model version was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The username of the user who created this registered model version.", "type": "string" }, "registeredModelId": { "description": "The ID of this registered model.", "type": "string" }, "registeredModelName": { "description": "The name of the registered model created from this vector database.", "type": "string" }, "registeredModelVersionId": { "description": "The ID of this version of this registered model.", "type": "string" }, "registeredModelVersionName": { "description": "The name of the registered model version created from this vector database.", "type": "string" }, "role": { "description": "The role that the requesting user has on this registered model version", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": [ "string", "null" ] }, "version": { "description": "The version number of this registered model version.", "type": "integer" } }, "required": [ "createdAt", "createdBy", "registeredModelId", "registeredModelName", "registeredModelVersionId", "registeredModelVersionName", "role", "version" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "data", "next", "previous" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [UseCaseVectorDatabaseRelatedRegisteredModel] | true | maxItems: 100 | The list of registered model versions created from this vector database. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | ## UseCaseWithShortenedInfoResponse ``` { "properties": { "advancedTour": { "description": "Advanced tour key.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp generated at record creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created the Use Case.", "type": [ "string", "null" ] }, "description": { "description": "The description of the Use Case.", "type": [ "string", "null" ] }, "formattedDescription": { "description": "The formatted description of the experiment container used as styled description.", "type": [ "string", "null" ], "x-versionadded": "v2.36" }, "id": { "description": "The ID of the Use Case.", "type": "string" }, "name": { "description": "The name of the Use Case.", "type": "string" }, "tenantId": { "description": "The ID of the tenant to associate this organization with.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp generated when the record was last updated.", "format": "date-time", "type": "string" }, "updatedBy": { "description": "The ID of the user who last updated the Use Case.", "type": [ "string", "null" ] }, "valueTrackerId": { "description": "The ID of the Value Tracker.", "type": [ "string", "null" ], "x-versionadded": "v2.36" } }, "required": [ "createdAt", "description", "formattedDescription", "id", "name", "tenantId", "updatedAt" ], "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | advancedTour | string,null | false | | Advanced tour key. | | createdAt | string(date-time) | true | | The timestamp generated at record creation. | | createdBy | string,null | false | | The ID of the user who created the Use Case. | | description | string,null | true | | The description of the Use Case. | | formattedDescription | string,null | true | | The formatted description of the experiment container used as styled description. | | id | string | true | | The ID of the Use Case. | | name | string | true | | The name of the Use Case. | | tenantId | string,null | true | | The ID of the tenant to associate this organization with. | | updatedAt | string(date-time) | true | | The timestamp generated when the record was last updated. | | updatedBy | string,null | false | | The ID of the user who last updated the Use Case. | | valueTrackerId | string,null | false | | The ID of the Value Tracker. | ## UseCasesApplicationsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the applications in this use case.", "items": { "properties": { "applicationId": { "description": "The application id of the application.", "type": "string" }, "applicationTemplateType": { "description": "The type of the application.", "type": [ "string", "null" ] }, "createdAt": { "description": "The timestamp generated at application creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "description": { "description": "The description of the application.", "type": [ "string", "null" ] }, "lastActivity": { "description": "The last activity details.", "properties": { "timestamp": { "description": "The time when this activity occurred.", "format": "date-time", "type": "string" }, "type": { "description": "The type of activity. Can be \"Added\" or \"Modified\".", "type": "string" } }, "required": [ "timestamp", "type" ], "type": "object" }, "name": { "description": "The name of the application.", "type": "string" }, "projectId": { "description": "The ID of the associated project", "type": [ "string", "null" ] }, "source": { "description": "The source used to create the application.", "type": [ "string", "null" ] }, "updatedAt": { "description": "The timestamp generated at application modification.", "format": "date-time", "type": "string" } }, "required": [ "applicationId", "applicationTemplateType", "createdAt", "createdBy", "description", "lastActivity", "name", "projectId", "source", "updatedAt" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ExperimentContainerApplicationResponse] | true | | The list of the applications in this use case. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCasesNotebooksListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the notebooks in this use case.", "items": { "properties": { "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at notebook creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity (same as ID of the notebook).", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "enum": [ "notebook" ], "type": "string" }, "experimentContainerId": { "description": "[DEPRECATED - replaced with use_case_id] The ID of the Use Case.", "type": "string", "x-versiondeprecated": "v2.32" }, "id": { "description": "The ID of the notebook.", "type": "string" }, "isDeleted": { "description": "Soft deletion flag for notebooks", "type": "boolean" }, "referenceId": { "description": "Original ID from DB", "type": "string" }, "tenantId": { "description": "The id of the tenant the notebook belongs to.", "type": [ "string", "null" ] }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] }, "useCaseId": { "description": "The ID of the Use Case.", "type": "string" }, "useCaseName": { "description": "Use Case name", "type": "string" } }, "required": [ "created", "createdAt", "entityId", "entityType", "experimentContainerId", "id", "isDeleted", "referenceId", "tenantId", "useCaseId" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ExperimentContainerNotebookResponse] | true | maxItems: 100 | The list of the notebooks in this use case. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCasesPlaygroundsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the playgrounds in this use case.", "items": { "properties": { "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at playground creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity (same as ID of the playground).", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "enum": [ "playground" ], "type": "string" }, "id": { "description": "The ID of the playground.", "type": "string" }, "isDeleted": { "description": "Soft deletion flag for playgrounds", "type": "boolean" }, "referenceId": { "description": "Original ID from DB", "type": "string" }, "tenantId": { "description": "The id of the tenant the playground belongs to.", "type": [ "string", "null" ] }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] } }, "required": [ "created", "createdAt", "entityId", "entityType", "id", "isDeleted", "referenceId", "tenantId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ExperimentContainerPlaygroundResponse] | true | | The list of the playgrounds in this use case. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## UseCasesProjectMigrationParam ``` { "properties": { "includeDataset": { "default": true, "description": "Include dataset migration when project is migrated.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.34" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | includeDataset | boolean | false | | Include dataset migration when project is migrated. | ## UseCasesVectorDatabasesListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of the vector databases in this use case.", "items": { "properties": { "created": { "description": "A user associated with a use case.", "properties": { "email": { "description": "The email address of the user.", "type": [ "string", "null" ] }, "fullName": { "description": "The full name of the user.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the user.", "type": "string" }, "userhash": { "description": "The user's gravatar hash.", "type": [ "string", "null" ] }, "username": { "description": "The username of the user.", "type": [ "string", "null" ] } }, "required": [ "email", "id" ], "type": "object" }, "createdAt": { "description": "The timestamp generated at vector database creation.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The ID of the user who created.", "type": [ "string", "null" ] }, "entityId": { "description": "The primary id of the entity (same as ID of the vector database).", "type": "string" }, "entityType": { "description": "The type of entity provided by the entity id.", "enum": [ "vector_database" ], "type": "string" }, "id": { "description": "The ID of the vector database.", "type": "string" }, "isDeleted": { "description": "Soft deletion flag for vector databases", "type": "boolean" }, "referenceId": { "description": "Original ID from DB", "type": "string" }, "tenantId": { "description": "The id of the tenant the vector database belongs to.", "type": [ "string", "null" ] }, "updatedBy": { "description": "The ID of the user who last updated.", "type": [ "string", "null" ] } }, "required": [ "created", "createdAt", "entityId", "entityType", "id", "isDeleted", "referenceId", "tenantId" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [ExperimentContainerVectorDatabaseResponse] | true | | The list of the vector databases in this use case. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ValueTrackerDrUserId ``` { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" } ``` DataRobot user information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | firstName | string,null | false | | The first name of the ValueTracker owner. | | id | string | true | | The DataRobot user ID. | | lastName | string,null | false | | The last name of the ValueTracker owner. | | username | string | false | | The username of the ValueTracker owner. | ## ValueTrackerHealthInformation ``` { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string,null(date-time) | true | | The end date for this health status. | | message | string,null | true | | Information about the health status. | | startDate | string,null(date-time) | true | | The start date for this health status. | | status | string,null | true | | The status of the value tracker. | ## ValueTrackerMonetaryValue ``` { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" } ``` Optional. Contains MonetaryValue objects. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | currency | string | true | | The ISO code of the currency. | | details | string,null | false | | Optional user notes. | | value | number | true | | The amount of value. | ### Enumerated Values | Property | Value | | --- | --- | | currency | [AED, BRL, CHF, EUR, GBP, JPY, KRW, UAH, USD, ZAR] | ## ValueTrackerResponse ``` { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` The value tracker information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyHealth | any | false | | The health of the accuracy. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerHealthInformation | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | businessImpact | integer,null | false | maximum: 5minimum: 1 | The expected effects on overall business operations. | | commentId | string | false | | The ID for this comment. | | content | string | false | | A string | | description | string,null | false | | The value tracker description. | | feasibility | integer,null | false | maximum: 5minimum: 1 | Assessment of how the value tracker can be accomplished across multiple dimensions. | | id | string | false | | The ID of the ValueTracker. | | inProductionWarning | string,null | false | | An optional warning to indicate that deployments are attached to this value tracker. | | mentions | [ValueTrackerDrUserId] | false | | The list of user objects. | | modelHealth | any | false | | The health of the model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerHealthInformation | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | | The name of the value tracker. | | notes | string,null | false | | The user notes. | | owner | ValueTrackerDrUserId | false | | DataRobot user information. | | permissions | [string] | false | | The permissions of the current user. | | potentialValue | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | | potentialValueTemplate | any | false | | Optional. Contains the template type and parameter information. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateClassification | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateRegression | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionTargets | [string] | false | | An array of prediction target name strings. | | predictionsCount | any | false | | The count of the number of predictions made. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [integer] | false | | The list of prediction counts. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | realizedValue | any | false | | Optional. Contains MonetaryValue objects. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | serviceHealth | any | false | | The health of the service. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerHealthInformation | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | string | false | | The current stage of the value tracker. | | targetDates | [ValueTrackerTargetDate] | false | | The array of TargetDate objects. | ### Enumerated Values | Property | Value | | --- | --- | | stage | [ideation, queued, dataPrepAndModeling, validatingAndDeploying, inProduction, retired, onHold] | ## ValueTrackerTargetDate ``` { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | date | string(date-time) | true | | The date of the target. | | stage | string | true | | The name of the target stage. | ### Enumerated Values | Property | Value | | --- | --- | | stage | [ideation, queued, dataPrepAndModeling, validatingAndDeploying, inProduction, retired, onHold] | ## ValueTrackerValueTemplateClassification ``` { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | ValueTrackerValueTemplateClassificationData | true | | The value tracker value data. | | templateType | string | true | | The value tracker value template type. | ### Enumerated Values | Property | Value | | --- | --- | | templateType | classification | ## ValueTrackerValueTemplateClassificationData ``` { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" } ``` The value tracker value data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyImprovement | number | true | | Accuracy improvement. | | decisionsCount | integer | true | | The estimated number of decisions per year. | | incorrectDecisionCost | number | true | | The estimated cost of an individual incorrect decision. | | incorrectDecisionsCount | integer | true | | The estimated number of incorrect decisions per year. | ## ValueTrackerValueTemplateRegression ``` { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | ValueTrackerValueTemplateRegressionData | true | | The value tracker value data. | | templateType | string | true | | The value tracker value template type. | ### Enumerated Values | Property | Value | | --- | --- | | templateType | regression | ## ValueTrackerValueTemplateRegressionData ``` { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" } ``` The value tracker value data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyImprovement | number | true | | Accuracy improvement. | | decisionsCount | integer | true | | The estimated number of decisions per year. | | targetValue | number | true | | The target value. | --- # User management URL: https://docs.datarobot.com/en/docs/api/reference/public-api/user_management.html > This page describes the endpoints for handling users, groups, and organizations. # User management This page describes the endpoints for handling users, groups, and organizations. ## Retrieve a list of access roles Operation path: `GET /api/v2/accessRoles/` Authentication requirements: `BearerAuth` Retrieve all access roles that the user has access to, optionally filtering by global access roles. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | organizationId | query | string | false | If provided, restricts results to roles only usable by the organization with the provided ID. Ignored when globalRoles=only. | | globalRoles | query | string | true | Whether to include roles that are available to all organizations, along with or instead of organization-specific results. | ### Enumerated Values | Parameter | Value | | --- | --- | | globalRoles | [included, excluded, only] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The access roles for this page of the query.", "items": { "properties": { "custom": { "description": "Whether the Access Role was defined by a user (and is editable) or by the system (and is not)", "type": "boolean" }, "id": { "description": "The ID of the access role.", "type": "string" }, "name": { "description": "The name of the access role.", "type": "string" }, "organizationId": { "description": "The organization that the Access Role is associated with. `null` indicates that the role is global and can be used by all organizations", "type": [ "string", "null" ] }, "permissions": { "description": "An overview of the permissions included in the access role.", "items": { "properties": { "active": { "description": "Whether the entity is usable in the system.", "type": "boolean" }, "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "entity": { "description": "The internal name of the entity.", "enum": [ "APPLICATION", "ARTIFACT", "ARTIFACT_REPOSITORY", "AUDIT_LOG", "COMPUTE_CLUSTERS_MANAGEMENT", "CUSTOM_APPLICATION", "CUSTOM_APPLICATION_SOURCE", "CUSTOM_ENVIRONMENT", "CUSTOM_MODEL", "DATASET_DATA", "DATASET_INFO", "DATA_SOURCE", "DATA_STORE", "DEPLOYMENT", "DYNAMIC_SYSTEM_CONFIG", "ENTITLEMENT_DEFINITION", "ENTITLEMENT_SET", "ENTITLEMENT_SET_LEASE", "EXPERIMENT_CONTAINER", "EXTERNAL_APPLICATION", "GROUP", "MODEL_PACKAGE", "NOTIFICATION_POLICY", "ORGANIZATION", "PREDICTION_ENVIRONMENT", "PREDICTION_SERVER", "PROJECT", "REGISTERED_MODEL", "RISK_MANAGEMENT_FRAMEWORK", "USER", "WORKLOAD" ], "type": "string" }, "entityName": { "description": "The readable name of the entity.", "type": "string" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "required": [ "active", "entity", "entityName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "required": [ "custom", "id", "name", "organizationId", "permissions" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The created role. | AccessRoleListResponse | ## Create a new custom access role Operation path: `POST /api/v2/accessRoles/` Authentication requirements: `BearerAuth` Create a new access role with custom permissions and associate it with a particular organization. ### Body parameter ``` { "properties": { "name": { "description": "The name of the new Access Role. Must be unique among roles in the organization.", "maxLength": 100, "type": "string" }, "organizationId": { "description": "The ID of the organization to associate the role with. Set to `null` to create a global role that is usable by all organizations.", "type": [ "string", "null" ] }, "permissions": { "description": "The permissions for the new Access Role. Each entity type accepts a specific subset of the valid permissions.", "properties": { "APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ARTIFACT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ARTIFACT_REPOSITORY": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "AUDIT_LOG": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "COMPUTE_CLUSTERS_MANAGEMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_APPLICATION_SOURCE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_ENVIRONMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_MODEL": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATASET_DATA": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATASET_INFO": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATA_SOURCE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATA_STORE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DEPLOYMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DYNAMIC_SYSTEM_CONFIG": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_DEFINITION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_SET": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_SET_LEASE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "EXPERIMENT_CONTAINER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "EXTERNAL_APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "GROUP": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "MODEL_PACKAGE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "NOTIFICATION_POLICY": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ORGANIZATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PREDICTION_ENVIRONMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PREDICTION_SERVER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PROJECT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "REGISTERED_MODEL": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "RISK_MANAGEMENT_FRAMEWORK": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "USER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "WORKLOAD": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" } }, "required": [ "name", "organizationId", "permissions" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | AccessRoleCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "custom": { "description": "Whether the Access Role was defined by a user (and is editable) or by the system (and is not)", "type": "boolean" }, "id": { "description": "The ID of the access role.", "type": "string" }, "name": { "description": "The name of the access role.", "type": "string" }, "organizationId": { "description": "The organization that the Access Role is associated with. `null` indicates that the role is global and can be used by all organizations", "type": [ "string", "null" ] }, "permissions": { "description": "An overview of the permissions included in the access role.", "items": { "properties": { "active": { "description": "Whether the entity is usable in the system.", "type": "boolean" }, "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "entity": { "description": "The internal name of the entity.", "enum": [ "APPLICATION", "ARTIFACT", "ARTIFACT_REPOSITORY", "AUDIT_LOG", "COMPUTE_CLUSTERS_MANAGEMENT", "CUSTOM_APPLICATION", "CUSTOM_APPLICATION_SOURCE", "CUSTOM_ENVIRONMENT", "CUSTOM_MODEL", "DATASET_DATA", "DATASET_INFO", "DATA_SOURCE", "DATA_STORE", "DEPLOYMENT", "DYNAMIC_SYSTEM_CONFIG", "ENTITLEMENT_DEFINITION", "ENTITLEMENT_SET", "ENTITLEMENT_SET_LEASE", "EXPERIMENT_CONTAINER", "EXTERNAL_APPLICATION", "GROUP", "MODEL_PACKAGE", "NOTIFICATION_POLICY", "ORGANIZATION", "PREDICTION_ENVIRONMENT", "PREDICTION_SERVER", "PROJECT", "REGISTERED_MODEL", "RISK_MANAGEMENT_FRAMEWORK", "USER", "WORKLOAD" ], "type": "string" }, "entityName": { "description": "The readable name of the entity.", "type": "string" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "required": [ "active", "entity", "entityName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "required": [ "custom", "id", "name", "organizationId", "permissions" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | The created role. | AccessRole | | 422 | Unprocessable Entity | Invalid organizationId; name is not unique within organization; or custom role limit exceeded (100 per organization). | None | ## Delete a custom access role by role ID Operation path: `DELETE /api/v2/accessRoles/{roleId}/` Authentication requirements: `BearerAuth` Delete a custom access role and remove it from any users, groups, or organizations it was assigned to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | roleId | path | string | true | The ID of the access role. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Role successfully deleted. | None | | 422 | Unprocessable Entity | Role is not a custom role. | None | ## Retrieve an access role by role ID Operation path: `GET /api/v2/accessRoles/{roleId}/` Authentication requirements: `BearerAuth` Retrieve details about an access role. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | roleId | path | string | true | The ID of the access role. | ### Example responses > 200 Response ``` { "properties": { "custom": { "description": "Whether the Access Role was defined by a user (and is editable) or by the system (and is not)", "type": "boolean" }, "id": { "description": "The ID of the access role.", "type": "string" }, "name": { "description": "The name of the access role.", "type": "string" }, "organizationId": { "description": "The organization that the Access Role is associated with. `null` indicates that the role is global and can be used by all organizations", "type": [ "string", "null" ] }, "permissions": { "description": "An overview of the permissions included in the access role.", "items": { "properties": { "active": { "description": "Whether the entity is usable in the system.", "type": "boolean" }, "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "entity": { "description": "The internal name of the entity.", "enum": [ "APPLICATION", "ARTIFACT", "ARTIFACT_REPOSITORY", "AUDIT_LOG", "COMPUTE_CLUSTERS_MANAGEMENT", "CUSTOM_APPLICATION", "CUSTOM_APPLICATION_SOURCE", "CUSTOM_ENVIRONMENT", "CUSTOM_MODEL", "DATASET_DATA", "DATASET_INFO", "DATA_SOURCE", "DATA_STORE", "DEPLOYMENT", "DYNAMIC_SYSTEM_CONFIG", "ENTITLEMENT_DEFINITION", "ENTITLEMENT_SET", "ENTITLEMENT_SET_LEASE", "EXPERIMENT_CONTAINER", "EXTERNAL_APPLICATION", "GROUP", "MODEL_PACKAGE", "NOTIFICATION_POLICY", "ORGANIZATION", "PREDICTION_ENVIRONMENT", "PREDICTION_SERVER", "PROJECT", "REGISTERED_MODEL", "RISK_MANAGEMENT_FRAMEWORK", "USER", "WORKLOAD" ], "type": "string" }, "entityName": { "description": "The readable name of the entity.", "type": "string" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "required": [ "active", "entity", "entityName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "required": [ "custom", "id", "name", "organizationId", "permissions" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The requested role. | AccessRole | ## Update a custom access role by role ID Operation path: `PATCH /api/v2/accessRoles/{roleId}/` Authentication requirements: `BearerAuth` Update the name or permissions of a custom access role. ### Body parameter ``` { "properties": { "name": { "description": "The new name for the Access Role. Must be unique among roles in the organization.", "maxLength": 100, "type": "string" }, "permissions": { "description": "The permissions for the new Access Role. Each entity type accepts a specific subset of the valid permissions.", "properties": { "APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ARTIFACT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ARTIFACT_REPOSITORY": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "AUDIT_LOG": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "COMPUTE_CLUSTERS_MANAGEMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_APPLICATION_SOURCE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_ENVIRONMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_MODEL": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATASET_DATA": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATASET_INFO": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATA_SOURCE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATA_STORE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DEPLOYMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DYNAMIC_SYSTEM_CONFIG": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_DEFINITION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_SET": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_SET_LEASE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "EXPERIMENT_CONTAINER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "EXTERNAL_APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "GROUP": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "MODEL_PACKAGE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "NOTIFICATION_POLICY": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ORGANIZATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PREDICTION_ENVIRONMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PREDICTION_SERVER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PROJECT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "REGISTERED_MODEL": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "RISK_MANAGEMENT_FRAMEWORK": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "USER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "WORKLOAD": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | roleId | path | string | true | The ID of the access role. | | body | body | AccessRoleUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "custom": { "description": "Whether the Access Role was defined by a user (and is editable) or by the system (and is not)", "type": "boolean" }, "id": { "description": "The ID of the access role.", "type": "string" }, "name": { "description": "The name of the access role.", "type": "string" }, "organizationId": { "description": "The organization that the Access Role is associated with. `null` indicates that the role is global and can be used by all organizations", "type": [ "string", "null" ] }, "permissions": { "description": "An overview of the permissions included in the access role.", "items": { "properties": { "active": { "description": "Whether the entity is usable in the system.", "type": "boolean" }, "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "entity": { "description": "The internal name of the entity.", "enum": [ "APPLICATION", "ARTIFACT", "ARTIFACT_REPOSITORY", "AUDIT_LOG", "COMPUTE_CLUSTERS_MANAGEMENT", "CUSTOM_APPLICATION", "CUSTOM_APPLICATION_SOURCE", "CUSTOM_ENVIRONMENT", "CUSTOM_MODEL", "DATASET_DATA", "DATASET_INFO", "DATA_SOURCE", "DATA_STORE", "DEPLOYMENT", "DYNAMIC_SYSTEM_CONFIG", "ENTITLEMENT_DEFINITION", "ENTITLEMENT_SET", "ENTITLEMENT_SET_LEASE", "EXPERIMENT_CONTAINER", "EXTERNAL_APPLICATION", "GROUP", "MODEL_PACKAGE", "NOTIFICATION_POLICY", "ORGANIZATION", "PREDICTION_ENVIRONMENT", "PREDICTION_SERVER", "PROJECT", "REGISTERED_MODEL", "RISK_MANAGEMENT_FRAMEWORK", "USER", "WORKLOAD" ], "type": "string" }, "entityName": { "description": "The readable name of the entity.", "type": "string" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "required": [ "active", "entity", "entityName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "required": [ "custom", "id", "name", "organizationId", "permissions" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Role successfully updated. | AccessRole | | 422 | Unprocessable Entity | Name is not unique within organization; or role is not a custom role. | None | ## Retrieve a list of the users using this access role by role ID Operation path: `GET /api/v2/accessRoles/{roleId}/users/` Authentication requirements: `BearerAuth` Retrieve all users that have this access role assigned to them, any of their groups, or their organization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | namePart | query | string | false | Restrict results to users whose name or username partially matches this string. | | roleId | path | string | true | The ID of the access role. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Information about the users on this page of the query.", "items": { "properties": { "firstName": { "description": "The user's first name.", "type": "string" }, "id": { "description": "The user's ID.", "type": "string" }, "isActive": { "description": "The user's status.", "type": "boolean" }, "lastName": { "description": "The user's last name.", "type": "string" }, "organizationId": { "description": "The ID of the user's organization.", "type": [ "string", "null" ] }, "scheduledForDeletion": { "description": "The user's permadelete status.", "type": "boolean" }, "username": { "description": "The user's username.", "type": "string" } }, "required": [ "firstName", "id", "isActive", "lastName", "organizationId", "username" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The list of users assigned to this access role. | AccessRoleUsersListResponse | ## Make multiple User creation requests in bulk Operation path: `POST /api/v2/bulkUserOperations/` Authentication requirements: `BearerAuth` Make multiple User creation requests in bulk from a list. ### Body parameter ``` { "properties": { "add": { "description": "List of User objects to create with associated RequestIds", "items": { "properties": { "attributionId": { "description": "User ID in external marketing systems (SFDC)", "type": "string" }, "company": { "description": "Company where the user works.", "type": "string" }, "country": { "description": "Country where the user lives.", "format": "ISO 3166-1 alpha-2 code", "type": "string" }, "defaultCatalogDatasetSampleSize": { "description": "Size of a sample to propose by default when enabling Fast Registration workflow.", "properties": { "type": { "description": "Sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "expirationDate": { "description": "Datetime, RFC3339, at which the user should expire.", "format": "date-time", "type": "string" }, "externalId": { "description": "User ID in external IdP.", "type": "string" }, "firstName": { "description": "First name of the user being created.", "type": "string" }, "jobTitle": { "description": "Job Title the user has.", "type": "string" }, "language": { "default": "en", "description": "Sets the language in the app for the user, and the language of the invite email, if the user is being invited (i.e. the password is omitted). Value must be a valid ISO-639-1 language code. Available options are: `en`, `ja`, `fr`, `ko`. System's default language will be used if omitted.", "enum": [ "ar_001", "de_DE", "en", "es_419", "fr", "ja", "ko", "pt_BR", "test", "uk_UA" ], "type": "string" }, "lastName": { "description": "Last name of the user being created.", "type": "string" }, "maxGpuWorkers": { "description": "Amount of user's available GPU workers.", "type": "integer" }, "maxIdleWorkers": { "description": "Amount of org workers that the user can utilize when idle.", "type": "integer" }, "maxUploadSize": { "description": "The upper limit for the allowed upload size for this user.", "type": "integer" }, "maxUploadSizeCatalog": { "description": "The upper limit for the allowed upload size in the AI catalog for this user.", "type": "integer" }, "maxWorkers": { "description": "Amount of user's available workers.", "type": "integer" }, "organizationId": { "description": "ID of the organization to add the user to.", "type": "string" }, "password": { "description": "Creates user with this password, if blank sends username invite.", "type": "string" }, "requestId": { "description": "Caller-provided unique identifier for matching user create request to result in response", "type": "string" }, "requireClickthroughAgreement": { "description": "Boolean to require the user to agree to a clickthrough.", "type": "boolean" }, "userType": { "description": "External name for UserType such as AcademicUser, BasicUser, ProUser.", "enum": [ "AcademicUser", "BasicUser", "ProUser", "Covid19TrialUser" ], "type": "string" }, "username": { "description": "Email address of new user.", "type": "string" } }, "required": [ "firstName", "requestId", "userType", "username" ], "type": "object" }, "maxItems": 50, "minItems": 1, "type": "array" } }, "required": [ "add" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserBulkCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "Number of results within the data field.", "minimum": 0, "type": "integer" }, "data": { "description": "List of User object results, corresponding to the creation request.", "items": { "properties": { "data": { "description": "Response message/data from this specific User creation request", "properties": { "message": { "description": "Error or other response message from the creation request if present.", "type": "string" }, "notifyStatus": { "description": "User notification values.", "properties": { "inviteLink": { "description": "The link the user can follow to complete their DR account.", "format": "uri", "type": "string" }, "sentStatus": { "description": "Boolean value whether an invite has been sent to the user or not.", "type": "boolean" } }, "required": [ "sentStatus" ], "type": "object" }, "userId": { "description": "The ID of the user if created.", "type": "string" }, "username": { "description": "The username of the user if created.", "type": "string" } }, "type": "object" }, "requestId": { "description": "Caller-provided unique identifier for matching user create request to result in response", "type": "string" }, "statusCode": { "description": "HTTP status code for this user creation request", "minimum": 100, "type": "integer" } }, "required": [ "data", "requestId", "statusCode" ], "type": "object" }, "maxItems": 50, "type": "array" } }, "required": [ "count", "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | List of responses from each User creation request. | UserBulkCreateResponse | | 422 | Unprocessable Entity | Invalid request payload. | None | ## Get the model deployment access control list by deployment ID Operation path: `GET /api/v2/deployments/{deploymentId}/sharedRoles/` Authentication requirements: `BearerAuth` Get a list of users, groups and organizations who have access to this deployment and their roles. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | deploymentId | path | string | true | The deployment ID. | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | SharingListV2Response | | 404 | Not Found | Either the deployment does not exist or the user does not have permissions to view the deployment. | None | | 422 | Unprocessable Entity | Both username and userId were specified | None | ## Update the model deployment access controls by deployment ID Operation path: `PATCH /api/v2/deployments/{deploymentId}/sharedRoles/` Authentication requirements: `BearerAuth` Set roles for users on this model deployment. ### Body parameter ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "An array of RoleRequest objects. May contain at most 100 such objects.", "items": { "oneOf": [ { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | deploymentId | path | string | true | The deployment ID. | | body | body | SharedRolesUpdateWithGrant | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Roles updated successfully. | None | | 409 | Conflict | The request would leave the project without an owner. | None | | 422 | Unprocessable Entity | One of the users in the request does not exist, or the request is otherwise invalid | None | ## Delete multiple user groups Operation path: `DELETE /api/v2/groups/` Authentication requirements: `BearerAuth` Delete the user groups. ### Body parameter ``` { "properties": { "groups": { "description": "The groups to remove.", "items": { "properties": { "groupId": { "description": "The identifier of the user group.", "type": "string" } }, "required": [ "groupId" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "groups" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserGroupBulkDelete | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 422 | Unprocessable Entity | Multiple user groups found by the identifier. | None | ## List user groups Operation path: `GET /api/v2/groups/` Authentication requirements: `BearerAuth` List the user groups that satisfy the query condition. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | namePart | query | string | false | User groups must contain the substring. | | orgId | query | any | false | List user groups for the organization. If the value is unowned, the route will filter for groups that aren't owned by any organization. | | userId | query | string | false | List user groups that the user is part of. | | excludeUserMembership | query | string | false | Indicates if the search by namePart should exclude the groups that the user with userId is a member. | | orderBy | query | string | false | The order that the results should be retrieved in. Prefix the attribute name with a dash to sort in descending order, e.g. orderBy=-name. | ### Enumerated Values | Parameter | Value | | --- | --- | | excludeUserMembership | [false, False, true, True] | | orderBy | [name, -name, orgName, -orgName, accessRoleName, -accessRoleName] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of user groups that match the query condition.", "items": { "properties": { "accessRoleId": { "description": "The identifier of the access role assigned to the group.", "type": [ "string", "null" ] }, "accessRoleName": { "description": "The name of the access role assigned to the group.", "type": [ "string", "null" ] }, "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions of this user group. Each key is a permission name.", "type": "object" }, "createdBy": { "description": "The identifier of the user who created this user group.", "type": [ "string", "null" ] }, "description": { "description": "The description of this user group.", "type": [ "string", "null" ] }, "email": { "description": "The email of this user group.", "type": [ "string", "null" ] }, "externalId": { "description": "The identifier of this group in the identity provider. Set when the group is managed via SCIM.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "id": { "description": "The identifier of the user group.", "type": "string" }, "maxCustomDeployments": { "description": "The number of maximum custom deployments available for users of this group.", "type": [ "integer", "null" ] }, "maxCustomDeploymentsLimit": { "description": "The upper limit for the number of maximum custom deployments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxEdaWorkers": { "description": "The upper limit for a number of EDA workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "maxRam": { "description": "The upper limit for amount of RAM available to users of this user group.", "type": [ "integer", "null" ] }, "maxUploadCatalogSizeLimit": { "description": "The upper limit for the maximum upload size in the AI catalog. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxUploadSize": { "description": "The maximum allowed upload size for users of this group.", "type": [ "integer", "null" ] }, "maxUploadSizeCatalog": { "description": "The maximum allowed upload size in the AI catalog for users in this group.", "type": [ "integer", "null" ] }, "maxUploadSizeLimit": { "description": "The upper limit for the maximum allowed upload size. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxWorkers": { "description": "The upper limit for a number of workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "membersCount": { "description": "The number of members in this user group.", "type": "integer" }, "name": { "description": "The name of the user group.", "type": "string" }, "orgId": { "description": "The identifier of the organization the user group belongs to.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization this user group belongs to.", "type": [ "string", "null" ] }, "scimIdpName": { "description": "The name of the identity provider managing this group via SCIM, e.g. `okta` or `entra_id`.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "scimManaged": { "description": "Set to `true` if this group is managed by an external identity provider via SCIM. Set to `false` if not.", "type": "boolean", "x-versionadded": "v2.44" } }, "required": [ "accessRoleId", "accessRoleName", "accountPermissions", "createdBy", "description", "email", "externalId", "id", "maxCustomDeployments", "maxCustomDeploymentsLimit", "maxEdaWorkers", "maxRam", "maxUploadCatalogSizeLimit", "maxUploadSize", "maxUploadSizeCatalog", "maxUploadSizeLimit", "maxWorkers", "membersCount", "name", "orgId", "orgName", "scimIdpName", "scimManaged" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL to the next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL to the previous page.", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items that match the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UserGroupsListResponse | ## Create a user group Operation path: `POST /api/v2/groups/` Authentication requirements: `BearerAuth` Create a new user group. ### Body parameter ``` { "properties": { "accessRoleId": { "description": "The identifier of the access role assigned to the group.", "type": "string" }, "description": { "description": "The description of this user group.", "maxLength": 1000, "type": "string" }, "email": { "description": "The email that can be used to contact this user group.", "type": "string" }, "name": { "description": "The name of the new user group. Must be unique.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The organization identifier of this user group.", "type": "string" } }, "required": [ "name", "orgId" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserGroupCreate | false | none | ### Example responses > 201 Response ``` { "properties": { "accessRoleId": { "description": "The identifier of the access role assigned to the group.", "type": [ "string", "null" ] }, "accessRoleName": { "description": "The name of the access role assigned to the group.", "type": [ "string", "null" ] }, "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions of this user group. Each key is a permission name.", "type": "object" }, "createdBy": { "description": "The identifier of the user who created this user group.", "type": [ "string", "null" ] }, "description": { "description": "The description of this user group.", "type": [ "string", "null" ] }, "email": { "description": "The email of this user group.", "type": [ "string", "null" ] }, "externalId": { "description": "The identifier of this group in the identity provider. Set when the group is managed via SCIM.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "id": { "description": "The identifier of the user group.", "type": "string" }, "maxCustomDeployments": { "description": "The number of maximum custom deployments available for users of this group.", "type": [ "integer", "null" ] }, "maxCustomDeploymentsLimit": { "description": "The upper limit for the number of maximum custom deployments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxEdaWorkers": { "description": "The upper limit for a number of EDA workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "maxRam": { "description": "The upper limit for amount of RAM available to users of this user group.", "type": [ "integer", "null" ] }, "maxUploadCatalogSizeLimit": { "description": "The upper limit for the maximum upload size in the AI catalog. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxUploadSize": { "description": "The maximum allowed upload size for users of this group.", "type": [ "integer", "null" ] }, "maxUploadSizeCatalog": { "description": "The maximum allowed upload size in the AI catalog for users in this group.", "type": [ "integer", "null" ] }, "maxUploadSizeLimit": { "description": "The upper limit for the maximum allowed upload size. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxWorkers": { "description": "The upper limit for a number of workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "membersCount": { "description": "The number of members in this user group.", "type": "integer" }, "name": { "description": "The name of the user group.", "type": "string" }, "orgId": { "description": "The identifier of the organization the user group belongs to.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization this user group belongs to.", "type": [ "string", "null" ] }, "scimIdpName": { "description": "The name of the identity provider managing this group via SCIM, e.g. `okta` or `entra_id`.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "scimManaged": { "description": "Set to `true` if this group is managed by an external identity provider via SCIM. Set to `false` if not.", "type": "boolean", "x-versionadded": "v2.44" } }, "required": [ "accessRoleId", "accessRoleName", "accountPermissions", "createdBy", "description", "email", "externalId", "id", "maxCustomDeployments", "maxCustomDeploymentsLimit", "maxEdaWorkers", "maxRam", "maxUploadCatalogSizeLimit", "maxUploadSize", "maxUploadSizeCatalog", "maxUploadSizeLimit", "maxWorkers", "membersCount", "name", "orgId", "orgName", "scimIdpName", "scimManaged" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | UserGroupResponse | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Delete a user group by group ID Operation path: `DELETE /api/v2/groups/{groupId}/` Authentication requirements: `BearerAuth` Delete the user group. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | groupId | path | string | true | The identifier of the user group. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 422 | Unprocessable Entity | Multiple user groups found by the identifier. | None | ## Retrieve a user group by group ID Operation path: `GET /api/v2/groups/{groupId}/` Authentication requirements: `BearerAuth` Retrieve the user group. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | groupId | path | string | true | The identifier of the user group. | ### Example responses > 200 Response ``` { "properties": { "accessRoleId": { "description": "The identifier of the access role assigned to the group.", "type": [ "string", "null" ] }, "accessRoleName": { "description": "The name of the access role assigned to the group.", "type": [ "string", "null" ] }, "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions of this user group. Each key is a permission name.", "type": "object" }, "createdBy": { "description": "The identifier of the user who created this user group.", "type": [ "string", "null" ] }, "description": { "description": "The description of this user group.", "type": [ "string", "null" ] }, "email": { "description": "The email of this user group.", "type": [ "string", "null" ] }, "externalId": { "description": "The identifier of this group in the identity provider. Set when the group is managed via SCIM.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "id": { "description": "The identifier of the user group.", "type": "string" }, "maxCustomDeployments": { "description": "The number of maximum custom deployments available for users of this group.", "type": [ "integer", "null" ] }, "maxCustomDeploymentsLimit": { "description": "The upper limit for the number of maximum custom deployments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxEdaWorkers": { "description": "The upper limit for a number of EDA workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "maxRam": { "description": "The upper limit for amount of RAM available to users of this user group.", "type": [ "integer", "null" ] }, "maxUploadCatalogSizeLimit": { "description": "The upper limit for the maximum upload size in the AI catalog. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxUploadSize": { "description": "The maximum allowed upload size for users of this group.", "type": [ "integer", "null" ] }, "maxUploadSizeCatalog": { "description": "The maximum allowed upload size in the AI catalog for users in this group.", "type": [ "integer", "null" ] }, "maxUploadSizeLimit": { "description": "The upper limit for the maximum allowed upload size. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxWorkers": { "description": "The upper limit for a number of workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "membersCount": { "description": "The number of members in this user group.", "type": "integer" }, "name": { "description": "The name of the user group.", "type": "string" }, "orgId": { "description": "The identifier of the organization the user group belongs to.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization this user group belongs to.", "type": [ "string", "null" ] }, "scimIdpName": { "description": "The name of the identity provider managing this group via SCIM, e.g. `okta` or `entra_id`.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "scimManaged": { "description": "Set to `true` if this group is managed by an external identity provider via SCIM. Set to `false` if not.", "type": "boolean", "x-versionadded": "v2.44" } }, "required": [ "accessRoleId", "accessRoleName", "accountPermissions", "createdBy", "description", "email", "externalId", "id", "maxCustomDeployments", "maxCustomDeploymentsLimit", "maxEdaWorkers", "maxRam", "maxUploadCatalogSizeLimit", "maxUploadSize", "maxUploadSizeCatalog", "maxUploadSizeLimit", "maxWorkers", "membersCount", "name", "orgId", "orgName", "scimIdpName", "scimManaged" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UserGroupResponse | | 422 | Unprocessable Entity | Multiple user groups found by the identifier. | None | ## Update a user group by group ID Operation path: `PATCH /api/v2/groups/{groupId}/` Authentication requirements: `BearerAuth` Update the user group. ### Body parameter ``` { "properties": { "accessRoleId": { "description": "The identifier of the access role to assign to the group.", "type": [ "string", "null" ] }, "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions to set for this user group. Each key is a permission name.", "type": "object" }, "description": { "description": "The description of this user group.", "maxLength": 1000, "type": "string" }, "email": { "description": "The email of this user group.", "type": "string" }, "name": { "description": "The new name for the user group.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The ID of the organization to assign the user group to.", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | groupId | path | string | true | The identifier of the user group. | | body | body | UserGroupUpdate | false | none | ### Example responses > 200 Response ``` { "properties": { "accessRoleId": { "description": "The identifier of the access role assigned to the group.", "type": [ "string", "null" ] }, "accessRoleName": { "description": "The name of the access role assigned to the group.", "type": [ "string", "null" ] }, "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions of this user group. Each key is a permission name.", "type": "object" }, "createdBy": { "description": "The identifier of the user who created this user group.", "type": [ "string", "null" ] }, "description": { "description": "The description of this user group.", "type": [ "string", "null" ] }, "email": { "description": "The email of this user group.", "type": [ "string", "null" ] }, "externalId": { "description": "The identifier of this group in the identity provider. Set when the group is managed via SCIM.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "id": { "description": "The identifier of the user group.", "type": "string" }, "maxCustomDeployments": { "description": "The number of maximum custom deployments available for users of this group.", "type": [ "integer", "null" ] }, "maxCustomDeploymentsLimit": { "description": "The upper limit for the number of maximum custom deployments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxEdaWorkers": { "description": "The upper limit for a number of EDA workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "maxRam": { "description": "The upper limit for amount of RAM available to users of this user group.", "type": [ "integer", "null" ] }, "maxUploadCatalogSizeLimit": { "description": "The upper limit for the maximum upload size in the AI catalog. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxUploadSize": { "description": "The maximum allowed upload size for users of this group.", "type": [ "integer", "null" ] }, "maxUploadSizeCatalog": { "description": "The maximum allowed upload size in the AI catalog for users in this group.", "type": [ "integer", "null" ] }, "maxUploadSizeLimit": { "description": "The upper limit for the maximum allowed upload size. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxWorkers": { "description": "The upper limit for a number of workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "membersCount": { "description": "The number of members in this user group.", "type": "integer" }, "name": { "description": "The name of the user group.", "type": "string" }, "orgId": { "description": "The identifier of the organization the user group belongs to.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization this user group belongs to.", "type": [ "string", "null" ] }, "scimIdpName": { "description": "The name of the identity provider managing this group via SCIM, e.g. `okta` or `entra_id`.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "scimManaged": { "description": "Set to `true` if this group is managed by an external identity provider via SCIM. Set to `false` if not.", "type": "boolean", "x-versionadded": "v2.44" } }, "required": [ "accessRoleId", "accessRoleName", "accountPermissions", "createdBy", "description", "email", "externalId", "id", "maxCustomDeployments", "maxCustomDeploymentsLimit", "maxEdaWorkers", "maxRam", "maxUploadCatalogSizeLimit", "maxUploadSize", "maxUploadSizeCatalog", "maxUploadSizeLimit", "maxWorkers", "membersCount", "name", "orgId", "orgName", "scimIdpName", "scimManaged" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UserGroupResponse | | 422 | Unprocessable Entity | Unable to process the request. | None | ## Remove users by group ID Operation path: `DELETE /api/v2/groups/{groupId}/users/` Authentication requirements: `BearerAuth` Remove users from the group. ### Body parameter ``` { "properties": { "users": { "description": "The users to change membership for.", "items": { "properties": { "username": { "description": "The name of the user.", "type": "string" } }, "required": [ "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "users" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | groupId | path | string | true | The identifier of the user group. | | body | body | ModifyUsersInGroup | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 422 | Unprocessable Entity | The user was not found. | None | ## List users by group ID Operation path: `GET /api/v2/groups/{groupId}/users/` Authentication requirements: `BearerAuth` List users in the group. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. | | namePart | query | string | false | User groups must contain the substring. | | orderBy | query | string | false | The order that the results should be retrieved in. Prefix the attribute name with a dash to sort in descending order, e.g. orderBy=-username. | | isActive | query | string | false | If specified, filters results by account activation status. | | isAdmin | query | string | false | If specified, filters results to admin users (system and org) or non-admin users. | | groupId | path | string | true | The identifier of the user group. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [username, -username, userGroup, -userGroup, lastName, -lastName, firstName, -firstName, status, -status, expirationDate, -expirationDate] | | isActive | [false, False, true, True] | | isAdmin | [false, False, true, True] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of users in the group.", "items": { "properties": { "expirationDate": { "description": "The expiration date of the user account, if applicable.", "type": [ "string", "null" ] }, "firstName": { "description": "The first name of the user.", "type": [ "string", "null" ] }, "lastName": { "description": "The last name of the user.", "type": [ "string", "null" ] }, "organization": { "description": "The name of the organization the user is part of.", "type": [ "string", "null" ] }, "scheduledForDeletion": { "description": "If the user is scheduled for deletion. Will be returned when an appropriate FF is enabled.", "type": "boolean" }, "status": { "description": "The status of the user, `active` if the has been activated, `inactive` otherwise.", "enum": [ "active", "inactive" ], "type": [ "string", "null" ] }, "userId": { "description": "The identifier of the user.", "type": [ "string", "null" ] }, "username": { "description": "The name of the user.", "type": "string" } }, "required": [ "firstName", "lastName", "organization", "status", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL to the next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL to the previous page.", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items that match the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ListUsersInGroupResponse | ## Add users by group ID Operation path: `POST /api/v2/groups/{groupId}/users/` Authentication requirements: `BearerAuth` Add users to the group. ### Body parameter ``` { "properties": { "users": { "description": "The users to change membership for.", "items": { "properties": { "username": { "description": "The name of the user.", "type": "string" } }, "required": [ "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "users" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | groupId | path | string | true | The identifier of the user group. | | body | body | ModifyUsersInGroup | false | none | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of users in the group.", "items": { "properties": { "expirationDate": { "description": "The expiration date of the user account, if applicable.", "type": [ "string", "null" ] }, "firstName": { "description": "The first name of the user.", "type": [ "string", "null" ] }, "lastName": { "description": "The last name of the user.", "type": [ "string", "null" ] }, "organization": { "description": "The name of the organization the user is part of.", "type": [ "string", "null" ] }, "scheduledForDeletion": { "description": "If the user is scheduled for deletion. Will be returned when an appropriate FF is enabled.", "type": "boolean" }, "status": { "description": "The status of the user, `active` if the has been activated, `inactive` otherwise.", "enum": [ "active", "inactive" ], "type": [ "string", "null" ] }, "userId": { "description": "The identifier of the user.", "type": [ "string", "null" ] }, "username": { "description": "The name of the user.", "type": "string" } }, "required": [ "firstName", "lastName", "organization", "status", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" } }, "required": [ "count", "data" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | AddUsersToGroupResponse | | 422 | Unprocessable Entity | Unable to process the request, or if the user does not belong to the group's organization, or if one is already in the maximum number of groups, or if the user is not found. | None | ## List organizations Operation path: `GET /api/v2/organizations/` Authentication requirements: `BearerAuth` List organizations available in the system. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | true | At most this many results are returned. | | id | query | array[string] | false | Query for organizations with these IDs. | | namePart | query | string | false | Only return the organizations whose names contain the given substring. | | includeDeleted | query | boolean | true | Allows the ability to retrieve deleted organizations. | | includePermadeleted | query | boolean | true | Allows the ability to retrieve deleted and permadeleted organizations. | | deletedOnly | query | boolean | true | Allows the ability to retrieve only deleted and permadeleted organizations. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items on the current page.", "type": "integer" }, "data": { "description": "The list of the requested organizations.", "items": { "properties": { "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions available to this organization. This will only be present if the corresponding feature flag is set.", "type": "object" }, "activeUsersCount": { "description": "The number of active users for this organization. This excludes deactivated users, aliased DataRobot accounts (in SaaS), and users with NON_BUILDER_USER seat license.", "type": "integer", "x-versionadded": "v2.41" }, "agreementStatus": { "description": "The status of the organization's agreement to the terms of DataRobot.", "enum": [ "NEEDED", "AGREED", "N/A" ], "type": "string" }, "customAppAutoStopSeconds": { "description": "Per-organization override for the cluster-default custom application auto-stop timeout (in seconds). When null, the cluster default is used.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.43" }, "datasetRefreshJobLimit": { "description": "How many enabled dataset refresh jobs are allowed per-dataset for this organization.", "type": [ "integer", "null" ] }, "datasetRefreshJobUserLimit": { "description": "How many enabled dataset refresh jobs are allowed per-user for this organization.", "type": [ "integer", "null" ] }, "defaultUserMaxGpuWorkers": { "description": "Maximum number of concurrent GPU workers assigned to a newly created user of this organization.", "type": [ "integer", "null" ] }, "defaultUserMaxWorkers": { "description": "Maximum number of concurrent workers assigned to a newly created user of this organization.", "type": [ "integer", "null" ] }, "deleted": { "description": "Indicates whether the organization is soft-deleted.", "type": [ "boolean", "null" ] }, "deletedAt": { "description": "Indicates when the organization was soft-deleted.", "format": "date-time", "type": [ "string", "null" ] }, "deletedBy": { "description": "Indicates who has soft-deleted the organization.", "type": [ "string", "null" ] }, "desiredCustomModelContainerSize": { "description": "The desired custom-model memory size. This will only be present if the corresponding feature flag is set.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "enableSso": { "description": "If the SSO is enabled for this organization. This will only be present if the corresponding feature flag is set.", "type": "boolean" }, "groupsCount": { "description": "The number of user groups belonging to this organization.", "type": "integer", "x-versionadded": "v2.17" }, "id": { "description": "The organization identifier.", "type": "string" }, "inactiveUsersCount": { "description": "The number of inactive users for this organization.", "type": "integer", "x-versionadded": "v2.21" }, "maxCodespaceFileUploadSize": { "description": "Maximum file upload size (MB) for Codespaces.", "maximum": 50000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.4" }, "maxCodespaces": { "description": "Maximum number of codespaces available to this organization.", "type": [ "integer", "null" ] }, "maxComputeServerlessPredictionApi": { "description": "The number of maximum allowed compute on a serverless platform for this organization", "minimum": 1, "type": [ "integer", "null" ] }, "maxConcurrentBatchPredictionsJob": { "description": "Maximum number of concurrent batch prediction jobs to this organization", "maximum": 10, "minimum": 1, "type": [ "integer", "null" ] }, "maxConcurrentNotebooks": { "description": "Maximum number of concurrent notebooks available to this organization.", "type": [ "integer", "null" ] }, "maxCustomModelContainerSize": { "description": "The maximum memory that might be allocated by the custom-model. This will only be present if the corresponding feature flag is set.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "maxCustomModelReplicasPerDeployment": { "description": "A fixed number of replicas that will be set for the given custom-model. This will only be present if the corresponding feature flag is set.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "maxCustomModelReplicasPerDeploymentForBatchPredictions": { "description": "The maximum custom inference model replicas per deployment for batch predictions. This will only be present if the corresponding feature flag is set.", "maximum": 8, "minimum": 1, "type": [ "integer", "null" ] }, "maxDailyBatchesPerDeployment": { "description": "The maximum number of monitoring batches that can be created per day on deployments that belong to this organization.", "minimum": 1, "type": [ "integer", "null" ] }, "maxDeploymentLimit": { "description": "The absolute limit on the number of deployments an organization is allowed to create. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "maxEdaWorkers": { "description": "Maximum number of EDA workers assigned available to this organization.", "type": [ "integer", "null" ] }, "maxEnabledNotebookSchedules": { "description": "Maximum number of enabled notebook schedules for this organization.", "minimum": 1, "type": [ "integer", "null" ] }, "maxExecutionEnvironments": { "description": "The upper limit for the number of maximum execution environments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "minimum": 0, "type": [ "integer", "null" ] }, "maxGpuWorkers": { "description": "Maximum number of concurrent GPU workers available to this organization.", "type": [ "integer", "null" ] }, "maxLlmApiCalls": { "description": "Maximum number of LLM calls.", "minimum": 1, "type": [ "integer", "null" ] }, "maxNotebookMachineSize": { "description": "Maximum size of notebook machine available to this organization.", "enum": [ "L", "M", "S", "XL", "XS", "XXL" ], "type": [ "string", "null" ] }, "maxNotebookSchedulingWorkers": { "description": "Maximum number of concurrently running notebooks schedules for this organization.", "maximum": 20, "minimum": 1, "type": [ "integer", "null" ] }, "maxPipelineModuleRuntimes": { "description": "Maximum number of Pipeline module runtimes available to this organization.", "type": [ "integer", "null" ] }, "maxUploadSize": { "description": "Maximum size for file uploads (MB). This will only be present if the corresponding feature flag is set.", "type": [ "integer", "null" ] }, "maxUploadSizeCatalog": { "description": "Maximum size for catalog file uploads (MB). This will only be present if the corresponding feature flag is set.", "type": [ "integer", "null" ] }, "maxVectorDatabases": { "description": "Maximum number of local vector databases.", "minimum": 1, "type": [ "integer", "null" ] }, "maxWorkers": { "description": "Maximum number of concurrent workers available to this organization.", "type": [ "integer", "null" ] }, "maximumActiveUsers": { "description": "The limit for active users for this organization. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "membersCount": { "description": "The number of members in this organization.", "type": "integer" }, "mlopsEventStorageRetentionDays": { "default": 0, "description": "The number of days to keep MLOps events in storage. Events with older timestamps will be removed.", "minimum": 0, "type": "integer" }, "name": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "notebooksSoftDeletionRetentionPeriod": { "description": "Retention window for soft-deleted notebooks and respective resources before NBX services hard-delete them.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "ormVersion": { "description": "On-demand Resource Manager (prediction service provisioning) version.", "enum": [ "v1", "v2", "v3", "CCM" ], "type": "string" }, "permadeleted": { "description": "Indicates whether the organization is permadeleted.", "type": [ "boolean", "null" ] }, "permadeletedAt": { "description": "Indicates when the organization was permadeleted.", "format": "date-time", "type": [ "string", "null" ] }, "permadeletedBy": { "description": "Indicates who has permadeleted the organization.", "type": [ "string", "null" ] }, "prepaidDeploymentLimit": { "description": "The number of deployments an organization can create under their contract. When it is reached more deployments can be made, but the user will be warned that this will result in a higher billing. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "restrictedSharing": { "description": "Whether sharing is only allowed within the organization. This will only be present if the corresponding feature flag is set.", "type": "boolean" }, "snapshotLimit": { "description": "The number of snapshots allowed for a dataset for this organization.", "type": [ "integer", "null" ] }, "supportEmail": { "description": "The support email of the organization.", "type": [ "string", "null" ] } }, "required": [ "activeUsersCount", "agreementStatus", "datasetRefreshJobLimit", "datasetRefreshJobUserLimit", "defaultUserMaxGpuWorkers", "defaultUserMaxWorkers", "groupsCount", "id", "inactiveUsersCount", "maxDeploymentLimit", "maxEdaWorkers", "maxExecutionEnvironments", "maxGpuWorkers", "maxLlmApiCalls", "maxPipelineModuleRuntimes", "maxVectorDatabases", "maxWorkers", "maximumActiveUsers", "membersCount", "name", "ormVersion", "prepaidDeploymentLimit", "snapshotLimit", "supportEmail" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OrganizationListResponse | ## Retrieve an organization by organization ID Operation path: `GET /api/v2/organizations/{organizationId}/` Authentication requirements: `BearerAuth` Retrieve the organization details. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | organizationId | path | string | true | Organization ID | ### Example responses > 200 Response ``` { "properties": { "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions available to this organization. This will only be present if the corresponding feature flag is set.", "type": "object" }, "activeUsersCount": { "description": "The number of active users for this organization. This excludes deactivated users, aliased DataRobot accounts (in SaaS), and users with NON_BUILDER_USER seat license.", "type": "integer", "x-versionadded": "v2.41" }, "agreementStatus": { "description": "The status of the organization's agreement to the terms of DataRobot.", "enum": [ "NEEDED", "AGREED", "N/A" ], "type": "string" }, "customAppAutoStopSeconds": { "description": "Per-organization override for the cluster-default custom application auto-stop timeout (in seconds). When null, the cluster default is used.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.43" }, "datasetRefreshJobLimit": { "description": "How many enabled dataset refresh jobs are allowed per-dataset for this organization.", "type": [ "integer", "null" ] }, "datasetRefreshJobUserLimit": { "description": "How many enabled dataset refresh jobs are allowed per-user for this organization.", "type": [ "integer", "null" ] }, "defaultUserMaxGpuWorkers": { "description": "Maximum number of concurrent GPU workers assigned to a newly created user of this organization.", "type": [ "integer", "null" ] }, "defaultUserMaxWorkers": { "description": "Maximum number of concurrent workers assigned to a newly created user of this organization.", "type": [ "integer", "null" ] }, "deleted": { "description": "Indicates whether the organization is soft-deleted.", "type": [ "boolean", "null" ] }, "deletedAt": { "description": "Indicates when the organization was soft-deleted.", "format": "date-time", "type": [ "string", "null" ] }, "deletedBy": { "description": "Indicates who has soft-deleted the organization.", "type": [ "string", "null" ] }, "desiredCustomModelContainerSize": { "description": "The desired custom-model memory size. This will only be present if the corresponding feature flag is set.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "enableSso": { "description": "If the SSO is enabled for this organization. This will only be present if the corresponding feature flag is set.", "type": "boolean" }, "groupsCount": { "description": "The number of user groups belonging to this organization.", "type": "integer", "x-versionadded": "v2.17" }, "id": { "description": "The organization identifier.", "type": "string" }, "inactiveUsersCount": { "description": "The number of inactive users for this organization.", "type": "integer", "x-versionadded": "v2.21" }, "maxCodespaceFileUploadSize": { "description": "Maximum file upload size (MB) for Codespaces.", "maximum": 50000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.4" }, "maxCodespaces": { "description": "Maximum number of codespaces available to this organization.", "type": [ "integer", "null" ] }, "maxComputeServerlessPredictionApi": { "description": "The number of maximum allowed compute on a serverless platform for this organization", "minimum": 1, "type": [ "integer", "null" ] }, "maxConcurrentBatchPredictionsJob": { "description": "Maximum number of concurrent batch prediction jobs to this organization", "maximum": 10, "minimum": 1, "type": [ "integer", "null" ] }, "maxConcurrentNotebooks": { "description": "Maximum number of concurrent notebooks available to this organization.", "type": [ "integer", "null" ] }, "maxCustomModelContainerSize": { "description": "The maximum memory that might be allocated by the custom-model. This will only be present if the corresponding feature flag is set.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "maxCustomModelReplicasPerDeployment": { "description": "A fixed number of replicas that will be set for the given custom-model. This will only be present if the corresponding feature flag is set.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "maxCustomModelReplicasPerDeploymentForBatchPredictions": { "description": "The maximum custom inference model replicas per deployment for batch predictions. This will only be present if the corresponding feature flag is set.", "maximum": 8, "minimum": 1, "type": [ "integer", "null" ] }, "maxDailyBatchesPerDeployment": { "description": "The maximum number of monitoring batches that can be created per day on deployments that belong to this organization.", "minimum": 1, "type": [ "integer", "null" ] }, "maxDeploymentLimit": { "description": "The absolute limit on the number of deployments an organization is allowed to create. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "maxEdaWorkers": { "description": "Maximum number of EDA workers assigned available to this organization.", "type": [ "integer", "null" ] }, "maxEnabledNotebookSchedules": { "description": "Maximum number of enabled notebook schedules for this organization.", "minimum": 1, "type": [ "integer", "null" ] }, "maxExecutionEnvironments": { "description": "The upper limit for the number of maximum execution environments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "minimum": 0, "type": [ "integer", "null" ] }, "maxGpuWorkers": { "description": "Maximum number of concurrent GPU workers available to this organization.", "type": [ "integer", "null" ] }, "maxLlmApiCalls": { "description": "Maximum number of LLM calls.", "minimum": 1, "type": [ "integer", "null" ] }, "maxNotebookMachineSize": { "description": "Maximum size of notebook machine available to this organization.", "enum": [ "L", "M", "S", "XL", "XS", "XXL" ], "type": [ "string", "null" ] }, "maxNotebookSchedulingWorkers": { "description": "Maximum number of concurrently running notebooks schedules for this organization.", "maximum": 20, "minimum": 1, "type": [ "integer", "null" ] }, "maxPipelineModuleRuntimes": { "description": "Maximum number of Pipeline module runtimes available to this organization.", "type": [ "integer", "null" ] }, "maxUploadSize": { "description": "Maximum size for file uploads (MB). This will only be present if the corresponding feature flag is set.", "type": [ "integer", "null" ] }, "maxUploadSizeCatalog": { "description": "Maximum size for catalog file uploads (MB). This will only be present if the corresponding feature flag is set.", "type": [ "integer", "null" ] }, "maxVectorDatabases": { "description": "Maximum number of local vector databases.", "minimum": 1, "type": [ "integer", "null" ] }, "maxWorkers": { "description": "Maximum number of concurrent workers available to this organization.", "type": [ "integer", "null" ] }, "maximumActiveUsers": { "description": "The limit for active users for this organization. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "membersCount": { "description": "The number of members in this organization.", "type": "integer" }, "mlopsEventStorageRetentionDays": { "default": 0, "description": "The number of days to keep MLOps events in storage. Events with older timestamps will be removed.", "minimum": 0, "type": "integer" }, "name": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "notebooksSoftDeletionRetentionPeriod": { "description": "Retention window for soft-deleted notebooks and respective resources before NBX services hard-delete them.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "ormVersion": { "description": "On-demand Resource Manager (prediction service provisioning) version.", "enum": [ "v1", "v2", "v3", "CCM" ], "type": "string" }, "permadeleted": { "description": "Indicates whether the organization is permadeleted.", "type": [ "boolean", "null" ] }, "permadeletedAt": { "description": "Indicates when the organization was permadeleted.", "format": "date-time", "type": [ "string", "null" ] }, "permadeletedBy": { "description": "Indicates who has permadeleted the organization.", "type": [ "string", "null" ] }, "prepaidDeploymentLimit": { "description": "The number of deployments an organization can create under their contract. When it is reached more deployments can be made, but the user will be warned that this will result in a higher billing. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "restrictedSharing": { "description": "Whether sharing is only allowed within the organization. This will only be present if the corresponding feature flag is set.", "type": "boolean" }, "snapshotLimit": { "description": "The number of snapshots allowed for a dataset for this organization.", "type": [ "integer", "null" ] }, "supportEmail": { "description": "The support email of the organization.", "type": [ "string", "null" ] } }, "required": [ "activeUsersCount", "agreementStatus", "datasetRefreshJobLimit", "datasetRefreshJobUserLimit", "defaultUserMaxGpuWorkers", "defaultUserMaxWorkers", "groupsCount", "id", "inactiveUsersCount", "maxDeploymentLimit", "maxEdaWorkers", "maxExecutionEnvironments", "maxGpuWorkers", "maxLlmApiCalls", "maxPipelineModuleRuntimes", "maxVectorDatabases", "maxWorkers", "maximumActiveUsers", "membersCount", "name", "ormVersion", "prepaidDeploymentLimit", "snapshotLimit", "supportEmail" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OrganizationRetrieve | ## List organization jobs by organization ID Operation path: `GET /api/v2/organizations/{organizationId}/jobs/` Authentication requirements: `BearerAuth` List currently running jobs belonging to this organization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. If 0, all results. | | organizationId | path | string | true | Organization ID | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items on the current page.", "type": "integer" }, "data": { "description": "The list of the requested jobs.", "items": { "properties": { "jobId": { "description": "The identifier of the submitted job (unique within a project)", "type": "string" }, "projectId": { "description": "The project identifier associated with this job", "type": "string" }, "projectOwnerUserId": { "description": "Identifier the user that owns the project associated with the job", "type": "string" }, "projectOwnerUsername": { "description": "The username of the user that owns the project associated with the job", "type": "string" }, "userId": { "description": "Identifies the user that submitted the job", "type": "string" }, "username": { "description": "The username of the user that submitted the job", "type": "string" } }, "required": [ "jobId", "projectId", "projectOwnerUserId", "projectOwnerUsername", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page.", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OrganizationJobListResponse | ## List organization users by organization ID Operation path: `GET /api/v2/organizations/{organizationId}/users/` Authentication requirements: `BearerAuth` List memberships in this organization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | true | This many results will be skipped. | | limit | query | integer | true | At most this many results are returned. If 0, all results. | | ids | query | array[string] | false | Query for users with these IDs. | | organizationId | path | string | true | Organization ID | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items on the current page.", "type": "integer" }, "data": { "description": "The list of users in the organization.", "items": { "properties": { "accessRoleIds": { "description": "The list of access role IDs assigned to the user.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "activated": { "description": "Whether the organization user is activated.", "type": "boolean" }, "expirationDate": { "description": "User expiration date", "format": "date-time", "type": [ "string", "null" ] }, "externalId": { "description": "The external user ID.", "type": "string" }, "firstName": { "description": "The first name of the user being created.", "type": [ "string", "null" ] }, "id": { "description": "The organization user identifier.", "type": "string" }, "isAliasedDatarobotAccount": { "description": "Determines whether or not the username uses aliasing in the email address for DataRobot accounts.", "type": "boolean", "x-versionadded": "v2.38" }, "lastName": { "description": "The last name of the user being created.", "type": [ "string", "null" ] }, "maxWorkers": { "description": "Maximum number of concurrent workers available to this user.", "type": "integer" }, "orgAdmin": { "description": "Is this user should be marked as an organizational admin", "type": "boolean", "x-versionadded": "v2.24" }, "organizationId": { "description": "The organization identifier.", "type": "string" }, "scheduledForDeletion": { "description": "Whether the user is scheduled for deletion. Only set when a specific feature flag is configured.", "type": "boolean" }, "username": { "description": "The username of the user to add to the organization.", "type": "string" } }, "required": [ "activated", "expirationDate", "externalId", "firstName", "id", "isAliasedDatarobotAccount", "lastName", "maxWorkers", "orgAdmin", "organizationId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OrganizationUserListResponse | ## Add user by organization ID Operation path: `POST /api/v2/organizations/{organizationId}/users/` Authentication requirements: `BearerAuth` Add user to an existing organization. A user can only be part of one organization. ### Body parameter ``` { "properties": { "accessRoleIds": { "description": "List of access role ids assigned to the user being created", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "create": { "default": false, "description": "Whether to create the user if not found.", "type": "boolean" }, "firstName": { "description": "The first name of the user being created.", "maxLength": 100, "type": "string" }, "language": { "description": "Language selection for the user being created.", "enum": [ "ar_001", "de_DE", "en", "es_419", "fr", "ja", "ko", "pt_BR", "test", "uk_UA" ], "type": "string" }, "lastName": { "description": "The last name of the user being created.", "maxLength": 100, "type": "string" }, "orgAdmin": { "default": false, "description": "Is this user should be marked as an organizational admin", "type": "boolean", "x-versionadded": "v2.24" }, "password": { "description": "Password for the user being created. Should be specified if `create` set to `true`", "maxLength": 512, "minLength": 8, "type": "string" }, "requireClickthrough": { "default": false, "description": "Require the user being created to agree to a clickthrough", "type": "boolean" }, "username": { "description": "The username of the user to add to the organization.", "type": "string" } }, "required": [ "username" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | organizationId | path | string | true | Organization ID | | body | body | OrganizationUser | false | none | ### Example responses > 201 Response ``` { "properties": { "userId": { "description": "The ID of the user added to the organization.", "type": "string" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | OrganizationUserCreatedResponse | | 422 | Unprocessable Entity | Invalid data. | None | ## Retrieve a user by organization ID Operation path: `GET /api/v2/organizations/{organizationId}/users/{userId}/` Authentication requirements: `BearerAuth` Retrieve the user from this organization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | organizationId | path | string | true | The organization the user is in | | userId | path | string | true | The user ID. | ### Example responses > 200 Response ``` { "properties": { "accessRoleIds": { "description": "The list of access role IDs assigned to the user.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "activated": { "description": "Whether the organization user is activated.", "type": "boolean" }, "expirationDate": { "description": "User expiration date", "format": "date-time", "type": [ "string", "null" ] }, "externalId": { "description": "The external user ID.", "type": "string" }, "firstName": { "description": "The first name of the user being created.", "type": [ "string", "null" ] }, "id": { "description": "The organization user identifier.", "type": "string" }, "isAliasedDatarobotAccount": { "description": "Determines whether or not the username uses aliasing in the email address for DataRobot accounts.", "type": "boolean", "x-versionadded": "v2.38" }, "lastName": { "description": "The last name of the user being created.", "type": [ "string", "null" ] }, "maxWorkers": { "description": "Maximum number of concurrent workers available to this user.", "type": "integer" }, "orgAdmin": { "description": "Is this user should be marked as an organizational admin", "type": "boolean", "x-versionadded": "v2.24" }, "organizationId": { "description": "The organization identifier.", "type": "string" }, "scheduledForDeletion": { "description": "Whether the user is scheduled for deletion. Only set when a specific feature flag is configured.", "type": "boolean" }, "username": { "description": "The username of the user to add to the organization.", "type": "string" } }, "required": [ "activated", "expirationDate", "externalId", "firstName", "id", "isAliasedDatarobotAccount", "lastName", "maxWorkers", "orgAdmin", "organizationId", "username" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | OrganizationUserResponse | ## Patch an organization user by organization ID Operation path: `PATCH /api/v2/organizations/{organizationId}/users/{userId}/` Authentication requirements: `BearerAuth` Patch organization's user. Only system or the organization admin can perform this operation. ### Body parameter ``` { "properties": { "accessRoleIds": { "description": "The list of access role IDs assigned to the user.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "activated": { "description": "Whether the organization user is activated.", "type": "boolean" }, "expirationDate": { "description": "The user expiration date.", "format": "date-time", "type": "string" }, "maxWorkers": { "description": "The user max workers.", "exclusiveMinimum": 0, "type": "integer" }, "orgAdmin": { "description": "Mark the user as an organizational admin.", "type": "boolean" }, "organizationId": { "description": "Organization to move the user into (destination org).", "type": "string" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | organizationId | path | string | true | The organization the user is in | | userId | path | string | true | The user ID. | | body | body | OrganizationUserPatch | false | none | ### Example responses > 204 Response ``` { "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | Empty | | 403 | Forbidden | Invalid permissions. | None | | 422 | Unprocessable Entity | Invalid data. | None | ## Get usage resources Operation path: `GET /api/v2/tenantUsageResources/` Authentication requirements: `BearerAuth` A unified route to retrieve aggregated resource usage over some period of time. For OrgAdmins, tenant_id is required. For SystemAdmins, tenant_id is optional. If tenant_id is omitted for SystemAdmins, returns usage for all tenants. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date) | true | The start date for the usage data. | | end | query | string(date) | true | The end date for the usage data. | | tenantId | query | string | false | The ID of the tenant. Optional for System Admins, required for Org Admins. | | userId | query | string | false | Only usage performed by this user will be retrieved. | | workloadCategory | query | string | false | The workload category. | ### Enumerated Values | Parameter | Value | | --- | --- | | workloadCategory | [all, All, ALL, gpuUsage, GpuUsage, GPU_USAGE, llmUsage, LlmUsage, LLM_USAGE, cpuUsage, CpuUsage, CPU_USAGE] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The usage records.", "items": { "properties": { "cloud": { "description": "Cloud provider where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "cloudRegion": { "description": "Cloud region where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "cost": { "description": "Cost for the usage", "type": "number", "x-versionadded": "v2.41" }, "currency": { "default": "USD", "description": "The price currency.", "enum": [ "usd", "Usd", "USD", "jpy", "Jpy", "JPY" ], "type": "string", "x-versionadded": "v2.41" }, "instanceType": { "description": "Instance type where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "price": { "description": "Numeric price for workload", "type": "number", "x-versionadded": "v2.41" }, "priceUnit": { "description": "The unit of price calculation.", "type": "string", "x-versionadded": "v2.41" }, "resourceBundleId": { "description": "The identifier of the resource bundle", "type": "string" }, "resourceBundleName": { "description": "Human readable name of the resource bundle", "type": "string" }, "usageUnit": { "description": "The unit of measurement for the usage value", "type": "string" }, "usageValue": { "description": "Numeric measure of the usage", "type": "number" }, "workloadId": { "description": "The identifier of the workload", "type": "string" }, "workloadName": { "description": "Human readable name of the workload", "type": "string" } }, "required": [ "resourceBundleId", "resourceBundleName", "usageUnit", "usageValue", "workloadId", "workloadName" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UsageResponse | ## Get active tenants Operation path: `GET /api/v2/tenantUsageResources/activeTenants/` Authentication requirements: `BearerAuth` A route to retrieve active tenants over a specified time period. Only admins can perform this operation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date) | true | The start date for the usage data. | | end | query | string(date) | true | The end date for the usage data. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The active organizations.", "items": { "properties": { "id": { "description": "The identifier of the organization", "type": "string" }, "name": { "description": "The name of the organization", "type": "string" }, "tenantId": { "description": "The identifier of the tenant", "type": "string" } }, "required": [ "id", "name", "tenantId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ActiveTenantsResponse | ## Get active users Operation path: `GET /api/v2/tenantUsageResources/activeUsers/` Authentication requirements: `BearerAuth` A unified route to retrieve active users over a specified time period. For OrgAdmins, tenant_id is required. For SystemAdmins, tenant_id is optional. If tenant_id is omitted for SystemAdmins with feature flag, returns all users in the cluster. If tenant_id is provided, returns only users in that tenant. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date) | true | The start date for the usage data. | | end | query | string(date) | true | The end date for the usage data. | | tenantId | query | string | false | The ID of the tenant. Optional for System Admins, required for Org Admins. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The active users.", "items": { "properties": { "fullname": { "description": "The display name of the user", "type": "string" }, "userId": { "description": "The identifier of the user", "type": "string" }, "username": { "description": "The username of the user", "type": "string" } }, "required": [ "fullname", "userId", "username" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ActiveUsersResponse | ## Get the available resource categories Operation path: `GET /api/v2/tenantUsageResources/categories/` Authentication requirements: `BearerAuth` A route to retrieve available resource categories for tenant usage. ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The resource categories.", "items": { "properties": { "id": { "description": "The identifier of the resource category", "type": "string" }, "name": { "description": "The display name of the resource category", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ResourceCategoriesResponse | ## Export usage Operation path: `GET /api/v2/tenantUsageResources/export/` Authentication requirements: `BearerAuth` A unified route to export resource usage over a specified time period. For OrgAdmins, tenant_id is required. For SystemAdmins, tenant_id is optional. If tenant_id is omitted for SystemAdmins, returns usage for all tenants. If tenant_id is provided, returns usage only for that tenant. If user_id is provided, returns usage only for that user. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date) | true | The start date for the usage data. | | end | query | string(date) | true | The end date for the usage data. | | userId | query | string | false | Only usage performed by this user will be retrieved. | | workloadCategory | query | string | false | The workload category. | | tenantId | query | string | false | The ID of the tenant. Optional for System Admins, required for Org Admins. | ### Enumerated Values | Parameter | Value | | --- | --- | | workloadCategory | [all, All, ALL, gpuUsage, GpuUsage, GPU_USAGE, llmUsage, LlmUsage, LLM_USAGE, cpuUsage, CpuUsage, CPU_USAGE] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The usage records.", "items": { "properties": { "amount": { "description": "The cost for the usage.", "type": "number", "x-versionadded": "v2.42" }, "cloud": { "description": "The cloud provider where the workload was executed.", "type": "string", "x-versionadded": "v2.41" }, "cloudRegion": { "description": "The cloud region where the workload was executed.", "type": "string", "x-versionadded": "v2.41" }, "cost": { "description": "The numeric price for the workload.", "type": "number", "x-versionadded": "v2.41" }, "costUnit": { "description": "The unit of price calculation.", "type": "string", "x-versionadded": "v2.42" }, "currency": { "default": "USD", "description": "The price currency.", "enum": [ "usd", "Usd", "USD", "jpy", "Jpy", "JPY" ], "type": "string", "x-versionadded": "v2.41" }, "resourceBundleId": { "description": "The identifier of the resource bundle", "type": "string" }, "resourceBundleName": { "description": "Human readable name of the resource bundle", "type": "string" }, "usageUnit": { "description": "The unit of measurement for the usage value", "type": "string" }, "usageValue": { "description": "Numeric measure of the usage", "type": "number" }, "userDisplayName": { "description": "The display name of the user", "type": "string" }, "userId": { "description": "The identifier of the user", "type": "string" }, "workloadId": { "description": "The identifier of the workload", "type": "string" }, "workloadName": { "description": "Human readable name of the workload", "type": "string" } }, "required": [ "resourceBundleName", "usageUnit", "usageValue", "userDisplayName", "userId", "workloadId", "workloadName" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ExportResponse | ## Get CPU/GPU resource utilization Operation path: `GET /api/v2/tenants/utilizationResources/` Authentication requirements: `BearerAuth` A route to retrieve aggregated CPU/GPU resources utilization for certain resource type. ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The available resource types.", "items": { "properties": { "id": { "description": "Resource type: CPU/GPU.", "type": "string" }, "name": { "description": "Resource type name: CPU usage/GPU usage.", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 2, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | TenantResourceTypeResponse | ## Export CPU/GPU resource utilization Operation path: `GET /api/v2/tenants/utilizationResources/export/` Authentication requirements: `BearerAuth` A route to retrieve aggregated CPU and GPU resources utilization. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date-time) | false | The start date for the usage data. | | end | query | string(date-time) | false | The end date for the usage data. | | resolution | query | string | false | The resolution for the usage data. | | filename | query | string | false | The filename for the usage data. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "Resource usage records grouped by workload group and category", "items": { "properties": { "metricDatetime": { "description": "The date and time for the resource usage metric.", "type": "string" }, "metricGroups": { "description": "The groups of workload.", "items": { "properties": { "group": { "description": "The metric group name.", "type": "string" }, "value": { "description": "The metric value.", "type": "number" } }, "required": [ "group", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "resourceType": { "description": "The resource type of the workload (GPU or CPU).", "type": "string" }, "total": { "description": "The resources utilized for all metric groups.", "type": "number" } }, "required": [ "metricDatetime", "metricGroups", "resourceType", "total" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "hash": { "description": "SHA-256 hash for file integrity verification.", "type": "string" }, "licenseLimit": { "description": "The license limit for the resource type.", "type": "integer" }, "maxGroupUsages": { "description": "Maximum resources utilized per groups of workload for period.", "items": { "properties": { "group": { "description": "The metric group name.", "type": "string" }, "value": { "description": "The metric value.", "type": "number" } }, "required": [ "group", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "maxUsage": { "description": "Maximum resources utilized for period.", "type": "number" }, "metricName": { "description": "The metric name.", "type": "string" } }, "required": [ "data", "licenseLimit", "maxGroupUsages", "maxUsage", "metricName" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | TenantResourceUtilizationResponse | ## Get CPU/GPU resource utilization by resourcetype Operation path: `GET /api/v2/tenants/utilizationResources/{resourceType}/` Authentication requirements: `BearerAuth` A route to retrieve aggregated CPU/GPU resources utilization for certain resource type. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date-time) | false | The start date for the usage data. | | end | query | string(date-time) | false | The end date for the usage data. | | resolution | query | string | false | The resolution for the usage data. | | resourceType | path | string | true | Resource type: CPU/GPU. | ### Enumerated Values | Parameter | Value | | --- | --- | | resourceType | [gpu, Gpu, GPU, cpu, Cpu, CPU] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "Resource usage records grouped by workload group and category", "items": { "properties": { "metricDatetime": { "description": "The date and time for the resource usage metric.", "type": "string" }, "metricGroups": { "description": "The groups of workload.", "items": { "properties": { "group": { "description": "The metric group name.", "type": "string" }, "value": { "description": "The metric value.", "type": "number" } }, "required": [ "group", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "resourceType": { "description": "The resource type of the workload (GPU or CPU).", "type": "string" }, "total": { "description": "The resources utilized for all metric groups.", "type": "number" } }, "required": [ "metricDatetime", "metricGroups", "resourceType", "total" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "hash": { "description": "SHA-256 hash for file integrity verification.", "type": "string" }, "licenseLimit": { "description": "The license limit for the resource type.", "type": "integer" }, "maxGroupUsages": { "description": "Maximum resources utilized per groups of workload for period.", "items": { "properties": { "group": { "description": "The metric group name.", "type": "string" }, "value": { "description": "The metric value.", "type": "number" } }, "required": [ "group", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "maxUsage": { "description": "Maximum resources utilized for period.", "type": "number" }, "metricName": { "description": "The metric name.", "type": "string" } }, "required": [ "data", "licenseLimit", "maxGroupUsages", "maxUsage", "metricName" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | TenantResourceUtilizationResponse | ## Get tenant active users by tenant ID Operation path: `GET /api/v2/tenants/{tenantId}/activeUsers/` Authentication requirements: `BearerAuth` DEPRECATED: This endpoint is deprecated. Use /activeUsers/ instead. A route to retrieve active tenant users over a specified time period. Only system or organization admins can perform this operation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date) | true | The start date for the usage data. | | end | query | string(date) | true | The end date for the usage data. | | tenantId | path | string | true | The ID of the tenant. | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The active users.", "items": { "properties": { "fullname": { "description": "The display name of the user", "type": "string" }, "userId": { "description": "The identifier of the user", "type": "string" }, "username": { "description": "The username of the user", "type": "string" } }, "required": [ "fullname", "userId", "username" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ActiveUsersResponse | ## Get the available resource categories by tenant ID Operation path: `GET /api/v2/tenants/{tenantId}/resourceCategories/` Authentication requirements: `BearerAuth` A route to retrieve available resource categories for tenant usage. ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The resource categories.", "items": { "properties": { "id": { "description": "The identifier of the resource category", "type": "string" }, "name": { "description": "The display name of the resource category", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ResourceCategoriesResponse | ## Get tenant usage by tenant ID Operation path: `GET /api/v2/tenants/{tenantId}/usage/` Authentication requirements: `BearerAuth` DEPRECATED: This endpoint is deprecated. Use `/tenantUsageResources/` instead. A route to retrieve aggregated or per-user tenant resource usage over some period of time. Only system or organization admins can perform this operation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date) | true | The start date for the usage data. | | end | query | string(date) | true | The end date for the usage data. | | userId | query | string | false | Only usage performed by this user will be retrieved. | | workloadCategory | query | string | false | The workload category. | | tenantId | path | string | true | The ID of the tenant. | ### Enumerated Values | Parameter | Value | | --- | --- | | workloadCategory | [all, All, ALL, gpuUsage, GpuUsage, GPU_USAGE, llmUsage, LlmUsage, LLM_USAGE, cpuUsage, CpuUsage, CPU_USAGE] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The usage records.", "items": { "properties": { "cloud": { "description": "Cloud provider where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "cloudRegion": { "description": "Cloud region where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "cost": { "description": "Cost for the usage", "type": "number", "x-versionadded": "v2.41" }, "currency": { "default": "USD", "description": "The price currency.", "enum": [ "usd", "Usd", "USD", "jpy", "Jpy", "JPY" ], "type": "string", "x-versionadded": "v2.41" }, "instanceType": { "description": "Instance type where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "price": { "description": "Numeric price for workload", "type": "number", "x-versionadded": "v2.41" }, "priceUnit": { "description": "The unit of price calculation.", "type": "string", "x-versionadded": "v2.41" }, "resourceBundleId": { "description": "The identifier of the resource bundle", "type": "string" }, "resourceBundleName": { "description": "Human readable name of the resource bundle", "type": "string" }, "usageUnit": { "description": "The unit of measurement for the usage value", "type": "string" }, "usageValue": { "description": "Numeric measure of the usage", "type": "number" }, "workloadId": { "description": "The identifier of the workload", "type": "string" }, "workloadName": { "description": "Human readable name of the workload", "type": "string" } }, "required": [ "resourceBundleId", "resourceBundleName", "usageUnit", "usageValue", "workloadId", "workloadName" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | UsageResponse | ## Export tenant usage by tenant ID Operation path: `GET /api/v2/tenants/{tenantId}/usageExport/` Authentication requirements: `BearerAuth` DEPRECATED: This endpoint is deprecated. Use /usageExport/ instead. A route to retrieve tenant resource usage over a specified time period. Only system or organization admins can perform this operation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string(date) | true | The start date for the usage data. | | end | query | string(date) | true | The end date for the usage data. | | userId | query | string | false | Only usage performed by this user will be retrieved. | | workloadCategory | query | string | false | The workload category. | | tenantId | path | string | true | The ID of the tenant. | ### Enumerated Values | Parameter | Value | | --- | --- | | workloadCategory | [all, All, ALL, gpuUsage, GpuUsage, GPU_USAGE, llmUsage, LlmUsage, LLM_USAGE, cpuUsage, CpuUsage, CPU_USAGE] | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The usage records.", "items": { "properties": { "amount": { "description": "The cost for the usage.", "type": "number", "x-versionadded": "v2.42" }, "cloud": { "description": "The cloud provider where the workload was executed.", "type": "string", "x-versionadded": "v2.41" }, "cloudRegion": { "description": "The cloud region where the workload was executed.", "type": "string", "x-versionadded": "v2.41" }, "cost": { "description": "The numeric price for the workload.", "type": "number", "x-versionadded": "v2.41" }, "costUnit": { "description": "The unit of price calculation.", "type": "string", "x-versionadded": "v2.42" }, "currency": { "default": "USD", "description": "The price currency.", "enum": [ "usd", "Usd", "USD", "jpy", "Jpy", "JPY" ], "type": "string", "x-versionadded": "v2.41" }, "resourceBundleId": { "description": "The identifier of the resource bundle", "type": "string" }, "resourceBundleName": { "description": "Human readable name of the resource bundle", "type": "string" }, "usageUnit": { "description": "The unit of measurement for the usage value", "type": "string" }, "usageValue": { "description": "Numeric measure of the usage", "type": "number" }, "userDisplayName": { "description": "The display name of the user", "type": "string" }, "userId": { "description": "The identifier of the user", "type": "string" }, "workloadId": { "description": "The identifier of the workload", "type": "string" }, "workloadName": { "description": "Human readable name of the workload", "type": "string" } }, "required": [ "resourceBundleName", "usageUnit", "usageValue", "userDisplayName", "userId", "workloadId", "workloadName" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ExportResponse | ## Users permanent delete Operation path: `POST /api/v2/userCleanupJobs/` Authentication requirements: `BearerAuth` Schedules a job which permanently delete the users represented by the provided users ids or organization id. Number of users to be deleted in one go is restricted by DELETED_USERS_BATCH_LIMIT system setting. ### Body parameter ``` { "properties": { "forceDeploymentDelete": { "default": false, "description": "If true, any deployment this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceNoCodeApplicationDelete": { "default": false, "description": "If true, any no code app this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceProjectDelete": { "default": false, "description": "If true, any project this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "orgId": { "default": null, "description": "Organization which's users to be permanently deleted.", "type": [ "string", "null" ] }, "orphansOwner": { "default": null, "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": [ "string", "null" ] }, "userIds": { "description": "Users to be permanently deleted.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UsersPermadelete | false | none | ### Example responses > 202 Response ``` { "properties": { "message": { "description": "Information about the users perma-deletion job submission.", "type": [ "string", "null" ] } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The users delete job status link. | UsersPermadeleteJobResponse | | 409 | Conflict | Self deletion or some users are already under deletion. | None | | 422 | Unprocessable Entity | Invalid data | None | | 500 | Internal Server Error | Failed to schedule users perma-delete job. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Cancel users perma-deletion by status ID Operation path: `DELETE /api/v2/userCleanupJobs/{statusId}/` Authentication requirements: `BearerAuth` Cancel permanent deletion of the selected users. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string | true | The ID of the status object. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 422 | Unprocessable Entity | Invalid data | None | ## Retrieve users perma-delete job status by status ID Operation path: `GET /api/v2/userCleanupJobs/{statusId}/` Authentication requirements: `BearerAuth` Get async status of the users perma-delete job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string | true | The ID of the status object. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "data": { "description": "Report id and associated status.", "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "reportId": { "description": "Report ID", "type": "string" }, "status": { "description": "The processing state of users perma-delete task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "reportId", "status" ], "type": "object" }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The processing state of the job.", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "created", "data", "message", "status", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Users perma-delete job status. | UsersPermadeleteJobStatusResponse | ## Users permanent delete preview Operation path: `POST /api/v2/userCleanupPreviewJobs/` Authentication requirements: `BearerAuth` Schedules a job for building preview for users selected for permanent deletion. Number of users to be deleted in one go is restricted by DELETED_USERS_BATCH_LIMIT system setting. ### Body parameter ``` { "properties": { "forceDeploymentDelete": { "default": false, "description": "If true, any deployment this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceNoCodeApplicationDelete": { "default": false, "description": "If true, any no code app this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceProjectDelete": { "default": false, "description": "If true, any project this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "orgId": { "default": null, "description": "Organization which's users to be permanently deleted.", "type": [ "string", "null" ] }, "orphansOwner": { "default": null, "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": [ "string", "null" ] }, "userIds": { "description": "Users to be permanently deleted.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UsersPermadelete | false | none | ### Example responses > 202 Response ``` { "properties": { "message": { "description": "Information about the users perma-deletion preview job submission.", "type": [ "string", "null" ] } }, "required": [ "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | The users delete preview job status link. | UsersPermadeletePreviewJobResponse | | 409 | Conflict | Self deletion or some users are already under deletion. | None | | 422 | Unprocessable Entity | Invalid data | None | | 500 | Internal Server Error | Failed to schedule perma-delete preview building job. | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 202 | Location | string | | A url that can be polled to check the status. | ## Cancel users perma-delete preview building by status ID Operation path: `DELETE /api/v2/userCleanupPreviewJobs/{statusId}/` Authentication requirements: `BearerAuth` Cancel scheduled users permanent delete preview building. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string | true | The ID of the status object. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 422 | Unprocessable Entity | Invalid data | None | ## Retrieve users perma-delete preview job status by status ID Operation path: `GET /api/v2/userCleanupPreviewJobs/{statusId}/` Authentication requirements: `BearerAuth` Get async status of the users perma-delete preview building job. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | statusId | path | string | true | The ID of the status object. | ### Example responses > 200 Response ``` { "properties": { "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "data": { "description": "Preview report id and associated status.", "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "reportId": { "description": "Report ID", "type": "string" }, "status": { "description": "The processing state of report building task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "reportId", "status" ], "type": "object" }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The processing state of the job.", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "created", "data", "message", "status", "statusId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Perma-delete preview job status. | UsersPermadeletePreviewJobStatusResponse | ## Delete users permanent delete report by report ID Operation path: `DELETE /api/v2/userCleanupPreviews/{reportId}/` Authentication requirements: `BearerAuth` Delete users permanent delete report. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | reportId | path | string | true | Report ID | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Report is deleted. | None | | 422 | Unprocessable Entity | Invalid data | None | ## Users permanent delete extended preview by report ID Operation path: `GET /api/v2/userCleanupPreviews/{reportId}/content/` Authentication requirements: `BearerAuth` Retrieve users permanent delete extended preview. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | reportId | path | string | true | Report ID | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The users delete extended preview file. | None | | 422 | Unprocessable Entity | Invalid data | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | attachment; filename="<"filename">".json JSON file with users delete extended preview. | | 200 | Content-Type | string | binary | application/json | ## Users permanent delete report parameters by report ID Operation path: `GET /api/v2/userCleanupPreviews/{reportId}/deleteParams/` Authentication requirements: `BearerAuth` Retrieve users permanent delete report parameters. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | reportId | path | string | true | Report ID | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The users cleanup delete parameters.", "properties": { "forceDeploymentDelete": { "default": false, "description": "If true, any deployment this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceNoCodeApplicationDelete": { "default": false, "description": "If true, any no code app this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceProjectDelete": { "default": false, "description": "If true, any project this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "orgId": { "default": null, "description": "Organization which's users to be permanently deleted.", "type": [ "string", "null" ] }, "orphansOwner": { "default": null, "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": [ "string", "null" ] }, "orphansOwnerName": { "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": "string" }, "userIds": { "description": "Users to be permanently deleted.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "type": "object" }, "message": { "description": "May contain further details.", "type": [ "string", "null" ] } }, "required": [ "data", "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The users delete report parameters. | UsersPermadeleteDeleteReportParamsResponse | | 422 | Unprocessable Entity | Invalid data | None | ## Users permanent delete preview statistics by report ID Operation path: `GET /api/v2/userCleanupPreviews/{reportId}/statistics/` Authentication requirements: `BearerAuth` Retrieve users permanent delete preview statistics. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | reportId | path | string | true | Report ID | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The users cleanup report statistics.", "properties": { "deploymentsWillBeAssignedNewOwner": { "description": "Number of deployments that will be assigned a new owner.", "type": "integer" }, "deploymentsWillBeDeleted": { "description": "Number of deployments that will be deleted.", "type": "integer" }, "deploymentsWillNotBeDeleted": { "description": "Number of deployments that will not be deleted.", "type": "integer" }, "errors": { "description": "Number of errors encountered during preview construction.", "type": "integer" }, "projectsWillBeAssignedNewOwner": { "description": "Number of projects that will be assigned a new owner.", "type": "integer" }, "projectsWillBeDeleted": { "description": "Number of projects that will be deleted.", "type": "integer" }, "projectsWillNotBeDeleted": { "description": "Number of projects that will not be deleted.", "type": "integer" }, "usersWillBeDeleted": { "description": "Number of users that will be deleted.", "type": "integer" }, "usersWillNotBeDeleted": { "description": "Number of users that will not be deleted.", "type": "integer" } }, "required": [ "deploymentsWillBeAssignedNewOwner", "deploymentsWillBeDeleted", "deploymentsWillNotBeDeleted", "errors", "projectsWillBeAssignedNewOwner", "projectsWillBeDeleted", "projectsWillNotBeDeleted", "usersWillBeDeleted", "usersWillNotBeDeleted" ], "type": "object" }, "message": { "description": "May contain further details.", "type": [ "string", "null" ] } }, "required": [ "data", "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The users delete preview statistics. | UsersPermadeletePreviewStatisticsResponse | | 422 | Unprocessable Entity | Invalid data | None | ## Delete user cleanup summaries by id Operation path: `DELETE /api/v2/userCleanupSummaries/{reportId}/` Authentication requirements: `BearerAuth` Delete users permanent delete report. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | reportId | path | string | true | Report ID | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Report is deleted. | None | | 422 | Unprocessable Entity | Invalid data | None | ## Users permanent delete extended summary by report ID Operation path: `GET /api/v2/userCleanupSummaries/{reportId}/content/` Authentication requirements: `BearerAuth` Retrieve users permanent delete extended summary. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | reportId | path | string | true | Report ID | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The users delete extended summary file. | None | | 422 | Unprocessable Entity | Invalid data | None | ### Response Headers | Status | Header | Type | Format | Description | | --- | --- | --- | --- | --- | | 200 | Content-Disposition | string | | attachment; filename="<"filename">".json JSON file with with users delete extended summary. | | 200 | Content-Type | string | binary | application/json | ## Retrieve delete params by id Operation path: `GET /api/v2/userCleanupSummaries/{reportId}/deleteParams/` Authentication requirements: `BearerAuth` Retrieve users permanent delete report parameters. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | reportId | path | string | true | Report ID | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The users cleanup delete parameters.", "properties": { "forceDeploymentDelete": { "default": false, "description": "If true, any deployment this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceNoCodeApplicationDelete": { "default": false, "description": "If true, any no code app this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceProjectDelete": { "default": false, "description": "If true, any project this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "orgId": { "default": null, "description": "Organization which's users to be permanently deleted.", "type": [ "string", "null" ] }, "orphansOwner": { "default": null, "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": [ "string", "null" ] }, "orphansOwnerName": { "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": "string" }, "userIds": { "description": "Users to be permanently deleted.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "type": "object" }, "message": { "description": "May contain further details.", "type": [ "string", "null" ] } }, "required": [ "data", "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The users delete report parameters. | UsersPermadeleteDeleteReportParamsResponse | | 422 | Unprocessable Entity | Invalid data | None | ## Users permanent delete summary statistics by report ID Operation path: `GET /api/v2/userCleanupSummaries/{reportId}/statistics/` Authentication requirements: `BearerAuth` Retrieve users permanent delete summary statistics. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | reportId | path | string | true | Report ID | ### Example responses > 200 Response ``` { "properties": { "data": { "description": "The users cleanup summary report statistics.", "properties": { "deploymentsAssignedNewOwner": { "description": "Number of deployments that are assigned a new owner.", "type": "integer" }, "deploymentsDeleted": { "description": "Number of deployments that are deleted.", "type": "integer" }, "deploymentsNotDeleted": { "description": "Number of deployments that are not deleted.", "type": "integer" }, "errors": { "description": "Number of errors encountered during users deletion.", "type": "integer" }, "projectsAssignedNewOwner": { "description": "Number of projects that are assigned a new owner.", "type": "integer" }, "projectsDeleted": { "description": "Number of projects that are deleted.", "type": "integer" }, "projectsNotDeleted": { "description": "Number of projects that are not deleted.", "type": "integer" }, "usersDeleted": { "description": "Number of users that are deleted.", "type": "integer" }, "usersNotDeleted": { "description": "Number of users that are not deleted.", "type": "integer" } }, "required": [ "deploymentsAssignedNewOwner", "deploymentsDeleted", "deploymentsNotDeleted", "errors", "projectsAssignedNewOwner", "projectsDeleted", "projectsNotDeleted", "usersDeleted", "usersNotDeleted" ], "type": "object" }, "message": { "description": "May contain further details.", "type": [ "string", "null" ] } }, "required": [ "data", "message" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The users delete summary statistics. | UsersPermadeleteSummaryReportStatisticsResponse | | 422 | Unprocessable Entity | Invalid data | None | ## Retrieve a list of existing users Operation path: `GET /api/v2/users/` Authentication requirements: `BearerAuth` Retrieve a list of existing users. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of results to skip. | | limit | query | integer | false | At most this many results are returned. The default may change without notice. | | namePart | query | string | false | When present, only return users with names that contain the given substring. | | username | query | string | false | Email address of the user. | | expirationDateLte | query | string(date-time) | false | Datetime, RFC3339. | | activated | query | string | false | Boolean return activated or deactivated users | | invited | query | string | false | Boolean return invited or joined users | | orderBy | query | string | false | Sort order to apply when listing users. | | tenantId | query | string | false | Query users scoped to the tenant | | attributionId | query | string | false | User ID in external marketing systems (SFDC) | ### Enumerated Values | Parameter | Value | | --- | --- | | activated | [false, False, true, True] | | invited | [false, False, true, True] | | orderBy | [registeredOn, -registeredOn, updatedAt, -updatedAt] | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Count of users returned, next/previous page URLs, and array 'data' of user objects. | None | | 403 | Forbidden | forbidden for non-administrators. | None | ## Create a User and add them Operation path: `POST /api/v2/users/` Authentication requirements: `BearerAuth` Create a User and add them to an organization. ### Body parameter ``` { "properties": { "attributionId": { "description": "User ID in external marketing systems (SFDC)", "type": "string" }, "company": { "description": "Company where the user works.", "type": "string" }, "country": { "description": "Country where the user lives.", "format": "ISO 3166-1 alpha-2 code", "type": "string" }, "defaultCatalogDatasetSampleSize": { "description": "Size of a sample to propose by default when enabling Fast Registration workflow.", "properties": { "type": { "description": "Sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "expirationDate": { "description": "Datetime, RFC3339, at which the user should expire.", "format": "date-time", "type": "string" }, "externalId": { "description": "User ID in external IdP.", "type": "string" }, "firstName": { "description": "First name of the user being created.", "type": "string" }, "jobTitle": { "description": "Job Title the user has.", "type": "string" }, "language": { "default": "en", "description": "Sets the language in the app for the user, and the language of the invite email, if the user is being invited (i.e. the password is omitted). Value must be a valid ISO-639-1 language code. Available options are: `en`, `ja`, `fr`, `ko`. System's default language will be used if omitted.", "enum": [ "ar_001", "de_DE", "en", "es_419", "fr", "ja", "ko", "pt_BR", "test", "uk_UA" ], "type": "string" }, "lastName": { "description": "Last name of the user being created.", "type": "string" }, "maxGpuWorkers": { "description": "Amount of user's available GPU workers.", "type": "integer" }, "maxIdleWorkers": { "description": "Amount of org workers that the user can utilize when idle.", "type": "integer" }, "maxUploadSize": { "description": "The upper limit for the allowed upload size for this user.", "type": "integer" }, "maxUploadSizeCatalog": { "description": "The upper limit for the allowed upload size in the AI catalog for this user.", "type": "integer" }, "maxWorkers": { "description": "Amount of user's available workers.", "type": "integer" }, "organizationId": { "description": "ID of the organization to add the user to.", "type": "string" }, "password": { "description": "Creates user with this password, if blank sends username invite.", "type": "string" }, "requireClickthroughAgreement": { "description": "Boolean to require the user to agree to a clickthrough.", "type": "boolean" }, "userType": { "description": "External name for UserType such as AcademicUser, BasicUser, ProUser.", "enum": [ "AcademicUser", "BasicUser", "ProUser", "Covid19TrialUser" ], "type": "string" }, "username": { "description": "Email address of new user.", "type": "string" } }, "required": [ "firstName", "userType", "username" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | UserCreate | false | none | ### Example responses > 200 Response ``` { "properties": { "notifyStatus": { "description": "User notification values.", "properties": { "inviteLink": { "description": "The link the user can follow to complete their DR account.", "format": "uri", "type": "string" }, "sentStatus": { "description": "Boolean value whether an invite has been sent to the user or not.", "type": "boolean" } }, "required": [ "sentStatus" ], "type": "object" }, "userId": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "userId", "username" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The username and userId of the newly created user. | UserCreateResponse | | 409 | Conflict | User already exists. | None | | 422 | Unprocessable Entity | Password invalid/required. | None | ## Invite multiple users by email Operation path: `POST /api/v2/users/invite/` Authentication requirements: `BearerAuth` Invite multiple users by email to join the platform. Users can have non-builder or regular roles.Resources can be specified to be shared with the invited users upon acceptance of the invite. ### Body parameter ``` { "properties": { "emails": { "description": "A list of user emails to invite.", "items": { "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" }, "language": { "default": "en", "description": "Language for the invite email.", "enum": [ "ar_001", "de_DE", "en", "es_419", "fr", "ja", "ko", "pt_BR", "test", "uk_UA" ], "type": "string" }, "orgId": { "description": "Organization ID to invite users to.", "type": "string" }, "resources": { "description": "A list of resources to share with the users.", "items": { "properties": { "resourceId": { "description": "ID of the resource to share with invited users.", "type": "string" }, "resourceType": { "description": "Resource type to share with invited users.", "enum": [ "app" ], "type": "string" } }, "required": [ "resourceId", "resourceType" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 20, "minItems": 1, "type": "array" }, "seatType": { "description": "Seat type for the invited users.", "enum": [ "Non-Builder User" ], "type": "string" } }, "required": [ "emails" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | BulkUserInvite | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | If the status code is 202, the header contains a 'Location' url to poll for the invite job status. [GET /api/v2/status/{statusId}/][get-apiv2statusstatusid]. | None | | 403 | Forbidden | Forbidden for non-administrators. | None | | 422 | Unprocessable Entity | Request data is invalid or users with the provided emails already exist. | None | ## Retrieve a single user by id by user ID Operation path: `GET /api/v2/users/{userId}/` Authentication requirements: `BearerAuth` Retrieve a single user by id. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | userId | path | string | true | The user identifier. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Info of requested user. | None | | 403 | Forbidden | Forbidden for non-administrators. | None | | 404 | Not Found | User not found. | None | # Schemas ## AccessControlV2 ``` { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the recipient. | | name | string | true | | The name of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | The type of the recipient. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## AccessRole ``` { "properties": { "custom": { "description": "Whether the Access Role was defined by a user (and is editable) or by the system (and is not)", "type": "boolean" }, "id": { "description": "The ID of the access role.", "type": "string" }, "name": { "description": "The name of the access role.", "type": "string" }, "organizationId": { "description": "The organization that the Access Role is associated with. `null` indicates that the role is global and can be used by all organizations", "type": [ "string", "null" ] }, "permissions": { "description": "An overview of the permissions included in the access role.", "items": { "properties": { "active": { "description": "Whether the entity is usable in the system.", "type": "boolean" }, "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "entity": { "description": "The internal name of the entity.", "enum": [ "APPLICATION", "ARTIFACT", "ARTIFACT_REPOSITORY", "AUDIT_LOG", "COMPUTE_CLUSTERS_MANAGEMENT", "CUSTOM_APPLICATION", "CUSTOM_APPLICATION_SOURCE", "CUSTOM_ENVIRONMENT", "CUSTOM_MODEL", "DATASET_DATA", "DATASET_INFO", "DATA_SOURCE", "DATA_STORE", "DEPLOYMENT", "DYNAMIC_SYSTEM_CONFIG", "ENTITLEMENT_DEFINITION", "ENTITLEMENT_SET", "ENTITLEMENT_SET_LEASE", "EXPERIMENT_CONTAINER", "EXTERNAL_APPLICATION", "GROUP", "MODEL_PACKAGE", "NOTIFICATION_POLICY", "ORGANIZATION", "PREDICTION_ENVIRONMENT", "PREDICTION_SERVER", "PROJECT", "REGISTERED_MODEL", "RISK_MANAGEMENT_FRAMEWORK", "USER", "WORKLOAD" ], "type": "string" }, "entityName": { "description": "The readable name of the entity.", "type": "string" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "required": [ "active", "entity", "entityName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "required": [ "custom", "id", "name", "organizationId", "permissions" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | custom | boolean | true | | Whether the Access Role was defined by a user (and is editable) or by the system (and is not) | | id | string | true | | The ID of the access role. | | name | string | true | | The name of the access role. | | organizationId | string,null | true | | The organization that the Access Role is associated with. null indicates that the role is global and can be used by all organizations | | permissions | [PermissionSetInfo] | true | maxItems: 100 | An overview of the permissions included in the access role. | ## AccessRoleCreate ``` { "properties": { "name": { "description": "The name of the new Access Role. Must be unique among roles in the organization.", "maxLength": 100, "type": "string" }, "organizationId": { "description": "The ID of the organization to associate the role with. Set to `null` to create a global role that is usable by all organizations.", "type": [ "string", "null" ] }, "permissions": { "description": "The permissions for the new Access Role. Each entity type accepts a specific subset of the valid permissions.", "properties": { "APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ARTIFACT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ARTIFACT_REPOSITORY": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "AUDIT_LOG": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "COMPUTE_CLUSTERS_MANAGEMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_APPLICATION_SOURCE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_ENVIRONMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_MODEL": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATASET_DATA": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATASET_INFO": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATA_SOURCE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATA_STORE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DEPLOYMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DYNAMIC_SYSTEM_CONFIG": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_DEFINITION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_SET": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_SET_LEASE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "EXPERIMENT_CONTAINER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "EXTERNAL_APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "GROUP": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "MODEL_PACKAGE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "NOTIFICATION_POLICY": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ORGANIZATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PREDICTION_ENVIRONMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PREDICTION_SERVER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PROJECT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "REGISTERED_MODEL": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "RISK_MANAGEMENT_FRAMEWORK": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "USER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "WORKLOAD": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" } }, "required": [ "name", "organizationId", "permissions" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 100 | The name of the new Access Role. Must be unique among roles in the organization. | | organizationId | string,null | true | | The ID of the organization to associate the role with. Set to null to create a global role that is usable by all organizations. | | permissions | Permissions | true | | The permissions for the new Access Role. Each entity type accepts a specific subset of the valid permissions. | ## AccessRoleListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The access roles for this page of the query.", "items": { "properties": { "custom": { "description": "Whether the Access Role was defined by a user (and is editable) or by the system (and is not)", "type": "boolean" }, "id": { "description": "The ID of the access role.", "type": "string" }, "name": { "description": "The name of the access role.", "type": "string" }, "organizationId": { "description": "The organization that the Access Role is associated with. `null` indicates that the role is global and can be used by all organizations", "type": [ "string", "null" ] }, "permissions": { "description": "An overview of the permissions included in the access role.", "items": { "properties": { "active": { "description": "Whether the entity is usable in the system.", "type": "boolean" }, "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "entity": { "description": "The internal name of the entity.", "enum": [ "APPLICATION", "ARTIFACT", "ARTIFACT_REPOSITORY", "AUDIT_LOG", "COMPUTE_CLUSTERS_MANAGEMENT", "CUSTOM_APPLICATION", "CUSTOM_APPLICATION_SOURCE", "CUSTOM_ENVIRONMENT", "CUSTOM_MODEL", "DATASET_DATA", "DATASET_INFO", "DATA_SOURCE", "DATA_STORE", "DEPLOYMENT", "DYNAMIC_SYSTEM_CONFIG", "ENTITLEMENT_DEFINITION", "ENTITLEMENT_SET", "ENTITLEMENT_SET_LEASE", "EXPERIMENT_CONTAINER", "EXTERNAL_APPLICATION", "GROUP", "MODEL_PACKAGE", "NOTIFICATION_POLICY", "ORGANIZATION", "PREDICTION_ENVIRONMENT", "PREDICTION_SERVER", "PROJECT", "REGISTERED_MODEL", "RISK_MANAGEMENT_FRAMEWORK", "USER", "WORKLOAD" ], "type": "string" }, "entityName": { "description": "The readable name of the entity.", "type": "string" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "required": [ "active", "entity", "entityName" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" } }, "required": [ "custom", "id", "name", "organizationId", "permissions" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [AccessRole] | true | maxItems: 100 | The access roles for this page of the query. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## AccessRoleUpdate ``` { "properties": { "name": { "description": "The new name for the Access Role. Must be unique among roles in the organization.", "maxLength": 100, "type": "string" }, "permissions": { "description": "The permissions for the new Access Role. Each entity type accepts a specific subset of the valid permissions.", "properties": { "APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ARTIFACT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ARTIFACT_REPOSITORY": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "AUDIT_LOG": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "COMPUTE_CLUSTERS_MANAGEMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_APPLICATION_SOURCE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_ENVIRONMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_MODEL": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATASET_DATA": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATASET_INFO": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATA_SOURCE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATA_STORE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DEPLOYMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DYNAMIC_SYSTEM_CONFIG": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_DEFINITION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_SET": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_SET_LEASE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "EXPERIMENT_CONTAINER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "EXTERNAL_APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "GROUP": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "MODEL_PACKAGE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "NOTIFICATION_POLICY": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ORGANIZATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PREDICTION_ENVIRONMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PREDICTION_SERVER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PROJECT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "REGISTERED_MODEL": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "RISK_MANAGEMENT_FRAMEWORK": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "USER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "WORKLOAD": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | maxLength: 100 | The new name for the Access Role. Must be unique among roles in the organization. | | permissions | Permissions | false | | The permissions for the new Access Role. Each entity type accepts a specific subset of the valid permissions. | ## AccessRoleUser ``` { "properties": { "firstName": { "description": "The user's first name.", "type": "string" }, "id": { "description": "The user's ID.", "type": "string" }, "isActive": { "description": "The user's status.", "type": "boolean" }, "lastName": { "description": "The user's last name.", "type": "string" }, "organizationId": { "description": "The ID of the user's organization.", "type": [ "string", "null" ] }, "scheduledForDeletion": { "description": "The user's permadelete status.", "type": "boolean" }, "username": { "description": "The user's username.", "type": "string" } }, "required": [ "firstName", "id", "isActive", "lastName", "organizationId", "username" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | firstName | string | true | | The user's first name. | | id | string | true | | The user's ID. | | isActive | boolean | true | | The user's status. | | lastName | string | true | | The user's last name. | | organizationId | string,null | true | | The ID of the user's organization. | | scheduledForDeletion | boolean | false | | The user's permadelete status. | | username | string | true | | The user's username. | ## AccessRoleUsersListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "Information about the users on this page of the query.", "items": { "properties": { "firstName": { "description": "The user's first name.", "type": "string" }, "id": { "description": "The user's ID.", "type": "string" }, "isActive": { "description": "The user's status.", "type": "boolean" }, "lastName": { "description": "The user's last name.", "type": "string" }, "organizationId": { "description": "The ID of the user's organization.", "type": [ "string", "null" ] }, "scheduledForDeletion": { "description": "The user's permadelete status.", "type": "boolean" }, "username": { "description": "The user's username.", "type": "string" } }, "required": [ "firstName", "id", "isActive", "lastName", "organizationId", "username" ], "type": "object", "x-versionadded": "v2.36" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [AccessRoleUser] | true | maxItems: 100 | Information about the users on this page of the query. | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## ActiveTenantsResponse ``` { "properties": { "data": { "description": "The active organizations.", "items": { "properties": { "id": { "description": "The identifier of the organization", "type": "string" }, "name": { "description": "The name of the organization", "type": "string" }, "tenantId": { "description": "The identifier of the tenant", "type": "string" } }, "required": [ "id", "name", "tenantId" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [OrganizationsResponse] | true | maxItems: 1000 | The active organizations. | ## ActiveUser ``` { "properties": { "fullname": { "description": "The display name of the user", "type": "string" }, "userId": { "description": "The identifier of the user", "type": "string" }, "username": { "description": "The username of the user", "type": "string" } }, "required": [ "fullname", "userId", "username" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | fullname | string | true | | The display name of the user | | userId | string | true | | The identifier of the user | | username | string | true | | The username of the user | ## ActiveUsersResponse ``` { "properties": { "data": { "description": "The active users.", "items": { "properties": { "fullname": { "description": "The display name of the user", "type": "string" }, "userId": { "description": "The identifier of the user", "type": "string" }, "username": { "description": "The username of the user", "type": "string" } }, "required": [ "fullname", "userId", "username" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [ActiveUser] | true | maxItems: 10000 | The active users. | ## AddUsersToGroupResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of users in the group.", "items": { "properties": { "expirationDate": { "description": "The expiration date of the user account, if applicable.", "type": [ "string", "null" ] }, "firstName": { "description": "The first name of the user.", "type": [ "string", "null" ] }, "lastName": { "description": "The last name of the user.", "type": [ "string", "null" ] }, "organization": { "description": "The name of the organization the user is part of.", "type": [ "string", "null" ] }, "scheduledForDeletion": { "description": "If the user is scheduled for deletion. Will be returned when an appropriate FF is enabled.", "type": "boolean" }, "status": { "description": "The status of the user, `active` if the has been activated, `inactive` otherwise.", "enum": [ "active", "inactive" ], "type": [ "string", "null" ] }, "userId": { "description": "The identifier of the user.", "type": [ "string", "null" ] }, "username": { "description": "The name of the user.", "type": "string" } }, "required": [ "firstName", "lastName", "organization", "status", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" } }, "required": [ "count", "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [UserInGroup] | true | maxItems: 1000 | List of users in the group. | ## AdminSampleSize ``` { "description": "Size of a sample to propose by default when enabling Fast Registration workflow.", "properties": { "type": { "description": "Sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" } ``` Size of a sample to propose by default when enabling Fast Registration workflow. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | Sample size can be specified only as a number of rows for now. | | value | integer | true | maximum: 1000000 | Number of rows to ingest during dataset registration. | ### Enumerated Values | Property | Value | | --- | --- | | type | rows | ## BulkUserInvite ``` { "properties": { "emails": { "description": "A list of user emails to invite.", "items": { "type": "string" }, "maxItems": 20, "minItems": 1, "type": "array" }, "language": { "default": "en", "description": "Language for the invite email.", "enum": [ "ar_001", "de_DE", "en", "es_419", "fr", "ja", "ko", "pt_BR", "test", "uk_UA" ], "type": "string" }, "orgId": { "description": "Organization ID to invite users to.", "type": "string" }, "resources": { "description": "A list of resources to share with the users.", "items": { "properties": { "resourceId": { "description": "ID of the resource to share with invited users.", "type": "string" }, "resourceType": { "description": "Resource type to share with invited users.", "enum": [ "app" ], "type": "string" } }, "required": [ "resourceId", "resourceType" ], "type": "object", "x-versionadded": "v2.41" }, "maxItems": 20, "minItems": 1, "type": "array" }, "seatType": { "description": "Seat type for the invited users.", "enum": [ "Non-Builder User" ], "type": "string" } }, "required": [ "emails" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | emails | [string] | true | maxItems: 20minItems: 1 | A list of user emails to invite. | | language | string | false | | Language for the invite email. | | orgId | string | false | | Organization ID to invite users to. | | resources | [ResourceShare] | false | maxItems: 20minItems: 1 | A list of resources to share with the users. | | seatType | string | false | | Seat type for the invited users. | ### Enumerated Values | Property | Value | | --- | --- | | language | [ar_001, de_DE, en, es_419, fr, ja, ko, pt_BR, test, uk_UA] | | seatType | Non-Builder User | ## Empty ``` { "type": "object" } ``` ### Properties None ## ExportItem ``` { "properties": { "amount": { "description": "The cost for the usage.", "type": "number", "x-versionadded": "v2.42" }, "cloud": { "description": "The cloud provider where the workload was executed.", "type": "string", "x-versionadded": "v2.41" }, "cloudRegion": { "description": "The cloud region where the workload was executed.", "type": "string", "x-versionadded": "v2.41" }, "cost": { "description": "The numeric price for the workload.", "type": "number", "x-versionadded": "v2.41" }, "costUnit": { "description": "The unit of price calculation.", "type": "string", "x-versionadded": "v2.42" }, "currency": { "default": "USD", "description": "The price currency.", "enum": [ "usd", "Usd", "USD", "jpy", "Jpy", "JPY" ], "type": "string", "x-versionadded": "v2.41" }, "resourceBundleId": { "description": "The identifier of the resource bundle", "type": "string" }, "resourceBundleName": { "description": "Human readable name of the resource bundle", "type": "string" }, "usageUnit": { "description": "The unit of measurement for the usage value", "type": "string" }, "usageValue": { "description": "Numeric measure of the usage", "type": "number" }, "userDisplayName": { "description": "The display name of the user", "type": "string" }, "userId": { "description": "The identifier of the user", "type": "string" }, "workloadId": { "description": "The identifier of the workload", "type": "string" }, "workloadName": { "description": "Human readable name of the workload", "type": "string" } }, "required": [ "resourceBundleName", "usageUnit", "usageValue", "userDisplayName", "userId", "workloadId", "workloadName" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | amount | number | false | | The cost for the usage. | | cloud | string | false | | The cloud provider where the workload was executed. | | cloudRegion | string | false | | The cloud region where the workload was executed. | | cost | number | false | | The numeric price for the workload. | | costUnit | string | false | | The unit of price calculation. | | currency | string | false | | The price currency. | | resourceBundleId | string | false | | The identifier of the resource bundle | | resourceBundleName | string | true | | Human readable name of the resource bundle | | usageUnit | string | true | | The unit of measurement for the usage value | | usageValue | number | true | | Numeric measure of the usage | | userDisplayName | string | true | | The display name of the user | | userId | string | true | | The identifier of the user | | workloadId | string | true | | The identifier of the workload | | workloadName | string | true | | Human readable name of the workload | ### Enumerated Values | Property | Value | | --- | --- | | currency | [usd, Usd, USD, jpy, Jpy, JPY] | ## ExportResponse ``` { "properties": { "data": { "description": "The usage records.", "items": { "properties": { "amount": { "description": "The cost for the usage.", "type": "number", "x-versionadded": "v2.42" }, "cloud": { "description": "The cloud provider where the workload was executed.", "type": "string", "x-versionadded": "v2.41" }, "cloudRegion": { "description": "The cloud region where the workload was executed.", "type": "string", "x-versionadded": "v2.41" }, "cost": { "description": "The numeric price for the workload.", "type": "number", "x-versionadded": "v2.41" }, "costUnit": { "description": "The unit of price calculation.", "type": "string", "x-versionadded": "v2.42" }, "currency": { "default": "USD", "description": "The price currency.", "enum": [ "usd", "Usd", "USD", "jpy", "Jpy", "JPY" ], "type": "string", "x-versionadded": "v2.41" }, "resourceBundleId": { "description": "The identifier of the resource bundle", "type": "string" }, "resourceBundleName": { "description": "Human readable name of the resource bundle", "type": "string" }, "usageUnit": { "description": "The unit of measurement for the usage value", "type": "string" }, "usageValue": { "description": "Numeric measure of the usage", "type": "number" }, "userDisplayName": { "description": "The display name of the user", "type": "string" }, "userId": { "description": "The identifier of the user", "type": "string" }, "workloadId": { "description": "The identifier of the workload", "type": "string" }, "workloadName": { "description": "Human readable name of the workload", "type": "string" } }, "required": [ "resourceBundleName", "usageUnit", "usageValue", "userDisplayName", "userId", "workloadId", "workloadName" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 10000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [ExportItem] | true | maxItems: 10000 | The usage records. | ## GrantAccessControlWithIdWithGrant ``` { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If role is NO_ROLE canShare is ignored. | | id | string | true | | The ID of the recipient. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## GrantAccessControlWithUsernameWithGrant ``` { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | canShare | boolean | false | | Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If role is NO_ROLE canShare is ignored. | | role | string | true | | The role of the recipient on this entity. | | shareRecipientType | string | true | | Describes the recipient type, either user, group, or organization. | | username | string | true | | The username of the user to update the access role for. | ### Enumerated Values | Property | Value | | --- | --- | | role | [ADMIN, CONSUMER, DATA_SCIENTIST, EDITOR, NO_ROLE, OBSERVER, OWNER, READ_ONLY, READ_WRITE, USER] | | shareRecipientType | [user, group, organization] | ## ListUsersInGroupResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of users in the group.", "items": { "properties": { "expirationDate": { "description": "The expiration date of the user account, if applicable.", "type": [ "string", "null" ] }, "firstName": { "description": "The first name of the user.", "type": [ "string", "null" ] }, "lastName": { "description": "The last name of the user.", "type": [ "string", "null" ] }, "organization": { "description": "The name of the organization the user is part of.", "type": [ "string", "null" ] }, "scheduledForDeletion": { "description": "If the user is scheduled for deletion. Will be returned when an appropriate FF is enabled.", "type": "boolean" }, "status": { "description": "The status of the user, `active` if the has been activated, `inactive` otherwise.", "enum": [ "active", "inactive" ], "type": [ "string", "null" ] }, "userId": { "description": "The identifier of the user.", "type": [ "string", "null" ] }, "username": { "description": "The name of the user.", "type": "string" } }, "required": [ "firstName", "lastName", "organization", "status", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL to the next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL to the previous page.", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items that match the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [UserInGroup] | true | maxItems: 1000 | List of users in the group. | | next | string,null(uri) | true | | The URL to the next page. | | previous | string,null(uri) | true | | The URL to the previous page. | | totalCount | integer | true | | The total number of items that match the query condition. | ## MetricGroups ``` { "properties": { "group": { "description": "The metric group name.", "type": "string" }, "value": { "description": "The metric value.", "type": "number" } }, "required": [ "group", "value" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | group | string | true | | The metric group name. | | value | number | true | | The metric value. | ## ModifyUsersInGroup ``` { "properties": { "users": { "description": "The users to change membership for.", "items": { "properties": { "username": { "description": "The name of the user.", "type": "string" } }, "required": [ "username" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "users" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | users | [Username] | true | maxItems: 100 | The users to change membership for. | ## NotifyStatus ``` { "description": "User notification values.", "properties": { "inviteLink": { "description": "The link the user can follow to complete their DR account.", "format": "uri", "type": "string" }, "sentStatus": { "description": "Boolean value whether an invite has been sent to the user or not.", "type": "boolean" } }, "required": [ "sentStatus" ], "type": "object" } ``` User notification values. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inviteLink | string(uri) | false | | The link the user can follow to complete their DR account. | | sentStatus | boolean | true | | Boolean value whether an invite has been sent to the user or not. | ## OrganizationJob ``` { "properties": { "jobId": { "description": "The identifier of the submitted job (unique within a project)", "type": "string" }, "projectId": { "description": "The project identifier associated with this job", "type": "string" }, "projectOwnerUserId": { "description": "Identifier the user that owns the project associated with the job", "type": "string" }, "projectOwnerUsername": { "description": "The username of the user that owns the project associated with the job", "type": "string" }, "userId": { "description": "Identifies the user that submitted the job", "type": "string" }, "username": { "description": "The username of the user that submitted the job", "type": "string" } }, "required": [ "jobId", "projectId", "projectOwnerUserId", "projectOwnerUsername", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jobId | string | true | | The identifier of the submitted job (unique within a project) | | projectId | string | true | | The project identifier associated with this job | | projectOwnerUserId | string | true | | Identifier the user that owns the project associated with the job | | projectOwnerUsername | string | true | | The username of the user that owns the project associated with the job | | userId | string | true | | Identifies the user that submitted the job | | username | string | true | | The username of the user that submitted the job | ## OrganizationJobListResponse ``` { "properties": { "count": { "description": "The number of items on the current page.", "type": "integer" }, "data": { "description": "The list of the requested jobs.", "items": { "properties": { "jobId": { "description": "The identifier of the submitted job (unique within a project)", "type": "string" }, "projectId": { "description": "The project identifier associated with this job", "type": "string" }, "projectOwnerUserId": { "description": "Identifier the user that owns the project associated with the job", "type": "string" }, "projectOwnerUsername": { "description": "The username of the user that owns the project associated with the job", "type": "string" }, "userId": { "description": "Identifies the user that submitted the job", "type": "string" }, "username": { "description": "The username of the user that submitted the job", "type": "string" } }, "required": [ "jobId", "projectId", "projectOwnerUserId", "projectOwnerUsername", "userId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page.", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items on the current page. | | data | [OrganizationJob] | true | maxItems: 1000 | The list of the requested jobs. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page. | ## OrganizationListResponse ``` { "properties": { "count": { "description": "The number of items on the current page.", "type": "integer" }, "data": { "description": "The list of the requested organizations.", "items": { "properties": { "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions available to this organization. This will only be present if the corresponding feature flag is set.", "type": "object" }, "activeUsersCount": { "description": "The number of active users for this organization. This excludes deactivated users, aliased DataRobot accounts (in SaaS), and users with NON_BUILDER_USER seat license.", "type": "integer", "x-versionadded": "v2.41" }, "agreementStatus": { "description": "The status of the organization's agreement to the terms of DataRobot.", "enum": [ "NEEDED", "AGREED", "N/A" ], "type": "string" }, "customAppAutoStopSeconds": { "description": "Per-organization override for the cluster-default custom application auto-stop timeout (in seconds). When null, the cluster default is used.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.43" }, "datasetRefreshJobLimit": { "description": "How many enabled dataset refresh jobs are allowed per-dataset for this organization.", "type": [ "integer", "null" ] }, "datasetRefreshJobUserLimit": { "description": "How many enabled dataset refresh jobs are allowed per-user for this organization.", "type": [ "integer", "null" ] }, "defaultUserMaxGpuWorkers": { "description": "Maximum number of concurrent GPU workers assigned to a newly created user of this organization.", "type": [ "integer", "null" ] }, "defaultUserMaxWorkers": { "description": "Maximum number of concurrent workers assigned to a newly created user of this organization.", "type": [ "integer", "null" ] }, "deleted": { "description": "Indicates whether the organization is soft-deleted.", "type": [ "boolean", "null" ] }, "deletedAt": { "description": "Indicates when the organization was soft-deleted.", "format": "date-time", "type": [ "string", "null" ] }, "deletedBy": { "description": "Indicates who has soft-deleted the organization.", "type": [ "string", "null" ] }, "desiredCustomModelContainerSize": { "description": "The desired custom-model memory size. This will only be present if the corresponding feature flag is set.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "enableSso": { "description": "If the SSO is enabled for this organization. This will only be present if the corresponding feature flag is set.", "type": "boolean" }, "groupsCount": { "description": "The number of user groups belonging to this organization.", "type": "integer", "x-versionadded": "v2.17" }, "id": { "description": "The organization identifier.", "type": "string" }, "inactiveUsersCount": { "description": "The number of inactive users for this organization.", "type": "integer", "x-versionadded": "v2.21" }, "maxCodespaceFileUploadSize": { "description": "Maximum file upload size (MB) for Codespaces.", "maximum": 50000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.4" }, "maxCodespaces": { "description": "Maximum number of codespaces available to this organization.", "type": [ "integer", "null" ] }, "maxComputeServerlessPredictionApi": { "description": "The number of maximum allowed compute on a serverless platform for this organization", "minimum": 1, "type": [ "integer", "null" ] }, "maxConcurrentBatchPredictionsJob": { "description": "Maximum number of concurrent batch prediction jobs to this organization", "maximum": 10, "minimum": 1, "type": [ "integer", "null" ] }, "maxConcurrentNotebooks": { "description": "Maximum number of concurrent notebooks available to this organization.", "type": [ "integer", "null" ] }, "maxCustomModelContainerSize": { "description": "The maximum memory that might be allocated by the custom-model. This will only be present if the corresponding feature flag is set.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "maxCustomModelReplicasPerDeployment": { "description": "A fixed number of replicas that will be set for the given custom-model. This will only be present if the corresponding feature flag is set.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "maxCustomModelReplicasPerDeploymentForBatchPredictions": { "description": "The maximum custom inference model replicas per deployment for batch predictions. This will only be present if the corresponding feature flag is set.", "maximum": 8, "minimum": 1, "type": [ "integer", "null" ] }, "maxDailyBatchesPerDeployment": { "description": "The maximum number of monitoring batches that can be created per day on deployments that belong to this organization.", "minimum": 1, "type": [ "integer", "null" ] }, "maxDeploymentLimit": { "description": "The absolute limit on the number of deployments an organization is allowed to create. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "maxEdaWorkers": { "description": "Maximum number of EDA workers assigned available to this organization.", "type": [ "integer", "null" ] }, "maxEnabledNotebookSchedules": { "description": "Maximum number of enabled notebook schedules for this organization.", "minimum": 1, "type": [ "integer", "null" ] }, "maxExecutionEnvironments": { "description": "The upper limit for the number of maximum execution environments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "minimum": 0, "type": [ "integer", "null" ] }, "maxGpuWorkers": { "description": "Maximum number of concurrent GPU workers available to this organization.", "type": [ "integer", "null" ] }, "maxLlmApiCalls": { "description": "Maximum number of LLM calls.", "minimum": 1, "type": [ "integer", "null" ] }, "maxNotebookMachineSize": { "description": "Maximum size of notebook machine available to this organization.", "enum": [ "L", "M", "S", "XL", "XS", "XXL" ], "type": [ "string", "null" ] }, "maxNotebookSchedulingWorkers": { "description": "Maximum number of concurrently running notebooks schedules for this organization.", "maximum": 20, "minimum": 1, "type": [ "integer", "null" ] }, "maxPipelineModuleRuntimes": { "description": "Maximum number of Pipeline module runtimes available to this organization.", "type": [ "integer", "null" ] }, "maxUploadSize": { "description": "Maximum size for file uploads (MB). This will only be present if the corresponding feature flag is set.", "type": [ "integer", "null" ] }, "maxUploadSizeCatalog": { "description": "Maximum size for catalog file uploads (MB). This will only be present if the corresponding feature flag is set.", "type": [ "integer", "null" ] }, "maxVectorDatabases": { "description": "Maximum number of local vector databases.", "minimum": 1, "type": [ "integer", "null" ] }, "maxWorkers": { "description": "Maximum number of concurrent workers available to this organization.", "type": [ "integer", "null" ] }, "maximumActiveUsers": { "description": "The limit for active users for this organization. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "membersCount": { "description": "The number of members in this organization.", "type": "integer" }, "mlopsEventStorageRetentionDays": { "default": 0, "description": "The number of days to keep MLOps events in storage. Events with older timestamps will be removed.", "minimum": 0, "type": "integer" }, "name": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "notebooksSoftDeletionRetentionPeriod": { "description": "Retention window for soft-deleted notebooks and respective resources before NBX services hard-delete them.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "ormVersion": { "description": "On-demand Resource Manager (prediction service provisioning) version.", "enum": [ "v1", "v2", "v3", "CCM" ], "type": "string" }, "permadeleted": { "description": "Indicates whether the organization is permadeleted.", "type": [ "boolean", "null" ] }, "permadeletedAt": { "description": "Indicates when the organization was permadeleted.", "format": "date-time", "type": [ "string", "null" ] }, "permadeletedBy": { "description": "Indicates who has permadeleted the organization.", "type": [ "string", "null" ] }, "prepaidDeploymentLimit": { "description": "The number of deployments an organization can create under their contract. When it is reached more deployments can be made, but the user will be warned that this will result in a higher billing. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "restrictedSharing": { "description": "Whether sharing is only allowed within the organization. This will only be present if the corresponding feature flag is set.", "type": "boolean" }, "snapshotLimit": { "description": "The number of snapshots allowed for a dataset for this organization.", "type": [ "integer", "null" ] }, "supportEmail": { "description": "The support email of the organization.", "type": [ "string", "null" ] } }, "required": [ "activeUsersCount", "agreementStatus", "datasetRefreshJobLimit", "datasetRefreshJobUserLimit", "defaultUserMaxGpuWorkers", "defaultUserMaxWorkers", "groupsCount", "id", "inactiveUsersCount", "maxDeploymentLimit", "maxEdaWorkers", "maxExecutionEnvironments", "maxGpuWorkers", "maxLlmApiCalls", "maxPipelineModuleRuntimes", "maxVectorDatabases", "maxWorkers", "maximumActiveUsers", "membersCount", "name", "ormVersion", "prepaidDeploymentLimit", "snapshotLimit", "supportEmail" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items on the current page. | | data | [OrganizationRetrieve] | true | maxItems: 1000 | The list of the requested organizations. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page) | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page | ## OrganizationRetrieve ``` { "properties": { "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions available to this organization. This will only be present if the corresponding feature flag is set.", "type": "object" }, "activeUsersCount": { "description": "The number of active users for this organization. This excludes deactivated users, aliased DataRobot accounts (in SaaS), and users with NON_BUILDER_USER seat license.", "type": "integer", "x-versionadded": "v2.41" }, "agreementStatus": { "description": "The status of the organization's agreement to the terms of DataRobot.", "enum": [ "NEEDED", "AGREED", "N/A" ], "type": "string" }, "customAppAutoStopSeconds": { "description": "Per-organization override for the cluster-default custom application auto-stop timeout (in seconds). When null, the cluster default is used.", "minimum": 1, "type": [ "integer", "null" ], "x-versionadded": "v2.43" }, "datasetRefreshJobLimit": { "description": "How many enabled dataset refresh jobs are allowed per-dataset for this organization.", "type": [ "integer", "null" ] }, "datasetRefreshJobUserLimit": { "description": "How many enabled dataset refresh jobs are allowed per-user for this organization.", "type": [ "integer", "null" ] }, "defaultUserMaxGpuWorkers": { "description": "Maximum number of concurrent GPU workers assigned to a newly created user of this organization.", "type": [ "integer", "null" ] }, "defaultUserMaxWorkers": { "description": "Maximum number of concurrent workers assigned to a newly created user of this organization.", "type": [ "integer", "null" ] }, "deleted": { "description": "Indicates whether the organization is soft-deleted.", "type": [ "boolean", "null" ] }, "deletedAt": { "description": "Indicates when the organization was soft-deleted.", "format": "date-time", "type": [ "string", "null" ] }, "deletedBy": { "description": "Indicates who has soft-deleted the organization.", "type": [ "string", "null" ] }, "desiredCustomModelContainerSize": { "description": "The desired custom-model memory size. This will only be present if the corresponding feature flag is set.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "enableSso": { "description": "If the SSO is enabled for this organization. This will only be present if the corresponding feature flag is set.", "type": "boolean" }, "groupsCount": { "description": "The number of user groups belonging to this organization.", "type": "integer", "x-versionadded": "v2.17" }, "id": { "description": "The organization identifier.", "type": "string" }, "inactiveUsersCount": { "description": "The number of inactive users for this organization.", "type": "integer", "x-versionadded": "v2.21" }, "maxCodespaceFileUploadSize": { "description": "Maximum file upload size (MB) for Codespaces.", "maximum": 50000, "minimum": 10, "type": [ "integer", "null" ], "x-versionadded": "v2.4" }, "maxCodespaces": { "description": "Maximum number of codespaces available to this organization.", "type": [ "integer", "null" ] }, "maxComputeServerlessPredictionApi": { "description": "The number of maximum allowed compute on a serverless platform for this organization", "minimum": 1, "type": [ "integer", "null" ] }, "maxConcurrentBatchPredictionsJob": { "description": "Maximum number of concurrent batch prediction jobs to this organization", "maximum": 10, "minimum": 1, "type": [ "integer", "null" ] }, "maxConcurrentNotebooks": { "description": "Maximum number of concurrent notebooks available to this organization.", "type": [ "integer", "null" ] }, "maxCustomModelContainerSize": { "description": "The maximum memory that might be allocated by the custom-model. This will only be present if the corresponding feature flag is set.", "maximum": 15032385536, "minimum": 134217728, "type": [ "integer", "null" ] }, "maxCustomModelReplicasPerDeployment": { "description": "A fixed number of replicas that will be set for the given custom-model. This will only be present if the corresponding feature flag is set.", "exclusiveMinimum": 0, "maximum": 25, "type": [ "integer", "null" ] }, "maxCustomModelReplicasPerDeploymentForBatchPredictions": { "description": "The maximum custom inference model replicas per deployment for batch predictions. This will only be present if the corresponding feature flag is set.", "maximum": 8, "minimum": 1, "type": [ "integer", "null" ] }, "maxDailyBatchesPerDeployment": { "description": "The maximum number of monitoring batches that can be created per day on deployments that belong to this organization.", "minimum": 1, "type": [ "integer", "null" ] }, "maxDeploymentLimit": { "description": "The absolute limit on the number of deployments an organization is allowed to create. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "maxEdaWorkers": { "description": "Maximum number of EDA workers assigned available to this organization.", "type": [ "integer", "null" ] }, "maxEnabledNotebookSchedules": { "description": "Maximum number of enabled notebook schedules for this organization.", "minimum": 1, "type": [ "integer", "null" ] }, "maxExecutionEnvironments": { "description": "The upper limit for the number of maximum execution environments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "minimum": 0, "type": [ "integer", "null" ] }, "maxGpuWorkers": { "description": "Maximum number of concurrent GPU workers available to this organization.", "type": [ "integer", "null" ] }, "maxLlmApiCalls": { "description": "Maximum number of LLM calls.", "minimum": 1, "type": [ "integer", "null" ] }, "maxNotebookMachineSize": { "description": "Maximum size of notebook machine available to this organization.", "enum": [ "L", "M", "S", "XL", "XS", "XXL" ], "type": [ "string", "null" ] }, "maxNotebookSchedulingWorkers": { "description": "Maximum number of concurrently running notebooks schedules for this organization.", "maximum": 20, "minimum": 1, "type": [ "integer", "null" ] }, "maxPipelineModuleRuntimes": { "description": "Maximum number of Pipeline module runtimes available to this organization.", "type": [ "integer", "null" ] }, "maxUploadSize": { "description": "Maximum size for file uploads (MB). This will only be present if the corresponding feature flag is set.", "type": [ "integer", "null" ] }, "maxUploadSizeCatalog": { "description": "Maximum size for catalog file uploads (MB). This will only be present if the corresponding feature flag is set.", "type": [ "integer", "null" ] }, "maxVectorDatabases": { "description": "Maximum number of local vector databases.", "minimum": 1, "type": [ "integer", "null" ] }, "maxWorkers": { "description": "Maximum number of concurrent workers available to this organization.", "type": [ "integer", "null" ] }, "maximumActiveUsers": { "description": "The limit for active users for this organization. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "membersCount": { "description": "The number of members in this organization.", "type": "integer" }, "mlopsEventStorageRetentionDays": { "default": 0, "description": "The number of days to keep MLOps events in storage. Events with older timestamps will be removed.", "minimum": 0, "type": "integer" }, "name": { "description": "The name of the organization.", "type": [ "string", "null" ] }, "notebooksSoftDeletionRetentionPeriod": { "description": "Retention window for soft-deleted notebooks and respective resources before NBX services hard-delete them.", "type": [ "string", "null" ], "x-versionadded": "v2.35" }, "ormVersion": { "description": "On-demand Resource Manager (prediction service provisioning) version.", "enum": [ "v1", "v2", "v3", "CCM" ], "type": "string" }, "permadeleted": { "description": "Indicates whether the organization is permadeleted.", "type": [ "boolean", "null" ] }, "permadeletedAt": { "description": "Indicates when the organization was permadeleted.", "format": "date-time", "type": [ "string", "null" ] }, "permadeletedBy": { "description": "Indicates who has permadeleted the organization.", "type": [ "string", "null" ] }, "prepaidDeploymentLimit": { "description": "The number of deployments an organization can create under their contract. When it is reached more deployments can be made, but the user will be warned that this will result in a higher billing. A value of zero means unlimited.", "type": "integer", "x-versionadded": "v2.21" }, "restrictedSharing": { "description": "Whether sharing is only allowed within the organization. This will only be present if the corresponding feature flag is set.", "type": "boolean" }, "snapshotLimit": { "description": "The number of snapshots allowed for a dataset for this organization.", "type": [ "integer", "null" ] }, "supportEmail": { "description": "The support email of the organization.", "type": [ "string", "null" ] } }, "required": [ "activeUsersCount", "agreementStatus", "datasetRefreshJobLimit", "datasetRefreshJobUserLimit", "defaultUserMaxGpuWorkers", "defaultUserMaxWorkers", "groupsCount", "id", "inactiveUsersCount", "maxDeploymentLimit", "maxEdaWorkers", "maxExecutionEnvironments", "maxGpuWorkers", "maxLlmApiCalls", "maxPipelineModuleRuntimes", "maxVectorDatabases", "maxWorkers", "maximumActiveUsers", "membersCount", "name", "ormVersion", "prepaidDeploymentLimit", "snapshotLimit", "supportEmail" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accountPermissions | object | false | | Account permissions available to this organization. This will only be present if the corresponding feature flag is set. | | » additionalProperties | boolean | false | | none | | activeUsersCount | integer | true | | The number of active users for this organization. This excludes deactivated users, aliased DataRobot accounts (in SaaS), and users with NON_BUILDER_USER seat license. | | agreementStatus | string | true | | The status of the organization's agreement to the terms of DataRobot. | | customAppAutoStopSeconds | integer,null | false | minimum: 1 | Per-organization override for the cluster-default custom application auto-stop timeout (in seconds). When null, the cluster default is used. | | datasetRefreshJobLimit | integer,null | true | | How many enabled dataset refresh jobs are allowed per-dataset for this organization. | | datasetRefreshJobUserLimit | integer,null | true | | How many enabled dataset refresh jobs are allowed per-user for this organization. | | defaultUserMaxGpuWorkers | integer,null | true | | Maximum number of concurrent GPU workers assigned to a newly created user of this organization. | | defaultUserMaxWorkers | integer,null | true | | Maximum number of concurrent workers assigned to a newly created user of this organization. | | deleted | boolean,null | false | | Indicates whether the organization is soft-deleted. | | deletedAt | string,null(date-time) | false | | Indicates when the organization was soft-deleted. | | deletedBy | string,null | false | | Indicates who has soft-deleted the organization. | | desiredCustomModelContainerSize | integer,null | false | maximum: 15032385536minimum: 134217728 | The desired custom-model memory size. This will only be present if the corresponding feature flag is set. | | enableSso | boolean | false | | If the SSO is enabled for this organization. This will only be present if the corresponding feature flag is set. | | groupsCount | integer | true | | The number of user groups belonging to this organization. | | id | string | true | | The organization identifier. | | inactiveUsersCount | integer | true | | The number of inactive users for this organization. | | maxCodespaceFileUploadSize | integer,null | false | maximum: 50000minimum: 10 | Maximum file upload size (MB) for Codespaces. | | maxCodespaces | integer,null | false | | Maximum number of codespaces available to this organization. | | maxComputeServerlessPredictionApi | integer,null | false | minimum: 1 | The number of maximum allowed compute on a serverless platform for this organization | | maxConcurrentBatchPredictionsJob | integer,null | false | maximum: 10minimum: 1 | Maximum number of concurrent batch prediction jobs to this organization | | maxConcurrentNotebooks | integer,null | false | | Maximum number of concurrent notebooks available to this organization. | | maxCustomModelContainerSize | integer,null | false | maximum: 15032385536minimum: 134217728 | The maximum memory that might be allocated by the custom-model. This will only be present if the corresponding feature flag is set. | | maxCustomModelReplicasPerDeployment | integer,null | false | maximum: 25 | A fixed number of replicas that will be set for the given custom-model. This will only be present if the corresponding feature flag is set. | | maxCustomModelReplicasPerDeploymentForBatchPredictions | integer,null | false | maximum: 8minimum: 1 | The maximum custom inference model replicas per deployment for batch predictions. This will only be present if the corresponding feature flag is set. | | maxDailyBatchesPerDeployment | integer,null | false | minimum: 1 | The maximum number of monitoring batches that can be created per day on deployments that belong to this organization. | | maxDeploymentLimit | integer | true | | The absolute limit on the number of deployments an organization is allowed to create. A value of zero means unlimited. | | maxEdaWorkers | integer,null | true | | Maximum number of EDA workers assigned available to this organization. | | maxEnabledNotebookSchedules | integer,null | false | minimum: 1 | Maximum number of enabled notebook schedules for this organization. | | maxExecutionEnvironments | integer,null | true | minimum: 0 | The upper limit for the number of maximum execution environments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level. | | maxGpuWorkers | integer,null | true | | Maximum number of concurrent GPU workers available to this organization. | | maxLlmApiCalls | integer,null | true | minimum: 1 | Maximum number of LLM calls. | | maxNotebookMachineSize | string,null | false | | Maximum size of notebook machine available to this organization. | | maxNotebookSchedulingWorkers | integer,null | false | maximum: 20minimum: 1 | Maximum number of concurrently running notebooks schedules for this organization. | | maxPipelineModuleRuntimes | integer,null | true | | Maximum number of Pipeline module runtimes available to this organization. | | maxUploadSize | integer,null | false | | Maximum size for file uploads (MB). This will only be present if the corresponding feature flag is set. | | maxUploadSizeCatalog | integer,null | false | | Maximum size for catalog file uploads (MB). This will only be present if the corresponding feature flag is set. | | maxVectorDatabases | integer,null | true | minimum: 1 | Maximum number of local vector databases. | | maxWorkers | integer,null | true | | Maximum number of concurrent workers available to this organization. | | maximumActiveUsers | integer | true | | The limit for active users for this organization. A value of zero means unlimited. | | membersCount | integer | true | | The number of members in this organization. | | mlopsEventStorageRetentionDays | integer | false | minimum: 0 | The number of days to keep MLOps events in storage. Events with older timestamps will be removed. | | name | string,null | true | | The name of the organization. | | notebooksSoftDeletionRetentionPeriod | string,null | false | | Retention window for soft-deleted notebooks and respective resources before NBX services hard-delete them. | | ormVersion | string | true | | On-demand Resource Manager (prediction service provisioning) version. | | permadeleted | boolean,null | false | | Indicates whether the organization is permadeleted. | | permadeletedAt | string,null(date-time) | false | | Indicates when the organization was permadeleted. | | permadeletedBy | string,null | false | | Indicates who has permadeleted the organization. | | prepaidDeploymentLimit | integer | true | | The number of deployments an organization can create under their contract. When it is reached more deployments can be made, but the user will be warned that this will result in a higher billing. A value of zero means unlimited. | | restrictedSharing | boolean | false | | Whether sharing is only allowed within the organization. This will only be present if the corresponding feature flag is set. | | snapshotLimit | integer,null | true | | The number of snapshots allowed for a dataset for this organization. | | supportEmail | string,null | true | | The support email of the organization. | ### Enumerated Values | Property | Value | | --- | --- | | agreementStatus | [NEEDED, AGREED, N/A] | | maxNotebookMachineSize | [L, M, S, XL, XS, XXL] | | ormVersion | [v1, v2, v3, CCM] | ## OrganizationUser ``` { "properties": { "accessRoleIds": { "description": "List of access role ids assigned to the user being created", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "create": { "default": false, "description": "Whether to create the user if not found.", "type": "boolean" }, "firstName": { "description": "The first name of the user being created.", "maxLength": 100, "type": "string" }, "language": { "description": "Language selection for the user being created.", "enum": [ "ar_001", "de_DE", "en", "es_419", "fr", "ja", "ko", "pt_BR", "test", "uk_UA" ], "type": "string" }, "lastName": { "description": "The last name of the user being created.", "maxLength": 100, "type": "string" }, "orgAdmin": { "default": false, "description": "Is this user should be marked as an organizational admin", "type": "boolean", "x-versionadded": "v2.24" }, "password": { "description": "Password for the user being created. Should be specified if `create` set to `true`", "maxLength": 512, "minLength": 8, "type": "string" }, "requireClickthrough": { "default": false, "description": "Require the user being created to agree to a clickthrough", "type": "boolean" }, "username": { "description": "The username of the user to add to the organization.", "type": "string" } }, "required": [ "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accessRoleIds | [string] | false | maxItems: 100 | List of access role ids assigned to the user being created | | create | boolean | false | | Whether to create the user if not found. | | firstName | string | false | maxLength: 100 | The first name of the user being created. | | language | string | false | | Language selection for the user being created. | | lastName | string | false | maxLength: 100 | The last name of the user being created. | | orgAdmin | boolean | false | | Is this user should be marked as an organizational admin | | password | string | false | maxLength: 512minLength: 8minLength: 8 | Password for the user being created. Should be specified if create set to true | | requireClickthrough | boolean | false | | Require the user being created to agree to a clickthrough | | username | string | true | | The username of the user to add to the organization. | ### Enumerated Values | Property | Value | | --- | --- | | language | [ar_001, de_DE, en, es_419, fr, ja, ko, pt_BR, test, uk_UA] | ## OrganizationUserCreatedResponse ``` { "properties": { "userId": { "description": "The ID of the user added to the organization.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | userId | string | false | | The ID of the user added to the organization. | ## OrganizationUserListResponse ``` { "properties": { "count": { "description": "The number of items on the current page.", "type": "integer" }, "data": { "description": "The list of users in the organization.", "items": { "properties": { "accessRoleIds": { "description": "The list of access role IDs assigned to the user.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "activated": { "description": "Whether the organization user is activated.", "type": "boolean" }, "expirationDate": { "description": "User expiration date", "format": "date-time", "type": [ "string", "null" ] }, "externalId": { "description": "The external user ID.", "type": "string" }, "firstName": { "description": "The first name of the user being created.", "type": [ "string", "null" ] }, "id": { "description": "The organization user identifier.", "type": "string" }, "isAliasedDatarobotAccount": { "description": "Determines whether or not the username uses aliasing in the email address for DataRobot accounts.", "type": "boolean", "x-versionadded": "v2.38" }, "lastName": { "description": "The last name of the user being created.", "type": [ "string", "null" ] }, "maxWorkers": { "description": "Maximum number of concurrent workers available to this user.", "type": "integer" }, "orgAdmin": { "description": "Is this user should be marked as an organizational admin", "type": "boolean", "x-versionadded": "v2.24" }, "organizationId": { "description": "The organization identifier.", "type": "string" }, "scheduledForDeletion": { "description": "Whether the user is scheduled for deletion. Only set when a specific feature flag is configured.", "type": "boolean" }, "username": { "description": "The username of the user to add to the organization.", "type": "string" } }, "required": [ "activated", "expirationDate", "externalId", "firstName", "id", "isAliasedDatarobotAccount", "lastName", "maxWorkers", "orgAdmin", "organizationId", "username" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page)", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page", "format": "uri", "type": [ "string", "null" ] } }, "required": [ "count", "data", "next", "previous" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items on the current page. | | data | [OrganizationUserResponse] | true | maxItems: 1000 | The list of users in the organization. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page) | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page | ## OrganizationUserPatch ``` { "properties": { "accessRoleIds": { "description": "The list of access role IDs assigned to the user.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "activated": { "description": "Whether the organization user is activated.", "type": "boolean" }, "expirationDate": { "description": "The user expiration date.", "format": "date-time", "type": "string" }, "maxWorkers": { "description": "The user max workers.", "exclusiveMinimum": 0, "type": "integer" }, "orgAdmin": { "description": "Mark the user as an organizational admin.", "type": "boolean" }, "organizationId": { "description": "Organization to move the user into (destination org).", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accessRoleIds | [string] | false | maxItems: 100 | The list of access role IDs assigned to the user. | | activated | boolean | false | | Whether the organization user is activated. | | expirationDate | string(date-time) | false | | The user expiration date. | | maxWorkers | integer | false | | The user max workers. | | orgAdmin | boolean | false | | Mark the user as an organizational admin. | | organizationId | string | false | | Organization to move the user into (destination org). | ## OrganizationUserResponse ``` { "properties": { "accessRoleIds": { "description": "The list of access role IDs assigned to the user.", "items": { "type": "string" }, "maxItems": 100, "type": "array" }, "activated": { "description": "Whether the organization user is activated.", "type": "boolean" }, "expirationDate": { "description": "User expiration date", "format": "date-time", "type": [ "string", "null" ] }, "externalId": { "description": "The external user ID.", "type": "string" }, "firstName": { "description": "The first name of the user being created.", "type": [ "string", "null" ] }, "id": { "description": "The organization user identifier.", "type": "string" }, "isAliasedDatarobotAccount": { "description": "Determines whether or not the username uses aliasing in the email address for DataRobot accounts.", "type": "boolean", "x-versionadded": "v2.38" }, "lastName": { "description": "The last name of the user being created.", "type": [ "string", "null" ] }, "maxWorkers": { "description": "Maximum number of concurrent workers available to this user.", "type": "integer" }, "orgAdmin": { "description": "Is this user should be marked as an organizational admin", "type": "boolean", "x-versionadded": "v2.24" }, "organizationId": { "description": "The organization identifier.", "type": "string" }, "scheduledForDeletion": { "description": "Whether the user is scheduled for deletion. Only set when a specific feature flag is configured.", "type": "boolean" }, "username": { "description": "The username of the user to add to the organization.", "type": "string" } }, "required": [ "activated", "expirationDate", "externalId", "firstName", "id", "isAliasedDatarobotAccount", "lastName", "maxWorkers", "orgAdmin", "organizationId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accessRoleIds | [string] | false | maxItems: 100 | The list of access role IDs assigned to the user. | | activated | boolean | true | | Whether the organization user is activated. | | expirationDate | string,null(date-time) | true | | User expiration date | | externalId | string | true | | The external user ID. | | firstName | string,null | true | | The first name of the user being created. | | id | string | true | | The organization user identifier. | | isAliasedDatarobotAccount | boolean | true | | Determines whether or not the username uses aliasing in the email address for DataRobot accounts. | | lastName | string,null | true | | The last name of the user being created. | | maxWorkers | integer | true | | Maximum number of concurrent workers available to this user. | | orgAdmin | boolean | true | | Is this user should be marked as an organizational admin | | organizationId | string | true | | The organization identifier. | | scheduledForDeletion | boolean | false | | Whether the user is scheduled for deletion. Only set when a specific feature flag is configured. | | username | string | true | | The username of the user to add to the organization. | ## OrganizationsResponse ``` { "properties": { "id": { "description": "The identifier of the organization", "type": "string" }, "name": { "description": "The name of the organization", "type": "string" }, "tenantId": { "description": "The identifier of the tenant", "type": "string" } }, "required": [ "id", "name", "tenantId" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the organization | | name | string | true | | The name of the organization | | tenantId | string | true | | The identifier of the tenant | ## PermissionSet ``` { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" } ``` Permission set for project entities. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | admin | boolean | false | | Whether admin access is granted. | | read | boolean | false | | Whether read access is granted. | | write | boolean | false | | Whether write access is granted. | ## PermissionSetInfo ``` { "properties": { "active": { "description": "Whether the entity is usable in the system.", "type": "boolean" }, "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "entity": { "description": "The internal name of the entity.", "enum": [ "APPLICATION", "ARTIFACT", "ARTIFACT_REPOSITORY", "AUDIT_LOG", "COMPUTE_CLUSTERS_MANAGEMENT", "CUSTOM_APPLICATION", "CUSTOM_APPLICATION_SOURCE", "CUSTOM_ENVIRONMENT", "CUSTOM_MODEL", "DATASET_DATA", "DATASET_INFO", "DATA_SOURCE", "DATA_STORE", "DEPLOYMENT", "DYNAMIC_SYSTEM_CONFIG", "ENTITLEMENT_DEFINITION", "ENTITLEMENT_SET", "ENTITLEMENT_SET_LEASE", "EXPERIMENT_CONTAINER", "EXTERNAL_APPLICATION", "GROUP", "MODEL_PACKAGE", "NOTIFICATION_POLICY", "ORGANIZATION", "PREDICTION_ENVIRONMENT", "PREDICTION_SERVER", "PROJECT", "REGISTERED_MODEL", "RISK_MANAGEMENT_FRAMEWORK", "USER", "WORKLOAD" ], "type": "string" }, "entityName": { "description": "The readable name of the entity.", "type": "string" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "required": [ "active", "entity", "entityName" ], "type": "object", "x-versionadded": "v2.36" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | active | boolean | true | | Whether the entity is usable in the system. | | admin | boolean | false | | Whether admin access is granted. | | entity | string | true | | The internal name of the entity. | | entityName | string | true | | The readable name of the entity. | | read | boolean | false | | Whether read access is granted. | | write | boolean | false | | Whether write access is granted. | ### Enumerated Values | Property | Value | | --- | --- | | entity | [APPLICATION, ARTIFACT, ARTIFACT_REPOSITORY, AUDIT_LOG, COMPUTE_CLUSTERS_MANAGEMENT, CUSTOM_APPLICATION, CUSTOM_APPLICATION_SOURCE, CUSTOM_ENVIRONMENT, CUSTOM_MODEL, DATASET_DATA, DATASET_INFO, DATA_SOURCE, DATA_STORE, DEPLOYMENT, DYNAMIC_SYSTEM_CONFIG, ENTITLEMENT_DEFINITION, ENTITLEMENT_SET, ENTITLEMENT_SET_LEASE, EXPERIMENT_CONTAINER, EXTERNAL_APPLICATION, GROUP, MODEL_PACKAGE, NOTIFICATION_POLICY, ORGANIZATION, PREDICTION_ENVIRONMENT, PREDICTION_SERVER, PROJECT, REGISTERED_MODEL, RISK_MANAGEMENT_FRAMEWORK, USER, WORKLOAD] | ## Permissions ``` { "description": "The permissions for the new Access Role. Each entity type accepts a specific subset of the valid permissions.", "properties": { "APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ARTIFACT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ARTIFACT_REPOSITORY": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "AUDIT_LOG": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "COMPUTE_CLUSTERS_MANAGEMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_APPLICATION_SOURCE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_ENVIRONMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "CUSTOM_MODEL": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATASET_DATA": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATASET_INFO": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATA_SOURCE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DATA_STORE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DEPLOYMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "DYNAMIC_SYSTEM_CONFIG": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_DEFINITION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_SET": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ENTITLEMENT_SET_LEASE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "EXPERIMENT_CONTAINER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "EXTERNAL_APPLICATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "GROUP": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "MODEL_PACKAGE": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "NOTIFICATION_POLICY": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "ORGANIZATION": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PREDICTION_ENVIRONMENT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PREDICTION_SERVER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "PROJECT": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "REGISTERED_MODEL": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "RISK_MANAGEMENT_FRAMEWORK": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "USER": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" }, "WORKLOAD": { "description": "Permission set for project entities.", "properties": { "admin": { "description": "Whether admin access is granted.", "type": "boolean" }, "read": { "description": "Whether read access is granted.", "type": "boolean" }, "write": { "description": "Whether write access is granted.", "type": "boolean" } }, "type": "object", "x-versionadded": "v2.36" } }, "type": "object", "x-versionadded": "v2.36" } ``` The permissions for the new Access Role. Each entity type accepts a specific subset of the valid permissions. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | APPLICATION | PermissionSet | false | | Permission set for project entities. | | ARTIFACT | PermissionSet | false | | Permission set for project entities. | | ARTIFACT_REPOSITORY | PermissionSet | false | | Permission set for project entities. | | AUDIT_LOG | PermissionSet | false | | Permission set for project entities. | | COMPUTE_CLUSTERS_MANAGEMENT | PermissionSet | false | | Permission set for project entities. | | CUSTOM_APPLICATION | PermissionSet | false | | Permission set for project entities. | | CUSTOM_APPLICATION_SOURCE | PermissionSet | false | | Permission set for project entities. | | CUSTOM_ENVIRONMENT | PermissionSet | false | | Permission set for project entities. | | CUSTOM_MODEL | PermissionSet | false | | Permission set for project entities. | | DATASET_DATA | PermissionSet | false | | Permission set for project entities. | | DATASET_INFO | PermissionSet | false | | Permission set for project entities. | | DATA_SOURCE | PermissionSet | false | | Permission set for project entities. | | DATA_STORE | PermissionSet | false | | Permission set for project entities. | | DEPLOYMENT | PermissionSet | false | | Permission set for project entities. | | DYNAMIC_SYSTEM_CONFIG | PermissionSet | false | | Permission set for project entities. | | ENTITLEMENT_DEFINITION | PermissionSet | false | | Permission set for project entities. | | ENTITLEMENT_SET | PermissionSet | false | | Permission set for project entities. | | ENTITLEMENT_SET_LEASE | PermissionSet | false | | Permission set for project entities. | | EXPERIMENT_CONTAINER | PermissionSet | false | | Permission set for project entities. | | EXTERNAL_APPLICATION | PermissionSet | false | | Permission set for project entities. | | GROUP | PermissionSet | false | | Permission set for project entities. | | MODEL_PACKAGE | PermissionSet | false | | Permission set for project entities. | | NOTIFICATION_POLICY | PermissionSet | false | | Permission set for project entities. | | ORGANIZATION | PermissionSet | false | | Permission set for project entities. | | PREDICTION_ENVIRONMENT | PermissionSet | false | | Permission set for project entities. | | PREDICTION_SERVER | PermissionSet | false | | Permission set for project entities. | | PROJECT | PermissionSet | false | | Permission set for project entities. | | REGISTERED_MODEL | PermissionSet | false | | Permission set for project entities. | | RISK_MANAGEMENT_FRAMEWORK | PermissionSet | false | | Permission set for project entities. | | USER | PermissionSet | false | | Permission set for project entities. | | WORKLOAD | PermissionSet | false | | Permission set for project entities. | ## PreviewPermadeleteStatus ``` { "description": "Preview report id and associated status.", "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "reportId": { "description": "Report ID", "type": "string" }, "status": { "description": "The processing state of report building task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "reportId", "status" ], "type": "object" } ``` Preview report id and associated status. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string,null | true | | May contain further information about the status. | | reportId | string | true | | Report ID | | status | string | true | | The processing state of report building task. | ### Enumerated Values | Property | Value | | --- | --- | | status | [ABORTED, BLOCKED, COMPLETED, CREATED, ERROR, EXPIRED, INCOMPLETE, INITIALIZED, PAUSED, RUNNING] | ## ResourceCategoriesResponse ``` { "properties": { "data": { "description": "The resource categories.", "items": { "properties": { "id": { "description": "The identifier of the resource category", "type": "string" }, "name": { "description": "The display name of the resource category", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [ResourceCategory] | true | maxItems: 1000 | The resource categories. | ## ResourceCategory ``` { "properties": { "id": { "description": "The identifier of the resource category", "type": "string" }, "name": { "description": "The display name of the resource category", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.37" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The identifier of the resource category | | name | string | true | | The display name of the resource category | ## ResourceShare ``` { "properties": { "resourceId": { "description": "ID of the resource to share with invited users.", "type": "string" }, "resourceType": { "description": "Resource type to share with invited users.", "enum": [ "app" ], "type": "string" } }, "required": [ "resourceId", "resourceType" ], "type": "object", "x-versionadded": "v2.41" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | resourceId | string | true | | ID of the resource to share with invited users. | | resourceType | string | true | | Resource type to share with invited users. | ### Enumerated Values | Property | Value | | --- | --- | | resourceType | app | ## SharedRolesUpdateWithGrant ``` { "properties": { "operation": { "description": "Name of the action being taken. The only operation is 'updateRoles'.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "An array of RoleRequest objects. May contain at most 100 such objects.", "items": { "oneOf": [ { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "The username of the user to update the access role for.", "type": "string" } }, "required": [ "role", "shareRecipientType", "username" ], "type": "object" }, { "properties": { "canShare": { "default": true, "description": "Whether the org/group/user should be able to share with others.If true, the org/group/user will be able to grant any role up to and includingtheir own to other orgs/groups/user. If `role` is `NO_ROLE` `canShare` is ignored.", "type": "boolean" }, "id": { "description": "The ID of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "NO_ROLE", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "Describes the recipient type, either user, group, or organization.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ] }, "maxItems": 100, "minItems": 1, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | Name of the action being taken. The only operation is 'updateRoles'. | | roles | [oneOf] | true | maxItems: 100minItems: 1 | An array of RoleRequest objects. May contain at most 100 such objects. | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithUsernameWithGrant | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | GrantAccessControlWithIdWithGrant | false | | none | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## SharingListV2Response ``` { "properties": { "count": { "description": "The number of items returned.", "type": "integer" }, "data": { "description": "The access control list.", "items": { "properties": { "id": { "description": "The identifier of the recipient.", "type": "string" }, "name": { "description": "The name of the recipient.", "type": "string" }, "role": { "description": "The role of the recipient on this entity.", "enum": [ "ADMIN", "CONSUMER", "DATA_SCIENTIST", "EDITOR", "OBSERVER", "OWNER", "READ_ONLY", "READ_WRITE", "USER" ], "type": "string" }, "shareRecipientType": { "description": "The type of the recipient.", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "name", "role", "shareRecipientType" ], "type": "object" }, "maxItems": 1000, "type": "array" }, "next": { "description": "The URL pointing to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The URL pointing to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items matching the condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned. | | data | [AccessControlV2] | true | maxItems: 1000 | The access control list. | | next | string,null | true | | The URL pointing to the next page. | | previous | string,null | true | | The URL pointing to the previous page. | | totalCount | integer | true | | The total number of items matching the condition. | ## SummaryPermadeleteStatus ``` { "description": "Report id and associated status.", "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "reportId": { "description": "Report ID", "type": "string" }, "status": { "description": "The processing state of users perma-delete task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "reportId", "status" ], "type": "object" } ``` Report id and associated status. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string,null | true | | May contain further information about the status. | | reportId | string | true | | Report ID | | status | string | true | | The processing state of users perma-delete task. | ### Enumerated Values | Property | Value | | --- | --- | | status | [ABORTED, BLOCKED, COMPLETED, CREATED, ERROR, EXPIRED, INCOMPLETE, INITIALIZED, PAUSED, RUNNING] | ## TenantResourceTypeItem ``` { "properties": { "id": { "description": "Resource type: CPU/GPU.", "type": "string" }, "name": { "description": "Resource type name: CPU usage/GPU usage.", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | Resource type: CPU/GPU. | | name | string | true | | Resource type name: CPU usage/GPU usage. | ## TenantResourceTypeResponse ``` { "properties": { "data": { "description": "The available resource types.", "items": { "properties": { "id": { "description": "Resource type: CPU/GPU.", "type": "string" }, "name": { "description": "Resource type name: CPU usage/GPU usage.", "type": "string" } }, "required": [ "id", "name" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 2, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [TenantResourceTypeItem] | true | maxItems: 2 | The available resource types. | ## TenantResourceUtilizationItem ``` { "properties": { "metricDatetime": { "description": "The date and time for the resource usage metric.", "type": "string" }, "metricGroups": { "description": "The groups of workload.", "items": { "properties": { "group": { "description": "The metric group name.", "type": "string" }, "value": { "description": "The metric value.", "type": "number" } }, "required": [ "group", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "resourceType": { "description": "The resource type of the workload (GPU or CPU).", "type": "string" }, "total": { "description": "The resources utilized for all metric groups.", "type": "number" } }, "required": [ "metricDatetime", "metricGroups", "resourceType", "total" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metricDatetime | string | true | | The date and time for the resource usage metric. | | metricGroups | [MetricGroups] | true | maxItems: 100 | The groups of workload. | | resourceType | string | true | | The resource type of the workload (GPU or CPU). | | total | number | true | | The resources utilized for all metric groups. | ## TenantResourceUtilizationResponse ``` { "properties": { "data": { "description": "Resource usage records grouped by workload group and category", "items": { "properties": { "metricDatetime": { "description": "The date and time for the resource usage metric.", "type": "string" }, "metricGroups": { "description": "The groups of workload.", "items": { "properties": { "group": { "description": "The metric group name.", "type": "string" }, "value": { "description": "The metric value.", "type": "number" } }, "required": [ "group", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 100, "type": "array" }, "resourceType": { "description": "The resource type of the workload (GPU or CPU).", "type": "string" }, "total": { "description": "The resources utilized for all metric groups.", "type": "number" } }, "required": [ "metricDatetime", "metricGroups", "resourceType", "total" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 10000, "type": "array" }, "hash": { "description": "SHA-256 hash for file integrity verification.", "type": "string" }, "licenseLimit": { "description": "The license limit for the resource type.", "type": "integer" }, "maxGroupUsages": { "description": "Maximum resources utilized per groups of workload for period.", "items": { "properties": { "group": { "description": "The metric group name.", "type": "string" }, "value": { "description": "The metric value.", "type": "number" } }, "required": [ "group", "value" ], "type": "object", "x-versionadded": "v2.39" }, "maxItems": 1000, "type": "array" }, "maxUsage": { "description": "Maximum resources utilized for period.", "type": "number" }, "metricName": { "description": "The metric name.", "type": "string" } }, "required": [ "data", "licenseLimit", "maxGroupUsages", "maxUsage", "metricName" ], "type": "object", "x-versionadded": "v2.39" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [TenantResourceUtilizationItem] | true | maxItems: 10000 | Resource usage records grouped by workload group and category | | hash | string | false | | SHA-256 hash for file integrity verification. | | licenseLimit | integer | true | | The license limit for the resource type. | | maxGroupUsages | [MetricGroups] | true | maxItems: 1000 | Maximum resources utilized per groups of workload for period. | | maxUsage | number | true | | Maximum resources utilized for period. | | metricName | string | true | | The metric name. | ## UsageItem ``` { "properties": { "cloud": { "description": "Cloud provider where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "cloudRegion": { "description": "Cloud region where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "cost": { "description": "Cost for the usage", "type": "number", "x-versionadded": "v2.41" }, "currency": { "default": "USD", "description": "The price currency.", "enum": [ "usd", "Usd", "USD", "jpy", "Jpy", "JPY" ], "type": "string", "x-versionadded": "v2.41" }, "instanceType": { "description": "Instance type where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "price": { "description": "Numeric price for workload", "type": "number", "x-versionadded": "v2.41" }, "priceUnit": { "description": "The unit of price calculation.", "type": "string", "x-versionadded": "v2.41" }, "resourceBundleId": { "description": "The identifier of the resource bundle", "type": "string" }, "resourceBundleName": { "description": "Human readable name of the resource bundle", "type": "string" }, "usageUnit": { "description": "The unit of measurement for the usage value", "type": "string" }, "usageValue": { "description": "Numeric measure of the usage", "type": "number" }, "workloadId": { "description": "The identifier of the workload", "type": "string" }, "workloadName": { "description": "Human readable name of the workload", "type": "string" } }, "required": [ "resourceBundleId", "resourceBundleName", "usageUnit", "usageValue", "workloadId", "workloadName" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloud | string | false | | Cloud provider where workload was executed | | cloudRegion | string | false | | Cloud region where workload was executed | | cost | number | false | | Cost for the usage | | currency | string | false | | The price currency. | | instanceType | string | false | | Instance type where workload was executed | | price | number | false | | Numeric price for workload | | priceUnit | string | false | | The unit of price calculation. | | resourceBundleId | string | true | | The identifier of the resource bundle | | resourceBundleName | string | true | | Human readable name of the resource bundle | | usageUnit | string | true | | The unit of measurement for the usage value | | usageValue | number | true | | Numeric measure of the usage | | workloadId | string | true | | The identifier of the workload | | workloadName | string | true | | Human readable name of the workload | ### Enumerated Values | Property | Value | | --- | --- | | currency | [usd, Usd, USD, jpy, Jpy, JPY] | ## UsageResponse ``` { "properties": { "data": { "description": "The usage records.", "items": { "properties": { "cloud": { "description": "Cloud provider where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "cloudRegion": { "description": "Cloud region where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "cost": { "description": "Cost for the usage", "type": "number", "x-versionadded": "v2.41" }, "currency": { "default": "USD", "description": "The price currency.", "enum": [ "usd", "Usd", "USD", "jpy", "Jpy", "JPY" ], "type": "string", "x-versionadded": "v2.41" }, "instanceType": { "description": "Instance type where workload was executed", "type": "string", "x-versionadded": "v2.41" }, "price": { "description": "Numeric price for workload", "type": "number", "x-versionadded": "v2.41" }, "priceUnit": { "description": "The unit of price calculation.", "type": "string", "x-versionadded": "v2.41" }, "resourceBundleId": { "description": "The identifier of the resource bundle", "type": "string" }, "resourceBundleName": { "description": "Human readable name of the resource bundle", "type": "string" }, "usageUnit": { "description": "The unit of measurement for the usage value", "type": "string" }, "usageValue": { "description": "Numeric measure of the usage", "type": "number" }, "workloadId": { "description": "The identifier of the workload", "type": "string" }, "workloadName": { "description": "Human readable name of the workload", "type": "string" } }, "required": [ "resourceBundleId", "resourceBundleName", "usageUnit", "usageValue", "workloadId", "workloadName" ], "type": "object", "x-versionadded": "v2.35" }, "maxItems": 1000, "type": "array" } }, "required": [ "data" ], "type": "object", "x-versionadded": "v2.35" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | [UsageItem] | true | maxItems: 1000 | The usage records. | ## UserBulkCreate ``` { "properties": { "add": { "description": "List of User objects to create with associated RequestIds", "items": { "properties": { "attributionId": { "description": "User ID in external marketing systems (SFDC)", "type": "string" }, "company": { "description": "Company where the user works.", "type": "string" }, "country": { "description": "Country where the user lives.", "format": "ISO 3166-1 alpha-2 code", "type": "string" }, "defaultCatalogDatasetSampleSize": { "description": "Size of a sample to propose by default when enabling Fast Registration workflow.", "properties": { "type": { "description": "Sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "expirationDate": { "description": "Datetime, RFC3339, at which the user should expire.", "format": "date-time", "type": "string" }, "externalId": { "description": "User ID in external IdP.", "type": "string" }, "firstName": { "description": "First name of the user being created.", "type": "string" }, "jobTitle": { "description": "Job Title the user has.", "type": "string" }, "language": { "default": "en", "description": "Sets the language in the app for the user, and the language of the invite email, if the user is being invited (i.e. the password is omitted). Value must be a valid ISO-639-1 language code. Available options are: `en`, `ja`, `fr`, `ko`. System's default language will be used if omitted.", "enum": [ "ar_001", "de_DE", "en", "es_419", "fr", "ja", "ko", "pt_BR", "test", "uk_UA" ], "type": "string" }, "lastName": { "description": "Last name of the user being created.", "type": "string" }, "maxGpuWorkers": { "description": "Amount of user's available GPU workers.", "type": "integer" }, "maxIdleWorkers": { "description": "Amount of org workers that the user can utilize when idle.", "type": "integer" }, "maxUploadSize": { "description": "The upper limit for the allowed upload size for this user.", "type": "integer" }, "maxUploadSizeCatalog": { "description": "The upper limit for the allowed upload size in the AI catalog for this user.", "type": "integer" }, "maxWorkers": { "description": "Amount of user's available workers.", "type": "integer" }, "organizationId": { "description": "ID of the organization to add the user to.", "type": "string" }, "password": { "description": "Creates user with this password, if blank sends username invite.", "type": "string" }, "requestId": { "description": "Caller-provided unique identifier for matching user create request to result in response", "type": "string" }, "requireClickthroughAgreement": { "description": "Boolean to require the user to agree to a clickthrough.", "type": "boolean" }, "userType": { "description": "External name for UserType such as AcademicUser, BasicUser, ProUser.", "enum": [ "AcademicUser", "BasicUser", "ProUser", "Covid19TrialUser" ], "type": "string" }, "username": { "description": "Email address of new user.", "type": "string" } }, "required": [ "firstName", "requestId", "userType", "username" ], "type": "object" }, "maxItems": 50, "minItems": 1, "type": "array" } }, "required": [ "add" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | add | [UserBulkCreateObject] | true | maxItems: 50minItems: 1 | List of User objects to create with associated RequestIds | ## UserBulkCreateObject ``` { "properties": { "attributionId": { "description": "User ID in external marketing systems (SFDC)", "type": "string" }, "company": { "description": "Company where the user works.", "type": "string" }, "country": { "description": "Country where the user lives.", "format": "ISO 3166-1 alpha-2 code", "type": "string" }, "defaultCatalogDatasetSampleSize": { "description": "Size of a sample to propose by default when enabling Fast Registration workflow.", "properties": { "type": { "description": "Sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "expirationDate": { "description": "Datetime, RFC3339, at which the user should expire.", "format": "date-time", "type": "string" }, "externalId": { "description": "User ID in external IdP.", "type": "string" }, "firstName": { "description": "First name of the user being created.", "type": "string" }, "jobTitle": { "description": "Job Title the user has.", "type": "string" }, "language": { "default": "en", "description": "Sets the language in the app for the user, and the language of the invite email, if the user is being invited (i.e. the password is omitted). Value must be a valid ISO-639-1 language code. Available options are: `en`, `ja`, `fr`, `ko`. System's default language will be used if omitted.", "enum": [ "ar_001", "de_DE", "en", "es_419", "fr", "ja", "ko", "pt_BR", "test", "uk_UA" ], "type": "string" }, "lastName": { "description": "Last name of the user being created.", "type": "string" }, "maxGpuWorkers": { "description": "Amount of user's available GPU workers.", "type": "integer" }, "maxIdleWorkers": { "description": "Amount of org workers that the user can utilize when idle.", "type": "integer" }, "maxUploadSize": { "description": "The upper limit for the allowed upload size for this user.", "type": "integer" }, "maxUploadSizeCatalog": { "description": "The upper limit for the allowed upload size in the AI catalog for this user.", "type": "integer" }, "maxWorkers": { "description": "Amount of user's available workers.", "type": "integer" }, "organizationId": { "description": "ID of the organization to add the user to.", "type": "string" }, "password": { "description": "Creates user with this password, if blank sends username invite.", "type": "string" }, "requestId": { "description": "Caller-provided unique identifier for matching user create request to result in response", "type": "string" }, "requireClickthroughAgreement": { "description": "Boolean to require the user to agree to a clickthrough.", "type": "boolean" }, "userType": { "description": "External name for UserType such as AcademicUser, BasicUser, ProUser.", "enum": [ "AcademicUser", "BasicUser", "ProUser", "Covid19TrialUser" ], "type": "string" }, "username": { "description": "Email address of new user.", "type": "string" } }, "required": [ "firstName", "requestId", "userType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributionId | string | false | | User ID in external marketing systems (SFDC) | | company | string | false | | Company where the user works. | | country | string(ISO 3166-1 alpha-2 code) | false | | Country where the user lives. | | defaultCatalogDatasetSampleSize | AdminSampleSize | false | | Size of a sample to propose by default when enabling Fast Registration workflow. | | expirationDate | string(date-time) | false | | Datetime, RFC3339, at which the user should expire. | | externalId | string | false | | User ID in external IdP. | | firstName | string | true | | First name of the user being created. | | jobTitle | string | false | | Job Title the user has. | | language | string | false | | Sets the language in the app for the user, and the language of the invite email, if the user is being invited (i.e. the password is omitted). Value must be a valid ISO-639-1 language code. Available options are: en, ja, fr, ko. System's default language will be used if omitted. | | lastName | string | false | | Last name of the user being created. | | maxGpuWorkers | integer | false | | Amount of user's available GPU workers. | | maxIdleWorkers | integer | false | | Amount of org workers that the user can utilize when idle. | | maxUploadSize | integer | false | | The upper limit for the allowed upload size for this user. | | maxUploadSizeCatalog | integer | false | | The upper limit for the allowed upload size in the AI catalog for this user. | | maxWorkers | integer | false | | Amount of user's available workers. | | organizationId | string | false | | ID of the organization to add the user to. | | password | string | false | | Creates user with this password, if blank sends username invite. | | requestId | string | true | | Caller-provided unique identifier for matching user create request to result in response | | requireClickthroughAgreement | boolean | false | | Boolean to require the user to agree to a clickthrough. | | userType | string | true | | External name for UserType such as AcademicUser, BasicUser, ProUser. | | username | string | true | | Email address of new user. | ### Enumerated Values | Property | Value | | --- | --- | | language | [ar_001, de_DE, en, es_419, fr, ja, ko, pt_BR, test, uk_UA] | | userType | [AcademicUser, BasicUser, ProUser, Covid19TrialUser] | ## UserBulkCreateResponse ``` { "properties": { "count": { "description": "Number of results within the data field.", "minimum": 0, "type": "integer" }, "data": { "description": "List of User object results, corresponding to the creation request.", "items": { "properties": { "data": { "description": "Response message/data from this specific User creation request", "properties": { "message": { "description": "Error or other response message from the creation request if present.", "type": "string" }, "notifyStatus": { "description": "User notification values.", "properties": { "inviteLink": { "description": "The link the user can follow to complete their DR account.", "format": "uri", "type": "string" }, "sentStatus": { "description": "Boolean value whether an invite has been sent to the user or not.", "type": "boolean" } }, "required": [ "sentStatus" ], "type": "object" }, "userId": { "description": "The ID of the user if created.", "type": "string" }, "username": { "description": "The username of the user if created.", "type": "string" } }, "type": "object" }, "requestId": { "description": "Caller-provided unique identifier for matching user create request to result in response", "type": "string" }, "statusCode": { "description": "HTTP status code for this user creation request", "minimum": 100, "type": "integer" } }, "required": [ "data", "requestId", "statusCode" ], "type": "object" }, "maxItems": 50, "type": "array" } }, "required": [ "count", "data" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | minimum: 0 | Number of results within the data field. | | data | [UserBulkCreateResponseObject] | true | maxItems: 50 | List of User object results, corresponding to the creation request. | ## UserBulkCreateResponseData ``` { "description": "Response message/data from this specific User creation request", "properties": { "message": { "description": "Error or other response message from the creation request if present.", "type": "string" }, "notifyStatus": { "description": "User notification values.", "properties": { "inviteLink": { "description": "The link the user can follow to complete their DR account.", "format": "uri", "type": "string" }, "sentStatus": { "description": "Boolean value whether an invite has been sent to the user or not.", "type": "boolean" } }, "required": [ "sentStatus" ], "type": "object" }, "userId": { "description": "The ID of the user if created.", "type": "string" }, "username": { "description": "The username of the user if created.", "type": "string" } }, "type": "object" } ``` Response message/data from this specific User creation request ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string | false | | Error or other response message from the creation request if present. | | notifyStatus | NotifyStatus | false | | User notification values. | | userId | string | false | | The ID of the user if created. | | username | string | false | | The username of the user if created. | ## UserBulkCreateResponseObject ``` { "properties": { "data": { "description": "Response message/data from this specific User creation request", "properties": { "message": { "description": "Error or other response message from the creation request if present.", "type": "string" }, "notifyStatus": { "description": "User notification values.", "properties": { "inviteLink": { "description": "The link the user can follow to complete their DR account.", "format": "uri", "type": "string" }, "sentStatus": { "description": "Boolean value whether an invite has been sent to the user or not.", "type": "boolean" } }, "required": [ "sentStatus" ], "type": "object" }, "userId": { "description": "The ID of the user if created.", "type": "string" }, "username": { "description": "The username of the user if created.", "type": "string" } }, "type": "object" }, "requestId": { "description": "Caller-provided unique identifier for matching user create request to result in response", "type": "string" }, "statusCode": { "description": "HTTP status code for this user creation request", "minimum": 100, "type": "integer" } }, "required": [ "data", "requestId", "statusCode" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | UserBulkCreateResponseData | true | | Response message/data from this specific User creation request | | requestId | string | true | | Caller-provided unique identifier for matching user create request to result in response | | statusCode | integer | true | minimum: 100 | HTTP status code for this user creation request | ## UserCreate ``` { "properties": { "attributionId": { "description": "User ID in external marketing systems (SFDC)", "type": "string" }, "company": { "description": "Company where the user works.", "type": "string" }, "country": { "description": "Country where the user lives.", "format": "ISO 3166-1 alpha-2 code", "type": "string" }, "defaultCatalogDatasetSampleSize": { "description": "Size of a sample to propose by default when enabling Fast Registration workflow.", "properties": { "type": { "description": "Sample size can be specified only as a number of rows for now.", "enum": [ "rows" ], "type": "string", "x-versionadded": "v2.27" }, "value": { "description": "Number of rows to ingest during dataset registration.", "exclusiveMinimum": 0, "maximum": 1000000, "type": "integer", "x-versionadded": "v2.27" } }, "required": [ "type", "value" ], "type": "object" }, "expirationDate": { "description": "Datetime, RFC3339, at which the user should expire.", "format": "date-time", "type": "string" }, "externalId": { "description": "User ID in external IdP.", "type": "string" }, "firstName": { "description": "First name of the user being created.", "type": "string" }, "jobTitle": { "description": "Job Title the user has.", "type": "string" }, "language": { "default": "en", "description": "Sets the language in the app for the user, and the language of the invite email, if the user is being invited (i.e. the password is omitted). Value must be a valid ISO-639-1 language code. Available options are: `en`, `ja`, `fr`, `ko`. System's default language will be used if omitted.", "enum": [ "ar_001", "de_DE", "en", "es_419", "fr", "ja", "ko", "pt_BR", "test", "uk_UA" ], "type": "string" }, "lastName": { "description": "Last name of the user being created.", "type": "string" }, "maxGpuWorkers": { "description": "Amount of user's available GPU workers.", "type": "integer" }, "maxIdleWorkers": { "description": "Amount of org workers that the user can utilize when idle.", "type": "integer" }, "maxUploadSize": { "description": "The upper limit for the allowed upload size for this user.", "type": "integer" }, "maxUploadSizeCatalog": { "description": "The upper limit for the allowed upload size in the AI catalog for this user.", "type": "integer" }, "maxWorkers": { "description": "Amount of user's available workers.", "type": "integer" }, "organizationId": { "description": "ID of the organization to add the user to.", "type": "string" }, "password": { "description": "Creates user with this password, if blank sends username invite.", "type": "string" }, "requireClickthroughAgreement": { "description": "Boolean to require the user to agree to a clickthrough.", "type": "boolean" }, "userType": { "description": "External name for UserType such as AcademicUser, BasicUser, ProUser.", "enum": [ "AcademicUser", "BasicUser", "ProUser", "Covid19TrialUser" ], "type": "string" }, "username": { "description": "Email address of new user.", "type": "string" } }, "required": [ "firstName", "userType", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attributionId | string | false | | User ID in external marketing systems (SFDC) | | company | string | false | | Company where the user works. | | country | string(ISO 3166-1 alpha-2 code) | false | | Country where the user lives. | | defaultCatalogDatasetSampleSize | AdminSampleSize | false | | Size of a sample to propose by default when enabling Fast Registration workflow. | | expirationDate | string(date-time) | false | | Datetime, RFC3339, at which the user should expire. | | externalId | string | false | | User ID in external IdP. | | firstName | string | true | | First name of the user being created. | | jobTitle | string | false | | Job Title the user has. | | language | string | false | | Sets the language in the app for the user, and the language of the invite email, if the user is being invited (i.e. the password is omitted). Value must be a valid ISO-639-1 language code. Available options are: en, ja, fr, ko. System's default language will be used if omitted. | | lastName | string | false | | Last name of the user being created. | | maxGpuWorkers | integer | false | | Amount of user's available GPU workers. | | maxIdleWorkers | integer | false | | Amount of org workers that the user can utilize when idle. | | maxUploadSize | integer | false | | The upper limit for the allowed upload size for this user. | | maxUploadSizeCatalog | integer | false | | The upper limit for the allowed upload size in the AI catalog for this user. | | maxWorkers | integer | false | | Amount of user's available workers. | | organizationId | string | false | | ID of the organization to add the user to. | | password | string | false | | Creates user with this password, if blank sends username invite. | | requireClickthroughAgreement | boolean | false | | Boolean to require the user to agree to a clickthrough. | | userType | string | true | | External name for UserType such as AcademicUser, BasicUser, ProUser. | | username | string | true | | Email address of new user. | ### Enumerated Values | Property | Value | | --- | --- | | language | [ar_001, de_DE, en, es_419, fr, ja, ko, pt_BR, test, uk_UA] | | userType | [AcademicUser, BasicUser, ProUser, Covid19TrialUser] | ## UserCreateResponse ``` { "properties": { "notifyStatus": { "description": "User notification values.", "properties": { "inviteLink": { "description": "The link the user can follow to complete their DR account.", "format": "uri", "type": "string" }, "sentStatus": { "description": "Boolean value whether an invite has been sent to the user or not.", "type": "boolean" } }, "required": [ "sentStatus" ], "type": "object" }, "userId": { "description": "The ID of the user.", "type": "string" }, "username": { "description": "The username of the user.", "type": "string" } }, "required": [ "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | notifyStatus | NotifyStatus | false | | User notification values. | | userId | string | true | | The ID of the user. | | username | string | true | | The username of the user. | ## UserGroupBulkDelete ``` { "properties": { "groups": { "description": "The groups to remove.", "items": { "properties": { "groupId": { "description": "The identifier of the user group.", "type": "string" } }, "required": [ "groupId" ], "type": "object" }, "maxItems": 100, "type": "array" } }, "required": [ "groups" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | groups | [UserGroupRetrieve] | true | maxItems: 100 | The groups to remove. | ## UserGroupCreate ``` { "properties": { "accessRoleId": { "description": "The identifier of the access role assigned to the group.", "type": "string" }, "description": { "description": "The description of this user group.", "maxLength": 1000, "type": "string" }, "email": { "description": "The email that can be used to contact this user group.", "type": "string" }, "name": { "description": "The name of the new user group. Must be unique.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The organization identifier of this user group.", "type": "string" } }, "required": [ "name", "orgId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accessRoleId | string | false | | The identifier of the access role assigned to the group. | | description | string | false | maxLength: 1000 | The description of this user group. | | email | string | false | | The email that can be used to contact this user group. | | name | string | true | maxLength: 100 | The name of the new user group. Must be unique. | | orgId | string | true | | The organization identifier of this user group. | ## UserGroupResponse ``` { "properties": { "accessRoleId": { "description": "The identifier of the access role assigned to the group.", "type": [ "string", "null" ] }, "accessRoleName": { "description": "The name of the access role assigned to the group.", "type": [ "string", "null" ] }, "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions of this user group. Each key is a permission name.", "type": "object" }, "createdBy": { "description": "The identifier of the user who created this user group.", "type": [ "string", "null" ] }, "description": { "description": "The description of this user group.", "type": [ "string", "null" ] }, "email": { "description": "The email of this user group.", "type": [ "string", "null" ] }, "externalId": { "description": "The identifier of this group in the identity provider. Set when the group is managed via SCIM.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "id": { "description": "The identifier of the user group.", "type": "string" }, "maxCustomDeployments": { "description": "The number of maximum custom deployments available for users of this group.", "type": [ "integer", "null" ] }, "maxCustomDeploymentsLimit": { "description": "The upper limit for the number of maximum custom deployments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxEdaWorkers": { "description": "The upper limit for a number of EDA workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "maxRam": { "description": "The upper limit for amount of RAM available to users of this user group.", "type": [ "integer", "null" ] }, "maxUploadCatalogSizeLimit": { "description": "The upper limit for the maximum upload size in the AI catalog. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxUploadSize": { "description": "The maximum allowed upload size for users of this group.", "type": [ "integer", "null" ] }, "maxUploadSizeCatalog": { "description": "The maximum allowed upload size in the AI catalog for users in this group.", "type": [ "integer", "null" ] }, "maxUploadSizeLimit": { "description": "The upper limit for the maximum allowed upload size. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxWorkers": { "description": "The upper limit for a number of workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "membersCount": { "description": "The number of members in this user group.", "type": "integer" }, "name": { "description": "The name of the user group.", "type": "string" }, "orgId": { "description": "The identifier of the organization the user group belongs to.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization this user group belongs to.", "type": [ "string", "null" ] }, "scimIdpName": { "description": "The name of the identity provider managing this group via SCIM, e.g. `okta` or `entra_id`.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "scimManaged": { "description": "Set to `true` if this group is managed by an external identity provider via SCIM. Set to `false` if not.", "type": "boolean", "x-versionadded": "v2.44" } }, "required": [ "accessRoleId", "accessRoleName", "accountPermissions", "createdBy", "description", "email", "externalId", "id", "maxCustomDeployments", "maxCustomDeploymentsLimit", "maxEdaWorkers", "maxRam", "maxUploadCatalogSizeLimit", "maxUploadSize", "maxUploadSizeCatalog", "maxUploadSizeLimit", "maxWorkers", "membersCount", "name", "orgId", "orgName", "scimIdpName", "scimManaged" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accessRoleId | string,null | true | | The identifier of the access role assigned to the group. | | accessRoleName | string,null | true | | The name of the access role assigned to the group. | | accountPermissions | object | true | | Account permissions of this user group. Each key is a permission name. | | » additionalProperties | boolean | false | | none | | createdBy | string,null | true | | The identifier of the user who created this user group. | | description | string,null | true | | The description of this user group. | | email | string,null | true | | The email of this user group. | | externalId | string,null | true | | The identifier of this group in the identity provider. Set when the group is managed via SCIM. | | id | string | true | | The identifier of the user group. | | maxCustomDeployments | integer,null | true | | The number of maximum custom deployments available for users of this group. | | maxCustomDeploymentsLimit | integer,null | true | | The upper limit for the number of maximum custom deployments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level. | | maxEdaWorkers | integer,null | true | | The upper limit for a number of EDA workers to be run for users of this user group. | | maxRam | integer,null | true | | The upper limit for amount of RAM available to users of this user group. | | maxUploadCatalogSizeLimit | integer,null | true | | The upper limit for the maximum upload size in the AI catalog. The limit is defined if the group is part of an organization, and the attribute is set on the organization level. | | maxUploadSize | integer,null | true | | The maximum allowed upload size for users of this group. | | maxUploadSizeCatalog | integer,null | true | | The maximum allowed upload size in the AI catalog for users in this group. | | maxUploadSizeLimit | integer,null | true | | The upper limit for the maximum allowed upload size. The limit is defined if the group is part of an organization, and the attribute is set on the organization level. | | maxWorkers | integer,null | true | | The upper limit for a number of workers to be run for users of this user group. | | membersCount | integer | true | | The number of members in this user group. | | name | string | true | | The name of the user group. | | orgId | string,null | true | | The identifier of the organization the user group belongs to. | | orgName | string,null | true | | The name of the organization this user group belongs to. | | scimIdpName | string,null | true | | The name of the identity provider managing this group via SCIM, e.g. okta or entra_id. | | scimManaged | boolean | true | | Set to true if this group is managed by an external identity provider via SCIM. Set to false if not. | ## UserGroupRetrieve ``` { "properties": { "groupId": { "description": "The identifier of the user group.", "type": "string" } }, "required": [ "groupId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | groupId | string | true | | The identifier of the user group. | ## UserGroupUpdate ``` { "properties": { "accessRoleId": { "description": "The identifier of the access role to assign to the group.", "type": [ "string", "null" ] }, "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions to set for this user group. Each key is a permission name.", "type": "object" }, "description": { "description": "The description of this user group.", "maxLength": 1000, "type": "string" }, "email": { "description": "The email of this user group.", "type": "string" }, "name": { "description": "The new name for the user group.", "maxLength": 100, "type": "string" }, "orgId": { "description": "The ID of the organization to assign the user group to.", "type": "string" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accessRoleId | string,null | false | | The identifier of the access role to assign to the group. | | accountPermissions | object | false | | Account permissions to set for this user group. Each key is a permission name. | | » additionalProperties | boolean | false | | none | | description | string | false | maxLength: 1000 | The description of this user group. | | email | string | false | | The email of this user group. | | name | string | false | maxLength: 100 | The new name for the user group. | | orgId | string | false | | The ID of the organization to assign the user group to. | ## UserGroupsListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "List of user groups that match the query condition.", "items": { "properties": { "accessRoleId": { "description": "The identifier of the access role assigned to the group.", "type": [ "string", "null" ] }, "accessRoleName": { "description": "The name of the access role assigned to the group.", "type": [ "string", "null" ] }, "accountPermissions": { "additionalProperties": { "type": "boolean" }, "description": "Account permissions of this user group. Each key is a permission name.", "type": "object" }, "createdBy": { "description": "The identifier of the user who created this user group.", "type": [ "string", "null" ] }, "description": { "description": "The description of this user group.", "type": [ "string", "null" ] }, "email": { "description": "The email of this user group.", "type": [ "string", "null" ] }, "externalId": { "description": "The identifier of this group in the identity provider. Set when the group is managed via SCIM.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "id": { "description": "The identifier of the user group.", "type": "string" }, "maxCustomDeployments": { "description": "The number of maximum custom deployments available for users of this group.", "type": [ "integer", "null" ] }, "maxCustomDeploymentsLimit": { "description": "The upper limit for the number of maximum custom deployments. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxEdaWorkers": { "description": "The upper limit for a number of EDA workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "maxRam": { "description": "The upper limit for amount of RAM available to users of this user group.", "type": [ "integer", "null" ] }, "maxUploadCatalogSizeLimit": { "description": "The upper limit for the maximum upload size in the AI catalog. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxUploadSize": { "description": "The maximum allowed upload size for users of this group.", "type": [ "integer", "null" ] }, "maxUploadSizeCatalog": { "description": "The maximum allowed upload size in the AI catalog for users in this group.", "type": [ "integer", "null" ] }, "maxUploadSizeLimit": { "description": "The upper limit for the maximum allowed upload size. The limit is defined if the group is part of an organization, and the attribute is set on the organization level.", "type": [ "integer", "null" ] }, "maxWorkers": { "description": "The upper limit for a number of workers to be run for users of this user group.", "type": [ "integer", "null" ] }, "membersCount": { "description": "The number of members in this user group.", "type": "integer" }, "name": { "description": "The name of the user group.", "type": "string" }, "orgId": { "description": "The identifier of the organization the user group belongs to.", "type": [ "string", "null" ] }, "orgName": { "description": "The name of the organization this user group belongs to.", "type": [ "string", "null" ] }, "scimIdpName": { "description": "The name of the identity provider managing this group via SCIM, e.g. `okta` or `entra_id`.", "type": [ "string", "null" ], "x-versionadded": "v2.44" }, "scimManaged": { "description": "Set to `true` if this group is managed by an external identity provider via SCIM. Set to `false` if not.", "type": "boolean", "x-versionadded": "v2.44" } }, "required": [ "accessRoleId", "accessRoleName", "accountPermissions", "createdBy", "description", "email", "externalId", "id", "maxCustomDeployments", "maxCustomDeploymentsLimit", "maxEdaWorkers", "maxRam", "maxUploadCatalogSizeLimit", "maxUploadSize", "maxUploadSizeCatalog", "maxUploadSizeLimit", "maxWorkers", "membersCount", "name", "orgId", "orgName", "scimIdpName", "scimManaged" ], "type": "object" }, "maxItems": 100, "type": "array" }, "next": { "description": "The URL to the next page.", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL to the previous page.", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items that match the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [UserGroupResponse] | true | maxItems: 100 | List of user groups that match the query condition. | | next | string,null(uri) | true | | The URL to the next page. | | previous | string,null(uri) | true | | The URL to the previous page. | | totalCount | integer | true | | The total number of items that match the query condition. | ## UserInGroup ``` { "properties": { "expirationDate": { "description": "The expiration date of the user account, if applicable.", "type": [ "string", "null" ] }, "firstName": { "description": "The first name of the user.", "type": [ "string", "null" ] }, "lastName": { "description": "The last name of the user.", "type": [ "string", "null" ] }, "organization": { "description": "The name of the organization the user is part of.", "type": [ "string", "null" ] }, "scheduledForDeletion": { "description": "If the user is scheduled for deletion. Will be returned when an appropriate FF is enabled.", "type": "boolean" }, "status": { "description": "The status of the user, `active` if the has been activated, `inactive` otherwise.", "enum": [ "active", "inactive" ], "type": [ "string", "null" ] }, "userId": { "description": "The identifier of the user.", "type": [ "string", "null" ] }, "username": { "description": "The name of the user.", "type": "string" } }, "required": [ "firstName", "lastName", "organization", "status", "userId", "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | expirationDate | string,null | false | | The expiration date of the user account, if applicable. | | firstName | string,null | true | | The first name of the user. | | lastName | string,null | true | | The last name of the user. | | organization | string,null | true | | The name of the organization the user is part of. | | scheduledForDeletion | boolean | false | | If the user is scheduled for deletion. Will be returned when an appropriate FF is enabled. | | status | string,null | true | | The status of the user, active if the has been activated, inactive otherwise. | | userId | string,null | true | | The identifier of the user. | | username | string | true | | The name of the user. | ### Enumerated Values | Property | Value | | --- | --- | | status | [active, inactive] | ## Username ``` { "properties": { "username": { "description": "The name of the user.", "type": "string" } }, "required": [ "username" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | username | string | true | | The name of the user. | ## UsersPermadelete ``` { "properties": { "forceDeploymentDelete": { "default": false, "description": "If true, any deployment this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceNoCodeApplicationDelete": { "default": false, "description": "If true, any no code app this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceProjectDelete": { "default": false, "description": "If true, any project this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "orgId": { "default": null, "description": "Organization which's users to be permanently deleted.", "type": [ "string", "null" ] }, "orphansOwner": { "default": null, "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": [ "string", "null" ] }, "userIds": { "description": "Users to be permanently deleted.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forceDeploymentDelete | boolean,null | false | | If true, any deployment this user group has a role on, including those shared outside the users, will be deleted. | | forceNoCodeApplicationDelete | boolean,null | false | | If true, any no code app this user group has a role on, including those shared outside the users, will be deleted. | | forceProjectDelete | boolean,null | false | | If true, any project this user group has a role on, including those shared outside the users, will be deleted. | | orgId | string,null | false | | Organization which's users to be permanently deleted. | | orphansOwner | string,null | false | | User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned. | | userIds | [string] | false | maxItems: 100minItems: 1 | Users to be permanently deleted. | ## UsersPermadeleteDeleteReportParamsResponse ``` { "properties": { "data": { "description": "The users cleanup delete parameters.", "properties": { "forceDeploymentDelete": { "default": false, "description": "If true, any deployment this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceNoCodeApplicationDelete": { "default": false, "description": "If true, any no code app this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceProjectDelete": { "default": false, "description": "If true, any project this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "orgId": { "default": null, "description": "Organization which's users to be permanently deleted.", "type": [ "string", "null" ] }, "orphansOwner": { "default": null, "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": [ "string", "null" ] }, "orphansOwnerName": { "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": "string" }, "userIds": { "description": "Users to be permanently deleted.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "type": "object" }, "message": { "description": "May contain further details.", "type": [ "string", "null" ] } }, "required": [ "data", "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | UsersPermadeleteResponse | true | | The users cleanup delete parameters. | | message | string,null | true | | May contain further details. | ## UsersPermadeleteJobResponse ``` { "properties": { "message": { "description": "Information about the users perma-deletion job submission.", "type": [ "string", "null" ] } }, "required": [ "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string,null | true | | Information about the users perma-deletion job submission. | ## UsersPermadeleteJobStatusResponse ``` { "properties": { "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "data": { "description": "Report id and associated status.", "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "reportId": { "description": "Report ID", "type": "string" }, "status": { "description": "The processing state of users perma-delete task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "reportId", "status" ], "type": "object" }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The processing state of the job.", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "created", "data", "message", "status", "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string(date-time) | true | | The time the status record was created. | | data | SummaryPermadeleteStatus | true | | Report id and associated status. | | message | string,null | true | | May contain further information about the status. | | status | string | true | | The processing state of the job. | | statusId | string | true | | The ID of the status object. | ### Enumerated Values | Property | Value | | --- | --- | | status | [ABORTED, COMPLETED, ERROR, EXPIRED, INITIALIZED, RUNNING] | ## UsersPermadeletePreviewJobResponse ``` { "properties": { "message": { "description": "Information about the users perma-deletion preview job submission.", "type": [ "string", "null" ] } }, "required": [ "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | message | string,null | true | | Information about the users perma-deletion preview job submission. | ## UsersPermadeletePreviewJobStatusResponse ``` { "properties": { "created": { "description": "The time the status record was created.", "format": "date-time", "type": "string" }, "data": { "description": "Preview report id and associated status.", "properties": { "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "reportId": { "description": "Report ID", "type": "string" }, "status": { "description": "The processing state of report building task.", "enum": [ "ABORTED", "BLOCKED", "COMPLETED", "CREATED", "ERROR", "EXPIRED", "INCOMPLETE", "INITIALIZED", "PAUSED", "RUNNING" ], "type": "string" } }, "required": [ "message", "reportId", "status" ], "type": "object" }, "message": { "description": "May contain further information about the status.", "type": [ "string", "null" ] }, "status": { "description": "The processing state of the job.", "enum": [ "ABORTED", "COMPLETED", "ERROR", "EXPIRED", "INITIALIZED", "RUNNING" ], "type": "string" }, "statusId": { "description": "The ID of the status object.", "type": "string" } }, "required": [ "created", "data", "message", "status", "statusId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | created | string(date-time) | true | | The time the status record was created. | | data | PreviewPermadeleteStatus | true | | Preview report id and associated status. | | message | string,null | true | | May contain further information about the status. | | status | string | true | | The processing state of the job. | | statusId | string | true | | The ID of the status object. | ### Enumerated Values | Property | Value | | --- | --- | | status | [ABORTED, COMPLETED, ERROR, EXPIRED, INITIALIZED, RUNNING] | ## UsersPermadeletePreviewStatistics ``` { "description": "The users cleanup report statistics.", "properties": { "deploymentsWillBeAssignedNewOwner": { "description": "Number of deployments that will be assigned a new owner.", "type": "integer" }, "deploymentsWillBeDeleted": { "description": "Number of deployments that will be deleted.", "type": "integer" }, "deploymentsWillNotBeDeleted": { "description": "Number of deployments that will not be deleted.", "type": "integer" }, "errors": { "description": "Number of errors encountered during preview construction.", "type": "integer" }, "projectsWillBeAssignedNewOwner": { "description": "Number of projects that will be assigned a new owner.", "type": "integer" }, "projectsWillBeDeleted": { "description": "Number of projects that will be deleted.", "type": "integer" }, "projectsWillNotBeDeleted": { "description": "Number of projects that will not be deleted.", "type": "integer" }, "usersWillBeDeleted": { "description": "Number of users that will be deleted.", "type": "integer" }, "usersWillNotBeDeleted": { "description": "Number of users that will not be deleted.", "type": "integer" } }, "required": [ "deploymentsWillBeAssignedNewOwner", "deploymentsWillBeDeleted", "deploymentsWillNotBeDeleted", "errors", "projectsWillBeAssignedNewOwner", "projectsWillBeDeleted", "projectsWillNotBeDeleted", "usersWillBeDeleted", "usersWillNotBeDeleted" ], "type": "object" } ``` The users cleanup report statistics. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentsWillBeAssignedNewOwner | integer | true | | Number of deployments that will be assigned a new owner. | | deploymentsWillBeDeleted | integer | true | | Number of deployments that will be deleted. | | deploymentsWillNotBeDeleted | integer | true | | Number of deployments that will not be deleted. | | errors | integer | true | | Number of errors encountered during preview construction. | | projectsWillBeAssignedNewOwner | integer | true | | Number of projects that will be assigned a new owner. | | projectsWillBeDeleted | integer | true | | Number of projects that will be deleted. | | projectsWillNotBeDeleted | integer | true | | Number of projects that will not be deleted. | | usersWillBeDeleted | integer | true | | Number of users that will be deleted. | | usersWillNotBeDeleted | integer | true | | Number of users that will not be deleted. | ## UsersPermadeletePreviewStatisticsResponse ``` { "properties": { "data": { "description": "The users cleanup report statistics.", "properties": { "deploymentsWillBeAssignedNewOwner": { "description": "Number of deployments that will be assigned a new owner.", "type": "integer" }, "deploymentsWillBeDeleted": { "description": "Number of deployments that will be deleted.", "type": "integer" }, "deploymentsWillNotBeDeleted": { "description": "Number of deployments that will not be deleted.", "type": "integer" }, "errors": { "description": "Number of errors encountered during preview construction.", "type": "integer" }, "projectsWillBeAssignedNewOwner": { "description": "Number of projects that will be assigned a new owner.", "type": "integer" }, "projectsWillBeDeleted": { "description": "Number of projects that will be deleted.", "type": "integer" }, "projectsWillNotBeDeleted": { "description": "Number of projects that will not be deleted.", "type": "integer" }, "usersWillBeDeleted": { "description": "Number of users that will be deleted.", "type": "integer" }, "usersWillNotBeDeleted": { "description": "Number of users that will not be deleted.", "type": "integer" } }, "required": [ "deploymentsWillBeAssignedNewOwner", "deploymentsWillBeDeleted", "deploymentsWillNotBeDeleted", "errors", "projectsWillBeAssignedNewOwner", "projectsWillBeDeleted", "projectsWillNotBeDeleted", "usersWillBeDeleted", "usersWillNotBeDeleted" ], "type": "object" }, "message": { "description": "May contain further details.", "type": [ "string", "null" ] } }, "required": [ "data", "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | UsersPermadeletePreviewStatistics | true | | The users cleanup report statistics. | | message | string,null | true | | May contain further details. | ## UsersPermadeleteResponse ``` { "description": "The users cleanup delete parameters.", "properties": { "forceDeploymentDelete": { "default": false, "description": "If true, any deployment this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceNoCodeApplicationDelete": { "default": false, "description": "If true, any no code app this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "forceProjectDelete": { "default": false, "description": "If true, any project this user group has a role on, including those shared outside the users, will be deleted.", "type": [ "boolean", "null" ], "x-versionadded": "v2.37" }, "orgId": { "default": null, "description": "Organization which's users to be permanently deleted.", "type": [ "string", "null" ] }, "orphansOwner": { "default": null, "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": [ "string", "null" ] }, "orphansOwnerName": { "description": "User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned.", "type": "string" }, "userIds": { "description": "Users to be permanently deleted.", "items": { "type": "string" }, "maxItems": 100, "minItems": 1, "type": "array" } }, "type": "object" } ``` The users cleanup delete parameters. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | forceDeploymentDelete | boolean,null | false | | If true, any deployment this user group has a role on, including those shared outside the users, will be deleted. | | forceNoCodeApplicationDelete | boolean,null | false | | If true, any no code app this user group has a role on, including those shared outside the users, will be deleted. | | forceProjectDelete | boolean,null | false | | If true, any project this user group has a role on, including those shared outside the users, will be deleted. | | orgId | string,null | false | | Organization which's users to be permanently deleted. | | orphansOwner | string,null | false | | User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned. | | orphansOwnerName | string | false | | User which becomes an owner of any projects or deployments that otherwise cause an error because they would be orphaned. | | userIds | [string] | false | maxItems: 100minItems: 1 | Users to be permanently deleted. | ## UsersPermadeleteSummaryReportStatistics ``` { "description": "The users cleanup summary report statistics.", "properties": { "deploymentsAssignedNewOwner": { "description": "Number of deployments that are assigned a new owner.", "type": "integer" }, "deploymentsDeleted": { "description": "Number of deployments that are deleted.", "type": "integer" }, "deploymentsNotDeleted": { "description": "Number of deployments that are not deleted.", "type": "integer" }, "errors": { "description": "Number of errors encountered during users deletion.", "type": "integer" }, "projectsAssignedNewOwner": { "description": "Number of projects that are assigned a new owner.", "type": "integer" }, "projectsDeleted": { "description": "Number of projects that are deleted.", "type": "integer" }, "projectsNotDeleted": { "description": "Number of projects that are not deleted.", "type": "integer" }, "usersDeleted": { "description": "Number of users that are deleted.", "type": "integer" }, "usersNotDeleted": { "description": "Number of users that are not deleted.", "type": "integer" } }, "required": [ "deploymentsAssignedNewOwner", "deploymentsDeleted", "deploymentsNotDeleted", "errors", "projectsAssignedNewOwner", "projectsDeleted", "projectsNotDeleted", "usersDeleted", "usersNotDeleted" ], "type": "object" } ``` The users cleanup summary report statistics. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentsAssignedNewOwner | integer | true | | Number of deployments that are assigned a new owner. | | deploymentsDeleted | integer | true | | Number of deployments that are deleted. | | deploymentsNotDeleted | integer | true | | Number of deployments that are not deleted. | | errors | integer | true | | Number of errors encountered during users deletion. | | projectsAssignedNewOwner | integer | true | | Number of projects that are assigned a new owner. | | projectsDeleted | integer | true | | Number of projects that are deleted. | | projectsNotDeleted | integer | true | | Number of projects that are not deleted. | | usersDeleted | integer | true | | Number of users that are deleted. | | usersNotDeleted | integer | true | | Number of users that are not deleted. | ## UsersPermadeleteSummaryReportStatisticsResponse ``` { "properties": { "data": { "description": "The users cleanup summary report statistics.", "properties": { "deploymentsAssignedNewOwner": { "description": "Number of deployments that are assigned a new owner.", "type": "integer" }, "deploymentsDeleted": { "description": "Number of deployments that are deleted.", "type": "integer" }, "deploymentsNotDeleted": { "description": "Number of deployments that are not deleted.", "type": "integer" }, "errors": { "description": "Number of errors encountered during users deletion.", "type": "integer" }, "projectsAssignedNewOwner": { "description": "Number of projects that are assigned a new owner.", "type": "integer" }, "projectsDeleted": { "description": "Number of projects that are deleted.", "type": "integer" }, "projectsNotDeleted": { "description": "Number of projects that are not deleted.", "type": "integer" }, "usersDeleted": { "description": "Number of users that are deleted.", "type": "integer" }, "usersNotDeleted": { "description": "Number of users that are not deleted.", "type": "integer" } }, "required": [ "deploymentsAssignedNewOwner", "deploymentsDeleted", "deploymentsNotDeleted", "errors", "projectsAssignedNewOwner", "projectsDeleted", "projectsNotDeleted", "usersDeleted", "usersNotDeleted" ], "type": "object" }, "message": { "description": "May contain further details.", "type": [ "string", "null" ] } }, "required": [ "data", "message" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | UsersPermadeleteSummaryReportStatistics | true | | The users cleanup summary report statistics. | | message | string,null | true | | May contain further details. | --- # Utilities URL: https://docs.datarobot.com/en/docs/api/reference/public-api/utilities.html > Use the endpoints described below to configure and manage encrypted credentials. # Utilities Use the endpoints described below to configure and manage encrypted credentials. ## Encrypt a string Operation path: `POST /api/v2/stringEncryptions/` Authentication requirements: `BearerAuth` DataRobot does not store your credentials when accessing external data stores. Instead you first encrypt your sensitive information with DataRobot and then, when accessing those data stores, supply the encrypted credentials. DataRobot will decrypt your credentials and use them to establish a connection to your data store. ### Body parameter ``` { "properties": { "plainText": { "description": "String to be encrypted. DataRobot will decrypt the string when needed to access data stores.", "type": "string" } }, "required": [ "plainText" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | StringEncryption | false | none | ### Example responses > 200 Response ``` { "properties": { "cipherText": { "description": "Encrypted version of plainText input.", "type": "string" } }, "required": [ "cipherText" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | The encrypted string. | StringEncryptionResponse | # Schemas ## StringEncryption ``` { "properties": { "plainText": { "description": "String to be encrypted. DataRobot will decrypt the string when needed to access data stores.", "type": "string" } }, "required": [ "plainText" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | plainText | string | true | | String to be encrypted. DataRobot will decrypt the string when needed to access data stores. | ## StringEncryptionResponse ``` { "properties": { "cipherText": { "description": "Encrypted version of plainText input.", "type": "string" } }, "required": [ "cipherText" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cipherText | string | true | | Encrypted version of plainText input. | --- # Value Tracker URL: https://docs.datarobot.com/en/docs/api/reference/public-api/value_tracker.html > Use the endpoints described below to manage Value Trackers, as well as add comments and host discussions. # Value Tracker Use the endpoints described below to manage Value Trackers, as well as add comments and host discussions. ## Post a comment Operation path: `POST /api/v2/comments/` Authentication requirements: `BearerAuth` Post a comment. ### Body parameter ``` { "properties": { "content": { "description": "Content of the comment, 10000 symbols max", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the entity to post the comment to", "type": "string" }, "entityType": { "description": "Type of the entity to post the comment to, currently only useCase is supported", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "mentions": { "description": "A list of users IDs mentioned in the content", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "content", "entityId", "entityType" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | Comment | false | none | ### Example responses > 200 Response ``` { "properties": { "content": { "description": "Content of the comment", "type": "string" }, "createdAt": { "description": "Timestamp when the comment was created", "type": "string" }, "createdBy": { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" }, "entityId": { "description": "ID of the entity the comment posted to", "type": "string" }, "entityType": { "description": "Type of the entity to post the comment to, currently only useCase is supported", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "id": { "description": "ID of the comment", "type": "string" }, "mentions": { "description": "A list of users objects (see below) mentioned in the content", "items": { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" }, "type": "array" }, "readonly": { "description": "If True, the comment cannot be updated or deleted, False otherwise", "type": "boolean", "x-versionadded": "v2.38" }, "updatedAt": { "description": "Timestamp when the comment was updated", "type": "string" } }, "required": [ "content", "createdAt", "createdBy", "entityId", "entityType", "id", "mentions", "readonly", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CommentRetrieve | | 201 | Created | The comment was successfully created | None | | 422 | Unprocessable Entity | The request was formatted improperly | None | ## Delete a comment by comment ID Operation path: `DELETE /api/v2/comments/{commentId}/` Authentication requirements: `BearerAuth` Delete a comment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | commentId | path | string | true | The ID of the comment | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | The use case comment was deleted | None | ## Retrieve a comment by comment ID Operation path: `GET /api/v2/comments/{commentId}/` Authentication requirements: `BearerAuth` Retrieve a comment. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | commentId | path | string | true | The ID of the comment | ### Example responses > 200 Response ``` { "properties": { "content": { "description": "Content of the comment", "type": "string" }, "createdAt": { "description": "Timestamp when the comment was created", "type": "string" }, "createdBy": { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" }, "entityId": { "description": "ID of the entity the comment posted to", "type": "string" }, "entityType": { "description": "Type of the entity to post the comment to, currently only useCase is supported", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "id": { "description": "ID of the comment", "type": "string" }, "mentions": { "description": "A list of users objects (see below) mentioned in the content", "items": { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" }, "type": "array" }, "readonly": { "description": "If True, the comment cannot be updated or deleted, False otherwise", "type": "boolean", "x-versionadded": "v2.38" }, "updatedAt": { "description": "Timestamp when the comment was updated", "type": "string" } }, "required": [ "content", "createdAt", "createdBy", "entityId", "entityType", "id", "mentions", "readonly", "updatedAt" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CommentRetrieve | ## Update a comment by comment ID Operation path: `PATCH /api/v2/comments/{commentId}/` Authentication requirements: `BearerAuth` Update a comment. ### Body parameter ``` { "properties": { "content": { "description": "Updated content of the comment, 10000 symbols max", "maxLength": 10000, "type": "string" }, "mentions": { "description": "A list of users IDs mentioned in the content", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "content" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | commentId | path | string | true | The ID of the comment | | body | body | CommentUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | The comment was successfully updated | None | | 422 | Unprocessable Entity | The request was formatted improperly | None | ## List comments by entitytype Operation path: `GET /api/v2/comments/{entityType}/{entityId}/` Authentication requirements: `BearerAuth` List comments. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | orderBy | query | string | false | Sort comments by a field of the comment. | | entityId | path | string | true | ID of the entity to retrieve comments of | | entityType | path | string | true | Type of the entity to retrieve a comments of, currently only useCase is supported | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [createdAt, -createdAt, updatedAt, -updatedAt] | | entityType | [useCase, model, catalog, experimentContainer, deployment, workloadDeployment, workload] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of comments", "items": { "properties": { "content": { "description": "Content of the comment", "type": "string" }, "createdAt": { "description": "Timestamp when the comment was created", "type": "string" }, "createdBy": { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" }, "entityId": { "description": "ID of the entity the comment posted to", "type": "string" }, "entityType": { "description": "Type of the entity to post the comment to, currently only useCase is supported", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "id": { "description": "ID of the comment", "type": "string" }, "mentions": { "description": "A list of users objects (see below) mentioned in the content", "items": { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" }, "type": "array" }, "readonly": { "description": "If True, the comment cannot be updated or deleted, False otherwise", "type": "boolean", "x-versionadded": "v2.38" }, "updatedAt": { "description": "Timestamp when the comment was updated", "type": "string" } }, "required": [ "content", "createdAt", "createdBy", "entityId", "entityType", "id", "mentions", "readonly", "updatedAt" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | CommentList | ## List available value tracker templates Operation path: `GET /api/v2/valueTrackerValueTemplates/` Authentication requirements: `BearerAuth` List available value tracker templates. ### Example responses > 200 Response ``` { "items": { "properties": { "description": { "description": "The description of the value template.", "type": "string" }, "schema": { "description": "Schema definition of all template parameters", "items": { "properties": { "parameterName": { "description": "Name of the parameter", "type": "string" }, "schema": { "description": "Schema entry defining the type and limits of a template parameter", "properties": { "elementType": { "description": "Possible types used for template variables", "enum": [ "integer", "float", "number" ], "type": "string" }, "label": { "description": "Label describing the represented value", "type": "string" }, "maximum": { "description": "Sets Maximum value if given", "type": [ "integer", "null" ] }, "minimum": { "description": "Sets Minimum value if given", "type": [ "integer", "null" ] }, "placeholder": { "description": "Text that can be used to prefill an input filed for the value", "type": "string" }, "unit": { "description": "The unit type (e.g., %).", "type": [ "string", "null" ] } }, "required": [ "elementType", "label", "maximum", "minimum", "placeholder", "unit" ], "type": "object" } }, "required": [ "parameterName", "schema" ], "type": "object" }, "type": "array" }, "templateType": { "description": "The name of the class ValueTracker value template to be retrieved", "enum": [ "classification", "regression" ], "type": "string" }, "title": { "description": "The title of the value template.", "type": "string" } }, "required": [ "description", "schema", "templateType", "title" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | Inline | ### Response Schema Status Code 200 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [ValueTrackerValueTemplateResponse] | false | | none | | » description | string | true | | The description of the value template. | | » schema | [ValueTrackerValueTemplateParameterWithSchema] | true | | Schema definition of all template parameters | | »» parameterName | string | true | | Name of the parameter | | »» schema | ValueTrackerValueTemplateSchema | true | | Schema entry defining the type and limits of a template parameter | | »»» elementType | string | true | | Possible types used for template variables | | »»» label | string | true | | Label describing the represented value | | »»» maximum | integer,null | true | | Sets Maximum value if given | | »»» minimum | integer,null | true | | Sets Minimum value if given | | »»» placeholder | string | true | | Text that can be used to prefill an input filed for the value | | »»» unit | string,null | true | | The unit type (e.g., %). | | » templateType | string | true | | The name of the class ValueTracker value template to be retrieved | | » title | string | true | | The title of the value template. | ### Enumerated Values | Property | Value | | --- | --- | | elementType | [integer, float, number] | | templateType | [classification, regression] | ## Get an individual value tracker value template by its name by templatetype Operation path: `GET /api/v2/valueTrackerValueTemplates/{templateType}/` Authentication requirements: `BearerAuth` Get an individual value tracker value template by its name. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | templateType | path | string | true | The name of the class ValueTracker value template to be retrieved | ### Enumerated Values | Parameter | Value | | --- | --- | | templateType | [classification, regression] | ### Example responses > 200 Response ``` { "properties": { "description": { "description": "The description of the value template.", "type": "string" }, "schema": { "description": "Schema definition of all template parameters", "items": { "properties": { "parameterName": { "description": "Name of the parameter", "type": "string" }, "schema": { "description": "Schema entry defining the type and limits of a template parameter", "properties": { "elementType": { "description": "Possible types used for template variables", "enum": [ "integer", "float", "number" ], "type": "string" }, "label": { "description": "Label describing the represented value", "type": "string" }, "maximum": { "description": "Sets Maximum value if given", "type": [ "integer", "null" ] }, "minimum": { "description": "Sets Minimum value if given", "type": [ "integer", "null" ] }, "placeholder": { "description": "Text that can be used to prefill an input filed for the value", "type": "string" }, "unit": { "description": "The unit type (e.g., %).", "type": [ "string", "null" ] } }, "required": [ "elementType", "label", "maximum", "minimum", "placeholder", "unit" ], "type": "object" } }, "required": [ "parameterName", "schema" ], "type": "object" }, "type": "array" }, "templateType": { "description": "The name of the class ValueTracker value template to be retrieved", "enum": [ "classification", "regression" ], "type": "string" }, "title": { "description": "The title of the value template.", "type": "string" } }, "required": [ "description", "schema", "templateType", "title" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerValueTemplateResponse | | 400 | Bad Request | Bad request | None | ## Calculate the value of the template by templatetype Operation path: `GET /api/v2/valueTrackerValueTemplates/{templateType}/calculation/` Authentication requirements: `BearerAuth` Calculate the value of the template with the given template parameters. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | accuracyImprovement | query | number | true | Accuracy improvement | | decisionsCount | query | integer | true | Estimated number of decisions per year | | incorrectDecisionsCount | query | integer | false | Estimated number of incorrect decisions per year. Required for templateType CLASSIFICATION | | incorrectDecisionCost | query | number | false | Estimated cost of an individual incorrect decision. Required for templateType CLASSIFICATION | | targetValue | query | number | false | Target value. Required for templateType REGRESSION | | templateType | path | string | true | The name of the class ValueTracker value template to be retrieved | ### Enumerated Values | Parameter | Value | | --- | --- | | templateType | [classification, regression] | ### Example responses > 200 Response ``` { "properties": { "averageValuePerDecision": { "description": "Estimated average value per decision", "type": "number" }, "savedAnnually": { "description": "Estimated amount saved annually", "type": "number" }, "template": { "description": "Template type and data used for calculating the result", "properties": { "data": { "description": "Data used for calculating the result", "oneOf": [ { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" } ] }, "templateType": { "description": "The name of the class ValueTracker value template", "enum": [ "classification", "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" } }, "required": [ "averageValuePerDecision", "savedAnnually", "template" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerValueTemplateCalculateResponse | | 400 | Bad Request | Bad request | None | ## List value trackers the requesting user has access to Operation path: `GET /api/v2/valueTrackers/` Authentication requirements: `BearerAuth` List value trackers the requesting user has access to. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | orderBy | query | string | false | Sort the value trackers by a field of the value tracker. | | namePart | query | string | false | Only return value trackers with names that match the given string. | | stage | query | string | false | Filter results by the current stage of the value trackers. | ### Enumerated Values | Parameter | Value | | --- | --- | | orderBy | [businessImpact, -businessImpact, potentialValue, -potentialValue, realizedValue, -realizedValue, feasibility, -feasibility, stage, -stage] | | stage | [ideation, queued, dataPrepAndModeling, validatingAndDeploying, inProduction, retired, onHold] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of value trackers that match the query.", "items": { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The number of items matching the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerListResponse | ## Create a new value tracker Operation path: `POST /api/v2/valueTrackers/` Authentication requirements: `BearerAuth` Create a new value tracker. ### Body parameter ``` { "properties": { "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "maxLength": 1024, "type": "string" }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of this description item.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "name": { "description": "The name of the value tracker.", "maxLength": 512, "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | ValueTracker | false | none | ### Example responses > 200 Response ``` { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": "string" }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerCreateResponse | ## Delete a value tracker by value tracker ID Operation path: `DELETE /api/v2/valueTrackers/{valueTrackerId}/` Authentication requirements: `BearerAuth` Delete a value tracker. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | valueTrackerId | path | string | true | The ID of the ValueTracker. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | The value tracker was deleted. | None | ## Retrieve a value tracker by value tracker ID Operation path: `GET /api/v2/valueTrackers/{valueTrackerId}/` Authentication requirements: `BearerAuth` Retrieve a value tracker. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | valueTrackerId | path | string | true | The ID of the ValueTracker. | ### Example responses > 200 Response ``` { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerResponse | ## Update a value tracker by value tracker ID Operation path: `PATCH /api/v2/valueTrackers/{valueTrackerId}/` Authentication requirements: `BearerAuth` Update a value tracker. ### Body parameter ``` { "properties": { "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "description": { "description": "The value tracker description.", "maxLength": 1024, "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "name": { "description": "The name of the value tracker.", "maxLength": 512, "type": "string" }, "notes": { "description": "The user notes.", "maxLength": 1024, "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Contains the template type and parameter information defined in the response of [GET /api/v2/valueTrackerValueTemplates/{templateType}/calculation/][get-apiv2valuetrackervaluetemplatestemplatetypecalculation]." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "type": "string" }, "type": "array" }, "realizedValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | valueTrackerId | path | string | true | The ID of the ValueTracker. | | body | body | ValueTrackerUpdate | false | none | ### Example responses > 200 Response ``` { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerResponse | ## Retrieve the activities of a value tracker by value tracker ID Operation path: `GET /api/v2/valueTrackers/{valueTrackerId}/activities/` Authentication requirements: `BearerAuth` Retrieve the activities of a value tracker. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | The number of records to skip over. Default 0. | | limit | query | integer | false | The number of records to return in the range from 1 to 100. Default 100. | | valueTrackerId | path | string | true | The ID of the ValueTracker. | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The activities of the value tracker.", "items": { "description": "DataRobot user information.", "properties": { "eventDescription": { "description": "Details of the activity. Content depends on activity type.", "properties": { "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of this description item.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "name": { "description": "The name of this event item.", "type": [ "string", "null" ] }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "eventType": { "description": "The type of activity.", "type": [ "string", "null" ] }, "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the ValueTracker activity.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "timestamp": { "description": "The timestamp of the activity.", "format": "date-time", "type": "string" }, "user": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "eventDescription", "eventType", "timestamp", "user" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The number of items matching the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerActivityListResponse | ## Get the list of resources attached by value tracker ID Operation path: `GET /api/v2/valueTrackers/{valueTrackerId}/attachments/` Authentication requirements: `BearerAuth` Get the list of resources attached to this value tracker. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | offset | query | integer | false | This many results will be skipped. | | limit | query | integer | false | This many results are returned. | | type | query | string | false | The value tracker attachment type. | | valueTrackerId | path | string | true | The ID of the ValueTracker. | ### Enumerated Values | Parameter | Value | | --- | --- | | type | [dataset, modelingProject, deployment, customModel, modelPackage, application] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of results returned.", "type": "integer" }, "data": { "description": "The array of attachment objects.", "items": { "properties": { "attachedObjectId": { "description": "The ID of the attached object.", "type": "string" }, "canView": { "description": "Indicates if the user has view access to the attached object.", "type": "boolean" }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "isAccessRequested": { "description": "Indicates if the user has requested access to the attached object.", "type": "boolean" }, "overview": { "description": "Detailed information depending on the type of the attachment", "oneOf": [ { "properties": { "accuracyHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "modelHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "name": { "description": "The name of the deployment.", "type": "string" }, "predictionServer": { "description": "The name of the prediction server.", "type": "string" }, "predictionUsage": { "description": "The value tracker attachment detail for deployment usage.", "properties": { "dailyRates": { "description": "The list of daily request counts.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Last prediction time.", "format": "date-time", "type": "string" } }, "required": [ "dailyRates", "lastTimestamp" ], "type": "object" }, "serviceHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" } }, "required": [ "accuracyHealth", "modelHealth", "name", "predictionServer", "predictionUsage", "serviceHealth" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the dataset was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The name of the user who created the dataset.", "type": "string" }, "lastModifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "lastModifiedBy": { "description": "The name of the user who did the last modification.", "type": "string" }, "name": { "description": "The name of the dataset.", "type": "string" } }, "required": [ "createdAt", "createdBy", "lastModifiedAt", "lastModifiedBy", "name" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the project was created.", "format": "date-time", "type": "string" }, "datasetName": { "description": "The name of the default dataset.", "type": "string" }, "name": { "description": "The name of the project.", "type": "string" }, "numberOfModels": { "description": "The number of models in the project.", "type": [ "integer", "null" ] }, "owner": { "description": "The name of the modeling project owner.", "type": [ "string", "null" ] }, "targetName": { "description": "The name of the modeling target.", "type": "string" }, "useCase": { "description": "Information about the use case that this modeling project is attached to.", "properties": { "id": { "description": "The ID of the use case this modeling project is attached to.", "type": "string" }, "name": { "description": "The name of the use case this modeling project is attached to.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "createdAt", "datasetName", "name", "numberOfModels", "owner", "targetName" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the custom model was created.", "format": "date-time", "type": "string" }, "modifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the custom model.", "type": "string" }, "owner": { "description": "The name of the custom model owner.", "type": "string" } }, "required": [ "createdAt", "modifiedAt", "name", "owner" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the model package was created.", "format": "date-time", "type": "string" }, "deploymentCount": { "description": "The number of deployments.", "type": "integer" }, "executionType": { "description": "The type of execution.", "type": "string" }, "name": { "description": "The name of the model package.", "type": "string" }, "targetName": { "description": "The name of the modeling target.", "type": "string" }, "targetType": { "description": "Type of modeling target", "type": "string" } }, "required": [ "createdAt", "deploymentCount", "executionType", "name", "targetName", "targetType" ], "type": "object" }, { "properties": { "appType": { "description": "The type of the application.", "type": "string" }, "createdAt": { "description": "The time when the application was created.", "format": "date-time", "type": "string" }, "lastModifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the application.", "type": "string" }, "version": { "description": "The version string of the app.", "type": "string" } }, "required": [ "appType", "createdAt", "lastModifiedAt", "name", "version" ], "type": "object" }, { "type": "null" } ] }, "type": { "description": "The value tracker attachment type.", "enum": [ "dataset", "modelingProject", "deployment", "customModel", "modelPackage", "application" ], "type": "string" }, "useCaseId": { "description": "The ID of the ValueTracker.", "type": "string" } }, "required": [ "attachedObjectId", "canView", "id", "isAccessRequested", "overview", "type", "useCaseId" ], "type": "object" }, "type": "array" }, "next": { "description": "The link to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The link to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of results available.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerAttachmentListResponse | | 404 | Not Found | Either the value tracker does not exist or the user does not have permissions to view this value tracker. | None | ## Attach the list of resources by value tracker ID Operation path: `POST /api/v2/valueTrackers/{valueTrackerId}/attachments/` Authentication requirements: `BearerAuth` Attach the list of resources to this value tracker. ### Body parameter ``` { "properties": { "attachedObjects": { "description": "The array of attachment objects.", "items": { "properties": { "attachedObjectId": { "description": "The ID of the attached object.", "type": "string" }, "type": { "description": "The value tracker attachment type.", "enum": [ "dataset", "modelingProject", "deployment", "customModel", "modelPackage", "application" ], "type": "string" } }, "required": [ "attachedObjectId", "type" ], "type": "object" }, "type": "array" } }, "required": [ "attachedObjects" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | valueTrackerId | path | string | true | The ID of the ValueTracker. | | body | body | CreateValueTrackerAttachment | false | none | ### Example responses > 201 Response ``` { "items": { "properties": { "attachedObjectId": { "description": "The ID of the attached object.", "type": "string" }, "type": { "description": "The value tracker attachment type.", "enum": [ "dataset", "modelingProject", "deployment", "customModel", "modelPackage", "application" ], "type": "string" } }, "required": [ "attachedObjectId", "type" ], "type": "object" }, "type": "array" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | none | Inline | | 404 | Not Found | Use case or object not found or value tracker not writable or object not readable. | None | | 422 | Unprocessable Entity | The request was formatted improperly. | None | ### Response Schema Status Code 201 | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | anonymous | [ValueTrackerAttachment] | false | | none | | » attachedObjectId | string | true | | The ID of the attached object. | | » type | string | true | | The value tracker attachment type. | ### Enumerated Values | Property | Value | | --- | --- | | type | [dataset, modelingProject, deployment, customModel, modelPackage, application] | ## Removes a resource by value tracker ID Operation path: `DELETE /api/v2/valueTrackers/{valueTrackerId}/attachments/{attachmentId}/` Authentication requirements: `BearerAuth` Removes a resource from a value tracker. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | valueTrackerId | path | string | true | The ID of the ValueTracker. | | attachmentId | path | string | true | The ID of the attachment. | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | none | None | | 403 | Forbidden | The user does not have permission to remove objects from this value tracker | None | | 404 | Not Found | Either the value tracker does not exist or the user does not have permissions to view this value tracker. | None | | 422 | Unprocessable Entity | The request was formatted improperly, or the requested object to delete was not found | None | ## Get a resource that is attached by value tracker ID Operation path: `GET /api/v2/valueTrackers/{valueTrackerId}/attachments/{attachmentId}/` Authentication requirements: `BearerAuth` Get a resource that is attached to a value tracker. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | valueTrackerId | path | string | true | The ID of the ValueTracker. | | attachmentId | path | string | true | The ID of the attachment. | ### Example responses > 200 Response ``` { "properties": { "attachedObjectId": { "description": "The ID of the attached object.", "type": "string" }, "canView": { "description": "Indicates if the user has view access to the attached object.", "type": "boolean" }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "isAccessRequested": { "description": "Indicates if the user has requested access to the attached object.", "type": "boolean" }, "overview": { "description": "Detailed information depending on the type of the attachment", "oneOf": [ { "properties": { "accuracyHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "modelHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "name": { "description": "The name of the deployment.", "type": "string" }, "predictionServer": { "description": "The name of the prediction server.", "type": "string" }, "predictionUsage": { "description": "The value tracker attachment detail for deployment usage.", "properties": { "dailyRates": { "description": "The list of daily request counts.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Last prediction time.", "format": "date-time", "type": "string" } }, "required": [ "dailyRates", "lastTimestamp" ], "type": "object" }, "serviceHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" } }, "required": [ "accuracyHealth", "modelHealth", "name", "predictionServer", "predictionUsage", "serviceHealth" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the dataset was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The name of the user who created the dataset.", "type": "string" }, "lastModifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "lastModifiedBy": { "description": "The name of the user who did the last modification.", "type": "string" }, "name": { "description": "The name of the dataset.", "type": "string" } }, "required": [ "createdAt", "createdBy", "lastModifiedAt", "lastModifiedBy", "name" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the project was created.", "format": "date-time", "type": "string" }, "datasetName": { "description": "The name of the default dataset.", "type": "string" }, "name": { "description": "The name of the project.", "type": "string" }, "numberOfModels": { "description": "The number of models in the project.", "type": [ "integer", "null" ] }, "owner": { "description": "The name of the modeling project owner.", "type": [ "string", "null" ] }, "targetName": { "description": "The name of the modeling target.", "type": "string" }, "useCase": { "description": "Information about the use case that this modeling project is attached to.", "properties": { "id": { "description": "The ID of the use case this modeling project is attached to.", "type": "string" }, "name": { "description": "The name of the use case this modeling project is attached to.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "createdAt", "datasetName", "name", "numberOfModels", "owner", "targetName" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the custom model was created.", "format": "date-time", "type": "string" }, "modifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the custom model.", "type": "string" }, "owner": { "description": "The name of the custom model owner.", "type": "string" } }, "required": [ "createdAt", "modifiedAt", "name", "owner" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the model package was created.", "format": "date-time", "type": "string" }, "deploymentCount": { "description": "The number of deployments.", "type": "integer" }, "executionType": { "description": "The type of execution.", "type": "string" }, "name": { "description": "The name of the model package.", "type": "string" }, "targetName": { "description": "The name of the modeling target.", "type": "string" }, "targetType": { "description": "Type of modeling target", "type": "string" } }, "required": [ "createdAt", "deploymentCount", "executionType", "name", "targetName", "targetType" ], "type": "object" }, { "properties": { "appType": { "description": "The type of the application.", "type": "string" }, "createdAt": { "description": "The time when the application was created.", "format": "date-time", "type": "string" }, "lastModifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the application.", "type": "string" }, "version": { "description": "The version string of the app.", "type": "string" } }, "required": [ "appType", "createdAt", "lastModifiedAt", "name", "version" ], "type": "object" }, { "type": "null" } ] }, "type": { "description": "The value tracker attachment type.", "enum": [ "dataset", "modelingProject", "deployment", "customModel", "modelPackage", "application" ], "type": "string" }, "useCaseId": { "description": "The ID of the ValueTracker.", "type": "string" } }, "required": [ "attachedObjectId", "canView", "id", "isAccessRequested", "overview", "type", "useCaseId" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerAttachmentResponse | | 403 | Forbidden | The user does not have permission to attach objects to this value tracker | None | | 404 | Not Found | Either the value tracker does not exist or the user does not have permissions to view this value tracker. | None | | 422 | Unprocessable Entity | The request was formatted improperly, or the requested object to attach was not found | None | ## Retrieve realized value information by value tracker ID Operation path: `GET /api/v2/valueTrackers/{valueTrackerId}/realizedValueOverTime/` Authentication requirements: `BearerAuth` Retrieve realized value information for a given value tracker over a period of time. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | start | query | string,null(date-time) | false | Start of the period to retrieve monitoring data, defaults to 7 days ago from the end of the period. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | end | query | string,null(date-time) | false | End of the period to retrieve monitoring data, defaults to the next top of the hour. Note: this field only accepts top of the hour RFC3339 datetime strings, for example: 2019-08-01T00:00:00Z. | | bucketSize | query | string(duration) | false | The time duration of a bucket. Needs to be multiple of one hour. Can not be longer than the total length of the period. If not set, a default value will be calculated based on the start and end time. | | valueTrackerId | path | string | true | The ID of the class ValueTracker | ### Example responses > 200 Response ``` { "properties": { "buckets": { "description": "An array of `bucket` objects representing prediction totals and realized value over time", "items": { "properties": { "period": { "description": "A `period` object describing the time this bucket covers", "properties": { "end": { "description": "RFC3339 datetime. End of time period to retrieve.", "format": "date-time", "type": "string" }, "start": { "description": "RFC3339 datetime. Start of time period to retrieve.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "predictionsCount": { "description": "Total number of predictions that happened in this bucket", "type": "integer" }, "realizedValue": { "description": "Total amount of value realised during this bucket", "type": "integer" } }, "required": [ "period", "predictionsCount" ], "type": "object" }, "type": "array" }, "previousTimeRangeSummary": { "description": "A `summary` object covering the time range before the given one with the same duration", "properties": { "averagePredictionsCount": { "description": "Average number of predictions per bucket during this time period", "type": "integer" }, "averageRealizedValue": { "description": "Average amount of value per bucket realised during this time period", "type": "integer" }, "period": { "description": "A `period` object describing the time this bucket covers", "properties": { "end": { "description": "RFC3339 datetime. End of time period to retrieve.", "format": "date-time", "type": "string" }, "start": { "description": "RFC3339 datetime. Start of time period to retrieve.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "totalPredictionsCount": { "description": "Total number of predictions that happened during this time period", "type": "integer" }, "totalRealizedValue": { "description": "Total amount of value realised during this time period", "type": "integer" } }, "required": [ "averagePredictionsCount", "period", "totalRealizedValue" ], "type": "object" }, "summary": { "description": "A `summary` object covering the time range before the given one with the same duration", "properties": { "averagePredictionsCount": { "description": "Average number of predictions per bucket during this time period", "type": "integer" }, "averageRealizedValue": { "description": "Average amount of value per bucket realised during this time period", "type": "integer" }, "period": { "description": "A `period` object describing the time this bucket covers", "properties": { "end": { "description": "RFC3339 datetime. End of time period to retrieve.", "format": "date-time", "type": "string" }, "start": { "description": "RFC3339 datetime. Start of time period to retrieve.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "totalPredictionsCount": { "description": "Total number of predictions that happened during this time period", "type": "integer" }, "totalRealizedValue": { "description": "Total amount of value realised during this time period", "type": "integer" } }, "required": [ "averagePredictionsCount", "period", "totalRealizedValue" ], "type": "object" } }, "required": [ "buckets", "previousTimeRangeSummary", "summary" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerRealizedValuesOverTimeResponse | ## Get the list of users, groups and organizations that have access by value tracker ID Operation path: `GET /api/v2/valueTrackers/{valueTrackerId}/sharedRoles/` Authentication requirements: `BearerAuth` Get the list of users, groups and organizations that have access to this value tracker. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | id | query | string | false | Only return roles for a user, group or organization with this identifier. | | offset | query | integer | true | This many results will be skipped | | limit | query | integer | true | At most this many results are returned | | name | query | string | false | Only return roles for a user, group or organization with this name. | | shareRecipientType | query | string | false | The list of access controls for recipients with this type. | | valueTrackerId | path | string | true | The ID of the class ValueTracker | ### Enumerated Values | Parameter | Value | | --- | --- | | shareRecipientType | [user, group, organization] | ### Example responses > 200 Response ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of sharing role objects", "items": { "properties": { "id": { "description": "The ID of the recipient", "type": "string" }, "name": { "description": "The name of the user, group, or organization", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "USER", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The number of items matching the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | ValueTrackerSharingListResponse | ## Share a value tracker by value tracker ID Operation path: `PATCH /api/v2/valueTrackers/{valueTrackerId}/sharedRoles/` Authentication requirements: `BearerAuth` Share a value tracker with a user, group or organization. ### Body parameter ``` { "properties": { "operation": { "description": "The name of the action being taken. Only 'updateRoles' is supported.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "A list of sharing role objects", "items": { "properties": { "id": { "description": "The ID of the recipient", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "USER", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "The name of the user, group, or organization", "type": "string" } }, "required": [ "role", "shareRecipientType" ], "type": "object" }, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | valueTrackerId | path | string | true | The ID of the class ValueTracker | | body | body | ValueTrackerSharingUpdate | false | none | ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | none | None | | 204 | No Content | The roles were updated successfully. | None | | 422 | Unprocessable Entity | The request was formatted improperly. | None | # Schemas ## Comment ``` { "properties": { "content": { "description": "Content of the comment, 10000 symbols max", "maxLength": 10000, "type": "string" }, "entityId": { "description": "ID of the entity to post the comment to", "type": "string" }, "entityType": { "description": "Type of the entity to post the comment to, currently only useCase is supported", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "mentions": { "description": "A list of users IDs mentioned in the content", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "content", "entityId", "entityType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | content | string | true | maxLength: 10000 | Content of the comment, 10000 symbols max | | entityId | string | true | | ID of the entity to post the comment to | | entityType | string | true | | Type of the entity to post the comment to, currently only useCase is supported | | mentions | [string] | false | maxItems: 100 | A list of users IDs mentioned in the content | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [useCase, model, catalog, experimentContainer, deployment, workloadDeployment, workload] | ## CommentList ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of comments", "items": { "properties": { "content": { "description": "Content of the comment", "type": "string" }, "createdAt": { "description": "Timestamp when the comment was created", "type": "string" }, "createdBy": { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" }, "entityId": { "description": "ID of the entity the comment posted to", "type": "string" }, "entityType": { "description": "Type of the entity to post the comment to, currently only useCase is supported", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "id": { "description": "ID of the comment", "type": "string" }, "mentions": { "description": "A list of users objects (see below) mentioned in the content", "items": { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" }, "type": "array" }, "readonly": { "description": "If True, the comment cannot be updated or deleted, False otherwise", "type": "boolean", "x-versionadded": "v2.38" }, "updatedAt": { "description": "Timestamp when the comment was updated", "type": "string" } }, "required": [ "content", "createdAt", "createdBy", "entityId", "entityType", "id", "mentions", "readonly", "updatedAt" ], "type": "object" }, "type": "array" }, "next": { "description": "The URL of the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "The URL of the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of items across all pages.", "type": "integer" } }, "required": [ "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | false | | The number of items returned on this page. | | data | [CommentRetrieve] | true | | A list of comments | | next | string,null(uri) | true | | The URL of the next page (if null, there is no next page). | | previous | string,null(uri) | true | | The URL of the previous page (if null, there is no previous page). | | totalCount | integer | true | | The total number of items across all pages. | ## CommentRetrieve ``` { "properties": { "content": { "description": "Content of the comment", "type": "string" }, "createdAt": { "description": "Timestamp when the comment was created", "type": "string" }, "createdBy": { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" }, "entityId": { "description": "ID of the entity the comment posted to", "type": "string" }, "entityType": { "description": "Type of the entity to post the comment to, currently only useCase is supported", "enum": [ "useCase", "model", "catalog", "experimentContainer", "deployment", "workloadDeployment", "workload" ], "type": "string" }, "id": { "description": "ID of the comment", "type": "string" }, "mentions": { "description": "A list of users objects (see below) mentioned in the content", "items": { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" }, "type": "array" }, "readonly": { "description": "If True, the comment cannot be updated or deleted, False otherwise", "type": "boolean", "x-versionadded": "v2.38" }, "updatedAt": { "description": "Timestamp when the comment was updated", "type": "string" } }, "required": [ "content", "createdAt", "createdBy", "entityId", "entityType", "id", "mentions", "readonly", "updatedAt" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | content | string | true | | Content of the comment | | createdAt | string | true | | Timestamp when the comment was created | | createdBy | CommentUser | true | | User object with information about the commenter | | entityId | string | true | | ID of the entity the comment posted to | | entityType | string | true | | Type of the entity to post the comment to, currently only useCase is supported | | id | string | true | | ID of the comment | | mentions | [CommentUser] | true | | A list of users objects (see below) mentioned in the content | | readonly | boolean | true | | If True, the comment cannot be updated or deleted, False otherwise | | updatedAt | string | true | | Timestamp when the comment was updated | ### Enumerated Values | Property | Value | | --- | --- | | entityType | [useCase, model, catalog, experimentContainer, deployment, workloadDeployment, workload] | ## CommentUpdate ``` { "properties": { "content": { "description": "Updated content of the comment, 10000 symbols max", "maxLength": 10000, "type": "string" }, "mentions": { "description": "A list of users IDs mentioned in the content", "items": { "type": "string" }, "maxItems": 100, "type": "array" } }, "required": [ "content" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | content | string | true | maxLength: 10000 | Updated content of the comment, 10000 symbols max | | mentions | [string] | false | maxItems: 100 | A list of users IDs mentioned in the content | ## CommentUser ``` { "description": "User object with information about the commenter", "properties": { "firstName": { "description": "First name of the commenter", "type": "string" }, "id": { "description": "User ID of the commenter", "type": "string" }, "lastName": { "description": "Last name of the commenter", "type": "string" }, "username": { "description": "Username of the commenter", "type": "string" } }, "required": [ "firstName", "id", "lastName", "username" ], "type": "object" } ``` User object with information about the commenter ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | firstName | string | true | | First name of the commenter | | id | string | true | | User ID of the commenter | | lastName | string | true | | Last name of the commenter | | username | string | true | | Username of the commenter | ## CreateValueTrackerAttachment ``` { "properties": { "attachedObjects": { "description": "The array of attachment objects.", "items": { "properties": { "attachedObjectId": { "description": "The ID of the attached object.", "type": "string" }, "type": { "description": "The value tracker attachment type.", "enum": [ "dataset", "modelingProject", "deployment", "customModel", "modelPackage", "application" ], "type": "string" } }, "required": [ "attachedObjectId", "type" ], "type": "object" }, "type": "array" } }, "required": [ "attachedObjects" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attachedObjects | [ValueTrackerAttachment] | true | | The array of attachment objects. | ## UseCaseInfo ``` { "description": "Information about the use case that this modeling project is attached to.", "properties": { "id": { "description": "The ID of the use case this modeling project is attached to.", "type": "string" }, "name": { "description": "The name of the use case this modeling project is attached to.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } ``` Information about the use case that this modeling project is attached to. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the use case this modeling project is attached to. | | name | string | true | | The name of the use case this modeling project is attached to. | ## ValueTracker ``` { "properties": { "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "maxLength": 1024, "type": "string" }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of this description item.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "name": { "description": "The name of the value tracker.", "maxLength": 512, "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "required": [ "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | businessImpact | integer,null | false | maximum: 5minimum: 1 | The expected effects on overall business operations. | | commentId | string | false | | The ID for this comment. | | content | string | false | | A string | | description | string | false | maxLength: 1024 | The value tracker description. | | feasibility | integer,null | false | maximum: 5minimum: 1 | Assessment of how the value tracker can be accomplished across multiple dimensions. | | id | string,null | false | | The ID of this description item. | | mentions | [ValueTrackerDrUserId] | false | | The list of user objects. | | name | string | true | maxLength: 512 | The name of the value tracker. | | notes | string,null | false | | The user notes. | | owner | ValueTrackerDrUserId | false | | DataRobot user information. | | permissions | [string] | false | | The permissions of the current user. | | potentialValue | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | | potentialValueTemplate | any | false | | Optional. Contains the template type and parameter information. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateClassification | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateRegression | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionTargets | [string] | false | | An array of prediction target name strings. | | realizedValue | any | false | | Optional. Contains MonetaryValue objects. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | string | false | | The current stage of the value tracker. | | targetDates | [ValueTrackerTargetDate] | false | | The array of TargetDate objects. | ### Enumerated Values | Property | Value | | --- | --- | | stage | [ideation, queued, dataPrepAndModeling, validatingAndDeploying, inProduction, retired, onHold] | ## ValueTrackerActivity ``` { "description": "DataRobot user information.", "properties": { "eventDescription": { "description": "Details of the activity. Content depends on activity type.", "properties": { "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of this description item.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "name": { "description": "The name of this event item.", "type": [ "string", "null" ] }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "eventType": { "description": "The type of activity.", "type": [ "string", "null" ] }, "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the ValueTracker activity.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "timestamp": { "description": "The timestamp of the activity.", "format": "date-time", "type": "string" }, "user": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "eventDescription", "eventType", "timestamp", "user" ], "type": "object" } ``` DataRobot user information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | eventDescription | ValueTrackerEventDescriptions | true | | Details of the activity. Content depends on activity type. | | eventType | string,null | true | | The type of activity. | | firstName | string,null | false | | The first name of the ValueTracker owner. | | id | string | false | | The ID of the ValueTracker activity. | | lastName | string,null | false | | The last name of the ValueTracker owner. | | timestamp | string(date-time) | true | | The timestamp of the activity. | | user | ValueTrackerDrUserId | true | | DataRobot user information. | | username | string | false | | The username of the ValueTracker owner. | ## ValueTrackerActivityListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The activities of the value tracker.", "items": { "description": "DataRobot user information.", "properties": { "eventDescription": { "description": "Details of the activity. Content depends on activity type.", "properties": { "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of this description item.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "name": { "description": "The name of this event item.", "type": [ "string", "null" ] }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "eventType": { "description": "The type of activity.", "type": [ "string", "null" ] }, "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The ID of the ValueTracker activity.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "timestamp": { "description": "The timestamp of the activity.", "format": "date-time", "type": "string" }, "user": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "eventDescription", "eventType", "timestamp", "user" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The number of items matching the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [ValueTrackerActivity] | true | | The activities of the value tracker. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | | totalCount | integer | true | | The number of items matching the query condition. | ## ValueTrackerAttachment ``` { "properties": { "attachedObjectId": { "description": "The ID of the attached object.", "type": "string" }, "type": { "description": "The value tracker attachment type.", "enum": [ "dataset", "modelingProject", "deployment", "customModel", "modelPackage", "application" ], "type": "string" } }, "required": [ "attachedObjectId", "type" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attachedObjectId | string | true | | The ID of the attached object. | | type | string | true | | The value tracker attachment type. | ### Enumerated Values | Property | Value | | --- | --- | | type | [dataset, modelingProject, deployment, customModel, modelPackage, application] | ## ValueTrackerAttachmentApplicationOverview ``` { "properties": { "appType": { "description": "The type of the application.", "type": "string" }, "createdAt": { "description": "The time when the application was created.", "format": "date-time", "type": "string" }, "lastModifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the application.", "type": "string" }, "version": { "description": "The version string of the app.", "type": "string" } }, "required": [ "appType", "createdAt", "lastModifiedAt", "name", "version" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | appType | string | true | | The type of the application. | | createdAt | string(date-time) | true | | The time when the application was created. | | lastModifiedAt | string(date-time) | true | | The time of the last modification. | | name | string | true | | The name of the application. | | version | string | true | | The version string of the app. | ## ValueTrackerAttachmentCustomModelOverview ``` { "properties": { "createdAt": { "description": "The time when the custom model was created.", "format": "date-time", "type": "string" }, "modifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the custom model.", "type": "string" }, "owner": { "description": "The name of the custom model owner.", "type": "string" } }, "required": [ "createdAt", "modifiedAt", "name", "owner" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The time when the custom model was created. | | modifiedAt | string(date-time) | true | | The time of the last modification. | | name | string | true | | The name of the custom model. | | owner | string | true | | The name of the custom model owner. | ## ValueTrackerAttachmentDatasetOverview ``` { "properties": { "createdAt": { "description": "The time when the dataset was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The name of the user who created the dataset.", "type": "string" }, "lastModifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "lastModifiedBy": { "description": "The name of the user who did the last modification.", "type": "string" }, "name": { "description": "The name of the dataset.", "type": "string" } }, "required": [ "createdAt", "createdBy", "lastModifiedAt", "lastModifiedBy", "name" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The time when the dataset was created. | | createdBy | string | true | | The name of the user who created the dataset. | | lastModifiedAt | string(date-time) | true | | The time of the last modification. | | lastModifiedBy | string | true | | The name of the user who did the last modification. | | name | string | true | | The name of the dataset. | ## ValueTrackerAttachmentDeploymentHealth ``` { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" } ``` Accuracy related health status. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string(date-time) | true | | The end date of the health status. | | message | string | true | | Health status related information. | | startDate | string(date-time) | true | | The start date of the health status. | | status | string | true | | Health status. | ## ValueTrackerAttachmentDeploymentOverview ``` { "properties": { "accuracyHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "modelHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "name": { "description": "The name of the deployment.", "type": "string" }, "predictionServer": { "description": "The name of the prediction server.", "type": "string" }, "predictionUsage": { "description": "The value tracker attachment detail for deployment usage.", "properties": { "dailyRates": { "description": "The list of daily request counts.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Last prediction time.", "format": "date-time", "type": "string" } }, "required": [ "dailyRates", "lastTimestamp" ], "type": "object" }, "serviceHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" } }, "required": [ "accuracyHealth", "modelHealth", "name", "predictionServer", "predictionUsage", "serviceHealth" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyHealth | ValueTrackerAttachmentDeploymentHealth | true | | Accuracy related health status. | | modelHealth | ValueTrackerAttachmentDeploymentHealth | true | | Accuracy related health status. | | name | string | true | | The name of the deployment. | | predictionServer | string | true | | The name of the prediction server. | | predictionUsage | ValueTrackerAttachmentDeploymentPredictionUsage | true | | The value tracker attachment detail for deployment usage. | | serviceHealth | ValueTrackerAttachmentDeploymentHealth | true | | Accuracy related health status. | ## ValueTrackerAttachmentDeploymentPredictionUsage ``` { "description": "The value tracker attachment detail for deployment usage.", "properties": { "dailyRates": { "description": "The list of daily request counts.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Last prediction time.", "format": "date-time", "type": "string" } }, "required": [ "dailyRates", "lastTimestamp" ], "type": "object" } ``` The value tracker attachment detail for deployment usage. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | dailyRates | [integer] | true | | The list of daily request counts. | | lastTimestamp | string(date-time) | true | | Last prediction time. | ## ValueTrackerAttachmentListResponse ``` { "properties": { "count": { "description": "The number of results returned.", "type": "integer" }, "data": { "description": "The array of attachment objects.", "items": { "properties": { "attachedObjectId": { "description": "The ID of the attached object.", "type": "string" }, "canView": { "description": "Indicates if the user has view access to the attached object.", "type": "boolean" }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "isAccessRequested": { "description": "Indicates if the user has requested access to the attached object.", "type": "boolean" }, "overview": { "description": "Detailed information depending on the type of the attachment", "oneOf": [ { "properties": { "accuracyHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "modelHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "name": { "description": "The name of the deployment.", "type": "string" }, "predictionServer": { "description": "The name of the prediction server.", "type": "string" }, "predictionUsage": { "description": "The value tracker attachment detail for deployment usage.", "properties": { "dailyRates": { "description": "The list of daily request counts.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Last prediction time.", "format": "date-time", "type": "string" } }, "required": [ "dailyRates", "lastTimestamp" ], "type": "object" }, "serviceHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" } }, "required": [ "accuracyHealth", "modelHealth", "name", "predictionServer", "predictionUsage", "serviceHealth" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the dataset was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The name of the user who created the dataset.", "type": "string" }, "lastModifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "lastModifiedBy": { "description": "The name of the user who did the last modification.", "type": "string" }, "name": { "description": "The name of the dataset.", "type": "string" } }, "required": [ "createdAt", "createdBy", "lastModifiedAt", "lastModifiedBy", "name" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the project was created.", "format": "date-time", "type": "string" }, "datasetName": { "description": "The name of the default dataset.", "type": "string" }, "name": { "description": "The name of the project.", "type": "string" }, "numberOfModels": { "description": "The number of models in the project.", "type": [ "integer", "null" ] }, "owner": { "description": "The name of the modeling project owner.", "type": [ "string", "null" ] }, "targetName": { "description": "The name of the modeling target.", "type": "string" }, "useCase": { "description": "Information about the use case that this modeling project is attached to.", "properties": { "id": { "description": "The ID of the use case this modeling project is attached to.", "type": "string" }, "name": { "description": "The name of the use case this modeling project is attached to.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "createdAt", "datasetName", "name", "numberOfModels", "owner", "targetName" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the custom model was created.", "format": "date-time", "type": "string" }, "modifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the custom model.", "type": "string" }, "owner": { "description": "The name of the custom model owner.", "type": "string" } }, "required": [ "createdAt", "modifiedAt", "name", "owner" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the model package was created.", "format": "date-time", "type": "string" }, "deploymentCount": { "description": "The number of deployments.", "type": "integer" }, "executionType": { "description": "The type of execution.", "type": "string" }, "name": { "description": "The name of the model package.", "type": "string" }, "targetName": { "description": "The name of the modeling target.", "type": "string" }, "targetType": { "description": "Type of modeling target", "type": "string" } }, "required": [ "createdAt", "deploymentCount", "executionType", "name", "targetName", "targetType" ], "type": "object" }, { "properties": { "appType": { "description": "The type of the application.", "type": "string" }, "createdAt": { "description": "The time when the application was created.", "format": "date-time", "type": "string" }, "lastModifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the application.", "type": "string" }, "version": { "description": "The version string of the app.", "type": "string" } }, "required": [ "appType", "createdAt", "lastModifiedAt", "name", "version" ], "type": "object" }, { "type": "null" } ] }, "type": { "description": "The value tracker attachment type.", "enum": [ "dataset", "modelingProject", "deployment", "customModel", "modelPackage", "application" ], "type": "string" }, "useCaseId": { "description": "The ID of the ValueTracker.", "type": "string" } }, "required": [ "attachedObjectId", "canView", "id", "isAccessRequested", "overview", "type", "useCaseId" ], "type": "object" }, "type": "array" }, "next": { "description": "The link to the next page.", "type": [ "string", "null" ] }, "previous": { "description": "The link to the previous page.", "type": [ "string", "null" ] }, "totalCount": { "description": "The total number of results available.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of results returned. | | data | [ValueTrackerAttachmentResponse] | true | | The array of attachment objects. | | next | string,null | true | | The link to the next page. | | previous | string,null | true | | The link to the previous page. | | totalCount | integer | true | | The total number of results available. | ## ValueTrackerAttachmentModelPackageOverview ``` { "properties": { "createdAt": { "description": "The time when the model package was created.", "format": "date-time", "type": "string" }, "deploymentCount": { "description": "The number of deployments.", "type": "integer" }, "executionType": { "description": "The type of execution.", "type": "string" }, "name": { "description": "The name of the model package.", "type": "string" }, "targetName": { "description": "The name of the modeling target.", "type": "string" }, "targetType": { "description": "Type of modeling target", "type": "string" } }, "required": [ "createdAt", "deploymentCount", "executionType", "name", "targetName", "targetType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The time when the model package was created. | | deploymentCount | integer | true | | The number of deployments. | | executionType | string | true | | The type of execution. | | name | string | true | | The name of the model package. | | targetName | string | true | | The name of the modeling target. | | targetType | string | true | | Type of modeling target | ## ValueTrackerAttachmentModelingProjectOverview ``` { "properties": { "createdAt": { "description": "The time when the project was created.", "format": "date-time", "type": "string" }, "datasetName": { "description": "The name of the default dataset.", "type": "string" }, "name": { "description": "The name of the project.", "type": "string" }, "numberOfModels": { "description": "The number of models in the project.", "type": [ "integer", "null" ] }, "owner": { "description": "The name of the modeling project owner.", "type": [ "string", "null" ] }, "targetName": { "description": "The name of the modeling target.", "type": "string" }, "useCase": { "description": "Information about the use case that this modeling project is attached to.", "properties": { "id": { "description": "The ID of the use case this modeling project is attached to.", "type": "string" }, "name": { "description": "The name of the use case this modeling project is attached to.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "createdAt", "datasetName", "name", "numberOfModels", "owner", "targetName" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | createdAt | string(date-time) | true | | The time when the project was created. | | datasetName | string | true | | The name of the default dataset. | | name | string | true | | The name of the project. | | numberOfModels | integer,null | true | | The number of models in the project. | | owner | string,null | true | | The name of the modeling project owner. | | targetName | string | true | | The name of the modeling target. | | useCase | UseCaseInfo | false | | Information about the use case that this modeling project is attached to. | ## ValueTrackerAttachmentResponse ``` { "properties": { "attachedObjectId": { "description": "The ID of the attached object.", "type": "string" }, "canView": { "description": "Indicates if the user has view access to the attached object.", "type": "boolean" }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "isAccessRequested": { "description": "Indicates if the user has requested access to the attached object.", "type": "boolean" }, "overview": { "description": "Detailed information depending on the type of the attachment", "oneOf": [ { "properties": { "accuracyHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "modelHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, "name": { "description": "The name of the deployment.", "type": "string" }, "predictionServer": { "description": "The name of the prediction server.", "type": "string" }, "predictionUsage": { "description": "The value tracker attachment detail for deployment usage.", "properties": { "dailyRates": { "description": "The list of daily request counts.", "items": { "type": "integer" }, "type": "array" }, "lastTimestamp": { "description": "Last prediction time.", "format": "date-time", "type": "string" } }, "required": [ "dailyRates", "lastTimestamp" ], "type": "object" }, "serviceHealth": { "description": "Accuracy related health status.", "properties": { "endDate": { "description": "The end date of the health status.", "format": "date-time", "type": "string" }, "message": { "description": "Health status related information.", "type": "string" }, "startDate": { "description": "The start date of the health status.", "format": "date-time", "type": "string" }, "status": { "description": "Health status.", "type": "string" } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" } }, "required": [ "accuracyHealth", "modelHealth", "name", "predictionServer", "predictionUsage", "serviceHealth" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the dataset was created.", "format": "date-time", "type": "string" }, "createdBy": { "description": "The name of the user who created the dataset.", "type": "string" }, "lastModifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "lastModifiedBy": { "description": "The name of the user who did the last modification.", "type": "string" }, "name": { "description": "The name of the dataset.", "type": "string" } }, "required": [ "createdAt", "createdBy", "lastModifiedAt", "lastModifiedBy", "name" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the project was created.", "format": "date-time", "type": "string" }, "datasetName": { "description": "The name of the default dataset.", "type": "string" }, "name": { "description": "The name of the project.", "type": "string" }, "numberOfModels": { "description": "The number of models in the project.", "type": [ "integer", "null" ] }, "owner": { "description": "The name of the modeling project owner.", "type": [ "string", "null" ] }, "targetName": { "description": "The name of the modeling target.", "type": "string" }, "useCase": { "description": "Information about the use case that this modeling project is attached to.", "properties": { "id": { "description": "The ID of the use case this modeling project is attached to.", "type": "string" }, "name": { "description": "The name of the use case this modeling project is attached to.", "type": "string" } }, "required": [ "id", "name" ], "type": "object" } }, "required": [ "createdAt", "datasetName", "name", "numberOfModels", "owner", "targetName" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the custom model was created.", "format": "date-time", "type": "string" }, "modifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the custom model.", "type": "string" }, "owner": { "description": "The name of the custom model owner.", "type": "string" } }, "required": [ "createdAt", "modifiedAt", "name", "owner" ], "type": "object" }, { "properties": { "createdAt": { "description": "The time when the model package was created.", "format": "date-time", "type": "string" }, "deploymentCount": { "description": "The number of deployments.", "type": "integer" }, "executionType": { "description": "The type of execution.", "type": "string" }, "name": { "description": "The name of the model package.", "type": "string" }, "targetName": { "description": "The name of the modeling target.", "type": "string" }, "targetType": { "description": "Type of modeling target", "type": "string" } }, "required": [ "createdAt", "deploymentCount", "executionType", "name", "targetName", "targetType" ], "type": "object" }, { "properties": { "appType": { "description": "The type of the application.", "type": "string" }, "createdAt": { "description": "The time when the application was created.", "format": "date-time", "type": "string" }, "lastModifiedAt": { "description": "The time of the last modification.", "format": "date-time", "type": "string" }, "name": { "description": "The name of the application.", "type": "string" }, "version": { "description": "The version string of the app.", "type": "string" } }, "required": [ "appType", "createdAt", "lastModifiedAt", "name", "version" ], "type": "object" }, { "type": "null" } ] }, "type": { "description": "The value tracker attachment type.", "enum": [ "dataset", "modelingProject", "deployment", "customModel", "modelPackage", "application" ], "type": "string" }, "useCaseId": { "description": "The ID of the ValueTracker.", "type": "string" } }, "required": [ "attachedObjectId", "canView", "id", "isAccessRequested", "overview", "type", "useCaseId" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | attachedObjectId | string | true | | The ID of the attached object. | | canView | boolean | true | | Indicates if the user has view access to the attached object. | | id | string | true | | The ID of the ValueTracker. | | isAccessRequested | boolean | true | | Indicates if the user has requested access to the attached object. | | overview | any | true | | Detailed information depending on the type of the attachment | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerAttachmentDeploymentOverview | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerAttachmentDatasetOverview | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerAttachmentModelingProjectOverview | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerAttachmentCustomModelOverview | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerAttachmentModelPackageOverview | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerAttachmentApplicationOverview | false | | none | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | type | string | true | | The value tracker attachment type. | | useCaseId | string | true | | The ID of the ValueTracker. | ### Enumerated Values | Property | Value | | --- | --- | | type | [dataset, modelingProject, deployment, customModel, modelPackage, application] | ## ValueTrackerBucket ``` { "properties": { "period": { "description": "A `period` object describing the time this bucket covers", "properties": { "end": { "description": "RFC3339 datetime. End of time period to retrieve.", "format": "date-time", "type": "string" }, "start": { "description": "RFC3339 datetime. Start of time period to retrieve.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "predictionsCount": { "description": "Total number of predictions that happened in this bucket", "type": "integer" }, "realizedValue": { "description": "Total amount of value realised during this bucket", "type": "integer" } }, "required": [ "period", "predictionsCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | period | ValueTrackerBucketPeriod | true | | A period object describing the time this bucket covers | | predictionsCount | integer | true | | Total number of predictions that happened in this bucket | | realizedValue | integer | false | | Total amount of value realised during this bucket | ## ValueTrackerBucketPeriod ``` { "description": "A `period` object describing the time this bucket covers", "properties": { "end": { "description": "RFC3339 datetime. End of time period to retrieve.", "format": "date-time", "type": "string" }, "start": { "description": "RFC3339 datetime. Start of time period to retrieve.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" } ``` A `period` object describing the time this bucket covers ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | end | string(date-time) | true | | RFC3339 datetime. End of time period to retrieve. | | start | string(date-time) | true | | RFC3339 datetime. Start of time period to retrieve. | ## ValueTrackerCreateResponse ``` { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": "string" }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` The value tracker information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyHealth | any | false | | The health of the accuracy. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerHealthInformation | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | businessImpact | integer,null | false | maximum: 5minimum: 1 | The expected effects on overall business operations. | | commentId | string | false | | The ID for this comment. | | content | string | false | | A string | | description | string | false | | The value tracker description. | | feasibility | integer,null | false | maximum: 5minimum: 1 | Assessment of how the value tracker can be accomplished across multiple dimensions. | | id | string | false | | The ID of the ValueTracker. | | inProductionWarning | string,null | false | | An optional warning to indicate that deployments are attached to this value tracker. | | mentions | [ValueTrackerDrUserId] | false | | The list of user objects. | | modelHealth | any | false | | The health of the model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerHealthInformation | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | | The name of the value tracker. | | notes | string,null | false | | The user notes. | | owner | ValueTrackerDrUserId | false | | DataRobot user information. | | permissions | [string] | false | | The permissions of the current user. | | potentialValue | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | | potentialValueTemplate | any | false | | Optional. Contains the template type and parameter information. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateClassification | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateRegression | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionTargets | [string] | false | | An array of prediction target name strings. | | predictionsCount | any | false | | The count of the number of predictions made. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [integer] | false | | The list of prediction counts. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | realizedValue | any | false | | Optional. Contains MonetaryValue objects. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | serviceHealth | any | false | | The health of the service. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerHealthInformation | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | string | false | | The current stage of the value tracker. | | targetDates | [ValueTrackerTargetDate] | false | | The array of TargetDate objects. | ### Enumerated Values | Property | Value | | --- | --- | | stage | [ideation, queued, dataPrepAndModeling, validatingAndDeploying, inProduction, retired, onHold] | ## ValueTrackerDrUserId ``` { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" } ``` DataRobot user information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | firstName | string,null | false | | The first name of the ValueTracker owner. | | id | string | true | | The DataRobot user ID. | | lastName | string,null | false | | The last name of the ValueTracker owner. | | username | string | false | | The username of the ValueTracker owner. | ## ValueTrackerEventDescriptions ``` { "description": "Details of the activity. Content depends on activity type.", "properties": { "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of this description item.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "name": { "description": "The name of this event item.", "type": [ "string", "null" ] }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` Details of the activity. Content depends on activity type. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | businessImpact | integer,null | false | maximum: 5minimum: 1 | The expected effects on overall business operations. | | commentId | string | false | | The ID for this comment. | | content | string | false | | A string | | description | string,null | false | | The value tracker description. | | feasibility | integer,null | false | maximum: 5minimum: 1 | Assessment of how the value tracker can be accomplished across multiple dimensions. | | id | string,null | false | | The ID of this description item. | | mentions | [ValueTrackerDrUserId] | false | | The list of user objects. | | name | string,null | false | | The name of this event item. | | notes | string,null | false | | The user notes. | | owner | ValueTrackerDrUserId | false | | DataRobot user information. | | permissions | [string] | false | | The permissions of the current user. | | potentialValue | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | | potentialValueTemplate | any | false | | Optional. Contains the template type and parameter information. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateClassification | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateRegression | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionTargets | [string] | false | | An array of prediction target name strings. | | realizedValue | any | false | | Optional. Contains MonetaryValue objects. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | string | false | | The current stage of the value tracker. | | targetDates | [ValueTrackerTargetDate] | false | | The array of TargetDate objects. | ### Enumerated Values | Property | Value | | --- | --- | | stage | [ideation, queued, dataPrepAndModeling, validatingAndDeploying, inProduction, retired, onHold] | ## ValueTrackerHealthInformation ``` { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | endDate | string,null(date-time) | true | | The end date for this health status. | | message | string,null | true | | Information about the health status. | | startDate | string,null(date-time) | true | | The start date for this health status. | | status | string,null | true | | The status of the value tracker. | ## ValueTrackerListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "The list of value trackers that match the query.", "items": { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The number of items matching the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [ValueTrackerResponse] | true | | The list of value trackers that match the query. | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | | totalCount | integer | true | | The number of items matching the query condition. | ## ValueTrackerMonetaryValue ``` { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" } ``` Optional. Contains MonetaryValue objects. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | currency | string | true | | The ISO code of the currency. | | details | string,null | false | | Optional user notes. | | value | number | true | | The amount of value. | ### Enumerated Values | Property | Value | | --- | --- | | currency | [AED, BRL, CHF, EUR, GBP, JPY, KRW, UAH, USD, ZAR] | ## ValueTrackerRealizedValuesOverTimeResponse ``` { "properties": { "buckets": { "description": "An array of `bucket` objects representing prediction totals and realized value over time", "items": { "properties": { "period": { "description": "A `period` object describing the time this bucket covers", "properties": { "end": { "description": "RFC3339 datetime. End of time period to retrieve.", "format": "date-time", "type": "string" }, "start": { "description": "RFC3339 datetime. Start of time period to retrieve.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "predictionsCount": { "description": "Total number of predictions that happened in this bucket", "type": "integer" }, "realizedValue": { "description": "Total amount of value realised during this bucket", "type": "integer" } }, "required": [ "period", "predictionsCount" ], "type": "object" }, "type": "array" }, "previousTimeRangeSummary": { "description": "A `summary` object covering the time range before the given one with the same duration", "properties": { "averagePredictionsCount": { "description": "Average number of predictions per bucket during this time period", "type": "integer" }, "averageRealizedValue": { "description": "Average amount of value per bucket realised during this time period", "type": "integer" }, "period": { "description": "A `period` object describing the time this bucket covers", "properties": { "end": { "description": "RFC3339 datetime. End of time period to retrieve.", "format": "date-time", "type": "string" }, "start": { "description": "RFC3339 datetime. Start of time period to retrieve.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "totalPredictionsCount": { "description": "Total number of predictions that happened during this time period", "type": "integer" }, "totalRealizedValue": { "description": "Total amount of value realised during this time period", "type": "integer" } }, "required": [ "averagePredictionsCount", "period", "totalRealizedValue" ], "type": "object" }, "summary": { "description": "A `summary` object covering the time range before the given one with the same duration", "properties": { "averagePredictionsCount": { "description": "Average number of predictions per bucket during this time period", "type": "integer" }, "averageRealizedValue": { "description": "Average amount of value per bucket realised during this time period", "type": "integer" }, "period": { "description": "A `period` object describing the time this bucket covers", "properties": { "end": { "description": "RFC3339 datetime. End of time period to retrieve.", "format": "date-time", "type": "string" }, "start": { "description": "RFC3339 datetime. Start of time period to retrieve.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "totalPredictionsCount": { "description": "Total number of predictions that happened during this time period", "type": "integer" }, "totalRealizedValue": { "description": "Total amount of value realised during this time period", "type": "integer" } }, "required": [ "averagePredictionsCount", "period", "totalRealizedValue" ], "type": "object" } }, "required": [ "buckets", "previousTimeRangeSummary", "summary" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | buckets | [ValueTrackerBucket] | true | | An array of bucket objects representing prediction totals and realized value over time | | previousTimeRangeSummary | ValueTrackerSummary | true | | A summary object covering the time range before the given one with the same duration | | summary | ValueTrackerSummary | true | | A summary object covering the time range before the given one with the same duration | ## ValueTrackerResponse ``` { "description": "The value tracker information.", "properties": { "accuracyHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the accuracy." }, "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "commentId": { "description": "The ID for this comment.", "type": "string" }, "content": { "description": "A string", "type": "string" }, "description": { "description": "The value tracker description.", "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "id": { "description": "The ID of the ValueTracker.", "type": "string" }, "inProductionWarning": { "description": "An optional warning to indicate that deployments are attached to this value tracker.", "type": [ "string", "null" ] }, "mentions": { "description": "The list of user objects.", "items": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "type": "array" }, "modelHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the model." }, "name": { "description": "The name of the value tracker.", "type": "string" }, "notes": { "description": "The user notes.", "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "permissions": { "description": "The permissions of the current user.", "items": { "type": "string" }, "type": "array" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains the template type and parameter information." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "description": "The name of the prediction target", "type": "string" }, "type": "array" }, "predictionsCount": { "anyOf": [ { "type": "string" }, { "type": "integer" }, { "description": "The list of prediction counts.", "items": { "type": "integer" }, "type": "array" } ], "description": "The count of the number of predictions made." }, "realizedValue": { "anyOf": [ { "type": "string" }, { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, { "type": "null" } ], "description": "Optional. Contains MonetaryValue objects." }, "serviceHealth": { "anyOf": [ { "type": "string" }, { "properties": { "endDate": { "description": "The end date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "message": { "description": "Information about the health status.", "type": [ "string", "null" ] }, "startDate": { "description": "The start date for this health status.", "format": "date-time", "type": [ "string", "null" ] }, "status": { "description": "The status of the value tracker.", "type": [ "string", "null" ] } }, "required": [ "endDate", "message", "startDate", "status" ], "type": "object" }, { "type": "null" } ], "description": "The health of the service." }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` The value tracker information. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyHealth | any | false | | The health of the accuracy. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerHealthInformation | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | businessImpact | integer,null | false | maximum: 5minimum: 1 | The expected effects on overall business operations. | | commentId | string | false | | The ID for this comment. | | content | string | false | | A string | | description | string,null | false | | The value tracker description. | | feasibility | integer,null | false | maximum: 5minimum: 1 | Assessment of how the value tracker can be accomplished across multiple dimensions. | | id | string | false | | The ID of the ValueTracker. | | inProductionWarning | string,null | false | | An optional warning to indicate that deployments are attached to this value tracker. | | mentions | [ValueTrackerDrUserId] | false | | The list of user objects. | | modelHealth | any | false | | The health of the model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerHealthInformation | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | | The name of the value tracker. | | notes | string,null | false | | The user notes. | | owner | ValueTrackerDrUserId | false | | DataRobot user information. | | permissions | [string] | false | | The permissions of the current user. | | potentialValue | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | | potentialValueTemplate | any | false | | Optional. Contains the template type and parameter information. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateClassification | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateRegression | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionTargets | [string] | false | | An array of prediction target name strings. | | predictionsCount | any | false | | The count of the number of predictions made. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [integer] | false | | The list of prediction counts. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | realizedValue | any | false | | Optional. Contains MonetaryValue objects. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | serviceHealth | any | false | | The health of the service. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerHealthInformation | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | stage | string | false | | The current stage of the value tracker. | | targetDates | [ValueTrackerTargetDate] | false | | The array of TargetDate objects. | ### Enumerated Values | Property | Value | | --- | --- | | stage | [ideation, queued, dataPrepAndModeling, validatingAndDeploying, inProduction, retired, onHold] | ## ValueTrackerSharingListData ``` { "properties": { "id": { "description": "The ID of the recipient", "type": "string" }, "name": { "description": "The name of the user, group, or organization", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "USER", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the recipient | | name | string | false | | The name of the user, group, or organization | | role | string | true | | The assigned role | | shareRecipientType | string | true | | The recipient type | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, USER, CONSUMER, NO_ROLE] | | shareRecipientType | [user, group, organization] | ## ValueTrackerSharingListResponse ``` { "properties": { "count": { "description": "The number of items returned on this page.", "type": "integer" }, "data": { "description": "A list of sharing role objects", "items": { "properties": { "id": { "description": "The ID of the recipient", "type": "string" }, "name": { "description": "The name of the user, group, or organization", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "USER", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type", "enum": [ "user", "group", "organization" ], "type": "string" } }, "required": [ "id", "role", "shareRecipientType" ], "type": "object" }, "type": "array" }, "next": { "description": "URL pointing to the next page (if null, there is no next page).", "format": "uri", "type": [ "string", "null" ] }, "previous": { "description": "URL pointing to the previous page (if null, there is no previous page).", "format": "uri", "type": [ "string", "null" ] }, "totalCount": { "description": "The number of items matching the query condition.", "type": "integer" } }, "required": [ "count", "data", "next", "previous", "totalCount" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of items returned on this page. | | data | [ValueTrackerSharingListData] | true | | A list of sharing role objects | | next | string,null(uri) | true | | URL pointing to the next page (if null, there is no next page). | | previous | string,null(uri) | true | | URL pointing to the previous page (if null, there is no previous page). | | totalCount | integer | true | | The number of items matching the query condition. | ## ValueTrackerSharingRoleUpdateData ``` { "properties": { "id": { "description": "The ID of the recipient", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "USER", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "The name of the user, group, or organization", "type": "string" } }, "required": [ "role", "shareRecipientType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | false | | The ID of the recipient | | role | string | true | | The assigned role | | shareRecipientType | string | true | | The recipient type | | username | string | false | | The name of the user, group, or organization | ### Enumerated Values | Property | Value | | --- | --- | | role | [OWNER, USER, CONSUMER, NO_ROLE] | | shareRecipientType | [user, group, organization] | ## ValueTrackerSharingUpdate ``` { "properties": { "operation": { "description": "The name of the action being taken. Only 'updateRoles' is supported.", "enum": [ "updateRoles" ], "type": "string" }, "roles": { "description": "A list of sharing role objects", "items": { "properties": { "id": { "description": "The ID of the recipient", "type": "string" }, "role": { "description": "The assigned role", "enum": [ "OWNER", "USER", "CONSUMER", "NO_ROLE" ], "type": "string" }, "shareRecipientType": { "description": "The recipient type", "enum": [ "user", "group", "organization" ], "type": "string" }, "username": { "description": "The name of the user, group, or organization", "type": "string" } }, "required": [ "role", "shareRecipientType" ], "type": "object" }, "type": "array" } }, "required": [ "operation", "roles" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | operation | string | true | | The name of the action being taken. Only 'updateRoles' is supported. | | roles | [ValueTrackerSharingRoleUpdateData] | true | | A list of sharing role objects | ### Enumerated Values | Property | Value | | --- | --- | | operation | updateRoles | ## ValueTrackerSummary ``` { "description": "A `summary` object covering the time range before the given one with the same duration", "properties": { "averagePredictionsCount": { "description": "Average number of predictions per bucket during this time period", "type": "integer" }, "averageRealizedValue": { "description": "Average amount of value per bucket realised during this time period", "type": "integer" }, "period": { "description": "A `period` object describing the time this bucket covers", "properties": { "end": { "description": "RFC3339 datetime. End of time period to retrieve.", "format": "date-time", "type": "string" }, "start": { "description": "RFC3339 datetime. Start of time period to retrieve.", "format": "date-time", "type": "string" } }, "required": [ "end", "start" ], "type": "object" }, "totalPredictionsCount": { "description": "Total number of predictions that happened during this time period", "type": "integer" }, "totalRealizedValue": { "description": "Total amount of value realised during this time period", "type": "integer" } }, "required": [ "averagePredictionsCount", "period", "totalRealizedValue" ], "type": "object" } ``` A `summary` object covering the time range before the given one with the same duration ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | averagePredictionsCount | integer | true | | Average number of predictions per bucket during this time period | | averageRealizedValue | integer | false | | Average amount of value per bucket realised during this time period | | period | ValueTrackerBucketPeriod | true | | A period object describing the time this bucket covers | | totalPredictionsCount | integer | false | | Total number of predictions that happened during this time period | | totalRealizedValue | integer | true | | Total amount of value realised during this time period | ## ValueTrackerTargetDate ``` { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | date | string(date-time) | true | | The date of the target. | | stage | string | true | | The name of the target stage. | ### Enumerated Values | Property | Value | | --- | --- | | stage | [ideation, queued, dataPrepAndModeling, validatingAndDeploying, inProduction, retired, onHold] | ## ValueTrackerUpdate ``` { "properties": { "businessImpact": { "description": "The expected effects on overall business operations.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "description": { "description": "The value tracker description.", "maxLength": 1024, "type": [ "string", "null" ] }, "feasibility": { "description": "Assessment of how the value tracker can be accomplished across multiple dimensions.", "maximum": 5, "minimum": 1, "type": [ "integer", "null" ] }, "name": { "description": "The name of the value tracker.", "maxLength": 512, "type": "string" }, "notes": { "description": "The user notes.", "maxLength": 1024, "type": [ "string", "null" ] }, "owner": { "description": "DataRobot user information.", "properties": { "firstName": { "description": "The first name of the ValueTracker owner.", "type": [ "string", "null" ] }, "id": { "description": "The DataRobot user ID.", "type": "string" }, "lastName": { "description": "The last name of the ValueTracker owner.", "type": [ "string", "null" ] }, "username": { "description": "The username of the ValueTracker owner.", "type": "string" } }, "required": [ "id" ], "type": "object" }, "potentialValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "potentialValueTemplate": { "anyOf": [ { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" }, { "type": "null" } ], "description": "Contains the template type and parameter information defined in the response of [GET /api/v2/valueTrackerValueTemplates/{templateType}/calculation/][get-apiv2valuetrackervaluetemplatestemplatetypecalculation]." }, "predictionTargets": { "description": "An array of prediction target name strings.", "items": { "type": "string" }, "type": "array" }, "realizedValue": { "description": "Optional. Contains MonetaryValue objects.", "properties": { "currency": { "description": "The ISO code of the currency.", "enum": [ "AED", "BRL", "CHF", "EUR", "GBP", "JPY", "KRW", "UAH", "USD", "ZAR" ], "type": "string" }, "details": { "description": "Optional user notes.", "type": [ "string", "null" ] }, "value": { "description": "The amount of value.", "type": "number" } }, "required": [ "currency", "value" ], "type": "object" }, "stage": { "description": "The current stage of the value tracker.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" }, "targetDates": { "description": "The array of TargetDate objects.", "items": { "properties": { "date": { "description": "The date of the target.", "format": "date-time", "type": "string" }, "stage": { "description": "The name of the target stage.", "enum": [ "ideation", "queued", "dataPrepAndModeling", "validatingAndDeploying", "inProduction", "retired", "onHold" ], "type": "string" } }, "required": [ "date", "stage" ], "type": "object" }, "type": "array" } }, "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | businessImpact | integer,null | false | maximum: 5minimum: 1 | The expected effects on overall business operations. | | description | string,null | false | maxLength: 1024 | The value tracker description. | | feasibility | integer,null | false | maximum: 5minimum: 1 | Assessment of how the value tracker can be accomplished across multiple dimensions. | | name | string | false | maxLength: 512 | The name of the value tracker. | | notes | string,null | false | maxLength: 1024 | The user notes. | | owner | ValueTrackerDrUserId | false | | DataRobot user information. | | potentialValue | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | | potentialValueTemplate | any | false | | Contains the template type and parameter information defined in the response of [GET /api/v2/valueTrackerValueTemplates/{templateType}/calculation/][get-apiv2valuetrackervaluetemplatestemplatetypecalculation]. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateClassification | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateRegression | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionTargets | [string] | false | | An array of prediction target name strings. | | realizedValue | ValueTrackerMonetaryValue | false | | Optional. Contains MonetaryValue objects. | | stage | string | false | | The current stage of the value tracker. | | targetDates | [ValueTrackerTargetDate] | false | | The array of TargetDate objects. | ### Enumerated Values | Property | Value | | --- | --- | | stage | [ideation, queued, dataPrepAndModeling, validatingAndDeploying, inProduction, retired, onHold] | ## ValueTrackerValueCalculateTemplate ``` { "description": "Template type and data used for calculating the result", "properties": { "data": { "description": "Data used for calculating the result", "oneOf": [ { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" } ] }, "templateType": { "description": "The name of the class ValueTracker value template", "enum": [ "classification", "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" } ``` Template type and data used for calculating the result ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | any | true | | Data used for calculating the result | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateClassificationData | false | | The value tracker value data. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ValueTrackerValueTemplateRegressionData | false | | The value tracker value data. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | templateType | string | true | | The name of the class ValueTracker value template | ### Enumerated Values | Property | Value | | --- | --- | | templateType | [classification, regression] | ## ValueTrackerValueTemplateCalculateResponse ``` { "properties": { "averageValuePerDecision": { "description": "Estimated average value per decision", "type": "number" }, "savedAnnually": { "description": "Estimated amount saved annually", "type": "number" }, "template": { "description": "Template type and data used for calculating the result", "properties": { "data": { "description": "Data used for calculating the result", "oneOf": [ { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" } ] }, "templateType": { "description": "The name of the class ValueTracker value template", "enum": [ "classification", "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" } }, "required": [ "averageValuePerDecision", "savedAnnually", "template" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | averageValuePerDecision | number | true | | Estimated average value per decision | | savedAnnually | number | true | | Estimated amount saved annually | | template | ValueTrackerValueCalculateTemplate | true | | Template type and data used for calculating the result | ## ValueTrackerValueTemplateClassification ``` { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "classification" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | ValueTrackerValueTemplateClassificationData | true | | The value tracker value data. | | templateType | string | true | | The value tracker value template type. | ### Enumerated Values | Property | Value | | --- | --- | | templateType | classification | ## ValueTrackerValueTemplateClassificationData ``` { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "incorrectDecisionCost": { "description": "The estimated cost of an individual incorrect decision.", "type": "number" }, "incorrectDecisionsCount": { "description": "The estimated number of incorrect decisions per year.", "type": "integer" } }, "required": [ "accuracyImprovement", "decisionsCount", "incorrectDecisionCost", "incorrectDecisionsCount" ], "type": "object" } ``` The value tracker value data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyImprovement | number | true | | Accuracy improvement. | | decisionsCount | integer | true | | The estimated number of decisions per year. | | incorrectDecisionCost | number | true | | The estimated cost of an individual incorrect decision. | | incorrectDecisionsCount | integer | true | | The estimated number of incorrect decisions per year. | ## ValueTrackerValueTemplateParameterWithSchema ``` { "properties": { "parameterName": { "description": "Name of the parameter", "type": "string" }, "schema": { "description": "Schema entry defining the type and limits of a template parameter", "properties": { "elementType": { "description": "Possible types used for template variables", "enum": [ "integer", "float", "number" ], "type": "string" }, "label": { "description": "Label describing the represented value", "type": "string" }, "maximum": { "description": "Sets Maximum value if given", "type": [ "integer", "null" ] }, "minimum": { "description": "Sets Minimum value if given", "type": [ "integer", "null" ] }, "placeholder": { "description": "Text that can be used to prefill an input filed for the value", "type": "string" }, "unit": { "description": "The unit type (e.g., %).", "type": [ "string", "null" ] } }, "required": [ "elementType", "label", "maximum", "minimum", "placeholder", "unit" ], "type": "object" } }, "required": [ "parameterName", "schema" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parameterName | string | true | | Name of the parameter | | schema | ValueTrackerValueTemplateSchema | true | | Schema entry defining the type and limits of a template parameter | ## ValueTrackerValueTemplateRegression ``` { "properties": { "data": { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" }, "templateType": { "description": "The value tracker value template type.", "enum": [ "regression" ], "type": "string" } }, "required": [ "data", "templateType" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | data | ValueTrackerValueTemplateRegressionData | true | | The value tracker value data. | | templateType | string | true | | The value tracker value template type. | ### Enumerated Values | Property | Value | | --- | --- | | templateType | regression | ## ValueTrackerValueTemplateRegressionData ``` { "description": "The value tracker value data.", "properties": { "accuracyImprovement": { "description": "Accuracy improvement.", "type": "number" }, "decisionsCount": { "description": "The estimated number of decisions per year.", "type": "integer" }, "targetValue": { "description": "The target value.", "type": "number" } }, "required": [ "accuracyImprovement", "decisionsCount", "targetValue" ], "type": "object" } ``` The value tracker value data. ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | accuracyImprovement | number | true | | Accuracy improvement. | | decisionsCount | integer | true | | The estimated number of decisions per year. | | targetValue | number | true | | The target value. | ## ValueTrackerValueTemplateResponse ``` { "properties": { "description": { "description": "The description of the value template.", "type": "string" }, "schema": { "description": "Schema definition of all template parameters", "items": { "properties": { "parameterName": { "description": "Name of the parameter", "type": "string" }, "schema": { "description": "Schema entry defining the type and limits of a template parameter", "properties": { "elementType": { "description": "Possible types used for template variables", "enum": [ "integer", "float", "number" ], "type": "string" }, "label": { "description": "Label describing the represented value", "type": "string" }, "maximum": { "description": "Sets Maximum value if given", "type": [ "integer", "null" ] }, "minimum": { "description": "Sets Minimum value if given", "type": [ "integer", "null" ] }, "placeholder": { "description": "Text that can be used to prefill an input filed for the value", "type": "string" }, "unit": { "description": "The unit type (e.g., %).", "type": [ "string", "null" ] } }, "required": [ "elementType", "label", "maximum", "minimum", "placeholder", "unit" ], "type": "object" } }, "required": [ "parameterName", "schema" ], "type": "object" }, "type": "array" }, "templateType": { "description": "The name of the class ValueTracker value template to be retrieved", "enum": [ "classification", "regression" ], "type": "string" }, "title": { "description": "The title of the value template.", "type": "string" } }, "required": [ "description", "schema", "templateType", "title" ], "type": "object" } ``` ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | The description of the value template. | | schema | [ValueTrackerValueTemplateParameterWithSchema] | true | | Schema definition of all template parameters | | templateType | string | true | | The name of the class ValueTracker value template to be retrieved | | title | string | true | | The title of the value template. | ### Enumerated Values | Property | Value | | --- | --- | | templateType | [classification, regression] | ## ValueTrackerValueTemplateSchema ``` { "description": "Schema entry defining the type and limits of a template parameter", "properties": { "elementType": { "description": "Possible types used for template variables", "enum": [ "integer", "float", "number" ], "type": "string" }, "label": { "description": "Label describing the represented value", "type": "string" }, "maximum": { "description": "Sets Maximum value if given", "type": [ "integer", "null" ] }, "minimum": { "description": "Sets Minimum value if given", "type": [ "integer", "null" ] }, "placeholder": { "description": "Text that can be used to prefill an input filed for the value", "type": "string" }, "unit": { "description": "The unit type (e.g., %).", "type": [ "string", "null" ] } }, "required": [ "elementType", "label", "maximum", "minimum", "placeholder", "unit" ], "type": "object" } ``` Schema entry defining the type and limits of a template parameter ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | elementType | string | true | | Possible types used for template variables | | label | string | true | | Label describing the represented value | | maximum | integer,null | true | | Sets Maximum value if given | | minimum | integer,null | true | | Sets Minimum value if given | | placeholder | string | true | | Text that can be used to prefill an input filed for the value | | unit | string,null | true | | The unit type (e.g., %). | ### Enumerated Values | Property | Value | | --- | --- | | elementType | [integer, float, number] | --- # Vector databases URL: https://docs.datarobot.com/en/docs/api/reference/public-api/vector_databases.html > The following endpoints outline how to manage vector databases. # Vector databases The following endpoints outline how to manage vector databases. ## List custom model embedding validations Operation path: `GET /api/v2/genai/customModelEmbeddingValidations/` Authentication requirements: `BearerAuth` List custom model embedding validations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | any | false | Only retrieve the custom model embedding validations associated with these use case IDs. | | playgroundId | query | any | false | Only retrieve the custom model embedding validations associated with this playground ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | search | query | any | false | Only retrieve the custom model embedding validations matching the search query. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name", "deploymentName", "userName", "creationDate". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | | completedOnly | query | boolean | false | If true, only retrieve the completed custom model embedding validations. The default is false. | | deploymentId | query | any | false | Only retrieve the custom model embedding validations associated with this deployment ID. | | modelId | query | any | false | Only retrieve the custom model embedding validations associated with this model ID. | | promptColumnName | query | any | false | Only retrieve the custom model embedding validations where the custom model uses this column name for prompt input. | | targetColumnName | query | any | false | Only retrieve the custom model embedding validations where the custom model uses this column name for prediction output. | ### Example responses > 200 Response ``` { "description": "Paginated list of custom model embedding validations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single custom model embedding validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelEmbeddingValidationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListCustomModelEmbeddingValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model embedding validations successfully retrieved. | ListCustomModelEmbeddingValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Validate custom model embedding Operation path: `POST /api/v2/genai/customModelEmbeddingValidations/` Authentication requirements: `BearerAuth` Validate an embedding model hosted in a custom model deployment for use in the playground. ### Body parameter ``` { "description": "The body of the \"Validate custom model\" request.", "properties": { "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model used in the deployment.", "title": "modelId" }, "name": { "default": "Untitled", "description": "The name to use for the validated custom model.", "maxLength": 5000, "title": "name", "type": "string" }, "predictionTimeout": { "default": 300, "description": "The timeout in seconds for the prediction when validating a custom model. Defaults to 300.", "maximum": 600, "minimum": 1, "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case to associate with the validated custom model.", "title": "useCaseId" } }, "required": [ "deploymentId", "promptColumnName", "targetColumnName" ], "title": "CreateCustomModelEmbeddingValidationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateCustomModelEmbeddingValidationRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for a single custom model embedding validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelEmbeddingValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Custom model embedding validation job successfully accepted. Follow the Location header to poll for job execution status. | CustomModelEmbeddingValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete custom model embedding validation by validation ID Operation path: `DELETE /api/v2/genai/customModelEmbeddingValidations/{validationId}/` Authentication requirements: `BearerAuth` Delete an existing custom model embedding validation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model embedding validation to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Custom model embedding validation successfully deleted. | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve custom model embedding validation status by validation ID Operation path: `GET /api/v2/genai/customModelEmbeddingValidations/{validationId}/` Authentication requirements: `BearerAuth` Retrieve the status of validating a custom model embedding. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model embedding validation to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single custom model embedding validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelEmbeddingValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model embedding validation status successfully retrieved. | CustomModelEmbeddingValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit custom model embedding validation by validation ID Operation path: `PATCH /api/v2/genai/customModelEmbeddingValidations/{validationId}/` Authentication requirements: `BearerAuth` Edit an existing custom model embedding validation. ### Body parameter ``` { "description": "The body of the \"Edit custom model validation\" request.", "properties": { "chatModelId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API.", "title": "chatModelId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the deployment associated with this custom model validation.", "title": "deploymentId" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the model associated with this custom model validation.", "title": "modelId" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the custom model validation to this value.", "title": "name" }, "predictionTimeout": { "anyOf": [ { "maximum": 600, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "If specified, sets the timeout in seconds for the prediction when validating a custom model.", "title": "predictionTimeout" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment.", "title": "promptColumnName" }, "targetColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment.", "title": "targetColumnName" } }, "title": "EditCustomModelValidationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model embedding validation to edit. | | body | body | EditCustomModelValidationRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single custom model embedding validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelEmbeddingValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model embedding validation successfully updated. | CustomModelEmbeddingValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Revalidate custom model embedding by validation ID Operation path: `POST /api/v2/genai/customModelEmbeddingValidations/{validationId}/revalidate/` Authentication requirements: `BearerAuth` Revalidate an existing custom model embedding validation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model embedding validation to revalidate. | ### Example responses > 200 Response ``` { "description": "API response object for a single custom model embedding validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelEmbeddingValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model embedding successfully revalidated. | CustomModelEmbeddingValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List custom model vector database validations Operation path: `GET /api/v2/genai/customModelVectorDatabaseValidations/` Authentication requirements: `BearerAuth` List custom model vector database validations. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | any | false | Only retrieve the custom model vector database validations associated with these use case IDs. | | playgroundId | query | any | false | Only retrieve the custom model vector database validations associated with this playground ID. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | search | query | any | false | Only retrieve the custom model vector database validations matching the search query. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name", "deploymentName", "userName", "creationDate". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | | completedOnly | query | boolean | false | If true, only retrieve the completed custom model vector database validations. The default is false. | | deploymentId | query | any | false | Only retrieve the custom model vector database validations associated with this deployment ID. | | modelId | query | any | false | Only retrieve the custom model vector database validations associated with this model ID. | | promptColumnName | query | any | false | Only retrieve the custom model vector database validations where the custom model uses this column name for prompt input. | | targetColumnName | query | any | false | Only retrieve the custom model vector database validations where the custom model uses this column name for prediction output. | ### Example responses > 200 Response ``` { "description": "Paginated list of custom model vector database validations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single custom model vector database validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelVectorDatabaseValidationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListCustomModelVectorDatabaseValidationsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model vector database validations successfully retrieved. | ListCustomModelVectorDatabaseValidationsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Validate custom model vector database Operation path: `POST /api/v2/genai/customModelVectorDatabaseValidations/` Authentication requirements: `BearerAuth` Validate a vector database hosted in a custom model deployment for use in the playground. ### Body parameter ``` { "description": "The body of the \"Validate custom model\" request.", "properties": { "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model used in the deployment.", "title": "modelId" }, "name": { "default": "Untitled", "description": "The name to use for the validated custom model.", "maxLength": 5000, "title": "name", "type": "string" }, "predictionTimeout": { "default": 300, "description": "The timeout in seconds for the prediction when validating a custom model. Defaults to 300.", "maximum": 600, "minimum": 1, "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case to associate with the validated custom model.", "title": "useCaseId" } }, "required": [ "deploymentId", "promptColumnName", "targetColumnName" ], "title": "CreateCustomModelVectorDatabaseValidationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateCustomModelVectorDatabaseValidationRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for a single custom model vector database validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelVectorDatabaseValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Custom model vector database validation job successfully accepted. Follow the Location header to poll for job execution status. | CustomModelVectorDatabaseValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Delete custom model vector database validation by validation ID Operation path: `DELETE /api/v2/genai/customModelVectorDatabaseValidations/{validationId}/` Authentication requirements: `BearerAuth` Delete an existing custom model vector database validation. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model vector database validation to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Successfully deleted custom model vector database validation | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve custom model vector database validation status by validation ID Operation path: `GET /api/v2/genai/customModelVectorDatabaseValidations/{validationId}/` Authentication requirements: `BearerAuth` Retrieve the status of validating a custom model vector database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model vector database validation to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single custom model vector database validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelVectorDatabaseValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model vector database validation status successfully retrieved. | CustomModelVectorDatabaseValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit custom model vector database validation by validation ID Operation path: `PATCH /api/v2/genai/customModelVectorDatabaseValidations/{validationId}/` Authentication requirements: `BearerAuth` Edit an existing custom model vector database validation. ### Body parameter ``` { "description": "The body of the \"Edit custom model validation\" request.", "properties": { "chatModelId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API.", "title": "chatModelId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the deployment associated with this custom model validation.", "title": "deploymentId" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the model associated with this custom model validation.", "title": "modelId" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the custom model validation to this value.", "title": "name" }, "predictionTimeout": { "anyOf": [ { "maximum": 600, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "If specified, sets the timeout in seconds for the prediction when validating a custom model.", "title": "predictionTimeout" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment.", "title": "promptColumnName" }, "targetColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment.", "title": "targetColumnName" } }, "title": "EditCustomModelValidationRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model vector database validation to edit. | | body | body | EditCustomModelValidationRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single custom model vector database validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelVectorDatabaseValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model vector database validation successfully updated. | CustomModelVectorDatabaseValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Revalidate custom model vector database by validation ID Operation path: `POST /api/v2/genai/customModelVectorDatabaseValidations/{validationId}/revalidate/` Authentication requirements: `BearerAuth` Revalidate an existing custom model vector database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | validationId | path | string | true | The ID of the custom model vector database validation to revalidate. | ### Example responses > 200 Response ``` { "description": "API response object for a single custom model vector database validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelVectorDatabaseValidationResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Custom model vector database successfully revalidated. | CustomModelVectorDatabaseValidationResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List vector databases Operation path: `GET /api/v2/genai/vectorDatabases/` Authentication requirements: `BearerAuth` List vector databases. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | useCaseId | query | any | false | Only retrieve the vector databases linked to these use case IDs. | | playgroundId | query | any | false | Only retrieve the vector databases linked to this playground ID. | | familyId | query | any | false | Only retrieve the vector databases linked to this family ID. | | parentsOnly | query | boolean | false | If true, only retrieve (root) parent vector databases. The default is false. | | offset | query | integer | false | Skip the specified number of values. | | limit | query | integer | false | Retrieve only the specified number of values. | | search | query | any | false | Only retrieve the vector databases with names matching the search query. | | sort | query | any | false | Apply this sort order to the results. Valid options are "name", "creationDate", "creationUserId", "embeddingModel", "datasetId", "chunkingMethod", "chunksCount", "size", "userName", "datasetName", "playgroundsCount", "source". Prefix the attribute name with a dash to sort in descending order, e.g., sort=-creationDate. | | completedOnly | query | boolean | false | If true, only retrieve the vector databases that have finished building. The default is false. | ### Example responses > 200 Response ``` { "description": "Paginated list of vector databases.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single vector database.", "properties": { "addedDatasetIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset IDs that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetIds" }, "addedDatasetNames": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset names that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetNames" }, "addedMetadataDatasetPairs": { "anyOf": [ { "items": { "description": "Pair of metadata dataset and dataset added to the vector database.", "properties": { "datasetId": { "description": "The ID of the dataset added to the vector database.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the dataset added to the vector database.", "title": "datasetName", "type": "string" }, "metadataDatasetId": { "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId", "type": "string" }, "metadataDatasetName": { "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName", "type": "string" } }, "required": [ "metadataDatasetId", "datasetId", "metadataDatasetName", "datasetName" ], "title": "MetadataDatasetPairApiFormatted", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Pairs of metadata dataset and dataset added to the vector database.", "title": "addedMetadataDatasetPairs" }, "chunkOverlapPercentage": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage that the vector database uses.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the text chunk (measured in tokens) that the vector database uses.", "title": "chunkSize" }, "chunkingLengthFunction": { "anyOf": [ { "description": "Supported length functions for text splitters.", "enum": [ "tokenizer_length_function", "approximate_token_count" ], "title": "ChunkingLengthFunctionNames", "type": "string" }, { "type": "null" } ], "default": "approximate_token_count", "description": "The length function to use for the text splitter." }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method the vector database uses." }, "chunksCount": { "description": "The number of text chunks in the vector database.", "title": "chunksCount", "type": "integer" }, "creationDate": { "description": "The creation date of the vector database (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationDuration": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The duration of the vector database creation.", "title": "creationDuration" }, "creationUserId": { "description": "The ID of the user that created this vector database.", "title": "creationUserId", "type": "string" }, "customChunking": { "description": "Whether the vector database uses custom chunking.", "title": "customChunking", "type": "boolean" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset the vector database was built from.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset this vector database was built from.", "title": "datasetName", "type": "string" }, "deploymentStatus": { "anyOf": [ { "description": "Sort order values for listing vector databases.", "enum": [ "Created", "Assembling", "Registered", "Deployed" ], "title": "VectorDatabaseDeploymentStatuses", "type": "string" }, { "type": "null" } ], "description": "How far along in the deployment process this vector database is." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model the vector database uses.", "title": "embeddingModel" }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model embedding (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the vector database creation error (in case of a creation error).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database creation error (in case of a creation error).", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnection", "milvus": "#/components/schemas/MilvusConnection", "pinecone": "#/components/schemas/PineconeConnection", "postgres": "#/components/schemas/PostgresConnection" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnection", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnection", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnection", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnection", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "familyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent.", "title": "familyId" }, "id": { "description": "The ID of the vector database.", "title": "id", "type": "string" }, "isSeparatorRegex": { "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "metadataColumns": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of metadata columns in the vector database.", "title": "metadataColumns" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId" }, "metadataDatasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName" }, "name": { "description": "The name of the vector database.", "title": "name", "type": "string" }, "organizationId": { "description": "The ID of the DataRobot organization this vector database belongs to.", "title": "organizationId", "type": "string" }, "parentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'.", "title": "parentId" }, "percentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "Vector database progress percentage.", "title": "percentage" }, "playgroundsCount": { "description": "The number of playgrounds that use this vector database.", "title": "playgroundsCount", "type": "integer" }, "separators": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The text chunking separators that the vector database uses.", "title": "separators" }, "size": { "description": "The size of the vector database (in bytes).", "title": "size", "type": "integer" }, "skippedChunksCount": { "description": "The number of text chunks skipped during vector database creation.", "title": "skippedChunksCount", "type": "integer" }, "source": { "description": "The source of the vector database.", "enum": [ "DataRobot", "External", "Pinecone", "Elasticsearch", "Milvus", "Postgres" ], "title": "VectorDatabaseSource", "type": "string" }, "tenantId": { "description": "The ID of the DataRobot tenant this vector database belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "description": "The ID of the use case the vector database is linked to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this vector database.", "title": "userName", "type": "string" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model vector database (in case of using a custom model vector database).", "title": "validationId" }, "version": { "description": "The version of the vector database linked to a certain family ID.", "title": "version", "type": "integer" } }, "required": [ "id", "name", "size", "useCaseId", "datasetId", "embeddingModel", "embeddingValidationId", "embeddingRegisteredModelId", "chunkSize", "chunkingMethod", "chunkOverlapPercentage", "chunksCount", "skippedChunksCount", "customChunking", "separators", "isSeparatorRegex", "creationDate", "creationUserId", "organizationId", "tenantId", "lastUpdateDate", "executionStatus", "errorMessage", "errorResolution", "source", "validationId", "parentId", "familyId", "addedDatasetIds", "version", "playgroundsCount", "datasetName", "userName", "addedDatasetNames" ], "title": "VectorDatabaseResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListVectorDatabasesResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Vector databases successfully retrieved. | ListVectorDatabasesResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create vector database Operation path: `POST /api/v2/genai/vectorDatabases/` Authentication requirements: `BearerAuth` Create a new vector database. ### Body parameter ``` { "description": "The body of the \"Create vector database\" request.", "properties": { "chunkingParameters": { "anyOf": [ { "description": "Chunking parameters for creating a vector database.", "properties": { "chunkOverlapPercentage": { "anyOf": [ { "maximum": 50, "minimum": 0, "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage to use for text chunking.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk size to use for text chunking (measured in tokens).", "title": "chunkSize" }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method to use." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model to use. If omitted, DataRobot will choose the default embedding model automatically." }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom embedding model (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "isSeparatorRegex": { "default": false, "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "separators": { "anyOf": [ { "items": { "maxLength": 20, "type": "string" }, "maxItems": 9, "type": "array" }, { "type": "null" } ], "description": "The list of separators to use for text chunking.", "title": "separators" } }, "title": "ChunkingParameters", "type": "object" }, { "type": "null" } ], "description": "The text chunking parameters to use for building the vector database." }, "datasetId": { "description": "The ID of the dataset to use for building the vector database.", "title": "datasetId", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnectionRequest", "milvus": "#/components/schemas/MilvusConnectionRequest", "pinecone": "#/components/schemas/PineconeConnectionRequest", "postgres": "#/components/schemas/PostgresConnectionRequest" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnectionRequest", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnectionRequest", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnectionRequest", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnectionRequest", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "metadataCombinationStrategy": { "description": "Strategy to use when the dataset and the metadata file have duplicate columns.", "enum": [ "replace", "merge" ], "title": "MetadataCombinationStrategy", "type": "string" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset to add metadata for building the vector database.", "title": "metadataDatasetId" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the vector database.", "title": "name" }, "parentVectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the parent vector database used as base for the re-building.", "title": "parentVectorDatabaseId" }, "updateDeployments": { "default": false, "description": "Whether to update the deployments that use the parent vector database.Can only be set to `true` if parent_vector_database_id is provided.", "title": "updateDeployments", "type": "boolean" }, "updateLlmBlueprints": { "default": false, "description": "Whether to update the LLM blueprints that use the parent vector database.Can only be set to `true` if parent_vector_database_id is provided.", "title": "updateLlmBlueprints", "type": "boolean" }, "useCaseId": { "description": "The ID of the use case to link the vector database to.", "title": "useCaseId", "type": "string" } }, "required": [ "datasetId", "useCaseId" ], "title": "CreateVectorDatabaseRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | CreateVectorDatabaseRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for a single vector database.", "properties": { "addedDatasetIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset IDs that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetIds" }, "addedDatasetNames": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset names that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetNames" }, "addedMetadataDatasetPairs": { "anyOf": [ { "items": { "description": "Pair of metadata dataset and dataset added to the vector database.", "properties": { "datasetId": { "description": "The ID of the dataset added to the vector database.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the dataset added to the vector database.", "title": "datasetName", "type": "string" }, "metadataDatasetId": { "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId", "type": "string" }, "metadataDatasetName": { "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName", "type": "string" } }, "required": [ "metadataDatasetId", "datasetId", "metadataDatasetName", "datasetName" ], "title": "MetadataDatasetPairApiFormatted", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Pairs of metadata dataset and dataset added to the vector database.", "title": "addedMetadataDatasetPairs" }, "chunkOverlapPercentage": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage that the vector database uses.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the text chunk (measured in tokens) that the vector database uses.", "title": "chunkSize" }, "chunkingLengthFunction": { "anyOf": [ { "description": "Supported length functions for text splitters.", "enum": [ "tokenizer_length_function", "approximate_token_count" ], "title": "ChunkingLengthFunctionNames", "type": "string" }, { "type": "null" } ], "default": "approximate_token_count", "description": "The length function to use for the text splitter." }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method the vector database uses." }, "chunksCount": { "description": "The number of text chunks in the vector database.", "title": "chunksCount", "type": "integer" }, "creationDate": { "description": "The creation date of the vector database (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationDuration": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The duration of the vector database creation.", "title": "creationDuration" }, "creationUserId": { "description": "The ID of the user that created this vector database.", "title": "creationUserId", "type": "string" }, "customChunking": { "description": "Whether the vector database uses custom chunking.", "title": "customChunking", "type": "boolean" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset the vector database was built from.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset this vector database was built from.", "title": "datasetName", "type": "string" }, "deploymentStatus": { "anyOf": [ { "description": "Sort order values for listing vector databases.", "enum": [ "Created", "Assembling", "Registered", "Deployed" ], "title": "VectorDatabaseDeploymentStatuses", "type": "string" }, { "type": "null" } ], "description": "How far along in the deployment process this vector database is." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model the vector database uses.", "title": "embeddingModel" }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model embedding (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the vector database creation error (in case of a creation error).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database creation error (in case of a creation error).", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnection", "milvus": "#/components/schemas/MilvusConnection", "pinecone": "#/components/schemas/PineconeConnection", "postgres": "#/components/schemas/PostgresConnection" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnection", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnection", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnection", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnection", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "familyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent.", "title": "familyId" }, "id": { "description": "The ID of the vector database.", "title": "id", "type": "string" }, "isSeparatorRegex": { "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "metadataColumns": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of metadata columns in the vector database.", "title": "metadataColumns" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId" }, "metadataDatasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName" }, "name": { "description": "The name of the vector database.", "title": "name", "type": "string" }, "organizationId": { "description": "The ID of the DataRobot organization this vector database belongs to.", "title": "organizationId", "type": "string" }, "parentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'.", "title": "parentId" }, "percentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "Vector database progress percentage.", "title": "percentage" }, "playgroundsCount": { "description": "The number of playgrounds that use this vector database.", "title": "playgroundsCount", "type": "integer" }, "separators": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The text chunking separators that the vector database uses.", "title": "separators" }, "size": { "description": "The size of the vector database (in bytes).", "title": "size", "type": "integer" }, "skippedChunksCount": { "description": "The number of text chunks skipped during vector database creation.", "title": "skippedChunksCount", "type": "integer" }, "source": { "description": "The source of the vector database.", "enum": [ "DataRobot", "External", "Pinecone", "Elasticsearch", "Milvus", "Postgres" ], "title": "VectorDatabaseSource", "type": "string" }, "tenantId": { "description": "The ID of the DataRobot tenant this vector database belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "description": "The ID of the use case the vector database is linked to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this vector database.", "title": "userName", "type": "string" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model vector database (in case of using a custom model vector database).", "title": "validationId" }, "version": { "description": "The version of the vector database linked to a certain family ID.", "title": "version", "type": "integer" } }, "required": [ "id", "name", "size", "useCaseId", "datasetId", "embeddingModel", "embeddingValidationId", "embeddingRegisteredModelId", "chunkSize", "chunkingMethod", "chunkOverlapPercentage", "chunksCount", "skippedChunksCount", "customChunking", "separators", "isSeparatorRegex", "creationDate", "creationUserId", "organizationId", "tenantId", "lastUpdateDate", "executionStatus", "errorMessage", "errorResolution", "source", "validationId", "parentId", "familyId", "addedDatasetIds", "version", "playgroundsCount", "datasetName", "userName", "addedDatasetNames" ], "title": "VectorDatabaseResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Vector database creation job successfully accepted. Follow the Location header to poll for job execution status. | VectorDatabaseResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create a vector databases from a custom model deployment Operation path: `POST /api/v2/genai/vectorDatabases/fromCustomModelDeployment/` Authentication requirements: `BearerAuth` Create a new vector database from a custom model deployment. ### Body parameter ``` { "anyOf": [ { "description": "The body of the \"Create vector database from validation ID\" request.", "properties": { "name": { "description": "The name of the vector database.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "useCaseId": { "description": "The ID of the use case to link the vector database to.", "title": "useCaseId", "type": "string" }, "validationId": { "description": "The validation ID of the custom model validation.", "title": "validationId", "type": "string" } }, "required": [ "name", "useCaseId", "validationId" ], "title": "CreateCustomModelVectorDatabaseFromValidationIdPayload", "type": "object" }, { "description": "The body of the \"Create vector database from custom model deployment\" request.", "properties": { "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "modelId": { "description": "The ID of the model in the custom model deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the vector database.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "useCaseId": { "description": "The ID of the use case to link the vector database to.", "title": "useCaseId", "type": "string" } }, "required": [ "name", "useCaseId", "promptColumnName", "targetColumnName", "deploymentId", "modelId" ], "title": "CreateCustomModelVectorDatabaseFromDeploymentRequest", "type": "object" } ], "title": "Payload" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | body | body | any | true | none | ### Example responses > 201 Response ``` { "description": "API response object for a single vector database.", "properties": { "addedDatasetIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset IDs that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetIds" }, "addedDatasetNames": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset names that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetNames" }, "addedMetadataDatasetPairs": { "anyOf": [ { "items": { "description": "Pair of metadata dataset and dataset added to the vector database.", "properties": { "datasetId": { "description": "The ID of the dataset added to the vector database.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the dataset added to the vector database.", "title": "datasetName", "type": "string" }, "metadataDatasetId": { "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId", "type": "string" }, "metadataDatasetName": { "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName", "type": "string" } }, "required": [ "metadataDatasetId", "datasetId", "metadataDatasetName", "datasetName" ], "title": "MetadataDatasetPairApiFormatted", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Pairs of metadata dataset and dataset added to the vector database.", "title": "addedMetadataDatasetPairs" }, "chunkOverlapPercentage": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage that the vector database uses.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the text chunk (measured in tokens) that the vector database uses.", "title": "chunkSize" }, "chunkingLengthFunction": { "anyOf": [ { "description": "Supported length functions for text splitters.", "enum": [ "tokenizer_length_function", "approximate_token_count" ], "title": "ChunkingLengthFunctionNames", "type": "string" }, { "type": "null" } ], "default": "approximate_token_count", "description": "The length function to use for the text splitter." }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method the vector database uses." }, "chunksCount": { "description": "The number of text chunks in the vector database.", "title": "chunksCount", "type": "integer" }, "creationDate": { "description": "The creation date of the vector database (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationDuration": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The duration of the vector database creation.", "title": "creationDuration" }, "creationUserId": { "description": "The ID of the user that created this vector database.", "title": "creationUserId", "type": "string" }, "customChunking": { "description": "Whether the vector database uses custom chunking.", "title": "customChunking", "type": "boolean" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset the vector database was built from.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset this vector database was built from.", "title": "datasetName", "type": "string" }, "deploymentStatus": { "anyOf": [ { "description": "Sort order values for listing vector databases.", "enum": [ "Created", "Assembling", "Registered", "Deployed" ], "title": "VectorDatabaseDeploymentStatuses", "type": "string" }, { "type": "null" } ], "description": "How far along in the deployment process this vector database is." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model the vector database uses.", "title": "embeddingModel" }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model embedding (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the vector database creation error (in case of a creation error).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database creation error (in case of a creation error).", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnection", "milvus": "#/components/schemas/MilvusConnection", "pinecone": "#/components/schemas/PineconeConnection", "postgres": "#/components/schemas/PostgresConnection" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnection", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnection", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnection", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnection", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "familyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent.", "title": "familyId" }, "id": { "description": "The ID of the vector database.", "title": "id", "type": "string" }, "isSeparatorRegex": { "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "metadataColumns": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of metadata columns in the vector database.", "title": "metadataColumns" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId" }, "metadataDatasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName" }, "name": { "description": "The name of the vector database.", "title": "name", "type": "string" }, "organizationId": { "description": "The ID of the DataRobot organization this vector database belongs to.", "title": "organizationId", "type": "string" }, "parentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'.", "title": "parentId" }, "percentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "Vector database progress percentage.", "title": "percentage" }, "playgroundsCount": { "description": "The number of playgrounds that use this vector database.", "title": "playgroundsCount", "type": "integer" }, "separators": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The text chunking separators that the vector database uses.", "title": "separators" }, "size": { "description": "The size of the vector database (in bytes).", "title": "size", "type": "integer" }, "skippedChunksCount": { "description": "The number of text chunks skipped during vector database creation.", "title": "skippedChunksCount", "type": "integer" }, "source": { "description": "The source of the vector database.", "enum": [ "DataRobot", "External", "Pinecone", "Elasticsearch", "Milvus", "Postgres" ], "title": "VectorDatabaseSource", "type": "string" }, "tenantId": { "description": "The ID of the DataRobot tenant this vector database belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "description": "The ID of the use case the vector database is linked to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this vector database.", "title": "userName", "type": "string" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model vector database (in case of using a custom model vector database).", "title": "validationId" }, "version": { "description": "The version of the vector database linked to a certain family ID.", "title": "version", "type": "integer" } }, "required": [ "id", "name", "size", "useCaseId", "datasetId", "embeddingModel", "embeddingValidationId", "embeddingRegisteredModelId", "chunkSize", "chunkingMethod", "chunkOverlapPercentage", "chunksCount", "skippedChunksCount", "customChunking", "separators", "isSeparatorRegex", "creationDate", "creationUserId", "organizationId", "tenantId", "lastUpdateDate", "executionStatus", "errorMessage", "errorResolution", "source", "validationId", "parentId", "familyId", "addedDatasetIds", "version", "playgroundsCount", "datasetName", "userName", "addedDatasetNames" ], "title": "VectorDatabaseResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 201 | Created | Custom model hosted vector database successfully added. Full representation is available in the response body. | VectorDatabaseResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List supported embedding models Operation path: `GET /api/v2/genai/vectorDatabases/supportedEmbeddings/` Authentication requirements: `BearerAuth` List the supported embedding models for building vector databases. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | datasetId | query | any | false | Only retrieve the embedding models compatible with this dataset ID. | | useCaseId | query | any | false | If specified, include the custom model embeddings available for this use case ID. | ### Example responses > 200 Response ``` { "description": "API response object for \"List supported embeddings\".", "properties": { "customModelEmbeddingValidations": { "description": "The list of validated custom embedding models.", "items": { "description": "API response object for a single custom embedding model.", "properties": { "id": { "description": "The validation ID of the custom embedding model.", "title": "id", "type": "string" }, "name": { "description": "The name of the custom embedding model.", "title": "name", "type": "string" } }, "required": [ "id", "name" ], "title": "SupportedCustomModelEmbeddings", "type": "object" }, "title": "customModelEmbeddingValidations", "type": "array" }, "defaultEmbeddingModel": { "description": "The name of the default embedding model.", "title": "defaultEmbeddingModel", "type": "string" }, "embeddingModels": { "description": "The list of embeddings models.", "items": { "description": "API response object for a single embedding model.", "properties": { "description": { "description": "The description of the embedding model.", "title": "description", "type": "string" }, "embeddingModel": { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, "languages": { "description": "The list of languages the embedding models supports.", "items": { "description": "The names of dataset languages.", "enum": [ "Afrikaans", "Amharic", "Arabic", "Assamese", "Azerbaijani", "Belarusian", "Bulgarian", "Bengali", "Breton", "Bosnian", "Catalan", "Czech", "Welsh", "Danish", "German", "Greek", "English", "Esperanto", "Spanish", "Estonian", "Basque", "Persian", "Finnish", "French", "Western Frisian", "Irish", "Scottish Gaelic", "Galician", "Gujarati", "Hausa", "Hebrew", "Hindi", "Croatian", "Hungarian", "Armenian", "Indonesian", "Icelandic", "Italian", "Japanese", "Javanese", "Georgian", "Kazakh", "Khmer", "Kannada", "Korean", "Kurdish", "Kyrgyz", "Latin", "Lao", "Lithuanian", "Latvian", "Malagasy", "Macedonian", "Malayalam", "Mongolian", "Marathi", "Malay", "Burmese", "Nepali", "Dutch", "Norwegian", "Oromo", "Oriya", "Panjabi", "Polish", "Pashto", "Portuguese", "Romanian", "Russian", "Sanskrit", "Sindhi", "Sinhala", "Slovak", "Slovenian", "Somali", "Albanian", "Serbian", "Sundanese", "Swedish", "Swahili", "Tamil", "Telugu", "Thai", "Tagalog", "Turkish", "Uyghur", "Ukrainian", "Urdu", "Uzbek", "Vietnamese", "Xhosa", "Yiddish", "Chinese" ], "title": "DatasetLanguages", "type": "string" }, "title": "languages", "type": "array" }, "maxSequenceLength": { "description": "The maximum input token sequence length that the embedding model can accept.", "title": "maxSequenceLength", "type": "integer" } }, "required": [ "embeddingModel", "description", "maxSequenceLength", "languages" ], "title": "EmbeddingModel", "type": "object" }, "title": "embeddingModels", "type": "array" }, "nimEmbeddingModels": { "description": "The list of NIM registered models.", "items": { "description": "API response object for a single registered NIM embedding model.", "properties": { "description": { "description": "The description of the registered NIM model.", "title": "description", "type": "string" }, "id": { "description": "The validation ID of the registered NIM model.", "title": "id", "type": "string" }, "name": { "description": "The name of the registered NIM model.", "title": "name", "type": "string" } }, "required": [ "id", "name", "description" ], "title": "SupportedNIMModelEmbeddings", "type": "object" }, "title": "nimEmbeddingModels", "type": "array" } }, "required": [ "embeddingModels", "defaultEmbeddingModel" ], "title": "SupportedEmbeddingsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Supported embeddings successfully retrieved. | SupportedEmbeddingsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List supported vector database retrieval settings Operation path: `GET /api/v2/genai/vectorDatabases/supportedRetrievalSettings/` Authentication requirements: `BearerAuth` List all vector database retrieval settings that are supported by LLM blueprints. ### Example responses > 200 Response ``` { "description": "API response object for \"Retrieve supported retrieval settings\".", "properties": { "settings": { "description": "The list of retrieval settings.", "items": { "description": "API response object for a single vector database setting parameter.", "properties": { "default": { "description": "The default value of the parameter.", "title": "default" }, "description": { "description": "The description of the parameter.", "title": "description", "type": "string" }, "enum": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of possible values for the parameter.", "title": "enum" }, "groupId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The identifier of the group the parameter belongs to.", "title": "groupId" }, "maximum": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the parameter.", "title": "maximum" }, "minimum": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the parameter.", "title": "minimum" }, "name": { "description": "The name of the parameter.", "title": "name", "type": "string" }, "settings": { "anyOf": [ { "items": "[Circular]", "type": "array" }, { "type": "null" } ], "description": "The list of available settings for the parameter.", "title": "settings" }, "title": { "description": "The title of the parameter.", "title": "title", "type": "string" }, "type": { "anyOf": [ { "description": "The types of vector database setting parameters.", "enum": [ "string", "integer", "boolean", "null", "number", "array" ], "title": "VectorDatabaseSettingTypes", "type": "string" }, { "items": { "description": "The types of vector database setting parameters.", "enum": [ "string", "integer", "boolean", "null", "number", "array" ], "title": "VectorDatabaseSettingTypes", "type": "string" }, "type": "array" } ], "description": "The type of the parameter.", "title": "type" } }, "required": [ "name", "type", "description", "title" ], "title": "VectorDatabaseSettingParameter", "type": "object" }, "title": "settings", "type": "array" } }, "required": [ "settings" ], "title": "SupportedRetrievalSettingsResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Supported vector database retrieval settings successfully retrieved. | SupportedRetrievalSettingsResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List supported text chunking methods Operation path: `GET /api/v2/genai/vectorDatabases/supportedTextChunkings/` Authentication requirements: `BearerAuth` List the supported text chunking methods for building vector databases. ### Example responses > 200 Response ``` { "description": "API response for \"List text chunking methods\".", "properties": { "textChunkingConfigs": { "description": "The list of text chunking configurations.", "items": { "description": "API response object for a single text chunking configuration.", "properties": { "defaultMethod": { "description": "The name of the default text chunking method.", "title": "defaultMethod", "type": "string" }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "description": "Model names for custom embedding models.", "enum": [ "custom-embeddings/default" ], "title": "CustomEmbeddingModelNames", "type": "string" } ], "description": "The name of the embedding model.", "title": "embeddingModel" }, "methods": { "description": "The list of text chunking methods.", "items": { "description": "API response object for a single text chunking method.", "properties": { "chunkingMethod": { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, "chunkingParameters": { "description": "The list of text chunking parameters.", "items": { "description": "API response object for a single text chunking parameter.", "properties": { "default": { "anyOf": [ { "type": "integer" }, { "items": { "type": "string" }, "type": "array" }, { "type": "boolean" } ], "description": "The default value of the parameter.", "title": "default" }, "description": { "description": "The description of the parameter.", "title": "description", "type": "string" }, "max": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the parameter (inclusive).", "title": "max" }, "min": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the parameter (inclusive).", "title": "min" }, "name": { "description": "The name of the parameter.", "title": "name", "type": "string" }, "type": { "description": "Supported parameter data types for text chunking parameters.", "enum": [ "int", "list[str]", "bool" ], "title": "ChunkingParameterTypes", "type": "string" } }, "required": [ "name", "type", "min", "max", "description", "default" ], "title": "TextChunkingParameterFields", "type": "object" }, "title": "chunkingParameters", "type": "array" }, "description": { "description": "The description of the text chunking method.", "title": "description", "type": "string" }, "title": { "description": "Supported user-facing friendly ames of text chunking methods.", "enum": [ "Recursive", "Semantic" ], "title": "ChunkingMethodNamesTitle", "type": "string" } }, "required": [ "chunkingMethod", "title", "chunkingParameters", "description" ], "title": "TextChunkingMethod", "type": "object" }, "title": "methods", "type": "array" } }, "required": [ "embeddingModel", "methods", "defaultMethod" ], "title": "TextChunkingConfig", "type": "object" }, "title": "textChunkingConfigs", "type": "array" } }, "required": [ "textChunkingConfigs" ], "title": "SupportedTextChunkingResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Supported text chunking methods successfully retrieved. | SupportedTextChunkingResponse | ## Delete vector database by vector database ID Operation path: `DELETE /api/v2/genai/vectorDatabases/{vectorDatabaseId}/` Authentication requirements: `BearerAuth` Delete an existing vector database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | path | string | true | The ID of the vector database to delete. | ### Example responses > 422 Response ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 204 | No Content | Vector database successfully deleted. | None | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve vector database by vector database ID Operation path: `GET /api/v2/genai/vectorDatabases/{vectorDatabaseId}/` Authentication requirements: `BearerAuth` Retrieve an existing vector database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | path | string | true | The ID of the vector database to retrieve. | ### Example responses > 200 Response ``` { "description": "API response object for a single vector database.", "properties": { "addedDatasetIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset IDs that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetIds" }, "addedDatasetNames": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset names that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetNames" }, "addedMetadataDatasetPairs": { "anyOf": [ { "items": { "description": "Pair of metadata dataset and dataset added to the vector database.", "properties": { "datasetId": { "description": "The ID of the dataset added to the vector database.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the dataset added to the vector database.", "title": "datasetName", "type": "string" }, "metadataDatasetId": { "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId", "type": "string" }, "metadataDatasetName": { "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName", "type": "string" } }, "required": [ "metadataDatasetId", "datasetId", "metadataDatasetName", "datasetName" ], "title": "MetadataDatasetPairApiFormatted", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Pairs of metadata dataset and dataset added to the vector database.", "title": "addedMetadataDatasetPairs" }, "chunkOverlapPercentage": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage that the vector database uses.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the text chunk (measured in tokens) that the vector database uses.", "title": "chunkSize" }, "chunkingLengthFunction": { "anyOf": [ { "description": "Supported length functions for text splitters.", "enum": [ "tokenizer_length_function", "approximate_token_count" ], "title": "ChunkingLengthFunctionNames", "type": "string" }, { "type": "null" } ], "default": "approximate_token_count", "description": "The length function to use for the text splitter." }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method the vector database uses." }, "chunksCount": { "description": "The number of text chunks in the vector database.", "title": "chunksCount", "type": "integer" }, "creationDate": { "description": "The creation date of the vector database (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationDuration": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The duration of the vector database creation.", "title": "creationDuration" }, "creationUserId": { "description": "The ID of the user that created this vector database.", "title": "creationUserId", "type": "string" }, "customChunking": { "description": "Whether the vector database uses custom chunking.", "title": "customChunking", "type": "boolean" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset the vector database was built from.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset this vector database was built from.", "title": "datasetName", "type": "string" }, "deploymentStatus": { "anyOf": [ { "description": "Sort order values for listing vector databases.", "enum": [ "Created", "Assembling", "Registered", "Deployed" ], "title": "VectorDatabaseDeploymentStatuses", "type": "string" }, { "type": "null" } ], "description": "How far along in the deployment process this vector database is." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model the vector database uses.", "title": "embeddingModel" }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model embedding (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the vector database creation error (in case of a creation error).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database creation error (in case of a creation error).", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnection", "milvus": "#/components/schemas/MilvusConnection", "pinecone": "#/components/schemas/PineconeConnection", "postgres": "#/components/schemas/PostgresConnection" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnection", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnection", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnection", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnection", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "familyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent.", "title": "familyId" }, "id": { "description": "The ID of the vector database.", "title": "id", "type": "string" }, "isSeparatorRegex": { "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "metadataColumns": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of metadata columns in the vector database.", "title": "metadataColumns" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId" }, "metadataDatasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName" }, "name": { "description": "The name of the vector database.", "title": "name", "type": "string" }, "organizationId": { "description": "The ID of the DataRobot organization this vector database belongs to.", "title": "organizationId", "type": "string" }, "parentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'.", "title": "parentId" }, "percentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "Vector database progress percentage.", "title": "percentage" }, "playgroundsCount": { "description": "The number of playgrounds that use this vector database.", "title": "playgroundsCount", "type": "integer" }, "separators": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The text chunking separators that the vector database uses.", "title": "separators" }, "size": { "description": "The size of the vector database (in bytes).", "title": "size", "type": "integer" }, "skippedChunksCount": { "description": "The number of text chunks skipped during vector database creation.", "title": "skippedChunksCount", "type": "integer" }, "source": { "description": "The source of the vector database.", "enum": [ "DataRobot", "External", "Pinecone", "Elasticsearch", "Milvus", "Postgres" ], "title": "VectorDatabaseSource", "type": "string" }, "tenantId": { "description": "The ID of the DataRobot tenant this vector database belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "description": "The ID of the use case the vector database is linked to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this vector database.", "title": "userName", "type": "string" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model vector database (in case of using a custom model vector database).", "title": "validationId" }, "version": { "description": "The version of the vector database linked to a certain family ID.", "title": "version", "type": "integer" } }, "required": [ "id", "name", "size", "useCaseId", "datasetId", "embeddingModel", "embeddingValidationId", "embeddingRegisteredModelId", "chunkSize", "chunkingMethod", "chunkOverlapPercentage", "chunksCount", "skippedChunksCount", "customChunking", "separators", "isSeparatorRegex", "creationDate", "creationUserId", "organizationId", "tenantId", "lastUpdateDate", "executionStatus", "errorMessage", "errorResolution", "source", "validationId", "parentId", "familyId", "addedDatasetIds", "version", "playgroundsCount", "datasetName", "userName", "addedDatasetNames" ], "title": "VectorDatabaseResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Vector database successfully retrieved. | VectorDatabaseResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Edit vector database by vector database ID Operation path: `PATCH /api/v2/genai/vectorDatabases/{vectorDatabaseId}/` Authentication requirements: `BearerAuth` Edit an existing vector database. ### Body parameter ``` { "description": "The body of the \"Edit vector database\" request.", "properties": { "credentialId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The new ID of the credential to access a connected vector database.", "title": "credentialId" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The new name of the vector database.", "title": "name" } }, "title": "EditVectorDatabaseRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | path | string | true | The ID of the vector database to edit. | | body | body | EditVectorDatabaseRequest | true | none | ### Example responses > 200 Response ``` { "description": "API response object for a single vector database.", "properties": { "addedDatasetIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset IDs that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetIds" }, "addedDatasetNames": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset names that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetNames" }, "addedMetadataDatasetPairs": { "anyOf": [ { "items": { "description": "Pair of metadata dataset and dataset added to the vector database.", "properties": { "datasetId": { "description": "The ID of the dataset added to the vector database.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the dataset added to the vector database.", "title": "datasetName", "type": "string" }, "metadataDatasetId": { "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId", "type": "string" }, "metadataDatasetName": { "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName", "type": "string" } }, "required": [ "metadataDatasetId", "datasetId", "metadataDatasetName", "datasetName" ], "title": "MetadataDatasetPairApiFormatted", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Pairs of metadata dataset and dataset added to the vector database.", "title": "addedMetadataDatasetPairs" }, "chunkOverlapPercentage": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage that the vector database uses.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the text chunk (measured in tokens) that the vector database uses.", "title": "chunkSize" }, "chunkingLengthFunction": { "anyOf": [ { "description": "Supported length functions for text splitters.", "enum": [ "tokenizer_length_function", "approximate_token_count" ], "title": "ChunkingLengthFunctionNames", "type": "string" }, { "type": "null" } ], "default": "approximate_token_count", "description": "The length function to use for the text splitter." }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method the vector database uses." }, "chunksCount": { "description": "The number of text chunks in the vector database.", "title": "chunksCount", "type": "integer" }, "creationDate": { "description": "The creation date of the vector database (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationDuration": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The duration of the vector database creation.", "title": "creationDuration" }, "creationUserId": { "description": "The ID of the user that created this vector database.", "title": "creationUserId", "type": "string" }, "customChunking": { "description": "Whether the vector database uses custom chunking.", "title": "customChunking", "type": "boolean" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset the vector database was built from.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset this vector database was built from.", "title": "datasetName", "type": "string" }, "deploymentStatus": { "anyOf": [ { "description": "Sort order values for listing vector databases.", "enum": [ "Created", "Assembling", "Registered", "Deployed" ], "title": "VectorDatabaseDeploymentStatuses", "type": "string" }, { "type": "null" } ], "description": "How far along in the deployment process this vector database is." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model the vector database uses.", "title": "embeddingModel" }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model embedding (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the vector database creation error (in case of a creation error).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database creation error (in case of a creation error).", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnection", "milvus": "#/components/schemas/MilvusConnection", "pinecone": "#/components/schemas/PineconeConnection", "postgres": "#/components/schemas/PostgresConnection" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnection", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnection", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnection", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnection", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "familyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent.", "title": "familyId" }, "id": { "description": "The ID of the vector database.", "title": "id", "type": "string" }, "isSeparatorRegex": { "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "metadataColumns": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of metadata columns in the vector database.", "title": "metadataColumns" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId" }, "metadataDatasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName" }, "name": { "description": "The name of the vector database.", "title": "name", "type": "string" }, "organizationId": { "description": "The ID of the DataRobot organization this vector database belongs to.", "title": "organizationId", "type": "string" }, "parentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'.", "title": "parentId" }, "percentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "Vector database progress percentage.", "title": "percentage" }, "playgroundsCount": { "description": "The number of playgrounds that use this vector database.", "title": "playgroundsCount", "type": "integer" }, "separators": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The text chunking separators that the vector database uses.", "title": "separators" }, "size": { "description": "The size of the vector database (in bytes).", "title": "size", "type": "integer" }, "skippedChunksCount": { "description": "The number of text chunks skipped during vector database creation.", "title": "skippedChunksCount", "type": "integer" }, "source": { "description": "The source of the vector database.", "enum": [ "DataRobot", "External", "Pinecone", "Elasticsearch", "Milvus", "Postgres" ], "title": "VectorDatabaseSource", "type": "string" }, "tenantId": { "description": "The ID of the DataRobot tenant this vector database belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "description": "The ID of the use case the vector database is linked to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this vector database.", "title": "userName", "type": "string" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model vector database (in case of using a custom model vector database).", "title": "validationId" }, "version": { "description": "The version of the vector database linked to a certain family ID.", "title": "version", "type": "integer" } }, "required": [ "id", "name", "size", "useCaseId", "datasetId", "embeddingModel", "embeddingValidationId", "embeddingRegisteredModelId", "chunkSize", "chunkingMethod", "chunkOverlapPercentage", "chunksCount", "skippedChunksCount", "customChunking", "separators", "isSeparatorRegex", "creationDate", "creationUserId", "organizationId", "tenantId", "lastUpdateDate", "executionStatus", "errorMessage", "errorResolution", "source", "validationId", "parentId", "familyId", "addedDatasetIds", "version", "playgroundsCount", "datasetName", "userName", "addedDatasetNames" ], "title": "VectorDatabaseResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Vector database successfully updated. | VectorDatabaseResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create custom model version by vector database ID Operation path: `POST /api/v2/genai/vectorDatabases/{vectorDatabaseId}/customModelVersions/` Authentication requirements: `BearerAuth` Export the specified Vector Database as a custom model version in Model Registry. ### Body parameter ``` { "description": "The body of the \"Create custom model version\" request.", "properties": { "modelName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the custom model that this job will create.", "title": "modelName" }, "promptColumnName": { "default": "promptText", "description": "The name of the column to use for prompt text input in the custom model.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "resources": { "anyOf": [ { "description": "The structure that describes resource settings for a custom model created from buzok.", "properties": { "maximumMemory": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum memory that can be allocated to the custom model.", "title": "maximumMemory" }, "networkEgressPolicy": { "default": "Public", "description": "Network egress policy for the custom model. Can be either Public or None.", "maxLength": 5000, "title": "networkEgressPolicy", "type": "string" }, "replicas": { "default": 1, "description": "A fixed number of replicas that will be created for the custom model.", "title": "replicas", "type": "integer" }, "resourceBundleId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "An identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "title": "resourceBundleId" } }, "title": "CustomModelResourcesRequest", "type": "object" }, { "type": "null" } ], "description": "The resources that the custom model will be provisioned with." }, "targetColumnName": { "default": "relevant", "description": "The name of the column to use for prediction output in the custom model.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Specifies the vector database retrieval settings in LLM blueprint API requests.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 128, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettingsRequest", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "title": "CreateVectorDatabaseCustomModelVersionRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | path | string | true | The ID of the vector database to create a custom model version for. | | body | body | CreateVectorDatabaseCustomModelVersionRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for the \"Create custom model version\" request.", "properties": { "customModelId": { "description": "The ID of the created custom model.", "title": "customModelId", "type": "string" } }, "required": [ "customModelId" ], "title": "CreateCustomModelVersionResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successful Response | CreateCustomModelVersionResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Export vector database dataset by vector database ID Operation path: `POST /api/v2/genai/vectorDatabases/{vectorDatabaseId}/datasetExportJobs/` Authentication requirements: `BearerAuth` Export an existing vector database as dataset to Data Registry. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | path | string | true | The ID of the vector database to retrieve. | ### Example responses > 202 Response ``` { "description": "API response object for exporting a vector database.", "properties": { "exportDatasetId": { "description": "The Data Registry dataset ID.", "title": "exportDatasetId", "type": "string" }, "jobId": { "description": "The ID of the export job.", "format": "uuid4", "title": "jobId", "type": "string" }, "vectorDatabaseId": { "description": "The ID of the vector database.", "title": "vectorDatabaseId", "type": "string" } }, "required": [ "jobId", "exportDatasetId", "vectorDatabaseId" ], "title": "VectorDatabaseExportResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Vector database export job successfully accepted.Follow the Location header to poll for job execution status. | VectorDatabaseExportResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Create new custom model version by vector database ID Operation path: `POST /api/v2/genai/vectorDatabases/{vectorDatabaseId}/deployments/` Authentication requirements: `BearerAuth` Export the specified vector database as a custom model version, register it, and deploy it on a new deployment. ### Body parameter ``` { "description": "The body of the \"Create vector database deployment\" request.", "properties": { "credentialId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the credential to access the connected vector database.", "title": "credentialId" }, "defaultPredictionServerId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of a prediction server for the new deployment to use. Cannot be used with predictionEnvironmentId.", "title": "defaultPredictionServerId" }, "modelName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the custom model that this job will create.", "title": "modelName" }, "predictionEnvironmentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the prediction environment for the new deployment to use. Cannot be used with defaultPredictionServerId.", "title": "predictionEnvironmentId" }, "promptColumnName": { "default": "promptText", "description": "The name of the column to use for prompt text input in the custom model.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "resources": { "anyOf": [ { "description": "The structure that describes resource settings for a custom model created from buzok.", "properties": { "maximumMemory": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum memory that can be allocated to the custom model.", "title": "maximumMemory" }, "networkEgressPolicy": { "default": "Public", "description": "Network egress policy for the custom model. Can be either Public or None.", "maxLength": 5000, "title": "networkEgressPolicy", "type": "string" }, "replicas": { "default": 1, "description": "A fixed number of replicas that will be created for the custom model.", "title": "replicas", "type": "integer" }, "resourceBundleId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "An identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "title": "resourceBundleId" } }, "title": "CustomModelResourcesRequest", "type": "object" }, { "type": "null" } ], "description": "The resources that the custom model will be provisioned with." }, "targetColumnName": { "default": "relevant", "description": "The name of the column to use for prediction output in the custom model.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Specifies the vector database retrieval settings in LLM blueprint API requests.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 128, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettingsRequest", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "title": "CreateVectorDatabaseDeploymentRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | path | string | true | The ID of the vector database to create a custom model version for. | | body | body | CreateVectorDatabaseDeploymentRequest | true | none | ### Example responses > 202 Response ``` { "description": "The response to the \"Create vector database deployment\" request.", "properties": { "jobId": { "description": "The ID of the job that will create the new vector database deployment.", "format": "uuid4", "title": "jobId", "type": "string" } }, "required": [ "jobId" ], "title": "CreateVectorDatabaseDeploymentResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Successful Response | CreateVectorDatabaseDeploymentResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Add documents by vector database ID Operation path: `PATCH /api/v2/genai/vectorDatabases/{vectorDatabaseId}/externalVectorDatabaseDocuments/` Authentication requirements: `BearerAuth` Add documents to a connected vector database. ### Body parameter ``` { "description": "The body of the \"Update connected vector database\" request.", "properties": { "datasetId": { "description": "The ID of the dataset to use for building the vector database.", "title": "datasetId", "type": "string" }, "metadataCombinationStrategy": { "description": "Strategy to use when the dataset and the metadata file have duplicate columns.", "enum": [ "replace", "merge" ], "title": "MetadataCombinationStrategy", "type": "string" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset to add metadata for building the vector database.", "title": "metadataDatasetId" } }, "required": [ "datasetId" ], "title": "UpdateConnectedVectorDatabaseRequest", "type": "object" } ``` ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | path | string | true | The ID of the vector database to edit. | | body | body | UpdateConnectedVectorDatabaseRequest | true | none | ### Example responses > 202 Response ``` { "description": "API response object for a single vector database.", "properties": { "addedDatasetIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset IDs that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetIds" }, "addedDatasetNames": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset names that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetNames" }, "addedMetadataDatasetPairs": { "anyOf": [ { "items": { "description": "Pair of metadata dataset and dataset added to the vector database.", "properties": { "datasetId": { "description": "The ID of the dataset added to the vector database.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the dataset added to the vector database.", "title": "datasetName", "type": "string" }, "metadataDatasetId": { "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId", "type": "string" }, "metadataDatasetName": { "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName", "type": "string" } }, "required": [ "metadataDatasetId", "datasetId", "metadataDatasetName", "datasetName" ], "title": "MetadataDatasetPairApiFormatted", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Pairs of metadata dataset and dataset added to the vector database.", "title": "addedMetadataDatasetPairs" }, "chunkOverlapPercentage": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage that the vector database uses.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the text chunk (measured in tokens) that the vector database uses.", "title": "chunkSize" }, "chunkingLengthFunction": { "anyOf": [ { "description": "Supported length functions for text splitters.", "enum": [ "tokenizer_length_function", "approximate_token_count" ], "title": "ChunkingLengthFunctionNames", "type": "string" }, { "type": "null" } ], "default": "approximate_token_count", "description": "The length function to use for the text splitter." }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method the vector database uses." }, "chunksCount": { "description": "The number of text chunks in the vector database.", "title": "chunksCount", "type": "integer" }, "creationDate": { "description": "The creation date of the vector database (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationDuration": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The duration of the vector database creation.", "title": "creationDuration" }, "creationUserId": { "description": "The ID of the user that created this vector database.", "title": "creationUserId", "type": "string" }, "customChunking": { "description": "Whether the vector database uses custom chunking.", "title": "customChunking", "type": "boolean" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset the vector database was built from.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset this vector database was built from.", "title": "datasetName", "type": "string" }, "deploymentStatus": { "anyOf": [ { "description": "Sort order values for listing vector databases.", "enum": [ "Created", "Assembling", "Registered", "Deployed" ], "title": "VectorDatabaseDeploymentStatuses", "type": "string" }, { "type": "null" } ], "description": "How far along in the deployment process this vector database is." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model the vector database uses.", "title": "embeddingModel" }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model embedding (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the vector database creation error (in case of a creation error).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database creation error (in case of a creation error).", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnection", "milvus": "#/components/schemas/MilvusConnection", "pinecone": "#/components/schemas/PineconeConnection", "postgres": "#/components/schemas/PostgresConnection" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnection", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnection", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnection", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnection", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "familyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent.", "title": "familyId" }, "id": { "description": "The ID of the vector database.", "title": "id", "type": "string" }, "isSeparatorRegex": { "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "metadataColumns": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of metadata columns in the vector database.", "title": "metadataColumns" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId" }, "metadataDatasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName" }, "name": { "description": "The name of the vector database.", "title": "name", "type": "string" }, "organizationId": { "description": "The ID of the DataRobot organization this vector database belongs to.", "title": "organizationId", "type": "string" }, "parentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'.", "title": "parentId" }, "percentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "Vector database progress percentage.", "title": "percentage" }, "playgroundsCount": { "description": "The number of playgrounds that use this vector database.", "title": "playgroundsCount", "type": "integer" }, "separators": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The text chunking separators that the vector database uses.", "title": "separators" }, "size": { "description": "The size of the vector database (in bytes).", "title": "size", "type": "integer" }, "skippedChunksCount": { "description": "The number of text chunks skipped during vector database creation.", "title": "skippedChunksCount", "type": "integer" }, "source": { "description": "The source of the vector database.", "enum": [ "DataRobot", "External", "Pinecone", "Elasticsearch", "Milvus", "Postgres" ], "title": "VectorDatabaseSource", "type": "string" }, "tenantId": { "description": "The ID of the DataRobot tenant this vector database belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "description": "The ID of the use case the vector database is linked to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this vector database.", "title": "userName", "type": "string" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model vector database (in case of using a custom model vector database).", "title": "validationId" }, "version": { "description": "The version of the vector database linked to a certain family ID.", "title": "version", "type": "integer" } }, "required": [ "id", "name", "size", "useCaseId", "datasetId", "embeddingModel", "embeddingValidationId", "embeddingRegisteredModelId", "chunkSize", "chunkingMethod", "chunkOverlapPercentage", "chunksCount", "skippedChunksCount", "customChunking", "separators", "isSeparatorRegex", "creationDate", "creationUserId", "organizationId", "tenantId", "lastUpdateDate", "executionStatus", "errorMessage", "errorResolution", "source", "validationId", "parentId", "familyId", "addedDatasetIds", "version", "playgroundsCount", "datasetName", "userName", "addedDatasetNames" ], "title": "VectorDatabaseResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 202 | Accepted | Connected vector database update job successfully accepted. Follow the Location header to poll for job execution status. | VectorDatabaseResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve vector database latest version by vector database ID Operation path: `GET /api/v2/genai/vectorDatabases/{vectorDatabaseId}/latestVersion/` Authentication requirements: `BearerAuth` Retrieve the latest vector database within the family associated with this vector database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | path | string | true | The ID of the vector database to retrieve the latest version. | | completedOnly | query | boolean | false | If true, only retrieve the vector databases that have finished building. The default is false. | ### Example responses > 200 Response ``` { "description": "API response object for a single vector database.", "properties": { "addedDatasetIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset IDs that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetIds" }, "addedDatasetNames": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset names that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetNames" }, "addedMetadataDatasetPairs": { "anyOf": [ { "items": { "description": "Pair of metadata dataset and dataset added to the vector database.", "properties": { "datasetId": { "description": "The ID of the dataset added to the vector database.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the dataset added to the vector database.", "title": "datasetName", "type": "string" }, "metadataDatasetId": { "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId", "type": "string" }, "metadataDatasetName": { "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName", "type": "string" } }, "required": [ "metadataDatasetId", "datasetId", "metadataDatasetName", "datasetName" ], "title": "MetadataDatasetPairApiFormatted", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Pairs of metadata dataset and dataset added to the vector database.", "title": "addedMetadataDatasetPairs" }, "chunkOverlapPercentage": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage that the vector database uses.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the text chunk (measured in tokens) that the vector database uses.", "title": "chunkSize" }, "chunkingLengthFunction": { "anyOf": [ { "description": "Supported length functions for text splitters.", "enum": [ "tokenizer_length_function", "approximate_token_count" ], "title": "ChunkingLengthFunctionNames", "type": "string" }, { "type": "null" } ], "default": "approximate_token_count", "description": "The length function to use for the text splitter." }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method the vector database uses." }, "chunksCount": { "description": "The number of text chunks in the vector database.", "title": "chunksCount", "type": "integer" }, "creationDate": { "description": "The creation date of the vector database (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationDuration": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The duration of the vector database creation.", "title": "creationDuration" }, "creationUserId": { "description": "The ID of the user that created this vector database.", "title": "creationUserId", "type": "string" }, "customChunking": { "description": "Whether the vector database uses custom chunking.", "title": "customChunking", "type": "boolean" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset the vector database was built from.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset this vector database was built from.", "title": "datasetName", "type": "string" }, "deploymentStatus": { "anyOf": [ { "description": "Sort order values for listing vector databases.", "enum": [ "Created", "Assembling", "Registered", "Deployed" ], "title": "VectorDatabaseDeploymentStatuses", "type": "string" }, { "type": "null" } ], "description": "How far along in the deployment process this vector database is." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model the vector database uses.", "title": "embeddingModel" }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model embedding (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the vector database creation error (in case of a creation error).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database creation error (in case of a creation error).", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnection", "milvus": "#/components/schemas/MilvusConnection", "pinecone": "#/components/schemas/PineconeConnection", "postgres": "#/components/schemas/PostgresConnection" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnection", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnection", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnection", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnection", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "familyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent.", "title": "familyId" }, "id": { "description": "The ID of the vector database.", "title": "id", "type": "string" }, "isSeparatorRegex": { "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "metadataColumns": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of metadata columns in the vector database.", "title": "metadataColumns" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId" }, "metadataDatasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName" }, "name": { "description": "The name of the vector database.", "title": "name", "type": "string" }, "organizationId": { "description": "The ID of the DataRobot organization this vector database belongs to.", "title": "organizationId", "type": "string" }, "parentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'.", "title": "parentId" }, "percentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "Vector database progress percentage.", "title": "percentage" }, "playgroundsCount": { "description": "The number of playgrounds that use this vector database.", "title": "playgroundsCount", "type": "integer" }, "separators": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The text chunking separators that the vector database uses.", "title": "separators" }, "size": { "description": "The size of the vector database (in bytes).", "title": "size", "type": "integer" }, "skippedChunksCount": { "description": "The number of text chunks skipped during vector database creation.", "title": "skippedChunksCount", "type": "integer" }, "source": { "description": "The source of the vector database.", "enum": [ "DataRobot", "External", "Pinecone", "Elasticsearch", "Milvus", "Postgres" ], "title": "VectorDatabaseSource", "type": "string" }, "tenantId": { "description": "The ID of the DataRobot tenant this vector database belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "description": "The ID of the use case the vector database is linked to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this vector database.", "title": "userName", "type": "string" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model vector database (in case of using a custom model vector database).", "title": "validationId" }, "version": { "description": "The version of the vector database linked to a certain family ID.", "title": "version", "type": "integer" } }, "required": [ "id", "name", "size", "useCaseId", "datasetId", "embeddingModel", "embeddingValidationId", "embeddingRegisteredModelId", "chunkSize", "chunkingMethod", "chunkOverlapPercentage", "chunksCount", "skippedChunksCount", "customChunking", "separators", "isSeparatorRegex", "creationDate", "creationUserId", "organizationId", "tenantId", "lastUpdateDate", "executionStatus", "errorMessage", "errorResolution", "source", "validationId", "parentId", "familyId", "addedDatasetIds", "version", "playgroundsCount", "datasetName", "userName", "addedDatasetNames" ], "title": "VectorDatabaseResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Latest vector database version successfully retrieved. | VectorDatabaseResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## List supported languages by vector database ID Operation path: `GET /api/v2/genai/vectorDatabases/{vectorDatabaseId}/supportedSyntheticDatasetGenerationLanguages/` Authentication requirements: `BearerAuth` List the languages for synthetic dataset generation that are supported by this vector database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | path | string | true | The ID of the vector database to retrieve supported languages. | ### Example responses > 200 Response ``` { "description": "API response object for \"List supported languages for Synthetic Dataset generation\".", "properties": { "recommendedLanguage": { "description": "The recommended language.", "title": "recommendedLanguage", "type": "string" }, "supportedLanguages": { "description": "The list of supported languages.", "items": { "type": "string" }, "title": "supportedLanguages", "type": "array" } }, "required": [ "recommendedLanguage", "supportedLanguages" ], "title": "SupportedLanguagesResponse", "type": "object" } ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Supported languages successfully retrieved. | SupportedLanguagesResponse | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ## Retrieve text chunks and embeddings by vector database ID Operation path: `GET /api/v2/genai/vectorDatabases/{vectorDatabaseId}/textAndEmbeddings/` Authentication requirements: `BearerAuth` Retrieve the text chunk and embeddings asset for an existing vector database. ### Parameters | Name | In | Type | Required | Description | | --- | --- | --- | --- | --- | | vectorDatabaseId | path | string | true | The ID of the vector database to retrieve the assets for. | | part | query | any | false | Retrieve the additional file for this addition number. | ### Example responses > 200 Response ``` {} ``` ### Responses | Status | Meaning | Description | Schema | | --- | --- | --- | --- | | 200 | OK | Text and embeddings asset successfully retrieved. | Inline | | 422 | Unprocessable Entity | Validation Error | HTTPValidationErrorResponse | ### Response Schema # Schemas ## ChunkingLengthFunctionNames ``` { "description": "Supported length functions for text splitters.", "enum": [ "tokenizer_length_function", "approximate_token_count" ], "title": "ChunkingLengthFunctionNames", "type": "string" } ``` ChunkingLengthFunctionNames ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ChunkingLengthFunctionNames | string | false | | Supported length functions for text splitters. | ### Enumerated Values | Property | Value | | --- | --- | | ChunkingLengthFunctionNames | [tokenizer_length_function, approximate_token_count] | ## ChunkingMethodNames ``` { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" } ``` ChunkingMethodNames ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ChunkingMethodNames | string | false | | Supported names of text chunking methods. | ### Enumerated Values | Property | Value | | --- | --- | | ChunkingMethodNames | [recursive, semantic] | ## ChunkingMethodNamesTitle ``` { "description": "Supported user-facing friendly ames of text chunking methods.", "enum": [ "Recursive", "Semantic" ], "title": "ChunkingMethodNamesTitle", "type": "string" } ``` ChunkingMethodNamesTitle ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ChunkingMethodNamesTitle | string | false | | Supported user-facing friendly ames of text chunking methods. | ### Enumerated Values | Property | Value | | --- | --- | | ChunkingMethodNamesTitle | [Recursive, Semantic] | ## ChunkingParameterTypes ``` { "description": "Supported parameter data types for text chunking parameters.", "enum": [ "int", "list[str]", "bool" ], "title": "ChunkingParameterTypes", "type": "string" } ``` ChunkingParameterTypes ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ChunkingParameterTypes | string | false | | Supported parameter data types for text chunking parameters. | ### Enumerated Values | Property | Value | | --- | --- | | ChunkingParameterTypes | [int, list[str], bool] | ## ChunkingParameters ``` { "description": "Chunking parameters for creating a vector database.", "properties": { "chunkOverlapPercentage": { "anyOf": [ { "maximum": 50, "minimum": 0, "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage to use for text chunking.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk size to use for text chunking (measured in tokens).", "title": "chunkSize" }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method to use." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model to use. If omitted, DataRobot will choose the default embedding model automatically." }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom embedding model (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "isSeparatorRegex": { "default": false, "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "separators": { "anyOf": [ { "items": { "maxLength": 20, "type": "string" }, "maxItems": 9, "type": "array" }, { "type": "null" } ], "description": "The list of separators to use for text chunking.", "title": "separators" } }, "title": "ChunkingParameters", "type": "object" } ``` ChunkingParameters ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkOverlapPercentage | any | false | | The chunk overlap percentage to use for text chunking. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 50minimum: 0 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkSize | any | false | | The chunk size to use for text chunking (measured in tokens). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkingMethod | any | false | | The text chunking method to use. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ChunkingMethodNames | false | | Supported names of text chunking methods. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | embeddingModel | any | false | | The name of the embedding model to use. If omitted, DataRobot will choose the default embedding model automatically. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | EmbeddingModelNames | false | | Embedding model names (matching the format of HuggingFace repositories). | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | embeddingRegisteredModelId | any | false | | The ID of registered model (in case of using NIM registered model for embeddings). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | embeddingValidationId | any | false | | The validation ID of the custom embedding model (in case of using a custom model for embeddings). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | isSeparatorRegex | boolean | false | | Whether the text chunking separator uses a regular expression. | | separators | any | false | | The list of separators to use for text chunking. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | maxItems: 9 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CreateCustomModelEmbeddingValidationRequest ``` { "description": "The body of the \"Validate custom model\" request.", "properties": { "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model used in the deployment.", "title": "modelId" }, "name": { "default": "Untitled", "description": "The name to use for the validated custom model.", "maxLength": 5000, "title": "name", "type": "string" }, "predictionTimeout": { "default": 300, "description": "The timeout in seconds for the prediction when validating a custom model. Defaults to 300.", "maximum": 600, "minimum": 1, "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case to associate with the validated custom model.", "title": "useCaseId" } }, "required": [ "deploymentId", "promptColumnName", "targetColumnName" ], "title": "CreateCustomModelEmbeddingValidationRequest", "type": "object" } ``` CreateCustomModelEmbeddingValidationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the custom model deployment. | | modelId | any | false | | The ID of the model used in the deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | maxLength: 5000 | The name to use for the validated custom model. | | predictionTimeout | integer | false | maximum: 600minimum: 1 | The timeout in seconds for the prediction when validating a custom model. Defaults to 300. | | promptColumnName | string | true | maxLength: 5000 | The name of the column the custom model uses for prompt text input. | | targetColumnName | string | true | maxLength: 5000 | The name of the column the custom model uses for prediction output. | | useCaseId | any | false | | The ID of the use case to associate with the validated custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CreateCustomModelVectorDatabaseFromDeploymentRequest ``` { "description": "The body of the \"Create vector database from custom model deployment\" request.", "properties": { "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "modelId": { "description": "The ID of the model in the custom model deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the vector database.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "useCaseId": { "description": "The ID of the use case to link the vector database to.", "title": "useCaseId", "type": "string" } }, "required": [ "name", "useCaseId", "promptColumnName", "targetColumnName", "deploymentId", "modelId" ], "title": "CreateCustomModelVectorDatabaseFromDeploymentRequest", "type": "object" } ``` CreateCustomModelVectorDatabaseFromDeploymentRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the custom model deployment. | | modelId | string | true | | The ID of the model in the custom model deployment. | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the vector database. | | promptColumnName | string | true | maxLength: 5000 | The name of the column the custom model uses for prompt text input. | | targetColumnName | string | true | maxLength: 5000 | The name of the column the custom model uses for prediction output. | | useCaseId | string | true | | The ID of the use case to link the vector database to. | ## CreateCustomModelVectorDatabaseFromValidationIdPayload ``` { "description": "The body of the \"Create vector database from validation ID\" request.", "properties": { "name": { "description": "The name of the vector database.", "maxLength": 5000, "minLength": 1, "title": "name", "type": "string" }, "useCaseId": { "description": "The ID of the use case to link the vector database to.", "title": "useCaseId", "type": "string" }, "validationId": { "description": "The validation ID of the custom model validation.", "title": "validationId", "type": "string" } }, "required": [ "name", "useCaseId", "validationId" ], "title": "CreateCustomModelVectorDatabaseFromValidationIdPayload", "type": "object" } ``` CreateCustomModelVectorDatabaseFromValidationIdPayload ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the vector database. | | useCaseId | string | true | | The ID of the use case to link the vector database to. | | validationId | string | true | | The validation ID of the custom model validation. | ## CreateCustomModelVectorDatabaseValidationRequest ``` { "description": "The body of the \"Validate custom model\" request.", "properties": { "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the model used in the deployment.", "title": "modelId" }, "name": { "default": "Untitled", "description": "The name to use for the validated custom model.", "maxLength": 5000, "title": "name", "type": "string" }, "predictionTimeout": { "default": 300, "description": "The timeout in seconds for the prediction when validating a custom model. Defaults to 300.", "maximum": 600, "minimum": 1, "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case to associate with the validated custom model.", "title": "useCaseId" } }, "required": [ "deploymentId", "promptColumnName", "targetColumnName" ], "title": "CreateCustomModelVectorDatabaseValidationRequest", "type": "object" } ``` CreateCustomModelVectorDatabaseValidationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the custom model deployment. | | modelId | any | false | | The ID of the model used in the deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | false | maxLength: 5000 | The name to use for the validated custom model. | | predictionTimeout | integer | false | maximum: 600minimum: 1 | The timeout in seconds for the prediction when validating a custom model. Defaults to 300. | | promptColumnName | string | true | maxLength: 5000 | The name of the column the custom model uses for prompt text input. | | targetColumnName | string | true | maxLength: 5000 | The name of the column the custom model uses for prediction output. | | useCaseId | any | false | | The ID of the use case to associate with the validated custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CreateCustomModelVersionResponse ``` { "description": "API response object for the \"Create custom model version\" request.", "properties": { "customModelId": { "description": "The ID of the created custom model.", "title": "customModelId", "type": "string" } }, "required": [ "customModelId" ], "title": "CreateCustomModelVersionResponse", "type": "object" } ``` CreateCustomModelVersionResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelId | string | true | | The ID of the created custom model. | ## CreateVectorDatabaseCustomModelVersionRequest ``` { "description": "The body of the \"Create custom model version\" request.", "properties": { "modelName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the custom model that this job will create.", "title": "modelName" }, "promptColumnName": { "default": "promptText", "description": "The name of the column to use for prompt text input in the custom model.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "resources": { "anyOf": [ { "description": "The structure that describes resource settings for a custom model created from buzok.", "properties": { "maximumMemory": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum memory that can be allocated to the custom model.", "title": "maximumMemory" }, "networkEgressPolicy": { "default": "Public", "description": "Network egress policy for the custom model. Can be either Public or None.", "maxLength": 5000, "title": "networkEgressPolicy", "type": "string" }, "replicas": { "default": 1, "description": "A fixed number of replicas that will be created for the custom model.", "title": "replicas", "type": "integer" }, "resourceBundleId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "An identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "title": "resourceBundleId" } }, "title": "CustomModelResourcesRequest", "type": "object" }, { "type": "null" } ], "description": "The resources that the custom model will be provisioned with." }, "targetColumnName": { "default": "relevant", "description": "The name of the column to use for prediction output in the custom model.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Specifies the vector database retrieval settings in LLM blueprint API requests.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 128, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettingsRequest", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "title": "CreateVectorDatabaseCustomModelVersionRequest", "type": "object" } ``` CreateVectorDatabaseCustomModelVersionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelName | any | false | | The name of the custom model that this job will create. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | string | false | maxLength: 5000 | The name of the column to use for prompt text input in the custom model. | | resources | any | false | | The resources that the custom model will be provisioned with. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelResourcesRequest | false | | The structure that describes resource settings for a custom model created from buzok. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetColumnName | string | false | maxLength: 5000 | The name of the column to use for prediction output in the custom model. | | vectorDatabaseId | any | false | | The ID of the vector database linked to this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseSettings | any | false | | A key/value dictionary of vector database settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseSettingsRequest | false | | Specifies the vector database retrieval settings in LLM blueprint API requests. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CreateVectorDatabaseDeploymentRequest ``` { "description": "The body of the \"Create vector database deployment\" request.", "properties": { "credentialId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the credential to access the connected vector database.", "title": "credentialId" }, "defaultPredictionServerId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of a prediction server for the new deployment to use. Cannot be used with predictionEnvironmentId.", "title": "defaultPredictionServerId" }, "modelName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The name of the custom model that this job will create.", "title": "modelName" }, "predictionEnvironmentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the prediction environment for the new deployment to use. Cannot be used with defaultPredictionServerId.", "title": "predictionEnvironmentId" }, "promptColumnName": { "default": "promptText", "description": "The name of the column to use for prompt text input in the custom model.", "maxLength": 5000, "title": "promptColumnName", "type": "string" }, "resources": { "anyOf": [ { "description": "The structure that describes resource settings for a custom model created from buzok.", "properties": { "maximumMemory": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum memory that can be allocated to the custom model.", "title": "maximumMemory" }, "networkEgressPolicy": { "default": "Public", "description": "Network egress policy for the custom model. Can be either Public or None.", "maxLength": 5000, "title": "networkEgressPolicy", "type": "string" }, "replicas": { "default": 1, "description": "A fixed number of replicas that will be created for the custom model.", "title": "replicas", "type": "integer" }, "resourceBundleId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "An identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "title": "resourceBundleId" } }, "title": "CustomModelResourcesRequest", "type": "object" }, { "type": "null" } ], "description": "The resources that the custom model will be provisioned with." }, "targetColumnName": { "default": "relevant", "description": "The name of the column to use for prediction output in the custom model.", "maxLength": 5000, "title": "targetColumnName", "type": "string" }, "vectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the vector database linked to this LLM blueprint.", "title": "vectorDatabaseId" }, "vectorDatabaseSettings": { "anyOf": [ { "description": "Specifies the vector database retrieval settings in LLM blueprint API requests.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 128, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettingsRequest", "type": "object" }, { "type": "null" } ], "description": "A key/value dictionary of vector database settings." } }, "title": "CreateVectorDatabaseDeploymentRequest", "type": "object" } ``` CreateVectorDatabaseDeploymentRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | The ID of the credential to access the connected vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultPredictionServerId | any | false | | The ID of a prediction server for the new deployment to use. Cannot be used with predictionEnvironmentId. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelName | any | false | | The name of the custom model that this job will create. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionEnvironmentId | any | false | | The ID of the prediction environment for the new deployment to use. Cannot be used with defaultPredictionServerId. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | string | false | maxLength: 5000 | The name of the column to use for prompt text input in the custom model. | | resources | any | false | | The resources that the custom model will be provisioned with. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomModelResourcesRequest | false | | The structure that describes resource settings for a custom model created from buzok. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetColumnName | string | false | maxLength: 5000 | The name of the column to use for prediction output in the custom model. | | vectorDatabaseId | any | false | | The ID of the vector database linked to this LLM blueprint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | vectorDatabaseSettings | any | false | | A key/value dictionary of vector database settings. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseSettingsRequest | false | | Specifies the vector database retrieval settings in LLM blueprint API requests. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CreateVectorDatabaseDeploymentResponse ``` { "description": "The response to the \"Create vector database deployment\" request.", "properties": { "jobId": { "description": "The ID of the job that will create the new vector database deployment.", "format": "uuid4", "title": "jobId", "type": "string" } }, "required": [ "jobId" ], "title": "CreateVectorDatabaseDeploymentResponse", "type": "object" } ``` CreateVectorDatabaseDeploymentResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | jobId | string(uuid4) | true | | The ID of the job that will create the new vector database deployment. | ## CreateVectorDatabaseRequest ``` { "description": "The body of the \"Create vector database\" request.", "properties": { "chunkingParameters": { "anyOf": [ { "description": "Chunking parameters for creating a vector database.", "properties": { "chunkOverlapPercentage": { "anyOf": [ { "maximum": 50, "minimum": 0, "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage to use for text chunking.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk size to use for text chunking (measured in tokens).", "title": "chunkSize" }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method to use." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model to use. If omitted, DataRobot will choose the default embedding model automatically." }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom embedding model (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "isSeparatorRegex": { "default": false, "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "separators": { "anyOf": [ { "items": { "maxLength": 20, "type": "string" }, "maxItems": 9, "type": "array" }, { "type": "null" } ], "description": "The list of separators to use for text chunking.", "title": "separators" } }, "title": "ChunkingParameters", "type": "object" }, { "type": "null" } ], "description": "The text chunking parameters to use for building the vector database." }, "datasetId": { "description": "The ID of the dataset to use for building the vector database.", "title": "datasetId", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnectionRequest", "milvus": "#/components/schemas/MilvusConnectionRequest", "pinecone": "#/components/schemas/PineconeConnectionRequest", "postgres": "#/components/schemas/PostgresConnectionRequest" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnectionRequest", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnectionRequest", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnectionRequest", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnectionRequest", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "metadataCombinationStrategy": { "description": "Strategy to use when the dataset and the metadata file have duplicate columns.", "enum": [ "replace", "merge" ], "title": "MetadataCombinationStrategy", "type": "string" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset to add metadata for building the vector database.", "title": "metadataDatasetId" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The name of the vector database.", "title": "name" }, "parentVectorDatabaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the parent vector database used as base for the re-building.", "title": "parentVectorDatabaseId" }, "updateDeployments": { "default": false, "description": "Whether to update the deployments that use the parent vector database.Can only be set to `true` if parent_vector_database_id is provided.", "title": "updateDeployments", "type": "boolean" }, "updateLlmBlueprints": { "default": false, "description": "Whether to update the LLM blueprints that use the parent vector database.Can only be set to `true` if parent_vector_database_id is provided.", "title": "updateLlmBlueprints", "type": "boolean" }, "useCaseId": { "description": "The ID of the use case to link the vector database to.", "title": "useCaseId", "type": "string" } }, "required": [ "datasetId", "useCaseId" ], "title": "CreateVectorDatabaseRequest", "type": "object" } ``` CreateVectorDatabaseRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkingParameters | any | false | | The text chunking parameters to use for building the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ChunkingParameters | false | | Chunking parameters for creating a vector database. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the dataset to use for building the vector database. | | externalVectorDatabaseConnection | any | false | | The external vector database connection to use. | anyOf - discriminator: type | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | PineconeConnectionRequest | false | | Pinecone vector database connection. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | ElasticsearchConnectionRequest | false | | Elasticsearch vector database connection. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | MilvusConnectionRequest | false | | Milvus vector database connection. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | PostgresConnectionRequest | false | | Postgres vector database connection. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metadataCombinationStrategy | MetadataCombinationStrategy | false | | The strategy to use when the dataset and the metadata file have duplicate columns. | | metadataDatasetId | any | false | | The ID of the dataset to add metadata for building the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | any | false | | The name of the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | parentVectorDatabaseId | any | false | | The ID of the parent vector database used as base for the re-building. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | updateDeployments | boolean | false | | Whether to update the deployments that use the parent vector database.Can only be set to true if parent_vector_database_id is provided. | | updateLlmBlueprints | boolean | false | | Whether to update the LLM blueprints that use the parent vector database.Can only be set to true if parent_vector_database_id is provided. | | useCaseId | string | true | | The ID of the use case to link the vector database to. | ## CustomEmbeddingModelNames ``` { "description": "Model names for custom embedding models.", "enum": [ "custom-embeddings/default" ], "title": "CustomEmbeddingModelNames", "type": "string" } ``` CustomEmbeddingModelNames ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomEmbeddingModelNames | string | false | | Model names for custom embedding models. | ### Enumerated Values | Property | Value | | --- | --- | | CustomEmbeddingModelNames | custom-embeddings/default | ## CustomModelEmbeddingValidationResponse ``` { "description": "API response object for a single custom model embedding validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelEmbeddingValidationResponse", "type": "object" } ``` CustomModelEmbeddingValidationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the custom model validation (ISO 8601 formatted). | | deploymentAccessData | any | true | | The parameters used for accessing the deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentAccessData | false | | Add authorization_header to avoid breaking change to API. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the custom model deployment. | | deploymentName | any | false | | The name of the custom model deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the validation error (if the validation failed). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model validation. | | modelId | string | true | | The ID of the model used in the deployment. | | name | string | true | | The name of the validated custom model. | | predictionTimeout | integer | true | | The timeout in seconds for the prediction API used in this custom model validation. | | promptColumnName | string | true | | The name of the column the custom model uses for prompt text input. | | targetColumnName | string | true | | The name of the column the custom model uses for prediction output. | | tenantId | string(uuid4) | true | | The ID of the tenant the custom model validation belongs to. | | useCaseId | any | true | | The ID of the use case associated with the validated custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | userId | string | true | | The ID of the user that created this custom model validation. | | userName | any | false | | The name of the user that created this custom model validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationStatus | CustomModelValidationStatus | true | | The status of the custom model validation. | ## CustomModelResourcesRequest ``` { "description": "The structure that describes resource settings for a custom model created from buzok.", "properties": { "maximumMemory": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum memory that can be allocated to the custom model.", "title": "maximumMemory" }, "networkEgressPolicy": { "default": "Public", "description": "Network egress policy for the custom model. Can be either Public or None.", "maxLength": 5000, "title": "networkEgressPolicy", "type": "string" }, "replicas": { "default": 1, "description": "A fixed number of replicas that will be created for the custom model.", "title": "replicas", "type": "integer" }, "resourceBundleId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "An identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint.", "title": "resourceBundleId" } }, "title": "CustomModelResourcesRequest", "type": "object" } ``` CustomModelResourcesRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maximumMemory | any | false | | The maximum memory that can be allocated to the custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | networkEgressPolicy | string | false | maxLength: 5000 | Network egress policy for the custom model. Can be either Public or None. | | replicas | integer | false | | A fixed number of replicas that will be created for the custom model. | | resourceBundleId | any | false | | An identifier that represents a bundle of resources: Memory, CPU, GPU, etc. A list of available bundles can be obtained via the resource bundles endpoint. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## CustomModelValidationStatus ``` { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } ``` CustomModelValidationStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | CustomModelValidationStatus | string | false | | Status of custom model validation. | ### Enumerated Values | Property | Value | | --- | --- | | CustomModelValidationStatus | [TESTING, PASSED, FAILED] | ## CustomModelVectorDatabaseValidationResponse ``` { "description": "API response object for a single custom model vector database validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelVectorDatabaseValidationResponse", "type": "object" } ``` CustomModelVectorDatabaseValidationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationDate | string(date-time) | true | | The creation date of the custom model validation (ISO 8601 formatted). | | deploymentAccessData | any | true | | The parameters used for accessing the deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | DeploymentAccessData | false | | Add authorization_header to avoid breaking change to API. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | string | true | | The ID of the custom model deployment. | | deploymentName | any | false | | The name of the custom model deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | false | | The error message associated with the validation error (if the validation failed). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the custom model validation. | | modelId | string | true | | The ID of the model used in the deployment. | | name | string | true | | The name of the validated custom model. | | predictionTimeout | integer | true | | The timeout in seconds for the prediction API used in this custom model validation. | | promptColumnName | string | true | | The name of the column the custom model uses for prompt text input. | | targetColumnName | string | true | | The name of the column the custom model uses for prediction output. | | tenantId | string(uuid4) | true | | The ID of the tenant the custom model validation belongs to. | | useCaseId | any | true | | The ID of the use case associated with the validated custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | userId | string | true | | The ID of the user that created this custom model validation. | | userName | any | false | | The name of the user that created this custom model validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | validationStatus | CustomModelValidationStatus | true | | The status of the custom model validation. | ## DatasetLanguages ``` { "description": "The names of dataset languages.", "enum": [ "Afrikaans", "Amharic", "Arabic", "Assamese", "Azerbaijani", "Belarusian", "Bulgarian", "Bengali", "Breton", "Bosnian", "Catalan", "Czech", "Welsh", "Danish", "German", "Greek", "English", "Esperanto", "Spanish", "Estonian", "Basque", "Persian", "Finnish", "French", "Western Frisian", "Irish", "Scottish Gaelic", "Galician", "Gujarati", "Hausa", "Hebrew", "Hindi", "Croatian", "Hungarian", "Armenian", "Indonesian", "Icelandic", "Italian", "Japanese", "Javanese", "Georgian", "Kazakh", "Khmer", "Kannada", "Korean", "Kurdish", "Kyrgyz", "Latin", "Lao", "Lithuanian", "Latvian", "Malagasy", "Macedonian", "Malayalam", "Mongolian", "Marathi", "Malay", "Burmese", "Nepali", "Dutch", "Norwegian", "Oromo", "Oriya", "Panjabi", "Polish", "Pashto", "Portuguese", "Romanian", "Russian", "Sanskrit", "Sindhi", "Sinhala", "Slovak", "Slovenian", "Somali", "Albanian", "Serbian", "Sundanese", "Swedish", "Swahili", "Tamil", "Telugu", "Thai", "Tagalog", "Turkish", "Uyghur", "Ukrainian", "Urdu", "Uzbek", "Vietnamese", "Xhosa", "Yiddish", "Chinese" ], "title": "DatasetLanguages", "type": "string" } ``` DatasetLanguages ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | DatasetLanguages | string | false | | The names of dataset languages. | ### Enumerated Values | Property | Value | | --- | --- | | DatasetLanguages | [Afrikaans, Amharic, Arabic, Assamese, Azerbaijani, Belarusian, Bulgarian, Bengali, Breton, Bosnian, Catalan, Czech, Welsh, Danish, German, Greek, English, Esperanto, Spanish, Estonian, Basque, Persian, Finnish, French, Western Frisian, Irish, Scottish Gaelic, Galician, Gujarati, Hausa, Hebrew, Hindi, Croatian, Hungarian, Armenian, Indonesian, Icelandic, Italian, Japanese, Javanese, Georgian, Kazakh, Khmer, Kannada, Korean, Kurdish, Kyrgyz, Latin, Lao, Lithuanian, Latvian, Malagasy, Macedonian, Malayalam, Mongolian, Marathi, Malay, Burmese, Nepali, Dutch, Norwegian, Oromo, Oriya, Panjabi, Polish, Pashto, Portuguese, Romanian, Russian, Sanskrit, Sindhi, Sinhala, Slovak, Slovenian, Somali, Albanian, Serbian, Sundanese, Swedish, Swahili, Tamil, Telugu, Thai, Tagalog, Turkish, Uyghur, Ukrainian, Urdu, Uzbek, Vietnamese, Xhosa, Yiddish, Chinese] | ## DeploymentAccessData ``` { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" } ``` DeploymentAccessData ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | authorizationHeader | string | false | | The Authorization header to use for the deployment. | | chatApiUrl | any | false | | The URL of the deployment's chat API. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datarobotKey | any | true | | The server key associated with the prediction API. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | inputType | DeploymentInputType | true | | The format of the input data. | | modelType | SupportedDeploymentType | true | | The type of the target output the deployment produces. | | predictionApiUrl | string | true | | The URL of the deployment's prediction API. | ## DeploymentInputType ``` { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" } ``` DeploymentInputType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | DeploymentInputType | string | false | | The format of the input data submitted to a DataRobot deployment. | ### Enumerated Values | Property | Value | | --- | --- | | DeploymentInputType | [CSV, JSON] | ## DistanceMetric ``` { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" } ``` DistanceMetric ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | DistanceMetric | string | false | | Distance metrics for vector databases. | ### Enumerated Values | Property | Value | | --- | --- | | DistanceMetric | [cosine, dot_product, euclidean, inner_product, manhattan] | ## EditCustomModelValidationRequest ``` { "description": "The body of the \"Edit custom model validation\" request.", "properties": { "chatModelId": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API.", "title": "chatModelId" }, "deploymentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the deployment associated with this custom model validation.", "title": "deploymentId" }, "modelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "If specified, changes the ID of the model associated with this custom model validation.", "title": "modelId" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "If specified, renames the custom model validation to this value.", "title": "name" }, "predictionTimeout": { "anyOf": [ { "maximum": 600, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "If specified, sets the timeout in seconds for the prediction when validating a custom model.", "title": "predictionTimeout" }, "promptColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment.", "title": "promptColumnName" }, "targetColumnName": { "anyOf": [ { "maxLength": 5000, "type": "string" }, { "type": "null" } ], "description": "If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment.", "title": "targetColumnName" } }, "title": "EditCustomModelValidationRequest", "type": "object" } ``` EditCustomModelValidationRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chatModelId | any | false | | The model ID to specify when calling the OpenAI chat completion API of the deployment. If this parameter is specified, the deployment must support the OpenAI chat completion API. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | deploymentId | any | false | | If specified, changes the ID of the deployment associated with this custom model validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | modelId | any | false | | If specified, changes the ID of the model associated with this custom model validation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | any | false | | If specified, renames the custom model validation to this value. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | predictionTimeout | any | false | | If specified, sets the timeout in seconds for the prediction when validating a custom model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 600minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | promptColumnName | any | false | | If specified, changes the name of the column that will be used to format the prompt text input for the custom model deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | targetColumnName | any | false | | If specified, changes the name of the column that will be used to extract the prediction response from the custom model deployment. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## EditVectorDatabaseRequest ``` { "description": "The body of the \"Edit vector database\" request.", "properties": { "credentialId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The new ID of the credential to access a connected vector database.", "title": "credentialId" }, "name": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The new name of the vector database.", "title": "name" } }, "title": "EditVectorDatabaseRequest", "type": "object" } ``` EditVectorDatabaseRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | any | false | | The new ID of the credential to access a connected vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | any | false | | The new name of the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ElasticsearchConnection ``` { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnection", "type": "object" } ``` ElasticsearchConnection ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudId | any | false | | The cloud ID of the elastic search connection. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | true | | The ID of the credential used to connect to the external vector database. | | credentialUserId | any | false | | The ID of the user supplying the credential used to connect to the external vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | distanceMetric | DistanceMetric | false | | The distance strategy to use for building the vector database. | | type | string | false | | The type of the external vector database. | | url | any | false | | The URL of the elastic search connection. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ElasticsearchConnectionRequest ``` { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnectionRequest", "type": "object" } ``` ElasticsearchConnectionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloudId | any | false | | The cloud ID of the elastic search connection. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | true | | The ID of the credential used to connect to the external vector database. | | credentialUserId | any | false | | The ID of the user supplying the credential used to connect to the external vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | distanceMetric | DistanceMetric | false | | The distance strategy to use for building the vector database. | | type | string | false | | The type of the external vector database. | | url | any | false | | The URL of the elastic search connection. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | maxLength: 5000minLength: 1minLength: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## EmbeddingModel ``` { "description": "API response object for a single embedding model.", "properties": { "description": { "description": "The description of the embedding model.", "title": "description", "type": "string" }, "embeddingModel": { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, "languages": { "description": "The list of languages the embedding models supports.", "items": { "description": "The names of dataset languages.", "enum": [ "Afrikaans", "Amharic", "Arabic", "Assamese", "Azerbaijani", "Belarusian", "Bulgarian", "Bengali", "Breton", "Bosnian", "Catalan", "Czech", "Welsh", "Danish", "German", "Greek", "English", "Esperanto", "Spanish", "Estonian", "Basque", "Persian", "Finnish", "French", "Western Frisian", "Irish", "Scottish Gaelic", "Galician", "Gujarati", "Hausa", "Hebrew", "Hindi", "Croatian", "Hungarian", "Armenian", "Indonesian", "Icelandic", "Italian", "Japanese", "Javanese", "Georgian", "Kazakh", "Khmer", "Kannada", "Korean", "Kurdish", "Kyrgyz", "Latin", "Lao", "Lithuanian", "Latvian", "Malagasy", "Macedonian", "Malayalam", "Mongolian", "Marathi", "Malay", "Burmese", "Nepali", "Dutch", "Norwegian", "Oromo", "Oriya", "Panjabi", "Polish", "Pashto", "Portuguese", "Romanian", "Russian", "Sanskrit", "Sindhi", "Sinhala", "Slovak", "Slovenian", "Somali", "Albanian", "Serbian", "Sundanese", "Swedish", "Swahili", "Tamil", "Telugu", "Thai", "Tagalog", "Turkish", "Uyghur", "Ukrainian", "Urdu", "Uzbek", "Vietnamese", "Xhosa", "Yiddish", "Chinese" ], "title": "DatasetLanguages", "type": "string" }, "title": "languages", "type": "array" }, "maxSequenceLength": { "description": "The maximum input token sequence length that the embedding model can accept.", "title": "maxSequenceLength", "type": "integer" } }, "required": [ "embeddingModel", "description", "maxSequenceLength", "languages" ], "title": "EmbeddingModel", "type": "object" } ``` EmbeddingModel ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | The description of the embedding model. | | embeddingModel | EmbeddingModelNames | true | | The name of the embedding model. | | languages | [DatasetLanguages] | true | | The list of languages the embedding models supports. | | maxSequenceLength | integer | true | | The maximum input token sequence length that the embedding model can accept. | ## EmbeddingModelNames ``` { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" } ``` EmbeddingModelNames ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | EmbeddingModelNames | string | false | | Embedding model names (matching the format of HuggingFace repositories). | ### Enumerated Values | Property | Value | | --- | --- | | EmbeddingModelNames | [intfloat/e5-large-v2, intfloat/e5-base-v2, intfloat/multilingual-e5-base, intfloat/multilingual-e5-small, sentence-transformers/all-MiniLM-L6-v2, jinaai/jina-embedding-t-en-v1, jinaai/jina-embedding-s-en-v2, cl-nagoya/sup-simcse-ja-base] | ## ExecutionStatus ``` { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" } ``` ExecutionStatus ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ExecutionStatus | string | false | | Job and entity execution status. | ### Enumerated Values | Property | Value | | --- | --- | | ExecutionStatus | [NEW, RUNNING, COMPLETED, REQUIRES_USER_INPUT, SKIPPED, ERROR] | ## HTTPValidationErrorResponse ``` { "properties": { "detail": { "items": { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" }, "title": "detail", "type": "array" } }, "title": "HTTPValidationErrorResponse", "type": "object" } ``` HTTPValidationErrorResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | detail | [ValidationError] | false | | none | ## ListCustomModelEmbeddingValidationResponse ``` { "description": "Paginated list of custom model embedding validations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single custom model embedding validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelEmbeddingValidationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListCustomModelEmbeddingValidationResponse", "type": "object" } ``` ListCustomModelEmbeddingValidationResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [CustomModelEmbeddingValidationResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListCustomModelValidationSortQueryParam ``` { "description": "Sort order values for listing custom model validations.", "enum": [ "name", "-name", "deploymentName", "-deploymentName", "userName", "-userName", "creationDate", "-creationDate" ], "title": "ListCustomModelValidationSortQueryParam", "type": "string" } ``` ListCustomModelValidationSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ListCustomModelValidationSortQueryParam | string | false | | Sort order values for listing custom model validations. | ### Enumerated Values | Property | Value | | --- | --- | | ListCustomModelValidationSortQueryParam | [name, -name, deploymentName, -deploymentName, userName, -userName, creationDate, -creationDate] | ## ListCustomModelVectorDatabaseValidationsResponse ``` { "description": "Paginated list of custom model vector database validations.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single custom model vector database validation.", "properties": { "creationDate": { "description": "The creation date of the custom model validation (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "deploymentAccessData": { "anyOf": [ { "description": "Add authorization_header to avoid breaking change to API.", "properties": { "authorizationHeader": { "default": "[REDACTED]", "description": "The `Authorization` header to use for the deployment.", "title": "authorizationHeader", "type": "string" }, "chatApiUrl": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL of the deployment's chat API.", "title": "chatApiUrl" }, "datarobotKey": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The server key associated with the prediction API.", "title": "datarobotKey" }, "inputType": { "description": "The format of the input data submitted to a DataRobot deployment.", "enum": [ "CSV", "JSON" ], "title": "DeploymentInputType", "type": "string" }, "modelType": { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" }, "predictionApiUrl": { "description": "The URL of the deployment's prediction API.", "title": "predictionApiUrl", "type": "string" } }, "required": [ "predictionApiUrl", "datarobotKey", "inputType", "modelType" ], "title": "DeploymentAccessData", "type": "object" }, { "type": "null" } ], "description": "The parameters used for accessing the deployment." }, "deploymentId": { "description": "The ID of the custom model deployment.", "title": "deploymentId", "type": "string" }, "deploymentName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the custom model deployment.", "title": "deploymentName" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the validation error (if the validation failed).", "title": "errorMessage" }, "id": { "description": "The ID of the custom model validation.", "title": "id", "type": "string" }, "modelId": { "description": "The ID of the model used in the deployment.", "title": "modelId", "type": "string" }, "name": { "description": "The name of the validated custom model.", "title": "name", "type": "string" }, "predictionTimeout": { "description": "The timeout in seconds for the prediction API used in this custom model validation.", "title": "predictionTimeout", "type": "integer" }, "promptColumnName": { "description": "The name of the column the custom model uses for prompt text input.", "title": "promptColumnName", "type": "string" }, "targetColumnName": { "description": "The name of the column the custom model uses for prediction output.", "title": "targetColumnName", "type": "string" }, "tenantId": { "description": "The ID of the tenant the custom model validation belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the use case associated with the validated custom model.", "title": "useCaseId" }, "userId": { "description": "The ID of the user that created this custom model validation.", "title": "userId", "type": "string" }, "userName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the user that created this custom model validation.", "title": "userName" }, "validationStatus": { "description": "Status of custom model validation.", "enum": [ "TESTING", "PASSED", "FAILED" ], "title": "CustomModelValidationStatus", "type": "string" } }, "required": [ "id", "deploymentId", "targetColumnName", "validationStatus", "modelId", "deploymentAccessData", "tenantId", "name", "useCaseId", "creationDate", "userId", "predictionTimeout", "promptColumnName" ], "title": "CustomModelVectorDatabaseValidationResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListCustomModelVectorDatabaseValidationsResponse", "type": "object" } ``` ListCustomModelVectorDatabaseValidationsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [CustomModelVectorDatabaseValidationResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## ListVectorDatabaseSortQueryParam ``` { "description": "Sort order values for listing vector databases.", "enum": [ "name", "-name", "creationUserId", "-creationUserId", "creationDate", "-creationDate", "embeddingModel", "-embeddingModel", "datasetId", "-datasetId", "chunkingMethod", "-chunkingMethod", "chunksCount", "-chunksCount", "size", "-size", "userName", "-userName", "datasetName", "-datasetName", "playgroundsCount", "-playgroundsCount", "source", "-source" ], "title": "ListVectorDatabaseSortQueryParam", "type": "string" } ``` ListVectorDatabaseSortQueryParam ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | ListVectorDatabaseSortQueryParam | string | false | | Sort order values for listing vector databases. | ### Enumerated Values | Property | Value | | --- | --- | | ListVectorDatabaseSortQueryParam | [name, -name, creationUserId, -creationUserId, creationDate, -creationDate, embeddingModel, -embeddingModel, datasetId, -datasetId, chunkingMethod, -chunkingMethod, chunksCount, -chunksCount, size, -size, userName, -userName, datasetName, -datasetName, playgroundsCount, -playgroundsCount, source, -source] | ## ListVectorDatabasesResponse ``` { "description": "Paginated list of vector databases.", "properties": { "count": { "description": "The number of records on this page.", "title": "count", "type": "integer" }, "data": { "description": "The list of records.", "items": { "description": "API response object for a single vector database.", "properties": { "addedDatasetIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset IDs that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetIds" }, "addedDatasetNames": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset names that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetNames" }, "addedMetadataDatasetPairs": { "anyOf": [ { "items": { "description": "Pair of metadata dataset and dataset added to the vector database.", "properties": { "datasetId": { "description": "The ID of the dataset added to the vector database.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the dataset added to the vector database.", "title": "datasetName", "type": "string" }, "metadataDatasetId": { "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId", "type": "string" }, "metadataDatasetName": { "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName", "type": "string" } }, "required": [ "metadataDatasetId", "datasetId", "metadataDatasetName", "datasetName" ], "title": "MetadataDatasetPairApiFormatted", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Pairs of metadata dataset and dataset added to the vector database.", "title": "addedMetadataDatasetPairs" }, "chunkOverlapPercentage": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage that the vector database uses.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the text chunk (measured in tokens) that the vector database uses.", "title": "chunkSize" }, "chunkingLengthFunction": { "anyOf": [ { "description": "Supported length functions for text splitters.", "enum": [ "tokenizer_length_function", "approximate_token_count" ], "title": "ChunkingLengthFunctionNames", "type": "string" }, { "type": "null" } ], "default": "approximate_token_count", "description": "The length function to use for the text splitter." }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method the vector database uses." }, "chunksCount": { "description": "The number of text chunks in the vector database.", "title": "chunksCount", "type": "integer" }, "creationDate": { "description": "The creation date of the vector database (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationDuration": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The duration of the vector database creation.", "title": "creationDuration" }, "creationUserId": { "description": "The ID of the user that created this vector database.", "title": "creationUserId", "type": "string" }, "customChunking": { "description": "Whether the vector database uses custom chunking.", "title": "customChunking", "type": "boolean" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset the vector database was built from.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset this vector database was built from.", "title": "datasetName", "type": "string" }, "deploymentStatus": { "anyOf": [ { "description": "Sort order values for listing vector databases.", "enum": [ "Created", "Assembling", "Registered", "Deployed" ], "title": "VectorDatabaseDeploymentStatuses", "type": "string" }, { "type": "null" } ], "description": "How far along in the deployment process this vector database is." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model the vector database uses.", "title": "embeddingModel" }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model embedding (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the vector database creation error (in case of a creation error).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database creation error (in case of a creation error).", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnection", "milvus": "#/components/schemas/MilvusConnection", "pinecone": "#/components/schemas/PineconeConnection", "postgres": "#/components/schemas/PostgresConnection" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnection", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnection", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnection", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnection", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "familyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent.", "title": "familyId" }, "id": { "description": "The ID of the vector database.", "title": "id", "type": "string" }, "isSeparatorRegex": { "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "metadataColumns": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of metadata columns in the vector database.", "title": "metadataColumns" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId" }, "metadataDatasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName" }, "name": { "description": "The name of the vector database.", "title": "name", "type": "string" }, "organizationId": { "description": "The ID of the DataRobot organization this vector database belongs to.", "title": "organizationId", "type": "string" }, "parentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'.", "title": "parentId" }, "percentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "Vector database progress percentage.", "title": "percentage" }, "playgroundsCount": { "description": "The number of playgrounds that use this vector database.", "title": "playgroundsCount", "type": "integer" }, "separators": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The text chunking separators that the vector database uses.", "title": "separators" }, "size": { "description": "The size of the vector database (in bytes).", "title": "size", "type": "integer" }, "skippedChunksCount": { "description": "The number of text chunks skipped during vector database creation.", "title": "skippedChunksCount", "type": "integer" }, "source": { "description": "The source of the vector database.", "enum": [ "DataRobot", "External", "Pinecone", "Elasticsearch", "Milvus", "Postgres" ], "title": "VectorDatabaseSource", "type": "string" }, "tenantId": { "description": "The ID of the DataRobot tenant this vector database belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "description": "The ID of the use case the vector database is linked to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this vector database.", "title": "userName", "type": "string" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model vector database (in case of using a custom model vector database).", "title": "validationId" }, "version": { "description": "The version of the vector database linked to a certain family ID.", "title": "version", "type": "integer" } }, "required": [ "id", "name", "size", "useCaseId", "datasetId", "embeddingModel", "embeddingValidationId", "embeddingRegisteredModelId", "chunkSize", "chunkingMethod", "chunkOverlapPercentage", "chunksCount", "skippedChunksCount", "customChunking", "separators", "isSeparatorRegex", "creationDate", "creationUserId", "organizationId", "tenantId", "lastUpdateDate", "executionStatus", "errorMessage", "errorResolution", "source", "validationId", "parentId", "familyId", "addedDatasetIds", "version", "playgroundsCount", "datasetName", "userName", "addedDatasetNames" ], "title": "VectorDatabaseResponse", "type": "object" }, "title": "data", "type": "array" }, "next": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the next page, or `null` if there is no such page.", "title": "next" }, "previous": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The URL to the previous page, or `null` if there is no such page.", "title": "previous" }, "totalCount": { "description": "The total number of records.", "title": "totalCount", "type": "integer" } }, "required": [ "totalCount", "count", "next", "previous", "data" ], "title": "ListVectorDatabasesResponse", "type": "object" } ``` ListVectorDatabasesResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | count | integer | true | | The number of records on this page. | | data | [VectorDatabaseResponse] | true | | The list of records. | | next | any | true | | The URL to the next page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | previous | any | true | | The URL to the previous page, or null if there is no such page. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | totalCount | integer | true | | The total number of records. | ## MetadataCombinationStrategy ``` { "description": "Strategy to use when the dataset and the metadata file have duplicate columns.", "enum": [ "replace", "merge" ], "title": "MetadataCombinationStrategy", "type": "string" } ``` MetadataCombinationStrategy ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | MetadataCombinationStrategy | string | false | | Strategy to use when the dataset and the metadata file have duplicate columns. | ### Enumerated Values | Property | Value | | --- | --- | | MetadataCombinationStrategy | [replace, merge] | ## MetadataDatasetPairApiFormatted ``` { "description": "Pair of metadata dataset and dataset added to the vector database.", "properties": { "datasetId": { "description": "The ID of the dataset added to the vector database.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the dataset added to the vector database.", "title": "datasetName", "type": "string" }, "metadataDatasetId": { "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId", "type": "string" }, "metadataDatasetName": { "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName", "type": "string" } }, "required": [ "metadataDatasetId", "datasetId", "metadataDatasetName", "datasetName" ], "title": "MetadataDatasetPairApiFormatted", "type": "object" } ``` MetadataDatasetPairApiFormatted ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the dataset added to the vector database. | | datasetName | string | true | | The name of the dataset added to the vector database. | | metadataDatasetId | string | true | | The ID of the dataset used to add metadata to the vector database. | | metadataDatasetName | string | true | | The name of the dataset used to add metadata to the vector database. | ## MilvusConnection ``` { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnection", "type": "object" } ``` MilvusConnection ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | true | | The ID of the credential used to connect to the external vector database. | | credentialUserId | any | false | | The ID of the user supplying the credential used to connect to the external vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | distanceMetric | DistanceMetric | false | | The distance strategy to use for building the vector database. | | type | string | false | | The type of the external vector database. | | uri | string | true | maxLength: 5000minLength: 1minLength: 1 | The URI of the Milvus connection. | ## MilvusConnectionRequest ``` { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnectionRequest", "type": "object" } ``` MilvusConnectionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | true | | The ID of the credential used to connect to the external vector database. | | credentialUserId | any | false | | The ID of the user supplying the credential used to connect to the external vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | distanceMetric | DistanceMetric | false | | The distance strategy to use for building the vector database. | | type | string | false | | The type of the external vector database. | | uri | string | true | maxLength: 5000minLength: 1minLength: 1 | The URI of the Milvus connection. | ## PineconeCloud ``` { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" } ``` PineconeCloud ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | PineconeCloud | string | false | | Supported cloud providers for Pinecone. | ### Enumerated Values | Property | Value | | --- | --- | | PineconeCloud | [aws, gcp, azure] | ## PineconeConnection ``` { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnection", "type": "object" } ``` PineconeConnection ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloud | PineconeCloud | true | | The cloud provider to create the index. | | credentialId | string | true | | The ID of the credential used to connect to the external vector database. | | credentialUserId | any | false | | The ID of the user supplying the credential used to connect to the external vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | distanceMetric | DistanceMetric | false | | The distance strategy to use for building the vector database. | | region | string | true | maxLength: 5000minLength: 1minLength: 1 | The region to create the index. | | type | string | false | | The type of the external vector database. | ## PineconeConnectionRequest ``` { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnectionRequest", "type": "object" } ``` PineconeConnectionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | cloud | PineconeCloud | true | | The cloud provider to create the index. | | credentialId | string | true | | The ID of the credential used to connect to the external vector database. | | credentialUserId | any | false | | The ID of the user supplying the credential used to connect to the external vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | distanceMetric | DistanceMetric | false | | The distance strategy to use for building the vector database. | | region | string | true | maxLength: 5000minLength: 1minLength: 1 | The region to create the index. | | type | string | false | | The type of the external vector database. | ## PostgresConnection ``` { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnection", "type": "object" } ``` PostgresConnection ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | true | | The ID of the credential used to connect to the external vector database. | | credentialUserId | any | false | | The ID of the user supplying the credential used to connect to the external vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | database | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the database. | | distanceMetric | DistanceMetric | false | | The distance strategy to use for building the vector database. | | host | string | true | maxLength: 5000minLength: 1minLength: 1 | The host for the Postgres connection. | | port | integer | false | maximum: 65535minimum: 0 | The port for the Postgres connection. | | schema | string | false | maxLength: 5000minLength: 1minLength: 1 | The name of the schema. | | sslmode | PostgresSSLMode | false | | The SSL mode for the Postgres connection. | | type | string | false | | The type of the external vector database. | ## PostgresConnectionRequest ``` { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnectionRequest", "type": "object" } ``` PostgresConnectionRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | credentialId | string | true | | The ID of the credential used to connect to the external vector database. | | credentialUserId | any | false | | The ID of the user supplying the credential used to connect to the external vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | database | string | true | maxLength: 5000minLength: 1minLength: 1 | The name of the database. | | distanceMetric | DistanceMetric | false | | The distance strategy to use for building the vector database. | | host | string | true | maxLength: 5000minLength: 1minLength: 1 | The host for the Postgres connection. | | port | integer | false | maximum: 65535minimum: 0 | The port for the Postgres connection. | | schema | string | false | maxLength: 5000minLength: 1minLength: 1 | The name of the schema. | | sslmode | PostgresSSLMode | false | | The SSL mode for the Postgres connection. | | type | string | false | | The type of the external vector database. | ## PostgresSSLMode ``` { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" } ``` PostgresSSLMode ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | PostgresSSLMode | string | false | | SSL modes for Postgres vector databases. | ### Enumerated Values | Property | Value | | --- | --- | | PostgresSSLMode | [prefer, require, verify-full] | ## RetrievalMode ``` { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" } ``` RetrievalMode ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | RetrievalMode | string | false | | Retrieval modes for vector databases. | ### Enumerated Values | Property | Value | | --- | --- | | RetrievalMode | [similarity, maximal_marginal_relevance] | ## SupportedCustomModelEmbeddings ``` { "description": "API response object for a single custom embedding model.", "properties": { "id": { "description": "The validation ID of the custom embedding model.", "title": "id", "type": "string" }, "name": { "description": "The name of the custom embedding model.", "title": "name", "type": "string" } }, "required": [ "id", "name" ], "title": "SupportedCustomModelEmbeddings", "type": "object" } ``` SupportedCustomModelEmbeddings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The validation ID of the custom embedding model. | | name | string | true | | The name of the custom embedding model. | ## SupportedDeploymentType ``` { "description": "The type of the target output a DataRobot deployment produces.", "enum": [ "TEXT_GENERATION", "VECTOR_DATABASE", "UNSTRUCTURED", "REGRESSION", "MULTICLASS", "BINARY", "NOT_SUPPORTED" ], "title": "SupportedDeploymentType", "type": "string" } ``` SupportedDeploymentType ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | SupportedDeploymentType | string | false | | The type of the target output a DataRobot deployment produces. | ### Enumerated Values | Property | Value | | --- | --- | | SupportedDeploymentType | [TEXT_GENERATION, VECTOR_DATABASE, UNSTRUCTURED, REGRESSION, MULTICLASS, BINARY, NOT_SUPPORTED] | ## SupportedEmbeddingsResponse ``` { "description": "API response object for \"List supported embeddings\".", "properties": { "customModelEmbeddingValidations": { "description": "The list of validated custom embedding models.", "items": { "description": "API response object for a single custom embedding model.", "properties": { "id": { "description": "The validation ID of the custom embedding model.", "title": "id", "type": "string" }, "name": { "description": "The name of the custom embedding model.", "title": "name", "type": "string" } }, "required": [ "id", "name" ], "title": "SupportedCustomModelEmbeddings", "type": "object" }, "title": "customModelEmbeddingValidations", "type": "array" }, "defaultEmbeddingModel": { "description": "The name of the default embedding model.", "title": "defaultEmbeddingModel", "type": "string" }, "embeddingModels": { "description": "The list of embeddings models.", "items": { "description": "API response object for a single embedding model.", "properties": { "description": { "description": "The description of the embedding model.", "title": "description", "type": "string" }, "embeddingModel": { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, "languages": { "description": "The list of languages the embedding models supports.", "items": { "description": "The names of dataset languages.", "enum": [ "Afrikaans", "Amharic", "Arabic", "Assamese", "Azerbaijani", "Belarusian", "Bulgarian", "Bengali", "Breton", "Bosnian", "Catalan", "Czech", "Welsh", "Danish", "German", "Greek", "English", "Esperanto", "Spanish", "Estonian", "Basque", "Persian", "Finnish", "French", "Western Frisian", "Irish", "Scottish Gaelic", "Galician", "Gujarati", "Hausa", "Hebrew", "Hindi", "Croatian", "Hungarian", "Armenian", "Indonesian", "Icelandic", "Italian", "Japanese", "Javanese", "Georgian", "Kazakh", "Khmer", "Kannada", "Korean", "Kurdish", "Kyrgyz", "Latin", "Lao", "Lithuanian", "Latvian", "Malagasy", "Macedonian", "Malayalam", "Mongolian", "Marathi", "Malay", "Burmese", "Nepali", "Dutch", "Norwegian", "Oromo", "Oriya", "Panjabi", "Polish", "Pashto", "Portuguese", "Romanian", "Russian", "Sanskrit", "Sindhi", "Sinhala", "Slovak", "Slovenian", "Somali", "Albanian", "Serbian", "Sundanese", "Swedish", "Swahili", "Tamil", "Telugu", "Thai", "Tagalog", "Turkish", "Uyghur", "Ukrainian", "Urdu", "Uzbek", "Vietnamese", "Xhosa", "Yiddish", "Chinese" ], "title": "DatasetLanguages", "type": "string" }, "title": "languages", "type": "array" }, "maxSequenceLength": { "description": "The maximum input token sequence length that the embedding model can accept.", "title": "maxSequenceLength", "type": "integer" } }, "required": [ "embeddingModel", "description", "maxSequenceLength", "languages" ], "title": "EmbeddingModel", "type": "object" }, "title": "embeddingModels", "type": "array" }, "nimEmbeddingModels": { "description": "The list of NIM registered models.", "items": { "description": "API response object for a single registered NIM embedding model.", "properties": { "description": { "description": "The description of the registered NIM model.", "title": "description", "type": "string" }, "id": { "description": "The validation ID of the registered NIM model.", "title": "id", "type": "string" }, "name": { "description": "The name of the registered NIM model.", "title": "name", "type": "string" } }, "required": [ "id", "name", "description" ], "title": "SupportedNIMModelEmbeddings", "type": "object" }, "title": "nimEmbeddingModels", "type": "array" } }, "required": [ "embeddingModels", "defaultEmbeddingModel" ], "title": "SupportedEmbeddingsResponse", "type": "object" } ``` SupportedEmbeddingsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | customModelEmbeddingValidations | [SupportedCustomModelEmbeddings] | false | | The list of validated custom embedding models. | | defaultEmbeddingModel | string | true | | The name of the default embedding model. | | embeddingModels | [EmbeddingModel] | true | | The list of embeddings models. | | nimEmbeddingModels | [SupportedNIMModelEmbeddings] | false | | The list of NIM registered models. | ## SupportedLanguagesResponse ``` { "description": "API response object for \"List supported languages for Synthetic Dataset generation\".", "properties": { "recommendedLanguage": { "description": "The recommended language.", "title": "recommendedLanguage", "type": "string" }, "supportedLanguages": { "description": "The list of supported languages.", "items": { "type": "string" }, "title": "supportedLanguages", "type": "array" } }, "required": [ "recommendedLanguage", "supportedLanguages" ], "title": "SupportedLanguagesResponse", "type": "object" } ``` SupportedLanguagesResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | recommendedLanguage | string | true | | The recommended language. | | supportedLanguages | [string] | true | | The list of supported languages. | ## SupportedNIMModelEmbeddings ``` { "description": "API response object for a single registered NIM embedding model.", "properties": { "description": { "description": "The description of the registered NIM model.", "title": "description", "type": "string" }, "id": { "description": "The validation ID of the registered NIM model.", "title": "id", "type": "string" }, "name": { "description": "The name of the registered NIM model.", "title": "name", "type": "string" } }, "required": [ "id", "name", "description" ], "title": "SupportedNIMModelEmbeddings", "type": "object" } ``` SupportedNIMModelEmbeddings ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | The description of the registered NIM model. | | id | string | true | | The validation ID of the registered NIM model. | | name | string | true | | The name of the registered NIM model. | ## SupportedRetrievalSettingsResponse ``` { "description": "API response object for \"Retrieve supported retrieval settings\".", "properties": { "settings": { "description": "The list of retrieval settings.", "items": { "description": "API response object for a single vector database setting parameter.", "properties": { "default": { "description": "The default value of the parameter.", "title": "default" }, "description": { "description": "The description of the parameter.", "title": "description", "type": "string" }, "enum": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of possible values for the parameter.", "title": "enum" }, "groupId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The identifier of the group the parameter belongs to.", "title": "groupId" }, "maximum": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the parameter.", "title": "maximum" }, "minimum": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the parameter.", "title": "minimum" }, "name": { "description": "The name of the parameter.", "title": "name", "type": "string" }, "settings": { "anyOf": [ { "items": "[Circular]", "type": "array" }, { "type": "null" } ], "description": "The list of available settings for the parameter.", "title": "settings" }, "title": { "description": "The title of the parameter.", "title": "title", "type": "string" }, "type": { "anyOf": [ { "description": "The types of vector database setting parameters.", "enum": [ "string", "integer", "boolean", "null", "number", "array" ], "title": "VectorDatabaseSettingTypes", "type": "string" }, { "items": { "description": "The types of vector database setting parameters.", "enum": [ "string", "integer", "boolean", "null", "number", "array" ], "title": "VectorDatabaseSettingTypes", "type": "string" }, "type": "array" } ], "description": "The type of the parameter.", "title": "type" } }, "required": [ "name", "type", "description", "title" ], "title": "VectorDatabaseSettingParameter", "type": "object" }, "title": "settings", "type": "array" } }, "required": [ "settings" ], "title": "SupportedRetrievalSettingsResponse", "type": "object" } ``` SupportedRetrievalSettingsResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | settings | [VectorDatabaseSettingParameter] | true | | The list of retrieval settings. | ## SupportedTextChunkingResponse ``` { "description": "API response for \"List text chunking methods\".", "properties": { "textChunkingConfigs": { "description": "The list of text chunking configurations.", "items": { "description": "API response object for a single text chunking configuration.", "properties": { "defaultMethod": { "description": "The name of the default text chunking method.", "title": "defaultMethod", "type": "string" }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "description": "Model names for custom embedding models.", "enum": [ "custom-embeddings/default" ], "title": "CustomEmbeddingModelNames", "type": "string" } ], "description": "The name of the embedding model.", "title": "embeddingModel" }, "methods": { "description": "The list of text chunking methods.", "items": { "description": "API response object for a single text chunking method.", "properties": { "chunkingMethod": { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, "chunkingParameters": { "description": "The list of text chunking parameters.", "items": { "description": "API response object for a single text chunking parameter.", "properties": { "default": { "anyOf": [ { "type": "integer" }, { "items": { "type": "string" }, "type": "array" }, { "type": "boolean" } ], "description": "The default value of the parameter.", "title": "default" }, "description": { "description": "The description of the parameter.", "title": "description", "type": "string" }, "max": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the parameter (inclusive).", "title": "max" }, "min": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the parameter (inclusive).", "title": "min" }, "name": { "description": "The name of the parameter.", "title": "name", "type": "string" }, "type": { "description": "Supported parameter data types for text chunking parameters.", "enum": [ "int", "list[str]", "bool" ], "title": "ChunkingParameterTypes", "type": "string" } }, "required": [ "name", "type", "min", "max", "description", "default" ], "title": "TextChunkingParameterFields", "type": "object" }, "title": "chunkingParameters", "type": "array" }, "description": { "description": "The description of the text chunking method.", "title": "description", "type": "string" }, "title": { "description": "Supported user-facing friendly ames of text chunking methods.", "enum": [ "Recursive", "Semantic" ], "title": "ChunkingMethodNamesTitle", "type": "string" } }, "required": [ "chunkingMethod", "title", "chunkingParameters", "description" ], "title": "TextChunkingMethod", "type": "object" }, "title": "methods", "type": "array" } }, "required": [ "embeddingModel", "methods", "defaultMethod" ], "title": "TextChunkingConfig", "type": "object" }, "title": "textChunkingConfigs", "type": "array" } }, "required": [ "textChunkingConfigs" ], "title": "SupportedTextChunkingResponse", "type": "object" } ``` SupportedTextChunkingResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | textChunkingConfigs | [TextChunkingConfig] | true | | The list of text chunking configurations. | ## TextChunkingConfig ``` { "description": "API response object for a single text chunking configuration.", "properties": { "defaultMethod": { "description": "The name of the default text chunking method.", "title": "defaultMethod", "type": "string" }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "description": "Model names for custom embedding models.", "enum": [ "custom-embeddings/default" ], "title": "CustomEmbeddingModelNames", "type": "string" } ], "description": "The name of the embedding model.", "title": "embeddingModel" }, "methods": { "description": "The list of text chunking methods.", "items": { "description": "API response object for a single text chunking method.", "properties": { "chunkingMethod": { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, "chunkingParameters": { "description": "The list of text chunking parameters.", "items": { "description": "API response object for a single text chunking parameter.", "properties": { "default": { "anyOf": [ { "type": "integer" }, { "items": { "type": "string" }, "type": "array" }, { "type": "boolean" } ], "description": "The default value of the parameter.", "title": "default" }, "description": { "description": "The description of the parameter.", "title": "description", "type": "string" }, "max": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the parameter (inclusive).", "title": "max" }, "min": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the parameter (inclusive).", "title": "min" }, "name": { "description": "The name of the parameter.", "title": "name", "type": "string" }, "type": { "description": "Supported parameter data types for text chunking parameters.", "enum": [ "int", "list[str]", "bool" ], "title": "ChunkingParameterTypes", "type": "string" } }, "required": [ "name", "type", "min", "max", "description", "default" ], "title": "TextChunkingParameterFields", "type": "object" }, "title": "chunkingParameters", "type": "array" }, "description": { "description": "The description of the text chunking method.", "title": "description", "type": "string" }, "title": { "description": "Supported user-facing friendly ames of text chunking methods.", "enum": [ "Recursive", "Semantic" ], "title": "ChunkingMethodNamesTitle", "type": "string" } }, "required": [ "chunkingMethod", "title", "chunkingParameters", "description" ], "title": "TextChunkingMethod", "type": "object" }, "title": "methods", "type": "array" } }, "required": [ "embeddingModel", "methods", "defaultMethod" ], "title": "TextChunkingConfig", "type": "object" } ``` TextChunkingConfig ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | defaultMethod | string | true | | The name of the default text chunking method. | | embeddingModel | any | true | | The name of the embedding model. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | EmbeddingModelNames | false | | Embedding model names (matching the format of HuggingFace repositories). | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | CustomEmbeddingModelNames | false | | Model names for custom embedding models. | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | methods | [TextChunkingMethod] | true | | The list of text chunking methods. | ## TextChunkingMethod ``` { "description": "API response object for a single text chunking method.", "properties": { "chunkingMethod": { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, "chunkingParameters": { "description": "The list of text chunking parameters.", "items": { "description": "API response object for a single text chunking parameter.", "properties": { "default": { "anyOf": [ { "type": "integer" }, { "items": { "type": "string" }, "type": "array" }, { "type": "boolean" } ], "description": "The default value of the parameter.", "title": "default" }, "description": { "description": "The description of the parameter.", "title": "description", "type": "string" }, "max": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the parameter (inclusive).", "title": "max" }, "min": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the parameter (inclusive).", "title": "min" }, "name": { "description": "The name of the parameter.", "title": "name", "type": "string" }, "type": { "description": "Supported parameter data types for text chunking parameters.", "enum": [ "int", "list[str]", "bool" ], "title": "ChunkingParameterTypes", "type": "string" } }, "required": [ "name", "type", "min", "max", "description", "default" ], "title": "TextChunkingParameterFields", "type": "object" }, "title": "chunkingParameters", "type": "array" }, "description": { "description": "The description of the text chunking method.", "title": "description", "type": "string" }, "title": { "description": "Supported user-facing friendly ames of text chunking methods.", "enum": [ "Recursive", "Semantic" ], "title": "ChunkingMethodNamesTitle", "type": "string" } }, "required": [ "chunkingMethod", "title", "chunkingParameters", "description" ], "title": "TextChunkingMethod", "type": "object" } ``` TextChunkingMethod ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkingMethod | ChunkingMethodNames | true | | The name of the text chunking method. | | chunkingParameters | [TextChunkingParameterFields] | true | | The list of text chunking parameters. | | description | string | true | | The description of the text chunking method. | | title | ChunkingMethodNamesTitle | true | | User-friendly label for the text chunking method. | ## TextChunkingParameterFields ``` { "description": "API response object for a single text chunking parameter.", "properties": { "default": { "anyOf": [ { "type": "integer" }, { "items": { "type": "string" }, "type": "array" }, { "type": "boolean" } ], "description": "The default value of the parameter.", "title": "default" }, "description": { "description": "The description of the parameter.", "title": "description", "type": "string" }, "max": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the parameter (inclusive).", "title": "max" }, "min": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the parameter (inclusive).", "title": "min" }, "name": { "description": "The name of the parameter.", "title": "name", "type": "string" }, "type": { "description": "Supported parameter data types for text chunking parameters.", "enum": [ "int", "list[str]", "bool" ], "title": "ChunkingParameterTypes", "type": "string" } }, "required": [ "name", "type", "min", "max", "description", "default" ], "title": "TextChunkingParameterFields", "type": "object" } ``` TextChunkingParameterFields ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | default | any | true | | The default value of the parameter. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | boolean | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | description | string | true | | The description of the parameter. | | max | any | true | | The maximum value of the parameter (inclusive). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | min | any | true | | The minimum value of the parameter (inclusive). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the parameter. | | type | ChunkingParameterTypes | true | | The data type of the parameter. | ## UpdateConnectedVectorDatabaseRequest ``` { "description": "The body of the \"Update connected vector database\" request.", "properties": { "datasetId": { "description": "The ID of the dataset to use for building the vector database.", "title": "datasetId", "type": "string" }, "metadataCombinationStrategy": { "description": "Strategy to use when the dataset and the metadata file have duplicate columns.", "enum": [ "replace", "merge" ], "title": "MetadataCombinationStrategy", "type": "string" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset to add metadata for building the vector database.", "title": "metadataDatasetId" } }, "required": [ "datasetId" ], "title": "UpdateConnectedVectorDatabaseRequest", "type": "object" } ``` UpdateConnectedVectorDatabaseRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetId | string | true | | The ID of the dataset to use for building the vector database. | | metadataCombinationStrategy | MetadataCombinationStrategy | false | | The strategy to use when the dataset and the metadata file have duplicate columns. | | metadataDatasetId | any | false | | The ID of the dataset to add metadata for building the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | ## ValidationError ``` { "properties": { "loc": { "items": { "anyOf": [ { "type": "string" }, { "type": "integer" } ] }, "title": "loc", "type": "array" }, "msg": { "title": "msg", "type": "string" }, "type": { "title": "type", "type": "string" } }, "required": [ "loc", "msg", "type" ], "title": "ValidationError", "type": "object" } ``` ValidationError ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | loc | [anyOf] | true | | none | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | msg | string | true | | none | | type | string | true | | none | ## VectorDatabaseDeploymentStatuses ``` { "description": "Sort order values for listing vector databases.", "enum": [ "Created", "Assembling", "Registered", "Deployed" ], "title": "VectorDatabaseDeploymentStatuses", "type": "string" } ``` VectorDatabaseDeploymentStatuses ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | VectorDatabaseDeploymentStatuses | string | false | | Sort order values for listing vector databases. | ### Enumerated Values | Property | Value | | --- | --- | | VectorDatabaseDeploymentStatuses | [Created, Assembling, Registered, Deployed] | ## VectorDatabaseExportResponse ``` { "description": "API response object for exporting a vector database.", "properties": { "exportDatasetId": { "description": "The Data Registry dataset ID.", "title": "exportDatasetId", "type": "string" }, "jobId": { "description": "The ID of the export job.", "format": "uuid4", "title": "jobId", "type": "string" }, "vectorDatabaseId": { "description": "The ID of the vector database.", "title": "vectorDatabaseId", "type": "string" } }, "required": [ "jobId", "exportDatasetId", "vectorDatabaseId" ], "title": "VectorDatabaseExportResponse", "type": "object" } ``` VectorDatabaseExportResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | exportDatasetId | string | true | | The Data Registry dataset ID. | | jobId | string(uuid4) | true | | The ID of the export job. | | vectorDatabaseId | string | true | | The ID of the vector database. | ## VectorDatabaseResponse ``` { "description": "API response object for a single vector database.", "properties": { "addedDatasetIds": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset IDs that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetIds" }, "addedDatasetNames": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of dataset names that were added to the vector database in addition to the initial creation dataset.", "title": "addedDatasetNames" }, "addedMetadataDatasetPairs": { "anyOf": [ { "items": { "description": "Pair of metadata dataset and dataset added to the vector database.", "properties": { "datasetId": { "description": "The ID of the dataset added to the vector database.", "title": "datasetId", "type": "string" }, "datasetName": { "description": "The name of the dataset added to the vector database.", "title": "datasetName", "type": "string" }, "metadataDatasetId": { "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId", "type": "string" }, "metadataDatasetName": { "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName", "type": "string" } }, "required": [ "metadataDatasetId", "datasetId", "metadataDatasetName", "datasetName" ], "title": "MetadataDatasetPairApiFormatted", "type": "object" }, "type": "array" }, { "type": "null" } ], "description": "Pairs of metadata dataset and dataset added to the vector database.", "title": "addedMetadataDatasetPairs" }, "chunkOverlapPercentage": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The chunk overlap percentage that the vector database uses.", "title": "chunkOverlapPercentage" }, "chunkSize": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The size of the text chunk (measured in tokens) that the vector database uses.", "title": "chunkSize" }, "chunkingLengthFunction": { "anyOf": [ { "description": "Supported length functions for text splitters.", "enum": [ "tokenizer_length_function", "approximate_token_count" ], "title": "ChunkingLengthFunctionNames", "type": "string" }, { "type": "null" } ], "default": "approximate_token_count", "description": "The length function to use for the text splitter." }, "chunkingMethod": { "anyOf": [ { "description": "Supported names of text chunking methods.", "enum": [ "recursive", "semantic" ], "title": "ChunkingMethodNames", "type": "string" }, { "type": "null" } ], "description": "The text chunking method the vector database uses." }, "chunksCount": { "description": "The number of text chunks in the vector database.", "title": "chunksCount", "type": "integer" }, "creationDate": { "description": "The creation date of the vector database (ISO 8601 formatted).", "format": "date-time", "title": "creationDate", "type": "string" }, "creationDuration": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "The duration of the vector database creation.", "title": "creationDuration" }, "creationUserId": { "description": "The ID of the user that created this vector database.", "title": "creationUserId", "type": "string" }, "customChunking": { "description": "Whether the vector database uses custom chunking.", "title": "customChunking", "type": "boolean" }, "datasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset the vector database was built from.", "title": "datasetId" }, "datasetName": { "description": "The name of the dataset this vector database was built from.", "title": "datasetName", "type": "string" }, "deploymentStatus": { "anyOf": [ { "description": "Sort order values for listing vector databases.", "enum": [ "Created", "Assembling", "Registered", "Deployed" ], "title": "VectorDatabaseDeploymentStatuses", "type": "string" }, { "type": "null" } ], "description": "How far along in the deployment process this vector database is." }, "embeddingModel": { "anyOf": [ { "description": "Embedding model names (matching the format of HuggingFace repositories).", "enum": [ "intfloat/e5-large-v2", "intfloat/e5-base-v2", "intfloat/multilingual-e5-base", "intfloat/multilingual-e5-small", "sentence-transformers/all-MiniLM-L6-v2", "jinaai/jina-embedding-t-en-v1", "jinaai/jina-embedding-s-en-v2", "cl-nagoya/sup-simcse-ja-base" ], "title": "EmbeddingModelNames", "type": "string" }, { "type": "string" }, { "type": "null" } ], "description": "The name of the embedding model the vector database uses.", "title": "embeddingModel" }, "embeddingRegisteredModelId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of registered model (in case of using NIM registered model for embeddings).", "title": "embeddingRegisteredModelId" }, "embeddingValidationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model embedding (in case of using a custom model for embeddings).", "title": "embeddingValidationId" }, "errorMessage": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The error message associated with the vector database creation error (in case of a creation error).", "title": "errorMessage" }, "errorResolution": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The suggested error resolution for the vector database creation error (in case of a creation error).", "title": "errorResolution" }, "executionStatus": { "description": "Job and entity execution status.", "enum": [ "NEW", "RUNNING", "COMPLETED", "REQUIRES_USER_INPUT", "SKIPPED", "ERROR" ], "title": "ExecutionStatus", "type": "string" }, "externalVectorDatabaseConnection": { "anyOf": [ { "discriminator": { "mapping": { "elasticsearch": "#/components/schemas/ElasticsearchConnection", "milvus": "#/components/schemas/MilvusConnection", "pinecone": "#/components/schemas/PineconeConnection", "postgres": "#/components/schemas/PostgresConnection" }, "propertyName": "type" }, "oneOf": [ { "description": "Pinecone vector database connection.", "properties": { "cloud": { "description": "Supported cloud providers for Pinecone.", "enum": [ "aws", "gcp", "azure" ], "title": "PineconeCloud", "type": "string" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "region": { "description": "The region to create the index.", "maxLength": 5000, "minLength": 1, "title": "region", "type": "string" }, "type": { "const": "pinecone", "default": "pinecone", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "cloud", "region" ], "title": "PineconeConnection", "type": "object" }, { "description": "Elasticsearch vector database connection.", "properties": { "cloudId": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The cloud ID of the elastic search connection.", "title": "cloudId" }, "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "elasticsearch", "default": "elasticsearch", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "url": { "anyOf": [ { "maxLength": 5000, "minLength": 1, "type": "string" }, { "type": "null" } ], "description": "The URL of the elastic search connection.", "title": "url" } }, "required": [ "credentialId" ], "title": "ElasticsearchConnection", "type": "object" }, { "description": "Milvus vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "type": { "const": "milvus", "default": "milvus", "description": "The type of the external vector database.", "title": "type", "type": "string" }, "uri": { "description": "The URI of the Milvus connection.", "maxLength": 5000, "minLength": 1, "title": "uri", "type": "string" } }, "required": [ "credentialId", "uri" ], "title": "MilvusConnection", "type": "object" }, { "description": "Postgres vector database connection.", "properties": { "credentialId": { "description": "The ID of the credential used to connect to the external vector database.", "title": "credentialId", "type": "string" }, "credentialUserId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the user supplying the credential used to connect to the external vector database.", "title": "credentialUserId" }, "database": { "description": "The name of the database.", "maxLength": 5000, "minLength": 1, "title": "database", "type": "string" }, "distanceMetric": { "description": "Distance metrics for vector databases.", "enum": [ "cosine", "dot_product", "euclidean", "inner_product", "manhattan" ], "title": "DistanceMetric", "type": "string" }, "host": { "description": "The host for the Postgres connection.", "maxLength": 5000, "minLength": 1, "title": "host", "type": "string" }, "port": { "default": 5432, "description": "The port for the Postgres connection.", "maximum": 65535, "minimum": 0, "title": "port", "type": "integer" }, "schema": { "default": "public", "description": "The name of the schema.", "maxLength": 5000, "minLength": 1, "title": "schema", "type": "string" }, "sslmode": { "description": "SSL modes for Postgres vector databases.", "enum": [ "prefer", "require", "verify-full" ], "title": "PostgresSSLMode", "type": "string" }, "type": { "const": "postgres", "default": "postgres", "description": "The type of the external vector database.", "title": "type", "type": "string" } }, "required": [ "credentialId", "host", "database" ], "title": "PostgresConnection", "type": "object" } ] }, { "type": "null" } ], "description": "The external vector database connection to use.", "title": "externalVectorDatabaseConnection" }, "familyId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent.", "title": "familyId" }, "id": { "description": "The ID of the vector database.", "title": "id", "type": "string" }, "isSeparatorRegex": { "description": "Whether the text chunking separator uses a regular expression.", "title": "isSeparatorRegex", "type": "boolean" }, "lastUpdateDate": { "description": "The date of the most recent update of this playground (ISO 8601 formatted).", "format": "date-time", "title": "lastUpdateDate", "type": "string" }, "metadataColumns": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of metadata columns in the vector database.", "title": "metadataColumns" }, "metadataDatasetId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the dataset used to add metadata to the vector database.", "title": "metadataDatasetId" }, "metadataDatasetName": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The name of the dataset used to add metadata to the vector database.", "title": "metadataDatasetName" }, "name": { "description": "The name of the vector database.", "title": "name", "type": "string" }, "organizationId": { "description": "The ID of the DataRobot organization this vector database belongs to.", "title": "organizationId", "type": "string" }, "parentId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'.", "title": "parentId" }, "percentage": { "anyOf": [ { "type": "number" }, { "type": "null" } ], "description": "Vector database progress percentage.", "title": "percentage" }, "playgroundsCount": { "description": "The number of playgrounds that use this vector database.", "title": "playgroundsCount", "type": "integer" }, "separators": { "anyOf": [ { "items": {}, "type": "array" }, { "type": "null" } ], "description": "The text chunking separators that the vector database uses.", "title": "separators" }, "size": { "description": "The size of the vector database (in bytes).", "title": "size", "type": "integer" }, "skippedChunksCount": { "description": "The number of text chunks skipped during vector database creation.", "title": "skippedChunksCount", "type": "integer" }, "source": { "description": "The source of the vector database.", "enum": [ "DataRobot", "External", "Pinecone", "Elasticsearch", "Milvus", "Postgres" ], "title": "VectorDatabaseSource", "type": "string" }, "tenantId": { "description": "The ID of the DataRobot tenant this vector database belongs to.", "format": "uuid4", "title": "tenantId", "type": "string" }, "useCaseId": { "description": "The ID of the use case the vector database is linked to.", "title": "useCaseId", "type": "string" }, "userName": { "description": "The name of the user that created this vector database.", "title": "userName", "type": "string" }, "validationId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The validation ID of the custom model vector database (in case of using a custom model vector database).", "title": "validationId" }, "version": { "description": "The version of the vector database linked to a certain family ID.", "title": "version", "type": "integer" } }, "required": [ "id", "name", "size", "useCaseId", "datasetId", "embeddingModel", "embeddingValidationId", "embeddingRegisteredModelId", "chunkSize", "chunkingMethod", "chunkOverlapPercentage", "chunksCount", "skippedChunksCount", "customChunking", "separators", "isSeparatorRegex", "creationDate", "creationUserId", "organizationId", "tenantId", "lastUpdateDate", "executionStatus", "errorMessage", "errorResolution", "source", "validationId", "parentId", "familyId", "addedDatasetIds", "version", "playgroundsCount", "datasetName", "userName", "addedDatasetNames" ], "title": "VectorDatabaseResponse", "type": "object" } ``` VectorDatabaseResponse ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addedDatasetIds | any | true | | The list of dataset IDs that were added to the vector database in addition to the initial creation dataset. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addedDatasetNames | any | true | | The list of dataset names that were added to the vector database in addition to the initial creation dataset. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addedMetadataDatasetPairs | any | false | | Pairs of metadata dataset and dataset added to the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [MetadataDatasetPairApiFormatted] | false | | [Pair of metadata dataset and dataset added to the vector database.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkOverlapPercentage | any | true | | The chunk overlap percentage that the vector database uses. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkSize | any | true | | The size of the text chunk (measured in tokens) that the vector database uses. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkingLengthFunction | any | false | | The length function to use for the text splitter. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ChunkingLengthFunctionNames | false | | Supported length functions for text splitters. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunkingMethod | any | true | | The text chunking method the vector database uses. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | ChunkingMethodNames | false | | Supported names of text chunking methods. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | chunksCount | integer | true | | The number of text chunks in the vector database. | | creationDate | string(date-time) | true | | The creation date of the vector database (ISO 8601 formatted). | | creationDuration | any | false | | The duration of the vector database creation. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | creationUserId | string | true | | The ID of the user that created this vector database. | | customChunking | boolean | true | | Whether the vector database uses custom chunking. | | datasetId | any | true | | The ID of the dataset the vector database was built from. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | datasetName | string | true | | The name of the dataset this vector database was built from. | | deploymentStatus | any | false | | How far along in the deployment process this vector database is. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseDeploymentStatuses | false | | Sort order values for listing vector databases. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | embeddingModel | any | true | | The name of the embedding model the vector database uses. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | EmbeddingModelNames | false | | Embedding model names (matching the format of HuggingFace repositories). | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | embeddingRegisteredModelId | any | true | | The ID of registered model (in case of using NIM registered model for embeddings). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | embeddingValidationId | any | true | | The validation ID of the custom model embedding (in case of using a custom model for embeddings). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorMessage | any | true | | The error message associated with the vector database creation error (in case of a creation error). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | errorResolution | any | true | | The suggested error resolution for the vector database creation error (in case of a creation error). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | executionStatus | ExecutionStatus | true | | The creation status of the vector database. | | externalVectorDatabaseConnection | any | false | | The external vector database connection to use. | anyOf - discriminator: type | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | any | false | | none | oneOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | PineconeConnection | false | | Pinecone vector database connection. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | ElasticsearchConnection | false | | Elasticsearch vector database connection. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | MilvusConnection | false | | Milvus vector database connection. | xor | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | »» anonymous | PostgresConnection | false | | Postgres vector database connection. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | familyId | any | true | | An ID associated with a family of vector databases, that is, a parent and all descendant vector databases. All vector databases that are descendants of the same root parent share a family ID.The family ID is equal to the vector database ID of the root parent.Like this each vector database knows it's direct parent and the root parent. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | id | string | true | | The ID of the vector database. | | isSeparatorRegex | boolean | true | | Whether the text chunking separator uses a regular expression. | | lastUpdateDate | string(date-time) | true | | The date of the most recent update of this playground (ISO 8601 formatted). | | metadataColumns | any | false | | The list of metadata columns in the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metadataDatasetId | any | false | | The ID of the dataset used to add metadata to the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | metadataDatasetName | any | false | | The name of the dataset used to add metadata to the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the vector database. | | organizationId | string | true | | The ID of the DataRobot organization this vector database belongs to. | | parentId | any | true | | The ID of the direct parent vector database.It is generated when a vector database is created from another vector database.For the root (parent), ID is 'None'. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | percentage | any | false | | Vector database progress percentage. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | number | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | playgroundsCount | integer | true | | The number of playgrounds that use this vector database. | | separators | any | true | | The text chunking separators that the vector database uses. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [any] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | size | integer | true | | The size of the vector database (in bytes). | | skippedChunksCount | integer | true | | The number of text chunks skipped during vector database creation. | | source | VectorDatabaseSource | true | | The source of the vector database. | | tenantId | string(uuid4) | true | | The ID of the DataRobot tenant this vector database belongs to. | | useCaseId | string | true | | The ID of the use case the vector database is linked to. | | userName | string | true | | The name of the user that created this vector database. | | validationId | any | true | | The validation ID of the custom model vector database (in case of using a custom model vector database). | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | version | integer | true | | The version of the vector database linked to a certain family ID. | ## VectorDatabaseRetrievers ``` { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } ``` VectorDatabaseRetrievers ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | VectorDatabaseRetrievers | string | false | | The method used to retrieve relevant chunks from the vector database. | ### Enumerated Values | Property | Value | | --- | --- | | VectorDatabaseRetrievers | [SINGLE_LOOKUP_RETRIEVER, CONVERSATIONAL_RETRIEVER, MULTI_STEP_RETRIEVER] | ## VectorDatabaseSettingParameter ``` { "description": "API response object for a single vector database setting parameter.", "properties": { "default": { "description": "The default value of the parameter.", "title": "default" }, "description": { "description": "The description of the parameter.", "title": "description", "type": "string" }, "enum": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "description": "The list of possible values for the parameter.", "title": "enum" }, "groupId": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "description": "The identifier of the group the parameter belongs to.", "title": "groupId" }, "maximum": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The maximum value of the parameter.", "title": "maximum" }, "minimum": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "description": "The minimum value of the parameter.", "title": "minimum" }, "name": { "description": "The name of the parameter.", "title": "name", "type": "string" }, "settings": { "anyOf": [ { "items": "[Circular]", "type": "array" }, { "type": "null" } ], "description": "The list of available settings for the parameter.", "title": "settings" }, "title": { "description": "The title of the parameter.", "title": "title", "type": "string" }, "type": { "anyOf": [ { "description": "The types of vector database setting parameters.", "enum": [ "string", "integer", "boolean", "null", "number", "array" ], "title": "VectorDatabaseSettingTypes", "type": "string" }, { "items": { "description": "The types of vector database setting parameters.", "enum": [ "string", "integer", "boolean", "null", "number", "array" ], "title": "VectorDatabaseSettingTypes", "type": "string" }, "type": "array" } ], "description": "The type of the parameter.", "title": "type" } }, "required": [ "name", "type", "description", "title" ], "title": "VectorDatabaseSettingParameter", "type": "object" } ``` VectorDatabaseSettingParameter ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | default | any | false | | The default value of the parameter. | | description | string | true | | The description of the parameter. | | enum | any | false | | The list of possible values for the parameter. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [string] | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | groupId | any | false | | The identifier of the group the parameter belongs to. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | string | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maximum | any | false | | The maximum value of the parameter. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | minimum | any | false | | The minimum value of the parameter. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | name | string | true | | The name of the parameter. | | settings | any | false | | The list of available settings for the parameter. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [VectorDatabaseSettingParameter] | false | | [API response object for a single vector database setting parameter.] | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | title | string | true | | The title of the parameter. | | type | any | true | | The type of the parameter. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | VectorDatabaseSettingTypes | false | | The types of vector database setting parameters. | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | [VectorDatabaseSettingTypes] | false | | [The types of vector database setting parameters.] | ## VectorDatabaseSettingTypes ``` { "description": "The types of vector database setting parameters.", "enum": [ "string", "integer", "boolean", "null", "number", "array" ], "title": "VectorDatabaseSettingTypes", "type": "string" } ``` VectorDatabaseSettingTypes ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | VectorDatabaseSettingTypes | string | false | | The types of vector database setting parameters. | ### Enumerated Values | Property | Value | | --- | --- | | VectorDatabaseSettingTypes | [string, integer, boolean, null, number, array] | ## VectorDatabaseSettingsRequest ``` { "description": "Specifies the vector database retrieval settings in LLM blueprint API requests.", "properties": { "addNeighborChunks": { "default": false, "description": "Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1.", "title": "addNeighborChunks", "type": "boolean" }, "maxDocumentsRetrievedPerPrompt": { "anyOf": [ { "maximum": 10, "minimum": 1, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of chunks to retrieve from the vector database.", "title": "maxDocumentsRetrievedPerPrompt" }, "maxTokens": { "anyOf": [ { "maximum": 51200, "minimum": 128, "type": "integer" }, { "type": "null" } ], "description": "The maximum number of tokens to retrieve from the vector database.", "title": "maxTokens" }, "maximalMarginalRelevanceLambda": { "default": 0.5, "description": "Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0).", "maximum": 1, "minimum": 0, "title": "maximalMarginalRelevanceLambda", "type": "number" }, "retrievalMode": { "description": "Retrieval modes for vector databases.", "enum": [ "similarity", "maximal_marginal_relevance" ], "title": "RetrievalMode", "type": "string" }, "retriever": { "description": "The method used to retrieve relevant chunks from the vector database.", "enum": [ "SINGLE_LOOKUP_RETRIEVER", "CONVERSATIONAL_RETRIEVER", "MULTI_STEP_RETRIEVER" ], "title": "VectorDatabaseRetrievers", "type": "string" } }, "title": "VectorDatabaseSettingsRequest", "type": "object" } ``` VectorDatabaseSettingsRequest ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | addNeighborChunks | boolean | false | | Add neighboring chunks to those that the similarity search retrieves, such that when selected, search returns i, i-1, and i+1. | | maxDocumentsRetrievedPerPrompt | any | false | | The maximum number of chunks to retrieve from the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 10minimum: 1 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maxTokens | any | false | | The maximum number of tokens to retrieve from the vector database. | anyOf | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | integer | false | maximum: 51200minimum: 128 | none | or | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | » anonymous | null | false | | none | continued | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | maximalMarginalRelevanceLambda | number | false | maximum: 1minimum: 0 | Adjust the retrieval of chunks when using maximal marginal relevance to favor diversity (0.0) or similarity (1.0). | | retrievalMode | RetrievalMode | false | | The retrieval mode to use. Similarity search or maximal marginal relevance. | | retriever | VectorDatabaseRetrievers | false | | The method used to retrieve relevant chunks from the vector database. | ## VectorDatabaseSource ``` { "description": "The source of the vector database.", "enum": [ "DataRobot", "External", "Pinecone", "Elasticsearch", "Milvus", "Postgres" ], "title": "VectorDatabaseSource", "type": "string" } ``` VectorDatabaseSource ### Properties | Name | Type | Required | Restrictions | Description | | --- | --- | --- | --- | --- | | VectorDatabaseSource | string | false | | The source of the vector database. | ### Enumerated Values | Property | Value | | --- | --- | | VectorDatabaseSource | [DataRobot, External, Pinecone, Elasticsearch, Milvus, Postgres] | --- # Python client changelog URL: https://docs.datarobot.com/en/docs/api/reference/sdk/CHANGES.html # Python client changelog ## 3.15.0 ### New features - Added MemorySpace to provide a basic CRUD functionality for Memory Spaces. - Added Session and Event to simplify building agentic applications with chat interfaces. ### Enhancements ### Bugfixes ### API changes ### Documentation changes ### Experimental changes ## 3.14.0rc2 ### New features - Extended the project advanced options available when setting a target to include new parameter: ‘custom_metrics_losses_info’ (part of the AdvancedOptions object). This parameter allows you to specify the Mongo ID of the custom metrics and losses metadata. - Added JdbcPreview for previewing data from a JDBC URL by executing SQL without creating a data store; use preview to get a JdbcPreviewData . - Promoted DataRobotFileSystem out of experimental. - Promoted FilesDetails out of experimental. - Promoted experimental methods from Files out of experimental. - Added OtelStats to retrieve OpenTelemetry record counts by service. - Added FEATURE_DISCOVERY_PRIVATE_PREVIEW to RecipeType for backward compatibility with legacy recipes that could not be migrated to the new version. ### Enhancements - Added checksum to File . - Added SAP_AI_CORE to PredictionEnvironmentPlatform . ### Bugfixes - Fixed an issue which caused an error when Feature Lists had an empty description in get_relationships_configuration - Fixed deserialization of ProjectOptions when loading project options from the server: feature_engineering_prediction_point , user_partition_col , and each entry in external_predictions are column names returned as strings by the API (the client previously expected Feature -shaped data, which could raise validation errors). ## 3.13.0 ### New features - Added OtelMetrics to retrieve and delete OpenTelemetry metric data. - Added delete to delete OpenTelemetry log entries. - Added the code_challenge_method property in OAuthProviderConfig to support the PKCE authorization code flow. client_secret was also made optional, as some providers do not require it in the PKCE flow. - Added support for Box JWT credentials via Credential.create_box_jwt and the CredentialTypes.BOX_JWT credential type. - Added get_agent_card , upload_agent_card , and delete_agent_card methods to Deployment for managing A2A agent cards. - Added is_a2a_agent filter parameter to DeploymentListFilters to filter deployments by whether they are A2A agents. ### Enhancements - Added MySQL support to the DataWranglingDialect enum. - Improved typing support for RecipePreview attribute result_schema . - Added the runtime_parameters field to CustomModelVersion.create_from_previous and Job.update methods. This supports a new API attribute that enables the creation of runtime parameters without requiring schema definitions in metadata files. - Added created_at to File . ### Bugfixes - Fixed an issue where VectorDatabase.deploy was not sending its parameters to the DataRobot API. - Fixed an issue where OAuthToken.from_dict was not populating id_token . - Fixed an issue where BatchPredictionJob.score sent improperly serialized columnNamesRemapping to the DataRobot API. ### API changes - Revert handling of deprecated parameter inputs on Recipe.from_dataset when an empty list is passed. Prior to release 3.10.0, an empty list was silently ignored, afterwards an empty list would cause an error. This release reverts the prior behavior. - CustomApplicationSource and CustomApplication no longer require the fields creator_first_name and creator_last_name to be set. ### Documentation changes - Add experimental documentation for DataRobotFileSystem , DataRobotFile and DataRobotFSMap . ### Experimental changes - Implement additional methods for DataRobot file system and Files API with fsspec implementation DataRobotFileSystem : - Added upload_from_url to FilesExperimental to load file(s) from a URL into a catalog item. - Create classes for working with user MCP servers: ## 3.12.0 ### Configuration changes - Added pytz and python-dateutil as dependencies. These were implicitly required by the client for date and time handling. - Updated datarobot[core] to require psutil>=7.2.1 (previously unpinned). This ensures compatibility in environments where an older psutil version may already be installed. ### New features - Added OtelSingleMetricValue to retrieve OpenTelemetry metric data for a single metric without configuration. - Added OtelMetricAggregatedValues to retrieve the latest OpenTelemetry metric data for configured metrics. - Added a new class ConfusionMatrix to interact with confusion matrix insights. ### Experimental changes - Added experimental package datarobot._experimental.fs to store DataRobot file system functionality. - Configured datarobot-early-access[fs] as an optional extra add-on to the datarobot-early-access package. - Added initial support for DataRobot file system and Files API with fsspec implementation DataRobotFileSystem . Added implementations for the following methods: ### Documentation changes - Added API reference documentation for ConfusionMatrix . - Added usage examples for model performance insights in a new guide showing how to use ConfusionMatrix.get() and ConfusionMatrix.compute() methods. ## 3.11.0 ### New features - Added Recipe.publish_to_dataset to recipe API for easy publishing to dataset. ### Enhancements - Added tag data and create/update/delete functions to Deployment . - Added PromptTemplateVersion.list_all to list prompt template versions across multiple templates with optional filtering by template IDs. - Extended LLM Settings to support creating LLM Blueprints for Agentic Workflows in LLMBlueprint llm_setting parameter now supports - Added the available_litellm_endpoints field to LLMGatewayCatalogEntry to define supported endpoints for each LLM gateway model (includes supports_chat_completions and supports_responses which correspond to /chat/completions and /responses ). - Edited LLMGatewayCatalog.list to include the optional parameter chat_completions_supported_only which filters to only list models that support the /chat/completions route. - Added support for external OAuth provider credentials, including the new CredentialTypes.EXTERNAL_OAUTH_PROVIDER enum value and the creation helper Credential.create_external_oauth_provider . ### Bugfixes - Made the prediction environment in Challenger optional, since it is not always provided by the server. - Restore multipart file uploads via client.request() broken in v3.10.0. ### Documentation changes - Added API reference documentation for RocCurve , LiftChart , and Residuals classes that were previously added in 3.7.0. They replace the deprecated methods Model.request_lift_chart and similar. - Added comprehensive usage examples for model performance insights in a new guide showing how to use RocCurve.get() , LiftChart.get() , and Residuals.get() methods. ## 3.10.0 ### New features - Added PromptTemplate to manage prompt templates with versioning support in the GenAI namespace. - Added PromptTemplateVersion to manage specific versions of prompt templates, including the ability to render prompts with variable substitution. - Added Dataset.get_raw_sample_data to dataset raw data as Pandas DataFrame. - Added the following data wrangling capabilities: ### Enhancements - Added PromptTemplateVersion.to_fstring to convert prompt templates from {{ variable }} format to Python f-string {variable} format for use with native Python string formatting. - Updated ExecutionEnvironment.list to accept additional parameter for is_public . - Updated MetricInsights.list to add additional parameters with_aggregation_types_only , production_only , and completed_only . - Exposed Recipe for import directly from the datarobot package. - Added RandomDownsamplingOperation and SmartDownsamplingOperation as wrappers to use when setting downsampling on a recipe. - Updated get_authorization_context to return an empty context if no authorization context is set instead of raising an error. - Made variables parameter optional in PromptTemplateVersion.create and PromptTemplate.create_version . Defaults to None , which sends an empty list to the API. ### Bugfixes - Fixed a bug on updating Recipe recipe_type . - Fixed a bug on updating Recipe downsampling to None . - Always camelize PUT request json body under the hood. ### API changes - Warning: Multipart file uploads via client.request() are no longer possible and will be addressed in a future release. - Removed pagination parameters from OtelMetricSummary.list . - Removed pagination values (e.g., count , next , and previous ) from OtelMetricSummary . - Added the name attribute to the Recipe class. - Deprecated the parameter operations from Recipe.get_sql . This should avoid confusion when converting arbitrary operations. This functionality has been duplicated to Recipe.generate_sql_for_operations . The operations parameter will be removed in 3.12. - Deprecated Recipe.retrieve_preview in favor of Recipe.get_preview . Recipe.get_preview will return the wrapper object RecipePreview . The RecipePreview object has a .df attribute containing the preview data as a Pandas DataFrame. - Added optional trace_id and span_id query parameters to OtelLogEntry.list . - Added optional trace_id and span_id to OtelLogEntry . - Deprecated parameter inputs on Recipe.from_dataset . Added new parameter sampling to use instead. ### Configuration changes - Removed black and pylint as dev dependencies. ruff is now used instead. ### Documentation changes - Updated documentation page for Recipes to include new methods. Created the new sections “Recipe Inputs”, “Recipe Operations”, and “Enums and Helpers” to better organize the Recipe components. - Updated documentation for recipe operations, related enums, and helper classes to document arguments and provide examples. - Improved documentation for recipe sampling operations. - Improved documentation for recipe creation methods Recipe.from_dataset and Recipe.from_data_store . - Improved documentation for the recipe input wrapper classes RecipeDatasetInput and JDBCTableDataSourceInput . ## 3.10.0b0 This was an experimental release and is subsumed by 3.10.0rc0. ## 3.9.1 ### New features - Added CustomApplication to manage custom applications with detailed resource information and operational controls. - Added CustomApplicationSource to manage custom application sources (templates for creating custom applications). - Added optional parameter version_id=None to Files.download and Files.list_contained_files to allow non-blocking upload. - Promoted FilesStage out of experimental. - Added Files.clone , Files.create_stage , Files.apply_stage and Files.copy , which were previously experimental. - Added DataRobotAppFrameworkBaseSettings as a Pydantic Settings class for managing configurations for agentic workflows and applications. ### Enhancements - Improved the string representation of RESTClientObject to include endpoint and client version. ## 3.9.0 ### New features - Added OtelMetricConfig to manage and control the display of OpenTelemetry metric configurations for an entity. - Added OtelLogEntry to list the OpenTelemetry logs associated with an entity. - Added OtelMetricSummary to list the reported OpenTelemetry metrics associated with an entity. - Added OtelMetricValue to list the OpenTelemetry metric values associated with an entity. - Added LLMGatewayCatalog to get available LLMs from the LLM Gateway. - Added ResourceBundle to list defined resource bundles. ### Enhancements - Added CustomTemplate.create to allow users to create a new CustomTemplate . - Updated CustomTemplate.list to accept additional parameters for publisher , category , and show_hidden for improved queries. - Updated CustomTemplate.update to accept additional parameters for file , enabled , and is_hidden to set additional fields. - Added is_hidden member to CustomTemplate to align with server data. - Added custom_metric_metadata member to TemplateMetadata to allow creating custom-metric templates using APIObjects. - Added CustomTemplate.download_content to allow users to download content associated with an items file. - Added new attribute spark_instance_size to RecipeSettings . - Added set_default_credential to DataStore.test to set the provided credential as default when the connection test is successful. - Added optional parameter data_type to Connector.list to list connectors which support specified data type. - Added optional parameter data_type to DataStore.list to list data stores which support specified data type. - Added optional parameters retrieval_mode and maximal_marginal_relevance_lambda to VectorDatabaseSettings to select the retrieval mode. - Added optional parameter wait_for_completion=True to Files.upload and Files.create_from_url to allow non-blocking upload. - Added support for file to UseCase.add and UseCase.remove . - Added GridSearchArguments with GridSearchArguments.to_api_payload for creating grid search arguments for advanced tuning jobs. - Added GridSearchSearchType . An enum to define supported grid search types. - Added GridSearchAlgorithm . An enum to define supported grid search algorithms. - Added optional parameters include_agentic , is_agentic , for_playground , and for_production to ModerationTemplate.list to include/filter for agentic templates and to fetch templates specific to playground or production. - Improved equality comparison for APIObject to only look at API related fields. - Added optional parameters tag_keys and tag_values to Deployment.list to filter search results by tags. ### Bugfixes - Fixed validator errors in EvaluationDatasetMetricAggregation . - Fixed Files.download() to download a correct file from the files container. - Fixed the enum values of ModerationGuardOotbType . - Fixed a bug where ModerationTemplate.find was unable to find a template when given a name. - Fixed a bug where OverallModerationConfig.find was unable to find a given entity’s overall moderation config. ### Documentation changes - Added OCREngineSpecificParameters, DataRobotOCREngineType and DataRobotArynOutputFormat to OCR job resources section ## 3.8.0 This release adds support for Notebooks and Codespaces and unstructured data in the Data Registry, and the Chunking Service v2. There are improvements related to playgrounds, vector databases, agentic workflows, incremental learning, and datasets. This release focuses heavily on file management capabilities. There are two new package extras: `auth` and `auth-authlib`. The `auth` extra provides OAuth2 support, while the `auth-authlib` extra provides OAuth2 support using the Authlib library. ### New features #### GenAI - Added AGENTIC_WORKFLOW target type. - Added VectorDatabase.send_to_custom_model_workshop to create a new custom model from a vector database. - Added VectorDatabase.deploy to create a new deployment from a vector database. - Added optional parameters vector_database_default_prediction_server_id , vector_database_prediction_environment_id , vector_database_maximum_memory , vector_database_resource_bundle_id , vector_database_replicas , and vector_database_network_egress_policy to LLMBlueprint.register_custom_model to allow specifying resources in cases where we automatically deploy a vector database when this function is called. - Added ReferenceToolCall for creating tool calls in the evaluation dataset. - Added ReferenceToolCalls to represent a list of tool calls in the evaluation dataset. - Added VectorDatabase.update_connected to add a dataset and optional additional metadata to a connected vector database. #### Notebooks and Codespaces The Notebook and Codespace APIs are now GA and the related classes have been promoted to the stable client. - Changed name of the Notebook run() method to be Notebook.run_as_job . - Added support for Codespaces to the Notebook.is_finished_executing method. - Added the NotebookKernel.get method. - Added the NotebookScheduledJob.cancel method. - Added the NotebookScheduledJob.list method. - Added the Notebook.list_schedules method. #### Unstructured Data The client now supports unstructured data in the Data Registry. - Added the Files class to manage files on the DataRobot platform. The class supports file metadata including the description, creation date, and creator information. - Added the FilesCatalogSearch class to represent file catalog search results with metadata such as catalog name, creator, and tags. - Added the File class to represent individual files within a Files archive. The class provides information about individual files such as name, size, and path within the archive. #### OAuth The client provides better support for OAuth2 authorization workflows in applications using the DataRobot platform. These features are available in the datarobot.auth module. - Added the methods set_authorization_context and get_authorization_context to handle context needed for OAuth access token management. - Added the decorator datarobot_tool_auth to inject OAuth access tokens into the agent tool functions. #### Other Features - Introduced support for Chunking Service V2. The chunking_service_v2 classes have been moved out of the experimental directory and are now available to all users. - Added Model.continue_incremental_learning_from_incremental_model to continue training of the incremental learning model. - Added optional parameter chunk_definition_id in Model.start_incremental_learning_from_sample to begin training using new chunking service. - Added a new attribute snapshot_policy to datarobot.models.RecipeDatasetInput to specify the snapshot policy to use. - Added a new attribute dataset_id to datarobot.models.JDBCTableDataSourceInput to specify the exact dataset ID to use. - Added Dataset.create_version_from_recipe to create a new dataset version based on the Recipe. ### Enhancements - Added the use_tcp_keepalive parameter to Client to enable TCP keep-alive packets when connections are timing out, enabled by default. - Enabled Playground to create agentic playgrounds via input param playground_type=PlaygroundType.AGENTIC . - Extended PlaygroundOOTBMetricConfiguration.create with additional reference column names for agentic metrics. - Updated CustomTemplate.list to return all custom templates when no offset is specified. - Extended MetricInsights.list with the option to pass llm_blueprint_ids . - Extended OOTBMetricConfigurationRequest and OOTBMetricConfigurationResponse with support for extra_metric_settings , which provides an additional configuration option for the Tool Call Accuracy metric. - Extended VectorDatabase.create to support creation of connected vector databases via input param external_vector_database_connection . - Extended VectorDatabase.create to support an additional metadata dataset via input params metadata_dataset_id and metadata_combination_strategy . - Extended VectorDatabase.update to support updating the credential used to access a connected vector database via input param credential_id . - Extended VectorDatabase.download_text_and_embeddings_asset to support downloading additional files via input param part . - Added a new attribute engine_specific_parameters to datarobot.models.OCRJobResource to specify OCR engine specific parameters. - Added docker_image_uri to datarobot.ExecutionEnvironmentVersion . - Added optional parameter docker_image_uri to ExecutionEnvironmentVersion.create . - Changed parameter docker_context_path in ExecutionEnvironmentVersion.create to be optional. - Added a new attribute image_id to datarobot.ExecutionEnvironmentVersion . ### Bugfixes - Fixed PlaygroundOOTBMetricConfiguration.create by using the right payload for customModelLLMValidationId instead of customModelLlmValidationId . - Fixed datarobot.models.RecipeDatasetInput to use correct fields for to_api . - Fixed EvaluationDatasetConfiguration.create to use the correct payload for is_synthetic_dataset . ### Deprecation summary - Remove unreleased Insight configuration routes. These were replaced with the new MetricInsights class, and insight specific configurations. - Deployment.create_from_learning_model method is deprecated. Please first register the leaderboard model with RegisteredModelVersion.create_for_leaderboard_item , then create a deployment with Deployment.create_from_registered_model_version . ### Documentation changes - Updated the example for GenAI to show creation of a metric aggregation job. ### Experimental changes - Added VectorDatabase with a new attribute external_vector_database_connection added to the VectorDatabase.create() method. - Added attribute version to DatasetInfo to identify the analysis version. - Added attribute dataset_definition_info_version to ChunkDefinition to identify the analysis information version. - Added a version query parameter to the DatasetDefinition class, allowing users to specify the analysis version in the get method. - Added DatasetDefinitionInfoHistory with the DatasetDefinitionInfoHistory.list method to retrieve a list of dataset information history records. - Added the DatasetDefinitionInfoHistory.list_versions method to retrieve a list of dataset information records. ## 3.7.0 ### New features - The DataRobot Python Client now supports Python 3.12 and Python 3.13. - Added Deployment.get_retraining_settings to retrieve retraining settings. - Added Deployment.update_retraining_settings to update retraining settings. - Updated RESTClientObject to retry requests when the server returns a 104 connection reset error. - Added support for datasphere as an intake and output type in batch predictions. - Added Deployment.get_accuracy_metrics_settings to retrieve accuracy metrics settings. - Added Deployment.update_accuracy_metrics_settings to update accuracy metrics settings. - Added CustomMetricValuesOverSpace to retrieve custom metric values over space. - Added CustomMetric.get_values_over_space to retrieve custom metric values over space. - Created ComplianceDocTemplateProjectType , an enum to define project type supported by the compliance documentation custom template. - Added attribute project_type to ComplianceDocTemplate to identify the template supported project type. - Added optional parameter project_type in ComplianceDocTemplate.get_default to retrieve the project type’s default template. - Added optional parameter project_type in ComplianceDocTemplate.create to specify the project type supported by the template to create. - Added optional parameter project_type in ComplianceDocTemplate.create_from_json_file to specify the project type supported by the template to create. - Added optional parameter project_type in ComplianceDocTemplate.update to allow updating an existing template’s project type. - Added optional parameter project_type in ComplianceDocTemplate.list to allow to filtering/searching by template’s project type. - Added ShapMatrix.get_as_csv to retrieve SHAP matrix results as a CSV file. - Added ShapMatrix.get_as_dataframe to retrieve SHAP matrix results as a dataframe. - Added a new class LiftChart to interact with lift chart insights. - Added a new class RocCurve to interact with ROC curve insights. - Added a new class Residuals to interact with residuals insights. - Added Project.create_from_recipe to create Feature Discovery projects using recipes. - Added an optional parameter recipe_type to datarobot.models.Recipe.from_dataset() to create Wrangling recipes. - Added an optional parameter recipe_type to datarobot.models.Recipe.from_data_store() to create Wrangling recipes. - Added Recipe.set_recipe_metadata to update recipe metadata. - Added an optional parameter snapshot_policy to datarobot.models.Recipe.from_dataset() to specify the snapshot policy to use. - Added new attributes prediction_point , relationships_configuration_id and feature_discovery_supervised_feature_reduction to RecipeSettings . - Added several optional parameters to ExecutionEnvironment for list , create and update methods. - Added optional parameter metadata_filter to ComparisonPrompt.create . - Added CustomInferenceModel.share to update access control settings for a custom model. - Added CustomInferenceModel.get_access_list to retrieve access control settings for a custom model. - Added new attribute latest_successful_version to ExecutionEnvironment . - Added Dataset.create_from_project to create datasets from project data. - Added ExecutionEnvironment.share to update access control settings for an execution environment. - Added ExecutionEnvironment.get_access_list to retrieve access control settings an execution environment. - Created ModerationTemplate to interact with LLM moderation templates. - Created ModerationConfiguration to interact with LLM moderation configuration. - Created CustomTemplate to interact with custom-templates elements. - Extended the advanced options available when setting a target to include parameter: ‘feature_engineering_prediction_point’(part of the AdvancedOptions object). - Added optional parameter substitute_url_parameters to DataStore for list and get methods. - Added Model.start_incremental_learning_from_sample to initialize the incremental learning model and begin training using the chunking service. Requires the “Project Creation from a Dataset Sample” feature flag. - Added NonChatAwareCustomModelValidation as the base class for CustomModelVectorDatabaseValidation and CustomModelEmbeddingValidation . In contrast, CustomModelLLMValidation now implements the create and update methods differently to interact with the deployments that support the chat completion API. - Added optional parameter chat_model_id to CustomModelLLMValidation.create and CustomModelLLMValidation.update to allow adding deployed LLMs that support the chat completion API. - Fixed ComparisonPrompt not being able to load errored comparison prompt results. - Added optional parameters retirement_date , is_deprecated , and is_active to LLMDefinition and added an optional parameter llm_is_deprecated to the MetricMetadata to expose LLM deprecation and retirement-related information. ### Enhancements - Added Deployment.share as an alias for Deployment.update_shared_roles . - Internally use the existing input argument max_wait in CustomModelVersion.clean_create , to set the READ request timeout. ### Bugfixes - Made user_id and username fields in management_meta optional for PredictionEnvironment to support API responses without these fields. - Fixed the enum values of ComplianceDocTemplateType . - Fixed the enum values of WranglingOperations . - Fixed the enum values of DataWranglingDialect . - Playground id parameter is no longer optional in EvaluationDatasetConfiguration.list - Copy insights path fixed in MetricInsights.copy_to_playground - Missing fields for prompt_type and warning were added to PromptTrace . - Fixed a query parameter name in SidecarModelMetricValidation.list . - Fix typo in attribute VectorDatabase : metadata_columns which was metada_columns - Do not camelCase metadata_filter dict in ChatPrompt.create - Fixed a Use Case query parameter name in CustomModelLLMValidation.list , CustomModelEmbeddingValidation.list , and CustomModelVectorDatabaseValidation.list . - Fixed featureDiscoverySettings parameter name in RelationshipsConfiguration.create and RelationshipsConfiguration.replace . ### API changes - Method CustomModelLLMValidation.create no longer requires the prompt_column_name and target_column_name parameters, and can accept an optional chat_model_id parameter. The parameter order has changed. If the custom model LLM deployment supports the chat completion API, it is recommended to use chat_model_id now instead of (or in addition to) specifying the column names. ### Deprecation summary - Removed the deprecated capabilities attribute of Deployment . - Method Model.request_lift_chart is deprecated and will be removed in favor of LiftChart.compute . - Method Model.get_lift_chart is deprecated and will be removed in favor of LiftChart.get . - Method Model.get_all_lift_charts is deprecated and will be removed in favor of LiftChart.list . - Method Model.request_roc_curve is deprecated and will be removed in favor of RocCurve.compute . - Method Model.get_roc_curve is deprecated and will be removed in favor of RocCurve.get . - Method Model.get_all_roc_curves is deprecated and will be removed in favor of RocCurve.list . - Method Model.request_residuals_chart is deprecated and will be removed in favor of Residuals.compute . - Method Model.get_residuals_chart is deprecated and will be removed in favor of Residuals.get . - Method Model.get_all_residuals_charts is deprecated and will be removed in favor of Residuals.list . ### Documentation changes - Starting with this release, Python client documentation will be available at https://docs.datarobot.com/ as well as on ReadTheDocs. Content has been reorganized to support this change. - Removed numpydoc as a dependency. Docstring parsing has been handled by sphinx.ext.napoleon since 3.6.0. - Fix issues with how the Table of Contents is rendered on ReadTheDocs. sphinx-external-toc is now a dev dependency. - Fix minor issues with formatting across the ReadTheDocs site. - Updated docs on Anomaly Assessment objects to remove duplicate information. ### Experimental changes - Added use_case and deployment_id properties to RetrainingPolicy class. - Added create and update_use_case methods to RetrainingPolicy class. - Renamed the method ‘train_first_incremental_from_sample’ to ‘start_incremental_learning_from_sample’. Added new parameters : ‘early_stopping_rounds’ and ‘first_iteration_only’. - Added the credentials_id parameter to the create method in ChunkDefinition . - Bugfix the next_run_time property of the NotebookScheduledJob class to be nullable. - Added the highlight_whitespace property to the NotebookSettings . - Create new directory specifically for notebooks in the experimental portion of the client. - Added methods to the Notebook class to work with session: start_session() , stop_session() , get_session_status() , is_running() . - Added methods to the Notebook in order to execute and check related execution status: execute() , get_execution_status() , is_finished_executing() . - Added Notebook.create_revision to the Notebook class in order to create revisions. - Moved ModerationTemplate class to ModerationTemplate . - Moved ModerationConfiguration class to ModerationConfiguration to interact with LLM moderation configuration. - Updates to Notebook.run method in the Notebook class in order to encourage proper usage as well as add more descriptive TypedDict as annotation. - Added NotebookScheduledJob.get_most_recent_run to the NotebookScheduledJob class to aid in more idiomatic code when dealing with manual runs. - Updates to Notebook.run method in the Notebook class in order to support Codespace Notebook execution as well as multiple related new classes and methods to expand API coverage which is needed for the underlying execution. - Added ExecutionEnvironment.assign_environment to the ExecutionEnvironment class, which gives the ability to assign or update a notebook’s environment. - Removed deprecated experimental method Model.get_incremental_learning_metadata . - Removed deprecated experimental method Model.start_incremental_learning . ## 3.6.0 ### New features - Added OCRJobResource for running OCR jobs. - Added new Jina V2 embedding model in VectorDatabaseEmbeddingModel. - Added new Small MultiLingual Embedding Model in VectorDatabaseEmbeddingModel. - Added Deployment.get_segment_attributes to retrieve segment attributes. - Added Deployment.get_segment_values to retrieve segment values. - Added AutomatedDocument.list_all_available_document_types to return a list of document types. - Added Model.request_per_class_fairness_insights to return per-class bias & fairness insights. - Added MLOpsEvent to report MLOps Events. Currently supporting moderation MLOps events only - Added Deployment.get_moderation_events to retrieve moderation events for that deployment. - Extended the advanced options available when setting a target to include new parameter: ‘number_of_incremental_learning_iterations_before_best_model_selection’(part of the AdvancedOptions object). This parameter allows you to specify how long top 5 models will run for prior to best model selection. - Add support for ‘connector_type’ in Connector.create . - Deprecate file_path for Connector.create and Connector.update . - Added DataQualityExport and Deployment.list_data_quality_exports to retrieve a list of data quality records. - Added secure config support for Azure Service Principal credentials. - Added support for categorical custom metrics in CustomMetric . - Added NemoConfiguration to manage Nemo configurations. - Added NemoConfiguration.create to create or update a Nemo configuration. - Added NemoConfiguration.get to retrieve a Nemo configuration. - Added a new class ShapDistributions to interact with SHAP distribution insights. - Added the MODEL_COMPLIANCE_GEN_AI value to the attribute document_type from DocumentOption to generate compliance documentation for LLMs in the Registry. - Added new attribute prompts_count to Chat . - Added Recipe modules for Data Wrangling. - Added RecipeOperation and a set of subclasses to represent a single Recipe.operations operation. - Added new attribute similarity_score to Citation . - Added new attributes retriever and add_neighbor_chunks to VectorDatabaseSettings . - Added new attribute metadata to Citation . - Added new attribute metadata_filter to ChatPrompt . - Added new attribute metadata_filter to ComparisonPrompt . - Added new attribute custom_chunking to ChunkingParameters . - Added new attribute custom_chunking to VectorDatabase . - Added a new class LLMTestConfiguration for LLM test configurations. - Added a new class LLMTestConfigurationSupportedInsights for LLM test configuration supported insights. - Added a new class LLMTestResult for LLM test results. - Added new attribute dataset_name to OOTBDatasetDict . - Added new attribute rows_count to OOTBDatasetDict . - Added new attribute max_num_prompts to DatasetEvaluationDict . - Added new attribute prompt_sampling_strategy to DatasetEvaluationDict . - Added a new class DatasetEvaluationRequestDict for Dataset Evaluations in create/edit requests. - Added new attribute evaluation_dataset_name to InsightEvaluationResult . - Added new attribute chat_name to InsightEvaluationResult . - Added new attribute llm_test_configuration_name to LLMTestResult . - Added new attribute creation_user_name to LLMTestResult . - Added new attribute pass_percentage to LLMTestResult . - Added new attribute evaluation_dataset_name to DatasetEvaluation . - Added new attribute datasets_compatibility to LLMTestConfigurationSupportedInsights . - Added a new class NonOOTBDataset for non out-of-the-box (OOTB) dataset entities. - Added a new class OOTBDataset for OOTB dataset entities. - Added a new class TraceMetadata to retrieve trace metadata. - Add new attributes to VectorDatabase : parent_id , family_id , metadata_columns , added_dataset_ids , added_dataset_names , and`version`. - Updated the method VectorDatabase.create to create a new vector database version. - Added a new class SupportedRetrievalSettings for supported vector database retrieval settings. - Added a new class SupportedRetrievalSetting for supported vector database retrieval setting. - Added a new class VectorDatabaseDatasetExportJob for vector database dataset export jobs. - Added new attribute playground_id to CostMetricConfiguration . - Added new attribute name to CostMetricConfiguration . - Added a new class SupportedInsights to support lists. - Added a new class MetricInsights for the new metric insights routes. - Added a new class PlaygroundOOTBMetricConfiguration for OOTB metric configurations. - Updated the schema for EvaluationDatasetMetricAggregation to include the new attributes ootb_dataset_name , dataset_id and dataset_name . - Updated the method EvaluationDatasetMetricAggregation.list with additional optional filter parameters. - Added new attribute warning to OOTBDataset . - Added new attribute warning to OOTBDatasetDict . - Added new attribute warnings to LLMTestConfiguration . - Added a new parameter playground_id to SidecarModelMetricValidation.create to support sidecar model metrics transition to playground. - Updated the schema for NemoConfiguration to include the new attributes prompt_pipeline_template_id and response_pipeline_template_id . - Added new attributes to EvaluationDatasetConfiguration : rows_count , playground_id . - Fix retrieving shap_remaining_total when requesting predictions with SHAP insights. This should return the remaining shap values when present. ### API changes - Updated ServerError ’s exc_message to be constructed with a request ID to help with debugging. - Added method Deployment.get_capabilities to retrieve a list of Capability objects containing capability details. - Advanced options parameters: modelGroupId , modelRegimeId , and modelBaselines were renamed into seriesId , forecastDistance , and forecastOffsets . - Added the parameter use_sample_from_dataset from Project.create_from_dataset . This parameter, when set, uses the EDA sample of the dataset to start the project. - Added the parameter quick_compute to functions in the classes ShapMatrix , ShapImpact , and ShapPreview . - Added the parameter copy_insights to Playground.create to copy the insights from existing Playground to the new one. - Added the parameter llm_test_configuration_ids , LLMBlueprint.register_custom_model , to run LLM compliance tests when a blueprint is sent to the custom model workshop. ### Enhancements - Added standard pagination parameters (e.g. limit , offset ) to Deployment.list , allowing you to get deployment data in smaller chunks. - Added the parameter base_path to get_encoded_file_contents_from_paths and get_encoded_image_contents_from_paths , allowing you to better control script behavior when using relative file paths. ### Bugfixes - Fixed field in CustomTaskVersion for controlling network policies. This is changed from outgoing_network_policy to outbound_network_policy . When performing a GET action, this field was incorrect and always resolved to None . When attempting a POST or PATCH action, the incorrect field would result in a 422. Also changed the name of datarobot.enums.CustomTaskOutgoingNetworkPolicy to datarobot.enums.CustomTaskOutboundNetworkPolicy to reflect the proper field name. - Fixed schema for DataSliceSizeInfo , so it now allows an empty list for the messages field. ### Deprecation summary - Removed the parameter in_use from ImageAugmentationList.create . This parameter was deprecated in v3.1.0. - Deprecated AutomatedDocument.list_available_document_types . Please use AutomatedDocument.list_all_available_document_types instead. - Deprecated Model.request_fairness_insights . Please use Model.request_per_class_fairness_insights instead, to return StatusCheckJob instead of status_id . - Deprecated Model.get_prime_eligibility . Prime models are no longer supported. - eligibleForPrime field will no longer be returned from Model.get_supported_capabilities and will be removed after version 3.8 is released. - Deprecated the property ShapImpact.row_count and it will be removed after version 3.7 is released. - Advanced options parameters: modelGroupId , modelRegimeId , and modelBaselines were renamed into seriesId , forecastDistance , and forecastOffsets and are deprecated and they will be removed after version 3.6 is released. - Renamed datarobot.enums.CustomTaskOutgoingNetworkPolicy to datarobot.enums.CustomTaskOutboundNetworkPolicy to reflect bug fix changes. The original enum was unusable. - Removed parameter user_agent_suffix in datarobot.Client . Please use trace_context instead. - Removed deprecated method DataStore.get_access_list . Please use DataStore.get_shared_roles instead. - Removed support for SharingAccess instances in DataStore.update_access_list . Please use SharingRole instances instead. ### Configuration changes - Removed upper bound pin on urllib3 package to allow versions 2.0.2 and above. - Upgraded the Pillow library to version 10.3.0. Users installing DataRobot with the “images” extra ( pip install datarobot[images] ) should note that this is a required library. ### Documentation changes - The API Reference page has been split into multiple sections for better usability. - Fixed docs for Project.refresh to clarify that it does not return a value. - Fixed code example for ExternalScores . - Added copy button to code examples in ReadTheDocs documentation, for convenience. - Removed the outdated ‘examples’ section from the documentation. Please refer to DataRobot’s API Documentation Home for more examples. - Removed the duplicate ‘getting started’ section from the documentation. - Updated to Sphinx RTD Theme v3. - Updated the description for the parameter: ‘number_of_incremental_learning_iterations_before_best_model_selection’ (part of the AdvancedOptions object). ### Experimental changes - Added the force_update parameter to the update method in ChunkDefinition . - Removed attribute select_columns from ChunkDefinition - Added initial experimental support for Chunking Service V2 - Added new method update to ChunkDefinition - Added experimental support for time series wrangling, including usage template: - Added the ability to use the Spark dialect when creating a recipe, allowing data wrangling support for files. - Added new attribute warning to Chat . - Moved all modules from datarobot._experimental.models.genai to datarobot.models.genai . - Added a new method ‘Model.train_first_incremental_from_sample’ that will train first incremental learning iteration from existing sample model. Requires “Project Creation from a Dataset Sample” feature flag. ## 3.5.0 ### New features - Added support for BYO LLMs using serverless predictions in CustomModelLLMValidation . - Added attribute creation_user_name to LLMBlueprint . - Added a new class HostedCustomMetricTemplate for hosted custom metrics templates. - Added Job.create_from_custom_metric_gallery_template to create a job from a custom metric gallery template. - Added a new class HostedCustomMetricTemplate for hosted custom metrics. - Added a new class datarobot.models.deployment.custom_metrics.HostedCustomMetricBlueprint for hosted custom metric blueprints. - Added Job.list_schedules to list job schedules. - Added a new class JobSchedule for the registry job schedule. - Added attribute credential_type to RuntimeParameter . - Added a new class EvaluationDatasetConfiguration for configuration of evaluation datasets. - Added a new class EvaluationDatasetMetricAggregation for metric aggregation results. - Added a new class SyntheticEvaluationDataset for synthetic dataset generation. Use SyntheticEvaluationDataset.create to create a synthetic evaluation dataset. - Added a new class SidecarModelMetricValidation for sidecar model metric validations. - Added experimental support for Chunking Service: ### Bugfixes - Updated the trafaret column prediction from TrainingPredictionsIterator for supporting extra list of strings. ### Configuration changes - Updated black version to 23.1.0. - Removes dependency on package mock , since it is part of the standard library. ### Documentation changes - Removed incorrect can_share parameters in Use Case sharing example - Added usage of external_llm_context_size in llm_settings in genai_example.rst . - Updated doc string for llm_settings to include attribute external_llm_context_size for external LLMs. - Updated genai_example.rst to link to DataRobot doc pages for external vector database and external LLM deployment creation. ### API changes - Remove ImportedModel object since it was API for SSE (standalone scoring engine) which is not part of DataRobot anymore. - Added number_of_clusters parameter to Project.get_model_records to filter models by number of clusters in unsupervised clustering projects. - Remove an unsupported NETWORK_EGRESS_POLICY.DR_API_ACCESS value for custom models. This value was used by a feature that was never released as a GA and is not supported in the current API. - Implemented support for dr-connector-v1 to DataStore and DataSource . - Added a new parameter name to DataStore.list for searching data stores by name. - Added a new parameter entity_type to the compute and create methods of the classes ShapMatrix , ShapImpact , ShapPreview . Insights can be computed for custom models if the parameter entity_type="customModel" is passed. See also the User Guide: :ref: SHAP insights overview . ### Experimental changes - Added experimental api support for Data Wrangling. See Recipe . - Added new experimental DataStore that adds get_spark_session for Databricks databricks-v1 data stores to get a Spark session. - Added attribute chunking_type to DatasetChunkDefinition . - Added OTV attributes to DatasourceDefinition . - Added DatasetChunkDefinition.patch_validation_dates to patch validation dates of OTV datasource definitions after sampling job. ## 3.4.1 ### New features ### Enhancements ### Bugfixes - Updated the validation logic of RelationshipsConfiguration to work with native database connections ### API changes ### Deprecation summary ### Configuration changes ### Documentation changes ### Experimental changes ## 3.4.0 ### New features - Added the following classes for generative AI. Importing these from datarobot._experimental.models.genai is deprecated and will be removed by the release of DataRobot 10.1 and API Client 3.5. - Extended the advanced options available when setting a target to include new parameter: incrementalLearningEarlyStoppingRounds (part of the AdvancedOptions object). This parameter allows you to specify when to stop for incremental learning automation. - Added experimental support for Chunking Service: - RuntimeParameter for retrieving runtime parameters assigned to CustomModelVersion . - RuntimeParameterValue to define runtime parameter override value, to be assigned to CustomModelVersion . - Added Snowflake Key Pair authentication for uploading datasets from Snowflake or creating a project from Snowflake data - Added Project.get_model_records to retrieve models. Method Project.get_models is deprecated and will be removed soon in favor of Project.get_model_records . - Extended the advanced options available when setting a target to include new parameter: chunkDefinitionId (part of the AdvancedOptions object). This parameter allows you to specify the chunking definition needed for incremental learning automation. - Extended the advanced options available when setting a target to include new Autopilot parameters: incrementalLearningOnlyMode and incrementalLearningOnBestModel (part of the AdvancedOptions object). These parameters allow you to specify how Autopilot is performed with the chunking service. - Added a new method DatetimeModel.request_lift_chart to support Lift Chart calculations for datetime partitioned projects with support of Sliced Insights. - Added a new method DatetimeModel.get_lift_chart to support Lift chart retrieval for datetime partitioned projects with support of Sliced Insights. - Added a new method DatetimeModel.request_roc_curve to support ROC curve calculation for datetime partitioned projects with support of Sliced Insights. - Added a new method DatetimeModel.get_roc_curve to support ROC curve retrieval for datetime partitioned projects with support of Sliced Insights. - Update method DatetimeModel.request_feature_impact to support use of Sliced Insights. - Update method DatetimeModel.get_feature_impact to support use of Sliced Insights. - Update method DatetimeModel.get_or_request_feature_impact to support use of Sliced Insights. - Update method DatetimeModel.request_feature_effect to support use of Sliced Insights. - Update method DatetimeModel.get_feature_effect to support use of Sliced Insights. - Update method DatetimeModel.get_or_request_feature_effect to support use of Sliced Insights. - Added a new method FeatureAssociationMatrix.create to support the creation of FeatureAssociationMatricies for Featurelists. - Introduced a new method Deployment.perform_model_replace as a replacement for Deployment.replace_model . - Introduced a new property, model_package , which provides an overview of the currently used model package in datarobot.models.Deployment . - Added new parameter prediction_threshold to BatchPredictionJob.score_with_leaderboard_model and BatchPredictionJob.score that automatically assigns the positive class label to any prediction exceeding the threshold. - Added two new enum values to datarobot.models.data_slice.DataSlicesOperators , “BETWEEN” and “NOT_BETWEEN”, which are used to allow slicing. - Added a new class Challenger for interacting with DataRobot challengers to support the following methods: Challenger.get to retrieve challenger objects by ID. Challenger.list to list all challengers. Challenger.create to create a new challenger. Challenger.update to update a challenger. Challenger.delete to delete a challenger. - Added a new method Deployment.get_challenger_replay_settings to retrieve the challenger replay settings of a deployment. - Added a new method Deployment.list_challengers to retrieve the challengers of a deployment. - Added a new method Deployment.get_champion_model_package to retrieve the champion model package from a deployment. - Added a new method Deployment.list_prediction_data_exports to retrieve deployment prediction data exports. - Added a new method Deployment.list_actuals_data_exports to retrieve deployment actuals data exports. - Added a new method Deployment.list_training_data_exports to retrieve deployment training data exports. - Manage deployment health settings with the following methods: - Added new enum value to datarobot.enums._SHARED_TARGET_TYPE to support Text Generation use case. - Added new enum value datarobotServerless to datarobot.enums.PredictionEnvironmentPlatform to support DataRobot Serverless prediction environments. - Added new enum value notApplicable to datarobot.enums.PredictionEnvironmentHealthType to support new health status from DataRobot API. - Added new enum value to datarobot.enums.TARGET_TYPE and datarobot.enums.CUSTOM_MODEL_TARGET_TYPE to support text generation custom inference models. - Updated datarobot.CustomModel to support the creation of text generation custom models. - Added a new class CustomMetric for interacting with DataRobot custom metrics to support the following methods: - Added CustomMetricValuesOverTime to retrieve custom metric over time information. - Added CustomMetricSummary to retrieve custom metric over time summary. - Added CustomMetricValuesOverBatch to retrieve custom metric over batch information. - Added CustomMetricBatchSummary to retrieve custom metric batch summary. - Added Job and JobRun to create, read, update, run, and delete jobs in the Registry. - Added KeyValue to create, read, update, and delete key values. - Added a new class PredictionDataExport for interacting with DataRobot deployment data export to support the following methods: - Added a new class ActualsDataExport for interacting with DataRobot deployment data export to support the following methods: - Added a new class TrainingDataExport for interacting with DataRobot deployment data export to support the following methods: - Added a new parameter base_environment_version_id to CustomModelVersion.create_clean for overriding the default environment version selection behavior. - Added a new parameter base_environment_version_id to CustomModelVersion.create_from_previous for overriding the default environment version selection behavior. - Added a new class PromptTrace for interacting with DataRobot prompt trace to support the following methods: - Added a new class InsightsConfiguration for describing available insights and configured insights for a playground. InsightsConfiguration.list to list the insights that are available to be configured. - Added a new class Insights for configuring insights for a playground. Insights.get to get the current insights configuration for a playground. Insights.create to create or update the insights configuration for a playground. - Added a new class CostMetricConfiguration for describing available cost metrics and configured cost metrics for a Use Case. CostMetricConfiguration.get to get the cost metric configuration. CostMetricConfiguration.create to create a cost metric configuration. CostMetricConfiguration.update to update the cost metric configuration. CostMetricConfiguration.delete to delete the cost metric configuration.Key - Added a new class LLMCostConfiguration for the cost configuration of a specific llm within a Use Case. - Added new classes ShapMatrix , ShapImpact , ShapPreview to interact with SHAP-based insights. See also the User Guide: :ref: SHAP insights overview ### API changes - Parameter Overrides: Users can now override most of the previously set configuration values directly through parameters when initializing the Client. Exceptions: The endpoint and token values must be initialized from one source (client params, environment, or config file) and cannot be overridden individually, for security and consistency reasons. The new configuration priority is as follows: - DATAROBOT_API_CONSUMER_TRACKING_ENABLED now always defaults to True . - Added Databricks personal access token and service principal (also shared credentials via secure config) authentication for uploading datasets from Databricks or creating a project from Databricks data. - Added secure config support for AWS long term credentials. - Implemented support for dr-database-v1 to DataStore , DataSource , and DataDriver . Added enum classes to support the changes. - You can retrieve the canonical URI for a Use Case using UseCase.get_uri . - You can open a Use Case in a browser using UseCase.open_in_browser . ### Enhancements - Added a new parameter to Dataset.create_from_url to support fast dataset registration: - Added a new parameter to Dataset.create_from_data_source to support fast dataset registration: - Job.get_result_when_complete returns datarobot.models.DatetimeModel instead of the datarobot.models.Model if a datetime model was trained. - Dataset.get_as_dataframe can handle downloading parquet files as well as csv files. - Implement support for dr-database-v1 in DataStore - Added two new parameters to BatchPredictionJobDefinition.list for paginating long job definitions lists: - Added two new parameters to BatchPredictionJobDefinition.list for filtering the job definitions: - Added new parameter to Deployment.validate_replacement_model to support replacement validation based on model package ID: - Added support for Native Connectors to Connector for everything other than Connector.create and Connector.update ### Deprecation summary - Removed Model.get_leaderboard_ui_permalink and Model.open_model_browser - Deprecated Project.get_models in favor of Project.get_model_records . - BatchPredictionJobDefinition.list will no longer return all job definitions after version 3.6 is released. To preserve current behavior please pass limit=0. - new_model_id parameter in Deployment.validate_replacement_model will be removed after version 3.6 is released. - Deployment.replace_model will be removed after version 3.6 is released. Method Deployment.perform_model_replace should be used instead. - CustomInferenceModel.assign_training_data was marked as deprecated in v3.2. The deprecation period has been extended, and the feature will now be removed in v3.5. Use CustomModelVersion.create_clean and CustomModelVersion.create_from_previous instead. ### Documentation changes - Updated genai_example.rst to utilize latest genAI features and methods introduced most recently in the API client. ### Experimental changes - Added new attribute, prediction_timeout to CustomModelValidation . - Added new attributes, feedback_result , metrics , and final_prompt to ResultMetadata . - Added use_case_id to CustomModelValidation . - Added llm_blueprints_count and user_name to Playground . - Added custom_model_embedding_validations to SupportedEmbeddings . - Added embedding_validation_id and is_separator_regex to VectorDatabase . - Added optional parameters, use_case , name , and model to CustomModelValidation.create . - Added a method CustomModelValidation.list , to list custom model validations available to a user with several optional parameters to filter the results. - Added a method CustomModelValidation.update , to update a custom model validation. - Added an optional parameter, use_case , to LLMDefinition.list , to include in the returned LLMs the external LLMs available for the specified use_case as well. - Added optional parameter, playground to VectorDatabase.list to list vector databases by playground. - Added optional parameter, comparison_chat , to ComparisonPrompt.list , to list comparison prompts by comparison chat. - Added optional parameter, comparison_chat , to ComparisonPrompt.create , to specify the comparison chat to create the comparison prompt in. - Added optional parameter, feedback_result , to ComparisonPrompt.update , to update a comparison prompt with feedback. - Added optional parameters, is_starred to LLMBlueprint.update to update the LLM blueprint’s starred status. - Added optional parameters, is_starred to LLMBlueprint.list to filter the returned LLM blueprints to those matching is_starred . - Added a new enum PromptType, PromptType to identify the LLMBlueprint’s prompting type. - Added optional parameters, prompt_type to LLMBlueprint.create , to specify the LLM blueprint’s prompting type. This can be set with PromptType . - Added optional parameters, prompt_type to LLMBlueprint.update , to specify the updated LLM blueprint’s prompting type. This can be set with PromptType . - Added a new class, ComparisonChat , for interacting with DataRobot generative AI comparison chats. ComparisonChat.get retrieves a comparison chat object by ID. ComparisonChat.list lists all comparison chats available to the user. ComparisonChat.create creates a new comparison chat. ComparisonChat.update updates the name of a comparison chat. ComparisonChat.delete deletes a single comparison chat. - Added optional parameters, playground and chat to ChatPrompt.list , to list chat prompts by playground and chat. - Added optional parameter, chat to ChatPrompt.create , to specify the chat to create the chat prompt in. - Added a new method, ChatPrompt.update , to update a chat prompt with custom metrics and feedback. - Added a new class, Chat , for interacting with DataRobot generative AI chats. Chat.get retrieves a chat object by ID. Chat.list lists all chats available to the user. Chat.create creates a new chat. Chat.update updates the name of a chat. Chat.delete deletes a single chat. - Removed the model_package module. Use RegisteredModelVersion instead. - Added new class UserLimits - Added new methods to the class Notebook which includes Notebook.run and Notebook.download_revision . See the documentation for example usage. - Added new class NotebookScheduledJob . - Added new class NotebookScheduledRun . - Added a new method Model.get_incremental_learning_metadata that retrieves incremental learning metadata for a model. - Added a new method Model.start_incremental_learning that starts incremental learning for a model. - Updated the API endpoint prefix for all GenerativeAI routes to align with the publicly documented routes. ### Bugfixes - Fixed how async url is build in Model.get_or_request_feature_impact - Fixed setting ssl_verify by env variables. - Resolved a problem related to tilde-based paths in the Client’s ‘config_path’ attribute. - Changed the force_size default of ImageOptions to apply the same transformations by default, which are applied when image archive datasets are uploaded to DataRobot. ## 3.3.0 ### New features - Added support for Python 3.11. - Added new library strenum to add StrEnum support while maintaining backwards compatibility with Python 3.7-3.10. DataRobot does not use the native StrEnum class in Python 3.11. - Added a new class PredictionEnvironment for interacting with DataRobot Prediction environments. - Extended the advanced options available when setting a target to include new parameters: modelGroupId , modelRegimeId , and modelBaselines (part of the AdvancedOptions object). These parameters allow you to specify the user columns required to run time series models without feature derivation in OTV projects. - Added a new method PredictionExplanations.create_on_training_data , for computing prediction explanation on training data. - Added a new class RegisteredModel for interacting with DataRobot registered models to support the following methods: - Added a new class RegisteredModelVersion for interacting with DataRobot registered model versions (also known as model packages) to support the following methods: - Added a new method Deployment.create_from_registered_model_version to support creating deployments from registered model version. - Added a new method Deployment.download_model_package_file to support downloading model package files (.mlpkg) of the currently deployed model. - Added support for retrieving document thumbnails: - Added support to retrieve document text extraction samples using: - Added new fields to CustomTaskVersion for controlling network policies. The new fields were also added to the response. This can be set with datarobot.enums.CustomTaskOutgoingNetworkPolicy . - Added a new method BatchPredictionJob.score_with_leaderboard_model to run batch predictions using a Leaderboard model instead of a deployment. - Set IntakeSettings and OutputSettings to use IntakeAdapters and OutputAdapters enum values respectively for the property type . - Added method Deployment.get_predictions_vs_actuals_over_time to retrieve a deployment’s predictions vs actuals over time data. ### Bugfixes - Payload property subset renamed to source in Model.request_feature_effect - Fixed an issue where Context.trace_context was not being set from environment variables or DR config files. - Project.refresh no longer sets Project.advanced_options to a dictionary. - Fixed Dataset.modify to clarify behavior of when to preserve or clear categories. - Fixed an issue with enums in f-strings resulting in the enum class and property being printed instead of the enum property’s value in Python 3.11 environments. ### Deprecation summary - Project.refresh will no longer set Project.advanced_options to a dictionary after version 3.5 is released. : All interactions with Project.advanced_options should be expected to be through the AdvancedOptions class. ### Experimental changes - Added a new class, VectorDatabase , for interacting with DataRobot vector databases. - Added a new class, CustomModelVectorDatabaseValidation , for validating custom model deployments for use as a vector database. - Added a new class, Playground , for interacting with DataRobot generative AI playgrounds. - Added a new class, LLMDefinition , for interacting with DataRobot generative AI LLMs. - Added a new class, LLMBlueprint , for interacting with DataRobot generative AI LLM blueprints. - Added a new class, ChatPrompt , for interacting with DataRobot generative AI chat prompts. - Added a new class, CustomModelLLMValidation , for validating custom model deployments for use as a custom model LLM. - Added a new class, ComparisonPrompt , for interacting with DataRobot generative AI comparison prompts. - Extended UseCase , adding two new fields to represent the count of vector databases and playgrounds. - Added a new method, ChatPrompt.create_llm_blueprint , to create an LLM blueprint from a chat prompt. - Added a new method, CustomModelLLMValidation.delete , to delete a custom model LLM validation record. - Added a new method, LLMBlueprint.register_custom_model , for registering a custom model from a generative AI LLM blueprint. ## 3.2.0 ### New features - Added new methods to trigger batch monitoring jobs without providing a job definition. - Added Deployment.submit_actuals_from_catalog_async to submit actuals from the AI Catalog. - Added a new class StatusCheckJob which represents a job for a status check of submitted async jobs. - Added a new class JobStatusResult represents the result for a status check job of a submitted async task. - Added DatetimePartitioning.datetime_partitioning_log_retrieve to download the datetime partitioning log. - Added method DatetimePartitioning.datetime_partitioning_log_list to list the datetime partitioning log. - Added DatetimePartitioning.get_input_data to retrieve the input data used to create an optimized datetime partitioning. - Added DatetimePartitioningId , which can be passed as a partitioning_method to Project.analyze_and_model . - Added the ability to share deployments. See :ref: deployment sharing for more information on sharing deployments. - Added new methods get_bias_and_fairness_settings and update_bias_and_fairness_settings to retrieve or update bias and fairness settings. - Added a new class UseCase for interacting with the DataRobot Use Cases API. - Added a new class Application for retrieving DataRobot Applications available to the user. - Added a new class SharingRole to hold user or organization access rights. - Added a new class BatchMonitoringJob for interacting with batch monitoring jobs. - Added a new class BatchMonitoringJobDefinition for interacting with batch monitoring jobs definitions. - Added a new methods for handling monitoring job definitions: list, get, create, update, delete, run_on_schedule and run_once - Added a new method to retrieve a monitoring job - Added the ability to filter return objects by a Use Case ID passed to the following methods: - Added the ability to automatically add a newly created dataset or project to a Use Case by passing a UseCase, list of UseCase objects, UseCase ID or list of UseCase IDs using the keyword argument use_cases to the following methods: - Added the ability to set a default UseCase for requests. It can be set in several ways. - Added the ability to configure the collection of client usage metrics to send to DataRobot. Note that this feature only tracks which DataRobot package methods are called and does not collect any user data. You can configure collection with the following settings: Currently the default value forenable_api_consumer_trackingisTrue. - Added methodDeployment.get_predictions_over_timeto retrieve deployment predictions over time data. - Added a new classFairnessScoresOverTimeto retrieve fairness over time information. - Added a new methodDeployment.get_fairness_scores_over_timeto retrieve fairness scores over time of a deployment. - Added a newuse_gpuparameter to the methodProject.analyze_and_modelto set whether the project should allow usage of GPU - Added a newuse_gpuparameter to the classProjectwith information whether project allows usage of GPU - Added a new classTrainingDatafor retrieving TrainingData assigned toCustomModelVersion. - Added a new classHoldoutDatafor retrieving HoldoutData assigned toCustomModelVersion. - Added the ability to retrieve the model and blueprint json using the following methods: -Model.get_model_blueprint_json-Blueprint.get_json- AddedCredential.updatewhich allows you to update existing credential resources. - Added a new optional parametertrace_contexttodatarobot.Clientto provide additional information on the DataRobot code being run. This parameter defaults toNone. - Updated methods inModelto support use of Sliced Insights: -Model.get_feature_effect-Model.request_feature_effect-Model.get_or_request_feature_effect-Model.get_lift_chart-Model.get_all_lift_charts-Model.get_residuals_chart-Model.get_all_residuals_charts-Model.request_lift_chart-Model.request_residuals_chart-Model.get_roc_curve-Model.get_feature_impact-Model.request_feature_impact-Model.get_or_request_feature_impact- Added support forSharingRoleto the following methods: -DataStore.share- Added new methods for retrievingSharingRoleinformation for the following classes: -DataStore.get_shared_roles- Added new method for calculating sliced roc curveModel.request_roc_curve- Added newDataSliceto support the following slices methods: -DataSlice.listto retrieve all data slices in a project. -DataSlice.createto create a new data slice. -DataSlice.deleteto delete the data slice calling this method. -DataSlice.request_sizeto submit a request to calculate a data slice size on a source. -DataSlice.get_size_infoto get the data slice’s info when applied to a source. -DataSlice.getto retrieve a specific data slice. - Added newDataSliceSizeInfoto define the result of a data slice applied to a source. - Added new method for retrieving all available feature impacts for the modelModel.get_all_feature_impacts. - Added new method for StatusCheckJob to wait and return the completed object once it is generateddatarobot.models.StatusCheckJob.get_result_when_complete() ### Enhancements - Improve error message of SampleImage.list to clarify that a selected parameter cannot be used when a project has not proceeded to the correct stage prior to calling this method. - Extended SampleImage.list by two parameters to filter for a target value range in regression projects. - Added text explanations data to PredictionExplanations and made sure it is returned in both datarobot.PredictionExplanations.get_all_as_dataframe() and datarobot.PredictionExplanations.get_rows() method. - Added two new parameters to Project.upload_dataset_from_catalog : - Implemented training and holdout data assignment for Custom Model Version creation APIs: - Extended CustomInferenceModel.create and CustomInferenceModel.update with the parameter is_training_data_for_versions_permanently_enabled . - Added value DR_API_ACCESS to the NETWORK_EGRESS_POLICY enum. - Added new parameter low_memory to Dataset.get_as_dataframe to allow a low memory mode for larger datasets - Added two new parameters to Project.list for paginating long project lists: ### Bugfixes - Fixed incompatibilities with Pandas 2.0 in DatetimePartitioning.to_dataframe . - Fixed a crash when using non-“latin-1” characters in Panda’s DataFrame used as prediction data in BatchPredictionJob.score . - Fixed an issue where failed authentication when invoking datarobot.client.Client() raises a misleading error about client-server compatibility. - Fixed incompatibilities with Pandas 2.0 in AccuracyOverTime.get_as_dataframe . The method will now throw a ValueError if an empty list is passed to the parameter metrics . ### API changes - Added parameter unsupervised_type to the class DatetimePartitioning . - The sliced insight API endpoint GET: api/v2/insights// returns a paginated response. This means that it returns an empty response if no insights data is found, unlike GET: api/v2/projects//models/// , which returns 404 NOT FOUND in this case. To maintain backwards-compatibility, all methods that retrieve insights data raise 404 NOT FOUND if the insights API returns an empty response. ### Deprecation summary - Model.get_feature_fit_metadata() has been removed. Use Model.get_feature_effect_metadata instead. - DatetimeModel.get_feature_fit_metadata() has been removed. Use DatetimeModel.get_feature_effect_metadata instead. - Model.request_feature_fit has been removed. Use Model.request_feature_effect instead. - DatetimeModel.request_feature_fit has been removed. Use DatetimeModel.request_feature_effect instead. - Model.get_feature_fit has been removed. Use Model.get_feature_effect instead. - DatetimeModel.get_feature_fit has been removed. Use DatetimeModel.get_feature_effect instead. - Model.get_or_request_feature_fit has been removed. Use Model.get_or_request_feature_effect instead. - DatetimeModel.get_or_request_feature_fit has been removed. Use DatetimeModel.get_or_request_feature_effect instead. - Deprecated the use of SharingAccess in favor of SharingRole for sharing in the following classes: - Deprecated the following methods for retrieving SharingAccess information. - CustomInferenceModel.assign_training_data was marked as deprecated and will be removed in v3.4. Use CustomModelVersion.create_clean and CustomModelVersion.create_from_previous instead. ### Configuration changes - Pins dependency on package urllib3 to be less than version 2.0.0. ### Deprecation summary - Deprecated parameter user_agent_suffix in datarobot.Client . user_agent_suffix will be removed in v3.4. Please use trace_context instead. ### Documentation changes - Fixed in-line documentation of DataRobotClientConfig . - Fixed documentation around client configuration from environment variables or config file. ### Experimental changes - Added experimental support for data matching: - Added new method DataMatchingQuery.get_result for returning data matching query results as pandas dataframes to DataMatchingQuery . - Changed behavior for returning results in the DataMatching . Instead of saving the results as a file, a pandas dataframe will be returned in the following methods: - Added experimental support for model lineage: ModelLineage - Changed behavior for methods that search for the closest data points in DataMatching . If the index is missing, instead of throwing the error, methods try to create the index and then query it. This is enabled by default, but if this is not the intended behavior it can be changed by passing False to the new build_index parameter added to the methods: - Added a new class Notebook for retrieving DataRobot Notebooks available to the user. - Added experimental support for data wrangling: ## 3.1.1 ### Configuration changes - Removes dependency on package contextlib2 since the package is Python 3.7+. - Update typing-extensions to be inclusive of versions from 4.3.0 to < 5.0.0. ## 3.1.0 ### Enhancements - Added new methods BatchPredictionJob.apply_time_series_data_prep_and_score and BatchPredictionJob.apply_time_series_data_prep_and_score_to_file that apply time series data prep to a file or dataset and make batch predictions with a deployment. - Added new methods DataEngineQueryGenerator.prepare_prediction_dataset and DataEngineQueryGenerator.prepare_prediction_dataset_from_catalog that apply time series data prep to a file or catalog dataset and upload the prediction dataset to a project. - Added new max_wait parameter to method Project.create_from_dataset . Values larger than the default can be specified to avoid timeouts when creating a project from Dataset. - Added new method for creating a segmented modeling project from an existing clustering project and model Project.create_segmented_project_from_clustering_model . Please switch to this function if you are previously using ModelPackage for segmented modeling purposes. - Added new method is_unsupervised_clustering_or_multiclass for checking whether the clustering or multiclass parameters are used, quick and efficient without extra API calls. PredictionExplanations.is_unsupervised_clustering_or_multiclass - Retry idempotent requests which result in HTTP 502 and HTTP 504 (in addition to the previous HTTP 413, HTTP 429 and HTTP 503) - Added value PREPARED_FOR_DEPLOYMENT to the RECOMMENDED_MODEL_TYPE enum - Added two new methods to the ImageAugmentationList class: ### Bugfixes - Added format key to Batch Prediction intake and output settings for S3, GCP and Azure ### API changes - The method PredictionExplanations.is_multiclass now adds an additional API call to check for multiclass target validity, which adds a small delay. - AdvancedOptions parameter blend_best_models defaults to false - AdvancedOptions parameter consider_blenders_in_recommendation defaults to false - DatetimePartitioning has parameter unsupervised_mode ### Deprecation summary - Deprecated method Project.create_from_hdfs . - Deprecated method DatetimePartitioning.generate . - Deprecated parameter in_use from ImageAugmentationList.create as DataRobot will take care of it automatically. - Deprecated property Deployment.capabilities from Deployment . - ImageAugmentationSample.compute was removed in v3.1. You can get the same information with the method ImageAugmentationList.compute_samples . - sample_id parameter removed from ImageAugmentationSample.list . Please use auglist_id instead. ### Documentation changes - Update the documentation to suggest that setting use_backtest_start_end_format of DatetimePartitioning.to_specification to True will mirror the same behavior as the Web UI. - Update the documentation to suggest setting use_start_end_format of Backtest.to_specification to True will mirror the same behavior as the Web UI. ## 3.0.3 ### Bugfixes - Fixed an issue affecting backwards compatibility in datarobot.models.DatetimeModel , where an unexpected keyword from the DataRobot API would break class deserialization. ## 3.0.2 ### Bugfixes - Restored Model.get_leaderboard_ui_permalink , Model.open_model_browser , These methods were accidentally removed instead of deprecated. - Fix for ipykernel < 6.0.0 which does not persist contextvars across cells ### Deprecation summary - Deprecated method Model.get_leaderboard_ui_permalink . Please use Model.get_uri instead. - Deprecated method Model.open_model_browser . Please use Model.open_in_browser instead. ## 3.0.1 ### Bugfixes - Added typing-extensions as a required dependency for the DataRobot Python API client. ## 3.0.0 ### New features - Version 3.0 of the Python client does not support Python 3.6 and earlier versions. Version 3.0 currently supports Python 3.7+. - The default Autopilot mode for project.start_autopilot has changed to Quick mode. - For datetime-aware models, you can now calculate and retrieve feature impact for backtests other than zero and holdout: - Added a backtest field to feature impact metadata: Model.get_or_request_feature_impact . This field is null for non-datetime-aware models and greater than or equal to zero for holdout in datetime-aware models. - You can use a new method to retrieve the canonical URI for a project, model, deployment, or dataset: - You can use a new method to open a class in a browser based on their URI (project, model, deployment, or dataset): - Added a new method for opening DataRobot in a browser: datarobot.rest.RESTClientObject.open_in_browser() . Invoke the method via dr.Client().open_in_browser() . - Altered method Project.create_featurelist to accept five new parameters (please see documentation for information about usage): - Added a new method to retrieve a feature list by name: Project.get_featurelist_by_name . - Added a new convenience method to create datasets: Dataset.upload . - Altered the method Model.request_predictions to accept four new parameters: - Added a new method to datarobot.models.Dataset , Dataset.get_as_dataframe , which retrieves all the originally uploaded data in a pandas DataFrame. - Added a new method to datarobot.models.Dataset , Dataset.share , which allows the sharing of a dataset with another user. - Added new convenience methods to datarobot.models.Project for dealing with partition classes. Both methods should be called before Project.analyze_and_model . - Added a new method to datarobot.models.Project Project.get_top_model which returns the highest scoring model for a metric of your choice. - Use the new method Deployment.predict_batch to pass a file, file path, or DataFrame to datarobot.models.Deployment to easily make batch predictions and return the results as a DataFrame. - Added support for passing in a credentials ID or credentials data to Project.create_from_data_source as an alternative to providing a username and password. - You can now pass in a max_wait value to AutomatedDocument.generate . - Added a new method to datarobot.models.Project Project.get_dataset which retrieves the dataset used during creation of a project. - Added two new properties to datarobot.models.Project : - Added a new Autopilot method to datarobot.models.Project Project.analyze_and_model which allows you to initiate Autopilot or data analysis against data uploaded to DataRobot. - Added a new convenience method to datarobot.models.Project Project.set_options which allows you to save AdvancedOptions values for use in modeling. - Added a new convenience method to datarobot.models.Project Project.get_options which allows you to retrieve saved modeling options. ### Enhancements - Refactored the global singleton client connection ( datarobot.client.Client() ) to use ContextVar instead of a global variable for better concurrency support. - Added support for creating monotonic feature lists for time series projects. Set skip_datetime_partition_column to True to create monotonic feature list. For more information see datarobot.models.Project.create_modeling_featurelist() . - Added information about vertex to advanced tuning parameters datarobot.models.Model.get_advanced_tuning_parameters() . - Added the ability to automatically use saved AdvancedOptions set using Project.set_options in Project.analyze_and_model . ### Bugfixes - Dataset.list no longer throws errors when listing datasets with no owner. - Fixed an issue with the creation of BatchPredictionJobDefinitions containing a schedule. - Fixed error handling in datarobot.helpers.partitioning_methods.get_class . - Fixed issue with portions of the payload not using camelCasing in Project.upload_dataset_from_catalog . ### API changes - The Python client now outputs a DataRobotProjectDeprecationWarning when you attempt to access certain resources (projects, models, deployments, etc.) that are deprecated or disabled as a result of the DataRobot platform’s migration to Python 3. - The Python client now raises a TypeError when you try to retrieve a labelwise ROC on a binary model or a binary ROC on a multilabel model. - The method Dataset.create_from_data_source now raises InvalidUsageError if username and password are not passed as a pair together. ### Deprecation summary - Model.get_leaderboard_ui_permalink has been removed. Use Model.get_uri instead. - Model.open_model_browser has been removed. Use Model.open_in_browser instead. - Project.get_leaderboard_ui_permalink has been removed. Use Project.get_uri instead. - Project.open_leaderboard_browser has been removed. Use Project.open_in_browser instead. - Enum VARIABLE_TYPE_TRANSFORM.CATEGORICAL has been removed - Instantiation of Blueprint using a dict has been removed. Use Blueprint.from_data instead. - Specifying an environment to use for testing with CustomModelTest has been removed. - CustomModelVersion ’s required_metadata parameter has been removed. Use required_metadata_values instead. - CustomTaskVersion ’s required_metadata parameter has been removed. Use required_metadata_values instead. - Instantiation of Feature using a dict has been removed. Use Feature.from_data instead. - Instantiation of Featurelist using a dict has been removed. Use Featurelist.from_data instead. - Instantiation of Model using a dict, tuple, or the data parameter has been removed. Use Model.from_data instead. - Instantiation of Project using a dict has been removed. Use Project.from_data instead. - Project ’s quickrun parameter has been removed. Pass AUTOPILOT_MODE.QUICK as the mode instead. - Project ’s scaleout_max_train_pct and scaleout_max_train_rows parameters have been removed. - ComplianceDocumentation has been removed. Use AutomatedDocument instead. - The Deployment method create_from_custom_model_image was removed. Use Deployment.create_from_custom_model_version instead. - PredictJob.create has been removed. Use Model.request_predictions instead. - Model.fetch_resource_data has been removed. Use Model.get instead. - The class CustomInferenceImage was removed. Use CustomModelVersion with base_environment_id instead. - Project.set_target has been deprecated. Use Project.analyze_and_model instead. ### Configuration changes - Added a context manager client_configuration that can be used to change the connection configuration temporarily, for use in asynchronous or multithreaded code. - Upgraded the Pillow library to version 9.2.0. Users installing DataRobot with the “images” extra ( pip install datarobot[images] ) should note that this is a required library. ### Experimental changes - Added experimental support for retrieving document thumbnails: - Added experimental support to retrieve document text extraction samples using: - Added experimental deployment improvements: - Added an experimental deployment improvement: - Added new methods to RetrainingPolicy : ## 2.29.0 ### New features - Added support to pass max_ngram_explanations parameter in batch predictions that will trigger the compute of text prediction explanations. - Added support to pass calculation mode to prediction explanations ( mode parameter in PredictionExplanations.create ) as well as batch scoring ( explanations_mode in BatchPredictionJob.score ) for multiclass models. Supported modes: - Added method datarobot.CalendarFile.create_calendar_from_dataset() to the calendar file that allows us to create a calendar from a dataset. - Added experimental support for n_clusters parameter in Model.train_datetime and DatetimeModel.retrain that allows to specify number of clusters when creating models in Time Series Clustering project. - Added new parameter clone to datarobot.CombinedModel.set_segment_champion() that allows to set a new champion model in a cloned model instead of the original one, leaving latter unmodified. - Added new property is_active_combined_model to datarobot.CombinedModel that indicates if the selected combined model is currently the active one in the segmented project. - Added new datarobot.models.Project.get_active_combined_model() that allows users to get the currently active combined model in the segmented project. - Added new parameters read_timeout to method ShapMatrix.get_as_dataframe . Values larger than the default can be specified to avoid timeouts when requesting large files. ShapMatrix.get_as_dataframe - Added support for bias mitigation with the following methods - Added new property status to datarobot.models.Deployment that represents model deployment status. - Added new Deployment.activate and Deployment.deactivate that allows deployment activation and deactivation - Added new Deployment.delete_monitoring_data to delete deployment monitoring data. ### Enhancements - Added support for specifying custom endpoint URLs for S3 access in batch predictions: See:endpoint_urlparameter. - Added guide on :ref:working with binary data - Added multithreading support to binary data helper functions. - Binary data helpers image defaults aligned with application’s image preprocessing. - Added the following accuracy metrics to be retrieved for a deployment - TPR, PPV, F1 and MCC :ref:Deployment monitoring ### Bugfixes - Don’t include holdout start date, end date, or duration in datetime partitioning payload when holdout is disabled. - Removed ICE Plot capabilities from Feature Fit. - Handle undefined calendar_name in CalendarFile.create_calendar_from_dataset - Raise ValueError for submitted calendar names that are not strings ### API changes - version field is removed from ImportedModel object ### Deprecation summary - Reason Codes objects deprecated in 2.13 version were removed. Please use Prediction Explanations instead. ### Configuration changes - The upper version constraint on pandas has been removed. ### Documentation changes - Fixed a minor typo in the example for Dataset.create_from_data_source. - Update the documentation to suggest that feature_derivation_window_end of datarobot.DatetimePartitioningSpecification class should be a negative or zero. ## 2.28.0 ### New features - Added new parameter upload_read_timeout to BatchPredictionJob.score and BatchPredictionJob.score_to_file to indicate how many seconds to wait until intake dataset uploads to server. Default value 600s. - Added the ability to turn off supervised feature reduction for Time Series projects. Option use_supervised_feature_reduction can be set in AdvancedOptions . - Allow maximum_memory to be input for custom tasks versions. This will be used for setting the limit to which a custom task prediction container memory can grow. - Added method datarobot.models.Project.get_multiseries_names() to the project service which will return all the distinct entries in the multiseries column - Added new segmentation_task_id attribute to datarobot.models.Project.set_target() that allows to start project as Segmented Modeling project. - Added new property is_segmented to datarobot.models.Project that indicates if project is a regular one or Segmented Modeling project. - Added method datarobot.models.Project.restart_segment() to the project service that allows to restart single segment that hasn’t reached modeling phase. - Added the ability to interact with Combined Models in Segmented Modeling projects. Available with new class:datarobot.CombinedModel. Functionality: -datarobot.CombinedModel.get()-datarobot.CombinedModel.get_segments_info()-datarobot.CombinedModel.get_segments_as_dataframe()-datarobot.CombinedModel.get_segments_as_csv()-datarobot.CombinedModel.set_segment_champion()- Added the ability to create and retrieve segmentation tasks used in Segmented Modeling projects. Available with new class:datarobot.SegmentationTask. Functionality: -datarobot.SegmentationTask.create()-datarobot.SegmentationTask.list()-datarobot.SegmentationTask.get()- Added new class:datarobot.SegmentInfothat allows to get information on all segments of Segmented modeling projects, i.e. segment project ID, model counts, autopilot status. Functionality: -datarobot.SegmentInfo.list()- Added new methods to baseAPIObjectto assist with dictionary and json serialization of child objects. Functionality: -APIObject.to_dict-APIObject.to_json- Added new methods toImageAugmentationListfor interacting with image augmentation samples. Functionality: -ImageAugmentationList.compute_samples-ImageAugmentationList.retrieve_samples- Added the ability to set a prediction threshold when creating a deployment from a learning model. - Added support for governance, owners, predictionEnvironment, and fairnessHealth fields when querying for a Deployment object. - Added helper methods for working with files, images and documents. Methods support conversion of file contents into base64 string representations. Methods for images provide also image resize and transformation support. Functionality: -get_encoded_file_contents_from_urls-get_encoded_file_contents_from_paths-get_encoded_image_contents_from_paths-get_encoded_image_contents_from_urls ### Enhancements - Requesting metadata instead of actual data of datarobot.PredictionExplanations to reduce the amount of data transfer ### Bugfixes - Fix a bug in Job.get_result_when_complete for Prediction Explanations job type to populate all attribute of of datarobot.PredictionExplanations instead of just one - Fix a bug in datarobot.models.ShapImpact where row_count was not optional - Allow blank value for schema and catalog in RelationshipsConfiguration response data - Fix a bug where credentials were incorrectly formatted in Project.upload_dataset_from_catalog and Project.upload_dataset_from_data_source - Rejecting downloads of Batch Prediction data that was not written to the localfile output adapter - Fix a bug in datarobot.models.BatchPredictionJobDefinition.create() where schedule was not optional for all cases ### API changes - User can include ICE plots data in the response when requesting Feature Effects/Feature Fit. Extended methods are ### Deprecation summary - attrs library is removed from library dependencies - ImageAugmentationSample.compute was marked as deprecated and will be removed in v2.30. You can get the same information with newly introduced method ImageAugmentationList.compute_samples - ImageAugmentationSample.list using sample_id - Deprecating scaleout parameters for projects / models. Includes scaleout_modeling_mode , scaleout_max_train_pct , and scaleout_max_train_rows ### Configuration changes - pandas upper version constraint is updated to include version 1.3.5. ### Documentation changes - Fixed “from datarobot.enums” import in Unsupervised Clustering example provided in docs. ## 2.27.0 ### New features - datarobot.UserBlueprint is now mature with full support of functionality. Users are encouraged to use the Blueprint Workshop instead of this class directly. - Added the arguments attribute in datarobot.CustomTaskVersion . - Added the ability to retrieve detected errors in the potentially multicategorical feature types that prevented the feature to be identified as multicategorical. Project.download_multicategorical_data_format_errors - Added the support of listing/updating user roles on one custom task. - Added a method datarobot.models.Dataset.create_from_query_generator() . This creates a dataset in the AI catalog from a datarobot.DataEngineQueryGenerator . - Added the new functionality of creating a user blueprint with a custom task version id. datarobot.UserBlueprint.create_from_custom_task_version_id() . - The DataRobot Python Client is no longer published under the Apache-2.0 software license, but rather under the terms of the DataRobot Tool and Utility Agreement. - Added a new class:datarobot.DataEngineQueryGenerator. This class generates a Spark SQL query to apply time series data prep to a dataset in the AI catalog. Functionality: -datarobot.DataEngineQueryGenerator.create()-datarobot.DataEngineQueryGenerator.get()-datarobot.DataEngineQueryGenerator.create_dataset() See the :ref:time series data prep documentation for more information. - Added the ability to upload a prediction dataset into a project from the AI catalogProject.upload_dataset_from_catalog. - Added the ability to specify the number of training rows to use in SHAP based Feature Impact computation. Extended method: -ShapImpact.create- Added the ability to retrieve and restore features that have been reduced using the time series feature generation and reduction functionality. The functionality comes with a new class:datarobot.models.restore_discarded_features.DiscardedFeaturesInfo. Functionality: -datarobot.models.restore_discarded_features.DiscardedFeaturesInfo.retrieve()-datarobot.models.restore_discarded_features.DiscardedFeaturesInfo.restore()- Added the ability to control class mapping aggregation in multiclass projects viaClassMappingAggregationSettingspassed as a parameter toProject.set_target- Added support for :ref:unsupervised clustering projects- Added the ability to compute and retrieve Feature Effects for a Multiclass model usingdatarobot.models.Model.request_feature_effects_multiclass(),datarobot.models.Model.get_feature_effects_multiclass()ordatarobot.models.Model.get_or_request_feature_effects_multiclass()methods. For datetime models use following methodsdatarobot.models.DatetimeModel.request_feature_effects_multiclass(),datarobot.models.DatetimeModel.get_feature_effects_multiclass()ordatarobot.models.DatetimeModel.get_or_request_feature_effects_multiclass()withbacktest_indexspecified - Added the ability to get and update challenger model settings for deployment class:datarobot.models.Deployment Functionality: -datarobot.models.Deployment.get_challenger_models_settings()-datarobot.models.Deployment.update_challenger_models_settings()- Added the ability to get and update segment analysis settings for deployment class:datarobot.models.Deployment Functionality: -datarobot.models.Deployment.get_segment_analysis_settings()-datarobot.models.Deployment.update_segment_analysis_settings()- Added the ability to get and update predictions by forecast date settings for deployment class:datarobot.models.Deployment Functionality: -datarobot.models.Deployment.get_predictions_by_forecast_date_settings()-datarobot.models.Deployment.update_predictions_by_forecast_date_settings()- Added the ability to specify multiple feature derivation windows when creating a Relationships Configuration usingRelationshipsConfiguration.create- Added the ability to manipulate a legacy conversion for a custom inference model, using the class:CustomModelVersionConversion Functionality: -CustomModelVersionConversion.run_conversion-CustomModelVersionConversion.stop_conversion-CustomModelVersionConversion.get-CustomModelVersionConversion.get_latest-CustomModelVersionConversion.list ### Enhancements - Project.get returns the query_generator_id used for time series data prep when applicable. - Feature Fit & Feature Effects can return datetime instead of numeric for feature_type field for numeric features that are derived from dates. - These methods now provide additional field rowCount in SHAP based Feature Impact results. - Improved performance when downloading prediction dataframes for Multilabel projects using: ### Bugfixes - Fix datarobot.CustomTaskVersion and datarobot.CustomModelVersion to correctly format required_metadata_values before sending them via API - Fixed response validation that could cause DataError when using datarobot.models.Dataset for a dataset with a description that is an empty string. ### API changes - RelationshipsConfiguration.create will include a new key data_source_id in data_source field when applicable ### Deprecation summary - Model.get_all_labelwise_roc_curves has been removed. You can get the same information with multiple calls of Model.get_labelwise_roc_curves , one per data source. - Model.get_all_multilabel_lift_charts has been removed. You can get the same information with multiple calls of Model.get_multilabel_lift_charts , one per data source. ### Documentation changes - This release introduces a new documentation organization. The organization has been modified to better reflect the end-to-end modeling workflow. The new “Tutorials” section has 5 major topics that outline the major components of modeling: Data, Modeling, Predictions, MLOps, and Administration. - The Getting Started workflow is now hosted at DataRobot’s API Documentation Home . - Added an example of how to set up optimized datetime partitioning for time series projects. ## 2.26.0 ### New features - Added the ability to use external baseline predictions for time series project. External dataset can be validated using datarobot.models.Project.validate_external_time_series_baseline() . Option can be set in AdvancedOptions to scale datarobot models’ accuracy performance using external dataset’s accuracy performance. See the :ref: external baseline predictions documentation for more information. - Added the ability to generate exponentially weighted moving average features for time series project. Option can be set in AdvancedOptions and controls the alpha parameter used in exponentially weighted moving average operation. - Added the ability to request a specific model be prepared for deployment using Project.start_prepare_model_for_deployment . - Added a new class: datarobot.CustomTask . This class is a custom task that you can use as part (or all) of your blue print for training models. It needs datarobot.CustomTaskVersion before it can properly be used. - Added a new class: datarobot.CustomTaskVersion . This class is for management of specific versions of a custom task. - Added the ability compute batch predictions for an in-memory DataFrame using BatchPredictionJob.score - Added the ability to specify feature discovery settings when creating a Relationships Configuration using RelationshipsConfiguration.create ### Enhancements - Improved performance when downloading prediction dataframes using: - Added new max_wait parameter to methods: ### Bugfixes - Model.get will return a DatetimeModel instead of Model whenever the project is datetime partitioned. This enables the ModelRecommendation.get_model to return a DatetimeModel instead of Model whenever the project is datetime partitioned. - Try to read Feature Impact result if existing jobId is None in Model.get_or_request_feature_impact . - Set upper version constraints for pandas. - RelationshipsConfiguration.create will return a catalog in data_source field - Argument required_metadata_keys was not properly being sent in the update and create requests for datarobot.ExecutionEnvironment . - Fix issue with datarobot.ExecutionEnvironment create method failing when used against older versions of the application - datarobot.CustomTaskVersion was not properly handling required_metadata_values from the API response ### API changes - Updated Project.start to use AUTOPILOT_MODE.QUICK when the autopilot_on param is set to True. This brings it in line with Project.set_target . - Updated project.start_autopilot to accept the following new GA parameters that are already in the public API: consider_blenders_in_recommendation , run_leakage_removed_feature_list ### Deprecation summary - The required_metadata property of datarobot.CustomModelVersion has been deprecated. required_metadata_values should be used instead. - The required_metadata property of datarobot.CustomTaskVersion has been deprecated. required_metadata_values should be used instead. ### Configuration changes - Now requires dependency on package scikit-learn rather than sklearn . Note: This dependency is only used in example code. See this scikit-learn issue for more information. - Now permits dependency on package attrs to be less than version 21. This fixes compatibility with apache-airflow. - Allow to setup Authorization: type header for OAuth2 Bearer tokens. ### Documentation changes - Update the documentation with respect to the permission that controls AI Catalog dataset snapshot behavior. ## 2.25.0 ### New features - There is a newAnomalyAssessmentRecordobject that implements public API routes to work with anomaly assessment insight. This also adds explanations and predictions preview classes. The insight is available for anomaly detection models in time series unsupervised projects which also support calculation of Shapley values. Functionality: - Initialize an anomaly assessment insight for the specified subset. -DatetimeModel.initialize_anomaly_assessment- Get anomaly assessment records, shap explanations, predictions preview: -DatetimeModel.get_anomaly_assessment_recordslist available records -AnomalyAssessmentRecord.get_predictions_previewget predictions preview for the record -AnomalyAssessmentRecord.get_latest_explanationsget latest predictions along with shap explanations for the most anomalous records. -AnomalyAssessmentRecord.get_explanationsget predictions along with shap explanations for the most anomalous records for the specified range. - Delete anomaly assessment record: -AnomalyAssessmentRecord.deletedelete record - Added an ability to calculate and retrieve Datetime trend plots forDatetimeModel. This includes Accuracy over Time, Forecast vs Actual, and Anomaly over Time. Plots can be calculated using a common method: -DatetimeModel.compute_datetime_trend_plots Metadata for plots can be retrieved using the following methods: -DatetimeModel.get_accuracy_over_time_plots_metadata-DatetimeModel.get_forecast_vs_actual_plots_metadata-DatetimeModel.get_anomaly_over_time_plots_metadata Plots can be retrieved using the following methods: -DatetimeModel.get_accuracy_over_time_plot-DatetimeModel.get_forecast_vs_actual_plot-DatetimeModel.get_anomaly_over_time_plot Preview plots can be retrieved using the following methods: -DatetimeModel.get_accuracy_over_time_plot_preview-DatetimeModel.get_forecast_vs_actual_plot_preview-DatetimeModel.get_anomaly_over_time_plot_preview- Support for Batch Prediction Job Definitions has now been added through the following class:BatchPredictionJobDefinition. You can create, update, list and delete definitions using the following methods: -BatchPredictionJobDefinition.list-BatchPredictionJobDefinition.create-BatchPredictionJobDefinition.update-BatchPredictionJobDefinition.delete ### Enhancements - Added a new helper function to create Dataset Definition, Relationship and Secondary Dataset used by Feature Discovery Project. They are accessible via DatasetDefinition Relationship SecondaryDataset - Added new helper function to projects to retrieve the recommended model. Project.recommended_model - Added method to download feature discovery recipe SQLs (limited beta feature). Project.download_feature_discovery_recipe_sqls . - Added docker_context_size and docker_image_size to datarobot.ExecutionEnvironmentVersion ### Bugfixes - Remove the deprecation warnings when using with latest versions of urllib3. - FeatureAssociationMatrix.get is now using correct query param name when featurelist_id is specified. - Handle scalar values in shapBaseValue while converting a predictions response to a data frame. - Ensure that if a configured endpoint ends in a trailing slash, the resulting full URL does not end up with double slashes in the path. - Model.request_frozen_datetime_model is now implementing correct validation of input parameter training_start_date . ### API changes - Arguments secondary_datasets now accept SecondaryDataset to create secondary dataset configurations - Arguments dataset_definitions and relationships now accept DatasetDefinition Relationship to create and replace relationships configuration - Argument required_metadata_keys has been added to datarobot.ExecutionEnvironment . This should be used to define a list of RequiredMetadataKey . datarobot.CustomModelVersion that use a base environment with required_metadata_keys must define values for these fields in their respective required_metadata - Argument required_metadata has been added to datarobot.CustomModelVersion . This should be set with relevant values defined by the base environment’s required_metadata_keys ## 2.24.0 ### New features - Partial history predictions can be made with time series time series multiseries models using the allow_partial_history_time_series_predictions attribute of the datarobot.DatetimePartitioningSpecification . See the :ref: Time Series documentation for more info. - Multicategorical Histograms are now retrievable. They are accessible via MulticategoricalHistogram or Feature.get_multicategorical_histogram . - Add methods to retrieve per-class lift chart data for multilabel models: Model.get_multilabel_lift_charts and Model.get_all_multilabel_lift_charts . - Add methods to retrieve labelwise ROC curves for multilabel models: Model.get_labelwise_roc_curves and Model.get_all_labelwise_roc_curves . - Multicategorical Pairwise Statistics are now retrievable. They are accessible via PairwiseCorrelations , PairwiseJointProbabilities and PairwiseConditionalProbabilities or Feature.get_pairwise_correlations , Feature.get_pairwise_joint_probabilities and Feature.get_pairwise_conditional_probabilities . - Add methods to retrieve prediction results of a deployment: : - Deployment.get_prediction_results - Deployment.download_prediction_results - Add method to download scoring code of a deployment using Deployment.download_scoring_code . - Added Automated Documentation: now you can automatically generate documentation about various entities within the platform, such as specific models or projects. Check out the - Create a new Dataset version for a given dataset by uploading from a file, URL or in-memory datasource. : - Dataset.create_version_from_file - Dataset.create_version_from_in_memory_data - Dataset.create_version_from_url - Dataset.create_version_from_data_source ### Enhancements - Added a new status called FAILED to from BatchPredictionJob as this is a new status coming to Batch Predictions in an upcoming version of DataRobot. - Added base_environment_version_id to datarobot.CustomModelVersion . - Support for downloading feature discovery training or prediction dataset using Project.download_feature_discovery_dataset . - Added datarobot.models.FeatureAssociationMatrix , datarobot.models.FeatureAssociationMatrixDetails and datarobot.models.FeatureAssociationFeaturelists that can be used to retrieve feature associations data as an alternative to Project.get_associations , Project.get_association_matrix_details and Project.get_association_featurelists methods. ### Bugfixes - Fixed response validation that could cause DataError when using TrainingPredictions.list and TrainingPredictions.get_all_as_dataframe methods if there are training predictions computed with explanation_algorithm . ### API changes - Remove desired_memory param from the following classes: datarobot.CustomInferenceModel , datarobot.CustomModelVersion , datarobot.CustomModelTest - Remove desired_memory param from the following methods: CustomInferenceModel.create , CustomModelVersion.create_clean , CustomModelVersion.create_from_previous , CustomModelTest.create and CustomModelTest.create ### Deprecation summary - class ComplianceDocumentation will be deprecated in v2.24 and will be removed entirely in v2.27. Use AutomatedDocument instead. To start off, see the ### Documentation changes - Remove reference to S3 for Project.upload_dataset since it is not supported by the server ## 2.23.0 ### New features - Calendars for time series projects can now be automatically generated by providing a country code to the method CalendarFile.create_calendar_from_country_code . A list of allowed country codes can be retrieved using CalendarFile.get_allowed_country_codes For more information, see the :ref: calendar documentation . - Added calculate_all_series`` param to [ DatetimeModel.compute_series_accuracy`](datarobot-models.html#datarobot.models.DatetimeModel.compute_series_accuracy). This option allows users to compute series accuracy for all available series at once, while by default it is computed for first 1000 series only. - Added ability to specify sampling method when setting target of OTV project. Option can be set in AdvancedOptions and changes a way training data is defined in autopilot steps. - Add support for custom inference model k8s resources management. This new feature enables users to control k8s resources allocation for their executed model in the k8s cluster. It involves in adding the following new parameters: network_egress_policy , desired_memory , maximum_memory , replicas to the following classes: datarobot.CustomInferenceModel , datarobot.CustomModelVersion , datarobot.CustomModelTest - Add support for multiclass custom inference and training models. This enables users to create classification custom models with more than two class labels. The datarobot.CustomInferenceModel class can now use datarobot.TARGET_TYPE.MULTICLASS for their target_type parameter. Class labels for inference models can be set/updated using either a file or as a list of labels. - Support for Listing all the secondary dataset configuration for a given project: : - SecondaryDatasetConfigurations.list - Add support for unstructured custom inference models. The datarobot.CustomInferenceModel class can now use datarobot.TARGET_TYPE.UNSTRUCTURED for its target_type parameter. target_name parameter is optional for UNSTRUCTURED target type. - All per-class lift chart data is now available for multiclass models using Model.get_multiclass_lift_chart . - AUTOPILOT_MODE.COMPREHENSIVE , a new mode , has been added to Project.set_target . - Add support for anomaly detection custom inference models. The datarobot.CustomInferenceModel class can now use datarobot.TARGET_TYPE.ANOMALY for its target_type parameter. target_name parameter is optional for ANOMALY target type. - Support for Updating and retrieving the secondary dataset configuration for a Feature discovery deployment: : - Deployment.update_secondary_dataset_config - Deployment.get_secondary_dataset_config - Add support for starting and retrieving Feature Impact information for datarobot.CustomModelVersion - Search for interaction features and Supervised Feature reduction for feature discovery project can now be specified : in AdvancedOptions . - Feature discovery projects can now be created using the Project.start method by providing relationships_configuration_id . - Actions applied to input data during automated feature discovery can now be retrieved using FeatureLineage.get Corresponding feature lineage id is available as a new datarobot.models.Feature field feature_lineage_id . - Lift charts and ROC curves are now calculated for backtests 2+ in time series and OTV models. The data can be retrieved for individual backtests using Model.get_lift_chart and Model.get_roc_curve . - The following methods now accept a new argument called credential_data, the credentials to authenticate with the database, to use instead of user/password or credential ID: : - Dataset.create_from_data_source - Dataset.create_project - Project.create_from_dataset - Add support for DataRobot Connectors, datarobot.Connector provides a simple implementation to interface with connectors. ### Enhancements - Running Autopilot on Leakage Removed feature list can now be specified in AdvancedOptions . By default, Autopilot will always run on Informative Features - Leakage Removed feature list if it exists. If the parameter run_leakage_removed_feature_list is set to False, then Autopilot will run on Informative Features or available custom feature list. - Method Project.upload_dataset and Project.upload_dataset_from_data_source support new optional parameter secondary_datasets_config_id for Feature discovery project. ### Bugfixes - added disable_holdout param in datarobot.DatetimePartitioning - Using Credential.create_gcp produced an incompatible credential - SampleImage.list now supports Regression & Multilabel projects - Using BatchPredictionJob.score could in some circumstances result in a crash from trying to abort the job if it fails to start - Using BatchPredictionJob.score or BatchPredictionJob.score would produce incomplete results in case a job was aborted while downloading. This will now raise an exception. ### API changes - New sampling_method param in Model.train_datetime , Project.train_datetime , Model.train_datetime and Model.train_datetime . - New target_type param in datarobot.CustomInferenceModel - New arguments secondary_datasets , name , creator_full_name , creator_user_id , created , : featurelist_id , credentials_ids , project_version and is_default in datarobot.models.SecondaryDatasetConfigurations - New arguments secondary_datasets , name , featurelist_id to : SecondaryDatasetConfigurations.create - Class FeatureEngineeringGraph has been removed. Use datarobot.models.RelationshipsConfiguration instead. - Param feature_engineering_graphs removed from Project.set_target . - Param config removed from SecondaryDatasetConfigurations.create . ### Deprecation summary - supports_binary_classification and supports_regression are deprecated : for datarobot.CustomInferenceModel and will be removed in v2.24 - Argument config and supports_regression are deprecated : for datarobot.models.SecondaryDatasetConfigurations and will be removed in v2.24 - CustomInferenceImage has been deprecated and will be removed in v2.24. : datarobot.CustomModelVersion with base_environment_id should be used in their place. - environment_id and environment_version_id are deprecated for CustomModelTest.create ### Documentation changes - feature_lineage_id is added as a new parameter in the response for retrieval of a datarobot.models.Feature created by automated feature discovery or time series feature derivation. This id is required to retrieve a datarobot.models.FeatureLineage instance. ## 2.22.1 ### New features - Batch Prediction jobs now support :ref: dataset as intake settings for BatchPredictionJob.score . - Create a Dataset from DataSource: Dataset.create_from_data_sourceDataSource.create_dataset - Added support for Custom Model Dependency Management. Please see :ref: custom model documentation . New features added: Added new argumentbase_environment_idto methodsCustomModelVersion.create_cleanandCustomModelVersion.create_from_previousNew fieldsbase_environment_idanddependenciesto classdatarobot.CustomModelVersionNew classdatarobot.CustomModelVersionDependencyBuildto prepare custom model versions with dependencies.Made argumentenvironment_idofCustomModelTest.createoptional to enable using custom model versions with dependenciesNew fieldimage_typeadded to classdatarobot.CustomModelTestDeployment.create_from_custom_model_versioncan be used to create a deployment from a custom model version. - Added new parameters for starting and re-running Autopilot with customizable settings within Project.start_autopilot . - Added a new method to trigger Feature Impact calculation for a Custom Inference Image: CustomInferenceImage.calculate_feature_impact - Added new method to retrieve number of iterations trained for early stopping models. Currently supports only tree-based models. Model.get_num_iterations_trained . ### Enhancements - A description can now be added or updated for a project. Project.set_project_description . - Added new parameters read_timeout and max_wait to method Dataset.create_from_file . Values larger than the default can be specified for both to avoid timeouts when uploading large files. - Added new parameter metric to datarobot.models.deployment.TargetDrift , datarobot.models.deployment.FeatureDrift , Deployment.get_target_drift and Deployment.get_feature_drift . - Added new parameter timeout to BatchPredictionJob.download to indicate how many seconds to wait for the download to start (in case the job doesn’t start processing immediately). Set to -1 to disable. This parameter can also be sent as download_timeout to BatchPredictionJob.score and BatchPredictionJob.score . If the timeout occurs, the pending job will be aborted. - Added new parameter read_timeout to BatchPredictionJob.download to indicate how many seconds to wait between each downloaded chunk. This parameter can also be sent as download_read_timeout to BatchPredictionJob.score and BatchPredictionJob.score . - Added parameter catalog to BatchPredictionJob to both intake and output adapters for type jdbc . - Consider blenders in recommendation can now be specified in AdvancedOptions . Blenders will be included when autopilot chooses a model to prepare and recommend for deployment. - Added optional parameter max_wait to Deployment.replace_model to indicate the maximum time to wait for model replacement job to complete before erroring. ### Bugfixes - Handle null values in predictionExplanationMetadata["shapRemainingTotal"] while converting a predictions response to a data frame. - Handle null values in customModel["latestVersion"] - Removed an extra column status from BatchPredictionJob as it caused issues with never version of Trafaret validation. - Make predicted_vs_actual optional in Feature Effects data because a feature may have insufficient qualified samples. - Make jdbc_url optional in Data Store data because some data stores will not have it. - The method Project.get_datetime_models now correctly returns all DatetimeModel objects for the project, instead of just the first 100. - Fixed a documentation error related to snake_case vs camelCase in the JDBC settings payload. - Make trafaret validator for datasets use a syntax that works properly with a wider range of trafaret versions. - Handle extra keys in CustomModelTests and CustomModelVersions - ImageEmbedding and ImageActivationMap now supports regression projects. ### API changes - The default value for the mode param in Project.set_target has been changed from AUTOPILOT_MODE.FULL_AUTO to AUTOPILOT_MODE.QUICK ### Documentation changes - Added links to classes with duration parameters such as validation_duration and holdout_duration to provide duration string examples to users. - The :ref: models documentation has been revised to include section on how to train a new model and how to run cross-validation or backtesting for a model. ## 2.21.0 ### New features - Added new arguments explanation_algorithm and max_explanations to method Model.request_training_predictions . New fields explanation_algorithm , max_explanations and shap_warnings have been added to class TrainingPredictions . New fields prediction_explanations and shap_metadata have been added to class TrainingPredictionsIterator that is returned by method TrainingPredictions.iterate_rows . - Added new arguments explanation_algorithm and max_explanations to method Model.request_predictions . New fields explanation_algorithm , max_explanations and shap_warnings have been added to class Predictions . Method Predictions.get_all_as_dataframe has new argument serializer that specifies the retrieval and results validation method ( json or csv ) for the predictions. - Added possibility to compute ShapImpact.create and request ShapImpact.get SHAP impact scores for features in a model. - Added support for accessing Visual AI images and insights. See the DataRobot Python Package documentation, Visual AI Projects, section for details. - User can specify custom row count when requesting Feature Effects. Extended methods are Model.request_feature_effect and Model.get_or_request_feature_effect . - Users can request SHAP based predictions explanations for a models that support SHAP scores using ShapMatrix.create . - Added two new methods to Dataset to lazily retrieve paginated responses. - It’s possible to create an Interaction feature by combining two categorical features together using Project.create_interaction_feature . Operation result represented by models.InteractionFeature. . Specific information about an interaction feature may be retrieved by its name using models.InteractionFeature.get - Added the DatasetFeaturelist class to support featurelists on datasets in the AI Catalog. DatasetFeaturelists can be updated or deleted. Two new methods were also added to Dataset to interact with DatasetFeaturelists. These are Dataset.get_featurelists and Dataset.create_featurelist which list existing featurelists and create new featurelists on a dataset, respectively. - Added model_splits to DatetimePartitioningSpecification and to DatetimePartitioning . This will allow users to control the jobs per model used when building models. A higher number of model_splits will result in less downsampling, allowing the use of more post-processed data. - Added support for :ref: unsupervised projects . - Added support for external test set. Please see :ref: testset documentation - A new workflow is available for assessing models on external test sets in time series unsupervised projects. More information can be found in the :ref: documentation . - Users can create payoff matrices for generating profit curves for binary classification projects using PayoffMatrix.create . - Deployment Improvements: - New arguments send_notification and include_feature_discovery_entities are added to Project.share . - Now it is possible to specify the number of training rows to use in feature impact computation on supported project types (that is everything except unsupervised, multi-class, time-series). This does not affect SHAP based feature impact. Extended methods: - A new class FeatureImpactJob is added to retrieve Feature Impact records with metadata. The regular Job still works as before. - Added support for custom models. Please see :ref: custom model documentation . Classes added: - Batch Prediction jobs now support forecast and historical Time Series predictions using the new argument timeseries_settings for BatchPredictionJob.score . - Batch Prediction jobs now support scoring to Azure and Google Cloud Storage with methods BatchPredictionJob.score_azure and BatchPredictionJob.score_gcp . - Now it’s possible to create Relationships Configurations to introduce secondary datasets to projects. A configuration specifies additional datasets to be included to a project and how these datasets are related to each other, and the primary dataset. When a relationships configuration is specified for a project, Feature Discovery will create features automatically from these datasets. ### Enhancements - Made creating projects from a dataset easier through the new Dataset.create_project . - These methods now provide additional metadata fields in Feature Impact results if called with with_metadata=True . Fields added: rowCount , shapBased , ranRedundancyDetection , count . - Secondary dataset configuration retrieve and deletion is easier now though new SecondaryDatasetConfigurations.delete soft deletes a Secondary dataset configuration. SecondaryDatasetConfigurations.get retrieve a Secondary dataset configuration. - Retrieve relationships configuration which is applied on the given feature discovery project using Project.get_relationships_configuration . ### Bugfixes - An issue with input validation of the Batch Prediction module - parent_model_id was not visible for all frozen models - Batch Prediction jobs that used other output types than local_file failed when using .wait_for_completion() - A race condition in the Batch Prediction file scoring logic ### API changes - Three new fields were added to the Dataset object. This reflects the updated fields in the public API routes at api/v2/datasets/ . The added fields are: ### Deprecation summary - datarobot.enums.VARIABLE_TYPE_TRANSFORM.CATEGORICAL for is deprecated for the following and will be removed in v2.22. ## 2.20.0 ### New features - There is a newDatasetobject that implements some of the public API routes atapi/v2/datasets/. This also adds two new feature classes and a details class. Functionality: - Create a Dataset by uploading from a file, URL or in-memory datasource. -Dataset.create_from_file-Dataset.create_from_in_memory_data-Dataset.create_from_url- Get Datasets or elements of Dataset with: -Dataset.listlists available Datasets -Dataset.getgets a specified Dataset -Dataset.updateupdates the Dataset with the latest server information. -Dataset.get_detailsgets the DatasetDetails of the Dataset. -Dataset.get_all_featuresgets a list of the Dataset’s Features. -Dataset.get_filedownloads the Dataset as a csv file. -Dataset.get_projectsgets a list of Projects that use the Dataset. - Modify, delete or un-delete a Dataset: -Dataset.modifyChanges the name and categories of the Dataset -Dataset.deletesoft deletes a Dataset. -Dataset.un_deleteun-deletes the Dataset. You cannot retrieve the IDs of deleted Datasets, so if you want to un-delete a Dataset, you need to store its ID before deletion. - You can also create a Project using aDatasetwith: -Project.create_from_dataset- It is possible to create an alternative configuration for the secondary dataset which can be used during the prediction -SecondaryDatasetConfigurations.createallow to create secondary dataset configuration - You can now filter the deployments returned by theDeployment.listcommand. You can do this by passing an instance of theDeploymentListFiltersclass to thefilterskeyword argument. The currently supported filters are: -role-service_health-model_health-accuracy_health-execution_environment_type-materiality- A new workflow is available for making predictions in time series projects. To that end,PredictionDatasetobjects now contain the following new fields: -forecast_point_range: The start and end date of the range of dates available for use as the forecast point, detected based on the uploaded prediction dataset -data_start_date: A datestring representing the minimum primary date of the prediction dataset -data_end_date: A datestring representing the maximum primary date of the prediction dataset -max_forecast_date: A datestring representing the maximum forecast date of this prediction dataset Additionally, users no longer need to specify aforecast_pointorpredictions_start_dateandpredictions_end_datewhen uploading datasets for predictions in time series projects. More information can be found in the :ref:time series predictionsdocumentation. - Per-class lift chart data is now available for multiclass models usingModel.get_multiclass_lift_chart. - Unsupervised projects can now be created using theProject.startandProject.set_targetmethods by providingunsupervised_mode=True, provided that the user has access to unsupervised machine learning functionality. Contact support for more information. - A new boolean attributeunsupervised_modewas added todatarobot.DatetimePartitioningSpecification. When it is set to True, datetime partitioning for unsupervised time series projects will be constructed for nowcasting:forecast_window_start=forecast_window_end=0. - Users can now configure the start and end of the training partition as well as the end of the validation partition for backtests in a datetime-partitioned project. More information and example usage can be found in the *ref:backtesting documentation . ### Enhancements - Updated the user agent header to show which python version. - Model.get_frozen_child_models can be used to retrieve models that are frozen from a given model - Added datarobot.enums.TS_BLENDER_METHOD to make it clearer which blender methods are allowed for use in time series projects. ### Bugfixes - An issue where uploaded CSV’s would loose quotes during serialization causing issues when columns containing line terminators where loaded in a dataframe, has been fixed - Project.get_association_featurelists is now using the correct endpoint name, but the old one will continue to work - Python API PredictionServer supports now on-premise format of API response. ## 2.19.0 ### New features - Projects can be cloned using Project.clone_project - Calendars used in time series projects now support having series-specific events, for instance if a holiday only affects some stores. This can be controlled by using new argument of the CalendarFile.create method. If multiseries id columns are not provided, calendar is considered to be single series and all events are applied to all series. - We have expanded prediction intervals availability to the following use-cases: More details on prediction intervals can be found in the :ref:prediction intervals documentation . - Allowed pairwise interaction groups can now be specified inAdvancedOptions. They will be used in GAM models during training. - New deployments features: - Update the label and description of a deployment usingDeployment.update. - *ref:Association ID settingcan be retrieved and updated. - Regression deployments now support :ref:prediction warnings. - For multiclass models now it’s possible to get feature impact for each individual target class usingModel.get_multiclass_feature_impact- Added support for new :ref:Batch Prediction API . - It is now possible to create and retrieve basic, oauth and s3 credentials withCredential. - It’s now possible to get feature association statuses for featurelists usingProject.get_association_featurelists- You can also pass a specific featurelist_id intoProject.get_associations ### Enhancements - Added documentation to Project.get_metrics to detail the new ascending field that indicates how a metric should be sorted. - Retraining of a model is processed asynchronously and returns a ModelJob immediately. - Blender models can be retrained on a different set of data or a different feature list. - Word cloud ngrams now has variable field representing the source of the ngram. - Method WordCloud.ngrams_per_class can be used to split ngrams for better usability in multiclass projects. - Method Project.set_target support new optional parameters featureEngineeringGraphs and credentials . - Method Project.upload_dataset and Project.upload_dataset_from_data_source support new optional parameter credentials . - Series accuracy retrieval methods ( DatetimeModel.get_series_accuracy_as_dataframe and DatetimeModel.download_series_accuracy_as_csv ) for multiseries time series projects now support additional parameters for specifying what data to retrieve, including: ### Bugfixes - An issue when using Feature.get and ModelingFeature.get to retrieve summarized categorical feature has been fixed. ### API changes - The datarobot package is now no longer a namespace package . - datarobot.enums.BLENDER_METHOD.FORECAST_DISTANCE is removed (deprecated in 2.18.0). ### Documentation changes - Updated :ref: Residuals charts documentation to reflect that the data rows include row numbers from the source dataset for projects created in DataRobot 5.3 and newer. ## 2.18.0 ### New features - - - - Deployment.submit_actuals can now be used to submit data about actual results from a deployed model, which can be used to calculate accuracy metrics. ### Enhancements - Monotonic constraints are now supported for OTV projects. To that end, the parameters monotonic_increasing_featurelist_id and monotonic_decreasing_featurelist_id can be specified in calls to Model.train_datetime or Project.train_datetime . - When retrieving information about features , information about summarized categorical variables is now available in a new keySummary . - For Word Clouds in multiclass projects, values of the target class for corresponding word or ngram can now be passed using the new class parameter. - Listing deployments using Deployment.list now support sorting and searching the results using the new order_by and search parameters. - You can now get the model associated with a model job by getting the model variable on the model job object . - The Blueprint class can now retrieve the recommended_featurelist_id , which indicates which feature list is recommended for this blueprint. If the field is not present, then there is no recommended feature list for this blueprint. - The Model class now can be used to retrieve the model_number . - The method Model.get_supported_capabilities now has an extra field supportsCodeGeneration to explain whether the model supports code generation. - Calls to Project.start and Project.upload_dataset now support uploading data via S3 URI and pathlib.Path objects. - Errors upon connecting to DataRobot are now clearer when an incorrect API Token is used. - The datarobot package is now a namespace package . ### Deprecation summary - datarobot.enums.BLENDER_METHOD.FORECAST_DISTANCE is deprecated and will be removed in 2.19. Use FORECAST_DISTANCE_ENET instead. ### Documentation changes - Various typo and wording issues have been addressed. - A new notebook showing regression-specific features is now been added to the examples_index. - Documentation for :ref: Access lists has been added. ## 2.17.0 ### New features - - Users can now list available prediction servers using PredictionServer.list . - When specifying datetime partitioning settings , :ref: time series projects can now mark individual features as excluded from feature derivation using the FeatureSettings.do_not_derive attribute. Any features not specified will be assigned according to the DatetimePartitioningSpecification.default_to_do_not_derive value. - Users can now submit multiple feature type transformations in a single batch request using Project.batch_features_type_transform . - - Information on feature clustering and the association strength between pairs of numeric or categorical features is now available. Project.get_associations can be used to retrieve pairwise feature association statistics and Project.get_association_matrix_details can be used to get a sample of the actual values used to measure association strength. ### Enhancements - number_of_do_not_derive_features has been added to the datarobot.DatetimePartitioning class to specify the number of features that are marked as excluded from derivation. - Users with PyYAML>=5.1 will no longer receive a warning when using the datarobot package - It is now possible to use files with unicode names for creating projects and prediction jobs. - Users can now embed DataRobot-generated content in a ComplianceDocTemplate using keyword tags. :ref: See here for more details. - The field calendar_name has been added to datarobot.DatetimePartitioning to display the name of the calendar used for a project. - - Previously, all backtests had to be run before :ref: prediction intervals for a time series project could be requested with predictions. Now, backtests will be computed automatically if needed when prediction intervals are requested. ### Bugfixes - An issue affecting time series project creation for irregularly spaced dates has been fixed. - ComplianceDocTemplate now supports empty text blocks in user sections. - An issue when using Predictions.get to retrieve predictions metadata has been fixed. ### Documentation changes - An overview on working with class ComplianceDocumentation and ComplianceDocTemplate has been created. :ref: See here for more details. ## 2.16.0 ### New features - Three new methods for Series Accuracy have been added to the DatetimeModel class. - Users can now access :ref: prediction intervals data for each prediction with a DatetimeModel . For each model, prediction intervals estimate the range of values DataRobot expects actual values of the target to fall within. They are similar to a confidence interval of a prediction, but are based on the residual errors measured during the backtesting for the selected model. ### Enhancements - Information on the effective feature derivation window is now available for :ref:time series projects to specify the full span of historical data required at prediction time. It may be longer than the feature derivation window of the project depending on the differencing settings used. Additionally, more of the project partitioning settings are also available on theDatetimeModelclass. The new attributes are: -effective_feature_derivation_window_start-effective_feature_derivation_window_end-forecast_window_start-forecast_window_end-windows_basis_unit- Prediction metadata is now included in the return ofPredictions.get ### Documentation changes - Various typo and wording issues have been addressed. - The example data that was meant to accompany the Time Series examples has been added to the zip file of the download in the examples_index. ## 2.15.1 ### Enhancements - CalendarFile.get_access_list has been added to the CalendarFile class to return a list of users with access to a calendar file. - A role attribute has been added to the CalendarFile class to indicate the access level a current user has to a calendar file. For more information on the specific access levels, see the :ref: sharing documentation. ### Bugfixes - Previously, attempting to retrieve the calendar_id of a project without a set target would result in an error. This has been fixed to return None instead. ## 2.15.0 ### New features - Previously available for only Eureqa models, Advanced Tuning methods and objects, including Model.start_advanced_tuning_session , Model.get_advanced_tuning_parameters , Model.advanced_tune , and AdvancedTuningSession , now support all models other than blender, open source, and user-created models. Use of Advanced Tuning via API for non-Eureqa models is in beta and not available by default, but can be enabled. - Calendar Files for time series projects can now be created and managed through the CalendarFile class. ### Enhancements - The dataframe returned from datarobot.PredictionExplanations.get_all_as_dataframe() will now have each class label class_X be the same from row to row. - The client is now more robust to networking issues by default. It will retry on more errors and respects Retry-After headers in HTTP 413, 429, and 503 responses. - Added Forecast Distance blender for Time-Series projects configured with more than one Forecast Distance. It blends the selected models creating separate linear models for each Forecast Distance. - Project can now be :ref: shared with other users. - Project.upload_dataset and Project.upload_dataset_from_data_source will return a PredictionDataset with data_quality_warnings if potential problems exist around the uploaded dataset. - relax_known_in_advance_features_check has been added to Project.upload_dataset and Project.upload_dataset_from_data_source to allow missing values from the known in advance features in the forecast window at prediction time. - cross_series_group_by_columns has been added to datarobot.DatetimePartitioning to allow users the ability to indicate how to further split series into related groups. - Information retrieval for ROC Curve has been extended to include fraction_predicted_as_positive , fraction_predicted_as_negative , lift_positive and lift_negative ### Bugfixes - Fixes an issue where the client would not be usable if it could not be sure it was compatible with the configured server ### API changes - Methods for creating datarobot.models.Project : create_from_mysql , create_from_oracle , and create_from_postgresql , deprecated in 2.11, have now been removed. Use datarobot.models.Project.create_from_data_source() instead. - datarobot.FeatureSettings attribute apriori , deprecated in 2.11, has been removed. Use datarobot.FeatureSettings.known_in_advance instead. - datarobot.DatetimePartitioning attribute default_to_a_priori , deprecated in 2.11, has been removed. Use datarobot.DatetimePartitioning.known_in_advance instead. - datarobot.DatetimePartitioningSpecification attribute default_to_a_priori , deprecated in 2.11, has been removed. Use datarobot.DatetimePartitioningSpecification.known_in_advance instead. ### Configuration changes - Now requires dependency on package requests to be at least version 2.21. - Now requires dependency on package urllib3 to be at least version 1.24. ### Documentation changes - Advanced model insights notebook extended to contain information on visualization of cumulative gains and lift charts. ## 2.14.2 ### Bugfixes - Fixed an issue where searches of the HTML documentation would sometimes hang indefinitely ### Documentation changes - Python3 is now the primary interpreter used to build the docs (this does not affect the ability to use the package with Python2) ## 2.14.1 ### Documentation changes - Documentation for the Model Deployment interface has been removed after the corresponding interface was removed in 2.13.0. ## 2.14.0 ### New features - The new method Model.get_supported_capabilities retrieves a summary of the capabilities supported by a particular model, such as whether it is eligible for Prime and whether it has word cloud data available. - New class for working with model compliance documentation feature of DataRobot: class ComplianceDocumentation - New class for working with compliance documentation templates: ComplianceDocTemplate - New class FeatureHistogram has been added to retrieve feature histograms for a requested maximum bin count - Time series projects now support binary classification targets. - Cross series features can now be created within time series multiseries projects using the use_cross_series_features and aggregation_type attributes of the datarobot.DatetimePartitioningSpecification . See the :ref: Time Series documentation for more info. ### Enhancements - Client instantiation now checks the endpoint configuration and provides more informative error messages. It also automatically corrects HTTP to HTTPS if the server responds with a redirect to HTTPS. - Project.upload_dataset and Project.create now accept an optional parameter of dataset_filename to specify a file name for the dataset. This is ignored for url and file path sources. - New optional parameter fallback_to_parent_insights has been added to Model.get_lift_chart , Model.get_all_lift_charts , Model.get_confusion_chart , Model.get_all_confusion_charts , Model.get_roc_curve , and Model.get_all_roc_curves . When True , a frozen model with missing insights will attempt to retrieve the missing insight data from its parent model. - New number_of_known_in_advance_features attribute has been added to the datarobot.DatetimePartitioning class. The attribute specifies number of features that are marked as known in advance. - Project.set_worker_count can now update the worker count on a project to the maximum number available to the user. - - Timeseries projects can now accept feature derivation and forecast windows intervals in terms of number of the rows rather than a fixed time unit. DatetimePartitioningSpecification and Project.set_target support new optional parameter windowsBasisUnit , either ‘ROW’ or detected time unit. - Timeseries projects can now accept feature derivation intervals, forecast windows, forecast points and prediction start/end dates in milliseconds. - DataSources and DataStores can now be :ref: shared with other users. - Training predictions for datetime partitioned projects now support the new data subset dr.enums.DATA_SUBSET.ALL_BACKTESTS for requesting the predictions for all backtest validation folds. ### API changes - The model recommendation type “Recommended” (deprecated in version 2.13.0) has been removed. ### Documentation changes - Example notebooks have been updated: - To supplement the embedded Python notebooks in both the PDF and HTML docs bundles, the notebook files and supporting data can now be downloaded from the HTML docs bundle. - Fixed a minor typo in the code sample for get_or_request_feature_impact ## 2.13.0 ### New features - The new method Model.get_or_request_feature_impact functionality will attempt to request feature impact and return the newly created feature impact object or the existing object so two calls are no longer required. - New methods and objects, including Model.start_advanced_tuning_session , Model.get_advanced_tuning_parameters , Model.advanced_tune , and AdvancedTuningSession , were added to support the setting of Advanced Tuning parameters. This is currently supported for Eureqa models only. - New is_starred attribute has been added to the Model class. The attribute specifies whether a model has been marked as starred by user or not. - Model can be marked as starred or being unstarred with Model.star_model and Model.unstar_model . - When listing models with Project.get_models , the model list can now be filtered by the is_starred value. - A custom prediction threshold may now be configured for each model via Model.set_prediction_threshold . When making predictions in binary classification projects, this value will be used when deciding between the positive and negative classes. - Project.check_blendable can be used to confirm if a particular group of models are eligible for blending as some are not, e.g. scaleout models and datetime models with different training lengths. - Individual cross validation scores can be retrieved for new models using Model.get_cross_validation_scores . ### Enhancements - Python 3.7 is now supported. - Feature impact now returns not only the impact score for the features but also whether they were detected to be redundant with other high-impact features. - A new is_blocked attribute has been added to the Job class, specifying whether a job is blocked from execution because one or more dependencies are not yet met. - The Featurelist object now has new attributes reporting its creation time, whether it was created by a user or by DataRobot, and the number of models using the featurelist, as well as a new description field. - Featurelists can now be renamed and have their descriptions updated with Featurelist.update and ModelingFeaturelist.update . - Featurelists can now be deleted with Featurelist.delete and ModelingFeaturelist.delete . - ModelRecommendation.get now accepts an optional parameter of type datarobot.enums.RECOMMENDED_MODEL_TYPE which can be used to get a specific kind of recommendation. - Previously computed predictions can now be listed and retrieved with the Predictions class, without requiring a reference to the original PredictJob . ### Bugfixes - The Model Deployment interface which was previously visible in the client has been removed to allow the interface to mature, although the raw API is available as a “beta” API without full backwards compatibility support. ### API changes - Added support for retrieving the Pareto Front of a Eureqa model. See ParetoFront . - A new recommendation type “Recommended for Deployment” has been added to ModelRecommendation which is now returns as the default recommended model when available. See :ref: model_recommendation . ### Deprecation summary - The feature previously referred to as “Reason Codes” has been renamed to “Prediction Explanations”, to provide increased clarity and accessibility. The old ReasonCodes interface has been deprecated and replaced with PredictionExplanations . - The recommendation type “Recommended” is deprecated and will no longer be returned in v2.14 of the API. ### Documentation changes - Added a new documentation section :ref: model_recommendation . - Time series projects support multiseries as well as single series data. They are now documented in the :ref: Time Series Projects documentation. ## 2.12.0 ### New features - Some models now have Missing Value reports allowing users with access to uncensored blueprints to retrieve a detailed breakdown of how numeric imputation and categorical converter tasks handled missing values. See the :ref: documentation for more information on the report. ## 2.11.0 ### New features - The new ModelRecommendation class can be used to retrieve the recommended models for a project. - A new helper method cross_validate was added to class Model. This method can be used to request Model’s Cross Validation score. - Training a model with monotonic constraints is now supported. Training with monotonic constraints allows users to force models to learn monotonic relationships with respect to some features and the target. This helps users create accurate models that comply with regulations (e.g. insurance, banking). Currently, only certain blueprints (e.g. xgboost) support this feature, and it is only supported for regression and binary classification projects. - DataRobot now supports “Database Connectivity”, allowing databases to be used as the source of data for projects and prediction datasets. The feature works on top of the JDBC standard, so a variety of databases conforming to that standard are available; a list of databases with tested support for DataRobot is available in the user guide in the web application. See :ref: Database Connectivity for details. - Added a new feature to retrieve feature logs for time series projects. Check datarobot.DatetimePartitioning.feature_log_list() and datarobot.DatetimePartitioning.feature_log_retrieve() for details. ### API changes - New attributes supporting monotonic constraints have been added to the AdvancedOptions , Project , Model , and Blueprint classes. See :ref: monotonic constraints for more information on how to configure monotonic constraints. - New parameters predictions_start_date and predictions_end_date added to Project.upload_dataset to support bulk predictions upload for time series projects. ### Deprecation summary - Methods for creating datarobot.models.Project : create_from_mysql , create_from_oracle , and create_from_postgresql , have been deprecated and will be removed in 2.14. Use datarobot.models.Project.create_from_data_source() instead. - datarobot.FeatureSettings attribute apriori , has been deprecated and will be removed in 2.14. Use datarobot.FeatureSettings.known_in_advance instead. - datarobot.DatetimePartitioning attribute default_to_a_priori , has been deprecated and will be removed in 2.14. datarobot.DatetimePartitioning.known_in_advance instead. - datarobot.DatetimePartitioningSpecification attribute default_to_a_priori , has been deprecated and will be removed in 2.14. Use datarobot.DatetimePartitioningSpecification.known_in_advance instead. ### Configuration changes - Retry settings compatible with those offered by urllib3’s Retry interface can now be configured. By default, we will now retry connection errors that prevented requests from arriving at the server. ### Documentation changes - “Advanced Model Insights” example has been updated to properly handle bin weights when rebinning. ## 2.9.0 ### New features - New ModelDeployment class can be used to track status and health of models deployed for predictions. ### Enhancements - DataRobot API now supports creating 3 new blender types - Random Forest, TensorFlow, LightGBM. - Multiclass projects now support blenders creation for 3 new blender types as well as Average and ENET blenders. - Models can be trained by requesting a particular row count using the new training_row_count argument with Project.train , Model.train and Model.request_frozen_model in non-datetime partitioned projects, as an alternative to the previous option of specifying a desired percentage of the project dataset. Specifying model size by row count is recommended when the float precision of sample_pct could be problematic, e.g. when training on a small percentage of the dataset or when training up to partition boundaries. - New attributes max_train_rows , scaleout_max_train_pct , and scaleout_max_train_rows have been added to Project . max_train_rows specified the equivalent value to the existing max_train_pct as a row count. The scaleout fields can be used to see how far scaleout models can be trained on projects, which for projects taking advantage of scalable ingest may exceed the limits on the data available to non-scaleout blueprints. - Individual features can now be marked as a priori or not a priori using the new feature_settings attribute when setting the target or specifying datetime partitioning settings on time series projects. Any features not specified in the feature_settings parameter will be assigned according to the default_to_a_priori value. - Three new options have been made available in the datarobot.DatetimePartitioningSpecification class to fine-tune how time-series projects derive modeling features. treat_as_exponential can control whether data is analyzed as an exponential trend and transformations like log-transform are applied. differencing_method can control which differencing method to use for stationary data. periodicities can be used to specify periodicities occurring within the data. All are optional and defaults will be chosen automatically if they are unspecified. ### API changes - Now training_row_count is available on non-datetime models as well as rowCount based datetime models. It reports the number of rows used to train the model (equivalent to sample_pct ). - Features retrieved from Feature.get now include target_leakage . ## 2.8.1 ### Bugfixes - The documented default connect_timeout will now be correctly set for all configuration mechanisms, so that requests that fail to reach the DataRobot server in a reasonable amount of time will now error instead of hanging indefinitely. If you observe that you have started seeing ConnectTimeout errors, please configure your connect_timeout to a larger value. - Version of trafaret library this package depends on is now pinned to trafaret>=0.7,<1.1 since versions outside that range are known to be incompatible. ## 2.8.0 ### New features - The DataRobot API supports the creation, training, and predicting of multiclass classification projects. DataRobot, by default, handles a dataset with a numeric target column as regression. If your data has a numeric cardinality of fewer than 11 classes, you can override this behavior to instead create a multiclass classification project from the data. To do so, use the set_target function, setting target_type=‘Multiclass’. If DataRobot recognizes your data as categorical, and it has fewer than 11 classes, using multiclass will create a project that classifies which label the data belongs to. - The DataRobot API now includes Rating Tables. A rating table is an exportable csv representation of a model. Users can influence predictions by modifying them and creating a new model with the modified table. See the :ref: documentation for more information on how to use rating tables. - scaleout_modeling_mode has been added to the AdvancedOptions class used when setting a project target. It can be used to control whether scaleout models appear in the autopilot and/or available blueprints. Scaleout models are only supported in the Hadoop environment with the corresponding user permission set. - A new premium add-on product, Time Series, is now available. New projects can be created as time series projects which automatically derive features from past data and forecast the future. See the - The Feature object now returns the EDA summary statistics (i.e., mean, median, minimum, maximum, and standard deviation) for features where this is available (e.g., numeric, date, time, currency, and length features). These summary statistics will be formatted in the same format as the data it summarizes. - The DataRobot API now supports Training Predictions workflow. Training predictions are made by a model for a subset of data from original dataset. User can start a job which will make those predictions and retrieve them. See the :ref: documentation for more information on how to use training predictions. - DataRobot now supports retrieving a :ref: model blueprint chart and a - With the introduction of Multiclass Classification projects, DataRobot needed a better way to explain the performance of a multiclass model so we created a new Confusion Chart. The API now supports retrieving and interacting with confusion charts. ### Enhancements - DatetimePartitioningSpecification now includes the optional disable_holdout flag that can be used to disable the holdout fold when creating a project with datetime partitioning. - When retrieving reason codes on a project using an exposure column, predictions that are adjusted for exposure can be retrieved. - File URIs can now be used as source data when creating a project or uploading a prediction dataset. The file URI must refer to an allowed location on the server, which is configured as described in the user guide documentation. - The advanced options available when setting the target have been extended to include the new parameter ‘events_count’ as a part of the AdvancedOptions object to allow specifying the events count column. See the user guide documentation in the web app for more information on events count. - PredictJob.get_predictions now returns predicted probability for each class in the dataframe. - PredictJob.get_predictions now accepts prefix parameter to prefix the classes name returned in the predictions dataframe. ### API changes - Add target_type parameter to set_target() and start(), used to override the project default. ## 2.7.2 ### Documentation changes - Updated link to the publicly hosted documentation. ## 2.7.1 ### Documentation changes - Online documentation hosting has migrated from PythonHosted to Read The Docs. Minor code changes have been made to support this. ## 2.7.0 ### New features - Lift chart data for models can be retrieved using the Model.get_lift_chart and Model.get_all_lift_charts methods. - ROC curve data for models in classification projects can be retrieved using the Model.get_roc_curve and Model.get_all_roc_curves methods. - Semi-automatic autopilot mode is removed. - Word cloud data for text processing models can be retrieved using Model.get_word_cloud method. - Scoring code JAR file can be downloaded for models supporting code generation. ### Enhancements - A __repr__ method has been added to the PredictionDataset class to improve readability when using the client interactively. - Model.get_parameters now includes an additional key in the derived features it includes, showing the coefficients for individual stages of multistage models (e.g. Frequency-Severity models). - When training a DatetimeModel on a window of data, a time_window_sample_pct can be specified to take a uniform random sample of the training data instead of using all data within the window. - Installing of DataRobot package now has an “Extra Requirements” section that will install all of the dependencies needed to run the example notebooks. ### Documentation changes - A new example notebook describing how to visualize some of the newly available model insights including lift charts, ROC curves, and word clouds has been added to the examples section. - A new section for Common Issues has been added to Getting Started to help debug issues related to client installation and usage. ## 2.6.1 ### Bugfixes - Fixed a bug with Model.get_parameters raising an exception on some valid parameter values. ### Documentation changes - Fixed sorting order in Feature Impact example code snippet. ## 2.6.0 ### New features - A new partitioning method (datetime partitioning) has been added. The recommended workflow is to preview the partitioning by creating a DatetimePartitioningSpecification and passing it into DatetimePartitioning.generate , inspect the results and adjust as needed for the specific project dataset by adjusting the DatetimePartitioningSpecification and re-generating, and then set the target by passing the final DatetimePartitioningSpecification object to the partitioning_method parameter of Project.set_target . - When interacting with datetime partitioned projects, DatetimeModel can be used to access more information specific to models in datetime partitioned projects. See - The advanced options available when setting the target have been extended to include the new parameters ‘offset’ and ‘exposure’ (part of the AdvancedOptions object) to allow specifying offset and exposure columns to apply to predictions generated by models within the project. See the user guide documentation in the web app for more information on offset and exposure columns. - Blueprints can now be retrieved directly by project_id and blueprint_id via Blueprint.get . - Blueprint charts can now be retrieved directly by project_id and blueprint_id via BlueprintChart.get . If you already have an instance of Blueprint you can retrieve its chart using Blueprint.get_chart . - Model parameters can now be retrieved using ModelParameters.get . If you already have an instance of Model you can retrieve its parameters using Model.get_parameters . - Blueprint documentation can now be retrieved using Blueprint.get_documents . It will contain information about the task, its parameters and (when available) links and references to additional sources. - The DataRobot API now includes Reason Codes. You can now compute reason codes for prediction datasets. You are able to specify thresholds on which rows to compute reason codes for to speed up computation by skipping rows based on the predictions they generate. See the reason codes ### Enhancements - A new parameter has been added to the AdvancedOptions used with Project.set_target . By specifying accuracyOptimizedMb=True when creating AdvancedOptions , longer-running models that may have a high accuracy will be included in the autopilot and made available to run manually. - A new option for Project.create_type_transform_feature has been added which explicitly truncates data when casting numerical data as categorical data. - Added 2 new blenders for projects that use MAD or Weighted MAD as a metric. The MAE blender uses BFGS optimization to find linear weights for the blender that minimize mean absolute error (compared to the GLM blender, which finds linear weights that minimize RMSE), and the MAEL1 blender uses BFGS optimization to find linear weights that minimize MAE + a L1 penalty on the coefficients (compared to the ENET blender, which minimizes RMSE + a combination of the L1 and L2 penalty on the coefficients). ### Bugfixes - Fixed a bug (affecting Python 2 only) with printing any model (including frozen and prime models) whose model_type is not ascii. - FrozenModels were unable to correctly use methods inherited from Model. This has been fixed. - When calling get_result for a Job, ModelJob, or PredictJob that has errored, AsyncProcessUnsuccessfulError will now be raised instead of JobNotFinished , consistently with the behavior of get_result_when_complete . ### Deprecation summary - Support for the experimental Recommender Problems projects has been removed. Any code relying on RecommenderSettings or the recommender_settings argument of Project.set_target and Project.start will error. - Project.update , deprecated in v2.2.32, has been removed in favor of specific updates: rename , unlock_holdout , set_worker_count . ### Documentation changes - The link to Configuration from the Quickstart page has been fixed. ## 2.5.1 ### Bugfixes - Fixed a bug (affecting Python 2 only) with printing blueprints whose names are not ascii. - Fixed an issue where the weights column (for weighted projects) did not appear in the advanced_options of a Project . ## 2.5.0 ### New features - Methods to work with blender models have been added. Use Project.blend method to create new blenders, Project.get_blenders to get the list of existing blenders and BlenderModel.get to retrieve a model with blender-specific information. - Projects created via the API can now use smart downsampling when setting the target by passing smart_downsampled and majority_downsampling_rate into the AdvancedOptions object used with Project.set_target . The smart sampling options used with an existing project will be available as part of Project.advanced_options . - Support for frozen models, which use tuning parameters from a parent model for more efficient training, has been added. Use Model.request_frozen_model to create a new frozen model, Project.get_frozen_models to get the list of existing frozen models and FrozenModel.get to retrieve a particular frozen model. ### Enhancements - The inferred date format (e.g. “%Y-%m-%d %H:%M:%S”) is now included in the Feature object. For non-date features, it will be None. - When specifying the API endpoint in the configuration, the client will now behave correctly for endpoints with and without trailing slashes. ## 2.4.0 ### New features - The premium add-on product DataRobot Prime has been added. You can now approximate a model on the leaderboard and download executable code for it. See documentation for further details, or talk to your account representative if the feature is not available on your account. - (Only relevant for on-premise users with a Standalone Scoring cluster.) Methods ( request_transferable_export and download_export ) have been added to the Model class for exporting models (which will only work if model export is turned on). There is a new class ImportedModel for managing imported models on a Standalone Scoring cluster. - It is now possible to create projects from a WebHDFS, PostgreSQL, Oracle or MySQL data source. For more information see the documentation for the relevant Project classmethods: create_from_hdfs , create_from_postgresql , create_from_oracle and create_from_mysql . - Job.wait_for_completion , which waits for a job to complete without returning anything, has been added. ### Enhancements - The client will now check the API version offered by the server specified in configuration, and give a warning if the client version is newer than the server version. The DataRobot server is always backwards compatible with old clients, but new clients may have functionality that is not implemented on older server versions. This issue mainly affects users with on-premise deployments of DataRobot. ### Bugfixes - Fixed an issue where Model.request_predictions might raise an error when predictions finished very quickly instead of returning the job. ### API changes - To set the target with quickrun autopilot, call Project.set_target with mode=AUTOPILOT_MODE.QUICK instead of specifying quickrun=True . ### Deprecation summary - Semi-automatic mode for autopilot has been deprecated and will be removed in 3.0. Use manual or fully automatic instead. - Use of the quickrun argument in Project.set_target has been deprecated and will be removed in 3.0. Use mode=AUTOPILOT_MODE.QUICK instead. ### Configuration changes - It is now possible to control the SSL certificate verification by setting the parameter ssl_verify in the config file. ### Documentation changes - The “Modeling Airline Delay” example notebook has been updated to work with the new 2.3 enhancements. - Documentation for the generic Job class has been added. - Class attributes are now documented in the API Reference section of the documentation. - The changelog now appears in the documentation. - There is a new section dedicated to configuration, which lists all of the configuration options and their meanings. ## 2.3.0 ### New features - The DataRobot API now includes Feature Impact, an approach to measuring the relevance of each feature that can be applied to any model. The Model class now includes methods request_feature_impact (which creates and returns a feature impact job) and get_feature_impact (which can retrieve completed feature impact results). - A new improved workflow for predictions now supports first uploading a dataset via Project.upload_dataset , then requesting predictions via Model.request_predictions . This allows us to better support predictions on larger datasets and non-ascii files. - Datasets previously uploaded for predictions (represented by the PredictionDataset class) can be listed from Project.get_datasets and retrieve and deleted via PredictionDataset.get and PredictionDataset.delete . - You can now create a new feature by re-interpreting the type of an existing feature in a project by using the Project.create_type_transform_feature method. - The Job class now includes a get method for retrieving a job and a cancel method for canceling a job. - All of the jobs classes ( Job , ModelJob , PredictJob ) now include the following new methods: refresh (for refreshing the data in the job object), get_result (for getting the completed resource resulting from the job), and get_result_when_complete (which waits until the job is complete and returns the results, or times out). - A new method Project.refresh can be used to update Project objects with the latest state from the server. - A new function datarobot.async.wait_for_async_resolution can be used to poll for the resolution of any generic asynchronous operation on the server. ### Enhancements - The JOB_TYPE enum now includes FEATURE_IMPACT . - The QUEUE_STATUS enum now includes ABORTED and COMPLETED . - The Project.create method now has a read_timeout parameter which can be used to keep open the connection to DataRobot while an uploaded file is being processed. For very large files this time can be substantial. Appropriately raising this value can help avoid timeouts when uploading large files. - The method Project.wait_for_autopilot has been enhanced to error if the project enters a state where autopilot may not finish. This avoids a situation that existed previously where users could wait indefinitely on their project that was not going to finish. However, users are still responsible to make sure a project has more than zero workers, and that the queue is not paused. - Feature.get now supports retrieving features by feature name. (For backwards compatibility, feature IDs are still supported until 3.0.) - File paths that have unicode directory names can now be used for creating projects and PredictJobs. The filename itself must still be ascii, but containing directory names can have other encodings. - Now raises more specific JobAlreadyRequested exception when we refuse a model fitting request as a duplicate. Users can explicitly catch this exception if they want it to be ignored. - A file_name attribute has been added to the Project class, identifying the file name associated with the original project dataset. Note that if the project was created from a data frame, the file name may not be helpful. - The connect timeout for establishing a connection to the server can now be set directly. This can be done in the yaml configuration of the client, or directly in the code. The default timeout has been lowered from 60 seconds to 6 seconds, which will make detecting a bad connection happen much quicker. ### Bugfixes - Fixed a bug (affecting Python 2 only) with printing features and featurelists whose names are not ascii. ### API changes - Job class hierarchy is rearranged to better express the relationship between these objects. See documentation for datarobot.models.job for details. - Featurelist objects now have a project_id attribute to indicate which project they belong to. Directly accessing the project attribute of a Featurelist object is now deprecated - Support INI-style configuration, which was deprecated in v2.1, has been removed. yaml is the only supported configuration format. - The method Project.get_jobs method, which was deprecated in v2.1, has been removed. Users should use the Project.get_model_jobs method instead to get the list of model jobs. ### Deprecation summary - PredictJob.create has been deprecated in favor of the alternate workflow using Model.request_predictions . - Feature.converter (used internally for object construction) has been made private. - Model.fetch_resource_data has been deprecated and will be removed in 3.0. To fetch a model from its ID, use Model.get. - The ability to use Feature.get with feature IDs (rather than names) is deprecated and will be removed in 3.0. - Instantiating a Project , Model , Blueprint , Featurelist , or Feature instance from a dict of data is now deprecated. Please use the from_data classmethod of these classes instead. Additionally, instantiating a Model from a tuple or by using the keyword argument data is also deprecated. - Use of the attribute Featurelist.project is now deprecated. You can use the project_id attribute of a Featurelist to instantiate a Project instance using Project.get . - Use of the attributes Model.project , Model.blueprint , and Model.featurelist are all deprecated now to avoid use of partially instantiated objects. Please use the ids of these objects instead. - Using a Project instance as an argument in Featurelist.get is now deprecated. Please use a project_id instead. Similarly, using a Project instance in Model.get is also deprecated, and a project_id should be used in its place. ### Configuration changes - Previously it was possible (though unintended) that the client configuration could be mixed through environment variables, configuration files, and arguments to datarobot.Client . This logic is now simpler - please see the Getting Started section of the documentation for more information. ## 2.2.33 ### Bugfixes - Fixed a bug with non-ascii project names using the package with Python 2. - Fixed an error that occurred when printing projects that had been constructed from an ID only or printing printing models that had been constructed from a tuple (which impacted printing PredictJobs). - Fixed a bug with project creation from non-ascii file names. Project creation from non-ascii file names is not supported, so this now raises a more informative exception. The project name is no longer used as the file name in cases where we do not have a file name, which prevents non-ascii project names from causing problems in those circumstances. - Fixed a bug (affecting Python 2 only) with printing projects, features, and featurelists whose names are not ascii. ## 2.2.32 ### New features - Project.get_features and Feature.get methods have been added for feature retrieval. - A generic Job entity has been added for use in retrieving the entire queue at once. Calling Project.get_all_jobs will retrieve all (appropriately filtered) jobs from the queue. Those can be cancelled directly as generic jobs, or transformed into instances of the specific job class using ModelJob.from_job and PredictJob.from_job , which allow all functionality previously available via the ModelJob and PredictJob interfaces. - Model.train now supports featurelist_id and scoring_type parameters, similar to Project.train . ### Enhancements - Deprecation warning filters have been updated. By default, a filter will be added ensuring that usage of deprecated features will display a warning once per new usage location. In order to hide deprecation warnings, a filter like warnings.filterwarnings('ignore', category=DataRobotDeprecationWarning) can be added to a script so no such warnings are shown. Watching for deprecation warnings to avoid reliance on deprecated features is recommended. - If your client is misconfigured and does not specify an endpoint, the cloud production server is no longer used as the default as in many cases this is not the correct default. - This changelog is now included in the distributable of the client. ### Bugfixes - Fixed an issue where updating the global client would not affect existing objects with cached clients. Now the global client is used for every API call. - An issue where mistyping a filepath for use in a file upload has been resolved. Now an error will be raised if it looks like the raw string content for modeling or predictions is just one single line. ### API changes - Use of username and password to authenticate is no longer supported - use an API token instead. - Usage of start_time and finish_time parameters in Project.get_models is not supported both in filtering and ordering of models - Default value of sample_pct parameter of Model.train method is now None instead of 100 . If the default value is used, models will be trained with all of the available training data based on project configuration, rather than with entire dataset including holdout for the previous default value of 100 . - order_by parameter of Project.list which was deprecated in v2.0 has been removed. - recommendation_settings parameter of Project.start which was deprecated in v0.2 has been removed. - Project.status method which was deprecated in v0.2 has been removed. - Project.wait_for_aim_stage method which was deprecated in v0.2 has been removed. - Delay , ConstantDelay , NoDelay , ExponentialBackoffDelay , RetryManager classes from retry module which were deprecated in v2.1 were removed. - Package renamed to datarobot . ### Deprecation summary - Project.update deprecated in favor of specific updates: rename , unlock_holdout , set_worker_count . ### Documentation changes - A new use case involving financial data has been added to the examples directory. - Added documentation for the partition methods. ## 2.1.31 ### Bugfixes - In Python 2, using a unicode token to instantiate the client will now work correctly. ## 2.1.30 ### Bugfixes - The minimum required version of trafaret has been upgraded to 0.7.1 to get around an incompatibility between it and setuptools . ## 2.1.29 ### Enhancements - Minimal used version of requests_toolbelt package changed from 0.4 to 0.6 ## 2.1.28 ### New features - Default to reading YAML config file from ~/.config/datarobot/drconfig.yaml - Allow config_path argument to client - wait_for_autopilot method added to Project. This method can be used to block execution until autopilot has finished running on the project. - Support for specifying which featurelist to use with initial autopilot in Project.set_target - Project.get_predict_jobs method has been added, which looks up all prediction jobs for a project - Project.start_autopilot method has been added, which starts autopilot on specified featurelist - The schema for PredictJob in DataRobot API v2.1 now includes a message . This attribute has been added to the PredictJob class. - PredictJob.cancel now exists to cancel prediction jobs, mirroring ModelJob.cancel - Project.from_async is a new classmethod that can be used to wait for an async resolution in project creation. Most users will not need to know about it as it is used behind the scenes in Project.create and Project.set_target , but power users who may run into periodic connection errors will be able to catch the new ProjectAsyncFailureError and decide if they would like to resume waiting for async process to resolve ### Enhancements - AUTOPILOT_MODE enum now uses string names for autopilot modes instead of numbers ### Deprecation summary - ConstantDelay , NoDelay , ExponentialBackoffDelay , and RetryManager utils are now deprecated - INI-style config files are now deprecated (in favor of YAML config files) - Several functions in the utils submodule are now deprecated (they are being moved elsewhere and are not considered part of the public interface) - Project.get_jobs has been renamed Project.get_model_jobs for clarity and deprecated - Support for the experimental date partitioning has been removed in DataRobot API, so it is being removed from the client immediately. ### API changes - In several places where AppPlatformError was being raised, now TypeError , ValueError or InputNotUnderstoodError are now used. With this change, one can now safely assume that when catching an AppPlatformError it is because of an unexpected response from the server. - AppPlatformError has gained a two new attributes, status_code which is the HTTP status code of the unexpected response from the server, and error_code which is a DataRobot-defined error code. error_code is not used by any routes in DataRobot API 2.1, but will be in the future. In cases where it is not provided, the instance of AppPlatformError will have the attribute error_code set to None . - Two new subclasses of AppPlatformError have been introduced, ClientError (for 400-level response status codes) and ServerError (for 500-level response status codes). These will make it easier to build automated tooling that can recover from periodic connection issues while polling. - If a ClientError or ServerError occurs during a call to Project.from_async , then a ProjectAsyncFailureError (a subclass of AsyncFailureError) will be raised. That exception will have the status_code of the unexpected response from the server, and the location that was being polled to wait for the asynchronous process to resolve. ## 2.0.27 ### New features - PredictJob class was added to work with prediction jobs - wait_for_async_predictions function added to predict_job module ### Deprecation summary - The order_by parameter of the Project.list is now deprecated. ## 0.2.26 ### Enhancements - Project.set_target will re-fetch the project data after it succeeds, keeping the client side in sync with the state of the project on the server - Project.create_featurelist now throws DuplicateFeaturesError exception if passed list of features contains duplicates - Project.get_models now supports snake_case arguments to its order_by keyword ### Deprecation summary - Project.wait_for_aim_stage is now deprecated, as the REST Async flow is a more reliable method of determining that project creation has completed successfully - Project.status is deprecated in favor of Project.get_status - recommendation_settings parameter of Project.start is deprecated in favor of recommender_settings ### Bugfixes - Project.wait_for_aim_stage changed to support Python 3 - Fixed incorrect value of SCORING_TYPE.cross_validation - Models returned by Project.get_models will now be correctly ordered when the order_by keyword is used ## 0.2.25 - Pinned versions of required libraries ## 0.2.24 Official release of v0.2 ## 0.1.24 - Updated documentation - Renamed parameter name of Project.create and Project.start to project_name - Removed Model.predict method - wait_for_async_model_creation function added to modeljob module - wait_for_async_status_service of Project class renamed to _wait_for_async_status_service - Can now use auth_token in config file to configure API Client ## 0.1.23 - Fixes a method that pointed to a removed route ## 0.1.22 - Added featurelist_id attribute to ModelJob class ## 0.1.21 - Removes model attribute from ModelJob class ## 0.1.20 - Project creation raises AsyncProjectCreationError if it was unsuccessful - Removed Model.list_prime_rulesets and Model.get_prime_ruleset methods - Removed Model.predict_batch method - Removed Project.create_prime_model method - Removed PrimeRuleSet model - Adds backwards compatibility bridge for ModelJob async - Adds ModelJob.get and ModelJob.get_model ## 0.1.19 - Minor bugfixes in wait_for_async_status_service ## 0.1.18 - Removes submit_model from Project until server-side implementation is improved - Switches training URLs for new resource-based route at /projects/ /models/ - Job renamed to ModelJob, and using modelJobs route - Fixes an inconsistency in argument order for train methods ## 0.1.17 - wait_for_async_status_service timeout increased from 60s to 600s ## 0.1.16 - Project.create will now handle both async/sync project creation ## 0.1.15 - All routes pluralized to sync with changes in API - Project.get_jobs will request all jobs when no param specified - dataframes from predict method will have pythonic names - Project.get_status created, Project.status now deprecated - Project.unlock_holdout created. - Added quickrun parameter to Project.set_target - Added modelCategory to Model schema - Add permalinks feature to Project and Model objects. - Project.create_prime_model created ## 0.1.14 - Project.set_worker_count fix for compatibility with API change in project update. ## 0.1.13 - Add positive class to set_target . - Change attributes names of Project , Model , Job and Blueprint - Model has now blueprint , project , and featurelist attributes. - Minor bugfixes. ## 0.1.12 - Minor fixes regarding rename Job attributes. features attributes now named processes , samplepct now is sample_pct . ## 0.1.11 (May 27, 2015) - Minor fixes regarding migrating API from under_score names to camelCase. ## 0.1.10 (May 20, 2015) - Remove Project.upload_file , Project.upload_file_from_url and Project.attach_file methods. Moved all logic that uploading file to Project.create method. ## 0.1.9 (May 15, 2015) - Fix uploading file causing a lot of memory usage. Minor bugfixes. --- # Package architecture URL: https://docs.datarobot.com/en/docs/api/reference/sdk/api-object.html # APIObject ### class datarobot.models.api_object.APIObject #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( T , bound= APIObject) --- # Application templates URL: https://docs.datarobot.com/en/docs/api/reference/sdk/application-templates.html # Custom templates ### class datarobot.models.custom_templates.CustomTemplate Template for custom activity (e.g., custom-metrics, applications). #### classmethod create(name, description, template_type, template_sub_type, template_metadata, default_environment, file, default_resource_bundle_id=None, enabled=None, is_hidden=None) Create the custom template. Added in version v3.9. - Parameters: - Return type: CustomTemplate > [!NOTE] Examples > ``` > from datarobot import CustomTemplate > from datarobot.models.custom_templates import DefaultEnvironment > def_env = DefaultEnvironment( > environment_id='679d47c8ce1ecd17326f3fdf', > environment_version_id='679d47c8ce1ecd17326f3fe3', > ) > template = template.create( > name="My new template", > default_environment=def_env, > description='Updated template with environment v17', > ) > ``` #### classmethod list(search=None, order_by=None, tag=None, template_type=None, template_sub_type=None, publisher=None, category=None, show_hidden=None, offset=None, limit=None) List all custom templates. Added in version v3.7. - Parameters: - Returns: templates - Return type: List[CustomTemplate] #### classmethod get(template_id) Get a custom template by ID. Added in version v3.7. - Parameters: template_id ( str ) – ID of the template. - Returns: template - Return type: CustomTemplate #### update(name=None, description=None, default_resource_bundle_id=None, template_type=None, template_sub_type=None, template_metadata=None, default_environment=None, file=None, enabled=None, is_hidden=None) Update the custom template. Added in version v3.7. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot import CustomTemplate > from datarobot.models.custom_templates import DefaultEnvironment > new_env = DefaultEnvironment( > environment_id='679d47c8ce1ecd17326f3fdf', > environment_version_id='679d47c8ce1ecd17326f3fe3', > ) > template = CustomTemplate.get(template_id='5c939e08962d741e34f609f0') > template.update(default_environment=new_env, description='Updated template with environment v17') > ``` #### delete() Delete this custom template. Added in version v3.7. - Return type: None #### download_content(index=None, filename=None) Retrieve the file content for the given item. The item can be identified by filename or index in the array of items. Added in version v3.9. - Parameters: - Return type: Bytes content of the file. #### upload_preview(filename) Upload the custom template preview image file. Added in version v3.10. - Parameters: filename ( str ) – The preview image filename. - Return type: None ### class datarobot.models.custom_templates.DefaultEnvironment Default execution environment. ### class datarobot.models.custom_templates.CustomMetricMetadata Metadata for custom metrics. ### class datarobot.models.custom_templates.TemplateMetadata Metadata for the custom templates. --- # Applications URL: https://docs.datarobot.com/en/docs/api/reference/sdk/applications.html # Applications ### class datarobot.Application An entity associated with a DataRobot Application. - Variables: #### classmethod list(offset=None, limit=None, use_cases=None) Retrieve a list of user applications. - Parameters: - Returns: applications – The requested list of user applications. - Return type: List[Application] #### classmethod get(application_id) Retrieve a single application. - Parameters: application_id ( str ) – The ID of the application to retrieve. - Returns: application – The requested application. - Return type: Application --- # Batch monitoring URL: https://docs.datarobot.com/en/docs/api/reference/sdk/batch-monitoring.html # Batch monitoring ### class datarobot.models.BatchMonitoringJob A Batch Monitoring Job is used to monitor data sets outside DataRobot app. - Variables: id ( str ) – the ID of the job #### classmethod get(project_id, job_id) Get batch monitoring job - Variables: job_id ( str ) – ID of batch job - Returns: Instance of BatchMonitoringJob - Return type: BatchMonitoringJob #### download(fileobj, timeout=120, read_timeout=660) Downloads the results of a monitoring job as a CSV. - Variables: #### classmethod run(deployment, intake_settings=None, output_settings=None, csv_settings=None, num_concurrent=None, chunk_size=None, abort_on_error=True, monitoring_aggregation=None, monitoring_columns=None, monitoring_output_settings=None, download_timeout=120, download_read_timeout=660, upload_read_timeout=600) Create new batch monitoring job, upload the dataset, and return a batch monitoring job. - Variables: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> job_spec = { > ... "intake_settings": { > ... "type": "jdbc", > ... "data_store_id": "645043933d4fbc3215f17e34", > ... "catalog": "SANDBOX", > ... "table": "10kDiabetes_output_actuals", > ... "schema": "SCORING_CODE_UDF_SCHEMA", > ... "credential_id": "645043b61a158045f66fb329" > ... }, > >>> "monitoring_columns": { > ... "predictions_columns": [ > ... { > ... "class_name": "True", > ... "column_name": "readmitted_True_PREDICTION" > ... }, > ... { > ... "class_name": "False", > ... "column_name": "readmitted_False_PREDICTION" > ... } > ... ], > ... "association_id_column": "rowID", > ... "actuals_value_column": "ACTUALS" > ... } > ... } > >>> deployment_id = "foobar" > >>> job = dr.BatchMonitoringJob.run(deployment_id, **job_spec) > >>> job.wait_for_completion() > ``` #### cancel(ignore_404_errors=False) Cancel this job. If this job has not finished running, it will be removed and canceled. - Return type: None #### get_status() Get status of batch monitoring job - Returns: Dict with job status - Return type: BatchMonitoringJob status data ### class datarobot.models.BatchMonitoringJobDefinition #### classmethod get(batch_monitoring_job_definition_id) Get batch monitoring job definition - Variables: batch_monitoring_job_definition_id ( str ) – ID of batch monitoring job definition - Returns: Instance of BatchMonitoringJobDefinition - Return type: BatchMonitoringJobDefinition > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> definition = dr.BatchMonitoringJobDefinition.get('5a8ac9ab07a57a0001be501f') > >>> definition > BatchMonitoringJobDefinition(60912e09fd1f04e832a575c1) > ``` #### classmethod list() Get job all monitoring job definitions - Returns: List of job definitions the user has access to see - Return type: List[BatchMonitoringJobDefinition] > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> definition = dr.BatchMonitoringJobDefinition.list() > >>> definition > [ > BatchMonitoringJobDefinition(60912e09fd1f04e832a575c1), > BatchMonitoringJobDefinition(6086ba053f3ef731e81af3ca) > ] > ``` #### classmethod create(enabled, batch_monitoring_job, name=None, schedule=None) Creates a new batch monitoring job definition to be run either at scheduled interval or as a manual run. - Variables: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> job_spec = { > ... "num_concurrent": 4, > ... "deployment_id": "foobar", > ... "intake_settings": { > ... "url": "s3://foobar/123", > ... "type": "s3", > ... "format": "csv" > ... }, > ... "output_settings": { > ... "url": "s3://foobar/123", > ... "type": "s3", > ... "format": "csv" > ... }, > ...} > >>> schedule = { > ... "day_of_week": [ > ... 1 > ... ], > ... "month": [ > ... "*" > ... ], > ... "hour": [ > ... 16 > ... ], > ... "minute": [ > ... 0 > ... ], > ... "day_of_month": [ > ... 1 > ... ] > ...} > >>> definition = BatchMonitoringJobDefinition.create( > ... enabled=False, > ... batch_monitoring_job=job_spec, > ... name="some_definition_name", > ... schedule=schedule > ... ) > >>> definition > BatchMonitoringJobDefinition(60912e09fd1f04e832a575c1) > ``` #### update(enabled, batch_monitoring_job=None, name=None, schedule=None) Updates a job definition with the changed specs. Takes the same input as [create()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-monitoring.html#datarobot.models.BatchMonitoringJobDefinition.create) - Variables: - Returns: Instance of the updated BatchMonitoringJobDefinition - Return type: BatchMonitoringJobDefinition > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> job_spec = { > ... "num_concurrent": 5, > ... "deployment_id": "foobar_new", > ... "intake_settings": { > ... "url": "s3://foobar/123", > ... "type": "s3", > ... "format": "csv" > ... }, > ... "output_settings": { > ... "url": "s3://foobar/123", > ... "type": "s3", > ... "format": "csv" > ... }, > ...} > >>> schedule = { > ... "day_of_week": [ > ... 1 > ... ], > ... "month": [ > ... "*" > ... ], > ... "hour": [ > ... "*" > ... ], > ... "minute": [ > ... 30, 59 > ... ], > ... "day_of_month": [ > ... 1, 2, 6 > ... ] > ...} > >>> definition = BatchMonitoringJobDefinition.create( > ... enabled=False, > ... batch_monitoring_job=job_spec, > ... name="updated_definition_name", > ... schedule=schedule > ... ) > >>> definition > BatchMonitoringJobDefinition(60912e09fd1f04e832a575c1) > ``` #### run_on_schedule(schedule) Sets the run schedule of an already created job definition. If the job was previously not enabled, this will also set the job to enabled. - Variables: schedule ( dict ) – Same as schedule in create() . - Returns: Instance of the updated BatchMonitoringJobDefinition with the new / updated schedule. - Return type: BatchMonitoringJobDefinition > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> definition = dr.BatchMonitoringJobDefinition.create('...') > >>> schedule = { > ... "day_of_week": [ > ... 1 > ... ], > ... "month": [ > ... "*" > ... ], > ... "hour": [ > ... "*" > ... ], > ... "minute": [ > ... 30, 59 > ... ], > ... "day_of_month": [ > ... 1, 2, 6 > ... ] > ...} > >>> definition.run_on_schedule(schedule) > BatchMonitoringJobDefinition(60912e09fd1f04e832a575c1) > ``` #### run_once() Manually submits a batch monitoring job to the queue, based off of an already created job definition. - Returns: Instance of BatchMonitoringJob - Return type: BatchMonitoringJob > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> definition = dr.BatchMonitoringJobDefinition.create('...') > >>> job = definition.run_once() > >>> job.wait_for_completion() > ``` #### delete() Deletes the job definition and disables any future schedules of this job if any. If a scheduled job is currently running, this will not be cancelled. > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> definition = dr.BatchMonitoringJobDefinition.get('5a8ac9ab07a57a0001be501f') > >>> definition.delete() > ``` - Return type: None --- # Batch predictions URL: https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html # Batch predictions ### class datarobot.models.BatchPredictionJob A Batch Prediction Job is used to score large data sets on prediction servers using the Batch Prediction API. - Variables: id ( str ) – the ID of the job #### classmethod score(deployment, intake_settings=None, output_settings=None, csv_settings=None, timeseries_settings=None, num_concurrent=None, chunk_size=None, passthrough_columns=None, passthrough_columns_set=None, max_explanations=None, max_ngram_explanations=None, explanation_algorithm=None, threshold_high=None, threshold_low=None, prediction_threshold=None, prediction_warning_enabled=None, include_prediction_status=False, skip_drift_tracking=False, prediction_instance=None, abort_on_error=True, column_names_remapping=None, include_probabilities=True, include_probabilities_classes=None, download_timeout=120, download_read_timeout=660, upload_read_timeout=600, explanations_mode=None) Create new batch prediction job, upload the scoring dataset and return a batch prediction job. The default intake and output options are both localFile which requires the caller to pass the file parameter and either download the results using the download() method afterwards or pass a path to a file where the scored data will be downloaded to afterwards. - Variables: #### classmethod apply_time_series_data_prep_and_score(deployment, intake_settings, timeseries_settings, **kwargs) Prepare the dataset with time series data prep, create new batch prediction job, upload the scoring dataset, and return a batch prediction job. The supported intake_settings are of type localFile or dataset. For timeseries_settings of type forecast the forecast_point must be specified. Refer to the [datarobot.models.BatchPredictionJob.score()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score) method for details on the other kwargs parameters. Added in version v3.1. - Variables: #### classmethod score_to_file(deployment, intake_path, output_path, **kwargs) Create new batch prediction job, upload the scoring dataset and download the scored CSV file concurrently. Will block until the entire file is scored. Refer to the [datarobot.models.BatchPredictionJob.score()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score) method for details on the other kwargs parameters. - Variables: - Returns: Instance of BatchPredictionJob - Return type: BatchPredictionJob #### classmethod apply_time_series_data_prep_and_score_to_file(deployment, intake_path, output_path, timeseries_settings, **kwargs) Prepare the input dataset with time series data prep. Then, create a new batch prediction job using the prepared AI catalog item as input and concurrently download the scored CSV file. The function call will return when the entire file is scored. For timeseries_settings of type forecast the forecast_point must be specified. Refer to the [datarobot.models.BatchPredictionJob.score()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score) method for details on the other kwargs parameters. Added in version v3.1. - Variables: #### classmethod score_s3(deployment, source_url, destination_url, credential=None, endpoint_url=None, **kwargs) Create new batch prediction job, with a scoring dataset from S3 and writing the result back to S3. This returns immediately after the job has been created. You must poll for job completion using get_status() or wait_for_completion() (see datarobot.models.Job) Refer to the [datarobot.models.BatchPredictionJob.score()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score) method for details on the other kwargs parameters. - Variables: - Returns: Instance of BatchPredictionJob - Return type: BatchPredictionJob #### classmethod score_azure(deployment, source_url, destination_url, credential=None, **kwargs) Create new batch prediction job, with a scoring dataset from Azure blob storage and writing the result back to Azure blob storage. This returns immediately after the job has been created. You must poll for job completion using get_status() or wait_for_completion() (see datarobot.models.Job). Refer to the [datarobot.models.BatchPredictionJob.score()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score) method for details on the other kwargs parameters. - Variables: - Returns: Instance of BatchPredictionJob - Return type: BatchPredictionJob #### classmethod score_gcp(deployment, source_url, destination_url, credential=None, **kwargs) Create new batch prediction job, with a scoring dataset from Google Cloud Storage and writing the result back to one. This returns immediately after the job has been created. You must poll for job completion using get_status() or wait_for_completion() (see datarobot.models.Job). Refer to the [datarobot.models.BatchPredictionJob.score()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score) method for details on the other kwargs parameters. - Variables: - Returns: Instance of BatchPredictionJob - Return type: BatchPredictionJob #### classmethod score_from_existing(batch_prediction_job_id) Create a new batch prediction job based on the settings from a previously created one - Variables: batch_prediction_job_id ( str ) – ID of the previous batch prediction job - Returns: Instance of BatchPredictionJob - Return type: BatchPredictionJob #### classmethod score_pandas(deployment, df, read_timeout=660, **kwargs) Run a batch prediction job, with a scoring dataset from a pandas dataframe. The output from the prediction will be joined to the passed DataFrame and returned. Use columnNamesRemapping to drop or rename columns in the output This method blocks until the job has completed or raises an exception on errors. Refer to the [datarobot.models.BatchPredictionJob.score()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob.score) method for details on the other kwargs parameters. - Variables: - Return type: Tuple [ BatchPredictionJob , DataFrame ] - Returns: #### classmethod score_with_leaderboard_model(model, intake_settings=None, output_settings=None, csv_settings=None, timeseries_settings=None, passthrough_columns=None, passthrough_columns_set=None, max_explanations=None, max_ngram_explanations=None, explanation_algorithm=None, threshold_high=None, threshold_low=None, prediction_threshold=None, prediction_warning_enabled=None, include_prediction_status=False, abort_on_error=True, column_names_remapping=None, include_probabilities=True, include_probabilities_classes=None, download_timeout=120, download_read_timeout=660, upload_read_timeout=600, explanations_mode=None) Creates a new batch prediction job for a Leaderboard model by uploading the scoring dataset. Returns a batch prediction job. The default intake and output options are both localFile, which requires the caller to pass the file parameter and either download the results using the download() method afterwards or pass a path to a file where the scored data will be downloaded to. - Variables: #### classmethod get(batch_prediction_job_id) Get batch prediction job - Variables: batch_prediction_job_id ( str ) – ID of batch prediction job - Returns: Instance of BatchPredictionJob - Return type: BatchPredictionJob #### download(fileobj, timeout=120, read_timeout=660) Downloads the CSV result of a prediction job - Variables: #### delete(ignore_404_errors=False) Cancel this job. If this job has not finished running, it will be removed and canceled. - Return type: None #### get_status() Get status of batch prediction job - Returns: Dict with job status - Return type: BatchPredictionJob status data #### classmethod list_by_status(statuses=None) Get jobs collection for specific set of statuses - Variables: statuses – List of statuses to filter jobs ([ABORTED|COMPLETED…]) if statuses is not provided, returns all jobs for user - Returns: List of job statuses dicts with specific statuses - Return type: BatchPredictionJob statuses ### class datarobot.models.BatchPredictionJobDefinition #### classmethod get(batch_prediction_job_definition_id) Get batch prediction job definition - Variables: batch_prediction_job_definition_id ( str ) – ID of batch prediction job definition - Returns: Instance of BatchPredictionJobDefinition - Return type: BatchPredictionJobDefinition > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> definition = dr.BatchPredictionJobDefinition.get('5a8ac9ab07a57a0001be501f') > >>> definition > BatchPredictionJobDefinition(60912e09fd1f04e832a575c1) > ``` #### classmethod list(search_name=None, deployment_id=None, limit=, offset=0) Get job all definitions - Parameters: - Returns: List of job definitions the user has access to see - Return type: List[BatchPredictionJobDefinition] > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> definition = dr.BatchPredictionJobDefinition.list() > >>> definition > [ > BatchPredictionJobDefinition(60912e09fd1f04e832a575c1), > BatchPredictionJobDefinition(6086ba053f3ef731e81af3ca) > ] > ``` #### classmethod create(enabled, batch_prediction_job, name=None, schedule=None) Creates a new batch prediction job definition to be run either at scheduled interval or as a manual run. - Variables: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> job_spec = { > ... "num_concurrent": 4, > ... "deployment_id": "foobar", > ... "intake_settings": { > ... "url": "s3://foobar/123", > ... "type": "s3", > ... "format": "csv" > ... }, > ... "output_settings": { > ... "url": "s3://foobar/123", > ... "type": "s3", > ... "format": "csv" > ... }, > ...} > >>> schedule = { > ... "day_of_week": [ > ... 1 > ... ], > ... "month": [ > ... "*" > ... ], > ... "hour": [ > ... 16 > ... ], > ... "minute": [ > ... 0 > ... ], > ... "day_of_month": [ > ... 1 > ... ] > ...} > >>> definition = BatchPredictionJobDefinition.create( > ... enabled=False, > ... batch_prediction_job=job_spec, > ... name="some_definition_name", > ... schedule=schedule > ... ) > >>> definition > BatchPredictionJobDefinition(60912e09fd1f04e832a575c1) > ``` #### update(enabled, batch_prediction_job=None, name=None, schedule=None) Updates a job definition with the changed specs. Takes the same input as [create()](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJobDefinition.create) - Variables: - Returns: Instance of the updated BatchPredictionJobDefinition - Return type: BatchPredictionJobDefinition > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> job_spec = { > ... "num_concurrent": 5, > ... "deployment_id": "foobar_new", > ... "intake_settings": { > ... "url": "s3://foobar/123", > ... "type": "s3", > ... "format": "csv" > ... }, > ... "output_settings": { > ... "url": "s3://foobar/123", > ... "type": "s3", > ... "format": "csv" > ... }, > ...} > >>> schedule = { > ... "day_of_week": [ > ... 1 > ... ], > ... "month": [ > ... "*" > ... ], > ... "hour": [ > ... "*" > ... ], > ... "minute": [ > ... 30, 59 > ... ], > ... "day_of_month": [ > ... 1, 2, 6 > ... ] > ...} > >>> definition = BatchPredictionJobDefinition.create( > ... enabled=False, > ... batch_prediction_job=job_spec, > ... name="updated_definition_name", > ... schedule=schedule > ... ) > >>> definition > BatchPredictionJobDefinition(60912e09fd1f04e832a575c1) > ``` #### run_on_schedule(schedule) Sets the run schedule of an already created job definition. If the job was previously not enabled, this will also set the job to enabled. - Variables: schedule ( dict ) – Same as schedule in create() . - Returns: Instance of the updated BatchPredictionJobDefinition with the new / updated schedule. - Return type: BatchPredictionJobDefinition > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> definition = dr.BatchPredictionJobDefinition.create('...') > >>> schedule = { > ... "day_of_week": [ > ... 1 > ... ], > ... "month": [ > ... "*" > ... ], > ... "hour": [ > ... "*" > ... ], > ... "minute": [ > ... 30, 59 > ... ], > ... "day_of_month": [ > ... 1, 2, 6 > ... ] > ...} > >>> definition.run_on_schedule(schedule) > BatchPredictionJobDefinition(60912e09fd1f04e832a575c1) > ``` #### run_once() Manually submits a batch prediction job to the queue, based off of an already created job definition. - Returns: Instance of BatchPredictionJob - Return type: BatchPredictionJob > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> definition = dr.BatchPredictionJobDefinition.create('...') > >>> job = definition.run_once() > >>> job.wait_for_completion() > ``` #### delete() Deletes the job definition and disables any future schedules of this job if any. If a scheduled job is currently running, this will not be cancelled. > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> definition = dr.BatchPredictionJobDefinition.get('5a8ac9ab07a57a0001be501f') > >>> definition.delete() > ``` - Return type: None ## Batch job ### class datarobot.models.batch_job.IntakeSettings Intake settings typed dict ### class datarobot.models.batch_job.OutputSettings Output settings typed dict ## Predict job ### datarobot.models.predict_job.wait_for_async_predictions(project_id, predict_job_id, max_wait=600) Given a Project id and PredictJob id poll for status of process responsible for predictions generation until it’s finished - Parameters: - Returns: predictions – Generated predictions. - Return type: pandas.DataFrame - Raises: ### class datarobot.models.PredictJob Tracks asynchronous work being done within a project - Variables: #### classmethod from_job(job) Transforms a generic Job into a PredictJob - Parameters: job ( Job ) – A generic job representing a PredictJob - Returns: predict_job – A fully populated PredictJob with all the details of the job - Return type: PredictJob - Raises: ValueError: – If the generic Job was not a predict job, e.g., job_type != JOB_TYPE.PREDICT #### classmethod get(project_id, predict_job_id) Fetches one PredictJob. If the job finished, raises PendingJobFinished exception. - Parameters: - Returns: predict_job – The pending PredictJob - Return type: PredictJob - Raises: #### classmethod get_predictions(project_id, predict_job_id, class_prefix='class_') Fetches finished predictions from the job used to generate them. > [!NOTE] Notes > The prediction API for classifications now returns an additional prediction_values > dictionary that is converted into a series of class_prefixed columns in the final > dataframe. For example, = 1.0 is converted to ‘class_1.0’. If you are on an > older version of the client (prior to v2.8), you must update to v2.8 to correctly pivot > this data. - Parameters: - Returns: predictions – Generated predictions - Return type: pandas.DataFrame - Raises: #### cancel() Cancel this job. If this job has not finished running, it will be removed and canceled. #### get_result(params=None) - Parameters: params ( dict or None ) – Query parameters to be added to request to get results. > [!NOTE] Notes > For featureEffects, source param is required to define source, > otherwise the default is training. - Returns:result– Return type depends on the job type : - for model jobs, a Model is returned - for predict jobs, a pandas.DataFrame (with predictions) is returned - for featureImpact jobs, a list of dicts by default (seewith_metadataparameter of theFeatureImpactJobclass and itsget()method). - for primeRulesets jobs, a list of Rulesets - for primeModel jobs, a PrimeModel - for primeDownloadValidation jobs, a PrimeFile - for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization - for predictionExplanations jobs, a PredictionExplanations - for featureEffects, a FeatureEffects. *Return type:object*Raises:*JobNotFinished– If the job is not finished, the result is not available. *AsyncProcessUnsuccessfulError– If the job errored or was aborted #### get_result_when_complete(max_wait=600, params=None) - Parameters: - Returns: result – Return type is the same as would be returned by Job.get_result. - Return type: object - Raises: #### refresh() Update this object with the latest job data from the server. #### wait_for_completion(max_wait=600) Waits for job to complete. - Parameters: max_wait ( Optional[int] ) – How long to wait for the job to finish. - Return type: None ## Prediction dataset ### class datarobot.models.PredictionDataset A dataset uploaded to make predictions Typically created via project.upload_dataset - Variables: #### classmethod get(project_id, dataset_id) Retrieve information about a dataset uploaded for predictions - Parameters: - Returns: dataset – A dataset uploaded to make predictions - Return type: PredictionDataset #### delete() Delete a dataset uploaded for predictions Will also delete predictions made using this dataset and cancel any predict jobs using this dataset. - Return type: None --- # Binary data helpers URL: https://docs.datarobot.com/en/docs/api/reference/sdk/binary_data_helpers.html # Binary data helpers ### datarobot.helpers.binary_data_utils.get_encoded_image_contents_from_urls(urls, custom_headers=None, image_options=None, continue_on_error=False, n_threads=None) Returns base64 encoded string of images located in addresses passed in input collection. Input collection should hold data of valid image url addresses reachable from location where code is being executed. Method will retrieve image, apply specified reformatting before converting contents to base64 string. Results will in same order as specified in input collection. - Parameters: - Raises: ContentRetrievalTerminatedError: – The error is raised when the flag continue_on_error is set to` False` and processing has been terminated due to an exception while loading the contents of the file. - Return type: List of base64 encoded strings representing reformatted images. ### datarobot.helpers.binary_data_utils.get_encoded_image_contents_from_paths(paths, image_options=None, continue_on_error=False, n_threads=None, base_path=None) Returns base64 encoded string of images located in paths passed in input collection. Input collection should hold data of valid image paths reachable from location where code is being executed. Method will retrieve image, apply specified reformatting before converting contents to base64 string. Results will in same order as specified in input collection. - Parameters: - Raises: ContentRetrievalTerminatedError: – The error is raised when the flag continue_on_error is set to` False` and processing has been terminated due to an exception while loading the contents of the file. - Return type: List of base64 encoded strings representing reformatted images. ### datarobot.helpers.binary_data_utils.get_encoded_file_contents_from_paths(paths, continue_on_error=False, n_threads=None, base_path=None) Returns base64 encoded string for files located under paths passed in input collection. Input collection should hold data of valid file paths locations reachable from location where code is being executed. Method will retrieve file and convert its contents to base64 string. Results will be returned in same order as specified in input collection. - Parameters: - Raises: ContentRetrievalTerminatedError: – The error is raised when the flag continue_on_error is set to` False` and processing has been terminated due to an exception while loading the contents of the file. - Return type: List of base64 encoded strings representing files. ### datarobot.helpers.binary_data_utils.get_encoded_file_contents_from_urls(urls, custom_headers=None, continue_on_error=False, n_threads=None) Returns base64-encoded string for files located in the URL addresses passed on input. Input collection holds data of valid file URL addresses reachable from location where code is being executed. Method will retrieve file and convert its contents to base64 string. Results will be returned in same order as specified in input collection. - Parameters: - Raises: ContentRetrievalTerminatedError: – The error is raised when the flag continue_on_error is set to` False` and processing has been terminated due to an exception while loading the contents of the file. - Return type: List of base64 encoded strings representing files. ### class datarobot.helpers.image_utils.ImageOptions Image options class. Class holds image options related to image resizing and image reformatting. should_resize: bool : Whether input image should be resized to new dimensions. force_size: bool : Whether the image size should fully match the new requested size. If the original and new image sizes have different aspect ratios, specifying True will force a resize to exactly match the requested size. This may break the aspect ratio of the original image. If False, the resize method modifies the image to contain a thumbnail version of itself, no larger than the given size, that preserves the image’s aspect ratio. image_size: Tuple[int, int] : New image size (width, height). Both values (width, height) should be specified and contain a positive value. Depending on the value of force_size, the image will be resized exactly to the given image size or will be resized into a thumbnail version of itself, no larger than the given size. image_format: ImageFormat | str : What image format will be used to save result image after transformations. For example (ImageFormat.JPEG, ImageFormat.PNG). Values supported are in line with values supported by DataRobot. If no format is specified by passing None value original image format will be preserved. image_quality: int or None : The image quality used when saving image. When None is specified, a value will not be passed and Pillow library will use its default. resample_method: ImageResampleMethod : What resampling method should be used when resizing image. keep_quality: bool : Whether the image quality is kept (when possible). If True, for JPEG images quality will be preserved. For other types, the value specified in image_quality will be used. --- # Blueprints URL: https://docs.datarobot.com/en/docs/api/reference/sdk/blueprints.html # Blueprints ## Blueprint ### class datarobot.models.Blueprint A Blueprint which can be used to fit models - Variables: #### classmethod get(project_id, blueprint_id) Retrieve a blueprint. - Parameters: - Returns: blueprint – The queried blueprint. - Return type: Blueprint #### get_json() Get the blueprint json representation used by this model. - Returns: Json representation of the blueprint stages. - Return type: BlueprintJson #### get_chart() Retrieve a chart. - Returns: The current blueprint chart. - Return type: BlueprintChart #### get_documents() Get documentation for tasks used in the blueprint. - Returns: All documents available for blueprint. - Return type: list of BlueprintTaskDocument #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( T , bound= APIObject) ### class datarobot.models.BlueprintTaskDocument Document describing a task from a blueprint. - Variables: ### class datarobot.models.BlueprintChart A Blueprint chart that can be used to understand data flow in blueprint. - Variables: #### classmethod get(project_id, blueprint_id) Retrieve a blueprint chart. - Parameters: - Returns: The queried blueprint chart. - Return type: BlueprintChart #### to_graphviz() Get blueprint chart in graphviz DOT format. - Returns: String representation of chart in graphviz DOT language. - Return type: unicode ### class datarobot.models.ModelBlueprintChart A Blueprint chart that can be used to understand data flow in model. Model blueprint chart represents reduced repository blueprint chart with only elements that used to build this particular model. - Variables: #### classmethod get(project_id, model_id) Retrieve a model blueprint chart. - Parameters: - Returns: The queried model blueprint chart. - Return type: ModelBlueprintChart #### to_graphviz() Get blueprint chart in graphviz DOT format. - Returns: String representation of chart in graphviz DOT language. - Return type: unicode ## User blueprints ### class datarobot.UserBlueprint A representation of a blueprint which may be modified by the user, saved to a user’s AI Catalog, trained on projects, and shared with others. It is recommended to install the python library called `datarobot_bp_workshop`, available via `pip`, for the best experience when building blueprints. Please refer to `http://blueprint-workshop.datarobot.com` for tutorials, examples, and other documentation. - Parameters: #### classmethod list(limit=100, offset=0, project_id=None) Fetch a list of the user blueprints the current user created - Parameters: - Raises: - Return type: list[UserBlueprint] #### classmethod get(user_blueprint_id, project_id=None) Retrieve a user blueprint - Parameters: - Raises: - Return type: UserBlueprint #### classmethod create(blueprint, model_type=None, project_id=None, save_to_catalog=True) Create a user blueprint - Parameters: - Raises: - Return type: UserBlueprint #### classmethod create_from_custom_task_version_id(custom_task_version_id, save_to_catalog=True, description=None) Create a user blueprint with a single custom task version - Parameters: - Raises: - Return type: UserBlueprint #### classmethod clone_project_blueprint(blueprint_id, project_id, model_type=None, save_to_catalog=True) Clone a blueprint from a project. - Parameters: - Raises: - Return type: UserBlueprint #### classmethod clone_user_blueprint(user_blueprint_id, model_type=None, project_id=None, save_to_catalog=True) Clone a user blueprint. - Parameters: - Raises: - Return type: UserBlueprint #### classmethod update(blueprint, user_blueprint_id, model_type=None, project_id=None, include_project_id_if_none=False) Update a user blueprint - Parameters: - Raises: - Return type: UserBlueprint #### classmethod delete(user_blueprint_id) Delete a user blueprint, specified by the userBlueprintId. - Parameters: user_blueprint_id ( string ) – Used to identify a specific user-owned blueprint. - Raises: - Return type: requests.models.Response #### classmethod get_input_types() Retrieve the input types which can be used with User Blueprints. - Raises: - Return type: UserBlueprintAvailableInput #### classmethod add_to_project(project_id, user_blueprint_ids) Add a list of user blueprints, by id, to a specified (by id) project’s repository. - Parameters: - Raises: - Return type: UserBlueprintAddToProjectMenu #### classmethod get_available_tasks(project_id=None, user_blueprint_id=None) Retrieve the available tasks, organized into categories, which can be used to create or modify User Blueprints. - Parameters: - Raises: - Return type: UserBlueprintAvailableTasks #### classmethod validate_task_parameters(output_method, task_code, task_parameters, project_id=None) Validate that each value assigned to specified task parameters are valid. - Parameters: - Raises: - Return type: UserBlueprintValidateTaskParameters #### classmethod list_shared_roles(user_blueprint_id, limit=100, offset=0, id=None, name=None, share_recipient_type=None) Get a list of users, groups and organizations that have an access to this user blueprint - Parameters: - Raises: - Return type: list[UserBlueprintSharedRolesResponseValidator] #### classmethod validate_blueprint(blueprint, project_id=None) Validate a user blueprint and return information about the inputs expected and outputs provided by each task. - Parameters: - Raises: - Return type: list[VertexContextItem] #### classmethod update_shared_roles(user_blueprint_id, roles) Share a user blueprint with a user, group, or organization - Parameters: - Raises: - Return type: requests.models.Response #### classmethod search_catalog(search=None, tag=None, limit=100, offset=0, owner_user_id=None, owner_username=None, order_by='-created') Fetch a list of the user blueprint catalog entries the current user has access to based on an optional search term, tags, owner user info, or sort order. - Parameters: - Return type: UserBlueprintCatalogSearch ### class datarobot.models.user_blueprints.models.UserBlueprintAvailableInput Retrieve the input types which can be used with User Blueprints. - Parameters: input_types ( list(UserBlueprintsInputType) ) – A list of associated pairs of an input types and their human-readable names. #### classmethod get_input_types() Retrieve the input types which can be used with User Blueprints. - Raises: - Return type: UserBlueprintAvailableInput ### class datarobot.models.user_blueprints.models.UserBlueprintAddToProjectMenu Add a list of user blueprints, by id, to a specified (by id) project’s repository. - Parameters: #### classmethod add_to_project(project_id, user_blueprint_ids) Add a list of user blueprints, by id, to a specified (by id) project’s repository. - Parameters: - Raises: - Return type: UserBlueprintAddToProjectMenu ### class datarobot.models.user_blueprints.models.UserBlueprintAvailableTasks Retrieve the available tasks, organized into categories, which can be used to create or modify User Blueprints. - Parameters: #### classmethod get_available_tasks(project_id=None, user_blueprint_id=None) Retrieve the available tasks, organized into categories, which can be used to create or modify User Blueprints. - Parameters: - Raises: - Return type: UserBlueprintAvailableTasks ### class datarobot.models.user_blueprints.models.UserBlueprintValidateTaskParameters Validate that each value assigned to specified task parameters are valid. - Parameters: errors ( list(UserBlueprintsValidateTaskParameter) ) – A list of the task parameters, their proposed values, and messages describing why each is not valid. #### classmethod validate_task_parameters(output_method, task_code, task_parameters, project_id=None) Validate that each value assigned to specified task parameters are valid. - Parameters: - Raises: - Return type: UserBlueprintValidateTaskParameters ### class datarobot.models.user_blueprints.models.UserBlueprintSharedRolesResponseValidator A list of SharedRoles objects. - Parameters: ### class datarobot.models.user_blueprints.models.VertexContextItem Info about, warnings about, and errors with a specific vertex in the blueprint. - Parameters: ### class datarobot.models.user_blueprints.models.UserBlueprintCatalogSearch An APIObject representing a user blueprint catalog entry the current user has access to based on an optional search term and/or tags. - Parameters: #### classmethod search_catalog(search=None, tag=None, limit=100, offset=0, owner_user_id=None, owner_username=None, order_by='-created') Fetch a list of the user blueprint catalog entries the current user has access to based on an optional search term, tags, owner user info, or sort order. - Parameters: - Return type: List [ UserBlueprintCatalogSearch ] ## Custom tasks ### class datarobot.CustomTask A custom task. This can be in a partial state or a complete state. When the latest_version is None, the empty task has been initialized with some metadata. It is not yet use-able for actual training. Once the first CustomTaskVersion has been created, you can put the CustomTask in UserBlueprints to train Models in Projects Added in version v2.26. - Variables: #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: CustomTask #### classmethod list(order_by=None, search_for=None) List custom tasks available to the user. Added in version v2.26. - Parameters: - Returns: a list of custom tasks. - Return type: List[CustomTask] - Raises: #### classmethod get(custom_task_id) Get custom task by id. Added in version v2.26. - Parameters: custom_task_id ( str ) – id of the custom task - Returns: retrieved custom task - Return type: CustomTask - Raises: #### classmethod copy(custom_task_id) Create a custom task by copying existing one. Added in version v2.26. - Parameters: custom_task_id ( str ) – id of the custom task to copy - Return type: CustomTask - Raises: #### classmethod create(name, target_type, language=None, description=None, calibrate_predictions=None, **kwargs) Creates only the metadata for a custom task. This task will not be use-able until you have created a CustomTaskVersion attached to this task. Added in version v2.26. - Parameters: #### update(name=None, language=None, description=None, **kwargs) Update custom task properties. Added in version v2.26. - Parameters: - Raises: - Return type: None #### refresh() Update custom task with the latest data from server. Added in version v2.26. - Raises: - Return type: None #### delete() Delete custom task. Added in version v2.26. - Raises: - Return type: None #### download_latest_version(file_path) Download the latest custom task version. Added in version v2.26. - Parameters: file_path ( str ) – the full path of the target zip file - Raises: - Return type: None #### get_access_list() Retrieve access control settings of this custom task. Added in version v2.27. - Return type: list of SharingAccess #### share(access_list) Update the access control settings of this custom task. Added in version v2.27. - Parameters: access_list ( list of SharingAccess ) – A list of SharingAccess to update. - Raises: - Return type: None > [!NOTE] Examples > Transfer access to the custom task from [old_user@datarobot.com](mailto:old_user@datarobot.com) to [new_user@datarobot.com](mailto:new_user@datarobot.com) > > ``` > import datarobot as dr > > new_access = dr.SharingAccess(new_user@datarobot.com, > dr.enums.SHARING_ROLE.OWNER, can_share=True) > access_list = [dr.SharingAccess(old_user@datarobot.com, None), new_access] > > dr.CustomTask.get('custom-task-id').share(access_list) > ``` ### class datarobot.models.custom_task_version.CustomTaskFileItem A file item attached to a DataRobot custom task version. Added in version v2.26. - Variables: ### class datarobot.enums.CustomTaskOutboundNetworkPolicy The way to set and view a CustomTaskVersions outbound network policy. ### class datarobot.CustomTaskVersion A version of a DataRobot custom task. Added in version v2.26. - Variables: #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: #### classmethod create_clean(custom_task_id, base_environment_id, maximum_memory=None, is_major_update=True, folder_path=None, required_metadata_values=None, outbound_network_policy=None) Create a custom task version without files from previous versions. Added in version v2.26. - Parameters: - Returns: created custom task version - Return type: CustomTaskVersion - Raises: #### classmethod create_from_previous(custom_task_id, base_environment_id, maximum_memory=None, is_major_update=True, folder_path=None, files_to_delete=None, required_metadata_values=None, outbound_network_policy=None) Create a custom task version containing files from a previous version. Added in version v2.26. - Parameters: - Returns: created custom task version - Return type: CustomTaskVersion - Raises: #### classmethod list(custom_task_id) List custom task versions. Added in version v2.26. - Parameters: custom_task_id ( str ) – the ID of the custom task - Returns: a list of custom task versions - Return type: List[CustomTaskVersion] - Raises: #### classmethod get(custom_task_id, custom_task_version_id) Get custom task version by id. Added in version v2.26. - Parameters: - Returns: retrieved custom task version - Return type: CustomTaskVersion - Raises: #### download(file_path) Download custom task version. Added in version v2.26. - Parameters: file_path ( str ) – path to create a file with custom task version content - Raises: #### update(description=None, required_metadata_values=None) Update custom task version properties. Added in version v2.26. - Parameters: - Raises: #### refresh() Update custom task version with the latest data from server. Added in version v2.26. - Raises: #### start_dependency_build() Start the dependency build for a custom task version and return build status. .. versionadded:: v2.27 - Returns: DTO of custom task version dependency build. - Return type: CustomTaskVersionDependencyBuild #### start_dependency_build_and_wait(max_wait) Start the dependency build for a custom task version and wait while pulling status. .. versionadded:: v2.27 - Parameters: max_wait ( int ) – max time to wait for a build completion - Returns: DTO of custom task version dependency build. - Return type: CustomTaskVersionDependencyBuild - Raises: #### cancel_dependency_build() Cancel custom task version dependency build that is in progress. .. versionadded:: v2.27 - Raises: #### get_dependency_build() Retrieve information about a custom task version’s dependency build. .. versionadded:: v2.27 - Returns: DTO of custom task version dependency build. - Return type: CustomTaskVersionDependencyBuild #### download_dependency_build_log(file_directory='.') Get log of a custom task version dependency build. .. versionadded:: v2.27 - Parameters: file_directory ( str (optional , default is "."``) ) – Directory path where downloaded file is to save. - Raises: ## Visual AI ### class datarobot.models.visualai.images.Image An image stored in a project’s dataset. - Variables: #### classmethod get(project_id, image_id) Get a single image object from project. - Parameters: - Return type: Image ### class datarobot.models.visualai.images.SampleImage A sample image in a project’s dataset. If `Project.stage` is `datarobot.enums.PROJECT_STAGE.EDA2` then the `target_*` attributes of this class will have values, otherwise the values will all be None. - Variables: #### classmethod list(project_id, feature_name, target_value=None, target_bin_start=None, target_bin_end=None, offset=None, limit=None) Get sample images from a project. - Parameters: - Return type: List [ SampleImage ] ### class datarobot.models.visualai.images.DuplicateImage An image that was duplicated in the project dataset. - Variables: #### classmethod list(project_id, feature_name, offset=None, limit=None) Get all duplicate images in a project. - Parameters: - Return type: List [ DuplicateImage ] ### class datarobot.models.visualai.insights.ImageEmbedding Vector representation of an image in an embedding space. A vector in an embedding space will allow linear computations to be carried out between images: for example computing the Euclidean distance of the images. - Variables: #### classmethod compute(project_id, model_id) Start the computation of image embeddings for the model. - Parameters: - Returns: URL to check for image embeddings progress. - Return type: str - Raises: datarobot.errors.ClientError – Server rejected creation due to client error. Most likely cause is bad project_id or model_id . #### classmethod models(project_id) For a given project_id, list all model_id - feature_name pairs with available Image Embeddings. - Parameters: project_id ( str ) – Id of the project to list model_id - feature_name pairs with available Image Embeddings for. - Returns: List of model and feature name pairs. - Return type: list( tuple(model_id , feature_name) ) #### classmethod list(project_id, model_id, feature_name) Return a list of ImageEmbedding objects. - Parameters: - Return type: List [ ImageEmbedding ] ### class datarobot.models.visualai.insights.ImageActivationMap Mark areas of image with weight of impact on training. This is a technique to display how various areas of the region were used in training, and their effect on predictions. Larger values in `activation_values` indicates a larger impact. - Variables: #### classmethod compute(project_id, model_id) Start the computation of activation maps for the given model. - Parameters: - Returns: URL to check for image embeddings progress. - Return type: str - Raises: datarobot.errors.ClientError – Server rejected creation due to client error. Most likely cause is bad project_id or model_id . #### classmethod models(project_id) For a given project_id, list all model_id - feature_name pairs with available Image Activation Maps. - Parameters: project_id ( str ) – Id of the project to list model_id - feature_name pairs with available Image Activation Maps for. - Returns: List of model and feature name pairs. - Return type: list( tuple(model_id , feature_name) ) #### classmethod list(project_id, model_id, feature_name, offset=None, limit=None) Return a list of ImageActivationMap objects. - Parameters: - Return type: List [ ImageActivationMap ] ### class datarobot.models.visualai.augmentation.ImageAugmentationOptions A List of all supported Image Augmentation Transformations for a project. Includes additional information about minimum, maximum, and default values for a transformation. - Variables: #### classmethod get(project_id) Returns a list of all supported transformations for the given project - Parameters: project_id ( str ) – sting The id of the project for which to return the list of supported transformations. - Return type: ImageAugmentationOptions - Returns: ImageAugmentationOptions : A list containing all the supported transformations for the project. ### class datarobot.models.visualai.augmentation.ImageAugmentationList A List of Image Augmentation Transformations - Variables: #### classmethod create(name, project_id, feature_name=None, initial_list=False, transformation_probability=0.0, number_of_new_images=1, transformations=None, samples_id=None) create a new image augmentation list - Return type: ImageAugmentationList #### classmethod list(project_id, feature_name=None) List Image Augmentation Lists present in a project. - Parameters: - Return type: list[ImageAugmentationList] #### update(name=None, feature_name=None, initial_list=None, transformation_probability=None, number_of_new_images=None, transformations=None) Update one or multiple attributes of the Image Augmentation List in the DataRobot backend as well on this object. - Parameters: - Returns: Reference to self. The passed values will be updated in place. - Return type: ImageAugmentationList #### retrieve_samples() Lists already computed image augmentation sample for image augmentation list. Returns samples only if they have been already computed. It does not initialize computation. - Return type: List of class ImageAugmentationSample #### compute_samples(max_wait=600) Initializes computation and retrieves list of image augmentation samples for image augmentation list. If samples exited prior to this call method, this will compute fresh samples and return latest version of samples. - Return type: List of class ImageAugmentationSample ### class datarobot.models.visualai.augmentation.ImageAugmentationSample A preview of the type of images that augmentations will create during training. - Variables: #### classmethod list(auglist_id=None) Return a list of ImageAugmentationSample objects. - Parameters: auglist_id ( str ) – ID for augmentation list to retrieve samples for - Return type: List of class ImageAugmentationSample --- # Challenger models URL: https://docs.datarobot.com/en/docs/api/reference/sdk/challenger-models.html # Challenger ### class datarobot.models.deployment.challenger.Challenger A challenger is an alternative model being compared to the model currently deployed - Variables: #### classmethod create(deployment_id, model_package_id, prediction_environment_id, name, max_wait=600) Create a challenger for a deployment - Parameters: - Return type: Challenger > [!NOTE] Examples > ``` > from datarobot import Challenger > challenger = Challenger.create( > deployment_id="5c939e08962d741e34f609f0", > name="Elastic-Net Classifier", > model_package_id="5c0a969859b00004ba52e41b", > prediction_environment_id="60b012436635fc00909df555" > ) > ``` #### classmethod get(deployment_id, challenger_id) Get a challenger for a deployment - Parameters: - Returns: The challenger object - Return type: Challenger > [!NOTE] Examples > ``` > from datarobot import Challenger > challenger = Challenger.get( > deployment_id="5c939e08962d741e34f609f0", > challenger_id="5c939e08962d741e34f609f0" > ) > > challenger.id > >>>'5c939e08962d741e34f609f0' > challenger.model_package['name'] > >>> 'Elastic-Net Classifier' > ``` #### classmethod list(deployment_id) List all challengers for a deployment - Parameters: deployment_id ( str ) – The ID of the deployment - Returns: challengers – A list of challenger objects - Return type: list > [!NOTE] Examples > ``` > from datarobot import Challenger > challengers = Challenger.list(deployment_id="5c939e08962d741e34f609f0") > > challengers[0].id > >>>'5c939e08962d741e34f609f0' > challengers[0].model_package['name'] > >>> 'Elastic-Net Classifier' > ``` #### delete() Delete a challenger for a deployment - Return type: None #### update(name=None, prediction_environment_id=None) Update name and prediction environment of a challenger - Parameters: - Return type: None ### class datarobot.models.deployment.champion_model_package.ChampionModelPackage Represents a champion model package. - Parameters: --- # Client setup URL: https://docs.datarobot.com/en/docs/api/reference/sdk/client-setup.html # Client setup ### datarobot.client.Client(token=None, endpoint=None, config_path=None, connect_timeout=None, ssl_verify=None, max_retries=None, use_tcp_keepalive=None, token_type=None, default_use_case=None, enable_api_consumer_tracking=None, trace_context=None) Configures the global API client for the Python SDK. The client will be configured in one of the following ways, in order of priority. > [!NOTE] Notes > Token and endpoint must be specified from one source only. This is a restriction > to prevent token leakage if environment variables or config file are used. > > The DataRobotClientConfig params will be looking up to find the configuration parameters > in one of the following ways, > > From call kwargs if specified; > From a YAML file at the path specified in the > config_path > kwarg; > From a YAML file at the path specified in the environment variables > DATAROBOT_CONFIG_FILE > ; > From environment variables; > From the default values in the default YAML file > at the path $HOME/.config/datarobot/drconfig.yaml. > > This can also have the side effect of setting a default Use Case for client API requests. - Parameters: - Return type: RESTClientObject - Returns: The RESTClientObject instance created. ### datarobot.client.get_client() Returns the global HTTP client for the Python SDK, instantiating it if necessary. - Return type: RESTClientObject ### datarobot.client.set_client(client) Configure the global HTTP client for the Python SDK. Returns previous instance. - Return type: Optional [ RESTClientObject ] ### datarobot.client.client_configuration(*args, **kwargs) This context manager can be used to temporarily change the global HTTP client. In multithreaded scenarios, it is highly recommended to use a fresh manager object per thread. DataRobot does not recommend nesting these contexts. - Parameters: > [!NOTE] Examples > ``` > from datarobot.client import client_configuration > from datarobot.models import Project > > with client_configuration(default_use_case=[]): > # Interact with all accessible projects, not just those associated > # with the current use case. > Project.list() > > with client_configuration(token="api-key-here", endpoint="https://host-name.com"): > # Interact with projects on a different DataRobot instance. > Project.list() > ``` > > ``` > from datarobot.client import Client, client_configuration > from datarobot.models import Project > > Client() # Interact with DataRobot using the default configuration. > Project.list() > > with client_configuration(config_path="/path/to/a/drconfig.yaml"): > # Interact with DataRobot using a different configuration. > Project.list() > ``` ### class datarobot.rest.RESTClientObject - Parameters: #### open_in_browser() Opens the DataRobot app in a web browser, or logs the URL if a browser is not available. - Return type: None --- # Compliance documentation URL: https://docs.datarobot.com/en/docs/api/reference/sdk/compliance-documentation.html # Compliance Documentation ## Automated documentation ### class datarobot.models.automated_documentation.AutomatedDocument An automated documentation object. Added in version v2.24. - Variables: #### classmethod list_available_document_types(cls) Get a list of all available document types and locales. This method is deprecated. - Returns: - Return type: List[DocumentOption] > [!NOTE] Examples > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > doc_types = dr.AutomatedDocument.list_available_document_types() > ``` #### classmethod list_all_available_document_types() Get a list of all available document types and locales. This method is direct replacement of list_available_document_types(). - Return type: List of dicts > [!NOTE] Examples > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > doc_types = dr.AutomatedDocument.list_all_available_document_types() > ``` #### property is_model_compliance_initialized : Tuple[bool, str] Check if model compliance documentation pre-processing is initialized. Model compliance documentation pre-processing must be initialized before generating documentation for a custom model. - Returns: - Return type: Tuple of (boolean , string) #### initialize_model_compliance() Initialize model compliance documentation pre-processing. Must be called before generating documentation for a custom model. - Returns: - Return type: Tuple of (boolean , string) > [!NOTE] Examples > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > > # NOTE: entity_id is either a model id or a model package (version) id > doc = dr.AutomatedDocument( > document_type="MODEL_COMPLIANCE", > entity_id="6f50cdb77cc4f8d1560c3ed5", > output_format="docx", > locale="EN_US") > > doc.initialize_model_compliance() > ``` #### generate(max_wait=600) Request generation of an automated document. Required attributes to request document generation: `document_type`, `entity_id`, and `output_format`. - Return type: requests.models.Response > [!NOTE] Examples > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > > doc = dr.AutomatedDocument( > document_type="MODEL_COMPLIANCE", > entity_id="6f50cdb77cc4f8d1560c3ed5", > output_format="docx", > locale="EN_US", > template_id="50efc9db8aff6c81a374aeec", > filepath="/Users/username/Documents/example.docx" > ) > > doc.generate() > doc.download() > ``` #### download() Download a generated Automated Document. Document ID is required to download a file. - Return type: requests.models.Response > [!NOTE] Examples > Generating and downloading the generated document: > > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > > doc = dr.AutomatedDocument( > document_type="AUTOPILOT_SUMMARY", > entity_id="6050d07d9da9053ebb002ef7", > output_format="docx", > filepath="/Users/username/Documents/Project_Report_1.docx" > ) > > doc.generate() > doc.download() > ``` > > Downloading an earlier generated document when you know the document ID: > > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > doc = dr.AutomatedDocument(id='5e8b6a34d2426053ab9a39ed') > doc.download() > ``` > > Notice that `filepath` was not set for this document. In this case, the file is saved > to the directory from which the script was launched. > > Downloading a document chosen from a list of earlier generated documents: > > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > > model_id = "6f5ed3de855962e0a72a96fe" > docs = dr.AutomatedDocument.list_generated_documents(entity_ids=[model_id]) > doc = docs[0] > doc.filepath = "/Users/me/Desktop/Recommended_model_doc.docx" > doc.download() > ``` #### delete() Delete a document using its ID. - Return type: requests.models.Response > [!NOTE] Examples > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > doc = dr.AutomatedDocument(id="5e8b6a34d2426053ab9a39ed") > doc.delete() > ``` > > If you don’t know the document ID, you can follow the same workflow to get the ID as in > the examples for the `AutomatedDocument.download` method. #### classmethod list_generated_documents(document_types=None, entity_ids=None, output_formats=None, locales=None, offset=None, limit=None) Get information about all previously generated documents available for your account. The information includes document ID and type, ID of the entity it was generated for, time of creation, and other information. - Parameters: - Return type: List [ AutomatedDocument ] - Returns: > [!NOTE] Examples > To get a list of all generated documents: > > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > docs = AutomatedDocument.list_generated_documents() > ``` > > To get a list of all `AUTOPILOT_SUMMARY` documents: > > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > docs = AutomatedDocument.list_generated_documents(document_types=["AUTOPILOT_SUMMARY"]) > ``` > > To get a list of 5 recently created automated documents in `html` format: > > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > docs = AutomatedDocument.list_generated_documents(output_formats=["html"], limit=5) > ``` > > To get a list of automated documents created for specific entities (projects or models): > > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > docs = AutomatedDocument.list_generated_documents( > entity_ids=["6051d3dbef875eb3be1be036", > "6051d3e1fbe65cd7a5f6fde6", > "6051d3e7f86c04486c2f9584"] > ) > ``` > > Note, that the list of results contains `AutomatedDocument` objects, which means that you > can execute class-related methods on them. Here’s how you can list, download, and then > delete from the server all automated documents related to a certain entity: > > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > > ids = ["6051d3dbef875eb3be1be036", "5fe1d3d55cd810ebdb60c517f"] > docs = AutomatedDocument.list_generated_documents(entity_ids=ids) > for doc in docs: > doc.download() > doc.delete() > ``` ### class datarobot.models.automated_documentation.DocumentOption ## Compliance documentation templates ### class datarobot.models.compliance_doc_template.ComplianceDocTemplate A [compliance documentation template](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/insights/automated_documentation.html#automated-documentation-overview). Templates are used to customize contents of [AutomatedDocument](https://docs.datarobot.com/en/docs/api/reference/sdk/compliance-documentation.html#datarobot.models.automated_documentation.AutomatedDocument). Added in version v2.14. > [!NOTE] Notes > Each `section` dictionary has the following schema: > > title > : title of the section > type > : type of section. Must be one of “datarobot”, “user” or “table_of_contents”. > > Each type of section has a different set of attributes described bellow. > > Section of type `"datarobot"` represent a section owned by DataRobot. DataRobot > sections have the following additional attributes: > > content_id > : The identifier of the content in this section. > You can get the default template with > get_default > for a complete list of possible DataRobot section content ids. > sections > : list of sub-section dicts nested under the parent section. > > Section of type `"user"` represent a section with user-defined content. > Those sections may contain text generated by user and have the following additional fields: > > regularText > : regular text of the section, optionally separated by > \n > to split paragraphs. > highlightedText > : highlighted text of the section, optionally separated > by > \n > to split paragraphs. > sections > : list of sub-section dicts nested under the parent section. > > Section of type `"table_of_contents"` represent a table of contents and has > no additional attributes. - Variables: #### classmethod get_default(template_type=None) Get a default DataRobot template. This template is used for generating compliance documentation when no template is specified. - Parameters: template_type ( str or None ) – Type of the template. Currently supported values are “normal” and “time_series” - Returns: template – the default template object with sections attribute populated with default sections. - Return type: ComplianceDocTemplate #### classmethod create_from_json_file(name, path, project_type=None) Create a template with the specified name and sections in a JSON file. This is useful when working with sections in a JSON file. Example: ``` default_template = ComplianceDocTemplate.get_default() default_template.sections_to_json_file('path/to/example.json') # ... edit example.json in your editor my_template = ComplianceDocTemplate.create_from_json_file( name='my template', path='path/to/example.json' ) ``` - Parameters: - Returns: template – The created template. - Return type: ComplianceDocTemplate #### classmethod create(name, sections, project_type=None) Create a template with the specified name and sections. - Parameters: - Returns: template – The created template. - Return type: ComplianceDocTemplate #### classmethod get(template_id) Retrieve a specific template. - Parameters: template_id ( str ) – the ID of the template to retrieve - Returns: template – the retrieved template - Return type: ComplianceDocTemplate #### classmethod list(name_part=None, limit=None, offset=None, project_type=None) Get a paginated list of compliance documentation template objects. - Parameters: - Returns: templates – The list of template objects. - Return type: list of ComplianceDocTemplate #### sections_to_json_file(path, indent=2) Save sections of the template to a json file at the specified path - Parameters: - Return type: None #### update(name=None, sections=None, project_type=None) Update the name or sections of an existing doc template. Note that default or non-existent templates can not be updated. - Parameters: - Return type: None #### delete() Delete the compliance documentation template. - Return type: None ### class datarobot.enums.ComplianceDocTemplateProjectType The project type supported by the template. ### class datarobot.enums.ComplianceDocTemplateType The type of default template and sections to create a template. #### classmethod to_project_type(template_type) Map from template type to project type supported by the template. - Return type: Optional [ ComplianceDocTemplateProjectType ] --- # Credentials URL: https://docs.datarobot.com/en/docs/api/reference/sdk/credentials.html # Credentials ### class datarobot.models.Credential #### classmethod list() Returns list of available credentials. - Returns: credentials – contains a list of available credentials. - Return type: list of Credential instances > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_sources = dr.Credential.list() > >>> data_sources > [ > Credential('5e429d6ecf8a5f36c5693e03', 'my_s3_cred', 's3'), > Credential('5e42cc4dcf8a5f3256865840', 'my_jdbc_cred', 'jdbc'), > ] > ``` #### classmethod get(credential_id) Gets the Credential. - Parameters: credential_id ( str ) – the identifier of the credential. - Returns: credential – the requested credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.get('5a8ac9ab07a57a0001be501f') > >>> cred > Credential('5e429d6ecf8a5f36c5693e03', 'my_s3_cred', 's3'), > ``` #### delete() Deletes the Credential the store. - Parameters: credential_id ( str ) – the identifier of the credential. - Returns: credential – the requested credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.get('5a8ac9ab07a57a0001be501f') > >>> cred.delete() > ``` #### classmethod create_basic(name, user, password, description=None) Creates the credentials. - Parameters: - Returns: credential – the created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_basic( > ... name='my_basic_cred', > ... user='username', > ... password='password', > ... ) > >>> cred > Credential('5e429d6ecf8a5f36c5693e03', 'my_basic_cred', 'basic'), > ``` #### classmethod create_oauth(name, token, refresh_token, description=None) Creates the OAUTH credentials. - Parameters: - Returns: credential – the created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_oauth( > ... name='my_oauth_cred', > ... token='XXX', > ... refresh_token='YYY', > ... ) > >>> cred > Credential('5e429d6ecf8a5f36c5693e03', 'my_oauth_cred', 'oauth'), > ``` #### classmethod create_s3(name, aws_access_key_id=None, aws_secret_access_key=None, aws_session_token=None, config_id=None, description=None) Creates the S3 credentials. - Parameters: - Returns: credential – the created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_s3( > ... name='my_s3_cred', > ... aws_access_key_id='XXX', > ... aws_secret_access_key='YYY', > ... aws_session_token='ZZZ', > ... ) > >>> cred > Credential('5e429d6ecf8a5f36c5693e03', 'my_s3_cred', 's3'), > ``` #### classmethod create_azure(name, azure_connection_string, description=None) Creates the Azure storage credentials. - Parameters: - Returns: credential – the created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_azure( > ... name='my_azure_cred', > ... azure_connection_string='XXX', > ... ) > >>> cred > Credential('5e429d6ecf8a5f36c5693e03', 'my_azure_cred', 'azure'), > ``` #### classmethod create_snowflake_key_pair(name, user=None, private_key=None, passphrase=None, config_id=None, description=None) Creates the Snowflake Key Pair credentials. - Parameters: - Returns: credential – the created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_snowflake_key_pair( > ... name='key_pair_cred', > ... user='XXX', > ... private_key='YYY', > ... passphrase='ZZZ', > ... ) > >>> cred > Credential('5e429d6ecf8a5f36c5693e03', 'key_pair_cred', 'snowflake_key_pair_user_account'), > ``` #### classmethod create_databricks_access_token(name, databricks_access_token, description=None) Creates the Databricks access token credentials. - Parameters: - Returns: credential – the created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_databricks_access_token( > ... name='access_token_cred', > ... databricks_access_token='XXX', > ... ) > >>> cred > Credential('5e429d6ecf8a5f36c5693e03', 'access_token_cred', 'databricks_access_token_account'), > ``` #### classmethod create_databricks_service_principal(name, client_id=None, client_secret=None, config_id=None, description=None) Creates the Databricks access token credentials. - Parameters: - Returns: credential – the created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_databricks_service_principal( > ... name='svc_principal_cred', > ... client_id='XXX', > ... client_secret='XXX', > ... ) > >>> cred > Credential('5e429d6ecf8a5f36c5693e03', 'svc_principal_cred', 'databricks_service_principal_account'), > ``` #### classmethod create_azure_service_principal(name, client_id=None, client_secret=None, azure_tenant_id=None, config_id=None, description=None) Creates the Azure service principal credentials. - Parameters: - Returns: credential – the created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_azure_service_principal( > ... name='my_azure_service_principal_cred', > ... client_id='XXX', > ... client_secret='YYY', > ... azure_tenant_id='ZZZ', > ... ) > >>> cred > Credential('66c9172d8b7a361cda126f5c', 'my_azure_service_principal_cred', 'azure_service_principal') > ``` #### classmethod create_adls_oauth(name, client_id=None, client_secret=None, oauth_scopes=None, config_id=None, description=None) Creates the ADLS OAuth credentials. - Parameters: - Returns: credential – The created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_adls_oauth( > ... name='my_adls_oauth_cred', > ... client_id='XXX', > ... client_secret='YYY', > ... oauth_scopes=['ZZZ'], > ... ) > >>> cred > Credential('66c91e0f03010d4790735220', 'my_adls_oauth_cred', 'adls_gen2_oauth') > ``` #### classmethod create_external_oauth_provider(name, authentication_id, description=None) Creates credentials for an external OAuth provider. - Parameters: - Returns: credential – The created credential. - Return type: Credential #### classmethod create_box_jwt(name, client_id, client_secret, enterprise_id, public_key_id, private_key_str, passphrase, description=None) Creates the Box JWT credentials. - Parameters: - Returns: credential – The created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_box_jwt( > ... name='my_box_jwt_cred', > ... client_id='XXX', > ... client_secret='YYY', > ... enterprise_id='ZZZ', > ... public_key_id='AAA', > ... private_key_str='-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----', > ... passphrase='BBB', > ... ) > >>> cred > Credential('5e429d6ecf8a5f36c5693e03', 'my_box_jwt_cred', 'box_jwt') > ``` #### classmethod create_gcp(name, gcp_key=None, description=None) Creates the GCP credentials. - Parameters: - Returns: credential – the created credential. - Return type: Credential > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> cred = dr.Credential.create_gcp( > ... name='my_gcp_cred', > ... gcp_key='XXX', > ... ) > >>> cred > Credential('5e429d6ecf8a5f36c5693e03', 'my_gcp_cred', 'gcp'), > ``` #### update(name=None, description=None, **kwargs) Update the credential values of an existing credential. Updates this object in place. Added in version v3.2. - Parameters: - Return type: None --- # Custom Application Sources URL: https://docs.datarobot.com/en/docs/api/reference/sdk/custom-application-sources.html # Custom Application Sources ### class datarobot.CustomApplicationSource A DataRobot custom application source that serves as a template for creating custom applications. Custom application sources define the code, configuration, and resource requirements used when creating custom applications. They can have resource configurations that determine the default resource allocation for applications created from this source. - Variables: > [!NOTE] Notes > Resource configuration is stored in the latest_version field and can be accessed > via the get_resources() method. #### classmethod list(offset=None, limit=None) Retrieve a list of custom application sources. - Parameters: - Returns: sources – The requested list of custom application sources. - Return type: List[CustomApplicationSource] #### classmethod get(source_id) Retrieve a single custom application source with full details. - Parameters: source_id ( str ) – The ID of the custom application source to retrieve. - Returns: source – The requested custom application source. - Return type: CustomApplicationSource #### classmethod create(name) Create a new custom application source. - Parameters: name ( str ) – The name for the new custom application source. - Returns: source – The newly created custom application source. - Return type: CustomApplicationSource #### update(name=None) Update this custom application source. - Parameters: name ( Optional[str] ) – New name for the source. If None, name will not be changed. - Returns: source – The updated custom application source. - Return type: CustomApplicationSource #### delete(hard_delete=False) Delete this custom application source. - Parameters: hard_delete ( bool , optional ) – If True, permanently delete the source and all its data. If False (default), soft delete the source (can be recovered). - Return type: None #### NOTE Deleting a source will affect all its versions. Applications created from this source will continue to run but cannot be recreated from the source. > [!NOTE] Examples > ``` > from datarobot import CustomApplicationSource > > source = CustomApplicationSource.get("source_id") > source.delete() # Soft delete > source.delete(hard_delete=True) # Permanent delete > ``` #### get_resources() Get resource configuration for applications created from this source. - Returns: resources – Resource configuration including replicas, resource bundle, and other settings. Returns None if no resources are configured. - Return type: Optional[Dict[str , Any]] #### get_resource_summary() Get a human-readable summary of resource configuration. - Returns: summary – A summary of resource configuration with readable descriptions. Returns None if no resources are configured. - Return type: Optional[Dict[str , Any]] #### property has_resources_configured : bool Check if this source has resource configuration. - Returns: True if resources are configured, False otherwise. - Return type: bool #### get_details() Get comprehensive details about this custom application source. - Returns: details – Comprehensive source details including metadata, version info, and resources. - Return type: Dict[str , Any] #### classmethod get_by_name(name) Find a custom application source by name. - Parameters: name ( str ) – The name of the custom application source to find. - Returns: source – The custom application source if found, None otherwise. - Return type: Optional[CustomApplicationSource] #### create_application(name, resources=None, environment_id=None) Create a Custom Application from this source. - Parameters: - Returns: application – The newly created Custom Application. - Return type: CustomApplication > [!NOTE] Examples > ``` > from datarobot import CustomApplicationSource > > source = CustomApplicationSource.get("source_id") > > # Create with default settings > app1 = source.create_application("My App") > > # Create with custom resources and environment > app2 = source.create_application( > "My App", > resources={"replicas": 2, "resourceLabel": "cpu.large"}, > environment_id="env_id_123" > ) > ``` #### get_versions() Get all versions of this custom application source. - Returns: versions – List of version information for this source. - Return type: List[Dict[str , Any]] #### get_version(version_id) Get details of a specific version of this source. - Parameters: version_id ( str ) – The ID of the version to retrieve. - Returns: version – Version details including files, environment, and configuration. - Return type: Dict[str , Any] #### update_resources(resource_label=None, replicas=None, session_affinity=None, service_web_requests_on_root_path=None) Update resource configuration for this source. - Parameters: - Returns: source – The updated custom application source. - Return type: CustomApplicationSource #### get_file(version_id, item_id) Retrieve a specific file from a version of this custom application source. - Parameters: - Returns: file_data – The file information and content as a JSON object. - Return type: Dict[str , Any] > [!NOTE] Examples > ``` > from datarobot import CustomApplicationSource > > source = CustomApplicationSource.get("source_id") > > # Get the latest version > versions = source.get_versions() > version_id = versions[0]["id"] > > # Get a file from that version > version_info = source.get_version(version_id) > item_id = version_info["items"][0]["id"] > file_data = source.get_file(version_id, item_id) > > print(f"File name: {file_data.get('fileName', 'Unknown')}") > if 'content' in file_data: > print(f"Content: {file_data['content']}") > ``` --- # Custom Applications URL: https://docs.datarobot.com/en/docs/api/reference/sdk/custom-applications.html # Custom Applications ### class datarobot.CustomApplication A DataRobot Custom Application with detailed resource information. This class provides access to the customApplications/ API endpoint which contains more detailed information than the basic applications/ endpoint, including resource allocation, status, and configuration details. - Variables: #### classmethod list(offset=None, limit=None) Retrieve a list of custom applications. - Parameters: - Returns: applications – The requested list of custom applications. - Return type: List[CustomApplication] #### classmethod get(application_id) Retrieve a single custom application with full details including resources. - Parameters: application_id ( str ) – The ID of the custom application to retrieve. - Returns: application – The requested custom application with resource details. - Return type: CustomApplication #### get_resources() Get resource allocation details for this custom application. - Returns: resources – Resource allocation including CPU, memory, replicas, and other settings. Returns None if no resources are allocated. - Return type: Optional[Dict[str , Any]] #### get_resource_summary() Get a human-readable summary of resource allocation. - Returns: summary – A summary of resource allocation with readable units. Returns None if no resources are allocated. - Return type: Optional[Dict[str , Any]] #### property is_running : bool Check if the custom application is currently running. - Returns: True if the application status is ‘running’, False otherwise. - Return type: bool #### get_details() Get comprehensive details about this custom application. - Returns: details – Comprehensive application details including metadata and resources. - Return type: Dict[str , Any] #### classmethod get_by_name(name) Find a custom application by name. - Parameters: name ( str ) – The name of the custom application to find. - Returns: application – The custom application if found, None otherwise. - Return type: Optional[CustomApplication] #### delete(hard_delete=False) Delete this custom application. - Parameters: hard_delete ( bool , optional ) – If True, permanently delete the application and all its data. If False (default), soft delete the application (can be recovered). - Return type: None > [!NOTE] Examples > ``` > from datarobot import CustomApplication > > app = CustomApplication.get("app_id") > app.delete() # Soft delete > app.delete(hard_delete=True) # Permanent delete > ``` #### get_logs() Retrieve logs and build information for this custom application. - Returns: logs_info – Dictionary containing: - Return type: Dict[str , Any] > [!NOTE] Examples > ``` > from datarobot import CustomApplication > > app = CustomApplication.get("app_id") > logs_info = app.get_logs() > > # Print runtime logs > for log_line in logs_info['logs']: > print(log_line) > > # Check build status > if 'build_status' in logs_info: > print(f"Build status: {logs_info['build_status']}") > > # Check for build errors > if 'build_error' in logs_info: > print(f"Build error: {logs_info['build_error']}") > ``` --- # Custom metrics URL: https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html # Custom metrics ### class datarobot.models.deployment.custom_metrics.CustomMetric A DataRobot custom metric. Added in version v3.4. - Variables: #### classmethod create(name, deployment_id, units, is_model_specific, aggregation_type, time_step='hour', directionality=None, description=None, baseline_value=None, value_column_name=None, sample_count_column_name=None, timestamp_column_name=None, timestamp_format=None, batch_column_name=None, categories=None, is_geospatial=None, geospatial_segment_attribute=None) Create a custom metric for a deployment - Parameters: - Returns: The custom metric object. - Return type: CustomMetric > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > from datarobot.enums import CustomMetricAggregationType, CustomMetricDirectionality > > custom_metric = CustomMetric.create( > deployment_id="5c939e08962d741e34f609f0", > name="Sample metric", > units="Y", > baseline_value=12, > is_model_specific=True, > aggregation_type=CustomMetricAggregationType.AVERAGE, > directionality=CustomMetricDirectionality.HIGHER_IS_BETTER > ) > ``` #### classmethod get(deployment_id, custom_metric_id) Get a custom metric for a deployment - Parameters: - Returns: The custom metric object. - Return type: CustomMetric > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > > custom_metric.id > >>>'65f17bdcd2d66683cdfc1113' > ``` #### classmethod list(deployment_id) List all custom metrics for a deployment - Parameters: deployment_id ( str ) – The ID of the deployment. - Returns: custom_metrics – A list of custom metrics objects. - Return type: list > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > > custom_metrics = CustomMetric.list(deployment_id="5c939e08962d741e34f609f0") > custom_metrics[0].id > >>>'65f17bdcd2d66683cdfc1113' > ``` #### classmethod delete(deployment_id, custom_metric_id) Delete a custom metric associated with a deployment. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > > CustomMetric.delete( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > ``` #### update(name=None, units=None, aggregation_type=None, directionality=None, time_step=None, description=None, baseline_value=None, value_column_name=None, sample_count_column_name=None, timestamp_column_name=None, timestamp_format=None, batch_column_name=None) Update metadata of a custom metric - Parameters: - Returns: The custom metric object. - Return type: CustomMetric > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > from datarobot.enums import CustomMetricAggregationType, CustomMetricDirectionality > > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > custom_metric = custom_metric.update( > deployment_id="5c939e08962d741e34f609f0", > name="Sample metric", > units="Y", > baseline_value=12, > is_model_specific=True, > aggregation_type=CustomMetricAggregationType.AVERAGE, > directionality=CustomMetricDirectionality.HIGHER_IS_BETTER > ) > ``` #### unset_baseline() Unset the baseline value of a custom metric - Return type: None > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > from datarobot.enums import CustomMetricAggregationType, CustomMetricDirectionality > > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > custom_metric.baseline_values > >>> [{'value': 12.0}] > custom_metric.unset_baseline() > custom_metric.baseline_values > >>> [] > ``` #### submit_values(data, model_id=None, model_package_id=None, dry_run=False) Submit aggregated custom metrics values from JSON. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > > # data for values over time > data = [{ > 'value': 12, > 'sample_size': 3, > 'timestamp': '2024-03-15T14:00:00' > }] > > # data witch association ID > data = [{ > 'value': 12, > 'sample_size': 3, > 'timestamp': '2024-03-15T14:00:00', > 'association_id': '65f44d04dbe192b552e752ed' > }] > > # data for batches > data = [{ > 'value': 12, > 'sample_size': 3, > 'batch': '65f44c93fedc5de16b673a0d' > }] > > # for deployment specific metrics > custom_metric.submit_values(data=data) > > # for model specific metrics pass model_package_id or model_id > custom_metric.submit_values(data=data, model_package_id="6421df32525c58cc6f991f25") > > # dry run > custom_metric.submit_values(data=data, model_package_id="6421df32525c58cc6f991f25", dry_run=True) > ``` #### submit_single_value(value, model_id=None, model_package_id=None, dry_run=False, segments=None) Submit a single custom metric value at the current moment. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > > # for deployment specific metrics > custom_metric.submit_single_value(value=121) > > # for model specific metrics pass model_package_id or model_id > custom_metric.submit_single_value(value=121, model_package_id="6421df32525c58cc6f991f25") > > # dry run > custom_metric.submit_single_value(value=121, model_package_id="6421df32525c58cc6f991f25", dry_run=True) > > # for segmented analysis > segments = [{"name": "custom_seg", "value": "val_1"}] > custom_metric.submit_single_value(value=121, model_package_id="6421df32525c58cc6f991f25", segments=segments) > ``` #### submit_values_from_catalog(dataset_id, model_id=None, model_package_id=None, batch_id=None, segments=None, geospatial=None) Submit aggregated custom metrics values from dataset (AI catalog). The names of the columns in the dataset should correspond to the names of the columns that were defined in the custom metric. In addition, the format of the timestamps should also be the same as defined in the metric. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > > # for deployment specific metrics > custom_metric.submit_values_from_catalog(dataset_id="61093144cabd630828bca321") > > # for model specific metrics pass model_package_id or model_id > custom_metric.submit_values_from_catalog( > dataset_id="61093144cabd630828bca321", > model_package_id="6421df32525c58cc6f991f25" > ) > > # for segmented analysis > segments = [{"name": "custom_seg", "column": "column_with_segment_values"}] > custom_metric.submit_values_from_catalog( > dataset_id="61093144cabd630828bca321", > model_package_id="6421df32525c58cc6f991f25", > segments=segments > ) > ``` #### get_values_over_time(start, end, model_package_id=None, model_id=None, segment_attribute=None, segment_value=None, bucket_size='P7D') Retrieve values of a single custom metric over a time period. - Parameters: - Returns: custom_metric_over_time – The queried custom metric values over time information. - Return type: CustomMetricValuesOverTime > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > from datetime import datetime, timedelta > > now=datetime.now() > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > values_over_time = custom_metric.get_values_over_time(start=now - timedelta(days=7), end=now) > > values_over_time.bucket_values > >>> {datetime.datetime(2024, 3, 22, 14, 0, tzinfo=tzutc()): 1.0, > >>> datetime.datetime(2024, 3, 22, 15, 0, tzinfo=tzutc()): 123.0}} > > values_over_time.bucket_sample_sizes > >>> {datetime.datetime(2024, 3, 22, 14, 0, tzinfo=tzutc()): 1, > >>> datetime.datetime(2024, 3, 22, 15, 0, tzinfo=tzutc()): 1}} > > values_over_time.get_buckets_as_dataframe() > >>> start end value sample_size > >>> 0 2024-03-21 16:00:00+00:00 2024-03-21 17:00:00+00:00 NaN NaN > >>> 1 2024-03-21 17:00:00+00:00 2024-03-21 18:00:00+00:00 NaN NaN > ``` #### get_values_over_space(start=None, end=None, model_id=None, model_package_id=None) Retrieve values of a custom metric over space. - Parameters: - Returns: custom_metric_over_space – The queried custom metric values over space information. - Return type: CustomMetricValuesOverSpace > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > values_over_space = custom_metric.get_values_over_space(model_package_id='6421df32525c58cc6f991f25') > ``` #### get_summary(start, end, model_package_id=None, model_id=None, segment_attribute=None, segment_value=None) Retrieve the summary of a custom metric over a time period. - Parameters: - Returns: custom_metric_summary – The summary of the custom metric. - Return type: CustomMetricSummary > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > from datetime import datetime, timedelta > > now=datetime.now() > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > summary = custom_metric.get_summary(start=now - timedelta(days=7), end=now) > > print(summary) > >> "CustomMetricSummary(2024-03-21 15:52:13.392178+00:00 - 2024-03-22 15:52:13.392168+00:00: > {'id': '65fd9b1c0c1a840bc6751ce0', 'name': 'Test METRIC', 'value': 215.0, 'sample_count': 13, > 'baseline_value': 12.0, 'percent_change': 24.02})" > ``` #### get_values_over_batch(batch_ids=None, model_package_id=None, model_id=None, segment_attribute=None, segment_value=None) Retrieve values of a single custom metric over batches. - Parameters: - Returns: custom_metric_over_batch – The queried custom metric values over batch information. - Return type: CustomMetricValuesOverBatch > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > # all batch metrics all model specific > values_over_batch = custom_metric.get_values_over_batch(model_package_id='6421df32525c58cc6f991f25') > > values_over_batch.bucket_values > >>> {'6572db2c9f9d4ad3b9de33d0': 35.0, '6572db2c9f9d4ad3b9de44e1': 105.0} > > values_over_batch.bucket_sample_sizes > >>> {'6572db2c9f9d4ad3b9de33d0': 6, '6572db2c9f9d4ad3b9de44e1': 8} > > values_over_batch.get_buckets_as_dataframe() > >>> batch_id batch_name value sample_size > >>> 0 6572db2c9f9d4ad3b9de33d0 Batch 1 - 03/26/2024 13:04:46 35.0 6 > >>> 1 6572db2c9f9d4ad3b9de44e1 Batch 2 - 03/26/2024 13:06:04 105.0 8 > ``` #### get_batch_summary(batch_ids=None, model_package_id=None, model_id=None, segment_attribute=None, segment_value=None) Retrieve the summary of a custom metric over a batch. - Parameters: - Returns: custom_metric_summary – The batch summary of the custom metric. - Return type: CustomMetricBatchSummary > [!NOTE] Examples > ``` > from datarobot.models.deployment import CustomMetric > > custom_metric = CustomMetric.get( > deployment_id="5c939e08962d741e34f609f0", > custom_metric_id="65f17bdcd2d66683cdfc1113" > ) > # all batch metrics all model specific > batch_summary = custom_metric.get_batch_summary(model_package_id='6421df32525c58cc6f991f25') > > print(batch_summary) > >> CustomMetricBatchSummary({'id': '6605396413434b3a7b74342c', 'name': 'batch metric', 'value': 41.25, > 'sample_count': 28, 'baseline_value': 123.0, 'percent_change': -66.46}) > ``` ### class datarobot.models.deployment.custom_metrics.CustomMetricValuesOverTime Custom metric over time information. Added in version v3.4. - Variables: #### classmethod get(deployment_id, custom_metric_id, start, end, model_id=None, model_package_id=None, segment_attribute=None, segment_value=None, bucket_size='P7D') Retrieve values of a single custom metric over a time period. - Parameters: - Returns: custom_metric_over_time – The queried custom metric values over time information. - Return type: CustomMetricValuesOverTime #### property bucket_values : Dict[datetime, int] The metric value for all time buckets, keyed by start time of the bucket. - Returns: bucket_values - Return type: Dict #### property bucket_sample_sizes : Dict[datetime, int] The sample size for all time buckets, keyed by start time of the bucket. - Returns: bucket_sample_sizes - Return type: Dict #### get_buckets_as_dataframe() Retrieves all custom metrics buckets in a pandas DataFrame. - Returns: buckets - Return type: pd.DataFrame ### class datarobot.models.deployment.custom_metrics.CustomMetricSummary The summary of a custom metric. Added in version v3.4. - Variables: #### classmethod get(deployment_id, custom_metric_id, start, end, model_id=None, model_package_id=None, segment_attribute=None, segment_value=None) Retrieve the summary of a custom metric over a time period. - Parameters: - Returns: custom_metric_summary – The summary of the custom metric. - Return type: CustomMetricSummary ### class datarobot.models.deployment.custom_metrics.CustomMetricValuesOverBatch Custom metric over batch information. Added in version v3.4. - Variables: #### classmethod get(deployment_id, custom_metric_id, batch_ids=None, model_id=None, model_package_id=None, segment_attribute=None, segment_value=None) Retrieve values of a single custom metric over batches. - Parameters: - Returns: custom_metric_over_batch – The queried custom metric values over batch information. - Return type: CustomMetricValuesOverBatch #### property bucket_values : Dict[str, int] The metric value for all batch buckets, keyed by batch ID - Returns: bucket_values - Return type: Dict #### property bucket_sample_sizes : Dict[str, int] The sample size for all batch buckets, keyed by batch ID. - Returns: bucket_sample_sizes - Return type: Dict #### get_buckets_as_dataframe() Retrieves all custom metrics buckets in a pandas DataFrame. - Returns: buckets - Return type: pd.DataFrame ### class datarobot.models.deployment.custom_metrics.CustomMetricBatchSummary The batch summary of a custom metric. Added in version v3.4. - Variables: metric ( Dict ) – The summary of the batch custom metric. #### classmethod get(deployment_id, custom_metric_id, batch_ids=None, model_id=None, model_package_id=None, segment_attribute=None, segment_value=None) Retrieve the summary of a custom metric over a batch. - Parameters: - Returns: custom_metric_summary – The batch summary of the custom metric. - Return type: CustomMetricBatchSummary ### class datarobot.models.deployment.custom_metrics.HostedCustomMetricTemplate Template for hosted custom metric. #### classmethod list(search=None, order_by=None, metric_type=None, offset=None, limit=None) List all hosted custom metric templates. - Parameters: - Returns: templates - Return type: List[HostedCustomMetricTemplate] #### classmethod get(template_id) Get a hosted custom metric template by ID. - Parameters: template_id ( str ) – ID of the template. - Returns: template - Return type: HostedCustomMetricTemplate ### class datarobot.models.deployment.custom_metrics.HostedCustomMetric Hosted custom metric. #### classmethod list(job_id, skip=None, limit=None) List all hosted custom metrics for a job. - Parameters: job_id ( str ) – ID of the job. - Returns: metrics - Return type: List[HostedCustomMetric] #### classmethod create_from_template(template_id, deployment_id, job_name, custom_metric_name, job_description=None, custom_metric_description=None, sidecar_deployment_id=None, baseline_value=None, timestamp=None, value=None, sample_count=None, batch=None, schedule=None, parameter_overrides=None) Create a hosted custom metric from a template. A shortcut for 2 calls: Job.from_custom_metric_template(template_id) HostedCustomMetrics.create_from_custom_job() - Parameters: - Returns: metric - Return type: HostedCustomMetric #### classmethod create_from_custom_job(custom_job_id, deployment_id, name, description=None, baseline_value=None, timestamp=None, value=None, sample_count=None, batch=None, schedule=None, parameter_overrides=None, geospatial_segment_attribute=None) Create a hosted custom metric from existing custom job. - Parameters: - Returns: metric - Return type: HostedCustomMetric #### update(name=None, description=None, units=None, directionality=None, aggregation_type=None, baseline_value=None, timestamp=None, value=None, sample_count=None, batch=None, schedule=None, parameter_overrides=None) Update the hosted custom metric. - Parameters: - Returns: metric - Return type: HostedCustomMetric #### delete() Delete the hosted custom metric. - Return type: None ### class datarobot.models.deployment.custom_metrics.DeploymentDetails Information about a hosted custom metric deployment. ### class datarobot.models.deployment.custom_metrics.MetricBaselineValue The baseline values for a custom metric. ### class datarobot.models.deployment.custom_metrics.SampleCountField A weight column used with columnar datasets if pre-aggregated metric values are provided. ### class datarobot.models.deployment.custom_metrics.ValueField A custom metric value source for when reading values from a columnar dataset like a file. ### class datarobot.models.deployment.custom_metrics.MetricTimestampSpoofing Custom metric timestamp spoofing. Occurs when reading values from a file, like a dataset. By default, replicates pd.to_datetime formatting behavior. ### class datarobot.models.deployment.custom_metrics.BatchField A custom metric batch ID source for when reading values from a columnar dataset like a file. ### class datarobot.models.deployment.custom_metrics.HostedCustomMetricBlueprint Hosted custom metric blueprints provide an option to share custom metric settings between multiple custom metrics sharing the same custom jobs. When a custom job of a hosted custom metric type is connected to the deployment, all the custom metric parameters from the blueprint are automatically copied. #### classmethod get(custom_job_id) Get a hosted custom metric blueprint. - Parameters: custom_job_id ( str ) – ID of the custom job. - Returns: blueprint - Return type: HostedCustomMetricBlueprint #### classmethod create(custom_job_id, directionality, units, type, time_step, is_model_specific, is_geospatial=None) Create a hosted custom metric blueprint. - Parameters: - Returns: blueprint - Return type: HostedCustomMetricBlueprint #### update(directionality=None, units=None, type=None, time_step=None, is_model_specific=None, is_geospatial=None) Update a hosted custom metric blueprint. - Parameters: - Returns: updated_blueprint - Return type: HostedCustomMetricBlueprint ### class datarobot.models.deployment.custom_metrics.CustomMetricValuesOverSpace Custom metric values over space. Added in version v3.7. - Variables: #### classmethod get(deployment_id, custom_metric_id, start=None, end=None, model_package_id=None, model_id=None) Retrieve custom metric values over space. - Parameters: - Returns: values_over_space – Custom metric values over geospatial hexagons. - Return type: CustomMetricValuesOverSpace --- # Custom models URL: https://docs.datarobot.com/en/docs/api/reference/sdk/custom-models.html # Custom models ### class datarobot.models.custom_model_version.CustomModelFileItem A file item attached to a DataRobot custom model version. Added in version v2.21. - Variables: ### class datarobot.CustomInferenceModel A custom inference model. Added in version v2.21. - Variables: #### classmethod list(is_deployed=None, search_for=None, order_by=None) List custom inference models available to the user. Added in version v2.21. - Parameters: - Returns: A list of custom inference models. - Return type: List[CustomInferenceModel] - Raises: #### classmethod get(custom_model_id) Get custom inference model by id. Added in version v2.21. - Parameters: custom_model_id ( str ) – The ID of the custom inference model. - Returns: Retrieved custom inference model. - Return type: CustomInferenceModel - Raises: #### download_latest_version(file_path) Download the latest custom inference model version. Added in version v2.21. - Parameters: file_path ( str ) – Path to create a file with custom model version content. - Raises: - Return type: None #### classmethod create(name, target_type, target_name=None, language=None, description=None, positive_class_label=None, negative_class_label=None, prediction_threshold=None, class_labels=None, class_labels_file=None, network_egress_policy=None, maximum_memory=None, replicas=None, is_training_data_for_versions_permanently_enabled=None) Create a custom inference model. Added in version v2.21. - Parameters: - Returns: Created a custom inference model. - Return type: CustomInferenceModel - Raises: #### classmethod copy_custom_model(custom_model_id) Create a custom inference model by copying existing one. Added in version v2.21. - Parameters: custom_model_id ( str ) – The ID of the custom inference model to copy. - Returns: Created a custom inference model. - Return type: CustomInferenceModel - Raises: #### update(name=None, language=None, description=None, target_name=None, positive_class_label=None, negative_class_label=None, prediction_threshold=None, class_labels=None, class_labels_file=None, is_training_data_for_versions_permanently_enabled=None) Update custom inference model properties. Added in version v2.21. - Parameters: - Raises: - Return type: None #### refresh() Update custom inference model with the latest data from server. Added in version v2.21. - Raises: - Return type: None #### delete() Delete custom inference model. Added in version v2.21. - Raises: - Return type: None #### assign_training_data(dataset_id, partition_column=None, max_wait=600) Assign training data to the custom inference model. Added in version v2.21. - Parameters: - Raises: - Return type: None #### get_access_list() Retrieve access control settings of this custom model. Added in version v2.36. - Return type: list of SharingAccess #### share(access_list) Update the access control settings of this custom model. Added in version v2.36. - Parameters: access_list ( list of SharingAccess ) – A list of SharingAccess to update. - Raises: - Return type: None > [!NOTE] Examples > Transfer access to the custom model from [old_user@datarobot.com](mailto:old_user@datarobot.com) to [new_user@datarobot.com](mailto:new_user@datarobot.com) > > ``` > import datarobot as dr > > new_access = dr.SharingAccess(new_user@datarobot.com, > dr.enums.SHARING_ROLE.OWNER, can_share=True) > access_list = [dr.SharingAccess(old_user@datarobot.com, None), new_access] > > dr.CustomInferenceModel.get('custom-model-id').share(access_list) > ``` ### class datarobot.CustomModelTest An custom model test. Added in version v2.21. - Variables: #### classmethod create(custom_model_id, custom_model_version_id, dataset_id=None, max_wait=600, network_egress_policy=None, maximum_memory=None, replicas=None) Create and start a custom model test. Added in version v2.21. - Parameters: - Returns: created custom model test - Return type: CustomModelTest - Raises: #### classmethod list(custom_model_id) List custom model tests. Added in version v2.21. - Parameters: custom_model_id ( str ) – the ID of the custom model - Returns: a list of custom model tests - Return type: List[CustomModelTest] - Raises: #### classmethod get(custom_model_test_id) Get custom model test by id. Added in version v2.21. - Parameters: custom_model_test_id ( str ) – the ID of the custom model test - Returns: retrieved custom model test - Return type: CustomModelTest - Raises: #### get_log() Get log of a custom model test. Added in version v2.21. - Raises: #### get_log_tail() Get log tail of a custom model test. Added in version v2.21. - Raises: #### cancel() Cancel custom model test that is in progress. Added in version v2.21. - Raises: #### refresh() Update custom model test with the latest data from server. Added in version v2.21. - Raises: ### class datarobot.CustomModelVersion A version of a DataRobot custom model. Added in version v2.21. - Variables: #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: CustomModelVersion #### classmethod create_clean(custom_model_id, base_environment_id=None, is_major_update=True, folder_path=None, files=None, network_egress_policy=None, maximum_memory=None, replicas=None, required_metadata_values=None, training_dataset_id=None, partition_column=None, holdout_dataset_id=None, keep_training_holdout_data=None, max_wait=600, runtime_parameter_values=None, base_environment_version_id=None) Create a custom model version without files from previous versions. > Create a version with training or holdout data: > If training/holdout data related parameters are provided, > the training data is assigned asynchronously. > In this case: > * if max_wait is not None, the function returns once the job is finished. > * if max_wait is None, the function returns immediately. Progress can be polled by the user (see examples).If training data assignment fails, new version is still created, > but it is not allowed to create a model package (version) for the model version and to deploy it. > To check for training data assignment error, check version.training_data.assignment_error[“message”]. Added in version v2.21. - Parameters: - Returns: Created custom model version. - Return type: CustomModelVersion - Raises: > [!NOTE] Examples > Create a version with blocking (default max_wait=600) training data assignment: > > ``` > import datarobot as dr > from datarobot.errors import TrainingDataAssignmentError > > dr.Client(token=my_token, endpoint=endpoint) > > try: > version = dr.CustomModelVersion.create_clean( > custom_model_id="6444482e5583f6ee2e572265", > base_environment_id="642209acc563893014a41e24", > training_dataset_id="6421f2149a4f9b1bec6ad6dd", > ) > except TrainingDataAssignmentError as e: > print(e) > ``` > > Create a version with non-blocking training data assignment: > > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > > version = dr.CustomModelVersion.create_clean( > custom_model_id="6444482e5583f6ee2e572265", > base_environment_id="642209acc563893014a41e24", > training_dataset_id="6421f2149a4f9b1bec6ad6dd", > max_wait=None, > ) > > while version.training_data.assignment_in_progress: > time.sleep(10) > version.refresh() > if version.training_data.assignment_error: > print(version.training_data.assignment_error["message"]) > ``` #### classmethod create_from_previous(custom_model_id, base_environment_id=None, is_major_update=True, folder_path=None, files=None, files_to_delete=None, network_egress_policy=None, maximum_memory=None, replicas=None, required_metadata_values=None, training_dataset_id=None, partition_column=None, holdout_dataset_id=None, keep_training_holdout_data=None, max_wait=600, runtime_parameter_values=None, base_environment_version_id=None, runtime_parameters=None) Create a custom model version containing files from a previous version. > Create a version with training/holdout data: > If training/holdout data related parameters are provided, > the training data is assigned asynchronously. > In this case: > * if max_wait is not None, function returns once job is finished. > * if max_wait is None, function returns immediately, progress can be polled by the user, see examples.If training data assignment fails, new version is still created, > but it is not allowed to create a model package (version) for the model version and to deploy it. > To check for training data assignment error, check version.training_data.assignment_error[“message”]. Added in version v2.21. - Parameters: - Returns: created custom model version - Return type: CustomModelVersion - Raises: > [!NOTE] Examples > Create a version with blocking (default max_wait=600) training data assignment: > > ``` > import datarobot as dr > from datarobot.errors import TrainingDataAssignmentError > > dr.Client(token=my_token, endpoint=endpoint) > > try: > version = dr.CustomModelVersion.create_from_previous( > custom_model_id="6444482e5583f6ee2e572265", > base_environment_id="642209acc563893014a41e24", > training_dataset_id="6421f2149a4f9b1bec6ad6dd", > ) > except TrainingDataAssignmentError as e: > print(e) > ``` > > Create a version with non-blocking training data assignment: > > ``` > import datarobot as dr > > dr.Client(token=my_token, endpoint=endpoint) > > version = dr.CustomModelVersion.create_from_previous( > custom_model_id="6444482e5583f6ee2e572265", > base_environment_id="642209acc563893014a41e24", > training_dataset_id="6421f2149a4f9b1bec6ad6dd", > max_wait=None, > ) > > while version.training_data.assignment_in_progress: > time.sleep(10) > version.refresh() > if version.training_data.assignment_error: > print(version.training_data.assignment_error["message"]) > ``` #### classmethod list(custom_model_id) List custom model versions. Added in version v2.21. - Parameters: custom_model_id ( str ) – The ID of the custom model. - Returns: A list of custom model versions. - Return type: List[CustomModelVersion] - Raises: #### classmethod get(custom_model_id, custom_model_version_id) Get custom model version by id. Added in version v2.21. - Parameters: - Returns: Retrieved custom model version. - Return type: CustomModelVersion - Raises: #### download(file_path) Download custom model version. Added in version v2.21. - Parameters: file_path ( str ) – Path to create a file with custom model version content. - Raises: - Return type: None #### update(description=None, required_metadata_values=None) Update custom model version properties. Added in version v2.21. - Parameters: - Raises: - Return type: None #### refresh() Update custom model version with the latest data from server. Added in version v2.21. - Raises: - Return type: None #### get_feature_impact(with_metadata=False) Get custom model feature impact. Added in version v2.23. - Parameters: with_metadata ( bool ) – The flag indicating if the result should include the metadata as well. - Returns: feature_impacts – The feature impact data. Each item is a dict with the keys ‘featureName’, ‘impactNormalized’, and ‘impactUnnormalized’, and ‘redundantWith’. - Return type: list of dict - Raises: #### calculate_feature_impact(max_wait=600) Calculate custom model feature impact. Added in version v2.23. - Parameters: max_wait ( Optional[int] ) – Max time to wait for feature impact calculation. If set to None - method will return without waiting. Defaults to 10 min - Raises: - Return type: None ### class datarobot.models.execution_environment.RequiredMetadataKey Definition of a metadata key that custom models using this environment must define Added in version v2.25. - Variables: ### class datarobot.models.CustomModelVersionConversion A conversion of a DataRobot custom model version. Added in version v2.27. - Variables: #### classmethod run_conversion(custom_model_id, custom_model_version_id, main_program_item_id, max_wait=None) Initiate a new custom model version conversion. - Parameters: - Returns: conversion_id – The ID of the newly created conversion entity. - Return type: str - Raises: #### classmethod stop_conversion(custom_model_id, custom_model_version_id, conversion_id) Stop a conversion that is in progress. - Parameters: - Raises: - Return type: Response #### classmethod get(custom_model_id, custom_model_version_id, conversion_id) Get custom model version conversion by id. Added in version v2.27. - Parameters: - Returns: Retrieved custom model version conversion. - Return type: CustomModelVersionConversion - Raises: #### classmethod get_latest(custom_model_id, custom_model_version_id) Get latest custom model version conversion for a given custom model version. Added in version v2.27. - Parameters: - Returns: Retrieved latest conversion for a given custom model version. - Return type: CustomModelVersionConversion or None - Raises: #### classmethod list(custom_model_id, custom_model_version_id) Get custom model version conversions list per custom model version. Added in version v2.27. - Parameters: - Returns: Retrieved conversions for a given custom model version. - Return type: List[CustomModelVersionConversion] - Raises: ### class datarobot.CustomModelVersionDependencyBuild Metadata about a DataRobot custom model version’s dependency build Added in version v2.22. - Variables: #### classmethod get_build_info(custom_model_id, custom_model_version_id) Retrieve information about a custom model version’s dependency build Added in version v2.22. - Parameters: - Returns: The dependency build information. - Return type: CustomModelVersionDependencyBuild #### classmethod start_build(custom_model_id, custom_model_version_id, max_wait=600) Start the dependency build for a custom model version dependency build Added in version v2.22. - Parameters: - Return type: Optional [ CustomModelVersionDependencyBuild ] #### get_log() Get log of a custom model version dependency build. Added in version v2.22. - Raises: - Return type: str #### cancel() Cancel custom model version dependency build that is in progress. Added in version v2.22. - Raises: - Return type: None #### refresh() Update custom model version dependency build with the latest data from server. Added in version v2.22. - Raises: - Return type: None ### class datarobot.ExecutionEnvironment An execution environment entity. Added in version v2.21. - Variables: #### classmethod create(name, description=None, programming_language=None, required_metadata_keys=None, is_public=None, use_cases=None) Create an execution environment. Added in version v2.21. - Parameters: - Returns: created execution environment - Return type: ExecutionEnvironment - Raises: #### classmethod list(search_for=None, is_own=None, use_cases=None, is_public=None, offset=0, limit=0) List execution environments available to the user. Added in version v2.21. - Parameters: - Returns: a list of execution environments. - Return type: List[ExecutionEnvironment] - Raises: #### classmethod get(execution_environment_id) Get execution environment by its ID. Added in version v2.21. - Parameters: execution_environment_id ( str ) – ID of the execution environment to retrieve - Returns: retrieved execution environment - Return type: ExecutionEnvironment - Raises: #### delete() Delete execution environment. Added in version v2.21. - Raises: - Return type: None #### update(name=None, description=None, required_metadata_keys=None, is_public=None, use_cases=None) Update execution environment properties. Added in version v2.21. - Parameters: - Raises: - Return type: None #### refresh() Update execution environment with the latest data from server. Added in version v2.21. - Raises: - Return type: None #### get_access_list() Retrieve access control settings of this environment. Added in version v2.36. - Return type: list of SharingAccess #### share(access_list) Update the access control settings of this execution environment. Added in version v2.36. - Parameters: access_list ( list of SharingAccess ) – A list of SharingAccess to update. - Raises: - Return type: None > [!NOTE] Examples > Transfer access to the execution environment from [old_user@datarobot.com](mailto:old_user@datarobot.com) to [new_user@datarobot.com](mailto:new_user@datarobot.com) > > ``` > import datarobot as dr > > new_access = dr.SharingAccess(new_user@datarobot.com, > dr.enums.SHARING_ROLE.OWNER, can_share=True) > access_list = [dr.SharingAccess(old_user@datarobot.com, None), new_access] > > dr.ExecutionEnvironment.get('environment-id').share(access_list) > ``` ### class datarobot.ExecutionEnvironmentVersion A version of a DataRobot execution environment. Added in version v2.21. - Variables: #### classmethod create(execution_environment_id, docker_context_path=None, docker_image_uri=None, label=None, description=None, max_wait=600) Create an execution environment version. Added in version v2.21. - Parameters: - Returns: created execution environment version - Return type: ExecutionEnvironmentVersion - Raises: #### classmethod list(execution_environment_id, build_status=None) List execution environment versions available to the user. .. versionadded:: v2.21 - Parameters: - Returns: a list of execution environment versions. - Return type: List[ExecutionEnvironmentVersion] - Raises: #### classmethod get(execution_environment_id, version_id) Get execution environment version by id. Added in version v2.21. - Parameters: - Returns: retrieved execution environment version - Return type: ExecutionEnvironmentVersion - Raises: #### download(file_path) Download execution environment version. Added in version v2.21. - Parameters: file_path ( str ) – path to create a file with execution environment version content - Returns: retrieved execution environment version - Return type: ExecutionEnvironmentVersion - Raises: #### get_build_log() Get execution environment version build log and error. Added in version v2.21. - Returns: retrieved execution environment version build log and error. If there is no build error - None is returned. - Return type: Tuple[str , str] - Raises: #### refresh() Update execution environment version with the latest data from server. Added in version v2.21. - Raises: - Return type: None ### class datarobot.models.custom_model_version.HoldoutData Holdout data assigned to a DataRobot custom model version. Added in version v3.2. - Variables: ### class datarobot.models.custom_model_version.TrainingData Training data assigned to a DataRobot custom model version. Added in version v3.2. - Variables: ### class datarobot.models.custom_model_version.RuntimeParameter Definition of a runtime parameter used for the custom model version, it includes : the override value if provided. Added in version v3.4.0. - Variables: #### to_dict() Serialize this parameter for use in the `runtime_parameters` creation argument. Returns a dict with snake_case keys that are converted to camelCase before sending to the API. Only fields relevant to parameter creation are included; server-computed fields ( `override_value`, `key_value_id`) are excluded. Optional fields with a `None` value are omitted so the server applies its own defaults. - Return type: Dict [ str , Any ] ### class datarobot.models.custom_model_version.RuntimeParameterValue The definition of a runtime parameter value used for the custom model version, this defines the runtime parameter override. Added in version v3.4.0. - Variables: --- # Data connectivity URL: https://docs.datarobot.com/en/docs/api/reference/sdk/data-connectivity.html # Database connectivity ### class datarobot.DataDriver A data driver - Variables: #### classmethod list(typ=None) Returns list of available drivers. - Parameters: typ ( DataDriverListTypes ) – If specified, filters by specified driver type. - Returns: drivers – contains a list of available drivers. - Return type: list of DataDriver instances > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> drivers = dr.DataDriver.list() > >>> drivers > [DataDriver('mysql'), DataDriver('RedShift'), DataDriver('PostgreSQL')] > ``` #### classmethod get(driver_id) Gets the driver. - Parameters: driver_id ( str ) – the identifier of the driver. - Returns: driver – the required driver. - Return type: DataDriver > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> driver = dr.DataDriver.get('5ad08a1889453d0001ea7c5c') > >>> driver > DataDriver('PostgreSQL') > ``` #### classmethod create(class_name, canonical_name, files=None, typ=None, database_driver=None) Creates the driver. Only available to admin users. - Parameters: - Returns: driver – the created driver. - Return type: DataDriver - Raises: ClientError – raised if user is not granted for Can manage JDBC database drivers feature > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> driver = dr.DataDriver.create( > ... class_name='org.postgresql.Driver', > ... canonical_name='PostgreSQL', > ... files=['/tmp/postgresql-42.2.2.jar'] > ... ) > >>> driver > DataDriver('PostgreSQL') > ``` #### update(class_name=None, canonical_name=None) Updates the driver. Only available to admin users. - Parameters: - Raises: ClientError – raised if user is not granted for Can manage JDBC database drivers feature - Return type: None > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> driver = dr.DataDriver.get('5ad08a1889453d0001ea7c5c') > >>> driver.canonical_name > 'PostgreSQL' > >>> driver.update(canonical_name='postgres') > >>> driver.canonical_name > 'postgres' > ``` #### delete() Removes the driver. Only available to admin users. - Raises: ClientError – raised if user is not granted for Can manage JDBC database drivers feature - Return type: None ### class datarobot.Connector A connector - Variables: #### classmethod list(data_type=None) Returns list of available connectors. - Parameters: data_type ( DataTypes ) – If specified, returns the connectors that support the specified data type. If not specified, it will default to DataTypes.ALL - Returns: connectors – contains a list of available connectors. - Return type: list of Connector instances > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> connectors = dr.Connector.list() > >>> connectors > [Connector('ADLS Gen2 Connector'), Connector('S3 Connector')] > ``` #### classmethod get(connector_id) Gets the connector. - Parameters: connector_id ( str ) – the identifier of the connector. - Returns: connector – the required connector. - Return type: Connector > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> connector = dr.Connector.get('5fe1063e1c075e0245071446') > >>> connector > Connector('ADLS Gen2 Connector') > ``` #### classmethod create(file_path=None, connector_type=None) Creates the connector from a jar file. Only available to admin users. - Parameters: - Returns: connector – the created connector. - Return type: Connector - Raises: ClientError – raised if user is not granted for Can manage connectors feature > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> connector = dr.Connector.create('/tmp/connector-adls-gen2.jar') > >>> connector > Connector('ADLS Gen2 Connector') > ``` #### update(file_path) Updates the connector with new jar file. Only available to admin users. - Parameters: file_path ( str ) – (Deprecated in version v3.6) the file path on file system file_path(s) for the java-based connector. - Returns: connector – the updated connector. - Return type: Connector - Raises: ClientError – raised if user is not granted for Can manage connectors feature > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> connector = dr.Connector.get('5fe1063e1c075e0245071446') > >>> connector.base_name > 'connector-adls-gen2.jar' > >>> connector.update('/tmp/connector-s3.jar') > >>> connector.base_name > 'connector-s3.jar' > ``` #### delete() Removes the connector. Only available to admin users. - Raises: ClientError – raised if user is not granted for Can manage connectors feature - Return type: None ### class datarobot.DataStore A data store. Represents database - Variables: #### classmethod list(typ=None, name=None, substitute_url_parameters=False, data_type=None) Returns list of available data stores. - Parameters: - Returns: data_stores – contains a list of available data stores. - Return type: list of DataStore instances > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_stores = dr.DataStore.list() > >>> data_stores > [DataStore('Demo'), DataStore('Airlines')] > ``` #### classmethod get(data_store_id, substitute_url_parameters=False) Gets the data store. - Parameters: - Returns: data_store – the required data store. - Return type: DataStore > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_store = dr.DataStore.get('5a8ac90b07a57a0001be501e') > >>> data_store > DataStore('Demo') > ``` #### classmethod create(data_store_type, canonical_name, driver_id=None, jdbc_url=None, fields=None, connector_id=None) Creates the data store. - Parameters: - Returns: data_store – the created data store. - Return type: DataStore > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_store = dr.DataStore.create( > ... data_store_type='jdbc', > ... canonical_name='Demo DB', > ... driver_id='5a6af02eb15372000117c040', > ... jdbc_url='jdbc:postgresql://my.db.address.org:5432/perftest' > ... ) > >>> data_store > DataStore('Demo DB') > ``` #### update(canonical_name=None, driver_id=None, connector_id=None, jdbc_url=None, fields=None) Updates the data store. - Parameters: - Return type: None > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_store = dr.DataStore.get('5ad5d2afef5cd700014d3cae') > >>> data_store > DataStore('Demo DB') > >>> data_store.update(canonical_name='Demo DB updated') > >>> data_store > DataStore('Demo DB updated') > ``` #### delete() Removes the DataStore - Return type: None #### test(username=None, password=None, credential_id=None, use_kerberos=None, credential_data=None, set_default_credential=False) Tests database connection. Changed in version v3.2: Added credential_id, use_kerberos and credential_data optional params and made username and password optional. Changed in version v3.9: If credential_id is provided and set_default_credential is True and the connection test is successful, the credential is set as the default for this data store. - Parameters: - Returns: message – message with status. - Return type: dict - Raises: CredentialsError – If unable to set the provided credential_id as default for this data store. > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_store = dr.DataStore.get('5ad5d2afef5cd700014d3cae') > >>> data_store.test(username='db_username', password='db_password') > {'message': 'Connection successful'} > ``` #### schemas(username, password) Returns list of available schemas. - Parameters: - Returns: response – dict with database name and list of str - available schemas - Return type: dict > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_store = dr.DataStore.get('5ad5d2afef5cd700014d3cae') > >>> data_store.schemas(username='db_username', password='db_password') > {'catalog': 'perftest', 'schemas': ['demo', 'information_schema', 'public']} > ``` #### tables(username, password, schema=None) Returns list of available tables in schema. - Parameters: - Returns: response – dict with catalog name and tables info - Return type: dict > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_store = dr.DataStore.get('5ad5d2afef5cd700014d3cae') > >>> data_store.tables(username='db_username', password='db_password', schema='demo') > {'tables': [{'type': 'TABLE', 'name': 'diagnosis', 'schema': 'demo'}, {'type': 'TABLE', > 'name': 'kickcars', 'schema': 'demo'}, {'type': 'TABLE', 'name': 'patient', > 'schema': 'demo'}, {'type': 'TABLE', 'name': 'transcript', 'schema': 'demo'}], > 'catalog': 'perftest'} > ``` #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: DataStore #### get_shared_roles() Retrieve what users have access to this data store Added in version v3.2. - Return type: list of SharingRole #### share(access_list) Modify the ability of users to access this data store Added in version v2.14. - Parameters: access_list ( list of SharingRole ) – the modifications to make. - Return type: None - Raises: datarobot.ClientError : – if you do not have permission to share this data store, if the user you’re sharing with doesn’t exist, if the same user appears multiple times in the access_list, or if these changes would leave the data store without an owner. > [!NOTE] Examples > The [SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) class is needed in order to > share a Data Store with one or more users. > > For example, suppose you had a list of user IDs you wanted to share this DataStore with. You could use > a loop to generate a list of [SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) objects for them, > and bulk share this Data Store. > > ``` > >>> import datarobot as dr > >>> from datarobot.models.sharing import SharingRole > >>> from datarobot.enums import SHARING_ROLE, SHARING_RECIPIENT_TYPE > >>> > >>> user_ids = ["60912e09fd1f04e832a575c1", "639ce542862e9b1b1bfa8f1b", "63e185e7cd3a5f8e190c6393"] > >>> sharing_roles = [] > >>> for user_id in user_ids: > ... new_sharing_role = SharingRole( > ... role=SHARING_ROLE.CONSUMER, > ... share_recipient_type=SHARING_RECIPIENT_TYPE.USER, > ... id=user_id, > ... can_share=True, > ... ) > ... sharing_roles.append(new_sharing_role) > >>> dr.DataStore.get('my-data-store-id').share(access_list) > ``` > > Similarly, a [SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) instance can be used to > remove a user’s access if the `role` is set to `SHARING_ROLE.NO_ROLE`, like in this example: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.sharing import SharingRole > >>> from datarobot.enums import SHARING_ROLE, SHARING_RECIPIENT_TYPE > >>> > >>> user_to_remove = "foo.bar@datarobot.com" > ... remove_sharing_role = SharingRole( > ... role=SHARING_ROLE.NO_ROLE, > ... share_recipient_type=SHARING_RECIPIENT_TYPE.USER, > ... username=user_to_remove, > ... can_share=False, > ... ) > >>> dr.DataStore.get('my-data-store-id').share(roles=[remove_sharing_role]) > ``` ### class datarobot.DataSource A data source. Represents data request - Variables: #### classmethod list(typ=None) Returns list of available data sources. - Parameters: typ ( DataStoreListTypes ) – If specified, filters by specified datasource type. If not specified it will default to DataStoreListTypes.DATABASES - Returns: data_sources – contains a list of available data sources. - Return type: list of DataSource instances > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_sources = dr.DataSource.list() > >>> data_sources > [DataSource('Diagnostics'), DataSource('Airlines 100mb'), DataSource('Airlines 10mb')] > ``` #### classmethod get(data_source_id) Gets the data source. - Parameters: data_source_id ( str ) – the identifier of the data source. - Returns: data_source – the requested data source. - Return type: DataSource > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_source = dr.DataSource.get('5a8ac9ab07a57a0001be501f') > >>> data_source > DataSource('Diagnostics') > ``` #### classmethod create(data_source_type, canonical_name, params) Creates the data source. - Parameters: - Returns: data_source – the created data source. - Return type: DataSource > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> params = dr.DataSourceParameters( > ... data_store_id='5a8ac90b07a57a0001be501e', > ... query='SELECT * FROM airlines10mb WHERE "Year" >= 1995;' > ... ) > >>> data_source = dr.DataSource.create( > ... data_source_type='jdbc', > ... canonical_name='airlines stats after 1995', > ... params=params > ... ) > >>> data_source > DataSource('airlines stats after 1995') > ``` #### update(canonical_name=None, params=None) Creates the data source. - Parameters: - Return type: None > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_source = dr.DataSource.get('5ad840cc613b480001570953') > >>> data_source > DataSource('airlines stats after 1995') > >>> params = dr.DataSourceParameters( > ... query='SELECT * FROM airlines10mb WHERE "Year" >= 1990;' > ... ) > >>> data_source.update( > ... canonical_name='airlines stats after 1990', > ... params=params > ... ) > >>> data_source > DataSource('airlines stats after 1990') > ``` #### delete() Removes the DataSource - Return type: None #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( TDataSource , bound= DataSource) #### get_access_list() Retrieve what users have access to this data source Added in version v2.14. - Return type: list of SharingAccess #### share(access_list) Modify the ability of users to access this data source Added in version v2.14. - Parameters: access_list ( list of SharingAccess ) – The modifications to make. - Return type: None - Raises: datarobot.ClientError: – If you do not have permission to share this data source, if the user you’re sharing with doesn’t exist, if the same user appears multiple times in the access_list, or if these changes would leave the data source without an owner. > [!NOTE] Examples > Transfer access to the data source from [old_user@datarobot.com](mailto:old_user@datarobot.com) to [new_user@datarobot.com](mailto:new_user@datarobot.com) > > ``` > from datarobot.enums import SHARING_ROLE > from datarobot.models.data_source import DataSource > from datarobot.models.sharing import SharingAccess > > new_access = SharingAccess( > "new_user@datarobot.com", > SHARING_ROLE.OWNER, > can_share=True, > ) > access_list = [ > SharingAccess("old_user@datarobot.com", SHARING_ROLE.OWNER, can_share=True), > new_access, > ] > > DataSource.get('my-data-source-id').share(access_list) > ``` #### create_dataset(username=None, password=None, do_snapshot=None, persist_data_after_ingestion=None, categories=None, credential_id=None, use_kerberos=None) Create a [Dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset) from this data source. Added in version v2.22. - Parameters: - Returns: response – The Dataset created from the uploaded data - Return type: Dataset ### class datarobot.DataSourceParameters Data request configuration - Variables: # JDBC data preview Preview data from a JDBC URL by executing SQL without creating a data store. ### class datarobot.JdbcPreview JDBC data preview API: run SQL against a JDBC URL and get a row-limited preview without creating a data store. Entry-point class with [preview()](https://docs.datarobot.com/en/docs/api/reference/sdk/data-connectivity.html#datarobot.JdbcPreview.preview) returning a [JdbcPreviewData](https://docs.datarobot.com/en/docs/api/reference/sdk/data-connectivity.html#datarobot.JdbcPreviewData). #### classmethod preview(jdbc_url, sql, max_rows=1000, parameters=None) Preview data from a JDBC URL by executing SQL without creating a data store. Executes the given SQL against the JDBC URL and returns a row-limited preview. Connection credentials and parameters may be specified in the JDBC URL and/or in the `parameters` dict (e.g.`user`, `password`, `ssl`, `timeout`). - Parameters: - Returns: Object with columns (list of column names), records (list of rows), and result_schema (list of JdbcResultSchemaEntry ), if returned by the server. - Return type: JdbcPreviewData > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> preview = dr.JdbcPreview.preview( > ... jdbc_url='jdbc:postgresql://localhost:5432/mydb', > ... sql='SELECT * FROM public.users', > ... max_rows=5, > ... parameters={'user': 'dbuser', 'password': 'secret'}, > ... ) > >>> preview.columns > ['id', 'name', 'email'] > >>> len(preview.records) > 5 > ``` ### class datarobot.JdbcPreviewData A JDBC data preview: columns, records, and optional result schema from running SQL against a JDBC URL. ### class datarobot.JdbcResultSchemaEntry Column metadata for one column in a JDBC data preview result schema. Returned as elements of the `result_schema` attribute of [JdbcPreviewData](https://docs.datarobot.com/en/docs/api/reference/sdk/data-connectivity.html#datarobot.JdbcPreviewData). Built via validation in [JdbcPreviewData](https://docs.datarobot.com/en/docs/api/reference/sdk/data-connectivity.html#datarobot.JdbcPreviewData). - Variables: # Data store ### class datarobot.models.data_store.TestResponse ### class datarobot.models.data_store.SchemasResponse ### class datarobot.models.data_store.TablesResponse --- # Data exports URL: https://docs.datarobot.com/en/docs/api/reference/sdk/data-exploration.html # Data exports ### class datarobot.models.deployment.data_exports.PredictionDataExport A prediction data export. Added in version v3.4. - Variables: #### classmethod list(deployment_id, status=None, model_id=None, batch=None, offset=0, limit=100) Retrieve a list of prediction data exports. - Parameters: - Returns: prediction_data_exports – A list of PredictionDataExport objects. - Return type: list > [!NOTE] Examples > ``` > from datarobot.models.deployment import PredictionDataExport > > prediction_data_exports = PredictionDataExport.list(deployment_id='5c939e08962d741e34f609f0') > ``` #### classmethod get(deployment_id, export_id) Retrieve a single prediction data export. - Parameters: - Returns: prediction_data_export – A prediction data export. - Return type: PredictionDataExport > [!NOTE] Examples > ``` > from datarobot.models.deployment import PredictionDataExport > > prediction_data_export = PredictionDataExport.get( > deployment_id='5c939e08962d741e34f609f0', export_id='65fbe59aaa3f847bd5acc75b' > ) > ``` #### classmethod create(deployment_id, start, end, model_id=None, batch_ids=None, max_wait=600) Create a deployment prediction data export. Waits until ready and fetches PredictionDataExport after the export finishes. This method is blocking. - Parameters: - Returns: prediction_data_export – A prediction data export. - Return type: PredictionDataExport > [!NOTE] Examples > ``` > from datetime import datetime, timedelta > from datarobot.models.deployment import PredictionDataExport > > now=datetime.now() > prediction_data_export = PredictionDataExport.create( > deployment_id='5c939e08962d741e34f609f0', start=now - timedelta(days=7), end=now > ) > ``` #### fetch_data() Return data from prediction export as datarobot Dataset. - Returns: prediction_datasets – List of datasets for a given export, most often it is just one. - Return type: List[Dataset] > [!NOTE] Examples > ``` > from datarobot.models.deployment import PredictionDataExport > > prediction_data_export = PredictionDataExport.get( > deployment_id='5c939e08962d741e34f609f0', export_id='65fbe59aaa3f847bd5acc75b' > ) > prediction_datasets = prediction_data_export.fetch_data() > ``` ### class datarobot.models.deployment.data_exports.ActualsDataExport An actuals data export. Added in version v3.4. - Variables: #### classmethod list(deployment_id, status=None, offset=0, limit=100) Retrieve a list of actuals data exports. - Parameters: - Returns: actuals_data_exports – A list of ActualsDataExport objects. - Return type: list > [!NOTE] Examples > ``` > from datarobot.models.deployment import ActualsDataExport > > actuals_data_exports = ActualsDataExport.list(deployment_id='5c939e08962d741e34f609f0') > ``` #### classmethod get(deployment_id, export_id) Retrieve a single actuals data export. - Parameters: - Returns: actuals_data_export – An actuals data export. - Return type: ActualsDataExport > [!NOTE] Examples > ``` > from datarobot.models.deployment import ActualsDataExport > > actuals_data_export = ActualsDataExport.get( > deployment_id='5c939e08962d741e34f609f0', export_id='65fb0a6c9bb187781cfdea36' > ) > ``` #### classmethod create(deployment_id, start, end, model_id=None, only_matched_predictions=None, max_wait=600) Create a deployment actuals data export. Waits until ready and fetches ActualsDataExport after the export finishes. This method is blocking. - Parameters: - Returns: actuals_data_export – An actuals data export. - Return type: ActualsDataExport > [!NOTE] Examples > ``` > from datetime import datetime, timedelta > from datarobot.models.deployment import ActualsDataExport > > now=datetime.now() > actuals_data_export = ActualsDataExport.create( > deployment_id='5c939e08962d741e34f609f0', start=now - timedelta(days=7), end=now > ) > ``` #### fetch_data() Return data from actuals export as datarobot Dataset. - Returns: actuals_datasets – List of datasets for a given export, most often it is just one. - Return type: List[Dataset] > [!NOTE] Examples > ``` > from datarobot.models.deployment import ActualsDataExport > > actuals_data_export = ActualsDataExport.get( > deployment_id='5c939e08962d741e34f609f0', export_id='65fb0a6c9bb187781cfdea36' > ) > actuals_datasets = actuals_data_export.fetch_data() > ``` ### class datarobot.models.deployment.data_exports.TrainingDataExport A training data export. Added in version v3.4. - Variables: #### classmethod list(deployment_id) Retrieve a list of successful training data exports. - Parameters: deployment_id ( str ) – The ID of the deployment. - Returns: training_data_exports – A list of TrainingDataExport objects. - Return type: list > [!NOTE] Examples > ``` > from datarobot.models.deployment import TrainingDataExport > > training_data_exports = TrainingDataExport.list(deployment_id='5c939e08962d741e34f609f0') > ``` #### classmethod get(deployment_id, export_id) Retrieve a single training data export. - Parameters: - Returns: training_data_export – A training data export. - Return type: TrainingDataExport > [!NOTE] Examples > ``` > from datarobot.models.deployment import TrainingDataExport > training_data_export = TrainingDataExport.get( > deployment_id='5c939e08962d741e34f609f0', export_id='65fbf2356124f1daa3acc522' > ) > ``` #### classmethod create(deployment_id, model_id=None, max_wait=600) Create a single training data export. Waits until ready and fetches TrainingDataExport after the export finishes. This method is blocking. - Parameters: - Return type: str - Returns: #### fetch_data() Return data from training data export as datarobot Dataset. - Returns: training_dataset – A datasets for a given export. - Return type: Dataset > [!NOTE] Examples > ``` > from datarobot.models.deployment import TrainingDataExport > > training_data_export = TrainingDataExport.get( > deployment_id='5c939e08962d741e34f609f0', export_id='65fbf2356124f1daa3acc522' > ) > training_data_export = training_data_export.fetch_data() > ``` ### class datarobot.models.deployment.data_exports.DataQualityExport A data quality export record. Added in version v3.6. - Variables: #### classmethod list(deployment_id, start, end, model_id=None, prediction_pattern=None, prompt_pattern=None, actual_pattern=None, order_by=None, order_metric=None, filter_metric=None, filter_value=None, offset=0, limit=100) Retrieve a list of data-quality export records for a given deployment. Added in version v3.6. - Parameters: - Returns: data_quality_exports – A list of DataQualityExport objects. - Return type: list > [!NOTE] Examples > ``` > from datarobot.models.deployment import DataQualityExport > > data_quality_exports = DataQualityExport.list( > deployment_id='5c939e08962d741e34f609f0', start_time='2024-07-01', end_time='2024-08-01 > ) > ``` --- # Data Registry URL: https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html # Datasets ### class datarobot.models.Dataset Represents a Dataset returned from the api/v2/datasets/ endpoints. - Variables: #### get_uri() - Returns: url – Permanent static hyperlink to this dataset in AI Catalog. - Return type: str #### classmethod upload(source) This method covers Dataset creation from local materials (file & DataFrame) and a URL. - Parameters: source ( str , pd.DataFrame or file object ) – Pass a URL, filepath, file or DataFrame to create and return a Dataset. - Returns: response – The Dataset created from the uploaded data source. - Return type: Dataset - Raises: InvalidUsageError – If the source parameter cannot be determined to be a URL, filepath, file or DataFrame. > [!NOTE] Examples > ``` > # Upload a local file > dataset_one = Dataset.upload("./data/examples.csv") > > # Create a dataset via URL > dataset_two = Dataset.upload( > "https://raw.githubusercontent.com/curran/data/gh-pages/dbpedia/cities/data.csv" > ) > > # Create dataset with a pandas Dataframe > dataset_three = Dataset.upload(my_df) > > # Create dataset using a local file > with open("./data/examples.csv", "rb") as file_pointer: > dataset_four = Dataset.create_from_file(filelike=file_pointer) > ``` #### classmethod create_from_file(cls, file_path=None, filelike=None, categories=None, read_timeout=600, max_wait=600, , use_cases=None) A blocking call that creates a new Dataset from a file. Returns when the dataset has been successfully uploaded and processed. Warning: This function does not clean up it’s open files. If you pass a filelike, you are responsible for closing it. If you pass a file_path, this will create a file object from the file_path but will not close it. - Parameters: - Returns: response – A fully armed and operational Dataset - Return type: Dataset #### classmethod create_from_in_memory_data(cls, data_frame=None, records=None, categories=None, read_timeout=600, max_wait=600, fname=None, , use_cases=None) A blocking call that creates a new Dataset from in-memory data. Returns when the dataset has been successfully uploaded and processed. The data can be either a pandas DataFrame or a list of dictionaries with identical keys. - Parameters: - Returns: response – The Dataset created from the uploaded data. - Return type: Dataset - Raises: InvalidUsageError – If neither a DataFrame or list of records is passed. #### classmethod create_from_url(cls, url, do_snapshot=None, persist_data_after_ingestion=None, categories=None, sample_size=None, max_wait=600, , use_cases=None) A blocking call that creates a new Dataset from data stored at a url. Returns when the dataset has been successfully uploaded and processed. - Parameters: - Returns: response – The Dataset created from the uploaded data - Return type: Dataset #### classmethod create_from_project(cls, project_id, categories=None, max_wait=600, , use_cases=None) A blocking call that creates a new dataset from project data. Returns when the dataset has been successfully created. - Parameters: - Returns: response – The dataset created from the project dataset. - Return type: Dataset #### classmethod create_from_datastage(cls, datastage_id, categories=None, max_wait=600, , use_cases=None) A blocking call that creates a new Dataset from data stored as a DataStage. Returns when the dataset has been successfully uploaded and processed. - Parameters: - Returns: response – The Dataset created from the uploaded data - Return type: Dataset #### classmethod create_from_data_source(cls, data_source_id, username=None, password=None, do_snapshot=None, persist_data_after_ingestion=None, categories=None, credential_id=None, use_kerberos=None, credential_data=None, sample_size=None, max_wait=600, , use_cases=None) A blocking call that creates a new Dataset from data stored at a DataSource. Returns when the dataset has been successfully uploaded and processed. Added in version v2.22. - Parameters: - Returns: response – The Dataset created from the uploaded data - Return type: Dataset #### classmethod create_from_query_generator(cls, generator_id, dataset_id=None, dataset_version_id=None, max_wait=600, , use_cases=None) A blocking call that creates a new Dataset from the query generator. Returns when the dataset has been successfully processed. If optional parameters are not specified the query is applied to the dataset_id and dataset_version_id stored in the query generator. If specified they will override the stored dataset_id/dataset_version_id, e.g., to prep a prediction dataset. - Parameters: - Returns: response – The Dataset created from the query generator - Return type: Dataset #### classmethod create_from_recipe(cls, recipe, name=None, do_snapshot=None, persist_data_after_ingestion=None, categories=None, credential=None, credential_id=None, use_kerberos=None, materialization_destination=None, max_wait=600, , use_cases=None) A blocking call that creates a new Dataset from the recipe. Returns when the dataset has been successfully uploaded and processed. Added in version 3.6. - Returns: response – The Dataset created from the uploaded data - Return type: Dataset #### classmethod get(dataset_id) Get information about a dataset. - Parameters: dataset_id ( string ) – the ID of the dataset - Returns: dataset – the queried dataset - Return type: Dataset #### classmethod delete(dataset_id) Soft deletes a dataset. You cannot get it or list it or do actions with it, except for un-deleting it. - Parameters: dataset_id ( string ) – The id of the dataset to mark for deletion - Return type: None #### classmethod un_delete(dataset_id) Un-deletes a previously deleted dataset. If the dataset was not deleted, nothing happens. - Parameters: dataset_id ( string ) – The id of the dataset to un-delete - Return type: None #### classmethod list(category=None, filter_failed=None, order_by=None, use_cases=None) List all datasets a user can view. - Parameters: - Returns: a list of datasets the user can view - Return type: list[Dataset] #### classmethod iterate(offset=None, limit=None, category=None, order_by=None, filter_failed=None, use_cases=None) Get an iterator for the requested datasets a user can view. This lazily retrieves results. It does not get the next page from the server until the current page is exhausted. - Parameters: - Yields: Dataset – An iterator of the datasets the user can view. - Return type: Generator [ TypeVar ( TDataset , bound= Dataset), None , None ] #### update() Updates the Dataset attributes in place with the latest information from the server. - Return type: None #### modify(name=None, categories=None) Modifies the Dataset name and/or categories. Updates the object in place. - Parameters: - Return type: None #### share(access_list, apply_grant_to_linked_objects=False) Modify the ability of users to access this dataset - Parameters: - Return type: None - Raises: datarobot.ClientError: – If you do not have permission to share this dataset, if the user you’re sharing with doesn’t exist, if the same user appears multiple times in the access_list, or if these changes would leave the dataset without an owner. > [!NOTE] Examples > Transfer access to the dataset from [old_user@datarobot.com](mailto:old_user@datarobot.com) to [new_user@datarobot.com](mailto:new_user@datarobot.com) > > ``` > from datarobot.enums import SHARING_ROLE > from datarobot.models.dataset import Dataset > from datarobot.models.sharing import SharingAccess > > new_access = SharingAccess( > "new_user@datarobot.com", > SHARING_ROLE.OWNER, > can_share=True, > ) > access_list = [ > SharingAccess( > "old_user@datarobot.com", > SHARING_ROLE.OWNER, > can_share=True, > can_use_data=True, > ), > new_access, > ] > > Dataset.get('my-dataset-id').share(access_list) > ``` #### get_details() Gets the details for this Dataset - Return type: DatasetDetails #### get_all_features(order_by=None) Get a list of all the features for this dataset. - Parameters: order_by ( string , optional ) – If unset, uses the server default: ‘name’. How the features should be ordered. Can be ‘name’ or ‘featureType’. - Return type: list[DatasetFeature] #### iterate_all_features(offset=None, limit=None, order_by=None) Get an iterator for the requested features of a dataset. This lazily retrieves results. It does not get the next page from the server until the current page is exhausted. - Parameters: - Yields: DatasetFeature - Return type: Generator [ DatasetFeature , None , None ] #### get_featurelists() Get DatasetFeaturelists created on this Dataset - Returns: feature_lists - Return type: list[DatasetFeaturelist] #### create_featurelist(name, features) Create a new dataset featurelist - Parameters: - Returns: featurelist – the newly created featurelist - Return type: DatasetFeaturelist > [!NOTE] Examples > ``` > dataset = Dataset.get('1234deadbeeffeeddead4321') > dataset_features = dataset.get_all_features() > selected_features = [feat.name for feat in dataset_features][:5] # select first five > new_flist = dataset.create_featurelist('Simple Features', selected_features) > ``` #### get_file(file_path=None, filelike=None) Retrieves all the originally uploaded data in CSV form. Writes it to either the file or a filelike object that can write bytes. Only one of file_path or filelike can be provided and it must be provided as a keyword argument (i.e., file_path=’path-to-write-to’). If a file-like object is provided, the user is responsible for closing it when they are done. The user must also have permission to download data. - Parameters: - Return type: None #### get_as_dataframe(low_memory=False) Retrieves all the originally uploaded data in a pandas DataFrame. Added in version v3.0. - Parameters: low_memory ( Optional[bool] ) – If True, use local files to reduce memory usage which will be slower. - Return type: pd.DataFrame #### get_projects() Retrieves the Dataset’s projects as ProjectLocation named tuples. - Returns: locations - Return type: list[ProjectLocation] #### get_raw_sample_data() Retrieves the raw sample data for the dataset as a pandas DataFrame. The raw sample dataset is a subset of the full dataset. Added in version v3.10. - Returns: A DataFrame with the dataset’s raw sample data. - Return type: pd.DataFrame #### create_project(project_name=None, user=None, password=None, credential_id=None, use_kerberos=None, credential_data=None, , use_cases=None) Create a [datarobot.models.Project](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project) from this dataset - Parameters: - Return type: Project #### classmethod create_version_from_file(dataset_id, file_path=None, filelike=None, categories=None, read_timeout=600, max_wait=600) A blocking call that creates a new Dataset version from a file. Returns when the new dataset version has been successfully uploaded and processed. Warning: This function does not clean up it’s open files. If you pass a filelike, you are responsible for closing it. If you pass a file_path, this will create a file object from the file_path but will not close it. Added in version v2.23. - Parameters: - Returns: response – A fully armed and operational Dataset version - Return type: Dataset #### classmethod create_version_from_in_memory_data(dataset_id, data_frame=None, records=None, categories=None, read_timeout=600, max_wait=600) A blocking call that creates a new Dataset version for a dataset from in-memory data. Returns when the dataset has been successfully uploaded and processed. The data can be either a pandas DataFrame or a list of dictionaries with identical keys. > Added in version v2.23. > *Parameters:*dataset_id(string) – The ID of the dataset for which new version to be created > *data_frame(DataFrame,optional) – The data frame to upload > *records(list[dict],optional) – A list of dictionaries with identical keys to upload > *categories(list[string],optional) – An array of strings describing the intended use of the dataset. The > current supported options are “TRAINING” and “PREDICTION”. > *read_timeout(Optional[int]) – The maximum number of seconds to wait for the server to respond indicating that the > initial upload is complete > *max_wait(Optional[int]) – Time in seconds after which project creation is considered unsuccessful > *Returns:response– The Dataset version created from the uploaded data > *Return type:Dataset*Raises:InvalidUsageError– If neither a DataFrame or list of records is passed. #### classmethod create_version_from_url(dataset_id, url, categories=None, max_wait=600) A blocking call that creates a new Dataset from data stored at a url for a given dataset. Returns when the dataset has been successfully uploaded and processed. Added in version v2.23. - Parameters: - Returns: response – The Dataset version created from the uploaded data - Return type: Dataset #### classmethod create_version_from_datastage(dataset_id, datastage_id, categories=None, max_wait=600) A blocking call that creates a new Dataset from data stored as a DataStage for a given dataset. Returns when the dataset has been successfully uploaded and processed. - Parameters: - Returns: response – The Dataset version created from the uploaded data - Return type: Dataset #### classmethod create_version_from_data_source(dataset_id, data_source_id, username=None, password=None, categories=None, credential_id=None, use_kerberos=None, credential_data=None, max_wait=600) A blocking call that creates a new Dataset from data stored at a DataSource. Returns when the dataset has been successfully uploaded and processed. Added in version v2.23. - Parameters: - Returns: response – The Dataset version created from the uploaded data - Return type: Dataset #### classmethod create_version_from_recipe(dataset_id, recipe, credential=None, credential_id=None, use_kerberos=None, max_wait=600) A blocking call that creates a new Dataset version from Recipe. Returns when the dataset has been successfully uploaded and processed. Added in version v3.8. - Parameters: - Returns: response – The Dataset version created from the uploaded data - Return type: Dataset #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( T , bound= APIObject) #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None ### class datarobot.DatasetDetails Represents a detailed view of a Dataset. The to_dataset method creates a Dataset from this details view. - Variables: #### classmethod get(dataset_id) Get details for a Dataset from the server - Parameters: dataset_id ( str ) – The id for the Dataset from which to get details - Return type: DatasetDetails #### to_dataset() Build a Dataset object from the information in this object - Return type: Dataset ### class datarobot.models.dataset.ProjectLocation ProjectLocation(url, id) #### id Alias for field number 1 #### url Alias for field number 0 ## Secondary datasets ### class datarobot.helpers.feature_discovery.SecondaryDataset A secondary dataset to be used for feature discovery Added in version v2.25. - Variables: > [!NOTE] Examples > ``` > import datarobot as dr > dataset_definition = dr.SecondaryDataset( > identifier='profile', > catalog_id='5ec4aec1f072bc028e3471ae', > catalog_version_id='5ec4aec2f072bc028e3471b1', > ) > ``` ## Secondary dataset configurations ### class datarobot.models.SecondaryDatasetConfigurations Create secondary dataset configurations for a given project Added in version v2.20. - Variables: #### classmethod create(project_id, secondary_datasets, name, featurelist_id=None) create secondary dataset configurations Added in version v2.20. - Parameters: - Return type: an instance of SecondaryDatasetConfigurations - Raises: ClientError – raised if incorrect configuration parameters are provided > [!NOTE] Examples > ``` > profile_secondary_dataset = dr.SecondaryDataset( > identifier='profile', > catalog_id='5ec4aec1f072bc028e3471ae', > catalog_version_id='5ec4aec2f072bc028e3471b1', > snapshot_policy='latest' > ) > > transaction_secondary_dataset = dr.SecondaryDataset( > identifier='transaction', > catalog_id='5ec4aec268f0f30289a03901', > catalog_version_id='5ec4aec268f0f30289a03900', > snapshot_policy='latest' > ) > > secondary_datasets = [profile_secondary_dataset, transaction_secondary_dataset] > new_secondary_dataset_config = dr.SecondaryDatasetConfigurations.create( > project_id=project.id, > name='My config', > secondary_datasets=secondary_datasets > ) > > >>> new_secondary_dataset_config.id > '5fd1e86c589238a4e635e93d' > ``` #### delete() Removes the Secondary datasets configuration Added in version v2.21. - Raises: ClientError – Raised if an invalid or already deleted secondary dataset config id is provided > [!NOTE] Examples > ``` > # Deleting with a valid secondary_dataset_config id > status_code = dr.SecondaryDatasetConfigurations.delete(some_config_id) > status_code > >>> 204 > ``` - Return type: None #### get() Retrieve a single secondary dataset configuration for a given id Added in version v2.21. - Returns: secondary_dataset_configurations – The requested secondary dataset configurations - Return type: SecondaryDatasetConfigurations > [!NOTE] Examples > ``` > config_id = '5fd1e86c589238a4e635e93d' > secondary_dataset_config = dr.SecondaryDatasetConfigurations(id=config_id).get() > >>> secondary_dataset_config > { > 'created': datetime.datetime(2020, 12, 9, 6, 16, 22, tzinfo=tzutc()), > 'creator_full_name': u'abc@datarobot.com', > 'creator_user_id': u'asdf4af1gf4bdsd2fba1de0a', > 'credential_ids': None, > 'featurelist_id': None, > 'id': u'5fd1e86c589238a4e635e93d', > 'is_default': True, > 'name': u'My config', > 'project_id': u'5fd06afce2456ec1e9d20457', > 'project_version': None, > 'secondary_datasets': [ > { > 'snapshot_policy': u'latest', > 'identifier': u'profile', > 'catalog_version_id': u'5fd06b4af24c641b68e4d88f', > 'catalog_id': u'5fd06b4af24c641b68e4d88e' > }, > { > 'snapshot_policy': u'dynamic', > 'identifier': u'transaction', > 'catalog_version_id': u'5fd1e86c589238a4e635e98e', > 'catalog_id': u'5fd1e86c589238a4e635e98d' > } > ] > } > ``` #### classmethod list(project_id, featurelist_id=None, limit=None, offset=None) Returns list of secondary dataset configurations. Added in version v2.23. - Parameters: - Returns: secondary_dataset_configurations – The requested list of secondary dataset configurations for a given project - Return type: list of SecondaryDatasetConfigurations > [!NOTE] Examples > ``` > pid = '5fd06afce2456ec1e9d20457' > secondary_dataset_configs = dr.SecondaryDatasetConfigurations.list(pid) > >>> secondary_dataset_configs[0] > { > 'created': datetime.datetime(2020, 12, 9, 6, 16, 22, tzinfo=tzutc()), > 'creator_full_name': u'abc@datarobot.com', > 'creator_user_id': u'asdf4af1gf4bdsd2fba1de0a', > 'credential_ids': None, > 'featurelist_id': None, > 'id': u'5fd1e86c589238a4e635e93d', > 'is_default': True, > 'name': u'My config', > 'project_id': u'5fd06afce2456ec1e9d20457', > 'project_version': None, > 'secondary_datasets': [ > { > 'snapshot_policy': u'latest', > 'identifier': u'profile', > 'catalog_version_id': u'5fd06b4af24c641b68e4d88f', > 'catalog_id': u'5fd06b4af24c641b68e4d88e' > }, > { > 'snapshot_policy': u'dynamic', > 'identifier': u'transaction', > 'catalog_version_id': u'5fd1e86c589238a4e635e98e', > 'catalog_id': u'5fd1e86c589238a4e635e98d' > } > ] > } > ``` ## Data engine query generator ### class datarobot.DataEngineQueryGenerator DataEngineQueryGenerator is used to set up time series data prep. Added in version v2.27. - Variables: #### classmethod create(generator_type, datasets, generator_settings) Creates a query generator entity. Added in version v2.27. - Parameters: - Returns: query_generator – The created generator - Return type: DataEngineQueryGenerator > [!NOTE] Examples > ``` > import datarobot as dr > from datarobot.models.data_engine_query_generator import ( > QueryGeneratorDataset, > QueryGeneratorSettings, > ) > dataset = QueryGeneratorDataset( > alias='My_Awesome_Dataset_csv', > dataset_id='61093144cabd630828bca321', > dataset_version_id=1, > ) > settings = QueryGeneratorSettings( > datetime_partition_column='date', > time_unit='DAY', > time_step=1, > default_numeric_aggregation_method='sum', > default_categorical_aggregation_method='mostFrequent', > ) > g = dr.DataEngineQueryGenerator.create( > generator_type='TimeSeries', > datasets=[dataset], > generator_settings=settings, > ) > g.id > >>>'54e639a18bd88f08078ca831' > g.generator_type > >>>'TimeSeries' > ``` #### classmethod get(generator_id) Gets information about a query generator. - Parameters: generator_id ( str ) – The identifier of the query generator you want to load. - Returns: query_generator – The queried generator - Return type: DataEngineQueryGenerator > [!NOTE] Examples > ``` > import datarobot as dr > g = dr.DataEngineQueryGenerator.get(generator_id='54e639a18bd88f08078ca831') > g.id > >>>'54e639a18bd88f08078ca831' > g.generator_type > >>>'TimeSeries' > ``` #### create_dataset(dataset_id=None, dataset_version_id=None, max_wait=600) A blocking call that creates a new Dataset from the query generator. Returns when the dataset has been successfully processed. If optional parameters are not specified the query is applied to the dataset_id and dataset_version_id stored in the query generator. If specified they will override the stored dataset_id/dataset_version_id, i.e., to prep a prediction dataset. - Parameters: - Returns: response – The Dataset created from the query generator - Return type: Dataset #### prepare_prediction_dataset_from_catalog(project_id, dataset_id, dataset_version_id=None, max_wait=600, relax_known_in_advance_features_check=None) Apply time series data prep to a catalog dataset and upload it to the project as a PredictionDataset. Added in version v3.1. - Parameters: - Returns: dataset – The newly uploaded dataset. - Return type: PredictionDataset #### prepare_prediction_dataset(sourcedata, project_id, max_wait=600, relax_known_in_advance_features_check=None) Apply time series data prep and upload the PredictionDataset to the project. Added in version v3.1. - Parameters: - Returns: dataset – The newly uploaded dataset. - Return type: PredictionDataset - Raises: ## Sharing access ### class datarobot.SharingAccess Represents metadata about whom a entity (e.g., a data store) has been shared with Added in version v2.14. Currently [DataStores](https://docs.datarobot.com/en/docs/api/reference/sdk/data-connectivity.html#datarobot.DataStore), [DataSources](https://docs.datarobot.com/en/docs/api/reference/sdk/data-connectivity.html#datarobot.DataSource), [Datasets](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset), [Projects](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project) (new in version v2.15) and [CalendarFiles](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.CalendarFile) (new in version 2.15) can be shared. This class can represent either access that has already been granted, or be used to grant access to additional users. - Variables: ## Sharing role ### class datarobot.models.sharing.SharingRole Represents metadata about a user who has been granted access to an entity. At least one of id or username must be set. - Variables: --- # Data wrangling URL: https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html # Recipes ### class datarobot.models.recipe.Recipe Data wrangling entity containing information required to transform one or more datasets and generate SQL. A recipe acts like a blueprint for creating a dataset by applying a series of operations (filters, aggregations, etc.) to one or more input datasets or datasources. - Variables: > [!NOTE] Examples > Create a recipe from a dataset or data source, > > ``` > >>> import datarobot as dr > >>> from datarobot.enums import DataWranglingDialect, RecipeType > >>> from datarobot.models.recipe_operation import RandomSamplingOperation > >>> my_use_case = dr.UseCase.list(search_params={"search": "My Use Case"})[0] > >>> dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f123') > >>> recipe = dr.Recipe.from_dataset( > ... use_case=my_use_case, > ... dataset=dataset, > ... dialect=DataWranglingDialect.SPARK, > ... recipe_type=RecipeType.WRANGLING, > ... sampling=RandomSamplingOperation(rows=500) > ... ) > ``` > > or use an existing recipe. > > ``` > >>> recipe = dr.Recipe.list(search="My Recipe")[0] > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > ``` > > Adjust the recipe’s name, description or other metadata fields. > > ``` > >>> recipe.update( > ... name='My updated recipe name', > ... description='Updated description for my recipe' > ... ) > ``` > > Then add additional datasets or data sources as inputs into the recipe. > > ``` > >>> from datarobot.models.recipe import RecipeDatasetInput > >>> my_other_dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f456') > >>> recipe.update( > ... inputs=[ > ... recipe.inputs[0], > ... RecipeDatasetInput.from_dataset( > ... dataset=my_other_dataset, > ... alias='dataset_B' > ... ) > ... ] > ... ) > ``` > > Apply wrangling operations to the recipe to join, filter, aggregate, or transform the recipe’s data, > > ``` > >>> from datarobot.models.recipe_operation import JoinOperation, FilterOperation, FilterCondition > >>> from datarobot.enums import JoinType, FilterOperationFunctions > >>> join_op = JoinOperation.join_dataset( > ... dataset=my_other_dataset, > ... join_type=JoinType.INNER, > ... right_prefix='B_', > ... left_keys=['id'], > ... right_keys=['id'] > ... ) > >>> filter_op = FilterOperation( > ... conditions=[ > ... FilterCondition( > ... column='B_value', > ... function=FilterOperationFunctions.GREATER_THAN, > ... function_arguments=[100] > ... ) > ... ] > ... ) > >>> recipe.update(operations=[join_op, filter_op]) > ``` > > or manually set the SQL for the recipe if you prefer to write your own SQL transformations: > > ``` > >>> recipe.update(sql=( > ... "SELECT A.*, B.value AS B_value FROM dataset_A AS A " > ... "INNER JOIN dataset_B AS B ON A.id = B.id " > ... "WHERE B.value > 100" > ... )) > ``` > > Then review the data preview generated for the recipe. > > ``` > >>> preview = recipe.get_preview() > >>> preview.df > ``` > > Finally, publish the recipe to create a new dataset. > > ``` > >>> published_dataset = recipe.publish_to_dataset( > ... name='My new Dataset built from recipe', > ... do_snapshot=True, > ... use_cases=my_use_case, > ... max_wait=600 > ... ) > ``` #### update(name=None, description=None, sql=None, recipe_type=None, inputs=None, operations=None, settings=None, **kwargs) Update the recipe. - Parameters: - Return type: None > [!NOTE] Examples > Update recipe metadata fields name and description: > > ``` > >>> import datarobot as dr > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> recipe.update( > ... name='My updated recipe name', > ... description='Updated description for my recipe' > ... ) > ``` > > Update recipe inputs to include 2 datasets to allow for joining data: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe import RecipeDatasetInput > >>> from datarobot.models.recipe_operation import RandomSamplingOperation > >>> primary_dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f123') > >>> secondary_dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f456') > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> recipe.update( > ... inputs=[ > ... RecipeDatasetInput.from_dataset( > ... dataset=primary_dataset, > ... sampling=RandomSamplingOperation(rows=500), > ... alias='dataset_A' > ... ), > ... RecipeDatasetInput.from_dataset( > ... dataset=secondary_dataset, > ... alias='dataset_B' > ... ) > ... ] > ... ) > ``` > > Update recipe operations to filter out users younger than 18 years old: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import FilterOperation, FilterCondition > >>> from datarobot.enums import FilterOperationFunctions > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> filter_op = FilterOperation( > ... conditions=[ > ... FilterCondition( > ... column='age', > ... function=FilterOperationFunctions.GREATER_THAN_OR_EQUAL, > ... function_arguments=[18] > ... ) > ... ] > ... ) > >>> recipe.update(operations=[filter_op]) > ``` > > Update recipe settings to change the column used for feature weights: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe import RecipeSettings > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> recipe.update( > ... settings=RecipeSettings( > ... weights_feature='observation_weights' > ... ) > ... ) > ``` > > Update downsampling to only keep 500 random rows when publishing: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import RandomDownsamplingOperation > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> recipe.update( > ... downsampling=RandomDownsamplingOperation(max_rows=500) > ... ) > ``` > [!NOTE] Notes > Setting the sql metadata field on a non-SQL type recipe will convert the recipe to a SQL type recipe. #### get_preview(max_wait=600, number_of_operations_to_use=None) Retrieve preview of sample data. Compute preview if absent. - Parameters: - Returns: preview – The preview of the application of the recipe. - Return type: RecipePreview > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> preview = recipe.get_preview() > >>> preview > RecipePreview( > columns=['feature_1', 'feature_2', 'feature_3'], > count=4, > data=[['5', 'true', 'James'], ['-7', 'false', 'Bryan'], ['2', 'false', 'Jamie'], ['4', 'true', 'Lyra']], > total_count=4, > byte_size=46, > result_schema=[ > {'data_type': 'INT_TYPE', 'name': 'feature_1'}, > {'data_type': 'BOOLEAN_TYPE', 'name': 'feature_2'}, > {'data_type': 'STRING_TYPE', 'name': 'feature_3'} > ], > stored_count=4, > estimated_size_exceeds_limit=False, > ) > >>> preview.df > feature_1 feature_2 feature_3 > 0 5 true James > 1 -7 false Bryan > 2 2 false Jamie > 3 4 true Lyra > ``` #### publish_to_dataset(name=None, do_snapshot=None, persist_data_after_ingestion=None, categories=None, credential=None, credential_id=None, use_kerberos=None, materialization_destination=None, max_wait=600, use_cases=None) A blocking call to publish the recipe to a new [Dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset). - Parameters: - Returns: dataset – The newly created dataset. - Return type: Dataset > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> dataset = recipe.publish_to_dataset( > ... name='Published Dataset from Recipe', > ... do_snapshot=True, > ... max_wait=600 > ... ) > ``` #### classmethod update_downsampling(recipe_id, downsampling) Set the downsampling operation for the recipe. Downsampling is applied during publishing. Consider using [update()](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) instead to update a Recipe instance. - Parameters: - Returns: recipe – Recipe with updated downsampling. - Return type: Recipe > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import RandomDownsamplingOperation > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> recipe = dr.Recipe.update_downsampling( > ... recipe_id=recipe.id, > ... downsampling=RandomDownsamplingOperation(max_rows=1000) > ... ) > ``` #### SEE ALSO [Recipe.update](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) #### retrieve_preview(max_wait=600, number_of_operations_to_use=None) Retrieve preview of sample data. Compute preview if absent. Deprecated since version 3.10: This method is deprecated and will be removed in 3.12. Use [Recipe.get_preview](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.get_preview) instead. - Parameters: - Returns: preview – Preview data computed. - Return type: Dict[str , Any] #### SEE ALSO [Recipe.get_preview](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.get_preview) #### retrieve_insights(max_wait=600, number_of_operations_to_use=None) Retrieve insights for the recipe sample data. Requires a preview of sample data to be computed first with .get_preview(). Computing the preview starts the insights job in the background automatically if it not already running. Will block thread until insights are ready or max_wait is exceeded. - Parameters: - Returns: insights – The insights for the recipe sample data. - Return type: Dict[str , Any] #### classmethod set_inputs(recipe_id, inputs) Set the inputs for the recipe. Inputs can be a dataset or a JDBC data source table. Consider using [update()](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) instead to update a Recipe instance. - Parameters: - Returns: recipe – Recipe with updated inputs. - Return type: Recipe > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe import RecipeDatasetInput > >>> from datarobot.models.recipe_operation import RandomSamplingOperation > >>> primary_dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f123') > >>> secondary_dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f456') > >>> recipe = dr.Recipe.set_inputs( > ... recipe_id='690bbf77aa31530d8287ae5f', > ... inputs=[ > ... RecipeDatasetInput.from_dataset( > ... dataset=primary_dataset, > ... sampling=RandomSamplingOperation(rows=500), > ... alias='dataset_A' > ... ), > ... RecipeDatasetInput.from_dataset( > ... dataset=secondary_dataset, > ... alias='dataset_B' > ... ) > ... ] > ... ) > ``` #### SEE ALSO [Recipe.update](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) #### classmethod set_operations(recipe_id, operations) Set the list of operations to use in the recipe. Operations are applied in order on the input(s). Consider using [update()](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) instead to update a Recipe instance. - Parameters: - Returns: recipe – Recipe with updated list of operations. - Return type: Recipe > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import FilterOperation, FilterCondition > >>> from datarobot.enums import FilterOperationFunctions > >>> recipe = dr.Recipe.get("690bbf77aa31530d8287ae5f") > >>> new_operations = [ > ... FilterOperation( > ... conditions=[ > ... FilterCondition( > ... column="column_A", > ... function=FilterOperationFunctions.GREATER_THAN, > ... function_arguments=[100] > ... ) > ... ] > ... ) > ... ] > >>> recipe = dr.Recipe.set_operations(recipe.id, operations=new_operations) > ``` #### SEE ALSO [Recipe.update](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) #### classmethod set_recipe_metadata(recipe_id, metadata) Update metadata for a recipe. Consider using [update()](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) instead to update a Recipe instance. - Parameters: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> recipe = dr.Recipe.get("690bbf77aa31530d8287ae5f") > >>> new_metadata = { > ... "name": "Updated Recipe Name", > ... "description": "This is an updated description for the recipe." > ... } > >>> recipe = dr.Recipe.set_recipe_metadata(recipe.id, metadata=new_metadata) > ``` - Returns: recipe – New recipe with updated metadata. - Return type: Recipe #### SEE ALSO [Recipe.update](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) > [!NOTE] Notes > Updating the sql metadata field on a non-SQL type recipe will convert the recipe to a SQL type recipe. #### classmethod set_settings(recipe_id, settings) Update the settings for a recipe. Consider using [update()](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) instead to update a Recipe instance. - Parameters: - Returns: recipe – Recipe with updated settings. - Return type: Recipe > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_settings import RecipeSettings > >>> recipe = dr.Recipe.get("690bbf77aa31530d8287ae5f") > >>> new_settings = RecipeSettings( > ... weights_feature="feature_weights" > ... ) > >>> recipe = dr.Recipe.set_settings(recipe.id, settings=new_settings) > ``` #### SEE ALSO [Recipe.update](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.update) #### classmethod list(search=None, dialect=None, status=None, recipe_type=None, order_by=None, created_by_user_id=None, created_by_username=None) List recipes. Apply filters to narrow down results. - Parameters: - Returns: recipes – List of recipes matching the filter criteria. - Return type: List[Recipe] > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> recipes = dr.Recipe.list() > >>> recipes > [Recipe( > dialect='spark', > id='690bbf77aa31530d8287ae5f', > name='Sample Recipe', > status='draft', > recipe_type='SQL', > inputs=[...], > operations=[...], > downsampling=..., > settings=..., > ), ...] > ``` #### SEE ALSO [Recipe.get](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.get) #### classmethod get(recipe_id) Retrieve a recipe by ID. - Parameters: recipe_id ( str ) – The ID of the recipe to retrieve. - Returns: recipe – The recipe with the specified ID. - Return type: Recipe > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> recipe = dr.Recipe.get("690bbf77aa31530d8287ae5f") > >>> recipe > Recipe( > dialect='spark', > id='690bbf77aa31530d8287ae5f', > name='Sample Recipe', > status='draft', > recipe_type='SQL', > inputs=[...], > operations=[...], > downsampling=..., > settings=..., > ) > ``` #### SEE ALSO [Recipe.list](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.list) #### get_sql(operations=None) Generate SQL for the recipe, taking into account its operations and inputs. This does not modify the recipe. - Parameters: operations ( Optional[List[WranglingOperation]] ) – If provided, generate SQL for the given list of operations instead of the recipe’s operations, using the recipe’s inputs as the base. .. deprecated:: 3.10 operations is deprecated and will be removed in 3.12. Use generate_sql_for_operations class method instead. - Returns: sql – Generated SQL string. - Return type: str > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import FilterOperation, FilterCondition > >>> from datarobot.enums import FilterOperationFunctions > >>> recipe = dr.Recipe.get("690bbf77aa31530d8287ae5f") > >>> recipe.update(operations=[ > ... FilterOperation( > ... conditions=[ > ... FilterCondition( > ... column="column_A", > ... function=FilterOperationFunctions.GREATER_THAN, > ... function_arguments=[100] > ... ) > ... ] > ... ) > ... ]) > >>> recipe.get_sql() > "SELECT `sample_dataset`.`column_A` FROM `sample_dataset` WHERE `sample_dataset`.`column_A` > 100" > ``` #### SEE ALSO [Recipe.generate_sql_for_operations](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.generate_sql_for_operations) #### classmethod generate_sql_for_operations(recipe_id, operations) Generate SQL for an arbitrary list of operations, using an existing recipe as a base. This does not modify the recipe. If you want to generate SQL for a recipe’s operations, use get_sql() instead. - Parameters: - Returns: sql – Generated SQL string. - Return type: str > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import FilterOperation, FilterCondition > >>> from datarobot.enums import FilterOperationFunctions > >>> dr.Recipe.generate_sql_for_operations( > ... recipe_id="690bbf77aa31530d8287ae5f", > ... operations=[ > ... FilterOperation( > ... conditions=[ > ... FilterCondition( > ... column="column_A", > ... function=FilterOperationFunctions.LESS_THAN, > ... function_arguments=[20] > ... ) > ... ] > ... ) > ... ] > ... ) > "SELECT `sample_dataset`.`column_A` FROM `sample_dataset` WHERE `sample_dataset`.`column_A` < 20" > ``` #### classmethod from_data_store(use_case, data_store, data_source_type, dialect, data_source_inputs, recipe_type=RecipeType.WRANGLING) Create a recipe using one or more data sources from a data store as input. - Parameters: - Returns: recipe – The recipe created. - Return type: Recipe > [!NOTE] Examples > Create a wrangling recipe with 2 Snowflake tables as inputs from a data store: > > ``` > >>> import datarobot as dr > >>> from datarobot.enums import DataWranglingDialect, DataWranglingDataSourceTypes, RecipeType > >>> from datarobot.models.recipe import DataSourceInput > >>> from datarobot.models.recipe_operation import LimitSamplingOperation > >>> from datetime import datetime > >>> my_use_case = dr.UseCase.list(search_params={"search": "My Use Case"})[0] > >>> data_store = dr.DataStore.list(name="Snowflake Data Store")[0] > >>> now = datetime.now().strftime("%Y%m%d_%H%M%S") > >>> primary_input = DataSourceInput( > ... canonical_name=f"Data source for stock_trades {now}", > ... table="stock_trades", > ... schema="PUBLIC", > ... sampling=LimitSamplingOperation(rows=1000) > ... ) > >>> secondary_input = DataSourceInput( > ... canonical_name=f"Data source for hist_stock_prices {now}", > ... table="hist_stock_prices", > ... schema="PUBLIC" > ... ) > >>> recipe = dr.Recipe.from_data_store( > ... use_case=my_use_case, > ... data_store=data_store, > ... data_source_type=DataWranglingDataSourceTypes.JDBC, > ... dialect=DataWranglingDialect.SNOWFLAKE, > ... data_source_inputs=[primary_input, secondary_input], > ... recipe_type=RecipeType.WRANGLING > ... ) > >>> recipe.update(name="My Snowflake wrangling recipe for stock trades and historical prices") > ``` #### classmethod from_dataset(use_case, dataset, dialect=None, inputs=None, recipe_type=RecipeType.WRANGLING, snapshot_policy=DataWranglingSnapshotPolicy.LATEST, sampling=None) Create a recipe using a dataset as input. - Parameters: > [!NOTE] Examples > Create a wrangling recipe to work with a snowflake dataset: > > ``` > >>> import datarobot as dr > >>> from datarobot.enums import DataWranglingDialect, RecipeType > >>> from datarobot.models.recipe_operation import RandomSamplingOperation > >>> my_use_case = dr.UseCase.list(search_params={"search": "My Use Case"})[0] > >>> dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f123') > >>> recipe = dr.Recipe.from_dataset( > ... use_case=my_use_case, > ... dataset=dataset, > ... dialect=DataWranglingDialect.SNOWFLAKE, > ... recipe_type=RecipeType.WRANGLING, > ... sampling=RandomSamplingOperation(rows=200) > ... ) > >>> recipe.update(name='My Snowflake wrangling recipe for dataset X') > ``` ### class datarobot.models.recipe.RecipeSettings Recipe settings for optional parameters that can be set to support or modify other recipe features or interactions. E.g. Using some downsampling strategies require target and weights_feature to be set. - Parameters: ### class datarobot.models.recipe.RecipeMetadata Recipe metadata for metadata fields that can be set on a recipe, e.g., name, description, etc. - Variables: ### class datarobot.models.recipe.RecipePreview A preview of data output from the application of a recipe. - Variables: ## Recipe Inputs Inputs are datasets or data sources fed into recipes to be joined, filtered, or otherwise transformed by recipe operations. Recipes have a single primary data input, which is used as the base of the recipe data, and can have multiple secondary data inputs. ### class datarobot.models.recipe.RecipeDatasetInput Dataset input configuration used to specify a dataset as an input to a recipe. - Parameters: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe import RecipeDatasetInput > >>> from datarobot.enums import RecipeInputType, DataWranglingSnapshotPolicy > >>> from datarobot.models.recipe_operation import LimitSamplingOperation > >>> input_dataset = RecipeDatasetInput( > ... input_type=RecipeInputType.DATASET, > ... dataset_id='5f43a1b2c9e77f0001e6f123', > ... snapshot_policy=DataWranglingSnapshotPolicy.LATEST, > ... sampling=LimitSamplingOperation(rows=250) > ... ) > ``` > > Create a [RecipeDatasetInput](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.RecipeDatasetInput) from a [Dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset). > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe import RecipeDatasetInput > >>> from datarobot.models.recipe_operation import LimitSamplingOperation > >>> dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f123') > >>> input_dataset = RecipeDatasetInput.from_dataset( > ... dataset=dataset, > ... sampling=LimitSamplingOperation(rows=250), > ... alias='my_dataset' > ... ) > ``` #### classmethod from_dataset(dataset, snapshot_policy=DataWranglingSnapshotPolicy.LATEST, sampling=None, alias=None) Create [RecipeDatasetInput](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.RecipeDatasetInput) configuration for a [Dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset). - Parameters: - Returns: The recipe dataset input created. - Return type: RecipeDatasetInput ### class datarobot.models.recipe.JDBCTableDataSourceInput JDBC data source input configuration used to specify a table from a JDBC data source as an input to a recipe. - Parameters: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe import JDBCTableDataSourceInput > >>> from datarobot.enums import RecipeInputType > >>> from datarobot.models.recipe_operation import LimitSamplingOperation > >>> data_store = dr.DataStore.list(name="Snowflake Connection")[0] > >>> data_source = DataSource.create( > ... data_source_type="jdbc", > ... canonical_name="My Snowflake connection", > ... params=dr.DataSourceParameters( > ... data_store_id=data_store.id, > ... schema="PUBLIC", > ... table="stock_prices", > ... ) > ... ) > >>> dataset = data_source.create_dataset(do_snapshot=True) > >>> input = JDBCTableDataSourceInput( > ... input_type=RecipeInputType.DATASOURCE, > ... data_source_id=data_source.id, > ... data_store_id=data_store.id, > ... dataset_id=dataset.id, > ... sampling=LimitSamplingOperation(rows=250), > ... alias='my_table_alias' > ... ) > >>> recipe = dr.Recipe.get('690e0ee89676e54e365b32e5') > >>> recipe.update(inputs=[input]) > ``` ### class datarobot.models.recipe.DataSourceInput Data source input configuration used to create a new recipe from a data store. - Parameters: > [!NOTE] Examples > Note: Canonical name must be unique to avoid collisions when creating a recipe from a data store. Append a unique > identifier if necessary. > > ``` > >>> from datarobot.models.recipe import DataSourceInput > >>> from datarobot.models.recipe_operation import RandomSamplingOperation > >>> from datetime import datetime > >>> now = datetime.now().strftime("%Y-%m-%d-%H_%M_%S") > >>> input_config = DataSourceInput( > ... canonical_name=f'Snowflake connection stock_prices_{now}', > ... table='stock_prices', > ... schema='PUBLIC', > ... sampling=RandomSamplingOperation(rows=500) > ... ) > ``` ### class datarobot.models.recipe.DatasetInput Wrapper for dataset input configuration passed when creating a new recipe from dataset. deprecated:: 3.10 : This class is deprecated and may be removed in 3.12 or later. Use the sampling parameter of [Recipe.from_dataset()](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe.Recipe.from_dataset) instead. - Parameters: sampling ( SamplingOperation ) – Sampling operation to apply to the dataset input. > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe import DatasetInput > >>> from datarobot.models.recipe_operation import LimitSamplingOperation > >>> from datarobot.enums import DataWranglingDialect, RecipeType > >>> my_use_case = dr.UseCase.list(search_params={"search": "My Use Case"})[0] > >>> dataset = dr.Dataset.get('5f43a1b2c9e77f0001e6f123') > >>> input_config = DatasetInput( > ... sampling=LimitSamplingOperation(rows=250) > ... ) > >>> recipe = dr.Recipe.from_dataset( > ... use_case=my_use_case, > ... dataset=dataset, > ... dialect=DataWranglingDialect.SPARK, > ... recipe_type=RecipeType.SQL, > ... inputs=[input_config] > ... ) > ``` ## Recipe Operations ### class datarobot.models.recipe_operation.BaseOperation Single base transformation unit in Data Wrangler recipe. ### Sampling Operations Sampling determines which rows from the recipe’s primary data input are used when generating a data preview. The primary data input is the first input in the list of recipe inputs. Only set sampling on the recipe’s primary data input. ### class datarobot.models.recipe_operation.SamplingOperation Base class for sampling operations. ### class datarobot.models.recipe_operation.RandomSamplingOperation A sampling technique that randomly selects the specified number of rows from the input when generating the sample data for a recipe. - Parameters: > [!NOTE] Examples > Using the default seed: > > ``` > >>> from datarobot.models.recipe_operation import RandomSamplingOperation > >>> op = RandomSamplingOperation(rows=500) > ``` > > Randomly generating a seed value: > > ``` > >>> from datarobot.models.recipe_operation import RandomSamplingOperation > >>> import random > >>> random_op = RandomSamplingOperation(rows=500, seed=random.randint(1, 10000)) > ``` ### class datarobot.models.recipe_operation.LimitSamplingOperation A sampling technique that samples the first N rows from the input when generating the sample data for a recipe. - Parameters: rows ( int ) – The number of rows to sample. > [!NOTE] Examples > Using the limit sampling operation to sample the first 100 rows: > > ``` > >>> from datarobot.models.recipe_operation import LimitSamplingOperation > >>> op = LimitSamplingOperation(rows=100) > ``` ### class datarobot.models.recipe_operation.DatetimeSamplingOperation A sampling technique that samples n rows by ordering rows based on a datetime partition column and selecting according to the strategy specified (e.g., latest, earliest). Supports multiseries data. - Parameters: > [!NOTE] Examples > Create a sampling operation to sample the latest 200 stock trades for tickers ‘AAPL’ and ‘MSFT’: > > ``` > >>> from datarobot.models.recipe_operation import DatetimeSamplingOperation > >>> from datarobot.enums import DatetimeSamplingStrategy > >>> op = DatetimeSamplingOperation( > ... datetime_partition_column='trade_date', > ... rows=200, > ... strategy=DatetimeSamplingStrategy.LATEST, > ... multiseries_id_column='ticker', > ... selected_series=['AAPL', 'MSFT'] > ... ) > ``` ### class datarobot.models.recipe_operation.TableSampleSamplingOperation A sampling technique that uses a table sample method to randomly select a percentage of rows from the input when generating the sample data for a recipe. Not supported for all data inputs. For data stores that support table sampling this method is generally more efficient than random sampling. - Parameters: > [!NOTE] Examples > Sample using 50% of the input datasource using the default seed: > > ``` > >>> from datarobot.models.recipe_operation import TableSampleSamplingOperation > >>> op = TableSampleSamplingOperation(percent=50) > ``` ### Downsampling Operations Downsampling reduces the size of the dataset published for faster experimentation. ### class datarobot.models.recipe_operation.DownsamplingOperation Base class for downsampling operations. ### class datarobot.models.recipe_operation.RandomDownsamplingOperation Downsampling that reduces the majority class via random sampling (i.e., each sample has equal probability of being chosen). - Parameters: > [!NOTE] Examples > Using the default seed: > > ``` > >>> from datarobot.models.recipe_operation import RandomDownsamplingOperation > >>> op = RandomDownsamplingOperation(max_rows=600) > ``` > > Randomly generating a seed value: > > ``` > >>> from datarobot.models.recipe_operation import RandomDownsamplingOperation > >>> import random > >>> random_op = RandomDownsamplingOperation(max_rows=600, seed=random.randint(1, 10000)) > ``` ### class datarobot.models.recipe_operation.SmartDownsamplingOperation A downsampling technique that relies on the distribution of target values to adjust size and specifies how much a specific class was sampled in a new column. For this technique to work, ensure the recipe has set target and weightsFeature in the recipe’s settings. - Parameters: > [!NOTE] Examples > ``` > >>> from datarobot.models.recipe_operation import SmartDownsamplingOperation, SmartDownsamplingMethod > >>> op = SmartDownsamplingOperation(max_rows=1000, method=SmartDownsamplingMethod.BINARY) > ``` ### Wrangling Operations ### class datarobot.models.recipe_operation.WranglingOperation Base class for data wrangling operations. ### class datarobot.models.recipe_operation.LagsOperation Data wrangling operation to create one or more lags for a feature based off of a datetime ordering feature. This operation will create a new column for each lag order specified. - Parameters: > [!NOTE] Examples > Create lags of orders 1, 5 and 30 in stock price data on opening price column “open_price”, ordered by datetime > column “date”. The data contains multiple time series identified by “ticker_symbol”: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import LagsOperation > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> lags_op = LagsOperation( > ... column="open_price", > ... orders=[1, 5, 30], > ... datetime_partition_column="date", > ... multiseries_id_column="ticker_symbol", > ... ) > >>> recipe.update(operations=[lags_op]) > ``` ### class datarobot.models.recipe_operation.WindowCategoricalStatsOperation Data wrangling operation to calculate categorical statistics for a rolling window. This operation will create a new column for each method specified. - Parameters: > [!NOTE] Examples > Create rolling categorical statistics to track the most frequent product category purchased by customers based on > their last 50 purchases: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import WindowCategoricalStatsOperation > >>> from datarobot.enums import CategoricalStatsMethods > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> window_cat_stats_op = WindowCategoricalStatsOperation( > ... column="product_category", > ... window_size=50, > ... methods=[CategoricalStatsMethods.MOST_FREQUENT], > ... datetime_partition_column="purchase_date", > ... multiseries_id_column="customer_id", > ... ) > >>> recipe.update(operations=[window_cat_stats_op]) > ``` ### class datarobot.models.recipe_operation.WindowNumericStatsOperation Data wrangling operation to calculate numeric statistics for a rolling window. This operation will create one or more new columns. - Parameters: > [!NOTE] Examples > Create rolling numeric statistics to track the maximum, minimum, and median stock prices over the last 7 trading > sessions: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import WindowNumericStatsOperation > >>> from datarobot.enums import NumericStatsMethods > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> window_num_stats_op = WindowNumericStatsOperation( > ... column="stock_price", > ... window_size=7, > ... methods=[ > ... NumericStatsMethods.MAX, > ... NumericStatsMethods.MIN, > ... NumericStatsMethods.MEDIAN, > ... ], > ... datetime_partition_column="trading_date", > ... multiseries_id_column="ticker_symbol", > ... ) > >>> recipe.update(operations=[window_num_stats_op]) > ``` ### class datarobot.models.recipe_operation.TimeSeriesOperation Data wrangling operation to generate a dataset ready for time series modeling: with forecast point, forecast distances, known in advance columns, etc. - Parameters: > [!NOTE] Examples > Create a time series operation for sales forecasting with forecast distances of 7 and 30 days, using the sale amount > as the target column, the date of the sale for datetime ordering, and “store_id” as the multiseries identifier. > The operation includes a task plan to compute lags of orders 1, 7, and 30 on the sales amount, and > specifies known in advance columns “promotion” and “holiday_flag”: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import TimeSeriesOperation, TaskPlanElement, Lags > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> task_plan = [ > ... TaskPlanElement( > ... column="sales_amount", > ... task_list=[Lags(orders=[1, 7, 30])] > ... ) > ... ] > >>> time_series_op = TimeSeriesOperation( > ... target_column="sales_amount", > ... datetime_partition_column="sale_date", > ... forecast_distances=[7, 30], > ... task_plan=[task_plan], > ... known_in_advance_columns=["promotion", "holiday_flag"], > ... multiseries_id_column="store_id" > ... ) > >>> recipe.update(operations=[time_series_op]) > ``` ### class datarobot.models.recipe_operation.ComputeNewOperation Data wrangling operation to create a new feature computed using a SQL expression. - Parameters: > [!NOTE] Examples > Create a new feature “total_sales” by summing the total of “online_sales” and “in_store_sales”, rounded to the > nearest dollar: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import ComputeNewOperation > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> compute_new_op = ComputeNewOperation( > ... expression="ROUND(online_sales + in_store_sales, 0)", > ... new_feature_name="total_sales" > ... ) > >>> recipe.update(operations=[compute_new_op]) > ``` ### class datarobot.models.recipe_operation.RenameColumnsOperation Data wrangling operation to rename one or more columns. - Parameters: column_mappings ( Dict [ str , str ]) – Mapping of original column names to new column names. > [!NOTE] Examples > Rename columns “old_name1” to “new_name1” and “old_name2” to “new_name2”: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import RenameColumnsOperation > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> rename_op = RenameColumnsOperation( > ... column_mappings={'old_name1': 'new_name1', 'old_name2': 'new_name2'} > ... ) > >>> recipe.update(operations=[rename_op]) > ``` ### class datarobot.models.recipe_operation.FilterOperation Data wrangling operation to filter rows based on one or more conditions. - Parameters: > [!NOTE] Examples > Filter input to only keep users older than 18: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import FilterOperation, FilterCondition > >>> from datarobot.enums import FilterOperationFunctions > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> condition = FilterCondition( > ... column="age", > ... function=FilterOperationFunctions.GREATER_THAN, > ... function_arguments=[18] > ... ) > >>> filter_op = FilterOperation(conditions=[condition], keep_rows=True) > >>> recipe.update(operations=[filter_op]) > ``` > > Filter input to filter out rows where “status” is either “inactive” or “banned”: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import FilterOperation, FilterCondition > >>> from datarobot.enums import FilterOperationFunctions > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> inactive_cond = FilterCondition( > ... column="status", > ... function=FilterOperationFunctions.EQUALS, > ... function_arguments=["inactive"] > ... ) > >>> banned_cond = FilterCondition( > ... column="status", > ... function=FilterOperationFunctions.EQUALS, > ... function_arguments=["banned"] > ... ) > >>> filter_op = FilterOperation( > ... conditions=[inactive_cond, banned_cond], > ... keep_rows=False, > ... operator="or" > ... ) > >>> recipe.update(operations=[filter_op]) > ``` ### class datarobot.models.recipe_operation.DropColumnsOperation Data wrangling operation to drop one or more columns. - Parameters: columns ( List [ str ]) – Columns to drop. > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import DropColumnsOperation > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> drop_op = DropColumnsOperation(columns=['col1', 'col2']) > >>> recipe.update(operations=[drop_op]) > ``` ### class datarobot.models.recipe_operation.DedupeRowsOperation Data wrangling operation to remove duplicate rows. Uses values from all columns. > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import DedupeRowsOperation > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> dedupe_op = DedupeRowsOperation() > >>> recipe.update(operations=[dedupe_op]) > ``` ### class datarobot.models.recipe_operation.FindAndReplaceOperation Data wrangling operation to find and replace strings in a column. - Parameters: > [!NOTE] Examples > Set Recipe operations to search for exact match of “old_value” in column “col1” and replace with “new_value”: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import FindAndReplaceOperation > >>> from datarobot.enums importFindAndReplaceMatchMode > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> find_replace_op = FindAndReplaceOperation( > ... column="col1", > ... find="old_value", > ... replace_with="new_value", > ... match_mode=FindAndReplaceMatchMode.EXACT, > ... is_case_sensitive=True > ... ) > >>> recipe.update(operations=[find_replace_op]) > ``` > > Set Recipe operations to use regular expression to replace names starting with “Brand” in column “name” and replace > with “Lyra”: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import FindAndReplaceOperation > >>> from datarobot.enums import FindAndReplaceMatchMode > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> find_replace_op = FindAndReplaceOperation( > ... column="name", > ... find="^Brand.*", > ... replace_with="Lyra", > ... match_mode=FindAndReplaceMatchMode.REGEX > ... ) > >>> recipe.update(operations=[find_replace_op]) > ``` ### class datarobot.models.recipe_operation.AggregationOperation Data wrangling operation to compute aggregate metrics for one or more features by grouping data by one or more columns. This operation will retain all group by columns in the output dataset and create a new column for each aggregation function applied to each feature chosen for aggregation. - Parameters: > [!NOTE] Examples > Create an aggregation operation to compute the total and average sales amounts, and total sales quantity per region. > This will create 3 new columns sales_amount_sum, sales_amount_avg, and sales_quantity_sum in the output > dataset: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe_operation import AggregationOperation, AggregateFeature > >>> from datarobot.enums import AggregationFunctions > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> agg_sales = AggregateFeature( > ... feature="sales_amount", > ... functions=[AggregationFunctions.SUM, AggregationFunctions.AVG] > ... ) > >>> agg_quantity = AggregateFeature( > ... feature="sales_quantity", > ... functions=[AggregationFunctions.SUM] > ... ) > >>> aggregation_op = AggregationOperation( > ... aggregations=[agg_sales, agg_quantity], > ... group_by_columns=["region"] > ... ) > >>> recipe.update(operations=[aggregation_op]) > ``` ### class datarobot.models.recipe_operation.JoinOperation Data wrangling operation to join an additional data input to the current data. The additional data input is treated as the right side of the join. The additional data input must be added to the recipe inputs when updating the recipe with this operation. The join condition only supports equality predicates. Multiple fields are combined with AND operators (e.g., JOIN A, B ON A.x = B.y AND A.z = B.z AND A.t = B.t). > [!NOTE] Examples > Join customer details with an additional dataset of credit card information using customer id: > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe import RecipeDatasetInput > >>> from datarobot.models.recipe_operation import JoinOperation > >>> from datarobot.enums import JoinTypes > >>> cc_dataset = dr.Dataset.get('5f43a1e2e4b0c123456789ab') > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> inputs = [recipe.inputs[0], RecipeDatasetInput.from_dataset(cc_dataset)] > >>> join_op = JoinOperation.join_dataset( > ... dataset=cc_dataset, > ... join_type=JoinTypes.INNER, > ... right_prefix='cc_', > ... left_keys=['customer_id'], > ... right_keys=['customer_id'] > ... ) > >>> recipe.update(operations=[join_op], inputs=inputs) > ``` > > Join sales data with a reference table of sales targets that applies to all stores (Cartesian join to broadcast > targets to every sales record): > > ``` > >>> import datarobot as dr > >>> from datarobot.models.recipe import JDBCTableDataSourceInput > >>> from datarobot.models.recipe_operation import JoinOperation > >>> from datarobot.enums import JoinTypes > >>> recipe = dr.Recipe.get('690bbf77aa31530d8287ae5f') > >>> data_source_id = "647873c5a721e5647c15bbdc" > >>> reference_table_input = JDBCTableDataSourceInput( > ... input_type=RecipeInputType.DATASOURCE, > ... data_source_id=data_source_id, > ... data_store_id="6418452b8a79f972e8ffe208", > ... alias="targets_table" > ... ) > >>> inputs = [recipe.inputs[0], reference_table_input] > >>> join_op = JoinOperation.join_jdbc_data_source_table( > ... data_source_id=data_source_id, > ... join_type=JoinTypes.CARTESIAN, > ... right_prefix='ref_' > ... ) > >>> recipe.update(operations=[join_op], inputs=inputs) > ``` #### classmethod join_dataset(dataset, join_type, right_prefix=None, left_keys=None, right_keys=None) Create a [JoinOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.JoinOperation) to join a dataset to the data in the recipe. - Parameters: - Return type: JoinOperation #### classmethod join_jdbc_data_source_table(data_source_id, join_type, right_prefix=None, left_keys=None, right_keys=None) Create a [JoinOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.JoinOperation) to join a JDBC table input from a data source to the data in the recipe. - Parameters: - Return type: JoinOperation ## Enums and Helpers ### class datarobot.models.recipe_operation.TaskPlanElement Represents a task plan element for a specific column in a time series operation. - Parameters: ### class datarobot.models.recipe_operation.BaseTimeAwareTask Base class for time-aware tasks in time series operation task plan. ### class datarobot.models.recipe_operation.CategoricalStats Time-aware task to compute categorical statistics for a rolling window. - Parameters: ### class datarobot.models.recipe_operation.NumericStats Time-aware task to compute numeric statistics for a rolling window. - Parameters: ### class datarobot.models.recipe_operation.Lags Time-aware task to create one or more lags for a feature. - Parameters: orders ( List [ int ]) – List of lag orders to create. ### class datarobot.enums.CategoricalStatsMethods Supported categorical stats methods for data wrangling. ### class datarobot.enums.NumericStatsMethods Supported numeric stats methods for data wrangling. ### class datarobot.models.recipe_operation.FilterCondition Condition to filter rows in a [FilterOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.FilterOperation). - Parameters: > [!NOTE] Examples > [FilterCondition](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.FilterCondition) to filter rows where “age” is between > 18 and 65: > > ``` > >>> from datarobot.models.recipe_operation import FilterCondition > >>> from datarobot.enums import FilterOperationFunctions > >>> condition = FilterCondition( > ... column="age", > ... function=FilterOperationFunctions.BETWEEN, > ... function_arguments=[18, 65] > ... ) > ``` ### class datarobot.enums.FilterOperationFunctions Operations supported in a FilterCondition. ### class datarobot.models.recipe_operation.AggregateFeature Feature to aggregate and the aggregation functions to apply in a [AggregationOperation](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.AggregationOperation). - Parameters: > [!NOTE] Examples > [AggregateFeature](https://docs.datarobot.com/en/docs/api/reference/sdk/data-wrangling.html#datarobot.models.recipe_operation.AggregateFeature) to compute the sum and average of > sales: > > ``` > >>> from datarobot.models.recipe_operation import AggregateFeature > >>> from datarobot.enums import AggregationFunctions > >>> aggregate_feature = AggregateFeature( > ... feature="sales_amount", > ... functions=[AggregationFunctions.SUM, AggregationFunctions.AVG] > ... ) > ``` ### class datarobot.enums.AggregationFunctions Supported aggregation functions for data wrangling. ### class datarobot.enums.FindAndReplaceMatchMode Find and replace modes used when searching for strings to replace. ### class datarobot.enums.DatetimeSamplingStrategy Supported datetime sampling strategies. ### class datarobot.enums.SmartDownsamplingMethod Smart downsampling methods. ### class datarobot.enums.DataWranglingSnapshotPolicy Data wrangling snapshot policy options. ### class datarobot.enums.RecipeType Data wrangling supported recipe types. ### class datarobot.enums.DataWranglingDialect Data wrangling supported dialects. ### class datarobot.enums.DataWranglingDataSourceTypes Data wrangling supported data source types. ### class datarobot.enums.RecipeInputType Data wrangling supported recipe input types. ### class datarobot.models.recipe.JDBCColumnResultSchema Schema information for a JDBC result column. All fields except ‘name’ and ‘data_type’ may not be present. --- # Models URL: https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html # DataRobot Models ## Generic models ### class datarobot.models.GenericModel GenericModel [ModelRecord] is the object that is returned from the /modelRecords list route. It contains most generic model information. ## Models ### class datarobot.models.Model A model trained on a project’s dataset capable of making predictions. All durations are specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. See [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Variables: #### classmethod get(project, model_id) Retrieve a specific model. - Parameters: - Returns: model – Queried instance. - Return type: Model - Raises: ValueError – passed project parameter value is of not supported type #### advanced_tune(params, description=None, grid_search_arguments=None) Generate a new model with the specified advanced-tuning parameters As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Parameters: - Returns: The created job to build the model - Return type: ModelJob #### continue_incremental_learning_from_incremental_model(chunk_definition_id, early_stopping_rounds=None) Submit a job to the queue to perform the first incremental learning iteration training on an existing sample model. This functionality requires the SAMPLE_DATA_TO_START_PROJECT feature flag to be enabled. - Parameters: - Returns: job – The model retraining job that is created. - Return type: ModelJob #### cross_validate() Run cross validation on the model. > [!NOTE] Notes > To perform Cross Validation on a new model with new parameters, use `train` instead. - Returns: The created job to build the model - Return type: ModelJob #### delete() Delete the model from the project leaderboard. - Return type: None #### download_scoring_code(file_name, source_code=False) Download the Scoring Code JAR. - Parameters: - Return type: None #### download_training_artifact(file_name) Retrieve trained artifact(s) from a model containing one or more custom tasks. Artifact(s) will be downloaded to the specified local filepath. - Parameters: file_name ( str ) – File path where trained model artifact(s) will be saved. #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Override the inherited method because the model must _not_ recursively change casing. - Parameters: #### get_advanced_tuning_parameters() Get the advanced-tuning parameters available for this model. As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Returns:A dictionary describing the advanced-tuning parameters for the current model. There are two top-level keys, tuning_description and tuning_parameters. tuning_description an optional value. If not None, then it indicates the user-specified description of this set of tuning parameter. tuning_parameters is a list of a dicts, each has the following keys * parameter_name :(str)name of the parameter (unique per task, see below) * parameter_id :(str)opaque ID string uniquely identifying parameter * default_value :(*)the actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on current_value) * current_value :(*)the single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range. * task_name :(str)name of the task that this parameter belongs to * constraints:(dict)see the notes below * vertex_id:(str)ID of vertex that this parameter belongs to *Return type:dict > [!NOTE] Notes > The type of default_value and current_value is defined by the constraints structure. > It will be a string or numeric Python type. > > constraints is a dict with at least one, possibly more, of the following keys. > The presence of a key indicates that the parameter may take on the specified type. > (If a key is absent, this means that the parameter may not take on the specified type.) > If a key on constraints is present, its value will be a dict containing > all of the fields described below for that key. > > ``` > "constraints": { > "select": { > "values": [] > }, > "ascii": {}, > "unicode": {}, > "int": { > "min": , > "max": , > "supports_grid_search": requested for this param> > }, > "float": { > "min": , > "max": , > "supports_grid_search": requested for this param> > }, > "intList": { > "min_length": , > "max_length": > "min_val": , > "max_val": > "supports_grid_search": requested for this param> > }, > "floatList": { > "min_length": , > "max_length": > "min_val": , > "max_val": > "supports_grid_search": requested for this param> > } > } > ``` > > The keys have meaning as follows: > > select: > Rather than specifying a specific data type, if present, it indicates that the parameter > is permitted to take on any of the specified values. Listed values may be of any string > or real (non-complex) numeric type. > ascii: > The parameter may be a unicode object that encodes simple ASCII characters. > (A-Z, a-z, 0-9, whitespace, and certain common symbols.) In addition to listed > constraints, ASCII keys currently may not contain either newlines or semicolons. > unicode: > The parameter may be any Python unicode object. > int: > The value may be an object of type int within the specified range (inclusive). > Please note that the value will be passed around using the JSON format, and > some JSON parsers have undefined behavior with integers outside of the range > [-(2**53)+1, (2**53)-1]. > float: > The value may be an object of type float within the specified range (inclusive). > intList, floatList: > The value may be a list of int or float objects, respectively, following constraints > as specified respectively by the int and float types (above). > > Many parameters only specify one key under constraints. If a parameter specifies multiple > keys, the parameter may take on any value permitted by any key. #### get_all_confusion_charts(fallback_to_parent_insights=False) Retrieve a list of all confusion matrices available for the model. - Parameters: fallback_to_parent_insights ( bool ) – (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent for any source that is not available for this model and if this has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent. - Returns: Data for all available confusion charts for model. - Return type: list of ConfusionChart #### get_all_feature_impacts(data_slice_filter=None) Retrieve a list of all feature impact results available for the model. - Parameters: data_slice_filter ( DataSlice , optional ) – A DataSlice used to filter the return values based on the DataSlice ID. By default, this function uses data_slice_filter.id == None, which returns an unsliced insight. If data_slice_filter is None, no data_slice filtering will be applied when requesting the ROC curve. - Returns: Data for all available model feature impacts, or an empty list if no data is found. - Return type: list of dicts > [!NOTE] Examples > ``` > model = datarobot.Model(id='model-id', project_id='project-id') > > # Get feature impact insights for sliced data > data_slice = datarobot.DataSlice(id='data-slice-id') > sliced_fi = model.get_all_feature_impacts(data_slice_filter=data_slice) > > # Get feature impact insights for unsliced data > data_slice = datarobot.DataSlice() > unsliced_fi = model.get_all_feature_impacts(data_slice_filter=data_slice) > > # Get all feature impact insights > all_fi = model.get_all_feature_impacts() > ``` #### get_all_lift_charts(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all Lift charts available for the model. - Parameters: - Returns: Data for all available model lift charts. Or an empty list if no data found. - Return type: list of LiftChart > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > > # Get lift chart insights for sliced data > sliced_lift_charts = model.get_all_lift_charts(data_slice_id='data-slice-id') > > # Get lift chart insights for unsliced data > unsliced_lift_charts = model.get_all_lift_charts(unsliced_only=True) > > # Get all lift chart insights > all_lift_charts = model.get_all_lift_charts() > ``` #### get_all_multiclass_lift_charts(fallback_to_parent_insights=False, data_slice_filter=, target_class=None) Retrieve a list of all Lift charts available for the model. - Parameters: - Returns: Data for all available model lift charts. - Return type: list of LiftChart #### get_all_residuals_charts(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all residuals charts available for the model. - Parameters: - Returns: Data for all available model residuals charts. - Return type: list of ResidualsChart > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > > # Get residuals chart insights for sliced data > sliced_residuals_charts = model.get_all_residuals_charts(data_slice_id='data-slice-id') > > # Get residuals chart insights for unsliced data > unsliced_residuals_charts = model.get_all_residuals_charts(unsliced_only=True) > > # Get all residuals chart insights > all_residuals_charts = model.get_all_residuals_charts() > ``` #### get_all_roc_curves(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all ROC curves available for the model. - Parameters: - Returns: Data for all available model ROC curves. Or an empty list if no RocCurves are found. - Return type: list of RocCurve > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > ds_filter=DataSlice(id='data-slice-id') > > # Get roc curve insights for sliced data > sliced_roc = model.get_all_roc_curves(data_slice_filter=ds_filter) > > # Get roc curve insights for unsliced data > data_slice_filter=DataSlice(id=None) > unsliced_roc = model.get_all_roc_curves(data_slice_filter=ds_filter) > > # Get all roc curve insights > all_roc_curves = model.get_all_roc_curves() > ``` #### get_confusion_chart(source, fallback_to_parent_insights=False) Retrieve a multiclass model’s confusion matrix for the specified source. - Parameters: - Returns: Model ConfusionChart data - Return type: ConfusionChart - Raises: ClientError – If the insight is not available for this model #### get_cross_class_accuracy_scores() Retrieves a list of Cross Class Accuracy scores for the model. - Return type: json #### get_cross_validation_scores(partition=None, metric=None) Return a dictionary, keyed by metric, showing cross validation scores per partition. Cross Validation should already have been performed using [cross_validate](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.cross_validate) or [train](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.train). > [!NOTE] Notes > Models that computed cross validation before this feature was added will need > to be deleted and retrained before this method can be used. - Parameters: - Returns: cross_validation_scores – A dictionary keyed by metric showing cross validation scores per partition. - Return type: dict #### get_data_disparity_insights(feature, class_name1, class_name2) Retrieve a list of Cross Class Data Disparity insights for the model. - Parameters: - Return type: json #### get_fairness_insights(fairness_metrics_set=None, offset=0, limit=100) Retrieve a list of Per Class Bias insights for the model. - Parameters: - Return type: json #### get_feature_effect(source, data_slice_id=None) Retrieve Feature Effects for the model. Feature Effects provides partial dependence and predicted vs. actual values for the top 500 features ordered by feature impact score. The partial dependence shows the marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. Requires that Feature Effects has already been computed with [request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect). See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the available sources. - Parameters: - Returns: feature_effects – The feature effects data. - Return type: FeatureEffects - Raises: ClientError – If the feature effects have not been computed or the source is not a valid value. #### get_feature_effect_metadata() Retrieve Feature Effects metadata. The response contains status and available model sources. - Feature Effect for the training partition is always available, with the exception of older projects that only supported Feature Effect for validation. - When a model is trained into validation or holdout without stacked predictions (i.e., no out-of-sample predictions in those partitions), Feature Effects is not available for validation or holdout. - Feature Effects for holdout is not available when holdout was not unlocked for the project. Use source to retrieve Feature Effects, selecting one of the provided sources. - Returns: feature_effect_metadata - Return type: FeatureEffectMetadata #### get_feature_effects_multiclass(source='training', class_=None) Retrieve Feature Effects for the multiclass model. Feature Effects provide partial dependence and predicted vs. actual values for the top 500 features ordered by feature impact score. The partial dependence shows the marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. Requires that Feature Effects has already been computed with [request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect). See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the available sources. - Parameters: - Returns: The list of multiclass feature effects. - Return type: list - Raises: ClientError – If Feature Effects have not been computed or the source is not a valid value. #### get_feature_impact(with_metadata=False, data_slice_filter=) Retrieve the computed Feature Impact results, a measure of the relevance of each feature in the model. Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged) and measuring how the error metric score for the predictions is affected. The ‘impactUnnormalized’ is how much worse the error metric score is when making predictions on this modified data. The ‘impactNormalized’ is normalized so that the largest value is 1. In both cases, larger values indicate more important features. If a feature is redundant, i.e., once other features are considered it does not contribute much in addition, the ‘redundantWith’ value is the name of the feature that has the highest correlation with this feature. Note that redundancy detection is only available for jobs run after the addition of this feature. When retrieving data that predates this functionality, a NoRedundancyImpactAvailable warning will be used. Only the top 1000 features are saved and can be returned. Elsewhere this technique is sometimes called ‘Permutation Importance’. Requires that Feature Impact has already been computed with [request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact). - Parameters: - Returns:The feature impact data response depends on the with_metadata parameter. The response is either a dict with metadata and a list with the actual data or just a list with that data. Each list item is a dict with the keysfeatureName,impactNormalized,impactUnnormalized,redundantWith, andcount. For the dict response, the available keys are: featureImpacts- Feature Impact data as a dictionary. Each item is a dict with : the keys:featureName,impactNormalized,impactUnnormalized, andredundantWith.shapBased- A boolean that indicates whether Feature Impact was calculated using : Shapley values.ranRedundancyDetection- A boolean that indicates whether redundant feature : identification was run while calculating this Feature Impact.rowCount- An integer or None that indicates the number of rows that were used to : calculate Feature Impact. For Feature Impact calculated with the default logic without specifying the rowCount, we return None here.count- An integer with the number of features underfeatureImpacts.Return type:listordictRaises:ClientError– If the feature impacts have not been computed.ValueError– If data_slice_filter is passed as None. #### get_features_used() Query the server to determine which features were used. Note that the data returned by this method may differ from the names of the features in the featurelist used by this model. This method returns the raw features that must be supplied for predictions to be generated on a new set of data. The featurelist, in contrast, also includes the names of derived features. - Returns: features – The names of the features used in the model. - Return type: List[str] #### get_frozen_child_models() Retrieve the IDs for all models that are frozen from this model. - Return type: A list of Models #### get_labelwise_roc_curves(source, fallback_to_parent_insights=False) Retrieve a list of LabelwiseRocCurve instances for a multilabel model for the given source and all labels. This method is valid only for multilabel projects. For binary projects, use Model.get_roc_curve API . Added in version v2.24. - Parameters: - Returns: Labelwise ROC Curve instances for source and all labels - Return type: list of LabelwiseRocCurve - Raises: ClientError – If the insight is not available for this model #### get_lift_chart(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve the model Lift chart for the specified source. - Parameters: - Returns: Model lift chart data - Return type: LiftChart - Raises: #### get_missing_report_info() Retrieve a report on missing training data that can be used to understand missing values treatment in the model. The report consists of missing values resolutions for features numeric or categorical features that were part of building the model. - Returns: The queried model missing report, sorted by missing count (DESCENDING order). - Return type: An iterable of MissingReportPerFeature #### get_model_blueprint_chart() Retrieve a diagram that can be used to understand data flow in the blueprint. - Returns: The queried model blueprint chart. - Return type: ModelBlueprintChart #### get_model_blueprint_documents() Get documentation for tasks used in this model. - Returns: All documents available for the model. - Return type: list of BlueprintTaskDocument #### get_model_blueprint_json() Get the blueprint json representation used by this model. - Returns: Json representation of the blueprint stages. - Return type: BlueprintJson #### get_multiclass_feature_impact() For multiclass models, feature impact can be calculated separately for each target class. The method of calculation is the same, computed in one-vs-all style for each target class. Requires that Feature Impact has already been computed with [request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact). - Returns: feature_impacts – The feature impact data. Each item is a dict with the keys ‘featureImpacts’ (list), ‘class’ (str). Each item in ‘featureImpacts’ is a dict with the keys ‘featureName’, ‘impactNormalized’, ‘impactUnnormalized’, and ‘redundantWith’. - Return type: list of dict - Raises: ClientError – If the multiclass feature impacts have not been computed. #### get_multiclass_lift_chart(source, fallback_to_parent_insights=False, data_slice_filter=, target_class=None) Retrieve model Lift chart for the specified source. - Parameters: - Returns: Model lift chart data for each saved target class - Return type: list of LiftChart - Raises: ClientError – If the insight is not available for this model #### get_multilabel_lift_charts(source, fallback_to_parent_insights=False) Retrieve model Lift charts for the specified source. Added in version v2.24. - Parameters: - Returns: Model lift chart data for each saved target class - Return type: list of LiftChart - Raises: ClientError – If the insight is not available for this model #### get_num_iterations_trained() Retrieve the number of estimators trained by early-stopping tree-based models. Added in version v2.22. - Returns: #### get_or_request_feature_effect(source, max_wait=600, row_count=None, data_slice_id=None) Retrieve Feature Effects for the model, requesting a new job if it has not been run previously. See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the source. - Parameters: - Returns: feature_effects – The Feature Effects data. - Return type: FeatureEffects #### get_or_request_feature_effects_multiclass(source, top_n_features=None, features=None, row_count=None, class_=None, max_wait=600) Retrieve Feature Effects for the multiclass model, requesting a job if it has not been run previously. - Parameters: - Returns: feature_effects – The list of multiclass feature effects data. - Return type: list of FeatureEffectsMulticlass #### get_or_request_feature_impact(max_wait=600, **kwargs) Retrieve feature impact for the model, requesting a job if it has not been run previously. Only the top 1000 features are saved and can be returned. - Parameters: - Returns: feature_impacts – The feature impact data. See get_feature_impact for the exact schema. - Return type: list or dict #### get_parameters() Retrieve the model parameters. - Returns: The model parameters for this model. - Return type: ModelParameters #### get_pareto_front() Retrieve the Pareto Front for a Eureqa model. This method is only supported for Eureqa models. - Returns: Model ParetoFront data - Return type: ParetoFront #### get_prime_eligibility() Check whether this model can be approximated with DataRobot Prime. - Returns: prime_eligibility – A dict indicating whether the model can be approximated with DataRobot Prime (key can_make_prime) and why it may be ineligible (key message). - Return type: dict #### get_residuals_chart(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve model residuals chart for the specified source. - Parameters: - Returns: Model residuals chart data - Return type: ResidualsChart - Raises: #### get_roc_curve(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve the ROC curve for a binary model for the specified source. This method is valid only for binary projects. For multilabel projects, use Model.get_labelwise_roc_curves. - Parameters: - Returns: Model ROC curve data - Return type: RocCurve - Raises: #### get_rulesets() List the rulesets that approximate this model, generated by DataRobot Prime. If this model has not been approximated yet, returns an empty list. Note that these are rulesets that approximate this model, not rulesets used to construct this model. - Returns: rulesets - Return type: list of Ruleset #### get_supported_capabilities() Retrieve a summary of the capabilities supported by a model. Added in version v2.14. - Returns: #### get_uri() Return the permanent static hyperlink to this model on the leaderboard. - Returns: url – The permanent static hyperlink to this model on the leaderboard. - Return type: str #### get_word_cloud(exclude_stop_words=False) Retrieve word cloud data for the model. - Parameters: exclude_stop_words ( Optional[bool] ) – Set to True if you want stopwords filtered out of response. - Returns: Word cloud data for the model. - Return type: WordCloud #### incremental_train(data_stage_id, training_data_name=None) Submit a job to the queue to perform incremental training on an existing model. See the train_incremental documentation. - Return type: ModelJob #### classmethod list(project_id, sort_by_partition='validation', sort_by_metric=None, with_metric=None, search_term=None, featurelists=None, families=None, blueprints=None, labels=None, characteristics=None, training_filters=None, number_of_clusters=None, limit=100, offset=0) Retrieve paginated model records, sorted by scores, with optional filtering. - Parameters: - Returns: generic_models - Return type: list of GenericModel #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### request_approximation() Request an approximation of this model using DataRobot Prime. This creates several rulesets that can be used to approximate this model. After comparing their scores and rule counts, the code used in the approximation can be downloaded and run locally. - Returns: job – The job that generates the rulesets. - Return type: Job #### request_cross_class_accuracy_scores() Request data disparity insights to be computed for the model. - Returns: status_id – A statusId of computation request. - Return type: str #### request_data_disparity_insights(feature, compared_class_names) Request data disparity insights to be computed for the model. - Parameters: - Returns: status_id – A statusId of computation request. - Return type: str #### request_external_test(dataset_id, actual_value_column=None) Request an external test to compute scores and insights on an external test dataset. - Parameters: - Returns: job – A job representing external dataset insights computation. - Return type: Job #### request_fairness_insights(fairness_metrics_set=None) Request fairness insights to be computed for the model. - Parameters: fairness_metrics_set ( Optional[str] ) – Can be one of . The fairness metric used to calculate the fairness scores. - Returns: status_id – A statusId of computation request. - Return type: str #### request_feature_effect(row_count=None, data_slice_id=None) Submit a request to compute Feature Effects for the model. See [get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect) for more information on the result of the job. - Parameters: - Returns: job – A job representing the feature effect computation. To get the completed feature effect data, use job.get_result or job.get_result_when_complete. - Return type: Job - Raises: JobAlreadyRequested – If the feature effects have already been requested. #### request_feature_effects_multiclass(row_count=None, top_n_features=None, features=None) Request Feature Effects computation for the multiclass model. See [get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effects_multiclass) for more information on the result of the job. - Parameters: - Returns: job – A job representing Feature Effect computation. To get the completed Feature Effect data, use job.get_result or job.get_result_when_complete. - Return type: Job #### request_feature_impact(row_count=None, with_metadata=False, data_slice_id=None) Request that feature impacts be computed for the model. See [get_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_impact) for more information on the result of the job. - Parameters: - Returns: job – A job representing the Feature Impact computation. To retrieve the completed Feature Impact data, use job.get_result or job.get_result_when_complete. - Return type: Job or status_id - Raises: JobAlreadyRequested – If the feature impacts have already been requested. #### request_frozen_datetime_model(training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None) Train a new frozen model with parameters from this model. Requires that this model belongs to a datetime partitioned project. If it does not, an error will occur when submitting the job. Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data. In addition to training_row_count and training_duration, frozen datetime models may be trained on an exact date range. Only one of training_row_count, training_duration, or training_start_date and training_end_date should be specified. Models specified using training_start_date and training_end_date are the only ones that can be trained into the holdout data (once the holdout is unlocked). All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Parameters: - Returns: model_job – The modeling job that trains a frozen model. - Return type: ModelJob #### request_frozen_model(sample_pct=None, training_row_count=None) Train a new frozen model with parameters from this model. > [!NOTE] Notes > This method only works if the project the model belongs to is not datetime > partitioned. If it is, use `request_frozen_datetime_model` instead. > > Frozen models use the same tuning parameters as their parent model instead of independently > optimizing them to allow efficiently retraining models on larger amounts of the training > data. - Parameters: - Returns: model_job – The modeling job that trains a frozen model. - Return type: ModelJob #### request_lift_chart(source, data_slice_id=None) Request the model Lift Chart for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_per_class_fairness_insights(fairness_metrics_set=None) Request per-class fairness insights be computed for the model. - Parameters: fairness_metrics_set ( Optional[str] ) – The fairness metric used to calculate the fairness scores. Value can be any one of . - Returns: status_check_job – The returned object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_predictions(dataset_id=None, dataset=None, dataframe=None, file_path=None, file=None, include_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None, max_ngram_explanations=None) Request predictions against a previously uploaded dataset. - Parameters: - Returns: job – The job computing the predictions. - Return type: PredictJob #### request_residuals_chart(source, data_slice_id=None) Request the model residuals chart for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_roc_curve(source, data_slice_id=None) Request the model Roc Curve for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_training_predictions(data_subset, explanation_algorithm=None, max_explanations=None) Start a job to build training predictions - Parameters: #### retrain(sample_pct=None, featurelist_id=None, training_row_count=None, n_clusters=None) Submit a job to the queue to train a blender model. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### set_prediction_threshold(threshold) Set a custom prediction threshold for the model. May not be used once `prediction_threshold_read_only` is True for this model. - Parameters: threshold ( float ) – only used for binary classification projects. The threshold to when deciding between the positive and negative classes when making predictions. Should be between 0.0 and 1.0 (inclusive). #### star_model() Mark the model as starred. Model stars propagate to the web application and the API, and can be used to filter when listing models. - Return type: None #### start_advanced_tuning_session(grid_search_arguments=None) Start an Advanced Tuning session. Returns an object that helps set up arguments for an Advanced Tuning model execution. As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Parameters: grid_search_arguments ( GridSearchArguments ) – Grid search arguments - Returns: Session for setting up and running Advanced Tuning on a model - Return type: AdvancedTuningSession #### start_incremental_learning_from_sample(early_stopping_rounds=None, first_iteration_only=False, chunk_definition_id=None) Submit a job to the queue to perform the first incremental learning iteration training on an existing sample model. This functionality requires the SAMPLE_DATA_TO_START_PROJECT feature flag to be enabled. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### train(sample_pct=None, featurelist_id=None, scoring_type=None, training_row_count=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=) Train the blueprint used in the model on a particular featurelist or amount of data. This method creates a new training job for the worker and appends it to the end of the queue for this project. After the job has finished, you can get the newly trained model by retrieving it from the project leaderboard or by retrieving the result of the job. Either sample_pct or training_row_count can be used to specify the amount of data to use, but not both. If neither is specified, a default of the maximum amount of data that can safely be used to train any blueprint without using the validation data will be selected. In smart-sampled projects, sample_pct and training_row_count are assumed to be in terms of rows of the minority class. > [!NOTE] Notes > For datetime partitioned projects, see [train_datetime](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.train_datetime) instead. - Parameters: - Returns: model_job_id – The ID of the created job; can be used as a parameter to ModelJob.get or the wait_for_async_model_creation function. - Return type: str > [!NOTE] Examples > ``` > project = Project.get('project-id') > model = Model.get('project-id', 'model-id') > model_job_id = model.train(training_row_count=project.max_train_rows) > ``` #### train_datetime(featurelist_id=None, training_row_count=None, training_duration=None, time_window_sample_pct=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=, use_project_settings=False, sampling_method=None, n_clusters=None) Train this model on a different featurelist or sample size. Requires that this model is part of a datetime partitioned project; otherwise, an error will occur. All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Parameters: - Returns: job – The created job to build the model. - Return type: ModelJob #### train_incremental(data_stage_id, training_data_name=None, data_stage_encoding=None, data_stage_delimiter=None, data_stage_compression=None) Submit a job to the queue to perform incremental training on an existing model using additional data. The ID of the additional data to use for training is specified with `data_stage_id`. Optionally, a name for the iteration can be supplied by the user to help identify the contents of the data in the iteration. This functionality requires the INCREMENTAL_LEARNING feature flag to be enabled. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### unstar_model() Unmark the model as starred. Model stars propagate to the web application and the API, and can be used to filter when listing models. - Return type: None ### class datarobot.models.model.AdvancedTuningParamsType ### class datarobot.models.model.BiasMitigationFeatureInfo ## Prime models ### class datarobot.models.PrimeModel Represents a DataRobot Prime model approximating a parent model with downloadable code. All durations are specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Variables: #### classmethod get(project_id, model_id) Retrieve a specific prime model. - Parameters: - Returns: model – The queried instance. - Return type: PrimeModel #### request_download_validation(language) Prep and validate the downloadable code for the ruleset associated with this model. - Parameters: language ( str ) – the language the code should be downloaded in - see datarobot.enums.PRIME_LANGUAGE for available languages - Returns: job – A job tracking the code preparation and validation - Return type: Job #### advanced_tune(params, description=None, grid_search_arguments=None) Generate a new model with the specified advanced-tuning parameters As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Parameters: - Returns: The created job to build the model - Return type: ModelJob #### continue_incremental_learning_from_incremental_model(chunk_definition_id, early_stopping_rounds=None) Submit a job to the queue to perform the first incremental learning iteration training on an existing sample model. This functionality requires the SAMPLE_DATA_TO_START_PROJECT feature flag to be enabled. - Parameters: - Returns: job – The model retraining job that is created. - Return type: ModelJob #### cross_validate() Run cross validation on the model. > [!NOTE] Notes > To perform Cross Validation on a new model with new parameters, use `train` instead. - Returns: The created job to build the model - Return type: ModelJob #### delete() Delete the model from the project leaderboard. - Return type: None #### download_scoring_code(file_name, source_code=False) Download the Scoring Code JAR. - Parameters: - Return type: None #### download_training_artifact(file_name) Retrieve trained artifact(s) from a model containing one or more custom tasks. Artifact(s) will be downloaded to the specified local filepath. - Parameters: file_name ( str ) – File path where trained model artifact(s) will be saved. #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### get_advanced_tuning_parameters() Get the advanced-tuning parameters available for this model. As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Returns:A dictionary describing the advanced-tuning parameters for the current model. There are two top-level keys, tuning_description and tuning_parameters. tuning_description an optional value. If not None, then it indicates the user-specified description of this set of tuning parameter. tuning_parameters is a list of a dicts, each has the following keys * parameter_name :(str)name of the parameter (unique per task, see below) * parameter_id :(str)opaque ID string uniquely identifying parameter * default_value :(*)the actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on current_value) * current_value :(*)the single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range. * task_name :(str)name of the task that this parameter belongs to * constraints:(dict)see the notes below * vertex_id:(str)ID of vertex that this parameter belongs to *Return type:dict > [!NOTE] Notes > The type of default_value and current_value is defined by the constraints structure. > It will be a string or numeric Python type. > > constraints is a dict with at least one, possibly more, of the following keys. > The presence of a key indicates that the parameter may take on the specified type. > (If a key is absent, this means that the parameter may not take on the specified type.) > If a key on constraints is present, its value will be a dict containing > all of the fields described below for that key. > > ``` > "constraints": { > "select": { > "values": [] > }, > "ascii": {}, > "unicode": {}, > "int": { > "min": , > "max": , > "supports_grid_search": requested for this param> > }, > "float": { > "min": , > "max": , > "supports_grid_search": requested for this param> > }, > "intList": { > "min_length": , > "max_length": > "min_val": , > "max_val": > "supports_grid_search": requested for this param> > }, > "floatList": { > "min_length": , > "max_length": > "min_val": , > "max_val": > "supports_grid_search": requested for this param> > } > } > ``` > > The keys have meaning as follows: > > select: > Rather than specifying a specific data type, if present, it indicates that the parameter > is permitted to take on any of the specified values. Listed values may be of any string > or real (non-complex) numeric type. > ascii: > The parameter may be a unicode object that encodes simple ASCII characters. > (A-Z, a-z, 0-9, whitespace, and certain common symbols.) In addition to listed > constraints, ASCII keys currently may not contain either newlines or semicolons. > unicode: > The parameter may be any Python unicode object. > int: > The value may be an object of type int within the specified range (inclusive). > Please note that the value will be passed around using the JSON format, and > some JSON parsers have undefined behavior with integers outside of the range > [-(2**53)+1, (2**53)-1]. > float: > The value may be an object of type float within the specified range (inclusive). > intList, floatList: > The value may be a list of int or float objects, respectively, following constraints > as specified respectively by the int and float types (above). > > Many parameters only specify one key under constraints. If a parameter specifies multiple > keys, the parameter may take on any value permitted by any key. #### get_all_confusion_charts(fallback_to_parent_insights=False) Retrieve a list of all confusion matrices available for the model. - Parameters: fallback_to_parent_insights ( bool ) – (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent for any source that is not available for this model and if this has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent. - Returns: Data for all available confusion charts for model. - Return type: list of ConfusionChart #### get_all_feature_impacts(data_slice_filter=None) Retrieve a list of all feature impact results available for the model. - Parameters: data_slice_filter ( DataSlice , optional ) – A DataSlice used to filter the return values based on the DataSlice ID. By default, this function uses data_slice_filter.id == None, which returns an unsliced insight. If data_slice_filter is None, no data_slice filtering will be applied when requesting the ROC curve. - Returns: Data for all available model feature impacts, or an empty list if no data is found. - Return type: list of dicts > [!NOTE] Examples > ``` > model = datarobot.Model(id='model-id', project_id='project-id') > > # Get feature impact insights for sliced data > data_slice = datarobot.DataSlice(id='data-slice-id') > sliced_fi = model.get_all_feature_impacts(data_slice_filter=data_slice) > > # Get feature impact insights for unsliced data > data_slice = datarobot.DataSlice() > unsliced_fi = model.get_all_feature_impacts(data_slice_filter=data_slice) > > # Get all feature impact insights > all_fi = model.get_all_feature_impacts() > ``` #### get_all_lift_charts(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all Lift charts available for the model. - Parameters: - Returns: Data for all available model lift charts. Or an empty list if no data found. - Return type: list of LiftChart > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > > # Get lift chart insights for sliced data > sliced_lift_charts = model.get_all_lift_charts(data_slice_id='data-slice-id') > > # Get lift chart insights for unsliced data > unsliced_lift_charts = model.get_all_lift_charts(unsliced_only=True) > > # Get all lift chart insights > all_lift_charts = model.get_all_lift_charts() > ``` #### get_all_multiclass_lift_charts(fallback_to_parent_insights=False, data_slice_filter=, target_class=None) Retrieve a list of all Lift charts available for the model. - Parameters: - Returns: Data for all available model lift charts. - Return type: list of LiftChart #### get_all_residuals_charts(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all residuals charts available for the model. - Parameters: - Returns: Data for all available model residuals charts. - Return type: list of ResidualsChart > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > > # Get residuals chart insights for sliced data > sliced_residuals_charts = model.get_all_residuals_charts(data_slice_id='data-slice-id') > > # Get residuals chart insights for unsliced data > unsliced_residuals_charts = model.get_all_residuals_charts(unsliced_only=True) > > # Get all residuals chart insights > all_residuals_charts = model.get_all_residuals_charts() > ``` #### get_all_roc_curves(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all ROC curves available for the model. - Parameters: - Returns: Data for all available model ROC curves. Or an empty list if no RocCurves are found. - Return type: list of RocCurve > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > ds_filter=DataSlice(id='data-slice-id') > > # Get roc curve insights for sliced data > sliced_roc = model.get_all_roc_curves(data_slice_filter=ds_filter) > > # Get roc curve insights for unsliced data > data_slice_filter=DataSlice(id=None) > unsliced_roc = model.get_all_roc_curves(data_slice_filter=ds_filter) > > # Get all roc curve insights > all_roc_curves = model.get_all_roc_curves() > ``` #### get_confusion_chart(source, fallback_to_parent_insights=False) Retrieve a multiclass model’s confusion matrix for the specified source. - Parameters: - Returns: Model ConfusionChart data - Return type: ConfusionChart - Raises: ClientError – If the insight is not available for this model #### get_cross_class_accuracy_scores() Retrieves a list of Cross Class Accuracy scores for the model. - Return type: json #### get_cross_validation_scores(partition=None, metric=None) Return a dictionary, keyed by metric, showing cross validation scores per partition. Cross Validation should already have been performed using [cross_validate](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.cross_validate) or [train](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.train). > [!NOTE] Notes > Models that computed cross validation before this feature was added will need > to be deleted and retrained before this method can be used. - Parameters: - Returns: cross_validation_scores – A dictionary keyed by metric showing cross validation scores per partition. - Return type: dict #### get_data_disparity_insights(feature, class_name1, class_name2) Retrieve a list of Cross Class Data Disparity insights for the model. - Parameters: - Return type: json #### get_fairness_insights(fairness_metrics_set=None, offset=0, limit=100) Retrieve a list of Per Class Bias insights for the model. - Parameters: - Return type: json #### get_feature_effect(source, data_slice_id=None) Retrieve Feature Effects for the model. Feature Effects provides partial dependence and predicted vs. actual values for the top 500 features ordered by feature impact score. The partial dependence shows the marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. Requires that Feature Effects has already been computed with [request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect). See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the available sources. - Parameters: - Returns: feature_effects – The feature effects data. - Return type: FeatureEffects - Raises: ClientError – If the feature effects have not been computed or the source is not a valid value. #### get_feature_effect_metadata() Retrieve Feature Effects metadata. The response contains status and available model sources. - Feature Effect for the training partition is always available, with the exception of older projects that only supported Feature Effect for validation. - When a model is trained into validation or holdout without stacked predictions (i.e., no out-of-sample predictions in those partitions), Feature Effects is not available for validation or holdout. - Feature Effects for holdout is not available when holdout was not unlocked for the project. Use source to retrieve Feature Effects, selecting one of the provided sources. - Returns: feature_effect_metadata - Return type: FeatureEffectMetadata #### get_feature_effects_multiclass(source='training', class_=None) Retrieve Feature Effects for the multiclass model. Feature Effects provide partial dependence and predicted vs. actual values for the top 500 features ordered by feature impact score. The partial dependence shows the marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. Requires that Feature Effects has already been computed with [request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect). See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the available sources. - Parameters: - Returns: The list of multiclass feature effects. - Return type: list - Raises: ClientError – If Feature Effects have not been computed or the source is not a valid value. #### get_feature_impact(with_metadata=False, data_slice_filter=) Retrieve the computed Feature Impact results, a measure of the relevance of each feature in the model. Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged) and measuring how the error metric score for the predictions is affected. The ‘impactUnnormalized’ is how much worse the error metric score is when making predictions on this modified data. The ‘impactNormalized’ is normalized so that the largest value is 1. In both cases, larger values indicate more important features. If a feature is redundant, i.e., once other features are considered it does not contribute much in addition, the ‘redundantWith’ value is the name of the feature that has the highest correlation with this feature. Note that redundancy detection is only available for jobs run after the addition of this feature. When retrieving data that predates this functionality, a NoRedundancyImpactAvailable warning will be used. Only the top 1000 features are saved and can be returned. Elsewhere this technique is sometimes called ‘Permutation Importance’. Requires that Feature Impact has already been computed with [request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact). - Parameters: - Returns:The feature impact data response depends on the with_metadata parameter. The response is either a dict with metadata and a list with the actual data or just a list with that data. Each list item is a dict with the keysfeatureName,impactNormalized,impactUnnormalized,redundantWith, andcount. For the dict response, the available keys are: featureImpacts- Feature Impact data as a dictionary. Each item is a dict with : the keys:featureName,impactNormalized,impactUnnormalized, andredundantWith.shapBased- A boolean that indicates whether Feature Impact was calculated using : Shapley values.ranRedundancyDetection- A boolean that indicates whether redundant feature : identification was run while calculating this Feature Impact.rowCount- An integer or None that indicates the number of rows that were used to : calculate Feature Impact. For Feature Impact calculated with the default logic without specifying the rowCount, we return None here.count- An integer with the number of features underfeatureImpacts.Return type:listordictRaises:ClientError– If the feature impacts have not been computed.ValueError– If data_slice_filter is passed as None. #### get_features_used() Query the server to determine which features were used. Note that the data returned by this method may differ from the names of the features in the featurelist used by this model. This method returns the raw features that must be supplied for predictions to be generated on a new set of data. The featurelist, in contrast, also includes the names of derived features. - Returns: features – The names of the features used in the model. - Return type: List[str] #### get_frozen_child_models() Retrieve the IDs for all models that are frozen from this model. - Return type: A list of Models #### get_labelwise_roc_curves(source, fallback_to_parent_insights=False) Retrieve a list of LabelwiseRocCurve instances for a multilabel model for the given source and all labels. This method is valid only for multilabel projects. For binary projects, use Model.get_roc_curve API . Added in version v2.24. - Parameters: - Returns: Labelwise ROC Curve instances for source and all labels - Return type: list of LabelwiseRocCurve - Raises: ClientError – If the insight is not available for this model #### get_lift_chart(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve the model Lift chart for the specified source. - Parameters: - Returns: Model lift chart data - Return type: LiftChart - Raises: #### get_missing_report_info() Retrieve a report on missing training data that can be used to understand missing values treatment in the model. The report consists of missing values resolutions for features numeric or categorical features that were part of building the model. - Returns: The queried model missing report, sorted by missing count (DESCENDING order). - Return type: An iterable of MissingReportPerFeature #### get_model_blueprint_chart() Retrieve a diagram that can be used to understand data flow in the blueprint. - Returns: The queried model blueprint chart. - Return type: ModelBlueprintChart #### get_model_blueprint_documents() Get documentation for tasks used in this model. - Returns: All documents available for the model. - Return type: list of BlueprintTaskDocument #### get_model_blueprint_json() Get the blueprint json representation used by this model. - Returns: Json representation of the blueprint stages. - Return type: BlueprintJson #### get_multiclass_feature_impact() For multiclass models, feature impact can be calculated separately for each target class. The method of calculation is the same, computed in one-vs-all style for each target class. Requires that Feature Impact has already been computed with [request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact). - Returns: feature_impacts – The feature impact data. Each item is a dict with the keys ‘featureImpacts’ (list), ‘class’ (str). Each item in ‘featureImpacts’ is a dict with the keys ‘featureName’, ‘impactNormalized’, ‘impactUnnormalized’, and ‘redundantWith’. - Return type: list of dict - Raises: ClientError – If the multiclass feature impacts have not been computed. #### get_multiclass_lift_chart(source, fallback_to_parent_insights=False, data_slice_filter=, target_class=None) Retrieve model Lift chart for the specified source. - Parameters: - Returns: Model lift chart data for each saved target class - Return type: list of LiftChart - Raises: ClientError – If the insight is not available for this model #### get_multilabel_lift_charts(source, fallback_to_parent_insights=False) Retrieve model Lift charts for the specified source. Added in version v2.24. - Parameters: - Returns: Model lift chart data for each saved target class - Return type: list of LiftChart - Raises: ClientError – If the insight is not available for this model #### get_num_iterations_trained() Retrieve the number of estimators trained by early-stopping tree-based models. Added in version v2.22. - Returns: #### get_or_request_feature_effect(source, max_wait=600, row_count=None, data_slice_id=None) Retrieve Feature Effects for the model, requesting a new job if it has not been run previously. See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the source. - Parameters: - Returns: feature_effects – The Feature Effects data. - Return type: FeatureEffects #### get_or_request_feature_effects_multiclass(source, top_n_features=None, features=None, row_count=None, class_=None, max_wait=600) Retrieve Feature Effects for the multiclass model, requesting a job if it has not been run previously. - Parameters: - Returns: feature_effects – The list of multiclass feature effects data. - Return type: list of FeatureEffectsMulticlass #### get_or_request_feature_impact(max_wait=600, **kwargs) Retrieve feature impact for the model, requesting a job if it has not been run previously. Only the top 1000 features are saved and can be returned. - Parameters: - Returns: feature_impacts – The feature impact data. See get_feature_impact for the exact schema. - Return type: list or dict #### get_parameters() Retrieve the model parameters. - Returns: The model parameters for this model. - Return type: ModelParameters #### get_pareto_front() Retrieve the Pareto Front for a Eureqa model. This method is only supported for Eureqa models. - Returns: Model ParetoFront data - Return type: ParetoFront #### get_prime_eligibility() Check whether this model can be approximated with DataRobot Prime. - Returns: prime_eligibility – A dict indicating whether the model can be approximated with DataRobot Prime (key can_make_prime) and why it may be ineligible (key message). - Return type: dict #### get_residuals_chart(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve model residuals chart for the specified source. - Parameters: - Returns: Model residuals chart data - Return type: ResidualsChart - Raises: #### get_roc_curve(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve the ROC curve for a binary model for the specified source. This method is valid only for binary projects. For multilabel projects, use Model.get_labelwise_roc_curves. - Parameters: - Returns: Model ROC curve data - Return type: RocCurve - Raises: #### get_rulesets() List the rulesets that approximate this model, generated by DataRobot Prime. If this model has not been approximated yet, returns an empty list. Note that these are rulesets that approximate this model, not rulesets used to construct this model. - Returns: rulesets - Return type: list of Ruleset #### get_supported_capabilities() Retrieve a summary of the capabilities supported by a model. Added in version v2.14. - Returns: #### get_uri() Return the permanent static hyperlink to this model on the leaderboard. - Returns: url – The permanent static hyperlink to this model on the leaderboard. - Return type: str #### get_word_cloud(exclude_stop_words=False) Retrieve word cloud data for the model. - Parameters: exclude_stop_words ( Optional[bool] ) – Set to True if you want stopwords filtered out of response. - Returns: Word cloud data for the model. - Return type: WordCloud #### incremental_train(data_stage_id, training_data_name=None) Submit a job to the queue to perform incremental training on an existing model. See the train_incremental documentation. - Return type: ModelJob #### classmethod list(project_id, sort_by_partition='validation', sort_by_metric=None, with_metric=None, search_term=None, featurelists=None, families=None, blueprints=None, labels=None, characteristics=None, training_filters=None, number_of_clusters=None, limit=100, offset=0) Retrieve paginated model records, sorted by scores, with optional filtering. - Parameters: - Returns: generic_models - Return type: list of GenericModel #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### request_cross_class_accuracy_scores() Request data disparity insights to be computed for the model. - Returns: status_id – A statusId of computation request. - Return type: str #### request_data_disparity_insights(feature, compared_class_names) Request data disparity insights to be computed for the model. - Parameters: - Returns: status_id – A statusId of computation request. - Return type: str #### request_external_test(dataset_id, actual_value_column=None) Request an external test to compute scores and insights on an external test dataset. - Parameters: - Returns: job – A job representing external dataset insights computation. - Return type: Job #### request_fairness_insights(fairness_metrics_set=None) Request fairness insights to be computed for the model. - Parameters: fairness_metrics_set ( Optional[str] ) – Can be one of . The fairness metric used to calculate the fairness scores. - Returns: status_id – A statusId of computation request. - Return type: str #### request_feature_effect(row_count=None, data_slice_id=None) Submit a request to compute Feature Effects for the model. See [get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect) for more information on the result of the job. - Parameters: - Returns: job – A job representing the feature effect computation. To get the completed feature effect data, use job.get_result or job.get_result_when_complete. - Return type: Job - Raises: JobAlreadyRequested – If the feature effects have already been requested. #### request_feature_effects_multiclass(row_count=None, top_n_features=None, features=None) Request Feature Effects computation for the multiclass model. See [get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effects_multiclass) for more information on the result of the job. - Parameters: - Returns: job – A job representing Feature Effect computation. To get the completed Feature Effect data, use job.get_result or job.get_result_when_complete. - Return type: Job #### request_feature_impact(row_count=None, with_metadata=False, data_slice_id=None) Request that feature impacts be computed for the model. See [get_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_impact) for more information on the result of the job. - Parameters: - Returns: job – A job representing the Feature Impact computation. To retrieve the completed Feature Impact data, use job.get_result or job.get_result_when_complete. - Return type: Job or status_id - Raises: JobAlreadyRequested – If the feature impacts have already been requested. #### request_lift_chart(source, data_slice_id=None) Request the model Lift Chart for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_per_class_fairness_insights(fairness_metrics_set=None) Request per-class fairness insights be computed for the model. - Parameters: fairness_metrics_set ( Optional[str] ) – The fairness metric used to calculate the fairness scores. Value can be any one of . - Returns: status_check_job – The returned object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_predictions(dataset_id=None, dataset=None, dataframe=None, file_path=None, file=None, include_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None, max_ngram_explanations=None) Request predictions against a previously uploaded dataset. - Parameters: - Returns: job – The job computing the predictions. - Return type: PredictJob #### request_residuals_chart(source, data_slice_id=None) Request the model residuals chart for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_roc_curve(source, data_slice_id=None) Request the model Roc Curve for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_training_predictions(data_subset, explanation_algorithm=None, max_explanations=None) Start a job to build training predictions - Parameters: #### retrain(sample_pct=None, featurelist_id=None, training_row_count=None, n_clusters=None) Submit a job to the queue to train a blender model. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### set_prediction_threshold(threshold) Set a custom prediction threshold for the model. May not be used once `prediction_threshold_read_only` is True for this model. - Parameters: threshold ( float ) – only used for binary classification projects. The threshold to when deciding between the positive and negative classes when making predictions. Should be between 0.0 and 1.0 (inclusive). #### star_model() Mark the model as starred. Model stars propagate to the web application and the API, and can be used to filter when listing models. - Return type: None #### start_advanced_tuning_session(grid_search_arguments=None) Start an Advanced Tuning session. Returns an object that helps set up arguments for an Advanced Tuning model execution. As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Parameters: grid_search_arguments ( GridSearchArguments ) – Grid search arguments - Returns: Session for setting up and running Advanced Tuning on a model - Return type: AdvancedTuningSession #### start_incremental_learning_from_sample(early_stopping_rounds=None, first_iteration_only=False, chunk_definition_id=None) Submit a job to the queue to perform the first incremental learning iteration training on an existing sample model. This functionality requires the SAMPLE_DATA_TO_START_PROJECT feature flag to be enabled. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### train_incremental(data_stage_id, training_data_name=None, data_stage_encoding=None, data_stage_delimiter=None, data_stage_compression=None) Submit a job to the queue to perform incremental training on an existing model using additional data. The ID of the additional data to use for training is specified with `data_stage_id`. Optionally, a name for the iteration can be supplied by the user to help identify the contents of the data in the iteration. This functionality requires the INCREMENTAL_LEARNING feature flag to be enabled. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### unstar_model() Unmark the model as starred. Model stars propagate to the web application and the API, and can be used to filter when listing models. - Return type: None ### Prime files ### class datarobot.models.PrimeFile Represents an executable file available for download of the code for a DataRobot Prime model - Variables: #### download(filepath) Download the code and save it to a file - Parameters: filepath ( string ) – the location to save the file to - Return type: None ## Blender models ### class datarobot.models.BlenderModel Represents blender model that combines prediction results from other models. All durations are specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Variables: #### classmethod get(project_id, model_id) Retrieve a specific blender. - Parameters: - Returns: model – The queried instance. - Return type: BlenderModel #### advanced_tune(params, description=None, grid_search_arguments=None) Generate a new model with the specified advanced-tuning parameters As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Parameters: - Returns: The created job to build the model - Return type: ModelJob #### continue_incremental_learning_from_incremental_model(chunk_definition_id, early_stopping_rounds=None) Submit a job to the queue to perform the first incremental learning iteration training on an existing sample model. This functionality requires the SAMPLE_DATA_TO_START_PROJECT feature flag to be enabled. - Parameters: - Returns: job – The model retraining job that is created. - Return type: ModelJob #### cross_validate() Run cross validation on the model. > [!NOTE] Notes > To perform Cross Validation on a new model with new parameters, use `train` instead. - Returns: The created job to build the model - Return type: ModelJob #### delete() Delete the model from the project leaderboard. - Return type: None #### download_scoring_code(file_name, source_code=False) Download the Scoring Code JAR. - Parameters: - Return type: None #### download_training_artifact(file_name) Retrieve trained artifact(s) from a model containing one or more custom tasks. Artifact(s) will be downloaded to the specified local filepath. - Parameters: file_name ( str ) – File path where trained model artifact(s) will be saved. #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Override the inherited method because the model must _not_ recursively change casing. - Parameters: #### get_advanced_tuning_parameters() Get the advanced-tuning parameters available for this model. As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Returns:A dictionary describing the advanced-tuning parameters for the current model. There are two top-level keys, tuning_description and tuning_parameters. tuning_description an optional value. If not None, then it indicates the user-specified description of this set of tuning parameter. tuning_parameters is a list of a dicts, each has the following keys * parameter_name :(str)name of the parameter (unique per task, see below) * parameter_id :(str)opaque ID string uniquely identifying parameter * default_value :(*)the actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on current_value) * current_value :(*)the single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range. * task_name :(str)name of the task that this parameter belongs to * constraints:(dict)see the notes below * vertex_id:(str)ID of vertex that this parameter belongs to *Return type:dict > [!NOTE] Notes > The type of default_value and current_value is defined by the constraints structure. > It will be a string or numeric Python type. > > constraints is a dict with at least one, possibly more, of the following keys. > The presence of a key indicates that the parameter may take on the specified type. > (If a key is absent, this means that the parameter may not take on the specified type.) > If a key on constraints is present, its value will be a dict containing > all of the fields described below for that key. > > ``` > "constraints": { > "select": { > "values": [] > }, > "ascii": {}, > "unicode": {}, > "int": { > "min": , > "max": , > "supports_grid_search": requested for this param> > }, > "float": { > "min": , > "max": , > "supports_grid_search": requested for this param> > }, > "intList": { > "min_length": , > "max_length": > "min_val": , > "max_val": > "supports_grid_search": requested for this param> > }, > "floatList": { > "min_length": , > "max_length": > "min_val": , > "max_val": > "supports_grid_search": requested for this param> > } > } > ``` > > The keys have meaning as follows: > > select: > Rather than specifying a specific data type, if present, it indicates that the parameter > is permitted to take on any of the specified values. Listed values may be of any string > or real (non-complex) numeric type. > ascii: > The parameter may be a unicode object that encodes simple ASCII characters. > (A-Z, a-z, 0-9, whitespace, and certain common symbols.) In addition to listed > constraints, ASCII keys currently may not contain either newlines or semicolons. > unicode: > The parameter may be any Python unicode object. > int: > The value may be an object of type int within the specified range (inclusive). > Please note that the value will be passed around using the JSON format, and > some JSON parsers have undefined behavior with integers outside of the range > [-(2**53)+1, (2**53)-1]. > float: > The value may be an object of type float within the specified range (inclusive). > intList, floatList: > The value may be a list of int or float objects, respectively, following constraints > as specified respectively by the int and float types (above). > > Many parameters only specify one key under constraints. If a parameter specifies multiple > keys, the parameter may take on any value permitted by any key. #### get_all_confusion_charts(fallback_to_parent_insights=False) Retrieve a list of all confusion matrices available for the model. - Parameters: fallback_to_parent_insights ( bool ) – (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent for any source that is not available for this model and if this has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent. - Returns: Data for all available confusion charts for model. - Return type: list of ConfusionChart #### get_all_feature_impacts(data_slice_filter=None) Retrieve a list of all feature impact results available for the model. - Parameters: data_slice_filter ( DataSlice , optional ) – A DataSlice used to filter the return values based on the DataSlice ID. By default, this function uses data_slice_filter.id == None, which returns an unsliced insight. If data_slice_filter is None, no data_slice filtering will be applied when requesting the ROC curve. - Returns: Data for all available model feature impacts, or an empty list if no data is found. - Return type: list of dicts > [!NOTE] Examples > ``` > model = datarobot.Model(id='model-id', project_id='project-id') > > # Get feature impact insights for sliced data > data_slice = datarobot.DataSlice(id='data-slice-id') > sliced_fi = model.get_all_feature_impacts(data_slice_filter=data_slice) > > # Get feature impact insights for unsliced data > data_slice = datarobot.DataSlice() > unsliced_fi = model.get_all_feature_impacts(data_slice_filter=data_slice) > > # Get all feature impact insights > all_fi = model.get_all_feature_impacts() > ``` #### get_all_lift_charts(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all Lift charts available for the model. - Parameters: - Returns: Data for all available model lift charts. Or an empty list if no data found. - Return type: list of LiftChart > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > > # Get lift chart insights for sliced data > sliced_lift_charts = model.get_all_lift_charts(data_slice_id='data-slice-id') > > # Get lift chart insights for unsliced data > unsliced_lift_charts = model.get_all_lift_charts(unsliced_only=True) > > # Get all lift chart insights > all_lift_charts = model.get_all_lift_charts() > ``` #### get_all_multiclass_lift_charts(fallback_to_parent_insights=False, data_slice_filter=, target_class=None) Retrieve a list of all Lift charts available for the model. - Parameters: - Returns: Data for all available model lift charts. - Return type: list of LiftChart #### get_all_residuals_charts(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all residuals charts available for the model. - Parameters: - Returns: Data for all available model residuals charts. - Return type: list of ResidualsChart > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > > # Get residuals chart insights for sliced data > sliced_residuals_charts = model.get_all_residuals_charts(data_slice_id='data-slice-id') > > # Get residuals chart insights for unsliced data > unsliced_residuals_charts = model.get_all_residuals_charts(unsliced_only=True) > > # Get all residuals chart insights > all_residuals_charts = model.get_all_residuals_charts() > ``` #### get_all_roc_curves(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all ROC curves available for the model. - Parameters: - Returns: Data for all available model ROC curves. Or an empty list if no RocCurves are found. - Return type: list of RocCurve > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > ds_filter=DataSlice(id='data-slice-id') > > # Get roc curve insights for sliced data > sliced_roc = model.get_all_roc_curves(data_slice_filter=ds_filter) > > # Get roc curve insights for unsliced data > data_slice_filter=DataSlice(id=None) > unsliced_roc = model.get_all_roc_curves(data_slice_filter=ds_filter) > > # Get all roc curve insights > all_roc_curves = model.get_all_roc_curves() > ``` #### get_confusion_chart(source, fallback_to_parent_insights=False) Retrieve a multiclass model’s confusion matrix for the specified source. - Parameters: - Returns: Model ConfusionChart data - Return type: ConfusionChart - Raises: ClientError – If the insight is not available for this model #### get_cross_class_accuracy_scores() Retrieves a list of Cross Class Accuracy scores for the model. - Return type: json #### get_cross_validation_scores(partition=None, metric=None) Return a dictionary, keyed by metric, showing cross validation scores per partition. Cross Validation should already have been performed using [cross_validate](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.cross_validate) or [train](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.train). > [!NOTE] Notes > Models that computed cross validation before this feature was added will need > to be deleted and retrained before this method can be used. - Parameters: - Returns: cross_validation_scores – A dictionary keyed by metric showing cross validation scores per partition. - Return type: dict #### get_data_disparity_insights(feature, class_name1, class_name2) Retrieve a list of Cross Class Data Disparity insights for the model. - Parameters: - Return type: json #### get_fairness_insights(fairness_metrics_set=None, offset=0, limit=100) Retrieve a list of Per Class Bias insights for the model. - Parameters: - Return type: json #### get_feature_effect(source, data_slice_id=None) Retrieve Feature Effects for the model. Feature Effects provides partial dependence and predicted vs. actual values for the top 500 features ordered by feature impact score. The partial dependence shows the marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. Requires that Feature Effects has already been computed with [request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect). See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the available sources. - Parameters: - Returns: feature_effects – The feature effects data. - Return type: FeatureEffects - Raises: ClientError – If the feature effects have not been computed or the source is not a valid value. #### get_feature_effect_metadata() Retrieve Feature Effects metadata. The response contains status and available model sources. - Feature Effect for the training partition is always available, with the exception of older projects that only supported Feature Effect for validation. - When a model is trained into validation or holdout without stacked predictions (i.e., no out-of-sample predictions in those partitions), Feature Effects is not available for validation or holdout. - Feature Effects for holdout is not available when holdout was not unlocked for the project. Use source to retrieve Feature Effects, selecting one of the provided sources. - Returns: feature_effect_metadata - Return type: FeatureEffectMetadata #### get_feature_effects_multiclass(source='training', class_=None) Retrieve Feature Effects for the multiclass model. Feature Effects provide partial dependence and predicted vs. actual values for the top 500 features ordered by feature impact score. The partial dependence shows the marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. Requires that Feature Effects has already been computed with [request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect). See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the available sources. - Parameters: - Returns: The list of multiclass feature effects. - Return type: list - Raises: ClientError – If Feature Effects have not been computed or the source is not a valid value. #### get_feature_impact(with_metadata=False, data_slice_filter=) Retrieve the computed Feature Impact results, a measure of the relevance of each feature in the model. Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged) and measuring how the error metric score for the predictions is affected. The ‘impactUnnormalized’ is how much worse the error metric score is when making predictions on this modified data. The ‘impactNormalized’ is normalized so that the largest value is 1. In both cases, larger values indicate more important features. If a feature is redundant, i.e., once other features are considered it does not contribute much in addition, the ‘redundantWith’ value is the name of the feature that has the highest correlation with this feature. Note that redundancy detection is only available for jobs run after the addition of this feature. When retrieving data that predates this functionality, a NoRedundancyImpactAvailable warning will be used. Only the top 1000 features are saved and can be returned. Elsewhere this technique is sometimes called ‘Permutation Importance’. Requires that Feature Impact has already been computed with [request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact). - Parameters: - Returns:The feature impact data response depends on the with_metadata parameter. The response is either a dict with metadata and a list with the actual data or just a list with that data. Each list item is a dict with the keysfeatureName,impactNormalized,impactUnnormalized,redundantWith, andcount. For the dict response, the available keys are: featureImpacts- Feature Impact data as a dictionary. Each item is a dict with : the keys:featureName,impactNormalized,impactUnnormalized, andredundantWith.shapBased- A boolean that indicates whether Feature Impact was calculated using : Shapley values.ranRedundancyDetection- A boolean that indicates whether redundant feature : identification was run while calculating this Feature Impact.rowCount- An integer or None that indicates the number of rows that were used to : calculate Feature Impact. For Feature Impact calculated with the default logic without specifying the rowCount, we return None here.count- An integer with the number of features underfeatureImpacts.Return type:listordictRaises:ClientError– If the feature impacts have not been computed.ValueError– If data_slice_filter is passed as None. #### get_features_used() Query the server to determine which features were used. Note that the data returned by this method may differ from the names of the features in the featurelist used by this model. This method returns the raw features that must be supplied for predictions to be generated on a new set of data. The featurelist, in contrast, also includes the names of derived features. - Returns: features – The names of the features used in the model. - Return type: List[str] #### get_frozen_child_models() Retrieve the IDs for all models that are frozen from this model. - Return type: A list of Models #### get_labelwise_roc_curves(source, fallback_to_parent_insights=False) Retrieve a list of LabelwiseRocCurve instances for a multilabel model for the given source and all labels. This method is valid only for multilabel projects. For binary projects, use Model.get_roc_curve API . Added in version v2.24. - Parameters: - Returns: Labelwise ROC Curve instances for source and all labels - Return type: list of LabelwiseRocCurve - Raises: ClientError – If the insight is not available for this model #### get_lift_chart(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve the model Lift chart for the specified source. - Parameters: - Returns: Model lift chart data - Return type: LiftChart - Raises: #### get_missing_report_info() Retrieve a report on missing training data that can be used to understand missing values treatment in the model. The report consists of missing values resolutions for features numeric or categorical features that were part of building the model. - Returns: The queried model missing report, sorted by missing count (DESCENDING order). - Return type: An iterable of MissingReportPerFeature #### get_model_blueprint_chart() Retrieve a diagram that can be used to understand data flow in the blueprint. - Returns: The queried model blueprint chart. - Return type: ModelBlueprintChart #### get_model_blueprint_documents() Get documentation for tasks used in this model. - Returns: All documents available for the model. - Return type: list of BlueprintTaskDocument #### get_model_blueprint_json() Get the blueprint json representation used by this model. - Returns: Json representation of the blueprint stages. - Return type: BlueprintJson #### get_multiclass_feature_impact() For multiclass models, feature impact can be calculated separately for each target class. The method of calculation is the same, computed in one-vs-all style for each target class. Requires that Feature Impact has already been computed with [request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact). - Returns: feature_impacts – The feature impact data. Each item is a dict with the keys ‘featureImpacts’ (list), ‘class’ (str). Each item in ‘featureImpacts’ is a dict with the keys ‘featureName’, ‘impactNormalized’, ‘impactUnnormalized’, and ‘redundantWith’. - Return type: list of dict - Raises: ClientError – If the multiclass feature impacts have not been computed. #### get_multiclass_lift_chart(source, fallback_to_parent_insights=False, data_slice_filter=, target_class=None) Retrieve model Lift chart for the specified source. - Parameters: - Returns: Model lift chart data for each saved target class - Return type: list of LiftChart - Raises: ClientError – If the insight is not available for this model #### get_multilabel_lift_charts(source, fallback_to_parent_insights=False) Retrieve model Lift charts for the specified source. Added in version v2.24. - Parameters: - Returns: Model lift chart data for each saved target class - Return type: list of LiftChart - Raises: ClientError – If the insight is not available for this model #### get_num_iterations_trained() Retrieve the number of estimators trained by early-stopping tree-based models. Added in version v2.22. - Returns: #### get_or_request_feature_effect(source, max_wait=600, row_count=None, data_slice_id=None) Retrieve Feature Effects for the model, requesting a new job if it has not been run previously. See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the source. - Parameters: - Returns: feature_effects – The Feature Effects data. - Return type: FeatureEffects #### get_or_request_feature_effects_multiclass(source, top_n_features=None, features=None, row_count=None, class_=None, max_wait=600) Retrieve Feature Effects for the multiclass model, requesting a job if it has not been run previously. - Parameters: - Returns: feature_effects – The list of multiclass feature effects data. - Return type: list of FeatureEffectsMulticlass #### get_or_request_feature_impact(max_wait=600, **kwargs) Retrieve feature impact for the model, requesting a job if it has not been run previously. Only the top 1000 features are saved and can be returned. - Parameters: - Returns: feature_impacts – The feature impact data. See get_feature_impact for the exact schema. - Return type: list or dict #### get_parameters() Retrieve the model parameters. - Returns: The model parameters for this model. - Return type: ModelParameters #### get_pareto_front() Retrieve the Pareto Front for a Eureqa model. This method is only supported for Eureqa models. - Returns: Model ParetoFront data - Return type: ParetoFront #### get_prime_eligibility() Check whether this model can be approximated with DataRobot Prime. - Returns: prime_eligibility – A dict indicating whether the model can be approximated with DataRobot Prime (key can_make_prime) and why it may be ineligible (key message). - Return type: dict #### get_residuals_chart(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve model residuals chart for the specified source. - Parameters: - Returns: Model residuals chart data - Return type: ResidualsChart - Raises: #### get_roc_curve(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve the ROC curve for a binary model for the specified source. This method is valid only for binary projects. For multilabel projects, use Model.get_labelwise_roc_curves. - Parameters: - Returns: Model ROC curve data - Return type: RocCurve - Raises: #### get_rulesets() List the rulesets that approximate this model, generated by DataRobot Prime. If this model has not been approximated yet, returns an empty list. Note that these are rulesets that approximate this model, not rulesets used to construct this model. - Returns: rulesets - Return type: list of Ruleset #### get_supported_capabilities() Retrieve a summary of the capabilities supported by a model. Added in version v2.14. - Returns: #### get_uri() Return the permanent static hyperlink to this model on the leaderboard. - Returns: url – The permanent static hyperlink to this model on the leaderboard. - Return type: str #### get_word_cloud(exclude_stop_words=False) Retrieve word cloud data for the model. - Parameters: exclude_stop_words ( Optional[bool] ) – Set to True if you want stopwords filtered out of response. - Returns: Word cloud data for the model. - Return type: WordCloud #### incremental_train(data_stage_id, training_data_name=None) Submit a job to the queue to perform incremental training on an existing model. See the train_incremental documentation. - Return type: ModelJob #### classmethod list(project_id, sort_by_partition='validation', sort_by_metric=None, with_metric=None, search_term=None, featurelists=None, families=None, blueprints=None, labels=None, characteristics=None, training_filters=None, number_of_clusters=None, limit=100, offset=0) Retrieve paginated model records, sorted by scores, with optional filtering. - Parameters: - Returns: generic_models - Return type: list of GenericModel #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### request_approximation() Request an approximation of this model using DataRobot Prime. This creates several rulesets that can be used to approximate this model. After comparing their scores and rule counts, the code used in the approximation can be downloaded and run locally. - Returns: job – The job that generates the rulesets. - Return type: Job #### request_cross_class_accuracy_scores() Request data disparity insights to be computed for the model. - Returns: status_id – A statusId of computation request. - Return type: str #### request_data_disparity_insights(feature, compared_class_names) Request data disparity insights to be computed for the model. - Parameters: - Returns: status_id – A statusId of computation request. - Return type: str #### request_external_test(dataset_id, actual_value_column=None) Request an external test to compute scores and insights on an external test dataset. - Parameters: - Returns: job – A job representing external dataset insights computation. - Return type: Job #### request_fairness_insights(fairness_metrics_set=None) Request fairness insights to be computed for the model. - Parameters: fairness_metrics_set ( Optional[str] ) – Can be one of . The fairness metric used to calculate the fairness scores. - Returns: status_id – A statusId of computation request. - Return type: str #### request_feature_effect(row_count=None, data_slice_id=None) Submit a request to compute Feature Effects for the model. See [get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect) for more information on the result of the job. - Parameters: - Returns: job – A job representing the feature effect computation. To get the completed feature effect data, use job.get_result or job.get_result_when_complete. - Return type: Job - Raises: JobAlreadyRequested – If the feature effects have already been requested. #### request_feature_effects_multiclass(row_count=None, top_n_features=None, features=None) Request Feature Effects computation for the multiclass model. See [get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effects_multiclass) for more information on the result of the job. - Parameters: - Returns: job – A job representing Feature Effect computation. To get the completed Feature Effect data, use job.get_result or job.get_result_when_complete. - Return type: Job #### request_feature_impact(row_count=None, with_metadata=False, data_slice_id=None) Request that feature impacts be computed for the model. See [get_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_impact) for more information on the result of the job. - Parameters: - Returns: job – A job representing the Feature Impact computation. To retrieve the completed Feature Impact data, use job.get_result or job.get_result_when_complete. - Return type: Job or status_id - Raises: JobAlreadyRequested – If the feature impacts have already been requested. #### request_frozen_datetime_model(training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None) Train a new frozen model with parameters from this model. Requires that this model belongs to a datetime partitioned project. If it does not, an error will occur when submitting the job. Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data. In addition to training_row_count and training_duration, frozen datetime models may be trained on an exact date range. Only one of training_row_count, training_duration, or training_start_date and training_end_date should be specified. Models specified using training_start_date and training_end_date are the only ones that can be trained into the holdout data (once the holdout is unlocked). All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Parameters: - Returns: model_job – The modeling job that trains a frozen model. - Return type: ModelJob #### request_frozen_model(sample_pct=None, training_row_count=None) Train a new frozen model with parameters from this model. > [!NOTE] Notes > This method only works if the project the model belongs to is not datetime > partitioned. If it is, use `request_frozen_datetime_model` instead. > > Frozen models use the same tuning parameters as their parent model instead of independently > optimizing them to allow efficiently retraining models on larger amounts of the training > data. - Parameters: - Returns: model_job – The modeling job that trains a frozen model. - Return type: ModelJob #### request_lift_chart(source, data_slice_id=None) Request the model Lift Chart for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_per_class_fairness_insights(fairness_metrics_set=None) Request per-class fairness insights be computed for the model. - Parameters: fairness_metrics_set ( Optional[str] ) – The fairness metric used to calculate the fairness scores. Value can be any one of . - Returns: status_check_job – The returned object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_predictions(dataset_id=None, dataset=None, dataframe=None, file_path=None, file=None, include_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None, max_ngram_explanations=None) Request predictions against a previously uploaded dataset. - Parameters: - Returns: job – The job computing the predictions. - Return type: PredictJob #### request_residuals_chart(source, data_slice_id=None) Request the model residuals chart for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_roc_curve(source, data_slice_id=None) Request the model Roc Curve for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_training_predictions(data_subset, explanation_algorithm=None, max_explanations=None) Start a job to build training predictions - Parameters: #### retrain(sample_pct=None, featurelist_id=None, training_row_count=None, n_clusters=None) Submit a job to the queue to train a blender model. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### set_prediction_threshold(threshold) Set a custom prediction threshold for the model. May not be used once `prediction_threshold_read_only` is True for this model. - Parameters: threshold ( float ) – only used for binary classification projects. The threshold to when deciding between the positive and negative classes when making predictions. Should be between 0.0 and 1.0 (inclusive). #### star_model() Mark the model as starred. Model stars propagate to the web application and the API, and can be used to filter when listing models. - Return type: None #### start_advanced_tuning_session(grid_search_arguments=None) Start an Advanced Tuning session. Returns an object that helps set up arguments for an Advanced Tuning model execution. As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Parameters: grid_search_arguments ( GridSearchArguments ) – Grid search arguments - Returns: Session for setting up and running Advanced Tuning on a model - Return type: AdvancedTuningSession #### start_incremental_learning_from_sample(early_stopping_rounds=None, first_iteration_only=False, chunk_definition_id=None) Submit a job to the queue to perform the first incremental learning iteration training on an existing sample model. This functionality requires the SAMPLE_DATA_TO_START_PROJECT feature flag to be enabled. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### train(sample_pct=None, featurelist_id=None, scoring_type=None, training_row_count=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=) Train the blueprint used in the model on a particular featurelist or amount of data. This method creates a new training job for the worker and appends it to the end of the queue for this project. After the job has finished, you can get the newly trained model by retrieving it from the project leaderboard or by retrieving the result of the job. Either sample_pct or training_row_count can be used to specify the amount of data to use, but not both. If neither is specified, a default of the maximum amount of data that can safely be used to train any blueprint without using the validation data will be selected. In smart-sampled projects, sample_pct and training_row_count are assumed to be in terms of rows of the minority class. > [!NOTE] Notes > For datetime partitioned projects, see [train_datetime](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.train_datetime) instead. - Parameters: - Returns: model_job_id – The ID of the created job; can be used as a parameter to ModelJob.get or the wait_for_async_model_creation function. - Return type: str > [!NOTE] Examples > ``` > project = Project.get('project-id') > model = Model.get('project-id', 'model-id') > model_job_id = model.train(training_row_count=project.max_train_rows) > ``` #### train_datetime(featurelist_id=None, training_row_count=None, training_duration=None, time_window_sample_pct=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=, use_project_settings=False, sampling_method=None, n_clusters=None) Train this model on a different featurelist or sample size. Requires that this model is part of a datetime partitioned project; otherwise, an error will occur. All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Parameters: - Returns: job – The created job to build the model. - Return type: ModelJob #### train_incremental(data_stage_id, training_data_name=None, data_stage_encoding=None, data_stage_delimiter=None, data_stage_compression=None) Submit a job to the queue to perform incremental training on an existing model using additional data. The ID of the additional data to use for training is specified with `data_stage_id`. Optionally, a name for the iteration can be supplied by the user to help identify the contents of the data in the iteration. This functionality requires the INCREMENTAL_LEARNING feature flag to be enabled. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### unstar_model() Unmark the model as starred. Model stars propagate to the web application and the API, and can be used to filter when listing models. - Return type: None ## Datetime models ### class datarobot.models.DatetimeModel Represents a model from a datetime partitioned project All durations are specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. Note that only one of training_row_count, training_duration, and training_start_date and training_end_date will be specified, depending on the data_selection_method of the model. Whichever method was selected determines the amount of data used to train on when making predictions and scoring the backtests and the holdout. - Variables: #### classmethod get(project, model_id) Retrieve a specific datetime model. If the project does not use datetime partitioning, a ClientError will occur. - Parameters: - Returns: model – the model - Return type: DatetimeModel #### score_backtests() Compute the scores for all available backtests. Some backtests may be unavailable if the model is trained into their validation data. - Returns: job – a job tracking the backtest computation. When it is complete, all available backtests will have scores computed. - Return type: Job #### cross_validate() Inherited from the model. DatetimeModels cannot request cross validation scores; use backtests instead. - Return type: NoReturn #### get_cross_validation_scores(partition=None, metric=None) Inherited from Model - DatetimeModels cannot request Cross Validation scores, Use `backtests` instead. - Return type: NoReturn #### request_training_predictions(data_subset, *args, **kwargs) Start a job that builds training predictions. - Parameters:data_subset(str) – data set definition to build predictions on. Choices are: dr.enums.DATA_SUBSET.HOLDOUT for holdout data set onlydr.enums.DATA_SUBSET.ALL_BACKTESTS for downloading the predictions for all : backtest validation folds. Requires the model to have successfully scored all backtests.Returns:an instance of created async jobReturn type:Job #### get_series_accuracy_as_dataframe(offset=0, limit=100, metric=None, multiseries_value=None, order_by=None, reverse=False) Retrieve series accuracy results for the specified model as a pandas.DataFrame. - Parameters: - Returns: A pandas.DataFrame with the Series Accuracy for the specified model. - Return type: data #### download_series_accuracy_as_csv(filename, encoding='utf-8', offset=0, limit=100, metric=None, multiseries_value=None, order_by=None, reverse=False) Save series accuracy results for the specified model in a CSV file. - Parameters: #### get_series_clusters(offset=0, limit=100, order_by=None, reverse=False) Retrieve a dictionary of series and the clusters assigned to each series. This is only usable for clustering projects. - Parameters: - Returns: A dictionary of the series in the dataset with their associated cluster - Return type: Dict - Raises: #### compute_series_accuracy(compute_all_series=False) Compute series accuracy for the model. - Parameters: compute_all_series ( Optional[bool] ) – Calculate accuracy for all series or only first 1000. - Returns: an instance of the created async job - Return type: Job #### retrain(time_window_sample_pct=None, featurelist_id=None, training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, sampling_method=None, n_clusters=None) Retrain an existing datetime model using a new training period for the model’s training set (with optional time window sampling) or a different feature list. All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Parameters: - Returns: job – The created job that is retraining the model - Return type: ModelJob #### get_feature_effect_metadata() Retrieve Feature Effect metadata for each backtest. Response contains status and available sources for each backtest of the model. - Each backtest is available for training and validation - If holdout is configured for the project it has holdout as backtestIndex. It has training and holdout sources available. Start/stop models contain a single response item with startstop value for backtestIndex. - Feature Effect of training is always available (except for the old project which supports only Feature Effect for validation). - When a model is trained into validation or holdout without stacked prediction (e.g., no out-of-sample prediction in validation or holdout), Feature Effect is not available for validation or holdout. - Feature Effect for holdout is not available when there is no holdout configured for the project. source is expected parameter to retrieve Feature Effect. One of provided sources shall be used. backtestIndex is expected parameter to submit compute request and retrieve Feature Effect. One of provided backtest indexes shall be used. - Returns: feature_effect_metadata - Return type: FeatureEffectMetadataDatetime #### request_feature_effect(backtest_index, data_slice_filter=) Request feature effects to be computed for the model. See [get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_feature_effect) for more information on the result of the job. See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_feature_effect_metadata) for retrieving information of backtest_index. - Parameters: backtest_index ( string , FeatureEffectMetadataDatetime.backtest_index. ) – The backtest index to retrieve Feature Effects for. - Returns: job – A Job representing the feature effect computation. To get the completed feature effect data, use job.get_result or job.get_result_when_complete. - Return type: Job - Raises: JobAlreadyRequested – If the feature effect have already been requested. #### get_feature_effect(source, backtest_index, data_slice_filter=) Retrieve Feature Effects for the model. Feature Effects provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score. The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. Requires that Feature Effects has already been computed with [request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect). See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_feature_effect_metadata) for retrieving information of source, backtest_index. - Parameters: - Returns: feature_effects – The feature effects data. - Return type: FeatureEffects - Raises: ClientError – If the feature effects have not been computed or source is not valid value. #### get_or_request_feature_effect(source, backtest_index, max_wait=600, data_slice_filter=) Retrieve Feature Effects computations for the model, requesting a new job if it hasn’t been run previously. See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.get_feature_effect_metadata) for retrieving information of source, backtest_index. - Parameters: - Returns: feature_effects – The feature effects data. - Return type: FeatureEffects #### request_feature_effects_multiclass(backtest_index, row_count=None, top_n_features=None, features=None) Request feature effects to be computed for the multiclass datetime model. See [get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effects_multiclass) for more information on the result of the job. - Parameters: - Returns: job – A Job representing Feature Effects computation. To get the completed Feature Effect data, use job.get_result or job.get_result_when_complete. - Return type: Job #### get_feature_effects_multiclass(backtest_index, source='training', class_=None) Retrieve Feature Effects for the multiclass datetime model. Feature Effects provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score. The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. Requires that Feature Effects has already been computed with [request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect). See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information the available sources. - Parameters: - Returns: The list of multiclass Feature Effects. - Return type: list - Raises: ClientError – If the Feature Effects have not been computed or source is not valid value. #### get_or_request_feature_effects_multiclass(backtest_index, source, top_n_features=None, features=None, row_count=None, class_=None, max_wait=600) Retrieve Feature Effects for a datetime multiclass model, and request a job if it hasn’t been run previously. - Parameters: - Returns: feature_effects – The list of multiclass feature effects data. - Return type: list of FeatureEffectsMulticlass #### calculate_prediction_intervals(prediction_intervals_size) Calculate prediction intervals for this DatetimeModel for the specified size. Added in version v2.19. - Parameters: prediction_intervals_size ( int ) – The prediction interval’s size to calculate for this model. See the prediction intervals documentation for more information. - Returns: job – a Job tracking the prediction intervals computation - Return type: Job #### get_calculated_prediction_intervals(offset=None, limit=None) Retrieve a list of already-calculated prediction intervals for this model Added in version v2.19. - Parameters: - Returns: A descending-ordered list of already-calculated prediction interval sizes - Return type: list[int] #### compute_datetime_trend_plots(backtest=0, source=SOURCE_TYPE.VALIDATION, forecast_distance_start=None, forecast_distance_end=None) Computes datetime trend plots (Accuracy over Time, Forecast vs Actual, Anomaly over Time) for this model Added in version v2.25. - Parameters: - Returns: job – a Job tracking the datetime trend plots computation - Return type: Job > [!NOTE] Notes > Forecast distance specifies the number of time steps > between the predicted point and the origin point. > For the multiseries models only first 1000 series in > alphabetical order and an average plot for them will be computed. > Maximum 100 forecast distances can be requested for > calculation in time series supervised projects. #### get_accuracy_over_time_plots_metadata(forecast_distance=None) Retrieve Accuracy over Time plots metadata for this model. Added in version v2.25. - Parameters: forecast_distance ( Optional[int] ) – Forecast distance to retrieve the metadata for. If not specified, the first forecast distance for this project will be used. Only available for time series projects. - Returns: metadata – a AccuracyOverTimePlotsMetadata representing Accuracy over Time plots metadata - Return type: AccuracyOverTimePlotsMetadata #### get_accuracy_over_time_plot(backtest=0, source=SOURCE_TYPE.VALIDATION, forecast_distance=None, series_id=None, resolution=None, max_bin_size=None, start_date=None, end_date=None, max_wait=600) Retrieve Accuracy over Time plots for this model. Added in version v2.25. - Parameters: - Returns: plot – a AccuracyOverTimePlot representing Accuracy over Time plot - Return type: AccuracyOverTimePlot > [!NOTE] Examples > ``` > import datarobot as dr > import pandas as pd > model = dr.DatetimeModel(project_id=project_id, id=model_id) > plot = model.get_accuracy_over_time_plot() > df = pd.DataFrame.from_dict(plot.bins) > figure = df.plot("start_date", ["actual", "predicted"]).get_figure() > figure.savefig("accuracy_over_time.png") > ``` #### get_accuracy_over_time_plot_preview(backtest=0, source=SOURCE_TYPE.VALIDATION, forecast_distance=None, series_id=None, max_wait=600) Retrieve Accuracy over Time preview plots for this model. Added in version v2.25. - Parameters: - Returns: plot – a AccuracyOverTimePlotPreview representing Accuracy over Time plot preview - Return type: AccuracyOverTimePlotPreview > [!NOTE] Examples > ``` > import datarobot as dr > import pandas as pd > model = dr.DatetimeModel(project_id=project_id, id=model_id) > plot = model.get_accuracy_over_time_plot_preview() > df = pd.DataFrame.from_dict(plot.bins) > figure = df.plot("start_date", ["actual", "predicted"]).get_figure() > figure.savefig("accuracy_over_time_preview.png") > ``` #### get_forecast_vs_actual_plots_metadata() Retrieve Forecast vs Actual plots metadata for this model. Added in version v2.25. - Returns: metadata – a ForecastVsActualPlotsMetadata representing Forecast vs Actual plots metadata - Return type: ForecastVsActualPlotsMetadata #### get_forecast_vs_actual_plot(backtest=0, source=SOURCE_TYPE.VALIDATION, forecast_distance_start=None, forecast_distance_end=None, series_id=None, resolution=None, max_bin_size=None, start_date=None, end_date=None, max_wait=600) Retrieve Forecast vs Actual plots for this model. Added in version v2.25. - Parameters: - Returns: plot – a ForecastVsActualPlot representing Forecast vs Actual plot - Return type: ForecastVsActualPlot > [!NOTE] Examples > ``` > import datarobot as dr > import pandas as pd > import matplotlib.pyplot as plt > > model = dr.DatetimeModel(project_id=project_id, id=model_id) > plot = model.get_forecast_vs_actual_plot() > df = pd.DataFrame.from_dict(plot.bins) > > # As an example, get the forecasts for the 10th point > forecast_point_index = 10 > # Pad the forecasts for plotting. The forecasts length must match the df length > forecasts = [None] * forecast_point_index + df.forecasts[forecast_point_index] > forecasts = forecasts + [None] * (len(df) - len(forecasts)) > > plt.plot(df.start_date, df.actual, label="Actual") > plt.plot(df.start_date, forecasts, label="Forecast") > forecast_point = df.start_date[forecast_point_index] > plt.title("Forecast vs Actual (Forecast Point {})".format(forecast_point)) > plt.legend() > plt.savefig("forecast_vs_actual.png") > ``` #### get_forecast_vs_actual_plot_preview(backtest=0, source=SOURCE_TYPE.VALIDATION, series_id=None, max_wait=600) Retrieve Forecast vs Actual preview plots for this model. Added in version v2.25. - Parameters: - Returns: plot – a ForecastVsActualPlotPreview representing Forecast vs Actual plot preview - Return type: ForecastVsActualPlotPreview > [!NOTE] Examples > ``` > import datarobot as dr > import pandas as pd > model = dr.DatetimeModel(project_id=project_id, id=model_id) > plot = model.get_forecast_vs_actual_plot_preview() > df = pd.DataFrame.from_dict(plot.bins) > figure = df.plot("start_date", ["actual", "predicted"]).get_figure() > figure.savefig("forecast_vs_actual_preview.png") > ``` #### get_anomaly_over_time_plots_metadata() Retrieve Anomaly over Time plots metadata for this model. Added in version v2.25. - Returns: metadata – a AnomalyOverTimePlotsMetadata representing Anomaly over Time plots metadata - Return type: AnomalyOverTimePlotsMetadata #### get_anomaly_over_time_plot(backtest=0, source=SOURCE_TYPE.VALIDATION, series_id=None, resolution=None, max_bin_size=None, start_date=None, end_date=None, max_wait=600) Retrieve Anomaly over Time plots for this model. Added in version v2.25. - Parameters: - Returns: plot – a AnomalyOverTimePlot representing Anomaly over Time plot - Return type: AnomalyOverTimePlot > [!NOTE] Examples > ``` > import datarobot as dr > import pandas as pd > model = dr.DatetimeModel(project_id=project_id, id=model_id) > plot = model.get_anomaly_over_time_plot() > df = pd.DataFrame.from_dict(plot.bins) > figure = df.plot("start_date", "predicted").get_figure() > figure.savefig("anomaly_over_time.png") > ``` #### get_anomaly_over_time_plot_preview(prediction_threshold=0.5, backtest=0, source=SOURCE_TYPE.VALIDATION, series_id=None, max_wait=600) Retrieve Anomaly over Time preview plots for this model. Added in version v2.25. - Parameters: - Returns: plot – a AnomalyOverTimePlotPreview representing Anomaly over Time plot preview - Return type: AnomalyOverTimePlotPreview > [!NOTE] Examples > ``` > import datarobot as dr > import pandas as pd > import matplotlib.pyplot as plt > > model = dr.DatetimeModel(project_id=project_id, id=model_id) > plot = model.get_anomaly_over_time_plot_preview(prediction_threshold=0.01) > df = pd.DataFrame.from_dict(plot.bins) > x = pd.date_range( > plot.start_date, plot.end_date, freq=df.end_date[0] - df.start_date[0] > ) > plt.plot(x, [0] * len(x), label="Date range") > plt.plot(df.start_date, [0] * len(df.start_date), "ro", label="Anomaly") > plt.yticks([]) > plt.legend() > plt.savefig("anomaly_over_time_preview.png") > ``` #### initialize_anomaly_assessment(backtest, source, series_id=None) Initialize the anomaly assessment insight and calculate Shapley explanations for the most anomalous points in the subset. The insight is available for anomaly detection models in time series unsupervised projects which also support calculation of Shapley values. - Parameters: - Return type: AnomalyAssessmentRecord #### get_anomaly_assessment_records(backtest=None, source=None, series_id=None, limit=100, offset=0, with_data_only=False) Retrieve computed Anomaly Assessment records for this model. Model must be an anomaly detection model in time series unsupervised project which also supports calculation of Shapley values. Records can be filtered by the data backtest, source and series_id. The results can be limited. Added in version v2.25. - Parameters: - Returns: records – a AnomalyAssessmentRecord representing Anomaly Assessment Record - Return type: list of AnomalyAssessmentRecord #### get_feature_impact(with_metadata=False, backtest=None, data_slice_filter=) Retrieve the computed Feature Impact results, a measure of the relevance of each feature in the model. Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged), and seeing how the error metric score for the predictions is affected. The ‘impactUnnormalized’ is how much worse the error metric score is when making predictions on this modified data. The ‘impactNormalized’ is normalized so that the largest value is 1. In both cases, larger values indicate more important features. If a feature is redundant, i.e., once other features are considered it does not contribute much in addition, the ‘redundantWith’ value is the name of the feature that has the highest correlation with this feature. Note that redundancy detection is only available for jobs run after the addition of this feature. When retrieving data that predates this functionality, a NoRedundancyImpactAvailable warning will be used. Only the top 1000 features are saved and can be returned. Elsewhere this technique is sometimes called ‘Permutation Importance’. Requires that Feature Impact has already been computed with [request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact). - Parameters: - Returns:The feature impact data response depends on the with_metadata parameter. The response is either a dict with metadata and a list with actual data or just a list with that data. Each List item is a dict with the keysfeatureName,impactNormalized, andimpactUnnormalized,redundantWithandcount. For dict response available keys are: featureImpacts- Feature Impact data as a dictionary. Each item is a dict with : keys:featureName,impactNormalized, andimpactUnnormalized, andredundantWith.shapBased- A boolean that indicates whether Feature Impact was calculated using : Shapley values.ranRedundancyDetection- A boolean that indicates whether redundant feature : identification was run while calculating this Feature Impact.rowCount- An integer or None that indicates the number of rows that was used to : calculate Feature Impact. For the Feature Impact calculated with the default logic, without specifying the rowCount, we return None here.count- An integer with the number of features under thefeatureImpacts.Return type:listordictRaises:ClientError– If the feature impacts have not been computed. #### request_feature_impact(row_count=None, with_metadata=False, backtest=None, data_slice_filter=) Request feature impacts to be computed for the model. See [get_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_impact) for more information on the result of the job. - Parameters: - Returns: job – A Job representing the feature impact computation. To get the completed feature impact data, use job.get_result or job.get_result_when_complete. - Return type: Job - Raises: JobAlreadyRequested – If the feature impacts have already been requested. #### get_or_request_feature_impact(max_wait=600, row_count=None, with_metadata=False, backtest=None, data_slice_filter=) Retrieve feature impact for the model, requesting a job if it hasn’t been run previously - Parameters: - Returns: feature_impacts – The feature impact data. See get_feature_impact for the exact schema. - Return type: list or dict #### request_lift_chart(source=None, backtest_index=None, data_slice_filter=) (New in version v3.4) Request the model Lift Chart for the specified backtest data slice. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### get_lift_chart(source=None, backtest_index=None, fallback_to_parent_insights=False, data_slice_filter=) (New in version v3.4) Retrieve the model Lift chart for the specified backtest and data slice. - Parameters: - Returns: Model lift chart data - Return type: LiftChart - Raises: #### request_roc_curve(source=None, backtest_index=None, data_slice_filter=) (New in version v3.4) Request the binary model Roc Curve for the specified backtest and data slice. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### get_roc_curve(source=None, backtest_index=None, fallback_to_parent_insights=False, data_slice_filter=) (New in version v3.4) Retrieve the ROC curve for a binary model for the specified backtest and data slice. - Parameters: - Returns: Model ROC curve data - Return type: RocCurve - Raises: #### advanced_tune(params, description=None, grid_search_arguments=None) Generate a new model with the specified advanced-tuning parameters As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Parameters: - Returns: The created job to build the model - Return type: ModelJob #### continue_incremental_learning_from_incremental_model(chunk_definition_id, early_stopping_rounds=None) Submit a job to the queue to perform the first incremental learning iteration training on an existing sample model. This functionality requires the SAMPLE_DATA_TO_START_PROJECT feature flag to be enabled. - Parameters: - Returns: job – The model retraining job that is created. - Return type: ModelJob #### delete() Delete the model from the project leaderboard. - Return type: None #### download_scoring_code(file_name, source_code=False) Download the Scoring Code JAR. - Parameters: - Return type: None #### download_training_artifact(file_name) Retrieve trained artifact(s) from a model containing one or more custom tasks. Artifact(s) will be downloaded to the specified local filepath. - Parameters: file_name ( str ) – File path where trained model artifact(s) will be saved. #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### get_advanced_tuning_parameters() Get the advanced-tuning parameters available for this model. As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Returns:A dictionary describing the advanced-tuning parameters for the current model. There are two top-level keys, tuning_description and tuning_parameters. tuning_description an optional value. If not None, then it indicates the user-specified description of this set of tuning parameter. tuning_parameters is a list of a dicts, each has the following keys * parameter_name :(str)name of the parameter (unique per task, see below) * parameter_id :(str)opaque ID string uniquely identifying parameter * default_value :(*)the actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on current_value) * current_value :(*)the single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range. * task_name :(str)name of the task that this parameter belongs to * constraints:(dict)see the notes below * vertex_id:(str)ID of vertex that this parameter belongs to *Return type:dict > [!NOTE] Notes > The type of default_value and current_value is defined by the constraints structure. > It will be a string or numeric Python type. > > constraints is a dict with at least one, possibly more, of the following keys. > The presence of a key indicates that the parameter may take on the specified type. > (If a key is absent, this means that the parameter may not take on the specified type.) > If a key on constraints is present, its value will be a dict containing > all of the fields described below for that key. > > ``` > "constraints": { > "select": { > "values": [] > }, > "ascii": {}, > "unicode": {}, > "int": { > "min": , > "max": , > "supports_grid_search": requested for this param> > }, > "float": { > "min": , > "max": , > "supports_grid_search": requested for this param> > }, > "intList": { > "min_length": , > "max_length": > "min_val": , > "max_val": > "supports_grid_search": requested for this param> > }, > "floatList": { > "min_length": , > "max_length": > "min_val": , > "max_val": > "supports_grid_search": requested for this param> > } > } > ``` > > The keys have meaning as follows: > > select: > Rather than specifying a specific data type, if present, it indicates that the parameter > is permitted to take on any of the specified values. Listed values may be of any string > or real (non-complex) numeric type. > ascii: > The parameter may be a unicode object that encodes simple ASCII characters. > (A-Z, a-z, 0-9, whitespace, and certain common symbols.) In addition to listed > constraints, ASCII keys currently may not contain either newlines or semicolons. > unicode: > The parameter may be any Python unicode object. > int: > The value may be an object of type int within the specified range (inclusive). > Please note that the value will be passed around using the JSON format, and > some JSON parsers have undefined behavior with integers outside of the range > [-(2**53)+1, (2**53)-1]. > float: > The value may be an object of type float within the specified range (inclusive). > intList, floatList: > The value may be a list of int or float objects, respectively, following constraints > as specified respectively by the int and float types (above). > > Many parameters only specify one key under constraints. If a parameter specifies multiple > keys, the parameter may take on any value permitted by any key. #### get_all_confusion_charts(fallback_to_parent_insights=False) Retrieve a list of all confusion matrices available for the model. - Parameters: fallback_to_parent_insights ( bool ) – (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent for any source that is not available for this model and if this has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent. - Returns: Data for all available confusion charts for model. - Return type: list of ConfusionChart #### get_all_feature_impacts(data_slice_filter=None) Retrieve a list of all feature impact results available for the model. - Parameters: data_slice_filter ( DataSlice , optional ) – A DataSlice used to filter the return values based on the DataSlice ID. By default, this function uses data_slice_filter.id == None, which returns an unsliced insight. If data_slice_filter is None, no data_slice filtering will be applied when requesting the ROC curve. - Returns: Data for all available model feature impacts, or an empty list if no data is found. - Return type: list of dicts > [!NOTE] Examples > ``` > model = datarobot.Model(id='model-id', project_id='project-id') > > # Get feature impact insights for sliced data > data_slice = datarobot.DataSlice(id='data-slice-id') > sliced_fi = model.get_all_feature_impacts(data_slice_filter=data_slice) > > # Get feature impact insights for unsliced data > data_slice = datarobot.DataSlice() > unsliced_fi = model.get_all_feature_impacts(data_slice_filter=data_slice) > > # Get all feature impact insights > all_fi = model.get_all_feature_impacts() > ``` #### get_all_lift_charts(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all Lift charts available for the model. - Parameters: - Returns: Data for all available model lift charts. Or an empty list if no data found. - Return type: list of LiftChart > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > > # Get lift chart insights for sliced data > sliced_lift_charts = model.get_all_lift_charts(data_slice_id='data-slice-id') > > # Get lift chart insights for unsliced data > unsliced_lift_charts = model.get_all_lift_charts(unsliced_only=True) > > # Get all lift chart insights > all_lift_charts = model.get_all_lift_charts() > ``` #### get_all_multiclass_lift_charts(fallback_to_parent_insights=False, data_slice_filter=, target_class=None) Retrieve a list of all Lift charts available for the model. - Parameters: - Returns: Data for all available model lift charts. - Return type: list of LiftChart #### get_all_residuals_charts(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all residuals charts available for the model. - Parameters: - Returns: Data for all available model residuals charts. - Return type: list of ResidualsChart > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > > # Get residuals chart insights for sliced data > sliced_residuals_charts = model.get_all_residuals_charts(data_slice_id='data-slice-id') > > # Get residuals chart insights for unsliced data > unsliced_residuals_charts = model.get_all_residuals_charts(unsliced_only=True) > > # Get all residuals chart insights > all_residuals_charts = model.get_all_residuals_charts() > ``` #### get_all_roc_curves(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all ROC curves available for the model. - Parameters: - Returns: Data for all available model ROC curves. Or an empty list if no RocCurves are found. - Return type: list of RocCurve > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > ds_filter=DataSlice(id='data-slice-id') > > # Get roc curve insights for sliced data > sliced_roc = model.get_all_roc_curves(data_slice_filter=ds_filter) > > # Get roc curve insights for unsliced data > data_slice_filter=DataSlice(id=None) > unsliced_roc = model.get_all_roc_curves(data_slice_filter=ds_filter) > > # Get all roc curve insights > all_roc_curves = model.get_all_roc_curves() > ``` #### get_confusion_chart(source, fallback_to_parent_insights=False) Retrieve a multiclass model’s confusion matrix for the specified source. - Parameters: - Returns: Model ConfusionChart data - Return type: ConfusionChart - Raises: ClientError – If the insight is not available for this model #### get_cross_class_accuracy_scores() Retrieves a list of Cross Class Accuracy scores for the model. - Return type: json #### get_data_disparity_insights(feature, class_name1, class_name2) Retrieve a list of Cross Class Data Disparity insights for the model. - Parameters: - Return type: json #### get_fairness_insights(fairness_metrics_set=None, offset=0, limit=100) Retrieve a list of Per Class Bias insights for the model. - Parameters: - Return type: json #### get_features_used() Query the server to determine which features were used. Note that the data returned by this method may differ from the names of the features in the featurelist used by this model. This method returns the raw features that must be supplied for predictions to be generated on a new set of data. The featurelist, in contrast, also includes the names of derived features. - Returns: features – The names of the features used in the model. - Return type: List[str] #### get_frozen_child_models() Retrieve the IDs for all models that are frozen from this model. - Return type: A list of Models #### get_labelwise_roc_curves(source, fallback_to_parent_insights=False) Retrieve a list of LabelwiseRocCurve instances for a multilabel model for the given source and all labels. This method is valid only for multilabel projects. For binary projects, use Model.get_roc_curve API . Added in version v2.24. - Parameters: - Returns: Labelwise ROC Curve instances for source and all labels - Return type: list of LabelwiseRocCurve - Raises: ClientError – If the insight is not available for this model #### get_missing_report_info() Retrieve a report on missing training data that can be used to understand missing values treatment in the model. The report consists of missing values resolutions for features numeric or categorical features that were part of building the model. - Returns: The queried model missing report, sorted by missing count (DESCENDING order). - Return type: An iterable of MissingReportPerFeature #### get_model_blueprint_chart() Retrieve a diagram that can be used to understand data flow in the blueprint. - Returns: The queried model blueprint chart. - Return type: ModelBlueprintChart #### get_model_blueprint_documents() Get documentation for tasks used in this model. - Returns: All documents available for the model. - Return type: list of BlueprintTaskDocument #### get_model_blueprint_json() Get the blueprint json representation used by this model. - Returns: Json representation of the blueprint stages. - Return type: BlueprintJson #### get_multiclass_feature_impact() For multiclass models, feature impact can be calculated separately for each target class. The method of calculation is the same, computed in one-vs-all style for each target class. Requires that Feature Impact has already been computed with [request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact). - Returns: feature_impacts – The feature impact data. Each item is a dict with the keys ‘featureImpacts’ (list), ‘class’ (str). Each item in ‘featureImpacts’ is a dict with the keys ‘featureName’, ‘impactNormalized’, ‘impactUnnormalized’, and ‘redundantWith’. - Return type: list of dict - Raises: ClientError – If the multiclass feature impacts have not been computed. #### get_multiclass_lift_chart(source, fallback_to_parent_insights=False, data_slice_filter=, target_class=None) Retrieve model Lift chart for the specified source. - Parameters: - Returns: Model lift chart data for each saved target class - Return type: list of LiftChart - Raises: ClientError – If the insight is not available for this model #### get_multilabel_lift_charts(source, fallback_to_parent_insights=False) Retrieve model Lift charts for the specified source. Added in version v2.24. - Parameters: - Returns: Model lift chart data for each saved target class - Return type: list of LiftChart - Raises: ClientError – If the insight is not available for this model #### get_num_iterations_trained() Retrieve the number of estimators trained by early-stopping tree-based models. Added in version v2.22. - Returns: #### get_parameters() Retrieve the model parameters. - Returns: The model parameters for this model. - Return type: ModelParameters #### get_pareto_front() Retrieve the Pareto Front for a Eureqa model. This method is only supported for Eureqa models. - Returns: Model ParetoFront data - Return type: ParetoFront #### get_prime_eligibility() Check whether this model can be approximated with DataRobot Prime. - Returns: prime_eligibility – A dict indicating whether the model can be approximated with DataRobot Prime (key can_make_prime) and why it may be ineligible (key message). - Return type: dict #### get_residuals_chart(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve model residuals chart for the specified source. - Parameters: - Returns: Model residuals chart data - Return type: ResidualsChart - Raises: #### get_rulesets() List the rulesets that approximate this model, generated by DataRobot Prime. If this model has not been approximated yet, returns an empty list. Note that these are rulesets that approximate this model, not rulesets used to construct this model. - Returns: rulesets - Return type: list of Ruleset #### get_supported_capabilities() Retrieve a summary of the capabilities supported by a model. Added in version v2.14. - Returns: #### get_uri() Return the permanent static hyperlink to this model on the leaderboard. - Returns: url – The permanent static hyperlink to this model on the leaderboard. - Return type: str #### get_word_cloud(exclude_stop_words=False) Retrieve word cloud data for the model. - Parameters: exclude_stop_words ( Optional[bool] ) – Set to True if you want stopwords filtered out of response. - Returns: Word cloud data for the model. - Return type: WordCloud #### incremental_train(data_stage_id, training_data_name=None) Submit a job to the queue to perform incremental training on an existing model. See the train_incremental documentation. - Return type: ModelJob #### classmethod list(project_id, sort_by_partition='validation', sort_by_metric=None, with_metric=None, search_term=None, featurelists=None, families=None, blueprints=None, labels=None, characteristics=None, training_filters=None, number_of_clusters=None, limit=100, offset=0) Retrieve paginated model records, sorted by scores, with optional filtering. - Parameters: - Returns: generic_models - Return type: list of GenericModel #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### request_approximation() Request an approximation of this model using DataRobot Prime. This creates several rulesets that can be used to approximate this model. After comparing their scores and rule counts, the code used in the approximation can be downloaded and run locally. - Returns: job – The job that generates the rulesets. - Return type: Job #### request_cross_class_accuracy_scores() Request data disparity insights to be computed for the model. - Returns: status_id – A statusId of computation request. - Return type: str #### request_data_disparity_insights(feature, compared_class_names) Request data disparity insights to be computed for the model. - Parameters: - Returns: status_id – A statusId of computation request. - Return type: str #### request_external_test(dataset_id, actual_value_column=None) Request an external test to compute scores and insights on an external test dataset. - Parameters: - Returns: job – A job representing external dataset insights computation. - Return type: Job #### request_fairness_insights(fairness_metrics_set=None) Request fairness insights to be computed for the model. - Parameters: fairness_metrics_set ( Optional[str] ) – Can be one of . The fairness metric used to calculate the fairness scores. - Returns: status_id – A statusId of computation request. - Return type: str #### request_frozen_datetime_model(training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None) Train a new frozen model with parameters from this model. Requires that this model belongs to a datetime partitioned project. If it does not, an error will occur when submitting the job. Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data. In addition to training_row_count and training_duration, frozen datetime models may be trained on an exact date range. Only one of training_row_count, training_duration, or training_start_date and training_end_date should be specified. Models specified using training_start_date and training_end_date are the only ones that can be trained into the holdout data (once the holdout is unlocked). All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Parameters: - Returns: model_job – The modeling job that trains a frozen model. - Return type: ModelJob #### request_per_class_fairness_insights(fairness_metrics_set=None) Request per-class fairness insights be computed for the model. - Parameters: fairness_metrics_set ( Optional[str] ) – The fairness metric used to calculate the fairness scores. Value can be any one of . - Returns: status_check_job – The returned object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_predictions(dataset_id=None, dataset=None, dataframe=None, file_path=None, file=None, include_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None, max_ngram_explanations=None) Request predictions against a previously uploaded dataset. - Parameters: - Returns: job – The job computing the predictions. - Return type: PredictJob #### request_residuals_chart(source, data_slice_id=None) Request the model residuals chart for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### set_prediction_threshold(threshold) Set a custom prediction threshold for the model. May not be used once `prediction_threshold_read_only` is True for this model. - Parameters: threshold ( float ) – only used for binary classification projects. The threshold to when deciding between the positive and negative classes when making predictions. Should be between 0.0 and 1.0 (inclusive). #### star_model() Mark the model as starred. Model stars propagate to the web application and the API, and can be used to filter when listing models. - Return type: None #### start_advanced_tuning_session(grid_search_arguments=None) Start an Advanced Tuning session. Returns an object that helps set up arguments for an Advanced Tuning model execution. As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Parameters: grid_search_arguments ( GridSearchArguments ) – Grid search arguments - Returns: Session for setting up and running Advanced Tuning on a model - Return type: AdvancedTuningSession #### start_incremental_learning_from_sample(early_stopping_rounds=None, first_iteration_only=False, chunk_definition_id=None) Submit a job to the queue to perform the first incremental learning iteration training on an existing sample model. This functionality requires the SAMPLE_DATA_TO_START_PROJECT feature flag to be enabled. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### train_datetime(featurelist_id=None, training_row_count=None, training_duration=None, time_window_sample_pct=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=, use_project_settings=False, sampling_method=None, n_clusters=None) Train this model on a different featurelist or sample size. Requires that this model is part of a datetime partitioned project; otherwise, an error will occur. All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Parameters: - Returns: job – The created job to build the model. - Return type: ModelJob #### train_incremental(data_stage_id, training_data_name=None, data_stage_encoding=None, data_stage_delimiter=None, data_stage_compression=None) Submit a job to the queue to perform incremental training on an existing model using additional data. The ID of the additional data to use for training is specified with `data_stage_id`. Optionally, a name for the iteration can be supplied by the user to help identify the contents of the data in the iteration. This functionality requires the INCREMENTAL_LEARNING feature flag to be enabled. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### unstar_model() Unmark the model as starred. Model stars propagate to the web application and the API, and can be used to filter when listing models. - Return type: None ## Frozen models ### class datarobot.models.FrozenModel Represents a model tuned with parameters which are derived from another model All durations are specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Variables: #### classmethod get(project_id, model_id) Retrieve a specific frozen model. - Parameters: - Returns: model – The queried instance. - Return type: FrozenModel ## Rating table models ### class datarobot.models.RatingTableModel A model that has a rating table. All durations are specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Variables: #### classmethod get(project_id, model_id) Retrieve a specific rating table model If the project does not have a rating table, a ClientError will occur. - Parameters: - Returns: model – the model - Return type: RatingTableModel #### classmethod create_from_rating_table(project_id, rating_table_id) Creates a new model from a validated rating table record. The RatingTable must not be associated with an existing model. - Parameters: - Returns: job – an instance of created async job - Return type: Job - Raises: #### advanced_tune(params, description=None, grid_search_arguments=None) Generate a new model with the specified advanced-tuning parameters As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Parameters: - Returns: The created job to build the model - Return type: ModelJob #### continue_incremental_learning_from_incremental_model(chunk_definition_id, early_stopping_rounds=None) Submit a job to the queue to perform the first incremental learning iteration training on an existing sample model. This functionality requires the SAMPLE_DATA_TO_START_PROJECT feature flag to be enabled. - Parameters: - Returns: job – The model retraining job that is created. - Return type: ModelJob #### cross_validate() Run cross validation on the model. > [!NOTE] Notes > To perform Cross Validation on a new model with new parameters, use `train` instead. - Returns: The created job to build the model - Return type: ModelJob #### delete() Delete the model from the project leaderboard. - Return type: None #### download_scoring_code(file_name, source_code=False) Download the Scoring Code JAR. - Parameters: - Return type: None #### download_training_artifact(file_name) Retrieve trained artifact(s) from a model containing one or more custom tasks. Artifact(s) will be downloaded to the specified local filepath. - Parameters: file_name ( str ) – File path where trained model artifact(s) will be saved. #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Override the inherited method because the model must _not_ recursively change casing. - Parameters: #### get_advanced_tuning_parameters() Get the advanced-tuning parameters available for this model. As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Returns:A dictionary describing the advanced-tuning parameters for the current model. There are two top-level keys, tuning_description and tuning_parameters. tuning_description an optional value. If not None, then it indicates the user-specified description of this set of tuning parameter. tuning_parameters is a list of a dicts, each has the following keys * parameter_name :(str)name of the parameter (unique per task, see below) * parameter_id :(str)opaque ID string uniquely identifying parameter * default_value :(*)the actual value used to train the model; either the single value of the parameter specified before training, or the best value from the list of grid-searched values (based on current_value) * current_value :(*)the single value or list of values of the parameter that were grid searched. Depending on the grid search specification, could be a single fixed value (no grid search), a list of discrete values, or a range. * task_name :(str)name of the task that this parameter belongs to * constraints:(dict)see the notes below * vertex_id:(str)ID of vertex that this parameter belongs to *Return type:dict > [!NOTE] Notes > The type of default_value and current_value is defined by the constraints structure. > It will be a string or numeric Python type. > > constraints is a dict with at least one, possibly more, of the following keys. > The presence of a key indicates that the parameter may take on the specified type. > (If a key is absent, this means that the parameter may not take on the specified type.) > If a key on constraints is present, its value will be a dict containing > all of the fields described below for that key. > > ``` > "constraints": { > "select": { > "values": [] > }, > "ascii": {}, > "unicode": {}, > "int": { > "min": , > "max": , > "supports_grid_search": requested for this param> > }, > "float": { > "min": , > "max": , > "supports_grid_search": requested for this param> > }, > "intList": { > "min_length": , > "max_length": > "min_val": , > "max_val": > "supports_grid_search": requested for this param> > }, > "floatList": { > "min_length": , > "max_length": > "min_val": , > "max_val": > "supports_grid_search": requested for this param> > } > } > ``` > > The keys have meaning as follows: > > select: > Rather than specifying a specific data type, if present, it indicates that the parameter > is permitted to take on any of the specified values. Listed values may be of any string > or real (non-complex) numeric type. > ascii: > The parameter may be a unicode object that encodes simple ASCII characters. > (A-Z, a-z, 0-9, whitespace, and certain common symbols.) In addition to listed > constraints, ASCII keys currently may not contain either newlines or semicolons. > unicode: > The parameter may be any Python unicode object. > int: > The value may be an object of type int within the specified range (inclusive). > Please note that the value will be passed around using the JSON format, and > some JSON parsers have undefined behavior with integers outside of the range > [-(2**53)+1, (2**53)-1]. > float: > The value may be an object of type float within the specified range (inclusive). > intList, floatList: > The value may be a list of int or float objects, respectively, following constraints > as specified respectively by the int and float types (above). > > Many parameters only specify one key under constraints. If a parameter specifies multiple > keys, the parameter may take on any value permitted by any key. #### get_all_confusion_charts(fallback_to_parent_insights=False) Retrieve a list of all confusion matrices available for the model. - Parameters: fallback_to_parent_insights ( bool ) – (New in version v2.14) Optional, if True, this will return confusion chart data for this model’s parent for any source that is not available for this model and if this has a defined parent model. If omitted or False, or this model has no parent, this will not attempt to retrieve any data from this model’s parent. - Returns: Data for all available confusion charts for model. - Return type: list of ConfusionChart #### get_all_feature_impacts(data_slice_filter=None) Retrieve a list of all feature impact results available for the model. - Parameters: data_slice_filter ( DataSlice , optional ) – A DataSlice used to filter the return values based on the DataSlice ID. By default, this function uses data_slice_filter.id == None, which returns an unsliced insight. If data_slice_filter is None, no data_slice filtering will be applied when requesting the ROC curve. - Returns: Data for all available model feature impacts, or an empty list if no data is found. - Return type: list of dicts > [!NOTE] Examples > ``` > model = datarobot.Model(id='model-id', project_id='project-id') > > # Get feature impact insights for sliced data > data_slice = datarobot.DataSlice(id='data-slice-id') > sliced_fi = model.get_all_feature_impacts(data_slice_filter=data_slice) > > # Get feature impact insights for unsliced data > data_slice = datarobot.DataSlice() > unsliced_fi = model.get_all_feature_impacts(data_slice_filter=data_slice) > > # Get all feature impact insights > all_fi = model.get_all_feature_impacts() > ``` #### get_all_lift_charts(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all Lift charts available for the model. - Parameters: - Returns: Data for all available model lift charts. Or an empty list if no data found. - Return type: list of LiftChart > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > > # Get lift chart insights for sliced data > sliced_lift_charts = model.get_all_lift_charts(data_slice_id='data-slice-id') > > # Get lift chart insights for unsliced data > unsliced_lift_charts = model.get_all_lift_charts(unsliced_only=True) > > # Get all lift chart insights > all_lift_charts = model.get_all_lift_charts() > ``` #### get_all_multiclass_lift_charts(fallback_to_parent_insights=False, data_slice_filter=, target_class=None) Retrieve a list of all Lift charts available for the model. - Parameters: - Returns: Data for all available model lift charts. - Return type: list of LiftChart #### get_all_residuals_charts(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all residuals charts available for the model. - Parameters: - Returns: Data for all available model residuals charts. - Return type: list of ResidualsChart > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > > # Get residuals chart insights for sliced data > sliced_residuals_charts = model.get_all_residuals_charts(data_slice_id='data-slice-id') > > # Get residuals chart insights for unsliced data > unsliced_residuals_charts = model.get_all_residuals_charts(unsliced_only=True) > > # Get all residuals chart insights > all_residuals_charts = model.get_all_residuals_charts() > ``` #### get_all_roc_curves(fallback_to_parent_insights=False, data_slice_filter=None) Retrieve a list of all ROC curves available for the model. - Parameters: - Returns: Data for all available model ROC curves. Or an empty list if no RocCurves are found. - Return type: list of RocCurve > [!NOTE] Examples > ``` > model = datarobot.Model.get('project-id', 'model-id') > ds_filter=DataSlice(id='data-slice-id') > > # Get roc curve insights for sliced data > sliced_roc = model.get_all_roc_curves(data_slice_filter=ds_filter) > > # Get roc curve insights for unsliced data > data_slice_filter=DataSlice(id=None) > unsliced_roc = model.get_all_roc_curves(data_slice_filter=ds_filter) > > # Get all roc curve insights > all_roc_curves = model.get_all_roc_curves() > ``` #### get_confusion_chart(source, fallback_to_parent_insights=False) Retrieve a multiclass model’s confusion matrix for the specified source. - Parameters: - Returns: Model ConfusionChart data - Return type: ConfusionChart - Raises: ClientError – If the insight is not available for this model #### get_cross_class_accuracy_scores() Retrieves a list of Cross Class Accuracy scores for the model. - Return type: json #### get_cross_validation_scores(partition=None, metric=None) Return a dictionary, keyed by metric, showing cross validation scores per partition. Cross Validation should already have been performed using [cross_validate](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.cross_validate) or [train](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.train). > [!NOTE] Notes > Models that computed cross validation before this feature was added will need > to be deleted and retrained before this method can be used. - Parameters: - Returns: cross_validation_scores – A dictionary keyed by metric showing cross validation scores per partition. - Return type: dict #### get_data_disparity_insights(feature, class_name1, class_name2) Retrieve a list of Cross Class Data Disparity insights for the model. - Parameters: - Return type: json #### get_fairness_insights(fairness_metrics_set=None, offset=0, limit=100) Retrieve a list of Per Class Bias insights for the model. - Parameters: - Return type: json #### get_feature_effect(source, data_slice_id=None) Retrieve Feature Effects for the model. Feature Effects provides partial dependence and predicted vs. actual values for the top 500 features ordered by feature impact score. The partial dependence shows the marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. Requires that Feature Effects has already been computed with [request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect). See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the available sources. - Parameters: - Returns: feature_effects – The feature effects data. - Return type: FeatureEffects - Raises: ClientError – If the feature effects have not been computed or the source is not a valid value. #### get_feature_effect_metadata() Retrieve Feature Effects metadata. The response contains status and available model sources. - Feature Effect for the training partition is always available, with the exception of older projects that only supported Feature Effect for validation. - When a model is trained into validation or holdout without stacked predictions (i.e., no out-of-sample predictions in those partitions), Feature Effects is not available for validation or holdout. - Feature Effects for holdout is not available when holdout was not unlocked for the project. Use source to retrieve Feature Effects, selecting one of the provided sources. - Returns: feature_effect_metadata - Return type: FeatureEffectMetadata #### get_feature_effects_multiclass(source='training', class_=None) Retrieve Feature Effects for the multiclass model. Feature Effects provide partial dependence and predicted vs. actual values for the top 500 features ordered by feature impact score. The partial dependence shows the marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. Requires that Feature Effects has already been computed with [request_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_effect). See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the available sources. - Parameters: - Returns: The list of multiclass feature effects. - Return type: list - Raises: ClientError – If Feature Effects have not been computed or the source is not a valid value. #### get_feature_impact(with_metadata=False, data_slice_filter=) Retrieve the computed Feature Impact results, a measure of the relevance of each feature in the model. Feature Impact is computed for each column by creating new data with that column randomly permuted (but the others left unchanged) and measuring how the error metric score for the predictions is affected. The ‘impactUnnormalized’ is how much worse the error metric score is when making predictions on this modified data. The ‘impactNormalized’ is normalized so that the largest value is 1. In both cases, larger values indicate more important features. If a feature is redundant, i.e., once other features are considered it does not contribute much in addition, the ‘redundantWith’ value is the name of the feature that has the highest correlation with this feature. Note that redundancy detection is only available for jobs run after the addition of this feature. When retrieving data that predates this functionality, a NoRedundancyImpactAvailable warning will be used. Only the top 1000 features are saved and can be returned. Elsewhere this technique is sometimes called ‘Permutation Importance’. Requires that Feature Impact has already been computed with [request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact). - Parameters: - Returns:The feature impact data response depends on the with_metadata parameter. The response is either a dict with metadata and a list with the actual data or just a list with that data. Each list item is a dict with the keysfeatureName,impactNormalized,impactUnnormalized,redundantWith, andcount. For the dict response, the available keys are: featureImpacts- Feature Impact data as a dictionary. Each item is a dict with : the keys:featureName,impactNormalized,impactUnnormalized, andredundantWith.shapBased- A boolean that indicates whether Feature Impact was calculated using : Shapley values.ranRedundancyDetection- A boolean that indicates whether redundant feature : identification was run while calculating this Feature Impact.rowCount- An integer or None that indicates the number of rows that were used to : calculate Feature Impact. For Feature Impact calculated with the default logic without specifying the rowCount, we return None here.count- An integer with the number of features underfeatureImpacts.Return type:listordictRaises:ClientError– If the feature impacts have not been computed.ValueError– If data_slice_filter is passed as None. #### get_features_used() Query the server to determine which features were used. Note that the data returned by this method may differ from the names of the features in the featurelist used by this model. This method returns the raw features that must be supplied for predictions to be generated on a new set of data. The featurelist, in contrast, also includes the names of derived features. - Returns: features – The names of the features used in the model. - Return type: List[str] #### get_frozen_child_models() Retrieve the IDs for all models that are frozen from this model. - Return type: A list of Models #### get_labelwise_roc_curves(source, fallback_to_parent_insights=False) Retrieve a list of LabelwiseRocCurve instances for a multilabel model for the given source and all labels. This method is valid only for multilabel projects. For binary projects, use Model.get_roc_curve API . Added in version v2.24. - Parameters: - Returns: Labelwise ROC Curve instances for source and all labels - Return type: list of LabelwiseRocCurve - Raises: ClientError – If the insight is not available for this model #### get_lift_chart(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve the model Lift chart for the specified source. - Parameters: - Returns: Model lift chart data - Return type: LiftChart - Raises: #### get_missing_report_info() Retrieve a report on missing training data that can be used to understand missing values treatment in the model. The report consists of missing values resolutions for features numeric or categorical features that were part of building the model. - Returns: The queried model missing report, sorted by missing count (DESCENDING order). - Return type: An iterable of MissingReportPerFeature #### get_model_blueprint_chart() Retrieve a diagram that can be used to understand data flow in the blueprint. - Returns: The queried model blueprint chart. - Return type: ModelBlueprintChart #### get_model_blueprint_documents() Get documentation for tasks used in this model. - Returns: All documents available for the model. - Return type: list of BlueprintTaskDocument #### get_model_blueprint_json() Get the blueprint json representation used by this model. - Returns: Json representation of the blueprint stages. - Return type: BlueprintJson #### get_multiclass_feature_impact() For multiclass models, feature impact can be calculated separately for each target class. The method of calculation is the same, computed in one-vs-all style for each target class. Requires that Feature Impact has already been computed with [request_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.request_feature_impact). - Returns: feature_impacts – The feature impact data. Each item is a dict with the keys ‘featureImpacts’ (list), ‘class’ (str). Each item in ‘featureImpacts’ is a dict with the keys ‘featureName’, ‘impactNormalized’, ‘impactUnnormalized’, and ‘redundantWith’. - Return type: list of dict - Raises: ClientError – If the multiclass feature impacts have not been computed. #### get_multiclass_lift_chart(source, fallback_to_parent_insights=False, data_slice_filter=, target_class=None) Retrieve model Lift chart for the specified source. - Parameters: - Returns: Model lift chart data for each saved target class - Return type: list of LiftChart - Raises: ClientError – If the insight is not available for this model #### get_multilabel_lift_charts(source, fallback_to_parent_insights=False) Retrieve model Lift charts for the specified source. Added in version v2.24. - Parameters: - Returns: Model lift chart data for each saved target class - Return type: list of LiftChart - Raises: ClientError – If the insight is not available for this model #### get_num_iterations_trained() Retrieve the number of estimators trained by early-stopping tree-based models. Added in version v2.22. - Returns: #### get_or_request_feature_effect(source, max_wait=600, row_count=None, data_slice_id=None) Retrieve Feature Effects for the model, requesting a new job if it has not been run previously. See [get_feature_effect_metadata](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect_metadata) for retrieving information on the source. - Parameters: - Returns: feature_effects – The Feature Effects data. - Return type: FeatureEffects #### get_or_request_feature_effects_multiclass(source, top_n_features=None, features=None, row_count=None, class_=None, max_wait=600) Retrieve Feature Effects for the multiclass model, requesting a job if it has not been run previously. - Parameters: - Returns: feature_effects – The list of multiclass feature effects data. - Return type: list of FeatureEffectsMulticlass #### get_or_request_feature_impact(max_wait=600, **kwargs) Retrieve feature impact for the model, requesting a job if it has not been run previously. Only the top 1000 features are saved and can be returned. - Parameters: - Returns: feature_impacts – The feature impact data. See get_feature_impact for the exact schema. - Return type: list or dict #### get_parameters() Retrieve the model parameters. - Returns: The model parameters for this model. - Return type: ModelParameters #### get_pareto_front() Retrieve the Pareto Front for a Eureqa model. This method is only supported for Eureqa models. - Returns: Model ParetoFront data - Return type: ParetoFront #### get_prime_eligibility() Check whether this model can be approximated with DataRobot Prime. - Returns: prime_eligibility – A dict indicating whether the model can be approximated with DataRobot Prime (key can_make_prime) and why it may be ineligible (key message). - Return type: dict #### get_residuals_chart(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve model residuals chart for the specified source. - Parameters: - Returns: Model residuals chart data - Return type: ResidualsChart - Raises: #### get_roc_curve(source, fallback_to_parent_insights=False, data_slice_filter=) Retrieve the ROC curve for a binary model for the specified source. This method is valid only for binary projects. For multilabel projects, use Model.get_labelwise_roc_curves. - Parameters: - Returns: Model ROC curve data - Return type: RocCurve - Raises: #### get_rulesets() List the rulesets that approximate this model, generated by DataRobot Prime. If this model has not been approximated yet, returns an empty list. Note that these are rulesets that approximate this model, not rulesets used to construct this model. - Returns: rulesets - Return type: list of Ruleset #### get_supported_capabilities() Retrieve a summary of the capabilities supported by a model. Added in version v2.14. - Returns: #### get_uri() Return the permanent static hyperlink to this model on the leaderboard. - Returns: url – The permanent static hyperlink to this model on the leaderboard. - Return type: str #### get_word_cloud(exclude_stop_words=False) Retrieve word cloud data for the model. - Parameters: exclude_stop_words ( Optional[bool] ) – Set to True if you want stopwords filtered out of response. - Returns: Word cloud data for the model. - Return type: WordCloud #### incremental_train(data_stage_id, training_data_name=None) Submit a job to the queue to perform incremental training on an existing model. See the train_incremental documentation. - Return type: ModelJob #### classmethod list(project_id, sort_by_partition='validation', sort_by_metric=None, with_metric=None, search_term=None, featurelists=None, families=None, blueprints=None, labels=None, characteristics=None, training_filters=None, number_of_clusters=None, limit=100, offset=0) Retrieve paginated model records, sorted by scores, with optional filtering. - Parameters: - Returns: generic_models - Return type: list of GenericModel #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### request_approximation() Request an approximation of this model using DataRobot Prime. This creates several rulesets that can be used to approximate this model. After comparing their scores and rule counts, the code used in the approximation can be downloaded and run locally. - Returns: job – The job that generates the rulesets. - Return type: Job #### request_cross_class_accuracy_scores() Request data disparity insights to be computed for the model. - Returns: status_id – A statusId of computation request. - Return type: str #### request_data_disparity_insights(feature, compared_class_names) Request data disparity insights to be computed for the model. - Parameters: - Returns: status_id – A statusId of computation request. - Return type: str #### request_external_test(dataset_id, actual_value_column=None) Request an external test to compute scores and insights on an external test dataset. - Parameters: - Returns: job – A job representing external dataset insights computation. - Return type: Job #### request_fairness_insights(fairness_metrics_set=None) Request fairness insights to be computed for the model. - Parameters: fairness_metrics_set ( Optional[str] ) – Can be one of . The fairness metric used to calculate the fairness scores. - Returns: status_id – A statusId of computation request. - Return type: str #### request_feature_effect(row_count=None, data_slice_id=None) Submit a request to compute Feature Effects for the model. See [get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effect) for more information on the result of the job. - Parameters: - Returns: job – A job representing the feature effect computation. To get the completed feature effect data, use job.get_result or job.get_result_when_complete. - Return type: Job - Raises: JobAlreadyRequested – If the feature effects have already been requested. #### request_feature_effects_multiclass(row_count=None, top_n_features=None, features=None) Request Feature Effects computation for the multiclass model. See [get_feature_effect](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_effects_multiclass) for more information on the result of the job. - Parameters: - Returns: job – A job representing Feature Effect computation. To get the completed Feature Effect data, use job.get_result or job.get_result_when_complete. - Return type: Job #### request_feature_impact(row_count=None, with_metadata=False, data_slice_id=None) Request that feature impacts be computed for the model. See [get_feature_impact](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model.get_feature_impact) for more information on the result of the job. - Parameters: - Returns: job – A job representing the Feature Impact computation. To retrieve the completed Feature Impact data, use job.get_result or job.get_result_when_complete. - Return type: Job or status_id - Raises: JobAlreadyRequested – If the feature impacts have already been requested. #### request_frozen_datetime_model(training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None) Train a new frozen model with parameters from this model. Requires that this model belongs to a datetime partitioned project. If it does not, an error will occur when submitting the job. Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data. In addition to training_row_count and training_duration, frozen datetime models may be trained on an exact date range. Only one of training_row_count, training_duration, or training_start_date and training_end_date should be specified. Models specified using training_start_date and training_end_date are the only ones that can be trained into the holdout data (once the holdout is unlocked). All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Parameters: - Returns: model_job – The modeling job that trains a frozen model. - Return type: ModelJob #### request_frozen_model(sample_pct=None, training_row_count=None) Train a new frozen model with parameters from this model. > [!NOTE] Notes > This method only works if the project the model belongs to is not datetime > partitioned. If it is, use `request_frozen_datetime_model` instead. > > Frozen models use the same tuning parameters as their parent model instead of independently > optimizing them to allow efficiently retraining models on larger amounts of the training > data. - Parameters: - Returns: model_job – The modeling job that trains a frozen model. - Return type: ModelJob #### request_lift_chart(source, data_slice_id=None) Request the model Lift Chart for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_per_class_fairness_insights(fairness_metrics_set=None) Request per-class fairness insights be computed for the model. - Parameters: fairness_metrics_set ( Optional[str] ) – The fairness metric used to calculate the fairness scores. Value can be any one of . - Returns: status_check_job – The returned object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_predictions(dataset_id=None, dataset=None, dataframe=None, file_path=None, file=None, include_prediction_intervals=None, prediction_intervals_size=None, forecast_point=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, explanation_algorithm=None, max_explanations=None, max_ngram_explanations=None) Request predictions against a previously uploaded dataset. - Parameters: - Returns: job – The job computing the predictions. - Return type: PredictJob #### request_residuals_chart(source, data_slice_id=None) Request the model residuals chart for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_roc_curve(source, data_slice_id=None) Request the model Roc Curve for the specified source. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob #### request_training_predictions(data_subset, explanation_algorithm=None, max_explanations=None) Start a job to build training predictions - Parameters: #### retrain(sample_pct=None, featurelist_id=None, training_row_count=None, n_clusters=None) Submit a job to the queue to train a blender model. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### set_prediction_threshold(threshold) Set a custom prediction threshold for the model. May not be used once `prediction_threshold_read_only` is True for this model. - Parameters: threshold ( float ) – only used for binary classification projects. The threshold to when deciding between the positive and negative classes when making predictions. Should be between 0.0 and 1.0 (inclusive). #### star_model() Mark the model as starred. Model stars propagate to the web application and the API, and can be used to filter when listing models. - Return type: None #### start_advanced_tuning_session(grid_search_arguments=None) Start an Advanced Tuning session. Returns an object that helps set up arguments for an Advanced Tuning model execution. As of v2.17, all models other than blenders, open source, prime, baseline and user-created support Advanced Tuning. - Parameters: grid_search_arguments ( GridSearchArguments ) – Grid search arguments - Returns: Session for setting up and running Advanced Tuning on a model - Return type: AdvancedTuningSession #### start_incremental_learning_from_sample(early_stopping_rounds=None, first_iteration_only=False, chunk_definition_id=None) Submit a job to the queue to perform the first incremental learning iteration training on an existing sample model. This functionality requires the SAMPLE_DATA_TO_START_PROJECT feature flag to be enabled. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### train(sample_pct=None, featurelist_id=None, scoring_type=None, training_row_count=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=) Train the blueprint used in the model on a particular featurelist or amount of data. This method creates a new training job for the worker and appends it to the end of the queue for this project. After the job has finished, you can get the newly trained model by retrieving it from the project leaderboard or by retrieving the result of the job. Either sample_pct or training_row_count can be used to specify the amount of data to use, but not both. If neither is specified, a default of the maximum amount of data that can safely be used to train any blueprint without using the validation data will be selected. In smart-sampled projects, sample_pct and training_row_count are assumed to be in terms of rows of the minority class. > [!NOTE] Notes > For datetime partitioned projects, see [train_datetime](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.DatetimeModel.train_datetime) instead. - Parameters: - Returns: model_job_id – The ID of the created job; can be used as a parameter to ModelJob.get or the wait_for_async_model_creation function. - Return type: str > [!NOTE] Examples > ``` > project = Project.get('project-id') > model = Model.get('project-id', 'model-id') > model_job_id = model.train(training_row_count=project.max_train_rows) > ``` #### train_datetime(featurelist_id=None, training_row_count=None, training_duration=None, time_window_sample_pct=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=, use_project_settings=False, sampling_method=None, n_clusters=None) Train this model on a different featurelist or sample size. Requires that this model is part of a datetime partitioned project; otherwise, an error will occur. All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Parameters: - Returns: job – The created job to build the model. - Return type: ModelJob #### train_incremental(data_stage_id, training_data_name=None, data_stage_encoding=None, data_stage_delimiter=None, data_stage_compression=None) Submit a job to the queue to perform incremental training on an existing model using additional data. The ID of the additional data to use for training is specified with `data_stage_id`. Optionally, a name for the iteration can be supplied by the user to help identify the contents of the data in the iteration. This functionality requires the INCREMENTAL_LEARNING feature flag to be enabled. - Parameters: - Returns: job – The created job that is retraining the model. - Return type: ModelJob #### unstar_model() Unmark the model as starred. Model stars propagate to the web application and the API, and can be used to filter when listing models. - Return type: None ## Clustering ### class datarobot.models.ClusteringModel ClusteringModel extends [Model](https://docs.datarobot.com/en/docs/api/reference/sdk/datarobot-models.html#datarobot.models.Model) class. It provides provides properties and methods specific to clustering projects. #### compute_insights(max_wait=600) Compute and retrieve cluster insights for model. This method awaits completion of job computing cluster insights and returns results after it is finished. If computation takes longer than specified `max_wait` exception will be raised. - Parameters: - Return type: List of ClusterInsight - Raises: #### property insights : List[ClusterInsight] Return actual list of cluster insights if already computed. - Return type: List of ClusterInsight #### property clusters : List[Cluster] Return actual list of Clusters. - Return type: List of Cluster #### update_cluster_names(cluster_name_mappings) Change many cluster names at once based on list of name mappings. - Parameters:cluster_name_mappings(Listoftuples) – Cluster names mapping consisting of current cluster name and old cluster name. Example: cluster_name_mappings=[("current cluster name 1","new cluster name 1"),("current cluster name 2","new cluster name 2")] * Return type: List of Cluster * Raises: datarobot.errors.ClientError – Server rejected update of cluster names. Possible reasons include: incorrect format of mapping, mapping introduces duplicates. #### update_cluster_name(current_name, new_name) Change cluster name from current_name to new_name. - Parameters: - Return type: List of Cluster - Raises: datarobot.errors.ClientError – Server rejected update of cluster names. ### class datarobot.models.cluster.Cluster Representation of a single cluster. - Variables: #### classmethod list(project_id, model_id) Retrieve a list of clusters in the model. - Parameters: - Return type: List of clusters #### classmethod update_multiple_names(project_id, model_id, cluster_name_mappings) Update many clusters at once based on list of name mappings. - Parameters: #### classmethod update_name(project_id, model_id, current_name, new_name) Change cluster name from current_name to new_name - Parameters: - Return type: List of Cluster ### class datarobot.models.cluster_insight.ClusterInsight Holds data on all insights related to feature as well as breakdown per cluster. - Parameters: #### classmethod compute(project_id, model_id, max_wait=600) Starts creation of cluster insights for the model and if successful, returns computed ClusterInsights. This method allows calculation to continue for a specified time and if not complete, cancels the request. - Parameters: - Return type: List[ClusterInsight] - Raises: ## Pareto front ### class datarobot.models.pareto_front.ParetoFront Pareto front data for a Eureqa model. The pareto front reflects the tradeoffs between error and complexity for particular model. The solutions reflect possible Eureqa models that are different levels of complexity. By default, only one solution will have a corresponding model, but models can be created for each solution. - Variables: #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: ### class datarobot.models.pareto_front.Solution Eureqa Solution. A solution represents a possible Eureqa model; however not all solutions have models associated with them. It must have a model created before it can be used to make predictions, etc. - Variables: #### create_model() Add this solution to the leaderboard, if it is not already present. ## Combined models See API reference for Combined Model in [Segmented Modeling API Reference](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#segmented-modeling-api) ## Advanced tuning ### class datarobot.models.advanced_tuning.AdvancedTuningSession A session enabling users to configure and run advanced tuning for a model. Every model contains a set of one or more tasks. Every task contains a set of zero or more parameters. This class allows tuning the values of each parameter on each task of a model, before running that model. This session is client-side only and is not persistent. Only the final model, constructed when run is called, is persisted on the DataRobot server. - Variables: description ( str ) – Description for the new advance-tuned model. Defaults to the same description as the base model. #### get_task_names() Get the list of task names that are available for this model - Returns: List of task names - Return type: list(str) #### get_parameter_names(task_name) Get the list of parameter names available for a specific task - Returns: List of parameter names - Return type: list(str) #### set_parameter(value, task_name=None, parameter_name=None, parameter_id=None) Set the value of a parameter to be used The caller must supply enough of the optional arguments to this function to uniquely identify the parameter that is being set. For example, a less-common parameter name such as ‘building_block__complementary_error_function’ might only be used once (if at all) by a single task in a model. In which case it may be sufficient to simply specify ‘parameter_name’. But a more-common name such as ‘random_seed’ might be used by several of the model’s tasks, and it may be necessary to also specify ‘task_name’ to clarify which task’s random seed is to be set. This function only affects client-side state. It will not check that the new parameter value(s) are valid. - Parameters: - Raises: - Return type: None #### get_parameters() Returns the set of parameters available to this model The returned parameters have one additional key, “value”, reflecting any new values that have been set in this AdvancedTuningSession. When the session is run, “value” will be used, or if it is unset, “current_value”. - Return type: AdvancedTuningParamsType - Returns: #### run() Submit this model for Advanced Tuning. - Returns: The created job to build the model - Return type: datarobot.models.modeljob.ModelJob ### class datarobot.models.advanced_tuning.GridSearchArguments Grid search arguments - Variables: #### to_api_payload() Convert the GridSearchArguments to an API payload - Return type: Dict [ str , Any ] ## Recommended models ### class datarobot.models.ModelRecommendation A collection of information about a recommended model for a project. - Variables: #### classmethod get(project_id, recommendation_type=None) Retrieves the default or specified by recommendation_type recommendation. - Parameters: - Returns: recommended_model - Return type: ModelRecommendation #### classmethod get_all(project_id) Retrieves all of the current recommended models for the project. - Parameters: project_id ( str ) – The project’s id. - Returns: recommended_models - Return type: list of ModelRecommendation #### classmethod get_recommendation(recommended_models, recommendation_type) Returns the model in the given list with the requested type. - Parameters: - Returns: recommended_model - Return type: ModelRecommendation or None if no model with the requested type exists #### get_model() Returns the Model associated with this ModelRecommendation. - Returns: recommended_model - Return type: Model or DatetimeModel if the project is datetime-partitioned ## Class mapping aggregation settings For multiclass projects with a lot of unique values in target column you can specify the parameters for aggregation of rare values to improve the modeling performance and decrease the runtime and resource usage of resulting models. ### class datarobot.helpers.ClassMappingAggregationSettings Class mapping aggregation settings. For multiclass projects allows fine control over which target values will be preserved as classes. Classes which aren’t preserved will be - aggregated into a single “catch everything else” class in case of multiclass - or will be ignored in case of multilabel. All attributes are optional, if not specified - server side defaults will be used. - Variables: ## Model jobs ### datarobot.models.modeljob.wait_for_async_model_creation(project_id, model_job_id, max_wait=600) Given a Project id and ModelJob id poll for status of process responsible for model creation until model is created. - Parameters: - Returns: model – Newly created model - Return type: Model - Raises: ### class datarobot.models.ModelJob Tracks asynchronous work being done within a project - Variables: #### classmethod from_job(job) Transforms a generic Job into a ModelJob - Parameters: job ( Job ) – A generic job representing a ModelJob - Returns: model_job – A fully populated ModelJob with all the details of the job - Return type: ModelJob - Raises: ValueError: – If the generic Job was not a model job, e.g., job_type != JOB_TYPE.MODEL #### classmethod get(project_id, model_job_id) Fetches one ModelJob. If the job finished, raises PendingJobFinished exception. - Parameters: - Returns: model_job – The pending ModelJob - Return type: ModelJob - Raises: #### classmethod get_model(project_id, model_job_id) Fetches a finished model from the job used to create it. - Parameters: - Returns: model – The finished model - Return type: Model - Raises: #### cancel() Cancel this job. If this job has not finished running, it will be removed and canceled. #### get_result(params=None) - Parameters: params ( dict or None ) – Query parameters to be added to request to get results. > [!NOTE] Notes > For featureEffects, source param is required to define source, > otherwise the default is training. - Returns:result– Return type depends on the job type : - for model jobs, a Model is returned - for predict jobs, a pandas.DataFrame (with predictions) is returned - for featureImpact jobs, a list of dicts by default (seewith_metadataparameter of theFeatureImpactJobclass and itsget()method). - for primeRulesets jobs, a list of Rulesets - for primeModel jobs, a PrimeModel - for primeDownloadValidation jobs, a PrimeFile - for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization - for predictionExplanations jobs, a PredictionExplanations - for featureEffects, a FeatureEffects. *Return type:object*Raises:*JobNotFinished– If the job is not finished, the result is not available. *AsyncProcessUnsuccessfulError– If the job errored or was aborted #### get_result_when_complete(max_wait=600, params=None) - Parameters: - Returns: result – Return type is the same as would be returned by Job.get_result. - Return type: object - Raises: #### refresh() Update this object with the latest job data from the server. #### wait_for_completion(max_wait=600) Waits for job to complete. - Parameters: max_wait ( Optional[int] ) – How long to wait for the job to finish. - Return type: None ## Registry jobs ### class datarobot.models.registry.job.Job A DataRobot job. Added in version v3.4. - Variables: #### classmethod create(name, environment_id=None, environment_version_id=None, folder_path=None, files=None, file_data=None, runtime_parameter_values=None) Create a job. Added in version v3.4. - Parameters: - Returns: created job - Return type: Job - Raises: #### classmethod list() List jobs. Added in version v3.4. - Returns: a list of jobs - Return type: List[Job] - Raises: #### classmethod get(job_id) Get job by id. Added in version v3.4. - Parameters: job_id ( str ) – The ID of the job. - Returns: retrieved job - Return type: Job - Raises: #### update(name=None, entry_point=None, environment_id=None, environment_version_id=None, description=None, folder_path=None, files=None, file_data=None, runtime_parameter_values=None, runtime_parameters=None) Update job properties. Added in version v3.4. - Parameters: - Raises: - Return type: None #### delete() Delete job. Added in version v3.4. - Raises: - Return type: None #### refresh() Update job with the latest data from server. Added in version v3.4. - Raises: - Return type: None #### classmethod create_from_custom_metric_gallery_template(template_id, name, description=None, sidecar_deployment_id=None) Create a job from a custom metric gallery template. - Parameters: - Returns: retrieved job - Return type: Job - Raises: #### list_schedules() List schedules for the job. - Returns: a list of schedules for the job. - Return type: List[JobSchedule] ### class datarobot.models.registry.job.JobFileItem A file item attached to a DataRobot job. Added in version v3.4. - Variables: ### class datarobot.models.registry.job_run.JobRun A DataRobot job run. Added in version v3.4. - Variables: #### classmethod create(job_id, max_wait=600, runtime_parameter_values=None) Create a job run. Added in version v3.4. - Parameters: - Returns: created job - Return type: Job - Raises: #### classmethod list(job_id) List job runs. Added in version v3.4. - Parameters: job_id ( str ) – The ID of the job. - Returns: A list of job runs. - Return type: List[Job] - Raises: #### classmethod get(job_id, job_run_id) Get job run by id. Added in version v3.4. - Parameters: - Returns: The retrieved job run. - Return type: Job - Raises: #### update(description=None) Update job run properties. Added in version v3.4. - Parameters: description ( str ) – new job run description - Raises: - Return type: None #### cancel() Cancel job run. Added in version v3.4. - Raises: - Return type: None #### refresh() Update job run with the latest data from server. Added in version v3.4. - Raises: - Return type: None #### get_logs() Get log of the job run. Added in version v3.4. - Raises: - Return type: Optional [ str ] #### delete_logs() Get log of the job run. Added in version v3.4. - Raises: - Return type: None ### class datarobot.models.registry.job_run.JobRunStatus Enum of the job run statuses ### class datarobot.models.registry.job.JobSchedule A job schedule. Added in version v3.5. - Variables: #### update(schedule=None, parameter_overrides=None) Update the job schedule. - Parameters: - Return type: JobSchedule #### delete() Delete the job schedule. :rtype: `None` #### classmethod create(custom_job_id, schedule, parameter_overrides=None) Create a job schedule. - Parameters: - Return type: JobSchedule ## Missing values report ### class datarobot.models.missing_report.MissingValuesReport Missing values report for model, contains list of reports per feature sorted by missing count in descending order. > [!NOTE] Notes > `Report per feature` contains: > > feature > : feature name. > type > : feature type – ‘Numeric’ or ‘Categorical’. > missing_count > : missing values count in training data. > missing_percentage > : missing values percentage in training data. > tasks > : list of information per each task, which was applied to feature. > > `task information` contains: > > id > : a number of task in the blueprint diagram. > name > : task name. > descriptions > : human readable aggregated information about how the task handles > missing values. The following descriptions may be present: what value is imputed for > missing values, whether the feature being missing is treated as a feature by the task, > whether missing values are treated as infrequent values, > whether infrequent values are treated as missing values, > and whether missing values are ignored. #### classmethod get(project_id, model_id) Retrieve a missing report. - Parameters: - Returns: The queried missing report. - Return type: MissingValuesReport ## Registered models ### class datarobot.models.RegisteredModel A registered model is a logical grouping of model packages (versions) that are related to each other. - Variables: #### classmethod get(registered_model_id) Get a registered model by ID. - Parameters: registered_model_id ( str ) – ID of the registered model to retrieve - Returns: registered_model – Registered Model Object - Return type: RegisteredModel > [!NOTE] Examples > ``` > from datarobot import RegisteredModel > registered_model = RegisteredModel.get(registered_model_id='5c939e08962d741e34f609f0') > registered_model.id > >>>'5c939e08962d741e34f609f0' > registered_model.name > >>>'My Registered Model' > ``` #### classmethod list(limit=100, offset=None, sort_key=None, sort_direction=None, search=None, filters=None) List all registered models a user can view. - Parameters: - Returns: registered_models – A list of registered models user can view. - Return type: List[RegisteredModel] > [!NOTE] Examples > ``` > from datarobot import RegisteredModel > registered_models = RegisteredModel.list() > >>> [RegisteredModel('My Registered Model'), RegisteredModel('My Other Registered Model')] > ``` > > ``` > from datarobot import RegisteredModel > from datarobot.models.model_registry import RegisteredModelListFilters > from datarobot.enums import RegisteredModelSortKey, RegisteredModelSortDirection > filters = RegisteredModelListFilters(target_type='Regression') > registered_models = RegisteredModel.list( > filters=filters, > sort_key=RegisteredModelSortKey.NAME.value, > sort_direction=RegisteredModelSortDirection.DESC.value > search='other') > >>> [RegisteredModel('My Other Registered Model')] > ``` #### classmethod archive(registered_model_id) Permanently archive a registered model and all of its versions. - Parameters: registered_model_id ( str ) – ID of the registered model to be archived - Return type: None #### classmethod update(registered_model_id, name) Update the name of a registered model. - Parameters: - Returns: registered_model – Updated registered model object - Return type: RegisteredModel #### get_shared_roles(offset=None, limit=None, id=None) Retrieve access control information for this registered model. - Parameters: - Return type: List [ SharingRole ] #### share(roles) Share this registered model or remove access from one or more user(s). - Parameters: roles ( List[SharingRole] ) – A list of SharingRole instances, each of which references a user and a role to be assigned. - Return type: None > [!NOTE] Examples > ``` > >>> from datarobot import RegisteredModel, SharingRole > >>> from datarobot.enums import SHARING_ROLE, SHARING_RECIPIENT_TYPE > >>> registered_model = RegisteredModel.get('5c939e08962d741e34f609f0') > >>> sharing_role = SharingRole( > ... role=SHARING_ROLE.CONSUMER, > ... share_recipient_type=SHARING_RECIPIENT_TYPE.USER, > ... username='jim.bob@datarobot.com' > ... ) > >>> registered_model.share(roles=[sharing_role]) > ``` #### get_version(version_id) Retrieve a registered model version. - Parameters: version_id ( str ) – The ID of the registered model version to retrieve. - Returns: registered_model_version – A registered model version object. - Return type: RegisteredModelVersion > [!NOTE] Examples > ``` > from datarobot import RegisteredModel > registered_model = RegisteredModel.get('5c939e08962d741e34f609f0') > registered_model_version = registered_model.get_version('5c939e08962d741e34f609f0') > >>> RegisteredModelVersion('My Registered Model Version') > ``` #### list_versions(filters=None, search=None, sort_key=None, sort_direction=None, limit=None, offset=None) Retrieve a list of registered model versions. - Parameters: - Returns: registered_model_versions – A list of registered model version objects. - Return type: List[RegisteredModelVersion] > [!NOTE] Examples > ``` > from datarobot import RegisteredModel > from datarobot.models.model_registry import RegisteredModelVersionsListFilters > from datarobot.enums import RegisteredModelSortKey, RegisteredModelSortDirection > registered_model = RegisteredModel.get('5c939e08962d741e34f609f0') > filters = RegisteredModelVersionsListFilters(tags=['tag1', 'tag2']) > registered_model_versions = registered_model.list_versions(filters=filters) > >>> [RegisteredModelVersion('My Registered Model Version')] > ``` #### list_associated_deployments(search=None, sort_key=None, sort_direction=None, limit=None, offset=None) Retrieve a list of deployments associated with this registered model. - Parameters: - Returns: deployments – A list of deployments associated with this registered model. - Return type: List[VersionAssociatedDeployment] ### class datarobot.models.RegisteredModelVersion Represents a version of a registered model. - Parameters: #### classmethod create_for_leaderboard_item(model_id, name=None, prediction_threshold=None, distribution_prediction_model_id=None, description=None, compute_all_ts_intervals=None, registered_model_name=None, registered_model_id=None, tags=None, registered_model_tags=None, registered_model_description=None) - Parameters: - Returns: regitered_model_version – A new registered model version object. - Return type: RegisteredModelVersion #### classmethod create_for_external(name, target, model_id=None, model_description=None, datasets=None, timeseries=None, registered_model_name=None, registered_model_id=None, tags=None, registered_model_tags=None, registered_model_description=None, geospatial_monitoring=None) Create a new registered model version from an external model. - Parameters: - Returns: registered_model_version – A new registered model version object. - Return type: RegisteredModelVersion #### classmethod create_for_custom_model_version(custom_model_version_id, name=None, description=None, registered_model_name=None, registered_model_id=None, tags=None, registered_model_tags=None, registered_model_description=None) Create a new registered model version from a custom model version. - Parameters: - Returns: registered_model_version – A new registered model version object. - Return type: RegisteredModelVersion #### list_associated_deployments(search=None, sort_key=None, sort_direction=None, limit=None, offset=None) Retrieve a list of deployments associated with this registered model version. - Parameters: - Returns: deployments – A list of deployments associated with this registered model version. - Return type: List[VersionAssociatedDeployment] ### class datarobot.models.model_registry.deployment.VersionAssociatedDeployment Represents a deployment associated with a registered model version. - Parameters: ### class datarobot.models.model_registry.RegisteredModelVersionsListFilters Filters for listing of registered model versions. - Parameters: ### class datarobot.models.model_registry.RegisteredModelListFilters Filters for listing registered models. - Parameters: ## Rulesets ### class datarobot.models.Ruleset Represents an approximation of a model with DataRobot Prime - Variables: #### request_model() Request training for a model using this ruleset Training a model using a ruleset is a necessary prerequisite for being able to download the code for a ruleset. - Returns: job – the job fitting the new Prime model - Return type: Job --- # Deployment management URL: https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html # Deployment Management # Deployments ### class datarobot.models.Deployment A deployment created from a DataRobot model. - Variables: #### classmethod create_from_learning_model(cls, model_id, label, description=None, default_prediction_server_id=None, importance=None, prediction_threshold=None, status=None, max_wait=600) Create a deployment from a DataRobot model. Added in version v2.17. - Parameters: - Returns: deployment – The created deployment - Return type: Deployment > [!NOTE] Examples > ``` > from datarobot import Project, Deployment > project = Project.get('5506fcd38bd88f5953219da0') > model = project.get_models()[0] > deployment = Deployment.create_from_learning_model(model.id, 'New Deployment') > deployment > >>> Deployment('New Deployment') > ``` #### classmethod create_from_leaderboard(model_id, label, description=None, default_prediction_server_id=None, importance=None, prediction_threshold=None, status=None, max_wait=600) Create a deployment from a Leaderboard. Added in version v2.17. - Parameters: - Returns: deployment – The created deployment - Return type: Deployment > [!NOTE] Examples > ``` > from datarobot import Project, Deployment > project = Project.get('5506fcd38bd88f5953219da0') > model = project.get_models()[0] > deployment = Deployment.create_from_leaderboard(model.id, 'New Deployment') > deployment > >>> Deployment('New Deployment') > ``` #### classmethod create_from_custom_model_version(custom_model_version_id, label, description=None, default_prediction_server_id=None, max_wait=600, importance=None) Create a deployment from a DataRobot custom model image. - Parameters: - Returns: deployment – The created deployment - Return type: Deployment #### classmethod create_from_registered_model_version(model_package_id, label, description=None, default_prediction_server_id=None, prediction_environment_id=None, importance=None, user_provided_id=None, additional_metadata=None, max_wait=600) Create a deployment from a DataRobot model package (version). - Parameters: - Returns: deployment – The created deployment - Return type: Deployment #### classmethod list(order_by=None, search=None, filters=None, offset=0, limit=0) List all deployments a user can view. Added in version v2.17. - Parameters: > [!NOTE] Examples > ``` > from datarobot import Deployment > deployments = Deployment.list() > deployments > >>> [Deployment('New Deployment'), Deployment('Previous Deployment')] > ``` > > ``` > from datarobot import Deployment > from datarobot.enums import DEPLOYMENT_SERVICE_HEALTH_STATUS > filters = DeploymentListFilters( > role='OWNER', > service_health=[DEPLOYMENT_SERVICE_HEALTH.FAILING] > ) > filtered_deployments = Deployment.list(filters=filters) > filtered_deployments > >>> [Deployment('Deployment I Own w/ Failing Service Health')] > ``` #### classmethod get(deployment_id) Get information about a deployment. Added in version v2.17. - Parameters: deployment_id ( str ) – the ID of the deployment - Returns: deployment – the queried deployment - Return type: Deployment > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > deployment.id > >>>'5c939e08962d741e34f609f0' > deployment.label > >>>'New Deployment' > ``` #### predict_batch(source, passthrough_columns=None, download_timeout=None, download_read_timeout=None, upload_read_timeout=None) A convenience method for making predictions with csv file or pandas DataFrame using a batch prediction job. For advanced usage, use [datarobot.models.BatchPredictionJob](https://docs.datarobot.com/en/docs/api/reference/sdk/batch-predictions.html#datarobot.models.BatchPredictionJob) directly. Added in version v3.0. - Parameters: - Returns: Prediction results in a pandas DataFrame. - Return type: pd.DataFrame - Raises: InvalidUsageError – If the source parameter cannot be determined to be a filepath, file, or DataFrame. > [!NOTE] Examples > ``` > from datarobot.models.deployment import Deployment > > deployment = Deployment.get("") > prediction_results_as_dataframe = deployment.predict_batch( > source="./my_local_file.csv", > ) > ``` #### get_uri() - Returns: url – Deployment’s overview URI - Return type: str #### update(label=None, description=None, importance=None) Update the label and description of this deployment. Added in version v2.19. - Return type: None #### delete() Delete this deployment. Added in version v2.17. - Return type: None #### activate(max_wait=600) Activates this deployment. When succeeded, deployment status become active. Added in version v2.29. - Parameters: max_wait ( Optional[int] ) – The maximum time to wait for deployment activation to complete before erroring - Return type: None #### deactivate(max_wait=600) Deactivates this deployment. When succeeded, deployment status become inactive. Added in version v2.29. - Parameters: max_wait ( Optional[int] ) – The maximum time to wait for deployment deactivation to complete before erroring - Return type: None #### replace_model(new_model_id, reason, max_wait=600, new_registered_model_version_id=None) Replace the model used in this deployment. To confirm model replacement eligibility, use : [validate_replacement_model()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.validate_replacement_model) beforehand. Added in version v2.17. Model replacement is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished. Predictions made against this deployment will start using the new model as soon as the request is completed. There will be no interruption for predictions throughout the process. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot import Deployment > from datarobot.enums import MODEL_REPLACEMENT_REASON > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > deployment.model['id'], deployment.model['type'] > >>>('5c0a979859b00004ba52e431', 'Decision Tree Classifier (Gini)') > > deployment.replace_model('5c0a969859b00004ba52e41b', MODEL_REPLACEMENT_REASON.ACCURACY) > deployment.model['id'], deployment.model['type'] > >>>('5c0a969859b00004ba52e41b', 'Support Vector Classifier (Linear Kernel)') > ``` #### perform_model_replace(new_registered_model_version_id, reason, max_wait=600) Replace the model used in this deployment. To confirm model replacement eligibility, use : [validate_replacement_model()](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.validate_replacement_model) beforehand. Added in version v3.4. Model replacement is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished. Predictions made against this deployment will start using the new model as soon as the request is completed. There will be no interruption for predictions throughout the process. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot import Deployment > from datarobot.enums import MODEL_REPLACEMENT_REASON > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > deployment.model_package['id'] > >>>'5c0a979859b00004ba52e431' > > deployment.perform_model_replace('5c0a969859b00004ba52e41b', MODEL_REPLACEMENT_REASON.ACCURACY) > deployment.model_package['id'] > >>>'5c0a969859b00004ba52e41b' > ``` #### validate_replacement_model(new_model_id=None, new_registered_model_version_id=None) Validate a model can be used as the replacement model of the deployment. Added in version v2.17. - Parameters: - Return type: Tuple [ str , str , Dict [ str , Any ]] - Returns: #### get_features() Retrieve the list of features needed to make predictions on this deployment. > [!NOTE] Notes > Each feature dict contains the following structure: > > name > : str, feature name > feature_type > : str, feature type > importance > : float, numeric measure of the relationship strength between > the feature and target (independent of model or other features) > date_format > : str or None, the date format string for how this feature was > interpreted, null if not a date feature, compatible with > https://docs.python.org/2/library/time.html#time.strftime > . > known_in_advance > : bool, whether the feature was selected as known in advance in > a time series model, false for non-time series models. - Returns: features – a list of feature dict - Return type: list > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > features = deployment.get_features() > features[0]['feature_type'] > >>>'Categorical' > features[0]['importance'] > >>>0.133 > ``` #### submit_actuals(data, batch_size=10000) Submit actuals for processing. The actuals submitted will be used to calculate accuracy metrics. - Parameters: - Raises: ValueError – if input data is not a list of dict-like objects or a pandas.DataFrame if input data is empty - Return type: None > [!NOTE] Examples > ``` > from datarobot import Deployment, AccuracyOverTime > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > data = [{ > 'association_id': '439917', > 'actual_value': 'True', > 'was_acted_on': True > }] > deployment.submit_actuals(data) > ``` #### submit_actuals_from_catalog_async(dataset_id, actual_value_column, association_id_column, dataset_version_id=None, timestamp_column=None, was_acted_on_column=None) Submit actuals from AI Catalog for processing. The actuals submitted will be used to calculate accuracy metrics. - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob - Raises: ValueError – if dataset_id not provided if actual_value_column not provided if association_id_column not provided > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > status_check_job = deployment.submit_actuals_from_catalog_async(data) > ``` #### get_predictions_by_forecast_date_settings() Retrieve predictions by forecast date settings of this deployment. Added in version v2.27. For time series deployments using the date/time format %Y-%m-%d %H:%M:%S.%f, DataRobot automatically populates a v2 in front of the timestamp format (forecast_date_format). Date/time values submitted in prediction data should not include this v2 prefix. Other timestamp formats are not affected. - Returns: settings – Predictions by forecast date settings of the deployment. - Return type: ForecastDateSettings #### update_predictions_by_forecast_date_settings(enable_predictions_by_forecast_date, forecast_date_column_name=None, forecast_date_format=None, max_wait=600) Update predictions by forecast date settings of this deployment. Added in version v2.27. Updating predictions by forecast date setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished. For time series deployments using the date/time format %Y-%m-%d %H:%M:%S.%f, DataRobot automatically populates a v2 in front of the timestamp format (forecast_date_format). If you are updating predictions by forecast date settings for a Time Series deployment with date/time format %Y-%m-%d %H:%M:%S.%f, you must add the v2 prefix to your submitted forecast_date_format parameter. Date/time values submitted in prediction data should not include this v2 prefix. Other timestamp formats are not affected. > [!NOTE] Examples > ``` > # To set predictions by forecast date settings to the same default settings you see when using > # the DataRobot web application, you use your 'Deployment' object like this: > deployment.update_predictions_by_forecast_date_settings( > enable_predictions_by_forecast_date=True, > forecast_date_column_name="date (actual)", > forecast_date_format="%Y-%m-%d", > ) > ``` - Parameters: - Return type: None #### get_challenger_models_settings() Retrieve challenger models settings of this deployment. Added in version v2.27. - Returns: settings - Return type: ChallengerModelsSettings #### update_challenger_models_settings(challenger_models_enabled, max_wait=600) Update challenger models settings of this deployment. Added in version v2.27. Updating challenger models setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished. - Parameters: - Return type: None #### get_segment_analysis_settings() Retrieve segment analysis settings of this deployment. Added in version v2.27. - Returns: settings - Return type: SegmentAnalysisSettings #### update_segment_analysis_settings(segment_analysis_enabled, segment_analysis_attributes=None, max_wait=600) Update segment analysis settings of this deployment. Added in version v2.27. Updating segment analysis setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished. - Parameters: - Return type: None #### get_bias_and_fairness_settings() Retrieve bias and fairness settings of this deployment. ..versionadded:: v3.2.0 - Returns: settings - Return type: BiasAndFairnessSettings #### update_bias_and_fairness_settings(protected_features, fairness_metric_set, fairness_threshold, preferable_target_value, max_wait=600) Update bias and fairness settings of this deployment. ..versionadded:: v3.2.0 Updating bias and fairness setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished. - Parameters: - Return type: None #### get_challenger_replay_settings() Retrieve challenger replay settings of this deployment. Added in version v3.4. - Returns: settings - Return type: ChallengerReplaySettings #### update_challenger_replay_settings(enabled, schedule=None) Update challenger replay settings of this deployment. Added in version v3.4. - Parameters: - Return type: None #### get_drift_tracking_settings() Retrieve drift tracking settings of this deployment. Added in version v2.17. - Returns: settings - Return type: DriftTrackingSettings #### update_drift_tracking_settings(target_drift_enabled=None, feature_drift_enabled=None, max_wait=600) Update drift tracking settings of this deployment. Added in version v2.17. Updating drift tracking setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished. - Parameters: - Return type: None #### get_association_id_settings() Retrieve association ID setting for this deployment. Added in version v2.19. - Returns: association_id_settings - Return type: str #### update_association_id_settings(column_names=None, required_in_prediction_requests=None, max_wait=600) Update association ID setting for this deployment. Added in version v2.19. - Parameters: - Return type: None #### get_predictions_data_collection_settings() Retrieve predictions data collection settings of this deployment. Added in version v2.21. - Returns: predictions_data_collection_settings – - Return type: dict in the following format: #### SEE ALSO [datarobot.models.Deployment.update_predictions_data_collection_settings](https://docs.datarobot.com/en/docs/api/reference/sdk/deployment-management.html#datarobot.models.Deployment.update_predictions_data_collection_settings): Method to update existing predictions data collection settings. #### update_predictions_data_collection_settings(enabled, max_wait=600) Update predictions data collection settings of this deployment. Added in version v2.21. Updating predictions data collection setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished. - Parameters: - Return type: None #### get_prediction_warning_settings() Retrieve prediction warning settings of this deployment. Added in version v2.19. - Returns: settings - Return type: PredictionWarningSettings #### update_prediction_warning_settings(prediction_warning_enabled, use_default_boundaries=None, lower_boundary=None, upper_boundary=None, max_wait=600) Update prediction warning settings of this deployment. Added in version v2.19. - Parameters: - Return type: None #### get_prediction_intervals_settings() Retrieve prediction intervals settings for this deployment. Added in version v2.19. > [!NOTE] Notes > Note that prediction intervals are only supported for time series deployments. - Returns: settings - Return type: PredictionIntervalsSettings #### update_prediction_intervals_settings(percentiles, enabled=True, max_wait=600) Update prediction intervals settings for this deployment. Added in version v2.19. > [!NOTE] Notes > Updating prediction intervals settings is an asynchronous process, which means some > preparatory work may be performed before the settings request is completed. This function > will not return until all work is fully finished. > > Note that prediction intervals are only supported for time series deployments. - Parameters: - Raises: - Return type: None #### get_health_settings() Retrieve health settings of this deployment. Added in version v3.4. - Returns: settings - Return type: HealthSettings #### update_health_settings(service=None, data_drift=None, accuracy=None, fairness=None, custom_metrics=None, predictions_timeliness=None, actuals_timeliness=None) Update health settings of this deployment. Added in version v3.4. - Parameters: - Return type: HealthSettings #### get_default_health_settings() Retrieve default health settings of this deployment. Added in version v3.4. - Returns: settings - Return type: HealthSettings #### get_service_stats(model_id=None, start_time=None, end_time=None, execution_time_quantile=None, response_time_quantile=None, slow_requests_threshold=None, segment_attribute=None, segment_value=None) Retrieves values of many service stat metrics aggregated over a time period. Added in version v2.18. - Parameters: - Returns: service_stats – the queried service stats metrics information - Return type: ServiceStats #### get_service_stats_over_time(metric=None, model_id=None, start_time=None, end_time=None, bucket_size=None, quantile=None, threshold=None, segment_attribute=None, segment_value=None) Retrieves values of a single service stat metric over a time period. Added in version v2.18. - Parameters: - Returns: service_stats_over_time – the queried service stats metric over time information - Return type: ServiceStatsOverTime #### get_target_drift(model_id=None, start_time=None, end_time=None, metric=None, segment_attribute=None, segment_value=None) Retrieve target drift information over a certain time period. Added in version v2.21. - Parameters: - Returns: target_drift – the queried target drift information - Return type: TargetDrift #### get_feature_drift(model_id=None, start_time=None, end_time=None, metric=None, segment_attribute=None, segment_value=None) Retrieve drift information for deployment’s features over a certain time period. Added in version v2.21. - Parameters: - Returns: feature_drift_data – the queried feature drift information - Return type: [FeatureDrift] #### get_predictions_over_time(model_ids=None, start_time=None, end_time=None, bucket_size=None, target_classes=None, include_percentiles=False, segment_attribute=None, segment_value=None) Retrieve stats of deployment’s prediction response over a certain time period. Added in version v3.2. - Parameters: - Returns: predictions_over_time – the queried predictions over time information - Return type: PredictionsOverTime > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > predictions_over_time = deployment.get_predictions_over_time() > predictions_over_time.buckets[0]['mean_predicted_value'] > >>>0.3772 > predictions_over_time.buckets[0]['row_count'] > >>>2000 > ``` #### get_accuracy(model_id=None, start_time=None, end_time=None, start=None, end=None, target_classes=None, segment_attribute=None, segment_value=None, metric=None, baseline_model_id=None) Retrieves values of many accuracy metrics aggregated over a time period. Added in version v2.18. - Parameters: - Returns: accuracy – the queried accuracy metrics information - Return type: Accuracy #### get_accuracy_over_time(metric=None, model_id=None, start_time=None, end_time=None, bucket_size=None, target_classes=None, segment_attribute=None, segment_value=None) Retrieves values of a single accuracy metric over a time period. Added in version v2.18. - Parameters: - Returns: accuracy_over_time – the queried accuracy metric over time information - Return type: AccuracyOverTime #### get_predictions_vs_actuals_over_time(model_ids=None, start_time=None, end_time=None, bucket_size=None, target_classes=None, segment_attribute=None, segment_value=None) Retrieve information for deployment’s predictions vs actuals over a certain time period. Added in version v3.3. - Parameters: - Returns: predictions_vs_actuals_over_time – The queried predictions vs actuals over time information. - Return type: PredictionsVsActualsOverTime > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > predictions_over_time = deployment.get_predictions_vs_actuals_over_time() > predictions_over_time.buckets[0]['mean_actual_value'] > >>>0.6673 > predictions_over_time.buckets[0]['row_count_with_actual'] > >>>500 > ``` #### get_fairness_scores_over_time(start_time=None, end_time=None, bucket_size=None, model_id=None, protected_feature=None, fairness_metric=None) Retrieves values of a single fairness score over a time period. Added in version v3.2. - Parameters: - Returns: fairness_scores_over_time – the queried fairness score over time information - Return type: FairnessScoresOverTime #### update_secondary_dataset_config(secondary_dataset_config_id, credential_ids=None) Update the secondary dataset config used by Feature discovery model for a given deployment. Added in version v2.23. - Parameters: - Return type: str > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment(deployment_id='5c939e08962d741e34f609f0') > config = deployment.update_secondary_dataset_config('5df109112ca582033ff44084') > config > >>> '5df109112ca582033ff44084' > ``` #### get_secondary_dataset_config() Get the secondary dataset config used by Feature discovery model for a given deployment. Added in version v2.23. - Returns: secondary_dataset_config – Id of the secondary dataset config - Return type: SecondaryDatasetConfigurations > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment(deployment_id='5c939e08962d741e34f609f0') > deployment.update_secondary_dataset_config('5df109112ca582033ff44084') > config = deployment.get_secondary_dataset_config() > config > >>> '5df109112ca582033ff44084' > ``` #### get_prediction_results(model_id=None, start_time=None, end_time=None, actuals_present=None, offset=None, limit=None) Retrieve a list of prediction results of the deployment. Added in version v2.24. - Parameters: - Returns: prediction_results – a list of prediction results - Return type: list[dict] > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > results = deployment.get_prediction_results() > ``` #### download_prediction_results(filepath, model_id=None, start_time=None, end_time=None, actuals_present=None, offset=None, limit=None) Download prediction results of the deployment as a CSV file. Added in version v2.24. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > results = deployment.download_prediction_results('path_to_prediction_results.csv') > ``` #### download_scoring_code(filepath, source_code=False, include_agent=False, include_prediction_explanations=False, include_prediction_intervals=False, max_wait=600) Retrieve scoring code of the current deployed model. Added in version v2.24. > [!NOTE] Notes > When setting include_agent or include_predictions_explanations or > include_prediction_intervals to True, > it can take a considerably longer time to download the scoring code. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > results = deployment.download_scoring_code('path_to_scoring_code.jar') > ``` #### download_model_package_file(filepath, compute_all_ts_intervals=False) Retrieve model package file (mlpkg) of the current deployed model. Added in version v3.3. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > deployment.download_model_package_file('path_to_model_package.mlpkg') > ``` #### delete_monitoring_data(model_id, start_time=None, end_time=None, max_wait=600) Delete deployment monitoring data. - Parameters: - Return type: None #### list_shared_roles(id=None, name=None, share_recipient_type=None, limit=100, offset=0) Get a list of users, groups and organizations that have an access to this user blueprint - Parameters: - Return type: List[DeploymentSharedRole] #### update_shared_roles(roles) Share a deployment with a user, group, or organization - Parameters: roles ( list(or(GrantAccessControlWithUsernameValidator , GrantAccessControlWithIdValidator , SharingRole)) ) – Array of GrantAccessControl objects, up to maximum 100 objects. - Return type: None #### share(roles) Share a deployment with a user, group, or organization - Parameters: roles ( list(SharingRole) ) – Array of SharingRole objects. - Return type: None #### list_challengers() Get a list of challengers for this deployment. Added in version v3.4. - Return type: list(Challenger) #### get_agent_card() Retrieve the agent card for this deployment. - Returns: agent_card – The agent card associated with this deployment. - Return type: dict > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > agent_card = deployment.get_agent_card() > ``` #### upload_agent_card(agent_card) Upload or replace the agent card for this deployment. This is only available for external deployments. - Parameters: agent_card ( dict ) – The agent card to upload for this deployment. - Returns: agent_card – The uploaded agent card. - Return type: dict > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > agent_card = deployment.upload_agent_card({"name": "My Agent", "version": "1.0.0"}) > ``` #### delete_agent_card() Delete the agent card for this deployment. This is only available for external deployments. This operation is idempotent — returns successfully even if no agent card exists. > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > deployment.delete_agent_card() > ``` - Return type: None #### get_champion_model_package() Get a champion model package for this deployment. - Returns: champion_model_package – A champion model package object. - Return type: ChampionModelPackage > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > champion_model_package = deployment.get_champion_model_package() > ``` #### list_prediction_data_exports(model_id=None, status=None, batch=None, offset=0, limit=100) Retrieve a list of asynchronous prediction data exports. - Parameters: - Returns: prediction_data_exports – A list of prediction data exports. - Return type: List[PredictionDataExport] #### list_actuals_data_exports(status=None, offset=0, limit=100) Retrieve a list of asynchronous actuals data exports. - Parameters: - Returns: actuals_data_exports – A list of actuals data exports. - Return type: List[ActualsDataExport] #### list_training_data_exports() Retrieve a list of successful training data exports. - Returns: training_data_export – A list of training data exports. - Return type: List[TrainingDataExport] #### list_data_quality_exports(start, end, model_id=None, prediction_pattern=None, prompt_pattern=None, actual_pattern=None, order_by=None, order_metric=None, filter_metric=None, filter_value=None, offset=0, limit=100) Retrieve a list of data-quality export records for a given deployment. Added in version v3.6. - Parameters: - Returns: data_quality_exports – A list of DataQualityExport objects. - Return type: list > [!NOTE] Examples > ``` > from datarobot import Deployment > > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > data_quality_exports = deployment.list_data_quality_exports(start_time='2024-07-01', end_time='2024-08-01') > ``` #### get_capabilities() Get a list capabilities for this deployment. Added in version v3.5. - Return type: list(Capability) > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > capabilities = deployment.get_capabilities() > ``` #### get_segment_attributes(monitoringType='serviceHealth') Get a list of segment attributes for this deployment. Added in version v3.6. - Parameters: monitoringType ( Optional[str] ) – The monitoring type for which segment attributes are being retrieved. - Return type: list(str) > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > segment_attributes = deployment.get_segment_attributes(DEPLOYMENT_MONITORING_TYPE.SERVICE_HEALTH) > ``` #### get_segment_values(segment_attribute=None, limit=100, offset=0, search=None) Get a list of segment values for this deployment. Added in version v3.6. - Parameters: - Return type: list(str) > [!NOTE] Examples > ``` > from datarobot import Deployment > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > segment_values = deployment.get_segment_values(segment_attribute=ReservedSegmentAttributes.CONSUMER) > ``` #### get_moderation_events(limit=100, offset=0) Get a list of moderation events for this deployment - Parameters: - Returns: events - Return type: List[MLOpsEvent] #### get_accuracy_metrics_settings() Get accuracy metrics settings for this deployment. - Returns: accuracy_metrics – A list of deployment accuracy metric names. - Return type: list(str) > [!NOTE] Examples > ``` > from datarobot import Deployment > > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > accuracy_metrics = deployment.get_accuracy_metrics_settings() > ``` #### update_accuracy_metrics_settings(accuracy_metrics) Update accuracy metrics settings for this deployment. - Parameters: accuracy_metrics ( list(str) ) – A list of accuracy metric names. - Returns: accuracy_metrics – A list of deployment accuracy metric names. - Return type: list(str) > [!NOTE] Examples > ``` > from datarobot import Deployment > from datarobot.enums import ACCURACY_METRIC > > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > payload = [ACCURACY_METRIC.AUC, ACCURACY_METRIC.LOGLOSS] > accuracy_metrics = deployment.update_accuracy_metrics_settings(payload) > ``` #### get_retraining_settings() Retrieve retraining settings of this deployment. Added in version v2.29. - Returns: settings - Return type: RetrainingSettings > [!NOTE] Examples > ``` > from datarobot import Deployment > > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > retraining_settings = deployment.get_retraining_settings() > ``` #### update_retraining_settings(retraining_user_id=, dataset_id=, prediction_environment_id=) Update retraining settings of this deployment. Added in version v2.29. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot import Deployment > > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > deployment.update_retraining_settings(retaining_user_id='5c939e08962d741e34f609f0') > ``` #### create_tag(name, value) Create a new deployment tag. - Parameters: - Return type: dict [ str , str ] #### update_tag(id, name, value) Update an existing deployment tag. - Parameters: - Return type: dict [ str , str ] #### delete_tag(id) Deletes the deployment tag specified by ID. - Parameters: id ( str ) – The ID of the deployment tag to delete. - Return type: None #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( T , bound= APIObject) #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None ### class datarobot.models.deployment.DeploymentListFilters Construct a set of filters to pass to `Deployment.list()` Added in version v2.20. - Parameters: > [!NOTE] Examples > Multiple filters can be combined in interesting ways to return very specific subsets of > deployments. > > Performing AND logic > > Providing multiple different parameters will result in AND logic between them. > For example, the following will return all deployments that I own whose service health > status is failing. > from > datarobot > import > Deployment > from > datarobot.models.deployment > import > DeploymentListFilters > from > datarobot.enums > import > DEPLOYMENT_SERVICE_HEALTH_STATUS > filters > = > DeploymentListFilters > ( > role > = > 'OWNER' > , > service_health > = > [ > DEPLOYMENT_SERVICE_HEALTH > . > FAILING > ] > ) > deployments > = > Deployment > . > list > ( > filters > = > filters > ) > > Performing OR logic > > Some filters support comma-separated lists (and will say so if they do). Providing a > comma-separated list of values to a single filter performs OR logic between those > values. For example, the following will return all deployments whose service health > is either > warning > OR > failing > . > from > datarobot > import > Deployment > from > datarobot.models.deployment > import > DeploymentListFilters > from > datarobot.enums > import > DEPLOYMENT_SERVICE_HEALTH_STATUS > filters > = > DeploymentListFilters > ( > service_health > = > [ > DEPLOYMENT_SERVICE_HEALTH > . > WARNING > , > DEPLOYMENT_SERVICE_HEALTH > . > FAILING > , > ] > ) > deployments > = > Deployment > . > list > ( > filters > = > filters > ) > > Performing OR logic across different filter types is not supported. > [!NOTE] Notes > In all cases, you may only retrieve deployments for which you have at least > the USER role for. Deployments for which you are a CONSUMER of will not be returned, > regardless of the filters applied. ### class datarobot.models.deployment.ServiceStats Deployment service stats information. - Variables: #### classmethod get(deployment_id, model_id=None, start_time=None, end_time=None, execution_time_quantile=None, response_time_quantile=None, segment_attribute=None, segment_value=None, slow_requests_threshold=None) Retrieve value of service stat metrics over a certain time period. Added in version v2.18. - Parameters: - Returns: service_stats – the queried service stats metrics - Return type: ServiceStats ### class datarobot.models.deployment.ServiceStatsOverTime Deployment service stats over time information. - Variables: #### classmethod get(deployment_id, metric=None, model_id=None, start_time=None, end_time=None, bucket_size=None, quantile=None, threshold=None, segment_attribute=None, segment_value=None) Retrieve information about how a service stat metric changes over a certain time period. Added in version v2.18. - Parameters: - Returns: service_stats_over_time – the queried service stat over time information - Return type: ServiceStatsOverTime #### property bucket_values : OrderedDict[str, int | float | None] The metric value for all time buckets, keyed by start time of the bucket. - Returns: bucket_values - Return type: OrderedDict ### class datarobot.models.deployment.TargetDrift Deployment target drift information. - Variables: #### classmethod get(deployment_id, model_id=None, start_time=None, end_time=None, metric=None, segment_attribute=None, segment_value=None) Retrieve target drift information over a certain time period. Added in version v2.21. - Parameters: - Returns: target_drift – the queried target drift information - Return type: TargetDrift > [!NOTE] Examples > ``` > from datarobot import Deployment, TargetDrift > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > target_drift = TargetDrift.get(deployment.id) > target_drift.period['end'] > >>>'2019-08-01 00:00:00+00:00' > target_drift.drift_score > >>>0.03423 > accuracy.target_name > >>>'readmitted' > ``` ### class datarobot.models.deployment.FeatureDrift Deployment feature drift information. - Variables: #### classmethod list(deployment_id, model_id=None, start_time=None, end_time=None, metric=None, segment_attribute=None, segment_value=None) Retrieve drift information for deployment’s features over a certain time period. Added in version v2.21. - Parameters: - Returns: feature_drift_data – the queried feature drift information - Return type: [FeatureDrift] > [!NOTE] Examples > ``` > from datarobot import Deployment, TargetDrift > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > feature_drift = FeatureDrift.list(deployment.id)[0] > feature_drift.period > >>>'2019-08-01 00:00:00+00:00' > feature_drift.drift_score > >>>0.252 > feature_drift.name > >>>'age' > ``` ### class datarobot.models.deployment.PredictionsOverTime Deployment predictions over time information. - Variables: #### classmethod get(deployment_id, model_ids=None, start_time=None, end_time=None, bucket_size=None, target_classes=None, include_percentiles=False, segment_attribute=None, segment_value=None) Retrieve information for deployment’s prediction response over a certain time period. Added in version v3.2. - Parameters: - Returns: predictions_over_time – the queried predictions over time information - Return type: PredictionsOverTime ### class datarobot.models.deployment.Accuracy Deployment accuracy information. - Variables: #### classmethod get(deployment_id, model_id=None, start_time=None, end_time=None, target_classes=None, segment_attribute=None, segment_value=None, metric=None, baseline_model_id=None) Retrieve values of accuracy metrics over a certain time period. Added in version v2.18. - Parameters: - Returns: accuracy – the queried accuracy metrics information - Return type: Accuracy > [!NOTE] Examples > ``` > from datarobot import Deployment, Accuracy > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > accuracy = Accuracy.get(deployment.id) > accuracy.period['end'] > >>>'2019-08-01 00:00:00+00:00' > accuracy.metric['LogLoss']['value'] > >>>0.7533 > accuracy.metric_values['LogLoss'] > >>>0.7533 > ``` #### property metric_values : Dict[str, int | None] The value for all metrics, keyed by metric name. - Returns: metric_values - Return type: Dict #### property metric_baselines : Dict[str, int | None] The baseline value for all metrics, keyed by metric name. - Returns: metric_baselines - Return type: Dict #### property percent_changes : Dict[str, int | None] The percent change of value over baseline for all metrics, keyed by metric name. - Returns: percent_changes - Return type: Dict ### class datarobot.models.deployment.AccuracyOverTime Deployment accuracy over time information. - Variables: #### classmethod get(deployment_id, metric=None, model_id=None, start_time=None, end_time=None, bucket_size=None, target_classes=None, segment_attribute=None, segment_value=None) Retrieve information about how an accuracy metric changes over a certain time period. Added in version v2.18. - Parameters: - Returns: accuracy_over_time – the queried accuracy metric over time information - Return type: AccuracyOverTime > [!NOTE] Examples > ``` > from datarobot import Deployment, AccuracyOverTime > from datarobot.enums import ACCURACY_METRICS > deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0') > accuracy_over_time = AccuracyOverTime.get(deployment.id, metric=ACCURACY_METRIC.LOGLOSS) > accuracy_over_time.metric > >>>'LogLoss' > accuracy_over_time.metric_values > >>>{datetime.datetime(2019, 8, 1): 0.73, datetime.datetime(2019, 8, 2): 0.55} > ``` #### classmethod get_as_dataframe(deployment_id, metrics=None, model_id=None, start_time=None, end_time=None, bucket_size=None) Retrieve information about how a list of accuracy metrics change over a certain time period as pandas DataFrame. In the returned DataFrame, the columns corresponds to the metrics being retrieved; the rows are labeled with the start time of each bucket. - Parameters: - Returns: accuracy_over_time - Return type: pd.DataFrame #### property bucket_values : Dict[datetime, int] The metric value for all time buckets, keyed by start time of the bucket. - Returns: bucket_values - Return type: Dict #### property bucket_sample_sizes : Dict[datetime, int] The sample size for all time buckets, keyed by start time of the bucket. - Returns: bucket_sample_sizes - Return type: Dict ### class datarobot.models.deployment.PredictionsVsActualsOverTime Deployment predictions vs actuals over time information. - Variables: #### classmethod get(deployment_id, model_ids=None, start_time=None, end_time=None, bucket_size=None, target_classes=None, segment_attribute=None, segment_value=None) Retrieve information for deployment’s predictions vs actuals over a certain time period. Added in version v3.3. - Parameters: - Returns: predictions_vs_actuals_over_time – the queried predictions vs actuals over time information - Return type: PredictionsVsActualsOverTime ### class datarobot.models.deployment.bias_and_fairness.FairnessScoresOverTime Deployment fairness over time information. - Variables: #### classmethod get(deployment_id, model_id=None, start_time=None, end_time=None, bucket_size=None, fairness_metric=None, protected_feature=None) Retrieve information for deployment’s fairness score response over a certain time period. Added in version FUTURE. - Parameters: - Returns: fairness_scores_over_time – the queried fairness score over time information - Return type: FairnessScoresOverTime ### class datarobot.models.deployment.DeploymentSharedRole - Parameters: ### class datarobot.models.deployment.DeploymentGrantSharedRoleWithId - Parameters: ### class datarobot.models.deployment.DeploymentGrantSharedRoleWithUsername - Parameters: ### class datarobot.models.deployment.deployment.FeatureDict ### class datarobot.models.deployment.deployment.ForecastDateSettings Forecast date settings of the deployment - Variables: ### class datarobot.models.deployment.deployment.ChallengerModelsSettings Challenger models settings of the deployment is a dict with the following format: - Variables: enabled ( bool ) – Is True if challenger models is enabled for this deployment. To update existing ‘’challenger_models’’ settings, see update_challenger_models_settings() ### class datarobot.models.deployment.deployment.SegmentAnalysisSettings Segment analysis settings of the deployment containing two items with keys : `enabled` and `attributes`, which are further described below. - Variables: ### class datarobot.models.deployment.deployment.BiasAndFairnessSettings Bias and fairness settings of this deployment - Variables: ### class datarobot.models.deployment.deployment.ChallengerReplaySettings Challenger replay settings of the deployment is a dict with the following format: - Variables: ### class datarobot.models.deployment.deployment.HealthSettings Health settings of the deployment containing seven nested dicts with keys - Variables: ### class datarobot.models.deployment.deployment.DriftTrackingSettings Drift tracking settings of the deployment containing two nested dicts with key : `target_drift` and `feature_drift`, which are further described below. - Variables: ### class datarobot.models.deployment.deployment.PredictionWarningSettings Prediction warning settings of the deployment - Variables: ### class datarobot.models.deployment.deployment.PredictionIntervalsSettings Prediction intervals settings of the deployment is a dict with the following format: - Variables: ### class datarobot.models.deployment.deployment.Capability ### class datarobot.enums.ACCURACY_METRIC ## Predictions ### class datarobot.models.Predictions Represents predictions metadata and provides access to prediction results. - Variables: > [!NOTE] Examples > List all predictions for a project > > ``` > import datarobot as dr > > # Fetch all predictions for a project > all_predictions = dr.Predictions.list(project_id) > > # Inspect all calculated predictions > for predictions in all_predictions: > print(predictions) # repr includes project_id, model_id, and dataset_id > ``` > > Retrieve predictions by id > > ``` > import datarobot as dr > > # Getting predictions by id > predictions = dr.Predictions.get(project_id, prediction_id) > > # Dump actual predictions > df = predictions.get_all_as_dataframe() > print(df) > ``` #### classmethod list(project_id, model_id=None, dataset_id=None) Fetch all the computed predictions metadata for a project. - Parameters: - Return type: A list of Predictions objects #### classmethod get(project_id, prediction_id) Retrieve the specific predictions metadata - Parameters: - Return type: Predictions - Returns: #### get_all_as_dataframe(class_prefix='class_', serializer='json') Retrieve all prediction rows and return them as a pandas.DataFrame. - Parameters: - Returns: dataframe - Return type: pandas.DataFrame - Raises: #### download_to_csv(filename, encoding='utf-8', serializer='json') Save prediction rows into CSV file. - Parameters: - Return type: None ## PredictionServer ### class datarobot.PredictionServer A prediction server can be used to make predictions. - Variables: #### classmethod list() Returns a list of prediction servers a user can use to make predictions. Added in version v2.17. - Returns: prediction_servers – Contains a list of prediction servers that can be used to make predictions. - Return type: list of PredictionServer instances > [!NOTE] Examples > ``` > prediction_servers = PredictionServer.list() > prediction_servers > >>> [PredictionServer('https://example.com')] > ``` ## Prediction environment ### class datarobot.models.PredictionEnvironment A prediction environment entity. Added in version v3.3.0. - Variables: #### classmethod list() Returns list of available external prediction environments. - Returns: prediction_environments – contains a list of available prediction environments. - Return type: list of PredictionEnvironment instances > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> prediction_environments = dr.PredictionEnvironment.list() > >>> prediction_environments > [ > PredictionEnvironment('5e429d6ecf8a5f36c5693e03', 'demo_pe', 'aws', 'env for demo testing'), > PredictionEnvironment('5e42cc4dcf8a5f3256865840', 'azure_pe', 'azure', 'env for azure demo testing'), > ] > ``` #### classmethod get(pe_id) Gets the PredictionEnvironment by id. - Parameters: pe_id ( str ) – the identifier of the PredictionEnvironment. - Returns: prediction_environment – the requested prediction environment object. - Return type: PredictionEnvironment > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> pe = dr.PredictionEnvironment.get('5a8ac9ab07a57a1231be501f') > >>> pe > PredictionEnvironment('5a8ac9ab07a57a1231be501f', 'my_predict_env', 'aws', 'demo env'), > ``` #### delete() Deletes the prediction environment. > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> pe = dr.PredictionEnvironment.get('5a8ac9ab07a57a1231be501f') > >>> pe.delete() > ``` - Return type: None #### classmethod create(name, platform, description=None, plugin=None, supported_model_formats=None, is_managed_by_management_agent=False, datastore=None, credential=None) Create a prediction environment. - Parameters: - Returns: prediction_environment – the prediction environment was created - Return type: PredictionEnvironment - Raises: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> pe = dr.PredictionEnvironment.create( > ... name='my_predict_env', > ... platform=PredictionEnvironmentPlatform.AWS, > ... description='demo prediction env', > ... ) > >>> pe > PredictionEnvironment('5e429d6ecf8a5f36c5693e99', 'my_predict_env', 'aws', 'demo prediction env'), > ``` --- # Errors URL: https://docs.datarobot.com/en/docs/api/reference/sdk/errors.html # Exceptions ### exception datarobot.errors.AppPlatformError Raised by `Client.request()` for requests that: : - Return a non-200 HTTP response, or - Connection refused/timeout or - Response timeout or - Malformed request - Have a malformed/missing header in the response. ### exception datarobot.errors.ServerError For 500-level responses from the server ### exception datarobot.errors.ClientError For 400-level responses from the server has json parameter for additional information to be stored about error if need be ### exception datarobot.errors.InputNotUnderstoodError Raised if a method is called in a way that cannot be understood ### exception datarobot.errors.InvalidUsageError Raised when methods are called with invalid or incompatible arguments ### exception datarobot.errors.AllRetriesFailedError Raised when the retry manager does not successfully make a request ### exception datarobot.errors.InvalidModelCategoryError Raised when method specific for model category was called from wrong model ### exception datarobot.errors.AsyncTimeoutError Raised when an asynchronous operation did not successfully get resolved within a specified time limit ### exception datarobot.errors.AsyncFailureError Raised when querying an asynchronous status resulted in an exceptional status code (not 200 and not 303) ### exception datarobot.errors.ProjectAsyncFailureError When an AsyncFailureError occurs during project creation or finalizing the project settings for modeling. This exception will have the attributes `status_code` indicating the unexpected status code from the server, and `async_location` indicating which asynchronous status object was being polled when the failure happened. ### exception datarobot.errors.AsyncProcessUnsuccessfulError Raised when querying an asynchronous status showed that async process was not successful ### exception datarobot.errors.AsyncModelCreationError Raised when querying an asynchronous status showed that model creation was not successful ### exception datarobot.errors.AsyncPredictionsGenerationError Raised when querying an asynchronous status showed that predictions generation was not successful ### exception datarobot.errors.PendingJobFinished Raised when the server responds with a 303 for the pending creation of a resource. ### exception datarobot.errors.JobNotFinished Raised when execution was trying to get a finished resource from a pending job, but the job is not finished ### exception datarobot.errors.DuplicateFeaturesError Raised when trying to create featurelist with duplicating features ### exception datarobot.errors.TrainingDataAssignmentError Raised when the training data assignment for a custom model version fails ### exception datarobot.errors.DataRobotDeprecationWarning Raised when using deprecated functions or using functions in a deprecated way #### SEE ALSO [PlatformDeprecationWarning](https://docs.datarobot.com/en/docs/api/reference/sdk/errors.html#datarobot.errors.PlatformDeprecationWarning) ### exception datarobot.errors.IllegalFileName Raised when trying to use a filename we can’t handle. ### exception datarobot.errors.JobAlreadyRequested Raised when the requested model has already been requested. ### exception datarobot.errors.ContentRetrievalTerminatedError Raised when due to content retrieval error process of data retrieval was terminated. ### exception datarobot.errors.CredentialsError Raised for errors related to credentials. ### exception datarobot.errors.UpdateAttributesError ### exception datarobot.errors.InvalidRatingTableWarning Raised when using interacting with rating tables that failed validation ### exception datarobot.errors.PartitioningMethodWarning Raised when interacting with project methods related to partition classes, i.e. Project.set_partitioning_method() or Project.set_datetime_partitioning(). ### exception datarobot.errors.NonPersistableProjectOptionWarning Raised when setting project options via Project.set_options if any of the options passed are not supported for POST requests to /api/v2/project/{project_id}/options/. All options that fall under this category can be found here: `datarobot.enums.NonPersistableProjectOptions()`. ### exception datarobot.errors.OverwritingProjectOptionWarning Raised when setting project options via Project.set_options if any of the options passed have already been set to a value in Project.advanced_options, or if a different value is already stored in the endpoint /api/v2/project/{project_id}/options/. Precedence is given to the new value you passed in. ### exception datarobot.errors.NoRedundancyImpactAvailable Raised when retrieving old feature impact data Redundancy detection was added in v2.13 of the API, and some projects, e.g., multiclass projects do not support redundancy detection. This warning is raised to make clear that redundancy detection is unavailable. ### exception datarobot.errors.ParentModelInsightFallbackWarning Raised when insights are unavailable for a model and insight retrieval falls back to retrieving insights for a model’s parent model ### exception datarobot.errors.ProjectHasNoRecommendedModelWarning Raised when a project has no recommended model. ### exception datarobot.errors.PlatformDeprecationWarning Raised when Deprecation header is returned in the API, for example a project may be deprecated as part of the 2022 Python 3 platform migration. #### SEE ALSO [DataRobotDeprecationWarning](https://docs.datarobot.com/en/docs/api/reference/sdk/errors.html#datarobot.errors.DataRobotDeprecationWarning) ### exception datarobot.errors.MultipleUseCasesNotAllowed Raised when a method decorated with add_to_use_case(allow_multiple=True) calls a method decorated with add_to_use_case(allow_multiple=False) with multiple UseCases passed --- # Data management URL: https://docs.datarobot.com/en/docs/api/reference/sdk/features.html # Features ### class datarobot.models.Feature A feature from a project’s dataset These are features either included in the originally uploaded dataset or added to it via feature transformations. In time series projects, these will be distinct from the [ModelingFeature](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#datarobot.models.ModelingFeature) s created during partitioning; otherwise, they will correspond to the same features. For more information about input and modeling features, see the [time series documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#input-vs-modeling). The `min`, `max`, `mean`, `median`, and `std_dev` attributes provide information about the distribution of the feature in the EDA sample data. For non-numeric features or features created prior to these summary statistics becoming available, they will be None. For features where the summary statistics are available, they will be in a format compatible with the data type, i.e., date type features will have their summary statistics expressed as ISO-8601 formatted date strings. - Variables: #### classmethod get(project_id, feature_name) Retrieve a single feature - Parameters: - Returns: feature – The queried instance - Return type: Feature #### get_multiseries_properties(multiseries_id_columns, max_wait=600) Retrieve time series properties for a potential multiseries datetime partition column Multiseries time series projects use multiseries id columns to model multiple distinct series within a single project. This function returns the time series properties (time step and time unit) of this column if it were used as a datetime partition column with the specified multiseries id columns, running multiseries detection automatically if it had not previously been successfully ran. - Parameters: - Returns:properties– A dict with three keys: time_series_eligible : bool, whether the column can be used as a partition columntime_unit : str or null, the inferred time unit if used as a partition columntime_step : int or null, the inferred time step if used as a partition columnReturn type:dict #### get_cross_series_properties(datetime_partition_column, cross_series_group_by_columns, max_wait=600) Retrieve cross-series properties for multiseries ID column. This function returns the cross-series properties (eligibility as group-by column) of this column if it were used with specified datetime partition column and with current multiseries id column, running cross-series group-by validation automatically if it had not previously been successfully ran. - Parameters: - Returns:properties– A dict with three keys: name : str, column nameeligibility : str, reason for column eligibilityisEligible : bool, is column eligible as cross-series group-byReturn type:dict #### get_multicategorical_histogram() Retrieve multicategorical histogram for this feature Added in version v2.24. - Return type: datarobot.models.MulticategoricalHistogram - Raises: #### get_pairwise_correlations() Retrieve pairwise label correlation for multicategorical features Added in version v2.24. - Return type: datarobot.models.PairwiseCorrelations - Raises: #### get_pairwise_joint_probabilities() Retrieve pairwise label joint probabilities for multicategorical features Added in version v2.24. - Return type: datarobot.models.PairwiseJointProbabilities - Raises: #### get_pairwise_conditional_probabilities() Retrieve pairwise label conditional probabilities for multicategorical features Added in version v2.24. - Return type: datarobot.models.PairwiseConditionalProbabilities - Raises: #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( T , bound= APIObject) #### get_histogram(bin_limit=None) Retrieve a feature histogram - Parameters: bin_limit ( int or None ) – Desired max number of histogram bins. If omitted, by default endpoint will use 60. - Returns: featureHistogram – The requested histogram with desired number or bins - Return type: FeatureHistogram ### class datarobot.models.ModelingFeature A feature used for modeling In time series projects, a new set of modeling features is created after setting the partitioning options. These features are automatically derived from those in the project’s dataset and are the features used for modeling. Modeling features are only accessible once the target and partitioning options have been set. In projects that don’t use time series modeling, once the target has been set, ModelingFeatures and Features will behave the same. For more information about input and modeling features, see the [time series documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#input-vs-modeling). As with the [Feature](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#datarobot.models.Feature) object, the min, max, `mean, median, and std_dev attributes provide information about the distribution of the feature in the EDA sample data. For non-numeric features, they will be None. For features where the summary statistics are available, they will be in a format compatible with the data type, i.e. date type features will have their summary statistics expressed as ISO-8601 formatted date strings. - Variables: #### classmethod get(project_id, feature_name) Retrieve a single modeling feature - Parameters: - Returns: feature – The requested feature - Return type: ModelingFeature ### class datarobot.models.DatasetFeature A feature from a project’s dataset These are features either included in the originally uploaded dataset or added to it via feature transformations. The `min`, `max`, `mean`, `median`, and `std_dev` attributes provide information about the distribution of the feature in the EDA sample data. For non-numeric features or features created prior to these summary statistics becoming available, they will be None. For features where the summary statistics are available, they will be in a format compatible with the data type, i.e., date type features will have their summary statistics expressed as ISO-8601 formatted date strings. - Variables: #### get_histogram(bin_limit=None) Retrieve a feature histogram - Parameters: bin_limit ( int or None ) – Desired max number of histogram bins. If omitted, by default endpoint will use 60. - Returns: featureHistogram – The requested histogram with desired number or bins - Return type: DatasetFeatureHistogram ### class datarobot.models.DatasetFeatureHistogram #### classmethod get(dataset_id, feature_name, bin_limit=None, key_name=None) Retrieve a single feature histogram - Parameters: - Returns: featureHistogram – The queried instance with plot attribute in it. - Return type: FeatureHistogram ### class datarobot.models.FeatureHistogram #### classmethod get(project_id, feature_name, bin_limit=None, key_name=None) Retrieve a single feature histogram - Parameters: - Returns: featureHistogram – The queried instance with plot attribute in it. - Return type: FeatureHistogram ### class datarobot.models.InteractionFeature Interaction feature data Added in version v2.21. - Variables: #### classmethod get(project_id, feature_name) Retrieve a single Interaction feature - Parameters: - Returns: feature – The queried instance - Return type: InteractionFeature ### class datarobot.models.MulticategoricalHistogram Histogram for Multicategorical feature. Added in version v2.24. > [!NOTE] Notes > `HistogramValues` contains: > > values.[].label > : string - Label name > values.[].plot > : list - Histogram for label > values.[].plot.[].label_relevance > : int - Label relevance value > values.[].plot.[].row_count > : int - Row count where label has given relevance > values.[].plot.[].row_pct > : float - Percentage of rows where label has given relevance - Variables: #### classmethod get(multilabel_insights_key) Retrieves multicategorical histogram You might find it more convenient to use [Feature.get_multicategorical_histogram](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#datarobot.models.Feature.get_multicategorical_histogram) instead. - Parameters: multilabel_insights_key ( string ) – Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via Feature.multilabel_insights_key . - Returns: The multicategorical histogram for multilabel_insights_key - Return type: MulticategoricalHistogram #### to_dataframe() Convenience method to get all the information from this multicategorical_histogram instance in form of a `pandas.DataFrame`. - Returns: Histogram information as a multicategorical_histogram. The dataframe will contain these columns: feature_name, label, label_relevance, row_count and row_pct - Return type: pandas.DataFrame ### class datarobot.models.PairwiseCorrelations Correlation of label pairs for multicategorical feature. Added in version v2.24. > [!NOTE] Notes > `CorrelationValues` contain: > > values.[].label_configuration > : list of length 2 - Configuration of the label pair > values.[].label_configuration.[].label > : str – Label name > values.[].statistic_value > : float – Statistic value - Variables: #### classmethod get(multilabel_insights_key) Retrieves pairwise correlations You might find it more convenient to use [Feature.get_pairwise_correlations](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#datarobot.models.Feature.get_pairwise_correlations) instead. - Parameters: multilabel_insights_key ( string ) – Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via Feature.multilabel_insights_key . - Returns: The pairwise label correlations - Return type: PairwiseCorrelations #### as_dataframe() The pairwise label correlations as a (num_labels x num_labels) DataFrame. - Returns: The pairwise label correlations. Index and column names allow the interpretation of the values. - Return type: pandas.DataFrame ### class datarobot.models.PairwiseJointProbabilities Joint probabilities of label pairs for multicategorical feature. Added in version v2.24. > [!NOTE] Notes > `ProbabilityValues` contain: > > values.[].label_configuration > : list of length 2 - Configuration of the label pair > values.[].label_configuration.[].relevance > : int – 0 for absence of the labels, > 1 for the presence of labels > values.[].label_configuration.[].label > : str – Label name > values.[].statistic_value > : float – Statistic value - Variables: #### classmethod get(multilabel_insights_key) Retrieves pairwise joint probabilities You might find it more convenient to use [Feature.get_pairwise_joint_probabilities](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#datarobot.models.Feature.get_pairwise_joint_probabilities) instead. - Parameters: multilabel_insights_key ( string ) – Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via Feature.multilabel_insights_key . - Returns: The pairwise joint probabilities - Return type: PairwiseJointProbabilities #### as_dataframe(relevance_configuration) Joint probabilities of label pairs as a (num_labels x num_labels) DataFrame. - Parameters:relevance_configuration(tupleoflength 2) – Valid options are (0, 0), (0, 1), (1, 0) and (1, 1). Values of 0 indicate absence of labels and 1 indicates presence of labels. The first value describes the presence for the labels in axis=0 and the second value describes the presence for the labels in axis=1. For example the matrix values for a relevance configuration of (0, 1) describe the probabilities of absent labels in the index axis and present labels in the column axis. E.g. The probability P(A=0,B=1) can be retrieved via:pairwise_joint_probabilities.as_dataframe((0,1)).loc['A', 'B']*Returns:The joint probabilities for the requestedrelevance_configuration. Index and column names allow the interpretation of the values. *Return type:pandas.DataFrame ### class datarobot.models.PairwiseConditionalProbabilities Conditional probabilities of label pairs for multicategorical feature. Added in version v2.24. > [!NOTE] Notes > `ProbabilityValues` contain: > > values.[].label_configuration > : list of length 2 - Configuration of the label pair > values.[].label_configuration.[].relevance > : int – 0 for absence of the labels, > 1 for the presence of labels > values.[].label_configuration.[].label > : str – Label name > values.[].statistic_value > : float – Statistic value - Variables: #### classmethod get(multilabel_insights_key) Retrieves pairwise conditional probabilities You might find it more convenient to use [Feature.get_pairwise_conditional_probabilities](https://docs.datarobot.com/en/docs/api/reference/sdk/features.html#datarobot.models.Feature.get_pairwise_conditional_probabilities) instead. - Parameters: multilabel_insights_key ( string ) – Key for multilabel insights, unique for a project, feature and EDA stage combination. The multilabel_insights_key can be retrieved via Feature.multilabel_insights_key . - Returns: The pairwise conditional probabilities - Return type: PairwiseConditionalProbabilities #### as_dataframe(relevance_configuration) Conditional probabilities of label pairs as a (num_labels x num_labels) DataFrame. The label names in the columns are the events, on which we condition. The label names in the index are the events whose conditional probability given the indexes is in the dataframe. E.g. The probability P(A=0|B=1) can be retrieved via: `pairwise_conditional_probabilities.as_dataframe((0, 1)).loc['A', 'B']` - Parameters:relevance_configuration(tupleoflength 2) – Valid options are (0, 0), (0, 1), (1, 0) and (1, 1). Values of 0 indicate absence of labels and 1 indicates presence of labels. The first value describes the presence for the labels in axis=0 and the second value describes the presence for the labels in axis=1. For example the matrix values for a relevance configuration of (0, 1) describe the probabilities of absent labels in the index axis given the presence of labels in the column axis. *Returns:The conditional probabilities for the requestedrelevance_configuration. Index and column names allow the interpretation of the values. *Return type:pandas.DataFrame ## Restoring Discarded Features ### class datarobot.models.restore_discarded_features.DiscardedFeaturesInfo An object containing information about time series features which were reduced during time series feature generation process. These features can be restored back to the project. They will be included into All Time Series Features and can be used to create new feature lists. Added in version v2.27. - Variables: #### classmethod restore(project_id, features_to_restore, max_wait=600) Restore discarded during time series feature generation process features back to the project. After restoration features will be included into All Time Series Features. Added in version v2.27. - Parameters: - Returns: status – information about features which were restored and which were not. - Return type: FeatureRestorationStatus #### classmethod retrieve(project_id) Retrieve the discarded features information for a given project. Added in version v2.27. - Parameters: project_id ( string ) - Returns: info – information about features which were discarded during feature generation process and limits how many features can be restored. - Return type: DiscardedFeaturesInfo ### class datarobot.models.restore_discarded_features.FeatureRestorationStatus Status of the feature restoration process. Added in version v2.27. - Variables: ## Feature lists ### class datarobot.DatasetFeaturelist A set of features attached to a dataset in the AI Catalog - Variables: #### classmethod get(dataset_id, featurelist_id) Retrieve a dataset featurelist - Parameters: - Returns: featurelist – the specified featurelist - Return type: DatasetFeatureList #### delete() Delete a dataset featurelist Featurelists configured into the dataset as a default featurelist cannot be deleted. - Return type: None #### update(name=None) Update the name of an existing featurelist Note that only user-created featurelists can be renamed, and that names must not conflict with names used by other featurelists. - Parameters: name ( Optional[str] ) – the new name for the featurelist - Return type: None ### class datarobot.models.Featurelist A set of features used in modeling - Variables: #### classmethod from_data(data) Overrides the parent method to ensure description is always populated - Parameters: data ( dict ) – the data from the server, having gone through processing - Return type: TypeVar ( TFeaturelist , bound= Featurelist) #### classmethod get(project_id, featurelist_id) Retrieve a known feature list - Parameters: - Returns: featurelist – The queried instance - Return type: Featurelist - Raises: ValueError – passed project_id parameter value is of not supported type #### delete(dry_run=False, delete_dependencies=False) Delete a featurelist, and any models and jobs using it All models using a featurelist, whether as the training featurelist or as a monotonic constraint featurelist, will also be deleted when the deletion is executed and any queued or running jobs using it will be cancelled. Similarly, predictions made on these models will also be deleted. All the entities that are to be deleted with a featurelist are described as “dependencies” of it. To preview the results of deleting a featurelist, call delete with dry_run=True When deleting a featurelist with dependencies, users must specify delete_dependencies=True to confirm they want to delete the featurelist and all its dependencies. Without that option, only featurelists with no dependencies may be successfully deleted and others will error. Featurelists configured into the project as a default featurelist or as a default monotonic constraint featurelist cannot be deleted. Featurelists used in a model deployment cannot be deleted until the model deployment is deleted. - Parameters: - Returns:result– A dictionary describing the result of deleting the featurelist, with the following keys : - dry_run : bool, whether the deletion was a dry run or an actual deletion - can_delete : bool, whether the featurelist can actually be deleted - deletion_blocked_reason : str, why the featurelist can’t be deleted (if it can’t) - num_affected_models : int, the number of models using this featurelist - num_affected_jobs : int, the number of jobs using this featurelist *Return type:dict #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( T , bound= APIObject) #### update(name=None, description=None) Update the name or description of an existing featurelist Note that only user-created featurelists can be renamed, and that names must not conflict with names used by other featurelists. - Parameters: - Return type: None ### class datarobot.models.ModelingFeaturelist A set of features that can be used to build a model In time series projects, a new set of modeling features is created after setting the partitioning options. These features are automatically derived from those in the project’s dataset and are the features used for modeling. Modeling features are only accessible once the target and partitioning options have been set. In projects that don’t use time series modeling, once the target has been set, ModelingFeaturelists and Featurelists will behave the same. For more information about input and modeling features, see the [time series documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#input-vs-modeling). - Variables: #### classmethod get(project_id, featurelist_id) Retrieve a modeling featurelist Modeling featurelists can only be retrieved once the target and partitioning options have been set. - Parameters: - Returns: featurelist – the specified featurelist - Return type: ModelingFeaturelist #### update(name=None, description=None) Update the name or description of an existing featurelist Note that only user-created featurelists can be renamed, and that names must not conflict with names used by other featurelists. - Parameters: - Return type: None #### delete(dry_run=False, delete_dependencies=False) Delete a featurelist, and any models and jobs using it All models using a featurelist, whether as the training featurelist or as a monotonic constraint featurelist, will also be deleted when the deletion is executed and any queued or running jobs using it will be cancelled. Similarly, predictions made on these models will also be deleted. All the entities that are to be deleted with a featurelist are described as “dependencies” of it. To preview the results of deleting a featurelist, call delete with dry_run=True When deleting a featurelist with dependencies, users must specify delete_dependencies=True to confirm they want to delete the featurelist and all its dependencies. Without that option, only featurelists with no dependencies may be successfully deleted and others will error. Featurelists configured into the project as a default featurelist or as a default monotonic constraint featurelist cannot be deleted. Featurelists used in a model deployment cannot be deleted until the model deployment is deleted. - Parameters: - Returns:result– A dictionary describing the result of deleting the featurelist, with the following keys : - dry_run : bool, whether the deletion was a dry run or an actual deletion - can_delete : bool, whether the featurelist can actually be deleted - deletion_blocked_reason : str, why the featurelist can’t be deleted (if it can’t) - num_affected_models : int, the number of models using this featurelist - num_affected_jobs : int, the number of jobs using this featurelist *Return type:dict ### class datarobot.models.featurelist.DeleteFeatureListResult ## Dataset definition ### class datarobot.helpers.feature_discovery.DatasetDefinition Dataset definition for the Feature Discovery Added in version v2.25. - Variables: > [!NOTE] Examples > ``` > import datarobot as dr > dataset_definition = dr.DatasetDefinition( > identifier='profile', > catalog_id='5ec4aec1f072bc028e3471ae', > catalog_version_id='5ec4aec2f072bc028e3471b1', > ) > > dataset_definition = dr.DatasetDefinition( > identifier='transaction', > catalog_id='5ec4aec1f072bc028e3471ae', > catalog_version_id='5ec4aec2f072bc028e3471b1', > primary_temporal_key='Date' > ) > ``` ## Relationships ### class datarobot.helpers.feature_discovery.Relationship Relationship between dataset defined in DatasetDefinition Added in version v2.25. - Variables: > [!NOTE] Examples > ``` > import datarobot as dr > relationship = dr.Relationship( > dataset1_identifier='profile', > dataset2_identifier='transaction', > dataset1_keys=['CustomerID'], > dataset2_keys=['CustomerID'] > ) > > relationship = dr.Relationship( > dataset2_identifier='profile', > dataset1_keys=['CustomerID'], > dataset2_keys=['CustomerID'], > feature_derivation_window_start=-14, > feature_derivation_window_end=-1, > feature_derivation_window_time_unit='DAY', > prediction_point_rounding=1, > prediction_point_rounding_time_unit='DAY' > ) > ``` ## Relationships configuration ### class datarobot.models.RelationshipsConfiguration A Relationships configuration specifies a set of secondary datasets as well as the relationships among them. It is used to configure Feature Discovery for a project to generate features automatically from these datasets. - Variables: #### classmethod create(dataset_definitions, relationships, feature_discovery_settings=None) Create a Relationships Configuration - Parameters: - Returns: relationships_configuration – Created relationships configuration - Return type: RelationshipsConfiguration > [!NOTE] Examples > ``` > dataset_definition = dr.DatasetDefinition( > identifier='profile', > catalog_id='5fd06b4af24c641b68e4d88f', > catalog_version_id='5fd06b4af24c641b68e4d88f' > ) > relationship = dr.Relationship( > dataset2_identifier='profile', > dataset1_keys=['CustomerID'], > dataset2_keys=['CustomerID'], > feature_derivation_window_start=-14, > feature_derivation_window_end=-1, > feature_derivation_window_time_unit='DAY', > prediction_point_rounding=1, > prediction_point_rounding_time_unit='DAY' > ) > dataset_definitions = [dataset_definition] > relationships = [relationship] > relationship_config = dr.RelationshipsConfiguration.create( > dataset_definitions=dataset_definitions, > relationships=relationships, > feature_discovery_settings = [ > {'name': 'enable_categorical_statistics', 'value': True}, > {'name': 'enable_numeric_skewness', 'value': True}, > ] > ) > >>> relationship_config.id > '5c88a37770fc42a2fcc62759' > ``` #### get() Retrieve the Relationships configuration for a given id - Returns: relationships_configuration – The requested relationships configuration - Return type: RelationshipsConfiguration - Raises: ClientError – Raised if an invalid relationships config id is provided. > [!NOTE] Examples > ``` > relationships_config = dr.RelationshipsConfiguration(valid_config_id) > result = relationships_config.get() > >>> result.id > '5c88a37770fc42a2fcc62759' > ``` #### replace(dataset_definitions, relationships, feature_discovery_settings=None) Update the Relationships Configuration which is not used in the feature discovery Project - Parameters: - Returns: relationships_configuration – the updated relationships configuration - Return type: RelationshipsConfiguration #### delete() Delete the Relationships configuration - Raises: ClientError – Raised if an invalid relationships config id is provided. > [!NOTE] Examples > ``` > # Deleting with a valid id > relationships_config = dr.RelationshipsConfiguration(valid_config_id) > status_code = relationships_config.delete() > status_code > >>> 204 > relationships_config.get() > >>> ClientError: Relationships Configuration not found > ``` ## Feature lineage ### class datarobot.models.FeatureLineage Lineage of an automatically engineered feature. - Variables:steps(list) – list of steps which were applied to build the feature. stepsstructure is: id - (int) : step id starting with 0.step_type: (str) : one of the data/action/json/generatedData.name: (str) : name of the step.description: (str) : description of the step.parents: (list[int]) : references to other steps id.is_time_aware: (bool) : indicator of step being time aware. Mandatory only foractionandjoinsteps.actionstep provides additional information about feature derivation window in the timeInfo field.catalog_id: (str) : id of the catalog for adatastep.catalog_version_id: (str) : id of the catalog version for adatastep.group_by: (list[str]) : list of columns which thisactionstep aggregated by.columns: (list) : names of columns involved into the feature generation. Available only fordatasteps.time_info: (dict) : description of the feature derivation window which was applied to thisactionstep.join_info: (list[dict]) :joinstep details. columnsstructure is data_type: (str) : the type of the feature, e.g., ‘Categorical’, ‘Text’is_input: (bool) : indicates features which provided data to transform in this lineage.name: (str) : feature name.is_cutoff: (bool) : indicates a cutoff column. time_infostructure is: latest: (dict) : end of the feature derivation window applied.duration: (dict) : size of the feature derivation window applied. latestand duration structure is: time_unit: (str) : time unit name like ‘MINUTE’, ‘DAY’, ‘MONTH’ etc.duration: (int) : value/size of this duration object. join_infostructure is: join_type - (str) : kind of join, left/right.left_table - (dict) : information about a dataset which was considered as left.right_table - (str) : information about a dataset which was considered as right. left_tableandright_tablestructure is: columns - (list[str]) : list of columns which datasets were joined by.datasteps - (list[int]) : list ofdatasteps id which brought thecolumnsinto the current step dataset. #### classmethod get(project_id, id) Retrieve a single FeatureLineage. - Parameters: - Returns: lineage – The queried instance - Return type: FeatureLineage ## OCR job resources ### class datarobot.models.ocr_job_resource.OCRJobResource An OCR job resource container. It is used to: - Get an existing OCR job resource. - List available OCR job resources. - Start an OCR job. - Check the status of a started OCR job. - Download the error report of a started OCR job. Added in version v3.6.0b0. - Variables: #### classmethod get(job_resource_id) Get an OCR job resource. - Parameters: job_resource_id ( str ) – identifier of OCR job resource - Returns: returned OCR job resource - Return type: OCRJobResource #### classmethod list(offset=0, limit=10) Get a list of OCR job resources. - Parameters: - Returns: A list of OCR job resources. - Return type: List[OCRJobResource] #### classmethod create(input_catalog_id, language, engine_specific_parameters=None) Create a new OCR job resource and return it. - Parameters: - Returns: The created OCR job resource. - Return type: OCRJobResource #### start_job() Start an OCR job with this OCR job resource. - Returns: The response of starting an OCR job. - Return type: StartOCRJobResponse #### get_job_status() Get status of the OCR job associated with this OCR job resource. - Returns: OCR job status enum - Return type: OCRJobStatusEnum #### download_error_report(download_file_path) Download the error report of the OCR job associated with this OCR job resource. - Parameters: download_file_path ( Path ) – path to download error report - Return type: None #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( TOCRJobResource , bound= OCRJobResource) ### class datarobot.models.ocr_job_resource.OCREngineSpecificParameters Container of Engine Specific Parameters. It is used to specify required OCR engine parameters when creating an OCR job resource. Added in version v3.8.0. - Variables: #### get_payload() return dict containing engine specific parameters whose values are not None - Return type: Dict [ str , Optional [ str ]] ### class datarobot.models.ocr_job_resource.OCRJobDatasetLanguage Supported OCR language ### class datarobot.models.ocr_job_resource.DataRobotOCREngineType Supported OCR engine type ### class datarobot.models.ocr_job_resource.DataRobotArynOutputFormat Supported ARYN OCR engine output format ### class datarobot.models.ocr_job_resource.OCRJobStatusEnum OCR Job status enum ### class datarobot.models.ocr_job_resource.StartOCRJobResponse Container of Start OCR Job API response ## Document text extraction ### class datarobot.models.documentai.document.FeaturesWithSamples FeaturesWithSamples(model_id, feature_name, document_task) #### document_task Alias for field number 2 #### feature_name Alias for field number 1 #### model_id Alias for field number 0 ### class datarobot.models.documentai.document.DocumentPageFile Page of a document as an image file. - Variables: #### property thumbnail_bytes : bytes Document thumbnail as bytes. - Returns: Document thumbnail. - Return type: bytes #### property mime_type : str ‘image/png’ - Returns: Mime image type of the document thumbnail. - Return type: str - Type: Mime image type of the document thumbnail. Example ### class datarobot.models.documentai.document.DocumentThumbnail Thumbnail of document from the project’s dataset. If `Project.stage` is `datarobot.enums.PROJECT_STAGE.EDA2` and it is a supervised project then the `target_*` attributes of this class will have values, otherwise the values will all be None. - Variables: #### classmethod list(project_id, feature_name, target_value=None, offset=None, limit=None) Get document thumbnails from a project. - Parameters: - Returns: documents – A list of DocumentThumbnail objects, each representing a single document. - Return type: List[DocumentThumbnail] > [!NOTE] Notes > Actual document thumbnails are not fetched from the server by this method. > Instead the data gets loaded lazily when `DocumentPageFile` object attributes > are accessed. > [!NOTE] Examples > Fetch document thumbnails for the given `project_id` and `feature_name`. > > ``` > from datarobot._experimental.models.documentai.document import DocumentThumbnail > > # Fetch five documents from the EDA SAMPLE for the specified project and specific feature > document_thumbs = DocumentThumbnail.list(project_id, feature_name, limit=5) > > # Fetch five documents for the specified project with target value filtering > # This option is only available after selecting the project target and starting modeling > target1_thumbs = DocumentThumbnail.list(project_id, feature_name, target_value='target1', limit=5) > ``` Preview the document thumbnail. ``` from datarobot._experimental.models.documentai.document import DocumentThumbnail from datarobot.helpers.image_utils import get_image_from_bytes # Fetch 3 documents document_thumbs = DocumentThumbnail.list(project_id, feature_name, limit=3) for doc_thumb in document_thumbs: thumbnail = get_image_from_bytes(doc_thumb.document.thumbnail_bytes) thumbnail.show() ``` ### class datarobot.models.documentai.document.DocumentTextExtractionSample Stateless class for computing and retrieving Document Text Extraction Samples. > [!NOTE] Notes > Actual document text extraction samples are not fetched from the server in the moment of > a function call. Detailed information on the documents, the pages and the rendered images of them > are fetched when accessed on demand (lazy loading). > [!NOTE] Examples > 1) Compute text extraction samples for a specific model, and fetch all existing document text > extraction samples for a specific project. > > ``` > from datarobot._experimental.models.documentai.document import DocumentTextExtractionSample > > SPECIFIC_MODEL_ID1 = "model_id1" > SPECIFIC_MODEL_ID2 = "model_id2" > SPECIFIC_PROJECT_ID = "project_id" > > # Order computation of document text extraction sample for specific model. > # By default `compute` method will await for computation to end before returning > DocumentTextExtractionSample.compute(SPECIFIC_MODEL_ID1, await_completion=False) > DocumentTextExtractionSample.compute(SPECIFIC_MODEL_ID2) > > samples = DocumentTextExtractionSample.list_features_with_samples(SPECIFIC_PROJECT_ID) > ``` 2) Fetch document text extraction samples for a specific model_id and feature_name, and display all document sample pages. ``` from datarobot._experimental.models.documentai.document import DocumentTextExtractionSample from datarobot.helpers.image_utils import get_image_from_bytes SPECIFIC_MODEL_ID = "model_id" SPECIFIC_FEATURE_NAME = "feature_name" samples = DocumentTextExtractionSample.list_pages( model_id=SPECIFIC_MODEL_ID, feature_name=SPECIFIC_FEATURE_NAME ) for sample in samples: thumbnail = sample.document_page.thumbnail image = get_image_from_bytes(thumbnail.thumbnail_bytes) image.show() ``` 3) Fetch document text extraction samples for specific model_id and feature_name and display text extraction details for the first page. This example displays the image of the document with bounding boxes of detected text lines. It also returns a list of all text lines extracted from page along with their coordinates. ``` from datarobot._experimental.models.documentai.document import DocumentTextExtractionSample SPECIFIC_MODEL_ID = "model_id" SPECIFIC_FEATURE_NAME = "feature_name" samples = DocumentTextExtractionSample.list_pages(SPECIFIC_MODEL_ID, SPECIFIC_FEATURE_NAME) # Draw bounding boxes for first document page sample and display related text data. image = samples[0].get_document_page_with_text_locations() image.show() # For each text block represented as bounding box object drawn on original image # display its coordinates (top, left, bottom, right) and extracted text value for text_line in samples[0].text_lines: print(text_line) ``` #### classmethod compute(model_id, await_completion=True, max_wait=600) Starts computation of document text extraction samples for the model and, if successful, returns computed text samples for it. This method allows calculation to continue for a specified time and, if not complete, cancels the request. - Parameters: - Raises: - Return type: None #### classmethod list_features_with_samples(project_id) Returns a list of features, model_id pairs with computed document text extraction samples. - Parameters: project_id ( str ) – The project ID to retrieve the list of computed samples for. - Return type: List[FeaturesWithSamples] #### classmethod list_pages(model_id, feature_name, document_index=None, document_task=None) Returns a list of document text extraction sample pages. - Parameters: - Return type: List[DocumentTextExtractionSamplePage] #### classmethod list_documents(model_id, feature_name) Returns a list of documents used for text extraction. - Parameters: - Return type: List[DocumentTextExtractionSampleDocument] ### class datarobot.models.documentai.document.DocumentTextExtractionSampleDocument Document text extraction source. Holds data that contains feature and model prediction values, as well as the thumbnail of the document. - Variables: #### classmethod list(model_id, feature_name, document_task=None) List available documents with document text extraction samples. - Parameters: - Return type: List[DocumentTextExtractionSampleDocument] ### class datarobot.models.documentai.document.DocumentTextExtractionSamplePage Document text extraction sample covering one document page. Holds data about the document page, the recognized text, and the location of the text in the document page. - Variables: #### classmethod list(model_id, feature_name, document_index=None, document_task=None) Returns a list of document text extraction sample pages. - Parameters: - Return type: List[DocumentTextExtractionSamplePage] #### get_document_page_with_text_locations(line_color='blue', line_width=3, padding=3) Returns the document page with bounding boxes drawn around the text lines as a PIL.Image. - Parameters: - Returns: Returns a PIL.Image with drawn text-bounding boxes. - Return type: Image --- # LLM blueprints URL: https://docs.datarobot.com/en/docs/api/reference/sdk/gen-llm-generation.html # LLM Generation ### class datarobot.models.genai.custom_model_validation.CustomModelValidation The validation record checking the ability of the deployment to serve as a custom model LLM, custom model vector database, or custom model embedding. - Variables: #### classmethod get(validation_id) Get the validation record by id. - Parameters: validation_id ( Union[CustomModelValidation , str] ) – The CustomModelValidation to retrieve, either CustomModelValidation or validation ID. - Return type: CustomModelValidation #### classmethod get_by_values(prompt_column_name, target_column_name, deployment_id, model_id) Get the validation record by field values. - Parameters: - Return type: CustomModelValidation #### classmethod list(prompt_column_name=None, target_column_name=None, deployment=None, model=None, use_cases=None, playground=None, completed_only=False, search=None, sort=None) List the validation records by field values. - Parameters: - Return type: List[CustomModelValidation] #### classmethod revalidate(validation_id) Revalidate an unlinked custom model vector database or LLM. This method is helpful when a deployment used as vector database or LLM is accidentally replaced with another model that stopped complying with the response schema requirements. Replace the deployment’s model with a complying model and call this method instead of creating a new custom model validation from scratch. Another application is if the API token used to create a validation record got revoked and no longer can be used to call the deployment. Calling revalidate will update the validation record with the token currently in use. - Parameters: validation_id ( str ) – The ID of the CustomModelValidation for revalidation. - Return type: CustomModelValidation #### delete() Delete the custom model validation. - Return type: None ### class datarobot.models.genai.custom_model_llm_validation.CustomModelLLMValidation Validation record checking the ability of the deployment to serve as a custom model LLM. - Variables: #### classmethod create(deployment_id, model=None, use_case=None, name=None, wait_for_completion=False, prediction_timeout=None, prompt_column_name=None, target_column_name=None, chat_model_id=None) Start the validation of the deployment that will serve as an LLM. - Parameters: - Return type: CustomModelLLMValidation #### update(name=None, prompt_column_name=None, target_column_name=None, chat_model_id=None, deployment=None, model=None, prediction_timeout=None) Update a custom model validation. - Parameters: - Return type: CustomModelLLMValidation ### class datarobot.models.genai.llm_blueprint.LLMBlueprint Metadata for a DataRobot GenAI LLM blueprint. - Variables: #### classmethod create(playground, name, prompt_type=PromptType.CHAT_HISTORY_AWARE, description='', llm=None, llm_settings=None, vector_database=None, vector_database_settings=None) Create a new LLM blueprint. - Parameters: - Returns: llm_blueprint – The created LLM blueprint. - Return type: LLMBlueprint #### classmethod create_from_llm_blueprint(llm_blueprint, name, description='') Create a new LLM blueprint from an existing LLM blueprint. - Parameters: - Returns: llm_blueprint – The created LLM blueprint. - Return type: LLMBlueprint #### classmethod get(llm_blueprint_id) Retrieve a single LLM blueprint. - Parameters: llm_blueprint_id ( str ) – The ID of the LLM blueprint you want to retrieve. - Returns: llm_blueprint – The requested LLM blueprint. - Return type: LLMBlueprint #### classmethod list(playground=None, llms=None, vector_databases=None, is_saved=None, is_starred=None, sort=None) Lists all LLM blueprints available to the user. If the playground is specified, then the results are restricted to the LLM blueprints associated with the playground. If the LLMs are specified, then the results are restricted to the LLM blueprints using those LLM types. If vector_databases are specified, then the results are restricted to the LLM blueprints using those vector databases. - Parameters: - Returns: playgrounds – A list of playgrounds available to the user. - Return type: list[Playground] #### update(name=None, description=None, llm=None, llm_settings=None, vector_database=None, vector_database_settings=None, is_saved=None, is_starred=None, prompt_type=None, remove_vector_database=False) Update the LLM blueprint. - Parameters: - Returns: llm_blueprint – The updated LLM blueprint. - Return type: LLMBlueprint #### delete() Delete the single LLM blueprint. - Return type: None #### register_custom_model(prompt_column_name=None, target_column_name=None, llm_test_configuration_ids=None, vector_database_default_prediction_server_id=None, vector_database_prediction_environment_id=None, vector_database_maximum_memory=None, vector_database_resource_bundle_id=None, vector_database_replicas=None, vector_database_network_egress_policy=None) Create a new CustomModelVersion. This registers a custom model from the LLM blueprint. If this LLM Blueprint uses a vector database and that vector database is not yet deployed, this will also deploy that vector database. - Parameters: - Returns: custom_model – The registered custom model. - Return type: CustomModelVersion ### class datarobot.models.genai.llm.LLMDefinition Metadata for a DataRobot GenAI LLM. - Variables: #### classmethod list(use_case=None, as_dict=True, moderation_supported_only=False) List all large language models (LLMs) available to the user. - Parameters: - Returns: llms – A list of large language models (LLMs) available to the user. - Return type: list[LLMDefinition] or list[LLMDefinitionDict] ### class datarobot.models.genai.llm.LLMDefinitionDict Dict representation of LLMDefinition. --- # Generative AI Moderation URL: https://docs.datarobot.com/en/docs/api/reference/sdk/gen-moderation.html # Generative AI Moderation ### class datarobot.models.moderation.configuration.ModerationConfiguration Details of overall moderation configuration for a model. #### classmethod get(config_id) Get a guard configuration by ID. Added in version v3.6. - Parameters: config_id ( str ) – ID of the configuration - Returns: retrieved configuration - Return type: ModerationConfiguration - Raises: #### classmethod list(entity_id, entity_type) List Guard Configurations. Added in version v3.6. - Parameters: - Returns: a list of configurations - Return type: List[ModerationConfiguration] - Raises: #### classmethod create(template_id, name, description, stages, entity_id, entity_type, intervention=None, llm_type=None, deployment_id=None, model_info=None, nemo_info=None, openai_api_key='', openai_api_base='', openai_deployment_id='', openai_credential='') Create a configuration. This is not a full create from scratch; it’s based on a template. Added in version v3.6. - Parameters: - Returns: created ModerationConfiguration - Return type: ModerationConfiguration - Raises: #### update(name=None, description=None, intervention=None, llm_type=None, deployment_id=None, model_info=None, nemo_info=None, openai_api_key='', openai_api_base='', openai_deployment_id='', openai_credential='') Update configuration. All fields are optional, and omitted fields are left unchanged. entity_id, entity_type, and stages cannot be modified for a guard configuration,. Added in version v3.6. - Parameters: - Raises: - Return type: None #### refresh() Update OverallModerationConfig with the latest data from server. Added in version v3.6. - Raises: - Return type: None #### delete() Delete configuration. Added in version v3.6. - Raises: - Return type: None ### class datarobot.models.moderation.intervention.GuardInterventionCondition Defines a condition for intervention. ### class datarobot.models.moderation.intervention.GuardInterventionForTemplate Defines the intervention conditions and actions a guard can take. Configuration schema differs slightly from template because changes were requested after templates were baked in. #### classmethod ensure_object(maybe_dict) intervention may arrive as an object, or as a dict. Return an object. - Return type: GuardInterventionForTemplate ### class datarobot.models.moderation.intervention.GuardInterventionForConfiguration Defines the intervention conditions and actions a guard can take. Configuration schema differs slightly from template because changes were requested after templates were baked in. #### classmethod ensure_object(maybe_dict) intervention may arrive as an object, or as a dict. Return an object. - Return type: GuardInterventionForConfiguration ### class datarobot.models.moderation.model_info.GuardModelInfo Model information for moderation templates and configurations. Omitted optional values are stored and presented as: * [] (for class names) * None (all others) #### to_dict() Convert the model information object to a dictionary. - Return type: Dict [ str , Union [ str , List [ str ], None ]] #### classmethod ensure_object(maybe_dict) intervention may arrive as an object, or as a dict. Return an object. - Return type: GuardModelInfo ### class datarobot.models.moderation.model_version_update.ModelVersionUpdate Implements the operation provided by “Save Configuration” in moderation UI. All guard configurations and overall config is saved to a new custom model version. #### classmethod new_custom_model_version_with_config(custom_model_id, overall_config, configs) Create a new custom model version with the provided moderation configuration :type custom_model_id: `str`:param custom_model_id: :type overall_config: [OverallModerationConfig](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-moderation.html#datarobot.models.moderation.overall.OverallModerationConfig):param overall_config: :type configs: `List` [ [ModerationConfiguration](https://docs.datarobot.com/en/docs/api/reference/sdk/gen-moderation.html#datarobot.models.moderation.configuration.ModerationConfiguration)] :param configs: - Return type: ID of the new custom model version. ### class datarobot.models.moderation.nemo_info.GuardNemoInfo Details of a NeMo Guardrails moderation guard. ### class datarobot.models.moderation.overall.OverallModerationConfig Details of overall moderation configuration for a model. #### classmethod find(entity_id, entity_type) Find overall configuration by entity ID and entity type. Each entity (such as a customModelVersion) may have at most 1 overall moderation configuration. Added in version v3.6. - Parameters: - Returns: an OverallModerationConfig or None - Return type: Optional[OverallModerationConfig] - Raises: #### classmethod locate(entity_id, entity_type) Find overall configuration by entity ID and entity type. This version of find() expects the object to exist. Its return type is not optional. Added in version v3.6. - Parameters: - Returns: a list of OverallModerationConfig - Return type: List[OverallModerationConfig] - Raises: #### classmethod create(timeout_sec, timeout_action, entity_id, entity_type) Create an OverallModerationConfig. Added in version v3.6. - Parameters: - Returns: created OverallModerationConfig - Return type: OverallModerationConfig - Raises: #### update(timeout_sec, timeout_action, entity_id, entity_type) Update an OverallModerationConfig. Added in version v3.6. - Parameters: - Returns: created OverallModerationConfig - Return type: OverallModerationConfig - Raises: #### refresh() Update OverallModerationConfig with the latest data from server. Added in version v3.6. - Raises: - Return type: None ### class datarobot.models.moderation.template.ModerationTemplate A DataRobot Moderation Template. Added in version v3.6. - Variables: #### classmethod get(template_id) Get Template by id. Added in version v3.6. - Parameters: template_id ( str ) – ID of the Template - Returns: retrieved Template - Return type: ModerationTemplate - Raises: #### classmethod list(include_agentic=None, is_agentic=None, for_playground=None, for_production=None, name=None) List Templates. Added in version v3.6. - Parameters: yet ( none ) - Returns: a list of Templates - Return type: List[ModerationTemplate] - Raises: #### classmethod find(name) Find Template by name. Added in version v3.6. - Parameters: name ( str ) – name of the Template - Returns: a list of Templates - Return type: List[ModerationTemplate] - Raises: #### classmethod create(name, description, type, allowed_stages, intervention=None, ootb_type=None, llm_type=None, model_info=None, nemo_info=None) Create a Template. Added in version v3.6. - Parameters: - Returns: created Template - Return type: ModerationTemplate - Raises: #### update(name=None, description=None, type=None, allowed_stages=None, intervention=None, ootb_type=None, llm_type=None, model_info=None, nemo_info=None) Update Template. All fields are optional, and omitted fields are left unchanged. Added in version v3.6. - Parameters: - Raises: - Return type: None #### refresh() Update Template with the latest data from server. Added in version v3.6. - Raises: - Return type: None #### delete() Delete Template. Added in version v3.6. - Raises: - Return type: None ### class datarobot.enums.ModerationGuardOotbType Defines the available OOTB guards. --- # Prompting URL: https://docs.datarobot.com/en/docs/api/reference/sdk/gen-prompting.html # Prompting ### class datarobot.models.genai.chat.Chat Metadata for a DataRobot GenAI chat. - Variables: #### classmethod create(name, llm_blueprint) Creates a new chat. - Parameters: - Returns: chat – The created chat. - Return type: Chat #### classmethod get(chat) Retrieve a single chat. - Parameters: chat ( Chat or str ) – The chat you want to retrieve. Accepts chat or chat ID. - Returns: chat – The requested chat. - Return type: Chat #### classmethod list(llm_blueprint=None, sort=None) List all chats available to the user. If the LLM blueprint is specified, results are restricted to only those chats associated with the LLM blueprint. - Parameters: - Returns: chats – Returns a list of chats. - Return type: list[Chat] #### delete() Delete the single chat. - Return type: None #### update(name) Update the chat. - Parameters: name ( str ) – The new name for the chat. - Returns: chat – The updated chat. - Return type: Chat ### class datarobot.models.genai.chat_prompt.ChatPrompt Metadata for a DataRobot GenAI chat prompt. - Variables: #### classmethod create(text, llm_blueprint=None, chat=None, llm=None, llm_settings=None, vector_database=None, vector_database_settings=None, wait_for_completion=False, metadata_filter=None) Create a new ChatPrompt. This submits the prompt text to the LLM. Either llm_blueprint or chat is required. - Parameters: #### update(custom_metrics=None, feedback_metadata=None) Update the chat prompt. - Parameters: - Returns: chat_prompt – The updated chat prompt. - Return type: ChatPrompt #### classmethod get(chat_prompt) Retrieve a single chat prompt. - Parameters: chat_prompt ( ChatPrompt or str ) – The chat prompt you want to retrieve, either ChatPrompt or chat prompt ID. - Returns: chat_prompt – The requested chat prompt. - Return type: ChatPrompt #### classmethod list(llm_blueprint=None, playground=None, chat=None) List all chat prompts available to the user. If the llm_blueprint, playground, or chat is specified then the results are restricted to the chat prompts associated with that entity. - Parameters: - Returns: chat_prompts – A list of chat prompts available to the user. - Return type: list[ChatPrompt] #### delete() Delete the single chat prompt. - Return type: None #### create_llm_blueprint(name, description='') Create a new LLM blueprint from an existing chat prompt. - Parameters: - Returns: llm_blueprint – The created LLM blueprint. - Return type: LLMBlueprint ### class datarobot.models.genai.comparison_chat.ComparisonChat Metadata for a DataRobot GenAI comparison chat. - Variables: #### classmethod create(name, playground) Creates a new comparison chat. - Parameters: - Returns: comparison_chat – The created comparison chat. - Return type: ComparisonChat #### classmethod get(comparison_chat) Retrieve a single comparison chat. - Parameters: comparison_chat ( ComparisonChat or str ) – The comparison chat you want to retrieve. Accepts ComparisonChat or comparison chat ID. - Returns: comparison_chat – The requested comparison chat. - Return type: ComparisonChat #### classmethod list(playground=None, sort=None) List all comparison chats available to the user. If the playground is specified, results are restricted to only those comparison chats associated with the playground. - Parameters: - Returns: comparison_chats – Returns a list of comparison chats. - Return type: list[ComparisonChat] #### delete() Delete the single comparison chat. - Return type: None #### update(name) Update the comparison chat. - Parameters: name ( str ) – The new name for the comparison chat. - Returns: comparison_chat – The updated comparison chat. - Return type: ComparisonChat ### class datarobot.models.genai.comparison_prompt.ComparisonPrompt Metadata for a DataRobot GenAI comparison prompt. - Variables: #### update(additional_llm_blueprints=None, wait_for_completion=False, feedback_result=None, **kwargs) Update the comparison prompt. - Parameters: additional_llm_blueprints ( list[LLMBlueprint or str] ) – The additional LLM blueprints you want to submit the comparison prompt. - Returns: comparison_prompt – The updated comparison prompt. - Return type: ComparisonPrompt #### classmethod create(llm_blueprints, text, comparison_chat=None, wait_for_completion=False, metadata_filter=None) Create a new ComparisonPrompt. This submits the prompt text to the LLM blueprints that are specified. - Parameters: #### classmethod get(comparison_prompt) Retrieve a single comparison prompt. - Parameters: comparison_prompt ( str ) – The comparison prompt you want to retrieve. Accepts entity or ID. - Returns: comparison_prompt – The requested comparison prompt. - Return type: ComparisonPrompt #### classmethod list(llm_blueprints=None, comparison_chat=None) List all comparison prompts available to the user that include the specified LLM blueprints or from the specified comparison chat. - Parameters: - Returns: comparison_prompts – A list of comparison prompts available to the user that use the specified LLM blueprints. - Return type: list[ComparisonPrompt] #### delete() Delete the single comparison prompt. - Return type: None ### class datarobot.models.genai.playground.Playground Metadata for a DataRobot GenAI playground. - Variables: #### classmethod create(name, description='', use_case=None, copy_insights=None, playground_type=PlaygroundType.RAG) Create a new playground. - Parameters: - Returns: playground – The created playground. - Return type: Playground #### classmethod get(playground_id) Retrieve a single playground. - Parameters: playground_id ( str ) – The ID of the playground you want to retrieve. - Returns: playground – The requested playground. - Return type: Playground #### classmethod list(use_case=None, search=None, sort=None) List all playgrounds available to the user. If the use_case is specified or can be inferred from the Context then the results are restricted to the playgrounds associated with the UseCase. - Parameters: - Returns: playgrounds – A list of playgrounds available to the user. - Return type: list[Playground] #### update(name=None, description=None) Update the playground. - Parameters: - Returns: playground – The updated playground. - Return type: Playground #### delete() Delete the playground. - Return type: None ### class datarobot.enums.PromptType Supported LLM blueprint prompting types. ### class datarobot.models.genai.user_limits.UserLimits Counts for user limits for LLM APIs and vector databases. #### classmethod get_vector_database_count() Get the count of vector databases for the user. - Return type: APIObject #### classmethod get_llm_requests_count() Get the count of LLMs requests made by the user. - Return type: APIObject ### class datarobot.models.genai.chat_prompt.ResultMetadata Metadata for the result of a chat prompt submission. - Variables: ### class datarobot.models.genai.prompt_trace.PromptTrace Prompt trace contains aggregated information about a prompt execution. - Variables: #### classmethod list(playground) List all prompt traces for a playground. - Parameters: playground ( str ) – The ID of the playground to list prompt traces for. - Returns: prompt_traces – List of prompt traces for the playground. - Return type: list[PromptTrace] #### classmethod export_to_ai_catalog(playground) Export prompt traces to AI Catalog as a CSV. - Parameters: playground ( str ) – The ID of the playground to export prompt traces for. - Returns: status_url – The URL where the status of the job can be monitored - Return type: str ### class datarobot.models.genai.prompt_trace.TraceMetadata Trace metadata contains information about all the users and chats that are relevant to this playground. - Variables: users ( list[dict] ) – The users who submitted the prompt. #### classmethod get(playground) Get trace metadata for a playground. - Parameters: playground ( str ) – The ID of the playground to get trace metadata for. - Returns: trace_metadata – The trace metadata for the playground. - Return type: TraceMetadata ### class datarobot.models.genai.prompt_template.PromptTemplate A prompt template that can have multiple versions. - Variables: #### classmethod create(name, description='') Create a new prompt template. - Parameters: - Returns: The created prompt template. - Return type: PromptTemplate - Raises: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> template = dr.genai.PromptTemplate.create( > ... name="Customer Support", > ... description="Template for support responses" > ... ) > ``` #### classmethod list(sort=None, search=None) List all prompt templates available to the user. - Parameters: - Returns: A list of prompt templates. - Return type: List[PromptTemplate] - Raises: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> templates = dr.genai.PromptTemplate.list(sort="-creationDate") > ``` #### classmethod get(prompt_template_id) Get a prompt template by ID. - Parameters: prompt_template_id ( str ) – The ID of the prompt template to retrieve. - Returns: The requested prompt template. - Return type: PromptTemplate - Raises: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> template = dr.genai.PromptTemplate.get("507f1f77bcf86cd799439011") > ``` #### create_version(prompt_text, variables=None, commit_comment='') Create a new version of this prompt template. - Parameters: - Returns: The created version. - Return type: PromptTemplateVersion - Raises: > [!NOTE] Examples > ``` > >>> from datarobot.models.genai.prompt_template import Variable > >>> template.create_version( > ... prompt_text="Hello {{ name }}, regarding {{ issue }}", > ... variables=[ > ... Variable(name="name", type="str", description="Customer name"), > ... Variable(name="issue", type="str", description="Issue type") > ... ], > ... commit_comment="Initial version" > ... ) > ``` #### list_versions() List all versions of this prompt template. - Returns: A list of versions for this template. - Return type: List[PromptTemplateVersion] - Raises: > [!NOTE] Examples > ``` > >>> versions = template.list_versions() > ``` #### get_version(prompt_template_version_id) Get a specific version of this prompt template. - Parameters: prompt_template_version_id ( str ) – The ID of the version to retrieve. - Returns: The requested version. - Return type: PromptTemplateVersion - Raises: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> template = dr.genai.PromptTemplate.get("507f1f77bcf86cd799439011") > >>> version = template.get_version("507f1f77bcf86cd799439012") > ``` #### get_latest_version() Get the latest version of this prompt template. - Returns: The latest version, or None if no versions exist. - Return type: Optional[PromptTemplateVersion] - Raises: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> template = dr.genai.PromptTemplate.get("507f1f77bcf86cd799439011") > >>> latest = template.get_latest_version() > >>> if latest: > ... print(f"Version {latest.version}") > ``` ### class datarobot.models.genai.prompt_template.PromptTemplateVersion A specific version of a prompt template. - Variables: #### classmethod get(prompt_template_id, prompt_template_version_id) Get a specific prompt template version by ID. - Parameters: - Returns: The requested prompt template version. - Return type: PromptTemplateVersion - Raises: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> version = dr.genai.PromptTemplateVersion.get("prompt_template_id", "prompt_template_version_id") > ``` #### classmethod create(prompt_template_id, prompt_text, variables=None, commit_comment='') Create a new version of a prompt template. - Parameters: - Returns: The created version. - Return type: PromptTemplateVersion - Raises: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> from datarobot.models.genai.prompt_template import Variable > >>> version = dr.genai.PromptTemplateVersion.create( > ... prompt_template_id="507f1f77bcf86cd799439011", > ... prompt_text="Hello {{ name }}, regarding {{ issue }}", > ... variables=[ > ... Variable(name="name", type="str", description="Customer name"), > ... Variable(name="issue", type="str", description="Issue type") > ... ], > ... commit_comment="Initial version" > ... ) > ``` #### classmethod list(prompt_template_id) List all versions of a prompt template. - Parameters: prompt_template_id ( str ) – The ID of the prompt template. - Returns: A list of versions for the template. - Return type: List[PromptTemplateVersion] - Raises: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> versions = dr.genai.PromptTemplateVersion.list( > ... prompt_template_id="507f1f77bcf86cd799439011" > ... ) > ``` #### classmethod list_all(prompt_template_ids=None) List prompt template versions across multiple templates. - Parameters: prompt_template_ids ( List[str] , optional ) – Filter versions to only those belonging to these prompt template IDs. If not provided, returns versions for all accessible templates. - Returns: A list of prompt template versions. - Return type: List[PromptTemplateVersion] - Raises: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> # List all versions across all templates > >>> versions = dr.genai.PromptTemplateVersion.list_all() > >>> # List versions for specific templates > >>> versions = dr.genai.PromptTemplateVersion.list_all( > ... prompt_template_ids=["template_id_1", "template_id_2"] > ... ) > ``` #### render(variables=None, **kwargs) Render the prompt text by substituting variables. - Parameters: - Returns: Rendered prompt text with variables substituted. - Return type: str - Raises: ValueError – If required variables are missing. > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> version = dr.genai.PromptTemplateVersion.get("template_id", "version_id") > >>> version.render(name="Alice", issue="billing") > 'Hello Alice, regarding billing...' > ``` #### to_fstring() Convert the prompt text from {{ variable }} format to Python f-string {variable} format. This method transforms template placeholders from double-brace format ({{ variable }}) to single-brace format ({variable}), making the template compatible with Python f-string syntax. Whitespace around variable names is automatically stripped. - Returns: Prompt text with variables in f-string format. - Return type: str - Raises: ValueError – If prompt_text is None. > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> version = dr.genai.PromptTemplateVersion.get("template_id", "version_id") > >>> # Original: "Hello {{ name }}, regarding {{ issue }}" > >>> fstring_template = version.to_fstring() > >>> # Result: "Hello {name}, regarding {issue}" > >>> # Can now be used with f-string evaluation: > >>> name = "Alice" > >>> issue = "billing" > >>> result = eval(f'f"{fstring_template}"') > ``` ### class datarobot.models.genai.prompt_template.Variable A variable used in prompt templates. - Variables: --- # LLM compliance tests URL: https://docs.datarobot.com/en/docs/api/reference/sdk/gen-testing.html # AI Robustness Tests ### class datarobot.models.genai.insights_configuration.InsightsConfiguration Configuration information for a specific insight. - Variables: #### classmethod from_data(data) Properly convert composition classes. - Return type: InsightsConfiguration ### class datarobot.models.genai.cost_metric_configurations.LLMCostConfiguration Cost configuration for a specific LLM model; used for cost metric calculation. Price-per-token is price/reference token count. - Variables: ### class datarobot.models.genai.cost_metric_configurations.CostMetricConfiguration Cost metric configuration for a use case. - Variables: #### classmethod get(cost_metric_configuration_id) Get cost metric configuration by ID. - Return type: CostMetricConfiguration #### update(cost_metric_configurations, name=None) Update the cost configurations. - Return type: CostMetricConfiguration #### classmethod create(use_case_id, playground_id, name, cost_metric_configurations) Create a new cost metric configuration. - Return type: CostMetricConfiguration #### delete() Delete the cost metric configuration. - Return type: None ### class datarobot.models.genai.evaluation_dataset_configuration.EvaluationDatasetConfiguration An evaluation dataset configuration used to evaluate the performance of LLMs. - Variables: #### classmethod get(id) Get an evaluation dataset configuration by ID. - Parameters: id ( str ) – The evaluation dataset configuration ID to fetch. - Returns: evaluation_dataset_configuration – The evaluation dataset configuration. - Return type: EvaluationDatasetConfiguration #### classmethod list(use_case_id, playground_id, evaluation_dataset_configuration_id=None, offset=0, limit=100, sort=None, search=None, correctness_only=False, completed_only=False) List all evaluation dataset configurations for a Use Case. - Parameters: - Returns: evaluation_dataset_configurations – A list of evaluation dataset configurations. - Return type: List[EvaluationDatasetConfiguration] #### classmethod create(name, use_case_id, dataset_id, prompt_column_name, playground_id, is_synthetic_dataset=False, response_column_name=None, tool_calls_column_name=None, agent_goals_column_name=None) Create an evaluation dataset configuration for an existing dataset. - Parameters: - Returns: evaluation_dataset_configuration – The created evaluation dataset configuration. - Return type: EvaluationDatasetConfiguration #### update(name=None, dataset_id=None, prompt_column_name=None, response_column_name=None, tool_calls_column_name=None, agent_goals_column_name=None) Update the evaluation dataset configuration. - Parameters: - Returns: evaluation_dataset_configuration – The updated evaluation dataset configuration. - Return type: EvaluationDatasetConfiguration #### delete() Delete the evaluation dataset configuration. - Return type: None ### class datarobot.models.genai.evaluation_dataset_metric_aggregation.EvaluationDatasetMetricAggregation Information about the aggregated metric results for one metric and one evaluation dataset. This class can list already computed aggregations or start the job computing the aggregations. Jobs will prompt an LLM blueprint or agentic workflow, compute metrics and aggregate the results across prompts. - Variables: #### classmethod create(chat_name, llm_blueprint_ids, evaluation_dataset_configuration_id, insights_configuration) Create a new evaluation dataset metric aggregation job. The job will run the specified metric for the specified LLM blueprint IDs using the prompt-response pairs in the evaluation dataset. - Parameters: - Returns: The ID of the evaluation dataset metric aggregation job. - Return type: str #### classmethod list(llm_blueprint_ids=None, chat_ids=None, evaluation_dataset_configuration_ids=None, metric_names=None, aggregation_types=None, current_configuration_only=False, sort=None, offset=0, limit=100, non_errored_only=True) List evaluation dataset metric aggregations. The results will be filtered by the provided LLM blueprint IDs and chat IDs. - Parameters: - Returns: A list of evaluation dataset metric aggregations. - Return type: List[EvaluationDatasetMetricAggregation] #### classmethod delete(llm_blueprint_ids=None, chat_ids=None) Delete the associated evaluation dataset metric aggregations. Either llm_blueprint_ids or chat_ids must be provided. If both are provided, only results matching both will be removed. - Parameters: - Return type: None ### class datarobot.models.genai.synthetic_evaluation_dataset_generation.SyntheticEvaluationDataset A synthetically generated evaluation dataset for LLMs. - Variables: #### classmethod create(llm_id, vector_database_id, llm_settings=None, dataset_name=None, language=None) Create a synthetic evaluation dataset generation job. This will create a synthetic dataset to be used for evaluation of a language model. - Parameters: - Returns: SyntheticEvaluationDataset - Return type: Reference to the synthetic evaluation dataset that was created. ### class datarobot.models.genai.sidecar_model_metric.SidecarModelMetricValidation A sidecar model metric validation for LLMs. - Variables: #### classmethod create(deployment_id, name, prediction_timeout, model_id=None, use_case_id=None, playground_id=None, prompt_column_name=None, target_column_name=None, response_column_name=None, citation_prefix_column_name=None, expected_response_column_name=None) Create a sidecar model metric validation. - Parameters: - Returns: The created sidecar model metric validation. - Return type: SidecarModelMetricValidation #### classmethod list(use_case_ids=None, offset=None, limit=None, search=None, sort=None, completed_only=True, deployment_id=None, model_id=None, prompt_column_name=None, target_column_name=None, citation_prefix_column_name=None) List sidecar model metric validations. - Parameters: - Returns: The list of sidecar model metric validations. - Return type: List[SidecarModelMetricValidation] #### classmethod get(validation_id) Get a sidecar model metric validation by ID. - Parameters: validation_id ( str ) – The ID of the validation to get. - Returns: The sidecar model metric validation. - Return type: SidecarModelMetricValidation #### revalidate() Revalidate the sidecar model metric validation. - Returns: The sidecar model metric validation. - Return type: SidecarModelMetricValidation #### update(name=None, prompt_column_name=None, target_column_name=None, response_column_name=None, expected_response_column_name=None, citation_prefix_column_name=None, deployment_id=None, model_id=None, prediction_timeout=None) Update the sidecar model metric validation. - Parameters: - Returns: The updated sidecar model metric validation. - Return type: SidecarModelMetricValidation #### delete() Delete the sidecar model metric validation. - Return type: None ### class datarobot.models.genai.llm_test_configuration.LLMTestConfiguration Metadata for a DataRobot GenAI LLM test configuration. - Variables: #### classmethod create(name, dataset_evaluations, llm_test_grading_criteria, use_case=None, description=None) Creates a new LLM test configuration. - Parameters: - Returns: llm_test_configuration – The created LLM test configuration. - Return type: LLMTestConfiguration #### classmethod get(llm_test_configuration) Retrieve a single LLM Test configuration. - Parameters: llm_test_configuration ( LLMTestConfiguration or str ) – The LLM test configuration to retrieve, either LLMTestConfiguration or LLMTestConfiguration ID. - Returns: llm_test_configuration – The requested LLM Test configuration. - Return type: LLMTestConfiguration #### classmethod list(use_case=None, test_config_type=None) List all LLM test configurations available to the user. If a Use Case is specified, results are restricted to only those configurations associated with that Use Case. - Parameters: - Returns: llm_test_configurations – Returns a list of LLM test configurations. - Return type: list[LLMTestConfiguration] #### update(name=None, description=None, dataset_evaluations=None, llm_test_grading_criteria=None) Update the LLM test configuration. - Parameters: - Returns: llm_test_configuration – The updated LLM test configuration. - Return type: LLMTestConfiguration #### delete() Delete a single LLM test configuration. - Return type: None ### class datarobot.models.genai.llm_test_configuration.LLMTestConfigurationSupportedInsights Metadata for a DataRobot GenAI LLM test configuration supported insights. - Variables: supported_insight_configurations ( list[InsightsConfiguration] ) – The supported insights for LLM test configurations. #### classmethod list(use_case=None, playground=None) List all supported insights for a LLM test configuration. - Parameters: - Returns: llm_test_configuration_supported_insights – Returns the supported insight configurations for the LLM test configuration. - Return type: LLMTestConfigurationSupportedInsights ### class datarobot.models.genai.llm_test_result.LLMTestResult Metadata for a DataRobot GenAI LLM test result. - Variables: #### classmethod create(llm_test_configuration, llm_blueprint) Create a new LLMTestResult. This executes the LLM test configuration using the specified LLM blueprint. To check the status of the LLM test, use the LLMTestResult.get method with the returned ID. - Parameters: - Returns: llm_test_result – The created LLM test result. - Return type: LLMTestResult #### classmethod get(llm_test_result) Retrieve a single LLM test result. - Parameters: llm_test_result ( LLMTestResult or str ) – The LLM test result to retrieve, specified by either LLM test result or test ID. - Returns: llm_test_result – The requested LLM test result. - Return type: LLMTestResult #### classmethod list(llm_test_configuration=None, llm_blueprint=None) List all LLM test results available to the user. If the LLM test configuration or LLM blueprint is specified, results are restricted to only those LLM test results associated with the LLM test configuration or LLM blueprint. - Parameters: - Returns: llm_test_results – Returns a list of LLM test results. - Return type: List[LLMTestResult] #### delete() Delete a single LLM test result. - Return type: None ### class datarobot.models.genai.llm_test_configuration.DatasetEvaluation Metadata for a DataRobot GenAI dataset evaluation. - Variables: ### class datarobot.models.genai.llm_test_result.InsightEvaluationResult Metadata for a DataRobot GenAI insight evaluation result. - Variables: ### class datarobot.models.genai.llm_test_configuration.OOTBDatasetDict ### class datarobot.models.genai.llm_test_configuration.DatasetEvaluationRequestDict ### class datarobot.models.genai.llm_test_configuration.DatasetEvaluationDict ### class datarobot.models.genai.nemo_configuration.NemoConfiguration Configuration for the Nemo Pipeline. - Variables: #### classmethod get(playground) Get the Nemo configuration for a playground. - Parameters: playground ( str or Playground ) – The playground to get the configuration for - Returns: The Nemo configuration for the playground. - Return type: NemoConfiguration #### classmethod upsert(playground, blocked_terms_file_contents, prompt_pipeline_metric_name=None, prompt_pipeline_files=None, prompt_llm_configuration=None, prompt_moderation_configuration=None, prompt_pipeline_template_id=None, response_pipeline_metric_name=None, response_pipeline_files=None, response_llm_configuration=None, response_moderation_configuration=None, response_pipeline_template_id=None) Create or update the nemo configuration for a playground. - Parameters: - Returns: The Nemo configuration for the playground. - Return type: NemoConfiguration ### class datarobot.models.genai.llm_test_configuration.OOTBDataset Metadata for a DataRobot GenAI out-of-the-box LLM compliance test dataset. - Variables: #### classmethod list() List all out-of-the-box datasets available to the user. - Returns: ootb_datasets – Returns a list of out-of-the-box datasets. - Return type: list[OOTBDataset] ### class datarobot.models.genai.llm_test_configuration.NonOOTBDataset Metadata for a DataRobot GenAI non out-of-the-box (OOTB) LLM compliance test dataset. #### classmethod list(use_case=None) List all non out-of-the-box datasets available to the user. - Returns: non_ootb_datasets – Returns a list of non out-of-the-box datasets. - Return type: list[NonOOTBDataset] ### class datarobot.models.genai.metric_insights.MetricInsights Metric insights for playground. #### classmethod list(playground, llm_blueprint_ids=None, with_aggregation_types_only=False, production_only=False, completed_only=False) Get metric insights for playground. - Parameters: - Returns: insights – Metric insights for playground. - Return type: list[InsightsConfiguration] #### classmethod copy_to_playground(source_playground, target_playground, add_to_existing=True, with_evaluation_datasets=False) Copy metric insights from one playground to another. - Parameters: - Return type: None ### class datarobot.models.genai.ootb_metric_configuration.PlaygroundOOTBMetricConfiguration OOTB metric configurations for a playground. - Variables: ootb_metric_configurations ( (List[OOTBMetricConfigurationResponse]) : The list of the OOTB metric configurations. ) #### classmethod get(playground_id) Get OOTB metric configurations for the playground. - Return type: PlaygroundOOTBMetricConfiguration #### classmethod create(playground_id, ootb_metric_configurations) Create a new OOTB metric configurations. - Return type: PlaygroundOOTBMetricConfiguration ### class datarobot.models.genai.evaluation_dataset_utils.ReferenceToolCall Reference tool call for an evaluation dataset. This is a convenience stand in for the Ragas ToolCall class. #### json() Convert the tool call to a JSON string. - Return type: str #### classmethod from_json(json_str) Create a ReferenceToolCall object from a JSON string. - Return type: ReferenceToolCall ### class datarobot.models.genai.evaluation_dataset_utils.ReferenceToolCalls Utility for creating a list of reference tool calls for an evaluation dataset. This class represents a list of tool calls for a single row in the evaluation dataset. Example usage: > df = pandas.DataFrame() > tool_calls_1 = ReferenceToolCalls([ > ReferenceToolCall(name=”get_weather”, args={“location”: “New York”}), > ReferenceToolCall(name=”get_news”, args={“topic”: “technology”}) > ]) > tool_calls_2 = ReferenceToolCalls([ > ReferenceToolCall(name=”get_weather”, args={“location”: “Los Angeles”}), > ReferenceToolCall(name=”get_news”, args={“topic”: “sports”}) > ]) > df[‘prompts’] = [‘what is the weather for the tech conference in NYC?’, > ‘what is the weather in LA?, and will it affect the game?’] > df[‘reference_tool_calls’] = [tool_calls_1.json(), tool_calls_2.json()] #### classmethod from_json(json_str) Create a ReferenceToolCalls object from a JSON string. - Return type: ReferenceToolCalls --- # Vector databases URL: https://docs.datarobot.com/en/docs/api/reference/sdk/gen-vector-databases.html # Vector Databases ### class datarobot.models.genai.vector_database.CustomModelVectorDatabaseValidation Validation record checking the ability of the deployment to serve as a vector database. - Variables: ### class datarobot.models.genai.vector_database.SupportedEmbeddings All supported embedding models including the recommended default model. - Variables: ### class datarobot.models.genai.vector_database.SupportedTextChunkings Supported text chunking configurations which includes a set of recommended chunking parameters for each supported embedding model. - Variables: text_chunking_configs – All supported text chunking configurations. ### class datarobot.models.genai.vector_database.VectorDatabase Metadata for a DataRobot vector database accessible to the user. - Variables: #### classmethod get_supported_embeddings(dataset_id=None, use_case=None) Get all supported and the recommended embedding models. - Parameters: - Returns: supported_embeddings – The supported embedding models. - Return type: SupportedEmbeddings #### submit_export_dataset_job() Submit the vector database dataset export job. - Returns: result – The result of the vector database dataset export job containing the exported dataset id. - Return type: VectorDatabaseDatasetExportJob #### classmethod get_supported_retrieval_settings() Get supported retrieval settings. - Returns: supported_retrieval_settings – The supported retriever settings. - Return type: SupportedRetrievalSettings #### classmethod create(dataset_id, chunking_parameters=None, use_case=None, name=None, parent_vector_database_id=None, update_llm_blueprints=None, update_deployments=None, external_vector_database_connection=None, metadata_dataset_id=None, metadata_combination_strategy=None) Create a new vector database. - Parameters: - Returns: vector database – The created vector database with execution status ‘new’. - Return type: VectorDatabase #### classmethod create_from_custom_model(name, use_case=None, validation_id=None, prompt_column_name=None, target_column_name=None, deployment_id=None, model_id=None) Create a new vector database from validated custom model deployment. - Parameters: - Returns: vector database – The created vector database. - Return type: VectorDatabase #### classmethod get(vector_database_id) Retrieve a single vector database. - Parameters: vector_database_id ( str ) – The ID of the vector database you want to retrieve. - Returns: vector database – The requested vector database. - Return type: VectorDatabase #### classmethod list(use_case=None, playground=None, search=None, sort=None, completed_only=None) List all vector databases associated with a specific use case available to the user. - Parameters: - Returns: vectorbases – A list of vector databases available to the user. - Return type: list[VectorDatabase] #### update(name=None, credential_id=None) Update the vector database. - Parameters: - Returns: vector database – The updated vector database. - Return type: VectorDatabase #### update_connected(dataset_id, metadata_dataset_id=None, metadata_combination_strategy=None) Update a connected vector database. - Parameters: - Returns: vector database – The updated vector database. - Return type: VectorDatabase #### delete() Delete the vector database. - Return type: None #### classmethod get_supported_text_chunkings() Get all supported text chunking configurations which includes a set of recommended chunking parameters for each supported embedding model. - Returns: supported_text_chunkings – The supported text chunking configurations. - Return type: SupportedTextChunkings #### download_text_and_embeddings_asset(file_path=None, part=None) Download a parquet file with text chunks and corresponding embeddings created by a vector database. - Parameters: - Return type: None #### send_to_custom_model_workshop(maximum_memory=None, resource_bundle_id=None, replicas=None, network_egress_policy=None) Create a new CustomModelVersion for this vector database. - Parameters: - Return type: CustomModelVersion #### deploy(default_prediction_server_id=None, prediction_environment_id=None, credential_id=None, maximum_memory=None, resource_bundle_id=None, replicas=None, network_egress_policy=None) Create a new Custom Model for this vector database and deploy it on a new Deployment. - Parameters: - Return type: Deployment ### class datarobot.models.genai.vector_database.SupportedRetrievalSetting A single supported retrieval setting. - Variables: ### class datarobot.models.genai.vector_database.VectorDatabaseDatasetExportJob Response for the vector database dataset export job. - Variables: ### class datarobot.models.genai.chat_prompt.Citation Citation for documents retrieved from a vector database. - Variables: ### class datarobot.models.genai.llm_blueprint.VectorDatabaseSettings Settings for a DataRobot GenAI vector database associated with an LLM blueprint. - Variables: ### class datarobot.models.genai.vector_database.ChunkingParameters Parameters defining how documents are split and embedded. - Variables: --- # Python API client URL: https://docs.datarobot.com/en/docs/api/reference/sdk/index.html # Python API client The DataRobot Python client is a library for working with the DataRobot API. To access other clients and additional information about DataRobot’s APIs, visit the [API documentation home](https://docs.datarobot.com/en/docs/api/index.html). The reference documentation outlines the functionality supported by the Python client. For information about specific endpoints, select a topic from the table of contents on the left. To get started with the Python client, reference DataRobot’s [API Quickstart guide](https://docs.datarobot.com/en/docs/api/api-quickstart/index.html). This guide outlines how to configure your environment to use the API. You can learn about use cases and experiment with code examples using the Python client in the [API user guide](https://docs.datarobot.com/en/docs/api/guide/index.html). In addition to code examples and use cases, you can browse [AI accelerators](https://docs.datarobot.com/en/docs/api/accelerators/index.html). AI accelerators are designed to help speed up model experimentation, development, and production readiness using the DataRobot API. They codify and package data science expertise in building and delivering successful machine learning projects into repeatable, code-first workflows and modular building blocks. --- # Insights URL: https://docs.datarobot.com/en/docs/api/reference/sdk/insights.html # Insights ## Model Performance Insights ### class datarobot.insights.RocCurve Class for ROC Curve calculations. Use the standard methods of BaseInsight to compute and retrieve: compute, create, list, get. Usage example: > ``pythonfrom datarobot.insights import RocCurve > RocCurve.compute("67643b2d87bb4954d7917323", data_slice_id="6764389b4bdd48581485a58b")RocCurve.get("67643b2d87bb4954d7917323", data_slice_id="6764389b4bdd48581485a58b")RocCurve.list("67643b2d87bb4954d7917323") > [, ...] > RocCurve.list("67643b2d87bb4954d7917323")[0].roc_points > [{'accuracy': 0.539375, 'f1_score': 0.0, 'false_negative_score': 737, 'true_negative_score': 863, ...}] > `` #### property kolmogorov_smirnov_metric : float Kolmogorov-Smirnov metric for the ROC curve values #### property auc : float AUC metric for the ROC curve values #### property positive_class_predictions : List[float] List of positive class prediction values for the ROC curve #### property negative_class_predictions : List[float] List of negative class prediction values for the ROC curve #### property roc_points : List[Dict[str, int | float]] List of ROC values for the ROC curve #### classmethod compute(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, **kwargs) Submit an insight compute request. You can use create if you want to wait synchronously for the completion of the job. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Status check job entity for the asynchronous insight calculation. - Return type: StatusCheckJob #### classmethod create(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, max_wait=600, **kwargs) Create an insight and wait for completion. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Entity of the newly or already computed insights. - Return type: Self #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Override from_server_data to handle paginated responses - Return type: Self #### classmethod get(entity_id, source=INSIGHTS_SOURCES.VALIDATION, quick_compute=None, **kwargs) Return the first matching insight based on the entity id and kwargs. - Parameters: - Returns: Previously computed insight. - Return type: Self #### get_uri() This should define the URI to their browser based interactions - Return type: str #### classmethod list(entity_id) List all generated insights. - Parameters: entity_id ( str ) – The ID of the entity queried for listing all generated insights. - Returns: List of newly or previously computed insights. - Return type: List[Self] #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### sort(key_name) Sorts insights data - Return type: None ### class datarobot.insights.LiftChart Class for Lift Chart calculations. Use the standard methods of BaseInsight to compute and retrieve: compute, create, list, get. Usage example: > ``pythonfrom datarobot.insights import LiftChart > LiftChart.compute("67643b2d87bb4954d7917323", data_slice_id="6764389b4bdd48581485a58b")LiftChart.list("67643b2d87bb4954d7917323") > [, ... ] > LiftChart.get("67643b2d87bb4954d7917323", data_slice_id="6764389b4bdd48581485a58b").bins > [{'actual': 0.4, 'predicted': 0.22727272727272724, 'bin_weight': 5.0}, ... ] > `` #### property bins : List[Dict[str, Any]] Lift chart bins. #### classmethod compute(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, **kwargs) Submit an insight compute request. You can use create if you want to wait synchronously for the completion of the job. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Status check job entity for the asynchronous insight calculation. - Return type: StatusCheckJob #### classmethod create(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, max_wait=600, **kwargs) Create an insight and wait for completion. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Entity of the newly or already computed insights. - Return type: Self #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Override from_server_data to handle paginated responses - Return type: Self #### classmethod get(entity_id, source=INSIGHTS_SOURCES.VALIDATION, quick_compute=None, **kwargs) Return the first matching insight based on the entity id and kwargs. - Parameters: - Returns: Previously computed insight. - Return type: Self #### get_uri() This should define the URI to their browser based interactions - Return type: str #### classmethod list(entity_id) List all generated insights. - Parameters: entity_id ( str ) – The ID of the entity queried for listing all generated insights. - Returns: List of newly or previously computed insights. - Return type: List[Self] #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### sort(key_name) Sorts insights data - Return type: None ### class datarobot.insights.Residuals Class for Residuals calculations. Use the standard methods of BaseInsight to compute and retrieve: compute, create, list, get. Usage example: > ``pythonfrom datarobot.insights import Residuals > Residuals.list("672e32de69b0b676ced54d9c") > [] > Residuals.compute("672e32de69b0b676ced54d9c", data_slice_id="677ae1249695103ba9feff97")Residuals.list("672e32de69b0b676ced54d9c") > [,] > Residuals.get("672e32de69b0b676ced54d9c", data_slice_id="677ae1249695103ba9feff97")Residuals.get("672e32de69b0b676ced54d9c", data_slice_id="677ae1249695103ba9feff97").histogram > [{'interval_start': -33.37288135593221, 'interval_end': -32.525000000000006, 'occurrences': 1}, ...] > `` #### property histogram : List[Dict[str, int | float]] Residuals histogram. #### property coefficient_of_determination : float Coefficient of determination. #### property residual_mean : float Residual mean. #### property standard_deviation : float Standard deviation. #### property chart_data : List[List[float]] The rows of Residuals chart data in [actual, predicted, residual, row number] form. #### classmethod compute(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, **kwargs) Submit an insight compute request. You can use create if you want to wait synchronously for the completion of the job. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Status check job entity for the asynchronous insight calculation. - Return type: StatusCheckJob #### classmethod create(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, max_wait=600, **kwargs) Create an insight and wait for completion. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Entity of the newly or already computed insights. - Return type: Self #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Override from_server_data to handle paginated responses - Return type: Self #### classmethod get(entity_id, source=INSIGHTS_SOURCES.VALIDATION, quick_compute=None, **kwargs) Return the first matching insight based on the entity id and kwargs. - Parameters: - Returns: Previously computed insight. - Return type: Self #### get_uri() This should define the URI to their browser based interactions - Return type: str #### classmethod list(entity_id) List all generated insights. - Parameters: entity_id ( str ) – The ID of the entity queried for listing all generated insights. - Returns: List of newly or previously computed insights. - Return type: List[Self] #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### sort(key_name) Sorts insights data - Return type: None ## SHAP Insights ### class datarobot.insights.ShapMatrix Class for SHAP Matrix calculations. Use the standard methods of BaseInsight to compute and retrieve: compute, create, list, get. #### property matrix : Any SHAP matrix values. #### property base_value : float SHAP base value for the matrix values #### property columns : List[str] List of columns associated with the SHAP matrix #### property link_function : str Link function used to generate the SHAP matrix #### classmethod compute(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, **kwargs) Submit an insight compute request. You can use create if you want to wait synchronously for the completion of the job. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Status check job entity for the asynchronous insight calculation. - Return type: StatusCheckJob #### classmethod create(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, max_wait=600, **kwargs) Create an insight and wait for completion. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Entity of the newly or already computed insights. - Return type: Self #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Override from_server_data to handle paginated responses - Return type: Self #### classmethod get(entity_id, source=INSIGHTS_SOURCES.VALIDATION, quick_compute=None, **kwargs) Return the first matching insight based on the entity id and kwargs. - Parameters: - Returns: Previously computed insight. - Return type: Self #### classmethod get_as_csv(entity_id, **kwargs) Retrieve a specific insight represented in CSV format. - Parameters: - Returns: The retrieved insight. - Return type: str #### classmethod get_as_dataframe(entity_id, **kwargs) Retrieve a specific insight represented as a pandas DataFrame. - Parameters: - Returns: The retrieved insight. - Return type: DataFrame #### get_uri() This should define the URI to their browser based interactions - Return type: str #### classmethod list(entity_id) List all generated insights. - Parameters: entity_id ( str ) – The ID of the entity queried for listing all generated insights. - Returns: List of newly or previously computed insights. - Return type: List[Self] #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### sort(key_name) Sorts insights data - Return type: None ### class datarobot.insights.ShapPreview Class for SHAP Preview calculations. Use the standard methods of BaseInsight to compute and retrieve: compute, create, list, get. #### property previews : List[Dict[str, Any]] SHAP preview values. - Returns: preview – A list of the ShapPreview values for each row. - Return type: List[Dict[str , Any]] #### property previews_count : int The number of shap preview rows. - Return type: int #### classmethod get(entity_id, source=INSIGHTS_SOURCES.VALIDATION, quick_compute=None, prediction_filter_row_count=None, prediction_filter_percentiles=None, prediction_filter_operand_first=None, prediction_filter_operand_second=None, prediction_filter_operator=None, feature_filter_count=None, feature_filter_name=None, **kwargs) Return the first matching ShapPreview insight based on the entity id and kwargs. - Parameters: - Returns: List of newly or already computed insights. - Return type: List[Any] #### classmethod compute(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, **kwargs) Submit an insight compute request. You can use create if you want to wait synchronously for the completion of the job. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Status check job entity for the asynchronous insight calculation. - Return type: StatusCheckJob #### classmethod create(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, max_wait=600, **kwargs) Create an insight and wait for completion. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Entity of the newly or already computed insights. - Return type: Self #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Override from_server_data to handle paginated responses - Return type: Self #### get_uri() This should define the URI to their browser based interactions - Return type: str #### classmethod list(entity_id) List all generated insights. - Parameters: entity_id ( str ) – The ID of the entity queried for listing all generated insights. - Returns: List of newly or previously computed insights. - Return type: List[Self] #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### sort(key_name) Sorts insights data - Return type: None ### class datarobot.insights.ShapImpact Class for SHAP Impact calculations. Use the standard methods of BaseInsight to compute and retrieve: compute, create, list, get. #### classmethod compute(entity_id, source=INSIGHTS_SOURCES.TRAINING, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, **kwargs) Submit an insight compute request. You can use create if you want to wait synchronously for the completion of the job. - Parameters: - Returns: Status check job entity for the asynchronous insight calculation. - Return type: StatusCheckJob #### classmethod create(entity_id, source=INSIGHTS_SOURCES.TRAINING, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, max_wait=600, **kwargs) Create an insight and wait for completion. - Parameters: - Returns: Entity of the newly or already computed insights. - Return type: Self #### sort(key_name='-impact_normalized') Sorts insights data by key name. - Parameters: key_name ( str ) – item key name to sort data. One of ‘feature_name’, ‘impact_normalized’ or ‘impact_unnormalized’. Starting with ‘-’ reverses sort order. Default ‘-impact_normalized’ - Return type: None #### property shap_impacts : List[List[Any]] SHAP impact values - Returns: A list of the SHAP impact values - Return type: shap impacts #### property base_value : List[float] A list of base prediction values #### property capping : Dict[str, Any] | None Capping for the models in the blender #### property link : str | None Shared link function of the models in the blender #### property row_count : int | None Number of SHAP impact rows. This is deprecated. #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Override from_server_data to handle paginated responses - Return type: Self #### classmethod get(entity_id, source=INSIGHTS_SOURCES.VALIDATION, quick_compute=None, **kwargs) Return the first matching insight based on the entity id and kwargs. - Parameters: - Returns: Previously computed insight. - Return type: Self #### get_uri() This should define the URI to their browser based interactions - Return type: str #### classmethod list(entity_id) List all generated insights. - Parameters: entity_id ( str ) – The ID of the entity queried for listing all generated insights. - Returns: List of newly or previously computed insights. - Return type: List[Self] #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None ### class datarobot.insights.ShapDistributions Class for SHAP Distributions calculations. Use the standard methods of BaseInsight to compute and retrieve: compute, create, list, get. #### property features : List[Dict[str, Any]] SHAP feature values - Returns: features – A list of the ShapDistributions values for each row - Return type: List[Dict[str , Any]] #### property total_features_count : int Number of shap distributions features - Return type: int #### classmethod compute(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, **kwargs) Submit an insight compute request. You can use create if you want to wait synchronously for the completion of the job. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Status check job entity for the asynchronous insight calculation. - Return type: StatusCheckJob #### classmethod create(entity_id, source=INSIGHTS_SOURCES.VALIDATION, data_slice_id=None, external_dataset_id=None, entity_type=ENTITY_TYPES.DATAROBOT_MODEL, quick_compute=None, max_wait=600, **kwargs) Create an insight and wait for completion. May be overridden by insight subclasses to accept additional parameters. - Parameters: - Returns: Entity of the newly or already computed insights. - Return type: Self #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Override from_server_data to handle paginated responses - Return type: Self #### classmethod get(entity_id, source=INSIGHTS_SOURCES.VALIDATION, quick_compute=None, **kwargs) Return the first matching insight based on the entity id and kwargs. - Parameters: - Returns: Previously computed insight. - Return type: Self #### get_uri() This should define the URI to their browser based interactions - Return type: str #### classmethod list(entity_id) List all generated insights. - Parameters: entity_id ( str ) – The ID of the entity queried for listing all generated insights. - Returns: List of newly or previously computed insights. - Return type: List[Self] #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### sort(key_name) Sorts insights data - Return type: None ## Types ### class datarobot.models.RocCurveEstimatedMetric Typed dict for estimated metric ### class datarobot.models.AnomalyAssessmentRecordMetadata Typed dict for record metadata ### class datarobot.models.AnomalyAssessmentPreviewBin Typed dict for preview bin ### class datarobot.models.ShapleyFeatureContribution Typed dict for shapley feature contribution ### class datarobot.models.AnomalyAssessmentDataPoint Typed dict for data points ### class datarobot.models.RegionExplanationsData Typed dict for region explanations ## Anomaly assessment ### class datarobot.models.anomaly_assessment.AnomalyAssessmentRecord Object which keeps metadata about anomaly assessment insight for the particular subset, backtest and series and the links to proceed to get the anomaly assessment data. Added in version v2.25. - Variables: #### classmethod list(project_id, model_id, backtest=None, source=None, series_id=None, limit=100, offset=0, with_data_only=False) Retrieve the list of the anomaly assessment records for the project and model. Output can be filtered and limited. - Parameters: - Returns: The anomaly assessment record. - Return type: AnomalyAssessmentRecord #### classmethod compute(project_id, model_id, backtest, source, series_id=None) Request anomaly assessment insight computation on the specified subset. - Parameters: - Returns: The anomaly assessment record. - Return type: AnomalyAssessmentRecord #### delete() Delete anomaly assessment record with preview and explanations. - Return type: None #### get_predictions_preview() Retrieve aggregated predictions statistics for the anomaly assessment record. - Return type: AnomalyAssessmentPredictionsPreview #### get_latest_explanations() Retrieve latest predictions along with shap explanations for the most anomalous records. - Return type: AnomalyAssessmentExplanations #### get_explanations(start_date=None, end_date=None, points_count=None) Retrieve predictions along with shap explanations for the most anomalous records in the specified date range/for defined number of points. Two out of three parameters: start_date, end_date or points_count must be specified. - Parameters: - Return type: AnomalyAssessmentExplanations #### get_explanations_data_in_regions(regions, prediction_threshold=0.0) Get predictions along with explanations for the specified regions, sorted by predictions in descending order. - Parameters: - Returns: dict in a form of {‘explanations’: explanations, ‘shap_base_value’: shap_base_value} - Return type: RegionExplanationsData ### class datarobot.models.anomaly_assessment.AnomalyAssessmentExplanations Object which keeps predictions along with shap explanations for the most anomalous records in the specified date range/for defined number of points. Added in version v2.25. - Variables: > [!NOTE] Notes > `DataPoint` contains: > > shap_explanation > : None or an array of up to 10 ShapleyFeatureContribution objects. > Only rows with the highest anomaly scores have Shapley explanations calculated. > Value is None if prediction is lower than prediction_threshold. > timestamp > (str) : ISO-formatted timestamp for the row. > prediction > (float) : The output of the model for this row. > > `ShapleyFeatureContribution` contains: > > feature_value > (str) : the feature value for this row. First 50 characters are returned. > strength > (float) : the shap value for this feature and row. > feature > (str) : the feature name. #### classmethod get(project_id, record_id, start_date=None, end_date=None, points_count=None) Retrieve predictions along with shap explanations for the most anomalous records in the specified date range/for defined number of points. Two out of three parameters: start_date, end_date or points_count must be specified. - Parameters: - Return type: AnomalyAssessmentExplanations ### class datarobot.models.anomaly_assessment.AnomalyAssessmentPredictionsPreview Aggregated predictions over time for the corresponding anomaly assessment record. Intended to find the bins with highest anomaly scores. Added in version v2.25. - Variables: > [!NOTE] Notes > `PreviewBin` contains: > > start_date > (str) : the ISO-formatted datetime of the start of the bin. > end_date > (str) : the ISO-formatted datetime of the end of the bin. > avg_predicted > (float or None) : the average prediction of the model in the bin. None if > there are no entries in the bin. > max_predicted > (float or None) : the maximum prediction of the model in the bin. None if > there are no entries in the bin. > frequency > (int) : the number of the rows in the bin. #### classmethod get(project_id, record_id) Retrieve aggregated predictions over time. - Parameters: - Return type: AnomalyAssessmentPredictionsPreview #### find_anomalous_regions(max_prediction_threshold=0.0) Sort preview bins by max_predicted value and select those with max predicted value : greater or equal to max prediction threshold. Sort the result by max predicted value in descending order. - Parameters: max_prediction_threshold ( Optional[float] ) – Return bins with maximum anomaly score greater or equal to max_prediction_threshold. - Returns: preview_bins – Filtered and sorted preview bins - Return type: list of preview_bin ## Confusion chart ### class datarobot.models.confusion_chart.ConfusionChart Confusion Chart data for model. > [!NOTE] Notes > `ClassMetrics` is a dict containing the following: > > class_name > (string) name of the class > actual_count > (int) number of times this class is seen in the validation data > predicted_count > (int) number of times this class has been predicted for the validation data > f1 > (float) F1 score > recall > (float) recall score > precision > (float) precision score > was_actual_percentages > (list of dict) one vs all actual percentages in format specified below. > : * > other_class_name > (string) the name of the other class > * > percentage > (float) the percentage of the times this class was predicted when is was actually class (from 0 to 1) > was_predicted_percentages > (list of dict) one vs all predicted percentages in format specified below. > : * > other_class_name > (string) the name of the other class > * > percentage > (float) the percentage of the times this class was actual predicted (from 0 to 1) > confusion_matrix_one_vs_all > (list of list) 2d list representing 2x2 one vs all matrix. > : * This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like: > * > [ [ True Negative, False Positive ], [ False Negative, True Positive ] ] - Variables: ## Lift chart (legacy) #### NOTE The Lift chart class below is from the legacy API. For new code, use [LiftChart](https://docs.datarobot.com/en/docs/api/reference/sdk/insights.html#datarobot.insights.LiftChart) documented above, which provides `compute()`, `get()`, `list()`, and `create()` methods. ### class datarobot.models.lift_chart.LiftChart Lift chart data for model. > [!NOTE] Notes > `LiftChartBin` is a dict containing the following: > > actual > (float) Sum of actual target values in bin > predicted > (float) Sum of predicted target values in bin > bin_weight > (float) The weight of the bin. For weighted projects, it is the sum of the weights of the rows in the bin. For unweighted projects, it is the number of rows in the bin. - Variables: #### classmethod from_server_data(data, keep_attrs=None, use_insights_format=False, **kwargs) Overwrite APIObject.from_server_data to handle lift chart data retrieved from either legacy URL or /insights/ new URL. - Parameters: ## Data slices ### class datarobot.models.data_slice.DataSlice Definition of a data slice - Variables: #### classmethod list(project, offset=0, limit=100) List the data slices in the same project - Parameters: - Returns: data_slices - Return type: list[DataSlice] > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> ... # set up your Client > >>> data_slices = dr.DataSlice.list("646d0ea0cd8eb2355a68b0e5") > >>> data_slices > [DataSlice(...), DataSlice(...), ...] > ``` #### classmethod create(name, filters, project) Creates a data slice in the project with the given name and filters - Parameters: > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> ... # set up your Client and retrieve a project > >>> data_slice = dr.DataSlice.create( > >>> ... name='yes', > >>> ... filters=[{'operand': 'binary_target', 'operator': 'eq', 'values': ['Yes']}], > >>> ... project=project, > >>> ... ) > >>> data_slice > DataSlice( > filters=[{'operand': 'binary_target', 'operator': 'eq', 'values': ['Yes']}], > id=646d1296bd0c543d88923c9d, > name=yes, > project_id=646d0ea0cd8eb2355a68b0e5 > ) > ``` #### delete() Deletes the data slice from storage > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> data_slice = dr.DataSlice.get('5a8ac9ab07a57a0001be501f') > >>> data_slice.delete() > ``` > > ``` > >>> import datarobot as dr > >>> ... # get project or project_id > >>> data_slices = dr.DataSlice.list(project) # project object or project_id > >>> data_slice = data_slices[0] # choose a data slice from the list > >>> data_slice.delete() > ``` - Return type: None #### request_size(source, model=None) Submits a request to validate the data slice’s filters and calculate the data slice’s number of rows on a given source - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> ... # get project or project_id > >>> data_slices = dr.DataSlice.list(project) # project object or project_id > >>> data_slice = data_slices[0] # choose a data slice from the list > >>> status_check_job = data_slice.request_size("validation") > ``` > > Model is required when source is ‘training’ > > ``` > >>> import datarobot as dr > >>> ... # get project or project_id > >>> data_slices = dr.DataSlice.list(project) # project object or project_id > >>> data_slice = data_slices[0] # choose a data slice from the list > >>> status_check_job = data_slice.request_size("training", model) > ``` #### get_size_info(source, model=None) Get information about the data slice applied to a source - Parameters: - Returns: slice_size_info – Information of the data slice applied to a source - Return type: DataSliceSizeInfo > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> ... # set up your Client > >>> data_slices = dr.DataSlice.list("646d0ea0cd8eb2355a68b0e5") > >>> data_slice = slices[0] # can be any slice in the list > >>> data_slice_size_info = data_slice.get_size_info("validation") > >>> data_slice_size_info > DataSliceSizeInfo( > data_slice_id=6493a1776ea78e6644382535, > messages=[ > { > 'level': 'WARNING', > 'description': 'Low Observation Count', > 'additional_info': 'Insufficient number of observations to compute some insights.' > } > ], > model_id=None, > project_id=646d0ea0cd8eb2355a68b0e5, > slice_size=1, > source=validation, > ) > >>> data_slice_size_info.to_dict() > { > 'data_slice_id': '6493a1776ea78e6644382535', > 'messages': [ > { > 'level': 'WARNING', > 'description': 'Low Observation Count', > 'additional_info': 'Insufficient number of observations to compute some insights.' > } > ], > 'model_id': None, > 'project_id': '646d0ea0cd8eb2355a68b0e5', > 'slice_size': 1, > 'source': 'validation', > } > ``` > > ``` > >>> import datarobot as dr > >>> ... # set up your Client > >>> data_slice = dr.DataSlice.get("6493a1776ea78e6644382535") > >>> data_slice_size_info = data_slice.get_size_info("validation") > ``` > > When using source=’training’, the model param is required. > > ``` > >>> import datarobot as dr > >>> ... # set up your Client > >>> model = dr.Model.get(project_id, model_id) > >>> data_slice = dr.DataSlice.get("6493a1776ea78e6644382535") > >>> data_slice_size_info = data_slice.get_size_info("training", model) > ``` > > ``` > >>> import datarobot as dr > >>> ... # set up your Client > >>> data_slice = dr.DataSlice.get("6493a1776ea78e6644382535") > >>> data_slice_size_info = data_slice.get_size_info("training", model_id) > ``` #### classmethod get(data_slice_id) Retrieve a specific data slice. - Parameters: data_slice_id ( str ) – The identifier of the data slice to retrieve. - Returns: data_slice – The required data slice. - Return type: DataSlice > [!NOTE] Examples > ``` > >>> import datarobot as dr > >>> dr.DataSlice.get('648b232b9da812a6aaa0b7a9') > DataSlice(filters=[{'operand': 'binary_target', 'operator': 'eq', 'values': ['Yes']}], > id=648b232b9da812a6aaa0b7a9, > name=test, > project_id=644bc575572480b565ca42cd > ) > ``` ### class datarobot.models.data_slice.DataSliceSizeInfo Definition of a data slice applied to a source - Variables: ## Datetime trend plots ### class datarobot.models.datetime_trend_plots.AccuracyOverTimePlotsMetadata Accuracy over Time metadata for datetime model. Added in version v2.25. - Variables: > [!NOTE] Notes > Backtest/holdout status is a dict containing the following: > > training: string > : Status backtest/holdout training. One of > datarobot.enums.DATETIME_TREND_PLOTS_STATUS > validation: string > : Status backtest/holdout validation. One of > datarobot.enums.DATETIME_TREND_PLOTS_STATUS > > Backtest/holdout metadata is a dict containing the following: > > training: dict > : Start and end dates for the backtest/holdout training. > validation: dict > : Start and end dates for the backtest/holdout validation. > > Each dict in the training and validation in backtest/holdout metadata is structured like: > > start_date: datetime.datetime or None > : The datetime of the start of the chart data (inclusive). None if chart data is not computed. > end_date: datetime.datetime or None > : The datetime of the end of the chart data (exclusive). None if chart data is not computed. ### class datarobot.models.datetime_trend_plots.AccuracyOverTimePlot Accuracy over Time plot for datetime model. Added in version v2.25. - Variables: > [!NOTE] Notes > Bin is a dict containing the following: > > start_date: datetime.datetime > : The datetime of the start of the bin (inclusive). > end_date: datetime.datetime > : The datetime of the end of the bin (exclusive). > actual: float or None > : Average actual value of the target in the bin. None if there are no entries in the bin. > predicted: float or None > : Average prediction of the model in the bin. None if there are no entries in the bin. > frequency: int or None > : Indicates number of values averaged in bin. > > Statistics is a dict containing the following: > > durbin_watson: float or None > : The Durbin-Watson statistic for the chart data. > Value is between 0 and 4. Durbin-Watson statistic > is a test statistic used to detect the presence of > autocorrelation at lag 1 in the residuals (prediction errors) > from a regression analysis. More info > https://wikipedia.org/wiki/Durbin%E2%80%93Watson_statistic > > Calendar event is a dict containing the following: > > name: string > : Name of the calendar event. > date: datetime > : Date of the calendar event. > series_id: string or None > : The series ID for the event. If this event does not specify a series ID, > then this will be None, indicating that the event applies to all series. ### class datarobot.models.datetime_trend_plots.AccuracyOverTimePlotPreview Accuracy over Time plot preview for datetime model. Added in version v2.25. - Variables: > [!NOTE] Notes > Bin is a dict containing the following: > > start_date: datetime.datetime > : The datetime of the start of the bin (inclusive). > end_date: datetime.datetime > : The datetime of the end of the bin (exclusive). > actual: float or None > : Average actual value of the target in the bin. None if there are no entries in the bin. > predicted: float or None > : Average prediction of the model in the bin. None if there are no entries in the bin. ### class datarobot.models.datetime_trend_plots.ForecastVsActualPlotsMetadata Forecast vs Actual plots metadata for datetime model. Added in version v2.25. - Variables: > [!NOTE] Notes > Backtest/holdout status is a dict containing the following: > > training: dict > : Dict containing each of > datarobot.enums.DATETIME_TREND_PLOTS_STATUS > as dict key, > and list of forecast distances for particular status as dict value. > validation: dict > : Dict containing each of > datarobot.enums.DATETIME_TREND_PLOTS_STATUS > as dict key, > and list of forecast distances for particular status as dict value. > > Backtest/holdout metadata is a dict containing the following: > > training: dict > : Start and end dates for the backtest/holdout training. > validation: dict > : Start and end dates for the backtest/holdout validation. > > Each dict in the training and validation in backtest/holdout metadata is structured like: > > start_date: datetime.datetime or None > : The datetime of the start of the chart data (inclusive). None if chart data is not computed. > end_date: datetime.datetime or None > : The datetime of the end of the chart data (exclusive). None if chart data is not computed. ### class datarobot.models.datetime_trend_plots.ForecastVsActualPlot Forecast vs Actual plot for datetime model. Added in version v2.25. - Variables: > [!NOTE] Notes > Bin is a dict containing the following: > > start_date: datetime.datetime > : The datetime of the start of the bin (inclusive). > end_date: datetime.datetime > : The datetime of the end of the bin (exclusive). > actual: float or None > : Average actual value of the target in the bin. None if there are no entries in the bin. > forecasts: list of float > : A list of average forecasts for the model for each forecast distance. > Empty if there are no forecasts in the bin. > Each index in the forecasts list maps to forecastDistances list index. > error: float or None > : Average absolute residual value of the bin. > None if there are no entries in the bin. > normalized_error: float or None > : Normalized average absolute residual value of the bin. > None if there are no entries in the bin. > frequency: int or None > : Indicates number of values averaged in bin. > > Calendar event is a dict containing the following: > > name: string > : Name of the calendar event. > date: datetime > : Date of the calendar event. > series_id: string or None > : The series ID for the event. If this event does not specify a series ID, > then this will be None, indicating that the event applies to all series. ### class datarobot.models.datetime_trend_plots.ForecastVsActualPlotPreview Forecast vs Actual plot preview for datetime model. Added in version v2.25. - Variables: > [!NOTE] Notes > Bin is a dict containing the following: > > start_date: datetime.datetime > : The datetime of the start of the bin (inclusive). > end_date: datetime.datetime > : The datetime of the end of the bin (exclusive). > actual: float or None > : Average actual value of the target in the bin. None if there are no entries in the bin. > predicted: float or None > : Average prediction of the model in the bin. None if there are no entries in the bin. ### class datarobot.models.datetime_trend_plots.AnomalyOverTimePlotsMetadata Anomaly over Time metadata for datetime model. Added in version v2.25. - Variables: > [!NOTE] Notes > Backtest/holdout status is a dict containing the following: > > training: string > : Status backtest/holdout training. One of > datarobot.enums.DATETIME_TREND_PLOTS_STATUS > validation: string > : Status backtest/holdout validation. One of > datarobot.enums.DATETIME_TREND_PLOTS_STATUS > > Backtest/holdout metadata is a dict containing the following: > > training: dict > : Start and end dates for the backtest/holdout training. > validation: dict > : Start and end dates for the backtest/holdout validation. > > Each dict in the training and validation in backtest/holdout metadata is structured like: > > start_date: datetime.datetime or None > : The datetime of the start of the chart data (inclusive). None if chart data is not computed. > end_date: datetime.datetime or None > : The datetime of the end of the chart data (exclusive). None if chart data is not computed. ### class datarobot.models.datetime_trend_plots.AnomalyOverTimePlot Anomaly over Time plot for datetime model. Added in version v2.25. - Variables: > [!NOTE] Notes > Bin is a dict containing the following: > > start_date: datetime.datetime > : The datetime of the start of the bin (inclusive). > end_date: datetime.datetime > : The datetime of the end of the bin (exclusive). > predicted: float or None > : Average prediction of the model in the bin. None if there are no entries in the bin. > frequency: int or None > : Indicates number of values averaged in bin. > > Calendar event is a dict containing the following: > > name: string > : Name of the calendar event. > date: datetime > : Date of the calendar event. > series_id: string or None > : The series ID for the event. If this event does not specify a series ID, > then this will be None, indicating that the event applies to all series. ### class datarobot.models.datetime_trend_plots.AnomalyOverTimePlotPreview Anomaly over Time plot preview for datetime model. Added in version v2.25. - Variables: > [!NOTE] Notes > Bin is a dict containing the following: > > start_date: datetime.datetime > : The datetime of the start of the bin (inclusive). > end_date: datetime.datetime > : The datetime of the end of the bin (exclusive). ## External scores and insights ### class datarobot.ExternalScores Metric scores on prediction dataset with target or actual value column in unsupervised case. Contains project metrics for supervised and special classification metrics set for unsupervised projects. Added in version v2.21. - Variables: > [!NOTE] Examples > List all scores for a dataset > > ``` > from datarobot.models.external_dataset_scores_insights.external_scores import ExternalScores > scores = ExternalScores.list(project_id, dataset_id=dataset_id) > ``` #### classmethod create(project_id, model_id, dataset_id, actual_value_column=None) Compute an external dataset insights for the specified model. - Parameters: - Returns: job – an instance of created async job - Return type: Job #### classmethod list(project_id, model_id=None, dataset_id=None, offset=0, limit=100) Fetch external scores list for the project and optionally for model and dataset. - Parameters: - Return type: List [ ExternalScores ] - Returns: A list of External Scores objects #### classmethod get(project_id, model_id, dataset_id) Retrieve external scores for the project, model and dataset. - Parameters: - Return type: ExternalScores - Returns: External Scores object ### class datarobot.ExternalLiftChart Lift chart for the model and prediction dataset with target or actual value column in unsupervised case. Added in version v2.21. `LiftChartBin` is a dict containing the following: > actual(float) Sum of actual target values in binpredicted(float) Sum of predicted target values in binbin_weight(float) The weight of the bin. For weighted projects, it is the sum of the weights of the rows in the bin. For unweighted projects, it is the number of rows in the bin.Variables:dataset_id(str) – id of the prediction dataset with target or actual value column for unsupervised casebins(listofdict) – List of dicts with schema described asLiftChartBinabove. #### classmethod list(project_id, model_id, dataset_id=None, offset=0, limit=100) Retrieve list of the lift charts for the model. - Parameters: - Return type: List [ ExternalLiftChart ] - Returns: A list of ExternalLiftChart objects #### classmethod get(project_id, model_id, dataset_id) Retrieve lift chart for the model and prediction dataset. - Parameters: - Return type: ExternalLiftChart - Returns: ExternalLiftChart object ### class datarobot.ExternalRocCurve ROC curve data for the model and prediction dataset with target or actual value column in unsupervised case. Added in version v2.21. - Variables: #### classmethod list(project_id, model_id, dataset_id=None, offset=0, limit=100) Retrieve list of the roc curves for the model. - Parameters: - Return type: List [ ExternalRocCurve ] - Returns: A list of ExternalRocCurve objects #### classmethod get(project_id, model_id, dataset_id) Retrieve ROC curve chart for the model and prediction dataset. - Parameters: - Return type: ExternalRocCurve - Returns: ExternalRocCurve object ## Feature association ### class datarobot.models.FeatureAssociationMatrix Feature association statistics for a project. > [!NOTE] Notes > Projects created prior to v2.17 are not supported by this feature. - Variables: > [!NOTE] Examples > ``` > import datarobot as dr > > # retrieve feature association matrix > feature_association_matrix = dr.FeatureAssociationMatrix.get(project_id) > feature_association_matrix.strengths > feature_association_matrix.features > > # retrieve feature association matrix for a metric, association type or a feature list > feature_association_matrix = dr.FeatureAssociationMatrix.get( > project_id, > metric=enums.FEATURE_ASSOCIATION_METRIC.SPEARMAN, > association_type=enums.FEATURE_ASSOCIATION_TYPE.CORRELATION, > featurelist_id=featurelist_id, > ) > ``` #### classmethod get(project_id, metric=None, association_type=None, featurelist_id=None) Get feature association statistics. - Parameters: - Returns: Feature association pairwise metric strength data, feature clustering data, and ordering data for Feature Association Matrix visualization. - Return type: FeatureAssociationMatrix #### classmethod create(project_id, featurelist_id) Compute the Feature Association Matrix for a Feature List - Parameters: - Returns: status_check_job – Object contains all needed logic for a periodical status check of an async job. - Return type: StatusCheckJob ## Feature association matrix details ### class datarobot.models.FeatureAssociationMatrixDetails Plotting details for a pair of passed features present in the feature association matrix. > [!NOTE] Notes > Projects created prior to v2.17 are not supported by this feature. - Variables: #### classmethod get(project_id, feature1, feature2, featurelist_id=None) Get a sample of the actual values used to measure the association between a pair of features Added in version v2.17. - Parameters: - Returns: The feature association plotting for provided pair of features. - Return type: FeatureAssociationMatrixDetails ## Feature association featurelists ### class datarobot.models.FeatureAssociationFeaturelists Featurelists with feature association matrix availability flags for a project. - Variables: #### classmethod get(project_id) Get featurelists with feature association status for each. - Parameters: project_id ( str ) – Id of the project of interest. - Returns: Featurelist with feature association status for each. - Return type: FeatureAssociationFeaturelists ## Feature effects ### class datarobot.models.FeatureEffects Feature Effects provides partial dependence and predicted vs actual values for top-500 features ordered by feature impact score. The partial dependence shows marginal effect of a feature on the target variable after accounting for the average effects of all other predictive features. It indicates how, holding all other variables except the feature of interest as they were, the value of this feature affects your prediction. - Variables: > [!NOTE] Notes > `featureEffects` is a dict containing the following: > > feature_name > (string) Name of the feature > feature_type > (string) dr.enums.FEATURE_TYPE, Feature type either numeric, categorical or datetime > feature_impact_score > (float) Feature impact score > weight_label > (string) optional, Weight label if configured for the project else null > partial_dependence > (List) Partial dependence results > predicted_vs_actual > (List) optional, Predicted versus actual results, may be omitted if there are insufficient qualified samples > > `partial_dependence` is a dict containing the following: > > is_capped > (bool) Indicates whether the data for computation is capped > data > (List) partial dependence results in the following format > > `data` is a list of dict containing the following: > > label > (string) Contains label for categorical and numeric features as string > dependence > (float) Value of partial dependence > > `predicted_vs_actual` is a dict containing the following: > > is_capped > (bool) Indicates whether the data for computation is capped > data > (List) pred vs actual results in the following format > > `data` is a list of dict containing the following: > > label > (string) Contains label for categorical features for numeric features contains range or numeric value. > bin > (List) optional, For numeric features contains labels for left and right bin limits > predicted > (float) Predicted value > actual > (float) Actual value. Actual value is null for unsupervised timeseries models > row_count > (int or float) Number of rows for the label and bin. Type is float if weight or exposure is set for the project. #### classmethod from_server_data(data, *args, use_insights_format=False, **kwargs) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing. - Parameters: ### class datarobot.models.FeatureEffectMetadata Feature Effect Metadata for model, contains status and available model sources. > [!NOTE] Notes > `source` is expected parameter to retrieve Feature Effect. One of provided sources > shall be used. ### class datarobot.models.FeatureEffectMetadataDatetime Feature Effect Metadata for datetime model, contains list of feature effect metadata per backtest. > [!NOTE] Notes > `feature effect metadata per backtest` contains: > > status > : str. > backtest_index > : str. > sources > : List[str]. > > `source` is expected parameter to retrieve Feature Effect. One of provided sources > shall be used. > > `backtest_index` is expected parameter to submit compute request and retrieve Feature Effect. > One of provided backtest indexes shall be used. - Variables: data ( list[FeatureEffectMetadataDatetimePerBacktest] ) – List feature effect metadata per backtest ### class datarobot.models.FeatureEffectMetadataDatetimePerBacktest Convert dictionary into feature effect metadata per backtest which contains backtest_index, status and sources. ## Payoff matrix ### class datarobot.models.PayoffMatrix Represents a Payoff Matrix, a costs/benefit scenario used for creating a profit curve. - Variables: > [!NOTE] Examples > ``` > import datarobot as dr > > # create a payoff matrix > payoff_matrix = dr.PayoffMatrix.create( > project_id, > name, > true_positive_value=100, > true_negative_value=10, > false_positive_value=0, > false_negative_value=-10, > ) > > # list available payoff matrices > payoff_matrices = dr.PayoffMatrix.list(project_id) > payoff_matrix = payoff_matrices[0] > ``` #### classmethod create(project_id, name, true_positive_value=1, true_negative_value=1, false_positive_value=-1, false_negative_value=-1) Create a payoff matrix associated with a specific project. - Parameters: project_id ( str ) – id of the project with which the payoff matrix will be associated - Returns: payoff_matrix – The newly created payoff matrix - Return type: PayoffMatrix #### classmethod list(project_id) Fetch all the payoff matrices for a project. - Parameters: project_id ( str ) – id of the project - Returns: A list of PayoffMatrix objects - Return type: List of PayoffMatrix - Raises: #### classmethod get(project_id, id) Retrieve a specified payoff matrix. - Parameters: - Return type: PayoffMatrix - Returns: - Raises: #### classmethod update(project_id, id, name, true_positive_value, true_negative_value, false_positive_value, false_negative_value) Update (replace) a payoff matrix. Note that all data fields are required. - Parameters: - Returns: PayoffMatrix with updated values - Return type: payoff_matrix - Raises: #### classmethod delete(project_id, id) Delete a specified payoff matrix. - Parameters: - Returns: response – Empty response (204) - Return type: requests.Response - Raises: #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( T , bound= APIObject) ## Prediction explanations ### class datarobot.PredictionExplanationsInitialization Represents a prediction explanations initialization of a model. - Variables: #### classmethod get(project_id, model_id) Retrieve the prediction explanations initialization for a model. Prediction explanations initializations are a prerequisite for computing prediction explanations, and include a sample what the computed prediction explanations for a prediction dataset would look like. - Parameters: - Returns: prediction_explanations_initialization – The queried instance. - Return type: PredictionExplanationsInitialization - Raises: ClientError – If the project or model does not exist or the initialization has not been computed. #### classmethod create(project_id, model_id) Create a prediction explanations initialization for the specified model. - Parameters: - Returns: job – an instance of created async job - Return type: Job #### delete() Delete this prediction explanations initialization. ### class datarobot.PredictionExplanations Represents prediction explanations metadata and provides access to computation results. > [!NOTE] Examples > ``` > prediction_explanations = dr.PredictionExplanations.get(project_id, explanations_id) > for row in prediction_explanations.get_rows(): > print(row) # row is an instance of PredictionExplanationsRow > ``` - Variables: #### classmethod get(project_id, prediction_explanations_id) Retrieve a specific prediction explanations metadata. - Parameters: - Returns: prediction_explanations – The queried instance. - Return type: PredictionExplanations #### classmethod create(project_id, model_id, dataset_id, max_explanations=None, threshold_low=None, threshold_high=None, mode=None) Create prediction explanations for the specified dataset. In order to create PredictionExplanations for a particular model and dataset, you must first: > Compute feature impact for the model viadatarobot.Model.get_feature_impact()Compute a PredictionExplanationsInitialization for the model viadatarobot.PredictionExplanationsInitialization.create(project_id, model_id)Compute predictions for the model and dataset viadatarobot.Model.request_predictions(dataset_id) `threshold_high` and `threshold_low` are optional filters applied to speed up computation. When at least one is specified, only the selected outlier rows will have prediction explanations computed. Rows are considered to be outliers if their predicted value (in case of regression projects) or probability of being the positive class (in case of classification projects) is less than `threshold_low` or greater than `thresholdHigh`. If neither is specified, prediction explanations will be computed for all rows. - Parameters: - Returns: job – an instance of created async job - Return type: Job #### classmethod create_on_training_data(project_id, model_id, dataset_id, max_explanations=None, threshold_low=None, threshold_high=None, mode=None, datetime_prediction_partition=None) Create prediction explanations for the the dataset used to train the model. This can be retrieved by calling `dr.Model.get().featurelist_id`. For OTV and timeseries projects, `datetime_prediction_partition` is required and limited to the first backtest (‘0’) or holdout (‘holdout’). In order to create PredictionExplanations for a particular model and dataset, you must first: > Compute Feature Impact for the model viadatarobot.Model.get_feature_impact()/Compute a PredictionExplanationsInitialization for the model viadatarobot.PredictionExplanationsInitialization.create(project_id, model_id).Compute predictions for the model and dataset viadatarobot.Model.request_predictions(dataset_id). `threshold_high` and `threshold_low` are optional filters applied to speed up computation. When at least one is specified, only the selected outlier rows will have prediction explanations computed. Rows are considered to be outliers if their predicted value (in case of regression projects) or probability of being the positive class (in case of classification projects) is less than `threshold_low` or greater than `thresholdHigh`. If neither is specified, prediction explanations will be computed for all rows. - Parameters: - Returns: job – An instance of created async job. - Return type: Job #### classmethod list(project_id, model_id=None, limit=None, offset=None) List of prediction explanations metadata for a specified project. - Parameters: - Returns: prediction_explanations - Return type: list[PredictionExplanations] #### get_rows(batch_size=None, exclude_adjusted_predictions=True) Retrieve prediction explanations rows. - Parameters: - Yields: prediction_explanations_row ( PredictionExplanationsRow ) – Represents prediction explanations computed for a prediction row. #### is_multiclass() Whether these explanations are for a multiclass project or a non-multiclass project #### is_unsupervised_clustering_or_multiclass() Clustering and multiclass XEMP always has either one of num_top_classes or class_names parameters set #### get_number_of_explained_classes() How many classes we attempt to explain for each row #### get_all_as_dataframe(exclude_adjusted_predictions=True) Retrieve all prediction explanations rows and return them as a pandas.DataFrame. Returned dataframe has the following structure: > row_id : row id from prediction datasetprediction : the output of the model for this rowadjusted_prediction : adjusted prediction values (only appears for projects that > utilize prediction adjustments, e.g., projects with an exposure column)class_0_label : a class level from the target (only appears for classification > projects)class_0_probability : the probability that the target is this class (only appears for > classification projects)class_1_label : a class level from the target (only appears for classification > projects)class_1_probability : the probability that the target is this class (only appears for > classification projects)explanation_0_feature : the name of the feature contributing to the prediction for > this explanationexplanation_0_feature_value : the value the feature took onexplanation_0_label : the output being driven by this explanation. For regression > projects, this is the name of the target feature. For classification projects, this > is the class label whose probability increasing would correspond to a positive > strength.explanation_0_qualitative_strength : a human-readable description of how strongly the > feature affected the prediction (e.g., ‘+++’, ‘–’, ‘+’) for this explanationexplanation_0_per_ngram_text_explanations : Text prediction explanations data in json > formatted string.explanation_0_strength : the amount this feature’s value affected the prediction…explanation_N_feature : the name of the feature contributing to the prediction for > this explanationexplanation_N_feature_value : the value the feature took onexplanation_N_label : the output being driven by this explanation. For regression > projects, this is the name of the target feature. For classification projects, this > is the class label whose probability increasing would correspond to a positive > strength.explanation_N_qualitative_strength : a human-readable description of how strongly the > feature affected the prediction (e.g., ‘+++’, ‘–’, ‘+’) for this explanationexplanation_N_per_ngram_text_explanations : Text prediction explanations data in json > formatted string.explanation_N_strength : the amount this feature’s value affected the prediction For classification projects, the server does not guarantee any ordering on the prediction values, however within this function we sort the values so that class_X corresponds to the same class from row to row. - Parameters: exclude_adjusted_predictions ( bool ) – Optional, defaults to True. Set this to False to include adjusted prediction values in the returned dataframe. - Returns: dataframe - Return type: pandas.DataFrame #### download_to_csv(filename, encoding='utf-8', exclude_adjusted_predictions=True) Save prediction explanations rows into CSV file. - Parameters: #### get_prediction_explanations_page(limit=None, offset=None, exclude_adjusted_predictions=True) Get prediction explanations. If you don’t want use a generator interface, you can access paginated prediction explanations directly. - Parameters: - Returns: prediction_explanations - Return type: PredictionExplanationsPage #### delete() Delete these prediction explanations. ### class datarobot.models.prediction_explanations.PredictionExplanationsRow Represents prediction explanations computed for a prediction row. > [!NOTE] Notes > `PredictionValue` contains: > > label > : describes what this model output corresponds to. For regression projects, > it is the name of the target feature. For classification projects, it is a level from > the target feature. > value > : the output of the prediction. For regression projects, it is the predicted > value of the target. For classification projects, it is the predicted probability the > row belongs to the class identified by the label. `PredictionExplanation` contains: - label : described what output was driven by this explanation. For regression projects, it is the name of the target feature. For classification projects, it is the class whose probability increasing would correspond to a positive strength of this prediction explanation. - feature : the name of the feature contributing to the prediction - feature_value : the value the feature took on for this row - strength : the amount this feature’s value affected the prediction - qualitative_strength : a human-readable description of how strongly the feature affected the prediction. A large positive effect is denoted ‘+++’, medium ‘++’, small ‘+’, very small ‘<+’. A large negative effect is denoted ‘—’, medium ‘–’, small ‘-’, very small ‘<-‘. - Variables: ### class datarobot.models.prediction_explanations.PredictionExplanationsPage Represents a batch of prediction explanations received by one request. - Variables: #### classmethod get(project_id, prediction_explanations_id, limit=None, offset=0, exclude_adjusted_predictions=True) Retrieve prediction explanations. - Parameters: - Returns: prediction_explanations – The queried instance. - Return type: PredictionExplanationsPage ### class datarobot.models.ShapMatrix Represents SHAP based prediction explanations and provides access to score values. - Variables: > [!NOTE] Examples > ``` > import datarobot as dr > > # request SHAP matrix calculation > shap_matrix_job = dr.ShapMatrix.create(project_id, model_id, dataset_id) > shap_matrix = shap_matrix_job.get_result_when_complete() > > # list available SHAP matrices > shap_matrices = dr.ShapMatrix.list(project_id) > shap_matrix = shap_matrices[0] > > # get SHAP matrix as dataframe > shap_matrix_values = shap_matrix.get_as_dataframe() > ``` #### classmethod create(cls, project_id, model_id, dataset_id) Calculate SHAP based prediction explanations against previously uploaded dataset. - Parameters: - Returns: job – The job computing the SHAP based prediction explanations - Return type: ShapMatrixJob - Raises: #### classmethod list(cls, project_id) Fetch all the computed SHAP prediction explanations for a project. - Parameters: project_id ( str ) – id of the project - Returns: A list of ShapMatrix objects - Return type: List of ShapMatrix - Raises: #### classmethod get(cls, project_id, id) Retrieve the specific SHAP matrix. - Parameters: - Return type: ShapMatrix object representing specified record #### get_as_dataframe(read_timeout=60) Retrieve SHAP matrix values as dataframe. - Return type: DataFrame - Returns: ### class datarobot.models.ClassListMode Calculate prediction explanations for the specified classes in each row. - Variables: class_names ( list ) – List of class names that will be explained for each dataset row. #### get_api_parameters(batch_route=False) Get parameters passed in corresponding API call - Parameters: batch_route ( bool ) – Batch routes describe prediction calls with all possible parameters, so to distinguish explanation parameters from others they have prefix in parameters. - Return type: dict ### class datarobot.models.TopPredictionsMode Calculate prediction explanations for the number of top predicted classes in each row. - Variables: num_top_classes ( int ) – Number of top predicted classes [1..10] that will be explained for each dataset row. #### get_api_parameters(batch_route=False) Get parameters passed in corresponding API call - Parameters: batch_route ( bool ) – Batch routes describe prediction calls with all possible parameters, so to distinguish explanation parameters from others they have prefix in parameters. - Return type: dict ## Rating table ### class datarobot.models.RatingTable Interface to modify and download rating tables. - Variables: #### classmethod from_server_data(data, should_warn=True, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: RatingTable #### classmethod get(project_id, rating_table_id) Retrieve a single rating table - Parameters: - Returns: rating_table – The queried instance - Return type: RatingTable #### classmethod create(project_id, parent_model_id, filename, rating_table_name='Uploaded Rating Table') Uploads and validates a new rating table CSV - Parameters: - Returns: job – an instance of created async job - Return type: Job - Raises: #### download(filepath) Download a csv file containing the contents of this rating table - Parameters: filepath ( str ) – The path at which to save the rating table file. - Return type: None #### rename(rating_table_name) Renames a rating table to a different name. - Parameters: rating_table_name ( str ) – The new name to rename the rating table to. - Return type: None #### create_model() Creates a new model from this rating table record. This rating table must not already be associated with a model and must be valid. - Returns: job – an instance of created async job - Return type: Job - Raises: ## ROC curve (legacy) #### NOTE The ROC curve class below is from the legacy API. For new code, use [RocCurve](https://docs.datarobot.com/en/docs/api/reference/sdk/insights.html#datarobot.insights.RocCurve) documented above, which provides `compute()`, `get()`, `list()`, and `create()` methods. ### class datarobot.models.roc_curve.RocCurve ROC curve data for model. - Variables: #### classmethod from_server_data(data, keep_attrs=None, use_insights_format=False, **kwargs) Overwrite APIObject.from_server_data to handle roc curve data retrieved from either legacy URL or /insights/ new URL. - Parameters: - Return type: RocCurve ### class datarobot.models.roc_curve.LabelwiseRocCurve Labelwise ROC curve data for one label and one source. - Variables: ## Word Cloud ### class datarobot.models.word_cloud.WordCloud Word cloud data for the model. > [!NOTE] Notes > `WordCloudNgram` is a dict containing the following: > > ngram > (str) Word or ngram value. > coefficient > (float) Value from [-1.0, 1.0] range, describes effect of this ngram on the target. Large negative value means strong effect toward negative class in classification and smaller target value in regression models. Large positive - toward positive class and bigger value respectively. > count > (int) Number of rows in the training sample where this ngram appears. > frequency > (float) Value from (0.0, 1.0] range, relative frequency of given ngram to most frequent ngram. > is_stopword > (bool) True for ngrams that DataRobot evaluates as stopwords. > class > (str or None) For classification - values of the target class for > corresponding word or ngram. For regression - None. - Variables: ngrams ( list of dict ) – List of dicts with schema described as WordCloudNgram above. #### most_frequent(top_n=5) Return most frequent ngrams in the word cloud. - Parameters: top_n ( int ) – Number of ngrams to return - Returns: Up to top_n top most frequent ngrams in the word cloud. If top_n bigger then total number of ngrams in word cloud - return all sorted by frequency in descending order. - Return type: list of dict #### most_important(top_n=5) Return most important ngrams in the word cloud. - Parameters: top_n ( int ) – Number of ngrams to return - Returns: Up to top_n top most important ngrams in the word cloud. If top_n bigger then total number of ngrams in word cloud - return all sorted by absolute coefficient value in descending order. - Return type: list of dict #### ngrams_per_class() Split ngrams per target class values. Useful for multiclass models. - Returns: Dictionary in the format of (class label) -> (list of ngrams for that class) - Return type: dict ### class datarobot.models.word_cloud.WordCloudNgram --- # Jobs URL: https://docs.datarobot.com/en/docs/api/reference/sdk/jobs.html # Job ### class datarobot.models.Job Tracks asynchronous work being done within a project - Variables: #### classmethod get(project_id, job_id) Fetches one job. - Parameters: - Returns: job – The job - Return type: Job - Raises: AsyncFailureError – Querying this resource gave a status code other than 200 or 303 #### cancel() Cancel this job. If this job has not finished running, it will be removed and canceled. #### get_result(params=None) - Parameters: params ( dict or None ) – Query parameters to be added to request to get results. > [!NOTE] Notes > For featureEffects, source param is required to define source, > otherwise the default is training. - Returns:result– Return type depends on the job type : - for model jobs, a Model is returned - for predict jobs, a pandas.DataFrame (with predictions) is returned - for featureImpact jobs, a list of dicts by default (seewith_metadataparameter of theFeatureImpactJobclass and itsget()method). - for primeRulesets jobs, a list of Rulesets - for primeModel jobs, a PrimeModel - for primeDownloadValidation jobs, a PrimeFile - for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization - for predictionExplanations jobs, a PredictionExplanations - for featureEffects, a FeatureEffects. *Return type:object*Raises:*JobNotFinished– If the job is not finished, the result is not available. *AsyncProcessUnsuccessfulError– If the job errored or was aborted #### get_result_when_complete(max_wait=600, params=None) - Parameters: - Returns: result – Return type is the same as would be returned by Job.get_result. - Return type: object - Raises: #### refresh() Update this object with the latest job data from the server. #### wait_for_completion(max_wait=600) Waits for job to complete. - Parameters: max_wait ( Optional[int] ) – How long to wait for the job to finish. - Return type: None ### class datarobot.models.TrainingPredictionsJob #### classmethod get(project_id, job_id, model_id=None, data_subset=None) Fetches one training predictions job. The resulting [TrainingPredictions](https://docs.datarobot.com/en/docs/api/reference/sdk/training_predictions.html#datarobot.models.training_predictions.TrainingPredictions) object will be annotated with model_id and data_subset. - Parameters: - Returns: job – The job - Return type: TrainingPredictionsJob #### refresh() Update this object with the latest job data from the server. #### cancel() Cancel this job. If this job has not finished running, it will be removed and canceled. #### get_result(params=None) - Parameters: params ( dict or None ) – Query parameters to be added to request to get results. > [!NOTE] Notes > For featureEffects, source param is required to define source, > otherwise the default is training. - Returns:result– Return type depends on the job type : - for model jobs, a Model is returned - for predict jobs, a pandas.DataFrame (with predictions) is returned - for featureImpact jobs, a list of dicts by default (seewith_metadataparameter of theFeatureImpactJobclass and itsget()method). - for primeRulesets jobs, a list of Rulesets - for primeModel jobs, a PrimeModel - for primeDownloadValidation jobs, a PrimeFile - for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization - for predictionExplanations jobs, a PredictionExplanations - for featureEffects, a FeatureEffects. *Return type:object*Raises:*JobNotFinished– If the job is not finished, the result is not available. *AsyncProcessUnsuccessfulError– If the job errored or was aborted #### get_result_when_complete(max_wait=600, params=None) - Parameters: - Returns: result – Return type is the same as would be returned by Job.get_result. - Return type: object - Raises: #### wait_for_completion(max_wait=600) Waits for job to complete. - Parameters: max_wait ( Optional[int] ) – How long to wait for the job to finish. - Return type: None ### class datarobot.models.ShapMatrixJob #### classmethod get(project_id, job_id, model_id=None, dataset_id=None) Fetches one SHAP matrix job. - Parameters: - Returns: job – The job - Return type: ShapMatrixJob - Raises: AsyncFailureError – Querying this resource gave a status code other than 200 or 303 #### refresh() Update this object with the latest job data from the server. - Return type: None #### cancel() Cancel this job. If this job has not finished running, it will be removed and canceled. #### get_result(params=None) - Parameters: params ( dict or None ) – Query parameters to be added to request to get results. > [!NOTE] Notes > For featureEffects, source param is required to define source, > otherwise the default is training. - Returns:result– Return type depends on the job type : - for model jobs, a Model is returned - for predict jobs, a pandas.DataFrame (with predictions) is returned - for featureImpact jobs, a list of dicts by default (seewith_metadataparameter of theFeatureImpactJobclass and itsget()method). - for primeRulesets jobs, a list of Rulesets - for primeModel jobs, a PrimeModel - for primeDownloadValidation jobs, a PrimeFile - for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization - for predictionExplanations jobs, a PredictionExplanations - for featureEffects, a FeatureEffects. *Return type:object*Raises:*JobNotFinished– If the job is not finished, the result is not available. *AsyncProcessUnsuccessfulError– If the job errored or was aborted #### get_result_when_complete(max_wait=600, params=None) - Parameters: - Returns: result – Return type is the same as would be returned by Job.get_result. - Return type: object - Raises: #### wait_for_completion(max_wait=600) Waits for job to complete. - Parameters: max_wait ( Optional[int] ) – How long to wait for the job to finish. - Return type: None ### class datarobot.models.FeatureImpactJob Custom Feature Impact job to handle different return value structures. The original implementation had just the the data and the new one also includes some metadata. In general, we aim to keep the number of Job classes low by just utilizing the job_type attribute to control any specific formatting; however in this case when we needed to support a new representation with the _same_ job_type, customizing the behavior of _make_result_from_location allowed us to achieve our ends without complicating the _make_result_from_json method. #### classmethod get(project_id, job_id, with_metadata=False) Fetches one job. - Parameters: - Returns: job – The job - Return type: Job - Raises: AsyncFailureError – Querying this resource gave a status code other than 200 or 303 #### cancel() Cancel this job. If this job has not finished running, it will be removed and canceled. #### get_result(params=None) - Parameters: params ( dict or None ) – Query parameters to be added to request to get results. > [!NOTE] Notes > For featureEffects, source param is required to define source, > otherwise the default is training. - Returns:result– Return type depends on the job type : - for model jobs, a Model is returned - for predict jobs, a pandas.DataFrame (with predictions) is returned - for featureImpact jobs, a list of dicts by default (seewith_metadataparameter of theFeatureImpactJobclass and itsget()method). - for primeRulesets jobs, a list of Rulesets - for primeModel jobs, a PrimeModel - for primeDownloadValidation jobs, a PrimeFile - for predictionExplanationInitialization jobs, a PredictionExplanationsInitialization - for predictionExplanations jobs, a PredictionExplanations - for featureEffects, a FeatureEffects. *Return type:object*Raises:*JobNotFinished– If the job is not finished, the result is not available. *AsyncProcessUnsuccessfulError– If the job errored or was aborted #### get_result_when_complete(max_wait=600, params=None) - Parameters: - Returns: result – Return type is the same as would be returned by Job.get_result. - Return type: object - Raises: #### refresh() Update this object with the latest job data from the server. #### wait_for_completion(max_wait=600) Waits for job to complete. - Parameters: max_wait ( Optional[int] ) – How long to wait for the job to finish. - Return type: None --- # Key-Values URL: https://docs.datarobot.com/en/docs/api/reference/sdk/key_values.html # Key-Values ### class datarobot.models.key_values.KeyValue A DataRobot Key-Value. Added in version v3.4. - Variables: #### classmethod get(key_value_id) Get Key-Value by id. Added in version v3.4. - Parameters: key_value_id ( str ) – ID of the Key-Value - Returns: retrieved Key-Value - Return type: KeyValue - Raises: #### classmethod list(entity_id, entity_type) List Key-Values. Added in version v3.4. - Parameters: - Returns: a list of Key-Values - Return type: List[KeyValue] - Raises: #### classmethod find(entity_id, entity_type, name) Find Key-Value by name. Added in version v3.4. - Parameters: - Returns: a list of Key-Values - Return type: List[KeyValue] - Raises: #### classmethod create(entity_id, entity_type, name, category, value_type, value=None, description=None) Create a Key-Value. Added in version v3.4. - Parameters: - Returns: created Key-Value - Return type: KeyValue - Raises: #### update(entity_id=None, entity_type=None, name=None, category=None, value_type=None, value=None, description=None, comment=None) Update Key-Value. Added in version v3.4. - Parameters: - Raises: - Return type: None #### refresh() Update Key-Value with the latest data from server. Added in version v3.4. - Raises: - Return type: None #### delete() Delete Key-Value. Added in version v3.4. - Raises: - Return type: None #### get_value() Get a value of Key-Value. Added in version v3.4. - Returns: value depending on the value type - Return type: Union[str , float , boolean] ### class datarobot.enums.KeyValueCategory Key-Value category ### class datarobot.enums.KeyValueEntityType Key-Value entity type ### class datarobot.enums.KeyValueType Key-Value type --- # Memory URL: https://docs.datarobot.com/en/docs/api/reference/sdk/memory.html # Memory ### class datarobot.models.memory.MemorySpace A container for chat sessions and their events. A memory space groups related sessions and memories together, providing an isolation boundary for conversational, semantic, and episodic memories in agentic applications. Added in version v3.15. - Variables: #### classmethod create(description=None, llm_model_name=None) Create a new memory space. Added in version v3.15. - Parameters: - Returns: The newly created memory space. - Return type: MemorySpace #### classmethod list(offset=None, limit=None) List memory spaces accessible to the current user. Added in version v3.15. - Parameters: - Returns: The available memory spaces. - Return type: list of MemorySpace #### classmethod get(memory_space_id) Get a memory space by its ID. Added in version v3.15. - Parameters: memory_space_id ( str ) – The ID of the memory space to retrieve. - Returns: The requested memory space. - Return type: MemorySpace #### update(description=, llm_model_name=) Update the memory space. If called without arguments, there is no effect. Pass `None` to clear a field. Added in version v3.15. - Parameters: - Return type: None #### delete() Delete the memory space. Added in version v3.15. - Return type: None ### class datarobot.models.memory.Session A chat session within a [MemorySpace](https://docs.datarobot.com/en/docs/api/reference/sdk/memory.html#datarobot.models.memory.MemorySpace). Sessions track conversations between participants and store the sequence of [Event](https://docs.datarobot.com/en/docs/api/reference/sdk/memory.html#datarobot.models.memory.Event) objects that reflect either a single message or a state. Added in version v3.15. - Variables: #### classmethod create(memory_space_id, participants, lifecycle_strategies=None, description=None, metadata=None) Create a new session in a memory space. Every session must have at least one lifecycle strategy. If `lifecycle_strategies` is not provided or is an empty list, the server attaches a default strategy. Added in version v3.15. - Parameters: - Returns: The newly created session. - Return type: Session #### classmethod list(memory_space_id, offset=None, limit=None, participants=None, description=None) List sessions within a single memory space. Added in version v3.15. - Parameters: - Returns: The matching sessions. - Return type: list of Session #### classmethod get(memory_space_id, session_id) Get a session by its ID. Added in version v3.15. - Parameters: - Returns: The requested session. - Return type: Session #### update(description=, metadata=) Update the session. If called without arguments, there is no effect. Pass `None` to clear a field. Added in version v3.15. - Parameters: - Return type: None #### delete() Delete the session. Added in version v3.15. - Return type: None #### post_event(body, emitter, event_type=None) Create an event in this session. Added in version v3.15. - Parameters: - Returns: The newly created event. - Return type: Event #### events(offset=None, limit=None, last_n=None, event_type=None) List events in this session. Provide either `offset` or `last_n`, but not both. Added in version v3.15. - Parameters: - Returns: The matching events. - Return type: list of Event #### update_event(sequence_id, body=, event_type=, emitter=, created_at=None) Update an event by its sequence ID. When `created_at` is provided, the server uses it for optimistic concurrency control. Specifically, if the event has been modified since that timestamp, the server rejects the update. The caller must handle this error and reload the event before retrying. Added in version v3.15. - Parameters: - Returns: The updated event, or None if called with no changes. - Return type: Event or None ### class datarobot.models.memory.Event A single action or chat message within a [Session](https://docs.datarobot.com/en/docs/api/reference/sdk/memory.html#datarobot.models.memory.Session). Events are always scoped to a session. Use [Session.post_event()](https://docs.datarobot.com/en/docs/api/reference/sdk/memory.html#datarobot.models.memory.Session.post_event), [Session.events()](https://docs.datarobot.com/en/docs/api/reference/sdk/memory.html#datarobot.models.memory.Session.events), and [Session.update_event()](https://docs.datarobot.com/en/docs/api/reference/sdk/memory.html#datarobot.models.memory.Session.update_event) to manage them. Added in version v3.15. - Variables: --- # MLOps event URL: https://docs.datarobot.com/en/docs/api/reference/sdk/mlops_event.html # MLOps event ### class datarobot.mlops.events.MLOpsEvent An MLOps Event Object: An object representing an important MLOps activity that happened. For example, health, service issues with the DataRobot deployment or a prediction environment or a particular phase in a long operation (like creation of deployment or processing training data) is completed or errored. This class allows the client to report such event to the DataRobot service. > [!NOTE] Notes > DataRobot backend support lots of events and all these events are categorized > into different categories. This class does not yet support ALL events, but > we will gradually add support for them > > Supported Event Categories: > : - moderation #### classmethod report_moderation_event(event_type, timestamp=None, title=None, message=None, deployment_id=None, org_id=None, guard_name=None, metric_name=None) Reports a moderation event - Parameters: - Return type: None - Raises: ValueError – If event_type is not one of the moderation event types. If fails to create the event. > [!NOTE] Examples > ``` > >>> from datarobot.mlops.events import MLOpsEvent > >>> MLOpsEvent.report_moderation_event( > ... event_type="moderationMetricCreationError", > ... title="Failed to create moderation metric", > ... message="Maximum number of custom metrics reached", > ... deployment_id="5c939e08962d741e34f609f0", > ... metric_name="Blocked Prompts", > ... ) > ``` --- # Notebooks URL: https://docs.datarobot.com/en/docs/api/reference/sdk/notebooks.html # Notebooks ### class datarobot.models.notebooks.enums.NotebookType Types of notebooks. ### class datarobot.models.notebooks.enums.RunType Types of notebook job runs. ### class datarobot.models.notebooks.enums.ManualRunType A subset of [RunType](https://docs.datarobot.com/en/docs/api/reference/sdk/notebooks.html#datarobot.models.notebooks.enums.RunType) To be used in API schemas. ### class datarobot.models.notebooks.enums.SessionType Types of notebook sessions. Triggered sessions include notebook job runs whether manually triggered or scheduled. ### class datarobot.models.notebooks.enums.ScheduleStatus Possible statuses for notebook schedules. ### class datarobot.models.notebooks.enums.ScheduledRunStatus Possible statuses for scheduled notebook runs. ### class datarobot.models.notebooks.enums.NotebookPermissions Permissions for notebooks. ### class datarobot.models.notebooks.enums.NotebookStatus Possible statuses for notebook sessions. ### class datarobot.models.notebooks.enums.KernelExecutionStatus Possible statuses for kernel execution. ### class datarobot.models.notebooks.enums.CellType Types of cells in a notebook. ### class datarobot.models.notebooks.enums.RuntimeLanguage Languages as used in notebook jupyter kernels. ### class datarobot.models.notebooks.enums.ImageLanguage Languages as used and supported in notebook images. ### class datarobot.models.notebooks.enums.KernelSpec Kernel specifications for Jupyter notebook kernels. ### class datarobot.models.notebooks.enums.KernelState Possible states for notebook kernels. ### exception datarobot.models.notebooks.exceptions.KernelNotAssignedError Raised when a Codespace notebook does not have a kernel assigned. ### class datarobot.models.notebooks.notebook.ManualRunPayload ### class datarobot.models.notebooks.notebook.Notebook Metadata for a DataRobot Notebook accessible to the user. - Variables: #### get_uri() - Returns: url – Permanent static hyperlink to this Notebook in its Use Case or standalone. - Return type: str #### classmethod get(notebook_id) Retrieve a single notebook. - Parameters: notebook_id ( str ) – The ID of the notebook you want to retrieve. - Returns: notebook – The requested notebook. - Return type: Notebook > [!NOTE] Examples > ``` > from datarobot.models.notebooks import Notebook > > notebook = Notebook.get(notebook_id='6556b00dcc4ea0bb7ea48121') > ``` #### create_revision(name=None, notebook_path=None, is_auto=False) Create a new revision for the notebook. - Parameters: - Returns: notebook_revision – Information about the created notebook revision. - Return type: NotebookRevision #### download_revision(revision_id, file_path=None, filelike=None) Downloads the notebook as a JSON (.ipynb) file for the specified revision. - Parameters: - Return type: None > [!NOTE] Examples > ``` > from datarobot.models.notebooks import Notebook > > notebook = Notebook.get(notebook_id='6556b00dcc4ea0bb7ea48121') > manual_run = notebook.run_as_job() > revision_id = manual_run.wait_for_completion() > notebook.download_revision(revision_id=revision_id, file_path="./results.ipynb") > ``` #### delete() Delete a single notebook > [!NOTE] Examples > ``` > from datarobot.models.notebooks import Notebook > > notebook = Notebook.get(notebook_id='6556b00dcc4ea0bb7ea48121') > notebook.delete() > ``` - Return type: None #### classmethod list(created_before=None, created_after=None, order_by=None, tags=None, owners=None, query=None, use_cases=None) List all Notebooks available to the user. - Parameters: - Returns: notebooks – A list of Notebooks available to the user. - Return type: List[Notebook] > [!NOTE] Examples > ``` > from datarobot.models.notebooks import Notebook > > notebooks = Notebook.list() > ``` #### is_running() Check if the notebook session is currently running. - Return type: bool #### get_session_status() Get the status of the notebook session. - Return type: NotebookStatus #### start_session(is_triggered_run=False, parameters=None, open_file_paths=None, clone_repository=None) Start a new session for the notebook. - Parameters: - Returns: notebook_session – The created notebook session. - Return type: NotebookSession > [!NOTE] Examples > ``` > from datarobot.models.notebooks import Notebook > > notebook = Notebook.get(notebook_id='6556b00dcc4ea0bb7ea48121') > session = notebook.start_session() > ``` #### stop_session() Stop the current session for the notebook. - Returns: notebook_session – The stopped notebook session. - Return type: NotebookSession > [!NOTE] Examples > ``` > from datarobot.models.notebooks import Notebook > > notebook = Notebook.get(notebook_id='6556b00dcc4ea0bb7ea48121') > session = notebook.stop_session() > ``` #### execute(notebook_path=None, cell_ids=None) Execute the notebook. Assumes session is already started. - Parameters: - Return type: None #### get_execution_status() Get the execution status information of the notebook. - Returns: execution_status – The notebook execution status information. - Return type: NotebookExecutionStatus #### is_finished_executing(notebook_path=None) Check if the notebook is finished executing. - Parameters: notebook_path ( Optional[str] ) – The path of the notebook the Codespace. Required only if the notebook is in a Codespace. Will raise an error if working with a standalone notebook. - Returns: is_finished_executing – Whether or not the notebook has finished executing. - Return type: bool - Raises: #### run_as_job(title=None, notebook_path=None, parameters=None, manual_run_type=ManualRunType.MANUAL) Create a manual scheduled job that runs the notebook. > [!NOTE] Notes > The notebook must be part of a Use Case. > If the notebook is in a Codespace then notebook_path is required. - Parameters: - Returns: notebook_scheduled_job – The created notebook schedule job. - Return type: NotebookScheduledJob - Raises: InvalidUsageError – If attempting to create a manual scheduled run for a Codespace without a notebook path. > [!NOTE] Examples > ``` > from datarobot.models.notebooks import Notebook > > notebook = Notebook.get(notebook_id='6556b00dcc4ea0bb7ea48121') > manual_run = notebook.run_as_job() > > # Alternatively, with title and parameters: > # manual_run = notebook.run_as_job(title="My Run", parameters=[{"name": "FOO", "value": "bar"}]) > > revision_id = manual_run.wait_for_completion() > ``` #### list_schedules(enabled_only=False) List all NotebookScheduledJobs associated with the notebook. - Parameters: enabled_only ( bool ) – Whether or not to return only enabled schedules. - Returns: notebook_schedules – A list of schedules for the notebook. - Return type: List[NotebookScheduledJob] - Raises: InvalidUsageError – If attempting to list schedules for a notebook not associated with a Use Case. > [!NOTE] Examples > ``` > from datarobot.models.notebooks import Notebook > > notebook = Notebook.get(notebook_id='6556b00dcc4ea0bb7ea48121') > enabled_schedules = notebook.list_schedules(enabled_only=True) > ``` ### class datarobot.models.notebooks.execution_environment.ExecutionEnvironmentAssignPayload Payload for assigning an execution environment to a notebook. ### class datarobot.models.notebooks.execution_environment.Image Execution environment image information. - Variables: ### class datarobot.models.notebooks.execution_environment.Machine Execution environment machine information. - Variables: ### class datarobot.models.notebooks.execution_environment.ExecutionEnvironment An execution environment associated with a notebook. - Variables: #### classmethod get(notebook_id) Get a notebook execution environment by its notebook ID. - Parameters: notebook_id ( str ) – The ID of the notebook. - Returns: The notebook execution environment. - Return type: ExecutionEnvironment #### classmethod assign_environment(notebook_id, payload) Assign execution environment values to a notebook. - Parameters: - Returns: The assigned execution environment. - Return type: ExecutionEnvironment > [!NOTE] Examples > ``` > from datarobot.models.notebooks import ExecutionEnvironment, ExecutionEnvironmentAssignPayload > > payload = ExecutionEnvironmentAssignPayload(machine_slug='medium', time_to_live=10) > exec_env = ExecutionEnvironment.assign_environment('67914bfab0279fd832dc3fd1', payload) > ``` ### class datarobot.models.notebooks.kernel.NotebookKernel A kernel associated with a codespace notebook. - Variables: ### class datarobot.models.notebooks.revision.CreateRevisionPayload Payload for creating a notebook revision. ### class datarobot.models.notebooks.revision.NotebookRevision Represents a notebook revision. - Variables: #### classmethod create(notebook_id, payload=None) Create a new notebook revision. - Parameters: - Returns: Information about the created notebook revision. - Return type: NotebookRevision ### class datarobot.models.notebooks.scheduled_job.NotebookScheduledJob DataRobot Notebook Schedule. A scheduled job that runs a notebook. - Variables: #### classmethod get(use_case_id, scheduled_job_id) Retrieve a single notebook schedule. - Parameters: scheduled_job_id ( str ) – The ID of the notebook schedule you want to retrieve. - Returns: notebook_schedule – The requested notebook schedule. - Return type: NotebookScheduledJob > [!NOTE] Examples > ``` > from datarobot.models.notebooks import NotebookScheduledJob > > notebook_schedule = NotebookScheduledJob.get( > use_case_id="654ad653c6c1e889e8eab12e", > scheduled_job_id="65734fe637157200e28bf688", > ) > ``` #### classmethod list(notebook_ids=None, statuses=None) List all NotebookScheduledJobs available to the user. - Parameters: - Returns: notebook_schedules – A list of NotebookScheduledJobs available to the user. - Return type: List[NotebookScheduledJob] #### cancel() Cancel a running notebook schedule. - Return type: None #### get_most_recent_run() Retrieve the most recent run for the notebook schedule. - Returns: notebook_scheduled_run – The most recent run for the notebook schedule, or None if no runs have been made. - Return type: Optional[NotebookScheduledRun] > [!NOTE] Examples > ``` > from datarobot.models.notebooks import NotebookScheduledJob > > notebook_schedule = NotebookScheduledJob.get( > use_case_id="654ad653c6c1e889e8eab12e", > scheduled_job_id="65734fe637157200e28bf688", > ) > most_recent_run = notebook_schedule.get_most_recent_run() > ``` #### get_job_history() Retrieve list of historical runs for the notebook schedule. Gets the most recent runs first. - Returns: notebook_scheduled_runs – The list of historical runs for the notebook schedule. - Return type: List[NotebookScheduledRun] > [!NOTE] Examples > ``` > from datarobot.models.notebooks import NotebookScheduledJob > > notebook_schedule = NotebookScheduledJob.get( > use_case_id="654ad653c6c1e889e8eab12e", > scheduled_job_id="65734fe637157200e28bf688", > ) > notebook_scheduled_runs = notebook_schedule.get_job_history() > ``` #### wait_for_completion(max_wait=600) Wait for the completion of a scheduled notebook and return the revision ID corresponding to the run’s output. - Parameters: max_wait ( int ) – The number of seconds to wait before giving up. - Returns: revision_id – Returns either revision ID or message describing current state. - Return type: str > [!NOTE] Examples > ``` > from datarobot.models.notebooks.notebook import Notebook > > notebook = Notebook.get(notebook_id='6556b00dcc4ea0bb7ea48121') > manual_run = notebook.run_as_job() > revision_id = manual_run.wait_for_completion() > ``` ### class datarobot.models.notebooks.scheduled_run.ScheduledJobParam DataRobot Schedule Job Parameter. - Variables: ### class datarobot.models.notebooks.scheduled_run.ScheduledJobPayload DataRobot Schedule Job Payload. - Variables: ### class datarobot.models.notebooks.scheduled_run.ScheduledRunRevisionMetadata DataRobot Notebook Revision Metadata specifically for a scheduled run. Both id and name can be null if for example the job is still running or has failed. - Variables: ### class datarobot.models.notebooks.scheduled_run.NotebookScheduledRun DataRobot Notebook Scheduled Run. A historical run of a notebook schedule. - Variables: ### class datarobot.models.notebooks.session.CloneRepositorySchema Schema for cloning a repository when starting a notebook session. ### class datarobot.models.notebooks.session.StartSessionParameters Parameters used as environment variables in a notebook session. ### class datarobot.models.notebooks.session.StartSessionPayload Payload for starting a notebook session. ### class datarobot.models.notebooks.session.NotebookExecutionStatus Notebook execution status information. - Variables: ### class datarobot.models.notebooks.session.CodespaceNotebookCell Represents a cell in a codespace notebook. ### class datarobot.models.notebooks.session.CodespaceNotebookState Notebook state information for a codespace notebook. - Variables: ### class datarobot.models.notebooks.session.NotebookSession Notebook session information. - Variables: #### classmethod get(notebook_id) Get a notebook session by its notebook ID. - Parameters: notebook_id ( str ) – The ID of the notebook. - Returns: The notebook session information. - Return type: NotebookSession #### classmethod start(notebook_id, payload) Start a notebook session. - Parameters: - Returns: The notebook session information. - Return type: NotebookSession #### classmethod stop(notebook_id) Stop a notebook session. - Parameters: notebook_id ( str ) – The ID of the notebook. - Returns: The notebook session information. - Return type: NotebookSession #### classmethod execute_notebook(notebook_id, cell_ids=None) Execute a notebook. - Parameters: - Return type: None #### classmethod execute_codespace_notebook(notebook_id, notebook_path, generation, cells) Execute a notebook. - Parameters: - Return type: None #### classmethod get_execution_status(notebook_id) Get the execution status information of a notebook. - Parameters: notebook_id ( str ) – The ID of the notebook. - Returns: The execution status information of the notebook. - Return type: NotebookExecutionStatus ### class datarobot.models.notebooks.settings.NotebookSettings Settings for a DataRobot Notebook. - Variables: ### class datarobot.models.notebooks.user.NotebookUser A user associated with a Notebook. - Variables: ### class datarobot.models.notebooks.user.NotebookActivity A record of activity (i.e., last run, updated, etc.) in a Notebook. - Variables: --- # Projects URL: https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html # Projects ## Project ### class datarobot.models.Project A project built from a particular training dataset - Variables: #### set_options(options=None, **kwargs) Update the advanced options of this project. Either accepts an AdvancedOptions object or individual keyword arguments. This is an inplace update. - Raises: ValueError – Raised if an object passed to the options parameter is not an AdvancedOptions instance, a valid keyword argument from the AdvancedOptions class, or a combination of an AdvancedOptions instance AND keyword arguments. - Return type: None #### get_options() Return the stored advanced options for this project. - Return type: AdvancedOptions #### classmethod get(project_id) Gets information about a project. - Parameters: project_id ( str ) – The identifier of the project you want to load. - Returns: project – The queried project - Return type: Project > [!NOTE] Examples > ``` > import datarobot as dr > p = dr.Project.get(project_id='54e639a18bd88f08078ca831') > p.id > >>>'54e639a18bd88f08078ca831' > p.project_name > >>>'Some project name' > ``` #### classmethod create(cls, sourcedata, project_name='Untitled Project', max_wait=600, read_timeout=600, dataset_filename=None, , use_case=None) Creates a project with provided data. Project creation is asynchronous process, which means that after initial request we will keep polling status of async process that is responsible for project creation until it’s finished. For SDK users this only means that this method might raise exceptions related to it’s async nature. - Parameters: - Returns: project – Instance with initialized data. - Return type: Project - Raises: > [!NOTE] Examples > ``` > p = Project.create('/home/datasets/somedataset.csv', > project_name="New API project") > p.id > >>> '5921731dkqshda8yd28h' > p.project_name > >>> 'New API project' > ``` #### classmethod encrypted_string(plaintext) Sends a string to DataRobot to be encrypted This is used for passwords that DataRobot uses to access external data sources - Parameters: plaintext ( str ) – The string to encrypt - Returns: ciphertext – The encrypted string - Return type: str #### classmethod create_from_hdfs(cls, url, port=None, project_name=None, max_wait=600) Create a project from a datasource on a WebHDFS server. - Parameters: - Return type: Project > [!NOTE] Examples > ``` > p = Project.create_from_hdfs('hdfs:///tmp/somedataset.csv', > project_name="New API project") > p.id > >>> '5921731dkqshda8yd28h' > p.project_name > >>> 'New API project' > ``` #### classmethod create_from_data_source(cls, data_source_id, username=None, password=None, credential_id=None, use_kerberos=None, credential_data=None, project_name=None, max_wait=600, , use_case=None) Create a project from a data source. Either data_source or data_source_id should be specified. - Parameters: - Raises: InvalidUsageError – Raised if either username or password is passed without the other. - Return type: Project #### classmethod create_from_dataset(cls, dataset_id, dataset_version_id=None, project_name=None, user=None, password=None, credential_id=None, use_kerberos=None, use_sample_from_dataset=None, credential_data=None, max_wait=600, , use_case=None) Create a Project from a [datarobot.models.Dataset](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.Dataset) - Parameters: - Return type: Project #### classmethod create_from_recipe(cls, recipe_id, , use_case=None) Create a project from a recipe - Parameters: recipe_id ( string ) – The ID of the recipe entry to use to create the project’s dataset. - Return type: Project #### classmethod create_segmented_project_from_clustering_model(cls, clustering_project_id, clustering_model_id, target, max_wait=600, , use_case=None) Create a new segmented project from a clustering model - Parameters: - Returns: project – The created project - Return type: Project #### classmethod from_async(async_location, max_wait=600) Given a temporary async status location poll for no more than max_wait seconds until the async process (project creation or setting the target, for example) finishes successfully, then return the ready project - Parameters: - Returns: project – The project, now ready - Return type: Project - Raises: #### classmethod start(cls, sourcedata, target=None, project_name='Untitled Project', worker_count=None, metric=None, autopilot_on=True, blueprint_threshold=None, response_cap=None, partitioning_method=None, positive_class=None, target_type=None, unsupervised_mode=False, blend_best_models=None, prepare_model_for_deployment=None, consider_blenders_in_recommendation=None, scoring_code_only=None, min_secondary_validation_model_count=None, shap_only_mode=None, relationships_configuration_id=None, autopilot_with_feature_discovery=None, feature_discovery_supervised_feature_reduction=None, unsupervised_type=None, autopilot_cluster_list=None, bias_mitigation_feature_name=None, bias_mitigation_technique=None, include_bias_mitigation_feature_as_predictor_variable=None, incremental_learning_only_mode=None, incremental_learning_on_best_model=None, number_of_incremental_learning_iterations_before_best_model_selection=None, , use_case=None) Chain together project creation, file upload, and target selection. > [!NOTE] Notes > While this function provides a simple means to get started, it does not expose > all possible parameters. For advanced usage, using `create`, `set_advanced_options` and `analyze_and_model` directly is recommended. - Parameters: - Returns: project – The newly created and initialized project. - Return type: Project - Raises: > [!NOTE] Examples > ``` > Project.start("./tests/fixtures/file.csv", > "a_target", > project_name="test_name", > worker_count=4, > metric="a_metric") > ``` > > This is an example of using a URL to specify the datasource: > > ``` > Project.start("https://example.com/data/file.csv", > "a_target", > project_name="test_name", > worker_count=4, > metric="a_metric") > ``` #### classmethod list(search_params=None, use_cases=None, offset=None, limit=None) Returns the projects associated with this account. - Parameters: > [!NOTE] Examples > List all projects > > ``` > p_list = Project.list() > p_list > >>> [Project('Project One'), Project('Two')] > ``` > > Search for projects by name > > ``` > Project.list(search_params={'project_name': 'red'}) > >>> [Project('Prediction Time'), Project('Fred Project')] > ``` > > List 2nd and 3rd projects > > ``` > Project.list(offset=1, limit=2) > >>> [Project('Project 2'), Project('Project 3')] > ``` #### refresh() Fetches the latest state of the project, and updates this object with that information. This is an in place update, not a new object. - Return type: None #### delete() Removes this project from your account. - Return type: None #### analyze_and_model(target=None, mode='quick', metric=None, worker_count=None, positive_class=None, partitioning_method=None, featurelist_id=None, advanced_options=None, max_wait=600, target_type=None, credentials=None, feature_engineering_prediction_point=None, unsupervised_mode=False, relationships_configuration_id=None, class_mapping_aggregation_settings=None, segmentation_task_id=None, unsupervised_type=None, autopilot_cluster_list=None, use_gpu=None) Set target variable of an existing project and begin the autopilot process or send data to DataRobot for feature analysis only if manual mode is specified. Any options saved using `set_options` will be used if nothing is passed to `advanced_options`. However, saved options will be ignored if `advanced_options` are passed. Target setting is an asynchronous process, which means that after initial request we will keep polling status of async process that is responsible for target setting until it’s finished. For SDK users this only means that this method might raise exceptions related to it’s async nature. When execution returns to the caller, the autopilot process will already have commenced (again, unless manual mode is specified). - Parameters: - Returns: project – The instance with updated attributes. - Return type: Project - Raises: #### SEE ALSO [datarobot.models.Project.start](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.start): combines project creation, file upload, and target selection. Provides fewer options, but is useful for getting started quickly. #### set_target(target=None, mode='quick', metric=None, worker_count=None, positive_class=None, partitioning_method=None, featurelist_id=None, advanced_options=None, max_wait=600, target_type=None, credentials=None, feature_engineering_prediction_point=None, unsupervised_mode=False, relationships_configuration_id=None, class_mapping_aggregation_settings=None, segmentation_task_id=None, unsupervised_type=None, autopilot_cluster_list=None) Set target variable of an existing project and begin the Autopilot process (unless manual mode is specified). Target setting is an asynchronous process, which means that after initial request DataRobot keeps polling status of an async process that is responsible for target setting until it’s finished. For SDK users, this method might raise exceptions related to its async nature. When execution returns to the caller, the Autopilot process will already have commenced (again, unless manual mode is specified). - Parameters: #### SEE ALSO [datarobot.models.Project.start](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.start): Combines project creation, file upload, and target selection. Provides fewer options, but is useful for getting started quickly. [datarobot.models.Project.analyze_and_model](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.analyze_and_model): the method replacing `set_target` after it is removed. #### get_model_records(sort_by_partition='validation', sort_by_metric=None, with_metric=None, search_term=None, featurelists=None, families=None, blueprints=None, labels=None, characteristics=None, training_filters=None, number_of_clusters=None, limit=100, offset=0) Retrieve paginated model records, sorted by scores, with optional filtering. - Parameters: #### get_models(order_by=None, search_params=None, with_metric=None, use_new_models_retrieval=False) List all completed, successful models in the leaderboard for the given project. - Parameters: > [!NOTE] Examples > ``` > Project.get('pid').get_models(order_by=['-sample_pct', > 'metric']) > > # Getting models that contain "Ridge" in name > Project.get('pid').get_models( > search_params={ > 'name': "Ridge" > }) > > # Filtering models based on 'starred' flag: > Project.get('pid').get_models(search_params={'is_starred': True}) > ``` > > ``` > # retrieve additional attributes for the model > model_records = project.get_models(use_new_models_retrieval=True) > model_record = model_records[0] > blueprint_id = model_record.blueprint_id > blueprint = dr.Blueprint.get(project.id, blueprint_id) > model_record.number_of_clusters > blueprint.supports_composable_ml > blueprint.supports_monotonic_constraints > blueprint.monotonic_decreasing_featurelist_id > blueprint.monotonic_increasing_featurelist_id > model = dr.Model.get(project.id, model_record.id) > model.prediction_threshold > model.prediction_threshold_read_only > model.has_empty_clusters > model.is_n_clusters_dynamically_determined > ``` #### recommended_model() Returns the default recommended model, or None if there is no default recommended model. - Returns: recommended_model – The default recommended model. - Return type: Model or None #### get_top_model(metric=None) Obtain the top ranked model for a given metric/ If no metric is passed in, it uses the project’s default metric. Models that display score of N/A in the UI are not included in the ranking (see [https://docs.datarobot.com/en/docs/modeling/reference/model-detail/leaderboard-ref.html#na-scores](https://docs.datarobot.com/en/docs/modeling/reference/model-detail/leaderboard-ref.html#na-scores)). - Parameters: metric ( Optional[str] ) – Metric to sort models - Returns: model – The top model - Return type: Model - Raises: ValueError – Raised if the project is unsupervised. Raised if the project has no target set. Raised if no metric was passed or the project has no metric. Raised if the metric passed is not used by the models on the leaderboard. > [!NOTE] Examples > ``` > from datarobot.models.project import Project > > project = Project.get("") > top_model = project.get_top_model() > ``` #### get_datetime_models() List all models in the project as DatetimeModels Requires the project to be datetime partitioned. If it is not, a ClientError will occur. - Returns: models – the datetime models - Return type: list of DatetimeModel #### get_prime_models() List all DataRobot Prime models for the project Prime models were created to approximate a parent model, and have downloadable code. - Returns: models - Return type: list of PrimeModel #### get_prime_files(parent_model_id=None, model_id=None) List all downloadable code files from DataRobot Prime for the project - Parameters: - Returns: files - Return type: list of PrimeFile #### get_dataset() Retrieve the dataset used to create a project. - Returns: Dataset used for creation of project or None if no catalog_id present. - Return type: Dataset > [!NOTE] Examples > ``` > from datarobot.models.project import Project > > project = Project.get("") > dataset = project.get_dataset() > ``` #### get_datasets() List all the datasets that have been uploaded for predictions - Returns: datasets - Return type: list of PredictionDataset instances #### upload_dataset(sourcedata, max_wait=600, read_timeout=600, forecast_point=None, predictions_start_date=None, predictions_end_date=None, dataset_filename=None, relax_known_in_advance_features_check=None, credentials=None, actual_value_column=None, secondary_datasets_config_id=None) Upload a new dataset to make predictions against - Parameters: - Returns: dataset – The newly uploaded dataset. - Return type: PredictionDataset - Raises: #### upload_dataset_from_data_source(data_source_id, username, password, max_wait=600, forecast_point=None, relax_known_in_advance_features_check=None, credentials=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, secondary_datasets_config_id=None) Upload a new dataset from a data source to make predictions against - Parameters: - Returns: dataset – the newly uploaded dataset - Return type: PredictionDataset #### upload_dataset_from_catalog(dataset_id, credential_id=None, credential_data=None, dataset_version_id=None, max_wait=600, forecast_point=None, relax_known_in_advance_features_check=None, credentials=None, predictions_start_date=None, predictions_end_date=None, actual_value_column=None, secondary_datasets_config_id=None) Upload a new dataset from a catalog dataset to make predictions against - Parameters: #### get_blueprints() List all blueprints recommended for a project. - Returns: menu – All blueprints in a project’s repository. - Return type: list of Blueprint instances #### get_features() List all features for this project - Returns: all features for this project - Return type: list of Feature #### get_modeling_features(batch_size=None) List all modeling features for this project Only available once the target and partitioning settings have been set. For more information on the distinction between input and modeling features, see the [time series documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#input-vs-modeling). - Parameters: batch_size ( Optional[int] ) – The number of features to retrieve in a single API call. If specified, the client may make multiple calls to retrieve the full list of features. If not specified, an appropriate default will be chosen by the server. - Returns: All modeling features in this project - Return type: list of ModelingFeature #### get_featurelists() List all featurelists created for this project - Returns: All featurelists created for this project - Return type: list of Featurelist #### get_associations(assoc_type, metric, featurelist_id=None) Get the association statistics and metadata for a project’s informative features Added in version v2.17. - Parameters: - Returns: association_data – Pairwise metric strength data, feature clustering data, and ordering data for Feature Association Matrix visualization - Return type: dict #### get_association_featurelists() List featurelists and get feature association status for each Added in version v2.19. - Returns: feature_lists – Dict with ‘featurelists’ as key, with list of featurelists as values - Return type: dict #### get_association_matrix_details(feature1, feature2) Get a sample of the actual values used to measure the association between a pair of features Added in version v2.17. - Parameters: - Returns: #### get_modeling_featurelists(batch_size=None) List all modeling featurelists created for this project Modeling featurelists can only be created after the target and partitioning options have been set for a project. In time series projects, these are the featurelists that can be used for modeling; in other projects, they behave the same as regular featurelists. See the [time series documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#input-vs-modeling) for more information. - Parameters: batch_size ( Optional[int] ) – The number of featurelists to retrieve in a single API call. If specified, the client may make multiple calls to retrieve the full list of features. If not specified, an appropriate default will be chosen by the server. - Returns: all modeling featurelists in this project - Return type: list of ModelingFeaturelist #### get_discarded_features() Retrieve discarded during feature generation features. Applicable for time series projects. Can be called at the modeling stage. - Returns: discarded_features_info - Return type: DiscardedFeaturesInfo #### restore_discarded_features(features, max_wait=600) Restore discarded during feature generation features. Applicable for time series projects. Can be called at the modeling stage. - Returns: status – information about features requested to be restored. - Return type: FeatureRestorationStatus #### create_type_transform_feature(name, parent_name, variable_type, replacement=None, date_extraction=None, max_wait=600) Create a new feature by transforming the type of an existing feature in the project Note that only the following transformations are supported: 1. Text to categorical or numeric 2. Categorical to text or numeric 3. Numeric to categorical 4. Date to categorical or numeric > [!NOTE] Notes > Special considerations when casting numeric to categorical > > There are two parameters which can be used for `variableType` to convert numeric > data to categorical levels. These differ in the assumptions they make about the input > data, and are very important when considering the data that will be used to make > predictions. The assumptions that each makes are: > > categorical > : The data in the column is all integral, and there are no missing > values. If either of these conditions do not hold in the training set, the > transformation will be rejected. During predictions, if any of the values in the > parent column are missing, the predictions will error. > categoricalInt > : > New in v2.6 > All of the data in the column should be considered categorical in its string form when > cast to an int by truncation. For example the value > 3 > will be cast as the string > 3 > and the value > 3.14 > will also be cast as the string > 3 > . Further, the > value > -3.6 > will become the string > -3 > . > Missing values will still be recognized as missing. > > For convenience these are represented in the enum `VARIABLE_TYPE_TRANSFORM` with the > names `CATEGORICAL` and `CATEGORICAL_INT`. - Parameters: - Returns: The data of the new Feature - Return type: Feature - Raises: #### get_featurelist_by_name(name) Creates a new featurelist - Parameters: name ( Optional[str] ) – The name of the Project’s featurelist to get. - Returns: featurelist found by name, optional - Return type: Featurelist > [!NOTE] Examples > ``` > project = Project.get('5223deadbeefdeadbeef0101') > featurelist = project.get_featurelist_by_name("Raw Features") > ``` #### create_featurelist(name=None, features=None, starting_featurelist=None, starting_featurelist_id=None, starting_featurelist_name=None, features_to_include=None, features_to_exclude=None) Creates a new featurelist - Parameters: - Returns: newly created featurelist - Return type: Featurelist - Raises: > [!NOTE] Examples > ``` > project = Project.get('5223deadbeefdeadbeef0101') > flists = project.get_featurelists() > > # Create a new featurelist using a subset of features from an > # existing featurelist > flist = flists[0] > features = flist.features[::2] # Half of the features > > new_flist = project.create_featurelist( > name='Feature Subset', > features=features, > ) > ``` > > ``` > project = Project.get('5223deadbeefdeadbeef0101') > > # Create a new featurelist using a subset of features from an > # existing featurelist by using features_to_exclude param > > new_flist = project.create_featurelist( > name='Feature Subset of Existing Featurelist', > starting_featurelist_name="Informative Features", > features_to_exclude=["metformin", "weight", "age"], > ) > ``` #### create_modeling_featurelist(name, features, skip_datetime_partition_column=False) Create a new modeling featurelist Modeling featurelists can only be created after the target and partitioning options have been set for a project. In time series projects, these are the featurelists that can be used for modeling; in other projects, they behave the same as regular featurelists. See the [time series documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#input-vs-modeling) for more information. - Parameters: - Returns: featurelist – the newly created featurelist - Return type: ModelingFeaturelist > [!NOTE] Examples > ``` > project = Project.get('1234deadbeeffeeddead4321') > modeling_features = project.get_modeling_features() > selected_features = [feat.name for feat in modeling_features][:5] # select first five > new_flist = project.create_modeling_featurelist('Model This', selected_features) > ``` #### get_metrics(feature_name) Get the metrics recommended for modeling on the given feature. - Parameters: feature_name ( str ) – The name of the feature to query regarding which metrics are recommended for modeling. - Returns: #### get_status() Query the server for project status. - Returns: status – Contains: - Return type: dict > [!NOTE] Examples > ``` > {"autopilot_done": False, > "stage": "modeling", > "stage_description": "Ready for modeling"} > ``` #### pause_autopilot() Pause autopilot, which stops processing the next jobs in the queue. - Returns: paused – Whether the command was acknowledged - Return type: boolean #### unpause_autopilot() Unpause autopilot, which restarts processing the next jobs in the queue. - Returns: unpaused – Whether the command was acknowledged. - Return type: boolean #### start_autopilot(featurelist_id, mode='quick', blend_best_models=False, scoring_code_only=False, prepare_model_for_deployment=True, consider_blenders_in_recommendation=False, run_leakage_removed_feature_list=True, autopilot_cluster_list=None) Start Autopilot on provided featurelist with the specified Autopilot settings, halting the current Autopilot run. Only one autopilot can be running at the time. That’s why any ongoing autopilot on a different featurelist will be halted - modeling jobs in queue would not be affected but new jobs would not be added to queue by the halted autopilot. - Parameters: #### train(trainable, sample_pct=None, featurelist_id=None, source_project_id=None, scoring_type=None, training_row_count=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=, n_clusters=None) Submit a job to the queue to train a model. Either sample_pct or training_row_count can be used to specify the amount of data to use, but not both. If neither are specified, a default of the maximum amount of data that can safely be used to train any blueprint without going into the validation data will be selected. In smart-sampled projects, sample_pct and training_row_count are assumed to be in terms of rows of the minority class. > [!NOTE] Notes > If the project uses datetime partitioning, use [Project.train_datetime](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.train_datetime) instead. - Parameters: > [!NOTE] Examples > Use a `Blueprint` instance: > > ``` > blueprint = project.get_blueprints()[0] > model_job_id = project.train(blueprint, training_row_count=project.max_train_rows) > ``` > > Use a `blueprint_id`, which is a string. In the first case, it is > assumed that the blueprint was created by this project. If you are > using a blueprint used by another project, you will need to pass the > id of that other project as well. > > ``` > blueprint_id = 'e1c7fc29ba2e612a72272324b8a842af' > project.train(blueprint, training_row_count=project.max_train_rows) > > another_project.train(blueprint, source_project_id=project.id) > ``` > > You can also easily use this interface to train a new model using the data from > an existing model: > > ``` > model = project.get_models()[0] > model_job_id = project.train(model.blueprint.id, > sample_pct=100) > ``` #### train_datetime(blueprint_id, featurelist_id=None, training_row_count=None, training_duration=None, source_project_id=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=, use_project_settings=False, sampling_method=None, n_clusters=None) Create a new model in a datetime partitioned project If the project is not datetime partitioned, an error will occur. All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Parameters: - Returns: job – the created job to build the model - Return type: ModelJob #### blend(model_ids, blender_method) Submit a job for creating blender model. Upon success, the new job will be added to the end of the queue. - Parameters: - Returns: model_job – New ModelJob instance for the blender creation job in queue. - Return type: ModelJob #### SEE ALSO [datarobot.models.Project.check_blendable](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.check_blendable): to confirm if models can be blended #### check_blendable(model_ids, blender_method) Check if the specified models can be successfully blended - Parameters: - Return type: EligibilityResult #### start_prepare_model_for_deployment(model_id) Prepare a specific model for deployment. The requested model will be trained on the maximum autopilot size then go through the recommendation stages. For datetime partitioned projects, this includes the feature impact stage, retraining on a reduced feature list, and retraining the best of the reduced feature list model and the max autopilot original model on recent data. For non-datetime partitioned projects, this includes the feature impact stage, retraining on a reduced feature list, retraining the best of the reduced feature list model and the max autopilot original model up to the holdout size, then retraining the up-to-the holdout model on the full dataset. - Parameters: model_id ( str ) – The model to prepare for deployment. - Return type: None #### get_all_jobs(status=None) Get a list of jobs This will give Jobs representing any type of job, including modeling or predict jobs. - Parameters:status(QUEUE_STATUS enum,optional) – If called with QUEUE_STATUS.INPROGRESS, will return the jobs that are currently running. If called with QUEUE_STATUS.QUEUE, will return the jobs that are waiting to be run. If called with QUEUE_STATUS.ERROR, will return the jobs that have errored. If no value is provided, will return all jobs currently running or waiting to be run. *Returns:jobs– Each is an instance of Job *Return type:list #### get_blenders() Get a list of blender models. - Returns: list of all blender models in project. - Return type: list of BlenderModel #### get_frozen_models() Get a list of frozen models - Returns: list of all frozen models in project. - Return type: list of FrozenModel #### get_combined_models() Get a list of models in segmented project. - Returns: list of all combined models in segmented project. - Return type: list of CombinedModel #### get_active_combined_model() Retrieve currently active combined model in segmented project. - Returns: currently active combined model in segmented project. - Return type: CombinedModel #### get_segments_models(combined_model_id=None) Retrieve a list of all models belonging to the segments/child projects of the segmented project. - Parameters: combined_model_id ( Optional[str] ) – Id of the combined model to get segments for. If there is only a single combined model it can be retrieved automatically, but this must be specified when there are > 1 combined models. - Returns: segments_models – A list of dictionaries containing all of the segments/child projects, each with a list of their models ordered by metric from best to worst. - Return type: list(dict) #### get_model_jobs(status=None) Get a list of modeling jobs - Parameters:status(QUEUE_STATUS enum,optional) – If called with QUEUE_STATUS.INPROGRESS, will return the modeling jobs that are currently running. If called with QUEUE_STATUS.QUEUE, will return the modeling jobs that are waiting to be run. If called with QUEUE_STATUS.ERROR, will return the modeling jobs that have errored. If no value is provided, will return all modeling jobs currently running or waiting to be run. *Returns:jobs– Each is an instance of ModelJob *Return type:list #### get_predict_jobs(status=None) Get a list of prediction jobs - Parameters:status(QUEUE_STATUS enum,optional) – If called with QUEUE_STATUS.INPROGRESS, will return the prediction jobs that are currently running. If called with QUEUE_STATUS.QUEUE, will return the prediction jobs that are waiting to be run. If called with QUEUE_STATUS.ERROR, will return the prediction jobs that have errored. If called without a status, will return all prediction jobs currently running or waiting to be run. *Returns:jobs– Each is an instance of PredictJob *Return type:list #### wait_for_autopilot(check_interval=20.0, timeout=86400, verbosity=1) Blocks until autopilot is finished. This will raise an exception if the autopilot mode is changed from AUTOPILOT_MODE.FULL_AUTO. It makes API calls to sync the project state with the server and to look at which jobs are enqueued. - Parameters: - Raises: - Return type: None #### rename(project_name) Update the name of the project. - Parameters: project_name ( str ) – The new name - Return type: None #### set_project_description(project_description) Set or Update the project description. - Parameters: project_description ( str ) – The new description for this project. - Return type: None #### unlock_holdout() Unlock the holdout for this project. This will cause subsequent queries of the models of this project to contain the metric values for the holdout set, if it exists. Take care, as this cannot be undone. Remember that best practice is to select a model before analyzing the model performance on the holdout set - Return type: None #### set_worker_count(worker_count) Sets the number of workers allocated to this project. Note that this value is limited to the number allowed by your account. Lowering the number will not stop currently running jobs, but will cause the queue to wait for the appropriate number of jobs to finish before attempting to run more jobs. - Parameters: worker_count ( int ) – The number of concurrent workers to request from the pool of workers. (New in version v2.14) Setting this to -1 will update the number of workers to the maximum available to your account. - Return type: None #### set_advanced_options(advanced_options=None, **kwargs) Update the advanced options of this project. > [!NOTE] Notes > Project options will not be stored at the database level, so the options > set via this method will only be attached to a project instance for the lifetime of a > client session (if you quit your session and reopen a new one before running autopilot, > the advanced options will be lost). > > Either accepts an AdvancedOptions object to replace all advanced options or individual keyword > arguments. This is an inplace update, not a new object. The options set will only remain for the > life of this project instance within a given session. - Parameters: - Return type: None #### list_advanced_options() View the advanced options that have been set on a project instance. Includes those that haven’t been set (with value of None). - Return type: dict of advanced options and their values #### set_partitioning_method(cv_method=None, validation_type=None, seed=0, reps=None, user_partition_col=None, training_level=None, validation_level=None, holdout_level=None, cv_holdout_level=None, validation_pct=None, holdout_pct=None, partition_key_cols=None, partitioning_method=None) Configures the partitioning method for this project. If this project does not already have a partitioning method set, creates a new configuration based on provided args. If the partitioning_method arg is set, that configuration will instead be used. > [!NOTE] Notes > This is an inplace update, not a new object. The options set will only remain for the > life of this project instance within a given session. You must still call `set_target` to make this change permanent for the project. Calling `refresh` without first calling `set_target` will invalidate this configuration. Similarly, calling `get` to retrieve a > second copy of the project will not include this configuration. > > Added in version v3.0. - Parameters: - Raises: - Returns: project – The instance with updated attributes. - Return type: Project #### get_uri() - Returns: url – Permanent static hyperlink to a project leaderboard. - Return type: str #### get_rating_table_models() Get a list of models with a rating table - Returns: list of all models with a rating table in project. - Return type: list of RatingTableModel #### get_rating_tables() Get a list of rating tables - Returns: list of rating tables in project. - Return type: list of RatingTable #### get_access_list() Retrieve users who have access to this project and their access levels Added in version v2.15. - Return type: list of SharingAccess #### share(access_list, send_notification=None, include_feature_discovery_entities=None) Modify the ability of users to access this project Added in version v2.15. - Parameters: - Return type: None - Raises: datarobot.ClientError : – if you do not have permission to share this project, if the user you’re sharing with doesn’t exist, if the same user appears multiple times in the access_list, or if these changes would leave the project without an owner > [!NOTE] Examples > Transfer access to the project from [old_user@datarobot.com](mailto:old_user@datarobot.com) to [new_user@datarobot.com](mailto:new_user@datarobot.com) > > ``` > import datarobot as dr > > new_access = dr.SharingAccess(new_user@datarobot.com, > dr.enums.SHARING_ROLE.OWNER, can_share=True) > access_list = [dr.SharingAccess(old_user@datarobot.com, None), new_access] > > dr.Project.get('my-project-id').share(access_list) > ``` #### batch_features_type_transform(parent_names, variable_type, prefix=None, suffix=None, max_wait=600) Create new features by transforming the type of existing ones. Added in version v2.17. > [!NOTE] Notes > The following transformations are only supported in batch mode: > > Text to categorical or numeric > Categorical to text or numeric > Numeric to categorical > > See {ref}`here ` for special considerations when casting > numeric to categorical. > Date to categorical or numeric transformations are not currently supported for batch > mode but can be performed individually usingcreate_type_transform_feature. - Parameters: #### clone_project(new_project_name=None, max_wait=600) Create a fresh (post-EDA1) copy of this project that is ready for setting targets and modeling options. - Parameters: - Return type: datarobot.models.Project #### create_interaction_feature(name, features, separator, max_wait=600) Create a new interaction feature by combining two categorical ones. Added in version v2.21. - Parameters: - Returns: The data of the new Interaction feature - Return type: datarobot.models.InteractionFeature - Raises: #### get_relationships_configuration() Get the relationships configuration for a given project Added in version v2.21. - Returns: relationships_configuration – relationships configuration applied to project - Return type: RelationshipsConfiguration #### download_feature_discovery_dataset(file_name, pred_dataset_id=None) Download Feature discovery training or prediction dataset - Parameters: - Return type: None #### download_feature_discovery_recipe_sqls(file_name, model_id=None, max_wait=600) Export and download Feature discovery recipe SQL statements .. versionadded:: v2.25 - Parameters: - Raises: - Return type: None #### validate_external_time_series_baseline(catalog_version_id, target, datetime_partitioning, max_wait=600) Validate external baseline prediction catalog. The forecast windows settings, validation and holdout duration specified in the datetime specification must be consistent with project settings as these parameters are used to check whether the specified catalog version id has been validated or not. See [external baseline predictions documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#external-baseline-predictions) for example usage. - Parameters: #### download_multicategorical_data_format_errors(file_name) Download multicategorical data format errors to the CSV file. If any format errors where detected in potentially multicategorical features the resulting file will contain at max 10 entries. CSV file content contains feature name, dataset index in which the error was detected, row value and type of error detected. In case that there were no errors or none of the features where potentially multicategorical the CSV file will be empty containing only the header. - Parameters: file_name ( str ) – File path where CSV file will be saved. - Return type: None #### get_multiseries_names() For a multiseries timeseries project it returns all distinct entries in the multiseries column. For a non timeseries project it will just return an empty list. - Returns: multiseries_names – List of all distinct entries in the multiseries column - Return type: List[str] #### restart_segment(segment) Restart single segment in a segmented project. Added in version v2.28. Segment restart is allowed only for segments that haven’t reached modeling phase. Restart will permanently remove previous project and trigger set up of a new one for particular segment. - Parameters: segment ( str ) – Segment to restart #### get_bias_mitigated_models(parent_model_id=None, offset=0, limit=100) List the child models with bias mitigation applied Added in version v2.29. - Parameters: - Returns: models - Return type: list of dict #### apply_bias_mitigation(bias_mitigation_parent_leaderboard_id, bias_mitigation_feature_name, bias_mitigation_technique, include_bias_mitigation_feature_as_predictor_variable) Apply bias mitigation to an existing model by training a version of that model but with bias mitigation applied. An error will be returned if the model does not support bias mitigation with the technique requested. Added in version v2.29. - Parameters: - Returns: the job of the model with bias mitigation applied that was just submitted for training - Return type: ModelJob #### request_bias_mitigation_feature_info(bias_mitigation_feature_name) Request a compute job for bias mitigation feature info for a given feature, which will include - if there are any rare classes - if there are any combinations of the target values and the feature values that never occur in the same row - if the feature has a high number of missing values. Note that this feature check is dependent on the current target selected for the project. Added in version v2.29. - Parameters: bias_mitigation_feature_name ( str ) – The feature name of the protected features that will be used in a bias mitigation task to attempt to mitigate bias - Returns: Bias mitigation feature info model for the requested feature - Return type: BiasMitigationFeatureInfo #### get_bias_mitigation_feature_info(bias_mitigation_feature_name) Get the computed bias mitigation feature info for a given feature, which will include - if there are any rare classes - if there are any combinations of the target values and the feature values that never occur in the same row - if the feature has a high number of missing values. Note that this feature check is dependent on the current target selected for the project. If this info has not already been computed, this will raise a 404 error. Added in version v2.29. - Parameters: bias_mitigation_feature_name ( str ) – The feature name of the protected features that will be used in a bias mitigation task to attempt to mitigate bias - Returns: Bias mitigation feature info model for the requested feature - Return type: BiasMitigationFeatureInfo #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( T , bound= APIObject) #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None #### set_datetime_partitioning(datetime_partition_spec=None, **kwargs) Set the datetime partitioning method for a time series project by either passing in a DatetimePartitioningSpecification instance or any individual attributes of that class. Updates `self.partitioning_method` if already set previously (does not replace it). This is an alternative to passing a specification to [Project.analyze_and_model](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.analyze_and_model) via the `partitioning_method` parameter. To see the full partitioning based on the project dataset, use [DatetimePartitioning.generate](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioning.generate). Added in version v3.0. - Parameters: datetime_partition_spec ( DatetimePartitioningSpecification ) – DatetimePartitioningSpecification , optional The customizable aspects of datetime partitioning for a time series project. An alternative to passing individual settings (attributes of the DatetimePartitioningSpecification class). - Returns: Full partitioning including user-specified attributes as well as those determined by DR based on the dataset. - Return type: DatetimePartitioning #### list_datetime_partition_spec() List datetime partitioning settings. This method makes an API call to retrieve settings from the DB if project is in the modeling stage, i.e., if analyze_and_model (autopilot) has already been called. If analyze_and_model has not yet been called, this method will instead simply print settings from project.partitioning_method. Added in version v3.0. - Return type: DatetimePartitioningSpecification or None ### class datarobot.helpers.eligibility_result.EligibilityResult Represents whether a particular operation is supported For instance, a function to check whether a set of models can be blended can return an EligibilityResult specifying whether or not blending is supported and why it may not be supported. - Variables: ## Advanced options ### class datarobot.helpers.AdvancedOptions Used when setting the target of a project to set advanced options of modeling process. - Parameters: > [!NOTE] Examples > ``` > import datarobot as dr > advanced_options = dr.AdvancedOptions( > weights='weights_column', > offset=['offset_column'], > exposure='exposure_column', > response_cap=0.7, > blueprint_threshold=2, > smart_downsampled=True, majority_downsampling_rate=75.0) > ``` #### get(_AdvancedOptions__key, _AdvancedOptions__default=None) Return the value for key if key is in the dictionary, else default. - Return type: Optional [ Any ] #### pop(_AdvancedOptions__key) If the key is not found, return the default if given; otherwise, raise a KeyError. - Return type: Optional [ Any ] #### update_individual_options(**kwargs) Update individual attributes of an instance of [AdvancedOptions](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.AdvancedOptions). - Return type: None #### collect_payload() a helper to collect data for payload - Return type: Dict [ str , Union [ Any , Dict [ str , str ]]] ## Partitioning ### class datarobot.RandomCV A partition in which observations are randomly assigned to cross-validation groups and the holdout set. - Parameters: ### class datarobot.StratifiedCV A partition in which observations are randomly assigned to cross-validation groups and the holdout set, preserving in each group the same ratio of positive to negative cases as in the original data. - Parameters: ### class datarobot.GroupCV A partition in which one column is specified, and rows sharing a common value for that column are guaranteed to stay together in the partitioning into cross-validation groups and the holdout set. - Parameters: ### class datarobot.UserCV A partition where the cross-validation folds and the holdout set are specified by the user. - Parameters: ### class datarobot.RandomTVH Specifies a partitioning method in which rows are randomly assigned to training, validation, and holdout. - Parameters: ### class datarobot.UserTVH Specifies a partitioning method in which rows are assigned by the user to training, validation, and holdout sets. - Parameters: ### class datarobot.StratifiedTVH A partition in which observations are randomly assigned to train, validation, and holdout sets, preserving in each group the same ratio of positive to negative cases as in the original data. - Parameters: ### class datarobot.GroupTVH A partition in which one column is specified, and rows sharing a common value for that column are guaranteed to stay together in the partitioning into the training, validation, and holdout sets. - Parameters: ### class datarobot.DatetimePartitioningSpecification Uniquely defines a DatetimePartitioning for some project Includes only the attributes of DatetimePartitioning that are directly controllable by users, not those determined by the DataRobot application based on the project dataset and the user-controlled settings. This is the specification that should be passed to [Project.analyze_and_model](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.analyze_and_model) via the `partitioning_method` parameter. To see the full partitioning based on the project dataset, use [DatetimePartitioning.generate](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioning.generate). All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. Note that either ( `holdout_start_date`, `holdout_duration`) or ( `holdout_start_date`, `holdout_end_date`) can be used to specify holdout partitioning settings. - Variables: #### collect_payload() Set up the dict that should be sent to the server when setting the target - Returns: partitioning_spec - Return type: dict #### prep_payload(project_id, max_wait=600) Run any necessary validation and prep of the payload, including async operations Mainly used for the datetime partitioning spec but implemented in general for consistency - Return type: None #### update(**kwargs) Update this instance, matching attributes to kwargs Mainly used for the datetime partitioning spec but implemented in general for consistency - Return type: None ### class datarobot.BacktestSpecification Uniquely defines a Backtest used in a DatetimePartitioning Includes only the attributes of a backtest directly controllable by users. The other attributes are assigned by the DataRobot application based on the project dataset and the user-controlled settings. There are two ways to specify an individual backtest: Option 1: Use `index`, `gap_duration`, `validation_start_date`, and `validation_duration`. All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. ``` import datarobot as dr partitioning_spec = dr.DatetimePartitioningSpecification( backtests=[ # modify the first backtest using option 1 dr.BacktestSpecification( index=0, gap_duration=dr.partitioning_methods.construct_duration_string(), validation_start_date=datetime(year=2010, month=1, day=1), validation_duration=dr.partitioning_methods.construct_duration_string(years=1), ) ], # other partitioning settings... ) ``` Option 2 (New in version v2.20): Use `index`, `primary_training_start_date`, `primary_training_end_date`, `validation_start_date`, and `validation_end_date`. In this case, note that setting `primary_training_end_date` and `validation_start_date` to the same timestamp will result with no gap being created. ``` import datarobot as dr partitioning_spec = dr.DatetimePartitioningSpecification( backtests=[ # modify the first backtest using option 2 dr.BacktestSpecification( index=0, primary_training_start_date=datetime(year=2005, month=1, day=1), primary_training_end_date=datetime(year=2010, month=1, day=1), validation_start_date=datetime(year=2010, month=1, day=1), validation_end_date=datetime(year=2011, month=1, day=1), ) ], # other partitioning settings... ) ``` All durations should be specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Variables: ### class datarobot.FeatureSettings Per feature settings - Variables: #### collect_payload(use_a_priori=False) - Parameters: use_a_priori ( bool ) – Switch to using the older a_priori key name instead of known_in_advance. Default False - Return type: BacktestSpecification dictionary representation ### class datarobot.Periodicity Periodicity configuration - Parameters: > [!NOTE] Examples > ``` > from datarobot as dr > periodicities = [ > dr.Periodicity(time_steps=10, time_unit=dr.enums.TIME_UNITS.HOUR), > dr.Periodicity(time_steps=600, time_unit=dr.enums.TIME_UNITS.MINUTE)] > spec = dr.DatetimePartitioningSpecification( > # ... > periodicities=periodicities > ) > ``` ### class datarobot.DatetimePartitioning Full partitioning of a project for datetime partitioning. To instantiate, use [DatetimePartitioning.get(project_id)](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioning.get). Includes both the attributes specified by the user, as well as those determined by the DataRobot application based on the project dataset. In order to use a partitioning to set the target, call [to_specification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioning.to_specification) and pass the resulting [DatetimePartitioningSpecification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioningSpecification) to [Project.analyze_and_model](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.analyze_and_model) via the `partitioning_method` parameter. The available training data corresponds to all the data available for training, while the primary training data corresponds to the data that can be used to train while ensuring that all backtests are available. If a model is trained with more data than is available in the primary training data, then all backtests may not have scores available. All durations are specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Variables: #### classmethod generate(cls, project_id, spec, max_wait=600, target=None) Preview the full partitioning determined by a DatetimePartitioningSpecification Based on the project dataset and the partitioning specification, inspect the full partitioning that would be used if the same specification were passed into [Project.analyze_and_model](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.analyze_and_model). - Parameters: - Returns: the full generated partitioning - Return type: DatetimePartitioning #### classmethod get(project_id) Retrieve the DatetimePartitioning from a project Only available if the project has already set the target as a datetime project. - Parameters: project_id ( str ) – the ID of the project to retrieve partitioning for - Returns: DatetimePartitioning - Return type: the full partitioning for the project #### classmethod generate_optimized(project_id, spec, target, max_wait=600) Preview the full partitioning determined by a DatetimePartitioningSpecification Based on the project dataset and the partitioning specification, inspect the full partitioning that would be used if the same specification were passed into Project.analyze_and_model. - Parameters: - Returns: the full generated partitioning - Return type: DatetimePartitioning #### classmethod get_optimized(project_id, datetime_partitioning_id) Retrieve an Optimized DatetimePartitioning from a project for the specified datetime_partitioning_id. A datetime_partitioning_id is created by using the [generate_optimized](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioning.generate_optimized) function. - Parameters: - Returns: DatetimePartitioning - Return type: the full partitioning for the project #### classmethod feature_log_list(project_id, offset=None, limit=None) Retrieve the feature derivation log content and log length for a time series project. The Time Series Feature Log provides details about the feature generation process for a time series project. It includes information about which features are generated and their priority, as well as the detected properties of the time series data such as whether the series is stationary, and periodicities detected. This route is only supported for time series projects that have finished partitioning. The feature derivation log will include information about: - Detected stationarity of the series: e.g., ‘Series detected as non-stationary’ - Detected presence of multiplicative trend in the series: e.g., ‘Multiplicative trend detected’ - Detected presence of multiplicative trend in the series: e.g., ‘Detected periodicities: 7 day’ - Maximum number of feature to be generated: e.g., ‘Maximum number of feature to be generated is 1440’ - Window sizes used in rolling statistics / lag extractors e.g., ‘The window sizes chosen to be: 2 months (because the time step is 1 month and Feature Derivation Window is 2 months)’ - Features that are specified as known-in-advance e.g., ‘Variables treated as apriori: holiday’ - Details about why certain variables are transformed in the input data e.g., ‘Generating variable “y (log)” from “y” because multiplicative trend is detected’ - Details about features generated as timeseries features, and their priority e.g., ‘Generating feature “date (actual)” from “date” (priority: 1)’ - Parameters: - Return type: Any #### classmethod feature_log_retrieve(project_id) Retrieve the feature derivation log content and log length for a time series project. The Time Series Feature Log provides details about the feature generation process for a time series project. It includes information about which features are generated and their priority, as well as the detected properties of the time series data such as whether the series is stationary, and periodicities detected. This route is only supported for time series projects that have finished partitioning. The feature derivation log will include information about: - Detected stationarity of the series: e.g., ‘Series detected as non-stationary’ - Detected presence of multiplicative trend in the series: e.g., ‘Multiplicative trend detected’ - Detected presence of multiplicative trend in the series: e.g., ‘Detected periodicities: 7 day’ - Maximum number of feature to be generated: e.g., ‘Maximum number of feature to be generated is 1440’ - Window sizes used in rolling statistics / lag extractors e.g., ‘The window sizes chosen to be: 2 months (because the time step is 1 month and Feature Derivation Window is 2 months)’ - Features that are specified as known-in-advance e.g., ‘Variables treated as apriori: holiday’ - Details about why certain variables are transformed in the input data e.g., ‘Generating variable “y (log)” from “y” because multiplicative trend is detected’ - Details about features generated as timeseries features, and their priority e.g., ‘Generating feature “date (actual)” from “date” (priority: 1)’ - Parameters: project_id ( str ) – project id to retrieve a feature derivation log for. - Return type: str #### to_specification(use_holdout_start_end_format=False, use_backtest_start_end_format=False) Render the DatetimePartitioning as a [DatetimePartitioningSpecification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioningSpecification) The resulting specification can be used when setting the target, and contains only the attributes directly controllable by users. - Parameters: - Returns: the specification for this partitioning - Return type: DatetimePartitioningSpecification #### to_dataframe() Render the partitioning settings as a dataframe for convenience of display Excludes project_id, datetime_partition_column, date_format, autopilot_data_selection_method, validation_duration, and number_of_backtests, as well as the row count information, if present. Also excludes the time series specific parameters for use_time_series, default_to_known_in_advance, default_to_do_not_derive, and defining the feature derivation and forecast windows. - Return type: DataFrame #### classmethod datetime_partitioning_log_retrieve(project_id, datetime_partitioning_id) Retrieve the datetime partitioning log content for an optimized datetime partitioning. The datetime partitioning log provides details about the partitioning process for an OTV or time series project. - Parameters: - Return type: Any #### classmethod datetime_partitioning_log_list(project_id, datetime_partitioning_id, offset=None, limit=None) Retrieve the datetime partitioning log content and log length for an optimized datetime partitioning. The Datetime Partitioning Log provides details about the partitioning process for an OTV or Time Series project. - Parameters: - Return type: Any #### classmethod get_input_data(project_id, datetime_partitioning_id) Retrieve the input used to create an optimized DatetimePartitioning from a project for the specified datetime_partitioning_id. A datetime_partitioning_id is created by using the [generate_optimized](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioning.generate_optimized) function. - Parameters: - Returns: DatetimePartitioningInput - Return type: The input to optimized datetime partitioning. ### class datarobot.helpers.partitioning_methods.DatetimePartitioningId Defines a DatetimePartitioningId used for datetime partitioning. This class only includes the datetime_partitioning_id that identifies a previously optimized datetime partitioning and the project_id for the associated project. This is the specification that should be passed to [Project.analyze_and_model](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.models.Project.analyze_and_model) via the `partitioning_method` parameter. To see the full partitioning use [DatetimePartitioning.get_optimized](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioning.get_optimized). - Variables: #### collect_payload() Set up the dict that should be sent to the server when setting the target - Returns: partitioning_spec - Return type: dict #### prep_payload(project_id, max_wait=600) Run any necessary validation and prep of the payload, including async operations Mainly used for the datetime partitioning spec but implemented in general for consistency - Return type: None #### update(**kwargs) Update this instance, matching attributes to kwargs Mainly used for the datetime partitioning spec but implemented in general for consistency - Return type: NoReturn ### class datarobot.helpers.partitioning_methods.Backtest A backtest used to evaluate models trained in a datetime partitioned project When setting up a datetime partitioning project, backtests are specified by a [BacktestSpecification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.BacktestSpecification). The available training data corresponds to all the data available for training, while the primary training data corresponds to the data that can be used to train while ensuring that all backtests are available. If a model is trained with more data than is available in the primary training data, then all backtests may not have scores available. All durations are specified with a duration string such as those returned by the [partitioning_methods.construct_duration_string](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.helpers.partitioning_methods.construct_duration_string) helper method. Please see [datetime partitioned project documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/datetime_partition.html#date-dur-spec) for more information on duration strings. - Variables: #### to_specification(use_start_end_format=False) Render this backtest as a [BacktestSpecification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.BacktestSpecification). The resulting specification includes only the attributes users can directly control, not those indirectly determined by the project dataset. - Parameters: use_start_end_format ( bool ) – Default False . If False , will use a duration-based approach for specifying backtests ( gap_duration , validation_start_date , and validation_duration ). If True , will use a start/end date approach for specifying backtests ( primary_training_start_date , primary_training_end_date , validation_start_date , validation_end_date ). In contrast, projects created in the Web UI will use the start/end date approach for specifying backtests. Set this parameter to True to mirror the behavior in the Web UI. - Returns: the specification for this backtest - Return type: BacktestSpecification #### to_dataframe() Render this backtest as a dataframe for convenience of display - Returns: backtest_partitioning – the backtest attributes, formatted into a dataframe - Return type: pandas.Dataframe ### class datarobot.helpers.partitioning_methods.FeatureSettingsPayload ### datarobot.helpers.partitioning_methods.construct_duration_string(years=0, months=0, days=0, hours=0, minutes=0, seconds=0) Construct a valid string representing a duration in accordance with ISO8601 A duration of six months, 3 days, and 12 hours could be represented as P6M3DT12H. - Parameters: - Returns: duration_string – The duration string, specified compatibly with ISO8601 - Return type: str ## Status check job ### class datarobot.models.StatusCheckJob Tracks asynchronous task status - Variables: job_id ( str ) – The ID of the status the job belongs to. #### wait_for_completion(max_wait=600) Waits for job to complete. - Parameters: max_wait ( Optional[int] ) – How long to wait for the job to finish. If the time expires, DataRobot returns the current status. - Returns: status – Returns the current status of the job. - Return type: JobStatusResult #### get_status() Retrieve JobStatusResult object with the latest job status data from the server. - Return type: JobStatusResult #### get_result_when_complete(max_wait=600) Wait for the job to complete, then attempt to convert the resulting json into an object of type self.resource_type :rtype: `A newly created resource` of `type self.resource_type` ### class datarobot.models.JobStatusResult JobStatusResult(status, status_id, completed_resource_url, message) #### status : Optional[str] Alias for field number 0 #### status_id : Optional[str] Alias for field number 1 #### completed_resource_url : Optional[str] Alias for field number 2 #### message : Optional[str] Alias for field number 3 ## Segmented modeling API Reference for entities used in Segmented Modeling. See dedicated [User Guide](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/segmented_modeling.html#segmented-modeling) for examples. ### class datarobot.CombinedModel A model from a segmented project. Combination of ordinary models in child segments projects. - Variables: #### classmethod get(project_id, combined_model_id) Retrieve combined model - Parameters: - Returns: The queried combined model. - Return type: CombinedModel #### classmethod set_segment_champion(project_id, model_id, clone=False) Update a segment champion in a combined model by setting the model_id that belongs to the child project_id as the champion. - Parameters: - Returns: combined_model_id – Id of the combined model that was updated - Return type: str #### get_segments_info() Retrieve Combined Model segments info - Returns: List of segments - Return type: list[SegmentInfo] #### get_segments_as_dataframe(encoding='utf-8') Retrieve Combine Models segments as a DataFrame. - Parameters: encoding ( Optional[str] ) – A string representing the encoding to use in the output csv file. Defaults to ‘utf-8’. - Returns: Combined model segments - Return type: DataFrame #### get_segments_as_csv(filename, encoding='utf-8') Save the Combine Models segments to a csv. - Parameters: - Return type: None #### train(sample_pct=None, featurelist_id=None, scoring_type=None, training_row_count=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=) Inherited from Model - CombinedModels cannot be retrained directly - Return type: NoReturn #### train_datetime(featurelist_id=None, training_row_count=None, training_duration=None, time_window_sample_pct=None, monotonic_increasing_featurelist_id=, monotonic_decreasing_featurelist_id=, use_project_settings=False, sampling_method=None, n_clusters=None) Inherited from Model - CombinedModels cannot be retrained directly - Return type: NoReturn #### retrain(sample_pct=None, featurelist_id=None, training_row_count=None, n_clusters=None) Inherited from Model - CombinedModels cannot be retrained directly - Return type: NoReturn #### request_frozen_model(sample_pct=None, training_row_count=None) Inherited from Model - CombinedModels cannot be retrained as frozen - Return type: NoReturn #### request_frozen_datetime_model(training_row_count=None, training_duration=None, training_start_date=None, training_end_date=None, time_window_sample_pct=None, sampling_method=None) Inherited from Model - CombinedModels cannot be retrained as frozen - Return type: NoReturn #### cross_validate() Inherited from Model - CombinedModels cannot request cross validation - Return type: NoReturn ### class datarobot.SegmentationTask A Segmentation Task is used for segmenting an existing project into multiple child projects. Each child project (or segment) will be a separate autopilot run. Currently only user defined segmentation is supported. Example for creating a new SegmentationTask for Time Series segmentation with a user defined id column: ``` from datarobot import SegmentationTask # Create the SegmentationTask segmentation_task_results = SegmentationTask.create( project_id=project.id, target=target, use_time_series=True, datetime_partition_column=datetime_partition_column, multiseries_id_columns=[multiseries_id_column], user_defined_segment_id_columns=[user_defined_segment_id_column] ) # Retrieve the completed SegmentationTask object from the job results segmentation_task = segmentation_task_results['completedJobs'][0] ``` - Variables: #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: SegmentationTask #### collect_payload() Convert the record to a dictionary - Return type: Dict [ str , str ] #### classmethod create(project_id, target, use_time_series=False, datetime_partition_column=None, multiseries_id_columns=None, user_defined_segment_id_columns=None, max_wait=600, model_package_id=None) Creates segmentation tasks for the project based on the defined parameters. - Parameters: - Returns: segmentation_tasks – Dictionary containing the numberOfJobs, completedJobs, and failedJobs. completedJobs is a list of SegmentationTask objects, while failed jobs is a list of dictionaries indicating problems with submitted tasks. - Return type: dict #### classmethod list(project_id) List all of the segmentation tasks that have been created for a specific project_id. - Parameters: project_id ( str ) – The id of the parent project - Returns: segmentation_tasks – List of instances with initialized data. - Return type: list of SegmentationTask #### classmethod get(project_id, segmentation_task_id) Retrieve information for a single segmentation task associated with a project_id. - Parameters: - Returns: segmentation_task – Instance with initialized data. - Return type: SegmentationTask ### class datarobot.SegmentInfo A SegmentInfo is an object containing information about the combined model segments - Variables: #### classmethod list(project_id, model_id) List all of the segments that have been created for a specific project_id. - Parameters: project_id ( str ) – The id of the parent project - Returns: segments – List of instances with initialized data. - Return type: list of datarobot.models.segmentation.SegmentInfo ### class datarobot.models.segmentation.SegmentationTask A Segmentation Task is used for segmenting an existing project into multiple child projects. Each child project (or segment) will be a separate autopilot run. Currently only user defined segmentation is supported. Example for creating a new SegmentationTask for Time Series segmentation with a user defined id column: ``` from datarobot import SegmentationTask # Create the SegmentationTask segmentation_task_results = SegmentationTask.create( project_id=project.id, target=target, use_time_series=True, datetime_partition_column=datetime_partition_column, multiseries_id_columns=[multiseries_id_column], user_defined_segment_id_columns=[user_defined_segment_id_column] ) # Retrieve the completed SegmentationTask object from the job results segmentation_task = segmentation_task_results['completedJobs'][0] ``` - Variables: #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: SegmentationTask #### collect_payload() Convert the record to a dictionary - Return type: Dict [ str , str ] #### classmethod create(project_id, target, use_time_series=False, datetime_partition_column=None, multiseries_id_columns=None, user_defined_segment_id_columns=None, max_wait=600, model_package_id=None) Creates segmentation tasks for the project based on the defined parameters. - Parameters: - Returns: segmentation_tasks – Dictionary containing the numberOfJobs, completedJobs, and failedJobs. completedJobs is a list of SegmentationTask objects, while failed jobs is a list of dictionaries indicating problems with submitted tasks. - Return type: dict #### classmethod list(project_id) List all of the segmentation tasks that have been created for a specific project_id. - Parameters: project_id ( str ) – The id of the parent project - Returns: segmentation_tasks – List of instances with initialized data. - Return type: list of SegmentationTask #### classmethod get(project_id, segmentation_task_id) Retrieve information for a single segmentation task associated with a project_id. - Parameters: - Returns: segmentation_task – Instance with initialized data. - Return type: SegmentationTask ### class datarobot.models.segmentation.SegmentationTaskCreatedResponse ## External baseline validation ### class datarobot.models.external_baseline_validation.ExternalBaselineValidationInfo An object containing information about external time series baseline predictions validation results. - Variables: #### classmethod get(project_id, validation_job_id) Get information about external baseline validation job - Parameters: - Returns: info – information about external baseline validation job - Return type: ExternalBaselineValidationInfo ## Calendar file ### class datarobot.CalendarFile Represents the data for a calendar file. For more information about calendar files, see the [calendar documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#calendar-files). - Variables: #### classmethod create(file_path, calendar_name=None, multiseries_id_columns=None) Creates a calendar using the given file. For information about calendar files, see the [calendar documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#calendar-files) The provided file must be a CSV in the format: ``` Date, Event, Series ID, Event Duration , , , , , , ``` A header row is required, and the “Series ID” and “Event Duration” columns are optional. Once the CalendarFile has been created, pass its ID with the [DatetimePartitioningSpecification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioningSpecification) when setting the target for a time series project in order to use it. - Parameters: - Returns: calendar_file – Instance with initialized data. - Return type: CalendarFile - Raises: AsyncProcessUnsuccessfulError – Raised if there was an error processing the provided calendar file. > [!NOTE] Examples > ``` > # Creating a calendar with a specified name > cal = dr.CalendarFile.create('/home/calendars/somecalendar.csv', > calendar_name='Some Calendar Name') > cal.id > >>> 5c1d4904211c0a061bc93013 > cal.name > >>> Some Calendar Name > > # Creating a calendar without specifying a name > cal = dr.CalendarFile.create('/home/calendars/somecalendar.csv') > cal.id > >>> 5c1d4904211c0a061bc93012 > cal.name > >>> somecalendar.csv > > # Creating a calendar with multiseries id columns > cal = dr.CalendarFile.create('/home/calendars/somemultiseriescalendar.csv', > calendar_name='Some Multiseries Calendar Name', > multiseries_id_columns=['series_id']) > cal.id > >>> 5da9bb21962d746f97e4daee > cal.name > >>> Some Multiseries Calendar Name > cal.multiseries_id_columns > >>> ['series_id'] > ``` #### classmethod create_calendar_from_dataset(dataset_id, dataset_version_id=None, calendar_name=None, multiseries_id_columns=None, delete_on_error=False) Creates a calendar using the given dataset. For information about calendar files, see the [calendar documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/modeling/spec/time_series.html#calendar-files) The provided dataset have the following format: ``` Date, Event, Series ID, Event Duration , , , , , , ``` The “Series ID” and “Event Duration” columns are optional. Once the CalendarFile has been created, pass its ID with the [DatetimePartitioningSpecification](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.DatetimePartitioningSpecification) when setting the target for a time series project in order to use it. - Parameters: - Returns: calendar_file – Instance with initialized data. - Return type: CalendarFile - Raises: AsyncProcessUnsuccessfulError – Raised if there was an error processing the provided calendar file. > [!NOTE] Examples > ``` > # Creating a calendar from a dataset > dataset = dr.Dataset.create_from_file('/home/calendars/somecalendar.csv') > cal = dr.CalendarFile.create_calendar_from_dataset( > dataset.id, calendar_name='Some Calendar Name' > ) > cal.id > >>> 5c1d4904211c0a061bc93013 > cal.name > >>> Some Calendar Name > > # Creating a calendar from a new dataset version > new_dataset_version = dr.Dataset.create_version_from_file( > dataset.id, '/home/calendars/anothercalendar.csv' > ) > cal = dr.CalendarFile.create( > new_dataset_version.id, dataset_version_id=new_dataset_version.version_id > ) > cal.id > >>> 5c1d4904211c0a061bc93012 > cal.name > >>> anothercalendar.csv > ``` #### classmethod create_calendar_from_country_code(country_code, start_date, end_date) Generates a calendar based on the provided country code and dataset start date and end dates. The provided country code should be uppercase and 2-3 characters long. See [CalendarFile.get_allowed_country_codes](https://docs.datarobot.com/en/docs/api/reference/sdk/projects.html#datarobot.CalendarFile.get_allowed_country_codes) for a list of allowed country codes. - Parameters: - Returns: calendar_file – Instance with initialized data. - Return type: CalendarFile #### classmethod get_allowed_country_codes(offset=None, limit=None) Retrieves the list of allowed country codes that can be used for generating the preloaded calendars. - Parameters: - Returns: A list dicts, each of which represents an allowed country codes. Each item has the following structure: - Return type: list #### classmethod get(calendar_id) Gets the details of a calendar, given the id. - Parameters: calendar_id ( str ) – The identifier of the calendar. - Returns: calendar_file – The requested calendar. - Return type: CalendarFile - Raises: DataError – Raised if the calendar_id is invalid, i.e., the specified CalendarFile does not exist. > [!NOTE] Examples > ``` > cal = dr.CalendarFile.get(some_calendar_id) > cal.id > >>> some_calendar_id > ``` #### classmethod list(project_id=None, batch_size=None) Gets the details of all calendars this user has view access for. - Parameters: - Returns: calendar_list – A list of CalendarFile objects. - Return type: list of CalendarFile > [!NOTE] Examples > ``` > calendars = dr.CalendarFile.list() > len(calendars) > >>> 10 > ``` #### classmethod delete(calendar_id) Deletes the calendar specified by calendar_id. - Parameters: calendar_id ( str ) – The id of the calendar to delete. The requester must have OWNER access for this calendar. - Raises: ClientError – Raised if an invalid calendar_id is provided. - Return type: None > [!NOTE] Examples > ``` > # Deleting with a valid calendar_id > status_code = dr.CalendarFile.delete(some_calendar_id) > status_code > >>> 204 > dr.CalendarFile.get(some_calendar_id) > >>> ClientError: Item not found > ``` #### classmethod update_name(calendar_id, new_calendar_name) Changes the name of the specified calendar to the specified name. The requester must have at least READ_WRITE permissions on the calendar. - Parameters: - Returns: status_code – 200 for success - Return type: int - Raises: ClientError – Raised if an invalid calendar_id is provided. > [!NOTE] Examples > ``` > response = dr.CalendarFile.update_name(some_calendar_id, some_new_name) > response > >>> 200 > cal = dr.CalendarFile.get(some_calendar_id) > cal.name > >>> some_new_name > ``` #### classmethod share(calendar_id, access_list) Shares the calendar with the specified users, assigning the specified roles. - Parameters: - Returns: status_code – 200 for success - Return type: int - Raises: > [!NOTE] Examples > ``` > # assuming some_user is a valid user, share this calendar with some_user > sharing_list = [dr.SharingAccess(some_user_username, > dr.enums.SHARING_ROLE.READ_WRITE)] > response = dr.CalendarFile.share(some_calendar_id, sharing_list) > response.status_code > >>> 200 > > # delete some_user from this calendar, assuming they have access of some kind already > delete_sharing_list = [dr.SharingAccess(some_user_username, > None)] > response = dr.CalendarFile.share(some_calendar_id, delete_sharing_list) > response.status_code > >>> 200 > > # Attempt to add an invalid user to a calendar > invalid_sharing_list = [dr.SharingAccess(invalid_username, > dr.enums.SHARING_ROLE.READ_WRITE)] > dr.CalendarFile.share(some_calendar_id, invalid_sharing_list) > >>> ClientError: Unable to update access for this calendar > ``` #### classmethod get_access_list(calendar_id, batch_size=None) Retrieve a list of users that have access to this calendar. - Parameters: - Returns: access_control_list – A list of SharingAccess objects. - Return type: list of SharingAccess - Raises: ClientError – Raised if user does not have access to calendar or calendar does not exist. ### class datarobot.models.calendar_file.CountryCode --- # Administration URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-admin.html # Administration These endpoints cover the requisite functionality for administrators to manage their organization’s DataRobot deployment and application. These workflows include authentication, user management and RBAC controls, resource management and monitoring, and notifications integration. --- # Applications URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-applications.html # Applications DataRobot offers various approaches for building applications that allow you to share machine learning projects. No-code applications are used to visualize, optimize, or otherwise interact with a machine learning model. They enable core DataRobot services without having to build and evaluate models. Use application templates to launch an end-to-end DataRobot experience. This can include aspects of importing data, model building, deployment, and monitoring. Custom applications are a simple method for building and running custom code within the DataRobot infrastructure. You can customize your application to fit whatever your organization’s needs might be—from something simple, such as tailoring the application UI to a specific design, or something more robust. --- # Client configuration URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-clientconfig.html # Client configuration This section provides configuration and troubleshooting insights that are fundamental to the Python API client. These pages include details on how to set up the API client, insight into error messages and exceptions, and an overview of the client's package architecture. --- # Collaboration URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-collaboration.html # Collaboration Teams can efficiently organize and deliver on their machine learning project goals with DataRobot’s collaborative capabilities, which enable business and engineering stakeholders to come together to solve specific business problems. DataRobot Use Cases offer a folder-like experience for organizing and sharing assets related to a specific ML project for rapid experimentation and streamlined productionization. --- # Data preparation URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-data-prep.html # Data preparation DataRobot’s data preparation offerings give you the ability to analyze, ingest, and transform your data for modeling and inference workflows. These endpoints include the functionality for creating connections to external data sources, ingesting and managing static and dynamic data assets in the Data Registry, and applying data transformations in preparation for modeling. --- # Data Registry URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-data-registry.html # Data Registry Read below to learn about managing data, including topics such as the Data Registry, datasets, feature lists, and blueprints. Reference the [Glossary](https://docs.datarobot.com/en/docs/reference/glossary/index.html) and UI documentation for more information about these topics. --- # Deliver URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-deliver.html # Deliver Deliver endpoints focus on the ways you can bring predictive and generative AI into production. This includes common deployment, monitoring, and model management functionality used to observe and mitigate the performance of models. --- # Deployments URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-deployment-management.html # Deployments This section contains endpoints that are the central hub for deployment management activity. It serves as a coordination point for all stakeholders involved in bringing models into production, including the information you supplied when creating the deployment and any model replacement activity. --- # Developer tools URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-dev-tools.html # Developer tools DataRobot offers native tools for accelerating code iteration and development for data scientists and app developers. DataRobot Notebooks and Codespaces provide a fully managed, hosted platform working with notebooks and other code assets. These endpoints include the functionality for creating and managing notebook and codespace assets. --- # Develop URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-develop.html # Develop Develop endpoints focus on the various ways you can build models of various types in DataRobot. These include LLM generation, end-to-end project development for machine learning models, and building user-defined custom models. --- # Generative AI URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-genai.html # Generative AI DataRobot’s Generative AI capabilities allow you to build enterprise-grade generative API applications using state-of-the-art LLMs and comprehensive evaluation tooling. These endpoints include the functionality for creating and managing vector databases, building and evaluating LLM blueprints, and prompt engineering and chat comparisons. --- # Secure and govern URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-govern.html # Secure and govern DataRobot offers a rich governance framework for organizations to ensure quality and regulatory compliance for their predictive and generative AI models in production. These endpoints control governance capabilities across model deployment approval workflows, compliance documentation, model lineage and traceability, and humility and fairness monitoring. --- # Mitigation URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-mitigation.html # Mitigation Machine learning models in production environments have a complex lifecycle; maintaining the predictive value of these models requires a robust and repeatable process to manage that lifecycle. Without proper management, models that reach production may deliver inaccurate data, poor performance, or unexpected results that can damage your business's reputation for AI trustworthiness. Lifecycle management is essential for creating a machine learning operations system that allows you to scale many models in production. --- # Modeling URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-ml.html # Modeling These sections describe aspects of preparing to build, building, and managing predictive AI models and projects with DataRobot. --- # Monitoring URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-observability.html # Monitoring To trust a model to power mission-critical operations, you must have confidence in all aspects of model deployment. By closely tracking the performance of models in production, you can identify potential issues before they impact business operations. Monitoring ranges from whether the service is reliably providing predictions in a timely manner and without errors to ensuring the predictions themselves are reliable. The predictive performance of a model typically starts to diminish as soon as it’s deployed. For example, someone might be making live predictions on a dataset with customer data, but the customer’s behavioral patterns might have changed due to an economic crisis, market volatility, natural disaster, or even the weather. Models trained on older data that no longer represents the current reality might not just be inaccurate, but irrelevant, leaving the prediction results meaningless or even harmful. Without dedicated production model monitoring, the user cannot know or detect when this happens. If model accuracy starts to decline without detection, the results can impact a business, expose it to risk, and destroy user trust. The endpoints in this section cover the many aspects of model monitoring. --- # Predictions URL: https://docs.datarobot.com/en/docs/api/reference/sdk/tag-predictions.html # Predictions These endpoints include the functionality to make predictions on your trained model to assess model performance prior to deployment on training data or external test data, as well as create and manage batch predictions for your deployed models. For more information on making real-time predictions with your deployed models, view the documentation on [real-time predictions with the API](https://docs.datarobot.com/en/docs/api/reference/predapi/index.html). --- # Training predictions URL: https://docs.datarobot.com/en/docs/api/reference/sdk/training_predictions.html # Training predictions ### class datarobot.models.training_predictions.TrainingPredictionsIterator Lazily fetches training predictions from DataRobot API in chunks of specified size and then iterates rows from responses as named tuples. Each row represents a training prediction computed for a dataset’s row. Each named tuple has the following structure: - Variables: > [!NOTE] Notes > Each `PredictionValue` dict contains these keys: > > label > : describes what this model output corresponds to. For regression > projects, it is the name of the target feature. For classification and multiclass > projects, it is a label from the target feature. > value > : the output of the prediction. For regression projects, it is the > predicted value of the target. For classification and multiclass projects, it is > the predicted probability that the row belongs to the class identified by the label. > > Each `PredictionExplanations` dictionary contains these keys: > > label (str) > : describes what output was driven by this prediction explanation. For regression > projects, it is the name of the target feature. For classification projects, it is the > class whose probability increasing would correspond to a positive strength of this > prediction explanation. > feature (str) > : the name of the feature contributing to the prediction > feature_value (object) > : the value the feature took on for this row. The type corresponds to the feature > (boolean, integer, number, string) > strength (float) > : algorithm-specific explanation value attributed to feature in this row > > `ShapMetadata` dictionary contains these keys: > > shap_remaining_total (float) > : The total of SHAP values for features beyond the > max_explanations > . This can be > identically 0 in all rows, if max_explanations is greater than the number of features > and thus all features are returned. > shap_base_value (float) > : the model’s average prediction over the training data. SHAP values are deviations from > the base value. > warnings (dict or None) > : SHAP values calculation warnings (e.g., additivity check failures in XGBoost models). > Schema described as > ShapWarnings > . > > `ShapWarnings` dictionary contains these keys: > > mismatch_row_count (int) > : the count of rows for which additivity check failed > max_normalized_mismatch (float) > : the maximal relative normalized mismatch value > [!NOTE] Examples > ``` > import datarobot as dr > > # Fetch existing training predictions by their id > training_predictions = dr.TrainingPredictions.get(project_id, prediction_id) > > # Iterate over predictions > for row in training_predictions.iterate_rows() > print(row.row_id, row.prediction) > ``` ### class datarobot.models.training_predictions.TrainingPredictions Represents training predictions metadata and provides access to prediction results. - Variables: > [!NOTE] Notes > Each element in `shap_warnings` has the following schema: > > partition_name (str) > : the partition used for the prediction record. > value (object) > : the warnings related to this partition. > > The objects in `value` are: > > mismatch_row_count (int) > : the count of rows for which additivity check failed. > max_normalized_mismatch (float) > : the maximal relative normalized mismatch value. > [!NOTE] Examples > Compute training predictions for a model on the whole dataset > > ``` > import datarobot as dr > > # Request calculation of training predictions > training_predictions_job = model.request_training_predictions(dr.enums.DATA_SUBSET.ALL) > training_predictions = training_predictions_job.get_result_when_complete() > print('Training predictions {} are ready'.format(training_predictions.prediction_id)) > > # Iterate over actual predictions > for row in training_predictions.iterate_rows(): > print(row.row_id, row.partition_id, row.prediction) > ``` > > List all training predictions for a project > > ``` > import datarobot as dr > > # Fetch all training predictions for a project > all_training_predictions = dr.TrainingPredictions.list(project_id) > > # Inspect all calculated training predictions > for training_predictions in all_training_predictions: > print( > 'Prediction {} is made for data subset "{}"'.format( > training_predictions.prediction_id, > training_predictions.data_subset, > ) > ) > ``` > > Retrieve training predictions by id > > ``` > import datarobot as dr > > # Getting training predictions by id > training_predictions = dr.TrainingPredictions.get(project_id, prediction_id) > > # Iterate over actual predictions > for row in training_predictions.iterate_rows(): > print(row.row_id, row.partition_id, row.prediction) > ``` #### classmethod list(project_id) Fetch all the computed training predictions for a project. - Parameters: project_id ( str ) – id of the project - Return type: A list of TrainingPredictions objects #### classmethod get(project_id, prediction_id) Retrieve training predictions on a specified data set. - Parameters: - Returns: object which is ready to operate with specified predictions - Return type: TrainingPredictions #### iterate_rows(batch_size=None) Retrieve training prediction rows as an iterator. - Parameters: batch_size ( Optional[int] ) – maximum number of training prediction rows to fetch per request - Returns: iterator – an iterator which yields named tuples representing training prediction rows - Return type: TrainingPredictionsIterator #### get_all_as_dataframe(class_prefix='class_', serializer='json') Retrieve all training prediction rows and return them as a pandas.DataFrame. Returned dataframe has the following structure: : - row_id : row id from the original dataset - prediction : the model’s prediction for this row - class_: the probability that the target is this class (only appears for classification and multiclass projects) - timestamp : the time of the prediction (only appears for out of time validation or time series projects) - forecast_point : the point in time used as a basis to generate the predictions (only appears for time series projects) - forecast_distance : how many time steps are between timestamp and forecast_point (only appears for time series projects) - series_id : he id of the series in a multiseries project or None for a single series project (only appears for time series projects) - Parameters: - Returns: dataframe - Return type: pandas.DataFrame #### download_to_csv(filename, encoding='utf-8', serializer='json') Save training prediction rows into CSV file. - Parameters: --- # Use cases URL: https://docs.datarobot.com/en/docs/api/reference/sdk/use-cases.html # Use cases ### class datarobot.UseCase Representation of a Use Case. - Variables: > [!NOTE] Examples > ``` > import datarobot > with UseCase.get("2348ac"): > print(f"The current use case is {dr.Context.use_case}") > ``` #### get_uri() - Returns: url – Permanent static hyperlink to this Use Case. - Return type: str #### classmethod get(use_case_id) Gets information about a Use Case. - Parameters: use_case_id ( str ) – The identifier of the Use Case you want to load. - Returns: use_case – The queried Use Case. - Return type: UseCase #### classmethod list(search_params=None) Returns the Use Cases associated with this account. - Parameters:search_params(dict,optional.) – If notNone, the returned projects are filtered by lookup. NotesCurrently, you can query use cases by:offset- The number of records to skip over. Default 0.limit- The number of records to return in the range from 1 to 100. Default 100.search- Only return Use Cases with names that match the given string.project_id- Only return Use Cases associated with the given project ID.application_id- Only return Use Cases associated with the given app.orderBy- The order to sort the Use Cases.orderByqueries can use the following options:idor-idnameor-namedescriptionor-descriptionprojects_countor-projects_countdatasets_countor-datasets_countfiles_countor-files_countnotebooks_countor-notebooks_countapplications_countor-applications_countcreated_ator-created_atcreated_byor-created_byupdated_ator-updated_atupdated_byor-updated_by - Returns: use_cases – Contains a list of Use Cases associated with this user account. - Return type: list of UseCase instances - Raises: TypeError – Raised if search_params parameter is provided, but is not of supported type. #### classmethod create(name=None, description=None) Create a new Use Case. - Parameters: - Returns: use_case – The created Use Case. - Return type: UseCase #### classmethod delete(use_case_id) Delete a Use Case. - Parameters: use_case_id ( str ) – The ID of the Use Case to be deleted. - Return type: None #### update(name=None, description=None) Update a Use Case’s name or description. - Parameters: - Returns: use_case – The updated Use Case. - Return type: UseCase #### add(entity=None, entity_type=None, entity_id=None) Add an entity (project, dataset, etc.) to a Use Case. Can only accept either an entity or an entity type and entity ID, but not both. Projects and Applications can only be linked to a single Use Case. Datasets can be linked to multiple Use Cases. There are some prerequisites for linking Projects to a Use Case which are explained in the [user guide](https://docs.datarobot.com/en/docs/api/dev-learning/python/use_cases/use_cases.html#add-project-to-a-use-case). - Parameters: - Returns: use_case_reference_entity – The newly created reference link between this Use Case and the entity. - Return type: UseCaseReferenceEntity #### remove(entity=None, entity_type=None, entity_id=None) Remove an entity from a Use Case. Can only accept either an entity or an entity type and entity ID, but not both. - Parameters: - Return type: None #### share(roles) Share this Use Case with or remove access from one or more user(s). - Parameters:roles(List[SharingRole]) – A list ofSharingRoleinstances, each of which references a user and a role to be assigned. Currently, the only supported roles for Use Cases are OWNER, EDITOR, and CONSUMER, and the only supported SHARING_RECIPIENT_TYPE is USER. To remove access, set a user’s role todatarobot.enums.SHARING_ROLE.NO_ROLE. *Return type:None > [!NOTE] Examples > The [SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) class is needed in order to > share a Use Case with one or more users. > > For example, suppose you had a list of user IDs you wanted to share this Use Case with. You could use > a loop to generate a list of [SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) objects for them, > and bulk share this Use Case. > > ``` > >>> from datarobot.models.use_cases.use_case import UseCase > >>> from datarobot.models.sharing import SharingRole > >>> from datarobot.enums import SHARING_ROLE, SHARING_RECIPIENT_TYPE > >>> > >>> user_ids = ["60912e09fd1f04e832a575c1", "639ce542862e9b1b1bfa8f1b", "63e185e7cd3a5f8e190c6393"] > >>> sharing_roles = [] > >>> for user_id in user_ids: > ... new_sharing_role = SharingRole( > ... role=SHARING_ROLE.CONSUMER, > ... share_recipient_type=SHARING_RECIPIENT_TYPE.USER, > ... id=user_id, > ... ) > ... sharing_roles.append(new_sharing_role) > >>> use_case = UseCase.get(use_case_id="5f33f1fd9071ae13568237b2") > >>> use_case.share(roles=sharing_roles) > ``` > > Similarly, a [SharingRole](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#datarobot.models.sharing.SharingRole) instance can be used to > remove a user’s access if the `role` is set to `SHARING_ROLE.NO_ROLE`, like in this example: > > ``` > >>> from datarobot.models.use_cases.use_case import UseCase > >>> from datarobot.models.sharing import SharingRole > >>> from datarobot.enums import SHARING_ROLE, SHARING_RECIPIENT_TYPE > >>> > >>> user_to_remove = "foo.bar@datarobot.com" > ... remove_sharing_role = SharingRole( > ... role=SHARING_ROLE.NO_ROLE, > ... share_recipient_type=SHARING_RECIPIENT_TYPE.USER, > ... username=user_to_remove, > ... ) > >>> use_case = UseCase.get(use_case_id="5f33f1fd9071ae13568237b2") > >>> use_case.share(roles=[remove_sharing_role]) > ``` #### get_shared_roles(offset=None, limit=None, id=None) Retrieve access control information for this Use Case. - Parameters: - Return type: List [ SharingRole ] #### list_projects() List all projects associated with this Use Case. - Returns: projects – All projects associated with this Use Case. - Return type: List[Project] #### list_datasets() List all datasets associated with this Use Case. - Returns: datasets – All datasets associated with this Use Case. - Return type: List[Dataset] #### list_applications() List all applications associated with this Use Case. - Returns: applications – All applications associated with this Use Case. - Return type: List[Application] #### classmethod from_data(data) Instantiate an object of this class using a dict. - Parameters: data ( dict ) – Correctly snake_cased keys and their values. - Return type: TypeVar ( T , bound= APIObject) #### classmethod from_server_data(data, keep_attrs=None) Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing - Parameters: - Return type: TypeVar ( T , bound= APIObject) #### open_in_browser() Opens class’ relevant web browser location. If default browser is not available the URL is logged. Note: If text-mode browsers are used, the calling process will block until the user exits the browser. - Return type: None ### class datarobot.models.use_cases.use_case.UseCaseUser Representation of a Use Case user. - Variables: ### class datarobot.models.use_cases.use_case.UseCaseReferenceEntity An entity associated with a Use Case. - Variables: --- # Self-Managed AI Platform API resources URL: https://docs.datarobot.com/en/docs/api/reference/self-managed.html > Learn about the resources available for self-managed DataRobot deployments. # Self-Managed AI Platform API resources To access current and past Python and R clients and documentation, use the following links: - Python - R The table below outlines which versions of DataRobot's SDKs correspond to DataRobot's Self-Managed AI Platform versions. | Self-Managed AI Platform version | Python API client version | R API client version | | --- | --- | --- | | v11.2 | v3.9 | v2.18.6 (GA) v2.31.2 (preview) | | v11.1 | v3.8 | v2.18.6 (GA) v2.31.2 (preview) | | v11.0 | v3.7 | v2.18.6 (GA) v2.31.2 (preview) | | v10.2 | v3.6 | v2.18.6 (GA) v2.31.2 (preview) | | v10.1 | v3.5 | v2.18.6 (GA) v2.31.2 (preview) | | v10.0 | v3.4 | v2.18.6 (GA) v2.31.2 (preview) | | v9.2 | v3.3 | v2.18.6 (GA) v2.31.2 (preview) | | v9.1 | v3.2 | v2.18.4 (GA) v2.31.2 (preview) | | v9.0 | v3.1 | v2.29 (preview) | | v8.0 | v2.28 | v2.18.2 | | v7.3 | v2.27.3 | v2.18.2 | | v7.2 | v2.26.0 | v2.18.2 | | v7.1 | v2.25.1 | v2.18.2 | | v7.0 | v2.24.0 | v2.18.2 | | v6.3 | v2.23.0 | v2.17.1 | | v6.2 | v2.22.1 | v2.17.1 | | v6.1 | v2.21.5 | v2.17.1 | | v6.0 | v2.20.2 | v2.17.1 | | v5.3 | v2.19.0 | v2.17.1 | | v5.2 | v2.18.0 | v2.17.1 | | v5.1 | v2.17.0 | v2.17.1 | | v5.0 | v2.15.1 | v2.15.0 | | v4.5 | v2.14.2 | v2.14.2 | | v4.4 | v2.13.3 | v2.13.1 | | v4.3 | v2.11.2 | v2.11.0 | | v4.2 | v2.9.3 | v2.9.0 | | v4.0 | v2.8.3 | v2.8.0 | | v3.1 | v2.7.3 | v2.7.1 | | v3.0 | v2.6.2 | v2.6.0 | | v2.9 | v2.4.3 | v2.4.0 | | v2.8 | v2.0.37 | v2.0.30 | > [!NOTE] Note > Both the backend and clients use versioning in the format Major.Minor.Patch (e.g., v2.3.1), but there is no relationship between the patch version of the backend and the patch version of the clients. There is a requirement, however, that the backend version has a major.minor version equal to or greater than the client version. For example, a v2.2 client can "talk" to either a v2.2 backend or a v2.4 backend, but cannot be used with a v2.0 backend. ## Install commands See the install commands for Python and R over different versions below. The commands are grouped by Major version (v5.x, 4.x, etc.). For install commands for Python versions past v8.0, access the corresponding versions from [PyPi](https://pypi.org/project/datarobot/#history). ### v8.0 Python: `pip install "datarobot>=2.28,<2.29"` R: ``` mkdir -p ~/datarobot_2.18.2 && tar -xvzf ~/Downloads/datarobot_2.18.2.tar.gz -C ~/datarobot_2.18.2 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.18.2/datarobot') ``` ### v7.x **v7.3:** Python: `pip install "datarobot>=2.27.4,<2.28"` R: ``` mkdir -p ~/datarobot_2.18.2 && tar -xvzf ~/Downloads/datarobot_2.18.2.tar.gz -C ~/datarobot_2.18.2 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.18.2/datarobot') ``` **v7.2:** Python: `pip install "datarobot>=2.26.0,<2.27"` R: ``` mkdir -p ~/datarobot_2.18.2 && tar -xvzf ~/Downloads/datarobot_2.18.2.tar.gz -C ~/datarobot_2.18.2 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.18.2/datarobot') ``` **v7.1:** Python: `pip install "datarobot>=2.25.1,<2.26"` R: ``` mkdir -p ~/datarobot_2.18.2 && tar -xvzf ~/Downloads/datarobot_2.18.2.tar.gz -C ~/datarobot_2.18.2 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.18.2/datarobot') ``` **v7.0:** Python: `pip install "datarobot>=2.24.0,<2.25.1"` R: ``` mkdir -p ~/datarobot_2.18.2 && tar -xvzf ~/Downloads/datarobot_2.18.2.tar.gz -C ~/datarobot_2.18.2 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.18.2/datarobot') ``` ### v6.x **v6.3:** Python: `pip install "datarobot>=2.23,<2.24"` R: ``` mkdir -p ~/datarobot_2.17.1 && tar -xvzf ~/Downloads/datarobot_2.17.1.tar.gz -C ~/datarobot_2.17.1 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.17.1/datarobot') ``` **v6.2:** Python: `pip install "datarobot>=2.22.1,<2.23"` R: ``` mkdir -p ~/datarobot_2.17.1 && tar -xvzf ~/Downloads/datarobot_2.17.1.tar.gz -C ~/datarobot_2.17.1 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.17.1/datarobot') ``` **v6.1:** Python: `pip install "datarobot>=2.21.5,<2.22.1"` R: ``` mkdir -p ~/datarobot_2.17.1 && tar -xvzf ~/Downloads/datarobot_2.17.1.tar.gz -C ~/datarobot_2.17.1 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.17.1/datarobot') ``` **v6.0:** Python: `pip install "datarobot>=2.20.2,<2.21.5"` R: ``` mkdir -p ~/datarobot_2.17.1 && tar -xvzf ~/Downloads/datarobot_2.17.1.tar.gz -C ~/datarobot_2.17.1 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.17.1/datarobot') ``` ### v5.x **v5.3:** Python: `pip install "datarobot>=2.19.0,<2.20"` R: ``` mkdir -p ~/datarobot_2.17.1 && tar -xvzf ~/Downloads/datarobot_2.17.1.tar.gz -C ~/datarobot_2.17.1 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.17.1/datarobot') ``` **v5.2:** Python: `pip install "datarobot>=2.18,<2.19"` R: ``` mkdir -p ~/datarobot_2.17.1 && tar -xvzf ~/Downloads/datarobot_2.17.1.tar.gz -C ~/datarobot_2.17.1 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.17.1/datarobot') ``` **v5.1:** Python: `pip install "datarobot>=2.17,<2.18"` R: ``` mkdir -p ~/datarobot_2.17.1 && tar -xvzf ~/Downloads/datarobot_2.17.1.tar.gz -C ~/datarobot_2.17.1 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.17.1/datarobot') ``` **v5.0:** Python: `pip install "datarobot>=2.15,<2.16"` R: ``` mkdir -p ~/datarobot_2.15.0 && tar -xvzf ~/Downloads/datarobot_2.15.0.tar.gz -C ~/datarobot_2.15.0 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.15.0/datarobot') ``` ### v4.x **v4.5:** Python: `pip install "datarobot>=2.14,<2.15"` R: ``` mkdir -p ~/datarobot_2.14.0 && tar -xvzf ~/Downloads/datarobot_2.14.0.tar.gz -C ~/datarobot_2.14.0 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.14.0/datarobot') ``` **v4.4:** Python: `pip install "datarobot>=2.13,<2.14"` R: ``` mkdir -p ~/datarobot_2.13.0 && tar -xvzf ~/Downloads/datarobot_2.13.0.tar.gz -C ~/datarobot_2.13.0 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.13.0/datarobot') ``` **v4.3.1:** Python: `pip install "datarobot>=2.12,<2.13"` R: ``` mkdir -p ~/datarobot_2.12.1 && tar -xvzf ~/Downloads/datarobot_2.12.1.tar.gz -C ~/datarobot_2.12.1 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.12.1/datarobot') ``` **v4.3:** Python: `pip install "datarobot>=2.11,<2.12"` R: ``` mkdir -p ~/datarobot_2.11.0 && tar -xvzf ~/Downloads/datarobot_2.11.0.tar.gz -C ~/datarobot_2.11.0 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.11.0/datarobot') ``` **v4.2:** Python: `pip install "datarobot>=2.9,<2.10"` R: ``` mkdir -p ~/datarobot_2.9.0 && tar -xvzf ~/Downloads/datarobot_2.9.0.tar.gz -C ~/datarobot_2.9.0 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.9.0/datarobot') ``` **v4.0:** Python: `pip install "datarobot>=2.8,<2.9"` R: ``` mkdir -p ~/datarobot_2.8.0 && tar -xvzf ~/Downloads/datarobot_2.8.0.tar.gz -C ~/datarobot_2.8.0 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.8.0/datarobot') ``` ### v3.x **v3.1:** Python: `pip install "datarobot>=2.7,<2.8"` R: ``` mkdir -p ~/datarobot_2.7.0 && tar -xvzf ~/Downloads/datarobot_2.7.0.tar.gz -C ~/datarobot_2.7.0 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.7.0/datarobot') ``` **v3.0:** Python: `pip install "datarobot>=2.8,<2.9"` R: ``` mkdir -p ~/datarobot_2.6.0 && tar -xvzf ~/Downloads/datarobot_2.6.0.tar.gz -C ~/datarobot_2.6.0 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.6.0/datarobot') ``` ### v2.x **v2.9:** Python: `pip install "datarobot>=2.4,<2.5"` R: ``` mkdir -p ~/datarobot_2.4.0 && tar -xvzf ~/Downloads/datarobot_2.4.0.tar.gz -C ~/datarobot_2.4.0 install.packages('devtools') # (If you don't already have devtools on your system.) devtools::install('~/datarobot_2.4.0/datarobot') ``` **v2.8:** Python: `pip install "datarobot>=2.0,<2.1"` R: `install.packages("datarobot", type="source")` --- # Feature Discovery support in No-Code AI Apps URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/app-preview/app-ft-cache.html > Create No-Code AI Apps from Feature Discovery projects. # Feature Discovery support in No-Code AI Apps > [!NOTE] Availability information > Feature Discovery support in No-Code AI Apps is off by default. Contact your DataRobot representative or administrator for information on enabling the feature. > > Feature flag: Enable Feature Cache for Feature Discovery You can create No-Code AI Apps from Feature Discovery projects (i.e., projects built with multiple datasets) with [feature cache](https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-preview/safer-ft-cache.html) enabled. Feature cache instructs DataRobot to source data from multiple datasets and generate new features in advance, storing this information in a "cache," which is then drawn from to make predictions. Before [creating an application](https://docs.datarobot.com/en/docs/classic-ui/app-builder/create-app.html), ensure the deployment has feature cache enabled in the deployment's Settings tab if the project contains multiple datasets. Once created, you can [use the app](https://docs.datarobot.com/en/docs/classic-ui/app-builder/use-apps/index.html) to build simulations and charts, run optimizations, and create what-if scenarios. ## Feature considerations Consider the following when enabling Feature Discovery support for No-Code AI Apps: - Postgres DB must be installed for feature cache to work properly. - Feature cache must be enabled to make single record predictions. - Feature cache only caches features for the most recent period. - You can only create apps that support Feature Discovery from deployed models. This feature is not supported by apps created from the Leaderboard. --- # Prefill application templates URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/app-preview/app-prefill.html > Prefill applications upon creation to more easily visualize the end-user experience. # Prefill application templates > [!NOTE] Availability information > Prefilled No-Code AI App templates are on by default. > > Feature flags: Enable Prefill NCA Templates with Training Data Now available for preview, you can choose whether or not to prefill application insights with training data, allowing you to more easily visualize the experience for the end-user. Previously, application widgets appeared blank until the app was used to make a prediction; now, with prefilled No-Code App templates enabled, the application can use the project's training data to populate the widgets. After creating a new application, DataRobot exports the training dataset to the AI Catalog, and if the dataset is larger than 50,000 rows, performs random downsampling. Once registered, DataRobot then performs a batch prediction job by scoring the training data. The All Rows widget populates using the training data as the prediction data source. Additionally, new tabs have been added so you can view training data, prediction data, or both. The Predictions data tab is only active after adding a batch prediction file or making a single-row prediction in the app. > [!NOTE] App prediction limit > The process of scoring the training data does not affect your application prediction limit. ## Set the prediction data source By default, the app uses only training data as the prediction data source. You can update the prediction data source in the app's settings. To update the settings: 1. Click Build in the upper-right corner. 2. Click Settings > General Configuration . 3. UnderPrediction Data Sourceselect a new option—Prediction Data Only, Training Data Only, or All Predictions Data. ## Feature considerations Prefilled templates are not available for time series applications. --- # AI App preview features URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/app-preview/index.html > Read preliminary documentation for app-related features currently in the DataRobot preview pipeline. # AI App preview features This section provides preliminary documentation for features currently in preview pipeline. If not enabled for your organization, the feature is not visible. Although these features have been tested within the engineering and quality environments, they should not be used in production at this time. Note that preview functionality is subject to change and that any Support SLA agreements are not applicable. > [!NOTE] Availability information > Contact your DataRobot representative or administrator for information on enabling or disabling preview features. ## Available application preview documentation **SaaS:** Feature Description Prefilled app templates Prefill applications upon creation to more easily visualize the end-user experience. Feature Discovery support in No-Code AI Apps Create No-Code AI Apps from Feature Discovery projects. **Self-Managed:** Feature Description Prefilled app templates Prefill applications upon creation to more easily visualize the end-user experience. --- # Create applications URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/create-app.html > Create No-Code AI Apps to enable core DataRobot services while using a no-code interface. # Create applications You can create applications in DataRobot from the [Applications](https://docs.datarobot.com/en/docs/classic-ui/app-builder/create-app.html#from-the-applications-tab) tab, a [model on the Leaderboard](https://docs.datarobot.com/en/docs/classic-ui/app-builder/create-app.html#from-the-leaderboard), or a [deployment](https://docs.datarobot.com/en/docs/classic-ui/app-builder/create-app.html#from-a-deployment). If you're creating an application from a time series deployment, see the documentation for [time series applications](https://docs.datarobot.com/en/docs/classic-ui/app-builder/ts-app.html). > [!NOTE] Multiclass projects with over 1000 classes > For [unlimited multiclass](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/multiclass.html#unlimited-multiclass) projects with more than 1000 classes, by default, DataRobot keeps the top 999 most frequent classes and aggregates the remainder into a single "other" bucket. You can, however, configure the aggregation parameters to ensure all classes necessary to your project are represented. > [!NOTE] Note > You can create multiple applications from the same deployment. ## Template options Before creating an application, consider the purpose of the app and review the template options—Predictor, What-if, or Optimizer—and if the deployment you intend to use is time series or non-time series. While templates only determine the initial configuration of the application and selecting a template does not mean the app can only be used for that purpose, time series applications require additional setup. See the documentation for [time series applications](https://docs.datarobot.com/en/docs/classic-ui/app-builder/ts-app.html). The table below describes each template option: | Template | Description | Default configuration | Time series | | --- | --- | --- | --- | | Predictor | Makes predictions for a target feature based on the information provided when the app is created and deployed. | Hides the What-if and Optimizer widget. | ✔ | | What-if | Creates and compares multiple prediction scenarios side-by-side to determine the option with the best outcome. | Displays the What-if and Optimizer widget with only the what-if functionality enabled. | ✔ | | Optimizer | Runs simulations to optimize an outcome for a given goal. This is most effective when you want to optimize for a single row. | Displays the What-if and Optimizer widget with only the optimizer functionality enabled.The All Rows widget displays an Optimized Prediction column. | | ## From the Applications tab When creating an application from the Applications tab, DataRobot uses an active deployment as the basis of the app. To create an application from the Applications tab: 1. Navigate to theApplicationstab. 2. The available application templates are listed at the top of the page. ClickUse templatenext to the template best suited for your use case. 3. A dialog box appears, prompting you to name the application and choose asharing option—Anyone With the Sharing Linkautomatically generates a link that can be shared with non-DataRobot users whileInvited Users Onlylimits sharing to other DataRobot users, groups, and organizations. The access option determines the initial configuration of the sharing permissions, which can be changed in theapplication settings. 4. ClickNext: select deployment. 5. Select a deployment for the application and clickCreate. Note that you must be an owner of the deployment in order to launch an application from it. You are then taken to theApplicationstab while the application builds. ## From the Leaderboard To create an application from a specific model on the Leaderboard: 1. After your models are built, navigate to theLeaderboardand select the model you want to use to build an application. 2. Click theBuild apptab and select the appropriate template for your use case. 3. Name the application and select asharing optionfrom the dropdown. ClickCreatewhen you're done. 4. The new application appears on theLeaderboardbelow the model's app templates as well as on theApplicationstab. ## From a deployment To create an application from a deployed model: 1. Navigate to theDeploymentinventory and select the deployment you want to launch the application from. 2. SelectCreate applicationfrom the action menu of your desired deployment. 3. Select the application template you would like to use and clickNext: add app info. 4. Name the application and choose asharing optionfrom the dropdown. When you're done, clickCreate. The application is available for use on the Applications tab. ### Deployments with an association ID When creating an application from a deployment with an association ID, note the following: - Accuracy and data drift are tracked for all single and batch predictions made using the application. - Accuracy and data drift are not tracked for synthetic predictions (simulations) made in the application using the What-If and Optimizer widget. - You cannot add an association ID to deployments that have already been used to create an application. In the deployment Settings, [add an association ID](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html#select-an-association-id). If Require association ID in prediction requests is enabled, this setting cannot be disabled after the application is created. If an application is created from a deployment with an association ID, the association ID is added as a required field to make single predictions in the application. This field cannot be removed in Build mode. --- # Manage applications URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/current-app.html > View, share, and delete current No-Code AI Apps. # Manage applications In addition to creating apps from the Applications tab, you can view all existing applications that you have created or have been shared with you. The table below describes the elements and available actions on the Applications tab when populated: | | Element | Description | | --- | --- | --- | | (1) | Templates | Deploys a new application using a template. For more information, see the section on templates and creating applications. | | (2) | Open | Opens an application where you can then access the following pages:Application: The end-user application where you test different configurations before sharing.Build page: Allows you to edit the configuration of an application.Settings page: Allows you to edit the general configuration and permissions, as well as view app usage information. | | (3) | Actions menu | Duplicates, shares, or deletes an application. | ## Duplicate applications The duplicate functionality allows you to create a copy of an existing application along with any predictions made in it. This is useful if you want to share an application with another user, but don't want their changes to affect your application, or when creating multiple iterations of an application. 1. Click the menu iconnext to the app you want to duplicate and selectDuplicate. 2. This opens theDuplicate Applicationwindow, where you can name the application and enter a description. 3. Select the box next toCopy Predictionsto carry over any predictions made with the original application. 4. To finish creating a copy of the application, click Duplicate . ## Share applications The sharing capability allows [appropriate user roles](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#role-priority-and-sharing) to manage permissions and share an application with users, groups, and organizations, as well as recipients outside of DataRobot. This is useful, for example, for allowing others to use your application without requiring them to have the expertise to create one. > [!WARNING] Warning > When multiple users have access to the same application, each user can see, edit, and overwrite changes or predictions made by another user, as well as view their uploaded datasets. You can access sharing functionality from three different areas: - The Applications tab. - The application's Home page. - The application's Settings > Permissions tab in Build mode. To share from the Applications tab, click the menu icon [https://docs.datarobot.com/en/docs/images/icon-menu.png](https://docs.datarobot.com/en/docs/images/icon-menu.png) next to the app you want to share and select Share [https://docs.datarobot.com/en/docs/images/icon-share.png](https://docs.datarobot.com/en/docs/images/icon-share.png). This opens the Share dialog, which lists each associated user and their role. Editors can share an application with one or more users or groups, or the entire organization. Additionally, you can share an application with non-DataRobot users with a sharing link. **Users:** To add a new user, enter their username in the Share with field. Choose their role from the dropdown. Select Send notification to send an email notification and Add note to add additional details to the notification. Click Share . **Groups and organizations:** Select either the Groups or Organizations tab in the Share dialog. Enter the group or organization name in the Share with field. Determine the role for permissions. Click Share . The app is shared with—and the role is applied to—every member of the designated group or organization. **Anyone With a Sharing Link:** The link that appears at the top of the Share dialog allows you to share No-Code AI Apps with end-users who don't have access to DataRobot. [https://docs.datarobot.com/en/docs/images/current-app-sharelink.png](https://docs.datarobot.com/en/docs/images/current-app-sharelink.png) You can revoke access to a sharing link by generating a new link in Permissions. To do so, open the application and click Build. Then, navigate to [Settings > Permissions](https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/app-settings.html). Under the sharing link, click Generate new link. The following actions are also available in the Share dialog: - To remove a user, click the X button to the right of their role. - To re-assign a user's role, click on the assigned role and assign a new one from the dropdown. See the [Sharing](https://docs.datarobot.com/en/docs/api/dev-learning/python/admin/sharing.html) section for more information. ## Delete an application If you have the appropriate [permissions](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#no-code-ai-app-roles), you can delete an application by opening the menu [https://docs.datarobot.com/en/docs/images/icon-menu.png](https://docs.datarobot.com/en/docs/images/icon-menu.png) and clicking Delete [https://docs.datarobot.com/en/docs/images/icon-delete.png](https://docs.datarobot.com/en/docs/images/icon-delete.png). This action initiates an email notification to all users with sharing privileges for the model or environment. --- # Upload custom applications URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/custom-apps/app-upload-custom.html > Create custom applications in DataRobot to share machine learning projects using web applications, including Streamlit and Dash. # Upload custom applications > [!NOTE] Availability information > Custom application upload is a premium feature. Contact your DataRobot representative or administrator for information on enabling the feature. Create custom applications in DataRobot to share machine learning projects using web applications, including Streamlit and Dash, from an image created in Docker. Once you create a custom machine learning app in Docker, you can upload it as an app in DataRobot and deploy it with secure data access and controls. Alternatively, you can [use the DRApps command line tool](https://docs.datarobot.com/en/docs/classic-ui/app-builder/custom-apps/custom-apps-hosting.html) to create your app code and push it to DataRobot, building the image automatically. > [!NOTE] Paused applications > Applications are paused after a period of inactivity. The first time you access a paused application, a loading screen appears while it restarts. To upload a custom application to DataRobot, first, you must create an app image in Docker: 1. InstallDocker. 2. Create an app (see examples forStreamlit,Flask, andAiohttp). 3. Exposeport8080in your Dockerfile for HTTP requests. 4. Build your image withdocker build [PATH] | [URL] --tag [IMAGE NAME]. 5. Test your app image locally withdocker run --publish 8080:8080 [IMAGE NAME]. When you are ready to upload your app image to DataRobot, create a new build and then export it with `docker save [IMAGE NAME] --output [PATH]`. Once you have your app (exported as a `tar`, `gz`, or `tgz` archive), upload the image to the [Applications](https://docs.datarobot.com/en/docs/classic-ui/app-builder/create-app.html) page. ## Create a custom application Once you have a custom application `tar`, `gz`, or `tgz` archive, you can upload the image to the [Applications](https://docs.datarobot.com/en/docs/classic-ui/app-builder/create-app.html) tab in DataRobot: 1. Navigate to theApplicationstab. 2. The available application templates are listed at the top of the page. ClickUse templatefor theCustomtemplate. 3. In theCreate Custom Appdialog box, review the custom application options, and then clickStart. TipIf you don't want to see these options again, you can scroll down and selectDon't show again. 4. On the next page of theCreate Custom Appdialog box, configure the following: The custom application image begins to upload. 5. Once the application is uploaded, clickCreate. The custom application is added to theApplicationstab. After it builds, clickOpento view the application. NoteYou can click the action menu () next to a custom application in the Applications tab toShareorDeletethe application. --- # Host custom applications URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/custom-apps/custom-apps-hosting.html > Host a custom application, such as a Streamlit app, in DataRobot using a DataRobot execution environment. # Host applications with DRApps DRApps is a simple command line interface (CLI) providing the tools required to host an application, such as a Streamlit app, in DataRobot using a DataRobot execution environment. This allows you to run apps without building your own Docker image. Applications don't provide any storage; however, you can access the full DataRobot API and other services. Alternatively, you can [upload an AI App (Classic)](https://docs.datarobot.com/en/docs/classic-ui/app-builder/custom-apps/app-upload-custom.html) or an [application (NextGen)](https://docs.datarobot.com/en/docs/classic-ui/app-builder/custom-apps/index.html) in a Docker container. > [!NOTE] Paused applications > Applications are paused after a period of inactivity. The first time you access a paused application, a loading screen appears while it restarts. ## Install the DRApps CLI tool To install the DRApps CLI tool, run either of the following commands: **Users:** ``` pip install git+https://github.com/datarobot/dr-apps ``` **Contributors:** First, clone the [dr-apps repository](https://github.com/datarobot/dr-apps/tree/main), then, run: ``` python setup.py install ``` ## Use the DRApps CLI After you install the DRApps CLI tool, you can use the `drapps --help` command to access the following information: ``` $ drapps --help Usage: drapps [OPTIONS] COMMAND [ARGS]... CLI tools for applications. You can use drapps COMMAND --help for getting more info about a command. Options: --help Show this message and exit. Commands: create Creates new custom application from docker image or... create-env Creates an execution environment and a first version. external-share Share a custom application with a user. logs Provides logs for custom application. ls Provides list of custom applications or execution... publish Updates a custom application. revert-publish Reverts updates to a custom application. terminate Stops custom application and removes it from the list. ``` Additionally, you can use `--help` for each command listed on this page. ### create command Creates a new application from a pre-built image (the output of `docker build` and `docker save`). If the application is created from a project folder, the application image is created or the existing application is updated. For more information, use the `drapps create --help` command: ``` $ drapps create --help Usage: drapps create [OPTIONS] APPLICATION_NAME Creates a new custom application from a pre-built image (the output of docker build and docker save). Options: -t, --token TEXT Pubic API access token. You can use DATAROBOT_API_TOKEN env instead. -E, --endpoint TEXT DataRobot Public API endpoint. You can use DATAROBOT_ENDPOINT instead. Default: https://app.datarobot.com/api/v2 -e, --base-env TEXT Name or ID for execution environment. -p, --path DIRECTORY Path to folder with files that should be uploaded. -i, --image FILE Path to tar archive with custom application docker images. --skip-wait Do not wait for ready status. --help Show this message and exit. ``` More detailed descriptions for each argument are provided in the table below: | Argument | Description | | --- | --- | | APPLICATION_NAME | Enter the name of your application. This name is also used to generate the name of the application image, adding the Image suffix. | | --token | Enter your API Key, found on the API keys and tools page of your DataRobot account. You can also provide your API Key using the DATAROBOT_API_TOKEN environment variable. | | --endpoint | Enter the URL for the DataRobot Public API. The default value is https://app.datarobot.com/api/v2. You can also provide the URL to Public API using the DATAROBOT_ENDPOINT environment variable. | | --base-env | Enter the UUID or name of execution environment used as base for your Streamlit app. The execution environment contains the libraries and packages required by your application. You can find list of available environments in the Custom Model Workshop on the Environments page. For a custom Streamlit application, use --base-env '[DataRobot] Python 3.9 Streamlit'. | | --path | Enter the path to a folder used to create the application. Files from this folder are uploaded to DataRobot and used to create the application image. The application is started from this image. To use the current working directory, use --path .. | | --image | Enter the path to an archive containing an application docker image. You can save your docker image to file with the docker save > .tar command. | | --skip-wait | Enables exiting the script immediately after the application creation request is sent, without waiting until the application setup completes. | ### logs command Returns the logs generated for an application. For more information, use the `drapps logs --help` command: ``` $ drapps logs --help Usage: drapps logs [OPTIONS] APPLICATION_ID_OR_NAME Provides logs for custom application. Options: -t, --token TEXT Pubic API access token. You can use DATAROBOT_API_TOKEN env instead. -E, --endpoint TEXT DataRobot Public API endpoint. You can use DATAROBOT_ENDPOINT instead. Default: https://app.datarobot.com/api/v2 -f, --follow Output append data as new log records appear. --help Show this message and exit. ``` | Argument | Description | | --- | --- | | APPLICATION_ID_OR_NAME | Enter the ID or the name of an application for which you want to view the logs. | | --token | Enter your API Key, found on the API keys and tools page of your DataRobot account. You can also provide your API Key using the DATAROBOT_API_TOKEN environment variable. | | --endpoint | Enter the URL for the DataRobot Public API. The default value is https://app.datarobot.com/api/v2. You can also provide the URL to Public API using the DATAROBOT_ENDPOINT environment variable. | | --follow | Enables the script to continue checking for new log records to display as they appear. | ### ls command Returns a list of applications or execution environments. For more information, use the `drapps ls --help` command: ``` $ drapps ls --help Usage: drapps ls [OPTIONS] {apps|envs} Provides list of custom applications or execution environments. Options: -t, --token TEXT Pubic API access token. You can use DATAROBOT_API_TOKEN env instead -E, --endpoint TEXT DataRobot Public API endpoint. You can use DATAROBOT_ENDPOINT instead. Default: https://app.datarobot.com/api/v2 --id-only Output only ids --help Show this message and exit. ``` | Argument | Description | | --- | --- | | --token | Enter your API Key, found on the API keys and tools page of your DataRobot account. You can also provide your API Key using the DATAROBOT_API_TOKEN environment variable. | | --endpoint | Enter the URL for the DataRobot Public API. The default value is https://app.datarobot.com/api/v2. You can also provide the URL to Public API using the DATAROBOT_ENDPOINT environment variable. | | --id-only | Enables showing only the IDs of the entity. This command can be useful with piping to terminate the command. | ### terminate command Stops the application and removes it from the applications list. For more information, use the `drapps terminate --help` command: ``` $ drapps terminate --help Usage: drapps terminate [OPTIONS] APPLICATION_ID_OR_NAME... Stops custom application and removes it from the list. Options: -t, --token TEXT Pubic API access token. You can use DATAROBOT_API_TOKEN env instead -E, --endpoint TEXT DataRobot Public API endpoint. You can use DATAROBOT_ENDPOINT instead. Default: https://app.datarobot.com/api/v2. --help Show this message and exit. ``` | Argument | Description | | --- | --- | | APPLICATION_ID_OR_NAME | Enter a space separated list of IDs or names of the applications to be removed. | | --token | Enter your API Key, found on the API keys and tools page of your DataRobot account. You can also provide your API Key using the DATAROBOT_API_TOKEN environment variable. | | --endpoint | Enter the URL for the DataRobot Public API. The default value is https://app.datarobot.com/api/v2. You can also provide the URL to Public API using the DATAROBOT_ENDPOINT environment variable. | ### external-share command Manages external users that can access an application. For more information, use the `drapps external-share --help` command: ``` $ drapps external-share --help Usage: drapps external-share [OPTIONS] APPLICATION_NAME Options: -t, --token TEXT Pubic API access token. You can use DATAROBOT_API_TOKEN env instead. -E, --endpoint TEXT Data Robot Public API endpoint. You can use DATAROBOT_ENDPOINT instead. Default: https://app.datarobot.com/api/v2 --set-external-sharing BOOLEAN --add-external-user TEXT --remove-external-user TEXT --help Show this message and exit. ``` | Argument | Description | | --- | --- | | -t, --token | (Text) Enter your API Key, found on the API keys and tools page of your DataRobot account. You can also provide your API Key using the DATAROBOT_API_TOKEN environment variable. | | -E, --endpoint | (Text) Enter the URL for the DataRobot Public API. The default value is https://app.datarobot.com/api/v2. You can also provide the URL to Public API using the DATAROBOT_ENDPOINT environment variable. | | --set-external-sharing | (Boolean) Determines whether or not external sharing is enabled for the application. | | --add-external-user | (Text) Grants the specified user access to the application. | | --remove-external-user | (Text) Revokes the specified user's access to the application. | | --help | Displays the available arguments for the command. | To enable external sharing for an app: ``` drapps external-share --set-external-sharing True ``` To enable external sharing (by ID) for an account: ``` drapps external-share --add-external-user user@datarobot.com ``` Enable external sharing (by name) for an account: ``` drapps external-share MyAwesomeApp --add-external-user user@email.com ``` To add two users from external sharing: ``` drapps external-share MyAwesomeApp --add-external-user user@email.com --add-external-user person@email.com ``` Add one user and remove one from external sharing: ``` drapps external-share MyAwesomeApp --add-external-user user@email.com --remove-external-user person@email.com ``` ## Deploy an example app First, clone the [dr-apps repository](https://github.com/datarobot/dr-apps/tree/main) so you can access example apps. You can then deploy an example Streamlit app using the following command from the root of the dr-apps repository: ``` drapps create -t -e "[Experimental] Python 3.9 Streamlit" -p ./examples/demo-streamlit DemoApp ``` This example script works as follows: 1. Finds the execution environment through the/api/v2/executionEnvironments/endpoint by the name or UUID you provided, verifying if the environment can be used for the application and retrieving the ID of the latest environment version. 2. Finds or creates the application image through the/api/v2/customApplicationImages/endpoint, named by adding theImagesuffix to the provided application name (i.e.,CustomApp Image). 3. Creates a new version of an application image through thecustomApplicationImages//versionsendpoint, uploading all files from the directory you provided and setting the execution environment version defined in the first step. 4. Starts a new application with the application image version created in the previous step. When this script runs successfully, a link to the app on the [Applications](https://app.datarobot.com/applications) page appears in the terminal. > [!NOTE] Application access > To access the application, you must be logged into the DataRobot instance and account associated with the application. ## Feature considerations Consider the following when creating an application: - The root directory of the application must contain astart-app.shfile, used as the entry point for starting your application server. - The web server of the application must listen on port8080. - The required packages can be listed in arequirements.txtfile in the application's root directory for automatic installation during application setup. - The application should authenticate with the DataRobot API through theDATAROBOT_API_TOKENenvironment variable using a key found in the DataRobotAPI keys and tools. The DataRobot package on PyPI already authenticates this way. This environment variable is added automatically to your running container by the application service. - The application should access the DataRobot Public API URL for the current environment through theDATAROBOT_ENDPOINTenvironment variable. The DataRobot package on PyPI already uses this route. This environment variable is added automatically to your running container by the application service. --- # Custom apps URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/custom-apps/index.html > Create custom applications in DataRobot to share machine learning projects using web applications, including Streamlit and Dash. # Custom apps The following sections describe the documentation available for custom AI Apps: | Topic | Description | | --- | --- | | Host custom applications | Host a custom application, such as a Streamlit app, in DataRobot using a DataRobot execution environment. | | Custom applications upload | Create custom AI Apps in DataRobot to share machine learning projects using web applications like Streamlit and Dash. | | Manage custom applications | Share, stop, or delete custom applications. | --- # Manage custom applications URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/custom-apps/manage-custom-apps.html > Create custom applications in DataRobot to share machine learning projects using web applications like Streamlit, Dash, and Plotly. # Manage custom applications The table below describes the elements and available actions from the Apps Workshop: | | Element | Description | | --- | --- | --- | | (1) | Application name | The application name. | | (2) | Open | Click to open an application. | | (3) | Actions menu | Shares, controls, or deletes an application. | ## Share applications The sharing capability allows you to manage permissions and share an application with users, groups, and organizations, as well as recipients outside of DataRobot. This is useful, for example, for allowing others to use your application without requiring them to have the expertise to create one. > [!WARNING] Warning > When multiple users have access to the same application, it's possible that each user can see, edit, and overwrite changes or predictions made by another user, as well as view their uploaded datasets. This behavior depends on the nature of the custom application. You can access sharing functionality from the actions menu in the Apps workshop. Click the Actions menu next to the app you want to share and select Share. This opens the Share dialog, which lists each associated user and their role. Editors can share an application with one or more users or groups, or the entire organization. Additionally, you can share an application externally with a sharing link. **Users:** To add a new user, enter their username in the Share with field. Choose their role from the dropdown. Select Send notification to send an email notification and Add note to add additional details to the notification. Click Share . **Groups and organizations:** Select either the Groups or Organizations tab in the Share dialog. Enter the group or organization name in the Share with field. Determine the role for permissions. Click Share . The app is shared with—and the role is applied to—every member of the designated group or organization. **External sharing:** To share a custom app with non-DataRobot users, toggle on Enable external sharing. The link that appears beneath the toggle allows you to share custom with end-users who don't have access to DataRobot. Before you can share that link with end users, you must specify the email domains and addresses that are permitted access to the app. You can revoke access to a sharing link by modifying this list or unselecting Enable external sharing. It may take up to 30 minutes for revoking this access to take effect. [https://docs.datarobot.com/en/docs/images/cl-manage-app-7.png](https://docs.datarobot.com/en/docs/images/cl-manage-app-7.png) You can also programmatically share custom applications using the [DRApps CLI](https://docs.datarobot.com/en/docs/classic-ui/app-builder/custom-apps/custom-apps-hosting.html#external-share-command). The following actions are also available in the Share dialog: - To remove a user, click the X button to the right of their role. - To re-assign a user's role, click the assigned role and assign a new one from the dropdown. ## Delete an application If you have the appropriate permissions, you can delete an application by opening the Actions menu and clicking Delete. --- # Pages URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/app-pages.html > Use pages in No-Code AI Apps to organize and group insights. # Pages Pages divide an application into separate sections that you can navigate between—allowing you to organize and group insights in a way that makes sense for your use case. By default, each non-time series application has the following pages: | Page | Description | | --- | --- | | Home | View the application landing page, where you can upload batch predictions and view individual prediction rows. | | Create Prediction / Create Optimization | Make single record predictions (non-time series only). | | Prediction Details | View prediction results for individual prediction rows. | To manage your pages, click the Pages panel icon on the left or open the Editing page dropdown and click Manage pages. The Editing page dropdown is also how you select the page you want to edit. In the Pages panel, you can: | | Element | Description | | --- | --- | --- | | (1) | + Add | Adds a new page to the application. | | (2) | Reorder | Modifies the order of the pages. | | (3) | Rename | Renames a page. | | (4) | Actions menu | Deletes or hides a page. | | (5) | Editing page | Controls the application page you are currently editing. | Pages are displayed at the top of the application. ## Create pages In addition to the default pages described above (Home, Create, Prediction Details), you can customize applications by creating pages new pages. You may want to do this, for example, to more intuitively group insights for your specific use case. To create a new page, open the Pages panel and click + Add. You can then click the pencil icon to rename the page (1) and drag-and-drop it to a new position (2). After publishing your changes and leaving Build mode, your new page is displayed along the top of the application. ## Delete pages If you want to remove a page from the end-user application, you can either hide or delete the page. Hiding the page, means it's no longer accessible when using the application, but the page and its contents are preserved. You may want to hide a page, for example, while you're working on it until it's ready to be shared publicly. Deleting a page removes it from the application entirely and it cannot be restored. To hide or remove a page, open the Pages panel and click the more actions icon. Then, select the Hide or Delete. > [!NOTE] Note > You cannot delete default pages, however, you can hide them from the end-user application. --- # Settings URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/app-settings.html > Edit general configuration details and sharing permissions, and view usage information for No-Code AI Apps. # Settings The Settings tab allows you to edit the application's general configuration details and sharing permissions, and view usage information. To access this tab, make sure you're in Build mode and click Settings. ## General Configuration The General Configuration tab allows you to edit the following settings: | Setting | Description | | --- | --- | | App name | Set the application name. | | App description | Add a description for the application. | | App logo | Upload a custom logo for the application. | | Prediction decimal places | Set the number of decimal places displayed for predictions. Affects the All Rows widget, Prediction Explanations, and What-if and Optimizer widget. | | CSV export | Toggle on Include BOM to include the byte order mark in exports. | Click Save to apply any changes made to the general configuration settings. ### Add a custom logo You can add a custom logo to your application, allowing you to keep the branding of the app consistent with that of your company before sharing it either externally or internally. To add a custom logo: 1. UnderApp logo, clickBrowse. Alternatively, you can drag-and-drop an image into the field. Upload requirementsThe image must be saved as a PNG, JPEG, or JPG, and the file size cannot exceed 100KB. 2. In the explorer window, locate and select the new image, and clickOpen. A notification (1) appears in the upper-right corner to let you know the upload was successful, and both the image preview (2) and app logo (3) update to reflect the new image. To remove a custom logo and revert back to the DataRobot logo, clickRemove file(4). ## Permissions The Permissions tab allows you to manage access to the application and share it with other users, groups, and organizations, including those without access to DataRobot. The options on this page vary depending on which option you've selected in the Who can access the app dropdown. - If you selectInvited user only, you can only share the application with users, groups, and organizations. - If you selectAnyone with the Sharing Link, you can share the application with users, groups, organizations, as well as users outside of DataRobot. Use the shareable link generated in the field belowLink sharing on(1). All users who access the app with this link haveConsumerpermissions. Note that you can revoke access to users accessing the link by clickingGenerate new link(2). You will need to share the new link to provide those users with access again. For more information on user roles, see [Roles and permissions](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#no-code-ai-app-roles). ## App Usage The App Usage tab displays the number of users who viewed the application over the specified time range, as well as user activity. To select a different time range for the chart, open the Time range dropdown and select a new option. The chart automatically updates. --- # Widgets URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/app-widgets.html > Add and configure widgets in No-Code AI Apps to create visual, interactive, and purpose-driven end-user applications. # Widgets Applications are composed of widgets that create visual, interactive, and purpose-driven end-user applications. There are two main categories of widgets: - Default widgets are included in every application, no matter the template, and cannot typically be removed. - Optional widgets can be added to customize an application for your specific use case. All optional widgets, which add visualizations, surface insights, or filter content, must be configured before using an application. If a widget is not configured or is configured incorrectly, DataRobot displays an error message. The tabs below further describe each widget type: **Default:** Applications automatically include the following [default widgets](https://docs.datarobot.com/en/docs/classic-ui/app-builder/reference/default-widgets.html) to make predictions and view prediction results. Note that [time series applications](https://docs.datarobot.com/en/docs/classic-ui/app-builder/ts-app.html) have a different set of default widgets. Widget Description Add Data Allows you to upload prediction files. All Rows Displays prediction history by row. Add New Row Allows you to make single record predictions. General Information Displays feature values you want to view for each prediction that don't necessarily impact the results. Prediction Information Displays feature values likely to impact the prediction, as well as Prediction Explanations. Prediction Explanations Displays a chart with prediction results and a table with Prediction Explanations. **Filter:** [Filter widgets](https://docs.datarobot.com/en/docs/classic-ui/app-builder/reference/optional-widgets.html#filters) provide additional filtering options within an application. The table below describes the available filter widgets: Widget Description Categories Filters by one or more categorical features. Dates Filters by date features. Numbers Filters by numeric features. You must define a Min and Max in the widget properties. **Chart:** [Chart widgets](https://docs.datarobot.com/en/docs/classic-ui/app-builder/reference/optional-widgets.html#charts) add visualizations to an application and can be configured to surface important insights in your data and prediction results. The table below describes the available chart widgets: Widget Description Line Displays a Line chart for the selected features—useful for visualizing trends, understanding the distribution of your data, comparing values in larger datasets, and understanding the relationship between value sets. Bar Displays a Bar chart for the selected features—useful for understanding the distribution of your data and comparing values in smaller datasets. Line + Bar Displays a Line and Bar chart for the selected features. You can toggle between the two in the open application. Area Displays an Area chart for the selected features—useful for visualizing the composition of data. Donut Displays a pie chart based on one dimension and one measure—useful for visualizing the composition of data, especially how individual parts compare to the whole. Single Value Displays the average value of the selected feature. **What-if and Optimizer:** The [What-if and Optimizer widget](https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/whatif-opt.html) can be a scenario comparison tool, a scenario optimizer tool, or both by providing two tools for interacting with prediction results. The initial configuration of this widget is based on the [template selected during app creation](https://docs.datarobot.com/en/docs/classic-ui/app-builder/create-app.html#template-options). ## Add widgets To add a widget to your application, open the Widget panel [https://docs.datarobot.com/en/docs/images/icon-widget.png](https://docs.datarobot.com/en/docs/images/icon-widget.png) in the upper-left corner, then drag-and-drop a widget from the left pane onto the canvas. ## Configure widgets To configure a widget, either click to select it or hover over the widget and click the pencil icon. Once selected, a panel opens on the left with the tabs described below: **Data tab:** The Data tab allows you to manage widget features, including adding and removing features, changing the feature display name, and setting constraints. [https://docs.datarobot.com/en/docs/images/app-data-1.png](https://docs.datarobot.com/en/docs/images/app-data-1.png) Name Element Description 1 Manage Adds or removes features from the widget, and add tooltips. 2 Set Constraints Adds feature constraints that instruct the app to only include values falling in the range determined by numeric constraints or specific values for a categorical feature. 3 Dimensions / Measures Displays the current feature selections for dimensions and measures. Click the pencil icon to change the display name of the feature in the widget. 4 Tooltips Displays current feature names and any tooltips manually added in the Manage Feature window. **Properties tab:** On the Properties tab, you can control the widget's behavior and appearance. You may use these customization options, for example, to fine-tune a widget to better suit your use case or change the appearance to match your company's branding. [https://docs.datarobot.com/en/docs/images/app-prop-1.png](https://docs.datarobot.com/en/docs/images/app-prop-1.png) > [!NOTE] Note > Configuration options are based on widget and project type, for example, multiclass projects include additional parameters for the What-if and Optimizer widget. > > See also [Default widgets](https://docs.datarobot.com/en/docs/classic-ui/app-builder/reference/default-widgets.html) or [Optional widgets](https://docs.datarobot.com/en/docs/classic-ui/app-builder/reference/optional-widgets.html) for a complete list of customization options. ### Manage widget features Configuring widget features is an important, and often necessary step when setting up an application. This controls, for example, chart widget visualizations and the features available when making single record predictions. For many widgets, you must select both a dimension and a feature: - Dimensions: Features that contain qualitative values used to categorize and reveal details in data. - Measures: Features with numeric, quantitative values that can be measured. To manage widget features: 1. Select the widget and click the Data tab in the left-hand panel. 2. ClickManage. TheManage Featurewindow opens. 3. In theDimensionstab, click the orange arrows next to one or more features you’d like to visualize on the x-axis. You can select categorical, date, and boolean features. Viewing feature detailsIn theManage Featurewindow, click a feature to view a histogram of the feature values in the training data.Instead of a histogram, location feature types display a static map with the training data represented by data points. 4. ClickMeasuresand click the arrow next to the feature you'd like to measure on the y-axis. TheMeasurestab only displays numeric and custom features. 5. ClickSaveto apply the configuration. > [!NOTE] Note > You must select at least one dimension and one measure to configure a widget (with the exception of the [Single Value](https://docs.datarobot.com/en/docs/classic-ui/app-builder/reference/optional-widgets.html#single-value) widget). > > If the widget displays a yellow error message stating there is no valid data, the application does not have access to the training data. You must create a project from the dataset in the AI Catalog. #### Custom features Similar to [feature transformations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html) in the main DataRobot platform, you can create custom features for chart widgets in your application. 1. In theManage Featurewindow, clickAdd custom feature. 2. Name the custom feature, then type the function and features using thesupported syntax. The example below measures the cost of shipments per kilogram. 3. ClickCreate. The custom feature appears in theMeasurestab of theManage Featureswindow. > [!NOTE] Note > You can only use numeric features to create custom feature expressions. #### Add a predicted class for multiclass projects For multiclass projects, you can add the predicted class field to the All Rows widget. 1. On theHomepage of the application, select theAll Rowswidget and clickManage. 2. Click the orange arrow next to the feature marked(Predicted Class). 3. ClickSave—the predicted class is now displayed as a column in theAll Rowswidget. --- # Edit no-code applications URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/index.html > Modify the configuration of current No-Code AI Apps using widgets. # Edit no-code applications On the Applications tab, click Open next to the application you want to manage and click Build. The Build page allows you to modify the configuration of an application using widgets. Before the app opens, you must sign in with DataRobot and authorize access. These sections describe the configurable elements of No-Code AI Apps: | Topic | Description | | --- | --- | | Pages | Add or remove pages to organize and group application insights. | | Widgets | Add, remove, and configure widgets—tools for surfacing insights, creating visualizations, and using applications. | | What-if and Optimizer widget | Configure the What-if and Optimizer widget—a single widget that provides scenario comparison and optimizer tools. | | Settings | Modify general configuration information and permissions, as well as view usage details. | ## UI overview | | Element | Description | | --- | --- | --- | | (1) | Pages panel | Allows you to rename, reorder, add, hide, and delete application pages. | | (2) | Widget panel | Allows you to add widgets to your application. | | (3) | Settings | Modifies general configurations and permissions as well as displays app usage. | | (4) | Documentation | Opens the DataRobot documentation for No-Code AI Apps. | | (5) | Editing page dropdown | Controls the application page you are currently editing. To view a different page, click the dropdown and select the page you want to edit. Click Manage pages to open the Pages panel. | | (6) | Preview | Previews the application on different devices. | | (7) | Go to app / Publish | Opens the end-user application, where you can make new predictions, as well as view prediction results and widget visualizations. After editing an application, this button displays Publish, which you must click to apply your changes. | | (8) | Widget actions | Moves, hides, edits, and deletes widgets. | --- # What-if and Optimizer URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/whatif-opt.html > Describes how to configure the What-if and Optimizer widget—a scenario comparison and optimizer tool. # What-if and Optimizer The What-if and Optimizer widget provides two tools for interacting with prediction results: - What-if: A decision-support tool that allows you to create and compare multiple prediction simulations to identify the option that provides the best outcome. You can also make a prediction, then change one or more inputs to create a new simulation, and see how those changes affect the target feature. - Optimizer: Identifies the maximum or minimum predicted value for a target or custom expression by varying the values of a selection of flexible features in the model. To edit the What-if and Optimizer widget, open the Editing page dropdown and select Prediction Details/Optimizer Details. Select the widget and click the Properties tab. The app offers a number of settings that enhance the output of predicted values for your target. The settings displayed are based on which tools are enabled as well as the project type. **Optimizer + What-if:** [https://docs.datarobot.com/en/docs/images/app-whatif-1.png](https://docs.datarobot.com/en/docs/images/app-whatif-1.png) **Optimizer only:** [https://docs.datarobot.com/en/docs/images/app-whatif-2.png](https://docs.datarobot.com/en/docs/images/app-whatif-2.png) **What-if only:** [https://docs.datarobot.com/en/docs/images/app-whatif-3.png](https://docs.datarobot.com/en/docs/images/app-whatif-3.png) **Multiclass:** [https://docs.datarobot.com/en/docs/images/app-multi-3.png](https://docs.datarobot.com/en/docs/images/app-multi-3.png) If you create an Optimizer application from a multiclass project, you need to select a target for the optimizer from the predicted class values. To do so, click the Properties tab and use the dropdown to select your target. Note that Enable scenario optimizer must be toggled on. The table below describes each configurable parameter: | Parameter | Description | | --- | --- | | What-if and Optimizer toggles | Enable scenario what-if: Toggle to enable or disable the comparison functionality.Enable scenario optimizer: Toggle to enable or disable the optimizer functionality. If optimizer is enabled, you must select an option under Outcome of optimal scenario and can optionally include a custom optimization expression. | | Select target for optimizer dropdown (multiclass only) | Sets a target for the optimizer from the predicted class values if Enable scenario optimizer is toggled on. | | Outcome of optimal scenario | Sets whether to minimize or maximize the predicted values for the target feature. Minimizing leads to the lowest outcome (e.g., custom churn), and maximizing the highest (e.g., sale price). | | Custom optimization expression | Creates an equation using curly braces that uses one or more features, such as {converted} * {renewal_price}. | | Set optimization algorithm | Sets an algorithm, when enabled; otherwise leaves the optimization choice to DataRobot. Choose from the algorithms listed and determine the number of simulations to run. Grid Search is an exhaustive, brute-force search of options on up to three flexible features. This may result in long run times because it tries many possibilities, even if prior iterations don't suggest a strong outcome. Particle Swarm is a metaheuristic strategy that tests a large number of options with up to 30 flexible features. It can be effective for numeric flexible features but may not be as effective for flexible categorical features.Hyperopt efficiently explores significantly fewer options on up to 20 flexible features. It is effective for categorical and numeric features. With this algorithm, you can set up to 400 simulations. More iterations may yield better results, but can result in longer run times as it takes many iterations to converge. | | Constrain sum of features | Sets output constraints. Constraints ensure that each record’s optimization iterations don’t output results that exceed a given value for the target feature. For example, if you are optimizing the price of a home, you may want to expand the gross living area by finishing part of the basement or adding a bedroom. You can use a sum constraint to limit the space each project is allowed to occupy in sq/ft. Choose Maximum (selected solutions must never exceed) or Equality (selected solutions must be equal) to the constrain value. | | Views | Displays the information as a chart, a table, or both. | > [!NOTE] Note > The default configuration is determined by the template selected during app creation. The What-if and Optimizer widget is disabled for the Predictor template but can be enabled by clicking the Eye icon. ## Custom optimization expressions For batch prediction optimization, use a field defined in the batch upload as part of the custom optimization expression the same way you use a feature from the dataset that the app is deployed from. For example, if you label a field in a spreadsheet `net_profit`, and you have a `time_to_market` feature in the project's underlying dataset, the following would be a valid custom expression: ``` {net_profit}/{time_to_market} ``` ## Constrain sum of features To select the features that you want to be part of the sum: 1. Turn onConstrain sum of featuresat the bottom of thePropertiestab. UnderPart of sum features, clickSelect. 2. In theManage Featureswindow, check the box next to at least two fixed or flexible features—the selected features must be numeric. This option is not available if you use theHyperopt algorithm. 3. ClickSavewhen you are finished selecting features. ## Flexible features The Data tab allows you to select the features that represent the factors you have control over when searching for your optimized outcome. For example, when optimizing the price of homes, some flexible features are the quality of the kitchen, the cost of the mortgage, and the size of the garage. To manage flexible features: 1. ClickManage. 2. Use the orange arrows to add or remove features. 3. ClickSaveto confirm your flexible features. ## Constraints When you have selected flexible features, you can apply constraints to them. This instructs the app to only include values falling in the range determined by numeric constraints or specific values for a categorical feature. To apply constraints to a feature, click Set Constraints on the Data tab. Selecting a flexible feature expands a dropdown displaying the feature distribution. **Categorical features:** For categorical features, open the Search from categories dropdown and choose which features to include in the simulation by checking the corresponding box. [https://docs.datarobot.com/en/docs/images/edit-app-4.png](https://docs.datarobot.com/en/docs/images/edit-app-4.png) **Numeric features:** For numeric features, you can enter individual values for the minimum and maximum numeric ranges or drag the boundaries on the histogram. [https://docs.datarobot.com/en/docs/images/edit-app-5.png](https://docs.datarobot.com/en/docs/images/edit-app-5.png) Toggle Integer values on to include only integer values (exclude decimals). Click Save to confirm your feature constraints. --- # AI Apps URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html > Create and configure AI-powered applications using a no-code interface to enable core DataRobot services without having to build models and evaluate their performance in DataRobot. # AI Apps No-Code AI Apps allow you to build and configure AI-powered applications using a no-code interface to enable core DataRobot services without having to build models and evaluate their performance in DataRobot. Applications are easily shared and do not require users to own full DataRobot licenses in order to use them. Applications also offer a great solution for broadening your organization's ability to use DataRobot's functionality. The following sections describe the documentation available for DataRobot No-Code AI Apps: | Topic | Description | | --- | --- | | Create applications | Create applications from the Applications tab or Deployment inventory. | | Manage applications | Launch, share, duplicate, or delete applications from the Applications tab. | | Edit applications | Configure your application widgets, pages, settings, and more. | | Use applications | Use a configured application to make predictions and interpret insights from your data. | | Time series applications | Create time series applications and consume insights in the What-if forecasting widget. | ## Feature considerations Consider the following before deploying an application. - The following project typesaresupported in DataRobot applications: - No-Code AI Apps do not support features generated by DataRobot. - Access to MLOps functionality in DataRobot is required. - Exponentiation (i.e.,**) is not a supported feature transformation for custom features. - Users accessing applications via a sharing link cannot: - Chart widgets display two types of data: - The following is not supported whencreating an application from a Leaderboard model: - Organizations are limited to 200 applications. To remove this limit, contact your DataRobot representative. - For users with Read access only, Prediction Explanations must be manually computed for the model. If a user has User access to the project, Prediction Explanations are automatically computed. - While there is no limit to the number of flexible features you can specify in Optimizer applications, if the Grid Search algorithm is selected, then the grid cannot contain more than 10000 points and will return an error message if it exceeds this limit. DataRobot does not recommend using Grid Search if you are optimizing more than three features. ### Time series applications - Before creating a time series application, make sure: - To include calendar events in the widget,add a calendar fileto your project and include calendar events for the timeline of the training dataset and forecasting window. - You cannot train a model on the Time Series Informative Features list after it has been deployed to production. --- # Default widgets URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/reference/default-widgets.html > Lists default widgets in No-Code AI Apps, by page, and their customization options. # Default widgets Applications automatically include several default widgets to make predictions and view prediction results. This section describes each default widget, including configuration options and differences for project types, grouped by which application page they can be found on. Use the Editing page dropdown at the top to edit a different page. ## Home page Home is the application landing page—the first page users see when opening an application. This is where you can upload batch prediction files as well as view prediction rows from batch predictions and single record predictions. ### Add Data Allows you to [upload batch prediction files](https://docs.datarobot.com/en/docs/classic-ui/app-builder/use-apps/app-make-pred.html#batch-predictions) —either local files or from the AI Catalog (available for signed-in users only). ### All Rows Displays prediction history by row. ## Create Prediction page The Create Prediction page, or Create Optimization if you selected the Optimizer template, is where you make single record predictions using any features in the Add New Row widget. ### Add New Row Allows you to [make single record predictions](https://docs.datarobot.com/en/docs/classic-ui/app-builder/use-apps/app-make-pred.html#single-record-predictions). If you want to make predictions using additional features in the project, see the information on [adding features to widgets](https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/app-widgets.html#manage-widget-features). ## Prediction Details page The Prediction Details page displays the results of individual predictions—allowing you to analyze prediction insights after making a single record prediction or selecting a row in the All Rows widget. ### Row Identifier Displays the prediction row you're currently viewing. > [!TIP] Customize apps with an association ID > With the Row Identifier widget selected, you can choose the association ID from the dropdown on the left to display this value on the prediction results page for each prediction. > > [https://docs.datarobot.com/en/docs/images/build-app-1.png](https://docs.datarobot.com/en/docs/images/build-app-1.png) ### General Information Displays, for each prediction, feature values that don't necessarily impact results. ### Prediction Information Displays feature values and identifies how the value impacted the prediction with XEMP or SHAP Prediction Explanations. ### Prediction Explanations Displays a chart with prediction results and a table with Prediction Explanations. --- # AI App reference URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/reference/index.html > Reference material for No-Code AI Apps. # AI App reference | Topic | Description | | --- | --- | | Default widgets | Lists each default widget, by page, and their customization options. | | Optional widgets | Lists each optional widget and their customization options. | --- # Optional widgets URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/reference/optional-widgets.html > Lists optional widgets in No-Code AI Apps and their customization options. # Optional widgets All optional widgets must be configured before they can be used in an application. ## Filters Filter widgets provide additional filtering options within an application, and if added to an application, they are accessible on every page (i.e., filter widgets are not page specific). Parameters specified in a filtering widget are applied to all visible chart widgets as well as the All Rows widget. > [!NOTE] Note > If there are no results in the filter dropdown in Consume mode, the dataset doesn’t include the required feature type. ### Categories Filters by one or more categorical features. On the Properties tab, customize the widget using the available parameters: | Parameter | Description | | --- | --- | | Widget Name | Rename the widget. Replaces the text displaying the widget type. | | Feature to filter by | Select a categorical feature from the dropdown. You can then filter the application by its feature values. | ### Dates Filters by date features. On the Properties tab, customize the widget using the available parameters: | Parameter | Description | | --- | --- | | Widget Name | Rename the widget. Replaces the text displaying the widget type. | | Feature to set dates for | Select a date feature from the dropdown. You can then filter the application by specific dates. | ### Numbers Filters by numeric features. On the Properties tab, customize the widget using the available parameters: | Parameter | Description | | --- | --- | | Widget Name | Rename the widget. Replaces the text displaying the widget type. | | Feature to set range for | Select a numeric feature from the dropdown. | | Min | Enter a value that represents the beginning your range (i.e., the minimum value). | | Max | Enter a value that represents the end of your range (i.e., the maximum value). | ## Charts Chart widgets add visualizations to an application and can be configured to surface important insights in your data and prediction results. ### Line Displays a Line chart for the selected features—useful for visualizing trends, understanding the distribution of your data, comparing values in larger datasets, and understanding the relationship between value sets. On the Properties tab, customize the widget using the available parameters: | Parameter | Description | | --- | --- | | Widget Name | Rename the widget. Replaces the text displaying the widget type. | | Add labels | Add labels that display the value being measured above each dimension. | | Show glyphs | Add glyphs to the line chart that highlight the value being measured above each dimension. If selected, you can choose a glyph type—a circle, square, or triangle. | | Color Settings | Customize the color of each dimension displayed in the chart. | | Y-axis Settings | Add a second measure to the Y-axis and specify color settings for each. Only available if more than one feature is added as a measure on the Data tab. | | Line Thickness | Add labels that display the average value of each dimension. | ### Bar Displays a Bar chart for the selected features—useful for understanding the distribution of your data and comparing values in smaller datasets. On the Properties tab, customize the widget using the available parameters: | Parameter | Description | | --- | --- | | Widget Name | Rename the widget. Replaces the text displaying the widget type. | | Orientation | Orients the charts vertically or horizontally. | | Bars Position | Increase the width of the bars on the bar chart. | | Color Settings | Customize the color of each dimension displayed in the chart. | | Y-axis Settings | Add a second measure to the Y-axis and specify color settings for each. Only available if more than one feature is added as a measure on the Data tab. | | Opacity | Control the opacity of the chart dimensions. | ### Line + Bar Displays both a line and a bar chart for the selected features. You can toggle between the two in the open application. On the Properties tab, customize the widget using the available parameters: | Parameter | Description | | --- | --- | | Widget Name | Rename the widget. Replaces the text displaying the widget type. | | Add labels | Add labels that display the value being measured above each dimension. | | Show glyphs | Add glyphs to the line chart that highlight the value being measured above each dimension. If selected, you can choose a glyph type—a circle, square, or triangle. | | Orientation | Orient the charts vertically or horizontally. | | Bars Position | Increase the width of the bars on the bar chart. | | Color Settings | Customize the color of each dimension displayed in the chart. | | Y-axis Settings | Add a second measure to the Y-axis and specify color settings for each. Only available if more than one feature is added as a measure on the Data tab. | | Display as Line | Add labels that display the average value of each dimension. | | Opacity of Bars | Control the opacity of the chart dimensions. | | Line Thickness | Add labels that display the average value of each dimension. | ### Area Displays an Area chart for the selected features—useful for visualizing the composition of data. On the Properties tab, customize the widget using the available parameters: | Parameter | Description | | --- | --- | | Widget Name | Rename the widget. Replaces the text displaying the widget type. | | Add labels | Add labels that display the value being measured above each dimension. | | Show glyphs | Add glyphs to the line chart that highlight the value being measured above each dimension. If selected, you can choose a glyph type—a circle, square, or triangle. | | Color Settings | Customize the color of each dimension displayed in the chart. | | Y-axis Settings | Add a second measure to the Y-axis and specify color settings for each. Only available if more than one feature is added as a measure on the Data tab. | | Line Thickness | Add labels that display the average value of each dimension. | | Opacity of area | Control the opacity of the chart dimensions. | ### Donut Displays a pie chart based on one dimension and one measure—useful for visualizing the composition of data, especially how individual parts compare to the whole. On the Properties tab, customize the widget using the available parameters: | Parameter | Description | | --- | --- | | Widget Name | Rename the widget. Replaces the text displaying the widget type. | | Add labels | Add labels that display the average value of each dimension. | | Color Settings | Customize the color of each dimension displayed in the chart. | | Inner Radius | Add negative space to the center of the donut chart. | | Opacity of fill | Control the opacity of the chart dimensions. | ### Single value By default, displays the average value of a selected numeric feature. Using the Set constraints functionality, you can configure the widget to show the count of distinct, min, and max, and the sum of the values of the feature. On the Properties tab, customize the widget using the available parameters: | Parameter | Description | | --- | --- | | Widget Name | Rename the widget. Replaces the text displaying the widget type. | | Assign categories to values | Create categories that group values by range. | --- # Time series applications URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/ts-app.html > Use No-Code AI Apps to consume time series insights. # Time series applications With No-Code AI Apps, you can create Predictor and What-if applications from time series deployments—single series and multiseries. Time series deployments require [additional setup](https://docs.datarobot.com/en/docs/classic-ui/app-builder/ts-app.html#configure-a-time-series-deployment) before creating an application and offer [unique insights](https://docs.datarobot.com/en/docs/classic-ui/app-builder/ts-app.html#what-if-widget) to time series use cases, including creating simulations that visualize how adjusting known in advance features affects a forecasted prediction and comparing predicted vs. actuals for a given time range. ## Configure a time series deployment When creating an application from a time series deployment, there some additional settings required. To configure the time series deployment, go to the Deployment inventory, select a time series deployment in the deployment, and navigate to the Settings tab. Use the table below to configure the appropriate deployment settings for a time series application: | Setting | Description | | --- | --- | | Association ID | Required. Enter the Series ID in the Association ID field. | | Require association ID in prediction requests | Toggle on to require an association ID in batch predictions. This prevents you from uploading a dataset without an association ID, which may affect the accuracy of the predictions. | | Enable target monitoring | Required. Must be toggled on for time series applications. | | Enable feature drift tracking | Required. Must be toggled on for time series applications. | | Enable automatic actuals feedback for time series models | Toggle on to have DataRobot add actuals based on prediction file data. | | Track attributes for segmented analysis of training data and predictions | Required. Must be toggled on for time series applications. | ## Create an application Before starting, review the [considerations](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html#feature-considerations) and [deployment settings](https://docs.datarobot.com/en/docs/classic-ui/app-builder/ts-app.html#configure-a-time-series-deployment) for time series applications because some options must be set up prior to model building.[Known in advance](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/nowcasting.html#features-known-in-advance) features are also required for What-if applications. [Create an application](https://docs.datarobot.com/en/docs/classic-ui/app-builder/create-app.html#from-a-deployment) from your time series deployment. Depending on the template you select—either Predictor or What-if—the application includes the following widgets: | Widget | Description | Predictor | What-if | | --- | --- | --- | --- | | Add Data | Allows you to upload and score prediction files. | ✔ | ✔ | | All Rows | Displays individual predictions. | ✔ | ✔ | | Time Series Forecasting Widget | Visualizes predictions, actuals, residuals, and forecast data on the Predicted vs Actuals and Prediction Explanations over time charts. | ✔ | ✔ | | What-if | Allows you to create, adjust, and save simulations using known in advance features. | | ✔ | ### Customize time series widgets All widgets are pre-configured based on the template you select, however, you can further customize each widget by clicking Build and selecting a widget. In addition to the customization options described in [Widgets](https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/app-widgets.html), each time series widget includes unique customization options: **What-if widget:** On the Data tab, you can specify which known in advance features can be used to create scenarios. Click Manage to add or remove features. [https://docs.datarobot.com/en/docs/images/ts-whatif-2.png](https://docs.datarobot.com/en/docs/images/ts-whatif-2.png) On the Properties tab, you can enable the option to add aggregate predictions for each scenario and choose the aggregation method. [https://docs.datarobot.com/en/docs/images/ts-whatif-3.png](https://docs.datarobot.com/en/docs/images/ts-whatif-3.png) **Time Series Forecasting Widget:** If event calendars were uploaded during project creation, you have the option to display events on the widget charts. To display events, enter Build mode. With the Time Series Forecasting Widget selected, click the Properties tab and select the box next to Show events. [https://docs.datarobot.com/en/docs/images/ts-app-6.png](https://docs.datarobot.com/en/docs/images/ts-app-6.png) ## Score predictions Initially, time series widgets do not display data or visualizations unless the deployment has already scored predictions. To score new predictions: 1. Drag-and-drop a prediction file into theAdd Datawidget. A prediction line appears on both charts and Prediction Explanations are calculated (Predicted vs Actualchart shown here). In this example, the file contains sales predictions for6/14/2014to6/20/2014. 2. ClickDeploymentsand select your time series deployment in theDeploymentinventory. 3. ClickSettings > Data. Scroll down toActualsandupload the dataset containing actuals. In this example, the file contains actuals for6/14/2014to6/20/2014. The actuals file must contain the association ID, which you can also find on the Settings page. NoteIf the forecast file contains actuals for the range of the initial prediction file, you do not need to upload actuals to the deployment and can proceed to step 5.In this example, the forecast file would need to contain actuals for6/14/2014to6/20/2014, the range of the initial prediction, in addition to predictions for6/21/2014to6/27/2014. The application displays anActualsline and calculatesResiduals—the difference between the prediction and the actuals for a given range—in the Time Series Forecasting widget. 4. Navigate back to your time series application. 5. Drag-and-drop a second prediction file, or forecast file, into theAdd Datawidget. A forecast line appears on both widgets. This forecast file contains predictions for6/21/2014to6/27/2014. ### What-if widget Once the application finishes scoring predictions, the What-if widget displays a forecast line and you can begin creating new scenarios; click Add Scenario. In the resulting window, select a date range for the scenario using the date selector (1), or by populating the date fields (2). The project's known in advance features are listed on the right side of the widget. In the time series What-if widget, these features serve as your [flexible features](https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/whatif-opt.html#flexible-features) —features you have control over, for example, launching a marketing campaign on a holiday versus a non-holiday. To create your scenario, enter new values for the features and click Save. Continue creating new scenarios one at a time by adjusting these values until you find one that maximizes the prediction score. #### Bulk edit scenarios After scoring predictions and adding scenarios to the What-if widget, if you need to modify the same known-in-advance feature for multiple scenarios, you can do so with the bulk edit feature. Click Manage Scenarios at the top of the What-if widget. Select the box next to the scenarios you want to edit or Select All to modify all existing scenarios. Then, click the pencil icon to the right of Select All. > [!NOTE] Note > If you are modifying a single scenario, click the pencil icon to the right of the scenario you're editing. If you're editing multiple scenarios, click the pencil icon to the right of Select All. When editing multiple scenarios, clicking the pencil icon to the right of a specific scenario only edits that scenario. Use the (1) slider to select a date range and (2) modify the known in advance features for the selected date range. Click (3) Save and (4) Update scenario. Once DataRobot finishes processing the batch prediction job, the updated scenarios appear on the What-if chart. ### Time Series Forecasting widget The Time Series Forecasting widget surfaces insights using two charts: Prediction vs Actual and Prediction Explanations over time. Use the tabs below to learn more about each chart: **Predicted vs Actual chart:** Similar to [Accuracy Over Time](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/aot-classic.html), the Predicted vs Actual chart helps you visualize how predictions change over time using predicted and actual vs time values based on forecast distances. [https://docs.datarobot.com/en/docs/images/ts-app-4.png](https://docs.datarobot.com/en/docs/images/ts-app-4.png) Setting Description 1 Filter data Hide target (actual) and residual information from the chart. Prediction information cannot be hidden. 2 Resolution View the results by day, week, month, quarter, or year. 3 Prediction line Represents scored predictions. 4 Actual line Represents actual values for the target. 5 Forecast line Represents the prediction based on time, into the future using recent inputs to predict future values. 6 Residuals Represents the difference between predicted and actual values. 7 Date range Drag the handles on the preview panel to bring specific areas into focus on the main chart. Hover over any point to view the date, prediction values, and top 3 Prediction Explanations. [https://docs.datarobot.com/en/docs/images/use-app-19.png](https://docs.datarobot.com/en/docs/images/use-app-19.png) **Prediction Explanations chart:** To view Prediction Explanations over time for your scored predictions, click the Prediction Explanations tab. Every point on the chart represents a separate prediction, and therefore has its own set of Prediction Explanations. Every Prediction Explanation also has its own unique color, allowing you to explore trends in the data. [https://docs.datarobot.com/en/docs/images/use-app-20.png](https://docs.datarobot.com/en/docs/images/use-app-20.png) Setting Description 1 Fade explanations Allows you to hide either all positive or negative Prediction Explanations. Select the box next to Fade explanations and select an option from the dropdown. 2 Highlight explanations Highlights specific features in the Prediction Explanations based on its unique color. Click Highlight explanations and select features from the dropdown. 3 Resolution View the results by day, week, month, quarter, or year. 4 Enable segment analysis Creates the specified number of additional rows for the forecast value. Select Enable segment analysis and choose a Forecast distance from the dropdown. 5 Prediction Explanations Each point represents a separate prediction and each prediction has its own set of explanations, which are grouped by color. 6 Prediction line Represents scored predictions. 7 Forecast line Represents the prediction based on time, into the future using recent inputs to predict future values. 8 Date range Drag the handles on the preview panel to bring specific areas into focus on the main chart. #### Forecast Details page The Forecast Details page allows you to view additional forecast details, including the average prediction values and up to 10 Prediction Explanations for a selected date, as well as segmented analysis for each forecast distance within the forecast window. In the Time Series Forecasting widget, click on a prediction in either the Predictions vs Actuals or Prediction Explanations chart to view the following forecast details for the selected date: | | Description | | --- | --- | | (1) | The average prediction value in the forecast window. | | (2) | Up to 10 Prediction Explanations for each prediction. | | (3) | Segmented analysis for each forecast distance within the forecast window. | | (4) | Prediction Explanations for each forecast distance included in the segmented analysis. | Click the arrow next to Forecast details to return to the main application. #### Download predictions After scoring a batch prediction, the Download button appears in the Time Series Forecasting widget, allowing you to download the prediction results as a CSV. > [!NOTE] Note > When downloading predictions from the Time Series Forecasting widget, non-KA features will always be empty. --- # View prediction results URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/use-apps/app-analyze-result.html > View prediction information and insights for individual predictions in No-Code AI Apps. # View prediction results The prediction results page displays prediction information and insights based on the values entered for an individual prediction. This page automatically opens after making a single record prediction, but you can also view prediction results by selecting a row in the All rows widget. The General Information widget displays helpful values for features that don't necessarily impact the prediction results. The Prediction Information widget, on the other hand, displays the values for features likely to impact the prediction, as well as Prediction Explanations. ## Prediction Explanations The Prediction Explanations widget displays a chart with your predictions results, as well as a table with [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) (either [XEMP-based](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/xemp-pe.html) or, as shown in the example below, [SHAP-based](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/shap-pe.html))—a measure of how features in the model impact a prediction based on their relationship to the target. | | Element | | --- | --- | | (1) | The predicted value for the row. | | (2) | The basis of the prediction classification, determined by the dataset the app deployed from. | | (3) | A table displaying the top 10 Prediction Explanations as well as a visualization representing how the feature is distributed in the training data. | > [!NOTE] Note > You must compute Prediction Explanations for the model before making a prediction. ### Prediction Explanation table The Prediction Explanation table displays the top 10 features with the largest impact on a prediction based on their relationship in the training data. See the table below for a description of each field: | Field | Description | | --- | --- | | Impact | The measured impact of a given feature on the prediction. For a description of the icons used, see the "qualitativeStrength" indicator description. | | Feature | The feature in the training data impacting the prediction. | | Value | The feature value causing the feature's measured impact on the prediction. | | Distribution | A histogram that represents the distribution of a given feature in the training data. Hover over the visualization for additional information that can then be used to add context to the Prediction Explanation. | ## What-if and Optimizer The What-if and Optimizer widget displays various scenarios in a chart or a table view. To learn how to configure this widget, see the documentation on [building applications](https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/whatif-opt.html) The table below describes the components of the What-if and Optimizer widget | | Description | | --- | --- | | (1) | Opens the Add scenario pane. | | (2) | Filters the display to show only actual, optimal, and all manually added scenarios. | | (3) | Displays prediction insights in chart or table view. | | (4) | Adds new scenarios to the widget. | | (5) | Selects features for the x- and y-axis. | > [!TIP] Tip > Check Show only manually added, optimal, and actual scenarios to more easily find your simulations. > > [https://docs.datarobot.com/en/docs/images/use-app-10.png](https://docs.datarobot.com/en/docs/images/use-app-10.png) ### Create simulations If the What-if functionality is enabled, you can manually add scenarios to the widget's display. To create a simulation, click Add scenario. Provide values for each variable selected on the Build page. When you have entered values for each feature, click Add. This triggers a prediction request to a DataRobot prediction server and returns a predicted value for your target feature. Selecting a point on the chart (data point) opens a pane on the right that displays prediction results and feature values for the selected scenario. If Prediction Explanations are available for your prediction request, they appear in the Prediction Explanations tab in the same pane. You can continue to make predictions by updating the variable inputs with new values and repeating this process. After making several predictions, click Table view. This view allows you to drag-and-drop scenarios for side-by-side comparison. ### Optimization simulation results If the Optimizer functionality is enabled, the chart displays an optimal scenario, as well as different outcomes based on the [flexible features](https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/whatif-opt.html#flexible-features) added to the widget's configuration. Once DataRobot completes running simulations, the chart populates the results. The y-axis measures the values of the target feature, and the x-axis indicates the simulation iteration. Each point on the graph represents the predicted value for each simulation run. The orange data point represents your prediction and the green data point represents the optimal scenario—the values of the feature that most often produce the optimal result for your target (the minimal or maximal, based on your settings). The selected values are those you selected for a given iteration, allowing you to compare the selected values for each iteration to the overall most optimal values determined by the app. --- # Make predictions URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/use-apps/app-make-pred.html > Make single record or batch predictions in No-Code AI Apps. # Make predictions There are two ways to make predictions in No-Code AI Apps: [batch predictions](https://docs.datarobot.com/en/docs/classic-ui/app-builder/use-apps/app-make-pred.html#batch-predictions) or [single record predictions](https://docs.datarobot.com/en/docs/classic-ui/app-builder/use-apps/app-make-pred.html#single-record-predictions). > [!NOTE] Note > All prediction requests are sent to DataRobot for processing, therefore, applications have the same prediction limits as the main DataRobot platform. ## Batch predictions To make multiple prediction requests at once from the Home page, click choose file or drag the files into the box. > [!NOTE] Note > Anonymous users (i.e., those accessing an application through a sharing link) can only submit batch predictions using a local file (CSV), while signed-in users can submit batch predictions using a local file (XLSX or CSV) or the AI Catalog. When signed-in users submit a batch prediction using a local XLSX file, it is automatically registered in the catalog. After adding new files, the application processes your predictions and displays them in the All Rows widget on the Home page. Click on any record to view the prediction results. ## Single record predictions To make a new prediction: 1. ClickAdd new row, bringing you to theCreate Predictionpage with theAdd New Rowwidget, which displays the features available to make a prediction. Why aren't some of my features showing up?Reason 1:By default, theAdd New Rowwidget only displays 10 features.To display additional features, clickShow moreat the bottom of the widget. If there are still features missing, you must add them to the widget inBuildmode. To add features, see the documentation onmanaging widget features.Reason 2:No-Code AI Apps only uses "prediction features," meaning features that impact the deployment's predictions. 2. Fill in the feature fields—at least one field must have a value— and theassociation IDif one was added for the deployment. If a field is left blank, the feature field displaysN/Aon the prediction results page. Alternatively, you can clickPopulate averagesto fill in the fields with the average value for each numeric feature and the first alphabetically ordered value of a categorical feature. Location features for geospatial projectsIf the dataset contains a location feature, a globe icon appears in the feature field. You can manually enter a feature value in the field, or click the globe icon to view a visual representation of the training data.The geometry type of the location feature determines the appearance of the training data on the map and affects which draw tool—Point, Polygon, or Path—you can use to highlight your prediction. In the example below, the location feature uses point geometry, so use thePointtool to add a new point to the map. With the point selected, clickSave selected location; the point is then converted to a geojson string to make your prediction. Click Add. After DataRobot completes the request, the prediction results page opens. To add or remove feature fields, click Build and navigate to the Create Prediction page. --- # Use no-code applications URL: https://docs.datarobot.com/en/docs/classic-ui/app-builder/use-apps/index.html > Test different No-Code AI App configurations before sharing the app with end-users. # Use no-code applications On the Applications tab, click Open next to the application you want to launch—from here you can test different application configurations before sharing it with users. > [!NOTE] Note > End-users must sign in with a DataRobot account or access the application via a [link that can be shared with users](https://docs.datarobot.com/en/docs/classic-ui/app-builder/edit-apps/app-settings.html#permissions) outside of DataRobot. These sections describe the actions available when using No-Code AI Apps: | Topic | Description | | --- | --- | | Make predictions | Make single record or batch predictions. | | Analyze prediction results | Analyze prediction information and insights for individual predictions. | ## UI overview | | Element | Description | | --- | --- | --- | | (1) | Application name | Displays the application name. Click to return to the app's Home page. | | (2) | Pages | Navigates between application pages. | | (3) | Build | Allows you to edit the application. | | (4) | Share | Shares the application with users, groups, or organizations within DataRobot. | | (5) | Add new row | Opens the Create Prediction page, where you can make single record predictions. | | (6) | Add Data | Uploads batch predictions—from the AI Catalog or a local file. | | (7) | All rows | Displays a history of predictions. Select a row to view prediction results for that entry. | --- # Work with assets URL: https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog-asset.html > How to work with individual catalog assets, including metadata and feature lists, view relationships and version history, and create snapshots. # Work with assets When you add a dataset, DataRobot ingests the source data and runs [EDA1](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda1) to register the asset and make it available from the catalog. This page describes how you can interact with your data once it's registered in DataRobot: - Update asset details (name, tags, and descriptions). - View and create feature lists. - View and manage configured relationships. - View version history. - Add comments and have discussions within individual assets. - Create a snapshot of a dynamic dataset. - Keep data up-to-date by scheduling snapshots. - Create a project from a catalog asset. See also: - Add data to the AI Catalog - Understand asset states - Schedule snapshots Additionally, when Composable ML is enabled, you can [save blueprints to the AI Catalog](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-catalog.html). From the catalog, a blueprint can be edited, used to train models in compatible projects, or shared. ## Find existing assets Once in the AI Catalog, there are a variety of tools to help quickly locate the data assets you want to work with. You can: **Search:** Search for a specific asset using the search query box. [https://docs.datarobot.com/en/docs/images/catalog-11.png](https://docs.datarobot.com/en/docs/images/catalog-11.png) **Sort:** Use the dropdown to modify the order of all existing assets. [https://docs.datarobot.com/en/docs/images/catalog-27.png](https://docs.datarobot.com/en/docs/images/catalog-27.png) The default sort option is Creation date, except after searching for a specific asset, in which case the default is Relevance. **Filter:** Under the search query box, you can filter assets by Source, Tags, and/or Owner. [https://docs.datarobot.com/en/docs/images/catalog-12.png](https://docs.datarobot.com/en/docs/images/catalog-12.png) For example, you can filter by any [tags](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog-asset.html#asset-details) manually added to an asset: [https://docs.datarobot.com/en/docs/images/catalog-13.png](https://docs.datarobot.com/en/docs/images/catalog-13.png) ## View asset information Click an asset in the catalog to view an overview of the asset's details as well as metadata. | | Element | Description | | --- | --- | --- | | (1) | Asset tabs | Select a tab to work with the asset (dataset): Info: View and edit basic information about the dataset. Update the name and description, and add tags to use for searches. Profile: Preview dataset column names and row data. Feature Lists: Create new feature lists and transformations from the dataset. Relationships: View relationships configured during Feature Discovery.Version History: List and view status for all versions of the dataset. Select a version to create a project or download.Comments: Add a comment to a dataset. Tag users in your comment and DataRobot sends them an email notification. | | (2) | Dataset Info | Update the name and description, and add tags to use for searches. The number of rows and features display on the right, along with other details. | | (3) | State badges | Displayed badges indicate the state of the asset—whether it's in the process of being registered, whether it's static or dynamic, generated from a Spark SQL query, or snapshotted. | | (4) | Create project | Create a machine learning project from the dataset. | | (5) | Share | Share assets with other users, groups, and organizations. | | (6) | Actions menu | Download, delete, or create a snapshot of the dataset. | | (7) | Renew Snapshot | Add a scheduled snapshot. | | (8) | Impact analysis | View how other DataRobot entities are related to—or dependent on—the current asset. | ### Impact analysis The [AI Catalog](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html) serves as a centralized collaboration hub for working with data and related assets. Impact analysis shows how other entities in the application are related to—or dependent on—the current asset. This is useful for a number of reasons, allowing you to: - View how popular an item is based on the number of projects in which it is used. - Understand which other entities might be affected if you were to makes changes or deletions. - Gain understanding on how the entity is used. All of the following associations are reported (with frequency values) as applicable: - Projects - Prediction datasets - Feature Discovery configurations - Time series calendars - Spark SQL queries - External model packages - Deployment retraining To view details, click on the asset title and tiles relevant to the selection display: Click on a tile for summary details and then click on the associated button for specific detail. For example, click on Project for a summary and Open Project for detail: In this example, DataRobot opens to the Start screen if [EDA1](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html) was the last step completed or the Data page if EDA2 completed. Each tile-type provides different (self-explanatory) details. If you do not have permission to access an asset, you can view an entry that represents the asset but the entry does not disclose any additional information. This functionality is also available from the Version History tab for an asset: ## Profile your data The Profile tab allows you to preview dataset column names and row data. It can be useful for finding or verifying column names when writing Spark SQL statements for [blended datasets](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/spark.html#create-blended-datasets). > [!NOTE] Info tab vs. Profile tab > The Info tab displays the data's total row count, feature count, and size. > > The Profile tab only displays a preview of the data based on a 1MB raw sample, and the feature types and details are based on a 500MB sample. > > Meaning the row count observed on the Profile tab may not match that displayed in the Info tab. Note that the preview is a random sample of up to 1MB of the data and may be ordered differently from the original data. To see the complete, original data, use the [Download Dataset](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog-asset.html#download-datasets) option. To preview a dataset, select it in the main catalog and click the pencil icon ( [https://docs.datarobot.com/en/docs/images/icon-pencil.png](https://docs.datarobot.com/en/docs/images/icon-pencil.png)) to access dataset information (if available). 1. Click theProfiletab to preview the contents of the dataset: 2. Use theColumnsdropdown to select the number of columns to display on the page and the scroll bars to scroll through those columns. Additionally, you can use theRowsdropdown to cycle through available data, 20 rows at a time. The Profile tab also displays details for all features in the dataset. To view details for a particular feature, scroll to it in the display and click. The Feature Details listed in the right panel update to reflect statistics for the feature. (These are the same statistics as those displayed on the [Data](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html) for EDA1.) ## View and create feature lists You can create new lists and feature transformations for features of any dataset in the catalog. To work with the tools, select the dataset in the main catalog and Feature Lists in the left panel. > [!NOTE] Note > To create feature lists, you must have Owner or Editor access to the dataset. When you create feature lists, they are copied to a project upon creation. You can then set the list to use for the project from the Feature List dropdown at the top of the Project Data list. See the section on working with [Feature Lists](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html) for complete details on creating, modifying, and understanding these lists. The Feature List tab also provides access to a tool for creating variable type feature transformations. While DataRobot bases variable type assignments on the values seen during EDA, there are times when you may need to change the type. Refer to [feature transformations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html) documentation for complete details. To create a feature list: 1. Use the checkboxes to the left of feature names to select a set of features. 2. Click theCreate new feature list from selectionlink, which becomes active after you select the first feature. 3. In the resulting dialog, provide a name for the new list and clickSubmit. The new list becomes available through the dropdown. You can delete or rename any feature list you created. You cannot make any changes to the DataRobot [default feature lists](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html#automatically-created-feature-lists). ## Manage relationships DataRobot’s Feature Discovery capability guides you through creating relationships, which define both the included datasets and how they are related to one another. The end product is a multitude of additional features that are a result of these linkings. The Feature Discovery engine analyzes the included datasets to determine a feature engineering “recipe” and, from that recipe, generates secondary features for training and predictions. Once these relationships are established, you can view them from the catalog. To view relationships, select the dataset in the main catalog and click the Relationships tab to view, modify, or delete existing relationships: See complete details of working with [relationships](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html#define-relationships) before modifying relationship details. ## View version history The Version History tab lists all versions of a selected asset. The Status column indicates the snapshot status—green if successful, red if failed, gray if the original version did not have a snapshot. Click a version to select it. Once selected, you can create a project from the version and download or delete the contents. ## Add comments The Comments tab allows you to add comments to—even host a discussion around—any item in the catalog that you have access to. Comment functionality is available in the AI Catalog (illustrated below), and also as a model tab from the Leaderboard and in [use case tracking](https://docs.datarobot.com/en/docs/classic-ui/modeling/value-tracker.html). With comments, you can: - Tag other users in a comment; DataRobot will then send them an email notification. - Edit or delete any comment you have added (you cannot edit or delete other users' comments). > [!NOTE] Versioning snapshot assets > Static assets can only be versioned by uploads of the same type; datasets created by local files are versioned from local file uploads, and datasets created from a data stage are versioned from data stage uploads. ## Create a snapshot You can uncheck Create Snapshot when adding external data connections, to meet certain security requirements, for example. Snapshotted [materialized](https://docs.datarobot.com/en/docs/reference/glossary/index.html#materialized) data is stored on disk; [unmaterialized](https://docs.datarobot.com/en/docs/reference/glossary/index.html#unmaterialized) data is stored remotely as your asset and only downloaded when needed. To determine whether an asset has been snapshotted, click on its catalog entry and check the details on the right. If it has been snapshotted, the last snapshot date displays; if not, a notification appears: To create a snapshot for unmaterialized data: 1. Select the asset from the main catalog listing. 2. Expand the menu in the upper right and selectCreate Snapshot. You cannot update the snapshot parameters that were defined when the catalog entry was added; snapshots are based on the original SQL. 3. DataRobot prompts for any credentials needed to access the data source. ClickYes, take snapshotto proceed. 4. DataRobot runs EDA. New snapshots are available from the version history, with the newest ("latest") snapshot becoming the one used by default for the dataset. Once EDA completes, the displayed status updates to "SNAPSHOT" and a message appears indicating that publishing is complete. If you want the asset to no longer be snapshotted, remove the asset and add it again, making sure to uncheck Create Snapshot. ## Create a project You can create new projects directly from the AI Catalog; you can also use listed datasets as a source for [predictions](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/predictions/predict.html). To create a project, from the catalog main listing, click on an asset to select it. In the upper right, click Create project. DataRobot runs [EDA1](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda1) and loads the project. When complete, DataRobot displays the [Start](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html) screen. --- # Load data URL: https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html > How to add external data using JDBC or a SQL query, configure fast registration, and upload calendars for time series projects. # Import data to the AI Catalog Import methods are the same for both legacy and catalog entry—that is, via local file, HDFS, URL, or JDBC data source. From the catalog, however, you can also add by blending datasets with Spark. When uploading through the catalog, DataRobot completes [EDA1](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda1) (for [materialized](https://docs.datarobot.com/en/docs/reference/glossary/index.html#materialized) assets), and saves the results for later re-use. For unmaterialized assets, DataRobot uploads and samples the data but does not save the results for later re-use. Additionally, you can [upload calendars](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html#upload-calendars) for use in time series projects and enable [personal data detection](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html#personal-data-detection). > [!NOTE] Dataset formatting > To avoid introducing unexpected line breaks or incorrectly separated fields during data import, if a dataset includes non-numeric data containing special characters—such as newlines, carriage returns, double quotes, commas, or other field separators—ensure that those instances of non-numeric data are wrapped in quotes ( `"`). Properly quoting non-numeric data is particularly important when the preview feature "Enable Minimal CSV Quoting" is enabled. To add data to the AI Catalog: 1. ClickAI Catalogat the top of DataRobot window. 2. ClickAdd to catalogand select an import method. The following table describes the methods: MethodDescriptionNew Data ConnectionConfigure a JBDC connectionto import from an external database of data lake.Existing Data ConnectionSelect a configured data sourceto import data. Select the account and the data you want to add.Local FileBrowse to upload a local dataset or drag and drop a datasetfor import.URLSpecify a URL.Spark SQLUseSpark SQL queries to select and prepare the datayou want to store. DataRobot registers the data after performing initial exploratory data analysis ( [EDA1](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda1)). Once registered, you can do the following: - View information about a dataset, including its history. - Blend the dataset with another dataset. - Create an AutoML project . - Use the additional tools to view, modify, and manage assets. ## From external connections Using JDBC, you can read data from external databases and add the data as assets to the AI Catalog for model building and predictions. See [Data connections](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/data-conn.html) for more information. 1. If you haven't already,create the connectionsand add data sources. 2. Select theAI Catalogtab, clickAdd to catalog, and selectExisting Data Connection. 3. Click the connection that holds the data you would like to add. 4. Select an account. Enteror use stored credentialsfor the connection to authenticate. 5. Once validated, select a source for data. ElementDescription1SchemasSelectSchemasto list all schemas associated with the database connection. Select a schema from the displayed list. DataRobot then displays all tables that are part of that schema. ClickSelectfor each table you want to add as a data source.2TablesSelectTablesto list all tables across all schemas. ClickSelectfor each table you want to add as a data source.3SQL QuerySelect data for your project with aSQL query.4SearchAfter you select how to filter the data sources (by schema, table, or SQL query), enter a text string to search.5Data source listClickSelectfor data sources you want to add. Selected tables (datasets) display on the right. Click thexto remove a single dataset orClear allto remove all entries.6PoliciesSelect a policy:Create snapshot: DataRobot takes a snapshot of the data.Create dynamic: DataRobot refreshes the data for future modeling and prediction activities. 6. Once the content is selected, clickProceed with registration. DataRobot registers the new tables (datasets) and you can then create projects from them or perform other operations, like sharing and querying with SQL. ## From a SQL Query You can use a SQL query to select specific elements of the named database and use them as your data source. DataRobot provides a web-based code editor with SQL syntax highlighting to help in query construction. Note that DataRobot’s SQL query option only supports SELECT-based queries. Also, SQL validation is only run on initial project creation. If you edit the query from the summary pane, DataRobot does not re-run the validation. To use the query editor: 1. Once you have added data from anexternal connection, click theSQL querytab. By default, theSettingstab is selected. 2. Enter your query in the SQL query box. 3. To validate that your entry is well-formed, make sure that theValidate SQL Querybox below the entry box is checked. NoteIn some scenarios, it can be useful to disable syntax validation as the validation can take a long time to complete for some complex queries. If you disable validation, no results display. You can skip running the query and proceed to registration. 4. Select whether to create asnapshot. 5. ClickRunto create a results preview. 6. Select theResultstab after computing completes. 7. Use the window-shade scroll to display more rows in the preview; if necessary, use the horizontal scroll bar to scroll through all columns of a row: When you are satisfied with your results, click Proceed with registration. DataRobot validates the query and begins data ingestion. When complete, the dataset is published to the catalog. From here you [can interact with the dataset](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog-asset.html) as with any other asset type. For more examples of working with the SQL editor, see [Prepare data in AI Catalog with Spark SQL](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/spark.html). ## Configure fast registration Fast registration allows you to quickly register large datasets in the AI Catalog by specifying the first N rows to be used for registration instead of the full dataset. This gives you faster access to data to use for testing and Feature Discovery. To configure fast registration: 1. In theAI Catalog, clickAdd to catalogand select your data source. Fast registration is only available when adding a dataset from a new data connection, an existing data connection, or a URL. 2. In the resulting window, enter the data source information (in this example, URL). 3. Select the appropriate policy for your use case—eitherCreate snapshotorCreate dynamic. For both snapshot and dynamic policies, the AI Catalog dataset calculates EDA1 using only the specified number of rows, taken from the start of the dataset. For example, it calculates using the first 1,000 rows in the dataset above. Where the two policies differ is that if you consume the snapshot dataset (for example, using it to create a project), the consumer of the dataset will only see the specified number of rows when consuming it, but the consumer of the dynamic dataset will see the full set of rows rather than the partial set of rows. 4. Select the fast registration data upload option. For snapshot, selectUpload data partially, and for dynamic, selectUse partial data for EDA. 5. Specify the number of rows to use for data ingest during registration and clickSave. ## Upload calendars [Calendars for time series](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/ts-adv-opt.html#calendar-files) projects can be uploaded either: - Directly to the catalog with the Add to catalog button, using any of the upload methods. Calendars uploaded as a local file are automatically added to the AI Catalog , where they can then be shared and downloaded. - From within the project using the Advanced options > Time Series tab. To upload calendar files from Advanced options on the Time Series tab: 1. When adding fromAdvanced options, use thechoose filedropdown and chooseAI Catalog. 2. A modal appears listing available calendars, which was determined based on the content of the dataset. Use the dropdown to sort the listing by type. DataRobot determines whether the calendar is single or multiseries based on the number of columns. If two columns, only one of which is a date, it is single series; three columns with just one being a date makes it multiseries. 3. Click on any calendar file to see the associated details and select the calendar for use with the project. The calendar file becomes part of the standardAI Cataloginventory and can be reused like any dataset. Calendars generated fromAdvanced optionsare saved to the catalog where you can then download them, apply further customization, and re-upload them. ## Personal data detection In some regulated and specific use cases, the use of personal data as a feature in a model is forbidden. DataRobot automates the detection of specific types of personal data to provide a layer of protection against the inadvertent inclusion of this information in a dataset and prevent its usage at modeling and prediction time. After a dataset is ingested through the AI Catalog, you have the option to check each feature for the presence of personal data. The result is a process that checks every cell in a dataset against patterns that DataRobot has developed for identifying this type of information. If found, a warning message is displayed in the AI Catalog's Info and Profile pages, informing you of the type of personal data detected for each feature and providing sample values to help you make an informed decision on how to move forward. Additionally, DataRobot creates a new [feature list](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html#automatically-created-feature-lists) —the equivalent of Informative Features but with all features containing any personal data removed. The new list is named Informative Features - Personal Data Removed. > [!WARNING] Warning > There is no guarantee that this tool has identified all instances of personal data. It is intended to supplement your own personal data detection controls. DataRobot currently supports detection of the following fields: - Email address - IPv4 address - US telephone number - Social security number To run personal data detection on a dataset in the AI Catalog, go to the Info page click Run Personal Data Detection on the banner that indicates successful dataset publishing:. If DataRobot detects personal data in the dataset, a warning message displays. Click Details to view more information about the personal data detected; click Dismiss to remove the warning and prevent it from being shown again. Warnings are also highlighted by column on the Profile tab: --- # AI Catalog URL: https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/index.html > The AI Catalog is a searchable collection of registered objects that contains definitions and relationships between various object types. Items stored in the catalog include data connections, data sources, data metadata, and blueprints. # AI Catalog The AI Catalog is a centralized collaboration hub for working with data and related assets. It enables seamlessly finding, sharing, tagging, and reusing data, helping to speed time to production. The catalog provides easy access to the data needed to answer a business problem while ensuring security, compliance, and consistency. The AI Catalog is comprised of three key functions: - Ingest : Data is imported into DataRobot and sanitized for use throughout the platform. - Storage : Reusable data assets are stored, accessed, and shared—allowing you to share data without sharing projects, decreasing risks and costs around data duplication. - Data Preparation : Clean, blend, transform, and enrich your data by leveraging SQL scripts for pinpointed results. The catalog also supports data security and governance, which reduces friction and speeds up model adoption, through selective addition to the catalog, role-based sharing, and an audit trail. | Topic | Description | | --- | --- | | Import datasets | | | Import data and create projects | Import data into the AI Catalog and from there, create a DataRobot project. | | Interact with catalog assets | | | Work with catalog assets | View and modify asset details, create snapshots, and create projects from a data entry. | | Manage catalog assets | Share, delete, and download data assets. | | Schedule data snapshots | Set up schedules for data snapshots in the AI Catalog to keep a dataset in sync with its source data. | | Prepare data | | | Prepare data with SparkSQL | Enrich, transform, shape, and blend together datasets using Spark SQL queries within the AI Catalog. | --- # Manage assets URL: https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/manage-asset.html > How to download, delete, and share actions individually or in bulk. # Manage assets In the AI Catalog, there are a number of ways you can interact with data assets, including downloading, sharing, and deleting datasets. ## Download To download a dataset, select it from the catalog list. From the dropdown menu in the upper right, select Download Dataset ( [https://docs.datarobot.com/en/docs/images/icon-down.png](https://docs.datarobot.com/en/docs/images/icon-down.png)) and in the resulting dialog, browse to a download location and click Save. > [!NOTE] Note > In most cases, only [snapshotted](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog-asset.html#create-a-snapshot) datasets can be downloaded. Additionally, there is a 10GB file size limit; attempting to download a dataset larger than 10GB will fail. ## Share Assets in the AI Catalog can be shared to users, groups, and organizations. | | Element | Description | | --- | --- | --- | | (1) | Allow sharing | The user you're sharing with can share the asset with other users. | | (2) | Can use data | The user you're sharing with can an use the data. How they use the data depends on their role. | | (3) | User list | Enter the user(s) with whom you are sharing the asset. | | (4) | Access level | Select from Users, by default. If your instance has Groups and Organizations configured, you can select from these categories. | | (5) | Role | Select a role for the users, groups, or organizations that are sharing the asset: Owner: Can view, edit, and administer the asset. Consumer: Can view the asset. Editor: Can view and edit the asset. | | (6) | Share | Select Share to perform the operation. | | (7) | Shared with | Shows the users, groups, and organizations that asset is shared with, along with their permission settings. | > [!TIP] Sharing with multiple users > When sharing a catalog asset with multiple users, DataRobot suggests [creating a user group](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-groups.html#manage-groups) first, and then sharing with that group instead of individual users. The catalog uses the same [sharing](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#sharing) window as other places in the application, with some fields specific to the data assets. ## Delete To delete a dataset, select the dataset from the catalog list. From the dropdown menu in the upper right, select Delete Dataset ( [https://docs.datarobot.com/en/docs/images/icon-delete.png](https://docs.datarobot.com/en/docs/images/icon-delete.png)). When prompted for confirmation, click Delete. ## Perform bulk actions You can share, tag, or delete multiple datasets at once using the bulk action functionality in the AI Catalog. Start by selecting the box next to the asset(s) you want to manage; select at least one asset to enable the bulk actions at the top. A counter also displays how many assets are actively selected. Once you're done selecting assets, choose the appropriate action from the following options: [Delete](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/manage-asset.html#delete-assets), [Tag](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog-asset.html#view-asset-information), or [Share](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/manage-asset.html#share-assets). --- # Schedule snapshots URL: https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/snapshot.html > To keep a dataset in sync with the data source, you can schedule snapshots at specified intervals through the AI Catalog. # Schedule snapshots > [!NOTE] Availability information > The AI Catalog must be enabled in order to schedule snapshot refreshes. For Self-Managed AI Platform installations, the Model Management Service must also be installed. To ensure that a dataset is always in sync with the data source, if desired, DataRobot provides an automated, scheduled refresh mechanism. Through the [AI Catalog](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html), users with dataset access above the [consumer](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html) level can schedule snapshots at daily, weekly, monthly, and annual intervals. You can refresh any data asset type (HDFS, JDBC, Spark, and URL) except for files. ## Schedule refresh tasks You can schedule multiple refresh tasks; [limits](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/snapshot.html#refresh-limit-settings) are applied to datasets and to users independently. To schedule snapshots for a dataset: 1. From the main catalog listing, select the asset for which you want to schedule one or more refresh tasks. 2. Click theSchedule refreshlink to expand the scheduler. 3. If the asset source is JDBC or HDFS, a login dialog results. Select the account credentials associated with the asset. DataRobot uses these credentials each time it runs the scheduled task. Once credentials are accepted (or if they were not required), the scheduler opens: 4. Complete the fields to set your task: FieldDescriptionName (1)Enter a name for the refresh job (or leave the default).Calendar picker(2)Sets the basis for the interval setting.Interval (3)Based on the calendar setting, the interval dropdown sets the frequency to daily, weekly, monthly, or annually. The time on the selected day is always set to the timestamp when the job was scheduled.Summary (4)Provides a summary of the selected scheduled task, including the interval and whether it is active or paused, supplied by DataRobot and updated with any changes to the job. 5. ClickSaveto schedule a refresh for the asset. DataRobot reports the last execution status under the scheduled job name. ### Use the calendar picker Use the calendar picker to select a date that will serve as the basis of the day-of-week, monthly date, or day of year for the refresh. Refreshes will start on or after (depending on the time set) the specific date. For example, if June 21 is the date selected, refreshes will begin: - daily at timestamp , either that day or the next day (June 22) - weekly on the set day (every Sunday at timestamp ) - monthly on that date of month (the 21st of each month at timestamp ) - Annually on that date (every June 21 at timestamp ). Click in the time picker. Use the arrows to change the time, setting the timestamp to the local time at which you want the snapshot to refresh. Click on the date to return to the full calendar view: ### Work with scheduled tasks Once scheduled, you can modify the task in a variety of ways. Use the menu associated with the task to access the options. - Pause job: Pauses the scheduled task indefinitely. When paused, the "Scheduled" label changes to "Paused" and the menu item changes to "Resume job". Use this action to re-enable the scheduled task. Paused jobs do not count against thetask limits. - Edit: Retrieves the scheduler interface, allowing you to change any aspect of the task configuration. - Manage credentials: Opens the credentials selection modal, allowing you to change the credentials associated with the dataset. - Delete: Deletes the scheduled task. ### Refresh limit settings The following table lists the defaults and maximums for refresh-related activities. > [!NOTE] Availability information > The default listed in the table is for the managed AI Platform. For Self-Managed AI Platform installations, consider the maximum setting as the default. | Parameter | Description | Default | Maximum | | --- | --- | --- | --- | | Enabled dataset refresh jobs for a user | The total number of refresh jobs a user can have across all AI Catalog datasets. | 100 | 100 | | Enabled dataset refresh jobs for a dataset | The total number of refresh jobs that can exist for a specific dataset for all users. | 5 | 100 | | Stored snapshots until a dataset refresh job is automatically disabled | The total number of stored snapshots that can exist for a specific dataset until the dataset refresh job is automatically disabled. | 100 | 1000 | --- # Prepare data with Spark SQL URL: https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/spark.html > On the Add menu, Prepare data with Spark SQL lets you prepare a new dataset from a single dataset or blend several datasets using a Spark SQL query. # Prepare data in AI Catalog with Spark SQL Using Prepare data with Spark SQL from the Add menu in the AI Catalog allows you to enrich, transform, shape, and blend datasets together using Spark SQL queries. The following sections describe the process of data preparation with Spark SQL: - Creating blended datasets. - Creating a query. - Previewing results. - Saving results to theAI Catalog. - Editing queries. ## Support information Supported version: - Spark SQL 3.4.1 Supported dataset types: - Static datasets created from local files. - Unmaterialized (dynamic) datasets created from JDBC data connections . - Snapshotted datasets created from JDBC data connections. ## Create blended datasets Using Spark SQL queries, you can pull in data from multiple sources to create a new dataset that can then be used for analysis and in visualizations. Blending datasets helps create more comprehensive datasets to compare relationships in data or address specific business problems, for example, combining highly related datasets to better predict customer behavior. 1. To create a new blended dataset, select Spark SQL from theAddmenu: 2. In the resulting dialog box, clickAdd datato open the “Select tables from the catalog for blending” modal. 3. DataRobot opens available datasets in a new modal. ClickSelectnext to one or more datasets from the list of assets. The right panel lists the selected datasets. 4. When you have finished adding datasets, clickAdd selected data. 5. Enter credentialsfor any datasets that require authentication and, when authenticated, clickComplete registrationto open the SQL editor. ### Add and edit datasets After initially adding datasets, you can add more or modify the dataset alias: - ClickAddto re-open the “Select tables from the catalog for blending” modal. Check marks indicate the datasets that are already included; clickSelectto add new datasets. You do not need to use all added datasets as part of the query. - ClickEditto rename the dataset alias or delete the dataset from the query. You can also do either of these tasks from the dataset's menu. > [!NOTE] Note > To conform to Spark SQL naming conventions (no special characters or spaces), DataRobot generates an alias by which to refer to each dataset in the SQL code. You’re welcome to choose your own alias or use the generated one. ## Create a query Once you have datasets loaded, the next step is to enter a valid [Spark SQL](https://spark.apache.org/docs/2.4.5/api/sql/index.html) query in the SQL input section. To access the Spark SQL documentation in DataRobot, click Spark Docs. To enter a query you can either manually enter the SQL query syntax into the editor or add some or all features using the menu next to the dataset name. > [!NOTE] Note > You must surround alias or feature names that contain non-ascii characters with backticks ( ` ). For example, a correctly escaped sequence might be `alias%name`.`feature@name`. ### Add features via the menu Click the menu next to the dataset name. DataRobot opens a pane that allows you to: - add features individually by clicking the arrow to the right of the feature name (1). - add a group of features by first selecting them in the checkbox to the left of the feature name and then choosing Add selected features to SQL (2). - select or deselect all features. When using the menu to add features, DataRobot moves the added feature(s) into the SQL editor at the point of your cursor. ## Preview results When the query is complete, click Run or if there are several queries in the editor, highlight a specific section and then click Run. After computing completes, if successful, DataRobot opens the Results tab. Use the window-shade scroll to display more rows in the preview; if necessary, use the horizontal scroll bar to scroll through all columns of a row: If the query was not successful, DataRobot returns a notification banner and returns details of the error in the Console: ### Preview considerations When running a query, preview results from the Run action are limited to 10,000 rows and/or 16MB. - If the preview exceeds 16MB, DataRobot returns:command document too large - If the preview exceeds 10,000 rows, DataRobot returns the message:Data engine query execution error: Output table is too large (more than 10000 rows). Please, use LIMIT or come up with another query Saving to the AI Catalog does not incur these limits. ## Save results to the AI Catalog Before saving your query and resulting dataset, you can optionally provide a name and/or description for the new dataset from the Settings tab, overwriting the default name "Untitled (blended dataset)". By default, DataRobot creates a [snapshot](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html#create-a-snapshot) of the new dataset. Uncheck "Create snapshot" (in Settings) to prevent this behavior. When you are satisfied with the naming and results, click Save to write the new blended dataset to the [AI Catalog](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html) and start the registration process. ## Edit queries Once you have saved your data asset, you can both view and edit the query from the asset's Info tab. Any errors from your previous run are displayed at the top of the page. To correct errors: 1. ClickEdit scriptto return to the query editor. All results and errors from the previous run are preloaded below the editor. 2. Make changes to the query andRunto validate results. 3. ClickSaveand choose to either edit your script ("Save new version") or save as a new dataset. NoteWhen using "Save new version," the new version must conform to the original dataset schema. If you must change output schema as part of the edit, use the "Save new dataset" option instead. If registration fails using the new query, use the Edit script link to return to the SQL editor, correct any problems, and save as a new version. ### Create a new version Additionally, you can use the "Create a new dataset from script" link in the Version History tab. Click the link and return to the query editor. When you Save, the entry is saved as a new data asset. --- # Analyze frequent values URL: https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/analyze-frequent-values.html # Analyze frequent values This page describes how to use the Frequent Values chart. The chart is a histogram that shows the number of rows containing each value of a feature and the percentage of rows for each value of the target. The sample dataset illustrated below contains patient data. With a goal to predict the likelihood of a patient's readmission to the hospital, use the target feature is `readmitted`. ## Overview The Frequent Values chart is the default display for categorical, text, and boolean features, although it is also available to other feature types. The display is dependent on the results of the [data quality](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#interpret-the-histogram-tab) check. With no data quality issues: In many cases, you can change the display using the Sort by dropdown. By default, DataRobot sorts by frequency ( Number of rows), from highest to lowest. You can also sort by < feature_name >, which displays either alphabetically or, in the case of numerics, from low to high. The [Exportlink](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/export-results.html) allows you to download an image of the Frequent Values chart as a PNG file. After EDA2 completes, the Frequent Values chart also displays an [average target value](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/analyze-frequent-values.html#average-target-values) overlay. ## Load and view your dataset After [importing your dataset](https://docs.datarobot.com/en/docs/classic-ui/data/import-data/index.html), navigate to the Project Data list and select a feature. For some features like categorical and boolean features, the Frequent Values tab is the default. For numeric features, the Frequent Values tab is to the right of the Histogram tab. The Feature Values chart displays each value that appears in the dataset for the feature and the number of rows with that value: For the `admission_type_id` feature, the most common values are Emergency and Urgent. ## View average target values After DataRobot begins calculating [EDA2](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda2), you can also view the average target values for features. 1. UnderWhat would you like to predict, enter your target feature. 2. ClickStart. As soon as DataRobot finishes analyzing features, you can view the average target values in the Frequent Values chart. 3. In theProject Datalist, select the feature you are analyzing. Notice the orange circles that overlay the histogram. The circles indicate the average target value for a bin. ## Related reading To learn more about the topics discussed on this page, see: - How DataRobot performs each stage of Exploratory Data Analysis (EDA). - How common data quality issues are detected and surfaced in the Data Quality Assessment. - Describes the checks DataRobot runs for the potential data quality issues. --- # Analyze features using histograms URL: https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/analyze-histogram.html > How to analyze numeric features using histograms, which let you analyze the distribution of values and view outlier values. # Analyze features using histograms DataRobot generates a histogram for each numeric feature so that you can analyze the distribution of the feature's values and view outlier values. This page describes how to analyze numeric features using histograms. The sample dataset illustrated below contains patient data. With a goal to predict the likelihood of a patient's readmission to the hospital, use the target feature is `readmitted`. ## Set feature distribution DataRobot breaks the data into several bins; the size of the bin depends on the number of rows in your dataset. You can change the number of bins to change the distribution range. The bin options depend largely on the number of unique values in the dataset. To change the distribution range use the dropdown. For classification projects, you can also (after EDA2) change the basis of the display to fill bins based on the number of rows or percentage of target value. The displays of the histogram and average target value overlay also change to match your selection. For numeric features, use the histogram to view a rough distribution of values: 1. Afterimporting your dataset, navigate to theProject Datalist and select a feature. For numeric features, the histogram displays equal-sized ranges (bins). The height of each bar represents the number of rows with values in that range. 2. Hover over a bin to view the range and the number of rows that fall within the range. Thetime_in_hospitalfeature is the number of days spent in the hospital. The histogram indicates that a visit of one to three days is most common. 3. Click theShowingdropdown menu on the bottom left to change the number of bins. When viewing the additional bins, a visit oftwo to threedays is most common. ## Calculate outliers Use the histogram to investigate a feature that has outlier values. 1. Select a feature that has outliers if one exists in your feature list. Locate outlier featuresUse theData Quality Assessmenttool to locate features with outliers. If a feature has outliers, a warning icon () displays in theData Qualitycolumn. The warning tip indicates the type of issue. 2. In the histogram that displays, toggleShow outlierson. The red dots at the top of the histogram represent outlier values. The gold box plot shows the middle quartiles for the data to help you determine whether the distribution is skewed. NoteDataRobot reshuffles the bin values based on the display. With outliers excluded, there are more bins and each contains a smaller number of rows. When toggled on, each bin contains a greater number of rows because the bin has expanded its range of values.The bin selection dropdown works as usual, regardless of the outlier display setting. 3. Hover over a red dot to view the value of the outlier. In this example, the outlier shown for thenum_medicationsfeature is 74.1—far from the median of 14. ## View average target values After DataRobot begins calculating [EDA2](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda2), you can also view the average target values for features. In the histogram, the orange circles indicate the average target value for a bin. In this example, hospital visits of 8 days result in the highest average target value— for 8-day visits, 46.12% of rows have `readmitted` = 1. The table below describes the information included in the bin range summary: | Element | Description | | --- | --- | | Value | Displays the bin range located on the X-axis. | | Rows | Displays the number of rows in the bin (located on the left Y-axis). | | Percentage | Displays the average target value (located on the right Y-axis). | ## Related reading To learn more about the topics discussed on this page, see: - How the histogram chart is generated. - How DataRobot performs each stage of Exploratory Data Analysis (EDA). - How common data quality issues are detected and surfaced in the Data Quality Assessment. - Checks DataRobot runs for the potential data quality issues. --- # Assess data quality with EDA URL: https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/assess-data-quality-eda.html > How DataRobot performs Exploratory Data Analysis (EDA) and how to assess the quality of your data at each stage of EDA. # Assess data quality with EDA Learn how DataRobot performs Exploratory Data Analysis (EDA) and how to assess the quality of your data at each stage of EDA— EDA1 and EDA2. Preparing your data is an iterative process. Even if you clean and prep your training data prior to uploading it to DataRobot, you can still improve its quality by assessing features during EDA. The sample dataset featured on this page contains patient data. The goal is to predict the likelihood of patient readmission to the hospital. The target feature is `readmitted`. ## Stages of EDA During EDA, DataRobot performs Data Quality Assessment. The assessment provides information about data quality issues that are relevant to the stage of model building you are performing. Click one of the following tabs to learn about the two EDA stages. **EDA1:** EDA1 (data ingest) occurs after you upload your data. EDA1 assesses the All Features list and detects issues like: Outliers Inliers Excess zeros Disguised missing values Inconsistent gaps in time series projects For more information on EDA1, see [Exploratory data Analysis](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda1). **EDA2:** Once you click Start on the Data page, DataRobot performs another round of EDA. During this stage, DataRobot detects [target leakage](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#target-leakage) and non-linear correlations between the features and the target, which helps you analyze [feature importance](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/assess-data-quality-eda.html#investigate-feature-importance). EDA2 reports on the selected feature list. If a feature list is not selected, EDA2 reports on the default All Features list. For more information on EDA2, see [Exploratory data Analysis](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda2). ## Load and view your dataset As soon as you load your dataset, creates a new project, and performs an initial EDA, generating summary statistics based on a sample of your data. View the progress in the Worker Queue on the right. Once you import your data, click Explore the data or scroll down to see the features in your dataset. DataRobot displays the features and provides summary information and statistics. | | Label | Description | | --- | --- | --- | | (1) | Var Type | The data type DataRobot identifies for the feature during EDA, for example, Numeric, Categorical, Boolean, Image, Text, and special features types like Date. | | (2) | Unique | The number of unique values for the feature. | | (3) | Missing | The number of missing values for the feature. | | (4) | Mean, Std Dev, Median, Min, Max | DataRobot calculates these statistics for numerical features. | ## Assess after EDA1 EDA1 helps you catch data issues before you start modeling. 1. Above your feature list and to the right, clickView info. The Data Quality Assessment dropdown menu displays. TipThe Data Quality Assessment provides the following issue status flags:Warning: Attention or action required.Informational: No action required.No issue. 2. (Optional) ClickFilter affected features by type of issue detectedand select particular issues to search for. 3. Scroll down to locate the features with issues. If a feature has an issue, the issue flag displays in theData Qualitycolumn. Hover over the flag to view the type of issue. 4. Click a feature that displays an issue flag, then use tools such as the Histogram, Frequent Values, and Feature Associations to explore further. ## Assess after EDA2 EDA2 kicks off after you set your target and start the modeling process. 1. UnderWhat would you like to predict, enter your target feature. Modeling modesYou can keep the mode set to the default,QuickAutopilot, or you can select a differentmodeling mode. You can also customize yourmodeling settings. 2. ClickStart. DataRobot performs a number of processing steps. Monitor the steps in the Worker Queue. As soon as DataRobot finishes analyzing features, you can take a look at feature importance. DataRobot continues with blueprint generation. ## Investigate feature importance The importance bars show the degree to which a feature is correlated with the target. Importance is calculated using an algorithm that measures the information content of the variable. This calculation is done independently for each feature in the dataset. Investigate feature importance to determine which features are most useful for building accurate models and which features you can remove from your training data. 1. In theDatatab, scroll down to the feature list. 2. Take a look at theImportancecolumn. The green bars indicate how closely a feature is related to the target. You might want to remove features that are unrelated to the target. ## Related reading To learn more about the topics discussed on this page, see: - How DataRobot performs each stage of Exploratory Data Analysis (EDA). - How common data quality issues are detected and surfaced in the Data Quality Assessment. - Describes the checks DataRobot runs for the potential data quality issues. --- # Data Quality Assessment URL: https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/data-quality.html > The Data Quality Assessment automatically detects and often handles data quality issues such as outliers, leading or trailing zeros, target leakage, and many more. # Data Quality Assessment The Data Quality Assessment capability automatically detects and surfaces common data quality issues and, often, handles them with minimal or no action on the part of the user. The assessment not only saves time finding and addressing issues, but provides transparency into automated data processing (you can see the automated processing that has been applied). It includes a warning level to help determine issue severity. Once EDA1 completes, the Data Quality Assessment appears just above the feature listing on the Data page. Once model building completes, you can view the [Data Quality Handling Report](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/dq-report.html) for additional imputation information. See the [data quality reference](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html) for a description of handling and detection logic for each check run. See the associated [considerations](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/data-quality.html#feature-considerations) for important additional information. ## Explore the assessment The Data Quality Assessment provides information about data quality issues that are relevant to your stage of model building. Initially run as part of EDA1 (data ingest), the results report on the All Features list. It runs again and updates after EDA2, displaying information for the selected feature list (or, by default, All Features). For checks that are not applicable to individual features (for example, Inconsistent Gaps), the report provides a general summary. Click View Info to view (and then Close Info to dismiss) the report: Each data quality check provides issue status flags, a short description of the issue, and a recommendation message, if appropriate: - Warning (): Attention or action required - Informational (): No action required - No issue () Because the results are feature-list based, it is possible that if you change the selected feature list on the Data page, new checks will appear or current checks will disappear from the assessment. For example, if feature list `List 1` contains a feature `problem`, which contains outliers, the outliers check will show in the assessment. If you change lists to `List 2` which does not include `problem` (or any other feature with outliers), the outliers check will report "no issue" ( [https://docs.datarobot.com/en/docs/images/icon-ok.png](https://docs.datarobot.com/en/docs/images/icon-ok.png)). From within the assessment modal, you can filter by issue type to see which features triggered the checks. Toggle on Show only affected features and check boxes next to the check names to select which checks to display: DataRobot then displays only features violating the selected data quality checks, and within the selected feature list, on the Data page. Hover on an icon for more detail: For multilabel and Visual AI projects, Preview Log displays at the top if the assessment detects [multicategorical format errors](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#multicategorical-format-errors) or [missing images](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#missing-images) in the dataset. Click Preview Log to open a window with a detailed view of each error, so you can more easily find and fix them in the dataset. Once EDA1 completes and you have, perhaps, filtered the display, view the list of features impacted by the issues you are interested in investigating. To see the values that triggered a warning or information notification, expand a feature and review the [Histogram](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#histogram-chart) and [Frequent Values](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#frequent-values-chart) visualizations. ## Feature considerations Consider the following when working with Data Quality Assessment capability: - For disguised missing values, inlier, and excess zero issues, automated handling is only enabled for linear and Keras blueprints, where they have proven to reduce model error. Detection is applied to all blueprints. - You cannot disable automated imputation handling. - A public API is not yet available. - Automated feature engineering runs on raw data (instead of removing all excess zeros and disguised missing values before calculating rolling averages). --- # Feature Associations URL: https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html > How to navigate and use the Feature Associations tab, which provides a matrix to help you track and visualize associations within your data. # Feature Associations Accessed from the Data page, the Feature Associations tab provides a matrix to help you track and visualize associations within your data. This information is derived from different metrics that: - Help to determine the extent to which features depend on each other. - Provide a protocol that partitions features into separate clusters or "families." The matrix is: - Created duringEDA2using thefeature importancescore. - Based on numeric and categorical features found in theInformative Featuresfeature list. To use the matrix, click the Feature Associations tab on the Data page. The page displays a [matrix](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html#view-the-matrix) (1) with an accompanying [details pane](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html#details-pane) (2) for more specific information on clusters, general associations, and association pairs. From the details pane, you can [view associations](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html#feature-association-pairs) and relationships between specific feature pairs (3). Below the matrix is a set of [matrix controls](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html#control-the-matrix-view) (4) to modify the view. The Feature Associations matrix provides information on [association strength](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html#what-are-associations) between pairs of numeric and categorical features (that is, num/cat, num/num, cat/cat) and feature clusters.Clusters, families of features denoted by color on the matrix, are features partitioned into groups based on their similarity. With the matrix's intuitive visualizations you can: - Quickly perform association analysis and better understand your data. - Gain understanding of the strength and nature of associations. - Detect families of pairwise association clusters. - Identify clusters of high-association features prior to model building (for example, to choose one feature in each group for model input while differencing the others). ## View the matrix Once EDA2 completes, the matrix becomes available. It lists up to the top 50 features, sorted by cluster, on both the X and Y axes. Look at the intersection of a feature pair for an indication of their level of co-occurrence. By default, the matrix displays by the [Mutual Information](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html#associations-tab) values. The following are some general takeaways from looking at the default matrix: - The target feature is bolded in white. - Each dot represents the association between two features (a feature pair). - Each cluster is represented by a different color. - The opacity of color indicates the level of co-occurrence (association or dependence) 0 to 1, between the feature pair. Levels are measured by the set metric, either mutual information or Cramer's V . - Shaded gray dots indicate that the two features, while showing some dependence, are not in the same cluster. - White dots represent features that were not categorized into a cluster. - The "Weaker ... Stronger" associations legend is a reminder that the opacity of the dots in the metric represent the strength of the metric score. Clicking points in the matrix updates the detail pane to the right. To reset to the default view, click again in the selected cell. Use the [controls](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html#control-the-matrix-view) beneath the matrix to change the display criteria. You can also filter the matrix by importance, which instead ranks your top 50 features by ACE ( [importance](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#data-summary-information)) score for binary classification, regression, and multiclass projects. ### Work with the display Click on any point in the matrix to highlight the association between the two features: Drag the cursor to outline any section of the matrix. DataRobot zooms the matrix to display only those points within your drawn boundary. Click Reset Zoom in the control pane to return to the full matrix view. Note that you can export either the zoomed or full matrix by clicking [https://docs.datarobot.com/en/docs/images/icon-export-button.png](https://docs.datarobot.com/en/docs/images/icon-export-button.png) in the upper left. ## Details pane By default, with no matrix cells selected, the details pane: - Displays the strongest associations ( Associations tab) found, ranked by association metric score. - Displays a list of all identified clusters ( Clusters tab) and their average metric score. - Provides access to charting of feature pair association details. The listings are based on internal [calculations](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html#how-associations-are-calculated) DataRobot runs when creating the matrix. ### Associations tab Once a cell is selected in the matrix, the Associations tab updates to reflect information specific to the selected feature pair: The table below describes the fields: | Category | Description | | --- | --- | | "feature_1" & "feature_2" | | | Cluster | The cluster that both features of the pair belong to, or if from different clusters, displays "None." | | Metric name | A measure of the dependence features have on each other. The value is dependent on the metric set, either Mutual Information or Cramer's V. | | Details for "feature_1" Details for "feature_2" | | | Importance | The normalized importance score, rounded to three digits, indicating a feature's importance to the target. This is the same value as that displayed on the Data page. | | Type | The feature's data type, either numeric or categorical. | | Mean | From the Data page, the mean of the feature value. | | Min/Max | From the Data page, the minimum and maximum values of the feature. | | Strong associations with "feature_1" | | | feature_1 | When you select a feature's intersection with itself on the matrix, a list of the five most strongly associated features, based on metric score. | ### Clusters tab By default DataRobot displays all found clusters, ranked by the average [metric](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html#more-about-metrics) score. These rankings illustrate the clusters with the strongest dependence on each other. The displayed name is based on the feature in the cluster with the highest importance score relative to the target. Clicking on a point in the matrix changes the Clusters tab display to report: - Score details for the cluster. - A list of all member features. ## Feature association pairs Click View Feature Association Pairs to open a modal that displays plots of the individual association between the two features of a feature pair. From the resulting insights, you can see the values that are impacting the calculation, the "metrics of association." Initially, the plots auto-populate to the points selected in the matrix (which are also those highlighted in the details pane). For each display, DataRobot displays the cluster that the feature with the highest metric score belongs to as well as the metric association score for the feature pair. You can change features directly from the modal (and the cluster and score update): The insight is the same whether accessed from the Clusters or the Associations tab. Once displayed, click Download PNG to save the insight. There are three types of plots that display, type being dependent on the data type: - Scatter plots for numeric vs. numeric features. - Box and whisker plots for numeric vs. categorical features. - Contingency tables for categorical vs. categorical features. The following shows an example of each type, with a brief "reading" of what you can learn from the insight. ### Scatter plots When comparing numeric features against each other, a scatter plot results with the X axis spanning the range of results. The dot size, or overlapping dots, represents the frequency of the value. For example, in the chart above you might assume there's no discernible dependence of 12m_interest on reviews_seasonal, and as a result, the mutual information they share is very low. ### Box and whisker plots Box and whisker plots graphically display upper and lower quartiles for a group of data. It is useful for helping to determine whether a distribution is skewed and/or whether the dataset contains a problematic number of outliers. Depending on the which feature sets the X or Y axis, the plot may rise vertically or lay horizontally. In either case, the end points represent the upper and lower extremes, with the box illustrating the highest occurrence of a value. DataRobot uses box and whisker plots to create insights for numeric and categorical feature pairs. In the example above, the plot shows most of the variation of the online_sites feature occurs in the E1 locality. Among the other localities, there is very little dispersion. ### Contingency tables When both features are categorical, DataRobot creates a contingency table which shows a frequency distribution of values for the selected features. The table can contain up to six bins, each representing a unique feature value. For features with more than five unique values, the top five are displayed with the rest accumulated in a bin named Other. Read the table as follows: The dots are all bigger in the 12 month bucket because there are more total reviews than in the 9 month bucket. Since there is not a lot of variation in the dot sizes across the reviews_department buckets, knowledge about the last_response doesn't improve knowledge about reviews_department. The result is a low metric score. ## Control the matrix view You can modify the matrix view by changing the sort criteria or the metric used to calculate the association. These controls are available below the matrix: The Sort by option allows you to sort by: - Cluster (the default). - Importance to the target (value from the Data page). - Alphabetically. The [Metric](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html#more-about-metrics) selection determines how DataRobot calculates the association between feature pairs, using either the Mutual Information or Cramer's V correlation algorithms. The Feature List selection allows you to compute feature association for any of the project's feature lists. If you select a list, the page refreshes and displays the matrix for the selected feature list. Additionally, if you previously highlighted a section of the matrix for closer observation, click Reset Zoom to return to the full matrix view. ## More info... The following sections include: - A general discussion about associations . - Understanding the mutual information and Cramer's V metrics . - How associations are calculated . ### What are associations? There is a lot of terminology to describe a feature pair's relationship to each other—feature associations, mutual dependence, levels of co-occurrence, and correlations (although technically this is somewhat different) to name the more common examples. The Feature Association tab is a tool to help visualize the association, both through a wide angle lens (the full matrix) and more close up (both matrix zoom and feature association pair details). Looking at the matrix, each dot tells you, "If I know the value of one of these features, how accurate will my guess be as to the value of the other?" The metric value puts a numeric value on that answer. The closer the metric value is to 0, the more independent the features are of each other. Knowing one doesn't tell you much about the other. A score of 1, on the other hand, says that if you know X, you know Y. Intermediate values indicate a pattern, but aren't completely reliable. The closer they are to "perfect mutual information" or 1, the higher their metric score and the darker their representation on the matrix. ### More about metrics The metric score is responsible for ordering and positioning of clusters and features in the matrix and the detail pane. You can select either the Mutual Information (the default) or Cramer's V metric. These metrics are well-documented on the internet: - A technical overview of Mutual Information on Wikipedia . - A longer discussion of Mutual Information on Scholarpedia , with examples. - A technical overview of Cramer's V on Wikipedia . - A Cramer's V tutorial of "what and why." Both metrics measure dependence between features and selection is largely dependent on preference and familiarity. Keep in mind that Cramer's V is more sensitive and, as such, when features depend weakly on each other it reports associations that Mutual Information may not. ### How associations are calculated When calculating associations, DataRobot selects the top 50 numeric and categorical features (or all features if fewer than 50). "Top" is defined as those features with the highest importance score, the value that represents a feature's association with the target. Data from those features is then randomly subsampled to a maximum of 10k rows. Note the following: - For associations, DataRobot performs quantile binning of numerical features and does no data imputation. Missing values are grouped as a new bin. - Outlying values are excluded from correlational analysis. - For clustering, features below an association threshold of 0.1 are eliminated. - If all features are relatively independent of each other—no distinct families—DataRobot displays the matrix but all dots are white. - Features missing over 90% of their values are excluded from calculations. - High-cardinality categorical features with more than 2000 values are excluded from calculations. --- # Feature details URL: https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html > How to work with a feature on the Data page, to view its details and also (in some cases) modify its type. # Feature details The Data page displays tags to indicate a variety of information that DataRobot uncovered while computing EDA1. You can also [click a feature name](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#view-feature-details) to view its details. ## Informational tags Informational tags on the Data page include: | Tag | Description | | --- | --- | | Duplicate | A feature column is duplicated in the ingest dataset. | | Empty | Column contains no values. | | Few values | Too few values, relative to the size of the dataset, for DataRobot to extrapolate meaningful information from the feature. Not an indicator of the number of unique values, but instead domination of a single value, making the feature inappropriate for modeling. Specifically: A numeric with no missing values and only one unique value. A variable in which >99.9% is the same value | | Too many values | Too many values, relative to the size of the dataset, for DataRobot to extrapolate meaningful information from the feature. For categorical features, the label is applied if: [ number of unique values ] > [ number of rows] / 2 \| | | Reference ID* | Column contains reference IDs (unique sequential numbers). | | Associated with Target | Column was derived from target column. | | Target leakage | Indicates a feature whose value cannot be known at the time of prediction. | ## Available feature details Once DataRobot displays features on the Data page, you can click a feature name to view its details and also (in some cases) modify its type. The options available are dependent on variable type: | Option | Description | Variable Type | | --- | --- | --- | | Tabs | | | | Histogram | Buckets numeric feature values into equal-sized ranges to show a rough distribution of the variable. | numeric, summarized categorical, multicategorical | | Frequent Values | Plots the counts of each individual value for the most frequent values of a feature. If there are more than 10 categories, DataRobot displays values that account for 95% of the data; the remaining 5% of values are bucketed into a single "All Other" category. | numeric, categorical, text, boolean | | Table | Provides a table of feature values and their occurrence counts. Note that if the value displayed contains a leading space, DataRobot includes a tag, leading space, to indicate as much. This is to help clarify why a particular value may show twice in the histogram (for example, 36 months and 36 months are both represented). | numeric, categorical, text, boolean, summarized categorical, multilabel | | Illustration | Shows how summarized categorical data—features that host a collection of categories—is represented as a feature. See also the summarized categorical tab differences for information on Overview and Histogram. | summarized categorical | | Category Cloud | After EDA2 completes, displays the keys most relevant to their corresponding feature in Word Cloud format. This is the same Word Cloud that is available from the Category Cloud on the Insights page. From the Data page you can more easily compare Clouds across features; on the Insights page you can compare Word Clouds for a project's categorically-based models. | summarized categorical | | Feature Statistics | Reports overall multilabel dataset characteristics, as well as pairwise statistics for pairs of labels and the occurrence percentage of each label in the dataset. | multilabel | | Over Time (time-aware only) | Identifies trends and potential gaps in data by displaying, for both the original modeling data and the derived data, how a feature changes over the primary date/time feature. | numeric, categorical, text, boolean | | Feature Lineage (time series) or (Feature Discovery) | Provides a visual description of how a derived feature was created. | numeric, categorical, text, boolean | | Actions | | | | Var Type Transform | Provides a dialog to modify the variable type. (Not shown if the variable type for this feature was previously transformed.) | numeric, categorical, text | | Transformation | Shows details for a selected transformed feature and a comparison of the transformed feature with the parent feature. (Applies to transformed features only.) | numeric, boolean | > [!NOTE] Note > The values and displays for a feature may differ between EDA1 and EDA2. For EDA1, the charts represent data straight from the dataset. After you have selected a target and built models, the data calculations may have fewer rows due to, for example, holdout or missing values. Additionally, after EDA2 DataRobot displays [average target values](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#average-target-values) which are not yet calculated for EDA1. ### Histogram chart The Histogram chart is the default display for numeric features. It "buckets" numeric feature values into equal-sized ranges to show frequency distribution of the variable—the target observation (left Y-axis) plotted against the frequency of the value (X-axis). The height of each bar represents the number of rows with values in that range. Initially, the display shows the bucketed data: Select the Show outliers checkbox to calculate and display outliers: The traditional box plot above the chart (shown in gold) highlights the middle quartiles for the data to help you determine whether the distribution is skewed. To determine whisker length, DataRobot uses [Ueda's algorithm](https://jsdajournal.springeropen.com/articles/10.1186/s40488-015-0031-y) to identify the outlier points—the whiskers depict the full range for the lowest and highest data points in the dataset excluding those outliers. This is useful for helping to determine whether a distribution is skewed and/or whether the dataset contains a problematic number of outliers. Note the change in the X-axis scale and compression of the box plot to allow for outlier display. Because there tend to be fewer rows recording an outlier value (it's what makes them outliers), the blue bar may not display. Hover on that column to display a tooltip with the actual row count. After EDA2 completes, the histogram also displays an [average target value](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#average-target-values) overlay. #### Calculate outliers Outliers, the observation points at the far ends of the sample mean, may be the result of data variability. They can also represent data error, in which case you may want to exclude them from the histogram. Outlier detection—run as part of [EDA1](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html) using a combination of heuristics—is strictly a histogram visualization tool and does not influence the modeling process. Check the Show outliers box and to initiate a calculation identifying the rows containing outliers. DataRobot then re-displays the histogram with outliers included: Outliers are generally calculated as a collection of two ranges: - p25 represents the values in the first quartile of a data distribution. - p75 represents the values in the third quartile of a data distribution. - IQR is the Interquartile Range, equal to the difference of the first quartile subtracted from the third quartile: IQR = p75-p25 . The ranges are then calculated as the first quartile minus IQR ( `p25-IQR`) and the third quartile plus IQR ( `p75+IQR`). Note that this is a general overview of outlier calculation. Additional calculations are required depending on how these ranges compare to the minimal and maximal values of the data distribution. There are also additional heuristics used for corner cases that cover how DataRobot calculates IQR and the final values of the outlier threshold. ### Frequent Values chart The [Frequent Values](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/analyze-frequent-values.html) chart, in addition to showing common values, reports inliers, disguised missing values, and excess zeros. ## Summarized categorical features The summarized categorical variable type is used for features that host a collection of categories (for example, the count of a product by category or department). If your original dataset does not have features of this type, DataRobot creates them (where appropriate as described below) as part of EDA2. The summarized categorical variable type offers unique feature details in its [Overview](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#overview-tab-for-summarized-categorical), [Histogram](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#histogram-tab-for-summarized-categorical), [Category Cloud](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#category-cloud-tab), and [Table](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#table-tab) tabs. > [!NOTE] Note > You cannot use summarized categorical features as your target for modeling. ### Required dataset formatting For features to be detected as the summarized categorical variable type (shown in the Var Type column on the Data tab), the column in your dataset must be a valid JSON-formatted dictionary: `"Key1": Value1, "Key2": Value2, "Key3": Value3, ...` - "Key": must be a string. - Value must be numeric (an integer or floating point value) and greater than 0. - Each key requires a corresponding value. If there is no value for a given key, the data will not be usable. - The column must be JSON-serializable. The following is an example of a valid summarized categorical column: `{“Book1”: 100, “Book2”: 13}` An invalid summarized categorical column can look like any of the following examples: - {‘Book1’: 100, ‘Book2’: 12} - {‘Book1’: ‘rate’,‘Book2’: ‘rate1’} - {“Book1”, “Book2”} ### Overview tab The Overview tab presents the top 50 most frequent keys for your feature. Each key displays the percentage of rows that it appears in, its mean, standard deviation, median, min, and max. You can sort the keys by any of these fields. Most of this information is available for other types of features in the columns on the Data page, but for summarized categorical features each individual key has its own values for these fields. | | Element | Description | | --- | --- | --- | | (1) | Export | Export the list of keys and their associated values as a PNG. You can choose to include the chart title in the image and edit the filename before you download it. | | (2) | Page control | Move through pages of listed keys (10 keys per page). | | (3) | Histogram icon | Access the histogram for a given key. | ### Histogram tab While most of the functionality for this tab is the same as described in the [working with histograms](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#histogram-chart) section above, there are some differences unique to this variable type. The histograms displayed in this tab correspond to the individual labels (keys) of a feature instead of a feature itself. The list of keys can be sorted by percentage of occurrence in the dataset's rows or alphabetically. | | Element | Description | | --- | --- | --- | | (1) | Search | Searches for labels. | | (2) | Showing | Changes the bin distribution. Select the number of bins to view. | | (3) | Target values | Sets the basis of the target value display. | | (4) | Scale Y-axis for large values | Reduces the number of rows measured in the Y-axis for large values. | | (5) | Export | Exports the histogram. | > [!NOTE] Note > DataRobot automatically filters out stopwords when calculating values for the histogram. #### Viewing large values The Scale the Y-axis for large values option reduces the number of rows measured in the Y-axis and improves the visualization of larger values—it is common that large numbers are only represented in a few rows. Resizing the histogram above results in: By scaling the Y-axis, you can see that the greatest value measured has been greatly reduced. As a result, the number of rows across all values are more evenly represented. ### Category Cloud The Category Cloud tab provides insights into [summarized categorical](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#summarized-categorical-features) features. It displays as a [word cloud](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/word-cloud-classic.html) and shows the keys that are most relevant to their corresponding feature. > [!NOTE] Category Cloud availability > The Category Cloud insight is available on the Models > Insights tab and on the Data tab. On the Insights page, you can compare word clouds for a project's categorically-based models. From the Data page you can more easily compare clouds across features. Note that the Category Cloud is not created when using a multiclass target. Keys are displayed in a color spectrum from blue to red, with blue indicating a negative effect and red indicating a positive effect. Keys that appear more frequently are displayed in a larger font size, and those that appear less frequently are displayed in smaller font sizes. Check the Filter stop words box to remove stopwords (commonly used terms that can be excluded from searches) from the display. Removing these words can improve interpretability if the words are not informative to the Auto-Tuned Summarized Categorical Model. Mouse over a key to display the coefficient value specific to that key and to read its full name (displayed with the information to the left of the cloud). Note that the names of keys are truncated to 20 characters when displayed in the cloud and limited to 100 characters otherwise. ### Illustration tab The Illustration tab shows how summarized categorical data is represented as a feature. For example, in the below image, the Values column contains five summarized categorical features displayed in JSON dictionary format (selected at random), as described above. Click Summary to display a box that visualizes how categorical values appeared in their initial state, prior to being engineered as summarized categorical features. ### Table tab The Table tab, which is the default tab for [multilabel](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/multilabel-classic.html) projects, displays a two-column table detailing counts for the top 50 most frequent label sets in the multicategorical feature. The table lists each key in the Values column, and the respective key's count in the Count column. > [!NOTE] Unicode text in the Values column > If you are using Unicode text and it appears abnormal in the Values column, make sure your text is UTF8 encoded. ## Average target values After EDA2, DataRobot displays orange circles as graph overlays on the Histogram and Frequent Values charts. The circles indicate the average target value for a bin. (These circles are connected for numeric features and not for categorical, since the ordering of categorical variables is arbitrary and histograms display a continuous range of values.) For example, consider the feature `num_lab_procedures`: In this example, there are 846 people who had between 44-49.999999 lab procedures. The average target value represented by the circle (in this case, the percent readmitted) is 37.23%. (The orange dots correspond to the right axis of the histogram.) ### How Exposure changes output If you used the [Exposure](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html#set-exposure) parameter when building models for the project, the Histogram and Frequent values tabs display the graphs adjusted to exposure. In this case: - The number of rows (1) in each bin. - The sum of exposure (2) in each bin. That is, the sum of the weights for all rows weighted by exposure. - The sum of target value divided by the sum of the exposure (3) in a bin. ### How Weight changes output If you set the Weight parameter for a project, DataRobot weights the number of rows and average target values by weight. --- # Analyze data URL: https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/index.html > Interpret the findings and visualizations created by DataRobot. # Analyze data These sections help you interpret the findings and visualizations created by DataRobot. | Topic | Description | | --- | --- | | Data quality reference | Read descriptions of each data quality check, as well as the logic DataRobot applies to detect, and often repair, common data quality issues. | | Post data ingest analysis (EDA1) | | | Assess data quality with EDA | Assess the quality of your data during each phase of Exploratory Data Analysis (EDA). | | Data Quality Assessment | Automatically detect and often handle data quality issues such as outliers, leading or trailing zeros, target leakage, and many more. | | Analyze features using histograms | Analyze the distribution of a feature's values and investigate outliers. | | Analyze frequent values | Look into the values that appear most and least frequently for a feature. | | Feature details | Interpret histograms, frequent values charts, and transformations. | | EDA1 | View summary statistics based on a sample of your data. | | ESDA | Interactively visualize, explore, and aggregate target, numeric, and categorical features on a map. | | Fast EDA for large datasets | Understand Fast Exploratory Data Analysis (EDA) for large datasets, and how to apply early target selection. | | Over Time chart | Review time-aware visualizations of how a feature changes over time feature (time-aware only). | | Post modeling analysis (EDA2) | | | EDA2 | View summary statistics based on the portion of the data used for EDA1, excluding rows that are also in the holdout data and rows where the target is N/A. | | Feature Associations | Interpret feature correlations. | | Use data pipelines for ingest and transformation | Learn how to create a reusable data pipeline to keep datasets cataloged inside DataRobot up-to-date and ready for experimentation, batch predictions, or automated retraining jobs. | --- # Exploratory Spatial Data Analysis (ESDA) URL: https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/lai-esda.html > DataRobot Location AI provides tools to conduct ESDA. The tools let you interactively visualize and aggregate target, numeric, and categorical features on a map. # Exploratory Spatial Data Analysis (ESDA) DataRobot Location AI provides a variety of tools for conducting ESDA within the DataRobot AutoML environment, including geometry map visualizations, categorical/numeric thematic maps, and smart aggregation of large geospatial datasets. Location AI’s modern web mapping tools allow you to interactively visualize, explore, and aggregate target, numeric, and categorical features on a map. ## Location visualization Within the Data tab, you can visualize and explore the spatial distribution of observations by expanding location features from the list and selecting the Geospatial Map link. Clicking Compute feature over map creates a chart showing the distribution on a map. By default, Location AI displays a Unique map visualization depicting individual rows in the dataset as unique geometries. You can: - Pan the map using a left-click hold (or equivalent touch gesture) and moving it. - Zoom in left by double-clicking (or equivalent touch gesture). - Use the zoom controls in the top-right corner of the map panel to zoom in and out. Within the Unique map view, rows from the input dataset that are co-located in space are aggregated; the map legend in the top-left corner of the map panel displays a color gradient that represents counts of co-located points at a given location. Hovering over a geometry produces a pop-up displaying the count of co-located points and the coordinates of the location at that geometry. The opacity of the data can be controlled in Visualization Settings. When the number or complexity of input geometries meets a certain threshold, Location AI automatically aggregates geometries into a Kernel density map to enhance the visualization experience and interpretability. ## Feature Over Space In addition to visualizing the spatial distribution of the input geometries, Location AI also displays distributions of numeric and categorical variables on the Geospatial Map. Within the Data tab, navigate to any numeric or categorical features, select Geospatial Map, and click Calculate Feature Over Map to create the visualization. By default, the Feature Over Space visualization displays a thematic map of unique locations with feature values depicted as colors. For geometries that are co-located spatially, the average value for the co-located locations are displayed. For numeric variables, you can change the metric used for the display by selecting “min”, “max”, or “avg” from the Aggregation dropdown menu at the bottom-left of the map panel. For categorical variables, the mode of the co-located categories is displayed. When the number of unique geometries grows large, DataRobot automatically aggregates individual geometries to enhance the visualization. ## Kernel density map A Kernel density map collects multiple observations within each given kernel and displays aggregated statistics with a color gradient. For location features, the count, min, max, and average can be selected from the Aggregation dropdown. For numeric features, the min, max, or average is available. For categorical features, the mode is displayed. Several visualization customizations are available in Visualization Settings. ## Hexagon map In addition to viewing kernel density and unique maps of features, you can also view hexagon map visualizations. Select Hexagon map from the Visualization dropdown at the bottom-left of the map panel. Once selected, the map visualization displays hexagon-shaped cells. For location features, the count, min, max, and average can be selected from the Aggregation dropdown. For numeric features, the min, max, or average is available. For categorical features, the mode is displayed. Use the Visualization settings in the bottom-right of the map panel to adjust the settings. ## Heat map You can also view heat map visualizations for geometry and numeric features. Heat map visualization is not available for categorical features. Select Heat map from the Visualization dropdown at the bottom-left of the map panel. Use the Visualization settings in the bottom-right of the map panel to adjust the settings. --- # Use data pipelines for ingest and transformation URL: https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/pipelines.html # Use data pipelines for ingest and transformation This workflow illustrates how a reusable data pipeline can be constructed to keep the datasets cataloged inside DataRobot up-to-date and ready for experimentation, batch predictions, or automated retraining jobs. It leverages DataRobot's Python client along with some configured environment variables to produce a pipeline that you can not only schedule, but also use as a template to ingest and prepare more datasets using a similar connection. This example assumes that an existing connection has already been configured for Snowflake, and that credentials are saved inside DataRobot. Reference the documentation to learn about [creating connections to your data stores](https://docs.datarobot.com/en/docs/data/connect-data/data-conn.html). Alternatively, you can use this workflow as a guide to transform and catalog data collected through an API or other programmatic tool. ## What is a data pipeline? A data pipeline is a series of processing tasks that configure data for analysis. Organizations can accumulate a significant amount of unprocessed data, collected from various sources, with the intention of performing analysis on that data; however, that raw data is often not useful. You must move, sort, filter, reformat, and analyze data for it to be effective. A data pipeline includes tools to identify and analyze patterns in data to inform actions. For example, a data pipeline might prepare data so that you can extract value from the data after examining it. ### Add environment variables Assuming a data store connection and credentials are available in DataRobot, you can supply the following secrets in your DataRobot Notebook. For more information, reference the documentation for [adding environment variables](https://docs.datarobot.com/en/docs/dr-notebooks/code-nb/dr-env-nb.html#environment-variables). | Variable | Description | | --- | --- | | connection_name | The name of the connection object in DataRobot. | | credential_name | The name of the credential object in DataRobot. | | datasource_name | The name of the data source object used to query data via the supplied connection (this does not need to be created ahead of time, and can be any string). | | ingest_table_name | The name of the table in the target data store that you want to ingest. | | raw_dataset_name | A name for the catalog item containing the raw data (this does not need to be created ahead of time, and can be any string*) | | transform_dataset_name | A name for the catalog item containing the final transformed data (this does not need to be created ahead of time, and can be any string). | ### Import libraries ``` import datarobot as dr import os ingest_table_name = os.environ['ingest_table_name'] connection_name = os.environ['connection_name'] datasource_name = os.environ['datasource_name'] transform_dataset_name = os.environ['transform_dataset_name'] raw_dataset_name = os.environ['raw_dataset_name'] credential_name = os.environ['credential_name'] ``` ### Gather or create a data source This cell searches for the appropriate data artifacts by the names supplied in the environment variables. The connection and credentials are required, and an exception will be raised if they are not available. If the datasource object is not already created, a new one will be made containing a query to fetch the target ingest table. ``` try: connection = [c for c in dr.DataStore.list() if connection_name in c.canonical_name][0] except: raise Exception("Specified connection is not configured.") try: credential = [c for c in dr.Credential.list() if credential_name in c.name][0] except: raise Exception("Specified credential is not configured.") try: datasource = [s for s in dr.DataSource.list() if datasource_name in s.canonical_name][0] except: query_train = ("SELECT * FROM " + ingest_table_name) ds_params = dr.DataSourceParameters(data_store_id=connection.id, query=query_train) datasource = dr.DataSource.create(data_source_type="jdbc", canonical_name=datasource_name, params=ds_params) ``` ### Collect the raw data Next, load the raw data into DataRobot's AI catalog. If the named dataset already exists, the pipeline will add a new version to the existing object. ``` try: raw_dataset = [x for x in dr.Dataset.list() if raw_dataset_name in x.name][0] raw_dataset = dr.Dataset.create_version_from_data_source(raw_dataset.id, data_source_id=datasource.id, credential_id=credential.credential_id) except: raw_dataset = dr.Dataset.create_from_data_source(data_source_id=datasource.id, do_snapshot=True, credential_id=credential.credential_id) raw_dataset.modify(name=raw_dataset_name) ``` ### Transform data This is where data transformations can be performed. The raw data is read into a dataframe that can then be manipulated as needed before updating the catalog with the final dataset. ``` df = raw_dataset.get_as_dataframe() df.drop(['policy_code', 'title', 'zip_code'], axis=1, inplace=True) df = df.astype({"revol_bal": int, "revol_util": float}) ``` ### Save transformed data Add the transformed data to the AI catalog for consumption in any downstream tasks. ``` try: transformed_dataset = [x for x in dr.Dataset.list() if transform_dataset_name in x.name][0] transformed_dataset = dr.Dataset.create_version_from_in_memory_data(transformed_dataset.id, df) except: transformed_dataset = dr.Dataset.create_from_in_memory_data(df) transformed_dataset.modify(name=transform_dataset_name) ``` ### Scheduling and future use DataRobot notebooks can be [scheduled](https://docs.datarobot.com/en/docs/workbench/wb-notebook/wb-manage-nb/wb-schedule-nb.html#notebook-scheduling) to run on a cadence that makes sense for the use case. Notebook scheduling allows the configured environment variables to be [overridden as parameters](https://docs.datarobot.com/en/docs/workbench/wb-notebook/wb-manage-nb/wb-schedule-nb.html#notebook-parameterization) for other jobs, so other tables can be ingested easily if they require the same transformations. --- # Data connections URL: https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/data-conn.html > Learn how to integrate with a variety of data sources using the # Data connections > [!NOTE] Note > If your database is protected by a network policy that only allows connections from specific IP addresses, have an administrator add all [allowed IPs for DataRobot](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/data-conn.html#allowed-source-ip-addresses) to your network policy. If the problem persists, contact your DataRobot representative. The DataRobot connectivity platform allows users to integrate with their data stores using either the DataRobot provided connectors or uploading the JDBC driver provided by the data store. The "self-service" database connectivity solution is a standardized, platform-independent solution that does not require complicated installation and configuration. Once configured, you can read data from production databases for model building and predictions. Connectivity to your data source allows you to quickly train and retrain models on that data, and avoids the unnecessary step of exporting data from your database to a CSV file for ingest into DataRobot. It allows access to more diverse data, which results in more accurate models. Users with the technical abilities and permissions can configure and establish data connections. Other users in the org can then leverage those connections to solve business problems. > [!NOTE] Availability information > The ability to [add, update, and remove JDBC drivers](https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/manage-drivers.html) and connectors is only available on Self-Managed AI Platform installations. > > Required permission: Can manage JDBC database drivers This page describes the following workflows: - An overview of the database connectivity workflow . - Steps for creating new connections . - Steps for adding data sources . - Steps for sharing data connections . ## Database connectivity workflow By default, users can create, modify (depending on their [role](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#shared-data-connection-and-data-asset-roles)), and share data connections. You can also create [data sources](https://docs.datarobot.com/en/docs/reference/glossary/index.html#data-source). DataRobot's database connectivity workflow, described below, has two fundamental components. First, the administrator uploads JDBC drivers and configures database connections for those drivers. Then, users can import data into DataRobot for project creation and predictions, as follows: 1. From theData Connectionspage, createdata connectionconfiguration(s). 2. From the sameStartscreen or theAI Catalog, createdata sources—from the data connections—to use for modeling and predictions. Once configured, your data sources are available for both ingest from theStartscreen and for predictions from theMake Predictionstab. 3. (Optional) Depending onrole,sharedata connections with others. There are additional opportunities to launch the data source creation dialogs, but these instructions describe the process used in all cases. ## Allowed source IP addresses Any connection initiated from DataRobot originates from an allowed IP addresses. See a full list at [Allowed source IP addresses](https://docs.datarobot.com/en/docs/reference/data-ref/allowed-ips.html). ## Create a new connection To create a new data connection: 1. From the account menu on the top right, selectData connections. You can also create a new data connection using theAI Catalogby selectingAdd to catalog>New Data Connection. All existing connections are displayed on the left. If you select a configured connection, its configuration options are displayed in the center. 2. To add a new data connection, clickAdd new connection. 3. Select the tile for the data store you want to use. Self-Managed AI Platform installationsFor Self-Managed AI Platform installations, you might not see any data stores listed. In that case, clickAdd a new driverand add a driver from thelist of supported connections. 4. Name the data connection (1), select an authentication method (2), and fill in the required fields (see the documentation for your specific data store). Note that the visible configuration options are the required parameters for the selected data store; therefore, these options vary for each data store. You can add more parameters underShow advanced options(3). Saved credentialsIf you previously added credentials for your datastore via theCredentials Managementpage, you can clickSelect saved credentialsand choose them from the list instead of adding them manually. 5. ClickAdd from connection; once selected, theSchematab opens. 6. TheSchematab lists the available schemas for your database—select a schema from the list. Once selected, theTablestab opens. To use a SQL query to select specific elements of the named database, click theSQL querytab. 7. Select the table(s) you want to register in the AI Catalog and clickProceed to confirmation. Each table will be registered as a separate catalog asset. 8. UnderSettings, select the appropriate policy (1) and data upload amount (2), then review and confirm your selections by clickingRegister in the AI Catalog. > [!NOTE] Note > Any connection that you create is only available to you unless you [share](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/data-conn.html#share-data-connections) it with others. #### Data connection with parameters The parameters provided for modification in the data connection configuration screen are dependent on the selected driver. Available parameters are dependent on the configuration done by the administrator who added the driver. Many other fields can be found in a searchable expanded field. If a desired field is not listed, open Show advanced options and click Add parameter to include it. Click the trash can icon ( [https://docs.datarobot.com/en/docs/images/icon-wb-delete.png](https://docs.datarobot.com/en/docs/images/icon-wb-delete.png)) to remove a listed parameter from the connection configuration. > [!NOTE] Note > Additional parameters may be required to establish a connection to your database. These parameters are not always pre-defined in DataRobot, in which case, they must be manually added. > > For more information on the required parameters, see the [documentation for your database](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/index.html). ### Test the connection Once your data connection is created, test the connection by clicking Test connection. In the resulting dialog box, enter or [use stored](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html) credentials for the database identified in the JDBC URL field or the parameter-based configuration of the data connection creation screen. Click Sign in and when the test passes successfully, click Close to return to the Data Connections page and create your [data sources](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/data-conn.html#add-data-sources). Snowflake and Google BigQuery users can set up a data connection using OAuth single sign-on. Once configured, you can read data from production databases to use for model building and [predictions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html). For information on setting up a data connection with OAuth, the required parameters, and troubleshooting steps, see the documentation for your database: [Snowflake](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-snowflake.html) or [BigQuery](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-bigquery.html). ### Modify a connection You can modify the name, JDBC URL, and, if the driver was configured with them, the parameters of an existing data source. 1. Select the data connection in the left-panel connections list. 2. In the updated main window, click in the box of the element you want to edit and enter new text. 3. ClickSave changes. ### Delete a connection You can delete any data connection that is not being used by an existing data source. If it is being used, you must first delete the dependencies. To delete a data connection: 1. From theData Connectionstab, select the data connection in the left-panel connections list. 2. Click theDeletebutton in the upper right (). 3. DataRobot prompts for confirmation. ClickDeleteto remove the data connection. If there are data sources dependent on the data connection, DataRobot returns a notification. 4. Once all dependent data sources are removedvia the API, try again to delete the data connection. ## Add data sources Your data sources specify, via SQL query or selected table and schema data, which data to extract from the data connection. It is the extracted data that you will use for modeling and predictions. You can point to entire database tables or use a SQL query to select specific data from the database. Any data source that you create is available only to you. > [!NOTE] Note > Once data sources are created, they cannot be modified and can only be deleted [via the API](https://datarobot-public-api-client.readthedocs-hosted.com/page/reference/data/database_connectivity.html). To add a data source, do one of the following: - From theStartscreen, clickData Sourceand select the connection that holds the data you would like to add. See how toimport from an existing data source. - From theAI Catalog, selectAdd to catalog>Existing Data Connection. See how toadd data from external connections. ## Share data connections Because the user creating a data connection and the end-user may not be the same, or there may be multiple end-users for the data connection, DataRobot provides the ability to set user-level permissions for each entity. You can accomplish scenarios like the following: - A user wants to set permissions on a selected data entity to control who has consumer-level, editor-level, or owner-level access. Or, the user wants to remove a particular user's access. - A user that has had a data connection shared with them wants the shared entity to appear under their list of available entities. When you invite a user, user group, or organization to share a data connection, DataRobot assigns the default role of Editor to each selected target (not all entities allow sharing beyond a specific user). You can change the role from the dropdown menu. To share data connections: 1. From the account menu on the top right, selectData Connections, select a data connection, and clickShare: 2. Enter the email address, group name, or organization you are adding and select arole. Check the box to grant sharing permission. 3. ClickShareto add the user, user group, or organization. 4. Add any number of collaborators and when finished, clickCloseto dismiss the sharing dialog box. Depending on your own permissions, you can remove any user or change access as described in the table of [roles and permissions](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html). > [!NOTE] Note > There must be at least one Owner for each entity; you cannot remove yourself or remove your sharing ability if you are the only collaborating Owner. --- # Data connections URL: https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/index.html > Connect to various data sources and manage stored credentials. # Data connections The AI Catalog is a browsable and searchable collection of registered objects that contains definitions and relationships between various object types. These definitions and relationships include data connections, data sources, data metadata, and blueprints. | Topic | Description | | --- | --- | | Stored data credentials | Add and manage securely stored credentials for reuse in accessing secure data sources. | | Share secure configurations | IT admins can configure OAuth-based authentication parameters for a data connection, and then securely share them with other users without exposing sensitive fields. | | Connect to data stores | Set up connections to data stores using DataRobot provided connectors or a “self-service” JDBC platform. | | Register data in the AI Catalog from a data connection | Register data in the catalog from a new or existing data connection. | | Supported data stores | View a list of supported and deprecated data stores along with the details on how to connect to them in DataRobot. | --- # Share secure configurations URL: https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/secure-config.html > Allows IT admins to configure OAuth-based authentication parameters for a data connection, and then securely share them with other users without exposing sensitive fields. # Share secure configurations IT admins can configure OAuth-based authentication parameters for a data connection, and then securely share them with other users without exposing sensitive fields. This allows users to easily connect to their data warehouse without needing to reach out to IT for data connection parameters. ## IT admins > [!NOTE] Availability information > Required user role: Organization administrator ### Prerequisites Before proceeding, make sure you have the following parameters depending on the secure configuration type: **OAuth 2.0:** Client ID Client Secret (Optional) Scopes Authorization endpoint URL Token URL > [!NOTE] Example > If your OAuth provider is Microsoft Entra ID, see the following examples: > > Authorization endpoint URL: > https://login.microsoftonline.com//oauth2/v2.0/authorize > Token URL: > https://login.microsoftonline.com//oauth2/v2.0/token > > For other OAuth providers, including Snowflake and Okta, see the following examples: > > Authorization endpoint URL: > https:///oauth/authorize > Token URL: > https:///oauth/token-request For more information, see [the documentation on connecting to Snowflake](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-snowflake.html). **Google Service Account:** Service Account Key (JSON string) **Key pair:** Username (required only for Snowflake connections) Private Key For more information, see the [documentation for connecting to Snowflake](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-snowflake.html#key-pair). **AWS Credentials:** AWS access Key ID AWS secret access key For more information, see the [documentation for connecting to AWS S3](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-s3.html) (available for preview). **Databricks Service Principal Account:** Client ID Client Secret **Azure Service Principal:** Client ID Client Secret Tenant ID **Azure OAuth 2.0:** Client ID Client Secret Scopes **NGC API Token:** Token ### Create a configuration To create a secure configuration: 1. Click your user icon in the upper-right corner and selectSecure Configurations. 2. ClickAdd a secure configuration. 3. Fill out the required parameters for your data connection by selecting a schema underSecure configuration typeand entering auniquename underSecure configuration display name. 4. ClickSave. ### Share a configuration Other users cannot access a secure configuration when setting up a data connection until it's been shared with them. To share a secure configuration: 1. On the Secure Configurations page, click the Share icon next to a configuration. 2. In the sharing modal, enter the user(s), group(s), or organization(s) you want to grant access to (1). Then, select the appropriate user role (2) and clickShare(3). Note that the role you select determines what configuration information the recipients can view. The table below describes each option: RoleDescriptionEnd usersConsumerCannot view sensitive fields (indicated in theAdd secure configurationmodal).AdministratorsEditorCan view and update sensitive fields.OwnerFull permissions for secure configurations, including the ability to delete existing configurations. > [!WARNING] Secure config exposure message > When Enable Secure Config Exposure is active for your organization, the Share modal displays a message that secrets from the shared secure configuration can be exposed. This means users who create credentials from this shared configuration can use those credentials as runtime parameters in custom models, applications, and jobs, and the actual secret values are injected into those runtimes. For details, see [Using credentials from shared secure configs in runtimes](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#using-credentials-from-shared-secure-configs). ### Secure config exposure > [!NOTE] Premium > Secure configuration exposure is a premium feature. Contact your DataRobot representative or administrator for information on enabling the feature. > > Required feature flag: Enable Secure Config Exposure Enable Secure Config Exposure is an organization-level setting that allows the exposure of secure configuration values when they are shared with users and referenced by credentials used in runtime parameters. When this feature is enabled, secure configuration values are injected directly into the runtime parameters of a custom model, application, or job executed in the cluster. When this feature is disabled, the credential is injected using only the configuration ID, without exposing the underlying secure configuration values. > [!WARNING] Secret exposure in runtimes > Enable Secure Config Exposure causes secret values to be exposed in the container's runtime. When this feature is enabled for your organization, any credential created from a shared secure configuration and used as a runtime parameter in a custom model, application, or job exposes actual secret values (such as access keys and tokens) by injecting them into the runtime. Those secrets are then present in the container's runtime and can be accessed by custom code.Do not enable or use this capability unless you accept the risks inherent in exposing secrets in an uncontrolled container runtime. Use only when necessary and with appropriate governance. When Enable Secure Config Exposure is active, the Share modal shows a warning that the secret might be exposed so admins are aware that shared configs can be used in this way. Organization administrators control whether Enable Secure Config Exposure is enabled. ### Manage secure configurations Once you've created a secure configuration, you can: **Update a configuration:** To update an existing configuration, click the name of the configuration you want to update. Update the fields that appear below the configuration name and click Save. [https://docs.datarobot.com/en/docs/images/sec-config-8.png](https://docs.datarobot.com/en/docs/images/sec-config-8.png) **Delete a configuration:** To delete an existing configuration, click the Actions menu next to the configuration you want to remove, and select Delete. [https://docs.datarobot.com/en/docs/images/sec-config-6.png](https://docs.datarobot.com/en/docs/images/sec-config-6.png) **Build credentials:** To build credentials from an existing configuration, click the Actions menu next to the configuration, and select Build credentials. [https://docs.datarobot.com/en/docs/images/sec-config-12.png](https://docs.datarobot.com/en/docs/images/sec-config-12.png) You can then define your [new credentials and associate a data connection](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/secure-config.html#associate-a-secure-configuration) with them. **Revoke access:** To revoke access to a shared secure configuration, click the Share icon next to the configuration and click the X next to the user, group, or organization. [https://docs.datarobot.com/en/docs/images/sec-config-5.png](https://docs.datarobot.com/en/docs/images/sec-config-5.png) ## Users With a shared secure configuration, you can quickly connect to an external database or data lake without going through the trouble of filling in the required fields and potentially exposing sensitive fields. To remove a secure configuration after it's been associated with a data connection, see the documentation on [stored data credentials](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#remove-stored-credentials). ### Prerequisites Before you can add a data connection with a secure configuration, your IT admin must share it with you. ### Associate a secure configuration You can apply secure configurations anywhere you have the option to create credentials in DataRobot, this includes the: **Secure configuration sharednotification:** When building a credential from a shared secure configuration, save the credential with a unique name and then select a data connection to associate with those credentials. To build credentials with shared secure configurations from the in-app notification: Open the notification center at the top of the page. In the Secure configuration shared notification, click Build credentials . In the Add credential modal, enter a unique name for your credentials under Display name . Click Save . The Credentials Management page opens with your new credentials highlighted. Click Add associated connection . Select the data connection you want to associate with your secure configuration and click Connect . Sign in with your database credentials. **Credentials Managementpage:** When adding a secure configuration from the Credentials Management page, you first add your credentials and then select a data connection to associate with those credentials: Click your user icon in the upper-right corner and select Credentials Management . Click + Add new . Fill in the available fields: Select the credential type associated with the secure configuration. Click Shared secure configurations . Select a secure configuration from the dropdown. Enter a unique display name. Click Save and sign in . Click Add associated connection . Select the data connection you want to associated with your secure configuration and click Connect . Sign in with your database credentials. **Data Connectionpage:** When adding secure configuration from the Data Connection page, you first select the data connection and then add your credentials: Click your user icon in the upper-right corner and select Data Connections . Select a data connection. Select Credentials and click + Add Credentials . In the Add Credentials modal, click + Create new . Fill in the available fields: Select the credential type associated with the secure configuration. Click Shared secure configurations . Select a secure configuration from the dropdown. Enter a unique display name. Click Save and sign in , and then sign in with your database credentials. --- # Data FAQ URL: https://docs.datarobot.com/en/docs/classic-ui/data/data-faq.html > Provides a list, with brief answers, of frequently asked data preparation and management questions. Answers links to more complete documentation. # Data FAQ --- # Data preview features URL: https://docs.datarobot.com/en/docs/classic-ui/data/data-preview/index.html > Read preliminary documentation for data-related features currently in the DataRobot preview pipeline. # Data preview features This section provides preliminary documentation for features currently in preview pipeline. If not enabled for your organization, the feature is not visible. Although these features have been tested within the engineering and quality environments, they should not be used in production at this time. Note that preview functionality is subject to change and that any Support SLA agreements are not applicable. > [!NOTE] Availability information > Contact your DataRobot representative or administrator for information on enabling or disabling preview features. ## Available data preview documentation **SaaS:** Feature Description Feature Discovery support in No-Code AI Apps Create No-Code AI Apps from Feature Discovery projects. Create feature lists in the Relationship Editor Create custom feature lists and transform features in the Relationship Editor. **Self-Managed:** Feature Description Create feature lists in the Relationship Editor Create custom feature lists and transform features in the Relationship Editor. --- # Create feature lists in the Relationship Editor URL: https://docs.datarobot.com/en/docs/classic-ui/data/data-preview/safer-rel-editor-feature-lists.html > Create custom feature lists and transform features in DataRobot's Relationship Editor. # Create feature lists in the Relationship Editor You can now create custom feature lists and transform features in the Relationship Editor. ## Access the Relationship Editor Open the Relationship Editor in either of the following ways: - Create a Feature Discovery project . Use this method if you haven't yet added secondary datasets and created relationships among the datasets. - Click Edit relationships on the right side of the Secondary Datasets section of the Data page. Use this method if you have already created a relationship configuration. ## Create a feature list 1. Hover over the menu on the right of the dataset tile from which you want to create a feature list and selectConfigure dataset. 2. In theDataset Editor, click+ Create new feature list. 3. Enter a name for the new feature list, select the features to be included, and clickCreate Feature List. A message indicating that the feature list has been successfully created displays in the top right. ## Transform features Just as you can [transform features](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html) on the Data tab, you can do so when you add them to a feature list in the Relationship Editor. To transform a feature in the Relationship Editor: 1. Open the Relationship Editor, hover over the menu on the right of the dataset tile, and selectConfigure dataset. 2. In theDataset Editor, click+ Create a new feature list. 3. Click the transform icon () for a feature you want to transform. There are different settings depending on the type of feature you are transforming. For example, for a categorical transform, you can choose to transform the category to a Text or Numeric feature. For a date feature, you can extract portions of a date and transform them. In this case, the month portion of the date is transformed into a categorical feature. 4. Once you have selected your transformed feature settings, optionally update the generated feature name in theNew Feature Namefield. In this example, a Numeric transform was selected. By default, DataRobot names the feature using the original feature name followed by the transform data type in parentheses. 5. ClickCreate Feature. A message indicating that the transformed feature has been successfully created displays in the top right. In theCreate feature listwindow, you can hover over the "i" to see that the feature was created as a result of a transform. --- # Import to DataRobot directly URL: https://docs.datarobot.com/en/docs/classic-ui/data/import-data/import-to-dr.html > This section provides detailed steps for importing without using the catalog, by drag and drop, URL, and HDFS. # Import to DataRobot directly This section describes detailed steps for importing data to DataRobot. Before you import data, review DataRobot's [data guidelines](https://docs.datarobot.com/en/docs/reference/data-ref/file-types.html) to understand dataset requirements, including file formats and sizes. ## Guidelines for imports Review the following data guidelines for AutoML, time series, and Visual AI projects prior to importing. > [!NOTE] Dataset formatting > To avoid introducing unexpected line breaks or incorrectly separated fields during data import, if a dataset includes non-numeric data containing special characters—such as newlines, carriage returns, double quotes, commas, or other field separators—ensure that those instances of non-numeric data are wrapped in quotes ( `"`). Properly quoting non-numeric data is particularly important when the preview feature "Enable Minimal CSV Quoting" is enabled. ### For AutoML projects - The data must be in a flat-file, tabular format. - You must have a column that includes the target you are trying to predict. ### For time series projects - The data must be in a flat-file, tabular format. - You must include a date/time feature for each row. - When using time series modeling, DataRobot detects the time step—the delta between rows measured as a number and a time-delta unit in the data, for example (15, “minutes”). Your dataset must have a row for each time-delta unit. For example, if you are predicting seven days in the future (time step equals 7, days), then your dataset must have row for each day for the entire date range; similarly, if you are forecasting out seven years, then your data must have one row for each year for the entire date range. - You must have a column that includes the target that you are trying to predict. ### For Visual AI projects - Set up folders that contain images for each class and name the folder for that class. Create a ZIP archive of that folder of folders and upload it to DataRobot. - You can also add tabular data if you include the links to the images within the top folder. You can find more information on that here. ## Create a project Before you can begin building models, you must create a new DataRobot project in either of the following ways: - Click the DataRobot logo in the upper left corner. - Open the Projectsfolder in the upper right corner and click the Create New Project link. Once the new project page is open, [select a method to import](https://docs.datarobot.com/en/docs/classic-ui/data/import-data/import-to-dr.html#import-methods) an acceptable file type to the page. (Accepted types are listed at the bottom of the screen.) If a particular upload method is disabled on your cluster, the corresponding ingest button will be grayed out. ## Import methods To import to DataRobot, navigate to the Begin project page by clicking the DataRobot logo on the top left. There are other methods of accessing this page depending on your account type. | | Import method | Description | | --- | --- | --- | | (1) | Drag and drop | Drag and drop a file from your computer onto the Begin a project page. | | (2) | Import from | Choose your import method. | | (3) | Browse | Browse the AI Catalog. You can import, store, blend, and share your data through the AI catalog. | | (4) | File types | View the accepted formats for imports. See Dataset requirements for more details. | The following table lists each import method: | Method | Description | | --- | --- | | Use an existing data source | Import from a configured data source. | | Import a dataset from a URL | Specify a URL from which to import data. | | Import local files | Browse to a local file and import. | | Import files from S3 | Upload from an AWS S3 bucket. | | Import files from Google Cloud Storage | Import directly from Google Cloud. | | Import files from Azure Blob Storage | Import directly from Azure Blob. | > [!NOTE] Note > A particular upload method may be disabled on your cluster, in which case a button for that method does not appear. Contact your system administrator for information about the configured import methods. > > Some import methods may need to be configured by an admin before use, as noted in the following sections. For larger datasets, DataRobot provides [special handling](https://docs.datarobot.com/en/docs/classic-ui/data/import-data/large-data/fast-eda.html) that lets you see your data earlier and select project options earlier. ### Upload a local file > [!NOTE] Availability information > The ability to load locally mounted files directly into DataRobot is not available for managed AI Platform users. Click Local file and browse for a file or drag a file directly onto the Begin a project page. You can also specify the URL link as `file:///local/file/location`. DataRobot ingests the file from the network storage drive connected to the cluster and creates a project. This import method needs to be configured for your organization's installation. > [!NOTE] Note > When dropping large files (greater than 100MB) the upload process may hang. If that happens: > > Try again. > Compress the file into a supported > format > and then try again. > Save the file to a remote data store (e.g., S3) and use URL ingest, which is more reliable for large files. > If security is a concern, use a temporarily signed S3 URL. ### Import from a URL Use a URL to import your data. It can be local, HTTP, HTTPS, Google Cloud Storage, Azure Blob Storage, or S3 (URL must use HTTP). 1. ClickURL. 2. Enter the URL to your data and clickCreate New Project. The URL can belocal, HTTP, HTTPS,Google Cloud Storage,Azure Blob Storage, orS3. DataRobot imports the data and creates a project. NoteThe ability to import from Google Cloud, Azure Blob Storage, or S3 using a URL needs to be configured for your organization's installation. Contact your system administrator for information about configured import methods. ### Import from a data source Before importing from a data source, [configure a JBDC connection](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/data-conn.html) to the external database. > [!NOTE] Note > When DataRobot ingests from the data source option, it makes a copy of the selected database rows for your use in the project. To import from an existing data source: 1. ClickData Source. 2. Search and select a data source. You can also choose toadd a new data connection. 3. Choose an account. 4. Select the data you want to connect to. 5. Click to create a project. DataRobot connects to the data and creates a project. ### Import files from S3 Self-Managed AI Platform installations with this import method configured can ingest S3 files via a URL by specifying the link to S3 as `s3:///` (instead of, for example, `https://s3.amazonaws.com/bucket/file?AWSAccessKeyId...`). This allows you to ingest files from S3 without setting your object and buckets to public. > [!NOTE] Note > This method is disabled for managed AI Platform users. Instead, import S3 files using one of the following methods: > > Using an Amazon S3 > data connection > . > Generate a pre-signed URL allowing public access to S3 buckets with authentication, then you can use a direct URL to ingest the dataset. ### Import files from Google Cloud Storage You can configure DataRobot to directly import files stored in Google Cloud Storage using the link `gs:///`. This import method needs to be configured for your organization's installation. > [!NOTE] Note > The ability to import files using the `gs:///` link is not available for managed AI Platform users. ### Import files from Azure Blob Storage It is possible to directly import files stored in Azure Blob Storage using the link `azure_blob:///`. This import method needs to be configured for your organization's installation. > [!NOTE] Note > The ability to import files using the `azure_blob:///` link is not available for managed AI Platform users. ## Project creation and analysis After you select a data source and import your data, DataRobot creates a new project. This first [exploratory data analysis](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html) step is known as EDA1. (See the section on ["Fast EDA"](https://docs.datarobot.com/en/docs/classic-ui/data/import-data/large-data/fast-eda.html) to understand how DataRobot handles larger datasets.) Progress messages indicate that the file is being processed. When [EDA1](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda1) completes, DataRobot displays the Start screen. From here you can scroll down or click the Explore link to view a data summary. You can also [specify the target feature](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html#set-the-target-feature) to use for predictions. Once you're in the data section, you can: - ClickView Raw Data(1) to display a modal presenting up to a 1MB random sample of the raw data table DataRobot will be building models with: - Set your target(2) by mousing over a feature name in the data display. - Work withfeature lists(3). You can also [view a histogram](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html) for each feature. The histogram provides several options for modifying the display to help explain the feature and its relationship to the dataset. More information becomes available once you set a target feature and begin your [model build](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html), which is the next step. --- # Import data URL: https://docs.datarobot.com/en/docs/classic-ui/data/import-data/index.html > DataRobot lets you import data using multiple methods, including uploading dataset files locally, uploading from URLs, and connecting to data sources. # Import data Data can be ingested into DataRobot from your local system, a URL, and through data connections to common databases and data lakes. A critical part of the data ingestion process is [Exploratory Data Analysis (EDA)](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html). EDA happens twice within DataRobot, once when data is ingested and again once a target has been selected and modeling has begun. You can import data directly into the DataRobot platform or you can import into the AI Catalog, a centralized collaboration hub for working with data and related assets. The catalog allows you to seamlessly find, understand, share, tag, and reuse data. The following sections provide guidelines and steps for importing data. | Topic | Description | | --- | --- | | Import directly into DataRobot | In DataRobot, you can import a dataset file, import from a URL, import from AWS S3, among other methods. | | Import into the AI Catalog | Import data into the AI Catalog and from there, create a DataRobot project. In the catalog, you can transform the data using SQL, and create and schedule snapshots of your data. | | Import large datasets | Methods of working with large datasets (greater than 10GB). | --- # Fast EDA for large datasets URL: https://docs.datarobot.com/en/docs/classic-ui/data/import-data/large-data/fast-eda.html > Overview of Fast Exploratory Data Analysis (EDA) for large datasets, and how to apply early target selection. # Early target selection The data ingestion process for large datasets can, optionally, be different than that used for smaller sets. (You can also use the same process by letting the project complete [EDA1](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda1).) When DataRobot optimizes for larger sets, it launches "[Fast (or preliminary) EDA](https://docs.datarobot.com/en/docs/classic-ui/data/import-data/large-data/fast-eda.html#fast-eda-application)," a subset of the full EDA1 process, which looks more like: 1. Dataset import begins. 2. DataRobot detects the need for, and launches, Fast EDA. 3. When Fast EDA completes, there is a window of time in which you can choose to participate in early target selection . This window is only valid between the time when Fast EDA completes and when EDA1 completes. As a result, for smaller datasets (less than 200MB) the window may be too small to take advantage of it. 4. If early target selection was enabled, DataRobot completes EDA1, partitions the data, and launches EDA2 using project criteria for early target selection. If it was not enabled, the standard ingest process resumes (select a target and options and press Start ). > [!TIP] Tip > When working with large datasets, you cannot create GLM, ENET, or PLS [blender models](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/creating-addl-models.html#create-a-blended-model). Median and average blenders are available. Also, Fast EDA is disabled in some cases, such as when the dataset has too many columns or uses too much RAM during ingest. ## Fast EDA application A dataset qualifies for Fast EDA if it is larger than 5MB, has fewer than 10,000 columns, and if when you begin to load data, 10 seconds elapse and the ingestion process is less than 75% complete. Note that the ingestion process is internal to DataRobot and may appear differently to you in the status bar. Fast EDA allows you to see preliminary EDA1 results and explore your data shortly after upload begins and while ingestion continues. Once Fast EDA completes, DataRobot continues calculating until full EDA1 completes. Fast EDA is particularly helpful with large datasets because it allows you to: - explore your data while ingestion continues. This is particularly helpful with large datasets. For example, it may take 15 minutes to ingest a 10GB file, but with Fast EDA you can see data information much more quickly. - use early target selection , described below, to set the target variable and advanced option settings earlier on in the upload process. > [!NOTE] Note > Fast EDA is calculated on the first X rows of the dataset, not a random sample. ## Fast EDA and early target selection Fast EDA paves the way for early target selection. Once you have chosen the target, DataRobot populates the project options (partitioning, downsampling, number of workers, etc.) with default values based on your Fast EDA data. You can change and save the options, then set the project to auto-start at the completion of full EDA1. In this way, you do not have to check repeatedly for ingestion completion, which can be time consuming with quite large datasets. If there is any kind of error in the settings or ingest, DataRobot notifies you by email with an informative error message (if configured to do so). Once you set the target and any advanced options, even if you close your browser, DataRobot saves your project selections. You can set the following at the completion of Fast EDA: - Target - Metric - Initial number of workers - Modeling mode - Advanced options Until full EDA1 completes, you cannot: - View feature importance - Create feature lists - Perform feature transformations ## Apply early target selection To use early target selection, keep an eye on the Start screen and the processing status reported in the right sidebar. Fast EDA is part of the ingestion process, but if your dataset is too small for early target selection to make sense, you won't be able to modify these selections and EDA1 will go on to complete. If early target selection is applicable to your project, you will see a change in the start screen that indicates early target selection is an option: To use early target selection: 1. Import your datasetto DataRobot. 2. When Fast EDA completes (part-way through the full EDA1 process), you are allowed to enter a target variable. Scroll down to explore your data and you see a yellow information message indicating, approximately, the amount of data used for the preliminary results: NoteThe informational message disappears after completion of EDA1. 3. Enter a target variable. TheDatapage displays the auto-start toggle: 4. Click theShow Advanced optionslink to set additional parameters. 5. If you choose to auto-start the model build process, toggle the auto-start andselect a modeling mode: When full EDA1 completes, DataRobot launches the model building process using the criteria you set. ## More info... When working with large datasets, there are some differences in behaviors that you should note. ### Train into validation and holdout If, when training models, you trained into the validation and/or holdout sets, those scores display `N/A` on the Leaderboard: With large datasets, DataRobot disables internal cross-validation when you train models into validation/holdout. For anything over 1.5GB DataRobot uses [TVH](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#training-validation-and-holdout-tvh) as the default validation method. This is because the validation/holdout rows are used for training the model, the scores are not likely to be an accurate representation of the model performance on unseen data (and thus, `N/A`). Some additional considerations for models displaying `N/A`: - They are not represented in the Learning Curves or Speed vs Accuracy tabs. - The Lift Chart and Feature Effects tabs are unavailable. - You cannot compute predictions using the Make Predictions tab. ### Change model sample size You can change model sample size either from the [Leaderboard](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/creating-addl-models.html#use-add-new-model) or the [Repository](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/repository.html#create-a-new-model). Additionally, you cannot change the sample size for blender models. If you do wish to change a blender sample size, you can: 1. Retrain each constituent model at the new sample size. 2. Blend the constituent models to make a new blender . ### Understand the messages DataRobot provides some notifications to help you interpret the preliminary data displayed and used for early target selection. For example: The [Smart Downsampling](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/smart-ds.html) setting is available (for binary classification or zero-boosted regression problems) after you set your target. The notification about the feature indicates the subset of data, in number of rows, that DataRobot will use in modeling. You can change the value in Advanced options: The dataset notification tells you the number of rows in your dataset that are missing the target variable (and are therefore excluded from model building/predictions): Additionally, auto-start returns a partitioning error when DataRobot bases preliminary calculations for partitioning settings on a subset of your dataset. If the cardinality of your partitioning column is outside the given range, auto-start returns an error. --- # Work with large datasets URL: https://docs.datarobot.com/en/docs/classic-ui/data/import-data/large-data/index.html > This section provides information on working with large datasets. Consider Fast EDA for large sets up to 10GB; use scalable ingest for sets up to 100GB. # Large datasets The following sections provide additional information on working with large datasets. Consider Fast EDA for large sets up to 10GB; use scalable ingest for sets up to 100GB. | Topic | Description | | --- | --- | | Fast EDA for large datasets | Details of the Fast EDA process. | --- # Data URL: https://docs.datarobot.com/en/docs/classic-ui/data/index.html > How to manage data for machine learning, including importing and transforming data, and connecting to data sources. # Data Data integrity and quality are cornerstones for creating highly accurate predictive models. These sections describe the tools and visualizations DataRobot provides to ensure that your project doesn't suffer the "garbage in, garbage out" outcome. See the associated [considerations](https://docs.datarobot.com/en/docs/classic-ui/data/index.html#feature-considerations) for important additional information. See also the [dataset requirements](https://docs.datarobot.com/en/docs/reference/data-ref/file-types.html). | Topic | Description | | --- | --- | | Dataset requirements | Dataset requirements, data type definitions, file formats and encodings, and special column treatments. | | Connect to data sources | Set up database connections and manage securely stored credentials for reuse when accessing secure data sources. | | AI Catalog | Import data into the AI Catalog and from there, you can transform data using SQL, as well as create and schedule snapshots of your data. Then, create a DataRobot project from a catalog asset. | | Import data | Import data from a variety of sources. | | Transform data | Transform primary datasets and perform Feature Discovery on multiple datasets. | | Analyze data | Investigate data using reports and visualizations created after EDA1 and EDA2. | | Data FAQ | A list of frequently asked data preparation and management questions with brief answers and links to more complete documentation. | ## How to build a feature store in DataRobot Feature stores serve as a central repository where frequently-used features are stored and organized for reuse and sharing. Using existing functionality, you can build a feature store in DataRobot. - Feature storage: Connect to and add data from external data sources using the Data Registry and AI Catalog , as well as saved credentials in Credentials Management . - Feature transformations: Build wrangling recipes in Workbench to apply transformations to your data. - Perform offline serving for batch processing by using wrangler recipe SQL and scheduling it within the AI Catalog . - Perform online serving for realtime processing using feature cache . - Data monitoring: Monitor your data with Workbench exploratory data insights (EDA) or via the jobs workshop . - Automation: Create custom jobs to implement automation. ## Feature considerations The following are the data-related considerations for working in DataRobot. ### General considerations For non-time series projects (see time series considerations [here](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/ts-consider.html)): - Ingestion of XLSX files often does not work as well as using the corresponding CSV format. The XLSX format requires loading the entire file into RAM before processing can begin, which can cause RAM availability errors. Even when successful, performance is poorer than CSV (which can begin processing before the entire file is loaded). As a result, XLSX file size limits are suggested. For larger file sizes than those listed below, convert your Excel file to CSV for importing. See thedataset requirementsfor more information. - When using the prediction API, there is a maximum 50MB body size limitation for real-time deployment prediction requests. - If you make a real-time deployment prediction request with a body larger than 50MB (in either Dedicated or Serverless environments), it will fail with theHTTP response HTTP 413: Entity Too Large. - Exportable Java scoring code uses extra RAM during model building and therefore, dataset size should be less than 8GB. ### 10GB Cloud ingest > [!NOTE] Availability information > The 10GB ingest option is only available for licensed users of the DataRobot Business Critical package and only available for AutoML (not time series) projects. Consider the following when working with the 10GB ingest option for AutoML projects: - Certain modeling activities may deliver less than 10GB availability, as described below. - The capability is available for regression, binary classification, and multiclass AutoML projects. - Project creation with datasets close to 10GB may take several hours, depending on the data structure and features enabled. In some situations, depending on the data or the nature of the modeling activity, 10GB datasets can cause out-of-memory (OOM) errors. The following conditions have resulted in OOM errors during testing: - Models built from the Repository; retry the model using a smaller sample size. - Feature Impact insights; rerun the Feature Impact job using a smaller sample size. - Using Advanced Tuning , particularly tunings that: a) add more trees to XGboost/LGBM models or b) deep grid searches of many parameters. - Retraining models at larger sample sizes. - Multiclass projects with more than 5-10 classes. - Feature Effects insight; try reducing the number of features. - Anomaly detection models, especially for datasets > 2.5GB. Specific areas of the application may have a limit lower than 10GB. Notably: - Location AI (geospatial modeling) is limited to 10,000,000 rows and 500 numeric columns. Datasets that exceed those limits will run as regular AutoML modeling projects but the Spatial Neighborhood Featurizer will not run (resulting in no geospatial-specific models). - Out-of-time validation (OTV) modeling supports datasets up to 5GB. --- # Interaction-based transformations URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-disc.html > Without a secondary dataset, Feature Discovery does not run. But with settings you can automatically create features using interactions in your primary dataset. # Interactions-based transformations If your project has no secondary dataset, the [Feature Discovery](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/index.html) process does not apply. For these cases, use the capability to search for interactions in a primary dataset to automatically create new features based on interactions between features from your primary dataset. These newly engineered features can provide additional insight that might be important for modeling. For example, if you were to provide the year a house was sold and the year a house was built, DataRobot could extract a new feature from the difference. This engineered feature, “age of house at sell date,” may prove more relevant than the build or sale dates alone. The [search for interactions](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-disc.html#search-for-interactions) functionality, run as part of the EDA2 process, results in not only new features but also new feature lists, both default lists and custom. The new features are represented in the following tabs: - Feature Impact if in the top 50 most impactful. - Feature Effects if they have more than zero influence on the model (based on the feature importance score). - Prediction Explanations , if applicable to the displayed reasons. If the search does not create any new features (or you have not enabled the option in [Advanced options](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html), there are no changes to the Data page list of features and no new feature lists are created. See the [considerations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-disc.html#feature-considerations) for feature availability. > [!NOTE] Note > DataRobot additionally provides automatic feature transformations for features of type date. This transformation, which occurs during EDA1 and is described as part of the [feature transformations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html) section, requires no manual settings. ## Search for interactions To enable interaction search for a primary dataset, after selecting a target, expand the Advanced options link and select the Additional tab. In the Automation Settings section, select Search for interactions: Return to the top of the page and click Start. As EDA2 runs, you can watch as the newly created features are added to the Data page. New features are named in a way that indicates the operation that created them: Note the Importance score of the new features, showing the strength of their relationship to the target. To improve efficiency when run, Autopilot does not search for differences/ratios for selected blueprints. This is because Search for Interactions, which is done at the EDA2 stage (before Autopilot is run), has already performed a similar search and added new features when applicable. ## Feature lists and created features DataRobot creates new feature lists—"Informative Features" and, if applicable, custom lists—with the created features and marks the lists with a plus (+) sign. Informative features: A custom list: When EDA2 completes, if DataRobot found and created new features, the selected modeling mode uses the new list to build models. A few things to note about feature lists: - The target feature is automatically added to every feature list. - If Autopilot is set to run on the "Informative Features" list, DataRobot createsInformative Features +. If set to run on a custom list, DataRobot creates both +andInformative Features +. - For custom lists, DataRobot only adds those features that make sense to the original content of the list. Also, DataRobot only creates a new custom list if the original custom list contains the parent of at least one newly derived feature. - Informative Features +may or may not have the same number of features as the original. This is because when deriving the new feature from the old, keeping both may result in redundancy. If that is the case, DataRobot removes one of the parent features. - Informative Features +is created based on the Informative Features with Leakage Removed feature list. - +is created based on the features in the custom list and any engineered features whose parents are in the custom list. ## Explore new features Once a new feature is created, the Transformation tab provides insights that explain the relationships. To view: 1. From the Data page click on the new feature name. 2. Select theTransformationtab. The display compares the transformed feature with the parent features and indicates the interaction (MINUS, EQUAL, or DIVIDED BY): To further investigate the newly engineered features, and how newly derived features affect model predictions, find them in the following insights: - Feature Impact - Feature Effects - Prediction Explanations In general, DataRobot considers an interaction between a pair "useful" only when the interaction satisfies criteria of both interpretability and accuracy. This is achieved through high correlation and significance checks. DataRobot fits a Generalized Linear Model with the derived features and then determines the significance of that feature (for example, using p-values or other statistical criteria). ## Feature considerations Search for Interactions typically adds additional insights, but can sometimes result in insights being slightly less accurate. That change in accuracy can lead to DataRobot selecting a different recommended model and also can change the runtime of the 80% model. Search for interactions on primary datasets is supported for: - Pure numeric - Special numeric (date, percentage, currency, length) And does not support the following: - Time series projects - Multiclass modeling --- # End-to-end Feature Discovery URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/enrich-data-using-feature-discovery.html > How Feature Discovery helps you combine datasets of different granularities and perform automated feature engineering. # End-to-end Feature Discovery This page describes how Feature Discovery helps you combine datasets of different granularities and perform automated feature engineering. More often than not, features are split across multiple data assets. Bringing these data assets together can take a lot of work—joining them and then running machine learning models on top. It's even more difficult when the datasets are of different granularities. In this case, you have to aggregate to join the data successfully. Feature Discovery solves this problem by automating the procedure of joining and aggregating your datasets. After defining how the datasets need to be joined, you leave feature generation and modeling to DataRobot. The examples below use data taken from Instacart, an online aggregator for grocery shopping. The business problem is to predict whether a customer is likely to purchase a banana. ## Takeaways This page shows how to: - Add datasets to a project - Define relationships - Set join conditions - Configure time-aware settings - Review features that are generated during Feature Discovery - Score models built using Feature Discovery ## Load the datasets to AI Catalog The examples on this page use these datasets: | Table | Description | | --- | --- | | Users | Information on users and whether or not they bought bananas on particular order dates. | | Orders | Historical orders made by a user. A User record is joined with multiple Order records. | | Transactions | Specific products bought by the user in an order. An Order record is joined with multiple Transaction records. | Each of these tables has a different unit of analysis, which defines the who or what you're predicting, as well as the level of granularity of the prediction. This shows how to join the tables together so that you have a suitable unit of analysis that produces good results. Start by loading the primary dataset—the dataset containing the target feature you want to predict. 1. Go to theAI Catalogand for each dataset you want to upload, clickAdd to catalog. You can add the data in various ways, for example, by connecting to a data source or uploading a local file. 2. Once all of your datasets are uploaded, select the dataset you want to be your primary dataset and clickCreate projectin the upper right. ## Add secondary datasets Once you upload your datasets to the AI Catalog, you can add the secondary datasets to the primary dataset in the project you created. 1. In the project you created, specify your target, then underSecondary Datasets, clickAdd datasets. 2. On theSpecify prediction pointpage of theRelationship editor, select the feature that indexes your primary dataset by time underSelect date feature to use as prediction point. Then clickSet up as prediction point. In this dataset, the date feature istime. 3. In theAdd datasetspage of theRelationship editor, selectAI Catalog. 4. In theAdd datasetswindow, clickSelectnext to each dataset you want to add, then clickAdd. 5. ClickContinueto finalize your selection. ## Define relationships Next, create relationships between your datasets by specifying the conditions for joining the datasets, for example, the columns on which they are joined. You can also configure time-aware settings if needed for your data. 1. On theDefine Relationshipspage, click a secondary dataset to highlight it, then click the plus sign that appears at the bottom of the primary dataset tile. 2. Set join conditions—in this case, specify the columns for joining. DataRobot recommends theuser_idcolumn for the join. ClickSave and configure time-aware. Build complex relationships with multiple join conditionsInstead of a single column, you can add a list of features for more complex joining operations. Click+ join conditionand select features to build complex relationships. 3. Select the time feature from the secondary dataset and the feature derivation window, and clickSave. SeeTime series modelingfor details on setting time-aware options. 4. Repeat these steps to add any other secondary datasets. In this example, the three datasets are joined with these relationships: ## Build your models Now that the secondary datasets are in place and DataRobot knows how to join them, you can go back to the project and begin modeling. 1. ClickContinue to projectin the top right. Back on the mainDatapage, you can see underSecondary Datasetsthat two relationships have been defined for theOrderssecondary dataset and one relationship has been defined for theTransactionssecondary dataset. 2. ClickStartto begin modeling. DataRobot loads the secondary datasets and discovers features: In the next section, you'll learn how to analyze them. ## Review derived features DataRobot automatically generates hundreds of features and removes features that might be redundant or have a low impact on model accuracy. > [!NOTE] Note > To prevent DataRobot from removing less informative features, turn off supervised feature reduction on the [Feature Reductiontab](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html#disable-feature-reduction) of the Feature Discovery Settings page. You can begin reviewing the derived features once EDA2 completes. 1. On theDatatab, click a derived feature and view theHistogramtab. Derived feature names include the dataset alias and the type of transformation. In this example, the transformation is the unique count of orders by the day of the month. 2. Click theFeature Lineagetab to see how this feature was created. 3. Scroll to the top of theDatapage and open theFeature Discoverytab. Click the menu iconand use the actions described below to learn more about how DataRobot processed Feature Discovery: **Download SQL:** To understand how the derived features are constructed, click Download SQL. [https://docs.datarobot.com/en/docs/images/tu-fd-download-sql.png](https://docs.datarobot.com/en/docs/images/tu-fd-download-sql.png) **Download dataset:** To download the new dataset with the derived features, click Download dataset. [https://docs.datarobot.com/en/docs/images/tu-fd-download-dataset.png](https://docs.datarobot.com/en/docs/images/tu-fd-download-dataset.png) **Feature Derivation log:** To understand the process DataRobot used to derive and prune the features, click Feature Derivation log. [https://docs.datarobot.com/en/docs/images/tu-fd-select-feature-derivation-log.png](https://docs.datarobot.com/en/docs/images/tu-fd-select-feature-derivation-log.png) The Feature Derivation Log shows information about the features processed, generated, and removed, along with the reasons why features were removed. You can optionally save the log by clicking Download: [https://docs.datarobot.com/en/docs/images/tu-fd-feature-derivation-log.png](https://docs.datarobot.com/en/docs/images/tu-fd-feature-derivation-log.png) ## Score models built with Feature Discovery When scoring models built with Feature Discovery, you need to ensure the secondary datasets are up-to-date and that feature derivation will complete without problems. To make predictions on models built with Feature Discovery: 1. In theModelspage, click theLeaderboardtab and click the model you selected for deployment. 2. ClickPredict, then underPrediction Datasets, clickImport data fromand import the scoring dataset. The dataset must have the same schema as the dataset used to create the project. The target column is optional and you don't need to upload secondary datasets at this point. 3. After the dataset is uploaded, clickCompute Predictions. 4. To change the default configuration for the secondary datasets, underSecondary datasets configuration, clickChange. Updating the secondary dataset configuration is necessary if the scoring data has a different time period and is not joinable with the secondary datasets used in the training phase. 5. To add a new configuration, clickcreate new. 6. To replace secondary dataset, on theSecondary Datasets Configurationwindow, locate the secondary dataset and clickReplace. > [!NOTE] Note > If you need to replace a secondary dataset, do so before uploading your scoring dataset to DataRobot. If not, DataRobot will use the default settings to compute the joins and perform feature derivation. --- # Feature Discovery settings URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-adv-opt.html > How to set Feature Discovery advanced options, including feature engineering controls and feature reduction. # Feature Discovery settings The Feature Discovery process uses a variety of heuristics to determine the list of [features to derive](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html) in a DataRobot project. In Feature Discovery Settings, you can control which transformations DataRobot will try when deriving new features ( [feature engineering controls](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-adv-opt.html#feature-engineering-controls)), as well as set DataRobot to automatically remove redundant features and those with low impact ( [feature reduction](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-adv-opt.html#feature-reduction)). To access Feature Discovery Settings, click the settings gear on the Define Relationships page. ## Feature engineering controls You can influence how DataRobot conducts feature engineering by setting feature engineering controls. You might want to do this to: - Use your domain knowledge to guide the feature engineering process and improve the quality of the derived features. - Speed up feature engineering. - Improve accuracy by deriving more features, for example, using categorical statistics , skewness, and kurtosis. - Exclude specific transforms that might be too complex to explain to business stakeholders. You can exclude these features post-modeling but that adds to the complexity of the modeling process. Set the feature engineering options in the relationship editor prior to [EDA2](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda2). In Feature Discovery Settings, click the Feature Engineering tab. Consider which feature engineering transformations make the most sense for your project and select the ones you want DataRobot to try when deriving new features. You can hover over a transformation to view a tool tip that describes it. When you're done, click Save changes. ## Feature reduction During Feature Discovery, DataRobot generates new features then removes the features that have low impact or are redundant. This is called feature reduction. You can instead include all features when building models by disabling feature reduction using the following method: In the relationship configuration (the Define Relationships page), click the settings ( [https://docs.datarobot.com/en/docs/images/icon-gear.png](https://docs.datarobot.com/en/docs/images/icon-gear.png)) gear. Select the Feature Reduction tab and toggle off Use supervised feature reduction: --- # Derived features URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html > Complete details on new features DataRobot derives during Feature Discovery, and how to work with these features on the Data page after EDA2 completes. # Derived features The Feature Discovery process uses a variety of heuristics to determine the list of features to derive in a DataRobot project. The results depend on a number of factors such as detected feature types, characteristics of the features, relationships between datasets, data size constraints, and more. See also [Feature engineering controls](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html#feature-engineering-controls) and [Feature reduction](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html#feature-reduction) sections. ## Analysis of derived features After [EDA2](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda2) completes, the [Data](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#data-summary-information) page lists newly discovered and derived features with their corresponding importance scores on the Project Data tab. All derived features are now listed. The name is comprised of the dataset alias and type of transformation. (See the [aggregation reference](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html#feature-aggregations) for more detail.) If the display is concatenated, you can hover on a feature to see the complete name: Some tabs available on the Data page function the same as projects that don't use Feature Discovery: - Transformations - Feature Lists - Feature Associations DataRobot provides additional tabs and tools available on the Data tab that help you analyze Feature Discovery projects: - Feature Lineage on the Project Data tab shows how your engineered features were derived. - The Feature Discovery tab provides a feature derivation log and a summary of dataset relationships. ### Feature Lineage The Feature Lineage tab is available when you access a feature on the Project Data tab. The Project Data tab provides a list of all available project features—original, user- or auto-transformed, and derived by the Feature Discovery process. Click to expand a feature and explore its characteristics. For each feature, depending on type, there are [a variety of sub-tabs](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html) available, one of which is the Feature Lineage tab. The Feature Lineage tab provides a visual description of how the feature was derived and the datasets that were involved in the feature derivation process. It visualizes the steps followed to generate the features (on the right) from the original dataset (on the left). Each element represents an action or a JOIN. Click a feature to expand it and then click the Feature Lineage tab. For example: You can work with the results as follows: - UnderOriginal, DataRobot displays the primary and secondary datasets. Click the name of the secondary dataset to see itsInfopage in theAI Catalog. - Hover on any info (i) icon to see details of the element. - Click on elements of the visualization to understand the lineage. Parent actions are to the left of the element you click. Click once on a feature to show its parent feature, click again to return to the full display. Clicking the yellow CustomerID, by contrast, illustrates the JOIN and resulting derived feature. - The white triangle indicates that the next action (e.g., max, count, etc.) will be performed on this feature. - Elements marked with the clock icon () are time-aware (i.e., derived using time index). ### Feature Discovery tab The Feature Discovery tab on the Data page provides [dataset relationship details](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html#dataset-relationship-details), a [feature derivation summary](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html#feature-derivation-summary), and a [feature derivation log](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html#feature-derivation-log). #### Dataset relationship details The Feature Discovery tab provides a visualization of the dataset relationships. The tab shows the number of secondary datasets, explored features, and derived features that resulted from Feature Discovery. Click Details in the menu on the dataset's tile for more information about the dataset. #### Feature derivation summary Before generating features for the full primary dataset, DataRobot evaluates a sample of the dataset to identify and discard: - Low impact features - Redundant features Click Show more in the Feature Discovery tab to display the feature engineering controls used to explore the features. In the example above, 200 features were evaluated (explored) and 132 were discarded in the feature reduction process, resulting in 68 derived features on the full dataset. DataRobot automatically adds those 68 derived features to the [Informative Features](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html#automatically-created-feature-lists) feature list. Click the Download dataset option in the menu on the right to download the dataset generated by the Feature Discovery process—that is, the multiple new features derived from the secondary datasets. The downloaded CSV contains the original dataset and the Feature Discovery-derived features; it excludes discarded features and those that resulted from the [Search for interaction](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-disc.html#search-for-interactions) option. #### Feature derivation log Click the Feature Derivation log option in the menu on the right for details of the feature generation and reduction process. The feature derivation log indicates: - Relationships between tables - Number of features processed in each secondary dataset - Removed features and reasons for removal Depending on the number of features in your dataset, the log may not display all activity and instead serves as a preview. Click Download to access the complete log contents. ### Feature aggregations When DataRobot creates new features as part of the feature derivation process, the feature name provides an indication of the action taken on the feature, as described and then illustrated below: - Primary table: Feature names begin with the name of the feature. The name of the primary table is not included. This also applies to date features that are used as the prediction point. - Secondary table(s): The table name is appended to the primary table feature name, with the secondary feature name indicated in brackets[ ]. The applied feature engineering is appended in parentheses( ). - Transformations: Automatic or user-created transformed features are prefaced with an info icon (). The following tables list aggregations that apply based on the detected feature type. These use a sample customer/sales dataset to provide examples. > [!NOTE] Note > You can enable and disable transformations for specific feature types during Feature Discovery. See [Feature engineering controls](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html#feature-engineering-controls) for details. #### General feature types | Aggregation | Example | | --- | --- | | Record count | Number of transactions for each customer | | Min count per intermediate entity | Minimum number of items per order across orders of each customer | | Max count per intermediate entity | Maximum number of items per order across orders of each customer | | Average count per intermediate entity | Average number of items per order across orders of each customer | | Latest | Most recent product bought by each customer | #### Numeric feature types | Aggregation | Example | | --- | --- | | Min | Minimum transaction amount, per customer | | Max | Maximum transaction amount, per customer | | Sum | Total amount from all transactions, per customer | | Average | Average number of items, per order, among customer orders | | Median | Median number of items, per order, among customer orders | | Missing count | Number of transactions, per customer, that have a missing amount | | Standard deviation (measures the variation of a set of values) | Std of item prices among orders, per customer | | Skewness (measure of the asymmetry of the frequency-distribution curve) | Asymmetry of the distribution of item prices among customer orders relative to the mean | | Kurtosis (measures the heaviness of a distribution's tails relative to a normal distribution) | "Tailedness" of the distribution of item prices among customer orders | #### Categorical feature types | Aggregation | Example | | --- | --- | | Most frequent | Most frequent merchant type in transactions, per customer | | Entropy | Entropy of merchant types in transactions, per customer | | Summarized counts | Count of transactions per merchant type for each customer | | Unique count | Number of unique merchant types for each customer | | Missing count | Number of transactions, per customer, with missing merchant type | #### Date feature types | Aggregation | Example | | --- | --- | | Interval from previous | Time since the last transaction by the same customer, per transaction | | Time since last | Time since the cutoff date of the last transaction of the customer | | Duration from creation date | Age of customer at profile creation date | | Entropy of date difference | Entropy of binned difference with cutoff date | | Pairwise date difference | Pairwise data difference within a secondary dataset (maximum of 10 different date columns) | #### Text feature types | Aggregation | Example | | --- | --- | | Word/character count | Length of remarks | | Summarized token counts | Counts of each word/character in the product descriptions of all transactions | #### Categorical Statistics Numeric features can be aggregated by common statistics like sum, min, max, count, and average but sometimes it makes more sense to aggregate these statistical groupings by other category column values. In the following business use case, the average spending by product type is more useful than the overall average amount of spending.Spending and Product_Type are features in a secondary dataset. The values of the Spending numeric feature correspond to the categories of the Product-Type categorical feature: If Categorical Statistics aggregation is enabled for Feature Discovery, DataRobot explores numeric statistics for each category of the Product-Type feature, for example: - Spending(30 days min) - Spending(30 days min by Product_Type = A) - Spending(30 days min by Product_Type = B) - Spending(30 days min by Product_Type = C) - ... Categorical Statistics aggregation is turned off by default. See [Feature engineering controls](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html#feature-engineering-controls) to learn how to enable it. > [!NOTE] Note > Feature Discovery only explores Categorical Statistics for categorical columns that have at most 50 unique values. --- # Set up Feature Discovery projects URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html > How to create a project from multiple datasets. You define the relationships. Feature Discovery aggregates the secondary datasets to enrich the primary dataset. # Set up Feature Discovery projects Feature Discovery is based on relationships—between datasets and the features within those datasets. DataRobot provides an intuitive relationship editor that allows you to build and visualize these relationships. The end product is a multitude of additional features that result from these linkages. These derived features can then train more accurate models and generate better predictions. DataRobot’s Feature Discovery engine analyzes the graphs and the included datasets to determine a feature engineering “recipe,” and from that recipe generates secondary features for training and predictions. > [!NOTE] Note > See the Feature Discovery [file requirements](https://docs.datarobot.com/en/docs/reference/data-ref/file-types.html#feature-discovery-file-import-sizes) for dataset sizes information. Review the next section to [get started with Feature Discovery](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html#get-started-with-feature-discovery) or skip to the step-by-step instructions that describe how to: 1. Add datasets to a project . 2. Create relationships . 3. Set join conditions . 4. Assess the quality of relationship configurations . 5. Start the project . You can also take a deeper dive into: - Feature Discovery integration with Snowflake . - Feature engineering controls and feature reduction . - Time-aware feature engineering . - Derived features . - Making predictions on models that have derived features . ## Get started with Feature Discovery In most cases, all you need to start a Feature Discovery project is a simple primary dataset that includes: - The target (column that you want to predict). - An identifier (for example, customer_id or transaction_id ) to link the dataset to additional related datasets. This key serves as the basis of dataset joins. - An optional time index—a date feature in the primary dataset—to support time-aware Feature Discovery . This date feature is used as the prediction point for generating new features. Each record of the primary dataset represents the desired unit of analysis. From this primary dataset, DataRobot guides you through creating relationships to additional datasets, called secondary datasets. Secondary datasets have features that can potentially enrich the primary dataset. While it may be the case that both primary and secondary datasets have one-to-one relationships when they are added, it is not required. In most cases, DataRobot aggregates and then summarizes features in the secondary datasets, and, from there, enriches the primary dataset. ### Sample use case The following sections use an example to illustrate how DataRobot automatically discovers new features from multiple datasets to predict whether a loan will default. In the primary dataset, CreditRisk - Loan Applications, the is-bad column is the project target. The relation between the datasets is the CustID column. Two additional relational datasets, CreditRisk - Credit Inquiries and CreditRisk - Tradeline Accounts, are the secondary datasets used for Feature Discovery. Once model building begins, DataRobot runs through EDA2, adding newly created features to the [Datapage](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html). The Data page provides a variety of information about all the resulting project data, both new and old. ## Add datasets From the AI Catalog, select the primary dataset and click Create project. Then, enter the target feature. > [!NOTE] Note > This procedure shows how to load datasets using the AI Catalog, so to begin, make sure all the assets are in the catalog. Alternatively, you can use the drag-and-drop method to upload datasets. If you do so, all datasets that you upload are automatically registered to the AI Catalog. A valid Feature Discovery project requires at least one secondary dataset —the following tabs describe how to load additional datasets into the project from both the Start page and the relationship editor: **From the Start page:** On the Start page, click Add datasets to add one or more additional datasets to the project. On the Specify prediction point page of the relationship editor , optionally Select a date feature to use as a prediction point . This date/time feature from the primary dataset serves as a reference date for feature derivation windows. Note The step to specify a prediction point does not display if you have already specified a prediction point for the project. For an in-app explanation of prediction points, expand Show Example . Click Set up as prediction point for a time-aware Feature Discovery project or Continue without prediction point for a non time-aware project. Note Although you can select the same date feature used for the out-of-time validation (OTV) partition as the prediction point, clicking Continue without prediction point automatically uses the OTV partition feature when generating new features. If you add or edit the prediction point , DataRobot accounts for that change when generating new features. In the Add datasets page of the relationship editor , select a data import method under Add Data From . This example shows how to add a dataset from the AI Catalog . From the AI Catalog , select the datasets you want to include by clicking Select . Use the search functionality to easily locate datasets for selection. When finished, click Add . Click Continue to finalize your selection. The secondary datasets you select on this page are immediately added to the configuration, so if you reload the page without clicking Continue , the data is not lost. The Define Relationships page displays the datasets. Best practice suggests continuing within this editor to define relationships. You can, however, click Continue to project to return to the Start screen. [https://docs.datarobot.com/en/docs/images/safer-secondary-datasets-on-start-page.png](https://docs.datarobot.com/en/docs/images/safer-secondary-datasets-on-start-page.png) The datasets display and you can see the number of relationships that have been defined. At any time, you can click Define relationships to return to the Define Relationships page. **From the relationship editor:** If your project has more than one secondary dataset, you can add more datasets after saving. From the Define Relationships page: Click Add datasets and select a data import method. This example shows how to add a dataset from the AI Catalog . From the AI Catalog , select the datasets you want to include by clicking Select . Use the search functionality to easily locate datasets for selection. When finished, click Add . The Define Relationships page displays the datasets. Each dataset displayed on the canvas has a menu with shortcuts to dataset-related tasks. See details of working with [primary datasets](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html#primary-datasets) and [secondary datasets](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html#secondary-datasets). After adding secondary datasets to your project, [define the relationships](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html#define-relationships) between the datasets. ### View dataset details You can access dataset details directly from the relationship editor using one of the following methods: **Brief description:** On the dataset tile, hover over the line beneath the dataset name to display metadata for the dataset. [https://docs.datarobot.com/en/docs/images/safer-dataset-brief-details.png](https://docs.datarobot.com/en/docs/images/safer-dataset-brief-details.png) **Detailed description:** Click the menu icon on the top right of the dataset tile and select Details to open the [Info](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog-asset.html#work-with-metadata) page in the AI Catalog. From here you can access the profile, feature lists, relationships, version history, and comments associated with the dataset. [https://docs.datarobot.com/en/docs/images/safer-dataset-details-menu-item.png](https://docs.datarobot.com/en/docs/images/safer-dataset-details-menu-item.png) You can also delete the dataset from this menu. [https://docs.datarobot.com/en/docs/images/safer-define-relationships-menu.png](https://docs.datarobot.com/en/docs/images/safer-define-relationships-menu.png) ## Manually define relationships Once all datasets are loaded, the next step is to define relationships on the Define Relationships page. The primary dataset is on the canvas while any secondary sets are listed in the left pane. After establishing a relationship between two datasets, you can define the relationship by setting [join conditions](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html#set-join-conditions) and [feature derivation windows (FDW)](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html#set-feature-derivation-windows) for time-aware feature engineering. To define relationships: 1. Click a secondary dataset to highlight it; notice the addition of a plus sign on the primary set. 2. Click the plus sign. DataRobot adds the selected secondary dataset to the canvas and opens the configuration editor. The following table describes the elements of theCreate new relationshippage: ElementDescription1Secondary dataset for joinSets the secondary dataset used in the join. Change via the dropdown to any added dataset. Changes are reflected in the canvas below.2Primary dataset for joinSets the primary dataset used in the join.3Suggested join conditionSets the join condition (feature) for the corresponding dataset (listed above the condition). DataRobot suggests up to five conditions, each of which is editable. Use the dropdown to select a new feature; use the trash icon () to delete the join.4Add join conditionProvides a manual join configuration option.5Save or Save and configure time-awareSaves the relationship configuration.Saveis the option if there is no date feature or you did not set a prediction point. If you did set aprediction pointfrom the primary dataset, theSave and configure time-awarebutton displays.6Canvas display controlsZooms in or out, or resets the default display size.7Dataset menu optionsProvides access to a variety of actions that can be enacted on aprimaryorsecondarydataset.8Join edit launchOpens the relationship editor, allowing you to define or modify the relationship between the datasets joined by the line you clicked.9Primary iconIndicates, with a bullseye icon, that this is the primary dataset.10Tour launchOpens a short tour that provides an overview of configuring Feature Discovery.11Continue to projectReturns to theStartscreen where you can revise your time-aware settings, set advanced options, set a modeling mode, and start the modeling process. ### Set join conditions If tables in your datasets are well-formed, DataRobot automatically detects compatible features and creates up to five "suggested" joins. You can modify the suggested join using the dropdowns associated with each join key. You can also manually create join keys by clicking Add join condition. In the resulting dialog, select a join feature from each dataset from the feature dropdown. Once you've added all of your secondary datasets and selected your relationship configuration settings, click Save and configure time-aware or Save for a non time-aware project. - If the project is not time-aware, the Start page displays. - If the project is time-aware, the Time-aware feature engineering page displays where you can configure FDWs . ### Set feature derivation windows After adding secondary datasets to a time-aware project, you can define the FDWs—a rolling window of past values used to generate features before the prediction point. The FDW constrains the time history—in the example below, no further back than 30 days, no more recent than 2 days. 1. ClickSelect time featureto choose a time index feature for the secondary dataset. 2. Configure the FDWs. You can configure up to three FDWs for each dataset, but each window must be unique. To add a FDW, clickAdd window. Once set, the FDW is reflected in the dataset's tile on the canvas: These time-aware settings ensure that the generated features are based only on records that occur before the prediction point. For more details, seeTime-aware feature engineering. ## Automatically generate relationships To automatically generate relationships in a Feature Discovery project, make sure all [secondary datasets are added](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html#add-datasets), and then click Generate Relationships at the top of the Define Relationships page. Once ARD is complete, relationships are automatically added to the primary dataset. > [!NOTE] Note > If you click Generate Relationships without adding any secondary datasets to the project, the button displays "Generating relationships" indefinitely. ## Work with datasets Once a dataset is added to the canvas, you can modify and refine its configuration. Primary datasets appear on the canvas by default, but all secondary datasets must be added. ### Primary datasets > [!NOTE] Note > Be sure to save your configuration before using the menu options. Unsaved changes are lost when you leave a page. Working from the canvas, you can select the menu option on the dataset tile. The primary dataset allows you to add a relationship or edit the prediction point: | Option | Description | | --- | --- | | Add relation | Choose Add relation when you don't have any previous relationships configured to open the Create new relationship page. This is the equivalent of selecting the dataset from the list on the left and clicking the plus sign on the primary's canvas tile. Once the page opens, select a secondary dataset from the dropdown and it is added to the canvas. | | Edit prediction point | Select Edit prediction point to choose a different date feature to use as your prediction point. | ### Secondary datasets When a secondary dataset has been selected and moved to the canvas, a menu option becomes available on its tile. The table below describes the options available from the menu: | Option | Description | | --- | --- | | Add relation | Opens the relationship editor and allows you to select a dataset (from any available in the left pane) to join with. | | Edit alias | Allows you to set an alias for the dataset. The string displays on the canvas as the secondary dataset name. The alias does not change the display in the left-pane dataset list or the relationship editor pages. | | Configure dataset | Opens the dataset configuration editor, where you can set dataset details. | | Configure time-awareness | Opens the time-aware feature engineering configuration dialog, where you can select a time index for the secondary dataset or confirm that the correct date/time feature is selected. | | Details | Click to open the Info window for the dataset in the AI Catalog. | | Delete | Deletes the dataset, and all its relationships, from the current relationship configuration. The dataset is still available to the configuration and listed in the left panel. | Selecting Configure dataset from a secondary dataset menu opens the Dataset Editor. From here you can: - Change the dataset alias. If not manually set, DataRobot auto-generates an alias based on the file name. Click in the box to modify the alias; the alias for the primary dataset cannot be modified. - Choose a snapshot policy, either Latest, Fixed, or Dynamic, to use for this project. By default, the selected snapshot policy will apply atprediction time. - Choose a feature list to apply against the corresponding dataset. Use this option to limit the size of the table by selecting relevant features. You can create new feature lists from theAI Catalog. ## Test relationship quality After configuring at least one secondary dataset, you can test the quality of those relationship configurations to identify and resolve potential problems early in the creation process. The Relationship Quality Assessment tool verifies join keys, dataset selection, and time-aware settings before EDA2 begins. Click the Review configuration button to trigger the Relationship Quality Assessment. A progress indicator (loading spinner) displays on each dataset and on the Review Configuration button, which is disabled, to indicate that an assessment is currently running. Once the assessment is complete, DataRobot marks all tested datasets. Those with identified issues display a yellow warning icon and those with no identified issues display a green tick. Select the warning icon to view a summary of the issues with suggested potential fixes. A summary of the issues identified during the assessment is displayed at the top of the window. > [!NOTE] Sampling percentage > To improve run times, DataRobot subsamples approximately 10% of the primary dataset, speeding up the computation without impacting the enrichment rate estimation accuracy or the results of the assessment. The sampling percentage is included at the top of the report. To open the detailed report, click the orange arrow on the right. DataRobot breaks down the assessment by category, providing additional information to diagnose the issue. If a secondary dataset has multiple FDWs, a detailed report is created for each one. To resolve warnings, click the orange link displayed below each warning— Review dataset, Review relationship, or Review window settings—and a pane appears at the top of the relationship editor allowing you to modify relationship configurations. After EDA2 completes and model building begins, you can view the most recent Relationship Quality Assessment in the Data > Feature Discovery tab. ## Start the project 1. Once you are happy with the definition of the relationship(s), clickContinue to projectto return to theStartscreen. TheSecondary Datasetssection provides visual queues that provide details about the secondary datasets. Visual queueIndicates1Datasets with blue textThe dataset is in use and part of the project.2Datasets with white textThe dataset is loaded but not part of the relationship definition.3Linked datasetsThe number of datasets linked with this dataset.4Number of datasets and relationshipsThe number of secondary datasets and how many have relationships defined. 2. ClickStart. DataRobot conducts feature engineering as part of EDA2 and begins generating model blueprints. ## Share assets As with any DataRobot project, you can share Feature Discovery projects (depending on your permissions). The assignable [roles](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html) provide different levels of permission for the recipient. Unique to Feature Discovery projects, however, is the ability to share engineering graphs and datasets as well. To share a project, click the share icon [https://docs.datarobot.com/en/docs/images/icon-share.png](https://docs.datarobot.com/en/docs/images/icon-share.png). For the recipient to interact with the project, they must have access to the additional assets. By default, assets are not shared. Check to enable sharing relationships and datasets, or DataRobot provides a warning: Note that in addition to the assigned role, the listing of project users also indicates whether project assets have been shared. --- # Predictions URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-predict.html > You can make predictions with models using engineered features the same way as with any other DataRobot model, using the Make Predictions or the Deploy tab. # Predictions You can make predictions with models using engineered features in the same way as you do with any other DataRobot model. To make predictions, use either: - Make Predictions to generate predictions, in-app, on a scoring dataset. - Deploy to create a deployment capable of batch predictions and prediction integrations. When using Feature Discovery with either method, the dataset configuration options available at prediction time are the same. The following describes that configuration followed by descriptions of each tab option. ## Select a secondary dataset configuration When applying a dataset configuration to prediction data, Feature Discovery allows you to use: - The default configuration. - An alternative, existing configuration. - A newly created configuration. If the feature list used by a model doesn’t rely on any of the secondary datasets supplied—because no features were derived or a custom feature list excluded the Feature Discovery features, for example—DataRobot supplies an informational message. ### Use the default configuration By default, DataRobot makes predictions using the secondary dataset configuration defined by the relationships used when building the project. You can view this configuration by clicking the Preview link. From Make Predictions: From Deploy: The default configuration cannot be modified or deleted from the predictions tabs. ### Use an alternate configuration To select an alternative to the default configuration—or to create a new configuration—click Change. When you create new secondary dataset configurations, they become available to all models in the project that use the same feature list. Note that you must make any changes to the secondary dataset configuration before uploading your prediction dataset. #### Apply an existing configuration To apply a different, existing configuration, click Change to open the Secondary Datasets Configuration modal: Expand the menu and select one of the following options: - To preview but not select the configuration, click on a configuration name and selectPreviewfrom the menu. TheSecondary Datasets Configurationsmodal opens. - To select a configuration other than the currently selected item, clickSelectfrom the menu. The item highlights and theSecondary Datasets Configurationsmodal opens. - ClickDeleteto remove a configuration. You cannot delete the default configuration. #### Create a new configuration To create a new configuration, click Change and then create new in the resulting Secondary Datasets Configurations modal: The [Secondary Datasets Configurations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-predict.html#secondary-datasets-configurations-modal) modal expands to include configuration fields. After changing or creating the configuration, you will see the entry listed in the secondary dataset configuration preview and also as the selected configuration on the Make Predictions or Deploy page. Click to select which configuration to use when making predictions with this model and click Apply. ### Secondary Datasets Configurations modal Access the modal by clicking Preview, Change > Preview, or Change > create new. Complete the fields as follows: | | Field | Description | | --- | --- | --- | | (1) | Configuration name | The name of the configuration, "New Configuration" by default. Click the pencil icon to change. | | (2) | Configuration datasets | The dataset(s) that make up the relationship for the new or existing secondary configuration. | | (3) | Dataset details | Basic dataset information, similar but less detailed than the information available from the AI Catalog. Click on a configuration dataset to display. | | (4) | Snapshot policy | The snapshot policy to apply to the dataset. By default, DataRobot applies the snapshot policy you defined in the graphs to your secondary datasets. | | (5) | Replace | A tool to choose which dataset(s) to use as the basis of the relationship to the primary dataset. Clicking "Replace" provides a list of available files in the AI Catalog. Click a dataset to view dataset details; click Use dataset in configuration to add the dataset and return to the previous screen. | | (6) | Required features | Lists the minimum features needed to support the configuration, based on the original relationships configured. If features are missing, DataRobot returns a validation error. See Features required in relationships, below. | | (7) | Create configuration | DataRobot automatically runs validation as you set up the configuration. When validated, click to create a new configuration. | ### Snapshot reference DataRobot applies snapshot policy for a secondary dataset as follows: - Dynamic: DataRobot pulls data from the associated data connection at the time you upload a new primary dataset. - Latest snapshot: DataRobot uses the latest snapshot available when you upload a new primary dataset. - Specific snapshot: DataRobot uses a specific snapshot, even if a more recent snapshot exists. Note that: 1. Changes apply to primary datasets that are uploaded after the changes are saved; you must review the secondary datasets before uploading a primary dataset. 2. Changes are only applicable while you are on theMake PredictionsorDeploypage. Leaving or refreshing the page causes the default snapshot policy to apply to a new primary dataset. ### Features required in relationships After the Feature Discovery and reduction workflow, DataRobot may prune some engineered features that were not used for modeling. This pruning may mean that some raw features are never used, and as such, are not necessary to include in the secondary datasets used for predictions. In other words, if only a subset of raw features are used in the final models, your secondary datasets do not need to include them. This allows you to collect and upload a subset of datasets and/or features, which can result in faster prediction and deployment times. When creating secondary dataset configurations, the modal initially displays the datasets used when building the model. Below the dataset entry, DataRobot reports the raw features used to engineer new features (the required raw features). Any replacement datasets must include those features: If you replace a dataset with an alternative that does not include the required features, DataRobot returns a validation error: ## Use Make Predictions If you accessed the secondary dataset configurations from the [Make Predictions](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/predictions/predict.html) tab, complete the remaining fields as you would with any other DataRobot model. When you upload [test data](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/predictions/predict.html) to run against the model, DataRobot runs validation that it includes the [required features](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-predict.html#features-required-in-relationships) and returns a validation error if it does not. If validation passes, the uploaded dataset is added to the list of available datasets and uses the new configuration. Remember that you must finalize the secondary dataset configuration before uploading a dataset: Once you upload a new scoring dataset, DataRobot extracts relevant data from secondary datasets that were used in the associated project relationships. Note that if the secondary datasets or selected snapshot policy are dynamic, DataRobot prompts for authentication. The Import data from...button is locked until credentials are provided. ## Use Deploy for batch predictions You can make batch predictions using the [Deploy](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html) tab. Once deployed, the model is added to the Deployments page and [model deployment and management](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html) functionality is available. To make batch predictions: 1. From the chosen model, selectPredict > Deploy. TheDeploytab opens for configuration. If the model is not the one chosen and prepared for deployment by DataRobot, consider using thePrepare for deploymentoption. 2. UnderDeploy model, clickRegister to deploy. 3. Configure themodel registration settingsand then clickAdd to registry. The model opens on theModel Registry > Registered Modelstab. 4. While the registered model builds, clickDeployand thenconfigure the deployment settings. 5. In thedeployment settings, clickShow advanced options, navigate to theAdvanced Predictions Configurationsection, and set theSecondary datasets configuration. Reference the details on working withthese configurations: 6. If the secondary dataset configuration was created with a dynamic snapshot policy, authenticate to proceed. When authentication succeeds, or if it is not required, click theCreate deploymentbutton (upper right corner). If the button is not activated, be sure you have correctly configured theassociation ID(If association ID is toggled on, you must supply the feature name containing the IDs.) 7. When deployment completes, DataRobot opens theOverviewpage for your deployment on the Deployments page. SelectPredictions > Prediction APItab, selectBatchandAPI Clientto access the snippet required for making batch predictions: 8. Click theCopy script to clipboardlink. Follow the sample and make the necessary changes when you want to integrate the model, via API, into your production application. 9. You can view the secondary dataset configuration used in the deployment from thePredictions > Settingstab. ClickPreviewto open a modal displaying the configuration. ### Batch prediction considerations - Only DataRobot models are supported, no external or custom model support. - Governance workflow and Feature Discovery model package export is not supported for Feature Discovery models. - You cannot replace a Feature Discovery model with a non-Feature Discovery model or vice versa. - You cannot change the configuration once a deployment is created. To use a different configuration, you must create a new deployment. - When a Feature Discovery model is replaced with another Feature Discovery model, the configuration used by the new model becomes the default configuration. - Feature Discovery predictions will be slower than other DataRobot models because feature engineering is also applied. --- # Snowflake integration URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-snowflake.html > How to set up an integration between DataRobot and Snowflake that allows joint users to both execute data science projects in DataRobot and perform computations in Snowflake. # Snowflake integration An integration between DataRobot and Snowflake allows joint users to both execute data science projects in DataRobot and perform computations in Snowflake as a way to optimize workload performance.Feature Discovery training and prediction workflows will push down relational inner-joins, projection, and filter operations to the Snowflake platform (via SQL). By natively conducting joins in the Snowflake database, data is filtered into smaller datasets for transfer across the network before loading into DataRobot. The smaller datasets reduce project runtimes. To enable integration with Snowflake, the following requirements must be met: - A Snowflake data connection is set up. - All secondary datasets are stored in Snowflake. - All Snowflake sources are stored in the same warehouse. - All datasets are configured as dynamic datasets in the AI Catalog. - You have write permissions to one of the schemas in use or one PUBLIC schema of the database in use. If the above requirements are met, DataRobot automatically establishes the integration and displays the Snowflake icon and Snowflake mode enabled, in blue, at the top of the Define Relationships page. --- # Time-aware feature engineering URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-time.html > How to configure time-aware feature engineering using only information available before the prediction point. # Time-aware feature engineering Time-based feature engineering in Feature Discovery projects involves use of a date feature in the primary table. This date prevents any feature derivation beyond the prediction point. In time-aware projects, the partition date column is, by default, used as the prediction point. > [!TIP] How time series features are derived > For information about how DataRobot derives time series features, see [time series feature derivation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/feature-eng.html). See also specific details about [derived date features](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-gen.html#date-feature-types). In non time-aware projects, where there is no partition date feature, you can set a prediction point to enable time-aware feature engineering. ## Prediction point and time indexes In most cases, the primary dataset includes a prediction point feature, describing when it would have been needed to make the prediction. For example, in a loan request the prediction point feature might be the "loan request date," because each time a customer requests a loan the model must generate a prediction to decide whether to approve or decline. In some cases, the primary dataset is built using one or multiple extracts done at some regular point in the past. For instance, to predict on the first of the month, you would want a monthly prediction point when building the training dataset (e.g., 2019-10-01, 2019-11-01, etc.). In this example, the prediction point feature might be “extract_date.” In both cases, you want to avoid using information from secondary datasets that was not available before the prediction point (for example, transactions that happened after the loan request). To avoid this "time travel paradox," DataRobot integrates time-aware feature engineering capabilities and allows you to configure a [feature derivation window (FDW)](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-adv-modeling/ts-customization.html#set-window-values), which defines a rolling window of past values that models use to generate features before the prediction point. With Feature Discovery, setting FDWs from the [relationship editor](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-time.html#configure-time-aware-feature-engineering) can be understood as: Using the loan application example, the loan request date would be the prediction point. If you only have a date (e.g., 02-14-20), and not a timestamp set, you don't know whether an event happened before or after the time of the specific loan request (in terms of the actual hour/minute/etc.). To be conservative, DataRobot excludes everything on that exact date so that the model doesn't coincidentally include data that happened "too early." Using time-aware settings, you can set a rolling window to ensure that the most relevant data is included. ### Configure time-aware feature engineering After a join is saved, if the added dataset has a date feature and if you set a prediction point, the Save and configure time-aware option becomes available. Click to open the Time-aware feature engineering editor. Set the date/time feature of the secondary dataset to ensure that it only uses records happening before the prediction point to generate features. Once set, the FDW settings can be modified. Set the boundaries of the FDW to determine how much historical data to use. By default, DataRobot sets the window to 30 to 0 days (e.g., transactions that happened in the 30 days before the "loan request date" of now). You can change the boundary by both entering a new value and also setting the increment. Keep in mind that using a larger FDW will slow down the Feature Discovery process. DataRobot also automatically calculates additional, smaller FDWs for the project, in addition to the window you specify. For example, if you set the FDW parameters to "30 to 0 Days," DataRobot selects additional candidate durations (perhaps 1 to 0 weeks, 1 to 0 days, and 6 to 0 hours) and derives features from those windows. The new candidate window sizes are based on an internal algorithm that: - Chooses additional windows between 50% and 0.5% of the original FDW size. - Ensures the additional windows do not use a time unit with a smaller granularity than what is relevant for the primary date/time feature format. If the time index doesn’t reflect the time when data is accessible, you can change the FDW end boundary to reflect the delay. For example, perhaps a secondary dataset is provided by an external data provider and that provider gives you access with a two day delay. You can specify a gap of two days (before the prediction point). The FDW is reflected in the dataset tile: ## Prediction point rounding If a prediction point has many distinct values, the Feature Discovery process may be slow. To speed up processing, DataRobot, by default, rounds down the prediction point to the nearest minute. For example, if a loan has a prediction point ("loan_request_date") of 2020-01-15 08:13:53, DataRobot will round that value down to 2020-01-15 08:13, dropping the `53` seconds. While rounding makes the Feature Discovery process faster, it does come at a cost of potentially losing fresh secondary dataset records. In this example, records that happened between 2020-01-15 08:13:00 and 2020-01-15 08:13:53. If your project is sensitive to that level of record loss, you can change the default rounding from nearest minute to a more suitable selection: ## Determine the final cutoff Once Feature Discovery applies prediction point rounding and the FDW end, DataRobot derives the final "cutoff" used for time-aware engineering. The cutoff point is the point at which DataRobot will not go forward when generating features. In other words, the FDW (the rolling window of past values) is comprised of the furthest time back and the nearest time, both modified by the rounding selection. For example, this setting: Can be understood conceptually as: --- # Feature Discovery URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/index.html > With DataRobot, you can automatically discover and generate new features from multiple datasets, without consolidating manually. # Feature Discovery To deploy AI across the enterprise and make the best use of predictive models, you must be able to access relevant features. Often, the starting point of your data does not contain the right set of features. Feature Discovery discovers and generates new features from multiple datasets so that you no longer need to perform manual feature engineering to consolidate various datasets into one. See the associated [considerations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/index.html#feature-considerations) for important additional information. Select topics from the following table to learn about the feature engineering workflow: | Topic | Description | | --- | --- | | End-to-end Feature Discovery | An end-to-end example that shows you how to enrich data using Feature Discovery. | | Feature Discovery projects | Create and configure projects with secondary datasets, including a simple use-case-based workflow overview. | | Snowflake integration | Set up an integration that allows joint users to both execute data science projects in DataRobot and perform computations in Snowflake. | | Feature Discovery settings | Configure advanced options for Feature Discovery projects, including feature engineering controls and feature reduction. | | Time-aware feature engineering | Configure time-aware feature engineering. | | Derived features | Introduction to the list of aggregations and the feature reduction process. | | Predictions | Score data with models created using secondary datasets. | ## Feature considerations When using Feature Discovery, consider the following: - JDBC drivers must be compatible with Java 1.8 and later. - For secondary datasets, only uploaded files and JDBC sources registered in theAI Catalogare supported. - The following features are not supported in Feature Discovery projects: - Maximum supported values: - If the primary dataset is larger than 40MB,CV partitioningis disabled by default. - Column names in Feature Discovery datasets cannot contain the following: - When there is an error during project start, you cannot return to defining relationships. You must restart the configuration. - There can be issues with the colors used in the visualization of linkages in the Feature Engineering relationship editor. - You must allow the IP addresses listed on theAllowed source IP addressespage to connect to the DataRobot JDBC connector. ### Batch prediction considerations - Only DataRobot models are supported; no external or custom model support. - Model package export is not supported for Feature Discovery models. - You cannot replace a Feature Discovery model with a non-Feature Discovery model or vice versa. - When a Feature Discovery model is replaced with another Feature Discovery model, the configuration used by the new model becomes the default configuration. - Feature discovery predictions will be slower than other DataRobot models because feature engineering is applied. - When Feature Discovery generates features using secondary datasets, the hash values of all the feature values (ROW_HASH) are used to break any ties (when applicable). The value of hash changes when applied to different datasets, so if you make predictions with another secondary configuration, you may receive different predictions. ### Feature Discovery compatibility The following table indicates which features are supported for Feature Discovery and describes any limitations. | Feature | Supported? | Limitations | | --- | --- | --- | | Monotonicity | Yes | Limited to features from the primary dataset used to start the project. Note: Users can start the project without specifying constraints. They can then manually constrain models from the Leaderboard and the Repository on eligible blueprints using discovered/generated features. | | Pairwise interaction in GA2M models | Yes | Limited to features from the primary dataset used to start the project. | | Positive class assignment | Yes | | | Smart downsampling | Yes | | | Supervised feature reduction | Yes | Only applies if secondary datasets are provided. | | Search for interactions | Yes | Automatically enabled. Cannot be disabled if secondary datasets are provided. | | Only blueprints with Scoring Code support | No | | | Create blenders from top models | Yes | | | Include only SHAP-supported blueprints | Yes | | | Recommend and prepare a model for deployment | Yes | | | Challenger models in MLOps | No | | | Include blenders when recommending a model | Yes | | | Use accuracy-optimized metablueprint | Yes | These models are extremely slow. | | Upperbound running time | Yes | | | Weight | Yes | Weight feature must be in the primary dataset used to start the project. | | Offset | Yes | Offset feature must be in the primary dataset used to start the project. | | Exposure | Yes | Exposure feature must be in the primary dataset used to start the project. | | Random seed | Yes | | | Count of events | Yes | Count of events feature must be in the primary dataset used to start the project. | --- # Manual transformations URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html > Manually create feature transformations using the natural logarithm, squaring, running functions on numeric data, or changing the variable type, if appropriate. # Manual transformations The following sections describe manual, user-created transformations. Transformed features do not replace the original, raw features; rather, they are provided as new, additional features for building models. > [!NOTE] Note > Transformed features (including numeric features created as user-defined functions) cannot be used for special variables, such as [Weight, Offset, Exposure, and Count of Events](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html#additional-weighting-details). ## Create transformations DataRobot supports different transformations that you can apply to your data, including taking the natural logarithm, squaring, and running functions on numeric data. (You can also change the [variable type](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html#variable-type-transformations) for features.) These transformations are only available when it is appropriate to the feature type. The following steps describe creating a user transformation. 1. Hover over a feature available for transformation and click the orange arrow to the left of the feature name to expose theTransformationsmenu: 2. Select a transformation. If you select the natural loglog()or squaring^2options, transformation is computed immediately and the new derived feature created. 3. If you select the function optionf(), a dialog for adding a new transformation appears. Note that you can also access this functionality from the menu: The transformed feature appears under the original feature in the Data page (all features). It can be included in any new feature lists and can also be used for modeling. When using a model that contains transformed features for predictions, DataRobot automatically includes the new feature in any uploaded dataset. As with other features, you can view the [histogram](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html), charted frequent values, and a table of values by clicking the feature name. However, instead of allowing further [variable type transformations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html#variable-type-transformations), the display compares the transformed feature with the parent feature: ## Variable type transformations DataRobot bases variable type assignment on the values seen during EDA and then lists the variable type for each feature in your dataset on the Data page. There are times, however, when you may need to change the type. For example, area codes may be interpreted as numeric but you would rather they map to categories. Or a categorical feature may be encoded as a number (that is intended to map to a feature value, such as `1=yes, 2=no`) but without transformation is interpreted as a number. There are certain cases where variable type transforms are not available. These include columns that DataRobot has identified as [special columns](https://docs.datarobot.com/en/docs/reference/data-ref/file-types.html#special-column-detection) for both integral and float values. (Date columns are a special case and do support transforms. See the description of [single feature transformations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html#single-feature-transformations).) Additionally, a column that is all numeric except for a single unique non-numeric value is treated as special. In this case, DataRobot converts the unique value to [NaN](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#missing-values) and disallows conversion to prevent losing the value. > [!NOTE] Note > When converting from numeric variable types to categorical, be aware that DataRobot drops any values after the decimal point. In other words, the value is truncated to become an integer. Also, when transforming floats with missing values to categorical, the new feature is converted, not rounded. For example, 9.9 becomes 9, not 10. > [!TIP] Tip > When making predictions DataRobot expects the columns in the prediction data to be the same as the original data. If a model uses the original variable plus the transformed variable, the prediction data must use the original feature name. DataRobot will calculate the derived features internally. You can transform the variable type of [many features](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html#multiple-feature-transformations) at the same time (using a batch transformation), or [one feature](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html#single-feature-transformations) at a time. ### Multiple feature transformations To modify the variable type for multiple features as a single batch operation, use the Change Variable Types option from the menu. This option is useful, for example, if you want to transform all features of one variable type. You can select to transform all features or multiple features for a specific variable type to another variable type. For example, you could change all Categorical features to Text, or you can pick specific Categorical features to transform to Text. All new features created using batch variable type transformations are available from the Data page, in the All Features list (although you can transform features from any feature list, when batch transformation completes, you need to view all features on the Data page). You can [add the new features to other feature lists](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html#create-feature-lists). > [!NOTE] Note > Keep the following in mind when transforming multiple features at the same time: > > A feature that is the result of a previous transformation operation cannot be selected for transformation. > All features selected for batch transformation must be of the same variable type. > > If DataRobot does not let you transform the features, you should correct the list of features and try the transformation again. 1. First, select the features to transform using one of the following methods: All features of that variable type are shown as selected in theDatapage (i.e., checks in the left-hand boxes). 2. From the menu, underActions, clickChange Variable Types. (If the link is disabled, there is an issue with the selected features. Hover over the disabled link to see the reason DataRobot cannot transform the selected features. SeeTransform options and syntaxfor details.) TheChange Variable Typedialog appears. NoteDataRobot supports transforming up to 500 features at a time. If you see a message indicating more than 500 features are selected for transformation, you need todeselect features. 3. Configure how to create the new features for the selected variable type. | Component | Description | | --- | --- | | Selected features (1) | Identifies the number of selected features, the variable type for the features, and the names of all features selected for transformation. | | Change variable type option (2) | Shows the selected variable and prompts you to select the target variable type for the transformation. DataRobot performs specific transformations for numeric variable types. | | Prefix for new features (3) | Provides a prefix to apply to the original feature names to create the transformed feature names. You can keep the default prefix (Updated_) or create your own. If creating a prefix, do not include - " . { } / \. The names of the new features must have a suffix, prefix, or both; if a suffix is defined, then a prefix is not required. | | Suffix for new features (4) | Provides a suffix to apply to the original feature names to create the transformed feature names. You can keep the default suffix, which is the new variable type, or create your own. (If creating a suffix, do not include - " . { } / \ . The names of the new features must have a suffix, prefix, or both; if a prefix is defined, then a suffix is not required. | | New Feature Names (5) | Shows how the new (transformed) features will be named: prefix_[original feature name]_suffix (using the actual prefix and/or suffix). | | Change (6) | Creates new features for all selected features, for the target variable type. | When you click Change, DataRobot creates a list of the selected features and submits them for variable type transformation. A message indicates features have been selected and transformation has started: DataRobot creates the transformed features in the background. As each new (transformed) feature completes finishes processing, it is shown in the Data page (all features). Depending on the number of features selected for transformation, it may take several minutes for all new features to finish transformation and become available. A message indicates when all transformations are complete: #### Feature transformation limit DataRobot supports transforming up to 500 features at a time and will show a message in the Change Variable Types dialog if you select more than 500: If this is the case, you need to deselect features so that only 500 or fewer features are selected. To do this, close the dialog and, in the Data page, deselect features: Then, when 500 or fewer features are selected for transformation, select Change Variable Type. ### Single feature transformations To modify the variable type for a single feature, use one of the following methods: - View the Transformations menu for the feature and click Change Var Type , or - View the histogram for the feature and click Var Type Transform . Both methods open the same dialog, which will vary depending on the variable type for the selected feature. The following table explains the settings for a categorical transformation: | Component | Description | | --- | --- | | Current variable type transformation (1) | Displays the current variable type assigned to the feature. | | Transformation options (2) | Selects a new feature type, via the dropdown, from the available variable types for the current feature. DataRobot performs specific transformations for numeric and categorical variable types. | | New Feature Name (3) | Provides a field to rename the new feature. By default, DataRobot uses the existing feature name with the new variable type appended. | | Feature list application (4) | Selects which feature list the new feature is added to. Select to add to "All Features" or use the dropdown (5) to add it to a specific list instead. | | Feature list selection (5) | Provides a dropdown selection of feature lists from the project, allowing you to select which list to add the feature to. | | Create Feature (6) | Creates the new feature. The new feature is then listed below the original on the Data page. | You can create any number of transformations from the same feature. By default, DataRobot applies a unique name to each transformation. If you inadvertently create duplicate features, DataRobot marks them as such and ignores them in processing. The following is an example of date transformation, which allows you to select which date-specific derivations to apply. You can also select whether the result should be considered a categorical or numeric value. Here's an example of a numeric to categorical transformation: ## Transform options and syntax DataRobot uses a subset of Python's [Numexp package](https://numexpr.readthedocs.io/en/latest/) to create user transformations of column values (features). When you select the function option, `f()`, from the [Transformations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html#create-transformations) menu, a dialog for entering the user transformation syntax appears. The following describes DataRobot's application of `Numexpr` and provides some examples. > [!NOTE] Note > The DataRobot API supports only variable type transformations. To create transformations, enter feature name(s) within curly braces `{}` and apply the appropriate function and operator. DataRobot provides auto-completion for feature names; if you click after the initial curly brace, you can select from the list of displayed features. Note that: - Feature names are case-sensitive. - You cannot transform features of variable type date. Instead, create new features out of the derived date features (for example, Timestamp (Hour of Day ). - You cannot do a feature transformation on the target. | Allowed functions | Description | | --- | --- | | log({feature}) | natural logarithm | | sqrt({feature}) | square root | | abs({feature}) | absolute value | | where({feature1} operator {feature2}, value-if-true, value-if-false) | if-then-else functionality | The following lists the allowed binary arithmetic operators. Use parentheses to group and order operations, for example `(1 + 2) * (3 + 4)`. You can reference multiple features in a single transform, for example `{number_inpatient} + {num_medications}`. Supported arithmetic operators are: - + (addition) - – (subtraction) - * (multiplication) - / (division) - ** (exponentiation) You can also use comparison operators, but they must be wrapped within a `where()` function (i.e., `where({feature1} operator {feature2}, value-if-true, value-if-false)`. Supported comparison operators are: - < , > (less than, greater than) - == (equal to) - != (not equal to) - <= , >= (less than or equal to, greater than or equal to) ### Comparison operators with missing values If there are missing values (NaN) in the dataset, applying transformations that compare feature values to the input values requires special consideration. If you do create statements using comparison operators with NaN values—and the goal is that the derived feature returns NaN if the original feature is NaN—be sure to compare results against the expected behavior. For example, transforming the feature `sales` to `excellent_sales` using the following statement will always return `False` if sales is `NaN`. Even if there are missing values in the data for the feature `sales`, missing values will not be returned in the result: ``` Excellent_Sales = where({Sales}>300000,1,0) ``` If this is not the desired result, consider an expression like the following: ``` Excellent_Sales = where(~({Sales} > 300000) & ~({Sales} <= 300000), {Sales}, where({Sales} > 300000, 1,0)) ``` Some transformation examples: - OrderOfMag = log({NumberOfLabs}) - Success = sqrt({sales} + 10) - CostBreakdown = abs({sales} - {costs}) - IsRich = where({YearlyIncome} > 1000000, 1, 0) --- # Transform data URL: https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/index.html > Perform transformations and feature discovery using DataRobot's feature engineering tools. # Transform data DataRobot supports multiple methods of feature engineering—automatic and manual feature transformations for single datasets, as well as Feature Discovery for multiple datasets. See the table below to learn about the feature transformation options in DataRobot. | Topic | Description | Dataset | Notes | | --- | --- | --- | --- | | Automatic transformations | | | | | Automatic feature transformations | Understand date-type feature transformations generated by DataRobot. | Primary | Calculated during EDA1. | | Interaction-based transformations | Transform features based in interactions within your primary dataset by enabling an advanced option. | Primary | Enabled in project and calculated during EDA2. | | Feature Discovery | Perform multi-dataset, interaction-based feature creation. | Secondary | Configured in project and calculated during EDA2. | | Automatic modeling transformations | Understand the automated feature engineering DataRobot performs as part of the modeling process. | All | Performed during modeling. | | Manual transformations | | | | | Manual feature transformations | Manually transform features in your dataset, including variable type transformations. | Primary | Transformed in project. | | AI Catalog transformations | | | | | Prepare data in AI Catalog with Spark SQL | Enrich, transform, shape, and blend together datasets using Spark SQL queries within the AI Catalog. | | | ## What is feature engineering? Feature engineering is the process of preparing a dataset for machine learning by changing existing features or deriving new features to improve model performance. Automated Feature Engineering uses AI to accelerate the transformation of data into machine learning assets, allowing you to build better machine learning models in less time. Feature engineering takes place after data preparation and ingest, and before model building. During EDA1, DataRobot analyzes and profiles every feature in each dataset—detecting feature types, [automatically transforming](https://docs.datarobot.com/en/docs/reference/data-ref/auto-transform.html) date-type features, and assessing feature quality. Before model building, you can take further advantage of Automated Feature Engineering by enabling [interaction-based transformations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-disc.html) for primary datasets or defining relationships between multiple datasets using [Feature Discovery](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html). You can also [manually transform features](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html) in your dataset, including variable type transformations, with functions. During EDA2, DataRobot uses these known interactions, or relationships, to discover relevant features for your ML models and automatically transforms them to address the unique requirements of each algorithm in the blueprint library. After model building, navigate to the Leaderboard and select a model. There are a few places you can view which [transformations](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#automated-feature-transformations) DataRobot performed for individual models during the modeling process: | Feature | Description | Location | | --- | --- | --- | | Blueprint | Displays preprocessing, modeling algorithms, and post-processing tasks for the selected model. | Click Describe > Blueprints. | | Data Quality Handling report | Displays feature and imputation information for supported blueprint tasks. | Click Describe > Data Quality Handling. | | Coefficients | Allows you to download coefficients and preprocessing information, including feature transformations, for supported model types. | Click Describe > Coefficients and click Export. | --- # Cell actions URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-action-nb.html > Describes the various actions available to control notebook cells. # Cell actions You can perform a variety of actions with each cell in a notebook, including most of the cell actions supported in classic Jupyter notebooks. To see the set of available cell actions in the notebook editor, click on the menu icon in the upper right corner of the cell. ## Modal editor DataRobot notebooks include a modal editor that changes keyboard shortcuts and cell actions based on which mode the notebook is in: [edit mode](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-action-nb.html#edit-mode) or [command mode](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-action-nb.html#command-mode). ### Edit mode Enter edit mode by clicking on the text editor portion of a cell. When a cell is in edit mode, you can enter text into the cell. This mode is indicated by a green border around the selected cell and text cursor inside of it: ### Command mode When you are in command mode, you are able to execute notebook-wide and cell-wide actions. However you cannot enter text into individual cells. Enter command mode by clicking the empty space outside of a DataRobot notebook's cells (where the pointer is in the following screenshot), or on the header or footer portion of the cell. This mode is indicated by a blue border around a cell: When a cell is currently selected in edit mode, you can switch to command mode via the keyboard shortcut Esc. ## Keyboard shortcuts Some cell actions have corresponding keyboard shortcuts. It is important to note that there are two different sets of keyboard shortcuts, one corresponding to edit mode and another set for command mode. Edit mode primarily contains shortcuts for editing text, while command mode shortcuts control actions on the cell itself (and not the cell’s text contents). The majority of the DataRobot Notebook keyboard shortcuts align with the classic Jupyter shortcuts you may already be familiar with. The tables below include a summary of all available cell actions. Some cell actions have corresponding keyboard shortcuts. To view the shortcuts in the notebook editor, select the keyboard icon from the sidebar. **Mac keyboard shortcuts:** Cell action Keyboard shortcut Description Command mode Switch to edit mode Enter Exits command mode and uses edit mode shortcuts. Run selected cells Shift + Enter Runs the currently selected cell. Run selected cells without advancing Cmd + Enter Runs the selected cell without progressing to the following cell. Insert code cell above A Inserts a code cell above the currently selected cell. Insert code cell below B Inserts a code cell below the currently selected cell. Change cell to code Y Changes a Markdown cell into a code cell. Change cell to Markdown M Changes a code cell into a Markdown cell. Stop execution (Interrupt kernel) Cmd + Shift + C Terminates the cell currently running in a notebook, as well as all queued cells. Select cell above K + Up Selects the cell above the one currently selected. Select cell below K + Down Selects the cell below the one currently selected. Delete cell(s) X Deletes the currently selected cell. Copy selected cell(s) C Copies the selected cell(s) to your clipboard. Paste cell(s) below V Pastes copied cells below the currently selected cell. Paste cell(s) above Shift + V Pastes copied cells above the currently selected cell. Undo delete D + D Undoes the previous cell deletion action. Merge cells Shift + M Merges the selected cell with the cell above or below. Toggle line numbers L Shows or hides the line numbers for the selected code cell. Toggle all line numbers Shift + L Shows or hides the line numbers for all cells. Toggle cell output O Shows or hides the output of a code cell. Toggle all outputs Shift + O Shows or hides the output of all code cells. Toggle code display E Shows or hides the code input for the currently selected cell. If hidden, only the cell output is displayed. Toggle all code displays Shift + E Shows or hides the code input for all cells. Edit mode Switch to command mode Esc Exits edit mode and uses command mode shortcuts. Autocomplete code Tab Triggers code completion suggestions. Select all text Cmd + A Selects all text in a cell. Undo text Cmd + Z Undoes the previously entered text. Split cell Cmd + Shift + - Splits the contents of a cell into two separate cells at the cursor location. Run selected cells Shift + Enter Runs the currently selected cell. Run selected cells without advancing Cmd + Enter Runs the selected cell without progressing to the following cell. Show inline documentation Shift + Tab For code cells, displays the docstrings tooltip for function documentation and parameters. Comment Insert a comment on the selected line. N/A Move cursor up Up Move cursor down Down Move one word right Option + Right Move one word left Option + Left Delete a line Cmd + D **Windows keyboard shortcuts:** Cell action Keyboard shortcut Description Command mode Switch to edit mode Enter Exits command mode and uses edit mode shortcuts. Run selected cells Shift + Enter Runs the currently selected cell. Run selected cells without advancing Ctrl + Enter Runs the selected cell without progressing to the following cell. Insert code cell above A Inserts a code cell above the currently selected cell. Insert code cell below B Inserts a code cell below the currently selected cell. Change cell to code +y+ Changes a Markdown cell into a code cell. Change cell to Markdown M Changes a code cell into a Markdown cell. Stop execution (Interrupt kernel) Ctrl + Shift + C Terminates the cell currently running in a notebook, as well as all queued cells. Select cell above K + Up Selects the cell above the one currently selected. Select cell below K + Down Selects the cell below the one currently selected. Delete cell(s) X Deletes the currently selected cell. Copy selected cell(s) C Copies the selected cell(s) to your clipboard. Paste cell(s) below V Pastes copied cells below the currently selected cell. Paste cell(s) above Shift + V Pastes copied cells above the currently selected cell. Undo delete D + D Undoes the previous cell deletion action. Merge cells Shift + M Merges the selected cell with the cell above or below. Toggle line numbers L Shows or hides the line numbers for the selected code cell. Toggle all line numbers Shift + L Shows or hides the line numbers for all cells. Toggle cell output O Shows or hides the output of a code cell. Toggle all outputs Shift + O Shows or hides the output of all code cells. Toggle code display E Shows or hides the code input for the currently selected cell. If hidden, only the cell output is displayed. Toggle all code displays Shift + E Shows or hides the code input for all cells. Edit mode Switch to command mode Esc Exits edit mode and uses command mode shortcuts. Autocomplete code Tab Triggers code completion suggestions. Select all text Ctrl + A Selects all text in a cell. Undo text Ctrl + Z Undoes the previously entered text. Split cell Ctrl + Shift + - Splits the contents of a cell into two separate cells at the cursor location. Run selected cells Shift + Enter Runs the currently selected cell. Run selected cells without advancing Ctrl + Enter Runs the selected cell without progressing to the following cell. Show inline documentation Shift + Tab For code cells, displays the docstrings tooltip for function documentation and parameters. Comment ?? Insert a comment on the selected line. Move cursor up Up Move cursor down Down Move one word right Option + Right Move one word left Option + Left Delete a line Ctrl + D ### Additional actions In addition to the actions outlined above, DataRobot offers some additional cell control capabilities. | Cell action | Description | | --- | --- | | Move cell | Moves a cell to a new location in the notebook via the drag icon in the top center of the cell. | | Run cells above | Runs all the cells above the currently selected cell (exclusive). | | Run cells below | Runs the currently selected cell and all cells below it (inclusive). | | Disable run | Disables a cell from executing after running. | | Move up | Moves the currently selected cell up one cell. | | Move down | Moves the currently selected cell down one cell. | | Duplicate cell | Duplicates the currently selected cell. | | Insert markdown cell above | Inserts a Markdown cell above the currently selected cell. | | Insert markdown cell below | Inserts a Markdown cell below the currently selected cell. | | Clear output | Clears the output of the selected code cell. | | Merge cell above or below | Merges the selected cell with the cell above or below. | ### Multi-cell selection Similar to Jupyter, you can select multiple adjacent cells at once to perform bulk actions on those cells. To select multiple cells, either: 1. Hold down the Shift key and use your mouse to select the cells. 2. Hold down Cmd-Shift and use the Up/Down arrows to select the cells. --- # Create and execute cells URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-cell-nb.html > Describes how to create and execute cells in DataRobot Notebooks. # Create and execute cells You can create and run code, text, and chart cells within your notebook. Before proceeding, be sure to review the guidelines for [configuring and initializing a notebook environment](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-env-nb.html). ### Use the DataRobot API Within DataRobot Notebooks you can easily use the DataRobot API. The environment images DataRobot provides come with the respective DataRobot Python and R clients preinstalled. DataRobot automatically fetches your endpoint and your API token and sets them as environment variables ( `DATAROBOT_ENDPOINT` and `DATAROBOT_API_TOKEN` for Python and `DATAROBOT_API_ENDPOINT` and `DATAROBOT_API_TOKEN` for R) within your notebook container, so that when the session starts up it automatically handles client instantiation without requiring manual authentication. **Python:** Import the DataRobot package to start using the Python client: `import datarobot as dr` **R:** Import the DataRobot library to start using the R client: `library(datarobot)` ### Create code cells The default image for the notebooks is a pre-built Python image with all the dependencies and common open-source software (OSS) libraries preinstalled. To see the list of all packages available in the default image, hover over that image in the Environment tab: > [!NOTE] Note > DataRobot notebooks are not polyglot; run Python code or R code in a notebook, but do not intermingle both. The language supported will depend on the image you’ve selected for the notebook’s environment configuration. For more information about configuring a notebook's environment, read about [environment management](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-env-nb.html). ### Add libraries Follow the instructions below to ad-hoc install additional libraries in Python or R. > [!NOTE] Note > Notebooks support rich outputs similar to Jupyter, so you can use plotting libraries of your choice (such as matplotlib, seaborn, etc.) and see the plotted charts inline within the cell output. You can run other shell commands for Python as well by using the `!` notation. You can install any additional packages you need into your environment at runtime during your notebook session. You can use Jupyter's magic commands (e.g., `!pip install `) from a notebook cell; however, when your session shuts down, packages installed at runtime do not persist. You must reinstall them the next time you restart the session. **Python:** To install and use a Python package that is not included in the default image, run the following within a code cell: `!pip install ` **R:** You can work with the R language in a notebook instead of Python by selecting the pre-built R image from the Environment tab. You can then write and execute R code in the code cells of the notebook with the R kernel. To install and use additional R packages, use [devtools](https://cran.r-project.org/web/packages/devtools/devtools.pdf) from a code cell or the following code: `install.packages()` ### Create text cells Markdown text cells support traditional Markdown syntax and [GitHub-flavored Markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) syntax. Render Markdown cells by executing the cell. To edit a rendered Markdown cell, double click on the cell. ### Create chart cells DataRobot allows you to create built-in, code-free chart cells within DataRobot Notebooks, enabling you to quickly visualize your data without coding your own plotting logic. > [!NOTE] Note > Note that chart cells are only supported for Python notebooks. They are unavailable for R notebooks. To add a code cell, hover your cursor outside of a cell in the notebook and select the Chart option. When you add a chart cell to a notebook, you first need to specify the data you want to visualize by selecting a DataFrame. Select the variable name corresponding to the DataFrame from the dropdown list in the chart cell. The cell lists all of the DataFrame objects that are currently in-memory. > [!NOTE] Note > Note that DataRobot only plots the first 5,000 rows of the DataFrame. After selecting a DataFrame, pick the type of chart you want to create. DataRobot offers configurations for bar charts, line charts, scatter plots, and area charts. They all require the same configuration fields, detailed below. | Field | Description | | --- | --- | | Show title | Displays a title for the chart. Once enabled, provide a name for the title. | | Display tooltips | Shows tooltips with more details on the data when hovering over the chart. | | Show legend | Displays legend mapping. | | X-axis (Dimension) | Select a column from the DataFrame (provided as a dropdown list) to encode as the X-axis. | | Y-axis (Measure) | Select a column from the DataFrame (provided as a dropdown list) to encode as the Y-axis. | | Color | Choose the color for the chart contents. | In addition to the configuration above, you can modify aspects of the X- and Y-axes by selecting the settings wheel next to each field. | Toggle | Description | | --- | --- | | Hide axis label | Hides the label for the axis from the chart display. | | Hide grid | Removes the grid pattern in the background of the chart. | | Hide in tooltip | Hides the value for the axis in the tooltip shown when hovering on points of the chart. | | Show point markers | Displays point markers along the points for the axis in the chart. | | Aggregation | Sets whether to aggregate values for the axis. You can aggregate by a variety of values, including the median, mean, sum, standard deviation, and more. | After configuring the chart, you can edit how the cell displays using the icons in the top-right corner. - Select the pencil icon to hide the editor menu for the chart cell. - Click the download icon to save a local copy of the chart as an SVG file. - Select the trash can icon to delete the chart cell. - Click the menu icon to access the actions available for the cell. ### Table of contents As you create and execute cells to develop your notebook, DataRobot automatically generates a table of contents for easier navigation. To access it, click the table of contents icon ( [https://docs.datarobot.com/en/docs/images/icon-toc.png](https://docs.datarobot.com/en/docs/images/icon-toc.png)) in the sidebar. The Markdown tab provides an autogenerated table of contents. Each entry maps to headings in the notebook's Markdown cells, corresponding to the Markdown heading level (#, ##, ###, etc.). Entries are hyperlinked, so clicking on an entry navigates you to the corresponding cell in the notebook. The Cells tab provides an overview of all cells within the notebook (both code and Markdown cells). You can perform [cell actions](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-action-nb.html) (and move cells around via drag-and-drop) from the cell list in the table of contents. ### Cell settings You can configure [notebook-wide cell settings](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/manage-nb/dr-settings-nb.html#notebook-settings) by accessing the gear icon menu at the top right of the Notebook header. ### Debug code DataRobot Notebooks offer built-in support for Python debugger ( [pdb](https://docs.python.org/3/library/pdb.html)) and IPython debugger ( [ipdb](https://pypi.org/project/ipdb/)) to interactively debug your Python code.`ipdb` uses the same debugging interface as `pdb`, but has some additional features such as syntax highlighting. One common debugging method for notebooks is to activate the debugger before executing code. To do so, import `ipdb` and set breakpoints using `ipdb.set_trace()` (you can also use `pdb` to do this). When you execute a cell containing `set_trace()`, the interactive debugger launches in the cell output. You can then step through the code line-by-line and inspect variables using the supported debugger commands such as `n(ext)`, `s(tep)`, `c(ontinue)`, and `p` in the input field. Note that when debugging code, notebook code execution is blocked on the cell that activated the debugger. Use `c(ontinue)` to finish executing the cell, or `q(uit)` to exit the debugger before running other cells. DataRobot also supports a retroactive debugging mode in cases where you want to launch the debugger after an exception occurs in the executed code. When code fails due to an error, run `%debug` [magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-debug) from a new cell to start the interactive debugger from the context of the last exception. This feature enables you to debug your code without having to backtrack, explicitly set breakpoints, and rerun the code to launch the interactive debugger. --- # Code intelligence URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-code-int.html > Describes the code intelligence capabilities available for code cells in DataRobot Notebooks. # Code intelligence This page describes the coding intelligence features included in the code cells of DataRobot Notebooks. ### Code snippets DataRobot provides a set of pre-defined code snippets, inserted as cells in a notebook, for commonly used methods in the DataRobot API as well as other data science tasks. These include connecting to external data sources, deploying a model, creating a model factory, and more. Access code snippets by selecting the code icon in the sidebar. ### Docstrings reference When using a specific method or class, use the `Shift + Tab` keyboard shortcut directly from the code editor to query docstrings. Additional documentation appears as an overlay. ### Autocomplete When [editing code](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-action-nb.html#modal-editor), you can autocomplete lines using the Tab key. To activate autocompletion, you must first [configure and start the notebook environment](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-env-nb.html). ### Syntax highlighting When writing code in code cells, DataRobot highlights syntax for the language set in the notebook environment. Below is a sample of syntax highlighting for Python: --- # Environment management URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-env-nb.html > Describes the environment management capabilities of the DataRobot Notebook platform. # Environment management This page outlines how to configure and start the notebook environment. ## Manage the notebook environment Before you create and execute code, click the environment icon to configure the notebook's environment. The [environment image](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-env-nb.html#built-in-environment-images) determines the coding language, dependencies, and open-source libraries used in the notebook. The default image for the DataRobot Notebooks is a pre-built Python image, but other options include GPU-optimized built-in environments. To see the list of all packages available in the default image, hover over that image in the Environment tab: The screenshot and table below outline the configuration options available for a notebook environment. | | Element | Description | | --- | --- | --- | | (1) | Start environment toggle | Starts or stops the notebook environment's kernel, which allows you to execute the notebook's code cells. | | (2) | Resource type | Represents a machine preset, specifying the CPU and RAM you have on your machine where the environment's kernel runs. | | (3) | Environment | Determines the coding language and associated libraries that the notebook uses. | | (4) | Runtime | Indicates the CPU usage, RAM usage, and elapsed runtime for the notebook's environment during an active session. | | (5) | Inactivity timeout | Limits the amount of inactivity time allowed before the environment stops running and the underlying machine is shut down. The default timeout on inactivity is 60 minutes; the maximum configurable timeout is 180 minutes. | | (6) | Exposed ports | Enables port forwarding to access web applications launched by tools and libraries like MLflow and Streamlit. When developing locally, the web application is accessible at http://localhost:PORT; however, when developing in a hosted DataRobot environment, the port that the web application is running on (in the session container) must be forwarded to access the application. For more information, see Manage exposed ports. | ### Manage session status To begin a notebook session where you create and run code, start the environment by toggling it on in the toolbar. Wait a moment for the environment to initialize, and once it displays the Started status, you can begin editing. If you upgrade any of the existing packages in the notebook environment during your session and want the upgraded version to be recognized, you need to restart the kernel. To do so, click the circular arrow icon in the toolbar. Note that restarting the kernel is different than restarting the environment session: when you stop the environment session (using the session toggle), this will stop the container your notebook is running in. The notebook state and any packages installed at runtime will be lost, as the next time you start the session, a new container will be spun up. Note that the session will automatically shut down on inactivity when the timeout is reached. The session is considered inactive when the notebook has no running cells and no changes have been made to the notebook contents in the time set by the session timeout value. From the notebook dashboard, you can view the status of all notebook environments. > [!NOTE] Note > Note that you can only run up to two active notebook sessions at the same time. ### Manage exposed ports To create a port for port forwarding, click + Add port, enter a Port number and optional Description, and then click the accept icon: > [!NOTE] Port limits > The maximum port number you can specify is `65535`, and the minimum port number is `1024`. Ports `8888`, `8889`, and `8022` are reserved and not allowed for use. You can expose up to five ports in one notebook or codespace. Self-managed users can override this limit. The port number dropdown includes options for the default port number options commonly used by OSS tools (MLFlow, Streamlit, etc.): Once you have added the port to forward, you can start the container session for the notebook or codespace. After launching a web application, access the web application via the URL for the port listed in the Exposed ports list: - ClickLinkto open a new browser tab with your application running. - Click the copy iconto copy the web application URL. - To access the URL for the web application running on a specified port, you must have access to the corresponding notebook or codespace. To change a port number, edit the port description or stop forwarding that port, click the actions menu: - ClickEditto modify thePort numberandDescription. - ClickDeleteto remove the exposed port, ending session forwarding to that port. > [!NOTE] Modify exposed ports > You can only modify (add, edit, or delete) forwarded ports when the notebook or codespace session is offline. #### Feature considerations and FAQ Review the following considerations and frequently asked questions before configuring port forwarding: **Considerations:** Your server application must be bound to 0.0.0.0 . Web servers launched by many libraries default to accepting connections only from the localhost / 127.0.0.1 . While this configuration works when using the library locally, it doesn't work in a DataRobot-hosted session. To allow the web server to accept connections from other machines, specify 0.0.0.0 as the host. For example, to launch the MLflow Tracking UI, you would run the following command from the DataRobot terminal: mlflow ui --host 0 .0.0.0 The port specified to run a web server must match the port exposed in the notebook or codespace environment configuration; otherwise, you will not be able to access the web application at the URL of the exposed port. For example, consider if you have enabled forwarding to port 8080 in the notebook or codespace environment configuration—MLflow's Tracking UI defaults to port 5000 . To access the MLflow Tracking UI in your web browser, override the default --port argument when launching the MLflow application from the DataRobot terminal: mlflow ui --port 8080 --host 0 .0.0.0. Only one server process can run on a given port at a time. For example, if you have the MLflow Tracking UI running on exposed port 8080 , terminate that process before running a new server to test a Streamlit application on that same port. **FAQ:** How do I write the contents of a script into a standalone notebook session? The Jupyter kernel provides the %%writefile command to save the content of a cell as a file: %%writefile app.py from fastapi import FastAPI from pydantic import BaseModel app = FastAPI () @app.get ( "/" ) async def echo () : return { "message" : "Hello, world!" } How do I access Streamlit in a DataRobot Notebook session? The default port for Streamlit is 8501 . If you exposed that port, use the following command: streamlit run app.py If you want to override the port, use the server.port argument: streamlit run app.py --server.port 8080 How do I access MLFlow in a DataRobot Notebook session? The default port for MLflow's Tracking UI is 5000 . If you enabled forwarding to port 8080 in the notebook or codespace environment configuration, to access the MLflow Tracking UI in your web browser, override the default --port argument when launching the MLflow application from the DataRobot terminal: mlflow ui --port 8080 --host 0 .0.0.0 MLFlow starts a separate set of processes to handle incoming connections. Sometimes these processes are not correctly terminated (for example, you run mlflow ui via terminal session and then abruptly close the session without stopping the process first). If the server processes are left in place, you can't reuse the port. To clean up these processes, use the following command: pkill -f mlflow.server How do I access Shiny in a DataRobot Notebook session? Shiny uses a random port and localhost . To override the default port, use the following command: options ( shiny.port = 7775 ) options ( shiny.host = "0.0.0.0" ) ui <- fluidPage ( "Hello, world!" ) server <- function ( input, output, session ) { } shinyApp ( ui, server ) How do I run Kedro-Viz in a DataRobot Notebook session? The default port for Kedro-Viz is 4141 . !kedro viz run --host 0 .0.0.0 How do I run TensorBoard in a DataRobot Notebook session? The default port for TensorBoard is 6006 . tensorboard --bind_all --logdir . How do I run a Gradio application in a DataRobot Notebook session? The default port for the Gradio UI is localhost:7680 . Before running the Gradio application, use the following code to mount it as a sub-application of another FastAPI application: app.py import gradio as gr from fastapi import FastAPI def greet ( name , intensity ): return "Hello, " + name + "!" * int ( intensity ) demo = gr . Interface ( fn = greet , inputs = [ "text" , "slider" ], outputs = [ "text" ], ) fapp = FastAPI ( root_path = "/notebook-sessions/ {NOTEBOOK_ID} /ports/ {YOUR_PORT} " ) gr . mount_gradio_app ( fapp , demo , f "/" , root_path = "/notebook-sessions/ {NOTEBOOK_ID} /ports/ {YOUR_PORT} " ) Then, run the script (in this example, app.py ) with uvicorn : uvicorn app:fapp --host 0 .0.0.0 --port 7860 --reload How do I run a NiceGUI application in a DataRobot Notebook session? The default port for the NiceGUI is 8080 . Use the following code to create a NiceGUI demo application: app.py from nicegui import ui class Demo : def __init__ ( self ): self . number = 1 demo = Demo () v = ui . checkbox ( 'visible' , value = True ) with ui . column () . bind_visibility_from ( v , 'value' ): ui . slider ( min = 1 , max = 3 ) . bind_value ( demo , 'number' ) ui . toggle ({ 1 : 'A' , 2 : 'B' , 3 : 'C' }) . bind_value ( demo , 'number' ) ui . number () . bind_value ( demo , 'number' ) ui . run () Then, run the script (in this example, app.py ): !python app.py Can I access a custom server? You can write and run your own TCP/HTTP servers. For example, the following creates a simple custom server written with FastAPI: from fastapi import FastAPI from pydantic import BaseModel app = FastAPI () @app.get ( "/" ) async def echo () : return { "message" : "Hello there" } Then, run the app with uvicorn : !uvicorn app:app --host 0 .0.0.0 --port 8501 --reload ## Custom environment images DataRobot Notebooks is integrated with [DataRobot custom environments](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-environment-workshop/nxt-add-custom-env.html), allowing you to define reusable custom Docker images for running notebook sessions. Custom environments provide full control over the environment configuration and the ability to leverage reproducible dependencies beyond those available in the built-in images. Note that only Python and R custom environments are supported for DataRobot Notebooks. ### Create a custom environment To add a custom environment, navigate to the Environment tab ( [https://docs.datarobot.com/en/docs/images/icon-env.png](https://docs.datarobot.com/en/docs/images/icon-env.png)) in the notebook sidebar and expand the Environment field dropdown. Scroll to the bottom of the list and select Create new environment. This brings you to the Environments page in Registry. On the Environments page, you can create and access environments for both notebooks and models. To create a new custom environment for a notebook and configure its details, click + Add environment. DataRobot strongly recommends accessing the [notebook built-in environment templates](https://github.com/datarobot/datarobot-user-models/tree/master/public_dropin_notebook_environments) to reference the requirements needed to create a Docker context that is compatible with running DataRobot Notebooks. In the Add environment version panel, provide the following: | Field | Description | | --- | --- | | Environment name | The name of the environment. | | Context file | The archive containing a Dockerfile and any other files needed to build the environment image. This file is not required if you supply a prebuilt image. | | Prebuilt image | (Optional) A prebuilt environment image saved as a tarball using the Docker save command. | | Programming Language | The language in which the environment was made. | | Description | (Optional) A description of the custom environment. | | Environment type | The DataRobot artifact types supported by the environment: Custom models or Notebooks. | When all fields are complete, click Add. The custom environment is ready for use. After you upload an environment, it is only available to you unless you [share](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-environment-workshop/nxt-add-custom-env.html#manage-environments) it with other individuals. To make changes to an existing environment, create a new [version](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-environment-workshop/nxt-add-custom-env.html#add-an-environment-version). You can click the Current Notebooks tab to view a list of any DataRobot notebooks that are configured to use versions of the custom environment as the notebook container image. To use the newly created custom environment with a notebook, return to the Environment tab ( [https://docs.datarobot.com/en/docs/images/icon-env.png](https://docs.datarobot.com/en/docs/images/icon-env.png)) in the notebook sidebar and expand the Environment field dropdown again. This time, you can select the custom environment listed under the CUSTOM header in the dropdown list. ### Use a custom environment for a notebook session To use a custom environment as the image to run for a notebook container session, open the notebook you want to use and navigate to the Environment tab ( [https://docs.datarobot.com/en/docs/images/icon-env.png](https://docs.datarobot.com/en/docs/images/icon-env.png)) in the notebook sidebar. In the Environment field dropdown, you can see all custom environments that you have access to that are compatible with DataRobot Notebooks. Select the custom environment you’d like to use. In the Version field, you can select the version of the environment to use. By default, the latest version of the environment is selected. Once you’ve configured your environment selection, you can start the notebook session. ## Built-in environment images DataRobot maintains a set of built-in Docker images that you can select from to use as the container image for a given notebook. DataRobot provides the following images: - Python 3.11 image: Contains Python version 3.11, theDataRobot Python client, and a suite of common data science libraries. - Python 3.9 image: Contains Python version 3.9, theDataRobot Python client, and a suite of common data science libraries. - R 4.3 image: Contains R version 4.3, theDataRobot R client, and a suite of common data science libraries. ## Environment variables If you need to reference sensitive strings in a notebook, rather than storing them in plain text within the notebook you can use environment variables to securely store the values. These values are stored encrypted by DataRobot. Environment variables are useful if you need to specify credentials for connecting to an external data source within your notebook, for instance. Whenever you start a notebook session, DataRobot sets the notebook's associated environment variables in the container environment, so you can reference them from your notebook code using the following code: **Python:** ``` import os KEY = os.environ['KEY'] # KEY variable now references your VALUE ``` **R:** ``` KEY = Sys.getenv("KEY") ``` To access environment variables, click the lock icon in the sidebar. Click Create new entry. In the dialog box, enter the key and value for a single entry, and provide an optional description. If you want to add multiple variables, select Bulk import. Use the following format on each line in the field: `KEY=VALUE # DESCRIPTION` > [!NOTE] Note > Any existing environment variable with the same key will have its value overwritten by the new value specified. When you have finished adding environment variables, click Save. ## Edit existing variables You can also edit and delete a notebook’s associated environment variables from the Environment variables panel: - Click the pencil icon on a variable to edit it. - Select the eye icon to view a hidden value. - Click the trash can icon to delete a variable. - Click Insert all to insert a code snippet that retrieves all of the notebook's environment variables and includes them in the notebook. --- # Azure OpenAI Service integration URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-openai-nb.html > Use Azure's OpenAI assistant to generate code in DataRobot Notebooks. # Azure OpenAI Service integration > [!NOTE] Availability information > For self-managed users, the Azure OpenAI integration is off by default. Contact your DataRobot representative or administrator for information on enabling this feature. > > Additionally, self-managed users must create their own Azure account and manage their own Azure OpenAI model deployment to configure usage with the Code Assist feature in DataRobot Notebooks. > > Feature flag: Enable Notebooks OpenAI Integration You can now power your code development workflows in DataRobot Notebooks by applying OpenAI large language models for assisting with code generation. With the Azure OpenAI Service integration in DataRobot Notebooks, you can leverage state-of-the-art generative models with Azure's enterprise-grade security and compliance capabilities. ## Use the Code Assistant When working in a code cell in a DataRobot Notebook, you can access the Code Assistant by selecting Assist. Once selected, complete the prompt by telling the assistant what you want to do. The example below requests Azure OpenAI Service to write code to create a pandas DataFrame with the Iris dataset and include comments. After completing the prompt, click Run OpenAI. Allow some time for the assistant to run—it then generates the result of the prompt in the cell. The prompt is maintained as the first comment in the cell. As the Code Assistant dynamically materializes the generated code in the cell's code editor, you can choose to speed up the process by clicking Finish Up!. You can iterate on the same code cell after generating the initial result. Click Assist, provide another prompt in the same code cell, and select Run OpenAI. For example, in the cell displayed above, you can prompt the Code Assistant to add comments in Spanish to each line. The resulting cell provides comments for each line of code in Spanish. When generation completes, you can evaluate the helpfulness of the Code Assistant's generation. Select the smiley face to provide an evaluation. From the modal, select whether the generated code was helpful, not helpful, or contains inappropriate or harmful content. --- # Notebook terminals URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-terminal-nb.html > Describes the terminal integration available for DataRobot Notebooks. # Notebook terminals DataRobot notebooks support integrated terminal windows. When you have a notebook session running, you can open one or more integrated terminals to execute terminal commands such as running .py scripts or installing packages. Terminal integration also allows you to have full support for a system shell (bash) so you can run installed programs. ## Create a terminal window To create a terminal window in a DataRobot notebook, first ensure that the [notebook environment is running](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/dr-env-nb.html#manage-the-notebook-environment). From the sidebar, select the terminal icon at the bottom of the page to create a new terminal window. After creating a window, the notebook page divides into two sections: one for the notebook itself, and another for the terminal. > [!NOTE] Note > Note that terminal windows only last for the duration of the notebook session and they will not persist when you access the notebook at a later time. ### Manage a terminal window You can manage the terminal window in a variety of ways: | | Element | Description | | --- | --- | --- | | (1) | Rename terminal window | To rename the terminal window window, select the pencil icon next to the window name. | | (2) | Add new terminal window | To add an additional terminal session, click the plus sign (+) next to the terminal window name. This will create an additional terminal window as a tab, allowing you to work in multiple windows simultaneously. | | (3) | Page divider | Use the page divider to manage the size of the notebook and terminal sections of the page. | | (4) | Fullscreen | Click to view the terminal on the full page and hide the notebook in session. | > [!NOTE] Note > Terminal instances and the notebook itself share the same current working directory. --- # Notebook coding experience URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/code-nb/index.html > Learn about the coding experience in DataRobot Notebooks. # Notebook coding experience These pages outline the coding experience when using DataRobot notebooks. Learn about common cell actions, keyboard shortcuts, and how to run notebooks. | Topic | Description | | --- | --- | | Environment management | How to configure and start the notebook environment. | | Create and execute cells | How to create and execute code and Markdown cells in a notebook. | | Cell actions | The actions and keyboard shortcuts available in a notebook. | | Code intelligence | The code intelligence features provided throughout the notebook coding experience. | | Notebook terminals | Use integrated terminals to execute commands such as .py scripts or package installations. | | Azure OpenAI Service integration | Leverage Azure's OpenAI Code Assistant to generate code in DataRobot Notebooks using ChatGPT. | --- # DataRobot Notebooks URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/index.html > Read documentation for DataRobot's notebook platform. # Notebooks ### Access to notebooks Notebooks offer an in-browser editor to create and execute code for data science analysis and modeling. They also display computation results in various formats, including text, images, graphs, plots, tables, and more. You can customize output display by using open-source plugins. Cells can also contain Markdown rich text for commentary and explanation of the coding workflow. For frequently asked questions, feature considerations, and additional reading, view the [notebook reference](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/notebook-faq-classic.html) documentation. ### Notebook workflow overview ``` graph TB A[Create a DataRobot notebook] A --> |New notebook|C[Add a new notebook] A --> |Existing notebook|D[Upload an .ipynb notebook]; C --> E{Configure the environment} D --> E E --> F[Start the notebook session] F --> G[Edit the notebook] G --> |Writing guidelines?|H[Create and edit Markdown cells] G --> |Coding?|I[Reference code snippets and create code cells] H --> J[Run the notebook] I --> J J --> K[Create a revision history] ``` ### Notebook management | Topic | Description | | --- | --- | | Create notebooks | How to create, import, and export notebooks. | | Notebook settings | The settings available for notebooks. | | Notebook versioning | How notebooks are versioned, and how to view the revision history of a notebook. | ### Notebook coding experience | Topic | Description | | --- | --- | | Environment management | How to configure and start the notebook's environment. | | Create and execute cells | How to create and execute code and Markdown cells in a notebook, and how to integrate DataRobot's API into your coding workflow. | | Cell actions | Understand the actions and keyboard shortcuts available in a notebook. | | Code intelligence | Learn about the code intelligence features provided throughout the notebook coding experience. | ## Browser compatibility > [!NOTE] DataRobot fully supports the latest version of Google Chrome > Other browsers such as Edge, Firefox, and Safari are not fully supported. As a result, certain features may not work as expected. DataRobot recommends using Chrome for the best experience. Ad block browser extensions may cause display or performance issues in the DataRobot web application. --- # Add notebooks URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/manage-nb/dr-create-nb.html > Learn how to create new DataRobot notebooks, import existing notebooks, and export notebooks as .ipynb files. # Add notebooks To add notebooks to DataRobot, navigate to the Notebooks page. This brings you the notebook dashboard, which hosts all notebooks currently available. ### Create notebooks You can create and manage notebooks across DataRobot. To get started, create a notebook. 1. ClickNotebooks > Create new notebook. 2. When you create a notebook, you are brought to the notebook editing interface. ElementDescription1Notebook nameThe name of the notebook displayed at the top of the page and in the dashboard. Click on the pencil iconto rename it.2SidebarHosts options for configuring notebook settings as well as additional notebook capabilities.3CellThe body of the notebook, where you write and edit code with full syntax highlighting (code cells) or include explanatory and procedural text (Markdown cells).4Menu barProvides the notebook environment status, display options, notebook management options, and a button to run the notebook. Reference the DataRobot documentation for more information aboutnotebook settingsandcell actions. ### Import notebooks To import existing Jupyter notebooks (in `.ipynb` format) into DataRobot, click Notebooks > Upload notebook. Upload the notebook by providing a URL or selecting a local file. If using a URL, note that it must be a public URL that is not blocked by authentication. Once uploaded, the notebook will appear in the dashboard alongside those you have already created. ### Export notebooks If you have a notebook that you would like to export, you can do so by downloading it as an `.ipynb` file. Click the Actions menu inside the notebook and then click Download. --- # Notebook versioning URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/manage-nb/dr-revise-nb.html > Learn how to maintain versions of DataRobot Notebooks. # Notebook versioning You can snapshot iterations of a notebook, allowing you to maintain older versions that you can revert back to in the future. These snapshots are called revisions (also known as checkpoints). Each saved revision contains the notebook's cells and their output in their state at the time of the snapshot. ### Revision types DataRobot Notebooks support both automatic and manual revisions. Automatic revisions are saved each time a notebook’s active session is shut down (from either your own termination of the session or via an inactivity timeout). Automatic revisions are defined by the creation timestamp and include an "Autosaved" label. You can create a manual revision at any time to save a checkpoint of a notebook. To do so, navigate to the Revision history tab in the sidebar. Click Create new revision. Provide a name for the revision in the a dialog box. If no name is specified, the timestamp of the checkpoint creation is used. Once named, click Create. ### Revision history View a list of all revisions (manual and automatic) for a notebook from the Revision history in the left panel. Click on the revision entry to view a preview of that version of the notebook: You can restore your current notebook to a specific revision by clicking Restore this revision at the bottom of the preview. Access additional actions for managing revisions by clicking the menu icon on the right of each entry. Note that you can update the names of manual revisions, while autosaved revisions will only have their timestamp as a name. This menu gives you options to: - Restore revision: Restore the current notebook to the state of the selected revision. After restoring to a given revision, saving will create new revisions at the top of the revision list. - Clone revision: Create a copy of the revision as a new notebook. - Delete the revision: Permanently delete an entry from the notebook’s revision history. --- # Notebook settings URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/manage-nb/dr-settings-nb.html > Learn about the options available for DataRobot Notebook settings. # Notebook settings Select the settings wheel to configure notebook settings. These settings allow you to control the display of: - Line numbers in code blocks. - Cell titles and outputs. - Cell output scrolling. Click the Actions menu to access additional notebook actions: - Download the notebook as an .ipynb file. - Duplicate the notebook. - Delete the notebook. Note that deleting a notebook will also delete its associated assets, such as the notebook's environment variables and any revision history. ### Notebook metadata Click the info icon [https://docs.datarobot.com/en/docs/images/icon-info-circle.png](https://docs.datarobot.com/en/docs/images/icon-info-circle.png) to edit the notebook's metadata: - Tags: (Optional) Enter one or more descriptive tags that you can use to filter notebooks when viewing them in the dashboard. - Description: (Optional) Enter a description of the notebook. --- # Manage notebooks URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/manage-nb/index.html > Learn how to create, configure, and manage DataRobot Notebooks. # Manage notebooks This section outlines the management capabilities available for DataRobot Notebooks. Access the dashboard that hosts the currently available notebooks and all functionality from the top-level Notebooks tab. | Topic | Description | | --- | --- | | Add notebooks | How to create new notebooks, import existing notebooks, and export notebooks as .ipynb files. | | Notebook settings | The settings available for notebooks. | | Notebook versioning | How notebooks are versioned and how to load the revision history of a notebook. | --- # Notebook FAQ URL: https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/notebook-faq-classic.html > Answers questions and provides tips for working with DataRobot Notebooks in Workbench. # Notebook FAQ ## Feature considerations Review the tables below to learn more about the limitations for DataRobot Notebooks. ### CPU and memory limits Review the limits below based on the machine size you are using. | Machine size | CPU limit | Memory limit | | --- | --- | --- | | XS | 1 CPU | 4 GB | | S | 2 CPU | 8 GB | | M* | 4 CPU | 16 GB | | L* | 8 CPU | 32 GB | * M and L machine sizes are available for users depending on their pricing tier (up to M for Enterprise tier users, and up to L for Business Critical tier). ### User limits You can maintain a maximum of four concurrent active notebook and/or codespace sessions per user in DataRobot. There is also a larger limit for the maximum number of concurrent notebook or codespace sessions that can be running across all users in an organization. If the limit has been reached, you can shut down an existing active session in order to launch another notebook or codespace in a session. Contact your DataRobot representative for more information on the limit for the total number of concurrent sessions that can run in parallel in your organization. ### Cell limits The table below outlines limits for cell execution time, cell source, and output sizes | Limit | Value | | --- | --- | | Max cell execution time | 24 hours | | Max cell output size | 10MB | | Max notebook cells count | 1000 cells | | Max cell source code size | 2MB | --- # Classic UI documentation URL: https://docs.datarobot.com/en/docs/classic-ui/index.html > Learn every aspect of the DataRobot workflow, from importing data to deploying and managing models. # Classic UI documentation Learn every aspect of the classic DataRobot workflow, from importing data to deploying and managing models. - Data¶ Ingest, transform, and store your data for modeling. - Modeling¶ Build, compare, and manage models. - Predictions¶ Make predictions with the UI or API. - MLOps¶ Deploy, monitor, manage, and govern models in production. - DataRobot Notebooks¶ Create interactive and executable computing documents. - Applications¶ Configure AI-powered applications and enable core DataRobot services. --- # Deploy models on AWS EKS URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/deploy-dr-models-on-aws.html > Deploy and monitor DataRobot models on AWS Elastic Kubernetes Service (EKS). # Deploy models on AWS EKS With DataRobot MLOps, you can deploy DataRobot models into your own AWS Elastic Kubernetes Service (EKS) clusters and still have the advantages of using the [monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/index.html) and [governance](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/index.html) capabilities of MLOps. These exportable DataRobot models are known as [Portable Prediction Servers (PPSs)](https://docs.datarobot.com/en/docs/reference/glossary/index.html#portable-prediction-server-pps). The models are embedded into Docker containers, providing flexibility and portability, and making them suitable for container orchestration tools such as Kubernetes. The following steps show how to deploy a DataRobot model on EKS. ## Before you start > [!NOTE] Availability information > To deploy a DataRobot model on EKS, you need to export a model package which requires the "Enable MMM model package export" flag. Contact your DataRobot representative for more information about enabling this feature. Before deploying to Amazon EKS, you need to create an EKS cluster. There are two approaches to spin up the cluster: - Using theeksctltool (CLI for Amazon EKS). This is the simplest and fastest way to create an EKS cluster. - Using AWS Management Console. This method provides more fine-grained tuning (for example, IAM role and VPC creation). This topic shows how to install using the eksctl tool. See [Getting started with Amazon EKS – eksctl](https://docs.aws.amazon.com/eks/latest/userguide/getting-started-eksctl.html) for detailed instructions. If any of the tools are already installed and configured, you can skip the corresponding steps. ### Install and configure AWS and EKS Follow the steps described below to install and configure Amazon Web Services CLI, EKS, and Kubernetes CLI. 1. Install the AWS CLI, version 2. aws --version 2. Configure your AWS CLI credentials. 3. Installeksctl. eksctl version 4. Install and configurekubectl(CLI for Kubernetes clusters). kubectl version --short --client ## Deploy models to EKS Deploying DataRobot models on a Kubernetes infrastructure consists of three main activities: - Preparing and pushing the Docker container with the MLOps package to the container registry - Creating the external deployment in DataRobot - Creating the Kubernetes cluster ### Configure and run the PPS Docker image To complete the following steps, you need to first generate your model and [create an MLOps model package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html). DataRobot provides a UI to help you configure the Portable Prediction Server and create the Docker image. Follow these steps: 1. Configure the Portable Prediction Server (PPS) . 2. Obtain the PPS Docker image . 3. Load the image to Docker . 4. Download the model package . 5. Run your Docker image . 6. Monitor your model . 7. Create an external deployment . When you create the deployment, make note of the MLOps model ID and the MLOps deployment ID. You’re going to need these IDs when you deploy the MLOps package to Kubernetes. ### Push the Docker image to Amazon ECR You need to upload the container image to Amazon Elastic Container Registry (ECR) so that your Amazon EKS cluster can download and run it. 1. Configure the Docker CLI tool to authenticate to Elastic Container Registry: awsecrget-login-password--regionus-east-1|dockerlogin--usernameXXX--password-stdin00000000000.xxx.ecr.us-east-1.amazonaws.com 2. Push the Docker image you just built to ECR: dockerpush00000000000.xxx.ecr.us-east-1.amazonaws.com/house-regression-model:latest ### Create an Amazon EKS cluster Now that the Docker image is stored in ECR and the external deployment is created, you can spin up an Amazon EKS cluster. The EKS cluster needs VPC with either of the following: - Two public subnets and two private subnets - A VPC with three public subnets The Amazon EKS requires subnets in at least two Availability Zones. A VPC with public and private subnets is recommended so that Kubernetes can create public load balancers in the public subnets to control traffic to the pods that run on nodes in private subnets. To create the Amazon EKS cluster: 1. (Optional) Create or choose two public and two private subnets in your VPC. Make sure that “Auto-assign public IPv4 address” is enabled for the public subnets. NoteTheeksctltool creates all necessary subnets behind the scenes if you don’t provide the corresponding--vpc-private-subnetsand--vpc-public-subnetsparameters. 2. Create the cluster: eksctlcreatecluster\--namehouse-regression\--ssh-access\--ssh-public-keymy-public-key.pub\--managed NotesUsage of the--managedparameter enablesAmazon EKS-managed nodegroups. This feature automates the provisioning and lifecycle management of nodes (EC2 instances) for Amazon EKS Kubernetes clusters. You can provision optimized groups of nodes for their clusters. EKS will keep their nodes up-to-date with the latest Kubernetes and host OS versions. Theeksctltool makes it possible to choose the specific size and instance type family via command line flags or config files.Although--ssh-public-keyis optional, it is highly recommended that you specify it when you create your node group with a cluster. This option enables SSH access to the nodes in your managed node group. Enabling SSH access allows you to connect to your instances and gather diagnostic information if there are issues. You cannot enable remote access after the node group is created.Cluster provisioning usually takes between 10 and 15 minutes and results in the following: 3. When your cluster is ready, test that your kubectl configuration is correct: kubectlgetsvc ### Deploy the MLOps package to Kubernetes To deploy the MLOps package to Kubernetes: 1. Create a Kubernetes namespace, for example: kubectlcreatenamespacehouse-regression-namespace 2. Save the following contents to ayamlfile on your local machine (in this case,house-regression-service.yaml), replacing the values for your project. Provide the values ofimage,DataRobot API token,model ID, anddeployment ID. (You should have saved the IDs when youcreated the external deployment in MLOps.) apiVersion:v1kind:Servicemetadata:name:house-regression-servicenamespace:house-regression-namespacelabels:app:house-regression-appspec:selector:app:house-regression-appports:-protocol:TCPport:80targetPort:8080---apiVersion:apps/v1kind:Deploymentmetadata:name:house-regression-deploymentnamespace:house-regression-namespacelabels:app:house-regression-appspec:replicas:3selector:matchLabels:app:house-regression-apptemplate:metadata:labels:app:house-regression-appspec:affinity:nodeAffinity:requiredDuringSchedulingIgnoredDuringExecution:nodeSelectorTerms:-matchExpressions:-key:beta.kubernetes.io/archoperator:Invalues:-amd64containers:-name:house-regression-modelimage:.ecr.us-east-1.amazonaws.com/house-regression-model:latestenv:-name:PORTABLE_PREDICTION_API_WORKERS_NUMBERvalue:"2"-name:PORTABLE_PREDICTION_API_MONITORING_ACTIVEvalue:"True"-name:PORTABLE_PREDICTION_API_MONITORING_SETTINGSvalue:spooler_type=filesystem;directory=/tmp;max_files=50;file_max_size=10240000;model_id=;deployment_id=-name:MONITORING_AGENTvalue:"True"-name:MONITORING_AGENT_DATAROBOT_APP_URLvalue:https://app.datarobot.com/-name:MONITORING_AGENT_DATAROBOT_APP_TOKENvalue:ports:-containerPort:80 3. Create a Kubernetes service and deployment: kubectlapply-fhouse-regression-service.yaml 4. View all resources that exist in the namespace: kubectlgetall-nhouse-regression-namespace ### Set up horizontal pod autoscaling The Kubernetes Horizontal Pod Autoscaler automatically scales the number of pods in a deployment, replication controller, or replica set based on that resource's CPU utilization. This can help your applications scale up to meet increased demand or scale back when resources are not needed, thus freeing up your nodes for other applications. When you set a target CPU utilization percentage, the Horizontal Pod Autoscaler scales your application up or back to try to meet that target. 1. Create a Horizontal Pod Autoscaler resource for the php-apache deployment. kubectlautoscaledeploymenthouse-regression-deployment-nhouse-regression-namespace--cpu-percent=80--min=1--max=5 2. View all resources that exist in the namespace. kubectlgetall-nhouse-regression-namespace Horizontal Pod Autoscaler appears in the resources list. ### Expose your model to the world (load balancing) Amazon EKS supports the Network Load Balancer and the Classic Load Balancer for pods running on Amazon EC2 instance nodes through the Kubernetes service of type LoadBalancer. 1. Tag the public subnets in your VPC so that Kubernetes knows to use only those subnets for external load balancers instead of choosing a public subnet in each Availability Zone (in lexicographical order by subnet ID): kubernetes.io/role/elb = 1 2. Tag the private subnets in the following way so that Kubernetes knows it can use the subnets for internal load balancers. kubernetes.io/role/internal-elb = 1 ImportantIf you use an Amazon EKS AWS CloudFormation template to create your VPC after March 26, 2020, then the subnets created by the template are tagged when they're created as explainedhere. 3. Use the kubectlexposecommand to generate a Kubernetes service for the deployment. kubectlexposedeploymenthouse-regression-deployment-nhouse-regression-namespace--name=house-regression-external--type=LoadBalancer--port80--target-port8080 4. Run the following command to get the service details: kubectlgetservice-nhouse-regression-namespace 5. Copy theEXTERNAL_IPaddress. 6. Score your model using theEXTERNAL_IPaddress: curl-XPOSThttp:///predictions-H"Content-Type: text/csv"--data-binary@kaggle_house_test_dataset.csv 7. Check the service health ofthe external deployment you created. The scoring request is now included in the statistics. ### Clean up 1. Remove the sample service, deployment, pods, and namespace: kubectldeletenamespacehouse-regression-namespace 2. Delete the cluster: eksctldeletecluster--namehouse-regression ## Wrap-up This walkthrough described how to deploy and monitor DataRobot models on the Amazon EKS platform via a Portable Prediction Server (PPS). A PPS is embedded into Docker containers alongside the MLOps agents, making it possible to acquire the principal IT (service health, number of requests, etc.) and ML (accuracy, data drift etc.) metrics in the cloud and monitor them on the centralized DataRobot MLOps dashboard. Using DataRobot PPSs allows you to avoid vendor lock-in and easily migrate between cloud environments or deploy models across them simultaneously. --- # Import data from AWS S3 URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/import-from-aws-s3.html > Import data from AWS S3 to start a DataRobot ML project. # Import data from AWS S3 This section shows how to ingest data from an Amazon Web Services S3 bucket into the DataRobot AI Catalog so that you can use it for ML modeling. To build an ML model based on an object saved in an S3 bucket: 1. Navigate to the dataset object in AWS S3 and copy the object’s URL. 2. Select theAI Catalogtab in DataRobot. 3. ClickAdd to catalogand selectURL. 4. In theAdd from URLwindow, paste the URL of the object and clickSave. DataRobot automatically reads the data and infers data types and the schema of the data, as it does when you upload a CSV file from your local machine. 5. Now that your data has been successfully uploaded, clickCreate projectin the upper right corner to start an ML project. ## Private S3 buckets You can also ingest data into DataRobot from private S3 buckets. For example, you can create a temporary link from a pre-signed S3 URL that DataRobot can then use to retrieve the file. A straightforward way to accomplish this is by using the [AWS Command Line Interface (CLI)](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-welcome.html). After you install and configure the CLI, use a command like the following: ``` aws s3 presign --expires-in 600 s3://bucket-name/path/to/file.csv https://bucket-name.s3.amazonaws.com/path/to/file.csv?AWSAccessKeyId= ``` The URL produced in this example allows whoever has it to read the private file, `file.csv`, from the private bucket, `bucket-name`. The `expires-in` parameter makes the signed link available for 600 seconds upon creation. If you have your own DataRobot installation, you can also: - Provide the application's DataRobot service account with IAM privileges to read private S3 buckets. DataRobot can then ingest from any S3 location that it has privileges to access. - Implement S3 impersonation of the user logging in to DataRobot to limit access to S3 data. This requires LDAP for authentication, with authorized roles for the user specified within LDAP attributes. Both of these options accept an `s3://` URI path. --- # AWS URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/index.html > Integrate DataRobot with Amazon Web Services. # AWS The sections described below provide techniques for integrating Amazon Web Services with DataRobot. | Topic | Description | | --- | --- | | Import data from AWS S3 | Importing data from AWS S3 to AI Catalog and creating an ML project. | | Deploy models on EKS | Deploying and monitor DataRobot models on AWS Elastic Kubernetes Service (EKS) clusters. | | Path-based routing to PPS on AWS | Using a single IP address for all Portable Prediction Servers through path-based routing. | | Score Snowflake data on AWS EMR Spark | Scoring Snowflake data via DataRobot models on AWS Elastic Map Reduce (EMR) Spark. | | Ingest data with AWS Athena | Ingesting AWS Athena and Parquet data for machine learning. | ## Lambda | Topic | Description | | --- | --- | | AWS Lambda reporting to MLOps | AWS Lambda serverless reporting of actuals to DataRobot MLOps. | | Use DataRobot Prime models with AWS Lambda | Using DataRobot Prime* models with AWS Lambda. | | Use Scoring Code with AWS Lambda | Making predictions using Scoring Code deployed on AWS Lambda. | * The ability to create new DataRobot Prime models has been removed from the application. This does not affect existing Prime models or deployments. ## SageMaker | Topic | Description | | --- | --- | | Deploy models on Sagemaker | Deploying on SageMaker and monitoring with MLOps agents. | | Use Scoring Code with AWS SageMaker | Making predictions using Scoring Code deployed on AWS SageMaker. | --- # Ingest data with AWS Athena URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/ingest-athena.html > Ingest AWS Athena and Parquet data for machine learning. # Ingest data with AWS Athena Multiple big data formats now offer different approaches to compressing large amounts of data for storage and analytics; some of these formats include Orc, Parquet, and Avro. Using and querying these datasets can present some challenges. This section shows one way for DataRobot to ingest data in Apache Parquet format that is sitting at rest in AWS S3. Similar techniques can be applied in other cloud environments. ## Parquet overview [Parquet](https://en.wikipedia.org/wiki/Apache_Parquet) is an open source columnar data storage format.  It is often misunderstood to be used primarily for compression. Additionally, note that using compressed data introduces a CPU cost to both compress and decompress it; there's no speed advantage when using all of the data either. Snowflake addresses this in an article [showing several approaches to load 10TB of benchmark data](https://community.snowflake.com/s/article/How-to-Load-Terabytes-Into-Snowflake-Speeds-Feeds-and-Techniques). Snowflake demonstrates the load of full data records to be far higher for a simple CSV format. So what is the advantage of Parquet? Columnar data storage offers little to no advantage when you are interested in a full record. The more columns requested, the more work must be done to read and uncompress them. This is why the full data exercise displayed above shows such a high performance for basic CSV files. However, selecting a subset of the data is where columnar really shines. If there are 50 columns of data in a loan dataset and the only one of interest is the `loan_id`, reading a CSV file will require reading 100% of the data file. However, reading a Parquet file requires reading only 1 of 50 columns. Assume for simplicity's sake that all of the columns take up exactly the same space—this would translate into needing to read only 2% of the data. You can make further read reductions by partitioning the data. To do so, create a path structure based on data values for a field. The SQL engine `WHERE` clause is applied to the folder path structure to decide whether a Parquet file inside it needs to be read. For example, you could partition and store daily files in a structure of `YYYY/MM/DD` for a loans datasource: `loans/2020/1/1/jan1file.parquet` `loans/2020/1/2/jan2file.parquet` `loans/2020/1/3/jan3file.parquet` The "hive style" of this would include the field name in the directory (partition): `loans/year=2020/month=1/day=1/jan1file.parquet` `loans/year=2020/month=1/day=2/jan2file.parquet` `loans/year=2020/month=1/day=3/jan3file.parquet` If the original program was only interested in the `loan_id` and specifically those `loan_id` values from January 2, 2020, then the 2% read would be reduced further still. Evenly distributed, this would reduce the read and decompress operation down to just 0.67% of the data, resulting in a faster read, a faster return of the data, and a lower bill for the resources required to retrieve the data. ## Data for project creation Find the data used in this page's examples [on GitHub](https://github.com/datarobot-community/ai_engineering/blob/master/databases/athena/athena_demo_file_create.ipynb). The dataset uses 20,000 records of loan data from LendingClub and was uploaded to S3 using the [AWS Command Line Interface](https://aws.amazon.com/cli/). ``` aws --profile support s3 ls s3://engineering/athena --recursive | awk '{print $4}' athena/ athena/ loan_credit /20k_loans_credit_data.csv athena/ loan_history /year=2015/1/1020de8e664e4584836c3ec603c06786.parquet athena/loan_history/year=2015/1/448262ad616e4c28b2fbd376284ae203.parquet athena/loan_history/year=2015/2/5e956232d0d241558028fc893a90627b.parquet athena/loan_history/year=2015/2/bd7153e175d7432eb5521608aca4fbbc.parquet athena/loan_history/year=2016/1/d0220d318f8d4cfd9333526a8a1b6054.parquet athena/loan_history/year=2016/1/de8da11ba02a4092a556ad93938c579b.parquet athena/loan_history/year=2016/2/b961272f61544701b9780b2da84015d9.parquet athena/loan_history/year=2016/2/ef93ffa9790c42978fa016ace8e4084d.parquet ``` `20k_loans_credit_data.csv` contains credit scores and histories for every loan. Loans are partitioned by year and month in impartial hive format to demonstrate the steps to work with either format within AWS Athena. Multiple parquet files are represented within the `YYYY/MM` structure, potentially representing different days a loan was created.  All `.parquet` files represent loan application and repayment. This data is in a bucket in the `AWS Region US-East-1` (N. Virginia). ## AWS Athena AWS Athena is a managed service on AWS that provides serverless access to use ANSI SQL against S3 objects. It uses Apache Presto and can read the following file formats: - CSV - TSV - JSON - ORC - Apache Parquet - Avro Athena also has support for compressed data in Snappy, Zlib, LZO, and GZIP formats. It charges on a pay-per-query model based on the amount of data read. AWS also provides [an article](https://aws.amazon.com/blogs/big-data/analyzing-data-in-s3-using-amazon-athena/) on using Athena against both regular text files, as well as parquet, describing the amount of data read, time taken, and cost spent for a query: ## AWS Glue AWS Glue is an Extract Transform Load (ETL) tool that supports the workflow captured in this example. ETL jobs are not constructed and scheduled out. Use Glue to discover files and structure on the hosted S3 bucket in order to apply a high-level schema against the contents, so that Athena is able to understand how to read and query the contents. Glue stores contents in a hive-like meta store. Hive DDL can be written explicitly, but this example assumes a large number of potentially different files and leverages Glue's [crawler](https://docs.aws.amazon.com/glue/latest/dg/add-crawler.html) to discover schemas and define tables. AWS Glue makes a crawler and points it at an S3 bucket. The crawler is set to output its data into an [AWS Glue Data Catalog](https://docs.aws.amazon.com/glue/latest/dg/populate-data-catalog.html) which is then leveraged by Athena. The Glue job should be created in the same region as the AWS S3 bucket ( `US-East-1` for the example on this page). This process is outlined below. 1. ClickAdd crawlerin the AWS Glue service in the AWS console to add a crawler job. 2. Name the crawler. 3. Choose theData storestype and specify the bucket of interest. 4. Choose or create an Identity and Access Management (IAM) role for the crawler. Note that managing IAM is out of scope for this example, but you can reference AWS documentation for more information about IAM privileges. 5. Set the frequency to run on demand, or update as necessary to meet requirements. 6. The crawler-discovered metadata needs to write to a destination. Choose a new or existing database to serve as a catalog. Crawler creation can now be completed. A prompt asks if the demand crawler should be run now; chooseYes. In this example, you can see the crawler has discovered and created two tables for the paths:loan_creditandloan_history. The log shows the created tables as well as partitions for theloan_history. The year partition was left in Hive format while the month was not, to show what happens if this methodology is not applied. Glue assigns it a generic name. 7. Navigate to tables and openloan_history. 8. Choose to edit the schema and click on the column name to rename the secondary partition to month and save. This table is now available for querying in Athena. ## Create a project in DataRobot This section outlines four methods for starting a project with data queried through Athena.  All programmatic methods will use the Python API client and some helper functions as defined in these [DataRobot Community GitHub examples](https://github.com/datarobot-community/ai_engineering/blob/master/databases/athena/athena_demo_create_project.ipynb). The four methods of providing data are: - JDBC Driver - Retrieve SQL results locally - Retrieve S3 CSV results locally - AWS S3 bucket with a signed URL ### JDBC Driver You can install JDBC drivers and use them with DataRobot to ingest data (contact Support for installation assistance for the driver, which is not addressed in this workflow). As of DataRobot 6.0 for the managed AI Platform offering, version 2.0 of the JDBC driver is available. Specifically, 2.0.5 is installed and available on the cloud. A catalog item dataset can be constructed by navigating to AI Catalog > Add New Data and then selecting Data Connection > Add a new data connection. For the purposes of this example, the Athena JDBC driver connection is set up to explicitly specify the address.`Awsregion` and `S3OutputLocation` (required) are also specified. Once configured, query results write to this location as a CSV file. Authentication takes place with an `AWSAccessKey` and `AWSSecretAccessKey` for user and password on the last step. As AWS users often have access to many services, including the ability to spin up many resources, a best practice is to create an IAM account within AWS with specific permissions for querying and then only work with Athena and S3. After creating the data connection, select it in the Add New Data from Data Connection modal and use it to create an item and project. ### Retrieve SQL results locally The snippet below sends a query to retrieve the first 100 records of loan history from the sample dataset. The results are provided back in a dictionary after paginating through the result set from Athena and loading it to local memory. You can then load the results to a dataframe, manipulate it to engineer new features, and push it into DataRobot to create a new project. The `s3_out` variable is a required parameter for Athena, which is where Athena writes CSV query results.  This file is used in subsequent examples. ``` athena_client = session.client('athena') database = 'community_athena_demo_db' s3_out = 's3://engineering/athena/output/' query = "select * from loan_history limit 100" query_results = fetchall_athena_sql(query, athena_client, database, s3_out) # Convert to dataframe to view and manipulate df = pd.DataFrame(query_results) df.head(2) proj = dr.Project.create(sourcedata=df, project_name='athena load query') # Continue work with this project via the DataRobot python package, or work in GUI using the link to the project printed below print(DR_APP_ENDPOINT[:-7] + 'projects/{}'.format(proj.id)) ``` DataRobot only recommends this method for smaller-sized datasets; it may be both easier and faster to simply download the data as a file rather than spool it back in paginated query results. The method uses a pandas DataFrame for convenience and ease of potential data manipulation and feature engineering; it is not required for working with the data or creating a DataRobot project. Additionally, note that the machine that this code runs on requires adequate memory to work with a pandas DataFrame for the size of the dataset being used in this example. ### Retrieve S3 CSV results locally The snippet below shows a more complicated query than the method above. It pulls all loans and joins CSV credit history data with Parquet loan history data. Upon completion, the S3 results file itself is downloaded to a local Python environment. From here, additional processing can be performed or the file can be pushed directly to DataRobot for a new project as shown in the snippet. ``` athena_client = session.client('athena') s3_client = session.client('s3') database = 'community_athena_demo_db' s3_out_bucket = 'engineering' s3_out_path = 'athena/output/' s3_out = 's3://' + s3_out_bucket + '/' + s3_out_path local_path = '/Users/mike/Documents/community/' local_path = !pwd local_path = local_path[0] query = "select lh.loan_id, " \ "lh.loan_amnt, lh.term, lh.int_rate, lh.installment, lh.grade, lh.sub_grade, " \ "lh.emp_title, lh.emp_length, lh.home_ownership, lh.annual_inc, lh.verification_status, " \ "lh.pymnt_plan, lh.purpose, lh.title, lh.zip_code, lh.addr_state, lh.dti, " \ "lh.installment / (lh.annual_inc / 12) as mnthly_paymt_to_income_ratio, " \ "lh.is_bad, " \ "lc.delinq_2yrs, lc.earliest_cr_line, lc.inq_last_6mths, lc.mths_since_last_delinq, lc.mths_since_last_record, " \ "lc.open_acc, lc.pub_rec, lc.revol_bal, lc.revol_util, lc.total_acc, lc.mths_since_last_major_derog " \ "from community_athena_demo_db.loan_credit lc " \ "join community_athena_demo_db.loan_history lh on lc.loan_id = lh.loan_id" s3_file = fetch_athena_file(query, athena_client, database, s3_out, s3_client, local_path) # get results file from S3 s3_client.download_file(s3_out_bucket, s3_out_path + s3_file, local_path + '/' + s3_file) proj = dr.Project.create(local_path + '/' + s3_file, project_name='athena load file') # further work with project via the python API, or work in GUI (link to project printed below) print(DR_APP_ENDPOINT[:-7] + 'projects/{}'.format(proj.id)) ``` ### AWS S3 bucket with a signed URL Another method for creating a project in DataRobot is to ingest data from S3 using URL ingest. There are several ways this can be done based on the data, environment, and configuration used. This example leverages a private dataset on the cloud and creates a Signed URL for use in DataRobot. | Dataset | DataRobot Environment | Approach | Description | | --- | --- | --- | --- | | Public | Local install, Cloud | Public | If a dataset is in a public bucket, the direct HTTP link to the file object can be ingested. | | Private | Local install | Global IAM role | You can install DataRobot with an IAM role granted to the DataRobot service account that has its own access privileges to S3. Any URL passed in that the DataRobot service account can see can be used to ingest data. | | Private | Local install | IAM impersonation | You can implement finer-grained security control by having DataRobot assume the role and S3 privileges of a user. This requires LDAP authentication and LDAP fields containing S3 information be made available. | | Private | Local install, Cloud | Signed S3 URL | AWS users can create a signed URL to an S3 object, providing a temporary link that expires after a specified amount of time. | The snippet below builds on the work presented in the method to [retrieve S3 CSV results locally](https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/ingest-athena.html#retrieve-s3-csv-results-locally). Rather than download the file to a local environment, you can use AWS credentials to sign the URL for temporary usage. The response variable contains a link to the results file, with an authentication string good for 3600 seconds. Anyone with the entire string URL will be able to access the file for the duration requested. In this way, rather than downloading the results locally, a DataRobot project can be initiated by referencing the URL value. ``` response = s3_client.generate_presigned_url('get_object', Params={'Bucket': s3_out_bucket, 'Key': s3_out_path + s3_file}, ExpiresIn=3600) proj = dr.Project.create(response, project_name='athena signed url') # further work with project via the python API, or work in GUI (link to project printed below) print(DR_APP_ENDPOINT[:-7] + 'projects/{}'.format(proj.id)) ``` Helper functions and full code is available in the [DataRobot Community GitHub repo](https://github.com/datarobot-community/ai_engineering/tree/master/databases/athena). ## Wrap-up After using any of the methods detailed above, your data should be ingested in DataRobot.  AWS Athena and Apache Presto have enabled SQL against varied data sources to produce results that can be used for data ingestion. Similar approaches can be used to work with this type of input data in Azure and Google cloud services as well. --- # AWS Lambda reporting to MLOps URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/lambda/aws-lambda-reporting-to-mlops.html > A serverless method of AWS Lambda reporting actuals to DataRobot MLOps. # AWS Lambda reporting to MLOps This topic describes a serverless method of reporting actuals data back to DataRobot once results are available for predicted items. Python 3.7 is used for the executable. ## Architecture The process works as follows: 1. CSV file(s) arrive at AWS S3 containing results to report back to DataRobot. These can be files created from any process. Examples above include a database writing results to S3 or a process sending a CSV file to S3. 2. Upon arrival in the monitored directory, a serverless compute AWS Lambda function is triggered. 3. The related deployment in DataRobot is specified in the S3 bucket path name to the CSV file, so the Lambda works generically for any deployment. 4. The Lambda parses out the deployment, reads through the CSV file, and reports results back to DataRobot for processing. You can then explore the results from various angles within the platform. ## Create or use an existing S3 bucket Actual CSV prediction results are written to a monitored area of an AWS S3 bucket. If one does not exist, create the new area to receive the results. Files are expected to be copied into this bucket from external sources such as servers, programs, or databases. To create a bucket, navigate to the S3 service within the AWS console and click Create bucket. Provide a name (like `datarobot-actualbucket`) and region for the bucket, then click Create bucket. Change the defaults if required for organizational policies. ## Create an IAM Role for the Lambda Navigate to Identity and Access Management (IAM). Under Roles, select Create role. Select AWS service and Lambda as a use case and then navigate to Next: Permissions. Search for and add the AWSLambdaBasicExecutionRole policy. Proceed with the next steps, provide the Role name `lambda_upload_actuals_role`. Complete the task by clicking Create role. Two policies must be attached to this role: - The AWS-managed policy, AWSLambdaBasicExecutionRole . - An inline policy used for accessing and managing the S3 objects/files associated with this Lambda. Specify the inline policy for monitoring the S3 bucket as follows: ``` { "Version": "2012-10-17", "Statement": [ { "Sid": "VisualEditor0", "Effect": "Allow", "Action": [ "s3:GetObject", "s3:ListBucket", "s3:DeleteObject" ], "Resource": [ "arn:aws:s3:::datarobot-actualbucket", "arn:aws:s3:::datarobot-actualbucket/*" ] } ] } ``` ## Create the Lambda Navigate to the AWS Lambda service in the GUI console and from the dashboard, click Create function. Provide a name, such as `lambda_upload_actuals`. In the Runtime environment section, choose Python 3.7. Expand the execution role section, select Use an existing role, and choose the `lambda_upload_actuals_role` created above. ## Add the Lambda trigger This Lambda will run any time a CSV file lands in the path it is monitoring. From the Designer screen, choose +Add trigger, and select S3 from the dropdown list. For the bucket, choose the one specified in the IAM role policy you created above. (Optional) Specify a prefix if the bucket is used for other purposes. For example, use the value `upload_actuals/` as a prefix if you want to only monitor objects that land in `s3://datarobot-actualbucket/upload_actuals/`. (Note that the data for this example would be expected to arrive similar to `s3://datarobot-actualbucket/upload_actuals/2f5e3433_DEPLOYMENT_ID_123/actuals.csv`.) Click Add to save the trigger. ## Create and add a Lambda layer Lambda layers provide the opportunity to build Lambda code on top of libraries, and separate that code from the delivery package. Although not required to separate the libraries, using layers simplifies the process of bringing in necessary packages and maintaining code. This code will require the requests and pandas libraries, which are not part of the base Amazon Linux image Lambda runs in, and must be added via a layer. This can be done by creating a virtual environment. In this example, the environment used to execute the code below is an Amazon Linux EC2 box. (See the instructions to install Python 3 on Amazon Linux [here](https://aws.amazon.com/premiumsupport/knowledge-center/ec2-linux-python3-boto3/).) Creating a ZIP file for a layer can then be done as follows: ``` python3 -m venv my_app/env source ~/my_app/env/bin/activate pip install requests pip install pandas deactivate ``` Per the [Amazon documentation](https://docs.aws.amazon.com/lambda/latest/dg/configuration-layers.html), this must be placed in the python or site-packages directory and expanded under `/opt`. ``` cd ~/my_app/env mkdir -p python/lib/python3.7/site-packages cp -r lib/python3.7/site-packages/* python/lib/python3.7/site-packages/. zip -r9 ~/layer.zip python ``` Copy the layer.zip file to a location on S3; this is required if the Lambda layer is larger than 10MB. ``` aws s3 cp layer.zip s3://datarobot-bucket/layers/layer.zip ``` Navigate to Lambda service > Layers > Create Layer. Provide a name and link to the file in S3. Note that this will be the Object URL of the uploaded ZIP. It is recommended but not necessary to set compatible environments. This will make them more easily accessible in a dropdown menu when adding them to a Lambda. Select to save the layer and its ARN. Navigate back to the Lambda and click Layers (below the Lambda title). Add a layer and provide the ARN from the previous step. ## Define the Lambda code ``` import boto3 import os import os.path import urllib.parse import pandas as pd import requests import json # 10,000 maximum allowed payload REPORT_ROWS = int(os.environ["REPORT_ROWS"]) DR_API_TOKEN = os.environ["DR_API_TOKEN"] DR_INSTANCE = os.environ["DR_INSTANCE"] s3 = boto3.resource("s3") def report_rows(list_to_report, url, total): print("reporting " + str(len(list_to_report)) + " records!") df = pd.DataFrame(list_to_report) # this must be provided as a string df["associationId"] = df["associationId"].apply(str) report_json = json.dumps({"data": df.to_dict("records")}) response = requests.post( url, data=report_json, headers={ "Authorization": "Bearer " + DR_API_TOKEN, "Content-Type": "application/json", }, ) print("response status code: " + str(response.status_code)) if response.status_code == 202: print("success! reported " + str(total) + " total records!") else: print("error reporting!") print("response content: " + str(response.content)) def lambda_handler(event, context): # get the object that triggered lambda bucket = event["Records"][0]["s3"]["bucket"]["name"] key = urllib.parse.unquote_plus(event["Records"][0]["s3"]["object"]["key"], encoding="utf-8") filenm = os.path.basename(key) fulldir = os.path.dirname(key) deployment = os.path.basename(fulldir) print("bucket is " + bucket) print("key is " + key) print("filenm is " + filenm) print("fulldir is " + fulldir) print("deployment is " + deployment) url = DR_INSTANCE + "/api/v2/deployments/" + deployment + "/actuals/fromJSON/" session = boto3.session.Session() client = session.client("s3") line_no = -1 total = 0 rows_list = [] for lines in client.get_object(Bucket=bucket, Key=key)["Body"].iter_lines(): # if the header, make sure the case sensitive required fields are present if line_no == -1: header = lines.decode("utf-8").split(",") col1 = header[0] col2 = header[1] expectedHeaders = ["associationId", "actualValue"] if col1 not in expectedHeaders or col2 not in expectedHeaders: print("ERROR: data must be csv with 2 columns, headers case sensitive: associationId and actualValue") break else: line_no = 0 else: input_dict = {} input_row = lines.decode("utf-8").split(",") input_dict.update({col1: input_row[0]}) input_dict.update({col2: input_row[1]}) rows_list.append(input_dict) line_no += 1 total += 1 if line_no == REPORT_ROWS: report_rows(rows_list, url, total) rows_list = [] line_no = 0 if line_no > 0: report_rows(rows_list, url, total) # delete the processed input s3.Object(bucket, key).delete() ``` ## Set Lambda environment variables Three variables need to be set for the Lambda. - DR_API_TOKEN is the API token of the account with access to the deployment, which will be used for submitting the actuals to the DataRobot environment. It is advised to use a service account for this configuration, rather than a personal user account. - DR_INSTANCE is the application server of the DataRobot instance that is being used. - REPORT_ROWS is the number of actuals records to upload in a payload; 10000 is the maximum. ## Set Lambda resource settings Edit the Basic settings to set configuration items for the Lambda. When reading input data to buffer and submit payloads, the Lambda uses a fairly low amount of local compute and memory. 512MB should be more than sufficient for memory settings, allocating half a vCPU accordingly. The Timeout is the amount of time allowed for the Lambda to not provide output, causing AWS to terminate it. This is most likely to happen when waiting for a response after submitting the payload. The default of 3 seconds is likely too short, especially when using the max size: 10,000-record payloads. Although 5-6 seconds is likely adequate, the configuration tested in this example was set to 30 seconds. ## Run the Lambda The Lambda is coded to expect a report-ready pair of data columns. It expects a CSV file with a header and case-sensitive columns `associationId` and `actualValue`.  Sample file contents are shown below for a Titanic passenger scoring model. ``` associationId,actualValue 892,1 893,0 894,0 895,1 896,1 ``` The following is an AWS CLI command to leverage the S3 service and copy a local file to the monitored directory: ``` aws s3 cp actuals.csv s3://datarobot-actualbucket/upload_actuals/deploy/ 5aa1a4e24eaaa003b4caa4 /actuals.csv ``` Note that the deployment ID is included as part of the path (shown above in red). This is the DataRobot deployment with which the actuals will be associated. Similarly, files from a process or database export can be written directly to S3. Consider that the maximum length of time for a Lambda to run is 15 minutes. In the testing for this article, this length of time was sufficient for 1 million records. In production usage, you may want to explore approaches that include more files with fewer records. Also, you may want to report actuals for multiple deployments simultaneously. It may be prudent to disable API rate limiting for the associated API token/service account reporting these values. Flesh out any additional error handling, such as sending an email, sending a queue data message, creating custom code to fit into an environment, moving the S3 file, etc. This Lambda deletes the input file upon successful processing and writes errors to the log in the event of failure. ## Wrap-up At this point, the Lambda is complete and ready to report any actuals data fed to it (i.e., the defined S3 location receives a file in the expected format). Set up a process to perform this operation once actual results arrive, then monitor and manage the model with DataRobot MLOps to understand how it’s performing for your use case. --- # AWS Lambda URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/lambda/index.html > Integrate DataRobot with AWS Lambda. # AWS Lambda The sections described below provide techniques for integrating AWS Lambda with DataRobot. | Topic | Description | | --- | --- | | AWS Lambda reporting to MLOps | AWS Lambda serverless reporting of actuals to DataRobot MLOps. | | Use DataRobot Prime models with AWS Lambda | Using DataRobot Prime* models with AWS Lambda. | | Use Scoring Code with AWS Lambda | Making predictions using Scoring Code deployed on AWS Lambda. | * The ability to create new DataRobot Prime models has been removed from the application. This does not affect existing Prime models or deployments. --- # Use DataRobot Prime with AWS Lambda URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/lambda/prime-lambda.html > Use DataRobot Prime to download a DataRobot model and deploy it using AWS Lambda. # Use DataRobot Prime models with AWS Lambda > [!NOTE] Availability information > The ability to create new DataRobot Prime models has been removed from the application. This does not affect existing Prime models or deployments. To export Python code in the future, use the Python Scoring Code export function in any RuleFit model. This page outlines how you can use the DataRobot Prime model functionality to download a DataRobot model and deploy it using AWS Lambda. DataRobot Prime can be initiated on most DataRobot models and allows you to download an approximation of a DataRobot model either as a Python or Java module. The code is then easily deployable in any environment and is not dependent on the DataRobot application. Before proceeding with this workflow, be sure to download a DataRobot Prime model as a Java module. ## Why deploy on AWS Lambda While DataRobot provides its own scalable prediction servers that are fully integrated with the platform, there are multiple reasons why you would want to deploy on AWS Lambda: - Company policy or governance decision. - Serverless architecture. - Cost reduction. - Custom functionality on top of the DataRobot model. - The ability to integrate models into systems that cannot communicate with the DataRobot API. ## Setup Follow the steps below to complete setup for this example. 1. Rename the Java file toPrediction.java. 2. Create a project, “lambda_prime_example”, in the IDE of your choice. 3. CopyPrediction.javato the project. 4. Compile the package using themvn packagecommand. 5. ClickCreate functionto create a new AWS function. 6. Choose Java 11 to create the Lambda function. 7. Give the function a name and choose the permissions. 8. Upload the compiled JAR file to Lambda. 9. Change the Lambda handler to the name of the Java package method: com.datarobot.examples.scoring.prime.PrimeLambdaExample::handleRequest Setup is now complete. Next, perform testing to verify that the deployment is working as intended. ## Test the Lambda function To test the Lambda function: 1. Go to the TEST event configuration page. 2. Put JSON with features as test event: 3. Click theTestbutton. Now, you can configure the integration with an AWS API Gateway or other services so that data is sent to the Lambda function and you receive results back. --- # Use Scoring Code with AWS Lambda URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/lambda/sc-lambda.html > Learn how to integrate Scoring Code models with AWS Lambda. # Use Scoring Code with AWS Lambda This topic describes how you can use DataRobot’s Scoring Code functionality to download a model's Scoring Code and deploy it using AWS Lambda. DataRobot automatically runs code generation for those models that support it, and indicates code availability with an icon on the Leaderboard. This option allows you to download validated Java Scoring Code for a predictive model without approximation; the code is easily deployable in any environment and is not dependent on the DataRobot application. ### Why deploy on AWS Lambda While DataRobot provides its own scalable prediction servers that are fully integrated with the platform, there are multiple reasons why you would want to deploy on AWS Lambda: - Company policy or governance decision. - Serverless architecture. - Cost reduction. - Custom functionality on top of the DataRobot model. - The ability to integrate models into systems that cannot communicate with the DataRobot API. In this case, AWS Lambda can be used either as a primary means of scoring for fully offline systems or as a backend for systems that are using the DataRobot API. ### Download Scoring Code The first step to deploying a DataRobot model to AWS Lambda is to download the Scoring Code JAR file from the [Leaderboard](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/sc-download-leaderboard.html) or the [deployment](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/sc-download-deployment.html). Next, create your own Lambda scoring project. In `pom.xml`, change the path to the downloaded JAR file. ### Compile the project After downloading Scoring Code and creating a Lambda project, finalize the setup by compiling the project. To start with, find this line in `CodeGenLambdaExample.java` and impute with your model ID: ``` public static String modelId = ""; ``` If you have a classification model, you need to use the IClassificationPredictor interface: ``` public static IClassificationPredictor model; ``` If it’s a regression model, use the IRegressionPredictor interface: ``` public static IRegressionPredictor model; ``` Now you can run the maven command `mvn package` to compile the code. The packaged JAR file will appear in the target folder of the project. ### Deploy to AWS Lambda To deploy to AWS Lambda, use the following steps: 1. SelectCreate functionfrom the AWS menu: 2. Choose Java 11 to create the Lambda function: 3. Enter a name for the function. 4. Configure Lambda permissions as shown below. 5. Upload the compiled JAR file to Lambda. View the upload options below. You can view the JAR file location below. 6. Choose the Lambda handler for your Java package name: The setup is now complete. DataRobot recommends testing the function to see that the deployment is working as intended. ### Test the Lambda function To test the Lambda function: 1. Go to the TEST event configuration page. 2. Add JSON with features as a test event. 3. Click theTestbutton. After testing is complete, you can integrate with the AWS API Gateway or other services so that data is sent to the Lambda function and it returns results. --- # Path-based routing to PPS URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/path-based-routing-to-pps-on-aws.html > Path-based routing to Portable Prediction Servers on AWS. # Path-based routing to PPSs on AWS Using DataRobot MLOps, users can deploy DataRobot models into their own Kubernetes clusters—managed or Self-Managed AI Platform—using Portable Prediction Servers (PPSs). A PPS is a Docker container that contains a DataRobot model with a monitoring agent, and can be deployed using container orchestration tools such as Kubernetes. Then you can use the [monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/index.html) and [governance](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/index.html) capabilities of MLOps. When deploying multiple PPSs in the same Kubernetes cluster, you often want to have a single IP address as the entry point to all of the PPSs. A typical approach to this is path-based routing, which can be achieved using different Kubernetes Ingress Controllers. Some of the existing approaches to this include [Traefik](https://github.com/traefik/traefik), [HAProxy](https://haproxy-ingress.github.io), and [NGINX](https://www.nginx.com/products/nginx/kubernetes-ingress-controller). The following sections describe how to use the NGINX Ingress controller for path-based routing to PPSs deployed on Amazon EKS. ## Before you start There are some prerequisites to interacting with AWS and the underlying services. If any (or all) of these tools are already installed and configured for you, you can skip the corresponding steps. See [Getting started with Amazon EKS – eksctl](https://docs.aws.amazon.com/eks/latest/userguide/getting-started-eksctl.html) for detailed instructions. ### Install necessary tools 1. Install the AWS CLI, version 2. 2. Configure your AWS CLI credentials. 3. Install eksctl. 4. Install and configure kubectl (CLI for Kubernetes clusters). 5. Check that you successfully installed the tools. ### Set up PPS containers This procedure assumes that you have created and locally tested PPS containers for DataRobot AutoML models and pushed them to Amazon Elastic Container Registry (ECR). See [Deploy models on AWS EKS](https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/deploy-dr-models-on-aws.html) for instructions. This walkthrough is based on two PPSs created with the models of linear regression and image classification use cases, using a Kaggle [housing prices dataset](https://www.kaggle.com/c/home-data-for-ml-course/data) and [Food 101 dataset](https://www.kaggle.com/dansbecker/food-101), respectively. The first PPS (housing prices) contains an eXtreme Gradient Boosted Trees Regressor (Gamma Loss) model. The second PPS (image binary classification - hot dog not hot dog), contains a SqueezeNet Image Pretrained Featurizer + Keras Slim Residual Neural Network Classifier using Training Schedule model. The latter model has been trained using DataRobot Visual AI functionality . ## Create an Amazon EKS cluster With the [Docker images stored in ECR](https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/deploy-dr-models-on-aws.html#push-the-docker-image-to-amazon-ecr), you can spin up an Amazon EKS cluster. The EKS cluster needs a VPC with either of the following: - Two public subnets and two private subnets - Three public subnets Amazon EKS requires subnets in at least two Availability Zones. A VPC with public and private subnets is recommended so that Kubernetes can create public load balancers in the public subnets to control traffic to the pods that run on nodes in private subnets. 1. (Optional) Create or choose two public and two private subnets in your VPC. Make sure that “Auto-assign public IPv4 address” is enabled for the public subnets. NoteTheeksctltool creates all necessary subnets behind the scenes if you don’t provide the corresponding--vpc-private-subnetsand--vpc-public-subnetsparameters. 2. Create the cluster. eksctlcreatecluster\--namemulti-app\--vpc-private-subnets=subnet-XXXXXXX,subnet-XXXXXXX\--vpc-public-subnets=subnet-XXXXXXX,subnet-XXXXXXX\--nodegroup-namestandard-workers\--node-typet3.medium\--nodes2\--nodes-min1\--nodes-max3\--ssh-access\--ssh-public-keymy-public-key.pub\--managed NotesUsage of the--managedparameter enablesAmazon EKS-managed nodegroups. This feature automates the provisioning and lifecycle management of nodes (EC2 instances) for Amazon EKS Kubernetes clusters. You can provision optimized groups of nodes for their clusters. EKS will keep their nodes up-to-date with the latest Kubernetes and host OS versions. Theeksctltool makes it possible to choose the specific size and instance type family via command line flags or config files.Although--ssh-public-keyis optional, it is highly recommended that you specify it when you create your node group with a cluster. This option enables SSH access to the nodes in your managed node group. Enabling SSH access allows you to connect to your instances and gather diagnostic information if there are issues. You cannot enable remote access after the node group is created.Cluster provisioning usually takes between 10 and 15 minutes and results in the following: 3. When your cluster is ready, test that your kubectl configuration is correct: kubectlgetsvc ## Deploy the NGINX Ingress controller AWS Elastic Load Balancing supports three types of load balancers: Application Load Balancers (ALB), Network Load Balancers (NLB), and Classic Load Balancers (CLB). See [Elastic Load Balancing features](https://aws.amazon.com/elasticloadbalancing/features/?nc=sn&loc=2) for details. The NGINX Ingress controller uses NLB on AWS. NLB is best suited for load balancing of TCP, UDP, and TLS traffic when extreme performance is required. Operating at the connection level ( [Layer 4 of the OSI model](https://en.wikipedia.org/wiki/OSI_model#Layer_4:_Transport_Layer)), NLB routes traffic to targets within Amazon VPC and is capable of handling millions of requests per second while maintaining ultra-low latencies. NLB is also optimized to handle sudden and volatile traffic patterns. Deploy the NGINX Ingress controller (this manifest file also launches the NLB): ``` kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress- nginx/master/deploy/static/provider/aws/deploy.yaml ``` ## Create and deploy services to Kubernetes 1. Create a Kubernetes namespace: kubectl create namespace aws-tlb-namespace 2. Save the following contents to ayamlfile on your local machine (in this case,house-regression-deployment.yaml), replacing the values for your project, for example: apiVersion:apps/v1kind:Deploymentmetadata:name:house-regression-deploymentnamespace:aws-tlb-namespacelabels:app:house-regression-appspec:replicas:3selector:matchLabels:app:house-regression-apptemplate:metadata:labels:app:house-regression-appspec:affinity:nodeAffinity:requiredDuringSchedulingIgnoredDuringExecution:nodeSelectorTerms:-matchExpressions:-key:beta.kubernetes.io/archoperator:Invalues:-amd64containers:-name:house-regression-modelimage:ports:-containerPort:80 3. Save the following contents to ayamlfile on your local machine (in this case,house-regression-service.yaml), replacing the values for your project, for example: apiVersion:v1kind:Servicemetadata:name:house-regression-servicenamespace:aws-tlb-namespacelabels:app:house-regression-appspec:selector:app:house-regression-appports:-protocol:TCPport:8080targetPort:8080type:NodePort 4. Create a Kubernetes service and deployment: kubectl apply -f house-regression-deployment.yaml kubectl apply -f house-regression-service.yaml 5. Save the following contents to ayamlfile on your local machine (in this case,hot-dog-deployment.yaml), replacing the values for your project, for example: apiVersion:apps/v1kind:Deploymentmetadata:name:hot-dog-deploymentnamespace:aws-tlb-namespacelabels:app:hot-dog-appspec:replicas:3selector:matchLabels:app:hot-dog-apptemplate:metadata:labels:app:hot-dog-appspec:affinity:nodeAffinity:requiredDuringSchedulingIgnoredDuringExecution:nodeSelectorTerms:-matchExpressions:-key:beta.kubernetes.io/archoperator:Invalues:-amd64containers:-name:hot-dog-modelimage:ports:-containerPort:80 6. Save the following contents to ayamlfile on your local machine (in this case,hot-dog-service.yaml), replacing the values for your project, for example: apiVersion:v1kind:Servicemetadata:name:hot-dog-servicenamespace:aws-tlb-namespacelabels:app:hot-dog-appspec:selector:app:hot-dog-appports:-protocol:TCPport:8080targetPort:8080type:NodePort 7. Create a Kubernetes service and deployment: kubectl apply -f hot-dog-deployment.yaml kubectl apply -f hot-dog-service.yaml 8. View all resources that exist in the namespace: kubectl get all -n aws-tlb-namespace ## Create and deploy Ingress resource for path-based routing 1. Save the following contents to ayamlfile on your local machine (in this case,nginx-redirect-ingress.yaml), replacing the values for your project, for example: apiVersion:networking.k8s.io/v1beta1kind:Ingressmetadata:name:nginx-redirect-ingressnamespace:aws-tlb-namespaceannotations:kubernetes.io/ingress.class:nginxnginx.ingress.kubernetes.io/rewrite-target:/$2labels:app:nginx-redirect-ingressspec:rules:-http:paths:-path:/house-regression(/|$)(.*)backend:serviceName:house-regression-serviceservicePort:8080-path:/hot-dog(/|$)(.*)backend:serviceName:hot-dog-serviceservicePort:8080 NoteThenginx.ingress.kubernetes.io/rewrite-targetannotation rewrites the URL before forwarding the request to the backend pods. As a result, the paths/house-regression/some-house-pathand/hot-dog/some-dog-pathtransform to/some-house-pathand/some-dog-path, respectively. 2. Create Ingress for path-based routing: kubectl apply -f nginx-redirect-ingress.yaml 3. Verify that Ingress has been successfully created: kubectl get ingress/nginx-redirect-ingress -n aws-tlb-namespace 4. (Optional) Use the following if you want to access the detailed output about this ingress: kubectl describe ingress/nginx-redirect-ingress -n aws-tlb-namespace Note the value ofAddressin the output for the next two scoring requests. 5. Score thehouse-regressionmodel: curl -X POST http://
/house-regression/predictions -H "Content-Type: text/csv" --data-binary @kaggle_house_test_dataset_10.csv 6. Score thehot-dogmodel: curl -X POST http://
/hot-dog/predictions -H "Content-Type: text/csv; charset=UTF-8" --data-binary @for_pred.csv Notefor_pred.csvis a CSV file containing one column with the header. The content for that column is a Base64 encoded image. Original photo for prediction (downloaded fromhere): The image is predicted to be a hot dog: Another photo for prediction (downloaded fromhere): The image is predicted not to be a hot dog: ## Clean up 1. Remove the sample services, deployments, pods, and namespaces: kubectl delete namespace aws-tlb-namespace kubectl delete namespace ingress-nginx 2. Delete the cluster: eksctl delete cluster --name multi-app ## Wrap-up The deployment of a few Kubernetes services behind the same IP address allows you to minimize the number of load balancers needed and facilitate the maintenance of the applications. Applying Kubernetes Ingress Controllers makes it possible. This walkthrough described how to develop the path-based routing to a few Portable Prediction Servers (PPSs) deployed on the Amazon EKS platform. This solution has been implemented via NGINX Ingress Controller. --- # Amazon SageMaker URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/sagemaker/index.html > Integrate DataRobot with Amazon SageMaker. # Amazon SageMaker The sections described below provide techniques for integrating Amazon SageMaker with DataRobot. | Topic | Description | | --- | --- | | Deploy models on Sagemaker | Deploying on SageMaker and monitoring with MLOps agents. | | Use Scoring Code with AWS SageMaker | Making predictions using Scoring Code deployed on AWS SageMaker. | --- # Deploy models on SageMaker URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/sagemaker/sagemaker-deploy.html > Deploy models on SageMaker and monitor them with MLOps Agents. # Deploy models on SageMaker This article showcases how to make predictions and monitor external models deployed on AWS SageMaker using DataRobot’s [Scoring Code](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/index.html) and [MLOps agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html). DataRobot automatically runs Scoring Code generation for models that support it and indicates code availability with an icon on the Leaderboard. This option allows you to download validated Java Scoring Code for a model without approximation; the code is easily deployable in any environment and is not dependent on the DataRobot application. ### Why deploy on AWS SageMaker While DataRobot provides its own scalable prediction servers that are fully integrated with the platform, there are multiple reasons why someone would want to deploy on AWS SageMaker: - Company policy or governance decision. - Custom functionality on top of the DataRobot model. - Low-latency scoring without the overhead of API calls. Java code is typically faster than scoring through the Python API. - The ability to integrate models into systems that can’t necessarily communicate with the DataRobot API. Note that data drift and accuracy tracking are unavailable out-of-the-box unless you configure the [MLOps agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html). You can leverage AWS SageMaker as a deployment environment for your Scoring Code. AWS SageMaker allows you to bring in your machine learning models (in several supported formats) and expose them as API endpoints. DataRobot packages the MLOps agent along with the model in a Docker container which will be deployed on AWS SageMaker. ### Download Scoring Code The first step to deploying a DataRobot model to AWS Sagemaker is to download the Scoring Code JAR file from the [Leaderboard](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/sc-download-leaderboard.html) or the [deployment](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/sc-download-deployment.html) found under the Downloads tab from within the model menu. ### Configure the MLOps agent The MLOps library provides a way for you to get the same monitoring features with your own models as you can with DataRobot models. The MLOps library provides an interface that you can use to report metrics to the MLOps service; from there, you can monitor deployment statistics and predictions, track feature drift, and get other insights to analyze model performance. For more information, reference the [MLOps agent documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html). You can use the MLOps library with any type of model, including Scoring Code models downloaded from DataRobot. DataRobot supports versions of the MLOps library in Java or Python (Python3). The MLOps agent can be [downloaded using the DataRobot API](https://app.datarobot.com/api/v2/mlopsInstaller), or as a tarball from the DataRobot UI. From the application, select your user icon and navigate to the Developer Tools page to find the tarball available for download. 1. Install the DataRobot MLOps agent and libraries. 2. Configure the agent. 3. Start the agent service. 4. Ensure that the agent buffer directory ( MLOPS_SPOOLER_DIR_PATH in the config file) exists. 5. Configure the channel you want to use for reporting metrics. (Note that the MLOps agent can be configured to work with a number of channels, including SQS, Google Pub Sub, Spool File and RabbitMQ. This example uses SQS. 6. Use the MLOps library to report metrics from your deployment. The MLOps library buffers the metrics locally, which enables high throughput without slowing down the deployment. It also forwards the metrics to the MLOps service so that you can monitor model performance via the deployment inventory. ### Create a deployment Helper scripts for creating deployments are available in the examples directories of the MLOps agent tarball. Every example has its own script to create the related deployment, and the `tools/create_deployment.py` script is available to create your own deployment. Deployment creation scripts interact with the MLOps service directly, so they must run on a machine with connectivity to the MLOps service. Every example has a description file ( `_deployment_info`) and a script to create a deployment. 1. Edit the description file to configure your deployment. 2. If you want to enable or disable feature drift tracking, configure the description file by adding or excluding the trainingDataset field. 3. Create a new deployment by running the script, _create_deployment.sh . Running this script returns a deployment ID and initial model ID that can be used to instrument your deployment. Alternatively, create the deployment from the DataRobot GUI. To create a deployment from the DataRobot GUI, use the following steps: 1. Log in to the DataRobot GUI. 2. Select Model Registry (1) and click Add New Package (2). 3. In the dropdown, selectNew external model package(3). 4. Complete all the information needed for your deployment, and then clickCreate package. 5. Select theDeploymentstab and clickDeploy Model Package, validate the details on this page, and clickCreate deployment(right-hand side at the top of the page). 6. You can use the toggle buttons to enable drift tracking, segment analysis of predictions, and more deployment settings. 7. Once you clickCreate Deploymentafter providing necessary details, the following dialog box appears. 8. You can see the deployment details of the newly created deployment. If you select theIntegrationstab for the deployment, you can see the monitoring code. When you scroll down through this monitoring code, you can seeDEPLOYMENT_IDandMODEL_IDwhich are used by the MLOps library to monitor the specific model deployment. ### Upload Scoring Code After downloading the Scoring Code JAR file, upload it to an AWS S3 bucket that is accessible to SageMaker. SageMaker expects a `tar.gz` archive format to be uploaded to the S3 bucket. Compress your model (the Scoring Code JAR file) using the following command: ``` tar -czvf 5e8471fa169e846a096d5137.jar.tar.gz 5e8471fa169e846a096d5137.jar ``` Note that if you are using macOS, the `tar` command adds hidden files in the `tar.gz` package that create problems during deployment; use the below command instead of the one shared above: ``` COPYFILE_DISABLE=1 tar -czvf 5e8471fa169e846a096d5137.jar.tar.gz 5e8471fa169e846a096d5137.jar ``` Once you have created the `tar.gz` archive, upload it to the S3 bucket. ### Customize the Docker image DataRobot has a published Docker image ( [scoring-inference-code-SageMaker:latest](https://hub.docker.com/r/datarobot/scoring-inference-code-sagemaker)) which contains the inference code to the Amazon ECR. You can use this Docker image as the base image, and then add a customized Docker container layer containing the MLOps agent. ``` FROM datarobotdev/scoring-inference-code-sagemaker RUN apk add --no-cache curl tar bash procps RUN apk add --no-cache --upgrade bash COPY agent-6.1.0.jar /agent/ COPY stdout.log4j2.properties /conf/ COPY mlops.agent.conf.yaml /conf/ COPY agent-entrypoint.sh / RUN chmod 755 /agent-entrypoint.sh ENTRYPOINT sh /agent-entrypoint.sh ``` The `agent-entrypoint.sh` shell script runs the Scoring Code as a JAR file and starts the MLOps agent JAR. ``` java -Dlog.file=${AGENT_LOG_PATH} \ -Dlog4j.configurationFile=file:${AGENT_LOG_PROPERTIES} \ -cp ${AGENT_JAR_PATH} com.datarobot.mlops.agent.Agent \ --config ${AGENT_CONFIG_YAML} Collapse & java -jar /opt/scoring/sagemaker-api.jar ``` By default, the MLOps configuration file reports metrics in the Amazon SQS service. Provide the URL for accessing SQS in the `mlops.agent.conf.yaml`: ``` - type: SQS_SPOOL - details: {name: "sqsSpool", queueUrl: " [https://sqs.us-east-1.amazonaws.com/123456789000/mlops-agent-sqs](https://sqs.us-east-1.amazonaws.com/123456789000/mlops-agent-sqs%C2%A0) "} ``` Now, create a Docker image from Dockerfile. Go to the directory containing the Dockerfile and run the following command: ``` docker build -t codegen-mlops-SageMaker ``` This creates a Docker image from the Dockerfile (a reference Dockerfile is shared with the source code). ### Publish the Docker image to Amazon ECR Next, publish the Docker image to the Amazon ECR: 1. Authenticate your Docker client to the Amazon ECR registry to which you intend to push the image. Authentication tokens must be obtained for each registry used, and the tokens are valid for 12 hours. You can refer to Amazon documentation for the various authentication options listed in this example. 2. This example uses token-based authentication: TOKEN=$(awsecrget-authorization-token--outputtext--query'authorizationData[].authorizationToken')curl-i-H"Authorization: Basic$TOKEN" 3. Create an Amazon ECR registry where you can push your image: awsecrcreate-repository--repository-nameSageMakerdemo This results in the output as shown below: You can also create a registry from the AWS Management console, fromECR Service>Create Repository(you must provide the repository name). 4. Identify the image to push. Run the Docker images command to list the images on your system: dockerimagels 5. Tag the image to push to AWS ECR. Find the Docker image's ID containing the inference code and the MLOps agent. 6. Tag the image with the Amazon ECR registry, repository, and the optional image tag name combination to use. The registry format isaws_account_id.dkr.ecr.region.amazonaws.com. The repository name should match the repository that you created for your image. If you omit the image tag, DataRobot uses the latest tag: dockertag"${account}.dkr.ecr.${region}.amazonaws.com/SageMakerdemo" 7. Push the image: dockerpush${account}.dkr.ecr.${region}.amazonaws.com/SageMakermlopsdockerized Once the image is pushed, you can validate from the AWS management console. ### Create a model 1. Sign into AWS and enter “SageMaker” in the search bar. Select the first result (Amazon SageMaker) to enter the SageMaker console and create a model. 2. In theIAM rolefield, selectCreate a new rolefrom the dropdown if you do not have an existing role on your account. This option creates a role with the required permissions and assigns it to your instance. 3. SelectAmazon SageMaker > Models > Create model. 4. For the Container input options field (1), selectProvide model artifacts and inference image location. Specify the location of the Scoring Code image (your model) in the S3 bucket (2) and the registry path to the Docker image containing the inference code (3). 5. ClickAdd containerbelow the fields when complete. Finally, your model configuration will look like this: 6. Open the dashboard on the left side and navigate to theEndpoint configurationspage to create a new endpoint configuration. Select the model you have uploaded. 7. Name the endpoint configuration (1) and provide an encryption key (2), if desired. When complete, select theCreate endpoint configurationat the bottom of the page. 8. Use the dashboard to navigate toEndpointsand create a new endpoint: 9. Name the endpoint (1) and opt to use an existing endpoint configuration (2). Select the configuration you just created (3) and clickSelect endpoint configuration.When endpoint creation is complete, you can make prediction requests with your model. When the endpoint is ready to service requests, theStatuswill change toInService. ### Making predictions Once the SageMaker endpoint status changes to InService, you can start making predictions against this endpoint. This example tests predictions using the Lending Club data. Test the endpoint from the command line to make sure the endpoint is responding. Use the following command to make test predictions and pass data into the body of a CSV string. Before using it, make sure you have installed AWS CLI. ``` aws SageMaker-runtime invoke-endpoint --endpoint-name mlops-dockerized-endpoint-new ``` You can also use a Python script (outlined below) to make predictions. This script uses the DataRobot MLOps library to report metrics back to the DataRobot application which you can see from the deployment you created. ``` import time import random import pandas as pd import json import boto3 from botocore.client import Config import csv import itertools from datarobot_mlops.mlops import MLOps import os from io import StringIO """ This is sample code and may not be production ready """ runtime_client = boto3.client('runtime.sagemaker') endpoint_name = 'mlops-dockerized-endpoint-new' cur_dir = os.path.dirname(os.path.abspath(__file__)) #dataset_filename = os.path.join(cur_dir, "CSV_10K_Lending_Club_Loans_cust_id.csv") dataset_filename = os.path.join(cur_dir, "../../data/sagemaker_mlops.csv") def _feature_df(num_samples): df = pd.read_csv(dataset_filename) return pd.DataFrame.from_dict(df) def _predictions_list(num_samples): with open(dataset_filename, 'rb') as f: payload = f.read() result = runtime_client.invoke_endpoint( EndpointName=endpoint_name, Body=payload, ContentType='text/csv', Accept='Accept' ) str_predictions = result['Body'].read().decode() df_predictions = pd.read_csv(StringIO(str_predictions)) #list_predictions = df_predictions['target_1_PREDICTION'].values.tolist() list_predictions = df_predictions.values.tolist() print("number of predictions made are : ",len(list_predictions)) return list_predictions def main(): num_samples = 10 # MLOPS: initialize mlops library # If deployment ID is not set, it will be read from MLOPS_DEPLOYMENT_ID environment variable. # If model ID is not set, it will be ready from MLOPS_MODEL_ID environment variable. mlops = MLOps().init() features_df = _feature_df(num_samples) #print(features_df.info()) start_time = time.time() predictions_array = _predictions_list(num_samples) print(len(predictions_array)) end_time = time.time() # MLOPS: report the number of predictions in the request and the execution time. mlops.report_deployment_stats(len(predictions_array), (end_time - start_time) * 1000) # MLOPS: report the prediction results. mlops.report_predictions_data(features_df=features_df, predictions=predictions_array) # MLOPS: release MLOps resources when finished. mlops.shutdown() if __name__ == "__main__": main() ``` ### Model monitoring Return to the deployment and check the Service Health tab to monitor the model. In this case, the MLOps Library is reporting predictions metrics to the Amazon SQS channel. The MLOps agent deployed on SageMaker along with Scoring Code reads these metrics from the SQS channel and reports them to the Service Health tab. --- # Use Scoring Code with AWS SageMaker URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/sagemaker/sc-sagemaker.html > Using Scoring Code models with AWS SageMaker. # Use Scoring Code with AWS SageMaker This topic describes how to make predictions using DataRobot’s Scoring Code deployed on AWS SageMaker. Scoring Code allows you to download machine learning models as JAR files which can then be deployed in the environment of your choice. AWS SageMaker allows you to bring in your machine-learning models and expose them as API endpoints, and DataRobot can export models in Java and Python. Once exported, you can deploy the model on AWS SageMaker. This example focuses on the DataRobot Scoring Code export, which provides a Java JAR file. Make sure the model you want to import supports [Scoring Code](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/index.html). Models that support Scoring Code export are indicated by the Scoring Code icon. ## Download Scoring Code The first step to deploying a DataRobot model to AWS SageMaker is to create a TAR.GZ archive that contains your model (the Scoring Code JAR file provided by DataRobot). You can download the JAR file from the [Leaderboard](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/sc-download-leaderboard.html) or from a [deployment](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/sc-download-deployment.html). > [!NOTE] Note > Depending on your DataRobot license, the code may only be available through the Deployments page. ## Upload Scoring Code to an AWS S3 bucket Once you have downloaded the Scoring Code JAR file, you need to upload your Codegen JAR file to an AWS S3 bucket so that SageMaker can access it. SageMaker expects the archive ( `tar.gz` format) to be uploaded to an S3 bucket. Compress your model as a `tar.gz` archive using one of the following commands: **Linux:** ``` tar -czvf 5e8471fa169e846a096d5137.jar.tar.gz 5e8471fa169e846a096d5137.jar ``` **MacOS:** MacOS adds hidden files to the `tar.gz` package that can introduce issues during deployment. To prevent these issues, use the following command: ``` COPYFILE\_DISABLE=1 tar -czvf 5e8471fa169e846a096d5137.jar.tar.gz 5e8471fa169e846a096d5137.jar ``` Once you have created the `tar.gz` archive, upload it to S3: 1. Enter the Amazon S3 console. 2. ClickUploadand provide yourtar.gzarchive to the S3 bucket. ## Publish a Docker image to Amazon ECR Next, publish a Docker image containing inference code to the Amazon ECR. In this example, you can download the DataRobot-provided Docker image with the following command: ``` docker pull datarobotdev/scoring-inference-code-sagemaker:latest ``` To publish the image to Amazon ECR: 1. Authenticate your Docker client to the Amazon ECR registry to which you intend to push your image. Authentication tokens must be obtained for each registry used, and the tokens are valid for 12 hours. You can refer to Amazon documentation for various authentication options listedhere. 2. Use token-based authentication: TOKEN=$(aws ecr get-authorization-token --output text --query 'authorizationData[].authorizationToken') curl -i -H "Authorization: Basic $TOKEN" 3. Next, create an Amazon ECR Registry where you can push your image: aws ecr create-repository --repository-name sagemakerdemo Using this command returns the output shown below: You can also create the repository from the AWS Management console: 1. Navigate toECR Service > Create Repositoryand provide the repository name. 2. Identify the image to push. Run the docker images command to list the images on your system. 3. Tag the image you want to push to AWS ECR. 4. Thexxxxxxxxplaceholder represents the image ID of the DataRobot-provided Docker image containing the inference code (scoring-inference-code-sagemaker:latest) that you downloaded from Docker Hub. 5. Tag the image with the Amazon ECR registry, repository, and optional image tag name combination to use. The registry format is*aws_account_id.dkr.ecr.region.amazonaws.com*. The repository name should match the repository that you created for the image. If you omit the image tag, then DataRobot assumes the tag is the latest. docker tag xxxxxxxx "${account}.dkr.ecr.${region}.amazonaws.com/sagemakerdemo" 6. Push the image: docker push ${account}.dkr.ecr.${region}.amazonaws.com/sagemakermlopsdockerized Once pushed, you can validate the image from the AWS management console. ## Create the model 1. Sign in to AWS and search forSageMaker. Select the first search result,Amazon SageMaker, to enter the SageMaker console and create a model. 2. In the IAM role field, selectCreate a new rolefrom the dropdown if you do not have an existing role on your account. This option creates a role with the required permissions and assigns it to your instance. 3. For theContainer input optionsfield (1), selectProvide model artifacts and inference image location. Then, specify the location of the Scoring Code image (your model) in the S3 bucket (2) and the registry path to the Docker image containing the inference code (3). 4. ClickAdd containerbelow the fields when complete. Your model configurations should match the example below: ## Create an endpoint configuration To set up an endpoint for predictions: 1. Open the dashboard on the left side and navigate to theEndpoint configurationspage to create a new endpoint configuration. Select the uploaded model. 2. Enter anEndpoint configuration name(1) and provide anEncryption keyif desired (2). When complete, selectCreate endpoint configurationat the bottom of the page. 3. Use the dashboard to navigate toEndpointsand create a new endpoint: Enter anEndpoint name(1) andUse an existing endpoint configuration(2). Then, click the configuration you just created (3). When finished, clickSelect endpoint configuration. When the endpoint creation is complete, you can make prediction requests with your model. Once the endpoint is ready to service requests, the status will change toInService: ## Make predictions Once the SageMaker endpoint status changes to InService you can start making predictions against the endpoint. Test the endpoint from the command line first to make sure the endpoint is responding. Use the command below to make a test prediction and pass the data in the body of the CSV string: ``` aws sagemaker-runtime invoke-endpoint --endpoint-name mlops-dockerized-endpoint-new ``` > [!NOTE] Note > To run the command above, ensure you have installed AWS CLI. ## Considerations Note the following when deploying on SageMaker: - There is no out-of-the-box data drift and accuracy tracking unless MLOps agents are configured. - You may experience additional time overhead as a result of deploying to AWS SageMaker. --- # Score Snowflake data on AWS EMR Spark URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/aws/score-snowflake-aws-emr-spark.html > Scoring Snowflake data using DataRobot Scoring Code on AWS EMR Spark. # Score Snowflake data on AWS EMR Spark DataRobot provides exportable Scoring Code that you can use to score millions of records on Spark. This topic shows how to do so with Snowflake as the data source and target. The steps can be used as a template you can modify to create Spark scoring jobs with different sources and targets. ## About the technologies Click a tab to learn about the technologies discussed in this topic. **AWS EMR and Apache Spark:** Apache Spark is an open source cluster computing framework considered to be in the "Big Data" family of technologies. Spark is used for large volumes of data in structured or semi-structured forms—in streaming or batch modes. Spark does not have its own persistent storage layer. It relies on file systems like HDFS, object storage like AWS S3, and JDBC interfaces for data. Popular Spark platforms include Databricks and AWS Elastic Map Reduce (EMR). The example in this topic shows how to score using EMR Spark. This is a Spark cluster that can be spun up for work as needed and shut down when work is completed. **AWS S3:** S3 is the object storage service of AWS. It is used in this example to store and retrieve the job's database query dynamically. S3 can also write to as a job completion target. In addition, cluster log files are written to S3. **AWS Secrets Manager:** Hardcoding credentials can be done during development or for ad-hoc jobs, although as a best practice it is ideal, even in development, to score these in a secure fashion. This is a requirement for safely protecting them in production scoring jobs. The Secrets Manager service will allow only trusted users or roles to be able to access securely stored secret information. **AWS Command Line Interface (CLI):** For brevity and ease of use, the AWS CLI will be used to perform command line operations for several activities related to AWS activities throughout this article.  These activities could also be performed manually via the GUI.  See [AWS Command Line Interface Documentation](https://docs.aws.amazon.com/cli/index.html) for more information on configuring the CLI. **Snowflake Database:** Snowflake is a cloud-based database platform designed for data warehouse and analytic workloads.  It allows for easy scale-up and scale-out capabilities for working on large data volume use cases and is available as a service across all major cloud platforms. For the scoring example in this topic, Snowflake is the source and target, although both can be swapped for other databases or storage platforms for Spark scoring jobs. **DataRobot Scoring Code:** You can quickly and easily deploy models in DataRobot for API hosting within the platform. In some cases, rather than bringing the data to the model in the API, it can be beneficial to bring the model to the data, for example, for very large scoring jobs. The example that follows scores three million Titanic passengers for survival probability from an enlarged [Kaggle dataset](https://www.kaggle.com/c/titanic). Although not typically an amount that would warrant considering using Spark over the API, here it serves as a good technical demonstration. You can export models from DataRobot in Java or Python as a rules-based approximation with DataRobot RuleFit models. A second export option is Scoring Code, which provides source code and a compiled Java binary JAR which holds the exact model chosen. **Programming Languages:** Structured Query Language (SQL) is used for the database, Scala for Spark.  Python/PySpark can also be leveraged for running jobs on Spark. ## Architecture ## Development Environment AWS EMR includes a Zeppelin Notebook service, which allows for interactive development of Spark code. To set up a development environment, first create an EMR cluster. You can do this via GUI options on AWS; the defaults are acceptable. Be sure to choose the Spark option. Note the advanced settings allow for more granular choices of software installation. Upon successful creation, when viewing the Summary tab on the cluster the AWS CLI export button provides a CLI script to recreate the instance, which can be saved and edited for the future.  An example is as follows: ``` aws emr create-cluster \ --applications Name=Spark Name=Zeppelin \ --configurations '[{"Classification":"spark","Properties":{}}]' \ --service-role EMR_DefaultRole \ --enable-debugging \ --release-label emr-5.30.0 \ --log-uri 's3n://mybucket/emr_logs/' \ --name 'Dev' \ --scale-down-behavior TERMINATE_AT_TASK_COMPLETION \ --region us-east-1 \ --tags "owner=doyouevendata" "environment=development" "cost_center=comm" \ --ec2-attributes '{"KeyName":"creds","InstanceProfile":"EMR_EC2_DefaultRole","SubnetId":"subnet-0e12345","EmrManaged SlaveSecurityGroup":"sg-123456","EmrManagedMasterSecurityGroup":"sg-01234567"}' \ --instance-groups '[{"InstanceCount":1,"EbsConfiguration":{"EbsBlockDeviceConfigs":[{"VolumeSpecification":{"SizeInGB" :32,"VolumeType":"gp2"},"VolumesPerInstance":2}]},"InstanceGroupType":"MASTER","InstanceType":"m5.xlarge","Name":"Master Instance Group"},{"InstanceCount":2,"EbsConfiguration":{"EbsBlockDeviceConfigs":[{"VolumeSpecification":{"SizeInGB":32," VolumeType":"gp2"},"VolumesPerInstance":2}]},"InstanceGroupType":"CORE","InstanceType":"m5.xlarge","Name":"Core Instance Group"}]' ``` You can find connectivity details about the cluster in the GUI. Log on to the server to provide additional configuration items. You can access a terminal via SSH; this requires a public-facing IP or DNS address, and that the VPC inbound ruleset applied to the EC2 cluster master instance allows incoming connections over SSH port 22. If connectivity is refused because the machine is unreachable, add source IP/subnets to the security group. ``` ssh -i ~/creds.pem hadoop@ec2-54-121-207-147.compute-1.amazonaws.com ``` Several packages are used to support database connectivity and model scoring. These JARs can be loaded to cluster nodes when the cluster is created to have them available in the environment. They can also be compiled into JARs for job submission, or they can be downloaded from a repository at run time. This example uses the last option. The AWS environment used in this article is based on EWS EMR 5.30 with Spark 2.11. Some changes may be necessary to follow along as new versions of referenced environments and packages are released. In addition to those already provided by AWS, two Snowflake and two DataRobot packages are used: - spark-snowflake - snowflake-jdbc - scoring-code-spark-api - datarobot-prediction To leverage these packages in the Zeppelin notebook environment, edit the `zeppelin-env` file to add the packages when the interpreter is invoked.  Edit this file on the master node. ``` sudo vi /usr/lib/zeppelin/conf/zeppelin-env.sh ``` Edit the `export SPARK_SUBMIT_OPTIONS` line at the bottom of the file and add the packages flag to the string value. ``` --packages net.snowflake:snowflake-jdbc:3.12.5,net.snowflake:spark- snowflake_2.11:2.7.1-spark_2.4,com.datarobot:scoring-code-spark-api_2.4.3:0.0.19,com.datarobot:datarobot- prediction:2.1.4 ``` If you make further edits while working in Zeppelin, you'll need to restart the interpreter within the Zeppelin environment for the edits to take effect. You can now establish an SSH tunnel to access the remote Zeppelin server from a local browser.  The following command forwards port 8890 on the master node to the local machine. Without using a public DNS entry, additional proxy configuration may be required. This statement leverages "Option 1" in the following topic. A proxy for the second option as well as additional ports and services can be found [here](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-web-interfaces.html). ``` ssh -i ~/creds.pem -L 8890:ec2-54-121-207-147.compute-1.amazonaws.com:8890 hadoop@ec2-54-121-207-147.compute-1.amazonaws.com -Nv ``` Navigating to port 8890 on the local machine now brings up the Zeppelin instance where a new note can be created along with the packages, as defined in the environment shell script. Several helper tools are provided on GitHub to aid in quickly and programmatically performing this process (and others described in this article) via the AWS CLI from a local machine. `env_config.sh` contains AWS environment variables, such as profile (if used), tags, VPCs, security groups, and other elements used in specifying a cluster. `snow_bootstrap.sh` is an optional file to perform tasks on the EMR cluster nodes after they are allocated, but before applications like Spark are installed. `create_dev_cluster.sh` uses the above to create a cluster and provides connectivity strings.  It takes no arguments. ## Create Secrets You can code credentials into variables during development, although this topic demonstrates how to create a production EMR job with auto-termination upon completion.  It is a good practice to store secret values such as database usernames and passwords in a trusted environment.  In this case, the IAM Role applied to the EC2 instances has been granted the privilege to interact with the [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/) service. The simplest form of a secret contains a string reference name and a string of values to store. The process for creating one is straightforward in the AWS GUI and will guide the creation of a secret, with a string representing provided keys and values in JSON. Some helper files are available to do this with the CLI. `secrets.properties` is a JSON list of secrets to store. Example contents: ``` { "dr_host":"https://app.datarobot.com", "dr_token":"N1234567890", "dr_project":"5ec1234567890", "dr_model":"5ec123456789012345", "db_host":"dp12345.us-east-1.snowflakecomputing.com", "db_user":"snowuser", "db_pass":"snow_password", "db_db":"TITANIC", "db_schema":"PUBLIC", "db_query_file":"s3://bucket/ybspark/snow.query", "db_output_table":"PASSENGERS_SCORED", "s3_output_loc":"s3a://bucket/ybspark/output/", "output_type":"s3" } ``` `create_secrets.sh` is a script which leverages the CLI to create (or update) the secret name specified within the script with the properties file. ## Source SQL Query Instead of putting a SQL extract statement into the code, instead it can be provided dynamically at runtime. It is not necessarily a secret and, given its potential length and complexity, it fits better as simply a file in S3. One of the secrets is pointing to this location, the `db_query_file` entry. The contents of this file on S3— `s3://bucket/ybspark/snow.query` is simply a SQL statement against a table with three million passenger records: ``` select * from passengers_3m ``` ## Spark Code (Scala) With supporting components in place, code to construct the model scoring pipeline can begin. It can be run on a spark-shell instance directly on the machine, with a helper to include the necessary packages with `run_spark-shell.sh` and `spark_env.sh`. This interactive session may assist in some quick debugging, but it only uses the master node and is not a friendly environment to iterate code development in. The Zeppelin notebook is a more friendly environment to do so in and runs the code in yarn-cluster mode, leveraging the multiple worker nodes available.  The code below can be copied or the note can simply be imported from the `snowflake_scala_note.json` in the GitHub repo for this project. ### Import package dependencies ``` import org.apache.spark.sql.functions.{col} import org.apache.spark.sql.{DataFrame, Dataset, SparkSession} import org.apache.spark.sql.SaveMode import java.time.LocalDateTime import com.amazonaws.regions.Regions import com.amazonaws.services.secretsmanager.AWSSecretsManagerClientBuilder import com.amazonaws.services.secretsmanager.model.GetSecretValueRequest import org.json4s.{DefaultFormats, MappingException} import org.json4s.jackson.JsonMethods._ import com.datarobot.prediction.spark.Predictors.{getPredictorFromServer, getPredictor} ``` ### Create helper functions to simplify process ``` /* get secret string from secrets manager */ def getSecret(secretName: String): (String) = { val region = Regions.US_EAST_1 val client = AWSSecretsManagerClientBuilder.standard() .withRegion(region) .build() val getSecretValueRequest = new GetSecretValueRequest() .withSecretId(secretName) val res = client.getSecretValue(getSecretValueRequest) val secret = res.getSecretString return secret } /* get secret value from secrets string once provided key */ def getSecretKeyValue(jsonString: String, keyString: String): (String) = { implicit val formats = DefaultFormats val parsedJson = parse(jsonString) val keyValue = (parsedJson \ keyString).extract[String] return keyValue } /* run sql and extract sql into spark dataframe */ def snowflakedf(defaultOptions: Map[String, String], sql: String) = { val spark = SparkSession.builder.getOrCreate() spark.read .format("net.snowflake.spark.snowflake") .options(defaultOptions) .option("query", sql) .load() } ``` ### Retrieve and parse secrets Next, retrieve and parse the secrets data stored in AWS to support the scoring job. ``` val SECRET_NAME = "snow/titanic" printMsg("db_log: " + "START") printMsg("db_log: " + "Creating SparkSession...") val spark = SparkSession.builder.appName("Score2main").getOrCreate(); printMsg("db_log: " + "Obtaining secrets...") val secret = getSecret(SECRET_NAME) printMsg("db_log: " + "Parsing secrets...") val dr_host = getSecretKeyValue(secret, "dr_host") val dr_project = getSecretKeyValue(secret, "dr_project") val dr_model = getSecretKeyValue(secret, "dr_model") val dr_token = getSecretKeyValue(secret, "dr_token") val db_host = getSecretKeyValue(secret, "db_host") val db_db = getSecretKeyValue(secret, "db_db") val db_schema = getSecretKeyValue(secret, "db_schema") val db_user = getSecretKeyValue(secret, "db_user") val db_pass = getSecretKeyValue(secret, "db_pass") val db_query_file = getSecretKeyValue(secret, "db_query_file") val output_type = getSecretKeyValue(secret, "output_type") ``` ### Read query into a variable Next, read the query hosted on S3 and specified in `db_query_file` into a variable. ``` printMsg("db_log: " + "Retrieving db query...") val df_query = spark.read.text(db_query_file) val query = df_query.select(col("value")).first.getString(0) ``` ### Retrieve the Scoring Code Next, retrieve the Scoring Code for the model from DataRobot. Although this can be done from a local JAR, the code here retrieves it from DataRobot on the fly. This model can be easily swapped out for another by changing the `dr_model` value referenced in the secrets. ``` printMsg("db_log: " + "Loading Model...") val spark_compatible_model = getPredictorFromServer(host=dr_host, projectId=dr_project, modelId=dr_model, token=dr_token) ``` ### Run the SQL Now, run the SQL retrieved against the database and bring it into a Spark dataframe. ``` printMsg("db_log: " + "Extracting data from database...") val defaultOptions = Map( "sfURL" -> db_host, "sfAccount" -> db_host.split('.')(0), "sfUser" -> db_user, "sfPassword" -> db_pass, "sfDatabase" -> db_db, "sfSchema" -> db_schema ) val df = snowflakedf(defaultOptions, query) ``` ### Score the dataframe The example below scores the dataframe through the retrieved DataRobot model. It creates a subset of the output containing just the identifying column (Passenger ID) and the probability towards the positive class label 1, i.e., the probability of survival for the passenger. ``` printMsg("db_log: " + "Scoring Model...") val result_df = spark_compatible_model.transform(df) val subset_df = result_df.select("PASSENGERID", "target_1_PREDICTION") subset_df.cache() ``` ### Write the results The value `output_type` dictates whether the scored data is written back to a table in the database or a location in S3. ``` if(output_type == "s3") { val s3_output_loc = getSecretKeyValue(secret, "s3_output_loc") printMsg("db_log: " + "Writing to S3...") subset_df.write.format("csv").option("header","true").mode("Overwrite").save(s3_output_loc) } else if(output_type == "table") { val db_output_table = getSecretKeyValue(secret, "db_output_table") subset_df.write .format("net.snowflake.spark.snowflake") .options(defaultOptions) .option("dbtable", db_output_table) .mode(SaveMode.Overwrite) .save() } else { printMsg("db_log: " + "Results not written to S3 or database; output_type value must be either 's3' or 'table'.") } printMsg("db_log: " + "Written record count - " + subset_df.count()) printMsg("db_log: " + "FINISH") ``` This approach works well for development and manual or ad-hoc scoring needs. You can terminate the EMR cluster when all work is complete.  AWS EMR can also be leveraged to create routinely run production jobs on a schedule as well. ## Productionalize the Pipeline A production job can be created to run this job on regular intervals. The process of creating an EMR instance is similar; however, the instance will be set to run some job steps after it comes online.  After the steps are completed, the cluster will be automatically terminated as well. The Scala code however cannot be run as a scripted step.  It must be compiled into a JAR for submission.  The open source build tool [sbt](https://www.scala-sbt.org/) is used for compiling Scala and Java code.  In the repo, sbt was installed already (using commands in the `snow_bootstrap.sh` script). Note this is only required for development to compile the JAR and can be removed from any production job run. Although the code does not need to be developed on the actual EMR master node, it does present a good environment for development because that is where the code will ultimately be run. The main files of interest in the project are: `snowscore/build.sbt` `snowscore/create_jar_package.sh` `snowscore/spark_env.sh` `snowscore/run_spark-submit.sh` `snowscore/src/main/scala/com/comm_demo/SnowScore.scala` - build.sbtcontains the prior referred to packages for Snowflake and DataRobot, and includes two additional packages for working with AWS resources. - create_jar_package.sh,spark_env.sh, andrun_spark-submit.share helper functions.  The first function runs a clean package build of the project, and the latter two functions allow for submission to the spark cluster of the built package JAR simply from the command line. - SnowScore.scalacontains the same code referenced above, arranged in a main class to be called when submit to the cluster for execution. Run the `create_jar_package.sh` to create the output package JAR, which calls `sbt clean` and `sbt package`.  This creates the JAR ready for job submission, `target/scala-2.11/score_2.11-0.1.0-SNAPSHOT.jar`. The JAR can be submitted with the `run_spark-submit.sh` script; however, to use it in a self-terminating cluster it needs to be hosted on S3. In this example, it has been copied over to `s3://bucket/ybspark/score_2.11-0.1.0-SNAPSHOT.jar`. If on a development EMR instance, after the JAR has been copied over to S3, the instance can be terminated. Lastly, the `run_emr_prod_job.sh` script can be run to call an EMR job using the AWS CLI to create an EMR instance, run a bootstrap script, install necessary applications, and execute a step function to call the main class of the S3 hosted package JAR. The `--steps` argument in the script creates the step to call the spark-submit job on the cluster. Note that the `--packages` argument submits the snapshot JAR and the main class that are specified in this attribute at runtime. Upon completion of the JAR, the EMR instance self-terminates. The production job creation is now complete. This may be run by various triggers or scheduling tools. By updating the `snow.query` file hosted on S3, the input can be modified; in addition, the output targets of tables in the database or object storage on S3 can also be modified. Different machine learning models from DataRobot can easily be swapped out as well, with no additional compilation or coding required. ### Performance and cost considerations Consider this example as a reference: A MacBook containing a i5-7360U CPU @ 2.30GHz and running a local (default option) Scoring Code CSV job scored at a rate of 5,660 rec/s. When using a system with m5.xlarge (4 vCPU 16GB RAM) for MASTER and CORE EMR nodes, running a few tests from 3 million to 28 million passenger records ran from 12,000–22,000 rec/s per CORE node. There is startup time required to construct an EMR cluster; this varies and takes 7+ minutes. There is additional overhead in simply running a Spark job. Scoring 418 records on a 2-CORE node system through the entire pipeline took 512 seconds total.  However, scoring 28 million on a 4-CORE node system took 671 seconds total.[Pricing](https://aws.amazon.com/emr/pricing/), another consideration, is based on instance hours for EC2 compute and EMR services. Examination of the scoring pipeline job alone as coded, without any tweaks to code, shows Spark, or EMR, scaling of 28 million records—from 694 seconds on 2 CORE nodes to 308 seconds on 4 CORE nodes. AWS EMR cost calculations can be a bit challenging due to the way they are measured with normalized instance hours and when the clock starts ticking for cluster payment. A GitHub project is available to create approximate costs for resources over a given time period or when given a specific cluster-id. This project can be found on [GitHub](https://github.com/marko-bast/aws-emr-cost-calculator). An estimate for the 28 million passenger scoring job with 4 CORE servers follows: ``` $ ./aws-emr-cost-calculator cluster --cluster_id=j-1D4QGJXOAAAAA CORE.EC2 : 0.16 CORE.EMR : 0.04 MASTER.EC2 : 0.04 MASTER.EMR : 0.01 TOTAL : 0.25 ``` Related code for this article can be found on [DataRobot Community GitHub](https://github.com/datarobot-community/dr_spark_examples/tree/master/scala/snowscore). --- # Deploy and monitor DataRobot models in Azure Kubernetes Service URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/azure/aks-deploy-and-monitor.html > Deploy and monitor DataRobot models in Azure Kubernetes Service # Deploy and monitor DataRobot models in Azure Kubernetes Service > [!NOTE] Availability information > The MLOps model package export feature is off by default. Contact your DataRobot representative or administrator for information on enabling this feature for DataRobot MLOps. > > Feature flag: Enable MMM model package export This page shows how to deploy machine learning models on Azure Kubernetes Services (AKS) to create production scoring pipelines with DataRobot's MLOps Portable Prediction Server (PPS). DataRobot Automated Machine Learning provides a dedicated prediction server as a low-latency, synchronous REST API suitable for real-time predictions. The DataRobot MLOps PPS extends this functionality to serve ML models in container images, giving you portability and control over your ML model deployment architecture. A containerized PPS is well-suited to deployment in a Kubernetes cluster, allowing you to take advantage of this deployment architecture's auto-scaling and high availability. The combination of PPS and Kubernetes is ideal for volatile, irregular workloads such as those you can find in IoT use cases. ## Create a model The examples on this page use the [public LendingClub dataset](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/10K_Lending_Club_Loans.csv) to predict the loan amount for each application. 1. To upload the training data to DataRobot, do either of the following: 2. Enterloan_amtas your target (what you want to predict)(1) and clickStart(2) to run Autopilot. 3. After Autopilot finishes, clickModelsand select a model at the top of the Leaderboard. 4. Under the model you selected, clickPredict > Deployto access theGet model packagedownload. 5. ClickDownload .mlpkgto start the.mlpkgfile download. > [!NOTE] Note > For more information, see the documentation on the [Portable Prediction Server](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html). ## Create an image with the model package After you [obtain the PPS base image](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html#obtain-the-pps-docker-image), create a new version of it by creating a Dockerfile with the content below: ``` FROM datarobot/datarobot-portable-prediction-api: AS build COPY . /opt/ml/model ``` > [!NOTE] Note > For more information on how to structure this Docker command, see the [Docker build](https://docs.docker.com/engine/reference/builder/) documentation. For the `COPY` command to work, you must have the `.mlpkg` file in the same directory as the Dockerfile. After creating your Dockerfile, run the command below to create a new image that includes the model: ``` docker build . --tag regressionmodel:latest ``` ## Create an Azure Container Registry Before deploying your image to AKS, push it to a container registry such as the Azure Container Registry (ACR) for deployment: 1. In the Azure Portal, clickCreate a resource > Containers, then clickContainer Registry. 2. On theCreate container registry blade, enter the following: FieldDescriptionRegistry nameEnter a suitable nameSubscriptionSelect your subscriptionResource groupUse your existing resource groupLocationSelect your regionAdmin userEnableSKUStandard 3. ClickCreate. 4. Navigate to your newly-generated registry and selectAccess Keys. 5. Copy the admin password to authenticate with Docker and push the.mlpkgimage to this registry. ## Push the model image to ACR To push your new image to Azure Container Registry (ACR), log in with the following command (replace `` with your previously-selected repository name): ``` docker login .azurecr.io ``` The password is the administrator password you created with the Azure Container Registry (ACR). Once logged in, make sure your Docker image is correctly tagged, and then push it to the repo with the following command (replace `` with your previously selected repository name): ``` docker tag regressionmodel .azurecr.io/regressionmodel docker push .azurecr.io/regressionmodel ``` ## Create an AKS cluster This section assumes you are familiar with AKS and Azure's Command Line Interface (CLI). > [!NOTE] Note > For more information on AKS, see [Microsoft's Quickstart tutorial](https://docs.microsoft.com/en-us/azure/aks/kubernetes-walkthrough). 1. If you don't have a running AKS cluster, create one: RESOURCE_GROUP=ai_success_engCLUSTER_NAME=AIEngineeringDemo azakscreate\--resource-group$RESOURCE_GROUP\--name$CLUSTER_NAME\-sStandard_B2s\--node-count1\--generate-ssh-keys\--service-principalXXXXXX\--client-secretXXXX\--enable-cluster-autoscaler\--min-count1\--max-count2 2. Create a secret Docker registry so that AKS can pull images from the private repository. In the command below, replace the following with your actual credentials: kubectlcreatesecretdocker-registry--docker-server=.azurecr.io--docker-username=--docker-password= 3. Deploy your Portable Prediction Server image. There are many ways to deploy applications, but the easiest method is via the Kubernetes dashboard. Start the Kubernetes dashboard with the following command: azaksbrowse--resource-group$RESOURCE_GROUP--name$CLUSTER_NAME ## Deploy a model to AKS To install and deploy the PPS containing your model: 1. Click CREATE > CREATE AN APP . 2. On theCREATE AN APPpage, specify the following: FieldValueApp namee.g.,portablepredictionserverContainer imagee.g.,aisuccesseng.azurecr.io/regressionmodel:latestNumber of podse.g.,1ServiceExternalPort,Target port, andProtocol8080,8080, andTCPImage pull secretpreviously createdCPU requirement (cores)1Memory requirement (MiB)250 3. ClickDeploy—it may take several minutes to deploy. ## Score predictions with Postman To test the model, download the [DataRobot PPS Examples](https://github.com/datarobot-community/ai_engineering/blob/master/mlops/DRMLOps_PortablePredictionServer_examples/DR%20MLOps%20Portable%20Prediction%20Server%20Public.postman_collection.json) a [Postman Collection](https://www.postman.com/collection/), and update the hostname from `localhost` to the external IP address assigned to your service. You can find the IP address in the Services tab on your Kubernetes dashboard: To make a prediction, execute the make predictions request: ## Configure autoscaling and high availability Kubernetes supports horizontal pod autoscaling to adjust the number of pods in a deployment depending on CPU utilization or other selected metrics. The Metrics Server provides resource utilization to Kubernetes and is automatically deployed in AKS clusters. In the previous sections, you deployed one pod for your service and defined only the minimum requirement for CPU and memory resources. To use the autoscaler, you must define CPU requests and utilization limits. By default, the Portable Prediction Server spins up one worker, which means it can handle only one HTTP request simultaneously. The number of workers you can run, and thus the number of HTTP requests that it can handle simultaneously, is tied to the number of CPU cores available for the container. Because you set the minimum CPU requirement to `1`, you can now set the limit to `2` in the `patchSpec.yaml` file: ``` apiVersion: apps/v1 kind: Deployment metadata: name: portablepredictionserver spec: selector: matchLabels: app: portablepredictionserver replicas: 1 template: metadata: labels: app: portablepredictionserver spec: containers: - name: portablepredictionserver image: aisuccesseng.azurecr.io/regressionmodel:latest ports: - containerPort: 8080 resources: requests: cpu: 1000m limits: cpu: 2000m imagePullSecrets: - name: aisuccesseng ``` Then run the following command: ``` kubectl patch deployment portablepredictionserver --patch "$(cat patchSpec.yaml)" ``` Alternatively, you can update the deployment directly in the Kubernetes dashboard by editing the JSON as shown below and clicking UPDATE. Now that the CPU limits are defined, you can configure autoscaling with the following command: ``` kubectl autoscale deployment portablepredictionserver --cpu-percent=50 --min=1 --max=10 ``` This enables Kubernetes to autoscale the number of pods in the `portablepredictionserver` deployment. If the average CPU utilization across all pods exceeds 50% of their requested usage, the autoscaler increases the pods from a minimum of one instance up to ten instances. To run a load test, download the sample JMeter test plan below and update the URLs/ authentication. Run it with the following command: ``` jmeter -n -t LoadTesting.jmx -l results.csv ``` The output will look similar to the following example: ## Report usage to DataRobot MLOps via monitoring agents After deploying your model to AKS, you can monitor this model, along with all of your other models, in one central dashboard by reporting the telemetrics for these predictions to your DataRobot MLOps server and dashboards. 1. Navigate to theModel Registry > Model Packages > Add New Packageand follow the instructions in thedocumentation. 2. SelectAdd new external model packageand specify a package name and description (1 and 2), upload the corresponding training data for drift tracking (3), and identify the model location (4), target (5), environment (6), and prediction type (7), then clickCreate package(8). 3. After creating the external model package, note the model ID in the URL as shown below (blurred in the image for security purposes). 4. While still on theModel Registrypage and within the expanded new package, select theDeploymentstab and clickCreate new deployment. The deployment page loads prefilled with information from the model package you created. 5. Complete any missing information for the deployment and clickCreate deployment. 6. Navigate toDeployments > Overviewand copy the deployment ID (from the URL). Now that you have your model ID and deployment ID, you can report predictions as described in the next section. ### Report prediction details To report prediction details to DataRobot, you need to provide a few environment variables to your Portable Prediction Server container. Update the deployment directly in the Kubernetes dashboard by editing the JSON and then clicking UPDATE: ``` "env": [ { "name": "PORTABLE_PREDICTION_API_WORKERS_NUMBER", "value": "2" }, { "name": "PORTABLE_PREDICTION_API_MONITORING_ACTIVE", "value": "True" }, { "name": "PORTABLE_PREDICTION_API_MONITORING_SETTINGS", "value": "output_type=output_dir;path=/tmp;max_files=50;file_max_size=10240000;model_id=;deployment_id=" }, { "name": "MONITORING_AGENT", "value": "False" }, { "name": "MONITORING_AGENT_DATAROBOT_APP_URL", "value": "https://app.datarobot.com/" }, { "name": "MONITORING_AGENT_DATAROBOT_APP_TOKEN", "value": "" } ] ``` Even though you deployed a model outside of DataRobot on a Kubernetes cluster (AKS), you can monitor it like any other model and track service health and data drift in one central dashboard (see below). --- # Run Batch Prediction jobs from Azure Blob Storage URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/azure/azure-blob-storage-batch-pred.html > Run Batch Prediction jobs from Azure Blob Storage # Run Batch Prediction jobs from Azure Blob Storage The DataRobot Batch Prediction API allows users to take in large datasets and score them against deployed models running on a Prediction Server. The API also provides flexible options for file intake and output. This page shows how you can set up a Batch Prediction job—using the [DataRobot Python Client package](https://datarobot-public-api-client.readthedocs-hosted.com/en/v2.24.0/) to call the Batch Prediction API—that will score files from Azure Blob storage and write the results back to Azure Blob storage. This method also works for Azure Data Lake Storage Gen2 accounts because the underlying storage is the same. All the code snippets on this page are part of a [Jupyter Notebook that you can download](https://github.com/datarobot-community/ai_engineering/blob/master/batch_predictions/azure_batch_prediction.ipynb) to get started. ## Prerequisites To run this code, you will need the following: - Python 2.7 or 3.4+ - The DataRobot Python Package (2.21.0+) (pypi)(conda) - A DataRobot deployment - An Azure storage account - An Azure storage container - A Scoring dataset (to use for scoring with your DataRobot deployment in the storage container) ## Create stored credentials in DataRobot The Batch Prediction job requires credentials to read and write to Azure Blob storage, including the name of the Azure storage account and an access key. To obtain these credentials: 1. In the Azure portal for the Azure Blob Storage account, clickAccess keys. 2. ClickShow keysto reveal the values of your access keys. You can use either of the keys shown (key1orkey2). 3. Use the following code to create a new credential object within DataRobot, used by the Batch Prediction job to connect to your Azure storage account. AZURE_STORAGE_ACCOUNT="YOUR AZURE STORAGE ACCOUNT NAME"AZURE_STORAGE_ACCESS_KEY="AZURE STORAGE ACCOUNT ACCESS KEY"DR_CREDENTIAL_NAME=f"Azure_{AZURE_STORAGE_ACCOUNT}"# Create an Azure-specific Credential# The connection string is also found below the access key in Azure if you want to copy that directly.credential=dr.Credential.create_azure(name=DR_CREDENTIAL_NAME,azure_connection_string=f"DefaultEndpointsProtocol=https;AccountName={AZURE_STORAGE_ACCOUNT};AccountKey={AZURE_STORAGE_ACCESS_KEY};")# Use this code to look up the ID of the credential object created.credential_id=Noneforcredindr.Credential.list():ifcred.name==DR_CREDENTIAL_NAME:credential_id=cred.credential_idbreakprint(credential_id) ## Set up and run a Batch Prediction job After creating a credential object, you can set up the Batch Prediction job. Set the intake settings and output settings to the azure type. Provide both attributes with the URL to the files in Blob storage that you want to read and write to (the output file does not need to exist already) and the ID of the credential object that previously set up. The code below creates and runs the Batch Prediction job and, and when finished, provide the status of the job. This code also demonstrates how to configure the job to return both Prediction Explanations and passthrough columns for the scoring data. ``` DEPLOYMENT_ID = 'YOUR DEPLOYMENT ID' AZURE_STORAGE_ACCOUNT = "YOUR AZURE STORAGE ACCOUNT NAME" AZURE_STORAGE_CONTAINER = "YOUR AZURE STORAGE ACCOUNT CONTAINER" AZURE_INPUT_SCORING_FILE = "YOUR INPUT SCORING FILE NAME" AZURE_OUTPUT_RESULTS_FILE = "YOUR OUTPUT RESULTS FILE NAME" # Set up your batch prediction job # Input: Azure Blob Storage # Output: Azure Blob Storage job = dr.BatchPredictionJob.score( deployment=DEPLOYMENT_ID, intake_settings={ 'type': 'azure', 'url': f"https://{AZURE_STORAGE_ACCOUNT}.blob.core.windows.net/{AZURE_STORAGE_CONTAINER}/{AZURE_INPUT_SCORING_FILE}", "credential_id": credential_id }, output_settings={ 'type': 'azure', 'url': "https://{AZURE_STORAGE_ACCOUNT}.blob.core.windows.net/{AZURE_STORAGE_CONTAINER}/{AZURE_OUTPUT_RESULTS_FILE}", "credential_id": credential_id }, # If explanations are required, uncomment the line below max_explanations=5, # If passthrough columns are required, use this line passthrough_columns=['column1','column2'] ) job.wait_for_completion() job.get_status() ``` When the job is complete, the output file is displayed in your Blob storage container. You now have a Batch Prediction job that can read and write from Azure Blob Storage via the DataRobot Python client package and the Batch Prediction API. ## More information - Community Github example code - DataRobot Python Client Batch Prediction Methods --- # Azure URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/azure/index.html > Integrate DataRobot with Azure cloud services. # Azure The sections within describe techniques for integrating Azure cloud services with DataRobot: | Topic | Description | | --- | --- | | Run Batch Prediction jobs from Azure Blob Storage | Running Batch Prediction jobs from Azure Blob Storage with the Batch Prediction API. | | Deploy and monitor DataRobot models in Azure Kubernetes Service | Deploying and monitoring DataRobot models on Azure Kubernetes Services (AKS) to create production scoring pipelines with the Portable Prediction Server (PPS). | | Deploy and monitor Spark models | Deploying and monitoring Spark models in DataRobot MLOps with the monitoring agent. | | Deploy and monitor ML.NET models | Deploying and monitoring ML.NET models in DataRobot MLOps with monitoring agent. | | Use Scoring Code with Azure ML | Importing Scoring Code models to Azure ML to make prediction requests with Azure. | --- # Deploy and monitor ML.NET models with DataRobot MLOps URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/azure/mlnet-deploy-and-monitor.html > Deploy and monitor ML.NET models with DataRobot MLOps # Deploy and monitor ML.NET models with DataRobot MLOps This page explores how models built with ML.NET can be deployed and monitored with DataRobot MLOps. ML.NET is an open-source machine learning framework created by Microsoft for the .NET developer platform. To learn more, see [What is ML.NET?](https://dotnet.microsoft.com/learn/ml-dotnet/what-is-mldotnet). The examples on this page use the [public LendingClub dataset](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/10K_Lending_Club_Loans.csv). You want to predict the likelihood of a loan applicant defaulting; in machine learning, this is referred to as a "binary classification problem." You can solve this with DataRobot AutoML, but in this case, you will create the model with ML.NET, deploy it into production, and monitor it with DataRobot MLOps. DataRobot MLOps allows you to monitor all of your models in one central dashboard, regardless of the source or programming language. Before deploying a model to DataRobot MLOps, you must create a new ML.NET model, and then create an ML.NET environment for DataRobot MLOps. > [!NOTE] Note > This DataRobot MLOps ML.NET environment only has to be created once, and if you only require support for binary classification and regression models, you can skip this step and use the existing "DataRobot ML.NET Drop-In" environment from the [DataRobot Community GitHub](https://github.com/datarobot-community/ai_engineering/tree/master/mlops/DRMLOps_MLNET_environment). ## Prerequisites To start building .NET apps, download and install the .NET software development kit (SDK). To install the SDK, follow the steps outlined in the [.NET intro](https://dotnet.microsoft.com/learn/dotnet/hello-world-tutorial/intro). 1. Once you've installed the .NET SDK, open a new terminal and run the following command: dotnet 2. If the previous command runs without error, you can proceed with the next step and install the ML.NET framework with the following command: dotnettoolinstall-gmlnet ### Create the ML.NET model If the installation of the ML.NET framework is successful, create the ML.NET model: ``` mkdir DefaultMLApp cd DefaultMLApp dotnet new console -o consumeModelApp dotnet classification --dataset "10K_Lending_Club_Loans.csv" --label-col "is_bad" --train-time 1000 ``` ### Evaluate your model After the ML.NET CLI selects the best model, it displays the experiment results—a summary of the exploration process—including how many models were explored in the given training time. While the ML.NET CLI generates code for the highest performing model, it also displays up to five models with the highest accuracy found during the given exploration time. It displays several evaluation metrics for those top models, including AUC, AUPRC, and F1-score. ### Test your model The ML.NET command-line interface (CLI) generates the machine learning model and adds the .NET apps and libraries needed to train and consume the model. The files created include the following: - A .NET console app (SampleBinaryClassification.ConsoleApp), which containsModelBuilder.cs(builds and trains the model) andProgram.cs(runs the model). - A .NET Standard class library (SampleBinaryClassification.Model), which containsModelInput.csandModelOutput.cs(the input and output classes for model training and consumption) andMLModel.zip(a generated serialized ML model). To test the model, run the console app ( `SampleBinaryClassification.ConsoleApp`), which predicts the likelihood of default for a single applicant: ``` cd SampleClassification/SampleClassification.ConsoleApp dotnet run ``` ## Create a DataRobot MLOps environment package While DataRobot provides many environment templates out of the box (including R, Python, Java, PyTorch, etc.), this section shows how to create your own runtime environment, from start to finish, using ML.NET. To make an easy-to-use, reusable environment, follow the below guidelines: - Your environment package must include a Dockerfile to install dependencies and an executablestart_server.shscript to start the model server. - Your custom models require a simple webserver to make predictions. The model server script can be co-located within the model package or separated into an environment package; however, it should be in a separate environment package, which allows you to use it for multiple models leveraging the same programming language. - A Dockerfile that copies all code and thestart_server.shscript to/opt/code/. You can download the code forthe MLOps environment packagefrom the DataRobot Community GitHub. - The web server must be listening on port8080and implement the following routes: DataRobot MLOps runs extensive tests before deploying a custom model to ensure reliability; therefore, it is essential that your webserver handles missing values and returns results in the expected response format as outlined above. As previously mentioned, you need to use port `8080` so that DataRobot can correctly identify the webserver. Therefore, in `appsettings.json`, specify port `8080` for the `Kestrel` web server, as shown below: ``` { "Kestrel": { "EndPoints": { "Http": { "Url": "http://0.0.0.0:8080" } } } } ``` Initialize the model code ( `mlContext`, `mlModel`, and `predEngine`) in the `Startup.cs` class. This allows .NET to recognize file changes whenever you create a new model package. ``` // Initialize MLContext MLContext ctx = new MLContext(); // Load the model DataViewSchema modelInputSchema; ITransformer mlModel = ctx.Model.Load(modelPath, out modelInputSchema); // Create a prediction engine & pass it to our controller predictionEngine = ctx.Model.CreatePredictionEngine(mlModel);​ ``` The `start_server.sh` shell script is responsible for starting the model server in the container. If you packaged the model and server together, you only need the compiled version, and the shell script runs `dotnet consumeModelApp.dll`. Since you have the model code and the server environment code separated for reusability, recompile from the source at container startup, as in the command below: ``` #!/bin/sh export DOTNET_CLI_HOME="/tmp/DOTNET_CLI_HOME" export DOTNET_CLI_TELEMETRY_OPTOUT="1" export TMPDIR=/tmp/NuGetScratch/ mkdir -p ${TMPDIR} rm -rf obj/ bin/ dotnet clean dotnet build dotnet run # to ensure Docker container keeps running tail -f /dev/null ``` Before uploading your custom environment to DataRobot MLOps, compress your custom environment code to a tarball, as shown below: ``` tar -czvf mlnetenvironment.tar.gz -C DRMLOps_MLNET_environment/. ``` ## Upload the DataRobot MLOps environment package To upload the new MLOps ML.NET environment, refer to the instructions on [creating a new custom model environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html) (see the screenshot below). ## Upload and test the ML.NET model in DataRobot MLOps Once the environment is created, create a new custom model entity and upload the actual model ( `MLModel.zip` and `ModelInput.cs`). To upload the new ML.NET model, refer to the instructions on [creating a new custom inference model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html). After creating the environment and model within DataRobot MLOps, upload test data to confirm it works as expected (as shown in the following screenshot). During this phase, DataRobot runs a test to determine how the model handles missing values and whether or not the internal webserver adheres to the response format. ## Make predictions with the new ML.NET model Once the tests are complete, deploy the custom model using the settings below: When this step is complete, you can make predictions with your new custom model as with any other DataRobot model (see also [Postman collection](https://github.com/datarobot-community/ai_engineering/blob/master/mlops/DRMLOps_MLNET_model%20samples/DR%20MLNET%20Custom%20Models.postman_collection.json)): Even though you built the model outside of DataRobot with ML.NET, you can use it like any other DataRobot model and track service health and data drift in one central dashboard, shown below: --- # Use Scoring Code with Azure ML URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/azure/sc-azureml.html > Import Scoring Code models to Azure ML to make prediction requests using Azure. # Use Scoring Code with Azure ML You must complete the following before importing Scoring Code models to Azure ML: - Install the Azure CLI client to configure your service to the terminal. - Install the Azure Machine Learning CLI extension . To import a Scoring Code model to Azure ML: 1. Login to Azure with the login command. az login 2. If you have not yet created a resource group, you can create one usingthis command: az group create --location --name [--subscription] [--tags] For example: az group create --location westus2 --name myresourcegroup 3. If you do not have an existing container registry that you want to use for storing custom Docker images, you must create one. If you want to use a DataRobot Docker image instead of building your own, you do not need to create a container registry. Instead, skip ahead to step 6. Create a container withthe following command: az acr create --name --resource-group --sku {Basic | Classic | Premium | Standard} [--admin-enabled {false | true}] [--default-action {Allow | Deny}] [--location] [--subscription] [--tags] [--workspace] For example: az acr create --name mycontainerregistry --resource-group myresourcegroup --sku Basic 4. Set up admin access usingthe following commands: az acr update --name --admin-enabled {false | true} For example: az acr update --name mycontainerregistry --admin-enabled true And print theregistry credentials: az acr credential show --name For example: az acr credential show --name mycontainerregistry Returns: { "passwords": [ { "name": "password", "value": }, { "name": "password2", "value": } ], "username": mycontainerregistry } 5. Upload a custom Docker image thatruns Java: az acr build --registry [--auth-mode {Default | None}] [--build-arg] [--file] [--image] [--no-format] [--no-logs] [--no-push] [--no-wait] [--platform] [--resource-group] [--secret-build-arg] [--subscription] [--target] [--timeout] [] For example: az acr build --registry mycontainerregistry --image myImage:1 --resource-group myresourcegroup --file Dockerfile . The following is an example of a custom Docker image. Reference theMicrosoft documentationto read more about building an image. FROM ubuntu:16.04 ARG CONDA_VERSION=4.5.12 ARG PYTHON_VERSION=3.6 ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 ENV PATH /opt/miniconda/bin:$PATH RUN apt-get update --fix-missing && \ apt-get install -y wget bzip2 && \ apt-get clean && \ rm -rf /var/lib/apt/lists/* RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-${CONDA_VERSION}-Linux-x86_64.sh -O ~/miniconda.sh && \ /bin/bash ~/miniconda.sh -b -p /opt/miniconda && \ rm ~/miniconda.sh && \ /opt/miniconda/bin/conda clean -tipsy RUN conda install -y conda=${CONDA_VERSION} python=${PYTHON_VERSION} && \ conda clean -aqy && \ rm -rf /opt/miniconda/pkgs && \ find / -type d -name __pycache__ -prune -exec rm -rf {} \; RUN apt-get update && \ apt-get upgrade -y && \ apt-get install software-properties-common -y && \ add-apt-repository ppa:openjdk-r/ppa -y && \ apt-get update -q && \ apt-get install -y openjdk-11-jdk && \ apt-get clean 6. If you have not already created a workspace,use the following command to create one. Otherwise, skip to step 7. az ml workspace create --workspace-name [--application-insights] [--container-registry] [--exist-ok] [--friendly-name] [--keyvault] [--location] [--resource-group] [--sku] [--storage-account] [--yes] For example: az ml workspace create --workspace-name myworkspace --resource-group myresourcegroup 7. Register your Scoring Code modelto the Azure model storage. NoteMake sure you have exported your Scoring Code JAR file from DataRobot before proceeding. You can download the JAR file from theLeaderboardor from adeployment.az ml model register --name [--asset-path][--cc] [--description][--experiment-name] [--gb][--gc] [--model-framework][--model-framework-version] [--model-path][--output-metadata-file] [--path][--property] [--resource-group][--run-id] [--run-metadata-file][--sample-input-dataset-id] [--sample-output-dataset-id][--tag] [--workspace-name][-v] For example, to register model namedcodegenmodel: az ml model register --name codegenmodel --model-path 5cd071deef881f011a334c2f.jar --resource-group myresourcegroup --workspace-name myworkspace 8. Prepare two configs and aPython entry scriptthat will execute the prediction. Below are some examples of configs with a Python entry script. 9. Create a new prediction endpoint: az ml model deploy --name [--ae] [--ai] [--ar] [--as] [--at] [--autoscale-max-replicas] [--autoscale-min-replicas] [--base-image] [--base-image-registry] [--cc] [--cf] [--collect-model-data] [--compute-target] [--compute-type] [--cuda-version] [--dc] [--description] [--dn] [--ds] [--ed] [--eg] [--entry-script] [--environment-name] [--environment-version] [--failure-threshold] [--gb] [--gc] [--ic] [--id] [--kp] [--ks] [--lo] [--max-request-wait-time] [--model] [--model-metadata-file] [--namespace] [--no-wait] [--nr] [--overwrite] [--path] [--period-seconds] [--pi] [--po] [--property] [--replica-max-concurrent-requests] [--resource-group] [--rt] [--sc] [--scoring-timeout-ms] [--sd] [--se] [--sk] [--sp] [--st] [--tag] [--timeout-seconds] [--token-auth-enabled] [--workspace-name] [-v] For example, to create a new endpoint with the namemyservice: az ml model deploy --name myservice --model codegenmodel:1 --compute-target akscomputetarget --ic inferenceconfig.json --dc deploymentconfig.json --resource-group myresourcegroup --workspace-name myworkspace 10. Get a tokento make prediction requests: az ml service get-keys --name [--path] [--resource-group] [--workspace-name] [-v] For example: az ml service get-keys --name myservice --resource-group myresourcegroup --workspace-name myworkspace This command returns a JSON response: { "primaryKey": , "secondaryKey": } You can now make prediction requests using Azure. --- # Deploy and monitor Spark models with DataRobot MLOps URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/azure/spark-deploy-and-monitor.html > Deploy and monitor Spark models with DataRobot MLOps # Deploy and monitor Spark models with DataRobot MLOps This page shows how to use DataRobot's Monitoring Agent (MLOps agent) to manage and monitor models from a central dashboard without deploying them within MLOps. You will explore how to manage and monitor remote models—models that are not running within DataRobot MLOps—deployed on your own infrastructure. Common examples are serverless deployments (AWS Lambda, Azure Functions) or deployments on Spark clusters (Hadoop, Databricks, AWS EMR). The sections below show how to take a DataRobot model to be deployed on a Databricks cluster and monitor this model with DataRobot MLOps in a central dashboard. This dashboard covers all of your models, regardless of where they were developed or deployed. This approach works for any model that runs within a Spark cluster. ## Create a model In this section, you are creating a model with DataRobot AutoML, then importing it into your Databricks cluster, using the [Lending Club dataset](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/10K_Lending_Club_Loans.csv). > [!NOTE] Note > If you already have a regression model that runs in your Spark cluster, you can skip this step and proceed to [Install the MLOps monitoring agent and library](https://docs.datarobot.com/en/docs/classic-ui/integrations/azure/spark-deploy-and-monitor.html#install-the-mlops-monitoring-agent-and-library). 1. To upload the training data to DataRobot, do either of the following: 2. Enterloan_amtas your target (1) (what you want to predict) and clickStart(2) to run Autopilot. 3. After Autopilot finishes, clickModels(1) and select a model with theSCORING CODElabel (2) at the top of the Leaderboard (3). 4. Under the model you selected, clickPredict(1) and clickDownloads(2) to access theScoring Code JARdownload. NoteThe ability to download Scoring Code for a model from the Leaderboard depends on the MLOps configuration for your organization. 5. ClickDownload(3) to start the JAR file download. For more information, see the documentation on [Scoring Code](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/index.html). ## Deploy a model To install Scoring Code, you can install the previously downloaded JAR file, along with Spark Wrapper, on the Databricks cluster as shown below. 1. ClickClustersto open the cluster settings. 2. Select the cluster to which you'd like to deploy the DataRobot model. 3. Click theLibrariestab. 4. ClickInstall New. 5. In theInstall Librarydialog box, with theLibrary Sourceset toUploadand theLibrary Typeset toJAR, drag-and-drop the Scoring Code JAR file (e.g.,5ed68d70455df33366ce0508.jar), 6. ClickInstall. Once the install is complete, repeat the same steps and install Spark Wrapper, which you can [download here](https://github.com/datarobot-community/ai_engineering/blob/master/mlops/DRMLOps_Spark_examples/scoring-code-spark-api.jar), or pull the latest version of it directly from [Maven](https://mvnrepository.com/artifact/com.datarobot/scoring-code-spark-api_2.4.3). ## Install the MLOps monitoring agent and library Remote models do not directly communicate with DataRobot MLOps. Instead, the communication is handled via DataRobot MLOps monitoring agents, which support many spooling mechanisms (e.g., flat files, AWS SQS, RabbitMQ). These agents are typically deployed in the external environment where the model is running. Libraries are available for all common programming languages to simplify communication with the DataRobot MLOps monitoring agent. The model is instructed to talk to the agent with the help of the MLOps library. The agent then collects all metrics from the model and relays them to the MLOps server and dashboards. In this example, the runtime environment is Spark. Therefore, you will install the MLOps library to your Spark cluster (Databricks) in the same way you installed the model itself previously (in Deploy a Model). You will also install the MLOps monitoring agent in an Azure Kubernetes Service (AKS) cluster alongside RabbitMQ, which is used as your queuing system. This process assumes that you are familiar with Azure Kubernetes Service and the Azure CLI. For more information, see [Microsoft's Quick Start Tutorial](https://docs.microsoft.com/en-us/azure/aks/kubernetes-walkthrough). ### Create an AKS cluster 1. If you don't have a running AKS cluster, create one, as shown below: RESOURCE_GROUP=ai_success_engCLUSTER_NAME=AIEngineeringDemo azakscreate\--resource-group$RESOURCE_GROUP\--name$CLUSTER_NAME\-sStandard_B2s\--node-count1\--generate-ssh-keys\--service-principalXXXXXX\--client-secretXXXX\--enable-cluster-autoscaler\--min-count1\--max-count2 2. Start the Kubernetes dashboard: azaksbrowse--resource-group$RESOURCE_GROUP--name$CLUSTER_NAME​ ### Install RabbitMQ There are many ways to deploy applications. The most direct way is via the Kubernetes dashboard. To install RabbitMQ: 1. ClickCREATE > CREATE AN APP(1). 2. On theCREATE AN APPpage (2), specify the following: FieldValue3App namee.g.,rabbitmqdemo4Container imagee.g.,rabbitmq:latest5Number of podse.g.,16ServiceExternal7PortandTarget port5672and567215672and15672 3. ClickDEPLOY(8). ### Download the MLOps monitoring agent To download the MLOps monitoring agent directly from your DataRobot cluster: 1. In the upper-right corner of DataRobot, click your profile icon (or the default avatar). 2. ClickAPI keys and tools. 3. UnderExternal Monitoring Agent, click the download icon. ### Install the MLOps monitoring agent You can install the agent anywhere; however, for this process, you will install it alongside RabbitMQ. 1. Copy the monitoring agent tarball you downloaded in the previous section to the container where RabbitMQ is running. To do this, run the following command: NoteYou may need to replace the filename of the tarball in the example below. kubectlcpdatarobot-mlops-agent-6.1.0.tar.gzdefault/rabbitmq-649ccbd8cb-qjb4l:/opt 2. ClickPods(1) and click the containerName(2) to connect to the CLI of the container. 3. ClickExec(3) to start the container. 4. In the container's CLI, begin to configure the agent. Review the tarball name and, if necessary, update the filename in the following commands, and then run them: cd/opt&&tar-xvzfmlops-agent-6.1.0.tar&&cdmlops-agent-6.1.0/conf 5. In the directory, update themlops.agent.conf.yamlconfiguration file to point to your DataRobot MLOps instance and message queue. 6. To update the configuration and run the agent, you must install Vim and Java with the following commands: apt-getupdate&&apt-getinstallvim&&apt-getinstalldefault-jdk 7. In this example, you are using RabbitMQ and the DataRobot managed AI Platform solution, so you must configure themlopsURLandapiToken(1) and thechannelConfigs(2) as shown below. NoteYou can obtain yourapiTokenfrom theDeveloper Tools. 8. Before starting the agent, you can enable the RabbitMQ Management UI and create a new user to monitor queues: ### Enable the RabbitMQ UIrabbitmq-pluginsenablerabbitmq_management&&### Add a user via the CLIrabbitmqctladd_user&&rabbitmqctlset_user_tagsadministrator&&rabbitmqctlset_permissions-p/".*"".*"".*" 9. Now that RabbitMQ is configured and the updated configuration is saved, switch to the/bindirectory and start the agent: cd../bin&&./start-agent.sh 10. Confirm that the agent is running correctly by checking its status: ./status-agent.sh 11. To ensure that everything is running as expected, check the logs located in the/logsdirectory. ### Install the MLOps library in a Spark cluster First, [Download the library from here](https://github.com/datarobot-community/ai_engineering/blob/master/mlops/DRMLOps_Spark_examples/datarobot-mlops-6.2.0.jar). To install the library in a Spark cluster (Databricks): 1. ClickClustersto open the cluster settings. 2. Select the cluster to which you'd like to deploy the DataRobot model. 3. Click theLibrariestab. 4. In theInstall Librarydialog box, with theLibrary Sourceset toUploadand theLibrary Typeset toJAR, drag-and-drop the MLOps JAR file (e.g.,MLOps.jar). 5. ClickInstall. ## Run your Model Now that all the prerequisites are in place, run your model to make predictions: ``` // Scala example (see also PySpark example in notebook references at the bottom) // 1) Use local DataRobot Model for Scoring import com.datarobot.prediction.spark.Predictors // referencing model_id, which is the same as the generated filename of the JAR file val DataRobotModel = com.datarobot.prediction.spark.Predictors.getPredictor("5ed68d70455df33366ce0508") // 2) read the scoring data val scoringDF = sql("select * from 10k_lending_club_loans_with_id_csv") // 3) Score the data and save results to spark dataframe val output = DataRobotModel.transform(scoringDF) // 4) Review/consume scoring results output.show(1,false) ``` To track the actual scoring time, wrap the scoring command, so the updated code would look like the following: ``` // to track the actual scoring time def time[A](f: => A): Double = { val s = System.nanoTime val ret = f val scoreTime = (System.nanoTime-s)/1e6 * 0.001 println("time: "+ scoreTime+"s") return scoreTime } // 1) Use local DataRobot Model for Scoring import com.datarobot.prediction.spark.Predictors // referencing model_id, which is the same as the generated filename of the JAR file val DataRobotModel = com.datarobot.prediction.spark.Predictors.getPredictor("5ed708a8fca6a1433abddbcb") // 2) read the scoring data val scoringDF = sql("select * from 10k_lending_club_loans_with_id_csv") val scoreTime = time { // Score the data and save results to spark dataframe val scoring_output = DataRobotModel.transform(scoringDF) scoring_output.show(1,false) scoring_output.createOrReplaceTempView("scoring_output") } ``` ## Report usage to MLOps via monitoring agents After using the model to predict the loan amount of an application, you can report the telemetrics around these predictions to your DataRobot MLOps server and dashboards. To do this, see the commands in the following sections. ### Create an external deployment Before you can report scoring details, you must create an external deployment within DataRobot MLOps. This only has to be done once and can be done via the UI in DataRobot MLOps: 1. ClickModel Registry(1), clickModel Packages(2), and then clickNew external model package(3). 2. Specify a package name and description (1 and 2), upload the corresponding training data for drift tracking (3), and identify the model location (4), target (5), environment (6), and prediction type (7), then clickCreate package(8). 3. After creating the external model package, note the model ID in the URL, as shown below (blurred in the image for security purposes). 4. ClickDeployments(1) and clickCreate new deployment(2). Once the deployment is created, theDeployments > Overviewpage is shown. 5. On theOverviewpage, copy the deployment ID (from the URL). Now that you have your model ID and deployment ID, you can report the predictions in the next section. ### Report prediction details To report prediction details to DataRobot, run the following code in your Spark environment. Make sure you update the input parameters. ``` import com.datarobot.mlops.spark.MLOpsSparkUtils val channelConfig = "OUTPUT_TYPE=RABBITMQ;RABBITMQ_URL=amqp://<>:5672;RABBITMQ_QUEUE_NAME=mlopsQueue" MLOpsSparkUtils.reportPredictions( scoringDF, // spark dataframe with actual scoring data "5ec3313XXXXXXXXX", // external DeploymentId "5ec3313XXXXXXXXX", // external ModelId channelConfig, // rabbitMQ config scoringTime, // actual scoring time Array("PREDICTION"), //target column "id" // AssociationId ) ``` ### Report actuals When you get actual values, you can report them to track accuracy over time. Report actuals using the function below: ``` import com.datarobot.mlops.spark.MLOpsSparkUtils val actualsDF = spark.sql("select id as associationId, loan_amnt as actualValue, null as timestamp from actuals") MLOpsSparkUtils.reportActuals( actualsDF, deploymentId, ModelId, channelConfig ) ``` Even though you deployed a model outside of DataRobot on a Spark cluster (Databricks), you can monitor it like any other model to track service health, data drift, and actuals in one central dashboard. For complete sample notebooks with code snippets for Scala and PySpark, go to [the DataRobot Community GitHub](https://github.com/datarobot-community/ai_engineering/tree/master/mlops/DRMLOps_Spark_examples). --- # Deploy and monitor models on GCP URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html > Deploy and monitor DataRobot models on Google Cloud Platform (GCP). # Deploy and monitor models on GCP > [!NOTE] Availability information > The MLOps model package export feature used in this procedure is off by default. Contact your DataRobot representative or administrator for information on enabling it. > > Feature flag: Enable MMM model package export The following describes the process of deploying a DataRobot model on the Google Cloud Platform (GCP) using the Google Kubernetes Engine (GKE). ## Overview [DataRobot MLOps](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html) provides a central hub to deploy, monitor, manage, and govern all your models in production. With MLOps, you aren't limited to serving DataRobot models on the dedicated scalable prediction servers inside the DataRobot cluster. You can also deploy DataRobot models into Kubernetes (K8s) clusters while maintaining the advantages of DataRobot's model monitoring capabilities. This exportable DataRobot model is called a [Portable Prediction Server](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html) (PPS) and is similar to Docker containers in the flexibility and portability it provides. A PPS is based on Docker containers and contains a DataRobot model with embedded monitoring agents. Using this approach, a DataRobot model is made available via a scalable deployment environment for usage, and associated data can be tracked in the centralized DataRobot MLOps dashboard with all of its monitoring and governance advantages. Unifying the portability of DataRobot model Docker images with the scalability of a K8s platform results in a powerful ML solution ready for production usage. ### Prerequisites You must complete the following steps before creating the main configuration. 1. Install Google Cloud SDK appropriate for your operating system (seeGoogle's documentation). 2. Run the following at a command prompt: gcloud init You will be asked to choose the existing project or to create a new one and also to select the compute zone. For example: 3. Install the Kubernetes command-line tool: gcloud components install kubectl The output will be similar to: ## Procedure The following sections, each a step in the process, describe the procedure for deploying and monitoring DataRobot models on the GCP platform via a PPS. The examples use the [Kaggle housing prices](https://www.kaggle.com/c/home-data-for-ml-course/data) dataset. ### Download a model package Build models using the housing prices dataset. Once Autopilot finishes, you can create and download the MLOps model package. To do this, navigate to the Models tab to select a model and click Predict > Deploy. In the MLOps Package section, select Generate & Download. DataRobot generates a model package (.mlpkg file) containing all the necessary information about the model. ### Create a Docker container image To create a Docker container image with the MLOps package: 1. After the model package download (started in the previous step) completes, download thePPS base image. 2. Once you have the PPS base image, use the following Dockerfile to generate an image that includes the DataRobot model package: NoteTo copy the.mlpkgfile into the Docker image, make sure the Dockerfile and the.mlpkgfile are in the same folder. FROMdatarobot/datarobot-portable-prediction-api: COPY.mlpkg/opt/ml/model 3. Set thePROJECT_IDenvironment variable to your Google Cloud project ID (the project ID you defined during the Google Cloud SDK installation). ThePROJECT_IDassociates the container image with your project's Container Registry: export PROJECT_ID= ai-XXXXXX-XXXXXX 4. Build and tag the Docker image. For example: docker build -t gcr.io/${PROJECT_ID}/house-regression-model:v1 5. Run thedocker imagescommand to verify that the build was successful: The generated image contains the DataRobot model and the monitoring agent used to transfer the service and model health metrics back to the DataRobot MLOps platform. ### Run Docker locally While technically an optional step, best practice advises always testing your image locally to save time and network bandwidth. To run locally: 1. Run your Docker container image: docker run --rm --name house-regression -p 8080:8080 -it gcr.io/${PROJECT_ID}/house-regression-model:v1 2. Score the data locally to test if the model works as expected: curl -X POST http://localhost:8080/predictions -H "Content-Type: text/csv" --data-binary @/Users/X.X/community/docker/kaggle_house_test_dataset.csv NoteUpdate the path to thekaggle_house_test_dataset.csvdataset to match the path locally on your workstation. ### Push Docker image to the Container Registry Once you have tested and validated the container image locally, upload it to a registry so that your Google Kubernetes Engine (GKE) cluster can download and run it. 1. Configure the Docker command-line tool to authenticate to Container Registry: gcloud auth configure-docker 2. Push the Docker imageyou builtto the Container Registry: docker push gcr.io/${PROJECT_ID}/house-regression-model:v1 > [!NOTE] Note > Pushing to the Container Registry may result in the `storage.buckets.create` permission issue. If you receive this error, contact the administrator of your GCP account. ### Create the GKE cluster After storing the Docker image in the Container Registry, you next create a GKE cluster, as follows: 1. Set your project ID and Compute Engine zone options for thegcloudtool: gcloud config set project $PROJECT_ID gcloud config set compute/zone europe-west1-b 2. Create the cluster: gcloud container clusters create house-regression-cluster This command finishes as follows: 3. After the command completes, run the following command to see the cluster worker instances: gcloud compute instances list The output is similar to: > [!NOTE] Note > Pushing to the Container Registry may result in the `gcloud.container.clusters.create` permission issue. If you receive this error, contact the administrator of your GCP account. ### Deploy the Docker image to GKE To deploy your image to GKE: 1. Create a Kubernetes deployment for your Docker image: kubectl create deployment house-regression-app --image=gcr.io/${PROJECT_ID}/house-regression-model:v1 2. Set the baseline number of deployment replicas to 3 (i.e., the deployment will always have 3 running pods). kubectl scale deployment house-regression-app --replicas=3 3. K8s provides the ability to manage resources in a flexible, automatic manner. For example, you can create a HorizontalPodAutoscaler resource for your deployment: kubectl autoscale deployment house-regression-app --cpu-percent=80 --min=1 --max=5 4. Run the following command to check that the pods you created are all operational and in a running state (e.g., you may to see up to 5 running pods as requested in the previous autoscale step): kubectl get pods The output is similar to: #### Expose your model The default service type in GKE is called ClusterIP, where the service gets an IP address reachable only from inside the cluster. To expose a Kubernetes service outside of the cluster, you must create a service of type `LoadBalancer`. This type of service spawns an External Load Balancer IP for a set of pods, reachable via the internet. 1. Use thekubectl exposecommand to generate a Kubernetes service for thehouse-regression-appdeployment: kubectl expose deployment house-regression-app --name=house-regression-app-service --type=LoadBalancer --port 80 --target-port 8080 Where: - --port is the port number configured on the Load Balancer - --target-portis the port number that thehouse-regression-appcontainer is listening on. - Run the following command to view service details: kubectl get service The output is similar to: - Copy theEXTERNAL-IPaddress from the service details. - Score your model using theEXTERNAL-IPaddress. curl -X POST http://XX.XX.XX.XX/predictions -H "Content-Type: text/csv" --data- binary @/Users/X.X/community/docker/kaggle_house_test_dataset.csv NoteUpdate the IP address placeholder above with theEXTERNAL-IPaddress you copied and update the path to thekaggle_house_test_dataset.csvdataset to match the path locally on your workstation. > [!NOTE] Note > The cluster is open to all incoming requests at this point. See the [Google documentation](https://cloud.google.com/kubernetes-engine/docs/concepts/access-control) to apply more fine-grained role-based access control (RBAC). ## Create an external deployment To create an external deployment in MLOps: 1. Navigate to theModel Registry > Model Packages > Add New Packageand follow the instructions in thedocumentation. ClickAdd new external model package. 2. Make a note of theMLOps model IDfound in the URL. You will use this whenlinking PPS and MLops. While still on theModel Registrypage and within the expanded new package, select theDeploymentstab and clickCreate new deployment. The deployment page loads prefilled with information from the model package you created. 3. Make a note of theMLOps deployment ID(earlier, you copied the model ID). You will use this whenlinking PPS and MLOps. ### Link PPS on K8s to MLOps Finally, update the K8s deployment configuration with the PPS and monitoring agent configuration. Add the following environment variables into the K8s Deployment configuration (see the complete configuration file [here](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/google-cloud-platform.html#K8s-configuration-files)): ``` PORTABLE_PREDICTION_API_WORKERS_NUMBER=2 PORTABLE_PREDICTION_API_MONITORING_ACTIVE=True PORTABLE_PREDICTION_API_MONITORING_SETTINGS=output_type=output_dir;path=/tmp;max_files=50;file_max_size=10240 000;model_id=;deployment_id= MONITORING_AGENT=True MONITORING_AGENT_DATAROBOT_APP_URL= MONITORING_AGENT_DATAROBOT_APP_TOKEN= ``` > [!NOTE] Note > You can obtain the `MONITORING_AGENT_DATAROBOT_APP_TOKEN` from the [Developer Tools](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html#api-key-management). ### Deploy new Docker image (optional) To upgrade the deployed Docker image, simply: 1. Create a new version of your Docker image: docker build -t gcr.io/${PROJECT_ID}/house-regression-model:v2 2. Push the new image to the Container Registry: docker push gcr.io/${PROJECT_ID}/house-regression-model:v2` 3. Apply a rolling update to the existing deployment with an image update: kubectl set image deployment/house-regression-app house-regression-model=gcr.io/${PROJECT_ID}/house-regression-model:v2 4. Watch the pods running the v1 image terminate, and new pods running the v2 image spin up: kubectl get pods ### Clean up To finish the process of setting up using the GCP platform via a Portable Prediction Server (PPS) for deployments, do the following. 1. Delete the service: kubectl delete service house-regression-app-service 2. Delete the cluster: gcloud container clusters delete house-regression-cluster ## K8s configuration files The following sections provide deployment and service configuration files for reference. ### Deployment configuration file ``` apiVersion: apps/v1 kind: Deployment metadata: annotations: deployment.kubernetes.io/revision: "2" creationTimestamp: "2020-07-08T12:47:27Z" generation: 8 labels: app: house-regression-app name: house-regression-app namespace: default resourceVersion: "14171" selfLink: /apis/apps/v1/namespaces/default/deployments/house-regression-app uid: 2de869fc-c119-11ea-8156-42010a840053 spec: progressDeadlineSeconds: 600 replicas: 5 revisionHistoryLimit: 10 selector: matchLabels: app: house-regression-app strategy: rollingUpdate: maxSurge: 25% maxUnavailable: 25% type: RollingUpdate template: metadata: creationTimestamp: null labels: app: house-regression-app spec: containers: - env: - name: PORTABLE_PREDICTION_API_WORKERS_NUMBER value: "2" - name: PORTABLE_PREDICTION_API_MONITORING_ACTIVE value: "True" - name: PORTABLE_PREDICTION_API_MONITORING_SETTINGS value: output_type=output_dir;path=/tmp;max_files=50;file_max_size=10240000;model_id=;deployment_id= - name: MONITORING_AGENT value: "True" - name: MONITORING_AGENT_DATAROBOT_APP_URL value: https://app.datarobot.com/ - name: MONITORING_AGENT_DATAROBOT_APP_TOKEN value: image: gcr.io/${PROJECT_ID}/house-regression-model:v1 imagePullPolicy: IfNotPresent name: house-regression-model resources: {} terminationMessagePath: /dev/termination-log terminationMessagePolicy: File dnsPolicy: ClusterFirst restartPolicy: Always schedulerName: default-scheduler securityContext: {} terminationGracePeriodSeconds: 30 status: availableReplicas: 5 conditions: - lastTransitionTime: "2020-07-08T12:47:27Z" lastUpdateTime: "2020-07-08T13:40:47Z" message: ReplicaSet "house-regression-app-855b44f748" has successfully progressed. reason: NewReplicaSetAvailable status: "True" type: Progressing - lastTransitionTime: "2020-07-08T13:41:39Z" lastUpdateTime: "2020-07-08T13:41:39Z" message: Deployment has minimum availability. reason: MinimumReplicasAvailable status: "True" type: Available observedGeneration: 8 readyReplicas: 5 replicas: 5 updatedReplicas: 5 ``` #### Service configuration file ``` apiVersion: v1 kind: Service metadata: creationTimestamp: "2020-07-08T12:58:13Z" labels: app: house-regression-app name: house-regression-app-service namespace: default resourceVersion: "5055" selfLink: /api/v1/namespaces/default/services/house-regression-app-service uid: aeb836cd-c11a-11ea-8156-42010a840053 spec: clusterIP: 10.31.242.132 externalTrafficPolicy: Cluster ports: - nodePort: 30654 port: 80 protocol: TCP targetPort: 8080 selector: app: house-regression-app sessionAffinity: None type: LoadBalancer status: loadBalancer: ingress: - ip: XX.XX.XXX.XXX ``` --- # Google URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/google/index.html > Deploying and monitoring DataRobot models on Google Cloud Platform (GCP) and Google Kubernetes Engine (GKE). # Google The following sections describe techniques for integrating the Google platforms and services with DataRobot: | Topic | Description | | --- | --- | | Ingest BigQuery data in DataRobot | In Workbench, retrieve BigQuery data to add it to your Use Case and register it in the Data Registry. | | Wrangle BigQuery data in DataRobot | In Workbench, build wrangling recipes and push down those transformations to BigQuery where they are applied to the source data by leveraging BigQuery SQL. | | Deploy and monitor models on GCP | Deploying and monitoring DataRobot models on the Google Cloud Platform (GCP). | | Deploy the MLOps agent on GKE | Deploying the MLOps agent on Google Kubernetes Engine (GKE) to monitor models. | --- # Deploy the MLOps agent on GKE URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/google/mlops-agent-with-gke.html > Deploy the MLOps agent on GKE to monitor DataRobot models. # Deploy the MLOps agent on GKE The following steps describe how to deploy the MLOps agent on Google Kubernetes Engine (GKE) with Pub/Sub as a spooler. This allows you to monitor a custom Python model developed outside DataRobot. The custom model will be scored at the local machine and will send the statistics to Google Cloud Platform (GCP) [Pub/Sub](https://cloud.google.com/pubsub#section-5). Finally, the agent (deployed on GKE) will consume this data and send it back to the DataRobot MLOps dashboard. ## Overview [DataRobot MLOps](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html) offers the ability to monitor all your ML models (trained in DataRobot or outside) in a centralized dashboard with the DataRobot [MLOps agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html). The agent, a Java utility running in parallel with the deployed model, can monitor models developed in Java, Python, and R programming languages. The MLOps agent communicates with the model via a spooler (i.e., file system, GCP Pub/Sub, AWS SQS, or RabbitMQ) and sends model statistics back to the MLOps dashboard. These can include the number of scored records, number of features, scoring time, data drift, and more. You can embed the agent can into a Docker image and deploy it on a Kubernetes cluster for scalability and robustness. ## Prerequisites You must complete the following steps before creating the main configuration. 1. Install the Google Cloud SDKspecific to your operating system. 2. Run the following at a command prompt: gcloud init You will be asked to choose an existing project or create a new one, as well as to select the compute zone. 3. Install the Kubernetes command-line tool: gcloud components install kubectl 4. Retrieve your Google Cloud service account credentials to call Google Cloud APIs. If you don’t have adefault service account, you can create it by following thisprocedure. 5. Once credentials are in place, download the JSON file that contains them. Later, when it is time to pass your credentials to the application that will call Google Cloud APIs, you can use one of these methods: ## Procedure The following sections, each a step in the process, describe the procedure for deploying the MLOps agent on GKE with the Pub/Sub. ### Create an external deployment First, [create an external deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-external-model.html). You will use the resulting model ID and deployment ID to configure communications with the agent (described in the instructions for [running Docker locally](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/mlops-agent-with-gke.html#run-docker-locally)). ### Create a Pub/Sub topic and subscription Second, create a Pub/Sub topic and subscription: 1. Go to your Google Cloud console Pub/Sub service and create a topic (i.e., a named resource where publishers can send messages). 2. Create a subscription—a named resource representing the stream of messages from a single, specific topic, to be delivered to the subscribing application. Use the Pub/Sub topic from the previous step and setDelivery typetoPull. This provides a Subscription ID. Additionally, you can configure message retention duration and other parameters. ### Embed MLOps agent in Docker To create a Docker image that embeds the agent: 1. Create the working directory on the machine where you will prepare the necessary files. 2. Create a directory namedconf. 3. Download and unzip the tarball file with the MLOps agent fromAPI keys and tools. 4. Copy themlops.log4j2.propertiesfile from/confto your. 5. Copy the filemlops.agent.conf.yamlto the working directory. Provide the following parameters (the example uses defaults for all other parameters): ParameterDefinitionmlopsUrlInstallation URL for Self-Managed AI Platform;app.datarobot.comfor managed AI PlatformapiTokenDataRobot keyprojectIdGCP ProjectIdtopicNameCreated in thePub/Sub section For example: mlopsUrl:"MLOPS-URL"apiToken:"YOUR-DR-API-TOKEN"channelConfigs:-type:"PUBSUB_SPOOL"details:{name:"pubsub",projectId:"YOUR-GOOGLE-PROJECT-ID",topicName:"YOUR-PUBSUB-TOPIC-ID-DEFINED-AT-STEP-2"} 6. Copy the/lib/mlops-agent-X.X.X.jarfile to your working directory. 7. In the working directory, create the Dockerfile using the following content: FROMopenjdk:8ENVAGENT_BASE_LOC=/opt/datarobot/maENVAGENT_LOG_PROPERTIES=mlops.log4j2.propertiesENVAGENT_CONF_LOC=$AGENT_BASE_LOC/conf/mlops.agent.conf.yamlCOPYmlops-agent-*.jar${AGENT_BASE_LOC}/mlops-agent.jarCOPYconf$AGENT_BASE_LOC/confCOPYentrypoint.sh/RUNchmod+x/entrypoint.shENTRYPOINT["./entrypoint.sh"] 8. Createentrypoint.shwith the following content: #!/bin/shecho"######## STARTING MLOPS-AGENT ########"echoexecjava-Dlog.file=$AGENT_BASE_LOC/logs/mlops.agent.log-Dlog4j.configurationFile=file:$AGENT_BASE_LOC/conf/$AGENT_LOG_PROPERTIES-cp$AGENT_BASE_LOC/mlops-agent.jarcom.datarobot.mlops.agent.Agent--config$AGENT_CONF_LOC 9. Create the Docker image, ensuring you include the period (.) at the end of the Docker build command. exportPROJECT_ID=ai-XXXXXXX-111111 dockerbuild-tgcr.io/${PROJECT_ID}/monitoring-agents:v1. 10. Run thedocker imagescommand to verify a successful build. ### Run Docker locally > [!NOTE] Note > While technically an optional step, best practice advises always testing your image locally to save time and network bandwidth. The monitoring agent tarball includes the necessary library (along with Java and R libraries) for sending statistics from the custom Python model back to MLOps. You can find the libraries in the `lib` directory. To run locally: 1. Install theDataRobot_MLOpslibrary for Python: pip install datarobot_mlops_package-/lib/datarobot_mlops--py2.py3-none-any.whl 2. Run your Docker container image. NoteYou will need the JSON file with credentials that you downloaded in theprerequisites(the step that describes downloading Google Cloud account credentials). dockerrun-it--rm--namema-v/path-to-you-directory/mlops.agent.conf.yaml:/opt/datarobot/ma/conf/mlops.agent.conf.yaml-v/path-to-your-directory/your-google-application-credentials.json:/opt/datarobot/ma/conf/gac.json-eGOOGLE_APPLICATION_CREDENTIALS="/opt/datarobot/ma/conf/gac.json"gcr.io/${PROJECT_ID}/monitoring-agents:v1 The following is the example of the Python code where your model is scored (all package import statements are omitted from this example): fromdatarobot_mlops.mlopsimportMLOpsDEPLOYMENT_ID="EXTERNAL-DEPLOYMENT-ID-DEFINED-AT-STEP-1"MODEL_ID="EXTERNAL-MODEL-ID-DEFINED-AT-STEP-1"PROJECT_ID="YOUR-GOOGLE-PROJECT-ID"TOPIC_ID="YOUR-PUBSUB-TOPIC-ID-DEFINED-AT-STEP-2"# MLOPS: initialize the MLOps instancemlops=MLOps()\.set_deployment_id(DEPLOYMENT_ID)\.set_model_id(MODEL_ID)\.set_pubsub_spooler(PROJECT_ID,TOPIC_ID)\.init()# Read your custom model pickle file (model has been trained outside DataRobot)model=pd.read_pickle('custom_model.pickle')# Read scoring datafeatures_df_scoring=pd.read_csv('features.csv')# Get predictionsstart_time=time.time()predictions=model.predict_proba(features_df_scoring)predictions=predictions.tolist()num_predictions=len(predictions)end_time=time.time()# MLOPS: report the number of predictions in the request and the execution timemlops.report_deployment_stats(num_predictions,end_time-start_time)# MLOPS: report the features and predictionsmlops.report_predictions_data(features_df=features_df_scoring,predictions=predictions)# MLOPS: release MLOps resources when finishedmlops.shutdown() 3. Set theGOOGLE_APPLICATION_CREDENTIALSenvironment variable: export GOOGLE_APPLICATION_CREDENTIALS="" 4. Score your data locally to test if the model works as expected. You will then be able to see a new record inmonitoring agent log: python score-your-model.py The statistics in the MLOps dashboard are updated as well: ### Push Docker image to the Container Registry After you have tested and validated the container image locally, upload it to a registry so that your Google Kubernetes Engine (GKE) cluster can download and run it. 1. Configure the Docker command-line tool to authenticate to Container Registry: gcloud auth configure-docker 2. Push the Docker imageyou builtto the Container Registry: docker push gcr.io/${PROJECT_ID}/monitoring-agents:v1 ### Create the GKE cluster After storing the Docker image in the Container Registry, you next create a GKE cluster, as follows: 1. Set your project ID and Compute Engine zone options for thegcloudtool: gcloud config set project $PROJECT_ID gcloud config set compute/zone europe-west1-b 2. Create a cluster. NoteThis example, for simplicity, creates a private cluster with unrestricted access to the public endpoint. For security, be sure to restrict access to the control plane for your production environment. Find detailed information about configuring different GKE private clustershere. gcloudcontainerclusterscreatemonitoring-agents-cluster\--networkdefault\--create-subnetworkname=my-subnet-0\--no-enable-master-authorized-networks\--enable-ip-alias\--enable-private-nodes\--master-ipv4-cidr172.16.0.32/28\--no-enable-basic-auth\--no-issue-client-certificate Where: ParameterResult--create-subnetwork name=my-subnet-0Causes GKE to automatically create a subnet namedmy-subnet-0.--no-enable-master-authorized-networksDisables authorized networks for the cluster.--enable-ip-aliasMakes the cluster VPC-native.--enable-private-nodesIndicates that the cluster's nodes do not have external IP addresses.--master-ipv4-cidr 172.16.0.32/28Specifies an internal address range for the control plane. This setting is permanent for this cluster.--no-enable-basic-authDisables basic auth for the cluster.--no-issue-client-certificateDisables issuing a client certificate. 3. Run the following command to see the cluster worker instances: gcloud compute instances list ### Create a cloud router The MLOps agent running on a GKE private cluster needs access to the DataRobot MLOps service. To do this, you must give the private nodes outbound access to the internet, which you can achieve using a NAT cloud router ( [Google documentation here](https://cloud.google.com/nat/docs/gke-example#gcloud_4)). 1. Create a cloud router: gcloudcomputerouterscreatenat-router\--networkdefault\--regioneurope-west1 2. Add configuration to the router. gcloudcomputeroutersnatscreatenat-config\--router-regioneurope-west1\--routernat-router\--nat-all-subnet-ip-ranges\--auto-allocate-nat-external-ips​ #### Create K8s ConfigMaps With the cloud router configured, you can now create K8s ConfigMaps to contain the MLOps agent configuration and Google credentials. You will need the downloaded JSON credentials file created during the [prerequisites](https://docs.datarobot.com/en/docs/classic-ui/integrations/google/mlops-agent-with-gke.html#prerequisites) stage. > [!NOTE] Note > Use K8s Secrets to save your configuration files for production usage. Use the following code to create ConfigMaps: ``` kubectl create configmap ma-configmap --from-file=mlops.agent.conf.yaml=your-path/mlops.agent.conf.yaml kubectl create configmap gac-configmap --from-file=gac.json=your-google-application-credentials.json ``` #### Create the K8s Deployment To create the deployment, create the `ma-deployment.yaml` file with the following content: > [!NOTE] Note > This example uses three always-running replicas; for autoscaling, use `kubectl autoscale deployment`. ``` apiVersion: apps/v1 kind: Deployment metadata: name: ma-deployment labels: app: ma spec: replicas: 3 selector: matchLabels: app: ma template: metadata: labels: app: ma spec: containers: - name: ma image: gcr.io/${PROJECT_ID}/monitoring-agents:v1 volumeMounts: - name: agent-conf-volume mountPath: /opt/datarobot/ma/conf/mlops.agent.conf.yaml subPath: mlops.agent.conf.yaml - name: gac-conf-volume mountPath: /opt/datarobot/ma/conf/gac.json subPath: gac.json env: - name: GOOGLE_APPLICATION_CREDENTIALS value: /opt/datarobot/ma/conf/gac.json ports: - containerPort: 80 volumes: - name: agent-conf-volume configMap: items: - key: mlops.agent.conf.yaml path: mlops.agent.conf.yaml name: ma-configmap - name: gac-conf-volume configMap: items: - key: gac.json path: gac.json name: gac-configmap ``` Next, create the deployment with the following command: `kubectl apply -f ma-deployment.yaml` Finally, check the running pods: `kubectl get pods` ### Score the model Score your local model and verify the output. 1. Score your local model: python score-your-model.py 2. Check the GKE Pod log; it shows that one record has been sent to DataRobot. 3. Check the Pub/Sub log. 4. Check the DataRobot MLOps dashboard. ### Clean up 1. Delete the NAT in the cloud router: gcloud compute routers nats delete nat-config --router=nat-router --router-region=europe-west1 2. Delete the cloud router: gcloud compute routers delete nat-router --region=europe-west1 3. Delete the cluster: gcloud container clusters delete monitoring-agents-cluster --- # Integrations URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/index.html > Step-by-step instructions to perform tasks within the DataRobot application as well as partners, cloud providers, and 3rd party vendors. # Integrations These sections provide step-by-step instructions on how to perform tasks within the DataRobot application as well as using partners, cloud providers, and 3rd party vendors:: | Topic | Description | | --- | --- | | AWS | How to integrate DataRobot with Amazon Web Services. | | Azure | How to integrate DataRobot with Azure cloud services. | | Google | How to integrate DataRobot with Google Kubernetes and cloud platforms. | | Snowflake | How to integrate DataRobot with Snowflake's Data Cloud. | --- # Snowflake URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/index.html > Guides around integrating DataRobot with Snowflake # Snowflake The articles in this section progress from data ingest to creating projects and machine learning models based on historic training sets, and finally to scoring new data through deployed models via several deployment methodologies: | Topic | Description | | --- | --- | | Data ingest | Retrieving data for Snowflake project creation. | | Wrangle Snowflake data in DataRobot | Using Workbench in DataRobot, build wrangling recipes and push down those transformations to Snowflake where they are applied to the source data by leveraging Snowflake SQL. | | Real-time predictions | Using the API to score Snowflake data. | | Server-side model scoring | Using the API to integrate your deployment with Snowflake to feed data to your model for predictions and to write those predictions back to your Snowflake Database. | | External functions and streams | Using external API call functions to create a Snowflake scoring pipeline. | | Generate Snowflake UDF Scoring Code | Using the DataRobot Scoring Code JAR as a user-defined function (UDF) on Snowflake. | --- # Real-time predictions URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/sf-client-scoring.html > Use the DataRobot Prediction API for real-time predictions. # Real-time predictions Once data is ingested, DataRobot provides several options for scoring model data. The most tightly integrated and feature-rich scoring method is using the Prediction API. The API can be leveraged and scaled horizontally to support both real-time scoring requests and batch scoring. A single API request can be sent with a data payload of one or more records, and many requests can be sent concurrently. DataRobot keeps track of data coming in for scoring requests and compares it to training data used to build a model as well. Using model management, technical performance statistics around the API endpoint are delivered along with data drift and model drift (associated with the health of the model itself). See the [DataRobot Prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html) documentation for more information. The information required to construct a successful API request can be collected from several places within DataRobot, although the quickest way to capture all values required is from the [Predictions > Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html#integration) tab. A sample Python script with relevant fields completed is available in the tab. The API endpoint accepts CSV and JSON data. The Content-Type header value must be set appropriately for the type of data being sent ( `text/csv` or `application/json`); the raw API request responds with JSON by default. To return CSV instead of JSON for real-time predictions, use `-H "Accept: text/csv"`. The following provides several examples of scoring data from Snowflake via a client request—from both a local or standing-server environment. ## Use the Prediction API Note the values in the following integration script: ``` API_KEY = 'YOUR API KEY' DEPLOYMENT_ID = 'YOUR DEPLOYMENT ID' DATAROBOT_KEY = 'YOUR DR KEY' ``` | Field | Description | | --- | --- | | USERNAME | Apply privileges associated with the named user. | | API_KEY | Authenticate web requests to the DataRobot API and Prediction API. | | DEPLOYMENT_ID | Specify the unique ID of the DataRobot deployment; this value sits in front of the model. | | DATAROBOT_KEY | (Managed AI Platform users only) Supply an additional engine access key. Self-Managed AI Platform installations can remove this option. | | DR_PREDICTION_HOST | (Self-Managed AI Platform users only) Supply the application host for locally installed clusters. (For Managed AI Platform users, the host defaults to app.datarobot.com or app.eu.datarobot.com.) | | Content-Type | Identify the type of input data being sent, either CSV and JSON, under headers. | | URL | Identify the hostname for scoring data. Typically, this is a load balancer in front of one or more prediction engines. | The following script snippet shows how to extract data from Snowflake via the Python connector and send it to DataRobot for scoring. It creates a single thread with a single request. Maximizing speed involves creating parallel request threads with appropriately sized data payloads to handle input of any size. Consider this basic example of creating a scoring request and working with results. ``` import snowflake.connector import datetime import sys import pandas as pd import requests from pandas.io.json import json_normalize # snowflake parameters SNOW_ACCOUNT = 'dp12345.us-east-1' SNOW_USER = 'your user' SNOW_PASS = 'your pass' SNOW_DB = 'TITANIC' SNOW_SCHEMA = 'PUBLIC' # create a connection ctx = snowflake.connector.connect( user=SNOW_USER, password=SNOW_PASS, account=SNOW_ACCOUNT, database=SNOW_DB, schema=SNOW_SCHEMA, protocol='https', application='DATAROBOT', ) # create a cursor cur = ctx.cursor() # execute sql sql = "select passengerid, pclass, name, sex, age, sibsp, parch, fare, cabin, embarked " \ + " from titanic.public.passengers" cur.execute(sql) # fetch results into dataframe df = cur.fetch_pandas_all() ``` Fields are all capitalized in accordance with ANSI standard SQL. Because DataRobot is case-sensitive to feature names, ensure that the fields in DataRobot match the data provided. Depending on the model-building workflow used, this may mean that database extractions via SQL require aliasing of the columns to match model-feature case. At this point, the data is in a Python script. Any pre-processing that occurred outside of DataRobot before model building can now be applied to the scoring. Once the data is ready, you can begin model scoring. ``` # datarobot parameters API_KEY = 'YOUR API KEY' USERNAME = 'mike.t@datarobot.com' DEPLOYMENT_ID = 'YOUR DEPLOYMENT ID' DATAROBOT_KEY = 'YOUR DR KEY' # replace with the load balancer for your prediction instance(s) DR_PREDICTION_HOST = 'https://app.datarobot.com' # replace app.datarobot.com with application host of your cluster if installed locally headers = { 'Content-Type': 'text/csv; charset=UTF-8', 'Datarobot-Key': DATAROBOT_KEY, 'Authorization': 'Bearer {}'.format(API_KEY) } predictions_response = requests.post( url, data=df.to_csv(index=False).encode("utf-8"), headers=headers, params={'passthroughColumns' : 'PASSENGERID'} ) if predictions_response.status_code != 200: print("error {status_code}: {content}".format(status_code=predictions_response.status_code, content=predictions_response.content)) sys.exit(-1) # first 3 records json structure predictions_response.json()['data'][0:3] ``` The above is a basic, straightforward call with little error handling; it is intended only as an example. The request includes a parameter value to request the logical or business key for the data being returned, along with the labels and scores. Note that `df.to_csv(index=False)` is required to remove the index column from the output, and `.encode("utf-8")` is required to convert the Unicode string to UTF-8 bytes (charset specified in `Content-Type`). The API always returns records in JSON format. Snowflake is flexible when working with JSON, allowing you to simply load the response into the database. ``` df_response = pd.DataFrame.from_dict(predictions_response.json()) df_response.head() ``` The following code creates a table and inserts the raw JSON. Note that it only does so with an abbreviated set of five records. For the sake of this demonstration, the records are being inserted one at a time via the Python Snowflake connector. This is not a best practice and is provided for demonstration only; when doing this yourself, make sure Snowflake instead ingests data via flat files and stage objects. ``` ctx.cursor().execute('create or replace table passenger_scored_json(json_rec variant)') df_head = df_response.head() # this is not the proper way to insert data into snowflake, but is used for quick demo convenience. # snowflake ingest should be done via snowflake stage objects. for _ind_, row in df5.iterrows(): escaped = str(row['data']).replace("'", "''") ctx.cursor().execute("insert into passenger_scored_json select parse_json('{rec}')".format(rec=escaped)) print(row['data']) ``` Use Snowflake's native JSON functions to parse and flatten the data. The code below retrieves all scores towards the positive class label `1` for survival from the binary classification model. ``` select json_rec:passthroughValues.PASSENGERID::int as passengerid , json_rec:prediction::int as prediction , json_rec:predictionThreshold::numeric(10,9) as prediction_threshold , f.value:label as prediction_label , f.value:value as prediction_score from titanic.public.passenger_scored_json , table(flatten(json_rec:predictionValues)) f where f.value:label = 1; ``` You can use a raw score and the output it provides against the threshold. In this example, passenger 892's chance of survival (11.69%) was less than the 50% threshold. As a result, the prediction towards the positive class survival label `1` was 0 (i.e., non-survival). The original response in Python can be flattened within Python as well. ``` df_results = json_normalize(data=predictions_response.json()['data'], record_path='predictionValues', meta = [['passthroughValues', 'PASSENGERID'], 'prediction', 'predictionThreshold']) df_results = df_results[df_results['label'] == 1] df_results.rename(columns={"passthroughValues.PASSENGERID": "PASSENGERID"}, inplace=True) df_results.head() ``` The above dataframe can be written to one or more CSVs or provided as compressed files in a Snowflake stage environment for ingestion into the database. --- # Snowflake external functions and streams URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/sf-function-streams.html > Use external API call functions to create a Snowflake scoring pipeline. # Snowflake external functions and streams With Snowflake, you can call out to [external APIs](https://docs.snowflake.com/en/sql-reference/external-functions-introduction.html) from user-defined functions (UDFs). Using a Snowflake scoring pipeline allows you to take advantage of these external API functions—leveraging Snowflake streams and tasks to create a streaming micro-batch ingestion flow that incorporates a DataRobot-hosted model. There are several requirements and considerations when exploring this approach: - Any API must be fronted by the trusted cloud native API service (in the case of AWS, the AWS API Gateway). - There are scaling, concurrency, and reliability considerations. - Max payload size for synchronous requests is 10MB for the API gateway and 6MB for Lambda (other cloud providers have different limitations). When deciding how to score your models, consider these questions. How does the total infrastructure react when scoring 10 rows vs. 10,000 rows vs. 10 million rows?  What kind of load is sent when a small 2-node cluster is vertically scaled to a large 8-node cluster or when it is scaled horizontally to 2 or 3 instances? What happens if a request times out or a resource is unavailable? Alternatives for executing large batch scoring jobs on Snowflake simply and efficiently are described in the [client-request](https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/sf-client-scoring.html) and [server-side](https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/sf-server-scoring.html)) scoring examples. Generally speaking, this type of scoring is best done as part of ETL or ELT pipelines. Low-volume streaming ingest using internal Snowflake streaming is a suitable application for leveraging external functions with a UDF. The following demonstrates an ETL pipeline using Snowpipe, Streams, and Tasks within Snowflake. The example scores records through a DataRobot-hosted model using Kaggle's [Titanic dataset](https://www.kaggle.com/c/titanic). It ingests data via a streaming pipeline with objects in an STG schema, scores it against the model, and then loads it to the `PUBLIC` schema presentation layer. ## Technologies used The examples uses the following technologies: Snowflake: - Storage Integration - Stage - Snowpipe - Streams - Tasks , Tables , and External Function UDF objects (to assemble a streaming scoring pipeline for data as it is ingested) AWS: - Lambda , as a serverless compute service that acts as the intermediary between Snowflake and DataRobot (which is currently a requirement for using an external function). - API Gateway , to provide an endpoint to front the Lambda function. - IAM policies to grant roles and privileges to necessary components. - Incoming data, which is placed in an S3 object store bucket. - An SQS queue. DataRobot: - The model was built and deployed on the AutoML platform and is available for scoring requests via the DataRobot Prediction API. In this case, the model is served on horizontally scalable DataRobot cluster member hardware, dedicated solely to serving these requests. ## External UDF architecture The following illustrates the Snowflake external API UDF architecture: Although a native UDF in Snowflake is written in JavaScript, the external function is executed remotely and can be coded in any language the remote infrastructure supports. It is then coupled with an API integration in Snowflake to expose it as an external UDF. This integration sends the payload to be operated on to an API proxy service (an AWS API Gateway in this case). The Gateway then satisfies this request through the remote service behind it—a microservice backed by a container or by a Lambda piece of code. ## Create the remote service (AWS Lambda) Hosting DataRobot models inside AWS Lambda takes advantage of AWS scalability features. For examples, see: - Using DataRobot Prime with AWS Lambda * - Using Scoring Code with AWS Lambda - Exporting a model outside of DataRobot as a Docker container * The ability to create new DataRobot Prime models has been removed from the application. This does not affect existing Prime models or deployments. This section provides an example of treating the gateway as a proxy for a complete passthrough and sending the scoring request to a DataRobot-hosted prediction engine.  Note that in this approach, scalability also includes horizontally scaling prediction engines on the DataRobot cluster. See the articles mentioned above for additional Lambda-creation workflows to gain familiarity with the environment and process. Create a Lambda named `proxy_titanic` with a Python 3.7 runtime environment.  Leverage an existing IAM role or create a new one with default execution permissions. Connecting to the DataRobot cluster requires some sensitive information: - The load balancing hostname in front of the DataRobot Prediction Engine (DPE) cluster. - The user's API token. - The deployment for the model to be scored. - The DataRobot key (managed AI Platform users only). These values can be stored in the Lambda Environment variables section. Lambda layers let you build Lambda code on top of libraries and separate that code from the delivery package. You don't have to separate the libraries, although using layers simplifies the process of bringing in necessary packages and maintaining code. This example requires the `requests` and `pandas` libraries, which are not part of the base Amazon Linux image, and must be added via a layer (by creating a virtual environment). In this example, the environment used is an Amazon Linux EC2 box. Instructions to install Python 3 on Amazon Linux are [here](https://aws.amazon.com/premiumsupport/knowledge-center/ec2-linux-python3-boto3/). Create a ZIP file for a layer as follows: ``` python3 -m venv my_app/env source ~/my_app/env/bin/activate pip install requests pip install pandas deactivate ``` Per the [Amazon documentation](https://docs.aws.amazon.com/lambda/latest/dg/configuration-layers.html), this must be placed in the `python` or `site-packages` directory and is expanded under `/opt`. ``` cd ~/my_app/env mkdir -p python/lib/python3.7/site-packages cp -r lib/python3.7/site-packages/* python/lib/python3.7/site-packages/. zip -r9 ~/layer.zip python ``` Copy the `layer.zip` file to a location on S3; this is required if the Lambda layer is > 10MB. ``` aws s3 cp layer.zip s3://datarobot-bucket/layers/layer.zip ``` Navigate to the Lambda service > Layers > Create Layer tab. Provide a name and link to the file in S3; note that this will be the Object URL of the uploaded ZIP. It is recommended, but not necessary, to set compatible environments, which makes them more easily accessible in a dropdown menu when adding them to a Lambda. Select to save the layer and its Amazon Resource Name (ARN). Navigate back to the Lambda and click Layers under the Lambda title; add a layer and provide the ARN from the previous step. Navigate back to the Lambda code. The following Python code will: 1. Accept a payload from Snowflake. 2. Pass the payload to DataRobot's Prediction API for scoring. 3. Return a Snowflake-compatible response. ``` import os import json #from pandas.io.json import json_normalize import requests import pandas as pd import csv def lambda_handler(event, context): # set default status to OK, no DR API error status_code = 200 dr_error = "" # The return value will contain an array of arrays (one inner array per input row). array_of_rows_to_return = [ ] try: # obtain secure environment variables to reach out to DataRobot API DR_DPE_HOST = os.environ['dr_dpe_host'] DR_USER = os.environ['dr_user'] DR_TOKEN = os.environ['dr_token'] DR_DEPLOYMENT = os.environ['dr_deployment'] DR_KEY = os.environ['dr_key'] # retrieve body containing input rows event_body = event["body"] # retrieve payload from body payload = json.loads(event_body) # retrieve row data from payload payload_data = payload["data"] # map list of lists to expected inputs cols = ['row', 'NAME', 'SEX', 'PCLASS', 'FARE', 'CABIN', 'SIBSP', 'EMBARKED', 'PARCH', 'AGE'] df = pd.DataFrame(payload_data, columns=cols) print("record count is: " + str(len(df.index))) # assemble and send scoring request headers = {'Content-Type': 'text/csv; charset=UTF-8', 'Accept': 'text/csv', 'datarobot-key': DR_KEY} response = requests.post(DR_DPE_HOST + '/predApi/v1.0/deployments/%s/predictions' % (DR_DEPLOYMENT), auth=(DR_USER, DR_TOKEN), data=df.to_csv(), headers=headers) # bail if anything other than a successful response occurred if response.status_code != 200: dr_error = str(response.status_code) + " - " + str(response.content) print("dr_error: " + dr_error) raise array_of_rows_to_return = [] row = 0 wrapper = csv.reader(response.text.strip().split('\n')) header = next(wrapper) idx = header.index('SURVIVED_1_PREDICTION') for record in wrapper: array_of_rows_to_return.append([row, record[idx]]) row += 1 # send data back in required snowflake format json_compatible_string_to_return = json.dumps({"data" : array_of_rows_to_return}) except Exception as err: # 400 implies some type of error. status_code = 400 # Tell caller what this function could not handle. json_compatible_string_to_return = 'failed' # if the API call failed, update the error message with what happened if len(dr_error) > 0: print("error") json_compatible_string_to_return = 'failed; DataRobot API call request error: ' + dr_error # Return the return value and HTTP status code. return { 'statusCode': status_code, 'body': json_compatible_string_to_return } ``` [Lambda code for this example is available in GitHub](https://github.com/datarobot-community/ai_engineering/tree/master/databases/snowflake/external_function). You can configure a test event to make sure the Lambda acts as expected. A DataRobot payload can be represented for this model with a few JSON records in the following format: ``` { "body": "{\"data\": [[0, \"test one\", \"male\", 3, 7.8292, null, 0, \"Q\", 0, 34.5 ], [1, \"test two\", \"female\", 3, 7, null, 1, \"S\", 0, 47 ] ] }" } ``` Once this event is created, select it from the test dropdown and click Test. The test returns a 200-level success response with a JSON-encapsulated list of lists, containing the 0-based row number and the returned model value. In this case, that model value is a score towards the positive class of label `1` (e.g., Titanic passenger survivability from a binary classifier model). You can set additional Lambda configuration under Basic Settings. Lambda serverless costs are based on RAM "used seconds" duration. The more RAM allowed, the more virtual CPU is allocated. This allows handling and manipulating larger-sized input loads and for processing inside the Lambda to occur more quickly. Note that this Lambda defers to DataRobot for the heavier work; it just needs to accommodate for the data movement. If a Lambda exits prematurely due to exceeding resources, these values may need to be edited. The timeout default is 3 seconds; if the response from DataRobot for the micro-batch of records the Lambda is responsible for takes longer to process than the default value, the Lambda does not detect activity and shuts down. DataRobot tested and recommends the following values: 256MB and a 10-second timeout. Actual usage for each executed Lambda can be found in the associated CloudWatch logs, available under the Monitoring tab of the Lambda. ## Configure the proxy service The following creates the AWS API Gateway proxy service. ### Create IAM role For a Snowflake-owned IAM user to be granted permission, you must create a role that the user can then assume within the AWS account. In the console, navigate to IAM > Roles > Create role. When asked to Select type of trusted entity, choose Another AWS account and fill in the Account ID box with the AWS Account ID for the currently logged-in account. This can be found in the ARN of other roles, from the My Account menu or from various other places. A Snowflake external ID for the account is applied later. Proceed through the next screens and save this role as `snowflake_external_function_role`. Save the role as Amazon Resource Name (ARN). ### Create API Gateway entry Navigate to the API Gateway service console and click Create API. Choose to build a REST API and select the REST protocol. Select to Create a New API. Create a friendly, readable name and click Create API. On the next screen, choose Actions > Create Resource. Set the resource name and path to score. Next, choose Actions> Create Method. In the dropdown menu under the endpoint, choose POST. Select the checkbox next to Use Lambda Proxy Integration, select the previously created Lambda, and save. Lastly, choose Actions > Deploy API. You must create a stage, such as test, and then click Deploy once you complete the form. > [!NOTE] Note > The Invoke URL field on the subsequent editor page will later be used in creating an integration with Snowflake. ### Secure the API Gateway endpoint Navigate back to the Resources of the created API (in the left menu above Stages). Click on POST under the endpoint to bring up the Method Execution. Click the Method Request, toggle the Authorization dropdown to AWS_IAM, and then click the checkmark to save. Navigate back to the Method Execution and note the ARN within the Method Request. Navigate to Resource Policy in the left menu. Add a policy that is populated with the AWS account number and the name of the previously created IAM role above (described in the [Snowflake documentation](https://docs.snowflake.com/en/sql-reference/external-functions-creating-aws.html#secure-your-aws-api-gateway-proxy-service-endpoint)). ### Create an API Integration object in Snowflake The API Integration object will map Snowflake to the AWS Account role. Provide the role ARN and set the allowed prefixes to include the Invoke URL from the stage referenced above (a privilege level of accountadmin is required to create an API Integration). ``` use role accountadmin; create or replace api integration titanic_external_api_integration api_provider=aws_api_gateway api_aws_role_arn='arn:aws:iam::123456789012:role/snowflake_external_function_role' api_allowed_prefixes=('https://76abcdefg.execute-api.us-east-1.amazonaws.com/test/') enabled=true; ``` Describe the integration: ``` describe integration titanic_external_api_integration; ``` Copy out the values for: `API_AWS_IAM_USER_ARN and API_AWS_EXTERNAL_ID` ## Configure the Snowflake-to-IAM role trust relationship Navigate back to the AWS IAM service > Roles, and to the `snowflake_external_function_role` role. At the bottom of the Summary page, choose the Trust relationships tab and click the Edit trust relationship button. This opens a policy document to edit. As per the [Snowflake documentation](https://docs.snowflake.com/en/sql-reference/external-functions-creating-aws.html#set-up-the-trust-relationship-s-between-snowflake-and-the-new-iam-role), edit the Principal attribute AWS key by replacing the existing value with the `API_AWS_IAM_USER_ARN` from Snowflake. Next to the `sts:AssumeRole` action, there will be a Condition key with an empty value between curly braces. Inside the braces, paste the following, replacing the `API_AWS_EXTERNAL_ID` with the value from Snowflake: ``` "StringEquals": { "sts:ExternalId": "API_AWS_EXTERNAL_ID" } ``` ## Create the external function You can now create the external function inside Snowflake. It will reference the trusted endpoint to invoke via the previously built API integration.  Be sure to match the expected parameter value in the function definition to the function the Lambda is expecting. ``` create or replace external function udf_titanic_score(name string, sex string, pclass int, fare numeric(10,5), cabin string, sibsp int, embarked string, parch int, age numeric(5,2)) returns variant api_integration = titanic_external_api_integration as 'https://76abcdefg.execute-api.us-east-1.amazonaws.com/test/score'; ``` The function is now ready for use. ## Call the external function You can call the function as expected. This code scores 100,000 Titanic passenger records: ``` select passengerid , udf_titanic_score(name, sex, pclass, fare, cabin, sibsp, embarked, parch, age) as score from passengers_100k; ``` In the above prediction, Passenger 7254024 has an 84.4% chance of Titanic survivability. ### External function performance considerations Some observations: - Full received payloads in this case contained ~1860 records. Payloads were roughly 0.029MB in size (perhaps Snowflake is limiting them to 0.03MB). - Whether scoring from an extra small-, small-, or medium-sized compute warehouse on Snowflake, the Lambda concurrency CloudWatch metrics dashboard always showed a concurrent execution peak of 8. Overall, this represents a rather gentle load on scoring infrastructure. - Performance should be satisfactory whether the model is run in the Lambda itself or offset to a DataRobot Prediction Engine. Note that for larger batch jobs and maximum throughput, other methods are still more efficient with time and resources. - Testing against an r4.xlarge Dedicated Prediction Engine on DataRobot produced a rate of roughly 13,800 records for this particular dataset and model. - Snowflake determines payload size and concurrency based on a number of factors .  A controllable payload ceiling can be specified with a MAX_BATCH_ROWS value during external function creation.  Future options may allow greater control over payload size, concurrency, and scaling with warehouse upsizing. ## Streaming ingest with streams and tasks There are multiple options to bring data into Snowflake using streaming. One option is to use Snowflake's native periodic data-loading capabilities with Snowpipe. By using Snowflake streams and tasks, you can handle new records upon arrival without an external driving ETL/ELT. ### Ingest pipeline architecture The following illustrates this ingest architecture: ### Create staging and presentation tables You must create tables to hold the newly arrived records loaded from Snowpipe and to hold the processed and scored records for reporting. In this example, a raw passengers table is created in an `STG` schema and a scored passengers table is presented in the `PUBLIC` schema. ``` create or replace TABLE TITANIC.STG.PASSENGERS ( PASSENGERID int, PCLASS int, NAME VARCHAR(100), SEX VARCHAR(10), AGE NUMBER(5,2), SIBSP int, PARCH int, TICKET VARCHAR(30), FARE NUMBER(10,5), CABIN VARCHAR(25), EMBARKED VARCHAR(5) ); create or replace TABLE TITANIC.PUBLIC.PASSENGERS_SCORED ( PASSENGERID int, PCLASS int, NAME VARCHAR(100), SEX VARCHAR(10), AGE NUMBER(5,2), SIBSP int, PARCH int, TICKET VARCHAR(30), FARE NUMBER(10,5), CABIN VARCHAR(25), EMBARKED VARCHAR(5), SURVIVAL_SCORE NUMBER(11,10) ); ``` ### Create the Snowpipe Snowflake needs to be connected to an external stage object. Use the [Snowflake documentation](https://docs.snowflake.com/en/user-guide/data-load-s3-config.html#option-1-configuring-a-snowflake-storage-integration) to set up a storage integration with AWS and IAM. ``` use role accountadmin; --note a replace will break all existing associated stage objects! create or replace storage integration SNOWPIPE_INTEGRATION type = EXTERNAL_STAGE STORAGE_PROVIDER = S3 STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::123456789:role/snowflake_lc_role' enabled = true STORAGE_ALLOWED_LOCATIONS = ('s3://bucket'); ``` Once the integration is available, you can use it to create a stage that maps to S3 and uses the integration to apply security. ``` CREATE or replace STAGE titanic.stg.snowpipe_passengers URL = 's3://bucket/snowpipe/input/passengers' storage_integration = SNOWPIPE_INTEGRATION; ``` Lastly, create the Snowpipe to map this stage to a table.  A file format is created for it below as well. ``` CREATE OR REPLACE FILE FORMAT TITANIC.STG.DEFAULT_CSV TYPE = 'CSV' COMPRESSION = 'AUTO' FIELD_DELIMITER = ',' RECORD_DELIMITER = '\n' SKIP_HEADER = 1 FIELD_OPTIONALLY_ENCLOSED_BY = '\042' TRIM_SPACE = FALSE ERROR_ON_COLUMN_COUNT_MISMATCH = TRUE ESCAPE = 'NONE' ESCAPE_UNENCLOSED_FIELD = '\134' DATE_FORMAT = 'AUTO' TIMESTAMP_FORMAT = 'AUTO' NULL_IF = (''); create or replace pipe titanic.stg.snowpipe auto_ingest=true as copy into titanic.stg.passengers from @titanic.stg.snowpipe_passengers file_format = TITANIC.STG.DEFAULT_CSV; ``` ### Automate the Snowpipe loading Snowflake provides options for loading new data as it arrives. This example applies option 1 (described in the [Snowflake documentation](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-auto-s3.html#step-1-create-a-stage-if-needed)) to use a Snowflake SQS queue directly. Note that step 4 to [create new file event notifications](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-auto-s3.html#step-4-configure-event-notifications) is required. To enable Snowpipe, navigate to the S3 bucket, click the Properties tab > Events tile, and then click Add notification. Create a notification to add a message to the specified SQS queue retrieved from the Snowflake pipe for every new file arrival. The pipe is now ready to accept and load data. ### Create the stream You can create two types of stream objects in Snowflake—standard and append-only. Standard stream objects capture any type of change to a table; append-only stream objects capture inserted rows. Use the former for general Change Data Capture (CDC) processing. Use the latter (used in this example) for simple new row ingest processing. In the append-only approach, think of the stream as a table that contains only records that are new since the last time any data was selected from it. Once a DML query that sources a stream is made, the rows returned are considered consumed and the stream becomes empty. In programming terms, this is similar to a queue. ``` create or replace stream TITANIC.STG.new_passengers_stream on table TITANIC.STG.PASSENGERS append_only=true; ``` ### Create the task A task is a step or series of cascading steps that can be constructed to perform an ELT operation. Tasks can be scheduled—similar to cron jobs— and set to run by days, times, or periodic intervals. The following basic task scores the Titanic passengers through the UDF and loads the scored data to the presentation layer. It checks to see if new records exist in the stream every 5 minutes; if records are found, the task runs. The task is created in a suspended state; enable the task by resuming it. Note that many timing options are available for scheduling based on days, times, or periods. ``` CREATE or replace TASK TITANIC.STG.score_passengers_task WAREHOUSE = COMPUTE_WH SCHEDULE = '5 minute' WHEN SYSTEM$STREAM_HAS_DATA('TITANIC.STG.NEW_PASSENGERS_STREAM') AS INSERT INTO TITANIC.PUBLIC.PASSENGERS_SCORED select passengerid, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked, udf_titanic_score(name, sex, pclass, fare, cabin, sibsp, embarked, parch, age) as score from TITANIC.STG.new_passengers_stream; ALTER TASK score_passengers_task RESUME; ``` ## Ingest and scoring pipeline complete The end-to-end pipeline is now complete. A `PASSENGERS.csv` file (available in [GitHub](https://github.com/datarobot-community/ai_engineering/tree/master/databases/snowflake/external_function)) can run the pipeline, copying it into the watched bucket. The file prefix results in the data being ingested into a staging schema, scored through a DataRobot model, and then loaded into the presentation schema—all without any external ETL tooling. ``` aws s3 cp PASSENGERS.csv s3://bucket/snowpipe/input/passengers/PASSENGERS.csv ``` --- # Large batch scoring URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/sf-large-batch.html > Use the DataRobot Batch Prediction API to mix and match source and target data sources. # DataRobot and Snowflake: Large batch scoring and object storage With DataRobot's Batch Prediction API, you can construct jobs to mix and match source and target data sources with scored data destinations (JDBC databases and cloud object storage options such as AWS S3, Azure Blob, and Google GCS) across various local files . For examples of leveraging the Batch Prediction API via the UI, as well as raw HTTP endpoint requests to create batch scoring jobs, see: - Server-side scoring with Snowflake - Batch Prediction API documentation - Python API client functions The critical path in a scoring pipeline is typically the amount of resources available to actually run a deployed machine learning model. Although you can extract data from a database quickly, scoring throughput is limited to available scoring compute. Inserts to shredded columnar cloud databases (e.g., Snowflake, Synapse) are also most efficient when done with native object storage bulk load operations, such as [COPY INTO](https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html) when using a Snowflake Stage. An added benefit, particularly in Snowflake, is that warehouse billing can be limited to running just a bulk load vs. a continual set of JDBC inserts during a job. This reduces warehouse running time and thus warehouse compute costs. Snowflake and Synapse adapters can leverage bulk extract and load operations to object storage, as well as object storage scoring pipelines. ## Snowflake adapter integration The examples provided below leverage some of the credential management Batch Prediction API helper code presented in the [server-side scoring](https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/sf-server-scoring.html) example. Rather than using the Python API client (which may be preferred for simplicity), this section demonstrates how to use the raw API with minimal dependencies. As scoring datasets grow larger, the object storage approach described here can be expected to reduce both the end-to-end scoring time and the database write time. Since the Snowflake adapter type leverages object storage as an intermediary, batch jobs require two sets of credentials: one for Snowflake and one for the storage layer, like S3. Also, similar to jobs, adapters can be mixed and matched. ## Snowflake JDBC to DataRobot to S3 stage to Snowflake This first example leverages the prior existing JDBC adapter intake type as well as the Snowflake adapter output type, which uses the Snowflake stage object on bulk load. Only job details are specified below; see the full code on [GitHub](https://github.com/datarobot-community/ai_engineering/blob/master/databases/snowflake/snowflake_adapter/Snowflake_Adapter_Type.ipynb). The job explicitly provides all values, although many have defaults that could be used without specification. This survival model scores Titanic passengers by specifying input tables. ``` job_details = { "deploymentId": DEPLOYMENT_ID, "numConcurrent": 16, "passthroughColumns": ["PASSENGERID"], "includeProbabilities": True, "predictionInstance" : { "hostName": DR_PREDICTION_HOST, "datarobotKey": DATAROBOT_KEY }, "intakeSettings": { "type": "jdbc", "dataStoreId": data_connection_id, "credentialId": snow_credentials_id, "table": "PASSENGERS_6M", "schema": "PUBLIC", }, 'outputSettings': { "type": "snowflake", "externalStage": "S3_SUPPORT", "dataStoreId": data_connection_id, "credentialId": snow_credentials_id, "table": "PASSENGERS_SCORED_BATCH_API", "schema": "PUBLIC", "cloudStorageType": "s3", "cloudStorageCredentialId": s3_credentials_id, "statementType": "insert" } } ``` ## Snowflake to S3 stage to DataRobot to S3 stage to Snowflake This second example uses the Snowflake adapter for both intake and output operations, with data dumped to an object stage, scored through an S3 pipeline, and loaded in bulk back from stage.This is the recommended flow for performance and cost. - The stage pipeline (from S3 to S3) will keep a constant flow of scoring requests against Dedicated Prediction Engine (DPE) scoring resources and will fully saturate their compute. - No matter how long the scoring component takes, the Snowflake compute resources only need to run for the duration of the initial extract and, once all data is scored, for a single final bulk load of the scored data. This maximizes the efficiency of the load, which is beneficial for the costs of running all Snowflake compute resources. In this example, the job is similar to the first example. To illustrate the option, a SQL query is used as input rather than the source table name. ``` job_details = { "deploymentId": DEPLOYMENT_ID, "numConcurrent": 16, "chunkSize": "dynamic", "passthroughColumns": ["PASSENGERID"], "includeProbabilities": True, "predictionInstance" : { "hostName": DR_PREDICTION_HOST, "datarobotKey": DATAROBOT_KEY }, "intakeSettings": { "type": "snowflake", "externalStage": "S3_SUPPORT", "dataStoreId": data_connection_id, "credentialId": snow_credentials_id, "query": "select * from PASSENGERS_6m", "cloudStorageType": "s3", "cloudStorageCredentialId": s3_credentials_id }, 'outputSettings': { "type": "snowflake", "externalStage": "S3_SUPPORT", "dataStoreId": data_connection_id, "credentialId": snow_credentials_id, "table": "PASSENGERS_SCORED_BATCH_API", "schema": "PUBLIC", "cloudStorageType": "s3", "cloudStorageCredentialId": s3_credentials_id, "statementType": "insert" } } ``` [Code for this exercise is available in GitHub](https://github.com/datarobot-community/ai_engineering/blob/master/databases/snowflake/snowflake_adapter/Snowflake_Adapter_Type.ipynb). If running large scoring jobs with Snowflake or Azure Synapse, it's best to take advantage of the related adapter. Using one of these adapters for both intake and output ensures the scoring pipelines scale as data volumes increase in size. --- # Data ingest and project creation URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/sf-project-creation.html > Retrieve data for Snowflake project creation. # Data ingest and project creation To create a project in DataRobot, you first need to ingest a training dataset. This dataset may or may not go through data engineering or feature engineering processes before being used for modeling. > [!NOTE] Note > The usage examples provided are not exclusive to Snowflake and can be applied in part or in whole to other databases. At a high level, there are two approaches for getting this data into DataRobot: - PUSH Send data to DataRobot and create a project with it.  Examples include dragging a supportable file type into the GUI or leveraging the DataRobot API. - PULL Create a project by pulling data from somewhere, such as the URL to a dataset, or via a database connection. Both examples are demonstrated below. Using the well-known [Kaggle Titanic Survival dataset](https://www.kaggle.com/c/titanic/data), a single tabular dataset is created with one new feature-engineered column, specifically: ``` total_family_size = sibsp + parch + 1 ``` ## PUSH: DataRobot Modeling API You can interact with DataRobot [via the UI](https://docs.datarobot.com/en/docs/classic-ui/data/import-data/import-to-dr.html) or programmatically through a REST API. The API is wrapped by an available [R API client](https://cran.r-project.org/web/packages/datarobot/index.html) or [Python API client](https://pypi.org/project/datarobot/), which simplifies calls and workflows with common multistep and asynchronous processes. The process below leverages Python 3 and the DataRobot Python API client for project creation. The data used in these examples is obtained via the [Snowflake Connector for Python](https://docs.snowflake.net/manuals/user-guide/python-connector.html). For easier data manipulation, the Pandas-compatible driver installation option is used to accommodate feature engineering using dataframes. First, import the necessary libraries and credentials; for convenience, they have been hardcoded into the script in this example. ``` import snowflake.connector import datetime import datarobot as dr import pandas as pd # snowflake parameters SNOW_ACCOUNT = 'my_creds.SNOW_ACCOUNT' SNOW_USER = 'my_creds.SNOW_USER' SNOW_PASS = 'my_creds.SNOW_PASS' SNOW_DB = 'TITANIC' SNOW_SCHEMA = 'PUBLIC' # datarobot parameters DR_API_TOKEN = 'YOUR API TOKEN' # replace app.datarobot.com with application host of your cluster if installed locally DR_ENDPOINT = 'https://app.datarobot.com/api/v2' DR_HEADERS = {'Content-Type': 'application/json', 'Authorization': 'token %s' % DR_API_TOKEN} ``` Below, the training dataset is loaded into the table `TITANIC.PUBLIC.PASSENGERS_TRAINING` and then retrieved and brought into a pandas DataFrame. ``` # create a connection ctx = snowflake.connector.connect( user=SNOW_USER, password=SNOW_PASS, account=SNOW_ACCOUNT, database=SNOW_DB, schema=SNOW_SCHEMA, protocol='https', application='DATAROBOT', ) # create a cursor cur = ctx.cursor() # execute sql sql = "select * from titanic.public.passengers_training" cur.execute(sql) # fetch results into dataframe df = cur.fetch_pandas_all() df.head() ``` You can then perform feature engineering within Python (in this case, using the Pandas library). > [!NOTE] Note > Feature names are uppercase because Snowflake follows the ANSI standard SQL convention of capitalizing column names and treating them as case-insensitive unless quoted. ``` # feature engineering a new column for total family size df['TOTAL_FAMILY_SIZE'] = df['SIBSP'] + df['PARCH'] + 1 df.head() ``` The data is then submitted to DataRobot to start a new modeling project. ``` # create a connection to datarobot dr.Client(token=DR_API_TOKEN, endpoint=DR_MODELING_ENDPOINT) # create project now = datetime.datetime.now().strftime('%Y-%m-%dT%H:%M') project_name = 'Titanic_Survival_{}'.format(now) proj = dr.Project.create(sourcedata=df, project_name=project_name) # further work with project via the python API, or work in GUI (link to project printed below) print(DR_MODELING_ENDPOINT[:-6] + 'projects/{}'.format(proj.id)) ``` You can interact further with the project using the SDK. ## PULL: Snowflake JDBC SQL Snowflake is cloud-native and publicly available by default. The DataRobot platform supports the installation of JDBC drivers to establish database connectivity. To connect from a locally hosted database, you must open firewall ports to provide DataRobot access and allow its IP addresses for incoming traffic. You might need to take additional similar steps if a service like [AWS PrivateLink](https://aws.amazon.com/privatelink/) is leveraged in front of a Snowflake instance. > [!NOTE] Note > If your database is protected by a network policy that only allows connections from specific IP addresses, contact [DataRobot Support](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html#allowed-source-ip-addresses) for a list of addresses that an administrator must add to your network policy. The DataRobot managed AI Platform has a JDBC driver installed and available; customers with a Self-Managed AI Platform installation must add the driver (contact DataRobot Support for assistance, if needed). You can establish a JDBC connection and initiate a project from the source via SQL using the [AI Catalog](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html). Use the JDBC connection to set up objects connected to Snowflake. 1. Create a Snowflake data connectionin DataRobot. The JDBC driver connection string's format can be found in theSnowflake documentation. In this example, the database is namedtitanic; if any parameters are left unspecified, the defaults associated with the Snowflake account login are used. 2. With the data connection created, you can now import assets into the AI Catalog. In theAI Catalog, clickAdd to Catalog > Existing Data Connection, choose the newly created connection, and respond to the credentials prompt. If you previously connected to the database, DataRobot provides the option to select from your saved credentials. When connectivity is established, DataRobot displays metadata of accessible objects. For this example, feature engineering of the new column will be done in SQL. 3. ChooseSQL queryrather than the object browsing option. The SQL to extract and create the new feature can be written and tested here. Note the [Create Snapshot](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html#create-a-snapshot) option. When checked, DataRobot extracts the data and materializes the dataset in the catalog (and adds a Snapshot label to the catalog listing). The snapshot can be shared among users and used to create projects, but it will not update from the database again unless you create a new snapshot for the dataset to refresh the materialized data with the latest dataset. Alternatively, you can add a Dynamic dataset. When dynamic, each subsequent use results in DataRobot re-executing the query against the database, pulling the latest data. When registration completes successfully, the dataset is published and available for use. Some additional considerations with this method: - The SQL for a dynamic dataset cannot be edited. - You might want to order the data because it can affect training dataset partitions from one project to the next. - A best practice for a dynamic dataset is to list each column of interest rather than using "*" for all columns. - For time series datasets, you need to order the data by grouping it and sorting on the time element. - You may want to connect to views with underlying logic that can be changed during project iterations. If implemented, that workflow may make it difficult to associate a project instance to a particular view logic at the time of extract. [Code for this example is available in GitHub](https://github.com/datarobot-community/ai_engineering/tree/master/databases/snowflake/project_creation/snowflake_api_push.ipynb). See [Real-time predictions](https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/sf-client-scoring.html) to learn about this model scoring techniques using DataRobot and Snowflake. --- # Server-side model scoring URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/sf-server-scoring.html > Use the DataRobot Batch Prediction API for server-side model scoring. # Server-side scoring The following describes advanced scoring options with Snowflake as a cloud-native database, leveraging the DataRobot Batch Prediction API from the UI or directly. The UI approach is good for ad-hoc use cases and smaller table sandbox scoring jobs.  As complexity grows, the API offers flexibility to run more complex, multistep pipeline jobs.  As data volumes grow, using S3 as an intermediate layer is one option for keeping strict control over resource usage and optimizing for cost efficiency. - DataRobot UI: Table scoring (JDBC supported by the API "behind the scenes") - DataRobot API: Query-as-source (JDBC, API) - DataRobot API: S3 Scoring with pre- or post-SQL (S3, API) Each option has trade-offs between simplicity and performance to meet business requirements.  Following is a brief overview of the Batch Prediction API and prerequisites universal to all scoring approaches. ## Batch Prediction API The Batch Prediction API allows a dataset of any size to be sent to DataRobot for scoring. This data is sliced up into individual HTTP requests and sent in parallel threads to saturate the [Dedicated Prediction Servers (DPSs)](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/intake-options.html) available to maximize scoring throughput. Source data and target data can be local files, S3/object storage, or JDBC data connections, and can be mixed and matched as well. See the [batch prediction documentation](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) for additional information. ## Feature considerations > [!NOTE] Note > Access and privileges for deployments vs. projects may differ. For example, an account may have the ability to [score](https://www.datarobot.com/wiki/scoring/) a model, but not be able to see the project or data that went into creating it. As a best practice, associate production workflows with a service account instead of a specific employee to abstract employees from your production scoring pipeline. - DataRobot Self-Managed AI Platform users may already have connectivity between their Snowflake account and their DataRobot environment. If additional network access is required, your infrastructure teams can fully control network connectivity. - DataRobot managed AI Platform users who want DataRobot to access their Snowflake instance may require additional infrastructure configuration; contact DataRobot support for assistance. Snowflake is, by default, publicly accessible. Customers may have set up easy/vanity local DNS entries (customer.snowflakecomputing.com) which DataRobot cannot resolve, or be leveraging AWS PrivateLink with the option to block public IPs. - The Snowflake write-back account requires CREATE TABLE , INSERT , and UPDATE privileges, depending on use case and workflow. Additionally, the JDBC driver requires the CREATE STAGE privilege to perform faster stage bulk inserts vs. regular array binding inserts . This creates a temporary stage object that can be used for the duration of the JDBC session. ## DataRobot UI ### Table scoring You can configure quick and simple batch scoring jobs directly within the DataRobot application. Jobs can be run ad-hoc or can be scheduled. Generally speaking, this scoring approach is the best option for use cases that only require scoring for reasonably small-sized tables. It enables you to perform some scoring and write back to the database, for example to sandbox/analysis area. See the documentation on [Snowflake prediction job examples](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/pred-job-examples-snowflake.html) for detailed workflows on setting up batch prediction job definitions for Snowflake using either a JDBC connector with Snowflake as an external data source or the Snowflake adapter with an external stage. ## DataRobot API DataRobot's Batch Prediction API can also be used programmatically. Benefits of using the API over the UI include: - The request code can be inserted into any production pipeline to sit between pre-and post-scoring steps. - The code can be triggered by an existing scheduler or in response to events as they take place. - It is not necessary to create an AI Catalog entry—the API will accept a table, view, or query. ### Query as source Consider the following when working with the API: - Batch prediction jobs must be initialized and then added to a job queue. - Jobs from local files do not begin until data is uploaded. - For a Snowflake-to-Snowflake job, both ends of the pipeline must be set with Snowflake source and target. Additional details about the job (deployment, prediction host, columns to be passed through) can be specified as well (see the [Batch Prediction API](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html) documentation for a full list of available options). Below is an example of how DataRobot's Batch Prediction API can be used to score Snowflake data via a basic JDBC connection. ``` import pandas as pd import requests import time from pandas.io.json import json_normalize import json import my_creds # datarobot parameters API_KEY = my_creds.API_KEY USERNAME = my_creds.USERNAME DEPLOYMENT_ID = my_creds.DEPLOYMENT_ID DATAROBOT_KEY = my_creds.DATAROBOT_KEY # replace with the load balancer for your prediction instance(s) DR_PREDICTION_HOST = my_creds.DR_PREDICTION_HOST DR_APP_HOST = 'https://app.datarobot.com' DR_MODELING_HEADERS = {'Content-Type': 'application/json', 'Authorization': 'token %s' % API_KEY} headers = {'Content-Type': 'text/plain; charset=UTF-8', 'datarobot-key': DATAROBOT_KEY} url = '{dr_prediction_host}/predApi/v1.0/deployments/{deployment_id}/'\ 'predictions'.format(dr_prediction_host=DR_PREDICTION_HOST, deployment_id=DEPLOYMENT_ID) # snowflake parameters SNOW_USER = my_creds.SNOW_USER SNOW_PASS = my_creds.SNOW_PASS ``` You can leverage an existing data connection to connect to a database (see the [data ingest page](https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/sf-project-creation.html) for an example using the UI). In the example below, the data connection uses a name lookup. ``` """ get a data connection by name, return None if not found """ def dr_get_data_connection(name): data_connection_id = None response = requests.get( DR_APP_HOST + '/api/v2/externalDataStores/', headers=DR_MODELING_HEADERS, ) if response.status_code == 200: df = pd.io.json.json_normalize(response.json()['data'])[['id', 'canonicalName']] if df[df['canonicalName'] == name]['id'].size > 0: data_connection_id = df[df['canonicalName'] == name]['id'].iloc[0] else: print('Request failed; http error {code}: {content}'.format(code=response.status_code, content=response.content)) return data_connection_id data_connection_id = dr_get_data_connection('snow_3_12_0_titanic') ``` A Batch Prediction job needs credentials specified; Snowflake user credentials can be saved securely to the server to run the job. Note that applied DataRobot privileges are established via the DataRobot API token in the header level of the request or session. These privileges will "own" the prediction job created and must be able to access the deployed model. You can create or look up credentials for the database with the following code snippets. ``` # get a saved credential set, return None if not found def dr_get_catalog_credentials(name, cred_type): if cred_type not in ['basic', 's3']: print('credentials type must be: basic, s3 - value passed was {ct}'.format(ct=cred_type)) return None credentials_id = None response = requests.get( DR_APP_HOST + '/api/v2/credentials/', headers=DR_MODELING_HEADERS, ) if response.status_code == 200: df = pd.io.json.json_normalize(response.json()['data'])[['credentialId', 'name', 'credentialType']] if df[(df['name'] == name) & (df['credentialType'] == cred_type)]['credentialId'].size > 0: credentials_id = df[(df['name'] == name) & (df['credentialType'] == cred_type)]['credentialId'].iloc[0] else: print('Request failed; http error {code}: {content}'.format(code=response.status_code, content=response.content)) return credentials_id # create credentials set def dr_create_catalog_credentials(name, cred_type, user, password, token=None): if cred_type not in ['basic', 's3']: print('credentials type must be: basic, s3 - value passed was {ct}'.format(ct=cred_type)) return None if cred_type == 'basic': json = { "credentialType": cred_type, "user": user, "password": password, "name": name } elif cred_type == 's3' and token != None: json = { "credentialType": cred_type, "awsAccessKeyId": user, "awsSecretAccessKey": password, "awsSessionToken": token, "name": name } elif cred_type == 's3' and token == None: json = { "credentialType": cred_type, "awsAccessKeyId": user, "awsSecretAccessKey": password, "name": name } response = requests.post( url = DR_APP_HOST + '/api/v2/credentials/', headers=DR_MODELING_HEADERS, json=json ) if response.status_code == 201: return response.json()['credentialId'] else: print('Request failed; http error {code}: {content}'.format(code=response.status_code, content=response.content)) # get or create a credential set def dr_get_or_create_catalog_credentials(name, cred_type, user, password, token=None): cred_id = dr_get_catalog_credentials(name, cred_type) if cred_id == None: return dr_create_catalog_credentials(name, cred_type, user, password, token=None) else: return cred_id credentials_id = dr_get_or_create_catalog_credentials('snow_community_credentials', 'basic', my_creds.SNOW_USER, my_creds.SNOW_PASS) ``` Create a session to define the job, which then submits the job and slots it to run asynchronously. DataRobot returns an HTTP 202 status code upon successful submission. You can retrieve the job state by querying the API for the current state of the job. ``` session = requests.Session() session.headers = { 'Authorization': 'Bearer {}'.format(API_KEY) } ``` A table to hold the results is created in Snowflake with the following SQL statement, reflecting the structure below: ``` create or replace TABLE PASSENGERS_SCORED_BATCH_API ( SURVIVED_1_PREDICTION NUMBER(10,9), SURVIVED_0_PREDICTION NUMBER(10,9), SURVIVED_PREDICTION NUMBER(38,0), THRESHOLD NUMBER(6,5), POSITIVE_CLASS NUMBER(38,0), PASSENGERID NUMBER(38,0) ); ``` The job specifies the following parameters: | Name | Description | | --- | --- | | Source | Snowflake JDBC | | Source data | Query results (simple select from passengers) | | Source fetch size | 100,000 (max fetch data size) | | Job concurrency | 4 prediction core threads requested | | Passthrough columns | Keep the surrogate key PASSENGERID | | Target table | PUBLIC.PASSENGERS_SCORED_BATCH_API | | statementType | insert (data will be inserted into the table) | ``` job_details = { "deploymentId": DEPLOYMENT_ID, "numConcurrent": 4, "passthroughColumns": ["PASSENGERID"], "includeProbabilities": True, "predictionInstance" : { "hostName": DR_PREDICTION_HOST, "sslEnabled": false, "apiKey": API_KEY, "datarobotKey": DATAROBOT_KEY, }, "intakeSettings": { "type": "jdbc", "fetchSize": 100000, "dataStoreId": data_connection_id, "credentialId": credentials_id, #"table": "PASSENGERS_500K", #"schema": "PUBLIC", "query": "select * from PASSENGERS" }, 'outputSettings': { "type": "jdbc", "table": "PASSENGERS_SCORED_BATCH_API", "schema": "PUBLIC", "statementType": "insert", "dataStoreId": data_connection_id, "credentialId": credentials_id } } ``` Upon successful job submission, the DataRobot response provides a link to check job state and details. ``` response = session.post( DR_APP_HOST + '/api/v2/batchPredictions', json=job_details ) ``` The job may or may not be in the queue, depending on whether other jobs are in front of it. Once launched, it proceeds to initialization and then runs through stages until aborted or completed. You can create a loop to repetitively check the state of the asynchronous job and hold control of a process until the job completes with an `ABORTED` or `COMPLETED` status. ``` if response.status_code == 202: job = response.json() print('queued batch job: {}'.format(job['links']['self'])) while job['status'] == 'INITIALIZING': time.sleep(3) response = session.get(job['links']['self']) response.raise_for_status() job = response.json() print('completed INITIALIZING') if job['status'] == 'RUNNING': while job['status'] == 'RUNNING': time.sleep(3) response = session.get(job['links']['self']) response.raise_for_status() job = response.json() print('completed RUNNING') print('status is now {status}'.format(status=job['status'])) if job['status'] != 'COMPLETED': for i in job['logs']: print(i) else: print('Job submission failed; http error {code}: {content}'.format(code=response.status_code, content=response.content)) ``` [Code for this exercise is available in GitHub](https://github.com/datarobot-community/ai_engineering/tree/master/databases/snowflake/batch_prediction_api/snow_batch_prediction_api_query.ipynb). ### S3 scoring with pre/post SQL (new records) This example highlights using an S3 pipeline between Snowflake sources and targets. Pre- or post-processing in SQL is not required. This example shows: - Scoring changes of new records based on pre-scoring retrieval of the last successful scoring run. - A post-scoring process that populates a target table and updates the successful ETL run history. In the example, data is loaded into an STG schema on Snowflake that exists to support an ETL/ELT pipeline. It is then updated into the target presentation table in the `PUBLIC` schema via a bulk update. It uses bulk updates because individual update statements would be very slow on Snowflake and other analytic databases vs. traditional row-store operational databases. The target presentation table contains a single field for reporting purposes from the scored results table (the `SURVIVAL` field). Using S3 allows using stage objects on data extract and load. Using discrete operations separate from scoring can minimize the time an ETL compute warehouse is up and running during the pipeline operations. Considerations that may result in S3 being part of a scoring pipeline include: - Leveraging Snowflake's native design to write to S3 (and possibly shred the data into multiple files). - Using the native bulk insert capability. - Currently, Snowflake compute warehouses charge based on the first 60 seconds of spin-up for a cluster, then each second after that. The prior methods (above) stream data out and in via JDBC and will keep a cluster active throughout the scoring process. Discretely separating out extract, scoring, and ingest steps may reduce the time the compute warehouse is actually running, which can result in cost reductions. - S3 inputs and scored sets could easily be used to create a point-in-time archive of data. In this example, a simple `ETL_HISTORY` table shows the scoring job history. The name of the job is `pass_scoring`, and the last three times it ran were March 3rd, 7th, and 11th. The next job scores any changed records greater than or equal to the last run, but before the current job run timestamp. Upon successful completion of the job, a new record will be placed into this table. Of the 500k records in this table that were scored: - Row 1 in this example will not be scored; it has not changed since the prior successful ETL run on the 11th. - Row 2 will be re-scored, as it was updated on the 20th. - Row 3 will be scored for the first time, as it was newly created on the 19th. Following are initial imports and various environment variables for DataRobot, Snowflake, and AWS S3: ``` import pandas as pd import requests import time from pandas.io.json import json_normalize import snowflake.connector import my_creds # datarobot parameters API_KEY = my_creds.API_KEY USERNAME = my_creds.USERNAME DEPLOYMENT_ID = my_creds.DEPLOYMENT_ID DATAROBOT_KEY = my_creds.DATAROBOT_KEY # replace with the load balancer for your prediction instance(s) DR_PREDICTION_HOST = my_creds.DR_PREDICTION_HOST DR_APP_HOST = 'https://app.datarobot.com' DR_MODELING_HEADERS = {'Content-Type': 'application/json', 'Authorization': 'token %s' % API_KEY} # snowflake parameters SNOW_ACCOUNT = my_creds.SNOW_ACCOUNT SNOW_USER = my_creds.SNOW_USER SNOW_PASS = my_creds.SNOW_PASS SNOW_DB = 'TITANIC' SNOW_SCHEMA = 'PUBLIC' # ETL parameters JOB_NAME = 'pass_scoring' ``` Similar to the previous example, you must specify credentials to leverage S3. You can create, save, or look up credentials for S3 access with the following code snippets. The account must have privileges to access the same area that the Snowflake Stage object is using to read/write data from (see Snowflake's [Creating the Stage](https://docs.snowflake.com/en/sql-reference/sql/create-stage.html) article for more information). ``` # get a saved credential set, return None if not found def dr_get_catalog_credentials(name, cred_type): if cred_type not in ['basic', 's3']: print('credentials type must be: basic, s3 - value passed was {ct}'.format(ct=cred_type)) return None credentials_id = None response = requests.get( DR_APP_HOST + '/api/v2/credentials/', headers=DR_MODELING_HEADERS, ) if response.status_code == 200: df = pd.io.json.json_normalize(response.json()['data'])[['credentialId', 'name', 'credentialType']] if df[(df['name'] == name) & (df['credentialType'] == cred_type)]['credentialId'].size > 0: credentials_id = df[(df['name'] == name) & (df['credentialType'] == cred_type)]['credentialId'].iloc[0] else: print('Request failed; http error {code}: {content}'.format(code=response.status_code, content=response.content)) return credentials_id # create credentials set def dr_create_catalog_credentials(name, cred_type, user, password, token=None): if cred_type not in ['basic', 's3']: print('credentials type must be: basic, s3 - value passed was {ct}'.format(ct=cred_type)) return None if cred_type == 'basic': json = { "credentialType": cred_type, "user": user, "password": password, "name": name } elif cred_type == 's3' and token != None: json = { "credentialType": cred_type, "awsAccessKeyId": user, "awsSecretAccessKey": password, "awsSessionToken": token, "name": name } elif cred_type == 's3' and token == None: json = { "credentialType": cred_type, "awsAccessKeyId": user, "awsSecretAccessKey": password, "name": name } response = requests.post( url = DR_APP_HOST + '/api/v2/credentials/', headers=DR_MODELING_HEADERS, json=json ) if response.status_code == 201: return response.json()['credentialId'] else: print('Request failed; http error {code}: {content}'.format(code=response.status_code, content=response.content)) # get or create a credential set def dr_get_or_create_catalog_credentials(name, cred_type, user, password, token=None): cred_id = dr_get_catalog_credentials(name, cred_type) if cred_id == None: return dr_create_catalog_credentials(name, cred_type, user, password, token=None) else: return cred_id credentials_id = dr_get_or_create_catalog_credentials('s3_community', 's3', my_creds.SNOW_USER, my_creds.SNOW_PASS) ``` Next, create a connection to Snowflake and use the last successful run time and current time to create the bounds for determining which newly created or recently updated rows must be scored: ``` # create a connection ctx = snowflake.connector.connect( user=SNOW_USER, password=SNOW_PASS, account=SNOW_ACCOUNT, database=SNOW_DB, schema=SNOW_SCHEMA, protocol='https', application='DATAROBOT', ) # create a cursor cur = ctx.cursor() # execute sql to get start/end timestamps to use sql = "select last_ts_scored_through, current_timestamp::TIMESTAMP_NTZ cur_ts " \ "from etl_history " \ "where job_nm = '{job}' " \ "order by last_ts_scored_through desc " \ "limit 1 ".format(job=JOB_NAME) cur.execute(sql) # fetch results into dataframe df = cur.fetch_pandas_all() start_ts = df['LAST_TS_SCORED_THROUGH'][0] end_ts = df['CUR_TS'][0] ``` Dump the data out to S3. ``` # execute sql to dump data into a single file in S3 stage bucket # AWS single file snowflake limit 5 GB sql = "COPY INTO @S3_SUPPORT/titanic/community/" + JOB_NAME + ".csv " \ "from " \ "( " \ " select passengerid, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked " \ " from passengers_500k_ts " \ " where nvl(updt_ts, crt_ts) >= '{start}' " \ " and nvl(updt_ts, crt_ts) < '{end}' " \ ") " \ "file_format = (format_name='default_csv' compression='none') header=true overwrite=true single=true;".format(start=start_ts, end=end_ts) cur.execute(sql) ``` Next, create a session to perform the DataRobot Batch Prediction API scoring job submission and monitor its progress. ``` session = requests.Session() session.headers = { 'Authorization': 'Bearer {}'.format(API_KEY) } ``` The job is defined to take the file dump from Snowflake as input and then create a file with `_scored` appended in the same S3 path. The example specifies a concurrency of `4` prediction cores with passthrough of the surrogate key `PASSENGERID` to be joined on later. ``` INPUT_FILE = 's3://'+ my_creds.S3_BUCKET + '/titanic/community/' + JOB_NAME + '.csv' OUTPUT_FILE = 's3://'+ my_creds.S3_BUCKET + '/titanic/community/' + JOB_NAME + '_scored.csv' job_details = { 'deploymentId': DEPLOYMENT_ID, 'passthroughColumns': ['PASSENGERID'], 'numConcurrent': 4, "predictionInstance" : { "hostName": DR_PREDICTION_HOST, "datarobotKey": DATAROBOT_KEY }, 'intakeSettings': { 'type': 's3', 'url': INPUT_FILE, 'credentialId': credentials_id }, 'outputSettings': { 'type': 's3', 'url': OUTPUT_FILE, 'credentialId': credentials_id } } ``` Submit the job for processing and retrieve a URL for monitoring. ``` response = session.post( DR_APP_HOST + '/api/v2/batchPredictions', json=job_details ) ``` Hold control until the job completes. ``` if response.status_code == 202: job = response.json() print('queued batch job: {}'.format(job['links']['self'])) while job['status'] == 'INITIALIZING': time.sleep(3) response = session.get(job['links']['self']) response.raise_for_status() job = response.json() print('completed INITIALIZING') if job['status'] == 'RUNNING': while job['status'] == 'RUNNING': time.sleep(3) response = session.get(job['links']['self']) response.raise_for_status() job = response.json() print('completed RUNNING') print('status is now {status}'.format(status=job['status'])) if job['status'] != 'COMPLETED': for log in job['logs']: print(log) else: print('Job submission failed; http error {code}: {content}'.format(code=response.status_code, content=response.content)) ``` Upon completion, load the staging table into the STG schema table `PASSENGERS_SCORED_BATCH_API` with the prediction results via a truncate and bulk load operation. ``` # multi-statement executions # https://docs.snowflake.com/en/user-guide/python-connector-api.html#execute_string # truncate and load STG schema table with scored results sql = "truncate titanic.stg.PASSENGERS_SCORED_BATCH_API; " \ " copy into titanic.stg.PASSENGERS_SCORED_BATCH_API from @S3_SUPPORT/titanic/community/" + JOB_NAME + "_scored.csv" \ " FILE_FORMAT = 'DEFAULT_CSV' ON_ERROR = 'ABORT_STATEMENT' PURGE = FALSE;" ctx.execute_string(sql) ``` Finally, create a transaction to update the presentation table with the latest survivability scores towards the positive class label `1` of survival. The ETL history is updated upon successful completion of all tasks. ``` # update target presentation table and ETL history table in transaction sql = \ "begin; " \ "update titanic.public.passengers_500k_ts trg " \ "set trg.survival = src.survived_1_prediction " \ "from titanic.stg.PASSENGERS_SCORED_BATCH_API src " \ "where src.passengerid = trg.passengerid; " \ "insert into etl_history values ('{job}', '{run_through_ts}'); " \ "commit; ".format(job=JOB_NAME, run_through_ts=end_ts) ctx.execute_string(sql) ``` Rows 2 and 3 are updated with new survival scores as expected. ETL history is updated and subsequent runs are now based on the (most recent) successful timestamp. [Code for this example is available in GitHub](https://github.com/datarobot-community/ai_engineering/tree/master/databases/snowflake/batch_prediction_api/snow_batch_prediction_api_query_s3.ipynb). Enhancements to consider: - Add error handling, scoring or otherwise, that suits your workflow and toolset. - Incorporate serverless technology, like AWS Lambda, into scoring workflows to kick off a job based on an event, like S3 object creation. - As data volumes grow, consider the following. Snowflake single statement dumps and ingests seem to perform best around 8 threads per cluster node, e.g., a 2-node Small will not ingest a single file any faster than 1-node XSmall instance . An XSmall would likely perform best with 8 or more file shreds. --- # Generate Snowflake UDF Scoring Code URL: https://docs.datarobot.com/en/docs/classic-ui/integrations/snowflake/snowflake-sc.html > Use the DataRobot Scoring Code JAR as a user-defined function (UDF) on Snowflake. # Generate Snowflake UDF Scoring Code > [!NOTE] Managed DataRobot installations > The information on this page is intended for Self-Managed DataRobot installations. For DataRobot-managed Snowflake prediction environments, see [Automated deployment and replacement of Scoring Code in Snowflake](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-prediction-environments/nxt-prediction-environment-integrations/nxt-snowflake-pred-env-integration.html). Scoring Code makes it easy for you to perform predictions on DataRobot models anywhere you want by exporting a model to a Java JAR file. Snowflake user-defined functions (UDFs) allow you to execute arbitrary Java code on Snowflake. DataRobot provides an API to Scoring Code for you to use Scoring Code as a UDF on Snowflake without writing any additional code. To download and execute scoring code with Snowflake UDFs, you must meet the following prerequisites: - Prepare a DataRobot model that supports Scoring Code for deployment andcreate a model packagewith it. - Register your Snowflake prediction environment to use with the model. ## Access Scoring Code When you have your model package and prediction environment prepared in DataRobot, you can deploy the model in order to access the Scoring Code for use with Snowflake. 1. Navigate to theModel Registryand select your model package. On theDeploymentstab, selectCreate new deployment. 2. Complete the fields andconfigure the deploymentas desired. To generate Snowflake UDF Scoring Code, specify your Snowflake prediction environment under theInferenceheader. 3. Once fully configured, selectCreate deploymentat the top of the screen. 4. After deploying the model, access the deployment from the inventory and navigate to thePredictions > Portable Predictionstab. This is where DataRobot hosts the Scoring Code for the model. 5. (Optional) Toggle onInclude prediction explanationsto include prediction explanations with the prediction results, then clickDownload. The Scoring Code JAR file appears in your browser bar. 6. When the Scoring Code download completes, copy the installation script and update it with your Snowflake warehouse, database, schema, and the path to the Scoring Code JAR file, then execute the script. Copy and paste (for regression)-- Replace with the warehouse to use USE WAREHOUSE my_warehouse; -- Replace with the database to use USE DATABASE my_database; -- Replace with the schema to use CREATE SCHEMA IF NOT EXISTS scoring_code_udf_schema; USE SCHEMA scoring_code_udf_schema; -- Update this path to match the Scoring Code JAR location PUT 'file:///path/to/downloaded_scoring_code.jar' '@~/jars/' AUTO_COMPRESS=FALSE; -- Create the UDF CREATE OR REPLACE FUNCTION datarobot_udf(RowValue OBJECT) RETURNS FLOAT LANGUAGE JAVA IMPORTS=('@~/jars/downloaded_scoring_code.jar') HANDLER='com.datarobot.prediction.simple.RegressionPredictor.score'; Copy and paste (for classification)-- Replace with the warehouse to use USE WAREHOUSE my_warehouse; -- Replace with the database to use USE DATABASE my_database; -- Replace with the schema to use CREATE SCHEMA IF NOT EXISTS scoring_code_udf_schema; USE SCHEMA scoring_code_udf_schema; -- Update this path to match the Scoring Code JAR location PUT 'file:///path/to/downloaded_scoring_code.jar' '@~/jars/' AUTO_COMPRESS=FALSE; -- Create the UDF CREATE OR REPLACE FUNCTION datarobot_udf(RowValue OBJECT) RETURNS OBJECT LANGUAGE JAVA IMPORTS=('@~/jars/downloaded_scoring_code.jar') HANDLER='com.datarobot.prediction.simple.ClassificationPredictor.score'; The script uploads the JAR file to a Snowflake stage and creates a UDF for making predictions with Scoring Code. NoteTo run these scripts, youmustuse the SnowSQL command provided in the next step (7). You can't execute these scripts in Snowflake's UI. 7. Execute the script in SnowSQL by providing your credentials and the script location: Copy and pastesnowsql --accountname $ACCOUNT_NAME --username $USERNAME --filename $SCRIPT_PATH 8. With your UDF successfully created, you can now use Snowflake to score data. Use the UDF in any manner supported by SQL. Copy and paste/* Scoring your data The Scoring Code UDF accepts rows of data as objects. The OBJECT_CONSTRUCT_KEEP_NULL method can be used to turn a table row into an object. */ -- Scoring without specifying columns. Data can contain nulls SELECT my_datarobot_model(OBJECT_CONSTRUCT_KEEP_NULL(*)) FROM source_table; 9. After scoring data, you canupload actuals via the Settings tabin order to enable accuracy monitoring and more. ## Feature Considerations Consider the following when using Scoring Code as a UDF on Snowflake. - Keras models cannot be executed in Snowflake. - Time series Scoring Code is not supported for Snowflake. - Scoring Code JARs created prior to the release of the Snowflake Scoring Code feature cannot be run in Snowflake. - To retrieve Prediction Explanations for Snowflake UDF Scoring Code, first toggle onInclude Prediction Explanationsin the deployment'sPortable Predictionstab. This tab includes code snipppets you can use to retrieve Prediction Explanations. You can use thescoreWithExplanations()method and customize the number of explanations that are returned. For example, configure the function like so:scoreWithExplanations(Map row, Integer maxCodes, Double thresholdLow, Double thresholdHigh) --- # Set up accuracy monitoring URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html > Configure accuracy monitoring on a deployment's Accuracy Settings tab. # Set up accuracy monitoring You can monitor a deployment for accuracy using the [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) tab, which lets you analyze the performance of the model deployment over time using standard statistical measures and exportable visualizations. You can enable accuracy monitoring on the Accuracy > Settings tab. To configure accuracy monitoring, you must: - Enable target monitoringin theData Drift Settings - Select an association IDin the Accuracy Settings - Add actualsin the Accuracy Settings On a deployment's Accuracy Settings page, you can configure the Association ID and Upload Actuals settings and the accuracy monitoring Definition and Notifications settings: | Field | Description | | --- | --- | | Association ID | | | Association ID | Defines the name of the column that contains the association ID in the prediction dataset for your model. Association IDs are required for setting up accuracy monitoring in a deployment. The association ID functions as an identifier for your prediction dataset so you can later match up outcome data (also called "actuals") with those predictions. | | Require association ID in prediction requests | Requires your prediction dataset to have a column name that matches the name you entered in the Association ID field. When enabled, you will get an error if the column is missing. This cannot be enabled with Enable automatic association ID generation for prediction rows. | | Enable automatic association ID generation for prediction rows | With an association ID column name defined, allows DataRobot to automatically populate the association ID values. This cannot be enabled with Require association ID in prediction requests. | | Enable automatic actuals feedback for time series models | For time series deployments that have indicated an association ID. Enables the automatic submission of actuals, so that you do not need to submit them manually via the UI or API. Once enabled, actuals can be extracted from the data used to generate predictions. As each prediction request is sent, DataRobot can extract an actual value for a given date. This is because when you send prediction rows to forecast, historical data is included. This historical data serves as the actual values for the previous prediction request. | | Upload Actuals | | | Drop file(s) here or choose file | Uploads a file with actuals to monitor accuracy by matching the model's predictions with actual values. Actuals are required to enable the Accuracy tab. | | Assigned features | Configures the Assigned features settings after you upload actuals. | | Definition | | | Set definition | Configures the metric, measurement, and threshold definitions for accuracy monitoring. | ## Enable target monitoring In order to enable accuracy monitoring, you must also [enable target monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html) in the Data Drift section of the Data Drift Settings tab. If target monitoring is turned off, a message displays on the Accuracy tab to remind you to enable target monitoring. ## Select an association ID To activate the [Accuracytab](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) for a deployment, first designate an association ID in the prediction dataset. The association ID is a [foreign key](https://www.tutorialspoint.com/Foreign-Key-in-RDBMS), linking predictions with future results (or [actuals](https://docs.datarobot.com/en/docs/reference/glossary/index.html#actuals)). It corresponds to an event for which you want to track the outcome; For example, you may want to track a series of loans to see if any of them have defaulted or not. > [!NOTE] Important: Association ID for monitoring agent and monitoring jobs > You must set an association ID before making predictions to include those predictions in accuracy tracking. For [agent-monitored](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html) external model deployments with challengers (and monitoring jobs for challengers), the association ID should be `__DataRobot_Internal_Association_ID__` to [report accuracy for the modelandits challengers](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent-use.html#report-accuracy-for-challengers). On the Accuracy > Settings tab of a deployment, the Association ID section has a field for the column name containing the association IDs. The column name you define in the Association ID field must match the name of the column containing the association IDs in the prediction dataset for your model. Each cell for this column in your prediction dataset should contain a unique ID that pairs with a corresponding unique ID that occurs in the actuals payload. In addition, you can enable Require association ID in prediction requests to throw an error if the column is missing from your prediction dataset when you make a prediction request. > [!NOTE] Association IDs for chat requests > For DataRobot-deployed text generation and agentic workflow custom models that use the Bolt-on Governance API (chat completions), an association ID can be specified directly in the chat request using the `extra_body` field. Set `datarobot_association_id` in `extra_body` to use a custom association ID instead of the auto-generated one. For more information, see the [chat()hook documentation](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#association-id). You can set the column name containing the association IDs on a deployment at any time, whether predictions have been made against that deployment or not. Once set, you can only update the association ID if you have not yet made predictions that include that ID. Once predictions have been made using that ID, you cannot change it. Association IDs (the contents in each row for the designated column name) must be shorter than 128 characters, or they will be truncated to that size. If truncated, uploaded actuals will require the truncated association IDs for your actuals in order to properly generate accuracy statistics. ### Association IDs for time series deployments For time series deployments, prediction requests already contain the data needed to uniquely identify individual predictions. Therefore, it is important to consider the feature used as an association ID, depending on the deployment type, consider the following guidelines: - Single-series deployments: DataRobot recommends using theForecast Datecolumn as the association ID because it is the date you are making predictions for. For example, if today is June 15th, 2022, and you are forecasting daily total sales for a store, you may wish to know what the sales will be on July 15th, 2022. You will have a single actual total sales figure for this date, so you can use “2022-07-15” as the association ID (the forecast date). - Multiseries deployments: DataRobot recommends using a custom column containingForecast Date + Series IDas the association ID. If a single model can predict daily total sales for a number of stores, then you can use, for example, the association ID “2022-07-15 1234” for sales on July 15th, 2022 for store #1234. - All time series deployments: You may want to forecast the same date multiple times as the date approaches. For example, you might forecast daily sales 30 days in advance, then again 14 days in advance, and again 7 days in advance. These forecasts all have the same forecast date, and therefore the same association ID. > [!NOTE] Important > Be aware that models may produce different forecasts when predicting closer to the forecast date. Predictions for multiple forecast distances are each tracked individually so that accuracy can be properly calculated for each forecast distance. After you designate an association ID, you can toggle Enable automatic actuals feedback for time series models to on. This setting automatically submits actuals so that you do not need to submit them manually via the UI or API. Once enabled, actuals can be extracted from the data used to generate predictions. As each prediction request is sent, DataRobot can extract an actual value for a given date. This is because when you send prediction rows to forecast, historical data is included. This historical data serves as the actual values for the previous prediction request. ## Add actuals You can directly upload datasets containing actuals to a deployment from the Accuracy > Settings tab (described here) or through the [API](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html#upload-actuals-with-the-api). The deployment's prediction data must correspond to the actuals data you upload. Review the [row limits](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html#actuals-upload-limit) for uploading actuals before proceeding. 1. To use actuals with your deployment, in theUpload Actualssection, clickChoose file. Either upload a file directly or select a file from theAI Catalog. If you upload a local file, it is added to theAI Catalogafter successful upload. Actuals must be snapshotted in the AI Catalog to use them with a deployment. 2. Once uploaded, complete the fields that populate in theActualssection. UnderAssigned features, each field has a dropdown menu that allows you to select any of the columns from your dataset: FieldDescriptionActual ResponseDefines the column in your dataset that contains the actual values.Association IDDefines the column that contains theassociation IDs.Timestamp (optional)Defines the column that contains the date/time when the actual values were obtained, formatted according toRFC 3339(for example, 2018-04-12T23:20:50.52Z).Keep actuals without matching predictionsDetermines if DataRobot stores uploaded actuals that don't match any existing predictions by association ID. Column name matchingThe column names for the association ID in the prediction and the actuals datasets do not need to match. The only requirement is that each dataset contains a column that includes an identifier that does match the other dataset. For example, if the columnstore_idcontains the association ID in the prediction dataset that you will use to identify a row and match it to the actual result, enterstore_idin theAssociation IDsection. In theUpload Actualssection underAssigned fields, in theAssociation IDfield, enter the name of the column in the actuals dataset that contains the identifiers associated with the identifiers in the prediction dataset. 3. After you configure theAssigned fields, clickSave. When you complete this configuration process andmake predictionswith a dataset containing the definedAssociation ID, theAccuracypage is enabled for your deployment. ## Upload actuals with the API This workflow outlines how to enable the Accuracy tab for deployments using the DataRobot API. 1. From theAccuracy > Settingstab, locate theAssociation IDsection. 2. In theAssociation IDfield, enter the column name containing the association IDs in your prediction dataset. 3. EnableRequire association ID in prediction requests. This requires your prediction dataset to have a column name that matches the name you entered in theAssociation IDfield. You will get an error if the column is missing. NoteYou can set an association ID andnottoggle on this setting if you are sending prediction requests that do not include the association ID and you do not want them to error; however, until it is enabled, you cannot monitor accuracy for your deployment. 4. Make predictionsusing a dataset that includes the association ID. 5. Submit the actual values via the DataRobot API (for details, refer to the API documentation by signing in to DataRobot, clicking the question mark on the upper right, and selectingAPI Documentation; in the API documentation, selectDeployments > Submit Actuals - JSON). You should review therow limitsfor uploading actuals before proceeding. NoteThe actuals payload must contain theassociationIdandactualValue, with the column names for those values in the dataset defined during the upload process. If you submit multiple actuals with the same association ID value, either in the same request or a subsequent request, DataRobot updates the actuals value; however, this update doesn't recalculate the metrics previously calculated using that initial actuals value. To recalculate metrics, you canclear the deployment statisticsand reupload the actuals (or create a new deployment). Use the following snippet in the API to submit the actual values: importrequestsAPI_TOKEN=''USERNAME='johndoe@datarobot.com'DEPLOYMENT_ID='5cb314xxxxxxxxxxxa755'LOCATION='https://app.datarobot.com'defsubmit_actuals(data,deployment_id):headers={'Content-Type':'application/json','Authorization':'Token{}'.format(API_TOKEN)}url='{location}/api/v2/deployments/{deployment_id}/actuals/fromJSON/'.format(deployment_id=deployment_id,location=LOCATION)resp=requests.post(url,json=data,headers=headers)ifresp.status_code>=400:raiseRuntimeError(resp.content)returnresp.contentdefmain():deployment_id=DEPLOYMENT_IDpayload={'data':[{'actualValue':1,'associationId':'5d8138fb9600000000000000',# str},{'actualValue':0,'associationId':'5d8138fb9600000000000001',},]}submit_actuals(payload,deployment_id)print('Done')if__name__=="__main__":main() After submitting at least 100 actuals for a non-time series deployment (there is no minimum for time series deployments) and making predictions with corresponding association IDs, theAccuracytab becomes available for your deployment. ## Define accuracy monitoring notifications For accuracy, the notification conditions relate to a [performance optimization metric](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html) for the underlying model in the deployment. Select from the same set of metrics that are available on the Leaderboard. You can visualize accuracy using the [Accuracy over Time graph](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html#accuracy-over-time-graph) and the [Predicted & Actual graph](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html#predicted-actual-graph). Accuracy monitoring is defined by a single accuracy rule. Every 30 seconds, the rule evaluates the deployment's accuracy. Notifications trigger when this rule is violated. Before configuring accuracy notifications and monitoring for a deployment, set an [association ID](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html#select-an-association-id). If not set, DataRobot displays the following message when you try to modify accuracy notification settings: > [!NOTE] Note > Only deployment Owners can modify accuracy monitoring settings. They can set no more than one accuracy rule per deployment.Consumers cannot modify monitoring or notification settings.Users can [configure the conditions under which notifications are sent to them](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/deploy-notifications.html) and see explained status information by hovering over the accuracy status icon: > > [https://docs.datarobot.com/en/docs/images/notify-8.png](https://docs.datarobot.com/en/docs/images/notify-8.png) To set up accuracy monitoring: 1. On theAccuracy Settingspage, in theDefinitionsection, configure the settings for monitoring accuracy: ElementDescription1MetricDefines the metric used to evaluate the accuracy of your deployment. The metrics available from the dropdown menu are the same as thosesupported by theAccuracytab.2MeasurementDefines the unit of measurement for the accuracy metric and its thresholds. You can selectvalueorpercentfrom the dropdown. Thevalueoption measures the metric and thresholds by specific values, and thepercentoption measures by percent changed. Thepercentoption is unavailable for model deployments that don't have training data.3"At Risk" / "Failing" thresholdsSets the values or percentages that, when exceeded, trigger notifications. Two thresholds are supported: when the deployment's accuracy is "At Risk" and when it is "Failing." DataRobot provides default values for the thresholds of the first accuracy metric provided (LogLoss for classification and RMSE for regression deployments) based on the deployment's training data. Deployments without training data populate default threshold values based on their prediction data instead. If you change metrics, default values are not provided. NoteChanges to thresholds affect the periods in which predictions are made across the entire history of a deployment. These updated thresholds are reflected in the performance monitoring visualizations on theAccuracytab. 2. After updating the accuracy monitoring settings, clickSave. ### Examples of accuracy monitoring settings Each combination of metric and measurement determines the expression of the rule. For example, if you use the LogLoss metric measured by value, the rule triggers notifications when accuracy "is greater than" the values of 5 or 10: However, if you change the metric to AUC and the measurement to percent, the rule triggers notifications when accuracy "decreases by" the values set for the threshold: --- # Configure challengers URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/challengers-settings.html > Configure deployments using the Challengers tab to store prediction request data at the row level and replay predictions on a schedule. # Configure challengers DataRobot can securely store prediction request data at the row level for deployments (not supported for external model deployments). This setting must be enabled on the Challengers > Settings tab for any deployment using the [Challengers](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html) tab. In addition to enabling challenger analysis, access to stored prediction request rows enables you to thoroughly audit the predictions and use that data to troubleshoot operational issues. For instance, you can examine the data to understand an anomalous prediction result or why a dataset was malformed. You may have enabled prediction rows storage during [deployment creation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-deploy-info.html). During deployment creation, this toggle appears under the [Challenger Analysis](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-deploy-info.html#challenger-analysis) section. Once enabled, prediction requests made for the deployment are collected by DataRobot. Prediction explanations are not stored. Contact your DataRobot representative to learn more about data security, privacy, and retention measures or to discuss prediction auditing needs. On a deployment's Challengers Settings page, you can configure the following settings to store prediction request data at the row level and replay predictions on a schedule: > [!NOTE] Important > Prediction requests are only collected if the prediction data is in a valid data format interpretable by DataRobot, such as CSV or JSON. Failed prediction requests with a valid data format are also collected (i.e., missing input features). | Field | Description | | --- | --- | | Challengers | | | Enable prediction row storage | Enables prediction data storage, a setting required to score predictions made by challenger and compare performance with the deployed model. | | Enable challenger analysis | Enables the use of challenger models, allowing you to compare models post-deployment and replace the champion model if necessary. | | Replay Schedule | | | Automatically replay challengers | Enables a recurring, scheduled challenger replay on stored predictions for retraining. | | Replay time | Displays the selected replay time in UTC. | | Local replay time | Displays the selected replay time converted to local time. If the selected replay time in UTC results in the replay time on a new day, a warning appears. | | Frequency / Time | Configures the replay schedule, selecting from the following options: Every hour: Each hour on the selected minute past the hour.Every day: Each day at the selected time.Every week: Each selected day at the selected time.Every month: Each month, on each selected day, at the selected time. The selected days in a month are provided as numbers (1 to 31) in a comma separated list.Every quarter: Each month of a quarter, on each selected day, at the selected time. The selected days in each month are provided as numbers (1 to 31) in a comma separated list.Every year: Each selected month, on each selected day, at the selected time. The selected days in each month are provided as numbers (1 to 31) in a comma separated list. | | Use advanced scheduler | Configures the replay schedule, entering values for the following advanced options: Minute: A comma-separated list of numbers between 0 and 59 or * for all.Hour: A comma-separated list of numbers between 0 and 23 or * for all.Day of month: A comma-separated list of numbers between 1 and 31 or * for all.Month: A comma-separated list of numbers between 1 and 12 or * for all.Day of week: A comma-separated list of numbers between 0 and 6 or * for all. | > [!NOTE] Note > The Time setting applies across all days selected. In other words, you cannot set checks to occur every 12 hours on Saturday and every 2 hours on Monday. --- # Set up custom metrics monitoring URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/custom-metrics-settings.html > Enable custom metrics monitoring by defining the # Set up custom metrics monitoring On a deployment's Custom Metrics > Settings tab, you can define "at risk" and "failing" thresholds to monitor the status of the custom metrics you created on the [Custom Metricstab](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html). | Field | Description | | --- | --- | | Association ID | | | Association ID | Defines the name of the column that contains the association ID in the prediction dataset for your model. Association IDs function as an identifier for your prediction dataset so you can later match up outcome data (also called "actuals") with those predictions. | | Require association ID in prediction requests | Requires your prediction dataset to have a column name that matches the name you entered in the Association ID field. When enabled, you will get an error if the column is missing. This cannot be enabled with Enable automatic association ID generation for prediction rows. | | Enable automatic association ID generation for prediction rows | With an association ID column name defined, allows DataRobot to automatically populate the association ID values. This cannot be enabled with Require association ID in prediction requests. | | Enable automatic actuals feedback for time series models | For time series deployments that have indicated an association ID. Enables the automatic submission of actuals, so that you do not need to submit them manually via the UI or API. Once enabled, actuals can be extracted from the data used to generate predictions. As each prediction request is sent, DataRobot can extract an actual value for a given date. This is because when you send prediction rows to forecast, historical data is included. This historical data serves as the actual values for the previous prediction request. | | Definition | | | At Risk / Failing | Enables DataRobot to apply logical statements to calculated custom metric values. You can define threshold statements for a custom metric to categorize the deployment as "at risk" or "failing" if either statement evaluates to true. | ## Define custom metric monitoring Configure thresholds to alert you when a deployed model is "at risk" or "failing" to meet the standards you set for the selected custom metric. > [!NOTE] Note > To access the settings in the Definition section, configure and save a metric on the [Custom Metricstab](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html). Only deployment Owners can modify custom metric monitoring settings; however, Users can [configure the conditions under which notifications are sent to them](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/deploy-notifications.html).Consumers cannot modify monitoring or notification settings. You can customize the rules used to calculate the custom metrics status for your deployment on the Custom Metrics Settings page: 1. In a deployment you want to monitor custom metrics for, clickCustom Metrics > Settings. 2. In theDefinitionsection, define logical statements for any of your custom metrics'At RiskandFailingthresholds: SettingDescriptionMetricSelect the custom metric to add a threshold definition for.ConditionSelect one of the following condition statements to set the custom metric's threshold forAt RiskorFailing:is less thanis less than or equal tois greater thanis greater than or equal toValueEnter a numeric value to set the custom metric's threshold forAt RiskorFailing. For example, a statement could be: "The ad campaign isAt Riskif theCampaign ROIisless than10000." 3. After adding custom metrics monitoring definitions, clickSave. --- # Set up data drift monitoring URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html > Configure data drift monitoring on a deployment's Data Drift Settings tab. # Set up data drift monitoring When deploying a model, there is a chance that the dataset used for training and validation differs from the prediction data. You can enable data drift monitoring on the Data Drift > Settings tab. DataRobot monitors both target and feature drift information and displays results on the [Data Drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html) tab. > [!NOTE] How does DataRobot track drift? > DataRobot tracks two types of drift: > > Target drift > : DataRobot stores statistics about predictions to monitor how the distribution and values of the target change over time. As a baseline for comparing target distributions, DataRobot uses the distribution of predictions on the holdout. > Feature drift > : DataRobot stores statistics about predictions to monitor how distributions and values of features change over time. The supported feature data types are numeric, categorical, and text. As a baseline for comparing distributions of features: > For training datasets larger than 500MB, DataRobot uses the distribution of a random sample of the training data. > For training datasets smaller than 500MB, DataRobot uses the distribution of 100% of the training data. > [!NOTE] Availability information > Data drift tracking is only available for deployments using deployment-aware prediction API routes (i.e., `https://example.datarobot.com/predApi/v1.0/deployments//predictions`). On a deployment's Data Drift Settings page, you can configure the following settings: | Field | Description | | --- | --- | | Data Drift | | | Enable feature drift tracking | Configures DataRobot to track feature drift in a deployment. Training data is required for feature drift tracking. | | Enable target monitoring | Configures DataRobot to track target drift in a deployment. Target monitoring is required for accuracy monitoring. | | Training data | | | Training data | Displays the dataset used as a training baseline while building a model. | | Inference data | | | DataRobot is storing your predictions | Confirms DataRobot is recording and storing the results of any predictions made by this deployment. DataRobot stores a deployment's inference data when a deployment is created. It cannot be uploaded separately. | | Inference data (external model) | | | DataRobot is recording the results of any predictions made against this deployment | Confirms DataRobot is recording and storing the results of any predictions made by the external model. | | Drop file(s) here or choose file | Uploads a file with prediction history data to monitor data drift. | | Definition | | | Set definition | Configures the drift and importance metric settings and threshold definitions for data drift monitoring. | > [!NOTE] Note > DataRobot monitors both target and feature drift information by default and displays results in the [Data Drift dashboard](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html). Use the Enable target monitoring and Enable feature drift tracking toggles to turn off tracking if, for example, you have sensitive data that should not be monitored in the deployment. The Enable target monitoring setting is also required to enable [accuracy monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html). ## Define data drift monitoring notifications Drift assesses how the distribution of data changes across all features for a specified range. The thresholds you set determine the amount of drift you will allow before a notification is triggered. > [!NOTE] Note > Only deployment Owners can modify data drift monitoring settings; however, Users can [configure the conditions under which notifications are sent to them](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/deploy-notifications.html).Consumers cannot modify monitoring or notification settings. Use the Definition section of the Data Drift > Settings tab to set thresholds for drift and importance: - Drift is a measure of how new prediction data differs from the original data used to train the model. - Importance allows you to separate the features you care most about from those that are less important. For both drift and importance, you can visualize the thresholds and how they separate the features on the [Data Drift tab](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html). By default, the data drift status for deployments is marked as "Failing" ( [https://docs.datarobot.com/en/docs/images/icon-red.png](https://docs.datarobot.com/en/docs/images/icon-red.png)) when at least one high-importance feature exceeds the set drift metric threshold; it is marked as "At Risk" ( [https://docs.datarobot.com/en/docs/images/icon-yellow.png](https://docs.datarobot.com/en/docs/images/icon-yellow.png)) when no high-importance features, but at least one low-importance feature exceeds the threshold. Deployment Owners can customize the rules used to calculate the drift status for each deployment. As a deployment Owner, you can: - Define or override the list of high or low-importance features to monitor features that are important to you or put less emphasis on less important features. - Exclude features expected to drift from drift status calculation and alerting so you do not get false alarms. - Customize what "At Risk" and "Failing" drift statuses mean to personalize and tailor the drift status of each deployment to your needs. To set up monitoring of drift status for a deployment: 1. On theData Drift Settingspage, in theDefinitionsection, configure the settings for monitoring data drift: ElementDescription1RangeAdjusts the time range of theReference period, which compares training data to prediction data. Select a time range from the dropdown menu.2Drift metricDataRobot only supports the Population Stability Index (PSI) metric. For more information, see the note onDrift metric supportbelow.3Importance metricDataRobot only supports the Permutation Importance metric. The importance metric measures the most impactful features in the training data.4Xexcluded featuresExcludes features (including the target) from drift status calculations. ClickXexcluded featuresto open a dialog box where you can enter the names of features to set asDrift exclusions. Excluded features do not affect drift status for the deployment but still display on the Feature Drift vs. Feature Importance chart. See anexample.5Xstarred featuresSets features to be treated as high importance even if they were initially assigned low importance. ClickXstarred featuresto open a dialog box where you can enter the names of features to set asHigh-importance stars. Once added, these features are assigned high importance. They ignore the importance thresholds, but still display on the Feature Drift vs. Feature Importance chart. See anexample.6Drift thresholdConfigures the thresholds of the drift metric. When drift thresholds are changed, theFeature Drift vs. Feature Importance chartupdates to reflect the changes.7Importance thresholdConfigures the thresholds of the importance metric. The importance metric measures the most impactful features in the training data. When importance thresholds are changed, theFeature Drift vs. Feature Importance chartupdates to reflect the changes. See anexample.8"At Risk" / "Failing" thresholdsConfigures the values that trigger drift statuses for "At Risk" () and "Failing" (). See anexample. NoteChanges to thresholds affect the periods of time in which predictions are made across the entire history of a deployment. These updated thresholds are reflected in the performance monitoring visualizations on theData Drifttab. 2. After updating the data drift monitoring settings, clickSave. ### Example of an excluded feature In the example below, the excluded feature, which appears as a gray circle, would normally change the drift status to "Failing" ( [https://docs.datarobot.com/en/docs/images/icon-red.png](https://docs.datarobot.com/en/docs/images/icon-red.png)). Because it is excluded, the status remains as Passing. ### Example of configuring the importance and drift thresholds In the example below, the chart has adjusted the importance and drift thresholds (indicated by the arrows), resulting in more features "At Risk" and "Failing" than the chart above. ### Example of starring a feature to assign high importance In the example below, the starred feature, which appears as a white circle, would normally cause drift status to be "At Risk" due to its initially low importance. However, since it is assigned high importance, the feature will change the drift status to "Failing" ( [https://docs.datarobot.com/en/docs/images/icon-red.png](https://docs.datarobot.com/en/docs/images/icon-red.png)). ### Example of setting a drift status rule The following example configures the rule for a deployment to mark its drift status as "At Risk" if one of the following is true: - The number of low-importance features above the drift threshold is greater than 1. - The number of high-importance features above the drift threshold is greater than 3. --- # Enable data exploration URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-exploration-settings.html > Enable prediction row storage for a deployment, allowing you to export the stored prediction and training data to compute and monitor custom business or performance metrics. # Enable data exploration You can enable prediction row storage to activate the [Data Exploration](https://docs.datarobot.com/en/docs/api/reference/sdk/data-exploration.html) tab, where you can export a deployment's stored training data, prediction data, and actuals to compute and monitor custom business or performance metrics on the [Custom Metrics tab](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html) or outside DataRobot. | Field | Description | | --- | --- | | Enable prediction row storage | Enables prediction data storage, a setting required to store and export a deployment's prediction data for use in custom metrics. | --- # Set up fairness monitoring URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/fairness-settings.html > Configure fairness monitoring on a deployment's Fairness Settings tab. # Set up fairness monitoring On a deployment's Fairness > Settings tab, you can define [Bias and Fairness](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html) settings for your deployment to identify any biases in a binary classification model's predictive behavior. If fairness settings are defined prior to deploying a model, the fields are automatically populated. For additional information, see the section on [defining fairness tests](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html#configure-metrics-and-mitigation). > [!NOTE] Note > To configure fairness settings, you must enable target monitoring for the deployment. Target monitoring allows DataRobot to monitor how the values and distributions of the target change over time by storing prediction statistics. If target monitoring is turned off, a message displays on the Fairness tab to remind you to enable [target monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html). Configuring fairness criteria and notifications can help you identify the root cause of bias in production models. On the Fairness tab for individual models, DataRobot calculates per-class bias and fairness over time for each protected feature, allowing you to understand why a deployed model failed the predefined acceptable bias criteria. For information on fairness metrics and terminology, see the [Bias and Fairness reference page](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/bias-ref.html). To measure the fairness of production models, you must configure bias and fairness testing in the Fairness > Settings tab of a deployed model. If bias and fairness testing was configured for the model prior to deployment, the fields are automatically populated. On a deployment's Fairness Settings page, you can configure the following settings: | Field | Description | | --- | --- | | Segmented Analysis | | | Track attributes for segmented analysis of training data and predictions | Enables DataRobot to monitor deployment predictions by segments, for example by categorical features. | | Fairness | | | Protected features | Selects each protected feature's dataset column to measure fairness of model predictions against; these features must be categorical. | | Primary fairness metric | Selects the statistical measure of parity constraints used to assess fairness. | | Favorable target outcome | Selects the outcome value perceived as favorable for the protected class relative to the target. | | Fairness threshold | Selects the fairness threshold to measure if a model performs within appropriate fairness bounds for each protected class. | | Association ID | | | Association ID | Defines the name of the column that contains the association ID in the prediction dataset for your model. An association ID is required to calculate two of the Primary fairness metric options: True Favorable Rate & True Unfavorable Rate Parity and Favorable Predictive & Unfavorable Predictive Value Parity. The association ID functions as an identifier for your prediction dataset so you can later match up outcome data (also called "actuals") with those predictions. | | Require association ID in prediction requests | Requires your prediction dataset to have a column name that matches the name you entered in the Association ID field. When enabled, you will get an error if the column is missing. This cannot be enabled with Enable automatic association ID generation for prediction rows. | | Enable automatic association ID generation for prediction rows | With an association ID column name defined, allows DataRobot to automatically populate the association ID values. This cannot be enabled with Require association ID in prediction requests. | | Definition | | | Set definition | Configures the number of protected classes below the fairness threshold required to trigger monitoring notifications. | ## Select a fairness metric DataRobot supports the following fairness metrics in MLOps: - Equal Parity - Proportional Parity - Prediction Balance - True FavorableandTrue UnfavorableRate Parity (True Positive Rate Parity and True Negative Rate Parity) - Favorable PredictiveandUnfavorable PredictiveValue Parity (Positive Predictive Value Parity and Negative Predictive Value Parity) If you are unsure of the appropriate fairness metric for your deployment, click [help me choose](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html#select-a-metric). > [!NOTE] Note > To calculate True Favorable Rate & True Unfavorable Rate Parity and Favorable Predictive & Unfavorable Predictive Value Parity, the deployment must provide an [association ID](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html#select-an-association-id). ## Define fairness monitoring notifications Configure notifications to alert you when a production model is at risk of or fails to meet predefined fairness criteria. You can visualize fairness status on the [Fairness](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/mlops-fairness.html) tab. Fairness monitoring uses a primary fairness metric and two thresholds—protected features considered to be "At Risk" and "Failing"—to monitor fairness. If not specified, DataRobot uses the default thresholds. > [!NOTE] Note > To access the settings in the Definition & Notifications section, configure and save the fairness settings. Only deployment Owners can modify fairness monitoring settings; however, Users can [configure the conditions under which notifications are sent to them](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/deploy-notifications.html).Consumers cannot modify monitoring or notification settings. To customize the rules used to calculate the fairness status for each deployment: 1. On theFairness Settingspage, in theDefinitionsection, clickSet definitionand configure the threshold settings for monitoring fairness: ThresholdDescriptionAt RiskDefines the number of protected features below the bias threshold that, when exceeded, classifies the deployment as "At Risk" and triggers notifications. The threshold forAt Riskshould be lower than the threshold forFailing.Default value:1FailingDefines the number of protected features below the bias threshold that, when exceeded, classifies the deployment as "Failing" and triggers notifications. The threshold forFailingshould be higher than the threshold forAt Risk.Default value:2 NoteChanges to thresholds affect the periods in which predictions are made across the entire history of a deployment. These updated thresholds are reflected in the performance monitoring visualizations on theFairnesstab. 2. After updating the fairness monitoring settings, clickSave. --- # Set up humility rules URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html > Configure humility rules which enable models to recognize, in real-time, when they make uncertain predictions or receive data they have not seen before. When they recognize conditions you specify in the rule, they perform desired actions you configure. # Set up humility rules > [!NOTE] Availability information > The Humility tab is only available for DataRobot MLOps users. Contact your DataRobot representative for more information about enabling this feature. To enable humility-over-time monitoring for a deployment (displayed on the [Summary](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html#view-humility-data-over-time) page), you must enable [Data Drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html) monitoring. MLOps allows you to create humility rules for deployments on the Humility > Settings tab. Humility rules enable models to recognize, in real-time, when they make uncertain predictions or receive data they have not seen before. Unlike data drift, model humility does not deal with broad statistical properties over time—it is instead triggered for individual predictions, allowing you to set desired behaviors with rules that depend on different triggers. Using humility rules to add triggers and corresponding actions to a prediction helps mitigate risk for models in production. Humility rules help to identify and handle data integrity issues during monitoring and to better identify the root cause of unstable predictions. The Humility tab contains the following sub-tabs: - Summary:View a summary of humility data over timeafter configuring humility rules and making predictions with humility monitoring enabled. - Settings:Create humility rulesto monitor for uncertainty and specify actions to manage it. - Prediction Warnings(for regression projects only):Configure prediction warningsto detect when deployments produce predictions with outlier values. Specific humility rules are available for multiseries projects detailed [below](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html#multiseries-humility-rules). While they follow the same general workflow for humility rules as AutoML projects, they have specific settings and options. ## Create humility rules To create humility rules for a deployment: 1. In theDeploymentsinventory, select a deployment and navigate to theHumility > Settingstab. 2. On theHumility Rules Settingspage, if you haven't enabled humility for a model, click theEnable humilitytoggle, then: 3. Click the pencil icon () to enter a name for the rule. Thenselect aTriggerandspecify anActionto take based on the selected trigger. The trigger detects a rule violation and the action handles the violating prediction. When rule configuration is complete, a rule explanation displays below the rule describing what happens for the configured trigger and respective action. 4. ClickAddto save the rule, and click+Add new ruleto add additional rules. 5. After adding andeditingthe rules, clickSubmit. WarningClickingSubmitis the only way to permanently save new rules and rule changes. If you navigate away from theHumilitytab without clickingSubmit, your rules and edits to rules are not saved. NoteIf a rule is a duplicate of an existing rule, you cannot save it. In this case, when you clickSubmit, a warning displays: After you save and submit the humility rules, DataRobot monitors the deployment using the new rules and any previously created rules. After a rule is created, the prediction response body returns the humility object. Refer to thePrediction API documentationfor more information. ### Choose a trigger for the rule Select a Trigger for the rule you want to create. There are three triggers available: Each trigger requires specific settings. The following table and subsequent sections describe these settings: | Trigger | Description | | --- | --- | | Uncertain Prediction | Detects whether a prediction's value violates the configured thresholds. You must set lower-bound and upper-bound thresholds for prediction values. | | Outlying Input | Detects if the input value of a numeric feature is outside of the configured thresholds. | | Low Observation Region | Detects if the input value of a categorical feature value is not included in the list of specified values. | #### Uncertain Prediction To configure the uncertain prediction trigger, set lower-bound and upper-bound thresholds for prediction values. You can either enter these values manually or click Calculate to use computed thresholds derived from the Holdout partition of the model (only available for DataRobot models). For regression models, the trigger detects any values outside of the configured thresholds. For binary classification models, the trigger detects any prediction's probability value that is inside the thresholds. You can view the type of model for your deployment from the Settings > Data tab. #### Outlying Input To configure the outlying input trigger, select a numeric feature and set the lower-bound and upper-bound thresholds for its input values. Enter the values manually or click Calculate to use computed thresholds derived from the training data of the model (only available for DataRobot models). #### Low Observation Region To configure the low observation region trigger, select a categorical feature and indicate one or more values. Any input value that appears in prediction requests that does not match the indicated values triggers an action. ### Choose an action for the rule Select an Action for the rule you are creating. DataRobot applies the action if the trigger indicates a rule violation. There are three actions available: | Action | Description | | --- | --- | | Override prediction | Modifies predicted values for rows violating the trigger with the value configured by the action. | | Throw error | Rows in violation of the trigger return a 480 HTTP error with the predictions, which also contributes to the data error rate on the Service Health tab. | | No operation | No changes are made to the detected prediction value. | #### Override prediction To configure the override action, set a value that will overwrite the returned value for predictions violating the trigger. For binary classification and multiclass models, the indicated value can be set to either of the model's class labels (e.g., "True" or "False"). For regression models, manually enter a value or use the maximum, minimum, or mean provided by DataRobot (only provided for DataRobot models). #### Throw error To configure the throw error action, you can use the default error message provided or specify your own custom error message. This error message will appear along a 480 HTTP error with the predictions. ## Edit humility rules You can edit or delete existing rules from the Humility > Rules tab if you have [Owner](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#deployment-roles) permissions. > [!NOTE] Note > Edits to humility rules can have significant impact on deployment predictions, as prediction values can be overwritten with new values or can return errors based on the rules configured. ### Edit a rule 1. Select the pencil icon for the rule. 2. Change thetrigger,action, and any associated values for the rule. When finished, clickSave Changes. 3. After editing the rules, clickSubmit. If you navigate away from theHumilitytab without clickingSubmit, your edits will be lost. ### Delete a rule 1. Select the trash can icon for the rule. 2. ClickSubmitto complete the delete operation. If you navigate away from theHumilitytab without clickingSubmit, your rules will not be deleted. ### Reorder a rule To [reorder](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html#rule-application-order) the rules listed, drag and drop them in the desired order and click Submit. #### Rule application order The displayed list order of your rules determines the order in which they are applied. Although every humility rule trigger is applied, if multiple rules match the trigger of a prediction response, DataRobot applies the first rule in the list that changes the prediction value. However, if any triggered rule has the "Throw Error" action, that rule takes priority. For example, consider a deployment with the following rules: | Trigger | Action | Thresholds | | --- | --- | --- | | Rule 1: Uncertain Prediction | Override the prediction value to 55. | Lower: 1 Upper: 50 | | Rule 2: Uncertain Prediction | Override the prediction value to 66. | Lower: 45 Upper: 50 | If a prediction returns the value 100, both rules will trigger, as both rules detect an uncertain prediction outside of their thresholds. The first rule, Rule 1, takes priority, so the prediction value is overwritten to 55. The action to overwrite the value to 66 (based on Rule 2) is ignored. In another example, consider a deployment with the following rules: | Trigger | Action | Thresholds | | --- | --- | --- | | Rule 1: Uncertain Prediction | Override the prediction value to 55. | Lower: 1 Upper: 55 | | Rule 2: Uncertain Prediction | Throw an error. | Lower: 45 Upper: 60 | If a prediction returns the value 50, both rules will trigger. However, Rule 2 takes priority over Rule 1 because it is configured to return an error. Therefore, the value is not overwritten, as the action to return an error is higher priority than the numerical order of the rules. ## Prediction warnings Enable prediction warnings for regression model deployments on the Humility > Prediction warnings tab. Prediction warnings allow you to mitigate risk and make models more robust by identifying when predictions do not match their expected result in production. This feature detects when deployments produce predictions with outlier values, summarized in a report that returns with your predictions. > [!NOTE] Prediction warnings availability > Prediction warnings are only available for deployments using regression models. This feature does not support classification or time series models. Prediction warnings provide the same functionality as the [Uncertain Prediction](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html#uncertain-prediction) trigger that is part of humility monitoring. You may want to enable both, however, because prediction warning results are integrated into the [Predictions Over Time chart](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html#predictions-over-time-chart) on the Data drift tab. ### Enable prediction warnings 1. To enable prediction warnings, navigate toHumility > Prediction warnings. 2. Enter aLower boundandUpper bound, or clickConfigureto have DataRobot calculate the prediction warning ranges. DataRobot derives thresholds for the prediction warning ranges from the Holdout partition of your model. These are the boundaries for outlier detection—DataRobot reports any prediction result outside these limits. You can choose to accept the Holdout-based thresholds or manually define the ranges instead. 3. After making any desired changes, clickSave ranges. After the humility rules are in effect, you can include prediction outlier warnings when you [make predictions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred.html#set-prediction-options). Prediction warnings are reported on the [Predictions Over Time chart](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html#predictions-over-time-chart) on the Data drift tab. > [!NOTE] Note > Prediction warnings are not retroactive. For example, if you set the upper-bound threshold for outliers to 40, a prediction with a value of 50, made prior to setting up thresholds, is not retroactively detected as an outlier. Prediction warnings will only return with prediction requests made after the feature is enabled. ### Recommended rules If you want DataRobot to recommend a set of rules for your deployment, click Use Recommended Rules when adding a new humility rule. This option generates two automatically configured humility rules: - Rule 1: The Uncertain prediction trigger and the No operation action. - Rule 2: The Outlying input trigger for the most important numeric feature (based on Feature Impact results) and the No operation action. Both recommended rules have automatically calculated upper- and lower-bound thresholds. ## Multiseries humility rules DataRobot supports multiseries blueprints that support feature derivation and predicting using partial history or no history at all—series that were not trained previously and do not have enough points in the training dataset for accurate predictions. This is useful, for example, in demand forecasting. When a new product is introduced, you may want initial sales predictions. In conjunction with “cold start modeling” (modeling on a series in which there is not sufficient historical data), you can predict on new series, but also keep accurate predictions for series with a history. With the support in place, you can set up [a humility rule](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/humble.html) that: - Triggers off a new series (unseen in training data). - Takes a specified action. - (Optional) Returns a custom error message. > [!NOTE] Note > If you [replace a model](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-replace.html) within a deployment using a model from a different project, the humility rule is disabled. If the replacement is a model from the same project, the rule is saved. ### Create multiseries humility rules 1. Select a deployment from yourDeploymentsinventory and clickHumility. ToggleEnable humilityto on. 2. Click+ Add new ruleto begin configuring the rule. Time series deployments can only have one humility rule applied. If you already have a rule, click it to edit if you want to make changes. 3. Select a trigger. To include new series data, selectNew seriesas the trigger. This rule detects if a series is present that was not available in the training data and does not have enough history in the prediction data for accurate predictions. 4. Select an action. Subsequent options are dependent on the selected action, as described in the following table: ActionIf a new series is encountered...Further actionNo operationDataRobot records the event but the prediction is unchanged.N/AUse model with new series supportThe prediction is overridden by the prediction from a selected model with new series support.Select a modelthat supports unseen series modeling. DataRobot preloads supported models in the dropdown.Use global most frequent class (binary classification only)The prediction value is replaced with the most frequent class across all series.N/AUse target mean for all series (regression only)The prediction value is overridden by the global target mean for all series.N/AOverride predictionThe prediction value is changed to the specified preferred value.Enter a numeric value to replace the prediction value for any new series.Return errorThe default or a custom error is returned with the 480 error.Use the default or click in the box to enter a custom error message. When finished, click the Add button to create the new rule or save changes. ### Select a model replacement When you expand the Model with new series support dropdown, DataRobot provides a list of models available from the [Model Registry](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/index.html), not the Leaderboard. Using models available from the registry decouples the model from the project and provides support for packages. In this way, you can use a backup model from any compatible project as long as it uses the same target and has the same series available. > [!NOTE] Note > If no packages are available, deploy a “new series support” model and add it to the Model Registry. You can identify qualifying models from the Leaderboard by the NEW SERIES OPTIMIZED badge. There is also a notification banner in the Make Predictions tab if you try to use a non-optimized model. ## Humility tab considerations Consider the following when using the Humility tab: - You cannot define more than 10 humility rules for a deployment. - Humility rules can only be defined by owners of the deployment. Users of the deployment can view the rules but cannot edit them or define new rules. - The "Uncertain Prediction" trigger is only supported for regression and binary classification models. - Multiclass models only support the "Override prediction" trigger. --- # Deployment settings URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/index.html > After you create and configure a deployment, you can use the settings tabs for individual features to add or update deployment functionality. # Deployment settings After you [create and configure a deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-deploy-info.html), you can use the settings tabs for individual features to add or update deployment functionality: | Topic | Describes | | --- | --- | | Set up service health monitoring | Enable segmented analysis to assess service health, data drift, and accuracy statistics by filtering them into unique segment attributes and values. | | Set up data drift monitoring | Enable data drift monitoring on a deployment's Data Drift Settings tab. | | Set up accuracy monitoring | Enable accuracy monitoring on a deployment's Accuracy Settings tab. | | Set up fairness monitoring | Enable fairness monitoring on a deployment's Fairness Settings tab. | | Set up humility rules | Enable humility monitoring by creating rules which enable models to recognize, in real-time, when they make uncertain predictions or receive data they have not seen before. | | Configure retraining | Enable Automated Retraining for a deployment by defining the general retraining settings and then creating retraining policies. | | Configure challengers | Enable challenger comparison by configuring a deployment to store prediction request data at the row level and replay predictions on a schedule. | | Review predictions settings | Review the Predictions Settings tab to view details about your deployment's inference data. | | Enable data exploration | Enable data exploration to export deployment data, allowing you to compute and monitor custom business or performance metrics. | | Set up custom metrics monitoring | Enable custom metrics monitoring by defining the "at risk" and "failing" thresholds for the custom metrics you created. | | Set up timeliness tracking | Enable timeliness tracking on a deployment's Usage Settings tab to reveal when deployment status indicators are based on old data. | | Set prediction intervals for time series deployments | Enable prediction intervals in the prediction response for deployed time series models. | --- # Configure predictions settings URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/predictions-settings.html > The Predictions Settings tab provides details about your deployment's inference (also known as scoring) data. # Configure predictions settings On a deployment's Predictions > Settings tab, you can view details about your deployment's inference (also known as scoring) data—the data containing prediction requests and results from the model. On the Predictions Settings page, you can access the following information: | Field | Description | | --- | --- | | Prediction environment | Displays the environment where predictions are generated. Prediction environments allow you to establish access controls and approval workflows. | | Prediction timestamp | Defines the method used for time-stamping prediction rows. To define the timestamp, select one of the following:Use time of prediction request: The timestamp of the prediction request.Use value from date/time feature: A date/time feature (e.g., forecast date) provided with the prediction data and defined by the Feature and Date format settings. Forecast date time-stamping is set automatically for time series deployments. It allows for a common time axis to be used between training data and the basis of data drift and accuracy statistics. The Feature and Date format settings cannot be changed after predictions are made. | | Batch monitoring | Enables viewing monitoring statistics organized by batch, instead of by time, with batch-enabled deployments | ## Set prediction autoscaling settings for DataRobot serverless deployments Autoscaling is a system that automatically adjusts the number of model replicas allocated to your deployment based on the incoming prediction request load. This ensures your deployment can handle peak traffic without manual intervention and scales down during quieter periods to optimize resource usage. For DataRobot models, DataRobot performs autoscaling based on CPU usage at a 40% threshold. To configure on-demand predictions on this environment, scroll down to Autoscaling and set the following options: | Field | Description | | --- | --- | | Minimum compute instances | (Premium feature) Set the minimum compute instances for the model deployment. If your organization doesn't have access to "always-on" predictions, this setting is set to 0 and isn't configurable. With the minimum compute instances set to 0, the inference server will be stopped after an inactivity period of 7 days. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. | | Maximum compute instances | Set the maximum compute instances for the model deployment to a value above the current configured minimum. To limit compute resource usage, set the maximum value equal to the minimum. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. | > [!NOTE] Premium feature: Always-on predictions > Always-on predictions are a premium feature. Contact your DataRobot representative or administrator for information on enabling the feature. > [!NOTE] Compute instance configurations > For DataRobot model deployments: > > The default minimum is 0 and default maximum is 3. > The minimum and maximum limits are taken from the organization's > max_compute_serverless_prediction_api > setting. > > For custom model deployments: > > The default minimum is 0 and default maximum is 1. > The minimum and maximum limits are taken from the organization's > max_custom_model_replicas_per_deployment > setting. > The minimum is always greater than 1 when running on GPUs (for LLMs). > > Additionally, for high availability scenarios: > > The minimum compute instances setting > must > be greater than or equal to 2. > This requires business critical or consumption-based pricing. ## Set feature discovery project settings Feature discovery projects use multiple datasets to generate new features, eliminating the need to perform manual feature engineering to consolidate those datasets into one. For deployed [feature discovery projects](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html), you can manage your Feature discovery configuration on the Predictions Settings page. To manage datasets for a feature discovery project: 1. On thePredictions Settingspage, locate theFeature discoverysection: 2. ClickPreviewto review theSecondary Datasets Configuration Infodialog box. 3. If you need to update your secondary datasets, clickChangeto open theSecondary Datasets Configurationdialog box, where you can: 4. ClickApply. ## Set prediction intervals for time series deployments Time series users have the additional capability to add a prediction interval to the prediction response of deployed models. When enabled, prediction intervals will be added to the response of any prediction call associated with the deployment. To enable prediction intervals, navigate to the Predictions > Prediction Intervals tab, click the Enable prediction intervals toggle, and select an Interval size (read more about prediction intervals [here](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-predictions.html#prediction-preview)): After you set an interval, copy the deployment ID from the [Overview tab](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/dep-overview.html), the deployment URL, or the snippet in the [Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab to check that the deployment was added to the database. You can compare the results from your API output with [prediction preview](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-predictions.html#prediction-preview) in the UI to verify results. For more information on working with prediction intervals via the API, access the API documentation by signing in to DataRobot, clicking the question mark on the upper right, and selecting API Documentation. In the API documentation, select Time Series Projects > Prediction Intervals. --- # Configure retraining URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/retraining-settings.html > To maintain model performance after deployment without extensive manual work, enable Automated Retraining by configuring the general retraining settings and then defining retraining policies. # Configure retraining To maintain model performance after deployment without extensive manual work, DataRobot provides an automatic retraining capability for deployments. Upon providing a retraining dataset registered in the [AI Catalog](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html), you can [define up to five retraining policies](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/set-up-auto-retraining.html) on each deployment. Before you define retraining policies, you must configure a deployment's general retraining settings on the Retraining > Settings tab. > [!NOTE] Note > Editing retraining settings requires [Owner](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html) permissions for the deployment. Those with `User` permissions can view the retraining settings for the deployment. On a deployment's Retraining Settings page, you can configure the following settings: | Element | Description | | --- | --- | | Retraining user | Selects a retraining user who has Owner access for the deployment. For resource monitoring, retraining policies must be run as a user account. | | Prediction environment | Selects the default prediction environment for scoring challenger models. | | Retraining data | Defines a retraining dataset for all retraining profiles. Drag or browse for a local file or select a dataset from the AI Catalog. | After you click Save and define these settings, you can [define a retraining policy](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/set-up-auto-retraining.html#set-up-retraining-policies). ## Select a retraining user When executed, scheduled retraining policies use the permissions and resources of an identified user (manually triggered policies use the resources of the user who triggers them.) The user needs the following: - For the retraining data, permission to use data and create snapshots. - Owner permissions for the deployment. [Modeling workers](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/worker-queue.html) are required to train the models requested by the retraining policy. Workers are drawn from the retraining user's pool, and each retraining policy requests 50% of the retraining user's total number of workers. For example, if the user has a maximum of four modeling workers and retraining policy A is triggered, it runs with two workers. If retraining policy B is triggered, it also runs with two workers. If policies A and B are running and policy C is triggered, it shares workers with the other two policies running. > [!NOTE] Note > Interactive user modeling requests do not take priority over retraining runs. If your workers are applied to retraining, and you initiate a new modeling run (manual or Autopilot), it shares workers with the retraining runs. For this reason, DataRobot recommends creating a user with a capped number of workers and designating this user for retraining jobs. ## Choose a prediction environment [Challenger analysis](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html) requires replaying predictions that were initially made with the champion model against the challenger models. DataRobot uses a defined schedule and prediction environment for replaying predictions. When a new challenger is added as a result of retraining, it uses the assigned prediction environment to generate predictions from the replayed requests. It is possible to later change the prediction environment any given challenger is using from the Challengers tab. While they are acting as challengers, models can only be deployed to DataRobot prediction environments. However, the champion model can use a different prediction environment from the challengers—either a DataRobot environment (for example, one marked for "Production" usage to avoid resource contention) or a remote environment (for example, AWS, OpenShift, or GCP). If a model is promoted from challenger to champion, it will likely use the prediction environment of the former champion. ## Provide retraining data All retraining policies on a deployment refer to the same AI Catalog dataset. When a retraining policy triggers, DataRobot uses the latest version of the dataset (for uploaded AI Catalog items) or creates and uses a new snapshot from the underlying data source (for catalog items using data connections or URLs). For example, if the catalog item uses a Spark SQL query, when the retraining policy triggers, it executes that query and uses the resulting rows as input to the modeling settings (including partitioning). For AI Catalog items with underlying data connections, if the catalog item already has the maximum number of snapshots (100), the retraining policy will delete the oldest snapshot before taking a new one. --- # Set up service health monitoring URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/service-health-settings.html > Configure segmented analysis to access drill-down analysis of service health, data drift, and accuracy statistics by filtering them into unique segment attributes and values. # Set up service health monitoring On a deployment's Service Health > Settings tab, you can enable [segmented analysis](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-segment.html) for service health; however, to use segmented analysis for data drift and accuracy, you must also enable the following deployment settings: - Enable target monitoring(required to enable data driftandaccuracy tracking) - Enable feature drift tracking(required to enable data drift tracking) Once you've enabled the tracking required for your deployment, configure segment analysis to access segmented analysis of [service health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-segment.html#service-health), [data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-segment.html#data-drift-and-accuracy), and [accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-segment.html#data-drift-and-accuracy) statistics by filtering them into unique segment attributes and values. On a deployment's Service Health > Settings tab, you can configure the Service Health Settings: | Field | Description | | --- | --- | | Segmented Analysis | | | Track attributes for segmented analysis of training data and predictions | Enables DataRobot to monitor deployment predictions by segments, for example by categorical features. | After enabling segmented analysis, you must specify the segment attributes to track in training and prediction data before making predictions. Selecting a segment attribute for tracking causes the model's data to be segmented by the attribute, allowing users to closely analyze the segment values that comprise the attributes selected for tracking. Attributes used for segmented analysis must be present in the training dataset for a deployed model, but they don't need to be features of the model. The list of segment attributes available for tracking is limited to categorical features, except the selected series ID used by multiseries deployments. To track an attribute, add it to the Track attributes for segmented analysis of training data and predictions field. The "Consumer" attribute (representing users making prediction requests) is always listed by default. For time series deployments with segmented analysis enabled, DataRobot automatically adds up to two segmented attributes: `Forecast Distance` and `series id` (the ID is only provided for multiseries models). Forecast distance is automatically available as a segment attribute without being explicitly present in the training dataset; it is inferred based on the forecast point and the date being predicted on. These attributes allow you to view accuracy and drift for a specific forecast distance, series, or other defined attribute. When you have finalized the attributes to track, click Save. [Make predictions](https://docs.datarobot.com/en/docs/classic-ui/predictions/index.html) and navigate to the tab you want to analyze for your deployment by segment: [Service Health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-segment.html#service-health), [Data Drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-segment.html#data-drift-and-accuracy), or [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-segment.html#data-drift-and-accuracy). > [!NOTE] Important > Segmented analysis is only available for predictions made after the segmented analysis is enabled. Service health tracks metrics about a deployment’s ability to respond to prediction requests quickly and reliably. You can view the service health status in the [deployment inventory](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html#color-coded-health-indicators) and visualize service health on the [Service Health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html) tab. Service health monitoring represents the occurrence of 4XX and 5XX errors in your prediction requests or prediction server: - 4xx errors indicate problems with the prediction request submission. - 5xx errors indicate problems with the DataRobot prediction server. | Color | Description | Action | | --- | --- | --- | | / Green | Passing: Zero 4xx or 5xx errors | No action needed. | | / Yellow | At risk: At least one 4xx error and zero 5xx errors | Concerns found but no immediate action needed; monitor. | | / Red | Failing: At least one 5xx error | Immediate action needed. | | / Gray | Unknown: No predictions made | Make predictions. | --- # Set up timeliness tracking URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/usage-settings.html > Configure timeliness tracking for predictions and actuals on the Usage Settings tab; define the timeliness interval frequency based on the prediction timestamp and the actuals upload time separately, depending on your organization's needs. # Set up timeliness tracking Timeliness indicators show if the prediction or actuals upload frequency meets the standards set by your organization. Configure timeliness tracking on the Usage > Settings tab for predictions and actuals. After enabling tracking, you can define the timeliness interval frequency based on the prediction timestamp and the actuals upload time separately, depending on your organization's needs. If timeliness is enabled for a deployment, the health indicators retain the most recently calculated health status, presented along with timeliness status indicators to reveal when they are based on old data. You can determine the appropriate timeliness intervals for your deployments on a case-by-case basis. To enable and define timeliness tracking: 1. From theDeploymentspage, do either of the following: 2. On theUsage Settingspage, configure the following settings: SettingDescriptionTrack timelinessEnable one or more ofTrack timeliness of predictionsandTrack timeliness of actuals. To track the timeliness of actuals,provide an association IDandenable target monitoringfor the deployment.Predictions timestamp definitionIf you enabled timeliness tracking for predictions, use the frequency selectors to define anExpected prediction frequencyinISO 8601format. The minimum granularity is one hour. To define time intervals directly in ISO 8601 notation (P1Y2M3DT1H), clickSwitch to advanced frequency.Actuals timestamp definitionIf you enabled timeliness tracking for actuals, use the frequency selectors to define theExpected actuals upload frequencyinISO 8601format. The minimum granularity is one hour. To define time intervals directly in ISO 8601 notation (P1Y2M3DT1H), clickSwitch to advanced frequency. TipYou can clickReset to defaultsto return to a daily expected frequency orP1D. 3. ClickSave. --- # Custom environments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html > Describes how to build a custom environment when a custom model requires something not contained in one of DataRobot's built-in environments. # Custom environments Once uploaded into DataRobot, custom models run inside of environments—Docker containers running in Kubernetes. In other words, DataRobot copies the uploaded files defining the custom task into the image container. In most cases, adding a custom environment is not required because there are a variety of built-in environments available in DataRobot. Python and/or R packages can be easily added to these environments by uploading a `requirements.txt` file with the task’s code. A custom environment is only required when a custom task: - Requires additional Linux packages. - Requires a different operating system. - Uses a language other than Python, R, or Java. This document describes how to build a custom environment for these cases. To assemble and test a custom environment locally, install both Docker Desktop and the [DataRobot User Models (DRUM) CLI tool](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html) on your machine. ## Custom environment guidelines > [!NOTE] Note > DataRobot recommends using an environment template and not building your own environment except for specific use cases. (For example, you don't want to use DRUM but you want to implement your own prediction server.) If you'd like to use a tool, language, or framework that is not supported by our template environments, you can make your own. DataRobot recommends modifying the provided environments to suit your needs; however, to make an easy-to-use, re-usable environment, you should adhere to the following guidelines: - Your environment must include a Dockerfile that installs any requirements you may want. - Custom models require a simple webserver to make predictions. DataRobot recommends putting this in your environment so you can reuse it with multiple models. The webserver must be listening on port8080and implement the following routes: NoteURL_PREFIXis an environment variable that is available at runtime. It must be added to the routes below. Mandatory endpointsDescriptionGET /URL_PREFIX/This route is used to check if your model's server is running.POST /URL_PREFIX/predict/This route is used to make predictions. Optional extension endpointsDescriptionGET /URL_PREFIX/stats/This route is used to fetch memory usage data for DataRobot Custom Model Testing.GET /URL_PREFIX/health/This route is used to check if model is loaded and functioning properly. If model loading fails error with 513 response code should be returned. Failing to handle this case may cause the backend k8s container to enter crash and enter a restart loop for several minutes. - An executablestart_server.shfile is required to start the model server. - Any code andstart_server.shshould be copied to/opt/code/by your Dockerfile. > [!NOTE] Note > To learn more about the complete API specification, you can review the [DRUM server APIyamlfile](https://github.com/datarobot/datarobot-user-models/blob/master/custom_model_runner/drum_server_api.yaml). ## Environment variables When you build a custom environment with DRUM, your custom model code can reference several environment variables injected to facilitate access to the [DataRobot Client](https://pypi.org/project/datarobot/) and [MLOps Connected Client](https://pypi.org/project/datarobot-mlops-connected-client/): | Environment Variable | Description | | --- | --- | | MLOPS_DEPLOYMENT_ID | If a custom model is running in deployment mode (i.e., the custom model is deployed), the deployment ID is available. | | DATAROBOT_ENDPOINT | If a custom model has public network access, the DataRobot endpoint URL is available. | | DATAROBOT_API_TOKEN | If a custom model has public network access, your DataRobot API token is available. | ## Create the environment Once DRUM is installed, begin your environment creation by copying one of the examples from [GitHub](https://github.com/datarobot/datarobot-user-models/tree/master/public_dropin_environments). Log in to GitHub before clicking this link. Make sure: 1. The environment code stays in a single folder. 2. You remove the env_info.json file. ### Add Linux packages To add Linux packages to an environment, add code at the beginning of `dockerfile`, immediately after the `FROM datarobot…` line. Use `dockerfile` syntax for an Ubuntu base. For example, the following command tells DataRobot which base to use and then to install packages `foo`, `boo`, and `moo` inside the Docker image: ``` FROM datarobot/python3-dropin-env-base RUN apt-get update --fix-missing && apt-get install foo boo moo ``` ### Add Python/R packages In some cases, you might want to include Python/R packages in the environment. To do so, note the following: - List packages to install inrequirements.txt. For R packages, do not include versions in the list. - Do not mix Python and R packages in the samerequirements.txtfile. Instead, create multiple files and adjustdockerfileso DataRobot can find and use them. ## Test the environment locally The following example illustrates how to quickly test your environment using Docker tools and DRUM. 1. To test a custom task with a custom environment, navigate to the local folder where the task content is stored. 2. Run the following, replacing placeholder names in< >brackets with actual names: drumfit--code-dir--docker--input--target-type--target--verbose ## Add a custom environment to DataRobot To add a custom environment, you must upload a compressed folder in `.tar`, `.tar.gz`, or `.zip` format. Be sure to review the guidelines for [preparing a custom environment folder](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html#custom-environment-guidelines) before proceeding. You may also consider creating a custom [drop-in environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/drop-in-environments.html) by adding Scoring Code and a `start_server.sh` file to your environment folder. Note the following environment limits and environment version limits: **SaaS:** Next to the Add new environment and the New version buttons, there is a badge indicating how many environments (or environment versions) you've added and how many environments (or environment versions) you can add in total. With the correct permissions, an administrator can set these limits at a [user](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#manage-execution-environment-limits) or [group](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-groups.html#manage-execution-environment-limits) level. The following status categories are available in this badge: **Self-Managed:** Next to the Add new environment and the New version buttons, there is a badge indicating how many environments (or environment versions) you've added and how many environments (or environment versions) you can add in total. With the correct permissions, an administrator can set these limits at a [user](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#manage-execution-environment-limits), [group](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-groups.html#manage-execution-environment-limits), or [organization](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-orgs.html#manage-execution-environment-limits) level. The following status categories are available in this badge: | Badge | Description | | --- | --- | | | The number of environments is less than 75% of the environment limit. | | | The number of environments is equal to or greater than 75% of the environment limit. | | | The number of environments is equal to the environment limit. You can't add more environments without removing an environment first. | Navigate to Model Registry > Custom Model Workshop and select the Environments tab. This tab lists the environments provided by DataRobot and those you have created. Click + Add new environment to configure the environment details and add it to the workshop. Complete the fields in the Add New Environment dialog box. | Field | Description | | --- | --- | | Environment name | The name of the environment. | | Context file | The archive containing a Dockerfile and any other files needed to build the environment image. This file is not required if you supply a prebuilt image. | | Prebuilt image | (Optional) A prebuilt environment image saved as a tarball using the Docker save command. | | Programming Language | The language in which the environment was made. | | Description | (Optional) A description of the custom environment. | | Environment type | The DataRobot artifact types supported by the environment: Custom models or Notebooks. | > [!NOTE] Context file and environment image upload > If you upload both a context and an image file, the priority is given to the image file. Even though a context file is not required when you upload an image file, you should still upload the context file, as your workflow flow may include functionality that requires context. When all fields are complete, click Add. The custom environment is ready for use in the Workshop. After you upload an environment, you are the only one who can access it unless you [share](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html#share-and-download-an-environment) it with other individuals. To make changes to an existing environment, create a new [version](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html#add-an-environment-version). ### Add an environment version Troubleshoot or update a custom environment by adding a new version of it to the Workshop. In the Versions tab, select New version. Upload the files for a new version and provide a brief description, then click Add. | Field | Description | | --- | --- | | Context file | The archive containing a Dockerfile and any other files needed to build the environment image. This file is not required if you supply a prebuilt image. | | Prebuilt image | (Optional) A prebuilt environment image saved as a tarball using the Docker save command. | | Description | (Optional) A description of the custom environment. | > [!NOTE] Context file and environment image upload > If you upload both a context and an image file, the priority is given to the image file. Even though a context file is not required when you upload an image file, you should still upload the context file, as your workflow flow may include functionality that requires context. The new version is available in the Version tab; all past environment versions are saved for later use. By default, when [creating a model version](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-versions.html), if the selected execution environment does not change, the version of that execution environment persists from the previous custom model version, even if a newer environment version is available. For more information on how to ensure the custom model version uses the latest version of the execution environment, see [Trigger base execution environment update](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html#trigger-base-execution-environment-update). ### View environment information There is a variety of information available for each custom and built-in environment. To view: 1. Navigate toModel Registry > Custom Model Workshop > Environments. The resulting list shows all environments available to your account, with summary information. 2. For more information on an individual environment, click to select: The versions tab lists a variety of version-specific information and provides a link for downloading that version's environment context file. 3. ClickCurrent Deploymentsto see a list of all deployments in which the current environment has been used. 4. ClickEnvironment Infoto view information about the general environment, not including version information. ### Share and download an environment You can share custom environments with anyone in your organization from the menu options on the right. These options are not available to built-in environments because all organization members have access and these environment options should not be removed. > [!NOTE] Note > An environment is not available in the model registry to other users unless it was explicitly shared. That does not, however, limit users' ability to use blueprints that include tasks that use that environment. See the description of [implicit sharing](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/cml-custom-tasks.html#implicit-sharing) for more information. From Model Registry > Custom Model Workshop > Environments, use the menu to [share and/or delete](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-actions.html) any custom environment that you have appropriate permissions for. (Note that the link points to custom model actions, but the options are the same for custom tasks and environments.) ## Self-Managed AI Platform admins The following is available only on the Self-Managed AI Platform. ### Environment availability Each custom environment is either public or private (the default availability). Making an environment public allows other users that are part of the same DataRobot installation to use it without the owner explicitly sharing it or users needing to create and upload their own version. Private environments can only be seen by the owner and the users that the environment has been shared with. Contact your DataRobot system administrator to make a custom environment public. --- # Drop-in environments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/drop-in-environments.html > Describes DataRobot's built-in custom model environments. # Drop-in environments DataRobot provides drop-in environments in the Custom Model Workshop. Drop-in environments contain the model requirements and the `start_server.sh` file for a custom model so that you don't need to provide them in the model's folder. The following table details the drop-in environments provided by DataRobot. Each environment is prefaced with [DataRobot]in the Environments tab of the Custom Model Workshop. You can select these drop-in environments when you [create a custom model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html). The available drop-in environments depend on your DataRobot installation; however, the table below lists commonly available public drop-in environments with [templates in the DRUM repository](https://github.com/datarobot/datarobot-user-models/tree/master/public_dropin_environments). Depending on your DataRobot installation, the Python version of these environments may vary, and additional non-public environments may be available for use. **Managed AI Platform (SaaS):** > [!NOTE] Drop-in environment security > Starting with the March 2025 Managed AI Platform release, most general purpose DataRobot custom model drop-in environments are security-hardened container images. When you require a security-hardened environment for running custom jobs, only shell code following the [POSIX-shell standard](https://pubs.opengroup.org/onlinepubs/9799919799/utilities/sh.html) is supported. Security-hardened environments following the POSIX-shell standard support a limited set of shell utilities. **Self-Managed AI Platform:** > [!NOTE] Drop-in environment security > Starting with the 11.0 Self-Managed AI Platform release, most general purpose DataRobot custom model drop-in environments are security-hardened container images. When you require a security-hardened environment for running custom jobs, only shell code following the [POSIX-shell standard](https://pubs.opengroup.org/onlinepubs/9799919799/utilities/sh.html) is supported. Security-hardened environments following the POSIX-shell standard support a limited set of shell utilities. | Environment name & example | Compatibility & artifact file extension | | --- | --- | | Python 3.X | Python-based custom models and jobs. You are responsible for installing all required dependencies through the inclusion of a requirements.txt file in your model files. | | Python 3.X GenAI Agents | Generative AI models (Text Generation or Vector Database target type) | | Python 3.X ONNX Drop-In | ONNX models and jobs (.onnx) | | Python 3.X PMML Drop-In | PMML models and jobs (.pmml) | | Python 3.X PyTorch Drop-In | PyTorch models and jobs (.pth) | | Python 3.X Scikit-Learn Drop-In | Scikit-Learn models and jobs (.pkl) | | Python 3.X XGBoost Drop-In | Native XGBoost models and jobs (.pkl) | | Python 3.X Keras Drop-In | Keras models and jobs backed by tensorflow (.h5) | | Java Drop-In | DataRobot Scoring Code models (.jar) | | R Drop-in Environment | R models trained using CARET (.rds) Due to the time required to install all libraries recommended by CARET, only model types that are also package names are installed (e.g., brnn, glmnet). Make a copy of this environment and modify the Dockerfile to install the additional, required packages. To decrease build times when you customize this environment, you can also remove unnecessary lines in the # Install caret models section, installing only what you need. Review the CARET documentation to check if your model's method matches its package name. (Log in to GitHub before clicking this link.) | > [!NOTE] scikit-learn > All Python environments contain scikit-learn to help with preprocessing (if necessary), but only scikit-learn can make predictions on `sklearn` models. ## Environment variables When you use a drop-in environment, your custom model code can reference several environment variables injected to facilitate access to the [DataRobot Client](https://pypi.org/project/datarobot/) and [MLOps Connected Client](https://pypi.org/project/datarobot-mlops-connected-client/): | Environment Variable | Description | | --- | --- | | MLOPS_DEPLOYMENT_ID | If a custom model is running in deployment mode (i.e., the custom model is deployed), the deployment ID is available. | | DATAROBOT_ENDPOINT | If a custom model has public network access, the DataRobot endpoint URL is available. | | DATAROBOT_API_TOKEN | If a custom model has public network access, your DataRobot API token is available. | --- # Custom model environments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/index.html > How to set up an environment for custom inference models created in the Custom Model Workshop. # Custom model environments To [create a custom inference model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html), you must select an environment that the model will use. By providing an environment separate from a custom model, DataRobot can build the environment for you. This allows you to reuse the environment for as many models as you want. An environment contains the packages required by a custom model, job, or application, along with any additional language and system libraries. You can select one of two types of environments: | Environment | Description | | --- | --- | | Drop-in environments | Contain the model requirements and the start_server.sh file for the custom model. They are provided by DataRobot in the Custom Model Workshop. | | Custom environments | Do not contain the model requirements or the start_server.sh file for the custom model. Instead these requirements must be provided in the folder of the custom model you intend to use with the environment. You can create your own environment in the Custom Model Workshop. You can also create a custom drop-in environment by including the Scoring Code and start_server.sh file in the environment folder. | --- # Create custom inference models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html > How to build a custom inference model in the Custom Model Workshop. # Create custom inference models Custom inference models are user-created, pre-trained models that you can upload to DataRobot (as a collection of files) via the Custom Model Workshop. You can then upload a model artifact to create, test, and deploy custom inference models to DataRobot's centralized deployment hub. You can assemble custom inference models in either of the following ways: - Create a custom modelwithoutproviding the model requirements andstart_server.shfile on theAssembletab. This type of custom modelmustuse a drop-in environment. Drop-in environments contain the requirements andstart_server.shfile used by the model. They areprovided by DataRobotin the Custom Model Workshop. You can alsocreate your owndrop-in custom environment. - Create a custom modelwiththe model requirements andstart_server.shfile on theAssembletab. This type of custom model can be paired with acustomordrop-inenvironment. Be sure to review the guidelines for [assembling a custom model](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/index.html) before proceeding. If any files overlap between the custom model and the environment folders, the model's files will take priority. > [!NOTE] Note > Once a custom model's file contents are assembled, you can [test the contents locally](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-local-test.html) for development purposes before uploading it to DataRobot. After you create a custom model in the Workshop, you can run a [testing suite](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-test.html) from the Assemble tab. ## Create a new custom model 1. To create a custom model, navigate toModel Registry>Custom Model Workshopand select theModelstab. This tab lists the models you have created. ClickAdd new model. 2. In theAdd Custom Inference Modelwindow, enter the fields described in the table below: ElementDescription1Model typeSelectCustom modelorProxy(external).2Model nameName the custom model.3Target type / Target nameSelect the target type (binary classification,regression,multiclass,text generation(premium feature),anomaly detection, orunstructured) and enter the name of the target feature.4Positive class label / Negative class labelFor binary classification models, specify the value to be used as the positive class label and the value to be used as the negative class label.For a multiclass classification model, these fields are replaced by a field to enter or upload the target classes in.csvor.txtformat. Target type support for proxy modelsIf you select theProxymodel type, you can't select theUnstructuredtarget type. 3. ClickShow Optional Fieldsand, if necessary, enter aPrediction threshold, theLanguageused to build the model, and aDescription. 4. After completing the fields, clickAdd Custom Model. 5. In theAssembletab, underModel Environmenton the right, select a model environment by clicking theBase Environmentdropdown menu on the right and selecting an environment. The model environment is used fortestinganddeployingthe custom model. NoteTheBase Environmentpulldown menu includesdrop-in model environments, if any exist, as well ascustom environmentsthat you can create. 6. UnderModelon the left, add content by dragging and dropping files or browsing. Alternatively, select aremote integrated repository. If you clickBrowse local file, you have the option of adding aLocal Folder. The local folder is for dependent files and additional assets required by your model, not the model itself. Even if the model file is included in the folder, it will not be accessible to DataRobot unless the file exists at the root level. The root file can then point to the dependencies in the folder. NoteYou must also upload the model requirements and astart_server.shfile to your model's folder unless you are pairing the model with adrop-in environment. ### View and edit runtime parameters When you add a `model-metadata.yaml` file with `runtimeParameterDefinitions` to DataRobot while creating a custom model, the Runtime Parameters section appears on the Assemble tab for that custom model. After you build the environment and create a new version, you can click View and Edit to configure the parameters: > [!NOTE] Note > Each change to a runtime parameter creates a new minor version of the custom model. After you [test a model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-test.html) with runtime parameters in the Custom Model Workshop, you can navigate to the Test > Runtime Parameters to view the model's parameters: If any runtime parameters have `allowEmpty: false` in the definition without a `defaultValue`, you must set a value before registering the custom model. For more information on how to define runtime parameters and use them in your custom model code, see the [Define custom mode runtime parameters](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html) documentation. ### Anomaly detection You can create custom inference models that support anomaly detection problems. If you choose to build one, reference the [DRUM template](https://github.com/datarobot/datarobot-user-models/tree/master/model_templates/python3_sklearn_anomaly). (Log in to GitHub before clicking this link.) When deploying custom inference anomaly detection models, note that the following functionality is not supported: - Data drift - Accuracy and association IDs - Challenger models - Humility rules - Prediction intervals --- # Manage custom models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-actions.html > How to use the Actions menu, which lets you share and delete custom models and environments. # Manage custom models There are several Actions available from the menu on the Model Registry > Custom Model Workshop page, such as [sharing](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-actions.html#share) and [deleting](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-actions.html#delete-a-deployment) custom models or environments. ## Share The sharing capability allows [appropriate user roles](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#custom-model-and-environment-roles) to grant permissions on a custom model or environment. This is useful, for example, for allowing others to use your models and environments without requiring them to have the expertise to create them. When you have created a custom model or environment and are ready to share it with others, open the action menu to the right of the Created header and select Share ( [https://docs.datarobot.com/en/docs/images/icon-share.png](https://docs.datarobot.com/en/docs/images/icon-share.png)). This takes you to the sharing modal, which lists each associated user and their role. To remove a user, click the X button to the right of their role. To re-assign a user's role, click on the assigned role and assign a new one from the dropdown. To add a new user, enter their username in the Share With field and choose their role from the dropdown. Then click Share. This action initiates an email notification. ## Delete a model or environment If you have the appropriate [permissions](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#custom-model-and-environment-roles), you can delete a custom model or environment from the Model Registry by clicking the trash can icon [https://docs.datarobot.com/en/docs/images/icon-delete.png](https://docs.datarobot.com/en/docs/images/icon-delete.png). This action initiates an email notification to all users with sharing privileges for the model or environment. --- # Manage custom model dependencies URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-dependencies.html > Describes how to manage these dependencies from the Workshop and update the base drop-in environments to support your model code. # Manage custom model dependencies Custom models can contain various machine learning libraries in the model code, but not every [drop-in environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/drop-in-environments.html) provided by DataRobot natively supports all libraries. However, you can manage these dependencies from the Workshop and update the base drop-in environments to support your model code. To manage model dependencies, you must include a `requirements.txt` file uploaded as part of your custom model. The text file must indicate the machine learning libraries used in the model code. For example, consider a custom R model that uses Caret and XGBoost libraries. If this model is added to the Workshop and the R drop-in environment is selected, the base environment will only support Caret, not XGBoost. To address this, edit `requirements.txt` to include the Caret and XGBoost dependencies. After editing and re-uploading the requirements file, the base environment includes XGBoost, making the model available within the environment. > [!NOTE] Important > Custom model dependencies aren't applied when testing a model locally with [DRUM](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html). List the following, depending on the model language, in `requirements.txt`: - For R models, list the machine learning library dependencies. - For Python models, list the dependenciesandany version constraints for the libraries. Supported constraint types include<,<=,==,>=,>, and multiple constraints can be issued in a single entry (for example,pandas >= 0.24, < 1.0). Once the requirements file is updated to include dependencies and constraints, navigate to your custom model's Assemble tab. Upload the file under the Model > Content header. The Model Dependencies field updates to display the dependencies and constraints listed in the file. From the Assemble tab, select a base drop-in environment under the Model Environment header. DataRobot warns you that a new environment must be built to account for the model dependencies. Select Build environment, and DataRobot installs the required libraries and constraints to the base environment. Once the base environment is updated, your custom model will be usable with the environment, allowing you to test, deploy, or register it. --- # GitHub Actions for custom models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-github-action.html > The custom models action manages custom inference models and deployments in DataRobot via GitHub CI/CD workflows. # GitHub Actions for custom models > [!NOTE] Availability information > GitHub Actions for custom models is a premium feature. Contact your DataRobot representative or administrator for information on enabling the feature. The custom models action manages custom inference models and their associated deployments in DataRobot via GitHub CI/CD workflows. These workflows allow you to create or delete models and deployments and modify settings. Metadata defined in YAML files enables the custom model action's control over models and deployments. Most YAML files for this action can reside in any folder within your custom model's repository. The YAML is searched, collected, and tested against a schema to determine if it contains the entities used in these workflows. For more information, see the [custom-models-action repository](https://github.com/datarobot-oss/custom-models-action). ## GitHub Actions quickstart This quickstart example uses a [Python Scikit-Learn model template](https://github.com/datarobot/datarobot-user-models/tree/master/model_templates/python3_sklearn) from the [datarobot-user-model repository](https://github.com/datarobot/datarobot-user-models/tree/master/model_templates). To set up a custom models action that will create a custom inference model and deployment in DataRobot from a custom model repository in GitHub, take the following steps: 1. In the.github/workflowsdirectory of your custom model repository, create a YAML file (with any filename) containing the following: 123456789101112131415161718192021222324252627282930313233name:Workflow CI/CDon:pull_request:branches:[master]push:branches:[master]# Allows you to run this workflow manually from the Actions tabworkflow_dispatch:jobs:datarobot-custom-models:# Run this job on any action of a PR, but skip the job upon merging to the main branch. This# will be taken care of by the push event.if:${{ github.event.pull_request.merged != true }}runs-on:ubuntu-lateststeps:-uses:actions/checkout@v3with:fetch-depth:0-name:DataRobot Custom Models Stepid:datarobot-custom-models-stepuses:datarobot-oss/custom-models-action@v1.6.0with:api-token:${{ secrets.DATAROBOT_API_TOKEN }}webserver:https://app.datarobot.com/branch:masterallow-model-deletion:trueallow-deployment-deletion:true Configure the following fields: 2. Commit the workflow YAML file and push it to the remote. After you complete this step, any push to the remote (or merged pull request) triggers the action. 3. In the folder for your DataRobot custom model, add a model definition YAML file (e.g.,model.yaml) containing the following YAML and update the field values according to your model's characteristics: user_provided_model_id:user/model-unique-id-1target_type:Regressionsettings:name:My Awesome GitHub Model 1 [GitHub CI/CD]target_name:Grade 2014version:# Make sure this is the environment ID is in your system.# This one is the '[DataRobot] Python 3 Scikit-Learn Drop-In' environmentmodel_environment_id:5e8c889607389fe0f466c72d Configure the following fields: 4. In any directory in your repository, add a deployment definition YAML file (with any filename) containing the following YAML: user_provided_deployment_id:user/my-awesome-deployment-iduser_provided_model_id:user/model-unique-id-1 Configure the following fields: 5. Commit these changes and push to the remote, then: > [!WARNING] Warning > Creating two commits (or merging two pull requests) in quick succession can result in a `ResourceNotFoundError`. For example, you add a model definition with a training dataset, make a commit, and push to the remote. Then, you immediately delete the model definition, make a commit, and push to the remote. The training data upload action may begin after model deletion, resulting in an error. To avoid this scenario, wait for an action's execution to complete before pushing new commits or merging new pull requests to the remote repository. ## Access commit information in DataRobot After your workflow creates a model and a deployment in DataRobot, you can access the commit information from the model's version info and the deployment's overview: **Model version info:** In the Model Registry , click Custom Model Workshop . On the Models tab, click a GitHub-sourced model from the list and then click the Versions tab. Under Manage Versions , click the version you want to view the commit for. Under Version Info , find the Git Commit Reference and then click the commit hash (or commit ID) to open the commit that created the current version. [https://docs.datarobot.com/en/docs/images/pp-cus-model-github2.png](https://docs.datarobot.com/en/docs/images/pp-cus-model-github2.png) **Registered model info:** In the Model Registry , on the Registered Models tab, click a GitHub-sourced model package from the list. On the Info tab, review the model information provided by your workflow, find the Git Commit Reference , and then click the commit hash (or commit ID) to open the commit that created the current model package. [https://docs.datarobot.com/en/docs/images/pp-cus-model-github4.png](https://docs.datarobot.com/en/docs/images/pp-cus-model-github4.png) **Deployment overview:** In the Deployments inventory, click a GitHub-sourced deployment from the list. On the deployment's Overview tab, review the model and deployment information provided by your workflow. In the Content group box, find the Git Commit Reference and click the commit hash (or commit ID) to open the commit that created the deployment. [https://docs.datarobot.com/en/docs/images/pp-cus-model-github1.png](https://docs.datarobot.com/en/docs/images/pp-cus-model-github1.png) --- # Register custom models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-reg.html > Register a custom model in the Model Registry. The Model Registry is an archive of your model packages where you can also deploy and share the packages. # Register custom models When you have successfully created and tested a custom inference model, you can register it in the Model Registry. > [!NOTE] Note > Although a model package can be created without testing the custom model, DataRobot recommends that you confirm the model passes testing before proceeding. Untested custom models prompt a dialog box warning that the custom model is not tested. To add a custom model as a registered model or version: 1. Navigate toModel Registry > Custom Model Workshop. 2. From theCustom Model Workshop, click the model you want to register and, on theAssembletab, clickRegister to deploy. 3. In theRegister new modeldialog box, configure the following: FieldDescriptionRegister modelSelect one of the following:Register new model:Create a new registered model. This creates the first version (V1).Save as a new version to existing model:Create a version of an existing registered model. This increments the version number and adds a new version to the registered model.Registered model name / Registered ModelDo one of the following:Registered model name:Enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, theModel registration failedwarning appears.Registered Model:Select the existing registered model you want to add a new version to.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister a new model.Optional settingsVersion descriptionDescribe the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add itemand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags do not apply to the registered model, just the versions within. Tags added when registering a new model are applied toV1. 4. ClickAdd to registry. --- # Add files from remote repos to custom models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-repos.html > Add files from remote repositories, including Bitbucket, GitHub, GitHub Enterprise, S3, GitLab, and GitLab Enterprise to the models you create in the Custom Model Workshop. # Add files from remote repos to custom models If you [add a model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html#create-a-new-custom-model) to the Custom Model Workshop, you can add files to that model from a wide range of repositories, including Bitbucket, GitHub, GitHub Enterprise, S3, GitLab, and GitLab Enterprise. After adding a repository to DataRobot, you can [pull files](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-repos.html#pull-files-from-the-repository) from the repository and include them in the custom model. ## Add a remote repository The following steps show how to add a remote repository so that you can pull files into a custom model: **From the Remote Repositories page:** On any page, click your profile avatar (or the default avatar ) in the upper-right corner of DataRobot, then click Remote repositories . On the Remote Repositories page, click Add repository , and then select a repository provider to integrate a new remote repository with DataRobot. **From the Assemble tab:** On the Model Registry > Custom Model Workshop tab, select a custom model you wish to add files to and navigate to the Assemble tab. On the Assemble tab, click Select remote repository . Click Add repository , and then select a repository provider to integrate a new remote repository with DataRobot. After you select the type of repository to register, follow the relevant process from the list below: - Bitbucket Server - GitHub - GitHub Enterprise - S3 - GitLab - GitLab Enterprise ### Bitbucket Server repository To register a Bitbucket Server repository, in the Add Bitbucket Server repository modal, configure the required fields: | Field | Description | | --- | --- | | Name | The name of the Bitbucket Server repository. | | Repository location | The URL for the Bitbucket Server repository that appears in the browser address bar when accessed. Alternatively, select Clone from the Bitbucket Server UI and paste the URL. | | Personal access token | The token used to grant DataRobot access to the Bitbucket Server repository. Generate this token from the Bitbucket Server UI. | | Description | (Optional) A description of the Bitbucket Server repository. | After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. ### GitHub repository To add a GitHub repository, in the Add GitHub repository modal, the steps for connecting to the repository depend on the connection method. **GitHub application:** The primary method for adding a GitHub repository is to authorize the DataRobot User Models Integration application for GitHub. Click Authorize GitHub app, then, configure the following fields: [https://docs.datarobot.com/en/docs/images/custom-repo-4.png](https://docs.datarobot.com/en/docs/images/custom-repo-4.png) Field Description Name The name of the GitHub repository. Repository Enter the GitHub repository URL. Start typing the repository name and repositories will populate in the autocomplete dropdown. To use a private repository, grant the GitHub app access to private repositories. When you grant access to a private repository , its URL is added to the Repository autocomplete dropdown. To use an external public GitHub repository, you must obtain the URL from the repo . Description (Optional) A description of the GitHub repository. Private repository permissions To use a private repository , click Edit repository permissions in the Add GitHub repository window. This gives the GitHub app access to your private repositories. You can give access to all current and future private repositories or a selected list of repositories External GitHub repositories To use an external public GitHub repository that is not owned by you or your organization, navigate to the repository in GitHub and click Code . Copy and paste the URL into the Repository field of the Add GitHub repository window. After access is granted, the private repositories appear in the autocomplete dropdown for the Repository field. **Personal access token:** The fallback method for adding a GitHub repository is to provide a repository location and personal access token. [https://docs.datarobot.com/en/docs/images/add-github-repo-classic.png](https://docs.datarobot.com/en/docs/images/add-github-repo-classic.png) Field Description Name The name of the GitHub repository. Repository location The URL for the GitHub repository that appears in the browser address bar when accessed. Alternatively, select Clone from the GitHub UI and paste the URL. Personal access token (Optional) The token used to grant DataRobot access to the GitHub repository. Generate this token from the GitHub UI. A token isn`t required for public repositories. Description (Optional) A description of the GitHub repository. After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. > [!TIP] GitHub repository organizations > You can add repositories from any [GitHub organization](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-repos.html#github-organization-repository-access) you belong to. #### GitHub organization repository access If you belong to a GitHub organization, you can request access to an organization's repository for use with DataRobot. A request for access notifies the GitHub admin, who then who approves or denies your access request. > [!NOTE] Organization repository access > If your admin approves a single user's access request, access is provided to all DataRobot users in that user's organization without any additional configuration. For more information, reference the [GitHub documentation](https://docs.github.com/en/github/setting-up-and-managing-organizations-and-teams/managing-access-to-your-organizations-repositories). ### GitHub Enterprise repository To register a GitHub Enterprise repository, in the Add GitHub Enterprise repository modal, configure the required fields: | Field | Description | | --- | --- | | Name | The name of the GitHub Enterprise repository. | | Repository location | The URL for the GitHub Enterprise repository that appears in the browser address bar when accessed. Alternatively, select Clone from the GitHub Enterprise UI and paste the URL. | | Personal access token | The token used to grant DataRobot access to the GitHub Enterprise repository. Generate this token from the GitHub UI. | | Description | (Optional) A description of the GitHub Enterprise repository. | After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. #### Git Large File Storage Git Large File Storage (LFS) is supported by default for GitHub integrations. Reference the [Git documentation](https://git-lfs.github.com) to learn more. Git LFS support for GitHub always requires having the GitHub application installed on the target repository, even if it's a public repository. Any non-authorized requests to the LFS API will fail with an HTTP 403. ### S3 repository To register an S3 repository, in the Add S3 repository modal, configure the required fields. | Field | Description | | --- | --- | | Name | The name of the S3 repository. | | Bucket name | The name of the S3 bucket. If you are adding a public S3 repository, this is the only field you must complete. | | Access key ID | The key used to sign programmatic requests made to AWS. Use with the AWS Secret Access Key to authenticate requests to pull from the S3 repository. Required for private S3 repositories. | | Secret access key | The key used to sign programmatic requests made to AWS. Use with the AWS Access Key ID to authenticate requests to pull from the S3 repository. Required for private S3 repositories. | | Session token | (Optional) A token that validates temporary security credentials when making a call to an S3 bucket. | | Description | (Optional) A description of the S3 repository. | After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. > [!NOTE] S3 credentials > AWS credentials are optional for public buckets. You can remove any S3 credentials by editing the repository connection. Select the connection and click Clear credentials. #### AWS S3 access configuration DataRobot requires the AWS S3 `ListBucket` and `GetObject` permissions in order to ingest data. These permissions should be applied as an additional AWS IAM Policy for the AWS user or role the cluster uses for access. For example, to allow ingestion of data from a private bucket named `examplebucket`, apply the following policy: ``` { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": ["s3:ListBucket"], "Resource": ["arn:aws:s3:::examplebucket"] }, { "Effect": "Allow", "Action": ["s3:GetObject"], "Resource": ["arn:aws:s3:::examplebucket/*"] } ] } ``` ### GitLab Cloud repository To add a GitLab repository, in the Add GitLab repository modal, the steps for connecting to the repository depend on the connection method. **GitLab application:** The primary method for adding a GitLab repository is to authorize the DataRobot User Models Integration application for GitLab. [https://docs.datarobot.com/en/docs/images/gitlab-1.png](https://docs.datarobot.com/en/docs/images/gitlab-1.png) Click Authorize GitLab app, grant access, and configure the following fields: [https://docs.datarobot.com/en/docs/images/gitlab-2.png](https://docs.datarobot.com/en/docs/images/gitlab-2.png) Field Description Name The name of the GitLab repository. Repository Enter the GitLab repository URL. Start typing the repository name and repositories will populate in the autocomplete dropdown. Description (Optional) A description of the GitLab repository. **Personal access token:** The fallback method for adding a GitLab repository is to provide a repository location and personal access token. [https://docs.datarobot.com/en/docs/images/add-gitlab-repo-classic.png](https://docs.datarobot.com/en/docs/images/add-gitlab-repo-classic.png) Field Description Name The name of the GitLab repository. Repository location The URL for the GitLab repository that appears in the browser address bar when accessed. Personal access token (Optional) Enter the token used to grant DataRobot access to the GitLab repository. Generate this token from GitLab. A token isn`t required for public repositories. Description (Optional) A description of the GitLab repository. After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. ### GitLab Enterprise repository To register a GitLab Enterprise repository, in the Add GitLab Enterprise repository modal, configure the required fields: | Field | Description | | --- | --- | | Name | The name of the GitLab Enterprise repository. | | Repository location | The URL for the GitLab Enterprise repository that appears in the browser address bar when accessed. | | Personal access token | (Optional) Enter the token used to grant DataRobot access to the GitLab Enterprise repository. Generate this token from GitLab Enterprise. A token isn`t required for public repositories. | | Description | (Optional) A description of the GitLab Enterprise repository. | After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. #### Create a personal access token for GitLab Enterprise To create a personal access token: 1. Navigate to GitLab. 2. Enter a name for the new token, set the mandatory scopes (read_apiandread_repository), and clickCreate personal access token. The newly generated token appears at the top of the page. 3. Enter the new token into thePersonal access tokenfield in theAdd GitLab Enterprise repositorywindow. ## Pull files from the repository After you add a repository to DataRobot, you can pull files from the repository and include them in the custom model. To pull files from a repository: 1. In the top navigation bar, clickModel Registry. 2. ClickCustom Model Workshop, click theModelstab, and select a model from the list. 3. UnderAssemble Model, clickSelect remote repository. Select a base environmentIf theModelgroup box is empty, select aBase Environmentfor the model. 4. In theSelect a remote repositorydialog box, select a repository in the list and clickSelect content. 5. In thePull from GitHub repositorydialog box, select the checkbox for any files or folders you want to pull into the custom model. You can also clickSelect allto select every file in the repository, or, after selecting one or more files, clickDeselect allto clear your selections. Repository typeThis step uses GitHub as an example; however, the process is the same for each repository type. TipYou can see how many files you have selected at the bottom of the dialog box (e.g.,+ 4 files will be added). 6. Once you select the files you want to pull into the custom model, clickPull. The added files appear under theModelheader as part of the custom model. --- # Manage custom model resources URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-resource-mgmt.html > Configure the resources the model consumes to facilitate smooth deployment and minimize potential environment errors in production. # Manage custom model resources After creating a custom inference model, you can configure the resources the model consumes to facilitate smooth deployment and minimize potential environment errors in production. To configure the resource allocation and access settings: 1. Navigate toModel Registry > Custom Model Workshop. 2. On theModelstab, click the model you want to manage and then click theAssembletab. 3. On the custom model'sAssemble Modelpage, under the deployment status, configure theResource Settings: NoteYou can also see these settings in the custom model's model package on theModel Registry > Model Packagespage. Click the custom model package, and then, on thePackage Infotab, scroll down to theResource Allocationsection. 4. Click the edit iconand configure the custom model's resource allocation and network access settings in theUpdate resource settingsdialog box: Resource settings accessUsers can determine the maximum memory allocated for a model, but onlyorganization adminscan configure additional resource settings. Imbalanced memory settingsDataRobot recommends configuring resource settings only when necessary. When you configure theMemorysetting below, you set the Kubernetes memory "limit" (the maximum allowed memory allocation); however, you can't set the memory "request" (the minimum guaranteed memory allocation). For this reason, it is possible to set the "limit" value too far above the default "request" value. An imbalance between the memory "request" and the memory usage allowed by the increased "limit" can result in the custom model exceeding the memory consumption limit. As a result, you may experience unstable custom model execution due to frequent eviction and relaunching of the custom model. If you require an increasedMemorysetting, you can mitigate this issue by increasing the "request" at the Organization level; for more information, contact DataRobot Support. SettingDescriptionMemoryDetermines the maximum amount of memory that may be allocated for a custom inference model. If a model allocates more than the configured maximum memory value, it is evicted by the system. If this occurs during testing, the test is marked as a failure. If this occurs when the model is deployed, the model is automatically launched again by Kubernetes.ReplicasSets the number of replicas executed in parallel to balance workloads when a custom model is running. Increasing the number of replicas may not result in better performance, depending on the custom model's speed.Network accessPremium feature. Configures the egress traffic of the custom model:Public: The default setting. The custom model can access any fully qualified domain name (FQDN) in a public network to leverage third-party services.None: The custom model is isolated from the public network and cannot access third party services.When public network access is enabled, your custom model can use theDATAROBOT_ENDPOINTandDATAROBOT_API_TOKENenvironment variables. These environment variables are available for any custom model using adrop-in environmentor acustom environmentbuilt onDRUM. Premium feature: Network accessEverynewcustom model you create haspublic network accessby default; however, when you create new versions of any custom model created before October 2023, those new versions remain isolated from public networks (access set toNone) until you enable public access for a new version (access set toPublic). From this point on, each subsequent version inherits the public access definition from the previous version. 5. Once you have configured the resource settings for the custom model, clickSave. This creates a new minor version of the custom model with edited resource settings applied. --- # Test custom models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-test.html > Follow the custom inference model testing workflow. Understand the types of tests employed and the insights available to verify performance, stability, and predictions. # Test custom models You can test custom models in the Custom Model Workshop. Alternatively, you can test custom models prior to uploading them by [testing locally with DRUM](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-local-test.html). ## Testing workflow Testing ensures that the custom model is functional before it is deployed by using the environment to run the model with prediction test data. Note that there are some differences in how predictions are made during testing and for a deployed custom model: - Testing bypasses the prediction servers, but predictions for a deployment are done by using the deployment's prediction server. - For both custom model testing and a custom model deployment, the model's target and partition columns are removed from prediction data before making predictions. - A deployment can be used to make predictions with a dataset containing an association ID. In this case, run custom model testing with a dataset that contains the association ID to make sure that the custom model is functional with the dataset. [Read below](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-test.html#testing-overview) for more details about the tests run for custom models. 1. To test a custom inference model, navigate to theTesttab. 2. SelectNew test. 3. Confirm the model version and upload the prediction test data. You can also configure theresource settings, which are only applied to the test (not the model itself). 4. After configuring the general settings, toggle the tests that you want to run. For more information about a test, reference thetesting overviewsection. When a test is toggled on, an unsuccessful check returns "Error", blocking the deployment of the custom model and aborting all subsequent tests. If toggled off, an unsuccessful check returns "Warning", but still permits deployment and continues the testing suite. Additionally, you can configure the tests' parameters (where applicable): 5. ClickStart Testto begin testing. As testing commences, you can monitor the progress and view results for individual tests under theSummary & Deploymentheader in theTesttab. For more information about a test, hover over the test name in the testing modal (displayed below) or reference thetesting overview. 6. When testing is complete, DataRobot displays the results. If all testing succeeds, the model is ready to be deployed. If you are satisfied with the configured resource settings, you canapply those changesfrom theAssembletab and create a new version of the model. To view any errors that occurred, selectView Full Log(the log is also available for download by selectingDownload Log). 7. After assessing any issues and fixing them locally for a model, upload the fixed file(s) and update the model version(s). Run testing again with the new model version. ## Testing overview The following table describes the tests performed on custom models to ensure they are ready for deployment. Note that [unstructured](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html) custom inference models only perform the "Startup Check" test, and skip all other procedures. | Test name | Description | | --- | --- | | Startup | Ensures that the custom model image can build and launch. If the image cannot build or launch, the test fails and all subsequent tests are aborted. | | Prediction error | Checks that the model can make predictions on the provided test dataset. If the test dataset is not compatible with the model or if the model cannot successfully make predictions, the test fails. | | Null imputation | Verifies that the model can impute null values. Otherwise, the test fails. The model must pass this test in order to support Feature Impact. | | Side effects | Checks that the batch predictions made on the entire test dataset match predictions made one row at a time for the same dataset. The test fails if the prediction results do not match. | | Prediction verification | Verifies predictions made by the custom model by comparing them to the reference predictions. The reference predictions are taken from the specified column in the selected dataset. | | Performance | Measures the time spent sending a prediction request, scoring, and returning the prediction results. The test creates 7 samples (from 1KB to 50MB), runs 10 prediction requests for each sample, and measures the prediction requests latency timings (minimum, mean, error rate etc.). The check is interrupted and marked as a failure if it elapses more than 10 seconds. | | Stability | Verifies model consistency. Specify the payload size (measured by row number), the number of prediction requests to perform as part of the check, and what percentage of them require 200 response code. You can extract insights with these parameters to understand where the model may have issues (for example, model failures respond with non-200 codes most of the time). | | Duration | Measures the time elapsed to complete the testing suite. | ### Testing insights #### Performance and stability checks Individual tests offer specific insights. Select See details on a completed test. The performance check insights display a table showing the prediction latency timings at different payload sample sizes. For each sample, you can see the minimal, average, and maximum prediction request time, along with the request per second (RPS) and error rate. Note that the prediction requests made to the model during testing bypass the prediction server, so the latency numbers will be slightly higher in a production environment as the prediction server will add some latency. Additionally, both Performance and Stability checks display a memory usage chart. This data requires the model to use a [DRUM-based execution environment](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-local-test.html) in order to display. The red line represents the maximum memory allocated for the model. The blue line represents how memory was consumed by the model. Memory usage is gathered from several replicas; the data displayed on the chart is coming from a different replica each time. The data displayed on the chart is likely to differ from multi-replica setups. For multi-replica setups, the memory usage chart is constructed by periodically pulling the memory usage stats from a random replica. This means that if the load is distributed evenly across all the replica, the chart shows the memory usage of each replica's model. Note that the model's usage can slightly exceed the maximum memory allocated because model termination logic depends on an underlying executor. Additionally, a model can be terminated even if the chart shows that its memory usage has not exceeded the limit, because the model is terminated before updated memory usage data is fetched from it. Memory usage data requires the model to use a [DRUM-based execution environment](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-local-test.html). #### Prediction verification check The insights for the prediction verification check display a histogram of differences between the model predictions and the reference predictions. Use the toggle to hide differences that represent matching predictions. In addition to the histogram, the prediction verification insights include a table containing rows for which model predictions do not match with reference predictions. The table values can be ordered by row number, or by the difference between a model prediction and a reference prediction. --- # Add training data to a custom model URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-training-data.html > How to assign training data to a custom model in the Custom Model Workshop. # Add training data to a custom model To enable feature drift tracking for a model deployment, you must add training data. To do this, assign training data to a model version. The method for providing training and holdout datasets for [unstructuredcustom inference models](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html) requires you to upload the training and holdout datasets separately. Additionally, these datasets cannot include a partition column. > [!WARNING] File size warning > The file size limit for custom model training data uploaded to DataRobot is 1.5GB. > [!WARNING] Considerations for training data prediction rows count > Training data uploaded to a custom model is used to compute Feature Impact, drift baselines, and Prediction Explanation previews. To perform these calculations, DataRobot automatically splits the uploaded training data into partitions for training, validation, and holdout (i.e., T/V/H) in a 60/20/20 ratio. Alternatively, you can manually provide a partition column in the training dataset to assign predictions, row-by-row, to the training ( `T`), validation ( `V`), or holdout ( `H`) partitions. > > Prediction Explanations require 100 rows in the validation partition, which—if you don’t define your own partitioning—requires the provided training dataset to contain a minimum of 500 rows. If the training data and partition ratio (defined automatically or manually) result in a validation partition containing fewer than 100 rows, Prediction Explanations are not calculated. While you can still register and deploy the model—and the deployment can make predictions—if you request predictions with explanations, the deployment returns an error. To assign training data to a custom model version: 1. InModel Registry > Custom Model Workshop, in theModelslist, select the model you want to add training data to. 2. On theAssembletab, next toDatasets: 3. In theAdd Training Data(orChange Training Data) dialog box, click and drag a training dataset file into theTraining Databox, or clickChoose fileand do either of the following: Include features required for scoringThe columns in a custom model's training data indicate which features are included in scoring requests to the deployed custom model; therefore, once training data is available, any features not included in the training dataset aren't sent to the model. Available as a preview feature, when youassemble a custom modelin the NextGen experience, you can disable this behavior using theColumn filtering setting. 4. (Optional)Specify the column name containing partitioning info for your data(based on training/validation/holdout partitioning). If you plan to deploy the custom model and monitor itsdata driftandaccuracy, specify the holdout partition in the column to establish an accuracy baseline. 5. When the upload is complete, clickAdd Training Data. Training data assignment errorIf the training data assignment fails, an error message appears in the new custom model version underDatasets. While this error is active, you can't create a model package to deploy the affected version. To resolve the error and deploy the model package, reassign training data to create a new version, or create a new version andthenassign training data. --- # Add custom model versions URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-versions.html > Update a model's contents to create a new version of the model due to new package versions, different preprocessing steps, hyperparameters, etc. # Add custom model versions If you want to update a model due to new package versions, different preprocessing steps, hyperparameters, and more, you can update the file contents to create a new version of the model. To upload a new version of a custom model environment, see [Add an environment version](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html#add-an-environment-version). ## Create a new minor version When you update the contents of a model, the minor version (1.1, 1.2, etc.) of the model automatically updates. To create a minor custom model version, select the model from the Custom Model Workshop and navigate to the Assemble tab. Under the Model header, click Add files and upload the files or folders you updated. The minor version is also updated if you delete a file. ## Create a new major version To create a new major version of a model (1.0, 2.0, etc.): 1. Select the model from the Custom Model Workshop and navigate to theAssembletab. 2. Under theModelheader, click+ New Version. 3. In theCreate new model versiondialog box, select a version creation strategy and configure the new version: SettingDescriptionCopy contents of previous versionAdd the contents of the current version to the new version of the custom model.Create empty versionDiscard the contents of the current version and add new files for the new version of the custom model.Base EnvironmentSelect the base execution environment of the new version. The execution environment of the current custom model version is selected by default. In addition, if the selected execution environment does not change, the version of that execution environment persists from the previous custom model version, even if a newer environment version is available. For more information on how to ensure the custom model version uses the latest version of the execution environment, seeTrigger base execution environment update.New version descriptionEnter a description of the new version. The version description is optional.Keep training data from previous versionEnable or disable adding the training data from the current version to the new custom model version. This setting is enabled by default. 4. ClickCreate new version. You can now use a new version of the model in addition to its previous versions. Select the iteration of the model that you want to use from theVersiondropdown. --- # Create custom model proxies for external models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/ext-model-proxy.html > Create custom model proxies for external models in the Custom Model Workshop. # Create custom model proxies for external models To create a custom model as a proxy for an external model, you can add a new proxy model to the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html). A proxy model contains proxy code created to connect with an external model, allowing you to use features like [compliance documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-compliance.html), [challenger analysis](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html), and [custom model tests](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-test.html) with a model running on infrastructure outside DataRobot. To create a proxy model: 1. (Optional)Add runtime parametersto the custom model through the model metadata (model-metadata.yaml). 2. Add proxy codeto the custom model through the custom model file (custom.py). 3. Create a proxy modelin the Custom Model Workshop. ## Add proxy code The custom model you create as a proxy for an external model should contain custom code in the `custom.py` file to connect the [proxy model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/ext-model-proxy.html#create-a-proxy-model) with the externally-hosted model; this code is the proxy code. See the [custom model assembly](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/index.html) documentation for more information on writing custom model code. The proxy code in the `custom.py` file should do the following: - Import the necessary modules and, optionally, theruntime parametersfrommodel-metadata.yaml. - Connect the custom model to an external model via an HTTPS connection or the network protocol required by your external model. - Request predictions and convert prediction data as necessary. To simplify the reuse of proxy code, you can add [runtime parameters](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-runtime-parameters.html) through your model metadata in the `model-metadata.yaml` file: ``` # model-metadata.yaml name: runtime-parameter-example type: inference targetType: regression runtimeParameterDefinitions: - fieldName: endpoint type: string description: The name of the endpoint. - fieldName: API_KEY type: credential description: The HTTP basic credential containing the endpoint's API key in the password field (the username field is ignored). ``` If you define runtime parameters in the model metadata, you can import them into the `custom.py` file to use in your proxy code. After importing these parameters, you can assign them to variables in your proxy code. This allows you to create a prediction request to connect to and retrieve prediction data from the external model. The following example outlines the basic structure of a `custom.py` file: ``` # custom.py # Import modules required to make a prediction request. import json import ssl import urllib.request import pandas as pd # Import SimpleNamespace to create an object to store runtime parameter variables. from types import SimpleNamespace # Import RuntimeParameters to use the runtime parameters set in the model metadata. from datarobot_drum import RuntimeParameters # Override the default load_model hook to read the runtime parameters. def load_model(code_dir): # Assign runtime parameters to variables. api_key = RuntimeParameters.get("API_KEY")["password"] endpoint = RuntimeParameters.get("endpoint") # Create scoring endpoint URL. url = f"https://{endpoint}.example.com/score" # Return an object containing the variables necessary to make a prediction request. return SimpleNamespace(**locals()) # Write proxy code to request and convert scoring data from the external model. def score(data, model, **kwargs): # Call make_remote_prediction_request. # Convert prediction data as necessary. def make_remote_prediction_request(payload, url, api_key): # Connect to the scoring endpoint URL. # Request predictions from the external model. ``` ## Create a proxy model To create a custom model as a proxy for an external model, you can add a new proxy model to the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html). A proxy model contains the proxy code you created to connect with your external model, allowing you to use features like [compliance documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-compliance.html), [challenger analysis](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html), and [custom model tests](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-test.html) with a model running on infrastructure outside DataRobot. To add a Proxy model through the Custom Model Workshop: 1. ClickModel Registry > Custom Model Workshop. 2. On theModelstab, click+ Add new model. 3. In theAdd Custom Inference Modeldialog box, selectProxy, and then add the model information. FieldDescriptionModel nameName the custom model.Target type / Target nameSelect the target type (binary classification,regression,multiclass classification,anomaly detection, ortext-generation(premium feature)) and enter the name of the target feature.Positive class label / Negative class labelThese fields only display for binary classification models. Specify the value to be used as the positive class label and the value to be used as the negative class label.For a multiclass classification model, these fields are replaced by a field to enter or upload the target classes in.csvor.txtformat. 4. ClickShow Optional Fieldsand, if necessary, enter aPrediction threshold, theLanguageused to build the model, and aDescription. 5. After completing the fields, clickAdd Custom Model. 6. In theAssembletab, underModel Environmenton the right, select a model environment by clicking theBase Environmentdropdown menu on the right and selecting an environment. The model environment is used fortestinganddeployingthe custom model. NoteTheBase Environmentpulldown menu includesdrop-in model environments, if any exist, as well ascustom environmentsthat you can create. 7. UnderModelon the left, add proxy model content by dragging and dropping files or browsing. Alternatively, select aremote integrated repository. If you clickBrowse local file, you have the option of adding aLocal Folder. The local folder is for dependent files and additional assets required by your model, not the model itself. Even if the model file is included in the folder, it will not be accessible to DataRobot unless the file exists at the root level. The root file can then point to the dependencies in the folder. NoteYou must also upload the model requirements and astart_server.shfile to your model's folder unless you are pairing the model with adrop-in environment. 8. On theAssembletab, next toResource settings, click the edit icon () to activate the requiredNetwork accessfor the proxy model. 9. If you provide runtime parameters in the model metadata, after you build the environment and create a new version, you can configure the parameters on theAssembletab underRuntime Parameters. 10. Finally, you canregister the custom modelto create a proxy model you can use to generatecompliance documentation. You can then deploy the proxy model to set upchallenger analysisand runcustom model testson the external model. --- # Custom Model Workshop URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html > Using custom inference models, you can bring your own pre-trained models into DataRobot. DataRobot supports models built with languages like Python, R, and Java. # Custom Model Workshop > [!NOTE] Availability information > The Custom Model Workshop is a feature exclusive to DataRobot MLOps. Contact your DataRobot representative for information on enabling it. The Custom Model Workshop allows you to upload model artifacts to create, test, and deploy custom inference models to a centralized model management and deployment hub. Custom inference models are pre-trained, user-defined models that support most of DataRobot's MLOps features. DataRobot supports custom inference models built in a variety of languages, including Python, R, and Java. If you've created a model outside of DataRobot and you want to upload your model to DataRobot, you need to define the model content and the model environment in the Custom Model Workshop. > [!NOTE] Important > Custom inference models are not custom DataRobot models—they are user-defined models created outside of DataRobot and assembled in the Custom Model Workshop for access to deployment, monitoring, and governance. To support the local development of the models that you want to bring into DataRobot through the Custom Model Workshop, the [DataRobot Model Runner](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html) provides you with tools to locally assemble, debug, test, and run the inference model before assembly in DataRobot. Before adding a custom model to the workshop, DataRobot recommends you reference the [custom model assembly guidelines](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/index.html). The following topics describe how you can manage custom model artifacts in DataRobot: | Topic | Describes | | --- | --- | | Create custom models | How to create custom inference models in the Custom Model Workshop. | | Manage custom model dependencies | How to manage model dependencies from the workshop and update the base drop-in environments to support your model code. | | Manage custom model resource usage | How to configure the resources a model consumes to facilitate smooth deployment and minimize potential environment errors in production. | | Add custom model versions | How to create a new version of the model and/or environment after updating the file contents with new package versions, different preprocessing steps, updated hyperparameters, and more. | | Add training data to a custom model | How to add training data to a custom inference model for deployment. | | Add files from a remote repo to a custom model | How to connect to a remote repository and pull custom model files into the Custom Model Workshop. | | Test a custom model in DataRobot | How to test custom inference models in the Custom Model Workshop. | | Manage custom models | How to delete or share custom models and custom model environments. | | Register custom models | How to register custom inference models in the Model Registry. | | Custom model proxy for external models | How to create custom model proxies for external models in the Custom Model Workshop. | | GitHub Actions for custom models | How to use the custom models action to manage custom inference models and deployments in DataRobot via GitHub CI/CD workflows. | Once deployed to a prediction server managed by DataRobot, you can [make predictions via the API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html) and [monitor your deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/index.html) with a suite of capabilities. --- # Prepare custom models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/index.html > Prepare to create deployments from custom models # Prepare custom models for deployment Custom inference models allow you to bring your own pre-trained models to DataRobot. By uploading a model artifact to the Custom Model Workshop, you can create, test, and deploy custom inference models to a centralized deployment hub. DataRobot supports models built with a variety of coding languages, including Python, R, and Java. If you've created a model outside of DataRobot and you want to upload your model to DataRobot, you need to define two components: - Model content: The compiled artifact, source code, and additional supporting files related to the model. - Model environment: The Docker image where the model will run. Model environments can be eitherdrop-inorcustom, containing a Docker file and any necessary supporting files. DataRobot provides a variety of built-in environments. Custom environments are only required to accommodate very specialized models and use cases. > [!NOTE] Note > Custom inference models are not custom DataRobot models. They are user-defined models created outside of DataRobot and assembled in the Custom Model Workshop for deployment, monitoring, and governance. See the associated [feature considerations](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/index.html#feature-considerations) for additional information. ## Custom Model Workshop | Topic | Describes | | --- | --- | | Custom Model Workshop | How you can bring your own pre-trained models into DataRobot as custom inference models and deploy these models to a centralized deployment hub. | | Create custom models | How to create custom inference models in the Custom Model Workshop. | | Manage custom model dependencies | How to manage model dependencies from the workshop and update the base drop-in environments to support your model code. | | Manage custom model resource usage | How to configure the resources a model consumes to facilitate smooth deployment and minimize potential environment errors in production. | | Add custom model versions | How to create a new version of the model and/or environment after updating the file contents with new package versions, different preprocessing steps, updated hyperparameters, and more. | | Add training data to a custom model | How to add training data to a custom inference model for deployment. | | Add files from a remote repo to a custom model | How to connect to a remote repository and pull custom model files into the Custom Model Workshop. | | Test a custom model in DataRobot | How to test custom inference models in the Custom Model Workshop. | | Manage custom models | How to delete or share custom models and custom model environments. | | Register custom models | How to register custom inference models in the Model Registry. | ## Custom model assembly | Topic | Describes | | --- | --- | | Custom model assembly | How to assemble the files required to run custom inference models. | | Custom model components | How to identify the components required to run custom inference models. | | Assemble structured custom models | How to use DRUM to assemble and validate structured custom models compatible with DataRobot. | | Assemble unstructured custom models | How to use DRUM to assemble and validate unstructured custom models compatible with DataRobot. | | DRUM CLI tool | How to download and install DataRobot User Models (DRUM) to work with Python, R, and Java custom models and to quickly test custom models, and custom environments locally before uploading into DataRobot. | | Test a custom model locally | How to test custom inference models in your local environment using the DataRobot Model Runner tool. | ## Custom model environments | Topic | Describes | | --- | --- | | Custom model environments | How to select a custom model environment from the drop-in environments or create additional custom environments. | | Drop-in environments | How to select the appropriate DataRobot drop-in environment when creating a custom model. | | Custom environments | How to assemble, validate, and upload a custom environment. | ## Feature considerations - The creation of deployments using model images cannot be canceled while in progress. - Inference models receive raw CSV data and must handle all preprocessing themselves. - A model's existing training data can only be changed if the model is not actively deployed. This restriction is not in place when adding training data for the first time. Also, training data cannot be unassigned; it can only be changed once assigned. - The target name can only be changed if a model has no training data and has not been deployed. - There is a per-user limit on the number of custom model deployments (30), custom environments (30), and custom environment versions (30) you can have. - Custom inference model server start-up is limited to 3 minutes. - The file size for training data is limited to 1.5GB. - Dependency management only works with packages in a proper index. Packages from URLs cannot be installed. - Unpinned python dependencies are not updated once the dependency image has been built. To update to a newer version, you will need to create a new requirements file with version constraints. DataRobot recommends always pinning versions. - SaaS AI Platform only : Custom inference models have no access to the internet and outside networks. --- # Configure deployment settings URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-deploy-info.html > When you add a deployment, configure the deployment by adding the prediction environment and enabling accuracy and data drift tracking, among other settings. # Configure a deployment Regardless of where you create a new deployment (the Leaderboard, the Model Registry, or the deployment inventory) or the type of artifact (DataRobot model, custom inference model, or remote mode), you are directed to the deployment information page where you can customize the deployment. The deployment information page outlines the capabilities of your current deployment based on the data provided, for example, [training data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#training-data), [prediction data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#prediction-data), or [actuals](https://docs.datarobot.com/en/docs/reference/glossary/index.html#actuals). It populates fields for you to provide details about the training data, inference data, model, and your outcome data. ## Standard options and information When you initiate model deployment, the Deployments tab opens to the Model information and the Prediction history and service health options: ## Model information The Model information section provides information about the model being used to make predictions for your deployment. DataRobot uses the files and information from the deployment to complete these fields, so they aren't editable. | Field | Description | | --- | --- | | Model name | The name of your model. | | Prediction type | The type of prediction the model is making. For example: Regression, Classification, Multiclass, Anomaly Detection, Clustering, etc. | | Threshold | The prediction threshold for binary classification models. Records above the threshold are assigned the positive class label and records below the threshold are assigned the negative class label. This field isn't available for Regression or Multiclass models. | | Target | The dataset column name the model will predict on. | | Positive / Negative classes | The positive and negative class values for binary classification models. This field isn't visible for Regression or Multiclass models. | | Model Package Id (Registered Model Version ID) | The id of the Model Package (Registered Model Version) in the Model Registry. | > [!NOTE] Note > If you are part of an organization with deployment limits, the Deployment billing section notifies you of the number of deployments your organization is using against the [deployment limit](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html#live-inventory-updates) and the deployment cost if your organization has exceeded the limit. > > [https://docs.datarobot.com/en/docs/images/deployment-billing.png](https://docs.datarobot.com/en/docs/images/deployment-billing.png) ## Prediction history and service health The Prediction history and service health section provides details about your deployment's inference (also known as scoring) data—the data that contains prediction requests and results from the model. > [!NOTE] Prediction environments for external models > External models run outside DataRobot and must be deployed to an external prediction environment. Do not deploy external models to a DataRobot Serverless prediction environment. For external environment setup instructions, see [Add external prediction environments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html) in the Classic UI documentation. | Setting | Description | | --- | --- | | Configure prediction environment | Environment where predictions are generated. Prediction environments allow you to establish access controls and approval workflows. | | Enable batch monitoring | Determines if predictions are grouped and monitored in batches, allowing you to compare batches of predictions or delete batches to retry predictions. For more information, see the Batch monitoring for deployment predictions documentation. | | Configure prediction timestamp | Determines the method used to time-stamp prediction rows for Data Drift and Accuracy monitoring. Use time of prediction request: Use the time you submitted the prediction request to determine the timestamp.Use value from date/time feature: Use the date/time provided as a feature with the prediction data (e.g., forecast date) to determine the timestamp. Forecast date time-stamping is set automatically for time series deployments. It allows for a common time axis to be used between training data and the basis of data drift and accuracy statistics. This setting doesn't apply to the Service Health prediction timestamp. The Service Health tab always uses the time the prediction server received the prediction request. For more information, see Time of Prediction below. This setting cannot be changed after the deployment is created and predictions are made. | | Set deployment importance | Determines the importance level of a deployment. These levels—Critical, High, Moderate, and Low—determine how a deployment is handled during the approval process. Importance represents an aggregate of factors relevant to your organization such as the prediction volume of the deployment, level of exposure, potential financial impact, and more. When a deployment is assigned an importance of Moderate or above, the Reviewers notification appears (under Model Information) to inform you that DataRobot will automatically notify users assigned as reviewers whenever the deployment requires review. | > [!NOTE] Time of Prediction > The Time of Prediction value differs between the [Data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html) and [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) tabs and the [Service health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html) tab: > > On the > Service health > tab, the "time of prediction request" is > always > the time the prediction server > received > the prediction request. This method of prediction request tracking accurately represents the prediction service's health for diagnostic purposes. > On the > Data drift > and > Accuracy > tabs, the "time of prediction request" is, > by default > , the time you > submitted > the prediction request, which you can override with the prediction timestamp in the > Prediction History and Service Health > settings. ## Advanced options Click Show advanced options: To configure the following deployment settings: - Data drift - Accuracy - Data exploration - Challenger analysis - Advanced predictions configuration(forFeature Discoverydeployments) - Advanced service health configuration - Fairness - Custom metrics ### Data drift When deploying a model, there is a chance that the dataset used for training and validation differs from the prediction data. To enable drift tracking you can configure the following settings: | Setting | Description | | --- | --- | | Enable feature drift tracking | Configures DataRobot to track feature drift in a deployment. Training data is required for feature drift tracking. | | Enable target monitoring | Configures DataRobot to track target drift in a deployment. Actuals are required for target monitoring, and target monitoring is required for accuracy monitoring. | | Training data | Required to enable feature drift tracking in a deployment. | > [!NOTE] How does DataRobot track drift? > DataRobot tracks two types of drift: > > Target drift > : DataRobot stores statistics about predictions to monitor how the distribution and values of the target change over time. As a baseline for comparing target distributions, DataRobot uses the distribution of predictions on the holdout. > Feature drift > : DataRobot stores statistics about predictions to monitor how distributions and values of features change over time. The supported feature data types are numeric, categorical, and text. As a baseline for comparing distributions of features: > For training datasets larger than 500MB, DataRobot uses the distribution of a random sample of the training data. > For training datasets smaller than 500MB, DataRobot uses the distribution of 100% of the training data. DataRobot monitors both target and feature drift information by default and displays results in the [Data Drift dashboard](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html). Use the Enable target monitoring and Enable feature drift tracking toggles to turn off tracking if, for example, you have sensitive data that should not be monitored in the deployment. You can customize how data drift is monitored. See the data drift page for more information on [customizing data drift status](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html) for deployments. > [!NOTE] Note > Data drift tracking is only available for deployments using deployment-aware prediction API routes (i.e., `https://example.datarobot.com/predApi/v1.0/deployments//predictions`). ### Accuracy Configuring the required settings for the [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) tab allows you to analyze the performance of the model deployment over time using standard statistical measures and exportable visualizations. | Setting | Description | | --- | --- | | Association ID | Specifies the column name that contains the association ID in the prediction dataset for your model. Association IDs are required for setting up accuracy tracking in a deployment. The association ID functions as an identifier for your prediction dataset so you can later match up outcome data (also called "actuals") with those predictions. If your deployment is for a time series project, see Association IDs for time series deployments to learn how to select or construct an effective association ID. | | Require association ID in prediction requests | Requires your prediction dataset to have a column name that matches the name you entered in the Association ID field. When enabled, you will get an error if the column is missing. Note that the Create deployment button is inactive until you enter an association ID or turn off this toggle. This cannot be enabled with Enable automatic association ID generation for prediction rows. | | Enable automatic association ID generation for prediction rows | With an association ID column name defined, allows DataRobot to automatically populate the association ID values. This cannot be enabled with Require association ID in prediction requests. | | Enable automatic actuals feedback for time series models | For time series deployments that have indicated an association ID. Enables the automatic submission of actuals, so that you do not need to submit them manually via the UI or API. Once enabled, actuals can be extracted from the data used to generate predictions. As each prediction request is sent, DataRobot can extract an actual value for a given date. This is because when you send prediction rows to forecast, historical data is included. This historical data serves as the actual values for the previous prediction request. | > [!NOTE] Important: Association ID for monitoring agent and monitoring jobs > You must set an association ID before making predictions to include those predictions in accuracy tracking. For [agent-monitored](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html) external model deployments with challengers (and monitoring jobs for challengers), the association ID should be `__DataRobot_Internal_Association_ID__` to [report accuracy for the modelandits challengers](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent-use.html#report-accuracy-for-challengers). ### Data exploration Enable prediction row storage to activate the [Data exploration](https://docs.datarobot.com/en/docs/api/reference/sdk/data-exploration.html) tab. From there, you can export a deployment's stored training data, prediction data, and actuals to compute and monitor custom business or performance metrics on the [Custom metrics](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html) tab or outside DataRobot. | Setting | Description | | --- | --- | | Enable prediction row storage | Enables prediction data storage, a setting required to store and export a deployment's prediction data for use in custom metrics. | ### Challenger analysis DataRobot can securely store prediction request data at the row level for deployments (not supported for external model deployments). This setting must be enabled for any deployment using the [Challengers](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html) tab. In addition to enabling challenger analysis, access to stored prediction request rows enables you to thoroughly audit the predictions and use that data to troubleshoot operational issues. For instance, you can examine the data to understand an anomalous prediction result or why a dataset was malformed. > [!NOTE] Note > Contact your DataRobot representative to learn more about data security, privacy, and retention measures or to discuss prediction auditing needs. | Setting | Description | | --- | --- | | Enable prediction rows storage for challenger analysis | Enables the use of challenger models, which allow you to compare models post-deployment and replace the champion model if necessary. Once enabled, prediction requests made for the deployment are collected by DataRobot. Prediction Explanations are not stored. | > [!NOTE] Important > Prediction requests are only collected if the prediction data is in a valid data format interpretable by DataRobot, such as CSV or JSON. Failed prediction requests with a valid data format are also collected (i.e., missing input features). ### Advanced predictions configuration In the Advanced predictions configuration section, you can configure settings dependent on the project type of the model being deployed and the prediction environment the model is being deployed to: - When you deploy a model to a DataRobot Serverless environment , you can configure the predictions autoscaling settings . - When you deploy a model from a Feature Discovery project , you can configure the secondary dataset configurations . #### Predictions autoscaling To configure on-demand predictions on this environment, click Show advanced options, scroll down to Advanced Predictions Configuration, and set the following Autoscaling options: Autoscaling automatically adjusts the number of replicas in your deployment based on incoming traffic. During high-traffic periods, it adds replicas to maintain performance. During low-traffic periods, it removes replicas to reduce costs. This eliminates the need for manual scaling while ensuring your deployment can handle varying loads efficiently. **Basic autoscaling:** To configure autoscaling, modify the following settings. Note that for DataRobot models, DataRobot performs autoscaling based on CPU usage at a 40% threshold.: [https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-configure.png](https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-configure.png) Field Description Minimum compute instances (Premium feature) Set the minimum compute instances for the model deployment. If your organization doesn't have access to "always-on" predictions, this setting is set to 0 and isn't configurable. With the minimum compute instances set to 0 , the inference server will be stopped after an inactivity period of 7 days. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. Maximum compute instances Set the maximum compute instances for the model deployment to a value above the current configured minimum. To limit compute resource usage, set the maximum value equal to the minimum. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. **Advanced autoscaling (custom models):** To configure autoscaling, select the metric that will trigger scaling: CPU utilization: Set a threshold for the average CPU usage across active replicas. When CPU usage exceeds this threshold, the system automatically adds replicas to provide more processing power. HTTP request concurrency: Set a threshold for the number of simultaneous requests being processed. For example, with a threshold of 5, the system will add replicas when it detects 5 concurrent requests being handled. When your chosen threshold is exceeded, the system calculates how many additional replicas are needed to handle the current load. It continuously monitors the selected metric and adjusts the replica count up or down to maintain optimal performance while minimizing resource usage. Review the settings for CPU utilization below. [https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-cpu.png](https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-cpu.png) Field Description CPU utilization (%) Set the target CPU usage percentage that triggers scaling. When CPU utilization reaches this threshold, the system adds more replicas. Cool down period (minutes) Set the wait time after a scale-down event before another scale-down can occur. This prevents rapid scaling fluctuations when metrics are unstable. Minimum compute instances (Premium feature) Set the minimum compute instances for the model deployment. If your organization doesn't have access to "always-on" predictions, this setting is set to 0 and isn't configurable. With the minimum compute instances set to 0 , the inference server will be stopped after an inactivity period of 7 days. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. Maximum compute instances Set the maximum compute instances for the model deployment to a value above the current configured minimum. To limit compute resource usage, set the maximum value equal to the minimum. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. Review the settings for HTTP request concurrency below. [https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-http.png](https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-http.png) Field Description HTTP request concurrency Set the number of simultaneous requests required to trigger scaling. When concurrent requests reach this threshold, the system adds more replicas. Cool down period (minutes) Set the wait time after a scale-down event before another scale-down can occur. This prevents rapid scaling fluctuations when metrics are unstable. Minimum compute instances (Premium feature) Set the minimum compute instances for the model deployment. If your organization doesn't have access to "always-on" predictions, this setting is set to 0 and isn't configurable. With the minimum compute instances set to 0 , the inference server will be stopped after an inactivity period of 7 days. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. Maximum compute instances Set the maximum compute instances for the model deployment to a value above the current configured minimum. To limit compute resource usage, set the maximum value equal to the minimum. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. > [!NOTE] Premium feature: Always-on predictions > Always-on predictions are a premium feature. Deployment autoscaling management is required to configure the minimum compute instances setting. Contact your DataRobot representative or administrator for information on enabling the feature. > > Feature flag: Enable Deployment Auto-Scaling Management > [!NOTE] Compute instance configurations > For DataRobot model deployments: > > The default minimum is 0 and default maximum is 3. > The minimum and maximum limits are taken from the organization's > max_compute_serverless_prediction_api > setting. > > For custom model deployments: > > The default minimum is 0 and default maximum is 1. > The minimum and maximum limits are taken from the organization's > max_custom_model_replicas_per_deployment > setting. > The minimum is always greater than 1 when running on GPUs (for LLMs). > > Additionally, for high availability scenarios: > > The minimum compute instances setting > must > be greater than or equal to 2. > This requires business critical or consumption-based pricing. > [!TIP] Update compute instances settings > If, after deployment, you need to update the number of compute instances available to the model, you can change these settings on the [Predictions Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/predictions-settings.html) tab. #### Secondary datasets for Feature Discovery [Feature Discovery](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html) identifies and generates new features from multiple datasets so that you no longer need to perform manual feature engineering to consolidate multiple datasets into one. This process is based on relationships between datasets and the features within those datasets. DataRobot provides an intuitive relationship editor that allows you to build and visualize these relationships. DataRobot’s Feature Discovery engine analyzes the graphs and the included datasets to determine a feature engineering “recipe” and, from that recipe, generates secondary features for training and predictions. While configuring the deployment settings, you can [change the selected secondary dataset configuration](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-predict.html#select-a-secondary-dataset-configuration). | Setting | Description | | --- | --- | | Secondary datasets configurations | Previews the dataset configuration or provides an option to change it. By default, DataRobot makes predictions using the secondary datasets configuration defined when starting the project. Click Change to select an alternative configuration before uploading a new primary dataset. | ### Advanced service health configuration [Segmented Analysis](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-segment.html) identifies operational issues with training and prediction data requests for a deployment. DataRobot enables drill-down analysis of data drift and accuracy statistics by filtering them into unique segment attributes and values. | Setting | Description | | --- | --- | | Track attributes for segmented analysis of training data and predictions | Enables DataRobot to monitor deployment predictions by segments (for example, by categorical features). This setting requires training data and is required to enable Fairness monitoring. | ### Fairness [Fairness](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/mlops-fairness.html) allows you to configure settings for your deployment to identify any biases in the model's predictive behavior. If fairness settings are defined prior to deploying a model, the fields are automatically populated. For additional information, see the section on [defining fairness tests](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html#configure-metrics-and-mitigation). | Setting | Description | | --- | --- | | Protected features | Identifies the dataset columns to measure fairness of model predictions against; must be categorical. | | Primary fairness metric | Defines the statistical measure of parity constraints used to assess fairness. | | Favorable target outcome | Defines the outcome value perceived as favorable for the protected class relative to the target. | | Fairness threshold | Defines the fairness threshold to measure if a model performs within appropriate fairness bounds for each protected class. | ### Custom metrics For generative AI deployments, configure these settings to monitor [data quality](https://docs.datarobot.com/en/docs/api/reference/sdk/data-exploration.html) and [custom metrics](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html). | Setting | Description | | --- | --- | | Association ID | Specifies the column name that contains the association ID in the prediction dataset for your model. Association IDs are required for setting up accuracy tracking in a deployment. The association ID functions as an identifier for your prediction dataset so you can later match up outcome data (also called "actuals") with those predictions. | | Require association ID in prediction requests | Requires your prediction dataset to have a column name that matches the name you entered in the Association ID field. When enabled, you will get an error if the column is missing. Note that the Create deployment button is inactive until you enter an association ID or turn off this toggle. This cannot be enabled with Enable automatic association ID generation for prediction rows. | | Enable automatic association ID generation for prediction rows | With an association ID column name defined, allows DataRobot to automatically populate the association ID values. This cannot be enabled with Require association ID in prediction requests. | | Track attributes for segmented analysis of training data and predictions | Enables DataRobot to monitor deployment predictions by segments (for example, by categorical features). This setting requires training data and is required to enable Fairness monitoring. | ## Deploy the model After you add the available data and your model is fully defined, click Deploy model at the top of the screen. > [!NOTE] Note > If the Deploy model button is inactive, be sure to either specify an association ID (required for [enabling accuracy monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html)) or toggle off Require association ID in prediction requests. The Creating deployment message appears, indicating that DataRobot is creating the deployment. After the deployment is created, the Overview tab opens. Click the arrow to the left of the deployment name to return to the deployment inventory. --- # Add prediction data post-deployment URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-prediction-data-post-deploy.html > How to add historical prediction data after a model is deployed. # Add prediction data post-deployment Users with the [Owner](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html) role can add historical prediction data to external model deployments if data drift is enabled. To do so, navigate to the [Data Drift > Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html) tab and click Choose file in the Inference data section to upload your prediction data in XLSX, CSV, or TXT format. You can also select prediction data from the AI Catalog. Training data is a critical component for calculating data drift. If you did not include training data when you created a deployment, or if there was an error when uploading that data, you can also add training data from the [Data Drift > Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html) tab. These datasets must meet the following requirements: - Historical prediction data: The uploaded data have the same features as the original prediction dataset. After uploading new data, DataRobot prompts you to confirm the addition because you cannot remove data from a deployment later. To use different prediction data, create a new deployment. - Missing training data: The uploaded data must include the same features as the prediction (scoring) dataset. You cannot replace training data. If you want a deployment to use different training data, create a new deployment with the appropriate data. --- # Deploy custom models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-custom-inf-model.html > How to deploy custom models, pre-trained models assembled in the Custom Model Workshop. # Deploy custom models After you [create a custom inference model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html) using the Custom Model Workshop, you can deploy it to a [custom model environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html). > [!NOTE] Note > While you can deploy your custom inference model to an environment without testing, DataRobot strongly recommends your model pass testing before deployment. ## Register and deploy a custom model To deploy an unregistered custom model: 1. Navigate toModel Registry > Custom Model Workshop > Modelsand select the model you want to deploy. 2. On theAssembletab, clickRegister to deployin the middle of the page. NoteDataRobot recommends testing that your model can make predictions before deploying. 3. In theRegister new modeldialog box, configure the following: FieldDescriptionRegister modelSelect one of the following:Register new model:Create a new registered model. This creates the first version (V1).Save as a new version to existing model:Create a version of an existing registered model. This increments the version number and adds a new version to the registered model.Registered model name / Registered ModelDo one of the following:Registered model name:Enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, theModel registration failederror message appears.Registered Model:Select the existing registered model you want to add a new version to.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister a new model.Optional settingsVersion descriptionDescribe the business problem these model packages solve, or, more generally, the relationship between them.TagsClick+ Add itemand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags do not apply to the registered model, just the versions within. Tags added when registering a new model are applied toV1. 4. ClickAdd to Registry. The model opens on theModel Registry > Registered Modelstab. 5. In the registered model version header, clickDeploy, and thenconfigure the deployment settings. Most information for your custom model is provided automatically. 6. ClickDeploy model. ## Deploy a registered custom model To deploy a registered custom model: 1. On theRegistered Modelspage, click the registered model containing the model version you want to deploy. 2. To open the registered model version, do either of the following: 3. In the version header, clickDeploy, and thenconfigure the deployment settings. 4. ClickDeploy model. TheCreating deploymentmodal appears, tracking the status of the deployment creation process, including the application of deployment settings and the calculation of the drift baseline. You canReturn to deploymentsor monitor the deployment progress from the modal, allowing you to access theCheck deployment's MLOps logslink if an error occurs: ### Make predictions Once a custom inference model is deployed, it can make predictions using API calls to a dedicated prediction server managed by DataRobot. You can find more information about [using the prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html) in the Predictions documentation. > [!NOTE] Training dataset considerations > When making predictions through a deployed model, the prediction dataset is handled as follows: > > Without > training data, only the target feature is removed from the prediction dataset. > With > training data, any features not in the training dataset are removed from the prediction dataset. ### Deployment status When DataRobot deploys a custom model, a Launching badge appears under the deployment name in the [deployment inventory](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html), and on any tab within the deployment. The following deployment status values are available for custom model deployments: | Status | Badge | | --- | --- | | | The custom model deployment process is still in progress. You can't currently make predictions through this deployment, or access deployment tabs that require an active deployment. | | | The custom model deployment process completed with errors. You may be unable to make predictions through this deployment; however, if you deactivate this deployment, you can't reactivate it until you resolve the deployment errors. You should check the MLOps Logs to troubleshoot the custom model deployment. | | | The custom model deployment process failed and the deployment is Inactive. You can't currently make predictions through this deployment, or access deployment tabs that require an active deployment. You should check the MLOps Logs to troubleshoot the custom model deployment. | From a deployment with an Errored or Warning status, you can access the Service Health MLOps logs link from the warning on any tab. This link takes you directly to the Service Health tab: On the Service Health tab, under Recent Activity, you can click the MLOps Logs tab to view the Event Details. In the Event Details, you can click View logs to access the [custom model deployment logs](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-custom-inf-model.html#deployment-logs) to diagnose the cause of the error: ### Deployment logs When you deploy a custom model, it generates log reports unique to this type of deployment, allowing you to debug custom code and troubleshoot prediction request failures from within DataRobot. To view the logs for a deployed model, navigate to the deployment, open the Actions menu, and select View Logs. You can access two types of logs: - Runtime Logsare used to troubleshoot failed prediction requests (via thePredictionstab or the API). The logs are captured from the Docker container running the deployed custom model and contain up to 1MB of data. The logs are cached for 5 minutes after you make a prediction request. You can re-request the logs by clickingRefresh. - Deployment logsare automatically captured if the custom model fails while deploying. The logs are stored permanently as part of the deployment. > [!NOTE] Note > DataRobot only provides logs from inside the Docker container from which the custom model runs. Therefore, it is possible in cases where a custom model fails to deploy or fails to execute a prediction request that no logs will be available. This is because the failures occurred outside of the Docker container. Use the Search bar to find specific references within the logs. Click Download Log to save a local copy of the logs. --- # Deploy external models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-external-model.html > How to deploy external models by registering and deploying a model package or by uploading training data for the external model directly. # Deploy external models To monitor models making predictions on external infrastructure, you can deploy external (remote) models using either of the following methods: - Deploy an external model package . - Deploy an external model by uploading historical training data . After you deploy, you can use the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html) to monitor the external deployment. ## Deploy a registered external model This section outlines how to deploy a registered external (remote) model. Before proceeding, make sure you have [registered your external model package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-external-models.html) in the Model Registry. > [!NOTE] Important > To send predictions, first configure the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html). Reference the agent's internal documentation for configuration information. You can deploy a registered external model at any time from the Registered Models page. To do that, you must open a registered model version: 1. On theRegistered Modelspage, click theregistered external modelcontaining the model version you want to deploy. 2. To open the registered model version, do either of the following: 3. In the version header, clickDeploy, and thenconfigure the deployment settings. 4. ClickDeploy model. ## Deploy an external model by uploading training data This section explains how to upload the training data for a model that made predictions in the past. Uploading the historical predictions directly to the deployment inventory enables you to analyze data drift and accuracy statistics in the past. Instrument the external deployment with the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html) to monitor future predictions and [add additional historical prediction data](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-prediction-data-post-deploy.html) after deployment. To create a deployment with training data: 1. Navigate toDeploymentsand click the+ Add deploymentlink. 2. Under theAdd a training datasetheader, selectbrowseand selectLocal Fileto upload your XLSX, CSV, or TXT formatted training data. You can also select training data from theAI Catalog. 3. After selecting your training dataset, provide information about the model that used the training data. Once completed, selectContinue to deployment detailsto further configure the deployment. 4. Adddeployment information and complete the deployment. ## Configure an external deployment After you create an external deployment, there are two options for additional configuration. You can: - Upload historical prediction datato the deployment to analyze data drift and accuracy in the past. - Configure the deployment with the monitoring agentusing the monitoring code snippet from thePredictions > Monitoringtabto monitor future predictions. ## Configure prediction data for time series scoring For time series predictions, if you add prediction data for scoring in the Predictions tab, you must include the following required features in the prediction dataset: | Feature | Description | | --- | --- | | Forecast Distance | Supplied by DataRobot when you download the .mlpkg file. | | dr_forecast_point | Supplied by DataRobot when you download the .mlpkg file. | | Datetime_column_name | Defines the date/time feature to use for time-stamping prediction rows. | | Series_column_name | Defines the feature (series ID) used for multiseries deployments (if applicable). | --- # Deploy DataRobot models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html > How to create new deployments from DataRobot AutoML models. # Deploy DataRobot models You can register and deploy models you build with DataRobot AutoML using the Model Registry. In most cases, before deployment, you should unlock holdout and [retrain your model](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/creating-addl-models.html#retrain-a-model) at 100% to improve predictive accuracy. Additionally, DataRobot automatically runs [Feature Impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html) for the model (this also calculates Prediction Explanations, if available). ## Register and deploy a model To register and deploy a model from the Leaderboard, you must first provide model registration details: 1. On theLeaderboard, select the model to use for generating predictions. DataRobot recommends a model with theRecommended for DeploymentandPrepared for Deploymentbadges. Themodel preparationprocess runs feature impact, retrains the model on a reduced feature list, and trains on a higher sample size, followed by the entire sample (latest data for date/time partitioned projects). ImportantTheDeploytab behaves differently in environments without a dedicated prediction server, as described in the section onshared modeling workers. 2. ClickPredict > Deploy. If the Leaderboard model doesn't have thePrepare for Deploymentbadge, DataRobot recommends you clickPrepare for Deploymentto run themodel preparationprocess for that model. TipIf you've already added the model to the Model Registry, the registered model version appears in theModel Versionslist. You can clickDeploynext to the model and skip the rest of this process. 3. UnderDeploy model, clickRegister to deploy. 4. In theRegister new modeldialog box, provide the following model information: FieldDescriptionRegister modelSelect one of the following:Register new model:Create a new registered model. This creates the first version (V1).Save as a new version to existing model:Create a version of an existing registered model. This increments the version number and adds a new version to the registered model.Registered model name / Registered ModelDo one of the following:Registered model name:Enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, theModel registration failedwarning appears.Registered Model:Select the existing registered model you want to add a new version to.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister a new model.Prediction thresholdFor binary classification models. Enter the value a prediction score must exceed to be assigned to the positive class. The default value is0.5. For more information, seePrediction thresholds.Optional settingsVersion descriptionDescribe the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add itemand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags do not apply to the registered model, just the versions within. Tags added when registering a new model are applied toV1.Include prediction intervalsFor time series models. Enable the computation of a model'stime series prediction intervals(from 1 to 100). Time series prediction intervals may take a long time to compute, depending on the number of series in the dataset, the number of features, the blueprint, etc. Consider if intervals are required in your deployment before enabling this setting. For more information see, theprediction intervals consideration. Prediction intervals in DataRobot serverless prediction environmentsIn a DataRobot serverless prediction environment, to make predictions with time-series prediction intervals included,you mustinclude pre-computed prediction intervals when registering the model package. If you don't pre-compute prediction intervals, the deployment resulting from the registered model doesn't supportenabling prediction intervals. Binary classification prediction thresholdsIf you set theprediction thresholdbefore thedeployment preparation process, the value does not persist. When deploying the prepared model, if you want it to use a value other than the default, set the value after the model has thePrepared for Deploymentbadge. Time series prediction intervals considerationWhen you deploy atime series model package with prediction intervals, thePredictions > Prediction Intervalstab is available in the deployment. For deployed model packages built without computing intervals, the deployment'sPredictions > Prediction Intervalstab is hidden; however, older time series deployments without computed prediction intervals may display thePrediction Intervalstab if they were deployed prior to August 2022. 5. ClickAdd to registry. The model opens on theModel Registry > Registered Modelstab. 6. While the registered model builds, clickDeployand thenconfigure the deployment settings. 7. ClickDeploy model. ## Deploy a registered model In the Model Registry, you can deploy a registered model at any time from the Registered Models page. To do that, you must open a registered model version: 1. On theRegistered Modelspage, click the registered model containing the model version you want to deploy. 2. To open the registered model version, do either of the following: 3. In the version header, clickDeploy, and thenconfigure the deployment settings. ## Use shared modeling workers If you do not have a dedicated prediction server instance, you can use a node that shares workers with your model-building activities. In this scenario, the deployment workflow has a different interface: 1. From theLeaderboard, select the model to use for generating predictions, and then clickPredict > Deploy Model API. 2. ClickShow Exampleto generate and display a usage example and define the following: FieldDescription1API_TOKENTheAPI key.2PROJECT_ID/MODEL_IDThe project and model IDs, available in the sample.3dr.Client(endpoint='https://app.datarobot.com/api/v2', token=API_TOKEN)The shared instance endpoint, available in the sample. The DataRobot Python client uses the API key you set for authentication so no key or username is required. 3. To execute the file, follow the instructions in the comments included in the example snippet. --- # Deploy models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/index.html > How to create deployments from DataRobot models, custom inference models, and external models. # Deploy models In DataRobot, the way you deploy a model to production depends on the type of model you start with and the prediction environment where the model will be used. The following sections describe how to add deployments for different types of artifacts, including models built in DataRobot, custom inference models, and remote models. **SaaS:** Topic Describes Deploy DataRobot models How to deploy DataRobot models from the Leaderboard or the Model Registry. Deploy custom models How to deploy custom models created in the Custom Model Workshop. Deploy external models How to deploy external (remote) models from the Model Registry or by uploading training data in the deployment inventory. MLOps agents How to monitor and manage deployments running in an external environment outside of DataRobot MLOps. Configure a deployment How to complete deployments by configuring inference options. Add prediction data post-deployment How to add historical prediction data to existing deployments. **Self-Managed:** Topic Describes Deploy DataRobot models How to deploy DataRobot models from the Leaderboard or the Model Registry. Deploy custom models How to deploy custom models created in the Custom Model Workshop. Deploy external models How to deploy external (remote) models from the Model Registry or by uploading training data in the deployment inventory. MLOps agents How to monitor and manage deployments running in an external environment outside of DataRobot MLOps. Configure a deployment How to complete deployments by configuring inference options. Add prediction data post-deployment How to add historical prediction data to existing deployments. Imported .mlpkg file How to import and deploy .mlpkg files from the Model Registry. --- # Custom model in a DataRobot environment URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-workflows/cus-model-dr-env.html > How to deploy a custom model in a DataRobot prediction environment. # Deploy a custom model in a DataRobot Environment Custom inference models allow you to bring your pre-trained models into DataRobot. To deploy a custom model to a DataRobot prediction environment, you can create a custom model in the Custom Model Workshop. Then, you can prepare, test, and register that model, and deploy it to a centralized deployment hub where you can monitor, manage, and govern it alongside your deployed DataRobot models. DataRobot supports custom models built in various programming languages, including Python, R, and Java. To create and deploy a custom model in DataRobot, follow the workflow outlined below: ``` graph TB A[Create a custom model] --> B{Use a custom model environment?} B --> |Yes|C[Create a custom model environment] B --> |No|D[Prepare the custom model]; C --> D D --> E{Test locally?} E --> |No|H[Test the custom model in DataRobot] E --> |Yes|F[Install the DataRobot Model Runner] F --> G[Test the custom model locally] G --> H H --> I[Register the custom model] I --> J[Deploy the custom model] ``` ## Create a custom model Custom inference models are user-created, pre-trained models (made up of a collection of files) uploaded to DataRobot via the Custom Model Workshop. You can assemble custom inference models in either of the following ways: - Create a custom modelwithoutproviding the model requirements andstart_server.shfile on theAssembletab. This type of custom modelmustuse a drop-in environment. Drop-in environments contain the requirements andstart_server.shfile used by the model. They areprovided by DataRobotin the Custom Model Workshop. You can alsocreate your owndrop-in custom environment. - Create a custom modelwiththe model requirements andstart_server.shfile on theAssembletab. This type of custom model can be paired with acustomordrop-inenvironment. [Create a custom model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html) ### (Optional) Create a custom model environment If you decide to use a custom environment or a custom drop-in environment, you must create that environment in the Custom Model Workshop. You can reuse these environments for other custom models. You can assemble custom model environments in either of the following ways: - Create a custom drop-in environmentwiththe model requirements andstart_server.shfile for the model. DataRobot provides severaldefault drop-in environmentsin the Custom Model Workshop. - Create a custom environmentwithoutthe model requirements andstart_server.shfile. Instead, you must provide the requirements and astart_server.shfile in the model folder for the custom model you intend to use with this environment. [Create a custom model environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html) ## Prepare the custom model Before adding custom models and environments to DataRobot, you must prepare and structure the files required to run them successfully. The tools and templates necessary to prepare custom models are hosted in the [DataRobot User Models GitHub Repository](https://github.com/datarobot/datarobot-user-models) (Log in to GitHub before clicking this link.). Once you verify the model's files and folder structure, you can proceed to test the model. [Prepare a custom model](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/index.html) ### (Optional) Test locally The [DataRobot Model Runner](https://pypi.org/project/datarobot-drum/) is a tool you can use to work locally with Python, R, and Java custom models. It can verify that a custom model can run and make predictions before you add it to DataRobot. However, this testing is only for development purposes, and DataRobot recommends that you use the Custom Model Workshop to test any model you intend to deploy. [Test a custom model locally](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-local-test.html) ### Test in DataRobot Testing the custom model in the Custom Model Workshop ensures that the model is functional before deployment. These tests use the model environment to run the model and make predictions with test data. > [!NOTE] Note > While you can deploy your custom inference model without testing, DataRobot strongly recommends that you ensure your model passes testing in the Custom Model Workshop before deployment. [Test a custom model in DataRobot](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-test.html) ## Register the custom model After successfully creating and testing a custom inference model in the Custom Model Workshop, you can add it to the Model Registry as a deployment-ready model package. [Register a custom model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-reg.html) ## Deploy the custom model After you register a custom inference model in the Model Registry, you can deploy it. Deployed custom models make predictions using API calls to a dedicated prediction server managed by DataRobot. [Deploy a custom model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-custom-inf-model.html). --- # Custom model in a PPS URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-workflows/cus-model-pps-env.html > How to deploy a custom model in a Portable Prediction Server. # Deploy a custom model in a Portable Prediction Server Custom inference models allow you to bring your pre-trained models into DataRobot through the Custom Model Workshop. DataRobot supports custom models built in various programming languages, including Python, R, and Java. Once you've created a custom model in DataRobot, you can deploy it to a containerized DataRobot prediction environment called a Portable Prediction Server (PPS). To deploy a custom model to a PPS, you can prepare and test it in the Custom Model Workshop, and then add it to the Model Registry. You can then deploy the custom model using a PPS bundle, which includes everything you need to deploy the model externally while monitoring it alongside models deployed within DataRobot. To create and deploy a custom model in a PPS, follow the workflow outlined below: ``` graph TB A[Create a custom model] --> B{Custom model environment?} B --> |Yes|C[Create a custom model environment] B --> |No|D[Prepare the custom model]; C --> D D --> E{Test locally?} E --> |No|H[Test the custom model in DataRobot] E --> |Yes|F[Install the DataRobot Model Runner] F --> G[Test the custom model locally] G --> H H --> I[Register the custom model] I --> J{Create an external prediction environment?} J --> |No|L[Deploy the custom model to an external prediction environment] J --> |Yes|K[Add an external prediction environment] K --> L L --> M[Deploy the custom model to a PPS] ``` ## Create a custom model Custom inference models are user-created, pre-trained models (made up of a collection of files) uploaded to DataRobot via the Custom Model Workshop. You can assemble custom inference models in either of the following ways: - Create a custom modelwithoutproviding the model requirements andstart_server.shfile on theAssembletab. This type of custom modelmustuse a drop-in environment. Drop-in environments contain the requirements andstart_server.shfile used by the model. They areprovided by DataRobotin the Custom Model Workshop. You can alsocreate your owndrop-in custom environment. - Create a custom modelwiththe model requirements andstart_server.shfile on theAssembletab. This type of custom model can be paired with acustomordrop-inenvironment. [Create a custom model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html) ### (Optional) Create a custom model environment If you decide to use a custom environment or a custom drop-in environment, you must create that environment in the Custom Model Workshop. You can reuse any environments you create this way for other custom models. You can assemble custom model environments in either of the following ways: - Create a custom drop-in environmentwiththe model requirements andstart_server.shfile for the model. - Create a custom environmentwithoutthe model requirements andstart_server.shfile. Instead, you must provide the requirements andstart_server.shfile in the model folder for the custom model you intend to use with this environment. [Create a custom model environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html) ## Prepare the custom model Before adding custom models and environments to DataRobot, you must prepare and structure the files required to run them successfully. The tools and templates necessary to prepare custom models are hosted in the [DataRobot User Models GitHub Repository](https://github.com/datarobot/datarobot-user-models) (Log in to GitHub before clicking this link.). Once you verify the model's files and folder structure, you can proceed to test the model. [Prepare a custom model](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/index.html) ### (Optional) Test locally The [DataRobot Model Runner](https://pypi.org/project/datarobot-drum/) is a tool you can use to work locally with Python, R, and Java custom models. It can verify that a custom model can run and make predictions before you add it to DataRobot. However, this testing is only for development purposes, and DataRobot recommends that you use the Custom Model Workshop to test any model you intend to deploy. [Test a custom model locally](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-local-test.html) ### Test in DataRobot Testing the custom model in the Custom Model Workshop ensures that the model is functional before deployment. These tests use the model environment to run the model and make predictions with test data. > [!NOTE] Note > While you can deploy your custom inference model without testing, DataRobot strongly recommends that you ensure your model passes testing in the Custom Model Workshop before deployment. [Test a custom model in DataRobot](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-test.html) ## Register the custom model After successfully creating and testing a custom inference model in the Custom Model Workshop, you can add it to the Model Registry as a deployment-ready model package. [Register a custom model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-reg.html) ## Deploy the custom model externally to a PPS The custom model Portable Prediction Server (PPS) is a solution for deploying a custom model to an external prediction environment. The PPS is a downloadable tarball containing a deployed custom model, a custom model environment, and the MLOps monitoring agent. Once running, the PPS container serves predictions via the DataRobot API. ### (Optional) Add an external prediction environment To create an MLOps custom model deployment compatible with the PPS bundle, you must add the custom model package to an external prediction environment. Create an external prediction environment if you don't already have one in DataRobot. [Add an external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html) ### Deploy the custom model package to an external prediction environment To create an MLOps custom model deployment with an external prediction environment, deploy your custom model package to an external prediction environment. [Deploy a custom model to an external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-custom-inf-model.html) ### Deploy the custom model to a PPS The custom model PPS bundle is provided for any custom model tagged as having an [external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html) in the deployment inventory. You can download the custom model PPS bundle to deploy and monitor the custom model. [Deploy a custom model to a PPS](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/custom-pps.html) --- # DataRobot model in a DataRobot environment URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-workflows/dr-model-dr-env.html > How to deploy a DataRobot model in a DataRobot environment. # Deploy a DataRobot model in a DataRobot environment DataRobot AutoML models allow you to deploy to a DataRobot-managed prediction environment. This deployment method is the most direct route to making predictions and monitoring, managing, and governing your model in a centralized deployment hub. To create and deploy an AutoML model on DataRobot, follow the workflow outlined below: ``` graph TB A{Deployment method?} --> |Leaderboard|B[Deploy a model from the Leaderboard]; A --> |Model registry|C[Register a model] C --> D[Deploy a model from the Model Registry] ``` ## Deploy a model from the Leaderboard DataRobot AutoML automatically generates models and displays them on the Leaderboard. The [model recommended for deployment](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html) appears at the top of the page. You can deploy this (or any other) model directly from the Leaderboard to start making and monitoring predictions. When you create a deployment from a model, DataRobot automatically creates a model package for the deployed model. You can access the model package at any time in the Model Registry. [Deploy from the Leaderboard](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html#register-and-deploy-a-model) ## Register a model If you don't want to deploy immediately from the Leaderboard, you can add a model package to the Model Registry to deploy later. > [!NOTE] Note > This method allows you to [share a model package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-action.html) or [generate compliance documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-compliance.html) before deploying a model. [Register a model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/dr-model-reg.html) ## Deploy a model from the Model Registry After you've added a model to the Model Registry, you can deploy it at any time to start making and monitoring predictions. [Deploy from the Model Registry](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html#deploy-a-registered-model) --- # DataRobot model in a PPS URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-workflows/dr-model-pps-env.html > How to deploy a DataRobot model in a Portable Prediction Server. # Deploy a DataRobot model in a Portable Prediction Server DataRobot AutoML models can be deployed to a containerized DataRobot prediction environment called a Portable Prediction Server (PPS). To deploy an AutoML model to a PPS, you can build models with AutoML, deploy a chosen model to an external prediction environment, and then deploy the model package in a PPS with monitoring enabled. Once deployed, you can monitor this portable model alongside models deployed in DataRobot prediction environments. To create and deploy an AutoML model in a PPS, follow the workflow outlined below: ``` graph TB A[Register a model] --> B{Create an external prediction environment?} B --> |No|C[Deploy the model to an external prediction environment] B --> |Yes|D[Add an external prediction environment] D --> C C --> E[Deploy the model package to a PPS] ``` ## Register a model DataRobot AutoML automatically generates models and displays them on the Leaderboard. The [model recommended for deployment](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html) appears at the top of the page. You can register this (or any other) model to the Model Registry directly from the Leaderboard. [Register a model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/dr-model-reg.html) ## Deploy the model externally to a PPS The Portable Prediction Server (PPS) is a solution for deploying a DataRobot model to an external prediction environment. You can download the PPS from the developer tools and use it to deploy a model package from the Model Registry. Once running, the PPS installation serves predictions via the DataRobot API. > [!NOTE] Note > Depending on the MLOps configuration for your organization, you may be able to [download the PPS model package from the Leaderboard](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html#leaderboard-download) for external deployment. However, without associating the model package with an external prediction environment, you won't be able to monitor the model's predictions. ### (Optional) Add an external prediction environment To create an MLOps model deployment compatible with the PPS, you must add the model package to an external prediction environment. Create an external prediction environment if you don't already have one in DataRobot. [Add an external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html) ### Deploy the model package to an external prediction environment To create an MLOps deployment with an external prediction environment, deploy a model package to an external prediction environment. [Deploy a model to an external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html#deploy-a-registered-model) ### Deploy the model package to a PPS The model's PPS model package ( `.mlpkg`) file and the command-line snippet used to initiate the PPS with monitoring are provided for any model tagged as having an [external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html) in the deployment inventory. You can download the model's PPS model package and use the provided docker commands to deploy the model with monitoring enabled. [Deploy a model to a PPS](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html) --- # Monitor an external model with the monitoring agent URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-workflows/ext-cus-model-ext-env.html > How to monitor an external model with the monitoring agent. # Monitor an external model with the MLOps agent With DataRobot MLOps you can register an external model, create an external prediction environment, and deploy the model to the external prediction environment you registered. Next, you can install and configure the monitoring agent alongside the external model, establishing a deployment scenario for that external model. Once installed and configured, the monitoring agent allows you to monitor models running externally as an MLOps deployment so that you can take advantage of DataRobot's powerful MLOps model management tools to monitor accuracy and data drift, prediction distribution, latency, and more. To install the MLOps agent to monitor an external model running in a prediction environment outside of DataRobot, follow the workflow outlined below: ``` graph TB A[Decide to monitor an existing external model] --> B[Register an external model package] B --> C{Create an external prediction environment?} C -->|No|E[Deploy the model to an external prediction environment] C --> |Yes|D[Add an external prediction environment] D --> E E --> F[Obtain the MLOps agent tarball and your API key] F --> G[Install and configure the monitoring agent] G --> H[Configure monitoring agent and MLOps library communication] ``` ## Decide to monitor an existing external model The monitoring agent is a solution for monitoring external models on your infrastructure while reporting statistics to DataRobot MLOps. The API used by the monitoring agent allows you to request specific data to report to a deployment you create in DataRobot. For more information, see the MLOps agent overview. [MLOps agent overview](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html) ## Register an external model package To report predictions metrics to an MLOps deployment in DataRobot, you must first register an external model's details as a model package in the DataRobot Model Registry; then, you can create an external MLOps deployment. [Register an external model package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-model-reg.html) ## Add an external prediction environment To create an external deployment, you need an external prediction environment. Create an external prediction environment if you don't already have one in DataRobot. [Add an external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html) ## Deploy the model to an external prediction environment To associate a model running externally with the external model package registered in the Model Registry, you must deploy the model from the Model Registry to an external prediction environment. After deploying this model externally, you can obtain the Model ID and Deployment ID from the external deployment's [Overview tab](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/dep-overview.html#content). The monitoring agent uses the Model ID and Deployment ID to report an external model's data to the deployment in DataRobot MLOps. [Deploy a model to an external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-external-model.html) ## Obtain the MLOps agent tarball and your API key The monitoring agent is a solution for monitoring external models on your infrastructure while reporting statistics to DataRobot MLOps. In the monitoring agent's configuration file, you must provide your MLOps URL and an API key. API keys are the preferred method for authenticating requests to the DataRobot API; they replace the legacy API token method. To use the monitoring agent, you must obtain the MLOps agent tarball and an API key from [DataRobot's developer tools](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html). [Get started with the MLOps agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html#mlops-agent-tarball) ## Install and configure the MLOps agent To monitor externally deployed models, you must implement the following software components included in the MLOps agent tarball download: - The MLOps library: Provides an API to communicate an external model's prediction data to the associated deployment in DataRobot (theexternal deploymentof anexternal modelyou created earlier). The function calls provided by the MLOps library allow you to request specific data that you want to report, including prediction time, the number of predictions, and other metrics and deployment statistics. The MLOps library writes this data to a spooler (or buffer) channel, from which the data can then be sent to DataRobot MLOps by either the monitoring agent or other MLOps library method calls. Libraries are available in Python 2, Python 3, and Java. - The MLOps agent: Monitors the spooler (or buffer) channel in a location you define when youconfigure MLOps agent and library communication. The MLOps agent reads data from the spooler and reports to the associated deployment in DataRobot (theexternal deploymentof anexternal modelyou created earlier). Depending on your configuration, the agent can read and report this data manually or automatically. [Install and configure the MLOps agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent.html) ## Configure the MLOps agent and library spooler The MLOps library communicates with the monitoring agent through a spooler, so it's essential that the library and the agent have matching spooler configurations. Some spooler configuration settings are required, and some are optional. You can configure these settings programmatically; settings configured through environment variables take precedence over those defined in configuration files. [Configure the monitoring agent and MLOps library spooler](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html) --- # Scoring Code in an external environment URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-workflows/ext-dr-model-ext-env.html > How to deploy an exported DataRobot model's Scoring Code in an external environment. # Deploy Scoring Code in an external environment With DataRobot MLOps you can register a DataRobot model, create a prediction environment, and deploy that model's Scoring Code package to an external prediction environment, establishing a deployment scenario for that model outside DataRobot. You can download the monitoring agent packaged with [Scoring Code](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/index.html) from a deployment's Portable Predictions tab or the Deployments inventory. > [!NOTE] Note > To access the Scoring Code package, make sure you train your model with Scoring Code enabled. Additionally, this package is only compatible with models running at the command line; it doesn't support models running on the [Portable Prediction Server](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html). The monitoring agent packaged with the Scoring Code JAR file is configured for the deployment, allowing you to quickly integrate the agent to report model monitoring statistics back to DataRobot MLOps. To create and deploy a Scoring Code enabled AutoML model in an external environment, follow the workflow outlined below: ``` graph TB A[Select a Scoring Code enabled model] --> B[Register the model] B --> C{Create an external prediction environment?} C --> |No|D[Deploy the model to an external prediction environment] C --> |Yes|E[Add an external prediction environment] E --> D D --> F[Download the Java Scoring Code and monitoring agent package] ``` ## Select a Scoring Code enabled model Only models compatible with scoring code (and trained with Scoring Code enabled) provide Scoring Code download as a Portable Prediction option. Scoring Code allows you to export DataRobot's AutoML-generated models as JAR files that you can use outside the platform. DataRobot automatically runs code generation for qualifying models and indicates Scoring Code availability with an [indicator badge](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/leaderboard-ref.html#tags-and-indicators) on the Leaderboard. [Select a Scoring Code enabled model](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/index.html) ## Register the model DataRobot AutoML automatically generates models and displays them on the Leaderboard. The [model recommended for deployment](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html) appears at the top of the page. To obtain the Scoring Code you need for this process, you can register this or any other model from the Leaderboard as long as the model has the Scoring Code indicator. [Register a model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/dr-model-reg.html) ## Deploy the model's Scoring Code externally To download a model's scoring code with the monitoring agent included and preconfigured, you must create an external MLOps deployment. ### Add an external prediction environment To create an external deployment, you need an external prediction environment. Create an external prediction environment if you don't already have one in DataRobot. [Add an external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html) ### Deploy the model to an external prediction environment Once you've added an external prediction environment, deploy your Scoring Code enabled model to that external prediction environment. [Deploy a model to an external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html#deploy-a-registered-model) ### Download the Java Scoring Code and monitoring agent package You can download the monitoring agent packaged with [Scoring Code](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/index.html) from a deployment's Portable Predictions tab or the Deployments inventory. The monitoring agent that comes packaged with the Scoring Code JAR file is already configured for the deployment, allowing you to quickly integrate the agent from the command line using a snippet provided on the Scoring Code download page. [Download the Scoring Code package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent-sc.html) --- # Deployment workflows URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-workflows/index.html > An overview of the most common DataRobot deployment workflows for various model and environment type combinations. # Deployment workflows DataRobot's MLOps monitoring is available for any models deployed in DataRobot prediction environments (including models on your own infrastructure using a Portable Prediction Server). With DataRobot MLOps, you can deploy models written in any open-source language or library and expose a production-quality REST API to support real-time or batch predictions. Custom inference models allow you to bring pre-trained models into DataRobot to make monitored predictions alongside DataRobot's models. In addition, you can configure monitoring for models running in external prediction environments with the MLOps agent. The workflows below provide high-level overviews of the most common deployment scenarios, including links to the relevant documentation for each step. ## Workflow types With the workflows provided for the common model and environment combinations below, you can learn to deploy DataRobot AutoML models and custom inference models to DataRobot prediction environments, either within DataRobot or containerized for external deployment. In addition, with the monitoring agent, you can monitor models deployed in completely external prediction environments: | Model Type | Environment Type | Workflow | | --- | --- | --- | | DataRobot model | DataRobot prediction environment | How to deploy a DataRobot model in a DataRobot prediction environment. | | DataRobot model | Portable Prediction Server | How to deploy a DataRobot model in a Portable Prediction Server (PPS). | | Custom model | DataRobot prediction environment | How to deploy a custom model in a DataRobot prediction environment. | | Custom model | Portable Prediction Server | How to deploy a custom model in a Portable Prediction Server (PPS). | | Scoring Code | External prediction environment | How to deploy exported DataRobot model Scoring Code in an external environment with monitoring agent enabled. | | External model | External prediction environment | How to deploy an external model in an external prediction environment with monitoring agent enabled. | ## Model types The model types referenced in the deployment workflows are defined below: | Model type | Description | | --- | --- | | DataRobot model | A standard DataRobot model. | | Custom model | An external (Python, Java, or R) model assembled in the Custom Model Workshop. | | Scoring Code | A method for downloading select DataRobot models from the Leaderboard for external deployments. Models downloaded this way are packaged as a Java Archive (JAR) file containing Java prediction calculation logic identical to the DataRobot API's calculation logic. However, Scoring Code predictions are made using a command-line interface (CLI) instead of API calls, allowing you to make low-latency predictions. | | External (remote) model | A model completely external to DataRobot, making predictions on local infrastructure or any other external environment. These models can be monitored by the MLOps agent, and deployment information can be reported to DataRobot MLOps. | ## Prediction environment types The prediction environments referenced in the deployment workflows are defined below: | Prediction environment type | Description | Evaluation | | --- | --- | --- | | DataRobot prediction environment | The default DataRobot prediction environment on DataRobot infrastructure. | Provides the most straightforward deployment, prediction, monitoring, and model replacement processes. However, predictions are subject to network performance limitations. | | Portable Prediction Server | A containerized (with all resources required to run on any infrastructure) DataRobot prediction environment for DataRobot models to make predictions on your infrastructure with MLOps monitoring. | You can make API-based predictions on local infrastructure to improve performance for low-latency predictions. However, the deployment, prediction, monitoring, and model replacement processes are more complex. | | Custom model Portable Prediction Server | A containerized (with all resources required to run on any infrastructure) DataRobot prediction environment for custom models to make predictions on your infrastructure with MLOps monitoring. The custom model PPS bundle contains a deployed custom model, a custom environment, and the monitoring agent. | You can make API-based predictions on local infrastructure to improve performance for low-latency predictions. However, the deployment, prediction, monitoring, and model replacement processes are more complex. | | External prediction environment | A prediction environment completely external to DataRobot and used to make predictions monitored by the monitoring agent and reported to DataRobot MLOps. | External predictions or Scoring Code predictions can be made on local infrastructure to improve performance for low-latency predictions. However, the deployment, prediction, monitoring, and model replacement processes are more complex. | --- # Register external models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-model-reg.html > Register an external model package in the Model Registry. The Model Registry is an archive of your model packages where you can also deploy and share the packages. # Register external models To register an external model monitored by the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html), add an external model as a registered model or version: 1. On theModel Registry > Registered Modelspage, clickRegister New Model > External model. 2. In theRegister new external modeldialog box, configure the following: FieldDescriptionModel version nameThe name of the model to be registered as a model version.Model version description (optional)Information to describe the model to be added as a registered model version.Model location (optional)The location of the model running outside of DataRobot. Describe the location as a filepath, such as folder1/opt/model.tar.Build EnvironmentThe programming language in which the model was built.Training data (optional)The filename of the training data, uploaded locally or via theAI Catalog. ClickClear selectionto upload and use a different file.Holdout data (optional)The filename of the holdout data, uploaded locally or via theAI Catalog. Use holdout data to set anaccuracy baselineand enable support for target drift and challenger models.TargetThe dataset's column name that the model will predict on.Prediction typeThe type of prediction the model makes. Depending on the prediction type, you must configure additional settings:Regression: No additional settings.Binary: For a binary classification model, enter thePositive classandNegative classlabels and a predictionThreshold.Multiclass: For a multiclass classification model, enter or upload (.csv, .txt) theTarget classesfor your target, one class per line. To ensure that the classes are applied correctly to your model's predictions, the classes should be in the same order as your model's predicted class probabilities.Multilable: For a multilabel model, enter or upload (.csv, .txt) theTarget labelsfor your target, one label per line. To ensure that the labels are applied correctly to your model's predictions, the labels should be in the same order as your model's predicted label probabilities.Text Generation:Premium feature. For more information, seeMonitoring support for generative models.Prediction columnThe column name in the holdout dataset containing the prediction result. If registering atime seriesmodel, select theThis is a time series modelcheckbox and configure the following fields: FieldDescriptionForecast date featureThe column in the training dataset that contains date/time values used by DataRobot to detect the range of dates (the valid forecast range) available for use as the forecast point.Date/time formatThe format used by the date/time features in the training dataset.Forecast point featureThe column in the training dataset that contains the point from which you are making a prediction.Forecast unitThe time unit (seconds, days, months, etc.) that comprise thetime step.Forecast distance featureThe column in the training dataset containing a unique time step—a relative position—within the forecast window. A time series model outputs one row for each forecast distance.Series identifier (optional, used formultiseries models)The column in the training dataset that identifies which series each row belongs to. Finally, configure the registered model settings: FieldDescriptionRegister modelSelect one of the following:Register new model:Create a new registered model. This creates the first version (V1).Save as a new version to existing model:Create a version of an existing registered model. This increments the version number and adds a new version to the registered model.Registered model name / Registered ModelDo one of the following:Registered model name:Enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, theModel registration failedwarning appears.Registered Model:Select the existing registered model you want to add a new version to.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister a new model.Optional settingsVersion descriptionDescribe the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add itemand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags do not apply to the registered model, just the versions within. Tags added when registering a new model are applied toV1. 3. Once all fields for the external model are defined, clickRegister. ## Set an accuracy baseline To set an accuracy baseline for external models (which enables target drift and challenger models when deployed), you must provide holdout data. This is because DataRobot cannot use the model to generate predictions that typically serve as a baseline, as the model is hosted in a remote prediction environment outside of the application. Provide holdout data when registering an external model package and specify the column containing predictions. --- # Manage prediction environments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env-manage.html > On the Prediction Environments page, you can edit, delete, or share external prediction environments. You can also deploy models to external prediction environments. # Manage predictions environments Models that run on your own infrastructure (outside of DataRobot) may be run in different environments and can have differing deployment permissions and approval processes. For example, while any user may have permission to deploy a model to a test environment, deployment to production may require a strict approval workflow and only be permitted by those authorized to do so. Prediction environments support this deployment governance by grouping deployment environments and supporting grouped deployment permissions and approval workflows. On the Deployments > Prediction Environments page, you can edit, delete, or share external prediction environments. You can also deploy models to external prediction environments. ## Edit a prediction environment To edit the prediction environment details you set when you created the environment and to assign a Service Account, navigate to the Deployments > Prediction Environments page and click the row containing the prediction environment you want to edit: - Name: Update the external prediction environment name you set when creating the environment. - Description: Update the external prediction environment description or add one if you haven't already. - Platform: Update the external platform you selected when creating the external prediction environment. - Service Account: Select the account that should have access to each deployment within this prediction environment. Only owners of the current prediction environment are available in the list of service accounts. > [!NOTE] Note > DataRobot recommends using an administrative service account as the account holder (an account that has access to each deployment that uses the configured prediction environment). ## Share a prediction environment The sharing capability allows [appropriate user roles](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#custom-model-and-environment-roles) to grant permissions for prediction environments. When you have created a prediction environment and want to share it with others, select Share ( [https://docs.datarobot.com/en/docs/images/icon-share.png](https://docs.datarobot.com/en/docs/images/icon-share.png)) from the dashboard. This takes you to the sharing window, which lists each associated user and their role. To remove a user, click the X button to the right of their role. To re-assign a user's role, click on the assigned role and assign a new one from the dropdown. To add a new user, enter their username in the Share with field and choose their role from the dropdown. Then click Share. This action initiates an email notification. ## Delete a prediction environment To delete a prediction environment, take the following steps: 1. Navigate to theDeployments > Prediction Environmentspage. 2. Next to the prediction environment you want to delete, click the delete icon. 3. In theDeletedialog box: --- # Add external prediction environments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html > You can manage and control user access to environments on the prediction environment dashboard and specify the prediction environment for any deployment. # Add external prediction environments Models that run on your own infrastructure (outside of DataRobot) may be run in different environments and can have differing deployment permissions and approval processes. For example, while any user may have permission to deploy a model to a test environment, deployment to production may require a strict approval workflow and only be permitted by those authorized to do so. External prediction environments support this deployment governance by grouping deployment environments and supporting grouped deployment permissions and approval workflows. ## Add an external prediction environment On the Prediction Environments page, you can review the DataRobot prediction environments available to you and create external prediction environments for both DataRobot models running on the [Portable Prediction Server](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html) and remote models monitored by the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html). To deploy models on external infrastructure, you create a custom external prediction environment: 1. ClickDeployments > Prediction Environmentsand then click+ Add prediction environment. 2. In theAdd prediction environmentdialog box, complete the following fields: FieldDescriptionNameEnter a descriptive prediction environment name.Description(Optional) Enter a description of the external prediction environment.PlatformSelect the external platform on which the model is running and making predictions. 3. UnderSupported Model Formats, select one or more formats to control which models can be deployed to the prediction environment, either manually or using the management agent. The available model formats areDataRobotorDataRobot Scoring Code,External Model, andCustom Model. ImportantYou can only select one ofDataRobotorDataRobot Scoring Code. 4. (Optional) If you want to manage your external model with DataRobot MLOps, clickUse Management Agentto allow theMLOps Management Agentto automate the deployment, replacement, and monitoring of models in this prediction environment. 5. Once you configure the environment settings, clickAdd environment. The environment is now available from the Prediction Environments page. ## Select a prediction environment for a deployment After you add a prediction environment to DataRobot, you can [deploy a model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/index.html) and use the prediction environment for the deployment. Specify the prediction environment in the Prediction History and Service Health section: > [!NOTE] Prediction environments for external models > External models run outside DataRobot and must be deployed to an external prediction environment. Do not deploy external models to a DataRobot Serverless prediction environment. > [!WARNING] Changing the prediction environment > After you specify a prediction environment and create the deployment, you cannot change the prediction environment. --- # Prepare external models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/index.html > Prepare to create deployments from external models # Prepare for external model deployment External prediction environments and model packages allow you to deploy external (or remote) models to DataRobot. These models can make predictions on local infrastructure or any other external environment while DataRobot performs monitoring and management through the MLOps agents. | Topic | Describes | | --- | --- | | Add an external prediction environment | How to set up prediction environments on your own infrastructure, group prediction environments, and configure permissions and approval workflows. | | Manage external prediction environments | How to view, edit, delete, and share external prediction environments, or deploy models to external prediction environments. | | Register external models | How to register external models in the Model Registry. | --- # Deployment URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/index.html > Use DataRobot MLOps to deploy DataRobot models, as well as custom and external models written in languages like Python and R, onto runtime environments. # Deployment With MLOps, the goal is to make model deployment easy. Regardless of your role—a business analyst, data scientist, data engineer, or member of an Operations team— you can easily create a deployment in MLOps. Deploy models built in DataRobot and those written in various programming languages like Python and R. The following sections describe how to deploy models to a production environment of your choice and use MLOps to [monitor](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/index.html) and [manage](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/index.html) those models. See the associated [deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/index.html#feature-considerations) and [custom model deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/index.html#feature-considerations) considerations for additional information. | Topic | Describes | | --- | --- | | Deployment workflows | How to deploy and monitor DataRobot AutoML models, custom inference models, and external models in various prediction environments. | | Register models | How to register DataRobot AutoML models, custom inference models, and external models in the Model Registry. | | Prepare custom models for deployment | How to create, test, and prepare custom inference models for deployment. | | Prepare for external model deployment | How to create and manage external models and prediction environments in preparation for deployment. | | Manage prediction environments | How to view DataRobot prediction environments and create, edit, delete, or share external prediction environments. | | Deploy models | How to deploy DataRobot models, custom inference models, and external models to DataRobot MLOps. | | MLOps agents | How to configure the monitoring and management agent for external models. | ## Feature considerations When curating a prediction request/response dataset from an external source: - Include the 25 most important features. - Follow the CSVfile size requirements. - For classification projects, classes must have a value of 0 or 1, or be text strings. Additionally, note that: - Self-Managed AI Platform only: By default, the 25 most important features and the target are tracked for data drift. - For real-time, deployment predictions, the maximum payload size is 50MB for both Dedicated and Serverless prediction environments. - TheMake Predictionstab is not available for external deployments. - DataRobot deployments only track predictions made against dedicated prediction servers bydeployment_id. - The first 1,000,000 predictions per deployment per hour are tracked for data drift analysis and computed for accuracy. Further predictions within an hour where this limit has been reached are not processed for either metric. However, there is no limit on predictions in general. - If you score larger datasets (up to 5GB), there will be a longer wait time for the predictions to become available, as multiple prediction jobs must be run. If you choose to navigate away from the predictions interface, the jobs will continue to run. - After making prediction requests, it can take 30 seconds or so for data drift and accuracy metrics to update. Note that the speed at which the metrics update depends on the model type (e.g., time series), the deployment configuration (e.g., segment attributes, number of forecast distances), and system stability. - DataRobot recommends that you do not submit multiple prediction rows that use the same association ID—an association ID is auniqueidentifier for a prediction row. If multiple prediction rows are submitted, only the latest prediction uses the associated actual value. All prior prediction rows are, in effect, unpaired from that actual value. Additionally,allpredictions made are included in data drift statistics, even the unpaired prediction rows. - If you want to write back your predictions to a cloud location or database, you must use thePrediction API. ### Time series deployments - To make predictions with a time series deployment, the amount of history needed depends on the model used: - ARIMA family and non-ARIMA cross-series models do not supportbatch predictions. - Classic only: The ability to create a job definition for all ARIMA and non-ARIMA cross-series models is disabled whenEnable cross-series feature generationis enabled. - All other time series models support batch predictions. For multiseries, input data must be sorted by series ID and timestamp. - There is no data limit for time series batch predictions on supported models except that a single series cannot exceed 50MB. - When scoring regression time series models usingintegrated enterprise databases, you may receive a warning that the target database is expected to contain the following column, which was not found:DEPLOYMENT_APPROVAL_STATUS. The column, which is optional, records whether the deployed model has been approved by an administrator. If your organization has configured adeployment approval workflow, you can: After taking either of the above actions, run the prediction job again, and the approval status appears in the prediction results. If you are not recording approval status, ignore the message, and the prediction job continues. - To ensure DataRobot can process your time series data fordeployment predictions, configure the dataset to meet the following requirements: For dataset examples, see therequirements for the scoring dataset. ### Multiclass deployments - Multiclass deployments of up to 100 classes support monitoring for target, accuracy, and data drift. - Multiclass deployments of up to 100 classes support retraining. - Multiclass deployments created before Self-Managed AI Platform version 7.0 with feature drift enabled don't have historical data for feature drift of the target; only new data is tracked. - DataRobot uses holdout data as a baseline for target drift. As a result, for multiclass deployments using certain datasets, rare class values could be missing in the holdout data and in the baseline for drift. In this scenario, these rare values are treated as new values. ### Challengers - To enableChallengersand replay predictions against them, the deployed model must support target drift trackingandnot be aFeature DiscoveryorUnstructured custom inferencemodel. - To replay predictions against Challengers, you must be in theorganizationassociated with the deployment. This restriction also applies to deploymentowners. ### Prediction results cleanup For each deployment, DataRobot periodically performs a cleanup job to delete the deployment's predicted and actual values from its corresponding prediction results table in Postgres. DataRobot does this to keep the size of these tables reasonable while allowing you to consistently generate accuracy metrics for all deployments and schedule replays for challenger models without the danger of hitting table size limits. The cleanup job prevents a deployment from reaching its "hard" limit for prediction results tables; when the table is full, predicted and actual values are no longer stored, and additional accuracy metrics for the deployment cannot be produced. The cleanup job triggers when a deployment reaches its "soft" limit, serving as a buffer to prevent the deployment from reaching the "hard" limit. The cleanup prioritizes deleting the oldest prediction rows already tied to a corresponding actual value. Note that the aggregated data used to power data drift and accuracy over time are unaffected. ### Managed AI Platform Managed AI Platform users have the following hourly limitations. Each deployment is allowed: - Data drift analysis: 1,000,000 predictions or, for each individual prediction instance, 100MB of total prediction requests. If either limit is reached, data drift analysis is halted for the remainder of the hour. - Prediction row storage: the first 100MB of total prediction requests per deployment per each individual prediction instance. If the limit is reached, no prediction data is collected for the remainder of the hour. --- # Agent event log URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/agent-event-log.html > On a deployment's Service Health tab, you can view Management and Monitoring events. # Agent event log On a deployment's Service Health tab, under Recent Activity, you can view Management events (e.g., deployment actions) and Monitoring events (e.g., spooler channel and rate limit events). The Monitoring events can help you quickly diagnose MLOps agent issues. The spooler channel error events can help you diagnose and fix [spooler configuration](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html) issues. The rate limit enforcement events can help you identify if service health stats, data drift values, or accuracy values aren't updating because you exceeded the API request rate limit. ## Enable agent event log To view Monitoring events, you must provide a `predictionEnvironmentID` in the agent configuration file ( `conf\mlops.agent.conf.yaml`) as shown below. If you haven't already installed and configured the MLOps agent, see the [Installation and configuration](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent.html) guide. | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | # This file contains configuration for the MLOps agent # URL to the DataRobot MLOps service mlopsUrl: "https://" # DataRobot API token apiToken: "" # Execute the agent once, then exit runOnce: false # When dryrun mode is true, do not report the metrics to MLOps service dryRun: false # When verifySSL is true, SSL certification validation will be performed when # connecting to MLOps DataRobot. When verifySSL is false, these checks are skipped. # Note: It is highly recommended to keep this config variable as true. verifySSL: true # Path to write agent stats statsPath: "/tmp/tracking-agent-stats.json" # Prediction Environment served by this agent. # Events and errors not specific to a single deployment are reported against this Prediction Environment. predictionEnvironmentId: "" # Number of times the agent will retry sending a request to the MLOps service on failure. httpRetry: 3 # Http client timeout in milliseconds (30sec timeout) httpTimeout: 30000 # Number of concurrent http request, default=1 -> synchronous mode; > 1 -> asynchronous httpConcurrentRequest: 10 # Number of HTTP Connections to establish with the MLOps service, Default: 1 numMLOpsConnections: 1 # Comment out and configure the lines below for the spooler type(s) you are using. # Note: the spooler configuration must match that used by the MLOps library. # Note: Spoolers must be set up before using them. # - For the filesystem spooler, create the directory that will be used. # - For the SQS spooler, create the queue. # - For the PubSub spooler, create the project and topic. # - For the Kafka spooler, create the topic. channelConfigs: - type: "FS_SPOOL" details: {name: "filesystem", directory: "/tmp/ta"} # - type: "SQS_SPOOL" # details: {name: "sqs", queueUrl: "your SQS queue URL", queueName: ""} # - type: "RABBITMQ_SPOOL" # details: {name: "rabbit", queueName: , queueUrl: "amqp://", # caCertificatePath: "", # certificatePath: "", # keyfilePath: ""} # - type: "PUBSUB_SPOOL" # details: {name: "pubsub", projectId: , topicName: , subscriptionName: } # - type: "KAFKA_SPOOL" # details: {name: "kafka", topicName: "", bootstrapServers: ",,..."} # The number of threads that the agent will launch to process data records. agentThreadPoolSize: 4 # The maximum number of records each thread will process per fetchNewDataFreq interval. agentMaxRecordsTask: 100 # Maximum number of records to aggregate before sending to DataRobot MLOps agentMaxAggregatedRecords: 500 # A timeout for pending records before aggregating and submitting agentPendingRecordsTimeoutMs: 5000 | ## View agent activity To view the agent event log, on the Service Health tab, navigate to the Recent Activity section. The most recent events appear at the top of the list. ### Event information Each event shows the time it occurred, a description, and an icon indicating its status: | Status icon | Description | | --- | --- | | Green / Passing | No action needed. | | Red / Failing | Immediate action needed. | | Gray / Informational | Details a deployment action (e.g., deployment launch has started). | ### Recent activity log In the Recent Activity log, you can filter the activity list and access additional information: | | Element | Description | | --- | --- | --- | | (1) | Filters | Set the Event Type filter to limit the list to Management events (e.g., deployment actions) or Monitoring events (e.g., spooler channel and rate limit events). | | (2) | Events | Click an event in the log to view additional Event Details for that event. The Event Details include the Event name, a Timestamp, a Channel Name, the event Type, the associated Prediction Environment, and an event Message. | | (3) | Event Details | Click the Prediction Environment name to open the Prediction Environments tab, where you can create, manage, and share prediction environments. | ### Monitoring events Monitoring events can help you diagnose and fix MLOps agent issues. Currently, the following events can appear in the Recent Activity log: | Event | Description | | --- | --- | | Monitoring Spooler Channel | Identify spooler configuration issues so you can resolve them. | | Rate limit was enforced | Identify when an operation exceeds API request rate limits, resulting in updates to service health stats, data drift calculations, or accuracy calculations stalling. This event reports how long the affected operation is suspended. Rate limits are applied per deployment, per operation. | --- # MLOps agents URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html > Use the MLOPS agents to monitor and manage models running outside of DataRobot MLOps, and report predictions from these models as part of MLOps deployments. Learn about the MLOps agent workflows for DataRobot deployments and for remote deployments. # MLOps agents > [!NOTE] Availability information > The MLOps agent feature is exclusive to DataRobot MLOps. Contact your DataRobot representative for information on enabling it. DataRobot MLOps provides powerful tools for tracking and managing models in production. But what if you already have—or need to have—deployments running in your own environment? How can you monitor external models that may have intermittent or no connectivity and so may report predictions sporadically? The MLOps agents allow you to monitor and manage external models—those running outside of DataRobot MLOps. With this functionality, predictions and information from these models can be reported as part of MLOps deployments. You can use the same powerful [model management tools](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html) to monitor accuracy, data drift, prediction distribution, latency, and more, regardless of where the model is running. Data provided to DataRobot MLOps provides valuable insight into the performance and health of those externally deployed models. The MLOps agents provide: - The ability to manage, monitor, and get insight from all model deployments in a single system - API and communications constructed to ensure little or no latency when monitoring external models - Support for deployments that are always connected to the network and the MLOps system, as well as partially or never-connected deployments - The MLOps library (available in Python and Java), used to monitor models written natively in those languages or to report the input and output of a model artifact in any language - Configuration with the Portable Prediction Server See the associated [feature considerations](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html#feature-considerations) for additional information. ## Monitoring agent | Topic | Describes | | --- | --- | | Installation and configuration | How to install and configure the monitoring agent. | | Examples directory | How to access and run monitoring agent code examples. | | Use cases | How to configure the monitoring agent to support various use cases. | | Environment variables | How to configure the monitoring agent environment variables, including those required for a containerized configuration. | | Library and agent spooler configuration | How to configure the MLOps library and agent to communicate through various spoolers (or buffers). | | Download Scoring Code | How to download model Scoring Code packaged with the monitoring agent. | | Monitor external multiclass deployments | How to monitor external multiclass deployments. | ## Management agent | Topic | Describes | | --- | --- | | Installation and configuration | How to install and configure the management agent. | | Configure environment plugins | How to use the example environment plugins as a starting point to configure the management agent for various prediction environments. | | Installation for Kubernetes | How to use a Helm chart to aid in the installation and configuration of the management agent and Kubernetes plugin. | | Deployment status and events | How to monitor the status and health of management agent deployments from the deployment inventory. | | Relaunch deployments | How to relaunch management agent deployments. | | Force delete deployments | How to delete a management agent deployment without waiting for the resolution of the deployment deletion request sent to the management agent. | ## Feature considerations - The MLOps agents run on Linux. - The MLOps agents don't support Windows environments. - The MLOps agents' releases are backward compatible with the last two versions of DataRobot (i.e., the monitoring agent version 9.2 is compatible DataRobot 9.0 and above). --- # Management agent URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/index.html > Automate model deployment to any type of infrastructure and monitor deployment events. # Management agent The MLOps management agent provides a standard mechanism to automate model deployment to any type of infrastructure. It pairs automated deployment with automated monitoring to ease the burden on remote models in production, especially with critical MLOps features such as challenger models and retraining. The agent, accessed from the DataRobot application, ships with an assortment of plugins that support custom configuration. ## Management agent setup To configure the management agent, you must prepare its various components, detailed below: - Register a prediction environment. - Download the agent tarball. - Configure an environment plugin. - Configure the management agent. - Create a deployment. ### Register a prediction environment You can use the management agent to automate the deployment, replacement, and monitoring of models in an external prediction environment. Management agent setup begins with configuring a prediction environment to use with deployments. Before proceeding, register the [prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html) with DataRobot. Once registered, navigate to Deployments > Prediction Environments. Select the prediction environment to use from the list and toggle on Use Management Agent. Once enabled, you must indicate the email address of the management agent service account holder. DataRobot recommends using an administrative service account as the account holder (an account that has access to each deployment that uses the configured prediction environment). ### Download the agent Access the management agent by downloading the MLOps agent tarball and installing it on the remote environment from which you are hosting models to make predictions. You can download it directly from the DataRobot application by clicking on your user icon and navigating to API keys and tools. Under the External Monitoring Agent header, click the download icon. The tarball appears in your browser's downloads bar when complete. ### Configure an environment plugin The management agent translates deployment events (model replacement, deployment launch, etc.) into processes for an environment plugin to run in response to that event. The tarball includes configurable, example environment plugins. These plugins can support various types of infrastructure used by remote models as is; however, you may need to modify these plugins to fully support your particular environment. Initially, you can choose, configure, and potentially modify the plugin that best supports your infrastructure. Advanced users can create new plugins, either completely customized or by using the provided plugins as a starting point. > [!NOTE] Note > The provided management agent plugins are examples. They are not intended to work for all use-cases; however, you can modify them to suit your needs. The MLOps management agent contains the following example plugins: - Docker plugin. - Filesystem plugin. - Kubernetes plugin. - Test plugin. > [!TIP] Tip > The tarball includes README files to help with the installation and configuration of the plugins. For more information, see [Configure management agent environment plugins](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/mgmt-agent-plugins.html). ### Configure the agent After downloading the tarball and configuring an agent plugin, edit the agent's config file: - Provide your DataRobot service URL and API key so the management agent can authenticate to DataRobot. - Provide the prediction environment id so the management agent can access it and any associated deployments. - Indicate which management agent plugin to use. For more information, see [Management agent installation and configuration](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/mgmt-agent-install.html). ### Create a deployment After configuring the prediction environment and the management agent for use, you can create an external deployment with events monitored by the agent. The deployment must use the prediction environment configured in the steps above in order to support the agent's monitoring functionality. To do so, DataRobot recommends [registering an external model package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html#register-external-model-packages) and [deploying it](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-external-model.html#deploy-an-external-model-package) from the Model Registry. Once deployed, you have a deployment fully configured with the management agent, capable of monitoring deployment events and automating actions in response to those events. --- # Force delete deployments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/mgmt-agent-delete.html > Delete a deployment without waiting for the resolution of the deployment deletion request sent to the management agent. # Force delete management agent deployments If the management agent is not running or has errored, you can delete a deployment without waiting for the resolution of the deployment deletion request sent to the management agent. > [!WARNING] Warning > This will remove the deployment from the deployments area for all users. This action cannot be undone. To force the deletion of a management agent deployment without waiting for the resolution of the deletion request sent to the agent, take the following steps: 1. On theDeploymentspage or any tab within a deployment, next to the name of the deployment you want to delete, click theActions menuand clickDelete. 2. In theDelete deploymentdialog box, clickIgnore Management Agent, and then clickDelete deployment. --- # Management agent deployment status and events URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/mgmt-agent-events-status.html > Monitor the status and health of MLOps management agent deployments. # Management agent deployment status and events To monitor the status and health of management agent deployments, you can view the overall deployment status and specific deployment service health events. ## Deployment status When the management agent is performing an action on an external deployment that it is managing, a badge appears under the deployment name in the [deployment inventory](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html), and on any tab within the deployment, to indicate the deployment status. The following four deployment status values are possible when an action is being taken on a deployment managed by the management agent: | Status | Badge | | --- | --- | | LAUNCHING | | | STOPPING | | | MODEL REPLACING | | | ERRORED | | ## Deployment events The management agent sends periodic updates about deployment health and status via the API. These are reported as MLOps events and are listed on the [Service Health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html) page. DataRobot allows you to monitor and work with deployment events for external deployments once set up with the management agent. From one place, you can: | Action | Example use case | | --- | --- | | Record and persist deployment-related events | Recording deployment actions, health changes, state changes, etc. | | View all related events | Auditing deployment events. | | Filter and search events | Viewing all model changes. | | Extract data | Reporting and offline storage. | | Receive notification of certain incidents | Receiving a Slack message for an outage. | | Enforce a retention policy | Ensuring that a log-retention policy is followed (90 days of retention guaranteed; older events may be purged). | To view an overview of deployment events, select the deployment from the inventory and navigate to the [Service Health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html) tab. All events are recorded under the Recent Activity > Agent Activity section: The most recent events are listed at the top of the list. Each event shows the time it occurred, a description, and an icon indicating its status: | Icon | Description | | --- | --- | | Green / Passing | No action needed. | | Yellow / At risk | Concerns found but no immediate action needed; continue monitoring. | | Red / Failing | Immediate action needed. | | Gray / Unknown | Unknown | | Informational | Details a deployment action (e.g., the deployment has launched). | > [!NOTE] Note > The management agent's most recently reported service health status is prioritized. For example, if data drift is green and passing on a deployment, but the management agent delivers an inferior status (red and failing), the list updates to reflect that condition. Select an event row to view its details on the right-side panel. --- # Installation and configuration URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/mgmt-agent-install.html > Install and configure the MLOps management agent. # Management agent installation and configuration The MLOps agent `.tar` file contains all artifacts required to run the management agent. You can run the management agent in either of the following configurations: - Inside a container. - On a host machine, as a standalone process. **Run in a container:** To build and install the management agent container, run the following commands to unpack the tarball in a suitable location and build the container image: tar -zxf datarobot_mlops_package-*.tar.gz cd datarobot_mlops_package-*/ cd tools/bosun_docker/ make build This tags the management agent image with the appropriate version tag and the latest tag. To build the management agent image and run the container such that the management agent is configurable from the command line, run the following: tar -zxf datarobot_mlops_package-*.tar.gz cd datarobot_mlops_package-*/ cd tools/bosun_docker/ make run Enter the mlopsUrl , the apiToken , and the ID of the prediction environment to monitor: Generate MLOps Management-Agent configuration file. Enter DataRobot App URL ( e.g. https://app.datarobot.com ) : > Enter DataRobot API Token: Enter DataRobot Prediction Environment ID: By default, the management agent uses the filesystem plugin. If you want to use a different plugin, you can configure the management agent configuration file to use that plugin and then map it to the container. For example, you can use the following commands to run the management agent with the Kubernetes plugin: cd datarobot_mlops_package-*/ docker run -it \ -v conf/mlops.bosun.conf.yaml:/opt/datarobot/mlops/bosun/conf/mlops.bosun.conf.yaml \ -v conf/plugin.k8s.conf.yaml:/opt/datarobot/mlops/bosun/conf/plugin.k8s.conf.yaml \ datarobot/mlops-management-agent **Run on a host machine:** To install and run the management agent on the host machine, Python 3.7+ and Java 11 must be installed on the system. Then, you can create a Python virtual environment to install the management agent plugins: mkdir /opt/management-agent-demo cd /opt/management-agent-demo python3 -m venv .venv source .venv/bin/activate tar -zxf datarobot_mlops_package-*.tar.gz cd datarobot_mlops_package-*/ pip install lib/datarobot_mlops-*-py2.py3-none-any.whl pip install lib/datarobot_mlops_connected_client-*-py3-none-any.whl pip install lib/datarobot_bosun-*-py3-none-any.whl Configure the management agent by modifying the configuration file: ./conf/mlops.bosun.conf.yaml Start the management agent: ./bin/start-bosun.sh To configure the management agent on the host machine, edit the management agent configuration file, conf/mlops.bosun.conf.yaml : Update the values for mlopsUrl and apiToken . Verify that points to the virtual environment created during installation (e.g., /opt/management-agent-demo/bin ). Specify the Prediction Environment ID at . Uncomment the appropriate command: line in the predictionEnvironments section to use the correct plugin. Ensure you comment out the command: line for any unused plugins. (Optional) You may need to configure the configuration file for the plugin you're using. For more information, see Configure management agent plugins . mlops.bosun.conf.yaml # This file contains configuration for the Management Agent # Items marked "Required" must be set. Other settings can use the defaults set below. # Required. URL to the DataRobot MLOps service. mlopsUrl : "https://" # Required. DataRobot API token. apiToken : "" # When true, verify SSL certificates when connecting to DR app. When false, SSL verification will not be # performed. It is highly recommended to keep this config variable as true. verifySSL : true # Whether to run management agent as the workload coordinator. The default value is true. isCoordinator : true # Whether to run management agent as worker. The default value is true. isWorker : true # When true, start a REST server. This will provide several API endpoints (worker health check enables) serverMode : false # The port to use for the above REST server serverPort : "12345" # The url where to reach REST server, will be use by external configuration services serverAddress : "http://localhost" # Specify the configuration service. This is 'internal' by default and the # workload coordinator and worker are expected to run in the same JVM. # When run in high availability mode, the configuration needs to be provided by # a service such as Consul. configurationService : tag : "tag" type : "internal" connectionDetail : "" # Path to write Bosun stats statsPath : "/tmp/management-agent-stats.json" # HTTP client timeout in milliseconds (30sec timeout). httpTimeout : 30000 # Number of times the agent will retry sending a request to the MLOps service after it receives a failure. httpRetry : 3 # Number of active workers to process management agent commands numActionWorkers : 2 # Timeout in seconds processing active commands, eg. launch, stop, replaceModel actionWorkerTimeoutSec : 300 # Timeout in seconds for requesting status of PE and the deployment statusWorkerTimeoutSec : 300 # How often (in seconds) status worker should update DR MLOps about the status of PE and deployments statusUpdateIntervalSec : 120 # How often (in seconds) to poll MLOps service for new deployment / PE Actions mlopsPollIntervalSec : 60 # Optional: Plugins directory in which all required plugin jars can be found. # If you are only using external commands to run plugin actions then there is # no need to use this option. # pluginsDir: "../plugins/" # Model Connector configuration modelConnector : type : "native" # Scratch place to work on, default "/tmp" scratchDir : "/tmp" # Config file for private / secret configuration, management agent will not read this file, just # forward the filename in configuration, optional secretsConfigFile : "/tmp/secrets.conf" # Python command that implements model connector. # mcrunner is installed as part the bosun python package. You should either # set your PATH to include the location of mcrunner, or provide the full path. command : "/bin/mcrunner" # prediction environments this service will monitor predictionEnvironments : # This Prediction Environment ID matches the one in DR MLOps service - id : "" type : "ExternalCommand" platform : "os" # Enable monitoring for this plugin, so that the MLOps information # (viz, url and token) can be forwarded to plugin, default: False # enableMonitoring : true # Provide the command to run the plugin: # You can either fix PATH to point to where bosun-plugin-runner is located, or # you can provide the full path below. # The filesystem plugin used in the example below if one of the built in plugins provided # by the bosun-plugin-runner command : "/bin/bosun-plugin-runner --plugin filesystem --private-config /plugin.filesystem.conf.yaml" # The following example will run the docker plugin # (one of the built in plugins provided by bosun-plugin runner) # command: "/bin/bosun-plugin-runner --plugin docker --private-config /plugin.docker.conf.yaml" # The following example will run the kubernetes plugin # (one of the built in plugins provided by bosun-plugin runner) # WARNING: this plugin is currently considered ALPHA maturity; please consult your account representative if you # are interested in trying it. # command: "/bin/bosun-plugin-runner --plugin k8s --private-config /plugin.k8s.conf.yaml" # If your plugin was installed as a python module (using pip), you can provide the name # of the module that contains the plugin class. For example --plugin sample_plugin.my_plugin # command: "/bin/bosun-plugin-runner --plugin sample_plugin.my_plugin --private-config /my_config.yaml" # If your plugin is in a directory, you can provide the name of the plugin as the path to the # file that contains your plugin. For example: --plugin sample_plugin/my_plugin.py # command: "/bin/bosun-plugin-runner --plugin sample_plugin/my_plugin.py --private-config /my_config.yaml" # Note: you can control the plugin logging via the --log-config option of bosun-plugin-runner **Run natively in Docker:** To run the management agent natively in Docker, first build the datarobot/mlops-management-agent image from the MLOps agent tarball: make build -C tools/bosun_docker Configure the monitoring agent in Docker, mounted to the default directory or a custom location: To run the management agent with the filesystem plugin and with the configuration mounted to the default directory: docker run \ -v /path/to/mlops.bosun.conf.yaml:/opt/datarobot/mlops/bosun/conf/mlops.bosun.conf.yaml \ -v /path/to/plugin.filesystem.conf.yaml:/opt/datarobot/mlops/bosun/conf/plugin.filesystem.conf.yaml \ datarobot/mlops-management-agent To run the management agent with the filesystem plugin and with agent configuration mounted to a custom location: docker run \ -v /path/to/mlops.bosun.conf.yaml:/var/tmp/mlops.bosun.conf.yaml \ -v /path/to/plugin.filesystem.conf.yaml:/opt/datarobot/mlops/bosun/conf/plugin.filesystem.conf.yaml \ -e MLOPS_AGENT_CONFIG_YAML=/var/tmp/mlops.bosun.conf.yaml \ datarobot/mlops-management-agent To use the Docker-based plugin while also running the management agent in a docker container, you will need to include a few extra options, and you will need to mount in the entire config directory since there are multiple files to modify: $ docker run \ -v ${PWD}/conf/:/opt/datarobot/mlops/bosun/conf/ \ -v /tmp:/tmp \ -v /var/run/docker.sock:/var/run/docker.sock \ --user root \ --network bosun \ datarobot/mlops-management-agent:latest --- # Install the management agent for Kubernetes URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/mgmt-agent-kubernetes.html > Install and configure the MLOps management agent to use a Kubernetes Namespace as a Prediction Environment # Management agent Helm installation for Kubernetes This process provides an example of a management agent use case, using a Helm chart to aid in the installation and configuration of the management agent and the Kubernetes plugin. > [!NOTE] Important > The Kubernetes plugin and Helm chart used in this process are examples; they may need to be modified to suit your needs. ## Overview The MLOps management agent provides a mechanism to automate model deployment to any infrastructure. Kubernetes is a popular solution for deploying and monitoring models outside DataRobot, orchestrated by the management and monitoring agents. To streamline the installation and configuration of the management agent and the Kubernetes plugin, you can use the contents of the `/tools/charts/datarobot-management-agent` directory in the agent tarball. The `/tools/charts/datarobot-management-agent` directory contains the files required for a [Helm chart](https://helm.sh/docs/topics/charts/) that you can modify to install and configure the management agent and its Kubernetes plugin for your preferred cloud environment: Amazon Web Services, Azure, Google Cloud Platform, or OpenShift. It also supports standard Docker Hub installation and configuration. This directory includes the default `values.yaml` file (located at `/tools/charts/datarobot-management-agent/values.yaml` in the agent tarball) and customizable example `values.yaml` files for each environment (located in the `/tools/charts/datarobot-management-agent/examples` directory of the agent tarball). You can copy and update the environment-specific `values.yaml` file you need and use `--values ` to overlay the default values. ### Architecture overviews **General overview:** [https://docs.datarobot.com/en/docs/images/mgmt-agent-helm-arch.png](https://docs.datarobot.com/en/docs/images/mgmt-agent-helm-arch.png) **Detailed overview:** [https://docs.datarobot.com/en/docs/images/mgmt-agent-helm-arch2.png](https://docs.datarobot.com/en/docs/images/mgmt-agent-helm-arch2.png) **Monitoring overview:** [https://docs.datarobot.com/en/docs/images/mgmt-agent-helm-arch3.png](https://docs.datarobot.com/en/docs/images/mgmt-agent-helm-arch3.png) The diagram above shows a detailed view of how the management agent deploys models into Kubernetes and enables model monitoring. **Docker image build overview:** [https://docs.datarobot.com/en/docs/images/mgmt-agent-helm-arch4.png](https://docs.datarobot.com/en/docs/images/mgmt-agent-helm-arch4.png) The diagram above shows the specifics of how DataRobot models are packaged into a deployable image for Kubernetes. This architecture leverages an open-source tool maintained by Google called [Kaniko](https://github.com/GoogleContainerTools/kaniko), designed to build Docker images inside a Kubernetes cluster securely. ## Prerequisites Before you begin, you must build and push the management agent Docker image to a registry accessible by your Kubernetes cluster. If you haven't done this, see the [MLOps management agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/index.html) overview. Once you have a management agent Docker image, set up a Kubernetes cluster with the following requirements: **Software Requirements:** Kubernetes clusters (version v1.21+) Nginx Ingress Docker Registry **Hardware Requirements:** 2+ CPUs 40+ GB of instance storage (image cache) 6+ GB of memory > [!NOTE] Important > All requirements are for the latest version of the management agent. ## Configure software requirements To install and configure the required software resources, follow the processes outlined below: **Kubernetes:** Any Kubernetes cluster running version 1.21 or higher is supported. Follow the documentation for your chosen distribution to create a new cluster. This process also supports OpenShift version 4.8 and above. **Nginx Ingress:** > [!NOTE] Important > If you are using OpenShift, you should skip this prerequisite. OpenShift uses the built-in [Ingress Controller](https://docs.openshift.com/container-platform/latest/networking/ingress-operator.html). Currently, the only supported ingress controller is the open-source [Nginx-Ingress](https://kubernetes.github.io/ingress-nginx/) controller (>=4.0.0). To install Nginx Ingress in your environment, see the Nginx Ingress documentation or try the example script below: ``` # Create a namespace for your ingress resources kubectl create namespace ingress-mlops # Add the ingress-nginx repository helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx helm repo update # Use Helm to deploy an NGINX ingress controller # # These settings should be considered sane defaults to help quickly get you started. # You should consult the official documentation to determine the best settings for # your expected prediction load. With Helm, it is trivial to change any of these # settings down the road. helm install nginx-ingress ingress-nginx/ingress-nginx \ --namespace ingress-mlops \ --set controller.ingressClassResource.name=mlops \ --set controller.autoscaling.enabled=true \ --set controller.autoscaling.minReplicas=2 \ --set controller.autoscaling.maxReplicas=3 \ --set controller.config.proxy-body-size=51m \ --set controller.config.proxy-read-timeout=605s \ --set controller.config.proxy-send-timeout=605s \ --set controller.config.proxy-connect-timeout=65s \ --set controller.metrics.enabled=true ``` **Docker Registry:** This process supports the major cloud vendor's managed registries (ECR, ACR, GCR) in addition to Docker Hub or any standard V2 Docker registry. If your registry requires pre-created repositories (i.e., ECR), you should create the following repositories: datarobot/mlops-management-agent datarobot/mlops-tracking-agent datarobot/datarobot-portable-prediction-api mlops/frozen-models > [!NOTE] Important > You must provide the management agent push access to the `mlops/frozen-model` repo. Examples of several common registry types are provided [below](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/mgmt-agent-kubernetes.html#configure-registry-credentials). If you are using GCR or OpenShift, the path for each Docker repository above must be modified to suit your environment. ## Configure registry credentials To configure the Docker Registry for your cloud solution, follow the relevant process outlined below. The section provides examples for the following registries: - Amazon Elastic Container Registry (ECR) - Microsoft Azure Container Registry (ACR) - Google Cloud Platform Container Registry (GCR) - OpenShift Integrated Registry - Generic Registry (Docker Hub) **ECR:** First, create all required repositories listed above using the ECR UI or using the following command: ``` repos="datarobot/mlops-management-agent datarobot/mlops-tracking-agent datarobot/datarobot-portable-prediction-api mlops/frozen-model" for repo in $repos; do aws ecr create-repository --repository-name $repo done ``` To provide push credentials to the agent, use an [IAM role for the service account](https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts.html): ``` eksctl create iamserviceaccount --approve \ --cluster \ --namespace datarobot-mlops \ --name datarobot-management-agent-image-builder \ --attach-policy-arn arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryPowerUser ``` Next, create a file called `config.json` with the following contents: ``` { "credsStore": "ecr-login" } ``` Use that JSON file to create a `ConfigMap`: ``` kubectl create configmap docker-config \ --namespace datarobot-mlops \ --from-file= ``` Update the `imageBuilder` section of the `values.yaml` file (located at `/tools/charts/datarobot-management-agent/values.yaml` in the agent tarball) to use the `configMap` you created and to configure `serviceAccount` with the IAM role you created: ``` imageBuilder: ... configMap: "docker-config" serviceAccount: create: false name: "datarobot-management-agent-image-builder" ``` **ACR:** First, in your ACR registry, under Settings > Access keys, enable the [Admin usersetting](https://docs.microsoft.com/en-us/azure/container-registry/container-registry-authentication?tabs=azure-cli#admin-account). Then, use one of the generated passwords to create a new secret: ``` kubectl create secret docker-registry registry-creds \ --namespace datarobot-mlops \ --docker-server=.azurecr.io \ --docker-username= \ --docker-password= ``` > [!NOTE] Note > This process assumes you already created the `datarobot-mlops` namespace. Next, update the `imageBuilder` section of the `values.yaml` file (located at `/tools/charts/datarobot-management-agent/values.yaml` in the agent tarball) to use the `secretName` for the secret you created: ``` imageBuilder: ... secretName: "registry-creds" ``` **GCR:** You should use Workload Identity in your GKE cluster to provide GCR push credentials to the Docker image building service. This process consists of the following steps: Enable Workload Identity Migrate existing node pools Authenticate with Workload Identity In this section, you can find the minimal configuration required to complete this guide. First, enable Workload Identity on your cluster and all of your node groups: ``` # Enable workload identity on your existing cluster gcloud container clusters update \ --workload-pool=.svc.id.goog # Enable workload identity on an existing node pool gcloud container node-pools update \ --cluster= \ --workload-metadata=GKE_METADATA ``` When the cluster is ready, create a new IAM Service Account and attach a role that provides all necessary permissions to the image builder service. The image builder service must be able to push new images into GCR, and the IAM Service Account must be able to bind to the GKE ServiceAccount created upon installation: ``` # Create Service Account gcloud iam service-accounts create gcr-push-user # Give user push access to GCR gcloud projects add-iam-policy-binding \ --member=serviceAccount:[gcr-push-user]@.iam.gserviceaccount.com \ --role=roles/cloudbuild.builds.builder # Link GKE ServiceAccount with the IAM Service Account gcloud iam service-accounts add-iam-policy-binding \ --role roles/iam.workloadIdentityUser \ --member "serviceAccount:.svc.id.goog[datarobot-mlops/datarobot-management-agent-image-builder]" \ gcr-push-user@.iam.gserviceaccount.com ``` Finally, update the `imageBuilder` section of the `values.yaml` file (located at `/tools/charts/datarobot-management-agent/values.yaml` in the agent tarball) to create a `serviceAccount` with the `annotations` and `name` created in previous steps: ``` imageBuilder: ... serviceAccount: create: true annotations: { iam.gke.io/gcp-service-account: gcr-push-user@.iam.gserviceaccount.com } name: datarobot-management-agent-image-builder ``` **OpenShift Integrated Registry:** OpenShift provides a [built-in registry solution](https://docs.openshift.com/container-platform/4.8/registry/index.html). This is the recommended container registry if you are using OpenShift. Later in this guide, you are required to push images built locally into the registry. To make this easier, use the following command to expose the registry externally: ``` oc patch configs.imageregistry.operator.openshift.io/cluster --patch '{"spec":{"defaultRoute":true}}' --type=merge ``` See the [OpenShift documentation](https://docs.openshift.com/container-platform/4.8/registry/securing-exposing-registry.html) to learn to log in to this registry to push images to it. In addition, you should create a dedicated Image Builder service account with [permission](https://docs.openshift.com/container-platform/4.8/authentication/managing-security-context-constraints.html) to run as `root` and to [push](https://docs.openshift.com/container-platform/4.8/registry/accessing-the-registry.html) to the integrated Docker registry: ``` oc new-project datarobot-mlops oc create sa datarobot-management-agent-image-builder # Allows the SA to push to the registry oc policy add-role-to-user registry-editor -z datarobot-management-agent-image-builder # Our Docker builds require the ability to run as `root` to build our images oc adm policy add-scc-to-user anyuid -z datarobot-management-agent-image-builder ``` When OpenShift created a Docker registry authentication secret, it created it in the incorrect format ( `kubernetes.io/dockercfg` instead of `kubernetes.io/dockerconfigjson`). To fix this, create a secret using the appropriate token. To do this, find the existing `Image pull secrets` assigned to the `datarobot-management-agent-image-builder` ServiceAccount: ``` $ oc describe sa/datarobot-management-agent-image-builder Name: datarobot-management-agent-image-builder Namespace: datarobot-mlops Labels: Annotations: Image pull secrets: datarobot-management-agent-image-builder-dockercfg-p6p5b Mountable secrets: datarobot-management-agent-image-builder-dockercfg-p6p5b datarobot-management-agent-image-builder-token-pj9ks Tokens: datarobot-management-agent-image-builder-token-p6dnc datarobot-management-agent-image-builder-token-pj9ks Events: ``` Next, track back from the pull secret back to the raw token: ``` $ oc describe secret $(oc get secret/datarobot-management-agent-image-builder-dockercfg-p6p5b -o jsonpath='{.metadata.annotations.openshift\.io/token-secret\.name}') Name: datarobot-management-agent-image-builder-token-p6dnc Namespace: datarobot-mlops Labels: Annotations: kubernetes.io/created-by: openshift.io/create-dockercfg-secrets kubernetes.io/service-account.name: datarobot-management-agent-image-builder kubernetes.io/service-account.uid: 34101931-d402-49bf-83df-7a60b31cdf44 Type: kubernetes.io/service-account-token Data ==== ca.crt: 11253 bytes namespace: 10 bytes service-ca.crt: 12466 bytes token: eyJhbGciOiJSUzI1NiIsImtpZCI6InJqcEx5LTFjOElpM2FKRzdOdDNMY... ``` ``` oc create secret docker-registry registry-creds \ --docker-server=image-registry.openshift-image-registry.svc:5000 \ --docker-username=imagebuilder \ --docker-password=eyJhbGciOiJSUzI1NiIsImtpZCI6InJqcEx5LTFjOElpM2FKRzdOdDNMY... ``` Update the `imageBuilder` section of the `values.yaml` file (located at `/tools/charts/datarobot-management-agent/values.yaml` in the agent tarball) to reference the `serviceAccount` created above: ``` imageBuilder: ... secretName: registry-creds rbac: create: false serviceAccount: create: false name: datarobot-management-agent-image-builder ``` It's common for the internal registry to be signed by an internal CA. To avoid this, skip TLS verification in the `values.yaml` configuration: ``` imageBuilder: ... skipSslVerifyRegistries: - image-registry.openshift-image-registry.svc:5000 ``` If you have the CA certificate, a more secure option would be to mount it as a `secret` or a `configMap` and then configure the `imageBuilder` to use it. Below we will show a third option of how you can obtain the CA directly from the underlying node: ``` imageBuilder: ... extraVolumes: - name: cacert hostPath: path: /etc/docker/certs.d extraVolumeMounts: - name: cacert mountPath: /certs/ readOnly: true extraArguments: - --registry-certificate=image-registry.openshift-image-registry.svc:5000=/certs/image-registry.openshift-image-registry.svc:5000/ca.crt ``` > [!NOTE] Note > The example above requires elevated SCC privileges. ``` oc adm policy add-scc-to-user hostmount-anyuid -z datarobot-management-agent-image-builder ``` **Docker Hub:** If you have a generic registry that uses a simple Docker username/password to log in, you can use the following procedure. Create a secret containing your Docker registry credentials: ``` kubectl create secret docker-registry registry-creds \ --namespace datarobot-mlops \ --docker-server=.your-company.com \ --docker-username= \ --docker-password= ``` Update the `imageBuilder` section of the `values.yaml` file (located at `/tools/charts/datarobot-management-agent/values.yaml` in the agent tarball) to use the new secret you created: ``` imageBuilder: ... secretName: "registry-creds" ``` If your registry is running on HTTP, you will need to add the following to the above example: ``` imageBuilder: ... secretName: "registry-creds" insecureRegistries: - .your-company.com ``` ## Install the management agent with Helm After the prerequisites are configured, install the MLOps management agent. In these steps, you will be building and pushing large docker images up to your remote registry. DataRobot recommends running these steps in parallel while downloads or uploads are happening. ### Fetch the Portable Prediction Server image The first step is to download the latest version of the [Portable Prediction Server Docker Image](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html#portable-prediction-server-docker-image) from DataRobot's Developer Tools. When the download completes, run the following commands: 1. Load the PPS Docker image: dockerload.tar.gz 2. Tag the PPS Docker image with an image name: NoteDon't uselatestas thetag. dockertagdatarobot/datarobot-portable-prediction-api:registry.your-company.com/datarobot/datarobot-portable-prediction-api: 3. Push the PPS Docker image to your remote registry: dockerpushyour-company.com/datarobot/datarobot-portable-prediction-api: ### Build the required Docker images First, build the management agent image with a single command: ``` make -C tools/bosun_docker REGISTRY=registry.your-company.com push ``` Next, build the monitoring agent with a similar command: > [!NOTE] Note > If you don't plan on enabling model monitoring, you can skip this step. ``` make -C tools/agent_docker REGISTRY=registry.your-company.com push ``` ### Create a new Prediction Environment To create a new prediction environment, see the [Prediction environments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/index.html) documentation. Record the Prediction Environment ID for later use. > [!NOTE] Note > Only the `DataRobot` and `Custom Model` model formats are currently supported. ### Install the Helm chart DataRobot recommends installing the agent into its own namespace. To do so, pre-create it and install the MLOps API key in it. ``` # Create a namespace to contain the agent and all the models it deploys kubectl create namespace datarobot-mlops # You can use an existing key or we recommend creating a key dedicated to the agent # by browsing here: # https://app.datarobot.com/account/developer-tools kubectl -n datarobot-mlops create secret generic mlops-api-key --from-literal=secret= ``` You can modify one of several common examples for the various cloud environments (located in the `/tools/charts/datarobot-management-agent/examples` directory of the agent tarball) to suit your account; then you can install the agent with the appropriate version of the following command: ``` helm upgrade --install bosun . \ --namespace datarobot-mlops \ --values ./examples/AKE_values.yaml ``` If none of the provided examples suit your needs, the minimum command to install the agent is as follows: ``` helm upgrade --install bosun . \ --namespace datarobot-mlops \ --set predictionServer.ingressClassName=mlops \ --set predictionServer.outfacingUrlRoot=http://your-company.com/deployments/ \ --set datarobot.apiSecretName=mlops-api-key \ --set datarobot.predictionEnvId= \ --set managementAgent.repository=registry.your-company.com/datarobot/mlops-management-agent \ --set trackingAgent.image=registry.your-company.com/datarobot/mlops-tracking-agent:latest \ --set imageBuilder.ppsImage=registry.your-company.com/datarobot/datarobot-portable-prediction-api: \ --set imageBuilder.generatedImageRepository=registry.your-company.com/mlops/frozen-models ``` There are several additional configurations to review in the `values.yaml` file (located at `/tools/charts/datarobot-management-agent/values.yaml` in the agent tarball) or using the following command: ``` helm show values . ``` --- # Configure environment plugins URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/mgmt-agent-plugins.html > Configure prediction environment plugins for the MLOps management agent. # Configure management agent environment plugins Management agent plugins deploy and manage models in a given prediction environment. The management agent submits commands to the plugin, and the plugin executes them and returns the status of the command to the management agent. To facilitate this interaction, you provide prediction environment details during plugin configuration, allowing the plugin to execute commands in that environment. For example, a Kubernetes plugin can launch a deployment (container) in a Kubernetes cluster, replace a model in the deployment, stop the container, etc. The MLOps management agent contains the following example plugins: - Filesystem plugin. - Docker plugin. - Kubernetes plugin. - Test plugin. > [!NOTE] Note > These example plugins are installed as part of the `datarobot_bosun-*-py3-none-any.whl` wheel file. ## Configure example plugins The following example plugins require additional configuration for use with the management agent: **Filesystem:** To enable communication between the management agent and the deployment, the filesystem plugin creates one directory per deployment in the local filesystem, and downloads each deployment's model package and configuration `.yaml` file into the deployment's local directory. These artifacts can then be used to serve predictions from a PPS container. ``` # The top-level directory that will be used to store each deployment directory baseDir: "." # Each deployment directory will be prefixed with the following string deploymentDirPrefix: "deployment_" # The name of the deployment config file to create inside the deployment directory. # Note: If working with the PPS, DO NOT change this name; the PPS expects this filename. deploymentInfoFile: "config.yml" # If defined, this string will be prefixed to the predictions URL for this deployment, # and the URL will be returned, with the deployment id suffixed to the end with the # /predict endpoint. deploymentPredictionBaseUrl: "http://localhost:8080" # If defined, create a yaml file with the kv of the deployment. # If the name of the file is the same as the deploymentInfoFile, # the key values are added to the same file as the other config. # deploymentKVFile: "kv.yaml" ``` **Docker:** The Docker plugin can deploy native DataRobot models and custom models on a Docker server. In addition, the plugin automatically runs the monitoring agent to monitor deployed models and uses the `traefik` reverse proxy to provide a single prediction endpoint for each deployment. The management agent's Docker plugin supports the use of the [Portable Prediction Server](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html), allowing a single Docker container to serve multiple models. It enables you to configure the PPS to indicate where models for each deployment are located and gives you the ability to start, stop, and manage deployments. The Docker plugin can: Retrieve a model package from DataRobot for a deployment. Launch the DataRobot model within the Docker container. Shut down and clean up the Docker container. Report status back via events. Monitor predictions using the monitoring agent. To configure the Docker plugin, take the following steps: Set up the environment required for the Docker plugin: docker pull rabbitmq:3-management docker pull traefik:2.3.3 docker network create bosun Build the monitoring agent container image: cd datarobot_mlops_package-*/ cd tools/agent_docker make build Download the Portable Prediction Server from the DataRobot UI. If you are planning to use a custom model image, make sure the image is built and accessible to the Docker service. Configure the Docker plugin configuration file: plugin.docker.conf.yaml # Docker network on which to run all containers. # This network must be created prior to running # the agent (i.e., 'docker network create `) dockerNetwork : "bosun" # Traefik image to use traefikImage : "traefik:2.3.3" # Address that will be reported to DataRobot outfacingPredictionURLPrefix : "http://10.10.12.22:81" # MLOps Agent image to use for monitoring agentImage : "datarobot/mlops-tracking-agent:latest" # RabbitMQ image to use for building a channel rabbitmqImage : "rabbitmq:3-management" # PPS base image ppsBaseImage : "datarobot/datarobot-portable-prediction-api:latest" # Prefix for generated images generatedImagePrefix : "mlops_" # Prefix for running containers containerNamePrefix : "mlops_" # Mapping of traefik proxy ports (not mandatory) traefikPortMapping : 80 : 81 8080 : 8081 # Mapping of RabbitMQ (not mandatory) rabbitmqPortMapping : 15672 : 15673 5672 : 5673 **Kubernetes:** DataRobot provides a plugin to deploy and manage models in your Kubernetes cluster without writing any additional code. For configuration information, see the README file in the `tools/charts/datarobot-management-agent` folder in the tarball. ``` ## The following settings are related to connecting to your Kubernetes cluster # # The name of the kube-config context to use (similar to --context argument of kubectl). There is a special # `IN_CLUSTER` string to be used if you are running the plugin inside a cluster. The default is "IN_CLUSTER" # kubeConfigContext: IN_CLUSTER # The namespace that you want to create and manage external deployments (similar to --namespace argument of kubectl). You # can leave this as `null` to use the "default" namespace, the namespace defined in your context, or (if running `IN_CLUSTER`) # manage resources in the same namespace the plugin is executing in. # kubeNamespace: ## The following settings are related to whether or not MLOps monitoring is enabled # # We need to know the location of the dockerized agent image that can be launched into your Kubernetes cluster. # You can build the image by running `make build` in the tools/agent_docker/ directory and retagging the image # and pushing it to your registry. # agentImage: "/mlops-tracking-agent:latest" ## The following settings are all related to accessing the model from outside the Kubernetes cluster # # The URL prefix used to access the deployed model, i.e., https://example.com/deployments/ # The model will be accessible via /predict outfacingPredictionURLPrefix: "" # We are still using the beta Ingress resource API, so a class must be provided. If your cluster # doesn't have a default ingress class, please provide one. # ingressClass: ## The following settings are all related to building the finalized model image (base image + mlpkg) # # The location of the Portable Prediction Server base image. You can download it from DataRobot's developer # tools section, retag it, and push it to your registry. ppsBaseImage: "/datarobot-portable-prediction-api:latest" # The Docker repo to which this plugin can push finalized models. The built images will be tagged # as follows: :m- generatedImageRepo: "/mlops-model" # We use Kaniko to build our finalized image. See https://github.com/GoogleContainerTools/kaniko#readme. # The default is to use the image below. # kanikoImage: "gcr.io/kaniko-project/executor:v1.5.2" # The name of the Kaniko ConfigMap to use. This provides the settings Kaniko will need to be able to push to # your registry type. See https://github.com/GoogleContainerTools/kaniko#pushing-to-different-registries. # The default is to not use any additional configuration. # kanikoConfigmapName: "docker-config" # The name of the Kaniko Secret to use. This provides the settings Kaniko will need to be able to push to # your registry type. See https://github.com/GoogleContainerTools/kaniko#pushing-to-different-registries. # The default is to not use any additional secrets. The secret must be of the type: kubernetes.io/dockerconfigjson # kanikoSecretName: "registry-credentials" # The name of a service account to use for running Kaniko if you want to run it in a more secure fashion. # See https://github.com/GoogleContainerTools/kaniko#security. # The default is to use the "default" service account in the namespace in which the pod runs. # kanikoServiceAccount: default ``` **Test:** To configure the test plugin, use the `--plugin test` option and set the temporary directory and sleep time (in seconds) for each action executed by the test plugin. For example, the deployment `launch_time_sec` set in the test plugin configuration below creates a temporary file for the deployment, sleeps for 1 second, and then returns. ``` tmp_dir: "/tmp" launch_time_sec: 1 stop_time_sec: 1 replace_model_time_sec: 1 pe_status_time_sec: 1 deployment_status_time_sec: 1 deployment_list_time_sec: 1 plugin_start_time: 1 plugin_stop_time: 1 ``` ## Create a custom plugin The management agent's plugin framework is flexible enough to accommodate custom plugins. This flexibility is helpful when you have a custom prediction environment (different from, for example, the standard Docker or Kubernetes environment) in which you deploy your models. You can implement a plugin for such a prediction environment either by modifying the existing plugin or by implementing one from scratch. You can use the filesystem plugin as a reference when creating a custom Python plugin. > [!NOTE] Note > Currently, custom Java plugins are not supported. If you decide to write a custom plugin, the following section describes the interface definition provided to write a Python plugin. ### Implement the plugin interface The management agent Python package defines the [abstract base class](https://docs.python.org/3/library/abc.html) `BosunPluginBase`. Each management agent plugin must inherit and implement the interface defined by this base class. To start implementing a custom plugin ( `SamplePlugin` below), inherit the `BosunPluginBase` base class. As an example, implement the plugin under `sample_plugin` directory in the file `sample_plugin.py`: ``` class SamplePlugin(BosunPluginBase): def __init__(self, plugin_config, private_config_file=None, pe_info=None, dry_run=False): ``` #### Python plugin arguments The constructor is invoked with the following arguments: | Argument | Definition | | --- | --- | | plugin_config | A dictionary containing general information about the plugin. We will go over the details in the following section. | | private_config_file | Path to the private configuration file for the plugin as passed in by the --private-config flag when calling the bosun-plugin-runner script. This file is optional and the contents are fully at the discretion of your custom plugin. | | pe_info | An instance of PEInfo, which contains information about the prediction environment. This parameter is unset for certain actions. | | dry_run | The invocation for dry run (development) or the actual run. | #### Python plugin methods This class implements the following methods: > [!NOTE] Note > The return type for each of the following functions must be `ActionStatusInfo`. ``` def plugin_start(self): ``` This method initializes the plugin; for example, it can check if the plugin can connect with the prediction environment (e.g., Docker, Kubernetes). In the case of the filesystem plugin, this method checks if the `baseDir` exists on the filesystem. Management agent invokes this method typically only once during the startup process. This method is guaranteed to be called before any deployment-specific action can be invoked. ``` def plugin_stop(self): ``` This method implements any tear-down process, for example, close client connections to the prediction environment. The management agent invokes this method typically only once during the shutdown process. This plugin method is guaranteed to be called after all deployment-specific actions are done. ``` def deployment_list(self): ``` This method returns the list of deployments already running in the given prediction environment. The management agent typically invokes this method during the startup to determine which deployments are already running in the prediction environment. The list of deployments is returned as a map of `deployment_id` -> Deployment Information, using the `data` field in the `ActionStatusInfo` (described below) ``` def deployment_start(self, deployment_info): ``` This method implements a deployment launch process. Management Agent invokes this method when deployment is created or activated in DataRobot. For example, this method can launch the container in the Kubernetes or Docker service. In the case of the filesystem plugin, this method creates a directory with the name `deployment_`. It then places the deployment's model and a YAML configuration file under the new directory. The plugin should ensure that the deployment in the prediction environment is uniquely identifiable by the deployment id and, ideally, by the paired deployment id and model id. For example, the built-in Docker plugin launches the container with the following name: `deployment__` ``` def deployment_stop(self, deployment_info): ``` This method implements a deployment stop process. Management Agent invokes this method when deployment is deactivated or deleted in DataRobot. For example, this method can stop the container in the Kubernetes or Docker service. The deployment id and model id from the `deployment_info` uniquely identifies the container that needs to be stopped. In the case of the filesystem plugin, this method removes the directory created for that deployment by the `deployment_start` method. ``` def deployment_replace_model(self, deployment_info): ``` This method implements a model replacement process in the deployment. The management agent invokes this method when a model is replaced in a deployment in DataRobot.`modelArtifact` contains the path to the new model, and `newModelId` contains the id of the new model to use for replacement. In the case of the Docker or Kubernetes plugin, a potential implementation of this method could stop the container with the old model id and then start a new container with the new model. In the case of filesystem plugin, it removes the old deployment directory and creates a new one with the new model. ``` def pe_status(self): ``` This method queries for the status of the prediction environment, for example, whether the Kubernetes or Docker service is still reachable. The management agent periodically invokes this method to ensure the prediction environment is in a good state. In order to improve the experience, the plugin can support queries for the status of the deployments running in the prediction environment in addition to the status of the prediction environment itself. In this case, the IDs of the deployments are included in the `deployments` field of the `peInfo` structure (described below), and the status of each deployment is returned using `data` field in the `ActionStatusInfo` object (described below). The deployment status is returned as a map of `deployment_id` to Deployment Information. ``` def deployment_status(self): ``` This method queries the status of the deployment deployed in a prediction environment, for example, whether the container corresponding to the deployment is still up and running. The management agent periodically invokes this method to ensure that the deployment is in a good state. ``` def deployment_relaunch(self, deployment_info): ``` This method implements the process of relaunching (stopping + starting) the deployment. The management agent Python package already provides a default implementation of this method by invoking `deployment_stop` followed by `deployment_start`; however, the plugin can implement its own relaunch mechanism if there is an optimal way to relaunch a deployment. #### Python plugin return value The return value for all these operations is an `ActionStatusInfo` object providing the status of the action: ``` class ActionStatusInfo: def __init__(self, status, msg=None, state=None, duration=None, data=None): ``` This object contains the following fields: | Field | Definition | | --- | --- | | status | Indicates the status of the action. Values: ActionStatus.OK, ActionStatus.WARN, ActionStatus.ERROR, and ActionStatus.UNKNOWN | | msg | Returns a string type message that the plugin can forward to the management agent, which in turn, will forward the message to the MLOps service (DataRobot). | | state | Indicates the state of the deployment after the execution of action. Values: ready, stopped, and errored. | | duration | Indicates the time the action took to execute. | | data | Returns information that plugin can forward to the management agent. Currently, deployment_list method uses this field to list the deployments in the form of a dictionary of deployment_id to Deployment Information. This field can also be used by the pe_status method to report the status of deployments running in the prediction environment in addition to the prediction environment status. | > [!NOTE] Note > The base class automatically adds the `timestamp` to the object to keep track of different action status values. ### Use the bosun-plugin-runner The management agent Python package provides the `bosun-plugin-runner` CLI tool, which allows you to invoke the custom plugin class and run a specific action. Using this tool, you can run your plugin in standalone mode while developing and debugging your plugin. For example: ``` bosun-plugin-runner \ --plugin sample_plugin/sample_plugin \ --action pe_status \ --config sample_configs/action_config_pe_status_only.yaml \ --private-config sample_configs/sample_plugin_config.yaml \ --status-file /tmp/status.yaml \ --show-status ``` The `bosun-plugin-runner` accepts the following arguments: | Argument | Definition | | --- | --- | | --plugin | Specifies the module containing the plugin class. In this case, we used sample_plugin/sample_plugin since the plugin class is inside the sample_plugin directory in the sample_plugin.py file. | | --action | Specifies the action to run. Here we use the pe_status action. Other supported actions are listed below. | | --config | Provides the configuration file to use for the action specified. We describe this in more detail in the next section. When your plugin runs as part of the Management agent service, this file will be generated for you but when testing specific actions manually via the bosun-plugin-runner you will have to generate the configuration file yourself. | | --private-config | Provides a plugin specific configuration file used only by plugin. | | --status-file | Provides a path for saving the plugin status that results from the action. | | --show-status | Shows the contents of the --status-file on stdout. | To view the list of actions supported by `bosun-plugin-runner` use the `--list-actions` option: ``` bosun-plugin-runner --list-actions # plugin_start # plugin_stop # deployment_start # deployment_stop # deployment_replace_model # deployment_status # pe_status # deployment_list ``` ### Create the action config file The `--config` flag is used to pass a YAML configuration file to the plugin. This is the structure of the configuration that the management agent prepares and invokes the plugin action with; however, during plugin development, you may need to write this configuration file yourself. The typical contents of such a config file are shown below: ``` pluginConfig: name: "ExternalCommand-1" type: "ExternalCommand" platform: "os" commandPrefix: "python3 sample_plugin.py" mlopsUrl: "https://app.datarobot.com" peInfo: id: "0x2345" name: "Sample-PE" description: "some description" createdOn: "iso formatted date" createdBy: "some username" deployments: ["deployment-1", "deployment-2"] keyValueConfig: max_models: 5 deploymentInfo: id: "deployment-1" name: "deployment-1" description: "Deployment 1 for testing" modelId: "model-A" modelArtifact: "/tmp/model-A.txt" modelExecutionType: "dedicated" keyValueConfig: key1: "some-value-for-key-1" ``` The action configuration file contains three sections: `pluginConfig`, `peInfo`, and `deploymentInfo`. The `pluginConfig` section contains general information about the plugin, for example, ID of the prediction environment, its type, and the platform. It may also contain the `mlopsUrl`, the address of the MLOps service (DataRobot) (in case the plugin would like to connect). This is the section that translates to the `pluginConfig` dictionary and is passed as a constructor argument. The `peInfo` section contains information about the prediction environment this action refers to. Typically, this information is used for `pe_status` action. If `deployments` key contains valid deployment ids, the plugin is expected to return not only the status of the prediction environment but also the status of the deployments listed under `deployments`. The `deploymentInfo` section contains the information about the deployment in the prediction environment this action refers to. All the deployment-related actions use this section to identify which deployment and model to work on. As this is a particularly important section of the config, let us go over some of the important fields: - id,name, anddescription: Provides information about the deployment as set in DataRobot. - modelId,modelArtifact: Indicates the ID of the model and the path where the model can be found. Note that the management agent will place the right model at this path before invokingdeployment_startordeployment_replace_model. - keyValueConfig: Lists the additional configuration for the deployment. Note that this additional config can be set on the deployment in DataRobot. For example, this can be used to specify how much memory the container corresponding to this deployment should use. ### Run actions with bosun-plugin-runner As covered above, during plugin development, you can use the `bosun-plugin-runner` to invoke the actions. For example, here is how a `deployment_start` action can be invoked. We will use the same config as described in the previous section and dump it to a file `sample_configs/config_deployment-1_model-A.yaml` file. ``` bosun-plugin-runner \ --plugin sample_plugin/sample_plugin \ --config sample_configs/action_config_deployment_1_model_A.yaml \ --private-config sample_configs/sample_plugin_config.yaml \ --action deployment_start \ --status-file /tmp/status.yaml \ --show-status ``` The status of this `deployment_start` action is captured in the file `/tmp/status.yaml` ### Configure the command prefix Now that your plugin is ready for the management agent, you can configure the `command` prefix in the management agent configuration file as: ``` command: "/bin/bosun-plugin-runner --plugin sample_plugin --private-config /plugin.sample_plugin_.conf.yaml" ``` --- # Relaunch deployments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/mgmt-agent/mgmt-agent-relaunch.html > Relaunch an MLOps management agent deployment without changes to the deployment's metadata, # Relaunch management agent deployments To manually relaunch a management agent deployment without changes to the deployment's metadata, you can trigger a relaunch from the deployment's [Actions menu](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/actions-menu.html). To manually relaunch a deployment, take the following steps: 1. On theDeploymentspage or any tab within a deployment, next to the name of the deployment you want to relaunch, click theActions menuand clickRelaunch. 2. In theRelaunch deploymentdialog box, clickRelaunch. --- # Examples directory URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent-ex.html > Use sample code available in the MLOps agent tarball as a starting point for creating and managing deployments. Examples include model configuration, data, and scripts used to create deployments and run the examples. # Examples directory The `examples` directory in the MLOps agent tarball contains both sample code (snippets for manual inspection) and example code (self-contained examples that you can run) in Python and Java. Navigate to the subdirectory for the language you wish to use and reference the respective `README` for further instruction. The examples directory includes model configuration, data, and scripts used to create deployments and run the examples, using Python to create the model package and deployment programmatically. Therefore, you must install the Python version of the MLOps library (described below). These examples also use the [MLOps Command Line Interface (mlops-cli)](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent-use.html#monitor-using-the-mlops-cli) to set up deployments and perform deployment actions. You must provide the `MLOPS_SERVICE_URL` and `MLOPS_API_TOKEN` environment variables to use the `mlops-cli`. In addition, most examples use the `mlops-cli` to upload monitoring data for faster setup; however, while the `mlops-cli` tool is suitable for simple use cases, you should use the agent for production scenarios. ## Run code examples with Python To run the Python code examples, you must install the dependencies used by the examples: ``` pip install -r examples/python/requirements.txt ``` See the `README` file in each example directory for further example-specific configuration requirements. In general, to run an example: 1. Initialize the model package and deployment: ./create_deployment.sh 2. Generate predictions and report statistics to DataRobot: ./run_example.sh 3. Verify that metrics were sent successfully: ./verify_example.sh 4. Delete resources created in the example: ./cleanup.sh --- # Monitoring external multiclass deployments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent-multi.html > How to configure the monitoring agent to monitor multiclass models deployed to external prediction environments. # Monitor external multiclass deployments Users with multiclass models deployed to external prediction environments can configure the monitoring agent to monitor their deployments. To do so, you must create a deployment in DataRobot with an [external model package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html#register-external-model-packages) for the multiclass model. When creating an external model package, you can indicate the prediction type for the model as multiclass: Once indicated, via direct text input or via upload of a CSV or TXT file, provide the target classes for your model (one class per line) in the Target classes field: Once all fields for the model package are defined, click Create package. The package populates in the Model Registry and is available for use. When you have configured an external model package for your multiclass model, follow the workflow to [create a deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-external-model.html#deploy-an-external-model-package) with the external model package and [configure it with the monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html). --- # Download Scoring Code URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent-sc.html > How to download a deployment’s monitoring agent with Scoring Code. # Download Scoring Code You can download the monitoring agent packaged with [Scoring Code](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/index.html) directly from a deployment. > [!NOTE] Scoring Code availability > The deployed model must be trained with Scoring Code enabled in order to access the package. Additionally, this package is only compatible with models running at the command line. The package does not support models running on [the Portable Prediction Server](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html). After deploying your Scoring Code model, Select Get Scoring Code from the [Actions](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/actions-menu.html) menu to download the Scoring Code JAR file. In addition to Scoring Code, you can now download a monitoring agent package preconfigured for the deployment. This allows you to quickly integrate the monitoring agent and report model monitoring statistics back to DataRobot. Reference the quickstart guide in the agent tarball for instruction on the initial setup after downloading the package. > [!NOTE] Java requirement > The MLOps monitoring library requires Java 11 or higher. Without monitoring, a model's Scoring Code JAR file requires Java 8 or higher; however, when using the MLOps library to instrument monitoring, a model's Scoring Code JAR file requires Java 11 or higher.For Self-managed AI platform installations, the Java 11 requirement applies to DataRobot v11.0 and higher. If you do not wish to integrate the monitoring agent, instead download just the Scoring Code, available in Source and Binary formats. --- # Monitoring agent use cases URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent-use.html > Investigate MLOPs reporting and monitoring use cases, including how to report metrics when the prediction environment isn't connected to DataRobot and how to monitor Spark environments. # Monitoring agent use cases Reference the use cases below for examples of how to apply the monitoring agent: - Enable large-scale monitoring - Perform advanced agent memory tuning for large workloads - Report accuracy for challengers - Report metrics - Monitor a Spark environment - Monitor using the MLOps CLI ## Enable large-scale monitoring To support large-scale monitoring, the MLOps library provides a way to calculate statistics from raw data on the client side. Then, instead of reporting raw features and predictions to the DataRobot MLOps service, the client can report anonymized statistics without the feature and prediction data. Reporting prediction data statistics calculated on the client side is the optimal (and highly performant) method compared to reporting raw data, especially at scale (billions of rows of features and predictions). In addition, because client-side aggregation only sends aggregates of feature values, it is suitable for environments where you don't want to disclose the actual feature values. To enable the large-scale monitoring functionality, you must set one of the feature type settings. These settings provide the dataset's feature types and can be configured programmatically in your code (using setters) or by defining environment variables. > [!NOTE] Note > If you configure these settings programmatically in your code and by defining environment variables, the environment variables take precedence. **Environment variables:** The following environment variables are used to configure large-scale monitoring: Variable Description MLOPS_FEATURE_TYPES_FILENAME The path to the file containing the dataset's feature types in JSON format. Example : "/tmp/feature_types.json" MLOPS_FEATURE_TYPES_JSON The JSON containing the dataset's feature types. Example : [{"name": "feature_name_f1","feature_type": "date", "format": "%m-%d-%y",}] Optional configuration MLOPS_STATS_AGGREGATION_MAX_RECORDS The maximum number of records in a dataset to aggregate. Example : 10000 MLOPS_STATS_AGGREGATION_PREDICTION_TS_COLUMN_NAME The name of the prediction timestamp column in the dataset you want to aggregate on the client side. Example : "ts" MLOPS_STATS_AGGREGATION_PREDICTION_TS_COLUMN_FORMAT The format of the prediction timestamp values in the dataset. Example : "%Y-%m-%d %H:%M:%S.%f" MLOPS_STATS_AGGREGATION_SEGMENT_ATTRIBUTES The custom attribute used to segment the dataset for segmented analysis of data drift and accuracy. Example : "country" MLOPS_STATS_AGGREGATION_AUTO_SAMPLING_PERCENTAGE The percentage of raw data to report to DataRobot using algorithmic sampling. This setting supports challengers and accuracy tracking by providing a sample of raw features, predictions, and actuals. The rest of the data is sent in aggregate format. In addition, you must define MLOPS_ASSOCIATION_ID_COLUMN_NAME to identify the column in the input data containing the data for sampling. Example : 20 MLOPS_ASSOCIATION_ID_COLUMN_NAME The column containing the association ID required for automatic sampling and accuracy tracking. Example : "rowID" **Setters:** The following code snippets show how you can configure large-scale monitoring settings programmatically: ``` mlops = MLOps() \ .set_stats_aggregation_feature_types_filename("/tmp/feature_types.json") \ .set_aggregation_max_records(10000) \ .set_prediction_timestamp_column("ts", "yyyy-MM-dd HH:mm:ss") \ .set_segment_attributes("country") \ .set_auto_sampling_percentage(20) \ .set_association_id_column_name("rowID") \ .init() ``` ``` mlops = MLOps() \ .set_stats_aggregation_feature_types_json([{"name": "feature_name_f1","feature_type": "date", "format": "%m-%d-%y",}]) \ .set_aggregation_max_records(10000) \ .set_prediction_timestamp_column("ts", "yyyy-MM-dd HH:mm:ss") \ .set_segment_attributes("country") \ .set_auto_sampling_percentage(20) \ .set_association_id_column_name("rowID") \ .init() ``` > [!TIP] Manual sampling > To support challenger models and accuracy monitoring, you must send raw features, predictions, and actuals; however, you don't need to use the automatic sampling feature. You can manually report a small sample of raw data and then send the remaining data in aggregate format. > [!NOTE] Prediction timestamp > If you don't provide the `MLOPS_STATS_AGGREGATION_PREDICTION_TS_COLUMN_NAME` and `MLOPS_STATS_AGGREGATION_PREDICTION_TS_COLUMN_FORMAT` environment variables, the timestamp is generated based on the current local time. The large-scale monitoring functionality is available for Python, the Java Software Development Kit (SDK), and the MLOps Spark Utils Library: **Python:** Replace calls to `report_predictions_data()` with calls to: ``` report_aggregated_predictions_data( self, features_df, predictions, class_names, deployment_id, model_id ) ``` **Java SDK:** Replace calls to `reportPredictionsData()` with calls to: ``` reportAggregatePredictionsData( Map> featureData, List predictions, List classNames ) ``` **MLOps Spark Utils Library:** Replace calls to `reportPredictions()` with calls to `predictionStatisticsParameters.report()`. The `predictionStatisticsParameters.report()` function has the following builder constructor: ``` PredictionStatisticsParameters.Builder() .setChannelConfig(channelConfig) .setFeatureTypes(featureTypes) .setDataFrame(df) .build(); predictionStatisticsParameters.report(); ``` > [!TIP] Tip > You can find an example of this use-case in the agent `.tar` file in `examples/java/PredictionStatsSparkUtilsExample`. ### Map supported feature types Currently, large-scale monitoring supports numeric and categorical features. When configuring this monitoring method, you must map each feature name to the corresponding feature type (either numeric or categorical). When mapping feature types to feature names, there is a method for Scoring Code models and a method for all other models. **Non Scoring Code:** Often, a model can output the feature name and the feature type using an existing access method; however, if access is not available, you may have to manually categorize each feature you want to aggregate as `Numeric` or `Categorical`. Map a feature type ( `Numeric` or `Categorical`) to each feature name using the `setFeatureTypes` method on `predictionStatisticsParameters`. **Scoring Code:** Map a feature type ( `Numeric` or `Categorical`) to each feature name after using the `getFeatures` query on the `Predictor` object to obtain the features. You can find an example of this use-case in the agent `.tar` file in `examples/java/PredictionStatsSparkUtilsExample/src/main/scala/com/datarobot/dr_mlops_spark/Main.scala`. ## Perform advanced agent memory tuning for large workloads The monitoring agent's default configuration is tuned to perform well for an average workload; however, as you increase the number of records the agent groups together for forwarding to DataRobot MLOps, the agent's total memory usage increases steadily to support the increased workload. To ensure the agent can support the workload for your use case, you can estimate the agent's total memory use and then set the agent's memory allocation or configure the maximum record group size. ### Estimate agent memory use When estimating the monitoring agent's approximate memory usage (in bytes), assume that each feature reported requires an average of `10 bytes` of memory. Then, you can estimate the memory use of each message containing raw prediction data from the number of features (represented by `num_features`) and the number of samples (represented by `num_samples`) reported. Each message uses approximately `10 bytes × num_features × num_samples` of memory. > [!NOTE] Note > Consider that the estimate of 10 bytes of memory per feature reported is most applicable to datasets containing a balanced mix of features. Text features tend to be larger, so datasets with an above-average amount of text features tend to use more memory per feature. When grouping many records at one time, consider that the agent groups messages together until reaching the limit set by the `agentMaxAggregatedRecords` setting. In addition, at that time, the agent will keep messages in memory up to the limit set by the `httpConcurrentRequest` setting. Combining the calculations above, you can estimate the agent's memory usage (and the necessary memory allocation) with the following formula: `memory_allocation = 10 bytes × num_features × num_samples × max_group_size × max_concurrency` Where the variables are defined as: - num_features: The number of features (columns) in the dataset. - num_samples: The number of rows reported in a single call to the MLOPS reporting function. - max_group_size: The number of records aggregated into each HTTP request, set byagentMaxAggregatedRecordsin theagent config file. - max_concurrency: The number of concurrent HTTP requests, set byhttpConcurrentRequestin theagent config file. Once you use the dataset and agent configuration information above to calculate the required agent memory allocation, this information can help you fine-tune the agent configuration to optimize the balance between performance and memory use. ### Set agent memory allocation Once you know the agent's memory requirement for your use case, you can increase the agent’s Java Virtual Machine (JVM) memory allocation using the `MLOPS_AGENT_JVM_OPT` [environment variable](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/env-var.html): ``` MLOPS_AGENT_JVM_OPT=-Xmx2G ``` > [!NOTE] Important > When running the agent in a container or VM, you should configure the system with at least 25% more memory than the `-Xmx` setting. ### Set the maximum group size Alternatively, to reduce the agent's memory requirement for your use case, you can decrease the agent's maximum group size limit set by `agentMaxAggregatedRecords` in the [agent config file](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent.html): ``` # Maximum number of records to group together before sending to DataRobot MLOps agentMaxAggregatedRecords: 10 ``` Lowering this setting to `1` disables record grouping by the agent. ## Report accuracy for challengers Because the agent may send predictions with an association ID, even if the deployment in DataRobot doesn’t have an association ID set, the agent's API endpoints recognize the `__DataRobot_Internal_Association_ID__` column as the association ID for the deployment. This enables accuracy tracking for models reporting prediction data through the agent; however, the `__DataRobot_Internal_Association_ID__` column isn't available to DataRobot when predictions are replayed for challenger models, meaning that DataRobot MLOps can't record accuracy for those models due to a missing association ID. To track accuracy for challenger models through the agent, set the association ID on the deployment to `__DataRobot_Internal_Association_ID__` during [deployment creation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-deploy-info.html#accuracy) or in the [accuracy settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html#select-an-association-id). Once you configure the association ID with this value, all future challenger replays will record accuracy for challenger models. ## Report metrics If your prediction environment cannot be network-connected to DataRobot, you can instead use monitoring agent reporting in a disconnected manner. 1. In the prediction environment, configure the MLOps library to use thefilesystemspooler type. The MLOps library will report metrics into its configured directory, e.g.,/disconnected/predictions_dir. 2. Run the monitoring agent on a machine thatisnetwork-connected to DataRobot. 3. Configure the agent to use thefilesystemspooler type and receive its input from a local directory, e.g.,/connected/predictions_dir. 4. Migrate the contents of the directory/disconnected/predictions_dirto the connected environment/connected/predictions_dir. ### Reports for Scoring Code models You can also use monitoring agent reporting to send monitoring metrics to DataRobot for downloaded Scoring Code models. Reference an example of this use case in the MLOps agent tarball at `examples/java/CodeGenExample`. ## Monitor a Spark environment A common use case for the monitoring agent is monitoring scoring in Spark environments where scoring happens in Spark and you want to report the predictions and features to DataRobot. Since Spark usually uses a multi-node setup, it is difficult to use the agent's `fileystem` spooler channel because a shared consistent file system is uncommon in Spark installations. To work around this, use a network-based channel like RabbitMQ or AWS SQS. These channels can work with multiple writers and single (or multiple) readers. The following example outlines how to set up agent monitoring on a Spark system using the MLOps Spark Util module, which provides a way to report scoring results on the Spark framework. Reference the documentation for the MLOpsSparkUtils module in the MLOps Java examples directory at `examples/java/SparkUtilsExample/`. The Spark example's source code performs three steps: 1. Given a scoring JAR file, it scores data and delivers results in a DataFrame. 2. Merges the feature's DataFrame and the prediction results into a single DataFrame. 3. Calls the mlops_spark_utils.MLOpsSparkUtils.reportPredictions helper to report the predictions using the merged DataFrame. You can use `mlops_spark_utils.MLOpsSparkUtils.reportPredictions` to report predictions generated by any model as long as the function retrieves the data via a DataFrame. This example uses RabbitMQ as the channel of communication and includes channel setup. Since Spark is a distributed framework, DataRobot requires a network-based channel like RabbitMQ or AWS SQS in order for the Spark workers to be able to send the monitoring data to the same channel regardless of the node the worker is running on. ### Spark prerequisites The following steps outline the prerequisites necessary to execute the Spark monitoring use case. 1. Run a spooler (RabbitMQ in this example) in a container: 2. Configure and start the monitoring agent. 3. If you are using mvn, install thedatarobot-mlopsJAR into your local mvn repository before testing the examples by running: ./examples/java/install_jar_into_maven.sh This command executes a shell script to install either themlops-utils-for-spark_2-.jarormlops-utils-for-spark_3-.jarfile, depending on the Spark version you're using (whererepresents the agent version). 4. Create the example JAR files, set theJAVA_HOMEenvironment variable, and then runmaketo compile. 5. Install and run Spark locally. NoteThe monitoring agent also supports Spark2. ### Spark use case After meeting the prerequisites outlined above, run the Spark example. 1. Create the model package and initialize the deployment: ./create_deployment.sh Alternatively,use the DataRobot UIto create an external model package and deploy it. 2. Set the environment variables for the deployment and the model returned from creating the deployment by copying and pasting them into your shell. exportMLOPS_DEPLOYMENT_ID=exportMLOPS_MODEL_ID= 3. Generate predictions and report statistics to DataRobot: run_example.sh 4. If you want to change the spooler type (the communication channel between the Spark job and the monitoring agent): ## Monitor using the MLOps CLI MLOps supports a command line interface (CLI) for interacting with the MLOps application. You can use the CLI for most MLOps actions, including creating deployments and model packages, uploading datasets, reporting metrics on predictions and actuals, and more. Use the MLOps CLI help page for a list of available operations and syntax examples: `mlops-cli [-h]` > [!NOTE] Monitoring agent vs. MLOps CLI > Like the monitoring agent, the MLOps CLI is also able to post prediction data to the MLOps service, but its usage is slightly different. MLOps CLI is a Python app that can send an HTTP request to the MLOps service with the current contents of the spool file. It does not run the monitoring agent or call any Java process internally, and it does not continuously poll or wait for new spool data; once the existing spool data is consumed, it exits. The monitoring agent, on the other hand, is a Java process that continuously polls for new data in the spool file as long as it is running, and posts that data in an optimized form to the MLOps service. --- # Installation and configuration URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent.html > How to install and configure the monitoring agent to forward buffered messages from the MLOps library to DataRobot MLOps. # Monitoring agent installation and configuration When the monitoring agent is running, it looks for buffered messages in the configured directory or a message queuing system and forwards them. To forward buffered messages from the MLOps library to DataRobot MLOps, install and configure the monitoring agent as indicated below. **Run on a host machine:** Unpack the MLOps .tar file: tar -xvf datarobot_mlops_package-*.tar.gz Update the configuration file: cd datarobot_mlops_package-* ; ./conf/mlops.agent.conf.yaml Configure the monitoring agent: In the agent configuration file, conf\mlops.agent.conf.yaml , you must update the values for mlopsUrl and apiToken . By default, the agent will use the filesystem channel. If you use the filesystem channel, make sure you create the spooler directory (by default, this is /tmp/ta ). Important For the filesystem spooler channel, the directory path you provide must be an absolute path (containing the complete directory list) for the agent to access the /tmp/ta directory (or a custom directory you create). If you want to use a different channel, follow the comments in the agent configuration file to update the path. mlops.agent.conf.yaml # This file contains configuration for the MLOps agent # URL to the DataRobot MLOps service mlopsUrl : "https://" # DataRobot API token apiToken : "" # Execute the agent once, then exit runOnce : false # When dryrun mode is true, do not report the metrics to MLOps service dryRun : false # When verifySSL is true, SSL certification validation will be performed when # connecting to MLOps DataRobot. When verifySSL is false, these checks are skipped. # Note: It is highly recommended to keep this config variable as true. verifySSL : true # Path to write agent stats statsPath : "/tmp/tracking-agent-stats.json" # Prediction Environment served by this agent. # Events and errors not specific to a single deployment are reported against this Prediction Environment. # predictionEnvironmentId: "" # Number of times the agent will retry sending a request to the MLOps service on failure. httpRetry : 3 # Http client timeout in milliseconds (30sec timeout) httpTimeout : 30000 # Number of concurrent http request, default=1 -> synchronous mode; > 1 -> asynchronous httpConcurrentRequest : 10 # Number of HTTP Connections to establish with the MLOps service, Default: 1 numMLOpsConnections : 1 # Comment out and configure the lines below for the spooler type(s) you are using. # Note: The spooler configuration must match that used by the MLOps library. # Note: The filesystem spooler directory must be an absolute path to the "/tmp/ta" directory. # Note: Spoolers must be set up before using them. # - For the filesystem spooler, create the directory that will be used. # - For the SQS spooler, create the queue. # - For the PubSub spooler, create the project and topic. # - For the Kafka spooler, create the topic. channelConfigs : - type : "FS_SPOOL" details : { name : "filesystem" , directory : "/tmp/ta" } # - type: "SQS_SPOOL" # details: {name: "sqs", queueUrl: "your SQS queue URL", queueName: ""} # - type: "RABBITMQ_SPOOL" # details: {name: "rabbit", queueName: , queueUrl: "amqp://", # caCertificatePath: "", # certificatePath: "", # keyfilePath: ""} # - type: "PUBSUB_SPOOL" # details: {name: "pubsub", projectId: , topicName: , subscriptionName: } # - type: "KAFKA_SPOOL" # details: {name: "kafka", topicName: "", bootstrapServers: ",,..."} # The number of threads that the agent will launch to process data records. agentThreadPoolSize : 4 # The maximum number of records each thread will process per fetchNewDataFreq interval. agentMaxRecordsTask : 100 # Maximum number of records to aggregate before sending to DataRobot MLOps agentMaxAggregatedRecords : 500 # A timeout for pending records before aggregating and submitting agentPendingRecordsTimeoutMs : 5000 **Run natively in Docker:** To run the monitoring agent natively in Docker, first build the datarobot/mlops-tracking-agent image from the MLOps agent tarball: make build -C tools/agent_docker Configure the monitoring agent in Docker, mounted to the default directory or a custom location: To run the monitoring agent with the configuration mounted to the default directory: docker run \ -v /path/to/mlops.agent.conf.yaml:/opt/datarobot/mlops/agent/conf/mlops.agent.conf.yaml \ datarobot/mlops-tracking-agent To run the monitoring agent with the configuration mounted to a custom location: docker run \ -v /path/to/mlops.agent.conf.yaml:/var/tmp/mlops.agent.conf.yaml \ -e MLOPS_AGENT_CONFIG_YAML = /var/tmp/mlops.agent.conf.yaml \ datarobot/mlops-tracking-agent ## Use the monitoring agent Once the monitoring agent is configured, you can run the agent, check the agent status, and shut down the agent. ### Run the monitoring agent Start the agent using the config file: ``` cd datarobot_mlops_package-*; ./bin/start-agent.sh ``` Alternatively, start the agent using environment variables: ``` export AGENT_CONFIG_YAML= export AGENT_LOG_PROPERTIES= export AGENT_JVM_OPT=-Xmx4G export AGENT_JAR_PATH= ./bin/start-agent.sh ``` For a complete reference of the available environment variables, see [MLOps agent environment variables](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/env-var.html). ### Check the agent's status To check the agent's status: ``` # Check status ./bin/status-agent.sh ``` ``` # Check status with real-time resource usage ./bin/status-agent.sh --verbose ``` ### Shut down the agent To shut down the agent: ``` ./bin/stop-agent.sh ``` --- # Environment variables URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/env-var.html > Describes the environment variables specific to operating the monitoring agent. # Monitoring agent environment variables In addition to the environment variables used to configure the attached [spooler](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html), you can configure the monitoring agent with the environment variables documented below. ### General configuration When you run the agent using the provided `start-agent.sh` script from the `bin/` directory, the following environment configuration options are available: | Variable | Description | | --- | --- | | MLOPS_AGENT_CONFIG_YAML | The full path to a custom configuration YAML file. | | MLOPS_AGENT_LOG_DIR | The directory for writing the agent log file and stdout/error. | | JAVA_HOME | The Java Virtual Machine (JVM) to run the agent code. If you don't provide a JVM, Java should be included in the system PATH. | | MLOPS_MAX_FEATURES_TO_MONITOR | The maximum number of features the monitoring agent can send to DataRobot for monitoring. This value should not exceed 300, as that's the maximum number of features DataRobot can be configured to receive. For more information, see How many features can DataRobot track? | . ### Containerized configuration When you run the agent using the provided `Dockerfile` from the `tools/agent_docker/` directory, the following environment configuration options are available: | Variable | Description | | --- | --- | | MLOPS_AGENT_CONFIG_YAML | The full path to a custom configuration YAML file. | | MLOPS_AGENT_LOG_DIR | The directory for writing the agent log file and stdout/error. | | MLOPS_AGENT_TMP_DIR | The directory for writing temporary files (a useful override if the container runs with a read-only root filesystem). | | MLOPS_SERVICE_URL | Specify the service URL to access MLOps via this environment variable instead of specifying it in the YAML configuration file. | | MLOPS_API_TOKEN | Provide your API token through this environment variable instead of specifying it in the YAML configuration file. | | Advanced configuration | | | MLOPS_AGENT_JVM_OPT | Configure to override the default JVM option -Xmx1G. | | MLOPS_AGENT_LOGGING_CONFIG | Specify a full path to a completely custom Log4J2 configuration file for the MLOps monitoring agent. | | MLOPS_AGENT_LOGGING_FORMAT | If using our default logging configuration, you can set the logging output format to either plain or json. | | MLOPS_AGENT_LOGGING_LEVEL | If using our default logging configuration, set the overall logging level for the agent (e.g. trace, debug, info, warning, error). | | MLOPS_AGENT_LOG_PROPERTIES | Configure to override the default path to mlops.log4j2.properties. | | MLOPS_AGENT_SERVER_PORT | Set a free port number to activate the embedded HTTP server; this is useful for health and metric monitoring. | --- # Monitoring agent URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html > Set up a remote environment so you can use the monitoring agent to monitor external models. # Monitoring agent When you enable the monitoring agent feature, you have access to the agent installation and MLOps components, all packaged within a single tarball. The image below illustrates the roles of these components in enabling DataRobot MLOps to monitor external models. > [!NOTE] Java requirement > The MLOps monitoring library requires Java 11 or higher. Without monitoring, a model's Scoring Code JAR file requires Java 8 or higher; however, when using the MLOps library to instrument monitoring, a model's Scoring Code JAR file requires Java 11 or higher.For Self-managed AI platform installations, the Java 11 requirement applies to DataRobot v11.0 and higher. | | Component | Description | | --- | --- | --- | | (1) | External model | External models are machine learning models running outside of DataRobot, within your environment. The deployments (running in Python or Java) score data and generate predictions along with other information, such as the number of predictions generated and the length of time to generate each. | | (2) | DataRobot MLOps library | The MLOps library, available in Python (v2 and v3) and Java, provides APIs to report prediction data and information from a specified deployment (identified by deployment ID and model ID). Supported library calls for the MLOps client let you specify which data to report to the MLOps service, including prediction time, number of predictions, and other metrics and deployment statistics. | | (3) | Spooler (Buffer) | The library-provided APIs pass messages to a configured spooler (or buffer). | | (4) | Monitoring agent | The monitoring agent detects data written to the target buffer location and reports it to the MLOps service. | | (5) | DataRobot MLOps service | If the monitoring agent is running as a service, it retrieves the data as soon as it’s available; otherwise, it retrieves prediction data when it is run manually. | If models are running in isolation and disconnected from the network, the MLOps library will not have networked access from the buffer directory. For these deployments, you can manually copy prediction data from the buffer location via USB drive as needed. The agent then accesses that data as configured and reports it to the MLOps service. Additional monitoring agent configuration settings specify where to read data from and report data to, how frequently to report the data, and so forth. The flexible monitoring agent design ensures support for a variety of deployment and environment requirements. Finally, from the [deployment inventory](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html) you can view your deployments and view and manage prediction statistics and metrics. ## Monitoring agent requirements To use the monitoring agent with a remote deployment environment, you must provide: - The URL of DataRobot MLOps. (ForSelf-Managed AI Platforminstallations, this is typically of the formhttps://10.0.0.1orhttps://my-server-name.) - An API key from DataRobot. You can configure this through the UI by going to theAPI keys and toolstabunder account settings and finding theAPI Keyssection. Additionally, reference the documentation for [creating](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html#register-external-model-packages) and [deploying](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-external-model.html#deploy-an-external-model-package) a model package. ## MLOps agent tarball You can download the MLOps agent tarball from two locations: - The API keys and tools page - The Predictions > Monitoring tab of a deployment configured to monitor an external model The MLOps agent tarball contains the MLOps libraries for you to install. See [monitoring agent and prediction reporting setup](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html#monitoring-agent-and-prediction-reporting-setup) to configure the monitoring agent. > [!NOTE] Python library public download > You can download the MLOps Python libraries from the public [Python Package Index site](https://pypi.org). Download and install the [DataRobot MLOps metrics reporting library](https://pypi.org/project/datarobot-mlops) and the [DataRobot MLOps Connected Client](https://pypi.org/project/datarobot-mlops-connected-client). These pages include instructions for installing the libraries. > [!NOTE] Java library public download > You can download the MLOps Java library and agent from the public [Maven Repository](https://mvnrepository.com/) with a `groupId` of `com.datarobot` and an `artifactId` of `datarobot-mlops` (library) and `mlops-agent` (agent). In addition, you can access the [DataRobot MLOps Library](https://mvnrepository.com/artifact/com.datarobot/datarobot-mlops) and [DataRobot MLOps Agent](https://mvnrepository.com/artifact/com.datarobot/mlops-agent) artifacts in the Maven Repository to view all versions and download and install the JAR file. In addition to the MLOps library, the tarball includes Python and Java API examples and accompanying datasets to: - Create a deployment that generates (example) predictions for both regression and classification models. - Report metrics from deployments using the MLOps library. The tarball also includes scripts to: - Start and stop the agent, as well as retrieve the current agent status. - Create a remote deployment that uploads a training dataset and returns the deployment ID and model ID for the deployment. ## How the agent works This section outlines the basic workflow for using the monitoring agent from different environments. Using DataRobot MLOps: 1. Use the Model Registry to create a model package with information about your model's metadata. 2. Deploy the model package . Create a deployment to display metrics about the running model. 3. Use the deployment Predictions tab to view a code snippet demonstrating how to instrument your prediction code with the monitoring agent to report metrics. Using a remote deployment environment: 1. Install the monitoring agent. 2. Use the MLOps library to report metrics from your prediction code, as demonstrated by the snippet. The MLOps library buffers the metrics in a spooler (i.e., Filesystem, Rabbit MQ, Kafka, among others), which enables high throughput without slowing down the deployment. 3. The monitoring agent forwards the metrics to DataRobot MLOps. 4. You can view the reported metrics via the DataRobot MLOps Deploymentinventory . ## Monitoring agent and prediction reporting setup The following sections outline how to configure both the machine using the monitoring agent to upload data, and the machine using the MLOps library to report predictions. ### Monitoring agent configuration Complete the following workflow for each machine using the monitoring agent to upload data to DataRobot MLOps. This setup only needs to be performed once for each deployment environment. 1. Ensure that Java (version 8) is installed. 2. Download the MLOps agent tarball, available through the API keys and tools tab. The tarball includes the monitoring agent and library software, example code, and associated scripts. 3. Change the directory to the unpacked directory. 4. Install the monitoring agent . 5. Configure the monitoring agent . 6. Run the agent service . ### Host predictions For each machine using the MLOps library to report predictions, ensure that appropriate libraries and requirements are installed. There are two locations where you can obtain the libraries: **MLOps agent tarball (for Java and Python):** Download the MLOps agent tarball and install the libraries: Java : The Java library is included in the .tar file in lib/datarobot-mlops-.jar . Python : The Python version of the library is included in the .tar file in lib\datarobot_mlops-*-py2.py3-none-any.whl . This works for both Python2 and Python3. You can install it using: pip install lib\datarobot_mlops-*-py2.py3-none-any.whl **Python Package Index (for Python):** Download the MLOps Python libraries from the [Python Package Index site](https://pypi.org): DataRobot MLOps metrics reporting library Download and then install: pip install datarobot-mlops DataRobot MLOps Connected client (mlops-cli) Download and then install: pip install datarobot-mlops-connected-client The MLOps agent `.tar` file includes [several end-to-end examples](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent-ex.html) in various languages. ### Create and deploy a model package A model package stores metadata about your external model: the problem type (e.g., regression), the training data used, and more. You can create a model package using the [Model Registry](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html) and deploy it. In the deployment's Integrations tab, you can view example code as well as the values for the `MLOPS_DEPLOYMENT_ID` and `MLOPS_MODEL_ID` that are necessary to report statistics from your deployment. If you wish to instead create a model package using the API, you can follow the pattern used in the [helper scripts](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent-ex.html) in the examples directory for creating model packages and deployments. Each example has its own `create_deployment.sh` script to create the related model package and deployment. This script interacts with DataRobot MLOps directly and so must be run on a machine with connectivity to it. When run, each script outputs a deployment ID and model ID that are then used by the `run_example.sh` script, in which the model inference and subsequent metrics reporting actually happens. When creating and deploying an external model package, you can upload the data used to train the model: the training dataset, the holdout dataset, or both. When you upload this data, it is used to monitor the model. The datasets you upload provide the following functionality: - Training dataset: Provides the baseline for feature drift monitoring. - Holdout dataset: Provides the predictions used as a baseline for accuracy monitoring. You can find examples of the expected data format for holdout datasets in the `examples/data` folder of the agent tar file: - mlops-example-lending-club-holdout.csv: Demonstrates the holdout data format for regression models. - mlops-example-iris-holdout.csv: Demonstrates the holdout data format for classification models. NoteFor classification models, you must provide the predictions for all classes. ### Instrument deployments with the monitoring agent To configure the monitoring agent with each deployment: 1. Locate the MLOps library and sample code. These are included within the MLOps .tar file distribution. 2. Configure the deployment ID and model ID in your environment. 3. Instrument your code with MLOps calls as shown in the sample code provided for your programming language. 4. To report results to DataRobot MLOps, you must configure the library to use the same channel as is configured in the agent. For testing, you can configure the library to output to stdout though these calls will not be forwarded to the agent or DataRobot MLOps. Configure the library via the :ref: mlops API . 5. You can view your deployment in the DataRobot MLOps UI under the Deployments tab. --- # Library and agent spooler configuration URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html > How to configure the MLOps library and monitoring agent spooler so that the library can communicate with the agent through the spooler. # MLOps library and agent spooler configuration The MLOps library communicates to the agent through a spooler, so it is important that the [agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#mlops-agent-configuration) and [library](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#mlops-library-configuration) spooler configurations match. When configuring the MLOps agent and library's spooler settings, some settings are required, and some are optional (optional settings are identified in each table under Optional configuration). The required settings can be configured programmatically or through the environment variables documented in the [General configuration](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#general-configuration) and [Spooler-specific configurations](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#spooler-specific-configurations) sections. If you configure any settings programmatically and by defining an environment variable, the environment variable takes precedence. > [!NOTE] Java requirement > The MLOps monitoring library requires Java 11 or higher. Without monitoring, a model's Scoring Code JAR file requires Java 8 or higher; however, when using the MLOps library to instrument monitoring, a model's Scoring Code JAR file requires Java 11 or higher.For Self-managed AI platform installations, the Java 11 requirement applies to DataRobot v11.0 and higher. MLOps agent and library communication can be configured to use any of the following spoolers: - Filesystem - Amazon SQS - RabbitMQ - Google Cloud Pub/Sub - Apache Kafka - Azure Event Hubs - DataRobot API ## MLOps agent configuration When running the monitoring agent as a separate service, specify the spooler configuration in `mlops.agent.conf.yaml` by uncommenting the `channelConfigs` section and entering the required configs. For more information on setting the `channelConfigs` see [Configure the monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/agent.html#configure-the-monitoring-agent). ## MLOps library configuration The MLOps library can be configured programmatically or by using environment variables. To configure the spooler programmatically, specify the spooler during the MLOps `init` call; for example, to configure the filesystem spooler using the Python library: ``` mlops = MLOps().set_filesystem_spooler("your_spooler_directory").init() ``` > [!NOTE] Note > You must create the directory specified in the code above; the program will not create it for you. Equivalent interfaces exist for other spooler types. To configure the MLOps library and agent using environment variables, see the [General configuration](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#general-configuration) and [Spooler-specific configurations](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#spooler-specific-configurations) sections. ## General configuration Use the following environment variables to configure the MLOps agent and library and to select a spooler type: | Variable | Description | | --- | --- | | MLOPS_DEPLOYMENT_ID | The deployment ID of the DataRobot deployment that should receive metrics from the MLOps library. | | MLOPS_MODEL_ID | The model ID of the DataRobot model that should be reported on by the MLOps library. | | MLOPS_SPOOLER_TYPE | The spooler type that the MLOps library will use to communicate with the monitoring agent. The following are valid spooler types: FILESYSTEM: Enable local filesystem spooler.SQS: Enable Amazon SQS spooler.RABBITMQ: Enable RabbitMQ spooler.KAFKA: Enable Apache Kafka or Azure Event Hubs spooler.PUBSUB: Enable Google Cloud Pub/Sub spooler.NONE: Disable MLOps library reporting.STDOUT: Print the reported metrics to stdout rather than forward them to the agent.API: Enable DataRobot API spooler.. | | Optional configuration | | | MLOPS_SPOOLER_DEQUEUE_ACK_RECORDS | Ensure that the monitoring agent does not dequeue a record until processing is complete. Set this option to true to ensure records are not dropped due to connection errors. Enabling this option is highly recommended. The dequeuing operation behaves as follows for the spooler channels: SQS: Deletes a message.RABBITMQ and PUBSUB: Acknowledges the message as complete.KAFKA and FILESYSTEM: Moves the offset. | | MLOPS_ASYNC_REPORTING | Enable the MLOps library to asynchronously report metrics to the spooler. | | MLOPS_FEATURE_DATA_ROWS_IN_ONE_MESSAGE | The number of feature rows that will be in a single message to the spooler. | | MLOPS_SPOOLER_CONFIG_RECORD_DELIMITER | The delimiter to replace the default value of ; between key-value pairs in a spooler configuration string (e.g., key1=value1;key2=value2 to key1=value1:key2=value2). | | MLOPS_SPOOLER_CONFIG_KEY_VALUE_SEPARATOR | The separator to replace the default value of = between keys and values in a spooler configuration string (e.g., key1=value1 to key1:value1). | > [!NOTE] Note > Setting the environment variable here takes precedence over variables definitions specified in the configuration file or configured programmatically. After setting a spooler type, you can configure the spooler-specific environment variables. ## Spooler-specific configurations Depending on the `MLOPS_SPOOLER_TYPE` you set, you can provide configuration information as environment variables unique to the supported spoolers. ### Filesystem Use the following environment variable to configure the `FILESYSTEM` spooler: | Variable | Description | | --- | --- | | MLOPS_FILESYSTEM_DIRECTORY | The directory to store the metrics to report to DataRobot. You must create this directory; the program will not create it for you. | | Optional configuration | | | MLOPS_FILESYSTEM_MAX_FILE_SIZE | Override the default maximum file size (in bytes). Default value: 1 GB | | MLOPS_FILESYSTEM_MAX_NUM_FILE | Override the default maximum number of files. Default value: 10 files | > [!NOTE] Programmatic configuration of the filesystem spooler > You can also [programmatically configure the filesystem spooler for the MLOps library](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#mlops-library-configuration). ### Amazon SQS When using Amazon `SQS` as a spooler, you can provide your credential set in either of two ways: - Set your credentials in theAWS_ACCESS_KEY_ID,AWS_SECRET_ACCESS_KEY, andAWS_REGIONorAWS_DEFAULT_REGIONenvironment variables. Only AWS software packages use these credentials; DataRobot doesn't access them. - If you are in an AWS environment, create anAWS IAM (Identity and Access Management) rolefor credential authentication. Use one of the following environment variables to configure the `SQS` spooler: | Variable | Description | | --- | --- | | MLOPS_SQS_QUEUE_URL | The URL of the SQS queue used for the spooler. | | MLOPS_SQS_QUEUE_NAME | The queue name of the SQS queue used for the spooler. | > [!NOTE] Note > When using the `SQS` spooler type, only provide the spooler name or the URL. ### RabbitMQ Use the following environment variables to configure the `RABBITMQ` spooler: | Variable | Description | | --- | --- | | MLOPS_RABBITMQ_QUEUE_URL | The URL of the RabbitMQ queue used for the spooler. | | MLOPS_RABBITMQ_QUEUE_NAME | The queue name of the RabbitMQ queue used for the spooler. | | Optional configuration | | | MLOPS_RABBITMQ_SSL_CA_CERTIFICATE_PATH | The path to the CA certificate file (.pem file). | | MLOPS_RABBITMQ_SSL_CERTIFICATE_PATH | The path to the client certificate (.pem file). | | MLOPS_RABBITMQ_SSL_KEYFILE_PATH | The path to the client key (.pem file). | | MLOPS_RABBITMQ_SSL_TLS_VERSION | The TLS version used for the client. The TLS version must match server version. | > [!NOTE] Note > RabbitMQ configuration requires keys in RSA format without a password. You can convert keys from PKCS8 to RSA as follows: > > `openssl rsa -in mykey_pkcs8_format.pem -text > mykey_rsa_format.pem` > > To generate keys, see [RabbitMQ TLS Support](https://www.rabbitmq.com/ssl.html#automated-certificate-generation). ### Google Cloud Pub/Sub When using Google Cloud `PUBSUB` as a spooler, you must provide the appropriate credentials in the `GOOGLE_APPLICATION_CREDENTIALS` environment variable. Only Google Cloud software packages use these credentials; DataRobot doesn't access them. Use the following environment variables to configure the `PUBSUB` spooler: | Variable | Description | | --- | --- | | MLOPS_PUBSUB_PROJECT_ID | The Pub/Sub project ID of the project used by the spooler; this should be the full path of the project ID. | | MLOPS_PUBSUB_TOPIC_NAME | The Pub/Sub topic name of the topic used by the spooler; this should be the topic name within the project, not the fully qualified topic name path that includes the project ID. | | MLOPS_PUBSUB_SUBSCRIPTION_NAME | The Pub/Sub subscription name of the subscription used by the spooler. | > [!NOTE] Pub/Sub service account permissions > The following service account permissions are required to use Google Cloud Pub/Sub as a spooler for the monitoring agent: > > Level > Permission(s) > Project > Project Viewer > Topic > Pub/Sub Publisher > Subscription > Pub/Sub Viewer, Pub/Sub Subscriber > > The base permissions required for communicating with the monitoring agent outside of DataRobot are Pub/Sub Publisher and Pub/Sub Subscriber.Pub/Sub Viewer and Project Viewer are required by [DataRobot prediction environments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/pred-env.html#add-a-new-external-prediction-environment). ### Apache Kafka Use the following environment variables to configure the Apache `KAFKA` spooler: | Variable | Description | | --- | --- | | MLOPS_KAFKA_TOPIC_NAME | The name of the specific Kafka topic to produce to or consume from. Apache Kafka Reference: Main Concepts and Terminology | | MLOPS_KAFKA_BOOTSTRAP_SERVERS | The list of servers that the agent connects to. Use the same syntax as the bootstrap.servers config used upstream. Apache Kafka Reference: bootstrap.servers | | Optional configuration | | | MLOPS_KAFKA_CONSUMER_POLL_TIMEOUT_MS | The amount of time to wait while consuming messages before processing them and sending them to DataRobot Default value: 3000 ms. | | MLOPS_KAFKA_CONSUMER_GROUP_ID | A unique string that identifies the consumer group this consumer belongs to. Default value: tracking-agent. Apache Kafka Reference: group.id | | MLOPS_KAFKA_CONSUMER_MAX_NUM_MESSAGES | The maximum number of messages to consume at one time before processing them and sending the results to DataRobot MLOps. Default value: 500 Apache Kafka Reference: max.poll.records | | MLOPS_KAFKA_SESSION_TIMEOUT_MS | The timeout used to detect client failures in the consumer group. Apache Kafka Reference: session-timeout.ms | | MLOPS_KAFKA_MESSAGE_BYTE_SIZE_LIMIT | The maximum chunk size when producing events to the channel. Default value: 1000000 bytes | | MLOPS_KAFKA_DELIVERY_TIMEOUT_MS | The absolute upper bound amount of time to send messages before considering it permanently failed. Apache Kafka Reference: delivery.timeout.ms | | MLOPS_KAFKA_REQUEST_TIMEOUT_MS | The maximum amount of time a client will wait for a response to a request before retrying. Apache Kafka Reference: request.timeout.ms | | MLOPS_KAFKA_METADATA_MAX_AGE_MS | The maximum amount of time (in ms) the client will wait before refreshing its cluster metadata. Apache Kafka Reference: metadata.max.age.ms | | MLOPS_KAFKA_SECURITY_PROTOCOL | Protocols used to connect to the brokers. Apache Kafka Reference: security.protocol valid values. | | MLOPS_KAFKA_SASL_MECHANISM | The mechanism clients use to authenticate with the broker. Apache Kafka Reference: sasl.mechanism | | MLOPS_KAFKA_SASL_JAAS_CONFIG (Java only) | Connection settings in a format used by JAAS configuration files. Apache Kafka Reference: sasl.jaas.config | | MLOPS_KAFKA_SASL_LOGIN_CALLBACK_CLASS (Java only) | A custom login handler class. Apache Kafka Reference: sasl.login.callback.handler.class | | MLOPS_KAFKA_CONNECTIONS_MAX_IDLE_MS (Java only) | The maximum amount of time (in ms) before the client closes an inactive connection. This value should be set lower than any timeouts your network infrastructure may impose. Apache Kafka Reference: connections.max.idle.ms | | MLOPS_KAFKA_SASL_USERNAME (Python only) | SASL username for use with the PLAIN and SASL-SCRAM-* mechanisms. Reference: See the sasl.username setting in librdkafka. | | MLOPS_KAFKA_SASL_PASSWORD (Python only) | SASL password for use with the PLAIN and SASL-SCRAM-* mechanisms. Reference: See the sasl.password setting in librdkafka | | MLOPS_KAFKA_SASL_OAUTHBEARER_CONFIG (Python only) | Custom configuration to pass the OAuth login callback. Reference: See the sasl.oauthbearer.config setting in librdkafka | | MLOPS_KAFKA_SOCKET_KEEPALIVE (Python only) | Enable TCP keep-alive on network connections, sending packets over those connections periodically to prevent the required connections from being closed due to inactivity. Reference: See the socket.keepalive.enable setting in librdkafka | ### DataRobot API The process to configure the DataRobot API spooler is different than typical spooler configuration. Usually, the monitoring agent connects to the spooler, gathers information, and sends that information to DataRobot MLOps. Using the DataRobot API, you do not actually connect to a spooler, and the calls you make to the MLOps library are unchanged. The calls do not go to a spooler or the monitoring agent, and instead go directly to DataRobot MLOps via HTTPS. In this case, you do not need to configure a complex spooler and monitoring agent. Use the following parameters to configure the DataRobot Python API spooler: | Parameter | Description | | --- | --- | | MLOPS_SERVICE_URL | Specify the service URL to access MLOps via this environment variable instead of specifying it in the YAML configuration file. | | MLOPS_API_TOKEN | The DataRobot API key. | | VERIFY SSL (Boolean) | (Optional) Determines whether the client should verify the SSL connection. Default value: True | | Optional configuration | | | MLOPS_HTTP_RETRY | The number of retries for a successful call. Default value: 3 | | API_POST_TIMEOUT_SECONDS | Sets the timeout value. Default value: 30 | | API_HTTP_RETRY_WAIT_SECONDS | Determines how long to wait after a timeout before retrying. Default value: 1 | ### Azure Event Hubs DataRobot allows you to use Microsoft Azure Event Hubs as a monitoring agent spooler by leveraging the existing [Kafka spooler type](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#kafka). To set this up, see [Using Azure Event Hubs from Apache Kafka applications](https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-for-kafka-ecosystem-overview). > [!NOTE] Note > Azure supports the Kafka protocol for Event Hubs only for the Standard and Premium pricing tiers. The Basic tier does not offer Kafka API support, so it is not supported as a spooler for the monitoring agent. See [Azure Event Hubs quotas and limits](https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-quotas) for details. To use Azure Event Hubs as a spooler, you need to set up authentication for the monitoring agent and MLOps library using one of these methods: - SAS-based authentication - Azure Active Directory OAuth 2.0 #### SAS-based authentication for Event Hubs To use Event Hubs SAS-based authentication for the monitoring agent and MLOps library, set the following environment variables using the example shell fragment below: ``` # Sample environment variables script for SAS-based authentication # Azure recommends setting the following values; see: # https://docs.microsoft.com/en-us/azure/event-hubs/apache-kafka-configurations export MLOPS_KAFKA_REQUEST_TIMEOUT_MS='60000' export MLOPS_KAFKA_SESSION_TIMEOUT_MS='30000' export MLOPS_KAFKA_METADATA_MAX_AGE_MS='180000' # Common configuration variables for both Java- and Python-based libraries. export MLOPS_KAFKA_BOOTSTRAP_SERVERS='XXXX.servicebus.windows.net:9093' export MLOPS_KAFKA_SECURITY_PROTOCOL='SASL_SSL' export MLOPS_KAFKA_SASL_MECHANISM='PLAIN' # The following setting is specific to the Java SDK (and the monitoring agent daemon) export MLOPS_KAFKA_SASL_JAAS_CONFIG='org.apache.kafka.common.security.plain.PlainLoginModule required username="$ConnectionString" password="Endpoint=sb://XXXX.servicebus.windows.net/;SharedAccessKeyName=XXXX;SharedAccessKey=XXXX";' # For the Python API client, you will need the following settings (in addition to the common ones above) export MLOPS_KAFKA_SASL_USERNAME='$ConnectionString' export MLOPS_KAFKA_SASL_PASSWORD='Endpoint=sb://XXXX.servicebus.windows.net/;SharedAccessKeyName=XXX;SharedAccessKey=XXXX' ``` > [!NOTE] Note > The environment variable values above use single-quotes ( `'`) to ensure that the special characters `$` and `"` are not interpreted by the shell when setting variables. If you are setting environment variables via DataBricks, you should follow their [guidelines](https://docs.microsoft.com/en-us/azure/databricks/kb/clusters/validate-environment-variable-behavior) on escaping special characters for the version of the platform you are using. #### Azure Active Directory OAuth 2.0 for Event Hubs DataRobot supports Azure Active Directory OAuth 2.0 for Event Hubs authentication. To use this authentication method, you must create a new Application Registration with the necessary permissions over your Event Hubs Namespace (i.e., Azure Event Hubs Data Owner). See [Authenticate an application with Azure AD to access Event Hubs resources](https://docs.microsoft.com/en-us/azure/event-hubs/authenticate-application) for details. To use Event Hubs Azure Active Directory OAuth 2.0 authentication, set the following environment variables using the example shell fragment below: ``` # Sample environment variables script for Azure AD OAuth 2.0 authentication # Azure recommends setting the following values; see: # https://docs.microsoft.com/en-us/azure/event-hubs/apache-kafka-configurations export MLOPS_KAFKA_REQUEST_TIMEOUT_MS='60000' export MLOPS_KAFKA_SESSION_TIMEOUT_MS='30000' export MLOPS_KAFKA_METADATA_MAX_AGE_MS='180000' # Common configuration variables for both Java- and Python-based libraries. export MLOPS_KAFKA_BOOTSTRAP_SERVERS='XXXX.servicebus.windows.net:9093' export MLOPS_KAFKA_SECURITY_PROTOCOL='SASL_SSL' export MLOPS_KAFKA_SASL_MECHANISM='OAUTHBEARER' # The following setting is specific to the Java SDK (and the tracking-agent daemon) export MLOPS_KAFKA_SASL_JAAS_CONFIG='org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required aad.tenant.id="XXXX" aad.client.id="XXXX" aad.client.secret="XXXX";' export MLOPS_KAFKA_SASL_LOGIN_CALLBACK_CLASS='com.datarobot.mlops.spooler.kafka.ActiveDirectoryAuthenticateCallbackHandler' # For the Python API client, you will need the following settings (in addition to the common ones above) export MLOPS_KAFKA_SASL_OAUTHBEARER_CONFIG='aad.tenant.id=XXXX-XXXX-XXXX-XXXX-XXXX, aad.client.id=XXXX-XXXX-XXXX-XXXX-XXXX, aad.client.secret=XXXX' ``` > [!NOTE] Note > Some environment variable values contain double quotes ( `"`). Take care when setting environment variables that include this special character (or others). ## Dynamically load required spoolers in a Java application To configure Monitoring Agent spoolers using third-party code, you can dynamically load a separate JAR file for the required spooler. This configuration is required for the [Amazon SQS](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#amazon-sqs), [RabbitMQ](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#rabbitmq), [Google Cloud Pub/Sub](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#google-cloud-pubsub), and [Apache Kafka](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html#apache-kafka) spoolers. The natively supported file system spooler is configurable without loading a JAR file. > [!NOTE] Note > Previously, the `datarobot-mlops` and `mlops-agent` packages included all spooler types by default; however, that configuration meant the code was always present, even if it was unused. ### Include spooler dependencies in the project object model To use a third-party spooler in your MLOps Java application, you must include the required spoolers as dependencies in your POM (Project Object Model) file, along with `datarobot-mlops`: ``` # Dependencies in a POM file 8.3.0 com.datarobot datarobot-mlops ${mlops.version} com.datarobot spooler-sqs ${mlops.version} com.datarobot spooler-rabbitmq ${mlops.version} com.datarobot spooler-pubsub ${mlops.version} com.datarobot spooler-kafka ${mlops.version} ``` ### Provide an executable JAR file for the spooler The spooler JAR files are included in the [MLOps agent tarball](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html#mlops-agent-tarball). They are also available individually as downloadable JAR files in the public Maven repository for the [DataRobot MLOps Agent](https://mvnrepository.com/artifact/com.datarobot/mlops-agent). To use a third-party spooler with the executable agent JAR file, add the path to the spooler to the classpath: ``` # Classpath without spooler java ... -cp path/to/mlops-agent-8.2.0.jar com.datarobot.mlops.agent.Agent ``` ``` # Classpath with Kafka spooler java ... -cp path/to/mlops-agent-8.3.0.jar:path/to/spooler-kafka-8.3.0.jar com.datarobot.mlops.agent.Agent ``` The `start-agent.sh` script provided as an example automatically performs this task, adding any spooler JAR files found in the `lib` directory to the classpath. If your spooler JAR files are in a different directory, set the `MLOPS_SPOOLER_JAR_PATH` environment variable. **Troubleshoot MLOps applications:** If a dynamic spooler is loaded successfully, the Monitoring Agent logs an INFO message: Creating spooler type : success. If loading a dynamic spooler fails, the Monitoring Agent logs an ERROR message: Creating spooler type : failed , followed by the reason (a class not found error, indicating a missing dependency) or more details (a system exception message, helping you diagnose the issue). If the class was not found, ensure the dependency for the spooler is included in the application's POM. Missing dependencies will not be discovered until runtime. **Troubleshooting the Monitoring Agent:** If a dynamic spooler is loaded successfully, the Monitoring Agent logs an INFO message: Creating spooler type : success. If loading a dynamic spooler fails, the Monitoring Agent logs an ERROR message: Creating spooler type : failed , followed by the reason (a class not found error, indicating a missing JAR file) or more details (a system exception message, helping you diagnose the issue). If the class was not found, ensure the matching JAR file for that spooler is included in the classpath of the java command that starts the agent. > [!TIP] Tip > If the agent is configured with a `predictionEnvironmentId` and can connect to DataRobot, the agent sends an `MLOps Spooler Channel Failed` event to DataRobot MLOps with information from the log message. These events appear in the [event log on the Service Health page of any deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/agent-event-log.html) associated with that prediction environment. You can also create a notification channel and policy to be notified (by email, Slack, or webhook) of these errors. --- # Add external prediction environments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/ext-pred-env.html > Manage and control user access to environments on the prediction environment dashboard and specify the prediction environment for any deployment. # Add external prediction environments Models that run on your own infrastructure (outside of DataRobot) may be run in different environments and can have differing deployment permissions and approval processes. For example, while any user may have permission to deploy a model to a test environment, deployment to production may require a strict approval workflow and only be permitted by those authorized to do so. External prediction environments support this deployment governance by grouping deployment environments and supporting grouped deployment permissions and approval workflows. ## Add an external prediction environment On the Prediction Environments page, you can review the DataRobot prediction environments available to you and create external prediction environments for both DataRobot models running on the [Portable Prediction Server](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html) and remote models monitored by the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html). To deploy models on external infrastructure, you create a custom external prediction environment: 1. ClickDeployments > Prediction Environmentsand then click+ Add prediction environment. 2. In theAdd prediction environmentdialog box, complete the following fields: FieldDescriptionNameEnter a descriptive prediction environment name.Description(Optional) Enter a description of the external prediction environment.PlatformSelect the external platform on which the model is running and making predictions. 3. UnderSupported Model Formats, select one or more formats to control which models can be deployed to the prediction environment, either manually or using the management agent. The available model formats areDataRobotorDataRobot Scoring Code,External Model, andCustom Model. ImportantYou can only select one ofDataRobotorDataRobot Scoring Code. 4. (Optional) If you want to manage your external model with DataRobot MLOps, clickUse Management Agentto allow theMLOps Management Agentto automate the deployment, replacement, and monitoring of models in this prediction environment. 5. Once you configure the environment settings, clickAdd environment. The environment is now available from the Prediction Environments page. ## Select a prediction environment for a deployment After you add a prediction environment to DataRobot, you can [deploy a model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/index.html) and use the prediction environment for the deployment. Specify the prediction environment in the Prediction History and Service Health section: > [!NOTE] Prediction environments for external models > External models run outside DataRobot and must be deployed to an external prediction environment. Do not deploy external models to a DataRobot Serverless prediction environment. > [!WARNING] Changing the prediction environment > After you specify a prediction environment and create the deployment, you cannot change the prediction environment. --- # Manage prediction environments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/index.html > On the Prediction Environment, view DataRobot prediction environments, and create, edit, delete, or share external prediction environments. # Manage prediction environments From the Prediction environments page, you can view DataRobot prediction environments, and create, edit, delete, or share serverless and external prediction environments. You can also review the DataRobot prediction environments available to your organization by locating the environments with DataRobot in the Platform column: These prediction environments are created by DataRobot and cannot be configured; however, you can [deploy models to these prediction environments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/pred-env-deploy.html) from this page. | Topic | Description | | --- | --- | | Add DataRobot Serverless prediction environments | Set up DataRobot Serverless prediction environments and deploy models to those environments to make predictions. | | Add external prediction environments | Set up prediction environments on your own infrastructure, group prediction environments, and configure permissions and approval workflows. | | Manage prediction environments | View, edit, delete, and share external prediction environments, or deploy models to external prediction environments. | | Deploy a model to a prediction environment | Access a prediction environment and deploy a model directly to the environment. | | Prediction environment integrations | Configure DataRobot-managed prediction environment integrations to deploy and replace DataRobot models. | --- # Deploy a model to a prediction environment URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/pred-env-deploy.html > On the Prediction Environments page, you can deploy a model directly to any prediction environment, DataRobot or external. # Deploy a model to a prediction environment On the Prediction Environments page, you can deploy a model directly to any prediction environment, DataRobot or external. To deploy a model directly to a prediction environment: 1. On thePrediction Environmentspage, click the environment you want to deploy a model to. 2. On theDetailstab, underUsages, in theDeploymentcolumn, click+ Add new deployment. 3. In theSelect model version from the registrydialog box, enter the name of the model you want to deploy in theSearchbox, click the model, and then click the model version you want to deploy. 4. ClickSelect model versionand thenconfigure the deployment settings. 5. ClickDeploy model. > [!TIP] Alternate deployment methods > If you don't want to deploy from the Prediction Environments page, you can deploy a model from the [Leaderboard](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html) or the [Model Registry](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-deploy.html). --- # Automated deployment and replacement of Scoring Code in AzureML URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/pred-env-integrations/azureml-sc-deploy-replace.html > Create a DataRobot-managed AzureML prediction environment to deploy and replace DataRobot Scoring Code in AzureML. # Automated deployment and replacement of Scoring Code in AzureML > [!NOTE] Premium > Automated deployment and replacement of Scoring Code in AzureML is a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable the Automated Deployment and Replacement of Scoring Code in AzureML ( Premium feature) Create a DataRobot-managed AzureML prediction environment to deploy DataRobot Scoring Code in AzureML. With DataRobot management enabled, the external AzureML deployment has access to MLOps features, including automatic Scoring Code replacement. > [!NOTE] Service health information for external models and monitoring jobs > Service health information is unavailable for external [agent-monitored deployments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html) and deployments with predictions uploaded through a [prediction monitoring job](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/pred-monitoring-jobs/index.html). ## Create an AzureML prediction environment To deploy a model in AzureML, you first create a custom AzureML prediction environment: 1. ClickDeployments > Prediction Environmentsand then clickAdd prediction environment. 2. In theAdd prediction environmentdialog box, configure the prediction environment settings: 3. Configure theAzure Subscription,Azure Resource Group, andAzureML Workspacefields accessible using the providedCredentials. 4. (Optional) If you want to connect to and retrieve data from Azure Event Hubs for monitoring, configure theEvent Hubs Namespace,Event Hubs Instance, andManaged Identitiesfields. This requires validCredentials, anAzure Subscription ID, and anAzure Resource Group. 5. (Optional) If you are using tags for governance and resource management in AzureML, clickAdd AzureML tagsand then+ Add new tagto add the required tags to the prediction environment. 6. After you configure the environment settings, clickAdd environment. The AzureML environment is now available from thePrediction Environmentspage. ## Deploy a model to the AzureML prediction environment Once you've created an AzureML prediction environment, you can deploy a model to it: 1. ClickModel Registry > Registered Modelsand select the Scoring Code enabled registered model version you want to deploy to the AzureML prediction environment. TipYou can also deploy a model to your AzureML prediction environment from theDeployments>Prediction Environmentstab by clicking+ Add new deploymentin the prediction environment. 2. On any tab in the registered model version, clickDeploy. 3. In theSelect Deployment Targetdialog box, underSelect deploy target, clickAzureML. NoteIf you can't click the AzureML deployment target, the selected model doesn't have Scoring Code available. 4. UnderSelect prediction environment, select the AzureML prediction environment you added, and then clickConfirm. 5. Configure the deploymentand, in thePrediction History and Service Healthsection, underEndpoint, click+ Add endpoint. 6. In theSelect endpointdialog box, define anOnlineorBatchendpoint, depending on your expected workload, and then clickNext. 7. (Optional) Define additionalEnvironment key-value pairsto provide extra parameters to the Azure deployment interface, then clickConfirm. 8. ClickDeploy model. While the deployment isLaunching, you can monitor the status events on theService Healthtab inRecent Activity > Agent Activity: ## Make predictions in AzureML After you deploy a model to an AzureML prediction environment, you can use the code snippet from the Predictions > Portable Predictions tab to score data in AzureML. Before you run the code snippet, you must provide the required credentials in either of the following ways: - Export the Azure Service Principal’s secrets as environment variables locally before running the snippet: Environment variableDescriptionAZURE_CLIENT_IDTheApplication IDin theApp registration > Overviewtab.AZURE_TENANT_IDTheDirectory IDin theApp registration > Overviewtab.AZURE_CLIENT_SECRETThe secret token generated in theApp registration > Certificates and secretstab. - Install theAzure CLI, and run theaz logincommand to allow the portable predictions snippet to use your personal Azure credentials. > [!NOTE] Important > Deployments to AzureML Batch and Online endpoints utilize different APIs than standard DataRobot deployments. > > Online endpoints support JSON or CSV as input and outputs results to JSON. > Batch endpoints support CSV input and output the results to a CSV file. --- # Prediction environment integrations URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/pred-env-integrations/index.html > Configure DataRobot-managed prediction environment integrations to deploy and replace DataRobot models. # Prediction environment integrations Configure DataRobot-managed prediction environment integrations to deploy and replace DataRobot models. | Integration | Description | | --- | --- | | AzureML | Create a DataRobot-managed AzureML prediction environment to deploy and replace DataRobot Scoring Code in AzureML. | | Sagemaker | Create a DataRobot-managed Sagemaker prediction environment to deploy and replace DataRobot custom models and Scoring Code in Sagemaker. | | Snowflake | Preview feature. Create a DataRobot-managed Snowflake prediction environment to deploy and replace DataRobot Scoring Code in Snowflake. | ## Feature considerations - Challenger models and model replacement are not supported (challenger prediction servers can't be set to an external or serverless prediction environment). - Only CSV files are supported for predictions. XLSX files are not supported by the code snippet. - On theService healthtab, information such as latency, throughput, and error rate is unavailable for external, agent-monitored deployments. --- # Automated deployment and replacement in Sagemaker URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/pred-env-integrations/sagemaker-cm-deploy-replace.html > Create a DataRobot-managed Sagemaker prediction environment to deploy and replace DataRobot custom models and Scoring Code in Sagemaker. # Automated deployment and replacement in Sagemaker > [!NOTE] Premium > Automated deployment and replacement of custom models and Scoring Code in Sagemaker is a premium feature. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable the Automated Deployment and Replacement of Custom Models in Sagemaker ( Premium feature) Now available for preview, you can create a DataRobot-managed Sagemaker prediction environment to deploy custom models and Scoring Code in Sagemaker with real-time inference and serverless inference. With DataRobot management enabled, the external Sagemaker deployment has access to MLOps management, including automatic model replacement. > [!NOTE] Service health information for external models and monitoring jobs > Service health information is unavailable for external [agent-monitored deployments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html) and deployments with predictions uploaded through a [prediction monitoring job](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/pred-monitoring-jobs/index.html). ## Create a Sagemaker prediction environment To deploy a model in Sagemaker, first create a custom Sagemaker prediction environment: 1. ClickDeployments > Prediction Environmentsand then clickAdd prediction environment. 2. In theAdd prediction environmentdialog box, configure the prediction environment settings: 3. After configuring the environment settings, clickAdd environment. The Sagemaker environment is now available from thePrediction Environmentspage. ## Deploy a model to the Sagemaker prediction environment Once you've created a Sagemaker prediction environment, you can deploy a model to it: 1. ClickModel Registry > Registered Modelsand select either the custom model or the Scoring Code enabled model you want to deploy to the Sagemaker prediction environment. TipYou can also deploy a model to your Sagemaker prediction environment from theDeployments > Prediction Environmentstab by clicking+ Add new deploymentin the prediction environment. 2. On any tab in the registered model version, clickDeploy. 3. In theSelect Deployment Targetdialog box, underSelect deploy target, clickSagemaker. 4. UnderSelect prediction environment, select the Sagemaker prediction environment you previously configured, and then clickConfirm. 5. Configure the deployment. When deploying to Sagemaker prediction environments, you must specify theReal-time inference instance typeandInitial instance countfields. When deployment configuration is complete, clickDeploy model. 6. Once the model is deployed to Sagemaker, you can use theScore your datacode snippet from thePredictions > Portable Predictionstab to score data in Sagemaker. ## Restart a Sagemaker prediction environment When you update database settings or credentials for the Sagemaker data connection used by the prediction environment, you can restart the environment to apply those changes to the environment: 1. ClickDeployments > Prediction Environmentspage, and then select the Sagemaker prediction environment from the list. 2. Below the prediction environment settings, underService Account, clickRestart Environment. --- # Manage prediction environments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/pred-env-manage.html > On the Prediction Environments page, you can view your organization's DataRobot prediction environments; edit, delete, or share external prediction environments; and deploy models directly to prediction environments. # Manage predictions environments On the Prediction Environments page, in addition to viewing your organization's DataRobot prediction environments and creating new external environments, you can edit, delete, or share external prediction environments. You can also [deploy models to prediction environments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/pred-env-deploy.html). ## Edit a prediction environment To edit the prediction environment details you set when you created the environment and to assign a Service Account, navigate to the Deployments > Prediction Environments page and click the row containing the prediction environment you want to edit: - Name: Update the external prediction environment name you set when creating the environment. - Description: Update the external prediction environment description or add one if you haven't already. - Platform: Update the external platform you selected when creating the external prediction environment. - Service Account: Select the account that should have access to each deployment within this prediction environment. Only owners of the current prediction environment are available in the list of service accounts. > [!NOTE] Note > DataRobot recommends using an administrative service account as the account holder (an account that has access to each deployment that uses the configured prediction environment). ## Share a prediction environment The sharing capability allows [appropriate user roles](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#custom-model-and-environment-roles) to grant permissions for prediction environments. When you have created a prediction environment and want to share it with others, select Share ( [https://docs.datarobot.com/en/docs/images/icon-share.png](https://docs.datarobot.com/en/docs/images/icon-share.png)) from the dashboard. This takes you to the sharing window, which lists each associated user and their role. To remove a user, click the X button to the right of their role. To re-assign a user's role, click on the assigned role and assign a new one from the dropdown. To add a new user, enter their username in the Share with field and choose their role from the dropdown. Then click Share. This action initiates an email notification. ## Delete a prediction environment To delete a prediction environment, take the following steps: 1. Navigate to theDeployments > Prediction Environmentspage. 2. Next to the prediction environment you want to delete, click the delete icon. 3. In theDeletedialog box: --- # Add DataRobot Serverless prediction environments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/pred-env.html > Review the DataRobot prediction environments available to you and create DataRobot Serverless prediction environments to make scalable predictions with configurable compute instance settings. # Add DataRobot Serverless prediction environments On the Prediction Environments page, you can review the DataRobot prediction environments available to you and create DataRobot Serverless prediction environments to make scalable predictions with configurable compute instance settings. **Managed AI Platform (SaaS):** > [!NOTE] Pre-provisioned DataRobot Serverless environments > Organizations created after November 2024 have access to a pre-provisioned DataRobot Serverless prediction environment on the Prediction Environments page. **Trial:** > [!NOTE] Pre-provisioned DataRobot Serverless environments > Trial accounts have access to a pre-provisioned DataRobot Serverless prediction environment on the Prediction Environments page. **Self-Managed AI Platform:** > [!NOTE] Pre-provisioned DataRobot Serverless environments > New Self-Managed organizations running DataRobot 10.2+ installations have access to a pre-provisioned DataRobot Serverless prediction environment on the Prediction Environments page. > [!WARNING] Prediction intervals in DataRobot serverless prediction environments > In a DataRobot serverless prediction environment, to make predictions with time-series prediction intervals included, you must [include pre-computed prediction intervals when registering the model package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/dr-model-reg.html). If you don't pre-compute prediction intervals, the deployment resulting from the registered model doesn't support [enabling prediction intervals](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/predictions-settings.html#set-prediction-intervals-for-time-series-deployments). To add a DataRobot Serverless prediction environment: 1. ClickDeployments > Prediction Environmentsand then click+ Add prediction environment. 2. In theAdd prediction environmentdialog box, complete the following fields: FieldDescriptionNameEnter a descriptive name for the prediction environment.Description(Optional) Enter a description of the external prediction environment.PlatformSelectDataRobot Serverless.Batch jobsMax Concurrent JobsDecrease the maximum number of concurrent jobs for this Serverless environment from the organization's defined maximum.PrioritySet the importance of batch jobs on this environment. How is the maximum concurrent job limit defined?There are two limits on max concurrent jobs and these limits depend on the details of your DataRobot installation. Each batch job is subject to both limits, meaning that the conditions of both must be satisfied for a batch job to run on the prediction environment. The first limit is theorganization-levellimit (default of30forSelf-Managedinstallations or10forSaaS) defined by an organization administrator; this should be the higher limit. The second limit is theenvironment-levellimit defined here by the prediction environment creator; this limit should be lower than the organization-level limit. 3. Once you configure the environment settings, clickAdd environment. The environment is now available from the Prediction Environments page. ## Deploy a model to the DataRobot Serverless prediction environment Using the pre-provisioned DataRobot Serverless environment, or a Serverless environment you created, you can deploy a model to make predictions. To deploy a model to the DataRobot Serverless prediction environment: 1. On thePrediction Environmentspage, in thePlatformrow, locate theDataRobot Serverlessprediction environments, and click the environment you want to deploy a model to. 2. On theDetailstab, underUsages, in theDeploymentcolumn, click+ Add new deployment. 3. In theSelect model version from the registrydialog box, enter the name of the model you want to deploy in theSearchbox, click the model, and then click theDataRobotmodel version you want to deploy. 4. ClickSelect model versionand thenconfigure the deployment settings. 5. To configure on-demand predictions on this environment, clickShow advanced options, scroll down toAdvanced Predictions Configuration, set the followingAutoscalingoptions, and then clickDeploy model: Autoscaling automatically adjusts the number of replicas in your deployment based on incoming traffic. During high-traffic periods, it adds replicas to maintain performance. During low-traffic periods, it removes replicas to reduce costs. This eliminates the need for manual scaling while ensuring your deployment can handle varying loads efficiently. **Basic autoscaling:** To configure autoscaling, modify the following settings. Note that for DataRobot models, DataRobot performs autoscaling based on CPU usage at a 40% threshold.: [https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-configure.png](https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-configure.png) Field Description Minimum compute instances (Premium feature) Set the minimum compute instances for the model deployment. If your organization doesn't have access to "always-on" predictions, this setting is set to 0 and isn't configurable. With the minimum compute instances set to 0 , the inference server will be stopped after an inactivity period of 7 days. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. Maximum compute instances Set the maximum compute instances for the model deployment to a value above the current configured minimum. To limit compute resource usage, set the maximum value equal to the minimum. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. **Advanced autoscaling (custom models):** To configure autoscaling, select the metric that will trigger scaling: CPU utilization: Set a threshold for the average CPU usage across active replicas. When CPU usage exceeds this threshold, the system automatically adds replicas to provide more processing power. HTTP request concurrency: Set a threshold for the number of simultaneous requests being processed. For example, with a threshold of 5, the system will add replicas when it detects 5 concurrent requests being handled. When your chosen threshold is exceeded, the system calculates how many additional replicas are needed to handle the current load. It continuously monitors the selected metric and adjusts the replica count up or down to maintain optimal performance while minimizing resource usage. Review the settings for CPU utilization below. [https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-cpu.png](https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-cpu.png) Field Description CPU utilization (%) Set the target CPU usage percentage that triggers scaling. When CPU utilization reaches this threshold, the system adds more replicas. Cool down period (minutes) Set the wait time after a scale-down event before another scale-down can occur. This prevents rapid scaling fluctuations when metrics are unstable. Minimum compute instances (Premium feature) Set the minimum compute instances for the model deployment. If your organization doesn't have access to "always-on" predictions, this setting is set to 0 and isn't configurable. With the minimum compute instances set to 0 , the inference server will be stopped after an inactivity period of 7 days. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. Maximum compute instances Set the maximum compute instances for the model deployment to a value above the current configured minimum. To limit compute resource usage, set the maximum value equal to the minimum. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. Review the settings for HTTP request concurrency below. [https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-http.png](https://docs.datarobot.com/en/docs/images/nxt-real-time-pred-http.png) Field Description HTTP request concurrency Set the number of simultaneous requests required to trigger scaling. When concurrent requests reach this threshold, the system adds more replicas. Cool down period (minutes) Set the wait time after a scale-down event before another scale-down can occur. This prevents rapid scaling fluctuations when metrics are unstable. Minimum compute instances (Premium feature) Set the minimum compute instances for the model deployment. If your organization doesn't have access to "always-on" predictions, this setting is set to 0 and isn't configurable. With the minimum compute instances set to 0 , the inference server will be stopped after an inactivity period of 7 days. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. Maximum compute instances Set the maximum compute instances for the model deployment to a value above the current configured minimum. To limit compute resource usage, set the maximum value equal to the minimum. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note. > [!NOTE] Premium feature: Always-on predictions > Always-on predictions are a premium feature. Deployment autoscaling management is required to configure the minimum compute instances setting. Contact your DataRobot representative or administrator for information on enabling the feature. > > Feature flag: Enable Deployment Auto-Scaling Management > [!NOTE] Compute instance configurations > For DataRobot model deployments: > > The default minimum is 0 and default maximum is 3. > The minimum and maximum limits are taken from the organization's > max_compute_serverless_prediction_api > setting. > > For custom model deployments: > > The default minimum is 0 and default maximum is 1. > The minimum and maximum limits are taken from the organization's > max_custom_model_replicas_per_deployment > setting. > The minimum is always greater than 1 when running on GPUs (for LLMs). > > Additionally, for high availability scenarios: > > The minimum compute instances setting > must > be greater than or equal to 2. > This requires business critical or consumption-based pricing. Depending on the availability of compute resources, it can take a few minutes after deployment for a prediction environment to be available for predictions. > [!TIP] Update compute instances settings > If, after deployment, you need to update the number of compute instances available to the model, you can change these settings on the [Predictions Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/predictions-settings.html) tab. ## Make predictions After you've created a DataRobot Serverless environment and deployed a model to that environment you can make real-time or batch predictions. > [!NOTE] Payload size limit > The maximum payload size for real-time deployment predictions on Serverless prediction environments is 50MB. For batch predictions, see [batch prediction limits](https://docs.datarobot.com/en/docs/api/reference/batch-prediction-api/index.html#limits). **Real-time predictions:** To make real-time predictions on the DataRobot Serverless prediction environment: In the Deployments inventory, locate and open a deployment associated with a DataRobot Serverless environment. To do this, click Filter , select DataRobot Serverless , and then click Apply filters . In a deployment associated with a DataRobot Serverless prediction environment, click Predictions > Prediction API . On the Prediction API Scripting Code page, under Prediction Type , click Real-time . Under Language , select Python or cURL , optionally enable Show secrets , and click Copy script to clipboard . Run the Python or cURL snippet to make a prediction request to the DataRobot Serverless deployment. **Batch predictions:** To make batch predictions on the DataRobot Serverless prediction environment, follow the standard process for [UI batch predictions](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/nxt-make-predictions.html) or [Prediction API scripting predictions](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/nxt-pred-api-snippets.html#batch-prediction-snippet-settings). --- # Register DataRobot models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/dr-model-reg.html > Register a model package in the Model Registry. The Model Registry is an archive of your model packages where you can also deploy and share the packages. # Register DataRobot models The Model Registry is an organizational hub for the variety of models used in DataRobot. Models are registered as deployment-ready model packages; the registry lists each package available for use. To add a DataRobot model as a registered model or version: 1. On theLeaderboard, select the model to use for generating predictions. DataRobot recommends a model with theRecommended for DeploymentandPrepared for Deploymentbadges. Themodel preparationprocess runs Feature Impact, retrains the model on a reduced feature list, and trains on a higher sample size, followed by the entire sample (latest data for date/time partitioned projects). ImportantTheDeploytab behaves differently in environments without a dedicated prediction server, as described in the section onshared modeling workers. 2. ClickPredict > Deploy. If the Leaderboard model doesn't have thePrepare for Deploymentbadge, DataRobot recommends you clickPrepare for Deploymentto run themodel preparationprocess for that model. 3. UnderDeploy model, clickRegister to deploy. 4. In theRegister new modeldialog box, provide the following model information: FieldDescriptionRegister modelSelect one of the following:Register new model:Create a new registered model. This creates the first version (V1).Save as a new version to existing model:Create a version of an existing registered model. This increments the version number and adds a new version to the registered model.Registered model name / Registered ModelDo one of the following:Registered model name:Enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, theModel registration failedwarning appears.Registered Model:Select the existing registered model you want to add a new version to.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister a new model.Prediction thresholdFor binary classification models. Enter the value a prediction score must exceed to be assigned to the positive class. The default value is0.5. For more information, seePrediction thresholds.Optional settingsVersion descriptionDescribe the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add itemand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags do not apply to the registered model, just the versions within. Tags added when registering a new model are applied toV1.Include prediction intervalsFor time series models. Enable the computation of a model'stime series prediction intervals(from 1 to 100). Time series prediction intervals may take a long time to compute, depending on the number of series in the dataset, the number of features, the blueprint, etc. Consider if intervals are required in your deployment before enabling this setting. For more information see, theprediction intervals consideration. Prediction intervals in DataRobot serverless prediction environmentsIn a DataRobot serverless prediction environment, to make predictions with time-series prediction intervals included,you mustinclude pre-computed prediction intervals when registering the model package. If you don't pre-compute prediction intervals, the deployment resulting from the registered model doesn't supportenabling prediction intervals. Binary classification prediction thresholdsIf you set theprediction thresholdbefore thedeployment preparation process, the value does not persist. When deploying the prepared model, if you want it to use a value other than the default, set the value after the model has thePrepared for Deploymentbadge. Time series prediction intervals considerationWhen you deploy atime series model package with prediction intervals, thePredictions > Prediction Intervalstab is available in the deployment. For deployed model packages built without computing intervals, the deployment'sPredictions > Prediction Intervalstab is hidden; however, older time series deployments without computed prediction intervals may display thePrediction Intervalstab if they were deployed prior to August 2022. 5. ClickAdd to registry. The model opens on theModel Registry > Registered Modelstab. You can laterdeploy the model from the Model Registry. --- # Register models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/index.html > The Model Registry organizes all models used in DataRobot as registered models containing deployment-ready model packages. # Register models In the [Model Registry](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html), models are listed as registered models containing deployment-ready model packages as versions. Each package functions the same way, regardless of the origin of its model. The Model Registry also contains the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html), where you can create, deploy, and register custom models. Custom inference model packages and external model packages are exclusive to MLOps. After you register a model to the Model Registry, you can generate [Compliance Documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-compliance.html) to provide evidence that the components of the model work as intended and the model is appropriate for its intended business purpose. You can also [deploy the model to production](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-deploy.html) from the Model Registry. > [!NOTE] Availability information > Contact your DataRobot representative for information on enabling MLOps-exclusive model package options. **SaaS:** Topic Describes Model Registry How DataRobot AutoML models, custom inference models, and external models are automatically or manually added to the Model Registry. Register DataRobot models How to add a DataRobot model to the Model Registry from the Leaderboard. Register custom models (MLOps only) How to register custom inference models in the Model Registry. Register external models (MLOps only) How to register external models in the Model Registry. Deploy registered models Deploy registered models at any time from the Registered Models tab in the Model Registry. Manage model packages How to deploy, share, or archive models from the Model Registry. Generate model compliance documentation How to generate model compliance documentation from model packages in the Model Registry. Customize compliance documentation with key values Build custom compliance documentation templates with references to key values, adding the associated data to the template and limiting the manual editing needed to complete the compliance documentation. Custom jobs Create custom jobs in the Model Registry to define tests for your models and deployments. Custom Model Workshop How to bring pre-trained models into the Model Registry as custom inference models. Model logs for model packages (legacy) View model logs for model packages from the Model Registry's legacy Model Packages tab to see successful operations (INFO status) and errors (ERROR status). **Self-Managed:** Topic Describes Model Registry How DataRobot AutoML models, custom inference models, and external models are automatically or manually added to the Model Registry. Register DataRobot models How to add a DataRobot model to the Model Registry from the Leaderboard. Register custom models (MLOps only) How to register custom inference models in the Model Registry. Register external models (MLOps only) How to register external models in the Model Registry. Deploy registered models Deploy registered models at any time from the Registered Models tab in the Model Registry. Manage model packages How to deploy, share, or archive models from the Model Registry. Generate model compliance documentation How to generate model compliance documentation from model packages in the Model Registry. Customize compliance documentation with key values Build custom compliance documentation templates with references to key values, adding the associated data to the template and limiting the manual editing needed to complete the compliance documentation. Custom jobs Create custom jobs in the Model Registry to define tests for your models and deployments. Custom Model Workshop How to bring pre-trained models into the Model Registry as custom inference models. Import .mlpkg files exported from DataRobot AutoML How to transfer .mlpkg files from DataRobot AutoML to DataRobot MLOps. Model logs for model packages (legacy) View model logs for model packages from the Model Registry's legacy Model Packages tab to see successful operations (INFO status) and errors (ERROR status). --- # View and manage registered models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-action.html > The Model Registry Actions menu allows users with appropriate permissions to share or delete registered models. # View and manage registered models In the Model Registry, model packages are grouped into registered models, allowing you to categorize them based on the business problem they solve. Once you add registered models, you can search, filter, and sort them. You can also view model and version info, share your registered models (and the versions they contain) with other users, and delete registered models. Registered models can contain the following artifacts as registered model versions: - DataRobot, custom, and external models - Challenger models (alongside the champion) - Automatically retrained models. ## View registered models On the Registered Models tab, you can sort registered models by Name or Last modified. In addition, within a registered model, on the Versions tab, you can sort the registered model versions by Name, Created at, Last updated at, or Model type: In the top-left corner of the Registered Models page, you can search and filter the list of registered models: - ClickSearchand enter the registered model name to locate it on theRegistered Modelspage. - ClickFiltersto enable, modify, or clear filters on theRegistered Modelspage. You can filter byTarget name,Target type,Created by,Created between, andModified between. This control filters on registered models: Once you locate the registered model or model version you are looking for, you can access a variety of information about the registered model or version. ### Model info Click a registered model to open the details panel. From that panel, you can access the following tabs: | Tab | Description | | --- | --- | | Versions | View all model versions for a registered model and the associated creation and status information. To open the version in the current tab, click the row for the version you want to access.To open the version in a new tab, click the open icon () next to the Type column for the version you want to access. | | Deployments | View all model deployments for a registered model and the associated creation and status information. You can click a name in the Deployment column to open that deployment. | | Model Info | View the registered model ID, Name, Latest Version, Created By username, Created date, Last Modified date, Target Type, and Target Name. You can click the pencil icon () next to Name to edit the registered model's name. | ### Version info To open the registered model version, do either of the following: - To open the version in the current tab, click the row for the version you want to access. - To open the version in a new tab, click the open icon () next to theTypecolumn for the version you want to access. > [!TIP] Tip > You can click Switch next to the name in the version header to select another version to view. | Tab | Description | | --- | --- | | Info | View general model information for the model version. In addition: For all model types, you can click + Add tag in the Tags field to apply additional tags to the version.For DataRobot models, you can click the Model Name to open the model in the Leaderboard.For custom models, you can click the Custom Model ID to open the model in the Custom Model Workshop.For custom models created via GitHub Actions, you can click the Git Commit Reference to open the commit that created the model package in GitHub. | | Key Values | Create key values for the model version. | | Compliance Documentation | Generate compliance documentation for the model version. | | Deployments | View all model deployments for a registered model version, in addition to the associated creation and status information. You can click a name in the Deployment column to open that deployment. | ## Manage registered models There are several options available in the Actions menu ( [https://docs.datarobot.com/en/docs/images/icon-menu.png](https://docs.datarobot.com/en/docs/images/icon-menu.png)), located in the last column for each registered model on the Registered Models tab. The available options depend on a variety of criteria, including user permissions and the data available to your model package: | Action | Description | | --- | --- | | Share | The sharing capability allows appropriate user roles to grant permissions on a model package. To share a model package, click the Share () action. You can only share up to your own access level (a consumer cannot grant an editor role, for example) and you cannot downgrade the access of a collaborator with a higher access level than your own. | | Delete | If you have the appropriate permissions, you can click Delete to remove a registered model from the model registry. | > [!NOTE] Changes to model sharing > With the introduction of the Registered Models page, registered models are the model artifact used for sharing, not model packages. When you share a registered model, you automatically share each model package contained in that registered model. --- # Generate model compliance documentation URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-compliance.html > Generate automated compliance documentation for models from the Model Registry. # Generate model compliance documentation After you [create a registered model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html) in the Model Registry (the inventory), you can generate automated compliance documentation for the model. The compliance documentation provides evidence that the components of the model work as intended, the model is appropriate for its intended business purpose, and the model is conceptually sound. This individualized model documentation is especially important for highly regulated industries. For the banking industry, for example, the report can help complete the Federal Reserve System's [SR 11-7: Guidance on Model Risk Management](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm). > [!TIP] Tip > You can also generate compliance documentation by selecting a model on the Leaderboard and clicking the [Compliance tab](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/compliance-classic/compliance-tab.html). After you generate the compliance documentation, you can view it or download it as a Microsoft Word (DOCX) file and edit it further. You can also create [specialized templates](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/compliance-classic/template-builder.html) for your organization. > [!NOTE] Note > When model packages are shared with users, all generated compliance documentation for the model is also shared. ## Generate compliance documentation 1. Create a model package if it is not yet in the inventory (Model Registry). To create a model package, you can: 2. ClickModel Registry > Registered Modelsand select a registered model. 3. In theVersionslist, to open the registered model version, do either of the following: 4. Click theCompliance Documentationtab, select aReport template, and then clickCreate Report. The default template is the Automated Compliance Document template. You can insteadcreate a custom report templateand select that template. Compliance documentation for custom models without null imputation supportTo generate the Sensitivity Analysis section of the defaultAutomated Compliance Documenttemplate, your custom model must support null imputation (the imputation of NaN values), or compliance documentation generation will fail. If the custom model doesn't support null imputation, you can use a specialized template to generate compliance documentation. In theReport templatedropdown list, selectAutomated Compliance Document (for models that do not impute null values). This template excludes the Sensitivity Analysis report and is only available for custom models. If this template option is not available for your version of DataRobot, you can download thecustom template for regression modelsor thecustom template for binary classification models. Generating Compliance Documentation requires DataRobot to execute many dependent insight tasks. This can take several minutes to complete. The documentation appears below when complete: 5. After the compliance documentation is generated, you can: ## Complete compliance documentation After you have generated the model compliance report, click Download and save the `.docx` file to your system. Open the file to review and complete the document. Areas of blue italic text are intended as guidance and instruction. They identify who should complete the section and provide detail of the required information. Areas of regular text are DataRobot's automatically generated model compliance text—preprocessing, performance, impact, task-specific, and DataRobot general information. ## Feature considerations Compliance documentation is available for the following project types: - Binary - Multiclass - Regression - Text generation - Anomaly detection for time series projects with DataRobot models, but not for non-time series unsupervised mode. --- # Model Registry URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html > How model packages are created and added to the Model Registry, manually or automatically. The Model Registry is an archive of your model packages where you can deploy and share packages. # Model Registry The Model Registry is an organizational hub for the variety of models used in DataRobot. Models are registered as deployment-ready model packages. Each registered model package functions the same way, regardless of the origin of its model. These model packages are grouped into registered models containing registered model versions, allowing you to categorize them based on the business problem they solve. Registered models can contain the following artifacts as registered model versions: - DataRobot, custom, and external models - Challenger models (alongside the champion) - Automatically retrained models. Custom models are [created and tested](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html) in the Model Registry on the [Custom Model Workshop tab](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html), and external models [operate outside of DataRobot](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html), associated with an [external model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-external-models.html) in the Model Registry. Once you add registered models, you can search, filter, and sort them. You can also share your registered models (and the versions they contain) with other users. Registered model packages (model artifacts with associated metadata) are listed on the Model Registry > Registered Models tab: In addition, from the Model Registry, you can [generate model compliance documentation from model packages](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-compliance.html) and [deploy, share, or archive models](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-action.html). ## Add registered models and versions You can register DataRobot, custom, and external model packages. When you add model packages to the Registered Models page, you can create a new registered model (version one) or save the model package as a new version of an existing registered model. Model packages added as versions of the same registered model must have the same target type, target name, and, if applicable, target classes and time series settings. To learn to add registered models to the mode registry, review the following documentation: | Topic | Describes | | --- | --- | | Register DataRobot models | How to add a DataRobot model to the Model Registry from the Leaderboard. | | Register custom models (MLOps only) | How to register custom inference models in the Model Registry. | | Register external models (MLOps only) | How to register external models in the Model Registry. | > [!NOTE] Important > Each registered model on the Registered Models page must have a unique name. If you choose a name that exists anywhere within your organization when creating a new registered model, the Model registration failed warning appears. Use a different name or add this model as a new version of the existing registered model. ## Access registered models and versions On the Registered Models page, you can sort registered models by Name or Last modified. In a registered model, on the Versions tab, you can sort versions by Name, Created at, Last updated at, or Model type: In the top-left corner of the Registered Models page, you can click Search and enter the registered model name to locate it on the Registered Models page or click Filters to enable, modify, or clear filters on the Registered Models page: For more information, see the [View and manage registered models](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-action.html) documentation. ## Access global models > [!NOTE] Availability information > Global models are a premium feature. Contact your DataRobot representative for information on enabling the feature. From the Registered Models page, you can deploy pre-trained, global models for predictive or generative use cases. These high-quality, open-source models are trained and ready for deployment, allowing you to make predictions without additional setup. For LLM use cases, you can find classifiers to identify prompt injection, toxicity, and sentiment, as well as a regressor to output a refusal score. To identify the global models created by DataRobot on the Model Registry > Registered Models page, click Filters, enter `global-models-robot@datarobot.com` in the Created by field, then click Apply filters: The following global models are available: | Model | Type | Target | Description | | --- | --- | --- | --- | | Prompt Injection Classifier | Binary | injection | Classifies text as prompt injection or legitimate. This guard model requires one column named text, containing the text to classify. For more information, see the deberta-v3-base-injection model details. | | Toxicity Classifier | Binary | toxicity | Classifies text as toxic or non-toxic. This guard model requires one column named text, containing the text to classify. For more information, see the toxic-comment-model details. | | Sentiment Classifier | Binary | sentiment | Classifies text sentiment as positive or negative. This model requires one column named text, containing the text to classify. For more information, see the distilbert-base-uncased-finetuned-sst-2-english model details. | | Emotions Classifier | Multiclass | target | Classifies text by emotion. This is a multilabel model, meaning that multiple emotions can be applied to the text. This model requires one column named text, containing the text to classify. For more information, see the roberta-base-go_emotions-onnx model details. | | Refusal Score | Regression | target | Outputs a maximum similarity score, comparing the input to a list of cases where an LLM has refused to answer a query because the prompt is outside the limits of what the model is configured to answer. | | Presidio PII Detection | Binary | contains_pii | Detects and replaces Personally Identifiable Information (PII) in text. This guard model requires one column named text, containing the text to be classified. The types of PII to detect can optionally be specified in a column, 'entities', as a comma-separated string. If this column is not specified, all supported entities will be detected. Entity types can be found in the PII entities supported by Presidio documentation. In addition to the detection result, the model returns an anonymized_text column, containing an updated version of the input with detected PII replaced with placeholders. For more information, see the Presidio: Data Protection and De-identification SDK documentation. | | Zero-shot Classifier | Binary | target | Performs zero-shot classification on text with user-specified labels. This model requires classified text in a column named text and class labels as a comma-separated string in a column named labels. It expects the same set of labels for all rows; therefore, the labels provided in the first row are used. For more information, see the deberta-v3-large-zeroshot-v1 model details. | | Python Dummy Binary Classification | Binary | target | Always yields 0.75 for the positive class. For more information, see the python3_dummy_binary model template. | --- # Custom jobs URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-custom-jobs.html > Create custom jobs in the Model Registry to define tests for your models and deployments. # Custom jobs Create custom jobs in the Model Registry to implement automation (for example, custom tests) for your models and deployments. Each job serves as an automated workload, and the exit code determines if it passed or failed. You can run the custom jobs you create for one or more models or deployments. The automated workloads you define through custom jobs can make prediction requests, fetch inputs, and store outputs using DataRobot's Public API. ## Register and assemble a new job When you register a custom job, you must assemble the required execution environment and files before running the job. Only the execution environment and an entry point file (typically `run.sh`) are required; however, you can designate any file you add as the entry point. If you add other files to create your job, the entry point file should reference those files. In addition, to configure runtime parameters, you should create or upload a `metadata.yaml` file containing the runtime parameter configuration for your job. To register and assemble a new custom job in the Model Registry: 1. ClickModel Registry > Custom Jobs, and then clickRegister new job. 2. In theRegister new jobdialog box, enter aJob nameandDescription (optional)and then clickCreate new job. The custom job opens in theWorkshoptab. 3. In theCustom Job Workshopfor the new job, underEnvironment and Description, select anExecution Environmentfor the job and (optionally) edit theDescription. 4. In theContentsection, assemble your custom job. You can use the options in this section to create or upload the files required to assemble a custom job: OptionDescriptionAdd filesUpload existing custom job files (run.sh,metadata.yaml, etc.).Create fileCreate a blank file and add contents in DataRobot.The default file name for a file created this way isrun.sh; however, you can edit that name before saving the file. If you've already added arun.shfile and you create another file without changing the default name, the new file overwrites the previously created or uploadedrun.shfile. 5. In theSettingssection, define theEntry pointfile. If you've only added one file (usuallyrun.sh), that file is the entry point. 6. (Optional) In theSettingssection, if you uploaded ametadata.yamlfile, configure theRuntime Parameters. ## Run and schedule jobs After you register and assemble a custom job with the required environment and files, you can run the job, schedule a run, or both. In the Model Registry, on the Custom Jobs > Workshop tab, locate the run options next to the Custom Jobs Workshop header: | Option | Description | | --- | --- | | Run | Starts a custom job run immediately and opens the run on the Runs tab, where you can view custom job run information. | | Schedule run | Opens the Schedule run (UTC) section, where you can select a frequency and enter a time (in UTC) to run your custom job. You can also click Use advanced scheduler to provide a more precise run schedule. Once you've configured a schedule, click Set schedule. | ## View and edit jobs info After you register a custom job, you can view and edit the registration info. In the Model Registry, on the Custom Jobs > Info tab, locate the Custom Job Info. You can view the custom job ID, and the created, updated, and last run information. You can also edit the Name and Description of the custom job. To edit those fields, click the edit icon ( [https://docs.datarobot.com/en/docs/images/icon-pencil.png](https://docs.datarobot.com/en/docs/images/icon-pencil.png)) next to the field contents: ## View and manage job runs When you run a custom job (or a scheduled run completes), the run information is recorded, allowing you to review the run information and view and manage run logs. This is helpful for diagnosing issues when a run fails. In the Model Registry, on the Custom Jobs > Runs tab, locate and click the run you want to view from the list and review the Custom job run info: > [!WARNING] Warning > Do not log sensitive information in your custom job code. The logs for a custom job run contain any information logged in that job's code. If you log sensitive information, you should delete your logs. | Field | Description | | --- | --- | | Job results | The Status and Duration of the selected job run. | | Description | A description of the job run. To edit the description, click the edit icon () next to the field contents. | | Logs of the run | Provides the following log controls: View full logs: Open the Console Logs window in DataRobot where you can Search, Download, and Refresh the log contents. You can view logs while the run is still in progress, clicking Refresh to keep them up to date. Download logs: Download the logs as a .txt file.Delete logs: Permanently delete the logs for a run, typically if you accidentally logged sensitive information. | If you uploaded a `metadata.yaml` file and configured Runtime Parameters during the custom jobs assembly process, you can view them in the Runtime Parameters table for a run: You can also initiate or schedule runs from the Runs tab: ## Delete a custom job To delete a custom job and all of its contents, in the Model Registry, on the Custom Jobs tab, locate the row for the custom job you want to delete, click the action menu ( [https://docs.datarobot.com/en/docs/images/icon-menu.png](https://docs.datarobot.com/en/docs/images/icon-menu.png)), and click Delete. Then, in the Delete Custom Job dialog box, click Delete again. --- # Register custom models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-custom-models.html > Register a custom model in the Model Registry. The Model Registry is an archive of your model packages where you can also deploy and share the packages. # Register custom models When you have successfully created and tested a custom inference model, you can register it in the Model Registry. > [!NOTE] Note > Although a model package can be created without testing the custom model, DataRobot recommends that you confirm the model passes testing before proceeding. Untested custom models prompt a dialog box warning that the custom model is not tested. To add a custom model as a registered model or version: 1. Navigate toModel Registry > Custom Model Workshop. 2. From theCustom Model Workshop, click the model you want to register and, on theAssembletab, clickRegister to deploy. 3. In theRegister new modeldialog box, configure the following: FieldDescriptionRegister modelSelect one of the following:Register new model:Create a new registered model. This creates the first version (V1).Save as a new version to existing model:Create a version of an existing registered model. This increments the version number and adds a new version to the registered model.Registered model name / Registered ModelDo one of the following:Registered model name:Enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, theModel registration failedwarning appears.Registered Model:Select the existing registered model you want to add a new version to.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister a new model.Optional settingsVersion descriptionDescribe the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add itemand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags do not apply to the registered model, just the versions within. Tags added when registering a new model are applied toV1. 4. ClickAdd to registry. --- # Deploy registered models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-deploy.html > Deploy a registered model at any time from the Registered Models page. # Deploy registered models You can deploy a registered model at any time from the Registered Models page. To do that, you must open a registered model version: 1. On theRegistered Modelspage, click the registered model containing the model version you want to deploy. 2. To open the registered model version, do either of the following: 3. In the version header, clickDeploy, and thenconfigure the deployment settings. 4. ClickDeploy model. --- # Register external models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-external-models.html > Register an external model package in the Model Registry. The Model Registry is an archive of your model packages where you can also deploy and share the packages. # Register external models To register an external model monitored by the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html), add an external model as a registered model or version: 1. On theModel Registry > Registered Modelspage, clickRegister New Model > External model. 2. In theRegister new external modeldialog box, configure the following: FieldDescriptionModel version nameThe name of the model to be registered as a model version.Model version description (optional)Information to describe the model to be added as a registered model version.Model location (optional)The location of the model running outside of DataRobot. Describe the location as a filepath, such as folder1/opt/model.tar.Build EnvironmentThe programming language in which the model was built.Training data (optional)The filename of the training data, uploaded locally or via theAI Catalog. ClickClear selectionto upload and use a different file.Holdout data (optional)The filename of the holdout data, uploaded locally or via theAI Catalog. Use holdout data to set anaccuracy baselineand enable support for target drift and challenger models.TargetThe dataset's column name that the model will predict on.Prediction typeThe type of prediction the model makes. Depending on the prediction type, you must configure additional settings:Regression: No additional settings.Binary: For a binary classification model, enter thePositive classandNegative classlabels and a predictionThreshold.Multiclass: For a multiclass classification model, enter or upload (.csv, .txt) theTarget classesfor your target, one class per line. To ensure that the classes are applied correctly to your model's predictions, the classes should be in the same order as your model's predicted class probabilities.Multilable: For a multilabel model, enter or upload (.csv, .txt) theTarget labelsfor your target, one label per line. To ensure that the labels are applied correctly to your model's predictions, the labels should be in the same order as your model's predicted label probabilities.Text Generation:Premium feature. For more information, seeMonitoring support for generative models.Prediction columnThe column name in the holdout dataset containing the prediction result. If registering atime seriesmodel, select theThis is a time series modelcheckbox and configure the following fields: FieldDescriptionForecast date featureThe column in the training dataset that contains date/time values used by DataRobot to detect the range of dates (the valid forecast range) available for use as the forecast point.Date/time formatThe format used by the date/time features in the training dataset.Forecast point featureThe column in the training dataset that contains the point from which you are making a prediction.Forecast unitThe time unit (seconds, days, months, etc.) that comprise thetime step.Forecast distance featureThe column in the training dataset containing a unique time step—a relative position—within the forecast window. A time series model outputs one row for each forecast distance.Series identifier (optional, used formultiseries models)The column in the training dataset that identifies which series each row belongs to. Finally, configure the registered model settings: FieldDescriptionRegister modelSelect one of the following:Register new model:Create a new registered model. This creates the first version (V1).Save as a new version to existing model:Create a version of an existing registered model. This increments the version number and adds a new version to the registered model.Registered model name / Registered ModelDo one of the following:Registered model name:Enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, theModel registration failedwarning appears.Registered Model:Select the existing registered model you want to add a new version to.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister a new model.Optional settingsVersion descriptionDescribe the business problem this model package solves, or, more generally, describe the model represented by this version.TagsClick+ Add itemand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags do not apply to the registered model, just the versions within. Tags added when registering a new model are applied toV1. 3. Once all fields for the external model are defined, clickRegister. ## Set an accuracy baseline To set an accuracy baseline for external models (which enables target drift and challenger models when deployed), you must provide holdout data. This is because DataRobot cannot use the model to generate predictions that typically serve as a baseline, as the model is hosted in a remote prediction environment outside of the application. Provide holdout data when registering an external model package and specify the column containing predictions. --- # Extend compliance documentation with key values URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-key-values.html > Build custom compliance documentation templates with references to key values, adding the associated data to the template and limiting the manual editing needed to complete the compliance documentation. # Extend compliance documentation with key values When you [create a model package in the Model Registry](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html), you can [generate automated compliance documentation for the model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-compliance.html). The compliance documentation provides evidence that the components of the model work as intended, the model is appropriate for its intended business purpose, and the model is conceptually sound. You can [build custom compliance documentation templates](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/compliance-classic/template-builder.html) with references to key values, adding the associated data to the template and limiting the manual editing needed to complete the compliance documentation. Key values associated with a model in the Model Registry are key-value pairs containing information about the registered model package. Each key-value pair has the following: - Name:The unique and descriptive name of the key (for the model package or version). - Value type:The data type of the value associated with the key. The possible types are string, numeric, boolean, URL, image, dataset, pickle, binary, JSON, or YAML. - Value:The stored data or file. You can include string, numeric, boolean, image, and dataset key values in custom compliance documentation templates. When you generate compliance documentation for a model package using a custom template referencing a supported key value, DataRobot inserts the matching values from the associated model package; for example, if the key value has an image attached, building the compliance documentation inserts that image, or if the key value refers to a dataset, it inserts the first 100 rows and 42 columns of the dataset. DataRobot automatically populates key values for registered models you create. These key values are read-only, and their names start with `datarobot.`. They are generated when you register a DataRobot model from the Leaderboard or a custom model from the Custom Model Workshop. - DataRobot models: DataRobot creates training parameters and metrics (such asdatarobot.parameter.max_depth), metrics (such asdatarobot.metric.AUC.validation), and tags (such asdatarobot.registered.model.version). The automatic training parameters and metrics depend on the particular DataRobot model. - Custom models: DataRobot creates runtime parameters from the model. The automatic key values depend on the custom model. Although you can't modify or delete these automatic key values, you can manually add key values as needed to supplement the system-provided values. Your compliance documentation template or integration workflow can use key values from either source. ## Create key values In the [Model Registry](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html), [open a registered model version](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-action.html#version-info) and access the Key values tab. In the table on this tab, you can view, add, edit, and delete key values, depending on your model package permissions. When you add a new key value, you can add a string, numeric, boolean, URL, JSON, or YAML key value without an attached file. To add a new key value: 1. Click+ Add key values > New key value 2. In theAdd new key valuedialog box, configure the following settings: SettingDescriptionCategorySelect one of the following categories for the new key value to organize your key values by purpose:Training parameterMetricTagArtifactRuntime parameterValue typeSelect one of the following value types for the new key value:DatasetImageStringPickleBinaryNumericBooleanURLJSONYAMLValueIf you selected one of the following value types, enter the appropriate data:String: Enter any string up to 4 KB.Numeric: Enter an integer or floating-point number.Boolean: SelectTrueorFalse.URL: A URL in the formatscheme://location; for example,https://example.com. DataRobot does not fetch the URL or provide a link to this URL in the user interface; however, in a downloaded compliance document, the URL may appear as a link.JSON: Enter or upload JSON as a string. This JSON must parse correctly; otherwise, DataRobot won't accept it.YAML: Enter or upload YAML as a string. DataRobot does not validate this YAML.UploadIf you selected one of the following value types, add an artifact by uploading a file:Upload a dataset: A CSV file or any other compatible data file ( with a.csv,.tsv,.dsv,.xls,.xlsx,.gz,.bz2,.tar,.tgz, or.zipextension). The file is added as a dataset in the AI catalog, so all file types supported there are also supported for key values.Upload an image: A JPEG or PNG file (with a.jpg,.jpeg, or.pngextension).Upload a pickle file: A Python PKL file (with the.pklextension). DataRobot only stores the file for the key value; you can't access or run objects or code in the file.Upload a binary file: A file with any file extension. DataRobot stores the file as an opaque object.NameEnter a descriptive name for the key in the key-value pair.Description(Optional) Enter a description of the key value's purpose. 3. ClickOKto save the key value. The new key appears in the table. ## View and manage key values On the Key values tab, you can view key value information and manage your custom key values. You can search key values by name (full or partial) and refresh the list. You can also filter by the categories shown. If the model package has more than 10 key values, the list is paginated. Additional controls are located in the Actions column: | Action | Description | | --- | --- | | View | You can view values in the following ways: String, numeric, and boolean values: View the values directly in the table.Image and dataset files: Click the preview icon (). Previewing an image key value shows the image in the browser. Previewing a dataset key value brings you to the dataset page in the AI catalog.Long strings, JSON, and YAML: Hover over the truncated value to view the full value.Pickle and binary files: Cannot be previewed. In these cases, the "value" is the original filename of the uploaded file. | | Edit | Click the edit icon () to edit a key value. You can only edit the name, value, or description. Leaving a comment in the comment field is optional. | | Delete | Click the delete icon () to delete a key value. If you delete an image, pickle, or binary key value, and no other key value is using the same file, DataRobot deletes the file from storage. Datasets remain in the AI catalog after deletion. | ## Customize a compliance documentation template with key values Once you've added key values, you can [build custom compliance documentation templates](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/compliance-classic/template-builder.html) with references to those key values. Referencing key values in a compliance documentation template adds the associated data to the generated compliance documentation, limiting the amount of manual editing needed to complete the compliance documentation. To reference key values in a compliance documentation template: 1. Click your profile avatar (or the default avatar) in the upper-right corner of DataRobot and, underApp Admin, clickTemplate Builder. 2. On theFlexible Documentation Templatespage,create or edit a compliance documentation template. 3. In the template, on theKey Valuespanel, select a model package with key values from theRegistered Model Versionlist. 4. To add a key value reference to the template, edit a section, click to place the cursor where you want to insert the reference, then click the key value in theKey Valuespanel. NoteYou can include string, numeric, boolean, image, and dataset key values in custom compliance documentation templates. After you add a value, you can remove it by deleting the reference from the section. 5. To preview the document, clickSave changesand then clickPreview template. As the template is not applied to any specific model package until you generate the compliance documentation, the preview displays placeholders for the key value. ## Copy key values from a previous model package version Registered models have versions; each is an individual model package with an independent set of key values. If you created key values in an earlier version, you can copy these to a newer version: 1. Navigate toModel Registry > Registered Modelspage and open the registered model containing the version you want to add key values to. 2. To open the registered model version, do either of the following: 3. Click+ Add key valuesand, underOther, clickCopy key values from previous version. 4. In theCopy key values to this registered versiondialog box, selectAll categoriesor a single category. 5. ClickOKto copy the key values. If a key value with the same name exists in the newer version and it is not read-only, the value from the older version will overwrite it. Otherwise, a new key value with that name is created in the newer version. Files for artifact key values (image, dataset, pickle, and binary) are not copied in bulk; instead, the new and old key values share the same file. If you edit either key value to use a different file, the other key value is unaffected, and the file is no longer shared. System key values are not included in bulk copy; for example,model.versionis not overwritten in a newer version with the old version's value. --- # Model logs for model packages (legacy) URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-model-pkg-logs.html > View the model logs for a model package to see a history of the successful operations (INFO status) and errors (ERROR status). # Model logs for model packages (legacy) > [!NOTE] Availability information > Model logs for model packages in the Model Registry are off by default, requiring the deprecated legacy Model Packages tab. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flags: Enable Legacy Model Registry On the deprecated legacy Model Packages tab in the Model Registry, the Model Logs tab displays information about the operations of the underlying model. This information can help you identify and fix errors. For example, compliance documentation requires DataRobot to execute many jobs, some of which run sequentially and some in parallel. These jobs may fail, and reading the logs can help you identify the cause of the failure (e.g., the Feature Effects job fails because a model does not handle null values). > [!NOTE] Model logs consideration > In the Model Registry, a model package's Model Logs tab only reports the operations of the underlying model, not the model package operations (e.g., model package deployment time). To view the model logs for a model package: 1. In theModel Registry, click theModel Packagestab. 2. Click a model package in the list, and then clickModel Logs. 3. On theModel Logstab, review the timestamped log entries: InformationDescription1Date / TimeThe date and time the model log event was recorded.2StatusThe status the log entry reports:INFO: Reports a successful operation.ERROR: Reports an unsuccessful operation.3MessageThe description of the successful operation (INFO), or the reason for the failed operation (ERROR). This information can help you troubleshoot the root cause of the error. 4. If you can't locate the log entry for the error you need to fix, it may be an older log entry not shown in the current view. ClickLoad older logsto expand theModel Logsview. View older logsLook for the older log entries at the top of theModel Logs; they are added to the top of the existing log history. --- # Import model packages into MLOps URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-transfer.html > Export a model created with DataRobot AutoML for import as a model package (.mlpkg file) in standalone MLOps environments. # Import model packages into MLOps > [!NOTE] Availability information > This feature is only available for Self-Managed AI Platform users that require MLOps and AutoML to run in separate environments. The process outlined requires multiple feature preview flags. Contact your DataRobot representative for more information about this configuration. > > Feature flags: Contact your DataRobot representative. Models created with DataRobot AutoML can be [exported](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-transfer.html#export-a-model-from-automl) as a model package (.mlpkg file). This allows you to [import](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-transfer.html#import-a-model-package-to-a-datarobot-mlops-only-environment) a model package into standalone environments like DataRobot MLOps to make predictions and monitor the model. You can also [create a new deployment in MLOps](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-transfer.html#deploy-a-model-package-in-mlops) by importing a model package. ## Export a model from AutoML You can export models created with AutoML from the Deploy tab on the model's Predict page. > [!NOTE] Note > The MLOps Package option on the Predict > Downloads tab directs you to Open the Deploy tab where you can deploy the model, add it to the Model Registry, or download the model package. To export your model a model package (.mlpkg) file from DataRobot AutoML, add it to the Model Registry, or deploy it directly to the Deployments inventory, take the following steps: 1. On theLeaderboard, click the model you want to export. 2. ClickPredict > Deploy. 3. On theDeploytab, clickDownload .mlpkgto prepare the model package for export. View your progress in the Worker Queue underProcessing. After DataRobot finishes generating the model package, the download begins automatically, appearing in the downloads bar when complete. You now have an exported model package fully capable of deployment to a different environment (such as DataRobot MLOps). ## Import a model package to a DataRobot MLOps-only environment To add an exported .mlpkg file to DataRobot MLOps as a [model package](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html): 1. ClickModel Registryand then clickModel Packages. 2. On theModel Packagestab, clickAdd new packageand then clickImport model package file (.mlpkg). 3. Browse for and upload, or drag-and-drop, the .mlpkg file you exported from DataRobot AutoML. The model package is uploaded and extracted. 4. When this process completes, DataRobot adds your model package to theModel Packagestab, complete with the metadata for your model package. ## Deploy a model package in MLOps To import your model into DataRobot MLOps, you can add it as a new [deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/index.html). 1. Navigate to theDeploymentspage and clickAdd deployment. 2. Under theAdd a modelheader, clickBrowseand clickLocal fileto upload your model package. You can also drag and drop a model package file into theAdd a modelbox. 3. After you upload a model, theDeploymentstab opens. NoteThe information under theModelheader appears automatically, as your model package contains that metadata. The model package also supplies the training data; you don't need to provide that information on this page. You can, however, add outcome data after you deploy the model. 4. Configure thedeployment creation settingsand decide if you want to allowdata drifttracking or require anassociation IDin prediction requests. 5. When you have added information about your data and your model is fully defined, you can clickDeploy modelat the top of the screen. --- # Model deployment approval workflow URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/dep-admin.html > DataRobot MLOps system administrators can specify security policies that control who can create or modify deployments and what kind of approval is required. # Approval process > [!NOTE] Availability information > The Model Deployment Approval Workflow is only available for DataRobot MLOps users. Contact your DataRobot representative for more information about enabling this feature. The approval workflow for deployment events is only prompted when the Model Deployment Approval Workflow is enabled. When you create a new or change an existing deployment, if the approval workflow is enabled, an MLOps administrator within your organization must approve your changes. [Approval policies](https://docs.datarobot.com/en/docs/platform/admin/deploy-approval.html) affect the users who have permissions to review deployments and provide automated actions when reviews time out. Approval policies also affect users whose deployment events are governed by a configured policy (e.g., new deployment creation, model replacement). Be sure to review the [deployment approval workflow considerations](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/dep-admin.html#feature-considerations) before proceeding. ## Deployment importance levels Before you deploy your model, you are prompted to assign an importance level to it: Critical, High, Moderate, or Low. Importance represents an aggregate of factors relevant to your organization such as the prediction volume of the deployment, level of exposure, potential financial impact, and more. ## Deployment creation approval Once the deployment is created, MLOps Admins are alerted via email that the deployment requires review. While awaiting review, the deployment is flagged as "NEEDS APPROVAL" in the [deployment inventory](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html). While a deployment with "NEEDS APPROVAL" status can still make predictions, DataRobot recommends contacting your MLOps administrator before using it to do so. Once the deployment is approved, the flag updates to "APPROVED" in the inventory. Additionally, predictions made after the deployment is approved are marked as “APPROVED” in the prediction server response metadata. ## Deployment event approval As a deployment owner, you can make changes to an existing deployment and include comments to detail the reason for the change. You also choose whether, after the change request has been approved, you want the change manually or automatically applied. You are always notified via email when your change request has been approved or requires changes. After approval, you can apply the changes; if changes are set to automatic, DataRobot applies changes immediately after approval. ## MLOps Admin: Approve deployment creation The MLOps administrator role offers access and governance to those within organizations who oversee deployments. Administrators are often responsible for monitoring deployments that make prediction requests, ensuring the quality of deployment performance, assisting with debugging, and reporting on deployment usage and activity. The MLOps administrator role, assigned by the system administrator, has User role permissions for all existing and newly created deployments within their organization. They receive email notifications to approve deployment events such as creation, model replacement, changes to importance levels, and deletion. > [!NOTE] Note > These elevated permissions only apply to deployments. Your primary function as an MLOps administrator is to review and approve deployment events within your organization. When a user within your organization creates a deployment, you are alerted via email that the deployment requires review. When you access a deployment that needs approval, DataRobot presents a notification banner on the [Overview tab](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/dep-overview.html) prompting you to begin the deployment review process. > [!NOTE] Important > You can't create and approve a deployment from the same account; therefore, if the deployment creator and MLOps Admin are the same, the This deployment needs review banner doesn't appear in the deployment overview. After you click Add review, a dialog box populates to submit approval or request updates for the deployment. Review the deployment and its importance level (chosen by the deployment creator). (Optional) You can include comments with your decision. If approved, DataRobot removes the NEEDS APPROVAL flag from the deployment inventory listing. If changes were requested, the flag remains until the changes are addressed and the deployment is approved. ## MLOps Admin: Approve changes to existing deployments In addition to reviewing deployment creations, MLOps administrators review and approve deployment events such as a model replacement, changes to importance levels, and deployment deletion. You will receive email notifications for these triggering events that require approval. Deployment owners are notified via email when you approve or request changes. As an MLOps administrator accessing a deployment that needs approval for a change, DataRobot presents a notification banner prompting you to begin the deployment review process. You can review the deployment's history of changes in the Overview tab under the Governance header. The Governance header details the history of changes made to a deployment, including the importance levels, the changes made, who made them, and who reviewed them. To approve a deployment change, select Add Review from the notification banner (also available under the Governance header): A dialog box populates, including a summary of the changes and any comments provided by the deployment owner making the change. Review the desired changes, include any comments with your decision, and submit, approve, or request updates for the deployment. After submitting your review, the notification banner updates if the changes were approved. If the deployment owner chooses to have the changes applied manually, the owner can click Apply Changes to do so. ## Feature considerations - A deployment Owner can choose to share a deployment withMLOps administratorsand grant either User or Owner permissions. When explicitly shared, Owner rights are the default. - For Self-Managed AI Platform installations: An MLOps administrator will be able to monitor actions taken by users in their organization. --- # Notifications tab URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/deploy-notifications.html > Enable notifications, which trigger emails for service health and data drift reporting. Configure Service Health, Data Drift, Accuracy, and Fairness monitoring. # Notifications tab DataRobot provides automated monitoring with a configurable notification system, alerting you when service health, data drift status, model accuracy, or fairness values exceed your defined acceptable levels. Notifications can trigger in-app alerts, emails, and webhooks. They are off by default but can be enabled by a deployment owner. Keep in mind that notifications only control whether emails are sent to subscribers; if notifications are disabled, monitoring of service health, data drift, accuracy, and fairness statistics still occurs. By default, notifications occur in real time for your configured status alerts, allowing your organization to quickly respond to changes in model health without waiting for scheduled health status notifications; however, you can choose to send notifications on the monitoring schedule. To set the types of notifications you want to receive and how you receive them, in the Deployments inventory, open a deployment and click the Notifications tab. > [!NOTE] Deployment consumer notifications > A deployment consumer only receives a notification when a deployment is shared with them and when a previously shared deployment is deleted. They are not notified about other events. > [!TIP] Schedule deployment reports > You can also [schedule deployment reports](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/deploy-reports.html) on the Notifications tab. ## Configure monitoring definitions Monitoring definitions are located on the deployment settings pages, and your control over those settings depends on your [deployment role](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#deployment-roles) — owner or user. Both roles can set personal notification settings; however, only deployment owners can set up schedules and thresholds to monitor the following: - Service health - Data drift status - Accuracy - Fairness ## Enable notification policies To configure deployment notifications through the creation of notification policies, you can configure and combine notification channels and templates. The notification template determines which events trigger a notification, and the channel determines which users are notified. When you create a notification policy for a deployment, you can use a policy template without changes or as the basis of a new policy with modifications. You can also create an entirely new notification policy. To use notification policies for a deployment, in the Notification configuration section, click Enable notifications configuration: ## Create notification policies After you enable notification configuration, click + Create a policy to add or define a policy for the deployment. You can use a policy template without changes or as the basis of a new policy with modifications. You can also create an entirely new notification policy: In the Create a policy dialog box, select one of the following, and then click Next: | Option | Description | | --- | --- | | Use template | Create a policy from a template, without changes. | | Create from template | Create a policy from a template, with changes. | | Create from scratch | Create a new policy and optionally save it as a template. | ### Use template In the Create a policy dialog box, select a policy, and then click Next: Enter a Policy name, then click Create policy. ### Create from template In the Create a policy dialog box, select a policy, and then click Next: On the Choose trigger tab, select an Event group or a Single event, then click Next: On the Choose channel tab, click Use existing channel and select a channel, or click Create new, enter a Channel name, and then configure the following fields: | Channel type | Fields | | --- | --- | | External | | | Webhook | Payload URL: Enter the URL that should receive notification payloads.Content type: Select application/json (when the payload URL requires JSON) or application/x-www-form-urlencoded (when the payload URL requires URL encoded data).Secret token: (Optional) If required by the webhook, enter the secret token.Enable SSL verification: By default, DataRobot verifies SSL certificates when delivering payloads and doesn't recommend disabling this option.Show advanced options: Define Custom headers for the HTTP request.Test connection: This type of connection must pass testing before it can be saved | | Email | Email address: Enter the email address that should receive notifications. | | Slack | Slack Incoming Webhook URL: Enter an incoming webhook URL, generated through your Slack workspace. For more information, see the Slack documentation.Test connection: This type of connection must pass testing before it can be saved | | Microsoft Teams | Microsoft Teams Incoming Webhook URL: Enter an incoming webhook URL, generated through your Microsoft Teams channel. For more information, see the Microsoft Teams documentation.Test connection: This type of connection must pass testing before it can be saved. | | DataRobot | | | User | Enter one or more existing DataRobot usernames to add those users to the channel. To remove a user, in the Username list, click the remove icon . | After you select or create a channel, click Next, confirm the Trigger, Channel, or Policy name, then click Create policy or Finish and save as template: ### Create from scratch On the Choose trigger tab, select an Event group or a Single event, then click Next: On the Choose channel tab, click Use existing channel and select a channel, or click Create new, enter a Channel name, and then configure the following fields: | Channel type | Fields | | --- | --- | | External | | | Webhook | Payload URL: Enter the URL that should receive notification payloads.Content type: Select application/json (when the payload URL requires JSON) or application/x-www-form-urlencoded (when the payload URL requires URL encoded data).Secret token: (Optional) If required by the webhook, enter the secret token.Enable SSL verification: By default, DataRobot verifies SSL certificates when delivering payloads and doesn't recommend disabling this option.Show advanced options: Define Custom headers for the HTTP request.Test connection: This type of connection must pass testing before it can be saved | | Email | Email address: Enter the email address that should receive notifications. | | Slack | Slack Incoming Webhook URL: Enter an incoming webhook URL, generated through your Slack workspace. For more information, see the Slack documentation.Test connection: This type of connection must pass testing before it can be saved | | Microsoft Teams | Microsoft Teams Incoming Webhook URL: Enter an incoming webhook URL, generated through your Microsoft Teams channel. For more information, see the Microsoft Teams documentation.Test connection: This type of connection must pass testing before it can be saved. | | DataRobot | | | User | Enter one or more existing DataRobot usernames to add those users to the channel. To remove a user, in the Username list, click the remove icon . | After you select or create a channel, click Next, confirm the Trigger, Channel, or Policy name, then click Create policy or Finish and save as template. --- # Deployment reports URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/deploy-reports.html > Learn about the deployment reports, which summarize details of a deployment, such as its owner, how the model was built, the model age, and the humility monitoring status. # Deployment reports Ongoing monitoring reports are a critical step in the deployment and governance process. DataRobot allows you to download deployment reports from MLOps, compiling deployment status, charts, and overall quality into a shareable report. Deployment reports are compatible with all deployment types. ## Generate a deployment report To generate a report for a deployment, select it from the inventory and navigate to the Overview tab. 1. SelectGenerate Report now. 2. Configure the report settings. Select the model used, the date range, and the date resolution (the granularity of comparison for the deployment's statistics within the specified time range). Once configured, clickGenerate report. 3. Allow some time for the report generation process to complete. Once finished, select the eye icon to view the report in your browser or the download icon to view it locally. ## Schedule deployment reports In addition to manual creation, DataRobot allows you to manage a schedule to generate deployment reports automatically: 1. In a deployment, click theNotificationstab. 2. SelectCreate new report schedule. 3. Complete the fields to fully configure the schedule: FieldDescription1FrequencyThe cadence at which deployment reports are generated for the deployment.2TimeThe time at which the deployment report is generated.3RangeThe period of time captured by the report.4ResolutionThe granularity of comparison for the deployment's statistics within the specified time range.5Additional recipients(Optional) The email addresses of those who should receive the deployment report, in addition to those who have access to the deployment. 4. After fully determining the report schedule, clickSave report schedule. The reports automatically generate at the configured dates and times. --- # Governance lens URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/gov-lens.html > Learn about the Governance lens, which summarizes details of a deployment such as the owner, how the model was built, model age, and humility monitoring status. # Governance lens The Governance lens summarizes the social and operational aspects of a deployment, such as the deployment owner, how the model was built, the model's age, and the [humility monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/humble.html) status. View the governance lens from the [deployment inventory](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html). The following table describes the information available from the Governance lens: | Category | Description | | --- | --- | | Deployment Name | The name assigned to a deployment at creation, the type of prediction server used, and the project name (DataRobot models only). | | Build Environment | The environment in which the model was built. | | Owners | The owner(s) of each deployment. To view the full list of owners, click on the names listed. A pop-up modal displays the owners with their associated email addresses. | | Model Age | The length of time the current model has been deployed. This value resets every time the model is replaced. | | Humility Monitoring | The status of prediction warnings and humility rules for each deployment. | | Fairness Monitoring | The status of fairness rules based on the number of protected features below the predefined fairness threshold for each deployment. | | Actions | Menu of additional model management activities, including adding data, replacing a model, setting data drift, and sharing and deleting deployments. | ## Build environments The build environment indicates the environment in which the model was built. The following table details the types of build environments displayed in the inventory for each type of model: | Deployed model | Available build environments | | --- | --- | | DataRobot model | DataRobot | | Custom model | Python, R, Java, or Other (if not specified). Custom models derive their build environment from the model's programming language. | | External model | DataRobot, Python, Java, R, or Other (if not specified). Specify an external model's build environment from the Model Registry when creating a model package. | ## Humility Monitoring indicators The Humility Monitoring column provides an at-a-glance indication of how [humility is configured](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html) for each deployment. To view more detailed information for an individual model, or enable humility monitoring, click on a deployment in the inventory list and navigate to the [Humility](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/humble.html) tab. The column indicates the status of two Humility Monitoring features: [prediction warnings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html#prediction-warnings) and [humility rules](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html). In the deployment inventory, interpret the color indicators for each humility feature as follows: | Color | Status | | --- | --- | | Blue | Enabled for the deployment. | | Light gray | Disabled for the deployment. | | Dark gray | Unavailable for the deployment. Humility Monitoring is only available for non-time-aware regression models and custom regression models that provide holdout data. | ## Fairness Monitoring indicators The Fairness column provides an at-a-glance indication of how each deployment is performing based on predefined [fairness](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/mlops-fairness.html) criteria. To view more detailed information for an individual model or enable fairness monitoring, click on a deployment in the inventory list and navigate to the Settings tab. In the deployment inventory, interpret the color indicators as follows: | Color | Status | | --- | --- | | Light gray | Fairness monitoring is not configured for this deployment. | | Green | All protected features are passing the fairness tests. | | Yellow | One protected feature is failing the fairness tests. Default is 1. | | Red | More than one protected feature is failing the fairness tests. Default is 2. | You can create rules for fairness monitoring in the [Definitionsection of theFairness > Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/fairness-settings.html#define-fairness-monitoring-notifications) tab. If no rules are specified, fairness monitoring uses the default values for "At Risk" and "Failing." --- # Humility tab URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/humble.html > After configuring rules and making predictions with humility monitoring enabled, you can view the humility data collected over time for a deployment from the Humility tab. # Humility tab After [configuring humility rules](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html) and making predictions with humility monitoring enabled, you can view the humility data collected over time for a deployment from the Humility > Summary tab. The X-axis measures the range of time that predictions have been made for the deployment. The Y-axis measures the number of times humility rules have triggered for the given period of time. The controls—model version and data time range selectors—work the same as those available on the [Data Drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html#configure-the-data-drift-dashboard) tab. --- # Governance URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/index.html > Model governance sets rules and controls for deployments, facilitates scaling of deployments, and provides legal and compliance reports. # Governance When machine learning models in production become critical to business functions, new requirements emerge to ensure quality and to comply with legal and regulatory obligations. The deployment and modification of models can have far-reaching impacts, so establishing clear practices can ensure consistent management and minimized risk. Model governance sets the rules and controls for deployments, including access control, testing and validation, change and access logs, and traceability of prediction results. With model governance in place, organizations can scale deployments and provide legal and compliance reports. Scaling the use and value of models in production requires a robust and repeatable production process, including clearly defined roles, procedures, and logging. A consistent process dramatically reduces an organization’s operational, legal, and regulatory risk. Additionally, logging shows that rules were followed and supports troubleshooting to resolve issues quickly, which increases trust and value from AI projects. ## Aspects of governance Model governance for MLOps includes various components: Roles and responsibilities: One of the first steps in production model governance is to establish clear [roles](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html) with duties within the production model lifecycle. Users may have more than one role.[MLOps admins](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/dep-admin.html) are central to maintaining model governance within an organization. Access control: To maintain control over production environments, access to production models and environments must be limited. Limitations can be implemented at the individual user level or via [role-based access control (RBAC)](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#role-based-access-control-for-users). In either case, a limited number of people will have the ability to update production data for model training, deploy production models, or modify production environments. Deployment testing and validation: To ensure quality in production, processes should include testing and validation of each new or refreshed model before deployment. These tests and their results should be logged to show that the model was deemed ready for production use. Testing information will be required for model approval. Model history: Models will change over time as they are updated and replaced in production. Maintenance of the complete [model history](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/dep-overview.html#history), including model artifacts and changelogs, is critical for legal and regulatory needs. The ability to understand when a change was made and by whom is critical for compliance but is also very useful for troubleshooting when something goes wrong. Humility rules: [Humility rules](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html) can be configured to allow models to be capable of recognizing, in real-time, when they make uncertain predictions or receive data they have not seen before. Unlike data drift, model humility does not deal with broad statistical properties over time—it is instead triggered for individual predictions, allowing you to set desired behaviors with rules that depend on different triggers. Fairness monitoring: [Fairness monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/fairness-settings.html) can be configured to allow models to be capable of recognizing when protected features fail to meet predefined fairness criteria. Testing the fairness of production models is triggered by individual predictions; however, any predictions made within the last 30 days are also taken into account. Traceable model results: Each model result must be attributable back to the model and model version that generated that result to meet legal and regulatory compliance obligations. Traceability is especially critical because of the dynamic nature of the production model lifecycle that results in frequent model updates. At the time of a legal or regulatory filing, which could be months after an individual model response, the model in production may not be the same as the model used to create the prediction in question. A record of request data and response values with date and time information satisfies this requirement. Also, a model ID should be provided as part of the model response to make the tracking process easier. --- # Fairness tab URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/mlops-fairness.html > Monitor the fairness of deployed production models over time. # Fairness tab > [!NOTE] Availability information > The Fairness tab is only available for DataRobot MLOps users. Contact your DataRobot representative for more information about enabling this feature. After you configure a deployment's [fairness settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/fairness-settings.html), you can use the Fairness tab to configure tests that allow models to monitor and recognize, in real time, when protected features in the dataset fail to meet predefined fairness conditions. When viewing the Deployment inventory with the [Governancelens](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/gov-lens.html), the Fairness column provides an at-a-glance indication of how each deployment is performing based on the fairness tests set up in the Settings > Data tab. To view more detailed information for an individual model or investigate why a model is failing fairness tests, click on a deployment in the inventory list and navigate to the Fairness tab. > [!NOTE] Note > To receive email notifications on fairness status, [configure notifications](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/deploy-notifications.html), [schedule monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/fairness-settings.html#schedule-fairness-monitoring-notifications), and [configure fairness monitoring settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/fairness-settings.html). ## Investigate bias The Fairness tab helps you understand why a deployment is failing fairness tests and which protected features are below the predefined fairness threshold. It provides two interactive and exportable visualizations that help identify which feature is failing fairness testing and why. | | Chart | Description | | --- | --- | --- | | (1) | Per-Class Bias chart | Uses the fairness threshold and fairness score of each class to determine if certain classes are experiencing bias in the model's predictive behavior. | | (2) | Fairness Over Time chart | Illustrates how the distribution of a protected feature's fairness scores have changed over time. | If a feature is marked as below threshold, the feature does not meet the predefined fairness conditions. Select the feature on the left to display fairness scores for each segmented attribute and better understand where bias exists within the feature. To further modify the display, see the documentation for the [version selector](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html). ### View per-class bias The Per-Class Bias chart helps to identify if a model is biased, and if so, how much and who it's biased towards or against. For more information, see the existing documentation on [per-class bias](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/per-class.html). Hover over a point on the chart to view its details: ### View fairness over time After configuring fairness criteria and making predictions with fairness monitoring enabled, you can view how [fairness scores](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/bias-ref.html) of the protected feature or feature values have changed over time for a deployment. The X-axis measures the range of time that predictions have been made for the deployment, and the Y-axis measures the fairness score. Hover over a point on the chart to view its details: You can also hide specific features or feature values from the chart by unchecking the box next to its name: The controls work the same as those available on the [Data Drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html) tab. ## Feature considerations - Bias and Fairness monitoring is only available for binary classification models and deployments. - To upload actuals for predictions, an association ID is required. It is also used to calculate True Positive & Negative Rate Parity and Positive & Negative Predictive Value Parity. --- # MLOps URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/index.html > DataRobot machine learning operations (MLOps) provides a central hub for you to deploy, monitor, manage, and govern your models in production. # MLOps DataRobot MLOps provides a central hub to [deploy](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/index.html), [monitor](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/index.html), [manage](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/index.html), and [govern](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/index.html) all your models in production, regardless of how they were created or when and where they were deployed. MLOps helps improve and maintain the quality of your models using health monitoring that accommodates changing conditions via continuous, automated model competitions ( [challenger models](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html)). It also ensures that all centralized production machine learning processes work under a robust governance framework across your organization, leveraging and sharing the burden of production model management. With MLOps, you can deploy any model to your production environment of choice. By instrumenting the [MLOps agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html), you can monitor any existing production model already deployed for live updates on behavior and performance from a single and centralized machine learning operations system. MLOps makes it easy to deploy models written in any open-source language or library and expose a production-quality, REST API to support real-time or batch predictions. MLOps also offers built-in [write-back integrations](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html) to systems such as Snowflake and Synapse. MLOps provides constant monitoring and production diagnostics to improve the performance of your existing models. Automated best practices enable you to track [service health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html), [accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html), and [data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html) to explain why your model is degrading. You can build your own challenger models or use Automated Machine Learning to build them for you and test them against your current champion model. This process of continuous learning and evaluation enables you to avoid surprise changes in model performance. The tools and capabilities of every deployment are determined by the data available to it: [training data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#training-data), [prediction data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#prediction-data), and outcome data (also referred to as [actuals](https://docs.datarobot.com/en/docs/reference/glossary/index.html#actuals)). | Topic | Description | | --- | --- | | Deployment | How to bring models to production by following the workflows provided for all kinds of starting artifacts. | | Deployment settings | How to use the settings tabs for individual MLOps features to add or update deployment functionality. | | Lifecycle management | Maintaining model health to minimize inaccurate data, poor performance, or unexpected results from models in production. | | Performance monitoring | Tracking the performance of models to identify potential issues, such as service errors or model accuracy decay, as soon as possible. | | Governance | Enacting workflow requirements to ensure quality and comply with regulatory obligations. | | MLOps FAQ | A list of frequently asked MLOps questions with brief answers linking to the relevant documentation. | --- # Manage deployments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/actions-menu.html > Manage deployments using the actions menu, which allows you to apply deployment settings, share deployments, create applications using the deployed model, replace models, and delete deployments, among other actions. # Manage deployments On the Deployments page, you can manage deployments using the actions menu. Access the actions menu to the right of each deployment or on the [Overview](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/dep-overview.html) tab: **Deployments page:** Click the menu icon to open the actions menu: [https://docs.datarobot.com/en/docs/images/deploy-menu-1.png](https://docs.datarobot.com/en/docs/images/deploy-menu-1.png) Or, click the Deployment name in the table to open the deployment [Overview](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-overview/nxt-overview.html), where you can view deployment information and access the actions menu. **Overview tab:** [https://docs.datarobot.com/en/docs/images/deploy-overview-1a.png](https://docs.datarobot.com/en/docs/images/deploy-overview-1a.png) ## Deployment actions The available options depend on a variety of criteria, including user permissions and the data available for your deployment. The table below briefly describes each option: | Option | Description | Availability | | --- | --- | --- | | Deployment access | Click Deployment name in the table to open that deployment to the Overview tab. From there, access deployment information, settings, and monitoring. If you have Consumer access to a deployment, you can't view, access, or act on that deployment. | User, Owner | | Replace model | Changes out the current model in the deployment with a newly specified model. | Owner | | Settings | Configures various settings for the deployment. Track target and feature drift in a deployment and activate additional features, such as prediction row storage for challenger models and segmented analysis. Use to enable data drift and add learning and inference data. | Owner | | Share | Provides sharing capabilities independent of project permissions. | User, Owner | | Create application | Launches a DataRobot application of your choice using the deployed model. | Owner | | Clear statistics | Reset the logged statistics for a deployment. | Owner | | Activate / Deactivate | Enables or disables a deployment's monitoring and prediction request capabilities. | Owner | | Relaunch | For management agent deployments, relaunch the deployment in the prediction environment managed by the agent. | Owner | | Get scoring code | Downloads Scoring Code (in JAR format) directly from the deployment. This action is only available for models that support Scoring Code. | User, Consumer, Owner | | View logs | For custom model deployments, opens the custom model runtime and startup logs for troubleshooting. | User, Owner | | Delete | Removes a deployment from the inventory. | Owner | ### Replace a model Prediction accuracy tends to degrade over time (which you can track in the [Data Drift dashboard](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html)) as conditions and data change. If you have the correct [permissions](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#deployment-roles), you can easily switch over to a new, better-adapted model using the [model replacement](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-replace.html) action. You can then incorporate the new model predictions into downstream applications. This action initiates an [email notification](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/actions-menu.html#deployment-email-notifications). ### Share a deployment The sharing capability allows [appropriate user roles](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#deployment-roles) to grant permissions on a deployment, independent of the project that created the deployed model. This is useful, for example, when the model creator regularly refreshes the model and wants the people using it to have access to the updated predictions but not to the model itself. To share a deployment, click Share. Then, enter the email address of the user you would like to share the deployment with, select their [role](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#deployment-roles), and click Share. You can later change the user's permissions by clicking on the current permission and selecting a new access level from the dropdown. This action initiates an [email notification](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/actions-menu.html#deployment-email-notifications). Additionally, deployment Owners and Users can share with groups and organizations. Select either the Groups or Organizations tab in the sharing modal. Enter the group or organization name in the Share With field, determine the role for permissions, and click Share. The deployment is shared with—and the role is applied to—every member of the designated group or organization. Additionally, deployment Owners and Users can share with groups and organizations (up to their own access level). ### Clear deployment statistics [Deployments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/index.html) collect various statistics for a model, including [accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html), [error rate](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html), [data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html), and [more](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/index.html). You may want to configure and test a deployed model before pushing a production workload on it to see if, for example, predictions perform well on data similar to that which you would upload for production. After testing a deployment, DataRobot allows you to reset the logged analytics, so you can separate testing data from live data without needing to recreate a deployment to start fresh. Choose a deployment for which you want to reset statistics from the inventory. Click the Actions menu and select Clear statistics. Complete the fields in the Clear Deployment Statistics window to configure the parameters of the reset. | Field | Description | | --- | --- | | Model version to clear from | Select the model version from which you want to clear statistics. If the model has not been replaced, there is only one option to choose from (the originally deployed model). | | Date range to clear from | Choose to either clear the entire history from the given model version, or specify a date range to wipe statistics from. | | Reason for clearance | (Optional) Describe why the statistics were reset. | | Confirm clearance | Select the toggle to confirm that you understand you are removing analytics from the selected deployment. | > [!NOTE] Note > If your organization has enabled the [deployment approval workflow](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/dep-admin.html), then approval must be given before any monitoring data can be removed from a deployment. After fully configuring the fields, click Clear statistics. DataRobot removes the monitoring data for the indicated time range from the deployment. ### Delete a deployment If you have the appropriate [permissions](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#deployment-roles), you can delete a deployment from the inventory by clicking the trash can icon [https://docs.datarobot.com/en/docs/images/icon-delete.png](https://docs.datarobot.com/en/docs/images/icon-delete.png). This action initiates an [email notification](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/actions-menu.html#deployment-email-notifications) to all users with sharing privileges to the deployment. ### Activate a deployment Deployments have capabilities, such as prediction requests and data monitoring, that consume a large amount of resources. You may want to test the prediction experience for a model or experiment with monitoring output settings without expending any resources or risking a production outage. Deployment activation allows you to control when these resource-intensive capabilities are enabled for individual deployments. Additionally, note that inactive deployments do not count towards your deployment limit. > [!NOTE] Availability information > Inactive deployment behavior depends on the [MLOps configuration](https://docs.datarobot.com/en/docs/release/deprecations-and-migrations/pricing.html) for your organization. A deployment's [Owner](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html) can activate its prediction requests and some monitoring capabilities. From the Actions menu for a deployment, select Activate. When created, a deployment is set to the active state by default; use the Actions menu to deactivate it. Once deactivated, you can still browse the deployment's monitoring tabs and edit its settings and metadata, but you cannot make predictions. Inactive deployments are indicated by an "INACTIVE" label in the deployment inventory: You can monitor the current number of active and inactive deployments from the tile at the top of the inventory: ### Deployment email notifications The following actions initiate an email notification. The availability of each action depends on each user's [role](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#deployment-roles). Additional notifications are available through the Notifications option of the [Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/deploy-notifications.html) tab. | Action | Description | | --- | --- | | Deployment Sharing | When you share a deployment with a user, the recipient receives an email, notifying them that permission has been granted. The email notification is only sent on the initial invite, not if permission levels have changed or been revoked. If the receiving user has the Consumer role on the deployment, the email will contain a deployment ID. Otherwise, for Users and Owners, the email will contain a link to view the deployment. | | Model Replacement | If a user with the appropriate role replaces a model within a deployment, DataRobot sends an email to all other users with the roles of Owner or User notifying them of the replacement. | | Deployment Deletion | When a user with the appropriate permission deletes a deployment, DataRobot sends a notifying email containing the deployment ID to all other subscribed users (all roles). | --- # Deployment inventory URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html > Learn about the deployment inventory, which displays all actively deployed models and lets you monitor deployed model performance and take necessary action. # Deployment inventory Once models are deployed, the deployment inventory is the central hub for deployment management activity. It serves as a coordination point for all stakeholders involved in operationalizing models. From the inventory, you can monitor deployed model performance and take action as necessary, as it provides an interface to all actively deployed models. ## Deployment lenses There are two unique deployment lenses that modify the information displayed in the inventory: - ThePrediction Health lenssummarizes prediction usage and model status for all active deployments. - TheGovernance lensreports the operational and social aspects of all active deployments. To change deployment lenses, click the active lens in the top right corner and select a lens from the dropdown. ### Prediction Health lens The Prediction Health lens is the default view of the deployment inventory, detailing prediction activity and model health for each deployment. Across the top of the inventory, the page summarizes the usage and status of all active deployments with [color-coded](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html#color-coded-health-indicators) health indicators. Beneath the summary is an individual report for each deployment. The following table describes the information available from the Prediction Health lens: | Category | Description | | --- | --- | | Deployment Name | Name assigned to the deployment at creation, the type of prediction server used, and the project name (DataRobot models only). | | Service | Service health of the individual deployment. The color-coded status indicates the presence or absence of errors in the last 24 hours. | | Drift | Data Drift occurring in the deployment. | | Accuracy | Model accuracy evaluated over time. | | Activity | A bar graph indicating the pattern of predictions over the past seven days. The starting point is the same for each deployment in the inventory. For example, a new deployment will plot that day's activity and six (blank) days previous. | | Avg. Predictions/Day | Average number of predictions per day over the last seven days. | | Last Prediction | Elapsed time since the last prediction was made against the model. | | Creation Date | Elapsed time since the deployment was created. | | Actions | Menu of additional model management activities, including adding data, replacing a model, setting data drift, and sharing and deleting deployments. | Click on any model entry in the table to [view details](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/dep-overview.html) about that deployment. Each model-specific page provides the above information in a status banner. ### Color-coded health indicators The [Service Health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html), [Data Drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html), and [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) summaries in the top part of the display provide an at-a-glance indication of health and accuracy for all deployed models. To view this more detailed information for an individual model, click on the model in the inventory list. The Service Health Summary tile measures the following error types over the last 24 hours. These are the Data Error Rate and System Error Rate errors recorded for an individual model on the Service Health tab. If you've enabled [timeliness tracking](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-usage.html#enable-timeliness-tracking) on the [Usage > Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/usage-settings.html) tab, you can view timeliness indicators in the inventory. Timeliness indicators show if the prediction or actuals upload frequency meets the standards set by your organization. Use the table below to interpret the color indicators for each deployment health category: | Color | Service Health | Data Drift | Accuracy | Timeliness | Action | | --- | --- | --- | --- | --- | --- | | Green / Passing | Zero 4xx or 5xx errors. | All attributes' distributions have remained similar since the model was deployed. | Accuracy is similar to when the model was deployed. | Prediction and/or actuals timeliness standards met. | No action needed. | | Yellow / At risk | At least one 4xx error and zero 5xx errors. | At least one lower-importance attribute's distribution has shifted since the model was deployed. | Accuracy has declined since the model was deployed. | N/A | Concerns found but no immediate action needed; monitor. | | Red / Failing | At least one 5xx error. | At least one higher-importance attribute's distribution has shifted since the model was deployed. | Accuracy has severely declined since the model was deployed. | Prediction and/or actuals timeliness standards not met. | Immediate action needed. | | Gray / Disabled | Unmonitored deployment. | Data drift tracking disabled. | Accuracy tracking disabled. | Timeliness tracking disabled. | Enable monitoring and make predictions. | | Gray / Not started | No service health events recorded. | Data drift tracking not started. | Accuracy tracking not started. | Timeliness tracking not started. | Make predictions. | | Gray / Unknown | No predictions made | Insufficient predictions made (min. 100 required). | Insufficient predictions made (min. 100 required). | N/A | Make predictions. | ## Live inventory updates The inventory automatically refreshes every 30 seconds and updates the following information: ### Active Deployments The Active Deployments tile indicates the number of deployments currently in use. The legend interprets the bar below the active deployment count: - Your active deployments (blue) - Other active deployments (white) - Available new deployments (gray) > [!NOTE] Inactive deployments > Inactive deployments do not count toward the deployments limit and do not support predictions, retraining, challengers, or model replacement. In the example above, the user's organization is allotted ten deployments. The user has seven active deployments, and there is one other active deployment in the organization. Users within the organization can create two more active deployments before reaching the limit. There are two inactive deployments not counted towards the deployment limit. If you're active in multiple organizations, under Your active deployments, you can see how many of those active deployments are in This organization or Other organizations: > [!NOTE] Deployments in other organizations > Your deployments in Other organizations do not count toward the allocated limit in the current organization. > [!NOTE] Availability information > The availability information shown on the Active Deployments tile depends on the [MLOps configuration](https://docs.datarobot.com/en/docs/release/deprecations-and-migrations/pricing.html) for your organization. ### Predictions The Predictions tile indicates the number of predictions made since the last refresh. Individual deployments show the number of predictions made on them during the last 30 seconds. If a deployment's service health, drift, or accuracy status changes to Failing, the individual deployment will flash red to draw attention to it. ### Sort deployments The deployment inventory is initially sorted by the most recent creation date (reported in the Creation Date column). You can click a different column title to sort by that metric instead. A blue arrow appears next to the sort column's title, indicating if the order is ascending or descending. > [!NOTE] Note > When you sort the deployment inventory, your most recent sort selection persists in your local settings until you clear your browser's local storage data. As a result, the deployment inventory is usually sorted by the column you selected last. You can sort in ascending or descending order by: - Deployment Name (alphabetically) - Service , Drift , and Accuracy (by status) - Avg. Predictions/Day (numerically) - Last Prediction (by date) - Build Environment (alphabetically) - Creation Date (by date) > [!NOTE] Note > The list is sorted secondarily by the time of deployment creation (unless the primary sort is by Creation Date). For example, if you sorted by drift status, all deployments whose status is passing would be ordered from most recent creation to oldest, followed by failing deployments most recent to oldest. ### Filter deployments To filter the deployment inventory, select Filters at the top of the inventory page. The filter menu opens, allowing you to select the criteria by which deployments are filtered. | Filter | Description | | --- | --- | | Ownership | Filters by deployment owner. Select Owned by me to display only those deployments for which you have the owner role. | | Activation status | Filters by deployment activation status. Active deployments are able to monitor and return new predictions. Inactive deployments can only show insights and statistics about past predictions. | | Fairness status | Filters by deployment fairness status. Choose to filter by passing , at risk , failing , unknown , and not started . | | Service status | Filters by deployment service health status. Choose to filter by passing , at risk , failing , unknown , and not started . If a deployment has never had service health enabled, then it will not be included when this filter is applied. | | Drift status | Filters by deployment data drift status. Choose to filter by passing , at risk , failing , unknown , and not started . If a deployment previously had data drift enabled and reported a status, then the last-reported status is used for filtering, even if you later disabled data drift for that deployment. If a deployment has never had drift enabled, then it will not be included when this filter is applied. | | Accuracy status | Filters by deployment accuracy status. Choose to filter by passing , at risk , failing , unknown , and not started . If a deployment does not have accuracy information available, it is excluded from results when you apply the filter. | | Predictions timeliness status | Filters by predictions timeliness status. Choose to filter by passing , failing , disabled , or not started . | | Actuals timeliness status | Filters by actuals timeliness status. Choose to filter by passing , failing , disabled , or not started . | | Importance | Filters by the criticality of deployments, based on prediction volume, exposure to regulatory requirements, and financial impact. Choices include Critical, High, Moderate, and Low. | | Build environment | Filters by the environment in which the model was built. | | Prediction environment platforms | Filters by the platform the prediction environment runs on. | After selecting the desired filters, click Apply Filters to update the deployment inventory. The Filters link updates to indicate the number of filters applied. You are notified if no deployments match your filters. To remove your filters, click the Clear all X filters shortcut, or open the filter dialog again and remove them manually. ## Self-Managed AI Platform deployments with monitoring disabled > [!NOTE] Availability information > This section is only applicable to the Self-Managed AI Platform. If you are a Self-Managed AI Platform administrator interested in enabling model monitoring for deployments by implementing the necessary hardware, contact DataRobot Support. The use of DataRobot's monitoring functionality depends on having hardware with PostgreSQL and rsyslog installed. If you don't have these services, you will still be able to create, manage, and make predictions against deployments, but all monitoring-related functionality will be disabled automatically. When Deployment Monitoring is disabled, the Deployments inventory is still accessible, but monitoring tools and statistics are disabled. A notification at the top of the page informs you of the monitoring status. The [actions menu](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/actions-menu.html) on the Deployments inventory page still allows you to [share](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/actions-menu.html#share-a-deployment) or [delete](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/actions-menu.html#delete-a-deployment) a deployment and [replace](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-replace.html) a model. When you select a deployment, you can still access the predictions code snippet from the [Predictions](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab. --- # Replace deployed models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-replace.html > How to replace deployment model packages, to keep models current and accurate. DataRobot uses training data to verify that the two models have the same target. # Replace deployed models Because model predictions tend to degrade in accuracy over time, DataRobot provides an easy way to switch models and model packages for deployments. This ensures that models are up-to-date and accurate. Using the model management capability to switch model packages for deployments allows model creators to keep models current without disrupting downstream consumers. It helps model validators and data science teams to track model history. And, it provides model consumers with confidence in their predictions without needing to know the details of the changeover. ## Replace a model package Use the Replace model functionality found in the [Actions](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/actions-menu.html) ( [https://docs.datarobot.com/en/docs/images/icon-menu.png](https://docs.datarobot.com/en/docs/images/icon-menu.png)) menu. The menu is available from the Deployments area of either the [Inventory](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html) or the [Overview](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/dep-overview.html) pages. You are redirected to the Overview tab of the deployment. Click Import from to choose your method of model replacement. - Local File: Upload a model package exported from DataRobot AutoML to replace an existing model package (standalone MLOps users only). - Model Registry: Select a model package from theModel Registryto replace an existing model package. - Paste AutoML URL: Copy the URL of the model from the Leaderboard and paste it into theReplacement Modelfield. When you have confirmed the model package for replacement, select the replacement reason and click Accept and replace. ## Model replacement considerations When replacing a deployed model, note the following: - Model replacement is available for all deployments. Each deployment's model is provided as a model package, which can be replaced with another model package, provided it iscompatible. NoteThe new model packagecannotbe the same Leaderboard model as an existing champion or challenger; each challengermustbe a unique model. If you create multiple model packages from the same Leaderboard model, you can't use those models as challengers in the same deployment. - While only the most current model is deployed, model history is maintained and can be used as a baseline for data drift. ### Model replacement validation DataRobot validates whether the new model is an appropriate replacement for the existing model and provides warning messages if issues are found. DataRobot compares the models to ensure that: - The target names and types match. For classification targets, the class names must match. - The feature types match. - There are no new features. If the new model has more features, the warning identifies the additional features. This is intended to help prevent prediction errors if the new model requires features not available in the old model. If the new model has fewer or the same number of features, there is no warning. - The replacement model supports all humility rules. - If the existing model is a time series model, the replacement model must also be a time series model and the series types must match (single series/multiseries). - If the model is a custom inference model, it must pass custom model tests. - Prediction intervals must be compatible if enabled for the deployment. - Segments must be compatible if segment analysis is enabled for the deployment. > [!NOTE] Note > DataRobot is only able to validate your model’s input features if you have assigned training data to both model packages (the existing model package for your deployment, and the one you selected to replace it with). Otherwise, DataRobot is unable to validate that the two model packages have the same target type and target name. A warning message informs you that model replacement is not allowed if the model, target type, and target name are not the same: > > [https://docs.datarobot.com/en/docs/images/replace-m-4.png](https://docs.datarobot.com/en/docs/images/replace-m-4.png) ### Model package replacement compatibility Consider the compatibility of each model package type (external and DataRobot) before proceeding with model package replacement for a deployment: **SaaS:** External model packages (monitored by the MLOps agent ) can only replace other external model packages. They cannot be replaced by DataRobot model packages. Custom model packages are DataRobot model packages. DataRobot model packages can only replace other DataRobot model packages. They cannot be replaced by external model packages. **Self-Managed:** External model packages (monitored by the MLOps agent ) can only replace other external model packages. They cannot be replaced by DataRobot model packages. Custom model packages and imported .mlpkg files are both DataRobot model package types. DataRobot model packages can only replace other DataRobot model packages. They cannot be replaced by external model packages. --- # Lifecycle management URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/index.html > Lifecycle management provides tools and a robust, repeatable process to scale models and manage the lifecycle of models in production environments. # Lifecycle management Machine learning models in production environments have a complex lifecycle, and the use and value of models requires a robust and repeatable process to manage that lifecycle. Without proper management, models that reach production may deliver inaccurate data, poor performance, or unexpected results that can damage your business’s reputation for AI trustworthiness. Lifecycle management is essential for creating a machine learning operations system that allows you to scale many models in production. The following sections describe how to manage models in production. Be sure to review the [deployment considerations](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/index.html#feature-considerations) before proceeding. | Topic | Description | | --- | --- | | Deployment inventory (Deployments page) | Coordinate deployments and view deployment inventory. | | Manage deployments | Understand the actions you can take with deployments. | | Replace deployed models | Replace the model used for a deployment. | | Set up Automated Retraining policies | Configure retraining policies to maintain model performance after deploying. | --- # Manage Automated Retraining policies URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/set-up-auto-retraining.html > Maintain model performance after deployment through Automated Retraining. # Manage Automated Retraining policies To maintain model performance after deployment without extensive manual work, DataRobot provides an automatic retraining capability for deployments. Upon providing a retraining dataset registered in the [AI Catalog](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html), you can define up to five retraining policies on each deployment, each consisting of a trigger, a modeling strategy, modeling settings, and a replacement action. When triggered, retraining will produce a new model based on these settings and notify you to consider promoting it. > [!TIP] Configure retraining settings > To configure a retraining policy, [use the NextGen UI](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-mitigation/nxt-retraining.html). ## Manage existing retraining policies You can start retraining policies or cancel retraining policies from the Classic UI. To edit or delete a retraining policy, [use the NextGen UI](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-mitigation/nxt-retraining.html). | | Element | Definition | | --- | --- | --- | | (1) | Manage retraining policies in the NextGen UI | Open the deployment's Mitigation > Retraining tab in the NextGen UI to edit or delete the retraining policy. | | (2) | Retraining policy row | Click on a retraining policy row to expand it and view the retraining policy runs. | | (3) | Run | Click the run button to start a policy manually. Alternatively, edit the policy by clicking the policy row and scheduling a run using the retraining trigger. | | (4) | Cancel | Click the cancel button to cancel a policy that is in progress or scheduled to run. You can't cancel a policy if it has finished successfully, reached the "Creating challenger" or "Replacing model" step, failed, or has already been canceled. | ## View retraining history You can view all previous runs of a training policy, successful or failed. Each run includes a start time, end time, duration, and—if the run succeeded—links to the resulting project and model package. While only the DataRobot-recommended model for each project is added automatically to the deployment, you may want to explore the project's Leaderboard to find or build alternative models. > [!NOTE] Note > Policies cannot be deleted or interrupted while they are running. If the retraining worker and organization have sufficient workers, multiple policies on the same deployment can be running at once. --- # MLOps FAQ URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-faq.html > Provides a list, with brief answers, of frequently asked MLOps deployment and monitoring questions. Answers link to complete documentation. # MLOps FAQ --- # MLOps preview features URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-preview/index.html > Read preliminary documentation for MLOps features currently in the DataRobot preview pipeline. # MLOps preview features This section provides preliminary documentation for features currently in preview pipeline. If not enabled for your organization, the feature is not visible. Although these features have been tested within the engineering and quality environments, they should not be used in production at this time. Note that preview functionality is subject to change and that any Support SLA agreements are not applicable. > [!NOTE] Availability information > Contact your DataRobot representative or administrator for information on enabling or disabling preview features. ## Available MLOps preview documentation **SaaS:** Feature Description Service health and accuracy history Service Health and accuracy history allows you to compare the current model and up to five previous models in one place and on the same scale. Automated deployment and replacement of Scoring Code in Snowflake Create a DataRobot-managed Snowflake prediction environment to deploy and replace DataRobot Scoring Code in Snowflake. Run the monitoring agent in DataRobot Run the monitoring agent within the DataRobot platform, one instance per prediction environment. MLOps reporting for unstructured models Report MLOps statistics from custom inference models created with an unstructured regression, binary, or multiclass target type. **Self-Managed:** Feature Description Service health and accuracy history Service Health and accuracy history allows you to compare the current model and up to five previous models in one place and on the same scale. Automated deployment and replacement of Scoring Code in Snowflake Create a DataRobot-managed Snowflake prediction environment to deploy and replace DataRobot Scoring Code in Snowflake. Run the monitoring agent in DataRobot Run the monitoring agent within the DataRobot platform, one instance per prediction environment. MLOps reporting for unstructured models Report MLOps statistics from custom inference models created with an unstructured regression, binary, or multiclass target type. --- # MLOps reporting for unstructured models URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-preview/mlops-unstructured-models.html > Report MLOps statistics from custom inference models created with an unstructured regression, binary, or multiclass target type. # MLOps reporting for unstructured models > [!NOTE] Availability information > MLOps Reporting from Unstructured Models is off by default. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable MLOps Reporting from Unstructured Models Now available for preview, you can report MLOps statistics from Python custom inference models [created in the Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html) with an Unstructured (Regression), Unstructured (Binary), or Unstructured (Multiclass) target type: > [!WARNING] Target type consideration > MLOps reporting for unstructured models is not supported for the Unstructured (Other) target type. With this feature enabled, when you [assemble an unstructured custom inference model](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html) in Python, you can read the `mlops` input argument from the `kwargs` as follows: ``` mlops = kwargs.get('mlops') ``` For an example of an unstructured Python custom model with MLOps reporting, see the [DataRobot User Models repository](https://github.com/datarobot/datarobot-user-models/tree/master/model_templates/python3_unstructured_with_mlops_reporting). ## Unstructured custom model reporting methods If the value of `mlops` is not `None`, you can access and use the following methods: ### report_deployment_stats Reports the number of predictions and execution time to DataRobot MLOps. ``` report_deployment_stats(num_predictions: int, execution_time: float) ``` | Argument | Description | | --- | --- | | num_predictions | The number of predictions. | | execution_time | The time, in milliseconds, that it took to calculate all predictions. | ### report_predictions_data Reports the features, along with their predictions and association IDs, to DataRobot MLOps. ``` report_predictions_data(features_df: pandas.DataFrame, predictions: list, association_ids: list, class_names: list) ``` | Argument | Description | | --- | --- | | features_df | A dataframe containing all features to track and monitor. Exclude any features you don't want to report from the dataframe. | | predictions | A list of predictions. For regression deployments, this is a 1-dimensional list containing prediction values (e.g., [1, 2, 4, 3, 2]).For classification deployments, this is a 2-dimensional list, where the inner list is the probabilities for each class type (e.g., [[0.2, 0.8], [0.3, 0.7]]). | | association_ids | (Optional) A list of association IDs corresponding to each prediction. Association IDs are used to calculate accuracy and must be unique for each reported prediction. The number of predictions should equal the number of association_ids in the list. | | class_names | (Classification only) A list of the names of predicted classes (e.g., ["class1", "class2", "class3"]). For classification deployments, the class names must be in the same order as the prediction probabilities reported. If the order isn't specified, the prediction order defaults to the order of the class names on the deployment. This argument is ignored for regression deployments. | ## Local testing To test an unstructured custom model with MLOps reporting locally, you must use the [drumutility](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/custom-model-drum.html) with the following input arguments (or the corresponding environment variables): | Input argument | Environment variable | Description | | --- | --- | --- | | --target-type | TARGET_TYPE | Must be unstructured. | | --webserver | EXTERNAL_WEB_SERVER_URL | The DataRobot external web server URL. | | --api-token | API_TOKEN | The DataRobot API token. | | --monitor-embedded | MLOPS_REPORTING_FROM_UNSTRUCTURED_MODELS | Enables a model to use MLOps library to report statistics. | | --deployment-id | DEPLOYMENT_ID | The deployment ID for monitoring model predictions. | | --model-id | MODEL_ID | The deployed model ID for monitoring predictions. | --- # Run the monitoring agent in DataRobot URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-preview/monitoring-agent-in-dr.html > Run the monitoring agent within the DataRobot platform, one instance per prediction environment. # Run the monitoring agent in DataRobot > [!NOTE] Preview > The monitoring agent in DataRobot is a preview feature, on by default. > > Feature flag: Disable the Monitoring Agent in DataRobot The [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html) typically runs outside of DataRobot, reporting metrics from a [configured spooler](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/spooler.html) populated by calls to the DataRobot MLOps library in the external model's code. Now available for preview, you can run the monitoring agent inside DataRobot by creating an [external prediction environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html) with an external spooler's credentials and configuration details. To create a prediction environment associated with an external spooler: 1. ClickDeployments > Prediction Environmentsand then clickAdd prediction environment. 2. In theAdd prediction environmentdialog box, complete the following fields: FieldDescriptionNameEnter a descriptive prediction environment name.Description(Optional) Enter a description of the external prediction environment.PlatformSelect the external platform on which the model is running and making predictions. 3. UnderSupported model formats, select one or more formats to control which models can be deployed to the prediction environment, either manually or using the management agent. The available model formats areDataRobotorDataRobot Scoring Code only,External Model, andCustom Model. ImportantYou cannot select bothDataRobotandDataRobot Scoring Code only. 4. UnderManaged by, selectManaged by DataRobot. The following options are provided: OptionDescriptionSelf-managedManually manage models on your infrastructure and report data manually to DataRobot.Managed by Management AgentManage models with theManagement Agenton your own infrastructure.Managed by DataRobotManage models with theManagement Agentinside DataRobot. This option is only available if thePlatformselected isAzure,Amazon Web Services (AWS), orSnowflake. 5. UnderSettings, select aQueue: Amazon SQSGoogle Pub/SubAzure Event HubsSelect theAWS S3 Credentialsfor your Amazon SQS spooler and configure the following Amazon SQS fields:FieldDescriptionRegionSelect the AWS region used for the queue.SQS Queue URLSelect the URL of the SQS queue used for the spooler.Visibility timeout(Optional) Thevisibility timeoutbefore the message is deleted from the queue. This is an Amazon SQS configuration not specific to the monitoring agent.After you configure theQueuesettings you can provide anyEnvironment variablesaccepted by the Amazon SQS spooler. For more information, see theAmazon SQS spooler reference.Select theGCP Credentialsfor your Google Pub/Sub spooler and configure the following Google Pub/Sub fields:FieldDescriptionPub/Sub projectSelect the Pub/Sub project used by the spooler.Pub/Sub topicSelect the Pub/Sub topic used by the spooler; this should be the topic name within the project, not the fully qualified topic name path that includes the project ID.Pub/Sub subscriptionSelect the Pub/Sub subscription name of the subscription used by the spooler.Pub/Sub acknowledgment deadline(Optional) Enter the amount of time (in seconds) for subscribers to process and acknowledge messages in the queue.After you configure theQueuesettings you can provide anyEnvironment variablesaccepted by the Google Pub/Sub spooler. For more information, see theGoogle Cloud Pub/Sub spooler reference.Select the Azure Service PrincipalCredentialsfor your Azure Event Hubs spooler and configure theAzure SubscriptionandAzure Resource Groupfields accessible using the providedCredentials:Azure Service Principal credentials requiredDataRobot management of Scoring Code in AzureML requires existing Azure Service PrincipalCredentials. If you don't have existing credentials, theAzure Service Principal credentials requiredalert appears, directing you toGo to Credentialstocreate Azure Service Principal credentials.To create the required credentials, forCredential type, selectAzure Service Principal. Then, enter aClient ID,Client Secret,Azure Tenant ID, and aDisplay name. To validate and save the credentials, clickSave and sign in.You can find these IDs and the display name on Azure'sApp registrations > Overviewtab (1). You can generate secrets on theApp registration > Certificates and secretstab(2):Next, configure the following Azure Event Hubs fields:FieldDescriptionEvent Hubs NamespaceSelect a validEvent Hubs namespaceretrieved from the Azure Subscription ID.Event Hub InstanceSelect anEvent Hubs instancewithin your namespace for monitoring data.After you configure theQueuesettings, you can provide anyEnvironment variablesaccepted by the Azure Event Hubs spooler. For more information, see theAzure Event Hubs spooler reference. 6. Once you configure the environment settings, clickAdd environment. The environment is now available from thePrediction Environmentspage. ## Manage an agent-monitored prediction environment When you add a prediction environment with monitoring settings configured, you can view the health and status of that prediction environment, edit the queue settings, and stop or start the agent. On the Deployments > Prediction Environments tab, locate the agent-monitored and managed environment. From the prediction environment inventory, you can view the Name, Platform, Added On date, Created By date, and Health. You can also edit, share, and delete prediction environments. For more information, see the [Manage prediction environments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/prediction-env/pred-env-manage.html) documentation. > [!TIP] Copy prediction environment ID > From the upper-left corner of either tab in a prediction environment, you can click the copy icon () to copy the Prediction Environment ID. From the Details tab, you can edit the prediction environment Name, Description, Platform, Supported model formats, Managed by setting, and associated Service Account. In the Usages section, you can view and access deployments associated with the environment: > [!NOTE] Deployment links > Deployment links are only provided for deployments you have access to. From the Monitoring tab, you can edit the Settings configured when creating the environment to modify the connection between the monitoring agent and the spooler. The agent Status must be inactive to edit these settings: From the Monitoring Agent section, you can view agent status information, enable or disable the agent, view prediction records, and view or download logs: --- # Service Health and Accuracy history URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-preview/pp-deploy-history.html > Service Health and Accuracy history allow you to compare the current model with previous models in one place, on the same scale. # Service Health and Accuracy history > [!NOTE] Availability information > Deployment history for service health and accuracy is off by default. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable Deployment History When analyzing a deployment, [Service Health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html) and [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) can provide critical information about the performance of current and previously deployed models. However, comparing these models can be a challenge as the charts are displayed separately, and the scale adjusts to the data. To improve the usability of the service health and accuracy comparisons, the [Service Health > History](https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-preview/pp-deploy-history.html#service-health-history) and [Accuracy > History](https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-preview/pp-deploy-history.html#accuracy-history) tabs (now available for preview) allow you to compare the current model with previously deployed models in one place, on the same scale. ## Service Health history The [Service Healthpage](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html) displays metrics you can use to assess a deployed model's ability to respond to prediction requests quickly and reliably. In addition, on the History tab, you can access visualizations representing the service health history of up to five of the most recently deployed models, including the currently deployed model. This history is available for each metric tracked in a model's service health, helping you identify bottlenecks and assess capacity, which is critical to proper provisioning. For example, if a deployment's response time seems to have slowed, the Service Health page for the model's deployment can help diagnose the issue. If the service health metrics show that median latency increases with an increase in prediction requests, you can then check the History tab to compare the currently deployed model with previous models. If the latency increased after replacing the previous model, you could consult with your team to determine whether to deploy a better-performing model. To access the Service Health > History tab: 1. ClickDeploymentsand select a deployment from the inventory. 2. On the selected deployment'sOverview, clickService Health. 3. On theService Health > Summarypage, clickHistory. TheHistorytab tracks the following metrics: MetricReportsTotal PredictionsThe number of predictions the deployment has made.Requests overxmsThe number of requests where the response time was longer than the specified number of milliseconds. The default is 2000 ms; click in the box to enter a time between 10 and 100,000 ms or adjust with the controls.Response Time (ms)The time (in milliseconds) DataRobot spent receiving a prediction request, calculating the request, and returning a response to the user. The report does not include time due to network latency. Select theMedianprediction request time or90th percentile,95th percentile, or99th percentile. The display reports a dash if you have made no requests against it or if it's an external deployment.Execution Time (ms)The time (in milliseconds) DataRobot spent calculating a prediction request. Select theMedianprediction request time or90th percentile,95th percentile, or99th percentile.Data Error Rate (%)The percentage of requests that result in a 4xx error (problems with the prediction request submission). This is a component of the value reported as the Service Health Summary in theDeploymentspage top banner.System Error Rate (%)The percentage of well-formed requests that result in a 5xx error (problem with the DataRobot prediction server). This is a component of the value reported as the Service Health Summary in theDeploymentspage top banner. 4. To view the details for a data point in a service health history chart, you can hover over the related bin on the chart: ## Accuracy history The [Accuracypage](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) analyzes the performance of model deployments over time using standard statistical measures and visualizations. Use this tool to analyze a model's prediction quality to determine if it is decaying and if you should consider replacing it. In addition, on the History page, you can access visualizations representing the accuracy history of up to five of the most recently deployed models, including the currently deployed model, allowing you to compare model accuracy directly. These accuracy insights are rendered based on the problem type and its associated optimization metrics. > [!NOTE] Note > Accuracy monitoring is not enabled for deployments by default. To enable it, first upload the data that contains predicted and actual values for the deployment collected outside of DataRobot. For more information, see the documentation on [setting up accuracy for deployments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html) by adding [actuals](https://docs.datarobot.com/en/docs/reference/glossary/index.html#actuals). To access the Accuracy > History tab: 1. ClickDeploymentsand select a deployment from the inventory. 2. On the selected deployment'sOverview, clickAccuracy. 3. On theAccuracy > Summarypage, clickHistory. TheHistorytab tracks the following: MetricReportsAccuracy Over TimeA line graph visualizing the change in the selected accuracy metric over time for up to five of the most recently deployed models, including the currently deployed model. The available accuracy metrics depend on the project type.Predictions vs Actuals Over TimeA line graph visualizing the difference between the average predicted values and average actual values over time for up to five of the most recently deployed models, including the currently deployed model. For classification projects, you can display results per-class. Accuracy Over TimePredictions vs Actuals Over TimeThe accuracy over time chart plots the selected accuracy metric for each prediction range along a timeline. The accuracy metrics available depend on the type of modeling project used for the deployment:Project typeAvailable metricsRegressionRMSE, MAE, Gamma Deviance, Tweedie Deviance, R Squared, FVE Gamma, FVE Poisson, FVE Tweedie, Poisson Deviance, MAD, MAPE, RMSLEBinary classificationLogLoss, AUC, Kolmogorov-Smirnov, Gini-Norm, Rate@Top10%, Rate@Top5%, TNR, TPR, FPR, PPV, NPV, F1, MCC, Accuracy, Balanced Accuracy, FVE BinomialYou can select an accuracy metric from theMetricdropdown list.The Predictions vs Actuals Over Time chart plots the average predicted value next to the average actual value for each prediction range along a timeline. In addition, the volume chart below the graph displays the number of predicted and actual values corresponding to the predictions made within each plotted time range. The shaded area represents the number of uploaded actuals, and the striped area represents the number of predictions without corresponding actuals.The timeline and bucketing work the same for classification and regression projects; however, for classification projects, you can use theClassdropdown to display results for that class. 4. To view the details for a data point in an accuracy history chart, you can hover over the related bin on the chart: --- # Automated deployment and replacement of Scoring Code in Snowflake URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-preview/pp-snowflake-sc-deploy-replace.html > Create a DataRobot-managed Snowflake prediction environment to deploy and replace DataRobot Scoring Code in Snowflake. # Automated deployment and replacement of Scoring Code in Snowflake > [!NOTE] Preview > Automated deployment and replacement of Scoring Code in Snowflake is off by default. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable the Automated Deployment and Replacement of Scoring Code in Snowflake Now available for preview, you can create a DataRobot-managed Snowflake prediction environment to deploy DataRobot Scoring Code in Snowflake. With DataRobot management enabled, the external Snowflake deployment has access to MLOps management, including automatic Scoring Code replacement. > [!NOTE] Service health information for external models and monitoring jobs > Service health information is unavailable for external [agent-monitored deployments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/monitoring-agent/index.html) and deployments with predictions uploaded through a [prediction monitoring job](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/pred-monitoring-jobs/index.html). ## Create a Snowflake prediction environment To deploy a model in Snowflake, you first create a custom Snowflake prediction environment: 1. ClickDeployments>Prediction Environmentsand then clickAdd prediction environment. 2. In theAdd prediction environmentdialog box, configure the prediction environment settings: 3. After you configure the environment settings, clickAdd environment. The Snowflake environment is now available from thePrediction Environmentspage. ## Deploy a model to the Snowflake prediction environment Once you've created a Snowflake prediction environment, you can deploy a model to it: 1. ClickModel Registry > Registered Modelsand select the Scoring Code enabled model you want to deploy to the Snowflake prediction environment. TipYou can also deploy a model to your Snowflake prediction environment from theDeployments>Prediction Environmentstab by clicking+ Add new deploymentin the prediction environment. 2. On any tab in the registered model version, clickDeploy. 3. In theSelect Deployment Targetdialog box, underSelect deploy target, clickSnowflake. NoteIf you can't click the Snowflake deployment target, the selected model doesn't have Scoring Code available. 4. UnderSelect prediction environment, select the Snowflake prediction environment you added, and then clickConfirm. 5. Configure the deployment, and then clickDeploy model. 6. Once the model is deployed to Snowflake, you can use theScore your datacode snippet from thePredictions > Portable Predictionstab to score data in Snowflake. ## Restart a Snowflake prediction environment When you update database settings or credentials for the Snowflake data connection used by the prediction environment, you can restart the environment to apply those changes to the environment: 1. ClickDeployments > Prediction Environmentspage, and then select the Snowflake prediction environment from the list. 2. Below the prediction environment settings, underService Account, clickRestart Environment. --- # Feature cache for Feature Discovery deployments URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-preview/safer-ft-cache.html > Schedule feature cache for Feature Discovery deployments # Feature cache for Feature Discovery deployments > [!NOTE] Availability information > Feature Cache for deployed Feature Discovery models is off by default. Contact your DataRobot representative or administrator for information on enabling the feature. > > Feature flag: Enable Feature Cache for Feature Discovery Now available for preview, you can schedule feature cache for Feature Discovery deployments, which instructs DataRobot to pre-compute and store features before making predictions. Currently, you can only make batch predictions with Feature Discovery projects; however, generating these features in advance makes single-record, low-latency scoring possible. Once feature cache is enabled and configured in the deployment's settings, DataRobot caches features and stores them in a database. When new predictions are made, the primary dataset is sent to the prediction endpoint, which enriches the data from the cache and returns the prediction response. The feature cache is then periodically updated based on the specified schedule. To enable feature cache, go to the [Predictions > Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/predictions-settings.html) tab of a [Feature Discovery project's](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html) deployment. Then, turn on the Enable Feature Cache toggle and choose a schedule for DataRobot to update cached features. > [!NOTE] Note > If you are configuring the settings for a new deployment, the creation process may take longer than usual as features are computed and stored for the first time during deployment creation. Once feature cache is enabled for a deployment, it cannot be disabled later on. You can change how often DataRobot caches features or monitor the [status](https://docs.datarobot.com/en/docs/classic-ui/mlops/mlops-preview/safer-ft-cache.html#general-statuses) of feature caching on the deployment's Predictions > Settings tab. ## General statuses In your deployment's settings, you can monitor the status of feature cache. The table below describes each possible status: | Status | Description | | --- | --- | | Not fetched | Feature cache was configured but data hasn't been populated into feature cache yet. Predictions are impossible at the moment. | | Outdated | Data was not populated during the last scheduled run. Outdated data still present in feature cache and predictions are possible but accuracy can be badly impacted. | | Configuration failed | Feature cache was enabled but failed to be configured. Predictions are impossible. | | Failed to fetch | Data failed to be stored in cache. Predictions are impossible. | | Updated | Last scheduled run was completed successfully. Predictions work as expected. | ## Considerations Consider the following when enabling feature cache for a Feature Discovery project: - The maximum number of prediction features is 300. - The scoring dataset can have a maximum of 200 rows. Datasets with more than 200 rows will not be scored with feature cache. - Feature cache is not compatible with data drift tracking. - Feature cache is only visible in the UI if DataRobot detects secondary datasets. - Feature cache cannot be disabled once it is enabled for a deployment. --- # Challengers tab URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/challengers.html > How to use the Challengers tab to submit challenger models that shadow a deployed model and replay predictions made against the deployed model. If a challenger outperforms the deployed model, you can replace the model. # Challengers tab > [!NOTE] Availability information > The Challengers tab is a feature exclusive to DataRobot MLOps users. Contact your DataRobot representative for information on enabling it. During model development, many models are often compared to one another until one is chosen to be deployed into a production environment. The Challengers tab provides a way to continue model comparison post-deployment. You can submit challenger models that shadow a deployed model and replay predictions made against the deployed model. This allows you to compare the predictions made by the challenger models to the currently deployed model (the "champion") to determine if there is a superior DataRobot model that would be a better fit. ## Enable challenger models To enable challenger models for a deployment, you must enable the Challengers tab and [prediction row storage](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/challengers-settings.html). To do so, configure the deployment's data drift settings either when [creating a deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-deploy-info.html#challenger-analysis) or on the [Challengers > Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/challengers-settings.html) tab. If you enable Challenger models, prediction row storage is automatically enabled for the deployment. It cannot be turned off, as it is required for challengers. > [!NOTE] Availability information > To enable challengers and replay predictions against them, the deployed model must support target drift tracking and not be a [Feature Discovery](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html) or [Unstructured custom inference](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/unstructured-custom-models.html) model. ## Select a challenger model Before adding a challenger model to a deployment, you must first build and select the model to be added as a challenger. Complete the [modeling process](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html) and choose a model from the Leaderboard, or deploy a [custom model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html#add-a-custom-inference-model) as a model package. When selecting a challenger model, consider the following: - It must have the same target type as the champion model. - It cannot be the same Leaderboard model as an existing champion or challenger; each challenger must be a unique model. If you create multiple model packages from the same Leaderboard model, you can't use those models as challengers in the same deployment. - It cannot be a Feature Discovery model. - It does not need to be trained on the same feature list as the champion model; however, it must share some features, and, to successfully replay predictions , you must send the union of all features required for champion and challengers. - It does not need to be built from the same project as the champion model. When you have selected a model to serve as a challenger, from the Leaderboard, navigate to Predict > Deploy and click Register to deploy. This creates a [registered model version](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/reg-create.html#access-registered-models-and-versions) for the selected model in the [Model Registry](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/registry/index.html), so you can add the model to a deployment as a challenger. ## Add challengers to a deployment To add a challenger model to a deployment, navigate to the Challengers tab and select + Add challenger model > Select existing model. You can add up to four challengers to each deployment. This means that in total, with the champion model included, up to five models can be compared during challenger analysis. > [!NOTE] Note > The selection list contains only model packages where the target type and name are the same as the champion model. The modal prompts you to select a model package from the registry to serve as the challenger model. Choose the model to add and click Select model version. DataRobot verifies that the model shares features and a target type with the champion model. Once verified, click Add Challenger. The model is now added to the deployment as a challenger. ## Replay predictions After adding a challenger model, you can replay stored predictions made with the champion model for all challengers, allowing you to compare performance metrics such as predicted values, accuracy, and data errors across each model. To replay predictions, select Update challenger predictions. The champion model computes and stores up to 100,000 prediction rows per hour. The challengers replay the first 10,000 rows of the prediction requests made for each hour within the time range specified by the [date slider](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html#use-the-time-range-and-resolution-dropdowns). Note that for time series deployments, this limit does not apply. All prediction data is used by the challengers to compare statistics. After predictions are made, click Refresh on the date slider to view an updated display of [performance metrics](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/challengers.html#challenger-performance-metrics) for the challenger models. ## Schedule prediction replay You can replay predictions with challengers on a periodic schedule instead of doing so manually. Navigate to a deployment's [Challengers > Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/challengers-settings.html) tab, enable the Automatically replay challengers toggle, and configure the preferred cadence and time of day for replaying predictions: > [!NOTE] Note > Only the deployment [Owner](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#deployment-roles) can schedule challenger replay. Once enabled, the replay will trigger at the configured time for all challengers. Note that if you have a deployment with prediction requests made in the past and choose to add challengers at the current time, the scheduled job scores the newly added challenger models upon the next run cycle. ## View challenger job history After adding one or more challenger models and replaying predictions, you can view challenger prediction jobs for a deployment's challengers on the [Deployments > Prediction Jobs](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html#manage-prediction-jobs) page. To view challenger prediction jobs, click Job History. The Prediction Jobs page opens and is filtered to display challenger jobs for the deployment you accessed the Job History from. ## Challenger models overview The Challengers tab displays information about the champion model and each challenger. | | Element | Description | | --- | --- | --- | | (1) | Display Name | The display name for each model. Use the pencil icon to edit the display name. This field is useful for describing the purpose or strategy of each challenger (e.g., "reference model," "former champion," "reduced feature list"). | | (2) | Challenger models | The list of challenger models. Each model is associated with a color. These colors allow you to compare the models using visualization tools. | | (3) | Model data | The metadata for each model, including the project name, model name, and the execution environment type. | | (4) | Prediction Environment | The environment a model uses to make deployment predictions. For more information, see Prediction environments. | | (5) | Accuracy | The model's accuracy metric calculation for the selected date range and, for challengers, a comparison with the champion's accuracy metric calculation. Use the Accuracy metric dropdown menu to compare different metrics. For more information on model accuracy, see the Accuracy chart. | | (6) | Training Data | The filename of the data used to train the model. | | (7) | Actions | The actions available for each model:Replace: Promotes a challenger to the champion (the currently deployed model) and demotes the current champion to a challenger model. Remove: Removes the model from the deployment as a challenger. Only challengers can be deleted; a champion must be demoted before it can be deleted. | ### Challenger performance metrics After prediction data is replayed for challenger models, you can examine the charts displayed below that capture the various performance metrics recorded for each model. Each model is listed with its corresponding color. Uncheck a model's box to stop displaying the model's performance data on the charts. #### Predictions chart The Predictions chart records the average predicted value of the target for each model over time. Hover over a point to compare the average value for each model at a specific point in time. For binary classification projects, use the Class dropdown to select the class for which you want to analyze the average predicted values. The chart also includes a toggle that allows you to switch between continuous and binary modes. Continuous mode shows the positive class predictions as probabilities between 0 and 1 without taking the prediction threshold into account. Binary mode takes the prediction threshold into account and shows, for all predictions made, the percentage for each possible class. #### Accuracy chart The Accuracy chart records the change in a selected [accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) metric value (LogLoss in this example) over time. These metrics are identical to those used for the evaluation of the model before deployment. Use the dropdown to change the accuracy metric. You can select from [any of the supported metrics](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html#available-accuracy-metrics) for the deployment's modeling type. > [!NOTE] Important > You must [set an association ID](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/accuracy-settings.html#select-an-association-id) before making predictions to include those predictions in accuracy tracking. #### Data Errors chart The Data Errors chart records the [data error rate](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html) for each model over time. Data error rate measures the percentage of requests that result in a 4xx error (problems with the prediction request submission). ## Challenger model comparisons MLOps allows you to compare challenger models against each other and against the currently deployed model (the "champion") to ensure that your deployment uses the best model for your needs. After evaluating DataRobot's model comparison visualizations, you can replace the champion model with a better-performing challenger. DataRobot renders visualizations based on a dedicated comparison dataset, which you select, ensuring that you're comparing predictions based on the same dataset and partition while still allowing you to train champion and challenger models on different datasets. For example, you may train a challenger model on an updated snapshot of the same data source used by the champion. > [!WARNING] Warning > Make sure your comparison dataset is out-of-sample for the models being compared (i.e., it doesn't include the training data from any models included in the comparison). ### Generate model comparisons After you [enable challengers](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html#enable-challenger-models) and [add one or more challengers](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html#add-challengers-to-a-deployment) to a deployment, you can generate comparison data and visualizations. 1. On theDeploymentspage, locate and expand the deployment with the champion and challenger models you want to compare. 2. Click theChallengerstab. 3. On theChallengers Summarytab, if necessary,add a challenger modelandreplay the predictionsfor challengers. 4. Click theModel Comparisontab. The following table describes the elements of theModel Comparisontab: ElementDescription1Model 1Defaults to the champion model—the currently deployed model. Click to select a different model to compare.2Model 2Defaults to the first challenger model in the list. Click to select a different model to compare. If the list doesn't contain a model you want to compare to Model 1, click theChallengers Summarytab to add a new challenger.3Open model packageClick to view the model's details. The details display in theModel Packagestab in the Model Registry.4Promote to championIf the challenger model in the comparison is the best model, clickPromote to championto replace the deployed model (the "champion") with this model.5Add comparison datasetSelect a dataset for generating insights on both models. Be sure to select a dataset that is out-of-sample for both models (seestacked predictions). Holdout and validation partitions for Model 1 and Model 2 are available as options if these partitions exist for the original model. By default, the holdout partition for Model 1 is selected. To specify a different dataset, click+ Add comparison datasetand choose a local file or asnapshotteddataset from the AI Catalog.6Prediction environmentSelect aprediction environmentfor scoring both models.7Model InsightsCompare model predictions, metrics, and more. 5. Scroll to theModel Insightssection of the Challengers page and clickCompute insights. You can generate new insights using a different dataset by clicking + Add comparison dataset, then selecting Compute insights again. ### View model comparisons Once you compute model insights, the Model Insights page displays the following tabs depending on the project type: > [!NOTE] Note > Multiclass classification projects only support accuracy comparison. | | Accuracy | Dual lift | Lift | ROC | Predictions Difference | | --- | --- | --- | --- | --- | --- | | Regression | ✔ | ✔ | ✔ | | ✔ | | Binary | ✔ | ✔ | ✔ | ✔ | ✔ | | Multiclass | ✔ | | | | | | Time series | ✔ | ✔ | ✔ | | ✔ | **Accuracy:** After DataRobot computes model insights for the deployment, you can compare model accuracy. Under Model Insights, click the Accuracy tab to compare accuracy metrics: [https://docs.datarobot.com/en/docs/images/challenger-compare-accuracy.png](https://docs.datarobot.com/en/docs/images/challenger-compare-accuracy.png) The two columns show the metrics for each model. Highlighted numbers represent favorable values. In this example, the champion, Model 1, outperforms Model 2 for most metrics shown. For time series projects, you can evaluate accuracy metrics by applying the following filters: Forecast distance : View accuracy for the selected forecast distance row within the forecast window range. For all x series : View accuracy scores by metric. This view reports scores in all available accuracy metrics for both models across the entire time series range ( x ). Per series : View accuracy scores by series within a multiseries comparison dataset. This view reports scores in a single accuracy metric (selected in the Metric dropdown menu) for each Series ID (e.g., store number) in the dataset for both models. For multiclass projects, you can evaluate accuracy metrics by applying the following filters: For all x classes : View accuracy scores by metric. This view reports scores in all available accuracy metrics for both models across the entire multiclass range ( x ). Per class : View accuracy scores by class within a multiclass classification problem. This view reports scores in a single accuracy metric (selected in the Metric dropdown menu) for each Class (e.g., buy, sell, or hold) in the dataset for both models. **Dual lift:** A [dual lift chart](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/other/model-compare.html#dual-lift-chart) is a visualization comparing two selected models against each other. This visualization can reveal how models underpredict or overpredict the actual values across the distribution of their predictions. The prediction data is evenly distributed into equal size bins in increasing order. To view the dual lift chart for the two models being compared, under Model Insights, click the Dual lift tab: [https://docs.datarobot.com/en/docs/images/challenger-compare-dual-lift.png](https://docs.datarobot.com/en/docs/images/challenger-compare-dual-lift.png) The curves for the two models represented on this chart maintain the color they were assigned when added to the deployment (as either a champion or challenger). To interact with the dual lift chart, you can hide the model curves and the actual curve. The + icons in the plot area of the chart represent the models' predicted values. Click the + icon next to a model name in the header to hide or show the curve for a particular model. The orange o icons in the plot area of the chart represent the actual values. Click the orange o icon next to Actual to hide or show the curve representing the actual values. **Lift:** A [lift chart](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/lift-chart-classic.html) depicts how well a model segments the target population and how capable it is of predicting the target, allowing you to visualize the model's effectiveness. To view the lift chart for the models being compared, under Model Insights, click the Lift tab: [https://docs.datarobot.com/en/docs/images/challenger-compare-lift.png](https://docs.datarobot.com/en/docs/images/challenger-compare-lift.png) The curves for the two models represented on this chart maintain the color they were assigned when added to the deployment (as either a champion or challenger). **ROC:** > [!NOTE] Note > The ROC tab is only available for binary classification projects. An [ROC curve](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/roc-curve-classic.html) plots the true-positive rate against the false-positive rate for a given data source. Use the ROC curve to explore classification, performance, and statistics for the models you're comparing. To view the ROC curves for the models being compared, under Model Insights, click the ROC tab: [https://docs.datarobot.com/en/docs/images/challenger-compare-roc.png](https://docs.datarobot.com/en/docs/images/challenger-compare-roc.png) The curves for the two models represented on this chart maintain the color they were assigned when added to the deployment (as either a champion or challenger). You can update the prediction thresholds for the models by clicking the pencil icons. **Predictions Difference:** Click the Predictions Difference tab to compare the predictions of two models on a row-by-row basis. The histogram shows the percentage of predictions that fall within the match threshold you specify in the Prediction match threshold field (along with the corresponding numbers of rows). The header of the histogram displays the percentage of predictions: Between the positive and negative values of the match threshold (shown in green) Greater than the upper (positive) match threshold (shown in red) Less than the lower (negative) match threshold (shown in red) [https://docs.datarobot.com/en/docs/images/challenger-compare-predictions-diff-1.png](https://docs.datarobot.com/en/docs/images/challenger-compare-predictions-diff-1.png) How are bin sizes calculated? The size of the Predictions Difference bins in the histogram depends on the Prediction match threshold you set. The value of the prediction match threshold bin is equal to the difference between the upper match threshold (positive) and the lower match threshold (negative). The default prediction match threshold value is 0.0025, so for that value, the center bin is 0.005 (0.0025 + |-0.0025|). The bins on either side of the central bin are ten times larger than the previous bin. The last bin on either end expands to fit the full Prediction Difference range. For example, based on the default Prediction match threshold , the bin sizes would be as follows (where x is the difference between 250 and the maximum Prediction Difference): Bin -5 Bin -4 Bin -3 Bin -2 Bin -1 Bin 0 Bin 1 Bin 2 Bin 3 Bin 4 Bin 5 Range (−250 + x) to −25 −25 to −2.5 −2.5 to −0.25 −0.25 to −0.025 −0.025 to −0.0025 −0.0025 to +0.0025 +0.0025 to +0.025 +0.025 to +0.25 +0.25 to +2.5 +2.5 to +25 +25 to (+250 + x) Size 225 + x 22.5 2.25 0.225 0.0225 0.005 0.0225 0.225 2.25 22.5 225 + x If many matches dilute the histogram, you can toggle Scale y-axis to ignore perfect matches to focus on the mismatches. The bottom section of the Predictions Difference tab shows the 1000 most divergent predictions (in terms of absolute value). [https://docs.datarobot.com/en/docs/images/challenger-compare-predictions-diff-2.png](https://docs.datarobot.com/en/docs/images/challenger-compare-predictions-diff-2.png) The Difference column shows how far apart the predictions are. ### Replace champion with challenger After comparing models, if you find a model that outperforms the deployed model, you can set it as the new champion. 1. Evaluate the comparison model insights to determine the best-performing model. 2. If a challenger model outperforms the deployed model, clickPromote to champion. 3. Select aReplacement Reasonand clickAccept and Replace. The challenger model is now the champion (deployed) model. ## Challengers for external deployments External deployments with [remote prediction environments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/ext-model-prep/ext-pred-env.html) can also use the Challengers tab. Remote models can serve as the champion model, and you can compare them to DataRobot and custom models serving as challengers. The [workflow](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html#enable-challenger-models) for adding challenger models is largely the same; however, there are unique differences for external deployments outlined below. ### Add challenger models to external deployments To enable challenger support, access an external deployment (one created with an external model package). In the Settings tab, under the Data Drift header, enable challenger models and [prediction row storage](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/challengers-settings.html). The Challengers tab is now accessible. To add challenger models to the deployment, navigate to the tab and click Add challenger model > Select existing model. Select a model package for the challenger you want to add (custom and DataRobot models only). Additionally, you must indicate a prediction environment used by the model package; this details where the model runs predictions. DataRobot or custom model can only use a DataRobot prediction environment for challengers models (unlike the champion model, deployed to an external prediction environment). When you have chosen the desired prediction environment, click Select. The tab updates to display the model package you wish to add, verifying that the features used in the model package match the deployed model. Select Add challenger. The model package is now serving as a challenger model for the remote deployment. ### Add external challenger comparison dataset To compare an external model challenger, you need to provide a dataset that includes the actuals and the prediction results. When you upload the comparison dataset, you can specify a column containing the prediction results. To add a comparison dataset for an external model challenger, follow the [Generate model comparisons](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/challengers.html#generate-model-comparions) process, and on the Model Comparison tab, upload your comparison dataset with a Prediction column identifier. Make sure the prediction dataset you provide includes the prediction results generated by the external model at the location identified by the Prediction column. ### Manage challengers for external deployments You can manage challenger models for remote deployments with various actions: - To edit the prediction environment used by a challenger, select the pencil icon and choose a new prediction environment from the dropdown. - To replace the deployed model with a challenger, the challenger must have a compatible prediction environment. Once replaced, the championdoes notbecome a challenger because remote models are ineligible. #### Challenger promotion to champion A deployment's champion can't switch between an external prediction environment and a DataRobot prediction environment. When a challenger replaces a champion running in an external prediction environment, that challenger inherits the external environment of the former champion. If the Management Agent isn't configured in the external prediction environment, you must manually deploy the new champion in the external environment to continue making predictions. #### Champion demotion to challenger If the former champion isn't an external model package, it is compatible with DataRobot hosting and can become a challenger. In that scenario, the former champion moves to a DataRobot prediction environment where the deployment can replay the champion's predictions against it. --- # Custom Metrics tab URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/custom-metrics.html > Create and monitor custom business or performance metrics in a deployment. # Custom Metrics tab On a deployment's Custom Metrics tab, you can use the data you collect from the [Data Explorationtab](https://docs.datarobot.com/en/docs/api/reference/sdk/data-exploration.html) (or data calculated through other custom metrics) to compute and monitor custom business or performance metrics. These metrics are recorded on the configurable Custom Metric Summary dashboard, where you monitor, visualize, and export each metric's change over time. This feature allows you to implement your organization's specialized metrics, expanding on the insights provided by built-in [Service Health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html), [Data Drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html), and [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) metrics. > [!NOTE] Custom metrics limits > You can have up to 50 custom metrics per deployment, and of those 50, 5 can be hosted custom metrics. To access custom metrics, in the top navigation bar, click Deployments and, on the Deployments tab, click on the deployment for which you want to create custom metrics. Then, in the deployment, click the Custom Metrics tab. The Summary tab opens: ## Add external custom metrics The Custom Metrics tab can track up to 50 metrics. To add custom metrics: 1. On theCustom Metrics > Summarytab, click+ Add Custom Metric. 2. In theAdd Custom Metricdialog box, clickCreate new external metric, then clickNext: 3. Configure the metric settings in theAdd custom metric dialog box: FieldDescriptionNameA descriptive name for the metric. This name appears on theCustom Metric Summarydashboard.Description(Optional) A description of the custom metric; for example, you could describe the purpose, calculation method, and more.Name of y-axisA descriptive name for the dependent variable. This name appears on the custom metric's chart on theCustom Metric Summarydashboard.Default intervalThe default interval used by the selectedAggregation type. OnlyHOURis supported.Baseline(Optional) The value used as a basis for comparison when calculating thex% betterorx% worsevalues.Aggregation typeIf the metric is calculated as aSum,Average, orGauge—a metric with a distinct value measured at single point in time.Metric directionThe directionality of the metric and changes how changes the metric are visualized. You can selectHigher is betterorLower is better. For example, if you chooseLower is bettera 10% decrease in the calculated value of your custom metric will be considered10% better, displayed in green.Is Model SpecificWhen enabled, links the metric to the model with theModel Package ID(Registered Model Version ID) provided in the dataset. This setting influences when values are aggregated (or uploaded). For example:Model-specific (enabled): Model accuracy metrics are model specific, so the values are aggregated completely separately. When you replace a model, the chart for your custom accuracy metric only shows data for the days after the replacement.Not model-specific (disabled): Revenue metrics aren't model specific, so the values are aggregated together. When you replace a model, the chart for your custom revenue metric doesn't change.This field can't be edited after you create the metric.Column names definitionTimestamp columnThe column in the dataset containing a timestamp.Value columnThe column in the dataset containing the values used for custom metric calculation.Date format(Optional) The date format used by the timestamp column. NoteYou can override theColumn names definitionsettings when youupload data to a custom metric. 4. ClickAdd custom metric. ### Upload data to custom metrics After you create a custom metric, you can provide data to calculate the metric: 1. On theCustom Metricstab, locate the custom metric for which you want to upload data, and then click theUpload Dataicon. 2. In theUpload Datadialog box, select an upload method, and then clickNext: Upload methodDescriptionUpload data as fileIn theChoose filedialog box, drag and drop file(s) to upload, or clickChoose file > Local fileto browse your local filesystem, and then clickSubmit data. You can upload up to 10GB uploaded in one file.Use AI CatalogIn theSelect a dataset from the AI Catalogdialog box, click a dataset from the list, and then clickSelect a dataset. The AI Catalog includes datasets from theData ExplorationAfter youcreate and configure a deployment, you can use the settings tabs for individual features to add or update deployment functionality: .Use APIIn theUse API Clientdialog box, clickCopy to clipboard, and then modify and use the API snippet to upload a dataset. You can upload up to 10,000 values in one API call. 3. In theSelect dataset columnsdialog box, configure the following: FieldDescriptionTimestamp columnThe column in the dataset containing a timestamp.Value columnThe column in the dataset containing the values used for custom metric calculation.Association IDThe row containing the association ID required by the custom metric to link predicted values to actuals.Date formatThe date format used by the timestamp column. 4. ClickUpload data. ### Report custom metrics via chat requests For DataRobot-deployed text generation and agentic workflow custom models that implement the `chat()` hook, custom metric values can be reported directly in chat completion requests using the `extra_body` field. This allows reporting custom metrics at the same time as making chat requests, without needing to upload data separately. > [!TIP] Manual chat request construction > The OpenAI client converts the `extra_body` parameter contents to top-level fields in the JSON payload of the chat `POST` request. When manually constructing a chat payload, without the OpenAI client, include `“datarobot_metrics": {...}` in the top level of the payload. To report custom metrics via chat requests: 1. Ensure the deployment has an association ID column defined and moderation configured. These are required for custom metrics to be processed. 2. Define custom metrics on theCustom Metricstab as described inAdd external custom metrics. 3. When making a chat completion request using the OpenAI client, includedatarobot_metricsin theextra_bodyfield with the metric names and values to report: ``` from openai import OpenAI openai_client = OpenAI( base_url="https:///api/v2/deployments/{deployment_id}/", api_key="", ) extra_body = { # These values pass through to the LLM "llm_id": "azure-gpt-6", # If set here, replaces the auto-generated association ID "datarobot_association_id": "my_association_id_0001", # DataRobot captures these for custom metrics "datarobot_metrics": { "field1": 24, "field2": 25 } } completion = openai_client.chat.completions.create( model="datarobot-deployed-llm", messages=[ {"role": "system", "content": "Explain your thoughts using at least 100 words."}, {"role": "user", "content": "What would it take to colonize Mars?"}, ], max_tokens=512, extra_body=extra_body ) print(completion.choices[0].message.content) ``` > [!NOTE] Custom metric requirements > A matching custom metric for each name in > datarobot_metrics > must already be defined for the deployment. > Custom metric values reported this way must be numeric. > The deployed custom model must have an association ID column defined and moderation configured for the metrics to be processed. For more information about using `extra_body` with chat requests, including how to specify association IDs, see the [chat()hook documentation](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#association-id). ## Add hosted custom metrics DataRobot offers [custom metrics](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html) for deployments to compute and monitor custom business or performance metrics. With hosted custom metrics, not only can you implement up to five of your organization's specialized metrics in a deployment, but also upload and host code using DataRobot Notebooks to easily add custom metrics to other deployments. > [!NOTE] Custom metrics limits > You can have up to 50 custom metrics per deployment, and of those 50, 5 can be hosted custom metrics. > [!WARNING] Time series support > The [DataRobot Model Metrics (DMM)](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/index.html) library does not support time series models, specifically [data export](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-data-sources.html#export-prediction-data) for time series models. To export and retrieve data, use the [DataRobot API client](https://datarobot-public-api-client.readthedocs-hosted.com/en/latest-release/reference/mlops/data_exports.html). To begin hosting custom metrics: 1. On theCustom Metricstab, click+ Add Custom Metric. 2. In theAdd Custom Metricdialog box, clickCreate new hosted metric, clickNext: 3. Configure the metric settings in theAdd custom metric dialog box: FieldDescriptionName(Required) A descriptive name for the metric. This name appears on theCustom Metric Summarydashboard.DescriptionA description of the custom metric; for example, you could describe the purpose, calculation method, and more.Name of y-axis(Required) A descriptive name for the dependent variable. This name appears on the custom metric's chart on theCustom Metric Summarydashboard.Default intervalDetermines the default interval used by the selectedAggregation type. OnlyHOURis supported.BaselineDetermines the value used as a basis for comparison when calculating thex% betterorx% worsevalues.Aggregation typeDetermines if the metric is calculated as aSum,Average, orGauge—a metric with a distinct value measured at single point in time.Metric directionDetermines the directionality of the metric, which controls how changes to the metric are visualized. You can selectHigher is betterorLower is better. For example, if you chooseLower is better, a 10% decrease in the calculated value of your custom metric will be considered10% better, displayed in green.Is model-specificWhen enabled, this setting links the metric to the model with theModel Package ID(Registered Model Version ID) provided in the dataset. This setting influences when values are aggregated (or uploaded). For example:Model-specific (enabled): Model accuracy metrics are model specific, so the values are aggregated separately. When you replace a model, the chart for your custom accuracy metric only shows data for the days after the replacement.Not model-specific (disabled): Revenue metrics aren't model specific, so the values are aggregated together. When you replace a model, the chart for your custom revenue metric doesn't change.This field can't be edited after you create the metric.ScheduleDefines when the custom metrics are populated. Select a frequency (hourly, daily, monthly, etc.) and a time. SelectAdvanced schedulefor more precise scheduling options. 4. ClickAdd custom metric from notebook. ### Test custom metrics with custom jobs > [!NOTE] Availability information > Notebooks for hosted custom metrics are off by default. Contact your DataRobot representative or administrator for information on enabling this feature. > > Feature flag: Enable Notebooks Custom Environments After configuring a custom metric, DataRobot loads the notebook that contains the code for it. The notebook contains one custom metric cell, a unique type of notebook cell that contains Python code defining how the metric is exported and calculated, code for scoring, and code to populate the metric. Modify the code in the custom metric cell as needed. Then, test the code by clicking Test custom metric code at the bottom of the cell. The test creates a custom job. - If the test runs successfully, click Deploy custom metric code to add the custom metric to your deployment. - If the code does not run properly, you will receive the Testing custom metric code failed warning after testing completes: ### Troubleshoot custom metric code To troubleshoot a custom metric's code, navigate to the Model Registry, select the Custom Jobs tab, and access the custom job that ran for testing. The job's Runs tab contains a log of the failed test, which you can browse by selecting View full logs. To troubleshoot failed tests, DataRobot recommends browsing the logs for each failed test. Additionally, the custom jobs interface allows you to modify the schedule for the custom metric from the Workshop tab by selecting Schedule run. ## Manage custom metrics On the Custom Metrics dashboard, after you've added your custom metrics, you can edit, arrange, or delete them. ### Edit or delete metrics To edit or delete a metric, on the Custom Metrics tab, locate the custom metric you want to manage, and then click the Actions menu: - To edit a metric, clickEdit, update any configurable settings, and clickUpdate custom metric. - To delete a metric, clickDelete. ### Arrange or hide metrics To arrange or hide metrics on the Custom Metric Summary dashboard, locate the custom metric you want to move or hide: - To move a metric, click the grid icon () on the left side of the metric tile and then drag the metric to a new location. - To hide a metric's chart, clear the checkbox next to the metric name. ## Configure the custom metric dashboard display settings Configure the following settings to specify the custom metric calculations you want to view on the dashboard: > [!TIP] Custom metrics for evaluation and moderation require an association ID > For the metrics added when you configure evaluations and moderations, to view data on the Custom metrics tab, ensure that you set an association ID and enable prediction storage before you start making predictions through the deployed LLM.If you don't set an association ID and provide association IDs alongside the LLM's predictions, the metrics for the moderations won't be calculated on the [Custom metrics](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-custom-metrics.html) tab.After you define the association ID, you can enable automatic association ID generation to ensure these metrics appear on the Custom metrics tab. You can enable this setting [during](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-deploy-models.html#custom-metrics) or [after](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-settings/nxt-custom-metrics-settings.html) deployment. | | Setting | Description | | --- | --- | --- | | (1) | Model | Select the deployment's model, current or previous, to show custom metrics for. | | (2) | Range (UTC) | Select the start and end dates of the period from which you want to view custom metrics. | | (3) | Resolution | Select the granularity of the date slider. Select from hourly, daily, weekly, and monthly granularity based on the time range selected. If the time range is longer than 7 days, hourly granularity is not available. | | (4) | Refresh | Refresh the custom metric dashboard. | | (5) | Reset | Reset the custom metric dashboard's display settings to the default. | --- # Data Drift tab URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html > How to use the Data Drift dashboard to analyze a deployed model's performance. It provides four interactive, exportable visualizations that communicate model health. # Data Drift tab As training and production data change over time, a deployed model loses predictive power. The data surrounding the model is said to be drifting. By leveraging the training data and prediction data (also known as inference data) that is added to your deployment, the Data Drift dashboard helps you analyze a model's performance after it has been deployed. > [!NOTE] How does DataRobot track drift? > DataRobot tracks two types of drift: > > Target drift > : DataRobot stores statistics about predictions to monitor how the distribution and values of the target change over time. As a baseline for comparing target distributions, DataRobot uses the distribution of predictions on the holdout. > Feature drift > : DataRobot stores statistics about predictions to monitor how distributions and values of features change over time. The supported feature data types are numeric, categorical, and text. As a baseline for comparing distributions of features: > For training datasets larger than 500MB, DataRobot uses the distribution of a random sample of the training data. > For training datasets smaller than 500MB, DataRobot uses the distribution of 100% of the training data. Target and feature tracking are enabled by default. You can control these drift tracking features by navigating to a deployment's [Data Drift > Settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html) tab. > [!NOTE] Availability information > If feature drift tracking is turned off, a message displays on the Data Drift tab to remind you to enable [feature drift tracking](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html). To receive email notifications on data drift status, [configure notifications](https://docs.datarobot.com/en/docs/classic-ui/mlops/governance/deploy-notifications.html), [schedule monitoring](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html#schedule-data-drift-monitoring-notifications), and [configure data drift monitoring settings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html#define-data-drift-monitoring-notifications). The Data Drift dashboard provides four interactive and exportable visualizations that help identify the health of a deployed model over a specified time interval. > [!NOTE] Note > The Export button allows you to download each chart on the Data Drift dashboard as a PNG, CSV, or ZIP file. | | Chart | Description | | --- | --- | --- | | (1) | Feature Drift vs. Feature Importance | Plots the importance of a feature in a model against how much the distribution of feature values has changed, or drifted, between one point in time and another. | | (2) | Feature Details | Plots percentage of records, i.e., the distribution, of the selected feature in the training data compared to the inference data. | | (3) | Drift Over Time | Illustrates the difference in distribution over time between the training dataset of the deployed model and the datasets used to generate predictions in production. This chart tracks the change in the Population Stability Index (PSI), which is a measure of data drift. | | (4) | Predictions Over Time | Illustrates how the distribution of a model's predictions has changed over time (target drift). The display differs depending on whether the project is regression or binary classification. | In addition to the visualizations above, you can use the [Data Drift > Drill Downtab](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html#drill-down-on-the-data-drift-tab) to compare data drift heat maps across the features in a deployment to identify drift trends. ## Configure the Data Drift dashboard You can [customize how a deployment calculates data drift status](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html) by configuring drift and importance thresholds and additional definitions on the Data Drift > Settings page. You can also use the following controls to configure the Data Drift dashboard as needed: | | Control | Description | | --- | --- | --- | | (1) | Model version selector | Updates the dashboard displays to reflect the model you selected from the dropdown. | | (2) | Date Slider | Limits the range of data displayed on the dashboard (i.e., zooms in on a specific time period). | | (3) | Range (UTC) | Sets the date range displayed for the deployment date slider. | | (4) | Resolution | Sets the time granularity of the deployment date slider. | | (5) | Selected Feature | Sets the feature displayed on the Feature Details chart and the Drift Over Time chart. | | (6) | Refresh | Initiates an on-demand update of the dashboard with new data. Otherwise, DataRobot refreshes the dashboard every 15 minutes. | | (7) | Reset | Reverts the dashboard controls to the default settings. | The Data Drift dashboard also supports [segmented analysis](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-segment.html), allowing you to view data drift while comparing a subset of training data to the predictions data for individual attributes and values using the Segment Attribute and Segment Value dropdowns. ## Feature Drift vs Feature Importance chart The Feature Drift vs. Feature Importance chart monitors the 25 most impactful numerical, categorical, and text-based features in your data. Use the chart to see if data is different at one point in time compared to another. Differences may indicate problems with your model or in the data itself. For example, if users of an auto insurance product are getting younger over time, the data that built the original model may no longer result in accurate predictions for your newer data. Particularly, drift in features with high importance can be a warning flag about your model accuracy. Hover over a point in the chart to identify the feature name and report the precise values for drift (Y-axis) and importance (X-axis). ### Feature Drift The Y-axis reports the Drift value for a feature. This value is a calculation of the [Population Stability Index (PSI)](https://www.kaggle.com/code/podsyp/population-stability-index/notebook), a measure of the difference in distribution over time. ### Feature Importance The X-axis reports the Importance score for a feature, calculated when ingesting the learning (or training) data. DataRobot calculates feature importance differently depending on the model type. For DataRobot models and custom models, the Importance score is calculated using [Permutation Importance](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html#permutation-based-feature-impact). For external models, the importance score is an [ACE Score](https://docs.datarobot.com/en/docs/reference/glossary/index.html#ace-scores). The dot resting at the Importance value of `1` is the target prediction [https://docs.datarobot.com/en/docs/images/icon-drift-pred.png](https://docs.datarobot.com/en/docs/images/icon-drift-pred.png). The most important feature in the model will also appear at 1 (as a solid green dot). ### Interpret the quadrants The quadrants represented in the chart help to visualize feature-by-feature data drift plotted against the feature's importance. Quadrants can be loosely interpreted as follows: | Quadrant | Read as... | Color indicator | | --- | --- | --- | | (1) | High importance feature(s) are experiencing high drift. Investigate immediately. | Red | | (2) | Lower importance feature(s) are experiencing drift above the set threshold. Monitor closely. | Yellow | | (3) | Lower importance feature(s) are experiencing minimal drift. No action needed. | Green | | (4) | High importance feature(s) are experiencing minimal drift. No action needed, but monitor features that approach the threshold. | Green | Note that points on the chart can also be gray or white. Gray circles represent features that have been excluded from drift status calculation, and white circles represent features set to high importance. If you are the project owner, you can click the gear icon in the upper right chart corner to reset the quadrants. By default, the drift threshold defaults to .15. The Y-axis scales from 0 to the higher of 0.25 and the highest observed drift value. These quadrants can be customized by [changing the drift and importance thresholds](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/data-drift-settings.html). ## Feature Details chart The Feature Details chart provides a histogram that compares the distribution of a selected feature in the training data to the distribution of that feature in the inference data. ### Numeric features For numeric data, DataRobot computes an efficient and precise approximation of the distribution of each feature. Based on this, drift tracking is conducted by comparing the normalized histogram for the training data to the scoring data using the selected drift metrics. The chart displays 13 bins for numeric features: - 10 bins capture the range of items observed in the training data. - Two bins capturevery highandvery lowvalues—extreme values in the scoring data that fall outside the range of the training data. For example, to define the high and low value bins, the values are compared against the training data ranges,min_trainingandmax_training. The low value bin contains values below themin_trainingrange and the high value bin contains values above themax_trainingrange. - One bin for themissingcount, containing all records withmissing feature values. ### Categorical features Unlike numeric data, where binning cutoffs for a histogram result from a data-dependent calculation, categorical data is inherently discrete in form (that is, not continuous), so binning is based on a defined category. Additionally, there could be missing or unseen category levels in the scoring data. The process for drift tracking of categorical features is to calculate the fraction of rows for each categorical level ("bin") in the training data. This results in a vector of percentages for each level. The 25 most frequent levels are directly tracked—all other levels are aggregated to an Other bin. This process is repeated for the scoring data, and the two vectors are compared using the selected drift metric. For categorical features, the chart includes two unique bins: - TheOtherbin contains all categorical features outside the 25 most frequent values. This aggregation is performed for drift tracking purposes; it doesn't represent the model's behavior. - TheNew levelbin only displays after you make predictions with data that has a new value for a feature not in the training data. For example, consider a dataset about housing prices with the categorical featureCity. If your inference data contains the valueBostonand your training data did not, theBostonvalue (and other unseen cities) are represented in the New level bin. To use the chart, select a feature from the dropdown. The list, which defaults to the target feature, includes any of the features tracked. Click a point in the Feature Drift vs. Feature Importance chart: ### Text features Text features are a high-cardinality problem, meaning the addition of new words does not have the impact of, for example, new levels found in categorical data. The method DataRobot uses to track drift of text features accounts for the fact that writing is subjective and cultural and may have spelling mistakes. In other words, to identify drift in text fields, it is more important to identify a shift in the whole language rather than in individual words. Drift tracking for a text feature is conducted by: 1. Detecting occurrences of the 1000 most frequent words from rows found in the training data. 2. Calculating the fraction of rows that contain these terms for that feature in the training data and separately in the scoring data. 3. Comparing the fraction in the scoring data to that in the training data. The two vectors of occurrence fractions (one entry per word) are compared with the available drift metrics. Prior to applying this methodology, DataRobot performs basic tokenization by splitting the text feature into words (or characters in the case of Japanese or Chinese). For text features, the [Feature Details chart](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html#feature-details-chart) replaces the feature drift bar chart with a word cloud visualizing data distributions for each token and revealing how much each individual token contributes to data drift in a feature. To access the feature drift word cloud for a text feature: 1. Open theData Drifttab of adrift-enableddeployment. 2. On theSummarytab, in theFeature Detailschart, select a text feature from dropdown list. NoteYou can also select a text feature from theSelected Featuredropdown list in theData Driftdashboard controls. 3. Use the dashboard controls toconfigure the Data Drift dashboard. 4. To interpret the feature drift word cloud for a text feature, you can hold the pointer over a token to view the following details: TipWhen your pointer is over the word cloud, you can scroll up to zoom in and view the text of smaller tokens. Chart elementDescriptionTokenThe tokenized text. Text size represents the token's drift contribution and text color represents the dataset prevalence. Stop words are hidden from this chart.Drift contributionHow much this particular token contributes to the feature's drift value, as reported in theFeature Drift vs. Feature ImportanceandDrift Over Timecharts.Data distributionHow much more often this particular token appears in the training data or the predictions data.Blue: This token appearsX% more often in training data.Red: This token appearsX% more often in predictions data. > [!NOTE] Note > Next to the Export button, you can click the settings icon ( [https://docs.datarobot.com/en/docs/images/icon-gear.png](https://docs.datarobot.com/en/docs/images/icon-gear.png)) and clear the Display text features as word cloud check box to disable the feature drift word cloud and view the standard chart: > > [https://docs.datarobot.com/en/docs/images/drift-word-cloud-disable.png](https://docs.datarobot.com/en/docs/images/drift-word-cloud-disable.png) ## Drift Over Time chart The Drift Over Time chart visualizes the difference in distribution over time between the training dataset of the deployed model and the datasets used to generate predictions in production. The drift away from the baseline established with the training dataset is measured using the Population Stability Index (PSI). As a model continues to make predictions on new data, the change in the PSI over time is visualized for each tracked feature, allowing you to identify [data drift](https://docs.datarobot.com/en/docs/reference/glossary/index.html#data-drift) trends. As data drift can decrease your model's predictive power, determining when a feature started drifting and monitoring how that drift changes (as your model continues to make predictions on new data) can help you estimate the severity of the issue. You can then compare data drift trends across the features in a deployment to identify correlated drift trends between specific features. In addition, the chart can help you identify seasonal effects (significant for time-aware models). This information can help you identify the cause of data drift in your deployed model, including data quality issues, changes in feature composition, or changes in the context of the target variable. The example below shows the PSI consistently increasing over time, indicating worsening data drift for the selected feature. The Drift Over Time chart includes the following elements and controls: | | Chart element | Description | | --- | --- | --- | | (1) | Selected Feature | Selects a feature for drift over time analysis, which is then reported in the Drift Over Time chart and the Feature Details chart. | | (2) | Time of Prediction / Sample size (X-axis) | Represents the time range of the predictions used to calculate the corresponding drift value (PSI). Below the X-axis, a bar chart represents the number of predictions made during the corresponding Time of Prediction. For more information on how time of prediction is represented in time series deployments, see the Time of prediction for time series deployments note. | | (3) | Drift (Y-axis) | Represents the range of drift values (PSI) calculated for the corresponding Time of Prediction. | | (4) | Training baseline | Represents the 0 PSI value of the training baseline dataset. | | (5) | Drift status information | Displays the drift status and threshold information for the selected feature. Drift status visualizations are based on the monitoring settings configured by the deployment owner. The deployment owner can also set the drift and importance thresholds in the Feature Drift vs Feature Importance chart settings. The possible drift status classifications are: Healthy (Green): The feature is experiencing minimal drift. No action needed, but monitor features that approach the threshold.At risk (Yellow): A lower importance feature is experiencing drift above the set threshold. Monitor closely.Failing (Red): A high importance feature is experiencing drift above the set threshold. Investigate immediately Feature importance is determined by comparing the feature impact score with the importance threshold value. For an important feature, the feature impact score is greater than or equal to the importance threshold. | | (6) | Export | Exports the Drift Over Time chart. | To view additional information on the Drift Over Time chart, hover over a marker in the chart to see the Time of Prediction, PSI, and Sample size: > [!TIP] Tip > The X-axis of the Drift Over Time chart aligns with the X-axis of the Predictions Over Time chart below to make comparing the two charts easier. In addition, the Sample size data on the Drift Over Time chart is equivalent to the Number of Predictions data from the Predictions Over Time chart. ## Predictions Over Time chart The Predictions Over Time chart provides an at-a-glance determination of how the model's predictions have changed over time. For example: > Dave sees that his model is predicting1(readmitted) noticeably more frequently over the past month. Because he doesn't know of a corresponding change in the actual distribution of readmissions, he suspects that the model has become less accurate. With this information, he investigates further whether he should consider retraining. Although the charts for binary classification and regression differ slightly, the takeaway is the same—are the plot lines relatively stable across time? If not, is there a business reason for the anomaly (for example, a blizzard)? One way to check this is to look at the bar chart below the plot. If the point for a binned period is abnormally high or low, check the histogram below to ensure there are enough predictions for this to be a reliable data point. > [!NOTE] Time of Prediction > The Time of Prediction value differs between the [Data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html) and [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) tabs and the [Service health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html) tab: > > On the > Service health > tab, the "time of prediction request" is > always > the time the prediction server > received > the prediction request. This method of prediction request tracking accurately represents the prediction service's health for diagnostic purposes. > On the > Data drift > and > Accuracy > tabs, the "time of prediction request" is, > by default > , the time you > submitted > the prediction request, which you can override with the prediction timestamp in the > Prediction History and Service Health > settings. Additionally, both charts have `Training` and `Scoring` labels across the X-axis. The `Training` label indicates the section of the chart that shows the distribution of predictions made on the holdout set of training data for the model. It will always have one point on the chart. The `Scoring` label indicates the section of the chart showing the distribution of predictions made on the deployed model.`Scoring` indicates that the model is in use to make predictions. It will have multiple points along the chart to indicate how prediction distributions change over time. ### For regression projects The Predictions Over Time chart for regression projects plots the average predicted value, as well as a visual indicator of the middle 80% range of predicted values for both training and prediction data. If training data is uploaded, the graph displays both the 10th-90th percentile and the mean value of the target ( [https://docs.datarobot.com/en/docs/images/icon-mean.png](https://docs.datarobot.com/en/docs/images/icon-mean.png)). Hover over a point on the chart to view its details: - Date : The starting date of the bin data. Displayed values are based on counts from this date to the next point along the graph. For example, if the date on point A is 01-07 and point B is 01-14, then point A covers everything from 01-07 to 01-13 (inclusive). - Average Predicted Value : For all points included in the bin, this is the average of their values. - Predictions : The number of predictions included in the bin. Compare this value against other points if you suspect anomalous data. - 10th-90th Percentile : Percentile of predictions for that time period. Note that you can also display this information for the mean value of the target by hovering on the point in the training data. ### For binary classification projects The Predictions Over Time chart for binary classification projects plots the class percentages based on the labels you set when you [added the deployment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/index.html) (in this example, `0` and `1`). It also reports the threshold set for prediction output. The threshold is set when adding your deployment to the inventory and cannot be revised. Hover over a point on the chart to view its details: - Date : The starting date of the bin data. Displayed values are based on counts from this date to the next point along the graph. For example, if the date on point A is 01-07 and point B is 01-14, then point A covers everything from 01-07 to 01-13 (inclusive). - : For all points included in the bin, the percentage of those in the "positive" class ( 0 in this example). - : For all points included in the bin, the percentage of those in the "negative" class ( 1 in this example). - Number of Predictions : The number of predictions included in the bin. Compare this value against other points if you suspect anomalous data. Additionally, the chart displays the mean value of the target in the training data. As with all plotted points, you can hover over it to see the specific values. The chart also includes a toggle in the upper-right corner that allows you to switch between continuous and binary modes (only for binary classification deployments): Continuous mode shows the positive class predictions as probabilities between 0 and 1, without taking the prediction threshold into account: Binary mode takes the prediction threshold into account and shows, of all predictions made, the percentage for each possible class: ### Prediction warnings integration If you have enabled [prediction warnings](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment-settings/humility-settings.html#prediction-warnings) for a deployment, any anomalous prediction values that trigger a warning are flagged in the [Predictions Over Time](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html#predictions-over-time-chart) bar chart. > [!NOTE] Prediction warnings availability > Prediction warnings are only available for deployments using regression models. This feature does not support classification or time series models. The yellow section of the bar chart represents the anomalous predictions for a point in time. To view the number of anomalous predictions for a specific time period, hover over the point on the plot corresponding to the flagged predictions in the bar chart. ## Use the version selector You can change the data drift display to analyze the current, or any previous, version of a model in the deployment. Initially, if there has been no model replacement, you only see the Current option. The models listed in the dropdown can also be found in the History section of the [Overview](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/dep-overview.html) tab. This functionality is only supported with deployments made with models or model images. ## Use the time range and resolution dropdowns The Range and Resolution dropdowns help diagnose deployment issues by allowing you to change the granularity of the three deployment monitoring tabs: Data Drift, [Service Health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html), and [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html). Expand the Range dropdown (1) to select the start and end dates for the time range you want to examine. You can specify the time of day for each date (to the nearest hour, rounded down) by editing the value after selecting a date. When you have determined the desired time range, click Update range (2). Select the Range reset icon ( [https://docs.datarobot.com/en/docs/images/icon-refresh.png](https://docs.datarobot.com/en/docs/images/icon-refresh.png)) (3) to restore the time range to the previous setting. > [!NOTE] Note > The date picker only allows you to select dates and times between the start date of the deployment's [current version](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html#use-the-version-selector) of a model and the current date. After setting the time range, use the Resolution dropdown to determine the granularity of the date slider. Select from hourly, daily, weekly, and monthly granularity based on the time range selected. The following Resolution settings are available, based on the selected range: | Resolution | Selected range requirement | | --- | --- | | Hourly | Less than 7 days. | | Daily | Between 1-60 days (inclusive). | | Weekly | Between 1-52 weeks (inclusive). | | Monthly | At least 1 month and less than 120 months. | When you choose a new value from the Resolution dropdown, the resolution of the date selection [slider](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html#use-the-date-slider) changes. Then, you can select start and end points on the slider to hone in on the time range of interest. Note that the selected slider range also carries across the [Service Health](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/service-health.html) and [Accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html) tabs (but not across deployments). ## Use the date slider The date slider limits the time range used for comparing prediction data to training data. The upper dates displayed in the slider, left and right edges, indicate the range currently used for comparison in the page's visualizations. The lower dates, left and right edges, indicate the full date range of prediction data available. The circles mark the "data buckets," which are determined by the time range. To use the slider, click a point to move the line or drag the endpoint left or right. The visualizations use predictions from the starting point of the updated time range as the baseline reference point, comparing them to predictions occurring up to the last date of the selected time range. You can also move the slider to a different time interval while maintaining the periodicity. Click anywhere on the slider between the two endpoints to drag it (you will see a hand icon on your cursor). In the example above, you see the slider spans a 3-month time interval. You can drag the slider and maintain the time interval of 3 months for different dates. By default, the slider is set to display the same date range that is used to calculate and display drift status. For example, if drift status captures the last week, then the default slider range will span from the last week to the current date. You can move the slider to any date range without affecting the data drift status display on the health dashboard. If you do so, a Reset button appears above the slider. Clicking it will revert the slider to the default date range that matches the range of the drift status. ## Use the class selector Multiclass deployments offer [class-based configuration](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html#class-selector) to modify the data displayed on the Data Drift graphs. Predictions over Time multiclass graph: Feature Details multiclass graph: ## Drill down on the Data Drift tab The Data Drift > Drill Down chart visualizes the difference in distribution over time between the training dataset of the deployed model and the datasets used to generate predictions in production. The drift away from the baseline established with the training dataset is measured using the Population Stability Index (PSI). As a model continues to make predictions on new data, the change in the drift status over time is visualized as a heat map for each tracked feature, allowing you to identify [data drift](https://docs.datarobot.com/en/docs/reference/glossary/index.html#data-drift) trends. Because data drift can decrease your model's predictive power, determining when a feature started drifting and monitoring how that drift changes (as your model continues to make predictions on new data) can help you estimate the severity of the issue. Using the Drill Down tab, you can compare data drift heat maps across the features in a deployment to identify correlated drift trends. In addition, you can select one or more features from the heat map to view a Feature Drift Comparison chart, comparing the change in a feature's data distribution between a reference time period and a comparison time period to visualize drift. This information helps you identify the cause of data drift in your deployed model, including data quality issues, changes in feature composition, or changes in the context of the target variable. To access the Drill Down tab: 1. ClickDeployments, and then select adrift-enableddeployment from theDeploymentsinventory. 2. In the deployment, clickData Drift, and then clickDrill Down: 3. On theDrill Downtab: ### Configure the drill down display settings The Drill Down tab includes the following display controls: | | Control | Description | | --- | --- | --- | | (1) | Model | Updates the heatmap to display the model you selected from the dropdown. | | (2) | Date slider | Limits the range of data displayed on the dashboard (i.e., zooms in on a specific time period). | | (3) | Range (UTC) | Sets the date range displayed for the deployment date slider. | | (4) | Resolution | Sets the time granularity of the deployment date slider. | | (5) | Reset | Reverts the dashboard controls to the default settings. | ### Use the feature drift heat map The Feature Drift for all features heat map includes the following elements and controls: | | Element | Description | | --- | --- | --- | | (1) | Prediction time (X-axis) | Represents the time range of the predictions used to calculate the corresponding drift value (PSI). Below the X-axis, the Prediction sample size bar chart represents the number of predictions made during the corresponding prediction time range. | | (2) | Feature (Y-axis) | Represents the features in a deployment's dataset. Click a feature name to generate the feature drift comparison below. | | (3) | Status heat map | Displays the drift status over time for each of a deployment's features. Drift status visualizations are based on the monitoring settings configured by the deployment owner. The deployment owner can also set the drift and importance thresholds in the Feature Drift vs Feature Importance chart settings. The possible drift status classifications are: Healthy (Green): The feature is experiencing minimal drift. No action needed, but monitor features that approach the threshold.At risk (Yellow): A lower importance feature is experiencing drift above the set threshold. Monitor closely.Failing (Red): A high importance feature is experiencing drift above the set threshold. Investigate immediately Feature importance is determined by comparing the feature impact score with the importance threshold value. For an important feature, the feature impact score is greater than or equal to the importance threshold. | | (4) | Prediction sample size | Displays the number of rows of prediction data used to calculate the data drift for the given time period. To view additional information on the prediction sample size, hover over a bin in the chart to see the time of prediction range and the sample size value. | ### Use the feature drift comparison chart The Feature Drift Comparison section includes the following elements and controls: | | Element | Description | | --- | --- | --- | | (1) | Reference period | Sets the date range of the period to use as a baseline for the drift comparison charts. | | (2) | Comparison period | Sets the date range of the period to compare data distribution against the reference period. You can also select an area of interest on the heat map to serve as the comparison period. | | (3) | Feature values (X-axis) | Represents the range of values in the dataset for the feature in the Feature Drift Comparison chart. | | (4) | Percentage of Records (y-axis) | Represents the percentage of the total dataset represented by a range of values and provides a visual comparison between the selected reference and comparison periods. | | (5) | Add a feature drift comparison chart | Generates a Feature Drift Comparison chart for a selected feature. | | (6) | Remove this chart | Removes a Feature Drift Comparison chart. | To view additional information on a Feature Drift Comparison chart, hover over a bar in the chart to see the range of values contained in that bar, the percentage of the total dataset those values represent in the Reference period, and the percentage of the total dataset those values represent in the Comparison period: --- # Data Exploration tab URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-exploration.html > Explore a deployment's stored prediction and training data to compute and monitor custom business or performance metrics. # Data Exploration tab You can export a deployment's stored training data, prediction data, and actuals to compute and monitor custom business or performance metrics on the [Custom Metrics tab](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html) or outside DataRobot. To export deployment data for custom metrics, make sure your deployment stores prediction data, generate data for a specified time range, and then view or download that data. To access deployment data export: 1. In the top navigation bar, clickDeployments. 2. On theDeploymentstab, click on the deployment from which you want to access stored training data, prediction data, or actuals. NoteTo access the Data Exploration tab, the deployment must store prediction data. Ensure that youEnable prediction rows storage for challenger analysisin the deployment settings. The Data Exploration tab doesn't store or export Prediction Explanations, even if they are requested with the predictions. 3. In the deployment, click theData Explorationtab. ## Access or download data To access or download prediction data, training data, or actuals: 1. Configure the following settings to specify the stored training data, prediction data, or actuals you want to export: SettingDescription1ModelSelect the deployment's model, current or previous, to export prediction data for.2Range (UTC)Select the start and end dates of the period you want to export prediction data from.3ResolutionSelect the granularity of the date slider. Select from hourly, daily, weekly, and monthly granularity based on the time range selected. If the time range is longer than 7 days, hourly granularity is not available.4ResetReset the data export settings to the default. 2. In theTraining Data,Prediction Data, andActualspanels: Availability informationCustom metric data export is off by default. Contact your DataRobot representative or administrator for information on enabling this feature.Feature flags:Enable Data Quality Table for Text Generation Target Types (Premium feature), Enable Actuals Storage for Generative Models(Premium feature) Prediction data and actuals considerationsWhen generating prediction data or actuals, consider the following:When generating prediction data, you can export up to 200,000 rows per export. If the time range you set exceeds 200,000 rows of prediction data, decrease the range.In the AI Catalog, you can have up to 100 prediction export items. If generating prediction data for export would cause the number of prediction export items in the AI Catalog to exceed that limit, delete old prediction export AI Catalog items.When generating prediction data for time series deployments, two prediction export items are added to the AI Catalog. One item is for the prediction data, and the other is for the prediction results. The Data Exploration tab links to the prediction results.When generating actuals, you can export up to 1,000,000 rows per export. If the time range you set exceeds 1,000,000 rows of actuals, decrease the time range.In the AI Catalog, you can have up to 100 actuals export items. If generating actuals data for export would cause the number of actuals export items in the AI Catalog to exceed that limit, delete old actuals export AI Catalog items.Up to 10,000,000 actuals are stored for a deployment; therefore, exporting old actuals can result in an error if no actuals are currently stored for that time period. The training data appears in theTraining Datapanel. Prediction data and actuals appear in the table below, identified byPrediction DataorActualsin theTypecolumn. 3. After the prediction data, training data, or actuals are generated: > [!NOTE] Note > You can also click Use data in Notebook to open a [DataRobot notebook](https://docs.datarobot.com/en/docs/classic-ui/dr-notebooks/index.html) with cells for exporting training data, prediction data, and actuals. ## Use exported deployment data for custom metrics To use the exported deployment data to create your own custom metrics, you can implement a script to read from the CSV file containing the exported data and then calculate metrics using the resulting values, including [columns automatically generated during the export process](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-exploration.html#datarobot-column-reference). This example uses the exported prediction data to calculate and plot the change in the `time_in_hospital` feature over a 30-day period using the DataRobot prediction timestamp ( `DR_RESERVED_PREDICTION_TIMESTAMP`) as the DateFrame index (or row labels). It also uses the exported training data as the plot's baseline: ``` import pandas as pd feature_name = "" training_df = pd.read_csv("") baseline = training_df[feature_name].mean() prediction_df = pd.read_csv("") prediction_df["DR_RESERVED_PREDICTION_TIMESTAMP"] = pd.to_datetime( prediction_df["DR_RESERVED_PREDICTION_TIMESTAMP"] ) predictions = prediction_df.set_index("DR_RESERVED_PREDICTION_TIMESTAMP")["time_in_hospital"] ax = predictions.rolling('30D').mean().plot() ax.axhline(y=baseline - 2, color="C1", label="training data baseline") ax.legend() ax.figure.savefig("feature_over_time.png") ``` ### DataRobot column reference DataRobot automatically adds the following columns to the prediction data generated for export: | Column | Description | | --- | --- | | DR_RESERVED_PREDICTION_TIMESTAMP | Contains the prediction timestamp. | | DR_RESERVED_PREDICTION | Identifies regression prediction values. | | DR_RESERVED_PREDICTION_/input.csv to set the source of the data. | | --output= | Yes Default: None | Sets the way to output results. Valid values are: --output=- to write the results to standard output --output=/path/to/output/csv/output.csv to save results to a file. The output file always contains the same number of rows as the original file, and they are always in the same order. Note that for files smaller than 1GB, you can specify the output file to be the same as the input file, causing it to replace the input with the scored file. | | --encoding= | No Default: Default system encoding | Sets the charset encoding used to read file content. Use one of the canonical names for java.io API and java.lang API. If the option is not set, the tool will be able to detect UTF8 and UTF16 BOM. | | --delimiter= | No Default: , (comma) | Specifies the delimiter symbol used in CSV files to split values between columns.Note: use the option --delimiter=“;” to use the semicolon ; as a delimiter (; is a reserved symbol in bash/shell). | | --passthrough_columns | No Default: None | Sets the input columns to include in the results file. For example, if the flag contains a set of columns (e.g., column1,column2), the output will contain predictive column(s) and the columns 1 and 2 only. To include all original columns, use All. The resulting file will contain columns in the same order, and will use the same format and the same value as the delimiters parameter. If this parameter is not specified, the command only returns the prediction column(s). | | --chunk_size= | No Default: min(1MB, {file_size}/{cores_number}) | "Slices" the initial dataset into chunks to score in a sequence as separate asynchronous tasks. In most cases, the default value will produce the best performance. Bigger chunks can be used to score very fast models and smaller chunks can be used to score very slow models. | | --workers_number= | No Default: Number of logical cores | Specifies the number of workers that can process chunks of work concurrently. By default, the value will match the number of logical cores and will produce the best performance. | | --log_level= | No Default: INFO | Sets the level of information to be output to the console. Available options are INFO, DEBUG, and TRACE. | | --pred_name= | No Default: DR_Score | For regression projects, this field sets the name of the prediction column in the output file. In classification projects, the prediction labels are the same as the class labels. | | --buffer_size= | No Default: 1000 | Controls the size of the asynchronous task queue. Set it to a smaller value if you are experiencing OutOfMemoryException errors while using this tool. This is an advanced parameter. | | --config= | No Default: The .jar file directory | Sets the location for the batch.properties file, which writes all config parameters to a single file. If you place it in the same directory as the .jar, you do not need to set this parameter. If you want to place batch.properties into another directory, you need to set the value of the parameter to be the path to the target directory. | | --with_explanations | No Default: Disabled | Turns on prediction explanation computations. | | --max_codes= | No Default: 3 | Sets the maximum number of explanations to compute. | | --threshold_low= | No Default: Null | Sets the low threshold for prediction rows to be included in the explanations. | | --threshold_high= | No Default: Null | Sets the high threshold for prediction rows to be included in the explanations. | | --enable_mlops | No Default: Enabled | Initializes an MLOps instance for tracking scores. | | --dr_token= | Yes if --enabled_mlops is set. Default: None | Specifies the authorization token for monitoring agent requests. | | --disable_agent | No Default: Enabled | When --enable_mlops is enabled, sets whether to allow offline tracking. | | Time series options | | | | --forecast_point= | No Default: None | Formatted date from which to forecast. | | --date_format= | No Default: None | Date format to use for output. | | --predictions_start_date= | No Default: None | Timestamp that indicates when to start calculating predictions. | | --predictions_end_date= | No Default: None | Timestamp that indicates when to stop calculating predictions. | | --with_intervals | No Default: None | Turns on prediction interval calculations. | | --interval_length= | No Default: None | Interval length as int value from 1 to 99. | | --time_series_batch_processing | No Default: Disabled | Enables performance-optimized batch processing for time series models. | > [!NOTE] Note > For more information, see [Scoring Code usage examples](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/quickstart-api.html). ## Batch properties file You can configure the `batch.properties` file to change the default values for [the command line options above](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/scoring-code/scoring-cli.html#command-line-options), allowing you to simplify the command line scoring process, as having too many options for a bash command can make it difficult to read. In addition, some command line options depend on your scoring environment, leading to duplicate options for some commands; to avoid these duplications, you can save those parameters to the `batch.properties` file and reuse them. The following properties are available in the `batch.properties` file, mapping to the listed command line option: | Batch property | Option mapping | | --- | --- | | com.datarobot.predictions.batch.encoding | --encoding | | com.datarobot.predictions.batch.passthrough.columns | --passthrough_columns | | com.datarobot.predictions.batch.chunk.size=150 | --chunk_size | | com.datarobot.predictions.batch.workers.number= | --workers_number | | com.datarobot.predictions.batch.log.level=INFO | --log_level | | com.datarobot.predictions.batch.pred.name=PREDICTION | --pred_name | | com.datarobot.predictions.batch.buffer.size=1000 | --buffer_size | | com.datarobot.predictions.batch.enable.mlops=false | --enable_mlops | | com.datarobot.predictions.batch.disable.agent | --disable_agent | | com.datarobot.predictions.batch.max.file.size=1000000000 | No option mapping To read and write to and from the same file, this property sets the maximum original file size, allowing the command line interface to read it all in memory before scoring. | | Time series parameters | | | com.datarobot.predictions.batch.forecast.point= | --forecast_point | | com.datarobot.predictions.batch.date.format=yyyy-MM-dd'T'HH:mm:ss.SSSSSS'Z' | --date_format | | com.datarobot.predictions.batch.start.timestamp= | --predictions_start_date | | com.datarobot.predictions.batch.end.timestamp= | --predictions_end_date | | com.datarobot.predictions.batch.with.interval | --with_intervals | | com.datarobot.predictions.batch.interval_length | --interval_length | | com.datarobot.predictions.batch.time.series.batch.proccessing | --time_series_batch_processing | ## Increase Java heap memory Depending on the model's binary size, you may have to increase the Java virtual machine (JVM) heap memory size. When scoring your model, if you receive an `OutOfMemoryError: Java heap space error` message, increase your Java heap size by calling `java -Xmx1024m` and adjusting the number as necessary to allocate sufficient memory for the process. To guarantee, in case of error, scoring result consistency and a non-zero exit code, run the application with the `-XX:+ExitOnOutOfMemoryError` flag. The following example increases heap memory to 2GB: ``` java -XX:+ExitOnOutOfMemoryError -Xmx2g -Dlog4j2.formatMsgNoLookups=true -jar 5cd071deef881f011a334c2f.jar csv --input=Iris.csv --output=Isis_out.csv ``` --- # Predictions reference URL: https://docs.datarobot.com/en/docs/classic-ui/predictions/pred-file-limits.html > Learn the file size limits for different methods of making predictions. Prediction file size limits depend on whether the model is deployed or not and whether you use the UI or an API. # Prediction reference DataRobot supports many methods of making predictions, including the DataRobot UI and APIs—for example, Python, R, and REST. The prediction methods you use depend on factors like the size of your prediction data, whether you're validating a model prior to deployment or using and monitoring it in production, whether you need immediate low-latency predictions, or if you want to schedule batch prediction jobs. This page hosts considerations, limits, and other helpful information to reference before making predictions. ## File size limits > [!NOTE] Self-Managed AI Platform limits > Prediction file size limits vary for Self-Managed AI Platform installations and limits are configurable. | Prediction method | Details | File size limit | | --- | --- | --- | | Leaderboard predictions | To make predictions on a non-deployed model using the UI, expand the model on the Leaderboard and select Predict > Make Predictions. Upload predictions from a local file, URL, data source, or the AI Catalog. You can also upload predictions using the modeling predictions API, also called the "V2 predictions API." Use this API to test predictions using your modeling workers on small datasets. Predictions can be limited to 100 requests per user, per hour, depending on your DataRobot package. | 1GB | | Batch predictions (UI) | To make batch predictions using the UI, deploy a model and navigate to the deployment's Make Predictions tab (requires MLOps). | 5GB | | Batch predictions (API) | The Batch Prediction API is optimized for high-throughput and contains production grade connectivity options that allow you to not only push data through the API, but also connect to the AI catalog, cloud storage, databases, or data warehouses (requires MLOps). | Unlimited | | Prediction API (real-time) Dedicated Prediction Environment (DPE) | To make real-time predictions on a deployed model, use the Prediction API. | 50MB | | Prediction API (real-time) Serverless Prediction Environment | To make real-time predictions on a deployed model, use the Prediction API. | 50MB | | Prediction monitoring | While the Batch Prediction API isn't limited to a specific file size, prediction monitoring is still subject to an hourly rate limit. | 100MB / hour | ## Monitor model health If you use any of the prediction methods mentioned above, DataRobot allows you to deploy a model and monitor its prediction output and performance over a selected time period. A critical part of the model management process is to identify when a model starts to deteriorate and to quickly address it. Once trained, models can then make predictions on new data that you provide. However, prediction data changes over time—businesses expand to new cities, new products enter the market, policy or processes change—any number of changes can occur. This can result in [data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html), the term used to describe when newer data moves away from the original training data, which can result in poor or unreliable prediction performance over time. Use the [MLOps deployment dashboard](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html) to analyze a model's performance metrics: prediction response time, model health, accuracy, data drift analysis, and more. When models deteriorate, the common action to take is to retrain a new model. Deployments allow you to replace models without re-deploying them, so not only do you not need to change your code, but DataRobot can track and represent the entire history of a model used for a particular use case. ## Avoiding common mistakes The section on [dataset guidelines](https://docs.datarobot.com/en/docs/reference/data-ref/file-types.html) provides important information about DataRobot's dataset requirements. In addition, consider: 1. Under-trained models. The most common prediction mistake is to use models in production without retraining them beyond the initial training set. Best practice suggests the following workflow: 2. File encoding issues. Be certain that you properly format your data to avoid prediction errors. For example, unquoted newline characters and commas in CSV files often cause problems. JSON can be a better choice for data that contains large amounts of text because JSON is more standardized than CSV. CSV can be faster than JSON, but only when it is properly formatted. 3. Insufficient cores. When making predictions, keep the number of threads or processes less than or equal to the number of prediction worker cores you have and make synchronous requests. That is, the number of concurrent predictions should generally not exceed the number of prediction worker cores on your dedicated prediction server(s). If you are not sure how many prediction cores you have, contactDataRobot Support. > [!WARNING] Warning > When performing predictions, the positive class has multiple representations that DataRobot can choose from, from the original positive class as written on the dataset, a user-specified choice in the frontend, or the positive class as provided by the prediction set. Currently DataRobot's internal rules regarding this are not obvious, which can lead to automation issues like `str("1.0")` being returned as the positive class instead of `int(1)`. This issue is being fixed by standardizing the internal ruleset in a future release. ## Prediction speed 1. Model scoring speed. Scoring time differs by model and not all models are fast enough for "real-time" scoring. Before going to production with a model, verify that the model you select is fast enough for your needs. Use theSpeed vs. Accuracytab to display model scoring time. 2. Understanding the model cache. A dedicated prediction server scores quickly because of its in-memory model cache. As a result, the first few requests using a new model may be slower because the model must first be retrieved. 3. Computing predictions with Prediction Explanations. Computing predictions with XEMP Prediction Explanations requires a significantly higher number of operations than only computing predictions. Expect higher runtimes, although actual speed is model-dependent. Reducing the number of features used or avoiding blenders and text variables may increase speed. Increased computation costsdo notapply to SHAP Prediction Explanations. --- # Predictions testing URL: https://docs.datarobot.com/en/docs/classic-ui/predictions/pred-test.html > To make predictions and assess model performance prior to deployment, you can make predictions on an external test dataset (i.e., external holdout) or on training data (i.e., validation and/or holdout). # Predictions on test and training data Use the [Make Predictions](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/predictions/predict.html) tab to make predictions and assess model performance prior to deployment. You can [make predictions on an external test dataset](https://docs.datarobot.com/en/docs/classic-ui/predictions/pred-test.html#make-predictions-on-an-external-test-dataset) (i.e., external holdout) or you can [make predictions on training data](https://docs.datarobot.com/en/docs/classic-ui/predictions/pred-test.html#make-predictions-on-training-data) (i.e., validation and/or holdout). ## Make predictions on an external test dataset To better evaluate model performance, you can upload any number of additional test datasets after project data has been partitioned and models have been trained. An external test dataset is one that: - Containsactuals(values for the target). - Isnotpart of the original dataset (you didn't train on any part of it). Using an external test dataset allows you to compare model accuracy against the predictions. By uploading an external dataset and using the original model's dataset partitions, you can compare metric scores and visualizations to ensure consistent performance prior to deployment. Select the external test set as if it were a partition in the original project data. > [!NOTE] Note > Support for external test sets is available for all project types except supervised time series. Unsupervised time series supports external test sets for anomaly detection but not clustering. To make predictions on an external test set: 1. Upload new test datain the same way you would upload a prediction dataset. For supervised learning, the external set must contain the target column and all columns present in the training dataset (although additional columnscan be added). The workflow is slightly different foranomaly detection projects. 2. Once uploaded, you'll see the labelEXTERNAL TESTbelow the dataset name. ClickRun external testto calculate predicted values and compute statistics that compare the actual target values to the predicted values. The external test is queued and job status appears in the Worker Queue on the right sidebar. 3. When calculations complete, clickDownload predictionsto save prediction results to a CSV file. NoteIn a binary classification project, when you clickRun external test, the current value of the Prediction Threshold is used for computation of the predicted labels. In the downloaded predictions, the labels correspond to that threshold, even if you updated the threshold between computing and downloading. DataRobot displays the threshold that was used in the calculation in the dataset listing. 4. To view external test scores, from the Leaderboard menu selectShow external test column. The Leaderboard now includes anExternal testcolumn. 5. From theExternal testcolumn, choose the test data to display results for or clickAdd external testto return to theMake Predictionstab to add additional test data. You can now sort models by external test scores or calculate scores for more models. ### Supply actual values for anomaly detection projects In [anomaly detection](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/unsupervised/anomaly-detection.html) (non-time series) projects, you must set an actuals column that identifies the outcome or future results to compare to predicted results. This provides a measure of accuracy for the event you are predicting on. The prediction dataset must contain the same columns as those in the training set with at least one column for known anomalies. Select the known anomaly column as the Actuals value. ### Compare insights with external test sets Expand the Data Selection dropdown to select an external test set as if it was a partition in the original project data. This option is available when using the following insights: - Lift Chart - ROC Curve - Profit Curve - Confusion Matrix - Accuracy Over Time (OTV only) - Stability (OTV only) - Residuals Note the following: - Insights are not computed if an external dataset has fewer than 10 rows; however, metric scores are computed and displayed on the Leaderboard. - TheROC Curveinsight is disabled if the external dataset only contains single class actuals. ## Make predictions on training data Less commonly (although there are [reasons](https://docs.datarobot.com/en/docs/classic-ui/predictions/pred-test.html#why-use-training-data-for-predictions)), you may want to download predictions for your original training data, which DataRobot automatically imports. From the dropdown, select the [partition(s)](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html) to use when generating predictions. For small datasets, predictions are calculated by doing [stacked predictions](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#what-are-stacked-predictions) and therefore can use all partitions. Because those calculations are too “expensive” to run on large datasets (750MB and higher by default), predictions are based on holdout and/or validation partitions, as long as the data wasn’t used in training. | Dropdown option | Description for small datasets | Description for large datasets | | --- | --- | --- | | All data | Predictions are calculated by doing stacked predictions on training, validation, and holdout partitions, regardless of whether they were used for training the model or if holdout has been unlocked. | Not available | | Validation and holdout | Predictions are calculated using the validation and holdout partitions. If validation was used in training, this option is disabled. | Predictions are calculated using the validation and holdout partitions. If validation was used in training or the project was created without a holdout partition, this option is not available. | | Validation | If the project was created without a holdout partition, this option replaces the Validation and holdout option. | If the project was created without a holdout partition, this option replaces the Validation and holdout option. | | Holdout | Predictions are calculated using the holdout partition only. If holdout was used in training, this option is not available (only the All data option is valid). | Predictions are calculated using the holdout partition only. If holdout was used in training, predictions are not available for the dataset. | > [!NOTE] Note > For [OTV](https://docs.datarobot.com/en/docs/reference/glossary/index.html#otv) projects, holdout predictions are generated using a model retrained on the holdout partition. If you upload the holdout as an external test dataset instead, the predictions are generated using the model from backtest 1. In this case, the predictions from the external test will not match the holdout predictions. Select Compute predictions to generate predictions for the selected partition on the existing dataset. Select Download predictions to save results as a CSV. > [!NOTE] Note > The `Partition` field of the exported results indicates the source partition name or fold number of the cross-validation partition. The value `-2` indicates the row was "discarded" (not used in [TVH](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#training-validation-and-holdout-tvh)). This could be because the target was missing, the [partition column](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/partitioning.html) (Date/Time-, Group, or Partition Feature-partitioned projects) was missing, or [smart downsampling](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/smart-ds.html) was enabled, and those rows were discarded from the majority class as part of downsampling. ### Why use training data for predictions? Although less common, there are times when you want to make predictions on your original training dataset. The most common application of the functionality is for use on large datasets. Because running [stacked predictions](https://docs.datarobot.com/en/docs/classic-ui/predictions/pred-test.html#stacked-predictions) on large datasets is often too computationally expensive, the Make Predictions tab allows you to download predictions using data from the validation and or holdout partitions (as long as they weren’t used in training). Some sample use cases: Clark the software developer needs to know the full distribution of his predictions, not just the mean. His dataset is large enough that stacked predictions are not available. With weekly modeling using the R API, he downloads holdout and validation predictions onto his local machine and loads them into R to produce the report he needs. Lois the data scientist wants to verify that she can reproduce model scores exactly as well in DataRobot as when using an in-house metric. She partitions the data, specifying holdout during modeling. After modeling completes, she unlocks holdout, selects the top model, and computes and downloads predictions for just the holdout set. She then compares predictions of that brief exercise to the result of her previous many-month-long project. --- # Prediction API snippets URL: https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html > How to adapt downloadable DataRobot Python code to submit a CSV or JSON file for scoring and integrate it into a production application via the Prediction API. # Prediction API snippets DataRobot provides sample Python code containing the commands and identifiers required to submit a CSV or JSON file for scoring. You can use this code with the [DataRobot Prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html). You can also read below for more information on: - Disabling data drift tracking for individual prediction requests - Using the monitoring snippet with deployments ## Prediction API scripting code To use the Prediction API Scripting Code, open the deployment you want to make predictions through and click Predictions > Prediction API. On the Prediction API Scripting Code page, you can choose from several scripts for Batch and Real-time predictions. Follow the sample provided and make the necessary changes when you want to integrate the model, via API, into your production application. To find and access the script required for your use case, configure the following settings: ### Batch prediction snippet settings | | Content | Description | | --- | --- | --- | | (1) | Prediction type | Determines the prediction method used. Select Batch. | | (2) | Interface | Determines the interface type of the batch prediction script you generate. Select one of the following interfaces:CLI: A standalone batch prediction script using the DataRobot API Client. Before using the CLI script, if you haven't already downloaded predict.py, click download CLI tools.API Client: An example batch prediction script using the DataRobot's Python package.HTTP: An example batch prediction script using the raw Python-based HTTP requests. | | (3) | Platform(only for CLI) | When you select the CLI interface option, the platform setting determines the OS on which you intend to run the generated CLI prediction script. Select one of the following platform types:Mac/LinuxWindows | | (4) | Copy script to clipboard | Copies the entire code snippet to your clipboard. | | (5) | Show secrets | Displays any secrets hidden by ***** in the code snippet. Revealing the secrets in a code snippet can provide a convenient way to retrieve your API key or datarobot-key; however, these secrets are hidden by default for security reasons, so ensure that you handle them carefully. | | (6) | Code overview screen | Displays the example code you can download and run on your local machine. Edit this code snippet to fit your needs. | ### Real-time prediction snippet settings | | Content | Description | | --- | --- | --- | | (1) | Prediction type | Determines the prediction method used. Select Real time. | | (2) | Language | Determines the language of the real-time prediction script generated. Select a format:Python: An example real-time prediction script using the DataRobot's Python package.cURL: A script using cURL, a command-line tool for transferring data using various network protocols, available by default in most Linux distributions and macOS. | | (3) | Copy script to clipboard | Copies the entire code snippet to your clipboard. | | (4) | Show secrets | Displays any secrets hidden by ***** in the code snippet. Revealing the secrets in a code snippet can provide a convenient way to retrieve your API key or datarobot-key; however, these secrets are hidden by default for security reasons, so ensure that you handle them carefully. | | (5) | Code overview screen | Displays the example code you can download and run on your local machine. Edit this code snippet to fit your needs. | To deploy the code, copy the sample and either: - Reviewdeployment options - Integrate for use with adedicated prediction server ### Disable data drift You can disable data drift tracking for individual prediction requests by applying a unique header to the request. This may be useful, for example, in the case where you are using synthetic data that does not have real-world consequences. Insert the header, `X-DataRobot-Skip-Drift-Tracking=1`, into the request snippet. For example: ``` headers['X-DataRobot-Skip-Drift-Tracking'] = '1' requests.post(url, auth=(USERNAME, API_KEY), data=data, headers=headers) ``` After you apply this header, drift tracking is not calculated for the request. However, service stats are still provided (data errors, system errors, execution time, and more). ### Monitoring snippet When you create an external model deployment, you are notified that the deployment requires the use of monitoring snippets to report deployment statistics with the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html). You can follow the link at the bottom of the page or navigate to Predictions > Monitoring for your deployment to view the snippet: The monitoring snippet is designed to configure your MLOps library to send a model's statistics to DataRobot MLOps and represent those statistics in the deployment. Use this functionality to report back Scoring Code metrics to your deployment. To instrument your Scoring Code with a deployment, select the Java language and copy the snippet to your clipboard when you are ready to use it. For further instructions, reference the Quick Start guide available in the monitoring agent's internal documentation. If you have not yet configured the monitoring agent to monitor your deployment, a download of the MLOps agent tarball is available from a link in the Monitoring tab. Additional documentation for setting up the monitoring agent is included in the tarball. ## Dormant prediction servers Prediction servers become dormant after a prolonged period of inactivity. If you see the Prediction server is dormant alert, please contact our support team at support@datarobot.com for prompt reactivation: --- # Real-time scoring methods URL: https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/index.html > Learn about DataRobot's available methods for making real-time predictions. # Real-time scoring methods Make real-time predictions by sending an HTTP request for a model via a synchronous call. After DataRobot receives the request, it immediately returns a response containing the prediction results. The simplest method for making real-time predictions is to [deploy a model from the Leaderboard](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/add-deploy-info.html) and make prediction requests with the [Prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html). After deploying a model, you can also navigate to a deployment's [Prediction API](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html) tab to access and configure scripting code that allows you to make simple requests to score data. The deployment also hosts [integration snippets](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/integration-code-snippets.html). --- # Qlik predictions URL: https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/integration-code-snippets.html > Submit Qlik data for scoring via the prediction API and a sample code snippet. # Qlik predictions To integrate with Qlik, DataRobot provides a code snippet containing the commands and identifiers necessary to submit Qlik data for scoring using the [Prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html). From a deployment's Predictions > Integrations tab, click the Qlik tile. To use the Qlik Integrations Code Snippet, follow the sample and make the necessary changes to integrate the model, via the API, into your production application. Click the Prediction Explanations checkbox to include prediction explanations (1) alongside the prediction results. Copy the sample code (2) and modify it as necessary. Once customized, your code snippet is ready for use with the Prediction API. --- # Foundational apps URL: https://docs.datarobot.com/en/docs/get-started/day0/apps-fundamentals.html > Learn about Foundational apps; DataRobot's solution to the challenge of scaling AI. # Foundational apps While getting started with AI is relatively straightforward, scaling these applications and ensuring they meet production standards introduces significant challenges. User authentication, data governance, and model observability must be addressed. This is where DataRobot's Foundational apps come into play, providing a comprehensive framework that simplifies the path to deploying reliable AI solutions. Foundational apps simplify the process of scaling AI from local experiments to production systems by leveraging industry-standard tools, including Git, GitHub Actions/GitLab Pipelines, and Terraform/Pulumi to provision production-grade resources. In the DataRobot platform, you can manage the complexities of modern AI applications while ensuring that governance, security, and observability are prioritized. ### The challenge of scaling AI Initially, AI tools are user-friendly and provide a great starting point for developers and data scientists. However, as your projects evolve, you encounter a range of complexities. These include ensuring sensitive data is handled securely, implementing user and API authentication to share with colleagues, scaling to handle hundreds of users, establishing governance controls, and maintaining observability over model performance. In many cases, significant amounts of time and resources are required to build out these components from scratch. A structured approach becomes necessary to not only meet these challenges but also to promote collaboration among colleagues. ### DataRobot's solution DataRobot addresses these scaling challenges by integrating industry-standard, infrastructure-as-code technologies, such as Terraform and Pulumi, into its Foundational apps. These applications allow organizations to easily manage multiple services and components while controlling their lifecycle—all while plugging into standard Git workflows and continuous integration and delivery solutions, like GitHub Actions and GitLab Pipelines. The following application templates are considered Foundational apps: - Forecast Assistant - Guarded RAG Assistant - Predictive Content Generator - Predictive AI Starter - Talk to My Data Agent Key features: - Open Source foundations: There are several open-source Foundational app Templates on GitHub in theDataRobot Community Repositoriesalong with the Declarative API provider via Terraform and Pulumi: DataRobot Community Repositories - All-in-one monorepos: A straightforward development environment for all the components of your end-to-end AI solution. - Web applications with built-in authentication: Easily create web applications that incorporate robust user and API authentication protocols, ensuring that access is both secure and user-friendly. - LLM Deployments with guardrails: As organizations grow, the complexity of managing large language models (LLMs) can increase significantly. Foundational apps provide governance and oversight that aid in the ethical deployment of LLMs. - Playgrounds for experimentation: Experiment and test LLM models in isolated environments, allowing for rapid iteration and better learning outcomes without impacting production systems. - Use Case tracking: Keep track of multiple Use Cases and their progress, enabling better project management and team collaboration as you scale up your AI efforts. - Notebooks and Codespaces: Facilitate training, experimentation, and development with integrated notebooks and coding environments, which help accelerate the learning curve and enhance productivity. --- # Suggested first steps URL: https://docs.datarobot.com/en/docs/get-started/day0/first-steps.html > Experiences to help you onboard quickly based on your goals, tech stack and skill set. # Suggested first steps Get started with a step-by-step generative or predictive AI how-to. Everything you need to complete these walkthroughs is included, so after completing one, come back and try the other. > [!NOTE] Note > Both tutorials are suitable for all levels, regardless of whether the technology is new to you. | Exercise | Estimated completion time | | --- | --- | | Generative AI (GenAI) playground | 20 minutes | | End-to-end predictive AI (in two parts) | 40 minutes | > [!NOTE] Availability information > DataRobot's Generative AI capabilities are a premium feature; contact your DataRobot representative for enablement information. However, you can try this functionality in a limited capacity by using the [DataRobot trial experience](https://www.datarobot.com/trial/?utm_source=productbanner&utm_medium=web&utm_campaign=FreeTrial23productcta). ## Exercise 1: Generative AI playground In the [GenAI how-to](https://docs.datarobot.com/en/docs/get-started/how-to/genai-walk-basic.html), you will create a GenAI pipeline, starting with raw documents and ending with multiple chatbot options from which you can deploy the best one. Specifically, you will break up pages of technical documentation into chunks and store those chunks as vectors for easy retrieval. Next, you'll add and configure a large language model (LLM), which you can then chat with. Finally, you'll compare how different LLMs respond to natural language questions about the documents in your vector database. [Download demo data](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/datarobot_english_documentation_5th_December.zip) Source: The provided ZIP file contain hundreds of pages of DataRobot product documentation. This is an example of how a company can build a chat agent for users to ask detailed questions about their product. In your case it might be a refrigerator, a sound mixing board, or a piece of advanced software. ## Exercise 2: End-to-end predictive AI After completing this tutorial, you'll have completed more work in less than an hour than a typical machine learning team can achieve in a week. This exercise is broken into two parts: | How-to | Description | | --- | --- | | Build | Prepare data, build multiple models, then evaluate and compare the performance. | | Operate and govern | Deploy the "best" model to an API endpoint, make batch predictions, and review deployment monitoring metrics. | These datasets come pre-loaded in DataRobot Trial accounts. Other DataRobot users can download it here: [Download training data](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/10k_diabetes.csv) [Download scoring data](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/10k_diabetes_scoring.csv) Source: The Hospital Readmissions sample data comes from a study of 70,000 inpatients with diabetes conducted by [BioMed Research International](https://www.hindawi.com/journals/bmri/2014/781670/). The researchers of the study collected this data from the Health Facts database provided by Cerner Corporation, which is a collection of clinical records across providers in the United States. Health Facts allows organizations that use Cerner’s electronic health system to voluntarily make their data available for research purposes. All the data was cleansed of PII in compliance with HIPAA. ## Ready for more? Visit the [sample assets](https://docs.datarobot.com/en/docs/get-started/day0/sample-assets.html) page for ready-to-use sample files and accompanying tutorials organized by problem type. Review the [how-tos](https://docs.datarobot.com/en/docs/get-started/how-to/index.html) for step-by-step exercises designed for learning various features of the platform. --- # Fundamentals of generative AI (GenAI) URL: https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-fundamentals.html > Learn the basics of building GenAI workflow— connect to your data, build and compare LLM blueprints, automate compliance, register and deploy. # Fundamentals of generative AI (GenAI) [Generative AI (GenAI)](https://docs.datarobot.com/en/docs/agentic-ai/index.html) available in Workbench, allows you to build, govern, and operate enterprise-grade generative AI solutions with confidence. The solution provides the freedom to rapidly innovate and adapt with the best-of-breed components of your choice (LLMs, vector databases, embedding models), across cloud environments. DataRobot's GenAI: - Safeguards proprietary data by extending your LLMs and monitoring cost of your generative AI experiments in real-time. - Shepherds you through creating, deploying, and maintaining safe, high-quality, generative AI applications and solutions in production, using your tool-of-choice. - Lets you confidently manage and govern LLMs in production, quickly detecting and correcting unexpected and unwanted behaviors. - Continuously improve GenAI applications with predictive modeling and user feedback. This page provides an overview of the functionality. > [!NOTE] For trial users > If you are a DataRobot trial user, see the [FAQ](https://docs.datarobot.com/en/docs/get-started/day0/trial-faq.html) for information on trial-specific capabilities. To start a DataRobot trial of predictive and generative AI, click Start a free trial at the top of this page. ## GenAI overview The DataRobot GenAI platform provides both API and GUI options, allowing you to experiment, compare, and assess the best GenAI components through qualitative and quantitative comparisons at an individual prompt and response level. The DataRobot AI Platform supports experimentation with common LLMs or you can bring your favorite libraries, bring or choose your LLMs, vector databases, and embeddings, and integrate third-party tools. ### GenAI problem statement With open source and GPU clusters offered by the cloud providers, it's possible to build GenAI workflows yourself, but it's hard to get those solutions into production in an enterprise setting. It's challenging to get the governance and guardrails in place to ensure you can confidently put your workflow into production while still ensuring that it meets your compliance and performance standards. With consultants that build an agentic application for you, you don't have the flexibility to adjust the implementation as your needs change over time. While there is a perception that "it's all about the model," in reality, the value depends more on the GenAI end-to-end strategy. Quality of the vector database (if used), prompting strategy, and monitoring, maintenance, and governance are all critical components of success. ### DataRobot solution Sitting between do-it-yourself open source options and the full white-glove treatment of consultants that build GenAI workflows and agents for you, DataRobot provides: - An AI platform that allows you to build whatever you need to support your business with Generative AI. - A suite of applications that show you how to implement end-to-end solutions for common use cases. - One unified experience for all users, no matter the ingestion data layer, cloud provider, or consumption layer. DataRobot is the "AI intelligence layer" that sits between the data infrastructure (unstructured data, on the left in the image below) and the consumption layer. DataRobot provides a comprehensive suite of tools to build and evaluate GenAI workflows. Out-of-the-box observability and state-of-the-art inference let you scale workflows and understand what's happening in production and across the entire lifecycle. Governance functionality ensures you're comfortable with your workflow in the enterprise setting. Application templates provide pre-built applications for common use cases, as well as the code associated with the implementation. This allows you to customize—now and in the future—how all aspects of the application work, ensuring that the needs of your use case are met. The general DataRobot architecture is used for both [generative](https://docs.datarobot.com/en/docs/agentic-ai/index.html) and [predictive](https://docs.datarobot.com/en/docs/workbench/index.html) AI, whether it's preparing data, building and optimizing, or sharing insight. From experimentation in Workbench through [deployment in Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/index.html), all models can be managed and monitored through [Console](https://docs.datarobot.com/en/docs/workbench/nxt-registry/index.html) production capabilities. This includes governance, ability to deploy and manage, and continuous and ongoing monitoring. > [!TIP] Tip > See the [generalized discussion](https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-workflow.html) of the steps to build generative models in a playground. Or, try it yourself with the [GenAI how-to](https://docs.datarobot.com/en/docs/get-started/how-to/genai-walk-basic.html). ## Read more - GenAI how-to compares multiple retrieval-augmented generation (RAG) pipelines ( video here ). When completed, you'll have multiple end-to-end pipelines with built-in evaluation, assessment, and logging, providing governance and guardrails. - What AI tools aren’t delivering for AI leaders (blog) - Everything You Need to Know About LLMOps (White paper) - 10 Key Considerations for Generative AI in Production (White paper) - The enterprise path to agentic AI (Blog) - Agentic AI: Real-world business impact, enterprise-ready solutions (Blog) - GenAI Accelerators GitHub repo --- # Transform forecasting insights URL: https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-welcome/forecasting-insights-genai.html > Convey forecasting predictions and methodologies for non-technical stakeholders, elevating visibility of this data and the underlying data science work across the organization. # Transform forecasting insights ### Fast facts | Characteristic | Value | | --- | --- | | Ease of Implementation | Medium | | Impact | High | | Impact Type | Efficiency/ Optimization | | Primary Users | Internal | | Type | Advanced summarization | | Includes Predictive AI | Yes | ## What it is In many industries, forecasts are still often created in spreadsheets for the sake of transparency and ease of forecast model interpretation. But these spreadsheet forecasts require a lot of manual effort, are resistant to backtesting, and are often difficult for non-technical stakeholders to understand. For these reasons, many organizations are already utilizing machine learning for time series forecasting. However, one of the problems with these predictive models persists. It still may be hard for non-technical or business stakeholders without a quantitative background to interpret these insights, even with advanced explainability that predictive AI can provide. Generative AI can bridge the gap by conveying predictions and methodologies for non-technical stakeholders, elevating visibility of this data and the underlying data science work across the organization. The solution would process the contextual information, as well as quantitative prediction insights, explaining the key drivers of these forecasts in human language and even learning to interpret and explain the underlying dynamics of the market at hand based on the data. This creates a seemingly all-knowing human-like assistant that’s able to back its decisions with the highest degree of quantitative data possible. ## How it works For this solution, prediction explanations (a quantitative indicator of the effect variables have on the predictions) from the forecasting model are fed to the generative AI model. Post-processed prediction data with prediction explanations for every time series is ingested and stored in a .csv format. Then it’s converted to a string when injected into the LLM prompt. But could easily also be stored in a database table and converted to a string later. Organizations may need to post-process the data and do aggregations up to the series-level in order to make it easier for the LLM to understand the prediction explanations, as the individual row level of predictions might be too granular, the series-level is easier to understand and work with. Since the generative AI model and its explanations are only as powerful as the underlying forecast, it’s important to use as many features in the underlying forecasting model as possible. Once the data is fed to the LLM, it summarizes prediction explanations into powerful narratives through an intricate prompting strategy. ## User experience The user can interact with the model through a number of ways, depending on how the solution is set up. It can be a standalone application that has access to different forecasting reports from the predictive model, with all of the data already pre-processed for the LLM. This application can also include prompting templates for the user to choose from and modify if necessary since, as you can see above, the prompting strategy can be complicated, depending on the forecasting needs. The organization may also choose to obfuscate some of the complicated prompting details by offering the user the ability to select necessary prompt elements via a dropdown, like the specific geos they might be interested in. This will then be automatically added to the final prompt. Once they’ve set up their prompt, they run the application and receive the final report within the application (text field, .pdf, or other formats). ## Why it might benefit your business Extremely powerful forecasting models built with predictive AI and backed by transparent explanations built with generative AI are extremely easy to understand. This improved decision-making processes by delivering reliable and understandable forecasts, which can have a multiplying positive effect on long-term business decisions, like investment choices and resource allocation. Getting the powerful predictive AI insights into the hands of consumers who otherwise would have been able to make decisions based on them can become a force multiplier for an organization. Such a comprehensive solution also Increases efficiency and productivity of analytical teams by augmenting and automating forecasting processes. These teams spend a lot of time interpreting the data, but a significant investment is also made in storytelling to explain these findings to decision makers. Generative AI can simplify this process, while simultaneously improving the robustness of insights. As a unified generative and predictive AI workflow, this can be a visible competitive advantage through improved velocity and accuracy of forecasting insights, as well as their transparency. What You Need To Implement This Use Case Successfully - A robust time series forecasting solution or framework that is able to provide context for its findings, “explaining” each prediction, row-by-row. - An elaborate post-processing pipeline for getting the predictive insights into the LLM workflow. - A solid prompting strategy that allows to shape the LLMs outputs in a digestible and useful manner. ## Potential risks Risks associated with the generated output: - Potential toxicity or readability issues associated with the prompt, as well as any potential cost implications, based on the scale of the user base and the complexity of inputs and outputs. - Data quality issues on the predictive side of the workflow, such as inaccurate or incomplete data, which can impact the accuracy and reliability of the predictions and lead to downstream effects for generative AI outputs. ### Baseline mitigation tactics - Custom metrics monitoring for toxicity, readability (Flesch Reading Score), as well as informative/ uninformative responses to ensure that responses are appropriate. Additional tokens/ cost monitoring ensures the financial viability of the solution. - Guard models that would prevent unwanted or unrelated outputs and ensure that the final answers don’t include any hallucinated answers by the LLM - Extensive testing of the LLM, its parameters, like the system prompts. - Ongoing monitoring of the underlying predictive model that supplies the forecasts (accuracy, data drift, etc.). --- # Use generative AI URL: https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-welcome/index.html > Create vector databases, create and compare LLM blueprints in the playground, and prepare LLMs for deployment. # Use generative AI > [!NOTE] Premium > DataRobot's GenAI capabilities are a premium feature; contact your DataRobot representative for enablement information. Try this functionality for yourself in a limited capacity in the DataRobot trial experience. This section provides a variety of GenAI use cases to help illustrate a variety of common business applications. | Topic | Description | | --- | --- | | GenAI in DataRobot overview | An overview of the DataRobot GenAI workflow. | | RFPbot use case | Leverages DataRobot's technology-agnostic framework to build a Request for Proposal Assistant. | | Forecasting insights | Convey forecasting predictions and methodologies for non-technical stakeholders, elevating visibility of this data and the underlying data science work across the organization. | | Invoice anomalies | Augment the invoice validation process to generate concise summaries of all detected anomalies and improve invoice approvals. | | Suspicious Activity Reporting (SAR) | Combine existing predictive AI workflows around suspicious activity monitoring with a GenAI component to streamline the reporting process. | | Legal and compliance answers | Learn how GenAI can address pain points for legal and compliance professionals in the exact context that these professionals require. | --- # Invoice anomaly detection and processing URL: https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-welcome/invoice-anomalies-genai.html > GenAI can augment the invoice validation process to generate concise summaries of all detected anomalies and improve invoice approvals. # Invoice anomaly detection and processing ### Fast facts | Characteristic | Value | | --- | --- | | Ease of Implementation | Medium | | Impact | Medium | | Impact Type | Efficiency/ Optimization | | Primary Users | Internal | | Type | Summarization | | Includes Predictive AI | Yes | ## What it is Manual invoice processing is costly, time-consuming, and prone to error. While predictive AI models can identify patterns and incongruencies in an organization’s invoicing data, generative AI can augment the validation process to generate concise summaries of all detected anomalies and improve invoice approvals. By crafting clear narratives around these invoice anomalies, generative AI summarization can help better communicate underlying causes and improve certainly around approval or rejection of any given invoice. This is another example of generative and predictive AI working together to deliver new efficiencies for an organization. ## How it works The data from the internal invoicing system, like SAP Concur, gets into the organization’s database (information filled out by the employee submitting the invoice). This information is then fed into a predictive model that utilizes [unsupervised learning for anomaly detection](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/unsupervised/anomaly-detection.html) by comparing every invoice against historical data from previously labeled invoices (training data, where invoices are categorized as “anomalous” and “non-anomalous”). Using [Prediction Explanations](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/shap-predex.html), a generative AI model is instructed through a system prompt to summarize the predictions for any given transaction in a concise and human-readable format, which are then fed back into the original invoicing system where the analyst or the financial manager is able to review the findings and make the decision (reject or approve). The process augments invoicing by improving anomaly detection rates for invoices and explaining the anomalies to the people making the decisions. It eliminates a lot of the manual steps required to review each and every invoice, by automating most of the reasoning processes involved in the review process. Two simple Python files can easily orchestrate this integration through simple functions and hooks that will be executed each time an invoice requires a prediction and its consecutive analysis. The first file has the credentials to connect with the generative AI model and contains the prompt to summarize the explanations and insights derived from the predictive model. The second file easily orchestrates the whole predictive and generative pipeline through a few simple hooks. ## User experience The end user can interact with the invoice anomaly detection solution via the front end user interface. They consume the generated insights within their invoicing system to make the final decision on each individual invoice. Everything in the backend is handled by the predictive model and the generative AI solution. ## Why it might benefit your business By automating anomaly detection organizations can accelerate invoice processing workflows, reduce the human capital required to handle manual invoice reviews and minimize disruptions created by invoicing errors. The additional benefit of this process is improved communication with external parties, like employees submitting invoices. Fewer legitimate invoices are being flagged due to the predictive AI pipeline, while more illegitimate ones get through the review process. Those that do get flagged are accompanied by an appropriate narrative explaining the organization’s decision to reject the invoice. Depending on the size of the organization and its invoicing backlog, the solution can save dozens, hundreds, and even thousands of hours on invoice processing, while also saving the organization money by detecting more anomalous invoices. ## Potential risks Risks associated with this use case span both generative and predictive AI components of the solution. - An inaccurately flagged invoice may lead to an incorrect decision by the user if the system prompt for the generative AI is not fine-tuned to appropriately explain the prediction. In this case, a bad prediction may be masked by a bad summarization with generative AI. The output may look convincing and the users may choose to just trust it to make the decision. - A system prompt that’s not fine-tuned may lead to unconventionally worded and structured summaries for invoices, which complicates the review and may also impact the user experience. For example, the system outputs an explanation for the prediction that’s too long for the invoicing system to display. ### Baseline mitigation tactics - Custom metrics monitoring for the generative AI model that tracks text quality parameters, like readability and complexity. - Extensive pre-production testing of the solution, such as feature selection, prompt engineering, and various LLMs. This also requires a human in the loop, i.e. the end user should be involved in evaluating the quality of various pipelines to identify the optimal solution. Since such solutions are integrated with existing invoicing tools, it’s important to make sure that the output of the model fits the UI of the system. - A retraining regiment that uses grounding data to improve the model’s outputs. However, this requires a new process, where the analyst can amend the automed report, which is then fed into a vector database (new infrastructure). --- # Generate legal and compliance answers URL: https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-welcome/legal-compliance-genai.html > Learn how GenAI can address pain points for legal and compliance professionals in the exact context that these professionals require. # Generate legal and compliance answers ### Fast facts | Characteristic | Value | | --- | --- | | Ease of Implementation | Medium | | Impact | High | | Impact Type | Efficiency/ Optimization | | Primary Users | Internal | | Type | RAG | | Includes Predictive AI | No | ## What it is It’s hard to find more document-intensive processes than legal and compliance. Finding answers to complex questions about the law and policies can involve a lot of time spent combing through dense and complicated documents. Any uncertainty here can halt operational and business processes. This kind of work involves highly paid professionals, which additionally increases the costs, as such processes take up a lot of their valuable time. Generative AI can address this pain point for legal and compliance professionals to scour documentation and find answers to pressing questions, in the exact context that these professionals require. After retrieving these relevant chunks of information, generative AI constructs and delivers a legible answer that the user can utilize in their decision making. The added benefit of this automation is that the LLM can uncover additional insights from those documents, things that a person “grinding” through the documents might miss. ## How it works The process involves feeding legal and compliance documents into a vector database, which is then utilized by the LLM to retrieve information for the user, chatbot-style. Most organizations already have stores of legal documents, in places like Microsoft SharePoint, which can be used as the source of the information. An important part of the process is ensuring that all of the possible LLM and vector database parameters are tested thoroughly before deployment, given the specific nature of “legalese.” Things like chunking and embedding strategies need to be reviewed rigorously. In many RAG cases, a standard publicly available LLM, called via an API, could work. But since the information for legal and compliance purposes can be highly sensitive, a locally-hosted open source LLM will be a better, more secure choice. The overall workflow will benefit from a predictive AI guard model, designed to monitor outputs and deliver confidence scores for each response, while also blocking unwanted, hallucinated outputs. The lower the confidence score, the more attention the user needs to pay to the answer. The users can also rate the answers or edit them, which then can be sent back to the original vector database for future reference. ## User experience There are multiple ways of approaching the deployment of this solution, but the most common one is implementing the chatbot directly in the standard corporate communications environment, like Microsoft Teams. The user would open the chat window and start asking questions, since the vector database already stores most of the necessary legal documents. For example: “What are the liability rules in the EU AI Act?”, “What are the rules around filing of civil appeals in the appellate court of {insert_state}?”, and so on. For this to work seamlessly, an additional internal data pipeline could be built to ensure that new documents could be added to the database quickly. For example, an automated solution that scans the location of legal files and automatically adds new ones to the vector database. The responses come back according to the given system prompt settings (format, length, etc.). An important addition to the output here would be to automatically ask the model to link to specific source documents that it's referencing. This increases trust and streamlines the user review process even more. ## Why it might benefit your business Organizations spend a lot of resources or, even, pay a high hourly rate to send legal and compliance professionals searching through large libraries of information. Generative AI chatbots can significantly reduce the time and labor it takes to find relevant information, realizing cost savings while freeing up those professionals to focus on more important work. This approach also reduces the risk of information being overlooked, resulting in faster, more comprehensive answers to the most pressing legal and compliance questions. ## Potential risks There are numerous risks associated with this solution, given the sensitive nature of the information that’s being processed and outputted. - Inaccurate or off-topic responses (hallucinations) to toxic responses, as well as outputs that divulge sensitive information that other stakeholders shouldn’t have access to. - A system prompt that’s not fine-tuned may lead to unconventionally worded and structured responses, which don’t satisfy the user, leading to a lot of manual edits or direct and potentially costly errors. ### Baseline mitigation tactics - Custom metrics monitoring for toxicity, readability (Flesch Reading Score), as well as informative/ uninformative responses to ensure that the responses are appropriate. This also extends to operational custom metrics, like tokens/ cost monitoring to ensure the financial viability of the solution. - Guard models that prevent unwanted generative AI outputs, based on the response parameters or halt any potentially inaccurate answers from being outputted to the user. Accuracy is paramount when legal language is involved and guard models can ensure that. This is also important to ensure that the users are utilizing the tool appropriately, as the guardrails in place can prevent users from sending irrelevant prompts, thus ballooning the costs of the solution. - A feedback loop by which the user can rate the generated response and edit, if necessary. The edited version then gets added back to the vector database to inform future responses, thus improving the system as time goes on. - Extensive testing of the LLM, its parameters, like the system prompts or the vector database to ensure that responses require minimal oversight and don’t lead to answers that misrepresent legal or compliance norms. --- # RFPbot use case URL: https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-welcome/rfpbot-genai.html > RFPBot has a predictive and a generative component and was built entirely within DataRobot in the course of a single afternoon. # RFPbot use case The following example illustrates how DataRobot leverages its own technology-agnostic, scalable, extensible, flexible, and repeatable framework to build an end-to-end generative AI solution at scale. This article showcases a Request for Proposal Assistant named RFPBot. RFPBot has a predictive and a generative component and was built entirely within DataRobot in the course of a single afternoon. In the image below, notice the content that follows the paragraph of generated text. There are four links to references, five subscores from the audit model, and an instruction to up-vote or down-vote the response. RFPBot uses an organization’s internal data to help salespeople generate RFP responses in a fraction of the usual time. The speed increase is attributable to three sources: 1. The custom knowledge base underpinning the solution. This stands in for the experts that would otherwise be tapped to answer the RFP. 2. The use of Generative AI to write the prose. 3. Integration with the organization’s preferred consumption environment (Slack, in this case). RFPBot integrates best-of-breed components during development. Post-development the entire solution is monitored in real-time. RFPBot both showcases the framework itself and the power of combining generative and predictive AI more generally to deliver business results. Note that the concepts and processes are transferable to any other use case that requires accurate and complete written answers to detailed questions. ### Applying the framework Within each major framework component, there are many choices of tools and technology. When implementing the framework, any choice is possible at each stage. Because organizations want to use best-of-breed—and which technology is best-of-breed will change over time—what really matters is flexibility and interoperability in a rapidly changing tech landscape. The icons shown are among the current possibilities. RFPBot uses the following. Each choice at each stage in the framework is independent. The role of the DataRobot AI Platform is to orchestrate, govern, and monitor the whole solution. - Word, Excel, and Markdown files as source content. - An embedding model from Hugging Face (all-MiniLM-L6-v2) - Facebook AI Similarity Search (FAISS) Vector Database - ChatGPT 3.5 Turbo - A Logistic Regression - A Streamlit application - A Slack integration. Generative and predictive models work together. Users are actually interacting with two models each time they type a question—a Query Response Model and an Audit Model. - The Query Response Model is generative: It creates the answer to the query. - The Audit Model is predictive: It evaluates the correctness of the answer given as a predicted probability. The citations listed as resources in the RFPBot example are citations of internal documents drawn from the knowledge base. The knowledge base was created by applying an [embedding model](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#embeddings) to a set of documents and files and storing the result in a [vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html). This step solves the problem of LLMs being stuck in time and lacking the context from private data. When a user queries RFPBot, context-specific information drawn from the knowledge base is made available to the LLM and shown to the user as a source for the generation. ### Orchestration and monitoring The entirety of the end-to-end solution integrating best-of-breed components is built in a DataRobot-hosted notebook, which has enterprise security, sharing, and version control. Once built, the solution is monitored using standard and [custom-defined metrics](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html). By abstracting away infrastructure and environment management tasks, a single person can create an application such as RFPBot in hours or days, not weeks or months. By using an open, extensible platform for developing GenAI applications and following a repeatable framework organizations avoid vendor lock-in and the accumulation of technical debt. They also vastly simplify model lifecycle management by being able to upgrade and replace individual components within the framework over time. Watch a video of creating RFPbot on [YouTube](https://www.youtube.com/watch?v=cOV5dss8xo0&t=2s). --- # Suspicious activity reporting (SAR) URL: https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-welcome/sar-genai.html > Combine existing predictive AI workflows around suspicious activity monitoring with a GenAI component to streamline the reporting process. # Suspicious activity reporting (SAR) | Characteristic | Value | | --- | --- | | Ease of Implementation | Low | | Impact | High | | Impact Type | Efficiency/ Optimization | | Primary Users | Internal | | Type | RAG, summarization | | Includes Predictive AI | Yes | ## What it is Financial organizations monitor suspicious activity as part of the industry’s regulatory framework. The industry is already using predictive AI to improve accuracy of suspicious activity detection, but the suspicious activity reports (SARs) that have to be prepared for each incident still require a lot of scrupulous work. Fraud analysts spend a lot of time preparing these reports for delivery to regulators. By combining the existing predictive AI workflows around suspicious activity monitoring with a generative AI component, the reporting process can be streamlined, improving the efficiency of Fraud/BSA/AML analysts. The existing predictive AI system flags a transaction as suspicious based on its parameters (amount of money, location, type of transaction, venue, etc.) and serves it to the fraud analyst. The analyst reviews the transaction and related context, such as previous alert history and check images, to determine whether it’s fraud. They instruct the generative AI application tied into the predictive AI workflow to automatically create the necessary, case-specific narrative for the report whether it’s determined to be fraud or not. ## How it works [Prediction Explanations](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/shap-predex.html) (a quantitative indicator of the effect variables have on the predictions) from the predictive model are fed to the generative AI model. This data is formatted as a JSON dictionary for the generative AI model to process. As such, there’s no vector database that the generative AI model interacts with. The JSON file is the source of truth for the LLM. A system prompt in the background controls the format of the output, based on the predefined template for how the financial institution needs to format their reports. The simplified transformation of predictive insights to the narrative within the report looks like this: variable A exceeds the threshold of X, thus it indicates this transaction as fraudulent. The analyst can then review the report and make necessary amendments, like elaborate on certain values that the predictive model highlighted, before passing the report along to the Financial Crimes Enforcement Network (FinCEN), which then routes it to the appropriate law enforcement agency for further investigation. ## User experience The fraud investigator in this scenario would interact with a pre-built application which connects to their existing suspicious activity alerting system, retrieves known relevant context such as transaction history and check images, and generates output from a separate predictive model based on the results of the alerting system. For each alert that has been flagged, the analyst would read a summary, created by generative AI, in natural language, detailing why an alert was tripped, its likelihood to truly be suspicious activity, and a compilation of pre-prepared relevant documents. Using human expertise and potentially seeking out additional information, the analyst reviews the case and makes the final determination on whether any given alert was correct or not. Once the analyst has confirmed the decision, the generative AI will construct the final narrative using all information given and format it into the predefined template that is consistent for the bank, explaining why a certain alert was truly suspicious or not. Once the report is generated within the app, the user can review it, make amendments if needed, and then generate the file in the format necessary for the reporting workflow. This file is then submitted into a different system, tied into FinCEN. ## Why it might benefit your business A single suspicious activity report can take hours to write, review, and finalize, especially due to the variety of all the information that requires review. Large financial institutions receive tens of thousands of suspicious activity alerts every day. So even a small improvement in how much time it might take to process one report, can have an incredibly powerful cumulative effect that can save tens of thousands of work hours. This also allows analysts to devote more of their time to actual investigation, not being pushed by the growing backlog of alerts to review, which improves their performance and minimizes human error. A reduction in processing time would also expedite approval for legitimate transactions. ## Potential risks Risks associated with this use case span both generative and predictive AI components of the solution. - Inaccurate flagging of transactions can result in inaccurate reports. - Generated reports may misrepresent predictive data or draw incorrect conclusions. - A poorly tuned system prompt can produce reports with unconventional wording and structure, requiring extensive manual amendments by analysts. ### Baseline mitigation tactics - Model monitoring for the predictive solution to ensure that it flags only the most relevant transactions. - Extensive pre-production testing of the LLM, its parameters, like the system prompts or the temperature of responses. - Consider a retraining regiment that uses grounding data to improve the model’s outputs. This might require a new process, where the user can amend the automated report, which is then fed into a retraining database. --- # GenAI workflow overview URL: https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-workflow.html > Review a generalized discussion of the generative LLM building workflow—vector databases, building and comparing LLM blueprints, and adding evaluation metrics. # GenAI workflow overview This section provides a generalized discussion of the generative LLM building workflow, which can include: - Creating and versioning vector databases . - Creating LLM blueprints . - Chatting with and compare LLM blueprints. - Applying evaluation metrics and creating compliance tests . - Preparing LLM blueprints for deployment . > [!TIP] Tip > For a hands-on experience, try the [GenAI how-to](https://docs.datarobot.com/en/docs/get-started/how-to/genai-walk-basic.html). See the [full documentation](https://docs.datarobot.com/en/docs/agentic-ai/index.html) for information on using your own data and LLMs, working with code instead of the UI, and working with NVIDIA NIM. ## Get started It all begins by [creating a Use Case](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/build-usecase.html) and [adding a RAG playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html#add-a-playground). A playground is a dedicated LLM-focused experimentation environment within Workbench, where you can build, review, compare, evaluate, and deploy. ## Create a vector database Once your playground is set up, optionally [add a vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/index.html). The role of the vector database is to enrich the prompt with relevant context before it is sent to the LLM. When creating a vector database, you: - Choose a provider. - Add data. - Set a basic configuration and text chunking details. [Vector databases can be versioned](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-versions.html#create-a-version) to make sure the most up-to-date data is available to ground LLM responses. ## Build LLM blueprints An LLM blueprint represents the full context for what is needed to generate a response from an LLM; the resulting output is what can then be compared within the playground. When you click to [create an LLM blueprint](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html), the playground opens. Select an LLM to get started and then set the configuration options. In the configuration panel, optionally add a vector database and set the prompting strategy. After you save, the new LLM blueprint is listed on the left. ## Chat and compare LLM blueprints Once the LLM blueprint configuration is saved, try sending it prompts (rag-chatting) to determine whether further refinements are needed. Then, add several blueprints to use the [comparison tool](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html) to test different LLM blueprints using the same prompt. This helps pick the best LLM blueprint for deployment. See the [best practices for prompt engineering](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/prompting-reference.html#best-practices-for-prompt-engineering) when chatting and doing comparisons. ## Use LLM evaluation tools Using [metrics and compliance tests](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html), DataRobot monitors how models are used in production, intervening and blocking bad outputs. Add metrics before or after configuring LLM blueprints: **Before:** [https://docs.datarobot.com/en/docs/images/gen-fund-9.png](https://docs.datarobot.com/en/docs/images/gen-fund-9.png) **After:** [https://docs.datarobot.com/en/docs/images/gen-fund-10.png](https://docs.datarobot.com/en/docs/images/gen-fund-10.png) Add evaluation datasets, or generate a synthetic dataset from within DataRobot, to create a systematic assessment of how well the model performs for its intended tasks. Combine evaluation metrics and an evaluation dataset to automate the detection of compliance issues through test prompt scenarios. Use DataRobot-supplied evaluations or create your own. ## Deploy an LLM Once you are satisfied with the LLM blueprint, you can [send it](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/deploy-llm.html) to the Registry's workshop from the playground. The [Registry workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/index.html) is where you test the LLM custom model and ultimately deploy it to [Console](https://docs.datarobot.com/en/docs/workbench/nxt-console/index.html), a centralized hub for monitoring and model management. ## What's next? - Try it - Watch it - Build your own --- # Start with GenAI URL: https://docs.datarobot.com/en/docs/get-started/day0/genai-start/index.html > Learn the basics of working with GenAI in DataRobot—an overview of the fundamentals and a generalized workflow. # Start with GenAI The following sections provide details for getting started with GenAI. See the full [UI](https://docs.datarobot.com/en/docs/agentic-ai/index.html) and API ( [REST](https://docs.datarobot.com/en/docs/api/reference/public-api/tag-genai.html) and [Python](https://docs.datarobot.com/en/docs/api/reference/sdk/tag-genai.html)) documentation for more information. | Topic | Description | | --- | --- | | GenAI fundamentals | Learn the basics of building a GenAI workflow—connect to your data, build and compare LLM blueprints, automate compliance, register and deploy. | | Generative workflow overview | Review a generalized discussion of the generative LLM building workflow—vector databases, building and comparing LLM blueprints, and adding evaluation metrics. | | Sample use cases | Review sample GenAI use cases that help illustrate a variety of common business applications. | --- # Start with Agent Assist URL: https://docs.datarobot.com/en/docs/get-started/day0/gs-agent-assist.html > Agent Assist (dr-assist) is an interactive AI assistant that helps users design, code, and deploy AI agents through natural conversation. # Start with Agent Assist DataRobot Agent Assist ( `dr-assist`) is an interactive AI assistant optimized for the development of AI agents. It helps users design, code, and deploy agents through natural conversation—users describe the agent they want, and the assistant helps build it on the foundation provided by the [Agentic Starter application template](https://github.com/datarobot-community/datarobot-agent-application). DataRobot Agent Assist integrates with the [DataRobot CLI](https://docs.datarobot.com/en/docs/agentic-ai/cli/index.html) as a plugin and uses the [DataRobot LLM gateway](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/dr-llm-gateway.html) for model access. During the design and code cycle, Agent Assist can outline which tools an agent should call based on the proposed functionality—for straightforward tools, it can implement the tool code; for more complex tools (such as those that consume API tokens or write to a database), it can scaffold the initial file structure for the human-in-the-loop to complete in the editor or development environment of their choice. > [!WARNING] Run dr assist in an empty directory > Only run `dr assist` from a dedicated and empty directory. Running this command in a directory containing code or other files is unsafe. When you use the agent assist coding workflow, the assistant clones the DataRobot Agent Application Template repository into the current directory. This action can overwrite or conflict with existing files, damaging the existing project and degrading the accuracy of the assistant's output. Before running `dr assist`, if you're not in a dedicated directory, create one and open the terminal there (for example, `mkdir my-agent && cd my-agent`, then run `dr assist`). DataRobot Agent Assist can: - Design AI agents by helping users think through specifications, ask clarifying questions, and produce an agent specification file ( agent_spec.md ). - Research solutions using file search and analysis (an internal agent can read files, list directories, grep, and glob). - Code AI agents by loading an existing agent_spec.md , cloning the DataRobot agent template repository, and implementing the agent with file edits and shell commands. - Simulate an agent from a specification before coding. In this simulation , the model chooses tools and arguments, but tool calls are not executed. Returns are generated by the LLM so you can validate design (which tools, I/O shapes, model behavior) without calling real deployments or datasets. - Deploy agents to DataRobot following the template’s deployment instructions. | Page | Description | | --- | --- | | Prerequisites and installation | System requirements, required tools and versions, installing the plugin or running standalone, verifying installation. | | Workflows and prompting | Welcome screen, slash commands, Design / Code / Deploy workflows, prompting tips. | | Environment and commands reference | Environment variables table, files and directories, slash commands. | | Troubleshooting | Plugin not discovered, dependency check failed, authentication errors, template bootstrap, LLM API errors, session interruption, and related fixes. | --- # First time here? URL: https://docs.datarobot.com/en/docs/get-started/day0/index.html > Get started with DataRobot's AI Platform. After a quick orientation, jump into suggested learning exercises or browse sample assets and tutorials. # First time here? > [!NOTE] Note > Prefer to learn with video? Visit the [video learning page](https://docs.datarobot.com/en/docs/get-started/day0/video-learning.html) for a list of videos with brief descriptions. Or, go straight to YouTube to view the [onboarding series](https://www.youtube.com/watch?v=LaQCLRX64qE&list=PLe-6XGmzriIjU4DyqNpKYZ6Eb_liRDhaV) to help get started with DataRobot. The DataRobot Agentic AI Platform seamlessly orchestrates the full lifecycle of AI agents across a wide range of deployment environments, including on-prem and private GPU clouds, sovereign GPU clouds, and major public cloud platforms. Optimized for the next-generation GPU architecture and tightly integrated with NVIDIA pre-built blueprints, DataRobot enables faster, more secure deployment of AI solutions that drive real business impact. The DataRobot interface provides a seamless transition from model experimentation in Workbench, to registering and managing models in Registry, to monitoring model deployments in Console. Key Highlights: - End-to-End orchestration and lifecycle management: Develop, deliver, and govern AI agents seamlessly. - Deployment flexibility: Available across cloud partners, sovereign GPU clouds, on-prem/private GPU clouds, and major public clouds. - NVIDIA-powered performance: Optimized for Blackwell GPUs, integrated with NIM and NeMo, with blueprints for rapid deployment. - Enterprise-grade governance: Ensures secure, compliant, and scalable AI deployments. - Validated design: Delivers streamlined, production-ready AI. ## Develop, deliver, govern 1. Build LLM or predictive blueprints and tune models inWorkbench. What is Workbench?Workbenchis an experiment-based user interface optimized to support iterative workflow. It enables users to group and share everything they need to solve a specific problem from a single location. Workbench is organized by Use Case, and each Use Case contains zero or more datasets, vector databases, playgrounds, models, notebooks, and applications. What is a blueprint?A blueprint is a graphical representation of the many steps involved in transforming input predictors and targets into a model. It represents the high-level end-to-end procedure for fitting the model, including any preprocessing steps, algorithms, and post-processing. Each box in a blueprint may represent multiple steps. You can view thegraphical representationof a blueprint by clicking on a model on the Leaderboard. 2. Register and manage deployment-ready model packages inRegistry. What is Registry?Registryis a centralized location where you access versioned, deployment-ready model packages. From there, you can create custom models and jobs, generate compliance documentation, and deploy models to production. 3. Monitor deployment activity from theConsolemanagement dashboard. What is Console?Consoleis a central hub for deployment management activity. Its dashboard provides access to deployed models for further monitoring and mitigation. It also provides access to prediction activities and allows you to view, create, edit, delete, or share serverless and external prediction environments To get started quickly in the DataRobot AI Platform, take a look at the overview and then jump directly into the two suggested learning exercises. Afterwards, continue your learning with more sample assets and tutorials. | Topic | Description | | --- | --- | | DataRobot AI Platform overview | Get a quick overview of the fundamentals of the DataRobot AI Platform: what it is, why you need it, and which user path to get started on. | | Foundational apps | Learn the basics of Foundational apps—DataRobot's solution to the challenge of scaling AI from local experiments to production systems. | | Start with Agent Assist | Learn about Agent Assist (dr-assist), an interactive AI assistant that helps users design, code, and deploy AI agents through natural conversation. | | Start with GenAI | Learn the basics of building a GenAI workflow—an overview of fundamental concepts and sample workflow. | | Start with predictive modeling | Understand the types of modeling projects you can create in DataRobot and review a generalized discussion of the steps to build predictive models. | | Suggested first steps | Try a generative or predictive walkthrough to get started. | | Sample assets | View sample assets by type with links to corresponding tutorials. | | Video learning | See a list of onboarding videos, with brief descriptions, to help get started with DataRobot. | | Trial FAQ | Questions and answers about the DataRobot Self-Service SaaS trial. | --- # DataRobot AI Platform overview URL: https://docs.datarobot.com/en/docs/get-started/day0/orientation.html > Learn how the DataRobot application is organized and which product areas you'll use for specific AI workflows. # DataRobot AI Platform overview The DataRobot AI Platform, organized by stages in the AI lifecycle, provides a unified experience for developing, delivering, and governing enterprise AI solutions. It is available on managed SaaS, virtual private cloud, or self-managed infrastructure. ### Develop Building great AI solutions—both generative and predictive models and agentic AI solutions—requires a lot of experimentation. Use [Workbench](https://docs.datarobot.com/en/docs/get-started/day0/predai-start/wb-overview.html) to quickly experiment, easily compare across experiments, and organize all your experiment assets in an intuitive Use Case container. ### Deliver Use [Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/index.html) to create deployment-ready model packages and generate compliance documentation for the purposes of enterprise governance. Registry ensures that all your AI assets are documented and under version control. With test results and metadata stored alongside each AI asset, you can deploy models to production with confidence, regardless of model origin—whether DataRobot models, user-defined models, or external models. ### Govern Use [Console](https://docs.datarobot.com/en/docs/workbench/nxt-console/index.html) to view the operating status of each deployed model. As your organization becomes more AI-driven, you’ll have tens or even hundreds of task-specific models. Console provides a centralized hub for observing the performance of these models, allowing you to configure numerous automated intervention and notification options to keep things running smoothly. ### One-click deployments But where do deployments fit into the structure above?The deployment process is your model's seamless transition from Registry to Console. Once the model is registered and tested in Registry, you’ll have the option to deploy it with a single click. Once you click to deploy, DataRobot’s automation creates an API endpoint for your model, in your selected prediction environment, and configures all the observability and monitoring. All four of these deployment options are supported: - A DataRobot model to a DataRobot serverless or dedicated prediction server. - A DataRobot model to an external prediction server. - A custom model to a DataRobot serverless or dedicated prediction server. - A custom model to an external prediction server. ### Generative AI DataRobot's generative AI offering builds off of DataRobot's predictive AI experience to enable you to bring your favorite libraries, choose your LLMs, and integrate third-party tools. You can embed or deploy AI wherever it will drive value for your business, and leverage built-in governance for each asset in the pipeline. Inside DataRobot there is very little distinction between generative and predictive AI. But, you will find that many tutorials and examples are organized along these lines. - Predictive AI includes time series, classification, regression, and unsupervised machine learning such as anomaly detection and clustering. - Generative AI (GenAI) includes text and image generation using foundational models. [Generative AI in DataRobot](https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-fundamentals.html) ### Applications DataRobot offers various approaches for building applications that allow you to share machine learning projects: custom applications, application templates, and no-code applications. - Custom applications are a simple method for building and running custom code. - Customizable application templates assist you by programmatically generating DataRobot resources that support your use case. - No-code applications enable core DataRobot services without having to build and evaluate models. [Experiment with the Talk to my Data Agent application template](https://docs.datarobot.com/en/docs/get-started/how-to/talk-data-walk.html) ## Which experience should you choose? When working in DataRobot, you have two interface choices: 1. Log into the platform via web browser and work with the graphical user interface (UI) . 2. Access the platform programmatically with the REST API or Python client packages. If you're leaning towards using DataRobot programmatically, it's recommended that you explore the workflows in the UI first. DataRobot is committed to full accessibility in both interfaces, so you are not locked into a single choice. Know that you can: - Flexibly switch between code and UI at any time. - Seamlessly collaborate with other users who are working with a different option. - When in code, use any development environment of your choice. ### NextGen An intuitive UI-based product comprised of Workbench for experiment-based iterative workflows, Registry for model evolution tracking and the centralized management of versioned models, and Console for monitoring and managing deployed models. NextGen provides a complete AI lifecycle platform, leveraging machine learning that has broad interoperability and end-to-end capabilities for ML experimentation and production. It is also the gateway for creating GenAI experiments, Notebooks, and applications. [Try a basic predictive walkthrough](https://docs.datarobot.com/en/docs/get-started/how-to/build-walk.html) ### Code A programmatic alternative for accessing DataRobot using the REST API or Python client packages. [See the API quickstart](https://docs.datarobot.com/en/docs/api/dev-learning/api-quickstart.html) ## Where to get help Help is everywhere you look: **Read the docs:** Onboarding resources provided in [Get started](https://docs.datarobot.com/en/get-started) are a small subset of all the available content. Try exploring the introductory tutorials and labs in this section before moving into the general reference sections. **Contact Support:** Email [DataRobot support](mailto:support@datarobot.com) to ask a question or report a problem. Existing customers can also visit the [Customer Support Portal](https://support.datarobot.com/login). **Contact your account team:** Your designated Customer Success Manager and/or Applied AI Expert are available to offer consultative advice, share best practices, and get you on the path to AI value with DataRobot. ## Next steps So what's next?The best way to learn DataRobot is hands-on. We recommend taking an hour to complete these [two suggested exercises](https://docs.datarobot.com/en/docs/get-started/day0/first-steps.html). Or, for more overview, watch [this rapid tour video](https://www.youtube.com/watch?v=o7Z2gHuBplY). It demonstrates: - Five major data types—numerics, categorical, text, geospatial, and images. - Nine major problem types—classification, regression, clustering, multilabel, anomaly detection, forecasting, time series clustering, time series anomaly detection, and generative AI. - More than 40 modeling techniques that are specific to each problem type. Each quick experiment demo was built with DataRobot's automation and results in a fully deployable machine learning pipeline. --- # Start with predictive modeling URL: https://docs.datarobot.com/en/docs/get-started/day0/predai-start/index.html > Understand the types of modeling projects you can build and see a generalized overview of the modeling process. # Start with predictive modeling The following sections provide details for getting started with predictive modeling. See the full [UI](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/index.html) and API ( [REST](https://docs.datarobot.com/en/docs/api/reference/public-api/index.html) and [Python](https://docs.datarobot.com/en/docs/api/reference/sdk/index.html)) documentation for more information. | Topic | Description | | --- | --- | | Predictive fundamentals | Understand the types of modeling projects you can create in DataRobot. Learn the general process of modeling, analyzing, and selecting models for deployment. | | Workbench overview | Learn the value and benefits of Workbench, as a generational leap from DataRobot Classic, as well as its components and architecture. | | Predictive workflow overview | Review a generalized discussion of the steps to build predictive models. | | Sample use cases | Read some 10,000-foot descriptions of how DataRobot customers work with predictive AI. | --- # Fundamentals of predictive modeling URL: https://docs.datarobot.com/en/docs/get-started/day0/predai-start/pred-fundamentals.html > Learn about modeling methods supported in DataRobot's predictive modeling, as well as the modeling lifecycle. # Fundamentals of predictive modeling This section describes DataRobot's predictive solutions; see [GenAI fundamentals](https://docs.datarobot.com/en/docs/get-started/day0/genai-start/genai-fundamentals.html) for an overview of working with generative AI-related tools and options. Predicitve AI uses automated machine learning (AutoML) to build models that solve real-world problems across domains and industries. DataRobot takes the data you provide, generates multiple machine learning (ML) models, and recommends the best model to put into use. You don't need to be a data scientist to build ML models using DataRobot, but an understanding of the basics will help you build better models. Your domain knowledge and DataRobot's AI expertise will lead to successful models that solve problems with speed and accuracy. DataRobot supports many different approaches to ML modeling—supervised learning, unsupervised learning, time series modeling, segmented modeling, multimodal modeling, and more. This section describes these approaches and also provides tips for analyzing and selecting the best models for deployment. This section describes predictive modeling methods. See the Workbench [predictive model training overview](https://docs.datarobot.com/en/docs/get-started/day0/predai-start/pred-workflow.html) for a generalized discussion of the steps to build predictive models. ## Predictive modeling methods ML modeling is the process of developing algorithms that learn by example from historical data. These algorithms predict outcomes and uncover patterns not easily discerned. DataRobot supports a variety of modeling methods, each suiting a specific type of data and problem type. ### Supervised and unsupervised learning The most basic form of machine learning is supervised learning. With supervised learning, you provide "labeled" data. A label in a dataset provides information to help the algorithm learn from the data. The label—also called the target —is what you're trying to predict. - In aregressionexperiment, the target is a numeric value. A regression model estimates a continuous dependent variable given a list of input variables (also referred to asfeaturesorcolumns). Examples of regression problems include financial forecasting, time series forecasting, maintenance scheduling, and weather analysis. Regression experiments can also be handled as classification by changing the target type from numeric to classification. - In aclassificationexperiment, the target is a category. A classification model groups observations into categories by identifying shared characteristics of certain classes. It compares those characteristics to the data you're classifying and estimates how likely it is that the observation belongs to a particular class. Classification experiments can bebinary(two classes) ormulticlass(three or more classes). For classification, DataRobot also supportsmultilabel modelingwhere the target feature has a variable number of classes orlabels; each row of the dataset is associated with one, several, or zero labels. Another form of machine learning is unsupervised learning. With unsupervised learning, the dataset is unlabeled and the algorithm must infer patterns in the data. - In ananomaly detectionexperiment, the algorithm detects unusual data points in your dataset. Potential uses include the detection of fraudulent transactions, faults in hardware, and human error during data entry. - In aclusteringexperiment, the algorithm splits the dataset into groups according to similarity. Clustering is useful for gaining intuition about your data. The clusters can also help label your data so that you can then use a supervised learning method on the dataset. ### Time-aware modeling Time data is a crucial component in solving prediction and forecasting problems. Models using time-relevant data make row-by-row predictions, time series forecasts, or current value predictions ("nowcasts"). An experiment becomes time-aware when, if the data is appropriate, the partitioning method is set to date/time. - Withtime series modeling, you can generate a forecast—a series of predictions for a period of time in the future. You train time series models on past data to predict future events. Predict a range of values in the future or usenowcastingto make a prediction at the current point in time. Use cases for time series modeling include predicting pricing and demand in domains such as finance, healthcare, and retail—basically, any domain where problems have a time component. - You can use time series modeling for a dataset containing a single series, but you can also build a model for a dataset that contains multiple series. For this type ofmultiseriesexperiment, one feature serves as theseries identifier. An example is a "store location" identifier that essentially divides the dataset into multiple series, one for each location. So you might have four store locations (e.g., Paris, Milan, Dubai, and Tokyo) and therefore four series for modeling. - With a multiseries experiment, you can choose to generate a model for each series usingsegmented modeling. In this case, DataRobot creates a deployment using the best model for each segment. - Sometimes, the dataset for the problem you're solving contains date and time information, but instead of generating a forecast as you do with time series modeling, you predict a target value on each individual row. This approach is calledtime-aware predictions. See [What is time-aware modeling?](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/whatis-time.html) for an in-depth discussion of these strategies. ### Specialized modeling workflows DataRobot provides specialized workflows to help you address a wide range of problems. - Image augmentationallows you to include images as features in your datasets. Use the image data alongside other data types to improve outcomes for various types of modeling experiments—regression, classification, anomaly detection, clustering, and more. - Witheditable blueprints, you can build and edit your own ML blueprints—the preprocessing steps (tasks), modeling algorithms, and post-processing steps that go into building a model—incorporating DataRobot preprocessing and modeling algorithms, as well as your own models. - For text features in your data, use Text AI insights likeWord Cloudsto understand their impact. - Location AIsupports geospatial analysis of modeling data. Use geospatial features to gain insights and visualize data using interactive maps before and after modeling. See the [generalized discussion](https://docs.datarobot.com/en/docs/get-started/day0/predai-start/pred-workflow.html) of the steps to build predictive models in Workbench. Or, try it yourself with the [model building how-to](https://docs.datarobot.com/en/docs/get-started/how-to/build-walk.html). --- # Predictive workflow overview URL: https://docs.datarobot.com/en/docs/get-started/day0/predai-start/pred-workflow.html > A generalized discussion of the steps to build predictive models in Workbench. # Predictive workflow overview This section provides a generalized discussion of the steps to build predictive models in Workbench. See the [fundamentals of predictive modeling](https://docs.datarobot.com/en/docs/get-started/day0/predai-start/pred-fundamentals.html) for a description of predictive modeling methods. ## Predictive model training workflow This section walks you through the steps to implement a DataRobot modeling experiment. 1. To begin the modeling process,import your dataorwrangle your datato provide a seamless, scalable, and secure way to access and transform data for modeling. 2. DataRobot conducts the first stage ofexploratory data analysis, (EDA1), where it analyzes data features. When registration is complete, theData previewtab shows feature details, including a histogram and summary statistics. 3. Next, for supervised modeling,select yourtargetand optionally change any other basic or advanced experiment configuration settings. Then,start modeling. DataRobot generatesfeature listsfrom which to build models. By default, it uses the feature list with the most informative features. Alternatively, you can select different generated feature lists orcreate your own. 4. DataRobot further evaluates the data during EDA2, determining which features correlate to the target (feature importance) and which features are informative, among other information. The application performsfeature engineering—transforming, generating, and reducing the feature set depending on the experiment type and selected settings. 5. DataRobot selectsblueprintsbased on the experiment type and builds candidate models. ## Analyze and select a model DataRobot automatically generates models and displays them on the [Leaderboard](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/leaderboard.html). The most accurate model is selected and trained on 100% of the data and is marked with the Prepared for Deployment badge. To analyze and select a model: 1. Compare models by selecting anoptimization metricfrom theMetricdropdown. 2. Analyze the model using the visualization tools that are best suited for the type of model you are building. Usemodel comparisonfor experiments within a single Use Case. See thelist of experiment types and associated visualizationsbelow. 3. Experiment with modeling settings to potentially improve the accuracy of your model. You can tryrerunning modelingusing a different feature list or modeling mode. 4. After analyzing your models, select the best andsend it to Registryto create a deployment-ready model package. TipIt's recommended that youtest predictionsbefore deploying. If you aren't satisfied with the results, you can revisit the modeling process and further experiment with feature lists and optimization settings. You might also find that gathering more informative data features can improve outcomes. 5. As part of the deployment process, youmake predictions. You can alsoset up a recurring batch prediction job. 6. DataRobot uses a variety of metrics tomonitoryour deployment. Use the application's visualizations to track data (feature) drift, accuracy, bias, service health, and many more. You can set up notifications so that you are regularly informed of the model's status. TipConsider enablingautomatic retrainingto automate an end-to-end workflow. With automatic retraining, DataRobot regularly testschallenger modelsagainst the current best model (thechampion model) and replaces the champion if a challenger outperforms it. ## Which visualizations should you use? Model insights help to interpret, explain, and validate what drives a model’s predictions. They are then used to assess what to do in your next experiment. While there are many visualizations available, not all are applicable to all modeling experiments—the visualizations you can access depend on your experiment type. The following table lists experiment types and examples of visualizations that are suited to their analysis. See the [full list of insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/index.html) to learn what you can access from your experiment's Leaderboard. | Experiment type | Analysis tools | | --- | --- | | All models | Feature Impact: Provides a high-level visualization that identifies which features are most strongly driving model decisions.Feature Effects: Visualizes the effect of changes in the value of each feature on the model’s predictions.Individual Prediction Explanations: Illustrates what drives predictions on a row-by-row basis, answering why a given model made a certain prediction. | | Regression | Lift Chart: Shows how well a model segments the target population and how capable it is of predicting the target. Residuals plot: Depicts the predictive performance and validity of a regression model by showing how linearly your models scale relative to the actual values of the dataset used. | | Classification | ROC Curve: Explores classification, performance, and statistics related to a selected model at any point on the probability scale. Confusion Matrix (binary experiments): Compares actual data values with predicted data values in binary experiments.Confusion Matrix (multiclass experiments): Compares actual data values with predicted data values in multiclass experiments. | | Time-aware modeling (time series and out-of-time validation) | Accuracy Over Time: Visualizes how predictions change over time.Forecast vs Actual: Compares how different predictions behave at different forecast points to different times in the future.Forecasting Accuracy: Provides a visual indicator of how well a model predicts at each forecast distance in the experiment’s forecast window.Stability: Provides an at-a-glance summary of how well a model performs on different backtests.Over Time chart: Identifies trends and potential gaps in your data by visualizing how features change over the primary date/time feature. The feature-over-time histogram displays once you select the ordering feature. | | Multiseries | Series Insights: Provides a histogram and table for series-specific information. | | Segmented modeling | Segmentation tab: Displays data about each segment of a Combined Model. | | Multilabel modeling | Metric values: Summarizes performance across labels for different values of the prediction threshold (which can be set from the page). | | Image augmentation | Image Embeddings: Projects images in two dimensions to see visual similarity between a subset of images and help identify outliers. Attention Maps: Highlights regions of an image according to its importance to a model's prediction.Neural Network VisualizerView a visual breakdown of each layer in the model's neural network. | | Text AI | Word Cloud: Visualizes variable keyword relevancy.Text Mining: Visualizes relevancy of words and short phrases. | | Geospatial AI | Anomaly Over Space: Displays anomalous score values on a unique map based on the validation partition. Accuracy Over Space: Provides a spatial residual mapping within an individual model. | | Clustering | Cluster Insights: Captures latent features in your data, surfacing and communicating actionable insights and identifying segments for further modeling. [Image Embeddings]/ml-image-embeddings){ target=_blank }: Displays a experimention of images onto a two-dimensional space defined by similarity. Attention Maps: Visualizes areas of images that a model is using when making predictions. | | Anomaly detection | Anomaly Over Time: Plots how anomalies occur across the timeline of your data . Anomaly Assessment: Plots data for the selected backtest and provides SHAP explanations for up to 500 anomalous points. | --- # Sample use cases URL: https://docs.datarobot.com/en/docs/get-started/day0/predai-start/sample-uses.html > Some 10,000-foot descriptions of how some of DataRobot customers work with predictive AI. # Sample use cases DataRobot was designed to unify complex enterprise environments across a variety of industries. The following descriptions provide 10,000-foot descriptions of how some DataRobot customers work with predictive AI. The fast facts reflect actual usage statistics from 2023. ## Insurance ### Use case 1 A large multinational insurance company headquartered in Brussels uses DataRobot across the business for a number of use cases including within fraud detection, claims processing, and underwriting. For model development they are very actively experimenting, testing models, and performing explainability checks on the DataRobot platform. Using DataRobot they are able to bring these models successfully through their risk management process and once in production have realized massive value. Fast facts - Projects/experiments: 1,438 - ML models built: 29,410 - Models in production: 4,436 - Predictions: 524 million (average of 43 million per month) ### Use case 2 A multinational insurer uses DataRobot in EMEA and Japan at considerable scale. DataRobot is key for their retention use cases, lead scoring and underwriting practice across the business. Fast facts - Projects/experiments: 682 - ML models built: 31,720 - Models in production: 1456 - Predictions: 258M prediction rows from 8804 prediction requests ### Use case 3 A large re-insurer is impressed with how DataRobot removes friction from building models and serving predictions. Use cases include predicting unpaid invoices to optimize overdue invoice collections and predicting market prices, taking quotes from quote aggregators to determine when competitors implemented pricing changes. They have used DataRobot's managed SaaS product, so they do not have to think about compute availability and can instead focus on data science outcomes for 157 users. Fast facts/2023 - Projects/experiments: 14,481 - ML models built: 177,475 - Predictions: 1.991 Billion on DataRobot's high-performance prediction servers ## Healthcare A global pharmaceuticals giant uses DataRobot to improve business efficiency. They use DataRobot to: - Forecast demand in North America and EMEA - Predict propensity to buy in the US - Predict IT tickets - Perform content recommendations. Fast facts - Projects/experiments: 3000+ per month - ML models built: 30,000+ per month - Models in production: 150 - Predictions: 3,000-7,000 per quarter ## Manufacturing One of the world’s largest building materials manufacturers uses DataRobot. They have more than 2000 plants globally and employ 60k+ employees. They deploy models to production in 60 of their manufacturing plants in order to predict equipment failure: kilns, fans, vertical roller mills, crushers etc. They leverage DataRobot MLOps possibility to deploy models outside of the platform in air-gapped environments, deploying using the Portable Prediction Server container to edge devices on-premise. Their predictive maintenance use cases help avoid stoppages along their production lines--critical because any stoppage leads to significant financial losses. Fast facts - Projects/experiments: 1000+ - ML models built: 15,000+ - Models in production: 150 ## Retail ### Use case 1 A freight company uses DataRobot to assist their freight, supply chain, and forwarding businesses, with 170+ active users on the platform from multiple divisions. They use DataRobot to forecast incoming calls and improve workforce planning, predict possible thefts in parcel centers, forecast total volume of packages entering certain countries, and predict financial KPIs. They have seen how the experiments and models built on DataRobot outperform forecasting models built outside the platform. They have experienced a 50%+ error reduction on key use cases. Accuracy improvements have allowed them to move from monthly to weekly—and even daily—predictions with granular breakdowns. Fast facts - Models in production: 200-250 models ### Use case 2 One of Europe’s largest media companies has been a DataRobot customer since 2018. They use their deployed models for demand optimization, targeted advertising, and content management. They recently extended their prediction environment to support large-scale batch predictions for audience segmentation and ad targeting. The team is leveraging the DataRobot/Snowflake integration capabilities to maintain a computationally intensive predictive pipeline and complete weekly scoring on time. DataRobot enables the small data science team to work more efficiently and with greater accuracy, bringing models live faster than before and achieving incremental revenue gain with optimized inventory management. Fast facts - Models in production: 20+ - Predictions: 160m+ rows weekly --- # Workbench overview URL: https://docs.datarobot.com/en/docs/get-started/day0/predai-start/wb-overview.html > Understand the components of the DataRobot Workbench interface, including the architecture, some sample workflows, and directory landing page. # Workbench overview The Workbench interface streamlines the modeling process, minimizing time-to-value while still leveraging cutting-edge ML and GenAI techniques. With Workbench: - Accelerate iteration and collaboration with repeatable, measured experiments. - Convert raw data into modeling-ready prepared, partitioned data. - Automate to quickly generate key insights and predictions from the best models. - Build, govern, and operate enterprise-gradegenerative AIsolutions; rapidly innovate and adapt with the best-of-breed components of your choice - Access from both an intuitive user interface and a notebook environment. Workbench is the launch point for predictive, [generative](https://docs.datarobot.com/en/docs/get-started/day0/predai-start/wb-overview.html#generative-ai), and [DataRobot Notebooks](https://docs.datarobot.com/en/docs/get-started/day0/predai-start/wb-overview.html#datarobot-notebooks). ## Predictive modeling Workbench is designed to match the data scientist's iterative workflows with easy project creation and model review, smooth navigation, and all key insights in one place. The interface lets you group, organize, and share your modeling assets to better leverage DataRobot for enhanced experimentation. These assets are housed within folder-like containers known as Use Cases. Because the modeling process extends beyond just model training, Workbench incorporates prepping data, training models, and leveraging results to make business decisions. It supports the idea of experiments to iterate through potential solutions until an outcome is reached. In other words, Workbench minimizes the time it takes to prep data, model, learn from modeling, prep more data, model again...and many iterations until a model is chosen and findings can be presented to stakeholders. Once models are built, use: - Registry to register and manage models, create custom models, jobs, and applications, and view and manage datasets. - Console for model and deployment monitoring and management. ## Generative AI Workbench is the launch point for building and iterating on your [GenAI](https://docs.datarobot.com/en/docs/agentic-ai/index.html) and [agentic](https://docs.datarobot.com/en/docs/agentic-ai/index.html) initiatives, with tools for working with vector databases, prompt management, and building RAG and agentic workflows. The DataRobot GenAI platform provides both API and GUI options, allowing you to experiment, compare, and assess the best GenAI components through qualitative and quantitative comparisons at an individual prompt and response level. Use the included common LLMs or bring your favorite libraries, bring or choose your LLMs, vector databases, and embeddings, and integrate third-party tools. ## DataRobot notebooks [Notebooks](https://docs.datarobot.com/en/docs/workbench/wb-notebook/index.html) offer an in-browser editor to create and execute code for data science analysis and modeling. Create for code development within the platform: standalone notebooks and codespaces. Standalone notebooks are a useful option for fast, lightweight notebook-based development and reports. Codespaces offer a persistent file system experience and allow you to work with both notebook and non-notebook files in the same session ## Navigation DataRobot provides breadcrumbs to help with navigation and asset selection. Click on any asset in the path to return to a location. For the final asset in the path, DataRobot provides a dropdown of same-type assets within the Use Case, to quickly access different assets without backtracking. ## Use Case assets A Use Case is composed of zero or more of the following assets: | Asset (symbol) | Read more | | --- | --- | | Datasets | Data preparation | | Vector databases | Vector databases | | Experiments | Experiments | | Playgrounds | Playgrounds | | Notebooks | Notebooks & codespaces | | Applications | Applications | | Deployments | Deployments | | Registered models | Registered models | ## Workbench directory To get started with Workbench if you are in DataRobot Classic, click DataRobot NextGen in the top navigation bar of the DataRobot application and select Workbench. DataRobot opens to your Workbench directory. The directory is the platform's landing page, providing a listing of Use Cases you are a member of and a button for creating new Use Cases. On first entry, the landing page provides a welcome and displays quick highlights of what you can do in Workbench. After your first Use Case is started, the directory lists all Use Cases either owned by or shared with you. See additional information on [creating, managing, and sharing Use Cases](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/build-usecase.html). ## Sample workflows The following images illustrate, at a high level, the predictive and generative workflows. ### Predictive workflow The following workflow shows different ways you can navigate through DataRobot's Workbench when using predictive modeling: ``` flowchart TB A((Open Workbench)) --> B{Create/open a Use Case}; B --> C[Add a dataset]; B --> D[Add an experiment]; B --> E[Add a notebook]; C -. optional .-> F[Wrangle your data]; E --> M[Create and execute code]; F -.-> G[Create an experiment]; G --> H[Set the target]; D --> L[Select a dataset]; L --> H; H --> I[Start modeling]; I --> J[Evaluate models]; J --> K[Make predictions]; J --> N[Build an application
from a model]; ``` ### Generative workflow The following workflow shows different ways you can work with vector databases, playgrounds, and LLM blueprints when using generative modeling: ``` flowchart TB A((Open Workbench)) --> B{Create/open a Use Case}; B -. optional .-> C[Add an
internal vector database]; B -. optional .-> D[Add an
external vector database]; C --> E[Configure vector database]; D --> E[Configure vector database]; E --> F[Add playground]; F --> G[Configure LLM blueprint]; G --> H[Chat]; H -. optional .-> I[Tune]; I -. optional .-> J[Compare]; J --> K[Deploy]; ``` ## Next steps From here, you can: - Build a Use Case . - Add and wrangle data . - Create experiments . --- # Sample assets URL: https://docs.datarobot.com/en/docs/get-started/day0/sample-assets.html > Learn DataRobot faster using these samples. In some cases, full tutorials using these assets are available for you to follow step-by-step. # Sample assets Learn DataRobot faster using these sample datasets. In some cases, full tutorials using these assets are available, allowing you to try it yourself, step-by-step. Datasets are organized by problem type. ## Generative | Name | Description | Usage | Asset link(s) | Learn more | | --- | --- | --- | --- | --- | | Space station research | A ZIP file of space station research papers and a CSV of evaluation prompts. | Retrieval augmented generation (RAG) | Download .zip | VideoWalkthrough | | Medical Research Abstracts | A ZIP file containing individual text files. Each text file is the abstract of a medical research paper. | RAG | Download .zip | AI Accelerator | | Technical Documentation | A ZIP file containing the technical documentation for DataRobot as of late 2023. | RAG | Download .zip | Walkthrough | | Kaggle "Wikipedia Movie Plots" | Several ZIP files, roughly 600 small text files each, containing movie plot summaries for some American, Japanese, and Indian movies from 1915 to 2017. | Build your own vector databases and LLM blueprints | Download dramas .zip Download random .zip Download romances .zip Download comedies .zip | Video walkthrough | ## Time series | Name | Description | Usage | Features | Asset link(s) | Learn more | | --- | --- | --- | --- | --- | --- | | Car Sales, GUI and Code | The monthly sales volume for many vehicle makes and models with additional contextual variables. | Multiseries, multivariate time series | Numeric | Short and fuller versions of data; a Python notebook | VideoWalkthrough | | Demand forecasting by SKU by store | Weekly units sold by store and SKU for 50 categorized products | SKU-level demand forecasting | Numeric, categorical | Training FileScoring File Calendar File | AI Accelerator | ## Regression | Name | Description | Usage | Features | Asset link(s) | Learn more | | --- | --- | --- | --- | --- | --- | | Fuel Efficiency | Predict the miles per gallon (MPG) based on other vehicle attributes. | Regression | Numeric | Training Data | API quickstart | | Wine Quality | Predict the quality score for white wines based on chemical composition. | Regression | Numeric | Training Data Scoring File | — | | Developer Salaries | Predict developer salaries based on the Stack Overflow Developer Survey 2019. | Regression | Numeric, categorical, text | Training Data | — | ## Classification | Name | Description | Usage | Features | Asset link(s) | Learn more | | --- | --- | --- | --- | --- | --- | | Hospital Readmissions | Predict whether a patient will be 'readmitted' to the hospital after being discharged. | Binary classification | Numeric, categorical, text | Training Data | Walkthrough | | Loan Default | Predict whether a loan 'is_bad' based on information provided on an application. | Binary classification | Numeric, categorical, text | Training Data Scoring File | Walkthrough | | Flight Delays | Predict whether an airline departure will be delayed by 30 minutes or more. | Binary classification | Numeric, categorical | Training Scoring | AI Accelerator | ## Multiclass / multilabel classification These projects can only be completed in DataRobot Classic. | Name | Description | Usage | Features | Asset link(s) | Learn more | | --- | --- | --- | --- | --- | --- | | Plant Disease | A ZIP file with several hundred images of plant leaves, organized into folders by disease class. | Multiclass | Images | Download | — | | Apparel Multilabel | Pictures of clothing which fit into multiple categories (for example, 'blue' and 'dress'). | Multilabel | Images | Download | — | --- # Trial FAQ URL: https://docs.datarobot.com/en/docs/get-started/day0/trial-faq.html > Questions and answers about DataRobot's self-service trial experience. # Trial FAQ --- # Video learning URL: https://docs.datarobot.com/en/docs/get-started/day0/video-learning.html > See a list of onboarding videos, with brief descriptions, to help get started with DataRobot. Check back often for new content. # Video learning The following table lists the series of onboarding videos available on YouTube. Check back often, as new content is added regularly. For those with "Try it here," you can use the provided data to follow along with the exercise. Visit the [DataRobot homepage on YouTube](https://www.youtube.com/@DataRobot/playlists) for additional demo videos, generative and agentic AI tutorials, customer use cases and stories, and more. | Title | Description | Try it | | --- | --- | --- | | Onboarding | | | | What is DataRobot? | A brief platform overview. | | | Getting oriented | A quick tour of the UI, including the cornerstone areas of building and deploying predictive and generative models—Workbench, Registry, Console, and the application gallery. | | | Starter exercises | Learn how to upload and preview data, build a predictive model, learn from insights, and register and deploy the model. | | | Walkthroughs | | | | Build LLM playgrounds | Learn how to use the RAG (Retrieval Augmented Generation) playground to compare multiple LLM blueprints side-by-side, create vector databases, then combine those vector databases with prompting strategies and LLM selections to create a complete RAG solution. | Walkthrough | | GenAI with governance | Create and compare LLM blueprints and RAG (Retrieval Augmented Generation) workflows. Then, compare multiple RAG pipelines with built-in evaluation, assessment, and logging, providing governance and guardrails. | Walkthrough | | Basic predictive modeling | Learn how to wrangle data with pushdown to Snowflake, run a model competition using Autopilot, and compare model insights and performance on the Leaderboard. | Walkthrough. | | Time series model building | Learn how to use time series modeling for a multivariate, multiseries forecasting use case. The step-by-step walkthrough exposes you to both the UI and using the DataRobot Python client to work in a data science notebook. | Walkthrough. | | RFPbot | See how the DataRobot framework allows you to securely use proprietary documents and data to provide context to LLMs by converting that information to custom knowledge bases. | | --- # Agents in the AM URL: https://docs.datarobot.com/en/docs/get-started/how-to/agents-am.html > A walkthrough for creating an agentic workflow, configuring metrics, and chatting with it. # Agents in the AM This how-to illustrates the ease of creating [agentic workflows](https://docs.datarobot.com/en/docs/agentic-ai/index.html) in DataRobot. After you complete it, try some additional how-tos, listed [at the end](https://docs.datarobot.com/en/docs/get-started/how-to/agents-am.html#try-this-next). In this walkthrough, you will: 1. Open a codespace and create a local environment. 2. Create and test an agent. 3. Send your agent to the Registry. 4. Configure metrics and connect to the playground. 5. Chat with the agent and review tracing. #### Prerequisites Download, and then unzip, the following file to try out the agentic functionality: [Download demo data](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/agents+in+the+am.zip) ### 1. Log in and navigate to Workbench Log in to DataRobot; you will land on the home page. Navigate to Workbench and create a new Use Case. Name it “Agents in the AM”. ### 2. Create a codespace From the Use Case getting started page, navigate to codespaces by clicking Notebooks & codespaces: Then, click to create a new codespace. And start a new session. ### 3. Load files to the codespace After the session initializes, drag-and-drop the unzipped file folder named `bloggeragent`, within the `storage` folder, that was downloaded as part of the [prerequisites](https://docs.datarobot.com/en/docs/get-started/how-to/agents-am.html#prerequisites). ### 4. Start a terminal session Click the Terminal tile to start a terminal session in the codespace. Change to the directory that contains the `bloggeragent` template: `cd storage/bloggeragent`. > [!NOTE] Note > Depending on your file structure, your command may be `cd bloggeragent`. Check the file browser hierarchy to confirm. ### 5. Create a local environment To create the local environment: 1. Run task start . 2. When prompted, choose agent_crewai ( 1 ) to select the CrewAI agent. 3. Enter Y to install dependencies locally. ### 6. Test the local agent Test the agent in your codespace. This is the same response as you will see in the playground, but faster. Use it to confirm that the agent is performing as expected. For example, try this command: ``` task agent:cli -- execute --user_prompt 'Hi, how are you?' ``` You can see the evolution of tasks: And then the final response: ### 7. Deploy the agent Send the agent to the Registry's workshop in preparation for deployment and monitoring. 1. Run task deploy to send the agent to the Registry. 2. Press Enter to create a new Pulumi stack. 3. Enter a stack name, such asagent_in_the_am_, andEnter. (.venv) [notebooks@kernel ~/storage/newsanalystagent]$ task deploy Running pulumi up with [DEPLOY] mode Using CPython 3.11.14 interpreter at: /usr/bin/python3 Creating virtual environment at: .venv Built pulumi-datarobot==0.10.24 Installed 84 packages in 151ms Please choose a stack, or create a new one: [Use arrows to move, type to filter] > 4. When prompted, selectyesto accept and perform Pulumi updates. This process can take 3-5 minutes and is an excellent time to read up on how to take agents fromPOC to production. ### 8. Go to Registry When you receive confirmation that the update was successful: 1. Go to Registry'sWorkshoptab. 2. Click the agent name to open the configuration area. ### 9. Configure metrics Next, scroll down the panel to the Evaluation and moderation section and click Configure. When you click to configure, the metric gallery opens. For this exercise, configure the prompt and response tokens: 1. Click on the Prompt Tokens tile. 2. Click Add . 3. Open and add theResponse Tokensmetric in the same way. The configuration summary panel shows the newly added metrics. 4. Save the configuration. ### 10. Connect the agent to the playground From the workshop, expand the Actions menu and select Connect to agentic playground. Use the dropdown to select: 1. The Use Case you created in Step 1. 2. The playground that was created by DataRobot with the stack name you entered in Step 7. Click Connect. ### 11. Chat with your agent Once connected, the agentic playground opens. Enter a prompt and click Send to test the agent. Initial output generation can take 3-5 minutes, so while it is "thinking," learn about [syftr](https://www.datarobot.com/blog/pareto-optimized-ai-workflows-syftr/), DataRobot's open-source framework for searching agentic workflow configurations to determine the optimal structure, components, and parameters for your data and use case. ### 12. Review tracing From within the response window, click Review tracing. Explore both the list and chart output. **List:** [https://docs.datarobot.com/en/docs/images/agents-am-21.png](https://docs.datarobot.com/en/docs/images/agents-am-21.png) **Chart:** [https://docs.datarobot.com/en/docs/images/agents-am-22.png](https://docs.datarobot.com/en/docs/images/agents-am-22.png) ### That's it! Congratulations—you now have a working agent. Feel free to leave the platform; the codespace will terminate by itself. Find your agent at any time in the Workbench Playgrounds tile. --- # Customize forecast analytics agents URL: https://docs.datarobot.com/en/docs/get-started/how-to/agents-ercot.html > A walkthrough to customize the behavior of an AI agent used in the ERCOT Forecast Analytics application. # Customize forecast analytics agents This how-to illustrates customizing the behavior of an AI agent used in the ERCOT Forecast Analytics application. The app is already deployed and running—you will connect your own personal agent to it and modify its YAML configuration directly in the DataRobot UI. The workflow removes the need to edit YAML files locally. All configuration changes will be performed in the custom model interface in DataRobot after your agent is deployed. Read more about the DataRobot [agentic workflow](https://docs.datarobot.com/en/docs/agentic-ai/index.html). After this walkthrough, you will have: - Deployed an AI agent to DataRobot. - Modified agent behavior in the DataRobot UI. - Customized role, goal, tone, structure, and data sources. - Added reasoning logic. - Updated deployments to use UI-based YAML edits. - Tested and compared different agent versions. - Connected custom agents to applications using deployment IDs. #### Prerequisites Prior to the workshop, DataRobot has: - Deployed and shared the ERCOT Forecast Analytics application. - Cloned the agent template repository. - Prepared sample data and configured tools. Download the agent template: [Download demo data](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/newsanalystagent.zip) ## Set up for agent experimentation To get started, unzip the agent template file you just downloaded. ### 1. Log in and navigate to Workbench Log in to DataRobot; you will land on the home page. Navigate to Workbench and create a new Use Case. Name it “Agents in the AM”. ### 2. Create a codespace From the Use Case getting started page, navigate to codespaces by clicking Notebooks & codespaces: Then, click to create a new codespace. And start a new session. ### 3. Load files to the codespace After the session initializes, drag-and-drop the unzipped file folder named `newsanalystagent` that was downloaded as part of the [prerequisites](https://docs.datarobot.com/en/docs/get-started/how-to/agents-ercot.html#prerequisites). ### 4. Start a terminal session Click the Terminal tile to start a terminal session in the codespace. Navigate to your agent folder: ``` cd newsanalystagent ``` ### 5. Deploy the agent Send the agent to the Registry workshop in preparation for deployment and monitoring. To do so: From `newsanalystagent` run: 1. Runtask deployto send the agent to the Registry. (.venv)[notebooks@kernel~/storage/newsanalystagent]$taskdeployRunningpulumiupwith[DEPLOY]modeUsingCPython3.11.14interpreterat:/usr/bin/python3Creatingvirtualenvironmentat:.venvBuiltpulumi-datarobot==0.10.24Installed84packagesin151msPleasechooseastack,orcreateanewone:[Usearrowstomove,typetofilter]> 2. PressEnterto create a new Pulumi stack. 3. Type a stack name, such asagent_in_the_am_, andEnter. 4. When prompted, selectyesto accept and perform Pulumi updates. This process can take 3-5 minutes and is an excellent time to read up on how to take agents fromPOC to production. During these steps, the following happens: - Pulumi packages the agent code. - DataRobot creates a custom model. - DataRobot creates a deployment (3–5 minutes) and returns a deployment ID. Save the deployment ID—you will need it in the next step. ### 6. Create an API key Click on your user icon and navigate to [API keys and tools](https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html#api-keys-and-tools) to access the area where you can create and manage API keys: Generate a new API key and name it `Agents in the AM`. ### 7. Connect to the application Duration: 5 minutes Open the shared application. A link to it is available in an email sent to the email you signed up with. Enter your "Agents in the AM" token and deployment ID from the previous step. Click Refresh. The app will confirm a successful connection. ### 8. Test the baseline agent Duration: 10 minutes Before modifying the agent YAML, see how the agent behaves in your application. 1. Set the following: FieldValueTrading hubHB_HOUSTONStart date7 days agoEnd dateToday 2. ClickRefreshto load data. 3. View the Forecast Chart and select an error point. 4. ClickAnalyze with AI. Document the output so that you can compare it against output after you make modifications. Specifically: - Tone - Length - Structure - Emphasis - Data sources ### 9. Open the agent YAML Duration: 20-30 minutes For the next steps, you will make configuration changes within DataRobot, not in your local files. 1. Navigate toRegistry > Workshop. 2. Open your agent and navigate to theFilessection. 3. Openagents.yamlby clicking thepencil icon to the right of the name. All YAML edits will be done here. The structure defines: And looks as follows: agents:forecast_analyst:role:"..."goal:"..."backstory:"..."tasks:error_analysis:description:"..."expected_output:"..." ## Experiment with the agent Based on the structure defined above, try making some changes to experiment with the agent output. - Learn how to create agent versions . - See the experiments section for sample changes. ### Create new agent versions To save and publish an edited YAML file, do the following with each change, or any combination of changes: 1. Save and publish your changes. 2. Register the updated agent. 3. Replace the model with the new version. 4. Test the new agent. #### Save and publish changes After making any changes to the YAML, while in the YAML editor, click Save. #### Register the updated agent Create a new version of the agent by registering the updates. 1. Go to Registry > Workshop . 2. Under the Model list, click on the agent name. 3. Click Register a workflow . #### Replace with the new version Replace the current deployment with the new version. 1. Navigate to Console > Deployments . 2. Select the deployment and under the actions menu selectReplace model. 3. Select the new version and replace by clickingSelect. #### Test the new agent Once deployed, re-run the analysis you did when [testing the baseline agent](https://docs.datarobot.com/en/docs/get-started/how-to/agents-ercot.html#8-test-the-baseline-agent). As you did in that step, compare and document the output for comparison. Consider: - Role - Tone - Length - Structure - News coverage - Insights ### Samples for experimenting Iterate and experiment! Try some, or all, of the following examples to use for comparison: - Change the role and goal . - Adjust the analysis depth . - Add new data sources . - Change the output structure . - Add a reasoning framework . Or, try your own. #### Experiment A. Change the agents section The default structure of the `agents` section defines the role, goal, and backstory in this way: ``` agents: forecast_analyst: role: "Energy Market Analyst" goal: "Interpret and explain ERCOT forecast errors through comprehensive narrative analysis" backstory: | You synthesize data into clear narratives that explain market dynamics. You interpret patterns and connect causes rather than simply listing facts. You understand how natural gas markets, international energy politics, geopolitical events, and regulatory changes impact electricity prices. You consider the interconnected nature of global energy markets and their influence on regional power markets like ERCOT. ``` Try replacing those values, for example, with one of the following: **Technical perspective:** ``` role: "Grid Operations Engineer" goal: "Diagnose forecast errors through technical analysis of grid operations" backstory: | You specialize in load balancing, renewable integration, and system constraints. ``` **Financial perspective:** ``` role: "Energy Trading Strategist" goal: "Identify market inefficiencies and trading opportunities from forecast errors" backstory: | You focus on arbitrage, market signals, and price movements. ``` #### Experiment B: Adjust the analysis depth Try varying the depth of the analysis: **Brief:** ``` expected_output: | A concise executive summary (3-4 sentences maximum) explaining the primary driver. ``` **Deep dive:** ```yaml expected_output: | A comprehensive analysis (12-15 sentences) covering immediate causes, market dynamics, external factors, interconnections, and implications. ``` #### Experiment C. Add new data sources In the YAML `description` section, expand your news search queries. Adding more queries gives the agent richer context. Replace lines 28-31 with the following: ``` Call fetch_energy_news with queries: - "ERCOT Texas electricity grid" - "natural gas prices energy markets" - "international energy politics natural gas LNG" - "renewable energy solar wind forecast Texas" - "power plant outages maintenance Texas ERCOT" - "electricity demand forecast weather Texas" - "Texas energy policy regulation changes" ``` #### Experiment D. Change the output structure Replace the narrative format with a structured, sectioned output: ``` expected_output: | Format your response with these sections: 1. EXECUTIVE SUMMARY 2. WEATHER CONDITIONS 3. MARKET DYNAMICS 4. EXTERNAL FACTORS 5. ROOT CAUSE ANALYSIS 6. APPENDICES ``` #### Experiment E. Add a reasoning framework Enhance the agent’s step-by-step reasoning: ``` description: | Analyze the forecast error using this reasoning process: STEP 1 - Quantify Impact STEP 2 - Immediate Causes STEP 3 - Market Context STEP 4 - External Factors STEP 5 - Synthesis ``` Congratulations—you now have a working agent, modified and applied for the ERCOT app. Find your agent at any time in the [WorkbenchPlaygroundstile](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html). --- # Business application briefs URL: https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/biz-app-briefs.html > A variety of quick summary applications with an accompanying No-Code AI App to provide an overview of possible uses. # Business application briefs This section provides a variety of quick use case summaries, with an accompanying [No-Code AI App](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html)), to provide examples of possible uses for predictive models in various industries: No-Code AI Apps allow you to build and configure AI-powered applications using a no-code interface to enable core DataRobot services without having to build models and evaluate their performance in DataRobot. Applications are easily shared and do not require users to own full DataRobot licenses in order to use them. Applications also offer a great solution for broadening your organization's ability to use DataRobot's functionality. - Flight delays - Parts failure predictions - Early loan payment predictions - Predictions for fantasy baseball ## Flight delays Airlines harness AI to optimize flight routes, enhance passenger experiences, and streamline operations. From predictive maintenance of aircraft to dynamic pricing strategies, AI empowers airlines to operate more efficiently and safely. Delays are particularly costly for airlines and their passengers. According to the Federal Aviation Administration (FAA), approximately 20% of flights in the United States experienced delays in 2019, resulting in an estimated $32.9 billion in costs to airlines, airports, and passengers. While the airline cannot avoid delays entirely, minimizing their magnitude saves money and customer frustration. A daily departure delay prediction could aid in the decision-making process of the tactical teams as they assess Air Traffic Control (ATC), maintenance, crew connections, ground handling, and scheduling integrity before a delay happens. DataRobot provides several use case opportunities for exploring flight delays. - To apply the trial experience flight delays guided walkthough as code, see this AI Accelerator . - For a code-based approach illustrating self-joins using this dataset, see the AI Accelerator on GitHub . Create AI measures for on-time performance (OTP) by modeling on factors such as: - Proportion of flight departures departing on time and those departing 30 minutes or more past scheduled departure. - Origin and destination airport. - Carrier. ## Parts failure predictions According to a study done by [Aberdeen Group](https://www.aberdeen.com/techpro-essentials/playing-russian-roulette-with-your-infrastructure-can-lead-to-big-downtime/), unplanned equipment failure can cost more than $260K an hour and can have associated health and safety risks. Existing best practices, such as scheduled preventative maintenance, can mitigate failure, but will not catch unusual, unexpected failures. Scheduled maintenance can also be dangerously conservative, resulting in excessive downtime and maintenance costs. A predictive model can signal your maintenance crew when an impending issue is likely to occur. This proactive approach to maintenance (automating related processes) allows operators to: 1. Identify subtle or unknown issues with equipment operation in the collected sensor data. 2. Schedule maintenance when maintenance is truly needed 3. Be automatically notified to intervene when a sudden failure is imminent. Leveraging collected sensor data not only saves your organization unintended down time, but also allows you to prevent unintended consequences of equipment failure. A sample app: ## Early loan payment predictions When a borrower takes out a 30-year mortgage, usually they won’t finish paying back the loan in exactly thirty years—it could be later or earlier, or the borrower may refinance. For regulatory purposes—and to manage liabilities—banks need to accurately forecast the effective duration of any given mortgage. Using DataRobot, mortgage loan traders can combine their practical experience with modeling insights to understand which mortgages are likely to be repaid early. Between general economic data and individual mortgage records, there’s plenty of data available to predict early loan prepayment. The challenge lies in figuring out which features, in which combination, with which modeling technique, will yield the most accurate model. Furthermore, federal regulations require that models be fully transparent so that regulators can verify that they are non-discriminatory and robust. A sample app: ## Predictions for fantasy baseball Millions of people play fantasy baseball using leagues that are typically draft- or auction-based. Choosing a team based on your favorite players—or simply on last year's performance without any regard for regression to the mean—is likely to field a weaker team. Because baseball is one of the most "documented" of all sports (statistics-wise), you can derive a better estimate of each player's true talent level and their likely performance in the coming year using machine learning. This allows for better drafting and helps avoid overpaying for players coming off of "career" seasons. When drafting players for fantasy baseball, you must make decisions based on the player's performance over their career to date, as well as variables like the effects of aging. Basing evaluation on personal interpretation of the player's performance is likely to cause you to overvalue a player's most recent performance. In other words, it's common to overvalue a player coming off a career year or undervalue a player coming off a bad year. The goal is to generate a better estimate of the player's value in the next year based on what he has done in prior years. If you build a machine learning model to predict a player's performance in the next year based on their previous performance, it will help you identify when over- or under-performance is a fluke, and when it is an indicator of that player’s future performance. A sample app: --- # Fraudulent claim detection URL: https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/fraud-claims.html > Improve the accuracy in predicting which insurance claims are fraudulent. # Fraudulent claim detection This page outlines the use case to improve the accuracy in predicting which insurance claims are fraudulent. It is captured below as a UI-based walkthrough. ### Business problem Because, on average, it takes roughly 20 days to process an auto insurance claim (which often frustrates policyholders), insurance companies look for ways to increase the efficiency of their claims workflows. Increasing the number of claim handlers is expensive, so companies have increasingly relied on automation to accelerate the process of paying or denying claims. Automation can increase Straight-Through Processing (STP) by more than 20%, resulting in faster claims processing and improved customer satisfaction. However, as insurance companies increase the speed by which they process claims, they also increase their risk of exposure to fraudulent claims. Unfortunately, most of the systems widely used to prevent fraudulent claims from being processed either require high amounts of manual labor or rely on static rules. ## Solution value While Business Rule Management Systems (BRMS) will always be required—they implement mandatory rules related to compliance—you can supplement these systems by improving the accuracy of predicting which incoming claims are fraudulent. Using historical cases of fraud and their associated features, AI can apply learnings to new claims to assess whether they share characteristics of the learned fraudulent patterns. Unlike BRMS, which are static and have hard-coded rules, AI generates a probabilistic prediction and provides transparency on the unique drivers of fraud for each suspicious claim. This allows investigators to not only route and triage claims by their likelihood of fraud, but also enables them to accelerate the review process as they know which vectors of a claim they should evaluate. The probabilistic predictions also allow investigators to set thresholds that automatically approve or reject claims. ### Problem framing Work with [stakeholders](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/fraud-claims.html#decision-stakeholders) to identify and prioritize the decisions for which automation will offer the greatest business value. In this example, stakeholders agreed that achieving over 20% STP in claims payment was a critical success factor and that minimizing fraud was a top priority. Working with subject matter experts, the team developed a shared understanding of STP in claims payment and built decision logic for claims processing: | Step | Best practice | | --- | --- | | Determine which decisions to automate. | Automate simple claims and send the more complex claims to a human claims processor. | | Determine which decisions will be based on business rules and which will be based on machine learning. | Manage decisions that rely on compliance and business strategy by rules. Use machine learning for decisions that rely on experiences, including whether a claim is fraudulent and how much the payment will be. | Once the decision logic is in good shape, it is time to build business rules and machine learning models. Clarifying the decision logic reveals the true data needs, which helps decision owners see exactly what data and analytics drive decisions. ### ROI estimation One way to frame the problem is to determine how to measure ROI. Consider: For ROI, multiple AI models are involved in an STP use case. For example, fraud detection, claims severity prediction, and litigation likelihood prediction are common use cases for models that can augment business rules and human judgment. Insurers implementing fraud detection models have reduced payments to fraud by 15% to 25% annually, saving $1 million to $3 million. To measure: 1. Identify the number of fraudulent claims that models detected but manual processing failed to identify (false negatives). 2. Calculate the monetary amount that would have been paid on these fraudulent claims if machine learning had not flagged them as fraud. 100 fraudulent claims * $20,000 each on average = $2 million per year 3. Identify fraudulent claims that manual investigation detected but machine learning failed to detect. 4. Calculate the monetary amount that would have been paid without manual investigation. 40 fraudulent claims * $5,000 each on average = $0.2 million per year The difference between these two numbers would be the ROI. ``` $2 million – $0.2 million = $1.8 million per year ``` ## Working with data For illustrative purposes, this guide uses a simulated dataset that resembles insurance company data. The dataset consists of 10,746 rows and 45 columns. ### Features and sample data The target variable for this use case is whether or not a claim submitted is fraudulent. It is a binary classification problem. In this dataset 1,746 of 10,746 claims (16%) are fraudulent. The target variable: - FRAUD ### Data preparation Below are examples of 44 features that can be used to train a model to identify fraud. They consist of historical data on customer policy details, claims data including free-text description, and internal business rules from national databases. These features help DataRobot extract relevant patterns to detect fraudulent claims. Beyond the features listed below, it might help to incorporate any additional data your organization collects that could be relevant to detecting fraudulent claims. For example, DataRobot is able to process image data as a feature together with numeric, categorical, and text features. Images of vehicles after an accident may be useful to detect fraud and help predict severity. Data from the claim table, policy table, customer table, and vehicle table are merged with customer ID as a key. Only data known before or at the time of the claim creation is used, except for the target variable. Each record in the dataset is a claim. ### Sample feature list | Feature name | Data type | Description | Data source | Example | | --- | --- | --- | --- | --- | | ID | Numeric | Claim ID | Claim | 156843 | | FRAUD | Numeric | Target | Claim | 0 | | DATE | Date | Date of Policy | Policy | 31/01/2013 | | POLICY_LENGTH | Categorical | Length of Policy | Policy | 12 month | | LOCALITY | Categorical | Customer’s locality | Customer | OX29 | | REGION | Categorical | Customer’s region | Customer | OX | | GENDER | Numeric | Customer’s gender | Customer | 1 | | CLAIM_POLICY_DIFF_A | Numeric | Internal | Policy | 0 | | CLAIM_POLICY_DIFF_B | Numeric | Internal | Policy | Policy | | CLAIM_POLICY_DIFF_C | Numeric | Internal | Policy | Policy | | CLAIM_POLICY_DIFF_D | Numeric | Internal | Policy | Policy | | CLAIM_POLICY_DIFF_E | Numeric | Internal | Policy | Policy | | POLICY_CLAIM_DAY_DIFF | Numeric | Number of days since policy taken | Policy, Claim | 94 | | DISTINCT_PARTIES_ON_CLAIM | Numeric | Number of people on claim | Claim | 4 | | CLM_AFTER_RNWL | Numeric | Renewal | History | Policy | | NOTIF_AFT_RENEWAL | Numeric | Renewal | History | Policy | | CLM_DURING_CAX | Numeric | Cancellation claim | Policy | 0 | | COMPLAINT | Numeric | Customer complaint | Policy | 0 | | CLM_before_PAYMENT | Numeric | Claim before premium paid | Policy, Claim | 0 | | PROP_before_CLM | Numeric | Claim History | Claim | 0 | | NCD_REC_before_CLM | Numeric | Claim History | Claim | 1 | | NOTIF_DELAY | Numeric | Delay in notification | Claim | 0 | | ACCIDENT_NIGHT | Numeric | Night time accident | Claim | 0 | | NUM_PI_CLAIM | Numeric | Number of personal injury claims | Claim | 0 | | NEW_VEHICLE_BEFORE_CLAIM | Numeric | Vehicle History | Vehicle, Claim | 0 | | PERSONAL_INJURY_INDICATOR | Numeric | Personal Injury flag | Claim | 0 | | CLAIM_TYPE_ACCIDENT | Numeric | Claim details | Claim | 1 | | CLAIM_TYPE_FIRE | Numeric | Claim details | Claim | 0 | | CLAIM_TYPE_MOTOR_THEFT | Numeric | Claim details | Claim | 0 | | CLAIM_TYPE_OTHER | Numeric | Claim details | Claim | 0 | | CLAIM_TYPE_WINDSCREEN | Numeric | Claim details | Claim | 0 | | LOCAL_TEL_MATCH | Numeric | Internal Rule Matching | Claim | 0 | | LOCAL_M_CLM_ADD_MATCH | Numeric | Internal Rule Matching | Claim | 0 | | LOCAL_M_CLM_PERS_MATCH | Numeric | Internal Rule Matching | Claim | 0 | | LOCAL_\NON\_CLM\_ADD\_MATCH\t | Numeric | Internal Rule Matching | Claim | 0 | | LOCAL_NON_CLM_PERS_MATCH | Numeric | Internal Rule Matching | Claim | 0 | | federal_TEL_MATCH | Numeric | Internal Rule Matching | Claim | 0 | | federal_CLM_ADD_MATCH | Numeric | Internal Rule Matching | Claim | 0 | | federal_CLM_PERS_MATCH | Numeric | Internal Rule Matching | Claim | 0 | | federal_NON_CLM_ADD_MATCH | Numeric | Internal Rule Matching | Claim | 0 | | federal_NON_CLM_PERS_MATCH | Numeric | Internal Rule Matching | Claim | 0 | | SCR_LOCAL_RULE_COUNT | Numeric | Internal Rule Matching | Claim | 0 | | SCR_NAT_RULE_COUNT | Numeric | Internal Rule Matching | Claim | 0 | | RULE MATCHES | Numeric | Internal Rule Matching | Claim | 0 | | CLAIM_DESCRIPTION | Text | Customer Claim Text | Claim | this via others themselves inc become within ours slow parking lot fast vehicle roundabout mall not indicating car caravan neck emergency | ## Modeling and insights DataRobot automates many parts of the modeling pipeline, including processing and partitioning the dataset, as described [here](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html). This section describes model interpretation. ### Feature Impact [Feature Impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html) reveals that the number of past personal injury claims ( `NUM_PI_CLAIM`) and internal rule matches ( `LOCAL_M_CLM_PERS_MATCH`, `RULE_MATCHES`, `SCR_LOCAL_RULE_COUNT`) are among the most influential features in detecting fraudulent claims. ### Feature Effects/partial dependence The [partial dependence plot](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-effects-classic.html#partial-dependence-calculations) in Feature Effects shows that the larger the number of personal injury claims ( `NUM_PI_CLAIM`), the higher the likelihood of fraud. As expected, when a claim matches internal red flag rules, its likelihood of being fraud increases greatly. Interestingly, `GENDER` and `CLAIM_TYPE_MOTOR_THEFT` (car theft) are also strong features. ### Word Cloud The current data includes `CLAIM_DESCRIPTION` as text. A [Word Cloud](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/word-cloud-classic.html) reveals that customers who use the term "roundabout," for example, are more likely to be committing fraud than those who use the term "emergency." (The size of a word indicates how many rows include the word; the deeper red indicates the higher association it has to claims scored as fraudulent. Blue words are terms associated with claims scored as non-fraudulent.) ### Prediction Explanations [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) provide up to 10 reasons for each prediction score. Explanations provide Send directly to Special Investigation Unit (SIU) agents and claim handlers with useful information to check during investigation. For example, DataRobot not only predicts that Claim ID 8296 has a 98.5% chance of being fraudulent, but it also explains that this high score is due to a specific internal rule match ( `LOCAL_M_CLM_PERS_MATCH`, `RULE_MATCHES`) and the policyholder’s six previous personal injury claims ( `NUM_PI_CLAIM`). When claim advisors need to deny a claim, they can provide the reasons why by consulting Prediction Explanations. ### Evaluate accuracy There are several vizualizations that help to evaluate accuracy. #### Leaderboard Modeling results show that the ENET Blender is the most accurate model, with 0.93 AUC on cross validation. This is an ensemble of eight single models. The high accuracy indicates that the model has learned signals to distinguish fraudulent from non-fraudulent claims. Keep in mind, however, that blenders take longer to score compared to single models and so may not be ideal for real-time scoring. The Leaderboard shows that the modeling accuracy is stable across Validation, Cross Validation, and Holdout. Thus, you can expect to see similar results when you deploy the selected model. #### Lift Chart The steep increase in the average target value in the right side of the [Lift Chart](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/lift-chart-classic.html) reveals that, when the model predicts that a claim has a high probability of being fraudulent (blue line), the claim tends to actually be fraudulent (orange line). #### Confusion matrix The [confusion matrix](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/confusion-matrix-classic.html) shows: - Of 2,149 claims in the holdout partition, the model predicted 372 claims as fraudulent and 1,777 claims as legitimate. - Of the 372 claims predicted as fraud, 275 were actually fraudulent (true positives), and 97 were not (false positives). - Of 1,777 claims predicted as non-fraud, 1,703 were actually not fraudulent (true negatives) and 74 were fraudulent (false negatives). Analysts can examine this table to determine if the model is accurate enough for business implementation. ### Post-processing To convert model predictions into decisions, you determine the best thresholds to classify a whether a claim is fraudulent. #### ROC Curve Set the [ROC Curve](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/roc-curve-tab-use.html) threshold depending on how you want to use model predictions and business constraints. Some examples: | If... | Then... | | --- | --- | | ...the main use of the fraud detection model is to automate payment | ...minimize the false negatives (the number of fraudulent claims mistakenly predicted as not fraudulent) by adjusting the threshold to classify prediction scores into fraud or not. | | ...the main use is to automate the transfer of the suspicious claims to SIU | ...minimize false positives (the number of non-fraudulent claims mistakenly predicted as fraudulent). | | ...you want to minimize the false negatives, but you do not want false positives to go over 100 claims because of the limited resources of SIU agents | ...lower the threshold just to the point where the number of false positives becomes 100. | #### Payoff matrix From the Profit Curve tab, use the [Payoff Matrix](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/profit-curve-classic.html) to set thresholds based on simulated profit. For example: | Payoff value | Description | | --- | --- | | True positive = $20,000 | Average payment associated with a fraudulent claim. | | False positive = -$20,000 | This is assuming that a false positive means that a human investigator will not be able to spend time detecting a real fraudulent claim. | | True negative = $100 | Leads to auto pay of claim and saves by eliminating manual claim processing. | | False negative = -$20,000 | Cost of missing fraudulent claims. | DataRobot then automatically calculates the threshold that maximizes profit. You can also measure DataRobot ROI by creating the same payoff matrix for your existing business process and subtracting the max profit of the existing process from that calculated by DataRobot. Once the threshold is set, model predictions are converted into fraud or non-fraud according to the threshold. These classification results are integrated into BRMS and become one of the many factors that determine the final decision. ## Predict and deploy After selecting the model that best learns patterns to predict fraud, you can deploy it into your desired decision environment. Decision environments are the ways in which the predictions generated by the model will be consumed by the appropriate organizational stakeholders, and how these stakeholders will make decisions using the predictions to impact the overall process. ### Decision stakeholders The following table lists potential decision stakeholders: | Stakeholder | Description | | --- | --- | | Decision executors | The decision logic assigns claims that require manual investigation to claim handlers (executors) and SIU agents based claim complexity. They investigate the claims referring to insights provided by DataRobot and decide whether to pay or deny. They report to decision authors the summary of claims received and their decisions each week. | | Decision managers | Managers monitor the KPI dashboard, which visualizes the results of following the decision logic. For example, they track the number of fraudulent claims identified and missed. They can discuss with decision authors how to improve the decision logic each week. | | Decision authors | Senior managers in the claims department examine the performance of the decision logic by receiving input from decision executors and decision managers. For example, decision executors will inform whether or not the fraudulent claims they receive are reasonable, and decision managers will inform whether or not the rate of fraud is as expected. Based on the inputs, decision authors update the decision logic each week. | ### Decision process This use case blends augmentation and automation for decisions. Instead of claim handlers manually investigating every claim, business rules and machine learning will identify simple claims that should be automatically paid and problematic claims that should be automatically denied. Fraud likelihood scores are sent to BRMS through the API and post-processed into high, medium, and low risk, based on set thresholds, and arrive at one of the following final decisions: | Action | Degree of risk | | --- | --- | | SIU | High | | Assign to claim handlers | Medium | | Auto pay | Low | | Auto deny | Low | Routing to claims handlers includes an intelligent triage, in which claims handlers receive fewer claims and just those which are better tailored to their skills and experience. For example, more complex claims can be identified and sent to more experienced claims handlers. SIU agents and claim handlers will decide whether to pay or deny the claims after investigation. ### Model deployment Predictions are deployed through the API and sent to the BRMS. ### Model monitoring Using DataRobot [MLOps](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html), you can monitor, maintain, and update models within a single platform. Each week, decision authors monitor the fraud detection model and retrain the model if [data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html) reaches a certain threshold. In addition, along with investigators, decision authors can regularly review the model decisions to ensure that data are available for future retraining of the fraud detection model. Based on the review of the model's decisions, the decision authors can also update the decision logic. For example, they might add a repair shop to the red flags list and improve the threshold to convert fraud scores into high, medium, or low risk. DataRobot provides tools for managing and monitoring the deployments, including accuracy and data drift. ### Implementation considerations Business goals should determine decision logic, not data. The project begins with business users building decision logic to improve business processes. Once decision logic is ready, true data needs will become clear. Integrating business rules and machine learning to production systems can be problematic. Business rules and machine learning models need to be updated frequently. Externalizing the rules engine and machine learning allows decision authors to make frequent improvements to decision logic. When the rules engine and machine learning are integrated into production systems, updating decision logic becomes difficult because it will require changes to production systems. Trying to automate all decisions will not work. It is important to decide which decisions to automate and which decisions to assign to humans. For example, business rules and machine learning cannot identify fraud 100% of the time; human involvement is still necessary for more complex claim cases. ### No-Code AI Apps Consider building a custom application where stakeholders can interact with the predictions and record the outcomes of the investigation. Once the model is deployed, predictions can be consumed for use in the [decision process](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/fraud-claims.html#decision-process). For example, this [No-Code AI App](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html) is an easily shareable, AI-powered application using a no-code interface: --- # Business solutions URL: https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/index.html > A catalog of UI-based, end-to-end how-tos that address common industry-specific problems. # Business solutions This section provides access to a catalog of UI-based, end-to-end how-tos, based on best practices and patterns, that address common industry-specific problems. | Use case | Description | | --- | --- | | Business application briefs | A variety of quick summary applications with an accompanying No-Code AI App to provide an overview of possible uses. | | Visual app development and process automation | Connects DataRobot deployments to SAP Build Process Automation. | | Purchase card fraud detection | Helps organizations that employ purchase cards for procurement to monitor for fraud and misuse. | | Likelihood of a loan default | Helps minimize risk by predicting the likelihood that a borrower will not repay their loan. | | Late shipment predictions | Helps supply chain managers can evaluate root cause and then implement short-term and long-term adjustments that prevent shipping delays. | | Reduce hospital readmission rates | Helps to reduce the 30-Day readmissions rate by predicting at-risk patients. | | Triage insurance claims | Helps insurers assess claim complexity and severity as early as possible for optimized routing and handling. | | Fraudulent claim detection | Helps reduce the risk of fraudulent claims while increasing claim efficiency processing. | | Anti-Money Laundering Alert Scoring | Build a model that uses historical data, including customer and transactional information, to identify which alerts resulted in a Suspicious Activity Report (SAR). | --- # Triage insurance claims URL: https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/insurance-claims.html > Evaluate the severity of an insurance claim in order to triage it effectively. # Triage insurance claims This page outlines a use case that assesses claim complexity and severity as early as possible to optimize claim routing, ensure the appropriate level of attention, and improve claimant communications. It is captured below as a UI-based walkthrough. ## Business problem Claim payments and claim adjustment are typically an insurance company’s largest expenses. For long-tail lines of business, such as workers’ compensation (which covers medical expenses and lost wages for injured workers), the true cost of a claim may not be known for many years until it is paid in full. However, claim adjustment activities start when a claim is made aware to the insurer. Typically when an employee gets injured at work ( `Accident Date`), the employer (insured) decides to file a claim to its insurance company ( `Report Date`) and a claim record is created in the insurer's claim system with all available information about the claim at the time of reporting. The claim is then assigned to a claim adjuster. This assignment could be purely random or based on roughly defined business rules. During the life cycle of a claim, assignment may be re-evaluated multiple times and re-assigned to a different claim adjuster. This process, however, has costly consequences: - It is well-known in insurance that 20% of claims account for 80% of the total claim payouts. Randomly assigning claims wastes resources. - Early intervention is critical to optimal claim results. Without the appropriate assignment of resources as early as possible, seemingly mild claims can become substantial. - Claims of low severity and complexity must wait to be processed alongside all other claims, often leading to a poor customer experience. - A typical claim adjuster can receive several hundred new claims every month, in addition to any existing open claims. When a claim adjuster is overloaded, it is unlikely they can process every assigned claim. If too much time passes, the claimant is more likely to obtain an attorney to assist in the process, driving up the cost of the claim unnecessarily. ## Solution value - Challenge:Help insurers assess claim complexity and severity as early as possible so that: - Desired Outcome - How can DataRobot help? | Topic | Description | | --- | --- | | Use case type | Insurance / Claim Triage | | Target audience | Claim adjusters | | Metrics / KPIs | False positive/negative rate Total expense savings (in terms of both labor and more accurate adjudication of claims)Customer satisfaction | | Sample dataset | Download here | ### Problem framing A machine learning model learns complex patterns from historically observed data. Those patterns can be used to make predictions on new data. In this use case, historical insurance claim data is used to build the model. When a new claim is reported, the model makes a prediction on it. Depending on how the problem is framed, the prediction can have different meanings. The goal of this claim triage use case is to have a model evaluate the workers' compensation claim severity as early as possible, ideally at the moment a claim is reported (the first notice of loss, or FNOL). The target feature is related to the total payment for a claim and the modeling unit is each individual claim. When the total payment for a claim is treated as the target, the use case is framed as a regression problem because you are predicting a quantity. The predicted total payment can then be compared with thresholds for low and high severity claims defined by business need, which classifies each claim as low-, medium-, or high-severity. Alternatively, you can frame this use case as a classification problem. To do so, apply the aforementioned thresholds to the total claim payment first and convert it to a categorical feature with levels "Low", "Medium" and "High". You can then build a classification model that uses this categorical variable as the target. The model instead predicts the probability a claim is going to be low-, medium- or high-severity. Regardless how the problem is framed, the ultimate goal is to route a claim appropriately. ### ROI estimation For this use case, direct return on investment (ROI) comes from improved claim handling results and expense savings. Indirect ROI stems from improved customer experience which in turn increases customer loyalty. The steps below focus on the direct ROI calculation based on the following assumptions: - 10,000 claims every month - Category I: 30% (3000) of claims are routed to straight through processing (STP) - Category II: 60% (6000) of claims are handled normally - Category III: 10% (1000) of claims are handled by experienced claim adjusters - Average Category I claim severity is 250 without the model; 275 with the model - Average Category II claim severity is 10K without the model; 9500 with the model - Saved labor: 3 full-time employees with an average annual salary of 65000 `Total annual ROI` = `65000 x 3 + [3000 x (250-275) + 1000 x (10000 - 9500)] x 12` = `$5295000` ## Working with data The sample data for this use case is a synthetic dataset from a worker compensation insurer's claims database, organized at the individual claim level. Most claim databases in an insurance company contain transactional data, i.e., one claim may have multiple records in the claims database. When the claim is first reported, a claim is recorded in the claims systems and initial information about the claim is recorded. Depending on the insurer's practice, a case reserve may be set up. The case reserve is adjusted accordingly when claim payments are made or additional information collected indicates a need to change the case reserve. Policy-level information can be predictive as well. This type of information includes class, industry, job description, employee tenure, size of the employer, and whether there is a return to work program. Policy attributes should be joined with the claims data to form the modeling dataset, although they are ignored in this example. When it comes to claim triage, insurers would like to know as early as possible how severe a claim potentially is, ideally at the moment a claim is reported (FNOL). However, an accurate estimate of a claim's severity may not be feasible at FNOL due to insufficient information. Therefore, in practice, a series of claim triage models are needed to predict the severity of a claim at different stages of that claim's life cycle, e.g., FNOL, 30 days, 60 days, 90 days, etc. For each of the models, the goal is to predict the severity of a claim; therefore, the target feature is the total payment on a claim. The features included in the training data are the claim attributes and policy attributes at different snapshots. For example, for an FNOL model, features are limited to what is known about a claim at FNOL. For insurers still using legacy systems which may not record the true FNOL data, an approximation is often made between 0-30 days. ### Features overview The following table outlines the prominent features in the [sample training dataset](https://s3.amazonaws.com/datarobot-doc-assets/DR_Demo_Statistical_Case_Estimates.csv). | Feature Name | Data Type | Description | Data Source | | --- | --- | --- | --- | | ReportingDelay | Numeric | Number of days between the accident date and report date | Claims | | AccidentHour | Numeric | Time of day that the accident occurred | Claims | | Age | Numeric | Age of claimant | Claims | | Weekly Rate | Numeric | Weekly salary | Claims | | Gender | Categorical | Gender of the claimant | Claims | | Marital Status | Categorical | Whether the claimant is married or not | Claims | | HoursWorkedPerWeek | Numeric | The usual number of hours worked per week by the claimant | Claims | | DependentChildren | Numeric | Claimant's number of dependent children | Claims | | DependentsOther | Numeric | Claimant's number of dependents who are not children | Claims | | PartTimeFullTime | Numeric | Whether the claimant works part time or full time | Claims | | DaysWorkedPerWeek | Numeric | Number of days per week worked by the claimant | Claims | | DateOfAccident | Date | Date that the accident occurred | Claims | | ClaimDescription | Text | Text description of the accident and injury | Claims | | ReportedDay | Numeric | Day of the week that the claim was reported to the insurer | Claims | | InitialCaseEstimate | Numeric | Initial case estimate set by claim staff | Claims | | Incurred | Numeric | target : final cost of the claim = all payments made by the insurer | Claims | ### Data preparation The example data is organized at the claim level; each row is a claim record, with all the claim attributes taken at FNOL. On the other hand, the target variable, `Incurred`, is the total payment for a claim when it is closed. So there are no open claims in the data. A workers’ compensation insurance carrier’s claim database is usually stored at the transaction level. That is, a new record will be created for each change to a claim, such as partial claim payments and reserve changes. This use case snapshots the claim (and all the attributes related to the claim) when it is first reported and then again when the claim is closed (from target to total payment). Policy-level information can be predictive as well, such as class, industry, job description, employee tenure, size of the employer, whether there is a return to work program, etc. Policy attributes should be joined with the claims’ data to form the modeling dataset. ### Data evaluation Once the modeling data is uploaded to DataRobot, [EDA](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html) produces a brief summary of the data, including descriptions of feature type, summary statistics for numeric features, and the distribution of each feature. A [data quality assessment](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/data-quality.html) helps ensure that only appropriate data is used in the modeling process. Navigate to the Data tab to learn more about your data. #### Exploratory Data Analysis Click each feature to see [histogram](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html) information such as the summary statistics (min, max, mean, std) of numeric features or a histogram that represents the relationship of a feature with the target. DataRobot automatically performs data quality checks. In this example, it has detected outliers for the target feature. Click Show Outliers to view them all (outliers are common in insurance claims data). To avoid bias introduced by the outlier, a common practice is to cap the target, such as capping it to the 95th percentile. This cap is especially important for linear models. #### Feature Associations Use the [Feature Associations](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html) tab to visualize the correlations between each pair of the input features. For example, in the plot below, the features `DaysWorkedPerWeek` and `PartTimeFullTime` (top-left corner) have strong associations and are therefore "clustered" together. Each color block in this matrix is a cluster. ## Modeling and insights After modeling completes, you can begin interpreting the model results. ### Feature Impact [Feature Impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html) reveals the association between each feature and the model target—the key drivers of the model.Feature Impact ranks features based on feature importance, from the most important to the least important, and also shows the relative importance of those features. In the example below we can see that `InitialCaseEstimate` is the most important feature for this model, followed by `ClaimDescription`, `WeeklyRate`, `Age`, `HoursWorkedPerWeek`, etc. This example indicates that features after `MaritalStatus` contribute little to the model. For example, `gender` has minimal contribution to the model, indicating that claim severity doesn't vary by the gender of the claimant. If you create a new feature list that does not include gender (and other features less impactful than `MaritalStatus`) and only includes the most impactful features, the model accuracy should not be significantly impacted. A natural next step is to [create a new feature list](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html) with only the top features and rerun the model. DataRobot automatically creates a new feature list, "DR Reduced Features", by including features that have a cumulative feature impact of 95%. ### Partial Dependence plot Once you know which features are important to the model, it is useful to know how each feature affects predictions. This can be seen in [Feature Effects](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-effects-classic.html) and in particular a model's partial dependence plot. In the example below, notice the partial dependence for the `WeeklyRate` feature. You can observe that claimants with lower weekly pay have lower claim severity, while claimants with higher weekly pay have higher claim severity. ### Prediction Explanations When a claims adjuster sees a low prediction for a claim, they are likely to initially ask what the drivers are behind such a low prediction. The [Prediction Explanation](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) insight, provided at an individual prediction level, can help claim adjusters understand how a prediction is made, increasing confidence in the model. By default, DataRobot provides the top three explanations for each prediction, but you can request up to 10 explanations. Model predictions and explanations can be downloaded as a CSV and you can control which predictions are populated in the CSV by specifying the thresholds for high and low predictions. The graph below shows the top three explanations for the 3 highest and lowest predictions. The graph shows that, generally, high predictions are associated with older claimants and higher weekly salary, while the low predictions are associated with a lower weekly salary. ### Word Cloud The feature `ClaimDescription` is an unstructured text field. DataRobot builds text mining models on textual features, and the output from those text-mining models is used as inputs into subsequent modeling processes. Below is a [Word Cloud](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/word-cloud-classic.html) for `ClaimDescription`, which shows the keywords parsed out by DataRobot. Size of the word indicates how frequently the word appears in the data: `strain` appears very often in the data while `fractured` does not appear as often. Color indicates severity: both `strain` and `fractured` (red words) are associated with high severity claims while `finger` and `eye` (blue words) are associated with low severity claims. ## Evaluate accuracy The following insights help evaluate accuracy. ### Lift Chart The [Lift Chart](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/lift-chart-classic.html) shows how effective the model is at differentiating lowest risks (on the left) from highest risks (on the right). In the example below, the blue curve represents the average predicted claim cost, and the orange curve indicates the average actual claim cost. The upward slope indicates the model has effectively differentiated the claims of low severity (close to 0) on the left and those of high severity (~45K) on the right. The fact that the actual values (orange curve) closely track the predicted values (blue curve) tells you that the model fits the data well. Note that DataRobot only displays lift charts on validation or holdout partitions. ## Post-processing A prediction for claim severity can be used for multiple different applications, requiring different post-processing steps for each. Primary insurers may use the model predictions for claim triage, initial case reserve determination, or reinsurance reporting. For example, for claim triage at FNOL, the model prediction can be used to determine where the claim should be routed. A workers’ compensation carrier may decide: - All claims with predicted severity under $5000 go to straight-through processing (STP). - Claims between $5000 and $20,000 go through the standard process. - Claims over $20,000 are assigned a nurse case manager. - Claims over $500,000 are also reported to a reinsurer, if applicable. Another carrier may decide to pass 40% of claims to STP; 55% to regular process; and 5% get assigned a nurse case manager so that thresholds can be determined accordingly. These thresholds can be programmed into the business process so that claims go through the predesigned pipeline once reported and then get routed appropriately. Note that companies with STP should carefully design their claim monitoring procedures to ensure unexpected claim activities are captured. In order to test these different assumptions, design single or multiple A/B tests and run them in sequence or parallel. Power analysis and p-value needs to be set before the tests in order to determine the number of observations required before stopping the test. In designing the test, think carefully about the drivers of profitability. Ideally you want to allocate resources based on the change they can effect, not just on the cost of the claim. For example, fatality claims are relatively costly but not complex, and so often can be assigned to a very junior claims handler. Finally, at the end of the A/B tests, you can identify the best combination based on the profit of each test. ## Predict and deploy You can use the DataRobot UI or REST API to deploy a model, depending on how ready it is to be put into production. However, before the model is fully integrated into production, a pilot may be beneficial for: - Testing the model performance using new claims data. - Monitoring unexpected scenarios so a formal monitoring process can be designed or modified accordingly. - Increasing the end users’ confidence in using the model outputs to assist business decision making. Once stakeholders feel comfortable about the model and also the process, integration of the model with production systems can maximize the value of the model. The outputs from the model can be customized to meet the needs of claim management. ### Decision process Deploy the selected model into your desired decision environment to embed the predictions into your regular business decisions. Insurance companies often have a separate system for claims management. For this particular use case, it may be in the best interest of the users to integrate the model with the claims management system, and with visualization tools such as Power BI or Tableau. If a model is integrated within an insurer’s claim management system when a new claim is reported, FNOL staff can record all the available information in the system. The model can then be run in the background to evaluate the ultimate severity. The estimated severity can help suggest initial case reserves and appropriate route for further claim handling (i.e., STP, regular claim adjusting, or experienced claim adjusters, possibly with nurse case manager involvement and/or reinsurance reporting). Carriers will want to include rules-based decisions as well, to capture decisions that are driven by considerations other than ultimate claim severity. Most carriers do not set initial reserves for STP claims. For those claims beyond STP, you can use model predictions to set initial reserves at the first notice of loss. Claims adjusters and nurse case managers will only be involved for claims over certain thresholds. The reinsurance reporting process may benefit from the model predictions as well; instead of waiting for claims to develop to very high severity, the reporting process may start at FNOL. Reinsurers will certainly appreciate the timely reporting of high severity claims, which will further improve the relationship between primary carriers and reinsurers. ### Decision stakeholders Consider the following to serve as decision stakeholders: - Claims management team - Claims adjusters - Reserving actuaries ### Model monitoring Carriers implementing a claims severity model usually have strictly defined business rules to ensure abnormal activities will be captured before they get out of control. Triggers based on abnormal behavior (for example, abnormally high predictions, too many missing inputs, etc.) can trigger manual reviews. Use the [performance monitoring capabilities](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/index.html) —especially service health, data drift, and accuracy to produce and distribute regular reports to stakeholders. ### Implementation considerations A claim severity model at FNOL should be one of a series of models built to monitor claim severity over time. Besides the FNOL Model, build separate models at different stages of a claim (e.g., 30 days, 90 days, 180 days) to leverage the additional information available and further evaluate the claim severity. Additional information comes in over time regarding medical treatments and diagnoses and missed work, allowing for improved accuracy as a claim matures. ### No-Code AI Apps Consider building a custom application where stakeholders can interact with the predictions and record the outcomes of the investigation. Once the model is deployed, predictions can be consumed for use in the [decision process](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/insurance-claims.html#decision-process). For example, this [No-Code AI App](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html) is an easily shareable, AI-powered application using a no-code interface: --- # Late shipment predictions URL: https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/late-ship.html > Helps supply chain managers can evaluate root cause and then implement short-term and long-term adjustments that prevent shipping delays. # Late shipment predictions With the inception of one-day and same-day delivery, customer standards on punctuality and speed have risen to levels unlike ever before. While a delayed delivery will usually be only a nuisance to the individual consumer, demands for speed ultimately flow upstream into the supply chain, where retailers and manufacturers are constantly being pressed on time. For these organizations, on-time performance is a matter of millions of dollars of customer orders or contractual obligations. Unfortunately, with the unavoidable challenges that come with managing variability in the supply chain, even the most well-known logistics carriers saw a 6.9 percent average delay across shipments made by 100 e-commerce retailers who collectively delivered more than 500,000 packages in the first quarter of 2019. Sample training data used in this use case, "Supply Chain Shipment Pricing Data": - SCMS_Delivery_History_Dataset.csv [Click here](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/late-ship.html#data) to jump directly to the hands-on sections that begin with working with data. Otherwise, the following several paragraphs describe the business justification and problem framing for this use case. ## Business problem A critical component of any supply chain network is to prevent parts shortages, especially when they occur at the last minute. Parts shortages not only lead to underutilized machines and transportation, but also cause a domino effect of late deliveries through the entire network. In addition, the discrepancies between the forecasted and actual number of parts that arrive on time prevent supply chain managers from optimizing their materials plans. To mitigate the impact delays will have on the supply chain, manufacturers adopt approaches such as holding excess inventory, optimizing product designs for more standardization, and moving away from single-sourcing strategies. However, most of these approaches add up to unnecessary costs for parts, storage, and logistics. In many cases, late shipments persist until supply chain managers can evaluate the root cause and then implement short-term and long-term adjustments that prevent them from occurring in the future. Unfortunately, supply chain managers have been unable to efficiently analyze historical data available in MRP systems because of the time and resources required. ## Solution value AI helps supply chain managers reduce parts shortages by predicting the occurrence of late shipments, which in turn gives them time to intervene. By learning from past cases of late shipments and their associated features, AI applies these patterns to future shipments to predict the likelihood that those shipments will also be delayed. Unlike complex MRP systems, AI provides supply chain managers with the statistical reasons behind each late shipment in an intuitive but scientific way. For example, when AI notifies supply chain managers of a late shipment, it will also explain why, offering reasons such as the shipment’s vendor, mode of transportation, or country. Using this information, supply chain managers can apply both-short term and long-term solutions to preventing late shipments. In the short term, based on their unique characteristics, shipment delays can be prevented by adjusting transportation or delivery routes. In the long term, supply chain managers can conduct aggregated root-cause analyses to discover and solve the systematic causes of delays. They can use this information to make strategic decisions, such as choosing vendors located in more accessible geographies or reorganizing shipment schedules and quantities. ## ROI estimation The ROI for implementing this solution can be estimated by considering the following factors: - Starting with the manufacturing company and production line stoppage, the cycle time of the production process can be used to understand how much of the production loss relates to part shortages. For example, if the cycle time (time taken to complete one part) is 60 seconds and each day 15 minutes of production are lost to part shortages, then total production loss is equivalent to 15 products, which can be translated to loss in profit of 15 products in a day. A similar calculation can be used to estimate annual loss due to part shortage. - For a logistic provider, predicting part shortages early can increase savings in terms of reduced inventory. This can be roughly measured by capturing the difference in maintaining parts stock before and after implementation of the AI solution. The difference in stock when multiplied by the holding and inventory cost per unit, calculates the overall ROI. Furthermore, in cases when the demand for parts is left unfulfilled (because of part shortages), the opportunity cost related to the unsatisfied demand could directly result in the loss of prospective business opportunities. ## Data This accelerator uses a [publicly-available dataset](https://www.kaggle.com/datasets/divyeshardeshana/supply-chain-shipment-pricing-data?select=SCMS_Delivery_History_Dataset.csv), provided by the President’s Emergency plan for AIDS relief (PEPFAR), to represent how a manufacturing or logistics company can leverage AI models to improve decision-making. This dataset provides supply chain health commodity shipment and pricing data. Specifically, it identifies Antiretroviral (ARV) and HIV lab shipments to supported countries. In addition, it provides the commodity pricing and associated supply chain expenses necessary to move the commodities to other countries for use. ### Features and sample data The features in the dataset represent some of the factors that are important in predicting delays. #### Target The target variable: - Late_delivery This feature represents whether or not a shipment would be delayed using values such as `True \ False`, `1 \ 0`, etc. This choice in target makes this a binary classification problem. The distribution of the target variable is imbalanced, with 11.4% being 1 (late delivery) and 88.6% being 0 (on-time delivery). #### Sample feature list The following shows sample features for this use case: | Feature name | Data type | Description | Data source | Example | | --- | --- | --- | --- | --- | | Vendor | Categorical | Name of the vendor who would be shipping the delivery | Purchase order | Ranbaxy, Sun Pharma etc. | | Item description | Text | Details of the part/item that is being shipped | Purchase order | 30mg HIV test kit, 600mg Lamivudine capsules | | Line item quantity | Numeric | Amount of item that was ordered | Purchase order | 1000, 300 etc. | | Line item value | Numeric | Unit price of the line item ordered | Purchase order | 0.39, 1.33 | | Manufacturing site | Categorical | Site of the vendor manufacturing (the same vendor can ship parts from different sites) | Invoice | Sun Pharma, India | | Product group | Categorical | Category of the product that is ordered | Purchase order | HRDT, ARV | | Shipment mode | Categorical | Mode of transport for part delivery | Invoice | Air, Truck | | Late delivery | Target (Binary) | Whether the delivery was late or on-time | ERP System, Purchase Order | 0 or 1 | In addition to the features listed above, incorporate any additional data that your organization collects that might be relevant to delays. (DataRobot is able to differentiate important/unimportant features if your selection would not improve modeling.) These features are generally stored across proprietary data sources available in the ERP systems of the organization. ### Data preparation The included dataset contains historical information on procurement transactions. Each row of analysis in the dataset is an individual order that is placed and whose delivery needs to be predicted. Every order has a scheduled delivery date and actual delivery date—the difference between these dates is used to define the target variable ( `Late_delivery`). If the delivery date surpassed the scheduled date, then the target variable had a value `1`, otherwise, the value is `0`. Overall, the dataset contains roughly 10,320 rows and 26 features, including the target variable. ## Modeling and insights DataRobot automates many parts of the modeling pipeline, including processing and partitioning the dataset, as described [here](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html). While this use case skips the modeling section and moves straight to model interpretation, it is worth noting that because the dataset is imbalanced, DataRobot automatically recommends using [LogLoss](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html#loglossweighted-logloss) as the optimization metric to identify the most accurate model, being that it is an error metric which penalizes wrong predictions. For this dataset, DataRobot found the most accurate model to be the Extreme Gradient Boosting Tree Classifier with unsupervised learning features using the open-source XGboost library. The following sections describe the insights available after a model is built. ### Feature Impact To provide transparency on how the model works, DataRobot provides both global and local levels of model explanations.[Feature Impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html) shows, at a high level, which features are driving model decisions (the relative importance of the features in the dataset in relation to the selected target variable). From the visualization, you can see that the model identified Pack Price, Country, Vendor, Vendor INCO Term, and Line item Insurance as some of the most critical factors affecting delays in the parts shipments: ### Prediction Explanations DataRobot also provides [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) to help understand the 10 key drivers for each prediction generated. This offers you the granularity you need to tailor your actions to the unique characteristics behind each part shortage. For example, if a particular country is a top reason for a shipment delay, you can take action by reaching out to vendors in these countries and closely monitoring the shipment delivery across these routes. Similarly, if there are certain vendors that are among the top reasons for delays, you can proactively reach out to these vendors and take corrective actions to avoid any delayed shipments that would affect the supply chain network. These insights help businesses make data-driven decisions to improve the supply chain process by incorporating new rules or alternative procurement sources. ### Word Cloud For text variables, such as `Part description` in the included dataset, use [Word Clouds](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/word-cloud-classic.html) to discover the words or phrases that are highly associated with delayed shipments. Although text features are generally the most challenging and time consuming to build models for, DataRobot automatically fits each individual text column as an individual classifier, which is directly preprocessed with natural language processing (NLP) techniques (tf-idf, n grams, etc.) In this cloud, you can see that the items described as nevirapine 10 mg are more likely to get delayed in comparison to other items. ### Evaluate accuracy To evaluate the performance of the model, DataRobot, by default, ran five-fold cross validation and the resulting AUC score was roughly 0.82. The AUC score on the Holdout set (unseen data) was nearly equivalent, indicating that the model is generalizing well and is not overfitting. The reason to use the AUC score for evaluating the model is because AUC ranks the output (i.e., the probability of delayed shipment) instead of looking at actual values. The [Lift Chart](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/lift-chart-classic.html), below, shows how the predicted values (blue line) compare to actual values (red line) when the data is sorted by predicted values. You can see that the model has slight under-predictions for the orders which are more likely to get delayed. But overall, the model performs well. Furthermore, depending on your ultimate problem framework, you can review the [Confusion Matrix](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/confusion-matrix-classic.html) for the selected model and, if required, adjust the prediction threshold to optimize for precision and recall. ## Predict and deploy After selecting a model, you can deploy it into your desired decision environment.Decision environments are the ways in which the predictions generated by the model will be consumed by the appropriate stakeholders in your organization, and how these stakeholders will make decisions using the predictions to impact the overall process. The predictions from this use case can augment the decisions of the supply chain managers as they foresee any upcoming delays in logistics. It acts as an intelligent machine that, combined with the decisions of the managers, help improve your entire supply chain network. ### Decision stakeholders The following table lists potential decision stakeholders | Stakeholder | Description | | --- | --- | | Decision Executors | Supply chain managers and procurement teams who are empowered with the information they need to ensure that the supply chain network is free from bottlenecks. These personnel have strong relationships with vendors and the ability to take corrective action using the model’s predictions. | | Decision Managers | Executive stakeholders who manage large-scale partnerships with key vendors. Based on the overall results, these stakeholders can perform quarterly reviews of the health of their vendor relationships to make strategic decisions on long-term investments and business partnerships. | | Decision Authors | Business analysts or data scientists who would build this decision environment. These analysts could be the engineers/analysts from the supply chain, engineering, or vendor development teams in the organization who usually work in collaboration with the supply chain managers and their teams. | ### Model deployment The model can be deployed using the DataRobot Prediction API. A REST API endpoint can bounce back predictions in near real time when new scoring data from new orders are received. ### No-Code AI Apps Once the model is deployed (in whatever way the organization decides), the predictions can be consumed in several ways. For example, a front-end application that acts as the supply chain’s reporting tool can be used to deliver new scoring data as an input to the model, which then returns predictions and Prediction Explanations in real-time for use in the [decision process](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/late-ship.html#decision-process). For example, this [No-Code AI App](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html) is an easily shareable, AI-powered application using a no-code interface: ### Decision process The action that managers and executive stakeholders could decide to take, based on the predictions and Prediction Explanations for identifying potential bottlenecks, is reaching out and collaborating with appropriate vendor teams in the supply chain network based on data-driven insights. The could make both long- and short-term decisions based on the severity of the impact of shortages on the business. ## Monitoring and management Tracking model health is one of the most critical components of proper model lifecycle management, similar to product lifecycle management. Use [MLOps](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html) to deploy, monitor (for data drift and accuracy), and manage all models across the organization through a centralized platform. ### Implementation considerations One of the major risks in implementing this solution in the real world is adoption at the ground level. Having strong and transparent relationships with vendors is also critical in taking corrective action. The risk is that vendors may not be ready to adopt a data-driven strategy and trust the model results. --- # Likelihood of a loan default URL: https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/loan-default.html > AI models for predicting the likelihood of a loan default can be deployed within the review process to score and rank all new flagged cases. # Likelihood of a loan default This page outlines the use case to reduce defaults and minimize risk by predicting the likelihood that a borrower will not repay their loan. It is captured below as a UI-based walkthrough. ## Business problem After the 2008 financial crisis, the IASB (International Accounting Standard Board) and FASB (Financial Accounting Standards Board) reviewed accounting standards. As a result, they updated policies to require estimated Expected Credit Loss (ECL) to maintain enough regulatory capital to handle any Unexpected Loss (UL). Now, every risk model undergoes tough scrutiny, and it is important to be aware of the regulatory guidelines while trying to deliver an AI model. This use case focuses on credit risk, which is defined as the likelihood that a borrower would not repay their lender. Credit risk can arise for individuals, SMEs, and large corporations and each is responsible for calculating ECL. Depending on the asset class, different companies take different strategies and components for calculation differ, but involve: - Probability of Default (PD) - Loss Given Default (LGD) - Exposure at Default (EAD) The most common approach for calculating the ECL is the following (see more about these factors in [problem framing](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/loan-default.html#problem-framing)): ``` ECL = PD * LGD * EAD ``` This use case builds a PD model for a consumer loan portfolio and provides some suggestions related LGD and EAD modeling. Sample training datasets for using some of the techniques described here are publicly available on [Kaggle](https://www.kaggle.com/c/home-credit-default-risk), but for interpretability, the examples do not exactly represent the Kaggle datasets. [Click here](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/loan-default.html#working-with-data) to jump directly to the hands-on sections that begin with working with data. Otherwise, the following several paragraphs describe the business justification and problem framing for this use case. ## Solution value Many credit decisioning systems are driven by scorecards, which are very simplistic rule-based systems. These are built by end-user organizations through industry knowledge or through simple statistical systems. Some organizations go a step further and obtain scorecards from third parties, which may not be customized for an individual organization’s book. An AI-based approach can help financial institutions learn signals from their own book and assess risk at a more granular level. Once the risk is calculated, a strategy may be implemented to use this information for interventions. If you can predict someone is going to default, this may lead to intervention steps, such as sending earlier notices or rejecting loan applications. ### Problem framing Banks deal with different types of risks, like credit risk, market risk, and operational risk. Calculating the ECL using `ECL = PD * LGD * EAD` is the most common approach. Risk is defined in financial terms as the chance that an outcome or investment’s actual gains will differ from an expected outcome or return. There are many ways you can position the problem, but in this specific use case you will be building Probability of Default (PD) models and will provide some guidance related to LGD and EAD modeling. For the PD model, the target variable is `is_bad`. In the training data, `0` indicates the borrower did pay and `1` indicates they defaulted. Here is additional guidance on the definition of each component of the ECL equation. Probability of Default (PD) - The borrower’s inability to repay their debt in full or on time. - Target is normally defined as 90 days delinquency. - Machine learning models generally give good results if adequate data is available for a particular asset class. Loss Given Default (LGD) - The proportion of the total exposure that cannot be recovered by the lender once a default has occurred. - Target is normally defined as the recovery rates and the value lies between 0 and 1. - Machine learning models required for this problem normally use Beta regression, which is not very common and therefore not supported in a lot of statistical software. We can divide this into two stages since a lot of the values in the target are zero. Exposure at Default (EAD) - The total value that a lender is exposed to when a borrower defaults. - Target is the proportion from the original amount of the loan that is still outstanding at the moment when the borrower defaulted. - Generally, machine learning models with MSE as loss are used. ### ROI estimation The ROI for implementing this solution can be estimated by considering the following factors: - ROI varies on the size of the business and the portfolio. For example, the ROI for secured loans would be quite different to those for a credit card portfolio. - If you are moving from one compliance framework to another, you need to take the appropriate considerations—whether to model a new and existing portfolio separately and, if so, make appropriate adjustments to the ROI calculations. - ROI depends on the decisioning system. If it is a binary (yes or no) decision on loan approval, you can assign dollar values to the amounts of true positives, false positives, true negatives, and false negatives. The sum total of that is the value at a given threshold. If there is an existing model, the difference in results between existing and new models is the ROI captured. - If the decisioning is non binary, then at every decision point, evaluate the difference between loan provided and the collections done. ## Working with data For illustrative purposes, this use case simplifies the sample datasets provided by Home Credit Group, which are publicly available on [Kaggle](https://www.kaggle.com/c/home-credit-default-risk). ### Sample feature list | Feature Name | Data Type | Description | Data Source | Example | | --- | --- | --- | --- | --- | | Amount_Credit | Numeric | Credit taken by a person | Application | 20,000 | | Flag_Own_Car | Categorical | Flag if applicant owns a car | Application | 1 | | Age | Numeric | Age of the applicant | Application | 25 | | CreditOverdue | Binomial | Whether credit is overdue | Bureau | TRUE | | Channel | Categorical | Channel through which credit taken | PreviousApplication | Online | | Balance | Numeric | Balance in credit card | CreditCard | 2,500 | | Is_Bad | Numeric (target) | Whether the borrower defaulted, 0 or 1 | Bureau | 1 (default) | ## Modeling and insights DataRobot automates many parts of the modeling pipeline, including processing and partitioning the dataset, as described [here](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html). This use case skips the modeling section and moves straight to model interpretation. DataRobot provides a variety of insights to [interpret results](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/loan-default.html#interpret-results) and [evaluate-accuracy](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/loan-default.html#evaluate-accuracy). ### Interpret results After automated modeling completes, the Leaderboard ranks each model. By default, DataRobot uses LogLoss as the evaluation metric. #### Feature Impact [Feature Impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html) reveals the association between each feature and the model target. For example: #### Feature Effects To understand the direction of impact and the default risk at different levels of the input feature, DataRobot provides partial dependence plots as part of the [Feature Effects](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-effects-classic.html) visualization. It depicts how the likelihood of default changes when the input feature takes different values. In this example, which is plotting for `AMT_CREDIT` (loan amount), as the loan amount increases above to $300K, the default risk increases in a step manner from 6% to 7% and then in another step to 7.8% when the loan about is around $500K. #### Prediction Explanations In the [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) visualization, DataRobot provides, for each alert scored and prioritized by the model, a human-interpretable rationale. In the example below, the record with ID=3606 gets a very high likelihood of turning into a loan default (prediction=51.2%). The main reasons are due to information from external sources ( `EXT_SOURCE_2` and `EXT_SOURCE_3`) and the source of income ( `NAME_INCOME_TYPE`) being `pension`. Prediction Explanations also help in maintaining regulatory compliance. It provides reasons why a particular loan decision was taken. ### Evaluate accuracy The following insights help evaluate accuracy. #### Lift Chart The [Lift Chart](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/lift-chart-classic.html) shows you how effective the model is at separating the default and non-default applications. Each record in the out-of-sample partition gets scored by the trained model and assigned with a default probability. In the Lift Chart, records are sorted based on the predicted probability, broken down into 10 deciles, and displayed from lowest to the highest. For each decile, DataRobot computes the average predicted risk (blue line/plus) as well as the average actual risk (orange line/circle), and displays the two lines together. In general, the steeper the actual line is, and the more closely the predicted line matches the actual line, the better the model is. A consistently increasing line is another good indicator. #### ROC Curve Once you know the model is performing well, select an explicit threshold to make a binary decision based on the continuous default risk predicted by DataRobot. The [ROC Curve](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/roc-curve-tab-use.html) tools provide a variety of information to help make some of the important decisions in selecting the optimal threshold: - The false negative rate has to be as small as possible. False negatives are the applications that are flagged as not defaults but actually end up defaulting on payment. Missing a true default is dangerous and expensive. - Ensure the selected threshold is working not only on the seen data, but on the unseen data too. ### Post-processing In some cases where there are fewer regulatory considerations, straight-through processing (SIP) may be possible, where an automated yes or no decision can be taken based on the predictions. But the more common approach is to convert the risk probability into a score (i.e., a credit score determined by organizations like Experian and TransUnion). The scores are derived based on exposure of probability buckets and on SME knowledge. Most of the machine learning models used for credit risk require approval from the Model Risk Management (MRM) team; to address this, the [Compliance Report](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/compliance-classic/compliance-tab.html) provides comprehensive evidence and rationale for each step in the model development process. ## Predict and deploy After finding the right model that best learns patterns in your data, you can deploy the model into your desired decision environment.Decision environments are the ways in which the predictions generated by the model will be consumed by the appropriate stakeholders in your organization, and how these stakeholders will make decisions using the predictions to impact the overall process. Decisions can be a blend of automated and straight-through processing or manual interventions. The degree of automation depends on the portfolio and business maturity. For example, retail loans or peer-to-peer portfolios in banks and fintechs are highly automated. Some fintechs promote their low loan-processing times. Unlike high ticket items, like mortgages, corporate loans may be a case of manual intervention. ### Decision stakeholders The following table lists potential decision stakeholders: | Stakeholder | Description | | --- | --- | | Decision Executors | The underwriting team will be the direct consumers of the predictions. These can be direct systems in the case of straight-through processing or an underwriting team sitting in front offices in the case of manual intervention. | | Decision Managers | Decisions often flow through to the Chief Risk Officer, who is responsible for the ultimate risk of the portfolio. However, there generally are intermediate managers (based on the structure of the organization). | | Decision Authors | Data scientists in credit risk teams drive the modeling. The model risk monitoring team is also be a key stakeholder. | ### Decision process Generally, models do not result in a direct yes or no decision being made, except in cases where models are used in less-regulated environments. Instead, the risk is converted to a score and, based on the score, impacts the interest or credit amount offered to the customer. ### Model monitoring Predictions are done in real time or batch mode based on the nature of the business. Regular monitoring and alerting is critical for [data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html). This is particularly important from a model risk perspective. These models are designed to be robust and last longer, so recalibration may be less frequent than in other industries. ### Implementation considerations - Credit risk models normally require integrations with third-party solutions like Experian and FICO. Ask about deployment requirements and if it is possible to move away from legacy tools. - Credit risk models require approval from the validation team, which can take significant time (for example, convincing them to adopt new model approval methods if they have not previously approved machine learning models). - Model validation teams can have strict requirements for specific assets. For example, if models need to generate an equation and therefore model code export. Be certain to discuss any questions in advance with the modeling team before taking the final model to validation teams. - Discuss alternative approaches for assets where the default rate was historically low, as the model might not be accurate enough to prove ROI. In addition to traditional risk analysis, [target leakage](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#target-leakage) may require attention in this use case. Target leakage can happen when information that should not be available at the time of prediction is being used to train the model. That is, particular features leak information about the eventual outcome, and that artificially inflates the performance of the model in training. While the implementation outlined in this document involves relatively few features, it is important to be mindful of target leakage whenever merging multiple datasets due to improper joins. DataRobot supports robust target leakage detection in the second round of exploratory data analysis (EDA) and the selection of the Informative Features feature list during Autopilot. ### No-Code AI Apps A [no-code](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html) or Streamlit app can be useful for showing aggregate results of the model (e.g., risky transactions at an entity level). Consider building a custom application where stakeholders can interact with the predictions and record the outcomes of the investigation. Once the model is deployed, predictions can be consumed for use in the [decision process](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/loan-default.html#decision-process). For example, this [No-Code AI App](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html) is an easily shareable, AI-powered application using a no-code interface: --- # Anti-Money Laundering (AML) Alert Scoring URL: https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/money-launder.html > Build a model that uses historical data, including customer and transactional information, to identify which alerts resulted in a Suspicious Activity Report (SAR). # Anti-Money Laundering (AML) Alert Scoring This use case builds a model that uses historical data, including customer and transactional information, to identify which alerts resulted in a Suspicious Activity Report (SAR). The model can then be used to assign a suspicious activity score to future alerts and improve the efficiency of an AML compliance program using rank ordering by score. It is captured below as a UI-based walkthrough. It is also available as a [Jupyter notebook](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/anti-money-laundering/Anti-Money%20Laundering%20(AML%29%20Alert%20Scoring.ipynb) that you can download and execute. [Download the dataset](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/DR_Demo_AML_Alert_train.csv) ## Business problem A key pillar of any AML compliance program is to monitor transactions for suspicious activity. The scope of transactions is broad, including deposits, withdrawals, fund transfers, purchases, merchant credits, and payments. Typically, monitoring starts with a rules-based system that scans customer transactions for red flags consistent with money laundering. When a transaction matches a predetermined rule, an alert is generated and the case is referred to the bank’s internal investigation team for manual review. If the investigators conclude the behavior is indicative of money laundering, then the bank will file a Suspicious Activity Report (SAR) with FinCEN. Unfortunately, the standard transaction monitoring system described above has costly drawbacks. In particular, the rate of false-positives (cases incorrectly flagged as suspicious) generated by this rules-based system can reach 90% or more. Since the system is rules-based and rigid, it cannot dynamically learn the complex interactions and behaviors behind money laundering. The prevalence of false-positives makes investigators less efficient as they have to manually weed out cases that the rules-based system incorrectly marked as suspicious. Compliance teams at financial institutions can have hundreds or even thousands of investigators, and the current systems prevent investigators from becoming more effective and efficient in their investigations. The cost of reviewing an alert ranges between `$30~$70`. For a bank that receives 100,000 alerts a year, this is a substantial sum; on average, penalties imposed for proven money laundering amount to `$145` million per case. A reduction in false positives could result in savings between `$600,000~$4.2` million per year. ## Solution value This use case builds a model that dynamically learns patterns in complex data and reduces false positive alerts. Financial crime compliance teams can then prioritize the alerts that legitimately require manual review and dedicate more resources to those cases most likely to be suspicious. By learning from historical data to uncover patterns related to money laundering, AI also helps identify which customer data and transaction activities are indicative of a high risk for potential money laundering. The primary issues and corresponding opportunities that this use case addresses include: | Issue | Opportunity | | --- | --- | | Potential regulatory fine | Mitigate the risk of missing suspicious activities due to lack of competency with alert investigations. Use alert scores to more effectively assign alerts—high risk alerts to more experienced investigators, low risk alerts to more junior team members. | | Investigation productivity | Increase investigators' productivity by making the review process more effective and efficient, and by providing a more holistic view when assessing cases. | Specifically: - Strategy/challenge: Help investigators focus their attention on cases that have the highest risk of money laundering while minimizing the time they spend reviewing false-positive cases. For banks with large volumes of daily transactions, improvements in the effectiveness and efficiency of their investigations ultimately results in fewer cases of money laundering that go unnoticed. This allows banks to enhance their regulatory compliance and reduce the volume of financial crime present within their network. - Business driver: Improve the efficiency of AML transaction monitoring and lower operational costs. With its ability to dynamically learn patterns in complex data, AI significantly improves accuracy in predicting which cases will result in a SAR filing. AI models for anti-money laundering can be deployed into the review process to score and rank all new cases. - Model solution: Assign a suspicious activity score to each AML alert, improving the efficiency of an AML compliance program. Any case that exceeds a predetermined threshold of risk is sent to the investigators for manual review. Meanwhile, any case that falls below the threshold can be automatically discarded or sent to a lighter review. Once AI models are deployed into production, they can be continuously retrained on new data to capture any novel behaviors of money laundering. This data will come from the feedback of investigators. Specifically, the model will use rules that trigger an alert whenever a customer requests a refund of any amount since small refund requests could be the money launderer’s way of testing the refund mechanism or trying to establish refund requests as a normal pattern for their account. The following table summarizes aspects of this use case. | Topic | Description | | --- | --- | | Use case type | Anti-money laundering (false positive reduction) | | Target audience | Data Scientist, Financial Crime Compliance Team | | Desired outcomes | Identify which customer data and transaction activity are indicative of a high risk for potential money laundering.Detect anomalous changes in behavior or nascent money laundering patterns before they spread.Reduce the false positive rate for the cases selected for manual review. | | Metrics/KPIs | Annual alert volumeCost per alertFalse positive reduction rate | | Sample dataset | https://s3.amazonaws.com/datarobot-use-case-datasets/DR_Demo_AML_Alert_train.csv | ### Problem framing The target variable for this use case is whether or not the alert resulted in a SAR after manual review by investigators, making this a binary classification problem. The unit of analysis is an individual alert—the model will be built on the alert level—and each alert will receive a score ranging from 0 to 1. The score indicates the probability of being a SAR. The goal of applying a model to this use case is to lower the false positive rate, which means resources are not spent reviewing cases that are eventually determined not to be suspicious after an investigation. In this use case, the False Positive Rate of the rules engine on the validation sample (1600 records) is: The number of `SAR=0` divided by the total number of records = `1436/1600` = `90%`. ### ROI estimation ROI can be calculated as follows: `Avoided potential regulatory fine + Annual alert volume * false positive reduction rate * cost per alert` A high-level measurement of the ROI equation involves two parts. 1. The total amount ofavoided potential regulatory fineswill vary depending on the nature of the bank and must be estimated on a case-by-case basis. 2. The second part of the equation is where AI can have a tangible impact on improving investigation productivity and reducing operational costs. Consider this example: Result: The annual ROI of implementing the solution will be100,000 * 70% * ($30~$70) = $2.1MM~$4.9MM. ## Working with data The linked synthetic dataset illustrates a credit card company’s AML compliance program. Specifically, the model detects the following money-laundering scenarios: - The customer spends on the card but overpays their credit card bill and seeks a cash refund for the difference. - The customer receives credits from a merchant without offsetting transactions and either spends the money or requests a cash refund from the bank. The unit of analysis in this dataset is an individual alert, meaning a rule-based engine is in place to produce an alert to detect potentially suspicious activity consistent with the above scenarios. ### Data preparation Consider the following when working with data: - Define the scope of analysis: Collect alerts from a specific analytical window to start with; it’s recommended that you use 12–18 months of alerts for model building. - Define the target: Depending on the investigation processes, the target definition could be flexible. In this walkthrough, alerts are classified asLevel1,Level2,Level3, andLevel3-confirmed. These labels indicate at which level of the investigation the alert was closed (i.e., confirmed as a SAR). To create a binary target, treatLevel3-confirmedas SAR (denoted by 1) and the remaining levels as non-SAR alerts (denoted by 0). - Consolidate information from multiple data sources: Below is a sample entity-relationship diagram indicating the relationship between the data tables used for this use case. Some features are static information—for example, `kyc_risk_score` and `state of residence` —these can be fetched directly from the reference tables. For transaction behavior and payment history, the information will be derived from a specific time window prior to the alert generation date. This case uses 90 days as the time window to obtain the dynamic customer behavior, such as `nbrPurchases90d`, `avgTxnSize90d`, or `totalSpend90d`. Below is an example of one row in the training data after it is merged and aggregated (it is broken into multiple lines for easier visualization). ### Features and sample data The features in the sample dataset consist of KYC (Know-Your-Customer) information, demographic information, transactional behavior, and free-form text information from notes taken by customer service representatives. To apply this use case in your organization, your dataset should contain, at a minimum, the following features: - Alert ID - Binary classification target ( SAR/no-SAR , 1/0 , True/False , etc.) - Date/time of the alert - "Know Your Customer" score used at the time of account opening - Account tenure, in months - Total merchant credit in the last 90 days - Number of refund requests by the customer in the last 90 days - Total refund amount in the last 90 days Other helpful features to include are: - Annual income - Credit bureau score - Number of credit inquiries in the past year - Number of logins to the bank website in the last 90 days - Indicator that the customer owns a home - Maximum revolving line of credit - Number of purchases in the last 90 days - Total spend in the last 90 days - Number of payments in the last 90 days - Number of cash-like payments (e.g., money orders) in last 90 days - Total payment amount in last 90 days - Number of distinct merchants purchased from in the last 90 days - Customer Service Representative notes and codes based on conversations with customer (cumulative) The table below shows a sample feature list: | Feature name | Data type | Description | Data source | Example | | --- | --- | --- | --- | --- | | ALERT | Binary | Alert Indicator | tbl_alert | 1 | | SAR | Binary(Target) | SAR Indicator (Binary Target) | tbl_alert | 0 | | kycRiskScore | Numeric | Account relationship (Know Your Customer) score used at time of account opening | tbl_customer | 2 | | income | Numeric | Annual income | tbl_customer | 32600 | | tenureMonths | Numeric | Account tenure in months | tbl_customer | 13 | | creditScore | Numeric | Credit bureau score | tbl_customer | 780 | | state | Categorical | Account billing address state | tbl_account | VT | | nbrPurchases90d | Numeric | Number of purchases in last 90 days | tbl_transaction | 4 | | avgTxnSize90d | Numeric | Average transaction size in last 90 days | tbl_transaction | 28.61 | | totalSpend90d | Numeric | Total spend in last 90 days | tbl_transaction | 114.44 | | csrNotes | Text | Customer Service Representative notes and codes based on conversations with customer (cumulative) | tbl_customer_misc | call back password call back card password replace atm call back | | nbrDistinctMerch90d | Numeric | Number of distinct merchants purchased at in last 90 days | tbl_transaction | 1 | | nbrMerchCredits90d | Numeric | Number of credits from merchants in last 90 days | tbl_transaction | 0 | | nbrMerchCredits-RndDollarAmt90d | Numeric | Number of credits from merchants in round dollar amounts in last 90 days | tbl_transaction | 0 | | totalMerchCred90d | Numeric | Total merchant credit amount in last 90 days | tbl_transaction | 0 | | nbrMerchCredits-WoOffsettingPurch | Numeric | Number of merchant credits without an offsetting purchase in last 90 days | tbl_transaction | 0 | | nbrPayments90d | Numeric | Number of payments in last 90 days | tbl_transaction | 3 | | totalPaymentAmt90d | Numeric | Total payment amount in last 90 days | tbl_account_bill | 114.44 | | overpaymentAmt90d | Numeric | Total amount overpaid in last 90 days | tbl_account_bill | 0 | | overpaymentInd90d | Numeric | Indicator that account was overpaid in last 90 days | tbl_account_bill | 0 | | nbrCustReqRefunds90d | Numeric | Number refund requests by the customer in last 90 days | tbl_transaction | 1 | | indCustReqRefund90d | Binary | Indicator that customer requested a refund in last 90 days | tbl_transaction | 1 | | totalRefundsToCust90d | Numeric | Total refund amount in last 90 days | tbl_transaction | 56.01 | | nbrPaymentsCashLike90d | Numeric | Number of cash like payments (e.g., money orders) in last 90 days | tbl_transaction | 0 | | maxRevolveLine | Numeric | Maximum revolving line of credit | tbl_account | 14000 | | indOwnsHome | Numeric | Indicator that the customer owns a home | tbl_transaction | 1 | | nbrInquiries1y | Numeric | Number of credit inquiries in the past year | tbl_transaction | 0 | | nbrCollections3y | Numeric | Number of collections in the past year | tbl_collection | 0 | | nbrWebLogins90d | Numeric | Number of logins to the bank website in the last 90 days | tbl_account_login | 7 | | nbrPointRed90d | Numeric | Number of loyalty point redemptions in the last 90 days | tbl_transaction | 2 | | PEP | Binary | Politically Exposed Person indicator | tbl_customer | 0 | ## Modeling and insights DataRobot automates many parts of the modeling pipeline, including processing and partitioning the dataset, as described [here](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html). This document starts with the visualizations available once modeling has started. ### Exploratory Data Analysis (EDA) Navigate to the Data tab to learn more about your data—summary statistics based on sampled data known as [EDA](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html). Click each feature to see a variety of information, including a [histogram](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html) that represents the relationship of the feature with the target. ### Feature Associations While DataRobot is running Autopilot to find the champion model, use the [Data > Feature Associations](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/feature-assoc.html) tab to view the feature association matrix and understand the correlations between each pair of input features. For example, the features `nbrPurchases90d` and `nbrDistinctMerch90d` (top-left corner) have strong associations and are, therefore, ‘clustered’ together (where each color block in this matrix is a cluster). DataRobot provides a variety of insights to [interpret results](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/money-launder.html#interpret-results) and [evaluate accuracy](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/money-launder.html#evaluate-accuracy). ### Leaderboard After Autopilot completes, the Leaderboard ranks each model based on the selected optimization metrics (LogLoss in this case). The outcome of Autopilot is not only a selection of best-suited models, but also the identification of a recommended model—the model that best understands how to predict the target feature `SAR`. Choosing the best model is a balance of accuracy, metric performance, and model simplicity. See the [model recommendation process](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html) description for more detail. Autopilot will continue building models until it selects the best predictive model for the specified target feature. This model is at the top of the Leaderboard, marked with the Recommended for Deployment badge. To reduce false positives, you can choose other metrics like Gini Norm to sort the Leaderboard based on how good the models are at giving SAR a higher rank than the non-SAR alerts. ### Interpret results There are many visualizations within DataRobot that provide insight into why an alert might be SAR. Below are the most relevant for this use case. #### Blueprint Click on a model to reveal the model [blueprint](https://docs.datarobot.com/en/docs/api/reference/public-api/blueprints.html) —the pipeline of preprocessing steps, modeling algorithms, and post-processing steps used to create the model. #### Feature Impact [Feature Impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html) reveals the association between each feature and the target. DataRobot identifies the top three most impactful features (which enable the machine to differentiate SAR from non-SAR alerts) as `total merchant credit in the last 90 days`, `number refund requests by the customer in the last 90 days`, and `total refund amount in the last 90 days`. #### Feature Effects To understand the direction of impact and the SAR risk at different levels of the input feature, DataRobot provides partial dependence graphs (within the [Feature Effects](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-effects-classic.html) tab) to depict how the likelihood of being a SAR changes when the input feature takes different values. In this example, the total merchant credit amount in the last 90 days is the most impactful feature, but the SAR risk is not linearly increasing when the amount increases. - When the amount is below $1000, the SAR risk remains relatively low. - SAR risk surges significantly when the amount is above $1000. - SAR risk increase slows when the amount approaches $1500. - SAR risk tilts again until it hits the peak and plateaus out at around $2200. The partial dependence graph makes it very straightforward to interpret the SAR risk at different levels of the input features. This could also be converted to a data-driven framework to set up risk-based thresholds that augment the traditional rule-based system. #### Prediction Explanations To turn the machine-made decisions into human-interpretable rationale, DataRobot provides [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) for each alert scored and prioritized by the machine learning model. In the example below, the record with `ID=1269` has a very high likelihood of being a suspicious activity (prediction=90.2%), and the three main reasons are: - Total merchant credit amount in the last 90 days is significantly greater than the others. - Total spend in the last 90 days is much higher than average. - Total payment amount in the last 90 days is much higher than average. Prediction Explanations can also be used to cluster alerts into subgroups with different types of transactional behaviors, which could help triage alerts to different investigation approaches. #### Word Cloud The [Word Cloud](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/word-cloud-classic.html) allows you to explore how text fields affect predictions. The Word Cloud uses a color spectrum to indicate the word's impact on the prediction. In this example, red words indicate the alert is more likely to be associated with a SAR. ### Evaluate accuracy The following insights help evaluate accuracy. #### Lift Chart The [Lift Chart](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/lift-chart-classic.html) shows how effective the model is at separating the SAR and non-SAR alerts. After an alert in the out-of-sample partition gets scored by the model, it is assigned a risk score that measures the likelihood of the alert being a SAR risk or becoming a SAR. In the Lift Chart, alerts are sorted based on the SAR risk, broken down into 10 deciles, and displayed from lowest to the highest. For each decile, DataRobot computes the average predicted SAR risk (blue plus) as well as the average actual SAR event (orange circle) and depicts the two lines together. For the champion model built for this false positive reduction use case, the SAR rate of the top decile is 55%, which is a significant lift from the ~10% SAR rate in the training data. The top three deciles capture almost all SARs, which means that the 70% of alerts with very low predicted SAR risk rarely result in SAR. #### ROC Curve Once you know the model is performing well, you select an explicit threshold to make a binary decision based on the continuous SAR risk predicted by DataRobot. The [ROC Curve](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/roc-curve-tab-use.html) tools provide a variety of information to help make some of the important decisions in selecting the optimal threshold: - The false negative rate has to be as small as possible. False negatives are the alerts that DataRobot determines are not SARs which then turn out to be true SARs. Missing a true SAR is very dangerous and would potentially result in an MRA (matter requiring attention) or regulatory fine. This case takes a conservative approach. To have a false negative rate of 0, the threshold has to be low enough to capture all the SARs. - Keep the alert volume as low as possible to reduce enough false positives. In this context, all alerts generated in the past that are not SARs are the de-facto false positives. The machine learning model is likely to assign a lower score to those non-SAR alerts; therefore, pick a high-enough threshold to reduce as many false positive alerts as possible. - Ensure the selected threshold is not only working on the seen data, but also on the unseen data, so that when the model gets deployed to the transaction monitoring system for ongoing scoring, it could still reduce false positives without missing any SARs. Different choices of thresholds using the cross-validation data (the data used for model training and validation) determines that `0.03` is the optimal threshold since it satisfies the first two criteria. On the one hand, the false negative rate is 0; on the other hand, the alert volume is reduced from `8000` to `2142`, reducing false positive alerts by 73% ( `5858/8000`) without missing any SARs. For the third criterion—does the threshold also work on the unseen alert—you can quickly validate it in DataRobot. By changing the data selection to Holdout and applying the same threshold ( `0.03`), the false negative rate remains 0, and the false positive reduction rate remains at 73% ( `1457/2000`). This proves that the model generalizes well and will perform as expected on unseen data. #### Payoff matrix From the Profit Curve tab, use the [Payoff Matrix](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/profit-curve-classic.html) to set thresholds based on simulated profit. If the bank has a specific risk tolerance for missing a small portion of historical SAR, they can also apply the Payoff Matrix to pick up the optimal threshold for the binary cutoff. For example: | Field | Example | Description | | --- | --- | --- | | False Negative | TP=-$200 | Reflects the cost of remediating a SAR that was not detected. | | False Positive | FP=-$50 | Reflects the cost of investigating an alert that proved a "false alarm." | | Metrics | False Positive Rate, False Negative Rate, and Average Profit | Provides standard statistics to help describe model performance at the selected display threshold. | By setting the cost per false positive to `$50` (cost of investigating an alert) and the cost per false negative to `$200` (cost of remediating a SAR that was not detected), the threshold is optimized at `0.1183` which gives a minimum cost of `$53k ($6.6 * 8000)` out of 8000 alerts and the highest ROI of `$347k ($50 * 8000 - $53k)`. On the one hand, the false negative rate remains low (only 5 SARs were not detected); on the other hand, the alert volume is reduced from 8000 to 1988, meaning the number of investigations is reduced by more than 75% (6012/8000). The threshold is optimized at `0.0619`, which gives the highest ROI of $300k out of 8000 alerts. By setting this threshold, the bank will reduce false positives by 74.3% ( `5940/8000`) at the risk of missing only 3 SARs. See the [deep dive](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/money-launder.html#deep-dive-imbalanced-targets) for information on handling class imbalance problems. ### Post-processing Once the modeling team decides on the champion model, they can download [compliance documentation](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/compliance-classic/index.html) for the model. The resulting Microsoft Word document provides a 360-degree view of the entire model-building process, as well as all the challenger models that are compared to the champion model. Most of the machine learning models used for the Financial Crime Compliance domain require approval from the Model Risk Management (MRM) team. The compliance document provides comprehensive evidence and rationale for each step in the model development process. ## Predict and deploy Once you identify the model that best learns patterns in your data to predict SARs, you can deploy it into your desired decision environment.Decision environments are the ways in which the predictions generated by the model will be consumed by the appropriate organizational [stakeholders](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/money-launder.html#decision-stakeholders), and how these stakeholders will make decisions using the predictions to impact the overall process. This is a critical step for implementing the use case, as it ensures that predictions are used in the real world to reduce false positives and improve efficiency in the investigation process. The following applications of the alert-prioritization score from the false positive reduction model both automate and augment the existing rule-based transaction monitoring system. - If the FCC (Financial Crime Compliance) team is comfortable with removing the low-risk alerts (very low prioritization score) from the scope of investigation, then the binary threshold selected during the model-building stage will be used as the cutoff to remove those no-risk alerts. The investigation team will only investigate alerts above the cutoff, which will still capture all the SARs based on what was learned from the historical data. - Often regulatory agencies will consider auto-closure or auto-removal as an aggressive treatment for production alerts. If auto-closing is not the ideal way to use the model output, the alert prioritization score can still be used to triage alerts into different investigation processes, improving the operational efficiency. ### Decision stakeholders The following table lists potential decision stakeholders: | Stakeholder | Description | | --- | --- | | Decision Executors | Financial Crime Compliance Team | | Decision Managers | Chief Compliance Officer | | Decision Authors | Data scientists or business analysts | ### Decision process Currently, the review process consists of a deep-dive analysis by investigators. The data related to the case is made available for review so that the investigators can develop a 360° view of the customer, including their profile, demographic, and transaction history. Additional data from third-party data providers and web crawling can supplement this information to complete the picture. For transactions that do not get auto-closed or auto-removed, the model can help the compliance team create a more effective and efficient review process by triaging their reviews. The predictions and their explanations also give investigators a more holistic view when assessing cases. Risk-based Alert Triage: Based on the prioritization score, the investigation team can take different investigation strategies. - For no-risk or low-risk alerts—alerts can be reviewed on a quarterly basis, instead of monthly. The frequently alerted entities without any SAR risk will be reviewed once every three months, which will significantly reduce the time of investigation. - For high-risk alerts with higher prioritization scores—investigations can fast-forward to the final stage in the alert escalation path. This will significantly reduce the effort spent on level 1 and level 2 investigations. - For medium-risk alerts—the standard investigation process can still be applied. Smart Alert Assignment: For an alert investigation team that is geographically dispersed, the alert prioritization score can be used to assign alerts to different teams in a more effective manner. High-risk alerts can be assigned to the team with the most experienced investigators, while low-risk alerts are assigned to the less-experienced team. This will mitigate the risk of missing suspicious activities due to a lack of competency during alert investigations. For both approaches, the definition of high/medium/low risk could be either a set of hard thresholds (for example, High: score>=0.5, Medium: 0.5>score>=0.3, Low: score<0.3), or based on the percentile of the alert scores on a monthly basis (for example, High: above 80th percentile, Medium: between 50th and 80th percentile, Low: below 50th percentile). ### Model deployment The predictions generated from DataRobot can be integrated with an alert management system which will let the investigation team know of high-risk transactions. ### Model monitoring DataRobot will continuously monitor the model deployed on the dedicated prediction server. With DataRobot [MLOps](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html), the modeling team can monitor and manage the alert prioritization model by tracking the distribution drift of the input features as well as the performance deprecation over time. ### Implementation considerations When operationalizing this use case, consider the following, which may impact outcomes and require model re-evaluation: - Change in the transactional behavior of the money launderers. - Novel information introduced to the transaction, and customer records that are not seen by the machine learning models. ### No-Code AI Apps Consider building a custom application where stakeholders can interact with the predictions and record the outcomes of the investigation. Once the model is deployed, predictions can be consumed for use in the [decision process](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/money-launder.html#decision-process). For example, this [No-Code AI App](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html) is an easily shareable, AI-powered application using a no-code interface: ### Notebook demo See the notebook version of this accelerator [here](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced-experimentation/customer_communication_datarobot_gen_ai/effective_customer_communication_datarobot_gen_ai.ipynb). ## Deep dive: Imbalanced targets In AML and Transaction Monitoring, the SAR rate is usually very low (1%–5%, depending on the detection scenarios); sometimes it could be even lower than 1% in extremely unproductive scenarios. In machine learning, such a problem is called class imbalance. The question becomes, how can you mitigate the risk of class imbalance and let the machine learn as much as possible from the limited known-suspicious activities? DataRobot offers different techniques to handle class imbalance problems. Some techniques: - Evaluate the model withdifferent metrics. For binary classification (the false positive reduction model here, for example), LogLoss is used as the default metric to rank models on the Leaderboard. Since the rule-based system is often unproductive, which leads to a very low SAR rate, it’s reasonable to take a look at a different metric, such as the SAR rate in the top 5% of alerts in the prioritization list. The objective of the model is to assign a higher prioritization score with a high risk alert, so it’s ideal to have a higher rate of SAR in the top tier of the prioritization score. In the example shown in the image below, the SAR rate in the top 5% of prioritization score is more than 70% (the original SAR rate is less than 10%), which indicates that the model is very effective in ranking the alert based on the SAR risk. - DataRobot also provides flexibility for modelers when tuning hyperparameters which could also help with the class imbalance problem. In the example below, the Random Forest Classifier is tuned by enabling the balance_boostrap (a random sample with an equal amount of SAR and non-SAR alerts in each decision tree in the forest); you can see the validation score of the new ‘Balanced Random Forest Classifier’ model is slightly better than the parent model. - You can also use Smart Downsampling (from the Advanced Options tab) to intentionally downsample the majority class (i.e., non-SAR alerts) in order to build faster models with similar accuracy. --- # Purchase card fraud detection URL: https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/p-card-detect.html > Helps organizations that employ purchase cards for procurement monitor for fraud and misuse. # Purchase card fraud detection In this use case you will build a model that can review 100% of purchase card transactions and identify the riskiest for further investigation via manual inspection. In addition to automating much of the resource-intensive tasks of reviewing transactions, this solution can also provide high-level insights such as aggregating predictions at the organization level to identify problematic departments and agencies to target for audit or additional interventions. Sample training data used in this use case: - synth_training_fe.csv.zip [Click here](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/p-card-detect.html#data) to jump directly to the hands-on sections that begin with working with data. Otherwise, the following several paragraphs describe the business justification and problem framing for this use case. ## Background Many auditor’s offices and similar fraud shops rely on business rules and manual processes to manage their operations of thousands of purchase card transactions each week. For example, an office reviews transactions manually in an Excel spreadsheet, leading to many hours of review and missed instances of fraud. They need a way to simplify this process drastically while also ensuring that instances of fraud are detected. They also need a way to seamlessly fold each transaction’s risk score into a front-end decision application that will serve as the primary way to process their review backlog for a broad range of users. Key use case takeaways: Strategy/challenge: Organizations that employ purchase cards for procurement have difficulty monitoring for fraud and misuse, which can comprise 3% or more of all purchases. Much of the time spent by examiners is quite manual and involves sifting through mostly safe transactions looking for clear instances of fraud or applying rules-based approaches that miss out on risky activity. Model solution: ML models can review 100% of transactions and identify the riskiest for further investigation. Risky transactions can be aggregated at the organization level to identify problematic departments and agencies to target for audit or additional interventions. ## Use case applicability The following table summarizes aspects of this use case: | Topic | Description | | --- | --- | | Use case type | Public Sector / Banking & Finance / Purchase Card Fraud Detection | | Target audience | Auditor’s office or fraud investigation unit leaders, fraud investigators or examiners, data scientists | | Desired outcomes | Identify additional fraudIncrease richness of fraud alertsProvide enterprise-level visibility into risk | | Metrics/KPIs | Current fraud ratePercent of investigated transactions that end in fraud determinationTotal cost of fraudulent transactions & estimated undetected fraudAnalyst hours spent reviewing fraudulent transactions | | Sample dataset | synth_training_fe.csv.zip | The solution proposed requires the following high-level technical components: - Extract, Transform, Load (ETL): Cleaning of purchase card data (feed established with bank or processing company, e.g., TSYS) and additional feature engineering. - Data science: Modeling of fraud risk using AutoML, selection/downweighting of features, tuning of prediction threshold, deployment of model and monitoring via MLOps. - Front-end app development: Embedding of data ingest and predictions into a front-end application (e.g., Streamlit). ## Solution value The primary issues and corresponding opportunities that this use case addresses include: | Issue | Opportunity | | --- | --- | | Government accountability / trust | Reviewing 100% of procurement transactions to increase public trust in government spending. | | Undetected fraudulent activity | Identifying 40%+ more risky transactions ($1M+ value, depending on organization size). | | Staff productivity | Increasing personnel efficiency by manually reviewing only the riskiest transactions. | | Organizational visibility | Providing high-level insight into areas of risk within the organization. | ## Sample ROI calculation Calculating ROI for this use case can be broken down into two main components: - Time saved by pre-screening transactions - Detecting additional risky transactions > [!NOTE] Note > As with any ROI or valuation exercise, the calculations are "ballpark" figures or ranges to help provide an understanding of the magnitude of the impact, rather than an exact number for financial accounting purposes. It is important to consider the calculation methodology and any uncertainty in the assumptions used as it applies to your use case. ### Time savings from pre-screening transactions Consider how much time can be saved by a model automatically detecting True Negatives (correctly identified as "safe"), in contrast to an examiner manually reviewing transactions. #### Input Variables | Variable | Value | | --- | --- | | Model's True Negative + False Negative Rate This is the number of transactions that will now be automatically reviewed (False Positives and True Positives still require manual review, and so do not have a time savings component) | 95% | | Number of transactions per year | 1M | | Percent (%) of transactions manually reviewed | 25% (assumes the other 75% are not reviewed) | | Average time spent on manual review (per transaction) | 2 minutes | | Hourly wage (fully loaded FTE) | $30 | #### Calculations | Variable | Formula | Value | | --- | --- | --- | | Transactions reviewed manually by examiner today | 1M * 25% | 250,000 | | Transactions pre-screened by model as not needing review | 1M * 95% | 950,000 | | Transactions identified by model as needing manual review | 1M - 950,000 | 50,000 | | Net transactions no longer needing manual review | 250,000 - 50,000 | 200,000 | | Hours of transactions reviewed manually per year | 200,000 * (2 minutes / 60 minutes) | 6,667 hours | | Cost savings per year | 6,667 * $30 | $200,000 | ### Calculating additional fraud detected annually #### Input Variables | Variable | Value | | --- | --- | | Number of transactions per year | 1M | | Percent (%) of transactions manually reviewed | 25% (assumes the other 75% are not reviewed) | | Average transaction amount | $300 | | Model True Positive rate | 2% (assume the model detects “risky” not necessarily fraud) | | Model False Negative rate | 0.5% | | Percent (%) of risky transactions that are actually fraud | 20% | #### Calculations | Variable | Formula | Value | | --- | --- | --- | | Number of transactions that are now reviewed by model that were not previously | 1M * (100%-25%) | 750,000 | | Number of transactions that are accurately identified as risky | 750k * 2% | 15,000 | | Percent (%) of risky transactions that are fraud | 15,000 * 20% | 3,000 | | Value ($) of newly identified fraud | 3,000 * $300 | $900,000 | | Number of transactions that are False Negatives (for risk of fraud) | 0.5% * 1M | 5,000 | | Number of False Negatives that would have been manually reviewed | 5,000 * 25% | 1,250 | | Number of False Negative transactions that are actually fraud | 1,250 * 20% | 250 | | Value ($) of missed fraud | 250 * $300 | $75,000 | | Net Value ($) | $900,000 - $75,000 | $825,000 | #### Total annual savings estimate: $1.025M > [!TIP] Tip > Communicate the model’s value in a range to convey the degree of uncertainty based on assumptions taken. For the above example, you might convey an estimated range of $0.8M - $1.1M. #### Considerations There may be other areas of value or even potential costs to implementing this model. - The model may find cases of fraud that were missed in the manual review by an examiner. - There may be additional cost to reviewing False Positives and True Positives that would not otherwise have been reviewed before. That said, this value is typically dwarfed by the time savings from the number of transactions that no longer need review. - To reduce the value lost from False Negatives, where the model misses fraud that an examiner would have found, a common strategy is to optimize your prediction threshold to reduce False Negatives so that these situations are less likely to occur. Prediction thresholding should closely follow the estimated cost of a False Negative versus a False Positive (in this case, the former is much more costly). ## Data The linked synthetic dataset illustrates a purchase card fraud detection program. Specifically, the model is detecting fraudulent transactions ( purchase card holders making non-approved/non-business related purchases). The unit of analysis in this dataset is one row per transaction. The dataset must contain transaction-level details, with itemization where available: - If no child items present, one row per transaction. - If child items present, one row for parent transaction and one row for each underlying item purchased with associated parent transaction features. ### Data preparation Consider the following when working with the data: Define the scope of analysis: For initial model training, the amount of data needed depends on several factors, such as the rate at which transactions occur or the seasonal variability in purchasing and fraud trends. This example case uses 6 months of labeled transaction data (or approximately 300,000 transactions) to build the initial model. Define the target: There are several options for setting the target, for example: - risky/not risky (as labeled by an examiner in an audit function). - fraud/not fraud (as recorded by actual case outcomes). - The target can also be multiclass/multilabel , with transactions marked as fraud , waste , and/or abuse . Other data sources: In some cases, other data sources can be joined in to allow for the creation of additional features. This example pulls in data from an employee resource management system as well as timecard data. Each data source must have a way to join back to the transaction level detail (e.g., Employee ID, Cardholder ID). ### Features and sample data Most of the features listed below are transaction or item-level fields derived from an industry-standard TSYS (DEF) file format. These fields may also be accessible via bank reporting sources. To apply this use case in your organization, your dataset should contain, minimally, the following features: Target: - risky/not risky (or an option as described above) Required features: - Transaction ID - Account ID - Transaction Date - Posting Date - Entity Name (akin to organization, department, or agency) - Merchant Name - Merchant Category Code (MCC) - Credit Limit - Single Transaction Limit - Date Account Opened - Transaction Amount - Line Item Details - Acquirer Reference Number - Approval Code Suggested engineered features: - Is_split_transaction - Account-Merchant Pair - Entity-MCC pair - Is_gift_card - Is_holiday - Is_high_risk_MCC - Num_days_to_post - Item Value Percent of Transaction - Suspicious Transaction Amount (multiple of $5) - Less than $2 - Near $2500 Limit - Suspicious Transaction Amount (whole number) - Suspicious Transaction Amount(ends in 595) - Item Value Percent of Single Transaction Limit - Item Value Percent of Account Limit - Transaction Value Percent of Account Limit - Average Transaction Value over last 180 days - Item Value Percentage of Average Transaction Value Other helpful features to include are: - Merchant City - Merchant ZIP - Cardholder City - Cardholder ZIP - Employee ID - Sales Tax - Transaction Timestamp - Employee PTO or Timecard Data - Employee Tenure (in current role) - Employee Tenure (in total) - Hotel Folio Data - Other common features - Suspicious Transaction timing (Employee on PTO) ## Exploratory Data Analysis (EDA) - Smart downsampling: For large datasets with few labeled samples of fraud, useSmart Downsamplingto reduce total dataset size by reducing the size of the majority class. (From theDatapage, chooseShow advanced options > Smart Downsamplingand toggle on Downsample Data.) - Time aware: For longer time spans,time-aware modelingcould be necessary and/or beneficial. Check for time dependence in your datasetYou can create a year+month feature from transaction time stamps and perform modeling to try to predict this. If the top model performs well, it is worthwhile to leverage time-aware modeling. - Data types: Your data may have transaction features encoded as numerics but they must betransformed to categoricals. For example, while Merchant Category Code (MCC) is a four-digit number used by credit card companies to classify businesses, there is not necessarily an ordered relationship to the codes (e.g., 1024 is not similar to 1025). Binary features must have either a categorical variable type or, if numeric, have values of0or1. In the sample data, several binary checks may result from feature engineering, such asis_holiday,is_gift_card,is_whole_num, etc. ## Modeling and insights After cleaning the data, performing feature engineering, uploading the dataset to DataRobot (AI Catalog or direct upload), and performing the EDA checks above, modeling can begin. For rapid results/insights, [Quick Autopilot mode](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#quick-autopilot) presents the best ratio of modeling approaches explored and time to results. Alternatively, use full Autopilot or Comprehensive modes to perform thorough model exploration tailored to the specific dataset and project type. Once the appropriate modeling mode has been selected from the dropdown, start modeling. The following sections describe the insights available after a model is built. ### Model blueprint The model [blueprint](https://docs.datarobot.com/en/docs/api/reference/public-api/blueprints.html), shown on the Leaderboard and sorted by a “survival of the fittest” scheme ranking by accuracy, shows the overall approach to model pipeline processing. The example below uses smart processing of raw data (e.g., text encoding, missing value imputation) and a robust algorithm based on a decision tree process to predict transaction riskiness. The resulting prediction is a fraud probability (0-100). ### Feature Impact [Feature Impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html) shows, at a high level, which features are driving model decisions. The Feature Impact chart above indicates: - Merchant information (e.g., MCC and its textual description) tend to be impactful features that drive model predictions. - Categorical and textual information tend to have more impact than numerical features. The chart provides a clear indication of over-dependence on at least one feature—Merchant Category Code (MCC). To effectively downweight the dependence, consider [creating a feature list](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html#create-feature-lists) with this feature excluded and/or blending top models. These steps can balance feature dependence with comparable model performance. For example, this use case creates an additional feature list that excluded the MCC and an additional engineered feature based on MCCs recognized as high risk by SMEs. Also, starting with a large number of engineered features may result in a Feature Impact plot that shows minimal amounts of reliance on many of the features. Retraining with reduced features may result in increased accuracy and will also reduce the computational demand of the model. The final solution used a blended model created from combining the top model from each of these two modified feature lists. It achieved comparable accuracy to the MCC-dependent model but with a more balanced Feature Impact plot. Compare the plot below to the one above: ### Confusion Matrix Leverage the [ROC Curve](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/index.html) to tune the prediction threshold based on, for example, the auditor’s office desired risk tolerance and capacity for review. The [Confusion Matrix](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/confusion-matrix-classic.html) and [Prediction Distribution](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/pred-dist-graph.html) graph provide excellent tools for experimenting with threshold values and seeing the effects on False Positive and False Negative counts/percentages. Because the model marks transactions as risky and in need of further review, the preferred threshold prioritizes minimizing false negatives. You can also use the ROC Curve tools to explain the tradeoff between optimization strategies. In this example, the solution mostly minimizes False Negatives (e.g., missed fraud) while slightly increasing the number of transactions needing review. You can see in the example above that the model outputs a probability of risk. - Anything above the set probability threshold marks the transaction as risky (or needs review) and vice versa. - Most predictions have low probability of being risky (left,Prediction Distributiongraph). - The best performance evaluators are Sensitivity and Precision (right,Confusion Matrixchart). - The defaultPrediction Distributiondisplay threshold of 0.41 balances the False Positive and False Negative amounts (adjustable depending on risk tolerance). ### Prediction Explanations With each transaction risk score, DataRobot provides two associated Risk Codes generated by [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html). These Risk Codes inform users which two features had the highest effect on that particular risk score and their relative magnitude. Inclusion of Prediction Explanations helps build trust by communicating the "why" of a prediction, which aids in confidence-checking model output and also identifying trends. ## Predict and deploy Use the tools above (blueprints on the Leaderboard, Feature Impact results, Confusion/Payoff matrices) to determine the best blueprint for the data/use case. Deploy the model that serves risk score predictions (and accompanying prediction explanations) for each transaction on a batch schedule to a database (e.g., Mongo) that your end application reads from. Confirm the ETL and prediction scoring frequency with your stakeholders. Often the TSYS DEF file is provided on a daily basis and contains transactions from several days prior to posting date. Generally daily scoring of the DEF is acceptable—the post-transaction review of purchases does not need to be executed in real-time. Point of reference, though—some cases can take up to 30 days post purchase to review transactions. A [no-code](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html) or Streamlit app can be useful for showing aggregate results of the model (e.g., risky transactions at an entity level). Consider building a custom application where stakeholders can interact with the predictions and record the outcomes of the investigation. A useful app will allow for intuitive and/or automated data ingestion and the review of individual transactions marked as risky, as well as organization- and entity-level aggregation. ## Monitoring and management Fraudulent behavior is dynamic as new schemes replace ones that have been mitigated. It is crucial to capture ground truth from SMEs/auditors to track model accuracy and verify the effectiveness of the model.[Data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html), as well as concept drift, can pose significant risks. For fraud detection, the process of retraining a model may require additional batches of data manually annotated by auditors. Communicate this process clearly and early in the project setup phase.[Champion/challenger](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/challengers.html) analysis suits this use-case well and should be enabled. For models trained with target data labeled as `risky` (as opposed to confirmed fraud), it could be useful in the future to explore modeling `confirmed fraud` as the amount of training data grows. The model threshold serves as a confidence knob that may increase across model iterations while maintaining low false negative rates. Moving to a model that predicts the actual outcome as opposed to risk of the outcome also addresses the potential difficulty when retraining with data primarily labeled as the actual outcome (collected from the end-user app). --- # Reduce 30-day readmissions rate URL: https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/readmit.html > Build ML models with the DataRobit UI to reduce the 30-Day readmissions rate by predicting at-risk patients. # Reduce 30-day readmissions rate This page outlines a use case to reduce the 30-day hospital readmission rate by predicting at-risk patients. Use case detail is captured below. Follow the [UI-based walkthrough](https://docs.datarobot.com/en/docs/get-started/how-to/build-walk.html) to try it yourself. ### Business problem A "readmission" event is when a patient is readmitted into the hospital within 30 days of being discharged. Readmissions are not only a reflection of uncoordinated healthcare systems that fail to sufficiently understand patients and their conditions, but they are also a tremendous financial strain on both healthcare providers and payers. In 2011, the United States Government estimated there were approximately 3.3 million cases of 30-day, all-cause hospital readmissions, incurring healthcare organizations a total cost of $41.3 billion. The foremost challenge in mitigating readmissions is accurately anticipating patient risk from the point of initial admission up until discharge. Although a readmission is caused by a multitude of factors, including a patient’s medical history, admission diagnosis, and social determinants, the existing methods (i.e., LACE and HOSPITAL scores) used to assess a patient’s likelihood of readmission do not effectively consider the variety of factors involved. By only including limited considerations, these methods result in suboptimal health evaluations and outcomes. ## Solution value AI provides clinicians and care managers with the information they need to nurture strong, lasting connections with their patients. It helps reduce readmission rates by predicting which patients are at risk and allowing clinicians to prescribe intervention strategies before, and after, the patient is discharged. AI models can ingest significant amounts of data and learn complex patterns behind why certain patients are likely to be readmitted. Model interpretability features offer personalized explanations for predictions, giving clinicians insight into the top risk drivers for each patient at any given time. By taking the form of an artificial clinician and augmenting the care they provide, along with other actions clinicians already take, AI enables them to conduct intelligent interventions to improve patient health. Using the information they learn, clinicians can decrease the likelihood of patient readmission by carefully walking through their discharge paperwork in-person, scheduling additional outpatient appointments (to give them more confidence about their health), and providing additional interventions that help reduce readmissions. ### Problem framing One way to frame the problem is to determine how to measure ROI for the use case. Consider: Current cost of readmissions: ``` Current readmissions annual rate x Annual hospital inpatient discharge volumes x Average cost of a hospital readmission ``` New cost of readmissions: ``` New readmissions annual rate x Annual hospital inpatient discharge volumes x Average cost of a hospital readmission ``` ROI: ``` New cost of readmissions - Current cost of readmissions ``` As a result, the top-down calculation for value estimates is: ROI: ``` Current costs of readmissions x improvement in readmissions rate ``` For example, at a US national level, calculating the top-down cost of readmissions for each healthcare provider is `$41.3 billion / 6,210 US providers = ~$6.7 million` For illustrative purposes, this tutorial uses a sample dataset provided by a [medical journal](https://www.hindawi.com/journals/bmri/2014/781670/#supplementary-materials) that studied readmissions across 70,000 inpatients with diabetes. The researchers of the study collected this data from the Health Facts database provided by Cerner Corporation, which is a collection of clinical records across providers in the United States. Health Facts allows organizations that use Cerner’s electronic health system to voluntarily make their data available for research purposes. All the data was cleansed of PII in compliance with HIPAA. ### Features and sample data The features for this use case represent key factors for predicting readmissions. They encompass each patient’s background, diagnosis, and medical history, which will help DataRobot find relevant patterns across the patient’s medical profile to assess their re-hospitalization risk. In addition to the features listed below, incorporate any additional data that your organization collects that might be relevant to readmission.(DataRobot is able to differentiate important/unimportant features if your selection would not improve modeling.) Relevant features are generally stored across proprietary data sources available in your EMR system (for example, Epic or Cerner) and include: - Patient data - Diagnosis data - Admissions data - Prescription data Other external data sources may also supply relevant data such as: - Seasonal data - Demographic data, - Social determinants data Each record in the data represents a unique patient visit. #### Target The target variable: - Readmitted This feature represents whether or not a patient was readmitted to the hospital within 30 days of discharge, using values such as `True \ False`, `1 \ 0`, etc. This choice in target makes this a binary classification problem. ### Sample feature list | Feature Name | Data Type | Description | Data Source | Example | | --- | --- | --- | --- | --- | | Readmitted | Binary (Target) | Whether or not the patient readmitted after 30 days | Admissions Data | False | | Age | Numeric | Patient age group | Patient Data | Female | | Weight | Categorical | Patient weight group | Patient Data | 50-75 | | Gender | Categorical | Patient gender | Patient Data | 50-60 | | Race | Categorical | Patient race | Patient Data | Caucasian | | Admissions Type | Categorical | Patient state during admission (Elective, Urgent, Emergency, etc.) | Admissions Data | Elective | | Discharge Disposition | Categorical | Patient discharge condition (Home, home with health services, etc.) | Admissions Data | Discharged to home | | Admission Source | Categorical | Patient source of admissions (Physician Referral, Emergency Room, Transfer, etc.) | Admissions Data | Physician Referral | | Days in Hospital | Numeric | Length of stay in hospital | Admissions Data | 1 | | Payer Code | Categorical | Unique code of patient’s payer | Admissions Data | CP | | Medical Specialty | Categorical | Medical specialty that patient is being admitted into | Admissions Data | Surgery-Neuro | | Lab Procedures | Numeric | Total lab procedures in the past | Admissions Data | 35 | | Procedures | Numeric | Total procedures in the past | Admissions Data | 4 | | Outpatient Visits | Numeric | Total outpatient visits in the past | Admissions Data | 0 | | ER Visits | Numeric | Total emergency room visits in the past | Admissions Data | 0 | | Inpatient Visits | Numeric | Total inpatient visits in the past | Admissions Data | 0 | | Diagnosis | Numeric | Total diagnosis | Diagnosis Data | 9 | | ICD10 Diagnosis Code(s) | Categorical | Patient’s ICD10 diagnosis on their condition; could be more than one (additional columns) | Diagnosis Data | M4802 | | ICD10 Diagnosis Description(s) | Categorical | Description on patient’s diagnosis; could be more than one (additional columns) | Diagnosis Data | Spinal stenosis, cervical region | | Medications | Numeric | Total number of medications prescribed to the patient | Prescription Data | 21 | | Prescribed Medication(s) | Binary | Whether or not the patient is prescribed to a medication; could be more than one (additional columns) | Prescription Data | Metformin – No | ### Data preparation The original raw data consisted of 74 million unique visits that include 18 million unique patients across 3 million providers. This data originally contained both inpatient and outpatient visits, as it included medical records from both integrated health systems and standalone providers. While the original data schema consisted of 41 tables with 117 features, the final dataset was filtered on relevant patients and features based on the use case. The patients included were limited to those with: - Inpatient encounters - Existing diabetic conditions - 1–14 days of inpatient stay - Lab tests performed during inpatient stay (or not) - Medications were prescribed during inpatient stay (or not) All other features were excluded due to lack of relevance and/or poor data integrity. Reference the [DataRobot documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/data/index.html) to see details on how to connect DataRobot to your data source, perform feature engineering, follow best-practice data science techniques, and more. ## Modeling and insights DataRobot automates many parts of the modeling pipeline, including processing and partitioning the dataset, as described [here](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html). This use case skips the modeling section and moves straight to model interpretation. Try the [UI-based walkthrough](https://docs.datarobot.com/en/docs/get-started/how-to/build-walk.html) to learn more about modeling. ### Feature Impact By taking a look at the [Feature Impact](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html) chart, you can see that a patient’s number of past inpatient visits, discharge disposition, and the medical specialty of their diagnosis are the top three most impactful features that contribute to whether a patient will readmit. ### Feature Effects/Partial Dependence In assessing the [partial dependence](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-effects-classic.html#partial-dependence-calculations) plots to further evaluate the marginal impact top features have on the predicted outcome, you can see that as a patient’s number of past inpatient visits increases from 0 to 2, their likelihood to readmit subsequently jumps from 37% to 53%. As the number of visits exceeds 4 the likelihood increases to roughly 59%. ### Prediction Explanations DataRobot’s [Prediction Explanations](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/pred-explain/index.html) provide a more granular view for interpreting model results—key drivers for each prediction generated. These explanations show why a given patient was predicted to readmit or not, based on the top predictive features. ### Post-processing For the prediction results to be intuitive for clinicians to consume, instead of displaying them as a probabilistic or binary number, they can be post-processed into different labels based on where they fall under predefined prediction thresholds. For instance, patients can be labeled as high risk, medium risk, and low risk depending on their risk of readmissions. ## Predict and deploy After selecting the model that best learns patterns in your data to predict readmissions, you can deploy it into your desired decision environment.Decision environments are the ways in which the predictions generated by the model will be consumed by the appropriate organizational [stakeholders](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/readmit.html#decision-stakeholders), and how these stakeholders will make decisions using the predictions to impact the overall process. This is a critical piece of implementing the use case as it ensures that predictions are used in the real-world for reducing hospital readmissions and generating clinical improvements. At its core, DataRobot empowers clinicians and care managers with the information they need to nurture strong and lasting connections with the people they care about most: their patients. While there are use cases where decisions can be automated in a data pipeline, a readmissions model is geared to augment the decisions of your clinicians. It acts as an intelligent machine that, combined with the expertise of your clinicians, will help improve patients’ medical outcomes. ### Decision stakeholders The following table lists potential decision stakeholders: | Stakeholder | Description | Examples | | --- | --- | --- | | Decision executors | Clinical stakeholders who will consume decisions on a daily basis to identify patients who are likely to readmit and understand the steps they can take to intervene. | Nurses, physicians, care managers | | Decision managers | Executive stakeholders who will monitor and manage the program to analyze the performance of the provider’s readmission improvement programs. | Chief medical officer, chief nursing officer, chief population health officer | | Decision authors | Technical stakeholders who will set up the decision flow in place. | Clinical operations analyst, business intelligence analyst, data scientists | ### Decision process You can set thresholds to determine whether a prediction constitutes a foreseen readmission or not. Assign clear action items for each level of threshold so that clinicians can prescribe the necessary intervention strategies. Low risk: Send an automated email or text that includes discharge paperwork, warning symptoms, and outpatient alternatives. Medium risk: Send multiple automated emails or texts that include discharge paperwork, warning symptoms, and outpatient alternatives, with multiple reminders. Follow up with the patient 10 days post-discharge through email to gauge their condition. High risk: Clinician briefs patient on their discharge paperwork in person. Send automated emails or texts that include discharge paperwork, warning symptoms, and outpatient alternatives, with multiple reminders. Follow up with the patient on a weekly basis post discharge through telephone or email to gauge their condition. ### Model deployment DataRobot provides clinicians with complete transparency on the top risk-drivers for every patient at any given time, enabling them to conduct intelligent interventions both before and after the patient is discharged. Reference the [DataRobot documentation](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html) for an overview of model deployment. #### No-Code AI Apps Consider building a custom application where stakeholders can interact with the predictions and record the outcomes of the investigation. Once the model is deployed, predictions can be consumed for use in the [decision process](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/readmit.html#decision-process). For example, this [No-Code AI App](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html) is an easily shareable, AI-powered application using a no-code interface: Click Add new row to enter patient data: #### Other business systems Predictions can also be integrated into other systems that are embedded in the provider’s day-to-day business workflow. Results can be integrated into the provider’s EMR system or BI dashboards. For the former, clinicians can easily see predictions as an additional column in the data they already view on a daily basis to monitor their assigned patients. They will be given transparent interpretability of the predictions to understand why the model predicts the patient to readmit or not. Some common integrations: - Display results through an Electronic Medical Record system (i.e., Epic) - Display results through a business intelligence tool (i.e., Tableau, Power BI) The following shows an example of how to integrate predictions with Microsoft Power BI to create a dashboard that can be accessed by clinicians to support decisions on which patients they should address to prevent readmissions. The dashboard below displays the probability of readmission for each patient on the floor. It shows the patient’s likelihood to readmit and top factors on why the model made the prediction. Nurses and physicians can consume a dashboard similar to this one to understand which patients are likely to readmit and why, allowing them to implement a prevention strategy tailored to each patient’s unique needs. ### Model monitoring Common decision operators—IT, system operations, and data scientists—would likely implement this use case as follows: Prediction Cadence: Batch predictions generated on a daily basis. Model Retraining Cadence: Models retrained once data drift reaches an assigned threshold; otherwise, retrain the models at the beginning of every new operating quarter. Use DataRobot's [performance monitoring capabilities](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/index.html) —especially service health, data drift, and accuracy to produce and distribute regular reports to stakeholders. ### Implementation considerations The following highlights some potential implementation risks, all of which are addressable once acknowledged: | Issue | Description | | --- | --- | | Access | Failure to make prediction results easy and convenient for clinicians to access (i.e., if they have to open a separate web browser to the EHR that they are already used to or have information overload). | | Understandability | Failure to make predictions intuitive for clinicians to understand. | | Interpretability | Failure to help clinicians interpret the predictions and why the model thought a certain way. | | Prescriptive | Failure to provide clinicians with prescriptive strategies to act on high risk cases. | ### Trusted AI In addition to traditional risk analysis, the following elements of AI Trust may require attention in this use case. Target leakage: Target leakage describes information that should not be available at the time of prediction being used to train the model. That is, particular features make leak information about the eventual outcome that will artificially inflate the performance of the model in training. This use case required the aggregation of data across 41 different tables and a wide timeframe, making it vulnerable to potential target leakage. In the design of this model and the preparation of data, it is pivotal to identify the point of prediction (discharge from the hospital) and ensure no data be included past that time. DataRobot additionally supports robust [target leakage detection](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#target-leakage) in the second round of exploratory data analysis and the selection of the Informative Features feature list during Autopilot. Bias & Fairness: This use case leverages features that may be categorized as protected or may be sensitive (age, gender, race). It may be advisable to assess the equivalency of the error rates across these protected groups. For example, compare if patients of different races have equivalent false negative and positive rates. The risk is if the system predicts with less accuracy for a certain protected group, failing to identify those patients as at risk of readmission. Mitigation techniques may be explored at various stages of the modeling process, if it is determined necessary. DataRobot's [bias and fairness resources](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/bias-resources.html) help identify bias before (or after) models are deployed. --- # Integration with SAP Build URL: https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/sap-build.html > Connects DataRobot deployments to SAP Build Process Automation. # Integration with SAP Build [SAP Build](https://www.sap.com/products/technology-platform/low-code.html) is a low-code platform for visual app development and process automation. SAP Build Process Automation provides a suite of tools to easily automate business processes including data extraction, processing, and routing, with built-in tools like Intelligent Robotic Process Automation (IRPA) bots. To connect your DataRobot deployments to SAP Build Process Automation, use the Custom Script function in Build Process Automation. In the example process below, the “extract invoice” step parses a PDF invoice with defined elements in the “data” dataArray, and is given the variable name `doxData`. These are passed as inputs to the `detectAnomaly` process. In the `detectAnomaly` process, the following elements call the DataRobot API using data from the previous step, and return the API response to be used downstream. To add a custom script, select it from the Automation Details Pane and drag on to the canvas Inside the custom script, read in your data from your input and call the DataRobot API with the input data as payload. To make the API call: - The url is the prediction endpoint from your DataRobot deployment - DataRobot-Key and Authorization are your API key and bearer token from the deployment, respectively. These can be found under the Predictions tab in your deployment. To complete the script, name the output variable that will return the DataRobot API response. Below, it is named `options`. Finally, add a step to `Call Web Service` from the Automation Details pane. Select as input the variable name for your output from the Custom Script ( `options` below). Again, define your output variable from the `Call Web Service` step ( `obj`) below. The response object from the DatraRobot API will now be available as input to subsequent steps. --- # Predictive model building URL: https://docs.datarobot.com/en/docs/get-started/how-to/build-walk.html > Use a hospital readmissions dataset to learn about wrangling data, building models, and creating no-code applications. # How-to: Predictive model building This walkthrough describes how to use DataRobot to identify at-risk patients, reduce readmission rates, maximize care, and minimize costs. Learn more about the use case [here](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/readmit.html) and watch a video version on [YouTube](https://www.youtube.com/watch?v=X7Gsfjz8s2w&t=12s). ## Assets for download To follow this walkthrough, download the two datasets linked below. [Download training data](https://s3.amazonaws.com/datarobot-doc-assets/10k_diabetes.csv) [Download scoring data](https://s3.amazonaws.com/datarobot-doc-assets/10k_diabetes_scoring.csv) ### 1. Create a Use Case DataRobot Use Cases are containers that group objects that are part of the Workbench experimentation flow, which can include datasets, models, experiments, No-Code AI Apps, and notebooks. To create a Use Case: 1. Open the DataRobot interface and clickWorkbench. 2. ClickCreate Use Case. 3. Give the Use Case a descriptive name in theUse Casefield. Now that the Use Case has been created, the data from the files linked [above](https://docs.datarobot.com/en/docs/get-started/how-to/build-walk.html#assets-for-download--assets-for-download-) can be uploaded to it. ### 2. Upload data files 1. From your open Use Case, locate thePredictive AIbox and clickData. 2. ClickUpload file. 3. Upload the training dataset file downloaded previously and wait for it to finish registering to the Use Case. ### 3. Preview the datasets All files within a Use Case are contained in the Data assets tab. To view them, click Data assets from the open Use Case. Click a dataset to explore its feature structure and values. In this example, select `10k_diabetes.csv`. Read more: [Working with data](https://docs.datarobot.com/en/docs/api/reference/sdk/data-registry.html#preview-a-dataset) ### 4. Wrangle data Click Data actions > Open in Wrangler to pull a random sample of data from the data source and begin transformation operations. Read more: [Wrangle data](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/index.html) ### 5. Build a recipe Click Add operation to build a wrangling "recipe." Each new operation updates the live sample to reflect the transformation. Note that if you wrangle your training dataset, you will want to apply the same operations to your scoring dataset to ensure you have the same columns. Read more: [Add operations](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/build-recipe/add-operation.html#add-operations) ### 6. Compute a new feature The recipe panel lists a variety of possible wrangling operations. Click Compute new feature to create a new output feature—perhaps one better representing your business problem—from existing dataset features. The Compute new feature window is where you add functions and subqueries that define the new feature. Enter the name and expression listed below and click Add to recipe when done. The transformation converts the age range into a single integer. New feature name: `convert_age_range_to_integer` Expression: ``CAST(regexp_extract(`age`, '\\[([0-9]+)', 1) AS INT )`` Read more: [Compute a new feature](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/build-recipe/add-operation.html#compute-a-new-feature) ### 7. Prepare for publishing When you are finished adding operations, assess the live sample to verify that the applied operations are ready for publishing. Click Recipe actions > Publish to configure the final publishing settings for the output dataset. Set the criteria for the final output dataset, such as the name and specifics of automatic downsampling (if enabled). Click Publish to apply the recipe to the source, creating a new output dataset, registering it in the Data Registry, and adding it to your Use Case. Read more: [Publish a recipe](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/pub-recipe.html) ### 8. Explore the new dataset The transformed, published dataset, identifiable by the wrangling time stamp, has been added to the Use Case’s Data assets tab. Click the dataset to see the final feature set, including the new wrangled feature, and explore feature insights. If the dataset needs further modification, you can choose to keep wrangling. Otherwise, from the new output dataset, click Data actions > Start modeling to set up a new experiment. Read more: [View data insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/explore-data/index.html#explore-data) ### 9. Create an experiment After DataRobot prepares the dataset, you can create a new experiment with the data. First, use the Learning type dropdown to select the type of experiment you want to run, Supervised or Unsupervised (see more about the different learning types in the [predictive experiments documentation](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/index.html)). For this example, select Supervised. Next, use the Target feature field to specify which column in the dataset to make predictions for. For this Use Case, enter the name `Readmitted`. DataRobot presents the target feature’s distribution in a histogram, with experiment settings summarized in the right panel. The list of features shown reflects the selected feature list. You can read more about the different features in the [feature list documentation](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-adv-experiment.html#feature-list). Read more: [Select a target](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-adv-experiment.html#select-target) ### 10. Apply optional settings Click Next to further refine your experiment. DataRobot sets default partitioning and validation based on your data; however, changing experiment parameters is a good way to iterate on a Use Case. Notice the experiment summary information in the right panel, then click Start modeling to launch Autopilot. Read more: [Customize settings](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-adv-experiment.html#customize-advanced-settings) ### 11. Start modeling Once modeling begins, Workbench begins to construct a model Leaderboard. Ultimately, DataRobot will select and retrain the most accurate model and mark it as prepared for deployment. While model building progresses, click any completed model to familiarize yourself with the insights available for model evaluation. Each model's landing page provides an overview that displays available insights for the model, which differ depending on the experiment type. Click Feature Impact (and compute if prompted) to visualize which features are driving model decisions. Read more: [Evaluate experiments](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/index.html) ### 12. View the modeling pipeline From the model you have selected, click the Details tab and select Blueprint. This allows you to view the pre- and post-processing steps that go into building a model. Read more: [Blueprints](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/model-blueprint.html) ### Next steps When you are done investigating, you can: **Take action from the Leaderboard:** From Model actions, you can access a variety of next-steps for your model. [https://docs.datarobot.com/en/docs/images/qs-model-actions.png](https://docs.datarobot.com/en/docs/images/qs-model-actions.png) Action Description Read more Register model Create versioned deployment-ready model packages. Operate and govern how-to Make predictions Make one-time predictions on new data, registered data, or training data to validate Leaderboard models. Make predictions from Workbench Create no-code application Use No-Code AI App templates to build applications that enable core DataRobot services and are shareable with other users, whether or not they have a DataRobot license. Create an application Delete model Permanently remove the selected model from the Use Case (and the associated Leaderboard). N/A **Try the Operate and govern how-to:** Register and monitor deployed models in the [operate and govern how-to](https://docs.datarobot.com/en/docs/get-started/how-to/production-walk.html). --- # Evaluate a regression model URL: https://docs.datarobot.com/en/docs/get-started/how-to/evaluate-regression-model.html > Evaluate a regression model using the DataRobot UI. # How-to: Evaluate a regression model This walkthrough uses machine learning to identify how different survey responses predict developer salaries. Think of this in the context of a Human Resources department determining the salary of an individual based on the experience needed for the position. Because the model needs to predict a number, this is a regression problem. ## Assets for download To follow this walkthrough, download the datasets that will be used to train and evaluate a regression model below. The first is the training dataset, which will be used to build the model. The second is the test dataset, which will be used to generate predictions. [Download the training dataset](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/StackOverflow.csv) [Download the test dataset](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/test_set_usd.csv) > [!NOTE] Important > Follow the steps detailed in the [Introduction to data analysis in DataRobot](https://docs.datarobot.com/en/docs/get-started/how-to/intro-to-eda.html) walkthrough to upload the dataset and prepare it for modeling. ### Stack Overflow survey data Stack Overflow runs an annual survey that captures the feedback of thousands of developers. The survey collects an array of information, including favorite technologies, preferences for job types, and even salaries. The data from this version of the survey: - Was collected in 2019. - Is anonymized and published online. - Contains over 90,000 responses. - Consists of many different information types (such as text and categoricals). - Is more than just a few hundred rows. ## Building a model Now that the data has been uploaded and analyzed, it is time to build a model. The steps in this section will build a model that can be used to predict the salary amount, which is indicated by the `CompTotal` feature. 1. ClickData actions > Start modeling. 2. In theSet up new experimentwindow, specifyCompTotalin theTarget featurefield. 3. Leave the remaining fields at their defaults and clickNext >. NoteFor more details on the additional settings, seeStart modeling setup. 4. Leave all partitioning changes fields at their defaults and clickStart modeling. 5. DataRobot begins building the models. 6. After a few moments, the Model Leaderboard appears and indicates the training progress. Model build timeModel build time can vary depending on the size of the dataset. When it completes, theWorkerspane displaysNo jobs currently running. For details on how to assess the various models after they are built, see [Compare models](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/compare-models.html). ## Model evaluation and interpretation Now that a set of models are ready for analysis, select the top model and explore its details. DataRobot flags the most accurate model as Prepared for deployment in the Model Leaderboard. Click the model to view more detailed information about it. Use the tabs in the Details pane to explore various insights, as highlighted below. These tabs provide a quick overview of the evaluation metrics available. Click Explanations > Individual Prediction Explanations and then Compute to have DataRobot generate the number of predictions for each row in the dataset. As seen in the graph above, the model shows the expected salary range based on the features in the dataset. The table below the graph provides a sample of five predictions from the model as an example of its results. Click one of the predictions to see its details. For more details on how to evaluate a model and explanations for what each insight means, see [Evaluate with model insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/index.html). ## Make predictions with the model When the most accurate model has been identified and selected, it can be used to make predictions. 1. ClickModel actions >Make predictions. 2. In theMake Predictionswindow, specify the dataset to use for predictions. In this case, use the test dataset by clickingChoose file > Upload a local file. Browse to the files downloaded in theAssets for downloadsection and select thetest_set_usd.csvfile. 3. Once the new data is uploaded and processed, clickCompute and download predictionsto generate the predictions. This process can take some time, depending on the size of the dataset. For a deeper dive into making predictions, see [Make predictions](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/make-predictions.html). ## Review the results Once the predictions are successfully generated, review them to see how well the model performed by opening the downloaded predictions file using a spreadsheet application. Alternatively, the predictions can be viewed in DataRobot by clicking Workbench and selecting your Use Case from the table. Once the uploaded file registers, click the new dataset to view the predictions. --- # GenAI with governance URL: https://docs.datarobot.com/en/docs/get-started/how-to/genai-space.html > Use International Space Station research papers to compare multiple retrieval-augmented generation (RAG) pipelines with evaluation metrics and governance. # How-to: GenAI with governance This generative AI use case compares multiple retrieval-augmented generation (RAG) pipelines. When completed, you'll have multiple end-to-end pipelines with built-in evaluation, assessment, and logging, providing governance and guardrails. Watch a video version on [YouTube](https://www.youtube.com/watch?v=WiEC5liBBEo). > [!TIP] Learn more > To learn more about generative AI at DataRobot, visit the [GenAI section](https://docs.datarobot.com/en/docs/agentic-ai/index.html) of the documentation. There you can find an overview and information about vector databases, playgrounds, and metrics, using both the UI and code. ## Assets for download To build this experiment as you follow along, first download the file `DataRobot+GenAI+Space+Research.zip` and unzip the archive. Inside you will find a TXT file, a CSV file, and another ZIP file, `Space_Station_Research.zip`. Do not unzip this inner ZIP archive. [Download files](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/DataRobot%2BGenAI%2BSpace%2BResearch.zip) ## 1. Create a Use Case From the Workbench directory, click Create Use Case in the upper right: and name it `Space research`. Read more: [Working with Use Cases](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/index.html) ## 2. Upload data Click Add data and then Upload on the resulting screen. From the assets you downloaded, upload the file named `Space_Station_Research.zip`. Do not unzip it.This is not the ZIP file you downloaded, but a ZIP within the original downloaded archive.DataRobot will begin registering the dataset. You can use this time to look at the documents you downloaded that are inside the ZIP file. Locally, unzip `Space_Station_Research.zip` and expand `Space_Station_Annual_Highlights`, which contains PDFs sharing highlights from the International Space Station's research programs from the last few years. Read more: [Upload local files](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/add-data/local-file.html) ## 3. Create a vector database After you upload data, you can create a vector database to enrich prompts with relevant context before they are sent to the LLM. To create a vector database: 1. There are two paths to creating a vector database‐both open the same creation page. You can use theAdddropdown on the right and clickVector database > Create vector databaseor, from theVector databasetab, clickCreate vector database. 2. Set the configuration using the following settings: | Field | Setting | Notes | | --- | --- | --- | | Name | Jina 256/20 | This name was selected to reflect the settings, but could be anything. | | Data source | Space_Station_Research.zip | All valid datasets uploaded to the Use Case will be available in the dropdown. | | Embedding model | jinaai/jina-embedding-t-en-v1 | Choose the recommended embedding model, Jina, for this exercise. | ``` ![](../../images/gai-walk-5.png) ``` 1. Text chunking is the process of splitting text documents into smaller text chunks that are then used to generate embeddings. You can use separator rules to divide content, set chunk overlap, and set the maximum number of tokens in each chunk. For this walkthrough, only change the chunk overlap percentage; leaveMax tokens per chunkon the recommended value of 256. Move the chunk overlap slider to 20%: 2. ClickCreate Vector Database; you are returned to the Use Case directory. While the vector database is building, add a second vector database for comparison purposes. This time, useintfloat/e5-base-v2as the embedding model. To compare it against theJinamodel, make theChunk overlapandMax tokens per chunksettings the same as those you set in step 2. That is, chunk overlap of 20% and max tokens of 256. Create any number of vector databases by iterating through this process. The best settings will depend on the type of text that you're working with and the objective of your use case. Read more: - Dataset types - Embeddings - Chunking settings ## 4. Add a playground The playground is where you create and compare LLM blueprints, configure metrics, and compare LLM blueprint responses before deployment. Create a playground using one of two methods. Read more: [Playground overview](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html) ## 5. Build an LLM blueprint Once in the playground, create an LLM blueprint: 1. In the Playground, on the LLM blueprints panel, clickCreate LLM blueprint: 2. In theConfigurationpanel,LLMtab, set the following: | Field | Setting | Notes | | --- | --- | --- | | LLM | Azure OpenAI GPT-3.5 Turbo | Alternatively, you can add a deployed LLM to the playground, which, when validated, is added to the Use Case and available to all associated playgrounds. | | Max completion tokens | 1024 (default) | The maximum number of tokens allowed in the completion. | | Temperature | .1 | Controls the randomness of model output. Change this to focus on truthfulness for scientific research papers. | | Top P | 1 (default) | Sets a threshold that controls the selection of words included in the response based on a cumulative probability cutoff for token selection | ``` ![](../../images/gai-walk-11.png) ``` 1. From theVector databasetab, choose the first vector database built,Jina 256/20and use the default configuration. 2. From thePromptingtab, chooseNo context. Context states control whether chat history is sent with the prompt to include relevant context for responses.No contextsends each prompt as independent input, without history from the chat. Then, enter the following prompt and then save the configuration: Your job is to help scientists write compelling pitches to have their talks accepted by conference organizers. You'll be given a proposed title for a presentation. Use details from the documents provided to write a one paragraph persuasive pitch for the presentation. Read more: - Playground overview - Create an LLM blueprint - Add a deployed LLM - LLM settings - Prompting strategies ## 6. Test the LLM blueprint Once saved, test the configuration with prompting (also known as "chatting"). Ideas are provided in the TXT file you downloaded. For example try these two prompts asking for a conference pitch in the Send a prompt dialog: - Blood flow and circulation in space. - Microgravity is weird. Next, click the edit icon next to the blueprint name to make it more descriptive, for example `Azure GPT 3.5 Turbo + Jina`, then click confirm. Read more: [Chatting with a single LLM blueprint](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#single-llm-blueprint-chat) ## 7. Create comparison blueprints To compare configuration settings, you must first create additional blueprints. To do this you can: - Follow the steps above to create a new LLM blueprint. - Make a copy of the existing blueprint and change one or more settings. You can do either of these actions from both the blueprint configuration area or the LLM blueprints panel. Because the intent is to compare blueprints, the following process copies the blueprint on the LLM blueprints panel of the Playground tile [https://docs.datarobot.com/en/docs/images/icon-playground-sparkle.png](https://docs.datarobot.com/en/docs/images/icon-playground-sparkle.png). > [!NOTE] Note > You can [navigate through the playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html#navigate-the-playground) using the icons in the far left. 1. From the named LLM blueprint, click theActions menuand selectCopy to new LLM blueprint. All settings from the first blueprint are carried over. 2. Change the vector database (1), save the configuration (2), and name the new blueprintAzure GPT 3.5 Turbo + E5(3). 3. Return to theLLM blueprintspanel to create a third blueprint. From the new LLM blueprint,Azure GPT 3.5 + E5, clickCopy to new LLM blueprintand this time change the LLM. For this walkthrough, chooseAmazon Titanand set theTemperaturevalue to0.1. Name the blueprintAmazon Titan + E5. Read more: [Copy LLM blueprints](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#copy-llm-blueprint) ## 8. Compare blueprints You can compare chats (responses) for up to three LLM blueprints from a single screen. The LLM blueprints tab lists all blueprints available for comparison—with filtering provided to simplify finding what you are interested in—as well as provides quick access to the chat history. To start the comparison, select all three blueprints by checking the box to the left of the name. Notice that a summary is available for each. Enter a new topic for exploration in the Send a prompt field. For example: `Monitoring astronaut health status.` Try a different prompt, for example, `Applications of ISS science results on earth`. The response that you prefer is subjective and depends on the use case, but there are some quantitative metrics to help you evaluate. Read more: [Compare LLMs](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html) ## 9. Evaluate responses One method of evaluating a response is to look at the basic information DataRobot returns with each prompt, summarized below the response. Expand the information panel for the LLM blueprint that used the Jina vector database; you can see that the response took seven seconds, had 173 response tokens, and scored 56.86% on the ROUGE-1 confidence metric. The ROUGE-1 metric represents how similar this LLM answer is to the citations provided to aid in its generation. To better understand the results, look at the citations. You can see a list of the chunks the generated answer from the LLM is based on: Scroll and read a few of the citations. This is the stage where you can see the impact of the chunk size you selected when you created the vector database. You may get better results with longer or shorter chunks, and could test that by creating additional vector databases. Read more: [Citations](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#citations) ## 10. Add an evaluation dataset The metrics described in the step above correspond to one LLM blueprint response, but only so much can be learned from evaluating a single prompt/response. To evaluate which LLM blueprint is the best overall, you will want aggregated metrics. Aggregation combines metrics across many prompts and/or responses, which helps to evaluate a blueprint at a high level and provides a more comprehensive approach to evaluation. First, in this step, you will add an evaluation dataset, which is required for aggregation. You will configure aggregation in [step 11](https://docs.datarobot.com/en/docs/get-started/how-to/genai-space.html#11-configure-aggregated-metrics). 1. Click theLLM evaluationiconin the upper left navigation. 2. From theLLM evaluationpage, click theEvaluation datasetstab and thenAdd evaluation dataset. 3. From theAdd evaluation datasetpanel, click the dataset namedSpace_research_evaluation_prompts.csv, which contains some additional conference titles to be used as a standard reference set. 4. Next, define thePrompt column nameand theResponse (target) column name, as shown below. Then, clickAdd evaluation dataset. FieldSettingPrompt column namequestionResponse (target) column nameanswer DataRobot returns to theEvaluation dataset metricsconfiguration page. Read more: [Evaluation datasets](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#add-evaluation-datasets) ## 11. Configure aggregated metrics After you add an evaluation dataset, to configure aggregation: 1. Click thePlaygroundiconto return to theLLM blueprint comparison, then, in the bottom left under the responses, clickConfigure aggregation. 2. In the configuration section, define aChat nameand select theEvaluation datasetadded in the previous section. 3. TheGenerate aggregated metricspage opens. Set theLatencyandROUGE-1metrics toAverage. Then, clickGenerate metrics. A notification in the lower right confirms that the aggregation job is queued. It can take some time for the aggregation request to process, but the metrics will appear as they complete. Read more: [Aggregated metrics](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#aggregated-metrics) ## 12. Interpret aggregated metrics When the aggregation job completes, on the LLM blueprint comparison page, open the Aggregated metrics tab. Note that these aggregate metrics are based on the rows in the evaluation dataset. To see the row-level details that contributed to these values, click an LLM blueprint. Notice on the left panel of the Chats tab, there is an entry named Aggregated metric chat (or a different chat name, as defined in the previous section), which contains all the responses to the prompts in the evaluation dataset. Scroll through the results to view the conference talks. You can provide feedback with the "thumbs" emojis. For example, for the question (prompt column name) "How are Lichen Liking Space?", give the response some positive feedback (thumbs up): ## 13. Tracing Tracing the execution of LLM blueprints is a powerful tool for understanding how most parts of the GenAI stack work. The tracing tab provides a log of all components and prompting activities used in generating LLM responses in the playground. Click the Tracing icon in the upper left navigation to access a log of all the components used in the LLM response generation. The table traces exactly which LLM parameters, which vector database, which system prompt, and which user prompt resulted in a particular generated response. Scroll the page to the far right to see the user feedback. You can use this information for LLM fine-tuning. You can also export the log to the DataRobot AI Catalog. From there, you can work with it in other ways, such as writing it to a database table or downloading it. Read more: [Tracing](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#tracing) ## Next steps After completing this walkthough, some suggested next steps are: - Deploy an LLM from the playground. - Create an end-to-end GenAI experiment with code --- # GenAI basic URL: https://docs.datarobot.com/en/docs/get-started/how-to/genai-walk-basic.html > A high-level overview of steps for using DataRobot GenAI with or without your own data. # How-to: GenAI basic This walkthrough shows you how to work with [generative AI (GenAI)](https://docs.datarobot.com/en/docs/agentic-ai/index.html) in DataRobot, where you can generate text content using a variety of pre-trained large language models (LLMs). Or, the content can be tailored to your domain-specific data by building vector databases and leveraging them in the LLM prompts. In this walkthrough, you will: 1. Create a playground. 2. Create a vector database. 3. Build and then compare LLM blueprints. > [!NOTE] Note > All LLMs available in playground are available out-of-the-box. If you encounter any errors or permissions issues, contact [DataRobot support](mailto:support@datarobot.com). #### Prerequisites Download the following demo dataset based on the DataRobot documentation to try out the GenAI functionality: [Download demo data](https://s3.amazonaws.com/datarobot-doc-assets/datarobot_english_documentation_5th_December.zip) ### 1. Add a playground A playground, another type of Use Case asset, is the space for creating and interacting with LLM blueprints, comparing the response of each to determine which to use in production. In Workbench, create a Use Case and then add a playground via the Playgrounds tab or the Add dropdown: Read more: [Create a Use Case](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/usecase-overview.html#create-a-use-case), [Add a playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html#add-a-playground) ### 2. Add internal data for the vector database To tailor results using domain-specific data, assign data to an LLM blueprint for leverage during RAG operations. (You can also test LLM blueprint responses without any grounding knowledge provided by a vector database. To do so, skip ahead to [step 4](https://docs.datarobot.com/en/docs/get-started/how-to/genai-walk-basic.html#4-add-and-configure-an-llm-blueprint) to configure the LLM blueprint.) You can add data directly to a Use Case either from a local file or data connections or from the AI Catalog. This walkthrough adds the data directly. Add `datarobot_english_documentation_5th_December.zip`, the data you downloaded above, to the Use Case you created. Read more: [Add internal data](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs-data.html#add-data-sources), [import from the AI Catalog](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog.html) ### 3. Add the vector database If you do not add a vector database, prompt responses are not augmented with relevant DataRobot documentation; the LLM response contains only details it was able to capture from its training on data found on the internet. Instead, associate the data you added above for more complete information. There are a variety of methods for adding a vector database. For example, from the Data tab in a Use Case: On the resulting Create vector database page, set or confirm the following and then click Create vector database: | Setting | Description | Set to... | | --- | --- | --- | | Name | Change the vector database name to reflect the settings. | datarobot docs --jina 20% / 256 | | Data source | Confirm this is the data you uploaded. | datarobot_english_documentation_5th_December.zip | | Embedding model | Use the recommended by model by keeping the pre-selected option. | jinaai/jina-embedding-t-en-v1 | | Chunk overlap | This value will help to maintain continuity when the DataRobot documentation is grouped into smaller chunks of text to embed in the vector database. | 20% | Read more: [Add a vector database](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#add-a-vector-database), [embeddings](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#embeddings) ### 4. Add and configure an LLM blueprint Open the playground you created in [step 1](https://docs.datarobot.com/en/docs/get-started/how-to/genai-walk-basic.html#1-add-a-playground) —it is from here that you access the controls for creating LLM blueprints, interacting with and fine-tuning them, and saving them for comparison and potential future deployment. From the playground, choose Create LLM blueprint and begin the configuration. Select an LLM from the Configuration dropdown to serve as the base model. This walkthrough uses Azure OpenAI GPT-3.5 Turbo model. Optionally, modify configuration settings. From the Vector database tab, choose the vector database you added in [step 3](https://docs.datarobot.com/en/docs/get-started/how-to/genai-walk-basic.html#3-add-an-vector-database). From the Prompting tab, optionally add a system prompt and change context awareness. This example leaves the blueprint configuration as context-aware, the default. Read more: [LLM blueprint configuration settings](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html), [context-awareness](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#context-aware-chatting) Save the configuration. ### 5. Send a prompt Chatting is the activity of sending prompts and receiving responses from the LLM. Once you have set the configuration for your LLM, on the Chats tab, send it a prompt (from the entry box in the lower center panel). For example: `Write a Python program to run DataRobot Autopilot`. Read more: [Chatting](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html) ### 6. Follow-up with additional prompts In the playground, you can ask follow-up questions to determine if configuration refinements are needed before saving the LLM blueprint. Because the LLM is context-aware, you can reference earlier conversation history to continue the "discussion" with additional prompts. From within the previous conversation, ask the LLM to make a change to "that code": ROUGE scores and citations reported with the response help provide a trust measure for the response. Read more: [ROUGE scores](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#rouge-scores) ### 7. Test and tune Use the configuration area to test and tune prompts until you are satisfied with the system prompt and settings. (You must save the configuration before submitting the first prompt.) Then, click the edit icon next to the blueprint name to make it more descriptive, for example `GPT 3.5 with VDB`, then click confirm. Read more: [Blueprint actions](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#actions-for-llm-blueprints) ### 8. Create a comparison LLM blueprint Create one or more LLM blueprints to run a comparison. (You can send prompts to a single blueprint in the comparison view.) These steps create an LLM blueprint that does not use a vector database. Click on the menu for the LLM blueprint you just built and select Copy to new LLM blueprint: Rename the copy, for example, `GPT3.5 without VDB`, and remove the vector database that is assigned. Click Save configuration. ### 9. Set up LLM blueprint comparison Now, with more than one LLM blueprint in your playground, you can compare responses side-by-side. Click Back to LLM blueprint comparison: Select up to three blueprints to compare. Notice that the LLM blueprint information indicates the version that does not leverage a vector database. Read more: [Compare blueprints](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html) ### 10. Compare LLM blueprints To start the comparison, send a prompt to both blueprints. The responses show that the blueprint with grounding knowledge from the vector database provides a more comprehensive and useful response. Notice that the response from the LLM blueprint that uses a vector database includes a citation link. Click the link to view the text chunks that the LLM blueprint retrieved to augment the prompt sent to the LLM. When you are satisfied with the LLM blueprint results, you can choose Send to the workshop from the LLM blueprint actions dropdown to register and ultimately deploy it. Read more: [Deploy an LLM](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/deploy-llm.html) --- # How-tos URL: https://docs.datarobot.com/en/docs/get-started/how-to/index.html > Get started with DataRobot's value-driven AI. Analyze data, create and deploy models, and leverage code-first notebooks. # How-tos Get started with DataRobot's value-driven AI in short summaries or task-oriented walkthroughs. | Topic | Description | | --- | --- | | Agentic AI | | | Agents in the A.M. | Create an agent in a few quick steps. | | Talk to my Data Agent | Learn how to build a Talk to my Data Agent application—from selecting the template in the Application Template Gallery to interacting with the AI agent. | | Use a DataRobot deployment in an application template | Learn how to create an LLM deployment using financial jargon to build a tailored Talk to my Data Agent application that can apply industry knowledge to its responses. | | GenAI basic | Create and compare LLM blueprints and RAG (Retrieval Augmented Generation) workflows in a high-level overview. | | GenAI with governance: Space research | Use International Space Station research papers to compare multiple retrieval-augmented generation (RAG) pipelines with evaluation metrics and governance. | | Predictive AI | | | Introduction to data analysis in DataRobot | Learn the basics of exploring and analyzing data in DataRobot. | | Model-building | Use a hospital readmissions dataset to learn about wrangling data, building models, and creating no-code applications. | | Evaluate a regression model | Learn how to evaluate a regression model with model insights. | | Predict a regression target | Learn how to predict a regression target using a DataRobot application. | | Time series forecasting part 1 | Learn about time series forecasting with a car sales dataset. | | Time series forecasting part 2 | Learn about time series forecasting with a car sales dataset using DataRobot Notebooks to create experiments. | | DataRobot Notebooks | Leveraging the DataRobot Notebooks platform, learn how to execute a code-first use case. | | Operate and govern | Leveraging the model building how-to, learn to register and monitor those models. | | Business solutions | | | Business solutions | End-to-end how-tos, with problem framing, that address common business problems. | --- # Analyze data in DataRobot URL: https://docs.datarobot.com/en/docs/get-started/how-to/intro-to-eda.html > A walkthrough for the basic steps of uploading and analyzing data in DataRobot. # How-to: Analyze data in DataRobot This walkthrough demonstrates how to upload a dataset, trigger DataRobot's Exploratory Data Analysis (EDA) process, and start assessing the data in DataRobot The steps outlined here provide the foundation for more advanced processes (such as generating and comparing predictive models, performing regression target predictions, etc.) covered in other walkthroughs in this section. > [!TIP] Learn more about EDA > For a deeper dive into DataRobot's EDA capabilities, see [EDA insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/explore-data/eda-insights.html). ## Assets for download To prepare a dataset for analysis, download it locally so that it can be uploaded to DataRobot by clicking the button below. Note that other walkthroughs in this section may require a different dataset, so be sure to download the correct one when following a different guide. [Download the dataset](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/StackOverflow.csv) ## Create a Use Case Use Cases are folder-like containers inside of DataRobot Workbench that group everything related to solving a specific business problem—datasets, models, experiments, applications, and notebooks—inside of a single, manageable entity. You can either share individual Use Case assets or the entire Use Case itself with other user accounts. To create a new Use Case: 1. Log in to DataRobot and click Workbench to access the Use Case directory. 2. Click+ Create Use Case. Enter a name for the Use Case and click the checkmark. DataRobot automatically opens the new Use Case, showing the Use Case assets tile. From here, the Use Case is ready for data to be uploaded. For additional information on Use Cases, see [Use Cases](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/usecase-overview.html). ## Import the dataset Begin by importing the dataset from the Use Case assets tile: 1. Locate thePredictive AItile and clickDatato access theData assetstile. This tile contains all of the data that has been uploaded to the Use Case. 2. ClickUpload file. 3. Locate theStackOverflow.csvfile and clickOpen. Depending on the size of the dataset, it will take a few moments to register after uploading. The progress is shown in the Data assets tile underneath the name of the uploaded dataset. Once the dataset is fully registered, click it to view the results of Exploratory Data Analysis (EDA). ## Analyze the data Each row in the table shown represents a survey response and each column represents that person's answers. Clicking the Show summary button (indicated below) displays the Data Quality Assessment Summary. From here, DataRobot provides a wide variety of details to provide a comprehensive overview of the data. The following sections provide a brief summary of several important features, but you should click through all the various tabs and fields to see everything DataRobot has to offer. For more details on data analysis, see [Analyze data insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/experiment-data.html#analyze-data-insights). ### View a histogram of a feature As depicted in the screenshot provided in the previous section, the Data preview tab displays a small histogram plot for each feature in the dataset. Clicking the histogram opens a view that shows additional details about the feature, as well as a larger version of the plot. As shown above, the Summary statistics area provides valuable insight into the feature data itself, including an assessment of whether the data has any issues. The histogram shows the distribution of the data on a per-education level basis, as the EdLevel feature is selected. From this view, it is clear that the majority of responses contained in the dataset are from individuals with a Bachelor's degree or higher. (For more details on data quality issues, see [#identify-data-quality-issues] later in this walkthrough.) The histogram can be toggled to show a table with more details about each category in the feature by clicking the Table button (indicated below). An even larger version of the histogram can be viewed by clicking the Go to feature button. This opens the feature in the Features tile. From here, all the features available in the dataset can be viewed by selecting the desired feature from the list provided in the left-hand pane. ### Identify data quality issues DataRobot performs several functions automatically, including highlighting potential issues with the data. Data quality issues can be isolated by clicking the Show details button. The Show only features with data quality issues button isolates the features that have potential problems, allowing you to focus specifically on attempting to resolve them. In this case, the remaining columns in the table contain data outliers, or data points that are significantly different from the rest of the data. Outliers can result in models that are not representative of the data, as they are not representative of the average population. Other examples of data quality issues include target leakage, inliers, and missing values. For additional details on data quality issues, see [Data quality checks](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html). ## Next steps Now that the data has been added and analyzed, proceed to one of the other tutorials in this section to learn how to build a model, as well as how to evaluate and deploy it. --- # Anti-Money Laundering (AML) alert scoring URL: https://docs.datarobot.com/en/docs/get-started/how-to/money-launder-tutorial.html > Step-by-step walkthrough to build a model that uses historical data, including customer and transactional information, to identify which alerts resulted in a Suspicious Activity Report (SAR). # How-to: Anti-money laundering (AML) alert scoring This walkthrough provides step-by-step instructions for building a model that uses historical data, including customer and transactional information, to identify which alerts resulted in a Suspicious Activity Report (SAR). The model can then be used to assign a suspicious activity score to future alerts and improve the efficiency of an AML compliance program using rank ordering by score. > [!TIP] Jupyter notebook version > While this walkthrough follows the DataRobot UI, an [equivalent Jupyter notebook is available](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/anti-money-laundering/Anti-Money%20Laundering%20(AML%29%20Alert%20Scoring.ipynb). For a deeper dive into the use case, see the [Anti-Money Laundering (AML) Alert Scoring](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/money-launder.html) guide and review it alongside working through this walkthrough. ## Assets for download To follow this walkthrough, download the dataset that will be used to train and evaluate the model below. The dataset contains a sample of alerts from a financial institution. [Download dataset](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/DR_Demo_AML_Alert_train.csv) > [!NOTE] Important > Follow the steps detailed in the [Introduction to data analysis in DataRobot](https://docs.datarobot.com/en/docs/get-started/how-to/intro-to-eda.html) walkthrough to upload the dataset and prepare it for modeling. ## Building a model Now that the data has been uploaded and analyzed, it is time to build a model. 1. ClickData actions > Start modeling. 2. In theSet up new experimentwindow, specifySARin theTarget featurefield. 3. Leave the remaining fields at their defaults and clickNext. NoteFor more details on the additional settings, seeStart modeling setup. 4. Leave all partitioning changes fields at their defaults and clickStart modeling. 5. DataRobot begins building the models. 6. After a few moments, the Model Leaderboard appears and indicates the training progress. Model build timeModel build time can vary depending on the size of the dataset. When it completes, theWorkerspane displaysNo jobs currently running. For details on how to assess the various models after they are built, see [Compare models](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/compare-models.html). ## Model evaluation and interpretation Now that a set of models are ready for analysis, select the top model and explore its details. DataRobot flags the most accurate model as Prepared for deployment in the Model Leaderboard. Click the model to view more detailed information about it. Use the tabs in the Details pane to explore various insights, as highlighted below. DataRobot provides a variety of tools that can be used to assess why a particular alert was flagged as suspicious. Review the following sections to understand how to use the most relevant ones for this use case. #### Blueprint From the model details page, click Details > Blueprint to reveal the model [blueprint](https://docs.datarobot.com/en/docs/api/reference/public-api/blueprints.html) —the pipeline of preprocessing steps, modeling algorithms, and post-processing steps used to create the model. #### Feature Impact Click [Explanations > Feature Impact](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/feature-impact.html) and then Compute to see the association between each feature in the dataset and the target. > [!NOTE] Note > Computing Feature Impact may take several minutes. DataRobot identifies the top three most impactful features (which enable the machine to differentiate SAR from non-SAR alerts) as `total merchant credit in the last 90 days`, `number refund requests by the customer in the last 90 days`, and `total refund amount in the last 90 days`. #### Feature Effects To understand the direction of impact and the SAR risk at different levels of the input feature, DataRobot provides partial dependence graphs on the [Feature Effects](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/feature-effects.html) tab. Click Explanations > Feature Effects and then Compute to depict how the likelihood of being a SAR changes when the input feature takes different values. > [!NOTE] Note > Computing Feature Effects may take several minutes. In this example, the total merchant credit amount in the last 90 days (on the x-axis) is the most impactful feature, but the SAR risk (on the y-axis) is not linearly increasing when the amount increases. The chart shows that the SAR risk remains relatively low when the amount is below $1000, surges significantly when the amount is above $1000, and then slows when the amount approaches $1500. #### Individual Prediction Explanations To view a breakdown of how each feature in the dataset contributes to the overall prediction outcome, click [Explanations > Individual Prediction Explanations](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/shap-predex.html). This tab shows the explanations for each alert scored and prioritized by the machine learning model. The image above demonstrates a sample of five random predictions from the model. You can click Predictions to sample below the chart to adjust the total number of predictions to be used to generate the chart. Click on a specific prediction in the predictions list to see which features contributed to the prediction. In the example below, the prediction with `ID=1789` has a very high likelihood of being a suspicious activity (prediction=91.3%) based on the total merchant credit amount in the last 90 days. #### Word Cloud Click [Explanations > Word Cloud](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/word-cloud.html) to explore how text fields affect predictions. The Word Cloud uses a color spectrum to indicate the word's impact on the prediction. In this example, red words indicate the alert is more likely to be associated with a SAR. Larger words indicate words that occur more frequently in the dataset. Click a word in the cloud to see the details about that particular word. ## Evaluate accuracy In addition to providing insights into the model's performance, DataRobot provides tools to evaluate the model's accuracy. This section describes several of the most relevant tools to this use case. ### Lift Chart Click [Performance > Lift Chart](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/lift-chart.html) to view how effective the model is at separating the SAR and non-SAR alerts. After an alert in the out-of-sample partition gets scored by the model, it is assigned a risk score that measures the likelihood of the alert being a SAR risk or becoming a SAR. In the Lift Chart, alerts are sorted based on the SAR risk, broken down into 10 deciles, and displayed from lowest to the highest. For each decile, DataRobot computes the average predicted SAR risk (as indicated by the blue plus signs) as well as the average actual SAR event (as indicated by the orange circle). It then connects the respective dots into two distinct line graphs for the predicted and actual SAR risk. The chart shows that the model has slight under-predictions for the alerts that are more likely to be a SAR, but overall, the model performs well. ### ROC Curve Now that you know the model is performing well, you can select an explicit threshold to make a binary decision based on the continuous SAR risk predicted by DataRobot. Click Performance > ROC Curve to access a variety of information to help make some of the important decisions in selecting the optimal threshold: For additional information on the ROC Curve, see [ROC Curve](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/roc-curve.html). ## Next steps Now that you have completed the basic process of building a model to analyze and make predictions on SAR alerts, you can move onto more detailed steps regarding this use case in the [Anti-Money Laundering (AML) Alert Scoring](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/money-launder.html) guide. --- # Execute a use case in DataRobot Notebooks URL: https://docs.datarobot.com/en/docs/get-started/how-to/notebook-walk.html > Leveraging the DataRobot Notebooks platform, learn how to execute a code-first use case. # How-to: Execute a use case in DataRobot Notebooks This walkthrough shows you how to execute a code-first use case using an AI accelerator with DataRobot Notebooks. You will: 1. Access and download an AI accelerator. 2. Create a Use Case in Workbench. 3. Upload the AI accelerator as a notebook in the Use Case. 4. Execute the accelerator in DataRobot Notebooks. #### Prerequisites Before proceeding with the workflow: - Review the API quickstart guide to get familiar with common API tasks and configuration. - Review the use case summary here . ### 1. Access the AI accelerator This walkthrough leverages the DataRobot API to quickly build multiple models that work together to predict common fantasy baseball metrics for each player. After reviewing the use case summary from the link in the prerequisites, [download a copy of the accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/model_factory_selfjoin_fantasy_baseball/fantasy_baseball_predictions_model_factory.ipynb) to your machine. ### 2. Create a Use Case in Workbench From the Workbench directory, click Create Use Case in the upper-right corner. Read more: [Use Cases](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/usecase-overview.html#create-a-use-case) ### 3. Upload the accelerator In the newly created Use Case, upload the AI accelerator as a notebook to work with it in DataRobot. Select Add > Notebook > Upload existing notebook. Select the local copy of the AI accelerator that you previously downloaded, then click Upload. When uploading completes, select Import. Once uploaded, the accelerator will open in DataRobot Notebooks as part of the Use Case. Read more: [Add notebooks](https://docs.datarobot.com/en/docs/workbench/wb-notebook/wb-manage-nb/wb-create-nb.html) ### 4. Configure the notebook environment To edit, create, or run the code in the accelerator, you must first configure and then run the notebook environment. The environment image determines the coding language, dependencies, and open-source libraries used in the notebook. To see the list of all packages available in the image, hover over it in the Environment tab: Review the available environments, and, for this walkthrough, select the Python 3.9.18 image and select the default environment settings. Read more: [Environment management](https://docs.datarobot.com/en/docs/workbench/wb-notebook/wb-code-nb/wb-env-nb.html) ### 5. Run the environment To begin working with the accelerator, start the environment by toggling it on in the toolbar. Wait a moment for the environment to initialize, and once it displays the Started status, you can begin coding, editing, and executing code. Read through the use case and execute the code cells as you go. To execute code, select the play button next to a cell. Read more: [Create and execute cells](https://docs.datarobot.com/en/docs/workbench/wb-notebook/wb-code-nb/wb-cell-nb.html) ### 6. Install and import libraries To install the pybaseball library, edit the cell to uncomment the `pip install` command, then run the cell. Run the cell that follows, which imports the notebook's required libraries. ### 7. Import data Run the "Import player batting data" cell to get the data used in this accelerator. The cells that follow structure and prepare the data for modeling. ### 8. Build models After preparing the data, the accelerator creates a DataRobot experiment to train many models against the assembled dataset. This accelerator leverages Feature Discovery, an automated feature engineering tool in DataRobot that uses the secondary dataset in previous steps to derive rolling, time-aware features about baseball players' recent performance history. Follow the cells to begin the modeling process. Read more: [Feature Discovery](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/index.html) ### 9. Make predictions After successfully building models, the accelerator points you to the Leaderboard to evaluate them. Select one of the top-performing models to test that it can successfully make predictions with test data. For this walkthrough, DataRobot recommends the AVG Blender model. Before making predictions, review the considerations, options, and maintenance for scoring baseball player data (detailed in the screenshot below), then execute the code. When predictions are returned, you can evaluate the top ten players' predicted batting averages. Read more: [DataRobot Prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html) ### 10. Plot a Lift Chart Lastly, use the code to create a Lift Chart that compares predicted batting averages to actual batting averages (actuals). The fact that the actual batting averages are running higher than the predicted batting averages is due to sampling biases. For example, only players with at least 250 plate appearances through mid-July are evaluated—the players likely being selected because they are playing better. Additionally, with only half a season of data, the volatility of outcomes is greater than had the model trained on a full season, inflating the highest batting averages in the chart. Read more: [Lift Chart](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/lift-chart-classic.html) --- # Predict a regression target URL: https://docs.datarobot.com/en/docs/get-started/how-to/predict-regression.html > Predict a regression target using a DataRobot application using a no-code application. # How-to: Predict a regression target This walkthrough uses machine learning to build an application that will use a predictive model to predict the fuel efficiency of a new car that has not yet been designed. Because the model needs to predict a number, this is a regression problem. The walkthrough describes how to frame, set up, evaluate, and interpret predictions for a continuous target. It then creates an application that can be used to make predictions based on adding new data to the application. ## Assets for download To follow this walkthrough, download the dataset that will be used below. [Download dataset](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/cars2020.csv) ### Car fuel economy data The dataset contains information about cars that have been designed and tested. The data is from the EPA's Fuel Economy Guide for 2020. Each row in this dataset represents information about a car, such as the make, model, drivetrain, and other specifications. The dataset is based on [public data](http://www.fueleconomy.gov/feg/epadata/20data.zip) from the [fueleconomy.gov](http://www.fueleconomy.gov/) website. > [!NOTE] Dataset notice > This dataset was cleaned and modified for use in this exercise. The data is from vehicle testing done at the EPA National Vehicle and Fuel Emissions Laboratory and from vehicle manufacturers. The data dictionary for each field is [also public](http://www.fueleconomy.gov/feg/ws/index.shtml#vehicle). ### Define the target The target is the fuel efficiency of the car, measured in miles per gallon (MPG). Notice that this is a continuous variable (i.e.,a number) rather than a binary True/False or Yes/No, making this a regression problem. The other columns contain information that will help us predict MPG. ## Set up the project Follow the steps provided in the [Introduction to data analysis in DataRobot](https://docs.datarobot.com/en/docs/get-started/how-to/intro-to-eda.html) walkthrough to set up the project. Use the `cars2020.csv` dataset in place of the dataset provided in the walkthrough. Once the project is set up, continue on to create an experiment. ## Create an experiment The steps in this section build an experiment that will help predict the fuel efficiency of the car, which is indicated by the `MPG` feature. 1. From the data view for the dataset, clickData actions > Start modeling. 2. In theSet up new experimentwindow, specifyMPGin theTarget featurefield. Also ensure that theTarget typeis set toRegression. 3. Leave the remaining fields at their defaults and clickNext >. NoteFor more details on the additional settings, seeStart modeling setup. 4. Leave all partitioning changes fields at their defaults and clickStart modeling. After a few moments, the Model Leaderboard appears and indicates the training progress. Model build timeModel build time can vary depending on the size of the dataset. When it completes, theWorkerspane displaysNo jobs currently running. 5. Once the models are built, the Model Leaderboard indicates the top model asPrepared for deployment. Click it to view the model's details. For details on how to assess the various models after they are built, see [Compare models](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/compare-models.html) and [Evaluate with model insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/index.html). ## Create an application and make a prediction Now that the best model has been identified, it can be registered and deployed to a production environment for use in making predictions. The steps below create an application that can be used to make predictions. 1. From the best model's details page, clickModel actions > Create a no-code application. 2. Once the application has been created, clickPredictionsin the left navigation pane. 3. The page refreshes to display all predictions data in the application. Scroll down to theSubmit Single Predictionsection and clickMake prediction. 4. In theAdd new predictionwindow, you can specify the features that should be used to make the new prediction. For this example, specify the values in the table below. FeatureValueDisplacement10Cylinders16TransmissionAuto(AM-S7)DriveAll wheel driveGears7Exhaust Valves Per Cyl2Intake Valves Per Cyl2Recommended FuelDieselModel Index121Max Ethanol10 5. ClickAdd prediction. 6. The new prediction is displayed in the first row of thePredictionssection. In this case, the predicted fuel efficiency is 11.152 MPG. Repeat the steps above to generate additional predictions as needed. If you would like to make multiple predictions at once, you can upload a CSV file with the predictions under the Batch Prediction section. For additional details on how to create and work with custom applications, see [Create custom applications](https://docs.datarobot.com/en/docs/wb-apps/custom-apps/upload-custom-app.html). --- # Deploy and govern a model URL: https://docs.datarobot.com/en/docs/get-started/how-to/production-walk.html > Leveraging the model building walkthrough, learn to register and monitor those models. # How-to: Deploy and govern This walkthrough shows you how to use DataRobot to predict airline take-off delays of 30 minutes or more. You can learn more about the use case [here](https://docs.datarobot.com/en/docs/get-started/how-to/biz-accelerators/biz-app-briefs.html#flight-delays). You will: 1. Register a model. 2. Create a deployment. 3. Make predictions. 4. Review monitoring metrics. #### Prerequisites To deploy and monitor your models, first either: - Complete the model building walkthrough to create models. - Open an existing experiment Leaderboard to start the registration process. ### 1. Register a model Registering models allows you to create a single source of truth for your entire AI landscape. For example, you can use model versioning to create a model management experience organized by problem type. To register a model, select it on the Leaderboard and choose Model actions > Register model. (The model that is prepared for deployment is a good choice for the walkthrough.) Read more: [DataRobot Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/index.html) ### 2. Configure registration details When registering a model, you specify whether to register it as a new model or a version of an existing registered model. For this walkthrough, register as a new model, optionally add a description, and click Register model. Read more: [Register DataRobot models](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-register-dr-models.html) ### 3. Start deployment creation While the registration process is ongoing, you can explore the lineage and metadata that DataRobot displays. This information is crucial for traceability. Click Deploy to proceed. Read more: [Deploy a model from Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-deploy-models.html) ### 4. Configure the deployment Prediction environments provide deployment governance by letting you control accessibility based on where the predictions are being made. Set an environment from this screen (an environment has been created for this trial). You can configure monitoring capabilities, including data drift (on by default), accuracy, fairness, and others by clicking Show advanced options. To continue, click Deploy model to create the deployment. Read more: [Configure advanced options](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-deploy-models.html#advanced-options) ### 5. Create the deployment DataRobot provides a status message as it applies your settings and establishes a data drift baseline for monitoring as it creates the deployment. The baseline is the basis of the Data Drift dashboard, which helps you analyze a model’s performance after it has been deployed. Once the deployment is ready, you will automatically be forwarded to the Console overview. Read more: [Console overview](https://docs.datarobot.com/en/docs/workbench/nxt-console/index.html) ### 6. Make predictions The Console overview provides a wealth of general information about the deployment. The other tabs at the top of the page configure and report on the settings that are critical to deploy, run, monitor, and optimize deployed models. Click the Predictions tab to make a one-time batch prediction. Read more: [Prediction options](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/index.html) ### 7. Upload prediction data To make a batch prediction, upload a prediction dataset. If you were to upload your own scoring data, it would be automatically stored in the Data Registry. Trial users have a provided dataset that has been pre-registered. Open the Choose file dropdown and select AI Catalog as the upload origin. From the catalog, locate and select the prediction dataset `HOSPITAL_READMISSIONS_SOCRING_DATA`. Click the dataset to select it, review the dataset metadata, and when ready, click Use this dataset. Read more: [Make one-time predictions](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/nxt-make-predictions.html#select-a-prediction-dataset) ### 8. Compute predictions Prediction options, both basic and advanced, allow you to fine-tune model output. Explore the controls, but for this walkthrough, stick with the defaults. Scroll to the bottom of the screen and click Compute and download predictions. After computing, DataRobot lists the entry in the recent predictions section. Read more: [Make predictions from a deployment](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-predictions/nxt-make-predictions.html#make-predictions-with-a-deployment) ### 9. Review Service Health The Service Health tab reports the deployed model’s operational health. Service Health tracks metrics about a deployment’s ability to respond to prediction requests quickly and reliably, helping to identify bottlenecks and assess capacity, which is critical to proper provisioning. Check total predictions, total requests, response time, and other service health metrics to evaluate model operational health. Then click Data Drift to check model performance. Read more: [Service health](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-service-health.html) ### 10. Review Data Drift As real world conditions change, the data distribution of the prediction data drifts away from the baseline set in the training data. By leveraging the training data and prediction data (also known as inference data) that is added to your deployment, the dashboard on Data drift tab helps you analyze a model's performance after it has been deployed. Read more: [Data drift](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-data-drift.html) --- # Talk to my Data Agent URL: https://docs.datarobot.com/en/docs/get-started/how-to/talk-data-walk.html > A high level walkthrough for the Talk to my Data Agent template that starts in the Application Template Gallery and ends with asking the agent a question. # How-to: Build Talk to my Data Agent Talk to my Data Agent allows you to upload raw data from your preferred data source, and then ask a question. The agent recommends business analyses—generating charts, tables, and code to help you interpret the results. You can view a video walkthrough at the [DataRobot Youtube channel](https://www.youtube.com/watch?v=HjSslAqfX6k). In this walkthrough, you will: - Select the Talk to my Data Agent template from the Application Template Gallery. - Configure the application template in a codespace. - Build the Pulumi stack and open the application. - Load data, automatically generating a data dictionary. - Interact with the agent, converting natural language queries into SQL/Python code to explain what the data shows, why patterns exist, and recommended next steps. ## Scalability When dealing with large datasets, connecting to Snowflake or BigQuery allows the analysis to run directly in the cloud using SQL. This is ideal for large datasets because the data remains in the cloud, utilizing cloud computing. ## Data quality The AI agent can talk to disparate datasets as if they were unified, performing joins or merges as it goes without explicit guidance. Upon ingestion, the agent also proactively cleans common data quality issues including data inconsistencies, special character problems, and formatting issues. ## Assets for download To recreate the visualizations shown in this walkthrough, download and the CSV file below. [Download training data](https://s3.amazonaws.com/datarobot-doc-assets/ontario_real_estate_market_2021.csv) ## Prerequisites To build a Talk to my Data Agent application from the Application Template Gallery, you need: - Access to GenAI and MLOps functionality in DataRobot - DataRobot API token - DataRobot Endpoint - Large language model (LLM) credentials for one of the following: ## 1. Open the Talk to my Data Agent template From Workbench, in the Use Case directory, click Browse application templates. Select Talk to my Data Agent and click Open in a codespace in the upper-right corner. This walkthrough focuses on working with application templates in a codespace, however, you can click Copy repository URL and paste the URL in your browser to open the template in GitHub. DataRobot opens and begins initializing a codespace. Once the session starts, the template files appear on the left and the `README` opens in the center. To learn more about the codespace interface, see [Codespace sessions](https://docs.datarobot.com/en/docs/workbench/wb-notebook/codespaces/session-cs.html). > [!TIP] Tip > DataRobot automatically creates a Use Case, so you can access this codespace (and any resulting assets) from the Use Case directory in the future. ## 2. Configure the codespace Follow the instructions included in the `README` file. In the `.env` file, accessed from the file browser on the left, the following fields are required: - DATAROBOT_API_TOKEN : Retrieved from User settings > API keys and tools in DataRobot. - DATAROBOT_ENDPOINT : Retrieved from User settings > API keys and tools in DataRobot. - PULUMI_CONFIG_PASSPHRASE : A self-selected alphanumeric passphrase. - LLM credentials. All application templates utilize generative AI, so DataRobot provides out-of-the-box support for Azure OpenAI, VertexAI (Google Cloud), and Anthropic on AWS. > [!NOTE] Note > Make sure to remove the `#` to the left of the populated LLM credentials. > > [https://docs.datarobot.com/en/docs/images/talk-walk-4.png](https://docs.datarobot.com/en/docs/images/talk-walk-4.png) > > In the example above, the `#` was manually removed from line 23 and 24. ## 3. Execute the Pulumi stack and open the application Click the Terminal tile in the left panel. In the resulting terminal pane, run `python quickstart.py YOUR_PROJECT_NAME`, replacing `YOUR_PROJECT_NAME` with a unique name. Then, press Enter. Executing the Pulumi stack can take several minutes. Once complete, DataRobot provides a URL at the bottom of the results in terminal. To view the deployed application, copy and paste the URL in your browser. > [!TIP] Tip > DataRobot also creates an application in Registry. To access this application again, you can navigate to the [Applications](https://docs.datarobot.com/en/docs/wb-apps/custom-apps/upload-custom-app.html) page. ## 4. Load and explore data Upload one or more datasets from a `.csv` or multi-tabbed excel file, or connect directly to a data source, including Snowflake, BigQuery, or the AI Catalog in DataRobot. Once ingested, the AI agent combines and cleans the data, automatically generating and opening a data dictionary for immediate analysis. The Dictionary is AI-generated, providing clear column definitions and metadata. You can edit columns to incorporate your own business perspective and then click Download Data Dictionary to export the updated definitions. The dataset in this example contains 2021 real estate information for Ontario, Canada, including information about real estate transactions, details about the properties sold (e.g., location, type, size, and features), as well as demographic and economic data for the areas where the properties are located. ## 5. Talk to the agent To begin talking to the AI agent, click AI Data Analyst. In the search bar at the bottom, enter a request in plain language. In this example, the request is: `Show me the average price of properties on a map, by city. Let's use one of those open street maps.` Immediately, the agent communicates that it understands that you want to see the average price of properties on a map, by city, using an open street map. After processing the request, the agent provides actionable insights and visualizations in the form of a table with the requested information, an interactive open street map, and a bar chart highlighting the most expensive regions at a glance. If you want more detail after interacting with the results, you can ask a follow up question. In this example, the follow up request is: `Break that down further by property type.` There's no need to start over, the agent uses the initial request as a starting point for the follow up request. You can even save valuable chats by clicking Save Chat on the left, or New Chat to start over again. --- # Time series forecasting in the GUI URL: https://docs.datarobot.com/en/docs/get-started/how-to/time-series-walk.html > Learn about DataRobot time series forecasting using a car sales dataset. # How-to: Time series forecasting in the GUI This walkthrough showcases a car sales forecasting example to learn about DataRobot time series. The dataset includes month-by-month sales for many makes and models of vehicles. On this page you will create an experiment and build models in the UI. Complete the second part of this walkthrough using a [code-first approach with the DataRobot Python client](https://docs.datarobot.com/en/docs/get-started/how-to/ts-car-sales-nb.html). In part 2, you create the same type of experiment but use Python loops to accomplish more modeling work, faster. Watch the full video of this use case on [YouTube](https://www.youtube.com/watch?v=Qh_sQ1OrT2A). ## Assets for download Download the following car sales-related assets—a shorter version of the data ( `FAST`), a fuller version with more segments ( `_Segments`), and a Python notebook ( `_Model_Factory.ipynb`): [Download assets](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/DataRobot%2BTime%2BSeries%2BCar%2BSales%2BForecasting.zip) ## 1: Create a Use Case From the Workbench directory, click Create Use Case in the upper right: and name it `Car sales`. Read more: [Working with Use Cases](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/index.html) ## 2: Upload data Click Add data > Upload to add the _ `FAST` dataset (included in the assets you downloaded to start) to your Use Case via [local file](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/add-data/local-file.html). While a dataset is being registered in Workbench, DataRobot performs EDA1—analyzing and profiling every feature to detect feature types, automatically transform date-type features, and assess feature quality. Once registration is complete, you can see what was uncovered while computing EDA1. After DataRobot finishes registering the dataset, click the dataset name to explore. Notice the following: | Characteristic | Supporting column | | --- | --- | | The time step in this time series dataset is monthly. | Date | | The target value you will forecast is the monthly sales volume. | Sales_Volume | | The dataset has ten models of cars in two major segments. | Model, Major_Segment | Additionally, there are five columns of contextual information, the average price and average dealer incentive, and three economic indicators. Read more: - Working with data - Time series file size requirements - Exploratory Data Insights in Workbench ## 3: Set basic modeling config After exploring, click Start > Modeling to build an experiment using the car sales data. Once the data is processed for modeling, review the information provided for a quick data quality check, such as the number of unique and missing values. Configure the following basic settings: | Field | Setting | | --- | --- | | Target | Sales Volume | | Modeling mode | Comprehensive Autopilot | | Optimization metric | Recommended (Gamma Deviance) | Until you start modeling, the configuration can be changed. Review the configuration so far in the experiment summary on the right: Click Next to continue configuring the experiment. Read more: - Create forecasting experiments - Add models to experiments - Optimization metric reference ## 4: Configure time series modeling Time-aware modeling is used when data must be kept in date order. This is true for all types of forecasting and also for certain classification and regression problems where out-of-time validation is used. To continue the configuration: 1. Select theTime series modelingtab and toggle on time series modeling: 2. Set the ordering feature and series identifier: In this example there is only one ordering feature—Date. In your experiments you may have to set this manually if there is more than one. DataRobot has also correctly detected the series identifier, which you confirm from the dropdown list. Alternatively you can choose another identifier. 3. Scroll down to configure window settings. Set the following values and note that the illustration to the right updates as you add values. Note that DataRobot automatically detected a monthly time step. Window settingValueDescriptionFeature derivation window values13 (left), 1 (right)Configures the periods of data that DataRobot uses to derive features for the modeling dataset. In this example, features are derived from data that is 13 months to 1 month prior to the forecast point.Forecast window2 (left), 3 (right)Defines the range (the forecast distance) of future predictions; DataRobot optimizes models for that range and ranks them on the Leaderboard on the average across that range. In this example, models will predict two, three, and four months into the future. The recommended model will be the one that minimizes the error for all makes and models of cars for all forecast distances. 4. Set features that are known in advance—features that do not vary with time and are known at prediction time. In this example,BrandandMajor_Segment. > [!NOTE] Note > When you toggle on time series modeling, DataRobot automatically enables date/time partitioning and configures backtest settings based on your dataset. You could do both of these tasks manually—for example if you want different backtest settings—from the Data partitioning tab. Click the tab to explore, if you choose. > > [https://docs.datarobot.com/en/docs/images/wb-ts-exp-15.png](https://docs.datarobot.com/en/docs/images/wb-ts-exp-15.png) All settings are now complete (see the updated experiment summary); click Start modeling to begin training models. DataRobot automation will derive time series features, apply other preprocessing to the data, select algorithms to test, and then start testing them on each appropriate model. Read more: - Time series framework - Multiseries modeling - Enable time-aware modeling - Set backtest partitions - Time series modeling data ## 5: Explore the Leaderboard As training commences, DataRobot populates the Leaderboard with models in the building and completed states—completed models display an accuracy score. The time required to finish training all models depends on the size of the starting data as well as the number of concurrent jobs your account allows. Expand the Jobs queue to see the status and queued models for your experiment. Once a model completes, click on it in the Leaderboard to begin exploring. You can star a model for easier identification later (for example, to find the model you ran computations on). For this walkthrough, star, and then select "Per Series Elastic Net Regressor with Forecast Distance Modeling." Click the Blueprint to see a graphical representation of the pipeline of preprocessing steps, modeling algorithms, and post-processing steps that go into building a model. Click a task to access the reference documentation for that task. Click Feature Impact for a high-level visualization that identifies which features are most strongly driving model decisions. Click to compute the insight, if prompted. The calculation is added to the queue. When calculations complete, click the Derived features tab. Investigate Accuracy Over Time by first clicking Compute for training. You can change backtest, series, forecast distance, and resolution. Notice the difference when training data is shown and hidden: Because this is a multiseries experiment, you can use Series Insights to view series-specific information. Compute accuracy scores to see beyond the first 1000 series; experiment with the dropdown settings to better understand the insight as the changes will help with interpreting the display. Read more: - Blueprints - Feature Impact - Accuracy Over Time - Series Insights ## 6: View experiment information At any point after model training, click View experiment information to see summary information including the derived data, to view and create features lists, and to access the blueprint repository (where you can access additional blueprints to train). Explore the tabs while insight calculations complete. The initial display, the Setup tab, provides summary information about the experiment. Click the Derived modeling data tab to see the data used for model training, after the feature derivation process was applied. Notice that the dataset has gone from 10 features, as shown in the Original data tab, to 107 features. You can also preview the derivation log or download the complete transformation record. DataRobot automatically creates time series feature—lags, average/max/median, rolling, and more—based on the characteristics of the data and configured windows. For example, in the car sales dataset DataRobot has created a number of lagged (and other) features based on average incentive`: Read more: [Time series feature derivation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/feature-eng.html) ## 7: Explore feature lists and blueprints After constructing time series features for the data, DataRobot automatically creates multiple feature lists, which are shown on the Feature lists tab. The initial selection of automatically created lists are, again, those most appropriate to your data. For example, DataRobot knows which algorithms require differencing and which do not, and creates appropriate lists containing those features. You can take a variety of actions for each list, as well as create your own feature lists. When creating a custom list, select features individually or use bulk actions; be sure to include the ordering feature. Provide a name, and optionally, description of your list. This walkthrough uses an automatically created list. The Blueprint repository tab provides a library of modeling blueprints available and relevant for a selected experiment. You can search for blueprints, change feature lists, and train one or more models from the repository. Read more: - Time series feature lists - Create a feature list - Blueprint repository ## 8: View and slice calculated insights Return to the Leaderboard, and your starred model, to view the view the insights you ran calculations for. 1. First, expandFeature Impact, which shows the relative contribution of features, both original and derived. By default the insight shows relative importance for all ten makes and models of cars in both identified segments. 2. Create a data slice for a finer-grained view. From theData slicedropdown in the bottom left of the insight, choose+ Create slice. Complete the modal as follows: FieldSettingSlice namePickupFilter featureMajor_Segment (actual)Operator=ValuePickup ClickSave slice. From theData slicedropdown, select the slicePickup; you will be prompted to recompute the insight using the configured subpopulation of a model data. 3. Open theFeature Effectsinsight, which shows the effect of changes in a feature's value. The insight is communicated in terms of partial dependence, an illustration of how changing a feature's value, while keeping all other features as they were, impacts a model's predictions. For example, you can view how a the commodity price index affects sales volume or view by the month of the year. If you change theSort bycriteria to order by effect size, you can see that the fewest cars are sold in January, while the most are sold in August and December. You can change the display to show only the features identified by thePickupdata slice, but changing the slice will require recomputing. 4. Next, scroll down toAccuracy Over Time. Predicted values are shown in blue and actual values in orange for the training (shaded blue) and validation (shaded green) periods. Use the controls to select change the series displayed, backtest or forecast distance. Use the preview selector at the bottom of the insight to zoom in on a specific time period. 5. Finally, look atSeries Insights. The tab shows a histogram and table of information specific to a selected, or all, series. Look at theBacktestcolumn, which shows the error for each series. You can see that the error is lowest for the Toyota Camry and highest for the Ford Focus. It's helpful to put errors in context with the actual sales volume—the target feature. Sort theTarget averagecolumn. If the vehicles with very high sales volumes are among the ones with the lower errors, it's a good indication that you may have found a suitable model. Conversely, if the errors are high for the high sales volume car, you would discard that model as a contender. Read more: - See the tip in step 5 - Sliced data - Feature Effects ## 9: Re-evaluate the Leaderboard To allow early investigation during this walkthrough, computations were run on a model selected before model building completed. Now, return to the Leaderboard and see the re-ordering that has occurred. If model building finished, DataRobot selected the most accurate individual, non-blender model and prepared it for deployment. That model is marked with a badge and the Feature Impact calculation has been run. Note that you can leave the Leaderboard, for example to work in other Use Cases or experiments, and return to this experiment at any time. Read more: - Model recommendation process - Remove redundant features ## Next steps After you have built a model in the UI, you can move onto [part 2 of the walkthrough](https://docs.datarobot.com/en/docs/get-started/how-to/ts-car-sales-nb.html) to use a code-first approach with the DataRobot Python client. Alternatively, to explore additional actions in the UI, reference the resources below: - Make predictions to test accuracy . - Register the model and ultimately deploy it. --- # Time series forecasting with code URL: https://docs.datarobot.com/en/docs/get-started/how-to/ts-car-sales-nb.html > Learn about DataRobot time series forecasting using a car sales dataset and a Jupyter notebook. # How-to: Time series forecasting with code This walkthrough, [a continuation of part 1](https://docs.datarobot.com/en/docs/get-started/how-to/time-series-walk.html), showcases a car sales forecasting example to learn about DataRobot time series. Watch the full video of this use case on [YouTube](https://www.youtube.com/watch?v=Qh_sQ1OrT2A). The dataset includes month-by-month sales for many makes and models of vehicles. You will create an experiment and build models in the UI and then import a Jupyter-formatted notebook to view more detailed segment analysis using DataRobot insights. Before proceeding with the workflow, review the [API quickstart guide]api-quickstart{ target=_blank } to get familiar with common API tasks and configuration. ### 1: Upload the notebook From the Notebooks tab of the Use Case folder, select Upload Notebook and navigate to the notebook file that provided with this walkthrough. Then click Import. Once uploaded, the accelerator will open in DataRobot Notebooks as part of the Use Case. Read more: - DataRobot-hosted notebooks - Codespaces ### 2: Configure the notebook environment To edit, create, or run the code in the accelerator, you must first configure and then run the notebook environment. The environment image determines the coding language, dependencies, and open-source libraries used in the notebook. To see the list of all packages available in the image, hover over it in the Environment tab: Review the available environments, and, for this walkthrough, select the Python 3.9.18 image and select the default environment settings. Read more: [Notebook environment management](https://docs.datarobot.com/en/docs/workbench/wb-notebook/wb-code-nb/wb-env-nb.html) ### 3: Run the environment To begin working with the accelerator, start the environment by toggling it on in the toolbar. Wait a moment for the environment to initialize, and once it displays the Started status, you can begin executing code. To execute code, select the play button next to a cell. Read more: [Create and execute cells documentation](https://docs.datarobot.com/en/docs/workbench/wb-notebook/wb-code-nb/wb-cell-nb.html) ### 4: Import libraries and data With the environment running, run the cells to import the required libraries, connect to DataRobot, and import the dataset. This walkthrough uses the fast version of the dataset, containing fewer vehicles and segments. To use a dataset with all vehicles, uncomment and use the alternative dataset path provided in the cell instead. After importing the data, reviewing the contents of the Pandas Dataframe. The proceeding cell selects five of the ten vehicle segments at random and plots them to display the shape of the data. ### 5: Configure the time series experiment Run the cell that defines the experiment settings (matching the configuration done in the DataRobot UI). This cell creates a time series experiment using all of the vehicles in the data frame. DataRobot will create an experiment for each vehicle segment. Reference some information about the features below: - The target is Sales_Volume . - The date feature is date . - The known in advance features are Brand and Major_Segment . - The multiseries column ID is Model . - The feature derivation window is from -13 to -1 . - The forecast window is from 2 to 4 months in the future. Read more: - Time series framework - Multiseries modeling - Enable time-aware modeling - Set backtest partitions - Time series modeling data ### 6: Run the experiment Next, create an experiment using all of the data in the Pandas Dataframe. This cell initiates DataRobot Autopilot for modeling. Allow some time for Autopilot to complete and build the experiment. You can reference the DataRobot GUI to monitor Autopilot's progress. ### 7: Create experiments for each segment The proceeding cells creates and an experiment for each unique value of `Major_Segment` in the Dataframe. In this walkthrough, you will create an experiment for the `Car` and `Pickup` segments. To do so, first subset the data by the segment. Then you will assign a name for each experiment created based on the vehicle segment. These experiments will leverage the time series settings specified in previous steps. Then, you will run Autopilot again for these experiments. This cell includes a print statement to inform you of which experiment DataRobot is currently building. ### Next steps When Autopilot completes, you will have a set of experiments for the major segments. This walkthrough demonstrates DataRobot's ability to automate experimentation using Python loops to accomplish exploratory work quickly. In addition to looping by segment, you can loop for individual models, individual forecast distance, or for different feature derivation windows. Reference the [model factory code example](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/python-multi.html) for more information. From here, you can also: - Create DataRobot Notebooks . - Leverage AI Accelerators . --- # Apply app templates to DataRobot deployments URL: https://docs.datarobot.com/en/docs/get-started/how-to/ttmd-deploy-walk.html > Learn how to create an LLM deployment using financial jargon to build a tailored Talk to my Data Agent application that can apply industry knowledge to its responses. # How-to: Apply app templates to DataRobot deployments Talk to my Data Agent allows you to upload raw data from your preferred data source and ask a question, after which the agent recommends business analyses. Instead of using an external LLM, DataRobot allows you to build and deploy a RAG model trained on a dataset containing industry- and company-specific definitions of terms and jargon. Doing so creates an AI agent that has the background knowledge of another member of your team, applying context to any responses it's trying to infer from your questions and data. In this walkthrough, you will: - Create a Use Case and add a .zip file of industry terms. - Create a vector database and set up a RAG playground. - Register and deploy the RAG LLM model. - Launch and the Talk to my Data Agent template in a codespace. - Customize the application template using the RAG model's deployment ID. - Build the Pulumi stack. ## Assets for download To follow this walkthrough, download and the `.zip` file linked below. [Download industry jargon dataset](https://datarobot-doc-assets.s3.us-east-1.amazonaws.com/finance_knowledge_md.md.zip) The `.zip` file above is list of common finance terms and definitions—generated using AI. ## Prerequisites For this walkthrough, you need: - Access to GenAI and MLOps functionality in DataRobot - DataRobot API token - DataRobot Endpoint - .zip file of industry-specific terms and definitions - A serverless prediction environment ## 1. Create a Use Case and upload data From Workbench, in the Use Case directory, click + Create Use Case. Before proceeding, you can give your Use Case a more descriptive name. In the left-hand navigation, click the Data assets tile and click Upload file. Then, select the `.zip` file saved to your computer. ## 2. Create the vector database Once the dataset is finished registering, in the left-hand navigation, click the Vector databases tile and then Create vector database. In the resulting window, populate the fields as follows: | | Field | Selection | | --- | --- | --- | | (1) | Vector database provider | DataRobot | | (2) | Name | A descriptive name (in this example, financial_jargon). | | (3) | Data source | finance_knowledge_md.md.zip (the .zip registered in the previous step). | For the rest of the fields (blurred in the above image), use the default settings. Then, click Create vector database. Note that building the vector database may take a minute or two. For more information, see [Vector databases](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/index.html). ## 3. Create the RAG playground Once the vector database is finished building, open the Actions menu and select Create playground from latest version. In the playground, select the Vector database tab in the right-hand pane, open the Vector database version dropdown, and select the database with the Latest badge. On the Prompting tab, enter the following in the System prompt field: `You are an expert financial analyst who is providing guidance on how to navigate data for analysis.` On the LLM tab, open the LLM dropdown and select Azure OpenAI GPT-4o. Click Save configuration, and then add a descriptive name—in this case, `ttmd-finance-terms`. In the playground, click Send to the workshop. Then, on the Vector database tab, choose a prediction environment and click Send to the workshop. For more information, see [Playground overview](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-overview.html). ## 4. Register and deploy the model from the workshop To register the RAG model, open Registry and select the Workshop tile. Select the model you sent over from the playground ( `ttmd-finance-terms`). Without changing the default settings, click Register a model. In the resulting window, click Register a model again. Once registration is complete, [clickDeploy model](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-deploy-models.html). Without changing the default settings, click Deploy model. A window appears—click Return to deployments to open Console and monitor the progress of the deployment. Open the deployment ( `ttmd-finance-terms`), and on the Overview tab, copy the Deployment ID. This ID will be used in the application template. ## 5. Launch Talk to my Data Agent template From Workbench, in the Use Case directory, click Browse application templates. Select Talk to my Data Agent and click Open in a codespace in the upper-right corner. This walkthrough focuses on working with application templates in a codespace; however, you can click Copy repository URL and paste the URL in your browser to open the template in GitHub. DataRobot opens and begins initializing a codespace. Once the session starts, the template files appear on the left and the `README` opens in the center. To learn more about the codespace interface, see [Codespace sessions](https://docs.datarobot.com/en/docs/workbench/wb-notebook/codespaces/session-cs.html). > [!TIP] Tip > DataRobot automatically creates a Use Case, so you can access this codespace (and any resulting assets) from the Use Case directory in the future. ## 6. Configure application template and add the LLM deployment Follow the instructions included in the `README` file. In the `.env` file, accessed from the file browser on the left, the following fields are required: - DATAROBOT_API_TOKEN : Retrieved from User settings > API keys and tools in DataRobot. - DATAROBOT_ENDPOINT : Retrieved from User settings > API keys and tools in DataRobot. - PULUMI_CONFIG_PASSPHRASE : A self-selected alphanumeric passphrase. - USE_DATAROBOT_LLM_GATEWAY : Set to true . - TEXTGEN_DEPLOYMENT_ID : The deployment ID previously copied . Comment out all other parameters by adding `#` to the beginning of the line. ## 7. Execute the Pulumi stack and open the application Click the Terminal tile in the left panel. In the resulting terminal pane, run `python quickstart.py YOUR_PROJECT_NAME`, replacing `YOUR_PROJECT_NAME` with a unique name. Then, press Enter. Executing the Pulumi stack can take several minutes. Once complete, DataRobot provides a URL at the bottom of the results in terminal. To view the deployed application, copy and paste the URL in your browser. > [!TIP] Tip > DataRobot also creates an application in Registry. To access this application again, you can navigate to the [Applications](https://docs.datarobot.com/en/docs/wb-apps/custom-apps/upload-custom-app.html) page. --- # Get started URL: https://docs.datarobot.com/en/docs/get-started/index.html > Get started with DataRobot's value-driven AI. Analyze data, create and deploy models, and leverage code-first notebooks. # Get started Access learning resources that help you get started in DataRobot, including workflow overviews, fundamental concept explanations, and video tutorials. - First time?¶ Get oriented then try some starter exercises. - How-tos¶ Follow step-by-step guidance to learn about using DataRobot. - Get help¶ Review platform troubleshooting steps and FAQs. --- # Where to find help URL: https://docs.datarobot.com/en/docs/get-started/troubleshooting/general-help.html > Use the following resources to get the help you need for success with the DataRobot end-to-end AI platform. # Where to find help Use the following resources to get the help you need for success with the DataRobot end-to-end AI platform. | For help with | Visit | | --- | --- | | AI Accelerators | AI Accelerators can be found as:Summary documentationCode on GitHub For additional assistance, email the team. | | Technical support | Email DataRobot Support or visit the Support site. | | Installation support | Email DataRobot Support or visit the Support site. | | Python client support | Visit PyPI or email the team. | | R client support | Visit CRAN or email the team. | | Documentation and education | | | Feature usage | DataRobot documentation (this site). | | New feature announcements | This month's SaaS platform deploymentsSelf-Managed AI Platform releases | | NextGen vs Classic feature availability | Capability matrix (deprecated) | --- # Get help URL: https://docs.datarobot.com/en/docs/get-started/troubleshooting/index.html > This help section provides basic account access troubleshooting and quick, task-based instructions for success in modeling. # Get help This section provides information on troubleshooting DataRobot authentication and access: | Topic | Description | | --- | --- | | Where to find help | Resources to get the help you need for success with the DataRobot end-to-end AI platform. | | Signing in | Things to try if you are having issues signing in. | | Check platform status | View and subscribe to platform status announcements. | --- # Need help signing in? URL: https://docs.datarobot.com/en/docs/get-started/troubleshooting/signin-help.html > This article addresses common questions related to signing in to the DataRobot AI Platform. # Need help signing in? This article addresses common questions related to signing in to the DataRobot AI Platform. ## Are you signing in to the correct AI Platform? Make sure that you are logging in to the appropriate region of the DataRobot AI Platform. You must log into the application based on the region selected when registering your account—either [app.datarobot.com](https://app.datarobot.com) or [app.eu.datarobot.com](https://app.eu.datarobot.com). ## Do you have the right password? If you are not sure if you are entering the right password, try a password reset. Note: - Make sure you have the right URL for your region before attempting a reset. - If you are using an SSO account, the SSO admin must perform the reset. - If you did not complete account setup, you will not receive a password reset email. - If you are using a Google account, you or your admin must reset the password in Google. ## Still having trouble? If these steps haven't worked and you're still having issues, try the following: - Make sure you are using the latest version of the Chrome browser, which DataRobot recommends for the best user experience. - Clear your browser cache and cookies and try accessing the domain again. - Try signing in with the browser in incognito mode. - Contact your administrator. - If you are in an office or public environment, there may be a firewall blocking website access. Try using a different network to access the site. ## Get more assistance If the suggestions above did not answer your question(s), contact your administrator or reach out to [support@datarobot.com](mailto:Support@datarobot.com). --- # Check platform status URL: https://docs.datarobot.com/en/docs/get-started/troubleshooting/status-help.html > Status page announcements provide information on service outages, scheduled maintenance, and historical uptime. # Check platform status DataRobot performs service maintenance regularly. Although most maintenance will occur unnoticed, some may cause a temporary impact. Status page announcements provide information on service outages, scheduled maintenance, and historical uptime. You can view and subscribe to notifications from the [DataRobot status page](https://status.datarobot.com/). You can also access the status page from the footer of any page on this documentation site: --- # DataRobot Product Documentation URL: https://docs.datarobot.com/en/docs/index.html > Public documentation for DataRobot’s end-to-end AI platform. Access platform and API docs, tutorial content, and more from a single location. # Welcome to DataRobot documentation Find all the information you need to succeed with DataRobot, in a style that suits you best. - Get Started¶ Analyze data, create models, and write code - Agentic AI¶ Connect and test agentic workflows, integrate tools, and implement evaluation metrics - NextGen¶ Docs for NextGen UI-based DataRobot use - Applications¶ Configure AI-powered applications and enable core DataRobot services - Developer docs¶ Docs for code-first DataRobot use - Releases¶ Managed AI Cloud and on-prem release information - Reference¶ Access reference content for DataRobot predictive and generative modeling - Account¶ Get help managing the DataRobot application - Install¶ Install the Self-Managed AI Platform Looking for DataRobot Classic?[Access documentation for the Classic DataRobot UI experience](https://docs.datarobot.com/en/docs/classic-ui/index.html) --- # Two-factor authentication URL: https://docs.datarobot.com/en/docs/platform/acct-settings/2fa.html > How to set up two-factor authentication (2FA), an opt-in feature that provides additional security for DataRobot users. # Two-factor authentication Two-factor authentication (2FA) is an opt-in feature that provides additional security for DataRobot users. 2FA in DataRobot is based on the Time-based One-Time Password algorithm ( [TOTP](https://en.wikipedia.org/wiki/Time-based_One-time_Password_algorithm)), the IETF RFC 6238 standard for many two-factor authentication systems. It works by generating a temporary, one-time password that must be manually entered into the app to authenticate access. To work with 2FA, you use an authentication app on your mobile device (for example, [Google Authenticator](https://support.google.com/accounts/answer/1066447?co=GENIE.Platform%3DAndroid&hl=en)). If you haven't already done so, install and register an app on your device. You will use the app to scan a DataRobot-provided QR code, which will, in turn, generate authentication and recovery codes. DataRobot provides a series of recovery codes for use if you lose access to your default authentication method. > [!WARNING] Warning > Before completing two-factor authentication, download, copy, or print these codes and save them to a secure location. > [!TIP] Tip > When you enable 2FA, all API endpoints that validate username and password require secondary authentication. See the [troubleshooting](https://docs.datarobot.com/en/docs/platform/acct-settings/2fa.html#troubleshooting) section for additional information. ## Set up 2FA The 2FA setup workflow varies depending on if you're a Self-managed or SaaS user: **SaaS:** To enable 2FA: Click the Authentication tab from the User Settings page and switch the Two-Factor Authentication toggle to on: Click Setup to start the setup process. DataRobot navigates back to the login screen, displaying a QR code to scan. Open the authenticator app on your device and select the option to scan a barcode. (On Google Authenticator, click the + sign and choose "Scan barcode.") Scan the QR code shown in the dialog box; your device displays a 6-digit code. If you have trouble scanning, see the alternate option . If you receive an error using either method, see the troubleshooting section. Enter the code (no spaces) into the box at the bottom of the screen and click the arrow to proceed. Once verified, DataRobot returns a recovery code for use if you lose access to the default authentication method. Save the code in a secure place. Select a method for saving your code and click the arrow to proceed. DataRobot briefly displays a notice that two-factor authentication is enabled. **Self-managed:** To enable 2FA: Click the Authentication tab from the User Settings page and switch the Two-Factor Authentication toggle to on: Click Setup to start the setup process. Open the authenticator app on your device and select the option that allows you to scan a barcode. (On Google Authenticator, click the + sign and choose "Scan barcode.") Scan the QR code shown in the dialog box; your device displays a 6-digit code. If you have trouble scanning, see the alternate option . If you receive an error using either method, see the troubleshooting section. Enter the code (no spaces) into the box at the bottom of the screen and click the arrow to proceed. Once verified, DataRobot returns a recovery code for use if you lose access to the default authentication method. Save the code in a secure place. Select a method for saving your codes and click Complete . DataRobot briefly displays a notice that two-factor authentication is enabled. ### Non-QR code method If you could not scan the QR code: 1. From the login screen, clickUse the recovery code: 2. Enter the recovery code provided when you configured two-factor authentication and clickVerify. Return tostep 4 of "Set up 2FA", above. Or, if you receive an error, see thetroubleshootingsection. If you do not have your recovery code, contact your DataRobot representative for assistance. ## Use 2FA After you enable and set up 2FA, you will be prompted for a code each time you log into DataRobot. (You are also prompted for an authentication code when requesting a password reset from the login page.) Open DataRobot and enter your email and password, or sign in with Google. You are prompted for an authentication code: If you have your mobile device available, open the authenticator app and enter the 6-digit code displayed. If you do not have your device, click Switch to recovery code and enter one of the codes from your saved list of codes. If you lose access to your phone and recovery codes, contact your administrator or DataRobot Support. ## Troubleshooting See the table below for troubleshooting assistance: | Problem | Solution | Cause | | --- | --- | --- | | I don't see the option to enable two-factor authentication in my user settings | When logging in to DataRobot, your account must use the username/password authentication method. Contact your DataRobot administrator for more information. | 2FA is only available for the username/password authentication method. | | I am receiving a message that my code is invalid | Rename or delete any DataRobot accounts listed in your authentication app. To do this with Google, for example, click the pencil icon and select all accounts registered to DataRobot. Select DELETE and when prompted, select REMOVE ACCOUNTS. (To reinstate the account, you can toggle "Enable two-factor authentication" in Settings and recapture the QR code). | Make sure that you have only one instance of DataRobot authentication in your authenticator app. Each time you scan the QR code, the authenticator app creates a new account based on that code. The code you enter must be associated with the QR code displayed, and with multiple entries, it can be unclear which code to enter. | | So many codes | When prompted for a code, enter the last DataRobot entry. | Some authentication systems (Google, for example) add new accounts to the bottom of the list. | | I lost my codes | If you lose access to your phone and recovery codes, contact your administrator or DataRobot Support. | N/A | | I no longer want to use 2FA | Toggle the feature off on the Settings page. Enter a 6-digit authentication code or a saved recovery code and click Disable. The feature is removed from your account, but you can re-enable it at any time. | N/A | | I forgot my password but I have my code | From the login page, click Don't Remember? and then on the next screen, click Reset Password. When prompted, enter your authentication app code or, if you don't have your mobile device, click Switch to recovery code and enter one of your saved codes. DataRobot will send a link to reset your password. | N/A | --- # Usage Explorer URL: https://docs.datarobot.com/en/docs/platform/acct-settings/acct-usage-explore.html > The Usage Explorer allows users to view GPU, CPU, and LLM API usage across the platform, providing general usage information broken down by service. # Usage Explorer The Usage Explorer provides users visibility into graphics processing unit (GPU), central processing unit (CPU), and large language model (LLM) API usage across the platform, providing general usage information broken down by service. The following services are tracked in the Usage Explorer: - Modeling - Inference - NVIDIA AI Enterprise - Vector Database Creation - GenAI Playground - Custom Models - Moderations - Data Management - Predictions > [!NOTE] Note > The services displayed in the Usage Explorer may vary depending on the type of usage being viewed. To access the Usage Explorer, open Account settings > Usage Explorer. From here, you can view resource consumption by service for a given date range, as well as export the report as a `.csv` file. | | Element | Description | | --- | --- | --- | | (1) | Date range selector | Use the two fields to display usage information for a specific date range. | | (2) | Export | Download the report as a .csv file. | | (3) | Usage options | Select the usage information to view—GPU, LLM, or CPU. | ### GPU usage The GPU Usage page reports data on GPU usage, providing general usage information broken down by service. GenAI features, for example, rely on GPU hardware for a range of workloads related to training, hosting, and running inference on LLMs. To access the GPU Usage page, click GPU Usage in the Usage Explorer. This page consists of a table that displays the name of the service using the resources, as well as the following details about each task under that service: | Field | Summary | | --- | --- | | Service | The service that used the GPU (e.g., Notebooks, Modeling, Vector Database Creation, etc.). | | Resource name | The number of GPUs, as well as the number of CPUs and amount of RAM/VRAM, used by the task. | | Cloud | The cloud provider executing the resource. | | Region | Region where costs are computed. | | Unit cost (per 1 hour) | The total cost of the resource each hour. | | Usage quantity | The amount of time each task used the resources within the specified time period. | | Amount | The current cost of the task. | ### LLM API usage The LLM API Usage page reports data on which LLM models are being used by which services, as well as how much each model is being used. The page provides general usage information broken down by service, tracking token consumption for each task. This detailed monitoring helps identify which services are consuming the most LLM resources across various services. To access the LLM API Usage page, click LLM API Usage in the Usage Explorer. This page consists of a table that displays the name of the service using the resources, as well as the following details about each task under that service: | Field | Summary | | --- | --- | | Service | The service that used the LLM API (e.g., Custom Models, GenAI Playground, etc.). | | Resource name | The name of the LLM model called via the API. | | Cloud | The cloud provider executing the resource. | | Region | Region where costs are computed. | | Unit cost (per 1 hour) | The total cost of the resource each hour. | | Usage quantity | The number of tokens used by the task. | | Amount | The current cost of the task. | ### CPU usage The CPU Usage page provides an overview of central processing unit (CPU) broken down by service. This allows for easy monitoring of exactly which users and services are consuming the most CPU resources, potentially helping to identify areas for optimization or budgetary concerns. To access the CPU Usage page, click CPU Usage in the Usage Explorer. This page consists of a table that displays the name of the service using the resources, as well as the following details about each task under that service: | Field | Summary | | --- | --- | | Service | The service that used the CPU (e.g., Custom Inference Model, Data Management, etc.) | | Resource name | The number of processors and amount of RAM used by the task. | | Cloud | The cloud provider executing the resource. | | Region | Region where costs are computed. | | Unit cost (per 1 hour) | The total cost of the resource each hour. | | Usage quantity | The amount of time each task used the CPU. | | Amount | The current cost of the task. | --- # API keys and tools URL: https://docs.datarobot.com/en/docs/platform/acct-settings/api-key-mgmt.html > How to use DataRobot's developer tools, including API key management, the MLOps agent tarball, and the Portable Prediction Server Docker image. # API keys and tools DataRobot provides multiple developer tools for you to use when making prediction requests or engaging with the DataRobot API. The currently available tools are: - API key management - The Monitoring agent tarball - Portable Prediction Server Docker image Click on your user icon and navigate to API keys and tools to access these features: ## API key management API keys are the preferred method for authenticating web requests to the DataRobot [APIs](https://docs.datarobot.com/en/docs/api/reference/index.html); they replace the legacy API token method. You can simply post the API key into the request header and the application requests you as a user. All DataRobot API endpoints use API keys as a mode of authentication. Generating multiple API keys allows you to create a new key, update your existing integrations, and then revoke the old key, all without disruption of service of your API calls. This also allows you to generate distinct keys for different integrations (script A using key A, script B using key B). > [!NOTE] Note > If you were previously using an API token, that token has been upgraded to an API key. > All existing integrations will continue to function as expected, both for the DataRobot API and the [Prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html). ### Access API key management You can register and manage—name, create, and delete—multiple API keys. To access this page, click on your user icon and navigate to the API keys and tools page. The Personal API keys tab lists your API keys with a variety of options available: | | Element | Description | | --- | --- | --- | | (1) | API endpoint | Copy the API endpoint to your clipboard (contents blurred out in this image) to paste elsewhere. | | (2) | Open documentation | Access multiple code-first resources, such as REST API documentation, Python client documentation, or AI accelerators. | | (3) | Key type | Toggle between tabs for the three kinds of API keys: API keys for personal use of the API, application API keys for use with custom applications, and agent API keys for use with agentic workflows. | | (4) | Search | Search the list of API keys by name or date created. | | (5) | + Create new key | Create a new personal API key. | | (6) | Copy | Copy a key to your clipboard (contents blurred out in this image) to paste elsewhere. | | (7) | Actions menu | Expand the actions menu to rename or delete an API key. | #### Personal API keys Each personal API key on the Personal API keys tab lists the key name, status, value, creation date, and when it was last used. To create a personal API key, from the Personal API keys tab on the API keys and tools page, click + Create new key. Then enter an API key name and click Create. This activates the new key, making it ready for use. #### Application API keys An application API key grants an application the necessary access to the DataRobot Public API, allowing you to grant users the ability to access and use data from within an application. A new key is automatically created each time you build a custom application. While sharing roles grant control over the application as an entity within DataRobot, application API keys grant control over the requests the app can make when a user accesses it. See [configuring application API keys for custom applications](https://docs.datarobot.com/en/docs/wb-apps/custom-apps/manage-custom-app.html#application-api-keys) for more information. Application API keys are listed in the Application API keys tab of the API keys and tools page. | Field | Description | | --- | --- | | Name | The name of the key. | | Key | The value of the API key. | | Role | The scope of access the application has to the public API granted by the key. | | Connected application | The name of the custom application that the key belongs to. Click the link to access the application. | | Data created | The date the key was created. | | Last used | The time elapsed since the last use of the key. | | Expiration | The time remaining until the key expires. | #### Agent API keys To differentiate between various applications and agents using a deployment, agent API keys are generated automatically when a new Agentic workflow deployment is created. Agent API keys are listed in the Agent API keys tab of the API keys and tools page, and can be edited (renamed) or deleted. | Field | Description | | --- | --- | | Name | The name of the key. | | Key | The value of the key. | | Connected deployment | The name of the deployment that the key belongs to. Click the link to access the deployment. | | Data created | The date the key was created. | | Last used | The time elapsed since the last use of the key. | ### Delete an existing key To delete an existing key, expand the actions menu for the key you wish to delete, then click Delete. This prompts a dialog box warning you about the impacts of deletion. Click Delete again to remove your key. ## Monitoring agent tarball DataRobot offers the [monitoring agent](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/mlops-agent/index.html) as a solution for monitoring external models outside of DataRobot and reporting back statistics. To monitor a deployment of this kind, you must first implement the following software components, provided by DataRobot: - MLOps library (available in Python, Java, and R) - The monitoring agent These components are part of an installer package available as a tarball in the DataRobot application. ### Download the monitoring agent tarball The monitoring agent tarball can be accessed from two locations: the API keys and tools section, and the [Predictions > Monitoring](https://docs.datarobot.com/en/docs/classic-ui/predictions/realtime/code-py.html#monitoring-snippet) tab for a deployment. Click on your user icon and navigate to API keys and tools. Under the Management and monitoring agents header, click the download icon. Additional documentation for setting up the Agent is included in the tarball. > [!NOTE] Note > You can also download the DataRobot Python libraries from the public [Python Package Index site](https://pypi.org). Download and install the [DataRobot metrics reporting library](https://pypi.org/project/datarobot-mlops) and the [DataRobot Connected Client](https://pypi.org/project/datarobot-mlops-connected-client). These pages include instructions for installing the libraries. ## Portable Prediction Server Docker image > [!NOTE] Availability information > The Portable Prediction Server image may not be available in some installations. Review the [availability guidelines](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html#obtain-the-pps-docker-image) for more information. Download the Portable Prediction Server Docker image from the API keys and tools page: You can see some important information about the image: | | Element | Description | | --- | --- | --- | | (1) | Image name | The name of the image archive file that will be downloaded. | | (2) | Image creation date | The date that the image was built. | | (3) | File size | The size of the compressed image to be downloaded. Be aware that the uncompressed image size can exceed 12GB. | | (4) | Docker Image ID | A shortened version of the Docker Image ID, as displayed by the docker images command. It is content-based so that regardless of the image tag, this value will remain the same. Use it to compare versions with the image you are currently running. | | (5) | Hash | Hash algorithm and content hash sum. Use to check file integrity after download (see example below). Currently SHA256 is used as a hash algorithm. | Click the download icon and wait for the file to download. Due to image size, download times may take minutes (or even hours) depending on your network speed. Once the download completes, check the file integrity using its hash sum. For example, on Linux: ``` sha256sum datarobot-portable-prediction-api-7.0.0-r1736.tar.gz 5bafef491c3575180894855164b08efaffdec845491678131a45f1646db5a99d datarobot-portable-prediction-api-7.0.0-r1736.tar.gz ``` If the checksum matches the value displayed in the image information (Hash value (5), above), the image was downloaded successfully and can be safely [loaded to Docker](https://docs.datarobot.com/en/docs/classic-ui/predictions/port-pred/pps/portable-pps.html#load-the-image-to-docker). --- # Account settings URL: https://docs.datarobot.com/en/docs/platform/acct-settings/index.html > How to access your DataRobot account settings, change your password, update your profile, find your API key, update your display language, and more. # Account settings To access profile information, [two-factor authentication](https://docs.datarobot.com/en/docs/platform/acct-settings/2fa.html), developer tools, data sources, and membership assignments, click your [profile icon](https://docs.datarobot.com/en/docs/platform/acct-settings/profile-settings.html) (or the default avatar) in the upper-right corner of DataRobot. The following table summarizes the options: | Topic | Description | | --- | --- | | User settings | Update your account information and avatar. | | Membership | View the organizations and groups you belong to and join groups. | | Feature access | Enable or disable features for tenants. | | Data connections | Add, delete, modify, and test data connections. | | Remote repositories | Add and manage connections to remote repositories. After adding a repository to DataRobot, you can pull files from the repository and include them in the custom model. | | OAuth provider management | Add, manage, and authorize OAuth providers. | | Credential management | Add and manage securely stored credentials to reuse when accessing secure data sources. | | Drivers | (Self-managed) Add and manage JDBC drivers. | | Usage Explorer | View your GPU, CPU, and LLM API usage across the platform, broken down by service. | | API keys and tools | | | API keys | Create and manage the keys necessary to connect to the DataRobot API. | | Management and monitoring agents | Download the agents to deploy and monitor remote models in production. | | Portable Prediction Server | Deploy models on your organization’s infrastructure with DataRobot’s Portable Prediction Server. | > [!NOTE] Note > The options available in the dropdown are dependent on your DataRobot permissions. --- # OAuth provider management URL: https://docs.datarobot.com/en/docs/platform/acct-settings/manage-oauth.html > How to add and manage OAuth providers. # OAuth provider management The OAuth providers tile allows you to configure, add, remove, or modify OAuth providers for your cluster. ## Configure an OAuth provider DataRobot supports integration with several external OAuth providers. Refer to the links below for configuration options. | Provider | Client setup instructions | Callback URI | | --- | --- | --- | | GitHub | Creating an OAuth app | DATAROBOT_BASE_URL/account/oauth-providers/?providerId=PROVIDER_ID | | GitLab | Configure GitLab as an OAuth 2.0 provider | DATAROBOT_BASE_URL/account/oauth-providers/?providerId=PROVIDER_ID | | Bitbucket | Use OAuth on Bitbucket Cloud | DATAROBOT_BASE_URL/account/oauth-providers/?providerId=PROVIDER_ID | | Google | Setting up OAuth 2.0 | DATAROBOT_BASE_URL/account/oauth-providers/?providerId=PROVIDER_ID | | Box | Setup with OAuth 2.0 | DATAROBOT_BASE_URL/account/oauth-providers/?providerId=PROVIDER_ID | | Microsoft | Register an application in Microsoft Entra ID | DATAROBOT_BASE_URL/account/oauth-providers/?providerId=PROVIDER_ID | | Confluence | OAuth 2.0 (3LO) apps | DATAROBOT_BASE_URL/account/oauth-providers/?providerId=PROVIDER_ID | | Jira | OAuth 2.0 (3LO) apps | DATAROBOT_BASE_URL/account/oauth-providers/?providerId=PROVIDER_ID | | SharePoint | OAuth 2.0 and OIDC authentication flow in the Microsoft identity platform | DATAROBOT_BASE_URL/account/oauth-providers/?providerId=PROVIDER_ID | > [!NOTE] Note > In the Callback URI field, substitude the URL of your deployment in place of `DATAROBOT_BASE_URL` (e.g., `app.DataRobot.com`). The Callback URI field is configured in the external OAuth provider's settings outside of DataRobot. For Google or Box configurations, the value for `PROVIDER_ID` must be updated in the URI field after creating the OAuth provider in DataRobot. Once added, the value is the one shown in the Provider id column for the provider in the DataRobot UI. ## Add a provider To add a new OAuth provider: 1. In the top-right corner, go toUser Settings > OAuth provider management. 2. ClickAdd OAuth provider. 3. Fill in the fields. Note that all fields are required: FieldDescriptionProvider typeSpecify the provider.Provider NameSpecify a name for the provider being added.Client IDEnter theClient IDfrom the provider you want to add.Client SecretEnter theClient Secretfrom the provider you want to add. 4. To add the new provider, clickAdd. > [!NOTE] Note > Only the user who added the provider will be able to access it for modifying or removal. ## Verify a provider Before an OAuth provider can be used in the cluster, it must be authorized for the user accessing the server. To verify an OAuth provider: 1. Open theActions menuon the right-hand side of theOAuth provider managementtable in the row containing your provider and selectAuthorize on NAME(NAME will vary based on the provider of the service). 2. Click through the subsequent steps required by the service to authorize your provider. The example below demonstrates the flow for GitHub, but other providers will differ. 3. After verification, the provider row displays anauthorizedbadge below its name. ## Modify a provider To edit a configured provider: 1. Open theActions menuon the right-hand side of theOAuth provider managementtable in the row containing your provider and selectEdit OAuth provider. 2. Edit the necessary fields and clickUpdateto finish. ## Remove a provider To remove a provider: 1. Open theActions menuon the right-hand side of theOAuth provider managementtable in the row containing your provider and selectRevoke authorization. 2. ClickRevoketo proceed. The provider now displays a Not authorized icon below it. > [!NOTE] Note > The default providers cannot be removed. ## Organization-wide consent for Microsoft OAuth This feature allows administrators to grant [tenant-wide consent](https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/configure-user-consent?pivots=portal#configure-user-consent-in-microsoft-entra-admin-center) for user permissions required by the DataRobot application in Microsoft Entra ID. In Azure, by default, user permissions are not granted to individual user resources, requiring personal user consent when authorizing a [Microsoft OAuth provider](https://learn.microsoft.com/en-us/entra/architecture/auth-oauth2). When enabled, users also no longer receive consent prompts. If the list of required permissions is changed by the DataRobot application, or the administrator account who granted consent is not active anymore, the administrator must grant consent again to provide the necessary permissions for the DataRobot application. > [!NOTE] Prerequisite > Before enabling this feature in DataRobot, in Microsoft Entra ID, a Cloud Application Administrator must [grant tenant-wide consent to the DataRobot application](https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/grant-admin-consent?pivots=portal). > > Managed AI Cloud (SaaS) administrators must contact DataRobot Support to enable this feature. To enable this feature in DataRobot: **Self-managed:** A system adminstrator must perform the following steps: Click the System admin icon in the management toolbar and select Organizations . On the Profile tab, scroll down to Grant organization-wide consent for global Microsoft OAuth providers . Select the checkbox to enable global authorization for all users in the organization. The resulting window describes next steps. After closing the window, click Save . Then, an organization administrator must perform the following steps: Open User settings > OAuth provider management . Click the Actions menu next to the OAuth provider you want to authorize and select Grant user consent on behalf of organization . A window appears describing action that must be taken in Azure Entra ID. Click Grant consent and then in Azure, an application administrator must enable consent on behalf of your organization for the DataRobot application. **SaaS:** > [!NOTE] Note > Before proceeding, contact DataRobot Support to enable this feature. An organization administrator must perform the following steps: Open User settings > OAuth provider management . Click the Actions menu next to the OAuth provider you want to authorize and select Grant consent on behalf of your organization . A window appears describing action that must be taken in Azure Entra ID. Click Grant consent and then in Azure, an application administrator must enable consent on behalf of your organization for the DataRobot application. --- # Data connections URL: https://docs.datarobot.com/en/docs/platform/acct-settings/nxt-data-connect.html > Learn how to integrate with a variety of data sources using the # Manage data connections The DataRobot connectivity platform allows users to integrate with their data stores using either the DataRobot provided connectors or uploading the JDBC driver provided by the data store. The "self-service" database connectivity solution is a standardized, platform-independent solution that does not require complicated installation and configuration. Once configured, you can read data from production databases for model building and predictions. Connectivity to your data source allows you to quickly train and retrain models on that data, and avoids the unnecessary step of exporting data from your database to a CSV file for ingest into DataRobot. It allows access to more diverse data, which results in more accurate models. Users with the technical abilities and permissions can configure and establish data connections. Other users in the organization can then leverage those connections to solve business problems. See also a [list of supported connections](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/index.html) in DataRobot. > [!NOTE] Availability information > The ability to [add, update, and remove JDBC drivers](https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/manage-drivers.html) and connectors is only available on Self-Managed AI Platform installations. Before users can import data into DataRobot, the administrator must upload JDBC drivers and configure database connections for those drivers. > > Required permission: Can manage JDBC database drivers This page describes how to work with data connections from the Account Settings > Data connections tile: | | Element | Description | | --- | --- | --- | | (1) | + Add connection | Allows you to add and configure a new data connection. | | (2) | List of connections | Lists all data connections associated with your DataRobot account. | | (3) | Connection Configuration | Displays the parameters used to establish a connection between DataRobot and the external data source. | | (4) | Data Sources | Displays a list of the datasets imported from the data connection. | | (5) | Credentials | Displays a list of authentication credentials associated with the data connection. | | (6) | Delete | Deletes the data connection. | | (7) | Test | Tests the data connection configuration, including authentication credentials. | | (8) | Share | Allows you to share the data connection with other users, groups, or organizations, as well as assign permissions. | | (9) | Save | Saves any changes made to the connection configuration. | | (10) | Show additional parameters | Allows you to add parameters to the connection configuration. | ## Database connectivity options By default, users can create, modify (depending on their [role](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#shared-data-connection-and-data-asset-roles)), and share data connections. You can also create [data sources](https://docs.datarobot.com/en/docs/reference/glossary/index.html#data-source). Below describes the various ways you can establish database connectivity in DataRobot NextGen: - From Account Settings > Data Connections , create data connection configuration(s). - From Registry > Data , click Add > Data connection . - From the Browse datamodal within a Use Case. (Optional) Depending on [role](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#shared-data-connection-and-data-asset-roles), you can also [share](https://docs.datarobot.com/en/docs/platform/acct-settings/nxt-data-connect.html#share-data-connections) data connections with others. ## Allowed source IP addresses Any connection initiated from DataRobot originates from an allowed IP addresses. See a full list at [Allowed source IP addresses](https://docs.datarobot.com/en/docs/reference/data-ref/allowed-ips.html). ## Create a new connection To create a new data connection, open your Account settings > Data connections. Then, click + Add connection. All existing connections are displayed on the left. If you select a configured connection, its configuration options are displayed in the center. While there are multiple methods to connect to a data source, the configuration process described [here](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/add-data/connect.html#connect-to-a-data-source) is used in all cases. ### Additional parameters The parameters provided for modification in the data connection configuration screen are dependent on the selected driver. Available parameters are dependent on the configuration done by the administrator who added the driver. Many other fields can be found in a searchable expanded field. If a desired field is not listed, open Show additional parameters and click Add parameter to include it. Click the delete icon to remove a listed parameter from the connection configuration. > [!NOTE] Note > Additional parameters may be required to establish a connection to your database. These parameters are not always pre-defined in DataRobot, in which case, they must be manually added. > > For more information on the required parameters, see the [documentation for your database](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/index.html). ## Test the connection Once your data connection is created, test the connection by clicking Test. In the resulting dialog box, enter or [use stored](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html) credentials for the database identified in the JDBC URL field or the parameter-based configuration of the data connection creation screen. Click Sign in and when the test passes successfully, click Close to return to the Data Connections tile and create your [data sources](https://docs.datarobot.com/en/docs/platform/acct-settings/nxt-data-connect.html#add-data-sources). Snowflake and Google BigQuery users can set up a data connection using OAuth single sign-on. Once configured, you can read data from production databases to use for model building and [predictions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html). For information on setting up a data connection with OAuth, the required parameters, and troubleshooting steps, see the documentation for your database: [Snowflake](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-snowflake.html) or [BigQuery](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-bigquery.html). ## Edit a connection You can modify existing data connections, including configuration parameters, as well as associated credentials and data sources. To edit a connection, click on the data connection in the left panel. See below for a description of each tab—what information is displayed on each and the available edit options: **Connection Configuration:** On the Connection Configuration tab, you can modify connection parameters, including adding new parameters and selecting or creating new credentials. [https://docs.datarobot.com/en/docs/images/edit-connectconfig-1.png](https://docs.datarobot.com/en/docs/images/edit-connectconfig-1.png) **Data Sources:** The Data Sources tab displays all data assets that have previously been accessed through this connection. Using this list, you can explore the most frequently used tables and SQL queries for a database, as well as file locations for blob (Binary Large Object) and document stores. When a dataset or file is added from this connection to the Data Registry or Use Case, a pointer to the data is automatically added to this tab. Additionally, you can add data sources directly in the connection settings. Note that this view can also support data governance workflows. From here, you can: [https://docs.datarobot.com/en/docs/images/wb-connectcred-2.png](https://docs.datarobot.com/en/docs/images/wb-connectcred-2.png) Element Description 1 Search Allows you to search for specific data sources. 2 Columns Displays the name and date when the data pointer was last updated. 3 Actions menu Provides access to the following actions: Share : Allows you to share the data source with a user. Delete : Removes the association between the data connection and data source—this does not remove the datasets/files created using this data source from the Data Registry or Use Case. **Credentials:** The Credentials tab displays all credentials compatible with this connection type that were added by you or shared through a shared secure configuration. From here, you can: [https://docs.datarobot.com/en/docs/images/wb-connectcred-3.png](https://docs.datarobot.com/en/docs/images/wb-connectcred-3.png) Element Description 1 Search Allows you to search for specific credentials. 2 Columns Displays the name, credential type, and date the credentials were first added. 3 Selected badge Indicates the credentials currently in use by the data connection. 4 Actions menu Provides access to the following actions: Select : Selects new credentials to use for authenticating the data connection. Test : Tests and authenticates the connection using the credentials. Edit : Expands the credentials, allowing you to edit the manual and/or shared secure configuration. You can also click on credentials to expand this panel. Delete : Deletes the credentials and removes them from all of your associated data connections. When you're done editing the connection, click Save. ## Delete a connection You can delete any data connection that is not being used by an existing data source. If it is being used, you must first delete the dependencies. To delete a data connection: 1. From theData Connectionstab, select the data connection in the left-panel connections list. 2. Click theDeletebutton in the upper right, or hover over the connection name in the left-panel and click the delete icon. 3. DataRobot prompts for confirmation. ClickDeleteto remove the data connection. If there are data sources dependent on the data connection, DataRobot returns a notification. 4. Once all dependent data sources are removed— either via the UI orAPI—try deleting the data connection again. ## Add data sources Your data sources specify, via SQL query or selected table and schema data, which data to extract from the data connection. It is the extracted data that you will use for modeling and predictions. You can point to entire database tables or use a SQL query to select specific data from the database. > [!NOTE] Note > Once data sources are created, they cannot be modified and can only be deleted [via the API](https://datarobot-public-api-client.readthedocs-hosted.com/page/reference/data/database_connectivity.html). To add a data source, do one of the following: **NextGen:** From a the Data assets tile in a Use Case, click Add data > Browse data and select the connection that holds the data you want to add. From Registry > Data , click Add data > Data connection and select the connection that holds the data you want to add. **DataRobot Classic:** From the Start screen, click Data Source and select the connection that holds the data you want to add. See how to import from an existing data source . From the AI Catalog , select Add to catalog > Existing Data Connection . See how to add data from external connections . ## Share data connections Because the user creating a data connection and the end-user may not be the same, or there may be multiple end-users for the data connection, DataRobot provides the ability to set user-level permissions for each entity. You can accomplish scenarios like the following: - A user wants to set permissions on a selected data entity to control who has consumer-level, editor-level, or owner-level access. Or, the user wants to remove a particular user's access. - A user that has had a data connection shared with them wants the shared entity to appear under their list of available entities. When you invite a user, user group, or organization to share a data connection, DataRobot assigns the default role of Editor to each selected target (not all entities allow sharing beyond a specific user). You can change the role from the dropdown menu. To share data connections: 1. From the account menu on the top right, selectData Connections, select a data connection, and clickSharein the upper-right corner. Alternatively, you can hover over the connection name in the left-panel and click the share icon. 2. Enter the email address, group name, or organization you are adding and select arole. Check the box to grant sharing permission. 3. ClickShareto add the user, user group, or organization. 4. Add any number of collaborators and when finished, clickCloseto dismiss the sharing dialog box. Depending on your own permissions, you can remove any user or change access as described in the table of [roles and permissions](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html). > [!NOTE] Note > There must be at least one Owner for each entity; you cannot remove yourself or remove your sharing ability if you are the only collaborating Owner. ## Stored credentials As an alternative to managing credentials from the Credentials management tile, you can interact with credentials when working with a data connection— on the [Credentials](https://docs.datarobot.com/en/docs/platform/acct-settings/nxt-data-connect.html#remove-credentials) tab, you can select and remove credentials associated with the connection, and on the [Connection Configuration](https://docs.datarobot.com/en/docs/platform/acct-settings/nxt-data-connect.html#create-credentials) tab, you can create new credentials. > [!NOTE] Note > You cannot edit stored credentials in the Data connections tile. To edit stored credentials, go to [Credentials Management](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#modify-credentials). ### Create credentials To create new saved credentials: 1. Select a data connection from the left panel and on theConnection Configurationtab, clickNew credential. 2. Select theCredential typeand whether you're manually configuring the credentials or using a shared secure configuration. 3. Enter the new credentials and click Save . Optionally, you can test your new credentials . ### Remove credentials From the Data connections tile, select the connection and then click the Credentials tab. Click the Actions menu next to the credentials you want to use for this connection and click Select. The credentials currently associated with the connection displays a "Selected" badge. --- # User settings URL: https://docs.datarobot.com/en/docs/platform/acct-settings/profile-settings.html > How to edit your DataRobot user settings, including account information, security settings, system settings, and notifications. # User settings > [!NOTE] Availability information > If your organization uses LDAP as an external account management system for single sign-on, you can't edit your profile in DataRobot. Contact your system administrator for assistance. To view your available account settings, click your profile avatar in the upper-right corner of DataRobot and click User settings. On the User settings tile, you can access the following tabs: | Tab | Description | | --- | --- | | Account | View and edit your private account information, public profile, and language selection. | | Authentication | Configure password, sign-in methods, and two-factor authentication to secure your DataRobot account. | | System | Update your display language, color theme, default DataRobot experience, and CSV export settings. | | Notifications | Mute email notifications and update Autopilot completion notifications. | ## Account To make changes to your DataRobot account information, edit any of the following fields and click Save changes: - First name - Last name - (Optional) Phone number Under Public profile, you can provide the following public profile and professional network information: - (Optional) Display name - (Optional) Company - (Optional) Job title - (Optional) Industry - (Optional) Country ### Upload picture > [!NOTE] Availability information > The custom avatar feature is only for DataRobot managed AI Platform deployments. To upload or change your avatar, upload a Gravatar at the top of the Account tab: 1. Click Upload Picture to open Gravatar in a new window. Log in to Gravatar or create an account. 2. Confirm that you registered your DataRobot email address with Gravatar. 3. If you haven't already, upload an avatar to your account. (Click My Gravatars > Add a new image .) 4. From the Manage Gravatars page, select a default image and click Confirm . 5. Return to DataRobot and refresh the page. If your new avatar does not appear, verify that the emails match . > [!TIP] Tip > To see your new avatar, you may need to clear your browser cache and refresh the page. If your avatar does not appear, wait approximately 10 minutes and try again (Gravatar doesn't instantaneously serve new images). ## Authentication To secure your DataRobot account, configure your password and two-factor authentication settings on the Authentication tab. ### Change password To change your password, enter your current password, enter a new password, and then confirm the new password. If your current password is incorrect, or the new password and confirmation password fields don't match, you receive an error message. DataRobot passwords must meet the following requirements: - Only printable ASCII characters - Minimum one uppercase letter - Minimum one number - Minimum 8 characters - Maximum 512 characters - Username and password cannot be the same ### Sign-in methods To add new methods to log in to your DataRobot account, click Add for either Google or Github SSO. ### Two-factor authentication Two-factor authentication (2FA) is an opt-in feature that provides additional security for DataRobot users. See the section on [2FA](https://docs.datarobot.com/en/docs/platform/acct-settings/2fa.html) for setup information. To enable 2FA, you must use the username/password account type. ## System Allows you to manage the following system-wide settings: | Setting | Description | | --- | --- | | Language | To change the language DataRobot displays, open the Language dropdown menu and click your preferred language. DataRobot reloads to display in your language. Available languages include English, Spanish, French, Japanese, Korean, and Portuguese. | | Theme | The DataRobot application displays in the dark theme by default. To change the color of the display, use the Theme dropdown menu to select between Dark and Light. | | Default experience | To change between the Classic and NextGen DataRobot experiences, use the toggle. Enable it to use the NextGen UI. Disable the toggle to use the Classic DataRobot experience. | | CSV export | To enable or disable BOM insertion in exported CSV files, under CSV export, switch the Include BOM toggle. This setting is disabled by default. | > [!NOTE] Note > Changing the DataRobot display language does not affect uploaded data, model names, and some other UI elements. ### Include byte order mark (BOM) The [byte order mark (BOM)](https://en.wikipedia.org/wiki/Byte_order_mark) is a byte sequence that indicates encoding by adding three bytes to the beginning of a file. DataRobot allows you to enable or disable the inclusion of this marker in your profile settings. Software recognizing the BOM is then able to display files correctly. When exported CSV files include non-ASCII characters, use the BOM to ensure compatibility with file editors that don't verify encoding. For example, without the BOM, Excel may misrepresent characters from languages other than English. Modern versions of Excel correctly display international characters if you include the BOM. ## Notifications On the Notifications tab, you can mute email notifications and configure Autopilot completion notifications: | Setting | Description | | --- | --- | | Mute all email notifications | To mute email notifications and configure Autopilot completion notifications, click your profile avatar in the upper-right corner and navigate to User Settings > Notifications. | | Enable email notification when Autopilot has finished | To mute all DataRobot email notifications, such as Autopilot notifications, deployment monitoring notifications, etc., enable the Mute all email notifications setting. | | Enable browser notification when Autopilot has finished | If you close your browser or log out, DataRobot continues building models in any projects that have started the model building phase. To allow DataRobot to send a notification when Autopilot completes—via browser alert or email—use the Enable email notification when Autopilot has finished and Enable browser notification when Autopilot has finished settings. | --- # Connect remote repositories URL: https://docs.datarobot.com/en/docs/platform/acct-settings/remote-repos.html > Connect remote repositories to DataRobot, including Bitbucket, GitHub, GitHub Enterprise, S3, GitLab, and GitLab Enterprise. # Connect remote repositories When you add a model to the Registry workshop, you can add files to that model from a wide range of repositories, including Bitbucket, GitHub, GitHub Enterprise, S3, GitLab, and GitLab Enterprise. After adding a repository to DataRobot, [pull files from the repository and include them in the custom model](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-create-custom-model.html#remote-repositories) or [create a codespace](https://docs.datarobot.com/en/docs/workbench/wb-notebook/codespaces/session-cs.html#use-git-with-codespaces). ## Add a remote repository The following steps show how to add a remote repository so that you can pull files into a custom model: 1. On any page, click your profile avatar (or the default avatar) in the upper-right corner of DataRobot, then clickRemote repositories. 2. On theRemote repositoriespage, clickAdd repository(or+ Add repositoryin the upper-right corner of the page), and then click a repository provider to integrate a new remote repository with DataRobot. 3. After you select the type of repository to register, follow the relevant process from the list below: ### Bitbucket Server repository To register a Bitbucket Server repository, in the Add Bitbucket Server repository modal, configure the required fields: | Field | Description | | --- | --- | | Name | The name of the Bitbucket Server repository. | | Repository location | The URL for the Bitbucket Server repository that appears in the browser address bar when accessed. Alternatively, select Clone from the Bitbucket Server UI and paste the URL. | | Personal access token | The token used to grant DataRobot access to the Bitbucket Server repository. Generate this token from the Bitbucket Server UI. | | Description | (Optional) A description of the Bitbucket Server repository. | After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. ### GitHub repository To add a GitHub repository, in the Add GitHub repository modal, the steps for connecting to the repository depend on the connection method. **GitHub application:** The primary method for adding a GitHub repository is to authorize the DataRobot User Models Integration application for GitHub. [https://docs.datarobot.com/en/docs/images/authorize-github-app.png](https://docs.datarobot.com/en/docs/images/authorize-github-app.png) Click Authorize GitHub app and grant access, then, configure the following fields: [https://docs.datarobot.com/en/docs/images/add-github-repo-app.png](https://docs.datarobot.com/en/docs/images/add-github-repo-app.png) Field Description Name The name of the GitHub repository. Repository Enter the GitHub repository URL. Start typing the repository name and repositories will populate in the autocomplete dropdown. To use a private repository, grant the GitHub app access to private repositories. When you grant access to a private repository , its URL is added to the Repository autocomplete dropdown. To use an external public GitHub repository, you must obtain the URL from the repo . Description (Optional) A description of the GitHub repository. Private repository permissions To use a private repository , click Edit repository permissions in the Add GitHub repository window. This gives the GitHub app access to your private repositories. You can give access to all current and future private repositories or a selected list of repositories External GitHub repositories To use an external public GitHub repository that is not owned by you or your organization, navigate to the repository in GitHub and click Code . Copy and paste the URL into the Repository field of the Add GitHub repository window. **Personal access token:** The fallback method for adding a GitHub repository is to provide a repository location and personal access token. [https://docs.datarobot.com/en/docs/images/add-github-repo.png](https://docs.datarobot.com/en/docs/images/add-github-repo.png) Field Description Name The name of the GitHub repository. Repository location The URL for the GitHub repository that appears in the browser address bar when accessed. Alternatively, select Clone from the GitHub UI and paste the URL. Personal access token (Optional) The token used to grant DataRobot access to the GitHub repository. Generate this token from the GitHub UI. A token isn`t required for public repositories. Description (Optional) A description of the GitHub repository. After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. > [!TIP] GitHub repository organizations > You can add repositories from any [GitHub organization](https://docs.datarobot.com/en/docs/platform/acct-settings/remote-repos.html#github-organization-repository-access) you belong to. #### GitHub organization repository access If you belong to a GitHub organization, you can request access to an organization's repository for use with DataRobot. A request for access notifies the GitHub admin, who then who approves or denies your access request. > [!NOTE] Organization repository access > If your admin approves a single user's access request, access is provided to all DataRobot users in that user's organization without any additional configuration. For more information, reference the [GitHub documentation](https://docs.github.com/en/github/setting-up-and-managing-organizations-and-teams/managing-access-to-your-organizations-repositories). ### GitHub Enterprise repository To register a GitHub Enterprise repository, in the Add GitHub Enterprise repository modal, configure the required fields: | Field | Description | | --- | --- | | Name | The name of the GitHub Enterprise repository. | | Repository location | The URL for the GitHub Enterprise repository that appears in the browser address bar when accessed. Alternatively, select Clone from the GitHub Enterprise UI and paste the URL. | | Personal access token | The token used to grant DataRobot access to the GitHub Enterprise repository. Generate this token from the GitHub UI. | | Description | (Optional) A description of the GitHub Enterprise repository. | After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. #### Git Large File Storage Git Large File Storage (LFS) is supported by default for GitHub integrations. Reference the [Git documentation](https://git-lfs.github.com) to learn more. Git LFS support for GitHub always requires having the GitHub application installed on the target repository, even if it's a public repository. Any non-authorized requests to the LFS API will fail with an HTTP 403. ### GitLab Cloud repository To add a GitLab repository, in the Add GitLab repository modal, the steps for connecting to the repository depend on the connection method. **GitLab application:** The primary method for adding a GitLab repository is to authorize the DataRobot User Models Integration application for GitLab. [https://docs.datarobot.com/en/docs/images/authorize-gitlab-app.png](https://docs.datarobot.com/en/docs/images/authorize-gitlab-app.png) Click Authorize GitLab app, grant access, and configure the following fields: [https://docs.datarobot.com/en/docs/images/add-gitlab-repo-app.png](https://docs.datarobot.com/en/docs/images/add-gitlab-repo-app.png) Field Description Name The name of the GitLab repository. Repository Enter the GitLab repository URL. Start typing the repository name and repositories will populate in the autocomplete dropdown. Description (Optional) A description of the GitLab repository. **Personal access token:** The fallback method for adding a GitLab repository is to provide a repository location and personal access token. [https://docs.datarobot.com/en/docs/images/add-gitlab-repo.png](https://docs.datarobot.com/en/docs/images/add-gitlab-repo.png) Field Description Name The name of the GitLab repository. Repository location The URL for the GitLab repository that appears in the browser address bar when accessed. Personal access token (Optional) Enter the token used to grant DataRobot access to the GitLab repository. Generate this token from GitLab. A token isn`t required for public repositories. Description (Optional) A description of the GitLab repository. After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. ### GitLab Enterprise repository To register a GitLab Enterprise repository, in the Add GitLab Enterprise repository modal, configure the required fields: | Field | Description | | --- | --- | | Name | The name of the GitLab Enterprise repository. | | Repository location | The URL for the GitLab Enterprise repository that appears in the browser address bar when accessed. | | Personal access token | (Optional) Enter the token used to grant DataRobot access to the GitLab Enterprise repository. Generate this token from GitLab Enterprise. A token isn`t required for public repositories. | | Description | (Optional) A description of the GitLab Enterprise repository. | After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. ### S3 repository To register an S3 repository, in the Add S3 repository modal, configure the required fields. | Field | Description | | --- | --- | | Name | The name of the S3 repository. | | Bucket name | The name of the S3 bucket. If you are adding a public S3 repository, this is the only field you must complete. | | Access key ID | The key used to sign programmatic requests made to AWS. Use with the AWS Secret Access Key to authenticate requests to pull from the S3 repository. Required for private S3 repositories. | | Secret access key | The key used to sign programmatic requests made to AWS. Use with the AWS Access Key ID to authenticate requests to pull from the S3 repository. Required for private S3 repositories. | | Session token | (Optional) A token that validates temporary security credentials when making a call to an S3 bucket. | | Description | (Optional) A description of the S3 repository. | After you configure the required fields, click Test to verify connection to the repository. Once you verify the connection, click Add repository. > [!NOTE] S3 credentials > AWS credentials are optional for public buckets. You can remove any S3 credentials by editing the repository connection. Select the connection and click Clear credentials. #### AWS S3 access configuration DataRobot requires the AWS S3 `ListBucket` and `GetObject` permissions in order to ingest data. These permissions should be applied as an additional AWS IAM Policy for the AWS user or role the cluster uses for access. For example, to allow ingestion of data from a private bucket named `examplebucket`, apply the following policy: ``` { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": ["s3:ListBucket"], "Resource": ["arn:aws:s3:::examplebucket"] }, { "Effect": "Allow", "Action": ["s3:GetObject"], "Resource": ["arn:aws:s3:::examplebucket/*"] } ] } ``` --- # Credentials management URL: https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html > How to add and manage securely stored credentials for reuse in accessing secure data sources. # Credentials management Because there is often a need to access secure data sources, DataRobot provides an option to securely store associated credentials for reuse. This capability is particularly useful for automating workflows that require access to secure sources or when combining many tables and sources that would each otherwise require individual authentication. The [Credentials management](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management) tile is where you can create new stored credentials, edit or remove existing ones, and add or remove associations with data connections. Application of stored credentials is available whenever you are prompted for them, such as when: - Creating a data source from the Data connections page or testing connections. - Using the Data Source option when beginning a project. - Using a data source or S3 to make batch predictions via the API. - Using a data connection to create a new dataset in the AI Catalog . - Taking a snapshot of a dataset in the AI Catalog . - Creating a project from a dataset in the AI Catalog . - Using the AI Catalog to select a prediction dataset using Make Predictions . To access this page, open Account Settings > Credentials management. > [!NOTE] FIPS validation requirements > Due to FIPS validation requirements, credentials with passwords of fewer than 14 characters may not work for connections to Snowflake systems. Snowflake keypair credentials must be a valid RSA with at least 2048 bits and a passphrase of at least 14 characters. For more information, see the [FIPS validation FAQ](https://docs.datarobot.com/en/docs/reference/misc-ref/fips-faq.html). ## Add new credentials From the Credentials management tile, click + Add new credential. In the Add new credentials dialog box, select a Credential type, manually enter the required credential fields, then click Save and sign in. The credential entry becomes available in the panel on the left. Click Add associated connection to add a data connection. Then, select the data connection you would like associated to these credentials and click Connect. ## Add credentials from a shared secure configuration IT admins can configure OAuth-based authentication parameters for a data connection, and then [securely share](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/secure-config.html) them with other users without exposing sensitive fields. This allows users to easily connect to their data warehouse without needing to reach out to IT for data connection parameters. When Enable Secure Config Exposure is active for your organization, these credentials can also be used as runtime parameters in custom models, applications, and jobs so that secret values are injected into those runtimes. For more information, see [Using credentials from shared secure configs in runtimes](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#using-credentials-from-shared-secure-configs). From the Credentials management tile, click + Add new credential. In the Add new credentials dialog box, select a Credential type, click the Shared secure configurations tab, then click Save and sign in. > [!TIP] Credential types for secure configurations > Not all credential types support shared secure configurations. Only those with the Shared secure configurations tab do. The credential entry becomes available in the panel on the left. ### Using credentials from shared secure configs > [!NOTE] Premium > Secure configuration exposure is a premium feature. Contact your DataRobot representative or administrator for information on enabling the feature. > > Required feature flag: Enable Secure Config Exposure When Enable Secure Config Exposure is enabled for your organization, credentials created from a shared secure configuration can be used as runtime parameters in: - Custom models: Use the credential as a runtime parameter when defining or deploying a custom model. The credential’s values are injected into the model’s runtime environment (for example, as environment variables in the container). - Custom applications: Use the credential as a runtime parameter in custom applications so the application can access the injected values at runtime. - Custom jobs: Use the credential as a runtime parameter in custom jobs so the job code can read the injected values when the job runs. When Enable Secure Config Exposure is active, shared secure configuration values are injected directly into the runtime parameters of the custom model, application, or job executed in the cluster. When it is disabled, the credential is injected using only the configuration ID, without exposing the underlying secure configuration values. In the enabled case, the actual secret values from the shared secure configuration (such as access keys or tokens) are exposed in the container's runtime so the code can use them. This differs from secure, back-end–only use (such as [Data Connections](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/data-conn.html)), where secret values are never sent to user-controlled code. > [!WARNING] Secret exposure in runtimes > Enable Secure Config Exposure causes secret values to be exposed in the container's runtime. When this feature is enabled for your organization, any credential created from a shared secure configuration and used as a runtime parameter in a custom model, application, or job exposes actual secret values (such as access keys and tokens) by injecting them into the runtime. Those secrets are then present in the container's runtime and can be accessed by custom code.Do not enable or use this capability unless you accept the risks inherent in exposing secrets in an uncontrolled container runtime. Use only when necessary and with appropriate governance. If your organization has Enable Secure Config Exposure active, admins are notified when sharing a secure configuration that the secret shared configuration values are exposed when used in runtimes. ## Manage credentials The Credentials management tile provides the ability to create new stored credentials as well as manage existing ones. Unlike other areas in DataRobot where you interact with stored credentials, you can only edit them from this tile. | | Element | Description | | --- | --- | --- | | (1) | Edit credential | Allows you to modify the username, password, and/or account name. When you edit credentials, the credentials are updated for all associated data connections. | | (2) | Delete | Removes the credentials from your list of stored credentials. | | (3) | Add associated connection | Establishes an association between a data connection and specific credentials, allowing you to quickly access them when working with the data connection. | | (4) | Remove association | Removes the association between a data connection and specific credentials. If you are removing all data connections for the associated credentials, you receive a message prompting you to delete the credentials. You can decide to remove the credentials or leave them without any associated connections. | --- # Membership URL: https://docs.datarobot.com/en/docs/platform/acct-settings/view-memberships.html > How to see which organization and groups your system administrator has assigned you to. # Membership From the Membership tile, you can see which organization and groups your system administrator has assigned you to and join other groups. Organization and group assignments allow system administrators to manage users, control project sharing, apply actions across user populations, etc. You may be a member of up to ten groups or not a member of any groups. Likewise, you may be a member of a single organization or not a member of any organization. System administrators choose how they want to configure user membership assignments. Only they can create and modify these memberships. --- # Overview URL: https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html > Overview of typical admin workflow for creating user accounts, defining groups, assigning access roles, monitoring and managing worker allocation, and more. # DataRobot overview and administrator workflow DataRobot sets up the basic deployment configuration, which defines the available system-wide features and resource allocations. The following describes the typical admin workflow for setting up users on DataRobot: **SaaS:** Log in using the default administrator account and create an Admin account. Create user accounts , starting with your own. Set user permissions and user roles . (Optional) Create groups and add multiple users or add users individually . (Optional) Manage personal worker allocation , which determines the maximum number of workers users can allocate to their project. See also: Monitoring and managing users **Self-Managed:** Log in using the default administrator account and create an Admin account. Create user accounts , starting with your own. Set user permissions and user roles . (Optional) Create groups and add multiple users or add users individually . (Optional) To control and allocate resources, create organizations and add multiple users or add users individually . (Optional) Configure SAML SSO. (Optional) Manage personal worker allocation , which determines the maximum number of workers users can allocate to their project. See also: Monitoring and managing the cluster and users Monitoring and managing users ## Important concepts The following sections explain concepts that are an important part of the Self-Managed AI Platform setup and configuration. Later sections assume you understand these elements: - Administrator roles - Workers and worker allocation - About groups - About organizations A DataRobot project is the combination of the dataset used for model training and the models built from that dataset. DataRobot builds a project through several distinct phases. During the first phase, DataRobot imports the specified dataset, reads the raw data, and performs EDA1 (Exploratory Data Analysis) to understand the data. The next phase, EDA2, begins when the user selects a target feature and starts the model building process. Once EDA2 completes, DataRobot ranks the resulting models by score on the model Leaderboard. ## System administrator vs. organization administrator DataRobot installations are managed by one of two types of administrator: - System administrator : Sys admins have cluster-wide management responsibilities that include creating organizations and allocating seats to each organization, managing users and groups, creating notification policies, custom user roles, and access policies. For SaaS users, this role is held by the DataRobot team. - Organization administrator : Org admins can monitor user activity, modify single sign-on settings, and manage users and groups within their own organization. They do not have permission to modify or monitor these resources outside of their organization. A single user account cannot be both a system administrator and an organization administrator. ## What are workers? Workers are the processing power behind the DataRobot platform, used for creating projects, training models, and making predictions. They represent the portion of processing power allocated to a task. DataRobot uses different types of workers for different phases of the project workflow, including DSS workers (Dataset Service workers), EDA workers, secure modeling workers, and quick workers. All workers, with the exception of modeling workers, are based on system and license settings. They are available to the installation's users on a first come, first served basis. Refer to the Installation and Configuration guide (provided with your release) for information about those worker types. This guide explains how to monitor and manage modeling workers. During EDA2, modeling workers train data on the target feature and build models. Modeling worker allocation is key to building models quickly; more modeling workers means faster build time. Because model development is time and resource intensive, the more models that are training at one time, the greater the chances for resource contention. ### Modeling worker allocation The admin and users each have some ability to modify modeling worker allocation. The admin [sets a total allocation](https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html#change-personal-worker-allocation) and the user has the ability to set per-project allocations, up to their assigned limit. Note that modeling worker allocation is independent of hardware resources in the cluster. Each user is allocated four workers by default. This "personal worker allocation" means, at any one time, no more than four workers (if left to the default) are processing a user's tasks. This task count applies across all projects in the cluster—multiple browser windows building models are all a part of the personal worker count, more windows does not provide more workers. The number of workers allocated when a project is created is the "project worker allocation." While this allocation stays with the project if it is shared, any user participating on the project is still restricted to their personal worker allocation. For example, a project owner may have 12 personal workers allocated to a project and share it with a user who has a four-worker personal allocation. The person invited to the project is still limited by their personal allocation, even if the project reflects a higher worker count. ### Change worker allocation The workers used during EDA1 (EDA workers) are set and controlled by the system; neither the admin or user can increase allocation of these workers. Increasing the displayed workers count during EDA does not affect how quickly data is analyzed or processed during this phase. During model development (EDA2), a user can increase the workers count as long as there are workers available to that user (based on personal worker allocation). Adjusting the worker toggle in the worker usage panel causes more workers to participate in processing. Users can read full details about [the Worker Queue](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/worker-queue.html) for a better understanding of how it works. > [!NOTE] Note > If the user's personal worker allocation is changed (increased or decreased), existing projects are not affected. ### Monitor and manage worker counts (Self-Managed only) For admins, the [Resource Monitor](https://docs.datarobot.com/en/docs/platform/admin/monitoring/resource-monitor.html) provides a dashboard showing modeling worker use across the cluster. This helps to monitor worker usage, ensure that workers are being shared as needed between DataRobot users, and determine when to make changes to a user's worker counts. To prevent resource contention and restrict worker access, the admin can add users to [organizations](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-orgs.html). ### Change personal worker allocation Admins can set the maximum number of workers each user can allocate to their project. 1. Expand the profile icon located in the upper right and clickAPP ADMIN > Usersfrom the dropdown menu. 2. Locate and select the user to open their profile page. 3. ClickPermissionsand scroll down toModeling workers limit. 4. Enter the worker limit in the field and clickSave changes. ## What are groups? You can create groups as a way to manage users, control project sharing, apply actions across user populations, and more. A user can be a member of up to ten groups, or not a member of any group. A group can be associated with a single organization, and a single organization can be the "parent organization" for multiple groups. Project owners can share their projects with a group; this makes the project accessible to all members of that group. Essentially, groups are a container of users that you can take bulk actions on. For example, if you share a project with a group, all users in the group can see the project (and work with it depending on the permission granted when sharing). Or, you can apply bulk permissions so that all users in a group have a permission set. See the section on [creating groups](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-groups.html) for information on setting up groups in your installation. ## What are organizations? To ensure workers are available as needed and prevent resource contention, an admin can add users to organizations. Organizations provide a way to help administrators manage DataRobot users and [groups](https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html#what-are-groups?). **SaaS:** You can use organizations to control worker allocation for groups of users to restrict the total number of workers available to all members of the organization. Project owners can share their projects with an organization; this makes the project accessible to all members of that organization. All users are required to belong to one and only one organization. Most commonly, organizations are used to set a cap on the total number of shared modeling workers the members of an organization can use in parallel. For example, you can create an organization of five users that has an allocation of ten workers. For this organization, the 5 users can collectively use up to 10 workers at one time, regardless of their personal worker allocations.This is not a cascading allocation; each user in the organization does not receive that allocation, they all share it. If a user with a personal allocation of 4 workers is defined in an organization with a worker allocation of 10 workers, the user can use no more than his personal allocation, i.e., 4 workers in this example. Organization membership is not a requirement for DataRobot users and users can be defined in only one organization at a time. The system admin manages the type of organization described above. Additionally there is a Organization User Admin, which is a user that has access to manage all users within their own organization and create groups for the organization. This type of admin does not have access to view other organizations or view users/groups outside of their organization. **Self-Managed:** You can use organizations to control worker allocation for groups of users to restrict the total number of workers available to all members of the organization. Project owners can share their projects with an organization; this makes the project accessible to all members of that organization. You can create organizations configured with a "restricted sharing" setting. When set, organization members cannot share projects with users and groups outside of their organizations. This is useful, for example, when a member needs to share a project with a customer support user who is outside of the organization. Most commonly, organizations are used to set a cap on the total number of shared modeling workers the members of an organization can use in parallel. For example, you can create an organization of five users that has an allocation of ten workers. For this organization, the 5 users can collectively use up to 10 workers at one time, regardless of their personal worker allocations.This is not a cascading allocation; each user in the organization does not receive that allocation, they all share it. If a user with a personal allocation of 4 workers is defined in an organization with a worker allocation of 10 workers, the user can use no more than his personal allocation, i.e., 4 workers in this example. Organization membership is not a requirement for DataRobot users and users can be defined in only one organization at a time. The system admin manages the type of organization described above. Additionally there is a Organization User Admin, which is a user that has access to manage all users within their own organization and create groups for the organization. This type of admin does not have access to view other organizations or view users/groups outside of their organization. See the section on [creating organizations](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-orgs.html) for information on setting up organizations in your installation. --- # Deployment approval policies URL: https://docs.datarobot.com/en/docs/platform/admin/deploy-approval.html > To enable effective governance and control across DataRobot, administrators can create and modify global approval policies and a monitoring plan for deployments. # Deployment approval policies > [!NOTE] Availability information > Required permission: Can Manage Approval Policies To enable effective governance of the model deployment, administrators can create approval policies for deployment-related activities and a monitoring plan to provide guidance on deployment configuration requirements. To create approval policies and a monitoring plan, navigate to the Governance management page: **Classic UI:** In the Classic UI, click the user icon and then, in the App Admin section, click Governance Management. [https://docs.datarobot.com/en/docs/images/classic-gov-manage-sys-menu.png](https://docs.datarobot.com/en/docs/images/classic-gov-manage-sys-menu.png) **NextGen UI:** In the NextGen UI, click the system administrator user icon and then click Governance Management. The Governance management page opens in the Classic UI, where you can manage approval policies and monitoring plans. After configuring these polices and plans, return to the NextGen UI using the dropdown in the upper-right corner of the navigation bar. [https://docs.datarobot.com/en/docs/images/nxt-gov-manage-sys-menu.png](https://docs.datarobot.com/en/docs/images/nxt-gov-manage-sys-menu.png) ## Create an approval policy Deployment approval policies ensure that deployments are created and configured safely with the necessary guardrails in place. Administrators can [create policies](https://docs.datarobot.com/en/docs/platform/admin/deploy-approval.html#create-an-approval-policy) from the Governance management > Approval policies page. Once created, you can perform a variety of [actions](https://docs.datarobot.com/en/docs/platform/admin/deploy-approval.html#manage-approval-policies) to manage approval policies. Note that the policies an administrator sees are specific to their organization—each organization may have its own approval policies configured. Approval policies affect users with permissions to review deployments and provide automated actions when reviews time out. Approval policies also affect users whose deployment events are governed by a configured policy (e.g., new deployment creation, model replacement). When a user deploys a model, they are prompted to assign an importance level to it: Critical, High, Moderate, or Low. Importance represents an aggregate of factors relevant to your organization such as the prediction volume of the deployment, the level of exposure, the potential financial impact, and more. These policies, the defined importance level, and the deployment monitoring plan serve as the basis of the approval process. To create a new approval policy: 1. On theApproval policiestab, click+ Create policy. 2. Define the required fields for the new approval policy: SettingDescriptionPolicy triggerDefine the deployment event that triggers the approval workflow for reviewers: deployment creation or deletion, importance changes, secondary dataset configuration changes, model replacement, status updates, or monitoring data deletion. You must also indicate the importance level required for the event to trigger the approval policy (critical, high, moderate, low, any, or all levels above low).Apply to groupsOptional. Add the user groups that can trigger the approval policy. Start typing a group name and select the groups you want to include. If no groups are specified, the policy applies to all users in the organization.ReviewersOptional. Add users or groups to review deployments after the policy is triggered. These users can review the deployment event to approve it or request changes. Once a user is added as a reviewer, they gain access to each deployment that triggers the policy and are notified when a review is requested. All MLOps Admins in the organization have reviewer permissions by default, and serve as the reviewers if none are specified.Automatically send reminders to reviewersAutomatically send reminders to reviewers. Set the reminder frequency (every 12 hours, 24 hours, 3 days, or 7 days).Automatically take action if change is not reviewedAssign an automatic action if a deployment event is not reviewed. Choose the action and when to apply it. For example, you can cancel a model replacement if the event was not reviewed within 7 days of the request for review.Policy nameDefine the name of the deployment approval policy. 3. ClickCreate Policy. The new policy appears on theApproval policiespage. ### Manage approval policies After creating one or more approval policies, you can perform the following actions on the Approval policies tab: | Element | Description | | --- | --- | | (1) | Click an approval policy row to access the following tabs:Policy details: View and edit policy details.Logs: View triggered policy events, event status, and the associated deployment. | | (2) | In the Policy details section, hover on the field you want to change and select Edit. After editing the field, click the save icon to apply the edits. | | (3) | From the Actions column, click one of the following:: Activate a paused deployment approval policy.: Pause the active deployment approval policy.: Permanently delete the deployment approval policy. A modal prompts you to confirm the deletion, warning that the automated action set up for the policy will automatically be applied to any deployments awaiting approval at the time of deletion. If no automated action was configured, the deployments need to be manually resolved. Confirm deletion by selecting Yes, remove policy. The approval policy will no longer appear on the Approval policies page. | ## Create a monitoring plan A deployment monitoring plan ensures the deployment owner is aware of the organization's requirements for deployment monitoring. Administrators can create a monitoring plan for deployments from the Governance management > Monitoring plan page. The defined monitoring plan appears in the Setup checklist panel on the deployment Overview. If a monitoring plan isn't defined, the checklist includes all available deployment settings. If a monitoring plan is defined, the checklist includes the settings defined in the monitoring plan and any additional guidance provided when you configured the checklist. Each setting in the checklist panel also includes the status of the setting: Not enabled, Partially enabled, or Enabled. Deployment owners can click a tile in the checklist to open the relevant [deployment setting](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-settings/index.html) page and configure the required settings. To create a monitoring plan: 1. On theMonitoring plantab, add settings to the plan: 2. After you add one or more settings rows, you can configure the following fields: FieldDescriptionSettingSelect adeployment settingto add to the deployment checklist.HintEnter a description of the configuration requirements for the selected setting. Remove a setting from the checklistTo remove a setting from the checklist, click the delete iconat the end of the setting row. 3. ClickSave changes. ## Feature considerations - A deployment Owner can choose to share a deployment withMLOps administratorsand grant either User or Owner permissions. When explicitly shared, Owner rights are the default. - MLOps administrators are eligible to perform reviews if the approval policy has no approvers group assigned. Otherwise, only members of the designated approvers group can review the deployment. - For Self-Managed AI Platform installations: An MLOps administrator will be able to monitor actions taken by users in their organization. --- # Administrator's guide URL: https://docs.datarobot.com/en/docs/platform/admin/index.html > Help for system administrators in managing DataRobot Self-Managed AI Platform deployments. # Administrator's guide > [!NOTE] Note > DataRobot performs service maintenance regularly. Although most maintenance will occur unnoticed, some may cause a temporary impact. Status page announcements provide information on service outages, scheduled maintenance, and historical uptime. You can view and subscribe to notifications from the [DataRobot status page](https://status.datarobot.com/). **SaaS:** The DataRobot Administrator's Guide is intended to help administrators manage their DataRobot application. Before starting to set up your users and monitoring tools, you may want to review the [overview](https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html) for a description of a typical admin workflow as well as important concepts for managing users in a DataRobot. You will work with some or all of the actions described in the following sections: Topic Description Admin Workflow overview Preview the workflow and learn about important admin concepts. Manage user accounts Learn about setting permissions, RBAC, passwords, and user activity. Manage groups Create, assign, and manage group memberships. Monitor activity Monitor user and system activity. Approval policies for deployments Configure approval policies for governance and control. Feature settings View and change feature settings. Notification service Integrate centralized notifications for change and incident management. Reference SSO Configure DataRobot and an external Identity Provider (IdP) for user authentication via single sign-on (SSO). Role-based access control (RBAC) Assign roles with designated privileges. User Activity Monitor (UAM) View Admin, App, and Prediction usage reports, as well as system information. **Self-Managed:** The DataRobot Administrator's Guide helps system administrators manage their DataRobot Self-Managed AI Platform deployments. > [!NOTE] Note > See the [end of support dates](https://docs.datarobot.com/en/docs/release/deprecations-and-migrations/onprem-eol.html) for Self-Managed AI Platform releases. Before setting up your users and monitoring tools, you may want to review the [overview](https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html) for a description of a typical admin workflow, as well as important concepts for managing users in DataRobot: Topic Description Workflow overview Preview the workflow and learn about important admin concepts. Manage user accounts Learn about setting permissions, role-based access controls (RBAC), passwords, and user activity. Manage groups Create, assign, and manage group memberships. Manage organizations Create and assign resources to organizations. Monitor activity Monitor user and system activity. Resource Monitor Monitor allocation and availability of EDA and modeling workers (compute resources), in a SAAS or standalone cluster environment. After setting up your users, continue to [Managing the cluster](https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/index.html). The following describes common ongoing admin activities: Topic Description Admin Manage the cluster Manage the application, deleted projects, and JDBC drivers; monitor system resources and user activity. Approval policies for deployments Configure approval policies for governance and control. Feature settings View and change feature settings. Notification service Integrate centralized notifications for change and incident management. Worker resource allocation Allocate worker resources, whether for individual users or across groups of users via organizations. Activity monitoring Monitor user and system activity with the User Activity Monitor. JDBC drivers Create and manage JDBC drivers , and for locked-down systems, restrict access to JDBC data stores. Worker allocation monitoring Monitor worker allocation with the Resource Monitor. User management Deactivate or reactivate users. Project management Delete or restore projects. Licenses Apply a new license. Reference SSO Configure DataRobot and an external Identity Provider (IdP) for user authentication via single sign-on (SSO). Role-based access control (RBAC) Assign roles with designated privileges. User Activity Monitor (UAM) View Admin, App, and Prediction usage reports, as well as system information. Other related documentation For information on installing and configuring your DataRobot cluster deployment (including config.yaml deployment settings), see the DataRobot Installation and Configuration Guide provided for your release. > [!NOTE] Note > `config.yaml` is the master configuration file for the DataRobot cluster. To modify the configuration, or better understand how your cluster is configured, contact DataRobot Customer Support. --- # Delete and restore projects URL: https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/delete-restore.html > For administrators, how to permanently delete a project, or restore a project temporarily deleted by a user. # Delete/restore projects > [!NOTE] Availability information > This feature is only available on the Self-Managed AI Platform. SaaS users must contact their DataRobot representative to permanently delete projects. > > Required permission: Can delete/restore projects As an administrator, you can delete and restore (or recover) projects. Deleting projects is a valuable tool that can help clear space for new projects. When the project owner deletes a project, it is not permanently deleted; only an administrator can permanently delete projects, or restore them if needed. ## Permanently delete projects To permanently delete a project: 1. Expand the profile icon located in the upper right and clickManage Deleted Projects: TheManage Deleted Projectstab shows all deleted projects for all users. 2. You can permanently delete one or more projects at a time, as follows: Then, fromMenuchoosePermanently Delete Selected Projects. A warning message appears prompting you to confirm that you want to delete the project(s). 1. Click to delete the project(s). > [!NOTE] Note > Once you delete a project from the Manage Deleted Projects page, it is permanently deleted and cannot be recovered. ## Restore projects Any projects listed in the Manage Deleted Projects page can be restored. 1. Open theManage Deleted Projectspage (if not open). TheManage Deleted Projectstab shows all deleted projects for all users. 2. You can restore one or more projects at a time, as follows: Then, fromMenuchooseRecover Selected Projects. A warning message appears prompting you to confirm that you want to restore the project(s). 3. Click to restore the project(s). Restored projects will be immediately available, to the owner and to anyone sharing the project. --- # Manage the cluster URL: https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/index.html > Summarizes the admin tasks for managing a deployed cluster, such as connecting to external systems, and monitoring resources, activity, access, and licenses. # Manage the cluster > [!NOTE] Availability information > Only Self-Managed AI Platform admins can manage their cluster. To manage the deployed cluster, you can perform the following activities: | Topic | Description | | --- | --- | | Configure external systems | Use the configuration management interface to ensure compatible configuration for LDAP and SMTP. | | Manage JDBC drivers | Add, delete, and control JDBC access. | | Manage access | Manage access to the cluster, including licenses and configuring SAML SSO. | | Delete/restore projects | Learn to delete and restore (or recover) projects. | | Apply a DataRobot license | Apply a DataRobot license for a user via the UI or the API. | --- # Apply a DataRobot license URL: https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/license.html > Learn how to apply a DataRobot license using either the user interface of DataRobot's API. # Apply a DataRobot license This page explains how to apply a DataRobot license using either the application interface or the DataRobot API. Note that this workflow is exclusive to administrators, and requires you to be logged into DataRobot with admin permissions. ## Apply a license in the DataRobot application 1. Access the DataRobot cluster in your browser. 2. Click the profile icon in the top-right corner of the page and selectLicense. 3. On theLicensepage, enter the license key in the field and clickValidate. 4. After validating, DataRobot lists the features and functionality included in the subscription (blurred in the image below). 5. Confirm that the validated license lists the correct subscription features, then clickSubmit. ### Seat licenses > [!NOTE] Premium > The ability to assign seat licenses is a premium feature. This capability is only available to: > > Multi-tenant SaaS organizations that have a seat license allocation. > Single-tenant SaaS/self-managed installations that have a license key with a cluster-level seat allocation, allowing system adminstrators for that installation to allocate seats to the organizations. > > Contact your DataRobot representative for enablement information. Seat licenses allow you to grant users specific permissions based on the number of individual licenses purchased for your organization. To apply any seat license(s) to user accounts: 1. From theLicensepage accessed in the previous section, click theSeat Licensesection. 2. ClickAllocate seatsin the resulting table. 3. In theAllocate seatsmodal, enter a name for your organization and specify the number of seats to be allocated. The seats are now allocated to your organization but remain unassigned. See [Assign seat licenses to users](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#assign-seat-licenses-to-users). ## Apply a license via the API Alternatively, administrators can validate a DataRobot license using DataRobot's REST API. To do so, provide your information in the snippet below and execute a REST API call. ``` # Set the URI of the DataRobot App node # Ex. https://datarobot.example.com # Ex. http://10.2.3.4 dr_app_node="http://10.2.3.4" # If you have a username and password, start here # Set the initial username admin_username=localadmin@datarobot.com # Set the local administrator user password admin_password="" # Read the license ldata=$(cat ./license.txt) # Apply the license with the following commands # Log in to the App Node curl --silent -X POST \ --cookie "/tmp/cookies.txt" \ --cookie-jar "/tmp/cookies.txt" \ -H "Content-Type: application/json" \ -d '{"username":"'"${admin_username}"'","password":"'"${admin_password}"'"}' \ ${dr_app_node}/account/login # Get an API key api_key=$(curl --silent -X POST \ --cookie "/tmp/cookies.txt" \ --cookie-jar "/tmp/cookies.txt" \ -H "Content-Type: application/json" \ -d '{"name":"apiKey"}' \ ${dr_app_node}/api/v2/account/apiKeys/ | cut -d ',' -f 5 | cut -d '"' -f 4) # Apply the license curl --silent -w "%{http_code}\n" -X PUT \ -H "Content-Type: application/json" \ -H "HTTP/1.1" \ -H "Authorization: Token ${api_key}}" \ -d '{"licenseKey":"'"${ldata}"'"}' \ ${dr_app_node}/api/v2/clusterLicense/ # Expected output: 200 ``` --- # Manage access and licenses URL: https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/manage-access.html > How to manage access to DataRobot, including setting up a DataRobot license and configuring SSO SAML for users. # Manage access To manage access to DataRobot, you may need to: - Manage your DataRobot license. - Configure SSO SAML for users. ## Manage licenses > [!NOTE] Availability information > This feature is only available on the Self-Managed AI Platform. A valid, unexpired DataRobot license is required for model building. Use the License page to apply a new license key to the deployed cluster when the current license is expiring or close to expiring. Contact Support to obtain a new DataRobot license key, when required. The application banner shows messages, visible to all users, when: - The current license is expiring within 30 days (by default); users can click the banner to snooze for four days at a time. - The license has expired. If your license expires before applying the new license, users continue to have access to DataRobot and can make predictions using existing models. They cannot build new models or new insights or generate Compliance Documentation; existing elements are still available in the UI, however. If model building (EDA2) is running for a project when the license expires, the current round finishes. When you apply the new license, existing projects resume and users can again create new models and use all features. ### Apply a new license > [!NOTE] Note > If you do not already have the new license key, you must first request it from Support. Once you have the license: 1. Expand the profile icon located in the upper right and clickLicense. The page shows license details, including expiration date, concurrent workers limit, maximum active users, prepaid deployment limit, maximum deployment limit, and in some instances, subscription features: 2. Copy the license key you receive from Support, paste it to theLicense Keyfield, and clickValidate. 3. Review the details associated with the provided license key and, if correct, clickSubmit. When successful, a message in the application banner shows the license is valid, and the tooltip shows the new license expiration date. It may take a few moments for the license change to take effect across your deployed cluster. > [!NOTE] Note > The details shown for a specific license are based on your subscription. For more information on license details, contact Support. ## Configure SSO SAML > [!NOTE] Availability information > Required cluster configuration. > > Required permission: Enable SAML SSO configuration management > [!WARNING] Warning > SSO SAML will be deprecated in an upcoming release. You must configure [Enhanced SAML Single Sign-on](https://docs.datarobot.com/en/docs/platform/admin/sso-ref.html) before DataRobot 7.1. If you need help configuring Enhanced SAML SSO, contact your DataRobot representative. DataRobot can use external services (Identity Providers, or IdP) for user authentication through Single Sign-On (SSO) technology. DataRobot supports SSO using the SAML (Security Assertion Markup Language) standard protocol. When users log in to a DataRobot cluster configured for SSO, they click a Single Sign-On button which redirects them to the IdP's authentication page. After signing in successfully, users are then redirected to DataRobot. If supported, configure the identity provider integration settings as follows. 1. Click the profile icon in the top right corner and selectAPP ADMIN > Manage Usersfrom the dropdown menu. 2. ClickManage SSOand configure the SSO settings described in the table below. When finished, clickCreate. | Field | Description | | --- | --- | | Entity Id | Unique identifier. Provided by Identity Provider | | IdP Metadata URL | Link to XML document with integration specific information | | Single Sign-On URL | DataRobot URL to redirect user after successful authentication on Identity Provider’s side | | Single Sign-Out URL | DataRobot URL to redirect user after sign out on Identity Provider’s side | | Verify IdP Metadata HTTPS Certificate | If selected, the public certificate of the Identity Provider is required | | User Session Length (sec) | Session cookie expiration time. Default is one month. | | SP Initiated Method | SAML method used to start authentication negotiation | | IdP Initiated Method | SAML method used to move user to DataRobot after successful authentication | | Identity Provider Metadata | XML document with integration specific information (needed if the Identity Provider doesn't provide IdP Metadata URL) | --- # Manage JDBC drivers URL: https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/manage-drivers.html > How to create predefined JDBC driver configurations, upload, modify, and delete drivers, and restrict access to JDBC data stores. # Manage JDBC drivers > [!NOTE] Availability information > This feature is only available on Self-Managed AI Platform installations. > > Required permission: Can manage JDBC database drivers You manage Java Database Connectivity (JDBC) drivers by: - Working with JDBC drivers - Restricting access for Kerberos authentication systems A driver allows DataRobot to provide a way for users to ingest data from a database via JDBC. The administrator can [upload JDBC driver files](https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/manage-drivers.html#add-drivers) (as a Java Archive (JAR) file) for their organization's users to access when creating data connections. As part of driver creation, the administrator can also upload additional JAR files containing library dependencies. Once uploaded, an administrator can delete or modify drivers. By default, all users have permissions to create, modify ( [depending on their roles](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html)), and share data connections and data sources. If needed, you can prevent access to data stores and data sources for a specific user with the ["Disable Database Connectivity"](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#additional-permissions-options) user permission; this prevents that user from creating new JDBC data connections or importing data from any defined JDBC data sources. Additionally, for cluster deployments using Kerberos authentication, you can control access to data stores through [validation and variable substitution](https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/manage-drivers.html#restrict-access-to-jdbc-data-stores). ## Predefined driver configurations When users create data connections for a selected JDBC driver, they specify how to retrieve the data. This may be a defined JDBC URL or a set of parameters. Because creating the JDBC URLs for data connections can be complicated, DataRobot provides predefined configurations for some supported drivers that have parameter support. Driver configurations specify the information users need to provide to retrieve data from their data sources. Each predefined configuration includes typical information needed to create connections using that type of driver. For example, while connections to Presto driver typically require the catalog and schema, connections to Snowflake driver often need the database and warehouse. Predefined configurations are available for the following drivers: - AWS Athena - Azure SQL - Azure Synapse - Google BigQuery - Intersystems - kdb+ - Microsoft SQL Server - MySQL - Postgres - Presto - Redshift - SAP HANA - Snowflake - Treasure Data: Hive When you add a new driver, you can select to use a predefined configuration (if one exists for that driver), or you can create a custom driver which does not include a configuration. ## Add drivers The steps below describe how to create a driver instance. 1. Click the profile icon in the top right corner of the application screen, and selectData Connectionsfrom the dropdown menu. 2. Select theConnectors & Driverstab. 3. In the left-panelDriverslist, clickAdd new driver. 4. In the displayed dialog, select the type of configuration you are using for this driver: 5. If you are adding a driver that has a configuration (Predefined), complete the following fields as prompted: FieldDescriptionConfigurationSelect the configuration to use for the driver you are creating. Configurations for some supported drivers with parameter support in DataRobot are provided for selection. If you don't see the configuration you want then create this as acustomdriver and specify driver name and driver class.Class name (Predefined)Shows the driver class name defined in this configuration.VersionEnter the version (user-defined) for the driver. This value is required. The combination of driver name and version are used to identify the driver configuration for users.Driver Files *Click+ UPLOAD JARto add the driver JAR file. Follow the same process for each additional library dependencies. When uploaded successfully, the JAR filename(s) appear in this field. 6. If you are adding a driver that does not include a configuration (Custom), complete the following fields as prompted: FieldDescriptionDriver nameEnter a display name for the driver.Class nameEnter the driver class name. If unknown, refer to the driver documentation.Driver Files *Click+ UPLOAD JARto add the driver JAR file. Follow the same process for each additional library dependencies. When uploaded successfully, the JAR filename(s) appear in this field. * The JAR driver and dependency file size limit is 100MB. That is, each file upload can be a maximum of 100MB, but totals may exceed that limit. NoteDataRobot does not validate uploaded drivers (other than simple extension checking). 7. ClickCreate driverto add the driver. The new driver is shown in the left-panelDriverslist, and the driver configuration is now available to all users in your organization. Drivers created with predefined configurations are named in the formatdriver name (version). ### Modify drivers If you are modifying drivers that use predefined configurations, you can change only the version and JAR files for the driver configuration. (The driver name is created automatically as a combination of selected configuration and version.) Also, note that removing the predefined configuration for a driver makes it a driver with a custom configuration (i.e., connections using this driver will require a JDBC URL). If you are modifying drivers that use custom configurations, you can change the driver name, configuration, class name, or JAR file(s). Adding a configuration to a driver makes it a driver with a predefined configuration. If you do select to add a configuration for the driver, DataRobot automatically verifies that any JDBC URLs for existing data connections are not affected. If that is not necessary, then you can select to skip the URL validation. > [!NOTE] Note > DataRobot recommends that you notify your users about driver configuration modifications that will affect JDBC URLs for existing data connections, so they can recreate them, if needed. 1. Select the driver from the left-panelDriverslist. The information for the driver configuration is added to the main window. 2. If this driver has a predefined configuration: 3. If this driver has a custom configuration: If there are existing connections to the driver and the new configuration will affect the JDBC URLs (and you still want to make this change), selectSkip data connections verification. This ensures DataRobot does not validate JDBC URLs for existing data connections. As shown in the below image, if existing JDBC URLs are affected by the new configuration andSkip data connections verificationisnotselected, the configuration cannot be added to the driver. 4. For either type of driver, you can clickUPDATE JAR FILEto replace the JAR file(s). 5. ClickSave changesto save modifications to this driver. You see the updated driver listing in the left-panelDriverslist. Any existing data connections for this driver are updated with these changes automatically. ### Delete drivers You can delete any driver that is not being used by existing data connections. If it is being used, you must first delete the dependencies. 1. Select the driver in the left-panel Drivers list. 2. In the upper right, click Delete Driver . If there are no existing data connections for the driver, DataRobot prompts for confirmation (if there are existing data connections using the driver, DataRobot provides a warning). 3. Click Delete to remove the driver. If you see the connector warning, the dependencies first need to be removed (in the Data Connections tab). Then, you can try again to delete the driver. ## Restrict access to JDBC data stores > [!NOTE] Availability information > Required permission: Can manage users, Can manage JDBC database drivers When using Kerberos authentication for JDBC, you can control access to data stores through validation and variable substitution. ### Control user access You can restrict the ability to create and modify JDBC data stores that utilize impersonation to only those users with the [admin setting](https://docs.datarobot.com/en/docs/platform/admin/user-settings.html)"Can create impersonated Data Store." Within cluster configuration ( `config.yaml`), you can define impersonated keywords that are used by any installed drivers that support impersonation. These keywords could be used to define operations considered "dangerous" and therefore permitted to only select users. By default, no impersonation keywords are defined. When a user attempts to create or modify a JDBC data store and there are impersonation keywords defined for the installation, DataRobot determines if the URI includes any of the keywords. If the URI includes one or more of the keywords and the "Can create impersonated Data Store" admin setting is enabled for the user, they are allowed to create or modify it. If keywords are included in the URI but the user does not have the "Can create impersonated Data Store" setting enabled, then DataRobot does not allow the request to create or modify it. ### Variable substitutions The variable substitution syntax, `${datarobot_read_username}`, provides another way to control access to drivers. If that variable is included in the URI when trying to ingest from the data source/data store, DataRobot replaces it with the impersonated account associated with the logged in user. --- # Configure external systems URL: https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/sys-config.html > How to configure external LDAP and SMTP systems easily, by using the configuration management interface in DataRobot. # Configure external systems > [!NOTE] Availability information > This feature is only available on the Self-Managed AI Platform. > > Required permission: Enable System Configuration To minimize iteration time when configuring external LDAP and SMTP systems, DataRobot provides a configuration management interface to ensure compatible configuration options. Using the new System Configuration interface, configuration values related to LDAP and SMTP can be controlled dynamically without the need to reconfigure the application, saving time and making the process more user friendly. All changes made through the interface are also recorded in the audit logs for future reference. To work with the interface, select System Configuration from the Settings dropdown: ## Change configuration settings The method for setting values is the same for all configuration options. See the full lists of [LDAP](https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/sys-config.html#ldap-settings) and [SMTP](https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/sys-config.html#smtp-settings) settings below. To change settings: 1. Select an option from theSystem Configurationpage (LDAP or SMTP). The list of configuration settings updates to display those specific to that option. The current configuration setting value is automatically populated in theOPTIONfield. The displayed value is based on the default and/or theconfig.yamlvalue. It is displayed in read-only mode. 2. To override a current configuration setting value, toggle the setting'sOVERRIDE. When toggled on, theOPTIONfield becomes editable. 3. Change theOPTIONfield to the desired value and clickSAVE. The system immediately updates to use the new configuration value across the application (and the toggle remains on). 4. To revert settings to the default/config.yamlvalue: ## Configuration-specific notes The following provide some option-specific details. ### LDAP and SMTP notes For LDAP and SMTP options, in addition to the Save and Reset buttons, there is also a Test button. Use it to validate the current configuration setting values and catch errors that could result from an invalid configuration (e.g., incorrect LDAP or SMTP authentication settings). It is highly recommended that you use the Test option to confirm no errors before saving changes to LDAP- and SMTP-related configuration settings. > [!NOTE] Note > Saving invalid configuration settings could result in users (including the Admin user): > > LDAP: being locked out of the application, requiring a fix from your support representative. > SMTP: losing the ability to generate email notifications. ## Configuration settings The following sections list settings by type. ### LDAP settings - USER_AUTH_LDAP_ATTR_EMAIL_ADDRESS - USER_AUTH_LDAP_ATTR_FIRST_NAME - USER_AUTH_LDAP_ATTR_LAST_NAME - USER_AUTH_LDAP_ATTR_UNIX_USER - USER_AUTH_LDAP_BIND_PASSWORD - USER_AUTH_LDAP_CONNECTION_OPTIONS - USER_AUTH_LDAP_DIST_NAME_TEMPLATE - USER_AUTH_LDAP_GLOBAL_OPTIONS - USER_AUTH_LDAP_GROUP_SEARCH_BASE_DN - USER_AUTH_LDAP_ORGANIZATION_NAME_ACCOUNT_ATTRIBUTE - USER_AUTH_LDAP_REQUIRED_GROUP - USER_AUTH_LDAP_REQUIRED_GROUP_ACCOUNT_ATTR - USER_AUTH_LDAP_REQUIRED_GROUP_MEMBER_ATTR - USER_AUTH_LDAP_SEARCH_BASE_DN - USER_AUTH_LDAP_SEARCH_FILTER - USER_AUTH_LDAP_SEARCH_SCOPE - USER_AUTH_LDAP_URI - USER_AUTH_SERVICE_USERNAMES - USER_AUTH_TYPE ### SMTP settings - DEFAULT_SENDER - DEFAULT_SUPPORT - SMTP_ADDRESS - SMTP_CONNECTION-_TIMEOUT_SECONDS - SMTP_MODE - SMTP_PASSWORD - SMTP_PORT - SMTP_USER --- # Manage entities URL: https://docs.datarobot.com/en/docs/platform/admin/manage-entities/index.html > Summarizes how to manage user accounts, groups, and organizations in DataRobot. # Manage entities Administrators can manage the following entities in DataRobot: | Topic | Description | | --- | --- | | Manage user accounts | Learn about setting permissions, role-based access controls (RBAC), passwords, and user activity. | | Manage groups | Create, assign, and manage group memberships. | | Manage organizations | Create and assign resources to organizations. | | Tenant isolation and collaboration | Learn how to add and manage collaborators within an organization. | --- # Manage groups URL: https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-groups.html > Learn about creating and deleting groups, adding users to groups, setting group permissions, and role-based access control (RBAC) for groups. # Manage groups **SaaS:** > [!NOTE] Availability information > Required permission: Org Admin **Self-Managed:** > [!NOTE] Availability information > Required permission: Can manage users ## Create a group Follow these steps to create a group: **Saas:** Expand the profile icon located in the upper right and click APP ADMIN > Groups . The displayed page lists all existing groups. From here you create, search, delete, and perform actions on one or more groups. Click Create a group . On the resulting page, enter a name and, optionally, description for the group. When the information is complete, click Create a group . The new group appears in the Manage Groups listing, including parent organization (enterprise only), number of members, and group description. **Self-Managed:** Expand the profile icon located in the upper right and click APP ADMIN > Groups . The displayed page lists all existing groups. From here you create, search, delete, and perform actions on one or more groups. Click Create a group . In the resulting modal, enter a name and, optionally, description for the group. If you want to associate this group with an organization, select that organization. This will be the "parent organization" for the group. Note If a group is assigned a parent organization, the assignment cannot be removed. When the information is complete, click Create a group . The new group appears in the groups listing, including parent organization (enterprise only), number of members, and group description. ## Add users to a group **SaaS:** Once a group is created, you add and remove one or more users from the Groups page. (After you configure a user's profile, you can also add or remove individuals from groups through their profile [Membership](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#manage-groups-and-organization-membership) tab.) **Self-Managed:** Once a group is created, you add and remove one or more users from the Groups page. (After you configure a user's profile, you can also add or remove individuals from groups through their profile [Membership](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#manage-groups-and-organization-membership) tab.) In LDAP-authenticated deployments, users can be added to LDAP groups. When a user logs into DataRobot, if there is a DataRobot user group whose name matches the LDAP group they are a part of, that user is automatically added to the DataRobot user group. > [!NOTE] Note > If the group has a parent organization, you are [restricted](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-orgs.html#restrict-orgs) to adding only users already associated with that organization. To add users through Manage Groups: 1. Open theManage Groups > All Groupspage. From the list of configured groups, click to select the group to add the user to. 2. When the profile opens, selectMembersto see all members in this group. 3. ClickAdd a user. 4. From the displayed page, type any part of the name of a user to add. As you type, DataRobot shows usernames containing those characters: 5. Select one or more users to add to the group. When done, clickAdd users. The Members list for the group updates to show all members and information for each, including first name, last name, organization (if any), and status ( [active/inactive](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#deactivate-user-accounts) in DataRobot). ## Delete groups You can delete one or more groups at a time. This removes the group profile only; members of that group continue to have access to DataRobot. Any projects shared with the deleted group are no longer shared with any users for that group. (If needed, re-share projects with individual users.) 1. View theGroupspage and do one of the following: 2. In the displayedConfirm Deletewindow, clickDelete group(s). Deleted groups are removed from the list of groups. ## Configure group permissions You can configure permissions and apply them to all users in an existing group in addition to managing each user individually. This allows for easier tracking and management of permissions for larger groups. > [!NOTE] Note > Note that a user's effective permissions are the union of those granted to their organization, any user groups they are a member of, and permissions granted to the user directly. A user who is added to a group obtains all permissions of that group. A user who is removed from a group does not maintain any permissions that were granted to them by that group. However, a user may still have the permissions granted by that group if the organization they belong to also grants those permissions. 1. To configure permissions for a group, select it from theGroupspage and navigate to thePermissionstab. 2. ThePermissionstab displays the premium products, optional products, preview features, and admin settings available for configuration for your group. Select the desired permissions to grant your group from the list. 3. When you have finished selecting the desired permissions for your group, clickSave Changes. Once saved, all existing users in your group and those added to it in the future are granted the configured permissions. A user can view their permissions on the Settings page. Hover over a permission to see what group(s) or organization(s) enabled it. ## RBAC for groups Role-based access (RBAC) controls access to the DataRobot application by assigning users roles with designated privileges. The assigned role controls both what the user sees when using the application and which objects they have access to. RBAC is additive, so a user's permissions will be the sum of all permissions set at the user and group level. Assign a default role for group members in a group's Group Permissions page: Review the [role and access definitions](https://docs.datarobot.com/en/docs/reference/misc-ref/rbac-ref.html) to understand the permissions enabled for each role. > [!NOTE] Note > RBAC overrides [sharing-based role permissions](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html). For example, consider a group member is assigned the Viewer role via RBAC, which only has Read access to objects. If this group member has a project shared with them that grants Owner permissions (which offers Read and Write access), the Viewer role takes priority and denies the user Write access. ## Manage execution environment limits The execution environment limit allows you to control how many custom model environments a user can add to the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html). In addition, the execution environment version limit allows you to control how many versions a user can add to each of those environments. These limits can be: 1. Directly applied to the user: Set in a user's permissions. Overrides the limits set in the group and organization permissions. 2. Inherited from a user group: Set in the permissions of the group a user belongs to. Overrides the limits set in organization permissions. 3. Inherited from an organization: Set in the permissions of the organization a user belongs to. If the environment or environment version limits are defined for an organization or a group, the users within that organization or group inherit the defined limits. However, a more specific definition of those limits at a lower level takes precedence. For example, an organization may have the environment limits set to 5, a group to 4, and the user to 3; in this scenario, the final limit for the individual user is 3. For more information on adding custom model execution environments, see the [Custom model environments documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html). To manage the execution environment limits in the platform settings: 1. Click your profile avatar (or the default avatar) in the upper-right corner of DataRobot, and then, underAPP ADMIN, clickGroups. 2. To configure permissions for a group, select it from theGroupspage and then clickPermissions. ImportantIf you access theMemberstab to set these permissions, you are setting the permissions on an individual user level, not the group level. 3. On thePermissionstab, clickPlatform, and then clickAdmin Controls. 4. UnderAdmin Controls, set either or both of the following settings: 5. ClickSave changes. --- # Manage organizations URL: https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-orgs.html > How to create an organization if you wish to restrict user access to workers. You can prevent members from sharing the organization's projects to outside users or groups. # Manage organizations > [!NOTE] Required permission > “Can manage users” Set an organization for users if you wish to restrict access to workers. See the [overview](https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html#what-are-organizations?) for a description of the organization functionality in DataRobot. Some notes on using organizations: - Users can belong to only one organization at a time; if you add a user to a new organization, they are automatically removed from the existing organization. - Assigning a parent organization to a group limitsgroupmembership to members of the parent organization (i.e., you are restricted to adding only users who are members of that parent organization). - You cannot change a group's parent organization once assigned. - Management of multiple organizations is available only in Self-Managed deployments. ## Create organizations Follow these steps to create an organization: 1. Expand the profile icon located in the upper right and clickAPP ADMIN > Organizations. The displayed page lists all existing organizations. From here you create and manage organizations. 2. ClickCreate new organization. 3. In the displayed dialog, enter a name and thenumber of workersto assign to the organization. 4. If you want torestrict usersin this organization to sharing projects only with other users and groups within this organization, selectRestrict sharing to organization members. 5. ClickCreate Organization. TheAll Organizationsprofile lists the new organization. 6. Click an organization to see its profile. In the below image, for example, the new ACME3 organization is allocated a maximum of 14 workers and members of the organization can share projects with users and groups outside of the ACME3 organization. > [!NOTE] Note > The worker resource allocation ("Workers limit") is independent from your cluster configuration. To avoid worker resource issues, don’t oversubscribe the physical capacity of the cluster. ## Add users to organizations You can add one or more users to (or remove users from) any defined organization using the Organizations pages. You can add or remove individual users from the [User Profile > Membership](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#manage-groups-and-organization-membership) page. 1. Open theOrganizationspage and select the organization to which you want to add a user. The profile page for that organization opens. 2. ClickMembersto list all members of that organization. 3. ClickAdd a userand from the displayed dialog, start typing the name of a user to add. As you type, DataRobot shows usernames containing those characters. 4. Select the intended user and repeat for each user you want to add. When done, clickAdd users. The displayed list shows all members of the organization and information for each, including first name, last name, and status ( [active/inactive](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#user-accounts-disable) in DataRobot). ## Delete organizations You can only delete an organization once all the members have been removed. Any projects previously shared with the organization (and therefore, to each user in the organization), are no longer shared with those (ex) members. If needed, re-share projects with individual users. To delete an organization: 1. Expand the profile icon located in the upper right and clickAPP ADMIN > Manage Organizations. In the displayed list of organizations, check theTotal Memberscolumn for the organization you want to delete to determine whether there are members assigned it. If the organization has members, you must remove them before you can delete the organization. 2. If the organization has members, do the following: 3. When the organization has no members, view theOrganizationspage and do one of the following: 4. In the displayedConfirm Deletewindow, clickDelete organization(orDelete organizationsif shown). Deleted organizations are removed from the list of organizations. ## Configure organization permissions You can configure permissions and apply them to all users in an existing organization in addition to managing each user individually. This allows for easier tracking and management of permissions for larger organizations. > [!NOTE] Note > Note that a user's effective permissions are the union of those granted to their organization, any user groups they are a member of, and permissions granted to the user directly. A user who is added to an organization obtains all permissions of that organization. A user who is removed from an organization does not maintain any permissions that were granted to them by the organization. However, a user may still have the permissions granted by that organization if a group they belong to also grants those permissions. 1. To configure permissions for an organization, select it from theOrganizationspage and navigate to thePermissionstab. 2. ThePermissionstab displays the premium products, optional products, preview features, and admin settings available for configuration for your organization. Select the desired permissions to grant your organization from the list. 3. When you have finished selecting the desired permissions for your organization, clickSave Changes. Once saved, all existing users in your organization and those added to it in the future are granted the configured permissions. A user can view their permissions on the Settings page. Hover over a permission to see what group(s) or organization(s) enabled it. ## Configure organization details ### Understand restricted sharing You can use a "restricted sharing" setting to prevent organization members from sharing projects from the organization to users or groups outside of it. This setting does not prevent users outside the organization from sharing projects to the organization. Users outside of a "restricted sharing" organization can share projects with any members and groups. After creating an organization, you can change its restricted sharing policy from the organization profile. If you set Restrict sharing to organization members after the organization was created, any project sharing that already exists is not affected; making this change only prevents the ability to start new project sharing with users or groups outside of the organization. ### Set custom app limits For custom application users, you can specify the time limit for external application re-authentication. If a user accesses the app within the specified time limit, they are taken directly to its landing page and the time limit resets. If a user does not open the app within the specified time limit, they will be prompted to log in before accessing the app. To manage this limit: 1. Click the system admin icon on the upper toolbar amd selectOrganizations. 2. On the Profile tab, scroll down toCustom app external user session TTL (days). 3. Define the time limit (in days) in the field. The maximum number of days you can enter is 90. 4. Click Save . ### Set execution environment limits The execution environment limit allows you to control how many custom model environments a user can add to the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html). In addition, the execution environment version limit allows you to control how many versions a user can add to each of those environments. These limits can be: 1. Directly applied to the user: Set in a user's permissions. Overrides the limits set in the group and organization permissions. 2. Inherited from a user group: Set in the permissions of the group a user belongs to. Overrides the limits set in organization permissions. 3. Inherited from an organization: Set in the permissions of the organization a user belongs to. If the environment or environment version limits are defined for an organization or a group, the users within that organization or group inherit the defined limits. However, a more specific definition of those limits at a lower level takes precedence. For example, an organization may have the environment limits set to 5, a group to 4, and the user to 3; in this scenario, the final limit for the individual user is 3. For more information on adding custom model execution environments, see the [Custom model environments documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html). To manage the execution environment limits in the platform settings: 1. Click your profile avatar (or the default avatar) in the upper-right corner of DataRobot, and then, underAPP ADMIN, clickOrganizations. 2. To configure permissions for an organization, select it from theOrganizationspage and then clickPermissions. ImportantIf you access theMemberstab to set these permissions, you are setting the permissions on an individual user level, not the organization level. 3. On thePermissionstab, clickPlatform, and then clickAdmin Controls. 4. UnderAdmin Controls, set either or both of the following settings: 5. ClickSave changes. ### Set custom model resource allocation For DataRobot MLOps users, you can determine the [resources allocated for each custom inference model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html#manage-custom) within an organization. Configuring these resources facilitates smooth deployment and minimizes potential environment errors in production. To manage these resources for an organization, navigate to the organization's Profile page and find Custom model resource allocation settings. Configure the fields: | Resource | Description | | --- | --- | | Desired memory | Determines the minimum reserved memory for the Docker container used by the custom inference model. | | Maximum memory | Determines the maximum amount of memory that may be allocated for a custom inference model. Note that if a model allocates more than the configured maximum memory value, it is evicted by the system. If this occurs during testing, the test is marked as a failure. If this occurs when the model is deployed, the model is automatically launched again by kubernetes. | | Maximum replicas | Sets the maximum number of replicas executed in parallel to balance workloads when a custom model is running. | When you have fully configured the resource settings, click Save. ### Set deployment limits For DataRobot MLOps users, you can determine the [maximum number of active deployments](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html#live-inventory-updates) users are allotted within an organization. To manage these resources for an organization: 1. Navigate to the organization's Profile page and locate Max deployments . 2. In the field, enter the number of active deployments allotted for users in the organization. This number cannot exceed the number listed underPrepaid deployments. ### Set compute instances You can adjust the maximum compute instances for an organization. If a limit is not specified, the maximum compute instances for an organization is 8. To manage compute resources: 1. Navigate to the organization's Profile page and locate Max compute for a serverless platform . 2. In the field, enter the number of resources you want to allocate for the selected organization. then, clickSave. ### Organization-wide consent for Microsoft OAuth This feature allows administrators to grant [tenant-wide consent](https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/configure-user-consent?pivots=portal#configure-user-consent-in-microsoft-entra-admin-center) for user permissions required by the DataRobot application in Microsoft Entra ID. In Azure, by default, user permissions are not granted to individual user resources, requiring personal user consent when authorizing a [Microsoft OAuth provider](https://learn.microsoft.com/en-us/entra/architecture/auth-oauth2). When enabled, users also no longer receive consent prompts. If the list of required permissions is changed by the DataRobot application, or the administrator account who granted consent is not active anymore, the administrator must grant consent again to provide the necessary permissions for the DataRobot application. > [!NOTE] Prerequisite > Before enabling this feature in DataRobot, in Microsoft Entra ID, a Cloud Application Administrator must [grant tenant-wide consent to the DataRobot application](https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/grant-admin-consent?pivots=portal). > > Managed AI Cloud (SaaS) administrators must contact DataRobot Support to enable this feature. To enable this feature in DataRobot: **Self-managed:** A system adminstrator must perform the following steps: Click the System admin icon in the management toolbar and select Organizations . On the Profile tab, scroll down to Grant organization-wide consent for global Microsoft OAuth providers . Select the checkbox to enable global authorization for all users in the organization. The resulting window describes next steps. After closing the window, click Save . Then, an organization administrator must perform the following steps: Open User settings > OAuth provider management . Click the Actions menu next to the OAuth provider you want to authorize and select Grant user consent on behalf of organization . A window appears describing action that must be taken in Azure Entra ID. Click Grant consent and then in Azure, an application administrator must enable consent on behalf of your organization for the DataRobot application. **SaaS:** > [!NOTE] Note > Before proceeding, contact DataRobot Support to enable this feature. An organization administrator must perform the following steps: Open User settings > OAuth provider management . Click the Actions menu next to the OAuth provider you want to authorize and select Grant consent on behalf of your organization . A window appears describing action that must be taken in Azure Entra ID. Click Grant consent and then in Azure, an application administrator must enable consent on behalf of your organization for the DataRobot application. --- # Manage user accounts URL: https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html > How to create LDAP or local authentication user accounts, set permissions, and manage membership of groups and organizations. # Manage user accounts The DataRobot deployment provides support for local authentication users. These are user accounts you create manually (through APP ADMIN > Manage Users). DataRobot provides restrictions for login and password settings. The login credentials for these locally authenticated users are stored as fully qualified domain names. **SaaS:** > [!NOTE] Availability information > Required permission: Org Admin **Self-Managed: LDAP:** > [!NOTE] Availability information > Required permission: Can manage users The DataRobot deployment provides support for three types of user accounts: User Account Type Description Internal This is the default DataRobot administrator account, which authenticates using admin@datarobot.com. This account has full administrator access to the deployed cluster. You cannot revoke administrator privileges; the only change you can make to this account is password updates. Local authentication These are user accounts you create manually (through APP ADMIN > Manage Users ). DataRobot provides restrictions for login and password settings. The login credentials for these locally authenticated users are stored as fully qualified domain names. LDAP authentication configuration These user accounts are created through an authentication integration with a defined LDAP directory service; you do not use the DataRobot UI to create these user accounts. LDAP accounts When LDAP users sign into DataRobot for the first time, their user profiles are created and saved in DataRobot but their passwords are not. Usernames for these LDAP-authenticated users are simple usernames and not fully qualified domain names. Passwords cannot be changed. Not that if a user is removed from the LDAP Directory server or group, they are not able to access DataRobot. The user account, however, remains intact. > [!NOTE] Note > [Local authentication](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#create-user-accounts) is not supported when LDAP is enabled (i.e., no "mixed mode"). See the instructions below for creating local authentication accounts. ## Create user accounts Both system and organization administrators can create and add new users to the DataRobot installation (org admins can only create users in their own organization). First, create a user account for yourself, so that you can access DataRobot as a user in addition to using the default administrator account. Use the following steps to create your own user account, and then repeat them for each additional user. See details on the [different types of administrators](https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html#administrator-role) for more information. > [!NOTE] Note > These steps show access to the flow using the NextGen UI, which has a separate menu for system admin features. In Classic UI, navigation can be performed by accessing the user profile icon at the top right of the screen. Once launched, the configuration happens within the Classic UI admin section; use the dropdown to return to NextGen. > > [https://docs.datarobot.com/en/docs/images/switch-ui-version.png](https://docs.datarobot.com/en/docs/images/switch-ui-version.png) **SaaS:** Click the admin icon, located to the left of your user icon, and select Users Click Create a user at the top of the displayed page. In the displayed modal, enter the username (i.e., email address), first name, and password for the new user (other account settings are optional at this point). Refer to Change your password for password requirements. Click Create user . If successful, you see the message "Account created successfully". Click View user profile to view and configure user settings for this user (such as organization membership or permissions), or click Close . **Self-Managed:** Expand the profile icon located in the upper right and click APP ADMIN > Users from the dropdown menu. Click Create a user at the top of the displayed page. In the displayed dialog, enter the username (i.e., email address), first name, and password for the new user (other account settings are optional at this point). If shown, selecting Require Clickthrough Agreement may be necessary for your cluster deployment. Refer to Change your password for password requirements. Click Create user . If successful, you see the message "Account created successfully" and the username for the new account. Click View user profile to view and configure user settings for this user, or click Close . The new user will now be listed in the Users table. You can open the User Profile to see some important information including the user's application-assigned ID. ## Invite users Both [system and organization administrators](https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html#administrator-role) can invite new users to join a DataRobot organization or installation (org admins can only create users in their own organization). You can invite up to twenty individuals at a time to join DataRobot as a regular or [non-builder user](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#non-builder-user-seats). 1. Click the admin icon, located to the left of your user icon, and selectUsers 2. ClickInvite usersat the top of the displayed page. 3. Enter a username (email address or username) for each individual you want to invite, and then, underOrganization, begin typing the name of the organization. Select an organization from the list that appears below. 4. You can either send invitations to join the DataRobot organization as a regular or non-builder user. Non-builder users can only interact with applications and do not count towards the organizations maximum active user limit. **User:** To send invitations to join as regular users, click Invite. **Non-builder user:** To send invitations to join as non-builder users, select the box next to Non-builder account. Under Available applications, enter the applications you want to share with the users, and then click Invite. By default, non-builder users can access all applications associated with the organization they're invited to join. [https://docs.datarobot.com/en/docs/images/invite-nonbuilder.png](https://docs.datarobot.com/en/docs/images/invite-nonbuilder.png) ## Set admin permissions for users **SaaS:** As an admin, you can set organization admin permissions for other DataRobot users within the application, including your personal user account. These permissions allow the recipient to enable or disable features per user, as needed. Visit the Settings page to see a list of available features; hover over a feature name for a brief description. Below are the steps to enable administrator access for any user. This user will have administrator access to all DataRobot functionality configured for the application. > [!NOTE] Note > Consider and control how you provide admin settings to non-administrator users. One way to do this is to add settings only on an as-needed basis and then remove those settings when related tasks are completed. From the Users page, locate the user and select to open the user's profile page. Click Membership to display the organization and groups that the user is a member of. Under the Organization header, check the box in the Org Admin column to enable organization admin permissions for the user. [https://docs.datarobot.com/en/docs/images/org-admin-2.png](https://docs.datarobot.com/en/docs/images/org-admin-2.png) This user can now modify settings for other users. At any point, if you want to disable these permissions for the user, uncheck the box; the user will no longer have administrator capabilities. **Self-Managed:** As an admin, you can set admin permissions for other DataRobot users within the application, including your personal user account. These permissions allow the recipient to enable or disable features per user, as needed. Visit the Settings page to see a list of available features; hover over a feature name for a brief description. Below are the steps to enable administrator access for any user. This user will have administrator access to all DataRobot functionality configured for the application. > [!NOTE] Note > Consider and control how you provide admin settings to non-administrator users. One way to do this is to add settings only on an as-needed basis and then remove those settings when related tasks are completed. From the Users page, locate the user and select to open the user's profile page. On User Profile , click Change Permissions to display the User Permissions > Manage Settings page for the user Select the Admin setting “Can manage users” and click Save . This user now can modify settings for other users. At any point, if you want to disable the “Can manage users” setting for this user, uncheck the box and click Save; the user will no longer have administrator capabilities. ## Assign seat licenses to users > [!NOTE] Premium > The ability to assign seat licenses is a premium feature. This capability is only available to: > > Multi-tenant SaaS organizations that have a seat license allocation. > Single-tenant SaaS/self-managed installations that have a license key with a cluster-level seat allocation, allowing system adminstrators for that installation to allocate seats to the organizations. > > Contact your DataRobot representative for enablement information. Users must be assigned seat licenses to grant them access to specific functionality. There are three main types of users that manage or use seat licenses: - System administrator (sys admin) : Provides overall license management, allocation, and assignment of seats across organizations within the installation. - Organization administrator (org admin) : Manages users, assignments, and assignment of seats within their organization. - User : Consumes assigned seats and accesses licensed features. > [!WARNING] Warning > A user account can only be assigned one role at a time, so the sys admin and org admin must be two separate accounts. ### Allocate seats to the organization (sys admin) > [!NOTE] Note > These steps show access to the flow using the NextGen UI, which has a separate menu for system admin features. In the Classic UI, navigate to this section through the user profile icon in the top-right corner of the screen. In both cases, the configuration process happens in the Classic UI admin section. Use the UI version dropdown to return to NextGen. > > [https://docs.datarobot.com/en/docs/images/switch-ui-version.png](https://docs.datarobot.com/en/docs/images/switch-ui-version.png) Before users can be assigned seats, the system administrator must allocate seats to the organization. For DataRobot-managed deployments, this is done by the DataRobot team. For self-managed deployments, this is done by the system administrator. > [!WARNING] Required feature flags > Seat licenses require the following feature flags to be enabled: `Enable MLOps agentic workflow`, `Enable MMM agentic workflow tracing`. 1. Click the admin icon, located to the left of your user icon, and selectLicense. 2. In theAllocate Seatstable, clickAllocate seats. 3. In theAllocate seatsmodal, select an organization and enter the number of seats to allocate. 4. ClickAllocate. #### Allocate additional seats As a system administrator, you can allocate additional seats to an organization that already has seat licenses allocated. 1. Navigate to the profile page of the organization that needs additional seats. 2. Under Organization Details , locate the Seat Licenses section. 3. Find the seat license you want to modify and enter the new seat count in the input field. 4. ClickSave. The organization now has additional seats allocated. ### Assign seats to users (system or org admin) Once the organization has been allocated seats, the licenses can be applied to individual user accounts by the system or organization administrator. To assign the licenses currently allocated for an organization: 1. Click the admin icon, located to the left of your user icon, and selectSeat License. 2. Click the seat license you want to assign. NoteThe licenses available will vary based on the type(s) of license allocated to the organization. 3. Click theAssign seatsbutton. WarningThe following step is only available for system administrators. Organization administrators can skip to step 6. 4. (System admin only) In theOrganizationfield, specify the organization that contains the users to be assigned seats. 5. In theUsernamesfield, specify the users to be assigned seats. 6. When you're done selecting users, clickAssign. Seats can be removed from users by clicking the Actions menu for the user in the Assigned Seats section and selecting Revoke seat. > [!NOTE] Note > The Organization field shown in the image above is only available for system administrators. ### Non-builder user seats Non-builder users can only interact with published applications—they do not have access to DataRobot functionality or public APIs. Using an application, non-builders can run predictions, add prompts, start chats, view/delete, and upload data. By default, non-builder users can access all applications associated with the organization they're invited to join as well as basic user settings. Non-builder users do not count towards an organization's maximum active user allocation. If you're a system admin, you must first [allocate seats to your organization](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#allocate-seats-to-the-organization-sys-admin). Then you can assign the license to specific users by: - Assigning seats to newly-created or existing users. - Inviting non-builder users. > [!NOTE] Non-builder seats vs Active user seats > If your organization has reached its maximum user allocation, you cannot create a non-builder user via the user creation flow—you must either assign a Non-builder seat to an existing user or invite a new user. ## Self-Managed AI Platform admins > [!NOTE] Note > The following feature is only available on the Self-Managed AI Platform. ### Additional permissions options To set permissions and supported features for users, repeat the previous process selecting the desired permissions from those listed in the user's User Permissions > Manage Settings page. See the settings and features description for information on the available admin settings and optional features. For each user you can also: - Set their maximum personal worker allocation . - Set their RAM usage limit. - Set their file upload size limit. - Set the rate at which the Deployment page refreshes (three second minimum). - Assign them to an organization (you must create the organization first). ## RBAC for users Role-based access (RBAC) controls access to the DataRobot application by assigning users roles with designated privileges. The assigned role controls both what the user sees when using the application and which objects they have access to. RBAC is additive, so a user's permissions will be the sum of all permissions set at the user and group level. To assign a user role: 1. From theUserspage, locate and select the user to open their profile page. 2. Click thePermissionstab to view a list of settings and permissions. 3. In the left panel, clickPlatform > Admin Controls. Then, open theUser roledropdown menu and select the appropriate role(s) for the user. 4. When you're done, clickSave changes. Review the [role and access definitions](https://docs.datarobot.com/en/docs/reference/misc-ref/rbac-ref.html) to understand the permissions enabled for each role. > [!TIP] Tip > Avoid granting access to specific features by assigning roles at the user-level because this makes managing permissions more difficult—causing you to have to modify several users, rather than a few groups, as well as increasing the possibility of having users with non-standardized levels of access. Make sure access to features required to complete work are defined at the group- or org-level, and that the user is a member. > [!NOTE] Note > Note that RBAC overrides [sharing-based role permissions](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html). For example, consider a user is assigned the Viewer role via RBAC, which only has Read access to objects. If this user has a project shared with them that grants Owner permissions (which offers Read and Write access), the Viewer role takes priority and denies the user Write access. ## Manage execution environment limits The execution environment limit allows you to control how many custom model environments a user can add to the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html). In addition, the execution environment version limit allows you to control how many versions a user can add to each of those environments. These limits can be: 1. Directly applied to the user: Set in a user's permissions. Overrides the limits set in the group and organization permissions. 2. Inherited from a user group: Set in the permissions of the group a user belongs to. Overrides the limits set in organization permissions. 3. Inherited from an organization: Set in the permissions of the organization a user belongs to. If the environment or environment version limits are defined for an organization or a group, the users within that organization or group inherit the defined limits. However, a more specific definition of those limits at a lower level takes precedence. For example, an organization may have the environment limits set to 5, a group to 4, and the user to 3; in this scenario, the final limit for the individual user is 3. For more information on adding custom model execution environments, see the [Custom model environments documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html). To manage the execution environment limits in the platform settings: 1. Click your profile avatar (or the default avatar) in the upper-right corner of DataRobot, and then, underAPP ADMIN, clickUsers. 2. From theUserspage, locate and select the user to open their profile page. 3. Click thePermissionstab to view a list of settings and permissions. 4. On thePermissionstab, clickPlatform, and then clickAdmin Controls. 5. UnderAdmin Controls, set either or both of the following settings: 6. ClickSave changes. ## Change your password You can change passwords for your own user accounts. If you need help generating a new password for the default administrator, contact Customer Support. To change your password: 1. Expand the profile icon located in the upper right and clickUser Settings. 2. On the User settings page, clickAuthentication. 3. On the displayed page, enter your current password and then the new password twice (to create and confirm). ClickChange Password. DataRobot enforces the following password requirements: - Only printable ASCII characters - Minimum one capital letter - Minimum one number - Minimum 8 characters - Maximum 512 characters - Username and password cannot be the same ## Manage groups and organization membership SaaS admins can manage groups; Self-Managed admins can manage groups and organizations. > [!NOTE] Note > Users can have membership in up to 50 groups. **Saas:** Configuring groups helps you to manage users across the DataRobot platform. For more information, see: Group overview Creating groups Once created, you can add one or more users as members from the group creation page. To add users individually, follow the steps below. > [!NOTE] Note > Users can see which groups they belong to from the Membership page, but they do not have permissions to make changes to those memberships. Browse to the Users page, select the user, and in User Profile click Membership. The User Membership page shows the currently configured groups for this user. [https://docs.datarobot.com/en/docs/images/org-admin-1.png](https://docs.datarobot.com/en/docs/images/org-admin-1.png) Work with the page as follows: Field Description 1 Add user to groups Opens a dialog where you can enter the name(s) of groups to add the user to. Note that if a group is assigned to an organization, you can only add members from that organization. 2 Group name Opens the group configuration to allow editing of the name and description. **Self-Managed:** Configuring groups and organizations helps you to manage users and resources across the DataRobot platform. For more information, see: Group overview Creating groups Organization overview Creating organizations Once created, you can add one or more users as members from the group and organization creation pages. To add users individually, follow the steps below. > [!NOTE] Note > Users can see which organization and groups they belong to from the Membership page, but they do not have permissions to make changes to those memberships. Browse to the Users page, select the user, and in User Profile click Membership. The User Membership page shows the currently configured organization and any groups for this user. [https://docs.datarobot.com/en/docs/images/admin-usermembership-unconfigured.png](https://docs.datarobot.com/en/docs/images/admin-usermembership-unconfigured.png) Work with the page as follows: Field Description 1 Organization Enter the name for the organization. Each user can be a member of only one organization. 2 Go to org profile Click to view the organization's profile. 3 Add user to groups Opens a dialog where you can enter the name(s) of groups to add the user to. If the user is a member of an organization, only groups also part of the same organization, or part of no organization, are available for selection. Users can have membership in up to 50 groups. 4 Opens the group configuration to allow editing of the name and description. When you next look at this user's profile, you see the organization for the user. [https://docs.datarobot.com/en/docs/images/admin-org-orgprofile.png](https://docs.datarobot.com/en/docs/images/admin-org-orgprofile.png) ## Deactivate user accounts You cannot delete a user account from DataRobot—this ensures that your company's data is not lost, regardless of employee movement. However, the admin can block a user's access to DataRobot while ensuring the data and projects they worked on remain intact. From APP ADMIN > Manage Users, locate the user: - To deactivate, click the padlock icon next to their name, changing it to locked . - To restore access, click the padlock icon to open . You can also change user account access from Users > User Profile by clicking Enable User or Disable User. ## View latest user activity From APP ADMIN > User Activity Monitor, you can quickly access the [user activity monitor](https://docs.datarobot.com/en/docs/platform/admin/monitoring/main-uam-overview.html#view-activity-and-events), which shows all app activities recorded for this user. --- # Tenant isolation and collaboration URL: https://docs.datarobot.com/en/docs/platform/admin/manage-entities/tenant-isolation-and-collaboration.html > How to collaborate within a tenant. # Tenant isolation and collaboration To reinforce data security, further protect your sensitive information, and streamline the sharing process, resources can now only be shared between users from the same organization. This page describes how you can add and manage collaborators within your organization. > [!NOTE] Note > October 2023 > > These changes will only impact Multi-Tenant SaaS users. This functionality will remain in place for Self-Managed OnPrem, Self-Managed VPC, and Single-Tenant SaaS users. If and when that changes, you will be notified and this page will be updated. ## Add a user to collaborate Before you can collaborate with other users, they must first be added as a user to your organization.This includes DataRobot employees. There are three ways to add users to your organization: ### 1: Create a user If you are a current DataRobot user, an admin can [create a new user account](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#create-user-accounts) within their organization. An organization admin must invite a DataRobot employee to their organization using the following format: `first_name.last_name+customer@datarobot.com`. This creates a DataRobot user that is unique to the organization. Make sure you select the box next to "Send invitation email" before clicking Create user. ### 2: Invite a user If you are in a trial or POV phase of DataRobot, open Workbench. Then, click the Onboarding Checklist icon at the top of the page and click Invite Colleagues. Alternatively, click the Account Settings icon and select Users. In the resulting dialogue, enter the email of the user you want to invite using the format shown in the image below. Then, click Send invite. ### 3: Submit a support ticket To [submit a ticket to DataRobot support](mailto:support@datarobot.com), click the?icon at the top of the page and choose Email Support. Indicate that you want to collaborate with a specific individual, and a support representative will add them as a user to your organization. ## Manage collaborators Once the user is added to your organization, you can manage their permissions via sharing and [role-based access control (RBAC)](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#rbac-for-users). Only share access to resources that collaborators require to do their job, also known as the least permissive approach. You can [deactivate/reactivate a collaborator](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#deactivate-user-accounts) by clicking the User Settings icon at the top of the page and selecting Users. Note that deactivating a collaborator does not delete the user, but it does prevent them from logging into your organization or accessing any resources. ## FAQ --- # Monitoring URL: https://docs.datarobot.com/en/docs/platform/admin/monitoring/index.html > Monitor usage across your organization, including user activity and various resources. # Monitoring To monitor usage across your organization, DataRobot provides the following tools: | Topic | Description | | --- | --- | | User Activity Monitor | Monitor user and system activity. | | Resource Monitor | (Self-managed only) Monitor allocation and availability of EDA and modeling workers (compute resources), in a SAAS or standalone cluster environment. | | Monitor usage | Monitor resource usage information, including GPUs and CPUs, for your organization, with the Usage Report (self-managed) and Usage Explorer (SaaS). | --- # User Activity Monitor URL: https://docs.datarobot.com/en/docs/platform/admin/monitoring/main-uam-overview.html > The User Activity Monitor (UAM) provides a means for accessing and analyzing various usage data and prediction statistics as online reports or via export. # User Activity Monitor DataRobot continuously collects user and system data and makes it available to you through the User Activity Monitor (UAM). The tool provides a means for accessing and analyzing various usage data and prediction statistics. You can view reports online or export the data as CSV files. System information about the deployed cluster is available as well. You can use this information to understand how DataRobot is being used, troubleshoot model or prediction errors, monitor user activities, and more. User activity data is available for review online and can be downloaded for offline access. Filters enable you to access and limit data records to specified time frames, users, and projects. The information provided in these reports proves invaluable to DataRobot Support when understanding your deployed system and resolving issues. You can also exclude sensitive [identifying information](https://docs.datarobot.com/en/docs/reference/misc-ref/uam-ref.html#hide-sensitive-information) from generated reports. ## User activity types Three types of user activity reports are available: Admin, App, and Prediction. See the [report reference](https://docs.datarobot.com/en/docs/reference/misc-ref/uam-ref.html) for fields and accompanying descriptions. **SaaS:** Report Type Description Admin Usage Provides a report of all administrator-initiated audited events. Information provided by this report can identify who modified an organization or an account and what changes were made. App Usage Provides information related to model development. This report can show models by user and identify the most commonly created types of models and projects, average time spent fitting each type of model, etc. Prediction Usage Provides a report with data around predictions and deployments. Information provided by this report can show how many models a user deployed, how predictions are being used, error codes generated for prediction requests, which model types generate the most predictions, and more. **Self-Managed:** Self-Managed AI Platform admins, you can also download a report with system information (no online preview available). Report Type Description Admin Usage Provides a report of all administrator-initiated audited events. Information provided by this report can identify who modified an organization or an account and what changes were made. App Usage Provides information related to model development. This report can show models by user and identify the most commonly created types of models and projects, average time spent fitting each type of model, etc. Prediction Usage Provides a report with data around predictions and deployments. Information provided by this report can show how many models a user deployed, how predictions are being used, error codes generated for prediction requests, which model types generate the most predictions, and more. System Information Provides a report with system information for the deployed cluster, such as installation type, operating system version, Python version, etc. Only accessible by download. ## Access the UAM Some ways to access the User Activity Monitor: - From the profile icon located in the upper right, clickUser Activity Monitorto access all data in all reports for any users. - From theUser Profilepage for a specific user, clickView Activityto view that user's events. - Once on an individual's activity page, remove the value in theUser IDfield and clickSearchto once again view all users. - From theUser Profileyou can quickly view thelast five app eventsfor the user. ## View activity and events When you open the User Activity Monitor, you see the 50 most recently recorded application events. (By default, the User Activity Monitor displays data in descending timestamp order). You can change the displayed report and view different report data. | Component | Description of use | | --- | --- | | Report view (1) | Selects the report view: App usage, Admin usage, or Prediction usage. (The System information report is not available to preview.) | | Timestamp—UTC (2) | Sorts all records, in all pages, in ascending or descending timestamp order. | | << or >> (3) | Pages forward or backward through the records. | | Export CSV (4) | Exports and downloads the user events and system data to CSV files. | ### Search report preview Use values in the Search dialog to filter the data returned for the selected online report view. Specify the filter values and click Search to apply the filter(s); the User Activity Monitor preview updates to show all records matching the filters. > [!NOTE] Note > Search values apply to the online report preview only. Click Reset to remove filters. > [!NOTE] Note > The [System Information report](https://docs.datarobot.com/en/docs/reference/misc-ref/uam-ref.html#system-information-report) is not available for online report preview. | Component | Description of use | | --- | --- | | Username or User ID (1) | Filters by username or user ID. For App Usage and Prediction Usage reports, you can additionally apply Project ID. If needed, you can copy the username, UID, or project ID values from the report preview and paste in the related search field. | | Project ID (2) | Filters by project; for App Usage and Prediction Usage reports, you can additionally apply Username or User ID. The Project ID field is disabled for the Admin Usage report. | | Include identifying fields (3) | Uncheck to hide "sensitive" information (columns display in the report without values). | | Time Range (4) | Limits the number of records shown in the preview (previous year of records by default). You can select one of the predefined time ranges or specify a custom time range using the date picker*. (See restrictions when previewing the Prediction Usage activity report.) | | Search or Reset | Generates the online preview of the report using the selected search filters or clears filters to view all available records. | * To specify a custom range, use the calendar controls to select the start and/or end dates for the records. All time values use the UTC standard. ### Prediction Usage preview DataRobot can display up to 24 hours of data for the Prediction Usage online report preview. When applying a time range search filter for this report, select Last day or Custom range (and select a specific day). Note that this applies only when previewing the Prediction Usage activity report online; when downloading Prediction Usage activity report data, you can select any of the Time Range values provided in the Export reports as CSV dialog. ## Download activity data Clicking Export CSV to generate and download selected usage activity reports prompts you to filter which records to include when downloading reports. You can apply the same filters you created for online report preview or set new filters. **SaaS:** [https://docs.datarobot.com/en/docs/images/uam-exportcsv-saas.png](https://docs.datarobot.com/en/docs/images/uam-exportcsv-saas.png) **Self-Managed:** [https://docs.datarobot.com/en/docs/images/useractivitymonitor-exportcsv.png](https://docs.datarobot.com/en/docs/images/useractivitymonitor-exportcsv.png) The Export reports as CSV dialog prompts you to configure and download reports of user activity data. | Component | Description | | --- | --- | | Data Selection* (1) | Select Queried Data to apply the same time range filters you set when previewing reports or All Data to ignore any time range filters set for online preview. If you select All Data, you can then set new time range filters for downloading data. | | Reports to include (2) | Select one or more reports to download. | | Time Range* (3) | If you select All Data, you can set this filter: Specify the time range of records you want to include in the reports. The end time for each of these ranges is the current day. For example, Last day creates reports with data recorded starting 24 hours ago and ending at the current time. The default selection downloads records generated over the past year. | | Include identifying fields (4) | If checked, downloaded reports include identifying information. | | Download CSV (5) | Click Download CSV to save the selected report(s) to your local machine. The file is named with a randomly generated hash value and the current date (year-month-day).The filename for each report includes the DataRobot Platform version number, current date, and type of data for the report. | * Fields do not apply to the System Information report. When DataRobot indicates the selected report(s) are ready, click the link at the top of the application window to download a ZIP archive of the usage report CSV files to your local machine. > [!NOTE] Note > The time to create and download reports depends on the time range for the data and number of reports. DataRobot creates the reports for export in the background and notifies you when the reports are ready for download. --- # Resource Monitor URL: https://docs.datarobot.com/en/docs/platform/admin/monitoring/resource-monitor.html > How to monitor allocation and availability of EDA and modeling workers (compute resources), in a SAAS or standalone cluster environment. # Resource Monitor > [!NOTE] Availability information > This feature is only available on the Self-Managed AI Platform. > > Required permission: Enable Resource Monitor The Resource Monitor provides visibility into DataRobot's active modeling and EDA workers across the installation, providing general information about the current state of the application and specific information about the status of components. With this service in place, you can easily track user activity on each project and know when DataRobot has available resources. Specifically, the Resource Monitor provides the number of currently running jobs, number of allowed concurrent jobs, and number of jobs waiting for a worker. Additionally, the tool provides information on which specific users are employing system resources. Additionally, monitoring resources over time helps to determine whether your organization has the correct number of workers to meet usage needs. ## Worker terminology The Resource Monitor reports on the system’s queue and workers, both overall and for individual users. See the [overview](https://docs.datarobot.com/en/docs/platform/admin/admin-overview.html#what-are-workers) for a discussion of workers; the following table describes the terminology used to describe DataRobot activity: | Term | Description | | --- | --- | | Jobs | The tasks DataRobot completes with workers, such as model building and certain calculations. Statistics are based on jobs. | | Modeling worker | Jobs displayed in the Worker Queue, such as model building or Feature Impact calculations. | | EDA worker | Jobs displayed in the Worker Queue, such as calculating EDA1 and other related tasks. | | In Progress (or running) | A job that has received a worker and is currently executing on the worker. | | Waiting for resources (or waiting for worker) | A job that is ready to execute, but has not yet received a worker. These jobs appear as “Waiting for worker” in the Processing section of the Worker Queue. | | Queued | A job that is in the queue but is not ready to execute. These jobs appear in the Queue section of the Worker Queue. | | Active User | A user that is the owner of at least one in-progress or waiting job. | ### Modeling resources The Modeling tab of the Resource Monitor reports the number of configured workers being used for modeling and related tasks. The following table describes the fields displayed on this tab. Use the [refresh option](https://docs.datarobot.com/en/docs/platform/admin/monitoring/resource-monitor.html#refresh-the-resource-monitor) to redisplay results. | Field | Description | | --- | --- | | Total | Total number of workers allocated to the installation. | | In use | Number of workers currently in use across the system. | | Not in use | Number of workers not currently in use and therefore available. | | Jobs waiting | Number of queued jobs. | | Users waiting | Number of individual users with at least one job waiting. | | Demand | Number of jobs trying to run vs. the number allowed by the organization's license (capacity). To calculate, add all In progress and Waiting jobs for all active users and divide that value by the total number of available workers. | | Active now | Number of users with a job running or waiting that requires a modeling worker. This value matches the total of the In use and Jobs waiting fields. | | Worker usage by user | Graphic and breakdown of per-active-user usage. (See Interpreting "Worker" usage by user.) | ### EDA resources The EDA tab of the Resource Monitor reports the number of configured workers being used for EDA1 and related tasks. The following table describes the fields displayed on this tab. Use the [refresh option](https://docs.datarobot.com/en/docs/platform/admin/monitoring/resource-monitor.html#refresh-the-resource-monitor) to redisplay results. | Field | Description | | --- | --- | | Jobs initialized | Number of queued jobs but are not yet scheduled to run. | | Jobs waiting | Number of jobs approved for execution and waiting for resources. | | Jobs in progress | Number of running jobs. | | Available workers | Number of workers not currently in use and therefore available. | | Total workers | Total number of workers allocated to the installation. | | Demand | Number of jobs trying to run vs. the number allowed by the organization's license (capacity). | | Users with initialized jobs | Number of individual users with at least one job initialized. | | Users with waiting jobs | Number of individual users with at least one job waiting. | | Users with running jobs | Number of individual users with at least one job running. | | Active now | Number of users with a job running or waiting that requires a modeling worker. This value matches the total of the In use and Jobs waiting fields. | | Worker usage by user | Graphic and breakdown of per-active-user usage. (See Interpreting "Worker" usage by user.) | ## Interpret "Worker usage by user" The Users and current activity section reports on users that are actively using the system. An active user is one that has at least one running or waiting job. The bar graph is a quick visual indicator of usage, with active users listed below it. For each user name, DataRobot displays: - In progress : the number of in-progress jobs. The number of jobs a user can have in progress is determined by both system availability and the individual allowance. - Waiting : the number of jobs awaiting an available worker. - Max workers : an individual's worker allowance. This value corresponds to the maximum setting of the Workers value at the top of the Worker Queue. ## Refresh the Resource Monitor DataRobot refreshes the Resource Monitor display at the interval selected from the dropdown. Expand the dropdown to change the interval or click the Refresh Now button to immediately update the page. --- # Monitor usage URL: https://docs.datarobot.com/en/docs/platform/admin/monitoring/usage-explorer.html > Describes the Usage report and Usage Explorer, which allow admins to monitor resource usage information, including GPUs and CPUs. # Monitor usage The [Usage report](https://docs.datarobot.com/en/docs/platform/admin/monitoring/usage-explorer.html#usage-report) (self-managed only) and [Usage Explorer](https://docs.datarobot.com/en/docs/platform/admin/monitoring/usage-explorer.html#usage-explorer) provide administrators visibility into an organization's resource usage. Monitoring these resources over time can help indicate whether the organization has the correct number of resources to meet usage needs. ## Usage report > [!NOTE] Self-managed only > The Usage Report is only available for system admins on self-managed installations. The Usage report provides system admins visibility into the organization's graphics processing unit (GPU) and central processing unit (CPU) usage across the platform. The following services are tracked in the Usage Report: - Modeling - Inference - NVIDIA AI Enterprise - Vector Database Creation - GenAI Playground - Custom Models - Moderations - Data Management - Predictions To access the Usage Report, open Admin settings > Usage Report. From here, you can view resource consumption for a given date range, as well as export the report as a `.csv` file. | | Element | Description | | --- | --- | --- | | (1) | Date range selector | Use the dropdown to display usage information for a specific time period. | | (2) | CPU Usage | View CPU Usage information, including max aggregate usage for all groups and a usage over time chart. | | (3) | GPU Usage | View GPU Usage information, including max aggregate usage for all groups and a usage over time chart. | | (4) | Download | Download the report as a .csv file. | ### CPU usage The CPU Usage section provides an overview of central processing unit (CPU) usage within the organization. This allows for easy monitoring of core usage, potentially helping to identify areas for optimization or budgetary concerns. | Element | Description | | --- | --- | | Max aggregate usage for all groups | Total core usage for all users across the platform. | | CPU utilization chart | A chart that displays total usage amount over the specified time period, as well as your organization's current license limit for core usage. Hover to over a point on the chart to view additional information. | ### GPU usage The GPU Usage section reports data on GPU usage, providing general usage information. GenAI features, for example, rely on GPU hardware for a range of workloads related to training, hosting, and running inference on LLMs. | Element | Description | | --- | --- | | Max aggregate usage for all groups | Total core usage for all users across the platform. | | GPU utilization chart | A chart that displays total usage amount over the specified time period, as well as your organization's current license limit for core usage. Hover over a point on the chart to view additional information. | ## Tenant Usage Explorer The Tenant Usage Explorer provides admins visibility into and organization's graphics processing unit (GPU), central processing unit (CPU), and large language model (LLM) API usage across the platform. Monitoring these resources over time can help indicate whether the organization has the correct number of resources to meet usage needs. For more information, see [Usage Explorer](https://docs.datarobot.com/en/docs/platform/acct-settings/acct-usage-explore.html) in the documentation for Account Settings. > [!NOTE] Note > The services displayed in the Tenant Usage Explorer may vary depending on the type of usage being viewed. To access the Tenant Usage Explorer, open Admin settings > Tenant Usage Explorer. From here, you can view resource consumption by user or service for a given date range, as well as export the report as a `.csv` file. | | Element | Description | | --- | --- | --- | | (1) | By services/By organizations/By users | View usage broken down by specific services, organizations (self-managed only), or users. | | (2) | Date range selector | Use the two fields to display usage information for a specific date range. | | (3) | Export | Download the report as a .csv file. | | (4) | Usage options | Select the usage information to view—GPU, LLM, or CPU. | Toggling the page to display By users allows you to view usage details for individual user accounts within the organization. Toggling the page to display By organizations allows you to view usage details for individual organizations within the cluster. > [!NOTE] Self-managed-only > The By organizations tab is only available for system admins on self-managed installations. --- # Network policies URL: https://docs.datarobot.com/en/docs/platform/admin/network-policy.html > Configure network policy controls to limit the public resources that users in your organization can access from within DataRobot. # Manage network policies > [!NOTE] Availability information > The ability to manage network policies for an organization is only available for Managed AI Cloud platform users. By default, some DataRobot capabilities, including Notebooks, have full public internet access from within the cluster DataRobot is deployed on. To limit the public resources users can access within DataRobot, you can set network access controls for all users within your organization. > [!NOTE] Running notebooks > Enabling/disabling the network policy control does not affect notebooks that are already running. To apply new policy controls to running notebooks, the notebook must be restarted. To manage network policies: 1. Open yourUser Settingsand under Admin, selectAccess Policies. 2. Enable the toggle to the left ofEnable network policy control. When this toggle is enabled, by default, users cannot access public resources from within DataRobot. 3. Enter the domains and IP addresses, one per line, that you willallowusers to access from within DataRobot. If the domain or IP address is incorrect, an error message appears when you click outside the text field. 4. To save your entries, clickUpdate allow listat the bottom of the page. --- # Reference URL: https://docs.datarobot.com/en/docs/platform/admin/reference/index.html # Reference The information in this section provides reference information for managing your DataRobot account. | Topic | Description | | --- | --- | | Role-based access control (RBAC) | View descriptions of each role in RBAC. | | Custom RBAC roles | System and organization administrators can create roles and define access at a more granular level, and assign them to users and groups. | | User Activity Monitor reference | View descriptions of the fields included in each activity report. | | Resource Monitor | Monitor allocation and availability of EDA and modeling workers (compute resources), in a SAAS or standalone cluster environment. | --- # Single sign-on URL: https://docs.datarobot.com/en/docs/platform/admin/sso-ref.html > How to configure DataRobot and an external Identity Provider (IdP) for user authentication via single sign-on (SSO). DataRobot supports the SAML 2.0 protocol. # SAML single sign-on DataRobot allows you to use external services (Identity Providers, known as IdPs) for user authentication through single sign-on (SSO) technology. SSO support in DataRobot is based on the SAML 2.0 protocol. To use SAML SSO in DataRobot, you must make changes to both the [IdP](https://docs.datarobot.com/en/docs/platform/admin/sso-ref.html#identity-provider-idp-configuration) and [service provider](https://docs.datarobot.com/en/docs/platform/admin/sso-ref.html#datarobot-configuration) (DataRobot) configurations. **SaaS:** > [!NOTE] Availability information > Availability of single sign-on (SSO) is dependent on your DataRobot package. If it is not enabled for your organization, contact your DataRobot representative. > > Required permission: Enable SAML SSO The basic workflow for configuring SAML SSO is as follows: Review and complete the prerequisites . Configure SSO in your identity provider and identify DataRobot as the service provider. Configure SSO in DataRobot: Choose a configuration option to set up the Entity ID and IdP metadata. Use mapping to define how attributes, groups, and roles are synchronized between DataRobot and the IdP. Set SSO requirements , including making SSO optional or required for all users. **Self-Managed:** The Self-Managed AI Platform provides enhanced SAML SSO : > [!NOTE] Availability information > Required permission: Enable Enhanced Global SAML SSO configuration management > > Required cluster configuration: > > ENABLE_SAML_SSO > = > False > ENABLE_ENHANCED_SAML_SSO > = > False > ENABLE_ENHANCED_GLOBAL_SAML_SSO > = > True The basic workflow for configuring SAML SSO is as follows: Review and complete the prerequisites . Configure SSO in your identity provider and identify DataRobot as the service provider. Configure SSO in DataRobot: Choose a configuration option to set up the Entity ID and IdP metadata. Use mapping to define how attributes, groups, and roles are synchronized between DataRobot and the IdP. Modify security parameters to increase or decrease the SAML protocol security strength. Set up advanced options . Set SSO requirements , including making SSO optional or required for all users. ## Authentication in DataRobot DataRobot ensures authentication and security using a variety of techniques. When using the database connectivity feature, for example, you are prompted for your database username and password credentials each time you perform an operation that accesses your organization's data sources. The password is encrypted before passing through DataRobot components and is only decrypted when DataRobot establishes a connection to the database. DataRobot does not store the username or password in any format. **SaaS:** To log into the application website, users can choose to authenticate by providing a username and password or they can delegate authentication to Google. The authentication process is handled over HTTPS using TLS 1.2 to the application server. When the user sets their password, it is securely stored in the database pictured above. Before the password is stored, it is hashed and uniquely salted using SHA-512 and further protected with Password-Based Key Derivation Function 2 (BPKDF2). The original password is discarded and never permanently stored. **Self-Managed:** To log into the application website, users can choose to authenticate by providing a username and password or delegate authentication to LDAP. SSO using SAML 2.0 is also supported. The authentication process is handled over HTTPS using TLS 1.2 to the application server. When the user sets their password, it is securely stored in the database pictured above. Before the password is stored, it is hashed and uniquely salted using SHA-512 and further protected with Password-Based Key Derivation Function 2 (BPKDF2). The original password is discarded and never permanently stored. DataRobot also provides enhancements to password-based authentication, including support for multifactor authentication (MFA) with software tokens generated using Time-based One-time Password (TOTP). All API communications use TLS 1.2 to protect the confidentiality of authentication materials. When interacting with the DataRobot API, authentication is performed using a bearer token contained in the HTTP Authorization header. Use the same authentication method when interacting with prediction servers via the API. While it is possible to authenticate using a username + API token (basic authentication) or just via an API token, these authentication methods are deprecated and not recommended. An additional HTTP Header named datarobot-key is also required to further limit access to the prediction servers. ## Prerequisites Make sure the following prerequisites are met before starting the SAML SSO configuration process: **SaaS:** SAML for SSO is enabled. The organization has at least one Org/System admin; the admin will be responsible for SAML SSO configuration once it is enabled. Contact your DataRobot representative to enable SAML SSO, and if necessary, to set up the first Org/System admin user (that user can then assign additional users to the Org/System admin role). **Self-Managed:** SAML for SSO is enabled. The organization has at least one System admin; the System admin will be responsible for SAML SSO configuration once it is enabled. Contact your DataRobot representative to enable SAML SSO, and if necessary, to set up the first System admin user (that user can then assign additional users to the System admin role). The following describes configuration necessary to enable SAML SSO for use with DataRobot. Admins can access the information required for setup on DataRobobt's SAML SSO configuration page, which can be accessed from Settings > Manage SSO: ## Identity Provider (IdP) configuration > [!NOTE] Note > Because configurations differ among IdPs, refer to your provider's documentation for related instructions. > DataRobot does not provide a file containing the metadata required for IdP configuration; you must manually configure this information. When configuring the IdP, you must create a new SAML application with your IdP and identify DataRobot as the service provider (SP) by providing SP sign-in and sign-out URLs. **SaaS:** To retrieve this information in DataRobot, go to Settings > Manage SSO and locate Service provider details, which lists URL details. [https://docs.datarobot.com/en/docs/images/sso-5.png](https://docs.datarobot.com/en/docs/images/sso-5.png) Use the root URL with the organization name appended. The organization name is the name assigned to your business by DataRobot, entered in lowercase with no spaces. URL type Root URL Description Okta example SP initiated login URL app.datarobot.com/sso/sign-in/< org_name > The endpoint URL that the IdP receives service provider requests from (where the requests originate). Recipient URL IdP initiated login URL app.datarobot.com/sso/signed-in/< org_name > The endpoint URL that receives the SAML sign-in request from the IdP. Single sign-on URL IdP initiated logout URL app.datarobot.com/sso/sign-out/< org_name > (Optional) The endpoint URL that receives the SAML sign-out request from the IdP. N/A **Self-Managed:** To retrieve this information in DataRobot, go to `/admin/organizations/manage//sso` and locate Service provider details, which lists URL details. [https://docs.datarobot.com/en/docs/images/self-provider-details.png](https://docs.datarobot.com/en/docs/images/self-provider-details.png) URL type Root URL Description Okta example SP initiated login URL https://app.datarobot.com/sso/sign-in/ The endpoint URL that the IdP receives service provider requests from (where the requests originate). Recipient URL IdP initiated login URL https://app.datarobot.com/sso/signed-in/ The endpoint URL that receives the SAML sign-in request from the IdP. Single sign-on URL IdP initiated logout URL https://app.datarobot.com/sso/sign-out/ (Optional) The endpoint URL that receives the SAML sign-out request from the IdP. N/A The tabs below provide example instructions for finishing IdP configuration in Okta, PingOne, and Azure Active Directory. > [!WARNING] Third-party application screenshots > The following images were accurate at the time they were taken, however, they may not reflect the current user interface of the third-party application. **Okta:** Make sure that the following required configuration is complete on the IdP side—this example uses Okta. If you don't already have an Okta developer account, sign up for free using your GitHub username or email. In Okta, click Applications > Applications in the left-hand navigation. Click Create App Integration , select SAML 2.0 , and click Next . On the General Settings tab, enter a name for the application and click Next . On the Configure SAML tab, fill in the following fields: Single sign-on URL Audience URI (SP Entity ID) Attribute Statement for username Note The Single sign-on URL has signed-in at the end. The attribute username must be set to user.email in order for SSO login to be successful with DataRobot. On the Feedback tab, select I’m a software vendor and click Finish . With your new application selected, click Applications > Assignments and assign People or Groups to your app. On the Sign On tab, locate the SAML Signing Certificates section . Next to SHA-2 , select Actions > View IdP metadata and copy the IdP metadata link address—you will need this to configure SSO in DataRobot. **PingOne:** Make sure that the following required configuration is complete on the IdP side—this example uses PingOne. Click the Add a SAML app tile. Click the + icon the right of the Applications . Name the application, select SAML Application , and click Save . Select Manually Enter ; then copy and paste the following: Copy the IdP initiated login URL from DataRobot and paste it in the ACS URLs field. Copy the Entity ID from DataRobot and paste it in the Entity ID field. Make sure to remove the leading https and trailing path. This must be an exact match when configuring PingOne in DataRobot. Click Save . Open the Configuration tab and click the pencil icon. Make sure Sign Assertion & Response is selected. Scroll down to the Subject Named Format dropdown. Click the dropdown and select urn:oasis:names:tc:SAML:2.0:name-id:transient . Click Save . Use the toggle to turn on the Example Ping1 integration with DataRobot PingOne application. Save the IDP Metadata URL . You will need this to configure SSO in DataRobot. Map Attributes (optional) If you decide to map attributes , you must complete both of the steps below. Typically, DataRobot uses an email address to identify users, however PingOne uses their username. Do not skip this step because it maps email addresses to usernames. Click the Attribute Mappings tab and click the pencil icon. Next to saml_subject , change the PingOne Mapping to Email Address . Click Add , enter username under Attributes , and select Email address for the PingOne Mapping . Click Save . **Azure AD:** Make sure that the following required configuration is complete on the IdP side—this example uses Azure Active Directory. Sign into Azure as a cloud application admin. Navigate to Azure Active Directory > Enterprise applications and click + Create your own application . Name the application, select Integrate any other application you don't find in the gallery (Non-gallery) , and click Add . On the Overview page, select Set up single sign on and select SAML as the single sign-on method. Click the pencil icon to the right of Basic SAML Configuration . Populate the following fields: For Identifier (Entity ID) , enter an arbitrary string. For Reply URL (Assertion Consumer Service URL) , copy the IdP initiated login URL from DataRobot and paste it in the field ( /sso/signed-in/ ). Click Save . Click the pencil icon to the right of User Attributes & Claims . Delete all default additional claims and add the following claims: username as Name (see the Mapping section for more information). Attribute as Source . user.userprincipalname as Source attribute . If the form prevents you from saving without a Namespace value, provide any string, click Save , and then edit it again to remove the Namespace value. After saving, the new claim appears in the table. Before proceeding, ensure the Signing Option is set to sign both the SAML response and assertion in the Azure Active Directory settings. Otherwise, you can encounter a SAML SSO authentication for a missing signature. In Azure's Set up Single Sign-On with SAML preview page, find the SAML Signing Certificate heading and click the Edit pencil icon to navigate to the SAML Signing Certificate page. In the Signing Option dropdown list, select Sign SAML response and assertion . Click Save to apply the new SAML signing certificate settings. To make sure the test account has access to the application, open Users and groups in the left-hand navigation and click Add user . Copy the Identifier (Entity ID) and App Federation Metadata URL —you will need these values to configure SSO in DataRobot. After configuring SSO in the IdP, you can now [configure SSO in DataRobot](https://docs.datarobot.com/en/docs/platform/admin/sso-ref.html#datarobot-configuration). ## DataRobot configuration Now, configure the IdP in DataRobot. > [!WARNING] Saving progress > At any point in your configuration, and at configuration completion, click Save. The button is only active when all required fields are complete. > > [https://docs.datarobot.com/en/docs/images/sso-10.png](https://docs.datarobot.com/en/docs/images/sso-10.png) After configuring the IdP, you must configure SSO in DataRobot by setting up an Entity ID and IdP Metadata for your organization. There are two Entity IDs—one from the service provider (DataRobot) and one from the IdP: - The Entity ID entered in the DataRobot SSO configuration is a unique string that serves as the service provider entity ID. This is what you enter when configuring service provider metadata for the DataRobot-specific SAML application on the IdP side. - If manually configuring IdP metadata for the DataRobot-side configuration, the Issuer field is the unique identifier of the Identity Provider (IdP), found on the IdP DataRobot-specific SAML application configuration. Normally, it is a URL of an identity provider. When logged in as an admin, open Settings > Manage SSO and click the Configure using dropdown to see the three options available to configure the IdP parameters (described in below). ### Testing process To conduct any troubleshooting or testing, click Test. This button is only available when changes are saved, and it will trigger the automated test of your changes to detect issues for SAML SSO connection. When you conduct a test, you are redirected to the IdP login page. Input the user credentials and perform the login process on the IdP side. When testing completes, you will receive either a success message or a warning message about what went wrong, with fields highlighting the incorrect values. ### Metadata URL Complete the following fields: **SaaS:** [https://docs.datarobot.com/en/docs/images/sso-2.png](https://docs.datarobot.com/en/docs/images/sso-2.png) Field Description Name Specify a meaningful name for the IdP configuration (for example, the organization name). This name will be used in the service provider details URL fields. Enter the name in lowercase, with no spaces. The value entered in this field updates the values provided in the Service provider details section. Entity ID An arbitrary, unique-per-organization string (for example, myorg_saml) that serves as the service provider Entity ID. Enter this value to establish a common identifier between DataRobot (SP) app and IdP SAML application. Metadata URL A URL provided by the IdP that points to an XML document with integration-specific information. The endpoint must be accessible to the DataRobot application. (For a local file, use the Metadata file option.) Verify IdP Metadata HTTPS Certificate If toggled on, the host certificate is validated for a given metadata URL. **Self-Managed:** [https://docs.datarobot.com/en/docs/images/sso-enhanced-2.png](https://docs.datarobot.com/en/docs/images/sso-enhanced-2.png) Field Description Entity ID An arbitrary, unique identifier of the SAML application created at Idp side, see SP Entity ID / Issuer / Audience above (some IdPs use Client ID term). Enter this value to establish a common identifier between the DataRobot (SP) app and the IdP SAML application. Metadata URL A URL provided by the IdP that points to an XML document with integration-specific information. The endpoint must be accessible to the DataRobot application. (For a local file, use the Metadata file option. In case the URLs triggers file downloading then also use the Metadata file option for downloaded metadata XML file.) Verify IdP Metadata HTTPS Certificate If toggled on, the host certificate is validated for a given metadata URL. ### Metadata file Select Metadata file to provide IdP metadata as XML content. **SaaS:** [https://docs.datarobot.com/en/docs/images/sso-3.png](https://docs.datarobot.com/en/docs/images/sso-3.png) Complete the following fields: Field Description Name Specify a meaningful name for the IdP configuration (for example, the organization name). This name will be used in the service provider details URL fields. Enter the name in lowercase, with no spaces. The value entered in this field updates the values provided in the Service provider details section. Entity ID An arbitrary, unique-per-organization string (for example, myorg_saml) that serves as the service provider Entity ID. Enter this value to set a matching between DataRobot (SP) app and IdP SAML application. Metadata file An XML document, provided by the IdP, with integration-specific information. Use this if the IdP did not provide a metadata URL. **Self-Managed:** [https://docs.datarobot.com/en/docs/images/sso-enhanced-3.png](https://docs.datarobot.com/en/docs/images/sso-enhanced-3.png) Complete the following fields: Field Description Entity ID An arbitrary, unique identifier provided by the identity provider. Enter this value to establish a common identifier between the DataRobot (SP) app and the IdP SAML application. Metadata file An XML document, provided by the IdP, with integration-specific information. Use this if the IdP did not provide a metadata URL. ### Manual settings Select Manual settings if IdP metadata is not available. **SaaS:** [https://docs.datarobot.com/en/docs/images/sso-4.png](https://docs.datarobot.com/en/docs/images/sso-4.png) Complete the following fields: Field Description Name Specify a meaningful name for the IdP configuration (for example, the organization name). This name will be used in the service provider details URL fields. Enter the name in lowercase, with no spaces. The value entered in this field updates the values provided in the Service provider details section. Entity ID An arbitrary, unique-per-organization string (for example, myorg_saml) that serves as the service provider Entity ID. Enter this value when manually configuring the IdP application for DataRobot. Identity Provider Single Sign-On URL The URL that DataRobot contacts to initiate login authentication for the user. This is obtained from the SAML application you created for DataRobot in the IdP configuration. Identity Provider Single Sign-Out URL (optional) The URL that DataRobot directs the user’s browser to after logout. This is obtained from the SAML application you created for DataRobot in the IdP configuration. If left blank, DataRobot redirects to the root DataRobot site. Issuer The IdP-provided Entity ID obtained from the SAML application you created for DataRobot in the IdP configuration. Note : Although the DataRobot UI shows this as optional, it is not and must be set correctly. Certificate The X.509 certificate, pasted or uploaded. Certificate is used for validating IdP signatures. This is obtained from the SAML application you created for DataRobot in the IdP configuration. **Self-Managed:** [https://docs.datarobot.com/en/docs/images/sso-enhanced-4.png](https://docs.datarobot.com/en/docs/images/sso-enhanced-4.png) Complete the following fields: Field Description Entity ID An arbitrary, unique identifier provided by the identity provider. Enter this value to set a matching between DataRobot (SP) app and IdP SAML application. Identity Provider Single Sign-On URL The URL that DataRobot contacts to initiate login authentication for the user. This is obtained from the SAML application you created for DataRobot in the IdP configuration. Identity Provider Single Sign-Out URL (optional) The URL that DataRobot directs the user’s browser to after logout. This is obtained from the SAML application you created for DataRobot in the IdP configuration. If left blank, DataRobot redirects to the root DataRobot site. Issuer The IdP-provided Entity ID obtained from the SAML application created for DataRobot in the IdP configuration. Note : Although the DataRobot UI shows this as optional, it is not and must be set correctly. Certificate The X.509 certificate, pasted or uploaded. Certificate is used for validating IdP signatures. This is obtained from the SAML application you created for DataRobot in the IdP configuration. [https://docs.datarobot.com/en/docs/images/sso-enhanced-5.png](https://docs.datarobot.com/en/docs/images/sso-enhanced-5.png) Auto-generate Users automatically adds new users to DataRobot upon initial sign on. ### Mapping All three configuration options allow you to define how attributes, groups, and roles are synchronized between DataRobot and the IdP. Mappings allow you to automatically provision users on DataRobot based on their settings in the IdP configuration. It also prevents individuals from teams not configured for DataRobot from entering the system. Adding mappings both adds more restrictions on who can access DataRobot and controls which assets users can access. Without mappings, anyone in your organization who was manually added to the DataRobot system by an administrator can access the platform. You can set up the following mappings: **Attributes:** Attribute mapping allows you to map DataRobot attributes (data about the user) to the fields of the SAML response. In other words, because DataRobot and the IdP may use different names, this section allows you to configure the name of the field in the SAML response where DataRobot updates the user's display name, username, first name, last name, and email. The Username field is required and critical for the configuration to allow DataRobot application to extract the correct username of the logged-in user. [https://docs.datarobot.com/en/docs/images/sso-6.png](https://docs.datarobot.com/en/docs/images/sso-6.png) **Organizations:** Organization mapping is available only on the Self-Managed AI Platform. It allows you to create multiple mappings between IdP organizations and existing DataRobot organizations. [https://docs.datarobot.com/en/docs/images/org-map-2.png](https://docs.datarobot.com/en/docs/images/org-map-2.png) Mappings can only be one-to-one. The list of mapped organizations is synced from IdP after every successful login. Unmapped organizations raise an exception for DataRobot, so you should be sure to map all possible organizations. [https://docs.datarobot.com/en/docs/images/org-map-1.png](https://docs.datarobot.com/en/docs/images/org-map-1.png) Field Description Organization attribute The name in the SAML response that identifies the string as an organization name. Organization The name of an existing DataRobot organization to which the user will be moved. Identity provider organization The name of the IdP organization to which the user belongs. If the organization is not provided from IdP, or the user doesn’t belong any organizations, the user is moved to the default organization after their first login. To define the default organization that a user is assigned to, navigate to System Configuration > Organization Management [https://docs.datarobot.com/en/docs/images/org-map-3.png](https://docs.datarobot.com/en/docs/images/org-map-3.png) **Groups:** Groups mapping allows you to create an unlimited number of mappings between IdP groups and existing [DataRobot groups](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-groups.html). Mappings can only be one-to-one. The list of mapped groups is synced from IdP after every successful login process. The unmapped groups can be used as unique entities in DataRobot, and they aren't changed after the login process. You can use [custom RBAC roles](https://docs.datarobot.com/en/docs/reference/misc-ref/custom-roles.html) to map one default role to each entry in the IdP group in DataRobot by creating a new role and assigning it to the desired group. [https://docs.datarobot.com/en/docs/images/sso-7.png](https://docs.datarobot.com/en/docs/images/sso-7.png) To configure, set: Field Description Group attribute The name, in the SAML response, that identifies the string as a group name. DataRobot group The name of an existing DataRobot group to which the user will be assigned. Identity provider group The name of the IdP group to which the user belongs. Self-managed users can also define separate group mappings for organizations with organization mapping. Otherwise, the mapping is tied to the default organization. [https://docs.datarobot.com/en/docs/images/org-map-4.png](https://docs.datarobot.com/en/docs/images/org-map-4.png) Field Description Group attribute The name in the SAML response that identifies the string as a group name. Group delimiter The delimiter between group names in the string. DataRobot group The name of an existing DataRobot group to which the user will be assigned. Identity provider group The name of the IdP group to which the user belongs. DataRobot organization The name of an existing DataRobot organization to which the user will be moved. Identity provider organization The name of the IdP organization to which the user belongs. **Roles:** Roles mapping allows you to create an unlimited number of one-to-one mappings between IdP and [DataRobot roles](https://docs.datarobot.com/en/docs/reference/misc-ref/rbac-ref.html). The list of mapped roles is synced from the IdP after every successful login process. Example: Role mapping behaviour In the IdP, you assign the role of Sample Role 1 to J_Doe. When you set up SSO in DataRobot, you map Sample Role 1 to the DataRobot role Data Scientist . Then, in DataRobot, you manually change J_Doe's role to App Admin . The next time J_Doe logs in to DataRobot, their role will change back to Data Scientist based on the specified role mapping. The unmapped groups can be used as unique entities in DataRobot, and they aren't changed after the login process. [https://docs.datarobot.com/en/docs/images/sso-8.png](https://docs.datarobot.com/en/docs/images/sso-8.png) To configure, set: Field Description Role attribute The name, in the SAML response, that identifies the string as a named user role. DataRobot role The name of the DataRobot role to assign to the user. Identity provider role The name of the role in the IdP configuration that is assigned to the user. Self-managed users can also define separate role mappings for organizations with organization mapping. Otherwise, the mapping is tied to the default organization. [https://docs.datarobot.com/en/docs/images/org-map-4.png](https://docs.datarobot.com/en/docs/images/org-map-4.png) Field Description Role attribute The name, in the SAML response, that identifies the string as a role name. Role delimiter The delimiter between role names in the string. DataRobot role The name of an existing DataRobot role to which the user will be assigned. Identity provider role The name of the IdP role to which the user belongs. DataRobot organization The name of an existing DataRobot organization to which the user will be moved. Identity provider organization The name of the IdP organization to which the user belongs. > [!NOTE] Self-Managed AI Platform admins > See the [Security Parameters](https://docs.datarobot.com/en/docs/platform/admin/sso-ref.html#security-parameters) section to modify the relationship between DataRobot and the IdP to either increase or decrease the SAML protocol security strength. ### Set SSO requirements After all fields are validated and connection is successful, choose whether to make SSO optional or required using the toggles. **SaaS:** Toggle Description Enable single sign-on Makes SSO optional for users. If enabled, users have the option to sign into DataRobot using SSO or another authentication method (i.e., username/password). Enforce single sign-on Makes SSO required for users. If enabled, users in the organization must sign in using SSO. > [!NOTE] Note > Do not enforce sign on until you have completed configuration and testing. Once SSO is configured, provide users with the `/sign-in/` to sign into DataRobot. In the following example, `https://app.eu.datarobot.com/sign-in/datarobot-saml-sso` is the SAML SSO link, `datarobot-saml-sso` is the custom name of the SAML SSO configuration, and `/sign-in/datarobot-saml-sso` is the URL to the organization SAML SSO login form that is provided to users. This must be used instead of the base login form. Managed AI Platform users cannot access SSO via the login screen at `app.datarobot.com`. [https://docs.datarobot.com/en/docs/images/sso-saas-1.png](https://docs.datarobot.com/en/docs/images/sso-saas-1.png) After clicking the SSO button in DataRobot, users are redirected to the IdP's authentication page and then redirected back to DataRobot after successful sign on. **Self-Managed:** Toggle Description Enable single sign-on Makes SSO optional for users. If enabled, users have the option to sign into DataRobot using SSO or another authentication method (i.e., username/password). Enforce single sign-on Makes SSO required for users. If enabled, users in the organization must sign in using SSO. > [!NOTE] Note > Do not enforce sign on until you have completed configuration and testing. After clicking the SSO button in DataRobot, users are redirected to the IdP's authentication page and then redirected back to DataRobot after successful sign on ## Self-Managed AI Platform admins The following is available only on the Self-Managed AI Platform. ### Security Parameters Security Parameters modify the relationship between DataRobot and the IdP to either increase or decrease the SAML protocol security strength. Use the following options to modify security strength: | Field | Description | | --- | --- | | Allow unsolicited | When SSO is initiated in DataRobot (SP-initiated request), DataRobot sends an auth request with a unique ID to the IdP. The Idp then sends a response back using the same unique ID. Enabling this parameter means the ID in the request and response do not need to match (e.g., in case of IdP-initiated authentication). | | Auth requests signed | DataRobot signs Authentication Requests before being sent to the IdP to make it possible to validate there was no third-party involvement. In Advanced Options > Client Config, configure a private key before enabling this parameter. | | Want assertions signed | DataRobot recommends keeping this parameter enabled as it makes the DataRobot application require the IdP to send signed assertions. Admins can disable signed assertions for testing and/or debugging. | | Want response signed | DataRobot recommends keeping this parameter enabled as it makes the DataRobot app require the IdP to send signed SAML responses. Admins can disable signed assertions for testing and/or debugging. | | Logout requests signed | DataRobot signs logout requests before sending them to the IdP to make it possible to validate there was no third-party involvement. Configure a private key before enabling this parameter. | See also the section on setting a [private key](https://docs.datarobot.com/en/docs/platform/admin/sso-ref.html#advanced-options) in Advanced Options > Client Config, which is required for the options `Auth requests signed` and `Logout requests signed`. ### Advanced Options You can configure the following advanced options: **Session & Binding:** Session & Binding controls how DataRobot and the IdP communicate—SAML requirements vary by IdP. [https://docs.datarobot.com/en/docs/images/sso-enhanced-10.png](https://docs.datarobot.com/en/docs/images/sso-enhanced-10.png) To configure, set: Field Description User Session Length (sec) Session cookie expiration time. The default length is one week. Reducing this number increases a rate of authentication requests to the IdP. SP Initiated Method The HTTP method used to start SAML authentication negotiation. IdP Initiated Method The HTTP method used to move user to DataRobot after successful authentication. **Client Config:** Client Config allows users to set private keys and certificates. This setting is exclusive to Self-Managed users. [https://docs.datarobot.com/en/docs/images/sso-enhanced-11.png](https://docs.datarobot.com/en/docs/images/sso-enhanced-11.png) To configure, set: Field Description Digest Algorithm A message digest algorithm used for calculating hash values. Signature Algorithm An algorithm used for producing signatures. SAML Config A JSON file that fine-tunes the SAML client configuration (for example, setting a private key). A private key must be set before DataRobot can sign SAML authentication requests (a requirement for the `Auth requests signed` and `Logout requests signed` options). To set a private key: Describe key pair to allow DataRobot to decipher IdP SAML responses. This is required in case the IdP cyphers its responses. The following JSON can also be provided to upload secrets as content: ``` { "key_file_value": "-----BEGIN RSA PRIVATE KEY-----\n...\n-----END RSA PRIVATE KEY-----", "cert_file_value": "-----BEGIN CERTIFICATE-----\n...\n-----END CERTIFICATE-----" } ``` Where there is an ellipsis in the example, insert the private key (in PEM format) as a single line. The same is valid for certificate file (use `cert_file_value` field in that case). Note that Okta requires an extra parameter ( `id_attr_name_crypto`) when the key pair is described (only required in the DataRobot v10.2 or older). The key pair can also be described by its content rather than its file paths. See the private key example above. --- # Feature settings URL: https://docs.datarobot.com/en/docs/platform/admin/user-settings.html > With the proper access and permissions, you can view and manage feature settings and permissions for your account and for other users. # Manage feature settings With the proper access and permissions, you can view and manage feature settings for your account and for other users. > [!NOTE] Availability information > The ability to manage feature settings is off for most users by default; however, Org Admin s may have access to a limited selection of feature settings as defined by your DataRobot configuration. Contact your DataRobot representative or administrator for information on enabling features. > > Required permission: Can manage users ## Manage your feature settings To manage feature settings for your account, click your profile avatar (or the default avatar [https://docs.datarobot.com/en/docs/images/icon-nextgen-settings.png](https://docs.datarobot.com/en/docs/images/icon-nextgen-settings.png)) in the upper-right corner of DataRobot and click Feature access. On the Feature access page, you can enable or disable features for a user and see which features are already enabled. Some features may not be available for pre-existing projects, in which case you could rebuild the project, or some models, to apply the new feature. ## Manage feature settings for users To manage feature settings for other users, click your profile avatar (or the default avatar [https://docs.datarobot.com/en/docs/images/icon-nextgen-settings.png](https://docs.datarobot.com/en/docs/images/icon-nextgen-settings.png)) in the upper-right corner of DataRobot and then User settings. On the All Users page, you can click an individual user in the list and click the Permissions tab to enable or disable features for their account. Some features may not be available for pre-existing projects, in which case the user could rebuild the project, or some models, to apply the new feature. If they are unsure, suggest that they recreate the project. ## Settings page sections The User settings (or Permissions) page is divided into product sections (i.e., MLDev, MLOps) and then maturity levels for the features within each product (i.e.GA, Preview). To navigate to a product, click the associated tab. The Platform section also includes an Admin Controls tab (for system and organization admins only). On the Settings page, you can do the following: - Search for a feature or permission by clicking the search box (or press Ctrl / Cmd + F ), and typing the feature name or label. - Display a tooltip describing a feature by hovering over the feature name; contact DataRobot Support if you need more detail. - Enable a feature or permission by turning on the associated toggle. > [!WARNING] Warning > When you search for features or permissions, the list is filtered. Be sure to clear the search bar to view all of the features and permissions again. ## Premium and enabled features **SaaS:** On the Settings page, you can see which Premium products and Enabled features are included in your DataRobot license. Contact DataRobot support if you need more information on premium and enabled features. **Self-Managed:** The Premium products available in a deployed cluster depend on your organization's DataRobot contract. When premium products are defined in the cluster configuration file ( `config.yaml`), you can enable them for any user in that cluster. If you need to change the available premium products, contact customer support. The Enabled features in a deployed cluster are defined in the cluster configuration file ( `config.yaml`). These features are available to all users, are not configurable via the UI, and cannot be set for individual users. If you need to change the enabled features, contact Customer Support. ## Self-Managed AI Platform admins The following is available only on the Self-Managed AI Platform. ### Cluster-wide features On the Settings page, you can see which Premium products and Enabled features are defined in your cluster configuration. > [!NOTE] Note > For more information on changing the cluster configuration in the `config.yaml` file, see the DataRobot Installation and Configuration Guide. ### Grant permissions to users If you are a system administrator, you can grant the Can manage users permission to another user, allowing them to manage their feature settings and those of other users. > [!NOTE] Note > Consider and control how you provide these permissions to non-administrator users. One way to do this is to add permissions on an "as-needed" basis and remove those permissions after the user completes the related tasks. --- # Notification service URL: https://docs.datarobot.com/en/docs/platform/admin/webhooks/index.html > How to use the Notification service to integrate flexible, centralized notifications into your organization's processes as webhooks, emails, and Slack messages. # Notification service The Notification service allows you to integrate flexible and centralized notifications into your organization's processes around change and incident management.[Notification channels](https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-notify.html), available as webhooks, emails, and Slack messages, allow users in an organization to subscribe to certain DataRobot events. When a notification event is triggered, an HTTP POST payload is sent to the webhook's configured URL, or an email is sent. Various DataRobot events, such as sharing projects, deployment activity, or Autopilot completing, generate notifications. When you configure a notification channel, you can choose which events you want to receive notifications for. Each event relates to a unique action within DataRobot. Choose to opt in to all events for a configuration, or subscribe to specific events. > [!NOTE] Note > The [notification center](https://docs.datarobot.com/en/docs/platform/toolbar/user-notif-center.html), available to users from the management tools in the upper right, is based on this notification service but is determined by a default system policy and is not configurable. These sections describe: | Topic | Description | | --- | --- | | Notification channels and policies | Create notification channels and policies. | | Webhook event payloads | Read about event payload configurations available for DataRobot webhooks. | | Mute deployment notifications | Stop receiving notifications for deployment-specific events tied to a configured policy. | | Maintenance notifications | Configure notifications that communicate service interruption. | ## Feature considerations Consider the following when using the Notification service: - Webhook channels do not support the ability to change a webhook payload to send notifications with specifically formatted messages. - Notification channels do not support Adaptive Card format for webhook messages, which means Microsoft Teams integration isn't currently possible. --- # Maintenance notifications URL: https://docs.datarobot.com/en/docs/platform/admin/webhooks/maintenance-notes.html > Learn how you as an administrator can configure notifications that communicate service interruptions to users. # Maintenance notifications Administrators can configure notifications that communicate service interruption to users. Admins will create a banner to notify users when the system is planning maintenance or is currently impacted by an incident. The banner communicates the incident start time, scheduled end time (optional), and a link for more details (also optional). Users will see the banner if they are logged-in during the incident or during the configured notification window. ## Create a new notification To create a maintenance notification: Click on your user icon and navigate to the Maintenance Notifications dashboard. This page is also accessible from the app administrator page. Select Add notification. A dialog box prompts you to provide information about the new notification. | Field | Description | | --- | --- | | Event type | The type of event to notify users about. Select "Incident" or "Maintenance" from the dropdown. | | Event start | The time when the event starts. Use the calendar modal to indicate the date and time. | | Event end (optional) | The time when the event ends. Use the calendar modal to indicate the date and time. Display notifications on | | Read more" link (optional) | The URL users can select to learn more information about the maintenance event. Appears as a clickable "Read more" link at the end of the notification. | When you have fully configured the fields, click Add. Note that these settings can be configured at a later time after the notification is saved. Your notification is available in the Maintenance Notifications dashboard. ### Edit a notification When you have created a maintenance notifications, you can edit them from the dashboard. Select a notification to expand it and edit the fields. When you have finished editing, click Save. If you wish to abandon the changes, click Discard changes. Select Preview to view the notification banner that will display at the configured time. ### Notification actions Notifications have two actions available: preview and deletion. - Select the eye icon () to preview the notification banner. - Select the trash icon () to permanently delete a notification. --- # Webhook event payloads URL: https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-events.html > Admins can configure notification channels to subscribe to some or all DataRobot event notifications, delivered by webhooks. Includes code samples. # Webhook event payloads Events generate notifications delivered by webhooks. When you configure a [notification channel](https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-notify.html#create-a-channel), you can choose which events you want to receive notifications for. Each event relates to a unique action within DataRobot. Choose to opt into all events for a configuration, or subscribe to specific events that are useful for you. This page details the event payload configurations available for DataRobot webhooks. Each event category includes an example. Before proceeding, review the [considerations](https://docs.datarobot.com/en/docs/platform/admin/webhooks/index.html#feature-considerations). ### Project events There are 4 available project event types: | Action | Payload format | | --- | --- | | Project created | project.created | | Project deleted | project.deleted | | Project shared | project.shared | | Autopilot completed | autopilot.complete | #### Example: Project deleted event ``` ```json { "event": { "deleted_by": "123a456b7c8e9f", "deletion_time": 1581504952, "entity_id": "123a456b7c8e9f", "uid": "" }, "event_type": "project.deleted", "project": { "active": 1, "default_dataset_id": "123a456b7c8e9f", "original_name": "https://s3.amazonaws.com/datarobot_public_datasets/DR_Demo_Store_Sales_Forecast_Train.xlsx", "project_id": "", "project_name": "DR_Demo_Store_Sales_Forecast_Train.xlsx", "stage": "modeling:" }, "timestamp": 1581504953 } ``` ``` #### Example: Autocomplete finished event ``` ```json { "event": { "dataset_id": "123a456b7c8e9f", "entity_id": "123a456b7c8e9f", "uid": "" }, "event_type": "autopilot.complete", "project": { "active": 1, "default_dataset_id": "123a456b7c8e9f", "original_name": "advanced_options.csv", "project_id": "", "project_name": "test-tvh-no-holdout-f2c6607d-544d-4e94-a488-c282b6aaa192", "stage": "modeling:" }, "timestamp": 1581507975 } ``` ``` ### Mongo fields: project events The following tables details all possible fields that can be included in project event payloads. | Field in Mongo | Required | Description | | --- | --- | --- | | uid | | N/A | | created | | N/A | | active | ✔ | Indicates whether the project is active. | | default_dataset_id | ✔ | Indicates the origin of the dataset in the AI Catalog. | | holdout_unlocked | | N/A | | originalName | ✔ | Contains the name of the file when it was uploaded to DataRobot. | | project_name | ✔ | Identifies the project name. | | stage | ✔ | Indicates the stage the project was in when the action was taken. | | is_deleted | | N/A | | deletion_time | ✔ | Indicates the deletion time (useful for troubleshooting delayed notifications). | | deleted_by | ✔ | Indicates the user who deleted the project. | ### Dataset events There are 3 available dataset event types: | Action | Payload format | | --- | --- | | Dataset created | dataset.created | | Dataset deleted | dataset.deleted | | Dataset shared | dataset.shared | #### Example: Dataset shared event ``` ```json { "dataset": { "catalog_type": "non_materialized_dataset", "dataset_id": "123a456b7c8e9f", "latest_catalog_version_id": "123a456b7c8e9f", "original_name": "amazon_de_reviews_small_80.csv", "version": 1 }, "event": { "entity_id": "123a456b7c8e9f", "shared_uids": [ "", "", "" ], "uid": "" }, "event_type": "dataset.shared", "timestamp": 1581508736 } ``` ``` ### Mongo fields: dataset events The following tables details all possible fields that can be included in dataset event payloads. | Field in Mongo | Required | Description | | --- | --- | --- | | uid | | N/A | | created | | N/A | | latest_catalog_version_id | ✔ | Indicates the version of the dataset used. | | originalName | ✔ | Contains the name of the file when it was uploaded to DataRobot. | | last_modified | | N/A | | last_modified_uid | | N/A | | catalog_type | ✔ | Determines the project type based on AI Catalog information. | | version | ✔ | Indicates the version of the dataset used. | | is_deleted | | N/A | | deletion_time | ✔ | Indicates the deletion time (useful for troubleshooting delayed notifications). | | deleted_by | ✔ | Indicates the user who deleted the project. | ### Model deployment events There are 10 available deployment event types: | Action | Payload format | | --- | --- | | Model Deployment Shared | model_deployments.deployment_sharing | | Model Deployment Replaced | model_deployments.model_replacement | | Model Deployment Created | model_deployments.deployment_creation | | Model Deployment Deleted | model_deployments.deployment_deletion | | Deployment Service Health Change: Green to Yellow | model_deployments.service_health_yellow_from_green | | Deployment Service Health Change: Red | model_deployments.service_health_red | | Deployment Data Drift Change: Green to Yellow | model_deployments.data_drift_yellow_from_green | | Deployment Data Drift Change: Red | model_deployments.data_drift_red | | Deployment Accuracy Health Change: Green to Yellow | model_deployments.accuracy_yellow_from_green | | Deployment Accuracy Health Change: Red | model_deployments.accuracy_red | #### Example: Deployment creation event ``` ```json { "event": { "entity_id": "123a456b7c8e9f", "model_id": "123a456b7c8e9f", "performer_uid": "", "status": "active" }, "event_type": "model_deployments.deployment_creation", “deployment": { "deployment_id": "123a456b7c8e9f", "model_id": "123a456b7c8e9f", "model_package_id": "123a456b7c8e9f", "project_id": "", "status": "active", "type": "dedicated", "user_id": "" }, "timestamp": 1581505115 } ``` ``` ### Mongo fields: deployment events The following tables details all possible fields that can be included in deployment event payloads. | Field in mongo | Required | Description | | --- | --- | --- | | created_at | | N/A | | deployed | | N/A | | description | | N/A | | export_target | | N/A | | instance_id | | N/A | | label | | N/A | | model_id | ✔ | N/A | | organization_id | | N/A | | project_id | ✔ | N/A | | service_id | | N/A | | updated_at | | N/A | | user_id | ✔ | N/A | | deleted | | N/A | --- # Mute deployment notifications URL: https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-mute.html > Learn how to mute notifications for a specific deployment, to stop receiving notifications for events tied to a configured notification policy. # Mute deployment notifications You can mute notifications for a specific deployment, allowing you to stop receiving notifications for the deployment-specific events tied to a configured [notification policy](https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-notify.html#create-a-notification-policy). Deployments can create a lot of noise if they often change health status or encounter issues with data drift or anomalous predictions. This can result in a large number of notifications. To mute deployment notifications, navigate to Deployments, select a deployment, and go to the Settings > Notifications tab. This tab lists all of the notification policies applied to the deployment. Identify the notification policy you wish to mute, and toggle on in Mute for channel. Once the toggle is turned on, you no longer receive notifications applied by the selected policy. --- # Notification channels and policies URL: https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-notify.html > Admins with permission can create, edit, and delete notification channels, view logs, and create, edit, enable, disable, or delete notification policies. # Notification channels and policies Although notifications are visible to all users, their configuration is limited to system and organization admins with permission to manage policies. - System-level admins are exclusive to Self-Managed AI Platform users. - Organization admins are exclusive to managed AI Platform users. This page describes how to configure various webhook, email, and Slack elements. For notification channels, you can: - Create a notification channel. - Edit a notification channel. - View notification logs. - Delete a notification channel. For notification policies, you can: - Create a notification policy. - Edit a notification policy. - Enable or disable a notification policy. - Delete a notification policy. Before proceeding, review the [considerations](https://docs.datarobot.com/en/docs/platform/admin/webhooks/index.html#feature-considerations). ## Create a channel Notification channels are mechanisms for delivering notifications created by admins. DataRobot supports email, webhook, and Slack notifications. You may want to set up several channels for each type of notification; for example, a webhook with a URL for deployment-related events, and a webhook for all project-related events. To create a notification channel: Click on your user icon and navigate to the Notification Management page. Navigate to the Notification Channels tab and click Add a channel. Before proceeding, select the channel type: [webhook](https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-notify.html#add-a-webhook-channel), [email](https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-notify.html#add-an-email-channel), or [Slack](https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-notify.html#add-a-slack-channel). Then, fill out the corresponding fields. ### Add a webhook channel A dialog box prompts you to provide information about the new webhook channel. | Field | Description | | --- | --- | | Channel name | The name of the notification channel being added. | | Payload URL | The URL of the server that will receive the webhook POST requests. For example: http://localhost:3527/payload | | Content type | Method for serializing the payload. Webhooks can be sent as different content types: json delivers the JSON payload directly as the body of the POST request form sends the JSON payload as a form parameter called payload. | | Secret token | A hashed secret token used to secure the connection of the webhook. It ensures that POST requests sent to the payload URL are from DataRobot. | | Enable SSL verification | Toggle on for DataRobot to validate SSL certificates against a certificate authority. Toggle off to allow unvalidated (or “self-signed”) certificates. | Note that these settings can be configured at a later time after the channel is saved. Select Show advanced options to add a custom header. Custom headers allow you to describe the event type that triggered the webhook delivery, provide a way to identify the delivery, and more. Select Add header and provide a name and value for the header you want to add. For example, you can use a custom header to detail a specific dataset with loan information being shared. The header would be formatted as: `loan-dataset-shared: 1` When your fields are completed, click the Test connection link. DataRobot tests the webhook connection and allows you to view the results before saving the completed fields. For the connection test, the body of the POST serves as the event template merged with placeholder values; you are unable to modify this content. The webhook will not be saved until it is successfully called in the test. When you have completed the required fields and passed the connection test, click Add channel. Your channel is available for use in the Notification Channels tab. #### Secret tokens Secret tokens help verify that notification requests are authentic and coming from DataRobot. To use secret tokens for your notification channel: 1. Create a secret key that will be known only to DataRobot and the client-side (where you need to make sure notifications are coming from DataRobot). 2. Add the created token to your notification channel configuration in DataRobot. 3. Implement additional logic on the client-side to implement HMAC digest creation using the SHA-1 hash function and the digest function ( hexdigest() if you use python) that returns hexadecimal digits as a result. 4. When a notification comes in, the new algorithm takes the secret token and the content of the received message, and passes them to the HMAC digest method. 5. Compare the result of the digest method with the DR-Signature header that comes with the notification. It should be done with the recommended function of HMAC ( compare_digest() if you use Python) instead of standard boolean operators to reduce the vulnerability to timing attacks. If they match, that means the notification was sent by DataRobot. > [!NOTE] Example > If you use Python, you can use the `hmac` module and the following code example: > > ``` > import hmac > import hashlib > > hexdigest = hmac.new(string_to_bytes(secret_token), string_to_bytes(msg), hashlib.sha1).hexdigest() > result = hmac.compare_digest(hexdigest, dr_signature): > ``` > > You must convert `secret_token` and `msg` to bytes before using `hmac.new`. ### Add an email channel Select the Email tab and a dialog box prompts you to provide information about the new email channel. | Field | Description | | --- | --- | | Channel name | The name of the notification channel being added. | | Email address | The email address that receives notifications. Only one email address can be entered into this field. If you need to send notifications to multiple emails, you must set up separate notification channels for each address. | You must verify the recipient email address. Click Send verification code to email. After receiving the email with the verification code, enter it in the corresponding field and click Verify. When you have completed the required fields and verified the email address, click Add channel. Your channel is available for use in the Notification Channels tab. ### Add a Slack channel Select the Slack tab. A dialog box prompts you to provide information about the new Slack notification channel. | Field | Description | | --- | --- | | Channel name | The name of the notification channel being added. | | Slack Incoming Webhook URL | A URL generated by Slack, found on Slack's workspace settings page, that DataRobot uses to send notifications to a specific workspace. On Slack's workplace settings, indicate a Slack app and a Slack channel where DataRobot will send notifications. Reference the Slack documentation for more information. | When your fields are completed, click the Test connection link. DataRobot tests the Slack workspace connection and allows you to view the results before saving the completed fields. If configured successfully, DataRobot delivers a Slack notification ( `connection test`) to the channel you configured on the Slack workplace settings page.. The channel does not save until the connection test successfully passes. If the test fails, verify that the Webhook URL is correct and verify the Slack workspace settings. When the connection test notification is successful, click Add channel. Your channel is available for use in the Notification Channels tab. ### Add a Microsoft Teams channel Select the Microsoft Teams tab. A dialog box prompts you to provide information about the new Microsoft Teams notification channel. | Field | Description | | --- | --- | | Channel name | A display name for the notification channel being added. | | Microsoft Teams Incoming Webhook URL | A URL generated by Microsoft Teams, found on the Microsoft Teams channel settings page. DataRobot uses the URL to send notifications to a specific workspace. On the Microsoft Teams channel settings page, indicate an Incoming Webhook app and a Webhook name to where DataRobot will send notifications. Reference the Microsoft Teams documentation for more information. | When your fields are completed, click the Test connection link. DataRobot tests the Microsoft Teams workspace connection and allows you to view the results before saving the completed fields. If configured successfully, DataRobot delivers a notification ( `connection test`) to the channel you configured on the Microsoft Teams channel settings page. The channel does not save until the connection test successfully passes. If the test fails, verify that the Webhook URL is correct and verify the Teams channel settings. When the connection test notification is successful, click Add channel. Your channel is available for use in the Notification Channels tab. ### Edit a channel You can edit the fields for an existing channel by selecting it and navigating to the Configuration tab. Make the desired changes and either retest your connection (for webhook notifications) or verify the new email address (for email notifications). When your test passes, click Save Changes. ### View notification logs Notification logs allow you to view the status of any system using a specific webhook, the number of times the system transmitted an event, and whether the transmission was successful. Notification logs are essential for debugging webhooks. Note that notification logs are not available for email notification channels. The notification logs list the 25 most recent [trigger](https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-notify.html#choose-a-trigger) events with their send date and time. Logs also include a copy of the HTTP request, the response code, and time to delivery. You can view notification logs for a channel by selecting it and navigating to the Logs tab. This tab lists every policy using the channel (1), the last 25 notification deliveries (2), and the header and payload for requests (3) and responses (4). If you have a failed delivery, you can choose to redeliver that notification (5). ### Delete a channel If you no longer wish to use a notification channel, you can remove it from the dashboard. Locate the channel you want to remove and select the trash can icon [https://docs.datarobot.com/en/docs/images/icon-delete.png](https://docs.datarobot.com/en/docs/images/icon-delete.png) to delete it. ## Create a notification policy A notification policy is a group of one or more alert conditions. A policy has two settings that apply to all of its conditions—incident preference and notification channels. Order of activity is as follows: 1. Create a notification channel. 2. Create a notification policy. 3. Add policy conditions. Policies can be organized by: - Architecture: Organizational-based policy structure. For example, Production website, Staging website, Production databases. - Teams: Team-based policy structure. For example, Team: Data Scientists, Team: IT Ops, Team: Model Validators. - Individuals: Notification policies set up for specific individuals. This configuration is useful for when users want to personally track a particular resource or metric. To create a notification policy: Click on your user icon and navigate to the Notification Channels dashboard. This page is also accessible from the app administrator page. Select Add policy. A dialog box prompts you to provide information to configure the new policy. #### Choose a trigger Choose a single event or a group of events that will trigger a notification. - The Event group tab displays groups of events organized by type. Select a group if you want the notification policy to be triggered by any of the events that belong to it. For example, the event group "Dataset-related events" consists of dataset creation, dataset sharing, and dataset deletion events. - The Single event tab displays every individual event available as a trigger for the notification policy. You can only select one. Once you have selected the event group or single event you wish to use to configure the notification policy, click Next. #### Choose a channel Select the [notification channel](https://docs.datarobot.com/en/docs/platform/admin/webhooks/web-notify.html#add-a-webhook-channel) for the policy to use from the dropdown. All notification channels that have been created or shared with you are available for selection (1). You can use the search bar (2) to find specific channels. When you have selected the channel you want to use, click Next. #### Name and review a policy Review the selected trigger and channel for the policy you are creating. If you want to go back and edit either of the selections, click the edit icon ( [https://docs.datarobot.com/en/docs/images/icon-rename.png](https://docs.datarobot.com/en/docs/images/icon-rename.png)) next to each selection. Then provide a name for your new policy. When you are satisfied with your policy configuration, click Create policy. You can view all created policies from the Notification Policies tab on the Notification Management page. ### Edit a policy You can edit an existing policy to re-configure the trigger and channel it uses, or to rename it. Locate the policy in the Notification Policies tab, and select the edit icon ( [https://docs.datarobot.com/en/docs/images/icon-rename.png](https://docs.datarobot.com/en/docs/images/icon-rename.png)). You can make any desired edits from the dialog box that appears. ### Enable or disable a policy Policies can be enabled or disabled at any time, allowing you to prevent notification fatigue, or bring attention to additional events. Locate the policy in the Notification Policies tab. Select the play icon to enable a policy and the pause icon to disable it. ### Delete a policy If you no longer wish to use a policy, you can remove it from the dashboard. Locate the policy you want to remove and select the trash can icon [https://docs.datarobot.com/en/docs/images/icon-delete.png](https://docs.datarobot.com/en/docs/images/icon-delete.png) to delete it. --- # Account management URL: https://docs.datarobot.com/en/docs/platform/index.html > This section provides information for administrators managing their organization in DataRobot as well as individual users managing their DataRobot accounts. # Account management This section provides information on how various users can manage accounts within DataRobot, including how administrators can manage organizations as well as how individual users can manage their own DataRobot settings. - Management toolbar¶ Learn about the upper-right navigation elements in DataRobot. - Administrator's guide¶ Admins can learn how to manage their organization, including SSO setup, user and group management, and governance policy creation. - Accounts settings¶ Users can learn how to manage account information, including 2FA, connection credentials, and API keys and tools. --- # Management toolbar URL: https://docs.datarobot.com/en/docs/platform/toolbar/index.html > This section introduces the management toolbar and includes links to information on how you can manage account settings. # Management toolbar This section describes how to use the management toolbar—the navigation elements in the upper-right. | | Option | Description | | --- | --- | --- | | (1) | Walkthroughs | Provides access to walkthroughs and information about new features. | | (2) | Help resources | Allows you to access learning content, including the documentation, YouTube channel, and Community GitHub Repositories, as well as contact product support. | | (3) | Notifications | Opens a modal that lists notifications sent from the DataRobot platform. | | (4) | Shortcuts menu | Opens the shortcuts menu, which allows you to quickly navigate the NextGen platform and access various assets. | | (5) | Admin settings | Provides access to administrator settings and features to help manage your DataRobot account. | | (6) | Account settings | Provides access to profile information, two-factor authentication and other settings, stored credentials, and your membership assignments. | --- # Shortcuts menu URL: https://docs.datarobot.com/en/docs/platform/toolbar/shortcut-menu.html > Learn how to open the shortcuts menu so that you can quickly navigate across the NextGen experience, as well as search for and open various assets. # Shortcuts menu The shortcuts menu allows you to quickly navigate across the NextGen experience in DataRobot, as well as search for and open various assets—this includes both Use Case and standalone (those existing outside of a Use Case) assets. There are two ways to access the shortcuts menu from NextGen: - On your keyboard, press Cmd+K . - Click the Search icon in the upper toolbar. The following menu displays: | Option | Description | | --- | --- | | Assets | Click to view a list of your most recently modified Use Case and standalone assets.To search for a specific asset, you can filter your assets using the Asset type dropdown or the search bar at the top of the menu. | | Navigation | Click to display navigation options and corresponding keyboard shortcuts.To search for a specific shortcut, use the search bar at the top of the menu. Note that you can only execute navigation keyboard shortcuts when this menu is open. | > [!NOTE] Search behavior > The search bar at the top of the shortcut menu searches the actively selected option. To search across both options, make sure neither Assets nor Navigation are selected. --- # Notification center URL: https://docs.datarobot.com/en/docs/platform/toolbar/user-notif-center.html # Notification center The alert icon provides access to notifications sent from the DataRobot platform. A numeric indicator on top of the alert icon indicates that you have unread notifications. Click the icon to see a list of notifications. Note that once you click on the icon, the indicator disappears. To remove a notification, hover on it and click the delete icon. If you do not delete them, the notification center lists up to the last 100 events. Notifications expire and are removed after one month. Notifications are delivered for the following events. Click the notification to open the experiment, project, or model in the deployment area that is related to the event. | Event name | Description | | --- | --- | | Autopilot has finished | Reports that Autopilot—either Quick or full mode—has completed. | | Experiment shared / Project shared | Reports that an experiment or project has been shared with you. DataRobot also delivers an email notification with a link to the experiment or project. | | New comment | Alerts that a comment has been added to an experiment or project you own, and displays the comment. | | New mention | Reports that you have been mentioned in an experiment or project. | | Data drift detected | Indicates that a deployed model has experienced data drift with a status of failing (red). | | Deployment is unhealthy | Indicates that service health for a deployed model—its ability to respond to prediction requests quickly and reliably—has severely declined since the model was deployed. | | Deployed model accuracy decreased | Indicates that model accuracy for a deployed model has severely declined since the model was deployed. | --- # Console reference URL: https://docs.datarobot.com/en/docs/reference/console-ref/index.html > Provide .... # Console reference The following sections provide reference content that supports working with predictive and time-aware experiments: | Topic | Description | | --- | --- | | text | text | --- # Allowed IP addresses URL: https://docs.datarobot.com/en/docs/reference/data-ref/allowed-ips.html > View a list of allowed source IP addresses in DataRobot. # Allowed source IP addresses Any connection initiated from DataRobot originates from one of the following IP addresses: | Host: https://app.datarobot.com | Host: https://app.eu.datarobot.com | Host: https://app.jp.datarobot.com | | --- | --- | --- | | 100.26.66.209 | 18.200.151.211 | 52.199.145.51 | | 54.204.171.181 | 18.200.151.56 | 52.198.240.166 | | 54.145.89.18 | 18.200.151.43 | 52.197.6.249 | | 54.147.212.247 | 54.78.199.18 | | | 18.235.157.68 | 54.78.189.139 | | | 3.211.11.187 | 54.78.199.173 | | | 52.1.228.155 | 18.200.127.104 | | | 3.224.51.250 | 34.247.41.18 | | | 44.208.234.185 | 99.80.243.135 | | | 3.214.131.132 | 63.34.68.62 | | | 3.89.169.252 | 34.246.241.45 | | | 3.220.7.239 | 52.48.20.136 | | | 52.44.188.255 | | | | 3.217.246.191 | | | > [!NOTE] Note > These IP addresses are reserved for DataRobot use only. The IP addresses listed on this page are only applicable to the multi-tenant SaaS (MTS) DataRobot Managed AI Platform for the US, EU, and JP regions. Self-managed AI Platform installations (single-tenant SaaS, VPC, and on-premise) use IP addresses dedicated and specific to each environment. --- # Asset states URL: https://docs.datarobot.com/en/docs/reference/data-ref/asset-state.html > Learn about the asset states in DataRobot. # Asset states Data assets within DataRobot can be one of the following: - Snapshot: DataRobot has imported and stored a copy of the dataset in the AI Catalog. - Dynamic: DataRobot has a "live" connection to the dataset and only pulls from the database when a copy of the dataset is needed. When you register a data asset in DataRobot, a badge is added to the entry to indicate the state and type of dataset. See the table below for a description of each badge: | State | Badge | Description | Supported ingest methods | | --- | --- | --- | --- | | Snapshot, Dynamic | SPARK | A dataset built from a Spark query. | Spark SQL | | Snapshot | SNAPSHOT | A dataset that has a snapshot. | URL, Database | | Snapshot | STATIC | A static file with a snapshot. Datasets uploaded using data stages also display the STATIC badge, however, the FROM field displays stage://{stageId}/{filename}. | Local file | | Dynamic | DYNAMIC | A dataset that has no snapshot. | URL, Database | ### Snapshot A snapshot captures the data at a specific point in time. When you import or create a snapshot, DataRobot pulls from the data asset and registers a copy of it in the catalog. Pros: - You can profile and model on the snapshot dataset. - You can access a version history of the dataset. Cons: - Data freshness—if the dataset is updated often, it can quickly become stale because the snapshotted data is disconnected from the underlying source data. - Data governance—when you create a snapshot, you are storing a copy of the data in DataRobot and can share access to that copy with users who do not have access to the original source data, bypassing an organization's strict controls over access to it. Additionally, security mechanisms used by DataRobot to protect the data may not be the same as what an organization uses for the data in the original source (e.g., encryption). > [!TIP] Schedule snapshots > To keep snapshot datasets up-to-date, they can be [automatically refreshed periodically](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/snapshot.html), and are also automatically versioned to preserve dataset lineage and enhance the overall governance capabilities of DataRobot. ### Dynamic Dynamic means there is a"live" connection to the source data, so when DataRobot adds the database table/view, it does not create a materialized data entry. When a copy of the data is needed—for example, to create a project or make predictions—DataRobot uses the most recent version of the data. Note that the dataset retains the `DYNAMIC` badge in the catalog. Pros: - When you create a project or make predictions, DataRobot is using the most up-to-date data. - Allows you to perform tasks like automatic retraining. Cons: - Profiling and versioning are not supported. > [!TIP] Create snapshots from dynamic datasets > If needed, you can manually [create a snapshot](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog-asset.html#create-a-snapshot) from a dynamic dataset. --- # Automatic transformations URL: https://docs.datarobot.com/en/docs/reference/data-ref/auto-transform.html > Learn about DataRobot's automatic transformations. Transformed features do not replace the original features, but are added as new features for building models. # Automatic transformations The following sections describe DataRobot's automatic transformations. Transformed features do not replace the original, raw features; rather, they are provided as new, additional features for building models. For information on automated feature transformations DataRobot performs during the modeling process, see the [Modeling process](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#data-transformation-information) documentation. > [!NOTE] Note > Transformed features (including numeric features created as user-defined functions) cannot be used for special variables, such as [Weight, Offset, Exposure, and Count of Events](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html#set-exposure). When DataRobot identifies a feature column as variable type date, it automatically creates transformations for qualifying features (see below the table) after EDA1 completes. When complete, the dataset can have up to four new features for each date column: | Feature variable | Description | Variable type | | --- | --- | --- | | Hour of Day | Numeric value representing a 24-hour period, 0-23. Data must contain one or more date columns and at least three different hours in the date field. | Numeric | | Day of week | Numeric and text value representing the day of the week, where 0 corresponds to Monday (for example, 0: Monday, 2: Wednesday, 5: Saturday). Data must contain at least three different weeks. | Categorical | | Day of Month | The day of the month, 1-31. Data must contain at least three different years. | Numeric | | Month | Numeric value representing the month, 1-12. Data must contain at least three different years. | Categorical | | Year | Data must contain at least three different years. | Numeric | Date features are not automatically extracted if: - there are 10 or more date and/or time columns in the dataset - transformed features would not be informative (e.g., if there is only 1 year of data there is no need to extract year) - transformed features risk overfitting (e.g., with 1 year of data, modeling on month cannot identify full seasonal effects) The new derived features are included in the [Informative Features](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html#feature-lists) feature list and used for Autopilot. DataRobot also maintains the original date column. Note, however, that the original raw date is excluded from Informative Features if all four features listed above were extracted (that is, the dataset included at least three years of data). The following is an example of a dataset that contains over 10 years' worth of data. As a result, DataRobot created new features for all four date columns: If any of the automatically-transformed date features are duplicates of existing features in the dataset, they are not included in the Informative Features list. As an example, assume you add a date-type column containing the manufacturing year, “MfgYear”, to the dataset prior to ingestion. DataRobot marks the transformed feature, "MfgYear(Year)”, as a duplicate and excludes it from Informative Features. If, however, the automatically-transformed feature has a different type than the original column, it is included in Informative Features. --- # Data augmentation methods URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-aug.html > Summarizes the various ways in which DataRobot augments datasets for different experiment types. # Data augmentation methods This page summarizes the various ways in which DataRobot augments datasets for different experiment types. ## Feature Discovery for derived features [Feature Discovery](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/enrich-data-using-feature-discovery.html) discovers and generates new features from multiple datasets so that you no longer need to perform manual feature engineering to consolidate various datasets into one. It automates the procedure of joining and aggregating datasets, using a variety of heuristics to determine the list of features to derive in a DataRobot project. The results depend on a number of factors such as detected feature types, characteristics of the features, relationships between datasets, data size constraints, and more. ## Time series feature derivation DataRobot time series uses a [feature engineering](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/feature-eng.html) and reduction process to create the time series modeling dataset. The modeling framework extracts relevant features from time-sensitive data, modifies them based on user-configurable forecasting needs, and creates an entirely new dataset derived from the original. DataRobot then uses standard, as well as time series-specific, machine learning algorithms for model building. The feature engineering process includes [time series data prep](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-modeling-data/ts-data-prep.html) capabilities and the ability to [restore features](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-modeling-data/restore-features.html) removed by the reduction process. ## Time-aware data wrangling [Time-aware wrangling](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/build-recipe/ts-wrangling.html) creates recipes of operations and applies them first to a sample and then, when verified, to a full dataset of time-aware data. This way, you can perform time series feature engineering during the data preparation phase. Executing operations like lags and rolling statistics on input data provides control over which time-based features are generated before modeling. By reviewing the preview that results from adding both time-aware and non-time-aware operations, you can adjust before publishing, preventing the need to rerun modeling if what would otherwise be done automatically doesn't fit your use case. ## Train-time image augmentation Train-time [image augmentation](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/visual-ai/tti-augment/index.html) is a processing step in the DataRobot blueprint that creates new images for training by randomly transforming existing images, thereby increasing the size of the training data. By creating new images for training by randomly transforming existing images, you can build insightful projects with datasets that might otherwise be too small. In addition, all image projects that use augmentation have the potential for smaller overall loss by improving the generalization of models on unseen data. ## Automated location feature engineering [Location AI](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/location-ai/index.html) provides the ability to ingest, autorecognize, and transform geospatial data unlocks powerful capabilities for DataRobot model blueprints. For example, geometric properties associated with row-level geometries can be powerful predictors in machine learning models. Location AI automatically derives features from the properties of the input geometries. DataRobot derives features for MultiPoints, Lines/MultiLines, Polygons/MultiPolygons --- # Data quality checks URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html > How DataRobot detects and handles each data quality issue, such as outliers, leading or trailing zeros, target leakage, etc. # Data quality checks The sections below detail the checks DataRobot runs for the potential data quality issues. The [Data Quality Check Logic](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#data-quality-check-logic-summary) summarizes this information. ## Data quality check logic summary The following table summarizes the logic behind each data quality check: | Check / Run | Detection logic | Handling | Reported in... | | --- | --- | --- | --- | | Outliers / EDA2 | Ueda's algorithm | Linear: Flag added to feature in blueprintTree: Handled automatically | Data > Histogram | | Multicategorical format error / EDA1 | Meets any of the following three conditions: Value is not valid JSON Value does not represent a list List entry contains an empty string | Feature is not identified as multicategorical | Data Quality Assessment log | | Inliers / EDA1 | Value is not an outlier; frequency is an outlier | Flag added feature in blueprint | Data > Frequent Values | | Excess zeros / EDA1 | Frequency is an outlier; value is 0 | Flag added feature in blueprint | Data > Frequent Values | | Disguised Missing Values / EDA1 | Meets the following three conditions: Value is an outlier Frequency is an outlier Value matches one of these patterns: Is two or more digits and all digits are the same begins with “1”, followed by multiple zeros -1, 98, or 97 | Median imputed; flag feature in blueprint | Data > Frequent Values | | Target leakage / EDA2 | Importance score for each feature, calculated using Gini Norm metric. Threshold levels for reporting are moderate-risk (0.85) or high-risk (0.975). | High-risk leaky features excluded from Autopilot (using "Leakage Removed" feature list) | Data page; optionally, filter by issue type | | Missing images / EDA1 | Empty cell, missing file, broken link | Links are fixed automatically | Data Quality Assessment log | | Imputation leakage / EDA2 (pre-feature derivation) | Target leakage with is_imputed as the target applied to KA features. Only checked for projects with time series data prep applied to the dataset. | Remove feature from KA features | Data page; optionally, filter by issue type | | Pre-derived lagged features / EDA2 | Features equal to target(t-1), target(t-2) ... target(t-8) | Excluded from derivation | Data page; optionally, filter by issue type | | Inconsistent gaps / EDA2 | [Irregular time steps]time steps | Model runs in a row-based mode | Message in the time-aware modeling configuration | | Leading/ trailing zeros / EDA2 | For series starting/ending with 0, compute probability of consecutive 0s; flag series with <5% probability | User correction | Data page; optionally, filter by issue type | | Infrequent negative values / EDA1 | Fewer than 2% of values are negative | User correction | Warning message | | New series in validation / EDA1 | More than 20% of series not seen in training data | User correction | Informational message | ## Outliers Outliers, the observation points at the far ends of the sample mean, may be the result of data variability. DataRobot automatically creates blueprints that handle outliers. Each blueprint applies an appropriate method for handling outliers, depending on the modeling algorithm used in the blueprint. For linear models, DataRobot adds a binary column inside of a blueprint to flag rows with outliers. Tree models handle outliers automatically. **How they are detected:** DataRobot uses its own implementation of [Ueda's algorithm](https://jsdajournal.springeropen.com/articles/10.1186/s40488-015-0031-y) for automatic detection of discordant outliers. **How they are handled:** The data quality tool checks for outliers; to view outliers use the feature's [histogram](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#histogram-chart). ## Multicategorical format errors Multilabel modeling is a classification task that allows each row to contain one, several, or zero labels. To create a training dataset that can be used for multilabel modeling, you must follow the [requirements for multicategorical features](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/multilabel-classic.html#create-the-dataset). **How they are detected:** From a sampling of 100 random rows, DataRobot checks every feature that might qualify as multicategorical, looking for at least one value with the proper multicategorical format. If found, each row is checked to determine whether it complies with the multicategorical format. If there is at least one row that does not, the "multicategorical format error" is reported for the feature. The logic for the check is: Value must be a valid JSON. Value must represent a list of non-empty strings. **How they are handled:** A selection of errors are reported to the data quality tool. If a feature has a multicategorical format error, it is not detected as multicategorical. View the assessment log for details of the error: [https://docs.datarobot.com/en/docs/images/dq-11.png](https://docs.datarobot.com/en/docs/images/dq-11.png) ## Inliers Inliers are values that are neither above nor below the range of common values for a feature, however, they are anomalously frequent compared to nearby values (for example, 55555 as a zip code value, entered by people who don't want to disclose their real zip code). If not handled, they could negatively affect model performance. **How they are detected:** For each value recorded for a feature, DataRobot computes the value's frequency for that feature and makes an array of the results. Inlier candidates are the outliers in that array. To reduce false positives, DataRobot then applies another condition, keeping as inliers only those values for which: frequency > 50 * (number of non-missing rows in the feature) / (number of unique non-missing values in the feature) The algorithm allows inlier detection in numeric features with many unique values where, due to the number of values, inliers wouldn’t be noticeable in a [histogram](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html#histogram-chart) plot. Note that this is a conservative approach for features with a smaller number of unique values. Additionally, it does not detect inliers in features with fewer than 50 unique values. **How they are handled:** A binary column is automatically added inside of a blueprint to flag rows with inliers. This allows the model to incorporate possible patterns behind abnormal values. No additional user action is required. ## Excess zeros Repeated zeros in a column could be regular values but could also represent missing values. For example, sales could be zero for a given item either because there was no demand for the item or due to no stock. Using 0s to impute missing values is often suboptimal, potentially leading to decreased model accuracy. **How they are detected:** Using the array described in [inliers](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#inliers), if the frequency of the value 0 is an outlier, DataRobot flags the feature. **How they are handled:** A binary column is automatically added inside of a blueprint to flag rows with excess zeros. This allows the model to incorporate possible patterns behind abnormal values. No additional user action is required. ## Disguised missing values A "disguised missing value" is the term applied to a situation when a value (for example, `-999`) is inserted to encode what would otherwise be a missing value. Because machine learning algorithms do not treat them automatically, these values could negatively affect model performance if not handled. **How they are detected:** DataRobot finds values that both repeat with greater frequency than other values and are also detected outliers. To be considered a disguised missing value, repeated outliers must meet one of the following heuristics: All digits in the value are the same and repeat at least twice (e.g., 99, 88, 9999). The value begins with 1 and is then followed by two or more zeros. The value is equal to -1, 98, or 97 . **How they are handled:** Disguised missing values are handled in the same way as standard [missing values](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#missing-values) —a median value is imputed and inserted and a binary column flags the rows where imputation occurred. ## Target leakage The goal of predictive modeling is to develop a model that makes accurate predictions on new data, unseen during training. Because you cannot evaluate the model on data you don’t have, DataRobot estimates model performance on unseen data by saving off a portion of the historical dataset to use for evaluation. A problem can occur, however, if the dataset uses information that is not known until the event occurs, causing target leakage. Target leakage refers to a feature whose value cannot be known at the time of prediction (for example, using the value for “churn reason” from the training dataset to predict whether a customer will churn). Including the feature in the model’s feature list would incorrectly influence the prediction and can lead to overly optimistic models. **How they are detected:** DataRobot checks for target leakage during [EDA2](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda2) by calculating ACE importance scores (Gini Norm metric) for each feature with regard to the target. Features that exceed the moderate-risk (0.85) threshold are flagged; features exceeding the high risk (0.975) threshold are removed. **How they are handled:** If the [advanced option](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html) for leakage removal is enabled (which it is by default), DataRobot automatically creates a feature list ( [Informative Features - Leakage Removed](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html#automatically-created-feature-lists)) that removes the high-risk problematic columns. Medium-risk features are marked with a yellow warning to alert you that you may want to investigate further. After DataRobot detects leakage and creates Informative Features - Leakage Removed, it behaves according to the Advanced Option [“Run Autopilot on feature list with target leakage removed”](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html) setting. If enabled (the default): - Quick, full, or Comprehensive Autopilot: DataRobot runs the newly created feature list unless you specified a user-created list. To run on one of the other default lists, rebuild models after the initial build with any list you select. - Manual mode: DataRobot makes the list available so that you can apply it, at your discretion, from the Repository. - The target leakage list will be available when adding models after the initial build. If disabled, DataRobot applies the above to Informative Features (with potential target leakage remaining) or any user-created list you specified. ## Imputation leakage The time series data prep tool can impute target and feature values for dates that are missing in the original dataset. The data quality check ensures that the imputed features are not leaking the imputed target. This is only a potential problem for features that are known in advance (KA), since the feature value is concurrent with the target value DataRobot is predicting. **How they are detected:** DataRobot derives a binary classification target `is_imputed = (aggregated_row_count == 0)`. Prior to deriving time series features, it applies the [target leakage](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#target-leakage) check to each KA feature, using `is_imputed` as the target. **How they are handled:** Any features identified as high or moderate risk for imputation leakage are removed from the set of KA features. Subsequently, time series feature derivation proceeds as normal. ## Pre-derived lagged feature When a time series project starts, DataRobot automatically creates multiple date/time-related features, like lags and rolling statistics. There are times, however, when you do not want to automate time-based feature engineering (for example, if you have extracted your own time-oriented features and do not want further derivation performed on them). In this case, you should flag those features as [Excluded from derivation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/ts-adv-opt.html#exclude-features-from-derivation) or [Known in advance](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/ts-adv-opt.html#set-known-in-advance-ka). The “Lagged feature” check helps to detect whether features that should have been flagged were not, which would lead to duplication of columns. **How they are detected:** DataRobot compares each non-target feature with target(t-1), target(t-2) ... target(t-8). **How they are handled:** All features detected as lags are automatically set as excluded from derivation to prevent "double derivation." Best practice suggests reviewing other uploaded features and setting all pre-derived features as “Excluded from derivation” or “Known in advance”, if applicable. ## Irregular time steps The “inconsistent gaps” check is flagged when a time series model has irregular [time steps](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-flow-overview.html#time-steps). These gaps cause inaccurate rolling statistics. Some examples: - Transactional data is not aggregated for a time series project and raw transactional data is used. - Transactional data is aggregated into a daily sales dataset, and dates with zero sales are not added to the dataset. **How they are detected:** DataRobot detects when there are expected timestamps missing. It is important to understand that gaps could be consistent (for example, no sales for each weekend). DataRobot accounts for that and only detects inconsistent or unexpected gaps. **How they are handled:** Because their inclusion is not good for rolling statistics, if greater than 20% of expected time steps are missing, the project runs in row-based mode (i.e., a regular project with out-of-time (OTV) validation). If that is not the intended behavior, make corrections in the dataset and recreate the project. ## Leading or trailing zeros Just as for excess zeros, this check works to detect zeros that are used to fill in missing values. It works for the special case where 0s are used to fill in missing values in the beginning or end of series that started later or finished earlier than others. **How they are detected:** DataRobot estimates a total rate for zeros in each series and performs a statistical test to identify the number of consecutive zeros that cannot be considered a natural sequence of zeros. **How they are handled:** If that is not the intended behavior, make corrections in the dataset and recreate the project. ## Infrequent negative values Data with excess zeros in the target can be modeled with a special [two-stage model](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#two-stage-models) for zero-inflated cases. This model is only available when the min value of the target is zero (that is, a single negative value will invalidate its use). In sales data, for example, this can happen when returns are recorded along with sales. This data quality check identifies a negative value when two-stage models are appropriate and provides a warning to correct the target if the desire is to enable zero-inflated modeling and other additional blueprints. **How they are detected:** If DataRobot detects that fewer than 2% of values are negative, it treats the project as zero-inflated. **How they are handled:** DataRobot surfaces a warning message. ## New series in validation Depending on the project settings (training and validation partition sizes), a multiseries project might be configured so that a new series is introduced at the end of the dataset and therefore isn't part of the training data. For example, this could happen when a new store opens. This check returns an information message indicating that the new series is not within the training data. **How they are detected:** If DataRobot detects that more than 20% of series are new (meaning that they are not in the training data). **How they are handled:** DataRobot surfaces an informational message. ## Missing images When an image dataset is used to build a [Visual AI project](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/visual-ai/index.html), the CSV contains paths to images contained in the provided ZIP archive. These paths can be missing, refer to an image that does not exist, or refer to an invalid image. A missing path is not necessarily an issue as a row could contain a variable number of images or simply not have an image for that row and column. Click Preview Log for a more detailed view: In this example, row 1 reports a file name referenced that did not exist in the uploaded file (1). Row 2 reports that a row was missing an image path (2). The log provides both the nature of the issue as well as the row in which the problem occurred. The log previews up to 100 rows; choose Download to export the log and view additional rows. **How they are detected:** DataRobot checks each image path provided to ensure it refers to an image that exists and is valid. **How they are handled:** For paths that fail to resolve, DataRobot attempts to find the intended image and replace the problematic path. In the event that an auto-correction is not possible, the problematic path is removed. If the image was invalid, the path is removed. All missing images, paths that fail to resolve (even when automatically fixed), and invalid images are logged and [available for viewing](https://docs.datarobot.com/en/docs/reference/data-ref/data-quality-ref.html#visual-ai-assessment-details). ## Feature considerations Consider the following when working with Data Quality Assessment capability: - For disguised missing values, inlier, and excess zero issues, automated handling is only enabled for linear and Keras blueprints, where they have proven to reduce model error. Detection is applied to all blueprints. - You cannot disable automated imputation handling. - A public API is not yet available. - Automated feature engineering runs on raw data (instead of removing all excess zeros and disguised missing values before calculating rolling averages). --- # Amazon Athena URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-athena.html # Amazon Athena ## Supported authentication - AWS Credential ## Prerequisites The following is required before connecting to Amazon Athena in DataRobot: - AWS account - Athena managed policies attached to the AWS account ## Required parameters The table below lists the minimum required fields to establish a connection with Amazon Athena: | Required field | Description | Documentation | | --- | --- | --- | | address | The service endpoint used to connect to AWS.Example: athena.us-east-1.amazonaws.com:448 | AWS documentation | | AwsRegion | Separate geographic areas that AWS uses to house its infrastructure.Example: us-east-1 | AWS documentation | | S3OutputLocation | Specifies the path to your data in Amazon S3. | AWS documentation | ## Feature considerations - Due to a limitation with the JDBC driver itself, the Existing Table tab is not available for Athena data connections. You must select SQL Query and enter a SQL query to retrieve data from Athena. - DataRobot only supports serverless Amazon Athena. ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Microsoft Azure URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-azure.html # Microsoft Azure ## Supported authentication - Azure SQL Server/Synapse username/password - Active Directory username/password ## Prerequisites The following is required before connecting to Azure in DataRobot: - Azure SQL account ## Required parameters The table below lists the minimum required fields to establish a connection with Azure: | Required field | Description | Documentation | | --- | --- | --- | | address | The connection URL that supplies connection information for Azure. | Microsoft documentation | Learn about additional [configuration options for Azure](https://docs.microsoft.com/en-us/sql/connect/jdbc/setting-the-connection-properties?view=azuresqldb-current). ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | | DataRobot returns the following error message when attempting to authenticate Azure credentials: Failed to authenticate. Check that this client has been granted access to your storage account and that your credentials are correct. Note that username/password authentication only works with organizational accounts. It will not work with personal accounts. | Check the user account type and make sure the user or group to which the user belongs is granted access via the asset's Access Control List (ACL). | The user account must be in Microsoft Azure Active Directory (AD)—Service Principal is not supported. If the user has an Azure AD account, grant them access to the asset via its ACL. | --- # Google BigQuery URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-bigquery.html # Google BigQuery > [!NOTE] Self-Managed AI Platform installations > The redirect URI for BigQuery OAuth has been updated to: `https://my.datarobot.com/account/google/google_authz_return`. The old redirect URI `https://my.datarobot.com/account/google/bigquery_authz_return` will be deprecated in an upcoming release. For more information, see the BigQuery & Google Drive section of the DataRobot Installation Guide. ## Supported authentication - OAuth - Google Cloud service account ## OAuth ### Prerequisites The following is required before connecting to Google BigQuery in DataRobot: - A Google account authenticated with OAuth - Data stored in Google BigQuery ### Set up a connection in DataRobot When connecting with OAuth parameters, you must create a new data connection. To set up a data connection using OAuth: 1. Follow the instructions forcreating a data connection—making sure the minimumrequired parametersare filled in—andtesting the connection. 2. After clickingTest Connection, a window appears. ClickSign in using Google. 3. Select the account you want to use. 4. To provide consent to the database client, clickAllow. If the connection is successful, the following message appears in DataRobot: ### Required parameters The table below lists the minimum required fields to establish a connection with Google BigQuery: | Required field | Description | Documentation | | --- | --- | --- | | Projectid | A globally unique identifier for your project. | Google Cloud documentation | Learn about additional configuration options for Google BigQuery in the Google Cloud documentation. ## Google Cloud service account ### Prerequisites The following is required before connecting to Google BigQuery in DataRobot: - A Google Cloud service account - Data stored in Google BigQuery ### Set up a connection in DataRobot When connecting with a service account, you must create a new data connection. To set up a data connection using service account: 1. Follow the instructions forcreating a data connectionandtesting the connection. 2. UnderCredential type, selectGoogle Cloud Service Accountand fill in therequired parametersfor manual configuration. 3. ClickSave and sign in. ### Required parameters The table below lists the minimum required fields to establish a connection with Google BigQuery: | Required field | Description | Notes | | --- | --- | --- | | Projectid | A globally unique identifier for your project. | See the Google Cloud documentation. | | Service Account Key | The public/private RSA key pair associated with each service account that can be provided as a JSON string or loaded from a file. | See the Google Cloud documentation, List and get service account keys. | | Display name | A unique identifier for your credentials DataRobot. | You can access and manage these credentials under this display name. | ## Feature considerations - You cannot use multiple Google accounts to authenticate Google BigQuery in DataRobot. Once a Google user is authenticated via OAuth, that Google account is used for all the BigQuery data connections for that DataRobot user. - If your Google account has a large number of projects, it may take a long time to list schemas, even if the project is filtered with the projectID parameter. - External tables created from Google Drive are not supported. - If you are connecting to both Google Drive and BigQuery with OAuth authentication, deleting the BigQuery OAuth credential bigquery-oauth will also remove the Google Drive OAuth credential (if it exists). ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | | Issue authenticating Google BigQueryNeed to reset the Google user assigned to authentication | Locate and remove the bigquery-oauth credential. | In DataRobot, navigate to the Credentials Management tab.Select the bigquery-oauth credential and click Delete.Select User Settings > Data Connections.Reauthenticate your Google BigQuery data connection. | | Issue authenticating Google BigQuery | Remove authentication consent in Google Cloud console. | Navigate to your Google Account permissions.Select DataRobot under third-party applications.Click Remove Access. | | redirect_uri mismatch (self-managed only ) | Update the redirect URI for BigQuery. | If you were previously using redirect URI https://my.datarobot.com/account/google/bigquery_authz_return, your admin must update the redirect URI to https://my.datarobot.com/account/google/google_authz_return in the Google OAuth configuration. If you need to continue using the old redirect URI, an admin can set the EngConfig BIGQUERY_OAUTH_USE_OLD_REDIRECT_URI to True. | --- # Box URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-box.html > Connect to Box to ingest unstructured data for vector database creation. # Box > [!NOTE] Self-Managed AI Platform installations > The Box connector is automatically installed and does not need to be manually added. ## Supported authentication - Box JSON Web Tokens (JWT) For more information about this authentication method, see the [Box documentation](https://developer.box.com/guides/authentication/jwt). ### Box JWT To establish a connection to Box with JWT, you can either upload a JSON config file using the Load key from file button or populate the required fields listed below: | Required field | Description | | --- | --- | | Client ID | The client ID for the application in Box. | | Client Secret | The client secret for the application in Box. | | Enterprise ID | The enterprise ID for your account in Box. | | Public Key ID | The public ID associated with your application in Box. | | Passphrase | The passphrase used to encrypt your private key. | | Private Key | The encrypted private key as a JSON string. | ## Prerequisites The following is required before connecting to Box in DataRobot: - A Box account authenticated with JWT - Resources stored in Box ## Set up a connection in DataRobot To connect to Box, create a vector database, and when you [select a data source](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#add-a-data-source), add Box as the connection. You can also set up a Box connection from the [Account Settings > Data connectionspage](https://docs.datarobot.com/en/docs/platform/acct-settings/nxt-data-connect.html). ### Required parameters Additional parameters are not required to connect to Box. ## Feature considerations - The Box connector only supports unstructured data and is only available during vector database creation. - You can only add and view the Box connector as part of the vector database create workflow and from Account settings > Data connections . You cannot view Box connections in other areas where you work with datasets (structured data), for example, the Browse data modal in NextGen or the AI Catalog in DataRobot Classic. ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Confluence URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-confluence.html > Connect to Confluence to import unstructured data for vector database creation. # Confluence > [!NOTE] Self-Managed AI Platform installations > The Confluence connector is automatically installed and does not need to be manually added. ## Supported authentication - Basic (username/password) > [!NOTE] Password requirement > When setting up the connection in DataRobot, enter an Atlassian API token in the password field. For more information, see the [Atlassian documentation](https://id.atlassian.com/manage-profile/security/api-tokens). ## Prerequisites The following is required before connecting to Confluence in DataRobot: - An Atlassian account with basic authentication - Data stored in Confluence ## Set up a connection in DataRobot To connect to Confluence, create a vector database, and when you [select a data source](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#add-a-data-source), add Confluence as the connection. You can also set up a Confluence connection from the [Account Settings > Data connectionspage](https://docs.datarobot.com/en/docs/platform/acct-settings/nxt-data-connect.html). ### Required parameters The table below lists the minimum required fields to establish a connection with Confluence: | Required field | Description | | --- | --- | | Domain Name | The unique URL of your Confluence instance in the following format: yourcompany.atlassian.net. | ## Feature considerations - The Confluence connector only supports unstructured data and is only available during vector database creation. - You can only add and view the Confluence connector as part of the vector database create workflow and from Account settings > Data connections . You cannot view Confluence connections in other areas where you work with datasets (structured data), for example, the Browse data modal in NextGen or the AI Catalog in DataRobot Classic. ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Databricks (JDBC) URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-databricks.html > How to connect to Databricks using JDBC. # Databricks (JDBC) Connecting to Databricks using JDBC is currently certified through Azure. > [!NOTE] Note > This page describes how to connect to the Databricks JDBC driver, not the [native Databricks connector](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-databricks.html). ## Supported authentication - Access token ## Prerequisites The following is required before connecting to Databricks in DataRobot: - A Databricks workspace in the Azure Portal app - Data stored in an Azure Databricks database ## Retrieve JDBC URL In Microsoft Azure: 1. Log into your Azure Databricks workspace. 2. On the cluster's Configuration tab, expand Advanced options , click the JDBC/ODBC tab, and copy the JDBC URL . See the [Azure Databricks documentation](https://learn.microsoft.com/en-us/azure/databricks/integrations/jdbc/compute). ## Generate personal access token In Microsoft Azure, generate a personal access token for your Databricks workspace. This token will be used to authenticate your connection to Databricks in DataRobot. See the [Azure Databricks documentation](https://learn.microsoft.com/en-us/azure/databricks/integrations/jdbc/authentication?source=recommendations#--azure-databricks-personal-access-token). ## Set up a connection in DataRobot To connect to Databricks in DataRobot: 1. Follow the instructions forcreating a data connectionusing the appropriate Databricks driver andJDBC URL. 2. ClickTest Connection, to open theCredentialswindow. Enter youraccess tokenand a display name to save your credentials in DataRobot. ## Required parameters The table below lists the minimum required fields to establish a connection with Databricks: | Required field | Description | Documentation | | --- | --- | --- | | Connection configuration | | | | JDBC URL | Combination of authentication settings, any driver capability settings, and compute resource settings. | Azure Databricks documentation | | Credentials | | | | Access token | Token used to authenticate your connection to Databricks in DataRobot. | Azure Databricks documentation | ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | | When using the Databricks driver for prediction writebacks, you receive the following error: java.sql.SQLException: [Databricks][JDBC](10040) Cannot use commit while Connection is in auto-commit mode. | Modify the JDBC URL. | Add ignoreTransactions=1 to the JDBC URL. | --- # Exasol URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-exasol.html # Exasol ## Supported authentication - Username/password ## Prerequisites The following is required before connecting to Exasol in DataRobot: - Exasol account ## Required parameters The table below lists the minimum required fields to establish a connection with Exasol: | Required field | Description | Documentation | | --- | --- | --- | | address | The connection URL that supplies connection information for Exasol. Example: jdbc:exa:testdb.exasol.com:5599 | Exasol documentation | | port | Specific port of network services with which Exasol databases may communicate.Auto-populated by DataRobot. | Exasol documentation | Learn about additional [configuration options for Exasol](https://docs.exasol.com/db/latest/connect_exasol/drivers/jdbc.htm). See also an end-to-end overview of [connecting to Exasol with DataRobot](https://community.exasol.com/t5/tech-blog/how-to-automate-your-machine-learning-with-datarobot-and-exasol/ba-p/2333) on the Exasol Community. ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Google Drive URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-gdrive.html # Google Drive > [!NOTE] Self-Managed AI Platform installations > The Google Drive connector will be automatically installed and does not need to be manually added. ## Supported authentication - Google OAuth - Google Service Account ### OAuth For self-managed AI platform users, Google OAuth must be configured by an administrator. For information on configuring OAuth in Google, see the following pages in Google's documentation: - Configure the OAuth consent screen and choose scopes - OAuth client ID credentials NoteNote the following when configuring OAuth consent and client ID credentials:Required scopes for Google Drive:https://www.googleapis.com/auth/driveAuthorized JavaScript origins URI:https://xxx.datarobot.comRedirect URI:https://xxx.datarobot.com/account/google/google_authz_return Your DataRobot admin must then configure OAuth credentials (client ID and secret) in the environment variables `GOOGLE_AUTH_CLIENT_ID` and `GOOGLE_AUTH_CLIENT_SECRET`. For more information, see the BigQuery & Google Drive section of the DataRobot Installation Guide. ### Service account For information on configuring these credentials, see [Service account credentials](https://developers.google.com/workspace/guides/create-credentials#service-account) in the Google documentation. When creating service account credentials, you also need to obtain credentials in the form of a key pair. For more information on creating a key pair, see [Create credentials for service account](https://developers.google.com/workspace/guides/create-credentials#create_credentials_for_a_service_account) in the Google documentation. ## Prerequisites The following is required before connecting to Google Drive in DataRobot: - A Google account authenticated with OAuth or a Google Service Account - Data stored in Google Drive ## Set up a connection in DataRobot To connect to Google Drive, create a vector database, and when you [select a data source](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#add-a-data-source), add Google Drive as the connection. Once you authenticate the connection with OAuth, a credential named `gdrive-oauth` is automatically created. You can also set up a Google Drive connection from the [Account Settings > Data connectionspage](https://docs.datarobot.com/en/docs/platform/acct-settings/nxt-data-connect.html). ## Required parameters Parameters are not required to connect to Google Drive. ## Feature considerations - The Google Drive connector only supports unstructured data and is only available during vector database creation. - You can only add and view the Google Drive connector as part of the vector database create workflow and from Account settings > Data connections . You cannot view Google Drive connections in other areas where you work with datasets (structured data), for example, the Browse data modal in NextGen or the AI Catalog in DataRobot Classic. - If you are connecting to both Google Drive and BigQuery with OAuth authentication, deleting the Google Drive OAuth credential gdrive-oauth will also remove the BigQuery OAuth credential (if it exists). ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | | Issue authenticating Google DriveNeed to reset the Google user assigned to authentication | Locate and remove the gdrive-oauth credential. | In DataRobot, navigate to the Credentials Management tab.Select the gdrive-oauth credential and click Delete.Select User Settings > Data Connections.Reauthenticate your Google Drive data connection. | | Issue authenticating Google Drive | Remove authentication consent in Google Cloud console. | Navigate to your Google Account permissions.Select DataRobot under third-party applications.Click Remove Access. | --- # Jira URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-jira.html > Connect to Jira to import unstructured data for vector database creation. # Jira > [!NOTE] Self-Managed AI Platform installations > The Jira connector is automatically installed and does not need to be manually added. ## Supported authentication - Basic (username/password) > [!NOTE] Password requirement > When setting up the connection in DataRobot, enter an Atlassian API token in the password field. For more information, see the [Atlassian documentation](https://id.atlassian.com/manage-profile/security/api-tokens). ## Prerequisites The following is required before connecting to Jira in DataRobot: - An Atlassian account with basic authentication - Data stored in Jira ## Set up a connection in DataRobot To connect to Jira, create a vector database and, when you [select a data source](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#add-a-data-source), add Jira as the connection. You can also set up a Jira connection from the [Account Settings > Data connectionspage](https://docs.datarobot.com/en/docs/platform/acct-settings/nxt-data-connect.html). ### Required parameters The table below lists the minimum required fields to establish a connection with Jira: | Required field | Description | | --- | --- | | Domain Name | The unique URL of your Jira instance in the following format: yourcompany.atlassian.net. | ## Browse with JQL For Jira connectors, you can use [Jira Query Language (JQL)](https://www.atlassian.com/software/jira/guides/jql/cheat-sheet#intro-to-jql) to browse and filter your projects. To filter with JQL, after configuring the connection, click Add from JQL query. Enter a JQL query in the query editor and click Add to Use Case to use the filtered results. ## Feature considerations - The Jira connector only supports unstructured data and is only available during vector database creation. - You can only add and view the Jira connector as part of the vector database create workflow and from Account settings > Data connections . You cannot view Jira connections in other areas where you work with datasets (structured data), for example, the Browse data modal in NextGen or the AI Catalog in DataRobot Classic. ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # kdb+ URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-kdb.html # kdb+ ## Supported authentication - Username/password ## Prerequisites The following is required before connecting to kdb+ in DataRobot: - kdb+ account ## Required parameters The table below lists the minimum required fields to establish a connection with kdb+: | Required field | Description | Documentation | | --- | --- | --- | | address | The connection URL used to connect to kdb+.Example: jdbc-cert-kdb+.cqz9ezythbf4.us-east-1.rds.amazonaws.com:3306 | kx documentation | ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Microsoft SQL Server URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-ms-sql-srvr.html # Microsoft SQL Server ## Supported authentication - Username/password ## Prerequisites The following is required before connecting to Microsoft SQL Server in DataRobot: - Microsoft SQL account ## Required parameters The table below lists the minimum required fields to establish a connection with Microsoft SQL Server: | Required field | Description | Documentation | | --- | --- | --- | | address | The service endpoint used to connect to SQL Server.Example: jdbc-cert-ms-sql-server-2016.cqz9ezetwbf4.us-east-1.rds.amazonaws.com:1489 | Microsoft documentation | ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # MySQL URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-mysql.html # MySQL ## Supported authentication - Username/password ## Prerequisites The following is required before connecting to MySQL in DataRobot: - MySQL account ## Required parameters The table below lists the minimum required fields to establish a connection with MySQL: | Required field | Description | Documentation | | --- | --- | --- | | address | The service endpoint used to connect to MySQL.Example: jdbc-cert-mysql.cqyt4ezythbf4.us-east-1.rds.amazonaws.com:3309 | MySQL documentation | For more information, see the [MySQL connector documentation](https://dev.mysql.com/doc/connector-j/8.0/en/connector-j-reference.html). ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Oracle URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-oracle.html # Oracle There are two data connection types for Oracle Database: Service Name and SID. Use the appropriate parameters for the connection path you use to connect to Oracle Database. ## Supported authentication - Username/password for both Service Name and SID ## Prerequisites The following is required before connecting to Oracle in DataRobot: - Oracle Database account ## Required parameters ### Service Name The table below lists the minimum required fields to establish a connection with Oracle (Service Name): | Required field | Description | Documentation | | --- | --- | --- | | address | The connection URL that supplies connection information for Oracle. | Oracle documentation | | serviceName | Specifies name used to connect to an instance. | Oracle documentation | ### SID The table below lists the minimum required fields to establish a connection with Oracle (SID): | Required field | Description | Documentation | | --- | --- | --- | | address | The connection URL that supplies connection information for Oracle.Example: | Oracle | | SID | A unique identifier for your database. | Oracle documentation | | port | A unique identifier for your database. | Oracle documentation | ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # PostgreSQL URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-postgresql.html # PostgreSQL ## Supported authentication - Username/password ## Prerequisites The following is required before connecting to PostgreSQL in DataRobot: - PostgreSQL account ## Required parameters The table below lists the minimum required fields to establish a connection with PostgreSQL: | Required field | Description | Documentation | | --- | --- | --- | | address | The server's address used to connect to PostgreSQL. | PostgreSQL documentation | ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Presto URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-presto.html # Presto You can use a Presto connector to access a wide variety of underlying [data sources and table formats](https://prestodb.github.io/docs/0.296/connector.html). Specifically you can use a Presto connector to access data stored in Hudi format (supported via [Presto’s Hudi connector](https://prestodb.io/docs/current/connector/hudi.html)) and Iceberg format (supported via [Presto’s Iceberg connector](https://prestodb.io/docs/current/connector/iceberg.html)). ## Supported authentication - Username/password ## Prerequisites The following is required before connecting to Presto in DataRobot: - Presto account ## Required parameters The table below lists the minimum required fields to establish a connection with Presto: | Required field | Description | Documentation | | --- | --- | --- | | address | The connection URL that supplies connection information for Presto. | Presto documentation | Learn about additional [configuration options for Presto](https://prestodb.io/docs/current/installation/jdbc.html#connecting). ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Amazon Redshift URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-redshift.html # Amazon Redshift ## Supported authentication - Username/password ## Prerequisites The following is required before connecting to Redshift in DataRobot: - Amazon Redshift account ## Required parameters The table below lists the minimum required fields to establish a connection with Redshift: | Required field | Description | Documentation | | --- | --- | --- | | address | The connection URL that supplies connection information for Redshift.Example: redshift.cjzu88438s1ja.us-east-1.redshift.amazonaws.com:7632 | Redshift documentation | | database | A unique identifier for your database. | Redshift documentation | Learn about additional [configuration options for Redshift](https://docs.aws.amazon.com/redshift/latest/mgmt/jdbc20-configuration-options.html). ## Feature considerations - DataRobot only supports serverless Amazon Redshift. ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # AWS S3 URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-s3.html # AWS S3 > [!NOTE] Self-Managed AI Platform installations > The S3 connector will be automatically installed and does not need to be manually added. ## Supported authentication - AWS credentials: Long-term credentials using aws_access_key_id and aws_secret_access_key . - AWS temporary credentials: Short-term credentials using aws_access_key_id , aws_secret_access_key , and aws_session_token . ## Prerequisites The following is required before connecting to AWS S3 in DataRobot: - AWS S3 account ## Required parameters The table below lists the minimum required fields to establish a connection with AWS S3: | Required field | Description | Documentation | | --- | --- | --- | | bucketName | A container that stores your data in AWS S3. | AWS documentation | Note that you can specify `bucketRegion` under Show advanced options, however this parameter is not required. ## Feature considerations - This connectordoes notsupport: - This connectordoessupport: --- # SAP HANA URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-sap-hana.html # SAP HANA ## Supported authentication - Username/password ## Prerequisites The following is required before connecting to SAP HANA in DataRobot: - SAP HANA account ## Required parameters The table below lists the minimum required fields to establish a connection with SAP HANA: | Required field | Description | Documentation | | --- | --- | --- | | address | The server's address used to connect to SAP HANA. | SAP documentation | For more information, see the [SAP HANA connector documentation](https://help.sap.com/docs/SAP_HANA_CLIENT/f1b440ded6144a54ada97ff95dac7adf/109397c2206a4ab2a5386d494f4cf75e.html). ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # SharePoint URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-sharepoint.html # SharePoint > [!NOTE] Self-Managed AI Platform installations > The SharePoint connector will be automatically installed and does not need to be manually added. ## Supported authentication - Azure OAuth (delegated access) - Azure service principal (app-only access) ## Prerequisites The following is required before connecting to SharePoint in DataRobot: - A SharePoint account authenticated with Azure OAuth or service principal - Data stored in SharePoint ## Generate credentials At the end of this section, you will have a fully configured application, including the required fields for your chosen authentication type, and the necessary permissions to access specific SharePoint sites. OAuth required fields: - Client ID - Client Secret - Scopes Service principal required fields: - Client ID - Client Secret - Tenant ID ### Create an application in Azure To support Azure OAuth or service principal, you must [create and register an application](https://learn.microsoft.com/en-us/entra/identity-platform/quickstart-register-app) for DataRobot in the Azure portal, and then configure its permissions. Use the appropriate configuration parameters based on your authentication type: **OAuth:** Configuration parameter Description Supported account types Accounts in any organizational directory and personal Microsoft accounts (multi-tenant). Accounts in any organizational directory (multi-tenant). Redirect URI Select Web and enter a redirect URI as follows: (SaaS) https://.datarobot.com/account/azure/azure_oauth_authz_return (Self-managed) https:///account/azure/azure_oauth_authz_return **Service principal:** Configuration parameter Description Supported account types Select Accounts in this organization directory only (single-tenant). Redirect URI N/A After registration is complete, go to the Overview page and copy the following information: - Application ID ( Client ID ) - Directory ID ( Tenant ID —service principal only) ### Configure the client secret 1. Navigate to your DataRobot application in the Azure portal app registrations (in Microsoft Entra ID > App registrations). 2. Select Certificates & secrets > Client secrets > New secret . 3. Add a description and expiration date, then click Add . 4. After saving the client secret, the value of the client secret is displayed. This value is only displayed once, so make sure you copy and store it. NoteEach client secret has an expiration date. To avoid OAuth outages, it is recommended that you periodically create a new client secret. Once you've created a new client secret, you must update all associated credentials. ### Configure permissions/scope **OAuth:** Navigate to your DataRobot application in the Azure portal app registrations (in Microsoft Entra ID > App registrations). In the left panel, select Manage > API Permissions > Add a permission . Select Microsoft Graph > Delegated permissions , then Sites.Selected/Sites.Read.All/Files.Read.All . Click Add permissions . The permissions are listed under Configured permissions . To view the scope for a specific permission, click on the permissions and copy the first URL shown in the resulting panel. You can add a list of required scopes—this represents the Scopes . Alternatively, you can use https://graph.microsoft.com/.default to include all permissions that have already been assigned to this app. Note that some permissions may require admin consent. **Service principal:** Navigate to your DataRobot application in the Azure portal app registrations (in Microsoft Entra ID > App registrations). In the left panel, select Manage > API Permissions > Add a permission . Select Microsoft Graph > Application permissions , select Sites.Selected/Sites.Read.All/Files.Read.All , and click Add permissions . The permissions are listed under Configured permissions . Note that some permissions may require admin consent. The required permissions and scopes depend on your specific use case. For more information, see the [Microsoft documentation](https://learn.microsoft.com/en-us/graph/permissions-reference). > [!NOTE] Note > Microsoft recently introduced an update affecting the delegated permission `Sites.Read.All`. For more information, see the [Microsoft documentation](https://learn.microsoft.com/en-us/entra/identity/enterprise-apps/manage-app-consent-policies?pivots=ms-graph#microsoft-recommended-current-settings). ### Assign the app permission to specific SharePoint sites This step is only required when using the `Sites.Selected` permission. An Azure admin must grant the DataRobot application access to the specific SharePoint sites using either the Microsoft Graph API or PowerShell. For each site the app needs to access, the admin must call the [create permission API](https://learn.microsoft.com/en-us/graph/api/site-post-permissions?view=graph-rest-1.0&tabs=http) and specify the roles as `read` in the request body to provide read-only access. To assign permissions, an admin can either use PowerShell or do the following: 1. Register another application in Microsoft Entra ID. 2. Configure a client secret for the app. 3. Configure the permission Sites.FullControl.All (Type=Application) for Graph API. Admin consent is required for this permission. 4. Write a small script (see examples here ) to add permission for the SharePoint site. To initialize the graph client, you can use the client credentials provider . ## Set up a connection in DataRobot To connect to SharePoint, create a vector database, and when you [select a data source](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/vector-dbs.html#add-a-data-source), add SharePoint as the connection. You can also set up a SharePoint connection from the [Account Settings > Data connectionspage](https://docs.datarobot.com/en/docs/platform/acct-settings/nxt-data-connect.html). ### Required parameters While parameters are not required to connect to SharePoint, depending on the authorizations given in the application and the credential type being used, you may need to configure the `Site ID` parameter under Show additional parameters. | Required field | Description | | --- | --- | | Sharepoint Site ID | A unique identifier of a SharePoint site, formatted as {hostname},{site collection GUID},{site (web) GUID}. | The following scenararios require the `Site ID` parameter: - OAuth with Sites.Read.All or Sites.Selected . - Service principal with Sites.Selected . ## Feature considerations - The SharePoint connector only supports unstructured data and is only available during vector database creation. - You can only add and view the SharePoint connector as part of the vector database create workflow and from Account settings > Data connections . You cannot view SharePoint connections in other areas where you work with datasets (structured data), for example, the Browse data modal in NextGen or the AI Catalog in DataRobot Classic. ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Snowflake URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-snowflake.html # Snowflake ## Supported authentication - Username/password - Key pair - Snowflake OAuth - External OAuth with Okta or Microsoft Entra ID (formerly, Azure AD) ## Username/password ### Prerequisites The following is required before connecting to Snowflake in DataRobot: - A Snowflake account > [!WARNING] OAuth with security integrations > If you create a security integration when configuring OAuth, you must specify the `OAUTH_REDIRECT_URI` as `https:///account/snowflake/snowflake_authz_return` ### Required parameters In addition to the required fields listed below, you can learn about other available configuration options in the [Snowflake documentation](https://docs.snowflake.com/en/user-guide/jdbc-parameters.html). | Required field | Description | Documentation | | --- | --- | --- | | address | A connection object that stores a secure connection URL to connect to Snowflake.Example: {account_name}.snowflakecomputing.com | Snowflake documentation | | warehouse | A unique identifier for your virtual warehouse. | Snowflake documentation | | db | A unique identifier for your database. | Snowflake documentation | ## Key pair ### Prerequisites The following is required before connecting to Snowflake in DataRobot: - A Snowflake account - A private key file (for instructions on generating a private key, see the Snowflake documentation ) ### Set up the connection in DataRobot The tabs below show how to configure a Snowflake data connection using key pair authentication: **DataRobot Classic:** When creating a Snowflake [data connection](https://docs.datarobot.com/en/docs/classic-ui/data/connect-data/data-conn.html#create-a-new-connection) in DataRobot Classic, select Key-pair as your credential type. Then, fill in the [required parameters](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-snowflake.html#required-parameters). [https://docs.datarobot.com/en/docs/images/snow-keypair-2.png](https://docs.datarobot.com/en/docs/images/snow-keypair-2.png) **Workbench:** When creating a Snowflake [data connection](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/add-data/connect.html) in Workbench, select Key-pair as your credential type. Then, fill in the [required parameters](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-snowflake.html#required-parameters). [https://docs.datarobot.com/en/docs/images/snow-keypair-1.png](https://docs.datarobot.com/en/docs/images/snow-keypair-1.png) ### Required parameters In addition to the required fields listed below, you can learn about other available configuration options in the [Snowflake documentation](https://docs.snowflake.com/en/user-guide/jdbc-parameters.html). | Required field | Description | | --- | --- | | Username | A unique identifier of a user inside a Snowflake account (i.e., the name you use to log into Snowflake). | | Private key | The string copied from your private key file. | | Display name | A unique identifier for your Snowflake credentials within DataRobot. | For more information on Snowflake key pair authentication, including generating private keys and configuring key pair authentication in Snowflake, see the [Snowflake documentation](https://docs.snowflake.com/en/user-guide/key-pair-auth). ## Snowflake OAuth ### Prerequisites The following is required before connecting to Snowflake in DataRobot: - A Snowflake account - Snowflake OAuth configured ### Set up the connection in DataRobot When connecting with OAuth parameters, you must create a new data connection. To set up a data connection using OAuth: 1. Follow the instructions forcreating a data connectionandtesting the connection. 2. After clickingTest Connection, a Credentials window appears. Enter your Snowflake client ID, client secret, and account name. SelectSnowflakeas the OAuth provider. 3. ClickSave and sign in. 4. Enter your Snowflake username and password. ClickSign in. 5. To provide consent to the database client, clickAllow. If the connection is successful, the following message appears in DataRobot: ### Required parameters In addition to the required fields listed below, you can learn about other available configuration options in the [Snowflake documentation](https://docs.snowflake.com/en/user-guide/jdbc-parameters.html). | Required field | Description | Documentation | | --- | --- | --- | | Required fields for data connection | | | | address | A connection object that stores a secure connection URL to connect to Snowflake.Example: {account_name}.snowflakecomputing.com | Snowflake documentation | | warehouse | A unique identifier for your virtual warehouse. | Snowflake documentation | | db | A unique identifier for your database. | Snowflake documentation | | Required fields for credentials | | | | Client ID | The public identifier for your application. | Snowflake documentation | | Client secret | A confidential identifier used to authenticate your application. | Snowflake documentation | | Snowflake account name | A unique identifier for your Snowflake account within an organization. | Snowflake documentation | ## Snowflake External OAuth ### Prerequisites The following is required before connecting to Snowflake in DataRobot using OAuth: **Okta:** A Snowflake account. External OAuth configured in Snowflake for Okta. > [!WARNING] External OAuth with security integrations > If using Okta as the external identity provider (IdP), you must specify `https:///account/snowflake/snowflake_authz_return` as a Sign-in redirect URI when [creating a new App integration in Okta](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-snowflake.html#external-idp-setup). **Microsoft Entra ID:** Microsoft Entra ID is the new name for Azure Active Directory. A Snowflake account. External OAuth configured in Snowflake for Microsoft Entra ID. > [!WARNING] External OAuth with security integrations > If using Entra ID as the external identity provider (IdP), you must specify `https:///account/snowflake/snowflake_authz_return` as a Redirect URI when [registering both applications in Entra ID](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-snowflake.html#external-idp-setup). ### External IdP setup > [!NOTE] Note > This section uses example configurations for setting up an external IdP. For information on setting up an external IdP based on your specific environment and requirements, see the documentation for Okta or Entra ID. In the appropriate external IdP, create the Snowflake application(s): **Okta:** Create a new App Integration in Okta: Go to Applications > Applications . Click Create App Integration . For the Sign-in method , select OIDC - OpenID Connect . For the Application type , select Web Application . Click Next . Enter a name for the new application. Under Grant type , make sure the following options are selected: Client Credentials Authorization Code Refresh Token Under LOGIN , add http:///account/snowflake/snowflake_authz_return to the Sign-in redirect URIs . Click Save —this generates your Client ID and Client secret . Create a new Authorization Server: Go to Security > API > Authorization Server . Click Add Authorization Server . Enter a name. Set Audience to https://.snowflakecomputing.com/ . Click Save . Go to Scopes > Add Scope . Set Name to session:role:public . public refers to the Snowflake role called Public. This can be a different role name, but it must also exist within Snowflake. For User Consent , set Required . Add Require user consent for this scope and Block services from requesting this scope . (Optional) Set the offline_access scope to require consent. Go to Access Policies and click Add Policy . For Assign to , select The following clients and select the application you just created. Click Create Policy . Click Add Rules . Enter a name and make sure the following options are selected: Add Check-in Client Credentials . Add Check-in Authorization Code . Click Create Rule . > [!WARNING] Required information > Before proceeding with Snowflake and DataRobot setup, make sure you've copied the following information in Okta: > > Client ID > Client secret > Okta Issuer URL > JWKS URI > Audience > > Audience is listed under the Settings tab, and to find the issuer URL and JWKS URI, click on the Metadata URI link. This loads the JSON items that includes the required information. (Optional) Test the application To test the application you just created in Okta: Go to Token Preview and fill in the Request Properties with the grant type, a user assigned to the application, and the specified scope. Click Preview Token . This results in the following: Issuer , for example, https://dev-11863425.okta.com/oauth2/aus15ca55wkdOxplJ5d7 . Auth Token for programmatic access to the Okta API. Auth server metadata JSON (found in Settings > Metadata URI ). Okta API calls Get current user curl --location --request GET 'https://.okta.com/api/v1/users/me' \ --header 'Accept: application/json' \ --header 'Content-Type: application/json' \ --header 'Authorization: SSWS ' Get the user's grants curl --location --request GET 'https://.okta.com/api/v1/users//clients//grants' \ --header 'Accept: application/json' \ --header 'Content-Type: application/json' \ --header 'Authorization: SSWS ' Revoke grant/consent curl --location --request DELETE 'https://.okta.com/api/v1/users//grants/' \ --header 'Accept: application/json' \ --header 'Content-Type: application/json' \ --header 'Authorization: SSWS ' **Microsoft Entra ID:** Register an application for Snowflake Resource in Microsoft Entra ID: Go to MS Azure > Microsoft Entra ID > App registrations . Click New registration . Under Name , enter Snowflake OAuth Resource . Under Supported account types , select Accounts in this organizational directory only . Under Redirect URI , select Web and enter https:///account/snowflake/snowflake_authz_return . Click Register . Expose the API. Click the set link next to Application ID URI make sure it is a unique ID (this does not need any change ). This will be the value. Click Add a scope to add a scope representing the Snowflake role. Using the name of the role in Snowflake, enter the scope as follows: session:scope: . If it's a custom role, set the role name in Snowflake, and enter the scope as follows: session:scope: . Add another scope name as session:role-any . Copy the value of the newly created scope. This will be value. Register an application for Snowflake Client App in Microsoft Entra ID: Go to MS Azure > Microsoft Entra ID > App registrations . Click New registration . Under Name , enter Snowflake OAuth Client . Under Supported account types , select Accounts in this organizational directory only . Under Redirect URI , select Web and enter https:///account/snowflake/snowflake_authz_return . Click Register . In the Overview section, copy the client ID from the Application (client) ID field. This will be known as the . Open Certificates & secrets and select New client secret . Click Add . Copy the secret. This will be known as the . Optional For programmatic clients that need to request an access token on behalf of a user, configure delegated permissions for applications as follows: Click API Permissions . Click Add Permission . Click on My APIs. Click on the Snowflake OAuth Resource that you created in Step 1: Configure the OAuth Resource in Entra ID. Click on the Delegated Permissions box. Check on the Permission related to the Scopes created in step 3 session:role-any Click Add Permissions. Collect the following information for the Snowflake integration: Click App Registrations . Click the Snowflake OAuth Resource . On the Overview page, click Endpoints . Copy the first part of the OAuth 2.0 token endpoint (v2) URL, e.g. https://login.microsoftonline.com/6064c47c-80e4-4a555b-82ee-1fc5643b37a2 . This will be value Copy the value of OpenID Connect metadata document and paste it on a new window. Locate the "jwks_uri" parameter, which will be the value (e.g., https://login.microsoftonline.com/6064c47c-80e4555b-82ee-1fc5643b37a2/discovery/v2.0/keys ). Copy the value of Federation metadata document and open the URL in a new window. Locate the "entityID" parameter, which will be the value, also known as (e.g., https://sts.windows.net/6064c47c-80e4-555582ee-1fc5643b37a2/ ). Make sure you've copied the following values: copied from Snowflake OAuth Resource . , , and values from the Overview and Endpoints view of Snowflake OAuth Resource . and copied from Snowflake OAuth Client . ### Snowflake setup > [!NOTE] Note > This section uses example configurations for setting up an external IdP in Snowflake. For information on setting up an external IdP in Snowflake based on your specific environment and requirements, see the Snowflake documentation. In Snowflake, execute the following commands to create an integration for the appropriate external IdP: **Okta:** ``` create security integration external_oauth_okta_2 type = external_oauth enabled = true external_oauth_type = okta external_oauth_issuer = '' external_oauth_jws_keys_url = '' external_oauth_audience_list = ('') external_oauth_token_user_mapping_claim = 'sub' external_oauth_snowflake_user_mapping_attribute = 'login_name'; CREATE OR REPLACE USER LOGIN_NAME = ''; alter user set DEFAULT_ROLE = 'PUBLIC'; ``` Reference values: OKTA_ISSUER : https://dev-11863425.okta.com/oauth2/aus15ca55wxplJ5d7 AUDIENCE : https://hl91180.us-east-2.aws.snowflakecomputing.com/ JWKS_URI : https://dev-11863425.okta.com/oauth2/aus15ca55wxplJ5d7/v1/keys (retrieved from Okta Auth server Metadata JSON) okta_user_name (retrieved from Okta > Directory > People , select a user, and then go to Profile > Username/login ) **Microsoft Entra ID:** > [!NOTE] Note > You must have the `accountadmin` role, or a role with the global `CREATE INTEGRATION` privilege to create the integration below. ``` create security integration external_oauth_azure_1 type = external_oauth enabled = true external_oauth_type = azure external_oauth_issuer = '' external_oauth_jws_keys_url = '' external_oauth_audience_list = ('') external_oauth_token_user_mapping_claim = 'upn' external_oauth_any_role_mode = 'ENABLE' external_oauth_snowflake_user_mapping_attribute = 'login_name'; ``` Reference values: : https://sts.windows.net/6064c47c-80e4-4a2b-4444-1fc5643b37a2/ : https://login.microsoftonline.com/6064c47c-80e4-4a2b-4444-1fc5643b37a2/discovery/v2.0/keys : api://8aa2572f-c9e6-4e91-4444-dcd84c856dd2 Grant access on the integration to the public role: `grant USE_ANY_ROLE on integration external_oauth_azure_1 to PUBLIC;` `grant USE_ANY_ROLE on integration external_oauth_azure_1 to ;` Ensure that the `LOGIN_NAME` of the user is the same as the Azure login. Verify using the following query in Snowflake: `DESC USER ` If the login names are different, Snowflake cannot validate the access token generated with Entra ID. In that case, use the command below to match Snowflake with Azure: `ALTER USER SET LOGIN_NAME=''` If you are using custom role and is not your default, use the command below to set it as your default to test connectivity: `ALTER USER SET DEFAULT ROLE='';` ### Set up the connection in DataRobot When connecting with external OAuth parameters, you must create a new data connection. To set up a Snowflake data connection using external OAuth: 1. Follow the instructions forcreating a data connectionandtesting the connection. 2. After clickingTest Connection, select your OAuth provider from the dropdown—either Okta or MS Azure AD— and fill in theadditional required fields.Then, clickSave and sign in. 3. In the OAuth modal, enter your Okta or Azure username and password. ClickSign in. 4. To provide consent to the database client, clickAllow. If the connection is successful, the following message appears in DataRobot: ### Required parameters In addition to the required fields listed below, you can learn about other available configuration options in the [Snowflake documentation](https://docs.snowflake.com/en/user-guide/jdbc-parameters.html). | Required field | Description | Documentation | | --- | --- | --- | | Required fields for data connection | | | | address | A connection object that stores a secure connection URL to connect to Snowflake.Example: {account_name}.snowflakecomputing.com | Snowflake documentation | | warehouse | A unique identifier for your virtual warehouse. | Snowflake documentation | | db | A unique identifier for your database. | Snowflake documentation | | Required fields for credentials | | | | Client ID | The public identifier for your application.In the Okta Admin console, go to Applications > Applications > Your OpenID Connect web app > Sign On tab > Sign On Methods.In Microsoft Entra ID/Azure AD, this is the value of Application(client) ID. You copied it as in the above instructions. | Okta or Entra ID documentation | | Client secret | A confidential identifier used to authenticate your application.In the Okta Admin console, go to Applications > Applications > Your OpenID Connect web app > Sign On tab > Sign On Methods.In Microsoft Entra ID/Azure AD, this is the client secret. You copied it as in the above instructions. | Okta or Entra ID documentation | | Snowflake account name | A unique identifier for your Snowflake account within an organization. | Snowflake documentation | | Issuer URL | A URL that uniquely identifies your SAML identity provider. "Issuer" refers to the Entity ID of your identity provider.Examples: Okta: https://.okta.com/oauth2/Microsoft Entra ID:You copied it as in the above instructions, e.g. https://login.microsoftonline.com/ | Okta or Entra ID documentation | | Scopes | Contains the name of your Snowflake role. Examples: Parameters for a Snowflake Analyst. Okta: session:role:analystMicrosoft Entra ID: e.g. api://8aa2572f-c9e6-4e91-9555-dcd84c856dd/session:role-any | Snowflake documentation | Reach out to your administrator for the appropriate values for these fields. ## Feature considerations - By default, Snowflake preserves the case of alphabetic characters when storing and resolving double-quoted identifiers; however, if you override this default in Snowflake, double-quoted identifiers are stored and resolved as uppercase letters. Because DataRobot is a case-sensitive platform, it's important to preserve the original case of the letters. - To avoid potential issues related to case-sensitivity, go to your Snowflake data connection in DataRobot, add the QUOTED_IDENTIFIERS_IGNORE_CASE parameter, and set the value to FALSE . See the Snowflake documentation for more details. - If you plan to set up scheduled jobs, such as refreshing datasets, key pair or basic (username/password) authentication are the recommended methods to use when connecting to Snowflake— not OAuth. When an access token is expired, it can be renewed with a refresh token without re-authentication. However, when the refresh token expires, you must re-authenticate. ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | | DataRobot returns the following message when testing external OAuth Snowflake connection with Microsoft Entra ID: AADSTS700016: Application with identifier 'aa2572f-c9e6-4e91-9eb1-dcd84c856dd2' was not found in the directory 'Azure directory "datarobot" ("azuresupportdatarobot")'. This can happen if the application has not been installed by the administrator of the tenant or consented to by any user in the tenant. You may have sent your authentication request to the wrong tenant. | Make sure scopes were created, granted, and assigned to the resource in Azure. | Refer to the Snowflake setup section for more details. | | DataRobot returns the following message when testing external OAuth after adding the data connection:JDBC connect failed for jdbc:snowflake://datarobot_partner.snowflakecomputing.com?CLIENT_TIMESTAMP_TYPE_MAPPING=TIMESTAMP_NTZ&db=SANDBOX&warehouse=DEMO_WH&application=DATAROBOT&CLIENT_METADATA_REQUEST_USE_CONNECTION_CTX=false. Original error: The role requested in the connection or the default role if none was requested in the connection (ACCOUNTADMIN) is not listed in the Access Token or was filtered. Please specify another role, or contact your OAuth Authorization server administrator. | Make sure the user who is establishing a connection with Azure has default role assigned. | The default role needs to be anything other than ACCOUNTADMIN, ORGADMIN, or SECURITYADMIN. If the session:scope is created with scope:role-any, the user can log in with any role other than the admin roles stated. | | DataRobot returns the following message when testing the connection: Invalid Request: The request tokens do not match the user context. Do not copy the user context values (cookies; form fields; headers) between different requests or user sessions; always maintain the ALL of the supplied values across a complete single user flow. Failure Reasons:[Token values do not match;] | Make sure the login name of the user matches the login name in both Snowflake and Azure to map user and create access tokens. | You can alter the login name in Snowflake to match the username of Azure if it does not already match. | | DataRobot returns the following error message when attempting to authenticate Snowflake credentials: Incorrect username or password was specified. | Confirm that your parameters are valid; if they are, use the recommended driver version. | Check the username, private key, and passphrase; if all parameters are valid, use the recommended driver version from the dropdown under Show additional parameters > Driver.If you are using driver version 3.13.9:Click Show additional parameters.Click Add parameter and select account.Enter your account name in the field.For more information, see the Snowflake community article. | --- # Treasure Data URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-treasure.html # Treasure Data ## Supported authentication - Username/password ## Prerequisites The following is required before connecting to Treasure Data (TD-Hive) in DataRobot: - Treasure Data account ## Required parameters The table below lists the minimum required fields to establish a connection with Treasure Data: | Required field | Description | Documentation | | --- | --- | --- | | address | The connection URL that supplies connection information for Treasure Data. | Treasure Data documentation | | database | A unique identifier for your database. | Treasure Data documentation | Learn about additional [configuration options for Treasure Data](https://docs.treasuredata.com/display/public/PD/JDBC+Driver+for+Hive+Query+Engine). ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Trino URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/dc-trino.html > How to connect to the native Trino connector. # Trino ## Supported authentication - Basic (username/password) ## Prerequisites The following is required before connecting to Trino in DataRobot: - Data stored in a Trino database ## Required parameters The table below lists the minimum required fields to establish a connection with Trino: | Required field | Description | Documentation | | --- | --- | --- | | Host | The hostname or IP address of your Trino coordinator. | Trino documentation | ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | ## Code examples The Python example below shows how to connect to and move data from Trino into DataRobot. Initialize the DataRobot client and define database details for later use: ``` api_token = '' endpoint = 'https://app.datarobot.com/api/v2' import datarobot as dr from datarobot.enums import DataStoreTypes dr.Client(token=api_token, endpoint=endpoint) TRINO_HOST = "datarobot.trino.galaxy.starburst.io" TRINO_PORT = 443 USE_SSL = "true" CATALOG = "" SCHEMA = "" TABLE = "
" QUERY = None TRINO_USERNAME = "" TRINO_PASSWORD = "" ``` Do one of the following to locate your Trino driver ID: - Create the Trino driver ID: trino_driver=dr.DataDriver.create(class_name=DataStoreTypes.DR_DATABASE_V1,canonical_name='Trino Driver',database_driver='trino-v1',) - Reference an existing Trino driver ID: trino_driver=dr.DataDriver.get('') Create (or reuse) Trino credentials and securely save them in DataRobot: ``` trino_credentials = dr.Credential.create_basic( name='Trino Credentials', user=TRINO_USERNAME, password=TRINO_PASSWORD, ) ``` Define a connection to the external data store: ``` datastore_fields = [ {"id": "host", "name": "Host Name", "value": TRINO_HOST}, {"id": "port", "name": "port", "value": str(TRINO_PORT)}, {"id": "ssl", "name": "ssl", "value": USE_SSL}, ] trino_datastore = dr.DataStore.create( data_store_type=DataStoreTypes.DR_DATABASE_V1, canonical_name='Trino Datastore', driver_id=trino_driver.id, fields=datastore_fields, ) ``` Point to a specific data source (table or query): ``` data_source_params = dr.DataSourceParameters( data_store_id=trino_datastore.id, catalog=CATALOG, schema=SCHEMA, table=TABLE, query=QUERY, ) trino_datasource = dr.DataSource.create( data_source_type=DataStoreTypes.DR_DATABASE_V1, canonical_name='Trino DataSource', params=data_source_params, ) ``` Pull the data from Trino and import a snapshotted version into DataRobot: ``` trino_dataset = trino_datasource.create_dataset( do_snapshot=True, credential_id=trino_credentials.id, ) ``` --- # Supported data stores URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/index.html > View the connectors and JDBC drivers currently supported by DataRobot, as well as a list of deprecated connections. # Supported data stores Below is a list of all the supported data stores, along with the latest certified version of their JDBC drivers. Some of the connectors are in preview. | Connection | Connection type | Version | Unstructured data support? | Credential type | Notes | | --- | --- | --- | --- | --- | --- | | ADLS Gen2 | Connector | 2023.11.221435 | Yes | OAuth, OAuth service principal | Required parameters and connection details | | Alibaba Cloud MaxCompute | Driver | 3.6.0 | No | Basic | Required parameters and connection details | | Amazon Redshift | Driver | 2.1.0.14 | No | Basic | Download JDBC driverRequired parameters and connection details | | AWS Athena | Driver | 2.0.35 | No | Basic | Download JDBC driverRequired parameters and connection details | | AWS S3 | Connector | 2022.1.1670354484 | Yes | AWS credentials (both temporary and not temporary) | Required parameters and connection details | | Azure SQL | Driver | 12.4.1 | No | Basic | Download the JDBC driverRequired parameters and connection details | | Azure Synapse | Driver | 12.4.1 | No | Basic | Download the JDBC driverRequired parameters and connection details | | Box | Connector | | Yes | JWT | Required parameters and connection details | | Confluence | Connector | | Yes | Basic (username/API token) | Required parameters and connection details | | Databricks | Driver | 2.6.40 | No | Basic, access token | Download the JDBC driverRequired parameters and connection details | | Databricks | Connector | | Yes | Personal access token, service principal | Required parameters and connection details | | Exasol | Driver | 7.1.2 | No | Basic | Download the JDBC driverRequired parameters and connection details | | Google BigQuery | Connector | | No | Google Cloud OAuth, service account | Required parameters and connection details | | Google Drive | Connector | | Yes | Google Cloud OAuth, service account | Required parameters and connection details | | InterSystems | Driver | 3.0.0 | No | Basic | Download the JDBC driver | | Jira | Connector | | Yes | Basic (username/API token) | Required parameters and connection details | | KDB+ * | Driver | 2019.11.11 | No | Basic | Download JDBC driver (Log in to GitHub before clicking this link.)Required parameters and connection details | | Managed cloud OAuth | Connector | | Yes | Managed cloud OAuth | OAuth provider management | | Microsoft SQL Server 6.4 | Driver | 12.2.0 | No | Basic | Download JDBC driverRequired parameters and connection details | | MySQL | Driver | 8.0.32 | No | Basic | Download JDBC driverRequired parameters and connection details | | Oracle 6 | Driver | oracle-xe_11.2.0-1.0 | No | Basic | Download JDBC driverRequired parameters and connection details | | Oracle 8 | Driver | 12.2.0.1 | No | Basic | Download JDBC driverRequired parameters and connection details | | PostgreSQL | Driver | 42.5.1 JDBC 4.2 | No | Basic | Download JDBC driverRequired parameters and connection details | | Presto | Driver | 0.216 | No | Basic | Download JDBC driverRequired parameters and connection details | | SAP Datasphere (Premium) | Connector | | No | Basic | Required parameters and connection details | | SAP HANA | Driver | 2.20.17 | No | Basic | Download JDBC driverRequired parameters and connection details | | SharePoint | Connector | | Yes | Azure OAuth, service principal | Required parameters and connection details | | Snowflake | Driver | 3.15.1 | No | Basic, key pair, OAuth, external OAuth | Download JDBC driverRequired parameters and connection details | | TD-Hive | Driver | 0.5.10 | No | Basic | Download JDBC driverRequired parameters and connection details | | Treasure Data | Driver | Select the query engine you contracted for | No | Basic | Configure the data integrationRequired parameters and connection details | | Trino | Connector | | No | Basic | Required parameters and connection details | * Only supported with JDBC, not with KDB native query language `q` > [!NOTE] Self-Managed AI Platform > See [Manage JDBC drivers](https://docs.datarobot.com/en/docs/platform/admin/manage-cluster/manage-drivers.html) for steps to upload JDBC drivers to your organization. ## Deprecated connections Support is deprecated for these drivers and connectors: | Connection | Connection type | Version | | --- | --- | --- | | ADLS Gen2 | JDBC | 2021.2.1634676262008; 2020.3.1605726437949 | | Apache Hive * | JDBC | All | | Amazon S3 | JDBC | 2020.3.1603724051432 | | Elasticsearch | JDBC | All | | Google BigQuery | JDBC | spark-1.2.23.1027 | | Microsoft SQL Server | JDBC | 6.0 | * The Apache Hive JDBC driver is only deprecated for multi-tenant SaaS installations. > [!NOTE] Note > Older driver versions may still exist, but DataRobot recommends that you use the latest supported version of a connection. --- # ADLS Gen2 URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-adls.html > How to connect to the native ADLS Gen2 connector. # ADLS Gen2 ## Supported authentication - OAuth - Azure service principal ## OAuth ### Register the DataRobot application in Azure For the Microsoft identity platform to provide OAuth 2.0 authentication and authorization services for an application and its users, the application must be registered in the Azure portal with the associated parameters configured. Once this step is done, you will have the following information required for setup in DataRobot: - Client ID - Client secret - Scope - Properly configured end-user permissions for role-based access control To register a DataRobot application in the Azure portal and configure its parameters, follow the instructions in the [Microsoft Entra documentation](https://learn.microsoft.com/en-us/entra/identity-platform/quickstart-register-app): 1. Under Supported account types , select Accounts in any organizational directory and personal Microsoft accounts or Accounts in any organizational directory . 2. After the initial registration is complete, copy the Application ID (Client ID) on the Overview page. 3. Configure a redirect URI . In Configure platform settings , select Web and enter a redirect URI as follows: https:///account/adls/adls_oauth_authz_return (e.g., `https://app.datarobot.com/account/adls/adls_oauth_authz_return). The first part is where you installed the DataRobot application. 4. Configure a client secret. InCertificates & secrets, select theClient secretstab and clickNew client secret. Copy the client secret value (you won't be able to copy this later). NoteEach client secret has an expiration date. To avoid OAuth outages, periodically create a new client secret. Once a new client secret is created, you must update all associated credentials. 5. Configure the permissions (scope): If the user already has access to the data in the storage account, you can skip [Configure access to the storage account](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-adls.html#configure-access-to-the-storage-account). ### Configure access to the storage account To allow the DataRobot app to access files or objects under a storage account on behalf of the user, the user must first be granted access to the storage account files and objects. Azure role-based access control (RBAC) is recommended. See the [Microsoft Azure documentation](https://learn.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-access-control-model) for more information. To set up RBAC, follow the instructions in the [Microsoft Azure documentation](https://learn.microsoft.com/en-us/azure/role-based-access-control/role-assignments-portal?tabs=delegate-condition) using the following parameters: - For Step 3: Select the appropriate role , choose Storage Blob Data Reader . - For Step 4: Select who needs access , choose the user or group you want to grant access to. ### Mark the application as publisher verified Mark the DataRobot application as publisher verified using the instructions in the [Microsoft Entra documentation](https://learn.microsoft.com/en-us/entra/identity-platform/mark-app-as-publisher-verified). ## Azure service principal ### Register the DataRobot application in Azure To support the Azure service principal account, you must create and register a DataRobot application in the Azure portal, and configure its permissions. Once this step is done, you will have the following information required for setup in DataRobot: - Client ID - Client secret - Tenant ID - Properly configured service principal permissions for role-based access control To register a DataRobot application in the Azure portal and configure its parameters, follow the instructions in the [Microsoft Entra documentation](https://learn.microsoft.com/en-us/entra/identity-platform/howto-create-service-principal-portal): > [!NOTE] Note > Configuring a redirect URI is optional for service principal connections. 1. Under Supported account types , select Accounts in this organizational directory only . Note that you will need the name of the application to assign permissions. 2. After the initial registration is complete, copy the Application ID (Client ID) and Directory ID (Tenant ID) on the Overview page. 3. Assign a role to the application . Set the role name to Storage Blob Data Reader . If you want to set permissions at the storage account level, select the appropriate storage account and follow the instructions. 4. Configure a client secret. InCertificates & secrets, select theClient secretstab and clickNew client secret. Copy the client secret value (you won't be able to copy this later). NoteEach client secret has an expiration date. To avoid OAuth outages, periodically create a new client secret. Once a new client secret is created, you must update all associated credentials. ## Set up a connection in DataRobot To connect to ADLS Gen2 in DataRobot (this example uses service principal): 1. Open Workbench and select a Use Case. 2. Follow the instructions for connecting to a data source . 3. Enter the Azure Storage Account Name , the subdomain name of your unique Azure URL. 4. UnderAuthentication, clickNew credentialsand select an authentication method. Then, enter therequired parametersretrieved in the previous sections, and a unique display name. If you've previously added credentials for this data source, you can select it from your saved credentials. 5. ClickSave. ## Required parameters The table below lists the minimum required fields to establish a connection with ADLS Gen2: **OAuth:** Required field Description Notes Azure storage account name A unique name for your Azure storage account, which contains all your Azure Storage data objects. Microsoft documentation Client ID A unique value that identifies an application in the Microsoft identity platform. Microsoft documentation Client Secret Credentials used by confidential client applications that access a web API. Microsoft documentation Scope Permissions-based access to web API resources for authorized users and client apps that access the API. Microsoft documentation **Service principal:** Required field Description Notes Azure storage account name A unique name for your Azure storage account, which contains all your Azure Storage data objects. Microsoft documentation Client ID A unique value that identifies an application. Microsoft documentation Client Secret Credentials used by confidential client applications that access a web API. Microsoft documentation Azure Tenant ID A unique identifier for your Microsoft Entra tenant, which represents an organization. Microsoft documentation > [!NOTE] Optional parameters > 'File System Name' and 'Data Store Root Directory' are optional parameters. If specified, you can browse the files and folders within the specified file system or root directory directly from DataRobot. ## Feature considerations Consider the following when connecting to ADLS Gen2 in DataRobot. - The ADLS Gen2 connector does not support: --- # Databricks URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-databricks.html > How to connect to the native Databricks connector. # Databricks The Databricks connector allows you to access data in Databricks on Azure or AWS. In addition to accessing Databricks tables, you can use the Databricks connector to access data stored in Delta Lake and Iceberg format as long as they are registered as tables in the [Databricks Unity Catalog](https://docs.databricks.com/aws/en/tables/managed). The native Databricks connector also supports ingesting unstructured data from the Databricks Unity Catalog volumes, allowing you to browse and ingest files stored in volumes when creating vector databases. > [!NOTE] Note > To ingest both structured and unstructured data from Databricks, you must add the connector twice. ## Supported authentication - Personal access token - Service principal ## Prerequisites In addition to either a [personal access token](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-databricks.html#generate-a-personal-access-token) or [service principal](https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-databricks.html#create-a-service-principal) for authentication, the following is required before connecting to Databricks in DataRobot: **Azure:** A Databricks workspace in the Azure Portal app Data stored in an Azure Databricks database **AWS:** A Databricks workspace in AWS Data stored in an AWS Databricks database To ingest unstructured data, you must also set up a volume in the [Databricks Unity Catalog](https://docs.databricks.com/aws/en/volumes). ### Generate a personal access token **Azure:** In the Azure Portal app, generate a personal access token for your Databricks workspace. This token will be used to authenticate your connection to Databricks in DataRobot. See the [Azure Databricks documentation](https://learn.microsoft.com/en-us/azure/databricks/dev-tools/auth#--azure-databricks-personal-access-tokens-for-workspace-users). **AWS:** In AWS, generate a personal access token for your Databricks workspace. This token will be used to authenticate your connection to Databricks in DataRobot. See the [Databricks on AWS documentation](https://docs.databricks.com/en/dev-tools/auth.html#databricks-personal-access-token-authentication). ### Create a service principal **Azure:** In the Azure Portal app, create an service principal for your Databricks workspace. The resulting client ID and client secret will be used to authenticate your connection to Databricks in DataRobot. See the [Azure Databricks documentation](https://learn.microsoft.com/en-us/azure/databricks/dev-tools/auth/oauth-m2m). In the linked instructions, copy the following information: Application ID: Entered in the client ID field during setup in DataRobot. OAuth secrets: Entered in the client secret field during setup in DataRobot. Make sure the service principal has permission to access the data you want to use. **AWS:** In AWS, create an service principal for your Databricks workspace. The resulting client ID and client secret will be used to authenticate your connection to Databricks in DataRobot. See the [Azure Databricks documentation](https://docs.databricks.com/en/dev-tools/auth/oauth-m2m.html). Make sure the service principal has permission to access the data you want to use. ## Set up a connection in DataRobot The example below shows how to connect to Databricks using Azure and an access token to ingest structured data. To connect to Databricks in DataRobot: 1. Open Workbench and select a Use Case. 2. Follow the instructions for connecting to a data source . 3. With the information retrieved in theprevious section, fill in therequired configuration parameters. 4. UnderAuthentication, clickNew credentials. Then, enter your access token and a unique display name. If you've previously added credentials for this data source, you can select it from your saved credentials. If you selected service principal as the authentication method, enter the client ID, client secret, and a unique display name. 5. ClickSave. ### Required parameters The table below lists the minimum required fields to establish a connection with Databricks: **Azure:** Required field Description Documentation Server Hostname The address of the server to connect to. Azure Databricks documentation HTTP Path The compute resources URL. Azure Databricks documentation **AWS:** Required field Description Documentation Server Hostname The address of the server to connect to. Databricks on AWS documentation HTTP Path The compute resources URL. Databricks on AWS documentation SQL warehouses are dedicated to execute SQL, and as a result, have less overhead than clusters and often provide better performance. It is recommended to use a SQL warehouse if possible. > [!NOTE] Note > If the `catalog` parameter is specified in a connection configuration, Workbench will only show a list of schemas in that catalog. If this parameter is not specified, Workbench lists all catalogs you have access to. ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | ## Feature considerations Review the feature considerations below before connecting to Databricks: **Structured data:** Predictions are not available using the native Databricks connector. You must connect to Databricks using the JDBC driver . **Unstructured data:** Unstructured data is only available during vector database creation. --- # Alibaba Cloud MaxCompute URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-maxcompute.html > How to connect to MaxCompute in DataRobot. # Alibaba Cloud MaxCompute ## Supported authentication - Username/password ## Prerequisites The following is required before connecting to MaxCompute in DataRobot: - Alibaba Cloud MaxCompute account - Alibaba Cloud MaxCompute project ## Required parameters The table below lists the minimum required fields to establish a connection with MaxCompute: | Required field | Description | Documentation | | --- | --- | --- | | address | The service endpoint used to connect to Alibaba Cloud MaxCompute.Example:http://service.us-east-1.maxcompute.aliyun.com/api | Alibaba Cloud MaxCompute endpoint documentation | | project | The MaxCompute project for storing data.Example:my_maxcompute_project | Alibaba Cloud MaxCompute project documentation | > [!TIP] Use a JDBC URL > You can also choose to use a JDBC URL to connect to MaxCompute. DataRobot notifies you about required parameters, but you can create a connection without them. The JDBC URL template is `jdbc:odps:?project=`. For example, `jdbc:odps:http://service.us-east-1.maxcompute.aliyun.com/api?project=jdbc_project&enableLimit=false`. For more information on the available parameters, see the [Alibaba Cloud MaxCompute JDBC parameters documentation](https://www.alibabacloud.com/help/en/maxcompute/user-guide/usage-notes-2#section-ue6-n1n-bpg) or the [aliyun-odps-jdbcpublic repository](https://github.com/aliyun/aliyun-odps-jdbc/tree/master?tab=readme-ov-file#connection-string-parameters). ## Feature considerations - By default, MaxCompute sets a 10000 row download limitation. To disable this limit, see theMaxCompute JDBC download control documentation. - For more information on the MaxCompute Java Database Connectivity (JDBC) driver, see theMaxCompute JDBC driver documentation. ### MaxCompute prediction considerations When using MaxCompute for predictions: - As a data destination, only the "insert"write strategyis supported. The writeback table and column names cannot contain special characters. These names can contain letters, digits, and underscores (_); however, they must start with a letter and cannot exceed 128 bytes in length. If the table and column names don't meet the requirements, an error is returned. - As a data source, the scoring data column names must be lowercase and cannot contain special characters. These names can contain letters, digits, and underscores (_); however, they must start with a letter and cannot exceed 128 bytes in length. If the column names don't meet the requirements, an error is returned. For more information on table creation and requirements in MaxCompute, see the [MaxCompute SQL table documentation](https://www.alibabacloud.com/help/en/maxcompute/user-guide/table-creation-and-deletion). ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # SAP Datasphere URL: https://docs.datarobot.com/en/docs/reference/data-ref/data-sources/wb-sap.html > How to connect to SAP Datasphere in DataRobot. # SAP Datasphere > [!NOTE] Premium feature > The SAP Datasphere connector is a premium feature. Contact your DataRobot representative or administrator for information on enabling the feature. ## Supported authentication - Username/password ## Prerequisites Before connecting to SAP Datasphere in DataRobot, make sure you've [created a database user](https://developers.sap.com/tutorials/data-warehouse-cloud-intro8-create-databaseuser.html) for the space you want to connect to. ## Required parameters The table below lists the minimum required fields to establish a connection with SAP Datasphere: | Required field | Description | | --- | --- | | hostname | The unique identifier of the device on a network. | | port | The unique identifier of the connection endpoint. | For more information on finding the required parameters, see the [SAP documentation](https://developers.sap.com/tutorials/data-warehouse-cloud-intro8-create-databaseuser.html). ## Feature considerations Note that Datasphere only exposes views through API for data consumption. If you want to bring in data from Datasphere to DataRobot for modeling, make sure the data is exposed as a view. ## Troubleshooting | Problem | Solution | Instructions | | --- | --- | --- | | When attempting to execute an operation in DataRobot, the firewall requests that you clear the IP address each time. | Add all allowed IPs for DataRobot. | See Allowed source IP addresses. If you've already added the allowed IPs, check the existing IPs for completeness. | --- # Exploratory Data Analysis (EDA) URL: https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html > EDA is a two-stage process that DataRobot employs to first analyze datasets and summarize their main characteristics and then build models. # Exploratory Data Analysis (EDA) Exploratory Data Analysis or EDA is DataRobot's approach to analyzing datasets and summarizing their main characteristics. Generally speaking, there are two stages of EDA—EDA1 and EDA2. EDA1 provides summary statistics based on a sample of your data. EDA2 is the step used for model building and uses the entire dataset, based on the options selected (see below). The following describes, in general terms, the DataRobot model building process for datasets under 1GB: 1. Import a dataset. 2. DataRobot launches EDA1 (and automatically creates feature transformations if date features are detected). 3. Upon completion of EDA1, select a target and clickStart. 4. DataRobot partitions the data. 5. DataRobot launches EDA2 and starts model building when it completes. The table below lists the components of EDA: | Analysis type | Analyzes... | | --- | --- | | Automatic data schema and data type | Numeric (numerical statistics, mean, standard deviation, median, min, max)CategoricalBooleanTextSpecial feature types dateCurrencyPercentageLengthImageGeospatial pointsGeospatial lines or polygons | | Data visualization | HistogramFrequency distribution for top 50 itemsOvertimeColumn validity for modeling (non-empty, non-duplicate)Average valueOutliersFeature correlation to the target | | Data quality checks | InliersOutliersDisguised missing valuesExcess zerosTarget leakageMissing imagesDuplicate images | | Feature association matrix | Support numerical and categorical data with metrics:Mutual informationCramer's VPearsonSpearman | ## EDA1 DataRobot calculates EDA1 on up to 500MB of your dataset, after any applicable conversion or expansion. If the expanded dataset is under 500MB, it uses the entire dataset; otherwise, it uses a 500MB random sample (meaning it takes a random sampling equaling 500MB when the dataset is over 500MB). > [!NOTE] Note > For larger datasets, Fast EDA runs during EDA1 and calculates early target selection using only a percentage of the input dataset. A message identifies the approximate percentage of data used. See [more information](https://docs.datarobot.com/en/docs/classic-ui/data/import-data/large-data/fast-eda.html#fast-eda-and-early-target-selection) on early target selection for large datasets. EDA1 returns: - Feature type - Special feature type - For numerics, numerical statistics - Frequency distribution for top 50 items - Column validity for modeling (non-empty, non-duplicate) ## EDA2 DataRobot calculates EDA2 on the portion of the data used for EDA1, excluding rows that are also in the holdout data (if there is a holdout) and rows where the target is `N/A`. DataRobot also does additional calculations on the target column using the entire dataset. EDA2 returns: - Recalculation of the numerical statistics originally calculated in EDA1. - Feature correlation to the target (initial feature importance calculation). The target data used is from the sampled portion used for all the other columns. Note that the following column types are flagged as "invalid/non-informative," cannot be transformed, and are not used in modeling: - Duplicate column(s). - Empty columns and columns lacking enough data to model. - Columns consisting of only unique identifiers (reference ID columns). --- # Dataset requirements URL: https://docs.datarobot.com/en/docs/reference/data-ref/file-types.html > Detailed dataset requirements for file size and format, rows, columns, encodings and characters sets, column length and name conversion, and more. # Dataset requirements This section provides information on dataset requirements: - General requirements - Ensuring acceptable file sizes - AutoML file import sizes - Time series (AutoTS) file import sizes - Feature Discovery file import sizes - Pipeline data requirements - File formats - Encodings and character sets - Special column detection - Length and name conversion - File download sizes See the associated [considerations](https://docs.datarobot.com/en/docs/classic-ui/data/index.html#feature-considerations) for important additional information. ## General requirements Consider the following dataset requirements for AutoML, [time series](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/index.html), and [Visual AI](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/visual-ai/index.html) projects. See additional information about [preparing your dataset](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/visual-ai/vai-model.html#prepare-the-dataset) for Visual AI. | Requirement | Solution | Dataset type | Visual AI | | --- | --- | --- | --- | | Dataset minimum row requirements for non-date/time projects: For regression projects, 20 data rows plus header rowFor binary classification projects: minimum 20 minority- and 20 majority-class rowsminimum 100 total data rows, plus header rowFor multiclass classification projects, minimum 100 total data rows, plus header row | Error displays number of rows found; add rows to the dataset until project meets the minimum rows (plus the header). | Training | Yes | | Date/time partitioning-based projects (Time series and OTV) have specific row requirements. | Error displays number of rows found; add rows to the dataset until project meets the minimum rows (plus the header). | Training | Yes | | Dataset used for predictions via the GUI must have at least one data row plus header row. | Error displays zero rows found; add a header row and one data row. | Predictions | Yes | | Dataset cannot have more than 20,000 columns. | Error message displays column count and limit; reduce the number of columns to less than 20,000. | Training, predictions | Yes | | Dataset must have headers. | Lack of header generally leads to bad predictions or ambiguous column names; add headers. | Training, predictions | Yes for CSV. If ZIP upload contains one folder of images per class, then technically there are not headers and so this is not always true for Visual AI. | | Dataset must meet deployment type and release size limits. | Error message displays dataset size and configured limit. Contact DataRobot Support for size limits; reduce dataset size by trimming rows and/or columns. | Training, predictions | Yes managed AI Platform: 5GB; 100k 224x224 pixels / 50kB images Self-Managed AI Platform: 10GB; 200k 224x224 pixels / 50kB images | | Number of columns in the header row must be greater or equal to the number of columns in all data rows. For any data row with fewer columns than the maximum, DataRobot assumes a value of NA/NULL for that row. Number of columns in the header row must be greater or equal to the number of columns in all data rows. | Error displays the line number of the first row that failed to parse; check the row reported in the error message. Quoting around text fields is a common reason for this error. | Training, predictions | Yes | | Dataset cannot have more than one blank (empty) column name. Typically the first blank column is the first column, due to the way some tools write the index column. | Error displays column index of the second blank column; add a label to the column. | Training, predictions | Yes | | Dataset cannot have any column names containing only whitespace. A single blank column name (no whitespace) is allowed, but columns such as “(space)” or "(space)(space)" are not allowed. | Error displays index of the column that contained only space(s); remove the space, or rename the column. | Training, predictions | Yes | | All dataset feature names must be unique. No feature name can be used for more than one column, and feature names must differ from each other beyond just their use of special characters (e.g., -, $, ., {, }, \n, \r, ", or '). | Error displays the two columns that resolved to the same name after sanitization; rename one column name. Example: robot.bar and robot$bar both resolve to robot\_bar. | Training, predictions | Yes | | Dataset must use a supported encoding. Because UTF-8 processes the fastest, it is the recommended encoding. | Error displays that the detected encoding is not supported or could not be detected; save the dataset to a CSV/delimited format, via another program, and change encoding. | Training, predictions | Yes | | Dataset files must have one of the following delimiters: comma (,), tab (\t), semicolon (;), or pipe ( \| ). | Error displays a malformed CSV/delimited message; save the dataset to another program (e.g., Excel) and modify to use a supported delimiter. A problematic delimiter that is one of the listed values indicates a quoting problem. For text datasets, if strings are not quoted there may be issues detecting the proper delimiter. Example: in a tab separated dataset, if there are commas in text columns that are not quoted, they may be interpreted as a delimiter. See this note for a related file size issue. | Training, predictions | Yes | | Excel datasets cannot have date times in the header. | Error displays the index of the column and approximation of the column name; rename the column (e.g., “date” or “date-11/2/2016”). Alternatively, save the dataset to CSV/delimited format. | Training, predictions | Yes | | Dataset must be a single file. | Error displays that the specified file contains more than one dataset. This most commonly occurs with archive files (tar and zip); uncompress the archive and make sure it contains only one file. | Training, predictions | Yes | | User must have read permissions to the dataset when using URL or HDFS ingest. | Error displays that user does not have permission to access the dataset. | Training, predictions | Yes | | All values in a date column must have the same format or be a null value. | Error displays the value that did not match and the format itself; find the unmatched value in the date column and change it. | Training, predictions | Yes, this applies to the dataset whenever there is a date column, with no dependence on an image column. | | Text features can contain up to 5 million characters (for a single cell); in some cases up to 10 million characters are accepted. In other words, no practical limit and the total size of the dataset is more likely the limiting factor. | N/A | Training, predictions | Yes, this applies to the dataset whenever there is a text column, with no dependence on an image column. | ## Ensure acceptable file import size > [!NOTE] Note > All file size limits represent the uncompressed size. When ingesting a dataset, its actual on-disk size might be different inside of DataRobot. - If the original dataset source is a CSV, then the size may differ slightly from the original size due to data preprocessing performed by DataRobot. - If the original dataset source isnota CSV (e.g., is SAS7BDAT, JDBC, XLSX, GEOJSON, Shapefile, etc.), the on-disk size will be that of the dataset when converted to a CSV. SAS7BDAT, for example, is a binary format that supports different encoding types. As a result, it is difficult to estimate the size of data when converted to CSV based only on the input size as a SAS7BDAT file. - XLSX, due to its structure, is read in as a single, whole document which can cause OOM issues when trying to parse. CSV, by contrast is read in chunks to reduce memory usage and prevent errors. Best practice recommends not exceeding 150MB for XLSX files. - If the original dataset source is an archive or a compressed CSV (e.g., .gzip, .bzip2, .zip, .tar, .tgz), the actual on-disk size will be that of the uncompressed CSV after preprocessing is performed. Keep the following in mind when considering file size: - Some of the preprocessing steps that are applied consist of converting the dataset encoding to UTF-8, adding quotation marks for the field data, normalizing missing value representation, converting geospatial fields, and sanitizing column names. - In the case of image archives or other similar formats, additional preprocessing will be done to add the images file contents to the resulting CSV. This potentially will make the size of the final CSV drastically different from the original uploaded file. - File size limitations are applied to filesonce they have been convertedto CSV. If you upload a zipped file into DataRobot, when DataRobot extracts the file, the file must be less than the file size limits. - If a delimited-CSV dataset (CSV, TSV, etc.) size is close to the upload limit prior to ingest, it is best to do the conversion outside of DataRobot. This helps ensure that the file import does not exceed the limit. If a non-comma delimited file is near to the limit size, it may be best to convert to a comma-delimited CSV outside of DataRobot as well. - When converting to CSV outside of DataRobot, be sure to use commas as the delimiter, newline as the record separator, and UTF-8 as the encoding type to avoid discrepancies in uploaded file size and size counted against DataRobot's maximum file size limit. - Consider modifying optional feature flags in some cases: ## AutoML file import sizes The following sections describe supported file import sizes. > [!NOTE] Note > File size upload is dependent on your DataRobot package and, in some cases, the number and size of servers deployed. See tips to ensure [acceptable file size](https://docs.datarobot.com/en/docs/reference/data-ref/file-types.html#ensure-acceptable-file-import-size) for more assistance. **SaaS:** File type Maximum size Notes CSV 5GB Essentials package CSV Up to 100GB See considerations XLSX 150MB See guidance * Up to 10GB applies to AutoML projects; [considerations apply](https://docs.datarobot.com/en/docs/classic-ui/data/index.html#feature-considerations). **Self-Managed:** File type Maximum size Release availability Notes CSV (training) Up to 100GB All Varies based on your DataRobot package and available hardware resources. XLS 150MB 3.0.1 and later ### Beyond 10GB ingest Ingest of up to 100GB training datasets provides large-scale modeling capabilities. When enabled, file ingest limit is increased from 10GB to 100GB, and models are trained using incremental learning methods. Consider the following when training with 100GB: - Available for binary classification, regression, multiclass, and multilabel experiments. Time series, anomaly detection, and clustering projects are API-only. - No support for Visual AI or Location AI projects. - Ingestion is only available from an external source ( data connection or URL); training data must be registered in the AI Catalog (20GB datasets cannot be directly uploaded from a local computer). - Sliced insights are disabled. - Feature Discovery is disabled. - By default, Feature Effects generates insights for the top 500 features (ranked by feature impact). With projects greater than 10GB, in consideration of runtime performance, Feature Effects generates insights for the top 100 features. ### OTV requirements For [out-of-time validation (OTV)](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/otv.html) modeling, maximum dataset size: Less than 5GB. OTV backtests require at least 20 rows in each of the validation and holdout folds and at least 100 rows in each training fold. If you set a number of backtests that results in any of the runs not meeting that criteria, DataRobot only runs the number of backtests that do meet the minimums (and marks the display with an asterisk). For example: - With one backtest, no holdout, minimum 100 training rows and 20 validation rows (120 total). - With one backtest and holdout, minimum 100 training rows, 20 validation rows, 20 holdout rows (140 total). ## Prediction file import sizes > [!NOTE] Self-Managed AI Platform limits > Prediction file size limits vary for Self-Managed AI Platform installations and limits are configurable. | Prediction method | Details | File size limit | | --- | --- | --- | | Leaderboard predictions | To make predictions on a non-deployed model using the UI, expand the model on the Leaderboard and select Predict > Make Predictions. Upload predictions from a local file, URL, data source, or the AI Catalog. You can also upload predictions using the modeling predictions API, also called the "V2 predictions API." Use this API to test predictions using your modeling workers on small datasets. Predictions can be limited to 100 requests per user, per hour, depending on your DataRobot package. | 1GB | | Batch predictions (UI) | To make batch predictions using the UI, deploy a model and navigate to the deployment's Make Predictions tab (requires MLOps). | 5GB | | Batch predictions (API) | The Batch Prediction API is optimized for high-throughput and contains production grade connectivity options that allow you to not only push data through the API, but also connect to the AI catalog, cloud storage, databases, or data warehouses (requires MLOps). | Unlimited | | Prediction API (real-time) Dedicated Prediction Environment (DPE) | To make real-time predictions on a deployed model, use the Prediction API. | 50MB | | Prediction API (real-time) Serverless Prediction Environment | To make real-time predictions on a deployed model, use the Prediction API. | 50MB | | Prediction monitoring | While the Batch Prediction API isn't limited to a specific file size, prediction monitoring is still subject to an hourly rate limit. | 100MB / hour | ## Time series file requirements When using [time series](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/index.html), datasets must be CSV format and meet the following size requirements: | Max file size: single series | Max file size: multiseries/segmented | Notes | | --- | --- | --- | | 500MB | 5GB | SaaS | | 500MB | 2.5GB | Self-Managed 6.0+, 30GB modeler configuration | | 500MB | 5GB | Self-Managed 6.0+, 60GB modeler configuration | If you set a number of backtests that results in any of the runs not meeting that criteria, DataRobot only runs the number of backtests that do meet the minimums (and marks the display with an asterisk). Specific features of time series: | Feature | Requirement | | --- | --- | | Minimum rows per backtest | | | Data ingest: Regression | 20 rows for training and 4 rows for validation | | Data ingest: Classification | 75 rows for training and 12 rows for validation | | Post-feature derivation: Regression | Minimum 35 rows | | Post-feature derivation: Classification | 100 rows | | Calendars | | | Calendar event files | Less than 1MB and 10K rows | | Multiseries modeling* | | | External baseline files for model comparison | Less than 5GB | * Self-Managed AI Platform versions 5.0 or later are limited to 100,000 series; versions 5.3 or later are limited to 1,000,000 series. > [!NOTE] Note > There are times that you may want to [partition without holdout](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-leaderboard.html#partition-without-holdout), which changes the minimum ingest rows and also the output of various visualizations. For releases 4.5, 4.4 and 4.3, datasets must be less than 500MB. For releases 4.2 and 4.0, datasets must be less than 10MB for time series and less than 500MB for OTV. Datasets must be less than 5MB for projects using Date/Time partitioning in earlier releases. ## Feature Discovery file import sizes When using Feature Discovery, the following requirements apply: - Secondary datasets must be either uploaded files or JDBC sources registered in theAI Catalog. - You can have a maximum of 30 datasets per project. - The sum of all dataset sizes (both primary and secondary) should not exceed 40GB, and individual dataset sizes should not exceed 20GB. Using larger datasets may impact performance and result in error. See the download limitsdownload limitsmentioned below. ## Data formats DataRobot supports the following formats and types for data ingestion. See also the supported [data types](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#data-summary-information). ### File formats - .csv, .dsv, or .tsv* (preferred formats) - database tables - .xls/.xlsx - PDF** - .sas7bdat - .parquet*** - .avro** *The file must be a comma-, tab-, semicolon-, or pipe-delimited file with a header for each data column. Each row must have the same number of fields, some of which may be blank. **These file types are preview. Contact your DataRobot representative for more information. ***Parquet files are typed data; if the file contains a string field with numeric values, DataRobot treats this field as categorical. #### Location AI file formats The following [Location AI](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/location-ai/lai-ingest.html) file types are supported only if enabled for users in your organization: - ESRI Shapefiles - GeoJSON - ESRI File Geodatabase - Well Known Text (embedded in table column) - PostGIS Databases (The file must be a comma-delimited, tab-delimited, semicolon-delimited, or pipe-delimited file and must have a header for each data column. Each row must have the same number of fields (columns), some of which may be blank.) ### Compression formats - .gz - .bz2 ### Archive format - .tar ### Compression and archive formats - .zip - .tar.gz/.tgz - .tar.bz2 Both compression and archive are accepted. Archive is preferred, however, because it allows DataRobot to know the uncompressed data size and therefore to be more efficient during data intake. ### Decimal separator The period (.) character is the only supported decimal separator—DataRobot does not support locale-specific decimal separators such as the comma (,). In other words, a value of `1.000` is equal to one (1), and cannot be used to represent one thousand (1000). If a different character is used as the separator, the value is treated as categorical. A numeric feature can be positive, negative, or zero, and must meet one of the following criteria: - Contains no periods or commas. - Contains a single period (values with more than one period are treated as categorical ). The table below provides sample values and their corresponding variable type: | Feature value | Data type | | --- | --- | | 1000000 | Numeric | | 0.1 | Numeric | | 0.1 | Numeric | | 1,000.000 | Categorical | | 1.000.000 | Categorical | | 1,000,000 | Categorical | | 0,1000 | Categorical | | 1000.000… | Categorical | | 1000,000… | Categorical | | (0,100) | Categorical | | (0.100) | Categorical | > [!TIP] Tip > Attempting a feature transformation (on features considered categorical based on the separator) from categorical to numeric will result in an empty numeric feature. ## Encodings and character sets Datasets must adhere to the following encoding requirements: - The data file cannot have any extraneous characters or escape sequences (from URLs). - Encoding must be consistent through the entire dataset. For example, if a datafile is encoded as UTF-8 for the first 100MB, but later in the file there are non-utf-8 characters, it can potentially fail due to incorrect detection from the first 100MB. Data must adhere to one of the following encodings: - ascii - cp1252 - utf-8 - utf-8-sig - utf-16 - utf-16-le - utf-16-be - utf-32 - utf-32-le - utf-32-be - Shift-JIS - ISO-2022-JP - EUC-JP - CP932 - ISO-8859-1 - ISO-8859-2 - ISO-8859-5 - ISO-8859-6 - ISO-8859-7 - ISO-8859-8 - ISO-8859-9 - windows-1251 - windows-1256 - KOI8-R - GB18030 - Big5 - ISO-2022-KR - IBM424 - windows-1252 ## Special column detection Note that these special columns will be detected if they meet the criteria described below, but `currency`, `length`, `percent`, and `date` cannot be selected as the target for a project. However, `date` can be selected as a partition feature. ### Date and time formats Columns are detected as date fields if they match any of the formats containing a date listed below. If they are strictly time formats, (for example, `%H:%M:%S`) they are detected as time. See the [Python definition table](https://docs.python.org/2/library/datetime#strftime-and-strptime-behavior) for descriptions of the directives. The following table provides examples using the date and time January 25, 1999 at 1:01 p.m. (specifically, 59 seconds and 000001 microseconds past 1:01 p.m.). | String | Example | | --- | --- | | %H:%M | 13:01 | | %H:%M:%S | 13:01:59 | | %I:%M %p | 01:01 PM | | %I:%M:%S %p | 01:01:59 PM | | %M:%S | 01:59 | | %Y %m %d | 1999 01 25 | | %Y %m %d %H %M %S | 1999 01 25 13 01 59 | | %Y %m %d %I %M %S %p | 1999 01 25 01 01 59 PM | | %Y%m%d | 19990125 | | %Y-%d-%m | 1999-25-01 | | %Y-%m-%d | 1999-01-25 | | %Y-%m-%d %H:%M:%S | 1999-01-25 13:01:59 | | %Y-%m-%d %H:%M:%S.%f | 1999-01-25 13:01:59.000000 | | %Y-%m-%d %I:%M:%S %p | 1999-01-25 01:01:59 PM | | %Y-%m-%d %I:%M:%S.%f %p | 1999-01-25 01:01:59.000000 PM | | %Y-%m-%dT%H:%M:%S | 1999-01-25T13:01:59 | | %Y-%m-%dT%H:%M:%S.%f | 1999-01-25T13:01:59.000000 | | %Y-%m-%dT%H:%M:%S.%fZ | 1999-01-25T13:01:59.000000Z | | %Y-%m-%dT%H:%M:%SZ | 1999-01-25T13:01:59Z | | %Y-%m-%dT%I:%M:%S %p | 1999-01-25T01:01:59 PM | | %Y-%m-%dT%I:%M:%S.%f %p | 1999-01-25T01:01:59.000000 PM | | %Y-%m-%dT%I:%M:%S.%fZ %p | 1999-01-25T01:01:59.000000Z PM | | %Y-%m-%dT%I:%M:%SZ %p | 1999-01-25T01:01:59Z PM | | %Y.%d.%m | 1999.25.01 | | %Y.%m.%d | 1999.01.25 | | %Y/%d/%m %H:%M:%S.%f | 1999/25/01 13:01:59.000000 | | %Y/%d/%m %H:%M:%S.%fZ | 1999/25/01 13:01:59.000000Z | | %Y/%d/%m %I:%M:%S.%f %p | 1999/25/01 01:01:59.000000 PM | | %Y/%d/%m %I:%M:%S.%fZ %p | 1999/25/01 01:01:59.000000Z PM | | %Y/%m/%d | 1999/01/25 | | %Y/%m/%d %H:%M:%S | 1999/01/25 13:01:59 | | %Y/%m/%d %H:%M:%S.%f | 1999/01/25 13:01:59.000000 | | %Y/%m/%d %H:%M:%S.%fZ | 1999/01/25 13:01:59.000000Z | | %Y/%m/%d %I:%M:%S %p | 1999/01/25 01:01:59 PM | | %Y/%m/%d %I:%M:%S.%f %p | 1999/01/25 01:01:59.000000 PM | | %Y/%m/%d %I:%M:%S.%fZ %p | 1999/01/25 01:01:59.000000Z PM | | %d.%m.%Y | 25.01.1999 | | %d.%m.%y | 25.01.99 | | %d/%m/%Y | 25/01/1999 | | %d/%m/%Y %H:%M | 25/01/1999 13:01 | | %d/%m/%Y %H:%M:%S | 25/01/1999 13:01:59 | | %d/%m/%Y %I:%M %p | 25/01/1999 01:01 PM | | %d/%m/%Y %I:%M:%S %p | 25/01/1999 01:01:59 PM | | %d/%m/%y | 25/01/99 | | %d/%m/%y %H:%M | 25/01/99 13:01 | | %d/%m/%y %H:%M:%S | 25/01/99 13:01:59 | | %d/%m/%y %I:%M %p | 25/01/99 01:01 PM | | %d/%m/%y %I:%M:%S %p | 25/01/99 01:01:59 PM | | %m %d %Y %H %M %S | 01 25 1999 13 01 59 | | %m %d %Y %I %M %S %p | 01 25 1999 01 01 59 PM | | %m %d %y %H %M %S | 01 25 99 13 01 59 | | %m %d %y %I %M %S %p | 01 25 99 01 01 59 PM | | %m-%d-%Y | 01-25-1999 | | %m-%d-%Y %H:%M:%S | 01-25-1999 13:01:59 | | %m-%d-%Y %I:%M:%S %p | 01-25-1999 01:01:59 PM | | %m-%d-%y | 01-25-99 | | %m-%d-%y %H:%M:%S | 01-25-99 13:01:59 | | %m-%d-%y %I:%M:%S %p | 01-25-99 01:01:59 PM | | %m.%d.%Y | 01.25.1999 | | %m.%d.%y | 01.25.99 | | %m/%d/%Y | 01/25/1999 | | %m/%d/%Y %H:%M | 01/25/1999 13:01 | | %m/%d/%Y %H:%M:%S | 01/25/1999 13:01:59 | | %m/%d/%Y %I:%M %p | 01/25/1999 01:01 PM | | %m/%d/%Y %I:%M:%S %p | 01/25/1999 01:01:59 PM | | %m/%d/%y | 01/25/99 | | %m/%d/%y %H:%M | 01/25/99 13:01 | | %m/%d/%y %H:%M:%S | 01/25/99 13:01:59 | | %m/%d/%y %I:%M %p | 01/25/99 01:01 PM | | %m/%d/%y %I:%M:%S %p | 01/25/99 01:01:59 PM | | %y %m %d | 99 01 25 | | %y %m %d %H %M %S | 99 01 25 13 01 59 | | %y %m %d %I %M %S %p | 99 01 25 01 01 59 PM | | %y-%d-%m | 99-25-01 | | %y-%m-%d | 99-01-25 | | %y-%m-%d %H:%M:%S | 99-01-25 13:01:59 | | %y-%m-%d %H:%M:%S.%f | 99-01-25 13:01:59.000000 | | %y-%m-%d %I:%M:%S %p | 99-01-25 01:01:59 PM | | %y-%m-%d %I:%M:%S.%f %p | 99-01-25 01:01:59.000000 PM | | %y-%m-%dT%H:%M:%S | 99-01-25T13:01:59 | | %y-%m-%dT%H:%M:%S.%f | 99-01-25T13:01:59.000000 | | %y-%m-%dT%H:%M:%S.%fZ | 99-01-25T13:01:59.000000Z | | %y-%m-%dT%H:%M:%SZ | 99-01-25T13:01:59Z | | %y-%m-%dT%I:%M:%S %p | 99-01-25T01:01:59 PM | | %y-%m-%dT%I:%M:%S.%f %p | 99-01-25T01:01:59.000000 PM | | %y-%m-%dT%I:%M:%S.%fZ %p | 99-01-25T01:01:59.000000Z PM | | %y-%m-%dT%I:%M:%SZ %p | 99-01-25T01:01:59Z PM | | %y.%d.%m | 99.25.01 | | %y.%m.%d | 99.01.25 | | %y/%d/%m %H:%M:%S.%f | 99/25/01 13:01:59.000000 | | %y/%d/%m %H:%M:%S.%fZ | 99/25/01 13:01:59.000000Z | | %y/%d/%m %I:%M:%S.%f %p | 99/25/01 01:01:59.000000 PM | | %y/%d/%m %I:%M:%S.%fZ %p | 99/25/01 01:01:59.000000Z PM | | %y/%m/%d | 99/01/25 | | %y/%m/%d %H:%M:%S | 99/01/25 13:01:59 | | %y/%m/%d %H:%M:%S.%f | 99/01/25 13:01:59.000000 | | %y/%m/%d %H:%M:%S.%fZ | 99/01/25 13:01:59.000000Z | | %y/%m/%d %I:%M:%S %p | 99/01/25 01:01:59 PM | | %y/%m/%d %I:%M:%S.%f %p | 99/01/25 01:01:59.000000 PM | | %y/%m/%d %I:%M:%S.%fZ %p | 99/01/25 01:01:59.000000Z PM | ### Percentages Columns that have numeric values ending with `%` are treated as percentages. ### Currencies Columns that contain values with the following currency symbols are treated as currency. - $ - EUR, USD, GBP - £ - ￡ (fullwidth) - € - ¥ - ￥ (fullwidth) Also, note the following regarding currency interpretation: - The currency symbol can be preceding ($1) or following (1EUR) the text but must be consistent across the feature. - Both comma ( , ) and period ( . ) can be used as a separator for thousands or cents, but must be consistent across the feature (e.g., 1000 dollars and 1 cent can be represented as 1,000.01 or 1.000,01). - Leading + and - symbols are allowed. ### Length Columns that contain values matching the convention < feet >’ < inches >” are displayed as variable type `length` on the Data page. DataRobot converts the length to a number in inches and then treats the value as a numeric in blueprints. If your dataset has other length values (for example, 12cm), the feature is treated as categorical. If a feature has mixed values that show the measurement (5m, 72in, and 12cm, for example), it is best to clean and normalize the dataset before uploading. ## Column name conversions During data ingestion, DataRobot converts the following characters to underscores ( `_`): `-`, `$`, `.` `{`, `}`, `"`, `\n`, and `\r`. Additionally, DataRobot removes all leading and trailing spaces. ## File download sizes Consider the following when downloading datasets: - There is a 10GB file size limit. - Datasets are downloaded as CSV files. - The downloaded dataset may differ from the one initially imported because DataRobot applies the conversions mentioned above. --- # Data URL: https://docs.datarobot.com/en/docs/reference/data-ref/index.html > Reference content that supports working with data and understanding data handling in DataRobot. # Data reference The following sections provide reference content that supports working with data and understanding data handling in DataRobot: | Topic | Description | | --- | --- | | Supported data stores | View a list of supported and deprecated data stores along with the details on how to connect to them in DataRobot. | | Allowed source IP addresses | View a list of allowed source IP addresses for DataRobot. | | EDA1 | View summary statistics based on a sample of your data. | | Automatic feature transformations | Understand date-type feature transformations generated by DataRobot. | | EDA2 | View summary statistics based on the portion of the data used for EDA1, excluding rows that are also in the holdout data and rows where the target is N/A. | | Data quality | See details of the checks DataRobot runs for the potential data quality issues. | | Asset states | Understand the difference between various asset states and the meaning of the corresponding badges displayed in DataRobot. | | Wrangle large Snowflake datasets | Tips for improving interactivity and performance when wrangling large Snowflake datasets in Workbench. | | Data augmentation methods | Summarizes the various ways in which DataRobot augments datasets for different experiment types. | | OCR for unstructured data | Structure data sources to become AI-consumable for agentic workflows. | --- # OCR for unstructured data URL: https://docs.datarobot.com/en/docs/reference/data-ref/ocr-ref.html > Structure data sources to become AI-consumable for agentic workflows. # OCR for unstructured data DataRobot’s OCR (optical character recognition) addresses one of the most significant bottlenecks in agentic workflows—the reliable transformation of messy unstructured data such as scanned PDFs, PowerPoints, and documents into AI-consumable formats. By automating the extraction of document hierarchies, tables, and figures without the need for custom parsing code or brittle scripts, using OCR ensures that AI agents can process real-world enterprise information at scale. The preservation of document structure is critical for retrieval-augmented generation (RAG) pipelines, as it prevents the "flattening" of data, which often leads to silent errors, inaccurate grounding, and loss of context during reasoning. With advanced parsing capabilities tht directly integrate into the REST ecosystem, developers can move from manual document preparation to production-ready agent deployment in a fraction of the time. The integration provides broad format coverage and standardized JSON outputs that flow seamlessly into DataRobot’s orchestration, evaluation, and monitoring tools. This unified approach eliminates the need for fragmented "glue code" and separate parsing tools, allowing organizations to build, operate, and govern sophisticated agents that can reason over complex knowledge bases with higher confidence and reduced maintenance overhead. ## Perform OCR To perform OCR with unstructured data, use Datarobot's Python client: ``` import datarobot as dr from datarobot import OCREngineSpecificParameters, OCRJobResource from datarobot import OCRJobDatasetLanguage dr_client = dr.Client( token='YOUR TOKEN', endpoint='ENGPOINT' ) # upload file file_path = 'OCR_files_integration/demo_files.zip' f = dr.Files.upload(file_path, use_archive_contents=True) resp = dr_client.post( url='ocrJobResources/', data={ 'dataset_id': input_file_id, 'language': 'ENGLISH', 'engine_specific_parameters': {'engine_type': 'ARYN', 'output_format': 'JSON'}, # we can try JSON or MARDOWN here } ) job_resource_id = resp.json()['id'] output_file_id = resp.json()['outputCatalogId'] job_resource = OCRJobResource.get(job_resource_id) start_resp = job_resource.start_job() output_file = dr.Files.get(output_file_id) output_file.list_contained_files(). # view all the files inside output_file.download('demo_files/file1.arynjson', file_path='output_file1.json') ``` --- # Wrangle large Snowflake datasets URL: https://docs.datarobot.com/en/docs/reference/data-ref/wrangle-snowflake.html > Tips for improving interactivity and performance when wrangling large Snowflake datasets in Workbench. # Wrangle large Snowflake datasets This page describes how to improve performance and interactivity when wrangling large Snowflake datasets in Workbench. ## Increase Snowflake warehouse size Snowflake warehouse size specifies the compute resources available per cluster, therefore, increasing your warehouse size will reduce the time it takes to execute wrangling queries. See the [Snowflake documentation on increasing warehouse size](https://docs.snowflake.com/en/user-guide/performance-query-warehouse-size). ## Change the sampling method When generating the live wrangling preview, DataRobot, by default, retrieves a random sample from the source table. To reduce the time it takes to execute the query in Snowflake and display the preview, you can change the sampling method so DataRobot retrieves the First-N Rows instead. For step-by-step instructions, see the documentation on [choosing a sampling method](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/build-recipe/build-recipe.html#configure-the-live-sample). ## Reduce the sample size To generate a live wrangling preview, DataRobot executes the query directly in Snowflake. By default, the preview uses 10000 random rows from the source table to generate insights, however, you can reduce the number of rows sampled to decrease the time it takes to execute the query in Snowflake. This method is particularly helpful for wide (hundreds of features) and heavy (many long text features) datasets where 10000 rows may require significant resources and time to process. For step-by-step instructions, see the documentation on [configuring the live sample](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/build-recipe/build-recipe.html#configure-the-live-sample). --- # GenAI feature considerations URL: https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-consider.html > Things to consider when working with DataRobot GenAI capabilities. # GenAI feature considerations The following sections describe things to consider when working with generative AI capabilities in DataRobot. Note that as the product continues to develop, some considerations may change. See [Troubleshooting](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-troubleshooting.html) for an overview of common errors and their handling. > [!NOTE] Considerations for trial users > See the considerations specific to the [DataRobot free trial](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-consider.html#trial-user-considerations), including [supported LLM base models](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html). > [!NOTE] LLM gateway rate limits > Depending on the configuration, your organization may be subject to rate limits on total number of chat completion calls. If the application returns a message that your maximum has been reached, it will reset in 24 hours. The time to reset is indicated in the error message. To remove the limit, contact your administrator or DataRobot representative to manage your organization's pricing plan. ## General considerations - If a multilingual dataset exceeds the limit associated with the multilingual model, DataRobot defaults to using thejinaai/jina-embedding-t-en-v1embedding model. - Deployments created from custom models with training data attached that have extra columns cannot be used unless column filtering is disabled on the custom model. - When using LLMs that are either BYO or deployed from the playground and require a runtime parameter to point to the endpoint associated with their credentials, be aware of the vendor's model versioning and end-of-life schedules. As a best practice, use only endpoints that are generally available when deploying to production. (Models provided in the playground manage this for you.) - Note that an API key named[Internal] DR API Access for GenAI Experimentationis created for you when you access the playground or vector database in the UI. - When using GPUs, BYO embeddings functionality is available for self-managed users only. Note that when many users run vector database creation jobs in parallel, if using BYO embeddings, LLM playground functionality may be degraded until vector database creation jobs complete. Using CPUs with a custom model that contains the embeddings model is supported in all environments. - Only one aggregated metric job can run at a time. If an aggregation job is currently running, theConfigure aggregationbutton is disabled and the "Aggregation job in progress; try again when it completes" tooltip appears. ## Vector database considerations The following describes considerations related to [vector databases](https://docs.datarobot.com/en/docs/agentic-ai/vector-database/index.html). See also the [supported dataset types](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html#supported-dataset-types), below. > [!NOTE] GPU usage for Self-Managed users > When working with datasets over 1GB, Self-Managed users who do not have GPU usage configured on their cluster may experience serious delays. Email [DataRobot Support](mailto:support@datarobot.com), or visit the [Support site](https://support.datarobot.com/), for installation guidance. - Creation: - Deployment: - Token budget: - Chunking: - Metadata filtering: ## Playground considerations - Playgroundscan be shared for viewing, and users with editor or owner access can perform additional actions within the shared playground, such as creating blueprints. While non-creators cannot prompt an LLM blueprint in the playground, they can make a copy and submit prompts to that copy. - You can only prompt LLM blueprints that you created (i.e., in both configuration and comparison view). To see the results of prompting another user’s LLM blueprint in a shared Use Case, copy the blueprint, and then you can chat with the same settings applied. - Deleted prompts and responses are counted toward daily limits, although only successful prompt/response pairs are counted. Bring-your-own (BYO) LLM calls are not part of the count. - For self-managed users (STS, VPC, on-premise): The number of prompts per day, across all LLMs, is dependent on your payment plan. For users on the consumption-based pricing plan, the limit is set by your organization's requirements. All others can submit 5,000 LLM prompts per day. Limits for trial users are different, as describedhere. ## Playground deployment considerations Consider the following when registering and deploying LLMs from the playground: - Setting API keys through the DataRobot credential management system is supported. Those credentials are accessed as environment variables in a deployment. - Registration and deployment is supported for: - The creation of a custom model version from an LLM blueprint associated with a large vector database (500MB+) can be time-consuming. You can leave the workshop while the model is being created and will not lose your progress. ### Bolt-on Governance API - When using the Bolt-on Governance API with a deployed LLM blueprint, seeLLM availabilityfor the recommended values of themodelparameter. Alternatively, specify a reserved value,model="datarobot-deployed-llm", to let the LLM blueprint select the relevant model ID automatically when calling the LLM provider's services. In Workbench, whenadding a deployed LLMthat implements thechatfunction, the playground uses the Bolt-on Governance API as the preferred communication method. Enter theChat model IDassociated with the LLM blueprint to set themodelparameter for requests from the playground to the deployed LLM. Alternatively, enterdatarobot-deployed-llmto let the LLM blueprint select the relevant model ID automatically when calling the LLM provider's services. - Configuringevaluation and moderationfor the custom model negates the effect of streaming responses in the chat completion API, since guardrails evaluate the complete response of the LLM and return the response text in one chunk. - The followingOpenAI parametersare not supported in the Bolt-on Governance API:functions,tool,tool_choice,logprobs,top_logprobs. ## LLM evaluation and moderation The following describes considerations related to [LLM evaluation and moderation](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html): - You cangenerate synthetic datasetsin both theUIand API. Use GPT-4, if possible, as it best follows the format DataRobot expects for the output format. Otherwise, the LLM might not generate question-answer pairs. - Metrics: - Moderations: - Aggregation: ## Trial user considerations The following considerations apply only to DataRobot free trial users: - You can create up to 15 DataRobot-hosted and third-party connected vector databases, computed across multiple Use Cases. Deleted vector databases are included in this count. BYO vector databases are not included in the count. - You can make 1,000 LLM API calls, where deleted prompts and responses are also counted. However, only successful prompt response pairs are counted. - Trial users do not have access toNVIDIA NIMor GPUs. --- # Troubleshooting URL: https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-troubleshooting.html > An overview of common errors and their handling in DataRobot GenAI functionality. # Troubleshooting When working with GenAI in DataRobot, you might encounter errors during creation, management, and use of the features that make up the capabilities. The sections below provide an overview of common errors and their handling. In case of errors, review the error messages and take appropriate action, such as revalidating external vector databases, creating duplicate LLM blueprints, or addressing issues with custom models. ## Vector database error handling The following issues apply to vector databases. ### Creation failure Errors might occur during the creation of a vector database, such as issues with data processing or model training. In this case, the system saves the execution status as `Error` and provides an error code and message to help identify and resolve the issue. ### Empty vector database The vector database might be empty if the documents in the dataset contain no text or only consist of images in PDF files. The system sends a notification of the issue. See the list of [supported dataset types](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html#supported-dataset-types) for more information. ### Retrieval failure Errors might occur while retrieving documents from the vector database, such as network glitches or internal errors in the custom model. If this occurs, follow the guidance in the error messages to diagnose and resolve the issue. ## LLM blueprint error handling The following issues apply to blueprints and the playground. ### Vector database unlinked If the connection with a vector database is broken, any LLM blueprint using it will be marked as `Unlinked`. Either revalidate/restore your external vector database connection or create a duplicate of the LLM blueprint and select a new database in the configuration. ### Vector database deleted If a vector database is deleted, any LLM blueprints using it become invalid. To proceed, create a duplicate of the LLM blueprint and select a new database. ### Deployment fails When exporting an LLM blueprint that includes a vector database to the [workshop](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-create-custom-model.html#configure-custom-model-resource-settings), if deployment fails, try increasing the resource bundle/memory. ### Custom model errors If the custom model used in an LLM blueprint encounters an error, such as a model replacement, deletion, or access removal, the LLM blueprint is marked with an appropriate error status. Follow the provided in-app guidance to proceed. When [adding a deployed LLM](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#add-a-deployed-llm) that supports the [Bolt-on Governance API](https://docs.datarobot.com/en/docs/api/code-first-tools/drum/structured-custom-models.html#chat), the playground uses the Bolt-on Governance API as the preferred communication method. Requests from the playground to the deployed LLM will [specify the value entered in theChat model IDfield as themodelparameter](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html). To disable the Bolt-on Governance API and use the predictions API instead, delete the `chat` function from the custom model and redeploy it. --- # GenAI URL: https://docs.datarobot.com/en/docs/reference/gen-ai-ref/index.html > Reference content that supports working with generative AI. # GenAI reference The following sections provide reference content that supports working with generative AI: | Topic | Description | | --- | --- | | Availability information | Describes support for the various elements that are part of GenAI model creation, including LLMs, embeddings, sharing and permissions, and supported dataset types. | | LLM reference | View brief descriptions of each available LLM. | | Feature considerations | Describes things to consider when working with GenAI capabilities in DataRobot. | | Troubleshooting | Provides an overview of common errors and their handling. | | Prompting strategy | Provides strategies for creating effective prompting. | | LLM metric reference | Provides detailed metric descriptions, including formulas for calculating DataRobot's custom metrics for LLM evaluation. | | LLM gateway administration | Details the steps for organizational admins to follow when adding LLMs to the LLM gateway. | --- # Availability information URL: https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html > The following sections describe support for the various elements that are part of GenAI model creation, including LLMs, embeddings, sharing and permissions, and supported dataset types. # Availability information The following sections describe support for the various elements that are part of GenAI model creation: - LLM availability , including deprecation and retirement information. - Embeddings availability , including multilingual language support. - Sharing and permissions . - Supported dataset types . > [!TIP] Trial users > See the considerations specific to the [DataRobot free trial](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-consider.html#trial-user-considerations). See also, for reference, [brief descriptions](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-reference.html) of each available LLM. ## LLM availability Note the following when working with LLMs and the LLM gateway: **LLM gateway availability:** Availability of the LLM gateway is based on your pricing package. When enabled, the specific LLMs available via the LLM gateway are ultimately controlled by the organization administrator. If you see an LLM listed below but do not see it as a selection option when [building LLM blueprints](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/build-llm-blueprints.html#select-an-llm), contact your administrator. See also the [LLM gateway service](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/dr-llm-gateway.html) documentation for information on the DataRobot API endpoint that can be used to interface with external LLM providers. LLM availability through the LLM gateway service is restricted to non-government regions. To integrate with LLMs not available through the LLM gateway service, see the notebook that outlines how to [build and validate an external LLM integration](https://docs.datarobot.com/en/docs/agentic-ai/genai-code/ext-llm.html) using the DataRobot Python client. **Rate limits:** Depending on the configuration, your organization may be subject to rate limits on total number of chat completion calls. If the application returns a message that your maximum has been reached, it will reset in 24 hours. The time to reset is indicated in the error message. To remove the limit, contact your administrator or DataRobot representative to manage your organization's pricing plan. **For org admins:** All LLMs that are part of the LLM gateway are disabled by default and can only be enabled by the organization administrator. To enable an LLM for a user or org, search for `LLM_` in the Feature access page; it will return the full list of available LLMs. These LLMs are supported for production usage in the DataRobot platform. Additionally, an org admin can toggle `Enable Fast-Track LLMs`, also in Feature access, to gain access to the newest LLMs from external LLM providers. These LLMs have not yet gone through the full DataRobot testing and approval process and are not recommended for production usage. **Region availability for self-managed:** Provider region availability information applies only to DataRobot's managed multi-tenant SaaS environments. It is not relevant for self-hosted (single-tenant SaaS, VPC, and on-premise) deployments where the provider region is dependent on the installation configuration. The following tables list LLMs by provider. - Amazon Bedrock - Azure OpenAI - Google VertexAI - Anthropic - Cerebras - TogetherAI In the tables below, which lists LLM availability by provider, note the following: | Indicator | Explanation | | --- | --- | | † | Due to EU regulations, model access is disabled for Cloud users on the EU platform. | | ‡ | Due to JP regulations, model access is disabled for Cloud users on the JP platform. | | Δ | The model ID the playground uses for calling the LLM provider's services. This value is also the recommended value for the model parameter when using the Bolt-on Governance API for deployed LLM blueprints. | | © | Meta Llama is licensed under the Meta Llama 4 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. | #### Amazon Bedrock #### Azure OpenAI #### Google VertexAI #### Anthropic #### Cerebras #### TogetherAI ### Deprecated and retired LLMs In the quickly advancing agentic AI landscape, LLMs are constantly improving, with new versions replacing older models. To address this, DataRobot's LLM deprecation process marks LLMs and LLM blueprints with a badge to indicate upcoming changes.Note that retirement dates are set by the provider and are subject to change. The following LLMs are currently, or will soon be, deprecated and removed: **Amazon Bedrock:** LLM Retirement date Anthropic Claude 2.1 Retired Anthropic Claude 3 Sonnet Retired Anthropic Claude Opus 3 Retired Anthropic Claude Sonnet 3.5 v1 Retired Anthropic Claude Sonnet 3.5 v2 Retired Anthropic Claude Sonnet 3.7 v1 2026-04-28 Anthropic Claude Haiku 3.5 v1 2026-06-19 Anthropic Claude Opus 4 Retired Cohere Command Light Text v14 Retired Cohere Command Text v14 Retired Titan Retired **Azure OpenAI:** LLM Retirement date GPT-3.5 Turbo Retired GPT-3.5 Turbo 16k Retired GPT-4 Retired GPT-4 32k Retired GPT-4 Turbo Retired GPT-4o Mini Retired o1-mini Retired **Google VertexAI:** LLM Retirement date Bison Retired Gemini 3 Pro Preview 2026-03-26 Gemini 2.0 Flash 2026-06-01 Gemini 2.0 Flash Lite 2026-06-01 Gemini 1.5 Flash Retired Gemini 1.5 Pro Retired Claude Opus 3 Retired Claude Sonnet 3.5 Retired Claude Sonnet 3.5 v2 Retired Claude Sonnet 3.7 2026-05-11 Claude Haiku 3.5 2026-07-05 Claude Haiku 3 2026-08-23 Claude Opus 4 2026-09-13 Claude Opus 4.1 2026-09-13 Mistral CodeStral 2501 Retired Mistral Large 2411 Retired **Anthropic:** LLM Retirement date Claude Opus 3 Retired Claude Sonnet 3.5 v1 Retired Claude Sonnet 3.5 v2 Retired Claude Sonnet 3.7 Retired Claude Haiku 3.5 Retired Claude Opus 4.1 2026-08-05 **Cerebras:** LLM Retirement date Cerebras Qwen 3 32B Retired Cerebras Llama 3.3 70B Retired **TogetherAI:** LLM Retirement date Arcee AI Virtuoso Large Retired Arcee AI Coder-Large Retired Arcee AI Maestro Reasoning Retired Meta Llama 3 70B Instruct Reference Retired Meta Llama 3.1 405B Instruct Turbo Retired Meta Llama 4 Scout Instruct Retired Mistral (7B) Instruct Retired Mistral (7B) Instruct v0.3 Retired Mistral (7B) Instruct v0.2 Retired Marin Community Marin 8B Instruct Retired Meta Llama 3 8B Instruct Lite Retired Meta Llama 3.1 8B Instruct Turbo Retired Meta Llama 3.2 3B Instruct Turbo Retired Meta Llama 4 Maverick Instruct Retired Mistral Small 3 Instruct (24B) Retired Mistral Mixtral-8x7B Instruct v0.1 Retired To help protect experiments and deployments from unexpected removal of provider support, badges for deprecated LLMs are shown in the LLM blueprint creation panel: Or if built, affected LLM blueprints are marked with a warning or notice, with dates provided on hover: **LLM selection:** When selecting an LLM for building LLM blueprints, in the selection panel LLMs are marked with a Deprecate badge to indicate that the end of support date for the LLM falls within 90 days. [https://docs.datarobot.com/en/docs/images/llm-select-button.png](https://docs.datarobot.com/en/docs/images/llm-select-button.png) **LLM blueprints:** Once LLM blueprints are built, they are displayed in the [LLM blueprintstab](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/compare-llm.html#llm-blueprints-tab). Deprecated or retired LLM blueprints are marked with a warning or notice, with dates provided on hover: [https://docs.datarobot.com/en/docs/images/llm-deprecate-badge.png](https://docs.datarobot.com/en/docs/images/llm-deprecate-badge.png) - When an LLM is in thedeprecationprocess, support for the LLM will be removed in 90 days. Badges and warnings are present, but functionality is not restricted. - Whenretired, assets created from the retired model are still viewable, but the creation of new assets is prevented. Retired LLMs cannot be used in single or comparison prompts. Some [evaluation metrics](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#configure-evaluation-metrics), for example faithfulness and correctness, use an LLM in their configuration. For those, messages are displayed when viewing or configuring the metrics, as well as in the prompt response. If an LLM has been deployed, because DataRobot does not have control over the credentials used for the underlying LLM, the deployment will fail to return predictions. If this happens, replace the deployed LLM with a new model. ## Embeddings availability DataRobot supports the following types of embeddings for encoding data; all are transformer models trained on a mixture of supervised and unsupervised data. | Embedding type | Description | Language | | --- | --- | --- | | cl-nagoya/sup-simcse-ja-base | A medium-sized language model from the Nagoya University Graduate School of Informatics ("Japanese SimCSE Technical Report"). It is a fast model for Japanese RAG.Input Dimension*: 512Output Dimension: 768Number of Parameters: 110M | Japanese | | huggingface.co/intfloat/multilingual-e5-base | A medium-sized language model from Microsoft Research ("Weakly-Supervised Contrastive Pre-training on large MultiLingual corpus") used for multilingual RAG performance across multiple languages. Input Dimension*: 512Output Dimension: 768Number of parameters: 278M | 100+, see ISO 639 | | huggingface.co/intfloat/multilingual-e5-small | A smaller-sized language model from Microsoft Research ("Weakly-Supervised Contrastive Pre-training on large MultiLingual corpus") used for multilingual RAG performance with faster performance than the MULTILINGUAL_E5_BASE. This embedding model is good for low-latency applications. Input Dimension*: 512Output Dimension: 384Number of parameters: 118M | 100+, see ISO 639 | | intfloat/e5-base-v2 | A medium-sized language model from Microsoft Research ("Weakly-Supervised Contrastive Pre-training on large English Corpus") for medium-to-high RAG performance. With fewer parameters and a smaller architecture, it is faster than E5_LARGE_V2. Input Dimension*: 512Output Dimension: 768Number of parameters: 110M | English | | intfloat/e5-large-v2 | A large language model from Microsoft Research ("Weakly-Supervised Contrastive Pre-training on large English Corpus") designed for optimal RAG performance. It is classified as slow due to its architecture and size.Input Dimension*: 512Output Dimension: 1024Number of parameters: 335M | English | | jinaai/jina-embedding-t-en-v1 | A tiny language model trained using Jina AI's Linnaeus-Clean dataset. It is pre-trained on the English corpus and is the fastest, and default, embedding model offered by DataRobot. Input Dimension*: 512Output Dimension: 384Number of parameters: 14M | English | | jinaai/jina-embedding-s-en-v2 | Part of the Jina Embeddings v2 family, this embedding model is the optimal choice for long-document embeddings (large chunk sizes, up to 8192). Input Dimension*: 8192Output Dimension: 384Number of parameters: 33M | English | | sentence-transformers/all-MiniLM-L6-v2 | A small language model fine-tuned on a 1B sentence-pairs dataset. It is relatively fast and pre-trained on the English corpus. It is not recommend for RAG, however, as it was trained on old data. Input Dimension*: 256Output Dimension: 384Number of parameters: 33M | English | * Input Dimension = `max_sequence_length` ## Sharing and permissions The following table describes GenAI component-related user permissions. All roles (Consumer, Editor, Owner) refer to the user's role in the Use Case; access to various function are based on the Use Case roles. For example, because sharing is handled on the Use Case level, you cannot share only a vector database (vector databases do not define any sharing rules). ## Supported dataset types The following describes requirements for vector databases and evalutation datasets. ### Vector database formats When uploading datasets for use in creating a vector database, the supported formats are either `.zip` or `.csv`. Two columns are mandatory for the files— `document` and `document_file_path`. Additional metadata columns, up to 50, can be added for use in filtering during prompt queries. Note that for purposes of [metadata filtering](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/rag-chatting.html#metadata-filtering), `document_file_path` is displayed as `source`. For `.zip` files, DataRobot processes the file to create a `.csv` version that contains text columns ( `document`) with an associated reference ID ( `document_file_path`) column. All content in the text column is treated as strings. The reference ID column is created automatically when the `.zip` is uploaded. All files should be either in the root of the archive or in a single folder inside an archive. Using a folder tree hierarchy is not supported. Regarding file types, DataRobot provides the following support: - .txtdocuments - PDF documents - .docxdocuments are supported but older.docformat is not supported. - .mddocuments, and the.markdownvariant, are supported. - A mix of all supported document types in a single dataset is allowed. ### Evaluation datasets Evaluation datasets serve as reference data for evaluation metrics and aggregated metrics. The evaluation dataset must be: - A CSV file. - In the Data Registry. - Have at least one text or categorical column. --- # LLM gateway model configuration URL: https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-gateway-config.html > Add approved LLMs to the LLM gateway to leverage agentic functionality in situations where company policy restricts the LLMs available to employees. # LLM gateway model configuration > [!NOTE] Note > The ability to add LLMs to the LLM gateway is a function controlled by the install administrator. For multi-tenant SaaS users, contact [DataRobot Support](mailto:support@datarobot.com) or visit the [Support site](https://support.datarobot.com/) to request assistance. A self-managed or single-tenant SaaS org admin can add LLMs to the LLM gateway of their installation, making them available for users. In this way, you can leverage the agentic functionality in situations where, for example, company policy restricts the LLMs available to its employees. DataRobot's GenAI service supports managed LLMs from a variety of providers. To enable these models, refer to the LLM provider documentation for provisioning LLM resources: - Azure OpenAI - Amazon Bedrock - Google Vertex AI - Anthropic - Cerebras - TogetherAI ## Set up LLM credentials Once LLMs are configured on the provider side, set up provider credentials in the DataRobot GenAI service to access the various LLMs. For user-managed credentials (always used for self-managed, and often, single-tenant SaaS installations), best practice suggests using [secure configurations](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-gateway-config.html#via-secure-configuration) in DataRobot for maximum flexibility and security. In certain cases, for instance a single-tenant SaaS environment with DataRobot-managed credentials, an admin can [set up credentials directly](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-gateway-config.html#directly-during-installation) in `values.yaml`. ### Via secure configuration Prior to DataRobot installation, ensure the respective provider is enabled under the `llmGateway` configuration section within the `buzok-llm-gateway` sub-chart, for example: ``` buzok-llm-gateway: enabled: true llmGateway: providers: : enabled: true credentialsSecureConfigName: genai--llm-credentials ``` When configuring, use the following list of supported providers and corresponding LLM credentials "`SecureConfig`" display names: ``` anthropic: credentialsSecureConfigName: genai-anthropic-llm-credentials aws: credentialsSecureConfigName: genai-aws-llm-credentials azure: credentialsSecureConfigName: genai-azure-llm-credentials cerebras: credentialsSecureConfigName: genai-cerebras-llm-credentials google: credentialsSecureConfigName: genai-gcp-llm-credentials togetherai: credentialsSecureConfigName: genai-togetherai-llm-credentials ``` > [!NOTE] Note > You can include the following providers' secure configurations, but they are not yet supported in DataRobot: > > cohere ( > genai-cohere-llm-credentials > ) > openai ( > genai-openai-llm-credentials > ) > groq ( > genai-groq-llm-credentials > ) After installation is complete, provide model credentials to DataRobot from the Secure Configuration page: **Access from NextGen:** From the system admin icon, to the left of the profile icon: [https://docs.datarobot.com/en/docs/images/secure-config-ng.png](https://docs.datarobot.com/en/docs/images/secure-config-ng.png) **Access from Classic:** From the user profile icon, in the APP ADMIN section: [https://docs.datarobot.com/en/docs/images/secure-config-classic.png](https://docs.datarobot.com/en/docs/images/secure-config-classic.png) 1. Click+ Add a secure configuration. 2. Complete the fields to create the configuration. FieldDescriptionSecure configuration typeFrom the dropdown, select the credential provider. For ease in locating provider options, enterGenAIin the search field. Example:[GenAI] Anthropic LLM CredentialsSecure configuration display nameEnter the display name, listed in eachprovider sectionbelow. Example:genai-anthropic-llm-credentialsLLM CredentialsAdd the credentials in JSON format by copying and pasting the JSON—from{to}—from theprovider-specific configuration sectionsbelow. Be sure to update with your actual credentials. 3. ClickSave. The new secure configuration, with credentials, is added to the list of secure configurations. 4. UnderActionsin the configuration table, click the share icon to share the newly created config with the GenAI Admin system user (genai-admin@datarobot.com). This user is automatically created during installation. You must assign either theOwnerorEditorrole to the admin. > [!NOTE] Note > The Generative AI Service caches credentials retrieved from secure configuration for a short period of time. When rotating credentials, it can take a few minutes for the new credentials to be applied. ### Directly during installation > [!NOTE] Note > Using this method, updating credentials requires reinstalling or updating DataRobot. Because the credentials are visible for the person performing the installation, this is considered a less secure and flexible way of setting up credentials for the service. Credentials can be specified directly in the `buzok-llm-gateway` configuration section. Secure configuration won't be used in this case. The example below is how a configuration for AWS credentials; exact keys differ for various providers, see the [provider-specific configuration](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-gateway-config.html#provider-specific-configuration) sections for credentials structure details: ``` buzok-llm-gateway: enabled: true llmGateway: providers: aws: enabled: true credentialsSecureConfigName: "" credentials: endpoints: - region: "" access_key_id: "" secret_access_key: "" ``` ## Provider-specific configuration The following sections describe configuration for supported providers: - Azure OpenAI - Amazon Bedrock - Google Vertex AI - Anthropic - Cerebras - TogetherAI ### Azure OpenAI Azure OpenAI models must be configured with specific deployment names, where deployment name can be: - Equal to the model name, for example, a deployment for the gpt-35-turbo-16k model can be named gpt-35-turbo-16k . - Different from the model name, such as creating a deployment named gpt-4o-2024-11-20 for the model gpt-4o . For a list of deployment names, see the chat model ID on the [Azure OpenAI LLM availability](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html#azure-openai) page. After configuring an Azure OpenAI LLM deployment, format the credentials using the following JSON structure: ``` { "endpoints": [ { "region": "", "api_type": "azure", "api_base": "https://.openai.azure.com/", "api_version": "2024-10-21", "api_key": "" } ] } ``` When creating a secure configuration with these credentials, use: | Field | Value | | --- | --- | | Secure configuration type | [GenAI] Azure OpenAI LLM Credentials | | Secure configuration display name | genai-azure-llm-credentials | > [!NOTE] Note > Currently, "2024-10-21" is the latest stable version of the Azure API. To use the latest models, you may be required to use a preview version of the API. Refer to the official [Azure API documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/api-version-lifecycle) for details on Azure Inference API versions. The secure configuration section for Azure should follow the YAML structure outlined below: ``` buzok-llm-gateway: enabled: true llmGateway: providers: azure: enabled: true credentialsSecureConfigName: genai-azure-llm-credentials ``` ### Amazon Bedrock The Generative AI Service supports the various first-party (e.g., `Amazon Nova`) and third-party (e.g., `Anthropic Claude`, `Meta Llama`, and `Mistral`) models from Amazon Bedrock. There are two options of setting up Amazon Bedrock models: - IAM roles - Static AWS credentials Using IAM roles ( [IRSA](https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts.html)) is recommended as it is a more secure option. This is because it uses dynamic short-lived AWS credentials. However, if your Kubernetes cluster is not in the AWS cloud, setting up this authorization mechanism requires setting up Workload Identity Federation. For this reason, the Generative AI Service also supports static AWS credentials. For security reasons, when using static credentials, it is recommended to create a separate AWS user that only has permissions to access AWS Bedrock. After enabling model access in AWS Bedrock, format the credentials using the following JSON structure for using static credentials: ``` { "endpoints": [ { "region": "", "access_key_id": "", "secret_access_key": "", "session_token": null, } ] } ``` Use the following JSON structure when using a role different from IRSA (ensure there is a policy that allows IRSA to assume this role): ``` { "endpoints": [ { "region": "", "role_arn": "" } ] } ``` Using IRSA to access Bedrock is available in 11.1.2 and later. Set `"use_web_identity": true` to use `AssumeRoleWithWebIdentity` from the pod's service account token. ``` { "endpoints": [ { "region": "", "role_arn": "", "use_web_identity": true } ] } ``` Using FIPS-enabled endpoints is available in 11.1.2 and later. Set `"use_fips_endpoint": true` to do that. Note, that model availability may vary between FIPS-enabled and regular endpoints. ``` { "endpoints": [ { "region": "", "role_arn": "", "use_fips_endpoint": true } ] } ``` #### Single-tenant SaaS (STS) configuration For single-tenant SaaS (STS) installations, the Bedrock credential configuration differs from the standard IRSA setup. Use the following JSON structure: ``` { "endpoints": [ { "region": "", "role_arn": "" } ] } ``` Note the following differences from the standard IRSA configuration: - The role_arn value must be the IAM role on the customer's AWS account—not the IAM role currently used by the STS cluster. - Do not include use_web_identity in the configuration. The STS cluster handles authentication through its own role assumption mechanism rather than web identity federation. When creating a secure configuration with these credentials, use: | Field | Value | | --- | --- | | Secure configuration type | [GenAI] AWS Bedrock LLM Credentials | | Secure configuration display name | genai-aws-llm-credentials | The secure configuration section for Amazon Bedrock should follow the YAML structure outlined below: ``` buzok-llm-gateway: enabled: true llmGateway: providers: aws: enabled: true credentialsSecureConfigName: genai-aws-llm-credentials ``` ### Google Vertex AI The Generative AI Service supports multiple models from Google Vertex AI, including first-party Gemini models and third-party Claude/Llama/Mistral models. After provisioning the model, you should receive a JSON file with access credentials. To provide credentials to DataRobot Generative AI, format the contents using the following JSON structure: ``` { "endpoints": [ { "region": "us-central1", "service_account_info": { "type": "service_account", "project_id": "", "private_key_id": "", "private_key": "----- -----\n", "client_email": ".iam.gserviceaccount.com", "client_id": "", "auth_uri": "https://accounts.google.com/o/oauth2/auth", "token_uri": "https://oauth2.googleapis.com/token", "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", "client_x509_cert_url": "https://.iam.gserviceaccount.com", "universe_domain": "googleapis.com" } } ] } ``` When creating a secure configuration with these credentials, use: | Field | Value | | --- | --- | | Secure configuration type | [GenAI] Google VertexAI LLM Credentials | | Secure configuration display name | genai-gcp-llm-credentials | The secure configuration section for Google Vertex AI should follow the YAML structure outlined below: ``` buzok-llm-gateway: enabled: true llmGateway: providers: google: enabled: true credentialsSecureConfigName: genai-gcp-llm-credentials ``` ### Anthropic The Generative AI Service provides first-party integration with Anthropic, giving access to various `Claude` models. To configure access to Anthropic, provide the API key using the following JSON structure: ``` { "endpoints": [ { "region": "us", "api_key": "" } ] } ``` When creating a secure configuration with these credentials, use: | Field | Value | | --- | --- | | Secure configuration type | [GenAI] Anthropic LLM Credentials | | Secure configuration display name | genai-anthropic-llm-credentials | The secure configuration section for Anthropic should follow the YAML structure outlined below: ``` buzok-llm-gateway: enabled: true llmGateway: providers: anthropic: enabled: true credentialsSecureConfigName: genai-anthropic-llm-credentials ``` ### Cerebras The Generative AI Service provides integration with Cerebras, giving access to high-performance inference models. To configure access to Cerebras, sign in to the [Cerebras platform](https://cloud.cerebras.ai/platform/) and create a new API key. Format the credentials using the following JSON structure: ``` { "endpoints": [ { "region": "global", "api_key": "" } ] } ``` When creating a Secure Configuration with these credentials, use: | Field | Value | | --- | --- | | Secure configuration type | [GenAI] Cerebras LLM Credentials | | Secure configuration display name | genai-cerebras-llm-credentials | The secure configuration section for Cerebras should follow the YAML structure outlined below: ``` buzok-llm-gateway: enabled: true llmGateway: providers: cerebras: enabled: true credentialsSecureConfigName: genai-cerebras-llm-credentials ``` ### TogetherAI The Generative AI Service provides integration with TogetherAI, offering access to a wide range of open-source language models. To configure access to TogetherAI, sign in to the [TogetherAI console](https://api.together.ai/) and create a new API key. Format the credentials using the following JSON structure: ``` { "endpoints": [ { "region": "global", "api_key": "" } ] } ``` When creating a Secure Configuration with these credentials, use: | Field | Value | | --- | --- | | Secure configuration type | [GenAI] TogetherAI LLM Credentials | | Secure configuration display name | genai-togetherai-llm-credentials | The secure configuration section for TogetherAI should follow the YAML structure outlined below: ``` buzok-llm-gateway: enabled: true llmGateway: providers: togetherai: enabled: true credentialsSecureConfigName: genai-togetherai-llm-credentials ``` --- # LLM reference URL: https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-reference.html > View brief descriptions of each available LLM. # LLM reference The following sections provide, for reference, brief descriptions of each available LLM. See the [LLM availability](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html) page for additional details of max context window, max completion tokens, and the chat model ID. Many descriptions came from the provider websites. --- # LLM metrics reference URL: https://docs.datarobot.com/en/docs/reference/gen-ai-ref/nxt-llm-custom-metric-reference.html > DataRobot's metrics for LLM evaluation provide basic information about prompts and responses, assess LLM performance, and help your organization report on prompt injection and hateful, toxic, or inappropriate content. # LLM metrics reference DataRobot's metrics for LLM evaluation provide basic information about prompts and responses, assess LLM performance, and help your organization report on prompt injection and hateful, toxic, or inappropriate content. These metrics can also safeguard against hallucinations, low-confidence responses, and the sharing of personally identifiable information (PII). | Name | Description | Requires | Output type | | --- | --- | --- | --- | | Performance | | | | | ROUGE-1 | Measures the similarity between the response generated from an LLM blueprint and the documents retrieved from the vector database. | Vector database | 0 to 100% | | Faithfulness | Evaluates whether a language model's generated answer is factually faithful (not a hallucination). | Vector database | 1 (faithful) or 0 (not faithful) | | Correctness | Evaluates the correctness and relevance of a generated answer against a reference answer. | Evaluation dataset of prompt and response pairs | 1 (worst) to 5 (best) | | Cosine Similarity | Calculates the mean, maximum, or minimum cosine similarity between each prompt vector and corresponding context vectors. This metric can help identify which prompts are the least covered by existing documentation. | | Mean, maximum, or minimum cosine similarity value | | Euclidean Distance | Calculates the mean, maximum, or minimum Euclidean distance between each prompt vector and corresponding context vectors. This metric can help identify which prompts are the least covered by existing documentation. | | Mean, maximum, or minimum Euclidean distance value | | Safety | | | | | Prompt Injection Classifier | Detects input manipulations, such as overwriting or altering system prompts, that are intended to modify the model's output. | Prompt Injection Classifier deployment | 0 (not likely prompt injection) to 1 (likely prompt injection) | | Sentiment Classifier [sidecar metric] | Classifies text sentiment as positive or negative using a pre-trained sentiment classification model. | Sentiment Classifier deployment | Score from 0 (negative sentiment) to 1 (positive sentiment) | | Sentiment Classifier (NLTK) | Calculates the sentiment of text using the Natural Language Toolkit (NLTK) library. | | -1 (negative sentiment) to 1 (positive sentiment) | | PII Detection | Identifies and anonymizes Personally Identifiable Information (PII) in text using the Microsoft Presidio Library to preserve individual privacy. | Presidio PII Detection deployment | 0 (likely no PII) to 1 (likely includes PII) | | Japanese PII Occurrence Count | Calculates the total number of occurrences of Personally Identifiable Information (PII) in Japanese text using the Microsoft Presidio analyzer library. | | Number of PII occurrences | | Toxicity[sidecar metric] | Measures the toxicity of text using a pretrained hate speech classification model to safeguard against harmful content. | Toxicity Classifier deployment | 0 (not likely toxic) to 1 (likely toxic) | | Readability | | | | | Dale-Chall Readability | Measures the U.S. grade level required to understand a text based on the percentage of difficult words and average sentence length. | Text must contain at least 100 words. | 0 (easy) to 10 (difficult) | | Flesch Reading Ease | Measures the ease of readability of text based on the average sentence length and average number of syllables per word. | Text must contain at least 100 words. | 0 (difficult) to 100 (easy) | | Operational | | | | | Token Count | Measures the number of tokens associated with the input to the LLM, output from the LLM, and/or retrieved text from a vector database. | | Number of tokens | | Cost | Estimates the financial cost of using the LLM by calculating the number of tokens in the input, output, and retrieved text, and then applying token pricing. | Token pricing information | Cost in USD | | Latency | Measures the response latency of the LLM blueprint. | | Time in seconds | | Completion Tokens Mean | Calculates the mean number of tokens in completions for the time period requested. The metric tracks the number of tokens using the tiktoken library. (See Token Count.) | | Average token count | | Prompt Tokens Mean | Calculates the mean number of tokens in prompts for the time period requested. The metric tracks the number of tokens using the tiktoken library. (See Token Count.) | | Average token count | | Tokens Mean | Calculates the mean number of tokens in prompts and completions. The metric tracks the number of tokens using the tiktoken library. (See Token Count.) | | Average token count | | Text | | | | | Completion Reading Time | Estimates the average time it takes a person to read text generated by the LLM. | | Time in seconds | | Character Count [Japanese] | Calculates the total number of Japanese characters in user prompts sent to the LLM. In DataRobot, by default the metric only analyzes prompt text, but the custom metric code can be edited to analyze completions as well. | | Number of Japanese characters | | Sentence Count | Calculates the total number of sentences in user prompts and text generated by the LLM. | | Number of sentences | | Syllable Count | Calculates the total number of syllables in the words in user prompts and text generated by the LLM. | | Number of syllables | | Word Count | Calculates the total number of words in user prompts and text generated by the LLM. | | Number of words | | NeMo evaluator metrics | | | | | Agent Goal Accuracy | Evaluates how well the agent fulfills the user's query. | NeMo evaluator deployment, LLM judge | 0 (goal not achieved) or 1 (goal achieved) | | Context Relevance | Measures how relevant the provided context is to the response. | NeMo evaluator deployment, LLM judge | 0 (not relevant) to 1 (highly relevant) | | Faithfulness (NeMo) | Evaluates whether the response stays faithful to the provided context. | NeMo evaluator deployment, LLM judge | 0 (not faithful) to 1 (fully faithful) | | LLM Judge | Uses a judge LLM to evaluate a user-defined metric. | NeMo evaluator deployment, LLM judge | User-defined; meaning and range depend on your judge prompt and schema | | Response Groundedness | Evaluates whether the response is grounded in the provided context. | NeMo evaluator deployment, LLM judge | 0 (not grounded) to 1 (fully grounded) | | Response Relevancy | Measures how relevant the response is to the user's query. | NeMo evaluator deployment, LLM judge, embedding deployment | 0 (not relevant) to 1 (highly relevant) | | Topic Adherence | Assesses whether the response adheres to the expected topics. | NeMo evaluator deployment, LLM judge, reference topics | 0 (does not adhere) to 1 (fully adheres) | | Topic control metrics | | | | | Stay on topic for inputs | Uses NVIDIA NeMo Guardrails to enforce topic boundaries so prompts stay on topic and avoid blocked terms. | NIM deployment (topic-control model), NeMo guardrails configuration | 0 (off topic or contains blocked terms) or 1 (on topic, passes) | | Stay on topic for output | Uses NVIDIA NeMo Guardrails to enforce topic boundaries so responses stay on topic and avoid blocked terms. | NIM deployment (topic-control model), NeMo guardrails configuration | 0 (off topic or contains blocked terms) or 1 (on topic, passes) | ## Performance Performance metrics evaluate the factual accuracy of LLM responses. ### ROUGE-1 [Recall-Oriented Understudy for Gisting Evaluation](https://aclanthology.org/W04-1013.pdf) ( ROUGE-1) measures the quality of text generated by an LLM by determining whether the generated response uses relevant information from the retrieved context in a vector database. Specifically, ROUGE-1 assesses the overlap of unigrams (single "words") between the reference text from the vector database and the generated text. This metric is calculated as follows: In this formula, the variables are as follows: - \(S\) : The set of reference summaries. - \(gram_1\) : The unigrams in the reference summaries. - \(Count\_{match}(gram_1)\) : The maximum number of unigrams co-occurring in the candidate summary and reference summary. - \(Count(gram_1)\) : The number of unigrams in the reference summary. In other words, ROUGE-1 is calculated as follows: ROUGE-1 scores range from 0 to 1 (or 0 to 100%), with higher scores indicating more information overlap between the generated response and the retrieved documents. DataRobot implements the metric using the [rouge-score](https://pypi.org/project/rouge-score/) library and returns the max of: - Precision: The fraction of unigrams in the generated text and the reference text. - Recall: The fraction of unigrams in the reference text that are also in the generated text. ### Faithfulness The Faithfulness metric evaluates if the answer generated by a language model is faithful to the source documents in the vector database or if the answer contains hallucinated information not supported by the sources. The metric uses the LlamaIndex [Faithfulness Evaluator](https://docs.llamaindex.ai/en/latest/examples/evaluation/faithfulness_eval/), which takes as input: - The generated answer. - The source documents/passages the answer should be based on. The evaluator uses a language model (e.g., GPT-4) to analyze if the answer can be supported by the provided sources. It outputs: - A binary "passing" score of 1 (Faithful) or 0 (Not faithful). - A text explanation of the reasoning behind the faithfulness assessment. Usage of Faithfulness in a playground counts towards user limits on LLM prompting (see GenAI considerations ). The use of Faithfulness as a deployment guardrail requires users to provide their own OpenAI credentials. ### Correctness The Correctness metric evaluates how well a generated answer matches a reference answer. It outputs a score between 1 and 5, where 1 is the worst and 5 is the best. The evaluation process uses the LlamaIndex [Correctness Evaluator](https://docs.llamaindex.ai/en/stable/examples/evaluation/correctness_eval/) to perform the following steps: 1. Input: The evaluator takes three inputs: a user query, a reference answer, and a generated answer. 2. Scoring: The evaluator uses a predefined scoring system to assign a score based on the relevance and correctness of the generated answer: 3. Output: The evaluator provides both a score and reasoning for the score. A score greater than or equal to a specified threshold (default is 4.0) is considered passing. The evaluation is conducted through a chat interface, where the system and user prompts are defined to guide the evaluation process. The system prompt instructs the evaluator on how to judge the answers, while the user prompt provides the specific query, reference answer, and generated answer for evaluation. In DataRobot, Correctness is only available in playgrounds as an [aggregated metric](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#aggregated-metrics) against an [evaluation dataset](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html#add-evaluation-datasets). Usage of Correctness also counts towards user limits on LLM prompting (see [GenAI considerations](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/genai-consider.html)). ## Safety Safety metrics are used to [monitor the security and privacy](https://www.datarobot.com/blog/how-to-safeguard-your-models-with-datarobot-a-comprehensive-guide/) of LLM responses and, in some cases, moderate output. ### Prompt Injection Classifier The Prompt Injection score uses the [deberta-v3-base-injection](https://huggingface.co/deepset/deberta-v3-base-injection) model to classify whether a given input contains a prompt injection attempt or not. (This model was fine-tuned on the [prompt-injections](https://huggingface.co/datasets/deepset/prompt-injections) dataset and achieves an accuracy of 99.14% on the evaluation set.) A Prompt Injection Score of 1 indicates the input likely contains a prompt injection attempt, while a score of 0 means the input appears to be a legitimate request. Using the score provides a layer of security to help prevent prompt injection attacks, but some prompt injection attempts may still bypass detection. In DataRobot, Prompt Injection score calculation requires a deployed Prompt Injection Classifier, available as a [global model in Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-global-models.html). ### Sentiment Classifier The Sentiment score uses the [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) model to classify text as either positive or negative sentiment. (This model was fine-tuned on the [Stanford Sentiment Treebank SST-2](https://huggingface.co/datasets/stanfordnlp/sst2) dataset and achieves an accuracy of 91.3% on the SST-2 dev set.) The model outputs a probability score between 0 and 1, with lower scores indicating more negative sentiment and higher scores indicating more positive sentiment. In DataRobot, Sentiment score calculation requires a deployed Sentiment Classifier, available as a [global model in Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-global-models.html). ### Sentiment Classifier (NLTK) The [NLTK Sentiment](https://www.nltk.org/howto/sentiment.html) score uses the [SentimentIntensityAnalyzer](https://www.nltk.org/api/nltk.sentiment.SentimentIntensityAnalyzer.html), a pre-trained model in the NLTK library, to determine the sentiment polarity (positive, negative, neutral) and intensity of a given text. The analyzer is based on the [VADER](https://github.com/cjhutto/vaderSentiment) (Valence Aware Dictionary and sEntiment Reasoner) lexicon. Sentiment scores range from -1 to 1: - -1 represents a completely negative sentiment. - 0 represents a neutral sentiment. - 1 represents a completely positive sentiment. The `SentimentIntensityAnalyzer` computes sentiment scores as follows: 1. The analyzer looks up each word of the input text in the VADER lexicon, an extensive list of words and their associated sentiment scores ranging from -1 (very negative) to +1 (very positive). 2. The analyzer considers linguistic features like capitalization, punctuation, negation, and degree modifiers to adjust the sentiment intensity. 3. The scores for each word are combined using a weighted average. In DataRobot, the Sentiment custom metric template only evaluates English prompts. ### PII Detection The PII Detection score uses the [Microsoft Presidio](https://microsoft.github.io/presidio/) library to detect and anonymize sensitive Personally Identifiable Information (PII) such as: - Names - Email addresses - Phone numbers - Credit card numbers - Social security numbers - Locations - Financial data Presidio uses regular expressions, rule-based logic, checksums, and named entity recognition models to detect PII with relevant context. By measuring the PII Detection score, you can assess how well a model preserves individual privacy by identifying information that needs to be protected before release or use in downstream applications, which is critical for complying with data protection laws and maintaining trust. In DataRobot, PII Detection score calculation requires a deployed Presidio PII Detection model, available as a [global model in Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-global-models.html). The PII Detection model also supports: - Specifying the types of PII to detect in a column, entities , as a comma-separated string. If this column is not specified, all supported entities are detected. Review the PII entities supported by Presidio documentation. - Returning the detection result with an anonymized_text column that contains a version of the input with detected PII replaced with placeholders. ### Japanese PII Occurrence Count The Japanese PII Occurrence Count metric uses the [Microsoft Presidio](https://microsoft.github.io/presidio/) library to quantify the presence of sensitive personal information from prompts written in Japanese text. The metric is useful for assessing privacy risks and compliance with data protection laws. This metric quantifies the presence of PII including, but not limited to: - Names - Addresses - Email addresses - Phone numbers - Passport numbers To calculate the metric: 1. Japanese text is passed to a Presidio analyzer that scans the text and detects instances of PII based on predefined entity recognition models for Japanese. 2. For each PII entity type detected, the analyzer returns the number of occurrences and sums up the counts across all PII entity types to get the total PII occurrence count. In DataRobot, by default, the metric only analyzes prompt text, but the custom metric code can be edited to analyze completions as well. ### Toxicity The Toxicity score uses the [martin-ha/toxic-comment-model](https://huggingface.co/martin-ha/toxic-comment-model) to classify the toxicity of text content. The model is a fine-tuned version of the [DistilBERT](https://huggingface.co/distilbert) model trained on data from a [Kaggle competition](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data). The model outputs a probability score between 0 and 1, with higher scores indicating more toxic content. Note that the model may perform poorly on text that mentions certain identity subgroups. In DataRobot, Toxicity score calculation requires a deployed Toxicity Classifier model, available as a [global model in Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-global-models.html). ## Readability Readability metrics generally measure how many years of education a reader needs to comprehend text, primarily based on word length or whether words are common in day-to-day use. ### Dale-Chall Readability The Dale-Chall Readability score is a [readability metric](https://pypi.org/project/py-readability-metrics/) that assesses the difficulty of English text by considering two main factors: the percentage of "difficult" words and the average sentence length. The formula uses approximately 3,000 words that are considered familiar to most 4th-grade American students. Any word not on this list is considered a "difficult word." The score is calculated using the following formula: If the percentage of difficult words is greater than 5%, an adjustment factor of 3.6365 is added to the raw score. The Dale-Chall readability score maps to grade levels as follows: | Score | U.S. Grade Level | | --- | --- | | 4.9 or lower | 4th grade or lower | | 5.0 - 5.9 | 5th-6th grade | | 6.0 - 6.9 | 7th-8th grade | | 7.0 - 7.9 | 9th-10th grade | | 8.0 - 8.9 | 11th-12th grade | | 9.0 - 9.9 | 13th-15th grade (college) | | 10.0 or higher | 16th grade or higher (college graduate) | In DataRobot, the text analyzed must contain at least 100 words to calculate the results. ### Flesch Reading Ease The Flesch Reading Ease score is a [readability metric](https://pypi.org/project/py-readability-metrics/) that indicates how easy text is to understand. It uses a formula based on the average number of syllables per word and the average number of words per sentence. The score is calculated using the following formula: Scores typically range from 0 to 100, with higher scores indicating easier readability. Scores can be interpreted as follows: | Score | Interpretation | U.S. Grade Level | | --- | --- | --- | | 90-100 | Very Easy | 5th grade | | 80-89 | Easy | 6th grade | | 70-79 | Fairly Easy | 7th grade | | 60-69 | Standard | 8th-9th grade | | 50-59 | Fairly Difficult | 10th-12th grade | | 30-49 | Difficult | College | | 0-29 | Very Difficult | College graduate | The score doesn't account for content complexity or technical jargon. In DataRobot, the text being analyzed must contain at least 100 words to calculate results. ## Operational Operational metrics are metrics measuring the LLM's system-related statistics. ### Token Count The Token Count metric tracks the number of tokens associated with text using the `cl100k_base` tokenization scheme provided by the [tiktoken](https://github.com/openai/tiktoken) library. This tokenization splits text into tokens in a way that is consistent with OpenAI's GPT-3 and GPT-4 language models. Tokens are the basic units that language models process, representing words or parts of words. In general, shorter text will have a lower token count than longer text; however, the exact number of tokens will depend on the specific words and characters used due to special rules for handling punctuation, rare words, and multibyte characters. The Token Count metric helps with managing the text processed by language models for: - Cost estimation: API calls are often priced based on token usage. - Token limit management: Ensures inputs don't exceed model token limits. - Performance monitoring: Token count affects processing time and resource usage. - Output length control: Helps manage the length of generated text. Different tokenization schemes can produce varying token counts for the same text, and there may be limitations when using the `cl100k_base` encoding with language models from other providers. In DataRobot, a different encoding can be specified as a runtime parameter. ### Cost The Cost metric estimates the expenses incurred when running language models. It considers the number of tokens in the input prompt to the model, the output generated by the model, and any text retrieved from a vector database. The metric uses the `cl100k_base` tokenization scheme from the [tiktoken](https://github.com/openai/tiktoken) library to count tokens (see [Token Count](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/nxt-llm-custom-metric-reference.html#token-count)) and applies two pricing variables: - Prompt token price: The cost per token for input and retrieved text. - Completion token price: The cost-per-token for the LLM's output. The metric is useful for managing expenses related to LLM usage by: - Estimating API usage costs before making calls to LLM services. - Budgeting and resource allocation. - Optimizing prompts and retrieval strategies to minimize costs. - Comparing different LLM configurations. In DataRobot, the token prices for prompts and completions should be specified as runtime parameters. Note that token pricing varies between different LLM providers and models, and tiered pricing or volume discounts are not accounted for in the metric by default. ## Text Text metrics provide basic information about prompt or completion text, for example, the number of words or sentences in a response. These metrics are more interpretable than the input and output token counts. ### Completion Reading Time The Completion Reading Time metric estimates the time required for an average person to read the text generated by a language model. This metric is useful for evaluating the length and complexity of text outputs in terms of human readability and time investment. The metric is calculated using the [readtime](https://pypi.org/project/readtime/) library with the following formula: In this formula, the variables are as follows: - \(\text{Word Count}\) : The number of words in the text. - \(\text{Words Per Minute}\) : Set to 265 for an average adult's reading speed. - \(\text{Image Count}\) : The number of images in the content (if applicable). - \(\text{Seconds Per Image}\) : The estimated time to process an image, starting at 12 seconds and decreasing one second with each image encountered, with a minimum of 3 seconds. The limitations of this metric are as follows: - The metric assumes an average reading speed, which may not accurately represent all users. - The complexity of the content is not considered, only its length. - The metric does not consider formatting or structure, which can affect actual reading time. ### Sentence Count The Sentence Count metric returns the sum of the number of sentences from prompts and completions. This metric is useful for evaluating the output of language models, ensuring that the generated text meets length and structure requirements. The metric is calculated using the [NLTK](https://www.nltk.org/api/nltk.tokenize.sent_tokenize.html) library, which uses natural language processing techniques to identify sentence boundaries based on punctuation and other linguistic cues. There may be limitations with accurate sentence detection when used with very short or informal texts or with unconventional writing styles. ### Syllable Count The Syllable Count metric calculates the total number of syllables in the words written while interacting with a language model. This metric is useful for evaluating the linguistic complexity and readability of text. The metric is calculated using the [NLTK](https://www.nltk.org/) library, which involves the following steps: 1. Tokenization: The text from prompts and completions is broken down into individual words using word_tokenize . 2. Syllable Counting: For each word, the number of syllables is determined using cmudict (Carnegie Mellon University Pronouncing Dictionary). This dictionary provides phonetic transcriptions of words for syllable counting. 3. Summation: The syllable counts for all words are summed. Note that there may be limitations with accurate syllable counts depending on the comprehensiveness of the cmudict dictionary. ### Word Count The Word Count metric calculates the total number of words written while interacting with a language model. This metric is useful for the length and complexity of text. The metric is computed using the [NLTK](https://www.nltk.org/) library by tokenizing the text into individual words with [word_tokenize](https://www.nltk.org/api/nltk.tokenize.word_tokenize.html) and then counting the tokens, excluding punctuation and other non-word characters. There may be limitations with accurate word counts depending on how the tokenizer handles punctuation, such as splitting contractions. ## NeMo Evaluator metrics NeMo Evaluator metrics are available in the NeMo metrics section when [configuring evaluation and moderation](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-configure-evaluation-moderation.html) for a custom text generation or agentic workflow model in Workshop. They require a NeMo evaluator workload deployment (set in NeMo evaluator settings in the Configuration summary sidebar) and an LLM judge (DataRobot deployment or LLM gateway). These metrics are Workshop-only and are not configurable in the playground. ### Agent Goal Accuracy Evaluates how well the agent fulfills the user's query.Inputs: User input (prompt string), response.Output: Binary (0 or 1).Requires: NeMo evaluator deployment, LLM judge.Applies to: Response. ### Context Relevance Measures how relevant the provided context is to the response.Inputs: User input (prompt string), retrieved context.Output: Float in range [0, 1].Requires: NeMo evaluator deployment, LLM judge.Applies to: Response. ### Faithfulness (NeMo) Evaluates whether the response stays faithful to the provided context.Inputs: User input (prompt string), response, retrieved context.Output: Float in range [0, 1].Requires: NeMo evaluator deployment, LLM judge.Applies to: Response. ### LLM Judge Uses a judge LLM to evaluate a user-defined metric.Inputs: User input (prompt string) and/or response.Output: Integer of the user's choice (parsed from the judge output).Requires: NeMo evaluator deployment, LLM judge.Configuration: System prompt, user prompt (must contain `{{ promptText }}` and/or `{{ responseText }}`), score parsing regex, optional directionality (higher is better / lower is better).Applies to: Prompt and/or Response. ### Response Groundedness Evaluates whether the response is grounded in the provided context.Inputs: Response, retrieved context.Output: Float in range [0, 1].Requires: NeMo evaluator deployment, LLM judge.Applies to: Response. ### Response Relevancy Measures how relevant the response is to the user's query.Inputs: User input (prompt string), response, optionally retrieved context.Output: Float in range [0, 1].Requires: NeMo evaluator deployment, LLM judge, embedding deployment.Applies to: Response. ### Topic Adherence Assesses whether the response adheres to the expected topics.Inputs: User input (prompt string), response, reference topics (static list configured at guard setup).Output: Float in range [0, 1].Requires: NeMo evaluator deployment, LLM judge.Configuration: Reference topics list, metric_mode (f1, recall, or precision).Applies to: Response. ## Topic control metrics Topic control metrics use a NIM deployment of the `llama-3.1-nemoguard-8b-topic-control` model (NVIDIA NeMo Guardrails). They do not use the NeMo evaluator workload deployment. Configure allowed and blocked topics in `prompts.yml` and blocked terms in `blocked_terms.txt`; the same `blocked_terms.txt` is shared between the input and output metrics. Topic control is available in the NeMo metrics section when [configuring evaluation and moderation](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-configure-evaluation-moderation.html) in the Workshop or [playground](https://docs.datarobot.com/en/docs/agentic-ai/playground-tools/playground-eval-metrics.html) (standard LLM playground only, not agentic). ### Stay on topic for inputs Uses NVIDIA NeMo Guardrails to enforce topic boundaries and blocked terms on the prompt.Inputs: User prompt.Output: 0 (off topic or contains blocked terms) or 1 (on topic, passes).Requires: NIM deployment of the topic-control model, NeMo guardrails configuration (e.g., `prompts.yml`, `blocked_terms.txt`).Applies to: Prompt. ### Stay on topic for output Uses NVIDIA NeMo Guardrails to enforce topic boundaries and blocked terms on the response.Inputs: LLM response.Output: 0 (off topic or contains blocked terms) or 1 (on topic, passes).Requires: NIM deployment of the topic-control model, NeMo guardrails configuration (e.g., `prompts.yml`, `blocked_terms.txt`).Applies to: Response. --- # Prompting strategy URL: https://docs.datarobot.com/en/docs/reference/gen-ai-ref/prompting-reference.html > Learn techniques for creating prompts that optimize chat results. # Prompting strategy The following sections provide both basic and advanced [few shot prompting](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/prompting-reference.html#few-shot-prompting) guidance. ## Best practices for prompt engineering Prompt engineering refers to the process of carefully crafting the input prompts that you give to an LLM to maximize the usefulness of the output it generates. This is a critical step in getting the most out of these models, as the way you phrase your prompt can significantly influence the response. The following are some best practices for prompt engineering: | Characteristic | Explanation | | --- | --- | | Specificity | Make prompts as specific as possible. Instead of asking “What’s the weather like?”, ask “What’s the current temperature in San Francisco, California?”. The latter is more likely to yield the information you’re looking for. | | Explicit instructions | If you have a specific format or type of answer in mind, make that clear in your prompt. For example, if you want a list, ask for a list. If you want a yes or no answer, ask for that. | | Contextual information | If relevant, provide some context to guide the model. For instance, if you’re asking for advice on writing a scientific type of content, make sure to mention that in your prompt. | | Use of examples | When you want the model to generate in a particular style or format, giving an example can help guide the output. For instance, if you want a rhyming couplet, you could include an example of one in your prompt. | | Prompt length | While it can be useful to provide context, remember that longer prompts may lead the model to focus more on the later parts of the prompt and disregard earlier information. Be concise and to the point. | | Bias and ethical considerations | Be aware that the way you phrase your prompt can influence the output in terms of bias and harmful response. Ensure your prompts are as neutral and fair as possible, and be aware that the model can reflect biases present in its training data. | | Temperature and Top P Settings | In addition to the prompt itself, you can also adjust the ‘temperature’ and ‘Top P’ settings. Higher temperature values make output more random, while lower values make it more deterministic. Top P controls the diversity of the output by limiting the model to consider only a certain percentile of most likely next words. | | Token limitations | Be aware of the maximum token limitations of the model. For example, GPT-3.5 Turbo has a maximum limit of 4096 tokens. If your prompt is too long, it could limit the length of the model’s response. | These are some of the key considerations in prompt engineering, but the exact approach depends on the specific use case, model, and kind of output you’re looking for. It’s often a process of trial and error and can require a good understanding of both your problem domain and the capabilities and limitations of the model. ## Effective prompting strategies This section uses the following example to identify elements of prompt engineering: You are a world-renowned poet in the early 1800s. Write a poem in the style of Edgar Allan Poe. It must be 10 sentences long and use “set up, rhyme” format. Consider the elements through the following images. #### Persona Persona provides a role or voice to ensure that answers resemble a specific counterpart throughout (e.g., profession, known person). #### Context and sensitivity Context is information or nuance that can steer the model toward a given setting (e.g., temporal, subject matter). Specificity provides additional details as part of the context that can lead to better results (e.g., tone and style). #### Instruction Instruction is a specific task you want the model to perform (e.g., write, translate, summarize). #### Rules Rules provide specifications to limit or otherwise restrain the response (e.g., word limit, topics to avoid). #### Output format The output format is the type and/or format of the output. Optionally, you can provide examples (e.g., question/answer, headline) to further refine the desired response. #### Example response ## Few-shot prompting Few-shot prompting is a technique for generating or classifying text based on a limited number of examples or prompts—"in-context learning." The examples, or "shots," condition a model to follow patterns in the provided context; it can then generate coherent and contextually relevant text even if it has never seen similar examples during training. This is in contrast to traditional machine learning, where models typically require a large amount of labeled training data. Few-shot prompting makes the model a good candidate for tasks like text generation, text summarization, translation, question-answering, and sentiment analysis without requiring fine-tuning on a specific dataset. A simple example of few-shot prompting is used in categorizing customer feedback as positive or negative. By showing the model three examples of positive and negative feedback, when the model sees unclassified feedback it can assign a rating based on the first three examples. Few-shot prompting is when you show the model 2 or more examples; zero-shot and one-shot prompting are similar techniques. The following shows the use of few-shot prompting in DataRobot. In the system prompt field, provide a prompt and some examples for learning: ``` ``` Given text in a customer support ticket text, determine the name of the product it refers to, as well as the issue type. The issue type can be "hardware" or "software". Format the response as JSON with two keys, "product" and "issue type". --------------- Examples: Input: I'm encountering a bug in TPS Report Generator Enterprise Edition. Whenever I click "Generate", the application crashes. Are there any updates or fixes available? Output: {"product": "TPS Report Generator Enterprise Edition", "issue_type": "software"} Input: The screen is flickering on my Acme Phone 5+, and I'm unable to use it. What should I do? I want to install a few games and performed a factory reset, hoping it would resolve the problem, but it didn't help. Output: {"product": "Acme Phone 5+", "issue_type": "hardware"} --------------- ``` ``` After providing the LLM with that context, try some example prompts: Prompt: I've noticed a peculiar error message popping up on my PrintPro 9000 screen. It says "PC LOAD LETTER". What does it mean? Prompt: I cannot install firmware v12.1 on my Print Pro 9002. It says "Incompatible product version". See the [MIT Prompt Engineering Guide](https://www.promptingguide.ai/techniques/fewshot) for more detailed information. --- # Glossary URL: https://docs.datarobot.com/en/docs/reference/glossary/index.html > The Glossary provides brief definitions of terms relevant to the DataRobot platform. # Glossary The DataRobot glossary provides brief definitions of terms relevant to the DataRobot platform. These terms span all phases of machine learning, from data to deployment. For agents, LLMs, RAG, and agentic workflows, use the Agentic AI filter below. [All](https://docs.datarobot.com/en/docs/reference/glossary/index.html#) [Agentic](https://docs.datarobot.com/en/docs/reference/glossary/index.html#) [API](https://docs.datarobot.com/en/docs/reference/glossary/index.html#) [Code-first](https://docs.datarobot.com/en/docs/reference/glossary/index.html#) [Data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#) [MLOps](https://docs.datarobot.com/en/docs/reference/glossary/index.html#) [Modeling](https://docs.datarobot.com/en/docs/reference/glossary/index.html#) [Predictions](https://docs.datarobot.com/en/docs/reference/glossary/index.html#) [Time-aware](https://docs.datarobot.com/en/docs/reference/glossary/index.html#) ## A ### Accuracy over space A model Leaderboard tab ( [Evaluate > Accuracy Over Space](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/location-ai/lai-insights.html)) and Location AI insight that provides a spatial residual mapping within an individual model. ### Accuracy over time A model Leaderboard tab ( [Evaluate > Accuracy Over Time](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/aot-classic.html)) that visualizes how predictions change over time. ### ACE scores Also known as Alternating Conditional Expectations. A univariate measure of correlation between the feature and the target. ACE scores detect non-linear relationships, but as they are univariate, they do not detect interaction effects. ### Actuals Actual values for an ML model that let you track its prediction outcomes. To generate accuracy statistics for a deployed model, you compare the model's predictions to real-world actual values for the problem. Both the prediction dataset and the actuals dataset must contain association IDs, which let you match up corresponding rows in the datasets to gauge the model's accuracy. ### Advanced tuning The ability to manually set model parameters after the model build, supporting experimentation with parameter settings to improve model performance. ### Agent An AI-powered component within DataRobot designed to execute complex, multi-step tasks autonomously. An agent can be configured with specific goals, LLMs, and a set of tools, allowing it to perform actions like orchestrating a data preparation workflow, running a modeling experiment, or generating an analysis without direct human intervention. Agents exhibit autonomous behavior, can reason about their environment, make decisions, and adapt their strategies based on feedback. Multiple agents can be combined in an agentic workflow to solve more sophisticated business problems through collaboration and coordination. ### Agent-based modeling Computational modeling approaches that simulate complex systems by modeling individual agents and their interactions. Agent-based modeling enables the study of emergent behaviors and system-level properties that arise from individual agent behaviors. In DataRobot's platform, agent-based modeling capabilities allow users to simulate business processes, test agent strategies, and understand how different agent configurations affect overall system performance. ### Agentic AI A paradigm of artificial intelligence where AI systems are designed to act as autonomous agents that can perceive their environment, reason about goals, plan actions, and execute tasks with minimal human oversight. Agentic AI systems are characterized by their ability to make independent decisions, learn from experience, and adapt their behavior to achieve objectives. In DataRobot's platform, agentic AI enables sophisticated automation of complex data science workflows, allowing AI systems to handle end-to-end processes from data preparation to model deployment and monitoring. ### Agentic workflow Systems that leverage AI agents to perform tasks and make decisions within a workflow, often with minimal human intervention. Agentic workflows can be built in a local IDE using DataRobot templates and a CLI and managed with real-time LLM intervention and moderation with out-of-the-box and custom guards, including integration with NVIDIA's NeMo for content safety and topical rails in the UI or with code. ### Agent Framework (AF) components Agent Framework (AF) components provide modular building blocks for constructing sophisticated AI agents. AF components include reasoning engines, memory systems, action planners, and communication modules that can be combined to create custom agent architectures. In DataRobot's platform, AF components enable rapid development of specialized agents with specific capabilities while maintaining consistency and interoperability across different agent implementations. ### Agent-to-Agent (A2A) Agent-to-Agent (A2A) refers to communication protocols and frameworks that enable direct interaction and coordination between AI agents. A2A systems facilitate information sharing, task delegation, and collaborative problem-solving among multiple agents. In DataRobot's agentic workflows, A2A capabilities enable agents to work together seamlessly, share context and knowledge, and coordinate complex multi-agent operations while maintaining security and governance controls. ### Aggregate image feature Used with Visual AI, a set of image features where each individual element of that set is a constituent image feature. For example, the set of image features extracted from an image might include a set of features indicating: 1. The colors of the individual pixels in the image. 2. Where edges are present in the image. 3. Where faces are present in the image. From the aggregate it may be possible to determine the impact of that feature on the output of a data analytics model and compare that impact to the impacts of the model's other features. ### AI catalog A browsable and searchable collection of registered objects that contains definitions and relationships between various objects types. Items stored in the catalog include: data connections, data sources, data metadata. ### AI tools Software applications, libraries, and frameworks designed to support the development, deployment, and management of artificial intelligence systems. In DataRobot, AI tools include built-in capabilities for model building, evaluation, deployment, and monitoring, as well as integrations with external AI services and frameworks. ### AIM The second phase of [Exploratory Data Analysis](https://docs.datarobot.com/en/docs/reference/glossary/index.html#eda-exploratory-data-analysis) (i.e., EDA2), that determines feature importance based on cross-correlation with the target feature. That data determines the "informative features" used for modeling during Autopilot. ### Alignment The critical process of steering an AI model's outputs and behavior to conform to an organization's specific ethical guidelines, safety requirements, and business objectives. In DataRobot, alignment is practically applied through features like guardrails, custom system prompts, and content moderation policies. This practice helps to mitigate risks from biased, unsafe, or off-topic model responses, ensuring the AI remains a trustworthy and reliable tool for the enterprise. ### Alternating conditional expectations See [ACE scores](https://docs.datarobot.com/en/docs/reference/glossary/index.html#ace-scores). ### Anomaly detection A form of [unsupervised learning](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/unsupervised/index.html) used to detect anomalies in data. Anomaly detection, also referred to as outlier or novelty detection, can be useful with data having a low percentage of irregularities or large amounts of unlabeled data. See also [unsupervised learning](https://docs.datarobot.com/en/docs/reference/glossary/index.html#unsupervised-learning). ### Apps See [No-Code AI Apps](https://docs.datarobot.com/en/docs/reference/glossary/index.html#no-code-ai-apps). ### ARIMA (AutoRegressive Integrated Moving Average) A time series modeling approach available in DataRobot time series that analyzes historical patterns to forecast future values. DataRobot's ARIMA implementation automatically handles parameter selection and optimization, making it accessible for users without deep statistical expertise while maintaining the mathematical rigor of traditional ARIMA models. ### Autoregressive A modeling approach where predictions are made sequentially, with each prediction depending on previous outputs. In DataRobot, autoregressive models are commonly used in time series forecasting and natural language processing tasks, where the model learns patterns from historical data to predict future values or generate text one step at a time. This technique enables coherent sequence generation and is particularly effective for time-dependent data where temporal relationships are crucial for accurate predictions. ### Asset One of the components of a Use Case that can be added, managed, and shared within Workbench. Components include data, vector databases, experiments, playgrounds, apps, and notebooks. ### Association ID An identifier that functions as a foreign key for your prediction dataset so you can later match up actual values (or "actuals") with the predicted values from the deployed model. An association ID is required for monitoring the accuracy of a deployed model. ### AUC (Area Under the Curve) A common error metric for binary classification that considers all possible thresholds and summarizes performance in a single value on the ROC Curve. It works by optimizing the ability of a model to separate the 1s from the 0s. The larger the area under the curve, the more accurate the model. ### Audit log A chronological, immutable record of all significant activities performed within the DataRobot platform by users and automated processes. It is essential for security audits, compliance reporting, and troubleshooting. Sometimes referred to as an "event log". ### Augmented intelligence DataRobot's enhanced approach to artificial intelligence, which expands current model building and deployment assistance practices. The DataRobot platform fully automates and governs the AI lifecycle from data ingest to model training and predictions to model-agnostic monitoring and governance. Guardrails ensure adherence to data science best practices when creating machine learning models and AI applications. Transparency across user personas and access to data wherever it resides avoids lock-in practices. ### Autonomy The ability of an AI agent to operate independently and make decisions without constant human oversight. Autonomous agents can plan, execute, and adapt their behavior based on changing conditions and feedback. In DataRobot's agentic workflows, autonomous capabilities are balanced with human oversight through guardrails and monitoring to ensure safe and effective operation. Autonomy enables agents to handle complex, multi-step processes while maintaining alignment with business objectives and safety requirements. ### Authentication The process of verifying the identity of users, applications, or systems before granting access to DataRobot's APIs and services. DataRobot supports multiple authentication methods, including API keys for programmatic access, OAuth 2.0 for web applications, and Single Sign-On (SSO) integration with enterprise identity providers. Authentication ensures secure access to projects, deployments, and platform resources while maintaining audit trails for compliance and security monitoring. ### Authorization The process of determining what actions or resources users or systems are permitted to access after authentication. ### Automated retraining Retraining strategies for MLOps that refresh production models based on a schedule or in response to an event (for example, a drop in accuracy or data drift). Automated Retraining also uses DataRobot's AutoML create and recommend new challenger models. When combined, these strategies maximize accuracy and enable timely predictions. ### AutoML (Automated Machine Learning) A software system that automates many of the tasks involved in preparing a dataset for modeling and performing a model selection process to determine the performance of each with the goal of identifying the best performing model for a specific use case. Used for predictive modeling; see also [time series](https://docs.datarobot.com/en/docs/reference/glossary/index.html#time-series) for forecasting. ### Autopilot (full Autopilot) The DataRobot "survival of the fittest" modeling mode that automatically selects the best predictive models for the specified target feature and runs them at ever-increasing sample sizes. In other words, it runs more models in the early stages on a small sample size and advances only the top models to the next stage. In full Autopilot, DataRobot runs models at 16% (by default) of total data and advances the top 16 models, then runs those at 32%. Taking the top 8 models from that run, DataRobot runs on 64% of the data (or 500MB of data, whichever is smaller). See also [Quick (Autopilot)](https://docs.datarobot.com/en/docs/reference/glossary/index.html#quick-autopilot), [Comprehensive](https://docs.datarobot.com/en/docs/reference/glossary/index.html#comprehensive), and [Manual](https://docs.datarobot.com/en/docs/reference/glossary/index.html#manual). ### AutoTS (Automated time series) A software system that automates all or most of the steps needed to build forecasting models, including featurization, model specification, model training, model selection, validation, and forecast generation. See also [time series](https://docs.datarobot.com/en/docs/reference/glossary/index.html#time-series). ### Average baseline The average of the target in the [Feature Derivation Window](https://docs.datarobot.com/en/docs/reference/glossary/index.html#feature-derivation-window); used in time series modeling. ## B ### Backend The server-side components of LLM and AI applications that handle data processing, model inference, business logic, and database operations. ### Backtesting The time-aware equivalent of cross-validation. Unlike cross-validation, however, backtests allow you to select specific time periods or durations for your testing instead of random rows, creating "trials" for your data. ### Baseline model Also known as a naive model. A simple model used as a comparison point to confirm that a generated ML or time series model is learning with more accuracy than a basic non-ML model. For example, generated ML models for a regression project should perform better than a baseline model that predicts the mean or median of the target. Generated ML models for a time series project should perform better than a baseline model that predicts the future using the most recent actuals (i.e., using today's actual value as tomorrow's prediction). For time series projects, baseline models are used to calculate the [MASE metric](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html#mase) (the ratio of the [MAE metric](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html#maeweighted-mae) over the baseline model). ### Batch predictions A method of making predictions with large datasets, in which you pass input data and get predictions for each row; predictions are written to output files. Users can make batch predictions with MLOps via the Predictions interface or can use the Batch Prediction API for automating predictions. Schedule batch prediction jobs by specifying the prediction data source and destination and determining when the predictions will be run. ### Bias mitigation Augments blueprints with a pre- or post-processing task intended to reduce bias across classes in a protected feature. Bias Mitigation is also a model Leaderboard tab ( [Bias and Fairness > Bias Mitigation](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html#retrain-with-mitigation)) where you can apply mitigation techniques after Autopilot has finished. ### Bias vs accuracy A Leaderboard tab that generates a chart to show the tradeoff between predictive accuracy and fairness, removing the need to manually note each model's accuracy score and fairness score for the protected features. ### Bias (AI bias) Systematic prejudice in AI model outputs that reflects unfair treatment of certain groups or individuals. AI bias can manifest in various forms, including gender bias, racial bias, or socioeconomic bias, and can result from biased training data, model architecture, or deployment contexts. DataRobot provides tools and practices to detect, measure, and mitigate bias in AI systems. ### Blind history "Blind history", used in time-aware modeling, captures the gap created by the delay of access to recent data (e.g., "most recent" may always be one week old). It is defined as the period of time between the smaller of the values supplied in the Feature Derivation Window and the forecast point. A gap of zero means "use data up to, and including, today;" a gap of one means "use data starting from yesterday" and so on. ### Blender A model that potentially increases accuracy by combining the predictions of between two and eight models. DataRobot can be configured to automatically create blender models as part of Autopilot, based on the top three regular Leaderboard models (for AVG, GLM, and ENET blenders). You can also create blenders manually (aka ensemble models). ### Blueprint A blueprint is a graphical representation of the many steps involved in transforming input predictors and targets into a model. It represents the high-level end-to-end procedure for fitting the model, including any preprocessing steps, algorithms, and post-processing. Each box in a blueprint may represent multiple steps. You can view the [graphical representation](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/model-blueprint.html) of a blueprint by clicking on a model on the Leaderboard. See also [user blueprints](https://docs.datarobot.com/en/docs/reference/glossary/index.html#user-blueprints). ## C ### Caching strategies Techniques for storing frequently accessed LLM responses, embeddings, or intermediate results to improve performance and reduce computational costs. ### Canary deployment A deployment strategy for LLM and AI models that gradually rolls out new versions to a small subset of users before full deployment, allowing for early detection of issues. ### "Can't operationalize" period The "can't operationalize" period, used in time series modeling, defines the gap of time immediately after the Forecast Point and extending to the beginning of the Forecast Window. It represents the time required for a model to be trained, deployed to production, and to start making predictions—the period of time that is too near-term to be useful. For example, predicting staffing needs for tomorrow may be too late to allow for taking action on that prediction. ### Catalog See [AI Catalog](https://docs.datarobot.com/en/docs/reference/glossary/index.html#ai-catalog). ### Centroid The center of a cluster generated using [unsupervised learning](https://docs.datarobot.com/en/docs/reference/glossary/index.html#unsupervised-learning). A centroid is the multi-dimensional average of a cluster, where the dimensions are observations (data points). ### CFDS (Customer Facing Data Scientist) A DataRobot employee responsible for the technical success of user and potential users. They assist with tasks like structuring data science problems to complete integration of DataRobot. CFDS are passionate about ensuring user success. ### Chain-of-thought A prompting technique that encourages language models to break down complex problems into step-by-step reasoning processes. In DataRobot's agentic workflows, chain-of-thought prompting enhances agent reasoning capabilities by requiring explicit intermediate steps in decision-making, leading to more transparent and reliable outcomes. This technique improves problem-solving accuracy and enables better debugging and validation of agent behavior in multi-step tasks. ### Challenger models Models that you can compare to a currently deployed model (the "champion" model) to continue model comparison post-deployment. Submit a challenger model to shadow a deployed model and replay predictions made against the champion to determine if there is a superior DataRobot model that would be a better fit. ### Champion model A model recommended by DataRobot—for a deployment (predictions) or for time series segmented modeling. In MLOps, you can replace the champion selected for a deployment yourself, or you can set up [Automated Retraining](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/set-up-auto-retraining.html), where DataRobot compares challenger models with the champion model and replaces the champion model if a challenger outperforms the champion. In the segmented modeling workflow, DataRobot builds a model for each segment. DataRobot recommends the best model for each segment—the segment champion. The segment champions roll up into a Combined Model. For each segment, you can select a different model as champion, which is then used in the Combined Model. ### Channel The connection between an output port of one module and an input port of another module. Data flows from one module's output port to another module's input port via a channel, represented visually by a line connecting the two. ### Chatting Sending prompts (and as a result, LLM payloads) to LLM endpoints based on a single [LLM blueprint](https://docs.datarobot.com/en/docs/reference/glossary/index.html#llm-blueprint) and receiving a response from the LLM. In this case, context from previous prompts/responses is sent along with the payload. ### Chunking The action of taking a body of [unstructured text](https://docs.datarobot.com/en/docs/reference/glossary/index.html#unstructured-text) and breaking it up into smaller pieces of unstructured text [(tokens)](https://docs.datarobot.com/en/docs/reference/glossary/index.html#tokens). ### Citation The chunks of text from the [vector database](https://docs.datarobot.com/en/docs/reference/glossary/index.html#vector-database) used during the generation of LLM responses. ### CI/CD pipelines Continuous Integration (CI) and Continuous Deployment (CD) pipelines that automate the building, testing, and deployment of LLM and AI applications to ensure reliable and consistent releases. ### Circuit breaker A crucial MLOps reliability pattern that safeguards a deployed model by monitoring for high error rates or latency. If a predefined failure threshold is breached, the circuit breaker automatically and temporarily redirects or pauses traffic to the unhealthy model instance. This action prevents a single failing model from causing a cascade failure across an application and allows the system time to recover, ensuring high availability for production AI services. ### Classification A DataRobot modeling approach that predicts categorical outcomes from your target feature. DataRobot supports three classification types: binary classification for two-class problems (like "churn" vs "retain"), multiclass classification for multiple discrete outcomes (like "buy", "sell", "hold"), and unlimited multiclass for projects with numerous possible classes. DataRobot automatically selects appropriate classification algorithms from the Repository and provides specialized evaluation metrics like AUC and confusion matrices to assess model performance. See also [regression](https://docs.datarobot.com/en/docs/reference/glossary/index.html#regression). ### CLI Command Line Interface (CLI) tools that enable programmatic interaction with DataRobot's agentic workflows and platform services. CLI tools provide scriptable access to agent configuration, workflow execution, and platform management functions. In DataRobot's agentic ecosystem, CLI tools support automation of agent deployment, monitoring, and maintenance tasks, enabling integration with CI/CD pipelines and automated workflows. ### Clustering A form of [unsupervised learning](https://docs.datarobot.com/en/docs/reference/glossary/index.html#unsupervised-learning) used to group similar data and identify natural segments. ### Cognitive architecture The underlying structural framework that defines how AI agents process information, make decisions, and interact with their environment. Cognitive architectures specify the components, processes, and relationships that enable intelligent behavior in agents. In DataRobot's agentic workflows, cognitive architectures provide the foundation for agent reasoning, memory management, learning, and decision-making capabilities, enabling sophisticated autonomous behavior. ### Codespace A fully configured Integrated Development Environment (IDE) hosted on the cloud. It provides tools for you to write, test, and debug code. It also offers file storage so that notebooks inside a codespace can reference Python utility scripts and other assets. ### Coefficients A model Leaderboard tab ( [Describe > Coefficients](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/coefficients-classic.html)) that provides a visual indicator of information that can help you refine and optimize your models. ### Combined model The final model generated in a time series segmented modeling workflow. With segmented modeling, DataRobot builds a model for each segment and combines the segment champions into a single Combined Model that you can deploy. ### Common event A data point is a common event if it occurs in a majority of weeks in data (for example, regular business days and hours would be common, but an occasional weekend data point would be uncommon). ### Compliance documentation Automated model development documentation that can be used for regulatory validation. The documentation provides comprehensive guidance on what constitutes effective model risk management. ### Compliance reporting The generation of reports and documentation required for regulatory compliance in LLM and AI deployments, including data usage, model performance, and security measures. ### Composable ML A code-centric feature, designed for data scientists, that allows applying custom preprocessing and modeling methods to create a blueprint for model training. Using built-in and [custom tasks](https://docs.datarobot.com/en/docs/reference/glossary/index.html#custom-task), you can compose and then integrate the new blueprint with other DataRobot features to augment and improve machine learning pipelines. ### Comprehensive A modeling mode that runs all Repository blueprints on the maximum Autopilot sample size to ensure more accuracy for models. ### Computer vision Use of computer systems to analyze and interpret image data, used with Visual AI. Computer vision tools generally use models that incorporate principles of geometry to solve specific problems within the computer vision domain. For example, computer vision models may be trained to perform object recognition (recognizing instances of objects or object classes in images), identification (identifying an individual instance of an object in an image), detection (detecting specific types of objects or events in images), etc. ### Computer vision tools/techniques Tools—for example, models, systems—that perform image preprocessing, feature extraction, and detection/segmentation functions. ### Connected vector database An external vector database accessed via a direct connection to a supported provider for vector database creation. The data source is stored locally in the Data Registry, configuration settings are applied, and the created vector database is written back to the provider. Connected vector databases maintain real-time synchronization with the platform and provide seamless access to embeddings and text chunks for grounding LLM responses. ### Configuration management The practice of managing LLM and AI system configurations across different environments (development, staging, production) to ensure consistency and reduce deployment errors. ### Confusion matrix A table that reports true versus predicted values. The name "confusion matrix" refers to the fact that the matrix makes it easy to see if the model is confusing two classes (consistently mislabeling one class as another class). The confusion matrix is available as part of the ROC Curve, Eureqa, and Confusion Matrix for multiclass model visualizations in DataRobot. ### Connection instance A connection that is configured with metadata about how to connect to a source system (e.g., instance of a Snowflake connection). ### Console [Console](https://docs.datarobot.com/en/docs/workbench/nxt-console/index.html) is a central hub for deployment management activity. Its dashboard provides access to deployed models for further monitoring and mitigation. It also provides access to prediction activities and allows you to view, create, edit, delete, or share serverless and external prediction environments ### Constraints A model Leaderboard tab ( [Describe > Constraints](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/monotonic.html)) that allows you to review monotonically constrained features if feature constraints were configured in Advanced Options prior to modeling. ### Container orchestration The automated management of containerized LLM and AI applications, including deployment, scaling, networking, and availability, typically using platforms like Kubernetes. ### Context window The limited amount of information, measured in tokens, that a large language model can hold in its active memory for a single chat conversation turn. This 'memory' includes the user's prompt, any recent conversation history provided, and data retrieved via Retrieval Augmented Generation (RAG). The size of the context window is a critical parameter in an LLM blueprint, as it dictates the model's ability to handle long documents or maintain coherence over extended dialogues; any information outside this window is not considered when generating the next response. ### Conversation memory The ability of an AI system to remember and reference previous interactions within a conversation session (meaning that the session contains one or more chat conversation turns). Conversation memory enables contextual continuity, allowing the AI to maintain awareness of earlier exchanges and build upon previous responses. In DataRobot's chat interfaces, conversation memory helps maintain coherent, contextually relevant dialogues. ### Cost allocation The process of assigning LLM and AI service costs to different teams, projects, or business units for budgeting and chargeback purposes. ### Credentials Information used to authenticate and authorize actions against data connections. The most common connection is through username and password, but alternate authentication methods include LDAP, Active Directory, and Kerberos. ### Cross-class accuracy A model Leaderboard tab ( [Bias and Fairness > Cross-Class Accuracy](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/cross-acc.html)) that helps to shows why the model is biased, and where in the training data it learned the bias from.[Bias and Fairness settings](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html) must be configured. ### Cross-class data disparity A model Leaderboard tab ( [Bias and Fairness > Cross-Class Data Disparity](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/cross-data.html)) that calculates, for each protected feature, evaluation metrics and ROC curve-related scores segmented by class.[Bias and Fairness settings](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html) must be configured. ### Cross-Validation (CV) DataRobot's validation approach that tests model performance by creating multiple training and validation partitions from your data. DataRobot automatically implements five-fold cross-validation by default, building separate models on different data subsets and using the remaining data for validation. This process generates more reliable performance estimates than single validation splits, and DataRobot displays the average cross-validation scores on the Leaderboard to help you select the best model. See also [validation](https://docs.datarobot.com/en/docs/reference/glossary/index.html#validation). ### Custom inference models User-created, pre-trained models uploaded as a collection of files via the Custom Model Workshop. Upload a model artifact to create, test, and deploy custom inference models to the centralized deployment hub in DataRobot. An inference model can have a predefined input/output schema or it can be unstructured. To customize prior to model training, use [custom tasks](https://docs.datarobot.com/en/docs/reference/glossary/index.html#custom-task). ### Custom model environment A versioned, containerized environment (e.g., a Docker image) that includes all the necessary libraries, packages, and dependencies required to run a custom model or task within DataRobot. Administrators manage these environments to ensure reproducibility and governance. ### Custom model workshop In the [Model Registry](https://docs.datarobot.com/en/docs/reference/glossary/index.html#model-registry), a location where you can upload user-created, pre-trained models as a collection of files. You can use these model artifacts to create, test, and deploy custom inference models to centralized deployment hub in DataRobot. ### Custom task A data transformation or ML algorithm, for example, XGBoost or One-hot encoding, that can be used as a step in an ML blueprint inside DataRobot and used for model training. Tasks are written in Python or R and are added via the Custom Model Workshop. Once saved, the task can be used when modifying a blueprint with [Composable ML](https://docs.datarobot.com/en/docs/reference/glossary/index.html#composable-ml). To deploy a pre-trained model where re-training is not required, use [custom inference models](https://docs.datarobot.com/en/docs/reference/glossary/index.html#custom-inference-models). ### CV See [Cross Validation](https://docs.datarobot.com/en/docs/reference/glossary/index.html#cross-validation). ## D ### Data classification The process of categorizing data based on sensitivity, regulatory requirements, and business value to determine appropriate handling, storage, and access controls for LLM and AI systems. DataRobot provides automated PII detection and data governance features to help organizations classify and protect sensitive information in their datasets. ### Data drift The difference between values in new inference data used to generate predictions for models in production and the training data initially used to train the deployed model. Predictive models learn patterns in training data and use that information to predict target values for new data. When the training data and the production data change over time, causing the model to lose predictive power, the data surrounding the model is said to be drifting. Data drift can happen for a variety of reasons, including data quality issues, changes in feature composition, and even changes in the context of the target variable. ### Data management The umbrella term related to loading, cleaning, transforming, and storing data within DataRobot. It also refers to the practices that companies follow when collecting, storing, using, and deleting data. ### Data preparation The process of transforming raw data to the point where it can be run through machine learning algorithms to uncover insights or make predictions. Also called "data preprocessing," this term covers a broad range of activities like normalizing data, standardizing data, statistically or mathematically transforming data, processing and/or preprocessing data, and feature engineering. ### Data Quality Handling Report A model Leaderboard tab ( [Describe > Data Quality Handling Report](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/dq-report.html)) that analyzes the training data and provides the following information for each feature: feature name, variable type, row count, percentage, and data transformation information. ### Data Registry In Workbench, a central catalog for datasets, allowing you to link them to specific Use Cases. ### Data residency The physical or geographical location where LLM and AI data is stored and processed, often subject to regulatory requirements and compliance standards. DataRobot supports various deployment options including cloud, on-premises, and hybrid configurations to meet specific data residency requirements. ### Data retention policies Policies that define how long LLM and AI data should be kept, when it should be archived, and when it should be deleted to comply with regulations and manage storage costs. ### Data wrangling Data preparation operations of a scope that ties to creating a dataset at an appropriate unit of analysis for a given machine learning use case. ### DataRobot Classic The original DataRobot value-driven AI product. It provides a complete AI lifecycle platform, leveraging machine learning that has broad interoperability and end-to-end capabilities for ML experimentation and production. DataRobot Classic is being migrated to the new user interface, [Workbench](https://docs.datarobot.com/en/docs/reference/glossary/index.html#workbench). ### DataRobot User Models (DRUM) A tool that allows you to test Python, R, and Java custom models and tasks locally. The test allows you to verify that a custom model can successfully run and make predictions in DataRobot before uploading it. ### Dataset Data, a file or the content of a data source, at a particular point in time. A data source can produce multiple datasets; an AI Catalog dataset has exactly one data source. In [AI Catalog](https://docs.datarobot.com/en/docs/reference/glossary/index.html#ai-catalog), a dataset is materialized data that is stored with a catalog version record. There may be multiple catalog version records associated with an entity, indicating that DataRobot has reloaded or refreshed the data. The older versions are stored to support existing projects, new projects use the most recent version. A dataset can be in one of two states: - A "snapshotted" (or materialized) dataset is an immutable snapshot of data that has previously been retrieved and saved. - A "remote" (or unmaterialized) dataset has been configured with a location from which data is retrieved on-demand (AI Catalog). ### Data connection A configured connection to a database—it has a name, a specified driver, and a JDBC URL. You can register data connections with DataRobot for ease of re-use. A data connection has one connector but can have many data sources. ### Data source A configured connection to the backing data (the location of data within a given endpoint). A data source specifies, via SQL query or selected table and schema data, which data to extract from the data connection to use for modeling or predictions. Examples include the path to a file on HDFS, an object stored in S3, and the table and schema within a database. A data source has one data connection and one connector but can have many datasets. It is likely that the features and columns in a datasource do not change over time, but that the rows within change as data is added or deleted. ### Data stage Intermediary storage that supports multipart upload of large datasets, reducing the chance of failure when working with large amounts of data. Upon upload, the dataset is uploaded in parts to the data stage, and once the dataset is whole and finalized, it is pushed to the AI Catalog or Batch Predictions. At any time after the first part is uploaded to the data stage, the system can instruct Batch Predictions to use the data from the data stage to fill in predictions. ### Data store A general term used to describe a remote location where your data is stored. A data store may contain one or more databases, or one or more files of varying formats. ### Data/time partitioning The only valid partitioning method for time-aware projects. With date/time, rows are assigned to [backtests](https://docs.datarobot.com/en/docs/reference/glossary/index.html#backtesting) chronologically instead of, for example, randomly. Backtests are configurable, including number, start and end times, and sampling method. ### Dashboard Visual monitoring interfaces that provide real-time insights into LLM and AI system performance, health, and operational metrics for administrators and stakeholders. DataRobot provides comprehensive dashboards for monitoring model performance, data drift, prediction accuracy, and system health across all deployments. ### Deep learning DataRobot's implementation of neural network architectures that process data through multiple computational layers. These algorithms power DataRobot's Visual AI capabilities for image analysis and are available as blueprints in the model Repository. Users can monitor training progress and layer performance through the Training Dashboard visualization, making deep learning accessible without requiring expertise in neural network architecture design. ### Deploying (from a playground) LLM blueprints and all their associated settings are registered in [Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/index.html) and can be deployed with DataRobot's production suite of products. ### Deployment inventory The central hub for managing deployments. Located on the Deployments page, the inventory serves as a coordination point for stakeholders involved in operationalizing models. From the inventory, you can monitor deployed model performance and take action as necessary, managing all actively deployed models from a single point. ### Detection/segmentation A computer vision technique that involves the selection of a subset of the input image data for further processing (for example, one or more images within a set of images or regions within an image). ### Directed acyclic graph (DAG) A mathematical structure used to represent workflows where nodes represent tasks or operations and edges represent dependencies between them. In AI workflows, DAGs ensure that tasks are executed in the correct order without circular dependencies, enabling efficient orchestration of complex multi-step processes like data preprocessing, model training, and deployment pipelines. ### Disaster recovery Plans and procedures for recovering LLM and AI services after system failures, natural disasters, or other catastrophic events to ensure business continuity. DataRobot provides backup and restore capabilities, along with high availability configurations to minimize downtime and ensure continuous model serving. ### Distributed tracing A technique for monitoring and troubleshooting LLM and AI applications by tracking requests as they flow through multiple services and components. ### Downloads tab A model Leaderboard tab ( [Predict > Downloads](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/predictions/download-classic.html)) where you can download model artifacts. ### Downsampling See [Smart downsampling](https://docs.datarobot.com/en/docs/reference/glossary/index.html#smart-downsampling). ### Driver The software that allows the DataRobot application to interact with a database; each data connection is associated with one driver (created and installed by your administrator). The driver configuration saves the JAR file storage location in DataRobot and any additional dependency files associated with the driver. DataRobot supports JDBC drivers. ### Dynamic dataset A dynamic dataset is a "live" connection to the source data, however, DataRobot samples the data for profile statistics (EDA1). The catalog stores a pointer to the data and pulls it upon request, for example, when you create a project. ## E ### EDA (Exploratory Data Analysis) The DataRobot approach to analyzing and summarizing the main characteristics of a dataset. Generally speaking, there are two stages of EDA: - EDA1 provides summary statistics based on a sample of data. In EDA1, DataRobot counts, categorizes, and applies automatic feature transformations (where appropriate) to data. - EDA2 is a recalculation of the statistics collected in EDA1 but using the entire dataset, excluding holdout. The results of this analysis are the criteria used for model building. ### Embedding A numerical (vector) representation of text, or a collection of numerical representations of text. The action of generating embeddings means taking a [chunk](https://docs.datarobot.com/en/docs/reference/glossary/index.html#chunking) of unstructured text and using a text embedding model to convert the text to a numerical representation. The chunk is the input to the embedding model and the embedding is the "prediction" or output of the model. ### Episodic memory Memory systems that store specific experiences, events, and contextual information about past interactions and situations. Episodic memory enables AI agents to recall specific instances, learn from particular experiences, and apply contextual knowledge to similar situations. In DataRobot's agentic workflows, episodic memory allows agents to remember specific user interactions, successful task executions, and contextual details that inform future decision-making. ### Endpoint A specific URL where a service can be accessed. In machine learning, an endpoint is typically used to send data to a deployed model and receive predictions. It is the primary interface for interacting with a model programmatically via an API. ### Ensemble models See [blender](https://docs.datarobot.com/en/docs/reference/glossary/index.html#blender). ### Environment A Docker container where a custom task runs. ### Environment management The practice of managing different environments (development, staging, production) for LLM and AI systems to ensure proper testing, deployment, and operational procedures. ### ESDA Exploratory Spatial Data Analysis (ESDA) is the exploratory data phase for Location AI. DataRobot provides a variety of tools for conducting ESDA within the DataRobot AutoML environment, including geometry map visualizations, categorical/numeric thematic maps, and smart aggregation of large geospatial datasets. ### Eureqa Model blueprints for Eureqa generalized additive models (Eureqa GAM), Eureqa regression, and Eureqa classification models. These blueprints use a proprietary Eureqa machine learning algorithm to construct models that balance predictive accuracy against complexity. ### Event streaming Real-time data processing systems that handle continuous streams of events from LLM and AI applications for monitoring, analytics, and operational insights. ### EWMA (Exponentially Weighted Moving Average) A moving average that places a greater weight and significance on the most recent data points, measuring trend direction over time. The "exponential" aspect indicates that the weighting factor of previous inputs decreases exponentially. This is important because otherwise a very recent value would have no more influence on the variance than an older value. ### Experiment An asset of a Use Case that is the result of having run the DataRobot modeling process. A Use Case can have zero or more experiments. ### Experiment tracking The process of recording and managing metadata, parameters, and results from machine learning experiments to enable reproducibility and comparison. ### Exploratory data insights See [EDA](https://docs.datarobot.com/en/docs/reference/glossary/index.html#eda-exploratory-data-analysis). ### External stage A designated location in a cloud storage provider (such as Amazon S3 or Azure) that is configured to act as an intermediary for loading and unloading data with a Snowflake database. When preparing data for a project in DataRobot, users may interact with an external stage to efficiently ingest large datasets from Snowflake or to publish transformed data back to the cloud environment. ## F ### Fairness score A numerical computation of model fairness against the protected class, based on the underlying fairness metric. ### Fairness threshold The measure of whether a model performs within appropriate fairness bounds for each protected class. It does not affect the fairness score or performance of any protected class. ### Fairness value Fairness scores normalized against the most favorable protected class (i.e., the class with the highest fairness score). ### Favorable outcome A value of the target that is treated as the favorable outcome for the model, used in bias and fairness modeling. Predictions from a binary classification model can be categorized as being a favorable outcome (i.e., good/preferable) or an unfavorable outcome (i.e., bad/undesirable) for the protected class. ### FDW See [Feature Derivation Window](https://docs.datarobot.com/en/docs/reference/glossary/index.html#feature-derivation-window). ### Feature A column in a dataset, also called "variable" or "feature variable." The target feature is the name of the column in the dataset that you would like to predict. ### Feature Derivation Window Also known as FDW; used in time series modeling. A rolling window of past values that models use to derive features for the modeling dataset. Consider the window relative to the [Forecast Point](https://docs.datarobot.com/en/docs/reference/glossary/index.html#forecast-point), it defines the number of recent values the model can use for forecasting. ### Feature Discovery A DataRobot capability that discovers and generates new features from multiple datasets, eliminating the need to perform manual feature engineering to consolidate multiple datasets into one. A relationship editor visualizes these relationships and the end product is additional, derived features that result from the created linkages. ### Feature Effects A model Leaderboard tab ( [Understand > Feature Effects](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-effects-classic.html)) that shows the effect of changes in the value of each feature on the model's predictions. It displays a graph depicting how a model "understands" the relationship between each feature and the target, with the features sorted by [Feature Impact](https://docs.datarobot.com/en/docs/reference/glossary/index.html#feature-impact). ### Feature engineering The generation of additional features in a dataset, which as a result, improve model accuracy and performance. Time series and Feature Discovery both rely on feature engineering as the basis of their functionality. ### Feature extraction Models that perform image preprocessing (or image feature extraction and image preprocessing) are also known as "image feature extraction models" or "image-specific models." ### Feature Extraction and Reduction (FEAR) The feature generation process for time series modeling (e.g., lags, moving averages). It extracts new features (now) and then reduces the set of extracted features (later). See [Time series feature derivation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/feature-eng.html). ### Feature flag A DataRobot mechanism that allows administrators to enable or disable specific features for certain users, organizations, or the entire platform. Feature flags are used to manage phased rollouts, beta testing, and custom configurations. Toggling a feature flag is performed by DataRobot Support for SaaS customers. ### Feature Impact A measurement that identifies which features in a dataset have the greatest effect on model decisions. In DataRobot, the measurement is reported as a visualization available from the Leaderboard. ### Feature imputation A mechanism in time series modeling that uses forward filling to enable imputation for all features (target and others) when using the time series data prep tool. This results in a dataset with no missing values (with the possible exception of leading values at the start of each series where there is no value to forward fill). ### Feature list A subset of features from a dataset used to build models. DataRobot creates several lists during EDA2 including all informative features, informative features excluding those with a leakage risk, a raw list of all original features, and a reduced list. Uses can create project-specific lists as well. ### Few-shot learning A capability of a model to learn to perform a task from a small number of examples provided in the prompt. ### Few-shot prompting A technique where a few examples are provided in the prompt (either in an input or system prompt) to guide the model's behavior and improve its performance on specific tasks. Few-shot prompting helps models understand the desired output format and style without requiring fine-tuning, making it useful for quick adaptation to new tasks or domains. ### Fine-tuning The process of adapting pre-trained foundation models to specific tasks or domains by continuing training on targeted datasets. In DataRobot's platform, fine-tuning enables users to customize large language models for particular use cases, improving performance on domain-specific tasks while preserving general capabilities. Unlike prompt engineering which works with existing model weights, fine-tuning modifies the model's internal parameters to create specialized versions optimized for particular applications, industries, or data types. ### Fitting See [model fitting](https://docs.datarobot.com/en/docs/reference/glossary/index.html#model-fitting). ### Forecast Distance A unique time step—a relative position—within the Forecast Window in a time series modeling project. A model outputs one row for each Forecast Distance. ### Forecast Point In time series modeling, the point you are making a prediction from; a relative time "if it was now..."; DataRobot trains models using all potential forecast points in the training data. In production, it is typically the most recent time. ### Forecast vs Actual A model Leaderboard tab ( [Evaluate > Forecast vs Actual](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/fore-act.html)) commonly used in time series projects that allows you to compare how different predictions behave from different forecast points to different times in the future. Although similar to the [Accuracy Over Time](https://docs.datarobot.com/en/docs/reference/glossary/index.html#accuracy-over-time) chart, which displays a single forecast at a time, the Forecast vs Actual chart shows multiple forecast distances in one view. ### Forecast Window Also known as FW; used in time series modeling. Beginning from the Forecast Point, defines the range (the Forecast Distance) of future predictions—"this is the range of time I care about." DataRobot then optimizes models for that range and ranks them on the Leaderboard on the average across that range. ### Forecasting Predictions based on time, into the future; use inputs from recent rows to predict future values. Forecasting is a subset of predictions, using trends in observation to characterize expected outcomes or expected responses. ### Foundation model A powerful, large-scale AI model, like GPT or Claude, that provides broad, general-purpose capabilities learned from massive datasets. In the DataRobot platform, these models act as the core component or 'foundation' of an LLM blueprint. Rather than being a ready-made solution, a foundation model is the versatile starting point that can be customized for specific business needs through techniques like prompting, RAG, or fine-tuning. ### Fast API A modern, high-performance web framework for building APIs with Python. FastAPI provides automatic API documentation, type validation, and high performance through async support. In DataRobot's ecosystem, FastAPI is used for building custom API endpoints, microservices, and integration layers that support agentic workflows and custom model deployments. ### Frozen run A process that "freezes" parameter settings from a model's early, small sample size-based run. Because parameter settings based on smaller samples tend to also perform well on larger samples of the same data. ### Function calling The capability of large language models to invoke external functions, tools, or APIs based on user requests and conversation context. In DataRobot's agentic workflows, function calling enables agents to perform actions beyond text generation, such as data retrieval, mathematical computations, API interactions, and system operations. This allows agents to execute complex tasks, integrate with enterprise systems, and provide dynamic responses based on real-time information. Function calling transforms conversational AI into actionable systems that can manipulate data and interact with external services. ### FW See [Forecast Window](https://docs.datarobot.com/en/docs/reference/glossary/index.html#forecast-window). ## G ### Generative AI (GenAI) A type of artificial intelligence that generates new content based on learned patterns from training data. In DataRobot's platform, GenAI capabilities include text generation, content creation, and intelligent responses through LLM blueprints. Unlike traditional predictive models that analyze existing data, GenAI creates novel outputs through prompting and can be integrated into DataRobot workflows for content generation, analysis, and automated decision-making processes. ### Governance lens A filtered view of DataRobot's deployment inventory on the Deployments page, summarizing the social and operational aspects of a deployment. These include the deployment owner, how the model was built, the model's age, and the humility monitoring status. ### GPU (graphics processing unit) A specialized processor designed for parallel computing tasks, particularly effective for deep learning and AI workloads. GPUs excel at matrix operations and parallel processing, making them ideal for training complex models on large datasets. In DataRobot, GPU acceleration is available for supported deep learning blueprints and can significantly reduce training time for models that process text, images, or other computationally intensive tasks. ### Guardrails Safety mechanisms that prevent AI systems from generating harmful or inappropriate content. Guardrails include content filtering, output validation, and behavioral constraints that ensure AI responses align with safety guidelines and organizational policies. In DataRobot, guardrails can be configured and help maintain responsible AI practices and prevent the generation of unsafe or unethical content. ### Grid search An exhaustive search method used for hyperparameters. ### Grounding The process of ensuring that language model responses are based on specific, verifiable data sources rather than relying solely on training data. In DataRobot's platform, grounding is achieved through Retrieval Augmented Generation (RAG) workflows that connect LLMs to vector databases containing relevant documents, knowledge bases, or enterprise data. This technique improves response accuracy, reduces hallucinations, and ensures that AI outputs are contextualized with current, relevant information from trusted sources. ### Group A collection of users who share common permissions and access to projects, deployments, and other resources within an organization. Groups simplify user management by allowing administrators to manage permissions for multiple users at once. ## H ### Hallucination When a language model generates information that is plausible-sounding but factually incorrect or not grounded in the provided data. ### Health checks Automated monitoring systems that verify the health and availability of LLM and AI services by periodically checking their status and responsiveness. ### High availability System design principles and practices that ensure LLM and AI services remain available and operational even during hardware failures, software issues, or other disruptions. ### High code A development approaches that emphasizes custom programming and fine-grained control over application behavior. High-code solutions provide maximum flexibility and customization capabilities for complex requirements. In DataRobot's agentic workflows, high-code capabilities enable advanced users to create highly specialized agents with custom logic, integrate with complex enterprise systems, and implement sophisticated decision-making algorithms. ### Holdout A subset of data that is unavailable to models during the training and validation process. Use the Holdout score for a final estimate of model performance only after you have selected your best model. See also [Validation](https://docs.datarobot.com/en/docs/reference/glossary/index.html#validation). ### HTTP Status Codes Standard response codes returned by DataRobot APIs to indicate the success or failure of requests. Common codes include 200 (success), 400 (bad request), 401 (unauthorized), 404 (not found), and 500 (server error). These codes help developers understand API responses and troubleshoot integration issues when working with DataRobot's REST APIs. ### Human in the loop (HILT) Integration patterns that incorporate human oversight, validation, and intervention into AI agent workflows. Human-in-the-loop systems enable humans to review agent decisions, provide feedback, correct errors, and guide agent behavior at critical decision points. In DataRobot's agentic workflows, human-in-the-loop capabilities ensure quality control, enable learning from human expertise, and maintain human authority over sensitive or high-stakes decisions. ### Humility A user-defined set of rules for deployments that allow models to be capable of recognizing, in real-time, when they make uncertain predictions or receive data they have not seen before. Unlike data drift, model humility does not deal with broad statistical properties over time—it is instead triggered for individual predictions, allowing you to set desired behaviors with rules that depend on different triggers. ## I ### Image data A sequence of digital images (e.g., video), a set of digital images, a single digital image, and/or one or more portions of any of these—data used as part of Visual AI. A digital image may include an organized set of picture elements ("pixels") stored in a file. Any suitable format and type of digital image file may be used, including but not limited to raster formats (e.g., TIFF, JPEG, GIF, PNG, BMP, etc.), vector formats (e.g., CGM, SVG, etc.), compound formats (e.g., EPS, PDF, PostScript, etc.), and/or stereo formats (e.g., MPO, PNS, JPS). ### Image preprocessing A computer vision technique, part of Visual AI. Some examples include image re-sampling, noise reduction, contrast enhancement, and scaling (e.g., generating a scale space representation). Extracted features may be: - Low-level: raw pixels, pixel intensities, pixel colors, gradients, textures, color histograms, motion vectors, edges, lines, corners, ridges, etc. - Mid-level: shapes, surfaces, volumes, etc. - High-level: objects, scenes, events, etc. ### Incremental learning A model training method specifically tailored for large datasets—those between 10GB and 100GB—that chunks data and creates training iterations. After model building begins, compare trained iterations and optionally assign a different active version or continue training. The active iteration is the basis for other insights and is used for making predictions. ### Infrastructure as Code (IaC) The practice of managing and provisioning LLM and AI infrastructure through machine-readable definition files rather than physical hardware configuration or interactive configuration tools. ### In-context learning The ability of LLMs to learn from examples provided in the prompt without requiring fine-tuning. In-context learning allows models to adapt their behavior based on the context and examples given in the current conversation, enabling them to perform new tasks or follow specific instructions without additional training. ### Inference data Data that is scored by applying an algorithmic model built from a historical dataset in order to uncover practical insights. See also [Scoring data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#scoring-data). ### In-sample predictions Predictions made on data that the model has already seen during its training process. This typically occurs when a model is trained on a very high percentage of the available data (e.g., above 80%), leaving little or no "unseen" data for validation. In such cases, the validation score is calculated from the same data used for training, which can result in an overly optimistic assessment of model performance. In DataRobot, these scores are marked with an asterisk on the Leaderboard to indicate that they may not reflect true generalization performance. Compare to [stacked](https://docs.datarobot.com/en/docs/reference/glossary/index.html#stacked-predictions) (out-of-sample) predictions. ### Integration patterns Common architectural patterns and best practices for integrating LLM and AI services with existing systems, applications, and data sources. ### Instruction tuning Training LLMs to follow specific instructions or commands by fine-tuning them on instruction-response pairs. Instruction tuning improves a model's ability to understand and execute user requests, making it more useful for practical applications where following directions is important. ### Irregular data Data in which no consistent spacing and no time step is detected. Used in time-aware modeling. ## J ### JSON A lightweight data format commonly used in DataRobot APIs for exchanging structured data between services. JSON is used throughout the DataRobot platform for configuration files, API responses, data transfer operations, and storing model metadata. The format provides a standardized way to represent complex data structures in a human-readable format that can be easily processed by applications. ## K ### KA See [Known in advance features](https://docs.datarobot.com/en/docs/reference/glossary/index.html#known-in-advance-features). ### Kernel Provides programming language support to execute the code in a notebook. ### Knowledge cutoff The date after which an LLM's training data ends, limiting its knowledge of historical events, information, and developments that occurred after that point. Knowledge cutoff dates are important for understanding the temporal scope of a model's information and determining when additional context or real-time data sources may be needed. ### Known in advance features Also known as KA; used in time series modeling. A variable for which you know the value in advance and does not need to be lagged, such as holiday dates. Or, for example, you might know that a product will be on sale next week and so you can provide the pricing information in advance. ## L ### Large language model (LLM) A deep learning model trained on extensive text datasets that can understand, generate, and process human language. In DataRobot's platform, LLMs form the core of LLM blueprints and can be configured with various settings, system prompts, and vector databases to create customized AI applications. These models enable DataRobot users to build intelligent chatbots, content generators, and analysis tools that can understand context and provide relevant responses. ### Latency The time delay between sending a request to a model or API and receiving a response, often measured in milliseconds. ### Leaderboard The list of trained blueprints (models) for a project, ranked according to a project metric. ### Leakage See [target leakage](https://docs.datarobot.com/en/docs/reference/glossary/index.html#target-leakage). ### Learning curves A graph to help determine whether it is worthwhile to increase the size of a dataset. The Learning Curve graph illustrates, for the top-performing models, how model performance varies as the sample size changes. ### License A commercial agreement that grants access to the DataRobot platform. The license defines the scope of usage, including the number of authorized users, available features, and limits on computational resources. ### Lift chart Depicts how well a model segments the target population and how capable it is of predicting the target to help visualize model effectiveness. ### Linkage keys (Feature Discovery) The features in the primary dataset used as keys to join and create relationships. ### LLM blueprint The saved blueprint, available to be used for [deployment](https://docs.datarobot.com/en/docs/reference/glossary/index.html#deploying-from-a-playground). LLM blueprints represent the full context for what is needed to generate a response from an LLM; the resulting output can be compared within the [playground](https://docs.datarobot.com/en/docs/reference/glossary/index.html#playground). This information is captured in the [LLM blueprint settings](https://docs.datarobot.com/en/docs/reference/glossary/index.html#llm-blueprint-settings). ### LLM blueprint components The entities that make up the [LLM blueprint settings](https://docs.datarobot.com/en/docs/reference/glossary/index.html#llm-blueprint-settings), this refers to the vector database, embedding model user to generate the vector database, LLM settings, system prompt, etc. These components can either be offered natively within DataRobot or can be brought in from external sources. ### LLM blueprint settings The parameters sent to the LLM to generate a response (in conjunction with the user-entered prompt). They include a single LLM, LLM settings, optionally a system prompt, and optionally a vector database. If no vector database is assigned, then the LLM uses its learnings from training to generate a response. LLM blueprint settings are configurable so that you can experiment with different configurations. ### LLM gateway A centralized service in DataRobot that manages access to multiple large language models from external providers with support for unified authentication, rate limiting, and request routing. The LLM gateway enables organizations to standardize their interactions with various LLM providers while maintaining security, monitoring, and cost controls across all model usage. ### LLM payload The bundle of contents sent to the LLM endpoint to generate a response. This includes the user prompt, LLM settings, system prompt, and information retrieved from the vector database. ### LLM responses Generated text from the LLM based on the payload sent to an LLM endpoint. ### LLM settings Parameters that define how an LLM intakes a user prompt and generates a response. They can be adjusted within the LLM blueprint to alter the response. These parameters are currently represented by the "Temperature", "Token selection probability cutoff (Top P)", and "Max completion tokens" settings. ### Load balancing The distribution of incoming requests across multiple LLM and AI service instances to optimize resource utilization, maximize throughput, minimize response time, and avoid overload. ### Location AI DataRobot's support for geospatial analysis by natively ingesting common geospatial formats and recognizing coordinates, allowing [ESDA](https://docs.datarobot.com/en/docs/reference/glossary/index.html#esda), and providing spatially-explicit modeling tasks and visualizations. ### Log A model Leaderboard tab ( [Describe > Log](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/log-classic.html)) that displays the status of successful operations with green INFO tags, along with information about errors marked with red ERROR tags. ### Log aggregation The centralized collection and storage of logs from multiple LLM and AI services to enable comprehensive monitoring, analysis, and troubleshooting. ### Loss function A method of evaluating how well a specific algorithm models the given data. It computes a number representing the "cost" of the model's predictions being wrong; the goal of training is to minimize this value. ### Low code A development approach that minimizes the amount of manual coding required to build applications and workflows. Low-code platforms provide visual interfaces, drag-and-drop components, and pre-built templates that enable rapid development. In DataRobot's agentic workflows, low-code capabilities allow users to create sophisticated AI agents and workflows through configuration interfaces rather than extensive programming, making agentic AI accessible to non-technical users. ## M ### Majority class If you have a categorical variable (e.g., `true` / `false` or `cat` / `mouse`), the value that's more frequent is the majority class. For example, if a dataset has 80 rows of value `cat` and 20 rows of value `mouse`, then `cat` is the majority class. See also [minority class](https://docs.datarobot.com/en/docs/reference/glossary/index.html#minority-class). ### Make Predictions tab A model Leaderboard tab ( [Predict > Make Predictions](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/predictions/predict.html)) that allows you to make predictions before deploying a model to a production environment. ### Management agent A downloadable client included in the MLOps agent tarball (accessed via API keys and tools) that allows you to manage external models (i.e., those running outside of DataRobot MLOps). This tool provides a standard mechanism to automate model deployment to any type of infrastructure. The management agent sends periodic updates about deployment health and status via the API and reports them as MLOps events on the Service Health page. ### Manual A modeling mode that causes DataRobot to complete EDA2 and prepare data for modeling, but does not execute model building. Instead, users select specific models to build from the model Repository. ### Materialized Data that DataRobot has pulled from the data asset and is currently keeping a copy of in the catalog. See also [snapshot](https://docs.datarobot.com/en/docs/reference/glossary/index.html#snapshot) and [unmaterialized](https://docs.datarobot.com/en/docs/reference/glossary/index.html#unmaterialized) data. ### Metadata Details of the data asset, such as creation and modification dates, number and types of features, snapshot status, and more. ### Metric See [optimization metric](https://docs.datarobot.com/en/docs/reference/glossary/index.html#optimization-metric). ### Metrics collection The systematic gathering of performance, business, and operational metrics from LLM and AI systems to enable monitoring, analysis, and decision-making. ### Minority class If you have a categorical variable (e.g., `true` / `false` or `cat` / `mouse`), the value that's less frequent is the minority class. For example, if a dataset has 80 rows of value `cat` and 20 rows of value `mouse`, then `mouse` is the minority class. See also [majority class](https://docs.datarobot.com/en/docs/reference/glossary/index.html#majority-class). ### MLOps (Machine Learning Operations) A scalable and governed means to rapidly deploy and manage ML applications in production environments. ### Multi-agent flow A workflow pattern where multiple AI agents collaborate to solve complex problems by dividing tasks among specialized agents. Each agent has specific capabilities and responsibilities, and they communicate and coordinate to achieve the overall objective. Multi-agent flows enable more sophisticated problem-solving by leveraging the strengths of different specialized agents. See also [Agentic workflow](https://docs.datarobot.com/en/docs/reference/glossary/index.html#agentic-workflow). ### MLOps agent The downloadable package (tarball) that contains two clients: the Monitoring Agent and the Management Agent. The MLOps Agent enables you to monitor and manage external models (i.e., those running outside of DataRobot MLOps) by providing these tools for deployment, monitoring, and reporting. See also [Monitoring Agent](https://docs.datarobot.com/en/docs/reference/glossary/index.html#monitoring-agent) and [Management Agent](https://docs.datarobot.com/en/docs/reference/glossary/index.html#management-agent). ### Model Context Protocol (MCP) server A Model Context Protocol (MCP) server provides standardized interfaces for AI agents to interact with external systems and data sources. MCP servers enable secure, controlled access to tools, databases, APIs, and other resources that agents need to accomplish their tasks. In DataRobot's agentic workflows, MCP servers facilitate seamless integration between agents and enterprise systems while maintaining security and governance controls. ### Model A trained machine learning model that can make predictions on new data. In DataRobot, models are built using various algorithms and can predict outcomes like customer churn, sales forecasts, or fraud detection. ### Model approval workflows Structured processes for reviewing, validating, and approving LLM and AI models before deployment to production, ensuring quality, compliance, and business alignment. ### Model catalog A centralized repository that provides a comprehensive view of all available LLM and AI models, including their versions, metadata, performance metrics, and deployment status. ### Model comparison A Leaderboard tab that allows you to compare two models using different evaluation tools, helping identify the model that offers the highest business returns or candidates for blender models. ### Model alignment Techniques to ensure AI models behave according to human values and intentions. Model alignment involves training and fine-tuning processes that help models produce outputs that are helpful, honest, and harmless, reducing risks of harmful or unintended behaviors in production environments. ### Model deprecation The process of phasing out and retiring old LLM and AI models from production use, including communication to stakeholders and migration strategies. ### Model fitting A measure of how well a model generalizes similar data to the data on which it was trained. A model that is well-fitted produces more accurate outcomes. A model that is overfitted matches the data too closely. A model that is underfitted doesn't match closely enough. ### Model Info A model Leaderboard tab ( [Describe > Model Info](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/model-info-classic.html)) that displays an overview for a given model, including model file size, prediction time, and sample size. ### Model lineage The complete history and provenance of LLM and AI models, including their training data, algorithms, parameters, and evolution over time for audit and compliance purposes. DataRobot tracks model lineage through the Model Registry, maintaining detailed records of training data, feature engineering steps, model versions, and deployment history for comprehensive audit trails. ### Model overview A page within an experiment that displays the model Leaderboard, and once a model is selected, displays visualizations for that model. ### Model package Archived model artifacts with associated metadata stored in the Model Registry. Model packages can be created manually or automatically, for example, through the deployment of a custom model. You can deploy, share, and permanently archive model packages. ### Model Registry An organizational hub for the variety of models used in DataRobot. Models are registered as deployment-ready model packages; Registry lists each package available for use. Each package functions the same way, regardless of the origin of its model. The Model Registry also contains the Custom Model Workshop where you can create and deploy custom models. Model packages can be created manually or automatically depending on the type of model. ### Model scoring The process of applying an optimization metric to a partition of the data and assigning a numeric score that can be used to evaluate a model performance. ### Model versioning The systematic tracking and management of different versions of LLM and AI models to enable rollbacks, comparisons, and controlled deployments. ### Modeling The process of building predictive models using machine learning algorithms. This involves training algorithms on historical data to identify patterns and relationships that can be used to predict future outcomes. DataRobot automates much of this process through AutoML, allowing users to build, evaluate, and deploy predictive models efficiently. ### Modeling dataset A transform of the original dataset that pre-shifts data to future values, generates lagged time series features, and computes time-series analysis metadata. Commonly referred to as feature derivation, it is used by time series but not OTV. See the [time series feature engineering reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/feature-eng.html) for a list of operators used and feature names created by the feature derivation process. See also [FEAR](https://docs.datarobot.com/en/docs/reference/glossary/index.html#feature-extraction-and-reduction-fear). ### Modeling mode A setting that controls the sample percentages of the training set that DataRobot uses to build models. DataRobot offers four modeling modes: [Autopilot](https://docs.datarobot.com/en/docs/reference/glossary/index.html#autopilot-full-autopilot), [Quick](https://docs.datarobot.com/en/docs/reference/glossary/index.html#quick-autopilot) (the default), [Manual](https://docs.datarobot.com/en/docs/reference/glossary/index.html#manual), and [Comprehensive](https://docs.datarobot.com/en/docs/reference/glossary/index.html#comprehensive). ### Moderation The process of monitoring and filtering model outputs to ensure they comply with safety, ethical, and policy guidelines. ### Monitoring agent A downloadable client included in the MLOps agent tarball (accessed via API keys and tools) that allows you to monitor external models (i.e., those running outside of DataRobot MLOps). With this functionality, predictions and information from these models can be reported as part of deployments. You can use this tool to monitor accuracy, data drift, prediction distribution, latency, and more, regardless of where the model is running. ### Monotonic modeling A method to force certain XGBoost models to learn only monotonic (always increasing or always decreasing) relationships between specific features and the target. ### Multiclass See [classification](https://docs.datarobot.com/en/docs/reference/glossary/index.html#classification). ### Multilabel A classification task where each row in a dataset is associated with one, several, or zero labels. Common multilabel classification problems are text categorization (a movie is both "crime" and "drama") and image categorization (an image shows a house and a car). ### Multimodal A model type that supports multiple var types at the same time, in the same model. ### Multiseries Datasets that contain multiple time series (for example, to forecast the sales of multiple stores) based on a common set of input features. ## N ### Naive model See [baseline model](https://docs.datarobot.com/en/docs/reference/glossary/index.html#baseline-model). ### NAT Neural Architecture Transfer (NAT) enables efficient transfer of learned representations and architectures between different AI models and tasks. NAT techniques allow agents to leverage pre-trained components and adapt them for specific use cases without full retraining. In DataRobot's agentic workflows, NAT capabilities enable rapid deployment of specialized agents by transferring knowledge from general-purpose models to domain-specific applications. ### NextGen The updated DataRobot user interface comprised of [Workbench](https://docs.datarobot.com/en/docs/reference/glossary/index.html#workbench) for experiment based iterative workflows, [Registry](https://docs.datarobot.com/en/docs/reference/glossary/index.html#registry) for model evolution tracking and the centralized management of versioned models, and [Console](https://docs.datarobot.com/en/docs/reference/glossary/index.html#console) for monitoring and managing deployed models. NextGen also provides the gateway for creating [agentic workflows](https://docs.datarobot.com/en/docs/reference/glossary/index.html#agentic), [GenAI experiments](https://docs.datarobot.com/en/docs/reference/glossary/index.html#genai), [Notebooks](https://docs.datarobot.com/en/docs/reference/glossary/index.html#notebook), and [apps](https://docs.datarobot.com/en/docs/reference/glossary/index.html#no-code-ai-apps). ### N-gram A sequence of words, where N is the number of words. For example, "machine learning" is a 2-gram. Text features are divided into n-grams to prepare for Natural Language Processing (NLP). ### NIM NVIDIA Inference Microservice (NIM) is a containerized AI model that provides optimized, high-performance inference with low latency and efficient resource utilization. In DataRobot's platform, a NIM can be integrated into an agentic workflow to provide specialized AI capabilities, enabling agents to leverage state-of-the-art models for specific tasks while maintaining optimal performance and scalability. ### No-Code AI Apps A no-code interface to create AI-powered applications that enable core DataRobot services without having to build models and evaluate their performance. Applications are easily shared and do not require consumers to own full DataRobot licenses in order to use them. ### Notebook An interactive, computational environment that hosts code execution and rich media. DataRobot provides its own in-app environment to create, manage, and execute Jupyter-compatible hosted notebooks. ### Nowcasting A method of time series modeling that predicts the current value of a target based on past and present data. Technically, it is a forecast window in which the start and end times are 0 (now). ## O ### Offset Feature(s) that should be treated as a fixed component for modeling (coefficient of 1 in generalized linear models or gradient boosting machine models). Offsets are often used to incorporate pricing constraints or to boost existing models. ### One-shot learning A capability of a model to learn to perform a task from only a single example. ### Orchestration The coordination of multiple AI components, tools, and workflows to achieve complex objectives. Orchestration involves managing the flow of data and control between different AI services, ensuring proper sequencing, error handling, and resource allocation. In DataRobot, orchestration enables the creation of sophisticated multi-step AI workflows that combine various capabilities and tools. ### Parameter efficient fine-tuning (PEFT) Methods to fine-tune large models using fewer parameters than full fine-tuning. PEFT techniques, such as LoRA (Low-Rank Adaptation) and adapter layers, allow for efficient model customization while maintaining most of the original model's performance and reducing computational requirements. ### Operation A single data manipulation instruction that specifies to either transform, filter, or pivot one or more records into zero or more records (e.g., find and replace or compute new feature). ### Optimization metric An error metric used in DataRobot to determine how well a model predicts actual values. After you choose a target feature, DataRobot selects an optimization metric based on the modeling task. ### Ordering feature The primary date/time feature that DataRobot will use for modeling. Options are detected during [EDA1](https://docs.datarobot.com/en/docs/reference/glossary/index.html#eda). ### Organization A top-level entity in DataRobot that represents a single customer or tenant. It serves as a container for all users, groups, projects, deployments, and other assets, enabling centralized billing and resource management. ### OTV Also known as out-of-time validation. A method for modeling time-relevant data. With OTV you are not forecasting, as with [time series](https://docs.datarobot.com/en/docs/reference/glossary/index.html#autots-automated-time-series). Instead, you are predicting the target value on each individual row. ### Overfitting A modeling issue where predictive models perform exceptionally well on training data but poorly on new data. DataRobot addresses overfitting through automated techniques like regularization, early stopping, and cross-validation. The platform's built-in safeguards help prevent overfitting by monitoring validation performance and automatically adjusting model complexity, ensuring models generalize well to unseen data while maintaining predictive accuracy. ## P ### Partition Segments of training data, broken down to maximize accuracy. The segments (splits) of the dataset. See also [training](https://docs.datarobot.com/en/docs/reference/glossary/index.html#training-data), [validation](https://docs.datarobot.com/en/docs/reference/glossary/index.html#validation), [cross-validation](https://docs.datarobot.com/en/docs/reference/glossary/index.html#cross-validation), and [holdout](https://docs.datarobot.com/en/docs/reference/glossary/index.html#holdout). ### Per-class bias A model Leaderboard tab ( [Bias and Fairness > Per-Class Bias](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/bias/per-class.html)) that helps to identify if a model is biased, and if so, how much and who it's biased towards or against.[Bias and Fairness settings](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/fairness-metrics.html) must be configured. ### Permissions A set of rights that control what actions a user or group can perform within DataRobot. Permissions are managed through roles and determine access to features like creating projects, deploying models, and managing system settings. ### PID (project identifier) An internal identifier used for uniquely identifying a project. ### PII Personal identifiable information, including name, pictures, home address, SSN or other identifying numbers, birth date, and more. DataRobot automates the detection of specific types of personal data to provide a layer of protection against the inadvertent inclusion of this information in a dataset. ### Pipeline A sequence of data processing and modeling steps, often automated, that transforms raw data into predictions or insights. ### Playground The place where you create and interact with [LLM blueprints](https://docs.datarobot.com/en/docs/reference/glossary/index.html#llm-blueprint) (LLMs and their associated settings), comparing the response of each to help determine which to use in production. Many LLM blueprints can live within a playground. A playground is an asset of a Use Case; multiple playgrounds can exist in a single Use Case. ### Playground compare The place to add LLM blueprints to the playground for comparison, submit prompts to these LLM blueprints, and evaluate the rendered responses. With [RAG](https://docs.datarobot.com/en/docs/reference/glossary/index.html#retrieval-augmented-generation-rag), a single prompt is sent to an LLM to generate a single response, without referencing previous prompts. This allows users to compare responses from multiple LLM blueprints. ### Port An interface that connects a DataRobot entity (a notebook, custom model, or custom app) to another network. ### Portable Prediction Server (PPS) A DataRobot execution environment for DataRobot model packages ( `.mlpkg` files) distributed as a self-contained Docker image. It can be run disconnected from main installation environments. ### Predicting For non-time-series modeling. Use information in a row to determine the target for that row. Predictions use explanatory variables to characterize expected outcomes or expected responses (e.g., a specific event in the future, gender, fraudulent transactions). For time series, see [Forecasting](https://docs.datarobot.com/en/docs/reference/glossary/index.html#forecasting). ### Prediction data Data that contains prediction requests and results from the model. ### Prediction environment An environment configured to manage deployment predictions on an external system, outside of DataRobot. Prediction environments allow you to configure deployment permissions and approval processes. Once configured, you can specify a prediction environment for use by DataRobot models running on the Portable Prediction Server and for remote models monitored by the MLOps monitoring agent. ### Prediction explanations A visualization that helps to illustrate what drives predictions on a row-by-row basis—they provide a quantitative indicator of the effect variables have on a model, answering why a given model made a certain prediction. It helps to understand why a model made a particular prediction so that you can then validate whether the prediction makes sense. See also [SHAP](https://docs.datarobot.com/en/docs/reference/glossary/index.html#shap-shapley-values), [XEMP](https://docs.datarobot.com/en/docs/reference/glossary/index.html#xemp-exemplar-based-explanations-of-model-predictions). ### Prediction intervals Prediction intervals help DataRobot assess and describe the uncertainty in a single record prediction by including an upper and lower bound on a point estimate (e.g., a single prediction from a machine learning model). The prediction intervals provide a probable range of values that the target may fall into on future data points. ### Prediction point The point in time when you made or will make a prediction. Plan your prediction point based on the production model (for example, "one month before renewal" or "loan application submission time"). Once defined, create that entry in the training data to help avoid lookahead bias. With [Feature Discovery](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-discovery/fd-overview.html), you define the prediction point to ensure the derived features only use data prior to that point. ### Prediction server The dedicated, scalable infrastructure responsible for hosting deployed models and serving real-time prediction requests via an API. It is optimized for low-latency and high-throughput scoring. ### Prepared dataset A dataset that has been [materialized](https://docs.datarobot.com/en/docs/reference/glossary/index.html#materialized) in its source after publishing a recipe. ### Primary dataset (Feature Discovery) The dataset used to start a project. ### Primary features (Feature Discovery) Features in the project's primary dataset. ### Privacy controls Mechanisms and policies for managing personal data in LLM and AI systems, including data anonymization, consent management, and compliance with privacy regulations. ### Project A referenceable item that includes a dataset, which is the source used for training, and any models built from the dataset. In DataRobot Classic, projects can be created and accessed from the home page, the project control center, and the AI Catalog. They can be shared to users, groups, and an organization. For DataRobot NextGen, see [Use Case](https://docs.datarobot.com/en/docs/reference/glossary/index.html#use-case). ### Prompt The input entered during chatting used to generate the LLM response. ### Prompt engineering The practice of designing and refining input prompts to guide a language model toward producing desired outputs. ### Prompt injection A security vulnerability where malicious prompts can override system instructions or safety measures. Prompt injection attacks attempt to manipulate AI systems into generating inappropriate content or performing unintended actions by crafting inputs that bypass the model's intended constraints and guidelines. ### Prompt template See [system prompt](https://docs.datarobot.com/en/docs/reference/glossary/index.html#system-prompt). ### Pulumi Infrastructure as Code (IaC) platform that enables developers to define and manage cloud infrastructure using familiar programming languages. Pulumi supports multiple cloud providers and provides a unified approach to infrastructure management. In DataRobot's agentic workflows, Pulumi enables automated provisioning and management of infrastructure resources needed for agent deployment, scaling, and monitoring across different environments. ### Protected class One categorical value of the protected feature, used in bias and fairness modeling. ### Protected feature The dataset column to measure fairness of model predictions against. Model fairness is calculated against the protected features from the dataset. Also known as "protected attribute." ### Publish Execution of the sequence of operations specified in a recipe resulting in the materialization of a dataset in a data source. ## Q ### Queue The system that manages the execution of jobs, such as model training and batch predictions. The queue prioritizes and allocates tasks to available workers based on system load and user permissions, ensuring efficient use of computational resources. ### Quick (Autopilot) A shortened version of the full Autopilot modeling mode that runs models directly at 64%. With Quick, the 16% and 32% sample sizes are not executed. DataRobot selects models to run based on a variety of criteria, including target and performance metric, but as its name suggests, chooses only models with relatively short training runtimes to support quicker experimentation. ## R ### Rate limiting A technique used to control the number of requests a client can make to an API within a specified time period, preventing abuse and ensuring fair usage. ### Rating table A model Leaderboard tab ( [Details > Rating Table](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/rating-tables.html)) where you can export the model's complete, validated parameters. ### Real-time predictions Method of making predictions when low latency is required. Use the Prediction API for real-time deployment predictions on a dedicated and/or a standalone prediction server. ### Receiver Operating Characteristic Curve See [ROC Curve](https://docs.datarobot.com/en/docs/reference/glossary/index.html#roc-curve). ### Recipe A user-defined sequence of transformation operations that are applied to the data. A recipe is uniquely identified and versioned by the system. It includes metadata identifying the input data's source and schema, the output data's schema, the Use Case Container ID, and user ID. ### Registry [Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/index.html) is a centralized location where you access versioned, deployment-ready model packages. From there, you can create custom models and jobs, generate compliance documentation, and deploy models to production. ### Regression A DataRobot modeling approach that predicts continuous numerical values from your target feature. DataRobot's regression capabilities handle various continuous outcomes like sales forecasts, price predictions, or risk scores. The platform automatically selects from regression algorithms in the Repository and provides evaluation metrics like RMSE, MAE, and R² to measure prediction accuracy. See also [classification](https://docs.datarobot.com/en/docs/reference/glossary/index.html#classification). ### Regularization A technique used to prevent model overfitting by adding a penalty term to the loss function. Common types are L1 (Lasso) and L2 (Ridge) regularization. ### Regular data Data is regular if rows in the dataset fall on an evenly spaced time grid (e.g., there's one row for every hour across the entire dataset). See also [time step](https://docs.datarobot.com/en/docs/reference/glossary/index.html#time%20step) and [semi-regular data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#semi-regular-data). ### Reinforcement learning from human feedback (RLHF) A training method that uses human feedback to improve model behavior. RLHF involves collecting human preferences on model outputs and using reinforcement learning techniques to fine-tune the model to produce responses that align with human values and preferences, improving safety and usefulness. ### ReAct A Reasoning and Acting (ReAct) framework combines reasoning capabilities with action execution in AI agents. ReAct enables agents to think through problems step-by-step, plan actions, execute them, and observe results to inform subsequent reasoning. In DataRobot's agentic workflows, ReAct capabilities allow agents to perform complex problem-solving by iteratively reasoning about situations, taking actions, and learning from outcomes to achieve their goals. ### Relationships (Feature Discovery) Relationships between datasets. Each relationship involves a pair of datasets, and a join key from each dataset. A key comprises one or more columns of a dataset. The keys from both datasets are ordered, and must have the same number of columns. The combination of keys is used to determine how two datasets are joined. ### Remote models Models running outside of DataRobot in external prediction environments, often monitored by the MLOps monitoring agent to report statistics back to DataRobot. ### Repository A library of modeling blueprints available for a selected project (based on the problem type). These models may be selected and built by DataRobot and also can be user-executed. ### Resource optimization The practice of optimizing LLM and AI resource usage for cost efficiency while maintaining performance and reliability requirements. ### Resource provisioning The allocation and management of computing resources (CPU, memory, storage, GPU) for LLM and AI workloads to ensure optimal performance and cost efficiency. ### Response time optimization Techniques and strategies for improving LLM response times, including caching, model optimization, and infrastructure improvements. ### Retrieval The process of finding relevant information from a knowledge base or database. In the context of RAG workflows, retrieval involves searching through vector databases or other knowledge sources to find the most relevant content that can be used to ground and inform AI responses, improving accuracy and reducing hallucination. ### Retrieval Augmented Generation (RAG) The process of sending a payload to an LLM that contains the prompt, system prompt, LLM settings, vector database (or subset of vector database), and the LLM returning corresponding text based on this payload. It includes the process of retrieving relevant information from a vector database and sending that along with the prompt, system prompt, and LLM settings to the LLM endpoint to generate a response grounded in the data in the vector database. This operation may optionally also incorporate orchestration to execute a chain of multiple prompts. ### Retrieval Augmented Generation (RAG) workflow An AI system that runs RAG, which includes data preparation, vector database creation, LLM configuration, and response generation. RAG workflows typically involve steps such as document chunking, embedding generation, similarity search, and context-aware response generation, all orchestrated to provide accurate, grounded responses to user queries. See also [Retrieval Augmented Generation (RAG)](https://docs.datarobot.com/en/docs/reference/glossary/index.html#retrieval-augmented-generation-rag). ### REST (Representational State Transfer) An architectural style for designing networked applications, commonly used for web APIs, that uses standard HTTP methods (GET, POST, PUT, DELETE) to access and manipulate resources. ### ROC Curve Also known as Receiver Operating Characteristic Curve. A visualization that helps to explore classification, performance, and statistics related to a selected model at any point on the probability scale. In DataRobot, the visualization is available from the Leaderboard. ### Role Roles—Owner, Consumer, and Editor—describe the capabilities provided to each user for a given dataset. This supports the scenarios when the user creating a data source or data connection and the enduser are not the same, or there are multiple endusers of the asset. ### Role-based access control (RBAC) A security model that restricts access to LLM and AI systems based on the roles of individual users, providing granular permission management and security control. DataRobot implements RBAC through user groups, permissions, and organization-level access controls to ensure secure and appropriate access to features and assets across the platform. ## S ### Sample The process of selecting a subset of data from a larger dataset for analysis, modeling, or preview purposes. DataRobot samples data in various contexts: - EDA1 sampling : DataRobot samples up to 500MB of data for initial exploratory data analysis. If the dataset is under 500MB, it uses the entire dataset; otherwise, it uses a 500MB random sample. - Live sample : During data wrangling, DataRobot retrieves a configurable number of rows (default 10,000) using different sampling methods (Random, First-N Rows, or Date/time for time series data) to provide interactive preview and analysis capabilities. - Feature Impact sampling : For calculating feature importance, DataRobot samples training records (default 2,500 rows, maximum 100,000) using different sampling strategies based on data characteristics (random sampling for balanced data, smart downsampling for imbalanced data). - Model evaluation sampling : Various model insights and evaluations use sampled data to balance computational efficiency with statistical accuracy. ### Sample size The percentage of the total training data used to build models. The percentage is based on the selected modeling mode or can be user-selected. ### Scoring See [Model scoring](https://docs.datarobot.com/en/docs/reference/glossary/index.html#model-scoring), [Scoring data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#scoring-data). ### Scoring code A method for using DataRobot models outside of the application. It is available for select models from the Leaderboard as a downloadable JAR file containing Java code that can be used to score data from the command line. An exportable JAR file, available for select models, that runs in Java. Scoring Code JARs contain prediction calculation logic identical to the DataRobot API—the code generation mechanism tests each model for accuracy as a part of the generation process. ### Scoring data The dataset provided to a deployed model to generate predictions. This is also known as inference data. For example, to predict housing prices, the scoring data would be a file containing new listings with all the model's required features (e.g., square footage, number of bedrooms) but without the final price. ### SDK (Software Development Kit) A collection of tools and libraries provided by a hardware or software vendor to enable developers to create applications for a specific platform. (e.g., the DataRobot Python SDK). ### Seasonality Repeating highs and lows observed at different times of year, within a week, day, etc. Periodicity. For example, temperature is very seasonal (hot in the summer, cold in the winter, hot during the day, cold at night). Applicable to time series modeling. ### Secondary dataset (Feature Discovery) A dataset that is added to a project and part of a relationship with the primary dataset. ### Secondary features (Feature Discovery) Features derived from a project's secondary datasets. ### Secure Single Sign-On Protocol (SSSOP) Secure Single Sign-On Protocol (SSSOP) that provides authentication and authorization services for AI agents and workflows. SSSOP ensures secure access control across distributed agent systems while maintaining user privacy and session management. In DataRobot's agentic platform, SSSOP enables seamless authentication for agents accessing external systems and provides audit trails for compliance and security monitoring. ### Segmented analysis A deployment utility that filters data drift and accuracy statistics into unique segment attributes and values. Useful for identifying operational issues with training and prediction request data. ### Segmented modeling A method of modeling [multiseries](https://docs.datarobot.com/en/docs/reference/glossary/index.html#multiseries) projects by generating a model for each segment. DataRobot selects the best model for each segment (the segment champion) and includes the segment champions in a single Combined Model that you can deploy. ### Segment ID A column in a dataset used to group series into segments for a multiseries project. A segment ID is required for the segmented modeling workflow, where DataRobot builds a separate model for each segment. See also [Segmented modeling](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-segmented.html). ### Semantic layer A semantic layer is a business representation of the source data that maps complex data to common business terms, helping you more easily understand what the data means and the information it represents. ### Semantic memory Memory systems that store general knowledge, facts, concepts, and relationships that are not tied to specific experiences. Semantic memory enables AI agents to maintain domain knowledge, understand concepts, and apply general principles to new situations. In DataRobot's agentic workflows, semantic memory allows agents to maintain knowledge about business processes, domain expertise, and general problem-solving strategies. ### Semantic search Search method that finds content based on meaning rather than exact keyword matches. Semantic search uses vector embeddings to understand the intent and context of queries, enabling more accurate and relevant results even when the exact words don't match. This approach is particularly useful in RAG systems for finding the most relevant information to ground AI responses. ### Short-term memory Temporary storage systems that AI agents use to maintain context and information during active task execution. Short-term memory enables agents to remember recent interactions, maintain conversation context, and track progress on current tasks. In DataRobot's agentic workflows, short-term memory allows agents to maintain coherence across multi-step processes and provides continuity in user interactions. ### Long-term memory Persistent storage systems that AI agents use to retain knowledge, experiences, and learned patterns across multiple sessions and tasks. Long-term memory enables agents to build upon previous experiences, maintain learned behaviors, and accumulate domain knowledge over time. In DataRobot's agentic workflows, long-term memory allows agents to improve performance through experience and maintain consistency across different use cases. ### Semi-regular data Data is semi-regular if most time steps are regular but there are some small gaps (e.g., business days, but no weekends). See also [regular data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#regular-data) and [time steps](https://docs.datarobot.com/en/docs/reference/glossary/index.html#time-step). ### Series ID A column in a dataset used to divide a dataset into series for a multiseries project. The column contains labels indicating which series each row belongs to. See also [Multiseries modeling](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/multiseries.html). ### Service health A performance monitoring component for deployments that tracks metrics about a deployment's ability to respond to prediction requests quickly and reliably. Useful for identifying bottlenecks and assessing prediction capacity. ### Service mesh A dedicated infrastructure layer for managing communication between LLM and AI microservices, providing features like load balancing, service discovery, and security. Service meshes enable fine-grained control over service-to-service communication, including traffic management, observability, and policy enforcement for complex AI application architectures. ### Streaming Real-time generation of text where output is displayed as it's being generated. Streaming provides immediate feedback to users by showing AI responses as they are produced, rather than waiting for the complete response. This approach improves user experience by reducing perceived latency and allowing users to see progress in real-time. ### Single agent flow A workflow pattern where a single AI agent handles all aspects of a task from start to finish. The agent receives input, processes it through its capabilities, and produces output without requiring coordination with other agents. Single agent flows are suitable for straightforward tasks that can be completed by one specialized agent. ### SHAP (Shapley Values) A fast, open-source methodology for computing Prediction Explanations for tree-based, deep learning, and linear-based models. SHAP estimates how much each feature contributes to a given prediction differing from the average. It is additive, making it easy to see how much top-N features contribute to a prediction. See also [Prediction Explanations](https://docs.datarobot.com/en/docs/reference/glossary/index.html#prediction-explanations), [XEMP](https://docs.datarobot.com/en/docs/reference/glossary/index.html#xemp-exemplar-based-explanations-of-model-predictions). ### Sidecar model A structural component that supports the LLM that is serving back responses. It helps to make determinations about whether a prompt was toxic, an injection attack, etc. With DataRobot, it uses hosted custom metrics to accomplish the monitoring. ### Single Sign-On (SSO) An authentication method that allows users to log in to DataRobot using their existing corporate identity provider (e.g., Okta, Azure AD). SSO simplifies user access by eliminating the need for separate DataRobot-specific credentials. ### Slim run A technique to improve time and memory use, slim runs apply to datasets exceeding 1.5GB. When triggered, models do no calculate an internal cross-validation and so do not have [stacked predictions](https://docs.datarobot.com/en/docs/reference/glossary/index.html#stacked-predictions). ### Smart downsampling A technique to reduce total dataset size by reducing the size of the majority class, enabling you to build models faster without sacrificing accuracy. When enabled, all analysis and model building is based on the new dataset size after smart downsampling. ### Snapshot An asset created from a data source. For example, with a database it represents either the entire database or a selection of (potentially joined) tables, taken at a particular point in time. It is taken from a live database but creates a static, read-only copy of data. DataRobot creates a snapshot of each data asset type, while allowing you to disable the snapshot when importing the data. ### Speed vs accuracy A Leaderboard tab that generates an analysis plot to show the tradeoff between runtime and predictive accuracy and help you choose the best model with the lowest overhead. ### Stability A model Leaderboard tab ( [Evaluate > Stability](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/stability-classic.html)) that provides an at-a-glance summary of how well a model performs on different backtests. The backtesting information in this chart is the same as that available from the [Model Info](https://docs.datarobot.com/en/docs/reference/glossary/index.html#model-info) tab. ### Stacked predictions A method for building multiple models on different subsets of the data. The prediction for any row is made using a model that excluded that data from training. In this way, each prediction is effectively an "out-of-sample" prediction. See an example in the [predictions documentation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#what-are-stacked-predictions). Compare to ["in-sample"](https://docs.datarobot.com/en/docs/reference/glossary/index.html#in-sample-predictions) predictions. See also [slim run](https://docs.datarobot.com/en/docs/reference/glossary/index.html#slim-run). ### Stationarity The mean of the series does not change over time. A stationary series does not have a trend or seasonal variation. Applicable to time series modeling. See also [trend](https://docs.datarobot.com/en/docs/reference/glossary/index.html#trend). ### Stop sequence A specific token or set of tokens that signals a language model to stop generating further output. ### Supervised learning Predictive modeling approach where your dataset includes a target feature with known values for training. DataRobot uses this labeled data to automatically build models that learn relationships between features and the target, enabling predictions on new data. This approach powers DataRobot's classification and regression projects, with the platform handling algorithm selection, hyperparameter tuning, and model evaluation automatically. See also [unsupervised learning](https://docs.datarobot.com/en/docs/reference/glossary/index.html#unsupervised-learning). ### Syftr An agent optimizer that uses multi-objective optimization to discover the best agentic workflows within a given budget or cost constraint. It automates the process of tuning agent configurations—including model selection, retrieval strategies, and prompt design—by systematically exploring the tradeoff space between quality and cost. Syftr integrates with popular LLM frameworks like LlamaIndex and uses Optuna to identify Pareto-optimal configurations that maximize performance for any given resource allocation. ### System prompt The system prompt, an optional field, is a "universal" prompt prepended to all individual prompts. It instructs and formats the LLM response. The system prompt can impact the structure, tone, format, and content that is created during the generation of the response. ## T ### Target The name of the column in the dataset that you would like to predict. ### Target leakage An outcome when using a feature whose value cannot be known at the time of prediction (for example, using the value for "churn reason" from the training dataset to predict whether a customer will churn). Including the feature in the model's feature list would incorrectly influence the prediction and can lead to overly optimistic models. ### Task An ML method, for example a data transformation such as one-hot encoding, or an estimation such as an XGBoost classifier, which is used to define a blueprint. There are hundreds of built-in tasks you can use, or you can define your own (custom) tasks. ### Temperature A parameter that controls the creativity and randomness of LLM responses. Lower temperature values (0.1-0.3) produce more focused, consistent outputs suitable for factual responses, while higher values (0.7-1.0) generate more creative and diverse content. DataRobot's playground interface allows you to experiment with different temperature values in LLM blueprint settings to find the optimal balance for your specific use case. ### Terminal A text-based interface used to interact with a server by entering commands. ### Template Pre-configured frameworks or structures that provide a starting point for creating agentic workflows, applications, or configurations. Templates in DataRobot include predefined agent configurations, workflow patterns, and code structures that accelerate development and ensure best practices. Templates can include agent goals, tool configurations, guardrails, and integration patterns, allowing users to quickly deploy sophisticated agentic systems without starting from scratch. ### Throughput The number of requests or predictions a system can process in a given period, often measured as requests per second (RPS) or tokens per second for LLMs. ### Time-aware predictions Assigns rows to backtests chronologically and makes row-by-row predictions. This method provides no feature engineering and can be used when forecasting is not needed. ### Time-aware predictions with feature engineering Assigns rows by forecast distance, builds separate models for each distance, and then makes row-by-row predictions. This method is best when combined with [time-aware wrangling](https://docs.datarobot.com/en/docs/reference/glossary/index.html#time-aware-wrangling), which provides transparent and flexible feature engineering. Use when forecasting is not needed, but predictions based on forecast distance and full transparency of the transformation process is desired. ### Time-aware wrangling Perform time series feature engineering during the data preparation phase by creating recipes of operations and applying them first to a sample and then, when verified, to a full dataset—time-aware data. This method provides control over which time-based features are generated before modeling to allow adjustment before publishing, preventing the need to rerun modeling if what would otherwise be done automatically doesn't fit the use case. ### Time series A series of data points indexed in time order—ordinarily a sequence of measurements taken at successive, equally spaced intervals.[Time series modeling](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/index.html) is a recommended practice for data science problems where conditions may change over time. ### Time series analysis Methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. ### Time series forecasting The use of a model to predict future values based on previously observed values. In practice, a forecasting model may combine time series features with other data. ### Time step The detected median time delta between rows in the time series; DataRobot determines the time unit. The time step consists of a number and a time-delta unit, for example (15, "minutes"). If a step isn't detected, the dataset is considered irregular and time series mode may be disabled. See also [regular data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#regular-data) and [semi-regular data](https://docs.datarobot.com/en/docs/reference/glossary/index.html#semi-regular-data). ### Token The smallest unit of text that LLMs process when parsing prompts/generating responses. In DataRobot's platform, tokens are used to measure input/output size of chats and calculate usage costs for LLM operations. When you send prompts to LLM blueprints, the system tokenizes your text and tracks consumption for billing and performance monitoring. Token usage is displayed in DataRobot's playground and deployment interfaces to help you optimize costs and stay within platform limits. ### Token usage The number of tokens consumed by an LLM for input and output, often used for billing and cost management. Token usage is a key metric for understanding the computational cost of AI operations, as most LLM providers charge based on the number of tokens processed. Monitoring token usage helps optimize costs and resource allocation in AI applications. ### Token usage tracking The monitoring and recording of LLM token consumption to track costs, usage patterns, and optimize resource allocation. DataRobot provides token usage analytics and cost management features to help organizations monitor and control their LLM API expenses across different models and deployments. ### Tokenization The process of breaking text into smaller units called tokens, which can be words, subwords, or characters, for processing by a language model. ### Tool A software component or service that provides specific functionality to AI agents or workflows. Tools can perform various tasks such as data retrieval, computation, API calls, or specialized processing. In DataRobot's agentic workflows, tools are modular components that agents can invoke to extend their capabilities and perform complex operations beyond their core functionality. ### Toolkit A collection of tools, utilities, and resources designed to support the development and deployment of agentic AI systems. Toolkits provide standardized interfaces, common functionality, and best practices for building AI agents. In DataRobot's platform, toolkits include pre-built tools for data processing, model training, API integration, and workflow orchestration, enabling rapid development of sophisticated agentic applications. ### Top-k A decoding parameter that limits the model's next-token choices to the k most likely options, sampling from only those candidates to generate more focused or creative responses. ### Top-p (nucleus sampling) A decoding parameter that limits the model's next-token choices to the smallest set whose cumulative probability exceeds a threshold p, allowing for dynamic selection of likely tokens. ### Toxicity The presence of harmful, offensive, or inappropriate language in model outputs, which safety and moderation systems aim to detect and prevent. ### Tracking agent See [MLOps agent](https://docs.datarobot.com/en/docs/reference/glossary/index.html#mlops-agent). ### Training The process of building models on data in which the target is known. ### Training dashboard A model Leaderboard tab ( [Evaluate > Training dashboard](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/training-dash.html)) that provides, for each executed iteration, information about a model's training and test loss, accuracy, learning rate, and momentum to help you get a better understanding about what may have happened during model training. ### Training data The portion (partition) of data used to build models. See also [validation](https://docs.datarobot.com/en/docs/reference/glossary/index.html#validation), [cross-validation](https://docs.datarobot.com/en/docs/reference/glossary/index.html#cross-validation), and [holdout](https://docs.datarobot.com/en/docs/reference/glossary/index.html#holdout). ### Transfer learning A project training on one dataset, extracting information that may be useful, and applying that learning to another. ### Trend An increase or decrease over time. Trends can be linear or non-linear and can show fluctuation. A series with a trend is not [stationary](https://docs.datarobot.com/en/docs/reference/glossary/index.html#stationary). ### Tuning A trial-and-error process by which you change some hyperparameters, run the algorithm on the data again, then compare performance to determine which set of hyperparameters results in the most accurate model. In DataRobot, this functionality is available from the Advanced Tuning tab. ## U ### Unit of analysis (Machine learning) The unit of observation at which you are making a prediction. ### Unlimited multiclass See [classification](https://docs.datarobot.com/en/docs/reference/glossary/index.html#classification). ### Unmaterialized Data that DataRobot samples for profile statistics, but does not keep. Instead, the catalog stores a pointer to the data and only pulls it upon user request at project start or when running batch predictions. See also [materialized](https://docs.datarobot.com/en/docs/reference/glossary/index.html#materialized) data. ### Unstructured text Text that cannot fit cleanly into a table. The most typical example is large blocks of text typically in some kind of document or form. ### Unsupervised learning A DataRobot modeling approach to discovering patterns in datasets without requiring a target feature. DataRobot offers unsupervised learning through anomaly detection projects, which identify unusual data points, and clustering projects, which group similar records together. These capabilities help users explore data structure, identify outliers, and segment populations without needing labeled training data. DataRobot automatically selects appropriate unsupervised algorithms and provides visualizations to interpret results. See also [supervised learning](https://docs.datarobot.com/en/docs/reference/glossary/index.html#supervised-learning). ### Use Case A container that groups objects that are part of the Workbench experimentation flow. ### User A DataRobot account that can be assigned to a specific user. Users can be assigned to one or more organizations and have specific permissions within those organizations. ### User blueprint A blueprint (and extra metadata) that has been created by a user and saved to the AI Catalog, where it can be both shared and further modified. This is not the same as a blueprint available from the Repository or via models on the Leaderboard, though both can be used as the basis for creation of a user blueprint. See also [blueprint](https://docs.datarobot.com/en/docs/reference/glossary/index.html#blueprint). ## V ### Validation The validation (or testing) partition is a subsection of data that is withheld from training and used to evaluate a model's performance. Since this data was not used to build the model, it can provide an unbiased estimate of a model's accuracy. You often compare the results of validation when selecting a model. See also [cross-validation](https://docs.datarobot.com/en/docs/reference/glossary/index.html#cross-validation). ### Variable See [feature](https://docs.datarobot.com/en/docs/reference/glossary/index.html#feature). ### Variance (Statistical) The variability of model prediction for a given data point. High-variance models are often too complex and are sensitive to the specific data they were trained on, leading to overfitting. ### Vector database A specialized database that stores text chunks alongside their numerical representations ( [embeddings](https://docs.datarobot.com/en/docs/reference/glossary/index.html#embedding)) for efficient similarity search. In DataRobot's platform, vector databases enable [RAG](https://docs.datarobot.com/en/docs/reference/glossary/index.html#retrieval-augmented-generation-rag) operations by allowing LLM blueprints to retrieve relevant information from large document collections. When you upload documents to DataRobot, the system automatically chunks the text, generates embeddings, and stores them in a vector database that can be connected to LLM blueprints for grounded, accurate responses based on your specific content. ### Visual AI DataRobot's ability to combine supported image types, either alone or in combination with other supported feature types, to create models that use images as input. The feature also includes specialized insights (e.g., image embeddings, attention maps, neural network visualizer) to help visually assess model performance. ## W ### Word cloud A model Leaderboard tab ( [Understand > Word Cloud](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/word-cloud-classic.html)) that displays the most relevant words and short phrases in word cloud format. ### Workbench [Workbench](https://docs.datarobot.com/en/docs/get-started/day0/predai-start/wb-overview.html) is an experiment-based user interface optimized to support iterative workflow. It enables users to group and share everything they need to solve a specific problem from a single location. Workbench is organized by Use Case, and each Use Case contains zero or more datasets, vector databases, playgrounds, models, notebooks, and applications. Workbench is the new generation of [DataRobot Classic](https://docs.datarobot.com/en/docs/reference/glossary/index.html#datarobot-classic). ### Worker The processing power behind the DataRobot platform, used for creating projects, training models, and making predictions. They represent the portion of processing power allocated to a task. DataRobot uses different types of workers for different phases of the project workflow, including DSS workers (Dataset Service workers), EDA workers, secure modeling workers, and quick workers. ### Wrangle A capability that enables you to import, explore, and transform data in an easy-to-use GUI environment. ### Webhook A user-defined HTTP callback that allows one system to send real-time data to another system when a specific event occurs. ## X ### XEMP (eXemplar-based Explanations of Model Predictions) A methodology for computing Prediction Explanations that works for all models. See also [Prediction Explanations](https://docs.datarobot.com/en/docs/reference/glossary/index.html#prediction-explanations), [SHAP](https://docs.datarobot.com/en/docs/reference/glossary/index.html#shap-shapley-values). ## Y ### YAML A human-readable configuration format used in DataRobot for defining model parameters, deployment settings, and workflow configurations. YAML files are commonly used in DataRobot projects to specify custom model environments, deployment configurations, and automation workflows, providing a clear and structured way to manage complex settings. ## Z ### Z score A metric measuring whether a given class of the protected feature is "statistically significant" across the population. used in bias and fairness modeling. ### Zero-shot learning A capability of a model to perform a task without having seen any examples of that task during training, relying on generalization from related knowledge. --- # Reference documentation URL: https://docs.datarobot.com/en/docs/reference/index.html > Create vector databases, create and compare LLM blueprints in the playground, and prepare LLMs for deployment. # Reference documentation Consult the resources in reference to understand the options, calculations, and processing behind DataRobot. - Data¶ Reference content that supports working with data and understanding data handling in DataRobot. - Agentic¶ Reference content that supports working with GenAI and agentic workflows. - Predictive AI¶ Reference content that supports working with predictive and time-aware experiments. - Platform and management¶ Reference content that supports working in the DataRobot platform as well as managing users and organizations. - Glossary¶ Brief definitions of terms relevant to the DataRobot platform. - ELI5¶ Commonly asked questions answered in Explain-it-like-I'm-5 format. --- # Accessibility URL: https://docs.datarobot.com/en/docs/reference/misc-ref/accessibility.html > Learn how DataRobot is committed to makes its tools and resources accessible to all users. # Accessibility in DataRobot DataRobot is committed to making its tools and resources accessible to all users. If you have any concerns, feedback, or issue to report, contact DataRobot Support at support@datarobot.com. ## Web content accessibility guidelines (WCAG) DataRobot continually works to improve the accessibility of the platform and strives to be compatible with the Web Content Accessibility Guidelines (WCAG) 2.21 Level A and AA standards. See the table below for more details: | Guideline | Implementation | | --- | --- | | Screen reader support | The platform is compatible with screen readers to assist users who rely on them. | | Keyboard navigation | All interactive elements of the platform are navigatable using the keyboard. | | Adjustable text size | Text can be resized without losing functionality or clarity. | | High contrast | Contrast between the text and background elements increase readability. | | Alternative text for images | All images include descriptive text to help users understand visual content. | | Consistent navigation | Navigation methods and elements are consistent throughout the platform. | | Light/dark themes | A light or dark theme can be applied to the platform to suit varying preferences and needs. | | Continuous improvements | DataRobot regularly checks for accessibility issues and stays up-to-date with the latest accessibility best practices to update features to enhance the user's experience on the platform. | ## Browser support > [!NOTE] DataRobot fully supports the latest version of Google Chrome > Other browsers such as Edge, Firefox, and Safari are not fully supported. As a result, certain features may not work as expected. DataRobot recommends using Chrome for the best experience. Ad block browser extensions may cause display or performance issues in the DataRobot web application. ## Data visualizations and resizing Due to the complex nature of data science, the platform includes numerous tables, grids, graphs, and data visualizations. While these elements have been designed to be as accessible as possible, some pages with extensive data may not reflow or resize perfectly on all devices or screen sizes. DataRobot is actively working on enhancing the responsiveness and accessibility of these features to provide a better experience for all users. ## Table keyboard navigation The platform features advanced, functional data tables that allow you to view and edit large amounts of complex data efficiently. To handle the instracacies of this complex data effectively and provide a consistent experience across DataRobot, tables in DataRobot support the following keyboard shortcuts: | Keyboard shortcut | Behaviour | | --- | --- | | Tab | Moves the focus to the first header cell of a table. | | Arrow keys | In a table, use the arrow keys to move between cells. | | Shift+Tab | When you exit and then return to a table, press Shift+Tab to go back to the last cell you viewed. | ## Blueprint graph keyboard navigation To navigate the blueprints graph using the keyboard, focus on any node and hold `Shift` while pressing the arrow keys. --- # Custom RBAC roles URL: https://docs.datarobot.com/en/docs/reference/misc-ref/custom-roles.html > Custom role-based access control (RBAC) roles are a solution for organizations with use cases that are not addressed by default roles in DataRobot. # Custom RBAC roles Custom role-based access control (RBAC) roles are a solution for organizations with use cases that are not addressed by [default roles](https://docs.datarobot.com/en/docs/reference/misc-ref/rbac-ref.html) in DataRobot. System and organization administrators can create roles and define access at a more granular level, and assign them to users and groups. You can access custom RBAC roles from User Settings > User Roles. The User Roles page lists each available role an admin can assign to a user in their organization, including DataRobot default roles. Before creating a custom role, review the [considerations](https://docs.datarobot.com/en/docs/reference/misc-ref/custom-roles.html#feature-considerations). ## Create custom roles To create a new role, click + Add a user role. Enter a name for the role and assign permissions. [DataRobot features](https://docs.datarobot.com/en/docs/reference/misc-ref/rbac-ref.html#object-types) are listed on the left. Role access options— [read, write, admin](https://docs.datarobot.com/en/docs/reference/misc-ref/rbac-ref.html#tiers-of-access), and entity-specific permissions—are listed on the right. Select an object, then select an access level for the object under Permissions. ## Manage roles Unlike default DataRobot roles, you can edit and delete any custom role created for an organization from the User Roles page. To determine if a role can be modified, refer to the Custom Role column; default roles display No and custom roles display Yes. From this page, you can: | | Element | Description | | --- | --- | --- | | (1) | Edit | Select a custom user role to modify its permissions. | | (2) | Duplicate | Click the duplicate icon and name the new role, then select the role to modify permissions. | | (3) | Delete | Click the Menu icon and select Delete. If the role is currently assigned to a user, a warning message appears before deleting the role. | ## Feature considerations - The RBAC feature flag must be enabled. - When system administrators create a custom role, the role is enabled system-wide across all organizations. When organization administrators create a custom role, the role is only enabled for that organization. - The feature flag for custom roles must be enabled system- or org-wide. - Feature flags cannot be configured through roles. - Multiple roles cannot be assigned to a user/group/organization at this time. --- # Enterprise architecture for deploying DataRobot in self-managed on-premise environments URL: https://docs.datarobot.com/en/docs/reference/misc-ref/enterprise-arch.html > Provides guidance for enterprise architects on deploying DataRobot to meet high availability (HA), disaster recovery (DR), and performance/scale requirements within a self-managed on-premise environment. # Enterprise architecture for deploying DataRobot in self-managed on-premise environments ## Overview This page provides guidance for enterprise architects on deploying DataRobot to meet high availability (HA), disaster recovery (DR), and performance/scale requirements within a self-managed on-premise environment. DataRobot is committed to ensure continuous operation, data integrity, and business continuity for all clients. The guide is structured according to the architectural layers in reverse (bottom-up) order: - Kubernetes - Persistent critical services - DataRobot platform ### Design considerations This page assumes the core physical infrastructure has been deployed with multiple data centers that provide hardware and networking for workloads to be deployed across these failure zones. The guidance provided here is learned from experience deploying and operating the DataRobot platform at scale across a diverse fleet of deployment options: - Multi-tenant SaaS (MTSaaS) on AWS within three regions. - Single-tenant SaaS (STSaaS) on AWS, GCP, and Azure in many regions. - Self-managed VPC installations on AWS, GCP, Azure. - Self-managed on-premise installations on a wide variety of data centers. When deployed within a cloud VPC environment, DataRobot leverages cloud-managed services for achieving high availability (HA), disaster recovery (DR), and performance/scale. HA focuses on keeping a system running with minimal interruptions, while DR focuses on recovering the workload after a major, destructive event. The DataRobot platform was designed according to cloud-native principles and applies them to meet the needs of self-managed on-premise customers. DataRobot adopts design guidelines from the major cloud providers’Well-Architected Framework reliability pillar in combination with Kubernetes best practices for achieving HA and DR. By applying these design guidelines and principles for all deployment options across the DataRobot fleet, the platform serves workloads that can recover from disruptions, meet demand, and mitigate failures. ### Eliminate single points of failure (SPOFs) - Use multiple Availability Zones (AZs): To mitigate the failure of a data center, workloads are distributed redundantly over multiple AZs within a cloud region. This is achieved in Kubernetes using zone-redundant node pools to deploy nodes across AZs, in conjunction with topology spread constraints that inform the Kubernetes scheduler to distribute pods across these nodes. This aims to keep workloads balanced across zones for fault tolerance to reduce the impact of outages. - Use load balancing: To automatically distribute incoming traffic and route it away from any unhealthy resources, an Application Load Balancer (ALB) handles ingress traffic. The Ingress Controller in Kubernetes deploys Nginx (or equivalent reverse proxy) redundantly across nodes and automatically manages the ALB’s routing rules to direct traffic to healthy pods. The Ingress Controller routes traffic to the API Gateway as a central front-door to handle access control across the platform. The API Gateway is deployed with a high availability configuration, and routes traffic to Kubernetes services for the DataRobot platform which themselves are load balanced with multiple pod replicas. - Employ redundancy: The DataRobot platform uses N+1 load balancing across multiple nodes. This provides higher throughput by distributing load across nodes to more effectively utilize the capacity and reduce latency by using pre-warmed nodes. This provides graceful degradation in the event of failures, and continues to service requests albeit with potentially less capacity. The system remains highly available with no downtime for maintenance since nodes can be taken offline without affecting overall service availability. In general, DataRobot designs services as stateless components that can be horizontally scaled using Horizontal Pod Autoscaler and KEDA for scale-to-zero. For latency-sensitive workloads, DataRobot utilizes a Daemonset across nodes to pre-pull container images for faster container starts when scaling up. For distributed workloads requiring consistency when performing tasks on shared resources, coordination among multiple replicas is achieved using built-in Kubernetes leader-election capabilities. Should the leader instance fail, the system will automatically elect a new leader and transition seamlessly to avoid disruption to service availability. DataRobot utilizes this capability for Kubernetes Operators which extend the Kubernetes control plane with AI-specific primitives and require consistency when deploying workloads to the Data Plane. ### Design for automation and self-healing - Automatic scaling: Within cloud environments, the platform uses cloud-managed services (EKS, GKE, AKS) which provision Kubernetes nodes using cloud instances elastically. In order to scale additional cluster capacity based on demand or scale-in nodes that are unutilized, DataRobot utilizes various cluster autoscaling amenities (Cluster Autoscaler, Karpenter, Descheduler). These autoscalers are tuned to provide extra idle capacity to handle rapid spikes in load while minimizing resource waste for efficiency. DataRobot workloads are designed using Kubernetes-native principles to handle pre-emption and pod eviction, allowing the Kubernetes scheduler to make optimal decisions about pod placement and node consolidation automatically. This is achieved using pod disruption budgets (PDBs) to ensure a minimal number of replicas are always available, topology spread constraints which place pods on different nodes and AZs, and pod anti-affinities to inform the scheduler to place pods on different nodes to reduce the likelihood of multiple replicas being disrupted at once. Stateful workloads, such as long-running batch jobs, use special labels to inform the scheduler and auto-scaler not to disrupt them or the nodes they are running on. Some long-running, stateful workloads are designed with point-in-time snapshot capabilities that checkpoint, persist and offload their state to a persistent volume claim rather than in ephemeral instance storage so that they can be recovered and resumed from the last checkpointed state should the node become unhealthy and the workload need to be retried on a healthy node. - Self-healing infrastructure: DataRobot aims to design the architecture for automatically detecting and recovering from failures without human intervention. This is achieved using resource schedulers (at the node or pod level) which monitor health using probes. Pods are required to implement liveness and readiness probes, where readiness probes determine that a pod is ready to process requests and liveness probes to detect whether a pod is stuck or in an unhealthy state that needs replacement. Kubernetes will only route traffic to healthy pods for a Service, and automatically updates the endpoints mapping for the Ingress Controller to make routing decisions. ### Implement disaster recovery strategies based on objectives - Recovery Time Objective (RTO): The maximum acceptable delay between a disaster event and the restoration of services. DataRobot is committed to quickly restoring availability of services within managed SaaS environments. This is achieved using high availability with redundancy across availability zones, in conjunction with Infrastructure as Code (IaC) and continuous delivery for deploying the platform in any infrastructure automatically. The SRE team in DataRobot has documented runbooks for performing failover operations, as needed. - Recovery Point Objective (RPO): The maximum amount of data loss the business can tolerate. This depends on how frequently data is backed up or replicated. DataRobot commits to achieving minimal data loss within managed SaaS environments. This is achieved using automated data backup and recovery testing performed on a schedule, with alerting for failures and data drift. The SRE team in DataRobot has documented runbooks for performing data recovery operations, as needed. - Multi-region recovery: The DataRobot platform can be deployed using multi-region strategies such as Pilot Light and Warm Standby. Pilot Light runs a minimal, scaled-down version of the application in a secondary region that would be scaled up during a failover when the primary region is experiencing an outage. Warm Standby would allow a faster failover by running a fully redundant cluster, but with a significantly higher cost for redundant resource consumption and data replication. ### Reference architecture for multi-tenant SaaS For reference on how DataRobot achieves HA, DR, and scale within managed SaaS environments, the image below depicts the high-level topology of multi-tenant SaaS deployments. Each region is physically disjointed and self-contained. This allows DataRobot to meet the security and compliance requirements (e.g. GDPR) for certain regions as it relates to data sovereignty, and provide a geo-localized service deployment for lower latency and better performance for customers within that area. In addition, this regional approach allows DataRobot to scale these deployments independently based on demand and capacity needs, and to exploit data locality for compute workloads. Within each region, the platform is deployed across multiple Kubernetes clusters: - Application cluster: Runs the DataRobot application plane (UI, API servers) and control plane (middleware services to deploying workloads). - Jobs cluster: Runs ephemeral worker jobs. - Deployed Workload cluster: Runs custom workload deployments (agentic workflows, custom models, applications). - Persistent critical services (PCS): Runs the critical services needed to power the DataRobot platform - object storage, databases, distributed caching, message brokers. This multi-cluster design enables independent scaling for these subsystems as well as network isolation. In addition, the application cluster utilizes a blue/green strategy for zero-downtime deployment where user traffic is routed to the “active” instance while test workloads are routed to the “inactive” instance. If tests pass on the inactive instance where newer code is deployed, then an automated switchover event is triggered to route all traffic to this instance as “active”. This deployment strategy could be utilized as the basis of an active-passive, pilot light or warm standby design for failover. Within each region, workloads are replicated across compute nodes running in different availability zones (AZ) for redundancy across data centers. Each Kubernetes cluster is configured with node groups (or node pools if using Karpenter) that span these AZs so that the Kubernetes scheduler can schedule pod replicas across them. For one of the MTSaaS production regions, the following are scale numbers to use as a reference over the trailing six month period: - Availability: 99.95% - Kubernetes node count: Mean: 280, Max: 460 - Kubernetes pod count: Mean: 10k, Max: 16k - New projects created: 225K+ - New models created: 3M+ - Active deployments: 3M+ - Prediction rows scored: 18B+ ## Kubernetes ### Performance/scale According to [Considerations for Large Clusters](https://kubernetes.io/docs/setup/best-practices/cluster-large/), Kubernetes is designed to handle: - No more than 5,000 nodes. - No more than 110 pods per node. - No more than 150,000 total pods. - No more than 300,000 total containers. These limits are determined based on the performance SLOs: - 99% of API calls return in less than 1 second. - 99% of Pods start within 5 seconds. Although possible to scale beyond these limits, the performance SLOs may no longer be achievable without significant optimization. #### Scaling the control plane - All control plane components (e.g. k8s API server) are designed to scale horizontally with multiple replicas. - Run one or two control plane instances per failure zone, scaling those instances vertically first (more CPU and RAM) and then scaling horizontally (more replicas across compute nodes) after reaching the point of diminishing returns with vertical scaling. - API server load scales based on the number of nodes and pods. - Node kubelets send heartbeats, events, node status and pod status updates to the API server. - Pods may interact directly with the API server, such as operators and controllers. - Watch operations may send a lot of event data to pods - Etcd performance must be able to scale to handle the volume of cluster state changes which must be persisted. This requiresetcdto be configured for high availability as well as with fast I/O for write operations. #### Cluster networking - IP address constraints:Since every pod gets its own unique cluster-scoped IP address, the available IP address space is a major constraint on the number of nodes and pods within a cluster. - Each node is given a CIDR block range from which to assign pod IPs. An individual node may exhaust its allocated CIDR range and not be able to launch new pods, even though the overall cluster IP space still has available addresses. - One technique used is IP masquerading with a private IP range to not interfere with the corporate network range and provide a larger assignable IP address space. ### High availability #### HA for the control plane - Run at least one instance per failure zone (availability zone in public cloud, or on-premise data center) to provide fault-tolerance. - Run control plane replicas across 3 or more compute nodes. - Use an odd number of control plane nodes to help with leader selection for failover. - Use a TCP-forwarding load balancer in front of the Kubernetes API server to distribute traffic to all healthy control plane nodes. - Refer to the Kubernetes High Availability documentation . When [running in multiple zones](https://kubernetes.io/docs/setup/best-practices/multiple-zones/): - Node labels are applied to identify the zone for zone-aware pod scheduling. - Topology spread constraints can be used to define how multiple pod replicas should be distributed across the node topology and ensure redundancy across failure zones. - Pod anti-affinity rules can be used to ensure pod replicas are not scheduled on the same compute node, reducing the likelihood of availability problems due to node failures. - Persistent volumes can have zone labels so that the scheduler can place pods in the same zone as the persistent volume being claimed. This minimizes network latency and improves I/O performance. #### Cluster networking - Configure zone-aware or topology-aware load balancing for routing traffic from kubelet running on the node to the k8s API server in the control plane within the same zone. This is achieved by configuring the control plane endpoint on every node kubelet to direct traffic to the load balancer. Make sure the load balancer fails over to routing traffic to the control plane within another zone in case the API server within the zone is not available. Zone-aware routing reduces cross-zone routing latency and network traffic costs. - Zone-aware networking is not enabled by default in Kubernetes and requires configuring the network plugin for cluster networking . ### Disaster recovery To back up a Kubernetes cluster, you must capture the cluster state by backing up the `etcd` database, and you should also back up your application-specific data and configurations, such as workloads, persistent volumes, and custom resources. A comprehensive backup strategy includes both these components to enable a full restore of the cluster and its applications in case of failure. #### Cluster state - etcd : This is the primary storage for all Kubernetes objects (configuration, secrets, API objects). Backing up etcd captures the entire state of your cluster. etcd can be backed up using the built-in snapshot tool or by taking a volume snapshot. See backing up anetcdcluster guide . - Secrets and ConfigMaps:These are critical for the application configuration. These will be backed up as part ofetcdsnapshot and should be stored securely in an encrypted format. - Consider storing these resources in an external secret store such as Hashicorp Vault and using the open source external-secrets-operator to sync these secrets and configmaps to the Kubernetes cluster. - Declarative configurations:Use Gitops and CI/CD tooling for version control of the Helm charts and resources deployed into the Kubernetes cluster. #### Application data - Persistent volumes: These are used for pod-level storage within the cluster for databases, logs and application data. Volume snapshots can be created and stored on an external storage system. ## Persistent critical services The database layer, a critical component, encompasses the following technologies each designed with robust HA and DR capabilities: - MongoDB for application data and metadata. - PostgreSQL for model monitoring data. - Redis for distributed caching. - RabbitMQ for message queue. - Elasticsearch for full-text indexing of GenAI data and app/agent-level telemetry. - Container registry for storing container images built as execution environments for custom workloads in DataRobot. - Object storage for raw object storage. When deploying persistent critical services on Kubernetes using the Bitnami distribution of Helm charts and container images, the following k8s design principles are used for HA and DR: - Using StatefulSets for data consistency (attach the correct PersistentVolume to the correct instance). - Use PodDisruptionBudgets to avoid pod eviction for node consolidation to disrupt the replica set and cause replication storms or trigger primary failover. Use multiple replicas with pod anti-affinity to avoid scheduling on the same node and topology spread constraints to place replicas in different failure zones. - Liveness/readiness probes to monitor health of replicas so that k8s can reschedule pods as needed and route traffic (via the Service) to healthy endpoints. ### MongoDB DataRobot leverages a MongoDB cluster replica set for high availability, consisting of a single primary replica and two secondary replicas. For Onprem, DataRobot recommends Mongo Enterprise Advanced (over the open source product) which comes with Ops Manager for monitoring and managing backup/restore. See the [Mongo Atlas Well-Architected Framework’s Reliability](https://www.mongodb.com/docs/atlas/architecture/current/reliability/) section for documentation covering HA and DR. #### High availability Data is replicated in near real-time between the replicas. In the event of a replica failure, a leader election protocol is initiated, automatically promoting one of the secondary replicas to primary. All subsequent traffic is then seamlessly redirected to the newly elected primary. This setup provides a near real-time recovery Point Objective (RPO) and recovery Time Objective (RTO) for the loss of a single replica. #### Disaster recovery In the rare event of a multi-replica failure, disaster recovery is achieved through established backup and restore procedures utilizing `mongodump` and `mongorestore` utilities. Clients can customize backup schedules to align with their specific RPO and RTO requirements. Backups should be persisted within a distributed object storage system. Additionally, when deployed on k8s, it's possible to snapshot the underlying persistent volumes as a backup mechanism. ### PostgreSQL High Availability and Fault Tolerance for PostgreSQL are achieved through a multi-node cluster configuration comprising a single primary and multiple secondary nodes. For Onprem deployments, DataRobot bundles [postgresql-ha](https://artifacthub.io/packages/helm/bitnami/postgresql-ha) Bitnami distribution for Helm charts and container images. This distribution provides both `pgPool` for connection pooling and load balancing (for reads only, writes are disabled to ensure data consistency) and repmgr for replication and managing the primary/secondary failover.`Repmgr` uses a Raft-like consensus algorithm for failover. #### High availability Datarobot employs Replication Manager (RepManager), an open-source tool, to manage replication, leader election, and automatic failovers within the PostgreSQL cluster. This ensures continuous operation and data consistency. #### Disaster recovery Backup and restore operations are performed using `pg_dump` and `pg_restore`. This provides clients with the flexibility to define their backup frequency based on their disaster recovery objectives. ### Redis #### High availability In on-premise deployments, Redis Sentinel is utilized to manage the high availability configuration. Redis Sentinel maintains a primary instance and two read-only secondary instances. This configuration ensures service continuity in the event of a primary instance failure by automatically promoting a secondary to primary. #### Disaster recovery Although it is possible to enable Redis with persistence (RDB snapshotting or append-only file), the application is designed to not require Redis state persistence. Redis is primarily used as a distributed cache (using a cache-aside strategy with MongoDB as the persistence storage backend) and for distributed coordination using Redis Lock. ### RabbitMQ RabbitMQ manages the asynchronous messaging between DataRobot services (like job queuing for AutoML). Its high availability focuses on preventing message loss and maintaining queue access. #### High availability DataRobot typically utilizes three RabbitMQ instances ( `replicaCount: 3`) within the cluster. The RabbitMQ cluster provides HA by allowing the application to distribute requests across the instances, preventing single points of failure. When deployed within a node autoscaling group, the nodes are dynamically adjusted with additional resources to maintain performance and availability, while load balancing distributes incoming requests across the RabbitMQ instances, preventing single points of failure. #### Disaster recovery Queues are declared as “durable” or “transient”. For durable queues, messages are stored on disk (within a mounted persistent volume) and are recovered when the RabbitMQ broker restarts. RabbitMQ is deployed on Kubernetes as a StatefulSet with Parallel policy to aid in cluster recovery, ensuring instances are consistently bound to the correct persistent volume for data integrity. Persistent volumes can be backed up to prevent message loss in RabbitMQ. ### Elasticsearch #### High availability Deploy Elasticsearch with three or more cluster nodes with multiple master-eligible nodes and data nodes distributed across failure zones. Elasticsearch is designed to scale horizontally by adding more data nodes and configuring the indices with an appropriate number of shards to distribute the query/indexing load. Indices are configured with multiple replicas (primary shard and replica shards) distributed across the available nodes and failure zones. #### Disaster recovery Asynchronous replication of replica shards ensures near zero RPO within the cluster, although this is influenced by replication lag if deployed across multiple data centers. Automated snapshots can be configured using snapshot lifecycle management (SLM) to snapshot indices and store them within a remote s3-compatible object store that is accessible across zones. ### Container registry The DataRobot platform requires a container registry for pulling and pushing container images. This is a “bring your own” (BYO) component and is not bundled with DataRobot. The container registry is needed to pull the images for the various DataRobot services, as well as building custom execution environments within the platform so that users can build and deploy their own custom workloads. Many customers utilize Harbor, Red Hat Quay, Gitlab container registry, or JFrog container registry within on-premise environments. DataRobot recommends following the vendor or open source project’s official documentation for achieving HA and DR. ### Object storage The DataRobot platform requires object storage to provide persistent volumes to the Kubernetes cluster, as well as for s3-compatible object storage used by the DataRobot platform. This is a “bring your own” (BYO) component and is not bundled with DataRobot. Object storage is used for storing raw customer data (e.g. uploaded training data) as well as derived assets (e.g. trained models, generated insights data, prediction results, etc.). Many customers utilize Minio or Ceph within on-premise environments. DataRobot recommends following the vendor or open source project’s official documentation for achieving HA and DR. --- # FIPS validation FAQ URL: https://docs.datarobot.com/en/docs/reference/misc-ref/fips-faq.html > Learn about FIPS validation and how it applies to DataRobot. # FIPS validation FAQ ## What is FIPS validation? Federal Information Processing Standards (FIPS) validation is a U.S. government standard that ensures cryptographic modules meet specific security requirements. It is designed to protect sensitive information and ensure secure communication in federal systems. FIPS validation requires that all credentials used in DataRobot, particularly Snowflake Basic credentials and key pairs, adhere to FIPS-compliant formats. ## What are the requirements for FIPS-compliant credentials? FIPS-compliant credentials must meet the following requirements: - RSA keys must be at least 2048 bits in length, and their passphrases must be at least 14 characters long with a salt length of at least 16 bytes (128 bits). - Snowflake key pair credentials must use a FIPS-approved algorithm and have a salt length of at least 16 bytes (128 bits). Credentials must be individually inspected by the user to ensure they meet the FIPS requirements. This can be done using the Credential management page: For details on how to manage credentials, see [Credentials management](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management). ## What is the impact? The credential requirements will be enforced in all production environments starting in July 2025 for multi-tenant SaaS users and with the 11.2 release for self-managed on-premise and single-tenant SaaS users. The UI will display a warning when creating a Basic credential password that is shorter than 14 characters. However, creating and updating Basic credentials that do not meet FIPS requirements is not blocked, as they may continue to be used for non-Snowflake connections. Any Snowflake connection using non-compliant credentials will fail, resulting in an error message indicating non-compliance as the cause. ## Will credentials be deleted? Credentials that do not meet FIPS requirements will not be deleted, but may not function for Snowflake connections. If you have questions or need assistance, contact [DataRobot Support](mailto:support@datarobot.com). --- # Platform and management URL: https://docs.datarobot.com/en/docs/reference/misc-ref/index.html > Reference content that supports working in the DataRobot platform as well as managing users and organizations. # Platform and management reference The following sections provide reference content that supports working in the DataRobot platform as well as managing users and organizations: | Topic | Description | | --- | --- | | Roles and permissions | Details roles and permissions at the architecture-, entity-, and authentication-level. | | Role-based access control (RBAC) | View descriptions of each role in RBAC. | | Custom RBAC roles | System and organization administrators can create roles and define access at a more granular level, and assign them to users and groups. | | User Activity Monitor | View descriptions of the fields included in each activity report. | | FIPS validation FAQ | Learn about Federal Information Processing Standards (FIPS) validation and how it applies to DataRobot. | --- # Role-based access control URL: https://docs.datarobot.com/en/docs/reference/misc-ref/rbac-ref.html > Admins can control access to the DataRobot application by assigning users roles with designated privileges. # Role-based access control [Role-based access control](https://en.wikipedia.org/wiki/Role-based_access_control) (RBAC) controls access to the DataRobot application by assigning users roles with designated privileges. Role-based permissions and role-role relationships make it simple to assign the appropriate permissions the specific ways in which users intend to use the application. System or organization admins can assign a role to specific users in [User Permissions](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-users.html#rbac-for-users), or to all members in a group in [Group Permissions](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/manage-groups.html#rbac-for-groups). The assigned role controls both what the user sees when using the application and which objects they have access to. RBAC is additive, so a user's permissions will be the sum of all permissions set at the user and group level. System or organization admins can assign the following roles: - Apps Admin - Apps Consumer - Data Admin - Data Consumer - Data Scientist - MLOps Admin - Prediction-only - Project Admin - Use Case Admin - Use Case Editor - Viewer The following objects also use the RBAC framework in the DataRobot application: - AI Applications - Custom Models and Environments - Database Connectivity - Dataset metadata - Datasets - Deployments - Execution Environments - Model Packages - Projects - Risk Management Framework ## Tiers of access Each role is granted a different degree of access for the various object types available within the application: | Access Level | Description | | --- | --- | | Read | Access to an object allows the user to access that area of the application for viewing but they cannot create these objects. | | Write | Access to an object type allows the user to create objects in that area of the application. There are no restrictions applied with write access aside from administrative permissions. | | Admin | Access to an object type grants a user access to all objects of a given type that belong to the user's organization. For example, if a user has admin access to projects, they can view every project created within their organization and make edits to them. | | No Access | Disables a user's access to an object type. This is indicated by the red "X" label displayed for a given permission. They will be unable to access that part of the application, create that type of object, or gain access to any of the objects of that type. | ## Object types You can grant any combination of the tiers of access described above for a variety of object types. The following sections describe the different object types and the permissions that can be granted for each. ### Application Controls access to DataRobot's AI-powered applications that provide business solutions and decision-making capabilities. These applications can include custom dashboards, automated workflows, predictive analytics tools, and interactive business intelligence solutions built on top of DataRobot's machine learning models. Users with access can view, create, modify, and delete applications that may integrate multiple models, data sources, and business logic to deliver end-to-end AI solutions for specific business use cases. To read more about applications in DataRobot, see [Applications](https://docs.datarobot.com/en/docs/wb-apps/index.html). ### Custom Environment Controls access to custom execution environments that define the runtime context for model deployment and inference. These environments specify the programming language, dependencies, libraries, and system configurations required for custom models to run properly in production. Users can create, modify, and manage environments that support various frameworks (Python, R, Java, etc.) and ensure consistent model execution across different deployment scenarios. To read more about custom environments in DataRobot, see [Create a custom environment](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-environment-workshop/nxt-create-custom-env.html). ### Custom Model Controls access to custom machine learning models that users create outside of DataRobot's AutoML capabilities. These models can be built using external frameworks (TensorFlow, PyTorch, scikit-learn, etc.) and uploaded to DataRobot for deployment and management. Users can view, create, modify, and delete custom models, including their associated code, dependencies, and metadata, enabling integration of specialized algorithms and domain-specific models. To read more about custom models in DataRobot, see [Create custom models](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-create-custom-model.html). ### Data Source Controls access to data sources, which are typically used to add and work with datasets. They contain a reference to a data store and the location of a data resource within the remote store. For example, this can be a SQL query in a database connection, a database table (with optional schema and catalog location), or a blob storage path (such as AWS S3 or Azure Blob Storage). To read more about data sources in DataRobot, see [the API reference](https://docs.datarobot.com/en/docs/api/reference/public-api/data_connectivity.html#post-apiv2externaldatasources). ### Data Store Controls access to configured data connections to external data systems and repositories, such as SQL databases, AWS S3, etc. Users can manage connection parameters, query configurations, and data source metadata. To read more about data stores in DataRobot, see [Data connections](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/add-data/connect.html). ### Dataset Data Controls access to the actual data content stored within datasets. Users with appropriate permissions can view the data in a dataset, as well as add more data by creating a new version. To read more about datasets in DataRobot, see [Explore data](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/explore-data/index.html). ### Dataset Info Controls access to dataset metadata, schema information, and data lineage. This includes data types, column descriptions, data quality metrics, version history, and other descriptive information about datasets without accessing the actual data content. Users can manage dataset documentation, tagging, categorization, and governance metadata. To read more about datasets in DataRobot, see [Metadata info](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-data-registry/nxt-explore-data.html#metadata-info). ### Deployment Controls access to deployed models. Deployments represent the operationalized versions of trained models that can receive prediction requests and return results. Users can manage deployment configurations, scaling parameters, monitoring settings, and the lifecycle of production models. To read more about deployments in DataRobot, see [Deployments dashboard](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-overview/nxt-dashboard.html#deployments-dashboard). ### Model Package Controls access to packaged model artifacts that contain the trained model, preprocessing logic, and all dependencies required for deployment. Model packages are self-contained units that can be deployed across different environments and include the model binary, feature engineering code, and runtime requirements. Users can manage package versions, dependencies, and deployment configurations. To read more about model packages in DataRobot, see [Import model packages into Registry](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-registry-model-transfer.html). ### Prediction Environment Controls access to the infrastructure and configuration settings for prediction services. These environments define the computational resources, networking, security, and operational parameters for serving model predictions. Users can manage prediction endpoints, load balancing, auto-scaling, and the overall prediction service infrastructure. To read more about prediction environments in DataRobot, see [Prediction environments](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-prediction-environments/index.html). ### Project Controls access to DataRobot projects, which are the primary workspaces for machine learning development. Projects contain model experiments, feature engineering, model training runs, and evaluation results. Users can create, manage, and collaborate on projects that represent the complete lifecycle of model development from data preparation to model validation. To read more about projects in DataRobot, see [Import projects to Workbench](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/asset-migration.html#import-projectss-to-datarobot-workbench). ### Registered Model Controls access to the model registry, which serves as a centralized repository for tracking, versioning, and managing trained models. Registered models include metadata about model performance, training parameters, feature lists, and lineage information. Users can manage model versions, approval workflows, and the governance process for model promotion to production. To read more about registered models in DataRobot, see [Register DataRobot models](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-register-dr-models.html). ### Risk Management Framework Controls access to DataRobot's risk management and governance framework that ensures responsible AI practices. This includes model monitoring, bias detection, explainability tools, compliance reporting, and governance workflows. Users can manage risk assessments, compliance documentation, audit trails, and governance policies that ensure AI systems meet organizational and regulatory requirements. To read more about the Risk Management Framework, see [Assess risk](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/assess-risk.html). ### Use Case Controls access to the Use Case Admin view on Workbench, which allows the user to view and manage all Use Cases in the organization. Use Cases contain model experiments, feature engineering, model training runs, and evaluation results. To read more about use cases in DataRobot, see [Use Case overview](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/usecase-overview.html). ## RBAC roles The sections below describe the permissions applied for each role provided with RBAC. ### Apps Admin Access: Can access every AI Application created across the system with admin permissions. Useful for: Debugging and reporting on usage and activity for any AI Application created in their organization. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | ✔ | ✔ | ✔ | | Custom Environment | | ✔ | ✔ | | Custom Model | | | | | Data Source | | ✔ | ✔ | | Data Store | | ✔ | ✔ | | Dataset Data | | ✔ | ✔ | | Dataset Info | | ✔ | ✔ | | Deployment | | ✔ | ✔ | | Model Package | | ✔ | ✔ | | Prediction Environment | | | | | Project | | ✔ | ✔ | | Registered Model | | ✔ | ✔ | | Risk Management Framework | ✔ | ✔ | ✔ | | Use Case | | ✔ | ✔ | ### Apps Consumer Access: Can consume the DataRobot AI-powered applications that are shared with them to help make business decisions. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | | ✔ | | | Custom Environment | | | | | Custom Model | | | | | Data Source | | ✔ | | | Data Store | | ✔ | | | Dataset Data | | ✔ | | | Dataset Info | | ✔ | | | Deployment | | | | | Model Package | | | | | Prediction Environment | | | | | Project | | | | | Registered Model | | | | | Risk Management Framework | | ✔ | | | Use Case | | ✔ | ✔ | ### Data Admin Access: Can access every dataset created across the system with admin permissions, including all metadata associated with each dataset. Useful for: Debugging and reporting on usage and activity for any data asset pulled into the AI Catalog. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | | | | | Custom Environment | | | | | Custom Model | | | | | Data Source | ✔ | ✔ | ✔ | | Data Store | ✔ | ✔ | ✔ | | Dataset Data | ✔ | ✔ | ✔ | | Dataset Info | ✔ | ✔ | ✔ | | Deployment | | | | | Model Package | | | | | Prediction Environment | | | | | Project | | | | | Registered Model | | | | | Risk Management Framework | ✔ | ✔ | ✔ | | Use Case | | ✔ | ✔ | ### Data Consumer Access: Can consume the datasets created across the system. Notes: To restrict users from being able to upload local files to a project directly, combine this role with the "Enable AI Catalog as File Source Limitation" feature flag. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | | ✔ | ✔ | | Custom Environment | | ✔ | | | Custom Model | | ✔ | ✔ | | Data Source | | ✔ | ✔ | | Data Store | | ✔ | ✔ | | Dataset Data | | ✔ | | | Dataset Info | | ✔ | | | Deployment | | ✔ | ✔ | | Model Package | | ✔ | ✔ | | Prediction Environment | | ✔ | | | Project | | ✔ | ✔ | | Registered Model | | ✔ | ✔ | | Risk Management Framework | | ✔ | | | Use Case | | ✔ | | ### Data Scientist Access: Can build or add models in the platform, both using AutoML and creating custom or remote models. Notes: Cannot perform any actions that will break production systems. This type of user can also build AI applications. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | | ✔ | ✔ | | Custom Environment | | ✔ | | | Custom Model | | ✔ | ✔ | | Data Source | | ✔ | ✔ | | Data Store | | ✔ | ✔ | | Dataset Data | | ✔ | ✔ | | Dataset Info | | ✔ | ✔ | | Deployment | | ✔ | | | Model Package | | ✔ | ✔ | | Prediction Environment | | ✔ | | | Project | | ✔ | ✔ | | Registered Model | | ✔ | ✔ | | Risk Management Framework | | ✔ | ✔ | | Use Case | | ✔ | ✔ | ### MLOps Admin Access: Can access every MLOps object on the system—deployments, model packages, custom models, and custom environments. Useful for: Debugging and reporting usage and activity for any MLOps object created in their organization. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | | ✔ | ✔ | | Custom Environment | ✔ | ✔ | ✔ | | Custom Model | ✔ | ✔ | ✔ | | Data Source | | ✔ | ✔ | | Data Store | | ✔ | ✔ | | Dataset Data | | ✔ | ✔ | | Dataset Info | | ✔ | ✔ | | Deployment | ✔ | ✔ | ✔ | | Model Package | ✔ | ✔ | ✔ | | Prediction Environment | ✔ | ✔ | ✔ | | Project | | ✔ | ✔ | | Registered Model | ✔ | ✔ | ✔ | | Risk Management Framework | ✔ | ✔ | ✔ | | Use Case | | ✔ | ✔ | ### Prediction-only Access: Can make predictions on a specified deployment and no other. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | | | | | Custom Environment | | | | | Custom Model | | | | | Data Source | | ✔ | | | Data Store | | ✔ | | | Dataset Data | | ✔ | | | Dataset Info | | ✔ | | | Deployment | | ✔ | | | Model Package | | | | | Prediction Environment | | ✔ | | | Project | | | | | Registered Model | | | | | Risk Management Framework | | ✔ | | | Use Case | | ✔ | ✔ | ### Project Admin Access: Can access every modeling project created across the system. Useful for: Debugging and reporting on usage and activity for any modeling project created in their organization. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | | ✔ | ✔ | | Custom Environment | | | | | Custom Model | | | | | Data Source | | ✔ | ✔ | | Data Store | | ✔ | ✔ | | Dataset Data | | ✔ | ✔ | | Dataset Info | | ✔ | ✔ | | Deployment | | | | | Model Package | | | | | Prediction Environment | | | | | Project | ✔ | ✔ | ✔ | | Registered Model | | | | | Risk Management Framework | ✔ | ✔ | ✔ | | Use Case | | ✔ | ✔ | ### Use Case Admin Access: Can access every Use Case created across the system. Useful for: Viewing and managing any Use Case created in their organization. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | ✔ | ✔ | ✔ | | Custom Environment | ✔ | ✔ | ✔ | | Custom Model | ✔ | ✔ | ✔ | | Data Source | ✔ | ✔ | ✔ | | Data Store | ✔ | ✔ | ✔ | | Dataset Data | ✔ | ✔ | ✔ | | Dataset Info | ✔ | ✔ | ✔ | | Deployment | ✔ | ✔ | ✔ | | Model Package | ✔ | ✔ | ✔ | | Prediction Environment | ✔ | ✔ | ✔ | | Project | ✔ | ✔ | ✔ | | Registered Model | ✔ | ✔ | ✔ | | Risk Management Framework | ✔ | ✔ | ✔ | | Use Case | ✔ | ✔ | ✔ | ### Use Case Editor Access: Can access Use Cases within Workbench. Useful for: Viewing and working with Use Cases. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | | ✔ | | | Custom Environment | | ✔ | | | Custom Model | | ✔ | | | Data Source | | ✔ | | | Data Store | | ✔ | | | Dataset Data | | ✔ | | | Dataset Info | | ✔ | | | Deployment | | ✔ | | | Model Package | | ✔ | | | Prediction Environment | | ✔ | | | Project | | ✔ | | | Registered Model | | ✔ | | | Risk Management Framework | | ✔ | ✔ | | Use Case | | ✔ | ✔ | ### Viewer Access: Can view any object across the system that they have access to, but cannot perform any actions beyond viewing datasets. | Object | Admin | Read | Write | | --- | --- | --- | --- | | Application | | ✔ | | | Custom Environment | | ✔ | | | Custom Model | | ✔ | | | Data Source | | ✔ | | | Data Store | | ✔ | | | Dataset Data | | ✔ | | | Dataset Info | | ✔ | | | Deployment | | ✔ | | | Model Package | | ✔ | | | Prediction Environment | | ✔ | | | Project | | ✔ | | | Registered Model | | ✔ | | | Risk Management Framework | | ✔ | | | Use Case | | ✔ | | --- # Roles and permissions URL: https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html > Describes the many layers of security DataRobot employs to help protect customer data through controlled user-assigned access levels. # Roles and permissions DataRobot employs many layers of security to help protect customer data—at the architecture, entity access, and authentication levels. The sections on this page provide details for roles and permissions at each level. ## General access guidance Access is comprised of roles and permissions.Roles categorize a user's access; permissions specify the function-based privileges associated with the role. ### Role definitions In general, role types have the following access: | Role | Access | | --- | --- | | Consumer/Observer | Read-only | | Editor/User | Read/Write | | Owner | Read/Write/Administer | ### Role priority and sharing Role-based access control (RBAC) controls access to the DataRobot application and is managed by organization administrators. The RBAC roles are named differently but covey the same read/write/admin permissions. The assigned role controls both what you can see when using the application and which objects you can access. RBAC overrides sharing-based role permissions. For example, let's say you share with another user who was assigned the RBAC Viewer role (Read-only access) by the admin. You grant them User permissions (Read/Write access). However, because the Viewer role takes priority, the user is denied Write access. A user can have multiple roles assigned for a single entity—the most permissive role takes precedence and is then updated according to RBAC. Consider: - A dataset is shared with an organization, with members assigned the consumer role. The dataset is then shared with a user in that organization and assigned the editor role. The user will have editor capabilities. Other organization members will be consumers. - A dataset is shared to a group, with members given owner permissions. You want one user in the group to have consumer access only. Remove that user from the group and reassign them individually to restrict their permissions. ## Project roles The following table describes the general capabilities allowed by each role. See also specific roles and privileges below. | Capability | Owner | User | Consumer | | --- | --- | --- | --- | | View everything | ✔ | ✔ | ✔ | | Launch IDEs | ✔ | ✔ | | | Make predictions | ✔ | ✔ | | | Create and edit feature lists | ✔ | ✔ | | | Set target | ✔ | ✔ | | | Delete jobs from queue | ✔ | ✔ | | | Run Autopilot | ✔ | ✔ | | | Share a project with others | ✔ | ✔ | | | Rename project | ✔ | ✔ | | | Delete project | ✔ | | | | Unlock holdout | ✔ | | | | Clone project | ✔ | ✔ | | ## Shared data connection and data asset roles The user roles below represent three levels of permissions to support nuanced access across collaborative data connections and data sources (entities). When you share entities, you must assign a role to the user(s) you share with: > [!NOTE] Note > Only an administrator can add database drivers. | User role | Description | | --- | --- | | Editor | An active user of an entity. This role has limitations based on the entity (read and write). | | Consumer | A passive user of an entity (read-only). | | Owner | The creator or assigned administrator of an entity. This role has the highest access and ability (read, write, administer). | The following table indicates which role is required for tasks associated with the AI Catalog. The table refers to the following roles: | User role | Code | | --- | --- | | Consumer | C | | Consumer w/ data access | CA | | Editor | E | | Editor w/ data access | EA | | Owner | O | | Task | Permission | | --- | --- | | Data store/Data connections | | | View data connections | C, CA, E, EA, O | | Test connections | C, CA, E, EA, O | | Create new data sources from a data connection | E, EA, O | | List schemas and tables | E, EA, O | | Edit and rename data connection | E, EA, O | | Delete data connection | O | | Dataset/Data asset | | | View metadata and collaborators | C, CA, E, EA, O | | Share | Collaborators can share with others, assigning a role as high as their own role. For example, a Consumer can share and assign the Consumer role but not the Editor role. The Owner role can assign any available roles. | | Download data sample | CA, EA, O | | Download dataset | CA, EA, O | | View sample data | CA, EA, O | | Use dataset for project creation | CA, EA, O | | Use dataset for custom model training | CA, EA, O | | Use dataset for predictions | CA, EA, O | | Modify metadata | E, EA, O | | Create a new version (remote or snapshot)* | EA, O | | Reload** | EA, O | | Delete dataset | O | * "Remote" refers to information on where to find data (e.g., a URL link); "snapshot" is actual data ** If the dataset is "remote," it is converted to a snapshot ## Deployment roles The following table defines the deployment permissions for each deployment role: | Capability | Owner | User | Consumer | | --- | --- | --- | --- | | Consume predictions | ✔ | ✔ | ✔* | | Get data via API | ✔ | ✔ | | | View deployment in inventory | ✔ | ✔ | | | Replace model | ✔ | | | | Edit deployment metadata | ✔ | | | | Delete deployment | ✔ | | | | Add user to deployment | ✔ | ✔ | | | Change permission levels of users | ✔ | ✔** | | | Remove users from shared deployment | ✔*** | ✔ | | * Consumers can make predictions using the deploy API route, but the deployment will not be part of their deployment inventory. ** To Consumer or User only. *** Can remove self only if there is another user with the Owner role. ## Shared deployment job roles Every user has full access to job definitions and batch jobs they created; however, shared job definitions and batch jobs are subject to role-based access controls. The following table defines the shared [prediction job definition](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/manage-pred-job-def.html) and [monitoring job definition](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/pred-monitoring-jobs/manage-monitoring-job-def.html) permissions for each deployment role: | Capability | Owner | User | Consumer | | --- | --- | --- | --- | | View prediction jobs and job definitions | ✔ | ✔ | | | View monitoring jobs and job definitions | ✔ | ✔ | | | Run prediction job definitions | ✔ | ✔ | | | Run monitoring job definitions | ✔ | ✔ | | | Clone prediction job definitions | ✔ | ✔ | | | Clone monitoring job definitions | ✔ | ✔ | | | Edit prediction job definitions | ✔ | | | | Edit monitoring job definitions | ✔ | | | | Delete prediction job definitions | ✔ | | | | Delete monitoring job definitions | ✔ | | | The following table defines the shared [batch job](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-jobs.html) permissions for each deployment role: | Capability | Owner | User | Consumer | | --- | --- | --- | --- | | View batch jobs and logs | ✔ | ✔ | | | Run batch jobs again | ✔ | ✔ | | | Create batch job definitions from jobs | ✔ | ✔ | | | Edit batch job definitions from jobs | ✔ | | | | Abort batch jobs | ✔ | | | ## Model Registry roles The following table defines the permissions for each model package role: | Option | Description | Availability | | --- | --- | --- | | View a model package | View the metadata for a model package, including the model target, prediction type, creation date, and more. | Owner, User, Consumer | | Deploy a model package | Creates a new deployment with the selected model package. | Owner, User, Consumer | | Share a model package | Provides sharing capabilities independent of project permissions. | Owner, User, Consumer | | Permanently archive a model package | Provides sharing capabilities independent of project permissions. | Owner | ## Custom Model and Environment roles The following tables define the permissions for each custom model or environment role: > [!NOTE] Note > There isn't an editor role for custom environments, only for custom models. #### Environment Roles and Permissions | Capability | Owner | Consumer | | --- | --- | --- | | Use and view the environment | ✔ | ✔ | | Update metadata and add new versions of the environment | ✔ | | | Delete the environment | ✔ | | #### Model roles and permissions | Capability | Owner | Editor | Consumer | | --- | --- | --- | --- | | Use and view the model | ✔ | ✔ | ✔ | | Update metadata and add new versions of the model | ✔ | ✔ | | | Delete the model | ✔ | ✔ | | *All roles can share an application by sharing the application link with an embedded authorization token. ## No-Code AI App roles The following table defines the permissions for each role supported for [Automated Applications](https://docs.datarobot.com/en/docs/classic-ui/app-builder/index.html). | Capability | Owner | Editor | Consumer | | --- | --- | --- | --- | | Make predictions | ✔ | ✔ | ✔ | | Deactivate an application | ✔ | ✔ | | | Share an application to other DataRobot licensed users | ✔ | | | | Delete an application | ✔ | | | | Upgrade an application | ✔ | ✔ | | | Update an application's settings | ✔ | ✔ | | ## GenAI roles Working with [Generative AI](https://docs.datarobot.com/en/docs/agentic-ai/index.html) (GenAI) in DataRobot can include creating vector databases, creating and comparing LLM blueprints in the playground, preparing LLM blueprints for deployment, working with metrics, and bringing your own LLM. The following table describes GenAI component-related user permissions. All roles (Consumer, Editor, Owner) refer to the user's role in the Use Case; access to various function are based on the Use Case roles. For example, because sharing is handled on the Use Case level, you cannot share only a vector database (vector databases do not define any sharing rules). --- # Sharing URL: https://docs.datarobot.com/en/docs/reference/misc-ref/sharing.html > Provides details on sharing assets in DataRobot. # Sharing You can share a variety of assets within your organization, including Use Cases, datasets, applications, and models. You may want to do this, for example, to get the assistance of an in-house data scientist who has offered to help optimize your data and models. Or, perhaps a colleague in a different group would benefit from your model's predictions. When you share an asset with a user, group, or organization, DataRobot assigns the default role of User or Editor to each selected target (See [Roles and permissions](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html) for more information). Note the following when sharing or removing access: - Not all entities allow sharing beyond a specific user. - You can only share with active accounts. - You can only share up to your own access level (a consumer cannot grant an editor role) and you cannot downgrade the access of a collaborator with a higher access level than your own. - Every entity must have at least one owner (entity creator by default). To remove the creator, that user must assign the owner role to one or more additional collaborators and then remove themselves. - Data connection and data asset entities must have at least one user who can share (based on the “Allow sharing” setting). > [!NOTE] Note > You can also [share custom models and environments](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-actions.html) as part of the MLOps workflow. ## Share assets To increase collaboration, you can share assets with other DataRobot users, groups within your org, and with the entire organization. However, note that it is required that users and groups must belong to the same organization when sharing. See [tenant isolation and collaboration](https://docs.datarobot.com/en/docs/platform/admin/manage-entities/tenant-isolation-and-collaboration.html) for details. The share modal functionality is largely the same across DataRobot, however, there can be minor variations depending on the asset type. Variations can include external sharing, the ability to add a note and/or send a notification, different sharing roles, and more. See below for examples: **Share Use Case:** When [adding team members to a Use Case](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/usecases/build-usecase.html#share), you can only add users and/or organizations. [https://docs.datarobot.com/en/docs/images/share-usecase-1.png](https://docs.datarobot.com/en/docs/images/share-usecase-1.png) **Share application:** When [sharing a custom application](https://docs.datarobot.com/en/docs/wb-apps/custom-apps/manage-custom-app.html#share-applications), you can enable an external sharing link and optionally, send a notification and/or add a note. [https://docs.datarobot.com/en/docs/images/share-app-1.png](https://docs.datarobot.com/en/docs/images/share-app-1.png) **Share deployment:** When [sharing a deployment from Console](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-overview/nxt-deployment-actions.html), you can choose from the following roles: Owner, User, Consumer. The User role replaces the more commonly seen Editor role. [https://docs.datarobot.com/en/docs/images/share-deployment-1.png](https://docs.datarobot.com/en/docs/images/share-deployment-1.png) If you encounter an error, review the [data asset roles](https://docs.datarobot.com/en/docs/reference/misc-ref/roles-permissions.html#shared-data-connection-and-data-asset-roles). ## Share dialog modal The fields of the Share dialog differ slightly. Generally: | Field | Description | | --- | --- | | Share with | Identifies the recipient(s). This can be any combination of user email, group, or organization (if supported). | | Role | Specifies recipient access level to the asset. You can grant access at the same or a lower level than your own access. | | Shared with | Lists the recipients and their assigned roles (and, for dataset, additional privileges). Use the dropdown to change the role, click the x to remove a recipient. | | Allow sharing | Provides the recipient with permission to re-share the asset with others (up to their level of access). | | Can use data | Allows the recipient to use the dataset for certain operations (e.g., download data, create project from dataset). | | Add note | Allows you to add a note that to be included in the notification sent to the recipient. | ## More info The following provides additional documentation related to sharing. - Tenant isolation and collaboration - Roles and permissions - Role priority and sharing - AI Catalog - Notification center - Project control center --- # User Activity Monitor URL: https://docs.datarobot.com/en/docs/reference/misc-ref/uam-ref.html > Use the User Activity Monitor to preview or download the Admin Usage and Prediction Usage reports. You can filter, hide sensitive fields, export CSV, and more. # User Activity Monitor reference The following sections describe the fields returned for the User Activity Monitor (UAM), based on the selected report view. See the [User Activity Monitoroverview](https://docs.datarobot.com/en/docs/platform/admin/monitoring/main-uam-overview.html) for information on using the tool. Each row of both the online preview and download of reports relates to a single audited event. Data is presented in ascending order, with the earliest data appearing at the top of the report. You can download the reports using [Export CSV](https://docs.datarobot.com/en/docs/platform/admin/monitoring/main-uam-overview.html#download-activity-data). When exporting reports, you are prompted to filter records for report download. The filters you apply when previewing the report apply only to the online preview. ## Hide sensitive information When viewing App Usage or Prediction Usage reports, you can hide or display identifying information with the "Include identifying fields" option. You may want to hide the information, for example, if Customer Support will be accessing a report. If unchecked, the columns display in the report without values. The fields considered sensitive are marked with an asterisk in the tables below. ## Admin Usage activity report The Admin Usage activity output reports the following data about administrator operations and activities. | Report field | Description | | --- | --- | | Timestamp—UTC | Timestamp (UTC time standard) when the administrator event occurred | | Event | Type of administrator event, such as Create Account, Organization created, Change Password, Update Account, etc. | | UID | ID for this user | | Username (*) | Username for this user | | Admin Org ID | ID of the administrator organization | | Admin Org Name | Name of the administrator's organization | | Org ID | ID of the user's organization | | Org Name (*) | Name of the user's organization | | Group ID | ID for this user's group | | Group Name | Name of the user's group | | Admin UID | ID for this administrator (if applicable) | | Admin Username | Username for this administrator (if applicable) | | Old values (*) | User account settings before the administrator made changes. For example, if the administrator activity changed the workers for the related user, this field shows the "max_workers" value before the change. | | New values (*) | User account settings after the administrator made changes. For example, if the administrator activity changed the workers for the related user, this field shows the "max_workers" value after the change. | (*) denotes an [identifying field](https://docs.datarobot.com/en/docs/reference/misc-ref/uam-ref.html#hide-sensitive-information) for this report ## App Usage activity report The App Usage activity output reports the following data about application events. | Report field | Description | | --- | --- | | Timestamp—UTC | Timestamp (UTC time standard) when the application event occurred | | Event | Type of application event, such as Add Model, Compliance Doc Generated, aiAPI Portal Login, Dataset Upload, etc. | | UID | ID of the user | | Username (*) | Name of the user | | Project ID | ID of the project | | Project Name (*) | Name of the project | | Org ID | ID of the user's organization | | Org Name (*) | Name of the user's organization | | Group ID | ID of the user's group | | Group Name (*) | Name of the user's group | | User Role | Role for the user who initiated the event; values include OWNER, USER, OBSERVER | | Project Type | Type for the related project; possible values include Binary Classification, Regression, Time Series—Regression, Multiclass Classification, etc. | | Metric | Optimization metric for the related project; potential values include LogLoss, RMSE, AUC, etc. | | Partition Method | Partition method for the related project (i.e., how data is partitioned for this project) | | Target Variable (*) | Target variable for the related project (i.e., what DataRobot will predict) | | Model ID | ID of the model | | Model Type | Type for the model; this also is the name of the model or blueprint | | Blender Model Types | Type of blender model, if applicable | | Sample Type | Type of training sample for the project; values may include Sample Percent, Row Count, Duration, etc. | | Sample Length | Amount of sample data for training the project; values are based on Sample Type and may be percentage, number of rows, or length of time | | Model Fit Time | Amount of time (in seconds) used to build the model | | Recommended Model | Identifies if this is the recommended model for deployment (true) or not (false) | | Insight Type | Type of insight requested for this model; possible values may include Variable Importance, Compute Series Accuracy, Compute Accuracy Over Time, Dual Lift, etc. | | Custom Template | Identifies if the compliance document (for the event) was developed with a custom template (true) or not (false); applies to Compliance Doc Generated events | | Deployment ID | ID for the deployment; applies to Replaced Model events | | Deployment Type | Type of deployment; applies to events such as Replaced Model and Deployment Added, and possible values include Dedicated Prediction (deployment to a dedicated prediction server) or Secure Worker (in-app modeling workers used for predictions) | | Client Type | Client (DataRobotPythonClient or DataRobotRClient) used to interface with DataRobot; applies to events such as DataSet Upload, Project Created, Project Target Selected, Select Model Metric, etc. | | Client Version | Version of the related client (DataRobotPythonClient or DataRobotRClient) | | Catalog ID | The ID of an item in the catalog. It can be used to address an individual item. Catalog ID is the same as a Dataset ID when the catalog item is a dataset. | | Catalog Version ID | An ID indicating a specific version of a catalog item. Catalog items can have multiple versions; by default the catalog uses the latest version. To work with an earlier version, you must use the specific version ID. | | Dataset ID | ID of dataset | | Dataset Name (*) | Name of dataset | | Dataset Size | Size of project dataset | | Snapshotted State | A dataset can be snapshotted (materialized) or not. If it is snapshotted, the data has been stored locally. If it is not, the data is requested from the source whenever it is used. | | Grantee | The UID of the user sharing the asset | | With Grant | Indicates whether the user receiving the new role is allowed to share with others | (*) denotes an [identifying field](https://docs.datarobot.com/en/docs/reference/misc-ref/uam-ref.html#hide-sensitive-information) for this report ## Prediction Usage activity report The Prediction Usage activity output reports the following data about prediction statistics. | Report field | Description | | --- | --- | | Timestamp—UTC | Timestamp (UTC time standard) when the prediction event occurred | | UID | ID for the user who initiated this event | | Username (*) | Name for the user who initiated this event | | Project ID | ID of the project | | Org ID | ID of the user's organization | | Org Name (*) | Name of the user's organization | | Group ID | ID of the user's group | | Group Name (*) | Name of the user's group | | User Role | Role for the user who initiated the prediction event; values include OWNER, USER, OBSERVER | | Model ID | ID of the model | | Model Type | Type for the model; this also is the name of the model or blueprint | | Blender Model Types | Type of blender model, if applicable | | Recommended Model | Identifies if this is the recommended model for deployment (true) or not (false) | | Project Type | Type for the project; possible values include Binary Classification, Regression, Time Series—Regression, Multiclass Classification, etc. | | Deployment ID | ID of the deployment | | Deployment Type | Type of deployment; possible values include dedicated (deployment to a dedicated prediction server) or Secure Worker (in-app modeling workers used for predictions) | | Dataset ID | ID of the dataset | | Prediction Method | Method for making predictions for the related project; possible values are Modeling Worker (predictions using modeling workers) or Dedicated Prediction (predictions using dedicated prediction server) | | Prediction Explanations | Identifies if Prediction Explanations were computed for this project model (true) or not (false) | | # of Requests | Number of prediction requests the deployment has received (where a single request can contain multiple prediction requests); provides statistics for deployment service health | | # Rows Scored | Number of dataset rows scored (for predictions) for this deployment | | User Errors | Number of user errors (4xx errors) for this deployment; provides statistics for deployment service health | | Server Errors | Number of server errors (5xx errors) for this deployment; provides statistics for deployment service health | | Average Execution Time | Average time (in milliseconds) DataRobot spent processing prediction requests for this deployment; provides statistics for deployment service health | (*) denotes an [identifying field](https://docs.datarobot.com/en/docs/reference/misc-ref/uam-ref.html#hide-sensitive-information) for this report ## Self-Managed AI Platform admins The system information report is available only on the Self-Managed AI Platform. ### System Information report System information is available for download only, using [Export CSV](https://docs.datarobot.com/en/docs/platform/admin/monitoring/main-uam-overview.html#download-activity-data) (online preview is not available). The System Information report provides information (key:value pairs) specific to the deployed cluster. The category of information is dependent on the type of deployed cluster. | Report field | Description | | --- | --- | | deployment | Python version used to deploy the cluster. | | install type | The type of installation for the deployed cluster. | | mongo | Mongo database configuration. Identifies whether secrets are enforced when communicating with the Mongo database and also database availability. | | redis | Redis queue service configuration. Identifies whether secrets are enforced when communicating with a Redis database and also database availability. | | postgresql | Postgres database configuration. Identifies whether secrets are enforced when communicating with the Postgres database. | | tls config | Identifies which services are set up to use transport layer security (TLS). | | modeling workers | Reports the number of modeling workers configured. If a cluster has unlimited modeling workers, this field is empty. | | dedicated predictions | Identifies whether a dedicated prediction environment is configured and if audit logs are enabled on the environment. | | smtp | Identifies whether SMTP integration is configured. | | product type | Lists DataRobot products (i.e. MLOps, AutoML, Time series) enabled for the deployed cluster. | Typical information for a report is shown below: --- # AI Report URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ai-report.html > Create an AI Report, a high-level overview of your modeling results and insights, to communicate the most important findings of your modeling project to stakeholders. # AI Report Once you complete an Autopilot run, you can generate an AI Report, which communicates the most important findings of your modeling project to stakeholders. The AI Report provides a high-level overview of your modeling results and insights, with particular focus on Trusted AI insights that fall under the dimensions of quality, accuracy, and interpretability. The report provides accuracy insights for the top performing model, including its speed and cross-validation scores. It also captures interpretability insights in the model's Feature Impact histogram, which helps to show which features are driving model decisions. The AI Report provides these summary details: - The target and the number of models trained. - The type of problem, for example, binary or regression. - The modeling mode, for example, Quick Autopilot. It also provides details about your data and its quality, including: - The number of rows of data. - The number and type of features (for example, categorical, numeric, text, etc.). - The number of features determined to be informative—DataRobot removes the features that are not informative. Availability is as follows: - Reports can be created for projects built with all modeling modes except manual mode. - The option is unavailable (the functionality is disabled) for time-aware, unsupervised, and multiclass/multilabel projects. ## Generating an AI Report To generate an AI Report for your Autopilot run, on the Leaderboard, click Menu and select AI Report > Generate new report. DataRobot automatically downloads the AI Report as a Word document after it is generated. ## Viewing an AI Report To view a previously generated AI Report, on the Leaderboard, click Menu and select AI Report > View report. ## Downloading an AI Report To download a previously generated AI Report, on the Leaderboard, click Menu and select AI Report > Download report. DataRobot downloads the AI Report as a Word document. --- # Leaderboard badges URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/badge-ref.html > Provides a description of the badges and indicators that display with models on the various pages of the Leaderboard and repository. # Leaderboard badges ## Leaderboard badges The following table describes the tags and indicators that display with models on the various pages of the [Leaderboard](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/leaderboard.html) and [repository](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/manage-experiments/blueprint-repo.html). | Display/name | Description | | --- | --- | | Exportable coefficients | Indicates a model from which you can export the coefficients and transformation parameters necessary to verify steps and make predictions outside of DataRobot. Blueprints that require complex preprocessing will not have the Beta tag because you can't export their preprocessing in a simple form (ridit transform for numerics, for example). Also note that when a blueprint has coefficients but is not marked with the Beta tag, it indicates that the coefficients are not exact (e.g., they may be rounded). | | Blender | Indicates a model that was created in Classic by combining the predictions of between two and eight models. | | BPxx Blueprint ID* | Displays a blueprint ID that represents an instance of a single model type (including version) and feature list. Models that share these characteristics within the same project have the same blueprint ID regardless of the sample size used to build them. Use the model ID to differentiate models when the blueprint ID is the same. Blender models indicate the blueprints used to create them (for example, BP6+17+20). | | External predictions | Indicates that a model is available to run a subset of DataRobot's evaluative insights for comparison against DataRobot models. | | Frozen parameters | Indicates that the model was produced using the frozen run feature. The badge also indicates the sample percent of the original model. | | GPU | Indicates that the model can be or was trained on GPU workers. | | Mxx Model ID* | Displays a unique ID for each model on the Leaderboard. The model ID represents a single instance of a model type, feature list, and sample size within a single project. Use the model ID to differentiate models when the blueprint ID is the same. | | Monotonic constraintsO | Indicates that the model supports monotonic constraints; available in the blueprint repository only. | | New Series Support | Indicates a model that supports unseen series modeling (new series support). | | Prepared for deployment | Indicates that the model has been through the Autopilot recommendation stages and is ready for deployment. | | Reference | Indicates that the model is a reference model. A reference model uses no special preprocessing; it is a basic model that you can use to measure performance increase provided by an advanced model. | | Scoring code | Indicates that the model has Scoring Code available for download. | | Time series baseline | Applicable to time series projects only. Indicates a baseline model built using the MASE metric. | | User-defined | Indicates that the model is a user-created, pretrained models that was uploaded to DataRobot, as a collection of files, via the Workshop. | * You cannot rely on blueprint or model IDs to be the same across projects. Model IDs represent the order in which the models were added to the queue when built; because different projects can have different models or a different order of models, these numbers can differ across projects. Similarly for blueprint IDs where blueprint IDs can be different based on different generated blueprints. If you want to check matching blueprints across projects, check the blueprint diagram—if the diagrams match, the blueprints are the same. --- # Bias and Fairness reference URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/bias-ref.html > The Bias and Fairness feature calculates fairness for a machine learning model and identifies any biases from the model's predictive behavior. # Bias and Fairness reference In DataRobot, bias represents the difference between a model's predictions for different populations (or groups) while fairness is the measure of the model's bias. More specifically, it provides methods to calculate fairness for a binary classification model and to identify any biases in the model's predictive behavior. Fairness metrics in modeling describe the ways in which a model can perform differently for distinct groups within data. Those groups, when they designate groups of people, might be identified by protected or sensitive characteristics, such as race, gender, age, and veteran status. The largest source of bias in an AI system is the data it was trained on. That data might have historical patterns of bias encoded in its outcomes. Bias might also be a product not of the historical process itself but of data collection or sampling methods misrepresenting the ground truth. See the [list of resource](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/bias-resources.html) for the settings and tools available on DataRobot to enable bias mitigation. ## Bias and Fairness terminology The following sections define terminology that is commonly used across the feature. ### Protected Feature The dataset column to measure fairness of model predictions against. That is, a model's fairness is calculated against the protected features from the dataset. Also known as "protected attribute." Examples: `age_group`, `gender`, `race`, `religion` Only categorical features can be marked as protected features. Each categorical value of the protected feature is referred to as a protected class or class of the feature. ### Protected Class One categorical value of the protected feature. Examples: `male` can be a protected class (or simply a class) of the feature `gender`; `asian` can be a protected class of the feature `race`. ### Favorable Outcome A value of the target that is treated as the favorable outcome for the model. Predictions from a binary classification model can be categorized as being a favorable outcome (i.e., good/preferable) or an unfavorable outcome (i.e., bad/undesirable) for the protected class. Example: To check gender discrimination for loan approvals, the target `is_bad` indicates whether the loan will default or not. In this case, the favorable outcome for the prediction is `No` (meaning the loan "is good") and therefore the value of `No` is the favorable (i.e., good) outcome for the loaner. Favorable target outcome is not always the same as the assigned positive class. For example, a common lending use case involves predicting whether or not an applicant will default on their loan. The positive class could be 1 (or "will default"), whereas the favorable target outcome would be 0 (or "will not default"). The favorable target outcome refers to the outcome that the protected individual would prefer to receive. ### Fairness Score A numerical computation of model fairness against the protected class, based on the underlying [fairness metric](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/bias-ref.html#fairness-metrics). Note: A model's fairness scores cannot be compared if the model uses different fairness metrics or if the fairness scores were calculated on different prediction data. ### Fairness Threshold The fairness threshold helps measure if a model performs within appropriate fairness bounds for each protected class and does not affect the fairness score or performance of any protected class. If not specified, the threshold defaults to 0.8. ### Fairness Value Fairness scores normalized against the most favorable protected class (i.e., the class with the highest fairness score). The fairness value will always be in the range `[0.0, 1.0]`, where `1.0` is assigned to the most favorable protected class. To ensure trust in the calculated fairness value for a given class of the protected feature, the tools determine if there was enough data in the sample to calculate fairness value with a high level of confidence (see Z score). ### Z Score A metric measuring whether a given class of the protected feature is "statistically significant" across the population. Example: To measure fairness against `gender` in a dataset with 10,000 rows identifying `male` and only 100 rows identifying `female`, the feature labels the `female` class as having insufficient data. In this case, use a different sample of the dataset to ensure trust of the fairness measures over this sample. ## Fairness Metrics Fairness metrics are statistical measures of parity constraints used to assess fairness. Each fairness metric result is calculated in two steps: 1. Calculating fairness scores for each protected class of the model's protected feature. 2. Normalizing fairness scores against the highest fairness score for the protected feature. Metrics that measure Fairness by Error evaluate whether the model's error rate is equivalent across each protected class. These metrics are best suited when you don't have control over the outcome or wish to conform to the ground truth, and simply want a model to be equally right between each protected group. Metrics that measure Fairness by Representation evaluate whether the model's predictions are equivalent across each protected class. These metrics are best suited when you have control over the target outcome or are willing to depart from ground truth in order for a model's predictions to exhibit more equal representation between protected groups, regardless of the target distribution in the training data. To help understand the ideal context/use case for applying a given fairness metric, this section covers hypothetical examples for each fairness metric. The examples are based on an HR hiring use case, where the fairness metrics evaluate a model that predicts the target `Hired` ( `Yes` or `No`). Disclaimer: The hypothetical examples do not reflect the views of DataRobot; they are meant solely for illustrative purposes. #### Notation - d : decision of the model (i.e., Yes or No ) - PF : protected feature - s : predicted probability scores - Y : target variable There are eight individual fairness metrics in total. Certain metrics are best used when paired with a related fairness metric. When applicable, these are noted in the descriptions below. ### Proportional Parity For each protected class, what is the probability of receiving favorable predictions from the model? This metric is based on equal representation of the model's target across protected classes. Also known as "statistical parity," "demographic parity," and "acceptance rate," it is used to score fairness for binary classification models. A common usage for Proportional Parity is the ["four-fifths"](https://www.eeoc.gov/laws/guidance/questions-and-answers-clarify-and-provide-common-interpretation-uniform-guidelines) (i.e. 4/5ths) rule in the Uniform Guidelines on Employee Selection Procedures in context of HR hiring. Required data: - Protected feature (i.e., gender with values male or female ) - Target with predicted decisions (i.e., Hired with values Yes or No ) Formula: Example: A company has a pool of `100` applicants, where: - 70 applicants are male - 30 applicants are female - 60 males are predicted to be hired and 5 females are predicted to be hired Calculate the probability of being hired ("hire rate for each protected class"). The following are the manually calculated fairness scores: ``` male hiring rate = (number of males hired) / (number of males) = 60/70 = 0.857 = 85.7% female hiring rate = (number of females hired) / (number of females) = 5/30 = 0.167 = 16.7% ``` Calculate the disparate impact (the fairness value) for females as follows: ``` disparate impact = (female hiring rate) / (male hiring rate) = 0.167/0.857 = 0.195 = 19.5% ``` Compare this relative fairness score ( `19.5%`) against a fairness threshold of `0.8` (i.e., 4/5 = 0.8 = 80%). The result ( `19.5% < 80%`) indicates that the model does not satisfy the four-fifths rule and is therefore unfairly treating the females in hiring. Example use case: According to the 4/5ths Rule in the US for regulating Human Resources, if you're selecting candidates for a job, the selection rate must be equal between protected classes (i.e. proportional parity) within a threshold of 0.8. If you interview 80% of men and only 40% of women, that violates this law. To rectify this bias, you would need to interview at least 64% of women (the 80% selection rate for men * 0.8 fairness threshold). ### Equal Parity For each protected class, what is the total number of records with favorable predictions from the model? This metric is based on equal representation of the model's target across protected classes. It is used for scoring fairness for binary classification models. Required data: - Protected feature (i.e., gender with values male or female ) - Target with predicted decisions (i.e., Hired with values Yes or No ) Formula: Example: Using the previous example, the fairness scores for male and female predicted hirings are: ``` males hired = 60 females hired = 5 ``` Example use case: In Europe, some countries require equal numbers of men and women on corporate boards. ### Prediction Balance The set of Prediction Balance fairness metrics include favorable and unfavorable class balance, described below. #### Favorable Class Balance For all actuals that were favorable outcomes, what is the average predicted probability for each protected class? This metric is based on equal representation of the model's average raw scores across each protected class and is part of the set of Prediction Balance fairness metrics. A common usage for Favorable Class Balance is ranking hiring candidates by the model's raw scores to select higher-scoring candidates. Required data: - Protected feature (i.e., gender with values male or female ) - Target with predicted probability scores (i.e., Hired with values in the range [0.0, 1.0] ) - Target with actual outcomes (i.e., Hired_actual with values Yes or No ) Formula: Example: A company has a pool of `100` applicants, where: - 70 applicants are male - 30 applicants are female - 50 males were actually hired and 20 females were actually hired - Range of predicted probability scores for males: [0.7, 0.9] - Range of predicted probability scores for females: [0.2, 0.4] Calculate the average for each protected class, based on a model's predicted probability scores, as follows: ``` hired males average score = sum(hired male predicted probability scores) / 50 = 0.838 hired females average score = sum(hired female predicted probability scores) / 20 = 0.35 ``` Example use case: In a hiring context, you can rank candidates by probability of passing hiring manager review and use the model's raw scores to filter out lower-scoring candidates, even if the model predicts that they should be hired. #### Unfavorable Class Balance For all actuals that were unfavorable outcomes, what is the average predicted probability for each protected class? This metric is based on equal representation of the model's average raw scores across each protected class and is part of the set of Prediction Balance fairness metrics. A common usage for Unfavorable Class Balance is ranking hiring candidates by the model's raw scores to filter out lower-scoring candidates. Required data: - Protected feature (i.e., gender with values male or female ) - Target with predicted probability scores (i.e., Hired with values in the range [0.0, 1.0] ) - Target with actual outcomes (i.e., Hired_actual with values Yes or No ) Formula: Example: A company has a pool of `100` applicants, where: - 70 applicants are male - 30 applicants are female - 20 males were actually not hired and 10 females were actually not hired - Range of predicted probability scores for males: [0.7, 0.9] - Range of predicted probability scores for females: [0.2, 0.4] Calculate the average for each protected class, based on a model's predicted probability scores, as follows: ``` non-hired males average score = sum(non-hired male predicted probability scores) / 20 = 0.70 non-hired females average score = sum(non-hired female predicted probability scores) / 10 = 0.20 ``` ### True Favorable Rate Parity For each protected class, what is the probability of the model predicting the favorable outcome for all actuals of the favorable outcome? This metric (also known as "True Positive Rate Parity") is based on equal error and is part of the set of True Favorable Rate & True Unfavorable Rate Parity fairness metrics. Required data: - Protected feature (i.e., gender with values male or female ) - Target with predicted decisions (i.e., Hired with values Yes or No ) - Target with actual outcomes (i.e., Hired_actual with values Yes or No ) Formula: Example: A company has a pool of `100` applicants, where: - 70 applicants are male - 30 applicants are female - 50 males were correctly predicted to be hired - 10 males were incorrectly predicted to be not hired - 8 females were correctly predicted to be hired - 12 females were incorrectly predicted to be not hired Calculate the True Favorable Rate for each protected class as follows: ``` male favorable rate = TP / (TP + FN) = 50 / (50 + 10) = 0.8333 female favorable rate = TP / (TP + FN) = 8 / (8 + 12) = 0.4 ``` Example use case: In healthcare, a model can be used to predict which medication a patient should receive. You would not want to give any protected class the wrong medication in order to ensure that everyone gets the same medication– instead, you want your model to give each protected class the right medication, and you don't want it to make significantly more errors on any group. ### True Unfavorable Rate Parity For each protected class, what is the probability of the model predicting the unfavorable outcome for all actuals of the unfavorable outcome? This metric (also known as "True Negative Rate Parity") is based on equal error and is part of the set of True Favorable Rate & True Unfavorable Rate Parity fairness metrics. Required data: - Protected feature (i.e., gender with values male or female ) - Target with predicted decisions (i.e., Hired with values Yes or No ) - Target with actual outcomes (i.e., Hired_actual with values Yes or No ) Formula: Example: A company has a pool of `100` applicants, where: - 70 applicants are male - 30 applicants are female - 5 males were correctly predicted to be not hired - 5 males were incorrectly predicted to be hired - 8 females were correctly predicted to be not hired - 2 females were incorrectly predicted to be hired Calculate the True Unfavorable Rate for each protected class as follows: ``` male unfavorable rate = TN / (TN + FP) = 5 / (5 + 5) = 0.5 female unfavorable rate = TN / (TN + FP) = 8 / (8 + 2) = 0.8 ``` ### Favorable Predictive Value Parity What is the probability of the model being correct (i.e., the actual results being favorable)? This metric (also known as "Positive Predictive Value Parity") is based on equal error and is part of the set of Favorable Predictive & Unfavorable Predictive Value Parity fairness metrics. Required data: - Protected feature (i.e., gender with values male or female ) - Target with predicted decisions (i.e., Hired with values Yes or No ) - Target with actual outcomes (i.e., Hired_actual with values Yes or No ) Formula: Example: A company has a pool of `100` applicants, where: - 70 applicants are male - 30 applicants are female - 50 males were correctly predicted to be hired - 5 males were incorrectly predicted to be hired - 8 females were correctly predicted to be hired - 2 females were incorrectly predicted to be hired Calculate the Favorable Predictive Value Parity for each protected class as follows: ``` male favorable predictive value = TP / (TP + FP) = 50 / (50 + 5) = 0.9091 female favorable predictive value = TP / (TP + FP) = 8 / (8 + 2) = 0.8 ``` Example use case: Insurance companies consider it ethical to charge men more than women, as men are considered significantly more reckless drivers than women. In this case, you would want the model to charge men the correct amount relative to their actual risk, even if the amount is different for women. ### Unfavorable Predictive Value Parity What is the probability of the model being correct (i.e., the actual results being unfavorable)? This metric (also known as "Negative Predictive Value Parity") is based on equal error and is part of the set of Favorable Predictive & Unfavorable Predictive Value Parity fairness metrics. Required data: - Protected feature (i.e., gender with values male or female ) - Target with predicted decisions (i.e., Hired with values Yes or No ) - Target with actual outcomes (i.e., Hired_actual with values Yes or No ) Formula: Example: A company has a pool of `100` applicants, where: - 70 applicants are male - 30 applicants are female - 5 males were correctly predicted to be not hired - 10 males were incorrectly predicted to be not hired - 8 females were correctly predicted to be not hired - 12 females were incorrectly predicted to be not hired Calculate the Unfavorable Predictive Value Parity for each protected class as follows: ``` male unfavorable predictive value = TN / (TN + FN) = 5 / (5 + 10) = 0.333 female unfavorable predictive value = TN / (TN + FN) = 8 / (8 + 12) = 0.4 ``` ## Additional resources - Blog post:Bias and Fairness as Dimensions of Trusted AIresource page for more detail. - Part one of a three-part series in partnership with Amazon Web Services:How to Build & Govern Trusted AI Systems - Podcast:Moving Past Artificial Intelligence To Augmented Intelligence --- # Blueprints in the AI Catalog URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-catalog.html > How to save, edit, share, and re-use blueprints from the AI Catalog. # Blueprints in the AI Catalog When Composable ML is enabled, you can save blueprints to the AI Catalog. From the catalog, a blueprint can be edited, used to train models in compatible projects, or shared. The list of blueprints available in the catalog are blueprints that: - Were shared with you. - You explicitly saved from the Leaderboard or Repository, via theBlueprinttab by clickingAdd to AI Catalog. - You saved via the Blueprint Workshop or API. ## Access catalog blueprints If you selected to save a blueprint from the Repository or Leaderboard, DataRobot presents a modal that allows you to rename it. When saving is complete, you can open the catalog to work with the blueprint or begin/continue editing from where you are. Once in the catalog, click Blueprints to display a list of all saved user blueprints. Click to select a blueprint. Once expanded, select one of the following tabs: | Tab | Description | | --- | --- | | Info | Display creation and modification information for the blueprint. Additionally, you can add or edit the blueprint name, description, or tags. | | Blueprint | Load the blueprint in a state where it is ready for additional editing. You can also train a model using the blueprint. | | Comments | Add comments to a blueprint. Any text will be visible to users who are granted access to the blueprint. | As with other catalog assets, you can share your blueprints. Click [Share](https://docs.datarobot.com/en/docs/classic-ui/data/ai-catalog/catalog-asset.html#share-assets) in the top right corner, and then choose who to share it with and assign permissions. ## Link project blueprints Some blueprints are meant to be used only with a specific project. For example, a blueprint preprocessing step might select features specific to the project. With DataRobot's automated project linking, feature selection is unavailable if the user blueprint is not linked to a project. This prevents a task from attempting to select a feature (column) that is not included in the project. Consider the following example: In a RandomForest Regressor blueprint, you want to select a specific column and reference a specific feature or features: Click Add to add the new step. You can then train the blueprint or add it to the AI Catalog. If you then open the blueprint in the catalog, because the blueprint references features in a specific dataset, DataRobot has automatically linked it to the project. If you then modify the linked project: DataRobot provides a warning that the required columns do not exist in the dataset: To use the blueprint, you must first edit the columns in the column selector step. If you do not want the blueprint linked to a project, use Unlink project in the project selection dropdown. If a project is not linked automatically (because there are not project dependencies), you can manually link it through the project dropdown: Finally, if you copy a user blueprint that is linked to a project, the link is also copied. Note that you can train a blueprint linked to a project ("project A") inside of another project ("project B"). DataRobot provides a warning if the project A blueprint refers to features that do not exist in project B. > [!NOTE] Note > The linked project name, and the project selection dropdown, are also available from the catalog Blueprint > Info tab. ## Train blueprints in bulk In the AI Catalog, you can apply bulk actions to user blueprints that [share the same target type](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-catalog.html#select-target-type). If selected blueprints don't have at least one common target type, DataRobot [prevents bulk training](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-catalog.html#target-type-validation). To train in bulk, select AI Catalog > Blueprints to list blueprints and select those you want to apply to a project. Once the blueprints are selected, the ability to train the blueprints becomes enabled. > [!NOTE] Note > When multiple target types are listed, the blueprint supports each listed target type. Click Train blueprints and the training modal for multiple blueprints opens. If any of the selected blueprints fail validation, the Train blueprints button is disabled and a [message](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-catalog.html#identify-errored-blueprints) identifies the errored blueprints. Blueprints that include warnings do not block training. Use the current project or change projects from the dropdown. Projects are filtered and available for selection based on commonality of target type with the selected blueprint (multiclass in this example): Complete the fields in the modal and click to train blueprints. DataRobot provides a notification that blueprints have started training and will be available on the Leaderboard. ### Select target type To make it easier to select appropriate blueprints, the Blueprints tab in the catalog provides a dropdown selector to filter blueprints by target type: Choose one or more types and the display changes to list only those blueprints matching at least one of the selected types. For example, if you select binary and a blueprint has a target type of binary and multiclass, it will be included in the list. ### Target type validation Because blueprints can only be applied to projects with a matching target type, DataRobot runs validation on selected blueprints and only allows training if there is a compatible target type. For example, if you select a blueprint with a binary target type and also choose a project with a binary target type, then you are able to proceed with training: When it is not valid, training is disabled: ### Identify errored blueprints The Train multiple blueprints modal displays a color-coded message that indicates status for the group of blueprints in the training request and the number of affected blueprints. - If all blueprints are valid, the message is green. - Yellow indicates that at least one blueprint contains a warning, but none are errored. - Red indicates that at least one blueprint contains an error (and as such, training is disabled). Click to expand the message and display the target type and a status indicator for each blueprint. Hover over the icons to display a tooltip containing information on addressing the error or warning. Or, deselect an errored blueprint to make the remaining blueprints available for training. Click the Deselect errors link to deselect all errored blueprints in the group. When errored blueprints are removed from the group, the message turns yellow or green, and the Train blueprints button is enabled. ## Delete blueprints in bulk To delete multiple blueprints with a single action: 1. Use the checkboxes to select the blueprints to delete. 2. Click Delete ( ) to open the confirmation modal, which tells you the number and type of blueprints that will be removed. Deleting a blueprint removes it from the AI Catalog, but note that: - Removing the blueprint from the catalog does not affect models that were created from that blueprint. - You can restore a deleted blueprint to the catalog from any model that uses it. --- # Composable ML considerations URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-consider.html > Platform support and considerations for working with Composable ML. # Composable ML considerations Consider the following when working with Composable ML. ### Environment support Custom tasks are supported on DataRobot’s managed AI Platform (US, EU). Composable ML blueprints with DataRobot tasks are available for all users. #### DRUM supported OSs - DRUM works on Mac OS, Linux, and Windows 10. Testing is on Linux only and therefore compatibility issues may occur when running on other platforms. #### Custom task languages - Python and R are supported. - SAS is not supported. #### Task limits | Component | Size | | --- | --- | | RAM | 60GB training / 14GB scoring | | CPU Cores | 4 | | GPU | not supported | | Storage | 350GB | | Artifact (Serialized trained model/transformer size) | 10GB | | Timeout | 72 hrs (for fit) | | Max custom tasks per blueprint | 3 | ### Modeling support The following describes modeling-specific capabilities. #### Modeling specifics The following are supported: - Predictive ML, including time-aware but not time series, and Feature Discovery. - Estimators, both built-in and custom, are available for binary classification, regression, multiclass, anomaly detection, and clustering. - Preprocessing, both built-in and custom. #### API - Python API client for blueprint generation. - Python client for custom task generation. #### Modeling options The following describes support for advanced modeling options with custom blueprints: - Metrics and loss function, where “loss function” is used to train a model and “metric” is used to evaluate models and for accuracy monitoring. Typically, the same measure is used for both. Support includes: - Exposure, Weight, Counts of Events, Offset NoteDataRobot does not indicate whether a task takes into account any of these options. As a result, using Composable ML on a project that uses those options is not recommended. (For example, if you train a custom blueprint on a project that uses exposure, there is no guarantee whether the model will useyory/exposureas the target.) On the other hand, when using blueprints generated by Autopilot, these options are taken into account correctly, in line with the project settings. - Monotonic constraints are not supported. - Hyperparameter tuning is supported for custom and built-in tasks. - Blenders are supported with the exception of custom estimators. You can howevermanuallycreate a blueprint that uses multiple estimators. #### Insights and compliance documentation - Model-agnosticinsights are supported: - Model-specificinsights offer limited support: - Compliance documentation is supported. ### Deployment options - All MLOps model monitoring and management features are supported. - Deployment of a custom blueprint inside the DataRobot platform is supported. - Deployment outside of the DataRobot platform (using Scoring Code or Portable Prediction Server) is only supported for blueprints without custom tasks. --- # Sentiment analysis example URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-sentiment-example.html > Apply Composable ML to capture sentiment from text. # Sentiment analysis example The model in this example includes reviews or tweets. The goal is to get an uplift for the model by capturing the sentiment in the text. To do this, simply [modify the blueprint](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/cml-blueprint-edit.html#access-the-blueprint-editor) (i.e., click Copy and Edit to start). The following is a simple blueprint.—text only—but the model could have features as well. Hover over either the Matrix of word-grams counts or Elastic-Net Classifier nodes to see: - The type of input required for that task. - The type of output returned. For example, the Matrix of word-grams counts task requires the input to be of type Text and it returns a data frame with all numeric features: To capture sentiments in a text feature for this example, hover over the Text variables node and click the [task selector](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/cml-blueprint-edit.html#use-the-task-selector) plus sign ( [https://docs.datarobot.com/en/docs/images/icon-plus.png](https://docs.datarobot.com/en/docs/images/icon-plus.png)). In the Select a task dialog box, expand Preprocessing > Text Preprocessing to see the options for text manipulations. (Some of these options are also available via [Advanced Tuning](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/adv-tuning.html), but others can only be accessed here.) Select to add TextBlob Sentiment Featurizer. The blueprint now shows a new node, outlined in red. When you hover over the node, you can see that it requires text: Note that the node's output is a data frame with numerical features ( `Data Type: Numeric`). Because the TextBlob Sentiment Featurizer is a preprocessing module, you must [connect it to the model task](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/cml-blueprint-edit.html#work-with-nodes). (Hover over the TextBlob node, drag the diagonal arrow ( [https://docs.datarobot.com/en/docs/images/icon-diagonal-arrow.png](https://docs.datarobot.com/en/docs/images/icon-diagonal-arrow.png)) icon to the Elastic-Net Classifier node, and click.) The new blueprint is [ready to be trained](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/cml-blueprint-edit.html#train-new-models). (Before training, you can change the feature list or the training sample size.) Here is the model on the Leaderboard, shown as one of the top four models. --- # Model metadata and validation schema URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-validation.html > How to use the model-metadata.yaml file to specify additional information about a custom task or a custom inference model. # Model metadata and validation schema The `model-metadata.yaml` file is used to specify additional information about a custom task or a custom inference model, such as: - Supported input/output data types that validate, when composing a blueprint, whether a task's input/output requirements match the neighboring tasks. - The environment ID/model ID of a task or model when runningdrum push. To define metadata, create a `model-metadata.yaml` file and put it in the top level of the task/model directory. In most cases, it can be skipped, but it is required for custom transform tasks when a custom task outputs non-numeric data. The `model-metadata.yaml` is located in the same folder as `custom.py`. The sections below show how to define metadata for custom models and tasks. For more information, you can review complete examples in the DRUM repository for [custom models](https://github.com/datarobot/datarobot-user-models/blob/master/model_templates/python3_sklearn/model-metadata.yaml) and [tasks](https://github.com/datarobot/datarobot-user-models/blob/master/task_templates/1_transforms/1_python_missing_values/model-metadata.yaml). ## General metadata parameters The following table describes options that are available to tasks and/or inference models. The parameters are required when using `drum push` to supply information about the model/task/version to create. Some of the parameters are also required outside of `drum push` for compatibility reasons. > [!NOTE] Note > The `modelID` parameter adds a new version to a pre-existing custom model or task with the specified ID. Because of this, all options that configure a new base-level custom model or task are ignored when passed alongside this parameter. However, at this time, these parameters still must be included. | Option | When required | Task or inference model | Description | | --- | --- | --- | --- | | name | Always | Both | A string, preferably unique for easy searching, that drum push uses as the custom model title. | | type | Always | Both | A string, either training (for custom tasks) or inference (for custom inference models). | | environmentID | Always | Both | A hash of the execution environment to use while running your custom model or task. You can find a list of available execution environments in Model Registry > Custom Model Workshop > Environments. Expand the environment and click on the Environment Info tab to view and copy the file ID. Required for drum push only. | | targetType | Always | Both | A string indicating the type of target. Must be one of: binaryregressionanomaly unstructured (inference models only)multiclasstextgeneration (inference models only)agenticworkflow (inference models only) transform (transform tasks only) | | modelID | Optional | Both | After creating a model or task, it is best practice to use versioning to add code while iterating. To create a new version instead of a new model or task, use this field to link the custom model/task you created. The ID (hash) is available from the UI, via the URL of the custom model or task. Used with drum push only. | | description | Optional | Both | A searchable field. If modelID is set, use the UI to change a model/task description. Used with drum push only. | | majorVersion | Optional | Both | Specifies whether the model version you are creating should be a major (True, the default) or minor (False) version update. For example, if the previous model version is 2.3, a major version update would create version 3.0; a minor version update would create version 2.4. Used for drum push only. | | targetName | For binary and multiclass (in inferenceModel) | Model | In inferenceModel, the name of the column the model predicts. For multiclass, use the same name as Target name in the Workshop and the same order of classes as Target classes for classLabels. | | positiveClassLabel / negativeClassLabel | For binary classification models | Model | In inferenceModel, when your model predicts probability, the positiveClassLabel dictates what class the prediction corresponds to. | | classLabels | For multiclass classification models | Model | In inferenceModel, a list of class names (strings). The list order must match the order of predicted class probabilities your model returns (for example, the column order of probability outputs). Use the same labels as the Target classes you configure for the custom model in the Workshop. | | predictionThreshold | Optional (binary classification models only). | Model | In inferenceModel, the cutoff point between 0 and 1 that dictates which label will be chosen as the predicted label. | | trainOnProject | Optional | Task | A hash with the ID of the project (PID) to train the model or version on. When using drum push to test and upload a custom estimator task, you have an option to train a single-task blueprint immediately after the estimator is successfully uploaded into DataRobot. The trainOnProject option specifies the project on which to train that blueprint. | ## Inference model metadata (inferenceModel) For structured inference models, target and class-label settings belong under the top-level key `inferenceModel` in `model-metadata.yaml`. If you omit fields that DataRobot or DRUM require for your `targetType`, builds, tests, or deployments can fail. | targetType | Required under inferenceModel | Notes | | --- | --- | --- | | binary | targetName, positiveClassLabel, negativeClassLabel | Optional: predictionThreshold. | | multiclass | targetName, classLabels | classLabels is a YAML list of class names in the same order as your model’s probability outputs. | | regression | (often none) | Many regression templates work without an inferenceModel block; follow your environment and DRUM requirements. | | anomaly, unstructured, textgeneration, … | Follow template / DRUM | See examples for your target type. | Workshop-generated file: On the Registry Workshop Assemble tab, Create model-metadata.yaml produces a starter file for your model’s target type. For multiclass, that file includes `inferenceModel` with `targetName` and `classLabels` (aligned with your Target classes), matching what you need for a successful deployment. ## Validation schema and fields The schema validation system, which is defined under the `typeSchema` field in `model_metadata.yaml`, is used to define the expected input and output data requirements for a given custom task. By including the optional `input_requirements` and `output_requirements` fields, you can specify exactly the kind of data a custom task expects or outputs. DataRobot displays the specified conditions in the blueprint editor to indicate whether the neighboring tasks match. It also uses them during blueprint training to validate whether the task's data format matches the conditions. Supported conditions include: - data type - data sparsity - number of columns - support of missing values > [!NOTE] Note > Be aware that `output_requirements` are only supported for custom transform tasks and must be omitted for estimators. The sections below describe allowed conditions and values. Unless noted otherwise, a single entry is all that is required for input and/or output requirements. ### data_types The `data_types` field specifies the data types that are expected, or those that are specifically disallowed. A single data type or a list is allowed for `input_requirements`; only a single data type is allowed as `output_requirements`. Allowed values are NUM, TXT, IMG, DATE, CAT, DATE_DURATION, COUNT_DICT, and GEO. The conditions used for `data_types` are: - EQUALS: All of the listed data types are required in the dataframe. Missing or unexpected types raise an error. - IN: All of the listed data types are supported, but not all are required to be present. - NOT_EQUALS: The data type for the input dataframe may not be this value. - NOT_IN: None of the listed data types are supported by the task. ### sparse The `sparse` field defines whether the task supports sparse data as an input or if the task can create output data that is in a sparse format. - A condition of EQUALS must always be included in sparsity specifications. For input, the following values apply: - FORBIDDEN: The task cannot handle a sparse matrix format, and will fail if one is provided. - SUPPORTED: The model must support both a dense dataframe and a sparse dataframe in CSR format. Either could be passed in from preceding tasks. - REQUIRED: This task only supports a sparse matrix as input and cannot use a dense matrix. DRUM will load the matrix into a sparse dataframe. For task output, the following values apply: - NEVER: The task can never output a sparse dataframe. - DYNAMIC: The task can output either a dense or sparse matrix. - ALWAYS: The task will always output a sparse matrix. - IDENTITY: The task can output either a sparse or dense matrix, and the sparsity will match the input matrix. ### number_of_columns The `number_of_columns` field specifies whether a specific minimum or maximum number of columns is required. The value should be a non-negative integer. For time-consuming tasks, specifying a maximum number of columns can help keep performance reasonable. The `number_of_columns` field allows multiple entries to create ranges of allowed values. Some conditions only allow a single entry (see the [example](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-validation.html#typeSchema-example)). The conditions used for `number_of_columns` in a dataframe are: - EQUALS: The number of columns must exactly match the value. No additional conditions allowed. - IN: Multiple possible acceptable values are possible. The values are provided as a list in the value field. No additional conditions allowed. - NOT_EQUALS: The number of columns must not be the specified value. - GREATER_THAN: The number of columns must be greater than the value provided. - LESS_THAN: The number of columns must be less than the value provided. - NOT_GREATER_THAN: The number of columns must be less than or equal to the value provided. - NOT_LESS_THAN: The number of columns must be greater than or equal to the value provided. The value must be a non-negative integer. ### contains_missing The `contains_missing` field specifies whether a task can accept missing data or whether a task can output missing values. - A condition of EQUALS must always be used. For input, the following values apply to the input dataframe: - FORBIDDEN: The task cannot accept missing values/NA. - SUPPORTED: The task is capable of dealing with missing values. For task output, the following values apply: - NEVER: The task can never output missing values. - DYNAMIC: The task can output missing values. ### Default schema When a schema isn't supplied for a task, DataRobot uses the default schema, which allows [sparse data](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-validation.html#sparse-data) and [missing values](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-validation.html#contains-missing) in the input. By default: ``` name: default-transform-model-metadata type: training targetType: transform typeSchema: input_requirements: - field: data_types condition: IN value: - NUM - CAT - TXT - DATE - DATE_DURATION - field: sparse condition: EQUALS value: SUPPORTED - field: contains_missing condition: EQUALS value: SUPPORTED output_requirements: - field: data_types condition: EQUALS value: NUM - field: sparse condition: EQUALS value: DYNAMIC - field: contains_missing condition: EQUALS value: DYNAMIC ``` The default output data type is NUM. If any of these values are not appropriate for the task, a schema must be supplied in `model-metadata.yaml` (which is required for custom transform tasks that output non-numeric data). ### Running checks locally When running `drum fit` or `drum push`, the full set of validation is run automatically. Verification first checks that the supplied `typeSchema` items meet the required format. Any format issues must be addressed before the task can be trained locally or on DataRobot. After format validation, the input dataset used for fit is compared against the supplied `input_requirements` specifications. Following task training, the output of the task is compared to the `output_requirements` and an error is reported if a mismatch is present. #### Ignore validation During task development, it might be useful to disable validation. To ignore errors, use the following with `drum fit` or `drum push`: ``` --disable-strict-validation ``` --- # Composable ML reference URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/index.html # Composable ML reference | Topic | Description | | --- | --- | | Blueprints in the AI Catalog | How to save, edit, share, and re-use blueprints from the AI Catalog. | | Validation schema | How to define the expected input and output data requirements for a given custom task. | | Sentiment analysis example | A tip for capturing Text sentiments using Composable ML. | | Composable ML considerations | Considerations to be aware of when working with Composable ML. | --- # Feature lists URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html > Create machine learning and time series experiments and iterate quickly to evaluate and select the best predictive and forecasting models. # Feature lists Feature lists control the subset of features that DataRobot uses to build models and make predictions. You can use one of the [automatically created lists](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html#automatically-created-feature-lists) or [create a custom feature list](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html#create-custom-feature-lists) by manually adding features. You can also review, rename, and delete custom feature lists. You might want to use feature lists to: - Remove features that cannot be used in the model for any reason, for example, a feature that is causing target leakage. - Make predictions faster by removing unimportant features (i.e., ones that don't improve the model's performance). ## Automatically created feature lists > [!NOTE] Time-aware feature lists > The information below applies to non-time-aware feature lists. For information on time-aware feature lists, see [Time series feature lists](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/ts-feature-lists.html). DataRobot automatically creates several feature lists for each dataset and experiment. Note that: - Time series feature lists differ from predictive feature lists. - Features created from a search for interactions result in different lists (appended with a plus (+) sign). - An experiment's target feature is automatically added to every feature list. The following describes the automatically created feature lists for non-time series experiments: | Feature list | Description | Availability | | --- | --- | --- | | All Features | While not a feature list (not available for use to build models), the All Features selection sets the Project Data display to list all columns in the dataset as well as any additional transformed features. | | | Informative Features | The default feature list if DataRobot does not detect target leakage. This list includes features that pass a "reasonableness" check that determines whether they contain information useful for building a generalizable model. For example, DataRobot excludes features it determines are low information or redundant, such as duplicate columns, a column containing all ones or reference IDs, a feature with too few values, and others. | After EDA1 | | Informative Features - Leakage Removed | The default feature list if DataRobot detects target leakage. This list excludes feature(s) that are at risk of causing target leakage and any features providing little or no information useful for modeling. To determine what was removed, you can see these features labeled in the Data table with All Features selected. | After EDA1 if target leakage is detected | | Informative Features + | If Autopilot is set to run on the Informative Features list and Search for interactions is enabled, DataRobot creates Informative Features +, which may not have the same number of features as the original because when deriving the new feature from the old, keeping both may result in redundancy. If that is the case, DataRobot removes one of the parent features. | (Classic only) After EDA2 with Search for interactions enabled | | Raw Features | All features in the dataset, excluding user-derived features and including those excluded from the Informative Features list (e.g., duplicates, high missing values). | After EDA1 | | Univariate Selections | Features that meet a certain threshold (an ACE score above 0.005) for non-linear correlation with the selected target. DataRobot calculates, for each entry in the Informative Features list, the feature’s individual relationship against the target. | After EDA2 | | DR Reduced Features | A subset of features, selected based on the Feature Impact calculation of the best non-blender model on the Leaderboard. DataRobot then automatically retrains the best non-blender model with this DR Reduced Features list, creating a new model. DataRobot compares the original and new models, selects the better one, and retrains this model at a higher sample size for model recommendation purposes. DR Reduced Features, in most cases, consists of the features that provide 95% of the accumulated impact for the model. If that number is greater than 100, only the top 100 features are included. If redundant feature identification is supported in the project, redundant features are excluded from DR Reduced Features. | After EDA2, but not for Quick mode | ## Create custom feature lists > [!NOTE] Required permissions > To create feature lists, you must have Owner or Editor access to the dataset. If you do not want to use one of the [automatically created](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html#automatically-created-feature-lists) feature lists, you can create customized feature lists and train your models on them to see if they yield a better model. The ability to create a custom feature list is available from: | Location | Description | | --- | --- | | Pre-modeling / After EDA1 | | | Data tab in Registry | Create custom feature lists for registered datasets prior to being added to a Use Case and used for modeling. From here, you can also perform variable type transformations on single features. | | Data explore page | Create custom feature lists for Use Case datasets after profiling the dataset but prior to modeling. Feature lists created at this stage appear in experiments based on the dataset. | | Post-modeling / After EDA2 | | | Data preview tile | Post-modeling features for predictive modeling and derived modeling data for time-aware modeling. | | Feature lists tile | Automatically created and custom lists available for the experiment. | | Feature Impact insight | Option for impact-based feature selection (predicitive only). | | Cluster Insights | Change the insight display or create lists from predictive clustering experiments. | Note that lists created from an experiment are: - Used, within an experiment, for retraining models or training new models from the blueprint repository . - Available only within that experiment, not across all experiments in the Use Case. - Not available in the data explore page. ### Add features To create a custom feature list, navigate to one of the tabs or insights listed in the table above and click + Create feature list. Then, you can: - Select features individually. - Use bulk actions to select multiple features. #### Select features individually **Non-time series:** To select features individually: Use the Show features from dropdown to change the displayed features that are available for selection. The default display lists features from the Raw Features list. All automatically generated and custom lists are available from the dropdown. Use the checkbox to the left of the feature name to add or clear selections. (Optional) Use the search field to update the display to show only those features, within the Show features from selection, that match the search string. Save the list . **Time series:** > [!NOTE] Note > You must include the [ordering feature](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-time-aware/ts-datetime.html#2-set-ordering-feature) when creating feature lists for time series model training. The ordering feature is not required if the list is not used directly for training, such as [monotonic constraint](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html#monotonic-feature-constraints) lists. To select features individually: Use the Show features from dropdown to change the displayed features that are available for selection. The default display list features from the Time Series Extracted Features list. All automatically generated and custom lists are available from the dropdown. (Optional) If you are using the new feature list to train models, you must add the ordering feature by clicking + Add ordering feature or selecting the checkbox to the left of the feature. Use the checkbox to the left of the feature name to add or clear selections. Save the list . #### Bulk feature list actions To add multiple features at a time, choose a method from the Bulk selection dropdown: **Select by variable type:** Use Select by variable type to create a list containing all features from the dataset that are of the selected variable type. While you can only select one variable type, afterwards, you can individually add any other features (of any type). [https://docs.datarobot.com/en/docs/images/wb-custom-fl-2-ts.png](https://docs.datarobot.com/en/docs/images/wb-custom-fl-2-ts.png) **Select by existing feature list:** Use Select by existing feature list to add all features in the chosen list. [https://docs.datarobot.com/en/docs/images/wb-custom-fl-3.png](https://docs.datarobot.com/en/docs/images/wb-custom-fl-3.png) Note that the bulk actions are secondary to the Show features from dropdown. For example, showing features from "Top5" lists the five features added in your custom list. If you then use Select by existing feature list > Informative features (or Time Series Informative Features), all features in "Top5" that are also in "Informative Features" are selected. Conversely, if you Show features from: Informative Features and Select by existing feature list > Top4, those five features are selected. [https://docs.datarobot.com/en/docs/images/wb-custom-fl-7.png](https://docs.datarobot.com/en/docs/images/wb-custom-fl-7.png) **Select N most important:** Use Select N most important to add the specified number of "most important" features from the features available in the list select in the Show features from dropdown. The [importance score](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#importance-score) indicates the degree to which a feature is correlated with the target—representing a measure of predictive power if you were to use only that variable to predict the target. [https://docs.datarobot.com/en/docs/images/wb-custom-fl-4.png](https://docs.datarobot.com/en/docs/images/wb-custom-fl-4.png) ### Save feature list Once all features for the list are selected, optionally rename the list and provide a description in the Feature list summary. The summary also provides count and type of features included in the list. Then, click the Create feature list button to save the information. The new list will display in the listing on the Feature lists tab. --- # Data partitioning and validation URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html > To maximize accuracy, DataRobot separates data into training, validation/cross-validation, and holdout data. # Data partitioning and validation Partitioning creates segments of training data, broken down to maximize accuracy. The following sections describe the segments that DataRobot creates. > [!NOTE] Note > To evaluate and select models, consider only the Validation and Cross-Validation scores. Use the Holdout score for a final estimate of model performance only after you have selected your best model. (DataRobot Classic only: To make sure that the Holdout score does not inadvertently affect your model selection, DataRobot “hides” the score behind the padlock icon.) After you have selected your optimal model, score it using the holdout data. ## Validation types To maximize accuracy, DataRobot separates data into training, validation, and holdout data. The segments (splits) of the dataset are defined as follows: | Split | Description | | --- | --- | | Training | The training set is data used to build the models. Things such as linear model coefficients and the splits of a decision tree are derived from information in the training set. | | Validation | The validation (or testing) set is data that is not part of the training set; it is used to evaluate a model’s performance using data it has not seen before. Since this data was not used to build the model, it can provide an unbiased estimate of a model’s accuracy. You often compare the results of validation when selecting a model. | | Holdout | Because the process of training a series of models and then selecting the “best” based on the validation score can yield an overly optimistic estimate of a model’s performance, DataRobot uses the holdout set as an extra check against this selection bias. The holdout data is unavailable to models during the training and validation process. After selecting a model, you can score your model using this data as another check. | When creating splits, DataRobot uses five folds by default. With each round of training, DataRobot uses increasing amounts of data, split across those folds. For full Autopilot, the first round uses a random sample comprised of 16% of the training data in the first four partitions. The next round uses 32% of the training data, and the final round 64%. The Validation and Holdout partitions never change. (Other [modeling modes](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html#set-the-modeling-mode) may use different sample sizes). You can visualize data partitioning like this: And cross-validation like this: DataRobot uses "stacked predictions" for the validation partition when creating "out-of-sample" predictions on training data. ### What are stacked predictions? Without some kind of manipulation, predictions from training data would appear to have misleadingly high accuracy. To address this, DataRobot uses a technique called stacked predictions for the training dataset. With stacked predictions, DataRobot builds multiple models on different subsets of the training data. The prediction for any row is made using a model that excluded that data from training. In this way, each prediction is effectively an "out-of-sample" prediction. To do this, DataRobot runs cross validation for each model and then "stacks" the out-of-fold predictions. For example, three-fold cross validation can be represented as follows. Note that every row from the training data is present in the stack, but because of the methodology, the "stack" provides out-of-sample predictions on the training data. Consider a sample of downloaded predictions: DataRobot makes it obvious which is the holdout partition—the validation partition is labeled as `0`. #### Stacked prediction considerations Any dataset that exceeds 1.5GB results in a project containing models that do not have stacked predictions (no out-of-sample predictions). For models that have not been trained into either validation or holdout, all scores and insights is available. Otherwise: - Validation scores are not available for models trained into Validation; Holdout scores are not available for models trained into Holdout. - For models trained into Validation but not into holdout: - For models trained into both Validation and Holdout (a model trained on 100% of the data): - Whether insights with in-sample predictions are computed is based on a variety of criteria; DataRobot displays a message explaining what is affected based on the sampling and partition into which the model was trained. - For models trained into Validation only or Validation and Holdout, thePrediction Explanationpreview is available but does not show a prediction distribution chart (because there are no stacked predictions, only in-sample predictions). ### Validation scores The Leaderboard lists all models that DataRobot created (automatically or manually) and the model's scores. Scores are displayed in all or some of the following columns: - Validation - Cross-Validation (CV) - Holdout The presence or absence of a particular column depends on the type of validation partition that you chose at the start of the project. By default, DataRobot creates a 20% holdout and five-fold cross-validation. If you use these defaults, DataRobot displays values in all three columns. Scores in the Validation column are calculated using a model's trained predictions against the first validation partition. That is, it uses a "single-fold" of data. The [Cross-Validation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#k-fold-cross-validation-cv) partition is a mean of the (by default) five scores calculated on five different training/validation partitions. ## Understand validation types Model validation has two important purposes. First, you use validation to pick the best model from all the models built for a given dataset. Then, once picked, validation helps you to decide whether the model is accurate enough to suit your needs. The following sections describe methods for using your data to validate models. ### K-fold cross-validation (CV) > [!NOTE] Note > DataRobot disables cross validation when dataset size is greater than 1.5GB. In those cases, train-validation-holdout ( [TVH](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#training-validation-and-holdout-tvh)) is the only supported partitioning method. The performance of a predictive model usually increases as the size of the training set increases. Also, model performance estimates are more consistent if the validation set is large. Therefore, it is best to use as much data as possible for both training and validation. CV is generally useful for smaller datasets where you would not otherwise have enough useful data using TVH. In other words, use this method to maximize the data available for each of these sets. This process involves: 1. Separating the data into two or more sections, called “folds.” 2. Creating one model per fold, with the data assigned to that fold used for validation and the rest of the data used for training. The benefit to this approach is that all of the data is used for scoring and if enough folds are used, most of the data is used for training. - Pros : This method provides a better estimate of model performance. - Cons : Because of its multiple passes, CV is computationally intensive and takes longer to run. To compensate for the overhead when working with large datasets, DataRobot first trains models on a smaller part of the data and uses only one cross-validation fold to evaluate model performance. Then, for the highest performing models, DataRobot increases the subset sizes. In the end, only the best models are trained on the total cross-validation partition. For those models, DataRobot completes k -fold cross-validation training and scoring. As a result, the mean score of complete cross-validation for a model is displayed in the Cross-Validation column. Those models that did not perform well will not have a cross-validation score. Instead, because they only had a "one-fold" validation, their score is reported in the Validation column. You can initiate complete CV model evaluation manually for those models by clicking Run in the model's Cross-Validation column. Notes on usage: - If the dataset is greater than or equal to 50k rows, DataRobot does not run cross-validation automatically. (This limit is configuration-dependent for self-managed users.) To initiate, clickRunin the model's Cross-Validation column. - CV requires: ### Training, validation, and holdout (TVH) With the TVH method, the default validation method for datasets larger than 1.5GB, DataRobot builds and evaluates predictive models by partitioning datasets into three distinct sections: training, validation, and holdout. Predictions are based on a single pass over the data. - Pros: This method is faster than cross-validation because it only makes one pass on each dataset to score the data. - Cons: For the same reason that it is faster, it is also moderately less accurate. For projects larger than 1.5GB (non-time-aware only), the training partition percentage is not scaled down. The validation and holdout partitions are set to default sizes of 80MB and 100MB respectively and do not change unless you manually do so (both have a maximum size of 400MB). The validation and holdout percentages, therefore, scale down with a larger training partition. The percentage of the training partition is comprised of the remaining percentage after accounting for the validation and holdout percentages. For example, say you have a 900MB project. If the validation and holdout partitions are at the default sizes of 80MB and 100MB respectively, then the validation percentage will be 9% and the holdout percentage will be 11.1%. The training partition will comprise the remaining 720MB as a percentage: 80%. Note that for time-aware projects, the TVH method is not applicable. They instead use [date/time partitioning](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-adv-modeling/ts-date-time.html). ## Examples: partitioning methods The examples below provide an illustration of how different partitioning methods work in DataRobot non-time-aware projects. All examples describe a binary classification problem: predicting loan defaults. ### Random partitioning Rows for each partition are selected at [random](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/partitioning.html#random-partitioning-random), without taking target values into account. | State | Loan_purpose | Is_bad_loan (target) | Possible outcome (TVH) | Possible outcome (5-fold CV) | | --- | --- | --- | --- | --- | | AR | debt consolidation | 0 | Training | Fold 1 | | AZ | debt consolidation | 0 | Training | Fold 5 | | AZ | home improvement | 1 | Validation | Fold 4 | | AZ | credit card | 1 | Training | Fold 4 | | CO | credit card | 0 | Training | Fold 3 | | CO | home improvement | 0 | Training | Fold 2 | | CO | home improvement | 0 | Validation | Fold 1 | | CT | small business | 1 | Training | Holdout | | G​A | credit card | 0 | Training | Fold 3 | | ID | small business | 0 | Training | Fold 2 | | IL | small business | 0 | Training | Holdout | | IN | home improvement | 1 | Holdout | Fold 5 | | IN | debt consolidation | 1 | Holdout | Fold 3 | | KY | credit card | 0 | Training | Holdout | ### Stratified partitioning For [stratified partitioning](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/partitioning.html#ratio-preserved-partitioning-stratified), each partition (T, V, H, or each CV fold) has a similar proportion of positive and negative target examples, unlike the previous example with random partitioning. | State | Loan_purpose | Is_bad_loan (target) | Possible outcome (TVH) | Possible outcome (5-fold CV) | | --- | --- | --- | --- | --- | | AR | debt consolidation | 1 | Training | Fold 1 | | AZ | debt consolidation | 0 | Training | Fold 5 | | AZ | home improvement | 1 | Validation | Fold 4 | | AZ | credit card | 0 | Training | Fold 4 | | CO | credit card | 1 | Training | Fold 3 | | CO | home improvement | 0 | Training | Fold 2 | | CO | home improvement | 0 | Validation | Fold 1 | | CT | small business | 1 | Training | Holdout | | G​A | credit card | 0 | Training | Fold 3 | | ID | small business | 1 | Training | Fold 2 | | IL | small business | 0 | Training | Holdout | | IN | home improvement | 1 | Holdout | Fold 5 | | IN | debt consolidation | 1 | Training | Fold 3 | | KY | credit card | 0 | Holdout | Holdout | ### Group partitioning In this example of [group partitioning](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/partitioning.html#group-partitioning-group), State is used as the group column. Note how the rows for the same state always end up in the same partition. | State | Loan_purpose | Is_bad_loan (target) | Possible outcome (TVH) | Possible outcome (5-fold CV) | | --- | --- | --- | --- | --- | | AR | debt consolidation | 1 | Training | Fold 1 | | AZ | debt consolidation | 0 | Training | Fold 5 | | AZ | home improvement | 1 | Training | Fold 5 | | AZ | credit card | 0 | Training | Fold 5 | | CO | credit card | 1 | Validation | Fold 3 | | CO | home improvement | 0 | Validation | Fold 3 | | CO | home improvement | 0 | Validation | Fold 3 | | CT | small business | 1 | Training | Fold 1 | | G​A | credit card | 0 | Training | Fold 2 | | ID | small business | 1 | Training | Fold 2 | | IL | small business | 0 | Holdout | Holdout | | IN | home improvement | 1 | Holdout | Fold 4 | | IN | debt consolidation | 1 | Training | Fold 4 | | KY | credit card | 0 | Training | Holdout | ### Partition feature partitioning The [partition feature](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/partitioning.html#column-based-partitioning-partition-feature) method uses either TVH or CV. TVH: The three unique values of “My_partition_id” directly correspond to assigned partitions. | State | Loan_purpose | Is_bad_loan (target) | My_partition_id (partition feature) | Outcome | | --- | --- | --- | --- | --- | | AR | debt consolidation | 1 | my_train | Training | | AZ | debt consolidation | 0 | my_train | Training | | AZ | home improvement | 1 | my_train | Training | | AZ | credit card | 0 | my_val | Validation | | CO | credit card | 1 | my_val | Validation | | CO | home improvement | 0 | my_val | Validation | | CO | home improvement | 0 | my_train | Training | | CT | small business | 1 | my_train | Training | | G​A | credit card | 0 | my_train | Training | | ID | small business | 1 | my_train | Training | | IL | small business | 0 | my_holdout | Holdout | | IN | home improvement | 1 | my_holdout | Holdout | | IN | debt consolidation | 1 | HO | Holdout | | KY | credit card | 0 | HO | Holdout | CV: The seven unique values of My_partition_id directly correspond to seven created partitions. | State | Loan_purpose | Is_bad_loan (target) | My_partition_id (partition feature) | Outcome | | --- | --- | --- | --- | --- | | AR | debt consolidation | 1 | P1 | Fold 1 | | AZ | debt consolidation | 0 | P1 | Fold 1 | | AZ | home improvement | 1 | P2 | Fold 2 | | AZ | credit card | 0 | P2 | Fold 2 | | CO | credit card | 1 | P3 | Fold 3 | | CO | home improvement | 0 | P3 | Fold 3 | | CO | home improvement | 0 | P4 | Fold 4 | | CT | small business | 1 | P4 | Fold 4 | | G​A | credit card | 0 | P5 | Fold 5 | | ID | small business | 1 | P5 | Fold 5 | | IL | small business | 0 | P6 | Fold 6 | | IN | home improvement | 1 | P6 | Fold 6 | --- # Tune Eureqa models URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/advanced-options.html > Customize Eureqa models by modifying various Advanced Tuning parameters and creating custom target expressions. # Tune Eureqa models You can customize Eureqa models by modifying various Advanced Tuning parameters and creating custom target expressions. Parameters you can adjust for your models include [building blocks](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/advanced-options.html#building-blocks), [target expressions](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/advanced-options.html#target-expressions), [error metrics](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/advanced-options.html#error-metrics), [row weighting](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/advanced-options.html#row-weights), and [prior solutions](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/advanced-options.html#prior-solutions). Additionally, you can customize how DataRobot [partitions data for the Eureqa model](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/advanced-options.html#data-partitioning-for-training-and-cross-validation). ## Building blocks Eureqa model expressions use building blocks (discrete sets of mathematical functions) for combining variables and creating new features from a dataset. Building blocks range from simple arithmetic functions (addition, subtraction) to complex functions (logistic or gaussian) and more. DataRobot creates Eureqa models using default sets of building blocks for preset problem types; however, certain problems may require different sets of building blocks. Advanced users dealing with systems that already have known or expected behavior may want to encourage certain model structures in DataRobot. For example, if you think that seasonality or some other cyclical trend may be a factor in your data, including the building blocks sin(x) and cos(x) will let DataRobot know to test those types of interactions against the data. See [Configuring building blocks](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/building-blocks.html) for information on selecting building blocks for the target expression. ### Building block complexity Complexity settings are additional weights DataRobot can apply to specific building blocks and terms to penalize related aspects of a given model. Changing the complexity given to certain building blocks or terms will affect which models will appear on the pareto frontier (in the [Eureqa Modelstab](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/eureqa-classic.html)) with the focus on finding the simplest possible models that achieve increasing levels of accuracy. The default complexity settings typically work well; however, if you have prior knowledge of the system you are trying to model, you may want to modify those settings. If there are particular building blocks that you know will be, or expect to be, part of a solution that accurately captures the core dynamics of a system, you might lower the complexity values of those building blocks to make it more likely that they will appear in the related Eureqa models. Similarly, if there are building blocks that you don't want to appear unless they significantly improve the fit of the models, you might raise the complexity values of those building blocks. See [Setting building block complexity](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/building-blocks.html#building-block-complexity) for more information. ## Target expressions The target expression tells DataRobot how to create the Eureqa model. Target expressions are comprised of variables that exist in your dataset and mathematical "building blocks". DataRobot creates the default target expression for a model using the selected target variable modeled as a function of all input variables. Here's an example default target expression: [https://docs.datarobot.com/en/docs/images/eureqaref-log-targetexpression.png](https://docs.datarobot.com/en/docs/images/eureqaref-log-targetexpression.png) You can customize the expression (model formula) to specify the type of relationship you want to model and incorporate your domain expertise of the fundamental behavior of the system. Complex expressions are possible and give you the power to tune for complex relationships, including: differential equations, polynomial equations, and binary classification. See [Customizing target expressions](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/custom-expressions.html) for more information. ## Error metrics DataRobot uses error metrics to guide how the quality of potential solutions is assessed. Each Eureqa model has default error metrics settings; however, advanced users can choose to optimize for different error metrics. Changing the error metric will change how DataRobot optimizes the solutions. See [Configuring error metrics](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/error-metrics.html) for more information. ## Row weights You can designate one of your variables as an indicator of how much relative weight (i.e., importance) you want Eureqa to give to the data in each row. For example, if the designated row weight variable has a value of 10 in the first row and 20 in the second row, data in the second row will be given twice the weight of the data in the first row when Eureqa is calculating how well a model performs on the data. Row weight can be specified by using a row weight variable or by using a row weight expression. See [Configuring row weighting blocks](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/row-weighting.html) for more information. ## Prior solutions Prior solutions "seed" DataRobot with solutions or partial solutions that express relationships that you believe will play some role in an eventual solution. Entering prior solutions for a Eureqa model may speed search performance by initializing that model with known information. The Prior Solutions parameter, prior_solutions, is available within the Prediction Model Parameters and can be specified as part of tuning your Eureqa models. You can specify multiple expressions, one per line, where each expression is a valid target expression (such as from a previous Eureqa model). The following shows an example of two prior solutions (expressions), sin(x1 - x2) and sin(x2), set for a model: If you have entered a custom target expression that uses multiple functions ( [as explained here](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/custom-expressions.html#multiple-functions)), enter a sub-expression for each function. Each f() is listed with its sub-expression, separated by a comma, on the same line. For example, if the expression contains two functions, such as Target = f0(x) * f1(x), and the prior model is Target = (x-1) * sin(2 * x), you will enter the prior solution as: Target = (x - 1), f1 = sin(2 * x) To specify multiple expressions from prior models, enter each set of functions on a new line. You can enter expressions for only some of the functions that exist in the target expression; if this is the case, DataRobot will fill in '0' as the seed for other functions. For example, if you enter: f1 = sin(2 * x) DataRobot will translate this to: f0 = 0, f1 = sin(2 * x) ## Data partitioning for training and cross-validation DataRobot performs its standard process for data partitioning ( [as explained here](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html)) for each Eureqa model. Then, it further subdivides the training set data into two more sets: a Eureqa internal training set and a Eureqa internal validation set. The data for these Eureqa internal sets is derived from the original DataRobot training set. (The original DataRobot validation set is never used as part of the Eureqa data partitioning process.) DataRobot uses the Eureqa internal training set to drive the core Eureqa evolutionary algorithm, and uses both the Eureqa internal training and validation sets to select which models are the "best" and, therefore, selected for inclusion in the final Eureqa Pareto Front (within the [Eureqa Models tab](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/describe/eureqa-classic.html)). ### Random split A random split will randomly assign rows for Eureqa internal training and Eureqa internal validation. Rows (within the original training set) are split based on the Eureqa internal training and Eureqa internal validation percentages. If the training and validation percentages total more than 100%, the overlapping percentage of rows will be assigned to both training and validation. Random split with 50% of data for Eureqa internal training and 50% for Eureqa internal validation is recommended for most (non-Time Series) modeling problems. For very small datasets (e.g., under a few hundred points) it is usually best to use overlapping Eureqa internal training/Eureqa internal validation datasets. When the data is extremely small, or has very little or no noise, you may want to use 50% of the original DataRobot training data for Eureqa internal training and 100% for Eureqa validation. In extreme cases, you may want to include 100% of the data for both Eureqa internal training/Eureqa internal validation datasets, and then limit your model selection to those with lower complexities. For large datasets (e.g., over 1,000 points) it is usually best to use a smaller fraction of data for the Eureqa training set. It is recommended to choose a fraction such that the size of the Eureqa training data is approximately 10,000 rows or less. Then, use all remaining data for the Eureqa validation set. ### In-order split An in-order split maintains the original order of the input data (i.e., the original DataRobot training set) and selects a percentage of rows, starting with the first row, to use for the Eureqa internal training set and a different percentage of rows, starting with the last row, to use for the Eureqa internal validation set. If the training and validation percentages total more than 100%, the overlapping percentage of rows will be assigned to both the Eureqa internal training and internal Eureqa validation sets. This option can be used if you have pre-arranged your data with rows you want to use for the Eureqa internal training set at the beginning of the dataset and rows you want to use for the Eureqa internal validation set at the end. In-order split is applied by default when performing data partitioning for Time Series and OTV models, as explained [here](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/advanced-options.html#eureqa-data-partitioning). ### Split by variable Split by variable allows you to manually indicate which rows to use for training and which to use for validation using variables that have been pre-defined in your project dataset. Rows are selected if the indicator variable has a value greater than 0. By default, the Eureqa internal training rows will be selected as the inverse of the Eureqa internal validation rows, unless a separate indicator is provided for training rows. You may include a validation data variable and/or a training data variable in your data before uploading it to Eureqa, or use Eureqa to create a derived variable that will be used to split the data. ### Split by expression Split by expression allows you to manually identify which rows to use for Eureqa internal training and which rows to use for Eureqa internal validation using expressions entered as part of the target expression. Rows are selected if the expression has a value greater than 0. By default, the Eureqa internal training set rows will be selected as the inverse of the Eureqa internal validation set rows, unless a separate expression is provided for training rows. ### Eureqa data partitioning You can modify the default Data Partitioning settings using the training_fraction and validation_fraction parameters. To adjust how DataRobot splits the data for the model, modify the split_mode parameter. Finally, to direct DataRobot to create the Eureqa internal training and internal validation sets based on custom expressions (rather than the default settings, explained previously), add those expressions to the training_split _expr and/or validation_split _expr parameters, as applicable. The default Eureqa data partitioning process (to create the Eureqa internal training and internal validation sets) differs between non-Time Series and Time Series models: - For non-Time Series models: DataRobot performs a 50/50 random split of the shuffled training set data and then uses the first half as the Eureqa internal training set and the second half as the Eureqa internal validation set (wheresplit_mode = 1, for random). - For datetime-partitioned models (i.e., models created as either Time Series Modeling and Out-of-Time Validation (OTV): DataRobot performs a 70/30 in-order split of the chronologically sorted training set data and then uses the first 70% as the Eureqa internal training set and the second 30% as the Eureqa internal validation set (wheresplit_mode = 2, for in-order). > [!TIP] Tip > If you selected random partitioning when you started your project (using Advanced options), it is strongly recommended that you do not select in-order split mode when tuning Eureqa models. ### Data Partitioning for cross-validation When performing cross-validation for Eureqa models, DataRobot uses only the first CV split for training; therefore, only that training data (from the first CV split) is split further into Eureqa internal training data and Eureqa internal validation data. ## Advanced tuning Advanced tuning allows you to manually set Eureqa model parameters, overriding the DataRobot defaults. Through advanced tuning you can control how gridsearch searches when multiple Eureqa hyperparameters are available for selection. Search types are `brute force` and `smart search`, as described in the general modeling [Advanced Tuning](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/adv-tuning.html#set-the-search-type) search type section. --- # Configure building blocks URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/building-blocks.html > Combine and configure building blocks to create a new Target Expression for Eureqa models. # Configure building blocks Building blocks are components of Eureqa models. As part of tuning a Eureqa model, you can combine and configure building blocks to create a new Target Expression for that model. For example, if you think that seasonality or some other cyclical trend may be a factor in your data you can use building block configurations to create target expressions that account for those types of trends. Building blocks provide mathematical functions and operators, including: - Constants - Variables - Operators (addition, subtraction, multiplication, division) - Functions (sin(), sqrt(), logistic(), etc.) ## How to choose building blocks The default building blocks have been selected based on extensive testing and are a good place to start. If you do decide to change which building blocks are selected, expert knowledge will help you. Which of the building blocks are typically used in your domain? Which ones are found in solutions to problems related to yours? Which ones are suggested by graphs of your data? Which ones just seem like good candidates based on your intuition (expert or otherwise)? ## Disable building blocks If there are building blocks you know you don't want DataRobot to include in model building, you can set them as Disabled. Although reducing the number of building blocks will speed up your search and may increase the likelihood that DataRobot will find an exact solution, disabling too many building blocks could preclude the discovery of an exact solution if a necessary operation is disabled. The DataRobot model building engine is extremely fast and can perform iterations quickly; we recommend additional iterations when trying to decide between actions. As needed, consult with your domain expertise when adjusting the building block settings to better reflect your use case and help DataRobot find models that provide better explaining power. ## Building block complexity Each building block is assigned a default complexity value which factors into the overall complexity of any model containing that block. The complexity weights indicate to DataRobot the relative importance of model interpretability vs. accuracy. If two different models have the same accuracy, but one uses building blocks with higher complexity values, DataRobot will favor the less complex model. If you know that some building blocks are more common within your specific problem type (such as power laws) than the default value would suggest, you can manually set the building block complexity value to a lower value. This value is also the “complexity penalty” for a building block. A building block complexity weight (or penalty) of 0 means the model can use that building block as needed without being penalized. Take care when setting building block complexity: models that use building blocks repeatedly and unnecessarily can become “cluttered”. To calculate model complexity, DataRobot will iterate over every building block in the model's target expression, read their individual complexity weights, and calculate the total complexity weight sum for that model. To set the complexity weight for a building block complexity: - Make sure the block is not set to Disabled . - Type the numeric value to use as the complexity weight. Typical complexity values are from 0-4. You should assign low complexity weights (such as 0) for blocks that can be used repeatedly during model processing without penalty, and assign higher complexity weights to blocks that should be used infrequently. ### Example of complexity metrics Assuming the following building block complexities: - Input variable: 1 - Addition: 1 - Multiplication: 0 | Expression | Complexity | | --- | --- | | a + b | 3 | | a * b | 2 | ## Available building blocks For information about all building blocks available to Eureqa models, including definitions and usage, refer to the [Building blocks reference page](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/eureqa-reference.html). To access further information about building block configuration, click the Documentation link provided in any Eureqa model blueprint (found in the upper right corner of any building block under the Advanced Tuning tab). --- # Custom target expressions URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/custom-expressions.html > Describes the many ways to customize target expressions for Eureqa models. # Custom target expressions Customizing target expressions provides one way to custom tune Eureqa models. Expressions may be any nested combination of [Eureqa model building blocks](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/building-blocks.html). For example, if a, b, and c are input variables, example expressions might include: - Target = 10 * a + b * c - Target = if( a > 10, b, c) + 15 ## What is the target expression? The target expression tells DataRobot what type of model to create. By default, the target expression is an equation that models your target variable as a function of all input variables. The target expression must be created in the form"Target = "regardless of the actual target variable name. For example, to create a target expression for target variable loan_is_bad (or default_rate, Sales, purchase_price, and so forth) you use the format"Target = f(...)". DataRobot automatically fills out the target expression for the selected Eureqa model type, using the defined target variable and input variables defined in the dataset. For a given target variable Target and 1 to n input variables of x, the default target expression for each of the search templates is: - Numeric search template: Target = f(x1 ... xn) - Classification search template: Target = logistic(f0() + f2() * f1(x1 ... xn)) - Exponential search template: Target = exp(f0(x1 ... xn)) + f1() More complex expressions are also possible and give advanced users the power to specify and search for complex relationships, including the modeling of polynomial equations, and binary classification. The Target Expression parameter, target_expression_string, is available within the Prediction Model Parameters and can be modified as part of tuning Eureqa models. ### Exponential search template When DataRobot detects an exponential trend in the dataset for a Eureqa model, it applies the exp() function. As part of this process, DataRobot automatically takes the log() of all input variables, manipulates the transformed variables to get the final target value, and then uses exp() to invert the log transform. The exp() building block is Disabled by default. If you are customizing the target expression in a model in a project whose data has an exponential trend, you may want to enable exp() for the model so that DataRobot will consider it during model building. > [!TIP] Tip > For Eureqa GAM models only: If you enable exp() support, you will want to select exponential as the variable for the EUREQA_target_expression_format [parameter](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/custom-expressions.html#constrain-the-target-expression-format-gam-only). ## Example expressions The following are some examples of basic and advanced expressions you could create as target expressions. The examples below assume the dataset contains four variables named: w, x, y, and z. ### Basic examples Model the Target variable as a function of variable x: Target = f(x) Model the Target variable as a function of two variables x and z: Target = f(x, z) Model the Target variable as a function of x and an expression, sin(z): Target = f(x, sin(z)) > [!NOTE] Note > As shown in this example, including sin(z) and not z means DataRobot has access to the data in variable z only after it passes through the sine function. ### Multiple functions To incorporate multiple functions into the target expression, use numbered functions starting with f0(). For example: Target = f0(x) + f1(w, z) Model the Target variable as a function of x, w, and the power law relationship: Target = f(x, w, x^f1(), w^f2()) Find a mechanism change with two known models: Target = if(x > f1(),exp(f2() * x), exp(f3() * x)) ### Constrain the target expression format (GAM only) For GAM models, you can enable the parameter EUREQA_target_expression_format if you want to constrain the expression format for the model. By default, there are no constraints to the expression format. - exponential constrains the target expression to an exponential format, similar to the following: Target = exp(f(...)) For example, if the default target expression would have been: Target = f(var1, var2, var3), the same target expression constrained to an exponential format would be: Target = exp(f(var1, var2, var3)). - feature_interaction constrains the target expression to contain 2-way interactions detected between features as functions. This ensures the feature interaction is declared explicitly. For example, if the model detects interaction of features x and y , the expression will be: Target = n + f(x, y) (where n identifies other features of the dataset) ### Fit coefficients You can represent an unknown constant or coefficient as a function with no arguments, f(). You can use multiple, no-argument functions, such as f1() to fit the coefficients of arbitrary nonlinear equations. For example, if you are looking for a polynomial of the form: Target = a * x + b * x^2 + c * x^3 use the following target expression: Target = f0() * x + f1() * x^2 + f2() * x^3 ### Nested functions Model the Target variable as the output of a recursive or iterated function to a depth of 3: Target = f(f(f(x))) ## Binary classification If y is a binary variable filled with 0s and 1s, model it using a squashing function, such as the logistic function. Using DataRobot for classification has a few advantages: - Finding models requires less data - Models can often extrapolate extremely well - Resulting models are simple to analyze, refit, and reuse - The structure of the models gives insight into the classification problem, allowing you to both predict as well as learn something about how the classification works ### Basic binary classification Model the Target variable as a binary function of x and w: Target = logistic(f(x, w)) Keep in mind that the logistic function will produce intermediate values between 0 and 1, such as 0.77 and 0.0001; therefore, you will need to threshold the value to get final 0 or 1 outputs. ## Model constraints You can also use the target expression to constrain the model output. You can include require and/or contains functions in target expressions to force specific model building or output behaviors. > [!TIP] Tip > Be aware that require and contains are very advanced, "experimental" settings that make it harder for DataRobot to find solutions and may significantly slow model search. To use these settings, we strongly suggest that you contact DataRobot for assistance as output behavior cannot be guaranteed. ### Add variables or terms If you need to force a certain variable or term to appear in Eureqa models, add a term that nests require or contains functions. Model y as a function of x, with all projects required to contain an x^2 term: Target = f(x) + 0 * require(contains(f(x),x^2)) For this to work, the first term of the contains operator must exactly match the functional term you are trying to fit (f(x) in this case). By multiplying the second term by 0, you guarantee that it won't impact the value produced by a particular solution f(x). ### Add a constraint You can also enforce a constraint on the model output if there are certain realities that need to be followed (e.g., price > cost). To do this, add a term with the require function. Model y as a function of x, with all solutions required to output values greater than 0: Target = f(x) + require( f(x) > 0 ) The following is a faster alternative: Target = max( f(x), 0 ) ### Force a condition There may be known relationships in your data that do not fully explain the data. For example, if you have a model that was generated based on academic theory, you can use DataRobot to fit the residual between that known model and the actual data. Model Target as a function of x, using existing knowledge of an x^2 relationship to model the residual: Target = f0(a, b, c, d, e) + f1() * x^2 DataRobot will interpret f1() as a coefficient and fit the term appropriately. --- # Error metrics URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/error-metrics.html > Eureqa error metrics measure how well a Eureqa model fits your data; DataRobot supports a variety of different error metrics for Eureqa models. # Error metrics Eureqa error metrics are measures of how well a Eureqa model fits your data. When DataRobot performs Eureqa model tuning, it searches for models that optimized error and complexity. The error metric that best defines a well-fit model will depend on the nature of the data and the objectives of the modeling exercise. DataRobot supports a variety of different error metrics for Eureqa models. Error metric selection and configuration determines how DataRobot will assess the quality of potential solutions. DataRobot sets default error metrics for each model, but advanced users can choose to optimize for different error metrics. Changing error metrics settings changes how DataRobot optimizes its solutions. The Error Metric parameter, error_metric, is available within the Prediction Model Parameters and can be modified as part of tuning your Eureqa models. Available error metrics are listed for selection. ## DataRobot versus Eureqa > [!NOTE] Note > There is some overlap in the DataRobot [optimization metrics](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html) and Eureqa error metrics. You may notice, however, that in some cases the metric formulas are expressed differently. For example, predictions may be expressed as `y^` versus `f(x)`. Both are correct, with the nuance being that `y^` often indicates a prediction generally, regardless of how you got there, while `f(x)` indicates a function that may represent an underlying equation. DataRobot provides the [optimization metrics](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html) for setting error metrics at the project level. The optimization metric is used to evaluate the error values shown on the Leaderboard entry for all models (including Eureqa), to compare different models, and to sort the Leaderboard. By contrast, the Eureqa error metric specifically governs how the Eureqa algorithm is optimized for the related solution and is not a project-level setting. When configuring these metrics, keep in mind they are fully independent and, in general, setting either metric does not influence the other metric. ## Choose a Eureqa error metric The best error metric for your problem will depend on your data and the objectives of your modeling analysis. For many problems there isn't one correct answer; therefore, DataRobot recommends running models with several different error metrics to see what types of models will be produced and which results best align with the modeling objectives. When choosing an error metric, consider these [suggestions for setting and configuring error metrics](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/guidance.html). ## Error metric parameters ### Mean Absolute The mean of the absolute value of the residuals. A smaller value indicates a lower error. Details: Mean Absolute Error is calculated as: How it's used: To minimize the residual errors. Mean Absolute Error is a good general purpose error metric, similar to Mean Squared Error but more tolerant of outliers. It is a common measure of forecast error in time series analyses. The value of this metric can be interpreted as the average distance predictions are from the actual values. Considerations: - Assumes the noise follows a double exponential distribution. - Compared to Mean Squared Error, Mean Absolute Error tends to be more permissive of outliers and can be a good choice if outliers are being given too much weight when optimizing for MSE. - Can be interpreted as the average error between predictions and actuals of the model. ### Mean Absolute Percentage The mean of the absolute percentage error. A smaller value indicates a lower error. Note that rows for which the actual value of the target variable = 0 are excluded from the error calculation. Details: Mean Absolute Percentage Error is calculated as: How it's used: To minimize the absolute percentage error. MAPE is a common measure of forecast error in time series forecasting analyses. The value of this metric can be interpreted as the average percentage that predictions vary from the actual values. Considerations: - Mean Absolute Percentage Error will be undefined when the actual value is zero. Eureqa’s calculation excludes rows for which the actual value is zero. - Mean Absolute Percentage Error is extremely sensitive to very small actual values - high percentage errors on small values may dominate the error metric calculation. - Can be interpreted as the average percentage that predicted values vary from actual values. - Mean Absolute Percentage Error may bias a model to underestimate actual values. ### Mean Squared The mean of the squared value of the residuals. A smaller value indicates a lower error. Details: Mean Squared Error is calculated as: How it's used: to minimize the squared residual errors. Mean Squared Error is the most common error metric. It emphasizes the extreme errors, so it's more useful if you have concerns about large errors with greater consequences. Considerations: - It assumes that the noise follows a normal distribution. - It's tolerant of small deviations, and sensitive to outliers. - For classification problems, logistic models optimized for Mean Squared Error produce values which can be interpreted as predicted probabilities. - Optimizing for Mean Squared Error is equivalent to optimizing for R^2. ### Root Mean Squared The mean of the squared value of the residuals. A smaller value indicates a lower error. Details: Root Mean Squared Error is calculated as: How it's used: to minimize the squared residual errors. Root Mean Squared Error is used similarly to Mean Squared Error. Root Mean Squared Error de-emphasizes extreme errors as compared to Mean Squared Error, so it is less likely to be swayed by outliers but more likely to favor models that have many records that do a little better and a few that do a lot worse. Considerations: - It assumes that the noise follows a normal distribution. ### R^2 Goodness of Fit (R^2) The percentage of variance in the target variable that can be explained by the model. A higher value indicates a better fit. Details: R^2 is calculated as: to give the fraction of variance explained. This value is multiplied by 100 to give the percentage of variance explained. SStot is proportional to the total variance, and SSres is the residual sum of squares (proportional to the unexplained variance). How it's used: to maximize the explained variance. It is the equivalent to optimizing Mean Squared Error, except the numbers are reported as a percentage. It is a good default error metric. Like Mean Squared Error, R^2 penalizes large errors more than small error, so it's useful if you have concerns about large errors with greater consequences. Considerations: - It assumes that the noise follows a normal distribution. - It has the same interpretation regardless of the scale of your data. - When R^2 is negative, it suggests that the model is not picking up a signal, is too simple, or is not useful for prediction. - Optimizing for R^2 is equivalent to optimizing for Mean Squared Error. - R^2 is closely related to the square of the correlation coefficient. ### Correlation Coefficient Measures how closely predictions made by the model and the actual target values follow a linear relationship. A higher value indicates a stronger positive correlation, with 0 representing no correlation, 1 representing a perfect positive correlation, and -1 representing a perfect negative correlation. Details: Correlation Coefficient is measured as: Where sf(x) and sy are the uncorrected sample standard deviations of the model and target variable. How it's used: to maximize normalized covariance. A commonly used error metric for feature exploration, to find patterns that explain the shape of the data. It's faster to optimize than other metrics because it does not require models to discover the scale and offset of the data. Considerations: - It ignores the magnitude and the offset of errors. This means that a model optimized for correlation coefficient alone will try to fit the same shape as the target variable, however actual predicted values may not be close to the actual values. - It is always on a [-1, 1] scale regardless of the scale of your data. - 1 is a perfect positive correlation, 0 is no correlation, and -1 is a perfect inverse correlation. ### Maximum Absolute The value of the largest absolute error. Smaller values are better. Details: Maximum Absolute Error is computed as: How it's used: to minimize the largest error. It is used when you only care about the single highest error, and when you are looking for an exact fit, such as symbolic simplification. It would typically be used when there is no noise in the data (e.g., processed, simulated, or generated data). Considerations: - The whole model's evaluation depends on a single data point. - It is best when the input data has little to no noise. ### Mean Logarithm Squared The mean of the logs of the squared residuals. A smaller value indicates a lower error. Details: Mean Logarithm Squared Error is computed as: where log is the natural log. How it's used: to minimize the squashed log error. It decreases the effect of large errors, which can help to decrease the role of outliers in shaping your model. Considerations: - It assumes diminishing marginal disutility of error with error size. ### Median Absolute The median of the residual values. A smaller value indicates lower error. Details: Median Absolute Error is calculated as: > [!NOTE] Note > For performance reasons, Eureqa models use an estimated (rather then exact) median value. For small datasets, the estimated median value may differ significantly from the actual value. How it's used: to minimize the median error value. If you expect your residuals to have a very skewed distribution, it is best at handling high noise and outliers. Considerations: - The scale of errors on either side of the median will have no effect. - It is the most robust error metric to outliers. - The estimated median may be inaccurate for very small datasets. ### Interquartile Mean Absolute The mean of the absolute error within the interquartile range (the middle 50%) of the residual errors. A smaller value indicates lower error. Details: The Interquartile Mean Absolute Error is calculated by taking the Mean Absolute Error of the middle 50% of residuals. How it's used: to minimize the error of the middle 50% error values. It is used when the target variable may contain large outliers, or when you care most about "on average" performance. It is similar to the median error in that it is very robust to outliers. Considerations: - It ignores the smallest and largest errors. ### AIC Absolute The Akaike Information Criterion (AIC) based on the Mean Absolute Error. It is a combination of the normalized Mean Absolute Error and a penalty based on the number of model parameters. A lower value indicates a better quality model. Unlike other error metrics, AIC may be a negative value, approaching negative infinity, for increasingly accurate models. Details: AIC Absolute Error is calculated as: where log is the natural logarithm, sy is the standard deviation of the target variable, MAE is the mean of the absolute value of the residuals: and k is the number of parameters in the model (including terms with a coefficient of 1). How it's used: to minimize the residual error and number of parameters. It penalizes complexity by including the number of parameters in the loss function. This metric can be useful if you want to directly limit and penalize the number of parameters in a model, for example when modeling physical systems or for other problems where solutions with fewer free parameters are preferred. It is similar to the AIC-Mean Squared Error metric but is more tolerant to outliers. Considerations: - Searches using this metric may produce fewer solutions and limit the number of very complex solutions. - Optimizing for this metric ensures you only get AIC-optimal models. - Assumes the noise follows a double exponential distribution. ### AIC Squared The Akaike Information Criterion (AIC). It is a combination of the normalized Mean Squared Error and a penalty based on the number of model parameters. A lower value indicates a better quality model. Unlike other error metrics, the value may be negative, approaching negative infinity, for increasingly accurate models. Details: AIC Squared Error is calculated as: where log is the natural logarithm, sy is the standard deviation of the target variable, MSE is the mean of the squared value of the residuals: and k is the number of parameters in the model (including terms with a coefficient of 1). How it's used: to minimize the squared residual error and number of parameters. It penalizes complexity by including number of parameters in the loss function. This metric can be useful if you want to directly limit and penalize the number of parameters in a model, for example when modeling physical systems or for other problems where solutions with fewer free parameters are preferred. Considerations: - Searches using this metric may produce fewer solutions and limit the number of very complex solutions. - Optimizing for this metric ensures you only get AIC-optimal models. - It assumes the residuals have a normal distribution. ### Rank Correlation (Rank-r) The Correlation Coefficient between the ranks of the predicted values and the actual values. A higher value indicates a stronger positive correlation, with 0 representing no correlation and 1 representing a perfect rank correlation. Details: The rank correlation is calculated as the correlation coefficient between the ranks of predicted values and ranks of actual values, meanings it optimizes models whose outputs can be used for ranking things. Ranks are computed by sorting each in ascending order and assigning incrementing values to each row based on its sorted position. How it's used: to maximize the correlation between predicted and actual rank. Use for problems where it is important that a model be able to predict a relative ordering between points, but where the actual predicted values are not important. Considerations: - The variables must be ordinal, interval, or ratio. - A rank correlation of 1 indicates that there is a monotonic relationship between the two variables. - It is always on a [-1,1] scale, regardless of the scale of your data. - If ranges for the actual and prediction values are vastly different, the resulting plots may not appear to display as expected. For example, prediction values of (29, 30, 32, 584, 9999, 10000) - or even (100001, 100002, 100003, 100004, 100005, 100006) - and actual values of (1, 2, 3, 4, 5, 6) have the same rank order of the predictions and actuals and so will result in a zero error. But, because the range of predictions vary wildly, they may not be visible on a plot of the actuals. ### Hinge Loss The mean “hinge loss” for classification predictions. Hinge loss increasingly penalizes wrong predictions as they get more confident but treats true predictions identically after they reach a minimum threshold value. A smaller value indicates lower error. Details: Hinge Loss Error is computed as: where, for this metric, binary classes typically represented as 0 and 1 are re-scaled to be -1 and 1 respectively: and With this calculation, when the prediction f(xi) and actual yi have the same sign (a correct prediction) and the predicted value >= 1, the error is considered 0, while when they have a different sign (an incorrect prediction), the error increases linearly with f(xi). How it's used: to minimize classification error. Hinge Loss Error is a one-sided metric that increasingly penalizes wrong predictions as they get more confident, but treats all true predictions identically after they reach a minimum threshold value. Considerations: - When using for classification problems, the target expression should not include the logistic() function. This error metric expects that predicted values will have a larger range. - It assumes a threshold value of 0.5 when turning a predicted score into a 0 or 1 prediction. ### Slope Absolute The mean absolute error of the predicted row-to-row deltas and the actual row-to-row deltas. A smaller value indicates a lower error. Details: Slope Absolute Error is computed as: For each side of the model equation (predicted values and actual values), this metric computes row-to-row "deltas" between the value of each row and its previous row. The Slope Absolute Error is the Mean Absolute Error between the actual deltas and the predicted deltas. How it's used: to minimize the error of the deltas. It is used for time series analysis where you are trying to predict the change from one time period to the next. Considerations: - It is an experimental (non-standard) error metric. - It fits the shape of a dataset; actual predicted values may not be in the same range as the actual values. --- # Building blocks reference URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/eureqa-reference.html > Provides the definitions and usage of all building blocks available to Eureqa models. # Building blocks reference This page provides the definitions and usage of all building blocks available to Eureqa models. To access further information about building block configuration, click the Documentation link provided in any Eureqa model blueprint (found in the upper right corner of any building block under the Advanced Tuning tab). ## Arithmetic | Building Block | Definition | Usage | | --- | --- | --- | | Addition | Returns the sum of x and y. | x+y or add(x,y) | | Constant | c is a real valued constant. | c | | Division | Returns the quotient of x and y. y must be non-zero. | x/y or div(x,y) | | Input Variable | x is a variable in your prepared dataset. | x | | Integer Constant | c is an integer constant. | c | | Multiplication | Returns the product of x and y. | x*y or mul(x,y) | | Negation | Returns the inverse of x. | -x | | Subtraction | Returns the difference of x and y. | x-y or sub(x,y) | ## Exponential | Building Block | Definition | Usage | | --- | --- | --- | | Exponential | Returns e^x. | exp(x) | | Factorial | Returns the product of all positive integers from 1 to x. | factorial(x) or x! | | Natural Logarithm | Returns the natural logarithm (base e) of x. | log(x) | | Power | Returns x raised to the power of y. x and y can be any expression. | x^y or pow(x,y) | | Square Root | Returns the square root of x. x must be positive. | sqrt(x) | ## Squashing functions Squashing functions take a continuous input variable and map it to a constrained output range. Recommended use: Depending on the shape of the particular squashing function, that function may be useful in identifying transition points in the data, and/or limiting the total impact of a particular term. | Building Block | Definition | Usage | | --- | --- | --- | | Complementary Error Function | 1.0 - erf(x) where erf(x) is the integral of the normal distribution. Returns a value between 2 and 0. | erfc(x) | | Error Function | Integral of the normal distribution. Returns a value between -1 and +1. | erf(x) | | Gaussian Function | Returns exp(-x^2). This is a bell-shaped squashing function. | gauss(x) | | Hyperbolic Tangent | The hyperbolic tangent of x. Hyperbolic tangent is a common squashing function that returns a value between -1 and +1. | tanh(x) | | Logistic Function | Returns 1/(1+exp(-x)). This is a common sigmoid (s-shaped) squashing function that returns a value between 0 and 1. | logistic(x) | | Step Function | Returns 1 if x is positive, 0 otherwise. | step(x) | | Sign | Returns -1 if x is negative, +1 if x is positive, and 0 if x is zero. | sgn(x) | ## Comparison/Boolean functions | Building Block | Definition | Usage | | --- | --- | --- | | Equal To | Returns 1 if x is numerically equal to y, 0 otherwise. | equal(x,y) or x=y | | Greater Than | Returns 1 if x>y, 0 otherwise. | greater(x,y) or x>y | | Greater Than or Equal To | Returns 1 if x>=y, 0 otherwise. | greater_or_equal(x,y) or x>=y | | If-Then-Else | Returns y if x is greater than 0, z otherwise. If x is nan, the function returns z. | if(x,y,z) | | Less Than | Returns 1 if x0) or (x>0 and y<=0), 0 otherwise. | xor(x,y) | | Logical Not | Returns 0 if x is greater than 0, 1 otherwise. | not( x ) | | Logical Or | Returns 1 if either x or y are greater than 0, 0 otherwise. | or(x,y) | ## Trigonometric functions Trigonometric functions are functions of an angle; they relate the angles of a triangle to the length of its sides. Recommended use: When modeling data from physical systems containing angles as inputs. | Building Block | Definition | Usage | | --- | --- | --- | | Cosine | The standard trigonometric cosine function. The angle (x) is in radians. | cos(x) | | Hyperbolic Cosine | The standard trigonometric hyperbolic cosine function. | cosh(x) | | Hyperbolic Sine | The standard trigonometric hyperbolic sine function. | sinh(x) | | Sine | The standard trigonometric sine function. The angle (x) is in radians. | sin(x) | | Tangent | The standard trigonometric tangent function. The angle (x) is in radians. | tan(x) | ## Inverse trigonometric functions | Building Block | Definition | Usage | | --- | --- | --- | | Arccosine | The standard trigonometric arccosine function. | acos(x) | | Arcsine | The standard trigonometric arcsine function. | asin(x) | | Arctangent | The standard trigonometric arctangent function. | atan(x) | | Inverse Hyperbolic Cosine | The standard inverse hyperbolic cosine function. | acosh(x) | | Inverse Hyperbolic Sine | The standard inverse hyperbolic sine function. | asinh(x) | | Inverse Hyperbolic Tangent | The standard inverse hyperbolic tangent function. | atanh(x) | | Two-Argument Arctangent | The standard trigonometric two-argument arctangent function. | atan2(y,x) | ## Other functions | Building Block | Definition | Usage | | --- | --- | --- | | Absolute Value | Returns the positive value of x, without regard for its sign. | abs(x) | | Ceiling | Returns the smallest integer not less than x. | ceil(x) | | Floor | Returns the largest integer not greater than x. | floor(x) | | Maximum | Returns the maximum (signed) result of x and y. | max(x,y) | | Minimum | Returns the minimum (signed) result of x and y. | min(x,y) | | Modulo | Returns the remainder of x/y. | mod(x,y) | | Round | Returns an integer of x rounded to the nearest integer. | round(x) | --- # Error metric guidance URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/guidance.html > Identifies the top Eureqa model error metrics for different types of problems. # Error metric guidance The good news is that there are common error metrics that work well on a large majority of problem types. Starting with one of these error metrics is usually a safe bet. This section identifies the top error metrics for different types of problems. ## Numeric and time series models ### Mean Absolute Error - By minimizing the absolute residual error, rather than squared residual error, Mean Absolute Error is also a good general-purpose error metric, but is more permissive to outliers than Mean Squared Error and R^2 Goodness of Fit Error. This can be a good choice if outliers in the data are likely to be due to noise, or if capturing an overall trend is more important than avoiding a few large errors. Mean Absolute Error can also be interpreted as "on average, predictions are off by this amount." ### Mean Absolute Percentage Error - Mean Absolute Percentage Error is a common error metric for time series forecasting. It can be interpreted as the average absolute percentage by which predicted values deviate from the actuals. It can be a good choice when relative errors are more important than absolute error values. Mean Absolute Percentage Error may not be a good choice if there are very small actual values in the dataset since small errors on these rows may dominate the metric calculation; any rows where the actual value is 0 are not included in the error metric calculation. ### R^2 Goodness of Fit Error (R^2) - R^2 is a standard measure of model fitness. It can be interpreted as the “percent of variance explained”. As a percentage, the R^2 can be compared across models and datasets since the scale is not dependent on the scale of the data. > [!NOTE] Note > R^2 Goodness of Fit Error is the Eureqa default for numeric and time series models. ### Mean Squared Error - Mean Squared Error is a common error metric. Optimizing for Mean Squared Error will be equivalent to optimizing for R^2; however, Mean Squared Error values will depend on the scale of the data. Because they depend on squared error, both Mean Squared Error and R^2 tend to be sensitive to outliers and a good choice when there is strong incentive to avoid individual large errors. ## Classification models ### Mean Squared Error for classification - Mean Squared Error for Classification is the default error metric for classification problems in Eureqa. This metric optimizes Mean Squared Error but with internal optimizations for classification problems. The output values of logistic models that have been optimized for Mean Squared Error can be interpreted as the probability of a 1 outcome. Mean Squared Error may not be the best error metric when trying to classify rare events since it attempts to minimize overall error rather than separation between positive and negative cases. ### Area Under ROC Curve Error (AUC) - AUC is a common error metric for classification and works by optimizing the ability of a model to separate the 1s from the 0s. AUC is not sensitive to the relative number of 0s and 1s in the target variable and can be a good choice for skewed classes. When optimized for AUC, predicted values will effectively order inputs from the most to least likely to be 1; however, they cannot be interpreted as a predicted probability. ## Error metrics and noise One consideration for choosing an error metric is the expected amount of noise in the data. Different error metrics effectively make different assumptions about the distribution of the noise in the observed output. For example, for very noisy systems you might select an error metric that would give relatively less weight to some large errors (e.g., Mean Absolute Error, IQR Error, Median Error) under the assumption that these large errors may be due to noise in the input data rather than poor model fit. On the contrary, when input data is expected to have very low noise you might select an error metric which heavily penalizes large errors (e.g., R^2 or Maximum Error). ### Noisy systems #### Mean Logarithm Squared Error - Mean Logarithm Squared Error uses the log function to squash error values and decrease the impact of large errors. #### Interquartile Mean Absolute Error (IQME) - By ignoring the smallest 25% and largest 75% of error values, IQME will not be impacted by a significant number of outliers and may work well if you are most interested in “on average” performance. #### Median Absolute Error - By ignoring all residual values except for the median, Median Absolute Error is the most permissive of outliers. ### Low-noise systems #### Maximum Absolute Error - By ignoring all but the maximum error value, Maximum Absolute Error can work well if you are expecting a perfect or nearly perfect fit; for example, if you are using the Eureqa model for symbolic simplification. ## Error Metrics for classification In addition to the common classification error metrics outlined above, the error metrics described in this section are specific to classification problems. ### Additional error metrics for classification #### Log Loss Error - Log Loss Error is a common metric for classification problems. The log transformation on the errors heavily penalizes a high confidence in wrong predictions. #### Maximum Classification Accuracy - Maximum Classification Accuracy optimizes the overall ability of a model to make correct 0 or 1 predictions. It may not work well for skewed classes (e.g., when only 1% of the data is ‘1’), since in these cases sometimes the highest predictive accuracy is achieved by simply predicting 0 all of the time. #### Hinge Loss Error - Hinge Loss Error is used to optimize classification models that will be used for 0 or 1 predictions. It is a one-sided metric that increasingly penalizes wrong predictions as they get more confident, but treats all true predictions identically after they reach a minimum threshold value. When optimizing Hinge Loss Error, logistic() ( building_block__logistic_function ) should not be used in the target expression since this metric expects a large range of predicted score values. ## Use case-specific error metrics ### Predicting rank Rank Correlation will measure a model based on its ability to rank-order observations rather than to predict a particular value. This can be useful when looking for a model that can predict a relative ranking, such as the finishing order of contestants in a race. --- # Eureqa advanced tuning URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/index.html > Eureqa models use expressions to represent mathematical relationships and transformations. DataRobot provides specialized workflows for tuning Eureqa models. # Eureqa advanced tuning Eureqa models use expressions to represent mathematical relationships and transformations. You can tune your Eureqa models by modifying building blocks, customizing the target expression, and modifying other model parameters, such as support for building blocks, error metrics, row weighting, and data splitting. To customize a Eureqa model, select the model from the Leaderboard and then click Evaluate > Advanced Tuning. The following sections detail specialized workflows for tuning Eureqa models: | Topic | Description | | --- | --- | | Tune Eureqa models | Customize Eureqa models by modifying Advanced Tuning parameters. | | Configure building blocks | Combine and configure building blocks to create a new target expression. | | Building blocks reference | Definitions and usage of building blocks available to Eureqa models. | | Customize target expressions | Custom tune Eureqa models by modifying the target expression. | | Configure error metrics | Optimize for different error metrics. | | Guidance for using error metrics | Understand the top error metrics for different problem types. | | Configure row weighting | Improve model performance with weighting. | --- # Row weighting blocks URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/row-weighting.html > Use the Row Weighting parameter as part of tuning Eureqa models. # Row weighting blocks You can configure row weight to help improve performance for your models. The Row Weighting parameter, weight_expr, is available within the Prediction Model Parameters and can be modified as part of tuning Eureqa models. > [!NOTE] Note > This Eureqa model-level row weight is separate from the DataRobot project-level row weighting (as set from [Advanced options](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html)). If set, the DataRobot project-level row weight affects how DataRobot calculates the validation score for the Eureqa models (e.g., performance on out-of-sample data) but has no effect on how these models are optimized. ## When to use a row weight The following are some common scenarios for which using a row weight may help to improve performance: - Suppose for each data point you have a confidence value that you determined while collecting the data or computed in some other program. Create a variable (i.e., a column) containing those confidence values and designate it as the row weight variable. DataRobot will weight the data accordingly, giving more weight to those values with higher confidence. - Suppose you want to give extra weight to a few important data points. You could give those points more weight by adding a new column to your data before you upload it to DataRobot. This new variable should label important rows with 10, 100, or 1000 (or some other weight) and set the remaining rows to 1. - Suppose you want to balance your data by giving more weight to rare events than to common ones. More specifically, suppose you want to model credit card fraud, and 99.99% of the data points are legitimate transactions while 0.01% are fraudulent. You could create a variable whose value is 1 in rows representing legitimate transactions and 9999 (i.e., 99.99% / 0.01%) in rows representing fraud, thereby creating equal pressure to model both legitimate and fraudulent cases. ## Row weight variable Include a row weight variable in your dataset before it is uploaded to DataRobot to reference it as a row weight variable during model creation. Then, when tuning the model, type the name of that variable as the row weight variable. This tells DataRobot to weight the rest of the data in each row in proportion to the value of the row weight variable in that row. ## Row weight expression Some row weighting schemes can be more easily achieved with a row weight expression than with a row weight variable. When defined, DataRobot will evaluate that row weight expression using the values in that row, and then weight the row with the result. ### 1 / occurrences (variable_name) This expression provides a quick way to balance data. To illustrate, let's imagine a toy dataset containing just three values of one variable: | x | 1 / occurrences (x) | | --- | --- | | 99 | 0.5 | | 99 | 0.5 | | 86 | 1 | The value returned by occurrences(x) is the number of times a particular value of x occurs in the dataset; in this case, it would return 2 in the first row, 2 in the second row, and 1 in the third row. Selecting 1/occurrences(x) as your row weight would therefore give the first row a weight of 1/2, the second row a weight of 1/2, and the third row a weight of 1. Returning to the credit card fraud example [(shown above)](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/row-weighting.html#when-to-use-a-row-weight), you could create variable z with a value of 0 in rows representing legitimate transactions and 1 in rows representing fraudulent ones. Selecting a row weight of 1/occurrences(x) would then automatically create equal pressure to model legitimate and fraudulent transactions. If new data is added, weights are automatically adjusted to maintain the balance. The special variable takes on the value of the row number. Using this as the row weight will give the first row a weight of 1, the second row a weight of 2, and so on. Row weighting can improve results for sparse datasets in which the target behavior only happens very rarely (such as for fraud and failures). Using this row weighting expression will help isolate and highlight those sparse signals in the data. ## Other row weight expressions Aside from the special row weighting variable, the best option for creating a custom row weight is to derive a new variable (feature), use a custom expression to populate the column automatically with the desired row weights, and use that new derived variable as the row weight variable directly. For information on deriving a new variable, see the documentation for [feature transformations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html). The following example expressions assume the dataset contains variables x and y: - abs( x ) gives row weights in proportion to the absolute value of x. - 1 / abs( x-y ) gives row weights in inverse proportion to the difference between x and y. - 1 / gives row 1 a weight of 1, row 2 a weight of 1/2, row 3 a weight of 1/3, ... - 0.5 + 0.5 * ( <= 100 ) gives row 1 through 100 a weight of 1 and the remaining rows a weight of 0.5. (Note that <= returns 1 if satisfied, 0 if unsatisfied.) --- # Export charts and data URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/export-results.html > DataRobot exports charts as PNG images and data as CSV files; use the export function to download this data. # Export charts and data Many of the DataRobot charts have images and data available for download. (You can also download all charts and data for a particular model using the Leaderboard [Downloads](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/predictions/download-classic.html) tab.) DataRobot exports charts as PNG images and data as CSV files. For charts that provide both, you can export images and data to a single ZIP file. Note that the PNG image DataRobot exports is generated from the chart as it appears in the application at the time of export. That is, it reflects any interaction you had with the chart. The table in the exported CSV file contains the data that was used to generate the chart you see at time of export. ### Export to a file To export chart data to a file, follow these steps: 1. Find the chart you want to export on the Leaderboard’s expanded model view or inInsightsand clickExport. 2. In theExportdialog box, select the content to export (not all options are available for all screens): 3. In some charts, such as the ROC Curve, you can select a single chart to export. Use theDownloadoption to export all charts in the insight. 4. Enter a file name or accept the default file name and clickDownload. ## Copy to the clipboard In addition to saving charts as PNG or CSV files, you can copy the chart image or data to the clipboard to paste into a document. Note that, in the instructions below, the text in the pop-up menus for copying may differ, depending on your browser. ### Copy charts To copy a chart to the clipboard, follow these steps: 1. ClickExporton the chart. 2. Select.pngto display the chart in the dialog. 3. Right-click on the image, and selectCopy Imagefrom the pop-up menu. 4. ClickCloseto close the dialog, then paste the image to the desired location. ### Copy CSV data To copy the CSV data of a chart to the clipboard, follow these steps: 1. ClickExporton the chart. 2. Select.csvto display the chart in the dialog. 3. Highlight some or all of the text, right-click on it, and selectCopyfrom the pop-up menu. 4. ClickCancelto close the dialog, then paste the file contents to the desired location. --- # Feature Associations URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/feature-associate.html > A deeper dive into the concepts related to Feature Associations and how it's calculated. # Feature Associations The sections below include a general discussion about associations, understanding the mutual information and Cramer's V metrics, and how associations are calculated. ## What are associations? There is a lot of terminology to describe a feature pair's relationship to each other—feature associations, mutual dependence, levels of co-occurrence, and correlations (although technically this is somewhat different) to name the more common examples. The Feature Association insight is a tool to help visualize the association, both through a wide angle lens (the full matrix) and more close up (both matrix zoom and feature association pair details). Looking at the matrix, each dot tells you, "If I know the value of one of these features, how accurate will my guess be as to the value of the other?" The metric value puts a numeric value on that answer. The closer the metric value is to 0, the more independent the features are of each other. Knowing one doesn't tell you much about the other. A score of 1, on the other hand, says that if you know X, you know Y. Intermediate values indicate a pattern, but aren't completely reliable. The closer they are to "perfect mutual information" or 1, the higher their metric score and the darker their representation on the matrix. ## More about metrics The metric score is responsible for ordering and positioning of clusters and features in the matrix and the detail pane. You can select either the Mutual Information (the default) or Cramer's V metric. These metrics are well-documented on the internet: - A technical overview of Mutual Information on Wikipedia . - A longer discussion of Mutual Information on Scholarpedia , with examples. - A technical overview of Cramer's V on Wikipedia . - A Cramer's V tutorial of "what and why." Both metrics measure dependence between features and selection is largely dependent on preference and familiarity. Keep in mind that Cramer's V is more sensitive and, as such, when features depend weakly on each other it reports associations that Mutual Information may not. ## How associations are calculated When calculating associations, DataRobot selects the top 50 numeric and categorical features (or all features if fewer than 50). "Top" is defined as those features with the highest importance score, the value that represents a feature's association with the target. Data from those features is then randomly subsampled to a maximum of 10k rows. Note the following: - For associations, DataRobot performs quantile binning of numerical features and does no data imputation. Missing values are grouped as a new bin. - Outlying values are excluded from correlational analysis. - For clustering, features below an association threshold of 0.1 are eliminated. - If all features are relatively independent of each other—no distinct families—DataRobot displays the matrix but all dots are white. - Features missing over 90% of their values are excluded from calculations. - High-cardinality categorical features with more than 2000 values are excluded from calculations. --- # GA2M output (from Rating Tables) URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ga2m.html > Overview and detailed explanations of the output for Generalized Additive Model (GA2M) models, available for download from the Rating Tables tab. # GA2M output (from Rating Tables) The following section helps to understand the output for Generalized Additive Model (GA2M) models. This output is available as a download from the [Rating Tables](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/rating-tables.html) tab. ## Read model output When examining the output, note the following: - Pairwise interactions found by the GA2M model have the following characteristics: - Feature Strengthdescribes the strength of each feature and pairwise interaction. Interaction strength is marginal and doesn't include the main effects strength. The Feature Strength is equal to the weighted average of the absolute value of the centered coefficients. - Transform1andValue1describe the preprocessing of the first variable in the pair;Transform2andValue2describe the preprocessing of the second variable in the pair. The coefficient applies to the product of the two values derived from the preprocessing of the two variables. - Weightis the sum of observations for each row of the table. If the project is using a weight variable, theWeightcolumn is the sum of weights. This can be used to quantify the (weighted) number of observations in the training data that correspond to each bin of numeric feature, each level of categorical feature, or each cell of pairwise interaction. The following is a sample excerpt from Generalized Additive Model output: In the sample table, the Intercept, Base, Loss distribution, and Link function parameters describe the model in general and not any particular feature. Each row in the table describes a feature and the transformations DataRobot applies to it. To compute the predictions, you can use either the `Coefficient` column or the `Relativity` column. Use the `Coefficient` column if you want the prediction to have the same precision as DataRobot predictions. For example, assume CRIM value equals 0.9 and LSTAT equals 8. Using the Coefficient column, read the sample as follows: | For... | Coefficient value | From line... | | --- | --- | --- | | Intercept | 3.080070 | 1 | | Coefficient for CRIM=0.9 | -0.005546 | 12 (bin includes CRIM values 0.60079503 to inf) | | Coefficient for LSTAT=8 | 0.257544 | 14 (bin includes LSTAT values -inf to 9.72500038) | | Get Coefficient for CRIM=0.9 and LSTAT=8 | 0.122927 | 20 (bin for Value1, CRIM, equal to 0.9 and Value2, LSTAT equal to 8) | Prediction = exp(3.08006971649 -0.00554623809222501 + 0.257543518013598 + 0.122926708231993) = 31.658089382684512 Using the Relativity column, read the sample as follows: | For... | Relativity value | From line... | | --- | --- | --- | | Base | 21.7599 | 2 | | Relativity for CRIM=0.9 | -0.9945 | 12 (bin includes CRIM values 0.60079503 to inf) | | Coefficient for LSTAT=8 | 1.2937 | 14 (bin includes LSTAT values -inf to 9.72500038) | | Get Coefficient for CRIM=0.9 and LSTAT=8 | 1.1308 | 20 (bin for Value1, CRIM, equal to 0.9 and Value2, LSTAT equal to 8) | Prediction = 21.7599193685 * 0.994469113891232 * 1.29374811110316 * 1.13080153946617 = 31.65808938265751 If the main model uses a [two-stage modeling process](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#two-stage-models) (Frequency-Severity Generalized Additive Model, for example), two additional columns— `Frequency_Coefficient` and `Severity_Coefficient` —provide the coefficients of each stage. ## Allowed pairwise interactions in GA2M You can choose to control which pairwise interactions are included in GA2M output (available in the [Rating Tables](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/rating-tables.html) tab) instead of using every interaction or none of them. This allows you to specify which interactions are permitted to interact during the training of a GA2M model in cases where there are certain features that are not permitted to interact due to regulatory constraints. > [!NOTE] Note > Specified pairwise interactions are not guaranteed to appear in a model's output. Only the interactions that add signal to a model according to the algorithm will be featured in the output. For example, if you specify an interaction group of features A, B, and C, then AxB, BxC, and AxC are the interactions considered during model training. If only AxB adds signal to the model, then only AxB is included in the model's output (excluding BxC and AxC). Use the [Feature Constraints](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/feature-con.html#pairwise-interactions) advanced option to specify the allowed pairwise interactions for a model. ## Define transformations for GA2M The following sections describe the routines DataRobot uses to reproduce predictions from a GAM. ### One-hot encoding Name: One-hot Value: string, or `Missing value`, or `Other categories` Value example: 'MA'Value example: Missing value One-Hot (or dummy-variable) transformation of categorical features: - Ifvalueis a string then derived feature will contain 1.0 whenever the original feature equalsvalue. - If value of the original feature is missing then "Binning" transformation with "Missing value" is equal to 1.0. - Ifvalueis "Other categories" then derived feature will contain 1.0 when the original feature doesn't match any of the above. ### Dummy encoding Name: Dummy Value: string Value example: 'MA' Derived feature will contain 1.0 whenever the original feature equals `value`. ### 1-Dummy encoding Name: 1-Dummy Value: string Value example: 'NOT MA' Derived feature will contain 1.0 whenever the original feature is different from `value` without the 4 characters 'NOT '. ### Binning Name: Binning Value: (a, b], or `Missing value` Value example: (-inf, 12.5]Value example: (12.5, 25]Value example: (25, inf) Value example: Missing value Transform numerical variables into non-uniform bins. The boundary of each bin is defined by the two numbers specified in `value`. Derived feature will equal to 1.0 if the original value `x` is within given interval: ``` a < x <= b ``` If `value` of the original feature is missing, then "Binning" transformation with "Missing value" is equal to 1.0. --- # Predictive AI URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/index.html > Provides a variety of reference material for model building, model insights, and specialized workflows. # Predictive AI reference The following sections provide reference content that supports working with predictive and time-aware experiments: | Topic | Description | | --- | --- | | Data partitioning | Describes validation types and data partitioning methods. | | Feature lists | Shows details of working with DataRobot-generated and custom feature lists, as well as where in the platform you can create and manage them. | | Modeling algorithms | Lists supervised and unsupervised modeling algorithms supported by DataRobot. | | Modeling process | Describes modeling modes, two-stage models, and data summary information. | | Model recommendation process | Describes the steps involved in DataRobot's selection of a recommended model. | | Leaderboard reference | Provides a reference table of the badges that display in the Leaderboard and the Blueprint repository, model icons, and other Leaderboard indicators. | | Optimization metrics | Briefly describes all metrics available for model building. | | SHAP reference | Provides details of SHapley Additive exPlanations, the coalitional game theory framework. | | Feature Associations | Explains about associations, understanding the mutual information and Cramer's V metrics, and how associations are calculated. | | Insurance-specific settings | Describes Exposure, Count of events, and Offset options, configured in advanced settings. | | Sliced insights | Describes sliced insights where you can view and compare insights based on segments of a project’s data. | | Bias and Fairness reference | Provides an overview of the methods used to calculate fairness and to identify biases in the model's predictive behavior. | | GA2M output | Describes and helps to understand the output for Generalized Additive Model (GA2M) models, available as a download from the Rating Tables tab. | | Time series reference | Provides reference material explaining the DataRobot framework for implementing time series modeling and see a variety of deep-dive reference material for DataRobot time series modeling. | | Eureqa advanced tuning | Describes how to modify building blocks, customize the target expression, and modify other model parameters for Eureqa models. | | Composable ML reference | Provides information on blueprints in the AI Catalog, model metadata, feature considerations, and a sentiment analysis example. | | Visual AI reference | Provides workflow and reference materials for including images as part of your DataRobot experiments. | | Export charts and data | Explains about downloading created insights. | | Worker queue (NextGen) | Helps to understand modeling workers and how to troubleshoot issues in NextGen. | | Worker queue (Classic) | Helps to understand modeling workers and how to troubleshoot issues in Classic. | | XEMP qualitative strength | Describes the calculations used to determine XEMP qualitative strength. | | AI Report (Classic only) | Describes how to create a report of modeling results and insights. | --- # Insurance settings URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/insurance-settings.html > Provides reference content for features that address frequent weighting needs of the insurance industry. # Insurance settings The following sections describe the weighting features available in advanced experiment setup. These settings are typically used by the insurance industry. Experiments built using the offset, exposure, and/or count of events parameters produce the same DataRobot insights as projects that do not. However, DataRobot excludes offset, exposure, and count of events columns from the predictive set. That is, the selected columns are not part of the [Coefficients](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/coefficients.html), [Individual Prediction Explanations](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/shap-predex.html), or [Feature Impact](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/feature-impact.html) visualizations; they are treated as special columns throughout the experiment. While the exposure, offset, and count of events columns do not appear in these displays as features, their values have been used in training. ## Exposure In regression problems, Exposure can be used to weight features in order to handle observations that are not of equal duration. It's commonly used in insurance use cases to introduce a measure of period duration. For example, in a use case where each row represents a policy-year, a policy that was applicable for half of the year will have an Exposure parameter of 0.5. DataRobot handles a feature selected for exposure as a special column, adding it to raw predictions when building or scoring a model. The selected column(s) must be present in any dataset later uploaded for predictions. Only [optimization metrics](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html#optimization-metric) with the log link function (Poisson, Gamma, or Tweedie deviance) can make use of exposure values in modeling. For these optimization metrics, DataRobot log transforms the value of the field you specify as an exposure (you do not need to do it). If you select otherwise, DataRobot returns an informative message. See [below](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/insurance-settings.html#offset-and-exposure-in-modeling) for more training and prediction application details. ## Count of events The Count of events parameter improves modeling of a zero-inflated target by adding information on the frequency of non-zero events. Frequency x Severity (two-stage) models handle it as a special column. The frequency stage uses the column to model the frequency of non-zero events. The severity stage normalizes the severity of non-zero events in the column and uses that value as the target. This improves interpretability of frequency and severity coefficients. The column is not used for making predictions on new data. The Count of events parameter is used in two-stage models—that is, Frequency-Severity and Frequency-Cost blueprints. Stages for each are described below. Frequency-Severity models 1. Model the frequency of events usingCount of eventsas the target. 2. Model the severity of non-zero events, where the target is the normalized target column (target divided byCount of events), and theCount of eventscolumn is used as the weight. Frequency-Cost 1. Model the frequency of events usingCount of eventsas the target. 2. Model the severity of events using the original target and predictions from stage 1 as an offset. The first stage of both these two stage models, Frequency, is always a poisson regression model. If you supply a count feature, that value is the stage one target. Otherwise, DataRobot creates a 0/1 target. ## Offset In regression and binary classification problems, the Offset parameter sets feature(s) that should be treated as a fixed component for modeling (coefficient of 1 in generalized linear models or gradient boosting machine models). Offsets are often used to incorporate pricing constraints or to boost existing models. DataRobot handles a feature selected for offset as a special column, adding it to raw predictions when building or scoring a model; the selected column(s) must be present in any dataset later uploaded for predictions. - Forregressionproblems, if theoptimization metricis Poisson, Gamma, or Tweedie deviance, DataRobot uses the log link function, in which case offsets should be log transformed in advance. Otherwise, DataRobot uses the identity link function and no transformation is needed for offsets. - Forbinary classificationproblems, DataRobot uses the logit link function, in which case offsets should be logit transformed in advance. See [below](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/insurance-settings.html#offset-and-exposure-in-modeling) for more training and prediction application details. ## Offset and exposure in modeling During training, offset and exposure are incorporated into modeling using the following logic: | Project metric | Modeling logic | | --- | --- | | RMSE | Y-offset ~ X | | Poisson/Tweedie/Gamma/RMSLE | ln(Y/Exposure) - offset ~ X | When making predictions, the following logic is applied: | Project metric | Prediction calculation logic | | --- | --- | | RMSE | model(X) + offset | | Poisson/Tweedie/Gamma/RMSLE | exp(model(X) + offset) * exposure | --- # Leaderboard reference URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/leaderboard-ref.html > A reference for the tags, icons, columns, and other aspects of the DataRobot model Leaderboard # Leaderboard reference The Leaderboard provides a wealth of summary information for each model built in a project. When models complete, DataRobot lists them on the Leaderboard with scoring and build information. The text below a model provides a brief description of the model type and version or whether it uses unaltered open-source code. Badges, tags, and columns, described below, provide quick model identifying and scoring information. ## Tags and indicators The following table describes the tags and indicators: | Display/name | Description | NextGen | Classic | | --- | --- | --- | --- | | Baseline / Time series baseline | Applicable to time series projects only. Indicates a baseline model built using the MASE metric. _Time series baseline in NextGen. | | ✔ | | Beta | Indicates a model from which you can export the coefficients and transformation parameters necessary to verify steps and make predictions outside of DataRobot. Blueprints that require complex preprocessing will not have the Beta tag because you can't export their preprocessing in a simple form (ridit transform for numerics, for example). Also note that when a blueprint has coefficients but is not marked with the Beta tag, it indicates that the coefficients are not exact (e.g., they may be rounded). | | ✔ | | Bias mitigation | Indicates that the model had bias mitigation techniques applied. The badge is added to the top three Autopilot Leaderboard models that DataRobot automatically attempted to mitigate bias for and any models to which mitigation techniques were manually applied. | | ✔ | | BPxx Blueprint ID* | Displays a blueprint ID that represents an instance of a single model type (including version) and feature list. Models that share these characteristics within the same project have the same blueprint ID regardless of the sample size used to build them. Use the model ID to differentiate models when the blueprint ID is the same. Blender models indicate the blueprints used to create them (for example, BP6+17+20). | ✔ | ✔ | | Exportable coefficients | Indicates a model from which you can export the coefficients and transformation parameters necessary to verify steps and make predictions outside of DataRobot. Blueprints that require complex preprocessing will not have the Beta tag because you can't export their preprocessing in a simple form (ridit transform for numerics, for example). Also note that when a blueprint has coefficients but is not marked with the Beta tag, it indicates that the coefficients are not exact (e.g., they may be rounded). | | ✔ | | External predictions | Indicates that a model is available to run a subset of DataRobot's evaluative insights for comparison against DataRobot models. | ✔ | | | Frozen run / Frozen parameters | Indicates that the model was produced using the frozen run feature. The badge also indicates the sample percent of the original model. | ✔ | ✔ | | GPU | Indicates that the model can be or was trained on GPU workers. | ✔ | | | Insights | Indicates that the model appears on the Insights page. | | ✔ | | Mxx Model ID* | Displays a unique ID for each model on the Leaderboard. The model ID represents a single instance of a model type, feature list, and sample size within a single project. Use the model ID to differentiate models when the blueprint ID is the same. | ✔ | ✔ | | Monotonic constraints | Indicates that the model supports monotonic constraints; available in the blueprint repository only. | ✔ | ✔ | | New series optimized / New series support | Indicates a model that supports unseen series modeling (new series support). | ✔ | ✔ | | Prepared for deployment | Indicates that the model has been through the Autopilot recommendation stages and is ready for deployment. | ✔ | ✔ | | Rating tables | Indicates that the model has rating tables available for download. | | ✔ | | Recommended for deployment | Indicates that this is the model DataRobot recommends for deployment, based on model accuracy and complexity. | | ✔ | | Ref / Reference | Indicates that the model is a reference model. A reference model uses no special preprocessing; it is a basic model that you can use to measure performance increase provided by an advanced model. | ✔ | ✔ | | Scoring code | Indicates that the model has Scoring Code available for download. | ✔ | ✔ | | Segment champion | Indicates that the model is the chosen segment champion in a multiseries segmented modeling project. | | ✔ | | SHAP | In Classic, indicates that the model was built with SHAP-based Prediction Explanations. If no badge, the model provides XEMP-based explanations. In Nextgen, because all models use SHAP, no badge is applied. | | ✔ | | Tuned | Indicates that the model has been tuned. | ✔ | ✔ | | Upper-bound running time | Indicates that the model exceeded the Upper Bound Running Time. | | ✔ | | User-defined | Indicates that the model is a user-created, pretrained models that was uploaded to DataRobot, as a collection of files, via the Registry workshop. | | | * You cannot rely on blueprint or model IDs to be the same across projects. Model IDs represent the order in which the models were added to the queue when built; because different projects can have different models or a different order of models, these numbers can differ across projects. Similarly for blueprint IDs where blueprint IDs can be different based on different generated blueprints. If you want to check matching blueprints across projects, check the blueprint diagram—if the diagrams match, the blueprints are the same. See also information on the [model recommendation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html) calculations. ## Model icons In addition to the tags, DataRobot displays a badge (icon) to the left of the model name indicating the type: - : specially tuned DataRobot implementation of a model - : blender model - : Eureqa model - : Keras model - : Light Gradient Boosting Machine model - : Python model - : R model - : Spark model - : TensorFlow model - : XGBoost model Text below the model provides a brief description of the model type and version, or whether it uses unaltered open source code. ### Model type and performance Some models sacrifice prediction speed to improve prediction accuracy. These models are best suited to batch predictions ( [one-time](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred.html) or [recurring](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html)), where prediction time and reliability aren't critical factors. Some use cases require a model to make low-latency (or real-time) predictions. For these performance-sensitive use cases, it is best to avoid deploying the following model types as they prioritize accuracy over prediction speed and prediction memory usage: - Keras models - Blender models (or ensemble models) - Advanced tuned models - Models generated using Comprehensive Autopilot mode ## Columns and tools Leaderboard columns give you at-a-glance information about a model's "specs": The following table describes the Leaderboard columns and tools: | Column | Description | | --- | --- | | Model Name and Description | Provides the model name (type) as well as identifiers and description. | | Feature List | Lists the name of the Feature List used to create the model. Click the Feature List label to get a count of the number of features in the list. | | Sample Size | Displays the sample size used to create the model. Click the Sample Size label to see the number of rows the sample size represents, or set the display to only selected sample sizes. By default, DataRobot displays all sample sizes run for a project. When a project includes an External predictions model, sample size displays N/A. | | Validation | Displays the Validation score of the model. This is the score derived from the first cross-validation fold. Some scores may be marked with an asterisk, indicating in-sample predictions. | | Cross-Validation | Displays the Cross-Validation score, if run. If the dataset is greater than 50,000 rows, DataRobot does not automatically start a cross-validation run. You can click the Run link to run cross-validation manually. Some scores may be marked with an asterisk, indicating in-sample predictions. If the dataset is larger than 1.5GB, cross-validation is not allowed. | | Holdout | Displays a lock icon that indicates whether holdout is unlocked for the model. When unlocked, some scores may be marked with an asterisk, indicating use of in-sample predictions to derive the score. | | Metric | Sets (and displays the selection of) an accuracy metric for the Leaderboard. Models display in order of their scoring (best to worst) for the metric chosen before the model building process. Click the orange arrow to access a dropdown that allows you to change the optimization metric. | | Menu | Provides quick access to comparing models, adding and deleting models, and creating blender models. | | Search | Searches for a model, as described below. | | Add New Model | Adds a model based on specific criteria that you set from the dialog. | | Filter | Filters by a variety of selection criteria. Alternatively, click a Leaderboard tag to filter by the selected tag. | | Export | Allows you to download the Leaderboard's contents as a CSV file, as described below. | ### Tag and filter models The Leaderboard offers filtering capabilities to make viewing and focusing on relevant models easier. - Tag or "star" one or more models on the Leaderboard, making it easier to refer back to them when navigating through the application. To star a model, hold the pointer over it and a star appears, which you can then click to select: To unselect the model, click again on the star. - Use theFiltersoption to only display models meeting the criteria you select. - Combine any of the filters withsearch filtering. First, search for a model type or blueprint number, for example, and then selectFiltersto find only those models of that type meeting the additional criteria. ### Use Leaderboard filters Use the Filters selection box to modify the Leaderboard display to match only those models matching the selected criteria. Available fields, and the settings for that field, are dependent on the project and/or model type. For example, non-date/time models offer sample size filtering while time-aware models offer training period: > [!NOTE] Note > Filters are inclusive. That is, results show models that match any of the filters, not all filters. Also, options available for selection only include those in which at least one model matching the criteria is on the Leaderboard. The following table describes all available Leaderboard filters. | Tag | Filters on | | --- | --- | | Model importance | Models that are manually marked with a star on the Leaderboard. | | Sample size | Selected sample size or N/A for External predictions models. Non time-aware only. | | Training period | Time periods, either duration or start/end date. Time-aware only. | | Feature list | Any feature list, manually or automatically created, that was used in at least one of the project's models. | | Model family | Models grouped by tasks, an extended functionality of the model icon badge. | | Model characteristics | Displayed model badges. | | Blueprint ID | All models that have the same ID—representing an instance of a single model type (including version). | | Model ID | A single, unique ID for a model on the Leaderboard. | | Build method | The method that added models to the Leaderboard. Autopilot: Models created using full, Quick, or Comprehensive Autopilot.Repository: Models added manually from the Repository.Composable ML: Custom models built using the blueprint editor.Advanced Tuning: Manually tuned models. Eureqa child: Manually added to the Leaderboard via Eureqa solutions. | #### Model characteristics options The following list includes the model characteristics available to search on. See the [table above](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/leaderboard-ref.html#tags-and-indicators) for brief descriptions or the linked pages for complete details. - Additional insights - Augmented - Baseline - Bias mitigation - Time-excluded - Deprecated - Exportable coefficients - External predictions - Frozen - Monotonic constraints - New series optimized - Rating table - Reference model - Scoring code - SHAP ### Use Repository filters The Filters option is also available from the model [Repository](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/repository.html) page: The following table describes all available Repository filters. | Tag | Filters on... | | --- | --- | | Blueprint characteristics | Blueprints based on the functionality they support. Options are Reference, Monotonic, Baseline, External Predictions, and SHAP. | | Blueprint family | The mathematical technique or algorithm the blueprint uses. | | Blueprint type | Blueprint origin, either DataRobot, Eureqa, or Composable ML. | | Blueprint ID | Models that have the same ID—representing an instance of a single model type (including version) and feature list. | ### Search the Leaderboard In addition to the Filter method, the Leaderboard provides a method to further limit the display to only those models matching your search criteria. ### Export the Leaderboard The Leaderboard allows you to download its contents as a CSV file. To do so, click the Export button on the action bar: Doing so prompts a preview screen: This screen displays the Leaderboard contents (1), which you can copy, and lets you rename the .csv file (2). Note that: - .csv is the only available file type for exporting the Leaderboard. - Holdout scores are only included in the report if holdout has been unlocked. Click Download to export the contents. ## Blender models A blender (or ensemble) model can increase accuracy by combining the predictions of between two and eight models. Use the [Create blenders from top models](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html) advanced option to enable DataRobot to add blenders to the Leaderboard automatically. If you did not select the Create blenders from top models option prior to model building, you can manually [create blender models](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/creating-addl-models.html#create-a-blended-model) when Autopilot has finished. To improve response times for blender models, DataRobot stores predictions for all models trained at the highest sample size used by Autopilot (typically 64%) and creates blenders from those results. Storing only the largest sample size (and therefore predictions from the best performing models) limits the disk space required. DataRobot has special logic in place for natural language processing (NLP) and image fine-tuner models. For example, fine-tuners do not support [stacked predictions](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#what-are-stacked-predictions). As a result, when blending stacked and non-stack-enabled models, the available blender methods are: AVG, MED, MIN, or MAX. DataRobot does not support other methods in this case because they may introduce target leakage. ## Asterisked scores > [!NOTE] Availability information > Asterisked partitions do not apply to time series or multiseries projects. Sometimes, the Leaderboard's Validation, Cross-Validation, or Holdout score displays an asterisk. Hover over the score for a tooltip explaining the reason for the asterisk: > [!NOTE] Note > The following training set percentage values are examples based on the default [data partitioning](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html) settings recommended by DataRobot (without downsampling). The default data partitions are [5-fold CV](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#k-fold-cross-validation-cv) with 20% holdout or, for larger datasets, [TVH](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#training-validation-and-holdout-tvh) 16% validation and 20% holdout. If you customize the data partitioning settings, the thresholds for training into validation change. For example, if you select a 10-fold CV with 20% holdout, your maximum training set sample size will be 72%, not 64%. By default, DataRobot uses up to 64% of the data for the training set. This is the largest sample size that does not include any data from the validation or holdout sets (16% of the data is reserved for the validation set and 20% for the holdout set). When model building finishes, you can manually train at larger sample sizes (for example, 80% or 100%). If you train above 64%, but under 80%, the model trains on data from the validation set. If you train above 80%, the model trains on data from the holdout set. As a result, if you train above 64%, DataRobot marks the Validation score with an asterisk to indicate that some in-sample predictions were used for that score. If you train above 80%, the Holdout score is also asterisked to indicate the use of in-sample predictions to derive the score. ## N/A scores Sometimes, the Leaderboard's Validation, Cross-Validation, or Holdout score displays N/A instead of a score. "Not available" scores occur if your project trains models into the validation or holdout sets and meets any of the following criteria: - The dataset exceeds 1.5GB resulting in a slim run project containing models that do not have stacked predictions . - The project is date/time partitioned (both OTV and time series), and all models do not have stacked predictions. - The project is multiclass with greater than ten classes. - The project uses Eureqa modeling , as Eureqa models do not have stacked predictions. --- # Modeling algorithms URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html > Provides a list of the supervised and unsupervised modeling algorithms DataRobot supports. # Modeling algorithms DataRobot supports a comprehensive library of pre- and post-processing (modeling) steps, which combine to make up the model [blueprint](https://docs.datarobot.com/en/docs/api/reference/public-api/blueprints.html). Which are run or available in the [model repository](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/repository.html) is dependent on the dataset. The comprehensive combination of pre- and post-processing steps allows DataRobot to confidently create a Leaderboard of your best modeling options. Some examples of the modeling flexibility include logistic regression with and without PCA as a pre-processor or random forests with and without a greedy search for interaction terms. The implication of this is that for every model in the list below, DataRobot likely runs two-to-five times, each with a different pre-processing and/or variable selection. The following sections list the relevant algorithms: - Pre-processing - Linear or additive models - Tree-based models - Deep learning and foundational models - Time series-specific models - Unsupervised models - Other model types ## Pre-processing tasks #### Categorical - Buhlman credibility estimates for high cardinality features - Categorical embedding - Category count - One-hot encoding - Ordinal encoding of categorical variables - Univariate credibility estimates with L2 - Efficient, sparse one-hot encoding for extremely high cardinality categorical variables #### Numerical - Binning of numerical variables - Constant splines - Missing values imputed - Numeric data cleansing - Partial Principal Components Analysis - Truncated Singular Values Decomposition - Normalizer #### Geospatial - Geospatial Location Converter - Spatial Neighborhood Featurizer #### Images - Greyscale Downscaled Image Featurizer - No Post Processing - OpenCV detect largest rectangle - OpenCV image featurizer - Pre-trained multi-level global average pooling image featurizer #### Text models - Character / word n-grams - Pretrained byte-pair encoders (best of both words for char-grams and n-grams) - Stopword removal - TF-IDF scaling (optional sublinear scaling and binormal separation scaling) - Hashing vectorizers for big data - Cosine similarity between pairs of text columns (on datasets with 2+ text columns) - Support for all languages, including English, Japanese, Chinese, Korean, French, Spanish, Chinese, Portuguese, Arabic, Ukrainian, Klingon, Elvish, Esperanto, etc. - Unsupervised Fasttext models - Linear n-gram models (character/word n-grams + TF-IDF + penalized linear/logistic regression) - SVD n-gram models (n-grams + TF-IDF + SVD) - Naive Bayes weighted SVM - TinyBERT / Roberta/ MiniLM embedding models - Text CNNs #### Generalized Linear Models - NA imputation (methods for missing at random and missing not at random), standardization, ridit transform - Search for best transformations - Efficient, sparse one-hot encoding for extremely high cardinality categorical variables ## Linear or additive models #### Generalized Linear Models - Penalty: L1 (Lasso), L2 (Ridge), ElasticNet, None (Logistic Regression) - Distributions: Binomial, Gaussian, Poisson, Tweedie, Gamma, Huber - Special Cases: 2-stage model (Binomial + Gaussian) for zero-inflated regression #### Support Vector Machines - Penalty: L1 (Lasso), L2 (Ridge), ElasticNet, None - Kernel: Linear, Nyström RFB, RBF - liblinear and libsvm #### Generalized Additive Models - GAM - GA2M ## Tree-based models - Decision Tree (or CART) - Random Forest - ExtraTrees (or Extremely Randomized Forests) - Gradient Boosted Trees (or GBM— Binomial, Gaussian, Poisson, Tweedie, Gamma, Huber) - Extreme Gradient Boosted Trees (or XGBoost— Binomial, Gaussian, Poisson) - LightGBM - AdaBoost - RuleFit ## Deep learning and foundational models - Keras MLPs with residual connections, adaptive learning rates and adaptive batch sizes - Keras self-normalizing MLPs with residual connections - Keras neural architecture search MLPs using hyperband - DeepCTR - Pretrained CNNs for images using foundational models (especially EfficientNet) - Pretrained + fine-tuned CNNs for images - Image augmentation - Pretrained TinyBERT models for text - Keras Text CNNs - Fastext models for text ## Time series-specific models - LSTMs - DeepAR models - AutoArima - ETS, aka exponential smoothing - TBATS - Prophet ## Unsupervised models #### Anomaly detection models - Isolation Forest - Local Outlier Factor - One Class SVM - Double Median Absolute Deviation - Mahalanobis Distance - Anomaly Detection Blenders - Keras Deep Autoencoder - Keras Deep Variational Autoencoder #### Clustering models - Kmeans - HDBScan ## Other model types - Eureqa (proprietary genetic algorithm for symbolic regression) - K-Nearest Neighbors (three distances) - Partial-least squares (used for blenders) - Isotonic Regression (used for calibrating predictions from other models) Click a [blueprint](https://docs.datarobot.com/en/docs/api/reference/public-api/blueprints.html) node to access full model documentation. Using [Composable ML](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/index.html), you can build models that best suit your needs using built-in tasks and custom Python/R code. --- # Model recommendation process URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html > As a result of the Autopilot modeling process, the most accurate individual, non-blender model is selected and then prepared for deployment. # Model recommendation process DataRobot provides an option to set the Autopilot modeling process to recommend a model for deployment. If you have enabled the [Recommend and prepare a model for deployment](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html) option, one of the models—the most accurate individual, non-blender model—is selected and then prepared for deployment. > [!NOTE] Note > When using [Workbench](https://docs.datarobot.com/en/docs/workbench/index.html) there is no model tagged with the Recommended for Deployment badge. This is because when comparing models across experiments on a multiproject Leaderboard, multiple models would be assigned the badge. DataRobot does prepare a model for each experiment however. The following tabs describe the process for each modeling mode when the Recommend and prepare a model for deployment option is enabled. **AutoML Quick (default):** The following describes the model recommendation process for Quick Autopilot mode in AutoML projects. Accuracy is based on the up-to-validation sample size (typically 64%). The resulting prepared model is marked with the Recommended for Deployment and Prepared for Deployment badges. You can also select any model from the Leaderboard and initiate the [deployment preparation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html#prepare-a-model-for-deployment) process. The following describes the preparation process: First, DataRobot calculates Feature Impact for the selected model and uses it to generate a reduced feature list . Next, the app retrains the selected model on the reduced feature list. If the new model performs better than the original model, DataRobot uses the new model for the next stage. Otherwise, the original model is used. Finally, DataRobot retrains the selected model as a frozen run using a 100% sample size and selects it as Recommended for Deployment . To apply the reduced feature list to the recommended model, manually retrain it—or any Leaderboard model—using the reduced feature list. Depending on the size of the dataset, the insights for the recommended model are either based on the up-to-holdout model or, if DataRobot can use [out-of-sample predictions](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#what-are-stacked-predictions), based on the 100%, recommended model. **AutoML (full):** The following describes the model recommendation process for full Autopilot and Comprehensive mode in AutoML projects. Accuracy is based on the up-to-validation sample size (typically 64%). The resulting prepared model is marked with the Recommended for Deployment and Prepared for Deployment badges. You can also select any model from the Leaderboard and initiate the [deployment preparation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html#prepare-a-model-for-deployment) process. You can also select any model from the Leaderboard and initiate the [deployment preparation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html#prepare-a-model-for-deployment) process. (Models manually prepared deployment will be only marked with the Prepared for Deployment badge.) The following describes the preparation process: First, DataRobot calculates Feature Impact for the selected model and uses it to generate a reduced feature list . Next, the app retrains the selected model on the reduced feature list. If the new model performs better than the original model, DataRobot uses the new model for the next stage. Otherwise, the original model is used. DataRobot then retrains the selected model at an up-to-holdout sample size (typically 80%). As long as the sample is under the frozen threshold (1.5GB), the stage is not frozen. Finally, DataRobot retrains the selected model as a frozen run (hyperparameters are not changed from the up-to-holdout run) using a 100% sample size and selects it as Recommended for Deployment . Depending on the size of the dataset, the insights for the recommended model are either based on the up-to-holdout model or, if DataRobot can use [out-of-sample predictions](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html#what-are-stacked-predictions), based on the 100%, recommended model. **Time-aware (Quick and full):** The following describes the model recommendation process for OTV and time series projects in both Quick and full Autopilot mode. The backtesting process for OTV works as follows (time series always runs on 100% training samples for each backtest). See the full details [here](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/multistep-ta.html): In full/comprehensive mode, the recommendation process runs iterative training at 25%, 50%, and 100% training samples of each backtest. In Quick mode, the process uses 100% of the maximum training size. When backtesting is finished, one of the models—the most accurate individual, non-blender model—is selected and then prepared for deployment. The resulting prepared model is marked with the Recommended for Deployment badge. The following describes the preparation process for time-aware projects: First, DataRobot calculates Feature Impact for the selected model and uses it to generate a reduced feature list . Next, the app retrains the selected model on the reduced feature list. If the new model performs better than the original model, DataRobot then retrains the better scoring model on the most recent data (using the same duration/row count as the original model). If using duration, and the equivalent period does not provide enough rows for training, DataRobot extends it until the minimum is met. Note that there are two exceptions for time series models: Feature reduction cannot be run for baseline (naive) or ARIMA models. This is because they only use date+naive predictions features (i.e., there is nothing to reduce). Because they don't use weights to train and don't need retraining, baseline (naive) models are not retrained on the most recent data. ## Prepare a model for deployment Although Autopilot recommends and prepares a single model for deployment, you can initiate the Autopilot recommendation and deployment preparation stages for any Leaderboard model. To do so, select a model from the Leaderboard and navigate to [Predict > Deploy](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-model.html). Click Prepare for Deployment. DataRobot begins running the recommendation stages described above for the selected model (view progress seen in the right panel). In other words, DataRobot runs Feature Impact, retrains the model on a reduced feature list, trains on a higher sample size, and then the full sample size (for non date/time partitioned projects) or most recent data (for [time-aware projects](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-adv-modeling/ts-date-time.html#recommended-time-series-models)). Once the process completes, DataRobot lists the prepared model, built with a 100% sample size, on the Leaderboard with the Prepared for Deployment badge. The originally recommended model also maintains its badge. From the Deploy tab of the original, limited sample size model you prepared for deployment, you can click Go to model to see the prepared, full sample size model on the Leaderboard. Click the new model's blueprint number to see the new feature list and sample sizes associated with the process: If you return to the model that you made the original request from (for example, the 64% sample size) and access the Deploy tab, you'll see that it is linked to the prepared model. ## Notes and considerations - When retraining the finalRecommended for Deploymentmodel at 100%, it is always executed as a frozen run. This makes model retraining faster, and also ensures that the 100% model uses the same settings as the 80% model. - If the model that is recommended for deployment has been trained into the validation set, DataRobots unlocks and displays theHoldout scorefor this model, but not the other Leaderboard models. Holdout can be unlocked for the other models from the right panel. - If the model that is recommended for deployment has been trained into the validation set, or the project was created without a holdout partition, the ability to compute predictions using validation and holdout data is not available. - The heuristic logic of automatic model recommendation may differ across different projects types. For example, retraining a model with non-redundant features is implemented in regression and binary classification while retraining a model at a higher sample size is implemented in regression, binary classification, and multiclass projects. - If you terminate a model that is being trained on a higher sample size, or training on a higher sample size does not successfully finish, that model will not be a candidate for theRecommended for Deploymentmodel. ### Deprecated badges Projects created prior to v6.1 may also have been tagged with the Most Accurate and/or Fast & Accurate badges. With improvements made to Autopilot automation, these badges are no longer necessary but are still visible, if they were assigned, to pre-v6.1 projects. Contact your DataRobot representative for code snippets that can help transition automation built around the deprecated badges. - The model markedMost Accurateis typically, but not always, a blender. As the name suggests, it is the most accurate model on the Leaderboard, determined by a ranking of validation or cross-validation scores. - TheFast & Accuratebadge, applicable only to non-blender models, is assigned to the model that is both the most accurateandis the fastest to make predictions. To evaluate, DataRobot uses prediction timing from: Not every project has a model tagged asFast & Accurate. This happens if the prediction time does not meet the minimum speed threshold determined by an internal algorithm. --- # Modeling process URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html > Provides details of DataRobot's modeling process, from selecting modeling modes, interpreting data summary information, and working with missing values. # Modeling process This section provides more detail to help understand DataRobot's initial model building process. - More on modeling modes, such as small datasets and Quick Autopilot - Two-stage models (Frequency and Severity models). - Data summary information - Handling project build failure - Working with missing values See also: - The data quality assessment , which automatically detects, and in some cases addresses, data quality issues. - The basic modeling process section for a workflow overview. - The list of modeling algorithms used by DataRobot. ## Modeling modes The exact action and options for a modeling mode are dependent on your data. In addition to the [standard description of mode behavior](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/model-data.html#modeling-modes-explained), the following sections describe circumstantial modeling behavior. ### Small datasets Autopilot for AutoML changes the sample percentages run depending on the number of rows in the dataset. The following table describes the criteria: | Number of rows | Percentages run | | --- | --- | | Less than 2000 | Final Autopilot stage only (64%) | | Between 2001 and 3999 | Final two Autopilot stages (32% and 64%) | | 4000 and larger | All stages of Autopilot (16%, 32%, and 64%) | ### Quick Autopilot Quick Autopilot is the default modeling mode, which has been optimized to ensure, typically, availability of more accurate models without sacrificing variety of tested options. As a result, reference models are not run. DataRobot runs [supported models](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html#modeling-modes) on a sample of data, depending on project type: | Project type | Sample size | | --- | --- | | AutoML | Typically 64% of data or 500MB, whichever is smaller. | | OTV | 100% of each backtest. | | Time series | Maximum training size for each backtest defined in the date/time partitioning. | With this shortened version of the full Autopilot, DataRobot selects models to run based on a variety of criteria, including target and performance metric, but as its name suggests, chooses only models with relatively short training runtimes to support quicker experimentation. The specific number of Quick models run varies by project and target type (e.g., some blueprints are only available for a specific target/target distribution). The [Average blender](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html), when enabled, is created from the top two models. To maximize runtime efficiency in Quick mode, DataRobot automatically creates the [DR Reduced Features](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/feature-lists.html#automatically-created-feature-lists) list but does not automatically fit the recommended (or any) model to it (fitting the reduced list requires retraining models). The steps involved in Quick mode are dependent on whether the [Recommend and prepare a model for deployment](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html) is checked. | Option state | Action | | --- | --- | | Checked | Run Quick mode at 64%Create a reduced feature list (if feature list can be reduced).Automatically retrain the recommended model at 100% (using the feature list of the 64% model). | | Unchecked | Run Quick mode at 64%. | For single column text datasets, DataRobot runs the following models: - Elastic Net (text-capable) - Single-column text models (for word clouds) - SVM on the document-term matrix For projects with [OffsetorExposure](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html#set-exposure) set, DataRobot runs the following: - XGBoost - Elastic Net (text-capable) - LightGBM - ASVM - Scikit learn GBM - GA2M + rating table - Eureqa GAM - Single-column text models (for word clouds) ## Two-stage models Some datasets result in a two-stage modeling process; these projects create additional models not otherwise available—Frequency and Severity models. Creation of this two-stage process, and the resulting additional model types, occurs in regression projects when the target is zero-inflated (that is, greater than 50% of rows in the dataset have a value of 0 for the target feature). These methods are most frequently applicable in insurance and operational risk and loss modeling—insurance claim, modeling foreclosure frequency with loss severity, and frequent flyer points redemption activity. For qualifying models (see below), you can view stage-related information in the following tabs: - In the Coefficients tab, DataRobot graphs parameters corresponding to the selected stage for linear models. Additionally, if you export the coefficients, two additional columns— Frequency_Coefficient and Severity_Coefficient —provide the coefficients at each stage. - In the Advanced Tuning tab, DataRobot graphs the parameters corresponding to the selected stage. DataRobot automatically runs some models built to support the frequency/severity methods as part of Autopilot; additional models are available in the [Repository](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/repository.html). The models in which the staging is available can be identified by the preface "Frequency-Cost" or "Frequency-Severity" and include the following: - XGBoost* - LightGBM* - Generalized Additive Models - Elastic Net * Coefficients are not available for these models Example use case: insurance Zach is building an insurance claim model based on frequency (the number of times that a policyholders made a claim) and severity (cost of the claim). Zach wants to predict the payout amount of claims for a potential policyholder in the coming year. Generally, most policyholders don't have accidents and so don't file claims. Therefore, a dataset where each row represents one policyholder—and the target is claim payouts—the target column for most rows will be $0. In Zach's dataset he has a zero-inflated target. Most policyholders represented in the training data have $0 as their target value. In this project, DataRobot will build several Frequency-Cost and Frequency-Severity models. ## Data summary information The following information assumes that you have selected a target feature and started the modeling process. After you select a target variable and begin modeling, DataRobot analyzes the data and presents this information in the Project Data tab of the Data page. Data features are listed in order of importance in predicting the target variable. DataRobot also detects the data (variable) type of each feature; supported data types are: - numeric - categorical - date - percentage - currency - length - text - summarized categorical - multicategorical - multilabel - date duration (OTV projects) - location (Location AI projects) Additional information on the Data page includes: - Unique and missing values - Mean, median, standard deviation, and minimum and maximum values - Informational tags - Feature importance - Access to tabs that allow you to work with feature lists and investigate feature associations ### Importance score The Importance bars show the degree to which a feature is correlated with the target. These bars are based on "Alternating Conditional Expectations" (ACE) scores. ACE scores are capable of detecting non-linear relationships with the target, but as they are univariate, they are unable to detect interaction effects between features.Importance is calculated using an algorithm that measures the information content of the variable; this calculation is done independently for each feature in the dataset. The importance score has two components— `Value` and `Normalized Value`: - Value : This shows the metric score you should expect (more or less) if you build a model using only that variable. For Multiclass, Value is calculated as the weighted average from the binary univariate models for each class. For binary classification and regression, Value is calculated from a univariate model evaluated on the validation set using the selected project metric. - Normalized Value : Value normalized; scores up to 1 (higher scores are better). 0 means accuracy is the same as predicting the training target mean. Scores of less than 0 mean the ACE model prediction is worse than the target mean model (overfitting). These scores represent a measure of predictive power for a simple model using only that variable to predict the target. (The score is adjusted by exposure if you set the [Exposure](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html#set-exposure) parameter.) Scores are measured using the project's accuracy metric. Features are ranked from most important to least important. The length of the green bar next to each feature indicates its relative importance—the amount of green in the bar compared to the total length of the bar, which shows the maximum potential feature importance (and is proportional to the `Normalized Value`)—the more green in the bar, the more important the feature. Hovering on the green bar shows both scores. These numbers represent the score in relation to the project metric for a model that uses only that feature (the metric selected when the project was run). Changing the metric on the Leaderboard has no effect on the tooltip scores. Click a feature name to [view details](https://docs.datarobot.com/en/docs/classic-ui/data/analyze-data/histogram.html) of the data values. While the values change between EDA1 and EDA2 (e.g., rows are removed because they are part of holdout or they are missing values), the meaning of the charts and the options are the same. ### Automated feature transformations Feature engineering is a key part of the modeling process. After pressing Start, DataRobot performs automated feature engineering on the given dataset to create derived variables in order to enhance model accuracy. See the table below for a list of feature engineering tasks DataRobot may perform during modeling for each feature type: | Feature type | Automated transformations | | --- | --- | | Numeric and categorical | Missing Imputation (Median, Arbitrary)StandardizationSearch for ratios Search for differencesRidit Transform DataRobot Smart Binning using a second modelPrincipal Components AnalysisK-Means ClusteringOne hot encodingOrdinal encodingCredibility intervalsCategory countsVariational AutoencoderCustom Feature Engineering for Numerics | | Date | Month-of-yearDay of weekDay of yearDay of monthHour of dayYearMonthWeek | | Text | Character / word ngramsPretrained TinyBERT featurizerStopword removalPart of Speech Tagging / RemovalTF-IDF scaling (optional sublinear scaling and binormal separation scaling)Hashing vectorizers for big dataSVD preprocessingCosine similarity between pairs of text columns (on datasets with 2+ text columns)Support for multiple languages, including English, Japanese, French, Korean, Spanish, Chinese, and Portuguese | | Images | DataRobot uses featurizers to turn images into numbers: Resnet50XceptionSqueezenetEfficientnetPreResnetDarknetMobileNetDataRobot also allows you to fine-tune these featurizers. | | Geospatial | DataRobot uses several techniques to automatically derive spatially-lagged features from the input dataset:Spatial Lag: A k-nearest neighbor approach to calculate mean neighborhood values of numeric features are varying spatial lags and neighborhood sizes.Spatial Kernel: Characterizes spatial dependence structure using a spatial kernel neighborhood technique. This technique characterizes spatial dependence structure for all numerica variables using varying kernel sizes, weighing by distanceDataRobot also derives local autocorrelation features using local indicators of spatial association to capture hot and cold spot of spatial similarity within the context of the entire input dataset.DataRobot derives features for the following geometric properties:MultiPoints: CentroidLines/MultiLines: Centroid, Length, Minimum bounding rectangle areaPolygons/MultiPolygons: Centroid, Perimeter, Area, Minimum bounding rectangle area | #### Text vs. categorical features DataRobot runs heuristics to differentiate text from categorical features, including the following: 1. If the number of unique rows is less than 5% of the column size, or if there are fewer than 60 unique rows, the column is classified ascategorical. 2. Using the Python language identifierlangid, DataRobot attempts to detect a language. If no language is detected, the column is classified ascategorical. 3. Languages are categorized as either Japanese/Chinese/Korean or English and all other languages ("English+"). If at least three of the following checks pass, the feature is classified as text: English+ Japanese/Chinese/Korean [Manual feature transformations](https://docs.datarobot.com/en/docs/classic-ui/data/transform-data/feature-transforms.html) allow you to override the automated assignment, but because this can cause errors, DataRobot provides a warning during the transformation process. #### Missing values DataRobot handles missing values differently, depending on the model and/or value type. The following are the codes DataRobot recognizes and treats as missing values: Special NaN Values for all feature types - null, NULL - na, NA, n/a, #N/A, N/A, #NA, #N/A N/A - 1.#IND, -1.#IND - NaN, nan, -NaN, -nan - 1.#QNAN, -1.#QNAN - ? - . - Inf, INF, inf, -Inf, -INF, -inf - None - One or more whitespace characters and empty cells are also treated as missing values. The following notes describe some specifics of DataRobot's value handling. > [!NOTE] Note > The missing value imputation method is fixed during training time. Either the median or arbitrary value set during training will be provided at prediction time. - Some models natively handle missing values so that no special preprocessing is needed. - For linear models (such as linear regression or an SVM), DataRobot's handling depends on the case: - For tree-based models, DataRobot imputes with an arbitrary value (e.g., -9999) rather than the median. This method is faster and gives just as accurate a result. - For categorical variables in all models, DataRobot treats missing values as another level in the categories. #### Numeric columns DataRobot assigns a var type to a value during EDA. For numeric columns, there are three types of values: 1. Numeric values: these can be integers or floating point numbers. 2. Special NaN values (listed in the table above): these are not numeric, but are recognized as representative of NaN. 3. All other values: for example, string or text data. Following are the rules DataRobot uses when determining if a particular column is treated as numeric, and how it handles the column at prediction time: - Strict Numeric: If a column has only numeric and special NaN values, DataRobot treats the column as numeric. At prediction time, DataRobot accepts any of the same special NaN values as missing and makes predictions. If an other value is present, DataRobot errors. - Permissive Numeric: If a column has numeric values, special NaN values and one (and only one) other value, DataRobot treats that other value as missing and treats the column as numeric. At prediction time, all other values are treated as missing (regardless of whether they differ from the first one). - Categorical: If DataRobot finds two or more other (non-numeric and non-NaN) values in a column during EDA, it treats the feature as categorical instead of numeric. - If DataRobot does not process any other value during EDA sampling and categorizes the feature as numeric, before training (but after EDA) it "looks" at the full dataset for that column. If any other values are seen for the full dataset, the column is treated as permissive numeric. If not, it is strict numeric. --- # Optimization metrics URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html > Provides a complete reference to the optimization metrics DataRobot employs during the modeling process. # Optimization metrics The following table lists all metrics, with a short description, available from the [Optimization Metric](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html#change-the-optimization-metric) dropdown. The sections [below the table](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html#datarobot-metrics) provide more detailed explanations, leveraging information from across the internet. > [!TIP] Tip > Remember that the metric DataRobot chooses for scoring models is usually the best selection. Changing the metric is advanced functionality and recommended only for those who understand the metrics and the algorithms behind them. For information on how recommendations are made, see [Recommended metrics](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html#recommended-metrics). For weighted metrics, the weights are the result of [smart downsampling](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/smart-ds.html) and/or specifying a value for the [Advanced options weights](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html) parameter. The metric then takes those weights into account. Metrics used are dependent on project type, either R (regression), C (binary classification), or M (multiclass). | Display | Full name | Description | Project type | | --- | --- | --- | --- | | Accuracy | Accuracy | Computes subset accuracy; the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true. | Binary classification, multiclass | | AUC/Weighted AUC | Area Under the (ROC) Curve | Measures the ability to distinguish the ones from the zeros; for multiclass, AUC is calculated for each class one-vs-all and then averaged, weighted by the class frequency. | Binary classification, multiclass, multilabel | | Area Under PR Curve | Area Under the Precision-Recall Curve | Approximation of the Area under the Precision-Recall Curve; summarizes precision and recall in one score. Well-suited to imbalanced targets. | Binary classification, multilabel | | Balanced Accuracy | Balanced Accuracy | Provides the average of the class-by-class one-vs-all accuracy. | Multiclass | | FVE Binomial/Weighted FVE Binomial | Fraction of Variance Explained | Measures deviance based on fitting on a binomial distribution. | Binary classification | | FVE Gamma/Weighted FVE Gamma | Fraction of Variance Explained | Provides FVE for gamma deviance. | Regression | | FVE Multinomial/Weighted FVE Multinomial | Fraction of Variance Explained | Measures deviance based on fitting on a multinomial distribution. | Multiclass | | FVE Poisson/Weighted FVE Poisson | Fraction of Variance Explained | Provides FVE for Poisson deviance. | Regression | | FVE Tweedie/Weighted FVE Tweedie | Fraction of Variance Explained | Provides FVE for Tweedie deviance. | Regression | | Gamma Deviance/Weighted Gamma Deviance | Gamma Deviance | Measures the inaccuracy of predicted mean values when the target is skewed and gamma distributed. | Regression | | Gini/Weighted Gini | Gini Coefficient | Measures the ability to rank. | Regression, binary classification | | Gini Norm/Weighted Gini Norm | Normalized Gini Coefficient | Measures the ability to rank. | Regression, binary classification | | KS | Kolmogorov-Smirnov | Measures the maximum distance between two non-parametric distributions. Used for ranking a binary classifier, KS evaluates models based on the degree of separation between true positive and false positive distributions. The KS value is displayed in the ROC Curvekolmogorov-smirnov-ks-metric). | Binary classification | | LogLoss/Weighted LogLoss | Logarithmic Loss | Measures the inaccuracy of predicted probabilities. | Binary classification, multiclass, multilabel | | MAE/Weighted MAE* | Mean Absolute Error | Measures the inaccuracy of predicted median values. | Regression | | MAPE/Weighted MAPE | Mean Absolute Percentage Error | Measures the percent inaccuracy of the mean values. | Regression | | MASE | Mean Absolute Scaled Error | Measures relative performance with respect to a baseline model. | Regression (time series only) | | Max MCC/Weighted Max MCC | Maximum Matthews correlation coefficient | Measures the maximum value of the Matthews correlation coefficient between the predicted and actual class labels. | Binary classification | | Poisson Deviance/Weighted Poisson Deviance | Poisson Deviance | Measures the inaccuracy of predicted mean values for count data. | Regression | | R Squared/Weighted R Squared | R Squared | Measures the proportion of total variation of outcomes explained by the model. | Regression | | Rate@Top5% | Rate@Top5% | Measures the response rate in the top 5% highest predictions. | Binary classification | | Rate@Top10% | Rate@Top10% | Measures the response rate in the top 10% highest predictions. | Binary classification | | Rate@TopTenth% | Rate@TopTenth% | Measures the response rate in the top tenth highest predictions. | Binary classification | | RMSE/Weighted RMSE | Root Mean Squared Error | Measures the inaccuracy of predicted mean values when the target is normally distributed. | Regression, binary classification | | RMSLE/Weighted RMSLE* | Root Mean Squared Log Error | Measures the inaccuracy of predicted mean values when the target is skewed and log-normal distributed. | Regression | | Silhouette Score | Silhouette score, also referred to as silhouette coefficient | Compares clustering models. | Clustering | | SMAPE/Weighted SMAPE | Symmetric Mean Absolute Percentage Error | Measures the bounded percent inaccuracy of the mean values. | Regression | | Synthetic AUC | Synthetic Area Under the Curve | Calculates AUC. | Unsupervised | | Theil's U | Henri Theil's U Index of Inequality | Measures relative performance with respect to a baseline model. | Regression (time series only) | | Tweedie Deviance/Weighted Tweedie Deviance | Tweedie Deviance | Measures the inaccuracy of predicted mean values when the target is zero-inflated and skewed. | Regression | * Because these metrics don't optimize for the mean, Lift Chart results (which show the mean) are misleading for most models that use them as a metric. ## Recommended metrics DataRobot recommends which optimization metric to use when scoring models; the recommended metric is usually the best option for the given circumstances. Changing the metric is advanced functionality, and only those who understand the other metrics (and the algorithms behind them) should use them for analysis. The table below outlines the general guidelines DataRobot follows when recommending a metric: | Project type | Recommended metric | | --- | --- | | Binary classification | LogLoss | | Multiclass classification | LogLoss | | Multilabel classification | LogLoss | | Regression | DataRobot will choose between RMSE, Tweedie Deviance, Poisson Deviance, and Gamma Deviance optimization metrics by applying heuristics informed by the properties of the target distribution, including percentiles, mean, variance, skew, and zero counts. | * Of the [EDA 1](https://docs.datarobot.com/en/docs/reference/data-ref/eda-explained.html#eda1) sample. ## DataRobot metrics The following sections describe the DataRobot optimization metrics in more detail. > [!NOTE] Note > There is some overlap in DataRobot optimization metrics and Eureqa error metrics. You may notice, however, that in some cases the metric formulas are expressed differently. For example, predictions may be expressed as `y^` versus `f(x)`. Both are correct, with the nuance being that `y^` indicates a prediction generally, regardless of how you got there, while `f(x)` indicates a function that may represent an underlying equation. ### Accuracy/Balanced Accuracy | Display | Full name | Description | Project type | | --- | --- | --- | --- | | Accuracy | Accuracy | Computes subset accuracy; the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true. | Binary classification, multiclass | | Balanced Accuracy | Balanced Accuracy | Provides the average of the class-by-class one-vs-all accuracy. | Multiclass | The Accuracy metric applies to classification problems and captures the ratio of the total count of correct predictions over the total count of all predictions, based on a given threshold. True positives (TP) and true negatives (TN) are correct predictions, false positives (FP) and false negatives (FN) are incorrect predictions. The formula is: Unlike Accuracy, which looks at the number of true positive and true negative predictions per class, Balanced Accuracy looks at the true positives (TP) and the false negatives (FN) for each class, also known as Recall. It is the sum of the recall values of each class divided by the total number of classes. (This formula matches the TPR formula.) For example, in the 3x3 matrix example below: Accuracy = `(TP_A + TP_B + TP_C) / Total prediction count` or, from the image above, `(9 + 60 + 30) / 200 = 0.495` Balanced Accuracy = `(Recall_A + Recall_B + Recall_C) / total number of classes`. ``` Recall_A = 9 / (9 + 1 + 0) = 0.9 Recall_B = 60 / (20 + 60 + 20) = 0.6 Recall_C = 30 / (25 + 35 + 30) = 0.333 ``` Balanced Accuracy = `(0.9 + 0.6 +0.333) / 3 = 0.611` Accuracy and Balanced Accuracy apply to both binary and multiclass classification. Using weights: Every cell of the confusion matrix will be the sum of the sample weights in that cell. If no weights are specified, the implied weight is 1, so the sum of the weights is also the count of observations. Accuracy does not perform well with imbalanced datasets. For example, if you have 95 negative and 5 positive samples, classifying all as negative gives 0.95 accuracy score.[Balanced Accuracy (bACC)](https://en.wikipedia.org/wiki/Precision_and_recall) overcomes this problem by normalizing true positive and true negative predictions by the number of positive and negative samples, respectively, and dividing their sum into two. This is equivalent to the following formula: ### AUC/Weighted AUC | Display | Full name | Description | Project type | | --- | --- | --- | --- | | AUC/Weighted AUC | Area Under the (ROC) Curve | Measures the ability to distinguish the ones from the zeros; for multiclass, AUC is calculated for each class one-vs-all and then averaged, weighted by the class frequency. | Binary classification, multiclass, multilabel | AUC for the ROC curve is a performance measurement for classification problems. ROC is a probability curve and AUC represents the degree or measure of separability. The metric ranges from 0 to 1 and indicates how much the model is capable of distinguishing between classes. The higher the AUC, the better the model is at predicting negatives (0s as 0s) and positives (1s as 1s). The ROC curve shows how the true positive rate (sensitivity) on the Y-axis and false positive rate (specificity) on the X-axis vary at each possible threshold. For a multiclass or multilabel model, you can plot n number of AUC/ROC Curves for n number classes using one-vs-all methodology. For example, if there are three classes named `X`, `Y` and `Z`, there will be one ROC for `X` classified against `Y` and `Z`, another ROC for `Y` classified against `X` and `Z`, and a third for `Z` classified against `Y` and `X`. To extend the ROC curve and the Area Under the Curve to multiclass or multilabel classification, it is necessary to binarize the output. For multiclass projects, the AUC score is the averaged AUC score for each single class (macro average), weighted by support (the number of true instances for each class). The Weighted AUC score is the averaged, sample-weighted AUC score for each single class (macro average), weighted according to the sample weights for each class `sum(sample_weights_for_class)/sum(sample_weights)`. For multilabel projects, the AUC score is the averaged AUC score for each single class (macro average). The Weighted AUC score is the averaged, sample-weighted AUC score for each single class (macro average). ### Area Under PR Curve | Display | Full name | Description | Project type | | --- | --- | --- | --- | | Area Under PR Curve | Area Under the Precision-Recall Curve | Approximation of the Area under the Precision-Recall Curve; summarizes precision and recall in one score. Well-suited to imbalanced targets. | Binary classification, multilabel | The Precision-Recall (PR) curve captures the tradeoff between a model's precision and recall at different probability thresholds. Precision is the proportion of positively labeled cases that are true positives (i.e., `TP / (TP + FP)`), and recall is the proportion of positive labeled cases that are recovered by the model ( `TP/ (TP + FN)`). The area under the PR curve cannot always be calculated exactly, so an approximation is used by means of a weighted mean of precisions at each threshold, weighted by the improvement in recall from the previous threshold: Area under the PR curve is very well-suited to problems with imbalanced classes where the minority class is the "positive" class of interest (it is important that this is encoded as such): precision and recall both summarize information about positive class retrieval, and neither is informed by True Negatives. For more reading about the relative merits of using the above approach as opposed to an interpolation of the area, see: - The Relationship Between Precision-Recall and ROC Curves - Precision-Recall-Gain Curves: PR Analysis Done Right For multilabel projects, the reported Area Under PR Curve score is the averaged Area Under PR Curve score for each single class (macro average). ### Deviance metrics | Display | Full name | Description | Project type | | --- | --- | --- | --- | | Gamma Deviance/ Weighted Gamma Deviance | Gamma Deviance | Measures the inaccuracy of predicted mean values when the target is skewed and gamma distributed. | Regression | | Poisson Deviance/Weighted Poisson Deviance | Poisson Deviance | Measures the inaccuracy of predicted mean values for count data. | Regression | | Tweedie Deviance/Weighted Tweedie Deviance | Tweedie Deviance | Measures the inaccuracy of predicted mean values when the target is zero-inflated and skewed. | Regression | [Deviance](https://en.wikipedia.org/wiki/Deviance_(statistics%29) is a measure of the goodness of model fit—how well your model fits the data. Technically, it is how well your fitted prediction model compares to a perfect (saturated) model from the observed values. This is usually defined as twice the log-likelihood function, parameters that are determined via a maximum likelihood estimation. Thus, the deviance is defined as the difference of likelihoods between the fitted model and the saturated model. As a consequence, the deviance is always larger than or equal to zero, where zero only applies if the fit is perfect. Deviance metrics are based on the principle of generalized linear models. That is, the deviance is some measure of the error difference between the target value and the predicted value, where the predicted value is run through a link function, denoted with: An example of a link function is the logit function, which is used in logistic regression to transform the prediction from a linear model into a probability between 0 and 1. In essence, each deviance equation is an error metric intended to work with a type of distribution deemed applicable for the target data. For example, a normal distribution for a target uses the sum of squared errors: And the Python implementation: `np.sum((y - pred) ** 2)` In this case, the deviance metric is just that—the sum of squared errors. For a Gamma distribution, where data is skewed to one side (say to the right for something like the distribution of how much customers spend at a store), deviance is: Python: `2 * np.mean(-np.log(y / pred) + (y - pred) / pred)` For a Poisson distribution, when interested in predicting counts or number of occurrences of something, the function is this: Python: `2 * np.mean(y * np.log(y / pred) - (y - pred))` For Tweedie, the function looks a little messier. Tweedie Deviance measures how well the model fits the data, assuming the target has a Tweedie distribution. Tweedie is commonly used in zero-inflated regression problems, where there are a relatively large number of 0s and the rest are continuous values. Smaller values of Deviance are more accurate models. As Tweedie Deviance is a more complicated metric, it may be easier to explain the model using FVE (Fraction of Variance Explained) Tweedie. This metric is equivalent to R^2, but for Tweedie distributions instead of Normal distributions. A score of 1 is a perfect explanation. Tweedie deviance attempts to differentiate between a variety of distribution families, including Normal, Poisson, Gamma, and some less familiar distributions. This includes a class of mixed compound Poisson–Gamma distributions that have positive mass at zero, but are otherwise continuous (e.g., zero-inflated distributions). In this case, [the function is](https://stats.stackexchange.com/questions/201339/what-if-explained-deviance-is-greater-than-1-0-or-100): Python: `2 * np.mean((y ** (2-p)) / ((1-p) * (2-p)) - (y * (pred ** (1-p))) / (1-p) + (pred ** (2-p)) / (2-p))` Where parameter `p` is an index value that differentiates between the distribution family. For example, 0 is Normal, 1 is Poisson, 1.5 is Tweedie, and 2 is Gamma. Interpreting these metric scores is not particularly intuitive.`y` and `pred` values are in the unit of target (e.g., dollars), but as can be seen above, log functions and scaling complicates it. You can transform this to a weighted deviance function simply by introducing a weights multiplier, for example for Poisson: > [!NOTE] Note > Because of log functions and predictions in the denominator in some calculations, this only works for positive responses. That is, predictions are enforced to be strictly positive `(max(pred, 1e-8))` and actuals are enforced to be either non-negative `(max(y, 0))` or strictly positive `(max(y, 1e-8))`, depending on the deviance function. ### FVE deviance metrics | Display | Full name | Description | Project type | | --- | --- | --- | --- | | FVE Binomial/Weighted FVE Binomial | Fraction of Variance Explained | Measures deviance based on fitting on a binomial distribution. | Binary classification | | FVE Gamma/Weighted FVE Gamma | Fraction of Variance Explained | Measures FVE for gamma deviance. | Regression | | FVE Multinomial/Weighted FVE Multinomial | Fraction of Variance Explained | Measures deviance based on fitting on a multinomial distribution. | Multiclass | | FVE Poisson/Weighted FVE Poisson | Fraction of Variance Explained | Measures FVE for Poisson deviance. | Regression | | FVE Tweedie/Weighted FVE Tweedie | Fraction of Variance Explained | Measures FVE for Tweedie deviance. | Regression | FVE is [fraction of variance explained](https://en.wikipedia.org/wiki/Explained_variation) (also sometimes referred to as "fraction of deviance explained"). That is, what proportion of the total deviance, or error, is captured by the model? This is defined as: To calculate the fraction of variance explained, three models are fit: - The "model analyzed," or the model actually constructed within DataRobot. - A "worst fit" model (a model fitted without any predictors, fitting only an intercept). - A "perfect fit" model (also called a "fully saturated" model), which exactly predicts every observation. "Null deviance" is the total deviance calculated between the "worst fit" model and the "perfect fit" model. "Residual deviance" is the total deviance calculated between the "model analyzed" and the "perfect fit" model. (See the [deviance formulas](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html#deviance-metrics) for more detail.) You can think of the "fraction of unexplained deviance" as the residual deviance (a measure of error between the "perfect fit" model and your model) divided by the null deviance (a measure of error between the "perfect fit" model and the "worst fit" model). The fraction of explained deviance is 1 minus the fraction of unexplained deviance. Gauge the model's performance improvement compared to the "worst fit" model by calculating an R²-style statistic, the Fraction of Variance Explained (FVE). Illustrated conceptually as: * Illustration courtesy of Eduardo García-Portugués, [Notes for Predictive Modeling](https://bookdown.org/egarpor/PM-UC3M/). Therefore, FVE equals traditional R-squared for linear regression models, but, unlike traditional R-squared, generalizes to exponential family regression models. By scaling the difference by the Null Deviance, the value of FVE should be between 0 and 1, but not always. It can be less than zero in the event the model predicts responses poorly for new observations and/or a cross-validated out of sample data is very different. For multiclass projects, FVE Multinomial computes `loss = logloss(act, pred)` and `loss_avg = logloss(act, act_avg)`, where: - act_avg is the one-hot encoded "actual" data. - each class (column) is averaged over N data points. Basically `act_avg` is a list containing the percentage of the data that belongs to each class. Then, the FVE is computed via `1 - loss / loss_avg`. ### Gini coefficient | Display | Full name | Description | Project type | | --- | --- | --- | --- | | Gini/Weighted Gini | Gini Coefficient | Measures the ability to rank. | Regression, binary classification | | Gini Norm/Weighted Gini Norm | Normalized Gini Coefficient | Measures the ability to rank. | Regression, binary classification | In machine learning, the [Gini Coefficient or Gini Index](https://en.wikipedia.org/wiki/Gini_coefficient) measures the ability of a model to accurately rank predictions. Gini is effectively the same as [AUC](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html#aucweighted-auc), but on a scale of -1 to 1 (where 0 is the score of a random classifier). If the Gini Norm is 1, then the model perfectly ranks the inputs. Gini can be useful when you care more about ranking your predictions, rather than the predicted value itself. Gini is defined as a ratio of normalized values between 0 and 1—the numerator as the area between the Lorenz curve of the distribution and the 45 degree uniform distribution line, discussed below. The [Gini coefficient](https://www.kaggle.com/batzner/gini-coefficient-an-intuitive-explanation) is thus defined as the blue area divided by the area of the lower triangle: The Gini coefficient is equal to the area below the line of perfect equality (0.5 by definition) minus the area below the Lorenz curve, divided by the area below the line of perfect equality. In other words, it is double the area between the Lorenz curve and the line of perfect equality. The line at 45 degrees thus represents perfect equality. The Gini coefficient can then be thought of as the ratio of the area that lies between the line of equality and the Lorenz curve (call that `A`) over the total area under the line of equality (call that `A + B`). So: `Gini = A / (A + B)` It is also equal to `2A` and to `1 − 2B` due to the fact that `A + B = 0.5` (since the axes scale from 0 to 1). It is alternatively defined as twice the area between the receiver operating characteristic (ROC) curve and its diagonal, in which case the AUC (Area Under the ROC Curve) measure of performance is given by `AUC = (G + 1)/2` or factored as `2 * AUC-1`. Its purpose is to normalize the AUC so that a random classifier scores 0, and a perfect classifier scores 1. Formally then, the range of possible Gini coefficient scores is [-1, 1] but in practice zero is typically the low end. You can also integrate the area between the perfect 45 degree line and the Lorenz curve to get the same Gini value, but the former is arguably easier. In economics, the [Gini coefficient](https://en.wikipedia.org/wiki/Gini_coefficient) is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents and is the most commonly used measure of inequality. A Gini coefficient of zero expresses perfect equality, where all values are the same (for example, where everyone has the same income). In this context, a Gini coefficient of 1 (or 100%) expresses maximal inequality among values (e.g., for a large number of people, where only one person has all the income or consumption, and all others have none, the Gini coefficient will be very nearly one). However, a value greater than 1 can occur if some persons represent negative contribution to the total (for example, having negative income or wealth). Using this economics example, the Lorenz curve shows income distribution by plotting the population percentile by income on the horizontal axis and cumulative income on the vertical axis. The Normalized Gini Coefficient adjusts the score by the theoretical maximum so that the maximum score is 1. Because the score is normalized, comparisons can be made between the Gini coefficient values of like entities such that values can be rank ordered. For example, [economic inequality by country](https://www.cia.gov/the-world-factbook/field/gini-index-coefficient-distribution-of-family-income/country-comparison) is commonly assessed with the Gini coefficient and is used to rank order the countries: | Rank | Country | Distribution of family income—Gini index | Date of information | | --- | --- | --- | --- | | 1 | LESOTHO | 63.2 | 1995 | | 2 | SOUTH AFRICA | 62.5 | 2013 EST. | | 3 | MICRONESIA, FEDERATED STATES OF | 61.1 | 2013 EST. | | 4 | HAITI | 60.8 | 2012 | | 5 | BOTSWANA | 60.5 | 2009 | One way to use the Gini index metric in a machine learning context is to compute it using the actual and predicted values, instead of using individual samples. If, using the example above, you generate the Gini index from the samples of individual incomes of people in a country, the Lorenz curve is a function of the population percentage by cumulative sum of incomes. In a machine learning context, you could generate the Gini from the actual and predicted values. One approach would be to pair the actual and predicted values and sort them by predicted. The Lorenz curve in that case is a function of the predicted values by the cumulative sum of actuals—the running total of the 1s of class 1 values. Then, calculate the Gini using one of the formulas above. The Weighted Gini metric uses the array of actual and predicted values multiplied by the weight and sorts the values by predicted. The metric is calculated as `(2*AUC - 1)`, where the AUC calculation is based on the cumulative sum of `actual * weight` and cumulative sum of weights. Weighted Gini Norm is Weighted Gini divided by the Weighted Gini value if predicted values are equal to actual. For an example of using the Gini metric, see the [Porto Seguro’s Safe Driver Kaggle competition](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction#evaluation) and the corresponding [explanation](https://www.kaggle.com/batzner/gini-coefficient-an-intuitive-explanation). ### Kolmogorov–Smirnov (KS) | Display | Full name | Description | Project type | | --- | --- | --- | --- | | KS | Kolmogorov-Smirnov | Measures the maximum distance between two non-parametric distributions. Used for ranking a binary classifier, KS evaluates models based on the degree of separation between true positive and false positive distributions. The KS value is displayed in the ROC Curvekolmogorov-smirnov-ks-metric). | Binary classification | The KS or Kolmogorov-Smirnov chart measures performance of classification models. More accurately, KS is a measure of the degree of separation between positive and negative distributions. The KS is 1 if the scores partition the population into two separate groups in which one group contains all the positives and the other all the negatives. On the other hand, If the model can’t differentiate between positives and negatives, then it is as if the model selects cases randomly from the population. In that case, the KS would be 0. In most classification models, the KS will fall between 0 and 1; the higher the value, the better the model is at separating the positive from negative cases. In [this paper](https://arxiv.org/pdf/1606.00496.pdf), in binary classification problems, it has been used as dissimilarity metric for assessing the classifier’s discriminant power measuring the distance that its score produces between the cumulative distribution functions (CDFs) of the two data classes, known as KS2 for this purpose (two samples). The usual metric for both purposes is the maximum vertical difference (MVD) between the CDFs (the Max_KS), which is invariant to score range and scale making it suitable for classifiers comparisons. The MVD is simply the vertical distance between the two curves at a single point on the X axis. The Max_KS is the single point [where this distance is the greatest](https://www.machinelearningplus.com/machine-learning/evaluation-metrics-classification-models-r/attachment/kolmogorov_smirnov_chart-2/). ### LogLoss/Weighted LogLoss | Display | Full name | Description | Project type | | --- | --- | --- | --- | | LogLoss/ Weighted LogLoss | Logarithmic Loss | Measures the inaccuracy of predicted probabilities. | Binary classification, multiclass, multilabel | Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Log loss increases as the predicted probability diverges from the actual label. So, for example, predicting a probability of .12 when the actual observation label is 1, or predicting .91 when the actual observation label is 0, would be “bad” and result in a higher loss value than misclassification probabilities closer to the true label value. A perfect model would have a log loss of 0. The graph above shows the range of possible loss values given a true observation (true = 1). As the predicted probability approaches 1, log loss slowly decreases. As the predicted probability decreases, however, the log loss increases rapidly. Log loss penalizes both types of errors, but especially those predictions that are confident and wrong. Cross-entropy and log loss are [slightly different depending on context](https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html), but in machine learning when calculating error rates between 0 and 1 they resolve to the same thing. In binary classification, the formula equals `-(ylog(p) + (1 - y)log(1 - p))` or: Where `p` is the predicted value of `y`. Similarly for multiclass and multilabel, take the sum of log loss values for each class prediction in the observation: You can transform this to a weighted loss function by introducing weights to a given class: Note that the reported log loss scores for multilabel are scaled by `1/number_of_unique_classes`. ### MAE/Weighted MAE | Display | Full name | Description | Project type | | --- | --- | --- | --- | | MAE/Weighted MAE | Mean Absolute Error | Measures the inaccuracy of predicted median values. | Regression | DataRobot implements a MAE metric using the median to measure absolute deviations, which is a more accurate calculation of absolute deviance (or rather stated, absolute error in this case). This is based on the fact that in optimizing the loss function for the absolute error, the best value that is derived turns out to be the median of the series. To see why, first assume a series of numbers that you want to summarize to an optimal value, ( `x1,x2,…,xn`)— the predictions. You want the summary to be a single number, `s`. How do you select `s` so that it summarizes the predictions, ( `x1,x2,…,xn`), effectively? Aggregate the error deviances between `xi` and `s` for each of `xi` into a single summary of the quality of a proposed value of `s`. To perform this aggregation, sum up the deviances over each of the `xi` and call the result `E`: Upon solving for the `s` that results in the smallest error, the `E` loss function optimizes to be the median, not the mean. Note that, likewise, the best value of the squared error loss function optimizes to be the mean. Thus, the mean squared error. While MAE stands for “mean absolute error,” it optimizes the model to predict the median correctly. This is similar to how RMSE is “root mean squared error,” but optimizes for predicting the mean correctly (not the square of the mean). You may notice some curious discrepancies in DataRobot, which are worth remembering, when you optimize for MAE. Most insights report for the mean. As such, all the Lift Charts look “off” because the model under- or over-predicts for every point along the distribution. The Lift Chart calculates a mean, whereas MAE optimizes for the median. You can transform this to a weighted loss function by introducing weights to observations: Unfortunately, the statistical literature has not yet adopted a standard notation, as both the mean absolute deviation around the mean (MAD) and the mean absolute error (what DataRobot calls “MAE”) have been denoted by their initials MAD in the literature, which may lead to confusion, since in general, they can have values considerably different from each other. ### MAPE/Weighted MAPE | Display | Full name | Description | Project type | | --- | --- | --- | --- | | MAPE/Weighted MAPE | Mean Absolute Percentage Error | Measures the percent inaccuracy of the mean values. | Regression | One problem with the MAE is that the relative size of the error is not always obvious. Sometimes it is hard to tell a large error from a small error. To deal with this problem, find the mean absolute error in percentage terms.[Mean Absolute Percentage Error (MAPE)](http://canworksmart.com/using-mean-absolute-error-forecast-accuracy/) allows you to compare forecasts of different series in different scales. For example, consider comparing the sales forecast accuracy of one store with the sales forecast of another, similar store, even though these stores may have different sales volumes. ### MASE | Display | Full name | Description | Project type | | --- | --- | --- | --- | | MASE | Mean Absolute Scaled Error | Measures relative performance with respect to a baseline model. | Regression (time series only) | MASE is a measure of the accuracy of forecasts and is a comparison of one model to a naïve baseline model—the simple ratio of the MAE of a model over the baseline model. This has the advantage of being easily interpretable and explainable in terms of relative accuracy gain, and is recommended when comparing models. In DataRobot time series projects, [the baseline model](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/ts-feature-lists.html#mase-and-baseline-models) is a model that uses the most recent value that matches the longest periodicity. That is, while a project could have multiple different naïve predictions with different periodicity, DataRobot uses the longest naïve predictions to compute the MASE score. Or in more detail: Where the numerator is the model of interest and the denominator is the naïve baseline model. ### Max MCC/Weighted Max MCC | Display | Full name | Description | Project type | | --- | --- | --- | --- | | Max MCC/Weighted Max MCC | Maximum Matthews correlation coefficient | Measures the maximum value of the Matthews correlation coefficient between the predicted and actual class labels. | Binary classification | [Matthews correlation coefficient](https://en.wikipedia.org/wiki/Matthews_correlation_coefficient) is a balanced metric for binary classification that takes into account all four entries in the [confusion matrix](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/evaluate/roc-curve-tab/confusion-matrix-classic.html). It can be calculated as: Where: | Outcome | Description | | --- | --- | | True positive (TP) | A positive instance that the model correctly classifies as positive. | | False positive (FP) | A negative instance that the model incorrectly classifies as positive. | | True negative (TN) | A negative instance that the model correctly classifies as negative. | | False negative (FN) | A positive instance that the model incorrectly classifies as negative. | The range of possible values is [-1, 1], where 1 represents perfect predictions. Since the entries in the confusion matrix depend on the prediction threshold, DataRobot uses the maximum value of MCC over possible prediction thresholds. ### R-Squared (R2)/Weighted R-Squared | Display | Full name | Description | Project type | | --- | --- | --- | --- | | R-Squared/Weighted R-Squared | R-Squared | Measures the proportion of total variation of outcomes explained by the model. | Regression | [R-squared is a statistical measure of goodness of fit](https://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit) —how close the data are to a fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression. As a description of the variance explained, it is the percentage of the response variable variation that is explained by a linear model. Typically R-squared is between 0 and 100%. 0% indicates that the model explains none of the variability of the response data around its mean. 100% indicates that the model explains all the variability of the response data around its mean. Note that there are circumstances that result in a negative R value, meaning that the model is predicting worse than the mean. This can happen, for example, due to problematic training data. For time-aware projects, R-squared has a higher chance to be negative due to mean changes over time—if you train a model on a high mean period, but test on a low mean period, a large negative R-squared value can result. (When partitioning is done via random sampling, the target mean for the train and test sets are roughly the same, so negative R-squared values are less likely.) Generally speaking, it is best to avoid models with a negative R-squared value. Where `SS_res` is the residual sum of squares, also called the explained sum of squares: `SS_tot` is the total sum of squares (proportional to the variance of the data) and: is the sample mean of `y`, calculated from the training data: For a weighted R-squared, `SS_res` becomes: And `SS_tot` becomes: Some key Limitations of R-squared: - R-squared cannot determine whether the coefficient estimates and predictions are biased, which is why you must assess the residual plots. - R-squared can be artificially made high. That is, you can increase the value of R-squared by simply adding more and more independent variables to the model. In other words, R-squared never decreases upon adding more independent variables. Sometimes, some of these variables might be very insignificant and can be really useless to the model. - R-squared does not indicate whether a regression model is adequate. You can have a low R-squared value for a good model, or a high R-squared value for a model that does not fit the data. To that end, R-squared values must be interpreted with caution. Low R-squared values aren’t inherently bad. In some fields, it is entirely expected that your R-squared values will be low. For example, any field that attempts to predict human behavior, such as psychology, typically has R-squared values lower than 50%. Humans are simply harder to predict than, say, physical processes. At the same time, high R-squared values aren’t inherently good. A high R-squared does not necessarily indicate that the model has a good fit. For example, the fitted line plot may indicate a good fit and seemingly express the high R-squared, but a look at the residual plot may show a systematic over and/or under prediction, indicative of high bias. DataRobot calculates on out-of-sample data, mitigating traditional critiques such as, for example, that adding more features increases the value or that R2 is not applicable to non-linear techniques. It is essentially treated as a scaled version of RMSE, allowing DataRobot to compare itself to the mean model (R2 = 0) and determine if it’s doing better (R2 >0) or worse (R2 <0). ### Rate@Top10%, Rate@Top5%, Rate@TopTenth% | Display | Full name | Description | Project type | | --- | --- | --- | --- | | Rate@Top5% | Rate@Top5% | Measures the response rate in the top 5% highest predictions. | Binary classification | | Rate@Top10% | Rate@Top10% | Measures the response rate in the top 10% highest predictions. | Binary classification | | Rate@TopTenth% | Rate@TopTenth% | Measures the response rate in the top 0.1% highest predictions. | Binary classification | Rate@Top5%, Rate@Top10%, and Rate@TopTenth% calculate the rate of positive labels in those confidence regions (top 5%, top 10% and top tenth% of highest predictions). For example, take a set of 100 predictions ordered from lowest to highest, something like: [.05, .08, .11, .12, .14 … .87, .89, .91, .93, .94 ]. Presuming the threshold is below .87, the top 5 predictions from .87 to .94 would be assigned to the positive class, 1. Now say the actual values for the top 5 are [1, 1, 0, 1, 1]. Then the Rate@Top5% measure of accuracy would be 80%. ### RMSE, Weighted RMSE & RMSLE, Weighted RMSLE | Display | Full name | Description | Project type | | --- | --- | --- | --- | | RMSE/ Weighted RMSE | Root Mean Squared Error | Measures the inaccuracy of predicted mean values when the target is normally distributed. | Regression, binary classification | | RMSLE/ Weighted RMSLE* | Root Mean Squared Log Error | Measures the inaccuracy of predicted mean values when the target is skewed and log-normal distributed. | Regression | The root mean squared error (RMSE) is another measure of accuracy somewhat similar to MAD in that they both take the difference between the actual and the predicted or forecast values. However, RMSE squares the difference rather than applying the absolute value, and then finds the square root. Thus, [RMSE is always non-negative](https://en.wikipedia.org/wiki/Root-mean-square_deviation) and a value of 0 indicates a perfect fit to the data. In general, a lower RMSE is better than a higher one. However, comparisons across different types of data would be invalid because the measure is dependent on the scale of the numbers used. RMSE is the square root of the average of squared errors. The effect of each error on RMSE is proportional to the size of the squared error. Thus, larger errors have a disproportionately large effect on RMSE. Consequently, RMSE is sensitive to outliers. The root mean squared log error (RMSLE), to avoid taking the natural log of zero, adds 1 to both actual and predicted before taking the natural logarithm. As a result, the function can be used if actual or predicted have zero-valued elements. Note that only the percent difference between the actual and prediction matter. For example, P = 1000 and A = 500 would give roughly the same error as when P = 100000 and A = 50000. You can transform this to a weighted function simply by introducing a weights multiplier: > [!NOTE] Note > For RMSLE, many model blueprints log transform the target and optimize for RMSE. This is equivalent to optimizing for RMSLE. If this occurs, the model's build information lists "log transformed response". ### Silhouette Score | Display | Full name | Description | Project type | | --- | --- | --- | --- | | Silhouette Score | Silhouette score, also referred to as silhouette coefficient | Compares clustering models. | Clustering | The silhouette score, also called the silhouette coefficient, is a metric used to compare clustering models. It is calculated using the mean intra -cluster distance (average distance between each point within a cluster) and the mean nearest -cluster distance (average distance between clusters). That is, it takes into account the distances between the clusters, but also the distribution of each cluster. If a cluster is condensed, the instances (points) have a high degree of similarity. The silhouette score ranges from -1 to +1. The closer to +1, the more separated the clusters are. Computing silhouette score for large datasets is very time-intensive—training a clustering model takes minutes but the metric computation can take hours. To address this, DataRobot performs stratified sampling to limit the dataset to 50000 rows so that models are trained and evaluated for large datasets in a reasonable timeframe while also providing a good estimation of the actual silhouette score. In time series, the silhouette score is a measure of the silhouette coefficient between different series calculated by comparing the similarity of the data points across the different series. Similar to non-time series use cases, the distance is calculated using the distances between the series; however, there is an important distinction in that the silhouette coefficient calculations do not account for location in time when considering similarity. While the silhouette score is generally useful, consider it with caution for time series. The silhouette score can identify series that have a high degree of similarity in the points contained within the series, but it does not account for periodicity and trends, or similarities across time. To understand the impact, examine the following two scenarios: #### Silhouette time series scenario 1 Consider these two series: - The first series has a large spike in the first 10 points, followed by 90 small to near-zero values. - The second series has 70 small to near-zero values followed by a moderate spike and several more near-zero values. In this scenario, the silhouette coefficient will likely be large between these two series. Given that time isn't taken into account, the values show a high degree of mathematical similarity. #### Silhouette time series scenario 2 Consider these three series: - The first series is a sine wave of magnitude 1. - The second series is a cosine wave of magnitude 1. - The third series is a cosine wave of magnitude 0.5. Potential clustering methods: - The first method adds the sine and cosine wave (both having a magnitude of 1) into a cluster and it adds the smaller cosine wave into a second cluster. - The second method adds the two cosine waves into a single cluster and the sine wave into a separate cluster. The first method will likely have a higher silhouette score than the second method. This is because the silhouette score does not consider the periodicity of the data and the fact that the peaks in the cosine waves likely have more meaning to each other. If the goal is to perform segmented modeling, take the silhouette score into consideration, but be aware of the following: - A higher silhouette score may not indicate a better segmented modeling performance. - Series grouped together based on periodicity, volatility, or other time-dependent features will likely return lower silhouette scores than series that have a higher similarity when considering only the magnitudes of values independent of time. ### SMAPE/Weighted SMAPE | Display | Full name | Description | Project type | | --- | --- | --- | --- | | SMAPE/Weighted SMAPE | Symmetric Mean Absolute Percentage Error | Measures the bounded percent inaccuracy of the mean values. | Regression | The Mean Absolute Percentage Error [(MAPE)](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html#mapeweighted-mape) allows you to compare forecasts of different series in different scales. However, MAPE cannot be used if there are zero values, and does not have an upper limit to the percentage error. In these cases, the [Symmetric Mean Absolute Percentage Error (SMAPE)](https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error) can be a good alternative. The SMAPE has a lower and upper boundary, and will always result in a value between 0% and 200%, which makes statistical comparisons between values easier. It is also a suitable function for use on data where values that are zero are present. That is, rows in which `Actual = Forecast = 0`, DataRobot replaces `0/0 = NaN` with zero before summing over all rows. ### Theil's U | Display | Full name | Description | Project type | | --- | --- | --- | --- | | Theil's U | Henri Theil's U Index of Inequality | Measures relative performance with respect to a baseline model. | Regression (time series only) | [Theil’s U](https://en.wikipedia.org/wiki/Uncertainty_coefficient), similar to [MASE](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html#mase), is a metric to evaluate the accuracy of a forecast relative to the forecast of the naïve model (a model that uses, for predictions, the most recent value that matches the longest periodicity). This has the advantage of being easily interpretable and explainable in terms of relative accuracy gain, and is recommended when comparing models. In DataRobot time series projects, [the baseline model](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/ts-feature-lists.html#mase-and-baseline-models) is a model that uses the most recent value that matches the longest periodicity. That is, while a project could have multiple different naïve predictions with different periodicity, DataRobot uses the longest naïve predictions to compute the Theil's U score. The comparison of the forecast model to the naïve model is a function of the ratio of the two. A value greater or less than 1 indicates the model is worse or better than the naïve model, respectively. Or, in more detail: Where the numerator is the model of interest and the denominator is the naïve baseline model. --- # SHAP reference URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/shap-ref.html > Provides reference content for understanding Shapley Values, the coalitional game theory framework by Lloyd Shapley, as used in SHAP Prediction Explanations. # SHAP reference SHAP (SHapley Additive exPlanations) is an open-source algorithm used to address the accuracy vs. explainability dilemma. SHAP is based on Shapley Values, the coalitional game theory framework by Lloyd Shapley, Nobel Prize-winning economist. The values are a unified measure of feature importance and are used to interpret predictions from machine learning models. SHAP values not only tell us about the importance of a feature, but also about the direction of the relationship (positive or negative). The SHAP values of a feature sum up to the difference between the prediction for the instance and the expected prediction (averaged across the dataset). If we consider a model that makes predictions based on several input features, the SHAP value for each feature would represent the average marginal contribution of that feature across all possible feature combinations. To understand the origination of the project: > Lloyd Shapley asked: How should we divide a payout among a cooperating team whose members made different contributions? Shapley values answers: - The Shapley value for member X is the amount of credit they get. - For every subteam, how much marginal value does member X add when they join the subteam? Shapley value is the weighted mean of this marginal value. - Total payout is the sum of Shapley values over members. Scott Lundberg is the primary author of the SHAP Python package, providing a programmatic way to explain predictions: > We can divide credit for model predictions among features! By assuming that each value of a feature is a “player” in a game, the prediction is the payout. SHAP explains how to fairly distribute the “payout” among features. SHAP has become increasingly popular due to the SHAP [open source package](https://github.com/slundberg/shap) that developed: - A high-speed exact algorithm for tree ensemble methods (called "TreeExplainer"). - A high-speed approximation algorithm for deep learning models (called "DeepExplainer"). - Several model-agnostic algorithms to estimate Shapley values for any model (including "KernelExplainer" and "PermutationExplainer"). The following key properties of SHAP make it particularly suitable for DataRobot machine learning: - Local accuracy : The sum of the feature attributions is equal to the output of the model DataRobot is "explaining." - Missingness : Features that are already missing have no impact. - Consistency : Changing a model to make a feature more important to the model will never decrease the SHAP attribution assigned to that feature. (For example, model A uses feature X. You then make a new model, B, that uses feature X more heavily (perhaps by doubling the coefficient for that feature and keeping everything else the same). Because of the consistency quality of SHAP, the SHAP importance for feature X in model B is at least as high as it was for feature X in model A.) Additional [readings](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/shap-ref.html#additional-reading) are listed below. SHAP contributes to model explainability by: - Feature Impact: SHAP shows, at a high level, which features are driving model decisions. Without SHAP, results are sensitive to sample size and can change when re-computed unless the sample is quite large. See thedeep dive. - Prediction explanations: There are certain types of data that don't lend themselves to producing results for all columns. This is especially problematic in regulated industries like banking and insurance. SHAP explanations reveal how much each feature is responsible for a given prediction being different from the average. For example, when a real estate record is predicted to sell for $X, SHAP prediction explanations illustrate how much each feature contributes to that price. - Feature Effects: > [!NOTE] Classic only > To retrieve the SHAP-based Feature Impact or Prediction Explanations visualizations, you must enable the [Include only models with SHAP value support](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/adv-opt/additional.html) advanced option prior to model building. The blueprint Repository will be filtered to include only blueprints that support SHAP values using some combination of `LinearExplainer`, `TreeExplainer`, and `DeepExplainer`. Blueprints that would use the `PermutationExplainer` won't be added. ## Feature Impact Feature Impact assigns importance to each feature ( `j`) used by a model. ### With SHAP Given a model and some observations (up to 5000 rows in the training data), Feature Impact for each feature `j` is computed as: sample average of `abs(shap_values for feature j)` Normalize values such that the top feature has an impact of 100%. ### With permutation Given a model and some observations (2500 by default and up to 100,000), calculate the metric for the model based on the actual data. For each column `j`: - Permute the values of column j . - Calculate metrics on permuted data. - Importance = metric_actual - metric_perm (Optional) Normalize by the largest resulting value. ## Prediction explanations SHAP prediction explanations are [additive](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/shap-ref.html#additivity-in-prediction-explanations). The sum of SHAP values is exactly equal to: ``` [prediction - average(training_predictions)] ``` DataRobot Classic only (Workbench is SHAP-only) When selecting between XEMP and SHAP, consider your need for accuracy versus interpretability and performance. With XEMP, because all blueprints are included in Autopilot, the results may produce slightly higher accuracy. This is only true in some cases, however, since SHAP supports all key blueprints, meaning that often the accuracy is the same. SHAP does provide higher interpretability and performance: - Results are intuitive. - SHAP is computed for all features. - Results often return 5-20 times faster. - SHAP is additive . - The open source nature provides transparency. ### Which explainer is used for which model? Within a blueprint, each modeling vertex uses the SHAP explainer that is most optimized to the model type: - Tree-based models (XGBoost, LightGBM, Random Forest, Decision Tree): TreeExplainer - Keras deep learning models: DeepExplainer - Linear models: LinearExplainer If a blueprint contains more than one modeling task, SHAP values are combined additively to yield the SHAP values for the overall blueprint. Some modeling tasks are not supported by any type-specific explainers. If a blueprint contains such tasks, the blueprint is explained as a whole unit by the model-agnostic `PermutationExplainer`. ### Additivity in prediction explanations In certain cases, you may notice that SHAP values do not add up to the prediction. The reason may be some or all of the following conditions: - Some models use a link function to convert the direct output of a modeling task to a different scale. This is very common in binary classification models, which often use thelogitlink function, and it also happens for regression models with skewed targets, which use theloglink function. If the SHAPPermutationExplaineris used, then the SHAP values are in the same units as the prediction—with the link transform already accounted for—and when summed together with the base value, they will reproduce the prediction. But if task-specific explainers are used, such asTreeExplainerorDeepExplainer, then the SHAP values are in the margin units before the link transform. To reproduce the prediction, you will need to sum the SHAP values together with the base value and then apply the inverse link transform as below. - You may have configured an offset, applied before the link, and/or an exposure, applied after the link. (See the Workbench configurationhereand the Classic configurationhere.) Offsets and exposures are not regular feature columns and do not get SHAP values; instead, they are applied to the model outputs as a post-processing step. SHAP values are computed as if all offsets are 0 and exposures are 1. - The model may "cap" or "censor" its predictions (for example, enforcing them to be non-negative). The following pseudocode can be used for verifying additivity in these cases. **Type-specific explainers:** In this example, SHAP values are in the margin units. ``` # shap_values = output from SHAP prediction explanations # If you obtained the base_value from the UI prediction distribution chart, first transform it by the link. base_value = api_shap_base_value or link_function(ui_shap_base_value) pred = base_value + sum(shap_values) if offset is not None: pred += offset if link_function == 'log': pred = exp(pred) elif link_function == 'logit: pred = exp(pred) / (1 + exp(pred)) if exposure is not None: pred *= exposure pred = predictions_capping(pred) # at this point, pred matches the prediction output from the model ``` **PermutationExplainer:** In this example, SHAP values are in the same units as the prediction. ``` if link_function == 'log': pred = (base_value + sum(shap_values)) * exposure * exp(offset) elif link_function == 'logit': # here expit(x) = exp(x) / (1 + exp(x)) pred = expit(offset + logit(base_value + sum(shap_values))) else: pred = offset + base_value + sum(shap_values) ``` ## How SHAP values are calculated for time-aware In time-aware experiments, SHAP explanations and impact are available when using Workbench (or via the API); calculations are based on input features, not the derived features that result from running time-aware modeling. The calculation method for this data is different than that used for predictive experiments. The sections below provide an overview. Generally, the way SHAP works is to take one row of the input data at a time and make a prediction, shuffle values, make another prediction, and so on, making a grid of results. Every time DataRobot steps to a row, it performs permutations on it and then moves to the next. Then, it centralizes all the results and uses them to calculate the SHAP values. However, because time-aware experiments do not make row-by-row predictions, this calculation method does not work. Time-aware is not designed to handle permutations of the same date and series, nor can it have duplicate series. With time-aware, you have a flow of dates across series and dates. To provide SHAP values for time-aware, DataRobot runs SHAP twice. First, it runs the data through to collect a set of prediction permutations SHAP might try. Then, instead of processing them individually, it stores them as parallel prediction caches. Each cache maintains chronological integrity and no single date exists more than once per cache. After predictions, results are reshuffled to present back to SHAP in the expected format. This creates the illusion for SHAP that it processed individual permutations, when in reality the methodology preserved time continuity. For example, for a dataset with 10 features, SHAP might want to create 10 variations of each row with different feature values included or excluded. If you have 10 features, DataRobot is going to make 10 copies of a column or 10 copies of a row, each with different settings. The results are then stored as 10 parallel caches. DataRobot predicts each cache independently and then reshuffles the data—SHAP "thinks" it has created 10 unique things, predicted them, and they returned with values. The number of permutations run by SHAP, and therefore the number of parallel caches, is `number of features x 2 - 1`. These are stored in parallel so that each cache basically has each permutation (e.g., January 2nd, 3rd, etc.,) but does not have a single date existing more than once per cache. This feature of SHAP compatibility prevents duplicate time steps from colliding with one another, as that would break the time series model. In predictive SHAP, values remain consistent regardless of row ordering—you can shuffle your data, calculate SHAP values, and then rearrange them back to their original order without affecting the results. Each row's SHAP values depend solely on its feature values. With time-aware SHAP, by contrast, values are position-dependent. Even if two data points have identical feature values (excluding the date itself), they will receive different SHAP values when positioned at different points in the time sequence. Moving data points earlier or later in the timeline changes their SHAP values because time series models inherently account for temporal relationships and patterns. This temporal dependency is an important consideration when interpreting time-aware SHAP values. The position of a data point within its chronological context directly impacts its importance and contribution to predictions. ## Classic only: SHAP compatibility matrix See the following for blueprint support with SHAP in DataRobot Classic. In NextGen, all models are supported. ## Additional reading The following public information provides additional information on open-source SHAP: - SHAP for explainable machine learning - SHAP (SHapley Additive exPlanations) - Explain Your Model with the SHAP Values - A Unified Approach to Interpreting Model Predictions - SHAP GitHub package --- # Sliced insights URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/sliced-insights.html > Using a filtered subset of the full data, DataRobot segments insights by subpopulation to provide better segment-based accuracy information. # Sliced insights Sliced insights provide the option to view a subpopulation of a model's data based on feature values—either raw or derived. Slices are, in effect, a filter for categorical, numeric, or both types of features. Slices are applied to the Training, Validation, Cross-validation, or Holdout (if unlocked) partitions, depending on the insight. Viewing and comparing insights based on segments of a project’s data helps to understand how models perform on different subpopulations. Use the segment-based accuracy information gleaned from sliced insights, or compare the segments to the "global" slice (all data), to improve training data, create individual models per segment, or augment predictions post-deployment. Some common uses of sliced insights: > A bank is building a model to predict loan default risk and wants to understand if there are segments of their data— demographic information, location, etc.—that their model performs either more or less accurately on. If they find that "slicing" the data shows some segments perform to their expectations, they may choose to create individual projects per segment.An advertising company wants to predict whether someone will click an ad. Their data contains multiple websites and they want to understand if the drivers are different between websites in their portfolio. They are interested in creating comparison groups, with each group consisting of some number of different values, to ultimately impact user behaviors in different ways for each site. To view insights for a segment of your data once models are trained, choose the preconfigured slice from the Slice dropdown. If the slice has been calculated for the chosen insight, DataRobot will load the insight. Otherwise, a button will be available to start further calculations. ## Supported insights Sliced insights are available for the following insights (where applicable) for binary classification and regression projects: In NextGen: - Feature Effects - Feature Impact - Individual Prediction Explanations - SHAP Distributions: Per Feature - Lift Chart - Residuals - ROC Curve In DataRobot Classic: - Feature Effects - Feature Impact - Lift Chart - Residuals (not available for time-aware) - ROC Curve See also the [sliced insight considerations](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/sliced-insights.html#feature-considerations). ## Create a slice You can create a slice to apply to insights from a supported [Leaderboard](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/sliced-insights.html#leaderboard-slices) insight. Each slice is made up of up to three [filters](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/sliced-insights.html#filter-configuration-reference) (connected, as needed, by the `AND` operator). > [!NOTE] Note > Features that can be used to create filters are based on all features, regardless of what is currently displayed on the Data tab or if you built a model using a list that doesn't include that feature. This is because while feature lists are based on columns, slices are based on rows. That is, the value of the selected feature appears in the row that is identified by the individual feature in the list feature. To create the first slice from the Leaderboard: 1. Select a model and open asupported insight. The insight loads using all data for the selected partition. 2. From theSlicedropdown, which defaults toAll Data, configure a new slice by selectingCreate slice. 3. Provide a name for the slice and configure the filters, as described in thefilter reference. 4. ClickSave sliceto finish and view theSliceswindow. TheSliceswindow lists all configured slices as well as summary text of the filters that define the slice. From here you can add a new slice or delete one or more configured slices. ClickCloseto close the configuration window. After a first slice is created, use Manage slices from the Data slice dropdown to create new or remove user-created slices. ### Filter configuration reference The following table describes the fields in the filter configuration: | Filter field | Description | | --- | --- | | Slice name | Enter a name for the filter. This is the name that will appear in the Slices dropdown of supported insights. | | Filter feature | Select the categorical or numeric feature to base the filter on. They are grouped in the dropdown by variable type. You cannot set the target as the filter type. | | Operator | Set the filter operator to define what comprises the subpopulation. That is, those rows in which the feature value: in: Falls within the range of the defined Value (categorical and boolean). =*: Is equivalent to the defined Value (see below). >: Is greater than the defined Value (numeric only). <: Is less than the defined Value (numeric only). between: Falls between the two values specified, inclusive (numeric only). not between: Is not within the two values specified, inclusive (numeric only). | | Values | Set the matching criteria for Filter type. For categorical features, all available values will be listed in the dropdown. For numerics, enter a value manually. | * If you select `=` as the operator, the Value must match exactly and you can choose only one value. If you set `in`, you can select multiple values. ### Adding multiple conditions Use Add filter to build a slice with multiple conditions. Note that: - You can mix categorical and numeric features in a single slice. - All conditions are processed with theANDoperator. ## Generate sliced insights When you first load an insight, DataRobot displays results for all data in the appropriate partition (unless further calculations are required). This is the equivalent of the global slice, or as referenced in the dropdown, `All Data`. For the Lift Chart, ROC Curve, and Residuals, once prediction calculations are run for the first slice, DataRobot stores them for re-use (assuming the same data partition). Feature Impact and Feature Effects, because they use a special [calculation process](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/sliced-insights.html#deep-dive-slice-calculations), do not benefit from caching and so recompute predictions for each slice. When viewing sliced data for a given model, you only have to generate predictions once for a selected partition—Validation, Cross-validation, or if calculated, Holdout. Note that this calculation is in addition to the original calculation DataRobot ran when fitting models for the project. > [!NOTE] Note > Feature Impact provides a [quick-compute option](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html#quick-compute) to control the sample size used in the calculation. ## Recompute sliced insights When using slices with either Feature Effects or Feature Impact, you must manually launch the calculation to compute a sliced version of the insight. The reason for this is to save compute resources—it allows you to determine whether a sliced insight has been created without automatically launching the associated jobs. The order of operations is: - When requested, DataRobot calculatesFeature Impacton all data. Then, you can initiate the calculation for any configured slice. - If you requestFeature EffectsbeforeFeature Impact, DataRobot calculatesFeature Impacton all data and then returnsFeature Effectsresults. You are not provided with the option to select a slice until after the initialFeature Impactjob is complete. - If off, the row count uses 100,000 rows or the number of rows available after a slice is applied, whichever is smaller. For unsliced Feature Impact, the quick-compute toggle replaces the [Adjust sample size](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/understand/feature-impact-classic.html#change-sample-size) option. In that case, possible outcomes are: - If on, DataRobot uses 2500 rows or the number of rows in the model training sample size, whichever is smaller. - If off, the row count uses 100,000 rows or the number of rows in the model training sample size. You may want to use this option, for example, to train Feature Impact at a sample size higher than the default 2500 rows (or less, if downsampled) in order to get more accurate and stable results. ### View a sliced insight To view a sliced insight, choose the appropriate slice from the Data slice dropdown. If you see a slice but are unsure of the filter conditions, click Manage Slices to view summary text of the filters that define the slice. The following example shows the ROC Curve tab without slices applied: Consider the same model with a slice applied that segments the data for females aged 70-80 who have had more than five diagnoses: > [!NOTE] Note > If the slice results in predictions that are either all positive or all negative, the ROC curve will be a straight line. The Confusion Matrix reports the same results in table form. The images below show Feature Impact with first the global, All data slice: And then a configured slice: Hover on a feature to compare the calculated impact between sliced views: ## Deep dive: Slice calculations For the Lift Chart, ROC Curve, and Residuals, once prediction calculations are run for the first slice, DataRobot stores them so that they can be re-used, assuming the same data partition. Specifically: - When you select a newslicefor the first time, within the same insight, DataRobot will generate the insight but will not need to rerun predictions (because predictions for the partition have already been computed). - When you change to another supportedinsight(other thanFeature Impact), the predictions are available and only the insight itself must be generated (because the partition's predictions were already computed by another supported insight). For Feature Impact and Feature Effects, DataRobot first runs predictions on the training sample chosen to fit the model. Then, DataRobot creates sliced-based synthetic prediction datasets and generates predictions for use in the respective insights. Each insight generates its own unique synthetic datasets. ## Feature considerations - Sliced insights are available for binary classification and regression projects for predictive and time-aware experiments. - Slices are not available for projects that useFeature Discovery. - Slices are not available in projects created with SHAP Feature Importance and SHAP-based Prediction Explanations. - You cannot edit slices. Instead, delete (if desired) the existing slice and create a new slice with the desired criteria. - You can add a maximum of three filter conditions to a single slice. - If you create an invalid slice, the slice is created, but when you apply it on supported insights, it will error. This could happen, for example, if there are not enough rows on the sliced data to compute the insight or the filter is invalid. For example, if setnum_procedures > 10and the maximum number for any row is6, DataRobot creates the slice but errors during the insight calculation if the slice is selected. - Row requirements: - ForFeature Impact: --- # Clustering algorithms URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/clustering-algos.html # Clustering algorithms Clustering is the ability to cluster time series within a multiseries dataset and then directly apply those clusters as segment IDs within a [segmented modeling](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-segmented.html) project. It allows you to group the most similar time series together to create more accurate segmented models, reducing time-to-value when creating complex multiseries projects. DataRobot uses the following algorithms for time series clustering: - Velocity clustering - K-means clustering with Dynamic Time Warping (DTW) The following table compares Velocity and K-means: | Characteristic | Velocity clustering | K-means clustering | | --- | --- | --- | | Speed | Fast | Slow | | Robust to irregular time steps | Yes | No | | Clusters based on series shape | No | Yes | | Series need to be the same length | No | No | While a single series may contain many unique values that vary over time, the time series variant looks for similarities across the different series to identify which series most closely relate to one another. Series are classified by being most closely associated with a specific barycenter (the time series clustering equivalent of a centroid), which is derived from the original dataset. ## Velocity clustering Velocity clustering is an unsupervised learning technique that splits series up based on summary statistics. The goal, as with most clustering, is to put similar series in the same cluster and series that differ significantly in different clusters. Specifically, it groups time series based on statistical properties such as the mean, standard deviation, and the percentage of zeros. The benefit of this approach is that time series with similar values within the feature derivation window are grouped together so that during segmented modeling, these features within the FDW have more signal. Calculation for each clustering feature is as follows: 1. Perform the given aggregation (i.e., mean, standard deviation) on all series. 2. Divide the resulting aggregations into quantiles representing the number of desired clusters. 3. Determine in which quantile the feature’s aggregation falls and assign the feature to that cluster. The cluster assigned the most features is the final cluster for the series. DataRobot implements four types of Velocity clustering: - Mean Aggregations - Standard Deviation Aggregations - Zero-Inflated & Mean Aggregations - Zero-Inflated & Standard Deviation Aggregations ## K-means with DTW Understanding the implementation of K-means clustering requires understanding how DataRobot measures the distance between two time series. This is done with Dynamic Time Warping (DTW). See the [K-Means deep dive](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/clustering-algos.html#deep-dive-k-means-dtw-clustering) (as it relates to clustering and DTW), below. ### Single-feature DTW DTW produces a similarity metric between two series by attempting to match the "shapes." The graph below shows an example where the [matching](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/clustering-algos.html#dtw-match-requirements) between the indices is not 1-to-1—it's warped. ### Multi-feature DTW The simple diagram above illustrates how to measure the distance between series focusing on a single feature. However, what if there are multiple features? For example, to calculate the distance between store A and store B using daily sales data and daily customer count data, do you calculate the average of the "DTW distance of sales data" and the "DTW distance of customer count data"? DTW is calculated as the distances measure between each feature. The value is then paired with K-means to do the actual clustering. Think of a series as a matrix of shape `m x n`, where `m` is each feature represented, and `n` is each point in time. Each series has its own `m x n` matrix. You then have a collection of series within a cluster. Note that the distance matrix itself is not the error metric. The error metric is calculated on top of the distance matrix. The distance matrix is then calculated for each `m` and `n` point across the series. Features are kept independent within DTW, resulting in a `2D` DTW representation instead of the `1D` representation in the image above. The actual K-means is an optimization of the resulting `2D` distance representations for each cluster. ### DTW match requirements There are four requirements to create a match: 1. Every index from the series A must be matched with at least one index from series B and vice versa. 2. The first index of sequence A must match with at least the first index of the sequence B . 3. The last index from sequence A is matched with last index from sequence B . 4. Mapping of the indices from sequence A to sequence B (and vice versa) must be monotonically increasing to prevent index mappings from crossing. How does DataRobot know if the match is accurate? To calculate distance with DTW: 1. Identify all matches between the two time series. 2. Calculate the sum of the squared differences between the values for each match. 3. Find the minimum squared sum across all matches. ## Deep dive: K-Means DTW clustering K-means is an unsupervised learning algorithm that clusters data by trying to separate samples into K groups of equal variance, minimizing a criterion known as “inertia” or “within-cluster sum-of-squares.” This algorithm requires the number of clusters to be specified in advanced. It scales well to large numbers of samples and has been used across a broad range of application areas in many different fields. To apply the K-means algorithm to a sequence, DataRobot initializes a cluster by selecting a series to serve as a barycenter. Specifically, DataRobot: 1. Identifies how many clusters to create (K). 2. Initializes clusters by randomly selecting K series as the barycenters. 3. Calculates the sum of the squared distance from each time series to all barycenters. 4. Assigns the time series to the cluster with the smallest sum. 5. Recalculates the barycenter of each cluster. 6. Repeats steps 4 & 5 until convergence or maximum iterations reached. ### DTW distance calculations A barycenter is a time series that is derived from other series, with the explicit goal of minimizing the distance between itself and other series. A barycenter ( `B`) is mathematically defined as the minimum sum of the squared distances ( `d`) to a reference time series ( `b`), when you are given a new time series dataset ( `x`): Clustering time series algorithms, i.e., K-means, computes these distances using different distance algorithms, such as Euclidean and DTW. With DTW K-means, DTW is used as the similarity measure between different series. One major advantage of DTW is that series do not need to be the same length, or aligned with one another in time. Instead two series can be compared after minimizing the temporal alignments between them. Compare this to Euclidean distance. In Euclidean distance, each point is compared exactly with the other point that occupies the same point in time between series ( `T_0` will always be compared with `T_0`). However if `T_10` is a peak in Series A, and `T_30` is a peak in Series B, than `T_10` will be compared with `T_30` when using DTW. Euclidean distance would compare `T_10` with `T_10`. While DTW is slightly slower than other methods, it provides a much more robust similarity metrics. This is especially important when considering that most multiseries datasets have a wide variety of characteristics, including differing start and stop times for each series. --- # Time series feature derivation URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/feature-eng.html > A comprehensive reference of the DataRobot time series feature derivation process. # Time series feature derivation The following tables document the feature derivation process—operators used and feature names created—that create the time series modeling dataset. For additional information, see the descriptions of: - Intra-month seasonality detection - Zero-inflated models - Automatically created feature lists ## Process overview When deriving new features, DataRobot passes each feature through zero or more preprocessors (some features are not preprocessed), then passes the result though one or more extractors, and then finally through postprocessors. Preprocessors are only run—although this step can be skipped—for target, date, and text columns (no feature columns): dataset --> preprocessor --> extractor --> postprocessor --> final Feature columns move from input to extractor or postprocessor: dataset --> extractor --> postprocessor --> final dataset --> extractor --> final More detailed, DataRobot: 1. Applies automatic feature transformation on date features during EDA1. These features are excluded from the EDA2 feature derivation process described below; only the original undergoes the process (i.e., transformed features are not further transformed). 2. Applies a preprocessor (e.g., CrossSeriesBinaryPreprocessor, NextEventType, Transform, and others). 3. Creates an "intermediate feature" for the target, date, or text features—a feature where preprocessing was applied but will not be complete until application of the post-processing operation. For example, Sales (log) is an intermediate step to the final Sales (log) (diff) (14 day min) and by itself is not a valid feature. 4. Uses an extractor step to consume the input from either the original dataset or the intermediate feature. The postprocessor (next step) consumes this output as input. 5. Applies postprocessing to the results of the extractor, creating the "final feature" to use for modeling. See the [visual representation](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/feature-eng.html#feature-reference) of feature generation for a quick reference. ### Feature reference The following provides a general overview of the derivation process. A sample input dataset: | Date | Target | | --- | --- | | 1/1/20 | 1 | | 2/1/20 | 2 | | 3/1/20 | 3 | The resulting time series modeling dataset: | Date (actual) | Target (actual) | Forecast distance | | --- | --- | --- | | 1/1/20 | 1 | 1 | | 2/1/20 | 2 | 1 | | 3/1/20 | 3 | 1 | | 1/1/20 | 1 | 2 | | 2/1/20 | 2 | 2 | | 3/1/20 | 3 | 2 | Example of target-derived feature Example of numeric feature Example of categorical feature Example of text feature Example of date feature ## Feature types Feature derivation acts on features based on their type. The examples and explanations below use these variables (for example, ``) to describe the interactions. | Component | Description | | --- | --- | | (intermediate) (final) | The feature selected at project start as the feature to predict. | | (final) | Any feature or target column from the dataset that is not of type date or text. Processing is the same as that done to the target if the feature is numeric; if the feature is categorical, there are differences (noted in the tables below). DataRobot does not apply preprocessing to non-target features. | | (intermediate) (final) | The primary date/time feature selected to enable time-aware modeling at project start. | | (intermediate) (final) | Any date feature, other than automatically transformed features during EDA1, that is not a primary date/time feature. | | (intermediate) | A text column. | The tables include information on: - Feature name patterns—the feature type followed by the pattern tag ("actual" if the feature is from the original uploaded dataset). This is the resulting feature name after all transformations are complete (for example: (diff) ). - Tags—characteristics of the feature. - Examples of the post-processed feature. ## Intermediate features The sections below detail the intermediate features created for target, primary date, date, and text features. **:** The sections below list each name pattern for target features. ` (log)` Description: A log-transformed target. Project type: Regression, multiplicative trend Tags: Target-derived Numeric Multiplicative Example(s): ``` sales (log) (naive latest value) sales (log) (diff) (1st lag) sales (log) (7 day diff) (35 day max) sales (log) (1 month diff) (2nd lag) ``` ` (diff)` Description: A diff-transformed target, created by calculating the difference between the current value and the previous single time step value. Time step is based on the interval in the uploaded dataset. Example: A quarterly dataset has a time step of 3 months. Project type: Regression, non-stationary Tags: Target-derived Numeric Stationarity Example(s): ``` sales (diff) (1st lag) sales (diff) (7 day mean) ``` ` ( diff)` Description: A diff-transformed target, created by calculating the difference between the current value and the previous value. Project type: Regression, seasonality Tags: Target-derived Numeric Seasonal Example(s): ``` sales (7 day diff) (1st lag) sales (7 day diff) (14 day mean) ``` ` (1 month diff)` Description: A diff-transformed target, created by calculating the difference between the current value and the previous month (same day of month) value. Project type: Regression, intramonth seasonality Tags: Target-derived Numeric Seasonal Example(s): ``` sales (1 month diff) (35 day mean) sales (1 month diff) (1st lag) ``` ` (1 month match end diff)` Description: A diff-transformed target, created by calculating the difference between the current value and the previous month (aligned to the end of the month) value. Project type: Regression, intramonth seasonality Tags: Target-derived Numeric Seasonal Example(s): ``` sales (1 month match end diff) (2nd lag) sales (1 month match end diff) (35 day max) ``` ` (1 month match weekly diff)` Description: A diff-transformed target, created by calculating the difference between the current value and the previous month (aligned to the week of the month and weekday) value. Project type: Regression, intramonth seasonality Tags: Target-derived Numeric Seasonal Example(s): ``` sales (1 month match weekly diff) (3th lag) sales (1 month match weekly diff) (35 day mean) ``` ` (1 month match weekly diff from end)` Description: A diff-transformed target, created by calculating the difference between the current value and the previous month (aligned to the weekday and the "week of the month from the end of the month") value. Project type: Regression, intramonth seasonality Tags: Target-derived Numeric Seasonal Example(s): ``` sales (1 month match weekly diff from end) (2nd lag) sales (1 month match weekly diff from end) (35 day min) ``` ` (total)` Description: Total target, for the given time, across all series. Project type: Cross series regression, total aggregation Tags: Target-derived Numeric Cross series Example(s): ``` sales (total) (2nd lag) sales (total) (35 day mean) sales (total) (3rd lag) (diff 35 day mean) sales (total) (7 day diff) (35 day mean) ``` ` (weighted total)` Description: Weighted total target, for the given time, across all series. Project type: Cross series regression, total aggregation, user-specified weights Tags: Target-derived Numeric Cross series Weighted Example(s): ``` sales (weighted total) (2nd lag) sales (weighted total) (35 day mean) sales (weighted total) (3rd lag) (diff 35 day mean) sales (weighted total) (7 day diff) (35 day mean) ``` ` ( total)` Description: Total target, for the given time, across all series within the same user-specified group. Project type: Cross series regression, total aggregation, user-specified groupby feature Tags: Target-derived Numeric Cross series Example(s): ``` sales (region total) (2nd lag) sales (region total) (35 day mean) sales (region total) (3rd lag) (diff 35 day mean) sales (region total) (7 day diff) (35 day mean) ``` ` ( weighted total)` Description: Weighted total target, for the given time, across all series within the same user-specified group. Project type: Cross series regression, total aggregation, user-specified groupby feature, user-specified weights Tags: Target-derived Numeric Cross series Weighted Example(s): ``` sales (region weighted total) (2nd lag) sales (region weighted total) (35 day mean) sales (region weighted total) (3rd lag) (diff 35 day mean) sales (region weighted total) (7 day diff) (35 day mean) ``` ` (average)` Description: Target average, for the given time, across all series. Project type: Cross series regression, average aggregation Tags: Target-derived Numeric Cross series Example(s): ``` sales (average) (2nd lag) sales (average) (35 day mean) sales (average) (3rd lag) (diff 35 day mean) sales (average) (7 day diff) (35 day mean) ``` ` (weighted average)` Description: Weighted target average, for the given time, across all series. Project type: Cross series regression, average aggregation, user-specified weights Tags: Target-derived Numeric Cross series Weighted Example(s): ``` sales (weighted average) (2nd lag) sales (weighted average) (35 day mean) sales (weighted average) (3rd lag) (diff 35 day mean) sales (weighted average) (7 day diff) (35 day mean) ``` ` ( average)` Description: Target average, for the given time, across all series within the same group. Project type: Cross series regression, average aggregation, user-specified cross-series groupby feature Tags: Target-derived Numeric Cross series Example(s): ``` sales (region average) (2nd lag) sales (region average) (35 day mean) sales (region average) (3rd lag) (diff 35 day mean) sales (region average) (7 day diff) (35 day mean) ``` ` ( weighted average)` Description: Weighted target average, for the given time, across all series within the same group. Project type: Cross series regression, total aggregation, user-specified groupby feature and weights Tags: Target-derived Numeric Cross series Weighted Example(s): ``` sales (region weighted average) (2nd lag) sales (region weighted average) (35 day mean) sales (region weighted average) (3rd lag) (diff 35 day mean) sales (region weighted average) (7 day diff) (35 day mean) ``` ` (proportion)` Description: Numeric target that specifies the proportion of the target across all series. Project type: Cross series regression, total aggregation, nonnegative target, sufficiently consistent series presence across timestamps Tags: Target-derived Numeric Cross series Example(s): ``` sales (proportion) (1st lag) sales (proportion) (14 day mean) sales (proportion) (30 day max) (diff 7 day mean) sales (proportion) (7 day diff) (1st lag) sales (proportion) (7 day diff) (30 day min) ``` ` (weighted proportion)` Description: Numeric target that specifies the weighted proportion of the target across all series. Project type: Cross series regression, total aggregation, nonnegative target, sufficiently consistent series presence across timestamps, user-specified weights Tags: Target-derived Numeric Cross series Weighted Example(s): ``` sales (weighted proportion) (1st lag) sales (weighted proportion) (14 day mean) sales (weighted proportion) (30 day max) (diff 7 day mean) sales (weighted proportion) (7 day diff) (1st lag) sales (weighted proportion) (7 day diff) (30 day min) ``` ` ( proportion)` Description: Numeric target that specifies the proportion of the target across all series within the same group. Project type: Cross series regression, total aggregation, nonnegative target, sufficiently consistent series presence across timestamps, user-specified cross-series groupby feature Tags: Target-derived Numeric Cross series Example(s): ``` sales (region proportion) (naive latest value) sales (region proportion) (2nd lag) sales (region proportion) (7 day mean) sales (region proportion) (1st lag) (diff 7 day mean) sales (region proportion) (7 day diff) (1st lag) sales (region proportion) (7 day diff) (30 day min) ``` ` ( weighted proportion)` Description: Numeric target that specifies the weighted proportion of the target across all series within the same group. Project type: Cross series regression, total aggregation, nonnegative target, sufficiently consistent series presence across timestamps, user-specified cross-series groupby feature and weights Tags: Target-derived Numeric Cross series Weighted Example(s): ``` sales (region weighted proportion) (naive latest value) sales (region weighted proportion) (2nd lag) sales (region weighted proportion) (7 day mean) sales (region weighted proportion) (1st lag) (diff 7 day mean) sales (region weighted proportion) (7 day diff) (1st lag) sales (region weighted proportion) (7 day diff) (30 day min) ``` ` (total equal